/usr/lib/python2.7/dist-packages/networkx/classes/function.py is in python-networkx 1.8.1-0ubuntu3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 | """Functional interface to graph methods and assorted utilities.
"""
# Copyright (C) 2004-2012 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
import itertools
__author__ = """\n""".join(['Aric Hagberg (hagberg@lanl.gov)',
'Pieter Swart (swart@lanl.gov)',
'Dan Schult(dschult@colgate.edu)'])
__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',
'all_neighbors','non_neighbors']
def nodes(G):
"""Return a copy of the graph nodes in a list."""
return G.nodes()
def nodes_iter(G):
"""Return an iterator over the graph nodes."""
return G.nodes_iter()
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)
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)
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)
def number_of_nodes(G):
"""Return the number of nodes in the graph."""
return G.number_of_nodes()
def number_of_edges(G):
"""Return the number of edges in the graph. """
return G.number_of_edges()
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 a graph without edges and 1 for a complete graph.
The density of multigraphs can be higher than 1.
Self loops are counted in the total number of edges so graphs with self
loops can have density higher than 1.
"""
n=number_of_nodes(G)
m=number_of_edges(G)
if m==0 or 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
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
def is_directed(G):
""" Return True if graph is directed."""
return G.is_directed()
def freeze(G):
"""Modify graph to prevent further change by adding or removing
nodes or edges.
Node and edge data can still be modified.
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
-----
To "unfreeze" a graph you must make a copy by creating a new graph object:
>>> graph = nx.path_graph(4)
>>> frozen_graph = nx.freeze(graph)
>>> unfrozen_graph = nx.Graph(frozen_graph)
>>> nx.is_frozen(unfrozen_graph)
False
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
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)
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
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
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
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)
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
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)
def all_neighbors(graph, node):
""" Returns all of the neighbors of a node in the graph.
If the graph is directed returns predecessors as well as successors.
Parameters
----------
graph : NetworkX graph
Graph to find neighbors.
node : node
The node whose neighbors will be returned.
Returns
-------
neighbors : iterator
Iterator of neighbors
"""
if graph.is_directed():
values = itertools.chain.from_iterable([graph.predecessors_iter(node),
graph.successors_iter(node)])
else:
values = graph.neighbors_iter(node)
return values
def non_neighbors(graph, node):
"""Returns the non-neighbors of the node in the graph.
Parameters
----------
graph : NetworkX graph
Graph to find neighbors.
node : node
The node whose neighbors will be returned.
Returns
-------
non_neighbors : iterator
Iterator of nodes in the graph that are not neighbors of the node.
"""
nbors = set(neighbors(graph, node)) | set([node])
return (nnode for nnode in graph if nnode not in nbors)
|