def test_multishift():
"""Test matrix multi-shifting."""
# multishift()
x = np.array([
[1, 2, 3, 4],
[5, 6, 7, 8]])
assert_equal(multishift(x, [1], axis=0), np.array([[0, 0, 0, 0],
[1, 2, 3, 4]]))
# multishift 3d, solution='valid'
x = np.ones((4, 4, 3))
x[..., 1] *= 2
x[..., 2] *= 3
xx = multishift(x, [-1, -2], reshape=True, solution='valid')
assert_equal(xx[..., 0], np.array([[1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1.]]))
assert_equal(xx[..., 1], np.array([[1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1.]]) * 2)
# relshift() 1d
y, y_ref = relshift([1, 2, 3, 4], [11, 12, 13, 14], [1], axis=0)
assert_equal(y.flatten(), [0, 1, 2, 3])
assert_equal(y_ref.flatten(), [0, 12, 13, 14])
y, y_ref = relshift(np.arange(1, 6), np.arange(1, 6), [0, 1], axis=0)
assert_equal(y, np.array([[1., 0.],
[2., 1.],
[3., 2.],
[4., 3.],
[5., 4.]]))
# relshift() 2d
x = [[1, 2, 3, 4],
[5, 6, 7, 8]]
x_ref = [[11, 12, 13, 14],
[15, 16, 17, 18]]
y, y_ref = relshift(x, x_ref, [0, 1])
assert_equal(y[..., 0], [[1, 2, 3, 4],
[5, 6, 7, 8]])
assert_equal(y[..., 1], [[0, 0, 0, 0],
[1, 2, 3, 4]])
assert_equal(y_ref[..., 0], [[11, 12, 13, 14],
[15, 16, 17, 18]])
assert_equal(y_ref[..., 1], [[0, 0, 0, 0],
[15, 16, 17, 18]])
def test_cca_crossvalidate_shifts():
"""Test CCA crossvalidation with shifts."""
n_times, n_trials = 10000, 2
x = np.random.randn(n_times, 20, n_trials)
y = np.random.randn(n_times, 8, n_trials)
# perfectly correlated
y[:, :2, :] = x[:, :2, :]
# 1/2 correlated
y[:, 2:4, :] = x[:, 2:4, :] + np.random.randn(n_times, 2, n_trials)
# 1/4 correlated
y[:, 4:6, :] = x[:, 4:6, :] + np.random.randn(n_times, 2, n_trials) * 3
# uncorrelated
y[:, 6:8, :] = np.random.randn(n_times, 2, n_trials)
xx = multishift(x, -np.arange(1, 4), reshape=True, solution='valid')
yy = multishift(y, -np.arange(1, 4), reshape=True, solution='valid')
# Test with shifts
A, B, R = cca_crossvalidate(xx, yy, shifts=[-3, -2, -1, 0, 1, 2, 3])