[Numpy-discussion] Newbie Question, Probability
svetosch at gmx.net
Sun Dec 24 03:54:52 CST 2006
Robert Kern schrieb:
> Rather, to put it accurately, numpy should not get large chunks of scipy
> functionality that require FORTRAN dependencies for reasons that should be
> obvious from that description. scipy.stats.distributions is just such a chunk.
I was probably not very clear, I was referring to "small" functions. As
you and others have pointed out, for p-value stuff this doesn't apply
> The ancillary point is that I think that, for those who do find the largeness
> and difficult-to-installness of scipy onerous, the best path forward is to work
> on the build process of scipy. And it will take *work* not wishes nor complaints
> nor <irony/> tags. And honestly, the more I see the latter, the less motivated I
> am to bother with the former.
My impression of the discussion was that many people said _nothing_ at
all should be added into numpy ever, which sounded kind of
fundamentalistic to me. And partly the given justification were some
features of scipy that don't exist yet (fair enough), and that nobody is
even working on. Of course I understand the lack of manpower, but then I
think this state of affairs should be properly taken into account when
arguing against moving (only small!) features from scipy into numpy.
Hence my earlier post.
I also try to contribute to open-source projects where I can, and
believe me, it would probably help my career more to just have my
faculty pay for matlab and forget about numpy et al. You know, users'
time is valuable, too. Unfortunately I don't have the skills to help
with modularizing scipy (nor the time to acquire those skills).
Btw, that's why I like the idea of paying for stuff like documentation
and other things that open-source projects often forget about because
it's not fun for the developers. (Hey, I'm an economist...) So I would
be willing to donate money for some of the dull tasks, for example. (I'm
fully aware that would not cover the real cost of the work, just like in
Travis' case with the numpy guide.)
Ok, that's enough,
More information about the Numpy-discussion