import numpy as np

from gatestats import gates, stats


def test_bootstrap_lower_bound_covers_true_mean_at_nominal_rate():
    rng = np.random.default_rng(1)
    X = rng.binomial(1, 0.3, size=(1000, 100)).astype(float)
    lb = stats.lower_bound(X @ stats.bootstrap_weights(100, 2000, rng).T, 0.05)
    assert 0.93 <= (lb <= 0.3).mean() <= 0.97


def test_two_sided_paired_ci_coverage():
    rng = np.random.default_rng(2)
    hits = 0
    for i in range(1000):
        a = rng.normal(0.5, 0.2, 100)
        b = a + rng.normal(0.01, 0.1, 100)
        _, lo, hi = stats.paired_bootstrap_ci(a, b, B=1000, seed=i)
        hits += lo <= 0.01 <= hi
    assert 0.93 <= hits / 1000 <= 0.97


def test_sign_flip_rejects_only_clear_effects():
    rng = np.random.default_rng(3)
    flips = rng.choice([-1.0, 1.0], size=(2000, 50))
    assert stats.sign_flip_pvalues(np.full(50, 0.1) + rng.normal(0, 0.05, 50), flips) < 0.01
    assert stats.sign_flip_pvalues(rng.normal(-0.1, 0.05, 50), flips) > 0.5


def test_gates_on_identical_systems():
    rng = np.random.default_rng(4)
    a = rng.binomial(1, 0.5, size=(200, 50)).astype(float)
    W = stats.bootstrap_weights(50, 1000, rng)
    assert gates.g3_paired(a, a, 0.01, W, 0.05).all()          # zero paired variance: always PASS
    assert not gates.g3u_unpaired(a, a, 0.01, W, stats.bootstrap_weights(50, 1000, rng), 0.05).any()


def test_wilson_holm_mde():
    lo, hi = stats.wilson_ci(5, 10)
    assert abs(lo - 0.2366) < 1e-3 and abs(hi - 0.7634) < 1e-3
    assert np.allclose(stats.holm([0.01, 0.04, 0.03]), [0.03, 0.06, 0.06])
    assert stats.required_n(0.3, stats.mde(0.3, 100)) in (100, 101)
    assert abs(stats.noninferiority_pass_prob(-0.02, 0.02, 0.3, 100) - 0.05) < 1e-12