Envi

importcartopy.crsasccrsimportcartopy.featureascfeatureimportmatplotlibasmplimportmatplotlib.hatchimportmatplotlib.pyplotaspltimportmplotutilsasmpuimportnumpyasnpfrommatplotlib.pathimportPathfromcartopy.mpl.tickerimportLongitudeFormatter, LatitudeFormatter

Function List

Please see the plot.py

Demo

Global IPCC

fromutilsimportplot

importxarrayasxrfile_name='data/ERA5temp_1978_monthly.nc'ds=xr.open_dataset(file_name)
lat=ds['latitude']
lon=ds['longitude']
ds=ds.rename_dims({'latitude':'lat','longitude':'lon'})
ds.coords['lat'] = ('lat', lat.to_numpy())
ds.coords['lon'] = ('lon', lon.to_numpy()) # 对维度lon指定新的坐标信息londs=ds.reset_coords(names=['latitude','longitude'], drop=True)
ds['t2m'] =ds['t2m'] -273.15ds['t2m']

importmatplotlib.pyplotaspltimportcartopy.crsasccrsimportnumpyasnpfig=plt.figure()
proj=ccrs.PlateCarree() #ccrs.Robinson()ccrs.Mollweide()Mollweide()ax=fig.add_subplot(111, projection=proj)
levels=np.linspace(-30, 30, num=19)
plot.one_map_flat(ds['t2m'][0], ax, levels=levels, cmap="BrBG_r", mask_ocean=False, add_coastlines=True, add_land=False, plotfunc="pcolormesh")

​
​

#import rioxarray as xrx#p = rxr.open_rasterio(filename)p=np.mean(ds['t2m'], 0) >-20

fig=plt.figure() proj=ccrs.PlateCarree() #ccrs.Robinson() #proj = ccrs.Robinson() ax=fig.add_subplot(111, projection=proj)
levels=np.linspace(-30, 30, num=19)
plot.one_map(ds['t2m'], ax, average='mean', dim='time', cmap="RdBu_r", levels=levels, mask_ocean=True, add_coastlines=True, add_land=True, plotfunc="pcolormesh", colorbar=True, getmean=True)
plot.hatch_map(ax, p, 3*".", label="Lack of model agreement", invert=True, linewidth=0.25, color="0.1")

​
​

at_warming_c= []
at_warming_c.append(ds['t2m'][5:8])
at_warming_c.append(ds['t2m'][9:12])
at_warming_c.append(ds['t2m'][0:3])
len(at_warming_c)
#fig = plt.figure() #proj = ccrs.Robinson() ##ax = fig.add_subplot(131, projection=proj)plot.at_warming_level_one(at_warming_c=at_warming_c, unit="Change (times as frequent)", title='drought frequency change w.r.t. 1850-1900', \
average="median", mask_ocean=True, colorbar=True, cmap="RdBu", dim='time', add_legend=False, hatch_data=None, levels=levels, plotfunc='pcolormesh', getmean=True)

import xarray
import salem
file_name='data/drop_data.nc'
ds=xarray.open_dataset(file_name, engine='netcdf4')
fig = plt.figure()
proj = ccrs.PlateCarree() #ccrs.Robinson()ccrs.Mollweide()Mollweide()
ax = fig.add_axes([0.1, 0.1, 0.9, 0.9], projection=proj)
levels = np.linspace(-30, 30, num=19)
plot.one_map_flat(ds['data'][0], ax, levels=levels, cmap="RdBu", mask_ocean=True, add_coastlines=True, add_land=False, colorbar=True, plotfunc="pcolormesh")
ax.set_ylim([-60, 90])
ax.set_title("test nc", fontsize=15, pad=8)
ax2 = fig.add_axes([1.05, 0.3, 0.15, 0.5])
plot.add_sta(ax2, ds['data'][0].salem.roi(shape=shpfile), [-80,20], 'lat')

Regional

importxarrayasxrfile_name='data/ET_trend.tif'ds=xr.open_dataset(file_name)
data=ds['band_data'][0]

file_name='data/ET_p.tif'ds=xr.open_dataset(file_name)
p=ds['band_data'][0]

datap=p<0.05lat=ds['y']
lon=ds['x']
datap=datap.swap_dims({'y':'lat','x':'lon'})
datap.coords['lat'] = ('lat',lat.to_numpy())
datap.coords['lon'] = ('lon',lon.to_numpy()) # 对维度lon指定新的坐标信息londatap=datap.reset_coords(names=['y','x'], drop=True)

datap

importsalemimportgeopandasasgpdshp_dir='data/Yangtze_4326.shp'shpfile=gpd.read_file(shp_dir)
pmaskregion=datap.salem.roi(shape=shpfile)

