docs: def relaxation(T, p0, obs, times=(1), k=None, ncv=None):
      r"""Relaxation experiment.
  
      The relaxation experiment describes the time-evolution
      of an expectation value starting in a non-equilibrium
      situation.
  
      Parameters
      ----------
      T : (M, M) ndarray or scipy.sparse matrix
          Transition matrix
      p0 : (M,) ndarray (optional)
          Initial distribution for a relaxation experiment
      obs : (M,) ndarray
          Observable, represented as vector on state space
      times : list of int (optional)
          List of times at which to compute expectation
      k : int (optional)
          Number of eigenvalues and eigenvectors to use for computation
      ncv : int (optional)
          The number of Lanczos vectors generated, `ncv` must be greater than k;
          it is recommended that ncv > 2*k
  
      Returns
      -------
      res : ndarray
          Array of expectation value at given times
  
      References
      ----------
      .. [1] Noe, F, S Doose, I Daidone, M Loellmann, M Sauer, J D
          Chodera and J Smith. 2010. Dynamical fingerprints for probing
          individual relaxation processes in biomolecular dynamics with
          simulations and kinetic experiments. PNAS 108 (12): 4822-4827.
  
      Notes
      -----
  
      **Relaxation**
  
      A relaxation experiment looks at the time dependent expectation
      value of an observable for a system out of equilibrium
  
      .. math:: \mathbb{E}_{w_{0}}[a(x, t)]=\sum_x w_0(x) a(x, t)=\sum_x w_0(x) \sum_y p^t(x, y) a(y).
  
      Examples
      --------
  
      >>> import numpy as np
      >>> from msmtools.analysis import relaxation
  
      >>> T = np.array([[0.9, 0.1, 0.0], [0.5, 0.0, 0.5], [0.0, 0.1, 0.9]])
      >>> p0 = np.array([1.0, 0.0, 0.0])
      >>> a = np.array([1.0, 1.0, 0.0])
      >>> times = np.array([1, 5, 10, 20])
  
      >>> rel = relaxation(T, p0, a, times=times)
      >>> rel
      array([ 1.        ,  0.8407    ,  0.71979377,  0.60624287])
  
      """
      # check if square matrix and remember size
      T = _types.ensure_ndarray_or_sparse(T, ndim=2, uniform=True, kind='numeric')
      n = T.shape[0]
      p0 = _types.ensure_ndarray(p0, ndim=1, size=n, kind='numeric')
      obs = _types.ensure_ndarray(obs, ndim=1, size=n, kind='numeric')
      times = _types.ensure_int_vector(times, require_order=True)
      # go
      if _issparse(T):
          return sparse.fingerprints.relaxation(T, p0, obs, k=k, times=times)
      else:
          return dense.fingerprints.relaxation(T, p0, obs, k=k, times=times)