On Wednesday, 5 August 2015 at 18:49:21 UTC, bachmeier wrote:
Yes. The question is whether we can put together a group of developers to build the infrastructure, which is a lot more than just code. That means, in particular, good documentation and using it for our own projects.
Right on! I would be willing to help with documentation if there were a concerted effort in this direction. There have been a number of failed individual efforts over the years. So how can a group effort be promoted?
Is the Dscience github project an adequate platform? How can other people get involved? Is a dedicated discussion group needed? Can we develop a plan of some sort rather than just a scatter of individual efforts?
Everyone these days talks about how Python is a powerhouse scientific programming language. A decade ago it was crap. I know, because I watched it for years wishing I could use it. There were some poorly documented, domain-specific, hacked-together libraries, but Python was not for the most part a suitable choice.
I started with Python in the years of the Numeric+numarray->NumPy transition. It was messy. Personally I think a unified library like NumPy, to underpin other more specialized libraries, is of paramount importance to any success of D in science.
Ideally there would be a NumPy/ndarray usage-compatible module in D. That would make D much more attractive to potential Python converts and lower the entry barrier considerably.
