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 a previous post, I explored the possibilities of running a GUI application inside a Docker container. In the current post, I will continue where we left off before, adding some details to make the process more convenient.
There are problems playing the pysolfc solitaire game on the latest release of Linux Mint 21 (Vanessa).
The game requires the formatter module from the Python
Standard Library, which had been deprecated since Python 3.4,
and has been removed in Python 3.10.
An easy, but ad-hoc workaround goes as follows:
Containers are not usually associated with GUI applications, but there may be times when one might still want to run such a program inside a container, for example to isolate the application’s dependencies. Installing a GUI application in a container means that not only the application, but also all its specific dependencies are encapsulated inside the container (respectively, the container image), and can therefore reliably be removed from the system in a single step.
The freedesktop project, arguably the most important Linux organization you’ve never heard of, has (among many other noble deeds) done an admirable job clearing up the mess of local cache and config files in one’s home directory. But how does one override their defaults, if this requires setting environment variables globally, for all processes, and outside an explicit shell environment?
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.
George H. Heilmeier, director of DARPA (the advanced-technology research agency of the US Defense Department) from 1975 to 1977, formulated a set of questions to help evaluate research proposals.
The Altitude Theorem or Geometric Mean Theorem is a result from high-school geometry. In a right triangle, the altitude $h$ on the hypotenuse divides the hypotenuse into two segments, $p$ and $q$. The theorem now states that $h^2 = pq$ or, equivalently, $h = \sqrt{pq}$: the altitude equals the geometric mean of the segments of the hypotenuse.
The content of this theorem is a bit surprising, because the altitude and the hypotenuse segments seem geometrically somewhat “unrelated”: it’s not clear how one could (geometrically) be transformed into the other. And although the theorem can be proven in many different ways, many of the proofs are at least partially algebraic, and therefore do not provide an intuitive, geometric sense why it is true. But it turns out that a very elegant, strictly geometric proof of this proposition can be constructed.
I just wasted one hour and five minutes, dealing with two of these opaque, unexpected, and almost undiagnosable showstopper roadblocks that Hugo will throw your way - much too often, in my opinion.
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.