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%global _empty_manifest_terminate_build 0
Name: python-smt
Version: 1.3.0
Release: 1
Summary: The Surrogate Modeling Toolbox (SMT)
License: BSD-3
URL: https://github.com/SMTorg/smt
Source0: https://mirrors.aliyun.com/pypi/web/packages/96/5c/be3c39df7bf93f312742722bb2bc5ced48bc871329e7fea4f98b0c442525/smt-1.3.0.tar.gz
%description
The surrogate modeling toolbox (SMT) is a Python package that contains
a collection of surrogate modeling methods, sampling techniques, and
benchmarking functions. This package provides a library of surrogate
models that is simple to use and facilitates the implementation of additional methods.
SMT is different from existing surrogate modeling libraries because of
its emphasis on derivatives, including training derivatives used for
gradient-enhanced modeling, prediction derivatives, and derivatives
with respect to the training data. It also includes new surrogate models
that are not available elsewhere: kriging by partial-least squares reduction
and energy-minimizing spline interpolation.
%package -n python3-smt
Summary: The Surrogate Modeling Toolbox (SMT)
Provides: python-smt
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-smt
The surrogate modeling toolbox (SMT) is a Python package that contains
a collection of surrogate modeling methods, sampling techniques, and
benchmarking functions. This package provides a library of surrogate
models that is simple to use and facilitates the implementation of additional methods.
SMT is different from existing surrogate modeling libraries because of
its emphasis on derivatives, including training derivatives used for
gradient-enhanced modeling, prediction derivatives, and derivatives
with respect to the training data. It also includes new surrogate models
that are not available elsewhere: kriging by partial-least squares reduction
and energy-minimizing spline interpolation.
%package help
Summary: Development documents and examples for smt
Provides: python3-smt-doc
%description help
The surrogate modeling toolbox (SMT) is a Python package that contains
a collection of surrogate modeling methods, sampling techniques, and
benchmarking functions. This package provides a library of surrogate
models that is simple to use and facilitates the implementation of additional methods.
SMT is different from existing surrogate modeling libraries because of
its emphasis on derivatives, including training derivatives used for
gradient-enhanced modeling, prediction derivatives, and derivatives
with respect to the training data. It also includes new surrogate models
that are not available elsewhere: kriging by partial-least squares reduction
and energy-minimizing spline interpolation.
%prep
%autosetup -n smt-1.3.0
%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-smt -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 1.3.0-1
- Package Spec generated
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