Fix linting

src/openeo_gfmap/inference/model_inference.py

@@ -116,11 +116,14 @@ def execute(self, inarr: xr.DataArray) -> xr.DataArray:
 
         # Load the model and the input_name parameters
         session = self.load_ort_session(self._parameters.get("model_url"))
-        
+
         input_name = self._parameters.get("input_name")
         if input_name is None:
             input_name = session.get_inputs()[0].name
-            udf_inspect(message=f"Input name not defined. Using name of parameters from the model session: {input_name}.", level="warning")
+            udf_inspect(
+                message=f"Input name not defined. Using name of parameters from the model session: {input_name}.",
+                level="warning",
+            )
 
         # Run the model inference on the input data
         input_data = inarr.values.astype(np.float32)
@@ -132,18 +135,12 @@ def execute(self, inarr: xr.DataArray) -> xr.DataArray:
         # Make the prediction
         output = self.apply_ml(input_data, session, input_name)
 
-        output = output.reshape(
-            len(self.output_labels()), height, width
-        )
+        output = output.reshape(len(self.output_labels()), height, width)
 
         return xr.DataArray(
             output,
             dims=["bands", "y", "x"],
-            coords={
-                "bands": self.output_labels(),
-                "x": inarr.x,
-                "y": inarr.y
-            }
+            coords={"bands": self.output_labels(), "x": inarr.x, "y": inarr.y},
         )
 
 

src/openeo_gfmap/preprocessing/cloudmasking.py

@@ -184,8 +184,7 @@ def max_score_selection(score):
     elif isinstance(period, list):
         udf_path = Path(__file__).parent / "udf_rank.py"
         rank_mask = bap_score.add_dimension(
-            name="bands",
-            label=BAPSCORE_HARMONIZED_NAME
+            name="bands", label=BAPSCORE_HARMONIZED_NAME
         ).apply_neighborhood(
             process=openeo.UDF.from_file(str(udf_path), context={"intervals": period}),
             size=[

src/openeo_gfmap/preprocessing/sar.py

@@ -0,0 +1,13 @@
+"""Routines to pre-process sar signals."""
+import openeo
+
+
+def compress_backscatter_uint16():
+    pass
+
+
+def multitemporal_speckle(cube: openeo.DataCube) -> openeo.DataCube:
+    _ = cube.filter_bands(
+        bands=filter(lambda band: band.startswith("S1"), cube.metadata.band_names)
+    )
+    pass

tests/test_openeo_gfmap/test_cloud_masking.py

@@ -122,13 +122,12 @@ def test_bap_quintad(backend: Backend):
     # Fetch the datacube
     s2_extractor = build_sentinel2_l2a_extractor(
         backend_context=backend_context,
-        bands=["S2-L2A-SCL"],
+        bands=["S2-L2A-B04", "S2-L2A-SCL"],
         fetch_type=FetchType.TILE,
         **fetching_parameters,
     )
 
     cube = s2_extractor.get_cube(connection, spatial_extent, temporal_extent)
-
     compositing_intervals = quintad_intervals(temporal_extent)
 
     expected_intervals = [

tests/test_openeo_gfmap/test_model_inference.py

@@ -1,32 +1,36 @@
 """Test on model inference implementations, both local and remote."""
 from pathlib import Path
 
-import xarray as xr
 import numpy as np
-
+import xarray as xr
 from openeo.udf import XarrayDataCube
 
-from openeo_gfmap import BoundingBoxExtent, TemporalContext, FetchType, Backend, BackendContext
-from openeo_gfmap.fetching.s2 import build_sentinel2_l2a_extractor
+from openeo_gfmap import (
+    Backend,
+    BackendContext,
+    BoundingBoxExtent,
+    FetchType,
+    TemporalContext,
+)
 from openeo_gfmap.backend import cdse_connection
+from openeo_gfmap.fetching.s2 import build_sentinel2_l2a_extractor
+from openeo_gfmap.inference.model_inference import (
+    ONNXModelInference,
+    apply_model_inference,
+)
 from openeo_gfmap.preprocessing.cloudmasking import mask_scl_dilation
 from openeo_gfmap.preprocessing.compositing import median_compositing
-from openeo_gfmap.inference.model_inference import ONNXModelInference, apply_model_inference, apply_model_inference_local
 
-
-spatial_context = BoundingBoxExtent(
-    west=5.0,
-    south=51.2,
-    east=5.025,
-    north=51.225,
-    epsg=4326
-)
+spatial_context = BoundingBoxExtent(west=5.0, south=51.2, east=5.025, north=51.225, epsg=4326)
 
 temporal_extent = TemporalContext(start_date="2018-05-01", end_date="2018-10-31")
 
-onnx_model_url = "https://artifactory.vgt.vito.be/artifactory/auxdata-public/gfmap/knn_model_rgbnir.onnx"
+onnx_model_url = (
+    "https://artifactory.vgt.vito.be/artifactory/auxdata-public/gfmap/knn_model_rgbnir.onnx"
+)
 dependency_url = "https://artifactory.vgt.vito.be/artifactory/auxdata-public/openeo/onnx_dependencies_1.16.3.zip"
 
+
 def test_onnx_inference_local():
     """Test the ONNX Model inference locally"""
     inds = xr.open_dataarray(Path(__file__).parent / "resources/test_inference_feats.nc")
@@ -41,8 +45,8 @@ def test_onnx_inference_local():
             "model_url": onnx_model_url,
             "input_name": "X",
             "output_labels": ["label"],
-            "GEO-EPSG": "whatever"
-        }
+            "GEO-EPSG": "whatever",
+        },
     )
 
     output = output.get_array()
@@ -80,9 +84,9 @@ def test_onnx_inference():
     cube = median_compositing(cube, period="year")
 
     # We remove the SCL mask
-    cube = cube.filter_bands(bands=['S2-L2A-B04', 'S2-L2A-B03', 'S2-L2A-B02', 'S2-L2A-B08'])
+    cube = cube.filter_bands(bands=["S2-L2A-B04", "S2-L2A-B03", "S2-L2A-B02", "S2-L2A-B08"])
 
-    cube = cube.ndvi(nir='S2-L2A-B08', red='S2-L2A-B04', target_band='S2-L2A-NDVI')
+    cube = cube.ndvi(nir="S2-L2A-B08", red="S2-L2A-B04", target_band="S2-L2A-NDVI")
 
     # Perform model inference
     cube = apply_model_inference(
@@ -96,7 +100,7 @@ def test_onnx_inference():
         size=[
             {"dimension": "x", "unit": "px", "value": 128},
             {"dimension": "y", "unit": "px", "value": 128},
-            {"dimension": "t", "value": 1}
+            {"dimension": "t", "value": 1},
         ],
     )
 
@@ -107,21 +111,19 @@ def test_onnx_inference():
         title="test_onnx_inference",
         out_format="GTiff",
         job_options={
-            "udf-dependency-archives": [
-                f"{dependency_url}#onnx_deps"
-            ],
-        }
+            "udf-dependency-archives": [f"{dependency_url}#onnx_deps"],
+        },
     )
     job.start_and_wait()
 
     for asset in job.get_results().get_assets():
         if asset.metadata["type"].startswith("application/x-netcdf"):
             asset.download(output_path)
             break
-    
+
     assert output_path.exists()
 
-    inds = xr.open_dataset(output_path).to_array(dim='bands')
+    inds = xr.open_dataset(output_path).to_array(dim="bands")
 
     assert inds.shape == (1, 256, 256)
     assert len(np.unique(inds.values)) == 3