code: def uncertainty_timescales(self):
          
          # drop the first eigenvalue
          u = self.eigenvalues_[1:]
          sigma_eigs = self.uncertainty_eigenvalues()[1:]
  
          sigma_ts = sigma_eigs / (u * np.log(u)**2)
          return sigma_ts
docs: Estimate of the element-wise asymptotic standard deviation
          in the model implied timescales.
  
          Returns
          -------
          sigma_timescales : np.array, shape=(n_timescales,)
              The estimated symptotic standard deviation in the implied
              timescales.
  
          References
          ----------
          .. [1] Hinrichs, Nina Singhal, and Vijay S. Pande. "Calculation of
             the distribution of eigenvalues and eigenvectors in Markovian state
             models for molecular dynamics." J. Chem. Phys. 126.24 (2007): 244101.