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%global _empty_manifest_terminate_build 0
Name: python-H-MCRLLM
Version: 0.0.24
Release: 1
Summary: H MCRLLM: Hierarchical Multivariate Curve Resolution by Log-Likelihood Maximization
License: MIT License
URL: https://pypi.org/project/H-MCRLLM/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/86/7a/4b013e02261bf14f2a137fe14c4101efc4ac6245cf2d90146f56bac96a5d/H_MCRLLM-0.0.24.tar.gz
BuildArch: noarch
%description
H MCRLLM: Hierarchical Multivariate Curve Resolution by Log-Likelihood Maximization
X = CS
where
X(nxk): Spectroscopic data where n spectra acquired over k energy levels
C(nxa): Composition map based on a MCRLLM components
S(axk): Spectra of the a components as computed by MCRLLM
# Method first presented in
Lavoie F.B., Braidy N. and Gosselin R. (2016) Including Noise Characteristics in MCR to improve Mapping and Component Extraction from Spectral Images, Chemometrics and Intelligent Laboratory Systems, 153, 40-50.
# Dataset
XPS dataset of Titanium, Vanadium and Chromium. Please refer to Lavoie et al. (2016) for further details on the sample.
%package -n python3-H-MCRLLM
Summary: H MCRLLM: Hierarchical Multivariate Curve Resolution by Log-Likelihood Maximization
Provides: python-H-MCRLLM
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-H-MCRLLM
H MCRLLM: Hierarchical Multivariate Curve Resolution by Log-Likelihood Maximization
X = CS
where
X(nxk): Spectroscopic data where n spectra acquired over k energy levels
C(nxa): Composition map based on a MCRLLM components
S(axk): Spectra of the a components as computed by MCRLLM
# Method first presented in
Lavoie F.B., Braidy N. and Gosselin R. (2016) Including Noise Characteristics in MCR to improve Mapping and Component Extraction from Spectral Images, Chemometrics and Intelligent Laboratory Systems, 153, 40-50.
# Dataset
XPS dataset of Titanium, Vanadium and Chromium. Please refer to Lavoie et al. (2016) for further details on the sample.
%package help
Summary: Development documents and examples for H-MCRLLM
Provides: python3-H-MCRLLM-doc
%description help
H MCRLLM: Hierarchical Multivariate Curve Resolution by Log-Likelihood Maximization
X = CS
where
X(nxk): Spectroscopic data where n spectra acquired over k energy levels
C(nxa): Composition map based on a MCRLLM components
S(axk): Spectra of the a components as computed by MCRLLM
# Method first presented in
Lavoie F.B., Braidy N. and Gosselin R. (2016) Including Noise Characteristics in MCR to improve Mapping and Component Extraction from Spectral Images, Chemometrics and Intelligent Laboratory Systems, 153, 40-50.
# Dataset
XPS dataset of Titanium, Vanadium and Chromium. Please refer to Lavoie et al. (2016) for further details on the sample.
%prep
%autosetup -n H-MCRLLM-0.0.24
%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-H-MCRLLM -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.24-1
- Package Spec generated
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