scipy.sparse.csgraph.

construct_dist_matrix#

scipy.sparse.csgraph.construct_dist_matrix(graph, predecessors, directed=True, null_value=np.inf)#

Construct distance matrix from a predecessor matrix

Added in version 0.11.0.

Parameters:

grapharray_like or sparse: The N x N matrix representation of a directed or undirected graph. If dense, then non-edges are indicated by zeros or infinities.
predecessorsarray_like: The N x N matrix of predecessors of each node (see Notes below).
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].
null_valuebool, optional: value to use for distances between unconnected nodes. Default is np.inf

Returns:

dist_matrixndarray: The N x N matrix of distances between nodes along the path specified by the predecessor matrix. If no path exists, the distance is zero.

Notes

The predecessor matrix is of the form returned by shortest_path. Row i of the predecessor matrix contains information on the shortest paths from point i: each entry predecessors[i, j] gives the index of the previous node in the path from point i to point j. If no path exists between point i and j, then predecessors[i, j] = -9999

Examples

>>> import numpy as np
>>> from scipy.sparse import csr_matrix
>>> from scipy.sparse.csgraph import construct_dist_matrix

>>> graph = [
... [0, 1, 2, 0],
... [0, 0, 0, 1],
... [0, 0, 0, 3],
... [0, 0, 0, 0]
... ]
>>> graph = csr_matrix(graph)
>>> print(graph)
   (np.int32(0), np.int32(1))       1
   (np.int32(0), np.int32(2))       2
   (np.int32(1), np.int32(3))       1
   (np.int32(2), np.int32(3))       3

>>> pred = np.array([[-9999, 0, 0, 2],
...                  [1, -9999, 0, 1],
...                  [2, 0, -9999, 2],
...                  [1, 3, 3, -9999]], dtype=np.int32)

>>> construct_dist_matrix(graph=graph, predecessors=pred, directed=False)
array([[0., 1., 2., 5.],
       [1., 0., 3., 1.],
       [2., 3., 0., 3.],
       [2., 1., 3., 0.]])