"""
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