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
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
Examples
Simple integer weights on edges:
>>> import numpy >>> A=numpy.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):
>>> import numpy >>> A = numpy.matrix([[1, 1], [1, 2]]) >>> G = nx.from_numpy_matrix(A, create_using = nx.MultiGraph()) >>> G[1][1] {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:
>>> import numpy >>> A = numpy.matrix([[1, 1], [1, 2]]) >>> temp = nx.MultiGraph() >>> G = nx.from_numpy_matrix(A, parallel_edges = True, create_using = temp) >>> G[1][1] {0: {'weight': 1}, 1: {'weight': 1}}
User defined compound data type on edges:
>>> import numpy >>> dt = [('weight', float), ('cost', int)] >>> A = numpy.matrix([[(1.0, 2)]], dtype = dt) >>> G = nx.from_numpy_matrix(A) >>> G.edges() [(0, 0)] >>> G[0][0]['cost'] 2 >>> G[0][0]['weight'] 1.0