Add point cloud gridding (#553)

geoutils/pointcloud.py

@@ -0,0 +1,77 @@
+"""Module for point cloud manipulation."""
+
+import warnings
+from typing import Literal
+
+import geopandas as gpd
+import numpy as np
+from scipy.interpolate import griddata
+
+from geoutils._typing import NDArrayNum
+
+
+def _grid_pointcloud(
+    pc: gpd.GeoDataFrame,
+    grid_coords: tuple[NDArrayNum, NDArrayNum],
+    data_column_name: str = "b1",
+    resampling: Literal["nearest", "linear", "cubic"] = "linear",
+    dist_nodata_pixel: float = 1.0,
+) -> NDArrayNum:
+    """
+    Grid point cloud (possibly irregular coordinates) to raster (regular grid) using delaunay triangles interpolation.
+
+    Based on scipy.interpolate.griddata combined to a nearest point search to replace values of grid cells further than
+    a certain distance (in number of pixels) by nodata values (as griddata interpolates all values in convex hull, no
+    matter the distance).
+
+    :param pc: Point cloud.
+    :param grid_coords: Grid coordinates for X and Y.
+    :param data_column_name: Name of data column for point cloud (only if passed as a geodataframe).
+    :param resampling: Resampling method within delauney triangles (defaults to linear).
+    :param dist_nodata_pixel: Distance from the point cloud after which grid cells are filled by nodata values,
+        expressed in number of pixels.
+    """
+
+    # 1/ Interpolate irregular point cloud on a regular grid
+
+    # Get meshgrid coordinates
+    xx, yy = np.meshgrid(grid_coords[0], grid_coords[1])
+
+    # Use griddata on all points
+    aligned_dem = griddata(
+        points=(pc.geometry.x.values, pc.geometry.y.values),
+        values=pc[data_column_name].values,
+        xi=(xx, yy),
+        method=resampling,
+        rescale=True,  # Rescale inputs to unit cube to avoid precision issues
+    )
+
+    # 2/ Identify which grid points are more than X pixels away from the point cloud, and convert to NaNs
+    # (otherwise all grid points in the convex hull of the irregular triangulation are filled, no matter the distance)
+
+    # Get the nearest point for each grid point
+    grid_pc = gpd.GeoDataFrame(
+        data={"placeholder": np.ones(len(xx.ravel()))}, geometry=gpd.points_from_xy(x=xx.ravel(), y=yy.ravel())
+    )
+    with warnings.catch_warnings():
+        warnings.filterwarnings("ignore", category=UserWarning, message="Geometry is in a geographic CRS.*")
+        near = gpd.sjoin_nearest(grid_pc, pc)
+        # In case there are several points at the same distance, it doesn't matter which one is used to compute the
+        # distance, so we keep the first index of closest point
+        index_right = near.groupby(by=near.index)["index_right"].min()
+
+    # Compute distance between points as a function of the pixel sizes in X and Y
+    res_x = np.abs(grid_coords[0][1] - grid_coords[0][0])
+    res_y = np.abs(grid_coords[1][1] - grid_coords[1][0])
+    dist = np.sqrt(
+        ((pc.geometry.x.values[index_right] - grid_pc.geometry.x.values) / res_x) ** 2
+        + ((pc.geometry.y.values[index_right] - grid_pc.geometry.y.values) / res_y) ** 2
+    )
+
+    # Replace all points further away than the distance of nodata by NaNs
+    aligned_dem[dist.reshape(aligned_dem.shape) > dist_nodata_pixel] = np.nan
+
+    # Flip Y axis of grid
+    aligned_dem = np.flip(aligned_dem, axis=0)
+
+    return aligned_dem

geoutils/raster/raster.py

@@ -3603,7 +3603,7 @@ def ij2xy(
 
     def coords(
         self, grid: bool = True, shift_area_or_point: bool | None = None, force_offset: str | None = None
-    ) -> tuple[NDArrayNum, ...]:
+    ) -> tuple[NDArrayNum, NDArrayNum]:
         """
         Get coordinates (x,y) of all pixels in the raster.
 
@@ -3631,7 +3631,7 @@ def coords(
         # If grid is True, return coordinate grids
         if grid:
             meshgrid = tuple(np.meshgrid(xx, np.flip(yy)))
-            return meshgrid
+            return meshgrid  # type: ignore
         else:
             return np.asarray(xx), np.asarray(yy)
 

