code: def NESS(xs, est_xs, ps):
      
  
      est_err = xs - est_xs
      ness = []
      for x, p in zip(est_err, ps):
          ness.append(np.dot(x.T, linalg.inv(p)).dot(x))
      return ness
docs: Computes the normalized estimated error squared test on a sequence
      of estimates. The estimates are optimal if the mean error is zero and
      the covariance matches the Kalman filter's covariance. If this holds,
      then the mean of the NESS should be equal to or less than the dimension
      of x.
  
      Examples
      --------
  
      .. code-block: Python
  
          xs = ground_truth()
          est_xs, ps, _, _ = kf.batch_filter(zs)
          NESS(xs, est_xs, ps)
  
      Parameters
      ----------
  
      xs : list-like
          sequence of true values for the state x
  
      est_xs : list-like
          sequence of estimates from an estimator (such as Kalman filter)
  
      ps : list-like
          sequence of covariance matrices from the estimator
  
      Returns
      -------
  
      ness : list of floats
         list of NESS computed for each estimate