automatic import of python-abcpy

3 files changed, 92 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..59d3319 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/abcpy-0.6.3.tar.gz
diff --git a/python-abcpy.spec b/python-abcpy.spec
new file mode 100644
index 0000000..9f9322a
--- /dev/null
+++ b/python-abcpy.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-abcpy
+Version:	0.6.3
+Release:	1
+Summary:	A framework for approximate Bayesian computation (ABC) that speeds up inference by parallelizing computation on single computers or whole clusters.
+License:	BSD-3
+URL:		https://github.com/eth-cscs/abcpy
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/f9/13/99179da46c2505482bb1395526679aa21865ad6af2fba28921b62653070d/abcpy-0.6.3.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-scipy
+Requires:	python3-scikit-learn
+Requires:	python3-glmnet
+Requires:	python3-sklearn
+Requires:	python3-sphinx
+Requires:	python3-sphinx-rtd-theme
+Requires:	python3-coverage
+Requires:	python3-mpi4py
+Requires:	python3-cloudpickle
+Requires:	python3-matplotlib
+Requires:	python3-tqdm
+Requires:	python3-pot
+
+%description
+ABCpy is a highly modular, scientific library for approximate Bayesian computation (ABC) written in Python. It is designed to run all included ABC algorithms in parallel, either using multiple cores of a single computer or using an Apache Spark or MPI enabled cluster. The modularity helps domain scientists to easily apply ABC to their research without being ABC experts; using ABCpy they can easily run large parallel simulations without much knowledge about parallelization, even without much additional effort to parallelize their code. Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment, and to extend the library with new algorithms. These benefits come mainly from the modularity of ABCpy.
+
+
+
+%package -n python3-abcpy
+Summary:	A framework for approximate Bayesian computation (ABC) that speeds up inference by parallelizing computation on single computers or whole clusters.
+Provides:	python-abcpy
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-abcpy
+ABCpy is a highly modular, scientific library for approximate Bayesian computation (ABC) written in Python. It is designed to run all included ABC algorithms in parallel, either using multiple cores of a single computer or using an Apache Spark or MPI enabled cluster. The modularity helps domain scientists to easily apply ABC to their research without being ABC experts; using ABCpy they can easily run large parallel simulations without much knowledge about parallelization, even without much additional effort to parallelize their code. Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment, and to extend the library with new algorithms. These benefits come mainly from the modularity of ABCpy.
+
+
+
+%package help
+Summary:	Development documents and examples for abcpy
+Provides:	python3-abcpy-doc
+%description help
+ABCpy is a highly modular, scientific library for approximate Bayesian computation (ABC) written in Python. It is designed to run all included ABC algorithms in parallel, either using multiple cores of a single computer or using an Apache Spark or MPI enabled cluster. The modularity helps domain scientists to easily apply ABC to their research without being ABC experts; using ABCpy they can easily run large parallel simulations without much knowledge about parallelization, even without much additional effort to parallelize their code. Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment, and to extend the library with new algorithms. These benefits come mainly from the modularity of ABCpy.
+
+
+
+%prep
+%autosetup -n abcpy-0.6.3
+
+%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-abcpy -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.3-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..bb945f7
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+d663cbda017d2d2258815c7af000404d  abcpy-0.6.3.tar.gz