%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