# Source code for networkx.algorithms.flow.utils

```
"""
Utility classes and functions for network flow algorithms.
"""
from collections import deque
import networkx as nx
__all__ = [
"CurrentEdge",
"Level",
"GlobalRelabelThreshold",
"build_residual_network",
"detect_unboundedness",
"build_flow_dict",
]
class CurrentEdge:
"""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:
"""Active and inactive nodes in a level."""
__slots__ = ("active", "inactive")
def __init__(self):
self.active = set()
self.inactive = set()
class GlobalRelabelThreshold:
"""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]
@nx._dispatchable(edge_attrs={"capacity": float("inf")}, returns_graph=True)
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.__networkx_cache__ = None # Disable caching
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(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
@nx._dispatchable(
graphs="R",
preserve_edge_attrs={"R": {"capacity": float("inf")}},
preserve_graph_attrs=True,
)
def detect_unboundedness(R, s, t):
"""Detect an infinite-capacity s-t path in R."""
q = deque([s])
seen = {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)
@nx._dispatchable(graphs={"G": 0, "R": 1}, preserve_edge_attrs={"R": {"flow": None}})
def build_flow_dict(G, R):
"""Build a flow dictionary from a residual network."""
flow_dict = {}
for u in G:
flow_dict[u] = {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
```