hy VS prysm

Not exactly the same (doesn't embed into the source like this did), but I believe Hylang[0] is the best Lisp package available for modern Python.

[0] https://github.com/hylang/hy

Isn't that a bit what hy (https://hylang.org/) tries to do ? AIUI it is a lisp interacting directly with the AST of Python, allowing seamless interop: Python modules can be used from hy and vice versa, everything is transparent.

I’ve been keeping loose tabs on this and Hy[1] for a while, but I’ve had some trouble figuring out the major differences between them and the use-cases for either. Would love to see an in-depth comparison in the form of a blog post sometime (though maybe the answer here is to do the research and write one up myself).

1: https://hylang.org

“Python is for scientists. Lisp is for engineers.”

Then what does that make Hy language?

https://hylang.org/

Re Languages with lots of example code and LLM’s

With translators or things like Hy lang, one could get the LLM’s to solve your problem in Python before converting it to another form. Then, you just need a translator. If lacking one, it’s easy to translate by hand.

The practicality of this concept will probably vary by use case. My experiments had GPT doing sketching, implementations, boilerplate, and even porting Python to Rust. A legally-clear LLM trained on multiple languages could probably be fine-tuned to do Python to LISP conversions. If not, Hy might be a stepping stone, too.

You could say so: I've been maintaining the compiler since 2016 ;). Infinitesimal Quest 2 + ε (SQ) exists more to advance Hy than for its own sake.

If you are using Python - you might find Hylang (https://hylang.org) interesting.

My current concept is to just combine zernike polynomials with a random factor and calculate the PSF from that, which can be somewhat easily be done with the prysm library. These PSFs can then be convolved with circular and gaussian kernels for modelling additional defocus and accounting for other stuff like the AA filter. Then I'd add chromatic aberration by offseting/scaling the PSFs for each channel. Some generated kernels already look pretty good when comparing them to stars in astrophotography images, but others not so much.

https://github.com/brandondube/prysm (caveat emptor: mine)

You can trace about 1 billion raysurfaces per second in pure python with CuPy, or a few million raysurfaces per second on CPU.

New paper from /u/BDube_Lensman using prysm to model NASA's Roman Coronagraph

This free book is what this free code is based on

Prysm Originally for diffraction type optics but seems to able to handle...everything? Performance as a priamary concern, GPU acceleration, proven JPL heritage :) Raytracing is however still experimental and without docs, generally whilst the library looks excellent if you're an optics person already I think I lack a bit of the base fundamental knowledge to really use it powerfully from just the API reference. I can see BDube has some raytracing example code in some of the issues I could probably adapt and muddle my way through at least. No guis is mildly annoying for a noob like myself, but I can work my way around matplotlib-ing just fine instead i'm sure.

You may want to steal my shim set since it lets you hot swap Numpy<-->cupy at runtime

I do computational diffraction with large manycore servers and GPUs at a FFRDC. The difference between MKL and not MKL is the difference between hitting enter and getting a result in an hour or two vs tomorrow.