from_numpy_matrix(A, parallel_edges=False, create_using=None)
The numpy matrix is interpreted as an adjacency matrix for the graph.
For directed graphs, explicitly mention create_using=nx.DiGraph, and entry i,j of A corresponds to an edge from i to j.
If :None:None:`create_using` is networkx.MultiGraph
or networkx.MultiDiGraph
, :None:None:`parallel_edges` is True, and the entries of A are of type int
, then this function returns a multigraph (constructed from :None:None:`create_using`) with parallel edges.
If :None:None:`create_using` indicates 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.
An adjacency matrix representation of a graph
If True, :None:None:`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 False, then the entries in the adjacency matrix are interpreted as the weight of a single edge joining the vertices.
Graph type to create. If graph instance, then cleared before populated.
Returns a graph from numpy matrix.
Simple integer weights on edges:
>>> import numpy as np
... A = np.array([[1, 1], [2, 1]])
... G = nx.from_numpy_matrix(A)
If :None:None:`create_using` indicates a multigraph and the matrix has only integer entries and :None:None:`parallel_edges` is False, then the entries will be treated as weights for edges joining the nodes (without creating parallel edges):
>>> A = np.array([[1, 1], [1, 2]])
... G = nx.from_numpy_matrix(A, create_using=nx.MultiGraph)
... G[1][1] AtlasView({0: {'weight': 2}})
If :None:None:`create_using` indicates a multigraph and the matrix has only integer entries and :None:None:`parallel_edges` is True, then the entries will be treated as the number of parallel edges joining those two vertices:
>>> A = np.array([[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.array([[(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"] 1.0See :
The following pages refer to to this document either explicitly or contain code examples using this.
networkx.convert_matrix.from_numpy_matrix
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