使用 Matplotlib 和 Cartopy 绘制卫星数据和海岸线

Question

我正在尝试将来自 DSCOVR 卫星的数据绘制到正交投影上，并将海岸线添加到图像中。我正在使用 Matplotlib 和 cartopy。我可以显示海岸线或数据，但不能同时显示。我怀疑问题的一部分是纬度和经度数据包括黑暗区域（地球周围的空间）并被赋予值 -999.0。

数据可在以下位置找到： https://asdc.larc.nasa.gov/data/DSCOVR/EPIC/L2_CLOUD_03/2021/12/ 该目录中的任何文件都可以使用。

这是我的代码：

import cartopy.crs as ccrs
import h5py
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import numpy as np


filename = 'DSCOVR_EPIC_L2_CLOUD_03_20211203163932_03.nc4'

def make_cloud_image(cloud_name):
    cloud = h5py.File(filename, 'r')
    cloud_data = np.array(cloud['geophysical_data/Cloud_Mask'])
    lat_data = np.array(cloud['geolocation_data/latitude'])
    lon_data = np.array(cloud['geolocation_data/latitude'])
    lat = cloud.attrs.__getitem__('centroid_mean_latitude')[0]
    lon = cloud.attrs.__getitem__('centroid_mean_longitude')[0]
    projection = ccrs.Orthographic(central_longitude=lon, central_latitude=lat, globe=None)    
    ax = plt.subplot(projection=projection)
    cmap = ListedColormap(["Black","dodgerblue", "lightblue", "lightyellow", "white"])
    ax.pcolormesh(lat_data,lon_data, cloud_data, cmap=cmap, transform=projection)
    ax.annotate(cloud_name[-30:-4],xy=(.5,.90),xycoords='axes fraction',
                color='white',fontsize=6, horizontalalignment='center')
    ax.set_global()
    plt.axis('off')
    ax.coastlines()

    plt.show
    

make_cloud_image(filename)

Answer 1

@kcw78，我不得不将非地球像素归零，因为它们被设置为“-999”的值，我还使用了之前评论中建议的变换。

#import cartopy.crs as ccrs

import h5py
import os, sys
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from matplotlib.patches import Patch
import numpy as np
import cartopy.crs as ccrs

filename = 'DSCOVR_EPIC_L2_CLOUD_03_20211203163932_03.nc4'
fontsiz = 6



def make_cloud_image(cloud_name):
    cloud = h5py.File(filename, 'r')
    data = np.array(cloud['geophysical_data/Cloud_Mask'])
    data_where = np.where(data==0,np.nan,data)
    lat_data = np.array(cloud['geolocation_data/latitude'])
    lat_where = np.where(lat_data==-999.0,0,lat_data)
    lon_data = np.array(cloud['geolocation_data/longitude'])
    lon_where = np.where(lon_data==-999.0,0,lon_data)
    lat = cloud.attrs.__getitem__('centroid_mean_latitude')[0]
    lon = cloud.attrs.__getitem__('centroid_mean_longitude')[0]
    lon_min = cloud.attrs.__getitem__('minimum_longitude')[0]
    lat_min = cloud.attrs.__getitem__('minimum_latitude')[0]    
    lon_max = cloud.attrs.__getitem__('maximum_longitude')[0]
    lat_max = cloud.attrs.__getitem__('maximum_latitude')[0]
    projection=ccrs.Orthographic(central_longitude=lon, central_latitude=lat,globe=None)
    
    ax = plt.subplot(projection=projection)
        
    cmap = ListedColormap(["dodgerblue", "lightblue", "lightyellow", "white"])
    plt.pcolormesh(lon_where,lat_where,data_where, cmap=cmap, transform=ccrs.PlateCarree(), zorder=2)
                     
    ax.coastlines(zorder=3)
    
    plt.savefig(f'{filename[:-4]}.png')
    
make_cloud_image(filename)