JensandClaude Opus 5
ea0e401690
The temporal timeline summarises every snapshot in a series. Each summary now also counts how many features the selection edge cuts, but a timeline point renders values only, so that was one database round trip per snapshot for a disclosure nobody sees. Make it opt-out and have the timeline opt out. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
549 lines
21 KiB
Python
549 lines
21 KiB
Python
from __future__ import annotations
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from datetime import datetime, timezone
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from pathlib import Path
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from types import SimpleNamespace
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from uuid import uuid4
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import pytest
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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, DatasetVersion
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from app.schemas.dataset import DatasetTemporalUpdate
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from app.schemas.temporal import TemporalComparisonRequest, TemporalObjectChanges
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from app.services.dataset_service import DatasetService
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from app.services.temporal_analysis_service import TemporalAnalysisService
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from app.services.vector_feature_service import VectorFeatureService
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ROOT = Path(__file__).parents[2]
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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 ScalarSession:
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def __init__(self, value: float):
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self.value = value
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def query(self, *args): # noqa: ANN002, ARG002
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return ScalarQuery(self.value)
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class SequenceScalarSession:
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def __init__(self, values: list[float]):
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self.values = iter(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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class FeatureRowsQuery:
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def __init__(self, rows: list[object]):
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self.rows = rows
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self.row_limit: int | None = None
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def filter(self, *args): # noqa: ANN002, ARG002
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return self
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def order_by(self, *args): # noqa: ANN002, ARG002
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return self
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def limit(self, value: int):
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self.row_limit = value
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return self
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def all(self):
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return self.rows[: self.row_limit]
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class SequentialFeatureSession:
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def __init__(self, row_sets: list[list[object]]):
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self.row_sets = iter(row_sets)
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def query(self, _model):
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return FeatureRowsQuery(next(self.row_sets))
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class VersionQuery:
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def __init__(self, latest: DatasetVersion | None):
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self.latest = latest
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def filter(self, *args): # noqa: ANN002, ARG002
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return self
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def order_by(self, *args): # noqa: ANN002, ARG002
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return self
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def first(self):
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return self.latest
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class TemporalUpdateSession:
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def __init__(self, dataset: Dataset, latest: DatasetVersion | None):
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self.dataset = dataset
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self.latest = latest
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self.added: list[object] = []
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def get(self, model, item_id): # noqa: ANN001
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return self.dataset if model is Dataset and item_id == self.dataset.id else None
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def query(self, model): # noqa: ANN001
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assert model is DatasetVersion
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return VersionQuery(self.latest)
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def add(self, item): # noqa: ANN001
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self.added.append(item)
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def commit(self):
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return None
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def refresh(self, _item):
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return None
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def temporal_dataset(*, project_id, observed_year: int, metric_method: str = "feature_count") -> Dataset:
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return Dataset(
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id=uuid4(),
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project_id=project_id,
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name=f"snapshot-{observed_year}.geojson",
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dataset_type="vector",
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source="official",
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dataset_role="reference",
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temporal_series_key="official:test:mol",
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observed_at=datetime(observed_year, 1, 1, tzinfo=timezone.utc),
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source_version=str(observed_year),
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source_metadata={
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"selection_aggregation": {
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"method": metric_method,
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"label": "Objecten",
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"unit": "objecten",
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}
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},
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)
