As many of you know, Travis has been working heroically on the next
generation Numeric, now called numpy
numpy's goal is to combine the best of Numeric and numarray and unify
the two camps of array users. Having two array packages has been a
major drag on scientific python, since packages available for one may
not work with the other, splitting developer resources and driving
folks to Ruby So getting everyone to agree on and use a single
array package will be a major step forward, and Travis has been
working with the numarray camp to make sure that numpy will have all
the features they need. As I understand it, Perry and Todd and crew
have decided to transition to numpy as it becomes feasible for them.
The latest release of matplotlib (many thanks to Charlie Moad for
building and testing across platforms) supports numpy in the numerix
layer (set numerix in your matplotlibrc file), and the windows build
has support for all three packages (Numeric, numarray and numpy) built
I encourage everyone to download the latest numpy and give it a test
drive with matplotlib, since ultimately we would like to stop
supporting three array packages and just adopt one. This will
significantly reduce compile times and binary distribution sizes, and
will make the code and build process cleaner. We don't have a
specific roadmap for when the numerix layer will become deprecated,
but we would like it to happen sooner rather than later.
You can profile your matplotlib scripts by using the numerix flags
from the prompt, eg
> time python myscript.py --numpy
> time python myscripy.py --Numeric
> time python myscripy.py --numarray
Also, note that when building matplotlib from source, the matplotlibrc
file is now autogenerated, setting the backend and numerix setting
depending on what was found at compile time. So make sure the
generated rc file is what you want, and if not edit it and move it to
your ~/.matplotlib dir.
Travis has written an excellent guide to numpy that can be purchased
at http://www.tramy.us . If you can afford it, it's a great way to
learn all the new features and support Travis (and his 5 kids!) and
scipy development. I think he will give discounts to the financially
challenged, so don't hesitate to contact him about it.
Thanks to Daishi, Eric and Andrew for their work on the numpy support.