JRuby VS NumPy

As someone who has looked at Shoes several times but never dove in, it's confusing how Shoes 4 has been the "preview version" of Shoes for, like, a decade or more. It made me actively avoid getting invested in Shoes 3 (the release promoted on the linked website) because Shoes 4 requires JRuby and I am happy with CRuby (the Ruby interpreter most people think of when they hear "Ruby").

https://github.com/shoes/shoes4/

http://www.rubydoc.info/github/shoes/shoes4

No disrespect to the developers but to me it feels like taking over a GUI toolkit created "to teach programming to everyone" (to quote the Shoes 4 readme) and making it depend upon a super-complicated enterprise-focused Ruby was sort of Missing The Point™ in a huge way.

Heck I couldn't even switch to JRuby if I wanted to because I <3 Ractors and JRuby still lacks CRuby 3.0 feature parity: https://github.com/jruby/jruby/issues/7459

__callee__ now properly returns the name under which a method was called, which will be the new name in the case of aliased methods. #2305, #7702

Issue tracker: https://github.com/jruby/jruby/issues

rdoc has been updated to 6.3.3 to fix all known CVEs. (#7396, #7404)

Altering the visibility of an included module method no longer changes what super method gets called. (#7240, #7343, #7344, #7356)

It looks like the readme is copy pasta from jruby: https://github.com/jruby/jruby

Homepage: https://www.jruby.org/

Chris: Yeah, that's exactly right. So I was working at Sun at the time. I remember the JRuby guys. I saw them speak at one of the Java conferences, and they came to work for Sun. Just listening to them talk about JRuby...and then a lot of it was obviously about Ruby on Rails at the time. And I was like, wow, this was just mind-blowing the way they talked about it.

In fact, I built its GUI twice with two different approaches, one using the up and coming Glimmer DSL for LibUI on CRuby relying on a multi-canvas-grid (LibUI area) approach, and one using the very mature Glimmer DSL for SWT on JRuby by relying on a button-grid approach.

In NumPy with @, dot() or matmul():

In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.

Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:

numpy: A library for scientific computing in Python

Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.

I was looking at all the big projects like numpy, pytorch, flask, etc.

I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]