From: A. M. A. <per...@gm...> - 2006-10-05 20:13:14
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On 05/10/06, Greg Willden <gre...@gm...> wrote: > On 10/5/06, Travis Oliphant <oli...@ee...> wrote: > > Perhaps that is the best way to move forward along with the work on a > > "pylab" super-package. > > That is exactly what I want. What is unsatisfactory about installing numpy+scipy+matplotlib? I've found they're generally pretty complete (except where no decent python alternative exists). > In the end I want a nice collection of functions, logically organized, that > let me analyze/filter/plot etc. etc. etc. > > The key for me is "logically organized". For the most part, the organization is pretty logical: * Basic array and matrix operations in numpy * linear algebra, differential equation, interpolation, etc. tools are in scipy, each in their own subpackage * weave is mysteriously in scipy * plotting tools are in matplotlib There are a few historical quirks, like window functions in numpy (they really belong in scipy), and some of the less-used scipy subpackages are a bit of a mess internally (scipy.interpolate for example), but otherwise I'm not sure what you want to be different. > And right now that means "So a newbie can find the function I need and the > function I need is there" > > I'm not criticising. I'd like to help get there. Install all three major packages. Use the window functions from scipy in matplotlib. Task-oriented documentation is so far a bit scant, although the scipy cookbook (http://www.scipy.org/Cookbook ) and the numpy examples list (http://www.scipy.org/Numpy_Example_List ) are a good start. A. M. Archibald |