Comparing Python Type Checkers: Typing Spec Conformance

Wow, quite surprising results. I have been working on a personal project with the astral stack (uv, ruff, ty) that's using extremely strict lint/type checking settings, you could call it an experiment in setting up a python codebase to work well with AI. I was not aware that ty's gaps were significant. I just tried with zuban + pyright. Both catch a half dozen issues that ty is ignoring. Zuban has one FP and one FN, pyright is 100% correct.

Looks like I will be converting to pyright. No disrespect to the astral team, I think they have been pretty careful to note that ty is still in early days. I'm sure I will return to it at some point - uv and ruff are excellent.

Article is a nice write up of https://htmlpreview.github.io/?https://github.com/python/typ...

(glad they include ty now)

Using VSCodium I was having issues with python type checkers for quite a while. I did the basedpyright thing for a while but that was painful. It's a bit too based for me, and I'm not sure i'd call it based. Right now I have uv, ruff, and ty and I'm happy with it. It's super easy to update and super fast. I didn't realize the coverage wasn't as good as some others but I still like it. I may have to try pyrefly. Never heard of it until this post, so thank you.

I've been using ty on some previously untyped codebases at work. It does a good job of being fast and easy to use while catching many issues without being overly draconian.

My teammates who were writing untyped Python previously don't seem to mind it. It's a good addition to the ecosystem!

Are there any good static (i.e. not runtime) type checkers for arrays and tensors? E.g. "16x64x256 fp16" in numpy, pytorch, jax, cupy, or whatever framework. Would be pretty useful for ML work.

I still can't get over the utter idiocy in Python's type hints being decorative. In what world does x: int = "thing" not give someone in the standardisation process pause?