"""
Functional interface to graph methods and assorted utilities.
"""
__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
'Pieter Swart (swart@lanl.gov)',
'Dan Schult(dschult@colgate.edu)'])
# Copyright (C) 2004-2010 by
# Aric Hagberg <hagberg@lanl.gov>
# Dan Schult <dschult@colgate.edu>
# Pieter Swart <swart@lanl.gov>
# All rights reserved.
# BSD license.
#
import networkx as nx
# functional style helpers
__all__ = ['nodes', 'edges', 'degree', 'degree_histogram', 'neighbors',
'number_of_nodes', 'number_of_edges', 'density',
'nodes_iter', 'edges_iter', 'is_directed','info',
'freeze','is_frozen','subgraph','create_empty_copy',
'set_node_attributes','get_node_attributes',
'set_edge_attributes','get_edge_attributes']
[docs]def nodes(G):
"""Return a copy of the graph nodes in a list."""
return G.nodes()
[docs]def nodes_iter(G):
"""Return an iterator over the graph nodes."""
return G.nodes_iter()
[docs]def edges(G,nbunch=None):
"""Return list of edges adjacent to nodes in nbunch.
Return all edges if nbunch is unspecified or nbunch=None.
For digraphs, edges=out_edges
"""
return G.edges(nbunch)
[docs]def edges_iter(G,nbunch=None):
"""Return iterator over edges adjacent to nodes in nbunch.
Return all edges if nbunch is unspecified or nbunch=None.
For digraphs, edges=out_edges
"""
return G.edges_iter(nbunch)
[docs]def degree(G,nbunch=None,weight=None):
"""Return degree of single node or of nbunch of nodes.
If nbunch is ommitted, then return degrees of *all* nodes.
"""
return G.degree(nbunch,weight)
def neighbors(G,n):
"""Return a list of nodes connected to node n. """
return G.neighbors(n)
[docs]def number_of_nodes(G):
"""Return the number of nodes in the graph."""
return G.number_of_nodes()
[docs]def number_of_edges(G):
"""Return the number of edges in the graph. """
return G.number_of_edges()
[docs]def density(G):
r"""Return the density of a graph.
The density for undirected graphs is
.. math::
d = \frac{2m}{n(n-1)},
and for directed graphs is
.. math::
d = \frac{m}{n(n-1)},
where `n` is the number of nodes and `m` is the number of edges in `G`.
Notes
-----
The density is 0 for an graph without edges and 1.0 for a complete graph.
The density of multigraphs can be higher than 1.
"""
n=number_of_nodes(G)
m=number_of_edges(G)
if m==0: # includes cases n==0 and n==1
d=0.0
else:
if G.is_directed():
d=m/float(n*(n-1))
else:
d= m*2.0/float(n*(n-1))
return d
[docs]def degree_histogram(G):
"""Return a list of the frequency of each degree value.
Parameters
----------
G : Networkx graph
A graph
Returns
-------
hist : list
A list of frequencies of degrees.
The degree values are the index in the list.
Notes
-----
Note: the bins are width one, hence len(list) can be large
(Order(number_of_edges))
"""
degseq=list(G.degree().values())
dmax=max(degseq)+1
freq= [ 0 for d in range(dmax) ]
for d in degseq:
freq[d] += 1
return freq
[docs]def is_directed(G):
""" Return True if graph is directed."""
return G.is_directed()
[docs]def freeze(G):
"""Modify graph to prevent addition of nodes or edges.
Parameters
-----------
G : graph
A NetworkX graph
Examples
--------
>>> G=nx.Graph()
>>> G.add_path([0,1,2,3])
>>> G=nx.freeze(G)
>>> try:
... G.add_edge(4,5)
... except nx.NetworkXError as e:
... print(str(e))
Frozen graph can't be modified
Notes
-----
This does not prevent modification of edge data.
To "unfreeze" a graph you must make a copy.
See Also
--------
is_frozen
"""
def frozen(*args):
raise nx.NetworkXError("Frozen graph can't be modified")
G.add_node=frozen
G.add_nodes_from=frozen
G.remove_node=frozen
G.remove_nodes_from=frozen
G.add_edge=frozen
G.add_edges_from=frozen
G.remove_edge=frozen
G.remove_edges_from=frozen
G.clear=frozen
G.frozen=True
return G
[docs]def is_frozen(G):
"""Return True if graph is frozen.
