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Both nodes and edgesÁ—HT can have arbitrary associated data, and edges may be either directed or undirected.ÁàW¸ # ExampleÁ¼ Ê ```rustÁÎ+ use petgraph::graph::{NodeIndex, UnGraph};ÁÚ.3 use petgraph::algo::{dijkstra, min_spanning_tree};Á‰6" use petgraph::data::FromElements;ÁÀ%" use petgraph::dot::{Dot, Config};Áæ%ŒT // Create an undirected graph with `i32` nodes and edges with `()` associated data.ÁW* let g = UnGraph::::from_edges(&[Áè- (1, 2), (2, 3), (3, 4),Á– (1, 4)]);Á¶ÈP // Find the shortest path from `1` to `4` using `1` as the cost for every edge.ÁÌS> let node_map = dijkstra(&g, 1.into(), Some(4.into()), |_| 1);Á A> assert_eq!(&1i32, node_map.get(&NodeIndex::new(4)).unwrap());ÁâA¤ M // Get the minimum spanning tree of the graph as a new graph, and check thatÁ¨ P // one edge was trimmed.Áù A let mst = UnGraph::<_, _>::from_elements(min_spanning_tree(&g));Á– D< assert_eq!(g.raw_edges().len() - 1, mst.raw_edges().len());ÁÛ ?›. // Output the tree to `graphviz` `DOT` formatÁŸ1B println!("{:?}", Dot::with_config(&mst, &[Config::EdgeNoLabel]));ÁÑE // graph {Á— // 0 [label="\"0\""]Á¦ // 1 [label="\"0\""]ÁÃ // 2 [label="\"0\""]Áà // 3 [label="\"0\""]Áý // 1 -- 2Áš // 3 -- 4Á¬ // 2 -- 3Á¾ // }ÁÐ ```ÁÙ á # Graph typesÁå ÷ ) * [`Graph`](./graph/struct.Graph.html) -Áû ,: An adjacency list graph with arbitrary associated data.Á¨=< * [`StableGraph`](./stable_graph/struct.StableGraph.html) -Áæ?C Similar to `Graph`, but it keeps indices stable across removals.Á¦F2 * [`GraphMap`](./graphmap/struct.GraphMap.html) -Áí5T An adjacency list graph backed by a hash table. The node identifiers are the keysÁ£W into the table.Áû< * [`MatrixGraph`](./matrix_graph/struct.MatrixGraph.html) -Á‘? An adjacency matrix graph.ÁÑ # * [`CSR`](./csr/struct.Csr.html) -Áò&B A sparse adjacency matrix graph with arbitrary associated data.Á™Eß ### Generic parametersÁãþS Each graph type is generic over a handful of parameters. All graphs share 3 commonÁ‚VQ parameters, `N`, `E`, and `Ty`. This is a broad overview of what those are. EachÁÙTA type's documentation will have finer detail on these parameters.Á®DóO `N` & `E` are called *weights* in this implementation, and are associated withÁ÷RY nodes and edges respectively. They can generally be of arbitrary type, and don't have toÁÊ\Y be what you might conventionally consider weight-like. For example, using `&str` for `N`Á§\S will work. Many algorithms that require costs let you provide a cost function thatÁ„VX translates your `N` and `E` weights into costs appropriate to the algorithm. Some graphÁÛ[2 types and choices do impose bounds on `N` or `E`.Á·5_ [`min_spanning_tree`](./algo/fn.min_spanning_tree.html) for example requires edge weights thatÁíb[ implement [`PartialOrd`](https://doc.rust-lang.org/stable/core/cmp/trait.PartialOrd.html).ÁÐ^[ [`GraphMap`](./graphmap/struct.GraphMap.html) requires node weights that can serve as hashÁ¯^I map keys, since that graph type does not create standalone node indices.ÁŽLÛO `Ty` controls whether edges are [`Directed`](./petgraph/enum.Directed.html) orÁßR0 [`Undirected`](./petgraph/enum.Unirected.html).Á²3æO `Ix` appears on graph types that use indices. It is exposed so you can controlÁêRV the size of node and edge indices, and therefore the memory footprint of your graphs.Á½YR Allowed values are `u8`, `u16`, `u32`, and `usize`, with `u32` being the default.Á—Uí ### Shorthand typesÁñ‰O Each graph type vends a few shorthand type definitions that name some specificÁRX generic choices. For example, [`DiGraph<_, _>`](./graph/type.DiGraph.html) is shorthandÁà[8 for [`Graph<_, _, Directed>`](graph/struct.Graph.html).Á¼ ;G [`UnMatrix<_, _>`](./matrix_graph/type.UnMatrix.html) is shorthand forÁø J] [`MatrixGraph<_, _, Undirected>`](./matrix_graph/struct.MatrixGraph.html). Each graph type'sÁÃ!`: module documentation lists the available shorthand types.Á¤"=â" # Crate featuresÁæ"û" * **serde-1** -Áÿ"G Defaults off. Enables serialization for ``Graph, StableGraph`` usingÁ“#JS [`serde 1.0`](https://crates.io/crates/serde). May require a more recent versionÁÞ#V of Rust than petgraph alone.Áµ$" * **graphmap** -ÁØ$F Defaults on. Enables [`GraphMap`](./graphmap/struct.GraphMap.html).Áí$I * **stable_graph** -Á·%P Defaults on. Enables [`StableGraph`](./stable_graph/struct.StableGraph.html).ÁÐ%S * **matrix_graph** -Á¤&P Defaults on. Enables [`MatrixGraph`](./matrix_graph/struct.MatrixGraph.html).Á½&S‘'˜'¹˜'›'Ë' äœ' ª'https://docs.rs/petgraph/0.4/Á¬'•'8 ¡Äâá ô ÃòË¼ÆýŽ«»ÂÜýƒÈ$á$ç$ô ¿Ü'ó“(·yû)¤ìÎ¶ž*Ì$Î¶ž*Í$¹¶¨*É$¹¶¨*Ê$Í¡3÷¦ž+û: È$üÏ'Ü'†(“(í)û)‹*&ž*ž*¨*¨*Á* ¶* Ú¶* ´*Å* ü E! š"& í',./<0 ”16 í7=>@ACBDGJù T ¦W\b] ^d glóz š{€é’“Ù © ”ª° ²·¹ ”ºÀ ¦ÁÆÇÚÛúÍ* Ñ*£¤· Ãñ î4 Ðå‚›÷ó ‚¡÷‚ ±›÷›÷ö èöó ó ‚ ‚ ·- º í$7î$7í$7èöî$7èö ½ ›÷¡÷¾ ôÄ ›- ÐåÚÕ+ íÕ+¯ €Ö+£ èö ¯ ¯ Í5 ÐåÚÕ+Ò íÕ+á €Ö+Õ èöÒ Ò á á —B š ï$7ð$7ï$7èöð$7èö¯¦¦ ›÷¡÷§ ô ƒ. ÐåÚÕ+ˆ íÕ+— €Ö+‹ èöˆ ˆ — — Æ2 É ñ$7ò$7ñ$7èöò$7èö¢Í ›÷¡÷Î ôÔ ™+ Ž+ ÚŽ+ ÷Œ+# Graph traits and graph traversals.Á&' ### The `Into-` TraitsÁ+FJ Graph traits like [`IntoNeighbors`][in] create iterators and use the sameÁJMJ pattern that `IntoIterator` does: the trait takes a reference to a graph,Á˜MJ and produces an iterator. These traits are quite composable, but with theÁæM; limitation that they only use shared references to graphs.Á´>ó ### Graph TraversalÁ÷D [`Dfs`](struct.Dfs.html), [`Bfs`][bfs], [`DfsPostOrder`][dfspo] andÁ“GJ [`Topo`][topo] are basic visitors and they use â€œwalkerâ€ methods: theÁÛMI visitors don't hold the graph as borrowed during traversal, only for theÁ©LA `.next()` call on the walker. They can be converted to iteratorsÁöD! through the [`Walker`][w] trait.Á»$àH There is also the callback based traversal [`depth_first_search`][dfs].ÁäK° [bfs]: struct.Bfs.htmlÁ´" [dfspo]: struct.DfsPostOrder.htmlÁÏ% [topo]: struct.Topo.htmlÁõ" [dfs]: fn.depth_first_search.htmlÁ’% [w]: trait.Walker.htmlÁ¸Ó ### Other Graph TraitsÁ×òK The traits are rather loosely coupled at the moment (which is intentional,ÁöNK but will develop a bit), and there are traits missing that could be added.ÁÅN” F Not much is needed to be able to use the visitors on a graph. A graphÁ˜ I= needs to define [`GraphBase`][gb], [`IntoNeighbors`][in] andÁâ @! [`Visitable`][vis] as a minimum.Á£ $È [gb]: trait.GraphBase.htmlÁÌ [in]: trait.IntoNeighbors.htmlÁë " [vis]: trait.Visitable.htmlÁŽ®¡+&P×ª½þÌ¿Ã³âÌð®§µ”‰ÐÙþœ¾º‰Ñ ªÃô ™ÄBuildHasherÁÿIÆšÔIÇšÚIÇ8®ÜÉÿ(µýÊù^Ò º!Ëñ¼µŽš#ÌËÌŸÍÝÍÏŽÁï È$Ï×·yú¤ÐÆìž¸•íÑë—u¯ âÒîÍæÓ÷È¸¨òÕ„»® ¥ÖÇ1Ù äå3úˆž6½"©Î7¡*¾ž9Ì1Ôû:ñ; ëæ<½J‡¿>µU¡Ê?‘Y±¢Cÿ ïˆEçˆó–+¨ó–+©—í 0ÁÑ: FÁÝæ\›Âõƒ¢‡Âþÿ™,ËÁò„·•Â„ƒ¼úÂóþî ´Áóþî µÁˆ‚ô éÁº…‡DžÂ†ß` §Â—‘˜ È—‘˜ É»“Ü@î»“Ü@ïì—Ú¸¤½Þ Ó ÚÓ ÿÑâ¬ ¹ È ¿ Î … Ï Ð Ý Ñ Ò Ü Ó ¹Ô × ûØ Þ à ä ê šë ï íð ö ÷ ƒ ø methodsÁù € Ï „ ‡ Š ½ =” Ÿ ¡ š¢ § Ù µ È ¶ Ïƒ· ¸ í¹ ¿ extraÁÀ Å ÏÆ Ê Í Ï ß Ïƒà á ã Á ù éõ+ú ” ˜ ž šŸ ¤ ¥ ® ¦ žô+§ ¯ öä ó õ · ÷ ø † ‡ ˆ ‰ Ž Š ¹‹ ž ¢ ¨ š© ® ¯ ¸ ° žô+± ¹ Â " Ï å Ž ë û ì ú í î ø ï Ÿƒð õ Ïö ù ý € ™ Š ‹ ’ Œ Ÿƒ “ š ¿ è À Î Á Â Ì Ã ŸƒÄ É ÏÊ Í Ð escapeÁÑ Ø _xÁÙ Û ÏÜ ß à æ á ¨èâ ã Ïä ç ê í ¨ Ú÷ Œ ¡ Ž ™ — ‘ Ÿƒ’ ˜ › œ Ÿ ¨èž ¢ © ¯ Û ° ¾ ± ² ¼ ³ Ÿƒ´ ¹ Ïº ½ À sectionÁÁ É æüÊ Ë íÌ Ò Ó Ù Ô ¨èÕ Ö Ï× Ú Ý à › Úê ÿ € ” Œ ‚ ƒ Š „ Ÿƒ… ‹ Ž ’ ¨è‘ “ • œ ¢ Å £ ± ¤ ¥ ¯ ¦ Ÿƒ§ ¬ Ï ° ³ ¨è´ µ Ï¶ ¹ º Ã » tailÁ¼ À ÏÁ Ä Ç Ê ‹ ÚÔ é ê „ ë ù ì í ô î Ÿƒï õ ÷ ¨èø û ü ‚ ý ‹„,þ ƒ … Œ † “ £ È © ¹ ª ¸ « ¬ ¶ Ÿƒ® ³ Ï´ · » ¾ × È É Ð Ê ŸƒË Ñ Ø Þ ÿ ß í à á ë â Ÿƒã è Ïé ì ï þ ñ ò û ó ‹„,ô ø Ïù ü „ Û Ž – Ÿƒ‘ — ™ Õ Ú§ ¼ ½ Ê ¾ ¿ Á Â È Ã ‹„,Ä É Ë Ü â … ã ñ ä å ï æ Ÿƒç ì Ïí ð ó ¨èô õ Ïö ù ú ƒ û ‹„,ü € Ï „ ‡ Š Å ö” £ ¤ ¾ ¥ ³ ¦ § ® ¨ Ÿƒ© ¯ ± ¨è² µ ¶ ¼ · ‹„,¸ ½ ¿ Æ Ë Ø Þ † ä ì å Ôæ ç ÷è î ñ ƒ û ü Ôý „ ˆ • ¡ Ê § ¯ ¨ Ô© ª ÷« ± ´ Ç ¾ ¿ À ÔÁ È § ´ Â ™ È Ø É Ê Ì Í Ö Î restÁÏ Ó ÏÔ × Ú Ý £ ¥ç ô ö ø ‘ ù ÿ !üú ü êþ € ê‚ ƒ … !ü† ê‰ Š §Œ “ ” š • •’,– › ¤ ª õ « í ¬ º ® ¸ ¯ paramÁ° µ Ï¶ ¹ » ½ self_typeÁ¾ Ç íÈ Í Ï self_wrapÁÐ Ù ÔÚ Ü Þ self_mapÁß ç íè ô ø þ šÿ ƒ í„ Š ‹ ˜ Œ Ž supÁ ’ í“ ™ › œ ® Ÿ more_supÁ ¨ í© ¯ ± ô ¾ ¿ ž É šü+Ê Ñ ì "Ò × assoc_name_extÁØ æ íç Ÿ © ª ¿ ´ ðÿ+µ "½ Ê Ë ´ Ù Ú ï Û Ü î Ý _assoc_attrÁÞ é ûê ð "þ ƒ assoc_nameÁ„ Ž í • – ¨ — ™ assoc_boundÁš ¥ Ô¦ © ª µ À ž Ÿ ë © ðÿ+ª ² ¿ À à Î Ï å Ð Ñ ä Ò _method_attrÁÓ ß ûà æ ô ÷ method_nameÁø ƒ í„ ‰ Å Š ¤ ‘ “ self_selftypeÁ” ¡ Ô¢ ¥ § ¨ Ã © ª margÁ« ¯ í° ¶ ¸ marg_tyÁ¹ À ÔÁ Ä Ç È Ô É Ì mretÁÍ Ñ ÔÒ Õ Ö á ì ± ² í ¼ ðÿ+½ nodelegateÁÅ Ø Ù â Ú ‹„,Û ß Ïà ã î ÷ ú — „ ˆ ‰ Š ‘ ‹ «•,Œ ’ “ • š– › Ÿ –, %ª ° ï•,± º ¼ š½ Â ‘ Ð Ñ ¹ ß à ª "ò ÷ Šœ,ø ƒ … ï•,† ‘ Šœ,’ œ « º È É › "Û à ö™,á ð ò ï•,ó ü þ ö™,ÿ œ ª « † ¹ º ÷ Ì Ï Ÿ,Ð Û Š Ü á â ó ã å ƒ ,æ ô ö ÷ ˆ ø ù ç ,ú þ € ¡, ‰ Œ – Ž ‘ þ¡,’ — ™ é ¯ ë–,° ¸ ¹ ¾ º ¿ À Ÿ,Á Ì × Í Î Ô Ï ç ,Ð Õ Ö ø ‡ ëï0FKO´µËéë‡ Ã&'»+4 þ&)Õ> ³&* %# 4 4 > > Ž š • ± ¡ Ô¢ £ ÷¤ ¨ ª Ñ¼ « ¬ ø ³ ¶ Þ ØÀ Ë Ì × Í ÔÎ Ï Ñ Ñ¼ Ò Ó Õ Ö Ø ß å ÿ æ Ôç è ÷é í ï Ñ¼ ð ñ ø ò ö ø qÁù ú ø û „ ’ Ž ” Ô• — Œ æü¥ § ª ‚ ¼ æü¿ À ºÁ Í Î Ð € æ æüí î ‚ ‡ æüŠ ‹ ÍŒ ˜ ™ › Ä ± Ñ¼ ² Æ Ë ô á ï°,â “ · î--‚øú¼ °ú./¼ ¼ Ð × ß,,.øúÒ% 2 Get the value in `Control::Break(_)`, if present.Á˜5 Ù °úÏá,,/øúå ã Control flow for callbacks.Á„ ¤ 7 The empty return value `()` is equivalent to continue.Á¨: í 000123123ÿ ‚ àž001àžœ Ÿ ó$7ó$7àž0¬ 02àž À Ã ô$7ô$7àž0Ð 03àžÑ ã 567 „ 445¦ © õ$7õ$74¶ 46· í ð ö$7ö$74ý 47þ ¥" î99½µ,ª °ú:;<ª ª Î Ñ °ú88:øúŒ ÷$7÷$7°ú8œ 8;øú £ ø$7ø$7°ú8° 8<øú± Ê4 d>£?>?‚åûÏ ‚êûß 0åûÒ žû@ABÏ Ï ß ß … ˆ žû==@åûêûÅ È ù$7ù$7žû=Õ =AåûêûÖ Ü ß ú$7ú$7žû=ì =Båûêûí ’ The default is `Continue`.Áó îDD½µ,— °úE— — · º °úCCEøú†:Ú A recursive depth first search.Áñ# • I Starting points are the nodes in the iterator `starts` (specify just oneÁ™L & start vertex *x* by using `Some(x)`).Áæ) K The traversal emits discovery and finish events for each reachable vertex,Á”N M and edge classification of each reachable edge. `visitor` is called for eachÁãP 1 event, see [`DfsEvent`][de] for possible values.Á´4 é U The return value should implement the trait `ControlFlow`, and can be used to changeÁíX the control flow of the search.ÁÆ# ê U `Control` Implements `ControlFlow` such that `Control::Continue` resumes the search.ÁîX K `Control::Break` will stop the visit early, returning the contained value.ÁÇ N L `Control::Prune` will stop traversing any additional edges from the currentÁ–!O 4 node and proceed immediately to the `Finish` event.Áæ!7 ž" N There are implementations of `ControlFlow` for `()`, and `Result` whereÁ¢"Q L `C: ControlFlow`. The implementation for `()` will continue until finished.Áô"O X For `Result`, upon encountering an `E` it will break, otherwise acting the same as `C`.ÁÄ#[ $ D ***Panics** if you attempt to prune a node from its `Finish` event.Á¤$G ì$ [de]: enum.DfsEvent.htmlÁð$ % # Example returning `Control`.Á‘%" ´% G Find a path from vertex 0 to 5, and exit the visit as soon as we reachÁ¸%J the goal vertex.Áƒ& ˜& õ®+œ& use petgraph::prelude::*;Á¤& & use petgraph::graph::node_index as n;ÁÂ&) ) use petgraph::visit::depth_first_search;Áì&, * use petgraph::visit::{DfsEvent, Control};Á™'- Ç' - let gr: Graph<(), ()> = Graph::from_edges(&[ÁË'0 (0, 1), (0, 2), (0, 3),Áü' (1, 3),Áœ( (2, 3), (2, 4),Á¬( (4, 0), (4, 5),ÁÄ( ]);ÁÜ( ä( 2 // record each predecessor, mapping node â†’ nodeÁè(5 ? let mut predecessor = vec![NodeIndex::end(); gr.node_count()];Áž)B let start = n(0);Áá) let goal = n(5);Á÷) / depth_first_search(&gr, Some(start), |event| {ÁŒ*2 . if let DfsEvent::TreeEdge(u, v) = event {Á¿*1 $ predecessor[v.index()] = u;Áñ*' if v == goal {Á™+ & return Control::Break(v);Á´+) }ÁÞ+ }Áì+ Control::ContinueÁö+ });Á, ˜, let mut next = goal;Áœ, let mut path = vec![next];Áµ, while next != start {ÁÔ, * let pred = predecessor[next.index()];Áî,- path.push(pred);Áœ- next = pred;Áµ- }ÁÊ- path.reverse();ÁÐ- . assert_eq!(&path, &[n(0), n(2), n(4), n(5)]);Áä-1 õ®+–. ž. # Example returning a `Result`.Á¢.# õ®+Æ. êÎ,Î.) ÄÎ,ø. œÏ,–/, ' use petgraph::visit::{DfsEvent, Time};ÁÃ/* î/ N let gr: Graph<(), ()> = Graph::from_edges(&[(0, 1), (1, 2), (1, 1), (2, 1)]);Áò/Q ïÒ,Ä0 let mut back_edges = 0;ÁÚ0 let mut discover_time = 0;Áö0 > // Stop the search, the first time a BackEdge is encountered.Á•1A < let result = depth_first_search(&gr, Some(start), |event| {Á×1? match event {Á—2 2 // In the cases where Ok(()) is returned,Á25 O // Result falls back to the implementation of Control on the value ().Áã2R J // In the case of (), this is to always return Control::Continue.Á¶3M " // continuing the search.Á„4% , DfsEvent::Discover(_, Time(t)) => {Áª4/ discover_time = t;ÁÚ4" Ok(())Áý4 ¤Õ,”5 & DfsEvent::BackEdge(_, _) => {Á¢5) back_edges += 1;ÁÌ5 = // the implementation of ControlFlow for Result,Áí5@ 8 // treats this Err value as Continue::BreakÁ®6; Err(event)Áê6 ¤Õ,…7 _ => Ok(()),Á“7 ºÕ,¬7 îÕ,¶7 ¾7 2 // Even though the graph has more than one cycle,ÁÂ75 F // The number of back_edges visited by the search should always be 1.Áø7I assert_eq!(back_edges, 1);ÁÂ8 / println!("discover time:{:?}", discover_time);Áá82 = println!("number of backedges encountered: {}", back_edges);Á”9@ % println!("back edge: {:?}", result);ÁÕ9( õ®+þ9 : Îÿ¯„óêGòHúIdJHJGI‚Îÿ : ‚£: ‚¯¦: ‚„ó©: ÃÎÿã: ³Îÿó: ä8…; å8Êÿ’; ð¯Ÿ€¬; ¯Ÿ€õ„óÊ; 0„óÔ; FÎÿ¯„ó8¬: åû¶: üûÅ: : : £: £: ¦: ¦: ©: ©: >… > &û$7ü$7ý$7þ$7ÎÿÊÿû$7ÍŽü$7×ÿý$7×ÿþ$7ã÷ÿêLúMdNLMNã> Êç,¬> ‚ÍŽ¯> ‚ÿ²> –è,Ì? ©è,Ü? ðÍŽŸ€î? ÍŽŸ€õÿŒ@ 0ÿ–@ È‰8º> Õ¢È> üûÚ> ¬ƒï> «„Œ? ”ü§? ¬> ¬> ¯> ¯> ²> ²> …H& ˆH &ÿ$7ÿ$7ã÷ã÷™H ¾ÿæü–H ù ý ›ú‡•ž§ì×PRŠâPSó–+¨PTó–+©PTë‡B ÃPV»ˆQÌPW»+hþPXÕr ³PYÎ¶‰Ì$PZÎ¶‰Í$PZõ©û!P[0++:BB B QQhhr r ‚‰—©• "¨]‰^]^‚–úš ‚±ü œ±üÆ üû_š š Õ Ø üû\\_–ú±üÓ¨a‰babíñ,Ø€ò,Û‡–ú÷‡–ú–úþ þ±ü–ú‘üûcefhjkØØÛÛ› jG Create a new **Dfs**, using the graph's visitor map, and put **start**Á¤J in the stack of nodes to visit.Áó#¢ õÉ–úüû`êdd`‚õÉ¦ ×õÉÚ ³õÉå õÉ·–úï õÉ´±üû c–ú±üõÉ8© î ³ ¦ ¦ 8- Create a `Dfs` from a vector and a visit mapÁè0¤ ê¥±üüû``e–ú±üŸƒ¯¬ƒ¾ £e Clear the visit stateÁ…ª€%7€%7üûõÉ`êgg³`ö,°£ö,Ý¶ö,èÉö,ò Þö,þf–ú±üõÉ¸8¾°°¯bB Create a new **Dfs** using the graph's visitor map, and no stack.ÁåE¶õÉüû`êii`ö,¼£ö,æ¶ö,ñÉö,û Þö,‡„¯8¿¼¼ô#? Keep the discovered map, but clear the visit stack and restartÁ…B the dfs from a particular node.ÁÌ#û%7%7üû–ú`ƒ`É¯ˆî Ž°`G Return the next node in the dfs, or **None** if the traversal is done.ÁáJ·‚%7‚%7üûõÉ·Á`êll¿`ö,¼ÃõÉöÉö,„ k–ú±üõÉÄ8Ê¼¼†+¨n‰onoíñ,‹€ò,Ž“ò,ÀËüp‹‹ŽŽÏÒËümmp–ú±üû¨r‰srsíñ,€ €ò,ƒ Õó,¨ èó,¯ ÿó,Â Ëütvxz{€ € ƒ ƒ Ð!jC Create a new `DfsPostOrder` using the graph's visitor map, and putÁÕ F( `start` in the stack of nodes to visit.Á !+×!õÉ–úËüqêuuqö,Û!£ö,"¶ö,š"Éö,¤" Þö,°"t–ú±üõÉ8Þ!î è!Û!Û!ï#bI Create a new `DfsPostOrder` using the graph's visitor map, and no stack.Áž#Lö#õÉËüqêwwqö,ü#£ö,¦$¶ö,±$Éö,»$ Þö,Ç$ðÏ8ÿ#ü#ü#•&eÄ÷,÷%œ&ƒ%7ƒ%7ËüõÉqêyy¥&qö,¢&£ö,Ï&¶ö,Ú&Éö,ä& Þö,ð&x–ú±üõÉª&8°&¢&¢&€)#L Keep the discovered and finished map, but clear the visit stack and restartÁ„(OÜû,Ø(#‡)„%7„%7Ëü–úq)qµÐ”)î š)À*`K Return the next node in the traversal, or `None` if the traversal is done.Áí)NÇ*…%7…%7ËüõÉ·Áqê||Ï*qö,Ì*žÿ,†+Éö,”+ {–ú±üõÉÔ*8Ú*Ì*Ì*À8"¨~‰~íñ,Å8€ò,È8“ò,ñ8 ý€Å8Å8È8È8€9ƒ9 ý}}€–ú±üƒ:¨‚‰ƒ‚ƒíñ,ˆ:€ò,‹:Õó,§:èó,®: ÿó,Á: ý„†ˆ:ˆ:‹:‹:Ë;jG Create a new **Bfs**, using the graph's visitor map, and put **start**ÁÔ:J¦ô,£;#Ò;õÉ–ú ýê……ö,Ö;£ö,Š<¶ö,•<Éö,Ÿ< Þö,«<„–ú±üõÉ8Ù;î ã;Ö;Ö;Ó>`G Return the next node in the bfs, or **None** if the traversal is done.Á„>JÚ>†%7†%7 ýõÉ·Áê‡‡â>ö,ß>žÿ,™?Éö,§? †–ú±üõÉç>8í>ß>ß>¾D#¨‰‰Š‰Šíñ,ÃD€ò,ÆD“ò,ðDåý‹ÃDÃDÆDÆDÿD‚Eåýˆˆ‹–ú±üýE¨‰ŽŽíñ,‚F€ò,…FÕó,¢Fèó,©F ÿó,¼Fåý‘”–˜‚F‚F…F…FÃGƒ@ Create a new `Topo`, using the graph's visitor map, and put allÁÏFC$ initial nodes in the to visit list.Á—G'ÊGõÉåýŒêŒö,ÎGâõÉøGÌõÉŽH¶ö,¦HÉö,°H Þö,¼H–ú±üõÉ8ÑGÎGÎGºIy½IP‡%7‡%7åýõÉŒê’’ÕIŒö,ÒIž—-ûI±—-‘J!Éö,§J ·ÿÚIÿŸàIÒIÒIÑK ‘È!“ÁÈ!“ÚÈ!““‘Ô‚²M^µMPõÉåýŒê••Œö,»M£ö,åM¶ö,ðMÉö,úM Þö,†Nñþ8¾M»M»MÑOˆE Clear visited state, and put all initial nodes in the to visit list.Á„OHØOˆ%7ˆ%7åýõÉŒê——áOŒö,ÞOž—-‹P±—-¡P¶ö,¹PÉö,ÃP Þö,ÏP––ú±üõÉæO8ìOÞOÞOŠTzD Return the next node in the current topological order traversal, orÁßQG' `None` if the traversal is at the end.Á«R*ÚRJ *Note:* The graph may not have a complete topological order, and the onlyÁâRMN way to know is to run the whole traversal and make sure it visits every node.Á´SQ‘T‰%7‰%7åýõÉ·ÁŒê™™™TŒö,–T±—-ÌT¶ö,äTÉö,îT Þö,úT˜–ú±üõÉžTÿŸ¤T–T–T²Y˜È!šÁÈ!šÚÈ!šš˜À•Ü\? A walker is a traversal state, but where part of the traversalÁñZB4 information is supplied manually to each next call.Á´[7ì[I This for example allows graph traversals that don't hold a borrow of theÁð[L graph they are traversing.Á½\æ\›kœœ›‚åží\›žŸžŸ‚àžåžü\ í\í\ü\ ]››¬]@ Advance to the next itemÁ‹]¯] Š%7Š%7àžåžõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê¬¥-›¹]›žàžåž¾]êÄ]´^s: Create an iterator out of the walker and given `context`.Áò]=·^àžåž“Ÿ››‚àž‰_×åž¡_Ÿàžåž¼^êÂ^”a›¡d¢¡¢‚âþ™a‚èþœa›âþèþ½a ×èþÏa®þ£¤¥™a™aœaœaÜaãa‹%7‹%7®þèþ ëa £âþèþìa¡b¨b Œ%7Œ%7®þŒ%7âþ ²b ¤âþèþ³báb%èb %7%7®þ%7âþ òb ¥âþèþ÷b«c(›§d¨¨§¾©-°cÑ©-³cä©-ác û©-óc®þ©ª°c°c³c³c€d …d¦¦ä«™d(œdŽ%7Ž%7®þõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê®þÙ9¦¡d¦ªâþèþ¦de.ü¬d›®¬®Ñ©-Še›Šÿèþ½e ÿ¯°†e†eŠeŠeeeÎe Óe««óçe9êe %7%7ÿèþõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÿèþ«ôe«°¬üèþŠÿùeêÿeÐf,ê²²Êç,Õf–è,Šg ©è,šg ™ÿ³´ÕfÕf«g °g±±ÊÿÆg9Ég %7%7™ÿÎÿõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê™ÿÎÿ±Óg±´ÎÿØgêÞg¦h5ê¶¶Êç,«h–è,éh ©è,ùh êÿ·¸«h«hŠi iµµÊÿ¥i9¨i ‘%7‘%7êÿÎÿõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êêÿÎÿµ²iµ¸Îÿ·iê½i…j,êººÊç,Šj–è,¿j ©è,Ïj ³€»¼ŠjŠjàj åj¹¹Êÿûj9þj ’%7’%7³€ÎÿõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê³€Îÿ¹ˆk¹¼Îÿkê“kÛk-ê¾¾Êç,àkÌÎÿ–l©è,®l ô€¿Ààkàk¿l Äl½½ÊÿÚl9Ýl “%7“%7ô€ÎÿõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êô€Îÿ½çl½ÀÎÿìlêòl « Ä Ñ â ô šš¦®®µµÄÒ þŽ½ÍÞ"ï ï úú‡•¡¯ ßæ–"¨® ® È$! A copyable reference to a graph.Á£$Ò××‡àžÜµàžã ×ï¾-Ü‚¿-ã ñüÙêÚÙÚ‚ÂúµÂ™ ßööúú§'üÜêÝÜÝç¿-°ú¿-Ü ñÞß¬¬°°íòÛÛ±¹ŠÛÛ¯ÀÇJ¨Ž¨Ý«Ä Œ¬´Í¶¦ üá¨â£ãïää ååâäáã‚ƒÐ‚–ƒÓ‚œƒÚ‚¢ƒÞ–ƒáüÖýœƒ â¢ƒ² ±‚æçÌÌÐÐÓÓáÚÚÞÞÃÈ àà––î šý‡›‘9œ¾ß5¶cz#ºáü–ƒ¢ƒü6ÿ ±‚±‚·±‚Äààçáüƒ–ƒœƒ¢ƒ‰–µø?ÝŽÝšàõ ŒáéÈ¸ë Ûüé¨ê£ëïìêìéëŠÃ-Ã-ˆ°Ã-‹ƒéü„‡ƒÅ«ƒÌÔIƒÒêÃ-àƒíîýýéˆˆ‹‹ðõ èèÞÞî âŸòãÔò`‚ßÛ”Ìéüƒœƒ¥6¨ ƒƒ·ƒÄèèîéüƒ–ƒœƒ²–µ¸‡%Düð¨ñ£òïóä ôðòôñóŠÃ-%Ã-“%°Ã-š%ÃÃ-ž%–ƒðü–%êÃ-Ú%ýÃ-ì% ˜„õöŒ%Œ%%%“%“%ðš%š%ž%ž%ý%‚& ïïÅ Å î É ý‡ÃÊ ‘9ÃË ¾ÃUÀIµš}ðü–ƒ¢ƒ¯&J²& ˜„³öÛÍ-ïïöðüƒ–ƒœƒ¢ƒ¼&–µÂ&§'Lüø¨ù£úïûä üøúüùûŠÃ-°'Ã-³'°Ã-º'ÃÃ-¾'–ƒøü¶'êÃ-‚(ýÃ-”( ô„ýþ¬'¬'°'°'³'³'øº'º'¾'¾'¥(ª(÷÷Å Å î É ý‡ÃÊ ‘9ÃË ¾ÃUÀIµš}øü–ƒ¢ƒß(€â(ô„³öÜÍÑ-÷÷þøüƒ–ƒœƒ¢ƒþ(–µŒ)‹)º*R›*Ž›*âž*·* ŒŸ*§*Í©*™* ü€¨£‚ïƒä „€‚„ƒŠÃ-Ã*Ã-Æ*°Ã-Í*ÃÃ-Ñ*–ƒ€üÉ*êÃ-›+ýÃ-+ †……†¿*¿*Ã*Ã*Æ*Æ*€Í*Í*Ñ*Ñ*¾+Ã+ÿÿ––î šý‡›‘9œ¾ß5¶cz#º€ü–ƒ¢ƒÿ+]‚,†…³öÜ†…Íÿÿ†€üƒ–ƒœƒ¢ƒ•,–µ›,‹´,¹-Gž-Žž-§¡-¶- Œ¢-ª-È¸¬- œ-üˆ¨‰£Šï‹ˆŠ‰‹ŠÃ-Â-Ã-É-°Ã-Ì-ƒˆüÅ-µÈ-Ž.ÈÈ-•.ÛÈ-›.êÃ-©.˜…Œ¾-¾-Â-Â-ˆÉ-É-Ì-Ì-¹.¾.‡‡ìì¬"ðŸòñ—Àòò•‡òóÔò¸Ç‡®uª“Çˆüƒœƒþ.L/˜…ƒÜ˜…Í‡‡ˆüƒ–ƒœƒ”/–µš/•‡ /¯?Jü¨£‘ï’ä “’‘“ŠÃ-¸?Ã-»?°Ã-Â?ÃÃ-Æ?–ƒü¾?êÃ-ˆ@ýÃ-š@ à…”•´?´?¸?¸?»?»?Â?Â?Æ?Æ?«@°@ŽŽÐÇÞ@3á@à…ÐÇŽŽ•üƒ–ƒœƒ¢ƒò@¿A4¨—£˜ï™ä š˜š—™íñ,ÄA¦¿,ÇA‚¸†ÊA‚¾†ÎAý¸†‚Bâ¾†”B ò…›ÄAÄAÇAÇAÊAÊAÎAÎA¥B¨B ”%7”%7ò…–³B–›–úêû¸†¾†´B‰CPêBŽêBÂíB†C ŒîBöBÍøBèB ü¨ž£Ÿï ä ¡ Ÿ¡žŠÃ-’CÃ-•C°Ã-œCÃÃ- C–ƒü˜CêÃ-èCýÃ-úC È†¢£ŽCŽC’C’C•C•CœCœC C C‹DDœœ¾¾¬µÂŸ6gìd°¢üƒ¢ƒÌD2ÏDÈ†È†ãœœ£üƒ–ƒœƒ¢ƒàDÓE:´EŽ´Eø·EÐE Œ¸EÀEÍÂE²E ¨¥£¦ï§ä ¨¨¥§¦íñ,ØE¦¿,ÛEà-ÞEÀà-âEÓà-œFæà-®F Ú†©ØEØEÛEÛEÞEÞEâEâE¿FÂF •%7•%7Ú†¤ÍF¤©–úêû¸†¾†ÎFƒJ An edge reference.Á›I²II Edge references are used by traits `IntoEdges` and `IntoEdgeReferences`.Á¶ILJªªï¾-–Jªï¾-–J«¬®¯°±«¬®¯°±‚àž«¡J‚àž¬²J‚àžÃJ¡J¦Jªª²J·JªªÃJÈJªªùJ! The source node of the edge.ÁÔJ üJ–%7–%7àžÿë-ªƒKª®àž„KÄK! The target node of the edge.ÁŸK ÇK—%7—%7àžÿë-ªÎKª¯àžÏK™L"' A reference to the weight of the edge.ÁêK*œL˜%7˜%7àž˜%7³ì-ª£Lª°àž¤LáL The edgeâ€™s identifier.ÁÀLäL™%7™%7àž™ì-ªçLª±àžèL‚M(ü³¨´£µ´µ³ŠÃ-‹MÃ-ŽMµÈ-¸Mú‡¶·¸¹º»¼‡M‡M‹M‹MŽMŽMÄMÉM²²ƒÙMÞM²²âÑóMøM²²–ƒ‰NŒNš%7š%7ú‡ƒ²“N²¹³üƒ–ƒ”NºN½N›%7›%7ú‡ƒ²ÄN²º³üƒ–ƒÅNëNîNœ%7œ%7ú‡œ%7–ƒ²õN²»³üƒ–ƒöNO O%7%7ú‡âÑ²£O²¼³üƒ–ƒ¤OîO A node reference.ÁØOøO½½ï¾-P½ï¾-P¾¿ÀÁ¾¿ÀÁ‚àž¾ŒP‚àž¿PŒP‘P½½P¢P½½®P±Pž%7ž%7àžŽø-½´P½ÀàžµPÐP"ÓPŸ%7Ÿ%7àžŸ%7¨ø-½ÚP½ÁàžÛPýS¨'ÃÃ‚¨ˆ‚T‡¨ˆ©T¦ˆÄÅÆÇ‚T‚TµTºTÂÂ¨ˆËTÐTÂÂáTäT %7 %7¦ˆ¦ˆ¾ÂçTÂÆ¨ˆèT™U!œU¡%7¡%7¦ˆ¡%7¦ˆ¿Â£UÂÇ¨ˆ¤UÅUÌUôU'üÊ¨'Ë›ÌÌÊË‚¶ˆýU‚ŠÿV‡¶ˆªV´ˆÍÎÏÐùUùUýUýUVV¶V»VÉÉ¶ˆÌVÑVÉÉŠÿáVäV¢%7¢%7´ˆ´ˆ¾ÉçVÉÏÊü¶ˆŠÿèV™W!œW£%7£%7´ˆ£%7´ˆ¿É£WÉÐÊü¶ˆŠÿ¤Wž[*ƒ[Žƒ[•†[›[ Œ‡[[È¸‘[ [¨Ò£ÓïÔÔÒÓíñ,£[¦¿,¦[à-©[Õó,Ö[èó,Ý[ Óà-ð[ßˆÕÖ£[£[¦[¦[©[©[€\…\ ÑÑ–ú™\ž\ ÑÑêû‚_4¨Ø£ÙïÚä ÛØÚÙÛíñ,‡_¦¿,Š_à-_Àà-‘_Óà-Å_æà-×_ ò…Ü‡_‡_Š_Š___‘_‘_è_ í_××¸† `:`Ž`Ð„`` Œ…``Í`ÿ_ ¨Þ£ßïàä áàßáÞíñ,¥`¦¿,¨`à-«`Àà-¯`Óà-é`æà-û` Ú†â¥`¥`¨`¨`«`«`¯`¯`Œa ‘aÝÝ¸†Àa/¥aŽ¥aí¨a½a Œ©a±aÈ¸³a £a¨ä£åïæäåæíñ,Åa¦¿,Èaà-Ëa¥–úýa Óà-bßˆçÅaÅaÈaÈaËaËa b ¥bãã¸†·bMüé¨ê£ëïìä ííêìéëŠÃ-ÀbÃ-Çb°Ã-ÎbÃÃ-ÒbƒéüÃb–ƒéüÊbêÃ-“cýÃ-¥c Ø‰îïð¼b¼bÀbÀbéÇbÇbéÎbÎbÒbÒb¶c»cèè§"§"…¥«"…Ã¬"QÃ®"Š&Ã°ô×+‚Ùéü–ƒ¢ƒêcïcèè××“*ÛŸÃ*Vø³`Céü–ƒ¢ƒ¦d0©dØ‰Ø‰‹èèðéüƒ–ƒœƒ¢ƒ¹d€m8¨ò£óïôä õõòôóíñ,…m¦¿,ˆmà-‹mÀà-mÓà-Çmæà-Ùm ò…ö÷ø…m…mˆmˆm‹m‹mmmêmím ¤%7¤%7ò…ñømñö–úêû¸†¾†ùm®n.