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Package is "python-Bottleneck" Sun Apr 21 20:29:52 2024 rev:15 rq:1169288 version:1.3.8 Changes: -------- --- /work/SRC/openSUSE:Factory/python-Bottleneck/python-Bottleneck.changes 2023-03-17 17:02:30.465128794 +0100 +++ /work/SRC/openSUSE:Factory/.python-Bottleneck.new.26366/python-Bottleneck.changes 2024-04-21 20:31:00.446127626 +0200 @@ -1,0 +2,8 @@ +Fri Apr 19 11:14:59 UTC 2024 - Ben Greiner <c...@bnavigator.de> + +- Update to 1.3.8 + * Maintenance updates +- Pin to numpy < 2: Fails to build otherwise. Upstream is working + on it. + +------------------------------------------------------------------- Old: ---- Bottleneck-1.3.7.tar.gz New: ---- Bottleneck-1.3.8.tar.gz ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Other differences: ------------------ ++++++ python-Bottleneck.spec ++++++ --- /var/tmp/diff_new_pack.sFylXj/_old 2024-04-21 20:31:00.962146569 +0200 +++ /var/tmp/diff_new_pack.sFylXj/_new 2024-04-21 20:31:00.962146569 +0200 @@ -1,7 +1,7 @@ # # spec file for package python-Bottleneck # -# Copyright (c) 2023 SUSE LLC +# Copyright (c) 2024 SUSE LLC # # All modifications and additions to the file contributed by third parties # remain the property of their copyright owners, unless otherwise agreed @@ -16,9 +16,8 @@ # -%define skip_python2 1 Name: python-Bottleneck -Version: 1.3.7 +Version: 1.3.8 Release: 0 Summary: A collection of fast NumPy array functions License: BSD-2-Clause AND BSD-3-Clause @@ -26,12 +25,14 @@ Source0: https://files.pythonhosted.org/packages/source/B/Bottleneck/Bottleneck-%{version}.tar.gz Source99: python-Bottleneck.rpmlintrc BuildRequires: %{python_module devel} -BuildRequires: %{python_module numpy-devel >= 1.16.0} +BuildRequires: %{python_module numpy-devel >= 1.16.0 with %python-numpy-devel < 2} +BuildRequires: %{python_module pip} BuildRequires: %{python_module pytest} BuildRequires: %{python_module setuptools} +BuildRequires: %{python_module wheel} BuildRequires: fdupes BuildRequires: python-rpm-macros -Requires: python-numpy >= 1.16.0 +Requires: (python-numpy >= 1.16.0 with python-numpy < 2) %python_subpackages %description @@ -42,10 +43,10 @@ %build export CFLAGS="%{optflags} -fno-strict-aliasing" -%python_build +%pyproject_wheel %install -%python_install +%pyproject_install %python_expand %fdupes %{buildroot}%{$python_sitearch} %check @@ -57,5 +58,5 @@ %license LICENSE %doc README.rst RELEASE.rst %{python_sitearch}/bottleneck/ -%{python_sitearch}/Bottleneck-%{version}*-info +%{python_sitearch}/Bottleneck-%{version}.dist-info ++++++ Bottleneck-1.3.7.tar.gz -> Bottleneck-1.3.8.tar.gz ++++++ diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/Bottleneck-1.3.7/Bottleneck.egg-info/PKG-INFO new/Bottleneck-1.3.8/Bottleneck.egg-info/PKG-INFO --- old/Bottleneck-1.3.7/Bottleneck.egg-info/PKG-INFO 2023-03-08 19:06:55.000000000 +0100 +++ new/Bottleneck-1.3.8/Bottleneck.egg-info/PKG-INFO 2024-02-25 01:30:22.000000000 +0100 @@ -1,6 +1,6 @@ Metadata-Version: 2.1 Name: Bottleneck -Version: 1.3.7 +Version: 1.3.8 Summary: Fast NumPy array functions written in C Home-page: https://github.com/pydata/bottleneck Download-URL: http://pypi.python.org/pypi/Bottleneck @@ -21,27 +21,40 @@ Classifier: Programming Language :: Python :: 3.6 Classifier: Programming Language :: Python :: 3.7 Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 Classifier: Topic :: Scientific/Engineering Classifier: Topic :: Scientific/Engineering Requires: numpy Description-Content-Type: