update dependencies

.github/workflows/main.yml

@@ -23,7 +23,7 @@ jobs:
     strategy:
       matrix:
         os: [Ubuntu]
-        python-version: ["3.9", "3.10", "3.11"]
+        python-version: ["3.10", "3.11"]
         include:
           - os: Ubuntu
             image: ubuntu-22.04

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "openeo-processes-dask"
-version = "2024.5.0"
+version = "2024.5.1"
 description = "Python implementations of many OpenEO processes, dask-friendly by default."
 authors = ["Lukas Weidenholzer <lukas.weidenholzer@eodc.eu>", "Sean Hoyal <sean.hoyal@eodc.eu>", "Valentina Hutter <valentina.hutter@eodc.eu>"]
 maintainers = ["EODC Staff <support@eodc.eu>"]
@@ -23,22 +23,22 @@ packages = [
 ]
 
 [tool.poetry.dependencies]
-python = ">=3.9,<3.12"
-geopandas = { version = ">=0.11.1,<1", optional = true }
+python = ">=3.10,<3.12"
+geopandas = { version = "^0.13.0", optional = true }
 xarray = { version = ">=2022.11.0", optional = true }
 dask = {extras = ["array", "dataframe"], version = ">=2023.4.0", optional = true}
 rasterio = { version = "^1.3.4", optional = true }
 dask-geopandas = { version = ">=0.2.0,<1", optional = true }
 xgboost = { version = ">=1.5.1", optional = true }
 rioxarray = { version = ">=0.12.0,<1", optional = true }
 openeo-pg-parser-networkx = { version = ">=2023.5.1", optional = true }
-odc-geo = { version = ">=0.4.1,<1", optional = true }
+odc-geo = { version = "^0.4.3", optional = true }
 stac_validator = { version = ">=3.3.1", optional = true }
 stackstac = { version = ">=0.4.3", optional = true }
 pystac_client = { version = ">=0.6.1", optional = true }
 planetary_computer = { version = ">=0.5.1", optional = true }
 scipy = "^1.11.3"
-xvec = { version = ">=0.1.0", optional = true }
+xvec = { version = "0.2.0", optional = true }
 joblib = { version = ">=1.3.2", optional = true }
 geoparquet = "^0.0.3"
 pyarrow = "^15.0.2"

tests/test_aggregate.py

@@ -95,99 +95,3 @@ def test_aggregate_temporal_period_numpy_equals_dask(
     assert_numpy_equals_dask_numpy(
         numpy_cube=numpy_cube, dask_cube=dask_cube, func=func
     )
-
-
-@pytest.mark.parametrize("size", [(30, 30, 30, 3)])
-@pytest.mark.parametrize("dtype", [np.int8])
-def test_aggregate_spatial(
-    random_raster_data,
-    bounding_box,
-    temporal_interval,
-    polygon_geometry_small,
-    process_registry,
-):
-    input_cube = create_fake_rastercube(
-        data=random_raster_data,
-        spatial_extent=bounding_box,
-        temporal_extent=temporal_interval,
-        bands=["B02", "B03", "B04"],
-        backend="dask",
-    )
-
-    reducer = partial(
-        process_registry["mean"].implementation,
-        data=ParameterReference(from_parameter="data"),
-    )
-
-    output_cube = aggregate_spatial(
-        data=input_cube, geometries=polygon_geometry_small, reducer=reducer
-    )
-
-    assert len(output_cube.dims) < len(input_cube.dims)
-
-    _process = partial(
-        process_registry["median"].implementation,
-        ignore_nodata=True,
-        data=ParameterReference(from_parameter="data"),
-    )
-
-    reduced_cube = reduce_dimension(data=input_cube, reducer=_process, dimension="t")
-
-    output_cube = aggregate_spatial(
-        data=reduced_cube, geometries=polygon_geometry_small, reducer=reducer
-    )
-
-    assert len(output_cube.dims) < len(reduced_cube.dims)
-
-    gdf = gpd.GeoDataFrame.from_features(polygon_geometry_small, crs="EPSG:4326")
-    xmin, ymin, xmax, ymax = gdf.total_bounds
-
-    expected_values = (
-        reduced_cube.sel(x=slice(xmin, xmax), y=slice(ymin, ymax))
-        .mean(["x", "y"])
-        .values
-    )
-
-    assert (output_cube.values == expected_values).all()
-
-    gdf = gpd.GeoDataFrame.from_features(polygon_geometry_small, crs="EPSG:4326")
-    gdf_equi7 = gdf.to_crs(
-        "+proj=aeqd +lat_0=53 +lon_0=24 +x_0=5837287.81977 +y_0=2121415.69617 +datum=WGS84 +units=m +no_defs"
-    )
-    output_cube_transform = aggregate_spatial(
-        data=reduced_cube, geometries=gdf_equi7, reducer=reducer
-    )
-    assert len(output_cube_transform.dims) == len(output_cube.dims)
-    assert output_cube_transform.shape == output_cube.shape
-
-    geometry_cube = xr.Dataset(
-        data_vars={"variable": (["geometry"], np.arange(len(gdf)))},
-        coords={"geometry": gdf["geometry"].values},
-    ).xvec.set_geom_indexes("geometry", crs=gdf.crs)
-    output_cube_transform = aggregate_spatial(
-        data=reduced_cube, geometries=geometry_cube, reducer=reducer
-    )
-    assert len(output_cube_transform.dims) == len(output_cube.dims)
-    assert output_cube_transform.shape == output_cube.shape
-
-    polygon_geometry_small["crs"] = 4326
-
-    output_cube = aggregate_spatial(
-        data=reduced_cube, geometries=polygon_geometry_small, reducer=reducer
-    )
-
-    assert len(output_cube.dims) < len(reduced_cube.dims)
-
-    geometry_url = "https://raw.githubusercontent.com/ValentinaHutter/polygons/master/polygons_small.json"
-    output_cube = aggregate_spatial(
-        data=reduced_cube, geometries=geometry_url, reducer=reducer
-    )
-
-    assert len(output_cube.geometry) == 38
-
-    geometry = {"type": "Polygon", "coordinates": [[[0, 0], [0, 1], [1, 1], [1, 0]]]}
-    output_cube = aggregate_spatial(
-        data=reduced_cube, geometries=geometry, reducer=reducer
-    )
-
-    assert np.isnan(output_cube.values).all()