multidiscover.py 文件源码

def main(args):
    if args.minimum_frequency is None:
        minimum_frequency = max((len(args.tables) + 1) // 2, 2)
    else:
        minimum_frequency = args.minimum_frequency
    logger.info('Minimum frequency set to %s', minimum_frequency)

    # Read in tables
    tables = []
    for path in args.tables:
        table = pd.read_csv(path, sep='\t')
        table = table[table.database_diff >= args.minimum_db_diff]
        table = table.dropna()
        tables.append(table)
        if len(table) == 0:
            logger.warn('Table read from %r is empty after filtering out sequences with database diff >= %s.', path, args.minimum_db_diff)

    # Count V sequence occurrences
    counter = Counter()
    for table in tables:
        counter.update(set(table.consensus))

    # Find most frequent occurrences and print result
    print('count', 'gene', 'database_diff', 'sequence', 'names', sep='\t')
    for sequence, frequency in counter.most_common():
        if frequency < minimum_frequency:
            break
        names = []
        gene = None
        for table in tables:
            matching_rows = table[table.consensus == sequence]
            if matching_rows.empty:
                continue
            names.extend(matching_rows.name)
            if gene is None:
                row = matching_rows.iloc[0]
                gene = row.gene
                database_diff = row.database_diff
                #shm = row['V_SHM']
        print(frequency, gene, database_diff, sequence, *names, sep='\t')