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106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
"""Flood risk must be a share of what was modelled, not of what was drawn.
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The share and fraction divided the inundated cells by every cell whose centre
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fell inside the selection, including cells where the VMM raster holds nodata
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because the area lies outside the modelled extent. An operator drawing a
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rectangle that reaches past the model coverage read "3% at risk" where the
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honest answer is "of the 40% we have a model for, 7.5% is at risk, and for the
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rest there is no model at all".
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Terrain, bathymetry and thematic raster analysis already divide by valid cells
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and report a coverage ratio; this brings flood hazard in line.
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"""
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from __future__ import annotations
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import pytest
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np = pytest.importorskip("numpy")
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from app.services.flood_hazard_analysis_service import ( # noqa: E402 - optional NumPy gate precedes service import
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FloodHazardCellStatistics,
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)
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NODATA = -9999.0
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def _stats(values, selected) -> FloodHazardCellStatistics:
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return FloodHazardCellStatistics.from_cells(
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np.asarray(values, dtype="float64"),
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np.asarray(selected, dtype=bool),
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nodata=NODATA,
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)
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def test_share_ignores_cells_the_model_does_not_cover() -> None:
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# Ten selected cells: four modelled (one of them wet), six nodata.
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values = [1.5, 0.0, 0.0, 0.0] + [NODATA] * 6
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selected = [True] * 10
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stats = _stats(values, selected)
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assert stats.selected_cell_count == 10
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assert stats.valid_cell_count == 4
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assert stats.no_data_cell_count == 6
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assert stats.inundated_cell_count == 1
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# 1 of 4 modelled cells, not 1 of 10 drawn cells.
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assert stats.inundated_fraction == pytest.approx(0.25)
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assert stats.data_coverage_ratio == pytest.approx(0.4)
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def test_cells_outside_the_drawn_selection_are_not_counted() -> None:
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values = [1.5, 1.5, 0.0, 0.0]
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selected = [True, False, True, False]
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stats = _stats(values, selected)
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assert stats.selected_cell_count == 2
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assert stats.valid_cell_count == 2
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assert stats.inundated_cell_count == 1
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assert stats.inundated_fraction == pytest.approx(0.5)
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def test_a_selection_without_any_model_data_reports_zero_coverage() -> None:
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stats = _stats([NODATA] * 4, [True] * 4)
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assert stats.valid_cell_count == 0
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assert stats.no_data_cell_count == 4
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assert stats.data_coverage_ratio == 0.0
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# No model, so no risk figure may be invented.
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assert stats.inundated_fraction is None
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def test_nan_is_treated_as_missing_model_data() -> None:
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stats = _stats([float("nan"), 2.0], [True, True])
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assert stats.valid_cell_count == 1
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assert stats.no_data_cell_count == 1
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assert stats.inundated_cell_count == 1
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def test_negative_depths_are_data_but_not_inundation() -> None:
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"""A modelled zero or negative depth means dry, not unknown."""
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stats = _stats([0.0, 0.0, 3.0], [True, True, True])
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assert stats.valid_cell_count == 3
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assert stats.inundated_cell_count == 1
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assert stats.inundated_fraction == pytest.approx(1 / 3)
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def test_depth_statistics_use_only_inundated_cells() -> None:
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stats = _stats([0.0, 2.0, 4.0, NODATA], [True] * 4)
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assert stats.depth_values.tolist() == [2.0, 4.0]
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assert stats.depth_values.mean() == pytest.approx(3.0)
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def test_areas_are_derived_from_the_matching_cell_populations() -> None:
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stats = _stats([1.0, 1.0, 0.0, NODATA], [True] * 4)
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# 100 m2 cells: 2 inundated, 3 modelled, 4 drawn.
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assert stats.inundated_area_ha(100.0) == pytest.approx(2 * 100.0 / 10_000.0)
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assert stats.analysed_area_ha(100.0) == pytest.approx(3 * 100.0 / 10_000.0)
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assert stats.selected_area_ha(100.0) == pytest.approx(4 * 100.0 / 10_000.0)
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