区域一般选择默认投影，因此不能修改投影

fromutilsimportplotimportmatplotlib.pyplotaspltimportcartopy.crsasccrsimportnumpyasnpfig=plt.figure()
proj=ccrs.PlateCarree() #ccrs.Robinson()ccrs.Mollweide()Mollweide()ax=fig.add_subplot(111, projection=proj)
levels=np.array([-1, -0.8, -0.4, 0, 0.4, 0.8, 1])#levels = np.linspace(-1, 1, num=19)plot.one_map_region(data, ax, levels=levels, extents=[89, 125, 23, 37], interval=[9, 7], mask_ocean=False, add_coastlines=False, add_land=False, add_gridlines=True, colorbar=True, plotfunc="pcolormesh")
plot.hatch_map(ax, pmaskregion, 3*"/", label="Lack of model agreement", invert=True, linewidth=0.25, color="0.1")
#plt.savefig("test.png", dpi=300)

​
​

在新的plot.one_map_region函数中，绘制全球格网经纬度地图，需要指定范围和间隔，而且不能改投影，不太方便

importxarrayasxrfile_name='data/ERA5temp_1978_monthly.nc'ds=xr.open_dataset(file_name)
lat=ds['latitude']
lon=ds['longitude']
ds=ds.rename_dims({'latitude':'lat','longitude':'lon'})
ds.coords['lat'] = ('lat', lat.to_numpy())
ds.coords['lon'] = ('lon', lon.to_numpy()) # 对维度lon指定新的坐标信息londs=ds.reset_coords(names=['latitude','longitude'], drop=True)
ds['t2m'] =ds['t2m'] -273.15ds['t2m']

importmatplotlib.pyplotaspltimportcartopy.crsasccrsimportnumpyasnpfig=plt.figure()
proj=ccrs.PlateCarree() #ccrs.Robinson()ccrs.Mollweide()Mollweide()ax=fig.add_subplot(111, projection=proj)
levels=np.linspace(-30, 30, num=19)
plot.one_map_region(ds['t2m'][0], ax, extents=[-180, 180, -90, 90], interval=[60, 30], levels=levels, cmap="RdBu", mask_ocean=False, add_coastlines=True, add_land=False, add_gridlines=True, colorbar=True, plotfunc="pcolormesh")

因此，有新的one_map_global_line函数，默认了格网经纬度

importxarrayasxrfile_name='data/r2.tif'ds=xr.open_dataset(file_name)
data=ds['band_data'][0]

fromutilsimportplotimportmatplotlib.pyplotaspltimportcartopy.crsasccrsimportnumpyasnpfig=plt.figure()
proj=ccrs.Robinson() #ccrs.Robinson()ccrs.Mollweide()Mollweide()ax=fig.add_subplot(111, projection=proj)
levels=np.linspace(0, 1, num=9)
plot.one_map_global_line(data, ax, levels=levels, cmap="RdBu", mask_ocean=False, add_coastlines=True, add_land=True, colorbar=True, plotfunc="pcolormesh")

​
​

fromutilsimportplotimportmatplotlib.pyplotaspltimportcartopy.crsasccrsimportnumpyasnpfig=plt.figure()
proj=ccrs.Robinson() #ccrs.Robinson()ccrs.Mollweide()Mollweide()ax=fig.add_subplot(111, projection=proj)
levels=np.linspace(0, 1, num=9)
plot.one_map_flat(data, ax, levels=levels, cmap="RdBu", mask_ocean=False, add_coastlines=True, add_land=True, colorbar=True, plotfunc="pcolormesh")