text/x-rst -Provides-Extra: doc License-File: LICENSE +Requires-Dist: numpy +Provides-Extra: doc +Requires-Dist: numpydoc; extra == "doc" +Requires-Dist: sphinx; extra == "doc" +Requires-Dist: gitpython; extra == "doc" Bottleneck is a collection of fast NumPy array functions written in C. -Let's give it a try. Create a NumPy array:: +Let's give it a try. Create a NumPy array: + +.. code-block:: pycon >>> import numpy as np >>> a = np.array([1, 2, np.nan, 4, 5]) -Find the nanmean:: +Find the nanmean: + +.. code-block:: pycon >>> import bottleneck as bn >>> bn.nanmean(a) 3.0 -Moving window mean:: +Moving window mean: + +.. code-block:: pycon >>> bn.move_mean(a, window=2, min_count=1) array([ 1. , 1.5, 2. , 4. , 4.5]) @@ -49,7 +62,9 @@ Benchmark ========= -Bottleneck comes with a benchmark suite:: +Bottleneck comes with a benchmark suite: + +.. code-block:: pycon >>> bn.bench() Bottleneck performance benchmark @@ -91,7 +106,9 @@ move_rank 847.1 1.2 1.4 2.3 2.6 You can also run a detailed benchmark for a single function using, for -example, the command:: +example, the command: + +.. code-block:: pycon >>> bn.bench_detailed("move_median", fraction_nan=0.3) @@ -122,21 +139,25 @@ Requirements: -======================== ==================================================== -Bottleneck Python 2.7, 3.5, 3.6, 3.7, 3.8; NumPy 1.16.0+ +======================== ============================================================================ +Bottleneck Python 2.7, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, 3.11; NumPy 1.16.0+ Compile gcc, clang, MinGW or MSVC Unit tests pytest Documentation sphinx, numpydoc -======================== ==================================================== +======================== ============================================================================ + +To install Bottleneck on Linux, Mac OS X, et al.: -To install Bottleneck on Linux, Mac OS X, et al.:: +.. code-block:: console $ pip install . To install bottleneck on Windows, first install MinGW and add it to your -system path. Then install Bottleneck with the command:: +system path. Then install Bottleneck with the command: - python setup.py install --compiler=mingw32 +.. code-block:: console + + $ python setup.py install --compiler=mingw32 Alternatively, you can use the Windows binaries created by Christoph Gohlke: http://www.lfd.uci.edu/~gohlke/pythonlibs/#bottleneck @@ -144,7 +165,9 @@ Unit tests ========== -After you have installed Bottleneck, run the suite of unit tests:: +After you have installed Bottleneck, run the suite of unit tests: + +.. code-block:: pycon In [1]: import bottleneck as bn diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/Bottleneck-1.3.7/PKG-INFO new/Bottleneck-1.3.8/PKG-INFO --- old/Bottleneck-1.3.7/PKG-INFO 2023-03-08 19:06:55.940163900 +0100 +++ new/Bottleneck-1.3.8/PKG-INFO 2024-02-25 01:30:22.989980500 +0100 @@ -1,6 +1,6 @@ Metadata-Version: 2.1 Name: Bottleneck -Version: 1.3.7 +Version: 1.3.8 Summary: Fast NumPy array functions written in C Home-page: https://github.com/pydata/bottleneck Download-URL: http://pypi.python.org/pypi/Bottleneck @@ -21,27 +21,40 @@ Classifier: Programming Language :: Python :: 3.6 Classifier: Programming Language :: Python :: 3.7 Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 Classifier: Topic :: Scientific/Engineering Classifier: Topic :: Scientific/Engineering Requires: numpy Description-Content-Type: text/x-rst -Provides-Extra: doc License-File: LICENSE +Requires-Dist: numpy +Provides-Extra: