def METHOD_NAME(self):
    levels = [-10, 1, 2, 10]
    scheme = 'nearest'
    result = extract_levels(self.cube, levels, scheme)
    expected = np.array([[[[_MDI, _MDI], [_MDI, _MDI]], [[4., 5.],
                                                         [6., 7.]],
                          [[8., 9.], [10., 11.]],
                          [[_MDI, _MDI], [_MDI, _MDI]]],
                         [[[_MDI, _MDI], [_MDI, _MDI]],
                          [[16., 17.], [18., 19.]], [[20., 21.],
                                                     [22., 23.]],
                          [[_MDI, _MDI], [_MDI, _MDI]]]])
    expected_mask = np.array([[[[True, True], [True, True]],
                               [[False, False], [False, False]],
                               [[False, False], [False, False]],
                               [[True, True], [True, True]]],
                              [[[True, True], [True, True]],
                               [[False, False], [False, False]],
                               [[False, False], [False, False]],
                               [[True, True], [True, True]]]])
    expected = np.ma.array(expected, mask=expected_mask)
    self.assert_array_equal(result.data, expected)
    self.shape[self.z_dim] = len(levels)
    self.assertEqual(result.shape, tuple(self.shape))