salem
Two options are offered to you when plotting geolocalised data on maps: you can use cartopy , or you can use salem's :py~Map object.
Plotting on maps using xarray and cartopy is extremely convenient.
With salem you can keep your usual plotting workflow, even with more exotic map projections:
python
import matplotlib.pyplot as plt import cartopy from salem import open_wrf_dataset, get_demo_file ds = open_wrf_dataset(get_demo_file('wrfout_d01.nc'))
ax = plt.axes(projection=cartopy.crs.Orthographic(70, 30)) ax.set_global(); ds.T2C.isel(time=1).plot.contourf(ax=ax, transform=ds.salem.cartopy()); @savefig cartopy_orthographic.png width=80% ax.coastlines();
You can also use the salem accessor to initialise the plot's map projection:
python
proj = ds.salem.cartopy() ax = plt.axes(projection=proj) ax.coastlines(); ax.add_feature(cartopy.feature.BORDERS, linestyle=':'); @savefig cartopy_base.png width=80% ax.set_extent(ds.salem.grid.extent, crs=proj);
Salem comes with a homegrown plotting tool. It is less flexible than cartopy, but it was created to overcome some of cartopy's limitations (e.g. the impossibility to add tick labels to lambert conformal maps), and to make nice looking regional maps:
python
@savefig salem_quickmap.png width=80% ds.T2C.isel(time=1).salem.quick_map()
Salem maps are different from cartopy's in that they don't change the matplotlib axes' projection. The map background is always going to be a call to imshow(), with an image size decided at instanciation:
python
plt.rcParams['figure.figsize'] = (7, 3) f = plt.figure(figsize=(7, 3))
python
from salem import mercator_grid, Map, open_xr_dataset
grid = mercator_grid(center_ll=(10.76, 46.79), extent=(9e5, 4e5)) grid.nx, grid.ny # size of the input grid smap = Map(grid, nx=500) smap.grid.nx, smap.grid.ny # size of the "image", and thus of the axes
@savefig map_central_europe.png width=100% smap.visualize(addcbar=False)
The map has it's own grid, wich is used internally in order to transform the data that has to be plotted on it:
python
ds = open_xr_dataset(get_demo_file('histalp_avg_1961-1990.nc')) smap.set_data(ds.prcp) # histalp is a lon/lat dataset @savefig map_histalp.png width=100% smap.visualize()
Refer to recipes for more examples on how to use salem's maps.