import polars as pl

from pipeline.download.ethnicity import _ethnicity_percentages


def test_ethnicity_percentages_recombines_predecessor_lads_by_population():
    rows = []
    for code, white, indian in [
        ("E07000026", 80, 20),
        ("E07000028", 10, 90),
    ]:
        total = white + indian
        rows.extend(
            [
                {
                    "Geography_code": code,
                    "Ethnicity_type": "ONS 2021 19+1",
                    "Ethnicity": "White British",
                    "Ethnic Population": white,
                    "Value1": white / total * 100,
                },
                {
                    "Geography_code": code,
                    "Ethnicity_type": "ONS 2021 19+1",
                    "Ethnicity": "Indian",
                    "Ethnic Population": indian,
                    "Value1": indian / total * 100,
                },
            ]
        )

    result = _ethnicity_percentages(pl.DataFrame(rows))

    cumberland = result.filter(pl.col("Geography_code") == "E06000063")
    assert cumberland.select("% White", "% South Asian").to_dicts() == [
        {"% White": 45.0, "% South Asian": 55.0}
    ]