Changelog for python-numexpr-2.4-1.1.i586.rpm :
Thu May 8 14:00:00 2014 toddrme2178AATTgmail.com
- Update to 2.4

* A new `contains()` function has been added for detecting substrings
in strings. Only plain strings (bytes) are supported for now. See
PR #135 and ticket #142. Thanks to Marcin Krol.

* New version of setup.py that allows better management of NumPy
dependency. See PR #133. Thanks to Aleks Bunin.

Wed Mar 12 13:00:00 2014 toddrme2178AATTgmail.com
- update to 2.3.1

* Added support for shift-left (<<) and shift-right (>>) binary operators.
See PR #131. Thanks to fish2000!

* Removed the rpath flag for the GCC linker, because it is probably
not necessary and it chokes to clang.
- update to 2.3

* Site has been migrated to https://github.com/pydata/numexpr. All
new tickets and PR should be directed there.

* [ENH] A `conj()` function for computing the conjugate of complex
arrays has been added. Thanks to David Menéndez. See PR #125.

* [FIX] Fixed a DeprecationWarning derived of using oa_ndim == 0 and
op_axes == NULL when using NpyIter_AdvancedNew() and NumPy 1.8.
Thanks to Mark Wiebe for advise on how to fix this properly.

Tue Oct 22 14:00:00 2013 toddrme2178AATTgmail.com
- update to 2.2.2

* The `copy_args` argument of `NumExpr` function has been brought
back. This has been mainly necessary for compatibility with
PyTables < 3.0, which I decided to continue to support. Fixed #115.

* The `__nonzero__` method in `ExpressionNode` class has been
commented out. This is also for compatibility with PyTables < 3.0.
See #24 for details.

* Fixed the type of some parameters in the C extension so that s390
architecture compiles. Fixes #116. Thank to Antonio Valentino for
reporting and the patch.
- update to 2.2.1

* Fixes a secondary effect of \"from numpy.testing import `
*`\", where
division is imported now too, so only then necessary functions from
there are imported now. Thanks to Christoph Gohlke for the patch.
- update to 2.2

* [LICENSE] Fixed a problem with the license of the
numexpr/win32/pthread.{c,h} files emulating pthreads on Windows
platforms. After persmission from the original authors is granted,
these files adopt the MIT license and can be redistributed without
problems. See issue #109 for details
(https://code.google.com/p/numexpr/issues/detail?id=110).

* [ENH] Improved the algorithm to decide the initial number of threads
to be used. This was necessary because by default, numexpr was
using a number of threads equal to the detected number of cores, and
this can be just too much for moder systems where this number can be
too high (and counterporductive for performance in many cases).
Now, the \'NUMEXPR_NUM_THREADS\' environment variable is honored, and
in case this is not present, a maximum number of
*8
* threads are
setup initially. The new algorithm is fully described in the Users
Guide now in the note of \'General routines\' section:
https://code.google.com/p/numexpr/wiki/UsersGuide#General_routines.
Closes #110.

* [ENH] numexpr.test() returns `TestResult` instead of None now.
Closes #111.

* [FIX] Modulus with zero with integers no longer crashes the
interpreter. It nows puts a zero in the result. Fixes #107.

* [API CLEAN] Removed `copy_args` argument of `evaluate`. This should
only be used by old versions of PyTables (< 3.0).

* [DOC] Documented the `optimization` and `truediv` flags of
`evaluate` in Users Guide
(https://code.google.com/p/numexpr/wiki/UsersGuide).

Mon May 6 14:00:00 2013 highwaystar.ruAATTgmail.com
- python3 package added
- update to 2.1

* New compatibility with Python 3:

* switch from PyString to PyBytes API (requires Python >= 2.6).

* fixed incompatibilities regarding the int/long API

* use the Py_TYPE macro

* use the PyVarObject_HEAD_INIT macro instead of PyObject_HEAD_INIT

* Dropped compatibility with Python < 2.6.

* Fixed several issues with different platforms not supporting
multithreading or subprocess properly (see tickets #75 and #77).

* Now, when trying to use pure Python boolean operators, \'and\',
\'or\' and \'not, an error is issued and suggesting that \'&\', \'|\'
and \'~\' should be used instead (fixes #24).

Tue Aug 14 14:00:00 2012 scorotAATTfree.fr
- fix requiements for SLE 11

Tue Jul 31 14:00:00 2012 toddrme2178AATTgmail.com
- Update to 2.0.1

* Added compatibility with Python 2.5 (2.4 is definitely not supported
anymore).

* `numexpr.evaluate` is fully documented now, in particular the new
`out`, `order` and `casting` parameters.

* Reduction operations are fully documented now.

* Negative axis in reductions are not supported (they have never been
actually), and a `ValueError` will be raised if they are used.
- Update to 2.0
- Added support for the new iterator object in NumPy 1.6 and later.
This allows for better performance with operations that implies
broadcast operations, fortran-ordered or non-native byte orderings.
Performance for other scenarios is preserved (except for very small
arrays).
- Division in numexpr is consistent now with Python/NumPy. Fixes #22
and #58.
- Constants like \"2.\" or \"2.0\" must be evaluated as float, not
integer. Fixes #59.
- `evaluate()` function has received a new parameter `out` for storing
the result in already allocated arrays. This is very useful when
dealing with large arrays, and a allocating new space for keeping
the result is not acceptable. Closes #56.
- Maximum number of threads raised from 256 to 4096. Machines with a
higher number of cores will still be able to import numexpr, but
limited to 4096 (which is an absurdly high number already).
- Update to 1.4.2
- Multithreaded operation is disabled for small arrays (< 32 KB).
This allows to remove the overhead of multithreading for such a
small arrays. Closes #36.
- Dividing int arrays by zero gives a 0 as result now (and not a
floating point exception anymore. This behaviour mimics NumPy.
Thanks to Gaëtan de Menten for the fix. Closes #37.
- When compiled with VML support, the number of threads is set to 1
for VML core, and to the number of cores for the native pthreads
implementation. This leads to much better performance. Closes #39.
- Fixed different issues with reduction operations (`sum`, `prod`).
The problem is that the threaded code does not work well for
broadcasting or reduction operations. Now, the serial code is used
in those cases. Closes #41.
- Optimization of \"compilation phase\" through a better hash. This can
lead up to a 25% of improvement when operating with variable
expressions over small arrays. Thanks to Gaëtan de Menten for the
patch. Closes #43.
- The ``set_num_threads`` now returns the number of previous thread
setting, as stated in the docstrings.

Fri Jul 1 14:00:00 2011 saschpeAATTsuse.de
- Require python-numpy

Fri Jul 1 14:00:00 2011 saschpeAATTsuse.de
- Initial version