code: def PMISc(S, method='JP'):
      
      S = remove_diagonal(S)
      weights, G, S, T = preprocess(S, coloring_method=method)
      return MIS(G, weights)
docs: C/F splitting using Parallel Modified Independent Set (in color).
  
      PMIS-c, or PMIS in color, improves PMIS by perturbing the initial
      random weights with weights determined by a vertex coloring.
  
      Parameters
      ----------
      S : csr_matrix
          Strength of connection matrix indicating the strength between nodes i
          and j (S_ij)
      method : string
          Algorithm used to compute the initial vertex coloring:
              * 'MIS' - Maximal Independent Set
              * 'JP'  - Jones-Plassmann (parallel)
              * 'LDF' - Largest-Degree-First (parallel)
  
      Returns
      -------
      splitting : array
          Array of length of S of ones (coarse) and zeros (fine)
  
      Examples
      --------
      >>> from pyamg.gallery import poisson
      >>> from pyamg.classical import PMISc
      >>> S = poisson((7,), format='csr') # 1D mesh with 7 vertices
      >>> splitting = PMISc(S)
  
      See Also
      --------
      MIS
  
      References
      ----------
      .. [7] David M. Alber and Luke N. Olson
         "Parallel coarse-grid selection"
         Numerical Linear Algebra with Applications 2007; 14:611-643.