(S, color=False)
return splitting
Compute a C/F splitting using the parallel CLJP algorithm.

    Parameters
    ----------
    S : csr_matrix
        Strength of connection matrix indicating the strength between nodes i
        and j (S_ij)
    color : bool
        use the CLJP coloring approach

    Returns
    -------
    splitting : array
        Array of length of S of ones (coarse) and zeros (fine)

    Examples
    --------
    >>> from pyamg.gallery import poisson
    >>> from pyamg.classical.split import CLJP
    >>> S = poisson((7,), format='csr') # 1D mesh with 7 vertices
    >>> splitting = CLJP(S)

    See Also
    --------
    MIS, PMIS, CLJPc

    References
    ----------
    .. [8] David M. Alber and Luke N. Olson
       "Parallel coarse-grid selection"
       Numerical Linear Algebra with Applications 2007; 14:611-643.
Compute a C/F splitting using the parallel CLJP algorithm.
Compute a C / F splitting using the parallel CLJP algorithm .
def CLJP(S, color=False):
    """Compute a C/F splitting using the parallel CLJP algorithm.

    Parameters
    ----------
    S : csr_matrix
        Strength of connection matrix indicating the strength between nodes i
        and j (S_ij)
    color : bool
        use the CLJP coloring approach

    Returns
    -------
    splitting : array
        Array of length of S of ones (coarse) and zeros (fine)

    Examples
    --------
    >>> from pyamg.gallery import poisson
    >>> from pyamg.classical.split import CLJP
    >>> S = poisson((7,), format='csr') # 1D mesh with 7 vertices
    >>> splitting = CLJP(S)

    See Also
    --------
    MIS, PMIS, CLJPc

    References
    ----------
    .. [8] David M. Alber and Luke N. Olson
       "Parallel coarse-grid selection"
       Numerical Linear Algebra with Applications 2007; 14:611-643.

    """
    if not isspmatrix_csr(S):
        raise TypeError('expected csr_matrix')
    S = remove_diagonal(S)

    colorid = 0
    if color:
        colorid = 1

    T = S.T.tocsr()  # transpose S for efficient column access
    splitting = np.empty(S.shape[0], dtype='intc')

    amg_core.cljp_naive_splitting(S.shape[0],
                                  S.indptr, S.indices,
                                  T.indptr, T.indices,
                                  splitting,
                                  colorid)

    return splitting
def CLJP ( S , color = False ) : if not isspmatrix_csr ( S ) : raise TypeError ( 'expected csr_matrix' ) S = remove_diagonal ( S ) colorid = 0 if color : colorid = 1 T = S . T . tocsr ( ) # transpose S for efficient column access splitting = np . empty ( S . shape [ 0 ] , dtype = 'intc' ) amg_core . cljp_naive_splitting ( S . shape [ 0 ] , S . indptr , S . indices , T . indptr , T . indices , splitting , colorid ) return splitting