docs: def stationary_distribution_from_eigenvector(T, ncv=None):
      r"""Compute stationary distribution of stochastic matrix T.
  
      The stationary distribution is the left eigenvector corresponding to the 1
      non-degenerate eigenvalue :math: `\lambda=1`.
  
      Input:
      ------
      T : numpy array, shape(d,d)
          Transition matrix (stochastic matrix).
      ncv : int (optional)
          The number of Lanczos vectors generated, `ncv` must be greater than k;
          it is recommended that ncv > 2*k
  
      Returns
      -------
      mu : numpy array, shape(d,)
          Vector of stationary probabilities.
  
      """
      vals, vecs = scipy.sparse.linalg.eigs(T.transpose(), k=1, which='LR', ncv=ncv)
      nu = vecs[:, 0].real
      mu = nu / np.sum(nu)
      return mu