建议收藏|Python调用270个NCL色带的cmaps库

import numpy as npimport matplotlib.pyplot as pltimport xarray as xrfrom cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatterimport cartopy.crs as ccrsimport cartopy.feature as cfeaturefrom cartopy.io.shapereader import Readerimport pandas as pdimport rioxarray as rxrimport geopandas as gpdimport rasteriofrom matplotlib import rcParamsconfig = {"font.family":'Times New Roman',"font.size":18,"mathtext.fontset":'stix'}rcParams.update(config)# 读取GMTED2010地形数据shapefile = r"./China_GS2025_5996_2.shp"raster_path = r"./GMTED2010_China.tif"boundary = gpd.read_file(shapefile).to_crs("EPSG:4326")with rasterio.open(raster_path) as src:    out_image, out_transform = rasterio.mask.mask(src, boundary.geometry, crop=True)    data = out_image[0]    nodata = src.nodatadata = np.ma.masked_invalid(data)if nodata is not None:    data = np.ma.masked_equal(data, nodata)data = np.ma.masked_less_equal(data, 0)nrows, ncols = data.shapex = np.arange(ncols) * out_transform[0] + out_transform[2]y = np.arange(nrows) * out_transform[4] + out_transform[5]X, Y = np.meshgrid(x, y)# 绘制中国区域地形图region=[70, 140, 15, 55]proj=ccrs.PlateCarree()fig=plt.figure(figsize=(16,9),dpi=600)ax = plt.axes(projection  = proj)ax.set_extent(region, crs = proj)ax.stock_img()ax.add_feature(cfeature.COASTLINE.with_scale('50m'),linewidth=0.5,zorder=0.5,color='k')# 添加海岸线ax.add_feature(cfeature.OCEAN.with_scale('50m'))ax.add_feature(cfeature.LAND.with_scale('50m'))ax.add_feature(cfeature.LAKES.with_scale('50m'))# 读取地形数据img=plt.imread('./ned.tif')ax.imshow(img,extent=[-180,180,-90,90],transform=ccrs.PlateCarree())# 调用NCL色带import cmapscustom_cmap=cmaps.MPL_RdYlGn_rtick=np.arange(0,6000.1,1000)cs=ax.pcolormesh(X[::4,::4],Y[::4,::4],data[::4,::4],transform=ccrs.PlateCarree(),cmap=custom_cmap,vmin=0,vmax=6000)import geopandas as gpdgdf=gpd.read_file('./China0.shp',encoding='utf-8')import cartopy.mpl.patch as cmppaths=cmp.geos_to_path(list(gdf['geometry']))import matplotlib.path as mpathclip_path=mpath.Path.make_compound_path(*paths)cs.set_clip_path(clip_path,transform=ax.transData)cbar=plt.colorbar(cs,ticks=tick,shrink=0.85,orientation='vertical',extend='both',pad=0.01,aspect=30)cbar.ax.tick_params(labelsize=18,direction='in',right=False)font3={'family':'SimHei','size':18,'color':'k'}cbar.set_label('海拔高度/(m)',fontdict=font3)ax.set_xticks(np.arange(region[0], region[1] + 1, 10), crs = proj)ax.set_yticks(np.arange(region[-2], region[-1] + 1, 10), crs = proj)ax.xaxis.set_major_formatter(LongitudeFormatter(zero_direction_label=False))ax.yaxis.set_major_formatter(LatitudeFormatter())ax.add_geometries(Reader(r'./China.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.5)ax.add_geometries(Reader(r'./nineline.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.5)ax.add_geometries(Reader(r'./ne_50m_lakes.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.2)ax.add_geometries(Reader(r'./1级河流.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='RoyalBlue',linewidth=0.4)ax.add_geometries(Reader(r'./2级河流.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='DodgerBlue',linewidth=0.3)ax.add_geometries(Reader(r'./3级河流.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='DeepSkyBlue',linewidth=0.2)ax.add_geometries(Reader(r'./4级河流.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='SkyBlue',linewidth=0.15)ax.add_geometries(Reader(r'./5级河流.