±n¥%7¥%7ò…Ÿ¼ñºnñ÷–úêû¸†¾†»néÞÁnün/ÿn ¦%7¦%7ò…ò…·ñŠoñø–úêû¸†¾†‹oéÞ‘oÒo?¨ú£ûïüä ýýúüûíñ,×o¦¿,Úoà-ÝoÀà-áoÓà- pæà-²p ò…×o×oÚoÚoÝoÝoáoáoÿp9 A mapping for storing the visited status for NodeId `N`.ÁÂp<‰qþ¨ÿþÿŠÃ-’qþ€€’q’q…r" Mark `a` as visited.Á›q¸q= Return **true** if this is the first visit, false otherwise.ÁÀq@ˆr§%7§%7àžƒþŽrþ€àžƒ“rš±™rár$, Return whether `a` has been visited before.Ár/är ¨%7©%7¨%7àž©%7ƒþïrþàžƒðrš±ör‰s7ä ƒƒ‚¡‹Žsâ¡‹Ïs °Š„…ŽsŽsàs4ãsª%7ª%7°ŠóŠ‚és‚„¡‹îsæüôs¾t6Át «%7¬%7«%7°Š¬%7óŠ‚Ìt‚…¡‹ÍtæüÓt¡u7ä ‡‡º›.¦uÍ›.çu °Šˆ‰¦u¦uøu4ûu%7%7°Š¯‹†v†ˆ¡‹†væüŒvÖv6Ùv ®%7¯%7®%7°Š¯%7¯‹†äv†‰¡‹åvæüëv¹w%ä ‹‹º›.¾wÍ›.íw °ŠŒ¾w¾wþw"x°%7°%7°Š¡‹Š‡xŠŒ¡‹Œxæü’xÊx$Íx ±%7²%7±%7°Š²%7¡‹ŠØxŠ¡‹Ùxæüßx›y(¨Í,íñ, y‚£Œ£yÔI–úÑyŒ–úØyÿI£Œãyñ‹‘’ y y£y£yöy!ùy³%7³%7ñ‹–úŽÿyŽ‘–ú£Œ„zæüŠz»z#¾z ´%7µ%7´%7ñ‹µ%7–úŽÉzŽ’–ú£ŒÊzæüÐzÞ~4¨”£•ï–ä —•—”–íñ,ã~¦¿,æ~à-é~Àà-í~æà-¡ ò…˜™ã~ã~æ~æ~é~é~í~í~²·““Ÿ¼Úß““å¼€4¨›£œïä žœž›íñ,†€¦¿,‰€à-Œ€Àà-€Óà-Ä€æà-Ö€ ò…Ÿ ¡†€†€‰€‰€Œ€Œ€€€ç€ì€šš°Šƒ"† ¶%7¶%7ò…°Ššš –úêû¸†¾†‘é(ì ·%7¸%7·%7ò…¸%7ò…´šöš¡–úêû¸†¾†÷àýø‚:Ù‚ŽÙ‚¥Ü‚õ‚ ŒÝ‚å‚Íç‚×‚ ¨££¤ï¥ä ¦¥¤¦£íñ,ý‚¦¿,€ƒà-ƒƒÀà-‡ƒæà-Áƒ Ú†§¨ý‚ý‚€ƒ€ƒƒƒƒƒ‡ƒ‡ƒÒƒ×ƒ¢¢Ÿ¼úƒÿƒ¢¢å¼Â„:£„Ž£„Ç¦„¿„ Œ§„¯„Í±„¡„ ¨ª£«ï¬ä ª¬«íñ,Ç„¦¿,Ê„à-Í„Àà-Ñ„Óà-‹…æà-… Ú†®¯°Ç„Ç„Ê„Ê„Í„Í„Ñ„Ñ„®…³…©©°ŠÊ…"Í… ¹%7¹%7Ú†°Š©×…©¯–úêû¸†¾†Ø…¯†(²† º%7»%7º%7Ú†»%7Ú†´©¼†©°–úêû¸†¾†½†àÃ†¾‡5Ÿ‡ŽŸ‡¢‡»‡ Œ£‡«‡Í‡‡ ¨²£³ï´ä µµ²´³íñ,Ã‡¦¿,Æ‡à-É‡Àà-Í‡Óà-‚ˆæà-”ˆ Ú†¶·Ã‡Ã‡Æ‡Æ‡É‡É‡Í‡Í‡¥ˆªˆ ±±–ú¾ˆÃˆ ±±êûóˆ/ØˆŽØˆ¯Ûˆðˆ ŒÜˆäˆÈ¸æˆ Öˆ¨¹£ºï»¹º»íñ,øˆ¦¿,ûˆà-þˆÕó,°‰èó,·‰ ßˆ¼½øˆøˆûˆûˆþˆþˆÈ‰Í‰¸¸–úÝ‰â‰¸¸¡â“Š/ø‰Žø‰Íû‰Š Œü‰„ŠÈ¸†Š ö‰¨¿£ÀïÁÀÁ¿íñ,˜Š¦¿,›Šà-žŠÕó,ÐŠ«–ú×Š££.ÝŠÓà-ëŠßˆÂÃÄ˜Š˜Š›Š›ŠžŠžŠûŠ€‹¾¾€–‹!™‹ ¼%7¼%7ßˆ€¾£‹¾Ã–úêû¸†¤‹ö‹(ù‹ ½%7¾%7½%7ßˆ¾%7ßˆ´¾ƒŒ¾Ä–úêû¸†„ŒàŠŒß’8‹’Ž‹’‘ Ž’£’ Œ’—’È¸™’ ‰’5 The `GraphMap` keeps an adjacency matrix internally.Á¦’8¨Æ£ÇïÈÈÆÇíñ,ä’¦¿,ç’à-ê’Õó,¥“Š¼.¬“££.²“Óà-À“ßˆÉÊËä’ä’ç’ç’ê’ê’Ð“Õ“ ÅÅ÷“ú“¿%7¿%7ßˆÅ‹”ÅÊ–úêû¸†Œ”§”1ª”À%7Á%7À%7ßˆÁ%7–ú–úÅ¶”ÅË–úêû¸†·”š±Å”ª±Ë”…• ‰•äˆ©¾Ôë‡¡±ÈÉîïº‰)ªÌÎL™ÌÏÖ´f‘ÌÐÖ´f’ÌÐó™…ÛÌÑ‰‹¡ðÌÓž§®ÌÔâŽ½ §ÌÕû†á µÌ×¾ì ”ÌØÙŒ÷‰ÌÙïˆ‹ ÐÌÚ»‰–ÙÌÛë‡© ÃÌÜ»ˆ¼ÌÌÝŠÓâÌÞô‹èþÌß‚Žü œÌàõ&‹½Ìá»+˜þÌâÕ¢ ³Ìã½ºî ÌÍÊ›º„ šÌÍÌÇ%ÑªÌÍÛzu¬ÌÍÄá·y‰¤ÌÍÆì¨z›°ÌÍÈõº â çÌÍÎÌŸñÝÌÍÏç þ ëÌÍÐ¼µúš#ÌÍËò„·•ÌÍÒÂ„ƒ¼úÌÍÓÂõƒÁ‡ÌÍÔÂ¬Âòá$ÌÍÖŽÁü È$ÌÍ×Î¶‡Ì$ÌÍØÎ¶‡Í$ÌÍØ¹¶‘É$ÌÍÙ¹¶‘Ê$ÌÍÙ×Â› ç$ÌÍÚ’_~ Ú!ÌÍÅãù^ º!ÌÍÇñ):LYfx…’5¡¡§§½ ½ Íáá á ì ì ÷÷‹ ‹ ––© © ¼¼ÓÓèèü ü ‹‹˜˜¢ ¢ Ï A graph filter for nodes.Á±Ù ä¨åäåŠÃ-ääææää§(2 Return true to have the node be part of the graphÁí5ªÂ%7Â%7àžƒä·äæàžƒ¸Q¾Óúè¨éèé•ã‰õˆŠÃ-Ûí•ã‰ÿ ‚•Ø•êØØÛÛ”$—Ã%7Ã%7•ƒç¤çê•ƒ¥–µ«™%> This filter includes the nodes that are contained in the set.Á×A¨ììíñ,žþ°Š–úÖ°Šížžé$ìÄ%7Ä%7°Š–úëùëí–úú–µ€÷*ÙÓ.µA¨ïÍ,ððïíñ,ü£.ÿþñ‹–ú»ñ‹ñüüÿÿÎ$ÑÅ%7Å%7ñ‹–úîÞîñ–ú£Œß–µåŠ &7Æ%¨óÆ%óŠÃ- þ°ŠƒÈ »ô Û $Þ Ç%7Ç%7»ƒòë òôÆ%7ƒì –µò §+7È%¨öÍ,÷È%ö÷ŠÃ-¬‚‡Ž¯þØƒìÐø¬¬¯¯ÿ$‚É%7É%7ÐƒõõøÈ%7ƒ‡Ž–µ–» úúêûúû•…Ã)õ‰ç¿-Ã ú¿-æ í•…Ã)ø žÓ.À ä™üÀ À Ã Ã Ù+< Create an `NodeFiltered` adaptor from the closure `filter`.Á•?àÂ•ä™ùùü•Â8èÆ0ò´+êþúÿþÿÊç,¹ðë,¼µÎÿí –Ž€¹¹¼¼þƒýýÊÿ› ýý¼Ï·7üƒê„ú…„…ƒç¿-Àðç,ÃÃÂü ä¯±¹’ïŽ†‡¼¼ÀÀÃÃ¯´ ‚‚ˆˆå3Œš4ÌŸÌŽÌCÌ÷ñÙNŒ$ƒü‹¹¯ð3ó ïŽ±¹ïŽÄ‚‚‡ƒüÂ¯ý–µƒï. A filtered neighbors iterator.ÁÌ"úü‰òŠú‹‰Š‹Ýç,”ðç,—¯‰üšå.Ëˆˆå3Œš4ÌŸÌŽÌCÌ÷ñÙNŒ$‰ü¯CÌ÷ñÙNŒ$ŒŽ””——‰¤¤Ìˆˆ¾¾ÌˆˆËËÌˆˆ‰ü¯Ø;üò‘ú’‘’Ýç,áðç,äØ9¡‡šŸ¸ä¯šŸÅº“”ÝÝááääà åšŸù(üÊ%7Ê%7ºõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êºÙ9”ü¯†›”È!•ÁÈ!•ÚÈ!••”¥ Õ?ü—ê˜ú™˜™—ç¿-Þðç,áÌÂ¢àá.Àš›ÚÚÞÞááÝâ––ˆˆå3Œš4ÌŸÌŽÌCÌ÷ñÙNŒ$—ü›Í¯®T±±¹ÜÍ––›—üÂ¯Ä–µÊ•‡Ø¹=üêžúŸžŸç¿-Âðç,ÅâÂ„àá. ¡ ¡¾¾ÂÂÅÅ½Âœœˆˆå3Œš4ÌŸÌŽÌCÌ÷ñÙNŒ$ü¡Æ¯Š2¡¡ãœœ¡üÂ¯žÙ<ü£ê¤ú¥¤¥£ç¿-âðç,åþÂ£àá.¾³¦§¨ÞÞââååÛà¢¢€†úÿ¢¢©©ž6š4Ì®ŸÌ¯Ì5X_«Ñ‚à£üŸÓ¯Á 0Ä ³³€¢¢¨£üÂ¯Ô ²"*% A filtered node references iterator.Á‰"(½"üªò«ú¬¬ª«Ýç,Ó"ðç,Ö"¯ªüÙ"Ðõ.Š#©©ž6š4Ì®ŸÌ¯Ì5X_«Ñ‚àªü¯5X_«Ñ‚à®¯Ï"Ï"Ó"Ó"Ö"Ö"ªã"ã"Ì©©ý"ý"Ì©©Š#Š#Ì©©ªü¯—#7ü±ò²ú³±²³Ýç, #ðç,£#è.Ü# è.ó#½šŸú#ä¯ò²Š$(Å´µœ#œ# # #£#£#º$ ¿$°°šŸÓ$(Ö$Ë%7Ë%7ÅõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÅÙ9°Û$°µ±ü¯à$õ%µÈ!¶ÁÈ!¶ÚÈ!¶¶µÌ¯´&<ü¸ê¹úº¸¹ºç¿-½&ðç,À&‰Âþ&àá.™'˜‘»¼½¹&¹&½&½&À&À&¶'»'··¨¸Õ'Ú'··¾¾Î7Ã8ÌÄŸÌÅÌ_fB½É¸üÂìØ¯¨(0«(˜‘˜‘‹··½¸üÂ¯»(–*6 A filtered edges iterator.Á÷)¡*ü¿êÀòÁúÂÀÂ¿Áç¿-À*‚õ‘Ã*‚û‘Æ*û‘¿üÉ*´€/û*¾¾Î7Ã8ÌÄŸÌÅÌ_fB½É¿üÂõ‘û‘_fB½ÉÃÄÅ¼*¼*À*À*Ã*Ã*Æ*Æ*¿Ó*Ó*Ì¾¾Èµî*î*Ì¾¾õ‘û*û*Ì¾¾¿üû‘ˆ+FüÇêÈòÉúÊÈÊÇÉõ‘Ù9¨¸ž,Ž€/”+¡€/—+äû‘±¹Ü+¿ü.ú+Ø9õ‘•,ç¿-‘+ª‘ËÌ++‘+‘+”+”+—+—+¸, ½,ÆÆõ‘Ù9Ñ,(Ô,Ì%7Ì%7ª‘õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êª‘Ù9ÆÙ,ÆÌÇüÂõ‘û‘Þ,¸-ÌÈ!ÍÁÈ!ÍÚÈ!ÍÍÌÞ¸Ž.3üÏêÐúÑÐÑÏç¿-—.ðç,š.ÐÂÏ. àá.á.…’ÒÓ“.“.—.—.š.š.þ. ƒ/ÎÎÔÔž9Ù8ÌÚš4ÌÛŸÌÜÌŠ¡ßÉ8}¬ÏüÂÈÂ¯¶/+¹/…’±¹…’ÑÎÎÓÏüÂ¯¿/š±Å/Á1-þ.¢1Ì1üÕêÖò×úØØÕ×Öç¿-â1Ž€/å1¡€/è1û‘Õüë1Ä‹/·2ÔÔž9Ù8ÌÚš4ÌÛŸÌÜÌŠ¡ßÉ8}¬ÕüÂõ‘û‘Š¡ßÉ8}¬ÙÚÛÜÞ1Þ1â1â1å1å1è1è1Õõ1õ1ÌÔÔÈµ22ÌÔÔª2ª2ÌÔÔõ‘·2·2ÌÔÔÕüû‘Ä2=üÞêßòàúáàßáÞÉƒ/È3Ž€/Ð2¡€/Ó2òƒ/3…ˆ/3 “„/¿3ç¿-Í2—’âãÉ2É2Í2Í2Ð2Ð2Ó2Ó2â3 ç3ÝÝ …/û3(þ3Í%7Í%7—’õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê—’Ù9Ýƒ4ÝãÞüÂõ‘û‘ˆ4¡5ãÈ!äÁÈ!äÚÈ!ääãÂÅà5)êæúçæçÊç,å5ðë,è5ÛÎÿ—6äÍŽÊÿ§6–Žèéå5å5è5è5Ä6DÇ6Î%7Î%7–Ž–Ž·õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÎ%7–ŽñåÓ6åèÎÿÍŽÔ6˜&Ú6Š8D8Ï%7Ï%7–Ž–Ž¶õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÏ%7–Žòå™8åéÎÿÍŽš8˜& 8ù8†9Ž9·9”9œ9•9 Ô–9—9 ÷˜9ž9¡9´9«9 Ô¬99Î®9µ9ç; A graph filter for edgesÁÊ;ñ; ë;ììë‚;ü;ëííü;ü;Â<+2 Return true to have the edge be part of the graphÁˆ<5Å<Ð%7Ð%7àž€™/ëÒ<ëíàž€™/Ó<û¾Ù<ñ<úï¨ððïæÒ.¦=ŠÃ-ù<‡Ó.= žÓ.ö<•ñö<ö<ù<ù<²=$µ=Ñ%7Ñ%7•ƒîÂ=îñ•ƒÃ=–µÉ=ÿ@úóêôôó•³çõ×Aç¿-‡A¿ü.ªAí•³çÅAžÓ.„Aüßõ„A„A‡A‡A§B+< Create an `EdgeFiltered` adaptor from the closure `filter`.ÁãA?®BÂ•üßòòõ•Â8¶BÆ0ÀB‚C+ê÷úøø÷Êç,‡Cðë,ŠC÷ß.»C ¬“ùú‡C‡CŠCŠCÌCÑCööÊÿéCîCöö¼Ï…D7üüêýúþüýþç¿-ŽDðç,‘D…ˆ/ÊD ë¯¨¸ÜDƒ”ÿ€ŠDŠDŽDŽD‘D‘DúDÿD ûû‡‡æ<‹ŸÌŒÌóÈó’÷üüÂ¯°E3³E ƒ”±¹ƒ”Äûû€üüÂ¯½E–µÃEÔF?ü‚êƒú„„‚ƒç¿-ÝFðç,àFÙÂ¡GÈ¡/»GÌ”…†ÙFÙFÝFÝFàFàFÙGÞG±±Ê?µŸÌ¶Ì·¹Ìaj¾É˜ò‚üÂ¯ŸHT¢HÌ”±¹ÜÌ”Í†‚üÂ¯µH–µ»H•‡ÉH²J.’ã.J"½Jüˆê‰úŠˆ‰Šç¿-×Jðç,ÚJ¯ˆüÝJ…ˆ/îJ Þ§/“K‡‡æ<‹ŸÌŒÌóÈó’÷ˆüÂ¯óÈó’÷‹ŒÓJÓJ×J×JÚJÚJˆÿJÿJÌ‡‡ÈÂ“K“KÌ‡‡ˆü¯ K;üŽêúŽç¿-©Kðç,¬KÈ¡/éK…ˆ/ˆL Þ”‘’¥K¥K©K©K¬K¬K™L žL±¹´L(·LÒ%7Ò%7Þ”õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÞ”Ù9¼L’ŽüÂ¯ÁL¨M’È!“ÁÈ!“ÚÈ!““’«èøN<ü•ê–ú—•–—ç¿-Oðç,„O¿ü.ÂOÈ¡/ÝO§•˜™šýNýNOO„O„OûO€P””¨¸šPŸP””¡¡¿>¦8Ì§ŸÌ¨Ì‘/lzîÌ•üÂìØ¯äP0çP§•§•‹””š•üÂ¯÷PªR3üœêúžœžç¿-³Rðç,¶R…ˆ/ëR È¡/ýR¹•Ÿ ¯R¯R³R³R¶R¶R›S S››¡¡¿>¦8Ì§ŸÌ¨Ì‘/lzîÌœüÂÈÂ¯ÓS+ÖS¹•±¹¹•Ñ›› œüÂ¯ÜS–µâSªU-þ.‹UµUü¢ê£ò¤ú¥¥¢¤£ç¿-ËUŽ€/ÎU¡€/ÑUû‘¢üÔU¿´/†V¡¡¿>¦8Ì§ŸÌ¨Ì‘/lzîÌ¢üÂõ‘û‘‘/lzîÌ¦§¨ÇUÇUËUËUÎUÎUÑUÑU¢ÞUÞUÌ¡¡ÈµùUùUÌ¡¡õ‘†V†VÌ¡¡¢üû‘“V=üªê«ò¬úª¬«Éƒ/¡WŽ€/ŸV¡€/¢Vëû‘¨¸ÞV¿ü.ýV“„/˜Wç¿-œVË•®¯˜V˜VœVœVŸVŸV¢V¢V»W ÀW©© …/ÔW(×WÓ%7Ó%7Ë•õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êË•Ù9©ÜW©¯ªüÂõ‘û‘áW®X¯È!°ÁÈ!°ÚÈ!°°¯‰õ†Y6( A filtered neighbors-directed iterator.ÁÚX+‘Yü²ê³ú´´²³ç¿-³Yðç,¶Y¯²ü¹Yº¤/ÊY±¼/ÿY±±Ê?µŸÌ¶Ì·¹Ìaj¾É˜ò²üÂ¯aj¾É˜òµ¶·¯Y¯Y³Y³Y¶Y¶Y²ãYãYÌ±±§ÄÿYÿYÌ±±²ü¯ZZÌ±±±¹¡ZCü¹êºú»¹º»ç¿-ªZðç,ZÈ¡/òZº¤/‘[¢–¼½¦Z¦ZªZªZZZª[ ¯[¸¸±¹Å[(È[Ô%7Ô%7¢–õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê¢–Ù9¸Í[¸½¹üÂ¯Ò[×\½È!¾ÁÈ!¾ÚÈ!¾¾½³ú‘•••ïˆ¨©¸¹ŽÁ È$¿ÁÎ¶Ì$¿ÂÎ¶Í$¿Â‰‹;ð¿ÄÇ%Aª¿Åû†J µ¿Æ¾U ”¿Çì`×¿ÈÙŒj‰¿Éïˆ~ Ð¿Ê»‰Ù¿Ëë‡ Ã¿Ì»ˆ¯Ì¿ÍŠÆâ¿Îô‹ßþ¿Ïžó®¿ÐâŽ‰ §¿Ñ‚Ž” œ¿ÒÕ£ ³¿Ó 'ˆ;;AAJ J U U ``jj~ ~ ¯¯ÆÆßßóó‰ ‰ ” ” £ £ Ô,êÕÕÊç,Ù÷ß.Ü É—Ö×ÙÙ‡ŒÔÔÊÿ¤©ÔÔ¼ÏÀ*êÙÙÊç,Å×ÎÿÈÉ—ÅÅ¥%êÛÛÊç,ªº-ØÉ—ÜÝªªõú ÚÚˆ• 8£ É—Êÿˆ•ÚÚÝÎÿ–µ³“-êßßÊç,˜º-ÎÉ—àá˜˜ëðÞÞˆ•žO¡É—ÊÿÜˆ•ÞÞáÎÿ´–µº‹È¬ !êããÊç,± ÙÎÿÛ É—äå± ± ô ù ââ¾™¦ .© É—É—·É—ÑââåÎÿ¯ š±µ ¸)êççÊç,½”Í/ïÉ—èé½½ˆ ææ¾™ÂGÅÉ—ƒÎ/ÜÎ/ææéÎÿÔš±Ú•‡ëó ,êëëÊç,ø ©è,û É—ìíîø ø ¦«êê×ÿ½À Õ%7Õ%7É—×ÿêÊêíÎÿË‚(… Ö%7×%7Ö%7É—×%7É—´êêîÎÿà–ô A reversed edges iterator.ÁÕÿ òðð‚÷˜Í˜èÔûûA:,½ñ––¿ïï÷˜¢%òóó½Ó/§Ø9÷˜Õªÿ ìÍ˜ôõ§§û €òòŠ¡«(®Ø%7Ø%7Í˜õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÍ˜Ù9ò³òõ÷˜¸ø éC÷÷‚°™ý™øùýý×!0 Return the original, unreversed edge reference.ÁŸ3Þ Ù%7Ù%7™Ù%7°™öìöø°™íÕ!C Consume `self` and return the original, unreversed edge reference.ÁŠFÜ™°™ööù°™ì§, An edge referenceÁ‘éCûûöÖ/¬ª°™á™üýþÿ€‚¬¬ðõúúâª’úúŒ¯ª¯úúÍÇ ÊÚ%7Ú%7™™«úÑúÿ°™ÒŒ Û%7Û%7™õÛ/ú–ú€°™—Ñ!ÔÜ%7Ü%7™Ü%7™úÛú°™Ü—šÝ%7Ý%7™™¬úú‚°™žÓ*ê„„Êç,Ø‰Îÿ‹É—…†‡ØØ¥ªƒƒ¸¸¹ì—»Î¿S†ª¶°jYýê ÛàƒƒÞ° 0£É—É—‹ƒƒ‡Îÿ³Ü$% A reversed edge references iterator.Á³(çò‰‰½Ó/þÚ™fb™¹xcÊÆŠþþ‡‡¿ˆˆ÷˜“.òŒŒ½Ó/˜ÌÔ/ÏßÔ/æÚ™Ž˜˜õ ú‹‹Š¡¥(¨Þ%7Þ%7Ú™õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÚ™Ù9‹‹Ž÷˜²š¢Ë¨°© Ôª« ÷¬²µÈ¿ ÔÀÁÎÂÉµ+ª+ Úª+ 4¨+9 Graph traits for associated data and graph construction.Á<½+ž¢ïòŠ“Ûùí—uP â‘î¼µŽš#“„»˜ ¥”ÌŸÞÝ•‰‹þð—âŽ„ §˜‚Ž œ™ó–ž¨šó–ž©šÿ(´ý›·yÉ¤œìBP |&ŽŽ˜ ˜ É Þï8þþ„ „ žž´ÂÉíú‚«ˆ‰ ÔŠ‹ ÷Œ’•¨Ÿ Ô ¡Î¢©¬ !: A graph that can be extended with further nodes and edgesÁî =¶ žžðàž½ §àžÄ ž£ì/½ ¶ì/Ä Ÿ ¡Ÿ ¡Ô A× ß%7ß%7àž– ÖÒžà ž £ å Š&ë ”‘B Add a new edge. If parallel edges (duplicate) are not allowed andÁšE( the edge already exists, return `None`.Áä+—à%7à%7àžÖÒÖÒ÷ÒùÑž©žˆ¥ ®š±¼ª±ÕŠ&î»F Add or update the edge from `a` to `b`. Return the id of the affectedÁß I edge.Á ¾á%7á%7àžÖÒÖÒ÷ÒÈÑžÓž‘ÑØš±æª±ÿŠ&˜ì! 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Œ·!¿!ÍÁ!±! ¨Ð£ÑïÒä ÓÐÒÑÓíñ,×!¦¿,Ú!à-Ý!Àà-á!Óà-—"æà-©" Ú†ÔÕÖ×!×!Ú!Ú!Ý!Ý!á!á!º"@½"í%7í%7Ú†Šƒ0¶‚0ÏÆ"ÏÔ–úêû¸†¾†Ë"Š&Ñ"¥#‘¨#î%7î%7Ú†¶‚0¶‚0Ý„0õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê‰„0Ïº#ÏÕ–úêû¸†¾†¿#š±Í#ª±æ#Š&ÿ#í$Œð$ï%7ï%7Ú†¶‚0¶‚0Ý„0‰„0Ï…%ÏÖ–úêû¸†¾†Š%š±˜%ª±±%Š&Ê%É&+®&Ž®&É9±&Æ& Œ²&º&È¸¼& ¬&¨Ø£ÙïÚØÙÚíñ,Î&¦¿,Ñ&à-Ô&Óà-ƒ'²Š.”' ßˆÛÜÝÎ&Î&Ñ&Ñ&Ô&Ô&¥'@¨'ð%7ð%7ßˆßˆñßˆ·×±'×Û–úêû¸†¶'Š&¼'(‘“(ñ%7ñ%7ßˆ¬™0¬™0ßˆòõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êßˆ¶×¥(×Ü–úêû¸†ª(š±¸(ª±Ñ(Š&ê(ò*Œõ*ò%7ò%7ßˆ¬™0¬™0¬š0öš0×Š+×Ý–úêû¸†+š±+ª±¶+Š&Ï+¾,1¨ß£àïáä âàâßáíñ,Ã,¦¿,Æ,à-É,Àà-Í,Óà-þ,æà-- ò…ãÃ,Ã,Æ,Æ,É,É,Í,Í,¡-4¤- ò…ÞÞã–úêû¸†¾†äy²-‘9À-¬.7.Ž.Œ;.©. Œ‘.™.Í›.‹. ¨å£æïçä èèåçæíñ,±.¦¿,´.à-·.Àà-».Óà-ò.æà-„/ Ú†é±.±.´.´.·.·.».».•/4˜/ Ú†ääé–úêû¸†¾†äy¦/‘9´/œ0,0Ž0»;„0™0 Œ…00È¸0 ÿ/¨ë£ìïíìíëíñ,¡0¦¿,¤0à-§0Óà-×0²Š.è0 ßˆî¡0¡0¤0¤0§0§0ù04ü0 ßˆêêî–úêû¸†äyŠ1‘9˜1ä5- Create a graph from an iterator of elements.Á³50î5ïï¢àžü5ï‰¦0ü5ðð‰6”Œ6 àžïòññïÝç,š6Ê¨-Ê6¼è,Ü6@å8ÎŸ é62ðàž« 6š6š6Ò;šÕ;ÎÿêóòôôóÊç,í;Ýç,ð;¢Îÿ’<œÎÿ›< ¼è,±<:å8Â° ¾<,òÎÿ« ó;í;í;ð;ð;¦=òÈ!õÁÈ!õÚÈ!õõòý² ý@7¨÷£øïùä úøú÷ùíñ,‚A¦¿,…Aà-ˆAÀà-ŒAÓà-ÃAæà-ÕA ò…û‚A‚A…A…AˆAˆAŒAŒAæA”éA ÿ¿ ò…öòüüö‚ÿ¿ ÷A‚ò…§Bä8ÿ¿ ¹B@ÿ¿ å8ùüŽœýŠ&þ;ÿô%€ – Š&#{½ª¯— º´ø/‡ú/ÆB2û–úêû¸†¾†ÿ¿ « úA÷A÷AÕC=¶CŽ¶C‹>¹CÒC ŒºCÂCÍÄC´C ¨þ£ÿï€ä €ÿþíñ,ÚC¦¿,ÝCà-àCÀà-äCÓà-¡Dæà-³D Ú†‚ÚCÚCÝCÝCàCàCäCäCÄD”ÇD ÿ¿ Ú†ýòƒƒýÎ¬0ÕD‚Ú†…Eô¬0—E@ÿ¿ å8ùüŽœýŠ&þ;ÿô%€ – Š&#{½ª¯— ºŠƒ0Ý„0¤E2‚–úêû¸†¾†ÿ¿ « ØDÕDÕD¯F2”FŽ”FÅ>—F¬F Œ˜F FÈ¸¢F ’F¨…£†ï‡…†‡íñ,´F¦¿,·Fà-ºFÓà-ðF²Š.G ßˆˆ´F´F·F·FºFºF’G”•G ¡Â ßˆ„ò‰‰„‚¡Â £G‚ßˆÓGä8¡Â åG@¡Â å8ùüŽœýŠ&þ;ÿô%€ – Š&#{½ª¯— º¢™0¬š0òG2ˆ–úêû¸†¡Â « ¦G£G£G’I?. Iterator adaptors for iterators of `Element`.ÁàH1œIŠ¨‹£ŒŒŠ‹Ø9àž³IàžÙ9ùüŽœýŠ&þ;ÿô%€ – Š&#{½ª¯— ºƒ–ƒ¼IŠÃ-¬IÃ-¯IŠ‡¸0³Iš¸0¼I¬I¬I¯I¯IÐMŽ8 Create an iterator adaptor that filters graph elements.ÁØI;˜JE The function `f` is called with each element and if its return valueÁ JH@ is `true` the element is accepted and if `false` it is removed.ÁíJCD `f` is called with mutable references to the node and edge weights,ÁµKG> so that they can be mutated (but the edge endpoints can not).ÁLAÇL= This filter adapts the edge source and target indices in theÁÏL@4 stream so that they are correct after the removals.Á”M7ÓMàžû‘æÄ ŠúŽŽŠ¡€/ãMÊ¨-¥Nô%7õ%7ðû‘ùüŽœýŠ&þ;ÿô%€ – Š&#{½ª¯— ºô%7ƒõ%7–ƒ·N&ô%7õ%7û‘õ¾0õÙNàžƒ–ƒû‘æMìMãMãMðO1¨£‘ò’‘’õ‘Ù9Ú›´P¦¿,øO“„/«Píñ,õOõ‘õOõOøOøOûOûOöQ) An iterator that filters graph elements.ÁÎP,ûP4 See [`.filter_elements()`][1] for more information.ÁÿP7·Q7 [1]: trait.ElementIterator.html#method.filter_elementsÁ»Q:Rò”ú•”•½Ó/Rðë,“R‚2æàð×VQ€–—˜™RR“R“RœRœR““÷˜©R©R ““ÀRÀR““æÅ ÕRÕR““ÍŽÞR2ò›úœ¨£žœž›÷˜Ù9íÇ §Sö%7÷%7ÍŽùüŽœýŠ&þ;ÿô%€ – Š&#{½ª¯— ºö%7µÈ ÷%7»È õçS‚µÈ éR‚»È ìRÌÔ/žS½Ó/ãRö%7÷%7ðÍŽàÆ0ÅS&ðë,æR‚Ÿ ãRãRæRæRéRéRìRìRóS øSššíÇ “T(–Tø%7ø%7‚õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê‚Ù9š›Tš ÷˜ÍŽµÈ »È TÄ+ Graph algorithms.ÁM It is a goal to gradually migrate the algorithms to be based on graph traitsÁPL so that they are generally applicable. For now, some of these still requireÁkO the `Graph` type.Á»Ì+¢ðòô÷ýˆŒ’”™ž ¯²¸»£ Ã Ä Ò Ó minÁõ¹¡Í`‘ ‡¡ÏŽÛ¡Ð—uÒ â¡Òî²Àó ¾$¡Óû†– µ¡Õì¡×¡ÖÙŒ«‰¡×ïˆ¿ Ð¡Øë‡Ê Ã¡Ù»ˆÙÌ¡ÚŠôâ¡Ûž‰®¡ÜâŽŸ §¡Ý‚Žª œ¡Þó–¹¨¡ßó–¹©¡ß»+Ãþ¡àÕÑ ³¡áÿ(êý¡âï›…ù¡ãòý¡ £¡äòý¡ ¤¡äÝ¾›¡åÂ‰‹Ùð¡çô‹ßþ¡èõ&ó½¡é¾[”ÔêÐm´ð ëãÏÛ ìí˜Ðêñîãô ‰ïp»ÇøG¡¶p»ÇûG¡¶AddÁÐÇÀ¡·½ºî ¡ÑÊ›º„ š¡ÑÌÇ%Ñª¡ÑÛzu¬¡ÑÄá·y‰¤¡ÑÆì¨z›°¡ÑÈõº â ç¡ÑÎÌŸñÝ¡ÑÏç þ ë¡ÑÐ¼µúš#¡ÑËò„·•¡ÑÒÂ„ƒ¼ú¡ÑÓÂõƒÁ‡¡ÑÔÂ¬Âòá$¡ÑÖŽÁü È$¡Ñ×Î¶‡Ì$¡ÑØÎ¶‡Í$¡ÑØ¹¶‘É$¡ÑÙ¹¶‘Ê$¡ÑÙ×Â› ç$¡ÑÚ’_~ Ú!¡ÑÅãù^ º!¡ÑÇñÒ, Compute dominators of a control-flow graph.Á/0 # The Dominance RelationÁ4QQ In a directed graph with a root node **R**, a node **A** is said to *dominate* aÁUT> node **B** iff every path from **R** to **B** contains **A**.ÁªAìQ The node **A** is said to *strictly dominate* the node **B** iff **A** dominatesÁðT **B** and **A â‰ B**.ÁÅàN The node **A** is said to be the *immediate dominator* of a node **B** iff itÁäQM strictly dominates **B** and there does not exist any node **C** where **A**Á¶P+ dominates **C** and **C** dominates **B**.Á‡.Ú ´¾¿ÅÆË˜ ·ÅÃñ¢£ŽæÛ¢¥ï™¢¦ˆÔI¢§ˆÚI¢§õƒ¢‡¢©Âû†° µ¢ªë‡» Ã¢«ÕÊ ³¢¬ÝÕ›¢Â»ÅÓ$ææïïýˆ“I¢¢° ° » » Ê Ê ÕÕ”¨¯¯íñ,™Õó,·¶£.¾££.ÃÖ°±²³™™–? Get the root node used to construct these dominance relations.ÁÏBù%7ù%7Ö–ú®¢®°–ú£ö 7/ Get the immediate dominator of the given node.ÁÍ2„ I Returns `None` for any node that is not reachable from the root, and forÁŒ L the root itself.ÁÝ ý ú%7ú%7Ö–ú·Á®‘ ®‘ë ’ Q˜ ÒE1 Iterate over the given node's strict dominators.Á¶4ïA If the given node is not reachable from the root, then `None` isÁ÷D returned.ÁÀ Ùû%7û%7Ö–úõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê´´ïP·ÑO¢¸Q¢Qf#oB˜÷üû%7–ú®ë®²–úìQòÑ>E Iterate over all of the given node's dominators (including the givenÁ‹H node itself).ÁØî˜å0öDåå0¿ Ø ü%7ü%7Ö–úõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê´´ïP·ÑO¢¸Q¢Qf#oB˜÷üü%7–ú®ã®³–úäQê“ " Iterator for a node's dominators.Áí%žüµ¨¶µ¶ŠÃ-±ƒµüÁµÈ-ÆŒƒÍÛÈ-ÒÃì0Þ´´ïP·ÑO¢¸Q¢Qf#oB˜÷üµüƒQf#oB˜÷ü·¸±±µÞÞ ¢´´µüíó ¢´´±ò •.üº¨»º»ŠÃ-žƒºüÑµÈ-Öáì0ÝÛÈ-â›ž¼½ššžžºî ó¹¹ƒ‚(…ý%7ý%7›žõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê›žÙ9¹Š¹½ºüƒºÀ ¢©šE This is an implementation of the engineered ["Simple, Fast DominanceÁçH+ Algorithm"][0] discovered by Cooper et al.Á°.ßR This algorithm is **O(|V|Â²)**, and therefore has slower theoretical running timeÁãUJ than the Lengauer-Tarjan algorithm (which is **O(|E| log |V|)**. However,Á¹ML Cooper et al found it to be faster in practice on control flow graphs of upÁ‡O to ~30,000 vertices.Á×ð1 [0]: http://www.cs.rice.edu/~keith/EMBED/dom.pdfÁô4°ÎÿÊÿó¨ êÀÀÊç,¼–è,€ ©è, ŒÊÿ¹ÔIÊÿ¾¿Îÿ8¿§PÉ¼¼Ì!¿È!ÁÁÈ!ÁÚÈ!ÁÁ¿ãƒ ‚)¿È!ÂÁÈ!ÂÚÈ!ÂÂ¿ì” Œ, ¿È!ÃÁÈ!ÃÚÈ!ÃÃ¿†™ ü0¿È!ÄÁÈ!ÄÚÈ!ÄÄ¿‡¤ Ö1SÙ1 ¢þ%7þ%7îÚ ï1û¾ ÑOã1 finger1Áý1finger2Á‘2œ4ÏŸ4¢ÿ%7€&7ÿ%7Ûä€&7ãÇ ¨Ç ÿ‘ ¨ÇÇÐ4íñ,¼4Õó,Ú5¶£.á5££.æ5Æ–úðµ Ä4 ¸¶ Ú4‰¶ Ž5¼4¼4š6ÆÈ!ÈÁÈ!ÈÚÈ!ÈÈÆ…É ó6ÆÈ!ÉÁÈ!ÉÚÈ!ÉÉÆãÍ ç7ÆÈ!ÊÁÈ!ÊÚÈ!ÊÊÆžÓ –9Ï™9¢ÎÿÊÿé© êÌÌÊç,°9–è,¢: ©è,²: ºö0Û:Íö0à:ïø 8¸9§PÆ9°9°9ë õþ'‘ ‘ ¬ÄÒ áó ‚Û– – ¡¡««¿ ¿ Ê Ê ÙÙôô‰‰Ÿ Ÿ ª ª ¹¹ÃÃÑ Ñ ãêø…’¡ °¾Ê1ÙÙßßóó†”£´Æ;Û Û êê‹% ÍeD \[Generic\] Return the number of connected components of the graph.Á³GûA For a directed graph, this is the *weakly* connected components.ÁÿDÕ£+Ä ö£+Ò use petgraph::Graph;ÁÞ* use petgraph::algo::connected_components;Á÷-ÄÎ,¥ Ã 5 let mut graph : Graph<(),(),Directed>= Graph::new();ÁÇ 83 let a = graph.add_node(()); // node with no weightÁ€ 6 let b = graph.add_node(());Á· let c = graph.add_node(());Á× let d = graph.add_node(());Á÷ let e = graph.add_node(());Á— let f = graph.add_node(());Á· let g = graph.add_node(());Á× let h = graph.add_node(());Á÷— graph.extend_with_edges(&[Á› (a, b),Áº (b, c),ÁÊ (c, d),ÁÚ (d, a),Áê (e, f),Áú (f, g),ÁŠ (g, h),Áš (h, e)Áª ËÑ,¹ // a ----> b e ----> fÁÁ // ^ | ^ |Áâ // | v | vÁƒ // d <---- c h <---- gÁ¤ Å, assert_eq!(connected_components(&graph),2);ÁÉ/ graph.add_edge(b,e,());Áù, assert_eq!(connected_components(&graph),1);Á•/õ®+ÅÔÎÿêññÊç,é®Îÿˆ‹ß/ŸðÎÿÿŸìéé ]? \[Generic\] Return `true` if the input graph contains a cycle.Á¥Bè0 Always treats the input graph as if undirected.Áì3§ÎÿêóóÊç,¼Ù¨0Ú ‹ß/êòÎÿÿŸ¿¼¼ûÆ< \[Generic\] Perform a topological sort of a directed graph.Á÷?·I If the graph was acyclic, return a vector of nodes in topological order:Á»L, each node is ordered before its successors.Áˆ/G Otherwise, it will return a `Cycle` error. Self loops are also cycles.Á¸JƒG To handle graphs with cycles, use the scc algorithms or `DfsPostOrder`Á‡J instead of this function.ÁÒðM If `space` is not `None`, it is used instead of creating a new workspace forÁôP2 graph traversal. The implementation is iterative.ÁÅ5‚&7ÎÿõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê&7ö† „êõõ«Êç,‹º-‰âÎÿ¡©è,· ôÎÿÿŸ“ò†‹‹øôÈ!öÁÈ!öÚÈ!ööôÈ…ƒ+hH \[Generic\] Return `true` if the input directed graph contains a cycle.ÁÝ)K©*F This implementation is recursive; use `toposort` if an alternative isÁ*I needed.Á÷*Š+ÎÿêøøÊç,+ø•1»+–è,Ñ+ ©è,á+ ÷ÎÿÿŸ +++ö+,…,…,™,™,Õ,÷È!üÁÈ!üÚÈ!üü÷³¬Å-Ê-¡êþþÊç,×-ö†×-×-Š/¨€‰€íñ,/€ò,’/Õó,³/èó,º/ ÿó,Í/àž‚//’/’/à/\ç/õÉàžÿêƒƒÿö,ë/£ö,‘0¶ö,œ0Éö,¦0 Þö,²0‚–ú±üõÉÿŸî/ë/ë/ô0'¨…‰†…†íñ,ù0€ò,ü0ÿó,ª1“ò,¸1àž‡ù0ù0ü0ü0Ç1Ê1àž„„‡–ú±ü3ž 3¡ƒ&7ÎÿõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êƒ&7ö†ÍŽ²¾ê‰úŠéC‹‰Š‹Æ3Êç,©3ðë,¬3‚²¾¯3¤É/õ3©è,€4 ‚&7óÍŽ‚&7™ÿ’4(‚&7ÍŽ·¡1õ²¾¹4ˆÎÿÍŽ²¾ÿŸ²3ò†¸3Ý3©3©3¬3¬3¯3¯3ö7·O \[Generic\] Check if there exists a path starting at `from` and reaching `to`.Á÷5RÊ6: If `from` and `to` are equal, this function returns true.ÁÎ6=Œ7»’17P graph traversal.Áá7ý7„&7ÎÿÊÿÊÿõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê„&7ö†êÙ8Êç,‘8–è,“9 ©è,£9 ŒÎÿÿŸ™8¹£8’½ ¸8ò†Ë8‘8‘8Ç9ŒÈ!ŽÁÈ!ŽÚÈ!ŽŽŒ‡Â˜:ŒÈ!ÁÈ!ÚÈ!ŒÁÇþ:p Renamed to `kosaraju_scc`.Á¯:Ð: ¤Ð: ôNÚ:û: ßÛ:à:renamed to kosaraju_sccÁâ:Î:/…;Ê§1Îÿ†Íê‘‘Êç,‰;º-¶;©è,Î; ø•1Ú;ÎÿÿŸŒ;‰;‰;œ@yY \[Generic\] Compute the *strongly connected components* using [Kosaraju's algorithm][1].Áˆ<\å<: [1]: https://en.wikipedia.org/wiki/Kosaraju%27s_algorithmÁé<=§=L Return a vector where each element is a strongly connected component (scc).Á«=OE The order of node ids within each scc is arbitrary, but the order ofÁû=H8 the sccs is their postorder (reverse topological sort).ÁÄ>;€?G For an undirected graph, the sccs are simply the connected components.Á„?JÏ?E This implementation is iterative and does two passes over the nodes.ÁÓ?H£@Îÿ†Íê““Êç,°@º-Ý@©è,õ@ ø•1AÊÌÿŸ³@°@°@MsW \[Generic\] Compute the *strongly connected components* using [Tarjan's algorithm][1].ÁáHZ¼IV [1]: https://en.wikipedia.org/wiki/Tarjan%27s_strongly_connected_components_algorithmÁÀIYšJÐª1žJO¨«1îJHù«1·K;óKÈ¬1÷KJÂLC This implementation is recursive and does one pass over the nodes.ÁÆLF”M Îÿ†Íê••Êç,ŸMø•1ÌM–è,âM Ù¨0òM ”ÎÿÿŸ¢MŸMŸM†Qk‰Q ¡…&7†&7ÊÿÎÿ…&7¸ ¸ ‰‹» …¡¼ äy¡½ Ÿƒ¡¾ ¦ ¡¬_'ÜË÷Ÿ†&7Îÿê——°QÊç,“Q–è,ÓQ Ù¨0ãQ œñÏ£–QÿŸ¤QÚEªQ“Q“Q€RR’R÷R R«R¡R Q¢R¦R ÷§RR°RìR ÚEÂRÆR äyÇRÌRÞR ÿŸÍRÎR ö*ÏR×RÝRØR QÙRíR±uŸ^ [Graph] Condense every strongly connected component into a single node and return the result.Á‰baëbU If `make_acyclic` is true, self-loops and multi edges are ignored, guaranteeing thatÁïbX the output is acyclic.ÁÈcÕ£+ãc ö£+ñcÿ‚1ýc" use petgraph::algo::condensation;Á–d%ÄÎ,¼dÚd6 let mut graph : Graph<(),(),Directed> = Graph::new();ÁÞd9¯„1˜e6î„1Ïe–…1ïe¾…1fæ…1¯fŽ†1Ïf¶†1ïfÞ†1g¯g‘‡1³g¸‡1ÒgÐ‡1âgè‡1òg€ˆ1‚h (b, e),Á’h˜ˆ1¢h°ˆ1²hÈˆ1Âhàˆ1ÒhËÑ,áhéh // a ----> b ----> e ----> fÁíh ‰1Ži Ö‰1¯i ÿ‰1Ði ñi1 let condensed_graph = condensation(graph,false);Áõi4 let A = NodeIndex::new(0);Áªj let B = NodeIndex::new(1);ÁÉj- assert_eq!(condensed_graph.node_count(), 2);Áèj0- assert_eq!(condensed_graph.edge_count(), 9);Á™k0P assert_eq!(condensed_graph.neighbors(A).collect::>(), vec![A, A, A, A]);ÁÊkSS assert_eq!(condensed_graph.neighbors(B).collect::>(), vec![A, B, B, B, B]);ÁžlVõ®+õlC If `make_acyclic` is true, self-loops and multi edges are ignored:ÁýlFÄmö£+Èm # use petgraph::Graph;ÁÔm$ # use petgraph::algo::condensation;Áïm' # use petgraph::prelude::*;Á—n #Á·n8 # let mut graph : Graph<(),(),Directed> = Graph::new();Á½n;5 # let a = graph.add_node(()); // node with no weightÁùn8 # let b = graph.add_node(());Á²o! # let c = graph.add_node(());ÁÔo! # let d = graph.add_node(());Áöo! # let e = graph.add_node(());Á˜p! # let f = graph.add_node(());Áºp! # let g = graph.add_node(());ÁÜp! # let h = graph.add_node(());Áþp!ÉÂ1 q # graph.extend_with_edges(&[Á¦q # (a, b),ÁÇq # (b, c),ÁØq # (c, d),Áéq # (d, a),Áúq # (b, e),Á‹r # (e, f),Áœr # (f, g),Ár # (g, h),Á¾r # (h, e)ÁÏr # ]);Áßr 9 let acyclic_condensed_graph = condensation(graph, true);Áér<ß½1¦s†¾1Ås5 assert_eq!(acyclic_condensed_graph.node_count(), 2);Áäs85 assert_eq!(acyclic_condensed_graph.edge_count(), 1);Át8O assert_eq!(acyclic_condensed_graph.neighbors(B).collect::>(), vec![A]);ÁÖtRõ®+©u¸uò…ÕÁ¨š£›ïœä šœ›íñ,Åu¦¿,Èuà-ËuÀà-ÏuÓà-´væà-Æv ™–úêû¸†¾†ÿŸØuÖÞôuÅuÅuÈuÈuËuËuÏuÏu˜ƒ¾: \[Generic\] Compute a *minimum spanning tree* of a graph.Áá~=Ÿ- The input graph is treated as if undirected.Á£0ÔG Using Kruskal's algorithm with runtime **O(|E| log |E|)**. We actuallyÁØJR return a minimum spanning forest, i.e. a minimum spanning tree for each connectedÁ£€U component of the graph.