Array Interface
Compiled Code
Older Packages
Download NumPy
Buy the Guide
NumPy
The fundamental package needed for scientific computing with Python is called NumPy. This package contains:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- basic linear algebra functions
- basic Fourier transforms
- sophisticated random number capabilities
- tools for integrating Fortran code.
- tools for integrating C/C++ code.
Numeric users should find the transition relatively easy (although not without some effort). There is a module (numpy.oldnumeric.alter_code1) that can make most of the necessary changes to your Python code that used Numeric to work with NumPy's Numeric compatibility module.
Users of numarray can also transition their code using a similar module (numpy.numarray.alter_code1) and the numpy.numarray compatibility layer.
C-code written to either package can be easily ported to NumPy using "numpy/oldnumeric.h" and "numpy/libnumarray.h" for the Numeric C-API and the Numarray C-API respectively. Sourceforge download site
There is a book for sale called "Guide to NumPy" which details the new system. Read the Sample Chapters
Questions? Ask them at the numpy-discussion@scipy.org mailing list.
Much of the documentation for Numeric and Numarray is applicable to the new NumPy package. However, there are significant feature improvements. A complete guide to the new system has been written by the primary developer, Travis Oliphant. If you want to fully understand the new system, or you just want to encourage further development on NumPy (or SciPy), you should purchase the documentation which is being sold for a relatively brief period of time to help offset the cost of writing the book and producing the Numeric/numarray hybrid, and to help raise money for future development. If the existence of this fee-based book concerns you, Travis has written some responses to FAQs.
Free Documentation is available at the scipy website and in the docstrings (which can be extracted using pydoc). Free Documentation for Numeric (most of which is still valid) is here or as a pdf file. Obviously you should replace references to Numeric in that document with numpy (i.e. instead of import Numeric, use import numpy).
For about 6 months at the end of 2005, the new package was called SciPy Core (not to be confused with the full SciPy package which remains a separate package), and so you will occasionally see references to SciPy Core floating around. It was decided in January 2006 to go with the historical name of NumPy for the new package. Realize that NumPy (module name numpy) is the new name. Because of the name-change, there were a lot of dicussions that took place on scipy-dev@scipy.org and scipy-user@scipy.org. If you have a question about the new system, you may wish to run a search on those mailing lists as well as the main NumPy list (numpy-discussion@lists.sourceforge.net)
The Array Interface
Out of developer discussions between developers of Numarray, Numeric,
and NumPy has arisen the concept of an array
interface. It is the
opinion of many involved that a default N-dimensional array interface
(even leading to a very simple N-dimensional array object and an
N-dimensional array protocol) is what should be pushed for placement in
the Python core. It is doubtful that the community
will want to slow down development of NumPy anytime soon by
placing it the Python core. But the array interface should become
a part of Python as soon as possible. Anybody wanting to push
this forward is welcome to help. The description of the array interface is here.
Interfacing to Compiled Code
There are several ways to use NumPy to interface with compiled code
including f2py, swig, ctypes, pyrex (cython), as well as the NumPy
C-API.
- f2py uses interface files which can be auto-generated and tweaked by hand to produce nice-looking interfaces to Fortran code.
- swig requires the numpy.i file in order to integrate with numpy. More information about the swig interface to numpy is here.
- ctypes is included in Python 2.5 and numpy has a ctypes attribute and a ctypes library which makes it easy to use NumPy array objects as generic chunks of memory for ctypes.
- pyrex is a Python-like language for writing C-extensions. Look at the mtrand code for an example of using NumPy arrays in pyrex extensions.
Older Array Packages
It may take months for the large code base that uses Numeric and/or Numarray to transition to the new NumPy system. Links to the older packages are provided here. New users should start out with NumPy.Numeric (version 24.2)
Numeric was the first arrayobject built for Python. It has been
quite successful and is used in a wide variety of settings and
applications. Maintenance
has ceased for Numeric, and users should
transisition to NumPy as quickly as possible. There is
a module called numpy.oldnumeric.alter_code1 in NumPy that can make the
transition to
NumPy easier (it will automatically perform the search-and-replace
style changes that need to be made to python code that uses Numeric to
make it work with
NumPy). Documentation for Numeric is here or as a pdf file
Sourceforge Numeric Download Page
Numarray
Numarray is another implementation of an arrayobject for Python written after Numeric and before NumPy. Sponsors of numarray have indicated they will be moving to NumPy as soon as is feasible for them so that eventually numarray will be phased out (probably sometime in 2007). This project shares some of the resources with the Numeric sourceforge site but maintains its own web page at http://www.stsci.edu/resources/software_hardware/numarraySourceforge Numarray Download Page
Links
NumPy Sourceforge Home PageSciPy Home Page
For web-page improvements, send mail to Travis Oliphant: oliphant.travis@ieee.org