Adding 'Detecting Deep Moist Convection' to openeo-processes-dask (#263)

* add ddmc and test

* update pydantic dependencies

* Adding ddmc to openeo-processes-dask

* Changing the version in the pyproject and changing the import statement to the right folder

* Deleting the unused file example%20file.geoparquet

openeo_processes_dask/process_implementations/cubes/__init__.py

@@ -1,6 +1,7 @@
 from ._filter import *
 from .aggregate import *
 from .apply import *
+from .ddmc import *
 from .general import *
 from .indices import *
 from .load import *

openeo_processes_dask/process_implementations/cubes/ddmc.py

@@ -0,0 +1,86 @@
+from openeo_processes_dask.process_implementations.arrays import array_element
+from openeo_processes_dask.process_implementations.cubes.general import add_dimension
+from openeo_processes_dask.process_implementations.cubes.merge import merge_cubes
+from openeo_processes_dask.process_implementations.cubes.reduce import reduce_dimension
+from openeo_processes_dask.process_implementations.data_model import RasterCube
+
+__all__ = ["ddmc"]
+
+
+def ddmc(
+    data: RasterCube,
+    nir08="nir08",
+    nir09="nir09",
+    cirrus="cirrus",
+    swir16="swir16",
+    swir22="swir22",
+    gain=2.5,
+    target_band=None,
+):
+    dimension = data.openeo.band_dims[0]
+    if target_band is None:
+        target_band = dimension
+
+    # Mid-Level Clouds
+    def MIDCL(data):
+        # B08 = array_element(data, label=nir08, axis = axis)
+
+        B08 = data.sel(**{dimension: nir08})
+
+        # B09 = array_element(data, label=nir09, axis = axis)
+
+        B09 = data.sel(**{dimension: nir09})
+
+        MIDCL = B08 - B09
+
+        MIDCL_result = MIDCL * gain
+
+        return MIDCL_result
+
+    # Deep moist convection
+    def DC(data):
+        # B10 = array_element(data, label=cirrus, axis = axis)
+        # B12 = array_element(data, label=swir22, axis = axis)
+
+        B10 = data.sel(**{dimension: cirrus})
+        B12 = data.sel(**{dimension: swir22})
+
+        DC = B10 - B12
+
+        DC_result = DC * gain
+
+        return DC_result
+
+    # low-level cloudiness
+    def LOWCL(data):
+        # B10 = array_element(data, label=cirrus, axis = axis)
+        # B11 = array_element(data, label=swir16, axis = axis)
+        B10 = data.sel(**{dimension: cirrus})
+        B11 = data.sel(**{dimension: swir16})
+
+        LOWCL = B11 - B10
+
+        LOWCL_result = LOWCL * gain
+
+        return LOWCL_result
+
+    # midcl = reduce_dimension(data, reducer=MIDCL, dimension=dimension)
+    midcl = MIDCL(data)
+    midcl = add_dimension(midcl, name=target_band, label="midcl", type=dimension)
+
+    # dc = reduce_dimension(data, reducer=DC, dimension=dimension)
+    dc = DC(data)
+    # dc = add_dimension(dc, target_band, "dc")
+    dc = add_dimension(dc, target_band, label="dc", type=dimension)
+
+    # lowcl = reduce_dimension(data, reducer=LOWCL, dimension=dimension)
+    lowcl = LOWCL(data)
+    lowcl = add_dimension(lowcl, target_band, label="lowcl", type=dimension)
+
+    # ddmc = merge_cubes(merge_cubes(midcl, dc), lowcl)
+    ddmc1 = merge_cubes(midcl, lowcl)
+    ddmc1.openeo.add_dim_type(name=target_band, type=dimension)
+    ddmc = merge_cubes(dc, ddmc1, overlap_resolver=target_band)
+
+    # return a datacube
+    return ddmc

pyproject.toml

@@ -32,7 +32,7 @@ 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 }
+openeo-pg-parser-networkx = { version = ">=2024.7", optional = true }
 odc-geo = { version = ">=0.4.1,<1", optional = true }
 stac_validator = { version = ">=3.3.1", optional = true }
 stackstac = { version = ">=0.4.3", optional = true }

tests/test_ddmc.py

@@ -0,0 +1,75 @@
+from functools import partial
+
+import numpy as np
+import pytest
+import xarray as xr
+from openeo_pg_parser_networkx.pg_schema import (
+    BoundingBox,
+    ParameterReference,
+    TemporalInterval,
+)
+
+from openeo_processes_dask.process_implementations.cubes.ddmc import ddmc
+from openeo_processes_dask.process_implementations.cubes.load import load_stac
+from openeo_processes_dask.process_implementations.cubes.reduce import (
+    reduce_dimension,
+    reduce_spatial,
+)
+from openeo_processes_dask.process_implementations.exceptions import (
+    ArrayElementNotAvailable,
+)
+from tests.general_checks import general_output_checks
+from tests.mockdata import create_fake_rastercube
+
+
+@pytest.mark.parametrize("size", [(30, 30, 20, 5)])
+@pytest.mark.parametrize("dtype", [np.float32])
+def test_ddmc_instance_dims(
+    temporal_interval: TemporalInterval, bounding_box: BoundingBox, random_raster_data
+):
+    input_cube = create_fake_rastercube(
+        data=random_raster_data,
+        spatial_extent=bounding_box,
+        temporal_extent=temporal_interval,
+        bands=["nir08", "nir09", "cirrus", "swir16", "swir22"],
+        backend="dask",
+    )
+
+    data = ddmc(input_cube)
+
+    assert isinstance(data, xr.DataArray)
+    assert set(input_cube.dims) == set(data.dims)
+
+
+@pytest.mark.parametrize("size", [(30, 30, 20, 5)])
+@pytest.mark.parametrize("dtype", [np.float32])
+def test_ddmc_target_band(
+    temporal_interval: TemporalInterval, bounding_box: BoundingBox, random_raster_data
+):
+    input_cube = create_fake_rastercube(
+        data=random_raster_data,
+        spatial_extent=bounding_box,
+        temporal_extent=temporal_interval,
+        bands=["nir08", "nir09", "cirrus", "swir16", "swir22"],
+        backend="dask",
+    )
+
+    data_band = ddmc(data=input_cube, target_band="ddmc")
+    assert "ddmc" in data_band.dims
+
+
+@pytest.mark.parametrize("size", [(30, 30, 20, 5)])
+@pytest.mark.parametrize("dtype", [np.float32])
+def test_ddmc_input_cube_exception(
+    temporal_interval: TemporalInterval, bounding_box: BoundingBox, random_raster_data
+):
+    input_cube_exception = create_fake_rastercube(
+        data=random_raster_data,
+        spatial_extent=bounding_box,
+        temporal_extent=temporal_interval,
+        bands=["b04", "nir09", "cirrus", "swir16", "swir22"],
+        backend="dask",
+    )
+
+    with pytest.raises(KeyError):
+        data = ddmc(input_cube_exception)