366 lines
18 KiB
Python
366 lines
18 KiB
Python
from __future__ import annotations
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import io
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import math
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from datetime import UTC, datetime
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from pathlib import Path
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from uuid import UUID
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from geoalchemy2.shape import to_shape
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from pyproj import Transformer
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from shapely.geometry import box, mapping
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from shapely.ops import transform as shapely_transform
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from app.core.config import Settings, get_settings
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from app.core.errors import AppError
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from app.models import Area, Dataset
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from app.schemas.flood_hazard import (
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FloodHazardMetric,
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FloodHazardPartitionSelectionRequest,
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FloodHazardSelectionRequest,
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FloodHazardSelectionResponse,
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FloodHazardSelectionSummary,
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)
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from app.services.flood_hazard_acquisition_service import FloodHazardAcquisitionService
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from app.services.raster_partition_analysis_service import RasterPartitionAnalysisService
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class FloodHazardAnalysisService:
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UNSUPPORTED_METRICS = [
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"bathymetry_depth_m",
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"permanent_water_volume_m3",
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"concurrent_flood_volume_m3",
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]
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LIMITATION = (
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"Alle waarden horen bij het gekozen VMM-overstromingsscenario. De diepte-oppervlakte-integraal telt lokale "
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"gemodelleerde maxima op en is geen gelijktijdig opgeslagen watervolume, actuele waterstand of bathymetrie."
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)
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@staticmethod
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def _load_dataset(db, project_id: UUID, dataset_id: UUID) -> Dataset:
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dataset = db.get(Dataset, dataset_id)
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if not dataset or dataset.project_id != project_id:
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raise AppError(code="DATASET_NOT_FOUND", message="Dataset not found", status_code=404)
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if dataset.dataset_type != "raster" or dataset.source_name != FloodHazardAcquisitionService.PROVIDER:
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raise AppError(
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code="INVALID_FLOOD_HAZARD_DATASET",
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message="Flood-hazard analysis requires a governed VMM flood-depth raster",
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status_code=400,
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)
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if dataset.status != "ready" or not dataset.storage_path or not Path(dataset.storage_path).is_file():
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raise AppError(code="DATASET_FILE_MISSING", message="Persisted VMM flood-depth raster is unavailable", status_code=404)
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return dataset
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@staticmethod
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def _selection_geometry(db, project_id: UUID, payload: FloodHazardSelectionRequest):
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selection = box(payload.bbox.min_x, payload.bbox.min_y, payload.bbox.max_x, payload.bbox.max_y)
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if payload.area_id is None:
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return selection
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area = db.get(Area, payload.area_id)
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if not area:
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raise AppError(code="AREA_NOT_FOUND", message="Area not found", status_code=404)
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if area.project_id != project_id:
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raise AppError(code="INVALID_DATASET_SCOPE", message="Area does not belong to this project", status_code=400)
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intersection = selection.intersection(to_shape(area.geometry))
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if intersection.is_empty or intersection.area <= 0:
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raise AppError(code="FLOOD_HAZARD_SELECTION_OUTSIDE_AREA", message="Selection does not overlap the selected work area", status_code=422)
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return intersection
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@staticmethod
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def analyze(
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db,
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project_id: UUID,
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dataset_id: UUID,
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payload: FloodHazardSelectionRequest,
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*,
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settings: Settings | None = None,
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) -> dict:
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resolved_settings = settings or get_settings()
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dataset = FloodHazardAnalysisService._load_dataset(db, project_id, dataset_id)
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selection_4326 = FloodHazardAnalysisService._selection_geometry(db, project_id, payload)
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try:
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import numpy as np
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import rasterio
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from rasterio.features import geometry_mask
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from rasterio.mask import mask
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except ImportError as exc:
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raise AppError(code="RASTER_PROCESSING_UNAVAILABLE", message="Rasterio and numpy are required for flood-hazard analysis", status_code=503) from exc
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source_metadata = dataset.source_metadata or {}
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product_key = str(source_metadata.get("product_key") or "")
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product = FloodHazardAcquisitionService._products().get(product_key)
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if product is None or str(source_metadata.get("normalized_value_unit") or "") != "m":
