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 0x7fbb8e51d540>

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()
Out[7]: <salem.graphics.Map at 0x7fbb8e51e350>

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

In [11]: t2_sub.salem.quick_map()
Out[11]: <salem.graphics.Map at 0x7fbb8e473640>

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

In [12]: t2_roi = t2_sub.salem.roi(shape=shdf)

In [13]: t2_roi.salem.quick_map()
Out[13]: <salem.graphics.Map at 0x7fbb8ef17ee0>


Maps can be embellished 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)

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()
{'imshow': <matplotlib.image.AxesImage at 0x7fbb8e5d6f80>,
 'contour': [],
 'contourf': []}

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')
{'imshow': <matplotlib.image.AxesImage at 0x7fbb8ede0be0>,
 'contour': [],
 'contourf': []}

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 0x7fbb8e94cee0>
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 0x7fbb8ed87be0>