tests/test_pointcloud.py

@@ -0,0 +1,107 @@
+"""Test module for point cloud functionalities."""
+
+import geopandas as gpd
+import numpy as np
+import rasterio as rio
+from shapely import geometry
+
+from geoutils import Raster
+from geoutils.pointcloud import _grid_pointcloud
+
+
+class TestPointCloud:
+    def test_grid_pc__chull(self) -> None:
+        """Test point cloud gridding."""
+
+        # 1/ Check gridding interpolation falls back exactly on original raster
+
+        # Create a point cloud from interpolating a grid, so we can compare back after to check consistency
+        rng = np.random.default_rng(42)
+        shape = (10, 12)
+        rst_arr = np.linspace(0, 10, int(np.prod(shape))).reshape(*shape)
+        transform = rio.transform.from_origin(0, shape[0] - 1, 1, 1)
+        rst = Raster.from_array(rst_arr, transform=transform, crs=4326, nodata=100)
+
+        # Generate random coordinates to interpolate, to create an irregular point cloud
+        points = rng.integers(low=1, high=shape[0] - 1, size=(100, 2)) + rng.normal(0, 0.15, size=(100, 2))
+        b1_value = rst.interp_points((points[:, 0], points[:, 1]))
+        pc = gpd.GeoDataFrame(data={"b1": b1_value}, geometry=gpd.points_from_xy(x=points[:, 0], y=points[:, 1]))
+        grid_coords = rst.coords(grid=False)
+
+        # Grid the point cloud
+        gridded_pc = _grid_pointcloud(pc, grid_coords=grid_coords)
+
+        # Compare back to raster, all should be very close (but not exact, some info is lost due to interpolations)
+        valids = np.isfinite(gridded_pc)
+        assert np.allclose(gridded_pc[valids], rst.data.data[valids], rtol=10e-5)
+
+        # 2/ Check the propagation of nodata values
+
+        # 2.1/ Grid points outside the convex hull of all points should always be nodata
+
+        # We convert the full raster to a point cloud, keeping all cells even nodata
+        rst_pc = rst.to_pointcloud(skip_nodata=False).ds
+
+        # We define a multi-point geometry from the individual points, and compute its convex hull
+        poly = geometry.MultiPoint([[p.x, p.y] for p in pc.geometry])
+        chull = poly.convex_hull
+
+        # We compute the index of grid cells interesting the convex hull
+        ind_inters_convhull = rst_pc.intersects(chull)
+
+        # We get corresponding 1D indexes for gridded output
+        i, j = rst.xy2ij(x=rst_pc.geometry.x.values, y=rst_pc.geometry.y.values)
+
+        # Check all values outside convex hull are NaNs
+        assert all(~np.isfinite(gridded_pc[i[~ind_inters_convhull], j[~ind_inters_convhull]]))
+
+        # 2.2/ For the rest of the points, data should be valid only if a point exists within 1 pixel of their
+        # coordinate, that is the closest rounded number
+        # TODO: Replace by check with distance, because some pixel not rounded can also be at less than 1 from a point
+
+        # Compute min distance to irregular point cloud for each grid point
+        list_min_dist = []
+        for p in rst_pc.geometry:
+            min_dist = np.min(np.sqrt((p.x - pc.geometry.x.values) ** 2 + (p.y - pc.geometry.y.values) ** 2))
+            list_min_dist.append(min_dist)
+
+        ind_close = np.array(list_min_dist) <= 1
+        # We get the indexes for these coordinates
+        iround, jround = rst.xy2ij(x=rst_pc.geometry.x.values[ind_close], y=rst_pc.geometry.y.values[ind_close])
+
+        # Keep only indexes in the convex hull
+        indexes_close = [(iround[k], jround[k]) for k in range(len(iround))]
+        indexes_chull = [(i[k], j[k]) for k in range(len(i)) if ind_inters_convhull[k]]
+        close_in_chull = [tup for tup in indexes_close if tup in indexes_chull]
+        iclosechull, jclosehull = list(zip(*close_in_chull))
+
+        # All values close  pixel in the convex hull should be valid
+        assert all(np.isfinite(gridded_pc[iclosechull, jclosehull]))
+
+        # Other values in the convex hull should not be
+        far_in_chull = [tup for tup in indexes_chull if tup not in indexes_close]
+        ifarchull, jfarchull = list(zip(*far_in_chull))
+
+        assert all(~np.isfinite(gridded_pc[ifarchull, jfarchull]))
+
+        # Check for a different distance value
+        gridded_pc = _grid_pointcloud(pc, grid_coords=grid_coords, dist_nodata_pixel=0.5)
+        ind_close = np.array(list_min_dist) <= 0.5
+
+        # We get the indexes for these coordinates
+        iround, jround = rst.xy2ij(x=rst_pc.geometry.x.values[ind_close], y=rst_pc.geometry.y.values[ind_close])
+
+        # Keep only indexes in the convex hull
+        indexes_close = [(iround[k], jround[k]) for k in range(len(iround))]
+        indexes_chull = [(i[k], j[k]) for k in range(len(i)) if ind_inters_convhull[k]]
+        close_in_chull = [tup for tup in indexes_close if tup in indexes_chull]
+        iclosechull, jclosehull = list(zip(*close_in_chull))
+
+        # All values close  pixel in the convex hull should be valid
+        assert all(np.isfinite(gridded_pc[iclosechull, jclosehull]))
+
+        # Other values in the convex hull should not be
+        far_in_chull = [tup for tup in indexes_chull if tup not in indexes_close]
+        ifarchull, jfarchull = list(zip(*far_in_chull))
+
+        assert all(~np.isfinite(gridded_pc[ifarchull, jfarchull]))