How to use the biosppy.metrics.squareform function in biosppy

Returns
    -------
    indices : array
        Indices that sort the computed mean distances.
    mdist : array
        Mean distance characterizing each data sample.

    """

    if metric_args is None:
        metric_args = {}

    # compute distances
    D = metrics.pdist(data, metric=metric, **metric_args)
    D = metrics.squareform(D)

    # compute mean
    mdist = np.mean(D, axis=0)

    # sort
    indices = np.argsort(mdist)

    return indices, mdist

“A Density-Based Algorithm for Discovering Clusters in Large Spatial
       Databases with Noise”, Proceedings of the 2nd International
       Conf. on Knowledge Discovery and Data Mining, pp. 226-231, 1996.

    """

    # check inputs
    if data is None:
        raise TypeError("Please specify input data.")

    if metric_args is None:
        metric_args = {}

    # compute distances
    D = metrics.pdist(data, metric=metric, **metric_args)
    D = metrics.squareform(D)

    # fit
    db = skc.DBSCAN(eps=eps, min_samples=min_samples, metric='precomputed')
    labels = db.fit_predict(D)

    # get cluster indices
    clusters = _extract_clusters(labels)

    return utils.ReturnTuple((clusters,), ('clusters',))

raise TypeError("Please specify the input co-association matrix.")

    if linkage not in ['average', 'complete', 'single', 'weighted']:
        raise ValueError("Unknown linkage criterion '%r'." % linkage)

    N = len(coassoc)
    if k > N:
        raise ValueError("Number of clusters 'k' is higher than the number of \
                          input samples.")

    if k < 0:
        k = 0

    # convert coassoc to condensed format, dissimilarity
    mx = np.max(coassoc)
    D = metrics.squareform(mx - coassoc)

    # build linkage
    Z = sch.linkage(D, method=linkage)

    # extract clusters
    if k == 0:
        # life-time
        labels = _life_time(Z, N)
    else:
        labels = sch.fcluster(Z, k, 'maxclust')

    # get cluster indices
    clusters = _extract_clusters(labels)

    return utils.ReturnTuple((clusters,), ('clusters',))