chore(#124): fixed linting issues

src/fusets/openeo/mogpr_band_udf.py

@@ -0,0 +1,21 @@
+from pathlib import Path
+
+from openeo.metadata import CollectionMetadata
+
+
+def apply_metadata(metadata: CollectionMetadata, context: dict) -> CollectionMetadata:
+    raise Exception(metadata)
+    extra_bands = [Band(f"{x}_STD", None, None) for x in metadata.bands]
+    for band in extra_bands:
+        metadata = metadata.append_band(band)
+    return metadata
+
+
+def load_mogpr_bands_udf() -> str:
+    """
+    Loads an openEO udf that applies mogpr.
+    @return:
+    """
+    import os
+
+    return Path(os.path.realpath(__file__)).read_text()

src/fusets/openeo/services/helpers.py

@@ -1,6 +1,6 @@
 import json
 import os
-from typing import Union, Any
+from typing import Any, Union
 
 import openeo
 from openeo.api.process import Parameter

src/fusets/openeo/services/publish_mogpr_s1_s2.py

@@ -20,33 +20,25 @@ def execute_udf():
         "type": "Polygon",
         "coordinates": [
             [
-                [
-                    12.502373837196238,
-                    42.06404350608216
-                ],
-                [
-                    12.502124488464212,
-                    42.03089916587777
-                ],
-                [
-                    12.571692784699895,
-                    42.031269589226014
-                ],
-                [
-                    12.57156811033388,
-                    42.06663507169753
-                ],
-                [
-                    12.502373837196238,
-                    42.06404350608216
-                ]
+                [12.502373837196238, 42.06404350608216],
+                [12.502124488464212, 42.03089916587777],
+                [12.571692784699895, 42.031269589226014],
+                [12.57156811033388, 42.06663507169753],
+                [12.502373837196238, 42.06404350608216],
             ]
         ],
     }
     temp_ext = ["2023-01-01", "2023-12-31"]
     mogpr = connection.datacube_from_flat_graph(
-        generate_cube(connection=connection, s1_collection='RVI DESC', s1_smoothing_lambda=WHITTAKER_DEFAULT_SMOOTHING,
-                      s2_collection='NDVI', polygon=spat_ext, date=temp_ext).flat_graph())
+        generate_cube(
+            connection=connection,
+            s1_collection="RVI DESC",
+            s1_smoothing_lambda=WHITTAKER_DEFAULT_SMOOTHING,
+            s2_collection="NDVI",
+            polygon=spat_ext,
+            date=temp_ext,
+        ).flat_graph()
+    )
     mogpr.execute_batch(
         "./result_mogpr_s1_s2_outputs.nc",
         title=f"FuseTS - MOGPR S1 S2 - Local - Outputs - DESC",
@@ -78,12 +70,17 @@ def _load_s1_grd_bands(connection, polygon, date, bands, orbit_direction):
     :param orbit_direction: Orbit direction to use
     :return:
     """
-    s1_grd = connection.load_collection("SENTINEL1_GRD", spatial_extent=polygon, temporal_extent=date, bands=bands,
-                                        properties={
-                                            "sat:orbit_state": lambda orbit_state: orbit_state == orbit_direction,
-                                            "resolution": lambda x: eq(x, 'HIGH'),
-                                            "sar:instrument_mode": lambda x: eq(x, 'IW')
-                                        })
+    s1_grd = connection.load_collection(
+        "SENTINEL1_GRD",
+        spatial_extent=polygon,
+        temporal_extent=date,
+        bands=bands,
+        properties={
+            "sat:orbit_state": lambda orbit_state: orbit_state == orbit_direction,
+            "resolution": lambda x: eq(x, "HIGH"),
+            "sar:instrument_mode": lambda x: eq(x, "IW"),
+        },
+    )
     return s1_grd.mask_polygon(polygon)
 