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='LightSkyBlue',linewidth=0.05)ax.add_geometries(Reader(r'./主要湖泊.shp').geometries(),ccrs.PlateCarree(),edgecolor='none',linewidth=0,facecolor='#BEE8FF')font3={'family':'SimHei','size':18,'color':'k'}ax.set_title('中国区域DEM地形图[国界，审图号：GS(2025)5996]',fontdict=font3)font6={'family':'SimHei','size':18,'color':'r'}ax.text(92,47,'中国国界审图号GS(2025)5996号',fontdict=font6,transform=ccrs.PlateCarree())plt.ylabel("纬度",fontdict=font3)plt.xlabel("经度",fontdict=font3)# 省会打点filename1=r'./city34_1.xlsx'df1=pd.read_excel(filename1)#读取文件plt.scatter(df1['lon'].values,df1['lat'].values,marker='o',s=10,color ="k")# 省会标注1filename2=r'./city34_2.xlsx'df2=pd.read_excel(filename2)#读取文件plt.scatter(df2['lon'].values,df2['lat'].values,marker='o',s=10,color ="k")font4={'family':'SimHei','size':8,'color':'k'}for i, j, k in list(zip(df2['lon'].values, df2['lat'].values, df2['name1'].values)):    plt.text(i+0.25,j-0.24,k,fontdict=font4)# 省会标注2filename2=r'./city34_3.xlsx'df2=pd.read_excel(filename2)#读取文件plt.scatter(df2['lon'].values,df2['lat'].values,marker='o',s=10,color ="k")font4={'family':'SimHei','size':8,'color':'k'}for i, j, k in list(zip(df2['lon'].values, df2['lat'].values, df2['name1'].values)):    plt.text(i-1.7,j-0.26,k,fontdict=font4)# 省会标注3，澳门filename2=r'./city34_5.xlsx'df2=pd.read_excel(filename2)#读取文件plt.scatter(df2['lon'].values,df2['lat'].values,marker='o',s=10,color ="k")font4={'family':'SimHei','size':6,'color':'k'}for i, j, k in list(zip(df2['lon'].values, df2['lat'].values, df2['name1'].values)):    plt.text(i-0.65,j-0.9,k,fontdict=font4)# 省会标注4，香港filename2=r'./city34_4.xlsx'df2=pd.read_excel(filename2)#读取文件plt.scatter(df2['lon'].values,df2['lat'].values,marker='o',s=10,color ="k")font4={'family':'SimHei','size':6,'color':'k'}for i, j, k in list(zip(df2['lon'].values, df2['lat'].values, df2['name1'].values)):    plt.text(i+0.18,j-0.5,k,fontdict=font4)# 绘制南海小地图ax2 = fig.add_axes([0.6313, 0.1645, 0.2, 0.2],projection=proj)leftlon, rightlon, lowerlat, upperlat = (105,124,0,24)extend_southsea=[leftlon, rightlon, lowerlat, upperlat]ax2.set_extent(extend_southsea, crs=ccrs.PlateCarree())ax2.stock_img()ax2.add_feature(cfeature.COASTLINE.with_scale('50m'),linewidth=0.5,zorder=0.5,color='k')# 添加海岸线ax2.add_feature(cfeature.LAKES.with_scale('50m'))ax2.add_feature(cfeature.RIVERS.with_scale('50m'))ax2.add_feature(cfeature.OCEAN.with_scale('50m'))ax2.add_feature(cfeature.LAND.with_scale('50m'))ax2.imshow(img,extent=[-180,180,-90,90],transform=ccrs.PlateCarree())ax2.add_geometries(Reader(r'./China.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.5)ax2.add_geometries(Reader(r'./nineline.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.5)ax2.add_geometries(Reader(r'./ne_50m_lakes.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='k',linewidth=0.2)ax2.add_geometries(Reader(r'./1级河流.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='RoyalBlue',linewidth=0.4)ax2.add_geometries(Reader(r'./2级河流.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='DodgerBlue',linewidth=0.3)ax2.add_geometries(Reader(r'./3级河流.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='DeepSkyBlue',linewidth=0.2)ax2.add_geometries(Reader(r'./4级河流.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='SkyBlue',linewidth=0.15)ax2.add_geometries(Reader(r'./5级河流.shp').geometries(),ccrs.PlateCarree(),facecolor='none',edgecolor='LightSkyBlue',linewidth=0.05)ax2.add_geometries(Reader(r'./主要湖泊.shp').geometries(),ccrs.PlateCarree(),edgecolor='none',linewidth=0,facecolor='#BEE8FF')css=ax2.pcolormesh(X[::4,::4],Y[::4,::4],data[::4,::4],transform=ccrs.PlateCarree(),cmap=custom_cmap,vmin=0,vmax=6000)import geopandas as gpdgdf2=gpd.read_file('./China_GS2025_5996_2_nanhai.shp',encoding='utf-8')import cartopy.mpl.patch as cmppaths2=cmp.geos_to_path(list(gdf2['geometry']))import matplotlib.path as mpathclip_path2=mpath.Path.make_compound_path(*paths2)css.set_clip_path(clip_path2,transform=ax2.transData)plt.savefig('./plot552.9.png',dpi=240,bbox_inches='tight',pad_inches=0)plt.show()