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File python-numexpr.changes of Package python-numexpr (Revision 7a35e6eed5dce95a28a6e40b19b5a44f)

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Tue Nov 12 11:14:53 UTC 2024 - Daniel Garcia <daniel.garcia@suse.com>

- Add upstream patch fix-test-max-threads-unset.patch
- Remove upstreamed patch numexpr-pr485-allow-numpy1.patch
- Update to 2.10.1:
  - The default number of 'safe' threads has been upgraded to 16
    (instead of previous 8). That means that if your CPU has > 16
    cores, the default is to use 16. You can always override this with
    the "NUMEXPR_MAX_THREADS" environment variable.
  - NumPy 1.23 is now the minimum supported.
  - Preliminary support for Python 3.13. Thanks to Karolina Surma.
  - Fix tests on nthreads detection (closes: #479). Thanks to
    @avalentino.
  - The build process has been modernized and now uses the
    pyproject.toml file for more of the configuration options.

-------------------------------------------------------------------
Sat May 11 19:48:33 UTC 2024 - Ben Greiner <code@bnavigator.de>

- Replace revert-to-numpy1.patch by numexpr-pr485-allow-numpy1.patch
  in order to be able to compile with either numpy major version

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Mon Apr 22 09:57:01 UTC 2024 - Markéta Machová <mmachova@suse.com>

- Add patch revert-to-numpy1.patch to restore compatibility with numpy v1
  * revert of upstream commit, drop when numpy v2 comes to Factory

-------------------------------------------------------------------
Fri Apr 19 11:57:33 UTC 2024 - Ben Greiner <code@bnavigator.de>

- Update to 2.10.0
  * Support for NumPy 2.0.0. This is still experimental, so please
    report any issues you find. Thanks to Clément Robert and Thomas
    Caswell for the work.
  * Avoid erroring when OMP_NUM_THREADS is empty string. Thanks to
    Patrick Hoefler.
  * Do not warn if OMP_NUM_THREAD set.

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Tue Feb  6 09:32:12 UTC 2024 - Dirk Müller <dmueller@suse.com>

- update to 2.9.0:
  * Support for PyPy (see PRs #467 and #740).  The full test suite
    should pass now, at least for the 3.10 version.
    providing help and additional fixes.
  * Fixed more sanitizer issues (see PR #469).
  * Modernized the test suite to avoid some warnings.

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Mon Dec 18 09:23:43 UTC 2023 - Dirk Müller <dmueller@suse.com>

- update to 2.8.8:
  * Fix re_evaluate not taking global_dict as argument.
  * Fix parsing of simple complex numbers.  Now,
    `ne.evaluate('1.5j')` works.
  * Fixes for upcoming NumPy 2.0

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Mon Nov 27 19:44:35 UTC 2023 - Dirk Müller <dmueller@suse.com>

- update to 2.8.7:
  * More permissive rules in sanitizing regular expression: allow to
    access digits after the . with scientific notation.
  * Don't reject double underscores that are not at the start or end of
    a variable name (pandas uses those), or scientific-notation numbers
    with digits after the decimal point.
  * Do not use numpy.alltrue in the test suite, as it has been
    deprecated (replaced by numpy.all).
  * Python 3.12 support

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Mon Sep  4 19:07:09 UTC 2023 - Ben Greiner <code@bnavigator.de>

- Revert to version 2.8.4: Patch release breaks its API and thus
  Pandas -- gh#pydata/numexpr#442, gh#pydata/numexpr#444
- Move to PEP517 build

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Sun Aug 27 21:31:12 UTC 2023 - Arun Persaud <arun@gmx.de>

- update to version 2.8.5:
  * A validate function has been added. This function checks the
    inputs, returning None on success or raising an exception on
    invalid inputs. This function was added as numerous projects seem
    to be using NumExpr for parsing user inputs. re_evaluate may be
    called directly following validate.
  * As an addendum to the use of NumExpr for parsing user inputs, is
    that NumExpr calls eval on the inputs. A regular expression is now
    applied to help sanitize the input expression string, forbidding
    '__', ':', and ';'. Attribute access is also banned except for
    '.r' for real and '.i' for imag.
  * Thanks to timbrist for a fix to behavior of NumExpr with integers
    to negative powers. NumExpr was pre-checking integer powers for
    negative values, which was both inefficient and caused parsing
    errors in some situations. Now NumExpr will simply return 0 as a
    result for such cases. While NumExpr generally tries to follow
    NumPy behavior, performance is also critical.
  * Thanks to peadar for some fixes to how NumExpr launches threads
    for embedded applications.
  * Thanks to de11n for making parsing of the site.cfg for MKL
    consistent among all shared platforms.

