Improve tests for subsampling in NuthKaab

tests/test_coreg/test_base.py

@@ -134,10 +134,9 @@ def test_get_subsample_on_valid_mask(self, subsample: float | int) -> None:
             subsample_val = subsample
         assert np.count_nonzero(subsample_mask) == min(subsample_val, np.count_nonzero(valid_mask))
 
-    # TODO: Activate NuthKaab once subsampling there is made consistent
     all_coregs = [
         coreg.VerticalShift,
-        # coreg.NuthKaab,
+        coreg.NuthKaab,
         coreg.ICP,
         coreg.Deramp,
         coreg.TerrainBias,
@@ -156,6 +155,8 @@ def test_subsample(self, coreg: Callable) -> None:  # type: ignore
         # But can be overridden during fit
         coreg_full.fit(**self.fit_params, subsample=10000, random_state=42)
         assert coreg_full._meta["subsample"] == 10000
+        # Check that the random state is properly set when subsampling explicitly or implicitly
+        assert coreg_full._meta["random_state"] == 42
 
         # Test subsampled vertical shift correction
         coreg_sub = coreg(subsample=0.1)

xdem/coreg/base.py

@@ -822,7 +822,7 @@ def fit(
             # In any case, override!
             self._meta["subsample"] = subsample
 
-        # Save random_state is a subsample is used
+        # Save random_state if a subsample is used
         if self._meta["subsample"] != 1:
             self._meta["random_state"] = random_state
 

xdem/examples.py

@@ -103,6 +103,10 @@ def process_coregistered_examples(name: str, overwrite: bool = False) -> None:
 
         nuth_kaab = xdem.coreg.NuthKaab()
         nuth_kaab.fit(reference_raster, to_be_aligned_raster, inlier_mask=inlier_mask, random_state=42)
+
+        # Check that random state is respected
+        assert nuth_kaab._meta["random_state"] == 42
+
         aligned_raster = nuth_kaab.apply(to_be_aligned_raster, resample=True)
 
         diff = reference_raster - aligned_raster