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import numpy as np
from numpy.testing import (assert_almost_equal, assert_allclose,
assert_array_equal)
from scipy import stats
import pytest
from mne.stats import fdr_correction, bonferroni_correction
def test_multi_pval_correction():
"""Test pval correction for multi comparison (FDR and Bonferroni)."""
rng = np.random.RandomState(0)
X = rng.randn(10, 1000, 10)
X[:, :50, 0] += 4.0 # 50 significant tests
alpha = 0.05
T, pval = stats.ttest_1samp(X, 0)
n_samples = X.shape[0]
n_tests = X.size / n_samples
thresh_uncorrected = stats.t.ppf(1.0 - alpha, n_samples - 1)
reject_bonferroni, pval_bonferroni = bonferroni_correction(pval, alpha)
thresh_bonferroni = stats.t.ppf(1.0 - alpha / n_tests, n_samples - 1)
assert pval_bonferroni.ndim == 2
assert reject_bonferroni.ndim == 2
assert_allclose(pval_bonferroni / 10000, pval)
reject_expected = pval_bonferroni < alpha
assert_array_equal(reject_bonferroni, reject_expected)
fwer = np.mean(reject_bonferroni)
assert_almost_equal(fwer, alpha, 1)
reject_fdr, pval_fdr = fdr_correction(pval, alpha=alpha, method='indep')
assert pval_fdr.ndim == 2
assert reject_fdr.ndim == 2
thresh_fdr = np.min(np.abs(T)[reject_fdr])
assert 0 <= (reject_fdr.sum() - 50) <= 50 * 1.05
assert thresh_uncorrected <= thresh_fdr <= thresh_bonferroni
pytest.raises(ValueError, fdr_correction, pval, alpha, method='blah')
assert np.all(fdr_correction(pval, alpha=0)[0] == 0)
reject_fdr, pval_fdr = fdr_correction(pval, alpha=alpha, method='negcorr')
thresh_fdr = np.min(np.abs(T)[reject_fdr])
assert 0 <= (reject_fdr.sum() - 50) <= 50 * 1.05
assert thresh_uncorrected <= thresh_fdr <= thresh_bonferroni
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