[SciPy-dev] SciPy Foundation
Sat Aug 1 07:47:36 CDT 2009
On Sat, Aug 1, 2009 at 5:57 AM, Dag Sverre
> I am going to play the devil's advocate here -- I'm not into this in order
> to make myself enemies, I just have some sincere questions.
> Joe Harrington wrote:
>> I define success as popular adoption in preference to commercial
>> packages. I believe in vote-with-your-feet: this goal will not be
>> reached until all aspects of the package and its presentation to the
>> world exceed those of our commercial competition. Scipy is now a
>> grass roots effort, but that takes it only so far. Other projects,
>> such as OpenOffice and Sage, don't follow this model and do produce
>> quality products that compete with commercial offerings, at least on
>> open-source platforms. Before we can even hope for that, we have to
>> do the following:
>> - Public communication
>> - A real marketing plan
>> - Executing on that plan
>> - Web site geared toward multiple audiences, run by experts at that
>> kind of communication
>> - More webinars, conference booths, training, aimed at all levels
>> - Demos, testimonials, topical forums, all showcased
> A thing OpenOffice.org and Sage both have is a very clear sense of
> direction and a clearly stated goal.
> SciPy might also have that for all I know, but I must admit I haven't
> understood what it is in the past year following the SciPy and NumPy
> lists, and reading the SciPy site. But I have seen email threads asking
> what the SciPy goal is, without any clear resolution (?).
> The website says this: "SciPy is open-source software for mathematics,
> science, and engineering."
> Which of course says nothing at all. Someone asked me what SciPy is the
> other day, and while I more or less "know" when I'd try to look in SciPy
> for an algorithm (instead of going to, say, R, or netlib.org, or
> whatever), I was more or less forced to say that it is a "dumping ground
> for various algorithms people have found useful, with the link being them
> being either written in Python or wrapped for Python".
I think scipy is a pretty much the same as a collection of matlab tool
boxes, either with more enhanced basic numerical algorithms (linalg,
special, optimize, interpolate, sparse, fft, spatial) or toolboxes
with wider applicability (stats including cluster, odr and maxentropy,
signal, ndimage+stsci?). This misses weave.
Which algorithms are actually included and some of the structure still
reflects the "dumping ground
for various algorithms people have found useful". And some parts
don't look very used.
There is still a lot of cleaning and testing to do, but the
description as analogy to matlab toolboxes is pretty accurate, if a
description by analogy is allowed. E.g. to understand more of
scipy.signal, I started to read the help for matlabs signal toolbox.
That's my impression of scipy after working my way through some parts
of it in the last year.
> That's probably an unfair description -- the point is: If one needs to
> formulate a two- or three-liner about SciPy, what would it be? Is it a
> goal to reimplement stuff in SciPy that's (for instance) already thriving
> in the open source R community, or is that not a goal? And so on.
For stats, I consider matlab and maybe gauss for econometrics as
benchmark, not the coverage of a specialized language/package like R,
but I'm no statistician and I don't know anyone personally that uses
> You might feel this is going off-topic, but I somehow feel that a very
> clear sense of direction is paramount when talking of these issues -- just
> look at the Sage project.
> Dag Sverre
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