Add governed VMM flood hazard scenarios
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from __future__ import annotations
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from pathlib import Path
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from uuid import uuid4
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import numpy as np
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import pytest
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import rasterio
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from fastapi.testclient import TestClient
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from pyproj import Transformer
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from rasterio.io import MemoryFile
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from rasterio.transform import from_origin
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from shapely.geometry import box
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from app.core.config import Settings
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from app.core.errors import AppError
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from app.db.session import get_db
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from app.main import app
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from app.models import Dataset, Job, Project
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from app.schemas.flood_hazard import FloodHazardAcquireRequest, FloodHazardSelectionRequest
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from app.schemas.assistant import AssistantQueryRequest
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from app.services.geo_assistant_service import GeoAssistantService
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from app.services.flood_hazard_acquisition_service import FloodHazardAcquisitionService
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from app.services.flood_hazard_analysis_service import FloodHazardAnalysisService
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ROOT = Path(__file__).resolve().parents[2]
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class FakeQuery:
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def __init__(self, result=None):
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self.result = result
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def filter(self, *_args):
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return self
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def order_by(self, *_args):
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return self
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def first(self):
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return self.result
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def all(self):
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return self.result if isinstance(self.result, list) else []
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class FakeSession:
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def __init__(self, rows=None, query_result=None):
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self.rows = rows or {}
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self.query_result = query_result
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self.added = []
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def get(self, model, row_id):
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row = self.rows.get((model, row_id))
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if row is not None:
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return row
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return next((item for item in self.added if isinstance(item, model) and item.id == row_id), None)
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def add(self, row):
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self.added.append(row)
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def commit(self):
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return None
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def rollback(self):
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return None
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def refresh(self, row):
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return row
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def query(self, _model):
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return FakeQuery(self.query_result)
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def flood_payload(*, product_key: str = "pluviaal_current_t100", side_m: float = 100.0) -> FloodHazardAcquireRequest:
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transformer = Transformer.from_crs("EPSG:31370", "EPSG:4326", always_xy=True)
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min_x, min_y = transformer.transform(200_000, 210_000)
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max_x, max_y = transformer.transform(200_000 + side_m, 210_000 + side_m)
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return FloodHazardAcquireRequest(
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bbox={"min_x": min_x, "min_y": min_y, "max_x": max_x, "max_y": max_y, "crs": "EPSG:4326"},
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product_key=product_key,
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resolution_m=5.0,
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force_refresh=True,
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)
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def depth_tiff(*, normalized_metres: bool = False) -> bytes:
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values = np.zeros((20, 20), dtype="float32")
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values[:, :10] = 1.0 if normalized_metres else 100.0
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with MemoryFile() as memory:
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with memory.open(
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driver="GTiff",
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width=20,
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height=20,
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count=1,
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dtype="float32",
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crs="EPSG:31370",
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transform=from_origin(200_000, 210_100, 5.0, 5.0),
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nodata=-9999.0 if normalized_metres else 0.0,
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) as output:
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if normalized_metres:
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values[:, 10:] = -9999.0
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output.write(values, 1)
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return memory.read()
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def test_flood_hazard_registry_is_complete_and_semantically_honest() -> None:
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products = FloodHazardAcquisitionService.list_products()
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assert len(products) == 12
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assert {item["mechanism"] for item in products} == {"pluviaal", "fluviaal"}
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assert {item["climate_context"] for item in products} == {"huidig klimaat", "klimaatprojectie 2050"}
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assert {item["return_period_years"] for item in products} == {10, 100, 1000}
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assert all(item["coverage_id"].startswith("Overstromingsgevaarkaarten-") for item in products)
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assert all(item["source_value_unit"] == "cm" and item["normalized_value_unit"] == "m" for item in products)
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assert all("geen bathymetrie" in item["limitation_message"] for item in products)
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def test_flood_hazard_request_is_bounded_and_rejects_arbitrary_products() -> None:
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settings = Settings(_env_file=None)
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prepared = FloodHazardAcquisitionService._prepared_request(flood_payload(), settings)
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product = prepared["product"]
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url = FloodHazardAcquisitionService._wcs_request_url(
