%global _empty_manifest_terminate_build 0 Name: python-Bottleneck Version: 1.3.7 Release: 1 Summary: Fast NumPy array functions written in C License: Simplified BSD URL: https://github.com/pydata/bottleneck Source0: https://mirrors.nju.edu.cn/pypi/web/packages/98/1f/e5c91a94a9e695fe12442aa3a1c0c8fa7b09b1091ab885e288a45733c089/Bottleneck-1.3.7.tar.gz Requires: python3-numpy Requires: python3-numpydoc Requires: python3-sphinx Requires: python3-gitpython %description Bottleneck comes with a benchmark suite:: >>> bn.bench() Bottleneck performance benchmark Bottleneck 1.3.0.dev0+122.gb1615d7; Numpy 1.16.4 Speed is NumPy time divided by Bottleneck time NaN means approx one-fifth NaNs; float64 used no NaN no NaN NaN no NaN NaN (100,) (1000,1000)(1000,1000)(1000,1000)(1000,1000) axis=0 axis=0 axis=0 axis=1 axis=1 nansum 29.7 1.4 1.6 2.0 2.1 nanmean 99.0 2.0 1.8 3.2 2.5 nanstd 145.6 1.8 1.8 2.7 2.5 nanvar 138.4 1.8 1.8 2.8 2.5 nanmin 27.6 0.5 1.7 0.7 2.4 nanmax 26.6 0.6 1.6 0.7 2.5 median 120.6 1.3 4.9 1.1 5.7 nanmedian 117.8 5.0 5.7 4.8 5.5 ss 13.2 1.2 1.3 1.5 1.5 nanargmin 66.8 5.5 4.8 3.5 7.1 nanargmax 57.6 2.9 5.1 2.5 5.3 anynan 10.2 0.3 52.3 0.8 41.6 allnan 15.1 196.0 156.3 135.8 111.2 rankdata 45.9 1.2 1.2 2.1 2.1 nanrankdata 50.5 1.4 1.3 2.4 2.3 partition 3.3 1.1 1.6 1.0 1.5 argpartition 3.4 1.2 1.5 1.1 1.6 replace 9.0 1.5 1.5 1.5 1.5 push 1565.6 5.9 7.0 13.0 10.9 move_sum 2159.3 31.1 83.6 186.9 182.5 move_mean 6264.3 66.2 111.9 361.1 246.5 move_std 8653.6 86.5 163.7 232.0 317.7 move_var 8856.0 96.3 171.6 267.9 332.9 move_min 1186.6 13.4 30.9 23.5 45.0 move_max 1188.0 14.6 29.9 23.5 46.0 move_argmin 2568.3 33.3 61.0 49.2 86.8 move_argmax 2475.8 30.9 58.6 45.0 82.8 move_median 2236.9 153.9 151.4 171.3 166.9 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:: >>> bn.bench_detailed("move_median", fraction_nan=0.3) Only arrays with data type (dtype) int32, int64, float32, and float64 are accelerated. All other dtypes result in calls to slower, unaccelerated functions. In the rare case of a byte-swapped input array (e.g. a big-endian array on a little-endian operating system) the function will not be accelerated regardless of dtype. %package -n python3-Bottleneck Summary: Fast NumPy array functions written in C Provides: python-Bottleneck BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-Bottleneck Bottleneck comes with a benchmark suite:: >>> bn.bench() Bottleneck performance benchmark Bottleneck 1.3.0.dev0+122.gb1615d7; Numpy 1.16.4 Speed is NumPy time divided by Bottleneck time NaN means approx one-fifth NaNs; float64 used no NaN no NaN NaN no NaN NaN (100,) (1000,1000)(1000,1000)(1000,1000)(1000,1000) axis=0 axis=0 axis=0 axis=1 axis=1 nansum 29.7 1.4 1.6 2.0 2.1 nanmean 99.0 2.0 1.8 3.2 2.5 nanstd 145.6 1.8 1.8 2.7 2.5 nanvar 138.4 1.8 1.8 2.8 2.5 nanmin 27.6 0.5 1.7 0.7 2.4 nanmax 26.6 0.6 1.6 0.7 2.5 median 120.6 1.3 4.9 1.1 5.7 nanmedian 117.8 5.0 5.7 4.8 5.5 ss 13.2 1.2 1.3 1.5 1.5 nanargmin 66.8 5.5 4.8 3.5 7.1 nanargmax 57.6 2.9 5.1 2.5 5.3 anynan 10.2 0.3 52.3 0.8 41.6 