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|
%global _empty_manifest_terminate_build 0
Name: python-cmdstanpy
Version: 1.1.0
Release: 1
Summary: Python interface to CmdStan
License: BSD License
URL: https://github.com/stan-dev/cmdstanpy
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/d5/2c/bc3216b7a3b0291bf74eac2387f5989477422bd4ad96663371639cb1c9f6/cmdstanpy-1.1.0.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-numpy
Requires: python3-tqdm
Requires: python3-xarray
Requires: python3-sphinx
Requires: python3-sphinx-gallery
Requires: python3-sphinx-rtd-theme
Requires: python3-numpydoc
Requires: python3-matplotlib
Requires: python3-flake8
Requires: python3-pylint
Requires: python3-pytest
Requires: python3-pytest-cov
Requires: python3-pytest-order
Requires: python3-mypy
Requires: python3-tqdm
Requires: python3-xarray
%description
# CmdStanPy
[](https://codecov.io/gh/stan-dev/cmdstanpy)
CmdStanPy is a lightweight pure-Python interface to CmdStan which provides access to the Stan compiler and all inference algorithms. It supports both development and production workflows. Because model development and testing may require many iterations, the defaults favor development mode and therefore output files are stored on a temporary filesystem. Non-default options allow all aspects of a run to be specified so that scripts can be used to distributed analysis jobs across nodes and machines.
CmdStanPy is distributed via PyPi: https://pypi.org/project/cmdstanpy/
or Conda Forge: https://anaconda.org/conda-forge/cmdstanpy
### Goals
- Clean interface to Stan services so that CmdStanPy can keep up with Stan releases.
- Provide access to all CmdStan inference methods.
- Easy to install,
+ minimal Python library dependencies: numpy, pandas
+ Python code doesn't interface directly with c++, only calls compiled executables
- Modular - CmdStanPy produces a MCMC sample (or point estimate) from the posterior; other packages do analysis and visualization.
- Low memory overhead - by default, minimal memory used above that required by CmdStanPy; objects run CmdStan programs and track CmdStan input and output files.
### Source Repository
CmdStanPy and CmdStan are available from GitHub: https://github.com/stan-dev/cmdstanpy and https://github.com/stan-dev/cmdstan
### Docs
The latest release documentation is hosted on https://mc-stan.org/cmdstanpy, older release versions are available from readthedocs: https://cmdstanpy.readthedocs.io
### Licensing
The CmdStanPy, CmdStan, and the core Stan C++ code are licensed under new BSD.
### Example
```python
import os
from cmdstanpy import cmdstan_path, CmdStanModel
# specify locations of Stan program file and data
stan_file = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.stan')
data_file = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.data.json')
# instantiate a model; compiles the Stan program by default
model = CmdStanModel(stan_file=stan_file)
# obtain a posterior sample from the model conditioned on the data
fit = model.sample(chains=4, data=data_file)
# summarize the results (wraps CmdStan `bin/stansummary`):
fit.summary()
```
%package -n python3-cmdstanpy
Summary: Python interface to CmdStan
Provides: python-cmdstanpy
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-cmdstanpy
# CmdStanPy
[](https://codecov.io/gh/stan-dev/cmdstanpy)
CmdStanPy is a lightweight pure-Python interface to CmdStan which provides access to the Stan compiler and all inference algorithms. It supports both development and production workflows. Because model development and testing may require many iterations, the defaults favor development mode and therefore output files are stored on a temporary filesystem. Non-default options allow all aspects of a run to be specified so that scripts can be used to distributed analysis jobs across nodes and machines.
CmdStanPy is distributed via PyPi: https://pypi.org/project/cmdstanpy/
or Conda Forge: https://anaconda.org/conda-forge/cmdstanpy
### Goals
- Clean interface to Stan services so that CmdStanPy can keep up with Stan releases.
- Provide access to all CmdStan inference methods.
- Easy to install,
+ minimal Python library dependencies: numpy, pandas
+ Python code doesn't interface directly with c++, only calls compiled executables
- Modular - CmdStanPy produces a MCMC sample (or point estimate) from the posterior; other packages do analysis and visualization.
- Low memory overhead - by default, minimal memory used above that required by CmdStanPy; objects run CmdStan programs and track CmdStan input and output files.
### Source Repository
CmdStanPy and CmdStan are available from GitHub: https://github.com/stan-dev/cmdstanpy and https://github.com/stan-dev/cmdstan
### Docs
The latest release documentation is hosted on https://mc-stan.org/cmdstanpy, older release versions are available from readthedocs: https://cmdstanpy.readthedocs.io
### Licensing
The CmdStanPy, CmdStan, and the core Stan C++ code are licensed under new BSD.
### Example
```python
import os
from cmdstanpy import cmdstan_path, CmdStanModel
# specify locations of Stan program file and data
stan_file = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.stan')
data_file = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.data.json')
# instantiate a model; compiles the Stan program by default
model = CmdStanModel(stan_file=stan_file)
# obtain a posterior sample from the model conditioned on the data
fit = model.sample(chains=4, data=data_file)
# summarize the results (wraps CmdStan `bin/stansummary`):
fit.summary()
```
%package help
Summary: Development documents and examples for cmdstanpy
Provides: python3-cmdstanpy-doc
%description help
# CmdStanPy
[](https://codecov.io/gh/stan-dev/cmdstanpy)
CmdStanPy is a lightweight pure-Python interface to CmdStan which provides access to the Stan compiler and all inference algorithms. It supports both development and production workflows. Because model development and testing may require many iterations, the defaults favor development mode and therefore output files are stored on a temporary filesystem. Non-default options allow all aspects of a run to be specified so that scripts can be used to distributed analysis jobs across nodes and machines.
CmdStanPy is distributed via PyPi: https://pypi.org/project/cmdstanpy/
or Conda Forge: https://anaconda.org/conda-forge/cmdstanpy
### Goals
- Clean interface to Stan services so that CmdStanPy can keep up with Stan releases.
- Provide access to all CmdStan inference methods.
- Easy to install,
+ minimal Python library dependencies: numpy, pandas
+ Python code doesn't interface directly with c++, only calls compiled executables
- Modular - CmdStanPy produces a MCMC sample (or point estimate) from the posterior; other packages do analysis and visualization.
- Low memory overhead - by default, minimal memory used above that required by CmdStanPy; objects run CmdStan programs and track CmdStan input and output files.
### Source Repository
CmdStanPy and CmdStan are available from GitHub: https://github.com/stan-dev/cmdstanpy and https://github.com/stan-dev/cmdstan
### Docs
The latest release documentation is hosted on https://mc-stan.org/cmdstanpy, older release versions are available from readthedocs: https://cmdstanpy.readthedocs.io
### Licensing
The CmdStanPy, CmdStan, and the core Stan C++ code are licensed under new BSD.
### Example
```python
import os
from cmdstanpy import cmdstan_path, CmdStanModel
# specify locations of Stan program file and data
stan_file = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.stan')
data_file = os.path.join(cmdstan_path(), 'examples', 'bernoulli', 'bernoulli.data.json')
# instantiate a model; compiles the Stan program by default
model = CmdStanModel(stan_file=stan_file)
# obtain a posterior sample from the model conditioned on the data
fit = model.sample(chains=4, data=data_file)
# summarize the results (wraps CmdStan `bin/stansummary`):
fit.summary()
```
%prep
%autosetup -n cmdstanpy-1.1.0
%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-cmdstanpy -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.0-1
- Package Spec generated
|
