automatic import of python-weightedstatsopeneuler20.03

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diff --git a/.gitignore b/.gitignore
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+/weightedstats-0.4.1.tar.gz
diff --git a/python-weightedstats.spec b/python-weightedstats.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-weightedstats
+Version:	0.4.1
+Release:	1
+Summary:	Mean, weighted mean, median, weighted median
+License:	MIT
+URL:		https://github.com/tinybike/weightedstats
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/da/a5/f5c0e601a610e4618316be3155febbbec98994788fcc0e9d8080369266ec/weightedstats-0.4.1.tar.gz
+BuildArch:	noarch
+
+
+%description
+Python functions to calculate the mean, weighted mean, median, and weighted median.
+Installation
+^^^^^^^^^^^^
+The easiest way to install WeightedStats is to use pip::
+    $ pip install weightedstats
+Usage
+^^^^^
+WeightedStats includes four functions (mean, weighted_mean, median, weighted_median) which accept lists as arguments, and two functions (numpy_weighted_mean, numpy weighted_median) which accept either lists or numpy arrays.
+Example:
+    import weightedstats as ws
+    my_data = [1, 2, 3, 4, 5]
+    my_weights = [10, 1, 1, 1, 9]
+    # Ordinary (unweighted) mean and median
+    ws.mean(my_data)    # equivalent to ws.weighted_mean(my_data)
+    ws.median(my_data)  # equivalent to ws.weighted_median(my_data)
+    # Weighted mean and median
+    ws.weighted_mean(my_data, weights=my_weights)
+    ws.weighted_median(my_data, weights=my_weights)
+    # Special weighted mean and median functions for use with numpy arrays
+    ws.numpy_weighted_mean(my_data, weights=my_weights)
+    ws.numpy_weighted_median(my_data, weights=my_weights)
+Tests
+^^^^^
+Unit tests are in the test/ directory.
+
+%package -n python3-weightedstats
+Summary:	Mean, weighted mean, median, weighted median
+Provides:	python-weightedstats
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-weightedstats
+Python functions to calculate the mean, weighted mean, median, and weighted median.
+Installation
+^^^^^^^^^^^^
+The easiest way to install WeightedStats is to use pip::
+    $ pip install weightedstats
+Usage
+^^^^^
+WeightedStats includes four functions (mean, weighted_mean, median, weighted_median) which accept lists as arguments, and two functions (numpy_weighted_mean, numpy weighted_median) which accept either lists or numpy arrays.
+Example:
+    import weightedstats as ws
+    my_data = [1, 2, 3, 4, 5]
+    my_weights = [10, 1, 1, 1, 9]
+    # Ordinary (unweighted) mean and median
+    ws.mean(my_data)    # equivalent to ws.weighted_mean(my_data)
+    ws.median(my_data)  # equivalent to ws.weighted_median(my_data)
+    # Weighted mean and median
+    ws.weighted_mean(my_data, weights=my_weights)
+    ws.weighted_median(my_data, weights=my_weights)
+    # Special weighted mean and median functions for use with numpy arrays
+    ws.numpy_weighted_mean(my_data, weights=my_weights)
+    ws.numpy_weighted_median(my_data, weights=my_weights)
+Tests
+^^^^^
+Unit tests are in the test/ directory.
+
+%package help
+Summary:	Development documents and examples for weightedstats
+Provides:	python3-weightedstats-doc
+%description help
+Python functions to calculate the mean, weighted mean, median, and weighted median.
+Installation
+^^^^^^^^^^^^
+The easiest way to install WeightedStats is to use pip::
+    $ pip install weightedstats
+Usage
+^^^^^
+WeightedStats includes four functions (mean, weighted_mean, median, weighted_median) which accept lists as arguments, and two functions (numpy_weighted_mean, numpy weighted_median) which accept either lists or numpy arrays.
+Example:
+    import weightedstats as ws
+    my_data = [1, 2, 3, 4, 5]
+    my_weights = [10, 1, 1, 1, 9]
+    # Ordinary (unweighted) mean and median
+    ws.mean(my_data)    # equivalent to ws.weighted_mean(my_data)
+    ws.median(my_data)  # equivalent to ws.weighted_median(my_data)
+    # Weighted mean and median
+    ws.weighted_mean(my_data, weights=my_weights)
+    ws.weighted_median(my_data, weights=my_weights)
+    # Special weighted mean and median functions for use with numpy arrays
+    ws.numpy_weighted_mean(my_data, weights=my_weights)
+    ws.numpy_weighted_median(my_data, weights=my_weights)
+Tests
+^^^^^
+Unit tests are in the test/ directory.
+
+%prep
+%autosetup -n weightedstats-0.4.1
+
+%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-weightedstats -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.1-1
+- Package Spec generated
diff --git a/sources b/sources
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index 0000000..4addd42
--- /dev/null
+++ b/sources
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+f5cac13564b15e49a4eee9ca6d195ca4  weightedstats-0.4.1.tar.gz