import numpy as np
import pytest

from pandas.errors import UnsupportedFunctionCall

from pandas import DataFrame, DatetimeIndex, Series, date_range
import pandas._testing as tm
from pandas.core.window import ExponentialMovingWindow


def test_doc_string():

    df = DataFrame({"B": [0, 1, 2, np.nan, 4]})
    df
    df.ewm(com=0.5).mean()


def test_constructor(frame_or_series):

    c = frame_or_series(range(5)).ewm

    # valid
    c(com=0.5)
    c(span=1.5)
    c(alpha=0.5)
    c(halflife=0.75)
    c(com=0.5, span=None)
    c(alpha=0.5, com=None)
    c(halflife=0.75, alpha=None)

    # not valid: mutually exclusive
    msg = "comass, span, halflife, and alpha are mutually exclusive"
    with pytest.raises(ValueError, match=msg):
        c(com=0.5, alpha=0.5)
    with pytest.raises(ValueError, match=msg):
        c(span=1.5, halflife=0.75)
    with pytest.raises(ValueError, match=msg):
        c(alpha=0.5, span=1.5)

    # not valid: com < 0
    msg = "comass must satisfy: comass >= 0"
    with pytest.raises(ValueError, match=msg):
        c(com=-0.5)

    # not valid: span < 1
    msg = "span must satisfy: span >= 1"
    with pytest.raises(ValueError, match=msg):
        c(span=0.5)

    # not valid: halflife <= 0
    msg = "halflife must satisfy: halflife > 0"
    with pytest.raises(ValueError, match=msg):
        c(halflife=0)

    # not valid: alpha <= 0 or alpha > 1
    msg = "alpha must satisfy: 0 < alpha <= 1"
    for alpha in (-0.5, 1.5):
        with pytest.raises(ValueError, match=msg):
            c(alpha=alpha)


@pytest.mark.parametrize("method", ["std", "mean", "var"])
def test_numpy_compat(method):
    # see gh-12811
    e = ExponentialMovingWindow(Series([2, 4, 6]), alpha=0.5)

    msg = "numpy operations are not valid with window objects"

    with pytest.raises(UnsupportedFunctionCall, match=msg):
        getattr(e, method)(1, 2, 3)
    with pytest.raises(UnsupportedFunctionCall, match=msg):
        getattr(e, method)(dtype=np.float64)


def test_ewma_times_not_datetime_type():
    msg = r"times must be datetime64\[ns\] dtype."
    with pytest.raises(ValueError, match=msg):
        Series(range(5)).ewm(times=np.arange(5))


def test_ewma_times_not_same_length():
    msg = "times must be the same length as the object."
    with pytest.raises(ValueError, match=msg):
        Series(range(5)).ewm(times=np.arange(4).astype("datetime64[ns]"))


def test_ewma_halflife_not_correct_type():
    msg = "halflife must be a string or datetime.timedelta object"
    with pytest.raises(ValueError, match=msg):
        Series(range(5)).ewm(halflife=1, times=np.arange(5).astype("datetime64[ns]"))


def test_ewma_halflife_without_times(halflife_with_times):
    msg = "halflife can only be a timedelta convertible argument if times is not None."
    with pytest.raises(ValueError, match=msg):
        Series(range(5)).ewm(halflife=halflife_with_times)


@pytest.mark.parametrize(
    "times",
    [
        np.arange(10).astype("datetime64[D]").astype("datetime64[ns]"),
        date_range("2000", freq="D", periods=10),
        date_range("2000", freq="D", periods=10).tz_localize("UTC"),
        "time_col",
    ],
)
@pytest.mark.parametrize("min_periods", [0, 2])
def test_ewma_with_times_equal_spacing(halflife_with_times, times, min_periods):
    halflife = halflife_with_times
    data = np.arange(10.0)
    data[::2] = np.nan
    df = DataFrame({"A": data, "time_col": date_range("2000", freq="D", periods=10)})
    result = df.ewm(halflife=halflife, min_periods=min_periods, times=times).mean()
    expected = df.ewm(halflife=1.0, min_periods=min_periods).mean()
    tm.assert_frame_equal(result, expected)


def test_ewma_with_times_variable_spacing(tz_aware_fixture):
    tz = tz_aware_fixture
    halflife = "23 days"
    times = DatetimeIndex(
        ["2020-01-01", "2020-01-10T00:04:05", "2020-02-23T05:00:23"]
    ).tz_localize(tz)
    data = np.arange(3)
    df = DataFrame(data)
    result = df.ewm(halflife=halflife, times=times).mean()
    expected = DataFrame([0.0, 0.5674161888241773, 1.545239952073459])
    tm.assert_frame_equal(result, expected)
