Hey everyone! I've been tasked with converting a substantial solution written in MATLAB (over 1000 lines) into a combination of internal tools and Python. The first challenge is to fully grasp what the original MATLAB code does. My go-to method is to read the code line by line and annotate it, but this approach often leads to a cluttered mess, making it hard to reference later. I'm reaching out to see if anyone has faced a similar challenge and has better techniques or methods for understanding someone else's code. I'd love to hear your thoughts and methods! Additionally, I'm a computational engineer with programming experience but lack formal training in software engineering, so I'm eager for any advice on how to document or annotate the code efficiently. Thanks!
3 Answers
When you're working with a large codebase, try breaking it down into smaller functions. Convert each tiny segment into Python and check that it produces the same results as MATLAB. When all the small pieces are working, gradually integrate them until the entire code is ported over.
Great tip! Also, think about writing unit tests in your destination language. If it's critical, consider doing them in MATLAB first. This could help catch bugs early on.
One method I find useful is running outputs first, then working backwards through the code. Setting up iterative unit tests can really help you compare outputs between your new code and the old one, especially with different input parameters.
Good point! Just be mindful of how rounding and precision are handled between both versions, that can trip you up if you're not careful.
For sure! Analyzing outputs before diving into the code helps cut down on confusion later!
If you're not familiar with array or matrix languages like MATLAB, try to get a grip on that first. You're going to rely heavily on libraries like Numpy in Python. Also, think about the goal of your conversion—could switching to something like Octave work instead? Just curious!
I totally get that! I've noticed a trend where many are moving MATLAB codebases into Python, especially since Python's tied to AI tools. Plus, Numpy can perform comparably to MATLAB, so it's a smart move for future maintenance.
Absolutely! I've used both MATLAB and Numpy for a while, and I’d definitely recommend keeping the maintainability and your customers' needs in mind.
Got it! So do you not usually annotate the parts that you might find tricky as you go? I feel like that could help a lot.