How to use the jellyfish.hamming_distance function in jellyfish

def percent_id(seq1,seq2):
    alignment = global_align(seq1,seq2)
    return (1. - hamming_distance(alignment[0],alignment[1]) / float(len(alignment[0]))) * 100.

Available algorithms:
        * levenshtein
        * damerau-levenshtein (DEFAULT)
        * hamming
        * jaro
        * jaro-winkler

    Return values:
        "levenshtein", "damerau-levenshtein" and "hamming" return integers
        "jaro" and "jaro-winkler" return floats in the range of 0.0 (completely
        different) to 1.0 (identical strings).
    '''
    algos = {
        "levenshtein":levenshtein_distance,
        "damerau-levenshtein":damerau_levenshtein_distance,
        "hamming":hamming_distance,
        "jaro":jaro_similarity,
        "jaro-winkler":jaro_winkler_similarity
    }

    if not method in list(algos.keys()):
        raise ValueError("Unsupported algorithm type: %s" % method)

    if str1 is None or str2 is None or not isinstance(str1, str) or not isinstance(str2, str):
        raise TypeError("Arguments must be strings.")

    distance_function = algos[method]

    # All the jellyfish distance functions expect unicode, which is the default
    # for Python3.  If we're running in Python2, we need to convert them.
    python_version = sys.version_info
    if python_version.major == 2:

def barcode_hamming(observed,barcodes):
    """Compute entropy of probabilistic barcode assignment.
    
    observed -- SeqRecord of the barcode
    barcodes -- list of barcode possibilities (python strings)
    """
    obs_seq = observed.seq.tostring()
    distances = [(barcode,hamming_distance(obs_seq,barcode)) for barcode in barcodes]
    closest = min(distances,key=lambda p: p[1])
    return closest  # tuple of (barcode, distance)