depth_first_order(csgraph, i_start, directed=True, return_predecessors=True)
Note that a depth-first order is not unique. Furthermore, for graphs with cycles, the tree generated by a depth-first search is not unique either.
The N x N compressed sparse graph. The input csgraph will be converted to csr format for the calculation.
The index of starting node.
If True (default), then operate on a directed graph: only move from point i to point j along paths csgraph[i, j]. If False, then find the shortest path on an undirected graph: the algorithm can progress from point i to j along csgraph[i, j] or csgraph[j, i].
If True (default), then return the predecesor array (see below).
The depth-first list of nodes, starting with specified node. The length of node_array is the number of nodes reachable from the specified node.
Returned only if return_predecessors is True. The length-N list of predecessors of each node in a depth-first tree. If node i is in the tree, then its parent is given by predecessors[i]. If node i is not in the tree (and for the parent node) then predecessors[i] = -9999.
Return a depth-first ordering starting with specified node.
>>> from scipy.sparse import csr_matrix
... from scipy.sparse.csgraph import depth_first_order
>>> 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
>>> depth_first_order(graph,0) (array([0, 1, 3, 2], dtype=int32), array([-9999, 0, 0, 1], dtype=int32))See :
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scipy.sparse.csgraph._traversal.depth_first_order
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