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Mon Jan  2 18:44:01 UTC 2023 - Dirk Müller <dmueller@suse.com>

- update to 2.8.4:
  * Support for Python 3.11 has been added.
  * Thanks to Tobias Hangleiter for an improved accuracy complex `expm1` function.
    While it is 25 % slower, it is significantly more accurate for the real component
    over a range of values and matches NumPy outputs much more closely.
  * Thanks to Kirill Kouzoubov for a range of fixes to constants parsing that was
    resulting in duplicated constants of the same value.
  * Thanks to Mark Harfouche for noticing that we no longer need `numpy` version
    checks. `packaging` is no longer a requirement as a result.

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Sun Jul 17 18:42:38 UTC 2022 - Ben Greiner <code@bnavigator.de>

- Fix requirements

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Sat Jul  9 13:38:01 UTC 2022 - Arun Persaud <arun@gmx.de>

- specfile:
  * require python >= 3.7

- update to version 2.8.3:
  * Support for Python 3.6 has been dropped due to the need to
    substitute the flag NPY_ARRAY_WRITEBACKIFCOPY for
    NPY_ARRAY_UPDATEIFCOPY. This flag change was initiated in NumPy
    1.14 and finalized in 1.23. The only changes were made to cases
    where an unaligned constant was passed in with a pre-allocated
    output variable:
    ```
        x = np.empty(5, dtype=np.uint8)[1:].view(np.int32) ne.evaluate('3', out=x)
    ```
    We think the risk of issues is very low, but if you are using
    NumExpr as a expression evaluation tool you may want to write a
    test for this edge case.
  * Thanks to Matt Einhorn (@matham) for improvements to the GitHub
    Actions build process to add support for Apple Silicon and
    aarch64.
  * Thanks to Biswapriyo Nath (@biswa96) for a fix to allow mingw
    builds on Windows.
  * There have been some changes made to not import platform.machine()
    on sparc but it is highly advised to upgrade to Python 3.9+ to
    avoid this issue with the Python core package platform.

- changes from version 2.8.2:
  * skipped due to an error in uploading to PyPi.

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Mon Feb  7 03:27:38 UTC 2022 - Arun Persaud <arun@gmx.de>

- specfile:
  * update copyright year
  * skip build for python2

- update to version 2.8.1:
  * Fixed dependency list.
  * Added "pyproject.toml" and modernize the "setup.py" script. Thanks
    to Antonio Valentino for the PR.

- changes from version 2.8.0:
  * Wheels for Python 3.10 are now provided.
  * Support for Python 2.7 and 3.5 has been discontinued.
  * All residual support for Python 2.X syntax has been removed, and
    therefore the setup build no longer makes calls to the `2to3`
    script. The `setup.py` has been refactored to be more modern.
  * The examples on how to link into Intel VML/MKL/oneAPI now use the
    dynamic library.

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Fri Mar 12 20:28:19 UTC 2021 - Dirk Müller <dmueller@suse.com>

- skip python3.6 build (no numpy)

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Wed Mar  3 19:08:44 UTC 2021 - Arun Persaud <arun@gmx.de>

- update to version 2.7.3:
  * Pinned Numpy versions to minimum supported version in an effort to
    alleviate issues seen in Windows machines not having the same MSVC
    runtime installed as was used to build the wheels.
  * ARMv8 wheels are now available, thanks to odidev for the pull
    request.

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Sat Jan  9 20:01:06 UTC 2021 - Arun Persaud <arun@gmx.de>

- specfile:
  * update copyright year

- update to version 2.7.2:
  * Support for Python 2.7 and 3.5 is deprecated and will be
    discontinued when cibuildwheels and/or GitHub Actions no longer
    support these versions.
  * Wheels are now provided for Python 3.7, 3.5, 3.6, 3.7, 3.8, and
    3.9 via GitHub Actions.
  * The block size is now exported into the namespace as
    numexpr.__BLOCK_SIZE1__ as a read-only value.
  * If using MKL, the number of threads for VML is no longer forced to
    1 on loading the module. Testing has shown that VML never runs in
    multi-threaded mode for the default BLOCKSIZE1 of 1024 elements,
    and forcing to 1 can have deleterious effects on NumPy functions
    when built with MKL. See issue #355 for details.
  * Use of ndarray.tostring() in tests has been switch to
    ndarray.tobytes() for future-proofing deprecation of .tostring(),
    if the version of NumPy is greater than 1.9.
  * Added a utility method get_num_threads that returns the (maximum)
    number of threads currently in use by the virtual machine. The
    functionality of set_num_threads whereby it returns the previous
    value has been deprecated and will be removed in 2.8.X.

