automatic import of python-alphatwirlopeneuler20.03

3 files changed, 128 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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+/alphatwirl-0.30.0.tar.gz
diff --git a/python-alphatwirl.spec b/python-alphatwirl.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-alphatwirl
+Version:	0.30.0
+Release:	1
+Summary:	A Python library for summarizing event data
+License:	BSD License
+URL:		https://github.com/alphatwirl/alphatwirl
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/af/ab/f12aeb7b1047d5edfca937668bb178f329c9f20bd5ce1b1b8015a716e224/alphatwirl-0.30.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-atpbar
+Requires:	python3-mantichora
+
+%description
+A Python library for summarizing event data into multivariate categorical data
+### Description
+_AlphaTwirl_ is a Python library that summarizes event data into multivariate categorical data as data frames. Event data, input to AlphaTwirl, are data with one entry (or row) for one event: for example, data in [ROOT](https://root.cern.ch/) [TTrees](https://root.cern.ch/doc/master/classTTree.html) with one entry per collision event of an [LHC](https://home.cern/topics/large-hadron-collider) experiment at [CERN](http://home.cern/). Event data are often large—too large to be loaded in memory—because they have as many entries as events. Multivariate categorical data, the output of AlphaTwirl, have one row for one category. They are usually small—small enough to be loaded in memory—because they only have as many rows as categories. Users can, for example, import them as data frames into [R](https://www.r-project.org/) and [pandas](http://pandas.pydata.org/), which usually load all data in memory, and can perform categorical data analyses with a rich set of data operations available in R and pandas.
+****
+### Quick start
+- Jupyter Notebook: [*Quick start of AlphaTwirl*](https://github.com/alphatwirl/notebook-tutorial-2019-02)<br />
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/alphatwirl/notebook-tutorial-2019-02/master?filepath=tutorial_01.ipynb)
+****
+### Publication
+- Tai Sakuma, *"AlphaTwirl: A Python library for summarizing event data into multivariate categorical data"*,
+  EPJ Web of Conferences **214**, 02001 (2019), [doi:10.1051/epjconf/201921402001](https://doi.org/10.1051/epjconf/201921402001),
+  [1905.06609](https://arxiv.org/abs/1905.06609)
+****
+### License
+- AlphaTwirl is licensed under the BSD license.
+*****
+### Contact
+- Tai Sakuma - tai.sakuma@gmail.com
+
+%package -n python3-alphatwirl
+Summary:	A Python library for summarizing event data
+Provides:	python-alphatwirl
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-alphatwirl
+A Python library for summarizing event data into multivariate categorical data
+### Description
+_AlphaTwirl_ is a Python library that summarizes event data into multivariate categorical data as data frames. Event data, input to AlphaTwirl, are data with one entry (or row) for one event: for example, data in [ROOT](https://root.cern.ch/) [TTrees](https://root.cern.ch/doc/master/classTTree.html) with one entry per collision event of an [LHC](https://home.cern/topics/large-hadron-collider) experiment at [CERN](http://home.cern/). Event data are often large&mdash;too large to be loaded in memory&mdash;because they have as many entries as events. Multivariate categorical data, the output of AlphaTwirl, have one row for one category. They are usually small&mdash;small enough to be loaded in memory&mdash;because they only have as many rows as categories. Users can, for example, import them as data frames into [R](https://www.r-project.org/) and [pandas](http://pandas.pydata.org/), which usually load all data in memory, and can perform categorical data analyses with a rich set of data operations available in R and pandas.
+****
+### Quick start
+- Jupyter Notebook: [*Quick start of AlphaTwirl*](https://github.com/alphatwirl/notebook-tutorial-2019-02)<br />
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/alphatwirl/notebook-tutorial-2019-02/master?filepath=tutorial_01.ipynb)
+****
+### Publication
+- Tai Sakuma, *"AlphaTwirl: A Python library for summarizing event data into multivariate categorical data"*,
+  EPJ Web of Conferences **214**, 02001 (2019), [doi:10.1051/epjconf/201921402001](https://doi.org/10.1051/epjconf/201921402001),
+  [1905.06609](https://arxiv.org/abs/1905.06609)
+****
+### License
+- AlphaTwirl is licensed under the BSD license.
+*****
+### Contact
+- Tai Sakuma - tai.sakuma@gmail.com
+
+%package help
+Summary:	Development documents and examples for alphatwirl
+Provides:	python3-alphatwirl-doc
+%description help
+A Python library for summarizing event data into multivariate categorical data
+### Description
+_AlphaTwirl_ is a Python library that summarizes event data into multivariate categorical data as data frames. Event data, input to AlphaTwirl, are data with one entry (or row) for one event: for example, data in [ROOT](https://root.cern.ch/) [TTrees](https://root.cern.ch/doc/master/classTTree.html) with one entry per collision event of an [LHC](https://home.cern/topics/large-hadron-collider) experiment at [CERN](http://home.cern/). Event data are often large&mdash;too large to be loaded in memory&mdash;because they have as many entries as events. Multivariate categorical data, the output of AlphaTwirl, have one row for one category. They are usually small&mdash;small enough to be loaded in memory&mdash;because they only have as many rows as categories. Users can, for example, import them as data frames into [R](https://www.r-project.org/) and [pandas](http://pandas.pydata.org/), which usually load all data in memory, and can perform categorical data analyses with a rich set of data operations available in R and pandas.
+****
+### Quick start
+- Jupyter Notebook: [*Quick start of AlphaTwirl*](https://github.com/alphatwirl/notebook-tutorial-2019-02)<br />
+&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/alphatwirl/notebook-tutorial-2019-02/master?filepath=tutorial_01.ipynb)
+****
+### Publication
+- Tai Sakuma, *"AlphaTwirl: A Python library for summarizing event data into multivariate categorical data"*,
+  EPJ Web of Conferences **214**, 02001 (2019), [doi:10.1051/epjconf/201921402001](https://doi.org/10.1051/epjconf/201921402001),
+  [1905.06609](https://arxiv.org/abs/1905.06609)
+****
+### License
+- AlphaTwirl is licensed under the BSD license.
+*****
+### Contact
+- Tai Sakuma - tai.sakuma@gmail.com
+
+%prep
+%autosetup -n alphatwirl-0.30.0
+
+%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-alphatwirl -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.30.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..a7001ee
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+100a1430ee00aa194c21baf74c59a2e1  alphatwirl-0.30.0.tar.gz