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// Copyright 2021 Travis Veazey // // Licensed under the Apache License, Version 2.0, <LICENSE-APACHE or // https://apache.org/licenses/LICENSE-2.0> or the MIT license <LICENSE-MIT or // https://opensource.org/licenses/MIT>, at your option. This file may not be // copied, modified, or distributed except according to those terms. //! Generate a Poisson disk distribution. //! //! This is an implementation of Bridson's ["Fast Poisson Disk Sampling"][Bridson] algorithm in //! arbitrary dimensions. //! //! * Iterator-based generation lets you leverage the full power of Rust's //! [Iterators](Iterator) //! * Lazy evaluation of the distribution means that even complex Iterator chains are as fast as //! O(N); with other libraries operations like mapping into another struct become O(N²) or more! //! * Using Rust's const generics allows you to consume the distribution with no additional //! dependencies //! //! # Features //! //! These are the optional features you can enable in your Cargo.toml: //! //! * `single_precision` changes the output, and all of the internal calculations, from using //! double-precision `f64` to single-precision `f32`. Distributions generated with the //! `single-precision` feature are *not* required nor expected to match those generated without //! it. //! * `small_rng` changes the internal PRNG used to generate the distribution: By default //! [`Xoshiro256StarStar`](rand_xoshiro::Xoshiro256StarStar) is used, but with this feature //! enabled then [`Xoshiro128StarStar`](rand_xoshiro::Xoshiro128StarStar) is used instead. This //! reduces the memory used for the PRNG's state from 256 bits to 128 bits, and may be more //! performant for 32-bit systems. //! * `derive_serde` automatically derives Serde's Serialize and Deserialize traits for `Poisson`, //! This relies on the [`serde_arrays`][sa] crate to allow (de)serializing the const generic arrays //! used by `Poisson`. //! //! # Requirements //! //! This library requires Rust 1.51.0 or later, as it relies on [const generics] to return //! fixed-length points (e.g. [x, y] or [x, y, z]) without adding additional external dependencies //! to your code. //! //! # Examples //! //! ``` //! use fast_poisson::Poisson2D; //! //! // Easily generate a simple `Vec` //! # // Some of these examples look a little hairy because we have to accomodate for the feature //! # // `single_precision` in doctests, which changes the type of the returned values. //! # #[cfg(not(feature = "single_precision"))] //! let points: Vec<[f64; 2]> = Poisson2D::new().generate(); //! # #[cfg(feature = "single_precision")] //! # let points: Vec<[f32; 2]> = Poisson2D::new().generate(); //! //! // To fill a box, specify the width and height: //! let points = Poisson2D::new().with_dimensions([100.0, 100.0], 5.0); //! //! // Leverage `Iterator::map` to quickly and easily convert into a custom type in O(N) time! //! // Also see the `Poisson::to_vec()` method //! # #[cfg(not(feature = "single_precision"))] //! struct Point { //! x: f64, //! y: f64, //! } //! # #[cfg(feature = "single_precision")] //! # struct Point { x: f32, y: f32 } //! let points = Poisson2D::new().iter().map(|[x, y]| Point { x, y }); //! //! // Distributions are lazily evaluated; here only 5 points will be calculated! //! let points = Poisson2D::new().iter().take(5); //! //! // `Poisson` can be directly consumed in for loops: //! for point in Poisson2D::new() { //! println!("X: {}; Y: {}", point[0], point[1]); //! } //! ``` //! //! Higher-order Poisson disk distributions are generated just as easily: //! ``` //! use fast_poisson::{Poisson, Poisson3D, Poisson4D}; //! //! // 3-dimensional distribution //! let points_3d = Poisson3D::new().iter(); //! //! // 4-dimensional distribution //! let mut points_4d = Poisson4D::new(); //! // To achieve desired levels of performance, you should set a larger radius for higher-order //! // distributions //! points_4d.with_dimensions([1.0; 4], 0.2); //! let points_4d = points_4d.iter(); //! //! // For more than 4 dimensions, use `Poisson` directly: //! let mut points_7d = Poisson::<7>::new(); //! points_7d.with_dimensions([1.0; 7], 0.6); //! let points_7d = points_7d.iter(); //! ``` //! //! # Upgrading //! //! ## 0.4.x //! //! This version is 100% backwards-compatible with 0.3.x and 0.2.0, however `fast_poisson` has been //! relicensed as of this version. //! //! Several bugs were identified and fixed in the underlying algorithms; as a result, distributions //! generated with 0.4.0 will *not* match those generated in earlier versions. //! //! ## 0.3.x //! //! This version adds no breaking changes and is backwards-compatible with 0.2.0. //! //! ## 0.2.0 //! //! This version adds some breaking changes: //! //! ### 2 