code: def remove_small_ns(taus, devs, deverrs, ns):
      
      ns_big_enough = ns > 1
  
      o_taus = taus[ns_big_enough]
      o_devs = devs[ns_big_enough]
      o_ns = ns[ns_big_enough]
      if isinstance(deverrs, list):
          assert len(deverrs) < 3
          o_deverrs = [deverrs[0][ns_big_enough], deverrs[1][ns_big_enough]]
      else:
          o_deverrs = deverrs[ns_big_enough]
      if len(o_devs)==0:
          print("remove_small_ns() nothing remains!?")
          raise UserWarning
           
      return o_taus, o_devs, o_deverrs, o_ns
docs: Remove results with small number of samples.
          If n is small (==1), reject the result
  
      Parameters
      ----------
      taus: array
          List of tau values for which deviation were computed
      devs: array
          List of deviations
      deverrs: array or list of arrays
          List of estimated errors (possibly a list containing two arrays :
          upper and lower values)
      ns: array
          Number of samples for each point
  
      Returns
      -------
      (taus, devs, deverrs, ns): tuple
          Identical to input, except that values with low ns have been removed.