code: def from_msm(cls, msm, n_macrostates, filter=1.1, save_all_maps=True,
                   n_proc=1, chunk_size=100):
          
          params = msm.get_params()
          lumper = cls(n_macrostates, filter, save_all_maps, n_proc,
                       chunk_size, **params)
  
          lumper.transmat_ = msm.transmat_
          lumper.populations_ = msm.populations_
          lumper.mapping_ = msm.mapping_
          lumper.countsmat_ = msm.countsmat_
          lumper.n_states_ = msm.n_states_
  
          if n_macrostates is not None:
              lumper._do_lumping()
  
          return lumper
docs: Create and fit lumped model from pre-existing MSM.
  
          Parameters
          ----------
          msm : MarkovStateModel
              The input microstate msm to use.
          n_macrostates : int
              The number of macrostates
  
          Returns
          -------
          lumper : cls
              The fit MVCA object.