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Wed Jan  8 18:04:17 UTC 2020 - Todd R <toddrme2178@gmail.com>

- Update to 2.7.1
  * Python 3.8 support has been added.
  * Python 3.4 support is discontinued.
  * The tests are now compatible with NumPy 1.18.
  * site.cfg.example was updated to use the libraries tag instead
    of mkl_libs, which is recommended for newer version of NumPy.
- Drop upstream-included fix_test.patch

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Sat Aug 31 04:46:34 UTC 2019 - Arun Persaud <arun@gmx.de>

- specfile:
  * update copyright year
  * added fix to call python in tests: fix_test.patch

- update to version 2.7.0:
  * The default number of 'safe' threads has been restored to the
    historical limit of 8, if the environment variable
    "NUMEXPR_MAX_THREADS" has not been set.
  * Thanks to @eltoder who fixed a small memory leak.
  * Support for Python 2.6 has been dropped, as it is no longer
    available via TravisCI.
  * A typo in the test suite that had a less than rather than greater
    than symbol in the NumPy version check has been corrected thanks
    to dhomeier.
  * The file site.cfg was being accidently included in the sdists on
    PyPi. It has now been excluded.

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Sat Dec 22 05:26:03 UTC 2018 - Todd R <toddrme2178@gmail.com>

- Update to 2.6.9
  * Thanks to Mike Toews for more robust handling of the thread-setting environment variables.
  * With Appveyor updating to Python 3.7.1, wheels for Python 3.7 are now available in addition to those for other OSes.

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Sun Sep  2 16:48:00 UTC 2018 - arun@gmx.de

- update to version 2.6.8:
  * Add check to make sure that f_locals is not actually f_globals
    when we do the f_locals clear to avoid the #310 memory leak issue.
  * Compare NumPy versions using distutils.version.LooseVersion to
    avoid issue #312 when working with NumPy development versions.
  * As part of multibuild, wheels for Python 3.7 for Linux and MacOSX
    are now available on PyPI.

- changes from version 2.6.7:
  * Thanks to Lehman Garrison for finding and fixing a bug that
    exhibited memory leak-like behavior. The use in numexpr.evaluate
    of sys._getframe combined with .f_locals from that frame object
    results an extra refcount on objects in the frame that calls
    numexpr.evaluate, and not evaluate's frame. So if the calling
    frame remains in scope for a long time (such as a procedural
    script where numexpr is called from the base frame) garbage
    collection would never occur.
  * Imports for the numexpr.test submodule were made lazy in the
    numexpr module.

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Mon Aug  6 20:04:09 UTC 2018 - toddrme2178@gmail.com

- Update to 2.6.6
  * Thanks to Mark Dickinson for a fix to the thread barrier that occassionally suffered from spurious wakeups on MacOSX.
- Update to 2.6.5
  * The maximum thread count can now be set at import-time by setting the environment variable ‘NUMEXPR_MAX_THREADS’. The default number of max threads was lowered from 4096 (which was deemed excessive) to 64.
  * A number of imports were removed (pkg_resources) or made lazy (cpuinfo) in order to speed load-times for downstream packages (such as pandas, sympy, and tables). Import time has dropped from about 330 ms to 90 ms. Thanks to Jason Sachs for pointing out the source of the slow-down.
  * Thanks to Alvaro Lopez Ortega for updates to benchmarks to be compatible with Python 3.
  * Travis and AppVeyor now fail if the test module fails or errors.
  * Thanks to Mahdi Ben Jelloul for a patch that removed a bug where constants in where calls would raise a ValueError.
  * Fixed a bug whereby all-constant power operations would lead to infinite recursion.

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Mon Oct  2 21:57:06 UTC 2017 - jengelh@inai.de

- Ensure neutrality of description.

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Sun Sep 24 21:29:49 UTC 2017 - arun@gmx.de

- update to version 2.6.4:
  * Christoph Gohkle noticed a lack of coverage for the 2.6.3 floor
    and ceil functions for MKL that caused seg-faults in

- changes from version 2.6.2:
  * Documentation now available at readthedocs.io.
  * Support for floor() and ceil() functions added by Caleb P. Burns.
  * NumPy requirement increased from 1.6 to 1.7 due to changes in
    iterator flags (#245).
  * Sphinx autodocs support added for documentation on
    readthedocs.org.
  * Fixed a bug where complex constants would return an error, fixing
    problems with sympy when using NumExpr as a backend.
  * Fix for #277 whereby arrays of shape (1,...) would be reduced as
    if they were full reduction. Behavoir now matches that of NumPy.
  * String literals are automatically encoded into 'ascii' bytes for
    convience (see #281).

