fix(map): make area analysis scale-aware

backend/tests/test_selection_partition_analysis.py

@@ -0,0 +1,103 @@
+from __future__ import annotations
+
+from uuid import uuid4
+
+import pytest
+from pydantic import ValidationError
+
+from app.api.routes.selection_partitions import select_vector_partitions
+from app.core.errors import AppError
+from app.models import Dataset
+from app.schemas.selection_partitions import VectorPartitionSelectionRequest
+from app.services.vector_feature_service import VectorFeatureService
+
+
+class DatasetQuery:
+    def __init__(self, datasets):
+        self.datasets = datasets
+
+    def filter(self, *_args):
+        return self
+
+    def all(self):
+        return self.datasets
+
+
+class DatasetSession:
+    def __init__(self, datasets):
+        self.datasets = datasets
+
+    def query(self, model):
+        assert model is Dataset
+        return DatasetQuery(self.datasets)
+
+
+def make_dataset(project_id, dataset_id, *, source_name="grb", product_key="buildings"):
+    return Dataset(
+        id=dataset_id,
+        project_id=project_id,
+        name=f"{source_name}-{product_key}",
+        dataset_type="vector",
+        source="official",
+        dataset_role="reference",
+        source_name=source_name,
+        reference_layer_name=product_key,
+        source_metadata={"product_key": product_key, "theme": product_key},
+        provenance_metadata={},
+        metadata_json={},
+        status="ready",
+    )
+
+
+def test_vector_partition_route_combines_one_governed_product(monkeypatch) -> None:
+    project_id = uuid4()
+    dataset_ids = [uuid4(), uuid4()]
+    db = DatasetSession([make_dataset(project_id, dataset_id) for dataset_id in dataset_ids])
+    captured = {}
+
+    def select_features(_db, **kwargs):
+        captured.update(kwargs)
+        return {
+            "selection_bbox": kwargs["bbox"],
+            "feature_count": 1,
+            "total_feature_count": 3,
+            "limit": kwargs["limit"],
+            "truncated": False,
+            "geojson": {"type": "FeatureCollection", "features": []},
+            "summary": None,
+        }
+
+    monkeypatch.setattr(VectorFeatureService, "select_features_by_bbox", select_features)
+    payload = VectorPartitionSelectionRequest(
+        dataset_ids=dataset_ids,
+        bbox={"min_x": 5.0, "min_y": 51.0, "max_x": 5.3, "max_y": 51.2},
+    )
+    response = select_vector_partitions(project_id, payload, db)
+
+    assert captured["dataset_ids"] == dataset_ids
+    assert captured["deduplicate_source_features"] is True
+    assert response["data"]["partition_count"] == 2
+    assert response["data"]["dataset_ids"] == dataset_ids
+
+
+def test_vector_partition_request_has_a_bounded_fan_out() -> None:
+    with pytest.raises(ValidationError):
+        VectorPartitionSelectionRequest(
+            dataset_ids=[uuid4() for _ in range(17)],
+            bbox={"min_x": 5.0, "min_y": 51.0, "max_x": 5.3, "max_y": 51.2},
+        )
+
+
+def test_vector_partition_route_rejects_mixed_source_products() -> None:
+    project_id = uuid4()
+    datasets = [
+        make_dataset(project_id, uuid4(), source_name="grb", product_key="buildings"),
+        make_dataset(project_id, uuid4(), source_name="spw_picc", product_key="picc_buildings"),
+    ]
+    payload = VectorPartitionSelectionRequest(
+        dataset_ids=[dataset.id for dataset in datasets],
+        bbox={"min_x": 5.0, "min_y": 51.0, "max_x": 5.3, "max_y": 51.2},
+    )
+    with pytest.raises(AppError) as exc_info:
+        select_vector_partitions(project_id, payload, DatasetSession(datasets))
+    assert getattr(exc_info.value, "code", None) == "VECTOR_PARTITION_SOURCE_MISMATCH"