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comparison planemo/lib/python3.7/site-packages/networkx/generators/ego.py @ 1:56ad4e20f292 draft

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"planemo upload commit 6eee67778febed82ddd413c3ca40b3183a3898f1"
author guerler
date Fri, 31 Jul 2020 00:32:28 -0400
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0:d30785e31577 1:56ad4e20f292
1 """
2 Ego graph.
3 """
4 # Copyright (C) 2010 by
5 # Aric Hagberg <hagberg@lanl.gov>
6 # Dan Schult <dschult@colgate.edu>
7 # Pieter Swart <swart@lanl.gov>
8 # All rights reserved.
9 # BSD license.
10 __author__ = """\n""".join(['Drew Conway <drew.conway@nyu.edu>',
11 'Aric Hagberg <hagberg@lanl.gov>'])
12 __all__ = ['ego_graph']
13
14 import networkx as nx
15
16
17 def ego_graph(G, n, radius=1, center=True, undirected=False, distance=None):
18 """Returns induced subgraph of neighbors centered at node n within
19 a given radius.
20
21 Parameters
22 ----------
23 G : graph
24 A NetworkX Graph or DiGraph
25
26 n : node
27 A single node
28
29 radius : number, optional
30 Include all neighbors of distance<=radius from n.
31
32 center : bool, optional
33 If False, do not include center node in graph
34
35 undirected : bool, optional
36 If True use both in- and out-neighbors of directed graphs.
37
38 distance : key, optional
39 Use specified edge data key as distance. For example, setting
40 distance='weight' will use the edge weight to measure the
41 distance from the node n.
42
43 Notes
44 -----
45 For directed graphs D this produces the "out" neighborhood
46 or successors. If you want the neighborhood of predecessors
47 first reverse the graph with D.reverse(). If you want both
48 directions use the keyword argument undirected=True.
49
50 Node, edge, and graph attributes are copied to the returned subgraph.
51 """
52 if undirected:
53 if distance is not None:
54 sp, _ = nx.single_source_dijkstra(G.to_undirected(),
55 n, cutoff=radius,
56 weight=distance)
57 else:
58 sp = dict(nx.single_source_shortest_path_length(G.to_undirected(),
59 n, cutoff=radius))
60 else:
61 if distance is not None:
62 sp, _ = nx.single_source_dijkstra(G,
63 n, cutoff=radius,
64 weight=distance)
65 else:
66 sp = dict(nx.single_source_shortest_path_length(G, n, cutoff=radius))
67
68 H = G.subgraph(sp).copy()
69 if not center:
70 H.remove_node(n)
71 return H