by Tuur Dutoit

What is Bridson?

The Bridson algorithm is used to generate so-called Poisson-disc distributions: random, but evenly distributed sets of points, inside a certain shape. Generating such a distribution seems easier than it is: at first, you’d think you can just generate some points using Math.random(). This works, but, unfortunately, results in a truly random distribution, which means there will be places where the points are nearly touching each other and places where they are too far apart. It doesn’t look good or natural.
That’s where Bridson comes in. It is able to generate random distributions that look even and are easier on the eye.

I’m not going to explain how it works, because Mike Bostock can do that much better than I ever could. He wrote a very interesting article, Visualizing algorithms, in which he explains the Bridson algorithm (and a few others) in great detail, accompanied by some amazing animations and illustrations, visualizing what he’s telling. It’s on this article that I based myself on when writing the bridson module.

Disclaimer: The code for this module isn’t very tidy and probably not as efficient as it could be. But it works. It works well, in fact. And I plan on making it the absolute best it can be, whenever I find a little bit of free time.

How it works

Basically, you give bridson a shape to work inside and a minimum distance between points, and it returns a distribution. That would be the basic workflow, but, of course, this module is much more flexible than that and can be customized a great deal. Let’s go over some options!

Note: You can find more technical docs on GitHub


But first, a note on points. In bridson, points are simply represented by two-element arrays: [x, y] is a point at (x, y). An array of points is thus an array of two-element arrays: [[x1, y1], [x2, y2]] is an array of two points at (x1, y1) and (x2, y2), respectively.

Minimum distance r

options.r basically defines the density of the distribution, but it’s also a little more than that. Explaining what it exactly does would lead us too far, though, so check out the article I mentioned above: it explains it well.


One of the most important options is the shape that contains the distributions. This option is really powerful with bridson: it not only works with rectangles, it works with any shape you can think of. Let’s take a look!


The most straightforward shape is a simple rectangle. Begin by specifying the origin of the rectangle in options.origin, as a point. I write ‘origin’ instead of ‘top-left corner’, because it actually doesn’t matter what coordinate system you use, as long you use it consistently.

Then specify either the opposite corner in options.max (as a point) or the width and height in options.width and options.height, respectively (in numbers).

Note: max takes precedence over width and height.

Custom shape

If a rectangle doesn’t work for you, you can specify a custom shape. This is achieved with just one options: isInside. This should be a function that takes two arguments, x and y, and returns a boolean indicating whether the point at (x, y) is inside your shape. It’s as simple as that.

When using a custom shape, don’t forget to specify a starting point (or more. Look further down for more details); as cool as it is that bridson works with custom shapes, unfortunately, it cannot choose a starting point by itself.

Starting point(s)

Set options.start to a point or array of points and bridson will use these as the active points when starting. If you don’t specify a starting point, bridson will choose a random one itself, but it can only do that when using a rectangle; it cannot do this when using a custom shape.

What you can do however, is using a custom shape and specifying a rectangle that is completely inside your shape; this will cause bridson to use the shape in the algorithm, but it will choose a random starting point inside the rectangle.

Other options

A few more options are available, like padding when using a rectangle, iterations to control whether or not to track the amount of iterations performed and candidates to control the amount of candidates generated in each iteration. Check the technical docs on GitHub for more info.