def gen_refined_corr_dfs(df_corr,
                         ang_corr_out_path=None,
                         subj_corr_out_path=None):
    """Generate print-ready angle/subject correlation tables for publication.

    Args:
        df_corr (pandas.DataFrame): general correlation table of all data
            streams generated by dataobj.data_utils.build_corr_table()
        ang_corr_out_path (str): output path for angle correlation CSV, if
            desired
        subj_corr_out_path (str): output path for subject correlation CSV, if
            desired

    Returns:
        pandas.DataFrame angle correlation table
        pandas.DataFrame subject correlation table
    """
    # extract only desired table rows (polyfits are omitted/nonsensical)
    df_corr_ref = df_corr.loc[[
        'emg-abs-bic', 'emg-abs-brd', 'us-csa', 'us-csa-dt', 'us-t', 'us-t-dt',
        'us-tr', 'us-tr-dt'
    ]]

    # rename with print-ready labels
    df_corr_ref.rename(index={
        'emg-abs-bic': 'sEMG-BIC',
        'emg-abs-brd': 'sEMG-BRD',
        'us-csa': 'CSA',
        'us-csa-dt': 'CSA-DT',
        'us-t': 'T',
        'us-t-dt': 'T-DT',
        'us-tr': 'AR',
        'us-tr-dt': 'AR-DT'
    },
                       inplace=True)

    # aggregate angle correlation data
    df_corr_ang = df_corr_ref[[
        'sub1wp1', 'sub1wp2', 'sub1wp5', 'sub1wp8', 'sub1wp10'
    ]]
    df_corr_ang.columns = ['25', '44', '69', '82', '97']
    if ang_corr_out_path:
        df_corr_ang.to_csv(ang_corr_out_path)

    df_corr_subj = df_corr_ref[[
        'sub1wp5', 'sub2wp5', 'sub3wp5', 'sub4wp5', 'sub5wp5'
    ]]
    df_corr_subj = df_corr_subj.loc[[
        'CSA', 'CSA-DT', 'T', 'T-DT', 'AR', 'AR-DT'
    ]]
    df_corr_subj.columns = ['Sub1', 'Sub2', 'Sub3', 'Sub4', 'Sub5']
    if subj_corr_out_path:
        df_corr_subj.to_csv(subj_corr_out_path)

    return df_corr_ang, df_corr_subj