218 lines
6.7 KiB
Python
218 lines
6.7 KiB
Python
# Copyright (C) 2004-2019 by
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# Aric Hagberg <hagberg@lanl.gov>
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# Dan Schult <dschult@colgate.edu>
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# Pieter Swart <swart@lanl.gov>
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# All rights reserved.
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# BSD license.
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#
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# Author: Aric Hagberg (hagberg@lanl.gov),
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# Pieter Swart (swart@lanl.gov),
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# Dan Schult(dschult@colgate.edu)
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"""View of Graphs as SubGraph, Reverse, Directed, Undirected.
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In some algorithms it is convenient to temporarily morph
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a graph to exclude some nodes or edges. It should be better
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to do that via a view than to remove and then re-add.
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In other algorithms it is convenient to temporarily morph
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a graph to reverse directed edges, or treat a directed graph
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as undirected, etc. This module provides those graph views.
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The resulting views are essentially read-only graphs that
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report data from the orignal graph object. We provide an
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attribute G._graph which points to the underlying graph object.
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Note: Since graphviews look like graphs, one can end up with
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view-of-view-of-view chains. Be careful with chains because
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they become very slow with about 15 nested views.
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For the common simple case of node induced subgraphs created
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from the graph class, we short-cut the chain by returning a
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subgraph of the original graph directly rather than a subgraph
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of a subgraph. We are careful not to disrupt any edge filter in
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the middle subgraph. In general, determining how to short-cut
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the chain is tricky and much harder with restricted_views than
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with induced subgraphs.
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Often it is easiest to use .copy() to avoid chains.
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"""
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from networkx.classes.coreviews import UnionAdjacency, UnionMultiAdjacency, \
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FilterAtlas, FilterAdjacency, FilterMultiAdjacency
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from networkx.classes.filters import no_filter
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from networkx.exception import NetworkXError
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from networkx.utils import not_implemented_for
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import networkx as nx
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__all__ = ['generic_graph_view', 'subgraph_view', 'reverse_view']
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def generic_graph_view(G, create_using=None):
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if create_using is None:
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newG = G.__class__()
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else:
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newG = nx.empty_graph(0, create_using)
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if G.is_multigraph() != newG.is_multigraph():
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raise NetworkXError("Multigraph for G must agree with create_using")
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newG = nx.freeze(newG)
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# create view by assigning attributes from G
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newG._graph = G
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newG.graph = G.graph
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newG._node = G._node
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if newG.is_directed():
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if G.is_directed():
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newG._succ = G._succ
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newG._pred = G._pred
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newG._adj = G._succ
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else:
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newG._succ = G._adj
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newG._pred = G._adj
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newG._adj = G._adj
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elif G.is_directed():
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if G.is_multigraph():
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newG._adj = UnionMultiAdjacency(G._succ, G._pred)
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else:
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newG._adj = UnionAdjacency(G._succ, G._pred)
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else:
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newG._adj = G._adj
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return newG
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def subgraph_view(G, filter_node=no_filter, filter_edge=no_filter):
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""" View of `G` applying a filter on nodes and edges.
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`subgraph_view` provides a read-only view of the input graph that excludes
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nodes and edges based on the outcome of two filter functions `filter_node`
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and `filter_edge`.
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The `filter_node` function takes one argument --- the node --- and returns
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`True` if the node should be included in the subgraph, and `False` if it
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should not be included.
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The `filter_edge` function takes two arguments --- the nodes describing an
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edge --- and returns `True` if the edge should be included in the subgraph,
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and `False` if it should not be included.
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Both node and edge filter functions are called on graph elements as they
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are queried, meaning there is no up-front cost to creating the view.
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Parameters
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----------
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G : networkx.Graph
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A directed/undirected graph/multigraph
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filter_node : callable, optional
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A function taking a node as input, which returns `True` if the node
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should appear in the view.
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filter_edge : callable, optional
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A function taking as input the two nodes describing an edge, which
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returns `True` if the edge should appear in the view.
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Returns
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-------
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graph : networkx.Graph
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A read-only graph view of the input graph.
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Examples
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--------
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>>> import networkx as nx
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>>> G = nx.path_graph(6)
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Filter functions operate on the node, and return `True` if the node should
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appear in the view:
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>>> def filter_node(n1):
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... return n1 != 5
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...
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>>> view = nx.subgraph_view(
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... G,
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... filter_node=filter_node
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... )
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>>> view.nodes()
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NodeView((0, 1, 2, 3, 4))
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We can use a closure pattern to filter graph elements based on additional
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data --- for example, filtering on edge data attached to the graph:
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>>> G[3][4]['cross_me'] = False
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>>> def filter_edge(n1, n2):
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... return G[n1][n2].get('cross_me', True)
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...
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>>> view = nx.subgraph_view(
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... G,
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... filter_edge=filter_edge
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... )
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>>> view.edges()
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EdgeView([(0, 1), (1, 2), (2, 3), (4, 5)])
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>>> view = nx.subgraph_view(
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... G,
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... filter_node=filter_node,
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... filter_edge=filter_edge,
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... )
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>>> view.nodes()
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NodeView((0, 1, 2, 3, 4))
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>>> view.edges()
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EdgeView([(0, 1), (1, 2), (2, 3)])
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"""
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newG = nx.freeze(G.__class__())
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newG._NODE_OK = filter_node
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newG._EDGE_OK = filter_edge
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# create view by assigning attributes from G
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newG._graph = G
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newG.graph = G.graph
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newG._node = FilterAtlas(G._node, filter_node)
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if G.is_multigraph():
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Adj = FilterMultiAdjacency
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def reverse_edge(u, v, k): return filter_edge(v, u, k)
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else:
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Adj = FilterAdjacency
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def reverse_edge(u, v): return filter_edge(v, u)
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if G.is_directed():
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newG._succ = Adj(G._succ, filter_node, filter_edge)
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newG._pred = Adj(G._pred, filter_node, reverse_edge)
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newG._adj = newG._succ
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else:
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newG._adj = Adj(G._adj, filter_node, filter_edge)
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return newG
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@not_implemented_for('undirected')
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def reverse_view(G):
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""" View of `G` with edge directions reversed
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`reverse_view` returns a read-only view of the input graph where
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edge directions are reversed.
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Identical to digraph.reverse(copy=False)
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Parameters
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----------
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G : networkx.DiGraph
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Returns
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-------
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graph : networkx.DiGraph
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Examples
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--------
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>>> import networkx as nx
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>>> G = nx.DiGraph()
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>>> G.add_edge(1, 2)
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>>> G.add_edge(2, 3)
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>>> G.edges()
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OutEdgeView([(1, 2), (2, 3)])
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>>> view = nx.reverse_view(G)
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>>> view.edges()
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OutEdgeView([(2, 1), (3, 2)])
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"""
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newG = generic_graph_view(G)
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newG._succ, newG._pred = G._pred, G._succ
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newG._adj = newG._succ
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return newG
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