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@@ -0,0 +1 @@ +/pystan-3.6.0.tar.gz diff --git a/python-pystan.spec b/python-pystan.spec new file mode 100644 index 0000000..626f9ab --- /dev/null +++ b/python-pystan.spec @@ -0,0 +1,162 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pystan +Version: 3.6.0 +Release: 1 +Summary: Python interface to Stan, a package for Bayesian inference +License: ISC +URL: https://mc-stan.org +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/b4/34/c82f306600df2812996689d1d3fc5acb5fa1fd627ad895ede29cd3ee9605/pystan-3.6.0.tar.gz +BuildArch: noarch + +Requires: python3-aiohttp +Requires: python3-httpstan +Requires: python3-pysimdjson +Requires: python3-numpy +Requires: python3-clikit +Requires: python3-setuptools + +%description +Install PyStan with ``pip install pystan``. PyStan runs on Linux and macOS. You will also need a C++ compiler such as gcc ≥9.0 or clang ≥10.0. +The following block of code shows how to use PyStan with a model which studied coaching effects across eight schools (see Section 5.5 of Gelman et al (2003)). This hierarchical model is often called the "eight schools" model. + import stan + schools_code = """ + data { + int<lower=0> J; // number of schools + real y[J]; // estimated treatment effects + real<lower=0> sigma[J]; // standard error of effect estimates + } + parameters { + real mu; // population treatment effect + real<lower=0> tau; // standard deviation in treatment effects + vector[J] eta; // unscaled deviation from mu by school + } + transformed parameters { + vector[J] theta = mu + tau * eta; // school treatment effects + } + model { + target += normal_lpdf(eta | 0, 1); // prior log-density + target += normal_lpdf(y | theta, sigma); // log-likelihood + } + """ + schools_data = {"J": 8, + "y": [28, 8, -3, 7, -1, 1, 18, 12], + "sigma": [15, 10, 16, 11, 9, 11, 10, 18]} + posterior = stan.build(schools_code, data=schools_data) + fit = posterior.sample(num_chains=4, num_samples=1000) + eta = fit["eta"] # array with shape (8, 4000) + df = fit.to_frame() # pandas `DataFrame` + +%package -n python3-pystan +Summary: Python interface to Stan, a package for Bayesian inference +Provides: python-pystan +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-pystan +Install PyStan with ``pip install pystan``. PyStan runs on Linux and macOS. You will also need a C++ compiler such as gcc ≥9.0 or clang ≥10.0. +The following block of code shows how to use PyStan with a model which studied coaching effects across eight schools (see Section 5.5 of Gelman et al (2003)). This hierarchical model is often called the "eight schools" model. + import stan + schools_code = """ + data { + int<lower=0> J; // number of schools + real y[J]; // estimated treatment effects + real<lower=0> sigma[J]; // standard error of effect estimates + } + parameters { + real mu; // population treatment effect + real<lower=0> tau; // standard deviation in treatment effects + vector[J] eta; // unscaled deviation from mu by school + } + transformed parameters { + vector[J] theta = mu + tau * eta; // school treatment effects + } + model { + target += normal_lpdf(eta | 0, 1); // prior log-density + target += normal_lpdf(y | theta, sigma); // log-likelihood + } + """ + schools_data = {"J": 8, + "y": [28, 8, -3, 7, -1, 1, 18, 12], + "sigma": [15, 10, 16, 11, 9, 11, 10, 18]} + posterior = stan.build(schools_code, data=schools_data) + fit = posterior.sample(num_chains=4, num_samples=1000) + eta = fit["eta"] # array with shape (8, 4000) + df = fit.to_frame() # pandas `DataFrame` + +%package help +Summary: Development documents and examples for pystan +Provides: python3-pystan-doc +%description help +Install PyStan with ``pip install pystan``. PyStan runs on Linux and macOS. You will also need a C++ compiler such as gcc ≥9.0 or clang ≥10.0. +The following block of code shows how to use PyStan with a model which studied coaching effects across eight schools (see Section 5.5 of Gelman et al (2003)). This hierarchical model is often called the "eight schools" model. + import stan + schools_code = """ + data { + int<lower=0> J; // number of schools + real y[J]; // estimated treatment effects + real<lower=0> sigma[J]; // standard error of effect estimates + } + parameters { + real mu; // population treatment effect + real<lower=0> tau; // standard deviation in treatment effects + vector[J] eta; // unscaled deviation from mu by school + } + transformed parameters { + vector[J] theta = mu + tau * eta; // school treatment effects + } + model { + target += normal_lpdf(eta | 0, 1); // prior log-density + target += normal_lpdf(y | theta, sigma); // log-likelihood + } + """ + schools_data = {"J": 8, + "y": [28, 8, -3, 7, -1, 1, 18, 12], + "sigma": [15, 10, 16, 11, 9, 11, 10, 18]} + posterior = stan.build(schools_code, data=schools_data) + fit = posterior.sample(num_chains=4, num_samples=1000) + eta = fit["eta"] # array with shape (8, 4000) + df = fit.to_frame() # pandas `DataFrame` + +%prep +%autosetup -n pystan-3.6.0 + +%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-pystan -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 3.6.0-1 +- Package Spec generated @@ -0,0 +1 @@ +2c70c720f8ecec0e3f238ec56283b47d pystan-3.6.0.tar.gz |