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|
%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 <Python_Bot@openeuler.org> - 0.2.1-1
- Package Spec generated
|
