1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308
//! `petgraph` is a graph data structure library. //! //! Graphs are collections of nodes, and edges between nodes. `petgraph` //! provides several [graph types](index.html#graph-types) (each differing in the //! tradeoffs taken in their internal representation), //! [algorithms](./algo/index.html#functions) on those graphs, and functionality to //! [output graphs](./doc/petgraph/dot/struct.Dot.html) in //! [`graphviz`](https://www.graphviz.org/) format. Both nodes and edges //! can have arbitrary associated data, and edges may be either directed or undirected. //! //! # Example //! //! ```rust //! use petgraph::graph::{NodeIndex, UnGraph}; //! use petgraph::algo::{dijkstra, min_spanning_tree}; //! use petgraph::data::FromElements; //! use petgraph::dot::{Dot, Config}; //! //! // Create an undirected graph with `i32` nodes and edges with `()` associated data. //! let g = UnGraph::<i32, ()>::from_edges(&[ //! (1, 2), (2, 3), (3, 4), //! (1, 4)]); //! //! // Find the shortest path from `1` to `4` using `1` as the cost for every edge. //! let node_map = dijkstra(&g, 1.into(), Some(4.into()), |_| 1); //! assert_eq!(&1i32, node_map.get(&NodeIndex::new(4)).unwrap()); //! //! // Get the minimum spanning tree of the graph as a new graph, and check that //! // one edge was trimmed. //! let mst = UnGraph::<_, _>::from_elements(min_spanning_tree(&g)); //! assert_eq!(g.raw_edges().len() - 1, mst.raw_edges().len()); //! //! // Output the tree to `graphviz` `DOT` format //! println!("{:?}", Dot::with_config(&mst, &[Config::EdgeNoLabel])); //! // graph { //! // 0 [label="\"0\""] //! // 1 [label="\"0\""] //! // 2 [label="\"0\""] //! // 3 [label="\"0\""] //! // 1 -- 2 //! // 3 -- 4 //! // 2 -- 3 //! // } //! ``` //! //! # Graph types //! //! * [`Graph`](./graph/struct.Graph.html) - //! An adjacency list graph with arbitrary associated data. //! * [`StableGraph`](./stable_graph/struct.StableGraph.html) - //! Similar to `Graph`, but it keeps indices stable across removals. //! * [`GraphMap`](./graphmap/struct.GraphMap.html) - //! An adjacency list graph backed by a hash table. The node identifiers are the keys //! into the table. //! * [`MatrixGraph`](./matrix_graph/struct.MatrixGraph.html) - //! An adjacency matrix graph. //! * [`CSR`](./csr/struct.Csr.html) - //! A sparse adjacency matrix graph with arbitrary associated data. //! //! ### Generic parameters //! //! Each graph type is generic over a handful of parameters. All graphs share 3 common //! parameters, `N`, `E`, and `Ty`. This is a broad overview of what those are. Each //! type's documentation will have finer detail on these parameters. //! //! `N` & `E` are called *weights* in this implementation, and are associated with //! nodes and edges respectively. They can generally be of arbitrary type, and don't have to //! be what you might conventionally consider weight-like. For example, using `&str` for `N` //! will work. Many algorithms that require costs let you provide a cost function that //! translates your `N` and `E` weights into costs appropriate to the algorithm. Some graph //! types and choices do impose bounds on `N` or `E`. //! [`min_spanning_tree`](./algo/fn.min_spanning_tree.html) for example requires edge weights that //! implement [`PartialOrd`](https://doc.rust-lang.org/stable/core/cmp/trait.PartialOrd.html). //! [`GraphMap`](./graphmap/struct.GraphMap.html) requires node weights that can serve as hash //! map keys, since that graph type does not create standalone node indices. //! //! `Ty` controls whether edges are [`Directed`](./petgraph/enum.Directed.html) or //! [`Undirected`](./petgraph/enum.Unirected.html). //! //! `Ix` appears on graph types that use indices. It is exposed so you can control //! the size of node and edge indices, and therefore the memory footprint of your graphs. //! Allowed values are `u8`, `u16`, `u32`, and `usize`, with `u32` being the default. //! //! ### Shorthand types //! //! Each graph type vends a few shorthand type definitions that name some specific //! generic choices. For example, [`DiGraph<_, _>`](./graph/type.DiGraph.html) is shorthand //! for [`Graph<_, _, Directed>`](graph/struct.Graph.html). //! [`UnMatrix<_, _>`](./matrix_graph/type.UnMatrix.html) is shorthand for //! [`MatrixGraph<_, _, Undirected>`](./matrix_graph/struct.MatrixGraph.html). Each graph type's //! module documentation lists the available shorthand