Changelog for python311-numexpr-2.10.0-1.1.x86_64.rpm :

* Mon Apr 22 2024 Markéta Machová - 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 2024 Ben Greiner - 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.
* Tue Feb 06 2024 Dirk Müller - 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.
* Mon Dec 18 2023 Dirk Müller - 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
* Mon Nov 27 2023 Dirk Müller - 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
* Mon Sep 04 2023 Ben Greiner - 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
* Sun Aug 27 2023 Arun Persaud - 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.
* Mon Jan 02 2023 Dirk Müller - 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.
* Sun Jul 17 2022 Ben Greiner - Fix requirements
* Sat Jul 09 2022 Arun Persaud - 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 (AATTmatham) for improvements to the GitHub Actions build process to add support for Apple Silicon and aarch64.
* Thanks to Biswapriyo Nath (AATTbiswa96) 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.
* Mon Feb 07 2022 Arun Persaud - 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.
* Fri Mar 12 2021 Dirk Müller - skip python3.6 build (no numpy)
* Wed Mar 03 2021 Arun Persaud - 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.
* Sat Jan 09 2021 Arun Persaud - 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.