test_caliber_distances.py 文件源码

def test_covariance_matrices():
    arr_1 = np.array([[[0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [0, 1, 1, 1, 1, 1, 0, 0, 0],
                       [0, 1, 1, 1, 1, 1, 0, 0, 0],
                       [0, 1, 1, 1, 1, 1, 2, 2, 0],
                       [0, 1, 1, 1, 1, 1, 2, 2, 0],
                       [0, 1, 1, 1, 1, 1, 0, 3, 3],
                       [0, 0, 0, 0, 0, 0, 0, 3, 3],
                       [0, 0, 0, 0, 0, 3, 3, 3, 3]],

                      [[0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [0, 0, 0, 0, 0, 0, 0, 0, 0],
                       [0, 1, 1, 1, 1, 1, 0, 0, 0],
                       [0, 1, 1, 1, 1, 1, 0, 0, 0],
                       [0, 1, 1, 1, 1, 1, 2, 2, 0],
                       [0, 1, 1, 1, 1, 1, 2, 2, 0],
                       [0, 1, 1, 1, 1, 1, 0, 0, 3],
                       [0, 0, 0, 0, 0, 0, 0, 3, 3],
                       [0, 0, 0, 0, 0, 0, 3, 3, 3]]
                      ])

    im1 = nib.Nifti1Image(arr_1, np.eye(4))

    cov =  covariance_matrices(im1, [1, 2, 3])
    assert len(cov) == 3
    for i in cov:
        assert_array_equal(i.shape, [3, 3])
        if np.count_nonzero(i - np.diag(np.diagonal(i))) == 0:
            assert_array_equal(np.diag(np.diag(i)), i)

    cov1 = covariance_matrices(im1, [1, 2, 3, 4])
    assert_array_equal(cov1[-1], np.nan * np.ones([3, 3]))