bdf67bc292175a81c63deca27a2056d566db050b,dipy/reconst/dti.py,,restore_fit_tensor,#Any#Any#Any#Any#Any#,1182

Before Change

        // Finally, converge on some solution and use it:
        this_dti = np.concatenate([np.ravel(x) for x in
                                   decompose_tensor(
                        from_lower_triangular(this_tensor[:6]).reshape(3,3))])

        dti_params[vox] = this_dti

After Change

                                             "sigma", this_sigma))

        // The parameters are the evals and the evecs:
        try:
            evals,evecs=decompose_tensor(from_lower_triangular(this_tensor[:6]))
            dti_params[vox, :3] = evals
            dti_params[vox, 3:] = evecs.ravel()

        // If leastsq failed to converge and produced nans, we"ll resort to the
        // OLS solution in this voxel:
        except np.linalg.LinAlgError:
            print(vox)
            dti_params[vox, :] = start_params


    dti_params.shape = data.shape[:-1] + (12,)
    restore_params = dti_params
    return restore_params

Instances