Áù€•[ The resulting graph has all the vertices of the input graph (with identical node indices),Á™^Q and **|V| - c** edges, where **c** is the number of connected components in `g`.ÁøTÍ‚C Use `from_elements` to create a graph from the resulting iterator.ÁÑ‚FŸƒÎÿÙ êŸŸÊç,±ƒ×øËéƒ×šÏƒ„±šÏšÏ‹„ þÎÿž„‹ß/³„Ù¨0È„ žÎÿÿŸ´ƒ±ƒ±ƒ£Š< An iterator producing a minimum spanning forest of a graph.Áã‰?®Šê¡¡Êç,¾ŠðÎÿÎŠ¿Ô1ÕŠÙ !& æ2<”Ñ¢£¤¥¦§¾Š¾ŠïŠïŠ¡ ÎÿýŠ#ýŠ¡ Üô¦‹¦‹ ¡ Þò Ç‹HÇ‹ ¡ Òñ•Œ•Œ¡ âõºŒºŒ ¡ ÐŒ'ê©©Êç,ÕŒ¿Ô1…Ù¨0š ÿÓ1¼§Ô1Ö Ù ª«ÕŒÕŒè í¨¨Â° Ž(£Ž‡&7‡&7Ù õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÙ Ù9¨¨Ž¨«ÎÿŽ²›¨íñ,·›½¡®·›·›œ50 Return a node id that participates in the cycleÁÉ›3ˆœˆ&7ˆ&7½¡–ú¬œ¬Õó,±œ®–ú‘œ‘®ìD \[Generic\] Compute shortest paths from node `source` to all other.ÁâGªžB Using the [Bellmanâ€“Ford algorithm][bf]; negative edge costs areÁ®žEC permitted, but the graph must not have a cycle of negative weightsÁôžF( (in that case it will return an error).Á»Ÿ+çŸI On success, return one vec with path costs, and another one which pointsÁëŸLA out the predecessor of a node along a shortest path. The vectorsÁ¸ D) are indexed by the graph's node indices.Áý ,ª¡C [bf]: https://en.wikipedia.org/wiki/Bellman%E2%80%93Ford_algorithmÁ®¡Fõ¡Õ£+ù¡ ö£+‡¢ÿ‚1“¢" use petgraph::algo::bellman_ford;Á¬¢%ÄÎ,Ò¢ð¢ let mut g = Graph::new();Áô¢/ let a = g.add_node(()); // node with no weightÁ’£2 let b = g.add_node(());ÁÅ£ let c = g.add_node(());Áá£ let d = g.add_node(());Áý£ let e = g.add_node(());Á™¤ let f = g.add_node(());Áµ¤ g.extend_with_edges(&[ÁÑ¤ (0, 1, 2.0),Áì¤ (0, 3, 4.0),Á¥ (1, 2, 1.0),Á–¥ (1, 5, 7.0),Á«¥ (2, 4, 5.0),ÁÀ¥ (4, 5, 1.0),ÁÕ¥ (3, 4, 1.0),Áê¥ËÑ,ÿ¥‡¦2 // Graph represented with the weight of each edgeÁ‹¦5 //ÁÁ¦ // 2 1ÁÈ¦ // a ----- b ----- cÁÝ¦ // | 4 | 7 |Áö¦ // d f | 5Á§ // | 1 | 1 |Áª§ // \------ e ------/ÁÃ§Ü§ let path = bellman_ford(&g, a);Áà§#L assert_eq!(path, Ok((vec![0.0 , 2.0, 3.0, 4.0, 5.0, 6.0],Á„¨OK vec![None, Some(a),Some(b),Some(a), Some(d), Some(e)]ÁÔ¨N ))Á£© );Á½©@ // Node f (indice 5) can be reach from a with a path costing 6.ÁÎ©CZ // Predecessor of f is Some(e) which predecessor is Some(d) which predecessor is Some(a).Á’ª]4 // Thus the path from a to f is a <-> d <-> e <-> fÁðª7¨«G let graph_with_neg_cycle = Graph::<(), f32, Undirected>::from_edges(&[Á¬«J (0, 1, -2.0),Á÷« (0, 3, -4.0),Á‘¬ (1, 2, -1.0),Á«¬ (1, 5, -25.0),ÁÅ¬ (2, 4, -5.0),Áà¬ (4, 5, -25.0),Áú¬ (3, 4, -1.0),Á•ËÑ,¯·J assert!(bellman_ford(&graph_with_neg_cycle, NodeIndex::new(0)).is_err());Á»Mõ®+‰®˜®ÎÿÊÿîÂ ê°°Êç,¥®§Îÿ ¯ ø•1¬¯ÐÎÿÂ¯ Ù¨0Î¯ »šÏð¯¯ÎÿÿŸ®ô%·®¥®¥®‚±¯È!±ÁÈ!±ÚÈ!±±¯â ’¼Õ` Return `true` if the graph is bipartite. A graph is bipartite if it's nodes can be divided intoÁÍ¹c[ two disjoint and indepedent sets U and V such that every edge connects U to one in V. ThisÁ±º^F algorithm implements 2-coloring algorithm based on the BFS algorithm.Á»IÚ»¸1Þ»3™¼Îÿƒê³¨´‰µ´µ³ Êç,±¼ŠÃ-´¼‚Î× ·¼¤É/à¼©è,ë¼Îÿ·ƒõ¼ Îÿ´Î× ½–è,½÷1›½ µÈ-¯½‡ƒƒ¶½ øGƒÂ½þÎ× ƒÛ½²ÎÿƒÎ× ÿŸ»¼î Á¼±¼±¼´¼´¼·¼·¼±Ç»ÇÆÇ ÐÇšÈR@ Associated data that can be used for measures (such as length).ÁÖÇC¤È¸¸øGàžÈ±àžàžµÈ ÀàžàžÂÈàžàžÂàžÌÈ ¤ò/ÝÈ×àžçÈ¸½ú1ÈÑú1µÈ éú1ÂÈû1ÌÈ ¤ò/ÝÈ¦û1çÈñÈÜZººß¢ß¢Âß¢¬É øGß¢É±ß¢ß¢˜É Àß¢ß¢¥É‚ß¢öÈœß¢ºÉ×ß¢ÄÉß¢öÈöÈìÉ& A floating-point measure.ÁÎÉöÉ»»¸àž„Êï¾-ŽÊ»²þ1„Êï¾-ŽÊ¼½¼½™ÊœÊàž»»¼àž°Ê³Êàž»»½àžÊÊ¿ÀêÊíÊ¾¾¿“Ë–Ë¾¾ÀÄËÂÃäËçËÁÁÂÌÌÁÁÃÒ+ Ö+ÔÚËä (œÄÆËä (ÄÆéä 2ŸÄÇéä 2 ÄÇ`R ‡ÄÉŽ^ÛÄÊxÔIÄËxÚIÄËÇ%’ªÄÍû†› µÄÎïˆ¦ ÐÄÏ»+±þÄÐÕ» ³ÄÑòýÚ £ÄÒòýÚ ¤ÄÒÈZ÷¸ÄÓ5((22?'R R ^^mxƒB’’› › ¦ ¦ ±±» » ËÚ ê÷÷ç( \[Generic\] A* shortest path algorithm.Á+T Computes the shortest path from `start` to `finish`, including the total path cost.Á±W‰[ `finish` is implicitly given via the `is_goal` callback, which should return `true` if theÁ^ given node is the finish node.Áì"Z The function `edge_cost` should return the cost for a particular edge. Edge costs must beÁ“] non-negative.Áñƒ] The function `estimate_cost` should return the estimated cost to the finish for a particularÁ‡`` node. For the algorithm to find the actual shortest path, it should be admissible, meaning thatÁèc] it should never overestimate the actual cost to get to the nearest goal node. Estimate costsÁÌ` must also be non-negative.ÁÌ; The graph should be `Visitable` and implement `IntoEdges`.ÁÐ>Õ£+“ õ®+¡ÿ‚1© use petgraph::algo::astar;ÁÂáôá1å let a = g.add_node((0., 0.));Áƒ ! let b = g.add_node((2., 0.));Á¥ ! let c = g.add_node((1., 1.));ÁÇ ! let d = g.add_node((0., 2.));Áé ! let e = g.add_node((3., 3.));Á‹ ! let f = g.add_node((4., 2.));Á !ä1Ï (a, b, 2),Áê (a, d, 4),Áý (b, c, 1),Á (b, f, 7),Á£ (c, e, 5),Á¶ (e, f, 1),ÁÉ (d, e, 1),ÁÜËÑ,ï÷ æ1û50 // Edges with '*' are part of the optimal path.Á±3ßæ1åïæ1ìç1 // | 4* | 7 |Áš Ñç1³ // | 1* | 1* |ÁÎ —è1ç €G let path = astar(&g, a, |finish| finish == f, |e| *e.weight(), |_| 0);Á„J/ assert_eq!(path, Some((6, vec![a, d, e, f])));ÁÏ2õ®+‚ŠZ Returns the total cost + the path of subsequent `NodeId` from start to finish, if one wasÁŽ] found.Áì þÎÿÊÿåû ÍŽ¦ù ‰“êÕúÖ®\×ÐØ¿\ÙØÕ×ÙÖÊç,„ðë,‡‚¦ù Š‚ßö ‚åû ò1´ ©è,À ðåû ÙÆ×åû ÙÆõëºö0€Íö0…ðÍŽŸ„’ÍŽŸ„õßö §ð¦ù ÙÆ±¦ù ÙÆõßö Å¸ßö Ï‡ßö ÙÔÎÿÍŽ¦ù ßö åû 8î «ÓŸÅëŸÞ … ô „„‡‡ŠŠÚ!á!ÄêÛÛÊç,í!÷ß.ý! ºö0—"Íö0œ"ø”Ëƒñ!\wÜí!í!¨"(¨" ÄÚÚŠ¥ Õ"êÞÞÊç,Ú"÷ß.ù" ºö0“#Íö0˜#ø”ßàáÚ"Ú"¤#§#Äø”ÝÝ½÷ “$C–$Ä‰&7‰&7ø”ÊÿÊÿÝ¦$Ý¸Š«$Q±$’¦Â$“%@–%ÄŠ&7Š&7ø”Êÿù Ýª%ÝÈü «%¹Ã±%Ý+@ Compressed Sparse Row (CSR) is a sparse adjacency matrix graph.ÁCå+† ˆ ‰ ð Ä Ü í ó ˆ!˜!¸uTÃâä·YÃñâå¢‚ t ´»âç´ñê½âèÖ´–‘âéÖ´–’âéIndexÁ²¤âëIndexMutÁ¹¨âìÆÃç²âí§—ÛÈÒâî‰‹øðâð¾þ ”âñÙŒ‰‰âòâŽ §âóÇ%¼ªâõû†Å µâöïˆÐ Ðâ÷ë‡Û Ãâø‚Žê œâùŠââûž¢®âüÕ¸ ³âýÝøÖ¿âþ†u… á€ à—u â î¬Â©á$âƒ ÿ(³ýâ„ àû½ƒâ… º‰Í€ªâ’ SliceIterÁÆƒ «Nâ› ITTYYht t ‰–§"²²¹¹ÃÃÏÛé>øøþ þ ‰‰ K¼¼Å Å Ð Ð Û Û ê ê þD¢¢¸ ¸ ÉÖö$… … ¡-©©³³½½û"& Csr node index type, a plain integer.ÁÑ)„ ä ‡ ‡ º›.Ž¡‹ŽŽÎ& Csr edge index type, a plain integer.Á¤)× ë!ñâÊ0¨‹ £Œ ï ä Ž Œ Ž ‹ íñ,Ï¦¿,Òà-ÕÀà-ÙÓà-‰æà-› ÷¢ ÏÏÒÒÕÕÙÙ¬¯÷¢Š Š –úêû¸†¾†àC¨‘ £’ ï“ ä ” ’ ” ‘ “ íñ,å¦¿,ïà-ùÀà-ý×–úè×êûò×¾†÷¢• ååïïùùýýª‹&7‹&7÷¢÷¢ ³ • –úêû¸†¾†´Ï$¨— £˜ ï™ ä š ˜ š — ™ íñ,Ô¦¿,×à-ÚÀà-ÞÓà-‚æà-” ÷¢› œ ÔÔ××ÚÚÞÞÄ Create an empty `Csr`.Á¥Ë÷¢– – «ÃãAa Create a new [`Csr`] with `n` nodes. `N` must implement [`Default`] for the weight of each node.Á¬d•O [`Default`]: https://doc.rust-lang.org/nightly/core/default/trait.Default.htmlÁR [`Csr`]: #struct.Csr.htmlÁô–Õ£+ž ö£+° use petgraph::csr::Csr;ÁÀÄÎ,à‚) let graph = Csr::::with_nodes(5);ÁŠ," assert_eq!(graph.node_count(),5);Á»%" assert_eq!(graph.edge_count(),0);Áå% assert_eq!(graph[0],0);Á— assert_eq!(graph[4],0);Á·õ®+×ê ÷¢– – œ–úœœ –úêû¸†¾†–µõ¾œ È! ÁÈ! ÚÈ! œ Ï¶&¨Ÿ £ ä ¡ ¡ Ÿ íñ,»¦¿,¾‚æ·Áâæ·ë êÚ¢ »»¾¾ÁÁæ"¯3 Create a new `Csr` from a sorted sequence of edgesÁü6·I Edges **must** be sorted and unique, where the sort order is the defaultÁ¿L8 order for the pair *(u, v)* in Rust (*u* has priority).Á;Ð# Computes in **O(|E| + |V|)** time.ÁØ&Õ£+ƒ ö£+• Ò³2¥ ÄÎ,Å ç / let graph = Csr::<(),()>::from_sorted_edges(&[Áï 2$ (0, 1), (0, 2),Á¦!', (1, 0), (1, 2), (1, 3),ÁÒ!/ (2, 0),Á†" (3, 1),Áª"ËÑ,Î"õ®+Ú"í"Œ&7Œ&7ú÷ ÷ž ;£ £ Œ#ž ‚ÁÖÿ"×ÁÖÍ#ƒÁÖêûÕ#+ÁÖêû…æ·é#¯·2$¢ –úêûæ·ÁÖ‘9…#ÿ"ÿ"ç$¢ È!¤ ÁÈ!¤ ÚÈ!¤ ¤ ¢ »×Ù4¢ È!¥ ÁÈ!¥ ÚÈ!¥ ¥ ¢ Œéó9$¨§ £¨ ï© ä ª ¨ ª § © íñ,ø9¦¿,û9à-þ9Àà-‚:Óà-¦:æà-¸: ÷¢« ¬ ® ¯ ° ± ² ³ ´ ¶ · ¸ ¹ º ø9ø9û9û9þ9þ9‚:‚:É:!Ð: &7&7÷¢¦ Û:¦ ¡´Ü:“;!š; Ž&7Ž&7÷¢¦ ¥;¦ ¬ –úêû¸†¾†¦;·ÁÍ\25 Iterator element type is `EdgeReference`.Á„]8È]›&7›&7÷¢¾†ó ó „9ø …âù ô%âú Ÿâû Ôâ}Ñ]³¤Ë£›&7êû¸†¾†¦ Î]¦ º –úêû¸†¾†Ï]š±Õ]€c@ü¼ £½ ï¾ ä ¿ ½ ¿ ¼ ¾ ¦¿,‰cà-ŒcÀà-c‡¾†”cà¦À …c…c‰c‰cŒcŒcccÇcÊcœ&7œ&7à¦à¦» Ðc» À ¼ üêû¸†¾†Ñcøc?üÂ £Ã ïÄ ä Å Å Â Ä Ã ¦¿,dà-„dÀà-ˆd¬Û2ŒdÌ§ýcýcdd„d„dˆdˆd¼d0üÇ ïÈ £É ä Ê È Ê Ç É ‚˜óÅdÃ-ÉdÀà-Ìdý˜óûdˆ!ˆ!…¥!…âŽ!ô%â!–â!Š&â‘!ÔâÌËúwZÓ3PÇ ü–ƒ˜ó¾†Ë ÁdÁdÅdÅdÉdÉdÌdÌd¯f Access the edgeâ€™s weight.Á‹e¯e3 **NOTE** that this method offers a longer lifetimeÁ·e65 than the trait (unfortunately they don't match yet).Áòe8¶f&7&7Ñß2Ç ü–ƒÆ ½fÆ Ë Ç ü˜ó–ƒ¾†¾fìf<üÍ £Î ïÏ ä Ð Ð Í Ï Î ¦¿,õfà-øfÀà-üfÓà-·gæà-Ég ¸¨Ñ Ò Ó Ô Õ Ö × ñfñfõfõføføfüfüfÚgßgÌ Ì ¾†ûg€hÌ Ì ˜hhÌ Ì êû®h ±hž&7ž&7¸¨¸¨«Ì ¸hÌ Ô Í üêû¸†¾†¹hïh òhŸ&7Ÿ&7¸¨¨æ2Ì ùhÌ Õ Í üêû¸†¾†úh°i³i &7 &7¸¨ &7êûÌ ºiÌ Ö Í üêû¸†¾†»içiêi¡&7¡&7¸¨¸¨¬Ì íiÌ × Í üêû¸†¾†îi¢j5üÙ £Ú ïÛ ä Ü Ú Ü Ù Û ¦¿,«jà-®jÀà-²jÓà-æjæà-øj ¤©Ý Þ §j§j«j«j®j®j²j²j‰k ŽkØ Ø ˆ!ˆ!…¥!…âŽ!ô%â!–â!Š&â‘!ÔâÌËúwZÓ3PÙ üêû¸†¾†·k(ºk¢&7¢&7¤©õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê¤©Ù9Ø ¿kØ Þ Ù üêû¸†¾†ÄkÿkÞ È!ß ÁÈ!ß ÚÈ!ß ß Þ ÏŽ¢n-¨á £â ïã ä ä â ä á ã íñ,§n¦¿,ªnà-nÀà-±nÓà-Þnæà-ðn ÷¢å æ §n§nªnªnnn±n±no†o à à –úšoŸo à à êû²oCüè ¨é £ê ïë ä ì è ê ì é ë ŠÃ-»oÃ-¾o°Ã-ÁoÃÃ-ÅoêÃ-„pýÃ-–p ªí î ï ·o·o»o»o¾o¾oÁoÁoÅoÅo§p¬pç ç ˆ!ˆ!…¥!…âŽ!ô%â!–â!Š&â‘!ÔâÌËúwZÓ3Pè ü–ƒœƒ¢ƒØpÝpç ç ð ð “*õ €gâö …â÷ œgâø µâù ‘9âú Ÿâû Ôâ”xÒÆþÒaè ü–ƒœƒ¢ƒ‘q0”qªª‹ç ç ï è üƒ–ƒœƒ¢ƒ¤qâs0ísüñ £ò ïó ä ô ò ô ñ ó ¦¿,€tà-‡tÀà-‹têûñ üƒt¾†ñ ütØö2uìö2ütð ð “*õ €gâö …â÷ œgâø µâù ‘9âú Ÿâû Ôâ”xÒÆþÒañ üêû¸†¾†”xÒÆþÒaõ ö ÷ ø ù ú û üsüs€t€tñ ‡t‡t‹t‹tñ ™t™tâð ð ¾†ºtºtâð ð Ìt*Ìtâð ð ´»´»¢‚ ¶»ŸŒ)·»÷Œ)‹mŸÉ™A3ÈÒÈÒ§—ËÒÏ£ýMÌÒÙ ýMñ“^¯6¦ñ üütütâð ð ñ üàÆuuâð ð ñ üÚì±u9±uâð ð ê½ê½´ñí½š±ç3î½ª±ç3ï½…ç3ð½°ç3ñ½ïñç3˜U`×qÑ«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+ñ ü¾†«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+ñ üêûðuðuâð ð ¾¸ˆv>üý £þ ïÿ ä € € ý ÿ þ ¦¿,‘và-”vÀà-˜vÓà-Õvæà-çv Š« ‚ vv‘v‘v”v”v˜v˜vøv ývü ü ˆ!ˆ!…¥!…âŽ!ô%â!–â!Š&â‘!ÔâÌËúwZÓ3Pý üêû¸†¾†¦w(©w£&7£&7Š«õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êŠ«Ù9ü ®wü ‚ ý üêû¸†¾†³w}:ü„ ¨… £† ï‡ ä ˆ ˆ … ‡ „ † ŠÃ-¦}Ã-©}°Ã-¬}ÃÃ-°}êÃ-æ}ýÃ-ø} ‰¬‰ Š ¢}¢}¦}¦}©}©}¬}¬}°}°}‰~ Ž~ƒ ƒ ó ó „9ø …âù ô%âú Ÿâû Ôâ}Ñ]³¤Ë£„ ü–ƒœƒ¢ƒ°~.³~‰¬‰¬·‰¬Ñƒ ƒ Š „ üƒ–ƒœƒ¢ƒ¹~š±¿~€2¨Œ £ ïŽ ä Œ Ž íñ,…¦¿,ˆà-‹Àà-Óà-Áæà-Ó ÷¢ ‘ ……ˆˆ‹‹äé‹ ‹ ¾†…€Š€‹ ‹ À€Í€Û€2¨” £• ï– ä — • — ” – íñ,à€¦¿,ã€à-æ€Àà-ê€Óà-œæà-® ÷¢˜ ™ š à€à€ã€ã€æ€æ€ê€ê€¿Ä“ “ °ŠÛ"Þ ¤&7¤&7÷¢°Š“ è“ ™ –úêû¸†¾†éÀ‚(Ã‚ ¥&7¦&7¥&7÷¢¦&7÷¢´“ Í‚“ š –úêû¸†¾†Î‚àÔ‚²ƒÆƒ Ä„+ü ä ž ž ‚Þ¬Í„âÞ¬þ„ £Ÿ ¡ É„É„Í„Í„… ”…œ œ Þ¬¯…(²…§&7§&7£õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê£Ù9œ ·…œ üÞ¬¼…‡†-Š† ¨&7¨&7£šòœ ”†œ ¡ üÞ¬•†Þ†>ü£ ¨¤ £¥ ï¦ ä § ¥ § ¤ ¦ £ ŠÃ-ç†Ã-ê†°Ã-í†ÃÃ-ñ†êÃ-«‡ýÃ-½‡ Þ¨ © ã†ã†ç†ç†ê†ê†í†í†ñ†ñ†Î‡Ó‡ ¢ ¢ ˜!˜!î ›!ŸâOMÐÚ¡Ýðª£ ü¢ƒ¨Š6, Return an iterator of all neighbors of `a`.Á÷‡/«ˆ2 - `Directed`: Targets of outgoing edges from `a`.Á³ˆ5B - `Undirected`: Opposing endpoints of all edges connected to `a`.ÁíˆE·‰øÖ2¿‰2* Iterator element type is `NodeIndex`.Áö‰-«Š ÞÞ·ÞÄ¢ ¢ © £ üƒ–ƒœƒ¢ƒµŠš±»Šº‹6¨« £¬ ï ä ® ¬ ® « íñ,¿‹¦¿,Â‹à-Å‹Àà-É‹Óà-ÿ‹æà-‘Œ ÷¢¯ ° ± ¿‹¿‹Â‹Â‹Å‹Å‹É‹É‹¢Œ¥Œ ©&7©&7÷¢ª °Œª ¯ –úêû¸†¾†±ŒæŒ,éŒª&7ª&7÷¢÷¢·ª òŒª ° –úêû¸†¾†óŒš±ùŒ±/´ «&7«&7÷¢Ô™3ª ¿ª ± –úêû¸†¾†ÀéÞÆ€Ž=¨³ £´ ïµ ä ¶ µ ´ ¶ ³ íñ,…Ž¦¿,ˆŽà-‹ŽÀà-ŽÓà-ÌŽæà-ÞŽ ÷¢…Ž…ŽˆŽˆŽ‹Ž‹ŽŽŽîŽ=¨¸ £¹ ïº ä » ¸ º ¹ » íñ,óŽ¦¿,öŽà-ùŽÀà-ýŽÓà-ºæà-Ì ÷¢¼ ½ óŽóŽöŽöŽùŽùŽýŽýŽÝâ· · –úó(ö€¬&7¬&7÷¢¾†¬&7–ú· ü· ½ –úêû¸†¾†ýéÞƒÎ@¨¿ £À ïÁ ä Â À Â ¿ Á íñ,Ó¦¿,Öà-ÙÀà-ÝÓà-‘æà-¯‘ ÷¢Ã ÓÓÖÖÙÙÝÝÀ‘4Ã‘ €&7&7÷¢¾†&7–ú¾ Í‘¾ Ã –úêû¸†¾†Ò‘éÞØ‘«’*¶’ä Å Å º›.Æ’˜®ÜÌDäÀ|¸Æ Ç Æ’Æ’Ü’Ü’âÄ Ä ±Œ ñ’ñ’âÄ Ä ‘‘’Ö´ß<Ž¤öÆÉ¡‹‰“)ä É É º›.Ž“Í›.Á“ ˜®Ê Ë Ì Ž“Ž“Ò“ ×“È È ¡‹ò“(õ“®&7®&7˜®õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê˜®Ù9È ú“È Ë ¡‹ÿ“Ë”-Î” ¯&7¯&7˜®šòÈ Ø”È Ì ¡‹Ù”Ÿ•DüÎ ¨Ï £Ð ïÑ ä Ò Ð Ò Ï Ñ Î ŠÃ-¨•Ã-«•°Ã-®•ÃÃ-²•êÃ-ò•ýÃ-„– Ô®Ó Ô ¤•¤•¨•¨•«•«•®•®•²•²••–š–Í Í ñÖÅ–2È–Ô®Ô®ãÍ Í Ô Î üƒ–ƒœƒ¢ƒÙ–é—2¨Ö £× ïØ ä Ù Ø × Ù Ö íñ,î—¦¿,ñ—à-ô—Àà-ø—Óà-ª˜æà-¼˜ ÷¢Ú î—î—ñ—ñ—ô—ô—ø—ø—Í˜Ð˜ °&7°&7÷¢Õ Û˜Õ Ú –úêû¸†¾†Ü˜“™2¨Ü £Ý ïÞ ä ß Ý ß Ü Þ íñ,˜™¦¿,›™à-ž™Àà-¢™Óà-Ô™æà-æ™ ÷¢à ˜™˜™›™›™ž™ž™¢™¢™÷™ ü™Û Û ¸†ê+î+ð Ëä (œá ã Ëä (á ã éä 2Ÿá ä éä 2 á ä `R ‡á æ Ž^Ûá ç xÔIá è xÚIá è Ç%’ªá ê ïˆ› Ðá ë »+¦þá ì Õ° ³á í ÈZÍ¸á î òýé £á ï òýé ¤á ï 5((22?'R R ^^mxƒ7’’› › ¦¦° ° ÀÍÚé ýÿ0 \[Generic\] Dijkstra's shortest path algorithm.Áõ3©H Compute the length of the shortest path from `start` to every reachableÁK node.Áù ƒH The graph should be `Visitable` and implement `IntoEdges`. The functionÁ‡KH `edge_cost` should return the cost for a particular edge, which is usedÁÓK8 to compute path costs. Edge costs must be non-negative.ÁŸ;ÛN If `goal` is not `None`, then the algorithm terminates once the `goal` node'sÁßQ cost is calculated.Á±É5 Returns a `HashMap` that maps `NodeId` to path cost.ÁÍ8Õ£+† ö£+”ÿ‚1 use petgraph::algo::dijkstra;Á¹!ÄÎ,Û use std::collections::HashMap;Áù"œîƒ1 8¯„1Ù6î„1–…1°¾…1Ðæ…1ðŽ†1 ¶†1° Þ†1Ð , // z will be in another connected componentÁð / let z = graph.add_node(());Á À ‘‡1Ä ¸‡1ã Ð‡1ó è‡1ƒ€ˆ1“˜ˆ1£ã»1³°ˆ1ÃÈˆ1Óàˆ1ãËÑ,òÇ¼1ú ‰1› Ö‰1¼ ÿ‰1Ý þ0 let expected_res: HashMap = [Á‚ 3 (a, 3),Á¶ (b, 0),ÁÇ (c, 1),ÁØ (d, 2),Áé (e, 1),Áú (f, 2),Á‹ (g, 3),Áœ (h, 4)Á! ].iter().cloned().collect();Á½$* let res = dijkstra(&graph,b,None, |_| 1);Áâ- assert_eq!(res, expected_res);Á"> // z is not inside res because there is not path from b to z.Á³Aõ®+õ„ÎÿÊÿÔúÍŽçïêñ úò Ðó ñ ò ó Êç,ðë,‚øÙ“ò1– ©è,¢ ºö0¼Íö0Á²˜2ÎÍŽŸ„õøÙã¸øÙí‡øÙ÷ð ÎÿÍŽøÙ8›î ©Üù¿ëŸà ““ø+( Simple graphviz dot file format output.Á+€,ƒŠŒŽ®µ¶¼½¤!Ÿ<–%ô ö xBýGô ÷ WriteÁKÕGô ø ‰‹lðô ú Ç%rªô û û†{ µô ü ¾† ”ô ý ì‘×ô þ ÙŒ›‰ô ÿ ô‹¯þô €‚ŽÇ œô õ&Ö½ô ‚1 <6BBKKXˆllrr{ { † † ‘‘››¯¯Ç Ç ÖÖÔ = `Dot` implements output to graphviz .dot format for a graph.Áã@¤B Formatting and options are rather simple, this is mostly intendedÁ¨E( for debugging. Exact output may change.Áî+š # ExamplesÁžõ®+±ÿ‚1¹®¥+Ò%ø' let mut graph = Graph::<_, ()>::new();Áü* graph.add_node("A");Á§ graph.add_node("B");ÁÀ graph.add_node("C");ÁÙ graph.add_node("D");Áò‘‡1‹ËÐ,ª (1, 2), (1, 3),ÁÊ (2, 3),ÁâËÑ,òúD println!("{:?}", Dot::with_config(&graph, &[Config::EdgeNoLabel]));ÁþGÆ, // In this case the output looks like this:ÁÊ/ßæ1ú // digraph {Á // 0 [label="\"A\""]Á’ // 1 [label="\"B\""]Á¯ // 2 [label="\"C\""]ÁÌ // 3 [label="\"D\""]Áé // 0 -> 1Á† // 0 -> 2Á˜ // 0 -> 3Áª // 1 -> 2Á¼ // 1 -> 3ÁÎ // 2 -> 3Áàä®+òûI // If you need multiple config options, just list them all in the slice.ÁÿLõ®+Ì ß ü„ê…„…ç¿-ç ¿ü.÷ ò.Œ „ü„üÎ 8€†„üŒ8¨¸„üÎ 8Â„üÎ 8ƒƒ–o†8ô ‡¹oô ˆÇoô ‰âoô <ñ‹0<ÊÃV„üÂ<ñ‹0<ÊÃV†‡ˆ‰ã ã ç ç ¦ ¦ ô ƒƒÂ´ ´ ô ƒƒ„üÌÎ 8Î ô ƒƒ„üíà„õà„è„„üŒ8Œô ƒƒ„üíû…õû…è„„üÉÐô Ýøÿô ÔÖ3ŒŸ¦ô ÕÖ3¾üê‘‘ç¿-Ç×Ââ¿ü.íò.‚ ƒƒ–o†8ô ‡¹oô ˆÇoô ‰âoô <ñ‹0<ÊÃVüÂ’“–ÃÃÇÇâ > Create a `Dot` formatting wrapper with default configuration.Áœ Aé ÂºØ3’üÂ8í ÷:= Create a `Dot` formatting wrapper with custom configuration.Á²@þÂüÌºØ3“üÂ8Š¹o”ä“È!”ÁÈ!”ÚÈ!””“Î„ü“È!•ÁÈ!•ÚÈ!••“é…¶Û½Â½Û3üíà„õà„è„üüíû…õû…è„üºØ3–üÂ8Ø¹oêÇoˆâoÊñü˜ê™˜™ç¿-úú¿-• ò.¡¿ü.¶ƒƒ–o†8ô ‡¹oô ˆÇoô ‰âoô <ñ‹0<ÊÃV˜üÂšööúúÐîÓ ô ·&7¸&7¹&7·&7Áß3Â¸&7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S¹&7ê–ð–Ôž—¦q›°qœºqÄqžœž›ö—‚ê–Ý‚ð–á‚ö–å‚ü–éœÂ‰ ò.™¿ü.®”ÂÍ ú¿-Ù ðÂï&Âñö–ôÂòü–…±&7²&7ðê–±&7ö–²&7³&7ð±±³&7õ±±Ôž²&7£F±&7²&7ê–ºä3õÔžÞ´&7µ&7ðð–´&7ü–µ&7¶&7ðÖÑ¶&7õÖÑÔžµ&7÷F´&7µ&7ð–Ðå3õÔž²š˜üÂê–ð–ö–ü–÷ÿŸ…“¿ƒ·ØƒÑÝÝááååééŒ šÈ!ŸÁÈ!ŸÚÈ!ŸŸš¨ò¢&šÈ! ÁÈ! ÚÈ! šÀýÏ('ü¢ê£¢£ç¿-Ø(¿ü.„)ò.™) ã3®) Çã3¾) ýG‹ÀÜ)ýGî½ý)š°¤Ô(Ô(Ø(Ø(‘*4”*º&7»&7¼&7º&7š°»&7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S¼&7Ôž¡˜*¡¤¢üÂ™*Ÿ*î*¤È!¥ÁÈ!¥ÚÈ!¥¥¤Û¢ý*¤È!¦ÁÈ!¦ÚÈ!¦¦¤¾£•+%ü¨ê©¨©ç¿-ž+¿ü.È+ò.Ý+ ã3ò+ Çã3‚, øG‹À , øGî½¿, ó°ªš+š+ž+ž+Ñ,4Ô,½&7¾&7¿&7½&7ó°¾&7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S¿&7Ôž§Ø,§ª¨üÂÙ,ß,Ó-ªÈ!«ÁÈ!«ÚÈ!««ª‰ªù-ªÈ!¬ÁÈ!¬ÚÈ!¬¬ªŸª¾.Å.ô ›¯¯¾©-Í.Ì±éñ³1¸s|®°âþÌ±¾.Å.ô âþÌ±®âþÍ.Í.Ð.Ð.ô âþÕ.!›²²¾©-Ú.ÕGâþ„/ Ì±³´Ú.Ú.–//™/ À&7Á&7À&7Ì±Á&7Ôž±£/±³âþ¨/ ®/ª000 Â&7Â&7Ì±Ôž±¸0±´âþ½0»êÃ0Š3‘3ô å··‚°²™3‚²k`<òT4ø¶¸°²‚²Š3‘3ô °²‚²µµ¶°²™3™3œ3œ3ô µµ°²¡3#åººÆö3¦3ýG°²Ò3‚²»¦3¦3æ34é3Ã&7Ä&7Å&7Ã&7‚²Ä&7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SÅ&7Ôž¹í3¹»°²î3ô3ë5ò5ô å¾¾Æö3û5º²™øî.ð‡½¿°²º²ë5ò5ô °²º²¼¼½°²û5û5þ5þ5ô ¼¼°²ƒ6$åÁÁÆö3ˆ6øG°²µ6 º²Âˆ6ˆ6Ç64Ê6Æ&7Ç&7È&7Æ&7º²Ç&7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SÈ&7ÔžÀÎ6ÀÂ°²Ï6Õ6´,¸, áâ‚„’¤¬°ÆÒ¶ Å Ü ã ý ž¨µâí°×îíî…"ú!Ú!Û!"§"º!»!“" ñÃÄŸ–%ÃÅ+ÔIÃÆ+ÚIÃÆŸ:Ë%ÃÇÖ´Q‘ÃÈÖ´Q’ÃÈÚ l¿ÃÉŠ 2„¤ÃË¦ 2‹¨ÃÌÆ•ç²ÃÍÞ ¦ž(ÃÎ¬Âºá$ÃÐŽÁÄ È$ÃÑÿ(ÏýÃÒÎ¶ÙÌ$ÃÓÎ¶ÙÍ$ÃÓàûãƒÃÔ¹¶õÉ$ÃÕ¹¶õÊ$ÃÕ×Âÿ ç$ÃÖ¤Ó¦ ÃØ¤Ó¦¡ÃØ¹Ô° ²ÃÙÿÓ¿«ÃÚÿÓ¿¬ÃÚÄøÜ ½ÃÛÇ%ùªÃÜïˆ• ÐÃÞ»‰ ÙÃßô‹³þÃà¸uòXÃÃÑ +5 :DQbly"„„‹‹••¡ ¦²XººÄ Ä ÏÏÙÙããõõÿ ÿ ‘7¦¦° ° ¿¿ÏÜ ëù†@• • ³³‚, The default integer type for graph indices.Áù/H `u32` is the default to reduce the size of the graph's data and improveÁ©K performance in the common case.Áõ#™B Used for node and edge indices in `Graph` and `StableGraph`, usedÁE for node indices in `Csr`.Áã‹ ÇND Trait for the unsigned integer type used for node and edge indices.ÁGå< Marked `unsafe` because: the trait must faithfully preserveÁé? and convert index values.Á©Ø ââï¾-ã¤ò/êÔIàžô«àžû½ú1 àžŽâï¾-ã¤ò/ê¼Œ4ôÏŒ4û½ú1 ìŒ4ŽãäåãäåœŸàžââãàžæü£º½É&7É&7àžâÃâäàžÄØÛàžââåàžíçèé© ¬ ææçæü° î ñ Ê&7Ê&7æ÷ æèø ¶ ¹ ææéì ëìí¦©êêëæüòõË&7Ë&7êûêìüÄÇêêíøïðñ² µ îîïæü¹ þ Ì&7Ì&7î‡îðˆÐÓîîñ„óôõ½ÀòòóæüÄˆ‹Í&7Í&7ò‘òô’ÚÝòòõ“!ä ÷÷º›.˜Í›.œ óŠøùúû˜˜ÉÐóŠöö¤ôæüÔ¦óŠööùå³ôûóŠööú¡‹º$½ ÃóŠ¯‹ööû¡‹È„.ä ýýº›.‰Í›. óŠþ‰‰¹¼¡‹óŠüüþ¡‹éÞÁò1ä €€º›.÷øG¡‹û óŠ÷÷ª4Î&7Ï&7Ð&7Î&7óŠÏ&7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SÐ&7Ôžÿ±ÿ¡‹²¸½?" Short version of `NodeIndex::new`Á—%Ä óŠä ƒƒº›.ÏÍ›.Ó üù…ÞÏÏÂ?" Short version of `EdgeIndex::new`Áœ%É ¯‹ä ……º›.ÔÍ›.Ø ®†…ãÔÔ§!ä ‡‡º›.¬Í›.° ¯‹ˆ‰Š‹¬¬Ýä¯‹††õæüèºÁ¯‹††ìéÇøF An invalid `EdgeIndex` used to denote absence of an edge, for exampleÁúI to end an adjacency list.ÁÈÿ¯‹††Š¡‹¾$Á Ã¯‹óŠ††‹¡‹Ìˆ.ä º›.Í›.‘ ¯‹Ž½À¡‹¯‹ŒŒŽ¡‹éÞÅö1ä º›.û‚›4ÿ ¯‹‘ûû®4±Ñ&7Ò&7Ó&7Ñ&7¯‹Ò&7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SÓ&7Ôžµ‘¡‹¶¼´# º# Ã¸Ò#È%!£•ä –•–‚ÔµÍ%3Ð%×Ôµ÷%‡Þ¬†&¢µ„"Í%Í%Ð%Ð%¹&"¨˜ä ™˜™íñ,¾&3Á&¡3Å& ä´š¾&¾&Á&Á&¸'8N Accessor for data structure internals: the first edge in the given direction.Áâ&Q¿' Ô&7Ô&7ä´Ü”‘—É'—š–úÞ¬Ê'•‡Ð'´*!£œä œà§4¹*3¼*ý§4ã*¨4ò*âµ‘"¹*¹*¼*¼*«+"£Ÿä Ÿà§4°+3³+¡3·+ âµ¡¢£°+°+³+³+ª,8N Accessor for data structure internals: the next edge for the given direction.ÁÔ+Q±, Õ&7Õ&7âµÜ”‘ž»,ž¡ÔµÞ¬¼,•‡Â,µ-% Return the source node index.Á-!¼-Ö&7Ö&7âµˆžÃ-ž¢ÔµÞ¬Ä-£.% Return the target node index.Áý-!ª.×&7×&7âµˆž±.ž£ÔµÞ¬².ùE5W `Graph` is a graph datastructure using an adjacency list representation.Áé.ZÄ/ `Graph` is parameterized over:ÁÈ/"ë/E - Associated data `N` for nodes and `E` for edges, called *weights*.Áï/H0 The associated data can be of arbitrary type.Á¸03U - Edge type `Ty` that determines whether the graph edges are directed or undirected.Áì0XC - Index type `Ix`, which determines the maximum size of the graph.ÁÅ1FŒ2K The `Graph` is a regular Rust collection and is `Send` and `Sync` (as longÁ2N% as associated data `N` and `E` are).Áß2(ˆ3M The graph uses **O(|V| + |E|)** space, and allows fast node and edge insert,ÁŒ3P- efficient graph search and graph algorithms.ÁÝ30M It implements **O(e')** edge lookup and edge and node removals, where **e'**ÁŽ4P% is some local measure of edge count.Áß4(0 Based on the graph datastructure used in rustc.Áˆ53¼5E Here's an example of building a graph with directed edges, and belowÁÀ5H? an illustration of how it could be rendered with graphviz (seeÁ‰6B" [`Dot`](../dot/struct.Dot.html)):ÁÌ6%ò6õ®+ö6ÿ‚1þ6—7+ let mut deps = Graph::<&str, &str>::new();Á›7.$ let pg = deps.add_node("petgraph");ÁÊ7'' let fb = deps.add_node("fixedbitset");Áò7*& let qc = deps.add_node("quickcheck");Á8)" let rand = deps.add_node("rand");ÁÇ8%" let libc = deps.add_node("libc");Áí8% deps.extend_with_edges(&[Á“9 (pg, fb), (pg, qc),Á±9* (qc, rand), (rand, libc), (qc, libc),ÁÍ9-ËÑ,û9õ®+ƒ:‹:K ![graph-example](https://bluss.github.io/ndarray/images/graph-example.svg)Á:NÞ: ### Graph IndicesÁâ:ø:C The graph maintains indices for nodes and edges, and node and edgeÁü:FJ weights may be accessed mutably. Indices range in a compact interval, forÁÃ;M: example for *n* nodes indices are 0 to *n* - 1 inclusive.Á‘<=Ï<Q `NodeIndex` and `EdgeIndex` are types that act as references to nodes and edges,ÁÓH4 removing an edge shifts the index of the last edge.Áþ>7. * Adding nodes or edges keeps indices stable.Á¶?1è?W The `Ix` parameter is `u32` by default. The goal is that you can ignore this parameterÁì?ZI completely unless you need a very big graph -- then you can use `usize`.ÁÇ@L”AT * The fact that the node and edge indices in the graph each are numbered in compactÁ˜AWN intervals (from 0 to *n* - 1 for *n* nodes) simplifies some graph algorithms.ÁðAQÂBQ * You can select graph index integer type after the size of the graph. A smallerÁÆBT" size may have better performance.Á›C%ÁCG * Using indices allows mutation while traversing the graph, see `Dfs`,ÁÅCJ and `.neighbors(a).detach()`.ÁD!²DF * You can create several graphs using the equal node indices but withÁ¶DI& differing weights or differing edges.Á€E)ªEC * Indices don't allow as much compile time checking as references.Á®EFõE„F¨¥£¦ï§ä ¨¨¥§¦íñ,ŠF¦¿,Fà-F Àà-ŸFò…1²Î"©©ª«ŠFŠFFFF FÎùŸFŸFµFµFÃ¤¤‚ºÒFÒFÃ¤¤»ïFïFÃ¤¤¾¸…H& A `Graph` with directed edges.Á‡G"ªGJ For example, an edge from *1* to *2* is distinct from an edge from *2* toÁ®GM *1*.ÁüGŽH¨£®ä ¯®¯íñ,–H¦¿,™Hƒ¹2œH¤¤·y©äyÃª‘9Ã«ÔÃ1²Î"©–úêûÎùæ·–H–H™H™HœHœHÒI&! A `Graph` with undirected edges.ÁÊH$ïHJ For example, an edge between *1* and *2* is equivalent to an edge betweenÁóHM *2* and *1*.ÁÁIÛI¨±£²ä ³±²³íñ,ãI¦¿,æIƒ¹2éI¤¤·y©äyÃª‘9Ã«ÔÃ1²Î"©–úêû…üæ·ãIãIæIæIéIéIÞJ;A The resulting cloned graph has the same graph indices as `self`.Á™JD¨µ£¶ï·ä ¸¸µ·¶íñ,ãJ¦¿,æJà-éJÀà-íJæà-ñJ â2§Kõ2µKò…¹ºãJãJæJæJéJéJíJíJÂKÅKØ&7Ø&7ò…ò…´ËK´¹–úêû¸†¾†ÌKèL$ëL Ù&7Ú&7Ù&7ò…Ú&7ò…´öL´º–úêû¸†¾†ûLŽ MˆN5¨¼£½ï¾ä ¿½¿¼¾íñ,N¦¿,Nà-“NÀà-—NøG–úËN øGêûÞN Óà-òNæà-„O ò…ÀNNNN“N“N—N—N•O4˜OÛ&7Ü&7Ý&7Û&7ò…Ü&7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SÝ&7Ôž»œO»À–úêû¸†¾†O£OÚSÀÈ!ÁÁÈ!ÁÚÈ!ÁÁÀ‰ºðUÀÈ!ÂÁÈ!ÂÚÈ!ÂÂÀì¿‰VÀÈ!ÃÁÈ!ÃÚÈ!ÃÃÀ¿¸WÀÈ!ÄÁÈ!ÄÚÈ!ÄÄÀÒÃÑWÀÈ!ÅÁÈ!ÅÚÈ!ÅÅÀÃ¬X±XÃåÇÇÆö3¶Xë+Ù›çA@nïÈÉÌÍÏÐÉÊË°²°²ë+ÍÎ°²ë+Ð¶X¶X¿X¿XÃÆÆÈ°²¿X¿XÃ°²°²ë+ÆÆÉ°²ÄXÄXÃÆÆ°²ÇXÇXÃÆÆ°²ÏXÏXÃÆÆÌ°²ÏXÏXÃ°²ë+ÆÆÍ°²ÓXÓXÃÆÆ°²ÛXÛXÃÆÆë+ÛXÛXÃÆÆë+èX òX©YD¬YÃÞ&7Þ&7êâÆÈÉÞ{ÊÎÃËÏÃÌÍÿ{ÎÎÃÏÐ³Ù›çA@nïÞ&7°²åÓÓÀYÆö3¸YÒ°²ää»Yš±ÊYª±ÔY¸Y¸Yë\ ¨Õ£ÖÕÖíñ,ð\¦¿,ó\¬é×ð\ð\ó\ó\ò^* Create a new `Graph` with directed edges.Á’]-Ä]Q This is a convenience method. Use `Graph::with_capacity` or `Graph::default` forÁÌ]TE a constructor that is generic in all the type parameters of `Graph`.Á¥^Hù^¬éÔÔ×–úêû‡`"¨Ù£ÚÙÚíñ,Œ`¦¿,`€íÛŒ`Œ```’b, Create a new `Graph` with undirected edges.Á°`/ä`óç4ì`TÐè4ÅaH™b€íØØÛ–úêû²c&¨Ý£Þïßä ààÝßÞíñ,·c¦¿,ºcà-½cÀà-ÁcÓà-çcæà-ùc ò…áâãäåæèêìíïñóô÷øùúûüýþÿ€ ‚ ƒ „ … † ‡ ˆ ‰ Š ‹ Œ Ž “ ” • – — ˜ ™ š › œ ž ¢ ¤ ¦ ® ´ ·c·cºcºc½c½cÁcÁcÀd8. Create a new `Graph` with estimated capacity.ÁŠd1Çd ò…ÜÜ´ùäyÕd‘9ãd€g!4 Return the number of nodes (vertices) in the graph.Á™f7Õf´Ñ2Ýf‡g ß&7ß&7ò…Ü’gÜÒÈ“g¤h!) Return the number of edges in the graph.ÁÈg,ùg´Ñ2h«h à&7à&7ò…Ü¶hÜñ·h¯i!- Whether the graph has directed edges or not.Áìh0¶iá&7á&7ò…ÜÂiÜ¿°Ãižl6L Add a node (also called vertex) with associated data `weight` to the graph.ÁøiOÌj´Ñ2Ôj÷j" Return the index of the new node.Áÿj%©kH **Panics** if the Graph is at the maximum number of nodes for its indexÁ±kK type (N/A if usize).Ál¥lâ&7â&7ò…–úŸ¼Ü®lÜøà³lŠ&¹l¹p9 Access the weight for node `a`.ÁÏo#÷o2 Also available with indexing syntax: `&graph[a]`.Áÿo5Àpã&7ã&7ò…Ÿ¼õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êã&7–úÜÌpÜÔÍpš±Óp›qæÈ!çÁÈ!çÚÈ!ççæî¬rE) Access the weight for node `a`, mutably.Áµq,æq6 Also available with indexing syntax: `&mut graph[a]`.Áîq9³rä&7ä&7ò…Ÿ¼õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êä&7–úÜÃrÜ®×Èrš±ÎržsèÈ!éÁÈ!éÚÈ!ééèñ†ÙwZ> Add an edge from `a` to `b` to the graph, with its associatedÁ¼sA data `weight`.Á‚t™t" Return the index of the new edge.Á¡t%Ët´Ñ2Ótöt0 **Panics** if any of the nodes don't exist.
