"""Flood risk must be a share of what was modelled, not of what was drawn.

The share and fraction divided the inundated cells by every cell whose centre
fell inside the selection, including cells where the VMM raster holds nodata
because the area lies outside the modelled extent. An operator drawing a
rectangle that reaches past the model coverage read "3% at risk" where the
honest answer is "of the 40% we have a model for, 7.5% is at risk, and for the
rest there is no model at all".

Terrain, bathymetry and thematic raster analysis already divide by valid cells
and report a coverage ratio; this brings flood hazard in line.
"""

from __future__ import annotations

import pytest

np = pytest.importorskip("numpy")

from app.services.flood_hazard_analysis_service import (  # noqa: E402 - optional NumPy gate precedes service import
    FloodHazardCellStatistics,
)


NODATA = -9999.0


def _stats(values, selected) -> FloodHazardCellStatistics:
    return FloodHazardCellStatistics.from_cells(
        np.asarray(values, dtype="float64"),
        np.asarray(selected, dtype=bool),
        nodata=NODATA,
    )


def test_share_ignores_cells_the_model_does_not_cover() -> None:
    # Ten selected cells: four modelled (one of them wet), six nodata.
    values = [1.5, 0.0, 0.0, 0.0] + [NODATA] * 6
    selected = [True] * 10

    stats = _stats(values, selected)

    assert stats.selected_cell_count == 10
    assert stats.valid_cell_count == 4
    assert stats.no_data_cell_count == 6
    assert stats.inundated_cell_count == 1
    # 1 of 4 modelled cells, not 1 of 10 drawn cells.
    assert stats.inundated_fraction == pytest.approx(0.25)
    assert stats.data_coverage_ratio == pytest.approx(0.4)


def test_cells_outside_the_drawn_selection_are_not_counted() -> None:
    values = [1.5, 1.5, 0.0, 0.0]
    selected = [True, False, True, False]

    stats = _stats(values, selected)

    assert stats.selected_cell_count == 2
    assert stats.valid_cell_count == 2
    assert stats.inundated_cell_count == 1
    assert stats.inundated_fraction == pytest.approx(0.5)


def test_a_selection_without_any_model_data_reports_zero_coverage() -> None:
    stats = _stats([NODATA] * 4, [True] * 4)

    assert stats.valid_cell_count == 0
    assert stats.no_data_cell_count == 4
    assert stats.data_coverage_ratio == 0.0
    # No model, so no risk figure may be invented.
    assert stats.inundated_fraction is None


def test_nan_is_treated_as_missing_model_data() -> None:
    stats = _stats([float("nan"), 2.0], [True, True])

    assert stats.valid_cell_count == 1
    assert stats.no_data_cell_count == 1
    assert stats.inundated_cell_count == 1


def test_negative_depths_are_data_but_not_inundation() -> None:
    """A modelled zero or negative depth means dry, not unknown."""

    stats = _stats([0.0, 0.0, 3.0], [True, True, True])

    assert stats.valid_cell_count == 3
    assert stats.inundated_cell_count == 1
    assert stats.inundated_fraction == pytest.approx(1 / 3)


def test_depth_statistics_use_only_inundated_cells() -> None:
    stats = _stats([0.0, 2.0, 4.0, NODATA], [True] * 4)

    assert stats.depth_values.tolist() == [2.0, 4.0]
    assert stats.depth_values.mean() == pytest.approx(3.0)


def test_areas_are_derived_from_the_matching_cell_populations() -> None:
    stats = _stats([1.0, 1.0, 0.0, NODATA], [True] * 4)

    # 100 m2 cells: 2 inundated, 3 modelled, 4 drawn.
    assert stats.inundated_area_ha(100.0) == pytest.approx(2 * 100.0 / 10_000.0)
    assert stats.analysed_area_ha(100.0) == pytest.approx(3 * 100.0 / 10_000.0)
    assert stats.selected_area_ha(100.0) == pytest.approx(4 * 100.0 / 10_000.0)