
SciPy 1.0: fundamental algorithms for scientific computing in Python - jonbaer
https://www.nature.com/articles/s41592-019-0686-2
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dragonsh
> _Our policy for evolving the API over time is that new functionality can be
> added, while removing or changing existing functionality can only be done if
> the benefits exceed the (often significant) costs to users and only after
> giving clear deprecation warnings to those users for at least one year. In
> general, we encourage changes that improve clarity in the API of the library
> but strongly discourage breaking backward compatibility, given our position
> near the base of the scientific Python computing stack._

This is one of the nuggets out of it. I have been using Python with scipy and
more extensively with it's sci-kit library since 2014. Started with it by
solving discrete _traveling sales man_ problem for solution of sitting
arrangements and was astonished with its ease of use of matrix algebra with
this libraries and got hooked to it. It helped me to do scientific computing
and create a user friendly user interface on web to help end-user to just
focus on siting arrangement and conference Organization. Can solve it with
Matlab or GNU octave, but couldn’t have developed easily the whole system with
user interface in them.

If Swift wanted to be used in scientific computing it needs to follow the
similar policy of managing its scientific stack.

Julia is good for scientific computing, but it’s UI and web development
libraries are isn’t that powerful. So will see how it develops.

I constantly see on HN mostly criticism of Python and something rewritten in
Rust as something revolutionary or ground breaking that it will change the
whole world. Earlier this fad was with go language. Probably in time to come
Rust will be used for some core library development which brings substantial
benefits like scipy, not something rewritten as wrapper around C library with
unsafe code which can be done with Python much better.

