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
