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
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