Examples

Subsetting and selecting data

Let’s open a WRF model output file:

In [1]: import salem

In [2]: from salem.utils import get_demo_file

In [3]: ds = salem.open_xr_dataset(get_demo_file('wrfout_d01.nc'))

Let’s take a time slice of the variable T2 for a start:

In [4]: t2 = ds.T2.isel(Time=2)

In [5]: t2.salem.quick_map()
Out[5]: <salem.graphics.Map at 0x7f15ec54f550>
_images/plot_wrf_t2.png

Although we are on a Lambert Conformal projection, it’s possible to subset the file using longitudes and latitudes:

In [6]: t2_sub = t2.salem.subset(corners=((77., 20.), (97., 35.)), crs=salem.wgs84)

In [7]: t2_sub.salem.quick_map()
I am densified (external_values, 49 elements)
Out[7]: <salem.graphics.Map at 0x7f15ec0ff390>
_images/plot_wrf_t2_corner_sub.png

It’s also possible to use geometries or shapefiles to subset your data:

In [8]: shdf = salem.read_shapefile(get_demo_file('world_borders.shp'))

In [9]: shdf = shdf.loc[shdf['CNTRY_NAME'].isin(['Nepal', 'Bhutan'])]  # GeoPandas' GeoDataFrame

In [10]: t2_sub = t2_sub.salem.subset(shape=shdf, margin=2)  # add 2 grid points
I am densified (external_values, 2 elements)

In [11]: t2_sub.salem.quick_map()
I am densified (external_values, 6 elements)
Out[11]: <salem.graphics.Map at 0x7f15ee28f828>
_images/plot_wrf_t2_country_sub.png

Based on the same principle, one can mask out the useless grid points:

In [12]: t2_roi = t2_sub.salem.roi(shape=shdf)
I am densified (external_values, 2 elements)

In [13]: t2_roi.salem.quick_map()
Out[13]: <salem.graphics.Map at 0x7f15ec8af5c0>
_images/plot_wrf_t2_roi.png

Plotting

Maps can be pimped with topographical shading, points of interest, and more:

In [14]: smap = t2_roi.salem.get_map(data=t2_roi-273.15, cmap='RdYlBu_r', vmin=-14, vmax=18)

In [15]: _ = smap.set_topography(get_demo_file('himalaya.tif'))

In [16]: smap.set_shapefile(shape=shdf, color='grey', linewidth=3)
I am densified (external_values, 2 elements)

In [17]: smap.set_points(91.1, 29.6)

In [18]: smap.set_text(91.2, 29.7, 'Lhasa', fontsize=17)

In [19]: smap.visualize()
_images/plot_wrf_t2_topo.png

Maps are persistent, which is useful when you have many plots to do. Plotting further data on them is possible, as long as the geolocalisation information is shipped with the data (in that case, the DataArray’s attributes are lost in the conversion from Kelvins to degrees Celsius so we have to set it explicitly):

In [20]: smap.set_data(ds.T2.isel(Time=1)-273.15, crs=ds.salem.grid)

In [21]: smap.visualize(title='2m temp - large domain', cbar_title='C')
_images/plot_wrf_t2_transform.png

Reprojecting data

Salem can also transform data from one grid to another:

In [22]: dse = salem.open_xr_dataset(get_demo_file('era_interim_tibet.nc'))

In [23]: t2_era_reproj = ds.salem.transform(dse.t2m)

In [24]: assert t2_era_reproj.salem.grid == ds.salem.grid

In [25]: t2_era_reproj.isel(time=0).salem.quick_map()
Out[25]: <salem.graphics.Map at 0x7f15ec74bef0>
_images/plot_era_repr_nn.png
In [26]: t2_era_reproj = ds.salem.transform(dse.t2m, interp='spline')

In [27]: t2_era_reproj.isel(time=0).salem.quick_map()
Out[27]: <salem.graphics.Map at 0x7f15ec3dd0f0>
_images/plot_era_repr_spline.png