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    structural_rank(graph)
    
    Compute the structural rank of a graph (matrix) with a given 
    sparsity pattern.

    The structural rank of a matrix is the number of entries in the maximum 
    transversal of the corresponding bipartite graph, and is an upper bound 
    on the numerical rank of the matrix. A graph has full structural rank 
    if it is possible to permute the elements to make the diagonal zero-free.

    .. versionadded:: 0.19.0

    Parameters
    ----------
    graph : sparse matrix
        Input sparse matrix.

    Returns
    -------
    rank : int
        The structural rank of the sparse graph.
    
    References
    ----------
    .. [1] I. S. Duff, "Computing the Structural Index", SIAM J. Alg. Disc. 
            Meth., Vol. 7, 594 (1986).
    
    .. [2] http://www.cise.ufl.edu/research/sparse/matrices/legend.html

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import structural_rank

    >>> graph = [
    ... [0, 1 , 2, 0],
    ... [1, 0, 0, 1],
    ... [2, 0, 0, 3],
    ... [0, 1, 3, 0]
    ... ]
    >>> graph = csr_matrix(graph)
    >>> print(graph)
      (0, 1)	1
      (0, 2)	2
      (1, 0)	1
      (1, 3)	1
      (2, 0)	2
      (2, 3)	3
      (3, 1)	1
      (3, 2)	3

    >>> structural_rank(graph)
    4

    
    reverse_cuthill_mckee(graph, symmetric_mode=False)
    
    Returns the permutation array that orders a sparse CSR or CSC matrix
    in Reverse-Cuthill McKee ordering.  
    
    It is assumed by default, ``symmetric_mode=False``, that the input matrix 
    is not symmetric and works on the matrix ``A+A.T``. If you are 
    guaranteed that the matrix is symmetric in structure (values of matrix 
    elements do not matter) then set ``symmetric_mode=True``.
    
    Parameters
    ----------
    graph : sparse matrix
        Input sparse in CSC or CSR sparse matrix format.
    symmetric_mode : bool, optional
        Is input matrix guaranteed to be symmetric.

    Returns
    -------
    perm : ndarray
        Array of permuted row and column indices.
 
    Notes
    -----
    .. versionadded:: 0.15.0

    References
    ----------
    E. Cuthill and J. McKee, "Reducing the Bandwidth of Sparse Symmetric Matrices",
    ACM '69 Proceedings of the 1969 24th national conference, (1969).

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import reverse_cuthill_mckee

    >>> graph = [
    ... [0, 1 , 2, 0],
    ... [0, 0, 0, 1],
    ... [2, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ]
    >>> graph = csr_matrix(graph)
    >>> print(graph)
      (0, 1)	1
      (0, 2)	2
      (1, 3)	1
      (2, 0)	2
      (2, 3)	3

    >>> reverse_cuthill_mckee(graph)
    array([3, 2, 1, 0], dtype=int32)
    
    numpy.core.multiarray failed to importmaximum_bipartite_matching (line 80)
    maximum_bipartite_matching(graph, perm_type='row')
    
    Returns an array of row or column permutations that makes
    the diagonal of a nonsingular square CSC sparse matrix zero free.  
    
    Such a permutation is always possible provided that the matrix 
    is nonsingular. This function looks at the structure of the matrix 
    only. The input matrix will be converted to CSC matrix format if
    necessary.

    Parameters
    ----------
    graph : sparse matrix
        Input sparse in CSC format
    perm_type : str, {'row', 'column'}
        Type of permutation to generate.

    Returns
    -------
    perm : ndarray
        Array of row or column permutations.

    Notes
    -----
    This function relies on a maximum cardinality bipartite matching 
    algorithm based on a breadth-first search (BFS) of the underlying 
    graph.

    .. versionadded:: 0.15.0

    References
    ----------
    I. S. Duff, K. Kaya, and B. Ucar, "Design, Implementation, and 
    Analysis of Maximum Transversal Algorithms", ACM Trans. Math. Softw.
    38, no. 2, (2011).

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import maximum_bipartite_matching

    >>> graph = [
    ... [0, 1 , 2, 0],
    ... [1, 0, 0, 1],
    ... [2, 0, 0, 3],
    ... [0, 1, 3, 0]
    ... ]
    >>> graph = csr_matrix(graph)
    >>> print(graph)
      (0, 1)	1
      (0, 2)	2
      (1, 0)	1
      (1, 3)	1
      (2, 0)	2
      (2, 3)	3
      (3, 1)	1
      (3, 2)	3

    >>> maximum_bipartite_matching(graph, perm_type='row')
    array([1, 0, 3, 2], dtype=int32)

    graph must be in CSC, CSR, or COO format.<strided and direct or indirect>Possibly singular input matrix.unable to allocate array data.reverse_cuthill_mckee (line 16)Input must be a sparse matrixmaximum_bipartite_matchingndarray is not C contiguousitemsize <= 0 for cython.arrayNo matching signature found_maximum_bipartite_matchingInvalid shape in axis %d: %d.structural_rank (line 307)Cannot index with type '%s'reverse_cuthill_mckee<contiguous and indirect>SparseEfficiencyWarning<MemoryView of %r at 0x%x><MemoryView of %r object>_reverse_cuthill_mckee<contiguous and direct><strided and indirect><strided and direct>cline_in_traceback__pyx_unpickle_Enumstructural_rank__setstate_cython____pyx_PickleErrordtype_is_objectallocate_bufferView.MemoryViewsymmetric_mode_reordering.pyxisspmatrix_csrisspmatrix_cscisspmatrix_coo__reduce_cython____pyx_getbuffertemp_degreesstringsourcescipy.sparse__pyx_checksumRuntimeErrorlevel_startPickleErrorMemoryErrorImportErrorsignaturesqueue_size__pyx_vtable____pyx_resultisspmatrixcsc_matrixValueErrorIndexErrortransposerow_match__reduce_ex__queue_ptrqueue_col__pyx_stateperm_typelevel_lenlevel_endenumerateTypeErrorwarnings__setstate__rev_inds__pyx_typepreviousnum_rowsnext_numitemsize__getstate__defaultsEllipsisvisitedmemviewint64_tint32_tindicesfortranfloat64argsortupdateunpackstruct__reduce__pickle__name__kwargsindptr__import__formatencodedegreecolumnzerostocsctemp2stripstartsplitshaperangequeueordernumpynrowsmatchint32graphflagserroremptydtype__class__N_oldITYPEDTYPEASCIIwarn__test__tempstopstepsizeseedrankptrspermpackndimnamemode__main__kindindseptr__dict__baseargssumrowptrobj__new__maxindcolanyzznpllkkjjiiid|()snjicTONBuffer dtype mismatch, expected %s%s%s but got %sBuffer dtype mismatch, expected '%s' but got %s in '%s.%s'memviewslice is already initialized!%.200s() takes no arguments (%zd given)%.200s() takes exactly one argument (%zd given)Bad call flags in __Pyx_CyFunction_Call. METH_OLDARGS is no longer supported!%.200s() takes no keyword arguments%.200s.%.200s is not a type object%.200s.%.200s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObject%s.%s size changed, may indicate binary incompatibility. Expected %zd from C header, got %zd from PyObjectShared Cython type %.200s is not a type objectShared Cython type %.200s has the wrong size, try recompilingUnable to initialize pickling for %s__annotations__ must be set to a dict object__kwdefaults__ must be set to a dict object__defaults__ must be set to a tuple objectfunction's dictionary may not be deletedsetting function's dictionary to a non-dict__qualname__ must be set to a string object__name__ must be set to a string objectDoes not understand character buffer dtype format string ('%c')Argument '%.200s' has incorrect type (expected %.200s, got %.200s)Cannot convert %.200s to %.200sord() expected string of length 1, but %.200s foundord() expected a character, but string of length %zd foundNeed at least one argument, 0 given.First argument should be of type %.200s, got %.200s.scipy/sparse/csgraph/_reordering.cView.MemoryView.memoryview.__str__View.MemoryView.memoryview.size.__get__View.MemoryView.memoryview.nbytes.__get__View.MemoryView.memoryview.itemsize.__get__View.MemoryView.memoryview.ndim.__get__View.MemoryView.memoryview.suboffsets.__get__View.MemoryView.memoryview.shape.__get__View.MemoryView.memoryview.setitem_indexedView.MemoryView.array.memview.__get__View.MemoryView.array.__getattr__View.MemoryView.array.__getitem__View.MemoryView.array.__setitem__Subscript deletion not supported by %.200s%s() got multiple values for keyword argument '%U'%.200s() keywords must be strings%s() got an unexpected keyword argument '%U'%.200s() takes %.8s %zd positional argument%.1s (%zd given)__int__ returned non-int (type %.200s).  The ability to return an instance of a strict subclass of int is deprecated, and may be removed in a future version of Python.__%.4s__ returned non-%.4s (type %.200s)View.MemoryView.memoryview.assign_item_from_objectView.MemoryView._memoryviewslice.assign_item_from_object'NoneType' object is not iterableneed more than %zd value%.1s to unpacktoo many values to unpack (expected %zd)Unexpected format string character: '%c'Expected a dimension of size %zu, got %zuExpected %d dimensions, got %dPython does not define a standard format string size for long double ('g')..Buffer dtype mismatch; next field is at offset %zd but %zd expectedBig-endian buffer not supported on little-endian compilerBuffer acquisition: Expected '{' after 'T'Cannot handle repeated arrays in format stringExpected a dimension of size %zu, got %dExpected a comma in format string, got '%c'Expected %d dimension(s), got %dUnexpected end of format string, expected ')'Buffer has wrong number of dimensions (expected %d, got %d)Item size of buffer (%zd byte%s) does not match size of '%s' (%zd byte%s)Acquisition count is %d (line %d)calling %R should have returned an instance of BaseException, not %Rraise: exception class must be a subclass of BaseExceptionView.MemoryView.array.__reduce_cython__View.MemoryView.array.__setstate_cython__View.MemoryView.memoryview.__reduce_cython__View.MemoryView.memoryview.__setstate_cython__View.MemoryView.memoryview.strides.__get__View.MemoryView._memoryviewslice.__reduce_cython__View.MemoryView._memoryviewslice.__setstate_cython__PyObject_GetBuffer: view==NULL argument is obsoleteView.MemoryView.array.__getbuffer__View.MemoryView.memoryview.__getbuffer__View.MemoryView.array.get_memviewView.MemoryView.memoryview_cwrapperBuffer exposes suboffsets but no stridesC-contiguous buffer is not indirect in dimension %dBuffer and memoryview are not contiguous in the same dimension.Buffer is not indirectly contiguous in dimension %d.Buffer is not indirectly accessible in dimension %d.Buffer not compatible with direct access in dimension %d.Item size of buffer (%zu byte%s) does not match size of '%s' (%zu byte%s)View.MemoryView.memoryview.is_sliceView.MemoryView.memoryview_fromsliceView.MemoryView.memoryview_copy_from_sliceView.MemoryView.memoryview.__repr__View.MemoryView.Enum.__reduce_cython__value too large to convert to intc-string too long to convert to PythonView.MemoryView.memoryview.convert_item_to_objectView.MemoryView._memoryviewslice.convert_item_to_object'NoneType' object is not subscriptablehasattr(): attribute name must be stringView.MemoryView.__pyx_unpickle_Enum__set_stateView.MemoryView.Enum.__setstate_cython__scipy.sparse.csgraph._reordering.reverse_cuthill_mckeeCannot transpose memoryview with indirect dimensionsView.MemoryView.transpose_memsliceView.MemoryView.memoryview_copyView.MemoryView.memoryview.T.__get__View.MemoryView.copy_data_to_tempView.MemoryView.memoryview_copy_contentsCannot copy memoryview slice with indirect dimensions (axis %d)View.MemoryView.array_cwrapperView.MemoryView.memoryview.copy_fortranView.MemoryView.memoryview.copyView.MemoryView.__pyx_unpickle_Enuminteger division or modulo by zerovalue too large to perform divisionView.MemoryView.pybuffer_indexView.MemoryView.memoryview.get_item_pointerView.MemoryView.get_slice_from_memviewView.MemoryView.assert_direct_dimensionsView.MemoryView.memoryview.setitem_slice_assign_scalarView.MemoryView.array.__cinit__Argument '%.200s' must not be Noneobject of type 'NoneType' has no len()expected bytes, NoneType foundView.MemoryView.memoryview.__setitem__scipy.sparse.csgraph._reordering.maximum_bipartite_matchingView.MemoryView.memoryview.__cinit__scipy.sparse.csgraph._reordering.structural_rankView.MemoryView.memoryview.setitem_slice_assignmentonly single character unicode strings can be converted to Py_UCS4, got length %zdscipy.sparse.csgraph._reordering.__pyx_fused_cpdefAll dimensions preceding dimension %d must be indexed and not slicedView.MemoryView.slice_memviewsliceStep may not be zero (axis %d)local variable '%s' referenced before assignmentView.MemoryView.memoryview.__getitem__Out of bounds on buffer access (axis %d)scipy.sparse.csgraph._reordering._node_degreesscipy.sparse.csgraph._reordering._reverse_cuthill_mckeescipy.sparse.csgraph._reordering._maximum_bipartite_matchingscipy.sparse.csgraph._reorderingcompiletime version %s of module '%.100s' does not match runtime version %sinit scipy.sparse.csgraph._reorderingscipy.sparse.csgraph._reordering._memoryviewsliceInternal class for passing memoryview slices to Pythonscipy.sparse.csgraph._reordering.memoryviewscipy.sparse.csgraph._reordering.Enumscipy.sparse.csgraph._reordering.array__pyx_fuse_1_maximum_bipartite_matching__pyx_fuse_0_maximum_bipartite_matching__pyx_fuse_1_reverse_cuthill_mckee__pyx_fuse_0_reverse_cuthill_mckeeð¿
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    structural_rank(graph)
    
