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Hello community,
here is the log from the commit of package python-numexpr for openSUSE:Factory
checked in at 2022-07-12 11:12:09
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Comparing /work/SRC/openSUSE:Factory/python-numexpr (Old)
and /work/SRC/openSUSE:Factory/.python-numexpr.new.1523 (New)
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Package is "python-numexpr"
Tue Jul 12 11:12:09 2022 rev:16 rq:988324 version:2.8.3
Changes:
--------
--- /work/SRC/openSUSE:Factory/python-numexpr/python-numexpr.changes
2022-02-09 20:38:18.354247647 +0100
+++ /work/SRC/openSUSE:Factory/.python-numexpr.new.1523/python-numexpr.changes
2022-07-12 11:12:16.563685617 +0200
@@ -1,0 +2,31 @@
+Sat Jul 9 13:38:01 UTC 2022 - Arun Persaud <a...@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.
+
+-------------------------------------------------------------------
Old:
----
numexpr-2.8.1.tar.gz
New:
----
numexpr-2.8.3.tar.gz
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Other differences:
------------------
++++++ python-numexpr.spec ++++++
--- /var/tmp/diff_new_pack.SI0vfh/_old 2022-07-12 11:12:17.899687365 +0200
+++ /var/tmp/diff_new_pack.SI0vfh/_new 2022-07-12 11:12:17.903687370 +0200
@@ -17,16 +17,15 @@
%{?!python_module:%define python_module() python-%{**} python3-%{**}}
-%global skip_python2 1
-%global skip_python36 1
Name: python-numexpr
-Version: 2.8.1
+Version: 2.8.3
Release: 0
Summary: Numerical expression evaluator for NumPy
License: MIT
Group: Development/Languages/Python
URL: https://github.com/pydata/numexpr/
Source:
https://files.pythonhosted.org/packages/source/n/numexpr/numexpr-%{version}.tar.gz
+BuildRequires: %{python_module base >= 3.7}
BuildRequires: %{python_module devel}
BuildRequires: %{python_module numpy-devel >= 1.6}
BuildRequires: %{python_module setuptools}
++++++ numexpr-2.8.1.tar.gz -> numexpr-2.8.3.tar.gz ++++++
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/numexpr-2.8.1/ANNOUNCE.rst
new/numexpr-2.8.3/ANNOUNCE.rst
--- old/numexpr-2.8.1/ANNOUNCE.rst 2021-12-10 21:06:49.000000000 +0100
+++ new/numexpr-2.8.3/ANNOUNCE.rst 2022-06-25 22:25:22.000000000 +0200
@@ -1,24 +1,42 @@
========================
-Announcing NumExpr 2.8.1
+Announcing NumExpr 2.8.3
========================
Hi everyone,
-This is another maintenance release to further modernize our install script
for
-distributions that do not include `setuptools` by default. Thanks to Antonio
-Valentino for the changes.
+Please find here another maintenance release of NumExpr. Support for Python
3.6
+has been dropped to enable support for NumPy 1.23 (and by extension Python
3.11
+when it is released). Wheels for ARM64 multilinux should be available again
after
+troubles with GitHub Actions and Apple Silicon wheels are also now available
on
+PyPi for download.
Project documentation is available at:
http://numexpr.readthedocs.io/
-Changes from 2.8.0 to 2.8.1
+Changes from 2.8.1 to 2.8.2
---------------------------
-* Fixed dependency list.
-* Added ``pyproject.toml`` and modernize the ``setup.py`` script. Thanks to
-Antonio Valentino for the PR.
+* 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`.
What's Numexpr?
