Yea this is 100% nonsense, and is borderline gatekeeping. Well roundedness allows me to identify a particular language/tech stack that is most suitable for a task and then I can just drill down on it and get productive very quickly.
Agreed. And to address the grandparent comment. Its entirely possible that he is approaching his coding with a professional mindset and wasting time on things like maintainability, scalability, or similar just out of habit.
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.