dimensions no longer assumed //! //! In version 0.1.0 you could directly instantiate `Poisson` and get a 2-dimensional distribution. //! Now you must specifiy that you want 2 dimensions using either `Poisson<2>` or [`Poisson2D`]. //! //! ### Returned points are arrays //! //! In version 0.1.0 the distribution was returned as an iterator over `(f64, f64)` tuples //! representing each point. To leverage Rust's new const generics feature and support arbitrary //! dimensions, the N-dimensional points are now `[f64; N]` arrays. //! //! ### Builder pattern //! //! Use the build pattern to instantiate new distributions. This will not work: //! ```compile_fail //! # use fast_poisson::Poisson2D; //! let poisson = Poisson2D { //! width: 100.0, //! height: 100.0, //! radius: 5.0, //! ..Default::default() //! }; //! let points = poisson.iter(); //! ``` //! Instead, leverage the new builder methods: //! ``` //! # use fast_poisson::Poisson2D; //! let mut poisson = Poisson2D::new(); //! poisson.with_dimensions([100.0; 2], 5.0); //! let points = poisson.iter(); //! ``` //! This change frees me to make additional changes to how internal state is stored without necessarily //! requiring additional changes to the API. //! //! [Bridson]: https://www.cct.lsu.edu/~fharhad/ganbatte/siggraph2007/CD2/content/sketches/0250.pdf //! [Tulleken]: http://devmag.org.za/2009/05/03/poisson-disk-sampling/ //! [const generics]: https://blog.rust-lang.org/2021/03/25/Rust-1.51.0.html#const-generics-mvp //! [small_rng]: https://docs.rs/rand/0.8.3/rand/rngs/struct.SmallRng.html //! [sa]: https://crates.io/crates/serde_arrays #[cfg(feature = "derive_serde")] use serde::{Deserialize, Serialize}; #[cfg(test)] mod tests; mod iter; pub use iter::{Iter, Point}; /// [`Poisson`] disk distribution in 2 dimensions pub type Poisson2D = Poisson<2>; /// [`Poisson`] disk distribution in 3 dimensions pub type Poisson3D = Poisson<3>; /// [`Poisson`] disk distribution in 4 dimensions pub type Poisson4D = Poisson<4>; #[cfg(not(feature = "single_precision"))] type Float = f64; #[cfg(feature = "single_precision")] type Float = f32; /// Poisson disk distribution in N dimensions /// /// Distributions can be generated for any non-negative number of dimensions, although performance /// depends upon the volume of the space: for higher-order dimensions you may need to [increase the /// radius](Poisson::with_dimensions) to achieve the desired level of performance. /// /// # Equality /// /// `Poisson` implements `PartialEq` but not `Eq`, because without a specified seed the output of /// even the same object will be different. That is, the equality of two `Poisson`s is based not on /// whether or not they were built with the same parameters, but rather on whether or not they will /// produce the same results once the distribution is generated. #[derive(Debug, Clone)] #[cfg_attr(feature = "derive_serde", derive(Serialize, Deserialize))] pub struct Poisson<const N: usize> { /// Dimensions of the box #[cfg_attr(feature = "derive_serde", serde(with = "serde_arrays"))] dimensions: [Float; N], /// Radius around each point that must remain empty radius: Float, /// Seed to use for the internal RNG seed: Option<u64>, /// Number of samples to generate and test around each point num_samples: u32, } impl<const N: usize> Poisson<N> { /// Create a new Poisson disk distribution /// /// By default, `Poisson` will sample each dimension from the semi-open range [0.0, 1.0), using /// a radius of 0.1 around each point, and up to 30 random samples around each; the resulting /// output will be non-deterministic, meaning it will be different each time. /// /// See [`Poisson::with_dimensions`] to change the range and radius, [`Poisson::with_samples`] /// to change the number of random samples for each point, and [`Poisson::with_seed`] to produce /// repeatable results. #[must_use] pub fn new() -> Self { Self::default() } /// Specify the space to be filled and the radius around each point /// /// To generate a 2-dimensional distribution in a 5×5 square, with no points closer than 1: /// ``` /// # use fast_poisson::Poisson2D; /// let mut points = Poisson2D::new().with_dimensions([5.0, 5.0], 1.0).iter(); /// /// assert!(points.all(|p| p[0] >= 0.0 && p[0] < 5.0 && p[1] >= 0.0 && p[1] < 5.0)); /// ``` /// /// To generate a 3-dimensional distribution in a 3×3×5 prism, with no points closer than 0.75: /// ``` /// # use fast_poisson::Poisson3D; /// let mut points = Poisson3D::new().with_dimensions([3.0, 3.0, 5.0], 0.75).iter(); /// /// assert!