dd55 at cornell.edu
Sun Feb 6 08:17:30 CST 2005
As a relatively new user, I would like to contribute my experience to the
discussion. Late in a graduate program, I caught the bug and wanted to try
out python. Being established in Matlab, I was hoping to find a similar
collection of capabilities for Python, something that I could experiment with
and start learning fairly quickly. When I discovered Matplotlib, I felt home
free. Without too much effort, I had a plot on screen and felt 'ok, this is
feasible.' With the recent effort from Matplotlib and IPython, this should be
even more true for newcomers.
On the downside, I have been somewhat confused by the state of things
concerning Numeric and numarray. I think I can trace this confusion back to
the pfdubios.com/numpy site (linked from numpy.sourceforge.net), which is the
first hit I get doing a google search for either numpy or numerical python.
"If you are new to Numerical Python, please use Numarray. The older module,
Numeric, is unsupported." There is no date associated with the site, but it
still advertises release 22.0. In the near future, I strongly suggest editing
websites for misleading information.
So now I have both Numeric and numarray on my system, linked to the full,
atlas-tuned, lapack and blas libraries. I started out using numarray,
thinking it was in my long term interest, but I guess this is open to debate.
I was also hesitant to use scipy, because it relied on Numeric. Being an
uninformed newbie, I just wasnt able to make an informed decision (I am still
struggling with it).
A lot of what I have read recently is very encouraging to me. I like the idea
of bridging the API gap between numarray and Numeric. I like the idea of a
multidimensional array getting into the core, something the community can
agree on. I dont care what array package I am using, I just want it to be the
accepted standard. I would like to hear what the numarray guys have to say
Finally, I like the idea of scipy. I hope the community will come to an
agreement and work within a framework that will result in the most efficient
use of everyones time, the most logical interplay between packages, the least
redundancy, and therefore the most gentle barrier to newcomers who want to
experiment with scientific computing in Python.
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