Áþt3H **Panics** if the Graph is at the maximum number of edges for its indexÁ¶uKÍõ4†v£vN **Note:** `Graph` allows adding parallel (â€œduplicateâ€) edges. If you wantÁ«vQP to avoid this, use [`.update_edge(a, b, weight)`](#method.update_edge) instead.ÁwSàwå&7å&7ò…Ÿ¼Ÿ¼êûå¼ÜéwÜâîwš±ôwª±†xŠ&˜x¹zêêœ‚]' Add or update an edge from `a` to `b`.Á¨*3 If the edge already exists, its weight is updated.Á×6’€' Return the index of the affected edge.Áš€*É€@ Computes in **O(e')** time, where **e'** is the number of edgesÁÑ€C? connected to `a` (and `b`, if the graph edges are undirected).Á™Bà, **Panics** if any of the nodes don't exist.Áè/£‚æ&7æ&7ò…Ÿ¼Ÿ¼êûå¼Ü¯‚Ü‘å´‚š±º‚ª±Ì‚Š&Þ‚Ð…9 Access the weight for edge `e`.Áæ„#Ž…2 Also available with indexing syntax: `&graph[e]`.Á–…5×…ç&7ç&7ò…å¼õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êç&7êûÜã…Ü¶Õä…Ôê…²†íÈ!îÁÈ!îÚÈ!îîíØ±Å‡E) Access the weight for edge `e`, mutably.ÁÎ†,ÿ†6 Also available with indexing syntax: `&mut graph[e]`.Á‡‡9Ì‡è&7è&7ò…å¼õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êè&7êûÜÜ‡Ü©Ùá‡Ôç‡·ˆïÈ!ðÁÈ!ðÚÈ!ððïý¶‹‰X, Access the source and target nodes for `e`.Á×ˆ/’‰é&7é&7ò…å¼Œ½Ü¡‰Üñ–úêû¸†¾†¢‰Ô¨‰¦ŠñÈ!òÁÈ!òÚÈ!òòñ¥¼é<? Remove `a` from the graph if it exists, and return its weight.ÁÒŠB1 If it doesn't exist in the graph, return `None`.Á™‹4Ò‹B Apart from `a`, this invalidates the last node index in the graphÁÚ‹E@ (that node will adopt the removed node index). Edge indices areÁ¤ŒC@ invalidated as they would be following the removal of each edgeÁìŒC with an endpoint in `a`.Á´ÕC Computes in **O(e')** time, where **e'** is the number of affectedÁÝFG edges, including *n* calls to `.remove_edge()` where *n* is the numberÁ¨ŽJB of edges with an endpoint in `a`, and including the edges with anÁ÷ŽE endpoint in the displaced node.ÁÁ#ðê&7ê&7ò…Ÿ¼·ÁÜüÜžÊš±‡—–šÃë&7ë&7ò…€¡å¼°ýÜµÜ®“º³‰È Ôïà‰‰ž ã¤žò©<I Remove an edge and return its edge weight, or `None` if it didn't exist.Áé¦Lº§B Apart from `e`, this invalidates the last edge index in the graphÁÂ§E/ (that edge will adopt the removed edge index).ÁŒ¨2Ã¨X Computes in **O(e')** time, where **e'** is the size of four particular edge lists, forÁË¨[? the vertices of `e` and the vertices of another affected edge.Á«©Bù©ì&7ì&7ò…å¼îôÜ…ªÜËŠªÔª¤®G§®Ãí&7í&7ò…å¼îôÜÂ®Ü§”Ç®ÔÍ®å·=@ Return an iterator of all nodes with an edge starting from `a`.Á‡´CÏ´‚Ö2×´** - `Undirected`: All edges from or to `a`.Á†µ-¸µ: Produces an empty iterator if the node doesn't exist.
ÁÀµ=„”3‚¶-´¶E Use [`.neighbors(a).detach()`][1] to get a neighbor walker that doesÁ¼¶H not borrow from the graph.Á‰·¬·) [1]: struct.Neighbors.html#method.detachÁ´·,ì· î&7î&7ò…Ÿ¼Å Å î É ý‡ÃÊ ‘9ÃË ¾ÃUÀIµš}î&7êû¾†Üö·Ü‡ÿ$÷·š±ý·ó¿VG Return an iterator of all neighbors that have an edge between them andÁÝ¸J! `a`, in the specified direction.Á¬¹$L If the graph's edges are undirected, this is equivalent to *.neighbors(a)*.ÁÕ¹O©º. - `Directed`, `Outgoing`: All edges from `a`.Á±º1, - `Directed`, `Incoming`: All edges to `a`.Áçº/œœ5›»-Í»ßœ5Õ»=„”3—¼-É¼G For a `Directed` graph, neighbors are listed in reverse order of theirÁÑ¼JE addition to the graph, so the most recently added edge's neighbor isÁ ½H? listed first. The order in an `Undirected` graph is arbitrary.Áí½B´¾S Use [`.neighbors_directed(a, dir).detach()`][1] to get a neighbor walker that doesÁ¼¾V’ž5—¿º¿Æž5Â¿,ú¿ï&7ï&7ò…Ÿ¼ÜÅ Å î É ý‡ÃÊ ‘9ÃË ¾ÃUÀIµš}ï&7êû¾†ÜÀÜî¡ŽÀš±”À•‡¦À“ÇH· 5ÅÂJ `a`, in either direction.Á”Ã¹¡5¶ÃOŠÄ9 - `Directed` and `Undirected`: All edges from or to `a`.Á’Ä<ÓÄßœ5ÛÄ=„”3Å-ÏÅP Use [`.neighbors_undirected(a).detach()`][1] to get a neighbor walker that doesÁ×ÅS’ž5¯ÆÒÆÆž5ÚÆ,‹ÇšÇð&7ð&7ò…Ÿ¼Å Å î É ý‡ÃÊ ‘9ÃË ¾ÃUÀIµš}ð&7êû¾†Ü¯ÇÜ…¤°Çš±¶Ç„Ì9ÃÕ2æÉ+–Ê‚Ö2žÊ*µÖ2ÍÊ/Ëßœ5‰Ë=1 Iterator element type is `EdgeReference`.ÁËË4‹Ìñ&7ñ&7ò…Ÿ¼ý ý „9‚ý‡Ãƒ‘9Ã„¾Ã†ú‹Ã‡ÔÃúÿ—ÁÐ=’ñ&7êû¸†¾†Ü‘ÌÜü–úêû¸†¾†’Ìš±˜ÌÑRD Return an iterator of all edges of `a`, in the specified direction.ÁôÌGÀÍž¢5ÈÍ1Ù¢5þÍ/Z - `Undirected`, `Outgoing`: All edges connected to `a`, with `a` being the source of eachÁ²Î] edge.Á”ÏZ - `Undirected`, `Incoming`: All edges connected to `a`, with `a` being the target of eachÁ¤Ï]Œ±5†Ð–Ð> Produces an empty iterator if the node `a` doesn't exist.
ÁžÐAˆ5äÐ4¤Ñò&7ò&7ò…Ÿ¼Üý ý „9‚ý‡Ãƒ‘9Ã„¾Ã†ú‹Ã‡ÔÃúÿ—ÁÐ=’ò&7êû¸†¾†Ü³ÑÜà±´Ñš±ºÑ•‡ÌÑ¡Ö> Return an iterator over all the edges connecting `a` and `b`.Á¯ÔAõÔ‚Ö2ýÔ*µÖ2¬Õ/àÕˆ5èÕ4¨Öó&7ó&7ò…Ÿ¼Ÿ¼ÈŒ”ÿŒÃ•‘9Ã–ÔÃÈ+SO?¼¾ó&7êû¸†¾†ÜÂÖÜþ–úêû¸†¾†ÃÖš±ÑÖª±ëÖ–ÚG, Lookup if there is an edge from `a` to `b`.ÁËØ/ÿØÈƒ5‡ÙC•„5ÏÙBÚ ô&7ô&7ò…Ÿ¼Ÿ¼Ü«ÚÜÿ–úêû¸†¾†¬Úš±²Úª±ÄÚÑÜT Lookup an edge from `a` to `b`.Á’Û#ºÛÈƒ5ÂÛC•„5ŠÜBØÜ õ&7õ&7ò…Ÿ¼Ÿ¼šãÜâÜÜý¨ãÜš±éÜª±ûÜùÝ € È! ÁÈ! ÚÈ! € óÁÑß„ÔßÃö&7÷&7ö&7ò…÷&7·ºŸ¼šãÜúßÜµÄûßQ‰àª±¥à¢å‹9 Lookup an edge between `a` and `b`, in either direction.ÁÒâ<“ãG If the graph is undirected, then this is equivalent to `.find_edge()`.Á›ãJêãF Return the edge index and its directionality, with `Outgoing` meaningÁòãI, from `a` to `b` and `Incoming` the reverse,ÁÀä/& or `None` if the edge does not exist.Áôä)©åø&7ø&7ò…Ÿ¼Ÿ¼•ÆÜÇåÜÁÈåš±Öåª±ðåÑç“ÔçÃù&7ú&7ù&7ò…ú&7·ºŸ¼•ÆÜüçÜ¦ÖýçQ‹èª±§èÌï?? Return an iterator over either the nodes without edges to themÁÏëB( (`Incoming`) or from them (`Outgoing`).Á–ì+Æì9 An *internal* node has both incoming and outgoing edges.ÁÎì<= The nodes in `.externals(Incoming)` are the source nodes andÁí@3 `.externals(Outgoing)` are the sinks of the graph.ÁÔí6îF For a graph with undirected edges, both the sinks and the sources areÁ—îI just the nodes without edges.Áåî!‹ï1 The whole iteration computes in **O(|V|)** time.Á“ï4Óï û&7û&7ò…Ü¶ ¶ Ü†» ŸÃ¼ •‡Ã½ ÔÃÜ½Hä$û&7–ú¸†¾†ÜÝïÜ… –úêû¸†¾†Þï•‡äïáô-7 Return an iterator over the node indices of the graph.Á–ñ:ÕñI For example, in a rare case where a graph algorithm were not applicable,ÁÝñL< the following code will iterate through all nodes to find aÁ®ò? specific index:ÁòòŠóõ®+’óÎÁ1žó) # let mut g = Graph::<&str, i32>::new();Á½ó, # g.add_node("book");ÁîóA let index = g.node_indices().find(|i| g[*i] == "book").unwrap();ÁŒôDõ®+Õôèôü&7ü&7ò…¶ðÜõôÜ®Åöô®÷;@ Return an iterator yielding mutable access to all node weights.ÁþõCÆöB The order in which weights are yielded matches the order of theirÁÎöE node indices.Á˜÷µ÷ý&7ý&7ò…¨¨§Ž¬äyÃüÅæÞ=P–ý&7–ú¾†ÜÆ÷Ü‡ –úêû¸†¾†Ë÷‚ù-6 Return an iterator over the edge indices of the graphÁÄø9‰ùþ&7þ&7ò…œöÜ–ùÜ¤Ñ—ùû65 Create an iterator over all edges, in indexed order.ÁŸú8Üúˆ5äú4¤ûÿ&7ÿ&7ò…××“*ÛŸÃ*Vø³`Cÿ&7êû¾†Ü´ûÜÊ×)µûåý;@ Return an iterator yielding mutable access to all edge weights.ÁµüCýüŠÍ5…ýE edge indices.ÁÏýìý€'7€'7ò…µµ—¹‘9ÃßÒjícI4‹€'7êû¾†ÜýýÜŠ –úêû¸†¾†‚þš€) Access the internal node array.Áòÿ#¡€ '7'7ò…'7™ÔÜ«€Ü‹ –úêû¸†¾†¬€) Access the internal edge array.Áå€#” ‚'7‚'7ò…‚'7ÏÊÜžÜŒ –úêû¸†¾†ŸŸ‚E? Convert the graph into a vector of Nodes and a vector of EdgesÁØB¦‚ò…ØÜÜ±¹·‚éƒSÜ¨4“ƒQðƒ ƒ'7ƒ'7ò…Ÿ¼ÜšãÜûƒÜŽ –úêû¸†¾†üƒš±‚„•‡”„Ü‡Rµ¬4†‡Qã‡ „'7„'7ò…å¼ÜšãÜí‡Ü –úêû¸†¾†î‡Ôô‡•‡†ˆß“5 Index the `Graph` by two indices, any combination ofÁøŠ8 node or edge indices is fine.Áµ‹!Û‹B **Panics** if the indices are equal or if they are out of bounds.Áã‹EŒõ®+µŒ! use petgraph::{Graph, Incoming};ÁÁŒ$ use petgraph::visit::Dfs;ÁêŒŒ let mut gr = Graph::new();Á” let a = gr.add_node(0.);Á· let b = gr.add_node(0.);ÁØ let c = gr.add_node(0.);Áù gr.add_edge(a, b, 3.);ÁšŽ gr.add_edge(b, c, 2.);Á¹Ž gr.add_edge(c, b, 1.);ÁØŽ÷Ž> // walk the graph and sum incoming edges into the node weightÁÿŽA let mut dfs = Dfs::new(&gr, a);ÁÅ#' while let Some(node) = dfs.next(&gr) {Áí*5 // use a walker -- a detached neighbors iteratorÁœ8D let mut edges = gr.neighbors_directed(node, Incoming).detach();ÁÙG2 while let Some(edge) = edges.next_edge(&gr) {Á¥‘57 let (nw, ew) = gr.index_twice_mut(node, edge);Áß‘: *nw += *ew;Áž’ºÕ,º’ ç×,È’Ò’ // check the resultÁÚ’ assert_eq!(gr[a], 0.);Áö’ assert_eq!(gr[b], 4.);Á•“ assert_eq!(gr[c], 2.);Á´“õ®+Ó“æ“…'7…'7ò…Ã”‚–…'7Ñ…'7âÜå‘ ä‚’ ‘ ’ …”Ü‚Ã”ö“‚‚–ù“¨ò…Ã”§•¨ò…‚–µ•âÃ”Í• â‚–ä• –úêû¸†¾†Ã”‚–Š”æ˜”Âª¦”ö“ö“ù“ù“³™# Reverse the direction of all edgesÁˆ™&º™†'7†'7ò…ÜÂ™Ü“ –úêû¸†¾†Ç™¸œ Remove all nodes and edgesÁ•œ¿œ‡'7‡'7ò…ÜÅœÜ” –úêû¸†¾†Êœ®˜Å2•µˆ'7ˆ'7ò…ÜÁÜ• –úêû¸†¾†Æ£Ÿ(8 Return the current node and edge capacity of the graph.Áãž;ªŸ‰'7‰'7ò…Ž£ Ü³ŸÜ– –úêû¸†¾†´Ÿô¡2I Reserves capacity for at least `additional` more nodes to be inserted inÁ LI the graph. Graph may reserve more space to avoid frequent reallocations.Áá L²¡2 **Panics** if the new capacity overflows `usize`.Áº¡5û¡ Š'7Š'7ò…Ü‰¢Ü— –úêû¸†¾†Ž¢þ¸”¢ À¤2I Reserves capacity for at least `additional` more edges to be inserted inÁÜ¢LÄí5£Lþ£¦î5†¤5Ç¤ ‹'7‹'7ò…ÜÕ¤Ü˜ –úêû¸†¾†Ú¤þ¸à¤ ß§8H Reserves the minimum capacity for exactly `additional` more nodes to beÁ¨¥K? inserted in the graph. Does nothing if the capacity is alreadyÁø¥B sufficient.Á¿¦Ó¦: Prefer `reserve_nodes` if future insertions are expected.ÁÛ¦=§¦î5¥§5æ§Œ'7Œ'7ò…Üú§Ü™ –úêû¸†¾†ÿ§þ¸…¨ Š«8H Reserves the minimum capacity for exactly `additional` more edges to beÁÓ¨K inserted in the graph.Á£©4 Does nothing if the capacity is already sufficient.ÁÂ©7þ©: Prefer `reserve_edges` if future insertions are expected.Á†ª=Èª¦î5Ðª5‘«'7'7ò…Ü¥«Üš –úêû¸†¾†ª«þ¸°« Ó¬%M Shrinks the capacity of the underlying nodes collection as much as possible.Áþ«PÚ¬Ž'7Ž'7ò…Üî¬Ü› –úêû¸†¾†ó¬ÿ%M Shrinks the capacity of the underlying edges collection as much as possible.ÁªP†®'7'7ò…Üš®Üœ –úêû¸†¾†Ÿ®•¯7 Shrinks the capacity of the graph as much as possible.ÁÖ®:œ¯ '7'7ò…Üª¯Ü –úêû¸†¾†¯¯±²p< Keep all nodes that return `true` from the `visit` closure,ÁŠ°? remove the others.ÁÎ°é°< `visit` is provided a proxy reference to the graph, so thatÁñ°?6 the graph can be walked and associated data modified.Áµ±9ó±. The order nodes are visited is not specified.Áû±1¸²’'7’'7ò…ðÈÜúŸ Ÿ È²Ü‚ðÈÅ²‘'7ððÈííîž¸ñÎÃcúK’í]_¢‘'7ò…Ÿ¼ö²*‘'7ðÈ¿6õœ³ž –úêû¸†¾†ðÈÍ²ª×²Å²Å²Î·p< Keep all edges that return `true` from the `visit` closure,Á§µ?½ý5ëµ†¶éý5Ž¶?²þ5Ò¶9·. The order edges are visited is not specified.Á˜·1Õ·”'7”'7ò…ðÈÜú¡ ¡ å·Ü™6â·“'7ððÈííîž¸ñÎÃcúK’í]_¢“'7ò…å¼“¸*“'7ðÈ¶…6õ¹¸ –úêû¸†¾†ðÈê·ªô·â·â·Á¾Í0 Create a new `Graph` from an iterable of edges.ÁÄº3üº, Node weights `N` are set to default values.Á„»/6 Edge weights `E` may either be specified in the list,Á¸»9( or they are filled with default values.Áö»+¦¼5 Nodes are inserted automatically to match the edges.Á®¼8ë¼õ®+ó¼ÿ‚1ÿ¼œ½) let gr = Graph::<(), i32>::from_edges(&[Á¤½,ËÐ,Õ½çË3ù½‡Ì3•¾ËÑ,©¾õ®+µ¾È¾ ÿ¿ ò…Üò£ £ ÜÎ¬0Ó¾ô¬0€¿ƒ¥åêûŸ¿Ï±æŸ¼æ¿¯·2†À¢ –úêû¸†¾†ÿ¿ « Ö¾Ó¾Ó¾£Ã×, Extend the graph from an iterable of edges.Á€Á/´Á·†6¼Á/ð†6ðÁ9³‡6®Â+ÞÂô‡6æÂ8ªÃ•'7•'7ò…ÿ¿ Üò¥ ¥ ¿ÃÜÎ¬0¼Ãô¬0ìÃ¾‹6‹ÄÖ‹6ÒÄ¯·2òÄ„ÞÄÃ« ÊÃ¼Ã¼Ã»Êæ) Create a new `Graph` by mapping node andÁýÈ, edge weights to new values.Á®ÉÒÉ8 The resulting graph has the same structure and the sameÁÚÉ; graph indices as `self`.ÁšÊÂÊ§ üò…ÝùãùÃÜü§ ú¨ ê© ß…ª é…« ¨ ª § © « Ü‚ÝùÊÊ‚ãùÍÊ‚®÷ÐÊ‚µ÷ÔÊðÝùŸ¼§ ü–úÑË!ÝùØ’6õ®÷ðËðãùå¼§ üêûÿË!ãù™“6õµ÷žÌ¦ –úêû¸†¾†§ üÝùãù®÷µ÷åÊ•Y÷ÊãƒËÆÊÆÊÊÊÊÊÍÊÍÊÐÊÐÊÔÊÔÊªÍ¦ È!¬ ÁÈ!¬ ÚÈ!¬ ¬ ¦ ·úØÎ¦ È! ÁÈ! ÚÈ! ¦ ÞþØÔý1 Create a new `Graph` by mapping nodes and edges.Á†Ð4: A node or edge may be mapped to `None` to exclude it fromÁ¿Ð= the resulting graph.ÁÑžÑ9 Nodes are mapped first with the `node_map` closure, thenÁ¦Ñ<B `edge_map` is called for the edges that have not had any endpointÁçÑE removed.Á±ÒÂÒK The resulting graph has the structure of a subgraph of the original graph.ÁÊÒNA If no nodes are removed, the resulting graph has compatible nodeÁÓD@ indices; if neither nodes nor edges are removed, the result hasÁæÓC" the same graph indices as `self`.Á®Ô%ßÔ ¯ üò…ÝùãùÃÜü¯ ú° ê± ß…² é…³ ° ² ¯ ± ³ Üý‘6îÔ‘’6ñÔ¥’6ôÔ¹’6øÔðÝùŸ¼¯ ü–úõÕ)Ýù†6õŒ©”Ö ðãùå¼¯ üêû«Ö)ãùÇ6õ°®ÊÖ ® –úêû¸†¾†¯ üÝùãù®÷µ÷‰Õ•Y›Õãƒ´ÕêÔêÔîÔîÔñÔñÔôÔôÔøÔøÔ±ß_J Convert the graph into either undirected or directed. No edge adjustmentsÁäÝMH are done, so you may want to go over the result to remove or add edges.Á¶ÞK†ß´Ñ2Žß¸ßò…Œ´ÜÏ†µ µ Ü‚¨³Çßý¨³‡à´ –úêû¸†¾†¨³ÎßÇßÇßÃé>F An iterator over either the nodes without edges to them or from them.ÁùèIÎé ü· ¨¸ ï¹ ä º ¸ º · ¹ ŠÃ-Üéà-ãéÀà-çéƒ· üßéæà-ëé ÷£6¨ ¾†· ü¨ ¶ ¶ Ü†» ŸÃ¼ •‡Ã½ ÔÃÜ½Hä$· üƒ¸†¾†Ü½Hä$» ¼ ½ ØéØéÜéÜé· ãéãéçéçéˆê3ˆêÃ¶ ¶ ´»´»¢‚ ¶»ŸŒ)·»÷Œ)‹mŸÉ™A3«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+· ü€·ÁêÁêÃ¶ ¶ ÜÕêÕêÃ¶ ¶ ¾¸íê=ü¿ ¨À ïÁ ä Â À Â ¿ Á ŠÃ-öêà-ýêÀà-ëƒ¿ üùêÓà-¹ëæà-Ëë Á¶Ã Ä òêòêöêöê¿ ýêýêëëÜë áë¾ ¾ Ÿ¼ûë+þë–'7–'7Á¶áÖ¾ ƒì¾ Ä ¿ üƒ¸†¾†ˆìó3' Iterator over the neighbors of a node.Á¶ð*áð„”3åð-“ñB Created with [`.neighbors()`][1], [`.neighbors_directed()`][2] orÁ—ñE [`.neighbors_undirected()`][3].ÁÝñ#ò( [1]: struct.Graph.html#method.neighborsÁ…ò+1 [2]: struct.Graph.html#method.neighbors_directedÁ±ò43 [3]: struct.Graph.html#method.neighbors_undirectedÁæò6¨ó üÆ £Ç ä È È Æ Ç ¦¿,¶óƒ¹2½óêûÆ ü¹óæ·Æ üÁóò®6™ô‡¯6™ôÅ Å î É ý‡ÃÊ ‘9ÃË ¾ÃUÀIµš}Æ üêûæ·UÀIµš}É Ê Ë ²ó²ó¶ó¶óÆ ½ó½óÆ úóúó ÃÅ Å ÚÌ™ô™ôÃÅ Å Æ ü…Æ·ô·ôÃÅ Å ÍÇÍôÔô1üÎ £Ï ä Ð Ð Î Ï ¦¿,Ýôƒ¹2àô–¹2”õ š·Ñ Ò ÙôÙôÝôÝôàôàô¥õ ªõÍ Í ÚÌÅõ+Èõ—'7—'7š·ÿÕÍ ÍõÍ Ò Î üêûæ·ÒõÈû.üÔ £Õ ä Ö Ô Õ Ö ¦¿,Ñûƒ¹2Ôû–¹2…ü ð·’"ÍûÍûÑûÑûÔûÔûÍü$üØ £Ù ä Ú Ø Ù Ú ¦¿,Öüƒ¹2Ùü–¹2€ý Å Å î É ý‡ÃÊ ‘9ÃË ¾ÃUÀIµš}Ø üêûæ·Û ÒüÒüÖüÖüÙüÙüžÿ)B Return a â€œwalkerâ€ object that can be used to step through theÁ‘ýE* neighbors and edges from the origin node.ÁÛý-þI Note: The walker does not borrow from the graph, this is to allow mixingÁ•þL0 edge walking with mutating the graph's weights.Áæþ3¥ÿ˜'7˜'7 ¶6Åá× ¬ÿ× Û Ø üêûæ·ÿ»€;Â€ÃüÝ £Þ ä ß Ý Þ ß ¦¿,Õ€ƒ¹2Ü€êûÝ üØ€–¹2à€ §»6ý€æ·Ý üý€Ü Ü ™‰à ‘9Ãá ¾Ãâ •‡Ã¾ýsº’bÝ üêûæ·¾ýsº’bà á â Ñ€Ñ€Õ€Õ€Ý Ü€Ü€ý€ý€ÃÜ Ü Ý ü…ÆŸŸÃÜ Ü ÎÇ¸¸ÃÜ Ü ÜË›ÎÃ™'7™'7÷ã”‘ÜÜ Ü ™‰à ‘9Ãá ¾Ãâ •‡Ã¾ýsº’b™'7ÔµÞ¬£ä ä å ä å òà§4ß3â¡3Ü‚ ã ÔµÞ¬‘9ë¾Š‚•‡£‚ßßââ”ƒ)üç £è ä é è é ç ¦¿,ƒƒ¹2 ƒ–¹2Ìƒ Ü Ü ™‰à ‘9Ãá ¾Ãâ •‡Ã¾ýsº’bç üêûæ·ê ì ™ƒ™ƒƒƒ ƒ ƒÝƒ3àƒ Ãš'7š'7¤Á6õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êš'7†Ææ êƒæ ê ç üêûæ·ïƒ«„ê È!ë ÁÈ!ë ÚÈ!ë ë ê Îå¿„?Â„Ã›'7›'7¤Á6õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÎÇ›'7†Ææ Ç„æ ì ç 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›˜š™›¦¿,’£à-•£Àà-™£Óà-×£æà-é£ Õ¹œŽ£Ž£’£’£•£•£™£™£ú£ ÿ£——§"§"…¥«"…Ã¬"QÃ®"Š&Ã°ô×+‚Ù˜üêû¾†¥¤6¨¤'7'7Õ¹õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê×ß6—¤—˜üêû¸†¾†²¤•¦(˜¦ Ã™ª™ªåŸŸÆö3¢¦ž°²æü©¦¢¦¢¦°¦»¦Û¦2ü££¤ï¥ä ¦¥¤¦£¦¿,ä¦à-ç¦Àà-ë¦æà-œ§ Óà-¯§®º§à¦à¦ä¦ä¦ç¦ç¦ë¦ë¦¿§Â§ž'7ž'7®º®º¢È§¢§£üêû¸†¾†É§Û©?6 Iterator yielding mutable access to all node weights.Á¡©9æ©ü©¨ªä «©ª«ŠÃ-ù©ƒ¹2€ªƒ©üü©–¹2„ª ôæ6¢( æ·©ü¢( ¨¨§Ž¬äyÃüÅæÞ=P–©üƒæ·üÅæÞ=P–¬õ©õ©ù©ù©©€ª€ª¡ª-¡ªÃ¨¨ÃNÃNóÞÆNÄýMÇNáýMÈNÇ ýM‘-bÂDÝÞ©üæ¯Óª6ü®¨¯ä °°®¯ŠÃ-Üªƒ¹2ßª–¹2˜« ›»±²´ØªØªÜªÜªßªßª©« ®«®üƒÅ«'È«Ÿ'7Ÿ'7›»õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êúê6Í«²®üƒæ·Ò«¬²È!³ÁÈ!³ÚÈ!³³²ù°±¬-´¬ '7 '7›»šò¾¬´®üƒæ·¿¬Ã?6 Iterator yielding mutable access to all edge weights.Á‰9Îü¶£·ä ¸¸¶·¦¿,áƒ¹2èêû¶üä–¹2ì œï6¢( æ·¶ü¢( µµ—¹‘9ÃßÒjícI4‹¶üêûæ·ßÒjícI4‹¹ÝÝáá¶èè‰®-‰®ÃµµÃNÃNóÞÆNÄýMÇNáýMÈNÇ ýM‘-bÂDÝÞ¶ü†Æ»®6ü»£¼ä ½¼½»¦¿,Ä®ƒ¹2Ç®–¹2€¯ Û»¾¿ÁÀ®À®Ä®Ä®Ç®Ç®‘¯ –¯ºº»üêû¯'°¯¡'7¡'7Û»õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê¢ó6ºµ¯º¿»üêûæ·º¯õ¯¿È!ÀÁÈ!ÀÚÈ!ÀÀ¿Ú·™°-œ° ¢'7¢'7Û»šòº¦°ºÁ»üêûæ·§°Ü±?9 Index the `Graph` by `NodeIndex` to access node weights.Áñ°<®±& **Panics** if the node doesn't exist.Á²±)¨Ã£ÄïÅä ÆÅÄÆÃíñ,á±¦¿,ä±à-ç±Àà-ë±Óà-ª²æà-¼² ò…ÇÈá±á±ä±ä±ç±ç±ë±ë±Í²Ò²ÂÂ–úâ²+å²€£'7£'7ò…Ÿ¼£'7–úÂë²ÂÈ–úêû¸†¾†ì²…ò²®´B¬õ6Ã³<€´þõ6„´)¨Ê£ËïÌä ÍÍÊÌËíñ,³´¦¿,¶´à-¹´Àà-½´Óà-ÿ´æà-‘µ ò…Î³´³´¶´¶´¹´¹´½´½´¢µ7¥µ €¤'7¤'7ò…Ÿ¼¤'7–úÉ¯µÉÎ–úêû¸†¾†´µ…ºµþ¶?9 Index the `Graph` by `EdgeIndex` to access edge weights.Á“¶<Ð¶& **Panics** if the edge doesn't exist.ÁÔ¶)¨Ð£ÑïÒä ÓÐÒÑÓíñ,ƒ·¦¿,†·à-‰·Àà-·Óà-Ì·æà-Þ· ò…ÔÕƒ·ƒ·†·†·‰·‰···ï·ô·ÏÏêû„¸+‡¸€¥'7¥'7ò…å¼¥'7êûÏ¸ÏÕ–úêû¸†¾†Ž¸…”¸Ð¹B€ý6å¸<¢¹Òý6¦¹)¨×£ØïÙä ÚØÚ×Ùíñ,Õ¹¦¿,Ø¹à-Û¹Àà-ß¹Óà-¡ºæà-³º ò…ÛÕ¹Õ¹Ø¹Ø¹Û¹Û¹ß¹ß¹Äº7Çº €¦'7¦'7ò…å¼¦'7êûÖÑºÖÛ–úêû¸†¾†Öº…ÜºÕ»2 Create a new empty `Graph`.Áµ»¨Ý£Þïßä ààÝßÞíñ,Ú»¦¿,Ý»à-à»Àà-ä»Óà-–¼æà-¨¼ ò…áÚ»Ú»Ý»Ý»à»à»ä»ä»¹¼¼¼ò…ÜÜá–úêû¸†¾†¨½) A `GraphIndex` is a node or edge index.Áû¼,²½ ââï¾-¾½âï¾-¾½ãäãäÜ½Ë½¹Ë½¯Î½Õ½ ÝÏ½É½ß½§'7§'7àžâå½âãàžæ½¾ü½¹ü½µ¯ÿ½†¾ Ý€¾ú½¾ ââäàž¬¾0ä ææº›.±¾Í›.µ¾ óŠçè±¾±¾ñ¾ô¾¨'7¨'7óŠåú¾åç¡‹û¾Ä¿Ç¿ ååè¡‹÷¿0ä êêº›.ü¿Í›.€À ¯‹ëìü¿ü¿¼À¿À©'7©'7¯‹éÅÀéë¡‹ÆÀÁ’Á ééì¡‹šÊP A â€œwalkerâ€ object that can be used to step through the edge list of a node.ÁÃÁS—ÂA Created with [`.detach()`](struct.Neighbors.html#method.detach).Á›ÂDàÂG The walker does not borrow from the graph, so it lets you step throughÁäÂJB neighbors or incident edges while also mutating graph weights, asÁ¯ÃE in the following example:ÁõÃ“Äõ®+—ÄäÜ5ŸÄ$’Ý5ÄÄâÄÅÝ5æÄíÝ5…Å“Þ5¢Å¹Þ5¿ÅßÞ5ÜÅƒß5÷Å§ß5’ÆÆF // step through the graph and sum incoming edges into the node weightÁ±ÆI¢à5ûÆ#Ïà5ŸÇ*' // use a detached neighbors walkerÁÊÇ*Åá5õÇG–â5½È5 gr[node] += gr[edge];ÁóÈ!ºÕ,•É ç×,ŸÉ¥Éâã5©Éƒä5ÁÉ§ä5ÜÉËä5÷Éõ®+’Ê¥Ê ä îîº›.³Ê¿½=æ'(‚ GEïð³Ê³Ê½Ê½Ê ÃííóŠÜÊÜÊÃíí•ÌòÊùÊ$ä óóº›.þÊÍ›.¬Ë ¿½ôþÊþÊ½ËÀËª'7ª'7¿½¿½òÆËòô¡‹ÇËÈÌ%ä ööº›.ÍÌÍ›.ÑÌ ¿½÷û€ÍÌÍÌ¼Ï„G Step to the next edge and its endpoint node in the walk for graph `g`.ÁôÌJÃÍD The next node indices are always the others than the starting pointÁËÍG- where the `WalkNeighbors` value was created.Á—Î0* For an `Outgoing` walk, the target nodes,ÁÌÎ-6 for an `Incoming` walk, the source nodes of the edge.ÁþÎ9ÃÏ«'7¬'7«'7¿½¬'7Ïáîàõ¨ø£ùïúøùúåÏõŠÃ-ÈÏÃ-ËÏ°Ã-ÎÏêÃ-ÒÏ•êêÏÿŸøÏÈÏÈÏËÏËÏÎÏÎÏêÖxñÖ '7®'7'7¿½®'7ÏáÌëõ¨ü£ýïþüýþ˜×õŠÃ-ûÖÃ-þÖ°Ã-×êÃ-…×û¡‹ƒ–ƒœƒ×ÿŸ«×ûÖûÖþÖþÖ××þ×ûÈ!ÿÁÈ!ÿÚÈ!ÿÿûãê’Øx™Ø ¯'7°'7¯'7¿½°'7Ïá¶ïõ¨£‚ïƒ‚ƒÀØõŠÃ-£ØÃ-¦Ø°Ã-©ØêÃ-Ø€¡‹ƒ–ƒœƒÅØÿŸÓØ£Ø£Ø¦Ø¦Ø©Ø©Ø¦Ù€È!„ÁÈ!„ÚÈ!„„€ÏîáÚ0ä ††º›.æÚÍ›.êÚ ý½‡ˆ‰æÚæÚ˜Û Û……óŠ¸Û(»Û±'7±'7ý½õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êý½Ù9…ÀÛ…ˆ¡‹ÅÛ”Ü-—Ü ²'7²'7ý½šò…¡Ü…‰¡‹¢ÜèÜ;ä ‹‹º›.íÜÍ›.ñÜ ý½ŒíÜíÜªÝ-Ý ³'7³'7ý½ñ¦7Š·ÝŠŒ¡‹¼ÝŽÞ9ä ŽŽº›.“ÞÍ›.—Þ ý½“Þ“Þõß0ä º›.úßÍ›.þß Ú¾‘’“úßúß¬à ±à¯‹Ìà(Ïà´'7´'7Ú¾õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÚ¾Ù9Ôà’¡‹Ùà¨á-«á µ'7µ'7Ú¾šòµá“¡‹¶áüá;ä ••º›.âÍ›.…â Ú¾–ââ¾â-Áâ ¶'7¶'7Ú¾†¬7”Ëâ”–¡‹Ðâ¢ã9ä ˜˜º›.§ãÍ›.«ã Ú¾§ã§ã–å=üš£›ä œœš›¦¿,Ÿåƒ¹2¢å–¹2¦å ‹À›å›åŸåŸå¢å¢åÚåÝå·'7·'7‹À‹À™ãå™šüêûæ·äå‹æ<üŸ£ ä ¡ ¡Ÿ¦¿,”æƒ¹2—æ–¹2›æ ßÀææ”æ”æ—æ—æÌæAü££¤ä ¥¤¥£¦¿,Õæƒ¹2Øæ–¹2Üæ ‡êûêû›ç ³Á¦ÑæÑæÕæÕæØæØæ¬ç ¯ç¸'7¹'7¸'7³Á¹'7³Á¢²ç¢¦£üêûæ·³çŽ ¹ç•èEü¨¨©£ªï«ä ¬¨ª¬©«ŠÃ-žèÃ-¡è°Ã-¤èÃÃ-¨èêÃ-éèýÃ-ûè ‹Â®¯šèšèžèžè¡è¡è¤è¤è¨è¨èŒé‘é§§³ö¨üƒ·é¼é§§°°ñ5´ŸÃ>¹/sðóf¨üƒ¢ƒìé0ïé‹Â‹Â€§§¯¨üƒ–ƒœƒ¢ƒÿé¤ë?$ Iterator over all nodes of a graph.