automatic import of python-cma

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diff --git a/.gitignore b/.gitignore
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+/cma-3.3.0.tar.gz
diff --git a/python-cma.spec b/python-cma.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-cma
+Version:	3.3.0
+Release:	1
+Summary:	CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical optimization in Python
+License:	BSD
+URL:		https://github.com/CMA-ES/pycma
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/46/b8/dc832cf0881641355c5121b390942de858079de08720bc2a6030c6c85153/cma-3.3.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-moarchiving
+Requires:	python3-matplotlib
+
+%description
+A stochastic numerical optimization algorithm for difficult (non-convex,
+ill-conditioned, multi-modal, rugged, noisy) optimization problems in
+continuous search spaces, implemented in Python.
+Typical domain of application are bound-constrained or unconstrained
+objective functions with:
+* search space dimension between, say, 5 and (a few) 100,
+* no gradients available,
+* at least, say, 100 times dimension function evaluations needed to
+  get satisfactory solutions,
+* non-separable, ill-conditioned, or rugged/multi-modal landscapes.
+The CMA-ES is quite reliable, however for small budgets (fewer function
+evaluations than, say, 100 times dimension) or in very small dimensions
+better (i.e. faster) methods are available.
+The ``pycma`` module provides two independent implementations of the 
+CMA-ES algorithm in the classes ``cma.CMAEvolutionStrategy`` and 
+``cma.purecma.CMAES``. 
+
+%package -n python3-cma
+Summary:	CMA-ES, Covariance Matrix Adaptation Evolution Strategy for non-linear numerical optimization in Python
+Provides:	python-cma
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-cma
+A stochastic numerical optimization algorithm for difficult (non-convex,
+ill-conditioned, multi-modal, rugged, noisy) optimization problems in
+continuous search spaces, implemented in Python.
+Typical domain of application are bound-constrained or unconstrained
+objective functions with:
+* search space dimension between, say, 5 and (a few) 100,
+* no gradients available,
+* at least, say, 100 times dimension function evaluations needed to
+  get satisfactory solutions,
+* non-separable, ill-conditioned, or rugged/multi-modal landscapes.
+The CMA-ES is quite reliable, however for small budgets (fewer function
+evaluations than, say, 100 times dimension) or in very small dimensions
+better (i.e. faster) methods are available.
+The ``pycma`` module provides two independent implementations of the 
+CMA-ES algorithm in the classes ``cma.CMAEvolutionStrategy`` and 
+``cma.purecma.CMAES``. 
+
+%package help
+Summary:	Development documents and examples for cma
+Provides:	python3-cma-doc
+%description help
+A stochastic numerical optimization algorithm for difficult (non-convex,
+ill-conditioned, multi-modal, rugged, noisy) optimization problems in
+continuous search spaces, implemented in Python.
+Typical domain of application are bound-constrained or unconstrained
+objective functions with:
+* search space dimension between, say, 5 and (a few) 100,
+* no gradients available,
+* at least, say, 100 times dimension function evaluations needed to
+  get satisfactory solutions,
+* non-separable, ill-conditioned, or rugged/multi-modal landscapes.
+The CMA-ES is quite reliable, however for small budgets (fewer function
+evaluations than, say, 100 times dimension) or in very small dimensions
+better (i.e. faster) methods are available.
+The ``pycma`` module provides two independent implementations of the 
+CMA-ES algorithm in the classes ``cma.CMAEvolutionStrategy`` and 
+``cma.purecma.CMAES``. 
+
+%prep
+%autosetup -n cma-3.3.0
+
+%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-cma -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu Mar 09 2023 Python_Bot <Python_Bot@openeuler.org> - 3.3.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..18e46b9
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+d9ddd1c7485f2c833c3aa2c5781c6e76  cma-3.3.0.tar.gz