'''
Momentum FSP numerical regressions.

'''
import numpy as np
import pytest

from piker.fsp._momo import ema


@pytest.mark.parametrize(
    'args,kwargs,expected',
    [
        ((), {}, [1., 1.5, 2.25]),
        ((None,), {}, [1., 1.5, 2.25]),
        ((None, None), {}, [1., 1.5, 2.25]),
        ((0.25,), {}, [1., 1.25, 1.6875]),
        ((), {'alpha': 0.25}, [1., 1.25, 1.6875]),
        ((), {'ylast': 4.}, [4., 3., 3.]),
        ((None, 4.), {}, [4., 3., 3.]),
        ((0.5, 4.), {}, [4., 3., 3.]),
    ],
)
def test_ema_optional_arguments(
    args: tuple[float|None, ...],
    kwargs: dict[str, float],
    expected: list[float],
) -> None:
    '''
    Accept omitted EMA defaults through the real Numba dispatcher.

    `ema()` exposed optional smoothing and seed arguments, but its
    eager signature accepted only explicit values or `None`.
    Numba's omitted-argument types therefore raised `TypeError`
    before the numerical kernel ran. Exercise positional and keyword
    omissions as well as explicit arguments, checking the resulting
    recurrence against hand-computed values. Import the production
    dispatcher so this catches signature regressions that a call to
    `ema.py_func` would miss.

    '''
    signal: np.ndarray = np.array([1., 2., 3.])
    result: np.ndarray = ema(signal, *args, **kwargs)

    np.testing.assert_allclose(result, expected)
    assert result.dtype == np.float64


def test_ema_single_sample_continuation() -> None:
    '''
    Preserve the previous EMA when advancing one realtime sample.

    The old one-sample path multiplied an absent seed by a float,
    failing instead of initializing from the sample. Removing the
    eager Numba signature must retain the existing initialization
    fix and the previous-value update used by realtime RSI. Use a
    distinct seed and smoothing factor so copying either the seed
    or sample fails; verify an omitted seed uses the sole sample.

    '''
    signal: np.ndarray = np.array([3.])

    np.testing.assert_allclose(ema(signal), [3.])
    np.testing.assert_allclose(ema(signal, 0.25, 7.), [6.])