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There are two applications that require using the NumPy array type in C extension modules: Access to numerical libraries: Extension modules can be used to make numerical libraries written in C (or languages linkable to C, such as Fortran) accessible to Python programs. The NumPy array type has the advantage of using the same data layout as arrays in C and Fortran. Mixed-language numerical code: In most numerical applications, only a small part of the total code is CPU time intensive. Only this part should thus be written in C, the rest can be written in Python. NumPy arrays are important for the interface between these two parts, because they provide equally simple access to their contents from Python and from C. This document is a tutorial for using NumPy arrays in C extensions.

What's the relation between these benefits and Stackless's implementation details? Here's a quick sketch: Continuations are the general-purpose concurrency construct. A continuation represents all the future computations of a particular program. Capturing all this control flow in a single conceptual object makes it programmable: It becomes possible to calculate or reason over the control flow. In particular, there's great scope for optimizing assignment of different calculations to different processes or threads or even hosts.

PQRC-2.4-A4-latest.pdf 30-Apr-2007 14:02 599K

""" a class for generating permutations from an array # choose 2 letters from 'abcd' c = Chooser(['a', 'b', 'c', 'd'], 2) while c.hasMoreElements(): print c.next() """ import time, copy class NoMoreChoices(StandardError): pass class Chooser:

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