Parameters
-----------
G : graph
A NetworkX graph
See Also
--------
freeze
"""
try:
return G.frozen
except AttributeError:
return False
def subgraph(G, nbunch):
"""Return the subgraph induced on nodes in nbunch.
Parameters
----------
G : graph
A NetworkX graph
nbunch : list, iterable
A container of nodes that will be iterated through once (thus
it should be an iterator or be iterable). Each element of the
container should be a valid node type: any hashable type except
None. If nbunch is None, return all edges data in the graph.
Nodes in nbunch that are not in the graph will be (quietly)
ignored.
Notes
-----
subgraph(G) calls G.subgraph()
"""
return G.subgraph(nbunch)
[docs]def create_empty_copy(G,with_nodes=True):
"""Return a copy of the graph G with all of the edges removed.
Parameters
----------
G : graph
A NetworkX graph
with_nodes : bool (default=True)
Include nodes.
Notes
-----
Graph, node, and edge data is not propagated to the new graph.
"""
H=G.__class__()
if with_nodes:
H.add_nodes_from(G)
return H
[docs]def info(G, n=None):
"""Print short summary of information for the graph G or the node n.
Parameters
----------
G : Networkx graph
A graph
n : node (any hashable)
A node in the graph G
"""
info='' # append this all to a string
if n is None:
info+="Name: %s\n"%G.name
type_name = [type(G).__name__]
info+="Type: %s\n"%",".join(type_name)
info+="Number of nodes: %d\n"%G.number_of_nodes()
info+="Number of edges: %d\n"%G.number_of_edges()
nnodes=G.number_of_nodes()
if len(G) > 0:
if G.is_directed():
info+="Average in degree: %8.4f\n"%\
(sum(G.in_degree().values())/float(nnodes))
info+="Average out degree: %8.4f"%\
(sum(G.out_degree().values())/float(nnodes))
else:
s=sum(G.degree().values())
info+="Average degree: %8.4f"%\
(float(s)/float(nnodes))
else:
if n not in G:
raise nx.NetworkXError("node %s not in graph"%(n,))
info+="Node % s has the following properties:\n"%n
info+="Degree: %d\n"%G.degree(n)
info+="Neighbors: "
info+=' '.join(str(nbr) for nbr in G.neighbors(n))
return info
[docs]def set_node_attributes(G,name,attributes):
"""Set node attributes from dictionary of nodes and values
Parameters
----------
G : NetworkX Graph
name : string
Attribute name
attributes: dict
Dictionary of attributes keyed by node.
Examples
--------
>>> G=nx.path_graph(3)
>>> bb=nx.betweenness_centrality(G)
>>> nx.set_node_attributes(G,'betweenness',bb)
>>> G.node[1]['betweenness']
1.0
"""
for node,value in attributes.items():
G.node[node][name]=value
[docs]def get_node_attributes(G,name):
"""Get node attributes from graph
Parameters
----------
G : NetworkX Graph
name : string
Attribute name
Returns
-------
Dictionary of attributes keyed by node.
Examples
--------
>>> G=nx.Graph()
>>> G.add_nodes_from([1,2,3],color='red')
>>> color=nx.get_node_attributes(G,'color')
>>> color[1]
'red'
"""
return dict( (n,d[name]) for n,d in G.node.items() if name in d)
[docs]def set_edge_attributes(G,name,attributes):
"""Set edge attributes from dictionary of edge tuples and values
Parameters
----------
G : NetworkX Graph
name : string
Attribute name
attributes: dict
Dictionary of attributes keyed by edge (tuple).
Examples
--------
>>> G=nx.path_graph(3)
>>> bb=nx.edge_betweenness_centrality(G, normalized=False)
>>> nx.set_edge_attributes(G,'betweenness',bb)
>>> G[1][2]['betweenness']
2.0
"""
for (u,v),value in attributes.items():
G[u][v][name]=value
[docs]def get_edge_attributes(G,name):
"""Get edge attributes from graph
Parameters
----------
G : NetworkX Graph
name : string
Attribute name
Returns
-------
Dictionary of attributes keyed by node.
Examples
--------
>>> G=nx.Graph()
>>> G.add_path([1,2,3],color='red')
>>> color=nx.get_edge_attributes(G,'color')
>>> color[(1,2)]
'red'
"""
return dict( ((u,v),d[name]) for u,v,d in G.edges(data=True) if name in d)