fixed issue to load nonEO_feature list from file

src/eo_processing/openeo/preprocessing.py

@@ -150,7 +150,7 @@ def extract_S1_datacube(
     #       therefore, the integration has to be this ugly and not only in the final warper functions
     if get_NVBT:
         nvbt_band = bands.filter_bands(bands.metadata.band_names[0]).count_time().rename_labels("bands",
-                                                                                                ["S1-NVBT"])
+                                                                                                ["S1-NVBT".lower()])
         nvbt_band = nvbt_band.apply(lambda x: if_(x.is_nodata(), 0, x))
 
     # Linearly interpolate missing values if wished
@@ -319,7 +319,7 @@ def extract_S2_datacube(
     # Note: we want to create the NVBT here before any possible linear interpolation is done
     #       therefore, the integration has to be this ugly and not only in the final warper functions
     if get_NVBT:
-        nvbt_band = bands.filter_bands(S2_bands[0]).count_time().rename_labels("bands", ["S2-NVBT"])
+        nvbt_band = bands.filter_bands(S2_bands[0]).count_time().rename_labels("bands", ["S2-NVBT".lower()])
         nvbt_band = nvbt_band.apply(lambda x: if_(x.is_nodata(), 0, x))
 
     # Linearly interpolate missing values if wished

src/eo_processing/openeo/processing.py

@@ -245,7 +245,7 @@ def calculate_features_cube(input_data: DataCube, chunk_size: int = CHUNK_SIZE)
                                                         "TileSize": chunk_size})
     # adapt the band names
     new_band_names = [
-        band + "_" + stat
+        band.lower() + "_" + stat.lower()
         for band in input_data.metadata.band_names
         for stat in ["p2", "p5", "p25", "median", "p75", "p95", "p98", "mean", "sd", "sum", "iqr", "iqr0595"]
     ]
@@ -254,11 +254,16 @@ def calculate_features_cube(input_data: DataCube, chunk_size: int = CHUNK_SIZE)
 
     # remove some bands which make no sense :)
     # mainly from S2REP --> sd, sum, iqr
+    #bands_keep = [band for band in features_cube.metadata.band_names if
+    #              band not in ['S2REP_sd', 'S2REP_sum', 'S2REP_iqr', 'S2REP_iqr0595' , 'VV_sum', 'VH_sum', 'VHVVD_sum',
+    #                           'S2-CLOUD-MASK_p2', 'S2-CLOUD-MASK_p5', 'S2-CLOUD-MASK_p25', 'S2-CLOUD-MASK_median',
+    #                           'S2-CLOUD-MASK_p75','S2-CLOUD-MASK_p95', 'S2-CLOUD-MASK_p98', 'S2-CLOUD-MASK_mean',
+    #                           'S2-CLOUD-MASK_sd','S2-CLOUD-MASK_iqr','S2-CLOUD-MASK_iqr0595', 'S2-CLOUD-MASK_sum']]
     bands_keep = [band for band in features_cube.metadata.band_names if
-                  band not in ['S2REP_sd', 'S2REP_sum', 'S2REP_iqr', 'S2REP_iqr0595' , 'VV_sum', 'VH_sum', 'VHVVD_sum',
-                               'S2-CLOUD-MASK_p2', 'S2-CLOUD-MASK_p5', 'S2-CLOUD-MASK_p25', 'S2-CLOUD-MASK_median',
-                               'S2-CLOUD-MASK_p75','S2-CLOUD-MASK_p95', 'S2-CLOUD-MASK_p98', 'S2-CLOUD-MASK_mean',
-                               'S2-CLOUD-MASK_sd','S2-CLOUD-MASK_iqr','S2-CLOUD-MASK_iqr0595', 'S2-CLOUD-MASK_sum']]
+                  band not in ['s2rep_sd', 's2rep_sum', 's2rep_iqr', 's2rep_iqr0595' , 'vv_sum', 'vh_sum', 'vhvvd_sum',
+                               's2-could-mask_p2', 's2-could-mask_p5', 's2-could-mask_p25', 's2-could-mask_median',
+                               's2-could-mask_p75','s2-could-mask_p95', 's2-could-mask_p98', 's2-could-mask_mean',
+                               's2-could-mask_sd','s2-could-mask_iqr','s2-could-mask_iqr0595', 's2-could-mask_sum']]
 
     features_cube = features_cube.filter_bands(bands=bands_keep)
 
@@ -492,7 +497,7 @@ def generate_nonEO_feature_cube(
                 # reduce the temporal domain since copernicus_30 collection is "special" and feature only are one time stamp
                 nonEO_feature_cube = nonEO_feature_cube.reduce_dimension(dimension='t', reducer=lambda x: x.last(ignore_nodata=True))
 
-        new_bands = [f"{collection}-{band}" for band in bands]
+        new_bands = [f"{collection.lower()}-{band.lower()}" for band in bands]
 
 
         # resample the cube to 10m and EPSG of corresponding 20x20km grid tile

src/eo_processing/utils/storage.py

@@ -815,6 +815,7 @@ def GenericQueryWithResult(self, sql_statement: str) -> List[tuple]:
             print('** get data from request')
             # create cursor
             cur = conn.cursor()
+            cur.itersize = 10000
             cur.execute(sql_statement)
             # get data
             vResult = cur.fetchall()
@@ -1355,7 +1356,7 @@ def upload_collection_to_catalog(self, collection: pystac.Collection, edit_flag:
         #get the auth
         auth_token = self.get_bearer_auth()
         #get catalog_url
-        catalog_url = self.get_catalog_url()
+        catalog_url = self.get_catalog_url().rstrip('/')
 
         # load the collection from the created collection.  
         coll = collection.to_dict()
@@ -1371,7 +1372,7 @@ def upload_collection_to_catalog(self, collection: pystac.Collection, edit_flag:
             # upload a new collection
             collection_url = f"{catalog_url}/collections/"
             resp = post(collection_url, auth=auth_token, json=coll)
-        if resp.status_code == 201:
+        if resp.status_code == 200 or resp.status_code == 201:
             coll_id = resp.json()["id"]
             if edit_flag:
                 print(f"Collection edited: {coll_id}")
@@ -1401,7 +1402,7 @@ def upload_items_to_collection(self, collection: pystac.Collection,
         #get the auth
         auth_token = self.get_bearer_auth()
         #get catalog_url
-        catalog_url = self.get_catalog_url()
+        catalog_url = self.get_catalog_url().rstrip('/')
 
         # check if there are items that need to be uploaded only if update is False
         if len(items_to_upload) == 0: