text: r"""
  ==============================================================
  Compressed Sparse Graph Routines (:mod:`scipy.sparse.csgraph`)
  ==============================================================
  
  .. currentmodule:: scipy.sparse.csgraph
  
  Fast graph algorithms based on sparse matrix representations.
  
  Contents
  ========
  
  .. autosummary::
     :toctree: generated/
  
     connected_components -- determine connected components of a graph
     laplacian -- compute the laplacian of a graph
     shortest_path -- compute the shortest path between points on a positive graph
     dijkstra -- use Dijkstra's algorithm for shortest path
     floyd_warshall -- use the Floyd-Warshall algorithm for shortest path
     bellman_ford -- use the Bellman-Ford algorithm for shortest path
     johnson -- use Johnson's algorithm for shortest path
     breadth_first_order -- compute a breadth-first order of nodes
     depth_first_order -- compute a depth-first order of nodes
     breadth_first_tree -- construct the breadth-first tree from a given node
     depth_first_tree -- construct a depth-first tree from a given node
     minimum_spanning_tree -- construct the minimum spanning tree of a graph
     reverse_cuthill_mckee -- compute permutation for reverse Cuthill-McKee ordering
     maximum_bipartite_matching -- compute permutation to make diagonal zero free
     structural_rank -- compute the structural rank of a graph
     NegativeCycleError
  
  .. autosummary::
     :toctree: generated/
  
     construct_dist_matrix
     csgraph_from_dense
     csgraph_from_masked
     csgraph_masked_from_dense
     csgraph_to_dense
     csgraph_to_masked
     reconstruct_path
  
  Graph Representations
  =====================
  This module uses graphs which are stored in a matrix format.  A
  graph with N nodes can be represented by an (N x N) adjacency matrix G.
  If there is a connection from node i to node j, then G[i, j] = w, where
  w is the weight of the connection.  For nodes i and j which are
  not connected, the value depends on the representation:
  
  - for dense array representations, non-edges are represented by
    G[i, j] = 0, infinity, or NaN.
  
  - for dense masked representations (of type np.ma.MaskedArray), non-edges
    are represented by masked values.  This can be useful when graphs with
    zero-weight edges are desired.
  
  - for sparse array representations, non-edges are represented by
    non-entries in the matrix.  This sort of sparse representation also
    allows for edges with zero weights.
  
  As a concrete example, imagine that you would like to represent the following
  undirected graph::
  
                G
  
               (0)
              /   \
             1     2
            /       \
          (2)       (1)
  
  This graph has three nodes, where node 0 and 1 are connected by an edge of
  weight 2, and nodes 0 and 2 are connected by an edge of weight 1.
  We can construct the dense, masked, and sparse representations as follows,
  keeping in mind that an undirected graph is represented by a symmetric matrix::
  
      >>> G_dense = np.array([[0, 2, 1],
      ...                     [2, 0, 0],
      ...                     [1, 0, 0]])
      >>> G_masked = np.ma.masked_values(G_dense, 0)
      >>> from scipy.sparse import csr_matrix
      >>> G_sparse = csr_matrix(G_dense)
  
  This becomes more difficult when zero edges are significant.  For example,
  consider the situation when we slightly modify the above graph::
  
               G2
  
               (0)
              /   \
             0     2
            /       \
          (2)       (1)
  
  This is identical to the previous graph, except nodes 0 and 2 are connected
  by an edge of zero weight.  In this case, the dense representation above
  leads to ambiguities: how can non-edges be represented if zero is a meaningful
  value?  In this case, either a masked or sparse representation must be used
  to eliminate the ambiguity::
  
      >>> G2_data = np.array([[np.inf, 2,      0     ],
      ...                     [2,      np.inf, np.inf],
      ...                     [0,      np.inf, np.inf]])
      >>> G2_masked = np.ma.masked_invalid(G2_data)
      >>> from scipy.sparse.csgraph import csgraph_from_dense
      >>> # G2_sparse = csr_matrix(G2_data) would give the wrong result
      >>> G2_sparse = csgraph_from_dense(G2_data, null_value=np.inf)
      >>> G2_sparse.data
      array([ 2.,  0.,  2.,  0.])
  
  Here we have used a utility routine from the csgraph submodule in order to
  convert the dense representation to a sparse representation which can be
  understood by the algorithms in submodule.  By viewing the data array, we
  can see that the zero values are explicitly encoded in the graph.
  
  Directed vs. Undirected
  -----------------------
  Matrices may represent either directed or undirected graphs.  This is
  specified throughout the csgraph module by a boolean keyword.  Graphs are
  assumed to be directed by default. In a directed graph, traversal from node
  i to node j can be accomplished over the edge G[i, j], but not the edge
  G[j, i].  In a non-directed graph, traversal from node i to node j can be
  accomplished over either G[i, j] or G[j, i].  If both edges are not null,
  and the two have unequal weights, then the smaller of the two is used.
  Note that a symmetric matrix will represent an undirected graph, regardless
  of whether the 'directed' keyword is set to True or False.  In this case,
  using ``directed=True`` generally leads to more efficient computation.
  
  The routines in this module accept as input either scipy.sparse representations
  (csr, csc, or lil format), masked representations, or dense representations
  with non-edges indicated by zeros, infinities, and NaN entries.
  """
  
  from __future__ import division, print_function, absolute_import
  
  __docformat__ = "restructuredtext en"
  
  __all__ = ['connected_components',
             'laplacian',
             'shortest_path',
             'floyd_warshall',
             'dijkstra',
             'bellman_ford',
             'johnson',
             'breadth_first_order',
             'depth_first_order',
             'breadth_first_tree',
             'depth_first_tree',
             'minimum_spanning_tree',
             'reverse_cuthill_mckee',
             'maximum_bipartite_matching',
             'structural_rank',
             'construct_dist_matrix',
             'reconstruct_path',
             'csgraph_masked_from_dense',
             'csgraph_from_dense',
             'csgraph_from_masked',
             'csgraph_to_dense',
             'csgraph_to_masked',
             'NegativeCycleError']
  
  from ._laplacian import laplacian
  from ._shortest_path import shortest_path, floyd_warshall, dijkstra,\
      bellman_ford, johnson, NegativeCycleError
  from ._traversal import breadth_first_order, depth_first_order, \
      breadth_first_tree, depth_first_tree, connected_components
  from ._min_spanning_tree import minimum_spanning_tree
  from ._reordering import reverse_cuthill_mckee, maximum_bipartite_matching, \
      structural_rank
  from ._tools import construct_dist_matrix, reconstruct_path,\
      csgraph_from_dense, csgraph_to_dense, csgraph_masked_from_dense,\
      csgraph_from_masked, csgraph_to_masked
  
  from scipy._lib._testutils import PytestTester
  test = PytestTester(__name__)
  del PytestTester
  