There is a compatibility problem, at least with the last formal release of scipy. Should we checkout and compile scipy from svn? Nadav. -----Original Message----- From: numpy-discussion-admin@lists.sourceforge.net on behalf of Bill Baxter Sent: Tue 14-Mar-06 13:52 To: numpy-discussion; SciPy Users List Cc: Subject: Re: [Numpy-discussion] [ANN] NumPy 0.9.6 released Just wondering, does this one also require an update to scipy? And in general do numpy updates always require an update to scipy, too? Or is it only when the numpy C API interface changes? --bb On 3/14/06, Travis Oliphant <oliphant.travis@ieee.org> wrote:

This post is to announce the release of NumPy 0.9.6 which fixes some important bugs and has several speed improvments.

NumPy is a multi-dimensional array-package for Python that allows rapid high-level array computing with Python. It is successor to both Numeric and Numarray. More information at http://numeric.scipy.org

The release notes are attached:

Best regards,

NumPy Developers

NumPy 0.9.6 is a bug-fix and optimization release with a few new features:

New features (and changes):

- bigndarray removed and support for Python2.5 ssize_t added giving full support in Python2.5 to very-large arrays on 64-bit systems.

- Strides can be set more arbitrarily from Python (and checking is done to make sure memory won't be violated).

- __array_finalize__ is now called for every array sub-class creation.

- kron and repmat functions added

- .round() method added for arrays

- rint, square, reciprocal, and ones_like ufuncs added.

- keyword arguments now possible for methods taking a single 'axis' argument

- Swig and Pyrex examples added in doc/swig and doc/pyrex

- NumPy builds out of the box for cygwin

- Different unit testing naming schemes are now supported.

- memusage in numpy.distutils works for NT platforms

- numpy.lib.math functions now take vectors

- Most functions in oldnumeric now return intput class where possible

Speed ups:

- x**n for integer n signficantly improved

- array(<python scalar>) much faster

- .fill() method is much faster

Other fixes:

- Output arrays to ufuncs works better.

- Several ma (Masked Array) fixes.

- umath code generation improved

- many fixes to optimized dot function (fixes bugs in matrix-sub-class multiply)

- scalartype fixes

- improvements to poly1d

- f2py fixed to handle character arrays in common blocks

- Scalar arithmetic improved to handle mixed-mode operation.

- Make sure Python intYY types correspond exactly with C PyArray_INTYY

-- William V. Baxter III OLM Digital Kono Dens Building Rm 302 1-8-8 Wakabayashi Setagaya-ku Tokyo, Japan 154-0023 +81 (3) 3422-3380