import polars as pl

from pipeline.transform.merge import (
    _AREA_COLUMNS,
    _STATIC_POI_DISTANCE_RENAMES,
    _is_dynamic_poi_metric_column,
    _less_deprived_percentile_expr,
)


def test_less_deprived_percentile_expr_preserves_direction_and_nulls() -> None:
    df = pl.DataFrame({"Income Score (rate)": [1.0, 2.0, 3.0, None]})

    result = df.lazy().with_columns(
        _less_deprived_percentile_expr("Income Score (rate)")
    ).collect()

    assert result["Income Score (rate)"].to_list() == [100.0, 50.0, 0.0, None]


def test_less_deprived_percentile_expr_uses_exact_scale_endpoints() -> None:
    df = pl.DataFrame({"Income Score (rate)": [1.0, 1.0, 2.0, 3.0, 3.0]})

    result = df.lazy().with_columns(
        _less_deprived_percentile_expr("Income Score (rate)")
    ).collect()

    assert result["Income Score (rate)"].to_list() == [100.0, 100.0, 50.0, 0.0, 0.0]


def test_dynamic_poi_metric_columns_are_area_level() -> None:
    assert _is_dynamic_poi_metric_column("Distance to nearest amenity (Cafe) (km)")
    assert _is_dynamic_poi_metric_column("Number of amenities (Cafe) within 2km")
    assert _is_dynamic_poi_metric_column("Number of amenities (Cafe) within 5km")
    assert not _is_dynamic_poi_metric_column("Number of restaurants within 2km")


def test_static_poi_distance_columns_are_renamed_to_configured_area_features() -> None:
    expected = {
        "parks_nearest_km": "Distance to nearest park (km)",
        "grocery_store_nearest_km": "Distance to nearest grocery store (km)",
        "cafe_nearest_km": "Distance to nearest cafe (km)",
        "pub_nearest_km": "Distance to nearest pub (km)",
        "restaurant_nearest_km": "Distance to nearest restaurant (km)",
    }

    assert _STATIC_POI_DISTANCE_RENAMES == expected
    assert set(expected.values()).issubset(_AREA_COLUMNS)