[Numpy-discussion] numpy's future (1.1 and beyond): whichdirection(s) ?

[Nadav Horesh]

I would like to see a unification of matrices and arrays. I often do calculation which involve both array processing and linear algebra, and the current solution of having function like dot and inv is not aesthetic. Switching between array and matrix types (or using .A attribute of a matrix) is not convinient either.

   Nadav.



-----הודעה מקורית-----
מאת: numpy-discussion-bounces at scipy.org בשם David Cournapeau
נשלח: ו 21-מרץ-08 06:35
אל: Discussion of Numerical Python
נושא: [Numpy-discussion] numpy's future (1.1 and beyond): whichdirection(s) ?
 
Hi,

	numpy 1.0.5 is on the way, and I was wondering about numpy's future. I
myself have some ideas about what could be done; has there been any
discussion behind what is on 1.1 trac's roadmap ? Some of the things I
would like to see myself:
	- a framework for plug-in architecture, that is the ability for numpy
to load/unload some libraries at runtime, plus a common api to access
the functions. Example: instead of calling directly atlas/etc..., it
would load the dll at runtime, so that other libraries can be loaded
instead (numpy itself could load different runtimes depending on the
CPU, for example: SSE vs SSE2 vs SSE3, multi-thread vs non
multi-thread). That would require the ability to build loadable
libraries (numscons, or a new numpy.distutils command).
	- a pure C core library for some common operations. For example, I
myself would really like to be able to use the fft in some C extensions.
Numpy has a fft, but I cannot access it from C (well, I could access the
python fft from C, but that would be... awkward); same for blas/lapack.
I really like the idea of a numpy "split" into a core C library reusable
by many C extensions, and python wrappers (in C, cython, ctypes,
whatever). That would be a huge work, of course, but hopefully can be
done gradually and smoothly. Only having fft + some basic blas/lapack
(dot, inv, det, etc...) and some basic functions (beta, gamma, digamma)
would be great, for example.
	- a highly optimized core library for memory copy, simple addition,
etc... basically, everything which can see huge improvements when using
MMX/SSE and co. This is somewhat linked to point 1. This would also
require more sophisticated memory allocator (aligned, etc...).

What do people think about this ? Is that a direction numpy developers
are interested in ?

cheers,

David

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