Initial public release

backend/tests/test_vector_selection_partial_features.py

@@ -0,0 +1,161 @@
+"""The object count and the area metric must describe the same selection.
+
+``intersection_area`` clips a feature to the drawn rectangle, but the object
+count treated any feature that merely touches the rectangle as wholly inside.
+For a rectangle across a built-up area that overstates the count at every
+edge, and the two headline numbers on the same panel then describe different
+populations: "1.000 gebouwen" next to the clipped area of rather fewer.
+
+The count now reports how many features lie entirely inside and how many are
+cut by the selection edge, and is marked as an estimate when any are.
+"""
+
+from __future__ import annotations
+
+from app.services.vector_feature_service import VectorFeatureService
+
+
+def test_a_count_without_partial_features_is_exact() -> None:
+    disclosure = VectorFeatureService.count_disclosure(
+        total_feature_count=120,
+        fully_covered_feature_count=120,
+    )
+
+    assert disclosure["partially_covered_feature_count"] == 0
+    assert disclosure["is_estimate"] is False
+    assert disclosure["warning"] is None
+
+
+def test_features_cut_by_the_selection_edge_are_reported() -> None:
+    disclosure = VectorFeatureService.count_disclosure(
+        total_feature_count=120,
+        fully_covered_feature_count=98,
+    )
+
+    assert disclosure["partially_covered_feature_count"] == 22
+    assert disclosure["is_estimate"] is True
+    assert "22" in disclosure["warning"]
+    assert "rand" in disclosure["warning"]
+
+
+def test_a_selection_of_only_partial_features_is_still_coherent() -> None:
+    disclosure = VectorFeatureService.count_disclosure(
+        total_feature_count=3,
+        fully_covered_feature_count=0,
+    )
+
+    assert disclosure["partially_covered_feature_count"] == 3
+    assert disclosure["is_estimate"] is True
+
+
+def test_an_empty_selection_makes_no_claim() -> None:
+    disclosure = VectorFeatureService.count_disclosure(
+        total_feature_count=0,
+        fully_covered_feature_count=0,
+    )
+
+    assert disclosure["partially_covered_feature_count"] == 0
+    assert disclosure["is_estimate"] is False
+    assert disclosure["warning"] is None
+
+
+def test_a_preclipped_full_area_selection_has_no_edge_effect() -> None:
+    """Selecting the whole work area cuts nothing; the count is exact."""
+
+    disclosure = VectorFeatureService.count_disclosure(
+        total_feature_count=500,
+        fully_covered_feature_count=None,
+    )
+
+    assert disclosure["partially_covered_feature_count"] is None
+    assert disclosure["is_estimate"] is False
+    assert disclosure["warning"] is None
+
+
+def test_an_inconsistent_covered_count_never_produces_a_negative() -> None:
+    disclosure = VectorFeatureService.count_disclosure(
+        total_feature_count=10,
+        fully_covered_feature_count=14,
+    )
+
+    assert disclosure["partially_covered_feature_count"] == 0
+    assert disclosure["is_estimate"] is False
+
+
+class _ScalarQuery:
+    def __init__(self, value):
+        self.value = value
+
+    def filter(self, *args):  # noqa: ANN002, ARG002
+        return self
+
+    def scalar(self):
+        return self.value
+
+
+class _SequenceSession:
+    """Answers the summary's scalar queries in order: covered count, then metrics."""
+
+    def __init__(self, values):
+        self.values = iter(values)
+
+    def query(self, *args):  # noqa: ANN002, ARG002
+        return _ScalarQuery(next(self.values))
+
+
+def _buildings_dataset():
+    from uuid import uuid4
+
+    from app.models import Dataset
+
+    return Dataset(
+        id=uuid4(),
+        project_id=uuid4(),
+        name="grb-buildings.geojson",
+        dataset_type="vector",
+        dataset_role="reference",
+        source_name="grb",
+        reference_layer_name="buildings",
+        source_metadata={"theme": "buildings"},
+    )
+
+
+BBOX = {"min_x": 5.0, "min_y": 51.1, "max_x": 5.2, "max_y": 51.3, "crs": "EPSG:4326"}
+
+
+def test_summary_reports_the_edge_cut_next_to_the_object_count() -> None:
+    summary = VectorFeatureService.summarize_features_by_bbox(
+        _SequenceSession([88, 125_000.0]),
+        dataset=_buildings_dataset(),
+        bbox=BBOX,
+        total_feature_count=100,
+    )
+
+    assert summary["feature_count"] == 100
+    assert summary["fully_covered_feature_count"] == 88
+    assert summary["partially_covered_feature_count"] == 12
+    assert "12 van de 100" in summary["selection_edge_warning"]
+
+    count_metric = next(
+        item for item in summary["metrics"] if item["aggregation_method"] == "feature_count"
+    )
+    assert count_metric["is_estimate"] is True
+    assert "doorgesneden" in count_metric["warning"]
+
+    # The clipped area metric is exact and must not inherit the count's caveat.
+    area_metric = next(
+        item for item in summary["metrics"] if item["aggregation_method"] == "intersection_area"
+    )
+    assert area_metric["is_estimate"] is False
+
+
+def test_summary_stays_exact_when_the_selection_cuts_nothing() -> None:
+    summary = VectorFeatureService.summarize_features_by_bbox(
+        _SequenceSession([100, 125_000.0]),
+        dataset=_buildings_dataset(),
+        bbox=BBOX,
+        total_feature_count=100,
+    )
+
+    assert summary["partially_covered_feature_count"] == 0
+    assert summary["selection_edge_warning"] is None