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| Name: python310-numexpr | Distribution: openSUSE:Factory:zSystems |
| Version: 2.10.1 | Vendor: openSUSE |
| Release: 1.1 | Build date: Tue Nov 12 12:14:53 2024 |
| Group: Development/Languages/Python | Build host: reproducible |
| Size: 561814 | Source RPM: python-numexpr-2.10.1-1.1.src.rpm |
| Packager: https://bugs.opensuse.org | |
| Url: https://github.com/pydata/numexpr/ | |
| Summary: Numerical expression evaluator for NumPy | |
Numexpr is a numerical expression evaluator for NumPy. It is a C++ module. With it, expressions that operate on arrays (like "3*a+4*b") can be accelerated and use less memory than doing the same calculation in Python.
MIT
* Tue Nov 12 2024 Daniel Garcia <[email protected]>
- 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 2024 Ben Greiner <[email protected]>
- Replace revert-to-numpy1.patch by numexpr-pr485-allow-numpy1.patch
in order to be able to compile with either numpy major version
* Mon Apr 22 2024 Markéta Machová <[email protected]>
- 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 <[email protected]>
- 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 <[email protected]>
- 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 <[email protected]>
- 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 <[email protected]>
- 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 <[email protected]>
- 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 <[email protected]>
- 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 <[email protected]>
- 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 <[email protected]>
- Fix requirements
* Sat Jul 09 2022 Arun Persaud <[email protected]>
- 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.
* Mon Feb 07 2022 Arun Persaud <[email protected]>
- 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 <[email protected]>
- skip python3.6 build (no numpy)
* Wed Mar 03 2021 Arun Persaud <[email protected]>
- 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 <[email protected]>
- 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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