depth_first_order(csgraph, i_start, directed=True, return_predecessors=True)¶
Return a depth-first ordering starting with specified node.
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.
New in version 0.11.0.
- csgraph : array_like or sparse matrix
The N x N compressed sparse graph. The input csgraph will be converted to csr format for the calculation.
- i_start : int
The index of starting node.
- directed : bool, optional
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].
- return_predecessors : bool, optional
If True (default), then return the predecesor array (see below).
- node_array : ndarray, one dimension
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.
- predecessors : ndarray, one dimension
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.
>>> 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))