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## iof-tools / networkxMiCe / networkx-master / networkx / algorithms / traversal / edgebfs.py @ 5cef0f13

 1 ```""" ``` ```============================= ``` ```Breadth First Search on Edges ``` ```============================= ``` ``` ``` ```Algorithms for a breadth-first traversal of edges in a graph. ``` ``` ``` ```""" ``` ```from collections import deque ``` ```import networkx as nx ``` ```FORWARD = 'forward' ``` ```REVERSE = 'reverse' ``` ```__all__ = ['edge_bfs'] ``` ```def edge_bfs(G, source=None, orientation=None): ``` ``` """A directed, breadth-first-search of edges in `G`, beginning at `source`. ``` ``` ``` ``` Yield the edges of G in a breadth-first-search order continuing until ``` ``` all edges are generated. ``` ``` ``` ``` Parameters ``` ``` ---------- ``` ``` G : graph ``` ``` A directed/undirected graph/multigraph. ``` ``` ``` ``` source : node, list of nodes ``` ``` The node from which the traversal begins. If None, then a source ``` ``` is chosen arbitrarily and repeatedly until all edges from each node in ``` ``` the graph are searched. ``` ``` ``` ``` orientation : None | 'original' | 'reverse' | 'ignore' (default: None) ``` ``` For directed graphs and directed multigraphs, edge traversals need not ``` ``` respect the original orientation of the edges. ``` ``` When set to 'reverse' every edge is traversed in the reverse direction. ``` ``` When set to 'ignore', every edge is treated as undirected. ``` ``` When set to 'original', every edge is treated as directed. ``` ``` In all three cases, the yielded edge tuples add a last entry to ``` ``` indicate the direction in which that edge was traversed. ``` ``` If orientation is None, the yielded edge has no direction indicated. ``` ``` The direction is respected, but not reported. ``` ``` ``` ``` Yields ``` ``` ------ ``` ``` edge : directed edge ``` ``` A directed edge indicating the path taken by the breadth-first-search. ``` ``` For graphs, `edge` is of the form `(u, v)` where `u` and `v` ``` ``` are the tail and head of the edge as determined by the traversal. ``` ``` For multigraphs, `edge` is of the form `(u, v, key)`, where `key` is ``` ``` the key of the edge. When the graph is directed, then `u` and `v` ``` ``` are always in the order of the actual directed edge. ``` ``` If orientation is not None then the edge tuple is extended to include ``` ``` the direction of traversal ('forward' or 'reverse') on that edge. ``` ``` ``` ``` Examples ``` ``` -------- ``` ``` >>> import networkx as nx ``` ``` >>> nodes = [0, 1, 2, 3] ``` ``` >>> edges = [(0, 1), (1, 0), (1, 0), (2, 0), (2, 1), (3, 1)] ``` ``` ``` ``` >>> list(nx.edge_bfs(nx.Graph(edges), nodes)) ``` ``` [(0, 1), (0, 2), (1, 2), (1, 3)] ``` ``` ``` ``` >>> list(nx.edge_bfs(nx.DiGraph(edges), nodes)) ``` ``` [(0, 1), (1, 0), (2, 0), (2, 1), (3, 1)] ``` ``` ``` ``` >>> list(nx.edge_bfs(nx.MultiGraph(edges), nodes)) ``` ``` [(0, 1, 0), (0, 1, 1), (0, 1, 2), (0, 2, 0), (1, 2, 0), (1, 3, 0)] ``` ``` ``` ``` >>> list(nx.edge_bfs(nx.MultiDiGraph(edges), nodes)) ``` ``` [(0, 1, 0), (1, 0, 0), (1, 0, 1), (2, 0, 0), (2, 1, 0), (3, 1, 0)] ``` ``` ``` ``` >>> list(nx.edge_bfs(nx.DiGraph(edges), nodes, orientation='ignore')) ``` ``` [(0, 1, 'forward'), (1, 0, 'reverse'), (2, 0, 'reverse'), (2, 1, 'reverse'), (3, 1, 'reverse')] ``` ``` ``` ``` >>> list(nx.edge_bfs(nx.MultiDiGraph(edges), nodes, orientation='ignore')) ``` ``` [(0, 1, 0, 'forward'), (1, 0, 0, 'reverse'), (1, 0, 1, 'reverse'), (2, 0, 0, 'reverse'), (2, 1, 0, 'reverse'), (3, 1, 0, 'reverse')] ``` ``` ``` ``` Notes ``` ``` ----- ``` ``` The goal of this function is to visit edges. It differs from the more ``` ``` familiar breadth-first-search of nodes, as provided by ``` ``` :func:`networkx.algorithms.traversal.breadth_first_search.bfs_edges`, in ``` ``` that it does not stop once every node has been visited. In a directed graph ``` ``` with edges [(0, 1), (1, 2), (2, 1)], the edge (2, 1) would not be visited ``` ``` if not for the functionality provided by this function. ``` ``` ``` ``` See Also ``` ``` -------- ``` ``` bfs_edges ``` ``` bfs_tree ``` ``` edge_dfs ``` ``` ``` ``` """ ``` ``` nodes = list(G.nbunch_iter(source)) ``` ``` if not nodes: ``` ``` return ``` ``` directed = G.is_directed() ``` ``` kwds = {'data': False} ``` ``` if G.is_multigraph() is True: ``` ``` kwds['keys'] = True ``` ``` # set up edge lookup ``` ``` if orientation is None: ``` ``` def edges_from(node): ``` ``` return iter(G.edges(node, **kwds)) ``` ``` elif not directed or orientation == 'original': ``` ``` def edges_from(node): ``` ``` for e in G.edges(node, **kwds): ``` ``` yield e + (FORWARD,) ``` ``` elif orientation == 'reverse': ``` ``` def edges_from(node): ``` ``` for e in G.in_edges(node, **kwds): ``` ``` yield e + (REVERSE,) ``` ``` elif orientation == 'ignore': ``` ``` def edges_from(node): ``` ``` for e in G.edges(node, **kwds): ``` ``` yield e + (FORWARD,) ``` ``` for e in G.in_edges(node, **kwds): ``` ``` yield e + (REVERSE,) ``` ``` else: ``` ``` raise nx.NetworkXError("invalid orientation argument.") ``` ``` if directed: ``` ``` neighbors = G.successors ``` ``` def edge_id(edge): ``` ``` # remove direction indicator ``` ``` return edge[:-1] if orientation is not None else edge ``` ``` else: ``` ``` neighbors = G.neighbors ``` ``` def edge_id(edge): ``` ``` return (frozenset(edge[:2]),) +edge[2:] ``` ``` check_reverse = directed and orientation in ('reverse', 'ignore') ``` ``` # start BFS ``` ``` visited_nodes = {n for n in nodes} ``` ``` visited_edges = set() ``` ``` queue = deque([(n, edges_from(n)) for n in nodes]) ``` ``` while queue: ``` ``` parent, children_edges = queue.popleft() ``` ``` for edge in children_edges: ``` ``` if check_reverse and edge[-1] == REVERSE: ``` ``` child = edge ``` ``` else: ``` ``` child = edge ``` ``` if child not in visited_nodes: ``` ``` visited_nodes.add(child) ``` ``` queue.append((child, edges_from(child))) ``` ``` edgeid = edge_id(edge) ``` ``` if edgeid not in visited_edges: ``` ``` visited_edges.add(edgeid) ``` ``` yield edge ```