acdb39c2f40fa90a033e895f09d8d02b02e85e9b,dipy/reconst/fwdti.py,,nlls_fit_tensor,#Any#Any#Any#Any#Any#Any#Any#,497

Before Change

            fw_params[vox, :3] = evals
            fw_params[vox, 3:] = evecs.ravel()
            fw_params[vox, 12] = start_params[7]
            fw_params[vox, 13] = np.exp(-start_params[6])

    fw_params.shape = data.shape[:-1] + (14,)
    return fw_params

After Change

        if cholesky:
            start_params = np.concatenate((lower_triangular_to_cholesky(dt),
                                           [-np.log(s0),
                                            np.arcsin(2*f - 1) + np.pi/2]),
                                          axis=0)
        else:
            start_params = np.concatenate((dt, [-np.log(s0), f]), axis=0)

        this_tensor, status = opt.leastsq(_nlls_err_func, start_params[:8],
                                          args=(design_matrix,
                                                flat_data[vox],
                                                Diso,
                                                weighting,
                                                sigma,
                                                cholesky))
                                                
        if cholesky:
            this_tensor[:6] = cholesky_to_lower_triangular(this_tensor[:6])
            this_tensor[7] =  0.5 * (1 + np.sin(this_tensor[7] - np.pi/2))

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

Instances