You may have noticed from some of my previous blog posts that I've fallen down a rabbit hole learning about Gray codes and Hilbert curves. They weren't just random topics. I wrote those articles in order to understand the topic of this one, how to easily convert a Hilbert Index to Hilbert Coordinates via a method described in a paper by John Skilling.
Skilling, J. (2004), "Programming the Hilbert Curve", AIP Conference Proceedings.
Regrettably I don't yet understand why it works to a level that I'm satisfied with. The paper describes the process and gives a summary of previous work by Butz and Lawder but doesn't really give a clear explanation of why the transform works. It may be perfectly clear to someone who works in the field, but I don't get it... yet.
What I am capable of doing though is summarising the information here with some graphics to explain the concept to the next person that gets nerd sniped by this. I'll start by recommending my last few blog posts as they will give you an idea as to my train of thought.
A basic concept to understand is how binary numbers are treated as coordinates. In the animation below, the index is 6 bits long and counts up from 000000 to 111111. This can be used to cover every coordinate on an 8 x 8 two dimensional grid if every second bit belongs to the coordinates of one dimension while the others belong to the other dimension. In this case the red bits belong to the x axis while the blue bits belong to the y axis. Everything I'll describe in this post is for two dimensions, but it works for any number of dimensions. For example an 8x8x8 grid could be covered with a 9 bit index where bits 0, 3, 6 belong to one axis, 1, 4, 7 to another, and 2, 5 ,8 to another.
|Binary indexing in 2 dimensions|
In the binary indexing animation you can see that there are diagonal lines. These occur because more than one bit of the index can change at once affecting both dimensions.
The first step in the Skilling Transform is to take the binary reflected Gray code of the index. This means that only one bit at a time can change. Therefore the value of only one axis can change at a time leaving only vertical and horizontal movements.
|Gray code indexing in 2 dimensions|
The addressing scheme detailed above is formally described in this section of the paper. I put this here just so we're all on the same page about the variable names.
$$$p$$$ is the number of bits in each axis
$$$n$$$ is the number of dimensions
Now onto the actual transform. It's minimal and easy to compute. It's not a recursive algorithm and takes a bit string that specifies the Hilbert index as an input and performs a series of swaps and inversions to return a bit string of the same length that describes the Hilbert coordinates.
To see if I could understand the process better I wanted to visualise the transform. The animation below show the transformation from Gray code indexing to the Hilbert curve. You can see how the algorithm starts the transformation on the smaller features first and works up to larger features at each step.
|Gray code to Hilbert curve via the Skilling transform in 2 dimensions.|
Hopefully the worked example below will help people understand the process a little better. The index is converted to its Gray code and the algorithm processes bits in a reverse order. The grey boxes refer to the bit $$$r$$$ shown in the algorithm above. The bits that are classified as low bits at each stage are underlined.
I can't really offer much of a conclusion here. The understanding I've gleaned so far is minimal and superficial at best. I 'll leave this one for a while and come back to it in the future when I have more time.
I have a love hate relationship with problems like this. I find them fascinating, but at the same time they remind me how much I miss formal study, and I get bummed out. Kinda sucks having no one to bounce ideas off either. Having said that though, I've finally generated some animations that I'm proud of. You can find the undocumented scratch code here.
If you come across this page and have a better understanding of why this works I'd be glad to hear from you.