poissondisc is a tiny package that implements Robert Bridson’s algorithm for fast poisson disc sampling. Poisson disc sampling is a method to produce a set of points that are random, but never closer to each other than some minimum distance. There are many applications in computer graphics, generative art, or geospatial analysis where you want a distribution of points that are roughly evenly spaced, but still pseudo-random. This is best illustrated by a graphic:
Three ways to generate a set of (x, y) points:
Currently this package only lives on github. You can install it like this:
# install.packages("devtools") devtools::install_github("will-r-chase/poissondisc")
This package contains only one function
poisson_disc(). The function
returns a dataframe with the x and y coordinates of the poisson disc
sampled points. You must specify the height and width within which you
want to generate your points (ie. your canvas size) and the minimum
distance between points. Optionally, you can specify the number of
samples before rejection, but it is recommended that you leave it at 30
unless you know what you’re doing (read
paper if you want to understand this parameter
#generate a set of points within a 400x400 canvas with a minimum distance of 20 pts <- poisson_disc(400, 400, 20) #plot the points library(ggplot) ggplot(pts, aes(x, y)) + geom_point()
I only plan to use this for generative art, but there are some real uses for poisson disc sampling. For my purposes, this simple implementation works fine, but it could be improved in several ways if you wanted to contribute. First, this algorithm is generalizable to any number of dimensions, but I’ve only implemented it in 2 dimensions. Second, this implementation is fast enough for my purposes, but if you wanted to speed it up you could re-write it in Rcpp, as it uses lots of for loops, it would be much faster that way. Third, I think the best use-case for this is geospatial analysis, but that might benefit from a different implementation. Mitchell’s best-candidate algorithm can be used to generate poisson disc distribution on a sphere, which might be useful for geospatial. If you want to contribute or you find a bug, please open an issue or submit a pull request. If you like this package, tell me about it on Twitter @W_R_Chase.
My to-do list:
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