automatic import of python-limbr

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diff --git a/python-limbr.spec b/python-limbr.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-LIMBR
+Version:	0.2.10
+Release:	1
+Summary:	Learning and Imputation for Mass-spec Bias Reduction
+License:	BSD-3
+URL:		https://github.com/aleccrowell/LIMBR
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/04/e3/7f3dca861a7dc47f1d12c4bb0b2aa9072db8980cb86a0ca12be21cc6474b/LIMBR-0.2.10.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-scipy
+Requires:	python3-sklearn
+Requires:	python3-statsmodels
+Requires:	python3-tqdm
+Requires:	python3-multiprocess
+Requires:	python3-matplotlib
+
+%description
+LIMBR provides a streamlined tool set for imputation of missing data followed by modelling and removal of batch effects.  The software was designed for proteomics datasets, with an emphasis on circadian 
+proteomics data, but can be applied to any time course or blocked experiments which produce large amounts of data, such as RNAseq. The two main classes are imputable, which performs missing data imputation, and sva, which performs 
+
+%package -n python3-LIMBR
+Summary:	Learning and Imputation for Mass-spec Bias Reduction
+Provides:	python-LIMBR
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-LIMBR
+LIMBR provides a streamlined tool set for imputation of missing data followed by modelling and removal of batch effects.  The software was designed for proteomics datasets, with an emphasis on circadian 
+proteomics data, but can be applied to any time course or blocked experiments which produce large amounts of data, such as RNAseq. The two main classes are imputable, which performs missing data imputation, and sva, which performs 
+
+%package help
+Summary:	Development documents and examples for LIMBR
+Provides:	python3-LIMBR-doc
+%description help
+LIMBR provides a streamlined tool set for imputation of missing data followed by modelling and removal of batch effects.  The software was designed for proteomics datasets, with an emphasis on circadian 
+proteomics data, but can be applied to any time course or blocked experiments which produce large amounts of data, such as RNAseq. The two main classes are imputable, which performs missing data imputation, and sva, which performs 
+
+%prep
+%autosetup -n LIMBR-0.2.10
+
+%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-LIMBR -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.2.10-1
+- Package Spec generated