h YlSrSSKrSSKJr SSKJr SSKJr SSK J r SSK J r SS K Jr \ r/S Qr\R""S S \"S 50S9SSj5r\R""S S \"S 50S9SSj5r\R""S S \"S 50S9SSj5r\R""S S \"S 50S9SSj5rg)zB Maximum flow (and minimum cut) algorithms on capacitated graphs. N)boykov_kolmogorov)dinitz) edmonds_karp) preflow_push)shortest_augmenting_path)build_flow_dict) maximum_flowmaximum_flow_value minimum_cutminimum_cut_valueflowGcapacityinf)graphs edge_attrsc Uc#U(a[R"S5e[n[U5(d[R"S5eU"XU4USS.UD6n[ X5nUR SU4$)aFind a maximum single-commodity flow. Parameters ---------- flowG : NetworkX graph Edges of the graph are expected to have an attribute called 'capacity'. If this attribute is not present, the edge is considered to have infinite capacity. _s : node Source node for the flow. _t : node Sink node for the flow. capacity : string Edges of the graph G are expected to have an attribute capacity that indicates how much flow the edge can support. If this attribute is not present, the edge is considered to have infinite capacity. Default value: 'capacity'. flow_func : function A function for computing the maximum flow among a pair of nodes in a capacitated graph. The function has to accept at least three parameters: a Graph or Digraph, a source node, and a target node. And return a residual network that follows NetworkX conventions (see Notes). If flow_func is None, the default maximum flow function (:meth:`preflow_push`) is used. See below for alternative algorithms. The choice of the default function may change from version to version and should not be relied on. Default value: None. kwargs : Any other keyword parameter is passed to the function that computes the maximum flow. Returns ------- flow_value : integer, float Value of the maximum flow, i.e., net outflow from the source. flow_dict : dict A dictionary containing the value of the flow that went through each edge. Raises ------ NetworkXError The algorithm does not support MultiGraph and MultiDiGraph. If the input graph is an instance of one of these two classes, a NetworkXError is raised. NetworkXUnbounded If the graph has a path of infinite capacity, the value of a feasible flow on the graph is unbounded above and the function raises a NetworkXUnbounded. See also -------- :meth:`maximum_flow_value` :meth:`minimum_cut` :meth:`minimum_cut_value` :meth:`edmonds_karp` :meth:`preflow_push` :meth:`shortest_augmenting_path` Notes ----- The function used in the flow_func parameter has to return a residual network that follows NetworkX conventions: 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']`. 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. Specific algorithms may store extra data in :samp:`R`. The function should supports an optional boolean parameter value_only. When True, it can optionally terminate the algorithm as soon as the maximum flow value and the minimum cut can be determined. Examples -------- >>> G = nx.DiGraph() >>> G.add_edge("x", "a", capacity=3.0) >>> G.add_edge("x", "b", capacity=1.0) >>> G.add_edge("a", "c", capacity=3.0) >>> G.add_edge("b", "c", capacity=5.0) >>> G.add_edge("b", "d", capacity=4.0) >>> G.add_edge("d", "e", capacity=2.0) >>> G.add_edge("c", "y", capacity=2.0) >>> G.add_edge("e", "y", capacity=3.0) maximum_flow returns both the value of the maximum flow and a dictionary with all flows. >>> flow_value, flow_dict = nx.maximum_flow(G, "x", "y") >>> flow_value 3.0 >>> print(flow_dict["x"]["b"]) 1.0 You can also use alternative algorithms for computing the maximum flow by using the flow_func parameter. >>> from networkx.algorithms.flow import shortest_augmenting_path >>> flow_value == nx.maximum_flow(G, "x", "y", flow_func=shortest_augmenting_path)[ ... 0 ... ] True QYou have to explicitly set a flow_func if you need to pass parameters via kwargs.flow_func has to be callable.Fr value_only flow_value)nx NetworkXErrordefault_flow_funccallabler graph)r_s_tr flow_funckwargsR flow_dicts R/var/www/html/env/lib/python3.13/site-packages/networkx/algorithms/flow/maxflow.pyr r sD ""; & I  >??%RO(uOOA)I GGL !9 --c Uc#U(a[R"S5e[n[U5(d[R"S5eU"XU4USS.UD6nURS$)aYFind the value of maximum single-commodity flow. Parameters ---------- flowG : NetworkX graph Edges of the graph are expected to have an attribute called 'capacity'. If this attribute is not present, the edge is considered to have infinite capacity. _s : node Source node for the flow. _t : node Sink node for the flow. capacity : string Edges of the graph G are expected to have an attribute capacity that indicates how much flow the edge can support. If this attribute is not present, the edge is considered to have infinite capacity. Default value: 'capacity'. flow_func : function A function for computing the maximum flow among a pair of nodes in a capacitated graph. The function has to accept at least three parameters: a Graph or Digraph, a