# scipy.sparse.csgraph.reconstruct_path¶

scipy.sparse.csgraph.reconstruct_path(csgraph, predecessors, directed=True)

Construct a tree from a graph and a predecessor list.

New in version 0.11.0.

Parameters
csgrapharray_like or sparse matrix

The N x N matrix representing the directed or undirected graph from which the predecessors are drawn.

predecessorsarray_like, one dimension

The length-N array of indices of predecessors for the tree. The index of the parent of node i is given by predecessors[i].

directedbool, 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 operate on an undirected graph: the algorithm can progress from point i to j along csgraph[i, j] or csgraph[j, i].

Returns
cstreecsr matrix

The N x N directed compressed-sparse representation of the tree drawn from csgraph which is encoded by the predecessor list.

Examples

>>> from scipy.sparse import csr_matrix
>>> from scipy.sparse.csgraph import reconstruct_path

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

>>> pred = np.array([-9999, 0, 0, 1], dtype=np.int32)

>>> cstree = reconstruct_path(csgraph=graph, predecessors=pred, directed=False)
>>> cstree.todense()
matrix([[ 0.,  1.,  2.,  0.],
[ 0.,  0.,  0.,  1.],
[ 0.,  0.,  0.,  0.],
[ 0.,  0.,  0.,  0.]])


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Spatial algorithms and data structures (scipy.spatial)