Incremental commit on warnings

tests/test_coreg/test_affine.py

@@ -163,7 +163,6 @@ def test_gradientdescending(self, subsample: int = 10000, inlier_mask: bool = Tr
             self.tba,
             inlier_mask=inlier_mask,
             verbose=verbose,
-            subsample=subsample,
             z_name="b1",
         )
         assert gds._meta["offset_east_px"] == pytest.approx(-0.496000, rel=1e-1, abs=0.1)

tests/test_fit.py

@@ -50,6 +50,9 @@ def test_robust_norder_polynomial_fit(self, pkg_estimator: str) -> None:
 
     def test_robust_norder_polynomial_fit_noise_and_outliers(self) -> None:
 
+        # Ignore sklearn convergence warnings
+        warnings.filterwarnings("ignore", category=UserWarning, message="lbfgs failed to converge")
+
         np.random.seed(42)
 
         # Define x vector

tests/test_spatialstats.py

@@ -365,9 +365,9 @@ def test_get_perbin_nd_binning(self) -> None:
             # Get the value at the random point for elevation, slope, aspect
             x = xrand[i]
             y = yrand[i]
-            h = self.ref.data[x, y]
-            slp = self.slope.data[x, y]
-            asp = self.aspect.data[x, y]
+            h = self.ref.data.filled(np.nan)[x, y]
+            slp = self.slope.data.filled(np.nan)[x, y]
+            asp = self.aspect.data.filled(np.nan)[x, y]
 
             if np.logical_or.reduce((np.isnan(h), np.isnan(slp), np.isnan(asp))):
                 continue

tests/test_volume.py

@@ -40,7 +40,7 @@ def test_bin_ddem(self) -> None:
         assert ddem_stds["value"].mean() < 50
         assert np.abs(np.mean(ddem_bins["value"] - ddem_bins_masked["value"])) < 0.01
 
-    def test_interpolate_ddem_bins(self) -> pd.Series:
+    def test_interpolate_ddem_bins(self) -> None:
         """Test dDEM bin interpolation."""
         ddem = self.dem_2009 - self.dem_1990
 
@@ -61,13 +61,18 @@ def test_interpolate_ddem_bins(self) -> pd.Series:
         # Check that no nans exist.
         assert not np.any(np.isnan(interpolated_bins))
 
-        # Return the value so that they can be used in other tests.
-        return interpolated_bins
-
     def test_area_calculation(self) -> None:
         """Test the area calculation function."""
-        ddem_bins = self.test_interpolate_ddem_bins()
 
+        ddem = self.dem_2009 - self.dem_1990
+
+        ddem_bins = xdem.volume.hypsometric_binning(ddem[self.mask], self.dem_2009[self.mask])
+
+        # Simulate a missing bin
+        ddem_bins.iloc[3, 0] = np.nan
+
+        # Interpolate the bins and exclude bins with low pixel counts from the interpolation.
+        interpolated_bins = xdem.volume.interpolate_hypsometric_bins(ddem_bins, count_threshold=200)
         # Test the area calculation with normal parameters.
         bin_area = xdem.volume.calculate_hypsometry_area(
             ddem_bins, self.dem_2009[self.mask], pixel_size=self.dem_2009.res[0]

xdem/coreg/base.py

@@ -1784,6 +1784,8 @@ def fit(
                 " individual steps of the pipeline. To silence this warning: only define 'subsample' in "
                 "either fit(subsample=...) or instantiation e.g., VerticalShift(subsample=...)."
             )
+            # Filter warnings of individual pipelines now that the one above was raised
+            warnings.filterwarnings("ignore", message="Subsample argument passed to*", category=UserWarning)
 
         # Pre-process the inputs, by reprojecting and subsampling, without any subsampling (done in each step)
         ref_dem, tba_dem, inlier_mask, transform, crs = _preprocess_coreg_fit(

xdem/coreg/biascorr.py

@@ -763,7 +763,7 @@ def _fit_rst_rst(  # type: ignore
             print("Estimating rotated coordinates.")
 
         x, _ = gu.raster.get_xy_rotated(
-            raster=gu.Raster.from_array(data=ref_elev, crs=crs, transform=transform),
+            raster=gu.Raster.from_array(data=ref_elev, crs=crs, transform=transform, nodata=-9999),
             along_track_angle=self._meta["angle"],
         )
 
@@ -807,7 +807,7 @@ def _fit_rst_pts(  # type: ignore
             print("Estimating rotated coordinates.")
 
         x, _ = gu.raster.get_xy_rotated(
-            raster=gu.Raster.from_array(data=rast_elev, crs=crs, transform=transform),
+            raster=gu.Raster.from_array(data=rast_elev, crs=crs, transform=transform, nodata=-9999),
             along_track_angle=self._meta["angle"],
         )
 
@@ -841,7 +841,7 @@ def _apply_rst(
 
         # Define the coordinates for applying the correction
         x, _ = gu.raster.get_xy_rotated(
-            raster=gu.Raster.from_array(data=elev, crs=crs, transform=transform),
+            raster=gu.Raster.from_array(data=elev, crs=crs, transform=transform, nodata=-9999),
             along_track_angle=self._meta["angle"],
         )
 

xdem/dem.py

@@ -459,7 +459,7 @@ def estimate_uncertainty(
         """
 
         # Elevation change
-        dh = other_dem.reproject(self) - self
+        dh = other_dem.reproject(self, silent=True) - self
 
         # If the precision of the other DEM is the same, divide the dh values by sqrt(2)
         # See Equation 7 and 8 of Hugonnet et al. (2022)

xdem/spatialstats.py

@@ -1191,16 +1191,17 @@ def _get_cdist_empirical_variogram(
 
     """
 
-    if subsample_method == "cdist_equidistant" and "runs" not in kwargs.keys() and "samples" not in kwargs.keys():
-
-        # We define subparameters for the equidistant technique to match the number of pairwise comparison
-        # that would have a classic "subsample" with pdist, except if those parameters are already user-defined
-        runs, samples, ratio_subsample = _choose_cdist_equidistant_sampling_parameters(**kwargs)
-
-        kwargs["runs"] = runs
-        # The "samples" argument is used by skgstat Metric subclasses (and not "subsample")
-        kwargs["samples"] = samples
-        kwargs["ratio_subsample"] = ratio_subsample
+    if subsample_method == "cdist_equidistant":
+
+        if "runs" not in kwargs.keys() and "samples" not in kwargs.keys():
+            # We define subparameters for the equidistant technique to match the number of pairwise comparison
+            # that would have a classic "subsample" with pdist, except if those parameters are already user-defined
+            runs, samples, ratio_subsample = _choose_cdist_equidistant_sampling_parameters(**kwargs)
+            kwargs["ratio_subsample"] = ratio_subsample
+            kwargs["runs"] = runs
+            # The "samples" argument is used by skgstat Metric subclasses (and not "subsample")
+            kwargs["samples"] = samples
+
         kwargs.pop("subsample")
 
     elif subsample_method == "cdist_point":