source node, and a target node. And return a residual network that follows NetworkX conventions (see Notes). If flow_func is None, the default maximum flow function (:meth:`preflow_push`) is used. See below for alternative algorithms. The choice of the default function may change from version to version and should not be relied on. Default value: None. kwargs : Any other keyword parameter is passed to the function that computes the maximum flow. Returns ------- flow_value : integer, float Value of the maximum flow, i.e., net outflow from the source. Raises ------ NetworkXError The algorithm does not support MultiGraph and MultiDiGraph. If the input graph is an instance of one of these two classes, a NetworkXError is raised. NetworkXUnbounded If the graph has a path of infinite capacity, the value of a feasible flow on the graph is unbounded above and the function raises a NetworkXUnbounded. See also -------- :meth:`maximum_flow` :meth:`minimum_cut` :meth:`minimum_cut_value` :meth:`edmonds_karp` :meth:`preflow_push` :meth:`shortest_augmenting_path` Notes ----- The function used in the flow_func parameter has to return a residual network that follows NetworkX conventions: 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']`. 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. Specific algorithms may store extra data in :samp:`R`. The function should supports an optional boolean parameter value_only. When True, it can optionally terminate the algorithm as soon as the maximum flow value and the minimum cut can be determined. Examples -------- >>> G = nx.DiGraph() >>> G.add_edge("x", "a", capacity=3.0) >>> G.add_edge("x", "b", capacity=1.0) >>> G.add_edge("a", "c", capacity=3.0) >>> G.add_edge("b", "c", capacity=5.0) >>> G.add_edge("b", "d", capacity=4.0) >>> G.add_edge("d", "e", capacity=2.0) >>> G.add_edge("c", "y", capacity=2.0) >>> G.add_edge("e", "y", capacity=3.0) maximum_flow_value computes only the value of the maximum flow: >>> flow_value = nx.maximum_flow_value(G, "x", "y") >>> flow_value 3.0 You can also use alternative algorithms for computing the maximum flow by using the flow_func parameter. >>> from networkx.algorithms.flow import shortest_augmenting_path >>> flow_value == nx.maximum_flow_value( ... G, "x", "y", flow_func=shortest_augmenting_path ... ) True rrTrr)rrrrrrrrrr r!r"s r$r r sqx ""; & I  >??%RN(tNvNA 77<  r%c `Uc#U(a[R"S5e[n[U5(d[R"S5eUR S5bU[ La[R"S5eU"XU4USS.UD6nUR SS9VVV s/sHupxoSU S :XdMXxU 4PM n nnn URU 5 [[[R"XbS 955n [U5U - U 4n URU 5 URS U 4$s sn nnf) a Compute the value and the node partition of a minimum (s, t)-cut. Use the max-flow min-cut theorem, i.e., the capacity of a minimum capacity cut is equal to the flow value of a maximum flow. Parameters ---------- flowG : NetworkX graph Edges of the graph are expected to have an attribute called 'capacity'. If this attribute is not present, the edge is considered to have infinite capacity. _s : node Source node for the flow. _t : node Sink node for the flow. capacity : string Edges of the graph G are expected to have an attribute capacity that indicates how much flow the edge can support. If this attribute is not present, the edge is considered to have infinite capacity. Default value: 'capacity'. flow_func : function A function for computing the maximum flow among a pair of nodes in a capacitated graph. The function has to accept at least three parameters: a Graph or Digraph, a source node, and a target node. And return a residual network that follows NetworkX conventions (see Notes). If flow_func is None, the default maximum flow function (:meth:`preflow_push`) is used. See below for alternative algorithms. The choice of the default function may change from version to version and should not be relied on. Default value: None. kwargs : Any other keyword parameter is passed to the function that computes the maximum flow. Returns ------- cut_value : integer, float Value of the minimum cut. partition : pair of node sets A partitioning of the nodes that defines a minimum cut. Raises ------ NetworkXUnbounded If the graph has a path of infinite capacity, all cuts have infinite capacity and the function raises a NetworkXError. See also -------- :meth:`maximum_flow` :meth:`maximum_flow_value` :meth:`minimum_cut_value` :meth:`edmonds_karp` :meth:`preflow_push` :meth:`shortest_augmenting_path` Notes ----- The function used in the flow_func parameter has to return a residual network that follows NetworkX conventions: 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']`. 