---------------
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/numexpr-2.8.1/PKG-INFO new/numexpr-2.8.3/PKG-INFO
--- old/numexpr-2.8.1/PKG-INFO 2021-12-10 23:03:16.315434500 +0100
+++ new/numexpr-2.8.3/PKG-INFO 2022-06-25 22:35:21.620025000 +0200
@@ -1,23 +1,17 @@
Metadata-Version: 2.1
Name: numexpr
-Version: 2.8.1
+Version: 2.8.3
Summary: Fast numerical expression evaluator for NumPy
Home-page: https://github.com/pydata/numexpr
Author: David M. Cooke, Francesc Alted, and others
-Author-email:
-david.m.co...@gmail.com
-fal...@gmail.com
Maintainer: Robert A. McLeod
Maintainer-email: robbmcl...@gmail.com
License: MIT
-Description: UNKNOWN
-Platform: UNKNOWN
Classifier: Development Status :: 6 - Mature
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
-Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
@@ -25,5 +19,193 @@
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS
-Requires-Python: >=3.6
-Description-Content-Type: text/markdown
+Requires-Python: >=3.7
+Description-Content-Type: text/x-rst
+License-File: LICENSE.txt
+License-File: AUTHORS.txt
+
+======================================================
+NumExpr: Fast numerical expression evaluator for NumPy
+======================================================
+
+:Author: David M. Cooke, Francesc Alted, and others.
+:Maintainer: Robert A. McLeod
+:Contact: robbmcl...@gmail.com
+:URL: https://github.com/pydata/numexpr
+:Documentation: http://numexpr.readthedocs.io/en/latest/
+:Travis CI: |travis|
+:GitHub Actions: |actions|
+:PyPi: |version|
+:DOI: |doi|
+:readthedocs: |docs|
+
+.. |actions| image::
https://github.com/pydata/numexpr/workflows/Build/badge.svg
+ :target: https://github.com/pydata/numexpr/actions
+.. |travis| image:: https://travis-ci.org/pydata/numexpr.png?branch=master
+ :target: https://travis-ci.org/pydata/numexpr
+.. |docs| image::
https://readthedocs.org/projects/numexpr/badge/?version=latest
+ :target: http://numexpr.readthedocs.io/en/latest
+.. |doi| image:: https://zenodo.org/badge/doi/10.5281/zenodo.2483274.svg
+ :target: https://doi.org/10.5281/zenodo.2483274
+.. |version| image:: https://img.shields.io/pypi/v/numexpr.png
+ :target: https://pypi.python.org/pypi/numexpr
+
+What is NumExpr?
+----------------
+
+NumExpr is a fast numerical expression evaluator for NumPy. With it,
+expressions that operate on arrays (like :code:`'3*a+4*b'`) are accelerated
+and use less memory than doing the same calculation in Python.
+
+In addition, its multi-threaded capabilities can make use of all your
+cores -- which generally results in substantial performance scaling compared
+to NumPy.
+
+Last but not least, numexpr can make use of Intel's VML (Vector Math
+Library, normally integrated in its Math Kernel Library, or MKL).
+This allows further acceleration of transcendent expressions.
+
+
+How NumExpr achieves high performance
+-------------------------------------
+
+The main reason why NumExpr achieves better performance than NumPy is
+that it avoids allocating memory for intermediate results. This
+results in better cache utilization and reduces memory access in
+general. Due to this, NumExpr works best with large arrays.
+
+NumExpr parses expressions into its own op-codes that are then used by
+an integrated computing virtual machine. The array operands are split
+into small chunks that easily fit in the cache of the CPU and passed
+to the virtual machine. The virtual machine then applies the
+operations on each chunk. It's worth noting that all temporaries and
+constants in the expression are also chunked. Chunks are distributed among
+the available cores of the CPU, resulting in highly parallelized code
+execution.
+
+The result is that NumExpr can get the most of your machine computing
+capabilities for array-wise computations. Common speed-ups with regard
+to NumPy are usually between 0.95x (for very simple expressions like
+:code:`'a + 1'`) and 4x (for relatively complex ones like :code:`'a*b-4.1*a >
+2.5*b'`), although much higher speed-ups can be achieved for some functions
+and complex math operations (up to 15x in some cases).
+
+NumExpr performs best on matrices that are too large to fit in L1 CPU cache.
+In order to get a better idea on the different speed-ups that can be achieved
+on your platform, run the provided benchmarks.
+
+Installation
+------------
+
+From wheels
+^^^^^^^^^^^
+
+NumExpr is available for install via `pip` for a wide range of platforms and
+Python versions (which may be browsed at:
https://pypi.org/project/numexpr/#files).