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raise AppError(code="INVALID_FLOOD_HAZARD_METADATA", message="VMM flood-hazard provenance is incomplete", status_code=409)
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try:
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with rasterio.open(dataset.storage_path) as source:
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if source.crs is None:
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raise AppError(code="INVALID_DATASET_CRS", message="VMM flood-depth raster CRS is missing", status_code=409)
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transformer = Transformer.from_crs("EPSG:4326", source.crs, always_xy=True)
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selection_metric = shapely_transform(transformer.transform, selection_4326)
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analysis_geometry = selection_metric.intersection(box(*source.bounds))
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if analysis_geometry.is_empty or analysis_geometry.area <= 0:
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raise AppError(code="FLOOD_HAZARD_SELECTION_OUTSIDE_DATASET", message="Selection does not overlap the persisted flood-depth raster", status_code=422)
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min_x, min_y, max_x, max_y = analysis_geometry.bounds
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expected_cells = math.ceil((max_x - min_x) / abs(source.res[0])) * math.ceil((max_y - min_y) / abs(source.res[1]))
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if expected_cells > resolved_settings.flood_hazard_max_pixels:
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raise AppError(
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code="FLOOD_HAZARD_SELECTION_TOO_LARGE",
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message="Flood-hazard analysis exceeds the configured raster cell limit",
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details={"pixel_count": expected_cells, "max_pixels": resolved_settings.flood_hazard_max_pixels},
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status_code=422,
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)
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clipped, clipped_transform = mask(source, [mapping(analysis_geometry)], crop=True, filled=False, indexes=[1])
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depth = np.ma.asarray(clipped[0], dtype="float64")
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raw = depth.filled(np.nan)
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selected_cells = geometry_mask([mapping(analysis_geometry)], out_shape=depth.shape, transform=clipped_transform, invert=True)
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nodata = source.nodata
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valid = selected_cells & ~np.ma.getmaskarray(depth) & np.isfinite(raw) & (raw > 0.0)
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if nodata is not None:
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valid &= raw != float(nodata)
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values = raw[valid]
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selected_cell_count = int(selected_cells.sum())
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inundated_cell_count = int(values.size)
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resolution_x = abs(float(source.res[0]))
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resolution_y = abs(float(source.res[1]))
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cell_area_m2 = resolution_x * resolution_y
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except AppError:
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raise
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except Exception as exc:
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raise AppError(
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code="FLOOD_HAZARD_ANALYSIS_FAILED",
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message="The persisted VMM flood-depth raster could not be analysed",
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details={"reason": str(exc)},
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status_code=500,
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) from exc
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def metric(key: str, label: str, value: float, unit: str, method: str) -> FloodHazardMetric:
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return FloodHazardMetric(
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metric_key=key,
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metric_label=label,
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metric_value=round(float(value), 4),
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metric_unit=unit,
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aggregation_method=method,
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)
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inundated_area_ha = inundated_cell_count * cell_area_m2 / 10_000.0
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metrics = [
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metric("modelled_inundated_area_ha", "Gemodelleerd overstroomd oppervlak", inundated_area_ha, "ha", "positive_depth_cells_times_cell_area"),
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metric(
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"modelled_inundated_share_pct",
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"Aandeel selectie met gemodelleerde diepte",
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inundated_cell_count / max(1, selected_cell_count) * 100.0,
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"%",
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"positive_depth_cells_divided_by_selected_cells",
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),
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]
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if inundated_cell_count:
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metrics.extend(
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[
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metric("modelled_depth_mean_m", "Gemiddelde gemodelleerde maximumdiepte", values.mean(), "m", "mean_positive_depth_cells"),
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metric("modelled_depth_p90_m", "90e percentiel gemodelleerde maximumdiepte", np.percentile(values, 90), "m", "percentile_90_positive_depth_cells"),
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metric("modelled_depth_max_m", "Hoogste gemodelleerde maximumdiepte", values.max(), "m", "maximum_positive_depth_cells"),
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metric(
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"modelled_max_depth_area_integral_m3",
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"Diepte-oppervlakte-integraal (geen gelijktijdig volume)",
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values.sum() * cell_area_m2,
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"m3",
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"sum_local_max_depth_times_cell_area",
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),
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]
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)
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primary = metrics[0]
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response = FloodHazardSelectionResponse(
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dataset_id=dataset.id,
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dataset_ids=[dataset.id],
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partition_count=1,
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product_key=product.key,
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mechanism=product.mechanism,
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climate_context=product.climate_context,
