Warning

This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

# Source code for networkx.algorithms.flow.utils

```
# -*- coding: utf-8 -*-
"""
Utility classes and functions for network flow algorithms.
"""
__author__ = """ysitu <ysitu@users.noreply.github.com>"""
# Copyright (C) 2014 ysitu <ysitu@users.noreply.github.com>
# All rights reserved.
# BSD license.
from collections import deque
import networkx as nx
__all__ = ['CurrentEdge', 'Level', 'GlobalRelabelThreshold',
'build_residual_network', 'detect_unboundedness', 'build_flow_dict']
class CurrentEdge(object):
"""Mechanism for iterating over out-edges incident to a node in a circular
manner. StopIteration exception is raised when wraparound occurs.
"""
__slots__ = ('_edges', '_it', '_curr')
def __init__(self, edges):
self._edges = edges
if self._edges:
self._rewind()
def get(self):
return self._curr
def move_to_next(self):
try:
self._curr = next(self._it)
except StopIteration:
self._rewind()
raise
def _rewind(self):
self._it = iter(self._edges.items())
self._curr = next(self._it)
class Level(object):
"""Active and inactive nodes in a level.
"""
__slots__ = ('active', 'inactive')
def __init__(self):
self.active = set()
self.inactive = set()
class GlobalRelabelThreshold(object):
"""Measurement of work before the global relabeling heuristic should be
applied.
"""
def __init__(self, n, m, freq):
self._threshold = (n + m) / freq if freq else float('inf')
self._work = 0
def add_work(self, work):
self._work += work
def is_reached(self):
return self._work >= self._threshold
def clear_work(self):
self._work = 0
[docs]def build_residual_network(G, capacity):
"""Build a residual network and initialize a zero flow.
The residual network :samp:`R` from an input graph :samp:`G` has the
same nodes as :samp:`G`. :samp:`R` is a DiGraph that contains a pair
of edges :samp:`(u, v)` and :samp:`(v, u)` iff :samp:`(u, v)` is not a
self-loop, and at least one of :samp:`(u, v)` and :samp:`(v, u)` exists
in :samp:`G`.
For each edge :samp:`(u, v)` in :samp:`R`, :samp:`R[u][v]['capacity']`
is equal to the capacity of :samp:`(u, v)` in :samp:`G` if it exists
in :samp:`G` or zero otherwise. If the capacity is infinite,
:samp:`R[u][v]['capacity']` will have a high arbitrary finite value
that does not affect the solution of the problem. This value is stored in
:samp:`R.graph['inf']`. For each edge :samp:`(u, v)` in :samp:`R`,
:samp:`R[u][v]['flow']` represents the flow function of :samp:`(u, v)` and
satisfies :samp:`R[u][v]['flow'] == -R[v][u]['flow']`.
The flow value, defined as the total flow into :samp:`t`, the sink, is
stored in :samp:`R.graph['flow_value']`. If :samp:`cutoff` is not
specified, reachability to :samp:`t` using only edges :samp:`(u, v)` such
that :samp:`R[u][v]['flow'] < R[u][v]['capacity']` induces a minimum
:samp:`s`-:samp:`t` cut.
"""
if G.is_multigraph():
raise nx.NetworkXError(
'MultiGraph and MultiDiGraph not supported (yet).')
R = nx.DiGraph()
R.add_nodes_from(G)
inf = float('inf')
# Extract edges with positive capacities. Self loops excluded.
edge_list = [(u, v, attr) for u, v, attr in G.edges_iter(data=True)
if u != v and attr.get(capacity, inf) > 0]
# Simulate infinity with three times the sum of the finite edge capacities
# or any positive value if the sum is zero. This allows the
# infinite-capacity edges to be distinguished for unboundedness detection
# and directly participate in residual capacity calculation. If the maximum
# flow is finite, these edges cannot appear in the minimum cut and thus
# guarantee correctness. Since the residual capacity of an
# infinite-capacity edge is always at least 2/3 of inf, while that of an
# finite-capacity edge is at most 1/3 of inf, if an operation moves more
# than 1/3 of inf units of flow to t, there must be an infinite-capacity
# s-t path in G.
inf = 3 * sum(attr[capacity] for u, v, attr in edge_list
if capacity in attr and attr[capacity] != inf) or 1
if G.is_directed():
for u, v, attr in edge_list:
r = min(attr.get(capacity, inf), inf)
if not R.has_edge(u, v):
# Both (u, v) and (v, u) must be present in the residual
# network.
R.add_edge(u, v, capacity=r)
R.add_edge(v, u, capacity=0)
else:
# The edge (u, v) was added when (v, u) was visited.
R[u][v]['capacity'] = r
else:
for u, v, attr in edge_list:
# Add a pair of edges with equal residual capacities.
r = min(attr.get(capacity, inf), inf)
R.add_edge(u, v, capacity=r)
R.add_edge(v, u, capacity=r)
# Record the value simulating infinity.
R.graph['inf'] = inf
return R
def detect_unboundedness(R, s, t):
"""Detect an infinite-capacity s-t path in R.
"""
q = deque([s])
seen = set([s])
inf = R.graph['inf']
while q:
u = q.popleft()
for v, attr in R[u].items():
if attr['capacity'] == inf and v not in seen:
if v == t:
raise nx.NetworkXUnbounded(
'Infinite capacity path, flow unbounded above.')
seen.add(v)
q.append(v)
def build_flow_dict(G, R):
"""Build a flow dictionary from a residual network.
"""
flow_dict = {}
for u in G:
flow_dict[u] = dict((v, 0) for v in G[u])
flow_dict[u].update((v, attr['flow']) for v, attr in R[u].items()
if attr['flow'] > 0)
return flow_dict
```