Visualization, Gnuplot, d3.js

A while back, I looked at the Diamond-Square Algorithm for terrain generation. That is a purely procedural algorithm that only attempts to generate realistic looking landscapes, without trying to model any physical or geological processes. By contrast, we will now look at an algorithm to generate realistic river networks, which is based on a (simplified) model of geological erosion.

Building open-source software from source is not necessarily hard: after all, typing make is fairly easy. But dealing with the tools and dependencies can be tedious, in particular, if you don’t use them all the time. In this post, I want to describe how to use Docker containers as convenient, clean-room build environments.

Over the holidays, I sat down and wrote a casual turn-based strategy game. Aside from the obvious fun aspect, one major motivation was to gain experience with the pyglet game library, which I wanted to evaluate as a potentially general, light-weight GUI library for Python.

Finding a realistic (or at least, realistic looking) initial configuration of game objects or simulation particles can be a challenge. The desired configuration should appear to be both “random” and at the same time “spatially uniform”, without objects clustering together or overlapping.

I recently got interested in algorithms for scaling pixel art images, such as icons or video game sprites. The Wikipedia page on the topic lists a handful of different algorithms that have been developed for that purpose.

In praise of what? Pic? Pic? Pic, the old diagram generation “little language” and half brother of roff (or troff or groff), from the days when Unix was young? Yes, indeed, that pic.

I occasionally see references to the HDF5 file format, but I have never encountered it in the wild. But a recent project generated multiple data sets simultaneously, in addition to metadata. Was there a better way than maintaining a collection of flat files? This prompted me to look at HDF5.

The Diamond-Square Algorithm is the natural first stop for generating artificial landscapes. The algorithm itself is beautifully simple (more details below, and on its Wikipedia page). But a casual implementation ended up not working at all, prompting me to look for an existing implementation to learn from. However, most implementations I found looked hideously complicated (or just hideous), not necessarily correct, and/or used out-of-date programming languages and styles. It therefore seemed like a good idea to create a clean, simple “reference” implementation of this algorithm, using a contemporary and widely known programming language and style.

If you are in a hurry to learn D3.js, the leading JavaScript library for web-based graphics and visualization, this book is for you. Written for technically savvy readers with a background in programming or data science, the book moves quickly, emphasizing unifying concepts and patterns. Anticipating common difficulties, the book teaches you how to apply D3 to your own problems.

Gnuplot in Action, 2nd Edition, is the authoritative guide to the gnuplot graphics and visualization program for developers, engineers, and scientists.