John Baez over at his new blog Azimuth has a post with an amazing looking fractal: the set of all roots of all polynomials with coefficients -1 or 1. Since it’s ``just’’ a set of points, it seemed like the perfect opportunity to try Facet on a large, good-looking dataset, and here is the result. I think it looks pretty nice. If you want to know more about the mathematics behind it, read Baez’s post. If you care about the visualization details of this, read on!

The original dataset used by Baez in the pictures is the set of all roots of polynomials of degree up to 24. That gives about 400 million points, and at 8 bytes per point, we’re talking 3.2GB of data. Not a good idea :) What I show here are the roots of polynomials of degree up to 15. It’s still fairly large, clocking at almost two million points. Still, the total amount of data being fetched from the server is only about 15MB. This would be hard to do in anything but WebGL, and would be painful to write in anything but Facet.

It’s worth mentioning that the whole thing is 180 lines of Javascript, of which about half is jQuery and GUI-related cruft, and the other half is Facet. The actual rendering is done in two passes. The first pass splats additive Gaussian blobs of adjustable size and weight onto a floating-point texture (so that we don’t get too much accumulation error). The shape of the gaussian blobs is computed in a fragment shader. Then, we read back the texture and pass it through a simple tonemapping and colormap on another shader. If you read the source, however, you’ll see that there’s no shaders being written anywhere: they’re all synthesized from the Javascript expressions.

The bit that took a lot of ugly parameter hacking was getting a pleasant tradeoff between a global look of the fractal structure, while still seeing details when zooming in. A fixed screen-space width for each blob looks bad (You can’t really see the points when deep zooming, they become too small), but a fixed world-space width for each blob looks bad too (the blobs never resolve into roots). The solution is to use, essentially, the geometric mean between those two sizes. It works well in practice, but I can’t really justify it theoretically.