JensandClaude Opus 5
39c12822bc
MapWorkspace.tsx opened with ~590 lines of theme catalogue, dataset matching and label formatting above a 3.200-line component. None of it is React, all of it is independently testable, and both render paths read from it, so it belongs beside the pure helpers that already live in mapWorkspaceUtils. The contract tests that read MapWorkspace.tsx would have gone red for a move that changes no behaviour at all — 24 of them. That is the brittleness the frontend_contract helper exists to remove, so it gains read_map_workspace(): the workspace is one feature spread over several modules, and a contract belongs to the feature rather than to whichever file currently holds it. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
363 lines
13 KiB
Python
363 lines
13 KiB
Python
from __future__ import annotations
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import re
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import uuid
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from pathlib import Path
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from types import SimpleNamespace
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from geoalchemy2.shape import from_shape, to_shape
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from shapely.geometry import Polygon, box
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from app.core.errors import AppError
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from app.models import Dataset, VectorFeature
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from app.services.vector_feature_service import VectorFeatureService
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from tests.frontend_contract import assert_calls, assert_mentions, assert_wired, read_map_workspace
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ROOT = Path(__file__).resolve().parents[2]
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class _FakeQuery:
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def __init__(self, rows: list[VectorFeature]) -> None:
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self.rows = rows
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self.limit_value: int | None = None
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def filter(self, *args, **kwargs): # noqa: ANN002, ANN003
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return self
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def order_by(self, *args, **kwargs): # noqa: ANN002, ANN003
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return self
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def limit(self, value: int):
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self.limit_value = value
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return self
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def all(self) -> list[VectorFeature]:
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if self.limit_value is None:
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return self.rows
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return self.rows[: self.limit_value]
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class _FakeSession:
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def __init__(self, rows: list[VectorFeature]) -> None:
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self.rows = rows
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def query(self, model): # noqa: ANN001
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assert model is VectorFeature
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return _FakeQuery(self.rows)
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def _feature_row(dataset_id: uuid.UUID, *, source_feature_id: str, name: str) -> VectorFeature:
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return VectorFeature(
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id=uuid.uuid4(),
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dataset_id=dataset_id,
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feature_class="parcel",
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source_feature_id=source_feature_id,
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properties_json={"name": name},
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geometry=from_shape(
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Polygon(
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[
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(5.0, 51.0),
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(5.001, 51.0),
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(5.001, 51.001),
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(5.0, 51.001),
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(5.0, 51.0),
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]
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),
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srid=4326,
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),
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)
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def test_vector_feature_service_extracts_bbox_geojson_from_persisted_rows() -> None:
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dataset_id = uuid.uuid4()
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rows = [_feature_row(dataset_id, source_feature_id="src-1", name="Test parcel")]
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result = VectorFeatureService.select_features_by_bbox(
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_FakeSession(rows),
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dataset_id=dataset_id,
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bbox={"min_x": 4.9, "min_y": 50.9, "max_x": 5.2, "max_y": 51.2, "crs": "EPSG:4326"},
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limit=25,
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)
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assert result["feature_count"] == 1
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assert result["total_feature_count"] == 1
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assert result["truncated"] is False
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assert result["geojson"]["type"] == "FeatureCollection"
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feature = result["geojson"]["features"][0]
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assert feature["type"] == "Feature"
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assert feature["properties"]["vector_feature_id"] == str(rows[0].id)
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assert feature["properties"]["source_feature_id"] == "src-1"
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assert feature["properties"]["feature_class"] == "parcel"
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assert feature["properties"]["name"] == "Test parcel"
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assert feature["geometry"]["type"] == "Polygon"
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def test_vector_feature_service_rejects_invalid_bbox() -> None:
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try:
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VectorFeatureService.select_features_by_bbox(
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_FakeSession([]),
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dataset_id=uuid.uuid4(),
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bbox={"min_x": 5.2, "min_y": 50.9, "max_x": 5.0, "max_y": 51.2, "crs": "EPSG:4326"},
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)
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except AppError as exc:
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assert exc.code == "INVALID_SELECTION_BBOX"
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else: # pragma: no cover
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raise AssertionError("Expected INVALID_SELECTION_BBOX")
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def test_vector_select_route_is_project_scoped_and_enveloped(monkeypatch) -> None:
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from app.api.routes import datasets as dataset_routes
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project_id = uuid.uuid4()
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dataset_id = uuid.uuid4()
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dataset = Dataset(id=dataset_id, project_id=project_id, dataset_type="vector", source="fixture", name="Vector")
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expected_payload = {
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"selection_bbox": {"min_x": 4.9, "min_y": 50.9, "max_x": 5.2, "max_y": 51.2, "crs": "EPSG:4326"},
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"feature_count": 0,
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"limit": 100,
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"truncated": False,
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"geojson": {"type": "FeatureCollection", "features": []},
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}
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monkeypatch.setattr(dataset_routes.DatasetService, "get_dataset", lambda db, selected_id: dataset)
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monkeypatch.setattr(
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dataset_routes.VectorFeatureService,
