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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
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