update on nonEO_feature_cube, to also allow external stac's.

src/eo_processing/openeo/processing.py

@@ -443,17 +443,17 @@ def generate_nonEO_feature_cube(
         **processing_options: Dict[str, Union[str, bool, int | float, List[str], List[int | float]]]) -> DataCube:
 
     """ Warper to generate the data cube of all nonEO data based on a collections  list of the form [(collection, [band1, band2, ...])]"""
-    temporal_extent = [start, end]
-    temporal_extent = None
 
     chunk_size: int = processing_options.get("openeo_chunk_size", CHUNK_SIZE)
 
-    for collection, bands, reproj, year in collections_list:
+    for collection, bands, reproj, year, stac_url in collections_list:
         #first need to distinguish between STAC and collection
         #we assume that they will allways be an url type of link in contrary with a collections which should just be a name
-        if temporal_extent:
+        if year:
             temporal_extent = [f"{year}-01-01T00:00:00Z", f"{year}-12-31T23:59:59Z"]
-        STAC_url = get_stac_collection_url(collection)
+        else:
+            temporal_extent = None
+        STAC_url = get_stac_collection_url(collection, stac_url)
         isSTAC = STAC_url is not None
 
         #secondly we know there are some specific case of reprojection EG DEM should be bilinear iso near

src/eo_processing/utils/jobmanager.py

@@ -782,6 +782,7 @@ def create_job_dataframe(gdf: Union[gpd.GeoDataFrame, List], year: int, file_nam
                          discriminator: Optional[str] = None, target_crs: Optional[int] = None,
                          version: Optional[str] = None,
                          model_ID: Optional[str] = None,
+                         nonEO_file: Optional[str] = None,
                          storage_options: Optional[storage_option_format] = None,
                          organization_id : Optional[int] = None, path_global_grid: Optional[str] = None,
                          feature_bbox: Optional[Tuple[float, float, float, float]] = None) -> gpd.GeoDataFrame:
@@ -817,11 +818,11 @@ def create_job_dataframe(gdf: Union[gpd.GeoDataFrame, List], year: int, file_nam
     if isinstance(gdf, gpd.GeoDataFrame):
         # we are preparing a inference or post-processing actions
         columns = ['name', 'tileID', 'target_epsg', 'bbox', 'file_prefix', 'start_date', 'end_date','export_workspace',
-                   's3_prefix', 'organization_id', 's2_tileid_list']
+                   's3_prefix', 'organization_id', 's2_tileid_list','nonEO_file']
         dtypes = {'name': 'string', 'tileID': 'string', 'target_epsg': 'UInt16',
                   'file_prefix': 'string', 'start_date': 'string', 'end_date': 'string', 's3_prefix': 'string',
                   'geometry': 'geometry', 'bbox': 'string', 'organization_id':'UInt16','s2_tileid_list':'string',
-                  'export_workspace':'string'}
+                  'export_workspace':'string','nonEO_file' : 'string'}
 
         job_df = gdf.copy()
 
@@ -862,6 +863,8 @@ def create_job_dataframe(gdf: Union[gpd.GeoDataFrame, List], year: int, file_nam
             job_df['s3_prefix'] = None
             job_df['export_workspace'] = None
 
+        # set no EO list  Should be later replaced by an extraction method straight out of the model metadata.
+        job_df['nonEO_file'] = nonEO_file
         # a fix since the "name" column has to be unique
         job_df['tileID'] = job_df[tile_col].copy()
         if discriminator: