from typing import Optional

from scripts.audit.neighbour_divergence import Neighbour, find_divergences


def _n(
    property_id: int,
    *,
    effective_sap: Optional[float],
    postcode: str = "AB1 2CD",
    property_type: str = "Flat",
    built_form: str = "Mid-Terrace",
    floor_area_m2: float = 60.0,
    main_fuel_type: Optional[int] = 26,  # default: mains gas
) -> Neighbour:
    return Neighbour(
        property_id=property_id,
        uprn=None,
        postcode=postcode,
        property_type=property_type,
        built_form=built_form,
        floor_area_m2=floor_area_m2,
        effective_sap=effective_sap,
        effective_band=None,
        main_fuel_type=main_fuel_type,
    )


class TestFindDivergences:
    def test_flags_the_outlier_neighbour(self) -> None:
        # Arrange — three identical-cohort flats, one sits a clear band below.
        cohort = [
            _n(1, effective_sap=70.0),
            _n(2, effective_sap=72.0),
            _n(3, effective_sap=50.0),  # Δ-20 vs median 70
        ]

        # Act
        result = find_divergences(cohort, min_gap=12.0)

        # Assert — only the outlier is flagged.
        assert [d.property_id for d in result] == [3]

    def test_silent_when_cohort_agrees(self) -> None:
        # Arrange — neighbours within a few SAP points of each other.
        cohort = [_n(1, effective_sap=70.0), _n(2, effective_sap=68.0)]

        # Act
        result = find_divergences(cohort, min_gap=12.0)

        # Assert
        assert result == []

    def test_singletons_never_flagged(self) -> None:
        # Arrange — one flat here, one house there: neither has a peer to diverge from.
        lonely = [
            _n(1, effective_sap=30.0, property_type="Flat"),
            _n(2, effective_sap=90.0, property_type="House"),
        ]

        # Act
        result = find_divergences(lonely, min_gap=12.0)

        # Assert
        assert result == []

    def test_different_postcode_is_a_different_cohort(self) -> None:
        # Arrange — same type/form but different postcodes: not neighbours.
        split = [
            _n(1, effective_sap=40.0, postcode="AB1 2CD"),
            _n(2, effective_sap=80.0, postcode="ZZ9 9ZZ"),
        ]

        # Act
        result = find_divergences(split, min_gap=12.0)

        # Assert
        assert result == []

    def test_electric_and_gas_are_separate_cohorts(self) -> None:
        # Arrange — same postcode/type/form, but an electric dwelling (fuel 29)
        # scores a band below its gas neighbours (fuel 26). Electricity is
        # inherently lower-SAP, so this is expected stock, not a divergence.
        mixed = [
            _n(1, effective_sap=70.0, main_fuel_type=26),  # gas
            _n(2, effective_sap=72.0, main_fuel_type=26),  # gas
            _n(3, effective_sap=52.0, main_fuel_type=29),  # electric — its own cohort
        ]

        # Act
        result = find_divergences(mixed, min_gap=12.0)

        # Assert — the electric singleton has no same-fuel peers, so nothing fires.
        assert result == []

    def test_electric_outlier_still_flagged_within_its_own_fuel_cohort(self) -> None:
        # Arrange — three electric dwellings; one genuinely diverges. Keying on
        # fuel must not blind the detector to real within-fuel outliers.
        electric = [
            _n(1, effective_sap=55.0, main_fuel_type=29),
            _n(2, effective_sap=57.0, main_fuel_type=30),  # 30 also electric-class
            _n(3, effective_sap=30.0, main_fuel_type=29),  # Δ-25 vs median
        ]

        # Act
        result = find_divergences(electric, min_gap=12.0)

        # Assert
        assert [d.property_id for d in result] == [3]