n-dimensional Poisson disk sampling for Julia

Installation

pkg> add PoissonDiskSampling

How to use

julia> using PoissonDiskSampling

julia> r = 0.1 # minimum distance between samples

julia> points = PoissonDiskSampling.generate(r, (0,5), (0,3));

julia> typeof(points)
Vector{Tuple{Float64, Float64}} (alias for Array{Tuple{Float64, Float64}, 1})

julia> using Plots

julia> scatter(points)

The default number of candidates per active sample is k=30. Increase k to try harder before giving up on an active sample:

julia> points = PoissonDiskSampling.generate(r, (0,5), (0,3); k=60);

Pass an RNG as the first argument to control the random stream:

julia> using Random

julia> rng = MersenneTwister(1234);

julia> points = PoissonDiskSampling.generate(rng, r, (0,5), (0,3));

For reproducible output, use an explicit RNG and leave threaded=false.

Set threaded=true to use multiple threads:

julia> points = PoissonDiskSampling.generate(r, (0,5), (0,3); threaded=true);

With threaded=true, reproducibility is not guaranteed. The result may differ from the single-threaded result and may change with thread count or scheduling, even when using the same RNG seed.

See ?PoissonDiskSampling.generate for details.