Áüê'¯ëü±¨²ä ³±²³ŠÃ-Âëƒ¹2Éëƒ±üÅë–¹2Íë ƒ»7¨ æ·±ü¨ °°ñ5´ŸÃ>¹/sðóf±üƒæ·>¹/sðóf´¾ë¾ëÂëÂë±ÉëÉëêë3êëÃ°°´»´»¢‚ ¶»ŸŒ)·»÷Œ)‹mŸÉ™A3«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+±üæ¯¢ì6ü¶¨·ä ¸¸¶·ŠÃ-«ìƒ¹2®ì–¹2çì Â¹º¼§ì§ì«ì«ì®ì®ìøì ýìµµÚÌ¶üƒ¡í(¤íº'7º'7ÂõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÂÙ9µ©íµº¶üƒæ·®íƒîºÈ!»ÁÈ!»ÚÈ!»»ºá¹î-¼î »'7»'7ÂšòµÆîµ¼¶üƒæ·ÇîïAü¾¨¿ä ÀÀ¾¿ŠÃ-™ïƒ¹2œï–¹2àï ÜÂÁ•ï•ï™ï™ïœïœïñï-ôï ¼'7¼'7ÜÂõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÜÂÙ9½þï½Á¾üƒæ·ƒðÝðÁÈ!ÂÁÈ!ÂÚÈ!ÂÂÁ¼¥‘ñ?üÄ¨Åä ÆÄÅÆŠÃ-šñƒ¹2ñ–¹2Ûñ ›Ã–ñ–ñšñšñññéñ(üÈä É£ÊÈÉÊ3òñÃ-öñ¡3 ò §"§"…¥«"…Ã¬"QÃ®"Š&Ã°ô×+‚ÙÈü–ƒÞ¬ËîñîñòñòñöñöñÕó‘à2±òÕòÄà2Ýò6ƒá2˜ó8Üó½'7½'7ùÇ7Èü–ƒÇãóÇËÈüÞ¬–ƒäó’ô4üÍä Î£ÏÍÎÏ3›ôÃ-Ÿô¡3Õô ÚÃÐÑÒÓÔÕÖ—ô—ô›ô›ôŸôŸôæôëôÌÌˆ‡õŒõÌÌ”‘¨õõÌÌ–ƒ¾õ Áõ¾'7¾'7ÚÃÚÃ«ÌÈõÌÓÍüÞ¬–ƒÉõ€ö ƒö¿'7¿'7ÚÃƒÍ7ÌŠöÌÔÍüÞ¬–ƒ‹öÂöÅöÀ'7À'7ÚÃÀ'7–ƒÌÌöÌÕÍüÞ¬–ƒÍöùöüöÁ'7Á'7ÚÃÚÃ¬ÌÿöÌÖÍüÞ¬–ƒ€÷Ü÷?$ Iterator over all edges of a graph.Á´÷'ç÷üØ£Ùä ÚØÙÚ¦¿,ú÷ƒ¹2øêûØüý÷–¹2…ø ½Ñ7¨ æ·Øü¨ ××“*ÛŸÃ*Vø³`CØüêûæ·*Vø³`CÛö÷ö÷ú÷ú÷Øøø¢ø3¢øÃ××´»´»¢‚ ¶»ŸŒ)·»÷Œ)‹mŸÉ™A3«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+Øü†ÆÚø6üÝ£Þä ßÝÞß¦¿,ãøƒ¹2æø–¹2Ÿù 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ííîž¸ñÎÃcúK’í]_¢¤ü¡„©È È Ì Ì Ï Ï Ò Ò Ö Ö à½‚Û5« 8ÄÛ5è !ŽûÛ5–EçÈ'7È'7ñ7’Æ˜ÆÈ'7ÛÆÈ'7ìÆ£åªä‚«ª«†£‚’Æ÷‚˜Æú¨¡„’ÆÕ ¨¡„˜Æã â’Æû â˜Æ’ ©¤üƒ–ƒœƒ¢ƒ’Æ˜Æ‹æ™Âª§÷÷úúÒßçíõî Ôïð ÷ñ÷ú„ Ô…†Î‡ŽÖÿ·ÿŽ·ÿ¥»ºÿÓÿ Œ»ÿÃÿÍÅÿµÿ 4 `StableGraph` keeps indices stable across removals.Á78' Depends on `feature = "stable_graph"`.Á<*gÞÿÝçë‘Ôæó‡–©¾ÚŒ#uñ®Ÿƒ–%¯Ÿ‘Ë%°Ö´¨‘±Ö´¨’±replaceÁÃÛ²Ú Ú¿³Š 2ò¤µ¦ 2ù¨¶Þ ž(·¬Â¡á$¹ŽÁ« È$ºÿ(¶ý»·yÀ¤¼ìÎ¶ÇÌ$½Î¶ÇÍ$½¹¶ÑÉ$¾¹¶ÑÊ$¾×ÂÛ ç$¿¤Ó‚ Á¤Ó‚¡Á¹ÔŒ ²ÂÿÓ›«ÃÿÓ›¬Ã¬Õ½½Ä©|ØÒÆ;å…"Çž¸ëíÈž¸ëîÈŽœóú!ÉÊ{ùÆÊ£xÿ ’ËÇ%¤ªÍÙŒ‰ÎïˆÁ ÐÏ»‰ÌÙÐô‹ßþÑ‚Žó œÒàûƒÓ¨w… „ÕÞïN‘ ‚Öß†u á×à’_¨ Ú!Øã÷‘³ âÙí—u¿ âÚîù^Ê º!ÛñÄøê ½Üpu~ƒŒ ‘›¨¹ÃÐÚçòòùùˆ ™M¡¡« « ¶¶ÀÀÇÇÑÑÛ Û í7‚‚Œ Œ ››ª½Ð:ØØååëëóóùùÿ ÿ s¤¤Á Á ÌÌßßó ó ‰ñe… … ‘ ‘ ¨ ¨ ³ ³ ¿ ¿ Ê Ê Ýê º;H `StableGraph` is a graph datastructure using an adjacencyÁ¦K list representation.Áò‹J The graph **does not invalidate** any unrelated node or edge indices whenÁM items are removed.ÁÝô% `StableGraph` is parameterized over:Áø(¡ J - Associated data `N` for nodes and `E` for edges, also called *weights*.Á¥ M‘²4ó 3Í²4§ X®³4€FÇL The graph uses **O(|V| + |E|)** space, and allows fast node and edge insertÁËO and efficient graph search.Á›»¶4¿P†·4 (¹ K - Nodes and edges are each numbered in an interval from *0* to some numberÁ½ NI *m*, but *not all* indices in the range are valid, since gaps are formedÁŒL by deletions.ÁÙëQ - You can select graph index integer type after the size of the graph. A smallerÁïT‚È4Ä%êG - Using indices allows mutation while traversing the graph, see `Dfs`.ÁîJ¹J - The `StableGraph` is a regular rust collection and is `Send` and `Sync`Á½M. (as long as associated data `N` and `E` are).Á‹1½C - Indices don't allow as much compile time checking as references.ÁÁFˆJ Depends on crate feature `stable_graph` (default). *Stable Graph is stillÁŒMG missing a few methods compared to Graph. You can contribute to help itÁÚJ achieve parity.*Á¥Å¨Þ£ßïàä áàßáÞíñ,Ñ¦¿,Ôà-× Àà-æÚ†Ûå‚üwf3âãäåæÑÑÔÔ× ×Îùææü&üÝÝ¼ô¨¨ ÝÝ¿¿ ÝÝÎÎ ÝÝŸ¼ìì ÝÝå¼,% A `StableGraph` with directed edges.Á‰(²¿Î4¶M•Ï4„– ¨è£éä êèéêíñ,¤¦¿,§ƒ¹2ªÝÝÌŸâÿŸãÎ$äÑaå˜ æ© Ûå‚üwf3–úêûÎùæ·¤¤§§ªªì,' A `StableGraph` with undirected edges.ÁÞ*‰ÐÑ4M¦Ò4Ûõ ¨ì£íä îìíîíñ,ƒ¦¿,†ƒ¹2‰ÝÝÌŸâÿŸãÎ$äÑaå˜ æ© Ûå‚üwf3–úêû…üæ·ƒƒ††‰‰¿;¨ð£ñïòä óðòñóíñ,Ä¦¿,Çà-ÊÀà-ÎÚÚ4ˆ íÚ4› Óà-¯æà-Á Ú†ôÄÄÇÇÊÊÎÎÒ4ÕÉ'7Ê'7Ë'7É'7Ú†Ê'7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SË'7ÔžïÙïô–úêû¸†¾†ÚàüôÈ!õÁÈ!õÚÈ!õõôß÷Í!ôÈ!öÁÈ!öÚÈ!ööô†úþ!ôÈ!÷ÁÈ!÷ÚÈ!÷÷ô²û”$ôÈ!øÁÈ!øÚÈ!øøôò‚%ôÈ!ùÁÈ!ùÚÈ!ùùô¯ð% ôÈ!úÁÈ!úÚÈ!úúôÝ&ôÈ!ûÁÈ!ûÚÈ!ûûôì¢¹'ôÈ!üÁÈ!üÚÈ!üüôƒˆµ(ôÈ!ýÁÈ!ýÚÈ!ýýôÊ†•) ôÈ!þÁÈ!þÚÈ!þþôó†¦)ôÈ!ÿÁÈ!ÿÚÈ!ÿÿôÒ¬ú*&¨£‚‚íñ,ÿ*¦¿,‚+ã°ƒÿ*ÿ*‚+‚+§-0 Create a new `StableGraph` with directed edges.Á§+3ß+? This is a convenience method. See `StableGraph::with_capacity`Áç+BG or `StableGraph::default` for a constructor that is generic in all theÁ®,J" type parameters of `StableGraph`.Áý,%®-ã°€€ƒ–úêûé-,¨…£†ï‡ä ˆˆ…‡†íñ,î-¦¿,ñ-à-ô-Àà-ø-Óà-¤.æà-¶. Ú†‰Š‹ŒŽ‘’“”—™œžŸ ¢£¤¥§¨©ª¬®¯°±²³¶¸º¼ÆÌÎÏÐÒî-î-ñ-ñ-ô-ô-ø-ø-ƒ/84 Create a new `StableGraph` with estimated capacity.ÁÇ.7Š/ Ú†„„éúäy˜/‘9¦/ä1(·ë5¤1;ë1Ì'7Ì'7Ú†Ž£ „ô1„Š–úêû¸†¾†õ1×2Ÿé5´2Þ2Í'7Í'7Ú†„ä2„‹–úêû¸†¾†é2½4˜Å2¤4Ä4Î'7Î'7Ú†„Ð4„Œ–úêû¸†¾†Õ4”8!×ï477é7´Ñ2ñ7›8 Ï'7Ï'7Ú†„¦8„®Ë§8·9!Šñ4Û8,Œ9´Ñ2”9¾9 Ð'7Ð'7Ú†„É9„¤ÑÊ9Á:!²ò4þ90È:Ñ'7Ñ'7Ú†„Ô:„ûîÕ:©=6Åó4Š;OÞ;´Ñ2æ;‰<Àô4‘<%»<F **Panics** if the `StableGraph` is at the maximum number of nodes forÁÃ×«ßœ5ß«=„”3¡¬-Ó¬ì©5Û¬S’ž5³ÖÆž5Þ,–®ï'7ï'7Ú†Ÿ¼––î šý‡›‘9œ¾ß5¶cz#ºï'7êû¾†„«®„©™¬®š±²®ù²9ÃÕ2Û°+‹±‚Ö2“±*µÖ2Â±/ö±ßœ5þ±=ˆ5À²4€³ð'7ð'7Ú†Ÿ¼ÔÔ„9Ùý‡Ú‘9Û¾Ýú‹ÞÔ§-¦Œøtð'7êû¸†¾†„†³„°–úêû¸†¾†‡³š±³’¸R¬¯5é³Gµ´ž¢5½´1Ù¢5ó´/¥°5§µ]Œ±5‰¶¡±5™¶]Œ±5û¶‹·¢²5“·Aˆ5Ù·4™¸ñ'7ñ'7Ú†Ÿ¼ÜÔÔ„9Ùý‡Ú‘9Û¾Ýú‹ÞÔ§-¦Œøtñ'7êû¸†¾†„¨¸„×¥©¸š±¯¸•‡Á¸š¿?åÁ5»B±Â5ä»+”¼òÂ5œ¼<¸Ã5Ý¼@‚Ä5¢½6Ý½ÎÄ5å½I¡Å5³¾!Ù¾ØÅ5á¾4¡¿ ò'7ò'7Ú†Ü‡‡Ü†ŒŸ•‡ŽÔ.MÛñ×cíò'7–ú¸†¾†„«¿„²–úêû¸†¾†¬¿•‡²¿¥Â; Index the `StableGraph` by two indices, any combination ofÁêÀ>ÄÛ5Á!ÓÁûÛ5ÛÁE¬Âó'7ó'7Ú†Ã”‚–ó'7§ºó'7¸º„å´ä‚µ´µËÂ„Šç5¼Âžç5¿Â¨Ú†Ã”íÃ¨Ú†‚–ûÃâç5“Ä öç5ªÄ ³–úêû¸†¾†Ã”‚–ÐÂæÞÂÂªìÂ¼Â¼Â¿Â¿ÂÕÍpôü5ÎÇ?½ý5’ÈÈéý5µÈ?²þ5ùÈ9·Éÿ5¿É1õÉG The node indices of the removed nodes are invalidated, but none other.ÁýÉJÐµ8ÌÊJ. each edge with an endpoint in a removed node.Á›Ë1ÑËN Computes in **O(n + e')** time, where **n** is the number of node indices andÁÙËQQ **e'** is the number of affected edges, including *n* calls to `.remove_edge()`Á¯ÌTE where *n* is the number of edges with an endpoint in a removed node.ÁˆÍHÜÍõ'7õ'7Ú†ðÈ„ú··ìÍ„™6éÍô'7ððÈííîž¸ñÎÃcúK’í]_¢ô'7Ú†Ÿ¼šÎ*ô'7ðÈíö8õÀÎ¶–úêû¸†¾†ðÈñÍªûÍéÍéÍßÔpö6ÆÐ?½ý5ŠÑ¥Ñéý5Ñ?²þ5ñÑ9¯Òƒ6·Ò1íÒF The edge indices of the removed edes are invalidated, but none other.ÁõÒIÃÓE Computes in **O(e'')** time, **e'** is the number of affected edges,ÁËÓH? including the calls to `.remove_edge()` for each removed edge.Á˜ÔBæÔ÷'7÷'7Ú†ðÈ„ú¹¹öÔ„™6óÔö'7ððÈííîž¸ñÎÃcúK’í]_¢ö'7Ú†å¼¤Õ*ö'7ðÈ…ü8õÊÕ¸–úêû¸†¾†ðÈûÔª…ÕóÔóÔõÛÍ6 Create a new `StableGraph` from an iterable of edges.ÁØ×9–Ø·†6žØ/ð†6ÒØ9³‡6Ù+ÀÙô‡6ÈÙ8…Úõ®+Ú) use petgraph::stable_graph::StableGraph;Á™Ú,ÊÚ/ let gr = StableGraph::<(), i32>::from_edges(&[ÁÒÚ2ËÐ,‰ÛçË3Û‡Ì3ÉÛËÑ,ÝÛõ®+éÛüÛ ÿ¿ Ú†„ò»»„Î¬0‡Üô¬0´Ü¾‹6ÓÜÖ‹6šÝ¯·2ºÝº–úêû¸†¾†ÿ¿ « ŠÜ‡Ü‡Üøßì/ Create a new `StableGraph` by mapping node andÁ´Þ2ËŽ6ëÞß€6—ß;Å6×ßÿß½üÚ†Ýùãù™à„ü½ú¾ê¿ß…Àé…ÁÀ½¿Á¾„ý‘6‡à‘’6Šà¥’6à¹’6‘àðÝùŸ¼½ü–ú”á!Ýù¨„9õ®÷³áðãùå¼½üêûÂá!ãùé„9õµ÷áá¼ –úêû¸†¾†½üÝùãù®÷µ÷¢à•Y´àãƒÍàƒàƒà‡à‡àŠàŠààà‘à‘à“â¼ È!Â ÁÈ!Â ÚÈ!ÂÂ¼ÍÝ®â¼ È!Ã ÁÈ!Ã ÚÈ!ÃÃ¼›åÏâ¼ È!Ä ÁÈ!Ä ÚÈ!ÄÄ¼€Þêâ¼ È!Å ÁÈ!Å ÚÈ!ÅÅ¼éëßèƒ7 Create a new `StableGraph` by mapping nodes and edges.Áää:ï•6£å=¶–6åå‚æä–6Šæ<ª—6ËæEù—6•ç¦ç›˜6®çNE Nodes and edges that are not removed maintain their old node or edgeÁèHÓÈ8Îèæè ÇüÚ†Ýùãù™à„üÇúÈêÉß…Êé…ËÈÊÇÉË„ý‘6õè‘’6øè¥’6ûè¹’6ÿèðÝùŸ¼Çü–ú‚ê)ÝùøŒ9õŒ©¡ê ðãùå¼Çüêû¸ê)ãù¹9õ°®×ê Æ –úêû¸†¾†ÇüÝùãù®÷µ÷é•Y¢éãƒ»éñèñèõèõèøèøèûèûèÿèÿèžú×¿‹6û÷/¯ø·†6·ø/ð†6ëø9³‡6©ù+Ùùô‡6áù8¥úø'7ø'7Ú†ÿ¿ „òÍÍºú„Î¬0·úô¬0çú¾‹6†ûÖ‹6Íû¯·2íûŸÙ¿ú« Åú·ú·úÔÿ-×ÿ ù'7ù'7Ú†ù'7ÀŸ„áÿ„¾íâÿª€-€ ú'7ú'7Ú†ú'7îŽ„·€„Ü†¸€€ƒ û'7û'7Ú†„Ž„ÂËïÐÈ!ÑÁÈ!ÑÚÈ!ÑÑÐ¬Ø‘ùŽùÎÖü‘ ”ý÷Û‘ü'7ü'7Ú†„ì‘„ Âí‘ŸžA±Ô4ÚD¨Ô£ÕïÖä ×Õ×ÔÖíñ,¤ž¦¿,§žà-ªžÀà-®žæà-²ž â2îžõ2üžÚ†ØÙ¤ž¤ž§ž§žªžªž®ž®ž‰ŸŒŸý'7ý'7Ú†Ú†Ó’ŸÓØ–úêû¸†¾†“Ÿ¡$¡ þ'7ÿ'7þ'7Ú†ÿ'7Ú†Ó›¡ÓÙ–úêû¸†¾† ¡Ž ¦¡õ£E? Index the `StableGraph` by `NodeIndex` to access node weights.Á„£BÇ£þõ6Ë£)¨Û£ÜïÝä ÞÝÜÞÛíñ,ú£¦¿,ý£à-€¤Àà-„¤Óà-É¤æà-Û¤ Ú†ßàú£ú£ý£ý£€¤€¤„¤„¤ì¤ñ¤ÚÚ–ú¥+„¥€€(7€(7Ú†Ÿ¼€(7–úÚŠ¥Úà–úêû¸†¾†‹¥…‘¥Ò¦H™9á¥B¤¦þõ6¨¦)¨â£ãïää ååâäãíñ,×¦¦¿,Ú¦à-Ý¦Àà-á¦Óà-©§æà-»§ Ú†æ×¦×¦Ú¦Ú¦Ý¦Ý¦á¦á¦Ì§7Ï§ €(7(7Ú†Ÿ¼(7–úáÙ§áæ–úêû¸†¾†Þ§…ä§©E? Index the `StableGraph` by `EdgeIndex` to access edge weights.Á¼¨Bÿ¨Òý6ƒ©)¨è£éïêä ëèêéëíñ,²©¦¿,µ©à-¸©Àà-¼©Óà-ªæà-“ª Ú†ìí²©²©µ©µ©¸©¸©¼©¼©¤ª©ªççêû¹ª+¼ª€‚(7‚(7Ú†å¼‚(7êûçÂªçí–úêû¸†¾†Ãª…ÉªŠ¬HÒ 9™«BÜ«Òý6à«)¨ï£ðïñä òðòïñíñ,¬¦¿,’¬à-•¬Àà-™¬Óà-á¬æà-ó¬ Ú†ó¬¬’¬’¬•¬•¬™¬™¬„7‡ €ƒ(7ƒ(7Ú†å¼ƒ(7êûî‘îó–úêû¸†¾†–…œš®8" Create a new empty `StableGraph`.Áô%¨õ£öï÷ä øøõ÷öíñ,Ÿ®¦¿,¢®à-¥®Àà-©®Óà-á®æà-ó® Ú†ùŸ®Ÿ®¢®¢®¥®¥®©®©®„¯‡¯Ú†ôôù–úêû¸†¾†ö°J' Convert a `Graph` into a `StableGraph`ÁÆ¯*ñ¯# Computes in **O(|V| + |E|)** time.Áõ¯&œ°: The resulting graph has the same node and edge indices asÁ °= the original graph.ÁÞ°¨û£üïýä þýüþûíñ,û°¦¿,þ°à-±Àà-…±Óà-Ï±æà-á± Ú†ÿû°û°þ°þ°±±…±…±ò±'õ±ò…Ú†úúÿ–úêû¸†¾†ÿŸú±È²ÿÈ!€ÁÈ!€ÚÈ!€€ÿÊþÉ³ÿÈ!ÁÈ!ÚÈ!ÿ«€ÏºJ' Convert a `StableGraph` into a `Graph`Á·*¸·º«9¼·&ã·L This translates the stable graph into a graph with node and edge indices inÁç·O= a compact interval without holes (like `Graph`s always are).Á·¸@ø¸M Only if the stable graph had no vacancies after deletions (if node bound wasÁü¸PM equal to node count, and the same for edges), would the resulting graph haveÁÍ¹P- the same node and edge indices as the input.Ážº0¨ƒ£„ï…ä †…„†ƒíñ,Ôº¦¿,×ºà-ÚºÀà-ÞºÓà-¨»æà-º» ò…‡ÔºÔº×º×ºÚºÚºÞºÞºË»1Î»Ú†ò…‚‚‡–úêû¸†¾†8Ó»£ÃKü‰¨Š£‹ïŒä ŠŒ‰‹ŠÃ-¬ÃÃ-¯Ã°Ã-²ÃÃÃ-¶ÃêÃ-ýÃýÃ-Ä âÐŽ¨Ã¨Ã¬Ã¬Ã¯Ã¯Ã²Ã²Ã¶Ã¶Ã Ä¥Äˆˆ³ö‰üƒËÄÐÄˆˆ‘‘ñ5•ŸÁ°>ËßÀ!i‰üƒ¢ƒ€Å0ƒÅâÐâÐ€ˆˆ‰üƒ–ƒœƒ¢ƒ“Å¶Æ?ý¸7ŽÆ'ÁÆü’¨“ä ””’“ŠÃ-ÔÆƒ¹2ÛÆƒ’ü×Æ–¹2ßÆ ½9¨ æ·’ü¨ ‘‘ñ5•ŸÁ°>ËßÀ!i’üƒæ·Á°>ËßÀ!i•ÐÆÐÆÔÆÔÆ’ÛÆÛÆüÆ;üÆ‘‘´»´»¢‚ ¶»ŸŒ)·»÷Œ)‹mŸÉ™A3«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+’üÌ¾¼Ç6ü—¨˜ä ™˜™—ŠÃ-ÅÇƒ¹2ÈÇ–¹2È ôÐš›žÁÇÁÇÅÇÅÇÈÇÈÇ’È —È––ÚÌ—üƒ»È(¾È„(7„(7ôÐõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êôÐÙ9–ÃÈ–›—üƒæ·ÈÈ‘É›È!œÁÈ!œÚÈ!œœ›¤¿¶É›È!ÁÈ!ÚÈ!›ÛÂßÉ-âÉ …(7…(7ôÐšò–ìÉ–ž—üƒæ·íÉÕÊAü ¨¡ä ¢ ¡¢ŠÃ-ÞÊƒ¹2áÊ–¹2¥Ë ³Ñ£ÚÊÚÊÞÊÞÊáÊáÊ¶Ë-¹Ë †(7†(7³ÑõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê³ÑÙ9ŸÃËŸ£ üƒæ·ÈË’Ì£È!¤ÁÈ!¤ÚÈ!¤¤£øÉ·Ì£È!¥ÁÈ!¥ÚÈ!¥¥£ÖÌšÎ=ü§£¨ä ©¨©§¦¿,£Îƒ¹2¦Î–¹2ªÎ ÆÒªŸÎŸÎ£Î£Î¦Î¦ÎÞÎáÎ‡(7‡(7ÆÒÆÒ¦çÎ¦ª§üêûæ·èÎÏ<ü¬£ä ®¬®¦¿,˜Ïƒ¹2›Ï–¹2ŸÏ šÓ”Ï”Ï˜Ï˜Ï›Ï›ÏÐÏAü°£±ä ²°±²¦¿,ÙÏƒ¹2ÜÏ–¹2àÏ ‚´7ŸÐ îÓ³ÕÏÕÏÙÏÙÏÜÏÜÏ°Ð ³Ðˆ(7‰(7ˆ(7îÓ‰(7îÓ¯¶Ð¯³°üêûæ··ÐŽ ½Ð™Ñ(üµä ¶£·µ¶·3¢ÑÃ-¦Ñ¡3ÐÑ Œ#Œ#…¥#…‘#Q“#Š&e“èñ$Ãµü–ƒÞ¬¸žÑžÑ¢Ñ¢Ñ¦Ñ¦Ñ…Ó‘à2áÑ…ÒÄà2Ò6ƒá2ÈÒ8ŒÓŠ(7Š(7çÏ9µü–ƒ´“Ó´¸µüÞ¬–ƒ”ÓÂÓ4üºä »£¼¼º»3ËÓÃ-ÏÓ¡3…Ô ÆÔ½¾¿ÀÁÂÃÇÓÇÓËÓËÓÏÓÏÓ–Ô›Ô¹¹ˆ·Ô¼Ô¹¹”‘ØÔÝÔ¹¹–ƒîÔ ñÔ‹(7‹(7ÆÔÆÔ«¹øÔ¹ÀºüÞ¬–ƒùÔ°Õ ³ÕŒ(7Œ(7ÆÔñÔ9¹ºÕ¹ÁºüÞ¬–ƒ»ÕòÕõÕ(7(7ÆÔ(7–ƒ¹üÕ¹ÂºüÞ¬–ƒýÕ©Ö¬ÖŽ(7Ž(7ÆÔÆÔ¬¹¯Ö¹ÃºüÞ¬–ƒ°ÖäÖBüÅ¨Æ£ÇïÈä ÉÈÅÇÉÆŠÃ-íÖÃ-ðÖ°Ã-óÖÃÃ-÷ÖêÃ-µ×ýÃ-Ç× šÕÊËéÖéÖíÖíÖðÖðÖóÖóÖ÷Ö÷ÖØ× Ý×ÄÄÔÔ„9Ùý‡Ú‘9Û¾Ýú‹ÞÔ§-¦ŒøtÅü–ƒœƒ¢ƒÿ×.‚ØšÕšÕ·šÕÑÄÄËÅüƒ–ƒœƒ¢ƒˆØš±ŽØÏØJüÍ¨Î£ÏïÐä ÑÐÍÏÑÎŠÃ-ØØÃ-ÛØ°Ã-ÞØÃÃ-âØêÃ-¨ÙýÃ-ºÙ ¬ÕÒÓÔØÔØØØØØÛØÛØÞØÞØâØâØËÙÐÙ ÌÌÔÔ„9Ùý‡Ú‘9Û¾Ýú‹ÞÔ§-¦ŒøtÍü–ƒœƒ¢ƒúÙOýÙ¬Õ¬Õ·Ü¬ÕÚÌÌÓÍüƒ–ƒœƒ¢ƒŒÚš±’Ú•‡£ÚªÛ3ÒÍ6ùÚ0µÛüÕ£Öï×ä ØØÕ×Ö¦¿,¿Ûà-ÆÛÀà-ÊÛêûÕüÂÛ¾†ÕüÎÛÓà-ìÛæà-þÛ ãá9ÑÜ øá9ÑÜ ÔÔ„9Ùý‡Ú‘9Û¾Ýú‹ÞÔ§-¦ŒøtÕüêû¸†¾†§-¦ŒøtÙÚÛÝÞ»Û»Û¿Û¿ÛÕÆÛÆÛÊÛÊÛÕ²Ü²Ü ÔÔŸ¼ÑÜ ÑÜÔÔÕüîŽ”Ý”ÝÔÔ°ýªÝ¢Þ¢Þ ÔÔÜ¼Þ¼ÞÔÔ¾¸ÔÞ5üà£áïâä ãàâáã¦¿,ÝÞà-àÞÀà-äÞÓà-˜ßæà-ªß ¾ÕäåÙÞÙÞÝÞÝÞàÞàÞäÞäÞ»ß ÀßßßŒ#Œ#…¥#…‘#Q“#Š&e“èñ$Ãàüêû¾†æß(éß(7(7¾ÕõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê¾ÕÙ9ßîßßåàüêû¸†¾†óßÜñ(ßñ ™ª™ªåççÆö3éñæ°²æüðñéñéñ÷ñ‚ò¢òSüë¨ì£íïîä ïíïìîëŠÃ-«òÃ-²ò°Ã-¹òÃÃ-½òƒëü®ò–ƒëüµòêÃ-„óýÃ-–ó «Öðñò§ò§ò«ò«òë²ò²òë¹ò¹ò½ò½ò§ó¬óêêŒ#Œ#…¥#…‘#Q“#Š&e“èñ$Ãëü–ƒ¢ƒÔóÙóêêóó“*÷Ÿ8e£ew$RÑëü–ƒ¢ƒ•õ0B Create an iterator over all edges in the graph, in indexed order.ÁŠôEÔôˆ5Üô4˜õ«Ö«Ö‹êêòëüƒ–ƒœƒ¢ƒ¨õÏö8·Ï7§ö'Úöüô£õä öôõö¦¿,íöƒ¹2ôöêûôüðöæ·ôüøöÝñ9¨ òñ9¨ óó“*÷Ÿ8e£ew$RÑôüêûæ·8e£ew$RÑ÷éöéöíöíöôôöôöôŽ÷;Ž÷óó´»´»¢‚ ¶»ŸŒ)·»÷Œ)‹mŸÉ™A3«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+ôü•£Î÷6üù£úä ûùúû¦¿,×÷ƒ¹2Ú÷–¹2“ø ½ÖüýÓ÷Ó÷×÷×÷Ú÷Ú÷¤ø ©øøøŒ#Œ#…¥#…‘#Q“#Š&e“èñ$Ãùüêûæ·Ïø(Òø(7(7½ÖõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê½ÖÙ9ø×øøýùüêûæ·Üø˜ùýÈ!þÁÈ!þÚÈ!þþý÷£Ëù ýÈ!ÿÁÈ!ÿÚÈ!ÿÿýí§ëúAü£‚ä ƒ‚ƒ¦¿,ôúƒ¹2÷ú–¹2»û üÖ„ðúðúôúôú÷ú÷úÌû-Ïû ‘(7‘(7üÖõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êüÖÙ9€Ùû€„üêûæ·Þû›ü„È!…ÁÈ!…ÚÈ!……„•Îü „È!†ÁÈ!†ÚÈ!††„ú¯¸þ>´¡6îýIÃþ üˆ¨‰ïŠä ‹ˆŠ‰‹ŠÃ-Ñþà-ØþÀà-ÜþƒˆüÔþæà-àþ Äþ9¨ ¾†ˆü¨ ‡‡Ü†ŒŸ•‡ŽÔ.MÛñ×cíˆüƒ¸†¾†.MÛñ×cíŒŽÍþÍþÑþÑþˆØþØþÜþÜþýþ;ýþ‡‡´»´»¢‚ ¶»ŸŒ)·»÷Œ)‹mŸÉ™A3«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+ˆü¹¶¾ÿ¾ÿ‡‡ÜÒÿÒÿ‡‡¾¸êÿ=ü¨‘ï’ä “’‘“ŠÃ-óÿà-úÿÀà-þÿƒüöÿÓà-¶€æà-È€ »×”•ïÿïÿóÿóÿúÿúÿþÿþÿÙ€ Þ€Ÿ¼ø€+û€’(7’(7»×áÖ€•üƒ¸†¾†…½†3ˆª6ä…*†& Iterator element type is `NodeIndex`.Á“†)È† ü—£˜ä ™˜™—¦¿,Ö†ƒ¹2Ý†êû—üÙ†æ·—üá†÷†:¹‡ Œ‡:¹‡ ––î šý‡›‘9œ¾ß5¶cz#º—üêûæ·ß5¶cz#ºš›œÒ†Ò†Ö†Ö†—Ý†Ý†—š‡š‡ ––ÚÌ¹‡ ¹‡––—üñÉß‡ß‡––ÍÇõ‡ü‡$üŸ£ ä ¡ ¡Ÿ¦¿,…ˆƒ¹2ˆˆ–¹2¯ˆ ––î šý‡›‘9œ¾ß5¶cz#ºŸüêûæ·¢ˆˆ…ˆ…ˆˆˆˆˆÍŠ)¹¶6ÀˆEˆ·6Š‰-¼‰Ë·6Ä‰L¡¸6•Š3ÔŠ“(7“(7ÊŠ:ÈžÛŠž¢Ÿüêûæ·ÜŠ«Œ1ü¤£¥ä ¦¤¥¦¦¿,´Œƒ¹2·Œ–¹2ëŒ ”Ø§¨°Œ°Œ´Œ´Œ·Œ·ŒüŒ ££ÚÌœ+Ÿ”(7”(7”ØÿÕ£¤£¨¤üêûæ·©”ØŽ7‡”SÛ”M See [*.detach()*](struct.Neighbors.html#method.detach) for more information.Áß”P°•›7´•Jï7ÿ•E¾‘7Å–ã–õ®+ç–’Ý5ï– use petgraph::Incoming;Á—êý8©—,Ö—! let mut gr = StableGraph::new();ÁÚ—$íÝ5ÿ—“Þ5œ˜¹Þ5¹˜ßÞ5Ö˜ƒß5ñ˜§ß5Œ™§™•“7«™I¢à5õ™#Ïà5™š*„”7Äš*Åá5ïšG–â5·›5Ô”7í›!ºÕ,œ ç×,™œŸœâã5£œƒä5»œ§ä5ÖœËä5ñœõ®+ŒŸ ä ªªº›.èØšV}Ki«··©©¿½Û/ä º›.àÍ›.ä èØ™#àà¹ž%ä ¯¯º›.¾žÍ›.Âž èØ°´¹¾ž¾ž¡ŠÁ™7åžJ´Ÿ¡š7¼ŸGòš7ˆ 0¬›7½ -ã›7ï 9´¡•(7–(7•(7èØ–(7ôæîà®¨±£²ï³±²³Ö¡®ŠÃ-¹¡Ã-¼¡°Ã-¿¡êÃ-Ã¡ÏèÛ¡ÿŸé¡¹¡¹¡¼¡¼¡¿¡¿¡ã¢~ê¢ —(7˜(7—(7èØ˜(7ôæÌë®¨µ£¶ï·µ¶·‘£®ŠÃ-ô¢Ã-÷¢°Ã-ú¢êÃ-þ¢´¡‹ƒ–ƒœƒ–£ÿŸ¤£ô¢ô¢÷¢÷¢ú¢ú¢ý£´È!¸ÁÈ!¸ÚÈ!¸¸´é‘¤~˜¤ ™(7š(7™(7èØš(7ôæ¶ï®¨º£»ï¼¼º»¿¤®ŠÃ-¢¤Ã-¥¤°Ã-¨¤êÃ-¬¤¹¡‹ƒ–ƒœƒÄ¤ÿŸÒ¤¢¤¢¤¥¤¥¤¨¤¨¤«¥¹È!½ÁÈ!½ÚÈ!½½¹¾ìì¥5+ Iterator over the node indices of a graph.Á½¥.÷¥ü¿¨Àä ÁÀÁ¿ŠÃ-‡¦ƒ¹2Ž¦ƒ¿üŠ¦æ·¿ü’¦þ :¨ “¡:¨ ¾¾¬µÂŸ6gìd°¢¿üƒæ·6gìd°¢Âƒ¦ƒ¦‡¦‡¦¿Ž¦Ž¦¿¨¦;¨¦¾¾´»´»¢‚ ¶»ŸŒ)·»÷Œ)‹mŸÉ™A3«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+¿üÌ¾è¦>üÄ¨Åä ÆÄÅÆŠÃ-ñ¦ƒ¹2ô¦–¹2ø¦ œÙÇÈÊí¦í¦ñ¦ñ¦ô¦ô¦§ ²§ÃÃÚÌÍ§(Ð§›(7›(7œÙõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êœÙÙ9ÃÕ§ÃÈÄüƒæ·Ú§–¨ÈÈ!ÉÁÈ!ÉÚÈ!ÉÉÈÆï½©-À© œ(7œ(7œÙšòÃÊ©ÃÊÄüƒæ·Ë©³ªIüÌ¨Íä ÎÌÍÎŠÃ-¼ªƒ¹2¿ª–¹2Ãª ÜÙÏ¸ª¸ª¼ª¼ª¿ª¿ªƒ«-†« (7(7ÜÙõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÜÙÙ9Ë«ËÏÌüƒæ·•«Ò«ÏÈ!ÐÁÈ!ÐÚÈ!ÐÐÏØ÷÷¬>¨Ò£ÓïÔä ÕÕÒÔÓíñ,ü¬¦¿,ÿ¬à-‚Àà-†Óà-Äæà-Ö Ú†ÖØÙü¬ü¬ÿ¬ÿ¬‚‚††¦®7 Return an upper bound of the node indices in the graphÁç:©® ž(7ž(7Ú†Ñ´®ÑÖ–úêû¸†¾†µ®ø®ÖÈ!×ÁÈ!×ÚÈ!××Öðý•¯.˜¯Ÿ(7Ÿ(7Ú†Ÿ¼Ñ¡¯ÑØ–úêû¸†¾†¢¯éÞ¨¯ã¯/æ¯ (7 (7Ú†¶‚0Ññ¯ÑÙ–úêû¸†¾†ò¯éÞø¯è°5+ Iterator over the edge indices of a graph.Á¹°.ó°üÛ£Üä ÝÜÝÛ¦¿,ƒ±ƒ¹2Š±êûÛü†±æ·ÛüŽ±ü²:¨ ‘³:¨ ÚÚ†·ÞŸ—“b°ËÅÌpÛüêûæ·—“b°ËÅÌpÞÿ°ÿ°ƒ±ƒ±ÛŠ±Š±Û¤±;¤±ÚÚ´»´»¢‚ ¶»ŸŒ)·»÷Œ)‹mŸÉ™A3«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+Ûü•£ä±>üà£áä âàáâ¦¿,í±ƒ¹2ð±–¹2ô± ¤Úãäæé±é±í±í±ð±ð±©² ®²ßßÎÇÉ²(Ì²¡(7¡(7¤ÚõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê¤ÚÙ9ßÑ²ßäàüêûæ·Ö²’³äÈ!åÁÈ!åÚÈ!ååä†… ¹´-¼´ ¢(7¢(7¤ÚšòßÆ´ßæàüêûæ·Ç´¯µIüè£éä êèéê¦¿,¸µƒ¹2»µ–¹2¿µ äÚë´µ´µ¸µ¸µ»µ»µÿµ-‚¶ £(7£(7äÚõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êäÚÙ9çŒ¶çëèüêûæ·‘¶Î¶ëÈ!ìÁÈ!ìÚÈ!ììëá £ƒ `Frozen` is a graph wrapper.Áíÿ Ž€: The `Frozen` only allows shared access (read-only) to theÁ’€=: underlying graph `G`, but it allows mutable access to itsÁÐ€= node and edge weights.ÁŽ©= This is used to ensure immutability of the graph's structureÁ@6 while permitting weights to be both read and written.Áî9¨‚A See indexing implementations and the traits `Data` and `DataMap`Á¬‚D. for read-write access to the graph's weights.Áñ‚1®ƒüïêððïç¿-¹ƒÂïü¼ƒÀÂ:Àƒ ííîž¸ñÎÃcúK’í]_¢ïüÂcúK’í]_¢îñïüÂßÂ:£ƒ®ƒÃµÃ:ßÂ:ííîïüÂµƒµƒ¹ƒ¹ƒïÀƒ Àƒ ÃííµÃ:á,Æ,ŽÆ,ÿíÉ,Þ, ŒÊ,Ò,È¸Ô, Ä,L `GraphMap` is a graph datastructure where node values are mappingÁO keys.ÁP é,š¥Þìü“®áâí˜°š#¦#³#ö n²òóIndexMapIterÁ®òõIndexMapIterMutÁ©Êòöõ‘ É§ò÷·áÃñòøŸô–%òùÛ‰¡IòûÔIòüÚIòü†¥à8òýÁÜÃ˜»òÿyËŠ9ò€Ö´ò‘òÖ´ò’òuŽÑòƒŠ 2•¤ò„¦ 2œ¨ò…¦ ·«Nò†¬ÂÊá$òˆŽÁÔ È$ò‰ÿ(ßýòŠÎ¶éÌ$ò‹Î¶éÍ$ò‹¹¶óÉ$òŒ¹¶óÊ$òŒ×Âý ç$òïN ‚òŽß·y»¤òìÙŒÕ‰ò‘ïˆé Ðò’žô®ò“Šžâò•ô‹³þò–âŽÇ §ò—‚ŽÒ œò˜àûíƒò™_nxAˆž©¿É×áïôý‰‚š¥·(ÃÃËËåòƒ"ŽŽ••œœ«·ÂFÊÊÔ Ô ßßééóóý ý »ÆCÕÕé é ôôQžž³³Ç Ç Ò Ò æí‹$ A `GraphMap` with undirected edges.Á€'¨ÐÑ4¬M¦Ò4ú” ¨›£œœ›íñ,Ÿ¦¿,¢š#š#¼µž#äyòŸ#‘9ò #ÔòfM{5Í{7Ó–úêû…üŸŸ¢¢Ä" A `GraphMap` with directed edges.ÁÃ%é¿Î4íM•Ï4»Í ¨ž£ŸžŸíñ,Ø¦¿,Ûš#š#¼µž#äyòŸ#‘9ò #ÔòfM{5Í{7Ó–úêûÎùØØÛÛþ^¨¡£¢ï£¡¢£íñ,ƒ¦¿,žà-¶£.†££.‹ÚÚ4’ íÚ4¡ Óà-±ßˆ¤ƒƒžžã4æ¤(7¥(7¦(7¤(7ßˆ¥(7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S¦(7Ôž ê ¤–úêû¸†ëññ&0 A trait group for `GraphMap`'s node identifier.Á½3û ¥¥ï¾-†ÏŒ4¼Œ4“¥ï¾-†ÏŒ4¼Œ4“›¨§§íñ, Õó,¼Š¼.Ã££.É–ú Ú)¼Û©ŠÜ¼Û¨¨©‹’·.¼Û«ì%ï§(7¨(7§(7¼Û¨(7Üªòª«óŽ ùÉ!¨£®ï¯®¯íñ,Î¦¿,Ñà-Ô²Š.ø Óà-‹ßˆ°±²³´µ·¸¹º»¼½ÀÃÄÅÆÇÈÉÊËÌÍÎÎÑÑÔÔ» Create a new `GraphMap`Á›Âßˆ¬¬°–úêû¸†®81 Create a new `GraphMap` with estimated capacity.Áõ4µ ßˆ¬¬žüäyÃ‘9ÑÔ(·ë5”;Û©(7©(7ßˆŽ£ ¬ä¬²–úêû¸†å£!¦ò–ú–ú¡â¬¬˜¸ š±¯ª±µê !& Whether the graph has directed edges.Á¼ )ñ ª(7ª(7ßˆ¬ý ¬´–úêû¸†þ µ&~3 Create a new `GraphMap` from an iterable of edges.Á³!6î!. Node values are taken directly from the list.Áö!1ð†6¬"9³‡6ê"+š#ô‡6¢#8ß#õ®+ç#$ use petgraph::graphmap::UnGraphMap;Áó#'Ÿ$% // Create a new undirected GraphMap.Á§$(9 // Use a type hint to have `()` be the edge weight type.ÁÔ$<, let gr = UnGraphMap::<_, ()>::from_edges(&[Á•%/ËÐ,É%çË3í%‡Ì3‰&ËÑ,&õ®+©&¼& ¡Â ßˆ¬ò¶¶¬ºµ0Ç&àµ0ô&ƒ‡¨ êû“'‡¨ êû…–ú§' µ–úêû¸†¡Â « Ê&Ç&Ç&˜(!) Return the number of nodes in the graph.Áç',Ÿ( «(7«(7ßˆ¬ª(¬•ÿ«(‘)!Šñ4à(,˜) ¬(7¬(7ßˆ¬£)¬ì–!¤)ü)Ÿé5Ù)ƒ*(7(7ßˆ¬‰*¬¹–úêû¸†Ž*ü*% Add node `n` to the graph.ÁÙ*ƒ+®(7®(7ßˆ–ú–ú¬Œ+¬àì‘+–µ—+ô,+' Return `true` if node `n` was removed.Áì+*›,I Computes in **O(V)** time, due to the removal of edges with other nodes.Á£,Lû,¯(7¯(7ßˆ–ú¬‡-¬»–úêû¸†Œ-–µ’-ì0)5 Return `true` if the node is contained in the graph.Á¯08ó0 °(7°(7ßˆ–ú¬1¬¼–úêû¸†‚1–µˆ1‚8>A Add an edge connecting `a` and `b` to the graph, with associatedÁÇ1DC data `weight`. For a directed graph, the edge is directed from `a`Á2F to `b`.ÁÛ2ë2G Inserts nodes `a` and/or `b` if they aren't already part of the graph.Áó2JÂ3? Return `None` if the edge did not previously exist, otherwise,ÁÊ3B= the associated data is updated and the old value is returnedÁ‘4@ as `Some(old_weight)`.ÁÖ4õ4õ®+ý4A // Create a GraphMap with directed edges, and add one edge to itÁ‰5D$ use petgraph::graphmap::DiGraphMap;ÁÒ5'þ5 let mut g = DiGraphMap::new();Á†6" g.add_edge("x", "y", -1);Á6 assert_eq!(g.node_count(), 2);ÁÏ6" assert_eq!(g.edge_count(), 1);Áö6"$ assert!(g.contains_edge("x", "y"));Á7'% assert!(!g.contains_edge("y", "x"));ÁÉ7(õ®+ö7‰8±(7±(7ßˆ–ú–úêûîô¬’8¬êï—8š±8ª±£8Š&©8Å:½È!¾ÁÈ!¾ÚÈ!¾¾½²Å È<½È!¿ÁÈ!¿ÚÈ!¿¿½ÇÈ ª>F>ò²(7³(7´(7²(7ßˆ³(7–ú´(7–úÜ¬À>¬“· Å>š±Ë>ª±Ò>•‡Ù>Ö@ÀÈ!ÁÁÈ!ÁÚÈ!ÁÁÀÐÖ ¹CÀÈ!ÂÁÈ!ÂÚÈ!ÂÂÀ‘Ú ÕI6G Remove edge from `a` to `b` from the graph and return the edge weight.ÁØEJ§F( Return `None` if the edge didn't exist.Á¯F+ßFõ®+çFH // Create a GraphMap with undirected edges, and add and remove an edge.ÁóFK…ä:ÃG'ïG let mut g = UnGraphMap::new();Á÷G"Áô:žHÀH) let edge_data = g.remove_edge("y", "x");ÁÈH,! assert_eq!(edge_data, Some(-1));ÁùH$ assert_eq!(g.edge_count(), 0);Á¢I"õ®+ÉIÜIµ(7µ(7ßˆ–ú–úîô¬èI¬Ã–úêû¸†íIš±óIª±ùIÉM/M Return `true` if the edge connecting `a` with `b` is contained in the graph.ÁôLPÐM ¶(7¶(7ßˆ–ú–ú¬ÞM¬Þ‡ßMš±åMª±ëM£O0 Return an iterator over the nodes of the graph.Á½N3õN Iterator element type is `N`.ÁýN!ªO·(7·(7ßˆ³#³#”·¶#Ÿò¤§áâÞ·(7–ú¬°O¬Å–úêû¸†±O¹R1µ›5˜PCàP‚Ö2èP*œœ5—Q-ÉQßœ5ÑQ=“‚;“R!ÀR ¸(7¸(7ßˆ–úÞÞî âŸòãÔò`‚ßÛ”Ì¸(7–ú¸†¬ÊR¬ˆ!ËRš±ÑR›XR· 5»TJ‹¡5ŠU$¹¡5³UO‡Vž¢5V1Ù¢5ÅV/œœ5ùV-«Wßœ5³W=“‚;õW!¢X¹(7¹(7ßˆ–úÜìì¬"ðŸòñ—Àòò•‡òóÔò¸Ç‡®uª“Ç¹(7–ú¸†¬µX¬Ç–úêû¸†¶Xš±¼X•‡ÂXÏ]/C Return an iterator of target nodes with an edge starting from `a`,ÁòZF+ paired with their respective edge weights.Á½[.ð[‚Ö2ø[*œœ5§\-Ù\ßœ5á\=$ Iterator element type is `(N, &E)`.Á£]'Ö]º(7º(7ßˆ–úüü„9¹ò‚‘9òƒŸò³ã^9a;ü.º(7–úêû¸†¬Ü]¬È–úêû¸†Ý]¹ã]‚`3B Return a reference to the edge weight connecting `a` with `b`, orÁ€_E0 `None` if the edge does not exist in the graph.ÁÊ_3‰`»(7»(7ßˆ–ú–úõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê»(7êû¬•`¬˜œ"–`š±œ`ª±¢`ûa?J Return a mutable reference to the edge weight connecting `a` with `b`, orÁñ`M»Œ;Ãa3‚b¼(7¼(7ßˆ–ú–úõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê¼(7êû¬’b¬ç "—bš±bª±£bd-U Return an iterator over all edges of the graph with their weight in arbitrary order.ÁúbX×c& Iterator element type is `(N, N, &E)`Áßc)”d ½(7½(7ßˆ““£Â˜š´ò™ÔòÅ;FJ*|D‡½(7–úêû¸†¬žd¬Ë–úêû¸†ŸdÛf8\ Return an iterator over all edges of the graph in arbitrary order, with a mutable referenceÁ¤e_ to their weight.Áˆf¡f* Iterator element type is `(N, N, &mut E)`Á©f-âf ¾(7¾(7ßˆ®®ýÃ³š´ò´Ôò¢S|¾„ç)Û¾(7–úêû¸†¬ðf¬Ì–úêû¸†õfla6 Return a `Graph` that corresponds to this `GraphMap`.Á„h9ÂhI 1. Note that node and edge indices in the `Graph` have nothing in commonÁÊhLK with the `GraphMap`s node weights `N`. The node weights `N` are used asÁ›iN/ node weights in the resulting `Graph`, too.Áîi2, 2. Note that the index type is user-chosen.Á¥j/Ùj- Computes in **O(|V| + |E|)** time (average).Ááj0–k= **Panics** if the number of nodes or edges does not fit withÁžk@" the resulting graph's index type.Áãk%”l ßˆò…¬ä ÎÎ¬Àà-Ÿlæà-ÖlÍ–úêû¸†¾†£lŸlŸl»q>·â:„q6¨Ð£ÑïÒòÓÐÒÑÓìÜêû…–úžr ¦¿,Ãqà-Æq‚ìÜÊqƒìÜêûŠríñ,Àq²Š.²r Óà-ÅrßˆÔÀqÀqÃqÃqÆqÆqÊqÊqÕrTØr ÿ¿ ßˆÏòÕÕÏÎ¬0ârô¬0sÿ¿ å8ìÜœsÔ–úêû¸†ìÜÿ¿ « årârârËu8¿‹6Þt/Žuô‡6’u8¨×£ØïÙòÚØÚ×Ùµ;¨v ¦¿,Óuà-Öuâ;Úuõ;”víñ,Ðu²Š.