code: def fingerprint_helper(egg, permute=False, n_perms=1000,
                         match='exact', distance='euclidean', features=None):
      
  
      if features is None:
          features = egg.dist_funcs.keys()
  
      inds = egg.pres.index.tolist()
      slices = [egg.crack(subjects=[i], lists=[j]) for i, j in inds]
  
      weights = _get_weights(slices, features, distdict, permute, n_perms, match,
                              distance)
      return np.nanmean(weights, axis=0)
docs: Computes clustering along a set of feature dimensions
  
      Parameters
      ----------
      egg : quail.Egg
          Data to analyze
  
      dist_funcs : dict
          Dictionary of distance functions for feature clustering analyses
  
      Returns
      ----------
      probabilities : Numpy array
        Each number represents clustering along a different feature dimension