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|
%global _empty_manifest_terminate_build 0
Name: python-Bottleneck
Version: 1.3.6
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/4a/4f/2ee4ee0494384891fa7784d774affbcf2ad6c9ddb33b1fd211da86739513/Bottleneck-1.3.6.tar.gz
Requires: python3-sphinx
Requires: python3-numpy
Requires: python3-numpydoc
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-1.3.6
%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
* Mon Mar 06 2023 Python_Bot <Python_Bot@openeuler.org> - 1.3.6-1
- Package Spec generated
|
