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#
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import pandas as pd

from superset.utils import pandas_postprocessing as pp
from superset.utils.pandas_postprocessing.utils import FLAT_COLUMN_SEPARATOR
from tests.unit_tests.fixtures.dataframes import timeseries_df


def test_flat_should_not_change():
    df = pd.DataFrame(
        data={
            "foo": [1, 2, 3],
            "bar": [4, 5, 6],
        }
    )

    assert pp.flatten(df).equals(df)


def test_flat_should_not_reset_index():
    index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"])
    index.name = "__timestamp"
    df = pd.DataFrame(index=index, data={"foo": [1, 2, 3], "bar": [4, 5, 6]})

    assert pp.flatten(df, reset_index=False).equals(df)


def test_flat_should_flat_datetime_index():
    index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"])
    index.name = "__timestamp"
    df = pd.DataFrame(index=index, data={"foo": [1, 2, 3], "bar": [4, 5, 6]})

    assert pp.flatten(df).equals(
        pd.DataFrame(
            {
                "__timestamp": index,
                "foo": [1, 2, 3],
                "bar": [4, 5, 6],
            }
        )
    )


def test_flat_should_flat_multiple_index():
    index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"])
    index.name = "__timestamp"
    iterables = [["foo", "bar"], [1, "two"]]
    columns = pd.MultiIndex.from_product(iterables, names=["level1", "level2"])
    df = pd.DataFrame(index=index, columns=columns, data=1)

    assert pp.flatten(df).equals(
        pd.DataFrame(
            {
                "__timestamp": index,
                FLAT_COLUMN_SEPARATOR.join(["foo", "1"]): [1, 1, 1],
                FLAT_COLUMN_SEPARATOR.join(["foo", "two"]): [1, 1, 1],
                FLAT_COLUMN_SEPARATOR.join(["bar", "1"]): [1, 1, 1],
                FLAT_COLUMN_SEPARATOR.join(["bar", "two"]): [1, 1, 1],
            }
        )
    )


def test_flat_should_drop_index_level():
    index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"])
    index.name = "__timestamp"
    columns = pd.MultiIndex.from_arrays(
        [["a"] * 3, ["b"] * 3, ["c", "d", "e"], ["ff", "ii", "gg"]],
        names=["level1", "level2", "level3", "level4"],
    )
    df = pd.DataFrame(index=index, columns=columns, data=1)

    # drop level by index
    assert pp.flatten(df.copy(), drop_levels=(0, 1,)).equals(
        pd.DataFrame(
            {
                "__timestamp": index,
                FLAT_COLUMN_SEPARATOR.join(["c", "ff"]): [1, 1, 1],
                FLAT_COLUMN_SEPARATOR.join(["d", "ii"]): [1, 1, 1],
                FLAT_COLUMN_SEPARATOR.join(["e", "gg"]): [1, 1, 1],
            }
        )
    )

    # drop level by name
    assert pp.flatten(df.copy(), drop_levels=("level1", "level2")).equals(
        pd.DataFrame(
            {
                "__timestamp": index,
                FLAT_COLUMN_SEPARATOR.join(["c", "ff"]): [1, 1, 1],
                FLAT_COLUMN_SEPARATOR.join(["d", "ii"]): [1, 1, 1],
                FLAT_COLUMN_SEPARATOR.join(["e", "gg"]): [1, 1, 1],
            }
        )
    )

    # only leave 1 level
    assert pp.flatten(df.copy(), drop_levels=(0, 1, 2)).equals(
        pd.DataFrame(
            {
                "__timestamp": index,
                FLAT_COLUMN_SEPARATOR.join(["ff"]): [1, 1, 1],
                FLAT_COLUMN_SEPARATOR.join(["ii"]): [1, 1, 1],
                FLAT_COLUMN_SEPARATOR.join(["gg"]): [1, 1, 1],
            }
        )
    )


def test_flat_should_not_droplevel():
    assert pp.flatten(timeseries_df, drop_levels=(0,)).equals(
        pd.DataFrame(
            {
                "index": pd.to_datetime(
                    ["2019-01-01", "2019-01-02", "2019-01-05", "2019-01-07"]
                ),
                "label": ["x", "y", "z", "q"],
                "y": [1.0, 2.0, 3.0, 4.0],
            }
        )
    )


def test_flat_integer_column_name():
    index = pd.to_datetime(["2021-01-01", "2021-01-02", "2021-01-03"])
    index.name = "__timestamp"
    columns = pd.MultiIndex.from_arrays(
        [["a"] * 3, [100, 200, 300]],
        names=["level1", "level2"],
    )
    df = pd.DataFrame(index=index, columns=columns, data=1)
    assert pp.flatten(df, drop_levels=(0,)).equals(
        pd.DataFrame(
            {
                "__timestamp": pd.to_datetime(
                    ["2021-01-01", "2021-01-02", "2021-01-03"]
                ),
                "100": [1, 1, 1],
                "200": [1, 1, 1],
                "300": [1, 1, 1],
            }
        )
    )