We are very pleased to announce the release of NumPy 1.0 available for download at http://www.numpy.org
This release is the culmination of over 18 months of effort to allow unification of the Numeric and Numarray communities. NumPy provides the features of both packages as well as comparable speeds in the domains where both were considered fast --- often beating both packages on certain problems. If there is an area where we can speed up NumPy then we are interested in hearing about the solution.
NumPy is essentially a re-write of Numeric to include the features of Numarray plus more. NumPy is a C-based extension module to Python that provides an N-dimensional array object (ndarray), a collection of fast math functions, basic linear algebra, array-producing random number generators, and basic Fourier transform capabilities.
Also included with NumPy are:
1) A data-type object. The data-type of all NumPy arrays are defined by a data-type object that describes how the block of memory that makes up an element of the array is to be interpreted. Supported are all basic C-types, structures containing C-types, arrays of C-types, and structures containing structures of C-types. Data-types can also be in big or little-endian order. NumPy arrays can therefore be constructed from any regularly-sized chunk of data. A chunk of data can also be a pointer to a Python object and therefore Object arrays can be constructed (including record arrays with object members).
2) Array scalars: there is a Python scalar object (inheriting from the standard object where possible) defined for every basic data-type that an array can have.
2) A matrix object so that '*' is re-defined as matrix-multiplication and '**' as matrix-power.
3) A character array object that can replace Numarray's similarly-named object. It is basically an array of strings (or unicode) with methods matching the string and unicode methods.
4) A record array that builds on the advanced data-type support of the basic array object to allow field access using attribute look-up as well as to provide more ways to build-up a record-array.
5) A memory-map object that makes it easier to use memory-mapped areas as the memory for an array object.
6) A basic container class that uses the ndarray as a member. This often facilitates multiple-inheritance.
7) A large collection of basic functions on the array.
8) Compatibility layer for Numeric including code to help in the conversion to NumPy and full C-API support.
9) Compatibility layer for NumPy including code to help in the conversion to NumPy and full C-API support.
NumPy can work with Numeric and Numarray installed and while the three array objects are different to Python, they can all share each other's data through the use of the array interface.
As the developers for Numeric we can definitively say development of Numeric has ceased as has effective support. You may still find an answer to a question or two and Numeric will be available for download as long as Sourceforge is around so and code written to Numeric will still work, but there will not be "official" releases of Numeric for future versions of Python (including Python2.5).
The development of NumPy has been supported by the people at STScI who created Numarray and support it. They have started to port their applications to NumPy and have indicated that support for Numarray will be phased out over the next year.
You are strongly encouraged to move to NumPy. The whole point of NumPy is to unite the Numeric/Numarray development and user communities. We have done our part in releasing NumPy 1.0 and doing our best to make the transistion as easy as possible. Please support us by adopting NumPy. If you have trouble with that, please let us know why so that we can address the problems you identify. Even better, help us in fixing the problems.
New users should download NumPy first unless they need an older package to work with third party code. Third-party package writers should migrate to use NumPy. Though it is not difficult, there are some things that have to be altered. Several people are available to help with that process, just ask (we will do it free for open source code and as work-for-hire for commercial code).
This release would not have been possible without the work of many people. Thanks go to (if we have missed your contribution please let us know):
* Travis Oliphant for the majority of the code adaptation (blame him for code problems )
* Jim Hugunin, Paul Dubois, Konrad Hinsen, David Ascher, Jim Fulton and many others for Numeric on which the code is based.
* Perry Greenfield, J Todd Miller, Rick White, Paul Barrett for Numarray which gave much inspiration and showed the way forward.
* Paul Dubois for Masked Arrays
* Pearu Peterson for f2py and numpy.distutils and help with code organization
* Robert Kern for mtrand, bug fixes, help with distutils, code organization, and much more.
* David Cooke for many code improvements including the auto-generated C-API and optimizations.
* Alexander Belopolsky (Sasha) for Masked array bug-fixes and tests, rank-0 array improvements, scalar math help and other code additions
* Francesc Altet for unicode and nested record tests and much help with rooting out nested record array bugs.
* Tim Hochberg for getting the build working on MSVC, optimization improvements, and code review
* Charles Harris for the sorting code originally written for Numarray and for improvements to polyfit, many bug fixes, and documentation strings.
* Robert Cimrman for numpy.distutils help and the set-operations for arrays
* David Huard for histogram code improvements including 2-d and d-d code
* Eric Jones for sundry subroutines borrowed from scipy_base
* Fernando Perez for code snippets, ideas, bugfixes, and testing.
* Ed Schofield for matrix.py patches, bugfixes, testing, and docstrings.
* John Hunter for code snippets (from matplotlib)
* Chris Hanley for help with records.py, testing, and bug fixes.
* Travis Vaught, Joe Cooper, Jeff Strunk for administration of numpy.org web site and SVN
* Andrew Straw for bug-reports and help with www.scipy.org
* Albert Strasheim for bug-reports, unit-testing and Valgrind runs
* Stefan van der Walt for bug-reports, regression-testing, and bug-fixes.
* Eric Firing for bugfixes.
* Arnd Baecker for 64-bit testing
* A.M. Archibald for code that decreases the number of times reshape makes a copy.
More information is available at http://numpy.scipy.org and http://www.scipy.org. Bug-reports and feature requests should be submitted as tickets to the Trac pages at http://projects.scipy.org/scipy/numpy/
As an anti-SPAM measure, you must create an account in order to post tickets.
Enjoy the new release,
The NumPy Developers
*Disclaimer*: The main author, Travis Oliphant, has written a 350+ page book entitled "Guide to NumPy" that documents the new system fairly thoroughly. The first two chapters of this book are available on-line for free, but the remainder must be purchased (until 2010 or a certain number of total sales has been reached). See http://www.trelgol.com for more details. There is plenty of free documentation available now for NumPy, however. Go to http://www.scipy.org for more details.