Sagemath uses maxima internally, so Jupyter notebook backed by Sagemath kernel might be useful?
From the front page:
> SageMath is a free open-source mathematics software system licensed under the GPL. It builds on top of many existing open-source packages: NumPy, SciPy, matplotlib, Sympy, Maxima, GAP, FLINT, R and many more. Access their combined power through a common, Python-based language or directly via interfaces or wrappers.
For once "many more" really means "many more".
By default Symbolic Calculus, which is a pretty common use-case since the space of integral-tables is huge and complicated, makes pretty heavy use of calls to Maxima as a backend, but it can slip over to SymPy, and some others pretty easily.
Sagemath is, a little rough to use as a novice-y math guy, really quirky especially since Python isn't as up-front with typing as I would like, and I've run into some lazy-evaluation confusion as well, but if your time is worthless, use-case restricted it can be an alternative to paying out the nose for Mathematica.
Yeah I mainly used it in my real analysis, complex analysis, and differential geometry classes. Also used it a bit in an intro cryptography class to play around with some rings and solve assignment problems.
I'm using Python right now in a neural networks class and I hate it. It's absolutely horrible to debug. You press run and then your neural network starts training and then craps out with divide-by-0 after a long time and no clue as to where the offending code might be.
Any pointers to how one would use it for real analysis?
Thanks.