def test_isc_corrmat_within_correct(): assert np.allclose(isc_within_diff(D[0:3], D[0:3]), 0)
import numpy as np def test_perm_test_is_permuting(): np.random.seed(1) fun = lambda A, B: sum(A) - sum(B) out = perm([0,0,0,0], [1,1,1,1], fun, nreps=1000) assert .010 < np.mean(np.array(out) == 4) < .018 C11 = np.ones([3,3]) * .3 C22 = np.ones([3,3]) * .5 C12 = np.ones([3,3]) * 0 C = np.hstack([np.vstack([C11, C12]), np.vstack([C12, C22])]) C[np.diag_indices_from(C)] = 1 D = corr_eig(None, 6, 20, C)[1].T a = isc_within_diff(D[0:3], D[3:]) b = isc_corrmat_within_diff(range(3), range(3, 6), C) #TODO wrap in setup func or class def test_isc_within_both_equiv(): assert np.allclose(a, b) def test_isc_within_correct(): assert np.allclose(isc_corrmat_within_diff(range(3), range(3), C), 0) def test_isc_corrmat_within_correct(): assert np.allclose(isc_within_diff(D[0:3], D[0:3]), 0) # this would hold if you were doing actual isc not subject-total corr #def test_isc_within_correct_diff(): # assert np.allclose(a, -.2) #
def test_perm_test_is_permuting(): np.random.seed(1) fun = lambda A, B: sum(A) - sum(B) out = perm([0, 0, 0, 0], [1, 1, 1, 1], fun, nreps=1000) assert .010 < np.mean(np.array(out) == 4) < .018 C11 = np.ones([3, 3]) * .3 C22 = np.ones([3, 3]) * .5 C12 = np.ones([3, 3]) * 0 C = np.hstack([np.vstack([C11, C12]), np.vstack([C12, C22])]) C[np.diag_indices_from(C)] = 1 D = corr_eig(None, 6, 20, C)[1].T a = isc_within_diff(D[0:3], D[3:]) b = isc_corrmat_within_diff(range(3), range(3, 6), C) #TODO wrap in setup func or class def test_isc_within_both_equiv(): assert np.allclose(a, b) def test_isc_within_correct(): assert np.allclose(isc_corrmat_within_diff(range(3), range(3), C), 0) def test_isc_corrmat_within_correct(): assert np.allclose(isc_within_diff(D[0:3], D[0:3]), 0)