def METHOD_NAME(dg):
    """
    $ ./setup.py --init_db
    scan daq directory, and augment with additional columns
    """
    dg.scan_daq_dir()
    # -- organize and augment the dg.fileDB DataFrame --
    # run 1 & 2 files don't match template
    dg.fileDB.query('run > 2', inplace=True)
    dg.fileDB.sort_values(['run','YYYYmmdd','hhmmss'], inplace=True)
    dg.fileDB.reset_index(drop=True, inplace=True)
    def get_cmap(row):
        run_str = f"{row['run']:0>4d}"
        if run_str not in dg.runDB:
            print("Warning: no runDB entry for run", run_str)
            row['cmap'] = ''
            return row
        row['cmap'] = dg.runDB[f"{row['run']:0>4d}"]["cmap"]
        return row
    dg.fileDB = dg.fileDB.apply(get_cmap, axis=1)
    dg.fileDB['runtype'] = dg.fileDB['rtp']
    dg.get_lh5_cols()
    # add columns for file size and processing group
    proc_thresh_GB = 128
    proc_LL_GB = 4 # lower limit
    sizes_GB, proc_groups = [], []
    size_sum, proc_group = 0, 0
    df = dg.fileDB
    for daq_path, daq_file in zip(df['daq_dir'], df['daq_file']):
        filename = os.path.expandvars('$LPGTA_DATA/daq')+daq_path+'/'+daq_file
        sizes_GB.append(os.path.getsize(filename) / (1024**3))
        # if this file pushes over the threshold, put in a new group
        if size_sum + sizes_GB[-1] > proc_thresh_GB and size_sum > proc_LL_GB:
            proc_group += 1
            size_sum = 0
        proc_groups.append(proc_group)
        size_sum += sizes_GB[-1]
        if size_sum > proc_thresh_GB:
            proc_group += 1
            size_sum = 0
    df['daq_size_GB'] = sizes_GB
    df['proc_group'] = proc_groups
    # save to file used by processing.py
    print(dg.fileDB)
    print(dg.fileDB.columns)
    print('FileDB location:', dg.config['fileDB'])
    ans = input('Save new fileDB? (y/n) ')
    if ans.lower() == 'y':
        dg.save_df(dg.config['fileDB'])