types. //! //! # Crate features //! //! * **serde-1** - //! Defaults off. Enables serialization for ``Graph, StableGraph`` using //! [`serde 1.0`](https://crates.io/crates/serde). May require a more recent version //! of Rust than petgraph alone. //! * **graphmap** - //! Defaults on. Enables [`GraphMap`](./graphmap/struct.GraphMap.html). //! * **stable_graph** - //! Defaults on. Enables [`StableGraph`](./stable_graph/struct.StableGraph.html). //! * **matrix_graph** - //! Defaults on. Enables [`MatrixGraph`](./matrix_graph/struct.MatrixGraph.html). //! #![doc(html_root_url = "https://docs.rs/petgraph/0.4/")] extern crate fixedbitset; #[cfg(feature = "graphmap")] extern crate indexmap; #[cfg(feature = "serde-1")] extern crate serde; #[cfg(feature = "serde-1")] #[macro_use] extern crate serde_derive; #[cfg(all(feature = "serde-1", test))] extern crate itertools; #[doc(no_inline)] pub use crate::graph::Graph; pub use crate::Direction::{Incoming, Outgoing}; #[macro_use] mod macros; mod scored; // these modules define trait-implementing macros #[macro_use] pub mod visit; #[macro_use] pub mod data; pub mod algo; mod astar; pub mod csr; mod dijkstra; pub mod dot; #[cfg(feature = "generate")] pub mod generate; mod graph_impl; #[cfg(feature = "graphmap")] pub mod graphmap; mod isomorphism; mod iter_format; mod iter_utils; #[cfg(feature = "matrix_graph")] pub mod matrix_graph; #[cfg(feature = "quickcheck")] mod quickcheck; #[cfg(feature = "serde-1")] mod serde_utils; mod simple_paths; mod traits_graph; pub mod unionfind; mod util; pub mod prelude; /// `Graph<N, E, Ty, Ix>` is a graph datastructure using an adjacency list representation. pub mod graph { pub use crate::graph_impl::{ edge_index, node_index, DefaultIx, DiGraph, Edge, EdgeIndex, EdgeIndices, EdgeReference, EdgeReferences, EdgeWeightsMut, Edges, EdgesConnecting, Externals, Frozen, Graph, GraphIndex, IndexType, Neighbors, Node, NodeIndex, NodeIndices, NodeReferences, NodeWeightsMut, UnGraph, WalkNeighbors, }; } #[cfg(feature = "stable_graph")] pub use crate::graph_impl::stable_graph; macro_rules! copyclone { ($name:ident) => { impl Clone for $name { #[inline] fn clone(&self) -> Self { *self } } }; } // Index into the NodeIndex and EdgeIndex arrays /// Edge direction. #[derive(Copy, Debug, PartialEq, PartialOrd, Ord, Eq, Hash)] #[repr(usize)] pub enum Direction { /// An `Outgoing` edge is an outward edge *from* the current node. Outgoing = 0, /// An `Incoming` edge is an inbound edge *to* the current node. Incoming = 1, } copyclone!(Direction); impl Direction { /// Return the opposite `Direction`. #[inline] pub fn opposite(self) -> Direction { match self { Outgoing => Incoming, Incoming => Outgoing, } } /// Return `0` for `Outgoing` and `1` for `Incoming`. #[inline] pub fn index(self) -> usize { (self as usize) & 0x1 } } #[doc(hidden)] pub use crate::Direction as EdgeDirection; /// Marker type for a directed graph. #[derive(Copy, Debug)] pub enum Directed {} copyclone!(Directed); /// Marker type for an undirected graph. #[derive(Copy, Debug)] pub enum Undirected {} copyclone!(Undirected); /// A graph's edge type determines whether it has directed edges or not. pub trait EdgeType { fn is_directed() -> bool; } impl EdgeType for Directed { #[inline] fn is_directed() -> bool { true } } impl EdgeType for Undirected { #[inline] fn is_directed() -> bool { false } } /// Convert an element like `(i, j)` or `(i, j, w)` into /// a triple of source, target, edge weight. /// /// For `Graph::from_edges` and `GraphMap::from_edges`. pub trait IntoWeightedEdge<E> { type NodeId; fn into_weighted_edge(self) -> (Self::NodeId, Self::NodeId, E); } impl<Ix, E> IntoWeightedEdge<E> for (Ix, Ix) where E: Default, { type NodeId = Ix; fn into_weighted_edge(self) -> (Ix, Ix, E) { let (s, t) = self; (s, t, E::default()) } } impl<Ix, E> IntoWeightedEdge<E> for (Ix, Ix, E) { type NodeId = Ix; fn into_weighted_edge(self) -> (Ix, Ix, E) { self } } impl<'a, Ix, E> IntoWeightedEdge<E> for (Ix, Ix, &'a E) where E: Clone, { type NodeId = Ix; fn into_weighted_edge(self) -> (Ix, Ix, E) { let (a, b, c) = self; (a, b, c.clone()) } } impl<'a, Ix, E> IntoWeightedEdge<E> for &'a (Ix, Ix) where Ix: Copy, E: Default, { type NodeId = Ix; fn into_weighted_edge(self) -> (Ix, Ix, E) { let (s, t) = *self; (s, t, E::default()) } } impl<'a, Ix, E> IntoWeightedEdge<E> for &'a (Ix, Ix, E) where Ix: Copy, E: Clone, { type NodeId = Ix; fn into_weighted_edge(self) -> (Ix, Ix, E) { self.clone() } }