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probability_class=product.probability_class,
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return_period_years=product.return_period_years,
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selection_bbox=payload.bbox,
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selection_area_id=payload.area_id,
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selected_cell_count=selected_cell_count,
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inundated_cell_count=inundated_cell_count,
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inundated_fraction=round(inundated_cell_count / max(1, selected_cell_count), 6),
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resolution_m=round(max(resolution_x, resolution_y), 4),
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summary=FloodHazardSelectionSummary(
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metric_label=primary.metric_label,
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metric_value=primary.metric_value,
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metric_unit=primary.metric_unit,
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aggregation_method=primary.aggregation_method,
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primary_metric_key=primary.metric_key,
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metrics=metrics,
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),
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unsupported_metrics=FloodHazardAnalysisService.UNSUPPORTED_METRICS,
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limitation_message=FloodHazardAnalysisService.LIMITATION,
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generated_at=datetime.now(UTC).isoformat(),
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)
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return response.model_dump(mode="json")
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@staticmethod
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def analyze_partitions(
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db,
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project_id: UUID,
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payload: FloodHazardPartitionSelectionRequest,
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*,
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settings: Settings | None = None,
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) -> dict:
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resolved_settings = settings or get_settings()
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product = FloodHazardAcquisitionService._products().get(payload.product_key.strip().lower())
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if product is None:
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raise AppError(
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code="FLOOD_HAZARD_PRODUCT_NOT_SUPPORTED",
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message="Select a governed VMM fluvial or pluvial flood-depth scenario",
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details={"product_key": payload.product_key},
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status_code=422,
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)
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selection_4326 = FloodHazardAnalysisService._selection_geometry(db, project_id, payload)
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partition = RasterPartitionAnalysisService.select(
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db,
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project_id,
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source_name=FloodHazardAcquisitionService.PROVIDER,
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product_key=product.key,
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selection_geometry_4326=selection_4326,
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nodata=FloodHazardAcquisitionService.NODATA,
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max_pixels=resolved_settings.flood_hazard_max_pixels,
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)
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try:
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import numpy as np
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except ImportError as exc:
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raise AppError(
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code="RASTER_PROCESSING_UNAVAILABLE",
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message="Numpy is required for partitioned flood-hazard analysis",
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status_code=503,
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) from exc
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raw = partition.values
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valid = (
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partition.selected_cells
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& np.isfinite(raw)
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& (raw != FloodHazardAcquisitionService.NODATA)
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& (raw > 0.0)
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)
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values = raw[valid]
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selected_cell_count = int(partition.selected_cells.sum())
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inundated_cell_count = int(values.size)
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cell_area_m2 = partition.resolution_x * partition.resolution_y
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def metric(key: str, label: str, value: float, unit: str, method: str) -> FloodHazardMetric:
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return FloodHazardMetric(
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metric_key=key,
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metric_label=label,
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metric_value=round(float(value), 4),
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metric_unit=unit,
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aggregation_method=method,
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)
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inundated_area_ha = inundated_cell_count * cell_area_m2 / 10_000.0
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metrics = [
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metric(
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"modelled_inundated_area_ha",
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"Gemodelleerd overstroomd oppervlak",
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inundated_area_ha,
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"ha",
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"positive_depth_cells_times_cell_area",
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),
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metric(
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"modelled_inundated_share_pct",
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"Aandeel selectie met gemodelleerde diepte",
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inundated_cell_count / max(1, selected_cell_count) * 100.0,
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"%",
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"positive_depth_cells_divided_by_selected_cells",
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),
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]
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if inundated_cell_count:
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metrics.extend(
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[
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metric("modelled_depth_mean_m", "Gemiddelde gemodelleerde maximumdiepte", values.mean(), "m", "mean_positive_depth_cells"),
