code: def rank_clusters(cluster_dict):
      
      # Figure out the relative rank of each cluster
      cluster_ranks = dict.fromkeys(cluster_dict.keys())
      for key in cluster_dict:
          cluster_ranks[key] = eval(string_avg(cluster_dict[key], binary=True))
  
      i = len(cluster_ranks)
      for key in sorted(cluster_ranks, key=cluster_ranks.get):
          cluster_ranks[key] = i
          i -= 1
  
      return cluster_ranks
docs: Helper function for clustering that takes a dictionary mapping cluster
      ids to lists of the binary strings that are part of that cluster and
      returns a dictionary mapping cluster ids to integers representing their
      "rank". Ranks provide an ordering for the clusters such that each
      cluster has its own rank, and clusters are ordered from simplest to
      most complex.