automatic import of python-root-numpy

3 files changed, 191 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..6a57385 100644
--- a/.gitignore
+++ b/.gitignore
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+/root_numpy-4.8.0.tar.gz
diff --git a/python-root-numpy.spec b/python-root-numpy.spec
new file mode 100644
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--- /dev/null
+++ b/python-root-numpy.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-root-numpy
+Version:	4.8.0
+Release:	1
+Summary:	The interface between ROOT and NumPy
+License:	new BSD
+URL:		http://scikit-hep.org/root_numpy
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/d5/5f/82f5111c22599676eb8b5f9b1bf85c38dcc7995d52cd6b4a8f5f5caa4659/root_numpy-4.8.0.tar.gz
+BuildArch:	noarch
+
+
+%description
+.. image:: https://img.shields.io/pypi/v/root_numpy.svg
+   :target: https://pypi.python.org/pypi/root_numpy
+.. image:: https://api.travis-ci.org/scikit-hep/root_numpy.png
+   :target: https://travis-ci.org/scikit-hep/root_numpy
+.. image:: https://coveralls.io/repos/github/scikit-hep/root_numpy/badge.svg?branch=master
+   :target: https://coveralls.io/github/scikit-hep/root_numpy?branch=master
+.. image:: https://landscape.io/github/scikit-hep/root_numpy/master/landscape.svg?style=flat
+   :target: https://landscape.io/github/scikit-hep/root_numpy/master
+.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.592881.svg
+   :target: https://doi.org/10.5281/zenodo.592881
+
+root_numpy is a Python extension module that provides an efficient interface
+between `ROOT <http://root.cern.ch/>`_ and `NumPy <http://www.numpy.org/>`_.
+root_numpy's internals are compiled C++ and can therefore handle large amounts
+of data much faster than equivalent pure Python implementations.
+
+With your ROOT data in NumPy form, make use of NumPy's `broad library
+<http://docs.scipy.org/doc/numpy/reference/>`_, including fancy indexing,
+slicing, broadcasting, random sampling, sorting, shape transformations, linear
+algebra operations, and more. See this `tutorial
+<https://docs.scipy.org/doc/numpy-dev/user/quickstart.html>`_ to get started.
+NumPy is the fundamental library of the scientific Python ecosystem. Using
+NumPy arrays opens up many new possibilities beyond what ROOT offers. Convert
+your TTrees into NumPy arrays and use `SciPy <http://www.scipy.org/>`_ for
+numerical integration and optimization, `matplotlib <http://matplotlib.org/>`_
+for plotting, `pandas <http://pandas.pydata.org/>`_ for data analysis,
+`statsmodels <http://statsmodels.sourceforge.net/>`_ for statistical modelling,
+`scikit-learn <http://scikit-learn.org/>`_ for machine learning, and perform
+quick exploratory analysis in a `Jupyter notebook <https://jupyter.org/>`_.
+
+At the core of root_numpy are powerful and flexible functions for converting
+`ROOT TTrees <https://root.cern.ch/doc/master/classTTree.html>`_ into
+`structured NumPy arrays
+<http://docs.scipy.org/doc/numpy/user/basics.rec.html>`_ as well as converting
+NumPy arrays back into ROOT TTrees. root_numpy can convert branches of strings
+and basic types such as bool, int, float, double, etc. as well as
+variable-length and fixed-length multidimensional arrays and 1D or 2D vectors
+of basic types and strings. root_numpy can also create columns in the output
+array that are expressions involving the TTree branches similar to
+``TTree::Draw()``.
+
+%package -n python3-root-numpy
+Summary:	The interface between ROOT and NumPy
+Provides:	python-root-numpy
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-root-numpy
+.. image:: https://img.shields.io/pypi/v/root_numpy.svg
+   :target: https://pypi.python.org/pypi/root_numpy
+.. image:: https://api.travis-ci.org/scikit-hep/root_numpy.png
+   :target: https://travis-ci.org/scikit-hep/root_numpy
+.. image:: https://coveralls.io/repos/github/scikit-hep/root_numpy/badge.svg?branch=master
+   :target: https://coveralls.io/github/scikit-hep/root_numpy?branch=master
+.. image:: https://landscape.io/github/scikit-hep/root_numpy/master/landscape.svg?style=flat
+   :target: https://landscape.io/github/scikit-hep/root_numpy/master
+.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.592881.svg
+   :target: https://doi.org/10.5281/zenodo.592881
+
+root_numpy is a Python extension module that provides an efficient interface
+between `ROOT <http://root.cern.ch/>`_ and `NumPy <http://www.numpy.org/>`_.
+root_numpy's internals are compiled C++ and can therefore handle large amounts
+of data much faster than equivalent pure Python implementations.