+Installation can be performed as::
+
+ pip install numexpr
+
+If you are using the Anaconda or Miniconda distribution of Python you may
prefer
+to use the `conda` package manager in this case::
+
+ conda install numexpr
+
+From Source
+^^^^^^^^^^^
+
+On most `Nix systems your compilers will already be present. However if you
+are using a virtual environment with a substantially newer version of Python
than
+your system Python you may be prompted to install a new version of `gcc` or
`clang`.
+
+For Windows, you will need to install the Microsoft Visual C++ Build Tools
+(which are free) first.The version depends on which version of Python you have
+installed:
+
+https://wiki.python.org/moin/WindowsCompilers
+
+For Python 3.6+ simply installating the latest version of MSVC build tools
should
+be sufficient. Note that wheels found via pip do not include MKL support.
Wheels
+available via `conda` will have MKL, if the MKL backend is used for NumPy.
+
+See `requirements.txt` for the required version of NumPy.
+
+NumExpr is built in the standard Python way::
+
+ python setup.py build install
+
+You can test `numexpr` with::
+
+ python -c "import numexpr; numexpr.test()"
+
+Do not test NumExpr in the source directory or you will generate import errors.
+
+Enable Intel?? MKL support
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+NumExpr includes support for Intel's MKL library. This may provide better
+performance on Intel architectures, mainly when evaluating transcendental
+functions (trigonometrical, exponential...).
+
+If you have Intel's MKL, copy the `site.cfg.example` that comes with the
+distribution to `site.cfg` and edit the latter file to provide correct paths
to
+the MKL libraries in your system. After doing this, you can proceed with the
+usual building instructions listed above.
+
+Pay attention to the messages during the building process in order to know
+whether MKL has been detected or not. Finally, you can check the speed-ups on
+your machine by running the `bench/vml_timing.py` script (you can play with
+different parameters to the `set_vml_accuracy_mode()` and
`set_vml_num_threads()`
+functions in the script so as to see how it would affect performance).
+
+Usage
+-----
+
+::
+
+ >>> import numpy as np
+ >>> import numexpr as ne
+
+ >>> a = np.arange(1e6) # Choose large arrays for better speedups
+ >>> b = np.arange(1e6)
+
+ >>> ne.evaluate("a + 1") # a simple expression
+ array([ 1.00000000e+00, 2.00000000e+00, 3.00000000e+00, ...,
+ 9.99998000e+05, 9.99999000e+05, 1.00000000e+06])
+
+ >>> ne.evaluate('a*b-4.1*a > 2.5*b') # a more complex one
+ array([False, False, False, ..., True, True, True], dtype=bool)
+
+ >>> ne.evaluate("sin(a) + arcsinh(a/b)") # you can also use functions
+ array([ NaN, 1.72284457, 1.79067101, ..., 1.09567006,
+ 0.17523598, -0.09597844])
+
+ >>> s = np.array([b'abba', b'abbb', b'abbcdef'])
+ >>> ne.evaluate("b'abba' == s") # string arrays are supported too
+ array([ True, False, False], dtype=bool)
+
+
+Documentation
+-------------
+
+Please see the official documentation at `numexpr.readthedocs.io
<https://numexpr.readthedocs.io>`_.
+Included is a user guide, benchmark results, and the reference API.
+
+
+Authors
+-------
+
+Please see `AUTHORS.txt
<https://github.com/pydata/numexpr/blob/master/AUTHORS.txt>`_.
+
+
+License
+-------
+
+NumExpr is distributed under the `MIT
<http://www.opensource.org/licenses/mit-license.php>`_ license.
+
+
+.. Local Variables:
+.. mode: text
+.. coding: utf-8
+.. fill-column: 70
+.. End:
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/numexpr-2.8.1/RELEASE_NOTES.rst
new/numexpr-2.8.3/RELEASE_NOTES.rst
--- old/numexpr-2.8.1/RELEASE_NOTES.rst 2021-12-10 21:07:17.000000000 +0100
+++ new/numexpr-2.8.3/RELEASE_NOTES.rst 2022-06-25 22:25:22.000000000 +0200
@@ -1,7 +1,32 @@
====================================
-Release notes for Numexpr 2.8 series
+Release notes for NumExpr 2.8 series
====================================
+Changes from 2.8.1 to 2.8.3
+---------------------------
+
+* 2.8.2 was skipped due to an error in uploading to PyPi.
