%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.aliyun.com/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 Jun 08 2023 Python_Bot - 0.6.3-1 - Package Spec generated