It’s a waste of time to use classes, if all you’re doing is hacking together 500 lines into a stable enough heap to generate the graphs for a paper.
Not all academic code is that bad, but the heft is definitely towards that end of the pool.
I got really familiar with the distinction, because I spent a while translating PhD project/demo code into actual products for $JOB. It’s definitely mostly in making things secure, stable, maintainable, and debuggable that you lose most of the time.
This a thousand times over. Academic code will take every shortcut and simplification possible. Hardcoded values, globals, no modularity, no error checking, shared-everything architecture assumptions, single-letter variables with mixed naming convention, anything to get the job done.
Once the paper is written, the data analyzed, the project is done. There is no such thing as "maintainability" because the code isn't used past publication.
In mathematical code, single letter variables are often the clearest. "Descriptive" names only obfuscate the meaning further, because the meaning is in the math.
I've translated several mathematical papers into code, and I must strongly disagree. The very first thing I do is translate glyphs into names relevant to the domain I'm applying the math to. It makes the rest of the process immensely easier.
That may make sense internal to a library, where you’ve established idioms.
But as a counterpoint, I only know what you meant by the first expression because I read the second expression.
I actually agree with you in large part, that short variable names can have more meaning within an established set of idioms because they allow you to parse whole statements at once. But there’s a trade-off involved, because mathematics can take symbology further than that’s useful.
Reasoning: k is the only subscript, so it can be dropped. Meaning of C and D are implicitly defined through their function wrt to state and input, so its OK not to name them. It also keeps the structure visible similar to that of the math.
Not all academic code is that bad, but the heft is definitely towards that end of the pool.
I got really familiar with the distinction, because I spent a while translating PhD project/demo code into actual products for $JOB. It’s definitely mostly in making things secure, stable, maintainable, and debuggable that you lose most of the time.