feat: add governed Mol nature value layer

backend/app/services/vector_feature_service.py

@@ -24,6 +24,7 @@ FULL_AREA_CLIPPED_OPERATOR_TOOLS = {
     "provision_regional_grb_buildings.py",
     "provision_regional_grb_context.py",
     "provision_waterinfo_station_history.py",
+    "provision_mol_bwk_natura2000.py",
 }
 
 
@@ -84,6 +85,7 @@ SEMANTIC_SELECTION_METRICS: dict[str, tuple[dict[str, Any], ...]] = {
             "warning": "GRB-percelen zijn een grafische referentie en vormen geen juridische grensopmeting.",
         },
     ),
+    "nature_value": (),
 }
 
 SEMANTIC_COUNT_LABELS = {
@@ -93,6 +95,7 @@ SEMANTIC_COUNT_LABELS = {
     "water": "Waterobjecten",
     "roads": "Wegsegmenten",
     "parcels": "Percelen",
+    "nature_value": "BWK-kaartvlakken",
 }
 
 
@@ -114,6 +117,10 @@ class VectorFeatureService:
             "waterways": "water",
             "road": "roads",
             "parcel": "parcels",
+            "nature": "nature_value",
+            "biodiversity": "nature_value",
+            "bwk": "nature_value",
+            "natura2000": "nature_value",
         }
         for candidate in candidates:
             if not isinstance(candidate, str) or not candidate.strip():
@@ -356,6 +363,17 @@ class VectorFeatureService:
             primary_config = semantic_metrics[0]
 
         metric_configs = [primary_config]
+        configured_metrics = source_metadata.get("selection_metrics")
+        if isinstance(configured_metrics, list):
+            existing_metric_keys = {str(primary_config.get("metric_key") or "")}
+            for configured_item in configured_metrics:
+                if not isinstance(configured_item, dict):
+                    continue
+                metric_key = str(configured_item.get("metric_key") or "").strip()
+                if not metric_key or metric_key in existing_metric_keys:
+                    continue
+                metric_configs.append(dict(configured_item))
+                existing_metric_keys.add(metric_key)
         for semantic_metric in semantic_metrics:
             signature = (semantic_metric["method"], semantic_metric["unit"])
             existing = {
@@ -419,6 +437,20 @@ class VectorFeatureService:
         metric_filter = selection_filter
         if dimension in {1, 2}:
             metric_filter += (func.ST_Dimension(VectorFeature.geometry) == int(dimension),)
+        filter_property = str(config.get("filter_property") or "").strip()
+        filter_values = config.get("filter_values")
+        if filter_property:
+            if not isinstance(filter_values, list) or not filter_values:
+                raise AppError(
+                    code="INVALID_SELECTION_AGGREGATION",
+                    message="Dataset selection metric filter requires one or more values",
+                    details={"dataset_id": str(dataset.id), "filter_property": filter_property},
+                    status_code=500,
+                )
+            normalized_filter_values = [str(value) for value in filter_values]
+            metric_filter += (
+                VectorFeature.properties_json.op("->>")(filter_property).in_(normalized_filter_values),
+            )
 
         if method == "intersection_area":
             measured_geometry = (
@@ -461,7 +493,7 @@ class VectorFeatureService:
             aggregate_function = func.avg if method == "mean" else func.sum
             aggregate_value = (
                 db.query(func.coalesce(aggregate_function(value_expression), 0.0))
-                .filter(*selection_filter)
+                .filter(*metric_filter)
                 .filter(VectorFeature.properties_json.op("->>")(property_name).isnot(None))
                 .scalar()
             )
@@ -469,16 +501,16 @@ class VectorFeatureService:
             if method == "area_weighted_sum" and not full_dataset_area:
                 partial_feature_count = (
                     db.query(func.count(VectorFeature.id))
-                    .filter(*selection_filter)
+                    .filter(*metric_filter)
                     .filter(coverage_ratio < 0.999999)
                     .scalar()
                 )
-                is_estimate = bool(partial_feature_count)
+                is_estimate = bool(config.get("is_estimate", False)) or bool(partial_feature_count)
                 if not is_estimate and config.get("warning_only_when_estimate", True):
                     warning = None
             elif method == "area_weighted_sum":
-                is_estimate = False
-                if config.get("warning_only_when_estimate", True):
+                is_estimate = bool(config.get("is_estimate", False))
+                if not is_estimate and config.get("warning_only_when_estimate", True):
                     warning = None
         elif method != "feature_count":
             raise AppError(