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Wed Apr 19 22:13:57 UTC 2017 - toddrme2178@gmail.com

- Source url must be https.

-------------------------------------------------------------------
Wed Apr 19 18:38:25 UTC 2017 - toddrme2178@gmail.com

- Update to 2.6.2
  * Updates to keep with API changes in newer NumPy versions (#228).
    Thanks to Oleksandr Pavlyk.
  * Removed several warnings (#226 and #227).  Thanks to Oleksander Pavlyk.
  * Fix bugs in function `stringcontains()` (#230).  Thanks to Alexander Shadchin.
  * Detection of the POWER processor (#232).  Thanks to Breno Leitao.
  * Fix pow result casting (#235).  Thanks to Fernando Seiti Furusato.
  * Fix integers to negative integer powers (#240).  Thanks to Antonio Valentino.
  * Detect numpy exceptions in expression evaluation (#240).  Thanks to Antonio Valentino.
  * Better handling of RC versions (#243).  Thanks to Antonio Valentino.
- Update to 2.6.1
  * Fixed a performance regression in some situations as consequence of
    increasing too much the BLOCK_SIZE1 constant.  After more careful
    benchmarks (both in VML and non-VML modes), the value has been set
    again to 1024 (down from 8192).  The benchmarks have been made with
    a relatively new processor (Intel Xeon E3-1245 v5 @ 3.50GHz), so
    they should work well for a good range of processors again.
  * Added NetBSD support to CPU detection.  Thanks to Thomas Klausner.
- Update to 2.6.0
  * Introduced a new re_evaluate() function for re-evaluating the
    previous executed array expression without any check.  This is meant
    for accelerating loops that are re-evaluating the same expression
    repeatedly without changing anything else than the operands.  If
    unsure, use evaluate() which is safer.
  * The BLOCK_SIZE1 and BLOCK_SIZE2 constants have been re-checked in
    order to find a value maximizing most of the benchmarks in bench/
    directory.  The new values (8192 and 16 respectively) give somewhat
    better results (~5%) overall.  The CPU used for fine tuning is a
    relatively new Haswell processor (E3-1240 v3).
  * The '--name' flag for `setup.py` returning the name of the package
    is honored now (issue #215).
- Update to 2.5.2
  * conj() and abs() actually added as VML-powered functions, preventing
    the same problems than log10() before (PR #212).  Thanks to Tom Kooij
    for the fix!
- Update to 2.5.1
  * Fix for log10() and conj() functions.  These produced wrong results
    when numexpr was compiled with Intel's MKL (which is a popular build
    since Anaconda ships it by default) and non-contiguous data (issue
    #210).  Thanks to Arne de Laat and Tom Kooij for reporting and
    providing a nice test unit.
  * Fix that allows numexpr-powered apps to be profiled with pympler.
    Thanks to @nbecker.
- Update to 2.5
  * Added locking for allowing the use of numexpr in multi-threaded
    callers (this does not prevent numexpr to use multiple cores
    simultaneously).  (PR #199, Antoine Pitrou, PR #200, Jenn Olsen).
  * Added new min() and max() functions (PR #195, CJ Carey).
- Implement single-spec version

-------------------------------------------------------------------
Mon Feb  1 11:39:24 UTC 2016 - toddrme2178@gmail.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).

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Thu May  7 18:37:17 UTC 2015 - benoit.monin@gmx.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

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Thu May  8 10:51:51 UTC 2014 - toddrme2178@gmail.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.

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Wed Mar 12 13:24:36 UTC 2014 - toddrme2178@gmail.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.

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Tue Oct 22 13:32:08 UTC 2013 - toddrme2178@gmail.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 06:22:06 UTC 2013 - highwaystar.ru@gmail.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 10:24:42 UTC 2012 - scorot@free.fr

- fix requiements for SLE 11

-------------------------------------------------------------------
Tue Jul 31 13:42:58 UTC 2012 - toddrme2178@gmail.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 08:16:08 UTC 2011 - saschpe@suse.de

- Require python-numpy

-------------------------------------------------------------------
Fri Jul  1 07:22:37 UTC 2011 - saschpe@suse.de

- Initial version

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