%global _empty_manifest_terminate_build 0 Name: python-celerite2 Version: 0.2.1 Release: 1 Summary: Fast and scalable Gaussian Processes in 1D License: MIT URL: https://celerite2.readthedocs.io Source0: https://mirrors.nju.edu.cn/pypi/web/packages/8c/1d/89293a96e3f99e6a30ce1405c4232200c1f9ba1a14253f91827a39b1a90a/celerite2-0.2.1.tar.gz Requires: python3-numpy Requires: python3-isort Requires: python3-black Requires: python3-black-nbconvert Requires: python3-coverage[toml] Requires: python3-pytest Requires: python3-pytest-cov Requires: python3-scipy Requires: python3-celerite Requires: python3-pep517 Requires: python3-twine Requires: python3-pre-commit Requires: python3-nbstripout Requires: python3-flake8 Requires: python3-sphinx Requires: python3-sphinx-material Requires: python3-sphinx-copybutton Requires: python3-rtds-action Requires: python3-nbsphinx Requires: python3-breathe Requires: python3-ipython Requires: python3-jax Requires: python3-jaxlib Requires: python3-pymc3 Requires: python3-aesara-theano-fallback Requires: python3-pep517 Requires: python3-twine Requires: python3-isort Requires: python3-black Requires: python3-black-nbconvert Requires: python3-coverage[toml] Requires: python3-pytest Requires: python3-pytest-cov Requires: python3-scipy Requires: python3-celerite Requires: python3-pymc3 Requires: python3-aesara-theano-fallback Requires: python3-jupytext Requires: python3-jupyter Requires: python3-nbconvert Requires: python3-matplotlib Requires: python3-scipy Requires: python3-emcee Requires: python3-pymc3 Requires: python3-aesara-theano-fallback Requires: python3-tqdm Requires: python3-numpyro %description # celerite2 _celerite_ is an algorithm for fast and scalable Gaussian Process (GP) Regression in one dimension and this library, _celerite2_ is a re-write of the original [celerite project](https://celerite.readthedocs.io) to improve numerical stability and integration with various machine learning frameworks. Documentation for this version can be found [here](https://celerite2.readthedocs.io/en/latest/). This new implementation includes interfaces in Python and C++, with full support for Theano/PyMC3 and JAX. This documentation won't teach you the fundamentals of GP modeling but the best resource for learning about this is available for free online: [Rasmussen & Williams (2006)](http://www.gaussianprocess.org/gpml/). Similarly, the _celerite_ algorithm is restricted to a specific class of covariance functions (see [the original paper](https://arxiv.org/abs/1703.09710) for more information and [a recent generalization](https://arxiv.org/abs/2007.05799) for extensions to structured two-dimensional data). If you need scalable GPs with more general covariance functions, [GPyTorch](https://gpytorch.ai/) might be a good choice. %package -n python3-celerite2 Summary: Fast and scalable Gaussian Processes in 1D Provides: python-celerite2 BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-celerite2 # celerite2 _celerite_ is an algorithm for fast and scalable Gaussian Process (GP) Regression in one dimension and this library, _celerite2_ is a re-write of the original [celerite project](https://celerite.readthedocs.io) to improve numerical stability and integration with various machine learning frameworks. Documentation for this version can be found [here](https://celerite2.readthedocs.io/en/latest/). This new implementation includes interfaces in Python and C++, with full support for Theano/PyMC3 and JAX. This documentation won't teach you the fundamentals of GP modeling but the best resource for learning about this is available for free online: [Rasmussen & Williams (2006)](http://www.gaussianprocess.org/gpml/). Similarly, the _celerite_ algorithm is restricted to a specific class of covariance functions (see [the original paper](https://arxiv.org/abs/1703.09710) for more information and [a recent generalization](https://arxiv.org/abs/2007.05799) for extensions to structured two-dimensional data). If you need scalable GPs with more general covariance functions, [GPyTorch](https://gpytorch.ai/) might be a good choice. %package help Summary: Development documents and examples for celerite2 Provides: python3-celerite2-doc %description help # celerite2 _celerite_ is an algorithm for fast and scalable Gaussian Process (GP) Regression in one dimension and this library, _celerite2_ is a re-write of the original [celerite project](https://celerite.readthedocs.io) to improve numerical stability and integration with various machine learning frameworks. Documentation for this version can be found [here](https://celerite2.readthedocs.io/en/latest/). This new implementation includes interfaces in Python and C++, with full support for Theano/PyMC3 and JAX. This documentation won't teach you the fundamentals of GP modeling but the best resource for learning about this is available for free online: [Rasmussen & Williams (2006)](http://www.gaussianprocess.org/gpml/). Similarly, the _celerite_ algorithm is restricted to a specific class of covariance functions (see [the original paper](https://arxiv.org/abs/1703.09710) for more information and [a recent generalization](https://arxiv.org/abs/2007.05799) for extensions to structured two-dimensional data). If you need scalable GPs with more general covariance functions, [GPyTorch](https://gpytorch.ai/) might be a good choice. %prep %autosetup -n celerite2-0.2.1 %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-celerite2 -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 10 2023 Python_Bot - 0.2.1-1 - Package Spec generated