Revert rioxarray usage and unify to DEM and landmask loading

pygmtsar/pygmtsar/AWS.py

@@ -25,6 +25,7 @@ def download_dem(self, geometry, filename=None, n_jobs=-1, product='1s', skip_ex
         dem.plot.imshow()
         """
         import xarray as xr
+        import rioxarray as rio
         import geopandas as gpd
         import numpy as np
         from tqdm.auto import tqdm
@@ -53,7 +54,8 @@ def job_tile(product, lon, lat):
             if response.status_code != 200:
                 return None
             with io.BytesIO(response.content) as f:
-                tile = xr.open_dataarray(f, engine='rasterio')\
+                #tile = xr.open_dataarray(f, engine='rasterio')
+                tile = rio.open_rasterio(f, chunks=self.chunksize)\
                     .squeeze(drop=True)\
                     .rename({'y': 'lat', 'x': 'lon'})\
                     .drop_vars('spatial_ref')\
@@ -69,7 +71,7 @@ def job_tile(product, lon, lat):
             tile_xarrays = joblib.Parallel(n_jobs=n_jobs)(joblib.delayed(job_tile)(product, x, y)\
                                 for x in range(left, right + 1) for y in range(lower, upper + 1))
 
-        dem = xr.combine_by_coords([tile for tile in tile_xarrays if tile is not None])['band_data']
+        dem = xr.combine_by_coords([tile for tile in tile_xarrays if tile is not None])
         bounds = self.get_bounds(geometry)
         dem = dem.sel(lat=slice(bounds[1], bounds[3]), lon=slice(bounds[0], bounds[2]))