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diff --git a/python-epsie.spec b/python-epsie.spec new file mode 100644 index 0000000..13b722c --- /dev/null +++ b/python-epsie.spec @@ -0,0 +1,123 @@ +%global _empty_manifest_terminate_build 0 +Name: python-epsie +Version: 1.0.0 +Release: 1 +Summary: An Embarrassingly Parallel Sampler for Inference Estimation. +License: GNU General Public License v3 (GPLv3) +URL: https://cdcapano.github.io/epsie +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/21/50/dcb58a1cdbbc9e62c8c059b1f5b5f3e2a6ca6ecbd5d996ceca2a1e540873/epsie-1.0.0.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-scipy +Requires: python3-six + +%description +EPSIE is a parallelized Markov chain Monte Carlo (MCMC) sampler for Bayesian +inference. It is meant for problems with complicated likelihood topology, +including multimodal distributions. It has support for both parallel tempering +and nested transdimensional problems. It was originally developed for +gravitational-wave parameter estimation, but can be used for any Bayesian +inference problem requring a stochastic sampler. +EPSIE is in many ways similar to `emcee +<https://emcee.readthedocs.io/en/stable/>`_ and other bring-your-own-likelihood +Python-based samplers. The primary difference from emcee is EPSIE +is not an ensemble sampler; i.e., the Markov chains used by EPSIE do not +attempt to share information between each other. Instead, to speed convergence, +multiple jump proposal classes are offered that can be customized to the +problem at hand. These include adaptive proposals that attempt to learn the +shape of the distribution during a burn-in period. The user can also easily +create their own jump proposals. +For more information, see the documentation at: +https://cdcapano.github.io/epsie + +%package -n python3-epsie +Summary: An Embarrassingly Parallel Sampler for Inference Estimation. +Provides: python-epsie +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-epsie +EPSIE is a parallelized Markov chain Monte Carlo (MCMC) sampler for Bayesian +inference. It is meant for problems with complicated likelihood topology, +including multimodal distributions. It has support for both parallel tempering +and nested transdimensional problems. It was originally developed for +gravitational-wave parameter estimation, but can be used for any Bayesian +inference problem requring a stochastic sampler. +EPSIE is in many ways similar to `emcee +<https://emcee.readthedocs.io/en/stable/>`_ and other bring-your-own-likelihood +Python-based samplers. The primary difference from emcee is EPSIE +is not an ensemble sampler; i.e., the Markov chains used by EPSIE do not +attempt to share information between each other. Instead, to speed convergence, +multiple jump proposal classes are offered that can be customized to the +problem at hand. These include adaptive proposals that attempt to learn the +shape of the distribution during a burn-in period. The user can also easily +create their own jump proposals. +For more information, see the documentation at: +https://cdcapano.github.io/epsie + +%package help +Summary: Development documents and examples for epsie +Provides: python3-epsie-doc +%description help +EPSIE is a parallelized Markov chain Monte Carlo (MCMC) sampler for Bayesian +inference. It is meant for problems with complicated likelihood topology, +including multimodal distributions. It has support for both parallel tempering +and nested transdimensional problems. It was originally developed for +gravitational-wave parameter estimation, but can be used for any Bayesian +inference problem requring a stochastic sampler. +EPSIE is in many ways similar to `emcee +<https://emcee.readthedocs.io/en/stable/>`_ and other bring-your-own-likelihood +Python-based samplers. The primary difference from emcee is EPSIE +is not an ensemble sampler; i.e., the Markov chains used by EPSIE do not +attempt to share information between each other. Instead, to speed convergence, +multiple jump proposal classes are offered that can be customized to the +problem at hand. These include adaptive proposals that attempt to learn the +shape of the distribution during a burn-in period. The user can also easily +create their own jump proposals. +For more information, see the documentation at: +https://cdcapano.github.io/epsie + +%prep +%autosetup -n epsie-1.0.0 + +%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-epsie -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.0-1 +- Package Spec generated |