[SciPy-user] Pros and Cons of Python verses other array environments
Steven H. Rogers
steve at shrogers.com
Sat Sep 30 09:50:28 CDT 2006
I believe the weaknesses you list are well understood and are being
addressed. I don't think it fair to say that SciPy looks like alpha
software. It may not have a slick interface suitable for the most naive
users, but it is quite powerful.
You'll have to decide about the "safety" of recommending Python/NumPy/SciPy
for your organization. I introduced Python into an engineering organization
about six years ago as a scripting language for a piece of equipment we
were developing and am just beginning to introduce NumPy. It's been a bumpy
road, but Python has gained wider adoption than I expected. Many of our
engineers can write an FFT in C or assembly, but balked at Python. Once
they really used it, most grew to like it and only drop down to lower level
code when necessary.
I'd suggest looking for a niche requirement that SciPy can fill as a
starting point. If it does well there, you can expand to other niches. If
not, the risk should be minimal.
David Finlayson wrote:
> I'm a lurker on this list, but this thread peaked my interests. I am a
> hydrographer (coastal mapping) not a computer scientist and I don't have
> much training in numerical computation (I did take the token applied
> math class in Matlab of course). So, my perspective on numeric Python is
> as an end user in a production environment. To me (and most of the
> people I support), data analysis environments like Matlab are black
> boxes. Maybe I am not the numeric Python target audience.
> We depend extensively on Matlab to do data analysis and plotting on our
> team. The vast majority of the scientists I work with struggle with
> programming and hand coding an FFT in FORTRAN would be impossible (for
> example). Matlab, or something like it is a necessary tool. Why not
> numeric Python?
> In a nutshell, it still looks like alpha software compared to Matlab.
> Documentation is not ready for end users (and not professionally
> published). Some of the numeric libraries have been around for ages, but
> that only adds to the confusion because there are numerous packages
> spread all over the Internet with a chain of dependencies that adds
> still more confusion. It still looks like a patchwork quilt rather than
> an organized system. Finally, most production environments outside of
> academia use Windows or maybe OSX. That means that (a) there is no
> compiler, its batteries included or its dead; and (b) there is a real
> need for an integrated environment because Emacs doesn't come installed
> on Windows.
> Python itself is making great headway on Windows at least. In my field
> (mapping), the big commercial vendor included Python as its macro
> language, so there has been an explosion of interest in python
> scripting. Recent publishing of IronPython for .NET was fully supported
> by Microsoft and there is every reason to believe that it will be a
> popular way to script and control the .NET framework. So, I think that
> average engineers and scientists are aware of Python the language and
> would be receptive to a data analysis package in Python so long as it
> was polished and well done. For example, the R statistical language has
> done a good job of packaging up R for Windows (I wish it were as well
> integrated into Gnome).
> I am not trying to take pot shots at numeric python here or FOSS or
> Linux. I use all of these personally. I just can't convince myself that
> this is a safe recommendation for folks I support.
> David Finlayson
Steven H. Rogers, Ph.D., steve at shrogers.com
"He who refuses to do arithmetic is doomed to talk nonsense."
-- John McCarthy
More information about the SciPy-user