allnan 15.1 196.0 156.3 135.8 111.2 rankdata 45.9 1.2 1.2 2.1 2.1 nanrankdata 50.5 1.4 1.3 2.4 2.3 partition 3.3 1.1 1.6 1.0 1.5 argpartition 3.4 1.2 1.5 1.1 1.6 replace 9.0 1.5 1.5 1.5 1.5 push 1565.6 5.9 7.0 13.0 10.9 move_sum 2159.3 31.1 83.6 186.9 182.5 move_mean 6264.3 66.2 111.9 361.1 246.5 move_std 8653.6 86.5 163.7 232.0 317.7 move_var 8856.0 96.3 171.6 267.9 332.9 move_min 1186.6 13.4 30.9 23.5 45.0 move_max 1188.0 14.6 29.9 23.5 46.0 move_argmin 2568.3 33.3 61.0 49.2 86.8 move_argmax 2475.8 30.9 58.6 45.0 82.8 move_median 2236.9 153.9 151.4 171.3 166.9 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:: >>> bn.bench_detailed("move_median", fraction_nan=0.3) Only arrays with data type (dtype) int32, int64, float32, and float64 are accelerated. All other dtypes result in calls to slower, unaccelerated functions. In the rare case of a byte-swapped input array (e.g. a big-endian array on a little-endian operating system) the function will not be accelerated regardless of dtype. %package help Summary: Development documents and examples for Bottleneck Provides: python3-Bottleneck-doc %description help Bottleneck comes with a benchmark suite:: >>> bn.bench() Bottleneck performance benchmark Bottleneck 1.3.0.dev0+122.gb1615d7; Numpy 1.16.4 Speed is NumPy time divided by Bottleneck time NaN means approx one-fifth NaNs; float64 used no NaN no NaN NaN no NaN NaN (100,) (1000,1000)(1000,1000)(1000,1000)(1000,1000) axis=0 axis=0 axis=0 axis=1 axis=1 nansum 29.7 1.4 1.6 2.0 2.1 nanmean 99.0 2.0 1.8 3.2 2.5 nanstd 145.6 1.8 1.8 2.7 2.5 nanvar 138.4 1.8 1.8 2.8 2.5 nanmin 27.6 0.5 1.7 0.7 2.4 nanmax 26.6 0.6 1.6 0.7 2.5 median 120.6 1.3 4.9 1.1 5.7 nanmedian 117.8 5.0 5.7 4.8 5.5 ss 13.2 1.2 1.3 1.5 1.5 nanargmin 66.8 5.5 4.8 3.5 7.1 nanargmax 57.6 2.9 5.1 2.5 5.3 anynan 10.2 0.3 52.3 0.8 41.6 allnan 15.1 196.0 156.3 135.8 111.2 rankdata 45.9 1.2 1.2 2.1 2.1 nanrankdata 50.5 1.4 1.3 2.4 2.3 partition 3.3 1.1 1.6 1.0 1.5 argpartition 3.4 1.2 1.5 1.1 1.6 replace 9.0 1.5 1.5 1.5 1.5 push 1565.6 5.9 7.0 13.0 10.9 move_sum 2159.3 31.1 83.6 186.9 182.5 move_mean 6264.3 66.2 111.9 361.1 246.5 move_std 8653.6 86.5 163.7 232.0 317.7 move_var 8856.0 96.3 171.6 267.9 332.9 move_min 1186.6 13.4 30.9 23.5 45.0 move_max 1188.0 14.6 29.9 23.5 46.0 move_argmin 2568.3 33.3 61.0 49.2 86.8 move_argmax 2475.8 30.9 58.6 45.0 82.8 move_median 2236.9 153.9 151.4 171.3 166.9 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:: >>> bn.bench_detailed("move_median", fraction_nan=0.3) Only arrays with data type (dtype) int32, int64, float32, and float64 are accelerated. All other dtypes result in calls to slower, unaccelerated functions. In the rare case of a byte-swapped input array (e.g. a big-endian array on a little-endian operating system) the function will not be accelerated regardless of dtype. %prep %autosetup -n Bottleneck-1.3.7 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-Bottleneck -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri Apr 07 2023 Python_Bot - 1.3.7-1 - Package Spec generated