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settings,
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product,
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tuple(prepared["bbox_epsg31370"]),
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prepared["resolution_m"],
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)
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assert "VERSION=1.1.0" in url
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assert "IDENTIFIER=Overstromingsgevaarkaarten-PLUVIAAL%3Awaterdiepte_PLU_noCC_T100" in url
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assert "GRIDOFFSETS=5%2C-5" in url
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assert prepared["width"] * prepared["height"] <= settings.flood_hazard_max_pixels
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with pytest.raises(AppError) as exc_info:
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FloodHazardAcquisitionService._prepared_request(flood_payload(product_key="custom"), settings)
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assert exc_info.value.code == "FLOOD_HAZARD_PRODUCT_NOT_SUPPORTED"
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def test_flood_hazard_normalization_converts_centimetres_and_clips_zero_values() -> None:
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payload = flood_payload()
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prepared = FloodHazardAcquisitionService._prepared_request(payload, Settings(_env_file=None))
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scope = box(
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payload.bbox.min_x,
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payload.bbox.min_y,
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payload.bbox.max_x,
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payload.bbox.max_y,
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)
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normalized, validation = FloodHazardAcquisitionService._normalize_raster(depth_tiff(), scope, prepared)
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assert validation["inundated_pixel_count"] == 200
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assert validation["minimum_depth_m"] == pytest.approx(1.0)
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assert validation["maximum_depth_m"] == pytest.approx(1.0)
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with MemoryFile(normalized) as memory, memory.open() as dataset:
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values = dataset.read(1, masked=True)
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assert dataset.crs.to_epsg() == 31370
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assert dataset.nodata == -9999.0
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assert values.count() == 200
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assert float(values.mean()) == pytest.approx(1.0)
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def test_flood_hazard_analysis_reports_scenario_metrics_without_claiming_waterbody_volume(tmp_path) -> None:
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project_id = uuid4()
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dataset_id = uuid4()
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path = tmp_path / "flood.tif"
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path.write_bytes(depth_tiff(normalized_metres=True))
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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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name="flood.tif",
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dataset_type="raster",
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source="VMM",
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source_name=FloodHazardAcquisitionService.PROVIDER,
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source_metadata={"product_key": "pluviaal_current_t100", "normalized_value_unit": "m"},
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status="ready",
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storage_path=str(path),
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)
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db = FakeSession({(Dataset, dataset_id): dataset})
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result = FloodHazardAnalysisService.analyze(
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db,
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project_id,
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dataset_id,
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FloodHazardSelectionRequest(bbox=flood_payload().bbox),
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settings=Settings(_env_file=None),
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)
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metrics = {item["metric_key"]: item for item in result["summary"]["metrics"]}
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assert result["inundated_cell_count"] == 200
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assert result["inundated_fraction"] == pytest.approx(0.5)
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assert metrics["modelled_inundated_area_ha"]["metric_value"] == pytest.approx(0.5)
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assert metrics["modelled_depth_mean_m"]["metric_value"] == pytest.approx(1.0)
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assert metrics["modelled_max_depth_area_integral_m3"]["metric_value"] == pytest.approx(5000.0)
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assert "concurrent_flood_volume_m3" in result["unsupported_metrics"]
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assert "geen gelijktijdig" in result["limitation_message"]
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def test_flood_hazard_renderer_returns_transparent_png(tmp_path) -> None:
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project_id = uuid4()
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dataset_id = uuid4()
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path = tmp_path / "flood.tif"
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path.write_bytes(depth_tiff(normalized_metres=True))
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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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name="flood.tif",
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dataset_type="raster",
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source="VMM",
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source_name=FloodHazardAcquisitionService.PROVIDER,
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source_metadata={"product_key": "pluviaal_current_t100", "normalized_value_unit": "m"},
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status="ready",
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storage_path=str(path),
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)
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db = FakeSession({(Dataset, dataset_id): dataset})
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assert FloodHazardAnalysisService.render_png(db, project_id, dataset_id).startswith(b"\x89PNG\r\n\x1a\n")
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def test_flood_hazard_api_uses_canonical_envelopes(monkeypatch) -> None:
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project_id = uuid4()
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output_dataset_id = uuid4()
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db = FakeSession({(Project, project_id): Project(id=project_id, name="Mol")})
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monkeypatch.setattr(
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FloodHazardAcquisitionService,
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"acquire",
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lambda *_args, **_kwargs: {"output_dataset_id": str(output_dataset_id), "provider": "vmm_flood_hazard", "reused": False},
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)
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monkeypatch.setattr(
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FloodHazardAnalysisService,
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"analyze",
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lambda *_args, **_kwargs: {
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"dataset_id": str(output_dataset_id),
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"inundated_cell_count": 4,
