automatic import of python-Bottleneck

3 files changed, 215 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..a8ecec1 100644
--- a/.gitignore
+++ b/.gitignore
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+/Bottleneck-1.3.6.tar.gz
diff --git a/python-Bottleneck.spec b/python-Bottleneck.spec
new file mode 100644
index 0000000..8a69331
--- /dev/null
+++ b/python-Bottleneck.spec
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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
diff --git a/sources b/sources
new file mode 100644
index 0000000..5acf932
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+b1ddbc83daf8456f4fef61c98d94ee80  Bottleneck-1.3.6.tar.gz