doc +Requires-Dist: numpydoc; extra == "doc" +Requires-Dist: sphinx; extra == "doc" +Requires-Dist: gitpython; extra == "doc" Bottleneck is a collection of fast NumPy array functions written in C. -Let's give it a try. Create a NumPy array:: +Let's give it a try. Create a NumPy array: + +.. code-block:: pycon >>> import numpy as np >>> a = np.array([1, 2, np.nan, 4, 5]) -Find the nanmean:: +Find the nanmean: + +.. code-block:: pycon >>> import bottleneck as bn >>> bn.nanmean(a) 3.0 -Moving window mean:: +Moving window mean: + +.. code-block:: pycon >>> bn.move_mean(a, window=2, min_count=1) array([ 1. , 1.5, 2. , 4. , 4.5]) @@ -49,7 +62,9 @@ Benchmark ========= -Bottleneck comes with a benchmark suite:: +Bottleneck comes with a benchmark suite: + +.. code-block:: pycon >>> bn.bench() Bottleneck performance benchmark @@ -91,7 +106,9 @@ move_rank 847.1 1.2 1.4 2.3 2.6 You can also run a detailed benchmark for a single function using, for -example, the command:: +example, the command: + +.. code-block:: pycon >>> bn.bench_detailed("move_median", fraction_nan=0.3) @@ -122,21 +139,25 @@ Requirements: -======================== ==================================================== -Bottleneck Python 2.7, 3.5, 3.6, 3.7, 3.8; NumPy 1.16.0+ +======================== ============================================================================ +Bottleneck Python 2.7, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, 3.11; NumPy 1.16.0+ Compile gcc, clang, MinGW or MSVC Unit tests pytest Documentation sphinx, numpydoc -======================== ==================================================== +======================== ============================================================================ + +To install Bottleneck on Linux, Mac OS X, et al.: -To install Bottleneck on Linux, Mac OS X, et al.:: +.. code-block:: console $ pip install . To install bottleneck on Windows, first install MinGW and add it to your -system path. Then install Bottleneck with the command:: +system path. Then install Bottleneck with the command: - python setup.py install --compiler=mingw32 +.. code-block:: console + + $ python setup.py install --compiler=mingw32 Alternatively, you can use the Windows binaries created by Christoph Gohlke: http://www.lfd.uci.edu/~gohlke/pythonlibs/#bottleneck @@ -144,7 +165,9 @@ Unit tests ========== -After you have installed Bottleneck, run the suite of unit tests:: +After you have installed Bottleneck, run the suite of unit tests: + +.. code-block:: pycon In [1]: import bottleneck as bn diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/Bottleneck-1.3.7/README.rst new/Bottleneck-1.3.8/README.rst --- old/Bottleneck-1.3.7/README.rst 2023-03-08 19:06:43.000000000 +0100 +++ new/Bottleneck-1.3.8/README.rst 2024-02-25 01:30:13.000000000 +0100 @@ -7,18 +7,24 @@ Bottleneck is a collection of fast NumPy array functions written in C. -Let's give it a try. Create a NumPy array:: +Let's give it a try. Create a NumPy array: + +.. code-block:: pycon >>> import numpy as np >>> a = np.array([1, 2, np.nan, 4, 5]) -Find the nanmean:: +Find the nanmean: + +.. code-block:: pycon >>> import bottleneck as bn >>> bn.nanmean(a) 3.0 -Moving window mean:: +Moving window mean: + +.. code-block:: pycon >>> bn.move_mean(a, window=2, min_count=1) array([ 1. , 1.5, 2. , 4. , 4.5]) @@ -26,7 +32,9 @@ Benchmark ========= -Bottleneck comes with a benchmark suite:: +Bottleneck comes with a benchmark suite: + +.. code-block:: pycon >>> bn.bench() Bottleneck performance