+* 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 2.8.0 to 2.8.1
---------------------------
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/numexpr-2.8.1/numexpr/interpreter.cpp
new/numexpr-2.8.3/numexpr/interpreter.cpp
--- old/numexpr-2.8.1/numexpr/interpreter.cpp 2018-07-12 19:49:43.000000000
+0200
+++ new/numexpr-2.8.3/numexpr/interpreter.cpp 2022-06-25 22:25:22.000000000
+0200
@@ -23,8 +23,11 @@
/* Some missing symbols and functions for Win */
#define fmax max
#define fmin min
-#define INFINITY (DBL_MAX+DBL_MAX)
-#define NAN (INFINITY-INFINITY)
+#define NE_INFINITY (DBL_MAX+DBL_MAX)
+#define NE_NAN (INFINITY-INFINITY)
+#else
+#define NE_INFINITY INFINITY
+#define NE_NAN NAN
#endif
#ifndef SIZE_MAX
@@ -1243,6 +1246,11 @@
/* A case with a single constant output */
+ PyArrayObject *singleton;
+ bool writeback;
+ // NOTE: cannot assign on declaration due to `goto` statements
+ singleton = NULL;
+ writeback = false;
if (n_inputs == 0) {
char retsig = get_return_sig(self->program);
@@ -1255,8 +1263,7 @@
goto fail;
}
}
- else {
- PyArrayObject *a;
+ else { // Use the provided output array
if (PyArray_SIZE(operands[0]) != 1) {
PyErr_SetString(PyExc_ValueError,
"output for a constant expression must have size 1");
@@ -1268,17 +1275,30 @@
goto fail;
}
Py_INCREF(dtypes[0]);
- a = (PyArrayObject *)PyArray_FromArray(operands[0], dtypes[0],
-
NPY_ARRAY_ALIGNED|NPY_ARRAY_UPDATEIFCOPY);
- if (a == NULL) {
+
+ // NumPy folks suggested using WRITEBACKIFCOPY to resolve issue
#397
+ singleton = (PyArrayObject *)PyArray_FromArray(operands[0],
dtypes[0],
+
NPY_ARRAY_ALIGNED|NPY_ARRAY_WRITEBACKIFCOPY);
+ if (singleton == NULL) {
goto fail;
}
+ writeback = true;
Py_DECREF(operands[0]);
- operands[0] = a;
+ operands[0] = singleton;
}
r = run_interpreter_const(self, PyArray_BYTES(operands[0]), &pc_error);
+ if (writeback) {
+ // Write-back our copy to the passed in output array if we had to
make a copy
+ // (which only happens if the input was not aligned)
+ int retval = PyArray_ResolveWritebackIfCopy(singleton);
+ if (retval < 0) {
+ // 1 means it copied the value, 0 means no copy, only -1 is an
error.
+ PyErr_Format(PyExc_ValueError, "Writeback to singleton failed
with error code: %d", retval);
+ goto fail;
+ }
+ }
ret = (PyObject *)operands[0];
Py_INCREF(ret);
goto cleanup_and_exit;
@@ -1409,7 +1429,7 @@
fill = PyLong_FromLong(1);
} else if (PyArray_DESCR(a)->kind == 'f') {
/* floating point min/max identity is NaN */
- fill = PyFloat_FromDouble(NAN);
+ fill = PyFloat_FromDouble(NE_NAN);
} else if (op >= OP_MIN && op < OP_MAX) {
/* integer min identity */
fill = PyLong_FromLong(LONG_MAX);
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/numexpr-2.8.1/numexpr/tests/test_numexpr.py
new/numexpr-2.8.3/numexpr/tests/test_numexpr.py
--- old/numexpr-2.8.1/numexpr/tests/test_numexpr.py 2021-12-03
21:14:17.000000000 +0100
+++ new/numexpr-2.8.3/numexpr/tests/test_numexpr.py 2022-06-25
22:25:22.000000000 +0200
@@ -481,6 +481,14 @@
else:
self.fail()
+ def test_unaligned_singleton(self):
+ # Test for issue #397 whether singletons outputs assigned to consts
must be
+ # aligned or not.