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"select_features_by_bbox",
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lambda db, dataset_id, bbox, limit=100: expected_payload,
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)
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response = dataset_routes.select_vector_features(
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project_id=project_id,
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dataset_id=dataset_id,
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payload=dataset_routes.VectorSelectionRequest(
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bbox=dataset_routes.VectorSelectionBBox(min_x=4.9, min_y=50.9, max_x=5.2, max_y=51.2),
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limit=100,
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),
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db=SimpleNamespace(),
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)
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assert response == {"data": expected_payload}
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def test_vector_select_route_rejects_non_vector_dataset(monkeypatch) -> None:
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from app.api.routes import datasets as dataset_routes
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project_id = uuid.uuid4()
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dataset_id = uuid.uuid4()
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dataset = Dataset(id=dataset_id, project_id=project_id, dataset_type="raster", source="fixture", name="Raster")
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monkeypatch.setattr(dataset_routes.DatasetService, "get_dataset", lambda db, selected_id: dataset)
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try:
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dataset_routes.select_vector_features(
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project_id=project_id,
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dataset_id=dataset_id,
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payload=dataset_routes.VectorSelectionRequest(
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bbox=dataset_routes.VectorSelectionBBox(min_x=4.9, min_y=50.9, max_x=5.2, max_y=51.2),
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limit=100,
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),
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db=SimpleNamespace(),
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)
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except AppError as exc:
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assert exc.code == "DATASET_NOT_VECTOR"
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else: # pragma: no cover
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raise AssertionError("Expected DATASET_NOT_VECTOR")
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def test_vector_select_route_uses_persisted_area_geometry_when_requested(monkeypatch) -> None:
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from app.api.routes import datasets as dataset_routes
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project_id = uuid.uuid4()
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dataset_id = uuid.uuid4()
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area_id = uuid.uuid4()
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dataset = Dataset(
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id=dataset_id,
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project_id=project_id,
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dataset_type="vector",
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source="fixture",
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name="Regional vector",
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source_metadata={"selection_aggregation": {"method": "feature_count"}},
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)
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area_geometry = from_shape(box(5.0, 51.0, 5.3, 51.3), srid=4326)
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area = SimpleNamespace(id=area_id, project_id=project_id, geometry=area_geometry)
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captured: dict[str, object] = {}
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class _AreaSession:
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@staticmethod
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def get(model, selected_id): # noqa: ANN001
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assert model is dataset_routes.Area
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assert selected_id == area_id
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return area
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def select_features(db, **kwargs): # noqa: ANN001
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captured["select"] = kwargs
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return {
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"selection_bbox": {"min_x": 4.9, "min_y": 50.9, "max_x": 5.2, "max_y": 51.2, "crs": "EPSG:4326"},
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"selection_area_id": str(area_id),
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"feature_count": 1,
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"total_feature_count": 1,
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"limit": 100,
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"truncated": False,
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"geojson": {"type": "FeatureCollection", "features": []},
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}
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def summarize_features(db, **kwargs): # noqa: ANN001
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captured["summary"] = kwargs
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return {
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"metric_label": "Gebouwen",
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"metric_value": 1,
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"metric_unit": "objecten",
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"aggregation_method": "feature_count",
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"feature_count": 1,
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"is_estimate": False,
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}
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monkeypatch.setattr(dataset_routes.DatasetService, "get_dataset", lambda db, selected_id: dataset)
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monkeypatch.setattr(dataset_routes.VectorFeatureService, "select_features_by_bbox", select_features)
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monkeypatch.setattr(dataset_routes.VectorFeatureService, "summarize_features_by_bbox", summarize_features)
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response = dataset_routes.select_vector_features(
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project_id=project_id,
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dataset_id=dataset_id,
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payload=dataset_routes.VectorSelectionRequest(
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bbox=dataset_routes.VectorSelectionBBox(min_x=4.9, min_y=50.9, max_x=5.2, max_y=51.2),
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area_id=area_id,
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limit=100,
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),
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db=_AreaSession(),
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)
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assert str(response["data"]["selection_area_id"]) == str(area_id)
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assert to_shape(captured["select"]["selection_geometry"]).equals(box(5.0, 51.0, 5.2, 51.2))
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assert captured["select"]["selection_area_id"] == area_id
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assert to_shape(captured["summary"]["selection_geometry"]).equals(box(5.0, 51.0, 5.2, 51.2))
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assert captured["select"]["full_dataset_area"] is False
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def test_vector_select_route_uses_bbox_for_dataset_preclipped_to_selected_area(monkeypatch) -> None:
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from app.api.routes import datasets as dataset_routes
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project_id = uuid.uuid4()
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dataset_id = uuid.uuid4()
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area_id = uuid.uuid4()
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dataset = Dataset(
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id=dataset_id,
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project_id=project_id,
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area_id=area_id,
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dataset_type="vector",
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source="fixture",
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name="Preclipped population",
