Rust library for sampling a Poisson disk distribution in multiple dimensions.

The Poisson disk distribution produces samples of which no two samples are too close to each other. This results in a more uniform distribution than from pure random sampling.

This library is an implementation of the algorithm introduced by Robert Bridson [1] which is O(N) for producing N samples. That is, the sampling time increases linearly with the number of produced samples. For two-dimensional sampling, the sampling time increases with the area and for three-dimensional sampling with the volume.

Examples

See the documentation for more information about the library.

Set-up

Add the library as a dependency to your project in Cargo.toml: toml [dependencies] # poisson-diskus = "1.0.0-pre.3"

Three dimensions

```rust use poisson_diskus::bridson;

let boxsize = [3.0, 5.0, 7.0]; let rmin = 0.5; let numattempts = 30; let use_pbc = false;

let coords = bridson(&boxsize, rmin, numattempts, use_pbc).unwrap(); ```

Larger number of dimensions

The generated points have the same number of dimensions as the input box size. Note that the sampling speed decreases very quickly as the number of dimensions is increased.

```rust use poisson_diskus::bridson;

let boxsize = [3.0, 5.0, 3.0, 2.0, 1.0]; let rmin = 1.0; let numattempts = 30; let use_pbc = false;

let coords = bridson(&boxsize, rmin, numattempts, use_pbc).unwrap();

for coord in coords { asserteq!(coord.len(), boxsize.len()); } ```

Periodic boundary conditions

Use the use_pbc parameter to control whether the algorithm should look for neighbours within the minimum distance in periodic images of the space. This is slightly slower: about 25%-35% for the same number of generated points.

Note on accuracy

While the generated results look alright when inspecting by eye the generated distribution is not verified to be accurate. Currently, computing a radial density distribution of samples show some weird behavior.

In short, I would currently recommend against using this for work where the distribution of points is critical. Or, at the very least, to inspect the results before use.

Citations

[1] Bridson, R. (2007). Fast Poisson disk sampling in arbitrary dimensions. SIGGRAPH sketches, 10, 1.

License

This library is offered under the permissive Blue Oak license. See LICENSE.md for more details.