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"summary": {"metric_value": 0.01, "metric_unit": "ha", "metrics": []},
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"unsupported_metrics": ["permanent_water_volume_m3"],
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},
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)
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app.dependency_overrides[get_db] = lambda: db
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try:
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client = TestClient(app)
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products = client.get(f"/api/v1/projects/{project_id}/datasets/flood-hazard/products")
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acquisition = client.post(
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f"/api/v1/projects/{project_id}/datasets/flood-hazard/acquire",
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json=flood_payload().model_dump(mode="json"),
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)
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selection = client.post(
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f"/api/v1/projects/{project_id}/datasets/{output_dataset_id}/raster/flood-hazard/select",
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json={"bbox": flood_payload().bbox.model_dump()},
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)
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finally:
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app.dependency_overrides.clear()
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assert products.status_code == 200 and set(products.json()) == {"data"}
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assert products.json()["data"]["total"] == 12
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assert acquisition.status_code == 200 and set(acquisition.json()) == {"data"}
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assert acquisition.json()["data"]["job_type"] == "raster.flood_hazard.acquire"
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assert selection.status_code == 200 and set(selection.json()) == {"data"}
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assert any(isinstance(item, Job) for item in db.added)
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def test_geo_assistant_receives_scenario_bound_flood_metrics(monkeypatch) -> None:
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project_id = uuid4()
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dataset_id = 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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name="pluvial.tif",
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dataset_type="raster",
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source="VMM",
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source_name=FloodHazardAcquisitionService.PROVIDER,
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source_metadata={
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"product_key": "pluviaal_current_t100",
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"product_display_name": "Pluviaal - huidig klimaat - middelgrote kans (T100)",
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},
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status="ready",
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)
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db = FakeSession({(Project, project_id): Project(id=project_id, name="Mol")}, query_result=[dataset])
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monkeypatch.setattr(
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FloodHazardAnalysisService,
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"analyze",
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lambda *_args, **_kwargs: {
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"product_key": "pluviaal_current_t100",
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"mechanism": "pluviaal",
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"climate_context": "huidig klimaat",
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"probability_class": "middelgrote kans",
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"return_period_years": 100,
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"summary": {
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"metrics": [
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{
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"metric_label": "Gemodelleerd overstroomd oppervlak",
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"metric_value": 12.5,
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"metric_unit": "ha",
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}
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]
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},
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"limitation_message": "Geen werkelijk of gelijktijdig volume.",
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},
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)
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payload = AssistantQueryRequest(question="Wat is het overstromingsgevaar?", bbox=flood_payload().bbox)
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context, metrics, _series, dataset_ids, warnings, _scope = GeoAssistantService(Settings(_env_file=None))._build_context(
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db,
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project_id=project_id,
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payload=payload,
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)
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assert warnings == []
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assert dataset_ids == [dataset_id]
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assert metrics[0].theme == "flood_hazard"
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assert "T100" in metrics[0].label
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assert context["rules"]["water_volume_available"] is False
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assert context["rules"]["flood_hazard_scenarios_available"] is True
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assert context["rules"]["flood_depth_area_integral_is_concurrent_volume"] is False
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def test_flood_hazard_runtime_contract_is_packaged() -> None:
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for path in (
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ROOT / ".env.example",
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ROOT / "docker-compose.yml",
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ROOT / "docker-compose.unraid.yml",
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ROOT / "deploy" / "unraid" / "geointel.env.example",
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):
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content = path.read_text(encoding="utf-8")
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assert "FLOOD_HAZARD_ENABLED" in content
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assert "FLOOD_HAZARD_WCS_URL" in content
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assert "FLOOD_HAZARD_MAX_PIXELS" in content
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operator = (ROOT / "scripts" / "provision_mol_flood_hazards.py").read_text(encoding="utf-8")
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readiness = (ROOT / "scripts" / "run_readiness_check.sh").read_text(encoding="utf-8")
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dockerfile = (ROOT / "deploy" / "unraid" / "Dockerfile.all-in-one").read_text(encoding="utf-8")
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frontend = (ROOT / "frontend" / "src" / "components" / "map" / "MapWorkspace.tsx").read_text(encoding="utf-8")
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assert "/datasets/flood-hazard/acquire" in operator
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assert "/raster/flood-hazard/select" in operator
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assert "concurrent_flood_volume_m3" in operator
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assert "py_compile scripts/provision_mol_flood_hazards.py" in readiness
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assert "COPY scripts/provision_mol_flood_hazards.py" in dockerfile
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assert "Overstromingsscenario" in frontend
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assert "floodHazardImageUrl" in frontend
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