207 lines
6.4 KiB
Python
207 lines
6.4 KiB
Python
|
"""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"]
|
||
|
|
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
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 (or three arguments if `G` is a
|
||
|
|
multi-graph) --- the nodes describing an edge, plus the edge-key if
|
||
|
|
parallel edges are possible --- 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 (plus the
|
||
|
|
edge-key if `G` is a multi-graph), 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
|
||
|
|
--------
|
||
|
|
>>> 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=None):
|
||
|
|
return filter_edge(v, u, k)
|
||
|
|
|
||
|
|
else:
|
||
|
|
Adj = FilterAdjacency
|
||
|
|
|
||
|
|
def reverse_edge(u, v, k=None):
|
||
|
|
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
|
||
|
|
|
||
|
|
|
||
|
|
@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
|
||
|
|
--------
|
||
|
|
>>> 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
|