¼v Óà-ÏvßˆÛÐuÐuÓuÓuÖuÖuÚuÚußvTâv¿(7¿(7ßˆÿ¿ ÖòÜÜìvÖÎ¬0évô¬0™wäŸ;¦wÛ–úêû¸†ìÜÿ¿ ñv« ÷vévévÓyày îyÝ~ôyßzõy šöyúy íüy‚zƒz„zz…z typarmÁ†zŒz Ïzz‘z’z %”zšz¬zœzz©zžz boundsÁŸz¥z Ï§zªz ³z·z¹z ºz¾z ÔÀzÂz ŸÉzÍzÏz ŸÐzÔz ÔÖzØzázäzÚ~% îz òzùz šúzÿz€{{‰{‚{ ˜¤;ƒ{Š{‹{Œ{ %Ž{”{•{{–{ ™¥;—{ž{ {Ã{ Ÿ®{²{´{ Ÿµ{¹{ Í{Ñ{Ò{Ó{Û{Ô{ ˜¤;Õ{Ü{Ý{Þ{ Ÿà{ é{í{ šî{ó{ô{õ{ý{ö{ ˜¤;÷{þ{ÿ{€| %Ž|”|•||–| ™¥;—|ž|¨|Ô~ "¶| ò»|À|Â| Ã|Ç|Õ|Ö|Ý| Œ×| ë| ¾î|ò|ü|ó| ô| ø|þ| µ}‡} ˆ}Œ} òŽ}’}”}Ã} ¦}ª} Ÿ«}¯} ¾°}´}µ}Ò}Ó}Ú} ŒÔ} è} âië} ô}ú}õ} ö}ü}ÿ}”~ ™€~…~ µ‡~~ ™Ž~“~–~Ê~ ¨~¬~ Ÿ~±~ âi²~ »~¼~Û~ê,õ üß¨àïáàáßŠÃ-ƒ€à-†€ƒßü¤€Óà-°€‚²;¨ ÞÞî âŸòãÔò`‚ßÛ”Ìßüƒ¸†`‚ßÛ”Ìâãÿÿƒ€ƒ€ß†€†€…üÀ€%À€òÞÞ«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+ßüÖ¾!ë€ë€òÞÞ¾¸ƒ1üå¨æïçåæçŠÃ-Œà-¥ƒÂ Óà-Õ¾ÝèéˆˆŒŒå êääƒøûÀ(7À(7¾Ý±ò ä€‚äéåüƒ¸†…‚ò‚éÈ!êÁÈ!êÚÈ!êêé«Ã!öƒ éÈ!ëÁÈ!ëÚÈ!ëëéØÅ!–„'¡„üí¨îïïíîïŠÃ-·„à-º„ƒíüË„Óà-×„£¹;¨ ìì¬"ðŸòñ—Àòò•‡òóÔò¸Ç‡®uª“Çíüƒ¸†¸Ç‡®uª“Çðñòó³„³„·„·„íº„º„ç„%ç„òìì«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+íüÖ¾!’… ’… òììƒ¥…¥…òììÜ¹…¹…òìì¾¸Ñ…9üõ¨öï÷õö÷ŠÃ-Ú…à-Ý…°µ;˜† Óà-«†‡ÞøùÖ…Ö…Ú…Ú…Ý…Ý…»† À†ôôƒÎ†Ñ†Á(7Á(7‡Þ±ò ôÖ†ôùõüƒ¸†Û†›ˆùÈ!úÁÈ!úÚÈ!úúù¯Ð!·Š ùÈ!ûÁÈ!ûÚÈ!ûûùÆÒ!×Š"âŠüý¨þ£ÿï€€ýÿþŠÃ-ìŠÃ-ïŠ°Ã-öŠ–ƒýüòŠƒýü‡‹°µ;Œ‹ êÃ-Ÿ‹ÊÁ;¼‹µÁ;¼‹üü„9¹ò‚‘9òƒŸò³ã^9a;ü.ýüƒ–ƒœƒ³ã^9a;ü.‚ƒèŠèŠìŠìŠýïŠïŠýöŠöŠ¯‹¯‹òüüƒ¼‹¼‹òüüýü©Þ!à‹à‹òüüÞÞî âŸòãÔò`‚ßÛ”Ìýüƒœƒÿ‹3ü…¨†£‡ïˆˆ…‡†ŠÃ-ˆŒÃ-‹Œ°Ã-ŽŒƒ…üÀŒ°µ;ÅŒ –ƒ…ü×ŒêÃ-ãŒæÞ‰Š„Œ„ŒˆŒˆŒ…‹Œ‹Œ…ŽŒŽŒóŒ øŒ„„ƒƒ…ü–ƒ’(•Â(7Â(7æÞõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êæÞÙ9„š„Š…üƒ–ƒœƒŸ—HüŒ¨£ŽïŒŽŠÃ- Ã-§°Ã-®ƒŒü£–ƒŒüª°µ;í êÃ-€‘½ß‘’œœ Œ§§Œ®®‘•‘‹‹ƒƒŒü–ƒ²‘·‘‹‹““£Â˜š´ò™ÔòÅ;FJ*|D‡Œüƒ–ƒœƒä‘0ç‘½ß½ß‹‹‹’Œüƒ–ƒœƒ÷‘¹’%Ä’ü”¨•£–ï—•—”–ŠÃ-Ñ’Ã-Ô’°Ã-Û’–ƒ”ü×’ƒ”üì’°µ;ñ’ ¬Î;¨ —Î;¨ 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–ƒÄüâ£êÃ-î£¼áÈþ¢þ¢‚£‚£Ä…£…£Äˆ£ˆ£þ£-¤ Ê(7Ê(7¼áõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê¼áÙ9Ã‹¤ÃÈÄüƒ–ƒœƒ¤ë¤ÈÈ!ÉÁÈ!ÉÚÈ!ÉÉÈµ•"œ¥?üË¨Ì£ÍïÎÍÌÎËŠÃ-¥¥Ã-¬¥°Ã-³¥ƒËü¨¥–ƒËü¯¥°µ;é¥ êÃ-ü¥‹âÏÐ¡¥¡¥¥¥¥¥Ë¬¥¬¥Ë³¥³¥Œ¦ ‘¦ÊÊüü„9¹ò‚‘9òƒŸò³ã^9a;ü.Ëüƒ–ƒœƒ²¦.µ¦‹â‹â·‹âÑÊÊÐËüƒ–ƒœƒ»¦š±Á¦½§37 Index `GraphMap` by node pairs to access edge weights.Á‚§:¨Ò£ÓïÔÔÒÓíñ,Â§¦¿,Å§à-È§²Š.þ§ Óà-‘¨ßˆÕÖÂ§Â§Å§Å§È§È§¡¨¦¨ÑÑêû¶¨$¹¨€Ë(7Ë(7ßˆ¡âË(7êûÑ¿¨ÑÖ–úêû¸†À¨…Æ¨·ª6ìò;ü©:¨Ø£ÙïÚØÙÚíñ,¼ª¦¿,¿ªà-Âª²Š.ûª Óà-Ž«ßˆÛ¼ª¼ª¿ª¿ªÂªÂªž«0¡« €Ì(7Ì(7ßˆ¡âÌ(7êû×««×Û–úêû¸†°«…¶«—- Create a new empty `GraphMap`.Áô¬"¨Ý£ÞïßÝÞßíñ,œ¦¿,Ÿà-¢²Š.Ò Óà-åßˆàœœŸŸ¢¢õøßˆÜÜà–úêû¸†ð°> A reference that is hashed and compared by its pointer value.Á»®Aý®8 `Ptr` is used for certain configurations of `GraphMap`,Á¯;9 in particular in the combination where the node type forÁ½¯<= `GraphMap` is something of type for example `Ptr(&Cell)`,Áú¯@1 with the `Cell` being `TypedArena` allocated.Á»°4û°•ÇãåääãíÕ+ƒ±¡÷ã•Ç†±Õÿ;Š± ááâ…ÇåÎîäG¯‡{ã•Ç¡÷îäG¯‡{âåã•Ç¡÷õÿ;ð°û°É€<õÿ;ááâã•Ç¡÷ÿ°ÿ°ƒ±ƒ±ãŠ± Š± 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äèÙÜÝàãæéßßãã©1°à(7à(7šÞ"äèÖ¨Ú£ÛÚÛ½ÖÓÇ0´æÇ0·ÙÛéÞ¬µÈ »È ÿŸº´´··‰!á(7á(7äèÖœÖÜÛéÞ¬œŒ£â(7ã(7â(7äèˆˆã(7šÞ"Ö¨Þ£ßÞß¿ÖÓÇ0°æÇ0³ÝÛéÞ¬µÈ »È Ä¹Ò’½ ïÿŸŠ°°³³½QÄä(7å(7ä(7äèˆå(7šÞ"Ö¨á£âáâÖÖÓÇ0ÐæÇ0ÓàÛéÞ¬µÈ »È Û¹áÿŸöÐÐÓÓÉ@Ðæ(7æ(7äè¯ ÖßÖãÛéÞ¬à†+æ êãÈ!äÁÈ!äÚÈ!ääã£–#òãÈ!åÁÈ!åÚÈ!ååãñ–#Ä?Ë ç(7ç(7äè¯ ÖÙÖæÛéÞ¬Ú†+à §!æÈ!çÁÈ!çÚÈ!ççæ•¤#¯"æÈ!èÁÈ!èÚÈ!èèæã¤#ƒ#AŠ#è(7è(7äè¯ Öš#ÖéÛéÞ¬›#†+¡# ©$éÈ!êÁÈ!êÚÈ!êêé±±#à$éÈ!ëÁÈ!ëÚÈ!ëëé›²#ð'‰B [Graph] Return `true` if the graphs `g0` and `g1` are isomorphic.Áý$EÃ%B Using the VF2 algorithm, only matching graph syntactically (graphÁÇ%E structure).Á&&& The graphs should not be multigraphs.Á¡&)Ë& **Reference**ÁÏ&á&B * Luigi P. Cordella, Pasquale Foggia, Carlo Sansone, Mario Vento;Áå&EA *A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs*Á«'D÷' é(7ê(7é(7ò…ê(7ò…¨í£îïïä ððíïî—(íñ,…(¦¿,ˆ(à-‹(Àà-(Óà-Ý(æà-ï( ì–úêû¸†¾†¸À#“(ËÀ#(…(…(ˆ(ˆ(‹(‹(((Š-ÛÅ<ƒ+EÉ+? Using the VF2 algorithm, examining both syntactic and semanticÁÍ+BH graph isomorphism (graph structure and matching node and edge weights).Á,KÜ,¥Ç<à,)‘-ï(7ð(7ï(7ò…ð(7ò…ðÈ›Æ#¨ò£óïôä õúöê÷õò÷ôöóÅ-íñ,¨-¦¿,«-à-®-Àà-²-™6¶-‚›Æ#¹-Óà-¿.æà-Ñ. ë(7ì(7ððÈë(7–úì(7–úã.ë(7ì(7ðÈóÐ<õô.í(7î(7ð›Æ#í(7êûî(7êû/í(7î(7›Æ#ØÑ<õ’/ñ–úêû¸†¾†ðÈ›Æ#¸À#Á-ËÀ#ß-ÆÉ#. áÉ#˜. ¨-¨-«-«-®-®-²-²-¶-¶-¹-¹-—11ËøåùøùíÕ+1øúûúû11¶1¹1Ëøøúàž¡÷Ð1'Ó1Ëñ(7ò(7ó(7ñ(7àžò(7¡÷ó(7¡÷øÖ1øûàž¡÷Û1á1è1û1‚2ËééZÿ0ÖK¥7ýû1‚2Ëüüéé”2.åÿÿÆö3™2éé€™2™2×2Ú2Ëþþ€°²”3&—3Ëô(7õ(7ö(7ô(7ééõ(7°²ö(7°²þš3þ°²Ÿ3Ó3#åƒú„„ƒ÷(7ø(7ÍŽ÷(7°²ø(7°²õ•4ðë,Û3÷(7ø(7ðÍŽùÙ<„4Æö3Ø3ÍŽ…†Ø3Ø3Û3Û3¯4²4Ë‚‚…°²ÍŽë4&î4Ëù(7ú(7û(7ù(7ÍŽú(7°²û(7°²‚ñ4‚†°²ÍŽö4š±ü4ª±ƒ5ì5¨ï5 Ë)7‚)7ƒ)7„)7…)7)7î¿#‚)7ò…ƒ)7ò…„)7ðÈ…)7›Æ#£À#¨ˆ£‰ïŠä ‹úŒêˆŠŒ‰‹š6 íñ,ù5¦¿,ü5à-ÿ5Àà-ƒ6™6‡6³Ð<Š6Óà-Â7æà-Ô7 øðÈ–úæ7ø›Æ#êû8Æ#¥±"–6¸À#º6ËÀ#Ø6ÆÉ#ö6 áÉ#Ž7 ù5ù5ü5ü5ÿ5ÿ5ƒ6ƒ6‡6‡6Š6Š6²6Ì=T‡È!ÁÈ!ÚÈ!‡ëä#é=‘H–‡È!‘ÁÈ!‘ÚÈ!‘‘‡ôà#®H‡L‡È!“ÁÈ!“ÚÈ!““‡„Ø$ÏN;‡È!”ÁÈ!”ÚÈ!””‡×Þ#ìN‡OÖP;‡È!—ÁÈ!—ÚÈ!——‡ùè#óPŽQŽSC‡È!šÁÈ!šÚÈ!šš‡€Ý#«SÆS„-ˆ- ¡«¬²ò#Ï%ž!žŸ7–%Ÿ%27`kú¢¢žÓ.t—+uQKlß,“¡£•—+`k•—+ ¡•ttww •€/ü¥ú¦ò§Ð¨îÓ©¨¥§©¦ ÍŽõõ‘Åõ‘å8ßö °†&Ü ¶—2‚°†&’íÍŽ½ ðë,‰ä8õ‘ÏŽ€/ŒøGßö ó øG°†&† šìª……‰‰ŒŒ’’˜4›†)7‡)7ˆ)7†)7šì‡)7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >Sˆ)7Ôž¤Ÿ¤ª¥üÍŽõ‘ßö °†& ¦¤¯åÆö3¸Îì½êõÆÍÖAV¬®°²Îì¤¯°²Îì««¬°²¸¸»»««°²Ä"å°°Æö3É¸ü3ô Îì±ÉÉ†4‰‰)7Š)7‹)7‰)7ÎìŠ)7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S‹)7Ôž¯¯±°²Ž”‰!“ ²²‡¸0¢²‡¸0¢³³±O´Œ)7àžŒ)7ò#ò#Á»õ#ß»ö#š´6:“Û¦*±UŒ)7àž²Ì²Ê¨-ú³àž»ó”&Á ô òµµ½Ó/ù ÌÔ/™ ÷˜ù ù ¦ ü·ò¸¸·Ýç,¯ è.Í ò#ò#Á»õ#ß»ö#š´6:“Û¦*±U·ü¹« « ¯ ¯ Ý à )7‘)7’)7)7Åô<‘)7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S’)7¯Ôž¶úººê ¶ðç,ç )7Ž)7)7ð¯)7šŸŽ)7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S)7¸3)7Ž)7)7¯»÷<õÔžà¹·ü¯ë ñ ¹¦ç ç °½ÉÂÏäÐÑâÒ fmt_traitÁÓ Ü íÝãæéÀóô¹ ‚†!ü‡‰ ò‹Œ ŸŽ‘“ ˆù<” ž Á»¢¨!ü©« ò® %À òÆÇ ŸÉÑ òéê òìð Ÿòõ÷ ˆù<ø ¯ ¡ Ÿ¤§Å ¨ © ¯°² ³ Ÿ·º „¼ Ç ŸÊÍ ÒÏÖ¡ ìð ªñ÷ øù Ÿûþ€ ˆù< Š ŸŒº•-™- ½ ½½‡¸0½‡¸0¾Á¾Ák’“)7“)7àžÍŽÂ¶&½ú¿éCÀÀ¿¤½ðë,žþ 1¡ðÍŽ¹´&ÛÍŽ¹´&õÂ¶&ð ¾àžÍŽ²¾©³žž¡¡Ž‘”)7”)7àžÍŽÂ¶&½úÂéCÃÂÃ¤½ðë,žþ 1¡™‚=Û°‚=ð Š9àž‰ÁàžÍŽ²¾©³žž¡¡ÑòÅÅ½Ó/ÖÌÔ/õ÷˜ÖÖÆ-§-Ž§-ê ª-Ã- Œ«-³-“Öµ-¥- b `MatrixGraph` is a graph datastructure backed by an adjacency matrix.ÁeÎ-èéëñýþŽ“”™ÃÑäüý‹«°±´µ·È£$¶$Ö´x‘ÆÇÖ´x’ÆÇŠ 2”¤ÆÉ¦ 2›¨ÆÊ°ñÆËmemÁ¾ºÆÌ»Ç(Ò‡ÆÍº‰îªÆÎ¬Âˆá$ÆÐŽÁ’ È$ÆÑÿ(ýÆÒàû§ƒÆÓ¹¶¹É$ÆÔ¹¶¹Ê$ÆÔ×ÂÃ ç$ÆÕGraphNodeIndexÁðº!ÆÖñ‰‹™ðÆØ±Ÿ¾ÆÙû†³ µÆÚ¾¾ ”ÆÛÙŒÉ‰ÆÜïˆÝ ÐÆÝë‡è ÃÆÞ»ˆûÌÆßŠ’âÆàô‹§þÆáž»®ÆââŽÕ §Æã‚Žà œÆäÕï ³ÆåªFžÆæ—u âçîkx‰””››«°¹¾ÈÒáî€Nˆˆ’ ’ §§¹¹Ã Ã Õð…ö™™ŸŸ³ ³ ¾ ¾ ÉÉÝ Ý è è ûû’’§§»»Õ Õ à à ï ï ‚Ÿ ¢ ÆÉ" Node identifier.Á´Ò ä êêº›.ÜóŠÜÜƒ‡Æì•Ÿììì®$åîîÆö3³¢î³³ÚåððÆö3ßÙîßßá Y\ Wrapper trait for an `Option`, allowing user-defined structs to be input as containers whenÁý_ defining a null element.ÁÝ ú _ Note: this trait is currently *sealed* and cannot be implemented for types outside this crate.Áþ bë ññ¤ò/õ ÏàžõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê¿¿&ÿ )ìàž«ñ¤ò/õ ƒ–=ÿ )Ú–=«òóôõöòóôõö‚¿¿&Ô ÔÃ¹Ãé¡ÆÍ ÝÇÁÙññú%é¹éë¡ìó Ýíçý¿¿&àžññóàžà™¸+§¹§ô¡ª± Ý«¥»•)7•)7àžõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê•)7¿¿&ñÂñú¾&Ãü3ë¹ë€¢îõ Ýïéÿ–)7–)7àžõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê–)7¿¿&ñ† ñõàž‹ È · ¹· Œ¢º Á Ý» µ Ë —)7—)7àžñÓ ñöàžÔ åøøÆö3’Ùîùúûü’’²·÷÷°²ÉÌ°²Ùî÷÷ú°²à™Ðƒ*†˜)7˜)7ÙîõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê˜)7Ùîò÷÷û°²ŽÑ2Ô™)7™)7ÙîõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê™)7© =÷Û÷ü°²àÏH `NotZero` is used to optimize the memory usage of edge weights `E` in aÁ¥KV [`MatrixGraph`](struct.MatrixGraph.html), replacing the default `Option` sentinel.ÁñYËG Pre-requisite: edge weight should implement [`Zero`](trait.Zero.html).ÁÏJš` Note that if you're already using the standard non-zero types (such as `NonZeroU32`), you don'tÁžcI have to use this wrapper and can leave the default `Null` type argument.Á‚LÚåÿÿÆö3â¢îí”ƒ<«¶þ€°²¢îÏÚÆ°²¢îýýþ°²ââååÆýý°²ê$å‚‚Æö3ïŽ°²ò¢îƒïï•˜¢îƒ°²Ð%å……Æö3Õå§=Ø¢î†‡ˆ‰ŠÕÕü„„°²“–°²¢î„„‡°²à™š¥¨š)7š)7¢î„°„ˆ°²±å*è›)7›)7¢îõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê›)7¢îò„ï„‰°²ð€2ƒœ)7œ)7¢îõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êœ)7é«=„Š„Š°²¥,åŒŒÆö3ªå§=¢îªªØÛ¢îÙî‹‹°²à’P Base trait for types that can be wrapped in a [`NotZero`](struct.NotZero.html).ÁàS´X Implementors must provide a singleton object that will be used to mark empty edges in aÁ¸[* [`MatrixGraph`](struct.MatrixGraph.html).Á”-ÂH Note that this trait is already implemented for the base numeric types.ÁÆKœŽŽŽòC Return the singleton object which can be used as a sentinel value.Á§FõàžŽŽàžÈ6 Return true if `self` is equal to the sentinel value.ÁŠ9Ë)7)7àžŽÓŽàžÔæó ì ‡• ˆ ¨è‰Š Ô‹Ž zÁ ÷‘—šé ¤ ŸÚ© ®² ¨è³µã Ã ùZÆÊËÍ ÐÕüç “´=è êí ¨èî ‹ ³ÚŽ • › – — ó ¥ Ù · ¼ ¿ À Ä ùZÆ Ê Ë ê ï ü ‹!â!‘!œ!’!“!™!”! ¨è•!–! Ô—!š!›!ž!¡!à!«!¬!Ù! ÂÚº! Ç!È!Î!É! ¨èÊ!Ë!ÎÍ!Ï!Ú!Ÿ#): Short version of `NodeIndex::new` (with Ix = `DefaultIx`)Á×"=¦# ¾Ô&“‡Ô&±#Þ.9% A `MatrixGraph` with directed edges.Áµ.(ç.¨•£–•Û—ä ˜˜•—–íñ,ð.¦¿,ó.‚žóö.Àà-ˆ/ÚÓ'ð.ð.ó.ó.ö.ö.îôˆ/ˆ/í/9' A `MatrixGraph` with undirected edges.ÁÂ/*ö/¨š£›•Ûœä šœ›íñ,ÿ/¦¿,‚0‘½=…0Àà-—0£$£$µ¾©$áÆª$™áÆ«$äyÆ¬$‹¿Æ$ÔÆ®$éÞÆ{Œg‡qU7…–úêû…üžó¾†ÿ/ÿ/‚0‚0…0…0îô—0—0Ó0h¨Ÿ£ ï¡•Û¢ä £ ¢Ÿ¡£ †ñòêûû0¦¿,Û0à-Þ0‚†ñì0ÃÃ-‰1Óà-â0ñ†ñò0íñ,Ø0ýÃ-1 ™ð¤¥¦§¨©ª«¬®¯°±²³´µ¶·¸ºØ0Ø0Û0Û0Þ0Þ0ì0ì0‰1‰1ˆ22> Create a new `MatrixGraph` with estimated capacity for nodes.ÁÂ1A2 ™ðžžÂí(™á2 è5Gë5Æž)7ž)7™ð³ö³öžü5ž ø&ý5š±ƒ6ª±•6·7 Remove all nodes and edges.Á“7¾7Ÿ)7Ÿ)7™ðžÄ7ž¦–úêû¸††ñ¢ƒÉ7ñ9!×ï4ü87¸9´Ñ2À9ø9 )7 )7™ðžƒ:žÊÄ'„:£;!Šñ4¹:,ê:´Ñ2ò:ª; ¡)7¡)7™ðžµ;ž¨–úêû¸††ñ¢ƒ¶;²%¬>T **Panics** if the MatrixGraph is at the maximum number of nodes for its index type.Á´>W—?£)7£)7™ð–ú³öž ?žÕÅ'¥?Š&«?ºA4 Remove `a` from the graph.Áƒ@¦@¨ì:®@Lÿ@ÄÍ2‡A.ÁA¤)7¤)7™ð³ö–úžÍAž«–úêû¸††ñ¢ƒÒAš±ØA´E?·EÆ¥)7¥)7™ð³öžÐEž‚á&ÕE¡…'ÛEÀGJÃGÆ¦)7¦)7™ð³ö³öžÜGžŠ‹'áGš±çGª±ùG„LYQ Update the edge from `a` to `b` to the graph, with its associated data `weight`.Á²IT‹J" Return the previous data, if any.Á“J%½J& Computes in **O(1)** time, best case.ÁÅJ)M Computes in **O(|V|^2)** time, worst case (matrix needs to be re-allocated).ÁóJPÈKí„5ÐK/‹L§)7§)7™ð³ö³öêûîôž—Lžè”'œLš±¢Lª±´LŠ&ÆLÝSIÑü4…OA›ý4ËOâOÁý4êO%”PßÐ=œP)‘Ñ=ÊPPŸQí„5§Q/6 **Panics** if an edge already exists from `a` to `b`.ÁÛQ9™Re **Note:** `MatrixGraph` does not allow adding parallel (â€œduplicateâ€) edges. If you want to avoidÁ¡RhG this, use [`.update_edge(a, b, weight)`](#method.update_edge) instead.ÁŽSJäS¨)7¨)7™ð³ö³öêûžíSž¾Æ'òSš±øSª±ŠTŠ&œTÂVF. Remove the edge from `a` to `b` to the graph.Á–U1ÌUí„5ÔU/2 **Panics** if no edge exists between `a` and `b`.ÁˆV5ÉV©)7©)7™ð³ö³öêûžÕVž°–úêû¸††ñ¢ƒÚVš±àVª±òV¯ZB5 Return true if there is an edge between `a` and `b`.Á¶Y8óYí„5ûY/¶Zª)7ª)7™ð³ö³öž¿ZžÎü(ÀZš±ÆZª±ØZø\1Öö4Ø[#€\÷4ˆ\5Â\þõ6Ê\)ÿ\«)7«)7™ð³ö«)7–úž‹]ž˜ò(Œ]š±’]ƒ_=Íù4Ö],‡^ú4^9Í^þõ6Õ^)Š_¬)7¬)7™ð³ö¬)7–úžš_žúó(Ÿ_š±¥_aC¯†5ñ_#™`è†5¡`5Û`Ù×=ã`5¤a)7)7™ð³ö³ö)7êûž°až÷(±aš±·aª±Éa‡dO®‰5Îb,ÿbð‰5‡c9ÅcÙ×=Íc5Žd®)7®)7™ð³ö³ö®)7êûžždžÓù(£dš±©dª±»d“hDµ›5ÄeCŒf‚Ö2”f*œœ5Ãf-õfßœ5ýf=L Iterator element type is [`NodeIndex`](../graph/struct.NodeIndex.html).Á¿gOšh ¯)7¯)7™ð³öüüýî ‚ÎÆhiü£SN¯)7¸††ñ¢ƒž¤hž¶–úêû¸††ñ¢ƒ¥hš±«h¤l<ÃÕ2ði+ j‚Ö2¨j*µÖ2×j/‹kßœ5“k=G Iterator element type is [`Edges`](../graph/struct.Edges.html).ÁÕkJ«l°)7°)7™ð³ö„9•ÿãÆ–áÆ—™áÆ˜îàÆ™µÆšÔÆ›éÞÆø;¢óÌþk°)7¸††ñ¢ƒž±lž·–úêû¸††ñ¢ƒ²lš±¸lÜqÍ6 Create a new `MatrixGraph` from an iterable of edges.Á¿m9ým·†6…n/ð†6¹n9³‡6÷n+§oô‡6¯o8ìoõ®+ôo) use petgraph::matrix_graph::MatrixGraph;Á€p,±p/ let gr = MatrixGraph::<(), i32>::from_edges(&[Á¹p2ËÐ,ðpçË3”q‡Ì3°qËÑ,Äqõ®+Ðqãq ·»'™ðžò¹¹ž‚·»'îqä8·»'›rƒø¿'êûºrÏûÀ'³ös¯·2¡s¸–úêû¸††ñ¢ƒ·»'« ñqîqîq´v×¿‹6‘t/Åt·†6Ít/ð†6u9³‡6¿u+ïuô‡6÷u8»v±)7±)7™ð·»'žò»»Ðvžóê=Ív†ë=ýv™ë=œw°ë=ãw¯·2ƒx»'Õv« ÛvÍvÍvœ{\¨½£¾•Û¿ä ÀÀ½¿¾žóòêû¶{¦¿,¤{‘½=§{Àà-Ä{ñžó{íñ,¡{æà-È{ ÚÓ'ÁÂ¡{¡{¤{¤{§{§{Ä{Ä{Ã€· 5ÿ{J‹¡5Î|$¹¡5÷|OË}" - `Outgoing`: All edges from `a`.ÁÓ}% - `Incoming`: All edges to `a`.Áý}#¥~ßœ5~=žá=ï~OÊ²)7²)7ÚÓ'Ÿ¼Üüüýî ‚ÎÆhiü£SN²)7Îùžó¾†¼æ¼Á–úêûžó¾†çš±õ‹€”…Y¬¯5Á‚Gƒëï=•ƒ%™ð=¿ƒ#çƒ¢²5ïƒAW Iterator element type is [`EdgeReference`](../graph/struct.EdgeReference.html).Áµ„Z›…³)7³)7ÚÓ'Ÿ¼Ü„9•ÿãÆ–áÆ—™áÆ˜îàÆ™µÆšÔÆ›éÞÆø;¢óÌþk³)7Îùžó¾†¼ª…¼Â–úêûžó¾†«…š±±…‹Ã…‘‰"/ Iterator over the node identifiers of a graph.Á›‡2Î‡K Created from a call to [`.node_identifiers()`][1] on a [`MatrixGraph`][2].ÁÒ‡N¡ˆG [1]: ../visit/trait.IntoNodeIdentifiers.html#tymethod.node_identifiersÁ¥ˆJ [2]: struct.MatrixGraph.htmlÁðˆ œ‰üÄä ÅÄÅ3°‰ÃÃû#ÆŸÆÇéÞÆB@vä tœÄüÞ¬B@vä tœÆÇ¬‰¬‰°‰°‰º‰º‰ÆÃÃÈÈüèÊ–éÆË©éÆÌ¼éÆ¦]oÏÄüÔ‰Ô‰ÆÃÃÔÞ'ì‰/üÉä ÊÉÊ3õ‰¡3ù‰ ÃÃû#ÆŸÆÇéÞÆB@vä tœÉüÞ¬Ëñ‰ñ‰õ‰õ‰¢Š$¥ŠÆÈÈüèÊ–éÆË©éÆÌ¼éÆ¦]oÏÉü¦ü=ÈÈËÉüÞ¬Ÿ©Šš‹<üÍä ÎÍÎ3£‹¡3§‹ •ñÏÐŸ‹Ÿ‹£‹£‹Ý‹ â‹ÌÌˆý‹(€Œ´)7´)7•ñõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê•ñÙ9Ì…ŒÌÐÍüÞ¬ŠŒÆŽ(ý¸7ÞŒ'†J Created from a call to [`.node_references()`][1] on a [`MatrixGraph`][2].ÁŠMØE [1]: ../visit/trait.IntoNodeReferences.html#tymethod.node_referencesÁÜHñ÷=¥Ž ÑŽüÒ¨Óä ÔÔÒÓŠÃ-äŽƒ¹2ëŽƒÒüçŽ±ƒ>õŽÑÑñ5ÕäyÆÖŸÆ×éÞÆ›Œ*/¤|Òüƒæ·›Œ*/¤|ÕÖ×àŽàŽäŽäŽÒëŽëŽõŽõŽÆÑÑÒüžä'’’ÆÑÑÈÈüèÊ–éÆË©éÆÌ¼éÆ¦]oÏÒü¬¬ÆÑÑþâ'Ä-üÙ¨Úä ÛÙÚÛŠÃ-Íƒ¹2ÔƒÙüÐÑÑñ5ÕäyÆÖŸÆ×éÞÆ›Œ*/¤|Ùüƒæ·ÜÉÉÍÍÙÔÔø'ûÆÙüžä'ž‡>ØØÜÙüƒæ·äyÿ¢‘EüÞ¨ßä ààÞßŠÃ-«‘ƒ¹2²‘ƒÞü®‘–¹2¶‘ Ûñáâ§‘§‘«‘«‘Þ²‘²‘î‘ ó‘ÝÝÚÌÞüƒ—’(š’µ)7µ)7ÛñõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÛñÙ9ÝŸ’ÝâÞüƒæ·¤’ù’âÈ!ãÁÈ!ãÚÈ!ããâÐæ'“•D·Ï7«“'Ó“J Created from a call to [`.edge_references()`][1] on a [`MatrixGraph`][2].Á×“M¥”E [1]: ../visit/trait.IntoEdgeReferences.html#tymethod.edge_referencesÁ©”Hñ÷=ò” ž•üåïæ•Ûçä èèåçæ˜ß2±•‘½=¿•Àà-Ô•¿ß2µ•žóåüÅ•ªï=Ê•î>–ää“*éîàÆêµÆëáÆì™áÆíÔÆîéÞÆXuËI!¶ßëåü˜óžó¾†XuËI!¶ßëéêëìíî••±•±•¿•¿•åÔ•Ô•Þ• Þ•Æääî• î•Æää––Æääåü¢Ö'£–£– Æää½–½–Æää•ì'Ö–Ö–ÆääÃì'î–Püðïñ•Ûòä óðòñó˜ß2÷–‘½=…—Àà-š—¿ß2û–žóðü‹—ªï=—ää“*éîàÆêµÆëáÆì™áÆíÔÆîéÞÆXuËI!¶ßëðü˜óžó¾†ôó–ó–÷–÷–…—…—ðš—š—Å—BÈ—Æðü¢Ö'ù”>ïïôðü˜óžó¾†áÌ—™áê— Ô™güöï÷•Ûøä ùø÷ùö˜ß2Ý™‘½=ë™Àà-û™¿ß2á™ªï=ñ™æà-ÿ™ ¯òúûÙ™Ù™Ý™Ý™ë™ë™û™û™Âš ÇšõõŸ¼Ÿ¼öü£ð'†›(‰›¶)7¶)7¯òõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê¯òÙ9õŽ›õûöü˜óžó¾†“›Ò¥?ˆª6¼£*ç£„”3ë£-™¤@ Created with [`.neighbors()`][1], [`.neighbors_directed()`][2].Á¤Cá¤. [1]: struct.MatrixGraph.html#method.neighborsÁå¤17 [2]: struct.MatrixGraph.html#method.neighbors_directedÁ—¥:Ý¥ üþïÿ•Û€ä €ÿþ˜ß2ë¥‘½=ù¥Àà-Ž¦¿ß2ï¥žóþüÿ¥ªï=„¦²ž>Éªüüýî ‚ÎÆhiü£SNþü˜óžó¾†hiü£SNý‚„9•ÿãÆ–áÆ—™áÆ˜îàÆ™µÆšÔÆ›éÞÆø;¢óÌþkþü˜óžó¾†Üž>Ò¥?Ý¥ Æ¹Ÿ>Üž>üüýþü˜óžó¾†ç¥ç¥ë¥ë¥ù¥ù¥þŽ¦Ž¦’¦’¦Æüü¹Ÿ>¦^ü„ï…•Û†ä ‡…‡„†˜ß2¶¦‘½=Ä¦Àà-Ô¦¿ß2º¦ªï=Ê¦æà-Ø¦ óˆ‰²¦²¦¶¦¶¦Ä¦Ä¦Ô¦Ô¦’§ —§ƒƒŸ¼²§(µ§·)7·)7óõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êóÙ9ƒº§ƒ‰„ü˜óžó¾†¿§÷§‰È!ŠÁÈ!ŠÚÈ!ŠŠ‰Ñ€(¨”¨Æ£…(:p`Š£]ŒŽ°¨°¨Æ‹‹£…(°¨°¨Æ‹‹£…(º¨º¨Æ‹‹£…(º¨º¨Æ‹‹£…( ª;ÒÍ6Æ¨0÷¨8 Created with [`.edges()`][1], [`.edges_directed()`][2].Áû¨;·©* [1]: struct.MatrixGraph.html#method.edgesÁ»©-3 [2]: struct.MatrixGraph.html#method.edges_directedÁé©6«ªü‘ï’•Û“ä ”’”‘“˜ß2µª‘½=ÃªÀà-Øª¿ß2¹ªžó‘üÉªªï=Îª†«>«„9•ÿãÆ–áÆ—™áÆ˜îàÆ™µÆšÔÆ›éÞÆø;¢óÌþk‘ü˜óžó¾†ø;¢óÌþk•–—˜™š›±ª±ªµªµªÃªÃª‘ØªØªâª%âªÆ£…(««Æ‘ü¢Ö'¯«¯« ÆÉ« É«ÆÙ« Ù«Æì«ì«Æ•ì'…¬…¬ÆÃì'¬Güïž•ÛŸä Ÿž˜ß2¦¬‘½=´¬Àà-É¬¿ß2ª¬žóüº¬ªï=¿¬„9•ÿãÆ–áÆ—™áÆ˜îàÆ™µÆšÔÆ›éÞÆø;¢óÌþkü˜óžó¾†¡¢¢¬¢¬¦¬¦¬´¬´¬É¬É¬ë¬Uî¬ Æü¢Ö'Á°>œœ¡ü˜óžó¾†îàù¬á…™á£ ¿¯UÂ¯ÆÄ²>Á°>œœ¢ü˜óžó¾†µÊ¯áÙ¯™á÷¯ Ž²Zü¤ï¥•Û¦ä §¥§¤¦˜ß2—²‘½=¥²Àà-µ²¿ß2›²ªï=«²æà-¹² ôó¨©“²“²—²—²¥²¥²µ²µ²ï² ô²££Ÿ¼Ÿ¼¤ü£ð'³³(¶³¸)7¸)7ôóõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êôóÙ9£»³£©¤ü˜óžó¾†À³ê³‰º`ŒºÆï¬¬¹<ªº¹<®º«Ûéîà¸ºµÄººÓºªºªº›¼ ž¼Æ¹)7¹)7î›(ï®å¯®¯è¼¹<·¼íÕ+Å¼¹<»¼œ¡÷È¼Ûé¡÷áÖ¼ó›(ù¼–œ(—½·¼·¼Å¼Å¼Ÿ¿S¢¿Æ™˜(îàÁ¿µÍ¿ºÜ¿›À“žÀÆº)7º)7¦(å²²ÛÀÆö3¸Àœ°²»À±°²áÉÀó›(ìÀ–œ(ŠÁ¸À¸À‹Ã‘ÃÆÈJ’È#ÆÌ˜(îà¶ÈµÂÈöÉzùÉÆ»)7»)7¦(å¶¶»ÊÆö3˜Êç¼>›Êµ°²á©Ê†±(ÌÊ˜Ê˜ÊîÌ6ñÌ Æ¼)7¼)7¦(å¸¸‹ÍÆö3üÌç¼>ÿÌ¢£(Ï£ˆÍÙ ˜ÍüÌüÌ½ÏåººÆö3ÂÏøô»¼½¾¿ÀÂÏÂÏØÏ)ÛÏ Æøô¹¹»°²‘ƒéÏ§Ñ&ªÑÆ½)7½)7øô°²¹®Ñ¹¼°²³ÑØ« ¹Ñ‰Ô$ŒÔÆ¾)7¾)7øô°²¹“Ô¹½°²˜Ô˜&žÔÖÖÆ¿)7¿)7øô¹–Ö¹¾°²›Ö›×ž×ÆÀ)7À)7øô¹¢×¹¿°²£×ñ× ô×ÆÁ)7Á)7øôÈÈüèÊ–éÆË©éÆÌ¼éÆ¦]oÏÁ)7¹ý×¹À°²þ×®Ù%åÂÂÆö3³ÙøôÃÄ³Ù³ÙÚÙßÙÁÁ°²ïÙ#òÙ€Â)7Â)7øôÂ)7°²ÁøÙÁÄ°²ùÙ…ÿÙÍÚ(åÆÆÆö3ÒÚøôÇÒÚÒÚüÚ/ÿÚ €Ã)7Ã)7øôÃ)7°²Å‰ÛÅÇ°²ŽÛ…”ÛæÛíÛ ÆüÉÉÈÈüèÊ–éÆË©éÆÌ¼éÆ¦]oÏÉü¦]oÏÊËÌøÛøÛ‚Ü‚ÜÆÈÈšÜ šÜÆÈÈÉü±È(ÀÜÀÜÆÈÈ¯ ÛÜ$üÎÎØõÏÐàÜàÜ†Ý ‹ÝÍÍžÝ(¡ÝÄ)7Ä)7ØõõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êØõÙ9Í¦ÝÍÐÎü«Ý×ât" Create a new empty `MatrixGraph`.Á±â%¨Ò£ÓïÔ•ÛÕä ÖÕÒÔÖÓ ìÁ=ÿâ¦¿,ßâà-ââ•Â=ðâÃÃ-ãÓà-æâ¼Â=öâíñ,ÜâýÃ-‘ã ™ð×ÜâÜâßâßâââââðâðâããÒãÕã™ðÑÑ×–úêû¸††ñ¢ƒ‘ä&¨Ù£ÚÙÚíñ,–ä¦¿,™äßî(Û–ä–ä™ä™ä¶æ0 Create a new `MatrixGraph` with directed edges.Á¾ä3öä] This is a convenience method. Use `MatrixGraph::with_capacity` or `MatrixGraph::default` forÁþä`K a constructor that is generic in all the type parameters of `MatrixGraph`.ÁãåN½æßî(ØØÛ–úêûõæ(¨Ý£ÞÝÞíñ,úæ¦¿,ýæ¿ð(ßúæúæýæýæžé2 Create a new `MatrixGraph` with undirected edges.Á¤ç5Þç½Ò>æç`§Ó>ËèN¥é¿ð(ÜÜß–úêûÙê? 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;&&//FP_R_Rtt‹ ‹ ›¦àÊ Returns iterator that produces all simple paths from `from` node to `to`, which contains at least `min_intermediate_nodes` nodesÁ´„S and at most `max_intermediate_nodes`, if given, limited by graph's order otherwiseÁ¹V( Simple path is path without repetitionsÁ+ Algorithm is adopted from https://networkx.github.io/documentation/stable/reference/algorithms/generated/networkx.algorithms.simple_paths.all_simple_paths.htmlÁ¼£çÂ±¹±¹¯ ¸®)ûôŠê‹Š‹‚Å¨)ø ç¿-„§Â¿ ýë.ÑŒ±¹÷ÔI±¹üà8Å¨)±¹’‰Å¨)Â8Œ¹š’½ ¯ù±)Â ²)åø ø „„ô‰È!ŒÁÈ!ŒÚÈ!ŒŒ‰›ª)Å‰È!ÁÈ!ÚÈ!‰µÃ)Ê.Î.º‰ªŽÿ(*ýŽ·yH¤Ž’ì—uO âŽ“îù^Z º!Ž”ñÌŸ ÝŽ•Ç%¿ªŽ–ÙŒü‰Ž˜‚Ž œŽ™±³¾Žš#*9+HHO O Z Z ‹ ±¿í1üü ¥ ³©=B The adjacency matrix for **Graph** is a bitmap that's computed byÁÈE `.adjacency_matrix()`.ÁŽ¨œ£ïžä ŸŸœžíñ,®¦¿,±à-´Àà-¸Óà-õæà-‡ ò… ¡¢®®±±´´¸¸˜ ››°Š»)¾×)7×)7ò…°Š›Ï›¡–úêû¸†¾†Ð–W™Ø)7Ù)7Ø)7ò…Ù)7°ŠŸ¼Ÿ¼›¥›¢–úêû¸†¾†¦…¬š±Âª±Ôì Cì Žì ÆÉï ˆ Œð ø Íú ê ¤·?‹ Eò·?Ñ ¨¤£¥ï¦ä §¥§¤¦íñ,ñ ¦¿,ô à-÷ Àà-û Óà-¾æà-Ð Ú†¨©ªñ ñ ô ô ÷ ÷ û û áæ ££°Š„)‡Ú)7Ú)7Ú†°Š£˜£©–úêû¸†¾†™ßWâÛ)7Ü)7Û)7Ú†Ü)7°ŠŸ¼Ÿ¼£î£ª–úêû¸†¾†ï…õš±‹ª±Ü.1 `UnionFind` is a disjoint-set data structure.Á4ä. ®°¾$—uH â«¬î·aÃñ«:H Wa‹ 8• «Ý)7Ý)7‡Œ*Ý)7›÷Ð¯¯ª ÚÕ+£ Û™*ð‹*¦ …° £ £ ” Dž «Þ)7Þ)7‡Œ*Þ)7›÷Ð±±· ÚÕ+° ¨*ð‹*³ …Á ° ° £Ð³³ÚÕ+¨â›÷Å Îú´µ¶·¸¹º¨¨/ Create a new `UnionFind` of `n` disjoint sets.ÁÖ2”Îú²²´›÷–µ˜–# Return the representative for `x`.Á· &â $ **Panics** if `x` is out of bounds.Áê 'ß)7ß)7Îú›÷›÷²¢²‡*£æü©Ë%õÆ?Þ&‰= Write back the found representative, flattening the internalÁ‘@9 datastructure in the process and quicken future lookups.ÁÖ<—¯Ç?Ÿ'Òà)7à)7Îú›÷›÷²Û²±*àæüæÜ6æ«á)7á)7Îú›÷›÷²ù²Š¡*þæüˆÏ'I Returns `true` if the given elements belong to the same set, and returnsÁãL `false` otherwise.Á´Öâ)7â)7Îú›÷›÷²Ü²¸›÷ÝæüãŸˆéö++ Unify the two sets containing `x` and `y`.Á©.ÜO Return `false` if the sets were already the same, `true` if they were unified.ÁäR»+ **Panics** if `x` or `y` is out of bounds.ÁÃ.ýã)7ã)7Îú›÷›÷²ƒ²¹›÷ˆæüŽŸˆ”í!(< Return a vector mapping each element to its representative.Á©!?ô! Îú½‚*²²º›÷†"ï.ó.½¿Ÿ Ë%»¼ [ ÷˜†Ì*ò¾¾½Ó/!ä8÷˜^½÷˜« $!!ÌsÓ÷˜£Î*Ï*òÀðøÁÀÁ½Ó/×‚£Î*Ú“Ó?ä8£Î*²¿÷˜£Î*æÝÂªã××ÚÚú. Commonly used items.Áõ®+ÄÎ,%õ®+C‚/zu¬Äá’_~ Ú!Åã·y‰¤Æìù^ º!Çñ¨z›°Èõ½ºî Ê¼µúš#Ë›º„ šÌº â çÎÌŸñÝÏç þ ëÐò„·•ÒÂ„ƒ¼úÓÂõƒÁ‡ÔÂ¬Âòá$ÖŽÁü È$×Î¶‡Ì$ØÎ¶‡Í$Ø¹¶‘É$Ù¹¶‘Ê$Ù×Â› ç$ÚÇ%ÑªÛf=uu~ ~ ‰‰ ››Ü3î î úú„ „ Ì@â â ññþ þ ¨&··¼¼ÁÁê<òòü ü ‡‡‘‘› › ÃÑç/ œ°4Œ/Zï/¨w 0 „ÞïN¬0 ‚ß†u¸0 áàzÃ0¬á;Ì0…"â’_Ò0 Ú!ã†·Ý0"ä…¥ê0 §"å“*1×æ—‘1µç„9¡1ý èÈŒ¨1éÜ†¹1 ¶ êž¸Ä1íë·yÌ1¤ì÷‘Û1 âí—uç1 âîî ò1 Å ïŽœý1ú!ðù^ƒ2 º!ñ¬µŽ2“"òñ5›2°ó§Ž³2¨ô¨zÃ2°õâ’Ì2 íöƒ0Ý 0 0 ¬0 ¬0 ¸0 ¸0 Ã0Ã0Ì0Ì0Ò0 Ò0 Ý0Ý0ê0 ê0 11‘1‘1¡1¡1¨1¨1¹1 ¹1 Ä1Ä1Ì1Ì1Û1 Û1 ç1 ç1 ò1 ò1 ý1ý1ƒ2 ƒ2 Ž2Ž2›2›2³2³2Ã2Ã2Ì2 Ì2 Ž3¡3°3½3 Ç3ð4Í3Ù3Î3 šÏ3Ó3 íÔ3Û3Þ3í4 è3 ií3 ó3÷3 šø3ý3ç4‹4Œ4“4 Œ4 ¡4 ¦¤4©4¯4ª4 «4±4 ´4¹4Ý4Ë4 Ì4î4ä7Üúû°8"! Return the opposite `Direction`.Áù7$·8ÜÜùù¨–À8‹:2 Return `0` for `Outgoing` and `1` for `Incoming`.ÁÃ95’:Üùù¦—˜:ç:û: =E A graph's edge type determines whether it has directed edges or not.Áä•>ÿÿ€Ã>…ü‚ô>÷>‚È@5 Convert an element like `(i, j)` or `(i, j, w)` intoÁ¦?8) a triple of source, target, edge weight.Áß?,Œ@4 For `Graph::from_edges` and `GraphMap::from_edges`.Á@7Ò@ƒ£„„ƒ¦¿,ã@ƒ…†…†‚àžêû…ì@ã@ã@ì@ñ@ƒƒý@?€AàžÐì?Ðì?êûƒƒ†àžêû“AÀA,ä ˆ£‰ˆ‰º›.ÅA¦¿,ÉAœêûúA¾üŠ‹ÅAÅAÉAÉA‰BŽB‡‡¡‹ B*£B¾üÏü‡‡‹¡‹êû¶BŽC/ä £ŽŽº›.“C¦¿,—CÏü“C“C—C—CÄCÉCŒŒ¡‹ÚC*ÝCÏüÏüŒŒ¡‹êûðCD7ü’ä “£””’“3¦DÃ-ªD×–ƒâDãü•–¢D¢D¦D¦DªDªDïDôD‘‘Þ¬…E*ˆEãü¥ý‘‘–’üÞ¬–ƒ›EóE4ü˜ä ™£š˜™š3üEÃ-€F¨4¶Fœ–ƒÃFý›œøEøEüEüE€F€FÒF×F——Þ¬èF*ëFý¥ý——œ˜üÞ¬–ƒþF×G7üžä Ÿ£ žŸ3àGÃ-äG¨4H˜ò?ªHœý¡¢ÜGÜGàGàGäGäG·H¼HÞ¬ÍH*ÐHœý¥ý¢žüÞ¬–ƒãHÃ Î Ð¥å¦¥¦ÚÕ+Ø íÕ+Û èöô(óÜ~f¤§¨›÷¡÷èöÃ Î ›÷¡÷èö££¤›÷¡÷Ø Ø Û Û Þ Þ ££›÷å å ££¡÷® ® Þ® ‚Ó® ñÐªå«ª«ÚÕ+Ø íÕ+Û ‡›÷® ‡¡÷® èöØ Ø Û Û ´ ´ Þ´ ˜Ó´ òÐå®®ÚÕ+Ø íÕ+Û ×›÷´ ×¡÷´ èö¯Ø Ø Û Û ´ ´ ä)7ä)7èöèö¬´ ¬¯›÷¡÷´ » » Þ» ÓÓ» óÐ±å²±²ÚÕ+Ø íÕ+Û øG›÷» ’<» èö³Ø Ø Û Û » » å)7æ)7ç)7å)7èöæ)7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >Sç)7Ôž°» °³›÷¡÷» » ã G Strictly monotonically increasing event time for a depth first search.ÁKJ î ã÷ãÊ_„3µ¶ã÷ã î ã÷´´µó ó ´´ Ÿ Ÿ Þ Ÿ ’Ô Ÿ ôã÷ ¥ ¥ Þ ¥ šÔ ¥ õã÷¹ ¥ ¥ è)7è)7ã÷ã÷¸ ¥ ¸¹¥ ¬ ¬ Þ¬ ¸Ô¬ öã÷»¬ ¬ é)7ê)7ë)7é)7ã÷ê)7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >Së)7Ôžº¬ º»¬ ¬ ³ ³ Þ³ àÔ³ ÷ã÷³ ³ Þ³ èÔ³ ÷ã÷¾³ ³ ì)7í)7ì)7ã÷í)7ã÷½³ ½¾³ ô³ ¾ ¾ Þ ¾ †Õ ¾ øã÷À ¾ ¾ î)7ï)7î)7ã÷ï)7ã÷¯¦¿ ¾ ¿À¾ ô ¾ Ê Ê ÞÊ ±ÕÊ ùã÷Ê Ê ÞÊ ¹ÕÊ ùã÷ÃÊ Ê ¹Ê ÂÕÊ Ê ÝÊ Ê Ê ÃÊ ÃÕÊ Ê €€ð)7ð)7ã÷ÂÊ ÂÃÊ Î Î ÞÎ ÔÕÎ úã÷ÅÎ Î ñ)7ò)7ñ)7ã÷ò)7ã÷¢ÄÎ ÄÅÎ ôÎ Ó Ó ÞÓ ûÕÓ ûã÷ÇÓ Ó ã÷ÆÆÇÜ Ü ÞÜ ’ÖÜ üã÷ÉÜ Ü ó)7ô)7ó)7ã÷ô)7úºÈûÊÊÜ È‚úºÜ ÜIúºÜ ÉúºÜ ï Ü Ü Ü Ì * A depth first search (DFS) visitor event.Á€- Õ ¨ÌÌíñ,Þ ëø3†=‡æÆBÍÎÑÒÕÖÙÚÝÞÎÏÐ–úã÷ëøÒÓÔ–ú–úëøÖ×Ø–ú–úëøÚÛÜ–ú–úëøÞßà–úã÷ëøÞ Þ ç ç ËËÍ–úç ç –úã÷ëøËËÎ–úð ð ËË–úó ó ËËã÷³ - An edge of the tree formed by the traversal.Áþ0 ³ ËËÑ–ú³ ³ –ú–úëøËËÒ–ú¼ ¼ ËË–ú¿ ¿ ËË–úó $ An edge to an already visited node.ÁÇ' ó ËËÕ–úó ó –ú–úëøËËÖ–úü ü ËË–úÿ ÿ ËË–úµ A cross or forward edge.