53 meteo dem extractor

src/openeo_gfmap/fetching/generic.py

@@ -0,0 +1,134 @@
+""" Generic extraction of features, supporting VITO backend.
+"""
+from functools import partial
+from typing import Callable
+
+import openeo
+from geojson import GeoJSON
+
+from openeo_gfmap.backend import Backend, BackendContext
+from openeo_gfmap.fetching import CollectionFetcher, FetchType
+from openeo_gfmap.fetching.commons import (
+    convert_band_names,
+    load_collection,
+    rename_bands,
+)
+from openeo_gfmap.spatial import SpatialContext
+from openeo_gfmap.temporal import TemporalContext
+
+BASE_DEM_MAPPING = {"DEM": "COP-DEM"}
+BASE_WEATHER_MAPPING = {
+    "dewpoint-temperature": "A5-dewtemp",
+    "precipitation-flux": "A5-precip",
+    "solar-radiation-flux": "A5-solrad",
+    "temperature-max": "A5-tmax",
+    "temperature-mean": "A5-tmean",
+    "temperature-min": "A5-tmin",
+    "vapour-pressure": "A5-vapour",
+    "wind-speed": "A5-wind",
+}
+
+
+def get_generic_fetcher(collection_name: str, fetch_type: FetchType) -> Callable:
+    if collection_name == "COPERNICUS_30":
+        BASE_MAPPING = BASE_DEM_MAPPING
+    elif collection_name == "AGERA5":
+        BASE_MAPPING = BASE_WEATHER_MAPPING
+    else:
+        raise Exception("Please choose a valid collection.")
+
+    def generic_default_fetcher(
+        connection: openeo.Connection,
+        spatial_extent: SpatialContext,
+        temporal_extent: TemporalContext,
+        bands: list,
+        **params,
+    ) -> openeo.DataCube:
+        bands = convert_band_names(bands, BASE_MAPPING)
+
+        cube = load_collection(
+            connection,
+            bands,
+            collection_name,
+            spatial_extent,
+            temporal_extent,
+            fetch_type,
+            **params,
+        )
+
+        # Apply if the collection is a GeoJSON Feature collection
+        if isinstance(spatial_extent, GeoJSON):
+            cube = cube.filter_spatial(spatial_extent)
+
+        return cube
+
+    return generic_default_fetcher
+
+
+def get_generic_processor(collection_name: str, fetch_type: FetchType) -> Callable:
+    """Builds the preprocessing function from the collection name as it stored
+    in the target backend.
+    """
+    if collection_name == "COPERNICUS_30":
+        BASE_MAPPING = BASE_DEM_MAPPING
+    elif collection_name == "AGERA5":
+        BASE_MAPPING = BASE_WEATHER_MAPPING
+    else:
+        raise Exception("Please choose a valid collection.")
+
+    def generic_default_processor(cube: openeo.DataCube, **params):
+        """Default collection preprocessing method for generic datasets.
+        This method renames bands and removes the time dimension in case the
+        requested dataset is DEM
+        """
+
+        cube = rename_bands(cube, BASE_MAPPING)
+
+        if collection_name == "COPERNICUS_30":
+            cube = cube.min_time()
+
+        return cube
+
+    return generic_default_processor
+
+
+OTHER_BACKEND_MAP = {
+    "AGERA5": {
+        Backend.TERRASCOPE: {
+            "fetch": partial(get_generic_fetcher, collection_name="AGERA5"),
+            "preprocessor": partial(get_generic_processor, collection_name="AGERA5"),
+        },
+        Backend.CDSE: {
+            "fetch": partial(get_generic_fetcher, collection_name="AGERA5"),
+            "preprocessor": partial(get_generic_processor, collection_name="AGERA5"),
+        },
+    },
+    "COPERNICUS_30": {
+        Backend.TERRASCOPE: {
+            "fetch": partial(get_generic_fetcher, collection_name="COPERNICUS_30"),
+            "preprocessor": partial(get_generic_processor, collection_name="COPERNICUS_30"),
+        },
+        Backend.CDSE: {
+            "fetch": partial(get_generic_fetcher, collection_name="COPERNICUS_30"),
+            "preprocessor": partial(get_generic_processor, collection_name="COPERNICUS_30"),
+        },
+    },
+}
+
+
+def build_generic_extractor(
+    backend_context: BackendContext,
+    bands: list,
+    fetch_type: FetchType,
+    collection_name: str,
+    **params,
+) -> CollectionFetcher:
+    """Creates a generic extractor adapted to the given backend. Currently only tested with VITO backend"""
+    backend_functions = OTHER_BACKEND_MAP.get(collection_name).get(backend_context.backend)
+
+    fetcher, preprocessor = (
+        backend_functions["fetch"](fetch_type=fetch_type),
+        backend_functions["preprocessor"](fetch_type=fetch_type),
+    )
+
+    return CollectionFetcher(backend_context, bands, fetcher, preprocessor, **params)

