automatic import of python-lea

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+%global _empty_manifest_terminate_build 0
+Name:		python-lea
+Version:	3.4.4
+Release:	1
+Summary:	Discrete probability distributions in Python
+License:	LGPL
+URL:		http://bitbucket.org/piedenis/lea
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/88/1d/bcd296e04f93d15eed58959a3209256252c8d0265c02ab343afa6e71cb15/lea-3.4.4.tar.gz
+BuildArch:	noarch
+
+
+%description
+Lea is a Python module aiming at working with discrete probability distributions in an intuitive way.
+
+It allows you modeling a broad range of random phenomena: gambling, weather, finance, etc. More generally, Lea may be used for any finite set of discrete values having known probability: numbers, booleans, date/times, symbols,... Each probability distribution is modeled as a plain object, which can be named, displayed, queried or processed to produce new probability distributions.
+
+Lea also provides advanced functions and Probabilistic Programming (PP) features; these include conditional probabilities, joint probability distributions, Bayesian networks, Markov chains and symbolic computation.
+
+All probability calculations in Lea are performed by a new exact algorithm, the Statues algorithm, which is based on variable binding and recursive generators. For problems intractable through exact methods, Lea provides on-demand approximate algorithms, namely MC rejection sampling and likelihood weighting.
+
+Beside the above-cited functions, Lea provides some machine learning functions, including Maximum-Likelihood and Expectation-Maximization algorithms.
+
+Lea can be used for AI, education (probability theory & PP), generation of random samples, etc.
+
+To install Lea 3.4.4, type the following command:
+::
+
+  pip install lea==3.4.4
+
+Please go on Lea project page (beside) for a comprehensive documentation.
+
+%package -n python3-lea
+Summary:	Discrete probability distributions in Python
+Provides:	python-lea
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-lea
+Lea is a Python module aiming at working with discrete probability distributions in an intuitive way.
+
+It allows you modeling a broad range of random phenomena: gambling, weather, finance, etc. More generally, Lea may be used for any finite set of discrete values having known probability: numbers, booleans, date/times, symbols,... Each probability distribution is modeled as a plain object, which can be named, displayed, queried or processed to produce new probability distributions.
+
+Lea also provides advanced functions and Probabilistic Programming (PP) features; these include conditional probabilities, joint probability distributions, Bayesian networks, Markov chains and symbolic computation.
+
+All probability calculations in Lea are performed by a new exact algorithm, the Statues algorithm, which is based on variable binding and recursive generators. For problems intractable through exact methods, Lea provides on-demand approximate algorithms, namely MC rejection sampling and likelihood weighting.
+
+Beside the above-cited functions, Lea provides some machine learning functions, including Maximum-Likelihood and Expectation-Maximization algorithms.
+
+Lea can be used for AI, education (probability theory & PP), generation of random samples, etc.
+
+To install Lea 3.4.4, type the following command:
+::
+
+  pip install lea==3.4.4
+
+Please go on Lea project page (beside) for a comprehensive documentation.
+
+%package help
+Summary:	Development documents and examples for lea
+Provides:	python3-lea-doc
+%description help
+Lea is a Python module aiming at working with discrete probability distributions in an intuitive way.
+
+It allows you modeling a broad range of random phenomena: gambling, weather, finance, etc. More generally, Lea may be used for any finite set of discrete values having known probability: numbers, booleans, date/times, symbols,... Each probability distribution is modeled as a plain object, which can be named, displayed, queried or processed to produce new probability distributions.
+
+Lea also provides advanced functions and Probabilistic Programming (PP) features; these include conditional probabilities, joint probability distributions, Bayesian networks, Markov chains and symbolic computation.
+
+All probability calculations in Lea are performed by a new exact algorithm, the Statues algorithm, which is based on variable binding and recursive generators. For problems intractable through exact methods, Lea provides on-demand approximate algorithms, namely MC rejection sampling and likelihood weighting.
+
+Beside the above-cited functions, Lea provides some machine learning functions, including Maximum-Likelihood and Expectation-Maximization algorithms.
+
+Lea can be used for AI, education (probability theory & PP), generation of random samples, etc.
+
+To install Lea 3.4.4, type the following command:
+::
+
+  pip install lea==3.4.4
+
+Please go on Lea project page (beside) for a comprehensive documentation.
+
+%prep
+%autosetup -n lea-3.4.4
+
+%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-lea -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 3.4.4-1
+- Package Spec generated