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source_metadata={
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"geometry_clipped_to_area": True,
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"selection_aggregation": {"method": "feature_count"},
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},
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)
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area = SimpleNamespace(
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id=area_id,
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project_id=project_id,
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geometry=from_shape(box(5.0, 51.0, 5.3, 51.3), srid=4326),
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)
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captured: dict[str, dict[str, object]] = {}
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class _AreaSession:
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@staticmethod
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def get(model, selected_id): # noqa: ANN001
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assert model is dataset_routes.Area
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assert selected_id == area_id
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return area
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def select_features(_db, **kwargs): # noqa: ANN001
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captured["select"] = kwargs
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return {
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"selection_bbox": {"min_x": 4.9, "min_y": 51.1, "max_x": 5.2, "max_y": 51.2, "crs": "EPSG:4326"},
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"selection_area_id": str(area_id),
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"feature_count": 1,
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"total_feature_count": 1,
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"limit": 25,
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"truncated": False,
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"geojson": {"type": "FeatureCollection", "features": []},
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}
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def summarize_features(_db, **kwargs): # noqa: ANN001
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captured["summary"] = kwargs
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return {
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"metric_label": "Inwoners",
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"metric_value": 1,
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"metric_unit": "inwoners",
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"aggregation_method": "feature_count",
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"feature_count": 1,
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"is_estimate": False,
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}
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monkeypatch.setattr(dataset_routes.DatasetService, "get_dataset", lambda _db, _id: dataset)
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monkeypatch.setattr(dataset_routes.VectorFeatureService, "select_features_by_bbox", select_features)
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monkeypatch.setattr(dataset_routes.VectorFeatureService, "summarize_features_by_bbox", summarize_features)
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dataset_routes.select_vector_features(
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project_id=project_id,
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dataset_id=dataset_id,
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payload=dataset_routes.VectorSelectionRequest(
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bbox=dataset_routes.VectorSelectionBBox(min_x=4.9, min_y=51.1, max_x=5.2, max_y=51.2),
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area_id=area_id,
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limit=25,
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),
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db=_AreaSession(),
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)
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assert captured["select"]["selection_geometry"] is None
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assert captured["summary"]["selection_geometry"] is None
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assert captured["select"]["selection_area_id"] == area_id
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assert captured["select"]["full_dataset_area"] is False
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def test_vector_select_route_rejects_area_from_another_project(monkeypatch) -> None:
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from app.api.routes import datasets as dataset_routes
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project_id = uuid.uuid4()
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dataset_id = uuid.uuid4()
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area_id = uuid.uuid4()
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dataset = Dataset(id=dataset_id, project_id=project_id, dataset_type="vector", source="fixture", name="Vector")
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other_area = SimpleNamespace(id=area_id, project_id=uuid.uuid4(), geometry=object())
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monkeypatch.setattr(dataset_routes.DatasetService, "get_dataset", lambda db, selected_id: dataset)
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try:
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dataset_routes.select_vector_features(
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project_id=project_id,
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dataset_id=dataset_id,
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payload=dataset_routes.VectorSelectionRequest(
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bbox=dataset_routes.VectorSelectionBBox(min_x=4.9, min_y=50.9, max_x=5.2, max_y=51.2),
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area_id=area_id,
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),
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db=SimpleNamespace(get=lambda model, selected_id: other_area),
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)
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except AppError as exc:
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assert exc.code == "AREA_NOT_FOUND"
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else: # pragma: no cover
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raise AssertionError("Expected AREA_NOT_FOUND")
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def test_frontend_exposes_map_bbox_selection_contracts() -> None:
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api_client = (ROOT / "frontend" / "src" / "services" / "api" / "datasets.ts").read_text(encoding="utf-8")
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map_workspace = read_map_workspace()
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geomap = (ROOT / "frontend" / "src" / "components" / "GeoMap.tsx").read_text(encoding="utf-8")
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app = (ROOT / "frontend" / "src" / "App.tsx").read_text(encoding="utf-8")
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extract_hook = (ROOT / "frontend" / "src" / "hooks" / "useMapSelectionExtract.ts").read_text(encoding="utf-8")
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theme_hook = (ROOT / "frontend" / "src" / "hooks" / "useMapThemeSelectionInsights.ts").read_text(encoding="utf-8")
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assert "selectVectorFeatures" in api_client
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assert "Area selection" in map_workspace
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assert "Teken rechthoek" in map_workspace
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assert "Objecten in gebied ophalen" in map_workspace
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assert "Gebiedsdownload bewaren" in map_workspace
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assert "bboxSelectionMode" in geomap
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assert "selection-bbox" in geomap
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assert "selection-result" in geomap
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assert "useMapSelectionExtract" in app
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assert "area_id: areaId" in extract_hook
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assert "area_id: areaId" in theme_hook
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# The saved area selection triggers analysis with its own area id.
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# The saved area bbox feeds the analysis path, now through a resolved
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# bbox rather than being passed positionally.
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assert_wired(map_workspace, "selectedAreaBbox")
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assert_calls(map_workspace, "analyzeSelection", first_argument="bbox")
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