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Safari | Python Developer's Handbook -> Scientific Computing





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Scientific Computing

Python is extensively used for scientific computing because it enables a rapid prototyping and execution of a number of functions. Scientists and engineers often have needs for high-performance computation tools that are also easy to use and modify. Many also want to be able to use a general-purpose language instead of a specialized tool, allowing them to integrate networking, GUI's, and so on in their high-performance work. Several modules have been developed to address these needs around the Python language.

In this section, I cover the Numeric Python extensions (NumPy), which provide efficient operations on large multidimensional arrays, and it has proven to be the right choice when talking about scientific computing with Python.

Besides NumPy, many other scientific tools are available. The Python community has created several extensions for manipulating data and functions, interfaces to data plotting libraries, storage solutions for scientific data, and much more. If you want to deeply discuss scientific computing with Python, you can look for the plot-sig (the Plotting Special Interest Group).

If you spend some time browsing around scientific Web pages, you will be surprised about the number of people who are really using Python for their projects.

For more information, visit the following Web sites:

Scientific computing topic at Python's Web site:

								
							http://www.python.org/topics/scicomp/
						
							

Simple Numerical Recipes in Python was written by William Park to describe few elementary numerical routines in Python:

								
							http://www.python.org/topics/scicomp/recipes_in_python.html
						
							

Python for ScienceAn Introduction to Python for Scientists:

								
							http://starship.python.net/crew/hinsen/
						
							

Numerical Extensions

The most powerful way to face scientific computing in Python systems is to use Python Numerical Extensions (commonly known as NumPy). The Numerical Python extensions were originally written by Jim Hugunin (JPython's author), but the responsibility to continue the project now belongs to a group of python users from the Lawrence Livermore National Laboratory. The languages that were used to guide the development of NumPy include Basis, MATLAB, FORTRAN, S and S+, and others.

The NumPy package adds a fast, compact multidimensional array language facility to Python. One-dimensional arrays are similar to standard Python sequences and two-dimensional arrays are similar to matrices from linear algebra. This package also includes tools for working with linear algebra, Fast Fourier Transforms (FFTs), random numbers, and so forth.

In addition, NumPy adds two new types to Python: A sequence type (to implement multidimensional arrays), and a new type of function called a universal function (ufunc). Numeric Python consists of a set of modules:

Numeric.py (and Its Helper Modules multiarray, umath, and fast_umath)

This module defines two new object types and a set of functions that manipulate these objects, as well as converting them and other Python types. The objects are the new array object (technically called multiarray objects), and universal functions (technically ufunc objects). The array objects are generally homogeneous collections of potentially large numbers of numbers. Universal functions (ufuncs) are functions that operate on arrays and other sequences.

The Numeric module provides, in addition to the functions needed to create the previous objects, a set of powerful functions to manipulate arrays, select subsets of arrays based on the contents of other arrays, and other array-processing operations. Note that only Numeric need be imported.

RandomArray.py (and Its Helper Module ranlib)

This module provides a high-level interface to a random-number generator (ranlib), which supplies a uniform distribution generator of pseudo-random numbers, as well as some convenience functions:

For more information, check out Additions to RandomArray Module, by Lee A. Barford:

										
									http://numpy.sourceforge.net/RandomArray-additions.html
								
									

FFT.py (and Its Helper Module fftpack)

This module provides a high-level interface to the fast Fourier transform routines implemented in the FFT-PACK library if it is available, or to the compatible but less optimized fftpack library that ships with Numeric Python.

The FFT module provides a high-level interface to the fast Fourier transform routines, which are implemented in the FFTPACK library. It performs one- and two-dimensional FFT's, forward and backwards (inverse FFTs), and includes efficient routines for FFTs of real-valued arrays. It is most efficient for arrays whose size is a power of two.

LinearAlgebra.py (and Its Helper Module lapack_litemodule)

This module provides a high-level interface to the linear algebra routines implemented in the LAPACK library if it is available, or to the compatible but less optimized lapack_lite library that ships with Numeric Python. It includes functions to solve systems of linear equations and linear least squares problems, invert matrices, compute eigenvalues and eig ... ... (Not finished, please download and read the complete file)

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