Á‡ ¨ G For an edge *(u, v)*, if the discover time of *v* is greater than *u*,Á°J . then it is a forward edge, else a cross edge.Áÿ1 µ ËËÙ–úµ µ –ú–úëøËËÚ–úÆ Æ ËË–úÉ É ËË–úƒ * All edges from a node have been reported.ÁÑ- ƒ ËËÝ–úƒ ƒ –úã÷ëøËËÞ–úŠ Š ËË–ú ËËã÷· · Þ· ãÖ· ý¨ââíñ,Þ Õó,· ëøÞ Þ ½ ½ Þ½ òÖ½ þ¨ääíñ,Þ â2½ ëøåÞ Þ ½ ½ õ)7õ)7ëøëøã½ ãå–ú½ Ä Ä ÞÄ ’ØÄ ÿ¨ççíñ,Þ ÚÚ4Ä ëøèÞ Þ Ä Ä ö)7÷)7ø)7ö)7ëø÷)7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >Sø)7ÔžæÄ æè–úÄ Ä ë 1 Control flow for `depth_first_search` callbacks.Á˜ 4 ô îêê½µ,ü °ú!)€ª9ëëìíîïðìîðñøú°úü ü ³ & Continue the DFS traversal as normal.Á…) ³ éé°ú³ ³ éé°úá G Prune the current node from the DFS traversal. No more edges from thisÁÁJ E node will be reported to the callback. A `DfsEvent::Finish` for thisÁH E node will still be reported. This can be returned in response to anyÁÝH / `DfsEvent`, except `Finish`, which will panic.Áª 2 á éé°úá á éé°úª 6 Stop the DFS traversal and return the provided value.Áì 9 ª ééïøúª ª øú°úééðøú° ° ééøúÖ Ö ÞÖ ¥ÙÖ €îóó½µ,ü ‡øúÖ °úü ü Ü Ü ÞÜ ´ÙÜ îõõ½µ,ü ×øúÜ °úöü ü Ü Ü ù)7ù)7°ú°úôÜ ôöøúÜ ã ã Þã ñÙã ‚îøø½µ,ü øGøúã °úùü ü ã ã ú)7û)7ü)7ú)7°úû)7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >Sü)7Ôž÷ã ÷ùøúã ã ƒ G Visit nodes of a graph in a depth-first-search (DFS) emitting nodes inÁ´J+ preorder (when they are first discovered).Áÿ.®H The traversal starts at a given node and only traverses nodes reachableÁ²K from it.Áþ‹ `Dfs` is not recursive.Á«H `Dfs` does not itself borrow the graph, and because of this you can runÁ¯KL a traversal over a graph while still retaining mutable access to it, if youÁûO# use it like the following example:ÁË&òõ®+öÿ‚1þ’Ý5—µ& let mut graph = Graph::<_,()>::new();Á¹) let a = graph.add_node(0);Áã‚# let mut dfs = Dfs::new(&graph, a);Á†&( while let Some(nx) = dfs.next(&graph) {Á+0 // we can access `graph` mutably here stillÁÙ3 graph[nx] += 1;Áç×,¥« assert_eq!(graph[a], 1);Á¯õ®+ÌÔF **Note:** The algorithm may not behave correctly if nodes are removedÁØIE during iteration. It may not necessarily visit added nodes or edges.Á¢H Ž ¨û‰üüûíñ,’ €ò,• üûHÄkËˆS ýþ’ ’ • • Ã The stack of nodes to visitÁŸ Ç úúê¥þ The map of discovered nodesÁÚ ‚ úú±ü3ô3ôÞ3ôµÝ3ô•¨€‰€íñ,’ €ò,• â23ô×±ü3ôüû‚’ ’ • • 3ô3ôý)7ý)7üûüûÿ3ôÿ‚–ú±üô4û4ûÞ4ûñÝ4û–¨„‰…„…íñ,’ €ò,• ÚÚ44ûøG±ü4ûüû†’ ’ • • 4û4ûþ)7ÿ)7€*7þ)7üûÿ)7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S€*7Ôžƒ4ûƒ†–ú±üû4û³F Visit nodes in a depth-first-search (DFS) emitting nodes in postorderÁŽI9 (each node after all its descendants have been emitted).ÁØ<•! `DfsPostOrder` is not recursive.Á™$¾ª®@ÂKþ®@Ž¾¨ˆ‰‰ˆ‰íñ,Ë€ò,ÎËüÊ¬t&ñ$kŠ‹ŒËËÎÎü“·@Ø€‡‡ê¥·Ú·@“» ‡‡±üñ The map of finished nodesÁÏõ‡‡±ü5¤5¤Þ5¤»Þ5¤—¨Ž‰Žíñ,Ë€ò,Îâ25¤‰º@5¤ËüËËÎÎ5¤5¤*7*7ËüËü5¤–ú±ü¤6«6«Þ6«‚ß6«˜¨’‰“’“íñ,Ë€ò,ÎÚÚ46«¡¼@6«Ëü”ËËÎÎ6«6«‚*7ƒ*7„*7‚*7Ëüƒ*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S„*7Ôž‘6«‘”–ú±ü«6«©7) A breadth first search (BFS) of a graph.Á®0,Û0ª®@ß0Kþ®@«1¸1 `Bfs` is not recursive.Á¼1Ø1H `Bfs` does not itself borrow the graph, and because of this you can runÁÜ1K¡°@¨2Où°@ø2&Ÿ3õ®+£3ÿ‚1«3 use petgraph::visit::Bfs;ÁÄ3â3å±@æ3)—²@4¯4# let mut bfs = Bfs::new(&graph, a);Á³4&( while let Some(nx) = bfs.next(&graph) {ÁÚ4+¬³@†53è³@º5ç×,Ò5Ø5 ´@Ü5õ®+ù56Ý´@…6I¯µ@Ï6H´7¨–‰—–—íñ,¸7€ò,»7 ýí]7“PÆ7˜™¸7¸7»7»7é7 The queue of nodes to visitÁÅ7í7••¢è©8Ú·@…88 ••±ü7¡77¡7Þ7¡7Îß7¡7™¨›‰œœ›íñ,¸7€ò,»7â27¡7‰º@7¡7 ý¸7¸7»7»77¡77¡7…*7…*7 ý ýš7¡7š–ú±ü¡7üC+ A topological order traversal for a graph.ÁàA.BD **Note** that `Topo` only visits nodes that are not part of cycles,Á“BGD i.e. nodes in a true DAG. Use other visitors like `DfsPostOrder` orÁÛBGD algorithms like kosaraju_scc to handle graphs with possible cycles.Á£CG ‡D¨Ÿ‰ Ÿíñ,ŒD€ò,DåýžÚ]¸\E_¶¡¢ŒDŒDDD™D™DPžžê¥®D®DPžž±ü8ôC8ôCÞ8ôC•à8ôCš¨¤‰¥¤¥íñ,ŒD€ò,Dâ28ôC‰º@8ôCåý¦ŒDŒDDD8ôC8ôC†*7†*7åýåý£8ôC£¦–ú±üôCÔ`3 A walker and its context wrapped into an iterator.Á…`6ß` ›¨d©¨©¾©-ê`Ñ©-í`®þÒ'ÑÀ&Ð‚ª«ê`ê`í`í`ö` ö`P§§âþ…a …aP§§èþ9Å`9Å`Þ9Å`Úà9Å`››d®®¾©-ê`Ñ©-í`×âþ9Å`û©-9Å`®þ¯ê`ê`í`í`9Å`9Å`‡*7‡*7®þ®þ¬9Å`¬¯âþèþÅ`:Ì`:Ì`Þ:Ì`–á:Ì`œ›±d²±²¾©-ê`Ñ©-í`øGâþ:Ì`øGèþ:Ì`®þ³ê`ê`í`í`:Ì`:Ì`ˆ*7‰*7Š*7ˆ*7®þ‰*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SŠ*7Ôž°:Ì`°³âþèþÌ`:Ì`;”Æ ³ ;§ ;Ù ;µ ;È ;¶ Ïƒ;· ;¸ í;¹ ;¿ éõ+;À ;Å Ï;Æ ;Ê ;Í ;Ï ;ß Ïƒ;à ;á ;ã ;Á ;ù éõ+;ú ;” ;˜ û†³ ½Å ‡ šü+ˆ› " ¤ • ¡ šü+¢©µ "ª ² ¯ » ðÿ+¼ ú¢,Ä # edge identifierÁÓ "ë ² ðö møý ¼ÿ ˆ# node identifierÁŽ "¦ ¤ «± m³¸ ¼º Ã;ž Í@¹Í@ÓáÍ@= Base graph trait: defines the associated node identifier andÁŸã@Í@;ˆ Ž¹ŽÕáŽ edge identifier types.Á”ä@Ž;ˆ ¤³ µµµ¶·¶·‚ÈÑë‡ÈÑø‡ÈÑÈÑÿ ‚ÖÒ¦‡ÖÒ³‡ÖÒÖÒº ëÓ¹ÓØáÓ§á@§á@ÓÓðµµ¦Ž¹ŽßáŽ“â@“â@ŽŽ«µµ\„Ž ¼ü¹êº¹ºç¿-[þ ú¿-\¼ ¹»¼[ú [ú [þ [þ \Û3 Z•¸¸±¹\Û3 Z¯¸¸¯À_„Ž ½ü¾ê¿¾¿ç¿-ˆú¿-_¼ ÍÀÁ„„ˆˆ_Û3 ^•½½±¹_Û3 ^¯½½¯À`”Æ ÿ `§ `Ù `µ `È `¶ Ïƒ`· `¸ í`¹ `¿ éõ+`À `Å Ï`Æ `Ê `Í `Ï `ß Ïƒ`à `á `ã `Á `ù éõ+`ú `” `˜ ë‡ÿ ì˜ê ž ðÿ+Ÿ "§ "° î µ ¾ ŸÀÈ òÉÍ ÎÒ ¤ ÔÚÛ á ðÿ+â ê#1 Return an iterator of the neighbors of node `a`.Áó4 ¬ ÿ ¯ ¸Ô ¹½ ¿Ã š±ÅÆ ÈÌ ¤ ÎÖ ÙÝ î ß è`ž¸ “(¹“(‘â“(% Access to the neighbors of each nodeÁ“ð@“(`ˆ ¼¹¼“â¼¼`ˆ À<¹À<•âÀ<9 The neighbors are, depending on the graphâ€™s edge type:Á¥ñ@À<`ˆ ý¹ý—âýý`ˆ 0¹0™â0- - `Directed`: All targets of edges from `a`.ÁËò@0`ˆ ²B¹²B›â²B? - `Undirected`: All other endpoints of edges connected to `a`.Á°ó@²B`ˆ Ïÿ ÃÃ×àžÃªõ@ÄÅÄÅ‚àžÄ°,Ø9åõ@Àåõ@Ù9ÖÒÉ°+µ ÃÃ¬=ó4¹ó4§âó4ôí@ôí@ó4ó4¯ àžÖÒåõ@ÃÃÅàž¹š±ÅŽ„Ž êüÇêÈÈÇç¿-þ Íá.Ž¼ †„ÉÊú ú þ þ Žò+ Œµ ÆÆ‹¹ŽÌJ Œ¯ †„†„·†„ÄÆÆÊÇüÂŽÜ š±ŒÅ”Æ š#§ Ù µ È ¶ Ïƒ· ¸ í¹ ¿ éõ+À Å ÏÆ Ê Í Ï ß Ïƒà á ã Á ù éõ+ú ” ˜ »ˆš#°# ë‡²# À#‚% Æ# ðÿ+Ç# "Ï# "Ø# ¬"Ý#î# Ÿð#ø# òù#ý# þ#‚$ ¤ „$Š$‹$ ‘$ ðÿ+’$ š$ £$ Å"¦$¸$Ü$ ¹$½$ –µ¿$À$ Â$Æ$ ¤ È$Î$ ‹Ð$Ñ$ ŽÁÓ$ æ$ é$í$ ¬"ï$€%ž¸ ¤ M¹¤ MÞâ¤ MJ Access to the neighbors of each node, through incoming or outgoing edges.ÁÝþ@¤ Mˆ ò ¹ò àâò ò ˆ ö Q¹ö Qââö QN Depending on the graphâ€™s edge type, the neighbors of a given directionalityÁ–€Aö Qˆ È!¹È!äâÈ! are:ÁAÈ!ˆ Ñ!¹Ñ!æâÑ!Ñ!ˆ Õ!<¹Õ!<èâÕ!<9 - `Directed`, `Outgoing`: All targets of edges from `a`.Á‘‚AÕ!<ˆ ’":¹’":êâ’":7 - `Directed`, `Incoming`: All sources of edges to `a`.ÁƒƒA’":ˆ Í"B¹Í"BìâÍ"B°ó@°ó@Í"Bˆ š#ÌÌÃàž²# Ì°…A²# ÍÎÍÎ‚àžÍØ#4Ø9ë…Að#ë…AÙ9ÖÒù#Ø#3Ý#ÌÌ£$^¦$àžÖÒÜë…AÌÌÎàž¹$–µ¿$‹Ð$½”Æ ¾3 ½§ ½Ù ½µ ½È ½¶ Ïƒ½· ½¸ í½¹ ½¿ éõ+½À ½Å Ï½Æ ½Ê ½Í ½Ï ½ß Ïƒ½à ½á ½ã ½Á ½ù éõ+½ú ½” ½˜ ïˆ¾3 È3 ÙŒÊ3Ý3 ë‡ß3 í3õ4 ó3 ðÿ+ô3 "ü3 "…4 „9Š44 Ÿ‘4™4 òš4ž4 Ÿ4£4 Ç%¥4¬44 ³4 ðÿ+´4 ¼4 Å4 ‘9È4Í4ã4 Î4Ò4 š±Ô4Õ4 ×4Û4 ¤ Ý4å4 è4ì4 „9î4ó4½ž¸ 0%¹0%‰ã0%" Access to the edges of each node.Á›ŒA0%½ˆ ¶0¹¶0‹ã¶0¶0½ˆ º08¹º08ãº085 The edges are, depending on the graphâ€™s edge type:Á¬Aº08½ˆ ó0¹ó0ãó0ó0½ˆ ÷0%¹÷0%‘ã÷0%" - `Directed`: All edges from `a`.ÁÐŽA÷0%½ˆ 1/¹1/“ã1/µÖ2µÖ21/½ˆ Í1¹Í1•ãÍ1Í1½ˆ Ñ1H¹Ñ1H—ãÑ1HE This is an extended version of the trait `IntoNeighbors`; the formerÁ›AÑ1H½ˆ š2D¹š2D™ãš2DA only iterates over the target node identifiers, while this traitÁ™‘Aš2D½ˆ ß23¹ß23›ãß230 yields edge references (trait [`EdgeRef`][er]).Á“’Aß23½ˆ “3¹“3ã“3“3½ˆ —3¹—3Ÿã—3 [er]: trait.EdgeRef.htmlÁ²“A—3½ˆ ¸¾3 ÐÐ‰àžÊ3°…Aß3 Ð‡•AÊ3°…Aß3 ÑÒÑÒ‚àžÑ…4)Ø9Ö•A‘4Ö•AÙ9àžŠš4…4(Š4ÐÐÅ4/È4àžÖÒÖ•AÐÐÒàžÎ4š±Ô4÷„Ž ÑüÔêÕÔÕç¿-öþ …ˆ/÷¼ ª…Ö×öú öú öþ öþ ÷ò+ õŠ4ÓÓÈÂ÷ÌJ õÈ4ª…ª…·ª…ÑÓÓ×ÔüÂ÷Ü š±õÔ4ù”Æ ç:ù§ ùÙ ùµ ùÈ ù¶ Ïƒù· ù¸ íù¹ ù¿ éõ+ùÀ ùÅ ÏùÆ ùÊ ùÍ ùÏ ùß Ïƒùà ùá ùã ùÁ ùù éõ+ùú ù” ù˜ »‰ç:ù: ïˆû: …; »ˆ‡;;Î< £; ðÿ+¤; "¬; "µ; ÒBº; Ç; ŸÉ;Ñ; òÒ;Ö; ×;Û; Ç%Ý;ä;å; ë; ðÿ+ì; ô; ý; çB€<Ž<´< <“< š±•<–< ˜<œ< ¤ ž<¤< •‡¦<©< ŽÁ«< ¶< ¹<½< ÒB¿< Ì<ùž¸ «5A¹«5Aàã«5A> Access to all edges of each node, in the specified direction.ÁµžA«5Aùˆ í5¹í5âãí5í5ùˆ ñ5J¹ñ5Jäãñ5JG The edges are, depending on the direction and the graphâ€™s edge type:ÁâŸAñ5Jùˆ ¼6¹¼6æã¼6¼6ùˆ À6¹À6èãÀ6À6ùˆ Ä61¹Ä61êãÄ61ž¢5ž¢5Ä61ùˆ ö6/¹ö6/ìãö6/Ù¢5Ù¢5ö6/ùˆ ¦7c¹¦7cîã¦7c` - `Undirected`, `Outgoing`: All edges connected to `a`, with `a` being the source of each edge.ÁÂ¢A¦7cùˆ Š8c¹Š8cðãŠ8c` - `Undirected`, `Incoming`: All edges connected to `a`, with `a` being the target of each edge.ÁÛ£AŠ8cùˆ î8¹î8òãî8î8ùˆ ò8P¹ò8Pôãò8PM This is an extended version of the trait `IntoNeighborsDirected`; the formerÁª¥Aò8Pùˆ Ã9D¹Ã9DöãÃ9D™‘A™‘AÃ9Dùˆ ˆ:3¹ˆ:3øãˆ:3“’A“’Aˆ:3ùˆ ¼:¹¼:úã¼:¼:ùˆ À:¹À:üãÀ:²“A²“AÀ:ùˆ øç:ÙÙÐàžû: Ìàž‡;Ù—©Aû: ª©A‡;ÚÛÚÛ‚àžÚµ;1Ø9ï©AÉ;ï©AÙ9‡–AÒ;µ;0º; ÙÙý;P€<àžÖÒÜï©AÙÙÛàž<š±•<•‡¦<¹„Ž ‘üÝêÞÝÞç¿-¸þ º¤/¹¼ ¼…ßà¸ú ¸ú ¸þ ¸þ ¹ò+ ·º; ÜÜ§Ä¹ÌJ ·€<¼…¼…·Ü¼…ÚÜÜàÝüÂ¹Ü š±·•<•‡·¦<»”Æ Í=»§ »Ù »µ »È »¶ Ïƒ»· »¸ í»¹ »¿ éõ+»À »Å Ï»Æ »Ê »Í »Ï »ß Ïƒ»à »á »ã »Á »ù éõ+»ú »” »˜ ŠÍ=á= ìã=ì=? ò= ðÿ+ó= "û= "„> û#‰>˜> Ÿš>¢> ò£>§> ¨>¬> ¤ ®>´>µ> »> ðÿ+¼> Ä> Í> ’$Ð>à>å> á>ç> ê>î> û#ð>ÿ>»ž¸ Œ=6¹Œ=6ÇäŒ=63 Access to the sequence of the graphâ€™s `NodeId`s.ÁÙ±AŒ=6»ˆ šÍ=ââªõ@ã=âªõ@ã=ãäãä‚àžã„>2Ø9ú³Aš>ú³AÙ9ÖÒ£>„>1‰>ââÍ>3Ð>àžú³Aââäàžá>Ý„Ž ³üæêçæçç¿-Üþ Œï.Ý¼ Î…èéÜú Üú Üþ Üþ Ýò+ Û‰>åå¡ÆÝÌJ ÛÐ>Î…Î…ãååéæüÂÝÜ â„Ž ¶üëêììëç¿-áþ ýë.â¼ Ä‡íîáú áú áþ áþ âò+ àÝ#êê›ÍâÌJ à¦$Ä‡Ä‡·ÜÄ‡ÍêêîëüÂâÜ –µà¿$‹àÐ$ä”Æ øGä§ äÙ äµ äÈ ä¶ Ïƒä· ä¸ íä¹ ä¿ éõ+äÀ äÅ ÏäÆ äÊ äÍ äÏ äß Ïƒäà äá äã äÁ äù éõ+äú ä” ä˜ ‰‹øGýG û†ÿG ‰HÇH H ðÿ+H "˜H "¡H ·(¦H °H "¶H É(»H ÅHäž¸ ¿G.¹¿G.¦å¿G.+ Define associated data for nodes and edgesÁò»A¿G.äˆ ¿øGðð‚¿-ÿG ð‚¿-ÿG ñòñò‚– ¡H‚÷Ò¶H¡H¦H ðð¶H»H ðð÷„Ž Éüôêõôõç¿-öþ äã3÷¼ Ö‡ö÷öú öú öþ öþ ÷ò+ õ¦H óóî½÷ò+ õ»H óó‹Àú„Ž Êüùêúùúç¿-€Iäã3ú¼ è‡ûüüHüH€I€Iúò+ ù¦H øøî½úò+ ù»H øø‹Àû”Æ ÄQû§ ûÙ ûµ ûÈ û¶ Ïƒû· û¸ íû¹ û¿ éõ+ûÀ ûÅ ÏûÆ ûÊ ûÍ ûÏ ûß Ïƒûà ûá ûã ûÁ ûù éõ+ûú û” û˜ ô‹ÄQ×Q ‰‹ÙQÞQ ŠàQôQÐS, úQ ðÿ+ûQ "ƒR "ŒR õ&‘R˜R õ&šR¡R ¤ ¢R¨R ©RR ¤ ¯RµR æ%·R½R ¾RÂR ·(ÄR ÎRÏR "ÕR ñ5ÚRèR ŸêRòR òóR÷R øRüR õ&þR…S†S ŒS ðÿ+S •S žS ‡6¡S°SµS ±S·S ºS¾S ñ5ÀSÎSûž¸ ˆQ1¹ˆQ1ØåˆQ1. Access to the sequence of the graphâ€™s nodesÁÅÇAˆQ1ûˆ ÔÄQþþ£ì/ÙQâàžàQþ£ì/ÙQ¹ÉAàQÿ€ÿ€‚àžÿŒRD½‚ÊAšR5‚ÊA¾ÖÒ¢R‚ÊA¿– ·R‚àž€ÕR2Ø9ÙÊAêRÙÊAÙ9‚ÊAóRŒRC‘RþþÕR1ÚRþþžS1¡SàžÙÊAþþàž±S±„Ž ÿüƒê„„ƒç¿-°þ ò.±¼ ”ˆ…†‡°ú °ú °þ °þ ±ò+ ¯‘R‚‚€†±ò+ ¯ÚR‚‚ŸÓ±ÌJ ¯¡S”ˆ”ˆ€‚‚‡ƒüÂ±Ü ³”Æ £X³§ ³Ù ³µ ³È ³¶ Ïƒ³· ³¸ í³¹ ³¿ éõ+³À ³Å Ï³Æ ³Ê ³Í ³Ï ³ß Ïƒ³à ³á ³ã ³Á ³ù éõ+³ú ³” ³˜ ÙŒ£X¶X ‰‹¸X½X ì¿XÈXÓZ2 ÎX ðÿ+ÏX "×X "àX Ç%åXìX Ç%îXõX ¤ öXüX ýXY ¤ ƒY‰Y ² ‹Y‘Y ’Y–Y ² ˜YžY æ%ºYÀY ÁYÅY É(ÇY ÑYÒY "ØY “*ÝYëY ŸíYõY òöYúY ûYÿY Ç%ZˆZ‰Z Z ðÿ+Z ˜Z ¡Z ©*¤Z³Z¸Z ´ZºZ ½ZÁZ “*ÃZÑZ³ž¸ çW1¹çW1œæçW1. Access to the sequence of the graphâ€™s edgesÁáÔAçW1³ˆ Š£X‰‰£ì/¸Xªõ@¿X‰£ì/¸Xªõ@¿XŠ‹ŒŠ‹Œ‚‡–AàXsª‡–AîXd‡–A«ÖÒöX‡–A¬ÈÑ‹Y‡–A÷ÒºY‚àž‹ØY2Ø9ú×AíYú×AÙ9‡–AöYàXråX‰‰ØY1ÝY‰‰¡Z1¤Zàžú×A‰‰Œàž´Zï„Ž »üŽêŽç¿-îþ ¿ü.ï¼ Íˆ‘’îú îú îþ îþ ïò+ íåX¨¸ïò+ íÝYìØïÌJ í¤ZÍˆÍˆ‹’ŽüÂïÜ ñ”Æ ‡] ñ§ ñÙ ñµ ñÈ ñ¶ Ïƒñ· ñ¸ íñ¹ ñ¿ éõ+ñÀ ñÅ ÏñÆ ñÊ ñÍ ñÏ ñß Ïƒñà ñá ñã ñÁ ñù éõ+ñú ñ” ñ˜ ¾‡] ‘] û†“] ]Þ^ £] ðÿ+¤] "¬]# The kind edges in the graph.Áµ] "Ú] ÿ(ß]ç] ÿ(é]ñ] ø] ðÿ+ù] ú¢,^ ^ ãa“^ž^¤^Ÿ^ ^¦^ ó©^®^Ü^¸^ ¹^½^ ÿ(¿^Ç^È^ ãaÊ^Õ^Ö^ñž¸ Ç\5¹Ç\5äæÇ\52 Edge kind property (directed or undirected edges)ÁžàAÇ\5ñˆ Ä‡] ””‚¿-“] ”‚¿-“] •–•–‚ÚÙÚ]ýÚÙé]Ú]µ] ¹µ] éæµ] ¸ÝA¸ÝAµ] µ] ß]””^“^‹*7‹*7àž”Ÿ^”–àž ^Œ„Ž Öü˜ê™˜™ç¿-‹þ Çã3Œ¼ ®‰š‹ú ‹ú ‹þ ‹þ Œò+ Šß]——Â•”Æ Óe § Ù µ È ¶ Ïƒ· ¸ í¹ ¿ éõ+À Å ÏÆ Ê Í Ï ß Ïƒà á ã Á ù éõ+ú ” ˜ ‚ŽÓe áe û†ãe íeùh ÷e ðÿ+øe €f#í¬:f:#% (suitable for the size of a bitmap).ÁÐf( g ä*„g Žgšg g“g•g –gœg ™Ÿg¤g#! Convert `a` to an integer index.Á®g$ Ûg ö*Þgægƒh çgëgíg îgòg š±ôgõg ÷gûg ¤ ýg…h ™ˆhh# Convert `i` to a node indexÁ—h ¿h †+Âh Ìhâh ÍhÑhÓh ÔhØh æÚhÛh ™Ýhäh çhëh ¤ íhóhž¸ še*¹še*çše*' The graphâ€™s `NodeId`s map to indicesÁ°ìAše*ˆ ÞÓe œœ‚¿-ãe œ‚¿-ãe žŸžŸg$f:¹f:”çf:í¬:í¬:f:f:Ðf(¹Ðf(–çÐf(©çA©çAÐf(Ðf(„g Œ*7Œ*7àžœ•gœàžgÛg3®g$¹®g$ ç®g$ÎèAÎèA®g$®g$Þg*7*7àžÖÒœígœžàžçgš±ôg¿h5—h¹—h¯ç—h±êA±êA—h—hÂh Ž*7Ž*7àžÖÒœÓhœŸàžÍhæÚh¶„Ž þü¡ê¢¡¢ç¿-µþ ã3¶¼ ê‰£¤¥µú µú µþ µþ ¶ÌJ ´„g *7*7ê‰ ´•g £¡üÂ¶Ü ¶ÌJ ´Þg*7*7ê‰ê‰· ´íg ¤¡üÂ¶Ü š±´ôg¶ÌJ ´Âh ‘*7‘*7ê‰ñôA ´Óh ¥¡üÂ¶Ü æ´Úhº”Æ âi º§ ºÙ ºµ ºÈ º¶ Ïƒº· º¸ íº¹ º¿ éõ+ºÀ ºÅ ÏºÆ ºÊ ºÍ ºÏ ºß Ïƒºà ºá ºã ºÁ ºù éõ+ºú º” º˜ âŽâi ìi û†îi øiµj þi ðÿ+ÿi ‡j j Î$“j j©j žj¢j¤j ¥j«j ™®j³jºž¸ ³i$¹³i$‡è³i$! A graph with a known node count.ÁºùA³i$ºˆ ‰âi §§‚¿-îi §‚¿-îi ¨¨j$“j ’*7’*7àž§¤j§¨àžžjÍ„Ž “üªê«ª«ç¿-Ìþ ¾²?Í¼ ü‰¬Ìú Ìú Ìþ Ìþ ÍÌJ Ë“j “*7“*7ü‰©Ë¤j©¬ªüÂÍÜ Ï”Æ žlÏ§ ÏÙ Ïµ ÏÈ Ï¶ ÏƒÏ· Ï¸ íÏ¹ Ï¿ éõ+ÏÀ ÏÅ ÏÏÆ ÏÊ ÏÍ ÏÏ Ïß ÏƒÏà Ïá Ïã ÏÁ Ïù éõ+Ïú Ï” Ï˜ žžl³l ‚Žµl Ãl âŽÅl ÏlÑlÏž¸ ëjE¹ëjE±èëjEB The graphâ€™s `NodeId`s map to indices, in a range without holes.Á‚€BëjEÏˆ ±k¹±k³è±k±kÏˆ µkB¹µkBµèµkB? The graph's node identifiers correspond to exactly the indicesÁ³BµkBÏˆ øk¹øk·èøk `0..self.node_bound()`.Á«‚BøkÏˆ ¤žl®®œàžµl ¶ì/Ål ®ÿƒBµl ¶ì/Ål ã„Ž §ü°ê±°±ç¿-âþ ®Âã¼ ŽŠâú âú âþ âþ ä”Æ î{ ä§ äÙ äµ äÈ ä¶ Ïƒä· ä¸ íä¹ ä¿ éõ+äÀ äÅ ÏäÆ äÊ äÍ äÏ äß Ïƒäà äá äã äÁ äù éõ+äú ä” ä˜ Õî{ ø{ û†ú{ „|»~ Š| ðÿ+‹| "“|# The associated map typeÁœ| "¼| ¾-Á|Ä| »+Æ|Î| Ï|Ó| ¤ Õ|Û|Ü| â| ðÿ+ã| ë|# Create a new visitor mapÁô| •} É-˜} ¡}} ¢}¦}¨} ©}¯} ²}¶} ¾-¸}»}#B Reset the visitor map (and resize to new size of graph if needed)ÁÁ}E ‹~ Ú-Ž~ —~¸~ ˜~œ~ž~ Ÿ~£~ à¥~¨~ª~ «~ ¯~³~ ¾-µ~¹~äž¸ •{N¹•{NÌè•{NK A graph that can create a map that tracks the visited status of its nodes.Á±ŒB•{Näˆ ¯î{ ³³‚¿-ú{ ³‚¿-ú{ ´µ¶´µ¶‚àž´¼|!þîŽBÖÒÆ|¼| œ|¹œ|Ñèœ|©‡B©‡Bœ|œ|Á|³³•}'ô|¹ô|Ùèô|ÞˆBÞˆBô|ô|˜} ”*7”*7àžîŽB³¨}³µàž¢}‹~/Á}E¹Á}EäèÁ}EŒŠBŒŠBÁ}EÁ}EŽ~ •*7–*7•*7àž–*7îŽB³ž~³¶àž˜~à¥~Ž„Ž Ðü¸ê¹¸¹ç¿-þ ³ÂŽ¼ ±Œº»¼ú ú þ þ Žò+ ŒÁ|··ôôŽÌJ Œ˜} —*7—*7±Œ±Œ´·Œ¨}·»¸üÂŽÜ ŽÌ? ŒŽ~ ˜*7™*7˜*7±Œ™*7”B·Œž~·¼¸üÂŽÜ àŒ¥~‘”Æ £Ž‘§ ‘Ù ‘µ ‘È ‘¶ Ïƒ‘· ‘¸ í‘¹ ‘¿ éõ+‘À ‘Å Ï‘Æ ‘Ê ‘Í ‘Ï ‘ß Ïƒ‘à ‘á ‘ã ‘Á ‘ù éõ+‘ú ‘” ‘˜ ±£Ž¶Ž û†¸Ž ÂŽÜ‘ ÈŽ ðÿ+ÉŽ "ÑŽ#% The associated adjacency matrix typeÁÚŽ( "‡ ‹0Œ • › ðÿ+œ ¤# Create the adjacency matrixÁ Ñ œ0Ôäð åéë ìò õù ‹0û „#B Return true if there is an edge from `a` to `b`, false otherwise.ÁŠE#Ô# Computes in O(1) time.ÁÜ û ´0þ‰‘Ñ‘ Š‘Ž‘‘ ‘‘•‘ …—‘‘Ÿ‘ ‘¤‘ ‹0¦‘ ¯‘ š±±‘²‘ ´‘¸‘ ¤ º‘À‘ ª±Â‘Ã‘ Å‘É‘ ¤ Ë‘Ó‘ óÖ‘Ú‘‘ž¸ ÑŒ5¹ÑŒ5©éÑŒ52 Create or access the adjacency matrix of a graph.Á›žBÑŒ5‘ˆ ‡¹‡«é‡‡‘ˆ ‹K¹‹Ké‹KH The implementor can either create an adjacency matrix, or it can returnÁÄŸB‹K‘ˆ ×A¹×A¯é×A> a placeholder if it has the needed representation internally.ÁÉ B×A‘ˆ à£Ž¾¾‚¿-¸Ž ¾‚¿-¸Ž ¿ÀÁ¿ÀÁ‚àž¿‡‡ÚŽ(¹ÚŽ(´éÚŽ(Ó—BÓ—BÚŽ(ÚŽ(Œ ¾¾Ñ4¹·éÒ˜BÒ˜BÔš*7š*7àžý¢B¾ë¾Ààžåû`ŠE¹ŠEÂéŠE‘šB‘šBŠEŠEÔ¹ÔÄéÔÔÔÜ¹ÜÆéÜðšBðšBÜÜþ›*7œ*7›*7àžœ*7ý¢BÖÒÖÒ¾‘¾ÁàžŠ‘…—‘š±±‘ª±Â‘À„Ž €üÃêÄÄÃç¿-¿þ ¾ÂÀ¼ ûŒÅÆÇ¿ú ¿ú ¿þ ¿þ Àò+ ¾Œ ÂÂñƒÀÌJ ¾Ô*7*7ûŒûŒ¿Â¾ëÂÆÃüÂÀÜ ÀÌJ ¾þž*7Ÿ*7ž*7ûŒŸ*7¹©BûŒ·ÄªBÂ¾‘ÂÇÃüÂÀÜ …¾—‘š±¾±‘ª±¾Â‘ A node-filtering graph adaptor.ÁË#/˜ êÊúËÊËÊç,¥ ðë,¨ –ŽõÑÿbç®ÉÌÍÎÿÍŽ–Ž ˜ ÎÿÍŽ–ŽÈÈÉÎÿÍŽ¥ ¥ ¨ ¨ « « ÈÈÎÿ² ² ÈÈÍŽÃøÃøÞÃø°êÃøƒêÏúÐÐÏÊç,¥ ðë,¨ ‡ÎÿÃø‡ÍŽÃø–Ž¥ ¥ ¨ ¨ ÄþÄþÞÄþÆêÄþ„êÒúÓÒÓÊç,¥ ðë,¨ ×ÎÿÄþ×ÍŽÄþ–ŽÔ¥ ¥ ¨ ¨ ÄþÄþ *7 *7–Ž–ŽÑÄþÑÔÎÿÍŽþÅ… Å… ÞÅ… ëÅ… …êÖú×Ö×Êç,¥ ðë,¨ øGÎÿÅ… øGÍŽÅ… –ŽØ¥ ¥ ¨ ¨ Å… Å… ¡*7¢*7£*7¡*7–Ž¢*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S£*7ÔžÕÅ… ÕØÎÿÍŽ… Å… È„Ž †êÚúÛÚÛÊç,Ñ9ðë,Ô9“Ö1È¼ –ŽÜÝÑ9Ñ9Ô9Ô9Èò+ Ç¦H ÙÙøËÈò+ Ç»H ÙÙšÏË„Ž ‡êßúààßÊç,™:ðë,œ:Ù¨0Ë¼ –Žáâã™:™:œ:œ:ËÌJ Ê„g ¤*7¤*7–ŽÞÊ•gÞáÎÿÍŽËÜ ËÌJ ÊÞg¥*7¥*7–Ž›”/ÞÊígÞâÎÿÍŽËÜ š±ÊôgËÌJ ÊÂh ¦*7¦*7–Ž›”/ÞÊÓhÞãÎÿÍŽËÜ æÊÚhÑ„Ž ‹êåúæåæÊç,Ý:ðë,à:”ÎÿÑ¼ –ŽçÝ:Ý:à:à:Ñò+ Ðß]ääÎÿ•Ô„Ž ŒêéúêéêÊç,¡;ðë,¤;©è,Ô¼ –Žëìí¡;¡;¤;¤;Ôò+ ÓÁ|èè×ÿÔÌJ Ó˜} §*7§*7–Ž–Ž´èÓ¨}èìÎÿÍŽÔÜ ÔÌ? ÓŽ~ ¨*7©*7¨*7–Ž©*7Ò½BèÓž~èíÎÿÍŽÔÜ àÓ¥~Ñ@! An edge-filtering graph adaptor.Áõ=$š>B The adaptor may filter out edges. The filter implements the traitÁž>E@ `FilterEdge`. Closures of type `Fn(G::EdgeRef) -> bool` alreadyÁä>C implement this trait.Á¨?Â?L The filter may use edge source, target, id, and weight to select whether toÁÆ?O include the edge or not.Á–@4Ü@êðúñðñÊç,é@ðë,ì@¬“=&„¶²ãïòóÎÿÍŽ¬“Ñ@Ü@ÎÿÍŽ¬“îîïÎÿÍŽé@é@ì@ì@ï@ï@îîÎÿö@ö@îîÍŽ×¼@×¼@Þ×¼@ñì×¼@êõúöõöÊç,é@ðë,ì@ë®B×¼@ÿ®B×¼@¬“é@é@ì@ì@ØÂ@ØÂ@ÞØÂ@‡íØÂ@êøúùøùÊç,é@ðë,ì@¬°BØÂ@À°BØÂ@¬“úé@é@ì@ì@ØÂ@ØÂ@ª*7ª*7¬“¬“÷ØÂ@÷úÎÿÍŽÂ@ÙÉ@ÙÉ@ÞÙÉ@ÂíÙÉ@‘êüúýüýÊç,é@ðë,ì@Ö²BÙÉ@ê²BÙÉ@¬“þé@é@ì@ì@ÙÉ@ÙÉ@«*7¬*7*7«*7¬“¬*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S*7ÔžûÙÉ@ûþÎÿÍŽÉ@ÙÉ@Ü„Ž ’ê€ú€Êç,€`ðë,ƒ`“Ö1Ü¼ ¬“‚ƒ€`€`ƒ`ƒ`Üò+ Û¦H ÿÿøËÜò+ Û»H ÿÿšÏß„Ž “ê…ú†…†Êç,Ä`ðë,Ç`‡»Bß¼ ¬“‡Ä`Ä`Ç`Ç`ßò+ Þß]„„é»Bâ„Ž ”ü‰êŠú‹‰Š‹ç¿-–aðç,™aŒï.â¼ …—Œ’a’a–a–a™a™aâò+ á‰>ˆˆ¡ÆâÌJ áÐ>…—…—ãˆˆ‰üÂ¯âÜ æ„Ž –üêú‘‘ç¿-ëaðç,îaò.æ¼ ——’“”çaçaëaëaîaîaæò+ å‘RŽŽ€†æò+ åÚRŽŽŸÓæÌJ å¡S————€ŽŽ”üÂ¯æÜ ê„Ž ˜ê–ú—–—Êç,¾bðë,Áb”1ê¼ ¬“¾b¾bÁbÁbí„Ž ™ê™úš™šÊç,‚cðë,…cññ1í¼ ¬“›‚c‚c…c…cíÌJ ì“j ®*7®*7¬“˜ì¤j˜›ÎÿÍŽíÜ ñ„Ž ›êúžžÊç,Êcðë,ÍcÙ¨0ñ¼ ¬“Ÿ ¡ÊcÊcÍcÍcñÌJ ð„g ¯*7¯*7¬“œð•gœŸÎÿÍŽñÜ ñÌJ ðÞg°*7°*7¬“¬“·œðígœ ÎÿÍŽñÜ š±ðôgñÌJ ðÂh ±*7±*7¬“€ØBœðÓhœ¡ÎÿÍŽñÜ æðÚh÷„Ž Ÿê£ú¤¤£Êç,Ždðë,‘d©è,÷¼ ¬“¥¦§ŽdŽd‘d‘d÷ò+ öÁ|¢¢×ÿ÷ÌJ ö˜} ²*7²*7¬“¬“´¢ö¨}¢¦ÎÿÍŽ÷Ü ÷Ì? öŽ~ ³*7´*7³*7¬“´*7™ÛB¢öž~¢§ÎÿÍŽ÷Ü àö¥~´! An edge-reversing graph adaptor.Á²$×7 All edges have the opposite direction with `Reversed`.ÁÛ:=¿êªªÊç,ÈÉ—¶ u5ë‰Ú©«ÎÿÉ—´¿ÎÿÉ—¨¨©ÎÿÈÈËË¨¨ÎÿúŸúŸÞúŸ§ðúŸ¢êÊç,Èë®BúŸÉ—ÈÈû¥û¥Þû¥¶ðû¥£ê¯¯Êç,È¬°Bû¥É—°ÈÈû¥û¥µ*7µ*7É—É—®û¥®°Îÿ¥ü¬ü¬Þü¬Þðü¬¤ê²²Êç,ÈÖ²Bü¬É—³ÈÈü¬ü¬¶*7·*7¸*7¶*7É—·*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S¸*7Ôž±ü¬±³Îÿ¬ü¬ÿ„Ž ¥êµµÊç,Š “Ö1ÿ¼ É—¶·Š Š ÿò+ þ¦H ´´øËÿò+ þ»H ´´šÏÏ# A reversed edge referenceÁ“?ÚéCººöÖ/ð™S†ª¶°jY¹»°™™Ï#Ú¿°™™¸¸¹°™ððóó¿¸¸°™€º€ºÞ€º§ñ€º¦éC½½öÖ/ð‡°™€º™ððÀÀÞÀ¶ñÀ§éC¿¿öÖ/ð×°™À™ÀððÀÀ¹*7¹*7™™¾À¾À°™À‚Ç‚ÇÞ‚ÇÞñ‚Ç¨éCÂÂöÖ/ðøG°™‚Ç™Ãðð‚Ç‚Çº*7»*7¼*7º*7™»*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S¼*7ÔžÁ‚ÇÁÃ°™Ç‚Ç…„Ž ©êÅÅÊç,Š Ù¨0…¼ É—ÆÇÈŠ Š …ÌJ „„g ½*7½*7É—Ä„•gÄÆÎÿ…Ü …ÌJ „Þg¾*7¾*7É—ƒÎ/Ä„ígÄÇÎÿ…Ü š±„ôg…ÌJ „Âh ¿*7¿*7É—ƒÎ/Ä„ÓhÄÈÎÿ…Ü æ„Úh‹„Ž êÊÊÊç,Š ”1‹¼ É—Š Š Ž„Ž ®êÌÌÊç,Š ø•1Ž¼ É—ÍÎŠ Š Žò+ ‰>ËË†¹ŽÌJ Ð>É—É—ãËËÎÎÿŽÜ ’„Ž °êÐÐÊç,Š ¿Ô1’¼ É—ÑÒÓŠ Š ’ò+ ‘‘RÏÏ…ù’ò+ ‘ÚRÏÏÂº’ÌJ ‘¡SÉ—É—€ÏÏÓÎÿ’Ü –„Ž ²êÕÕÊç,Š ‡»B–¼ É—ÖŠ Š –ò+ •ß]ÔÔé»B™„Ž ³êØØÊç,Š ññ1™¼ É—ÙŠ Š ™ÌJ ˜“j À*7À*7É—×˜¤j×ÙÎÿ™Ü ›”Æ ¥›§ ›Ù ›µ ›È ›¶ Ïƒ›· ›¸ í›¹ ›¿ éõ+›À ›Å Ï›Æ ›Ê ›Í ›Ï ›ß Ïƒ›à ›á ›ã ›Á ›ù éõ+›ú ›” ›˜ ó™¥ ‰‹¯´è º ðÿ+» Ã Ì Æ:ÏÚø Ûßá âæ ˜&èê ìð ¤ òú µýƒ„ …‰ ·(‹ •– œ Ù:ŸªÈ «¯± ²¶ ˜&¸º ¼À ² ÂÊ µÍÓÔ ÕÙ É(Û åæ›ž¸ ç3¹ç3Ìóç30 Access node and edge weights (associated data).ÁïüBç3›ˆ ¼¥ÛÛ£ì/¯Û£ì/¯ÜÝÜÝÌKÏÁ*7Á*7àžÖÒõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÁ*7– ÛáÛÜàžÛ˜&èœKŸÂ*7Â*7àžÈÑõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÂ*7÷ÒÛ±ÛÝàž«˜&¸À„Ž Øüßêààßç¿-¿þ ÛÂÀ¼ ¼šáâ¿ú ¿ú ¿þ ¿þ ÀÌJ ¾ÏÃ*7Ã*7¼š¼š·õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÃ*7¼šñÞ¾áÞáßüÂÀÜ ˜&¾èÀÌJ ¾ŸÄ*7Ä*7¼š¼š¶õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÄ*7¼šòÞ¾±ÞâßüÂÀÜ ˜&¾¸Å„Ž Ûüäêåäåç¿-è¯‚CÅ¼ ÎšæçääèèÅÌJ ÄÏÅ*7Å*7ÎšÎš·õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÅ*7ÎšñãÄáãæäüÂÅÜ ˜&ÄèÅÌJ ÄŸÆ*7Æ*7ÎšÎš¶õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÆ*7ÎšòãÄ±ãçäüÂÅÜ ˜&Ä¸Ê„Ž ÞêééÊç,Š ‹“/Ê¼ É—êëŠ Š ÊÌJ ÉÏÇ*7Ç*7É—ƒÎ/õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÇ*7É—ñèÉáèêÎÿÊÜ ˜&ÉèÊÌJ ÉŸÈ*7È*7É—É—¶õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÈ*7É—òèÉ±èëÎÿÊÜ ˜&É¸Í”Æ ‹ Í§ ÍÙ Íµ ÍÈ Í¶ ÏƒÍ· Í¸ íÍ¹ Í¿ éõ+ÍÀ ÍÅ ÏÍÆ ÍÊ ÍÍ ÍÏ Íß ÏƒÍà Íá Íã ÍÁ Íù éõ+Íú Í” Í˜ ‚›‹ – ó™˜ ì ¦ ðÿ+§ ¯ ¸ œH»Êì ËÏÑ Ò ÖÚ ˜&ÜÞ àä ¤ æî µñ÷ø ù ý ·(ƒ Ž ” ³H—¦È §« ® ²¶ ˜&¸º ¼À ² ÂÊ µÍÓÔ Õ ÙÝ É(ß éêÍž¸ ×)¹×)¸õ×)& Access node and edge weights mutably.Á²”C×)Íˆ è‹ ííÛàž˜í”–C˜îïîï¸W»É*7É*7àžÖÒõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÉ*7– íÑíîàžË˜&Ü”W—Ê*7Ê*7àžÈÑõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÊ*7÷Òííïàž§˜&¸ó„Ž …üñêòñòç¿-’ íÂó¼ èšóôŽ Ž ’ ’ óÌJ ò»Ë*7Ë*7èšèš·õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êË*7èšñðòÑðóñüÂóÜ ˜&òÜóÌJ ò—Ì*7Ì*7èšèš¶õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÌ*7èšòðòðôñüÂóÜ ˜&ò¸ø„Ž ˆêööÊç,Š íÎÿø¼ É—÷øŠ Š øÌJ ÷»Í*7Í*7É—ƒÎ/õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÍ*7„ŒCõ÷Ñõ÷ÎÿøÜ ˜&÷ÜøÌJ ÷—Î*7Î*7É—ùŒCõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷êÎ*7ÍCõ÷õøÎÿøÜ ˜&÷¸€4 A graph element.Áã1ø1I A sequence of Elements, for example an iterator, is laid out as follows:Áü1LJ Nodes are implicitly given the index of their appearance in the sequence.ÁÉ2M> The edgesâ€™ source and target fields refer to these indices.Á—3AN‰4¨ú£ûúûíñ,‘4¦¿,”4Ú›#{½ª¯— ºüþýÿ€ ‘4‘4”4”4³4 A graph node.Á4N³4ùùÚ›º4 º4ùù–úá4 A graph edge.ÁË4Ná4ùùÚ›ð4 ð4ùù‡5 ‡5ùùž5 ž5ùùêûÿâ3ÿâ3Þÿâ3†÷ÿâ3¨ƒ £„ „ ƒ íñ,‘4¦¿,”4â2ÿâ3õ2ÿâ3Ú›… ‘4‘4”4”4ÿâ3ÿâ3Ï*7Ï*7Ú›Ú›‚ ÿâ3‚ … –úêûâ3€é3€é3Þ€é3ç÷€é3Ž¨‡ £ˆ ˆ ‡ íñ,‘4¦¿,”4ÚÚ4€é3íÚ4€é3Ú›‰ ‘4‘4”4”4€é3€é3Ð*7Ñ*7Ò*7Ð*7Ú›Ñ*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SÒ*7Ôž† €é3† ‰ –úêûé3€é3ð3 ð3 Þð3 Âøð3 ¨‹ £Œ Œ ‹ íñ,‘4¦¿,”4Ú›‘4‘4”4”4ð3 ð3 Þð3 Ðøð3 ¨Ž £ Ž íñ,‘4¦¿,”4èó,ð3 ‚´7ð3 Ú› ‘4‘4”4”4ð3 ð3 Ó*7Ô*7Ó*7Ú›Ô*7Ú› ð3 –úêûð3 ôð3 ‚û3‚û3Þ‚û3áù‚û3¨’ £“ ’ “ íñ,‘4¦¿,”4Ú›‘4‘4”4”4‚û3‚û3Þ‚û3ïù‚û3¨• £– • – íñ,‘4¦¿,”4¶£.‚û3Œêû‚û3Ú›— ‘4‘4”4”4‚û3‚û3¹‚û3†ú‚û3‚û3 Ý‚û3‚û3‚û3Ã‚û3‡ú‚û3‚û3€€Õ*7Õ*7Ú›” ‚û3” — –úêûû3«0 The dominance relation for some graph and root.Áß3O¶ ¨™ ™ íñ,ÁÕó,Ñ¶£.Ø££.ÝÖój·Ì?,Þš › ÁÁéé¢˜ ˜ –úöö ¢˜ ˜ àß ƒœƒœÞƒœ¾úƒœ‘¨ íñ,ÁÚÚ4ƒœÕó,Ñ¶£.Ø££.ÝÖž ÁÁƒœƒœÖ*7×*7Ø*7Ö*7Ö×*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SØ*7Ôžœ ƒœœ ž –úœƒœ„£„£Þ„£ûú„£’¨ íñ,Áâ2„£Õó,Ñ¶£.Ø££.ÝÖ¡ ÁÁ„£„£Ù*7Ù*7ÖÖŸ „£Ÿ ¡ –ú£ß/¿È!¢ ÁÈ!¢ ÚÈ!¢ ¢ ¿òž Õ.! Workspace for a graph traversal.Á˜.$Wà.¨¤ ‰¥ ¤ ¥ íñ,é.€ò,ì.àžå(Q¬ š¦ é.é.ì.ì.ö.ö.¡£ £ üûžÆ.žÆ.ÞžÆ.ÇüžÆ.Ÿ¨¨ ‰© ¨ © íñ,é.€ò,ì.â2žÆ.‰º@žÆ.àžª é.é.ì.ì.žÆ.žÆ.Ú*7Ú*7àžàž§ žÆ.§ ª –ú±üÆ.ŸÍ.ŸÍ.ÞŸÍ.øüŸÍ. ¨¬ ‰ ¬ íñ,é.€ò,ì.ÚÚ4ŸÍ.¡¼@ŸÍ.àž® é.é.ì.ì.ŸÍ.ŸÍ.Û*7Ü*7Ý*7Û*7àžÜ*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SÝ*7Ôž« ŸÍ.« ® –ú±üÍ.