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Source code for networkx.algorithms.traversal.breadth_first_search

"""
====================
Breadth-first search
====================

Basic algorithms for breadth-first searching.
"""
import networkx as nx
from collections import defaultdict, deque
__author__ = """\n""".join(['Aric Hagberg <aric.hagberg@gmail.com>'])
__all__ = ['bfs_edges', 'bfs_tree', 'bfs_predecessors', 'bfs_successors']

[docs]def bfs_edges(G, source, reverse=False): """Produce edges in a breadth-first-search starting at source. Parameters ---------- G : NetworkX graph source : node, optional Specify starting node for breadth-first search and return edges in the component reachable from source. reverse : bool, optional If True traverse a directed graph in the reverse direction Returns ------- edges: generator A generator of edges in the breadth-first-search. Examples -------- >>> G = nx.Graph() >>> G.add_path([0,1,2]) >>> print(list(nx.bfs_edges(G,0))) [(0, 1), (1, 2)] Notes ----- Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py by D. Eppstein, July 2004. If a source is not specified then a source is chosen arbitrarily and repeatedly until all components in the graph are searched. """ if reverse and isinstance(G, nx.DiGraph): neighbors = G.predecessors_iter else: neighbors = G.neighbors_iter visited = set([source]) queue = deque([(source, neighbors(source))]) while queue: parent, children = queue[0] try: child = next(children) if child not in visited: yield parent, child visited.add(child) queue.append((child, neighbors(child))) except StopIteration: queue.popleft()
[docs]def bfs_tree(G, source, reverse=False): """Return an oriented tree constructed from of a breadth-first-search starting at source. Parameters ---------- G : NetworkX graph source : node, optional Specify starting node for breadth-first search and return edges in the component reachable from source. reverse : bool, optional If True traverse a directed graph in the reverse direction Returns ------- T: NetworkX DiGraph An oriented tree Examples -------- >>> G = nx.Graph() >>> G.add_path([0,1,2]) >>> print(list(nx.bfs_edges(G,0))) [(0, 1), (1, 2)] Notes ----- Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py by D. Eppstein, July 2004. If a source is not specified then a source is chosen arbitrarily and repeatedly until all components in the graph are searched. """ T = nx.DiGraph() T.add_node(source) T.add_edges_from(bfs_edges(G,source,reverse=reverse)) return T
[docs]def bfs_predecessors(G, source): """Return dictionary of predecessors in breadth-first-search from source. Parameters ---------- G : NetworkX graph source : node, optional Specify starting node for breadth-first search and return edges in the component reachable from source. Returns ------- pred: dict A dictionary with nodes as keys and predecessor nodes as values. Examples -------- >>> G = nx.Graph() >>> G.add_path([0,1,2]) >>> print(nx.bfs_predecessors(G,0)) {1: 0, 2: 1} Notes ----- Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py by D. Eppstein, July 2004. If a source is not specified then a source is chosen arbitrarily and repeatedly until all components in the graph are searched. """ return dict((t,s) for s,t in bfs_edges(G,source))
[docs]def bfs_successors(G, source): """Return dictionary of successors in breadth-first-search from source. Parameters ---------- G : NetworkX graph source : node, optional Specify starting node for breadth-first search and return edges in the component reachable from source. Returns ------- succ: dict A dictionary with nodes as keys and list of succssors nodes as values. Examples -------- >>> G = nx.Graph() >>> G.add_path([0,1,2]) >>> print(nx.bfs_successors(G,0)) {0: [1], 1: [2]} Notes ----- Based on http://www.ics.uci.edu/~eppstein/PADS/BFS.py by D. Eppstein, July 2004. If a source is not specified then a source is chosen arbitrarily and repeatedly until all components in the graph are searched. """ d = defaultdict(list) for s,t in bfs_edges(G,source): d[s].append(t) return dict(d)