code: def apseudo(Ss, ipar, sigma):
      
  #
      Is = random.randint(0, len(Ss) - 1, size=len(Ss))  # draw N random integers
      #Ss = np.array(Ss)
      if not ipar: # ipar == 0:
          BSs = Ss[Is]
      else:  # need to recreate measurement - then do the parametric stuffr
          A, B = design(6)  # get the design matrix for 6 measurementsa
          K, BSs = [], []
          for k in range(len(Ss)):
              K.append(np.dot(A, Ss[k][0:6]))
          Pars = np.random.normal(K, sigma)
          for k in range(len(Ss)):
              BSs.append(np.dot(B, Pars[k]))
      return np.array(BSs)
docs: draw a bootstrap sample of Ss