forked from pool/python-numexpr
ee0eb1187e
I intend to submit python-pandas to openSUSE:Factory. python-pandas is a tool that makes it easy to do complex data import, export, and manipulation. It has become one of the critical tools for data analysis in python, like python-numpy, python-scipy, python-matplotlib, and IPython (all of which are in openSUSE:Factory). This is a dependency of python-pandas. This package is also useful on its own for greatly increasing the speed of python-numpy operations. (I know I said the same thing about python-Bottleneck, both accelerate different types of numpy operations in different ways, that is why they are both used here). OBS-URL: https://build.opensuse.org/request/show/241241 OBS-URL: https://build.opensuse.org/package/show/openSUSE:Factory/python-numexpr?expand=0&rev=1
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154 lines
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Thu May 8 10:51:51 UTC 2014 - toddrme2178@gmail.com
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- Update to 2.4
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* A new `contains()` function has been added for detecting substrings
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in strings. Only plain strings (bytes) are supported for now. See
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PR #135 and ticket #142. Thanks to Marcin Krol.
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* New version of setup.py that allows better management of NumPy
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dependency. See PR #133. Thanks to Aleks Bunin.
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Wed Mar 12 13:24:36 UTC 2014 - toddrme2178@gmail.com
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- update to 2.3.1
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* Added support for shift-left (<<) and shift-right (>>) binary operators.
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See PR #131. Thanks to fish2000!
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* Removed the rpath flag for the GCC linker, because it is probably
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not necessary and it chokes to clang.
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- update to 2.3
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* Site has been migrated to https://github.com/pydata/numexpr. All
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new tickets and PR should be directed there.
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* [ENH] A `conj()` function for computing the conjugate of complex
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arrays has been added. Thanks to David Menéndez. See PR #125.
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* [FIX] Fixed a DeprecationWarning derived of using oa_ndim == 0 and
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op_axes == NULL when using NpyIter_AdvancedNew() and NumPy 1.8.
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Thanks to Mark Wiebe for advise on how to fix this properly.
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Tue Oct 22 13:32:08 UTC 2013 - toddrme2178@gmail.com
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- update to 2.2.2
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* The `copy_args` argument of `NumExpr` function has been brought
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back. This has been mainly necessary for compatibility with
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PyTables < 3.0, which I decided to continue to support. Fixed #115.
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* The `__nonzero__` method in `ExpressionNode` class has been
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commented out. This is also for compatibility with PyTables < 3.0.
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See #24 for details.
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* Fixed the type of some parameters in the C extension so that s390
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architecture compiles. Fixes #116. Thank to Antonio Valentino for
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reporting and the patch.
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- update to 2.2.1
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* Fixes a secondary effect of "from numpy.testing import `*`", where
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division is imported now too, so only then necessary functions from
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there are imported now. Thanks to Christoph Gohlke for the patch.
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- update to 2.2
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* [LICENSE] Fixed a problem with the license of the
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numexpr/win32/pthread.{c,h} files emulating pthreads on Windows
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platforms. After persmission from the original authors is granted,
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these files adopt the MIT license and can be redistributed without
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problems. See issue #109 for details
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(https://code.google.com/p/numexpr/issues/detail?id=110).
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* [ENH] Improved the algorithm to decide the initial number of threads
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to be used. This was necessary because by default, numexpr was
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using a number of threads equal to the detected number of cores, and
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this can be just too much for moder systems where this number can be
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too high (and counterporductive for performance in many cases).
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Now, the 'NUMEXPR_NUM_THREADS' environment variable is honored, and
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in case this is not present, a maximum number of *8* threads are
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setup initially. The new algorithm is fully described in the Users
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Guide now in the note of 'General routines' section:
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https://code.google.com/p/numexpr/wiki/UsersGuide#General_routines.
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Closes #110.
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* [ENH] numexpr.test() returns `TestResult` instead of None now.
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Closes #111.
