Changelog for python-numexpr-2.4.6-1.3.x86_64.rpm :

* Mon Feb 01 2016 toddrme2178AATTgmail.com- update to version 2.4.6:
* Fixed some UserWarnings in Solaris (PR #189, Graham Jones).
* Better handling of MSVC defines. (#168, Francesc Alted).- update to version 2.4.5:
* Undone a \'fix\' for a harmless data race. (#185 Benedikt Reinartz, Francesc Alted).
* Ignore NumPy warnings (overflow/underflow, divide by zero and others) that only show up in Python3. Masking these warnings in tests is fine because all the results are checked to be valid. (#183, Francesc Alted).- update to version 2.4.4:
* Honor OMP_NUM_THREADS as a fallback in case NUMEXPR_NUM_THREADS is not set. Fixes #161. (PR #175, Stefan Erb).
* Added support for AppVeyor (PR #178 Andrea Bedini)
* Fix to allow numexpr to be imported after eventlet.monkey_patch(), as suggested in #118 (PR #180 Ben Moran).
* Fix harmless data race that triggers false positives in ThreadSanitizer. (PR #179, Clement Courbet).
* Fixed some string tests on Python 3 (PR #182, Antonio Valentino).
* Thu May 07 2015 benoit.moninAATTgmx.fr- update to version 2.4.3:
* Comparisons with empty strings work correctly now. Fixes #121 and PyTables #184.- additional changes from version 2.4.2:
* Improved setup.py so that pip can query the name and version without actually doing the installation. Thanks to Joris Borgdorff.- additional changes from version 2.4.1:
* Added more configuration examples for compiling with MKL/VML support. Thanks to Davide Del Vento.
* Symbol MKL_VML changed into MKL_DOMAIN_VML because the former is deprecated in newer MKL. Thanks to Nick Papior Andersen.
* Better determination of methods in cpuinfo module. Thanks to Marc Jofre.
* Improved NumPy version determination (handy for 1.10.0). Thanks to Åsmund Hjulstad.
* Benchmarks run now with both Python 2 and Python 3. Thanks to Zoran Plesivčak.- remove shebang of cpuinfo.py instead of setting it executable- remove unneeded clean section
* Thu May 08 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 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 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 06 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 2012 scorotAATTfree.fr- fix requiements for SLE 11
* Tue Jul 31 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 01 2011 saschpeAATTsuse.de- Require python-numpy
* Fri Jul 01 2011 saschpeAATTsuse.de- Initial version