JensandClaude Opus 5
6572e4ad5f
93 test files read a single frontend source and asserted identifiers in it. The
MapWorkspace split showed what that costs: 24 tests went red for a move that
changed no behaviour at all. A contract belongs to the feature — a container,
its hooks, its domain layer — not to whichever file currently holds it.
232 read sites now resolve through read_feature(). The distinction that makes
this safe is direction: a *positive* contract ("this is wired") may widen,
because the identifier must still exist somewhere in the feature; a *negative*
one ("this component performs no transport") is a statement about one file, and
widening it would quietly weaken the check. The 73 single-file reads that
remain are exactly those, and a guard now enforces the rule for new tests.
Verified rather than assumed: of the 732 migrated positive assertions, 644 still
match exactly one module — as specific as before — and the other 86 already
spanned a container and its hook by nature. Two apparent misses are an artefact
of the checking regex reading an escaped newline literally.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
187 lines
6.6 KiB
Python
187 lines
6.6 KiB
Python
from __future__ import annotations
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from pathlib import Path
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from uuid import uuid4
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from app.models import Dataset
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from app.schemas.operations import VectorSelectionSummary
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from app.services.vector_feature_service import VectorFeatureService
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from tests.frontend_contract import read_feature
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ROOT = Path(__file__).parents[2]
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BBOX = {"min_x": 5.0, "min_y": 51.1, "max_x": 5.2, "max_y": 51.3, "crs": "EPSG:4326"}
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class ScalarQuery:
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def __init__(self, value: float):
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self.value = value
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def filter(self, *args): # noqa: ANN002, ARG002
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return self
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def scalar(self):
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return self.value
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class SequenceScalarSession:
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"""Answers scalar queries in the order the summary issues them.
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The first query is the fully-covered feature count that produces the
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selection-edge disclosure; ``covered_count`` defaults to the full
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population, i.e. a selection that cuts nothing.
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"""
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def __init__(self, values: list[float], covered_count: float | None = None):
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self.values = iter(([covered_count] if covered_count is not None else []) + values)
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def query(self, *args): # noqa: ANN002, ARG002
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return ScalarQuery(next(self.values))
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def themed_dataset(theme: str, *, method: str = "feature_count") -> Dataset:
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return Dataset(
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id=uuid4(),
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project_id=uuid4(),
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name=f"regional-{theme}.geojson",
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dataset_type="vector",
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dataset_role="reference",
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source_name="grb",
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reference_layer_name=theme,
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source_metadata={
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"theme": theme,
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"selection_aggregation": {
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"method": method,
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"label": theme.title(),
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"unit": "objecten",
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},
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},
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)
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def test_building_selection_promotes_footprint_area_and_retains_object_count() -> None:
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result = VectorFeatureService.summarize_features_by_bbox(
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SequenceScalarSession([125_000.0], covered_count=40),
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dataset=themed_dataset("buildings"),
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bbox=BBOX,
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total_feature_count=40,
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)
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assert result["primary_metric_key"] == "footprint_area"
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assert result["metric_label"] == "Bebouwde grondoppervlakte"
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assert result["metric_value"] == 12.5
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assert result["metric_unit"] == "ha"
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assert [(item["metric_key"], item["metric_value"]) for item in result["metrics"]] == [
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("footprint_area", 12.5),
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("feature_count", 40.0),
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]
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assert "niet de totale vloeroppervlakte" in result["warning"]
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VectorSelectionSummary(**result)
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def test_water_selection_reports_surface_length_and_honest_volume_limitation() -> None:
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result = VectorFeatureService.summarize_features_by_bbox(
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SequenceScalarSession([52_500.0, 12_750.0], covered_count=23),
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dataset=themed_dataset("water"),
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bbox=BBOX,
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total_feature_count=23,
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)
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assert result["metric_value"] == 5.25
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assert result["metric_unit"] == "ha"
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assert [(item["metric_key"], item["metric_value"], item["metric_unit"]) for item in result["metrics"]] == [
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("water_area", 5.25, "ha"),
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("watercourse_length", 12.75, "km"),
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("feature_count", 23.0, "objecten"),
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]
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assert "Watervolume is niet berekenbaar" in result["warning"]
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def test_population_keeps_configured_metric_and_adds_sector_count() -> None:
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dataset = themed_dataset("population", method="sum")
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dataset.source_metadata["selection_aggregation"].update(
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{"metric_key": "population", "property": "population_total", "label": "Inwoners", "unit": "inwoners"}
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)
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result = VectorFeatureService.summarize_features_by_bbox(
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SequenceScalarSession([86_458.0], covered_count=733),
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dataset=dataset,
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bbox=BBOX,
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total_feature_count=733,
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)
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assert result["primary_metric_key"] == "population"
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assert result["metric_value"] == 86_458.0
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assert result["metrics"][1] == {
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"metric_key": "feature_count",
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"metric_label": "Statistische sectoren",
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"metric_value": 733.0,
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"metric_unit": "objecten",
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"aggregation_method": "feature_count",
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"is_estimate": False,
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"warning": None,
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}
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def test_station_measurement_uses_numeric_mean_without_area_extrapolation() -> None:
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dataset = themed_dataset("water", method="mean")
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dataset.source_name = "waterinfo"
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dataset.source_metadata.update(
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{
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"semantic_metrics": False,
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"selection_aggregation": {
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"metric_key": "water_level",
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"method": "mean",
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"property": "annual_mean_water_level_m",
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"label": "Jaargemiddelde waterstand",
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"unit": "m",
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"warning": "Puntmeting; geen gebiedsdekkend watervolume.",
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},
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}
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)
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result = VectorFeatureService.summarize_features_by_bbox(
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SequenceScalarSession([30.455], covered_count=1),
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dataset=dataset,
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bbox=BBOX,
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total_feature_count=1,
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)
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assert result["metric_value"] == 30.455
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assert result["aggregation_method"] == "mean"
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assert result["metric_unit"] == "m"
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assert result["warning"] == "Puntmeting; geen gebiedsdekkend watervolume."
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def test_regional_historical_polygons_do_not_emit_irrelevant_line_metrics() -> None:
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dataset = themed_dataset("water", method="intersection_area")
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dataset.source_metadata["selection_aggregation"].update(
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{"metric_key": "water_area", "label": "Historische wateroppervlakte", "unit": "ha"}
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)
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dataset.provenance_metadata = {"operator_tool": "provision_regional_historical_landuse.py"}
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result = VectorFeatureService.summarize_features_by_bbox(
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SequenceScalarSession([52_500.0], covered_count=23),
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dataset=dataset,
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bbox=BBOX,
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total_feature_count=23,
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)
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assert [(item["metric_key"], item["metric_unit"]) for item in result["metrics"]] == [
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("water_area", "ha"),
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("feature_count", "objecten"),
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]
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def test_future_regional_imports_persist_semantic_aggregation_configuration() -> None:
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buildings = (ROOT / "scripts/provision_regional_grb_buildings.py").read_text(encoding="utf-8")
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context = (ROOT / "scripts/provision_regional_grb_context.py").read_text(encoding="utf-8")
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frontend = read_feature("map_workspace")
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assert '"method": "intersection_area"' in buildings
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assert '"label": "Bebouwde grondoppervlakte"' in buildings
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assert 'metric_method="intersection_length"' in context
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assert 'metric_label="Wateroppervlakte"' in context
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assert 'metric_label="Perceeloppervlakte"' in context
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assert 'aria-label="Aanvullende gebiedsmetingen"' in frontend
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assert "activeSelectionResult.summary.warning" in frontend
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