Alright folks, let me tell you about this little project I’ve been messing around with called “van wells.” It all started last week, I was bored, surfing the net, and I stumbled upon some obscure article about data structures, specifically mentioning something about Voronoi diagrams and how they could be used for, like, totally random stuff. So, naturally, I thought, “Hey, I should try that!”
First things first, I needed to figure out what a Voronoi diagram actually was. Turns out, it’s basically a way of dividing up a space into regions based on the distance to a set of points. Imagine dropping a bunch of pushpins on a map, then drawing lines around each pin so that everything inside a given region is closer to that pin than any other. That’s the gist of it.
Okay, got the theory. Now to the coding! I decided to use Python, because, well, Python’s my go-to for quick and dirty experiments. I started by installing a couple of libraries: scipy for the Voronoi diagram calculations and matplotlib for visualizing the results. The module has a Voronoi function that does all the heavy lifting.
The first step was generating some random points. I used numpy for that. Just a simple array of (x, y) coordinates. I fed those points into the Voronoi function, and boom! I had a Voronoi diagram object. The object contains information about the vertices (the corners of the regions) and the ridges (the lines that separate the regions).
Next up, visualizing the thing. This is where matplotlib came in handy. I basically looped through the ridges and drew lines connecting the corresponding vertices. It took a bit of fiddling to get the colors right and make it look decent, but eventually, I had a nice-looking Voronoi diagram staring back at me from the screen.
But that wasn’t enough. I wanted to do something with it. So I started thinking about applying it to some problem. After a bit of head-scratching, I decided to try using it for pathfinding. The idea was to treat the Voronoi ridges as a network of paths. You could then use A or Dijkstra’s algorithm to find the shortest path between two points along these ridges.
This was the tricky part. I needed to build a graph from the Voronoi diagram. Each vertex became a node in the graph, and each ridge became an edge. The weight of each edge was simply the distance between the vertices. I used the networkx library to create and manipulate the graph.
I implemented Dijkstra’s algorithm (because I’m a sucker for the classics) to find the shortest path in the graph. Then, I visualized the path on top of the Voronoi diagram. And you know what? It actually worked! It wasn’t perfect, and sometimes the path looked a little wonky, but it generally found a reasonable route between two points.
I spent the next few days tweaking the code, trying different point distributions, experimenting with different pathfinding algorithms, and just generally messing around. It was a fun little project, and I learned a lot about Voronoi diagrams and their applications. It’s amazing how something that looks so simple can be used for so many different things.
So, that’s the story of “van wells.” It’s not a groundbreaking piece of software, but it was a good exercise in learning and experimentation. And who knows, maybe I’ll come back to it someday and turn it into something more useful. For now, it’s just a fun little project that I can say I built from scratch.