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def test_temporal_series_keeps_only_latest_snapshot_per_observation_date() -> None:
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project_id = uuid4()
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old = temporal_dataset(project_id=project_id, observed_year=2025)
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old.imported_at = datetime(2026, 7, 19, tzinfo=timezone.utc)
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latest = temporal_dataset(project_id=project_id, observed_year=2025)
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latest.imported_at = datetime(2026, 7, 21, tzinfo=timezone.utc)
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earlier = temporal_dataset(project_id=project_id, observed_year=2022)
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earlier.imported_at = datetime(2026, 7, 21, tzinfo=timezone.utc)
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canonical = TemporalAnalysisService._canonical_observation_snapshots([old, latest, earlier])
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assert [dataset.id for dataset in canonical] == [earlier.id, latest.id]
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def governed_grb_dataset(*, project_id, observed_day: int) -> Dataset:
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dataset = temporal_dataset(project_id=project_id, observed_year=2026, metric_method="intersection_area")
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dataset.observed_at = datetime(2026, 7, observed_day, tzinfo=timezone.utc)
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dataset.source_version = f"2026-07-{observed_day:02d}"
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dataset.source_name = "grb"
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dataset.reference_layer_name = "buildings"
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dataset.temporal_series_key = "grb:buildings:kempen-transport-region"
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dataset.source_metadata = {
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"authority_level": "authoritative",
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"collection": "GRB/GBG",
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"coverage_scope": "kempen-transport-region",
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"scope_type": "transport_region",
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"member_count": 28,
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"partition_count": 28,
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"partition_strategy": "municipality_bbox_maximum_boundary_intersection",
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"selection_aggregation": {
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"method": "intersection_area",
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"label": "Bebouwde grondoppervlakte",
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"unit": "ha",
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},
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}
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dataset.provenance_metadata = {
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"operator_tool": "provision_regional_grb_buildings.py",
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"reference_truncated": False,
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"manifest_path": "/storage/operator/grb/manifest.json",
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"source_url": "https://geo.api.vlaanderen.be/GRB/ogc/features/v1/collections/GBG/items",
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"artifact_sha256": "a" * 64,
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"partition_checksums": {f"{index:05d}": "b" * 64 for index in range(28)},
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}
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if observed_day > 14:
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dataset.source_metadata["geometry_clipped_to_area"] = True
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return dataset
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def persisted_feature(dataset_id, source_feature_id: str | None, polygon: Polygon):
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return SimpleNamespace(
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id=uuid4(),
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dataset_id=dataset_id,
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source_feature_id=source_feature_id,
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properties_json={},
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geometry=from_shape(polygon, srid=4326),
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)
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def test_temporal_migration_and_models_align() -> None:
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migration = (ROOT / "backend/alembic/versions/202607140001_temporal_dataset_foundation.py").read_text(encoding="utf-8")
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for field in (
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"temporal_series_key",
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"observed_at",
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"valid_from",
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"valid_to",
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"temporal_granularity",
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"source_version",
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):
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assert field in migration
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assert hasattr(Dataset, field)
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assert "ix_vector_features_dataset_source_feature" in migration
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assert 'down_revision = "202606120900"' in migration
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def test_temporal_metadata_requires_an_explicit_series_and_observation_date() -> None:
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with pytest.raises(AppError, match="observed_at is required"):
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DatasetService._validate_temporal_metadata(
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temporal_series_key="official:test:mol",
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observed_at=None,
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valid_from=None,
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valid_to=None,
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temporal_granularity="year",
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source_version="2024",
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)
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with pytest.raises(AppError, match="valid_to must be"):
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DatasetService._validate_temporal_metadata(
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temporal_series_key="official:test:mol",
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observed_at=datetime(2024, 1, 1, tzinfo=timezone.utc),
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valid_from=datetime(2024, 12, 31, tzinfo=timezone.utc),
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valid_to=datetime(2024, 1, 1, tzinfo=timezone.utc),
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temporal_granularity="year",
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source_version="2024",