benchmark @@ -68,7 +76,9 @@ move_rank 847.1 1.2 1.4 2.3 2.6 You can also run a detailed benchmark for a single function using, for -example, the command:: +example, the command: + +.. code-block:: pycon >>> bn.bench_detailed("move_median", fraction_nan=0.3) @@ -99,21 +109,25 @@ Requirements: -======================== ==================================================== -Bottleneck Python 2.7, 3.5, 3.6, 3.7, 3.8; NumPy 1.16.0+ +======================== ============================================================================ +Bottleneck Python 2.7, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, 3.11; NumPy 1.16.0+ Compile gcc, clang, MinGW or MSVC Unit tests pytest Documentation sphinx, numpydoc -======================== ==================================================== +======================== ============================================================================ + +To install Bottleneck on Linux, Mac OS X, et al.: -To install Bottleneck on Linux, Mac OS X, et al.:: +.. code-block:: console $ pip install . To install bottleneck on Windows, first install MinGW and add it to your -system path. Then install Bottleneck with the command:: +system path. Then install Bottleneck with the command: - python setup.py install --compiler=mingw32 +.. code-block:: console + + $ python setup.py install --compiler=mingw32 Alternatively, you can use the Windows binaries created by Christoph Gohlke: http://www.lfd.uci.edu/~gohlke/pythonlibs/#bottleneck @@ -121,7 +135,9 @@ Unit tests ========== -After you have installed Bottleneck, run the suite of unit tests:: +After you have installed Bottleneck, run the suite of unit tests: + +.. code-block:: pycon In [1]: import bottleneck as bn diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/Bottleneck-1.3.7/RELEASE.rst new/Bottleneck-1.3.8/RELEASE.rst --- old/Bottleneck-1.3.7/RELEASE.rst 2023-03-08 19:06:43.000000000 +0100 +++ new/Bottleneck-1.3.8/RELEASE.rst 2024-02-25 01:30:13.000000000 +0100 @@ -6,6 +6,16 @@ These are the major changes made in each release. For details of the changes see the commit log at https://github.com/pydata/bottleneck +Bottleneck 1.3.8 +================ + +*Release date: 2024-02-04* + +Enhancements +~~~~~~~~~~~~ +- Python 3.12 wheel available +- Update CI config + Bottleneck 1.3.7 ================ diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/Bottleneck-1.3.7/bottleneck/_version.py new/Bottleneck-1.3.8/bottleneck/_version.py --- old/Bottleneck-1.3.7/bottleneck/_version.py 2023-03-08 19:06:55.940163900 +0100 +++ new/Bottleneck-1.3.8/bottleneck/_version.py 2024-02-25 01:30:22.989980500 +0100 @@ -8,11 +8,11 @@ version_json = ''' { - "date": "2023-02-20T11:39:37+0100", + "date": "2024-02-25T01:15:02+0100", "dirty": false, "error": null, - "full-revisionid": "22f9dd130cdcd70d86f6f8fabf1412d927e49864", - "version": "1.3.7" + "full-revisionid": "bd70f840ac1e0bc220b9bc6bfee6b42ffa57464a", + "version": "1.3.8" } ''' # END VERSION_JSON diff -urN '--exclude=CVS' '--exclude=.cvsignore' '--exclude=.svn' '--exclude=.svnignore' old/Bottleneck-1.3.7/setup.py new/Bottleneck-1.3.8/setup.py --- old/Bottleneck-1.3.7/setup.py 2023-03-08 19:06:43.000000000 +0100 +++ new/Bottleneck-1.3.8/setup.py 2024-02-25 01:30:13.000000000 +0100 @@ -166,6 +166,9 @@ "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", + "Programming Language :: Python :: 3.9", + "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", "Topic :: Scientific/Engineering", "Topic :: Scientific/Engineering", ]
