SciD: the humble beginning

[dsimcha]

== Quote from Lars T. Kyllingstad (public at kyllingen.NOSPAMnet)'s article
> I've just created a new dsource project and uploaded my collection of
> numerical routines. You can find it here:
>    http://www.dsource.org/projects/scid
> Hopefully others will find it useful as well. Most of the code is
> written with a "get it to work - NOW" philoshophy, and as such there is
> a lot of potential for improvement and polish, not least when it comes
> to performance. I am aware of this, so there is really no need to
> benchmark it against established scientific libraries quite yet. ;) More
> routines will be added as I write them (which is usually when I need
> them for work), and the existing ones will be polished whenever I have
> time to do so.
> Huge thanks to Bill Baxter for letting me use his BLAS and LAPACK bindings!
> Requirements:
>    DMD 2.037
>    BLAS and LAPACK libraries
> -Lars

This looks absolutely terrific given that it's a work in progress and all.  I
agree that we shouldn't be too obsessed with performance yet because:

1.  **Make it work**, make it right, make it fast.  We can always optimize it
later provided that there aren't any ridiculously bad design decisions.
2.  At least some of the time people (myself included) would rather a nice API and
decent performance than an ugly API (read:  a straight Fortran or C API) and
lightening quick performance.

We've got the beginnings of a good matrix/numerics lib in your work.  I believe
that my dstats lib would provide just about all the statistical functionality a
good scientific lib would need, and I'm getting close to declaring it
beta-quality.  I'd say based on a quick look at what scipy has that we also need
the following for a credible full-fledged scientific lib:

Machine learning, i.e. classification and clustering.  (I'd probably be qualified
to write that and was halfway thinking of breaking ground on it over Christmas
break.  However, I'm not committing to this yet, so if someone else already has a
work in progress, let me know.  Also, there's a Java machine learning library
called Weka that I've used before.  It's very complete but the API is painful and
it's GPL, so IDK if it'd be worth it to port to D.)

Optimization.  (A few people have tried but IDK if they've actually gotten far off
the ground with it.)

Basic image I/O and processing.

Plotting.  I've considered doing this a few times, but I've decided it needs to be
put off until D2 is stable and the GUI toolkits for it are reasonably stable.  One
layer of instability (D2 itself) is workable, but two layers (D2 and the GUI libs)
is not.