def test_f_twoway_rm():
    """ Test 2-way anova """
    rng = np.random.RandomState(42)
    iter_params = product([4, 10], [2, 15], [4, 6, 8],
                          ['A', 'B', 'A:B'],
                          [False, True])
    _effects = {
        4: [2, 2],
        6: [2, 3],
        8: [2, 4]
    }
    for params in iter_params:
        n_subj, n_obs, n_levels, effects, correction = params
        data = rng.random_sample([n_subj, n_levels, n_obs])
        fvals, pvals = f_mway_rm(data, _effects[n_levels], effects,
                                 correction=correction)
        assert_true((fvals >= 0).all())
        if pvals.any():
            assert_true(((0 <= pvals) & (1 >= pvals)).all())
        n_effects = len(_map_effects(n_subj, effects)[0])
        assert_true(fvals.size == n_obs * n_effects)
        if n_effects == 1:  # test for principle of least surprise ...
            assert_true(fvals.ndim < 1)

        fvals_ = f_threshold_mway_rm(n_subj, _effects[n_levels], effects)
        assert_true((fvals_ >= 0).all())
        assert_true(fvals_.size == n_effects)

    data = rng.random_sample([n_subj, n_levels, 1])
    assert_raises(ValueError, f_mway_rm, data, _effects[n_levels],
                  effects='C', correction=correction)
    data = rng.random_sample([n_subj, n_levels, n_obs, 3])
    # check for dimension handling
    f_mway_rm(data, _effects[n_levels], effects, correction=correction)

    # now check against external software results
    test_data = generate_data(n_subjects=20, n_conditions=6)
    fvals, pvals = f_mway_rm(test_data, [2, 3])

    assert_array_almost_equal(fvals, test_external['spss_fvals'], 3)
    assert_array_almost_equal(pvals, test_external['spss_pvals_uncorrected'],
                              3)
    assert_array_almost_equal(fvals, test_external['r_fvals'], 4)
    assert_array_almost_equal(pvals, test_external['r_pvals_uncorrected'], 3)

    _, pvals = f_mway_rm(test_data, [2, 3], correction=True)
    assert_array_almost_equal(pvals, test_external['spss_pvals_corrected'], 3)

    test_data = generate_data(n_subjects=20, n_conditions=8)
    fvals, _ = f_mway_rm(test_data, [2, 2, 2])
    assert_array_almost_equal(fvals, test_external['r_fvals_3way'], 5)

    fvals, _ = f_mway_rm(test_data, [8], 'A')
    assert_array_almost_equal(fvals, test_external['r_fvals_1way'], 5)
dataset/ETHPy150Open mne-tools/mne-python/mne/stats/tests/test_parametric.py/test_f_twoway_rm/operator `==` -> `<`