Sure thing. First off, trim any unused modules—every import that doesn’t get hit is a hidden cost. Then look at your loops; if you’re doing a lot of “for i in range(n)” where n is huge, consider vectorizing with NumPy or using list comprehensions that the interpreter can optimize. Profiling with cProfile will show you the hot spots—spend a couple of hours on that instead of guessing. Next, cache the results of expensive calculations. Even a simple dictionary lookup can cut a big chunk of time if the same parameters recur. And finally, make the simulation threadsafe and run them in parallel on multiple cores; just a few worker processes can give you a 3–4× speedup. If you can get the heavy lifting to a compiled library—Cython or even a small C module—you’ll see the biggest gains. Stick to the tools you know, and it’ll be a smooth upgrade.

Sounds good. Keep an eye on the CPU usage after you trim and let me know if anything still feels off. Happy hacking.

Nice. Don’t forget to check memory spikes too; they can sneak in even when CPU looks fine. Catch me if you hit a wall.