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metric("modelled_depth_p90_m", "90e percentiel gemodelleerde maximumdiepte", np.percentile(values, 90), "m", "percentile_90_positive_depth_cells"),
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metric("modelled_depth_max_m", "Hoogste gemodelleerde maximumdiepte", values.max(), "m", "maximum_positive_depth_cells"),
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metric(
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"modelled_max_depth_area_integral_m3",
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"Diepte-oppervlakte-integraal (geen gelijktijdig volume)",
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values.sum() * cell_area_m2,
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"m3",
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"sum_local_max_depth_times_cell_area",
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),
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]
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)
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primary = metrics[0]
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first_dataset = partition.datasets[0]
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response = FloodHazardSelectionResponse(
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dataset_id=first_dataset.id,
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dataset_ids=[dataset.id for dataset in partition.datasets],
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partition_count=len(partition.datasets),
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product_key=product.key,
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mechanism=product.mechanism,
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climate_context=product.climate_context,
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probability_class=product.probability_class,
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return_period_years=product.return_period_years,
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selection_bbox=payload.bbox,
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selection_area_id=payload.area_id,
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selected_cell_count=selected_cell_count,
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inundated_cell_count=inundated_cell_count,
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inundated_fraction=round(inundated_cell_count / max(1, selected_cell_count), 6),
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resolution_m=round(max(partition.resolution_x, partition.resolution_y), 4),
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summary=FloodHazardSelectionSummary(
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metric_label=primary.metric_label,
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metric_value=primary.metric_value,
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metric_unit=primary.metric_unit,
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aggregation_method=primary.aggregation_method,
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primary_metric_key=primary.metric_key,
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metrics=metrics,
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),
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unsupported_metrics=FloodHazardAnalysisService.UNSUPPORTED_METRICS,
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limitation_message=(
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f"{FloodHazardAnalysisService.LIMITATION} De selectie werd exact berekend over "
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f"{len(partition.datasets)} persistente gemeentelijke rasterpartities."
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),
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generated_at=datetime.now(UTC).isoformat(),
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)
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return response.model_dump(mode="json")
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@staticmethod
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def render_png(db, project_id: UUID, dataset_id: UUID, *, max_dimension: int = 1800) -> bytes:
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dataset = FloodHazardAnalysisService._load_dataset(db, project_id, dataset_id)
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try:
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import numpy as np
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import rasterio
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from PIL import Image
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from rasterio.enums import Resampling
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except ImportError as exc:
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raise AppError(code="RASTER_PROCESSING_UNAVAILABLE", message="Rasterio, numpy and Pillow are required for flood-hazard rendering", status_code=503) from exc
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try:
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with rasterio.open(dataset.storage_path) as source:
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scale = min(1.0, max_dimension / max(source.width, source.height))
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width = max(1, round(source.width * scale))
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height = max(1, round(source.height * scale))
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data = source.read(1, out_shape=(height, width), masked=True, resampling=Resampling.bilinear)
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values = np.asarray(data.filled(np.nan), dtype="float64")
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valid = np.isfinite(values) & ~np.ma.getmaskarray(data) & (values > 0.0)
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normalized = np.clip(values / 2.0, 0.0, 1.0)
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normalized = np.where(valid, normalized, 0.0)
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stops = np.asarray([0.0, 0.15, 0.35, 0.65, 1.0])
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colors = np.asarray(
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[[190, 228, 255], [105, 184, 235], [42, 132, 201], [19, 83, 154], [8, 36, 92]],
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dtype="float64",
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)
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rgba = np.zeros((height, width, 4), dtype="uint8")
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for channel in range(3):
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rgba[:, :, channel] = np.interp(normalized, stops, colors[:, channel]).astype("uint8")
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rgba[:, :, 3] = np.where(valid, np.clip(150 + normalized * 90, 0, 235), 0).astype("uint8")
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output = io.BytesIO()
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Image.fromarray(rgba).save(output, format="PNG", optimize=True)
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return output.getvalue()
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except AppError:
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raise
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except Exception as exc:
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raise AppError(
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code="FLOOD_HAZARD_PREVIEW_FAILED",
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message="The persisted VMM flood-depth raster could not be rendered",
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details={"reason": str(exc)},
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status_code=500,
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) from exc
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