import numpy as np
import pytest

from pandas import DataFrame, Series, concat
import pandas._testing as tm


@pytest.mark.parametrize("func", ["cov", "corr"])
def test_ewm_pairwise_cov_corr(func, frame):
    result = getattr(frame.ewm(span=10, min_periods=5), func)()
    result = result.loc[(slice(None), 1), 5]
    result.index = result.index.droplevel(1)
    expected = getattr(frame[1].ewm(span=10, min_periods=5), func)(frame[5])
    expected.index = expected.index._with_freq(None)
    tm.assert_series_equal(result, expected, check_names=False)


@pytest.mark.parametrize("name", ["cov", "corr"])
def test_ewm_corr_cov(name):
    A = Series(np.random.randn(50), index=np.arange(50))
    B = A[2:] + np.random.randn(48)

    A[:10] = np.NaN
    B[-10:] = np.NaN

    result = getattr(A.ewm(com=20, min_periods=5), name)(B)
    assert np.isnan(result.values[:14]).all()
    assert not np.isnan(result.values[14:]).any()


@pytest.mark.parametrize("min_periods", [0, 1, 2])
@pytest.mark.parametrize("name", ["cov", "corr"])
def test_ewm_corr_cov_min_periods(name, min_periods):
    # GH 7898
    A = Series(np.random.randn(50), index=np.arange(50))
    B = A[2:] + np.random.randn(48)

    A[:10] = np.NaN
    B[-10:] = np.NaN

    result = getattr(A.ewm(com=20, min_periods=min_periods), name)(B)
    # binary functions (ewmcov, ewmcorr) with bias=False require at
    # least two values
    assert np.isnan(result.values[:11]).all()
    assert not np.isnan(result.values[11:]).any()

    # check series of length 0
    empty = Series([], dtype=np.float64)
    result = getattr(empty.ewm(com=50, min_periods=min_periods), name)(empty)
    tm.assert_series_equal(result, empty)

    # check series of length 1
    result = getattr(Series([1.0]).ewm(com=50, min_periods=min_periods), name)(
        Series([1.0])
    )
    tm.assert_series_equal(result, Series([np.NaN]))


@pytest.mark.parametrize("name", ["cov", "corr"])
def test_different_input_array_raise_exception(name):
    A = Series(np.random.randn(50), index=np.arange(50))
    A[:10] = np.NaN

    msg = "Input arrays must be of the same type!"
    # exception raised is Exception
    with pytest.raises(Exception, match=msg):
        getattr(A.ewm(com=20, min_periods=5), name)(np.random.randn(50))


def create_mock_weights(obj, com, adjust, ignore_na):
    if isinstance(obj, DataFrame):
        if not len(obj.columns):
            return DataFrame(index=obj.index, columns=obj.columns)
        w = concat(
            [
                create_mock_series_weights(
                    obj.iloc[:, i], com=com, adjust=adjust, ignore_na=ignore_na
                )
                for i, _ in enumerate(obj.columns)
            ],
            axis=1,
        )
        w.index = obj.index
        w.columns = obj.columns
        return w
    else:
        return create_mock_series_weights(obj, com, adjust, ignore_na)


def create_mock_series_weights(s, com, adjust, ignore_na):
    w = Series(np.nan, index=s.index)
    alpha = 1.0 / (1.0 + com)
    if adjust:
        count = 0
        for i in range(len(s)):
            if s.iat[i] == s.iat[i]:
                w.iat[i] = pow(1.0 / (1.0 - alpha), count)
                count += 1
            elif not ignore_na:
                count += 1
    else:
        sum_wts = 0.0
        prev_i = -1
        count = 0
        for i in range(len(s)):
            if s.iat[i] == s.iat[i]:
                if prev_i == -1:
                    w.iat[i] = 1.0
                else:
                    w.iat[i] = alpha * sum_wts / pow(1.0 - alpha, count - prev_i)
                sum_wts += w.iat[i]
                prev_i = count
                count += 1
            elif not ignore_na:
                count += 1
    return w


@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
def test_ewm_consistency_mean(consistency_data, adjust, ignore_na, min_periods):
    x, is_constant, no_nans = consistency_data
    com = 3.0

    result = x.ewm(
        com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
    ).mean()
    weights = create_mock_weights(x, com=com, adjust=adjust, ignore_na=ignore_na)
    expected = (
        x.multiply(weights).cumsum().divide(weights.cumsum()).fillna(method="ffill")
    )
    expected[
        x.expanding().count() < (max(min_periods, 1) if min_periods else 1)
    ] = np.nan
    tm.assert_equal(result, expected.astype("float64"))


@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
def test_ewm_consistency_consistent(consistency_data, adjust, ignore_na, min_periods):
    x, is_constant, no_nans = consistency_data
    com = 3.0

    if is_constant:
        count_x = x.expanding().count()
        mean_x = x.ewm(
            com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
        ).mean()
        # check that correlation of a series with itself is either 1 or NaN
        corr_x_x = x.ewm(
            com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
        ).corr(x)
        exp = x.max() if isinstance(x, Series) else x.max().max()

        # check mean of constant series
        expected = x * np.nan
        expected[count_x >= max(min_periods, 1)] = exp
        tm.assert_equal(mean_x, expected)

