Envi
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib as mpl
import matplotlib.hatch
import matplotlib.pyplot as plt
import mplotutils as mpu
import numpy as np
from matplotlib.path import Path
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatterPlease see the plot.py
from utils import plotimport xarray as xr
file_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lon
ds = ds.reset_coords(names=['latitude','longitude'], drop=True)
ds['t2m'] = ds['t2m'] - 273.15
ds['t2m']import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
fig = 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) > -20fig = 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')
import xarray as xr
file_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 < 0.05
lat = 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lon
datap = datap.reset_coords(names=['y','x'], drop=True)datapimport salem
import geopandas as gpd
shp_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
from utils import plot
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
fig = 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
import xarray as xr
file_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lon
ds = ds.reset_coords(names=['latitude','longitude'], drop=True)
ds['t2m'] = ds['t2m'] - 273.15
ds['t2m']import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
fig = 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
import xarray as xr
file_name='data/r2.tif'
ds=xr.open_dataset(file_name)
data = ds['band_data'][0]from utils import plot
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
fig = 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")
from utils import plot
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
fig = 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
import xarray as xr
file_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 < 0.05
lat = 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lon
datap = datap.reset_coords(names=['y','x'], drop=True)import salem
import geopandas as gpd
shp_dir='data/GRDC.shp'
shpfile=gpd.read_file(shp_dir)
pmaskregion=datap.salem.roi(shape=shpfile)from utils import plotimport matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
import cmaps
fig = 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) 
中国
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib as mpl
import matplotlib.hatch
import matplotlib.pyplot as plt
import mplotutils as mpu
import numpy as np
from matplotlib.path import Path
from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatterimport xarray as xr
import numpy as np
file_name = 'D:/Onedrive/data/tp/tmp_2022.nc'
ds=xr.open_dataset(file_name)
da = np.mean(ds['tmp'], 0) * 0.1import salem
import geopandas as gpd
shp_dir='data/china.shp'
shpfile=gpd.read_file(shp_dir)
damask=da.salem.roi(shape=shpfile)from utils import plot
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
import cmaps
fig = 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 > -2
pamask=pa.salem.roi(shape=shpfile)from utils import plot
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
import cmaps
fig = 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")
