def METHOD_NAME(data_files, bval_files, bvec_files, mask=None,
            out_dir=None, file_prefix=None, b0_threshold=50):
    """
    Fit the DTI model using default settings, save files with derived maps.
    Parameters
    ----------
    data_files : str or list
        Files containing DWI data. If this is a str, that's the full path to a
        single file. If it's a list, each entry is a full path.
    bval_files : str or list
        Equivalent to `data_files`.
    bvec_files : str or list
        Equivalent to `data_files`.
    mask : ndarray, optional
        Binary mask, set to True or 1 in voxels to be processed.
        Default: Process all voxels.
    out_dir : str, optional
        A full path to a directory to store the maps that get computed.
        Default: maps get stored in the same directory as the last DWI file
        in `data_files`.
    b0_threshold : float
    Returns
    -------
    file_paths : dict
        A dict with the derived maps that were computed and full-paths
        to the files containing these maps.
    Notes
    -----
    Maps that are calculated: FA, MD, AD, RD
    """
    img, data, gtab, mask = ut.prepare_data(data_files, bval_files,
                                            bvec_files, mask=mask,
                                            b0_threshold=b0_threshold)
    # In this case, we dump the fit object
    dtf = _fit(gtab, data, mask=mask)
    FA, MD, AD, RD, params = dtf.fa, dtf.md, dtf.ad, dtf.rd, dtf.model_params
    maps = [FA, MD, AD, RD, params]
    names = ['FA', 'MD', 'AD', 'RD', 'params']
    if out_dir is None:
        if isinstance(data_files, list):
            out_dir = op.join(op.split(data_files[0])[0], 'dti')
        else:
            out_dir = op.join(op.split(data_files)[0], 'dti')
    if file_prefix is None:
        file_prefix = ''
    if not op.exists(out_dir):
        os.makedirs(out_dir)
    aff = img.affine
    file_paths = {}
    for m, n in zip(maps, names):
        file_paths[n] = op.join(out_dir, file_prefix + 'dti_%s.nii.gz' % n)
        nib.save(nib.Nifti1Image(m, aff), file_paths[n])
    return file_paths