+ a = np.empty(5, dtype=np.uint8)[1:].view(np.int32)
+ evaluate('3', out=a)
+ assert_equal(a, 3)
+
+
def test_ex_uses_vml(self):
vml_funcs = [ "sin", "cos", "tan", "arcsin", "arccos", "arctan",
"sinh", "cosh", "tanh", "arcsinh", "arccosh", "arctanh",
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/numexpr-2.8.1/numexpr/utils.py
new/numexpr-2.8.3/numexpr/utils.py
--- old/numexpr-2.8.1/numexpr/utils.py 2021-11-06 01:29:32.000000000 +0100
+++ new/numexpr-2.8.3/numexpr/utils.py 2022-06-25 22:25:22.000000000 +0200
@@ -17,6 +17,7 @@
from numexpr.interpreter import _set_num_threads, _get_num_threads, MAX_THREADS
from numexpr import use_vml
+from . import version
if use_vml:
from numexpr.interpreter import (
@@ -123,7 +124,7 @@
the virtual machine.
"""
# Any platform-specific short-circuits
- if 'sparc' in platform.machine():
+ if 'sparc' in version.platform_machine:
log.warning('The number of threads have been set to 1 because problems
related '
'to threading have been reported on some sparc machine. '
'The number of threads can be changed using the
"set_num_threads" '
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/numexpr-2.8.1/numexpr/version.py
new/numexpr-2.8.3/numexpr/version.py
--- old/numexpr-2.8.1/numexpr/version.py 2021-12-10 23:03:15.000000000
+0100
+++ new/numexpr-2.8.3/numexpr/version.py 2022-06-25 22:35:20.000000000
+0200
@@ -1,3 +1,4 @@
# THIS FILE IS GENERATED BY `SETUP.PY`
-version = '2.8.1'
-numpy_build_version = '1.20.2'
+version = '2.8.3'
+numpy_build_version = '1.21.6'
+platform_machine = 'AMD64'
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/numexpr-2.8.1/numexpr.egg-info/PKG-INFO
new/numexpr-2.8.3/numexpr.egg-info/PKG-INFO
--- old/numexpr-2.8.1/numexpr.egg-info/PKG-INFO 2021-12-10 23:03:15.000000000
+0100
+++ new/numexpr-2.8.3/numexpr.egg-info/PKG-INFO 2022-06-25 22:35:20.000000000
+0200
@@ -1,23 +1,17 @@
Metadata-Version: 2.1
Name: numexpr
-Version: 2.8.1
+Version: 2.8.3
Summary: Fast numerical expression evaluator for NumPy
Home-page: https://github.com/pydata/numexpr
Author: David M. Cooke, Francesc Alted, and others
-Author-email:
-david.m.co...@gmail.com
-fal...@gmail.com
Maintainer: Robert A. McLeod
Maintainer-email: robbmcl...@gmail.com
License: MIT
-Description: UNKNOWN
-Platform: UNKNOWN
Classifier: Development Status :: 6 - Mature
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
-Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
@@ -25,5 +19,193 @@
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS
-Requires-Python: >=3.6
-Description-Content-Type: text/markdown
+Requires-Python: >=3.7
+Description-Content-Type: text/x-rst
+License-File: LICENSE.txt
+License-File: AUTHORS.txt
+
+======================================================
+NumExpr: Fast numerical expression evaluator for NumPy
+======================================================
+
+:Author: David M. Cooke, Francesc Alted, and others.
+:Maintainer: Robert A. McLeod
+:Contact: robbmcl...@gmail.com
+:URL: https://github.com/pydata/numexpr
+:Documentation: http://numexpr.readthedocs.io/en/latest/
+:Travis CI: |travis|
+:GitHub Actions: |actions|
+:PyPi: |version|
+:DOI: |doi|
+:readthedocs: |docs|
+
+.. |actions| image::
https://github.com/pydata/numexpr/workflows/Build/badge.svg
+ :target: https://github.com/pydata/numexpr/actions
+.. |travis| image:: https://travis-ci.org/pydata/numexpr.png?branch=master
+ :target: https://travis-ci.org/pydata/numexpr
+.. |docs| image::
https://readthedocs.org/projects/numexpr/badge/?version=latest
+ :target: http://numexpr.readthedocs.io/en/latest
+.. |doi| image:: https://zenodo.org/badge/doi/10.5281/zenodo.2483274.svg
+ :target: https://doi.org/10.5281/zenodo.2483274
+.. |version| image:: https://img.shields.io/pypi/v/numexpr.png
+ :target: https://pypi.python.org/pypi/numexpr
+
+What is NumExpr?
