import pytest


@pytest.fixture()
def example_data():
    import pandas as pd

    data = [
        ("A", "a", 1, 1, 100, 5),
        ("A", "a", 1, 2, 100, 2),
        ("A", "a", 1, 3, 100, 0),
        ("A", "b", 1, 1, 200, 0),
        ("A", "b", 1, 2, 200, 3),
        ("A", "b", 1, 3, 200, 0),
        ("B", "a", 1, 1, 300, 0),
        ("B", "a", 1, 2, 300, 2),
        ("B", "a", 1, 3, 300, 0),
    ]
    columns = [
        "Analysis.Name",
        "Well.Name",
        "Pos.Id",
        "Exposure.Id",
        "Spot.Mean",
        "Spot.Saturation",
    ]
    return pd.DataFrame(data, columns=columns)


@pytest.mark.parametrize(
    ("analysis", "expected_cy3"), [("dry1", 100), ("hyb", 200)]
)
def test_add_exposure_info(example_data, analysis, expected_cy3):
    from conda_helpers.mbp import add_exposure_info

    result = add_exposure_info(example_data, analysis=analysis)

    assert "Exposure.Channel" in result.columns
    assert "Exposure.Time" in result.columns
    assert "Exposure.Time.Normalized" in result.columns

    for i, channel, time in [
        (1, "Cy3", expected_cy3),
        (2, "Cy5", 150),
        (3, "Cy5", 15),
    ]:
        selection = result["Exposure.Id"] == i
        selected = result[selection].copy()
        assert list(selected["Exposure.Channel"].unique()) == [channel]
        assert list(selected["Exposure.Time"].unique()) == [time]


def test_test_overflow(example_data):
    from conda_helpers.mbp import TEST_OVERFLOW, test_overflow

    result = test_overflow(example_data)

    assert list(result[TEST_OVERFLOW]) == [
        True,
        False,
        False,
        False,
        True,
        False,
        False,
        False,
        False,
    ]


def test_normalize(example_data):
    from conda_helpers.mbp import normalize

    result = normalize(example_data)
    assert "Spot.Mean.Normalized" in result.columns

    assert list(result["Spot.Mean.Normalized"]) == [
        100,
        200,
        300,
        100 * 25 / 150,
        300 * 25 / 150,
        200 * 25 / 15,
    ]