ŸÍ.©NX°N¡¨Ÿôæx 36ÞË° ± ² ÃNÃN¡¯ ¯ ¯ áNáN¡¯ ¯ ùNùN¡¯ ¯ N NÞ N½ý N¡¨Ÿ¡–N¡–NÞ¡–NÆý¡–N¢¨Ÿµ ¡–N¡–NÞ*7Þ*7¨Ÿ¨Ÿ´ ¡–N´ µ –N¢N¢NÞ¢Nùý¢N£¨Ÿ· ¢N¢Nß*7à*7á*7ß*7¨Ÿà*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >Sá*7Ôž¶ ¢N¶ · N¢N©OY°O¡ü¹ êº ¹ º ç¿-¹O ã3ÑO ±¹¹ üóO²ÈCØP!¸ ¸ ‰‹» …¡¼ äy¡½ Ÿƒ¡¾ ¦ ¡¬_'ÜË÷Ÿ¹ üÂ¬_'ÜË÷Ÿ» ¼ ½ ¾ µOµO¹O¹O…P…P¡¸ ¸ ›P›P¡¸ ¸ Òó¹P¹P¡¸ ¸ ÷úØP!ØP¡¸ ¸ ¹ üƒü£O£OÞ£OÑþ£O¤üÀ êÁ À Á ç¿-¹OøGÂ£O ã3ÑO ±¹À üóOøG±¹£OýŸÂ µOµO¹O¹O£O£Oâ*7ã*7ä*7â*7ýŸã*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >Sä*7Ôž¿ £O¿ Â À üÂO£O™›4 An algorithm error: a cycle was found in the graph.Á¾š7j¤›¨Å Å íñ,ª›½¡È§ndé”< Ä Æ –ú½¡™›¤›¡–ú½¡Ã Ã Ä –úª›ª›››¡Ã Ã –ú¸ÿš¸ÿšÞ¸ÿšÙ€¸ÿš¨¨È È íñ,ª›â2¸ÿš½¡É ª›ª›¸ÿš¸ÿšå*7å*7½¡½¡Ç ¸ÿšÇ É –úÿš¹†›¹†›Þ¹†›¹†›©¨Ë Ë íñ,ª›ÚÚ4¹†›½¡Ì ª›ª›¹†›¹†›æ*7ç*7è*7æ*7½¡ç*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >Sè*7ÔžÊ ¹†›Ê Ì –ú†›¹†›º› º› Þº› °º› ª¨Î Î íñ,ª›½¡ª›ª›º› º› Þº› »º› ª¨Ð Ð íñ,ª›èó,º› ½¡Ñ ª›ª›º› º› é*7ê*7é*7½¡ê*7½¡Ï º›Ï Ñ –ú› ôº› ÃH An algorithm error: a cycle of negative weights was found in the graph.ÁÔœKkÎ ¢d"yN+ {Ó Ô ¢ÃÎ ¡¢Ò Ò Ó ÜÜ¡Ò Ò »©»©Þ»©æ»©«¢Ö »©»©ë*7ë*7¢¢Õ »©Õ Ö ©¼°¼°Þ¼°…‚¼°¬¢Ø ¼°¼°ì*7í*7î*7ì*7¢í*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >Sî*7Ôž× ¼°× Ø °¼°½· ½· Þ½· ‚½· ¢½· ½· Þ½· µ‚½· ¢Û ½· ½· ï*7ð*7ï*7¢ð*7¢Ú ½·Ú Û · ô½· ·=D Compressed Sparse Row ([`CSR`]) is a sparse adjacency matrix graph.Á”GÜ `CSR` is parameterized over:Áà À±4…H‘²4Î3Í²4‚X®³4ÛF¢ ¦ Using **O(|E| + |V|)** space.Áª !Ì + Self loops are allowed, no parallel edges.ÁÐ .ÿ 2 Fast iteration of the outgoing edges of a vertex.Áƒ 5¹ e [`CSR`]: https://en.wikipedia.org/wiki/Sparse_matrix#Compressed_sparse_row_(CSR,_CRS_or_Yale_format)Á½ hqÂ¨Ý £Þ ïß ä à à Ý ß Þ íñ,Æ¦¿,Îà-Ö Àà-å÷¢FORÅ3Ó'Šá â ã ä å æ ÆÆÎÎÖ ÖÎùåå——âÜ Ü ûµê êâÜ Ü Þ¶ì ì âÜ Ü æÅ âÜ Ü ê¥›› âÜ Ü ²²âÜ Ü ¾¸É¯É¯ÞÉ¯½ƒÉ¯²¨è £é ïê ä ë è ê é ë íñ,Æ¦¿,Îà-ÖÀà-åÚÚ4É¯íÚ4É¯øG¸†É¯øG¾†É¯÷¢ì ÆÆÎÎÖÖååÉ¯É¯ñ*7ò*7ó*7ñ*7÷¢ò*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >Só*7Ôžç É¯ç ì –úêû¸†¾†¯É¯ö4 Csr creation error: edges were not in sorted order.Á¦7wú£æç¿ÃÜÄY³î ––âí í Ž£ ÔçÔçÞÔçÞ„Ôç¸ú£ð ÔçÔçô*7ô*7ú£ú£ï Ôçï ð çÕîÕîÞÕîý„Õî¹ú£ò ÕîÕîõ*7ö*7÷*7õ*7ú£ö*7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S÷*7Ôžñ Õîñ ò îÕîí_>zø_üô £õ ïö ä ÷ õ ÷ ô ö ¦¿,‚`à-‰` Àà-˜`êûô ü…`¾†ô üœ`›ñC¨ ¯ñC¨ ó ó „9ø …âù ô%âú Ÿâû Ôâ}Ñ]³¤Ë£ô üêû¸†¾†}Ñ]³¤Ë£ø ù ú û þ_þ_‚`‚`ô ‰` ‰`Îù˜`˜`ô ²`²`âó ó Ä`Ä`âó ó ¾†ß`9ß`âó ó ê½ê½´ñí½š±ç3î½ª±ç3ï½…ç3ð½°ç3ñ½ïñç3˜U`×qÑ«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+ô ü¾†«N«N¦ ®NÄýM¯NáýM°NÇ ýMj™Æg(Ò+ô üêûžažaâó ó ¾¸ÛÞ_ÛÞ_ÞÛÞ_§†ÛÞ_¼üý £þ ïÿ ä €!€!ý ÿ þ ¦¿,‚`à-‰`Àà-˜`õ2ÛÞ_êûý ü…`×¸†ÛÞ_ˆ®2ÛÞ_¾†ý üœ`²¤!þ_þ_‚`‚`ý ‰`‰`˜`˜`ý ÛÞ_ÛÞ_ø*7ø*7²¤²¤ü ÛÞ_ü !ý üêû¸†¾†Þ_Üå_Üå_ÞÜå_‡‡Üå_½üƒ!£„!ï…!ä 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(will be expanded)Á»G}¬ö®’×•è'vd¥!¦!§!¨!©!ª!«!¬!!®!¯!°!¦!¨!ª!¬!®!°!±!ö®Þ Use indices for node labels.Á¹ }Þ¤!¤!ö®ÞÞ¤!¤!ö®— Use indices for edge labels.Áò }—¤!¤!ö®——¤!¤!ö®Ç Use no edge labels.Á«}Ç¤!¤!ö®ÇÇ¤!¤!ö®ô Use no node labels.ÁØ}ô¤!¤!ö®ôô¤!¤!ö®´' Do not print the graph/digraph string.Á…*}´¤!¤!ö®´´¤!¤!ö®ÝÌ¹ÌÆ‰ÏÖ ÝÐÊ}Ý¤!¤!¯!ÝÝö®¤!¤!°!éé¤!¤!àŒàŒÞàŒË‰àŒÁö®³!àŒàŒƒ+7„+7…+7ƒ+7ö®„+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S…+7Ôž²!àŒ²!³!ŒàŒá“ á“ Þá“ ºŠá“ Âö®á“ á“ Þá“ ÂŠá“ Âö®¶!á“ á“ †+7‡+7†+7ö®‡+7ö®µ!á“µ!¶!“ ôá“ âžâžÞâž‹âžÃö®âžâžÞâž•‹âžÃö®¹!âžâž¹âžž‹âžâž ÝâžâžâžÃâžŸ‹âžâž€€ˆ+7ˆ+7ö®¸!âž¸!¹!žè$Ÿ=Œó ä ¼!¼!º›.ýóŠ#´»Ð$é'Ž»!½!¡‹óŠè$ó Ã¡‹óŠº!º!»!¡‹ýýÃº!º!¡‹ÿ«ÿ«Þÿ«ÙŒÿ«Òä ¿!¿!º›.ý‡¡‹ÿ«óŠýý€±€±Þ€±èŒ€±Óä Á!Á!º›.ý×¡‹€±óŠÂ!ýý€±€±‰+7‰+7óŠóŠÀ!€±À!Â!¡‹±¸¸Þ¸¸Ôä Ä!Ä!º›.ýœ¡‹¸óŠÅ!ýý¸¸óŠÃ!Ã!Å!¡‹‚Á ‚Á Þ‚Á °‚Á Õä Ç!Ç!º›.ýóŠýý‚Á ‚Á Þ‚Á »‚Á Õä É!É!º›.ý‡¡‹¡‹‚Á óŠÊ!ýý‚Á ‚Á Š+7‹+7Š+7óŠ‹+7óŠÈ!‚ÁÈ!Ê!¡‹Á ô‚Á ƒÌ ƒÌ ÞƒÌ âƒÌ Öä Ì!Ì!º›.ý±¡‹¡‹ƒÌ óŠÍ!ýýƒÌ ƒÌ Œ+7+7Œ+7óŠ+7óŠ¯¦Ë!ƒÌË!Í!¡‹Ì ôƒÌ „Ø„ØÞ„Ø–Ž„Ø×ä Ï!Ï!º›.ýóŠýý„Ø„ØÞ„Ø¡Ž„Ø×ä Ñ!Ñ!º›.ýŒ¡‹„ØóŠÒ!ýý„Ø„Ø¹„Ø±Ž„Ø„Ø Ý„Ø„Ø„ØÃ„Ø²Ž„Ø„Ø€€Ž+7Ž+7óŠÐ!„ØÐ!Ò!¡‹Ø…Ü…ÜÞ…ÜÃŽ…ÜØä Ô!Ô!º›.ý«¡‹…ÜóŠÕ!ýý…Ü…Ü+7+7+7óŠ+7óŠ¢Ó!…ÜÓ!Õ!¡‹Üô…Ü†á†áÞ†áóŽ†áÙä ×!×!º›.ýÔI¡‹†áóŠØ!ýý†á†á‘+7’+7‘+7óŠ’+7ÊçÖ!ûÙ!Ù!†áÖ!‚Êç†áÜIÊç†áØ!¡‹Êçáï †á†á†áü$ Edge identifier.Á¡Ž‡ ä Ü!Ü!º›.‘¯‹ÀƒÿM3æÛ!Ý!¡‹¯‹ü$‡ Ã¡‹¯‹Ú!Ú!Û!¡‹‘‘¡¡ÃÚ!Ú!¡‹‰¿‰¿Þ‰¿±‰¿Ûä ß!ß!º›.‘… D‰¿¯‹‘‘ŠÅŠÅÞŠÅÀŠÅÜä á!á!º›.‘ƒ¡DŠÅ¯‹â!‘‘ŠÅŠÅ“+7“+7¯‹¯‹à!ŠÅà!â!¡‹Å‹Ì‹ÌÞ‹Ìè‹ÌÝä ä!ä!º›.‘å¢D‹Ì¯‹å!‘‘‹Ì‹Ì¯‹ã!ã!å!¡‹ŒÕ ŒÕ ÞŒÕ ˆŒÕ Þä ç!ç!º›.‘¯‹‘‘ŒÕ ŒÕ ÞŒÕ “ŒÕ Þä é!é!º›.‘¥DŒÕ ¯‹ê!‘‘ŒÕ ŒÕ ”+7•+7”+7¯‹•+7¯‹è!ŒÕè!ê!¡‹Õ ôŒÕ à à Þà ºà ßä ì!ì!º›.‘§Dà ¯‹í!‘‘à à –+7—+7–+7¯‹—+7¯‹¯¦ë!àë!í!¡‹à ôà ŽìŽìÞŽìîŽìàä ï!ï!º›.‘¯‹‘‘ŽìŽìÞŽìùŽìàä ñ!ñ!º›.‘ÿ©DŽì¯‹ò!‘‘ŽìŽì¹Žì‰‘ŽìŽì ÝŽìŽìŽìÃŽìŠ‘ŽìŽì€€˜+7˜+7¯‹ð!Žìð!ò!¡‹ìððÞð›‘ðáä ô!ô!º›.‘Æ¬Dð¯‹õ!‘‘ðð™+7š+7™+7¯‹š+7¯‹¢ó!ðó!õ!¡‹ðôðõõÞõË‘õâä ÷!÷!º›.‘Ç®Dõ¯‹ø!‘‘õõ›+7œ+7›+7¯‹œ+7Êçö!ûù!ù!õö!ë¯Dõÿ¯Dõø!¡‹Êçõï õõõš$" The graph's node type.Áî#¥$¨û!ä ü!ü!û!íñ,ª$3$ä´;’âÔ…G5oý!þ!ª$ª$$$á$ Associated node data.ÁÃ$å$ú!ú!–ú«%«%Ãú!ú!“‘Á%“’$“’$Þ“’$‘’“’$ä¨"ä ‚""‚"íñ,ª$3$ÚÚ4“’$¹ŒD“’$ä´ƒ"ª$ª$$$“’$“’$+7ž+7Ÿ+7+7ä´ž+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SŸ+7Ôž€"“’$€"ƒ"–úÞ¬’$“’$”T”W +7 +7¢µ¢µ””]”„"ÔµÞ¬”^Ç(" The graph's edge type.Á›(’Ò(£†"ä ‡"†"‡"à§4×(3Ú(âµrÚ˜ýR~/7ˆ"‰"‹"×(×(Ú(Ú(Ž) Associated edge data.Áð(’)…"…"ÔµØ)Ø)Ã…"…"“‘î)—*—*Ã…"…"‡*•¿(•¿(Þ•¿(ü’•¿(å£Ž"ä "Ž""à§4×(3Ú(øGÔµ•¿(¹ŒD•¿(âµ"×(×(Ú(Ú(•¿(•¿(¡+7¢+7£+7¡+7âµ¢+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S£+7Ôž"•¿(""ÔµÞ¬¿(•¿(–T–W¤+7¤+7âµâµ›–]›‘"ÔµÞ¬–^¹T¹W¥+7¥+7ð·ð·Ó ¹]Ó ’"Ô üêûæ·¹^ÿÙ&ñž:¸Ù.ŸŠÚä ”"”"º›.–Úý½sz®‚¬‹~•"–"–Ú–Ú¬Ú¬ÚÃ“"“"±Œ ÁÚÁÚÃ“"“"ÁòºðÙºðÙÞºðÙá•ºðÙõä ˜"˜"º›.–Úƒ¡DºðÙý½™"–Ú–ÚºðÙºðÙ¦+7¦+7ý½ý½—"ºðÙ—"™"¡‹ðÙ»÷Ù»÷ÙÞ»÷Ù”–»÷Ùöä ›"›"º›.–Ú‚›4»÷Ùý½œ"–Ú–Ú»÷Ù»÷Ù§+7¨+7©+7§+7ý½¨+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S©+7Ôžš"»÷Ùš"œ"¡‹÷Ù»÷Ù“ß&ï°:ÌÞ. žßä ž"ž"º›.ªßÚ¾Ú¨@çñHÍŸ" "ªßªßÀßÀßÃ""±Œ ÕßÕßÃ""Áò¼„ß¼„ßÞ¼„ßØ–¼„ß÷ä ¢"¢"º›.ªßƒ¡D¼„ßÚ¾£"ªßªß¼„ß¼„ßª+7ª+7Ú¾Ú¾¡"¼„ß¡"£"¡‹„ß½‹ß½‹ßÞ½‹ß‹—½‹ßøä ¥"¥"º›.ªß‚›4½‹ßÚ¾¦"ªßªß½‹ß½‹ß«+7¬+7+7«+7Ú¾¬+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S+7Ôž¤"½‹ß¤"¦"¡‹‹ß½‹ß’ä3 Reference to a `Graph` edge.Áàã ¡ä ü¨"£©"ä ª"¨"©"ª"¦¿,¯äƒ¹2¶äêû¨"ü²ä«ÞD„å §"§"…¥«"…Ã¬"QÃ®"Š&Ã°ô×+‚Ù¨"üêûæ·°ô×+‚Ù«"¬"®"«ä«ä¯ä¯ä¨"¶ä¶äÌäÌäÃ§"§"ÎÇæäæäÃ§"§"ÙÌüä„å „åÃ§"§"¨"üêû¾Šä¾ŠäÞ¾ŠäÙ—¾Šäùü°"£±"ä ²"°"±"²"¦¿,¯äƒ¹2¶äíÚ4¾Šäêû°"ü²äøGæ·¾Šä·¿³"«ä«ä¯ä¯ä°"¶ä¶ä¾Šä¾Šä®+7¯+7°+7®+7·¿¯+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S°+7Ôž¯"¾Šä¯"³"°"üêûæ·Šä¾ŠäÁ„Ž úüµ"ê¶"µ"¶"ç¿-®äã3Á¼ ³Ç·"¸"ªª®®Áò+ À¦H ´"´"î½Áò+ À»H ´"´"‹ÀÄ„Ž ûüº"ê»"º"»"ç¿-ð¯‚CÄ¼ óÇ¼"½"ììððÄÌJ ÃÏ±+7±+7óÇóÇ·õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê±+7óÇñ¹"Ãá¹"¼"º"üÂÄÜ ˜&ÃèÄÌJ ÃŸ²+7²+7óÇóÇ¶õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê²+7óÇò¹"Ã±¹"½"º"üÂÄÜ ˜&Ã¸É„Ž þü¿"êÀ"À"¿"ç¿-µð™CÉ¼ ³ÈÁ"Â"±±µµÉÌJ È»³+7³+7³È³È·õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê³+7³Èñ¾"ÈÑ¾"Á"¿"üÂÉÜ ˜&ÈÜÉÌJ È—´+7´+7³È³È¶õÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê´+7³Èò¾"È¾"Â"¿"üÂÉÜ ˜&È¸Î„Ž üÄ"êÅ"Ä"Å"ç¿-þž¨BÎ¼ óÈÆ"Ç"È"úúþþÎò+ ÍŒ Ã"Ã"ñƒÎÌJ ÍÔµ+7µ+7óÈóÈ¿Ã"ÍëÃ"Ç"Ä"üÂÎÜ ÎÌJ Íþ¶+7·+7¶+7óÈ·+7œðDóÈ·§ñDÃ"Í‘Ã"È"Ä"üÂÎÜ …Í—‘š±Í±‘ª±ÍÂ‘Ó„Ž „ 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þÁ|†#†#ôôÿÌJ þ˜} ¼+7¼+7ÂÏÂÏ´†#þ¨}†#Š#‡#üÂÿÜ ÿÌ? þŽ~ ½+7¾+7½+7ÂÏ¾+7â‹E†#þž~†#‹#‡#üÂÿÜ àþ¥~–Í3# Reference to a `StableGraph` edge.ÁÞÌ&Â¡Í ü#£Ž#ä ##Ž##¦¿,³Íƒ¹2ºÍêû#ü¶Í»ŽEˆÎ Œ#Œ#…¥#…‘#Q“#Š&e“èñ$Ã#üêûæ·e“èñ$Ã#‘#“#¯Í¯Í³Í³Í#ºÍºÍÐÍÐÍŒ#Œ#ÎÇêÍêÍŒ#Œ#ÙÌ€ÎˆÎ ˆÎŒ#Œ##üêûÆ ŽÍÆ ŽÍÞÆ ŽÍÁ¡Æ ŽÍÄ ü•#£–#ä —#•#–#—#¦¿,³Íƒ¹2ºÍíÚ4Æ ŽÍêû•#ü¶ÍšâDÆ ŽÍòÑ˜#¯Í¯Í³Í³Í•#ºÍºÍÆ ŽÍÆ ŽÍ¿+7À+7Á+7¿+7òÑÀ+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SÁ+7Ôž”#Æ ŽÍ”#˜#•#üêûæ·ŽÍÆ ŽÍÓ TÓ WÂ+7Â+7èØèØ¬Ó ]¬™#¡‹Ó ^îI `GraphMap` is a graph datastructure using an associative arrayÁûL of its node weights `N`.ÁÈ å ? It uses an combined adjacency list and sparse adjacency matrixÁé BJ representation, using **O(|V| + |E|)** space, and allows testing for edgeÁ¬ M existence in constant time.Áú š" `GraphMap` is parameterized over:Áž%ÄÀ±4ÈHE - The node weight `N` must implement `Copy` and will be used as nodeÁ‘HB identifier, duplicated into several places in the data structure.ÁÚED It must be suitable as a hash table key (implementing `Eq + Hash`).Á GG The node type must also implement `Ord` so that the implementation canÁè JL order the pair (`a`, `b`) for an edge connecting any two nodes `a` and `b`.Á³O - `E` can be of arbitrary type.Áƒ#I - Edge type `Ty` that determines whether the graph edges are directed orÁ§L undirected.Áô„L You can use the type aliases `UnGraphMap` and `DiGraphMap` for convenience.ÁˆOØF `GraphMap` does not allow parallel edges, but self loops are allowed.ÁÜI¦/ Depends on crate feature `graphmap` (default).Áª2Êù¨›#£œ#ï#œ##›#íñ,‚¦¿,…à-ˆßˆfM{5Í{7Óž#Ÿ# #‚‚……ˆˆ’.’òš#š#ë’ ÆÆòš#š#¾” ææòš#š#¾¸Ô æÔ æÞÔ æð¢Ô æË ¨¢#££#ï¤#¤#¢#£#íñ,‚¦¿,…à-ˆâ2Ô æõ2Ô æÑ÷CÔ æßˆ¥#‚‚……ˆˆÔ æÔ æÃ+7Ã+7ßˆßˆ¡#Ô æ¡#¥#–úêû¸†æ£Ë¨ò¼ÛÄ¡gó~Ã§#¨#©#ª#¨#ª#¿Ë¿ò¦#¦#¼Û¿¿ò¦#¦#¼ÛÍËÍò¦#¦#¼ÛÍÍò¦#¦#¼ÛÕ ƒÕ ƒÞÕ ƒÅ£Õ ƒÌ ¼ÛÖ ‰Ö ‰ÞÖ ‰Í£Ö ‰Í ¼Û#Ö ‰Ö ‰Ä+7Ä+7¼Û¼Û¬#Ö ‰¬##‰× × Þ× â£× Î ¼Û¯#× × Å+7Æ+7Ç+7Å+7¼ÛÆ+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SÇ+7Ôž®#× ®#¯#× Ø — Ø — ÞØ — ‘¤Ø — Ï ¼ÛØ — Ø — ÞØ — ™¤Ø — Ï ¼Û²#Ø — Ø — È+7É+7È+7¼ÛÉ+7¼Û±#Ø —±#²#— ôØ — Ý îzVÍõ~ü´#¨µ#´#µ#ŠÃ-€ƒ´#üŽ°µ;“ È¬E¨ ³#³#”·¶#Ÿò¤§áâÞ´#üƒ¤§áâÞ¶#ü~ü~€€´#Ý ®{Ý ®{ò³#³#˜»˜»ÁÜš»ÑÜÇ'[›oN{c²²ö ¶Ÿ>=`ºŠ¡,Ó›´#üƒ¡¾!Ý Í{4Íü¸#¨¹#¸#¹#ŠÃ-€ƒ¸#üŽ°µ;“ ‚Ýº#»#¼#ü~ü~€€¸#Ý ¶| Ý »|·#·#ƒÝ ë|(Ý î|Ê+7Ê+7‚ÝõÄ÷ÄøÄ³ùÄúÄßûÄÎÕÐ{-áP÷ê‚ÝÙ9·#Ý ó|·#»#¸#üƒÝ ø|Ý è}-Ý ë} Ë+7Ë+7‚Ýšò·#Ý õ}·#¼#¸#üƒÝ ö}»ÏÂËï¾#ä ¿#¾#¿#¹<Ë3ÏäèA\Þc£ÖÀ#Á#Â#Ã#Ä#Å#Æ#Ç#ËËÏÏÊÊË½#½#êË"¡¡Ë½#½#æÅ ½½Ë½#½#æÅ ÒÒË½#½#ççË½#½#üüË½#½#°ŠŸŸ Ë½#½#¶¶Ë½#½#ÆÎ"á ³á ³Þá ³à¥á ³Ó ïÉ#ä Ê#É#Ê#¹<Ë3ÏøGÛéá ³¹ŒDá ³äèË#ËËÏÏá ³á ³Ì+7Í+7Î+7Ì+7äèÍ+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SÎ+7ÔžÈ#á ³È#Ë#ÛéÞ¬³á ³ ; Ð¥;Ë¢êR$§ð¥õë‰Í#Î#Ï#Ð#Ñ#Ò#Î#Ð#Ò#¸;Ð¸;ËÌ#Ì#¢ê¸;¸;ËÌ#Ì#¢êÅ;ÐÅ;ËÌ#Ì#¢êÅ;Å;ËÌ#Ì#¢êÑ;ÐÑ;ËÌ#Ì#¢êÑ;Ñ;ËÌ#Ì#¢êâ ü:â ü:Þâ ü:Ü¦â ü:Ô ¢êã ‚;ã ‚;Þã ‚;å¦ã ‚;Õ ¢êÕ#ã ‚;ã ‚;Ï+7Ï+7¢ê¢êÔ#ã ‚;Ô#Õ#‚;ä ‰; ä ‰; Þä ‰; û¦ä ‰; Ö ¢êä ‰; ä ‰; Þä ‰; „§ä ‰; Ö ¢êØ#ä ‰; ä ‰; Ð+7Ñ+7Ð+7¢êÑ+7¢ê×#ä ‰;×#Ø#‰; ôä ‰; å ”;å ”;Þå ”;µ§å ”;× ¢êÚ#å ”;å ”;Ò+7Ó+7Ô+7Ò+7¢êÓ+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SÔ+7ÔžÙ#å ”;Ù#Ú#”;å ”;Š<Ñ<Ë¨Ü#Ü#íñ,•<Õó,˜<ëí^ï`AÇîÞÝ#Þ#ß#ã#Þ#à#â#ä#æ#•<•<°<Ñ°<ËÛ#Û#ë°<°<ËÛ#Û#ë¿<Ñ¿<ËÛ#Û#ëÇ< Ç<ËÛ#Û#Ê—$Ò<Ö<Ö< ËÛ#Û#¢êõ<Ñõ<ËÛ#Û#ëþ< þ<ËÛ#Û#Ê—$‰=== ËÛ#Û#¢êæ ì;æ ì;Þæ ì;†¨æ ì;Ø ¨è#è#íñ,•<â2æ ì;Õó,˜<ëé#•<•<æ ì;æ ì;Õ+7Õ+7ëëç#æ ì;ç#é#–úì;ç ó; ç ó; Þç ó; é¨ç ó; Ù ¨ë#ë#íñ,•<Õó,˜<ë•<•<ç ó; ç ó; Þç ó; ø¨ç ó; Ù ¨í#í#íñ,•<èó,ç ó; Õó,˜<ëî#•<•<ç ó; ç ó; Ö+7×+7Ö+7ë×+7ëì#ç ó;ì#î#–úó; ôç ó; è þ;è þ;Þè þ;ý©è þ;Ú ¨ð#ð#íñ,•<ÚÚ4è þ;Õó,˜<ëñ#•<•<è þ;è þ;Ø+7Ù+7Ú+7Ø+7ëÙ+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SÚ+7Ôžï#è þ;ï#ñ#–úþ;è þ;ð×ûüó#òô#ô#ó#Ýç,†ò#ò#Á»õ#ß»ö#š´6:“Û¦*±Uó#ü6:“Û¦*±Uõ#ö#‚‚††ò#ò#ó#üëëò#ò#¯&÷ è÷ èÞ÷ è¨«÷ èá üø#òù#ø#ù#Ýç,†×÷ è‚íú#‚‚††÷ è÷ èÛ+7Û+7‚í‚í÷#÷ è÷#ú#ø#üèû ‚-Ùüü#òý#ü#ý#Ýç,û ‹è.û ÉøGšŸû òÒíþ#û ‡û ‡û ‹û ‹û ¡4û ¤Ü+7Ý+7Þ+7Ü+7ÒíÝ+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >SÞ+7Ôžû#û ¨û#þ#ü#üû ©û ¯ÿ ¤Àã €$$ÿ Ãÿ Æÿ#ÿ#€$ÿ ‹ ÿ Ž ß+7ß+7ÿ#ÿ – ÿ#$ÿ — € ¤Àä ƒ$„$€ Ã€ Æ‚$‚$ƒ$€ ‹ € Ž à+7à+7‚$€ – ‚$„$€ — ¤Àå †$‡$ Ã Æ…$…$†$ ‹ Ž á+7á+7…$ – …$‡$ — ‚ ¤Àæ ‰$Š$‚ Ã‚ Æˆ$ˆ$‰$‚ ‹ ‚ Ž â+7â+7ˆ$‚ – ˆ$Š$‚ — ƒ ¤Àç Œ$$ƒ Ãƒ Æ‹$‹$Œ$ƒ ‹ ƒ Ž ã+7ã+7‹$ƒ – ‹$$ƒ — … ¤Àè $$… Ã… ÆŽ$Ž$$… ‹ … Ž ä+7ä+7Ž$… – Ž$$… — † ¤Àé ’$“$† Ã† Æ‘$‘$’$† ‹ † Ž å+7å+7‘$† – ‘$“$† — ‡ ¤Àê •$–$‡ Ã‡ Æ”$”$•$‡ ‹ ‡ Ž æ+7æ+7”$‡ – ”$–$‡ — ˆ ¤Àë ˜$™$ˆ Ãˆ Æ—$—$˜$ˆ ‹ ˆ Ž ç+7ç+7—$ˆ – —$™$ˆ — ‰ ¤Àì ›$œ$‰ Ã‰ Æš$š$›$‰ ‹ ‰ Ž è+7è+7š$‰ – š$œ$‰ — ‹ ¤Àí ž$Ÿ$‹ Ã‹ Æ$$ž$‹ ‹ ‹ Ž é+7é+7$‹ – $Ÿ$‹ — Œ ¤Àî ¡$¢$Œ ÃŒ Æ $ $¡$Œ ‹ Œ Ž ê+7ê+7 $Œ – $¢$Œ — °,dQ `MatrixGraph` is a graph datastructure using an adjacency matrixÁå#T representation.Áº$Î$% `MatrixGraph` is parameterized over:ÁÒ$(û$À±4ÿ$H‘²4È%3Í²4ü%X] - Nullable type `Null`, which denotes the edges' presence (defaults to `Option`). You mayÁÕ&`^ specify [`NotZero`](struct.NotZero.html) if you want to use a sentinel value (such as 0)Á¶'a" to mark the absence of an edge.Á˜(%V - Index type `Ix` that sets the maximum size for the graph (defaults to `DefaultIx`).Á¾(Y˜)` The graph uses **O(|V^2|)** space, with fast edge insertion & amortized node insertion, as wellÁœ)c@ as efficient graph search and graph algorithms on dense graphs.Á€*CÄ*_ This graph is backed by a flattened 2D array. For undirected graphs, only the lower triangularÁÈ*b^ matrix is stored. Since the backing array stores edge weights, it is recommended to box largeÁ«+a edge weights.Á,Þ»,¨¤$£¥$ï¦$•Û§$ä ¨$¨$¥$§$¤$¦$íñ,Ç,¦¿,Ê,à-Í, •Â=Ü,'ÃÃ-…-¼Â=â,ìÁ=ë,™ð{Œg‡qU7…©$ª$«$¬$$®$Ç,Ç,Ê,Ê,Í, Í,ÎùÜ,'Ü,îô…-…-›-›-Æ£$£$ºÕ&¼-¼- Æ£$£$Ö-Ö-Æ£$£$ßÖ&ï-ï-Æ£$£$„.„.Æ£$£$¾¸..Æ£$£$¦Ö ¨, ¨,Þ ¨,Ç¯ ¨,ï ¨°$£±$ï²$•Û³$ä ´$°$²$´$±$³$ìÁ=ë,¦¿,Ê,à-Í,•Â=Ü,ÃÃ-…-â2 ¨,õ2 ¨,Ñ÷C ¨,×†ñ ¨,¼Â=â,íñ,Ç,×¢ƒ ¨,™ðµ$Ç,Ç,Ê,Ê,Í,Í,Ü,Ü,…-…- ¨, ¨,ë+7ë+7™ð™ð¯$ ¨,¯$µ$–úêû¸††ñ¢ƒ¨,ÌÎãÓÎ Æå·$·$Æö3ÝÎøôOÔÈ±èlQ)¸$¹$º$ÝÎÝÎæÎæÎÆ¶$¶$†Ç(„Ï„ÏÆ¶$¶$œÏœÏÆ¶$¶$±È(˜ ÄÎ˜ ÄÎÞ˜ ÄÎ•±˜ ÄÎô å¼$¼$Æö3ÝÎ×°²˜ ÄÎøô½$ÝÎÝÎ˜ ÄÎ˜ ÄÎì+7ì+7øôøô»$˜ ÄÎ»$½$°²ÄÎßZ `UnionFind` is a disjoint-set data structure. It tracks set membership of *n* elementsÁl]\ indexed from *0* to *n - 1*. The scalar type is `K` which must be an unsigned integer type.ÁÊ_ª; Á®>í Too awesome not to quote:ÁñK â€œThe amortized time per operation is **O(Î±(n))** where **Î±(n)** is theÁ“Na inverse of **f(x) = A(x, x)** with **A** being the extremely fast-growing Ackermann function.â€Áâdåê Ð¿$¿$ÚÕ+ôÎúÖ¦ºolÀŒÀ$Á$ôôÚÚ«¾$¾$½‚*ï ï«¾$¾$Çƒ*› Ð› ÐÞ› Ðô±› Ðö ÐÃ$Ã$ÚÕ+ô‡ÿ?› ÐÎúÄ$ôô› Ð› Ðí+7î+7ï+7í+7Îúî+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >Sï+7ÔžÂ$› ÐÂ$Ä$›÷Ð› Ðœ ×œ ×Þœ ×©²œ ×÷ ÐÆ$Æ$ÚÕ+ôæü?œ ×ÎúÇ$ôôœ ×œ ×ð+7ð+7ÎúÎúÅ$œ ×Å$Ç$›÷×„6 Edge direction.Á¤5ë6 ÜŠòeÊY»…É$Ê$Ì$Í$Ë$Ê$Î$Í$ä6? An `Outgoing` edge is an outward edge *from* the current node.Á6Bëä6È$È$Üä6ä6È$È$Üï6»7= An `Incoming` edge is an inbound edge *to* the current node.Áö6@ë»7È$È$Ü»7»7È$È$ÜÆ7« Á5« Á5Þ« Á5Ä³« Á5ý Ü¬ Ç5¬ Ç5Þ¬ Ç5Ì³¬ Ç5þ ÜÑ$¬ Ç5¬ Ç5ñ+7ò+7ó+7ñ+7Üò+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >Só+7ÔžÐ$¬ Ç5Ð$Ñ$Ç5¬ Ç5 Î5 Î5 Þ Î5 û³ Î5 ÿ Ü Î5 Î5 Þ Î5 ƒ´ Î5 ÿ ÜÔ$ Î5 Î5 ô+7õ+7ô+7Üõ+7ÜÓ$ Î5Ó$Ô$Î5 ô Î5 ® Ù5 ® Ù5 Þ® Ù5 ³´® Ù5 € ÜÖ$® Ù5 ® Ù5 ö+7÷+7ö+7Ü÷+7Ü¯¦Õ$® Ù5Õ$Ö$Ù5 ô® Ù5 ¯ å5¯ å5Þ¯ å5ð´¯ å5 ÜØ$¯ å5¯ å5ø+7ù+7ø+7Üù+7Ü¢×$¯ å5×$Ø$å5ô¯ å5° ê5° ê5Þ° ê5©µ° ê5‚ Ü° ê5° ê5Þ° ê5±µ° ê5‚ ÜÛ$° ê5° ê5¹° ê5ºµ° ê5° ê5 Ý° ê5° ê5° ê5Ã° ê5»µ° ê5° ê5€€ú+7ú+7ÜÚ$° ê5Ú$Û$ê5± î5± î5Þ± î5Ãµ± î5ƒ ÜÝ$± î5± î5û+7ü+7û+7Üü+7úºÜ$ûÞ$Þ$± î5Ü$”@± î5§@± î5Ý$úºî5ï ± î5± î5± î5² è3ìÜà$² ¡4² ¤4ý+7ý+7ÜÜß$² ª4ß$à$² «4È;" Marker type for a directed graph.Á‹;%íÑ;Îùê'õ‚JY³ º;³ º;Þ³ º;†¶³ º;„ Îù´ À;´ À;Þ´ À;Ž¶´ À;… Îùä$´ À;´ À;þ+7ÿ+7€,7þ+7Îùÿ+7ßGßG„áG˜³EâG—³EãGš³EäGº³EåGª³EæG¢Ÿ³EQ Í!Ý >S€,7Ôžã$´ À;ã$ä$À;´ À;µ è3îÎùæ$µ ¡4µ ¤4,7,7ÎùÎùå$µ ª4å$æ$µ 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/™ ‘Ø /ÅFG /“‘0 0…‘ð 6‰‰È“DWh ‘è ™ ‘ð ‰;‘ø ‘€ %%‘7 3‰ÈDEW• ‘ Ÿ™ ‰ÈÑ¯V3I¸VDW ‘˜ ‘„V ‘ ‘ÅF ‘¨ ‘‘V%‘¿ ’ƒ;‘è “‘Ð –ë%‘þ ˜À‘ˆ ˜‰‘ ™‰ÈDxWA ‘ 5„VÈD~W` ‘ *„V ‘ *‰2 , /‘ +/‘(7ÈDNW„ ‘( Ù„V ‘0 ÙÅFÈD{WýH#Lë‘#Lƒ;‘à#V—:DWH#^ëD;W,‘#1Š;DWV‘~2DDLWk‘&8DDŠW€‘rOD1ê4X /‘|ú415V /‘t51$5V /‘x45DZW•‘-DDWª‘IDDþW,‘Ø ¥øY‘à ¥‰‰ÀÁGHbGDHSZLZEZ>ZL\X HÅF7Z[ÕZ¡ZïZ [ÈZ»ZüZ®ZâZœI‰bGÁGH…ÊP—:T-²,ÊP¬Pp<“\‰‰Ú=¥>éDp<L@¹@XE5=Ç>,F5Fü ÿÿÿÿ ( “ ! 5J ############–¦,V¦'$4QPU2N‰ =‚kÃœb@#®6q ‰‚‘ _ ¶MEx~N Þ{;)LŠZþaù #4§HÁHYi•454554545566bclang LLVM (rustc version 1.72.0 (5680fa18f 2023-08-23))/home/steffen/.cargo/registry/src/index.crates.io-6f17d22bba15001f/petgraph-0.5.1/src/lib.rs/@/petgraph.5cf8a40f1173021-cgu.0/home/steffen/.cargo/registry/src/index.crates.io-6f17d22bba15001f/petgraph-0.5.1<&usize as core::fmt::Debug>::{vtable}drop_in_place()*const ()sizeusizealign__method3&usize<&usize as core::fmt::Debug>::{vtable_type}> as core::ops::function::FnMut<(usize)>>::{vtable}__method4hashbrownrawinner{impl#7}find_ref__eqindexmapmapcoreequivalentkeyentriesdata_ptrKVhash__0HashValuevalueBucketlength&[indexmap::Bucket]{closure_env#0}&mut indexmap::map::core::equivalent::{closure_env#0}_ref__selfTallocGlobalAtablebucket_maskctrlptrnon_nullu8pointer*const u8NonNullgrowth_leftitemsRawTableInnermarkerPhantomDataRawTable&hashbrown::raw::inner::RawTable{closure_env#0}>> as core::ops::function::FnMut<(usize)>>::{vtable_type}petgraphvisit{impl#13}weightDUMMY_ZN66_$LT$$LP$Id$C$$LP$$RP$$RP$$u20$as$u20$petgraph..visit..NodeRef$GT$6weight5DUMMY17ha1c70aa38c0133b4EdotTYPE&str__ARRAY_SIZE_TYPE___ZN8petgraph3dot4TYPE17he7dd9f76d7d8601cEEDGE_ZN8petgraph3dot4EDGE17h2260a18bf2d926b5EINDENT_ZN8petgraph3dot6INDENT17hfa3deaeb5bc2fedcE as core::fmt::Debug>::{vtable}optionu64NoneSomeOption as core::fmt::Debug>::{vtable_type}::{vtable}::{vtable_type}<&bool as core::fmt::Debug>::{vtable}bool&bool<&bool as core::fmt::Debug>::{vtable_type}<&() as core::fmt::Debug>::{vtable}&()<&() as core::fmt::Debug>::{vtable_type}<&(usize, usize) as core::fmt::Debug>::{vtable}__1(usize, usize)&(usize, usize)<&(usize, usize) as core::fmt::Debug>::{vtable_type}fmtrtLeftRightCenterUnknownAlignmentcmpi8LessEqualGreaterOrderingpanickingEqNeMatchAssertKindgraphmapOutgoingIncomingCompactDirectionDirectionisomorphismtry_matchOutInOtherOpenListbitmask{impl#1}{impl#2}{impl#12}{impl#53}{impl#25}impls{impl#55}{impl#28}{impl#22}{impl#33}{impl#34}{impl#29}{impl#30}{impl#31}{impl#23}{impl#24}{impl#27}{impl#21}{impl#9}num{impl#89}pieces&[&str]positionfillcharflagsu32precisionIsParamImpliedCountwidthPlaceholder&[core::fmt::rt::Placeholder]Option<&[core::fmt::rt::Placeholder]>args{extern#0}Opaque&core::fmt::rt::{extern#0}::OpaqueformatterresultOkErrorEErrResult<(), core::fmt::Error>bufdyn core::fmt::Writevtable&[usize; 3]&mut dyn core::fmt::WriteFormatter&mut core::fmt::Formatterfn(&core::fmt::rt::{extern#0}::Opaque, 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indexmap::Bucket_ZN4core5slice3raw14from_raw_parts17h426685f199be70c7Efrom_raw_parts>vecraw_vecuniqueNonNull>_markerPhantomData>Unique>capRawVec, alloc::alloc::Global>Vec, alloc::alloc::Global>_ZN5alloc3vec16Vec$LT$T$C$A$GT$6as_ptr17hd279c27c2c5da8f6Eas_ptr, alloc::alloc::Global>&alloc::vec::Vec, alloc::alloc::Global>_ZN5alloc7raw_vec19RawVec$LT$T$C$A$GT$3ptr17h7a384935e6751282Eptr, alloc::alloc::Global>*mut indexmap::Bucket&alloc::raw_vec::RawVec, alloc::alloc::Global>_ZN4core3ptr6unique15Unique$LT$T$GT$6as_ptr17h3964a6e56d06b245Eas_ptr>U_ZN4core3ptr9const_ptr33_$LT$impl$u20$$BP$const$u20$T$GT$4cast17hd023ffbf9edc5989Ecast, ()>_ZN4core3ptr20slice_from_raw_parts17h5ac0d9f0a4c41c5fEslice_from_raw_parts>*const [indexmap::Bucket]metadata_ZN4core3ptr8metadata14from_raw_parts17h117c9e364b0e7249Efrom_raw_parts<[indexmap::Bucket]>data_address{impl#40}{impl#4}_ZN67_$LT$core..hash..sip..SipHasher13$u20$as$u20$core..hash..Hasher$GT$5write17hc83954330334d80eEwritehasherSip13RoundsSk0k1statev0v3StatetailntailPhantomDataHasherSipHasher13&mut core::hash::sip::SipHasher13msgstdcollections{impl#86}_ZN67_$LT$core..hash..sip..SipHasher13$u20$as$u20$core..hash..Hasher$GT$6finish17h5ada714d07518c9aEfinish&core::hash::sip::SipHasher13{impl#41}_ZN4core4hash3sip15Hasher$LT$S$GT$13new_with_keys17h576aaf28f1017470Enew_with_keyskey0key1_ZN4core4hash3sip11SipHasher1313new_with_keys17h6f773dbe0bcaaef9Enew_with_keys_ZN4core4hash3sip15Hasher$LT$S$GT$5reset17h474acc29ad9c2ea4Ereset&mut core::hash::sip::Hasher{impl#83}RandomStateindicesIndexMapCorehash_builderIndexMapQ_ZN8indexmap3map25IndexMap$LT$K$C$V$C$S$GT$12contains_key17h09e76419553ad02aEcontains_key&indexmap::map::IndexMap_ZN8indexmap3map25IndexMap$LT$K$C$V$C$S$GT$12get_index_of17h5a7e9de8b8151092Eget_index_of_ZN8indexmap3map25IndexMap$LT$K$C$V$C$S$GT$3len17h2c1b01a1abfc7d6bElen_ZN8indexmap3map25IndexMap$LT$K$C$V$C$S$GT$4hash17hd17b24b63f19c398Ehash_ZN8indexmap3map25IndexMap$LT$K$C$V$C$S$GT$8is_empty17h877aa8a8265e6f1eEis_empty_ZN8indexmap3map4core25IndexMapCore$LT$K$C$V$GT$12get_index_of17h1ea1357173bb9acbEget_index_of&indexmap::map::core::IndexMapCore_ZN8indexmap3map4core25IndexMapCore$LT$K$C$V$GT$3len17h776fcc41f28699f0ElensetIndexSet_ZN8indexmap3set21IndexSet$LT$T$C$S$GT$8contains17h2c992cf8195d94c2Econtains&indexmap::set::IndexSet*const usizeNonNullBucket_ZN9hashbrown3raw5inner15Bucket$LT$T$GT$6as_ref17hd952a64f049c0c8fEas_ref&hashbrown::raw::inner::Bucketimpl FnMut(&T) -> bool_ZN9hashbrown3raw5inner21RawTable$LT$T$C$A$GT$3get17hb29ac5571989e6d5Eget>_ZN9hashbrown3raw5inner21RawTable$LT$T$C$A$GT$3len17hcf5310dadd09bd27Elen_ZN9hashbrown3raw5inner21RawTable$LT$T$C$A$GT$4find17h7d1890512ca3335bEfind>Option>_ZN9hashbrown3raw5inner21RawTable$LT$T$C$A$GT$6bucket17h630d0c99163df9d9Ebucket_ZN9hashbrown3raw5inner21RawTable$LT$T$C$A$GT$7buckets17h7be771cb654e33ecEbuckets_ZN9hashbrown3raw5inner22RawTableInner$LT$A$GT$10find_inner17h2ab1d930248ebd40Efind_inner&hashbrown::raw::inner::RawTableInnerdyn core::ops::function::FnMut<(usize), Output=bool>&mut dyn core::ops::function::FnMut<(usize), 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