Nume MacAroon Ⓥ @nm

**nen** @nen@mementomori.social · Apr 22 *

SymPy in Julia is actually awesome! It feels more ergonomic to use in Pluto notebook than the native Python package does in Jupyter, which is the opposite of what I expected.

Screenshot of a Pluto notebook. Cells contain text and code, and outputs are rendered as LaTeX math.

Symbolic math with SymPy in Julia+Pluto

using SymPy
@syms x y a
eq1 = x^2 + y^2 ~ 1
eq2 = a*x - y ~ 1/sqrt(Sym(3)) * Rational(1, 3)

#Julia #JuliaLang #Pluto

**David Bremner** @bremner@mathstodon.xyz · Apr 8

Apr 8

David Bremner @bremner@mathstodon.xyz

It's been a long time since I tried #SymPy, but yesterday I went back to it to do some simple symbolic linear algebra, and I have to say it is pretty good these days.

The same project was also the first time I used #orgmode source blocks to insert generated output (from SymPy i this case) into a document. It took me a few times returning to the docs, but it is pretty nice for sharing calculations with fellow researchers.

**nen** @nen@mementomori.social · Dec 25, 2024 *

Dec 25, 2024 *

nen @nen@mementomori.social

I like to do math, small prototypes and other experimenting with Python in Jupyter notebooks. NumPy, SciPy, SymPy and Matplotlib have been very handy.

But recently Python was too slow for what I was doing and I had trouble juggling Numpy arrays like I often have (it is required for speed). I know some #Rust so I decided to give #evcxr a go because it has a Jupyter kernel. And omg, I wasn't disappointed! It was mindbogglingly fast compared to what I'm used to and it also made the hard part of my code way easier to get right.

However, I only needed math functions provided by std this time. Now I'm trying to figure out if there are science Rust libs that are convenient enough to replace most Python in my notebooks. I know it's a fragmented space, but maybe one could already gather a comprehensive collection of crates manually.

Any recommendations?

$End of a long Jupyter notebook cell in VSCodium with Rust code in it. Running it took 6.3s. The uncommented code that is visible is computing something in parallel using rayon: x.par_iter_mut().enumerate().for_each(|(n, xn)| { let n = n as i32; for k in n-N+1..=n { *xn += a(k, lam, t1, t4, sr) * (-lam/(sr as f64)*((n-k) as f64)).exp() * (omega[k.max(0) as usize]/(sr as f64) * ((n-k) as f64) + phi[k.max(0) as usize]).cos(); } });$

#RustLang #SciPy #NumPy