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import numpy as np
from numpy.testing import assert_array_equal, assert_almost_equal
from scipy import stats
from mne.stats.permutations import permutation_t_test
def test_permutation_t_test():
"""Test T-test based on permutations
"""
# 1 sample t-test
np.random.seed(10)
n_samples, n_tests = 30, 5
X = np.random.randn(n_samples, n_tests)
X[:, :2] += 1
T_obs, p_values, H0 = permutation_t_test(X, n_permutations=999, tail=0)
is_significant = p_values < 0.05
assert_array_equal(is_significant, [True, True, False, False, False])
T_obs, p_values, H0 = permutation_t_test(X, n_permutations=999, tail=1)
is_significant = p_values < 0.05
assert_array_equal(is_significant, [True, True, False, False, False])
T_obs, p_values, H0 = permutation_t_test(X, n_permutations=999, tail=-1)
is_significant = p_values < 0.05
assert_array_equal(is_significant, [False, False, False, False, False])
X = np.random.randn(18, 1)
T_obs, p_values, H0 = permutation_t_test(X[:, [0]], n_permutations='all')
T_obs_scipy, p_values_scipy = stats.ttest_1samp(X[:, 0], 0)
assert_almost_equal(T_obs[0], T_obs_scipy, 8)
assert_almost_equal(p_values[0], p_values_scipy, 2)
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