src/openeo_gfmap/fetching/s1.py

@@ -5,7 +5,6 @@
 
 import openeo
 from geojson import GeoJSON
-from openeo.processes import array_create, if_, is_nodata, power
 
 from openeo_gfmap.backend import Backend, BackendContext
 from openeo_gfmap.spatial import SpatialContext

src/openeo_gfmap/preprocessing/compositing.py

@@ -39,14 +39,12 @@ def max_ndvi_selection(ndvi: openeo.DataCube):
             size=[
                 {"dimension": "x", "unit": "px", "value": 1},
                 {"dimension": "y", "unit": "px", "value": 1},
-                {"dimension": "t", "value": period}
+                {"dimension": "t", "value": period},
             ],
-            overlap=[]
+            overlap=[],
         )
 
-        cube = cube.mask(mask=rank_mask).aggregate_temporal_period(
-            period, "first"
-        )
+        cube = cube.mask(mask=rank_mask).aggregate_temporal_period(period, "first")
 
     else:
         raise ValueError(

src/openeo_gfmap/preprocessing/sar.py

@@ -1,11 +1,13 @@
 """Routines to pre-process sar signals."""
 import openeo
-from openeo.processes import if_, power, is_nodata, array_create
+from openeo.processes import array_create, if_, is_nodata, power
 
-from openeo_gfmap import BackendContext, Backend
+from openeo_gfmap import Backend, BackendContext
 
 
-def compress_backscatter_uint16(backend_context: BackendContext, cube: openeo.DataCube) -> openeo.DataCube:
+def compress_backscatter_uint16(
+    backend_context: BackendContext, cube: openeo.DataCube
+) -> openeo.DataCube:
     """
     Scaling the bands from float32 power values to uint16 for memory optimization. The scaling
     casts the values from power to decibels and applies a linear scaling from 0 to 65534.

src/openeo_gfmap/spatial.py

@@ -42,7 +42,7 @@ def __iter__(self):
 
     def to_geometry(self) -> Polygon:
         return box(self.west, self.south, self.east, self.north)
-    
+
     def to_geojson(self) -> GeoJSON:
         return self.to_geometry().__geo_interface__
 

tests/test_openeo_gfmap/test_feature_extractors.py

@@ -12,7 +12,6 @@
     BackendContext,
     cdse_connection,
 )
-from openeo_gfmap.preprocessing.sar import compress_backscatter_uint16
 from openeo_gfmap.features import (
     PatchFeatureExtractor,
     apply_feature_extractor,
@@ -22,6 +21,7 @@
     build_sentinel1_grd_extractor,
     build_sentinel2_l2a_extractor,
 )
+from openeo_gfmap.preprocessing.sar import compress_backscatter_uint16
 
 SPATIAL_CONTEXT = BoundingBoxExtent(
     west=4.261,

tests/test_openeo_gfmap/test_s1_fetchers.py

@@ -21,9 +21,7 @@
     FetchType,
     build_sentinel1_grd_extractor,
 )
-from openeo_gfmap.preprocessing.sar import (
-    compress_backscatter_uint16
-)
+from openeo_gfmap.preprocessing.sar import compress_backscatter_uint16
 from openeo_gfmap.utils import (
     array_bounds,
     arrays_cosine_similarity,