from_numpy_array#
- from_numpy_array(A, parallel_edges=False, create_using=None, edge_attr='weight')[source]#
Returns a graph from a 2D NumPy array.
The 2D NumPy array is interpreted as an adjacency matrix for the graph.
- Parameters:
- Aa 2D numpy.ndarray
An adjacency matrix representation of a graph
- parallel_edgesBoolean
If this is True,
create_using
is a multigraph, andA
is an integer array, then entry (i, j) in the array 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 array are interpreted as the weight of a single edge joining the vertices.- create_usingNetworkX graph constructor, optional (default=nx.Graph)
Graph type to create. If graph instance, then cleared before populated.
- edge_attrString, optional (default=”weight”)
The attribute to which the array values are assigned on each edge. If it is None, edge attributes will not be assigned.
See also
Notes
For directed graphs, explicitly mention create_using=nx.DiGraph, and entry i,j of A corresponds to an edge from i to j.
If
create_using
isnetworkx.MultiGraph
ornetworkx.MultiDiGraph
,parallel_edges
is True, and the entries ofA
are of typeint
, then this function returns a multigraph (of the same type ascreate_using
) with parallel edges.If
create_using
indicates an undirected multigraph, then only the edges indicated by the upper triangle of the arrayA
will be added to the graph.If
edge_attr
is Falsy (False or None), edge attributes will not be assigned, and the array data will be treated like a binary mask of edge presence or absence. Otherwise, the attributes will be assigned as follows:If the NumPy array has a single data type for each array entry it will be converted to an appropriate Python data type.
If the NumPy array has a user-specified compound data type the names of the data fields will be used as attribute keys in the resulting NetworkX graph.
Examples
Simple integer weights on edges:
>>> import numpy as np >>> A = np.array([[1, 1], [2, 1]]) >>> G = nx.from_numpy_array(A) >>> G.edges(data=True) EdgeDataView([(0, 0, {'weight': 1}), (0, 1, {'weight': 2}), (1, 1, {'weight': 1})])
If
create_using
indicates a multigraph and the array has only integer entries andparallel_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_array(A, create_using=nx.MultiGraph) >>> G[1][1] AtlasView({0: {'weight': 2}})
If
create_using
indicates a multigraph and the array has only integer entries andparallel_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_array(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_array(A) >>> G.edges() EdgeView([(0, 0)]) >>> G[0][0]["cost"] 2 >>> G[0][0]["weight"] 1.0