​
​

由于区域尺度特殊性，不容易看出区域的位置，因此又添加了add_river,add_lake,add_stock函数

上述函数分别决定是否添加河流、湖泊和背景影像图

importxarrayasxrfile_name='data/data_trend.tif'ds=xr.open_dataset(file_name)
data=ds['band_data'][0]

file_name='data/data_p.tif'ds=xr.open_dataset(file_name)
p=ds['band_data'][0]

datap=p<0.05lat=ds['y']
lon=ds['x']
datap=datap.swap_dims({'y':'lat','x':'lon'})
datap.coords['lat'] = ('lat',lat.to_numpy())
datap.coords['lon'] = ('lon',lon.to_numpy()) # 对维度lon指定新的坐标信息londatap=datap.reset_coords(names=['y','x'], drop=True)

importsalemimportgeopandasasgpdshp_dir='data/GRDC.shp'shpfile=gpd.read_file(shp_dir)
pmaskregion=datap.salem.roi(shape=shpfile)

fromutilsimportplot

importmatplotlib.pyplotaspltimportcartopy.crsasccrsimportnumpyasnpimportcmapsfig=plt.figure()
proj=ccrs.PlateCarree() #ccrs.Robinson()ccrs.Mollweide()Mollweide()ax=fig.add_subplot(111, projection=proj)
levels=np.array([-10, -5, -3, -1, 0, 1, 3, 5, 10])#levels = np.linspace(-1, 1, num=19)plot.one_map_region(data, ax, cmap=cmaps.temp_19lev_r, levels=levels, extents=[30, 130, 22, 58], interval=[20, 18], mask_ocean=False, add_coastlines=True, add_land=False, add_river=True, add_lake=True, add_stock=True, add_gridlines=True, colorbar=True, plotfunc="pcolormesh")
plot.hatch_map(ax, pmaskregion, 3*"/", label="Lack of model agreement", invert=True, linewidth=0.25, color="black")
#cmap='RdYlGn' colors=mycolor#plt.savefig('temp.png', dpi=300) 

China

中国

importcartopy.crsasccrsimportcartopy.featureascfeatureimportmatplotlibasmplimportmatplotlib.hatchimportmatplotlib.pyplotaspltimportmplotutilsasmpuimportnumpyasnpfrommatplotlib.pathimportPathfromcartopy.mpl.tickerimportLongitudeFormatter, LatitudeFormatter

importxarrayasxrimportnumpyasnpfile_name='D:/Onedrive/data/tp/tmp_2022.nc'ds=xr.open_dataset(file_name)
da=np.mean(ds['tmp'], 0) *0.1

importsalemimportgeopandasasgpdshp_dir='data/china.shp'shpfile=gpd.read_file(shp_dir)
damask=da.salem.roi(shape=shpfile)

fromutilsimportplotimportmatplotlib.pyplotaspltimportcartopy.crsasccrsimportnumpyasnpimportcmapsfig=plt.figure()
#proj = ccrs.PlateCarree() #ccrs.Robinson()ccrs.Mollweide()Mollweide()proj=ccrs.LambertConformal(central_longitude=105, central_latitude=40,
standard_parallels=(25.0, 47.0))
ax=fig.add_subplot(111, projection=proj)
levels=np.array([-6, -4, -2, 0, 2, 4, 8, 15, 20, 25])#levels = np.linspace(-1, 1, num=19)plot.one_map_china(damask, ax, cmap=cmaps.temp_19lev, levels=levels, mask_ocean=False, add_coastlines=True, add_land=False, add_river=True, add_lake=True, add_stock=False, add_gridlines=True, colorbar=True, plotfunc="pcolormesh")
ax2=fig.add_axes([0.708, 0.174, 0.2, 0.3], projection=proj)
plot.sub_china_map(damask, ax2, add_coastlines=True, add_land=False)

​
​

pa=da>-2pamask=pa.salem.roi(shape=shpfile)

fromutilsimportplotimportmatplotlib.pyplotaspltimportcartopy.crsasccrsimportnumpyasnpimportcmapsfig=plt.figure()
proj=ccrs.PlateCarree() #ccrs.Robinson()ccrs.Mollweide()Mollweide()ax=fig.add_subplot(111, projection=proj)
levels=np.array([-6, -4, -2, 0, 2, 4, 8, 15, 20, 25])#levels = np.linspace(-1, 1, num=19)plot.one_map_china(damask, ax, cmap=cmaps.temp_19lev, levels=levels, mask_ocean=False, add_coastlines=True, add_land=False, add_river=True, add_lake=True, add_stock=True, add_gridlines=True, colorbar=True, plotfunc="pcolormesh")
plot.hatch_map(ax, pamask, 3*"/", label="Lack of model agreement", invert=False, linewidth=0.25, color="black")
ax2=fig.add_axes([0.71, 0.196, 0.2, 0.3], projection=proj)
plot.sub_china_map(damask, ax2, add_coastlines=True, add_land=False, add_stock=True)
plot.hatch_map(ax2, pamask, 3*"/", label="Lack of model agreement", invert=False, linewidth=0.25, color="black")

​
​