+----------------
+
+NumExpr is a fast numerical expression evaluator for NumPy. With it,
+expressions that operate on arrays (like :code:`'3*a+4*b'`) are accelerated
+and use less memory than doing the same calculation in Python.
+
+In addition, its multi-threaded capabilities can make use of all your
+cores -- which generally results in substantial performance scaling compared
+to NumPy.
+
+Last but not least, numexpr can make use of Intel's VML (Vector Math
+Library, normally integrated in its Math Kernel Library, or MKL).
+This allows further acceleration of transcendent expressions.
+
+
+How NumExpr achieves high performance
+-------------------------------------
+
+The main reason why NumExpr achieves better performance than NumPy is
+that it avoids allocating memory for intermediate results. This
+results in better cache utilization and reduces memory access in
+general. Due to this, NumExpr works best with large arrays.
+
+NumExpr parses expressions into its own op-codes that are then used by
+an integrated computing virtual machine. The array operands are split
+into small chunks that easily fit in the cache of the CPU and passed
+to the virtual machine. The virtual machine then applies the
+operations on each chunk. It's worth noting that all temporaries and
+constants in the expression are also chunked. Chunks are distributed among
+the available cores of the CPU, resulting in highly parallelized code
+execution.
+
+The result is that NumExpr can get the most of your machine computing
+capabilities for array-wise computations. Common speed-ups with regard
+to NumPy are usually between 0.95x (for very simple expressions like
+:code:`'a + 1'`) and 4x (for relatively complex ones like :code:`'a*b-4.1*a >
+2.5*b'`), although much higher speed-ups can be achieved for some functions
+and complex math operations (up to 15x in some cases).
+
+NumExpr performs best on matrices that are too large to fit in L1 CPU cache.
+In order to get a better idea on the different speed-ups that can be achieved
+on your platform, run the provided benchmarks.
+
+Installation
+------------
+
+From wheels
+^^^^^^^^^^^
+
+NumExpr is available for install via `pip` for a wide range of platforms and
+Python versions (which may be browsed at:
https://pypi.org/project/numexpr/#files).
+Installation can be performed as::
+
+ pip install numexpr
+
+If you are using the Anaconda or Miniconda distribution of Python you may
prefer
+to use the `conda` package manager in this case::
+
+ conda install numexpr
+
+From Source
+^^^^^^^^^^^
+
+On most `Nix systems your compilers will already be present. However if you
+are using a virtual environment with a substantially newer version of Python
than
+your system Python you may be prompted to install a new version of `gcc` or
`clang`.
+
+For Windows, you will need to install the Microsoft Visual C++ Build Tools
+(which are free) first.The version depends on which version of Python you have
+installed:
+
+https://wiki.python.org/moin/WindowsCompilers
+
+For Python 3.6+ simply installating the latest version of MSVC build tools
should
+be sufficient. Note that wheels found via pip do not include MKL support.
Wheels
+available via `conda` will have MKL, if the MKL backend is used for NumPy.
+
+See `requirements.txt` for the required version of NumPy.
+
+NumExpr is built in the standard Python way::
+
+ python setup.py build install
+
+You can test `numexpr` with::
+
+ python -c "import numexpr; numexpr.test()"
+
+Do not test NumExpr in the source directory or you will generate import errors.
+
+Enable Intel?? MKL support
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+NumExpr includes support for Intel's MKL library. This may provide better
+performance on Intel architectures, mainly when evaluating transcendental
+functions (trigonometrical, exponential...).
+
+If you have Intel's MKL, copy the `site.cfg.example` that comes with the
+distribution to `site.cfg` and edit the latter file to provide correct paths
to
+the MKL libraries in your system. After doing this, you can proceed with the
+usual building instructions listed above.
+
+Pay attention to the messages during the building process in order to know
+whether MKL has been detected or not. Finally, you can check the speed-ups on
+your machine by running the `bench/vml_timing.py` script (you can play with
+different parameters to the `set_vml_accuracy_mode()` and
`set_vml_num_threads()`
+functions in the script so as to see how it would affect performance).