        # check correlation of constant series with itself is NaN
        expected[:] = np.nan
        tm.assert_equal(corr_x_x, expected)


@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
def test_ewm_consistency_var_debiasing_factors(
    consistency_data, adjust, ignore_na, min_periods
):
    x, is_constant, no_nans = consistency_data
    com = 3.0

    # check variance debiasing factors
    var_unbiased_x = x.ewm(
        com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
    ).var(bias=False)
    var_biased_x = x.ewm(
        com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
    ).var(bias=True)

    weights = create_mock_weights(x, com=com, adjust=adjust, ignore_na=ignore_na)
    cum_sum = weights.cumsum().fillna(method="ffill")
    cum_sum_sq = (weights * weights).cumsum().fillna(method="ffill")
    numerator = cum_sum * cum_sum
    denominator = numerator - cum_sum_sq
    denominator[denominator <= 0.0] = np.nan
    var_debiasing_factors_x = numerator / denominator

    tm.assert_equal(var_unbiased_x, var_biased_x * var_debiasing_factors_x)


@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
@pytest.mark.parametrize("bias", [True, False])
def test_moments_consistency_var(
    consistency_data, adjust, ignore_na, min_periods, bias
):
    x, is_constant, no_nans = consistency_data
    com = 3.0

    mean_x = x.ewm(
        com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
    ).mean()
    var_x = x.ewm(
        com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
    ).var(bias=bias)
    assert not (var_x < 0).any().any()

    if bias:
        # check that biased var(x) == mean(x^2) - mean(x)^2
        mean_x2 = (
            (x * x)
            .ewm(com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na)
            .mean()
        )
        tm.assert_equal(var_x, mean_x2 - (mean_x * mean_x))


@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
@pytest.mark.parametrize("bias", [True, False])
def test_moments_consistency_var_constant(
    consistency_data, adjust, ignore_na, min_periods, bias
):
    x, is_constant, no_nans = consistency_data
    com = 3.0
    if is_constant:
        count_x = x.expanding(min_periods=min_periods).count()
        var_x = x.ewm(
            com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
        ).var(bias=bias)

        # check that variance of constant series is identically 0
        assert not (var_x > 0).any().any()
        expected = x * np.nan
        expected[count_x >= max(min_periods, 1)] = 0.0
        if not bias:
            expected[count_x < 2] = np.nan
        tm.assert_equal(var_x, expected)


@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
@pytest.mark.parametrize("bias", [True, False])
def test_ewm_consistency_std(consistency_data, adjust, ignore_na, min_periods, bias):
    x, is_constant, no_nans = consistency_data
    com = 3.0
    var_x = x.ewm(
        com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
    ).var(bias=bias)
    std_x = x.ewm(
        com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
    ).std(bias=bias)
    assert not (var_x < 0).any().any()
    assert not (std_x < 0).any().any()

    # check that var(x) == std(x)^2
    tm.assert_equal(var_x, std_x * std_x)


@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
@pytest.mark.parametrize("bias", [True, False])
def test_ewm_consistency_cov(consistency_data, adjust, ignore_na, min_periods, bias):
    x, is_constant, no_nans = consistency_data
    com = 3.0
    var_x = x.ewm(
        com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
    ).var(bias=bias)
    assert not (var_x < 0).any().any()

    cov_x_x = x.ewm(
        com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
    ).cov(x, bias=bias)
    assert not (cov_x_x < 0).any().any()

    # check that var(x) == cov(x, x)
    tm.assert_equal(var_x, cov_x_x)


@pytest.mark.parametrize("min_periods", [0, 1, 2, 3, 4])
@pytest.mark.parametrize("bias", [True, False])
def test_ewm_consistency_series_cov_corr(
    consistency_data, adjust, ignore_na, min_periods, bias
):
    x, is_constant, no_nans = consistency_data
    com = 3.0

    if isinstance(x, Series):
        var_x_plus_y = (
            (x + x)
            .ewm(com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na)
            .var(bias=bias)
        )
        var_x = x.ewm(
            com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
        ).var(bias=bias)
        var_y = x.ewm(
            com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
        ).var(bias=bias)
        cov_x_y = x.ewm(
            com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
        ).cov(x, bias=bias)
        # check that cov(x, y) == (var(x+y) - var(x) -
        # var(y)) / 2
        tm.assert_equal(cov_x_y, 0.5 * (var_x_plus_y - var_x - var_y))

        # check that corr(x, y) == cov(x, y) / (std(x) *
        # std(y))
        corr_x_y = x.ewm(
            com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
        ).corr(x, bias=bias)
        std_x = x.ewm(
            com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
        ).std(bias=bias)
        std_y = x.ewm(
            com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
        ).std(bias=bias)
        tm.assert_equal(corr_x_y, cov_x_y / (std_x * std_y))

        if bias:
            # check that biased cov(x, y) == mean(x*y) -
            # mean(x)*mean(y)
            mean_x = x.ewm(
                com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
            ).mean()
            mean_y = x.ewm(
                com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
            ).mean()
            mean_x_times_y = (
                (x * x)
                .ewm(
                    com=com, min_periods=min_periods, adjust=adjust, ignore_na=ignore_na
                )
                .mean()
            )
            tm.assert_equal(cov_x_y, mean_x_times_y - (mean_x * mean_y))
