--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/planemo/lib/python3.7/site-packages/networkx/generators/ego.py	Fri Jul 31 00:32:28 2020 -0400
@@ -0,0 +1,71 @@
+"""
+Ego graph.
+"""
+#    Copyright (C) 2010 by
+#    Aric Hagberg <hagberg@lanl.gov>
+#    Dan Schult <dschult@colgate.edu>
+#    Pieter Swart <swart@lanl.gov>
+#    All rights reserved.
+#    BSD license.
+__author__ = """\n""".join(['Drew Conway <drew.conway@nyu.edu>',
+                            'Aric Hagberg <hagberg@lanl.gov>'])
+__all__ = ['ego_graph']
+
+import networkx as nx
+
+
+def ego_graph(G, n, radius=1, center=True, undirected=False, distance=None):
+    """Returns induced subgraph of neighbors centered at node n within
+    a given radius.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX Graph or DiGraph
+
+    n : node
+      A single node
+
+    radius : number, optional
+      Include all neighbors of distance<=radius from n.
+
+    center : bool, optional
+      If False, do not include center node in graph
+
+    undirected : bool, optional
+      If True use both in- and out-neighbors of directed graphs.
+
+    distance : key, optional
+      Use specified edge data key as distance.  For example, setting
+      distance='weight' will use the edge weight to measure the
+      distance from the node n.
+
+    Notes
+    -----
+    For directed graphs D this produces the "out" neighborhood
+    or successors.  If you want the neighborhood of predecessors
+    first reverse the graph with D.reverse().  If you want both
+    directions use the keyword argument undirected=True.
+
+    Node, edge, and graph attributes are copied to the returned subgraph.
+    """
+    if undirected:
+        if distance is not None:
+            sp, _ = nx.single_source_dijkstra(G.to_undirected(),
+                                              n, cutoff=radius,
+                                              weight=distance)
+        else:
+            sp = dict(nx.single_source_shortest_path_length(G.to_undirected(),
+                                                            n, cutoff=radius))
+    else:
+        if distance is not None:
+            sp, _ = nx.single_source_dijkstra(G,
+                                              n, cutoff=radius,
+                                              weight=distance)
+        else:
+            sp = dict(nx.single_source_shortest_path_length(G, n, cutoff=radius))
+
+    H = G.subgraph(sp).copy()
+    if not center:
+        H.remove_node(n)
+    return H