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#
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from datetime import datetime

import pytest
from numpy import nan
from numpy.testing import assert_array_equal
from pandas import DataFrame

from superset.exceptions import InvalidPostProcessingError
from superset.utils.core import DTTM_ALIAS, PostProcessingContributionOrientation
from superset.utils.pandas_postprocessing import contribution


def test_contribution():
    df = DataFrame(
        {
            DTTM_ALIAS: [
                datetime(2020, 7, 16, 14, 49),
                datetime(2020, 7, 16, 14, 50),
                datetime(2020, 7, 16, 14, 51),
            ],
            "a": [1, 3, nan],
            "b": [1, 9, nan],
            "c": [nan, nan, nan],
        }
    )
    with pytest.raises(InvalidPostProcessingError, match="not numeric"):
        contribution(df, columns=[DTTM_ALIAS])

    with pytest.raises(InvalidPostProcessingError, match="same length"):
        contribution(df, columns=["a"], rename_columns=["aa", "bb"])

    # cell contribution across row
    processed_df = contribution(
        df,
        orientation=PostProcessingContributionOrientation.ROW,
    )
    assert processed_df.columns.tolist() == [DTTM_ALIAS, "a", "b", "c"]
    assert_array_equal(processed_df["a"].tolist(), [0.5, 0.25, nan])
    assert_array_equal(processed_df["b"].tolist(), [0.5, 0.75, nan])
    assert_array_equal(processed_df["c"].tolist(), [0, 0, nan])

    # cell contribution across column without temporal column
    df.pop(DTTM_ALIAS)
    processed_df = contribution(
        df, orientation=PostProcessingContributionOrientation.COLUMN
    )
    assert processed_df.columns.tolist() == ["a", "b", "c"]
    assert_array_equal(processed_df["a"].tolist(), [0.25, 0.75, 0])
    assert_array_equal(processed_df["b"].tolist(), [0.1, 0.9, 0])
    assert_array_equal(processed_df["c"].tolist(), [nan, nan, nan])

    # contribution only on selected columns
    processed_df = contribution(
        df,
        orientation=PostProcessingContributionOrientation.COLUMN,
        columns=["a"],
        rename_columns=["pct_a"],
    )
    assert processed_df.columns.tolist() == ["a", "b", "c", "pct_a"]
    assert_array_equal(processed_df["a"].tolist(), [1, 3, nan])
    assert_array_equal(processed_df["b"].tolist(), [1, 9, nan])
    assert_array_equal(processed_df["c"].tolist(), [nan, nan, nan])
    assert processed_df["pct_a"].tolist() == [0.25, 0.75, 0]