foundOnTheWeb by rjb & Helma

Displaying items 1 - 5 of 212:

• The Hitchhiker’s Guide to Python! 
  This opinionated guide exists to provide both novice and expert Python developers a best-practice handbook to the installation, configuration, and usage of Python on a daily basis.
  Added: Thu May 10 2012; URL: http://docs.python-guide.org/en/latest/index.html; id = 212
• The Julia Language 
  Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments. It provides a sophisticated compiler, distributed parallel execution, numerical accuracy, and an extensive mathematical function library. The library, mostly written in Julia itself, also integrates mature, best-of-breed C and Fortran libraries for linear algebra, random number generation, FFTs, and string processing. More libraries continue to be added over time. Julia programs are organized around defining functions, and overloading them for different combinations of argument types (which can also be user-defined).
  Added: Sun Apr 22 2012; URL: http://julialang.org/; id = 211
• An introduction to scientific computing with Python 
  This course will give a general introduction to Python programming, useful for all physics postgrads. (PDF slides)
  Added: Wed Apr 18 2012; URL: http://stevenbamford.com/teaching/python_mpags_2011/; id = 210
• Numeric Javascript 
  Numeric Javascript is a library for numerical computations in Javascript. You can write Javascript programs that manipulate matrices, solve linear problems, find eigenvalues and solve optimization problems in the browser using Numeric Workshop or in your own web pages by downloading numeric.js. You can also use Numeric Javascript outside the browser in any Javascript environment.
  Added: Fri Feb 03 2012; URL: http://www.numericjs.com/; id = 209
• pandas: powerful Python data analysis toolkit 
  pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way toward this goal.
  Added: Sat Dec 24 2011; URL: http://pandas.sourceforge.net/; id = 208