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* [FIX] Modulus with zero with integers no longer crashes the
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interpreter. It nows puts a zero in the result. Fixes #107.
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* [API CLEAN] Removed `copy_args` argument of `evaluate`. This should
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only be used by old versions of PyTables (< 3.0).
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* [DOC] Documented the `optimization` and `truediv` flags of
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`evaluate` in Users Guide
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(https://code.google.com/p/numexpr/wiki/UsersGuide).
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Mon May 6 06:22:06 UTC 2013 - highwaystar.ru@gmail.com
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- python3 package added
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- update to 2.1
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* New compatibility with Python 3:
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* switch from PyString to PyBytes API (requires Python >= 2.6).
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* fixed incompatibilities regarding the int/long API
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* use the Py_TYPE macro
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* use the PyVarObject_HEAD_INIT macro instead of PyObject_HEAD_INIT
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* Dropped compatibility with Python < 2.6.
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* Fixed several issues with different platforms not supporting
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multithreading or subprocess properly (see tickets #75 and #77).
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* Now, when trying to use pure Python boolean operators, 'and',
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'or' and 'not, an error is issued and suggesting that '&', '|'
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and '~' should be used instead (fixes #24).
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Tue Aug 14 10:24:42 UTC 2012 - scorot@free.fr
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- fix requiements for SLE 11
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Tue Jul 31 13:42:58 UTC 2012 - toddrme2178@gmail.com
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- Update to 2.0.1
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*Added compatibility with Python 2.5 (2.4 is definitely not supported
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anymore).
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*`numexpr.evaluate` is fully documented now, in particular the new
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`out`, `order` and `casting` parameters.
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*Reduction operations are fully documented now.
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*Negative axis in reductions are not supported (they have never been
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actually), and a `ValueError` will be raised if they are used.
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- Update to 2.0
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- Added support for the new iterator object in NumPy 1.6 and later.
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This allows for better performance with operations that implies
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broadcast operations, fortran-ordered or non-native byte orderings.
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Performance for other scenarios is preserved (except for very small
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arrays).
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- Division in numexpr is consistent now with Python/NumPy. Fixes #22
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and #58.
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- Constants like "2." or "2.0" must be evaluated as float, not
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integer. Fixes #59.
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- `evaluate()` function has received a new parameter `out` for storing
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the result in already allocated arrays. This is very useful when
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dealing with large arrays, and a allocating new space for keeping
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the result is not acceptable. Closes #56.
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- Maximum number of threads raised from 256 to 4096. Machines with a
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higher number of cores will still be able to import numexpr, but
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limited to 4096 (which is an absurdly high number already).
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- Update to 1.4.2
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- Multithreaded operation is disabled for small arrays (< 32 KB).
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This allows to remove the overhead of multithreading for such a
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small arrays. Closes #36.
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- Dividing int arrays by zero gives a 0 as result now (and not a
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floating point exception anymore. This behaviour mimics NumPy.
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Thanks to Gaëtan de Menten for the fix. Closes #37.
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- When compiled with VML support, the number of threads is set to 1
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for VML core, and to the number of cores for the native pthreads
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implementation. This leads to much better performance. Closes #39.
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- Fixed different issues with reduction operations (`sum`, `prod`).
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The problem is that the threaded code does not work well for
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broadcasting or reduction operations. Now, the serial code is used
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in those cases. Closes #41.
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- Optimization of "compilation phase" through a better hash. This can
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lead up to a 25% of improvement when operating with variable
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expressions over small arrays. Thanks to Gaëtan de Menten for the
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patch. Closes #43.
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- The ``set_num_threads`` now returns the number of previous thread
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setting, as stated in the docstrings.
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-------------------------------------------------------------------
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Fri Jul 1 08:16:08 UTC 2011 - saschpe@suse.de
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- Require python-numpy
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-------------------------------------------------------------------
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Fri Jul 1 07:22:37 UTC 2011 - saschpe@suse.de
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- Initial version
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