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)
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def test_temporal_metadata_update_appends_provenance_version_and_is_idempotent() -> None:
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project_id = uuid4()
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dataset = temporal_dataset(project_id=project_id, observed_year=2024)
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dataset.status = "ready"
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dataset.metadata_json = {}
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latest = DatasetVersion(
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dataset_id=dataset.id,
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version=3,
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observed_at=dataset.observed_at,
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source_version="2024",
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)
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session = TemporalUpdateSession(dataset, latest)
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payload = DatasetTemporalUpdate(
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temporal_series_key="official:test:mol",
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observed_at=datetime(2025, 1, 1, tzinfo=timezone.utc),
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temporal_granularity="year",
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source_version="2025",
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)
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updated = DatasetService.update_temporal_metadata(session, dataset.id, payload)
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assert updated.observed_at == payload.observed_at
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assert latest.version == 3
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assert latest.observed_at == datetime(2024, 1, 1, tzinfo=timezone.utc)
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assert len(session.added) == 2
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appended = session.added[1]
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assert isinstance(appended, DatasetVersion)
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assert appended.version == 4
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assert appended.observed_at == payload.observed_at
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session.added.clear()
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DatasetService.update_temporal_metadata(session, dataset.id, payload)
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assert session.added == []
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def test_selection_area_aggregation_returns_hectares_without_loading_all_features() -> None:
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project_id = uuid4()
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dataset = temporal_dataset(project_id=project_id, observed_year=1969, metric_method="intersection_area")
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dataset.source_metadata["selection_aggregation"].update({"label": "Oppervlakte", "unit": "ha"})
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result = VectorFeatureService.summarize_features_by_bbox(
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ScalarSession(125_000.0),
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dataset=dataset,
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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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total_feature_count=40,
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)
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assert result["metric_value"] == 12.5
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assert result["metric_unit"] == "ha"
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assert result["feature_count"] == 40
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def test_population_area_weighting_is_exact_for_full_features_and_estimated_for_partial_features() -> None:
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dataset = temporal_dataset(project_id=uuid4(), observed_year=2025, metric_method="area_weighted_sum")
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dataset.source_metadata["selection_aggregation"].update(
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{
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"property": "population_total",
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"label": "Inwoners",
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"unit": "inwoners",
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"warning": "Partial-sector estimate",
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"warning_only_when_estimate": True,
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}
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)
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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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full = VectorFeatureService.summarize_features_by_bbox(
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SequenceScalarSession([49, 38_675.0]),
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dataset=dataset,
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bbox=bbox,
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total_feature_count=49,
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)
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partial = VectorFeatureService.summarize_features_by_bbox(
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SequenceScalarSession([1, 1_250.5]),
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dataset=dataset,
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bbox=bbox,
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total_feature_count=3,
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)
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assert full["metric_value"] == 38_675.0
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assert full["is_estimate"] is False
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assert full["warning"] is None
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assert partial["metric_value"] == 1_250.5
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assert partial["is_estimate"] is True
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assert partial["warning"] == "Partial-sector estimate"
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def test_temporal_compare_returns_delta_and_canonical_change_payload(monkeypatch) -> None:
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project_id = uuid4()
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earlier = temporal_dataset(project_id=project_id, observed_year=2021)
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later = temporal_dataset(project_id=project_id, observed_year=2024)
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def get_dataset(_db, _project_id, dataset_id, _label):
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return earlier if dataset_id == earlier.id else later
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def summarize(_db, *, dataset, bbox, disclose_selection_edge=True): # noqa: ARG001
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value = 100.0 if dataset.id == earlier.id else 115.0
