extending test cases to provide a higher coverage

tests/core/test_landsat.py

@@ -3,6 +3,7 @@
 '''
 
 import geopandas as gpd
+import numpy as np
 import pytest
 
 from datetime import datetime
@@ -52,3 +53,48 @@ def test_landsat_from_usgs():
         for band_name in landsat.band_names[:-1]:
             assert 0 < landsat[band_name].values.min() <= 1, 'wrong value'
             assert 0 < landsat[band_name].values.max() <= 1, 'wrong value'
+
+        # get the cloud mask -> this should fail because we didn't
+        # read the qa bands
+        with pytest.raises(KeyError):
+            cloud_mask = landsat.get_cloud_and_shadow_mask()
+
+        # repeat the reading of the scene WITH the qa bands
+        band_selection = ['blue', 'green', 'red', 'nir08', 'swir12']
+        landsat = Landsat.from_usgs(
+            in_dir=landsat_scene_item['assets'],
+            vector_features=gdf,
+            read_qa=True,
+            read_atcor=False,
+            band_selection=band_selection
+        )
+        assert 'qa_pixel' in landsat.band_names, 'missing quality band'
+
+        # now the generation of a binary cloud mask should work
+        cloud_mask = landsat.get_cloud_and_shadow_mask()
+        assert cloud_mask.values.dtype == bool, 'expected boolean'
+
+        water_mask = landsat.get_water_mask()
+        assert water_mask.values.dtype == 'bool', 'expected boolean'
+
+        # mask clouds and shadows -> check if the mask has an effect
+        landsat.mask_clouds_and_shadows(inplace=True)
+        assert landsat['blue'].is_masked_array, 'expected as masked array'
+
+        # calculate the NDVI
+        landsat.calc_si(si_name='NDVI', inplace=True)
+        assert 'NDVI' in landsat.band_names
+        assert -1 <= landsat['NDVI'].values.min() <= 1
+        assert -1 <= landsat['NDVI'].values.max() <= 1
+        assert np.isnan(landsat['NDVI'].nodata)
+
+        # repeat the reading of the scene WITH the atcor bands
+        band_selection = ['blue', 'green', 'red', 'nir08', 'swir12']
+        landsat = Landsat.from_usgs(
+            in_dir=landsat_scene_item['assets'],
+            vector_features=gdf,
+            read_qa=True,
+            read_atcor=True,
+            band_selection=band_selection
+        )
+        assert 'lwir' in landsat.band_names

tests/core/test_sentinel2.py

@@ -179,6 +179,10 @@ def test_read_from_safe_with_mask_l2a(datadir, get_s2_safe_l2a, get_polygons, ge
     # make sure meta information was saved correctly
     assert handler['scl'].meta['dtype'] == 'uint8', 'wrong data type for SCL in meta'
 
+    # get a binary cloud mask
+    cloud_mask = handler.get_cloud_and_shadow_mask()
+    assert cloud_mask.values.dtype == bool, 'expected boolean'
+
     # to_xarray should fail because of different spatial resolutions
     with pytest.raises(ValueError):
         handler.to_xarray()
@@ -413,7 +417,7 @@ def test_read_from_safe_l2a(datadir, get_s2_safe_l2a):
         inplace=True
     )
 
-    assert reader.is_bandstack(), 'data should fulfill bandstack criteria but doesnot'
+    assert reader.is_bandstack(), 'data should fulfill bandstack criteria but does not'
     assert reader['blue'].crs == 32633, 'projection was not updated'
 
     # try writing bands to output file