array-operations VS hissp

Personally, I've been relying on the stream-based method using py4cl/2, mostly because I did not - and perhaps do not - have the knowledge and time to dig into the CFFI based method. The limitation is that this would get you less than 10000 python interactions per second. That is sufficient if you will be running a long running python task - and I have successfully run trivial ML programs using it, but any intensive array processing gets in the way. For this later task, there are a few emerging libraries like numcl and array-operations without SIMD (yet), and numericals using SIMD. For reasons mentioned on the readme, I recently cooked up dense-arrays. This has interchangeable backends and can also use cl-cuda. But barring that, the developer overhead of actually setting up native-CFFI ecosystem is still too high, and I'm back to py4cl/2 for tasks beyond array processing.

array-operations - a collection of functions and macros for manipulating Common Lisp arrays and performing numerical calculations with them. [MIT][200].

Hissp is a Python library that can compile a whole program into one Python expression.

If you want Python to be as customizable as Lissp, check out Hissp (and Hebigo).

Hissp is really cool for metaprogramming Python. There's also macropy, but it's harder to use.

I prefer https://github.com/gilch/hissp, where Hy has to use shims to pretend statements are expressions, Hissp just targets the expression subset in the first place. (though as you mentioned, hy has a lot of literature and support around it, where as you're going to have to find your own way around hissp)

No shortage of options, e.g. Dg, Mochi, Coconut, and Hebigo (based on Hissp[1]).

[1]: https://github.com/gilch/hissp

If you want Lisp metaprogramming plus Python ecosystem, check out Hissp

Hissp is really interesting. Read through the docs and you'll understand Python more deeply. It works well with Toolz and Pyrsistent.