+
+Usage
+-----
+
+::
+
+ >>> import numpy as np
+ >>> import numexpr as ne
+
+ >>> a = np.arange(1e6) # Choose large arrays for better speedups
+ >>> b = np.arange(1e6)
+
+ >>> ne.evaluate("a + 1") # a simple expression
+ array([ 1.00000000e+00, 2.00000000e+00, 3.00000000e+00, ...,
+ 9.99998000e+05, 9.99999000e+05, 1.00000000e+06])
+
+ >>> ne.evaluate('a*b-4.1*a > 2.5*b') # a more complex one
+ array([False, False, False, ..., True, True, True], dtype=bool)
+
+ >>> ne.evaluate("sin(a) + arcsinh(a/b)") # you can also use functions
+ array([ NaN, 1.72284457, 1.79067101, ..., 1.09567006,
+ 0.17523598, -0.09597844])
+
+ >>> s = np.array([b'abba', b'abbb', b'abbcdef'])
+ >>> ne.evaluate("b'abba' == s") # string arrays are supported too
+ array([ True, False, False], dtype=bool)
+
+
+Documentation
+-------------
+
+Please see the official documentation at `numexpr.readthedocs.io
<https://numexpr.readthedocs.io>`_.
+Included is a user guide, benchmark results, and the reference API.
+
+
+Authors
+-------
+
+Please see `AUTHORS.txt
<https://github.com/pydata/numexpr/blob/master/AUTHORS.txt>`_.
+
+
+License
+-------
+
+NumExpr is distributed under the `MIT
<http://www.opensource.org/licenses/mit-license.php>`_ license.
+
+
+.. Local Variables:
+.. mode: text
+.. coding: utf-8
+.. fill-column: 70
+.. End:
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/numexpr-2.8.1/setup.cfg new/numexpr-2.8.3/setup.cfg
--- old/numexpr-2.8.1/setup.cfg 2021-12-10 23:03:16.315434500 +0100
+++ new/numexpr-2.8.3/setup.cfg 2022-06-25 22:35:21.621027200 +0200
@@ -1,16 +1,13 @@
[metadata]
name = numexpr
-version = 2.8.1
+version = 2.8.3
description = Fast numerical expression evaluator for NumPy
author = David M. Cooke, Francesc Alted, and others
-author_email =
- david.m.co...@gmail.com
- fal...@gmail.com
maintainer = Robert A. McLeod
maintainer_email = robbmcl...@gmail.com
url = https://github.com/pydata/numexpr
-long_description = file: README.md
-long_description_content_type = text/markdown
+long_description = file: README.rst
+long_description_content_type = text/x-rst
license = MIT
classifiers =
Development Status :: 6 - Mature
@@ -18,7 +15,6 @@
Intended Audience :: Science/Research
License :: OSI Approved :: MIT License
Programming Language :: Python :: 3
- Programming Language :: Python :: 3.6
Programming Language :: Python :: 3.7
Programming Language :: Python :: 3.8
Programming Language :: Python :: 3.9
@@ -30,7 +26,7 @@
[options]
zip_safe = False
packages = find:
-python_requires = >=3.6
+python_requires = >=3.7
[egg_info]
tag_build =
diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn'
'--exclude=.svnignore' old/numexpr-2.8.1/setup.py new/numexpr-2.8.3/setup.py
--- old/numexpr-2.8.1/setup.py 2021-12-08 01:21:59.000000000 +0100
+++ new/numexpr-2.8.3/setup.py 2022-06-25 22:25:22.000000000 +0200
@@ -26,6 +26,7 @@
fh.write("numpy_build_version = '%s'\n" % np.__version__)
except ImportError:
pass
+ fh.write("platform_machine = '%s'\n" % platform.machine())
lib_dirs = []
inc_dirs = [np.get_include(), op.join('framestream')]
@@ -39,7 +40,9 @@
extra_link_args = []
if platform.uname().system == 'Windows':
- extra_cflags = ['/O2']
+ # For MSVC only
+ if "MSC" in platform.python_compiler():
+ extra_cflags = ['/O2']
extra_link_args = []
sources.append('numexpr/win32/pthread.c')
else:
