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networkx.convert_matrix.from_numpy_matrix

from_numpy_matrix(A, parallel_edges=False, create_using=None)[source]

Return a graph from numpy matrix.

The numpy matrix is interpreted as an adjacency matrix for the graph.

Parameters:
  • A (numpy matrix) – An adjacency matrix representation of a graph
  • parallel_edges (Boolean) – If this is True, create_using is a multigraph, and A is an integer matrix, then entry (i, j) in the matrix is interpreted as the number of parallel edges joining vertices i and j in the graph. If it is False, then the entries in the adjacency matrix are interpreted as the weight of a single edge joining the vertices.
  • create_using (NetworkX graph) – Use specified graph for result. The default is Graph()

Notes

If create_using is an instance of networkx.MultiGraph or networkx.MultiDiGraph, parallel_edges is True, and the entries of A are of type int, then this function returns a multigraph (of the same type as create_using) with parallel edges.

If create_using is an undirected multigraph, then only the edges indicated by the upper triangle of the matrix A will be added to the graph.

If the numpy matrix has a single data type for each matrix entry it will be converted to an appropriate Python data type.

If the numpy matrix has a user-specified compound data type the names of the data fields will be used as attribute keys in the resulting NetworkX graph.

See also

to_numpy_matrix(), to_numpy_recarray()

Examples

Simple integer weights on edges:

>>> import numpy as np
>>> A = np.matrix([[1, 1], [2, 1]])
>>> G = nx.from_numpy_matrix(A)

If create_using is a multigraph and the matrix has only integer entries, the entries will be interpreted as weighted edges joining the vertices (without creating parallel edges):

>>> A = np.matrix([[1, 1], [1, 2]])
>>> G = nx.from_numpy_matrix(A, create_using=nx.MultiGraph())
>>> G[1][1]
AtlasView({0: {'weight': 2}})

If create_using is a multigraph and the matrix has only integer entries but parallel_edges is True, then the entries will be interpreted as the number of parallel edges joining those two vertices:

>>> A = np.matrix([[1, 1], [1, 2]])
>>> temp = nx.MultiGraph()
>>> G = nx.from_numpy_matrix(A, parallel_edges=True, create_using=temp)
>>> G[1][1]
AtlasView({0: {'weight': 1}, 1: {'weight': 1}})

User defined compound data type on edges:

>>> dt = [('weight', float), ('cost', int)]
>>> A = np.matrix([[(1.0, 2)]], dtype=dt)
>>> G = nx.from_numpy_matrix(A)
>>> list(G.edges())
[(0, 0)]
>>> G[0][0]['cost']
2
>>> G[0][0]['weight']
1.0