Visualization, Gnuplot, d3.js

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most implementations I found looked hideously complicated (or just hideous), not necessarily correct, and/or used out-of-date programming languages and styles.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.

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Gnuplot in Action is a comprehensive tutorial written for all gnuplot users: data analysts, computer professionals, scientists, researchers, and others. It shows how to apply gnuplot to data analysis problems. It gets into tricky and poorly documented areas.