Issue #16 cleanup trailing spaces

src/openeo_gfmap/fetching/s1.py

@@ -94,7 +94,7 @@ def get_s1_grd_default_processor(
     def s1_grd_default_processor(cube: openeo.DataCube, **params):
         """Default collection preprocessing method.
         This method performs:
-        
+
         * Compute the backscatter of all the S1 products. By default, the
         "sigma0-ellipsoid" method is used with "COPERNICUS_30" DEM, but those
         can be changed by specifying "coefficient" and "elevation_model" in
@@ -115,7 +115,7 @@ def s1_grd_default_processor(cube: openeo.DataCube, **params):
         cube = resample_reproject(
             cube,
             params.get("target_resolution", 10.0),
-            params.get("target_crs", None) 
+            params.get("target_crs", None)
         )
 
         cube = rename_bands(cube, BASE_SENTINEL1_GRD_MAPPING)

src/openeo_gfmap/preprocessing/cloudmasking.py

@@ -70,7 +70,7 @@ def get_bap_score(cube: openeo.DataCube, **params: dict) -> openeo.DataCube:
     * Coverage Score: Per date, the percentage of all pixels that are classified
       as a cloud over the entire spatial extent is calculated. The Coverage
       Score is then equal to 1 - the cloud percentage.
-    * Date Score: In order to favor pixels that are observed in the middle of a 
+    * Date Score: In order to favor pixels that are observed in the middle of a
       month, a date score is calculated, which follows a Gaussian shape. I.e.
       the largest scores are given for days in the middle of the month, the
       lowest scores are given for days at the beginning and end of the month.

src/openeo_gfmap/preprocessing/udf_rank.py

@@ -31,7 +31,7 @@ def select_maximum(score: xr.DataArray):
     elif isinstance(intervals, list):
         # Convert YYYY-mm-dd to datetime64 objects
         time_bins = [np.datetime64(interval[0]) for interval in intervals]
-        
+
         rank_mask = bap_score.groupby_bins('t', bins=time_bins).map(
             select_maximum
         )

src/openeo_gfmap/utils/catalogue.py

@@ -46,7 +46,7 @@ def _check_cdse_catalogue(
     # The date format should be YYYY-MM-DD
     start_date = f'{temporal_extent.start_date}T00:00:00Z'
     end_date = f'{temporal_extent.end_date}T00:00:00Z'
-    
+
     url = (
         f"https://catalogue.dataspace.copernicus.eu/resto/api/collections/"
         f"{collection}/search.json?box={minx},{miny},{maxx},{maxy}"
@@ -69,4 +69,4 @@ def _check_cdse_catalogue(
         filter(lambda feature: feature["properties"]["productType"].contains("GRD"), body["features"])
     )
 
-    return len(grd_tiles) > 0 
+    return len(grd_tiles) > 0

src/openeo_gfmap/utils/intervals.py

@@ -1,4 +1,4 @@
-"""Utilitary function for intervals, useful for temporal aggregation 
+"""Utilitary function for intervals, useful for temporal aggregation
 methods.
 """
 
@@ -27,7 +27,7 @@ def quintad_intervals(temporal_extent: TemporalContext) -> list:
         offset = (start_date - timedelta(days=1)).day % 5
         current_date = current_date - timedelta(days=offset)
     else:
-        offset = 0    
+        offset = 0
 
     while current_date <= end_date:
         # Get the last day of the current month

src/openeo_gfmap/utils/tile_processing.py

@@ -15,7 +15,7 @@ def normalize_array(
     minimum = inarr.min(dim=["x", "y", "t"])
 
     inarr = (inarr - minimum) / (quantile_value - minimum)
-    
+
     # Perform clipping on values that are higher than the computed quantile
     return inarr.where(inarr < 1.0, 1.0)
 
@@ -60,7 +60,7 @@ def arrays_cosine_similarity(
 ) -> float:
     """Returns a similarity score based on normalized cosine distance. The
     input arrays must have similar ranges to obtain a valid score.
-    1.0 represents the best score (same tiles), while 0.0 is the worst score. 
+    1.0 represents the best score (same tiles), while 0.0 is the worst score.
     """
     dot_product = np.sum(first_array * second_array)
     first_norm = np.linalg.norm(first_array)

tests/test_openeo_gfmap/test_cloud_masking.py

@@ -59,7 +59,7 @@ def test_bap_score(backend: Backend):
     # Compute the BAP score
     bap_score = get_bap_score(cube, **preprocessing_parameters)
     ndvi = cube.ndvi(nir="S2-B08", red="S2-B04")
-    
+
     cube = bap_score.merge_cubes(ndvi).rename_labels(
         'bands', ['S2-BAPSCORE', 'S2-NDVI']
     )
@@ -70,7 +70,7 @@ def test_bap_score(backend: Backend):
     )
 
     job.start_and_wait()
-    
+
     for asset in job.get_results().get_assets():
         if asset.metadata["type"].startswith("application/x-netcdf"):
             asset.download(
@@ -101,7 +101,7 @@ def test_bap_masking(backend: Backend):
 
     cube = cube.linear_scale_range(0, 65535, 0, 65535)
 
-    # Perform masking with BAP, masking optical bands 
+    # Perform masking with BAP, masking optical bands
     cube = bap_masking(cube, period="dekad")
 
     # Perform compositing, the cube should be only composed of optical bands
@@ -137,7 +137,7 @@ def test_bap_quintad(backend: Backend):
         "fetching_resolution": 10.0
     }
     preprocessing_parameters = {
-        "apply_scl_dilation": True 
+        "apply_scl_dilation": True
     }
 
     # Fetch the datacube

tests/test_openeo_gfmap/test_intervals.py

@@ -75,7 +75,7 @@ def test_quintad_four_months():
     end_date = "2023-04-30"
 
     temporal_extent = TemporalContext(start_date, end_date)
-    
+
     expected = [
         ("2023-01-01", "2023-01-05"),
         ("2023-01-06", "2023-01-10"),

tests/test_openeo_gfmap/test_s1_fetchers.py

@@ -131,7 +131,7 @@ def compare_sentinel1_tiles():
                     dtype = array.dtype
                 else:
                     assert dtype == array.dtype
-            
+
             bounds = None
             for tile in loaded_tiles:
                 tile_bounds = array_bounds(tile)
@@ -202,7 +202,7 @@ def sentinel1_grd_point_based(
                 if band in col:
                     exists = True
             assert exists, f"Couldn't find a single column for band {band}"
-        
+
         assert len(df.columns) % len(bands) == 0, (
             f"The number of columns ({len(df.columns)}) should be a multiple"
             f"of the number of bands ({len(bands)})"