automatic import of python-halla

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+%global _empty_manifest_terminate_build 0
+Name:		python-HAllA
+Version:	0.8.20
+Release:	1
+Summary:	HAllA: Hierarchical All-against All Association Testing
+License:	MIT
+URL:		https://github.com/biobakery/halla
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/4f/c1/a5d48566d0b415b38e58a2c6d8b9e9f6d2d20201b489138fc51657ccaca5/HAllA-0.8.20.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-jenkspy
+Requires:	python3-matplotlib
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-PyYAML
+Requires:	python3-rpy2
+Requires:	python3-scikit-learn
+Requires:	python3-scipy
+Requires:	python3-seaborn
+Requires:	python3-sklearn
+Requires:	python3-statsmodels
+Requires:	python3-tqdm
+
+%description
+Given two high-dimensional 'omics datasets X and Y (continuous and/or categorical features) from the same n biosamples, HAllA (Hierarchical All-against-All Association Testing) discovers densely-associated blocks of features in the X vs. Y association matrix where: 1) each block is defined as all associations between features in a subtree of X hierarchy and features in a subtree of Y hierarchy and 2) a block is densely associated if (1 - FNR)% of pairwise associations are FDR significant (FNR is the pre-defined expected false negative rate)
+
+
+
+
+%package -n python3-HAllA
+Summary:	HAllA: Hierarchical All-against All Association Testing
+Provides:	python-HAllA
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-HAllA
+Given two high-dimensional 'omics datasets X and Y (continuous and/or categorical features) from the same n biosamples, HAllA (Hierarchical All-against-All Association Testing) discovers densely-associated blocks of features in the X vs. Y association matrix where: 1) each block is defined as all associations between features in a subtree of X hierarchy and features in a subtree of Y hierarchy and 2) a block is densely associated if (1 - FNR)% of pairwise associations are FDR significant (FNR is the pre-defined expected false negative rate)
+
+
+
+
+%package help
+Summary:	Development documents and examples for HAllA
+Provides:	python3-HAllA-doc
+%description help
+Given two high-dimensional 'omics datasets X and Y (continuous and/or categorical features) from the same n biosamples, HAllA (Hierarchical All-against-All Association Testing) discovers densely-associated blocks of features in the X vs. Y association matrix where: 1) each block is defined as all associations between features in a subtree of X hierarchy and features in a subtree of Y hierarchy and 2) a block is densely associated if (1 - FNR)% of pairwise associations are FDR significant (FNR is the pre-defined expected false negative rate)
+
+
+
+
+%prep
+%autosetup -n HAllA-0.8.20
+
+%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-HAllA -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.8.20-1
+- Package Spec generated