 
@@ -235,29 +232,32 @@ def load_s1_collection(connection, collection, smoothing_lambda, polygon, date):
     for option in [
         {
             "label": "grd desc",
-            "function": _load_s1_grd_bands(connection=connection, polygon=polygon, date=date, bands=["VV", "VH"],
-                                           orbit_direction='DESCENDING'),
+            "function": _load_s1_grd_bands(
+                connection=connection, polygon=polygon, date=date, bands=["VV", "VH"], orbit_direction="DESCENDING"
+            ),
         },
         {
             "label": "grd asc",
-            "function": _load_s1_grd_bands(connection=connection, polygon=polygon, date=date, bands=["VV", "VH"],
-                                           orbit_direction='ASCENDING'),
+            "function": _load_s1_grd_bands(
+                connection=connection, polygon=polygon, date=date, bands=["VV", "VH"], orbit_direction="ASCENDING"
+            ),
+        },
+        {
+            "label": "rvi desc",
+            "function": _load_rvi(connection=connection, polygon=polygon, date=date, orbit_direction="DESCENDING"),
+        },
+        {
+            "label": "rvi asc",
+            "function": _load_rvi(connection=connection, polygon=polygon, date=date, orbit_direction="ASCENDING"),
         },
-        {"label": "rvi desc",
-         "function": _load_rvi(connection=connection, polygon=polygon, date=date, orbit_direction='DESCENDING')},
-        {"label": "rvi asc",
-         "function": _load_rvi(connection=connection, polygon=polygon, date=date, orbit_direction='ASCENDING')},
         {"label": "gamma0", "function": _load_gamma0(connection=connection, polygon=polygon, date=date)},
         {"label": "coherence", "function": _load_coherence(connection=connection, polygon=polygon, date=date)},
     ]:
         collections = _build_collection_graph(
             collection=collection, label=option["label"], callable=option["function"], reject=collections
         )
 
-    smoothed = generate_whittaker_cube(
-        input_cube=collections,
-        smoothing_lambda=smoothing_lambda
-    )
+    smoothed = generate_whittaker_cube(input_cube=collections, smoothing_lambda=smoothing_lambda)
     return smoothed
 
 
@@ -332,15 +332,22 @@ def generate_mogpr_s1_s2_udp(connection):
         "s2_collection", "S2 data collection to use for fusing the data", S2_COLLECTIONS[0], S2_COLLECTIONS
     )
     s1_smoothing_lambda = Parameter.number(
-        "s1_smoothing_lambda", "Smoothing factor (Whittaker) to smooth the S1 data (0 = no smoothing)",
-        WHITTAKER_DEFAULT_SMOOTHING
+        "s1_smoothing_lambda",
+        "Smoothing factor (Whittaker) to smooth the S1 data (0 = no smoothing)",
+        WHITTAKER_DEFAULT_SMOOTHING,
+    )
+    process = generate_cube(
+        connection=connection,
+        s1_collection=s1_collection,
+        s2_collection=s2_collection,
+        polygon=polygon,
+        date=date,
+        s1_smoothing_lambda=s1_smoothing_lambda,
     )
-    process = generate_cube(connection=connection, s1_collection=s1_collection, s2_collection=s2_collection,
-                            polygon=polygon, date=date, s1_smoothing_lambda=s1_smoothing_lambda)
     return publish_service(
         id="mogpr_s1_s2",
         summary="Integrates timeseries in data cube using multi-output gaussian "
-                "process regression with a specific focus on fusing S1 and S2 data.",
+        "process regression with a specific focus on fusing S1 and S2 data.",
         description=description,
         parameters=[
             polygon.to_dict(),

src/fusets/openeo/services/publish_whittaker.py

@@ -3,17 +3,16 @@
 
 from openeo import DataCube
 from openeo.api.process import Parameter
-from openeo.processes import apply_dimension, run_udf, ProcessBuilder
+from openeo.processes import ProcessBuilder, apply_dimension, run_udf
 
 from fusets.openeo import load_whittakker_udf
-from fusets.openeo.services.helpers import publish_service, read_description, get_context_value
+from fusets.openeo.services.helpers import get_context_value, publish_service, read_description
 
