automatic import of python-dit

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+%global _empty_manifest_terminate_build 0
+Name:		python-dit
+Version:	1.5
+Release:	1
+Summary:	Python package for information theory.
+License:	BSD
+URL:		http://dit.io
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/9a/44/4c9ca52132947daafccf1f52f5b0cebc7f1bb62e34ae7723943682b5e686/dit-1.5.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-boltons
+Requires:	python3-debtcollector
+Requires:	python3-lattices
+Requires:	python3-networkx
+Requires:	python3-numpy
+Requires:	python3-PLTable
+Requires:	python3-scipy
+Requires:	python3-pypoman
+
+%description
+Information theory is a powerful extension to probability and statistics, quantifying dependencies
+among arbitrary random variables in a way that is consistent and comparable across systems and
+scales. Information theory was originally developed to quantify how quickly and reliably information
+could be transmitted across an arbitrary channel. The demands of modern, data-driven science have
+been coopting and extending these quantities and methods into unknown, multivariate settings where
+the interpretation and best practices are not known. For example, there are at least four reasonable
+multivariate generalizations of the mutual information, none of which inherit all the
+interpretations of the standard bivariate case. Which is best to use is context-dependent. ``dit``
+implements a vast range of multivariate information measures in an effort to allow information
+practitioners to study how these various measures behave and interact in a variety of contexts. We
+hope that having all these measures and techniques implemented in one place will allow the
+development of robust techniques for the automated quantification of dependencies within a system
+and concrete interpretation of what those dependencies mean.
+
+%package -n python3-dit
+Summary:	Python package for information theory.
+Provides:	python-dit
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-dit
+Information theory is a powerful extension to probability and statistics, quantifying dependencies
+among arbitrary random variables in a way that is consistent and comparable across systems and
+scales. Information theory was originally developed to quantify how quickly and reliably information
+could be transmitted across an arbitrary channel. The demands of modern, data-driven science have
+been coopting and extending these quantities and methods into unknown, multivariate settings where
+the interpretation and best practices are not known. For example, there are at least four reasonable
+multivariate generalizations of the mutual information, none of which inherit all the
+interpretations of the standard bivariate case. Which is best to use is context-dependent. ``dit``
+implements a vast range of multivariate information measures in an effort to allow information
+practitioners to study how these various measures behave and interact in a variety of contexts. We
+hope that having all these measures and techniques implemented in one place will allow the
+development of robust techniques for the automated quantification of dependencies within a system
+and concrete interpretation of what those dependencies mean.
+
+%package help
+Summary:	Development documents and examples for dit
+Provides:	python3-dit-doc
+%description help
+Information theory is a powerful extension to probability and statistics, quantifying dependencies
+among arbitrary random variables in a way that is consistent and comparable across systems and
+scales. Information theory was originally developed to quantify how quickly and reliably information
+could be transmitted across an arbitrary channel. The demands of modern, data-driven science have
+been coopting and extending these quantities and methods into unknown, multivariate settings where
+the interpretation and best practices are not known. For example, there are at least four reasonable
+multivariate generalizations of the mutual information, none of which inherit all the
+interpretations of the standard bivariate case. Which is best to use is context-dependent. ``dit``
+implements a vast range of multivariate information measures in an effort to allow information
+practitioners to study how these various measures behave and interact in a variety of contexts. We
+hope that having all these measures and techniques implemented in one place will allow the
+development of robust techniques for the automated quantification of dependencies within a system
+and concrete interpretation of what those dependencies mean.
+
+%prep
+%autosetup -n dit-1.5
+
+%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-dit -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 1.5-1
+- Package Spec generated