[majoe]

I had a similar problem, when I was working as a PhD student a few years ago, where I needed to match the voxel representation of a 3D printer with the tetrahedral mesh of our rendering application.

My first attempt in Python was both prohibitively slow and more complicated than necessary, because I tried to use vectorized numpy, where possible.

Since this was only a small standalone script, I rewrote it in Julia in a day. The end result was ca. 100x faster and the code a lot cleaner, because I could just implement the core logic for one tetrahedron and then use Julia's broadcast to apply it to the array of tetrahedrons.

Anyway, Julia's long startup time often prohibits it from being used inside other languages (even though the Python/Julia interoperability is good). On the contrary the Rust/Python interop presented here seems to be pretty great. Another reason I should finally invest the time to learn Rust.

[hgomersall]

Numba is great if you want to write a naive loop approach in python.

[dunefox]

Long startup time is relative. I believe it's much lower now than a couple of versions ago. 0.15s or so? Interop between python and rust will also take time.

[xgdgsc]

Julia 1.9 is fast. And you can use https://github.com/Suzhou-Tongyuan/jnumpy to write python extension in Julia now. So I think after 1.9 release julia would be much more usable.