How to use the fastcluster.average function in fastcluster

def calc_dendrograms(HC, D, linkage_type='single'):
  linkage_types = { 'single'   : fc.single,
                    'complete' : fc.complete,
                    'average'  : fc.average,
                    'weighted' : fc.weighted,
                    'centroid' : fc.centroid,
                    'median'   : fc.median,
                    'ward'     : fc.ward };
  T = {};
  print "Calculating linkages. This will take a while!";
  nk = len(D.keys());
  for (i, dk) in enumerate(D.keys()):
    print "\r%d/%d" % (i+1, nk),
    sys.stdout.flush();
    L = linkage_types[linkage_type](D[dk]);
    T[dk] = construct_dendrogram(L, HC[dk]);
  #efor

  return T;

The input nodes are labeled 0, . . . , N - 1, and the newly generated nodes have the labels N, . . . , 2N - 2.
    The third column contains the distance between the two nodes at each step, ie. the
    current minimal distance at the time of the merge. The fourth column counts the
    number of points which comprise each new node.

    :param pairwise_estimates: dictionary with data frames with pairwise estimates of Ks, Ka and Ka/Ks
        (or at least Ks), as returned by :py:func:`analyse_family`.
    :return: average linkage clustering as performed with ``fastcluster.average``.
    """
    if pairwise_estimates is None:
        return None

    if pairwise_estimates['Ks'].shape[0] < 2:
        return None

    clustering = fastcluster.average(pairwise_estimates['Ks'])

    return clustering

distance between the two nodes at each step, ie. the current minimal
    distance at the time of the merge. The fourth column counts the number of
    points which comprise each new node.

    :param pairwise_estimates: dictionary with data frames with pairwise
        estimates of Ks, Ka and Ka/Ks (or at least Ks), as returned by
        :py:func:`analyse_family`.
    :return: average linkage clustering as performed with
        ``fastcluster.average``.
    """
    # fill NaN values with something larger than all the rest, not a foolproof
    # approach, but should be reasonable in most cases
    if np.any(np.isnan(pairwise_estimates)):
        logging.warning("Ks matrix contains NaN values, replaced with 1000")
        pairwise_estimates.fillna(1000, inplace=True)
    clustering = fastcluster.average(pairwise_estimates)

    return clustering