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return {
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"metric_label": "Inwoners",
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"metric_value": value,
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"metric_unit": "inwoners",
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"aggregation_method": "area_weighted_sum",
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"feature_count": 10,
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"is_estimate": True,
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"warning": "Areal weighting",
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}
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monkeypatch.setattr(TemporalAnalysisService, "_get_temporal_dataset", staticmethod(get_dataset))
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monkeypatch.setattr(VectorFeatureService, "summarize_features_by_bbox", staticmethod(summarize))
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monkeypatch.setattr(
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TemporalAnalysisService,
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"_compare_identity_features",
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staticmethod(
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lambda *args, **kwargs: (
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TemporalObjectChanges(available=True, added_count=1, removed_count=0, modified_count=2, unchanged_count=7),
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{"type": "FeatureCollection", "features": []},
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[],
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)
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),
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)
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result = TemporalAnalysisService.compare(
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SimpleNamespace(),
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project_id=project_id,
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payload=TemporalComparisonRequest(
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earlier_dataset_id=earlier.id,
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later_dataset_id=later.id,
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bbox={"min_x": 5.0, "min_y": 51.1, "max_x": 5.2, "max_y": 51.3},
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),
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)
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assert result.metric.absolute_change == 15.0
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assert result.metric.percent_change == 15.0
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assert result.metric.is_estimate is True
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assert result.object_changes.modified_count == 2
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assert result.geojson["type"] == "FeatureCollection"
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def test_temporal_comparison_clips_cross_boundary_bbox_to_selected_area(monkeypatch) -> None:
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project_id = uuid4()
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area_id = uuid4()
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earlier = temporal_dataset(project_id=project_id, observed_year=2021)
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later = temporal_dataset(project_id=project_id, observed_year=2024)
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area_shape = box(5.0, 51.0, 5.2, 51.2)
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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(area_shape, srid=4326),
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)
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captured_geometries = []
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identity_capture = {}
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monkeypatch.setattr(
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TemporalAnalysisService,
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"_get_temporal_dataset",
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staticmethod(lambda _db, _project_id, dataset_id, _label: earlier if dataset_id == earlier.id else later),
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)
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monkeypatch.setattr(
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TemporalAnalysisService,
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"_get_selection_area",
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staticmethod(lambda _db, _project_id, requested_area_id: area if requested_area_id == area_id else None),
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)
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def summarize(_db, *, dataset, bbox, selection_geometry, full_dataset_area, disclose_selection_edge=True): # noqa: ARG001
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captured_geometries.append(selection_geometry)
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return {
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"metric_label": "Oppervlakte",
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"metric_value": 10.0 if dataset.id == earlier.id else 12.0,
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"metric_unit": "ha",
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"aggregation_method": "intersection_area",
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"feature_count": 1,
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"is_estimate": False,
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"warning": None,
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}
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def compare_identity(*_args, **kwargs):
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identity_capture.update(kwargs)
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return (
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TemporalObjectChanges(available=False),
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{"type": "FeatureCollection", "features": []},
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[],
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)
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monkeypatch.setattr(VectorFeatureService, "summarize_features_by_bbox", staticmethod(summarize))
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monkeypatch.setattr(TemporalAnalysisService, "_compare_identity_features", staticmethod(compare_identity))
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result = TemporalAnalysisService.compare(
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SimpleNamespace(),
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project_id=project_id,
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payload=TemporalComparisonRequest(
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earlier_dataset_id=earlier.id,
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later_dataset_id=later.id,
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area_id=area_id,
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bbox={"min_x": 4.9, "min_y": 51.1, "max_x": 5.1, "max_y": 51.3},
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),
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)
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expected = box(5.0, 51.1, 5.1, 51.2)
