Sponsorised links
October 2007
Veusz: a scientific plotting package written in Python.
by pvergain
Veusz is a scientific plotting package written in Python. It uses PyQt (Wiki) and Numpy. Veusz is designed to produce publication-ready Postscript output.
July 2007
Elefant - What is Elefant
by ogrisel
Elefant (Efficient Learning, Large-scale Inference, and Optimisation Toolkit) is an open source library for machine learning
Elefant include modules for many common optimisation problems arising in machine learning and inference. It is designed to be modular and easy to use. Framework provides easy to use python interface, which can be use for quick prototyping and testing inference algorithms.
CVXOPT: A Python Package for Convex Optimization — CVXOPT
by ogrisel
CVXOPT is a free software package for convex optimization based on the Python programming language. It can be used with the interactive Python interpreter, on the command line by executing Python scripts, or integrated in other software via Python extension modules. Its main purpose is to make the development of software for convex optimization applications straightforward by building on Python's extensive standard library and on the strengths of Python as a high-level programming language.
Sponsorised links
October 2006
PerformancePython - SciPy.org
by YukuanMark
A comparison of weave with NumPy, Pyrex, Psyco, Fortran and C++ for solving Laplace's equation. This article was originally written by Prabhu Ramachandran.
September 2006
17. RandomArray Reference
by YukuanMark
The RandomArray.py module (in conjunction with the ranlibmodule.c file) provides a high-level interface to the ranlib module, which provides a good quality C implementation of a random-number generator.
NumPy for Matlab Users - SciPy.org
by YukuanMark
MATLAB® and NumPy/SciPy have a lot in common. But there are many differences. NumPy and SciPy were created to do numerical and scientific computing in the most natural way with Python, not to be MATLAB® clones. This page is intended to be a place to col
April 2006
1
(10 marks)