(points.all(|p| { /// p[0] >= 0.0 && p[0] < 3.0 /// && p[1] >= 0.0 && p[1] < 3.0 /// && p[2] >= 0.0 && p[2] < 5.0 /// })); /// ``` pub fn with_dimensions(&mut self, dimensions: [Float; N], radius: Float) -> &mut Self { self.dimensions = dimensions; self.radius = radius; self } /// Specify the PRNG seed for this distribution /// /// If no seed is specified then the internal PRNG will be seeded from entropy, providing /// non-deterministic and non-repeatable results. /// /// ``` /// # use fast_poisson::Poisson2D; /// let points = Poisson2D::new().with_seed(0xBADBEEF).iter(); /// ``` pub fn with_seed(&mut self, seed: u64) -> &Self { self.seed = Some(seed); self } /// Specify the maximum samples to generate around each point /// /// Note that this is not specifying the number of samples in the resulting distribution, but /// rather sets the maximum number of attempts to find a new, valid point around an existing /// point for each iteration of the algorithm. /// /// A higher number may result in better space filling, but may also slow down generation. /// /// ``` /// # use fast_poisson::Poisson3D; /// let points = Poisson3D::new().with_samples(40).iter(); /// ``` pub fn with_samples(&mut self, samples: u32) -> &Self { self.num_samples = samples; self } /// Returns an iterator over the points in this distribution /// /// ``` /// # use fast_poisson::Poisson3D; /// let points = Poisson3D::new(); /// /// for point in points.iter() { /// println!("{:?}", point); /// } /// ``` #[must_use] pub fn iter(&self) -> Iter<N> { Iter::new(self.clone()) } /// Generate the points in this Poisson distribution, collected into a [`Vec`](std::vec::Vec). /// /// Note that this method does *not* consume the `Poisson`, so you can call it multiple times /// to generate multiple `Vec`s; if you have specified a seed, each one will be identical, /// whereas they will each be unique if you have not (see [`Poisson::with_seed`]). /// /// ``` /// # use fast_poisson::Poisson2D; /// let mut poisson = Poisson2D::new(); /// /// let points1 = poisson.generate(); /// let points2 = poisson.generate(); /// /// // These are not identical because no seed was specified /// assert!(points1.iter().zip(points2.iter()).any(|(a, b)| a != b)); /// /// poisson.with_seed(1337); /// /// let points3 = poisson.generate(); /// let points4 = poisson.generate(); /// /// // These are identical because a seed was specified /// assert!(points3.iter().zip(points4.iter()).all(|(a, b)| a == b)); /// ``` pub fn generate(&self) -> Vec<Point<N>> { self.iter().collect() } /// Generate the points in the Poisson distribution, as a [`Vec<T>`](std::vec::Vec). /// /// This is a shortcut to translating the arrays normally generated into arbitrary types, /// with the precondition that the type `T` must implement the `From` trait. This is otherwise /// identical to the [`generate`][Poisson::generate] method. /// /// ``` /// # use fast_poisson::Poisson2D; /// # #[cfg(not(feature = "single_precision"))] /// struct Point { /// x: f64, /// y: f64, /// } /// # #[cfg(feature = "single_precision")] /// # struct Point { x: f32, y: f32 } /// /// # #[cfg(not(feature = "single_precision"))] /// impl From<[f64; 2]> for Point { /// fn from(point: [f64; 2]) -> Point { /// Point { /// x: point[0], /// y: point[1], /// } /// } /// } /// # #[cfg(feature = "single_precision")] /// # impl From<[f32; 2]> for Point { /// # fn from(point: [f32; 2]) -> Point { /// # Point { /// # x: point[0], /// # y: point[1], /// # } /// # } /// # } /// /// let points: Vec<Point> = Poisson2D::new().to_vec(); /// ``` pub fn to_vec<T>(&self) -> Vec<T> where T: From<[Float; N]>, { self.iter().map(|point| point.into()).collect() } } /// No object is equal, not even to itself, if the seed is unspecified impl<const N: usize> PartialEq for Poisson<N> { fn eq(&self, other: &Self) -> bool { self.seed.is_some() && other.seed.is_some() && self.dimensions == other.dimensions && self.radius == other.radius && self.seed == other.seed && self.num_samples == other.num_samples } } impl<const N: usize> Default for Poisson<N> { fn default() -> Self { Poisson::<N> { dimensions: [1.0; N], radius: 0.1, seed: None, num_samples: 30, } } } impl<const N: usize> IntoIterator for Poisson<N> { type Item = Point<N>; type IntoIter = Iter<N>; fn into_iter(self) -> Self::IntoIter { Iter::new(self) } } impl<const N: usize> IntoIterator for &Poisson<N> { type Item = Point<N>; type IntoIter = Iter<N>; fn into_iter(self) -> Self::IntoIter { self.iter() } } /// For convenience allow converting to a Vec directly from Poisson impl<T, const N: usize> From<Poisson<N>> for Vec<T> where T: From<[Float; N]>, { fn from(poisson: Poisson<N>) -> Vec<T> { poisson.to_vec() } } // Hacky way to include README in doc-tests, but works until #[doc(include...)] is stabilized // https://github.com/rust-lang/cargo/issues/383#issuecomment-720873790 #[cfg(doctest)] mod test_readme { macro_rules! external_doc_test { ($x:expr) => { #[doc = $x] extern "C" {} }; } external_doc_test!(include_str!("../README.md")); }