[sbinet]

others have already mentioned Gonum:

- https://gonum.org

Gonum is almost on par with _e.g._ NumPy/SciPy (most notably lacking: ODEs). So still ways to Go but it's getting there.

Go-HEP is my attempt to bring a few High Energy Physics oriented packages to particle physicists:

- https://go-hep.org

I've also written a few words on why I think Go is great for science:

- https://sbinet.github.io/posts/2018-07-31-go-hep-manifesto/

TL;DR: Go is great b/c it brings great s/w engineering practices and a s/w engineering-friendly environment to scientists.

Admittedly, generics will change how packages are written. So some code churn will take place when/if they land, but the Go community learned the lessons from Python2/3 and Perl5/6. Expect a better migration path.

Lastly, I guess the 2 remaining weak points of Go are:

- runtime performances sub-par wrt C++ or Rust

- GUIs (which may or may not fall into "interactive visualization")

That said, the Go community worked on a Go kernel for Jupyter:

- https://github.com/gopherdata/gophernotes

- http://gopherdata.io/

hth, -s

[tptacek]

Gonum is neat, but to the previously-made point about Go's type system making stuff more painful than it needs to be in this application: gonum's linear algebra is defined over float64 and int, which is problematic if you need arbitrary precision.

[sbinet]

I guess it's scientific domain dependent. I've very rarely needed something else than float/double in my C++ days or REAL*8 in my F77 ones.

sure, when you need it, you need it. but float64 caters for a good 99% of my usual work day.

From a user POV, seamless installation of packages is a great boon. From a grid/cloud operator POV, static binaries are great too.

[mbo]

What's wrong with Julia, a language purpose built for scientific computing?

[chewxy]

There's Gorgonia too! :)