Resample cube update (#300)

* draft: test

* fix resample cube spatial, not crop data

* pre-commit run

openeo_processes_dask/process_implementations/cubes/resample.py

@@ -43,6 +43,30 @@ def resample_spatial(
 ):
     """Resamples the spatial dimensions (x,y) of the data cube to a specified resolution and/or warps the data cube to the target projection. At least resolution or projection must be specified."""
 
+    if data.openeo.y_dim is None or data.openeo.x_dim is None:
+        raise DimensionMissing(f"Spatial dimension missing for dataset: {data} ")
+
+    methods_list = [
+        "near",
+        "bilinear",
+        "cubic",
+        "cubicspline",
+        "lanczos",
+        "average",
+        "mode",
+        "max",
+        "min",
+        "med",
+        "q1",
+        "q3",
+    ]
+
+    if method not in methods_list:
+        raise Exception(
+            f'Selected resampling method "{method}" is not available! Please select one of '
+            f"[{', '.join(methods_list)}]"
+        )
+
     # Assert resampling method is correct.
     if method == "near":
         method = "nearest"
@@ -53,18 +77,9 @@ def resample_spatial(
             f"[{', '.join(resample_methods_list)}]"
         )
 
-    dims = list(data.dims)
+    dim_order = data.dims
 
-    known_dims = []
-    if len(data.openeo.band_dims) > 0:
-        known_dims.append(data.openeo.band_dims[0])
-    if len(data.openeo.temporal_dims) > 0:
-        known_dims.append(data.openeo.temporal_dims[0])
-    known_dims += [data.openeo.y_dim, data.openeo.x_dim]
-
-    other_dims = [dim for dim in dims if dim not in known_dims]
-
-    data_cp = data.transpose(*other_dims, *known_dims)
+    data_cp = data.transpose(..., data.openeo.y_dim, data.openeo.x_dim)
 
     if projection is None:
         projection = data_cp.rio.crs
@@ -89,6 +104,8 @@ def resample_spatial(
     if reprojected.openeo.y_dim != data.openeo.y_dim:
         reprojected = reprojected.rename({reprojected.openeo.y_dim: data.openeo.y_dim})
 
+    reprojected = reprojected.transpose(*dim_order)
+
     reprojected.attrs["crs"] = data_cp.rio.crs
 
     return reprojected
@@ -97,67 +114,29 @@ def resample_spatial(
 def resample_cube_spatial(
     data: RasterCube, target: RasterCube, method="near", options=None
 ) -> RasterCube:
-    methods_list = [
-        "near",
-        "bilinear",
-        "cubic",
-        "cubicspline",
-        "lanczos",
-        "average",
-        "mode",
-        "max",
-        "min",
-        "med",
-        "q1",
-        "q3",
-    ]
-
-    if (
-        data.openeo.y_dim is None
-        or data.openeo.x_dim is None
-        or target.openeo.y_dim is None
-        or target.openeo.x_dim is None
-    ):
+    if target.openeo.y_dim is None or target.openeo.x_dim is None:
         raise DimensionMissing(
-            f"Spatial dimension missing from data or target. Available dimensions for data: {data.dims} for target: {target.dims}"
+            f"Spatial dimension missing for target dataset: {target} "
         )
 
-    # ODC reproject requires y to be before x
-    required_dim_order = (..., data.openeo.y_dim, data.openeo.x_dim)
-
-    data_reordered = data.transpose(*required_dim_order, missing_dims="ignore")
-    target_reordered = target.transpose(*required_dim_order, missing_dims="ignore")
-
-    if method == "near":
-        method = "nearest"
-
-    elif method not in methods_list:
-        raise Exception(
-            f'Selected resampling method "{method}" is not available! Please select one of '
-            f"[{', '.join(methods_list)}]"
-        )
-
-    resampled_data = data_reordered.odc.reproject(
-        target_reordered.odc.geobox, resampling=method
+    target_resolution, target_crs = None, None
+    if hasattr(target, "rio"):
+        if hasattr(target.rio, "resolution"):
+            if type(target.rio.resolution()) in [tuple, list]:
+                target_resolution = target.rio.resolution()[0]
+            else:
+                target_resolution = target.rio.resolution()
+        if hasattr(target.rio, "crs"):
+            target_crs = target.rio.crs
+    if not target_crs:
+        raise OpenEOException(f"Projection not found in target dataset: {target} ")
+    if not target_resolution:
+        raise OpenEOException(f"Resolution not found in target dataset: {target} ")
+
+    resampled_data = resample_spatial(
+        data=data, projection=target_crs, resolution=target_resolution, method=method
     )
 
-    resampled_data.rio.write_crs(target_reordered.rio.crs, inplace=True)
-
-    try:
-        # odc.reproject renames the coordinates according to the geobox, this undoes that.
-        resampled_data = resampled_data.rename(
-            {"longitude": data.openeo.x_dim, "latitude": data.openeo.y_dim}
-        )
-    except ValueError:
-        pass
-
-    # Order axes back to how they were before
-    resampled_data = resampled_data.transpose(*data.dims)
-
-    # Ensure that attrs except crs are copied over
-    for k, v in data.attrs.items():
-        if k.lower() != "crs":
-            resampled_data.attrs[k] = v
     return resampled_data
 
 

tests/test_resample.py

@@ -140,6 +140,48 @@ def test_resample_cube_spatial(
     assert output_cube.odc.spatial_dims == ("y", "x")
 
 
+@pytest.mark.parametrize(
+    "output_crs",
+    [
+        3587,
+        "32633",
+        "+proj=aeqd +lat_0=53 +lon_0=24 +x_0=5837287.81977 +y_0=2121415.69617 +datum=WGS84 +units=m +no_defs",
+    ],
+)
+@pytest.mark.parametrize("output_res", [5, 30, 60])
+@pytest.mark.parametrize("size", [(30, 30, 20, 4)])
+@pytest.mark.parametrize("dtype", [np.float32])
+def test_resample_cube_spatial_small(
+    output_crs, output_res, temporal_interval, bounding_box, random_raster_data
+):
+    """Test to ensure resolution gets changed correctly."""
+    input_cube = create_fake_rastercube(
+        data=random_raster_data,
+        spatial_extent=bounding_box,
+        temporal_extent=temporal_interval,
+        bands=["B02", "B03", "B04", "B08"],
+        backend="dask",
+    )
+
+    resampled_cube = resample_spatial(
+        data=input_cube, projection=output_crs, resolution=output_res
+    )
+
+    output_cube = resample_cube_spatial(
+        data=input_cube, target=resampled_cube[10:60, 20:150, :, :], method="average"
+    )
+
+    general_output_checks(
+        input_cube=input_cube,
+        output_cube=output_cube,
+        expected_dims=input_cube.dims,
+        verify_attrs=False,
+        verify_crs=False,
+    )
+
+    assert list(output_cube.shape) == list(resampled_cube.shape)
+
+
 @pytest.mark.parametrize("size", [(6, 5, 30, 4)])
 @pytest.mark.parametrize("dtype", [np.float64])
 @pytest.mark.parametrize(