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assert result.metric.absolute_change == 2.0
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assert all(to_shape(geometry).equals(expected) for geometry in captured_geometries)
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assert to_shape(identity_capture["selection_geometry"]).equals(expected)
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assert identity_capture["earlier_full_dataset_area"] is False
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assert identity_capture["later_full_dataset_area"] is False
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def test_unstable_temporal_identity_returns_clear_end_user_warning() -> None:
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project_id = uuid4()
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earlier = temporal_dataset(project_id=project_id, observed_year=2021)
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later = temporal_dataset(project_id=project_id, observed_year=2025)
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earlier.source_metadata["identity_stable"] = False
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later.source_metadata["identity_stable"] = False
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changes, geojson, warnings = TemporalAnalysisService._compare_identity_features(
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SimpleNamespace(),
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earlier=earlier,
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later=later,
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bbox={"min_x": 5.0, "min_y": 51.1, "max_x": 5.2, "max_y": 51.3},
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preview_limit=100,
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)
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assert changes.available is False
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assert geojson == {"type": "FeatureCollection", "features": []}
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assert warnings == ["Wijzigingen van individuele objecten kunnen voor deze bron niet betrouwbaar worden gevolgd."]
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def test_governed_legacy_grb_snapshots_use_verified_official_feature_identity() -> None:
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project_id = uuid4()
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earlier = governed_grb_dataset(project_id=project_id, observed_day=14)
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later = governed_grb_dataset(project_id=project_id, observed_day=15)
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original = Polygon([(5.1, 51.1), (5.101, 51.1), (5.101, 51.101), (5.1, 51.101)])
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changed = Polygon([(5.1, 51.1), (5.102, 51.1), (5.102, 51.101), (5.1, 51.101)])
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added = Polygon([(5.11, 51.11), (5.111, 51.11), (5.111, 51.111), (5.11, 51.111)])
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session = SequentialFeatureSession(
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[
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[persisted_feature(earlier.id, "GBG.1", original)],
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[persisted_feature(later.id, "GBG.1", changed), persisted_feature(later.id, "GBG.2", added)],
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]
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)
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changes, geojson, warnings = TemporalAnalysisService._compare_identity_features(
|
|
session,
|
|
earlier=earlier,
|
|
later=later,
|
|
bbox={"min_x": 5.0, "min_y": 51.0, "max_x": 5.2, "max_y": 51.2},
|
|
preview_limit=100,
|
|
)
|
|
|
|
assert changes.available is True
|
|
assert changes.added_count == 1
|
|
assert changes.removed_count == 0
|
|
assert changes.modified_count == 1
|
|
assert changes.unchanged_count == 0
|
|
assert {feature["properties"]["change_type"] for feature in geojson["features"]} == {"added", "modified"}
|
|
assert warnings == []
|
|
|
|
|
|
def test_governed_grb_object_history_fails_closed_for_unverified_identity() -> None:
|
|
project_id = uuid4()
|
|
earlier = governed_grb_dataset(project_id=project_id, observed_day=14)
|
|
later = governed_grb_dataset(project_id=project_id, observed_day=15)
|
|
polygon = Polygon([(5.1, 51.1), (5.101, 51.1), (5.101, 51.101), (5.1, 51.101)])
|
|
fallback_hash = "a" * 64
|
|
session = SequentialFeatureSession(
|
|
[
|
|
[persisted_feature(earlier.id, fallback_hash, polygon)],
|
|
[persisted_feature(later.id, fallback_hash, polygon)],
|
|
]
|
|
)
|
|
|
|
changes, geojson, warnings = TemporalAnalysisService._compare_identity_features(
|
|
session,
|
|
earlier=earlier,
|
|
later=later,
|
|
bbox={"min_x": 5.0, "min_y": 51.0, "max_x": 5.2, "max_y": 51.2},
|
|
preview_limit=100,
|
|
)
|
|
|
|
assert changes.available is False
|
|
assert geojson["features"] == []
|
|
assert warnings == ["De geselecteerde objecten bevatten geen volledig verifieerbare stabiele bronidentiteit."]
|
|
|
|
|
|
def test_legacy_grb_identity_requires_complete_partition_evidence() -> None:
|
|
dataset = governed_grb_dataset(project_id=uuid4(), observed_day=14)
|
|
dataset.provenance_metadata["partition_checksums"] = {"13025": "b" * 64}
|
|
|
|
assert TemporalAnalysisService._identity_contract(dataset) is None
|
|
|
|
|
|
def test_temporal_frontend_and_official_operator_contracts_exist() -> None:
|
|
workspace = (ROOT / "frontend/src/components/map/MapWorkspace.tsx").read_text(encoding="utf-8")
|
|
temporal_api = (ROOT / "frontend/src/services/api/temporal.ts").read_text(encoding="utf-8")
|
|
population = (ROOT / "scripts/provision_mol_population_history.py").read_text(encoding="utf-8")
|
|
landuse = (ROOT / "scripts/provision_mol_historical_landuse.py").read_text(encoding="utf-8")
|
|
dockerfile = (ROOT / "deploy/unraid/Dockerfile.all-in-one").read_text(encoding="utf-8")
|
|
|
|
assert "Laatste toestand" in workspace
|
|
assert "Evolutie" in workspace
|
|
assert "Vergelijk periode" in workspace
|
|
assert "temporalRangeLabel" in workspace
|
|
assert "Dagelijkse GRB-edities tonen wijzigingen in de officiële registratie" in workspace
|
|
assert "/temporal/compare" in temporal_api
|
|
assert "Statbel" in population and "area_weighted_sum" in population
|
|
assert '"identity_stable": False' in population
|
|
assert "HistLandgebruik" in landuse and "intersection_area" in landuse
|
|
assert "<wfs:GetFeature" in landuse and "session.post(" in landuse
|
|
assert 'lambda value: value.startswith("weg"), "weg*"' in landuse
|
|
assert "provision_mol_population_history.py" in dockerfile
|
|
assert "provision_mol_historical_landuse.py" in dockerfile
|
|
assert "fake" not in population.lower()
|
|
|
|
|
|
def test_tower_deploy_waits_for_startup_migration_before_live_smoke() -> None:
|
|
for relative_path in ("scripts/deploy_tower.ps1", "scripts/deploy_tower.sh"):
|
|
script = (ROOT / relative_path).read_text(encoding="utf-8")
|
|
assert "bash deploy/unraid/deploy-release.sh" in script
|
|
|
|
release_script = (ROOT / "deploy/unraid/deploy-release.sh").read_text(encoding="utf-8")
|
|
wait_position = release_script.index("wait_for_geointel_health")
|
|
invocation_position = release_script.index("\n wait_for_geointel_health", wait_position)
|
|
smoke_position = release_script.index("LIVE_SMOKE_CONTAINER=geointel bash scripts/live_migration_smoke.sh")
|
|
assert "docker inspect --format" in release_script
|
|
assert invocation_position < smoke_position
|