post_processing.py 文件源码

def sort_eigensystem(parameters_dict):
        eigenvectors = np.stack(tensor_spherical_to_cartesian(np.squeeze(parameters_dict['theta']),
                                                              np.squeeze(parameters_dict['phi']),
                                                              np.squeeze(parameters_dict['psi'])), axis=0)

        eigenvalues = np.atleast_2d(np.squeeze(np.dstack([parameters_dict['d'],
                                                          parameters_dict['dperp0'],
                                                          parameters_dict['dperp1']])))

        ranking = np.atleast_2d(np.squeeze(np.argsort(eigenvalues, axis=1, kind='mergesort')[:, ::-1]))
        voxels_range = np.arange(ranking.shape[0])
        sorted_eigenvalues = np.concatenate([eigenvalues[voxels_range, ranking[:, ind], None]
                                             for ind in range(ranking.shape[1])], axis=1)
        sorted_eigenvectors = np.stack([eigenvectors[ranking[:, ind], voxels_range, :]
                                        for ind in range(ranking.shape[1])])

        return sorted_eigenvalues, sorted_eigenvectors, ranking