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. Specific algorithms may store extra data in :samp:`R`. The function should supports an optional boolean parameter value_only. When True, it can optionally terminate the algorithm as soon as the maximum flow value and the minimum cut can be determined. Examples -------- >>> G = nx.DiGraph() >>> G.add_edge("x", "a", capacity=3.0) >>> G.add_edge("x", "b", capacity=1.0) >>> G.add_edge("a", "c", capacity=3.0) >>> G.add_edge("b", "c", capacity=5.0) >>> G.add_edge("b", "d", capacity=4.0) >>> G.add_edge("d", "e", capacity=2.0) >>> G.add_edge("c", "y", capacity=2.0) >>> G.add_edge("e", "y", capacity=3.0) minimum_cut computes both the value of the minimum cut and the node partition: >>> cut_value, partition = nx.minimum_cut(G, "x", "y") >>> reachable, non_reachable = partition 'partition' here is a tuple with the two sets of nodes that define the minimum cut. You can compute the cut set of edges that induce the minimum cut as follows: >>> cutset = set() >>> for u, nbrs in ((n, G[n]) for n in reachable): ... cutset.update((u, v) for v in nbrs if v in non_reachable) >>> print(sorted(cutset)) [('c', 'y'), ('x', 'b')] >>> cut_value == sum(G.edges[u, v]["capacity"] for (u, v) in cutset) True You can also use alternative algorithms for computing the minimum cut by using the flow_func parameter. >>> from networkx.algorithms.flow import shortest_augmenting_path >>> cut_value == nx.minimum_cut(G, "x", "y", flow_func=shortest_augmenting_path)[0] True rrcutoffcutoff should not be specified.Tr)dataflowr)targetr) rrrrgetredgesremove_edges_fromsetdictshortest_path_lengthadd_edges_fromr) rrrrr r!r"uvdcutset non_reachable partitions r$r r 3s)J ""; & I  >?? zz('I,E@AA%RN(tNvNA'(wwDw'9 X'9GA!vY!J-=WiqQi'9F X R44QBCDMUm+];IV GGL !9 --Ys D)4D)c 4Uc#U(a[R"S5e[n[U5(d[R"S5eUR S5bU[ La[R"S5eU"XU4USS.UD6nUR S$)aCompute the value of a minimum (s, t)-cut. Use the max-flow min-cut theorem, i.e., the capacity of a minimum capacity cut is equal to the flow value of a maximum flow. Parameters ---------- flowG : NetworkX graph Edges of the graph are expected to have an attribute called 'capacity'. If this attribute is not present, the edge is considered to have infinite capacity. _s : node Source node for the flow. _t : node Sink node for the flow. capacity : string Edges of the graph G are expected to have an attribute capacity that indicates how much flow the edge can support. If this attribute is not present, the edge is considered to have infinite capacity. Default value: 'capacity'. flow_func : function A function for computing the maximum flow among a pair of nodes in a capacitated graph. The function has to accept at least three parameters: a Graph or Digraph, a source node, and a target node. And return a residual network that follows NetworkX conventions (see Notes). If flow_func is None, the default maximum flow function (:meth:`preflow_push`) is used. See below for alternative algorithms. The choice of the default function may change from version to version and should not be relied on. Default value: None. kwargs : Any other keyword parameter is passed to the function that computes the maximum flow. Returns ------- cut_value : integer, float Value of the minimum cut. Raises ------ NetworkXUnbounded If the graph has a path of infinite capacity, all cuts have infinite capacity and the function raises a NetworkXError. See also -------- :meth:`maximum_flow` :meth:`maximum_flow_value` :meth:`minimum_cut` :meth:`edmonds_karp` :meth:`preflow_push` :meth:`shortest_augmenting_path` Notes ----- The function used in the flow_func parameter has to return a residual network that follows NetworkX conventions: 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']`. 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. Specific algorithms may store extra data in :samp:`R`. The function should supports an optional boolean parameter value_only. When True, it can optionally terminate the algorithm as soon as the maximum flow value and the minimum cut can be determined. Examples -------- >>> G = nx.DiGraph() >>> G.add_edge("x", "a", capacity=3.0) >>> G.add_edge("x", "b", capacity=1.0) >>> G.add_edge("a", "c", capacity=3.0) >>> G.add_edge("b", "c", capacity=5.0) >>> G.add_edge("b", "d", capacity=4.0) >>> G.add_edge("d", "e", capacity=2.0) >>> G.add_edge("c", "y", capacity=2.0) >>> G.add_edge("e", "y", capacity=3.0) minimum_cut_value computes only the value of the minimum cut: >>> cut_value = nx.minimum_cut_value(G, "x", "y") >>> cut_value 3.0 You can also use alternative algorithms for computing the minimum cut by using the flow_func parameter. >>> from networkx.algorithms.flow import shortest_augmenting_path >>> cut_value == nx.minimum_cut_value( ... G, "x", "y", flow_func=shortest_augmenting_path ... ) True rrr)r*Trr)rrrrr.rrr's r$r r sr ""; & I  >?? zz('I,E@AA%RN(tNvNA 77<  r%)rN)__doc__networkxrboykovkolmogorovr dinitz_algr edmondskarpr preflowpushrshortestaugmentingpathrutilsr r__all__ _dispatchablefloatr r r r r%r$rHs/%%<"! Tj%,-GHO.IO.dj%,-GHH!IH!Vj%,-GH_.I_.Dj%,-GHH!IH!r%