automatic import of python-pypmcopeneuler20.03

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diff --git a/.gitignore b/.gitignore
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+/pypmc-1.2.tar.gz
diff --git a/python-pypmc.spec b/python-pypmc.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-pypmc
+Version:	1.2
+Release:	1
+Summary:	A toolkit for adaptive importance sampling featuring implementations of variational Bayes, population Monte Carlo, and Markov chains.
+License:	GPLv2
+URL:		https://github.com/pypmc/pypmc
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/9f/5c/79342ebacdf9005a1461c2d023338e9573eeb7f2ec9ea10797d0ca10a192/pypmc-1.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-mpi4py
+Requires:	python3-matplotlib
+Requires:	python3-nose
+
+%description
+``pypmc`` is a python package focusing on adaptive importance
+sampling.  It can be used for integration and sampling from a
+user-defined target density. A typical application is Bayesian
+inference, where one wants to sample from the posterior to marginalize
+over parameters and to compute the evidence. The key idea is to create
+a good proposal density by adapting a mixture of Gaussian or student's
+t components to the target density. The package is able to efficiently
+integrate multimodal functions in up to about 30-40 dimensions at the
+level of 1% accuracy or less. For many problems, this is achieved
+without requiring any manual input from the user about details of the
+function. Importance sampling supports parallelization on multiple
+machines via ``mpi4py``.
+
+Useful tools that can be used stand-alone include:
+
+* importance sampling (sampling & integration)
+* adaptive Markov chain Monte Carlo (sampling)
+* variational Bayes (clustering)
+* population Monte Carlo (clustering)
+
+
+
+
+%package -n python3-pypmc
+Summary:	A toolkit for adaptive importance sampling featuring implementations of variational Bayes, population Monte Carlo, and Markov chains.
+Provides:	python-pypmc
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-pypmc
+``pypmc`` is a python package focusing on adaptive importance
+sampling.  It can be used for integration and sampling from a
+user-defined target density. A typical application is Bayesian
+inference, where one wants to sample from the posterior to marginalize
+over parameters and to compute the evidence. The key idea is to create
+a good proposal density by adapting a mixture of Gaussian or student's
+t components to the target density. The package is able to efficiently
+integrate multimodal functions in up to about 30-40 dimensions at the
+level of 1% accuracy or less. For many problems, this is achieved
+without requiring any manual input from the user about details of the
+function. Importance sampling supports parallelization on multiple
+machines via ``mpi4py``.
+
+Useful tools that can be used stand-alone include:
+
+* importance sampling (sampling & integration)
+* adaptive Markov chain Monte Carlo (sampling)
+* variational Bayes (clustering)
+* population Monte Carlo (clustering)
+
+
+
+
+%package help
+Summary:	Development documents and examples for pypmc
+Provides:	python3-pypmc-doc
+%description help
+``pypmc`` is a python package focusing on adaptive importance
+sampling.  It can be used for integration and sampling from a
+user-defined target density. A typical application is Bayesian
+inference, where one wants to sample from the posterior to marginalize
+over parameters and to compute the evidence. The key idea is to create
+a good proposal density by adapting a mixture of Gaussian or student's
+t components to the target density. The package is able to efficiently
+integrate multimodal functions in up to about 30-40 dimensions at the
+level of 1% accuracy or less. For many problems, this is achieved
+without requiring any manual input from the user about details of the
+function. Importance sampling supports parallelization on multiple
+machines via ``mpi4py``.
+
+Useful tools that can be used stand-alone include:
+
+* importance sampling (sampling & integration)
+* adaptive Markov chain Monte Carlo (sampling)
+* variational Bayes (clustering)
+* population Monte Carlo (clustering)
+
+
+
+
+%prep
+%autosetup -n pypmc-1.2
+
+%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-pypmc -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.2-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..75e5a9c
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+179299a5a3c6bd86af6b6c95c9c73fc0  pypmc-1.2.tar.gz