Update interp_points input with new geoutils release

dev-environment.yml

@@ -13,7 +13,7 @@ dependencies:
   - tqdm
   - scikit-image=0.*
   - scikit-gstat>=1.0,<1.1
-  - geoutils=0.1.5
+  - geoutils=0.1.6
 
   # Development-specific, to mirror manually in setup.cfg [options.extras_require].
   - pip

environment.yml

@@ -13,7 +13,7 @@ dependencies:
   - tqdm
   - scikit-image=0.*
   - scikit-gstat>=1.0,<1.1
-  - geoutils=0.1.5
+  - geoutils=0.1.6
   - pip
 
    # To run CI against latest GeoUtils

requirements.txt

@@ -11,5 +11,5 @@ scipy>=1.0,<1.13
 tqdm
 scikit-image==0.*
 scikit-gstat>=1.0,<1.1
-geoutils==0.1.5
+geoutils==0.1.6
 pip

xdem/coreg/affine.py

@@ -561,7 +561,7 @@ def _fit_rst_pts(
             for key, raster in [("nx", normal_east), ("ny", normal_north), ("nz", normal_up)]:
                 raster.tags["AREA_OR_POINT"] = "Area"
                 point_elev[key] = raster.interp_points(
-                    point_elev[["E", "N"]].values,
+                    (point_elev["E"].values, point_elev["N"].values),
                     shift_area_or_point=True,
                 )
 
@@ -906,8 +906,8 @@ def _fit_rst_pts(
         x_coords -= resolution / 2
         y_coords += resolution / 2
 
-        pts = np.array((x_coords, y_coords)).T
-        # This needs to be consistent, so it's cardcoded here
+        pts = (x_coords, y_coords)
+        # This needs to be consistent, so it's hardcoded here
         area_or_point = "Area"
         # Make a new DEM which will be modified inplace
         aligned_dem = rst_elev.copy()
@@ -934,7 +934,7 @@ def _fit_rst_pts(
         tba_pts = aligned_dem.interp_points(pts, shift_area_or_point=True)
 
         # Treat new_pts as a window, every time we shift it a little bit to fit the correct view
-        new_pts = pts.copy()
+        new_pts = (pts[0].copy(), pts[1].copy())
 
         elevation_difference = point_elev[z_name].values - tba_pts
         vshift = float(np.nanmedian(elevation_difference))
@@ -965,7 +965,7 @@ def _fit_rst_pts(
 
             # Assign offset to the coordinates of the pts
             # Treat new_pts as a window, every time we shift it a little bit to fit the correct view
-            new_pts += [east_diff * resolution, north_diff * resolution]
+            new_pts = (new_pts[0] + east_diff * resolution, new_pts[1] + north_diff * resolution)
 
             # Get new values
             tba_pts = aligned_dem.interp_points(new_pts, shift_area_or_point=True)

xdem/coreg/base.py

@@ -189,7 +189,9 @@ def _df_sampling_from_dem(
     return df
 
 
-def _mask_dataframe_by_dem(df: pd.DataFrame | NDArrayf, dem: RasterType) -> pd.DataFrame | NDArrayf:
+def _mask_dataframe_by_dem(
+    df: pd.DataFrame | tuple[NDArrayf, NDArrayf], dem: RasterType
+) -> pd.DataFrame | tuple[NDArrayf, NDArrayf]:
     """
     Mask out the dataframe (has 'E','N' columns), or np.ndarray ([E,N]) by DEM's mask.
 
@@ -200,12 +202,15 @@ def _mask_dataframe_by_dem(df: pd.DataFrame | NDArrayf, dem: RasterType) -> pd.D
     mask_raster = dem.copy(new_array=final_mask.astype(np.float32))
 
     if isinstance(df, pd.DataFrame):
-        pts = np.array((df["E"].values, df["N"].values)).T
-    elif isinstance(df, np.ndarray):
-        pts = df
+        pts = (df["E"].values, df["N"].values)
+    elif isinstance(df, tuple):
+        pts = df  # type: ignore
 
     ref_inlier = mask_raster.interp_points(pts)
-    new_df = df[ref_inlier.astype(bool)].copy()
+    if isinstance(df, pd.DataFrame):
+        new_df = df[ref_inlier.astype(bool)].copy()
+    else:
+        new_df = (pts[0][ref_inlier.astype(bool)], pts[1][ref_inlier.astype(bool)])
 
     return new_df, ref_inlier.astype(bool)
 

xdem/coreg/biascorr.py

@@ -380,7 +380,7 @@ def _fit_rst_pts(  # type: ignore
         pts_elev = ref_elev if isinstance(ref_elev, gpd.GeoDataFrame) else tba_elev
         rst_elev = ref_elev if not isinstance(ref_elev, gpd.GeoDataFrame) else tba_elev
 
-        pts = np.array((pts_elev.geometry.x.values, pts_elev.geometry.y.values)).T
+        pts = (pts_elev.geometry.x.values, pts_elev.geometry.y.values)
 
         # Get valid mask ahead of subsampling to have the exact number of requested subsamples by user
         if bias_vars is not None:
@@ -397,10 +397,10 @@ def _fit_rst_pts(  # type: ignore
 
         # If there is a subsample, it needs to be done now on the point dataset to reduce later calculations
         subsample_mask = self._get_subsample_on_valid_mask(valid_mask=valid_pts, verbose=verbose)
-        pts = pts[subsample_mask]
+        pts = (pts[0][subsample_mask], pts[1][subsample_mask])
 
         # Now all points should be valid, we can pass an inlier mask completely true
-        inlier_pts_alltrue = np.ones(len(pts), dtype=bool)
+        inlier_pts_alltrue = np.ones(len(pts[0]), dtype=bool)
 
         # Below, we derive 1D arrays for the rst_rst function to take over after interpolating to the point coordinates
         # (as rst_rst works for 1D arrays as well as 2D arrays, as long as coordinates match)