code: def normalized_mutation_entropy(counts, total_cts=None):
      
      cts = np.asarray(counts, dtype=float)
      if total_cts is None:
          total_cts = np.sum(cts)
      if total_cts > 1:
          p = cts / total_cts
          ent = shannon_entropy(p)
          max_ent = max_shannon_entropy(total_cts)
          norm_ent = ent / max_ent
      else:
          norm_ent = 1.0
      return norm_ent
docs: Calculate the normalized mutation entropy based on a list/array
      of mutation counts.
  
      Note: Any grouping of mutation counts together should be done before hand
  
      Parameters
      ----------
      counts : np.array_like
          array/list of mutation counts
  
      Returns
      -------
      norm_ent : float
          normalized entropy of mutation count distribution.