    Compute the structural rank of a graph (matrix) with a given 
    sparsity pattern.

    The structural rank of a matrix is the number of entries in the maximum 
    transversal of the corresponding bipartite graph, and is an upper bound 
    on the numerical rank of the matrix. A graph has full structural rank 
    if it is possible to permute the elements to make the diagonal zero-free.

    .. versionadded:: 0.19.0

    Parameters
    ----------
    graph : sparse matrix
        Input sparse matrix.

    Returns
    -------
    rank : int
        The structural rank of the sparse graph.
    
    References
    ----------
    .. [1] I. S. Duff, "Computing the Structural Index", SIAM J. Alg. Disc. 
            Meth., Vol. 7, 594 (1986).
    
    .. [2] http://www.cise.ufl.edu/research/sparse/matrices/legend.html

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import structural_rank

    >>> graph = [
    ... [0, 1 , 2, 0],
    ... [1, 0, 0, 1],
    ... [2, 0, 0, 3],
    ... [0, 1, 3, 0]
    ... ]
    >>> graph = csr_matrix(graph)
    >>> print(graph)
      (0, 1)	1
      (0, 2)	2
      (1, 0)	1
      (1, 3)	1
      (2, 0)	2
      (2, 3)	3
      (3, 1)	1
      (3, 2)	3

    >>> structural_rank(graph)
    4

    
    Maximum bipartite matching of a graph in CSC format.
    
    Reverse Cuthill-McKee ordering of a sparse symmetric CSR or CSC matrix.  
    We follow the original Cuthill-McKee paper and always start the routine
    at a node of lowest degree for each connected component.
    
    maximum_bipartite_matching(graph, perm_type='row')
    
    Returns an array of row or column permutations that makes
    the diagonal of a nonsingular square CSC sparse matrix zero free.  
    
    Such a permutation is always possible provided that the matrix 
    is nonsingular. This function looks at the structure of the matrix 
    only. The input matrix will be converted to CSC matrix format if
    necessary.

    Parameters
    ----------
    graph : sparse matrix
        Input sparse in CSC format
    perm_type : str, {'row', 'column'}
        Type of permutation to generate.

    Returns
    -------
    perm : ndarray
        Array of row or column permutations.

    Notes
    -----
    This function relies on a maximum cardinality bipartite matching 
    algorithm based on a breadth-first search (BFS) of the underlying 
    graph.

    .. versionadded:: 0.15.0

    References
    ----------
    I. S. Duff, K. Kaya, and B. Ucar, "Design, Implementation, and 
    Analysis of Maximum Transversal Algorithms", ACM Trans. Math. Softw.
    38, no. 2, (2011).

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import maximum_bipartite_matching

    >>> graph = [
    ... [0, 1 , 2, 0],
    ... [1, 0, 0, 1],
    ... [2, 0, 0, 3],
    ... [0, 1, 3, 0]
    ... ]
    >>> graph = csr_matrix(graph)
    >>> print(graph)
      (0, 1)	1
      (0, 2)	2
      (1, 0)	1
      (1, 3)	1
      (2, 0)	2
      (2, 3)	3
      (3, 1)	1
      (3, 2)	3

    >>> maximum_bipartite_matching(graph, perm_type='row')
    array([1, 0, 3, 2], dtype=int32)

    
    reverse_cuthill_mckee(graph, symmetric_mode=False)
    
    Returns the permutation array that orders a sparse CSR or CSC matrix
    in Reverse-Cuthill McKee ordering.  
    
    It is assumed by default, ``symmetric_mode=False``, that the input matrix 
    is not symmetric and works on the matrix ``A+A.T``. If you are 
    guaranteed that the matrix is symmetric in structure (values of matrix 
    elements do not matter) then set ``symmetric_mode=True``.
    
    Parameters
    ----------
    graph : sparse matrix
        Input sparse in CSC or CSR sparse matrix format.
    symmetric_mode : bool, optional
        Is input matrix guaranteed to be symmetric.

    Returns
    -------
    perm : ndarray
        Array of permuted row and column indices.
 
    Notes
    -----
    .. versionadded:: 0.15.0

    References
    ----------
    E. Cuthill and J. McKee, "Reducing the Bandwidth of Sparse Symmetric Matrices",
    ACM '69 Proceedings of the 1969 24th national conference, (1969).

    Examples
    --------
    >>> from scipy.sparse import csr_matrix
    >>> from scipy.sparse.csgraph import reverse_cuthill_mckee

    >>> graph = [
    ... [0, 1 , 2, 0],
    ... [0, 0, 0, 1],
    ... [2, 0, 0, 3],
    ... [0, 0, 0, 0]
    ... ]
    >>> graph = csr_matrix(graph)
    >>> print(graph)
      (0, 1)	1
      (0, 2)	2
      (1, 3)	1
      (2, 0)	2
      (2, 3)	3

    >>> reverse_cuthill_mckee(graph)
    array([3, 2, 1, 0], dtype=int32)
    
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