# Source code for networkx.classes.graphviews

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