import numpy as np

from repro_beyond_model_ranking.spectral import linear_utilization_ratio, scp


def test_scp_separates_coherent_signal_from_independent_noise():
    rng = np.random.default_rng(7)
    n = 8192
    x = rng.normal(size=n)
    coherent_y = 0.8 * x + 0.2 * rng.normal(size=n)
    independent_y = rng.normal(size=n)

    coherent = scp(x, coherent_y)
    independent = scp(x, independent_y)

    assert coherent.p_coherence > 0.85
    assert independent.p_coherence < 0.35
    assert coherent.p_coherence > independent.p_coherence + 0.5


def test_scp_matches_gaussian_intrinsic_predictability_as_n_grows():
    rng = np.random.default_rng(11)
    n = 65536
    coefficient = 0.75
    noise_std = 0.5
    x = rng.normal(size=n)
    y = coefficient * x + noise_std * rng.normal(size=n)
    theoretical = coefficient**2 / (coefficient**2 + noise_std**2)

    # A smaller fractional window increases the number of Welch segments,
    # which is the effective sample size referenced by the consistency claim.
    estimate = scp(x, y, win_frac=1 / 64)

    assert abs(estimate.p_coherence - theoretical) < 0.04


def test_paper_formula_can_leave_unit_interval_with_boundary_mean_shift():
    rng = np.random.default_rng(19)
    x = rng.normal(0.0, 1.0, size=8192)
    y = x + 4.0

    estimate = scp(x, y)

    assert estimate.mean_shift_mse > estimate.var_y
    assert estimate.p_xy_raw < 0.0
    assert estimate.p_xy_clipped == 0.0
    assert estimate.bound_violated


def test_lur_distinguishes_underuse_saturation_and_beyond_linear():
    rng = np.random.default_rng(23)
    n = 8192
    x = rng.normal(size=n)
    y = x + 0.5 * rng.normal(size=n)
    under = rng.normal(size=n)
    saturated = x
    beyond = y

    under_lur = linear_utilization_ratio(x, y, under).lur
    saturated_lur = linear_utilization_ratio(x, y, saturated).lur
    beyond_lur = linear_utilization_ratio(x, y, beyond).lur

    assert under_lur < 0.5
    assert 0.9 < saturated_lur < 1.1
    assert beyond_lur > 1.1


def test_band_lur_returns_frequency_resolved_values():
    n = 8192
    t = np.arange(n)
    low = np.sin(2 * np.pi * 0.02 * t)
    high = 0.5 * np.sin(2 * np.pi * 0.32 * t)
    x = low + high
    y = low + high
    prediction = low

    result = linear_utilization_ratio(
        x,
        y,
        prediction,
        bands=((0.0, 0.1), (0.1, 0.5)),
    )

    assert len(result.band_lur) == 2
    assert result.band_lur[0] > result.band_lur[1]
    assert result.band_lur[0] > 0.9
    assert result.band_lur[1] < 0.2