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%global _empty_manifest_terminate_build 0
Name: python-tdub
Version: 0.0.79
Release: 1
Summary: tW analysis tools.
License: BSD 3-clause
URL: https://github.com/douglasdavis/tdub
Source0: https://mirrors.aliyun.com/pypi/web/packages/95/eb/57e2f7c6f6f6f2089b5c4a5c3b235e327876a25015d0aec35d0bd7a2bd85/tdub-0.0.79.tar.gz
BuildArch: noarch
Requires: python3-click
Requires: python3-formulate
Requires: python3-joblib
Requires: python3-lz4
Requires: python3-matplotlib
Requires: python3-numexpr
Requires: python3-pandas
Requires: python3-pycondor
Requires: python3-pygram11
Requires: python3-pyyaml
Requires: python3-scikit-learn
Requires: python3-uproot
Requires: python3-xxhash
Requires: python3-requests
%description
# tdub
[](https://github.com/douglasdavis/tdub/actions)
[](https://tdub.readthedocs.io/)
[](https://pypi.org/project/tdub/)
[](https://pypi.org/project/tdub/)
`tdub` is a Python project for handling some downstsream steps in the
ATLAS Run 2 *tW* inclusive cross section analysis. The project provides
a simple command line interface for performing standard analysis tasks
including:
- BDT feature selection and hyperparameter optimization.
- Training BDT models on our Monte Carlo.
- Applying trained BDT models to our data and Monte Carlo.
- Generating plots from various raw sources (our ROOT files and
Classifier training output).
- Generating plots from the output of
[`TRExFitter`](https://gitlab.cern.ch/TRExStats/TRExFitter/).
For potentially finer-grained tasks the API is fully documented. The
API mainly provides quick and easy access to pythonic representations
(i.e. dataframes or NumPy arrays) of our datasets (which of course
originate from [ROOT](https://root.cern/) files), modularized ML
tasks, and a set of utilities tailored for interacting with our
specific datasets.
%package -n python3-tdub
Summary: tW analysis tools.
Provides: python-tdub
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-tdub
# tdub
[](https://github.com/douglasdavis/tdub/actions)
[](https://tdub.readthedocs.io/)
[](https://pypi.org/project/tdub/)
[](https://pypi.org/project/tdub/)
`tdub` is a Python project for handling some downstsream steps in the
ATLAS Run 2 *tW* inclusive cross section analysis. The project provides
a simple command line interface for performing standard analysis tasks
including:
- BDT feature selection and hyperparameter optimization.
- Training BDT models on our Monte Carlo.
- Applying trained BDT models to our data and Monte Carlo.
- Generating plots from various raw sources (our ROOT files and
Classifier training output).
- Generating plots from the output of
[`TRExFitter`](https://gitlab.cern.ch/TRExStats/TRExFitter/).
For potentially finer-grained tasks the API is fully documented. The
API mainly provides quick and easy access to pythonic representations
(i.e. dataframes or NumPy arrays) of our datasets (which of course
originate from [ROOT](https://root.cern/) files), modularized ML
tasks, and a set of utilities tailored for interacting with our
specific datasets.
%package help
Summary: Development documents and examples for tdub
Provides: python3-tdub-doc
%description help
# tdub
[](https://github.com/douglasdavis/tdub/actions)
[](https://tdub.readthedocs.io/)
[](https://pypi.org/project/tdub/)
[](https://pypi.org/project/tdub/)
`tdub` is a Python project for handling some downstsream steps in the
ATLAS Run 2 *tW* inclusive cross section analysis. The project provides
a simple command line interface for performing standard analysis tasks
including:
- BDT feature selection and hyperparameter optimization.
- Training BDT models on our Monte Carlo.
- Applying trained BDT models to our data and Monte Carlo.
- Generating plots from various raw sources (our ROOT files and
Classifier training output).
- Generating plots from the output of
[`TRExFitter`](https://gitlab.cern.ch/TRExStats/TRExFitter/).
For potentially finer-grained tasks the API is fully documented. The
API mainly provides quick and easy access to pythonic representations
(i.e. dataframes or NumPy arrays) of our datasets (which of course
originate from [ROOT](https://root.cern/) files), modularized ML
tasks, and a set of utilities tailored for interacting with our
specific datasets.
%prep
%autosetup -n tdub-0.0.79
%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-tdub -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.79-1
- Package Spec generated
|
