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