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:

- Method 1: sample x and y positions from a uniform distribution. These points will be random, but they end up forming lots of clusters and empty parts.
- Method 2: calculate x and y positions to be a perfect grid with exactly some distance between each point. These will be perfectly spaced, but it has no randomness.
- Method 3: Poisson disc sampling. These points will be roughly evenly spaced, but jittered in a natural looking way

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
Bridson’s
paper if you want to understand this parameter
better).

```
#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:

- Write more tests :)
- Implement helper-function to restrict points inside polygon w/
`mgcv::inSide()`

- Add travis/appveyor CI

will-r-chase/poissondisc documentation built on Nov. 6, 2019, 5:28 p.m.

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