 WHITTAKER_DEFAULT_SMOOTHING = 10000
 
 
 def generate_whittaker_cube(
-        input_cube: Union[DataCube, ProcessBuilder, Parameter],
-        smoothing_lambda: Union[float, Parameter]
+    input_cube: Union[DataCube, ProcessBuilder, Parameter], smoothing_lambda: Union[float, Parameter]
 ):
     context = {"smoothing_lambda": get_context_value(smoothing_lambda)}
     return apply_dimension(
@@ -28,19 +27,17 @@ def generate_whittaker_udp():
 
     input_cube = Parameter.raster_cube()
     lambda_param = Parameter.number(
-        name="smoothing_lambda", default=WHITTAKER_DEFAULT_SMOOTHING,
-        description="Lambda parameter to change the Whittaker smoothing"
+        name="smoothing_lambda",
+        default=WHITTAKER_DEFAULT_SMOOTHING,
+        description="Lambda parameter to change the Whittaker smoothing",
     )
 
-    process = generate_whittaker_cube(
-        input_cube=input_cube,
-        smoothing_lambda=lambda_param
-    )
+    process = generate_whittaker_cube(input_cube=input_cube, smoothing_lambda=lambda_param)
 
     return publish_service(
         id="whittaker",
         summary="Execute a computationally efficient reconstruction method for "
-                "smoothing and gap-filling of time series.",
+        "smoothing and gap-filling of time series.",
         description=description,
         parameters=[input_cube.to_dict(), lambda_param.to_dict()],
         process_graph=process,

src/fusets/temporal_outliers.py

@@ -67,4 +67,4 @@ def temporal_outliers_f(x: Sequence[datetime], y: np.ndarray, window: Union[int,
     ts_zscore = timeseries.sub(ts_mean).div(ts_std)
     ts_mask = ts_zscore.between(-threshold, threshold)
 
-    return timeseries.where(ts_mask, ts_mean).to_numpy(dtype='float32')
+    return timeseries.where(ts_mask, ts_mean).to_numpy(dtype="float32")

tests/conftest.py

@@ -71,12 +71,13 @@ def generate_data(xs: np.array):
 @pytest.fixture
 def outlier_timeseries():
     rng = np.random.default_rng(42)
-    dates = pd.date_range('2019-01-01', '2019-12-31', periods=300)
-    values = np.sin(np.linspace(0, 4*np.pi, len(dates))) + rng.random(len(dates))*0.2
+    dates = pd.date_range("2019-01-01", "2019-12-31", periods=300)
+    values = np.sin(np.linspace(0, 4 * np.pi, len(dates))) + rng.random(len(dates)) * 0.2
     values[rng.choice(range(len(dates)), 4).astype(int)] += rng.choice([-1, 1], 4) * 5
 
     return xarray.DataArray(data=values, dims=["time"], coords=dict(time=dates))
 
+
 @pytest.fixture
 def areas():
     return {

tests/fusets_openeo_tests/conftest.py

@@ -9,18 +9,15 @@
 
 @pytest.fixture
 def benchmark_features():
-    aoi_dir = RESOURCES / 'aois'
-    geojson_files = [file for file in os.listdir(aoi_dir) if file.endswith('.geojson')]
+    aoi_dir = RESOURCES / "aois"
+    geojson_files = [file for file in os.listdir(aoi_dir) if file.endswith(".geojson")]
     result = []
     for file in geojson_files:
-        with open(aoi_dir / file, 'r') as input:
+        with open(aoi_dir / file, "r") as input:
             data = json.load(input)
-            aois = data['features']
+            aois = data["features"]
             for feature in aois:
-                feature["properties"] = {
-                    **feature["properties"],
-                    "jobname": file.split('.')[0]
-                }
+                feature["properties"] = {**feature["properties"], "jobname": file.split(".")[0]}
             result += aois
             input.close()
     return gpd.GeoDataFrame.from_features(result)