+
+With your ROOT data in NumPy form, make use of NumPy's `broad library
+<http://docs.scipy.org/doc/numpy/reference/>`_, including fancy indexing,
+slicing, broadcasting, random sampling, sorting, shape transformations, linear
+algebra operations, and more. See this `tutorial
+<https://docs.scipy.org/doc/numpy-dev/user/quickstart.html>`_ to get started.
+NumPy is the fundamental library of the scientific Python ecosystem. Using
+NumPy arrays opens up many new possibilities beyond what ROOT offers. Convert
+your TTrees into NumPy arrays and use `SciPy <http://www.scipy.org/>`_ for
+numerical integration and optimization, `matplotlib <http://matplotlib.org/>`_
+for plotting, `pandas <http://pandas.pydata.org/>`_ for data analysis,
+`statsmodels <http://statsmodels.sourceforge.net/>`_ for statistical modelling,
+`scikit-learn <http://scikit-learn.org/>`_ for machine learning, and perform
+quick exploratory analysis in a `Jupyter notebook <https://jupyter.org/>`_.
+
+At the core of root_numpy are powerful and flexible functions for converting
+`ROOT TTrees <https://root.cern.ch/doc/master/classTTree.html>`_ into
+`structured NumPy arrays
+<http://docs.scipy.org/doc/numpy/user/basics.rec.html>`_ as well as converting
+NumPy arrays back into ROOT TTrees. root_numpy can convert branches of strings
+and basic types such as bool, int, float, double, etc. as well as
+variable-length and fixed-length multidimensional arrays and 1D or 2D vectors
+of basic types and strings. root_numpy can also create columns in the output
+array that are expressions involving the TTree branches similar to
+``TTree::Draw()``.
+
+%package help
+Summary:	Development documents and examples for root-numpy
+Provides:	python3-root-numpy-doc
+%description help
+.. image:: https://img.shields.io/pypi/v/root_numpy.svg
+   :target: https://pypi.python.org/pypi/root_numpy
+.. image:: https://api.travis-ci.org/scikit-hep/root_numpy.png
+   :target: https://travis-ci.org/scikit-hep/root_numpy
+.. image:: https://coveralls.io/repos/github/scikit-hep/root_numpy/badge.svg?branch=master
+   :target: https://coveralls.io/github/scikit-hep/root_numpy?branch=master
+.. image:: https://landscape.io/github/scikit-hep/root_numpy/master/landscape.svg?style=flat
+   :target: https://landscape.io/github/scikit-hep/root_numpy/master
+.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.592881.svg
+   :target: https://doi.org/10.5281/zenodo.592881
+
+root_numpy is a Python extension module that provides an efficient interface
+between `ROOT <http://root.cern.ch/>`_ and `NumPy <http://www.numpy.org/>`_.
+root_numpy's internals are compiled C++ and can therefore handle large amounts
+of data much faster than equivalent pure Python implementations.
+
+With your ROOT data in NumPy form, make use of NumPy's `broad library
+<http://docs.scipy.org/doc/numpy/reference/>`_, including fancy indexing,
+slicing, broadcasting, random sampling, sorting, shape transformations, linear
+algebra operations, and more. See this `tutorial
+<https://docs.scipy.org/doc/numpy-dev/user/quickstart.html>`_ to get started.
+NumPy is the fundamental library of the scientific Python ecosystem. Using
+NumPy arrays opens up many new possibilities beyond what ROOT offers. Convert
+your TTrees into NumPy arrays and use `SciPy <http://www.scipy.org/>`_ for
+numerical integration and optimization, `matplotlib <http://matplotlib.org/>`_
+for plotting, `pandas <http://pandas.pydata.org/>`_ for data analysis,
+`statsmodels <http://statsmodels.sourceforge.net/>`_ for statistical modelling,
+`scikit-learn <http://scikit-learn.org/>`_ for machine learning, and perform
+quick exploratory analysis in a `Jupyter notebook <https://jupyter.org/>`_.
+
+At the core of root_numpy are powerful and flexible functions for converting
+`ROOT TTrees <https://root.cern.ch/doc/master/classTTree.html>`_ into
+`structured NumPy arrays
+<http://docs.scipy.org/doc/numpy/user/basics.rec.html>`_ as well as converting
+NumPy arrays back into ROOT TTrees. root_numpy can convert branches of strings
+and basic types such as bool, int, float, double, etc. as well as
+variable-length and fixed-length multidimensional arrays and 1D or 2D vectors
+of basic types and strings. root_numpy can also create columns in the output
+array that are expressions involving the TTree branches similar to
+``TTree::Draw()``.
+
+%prep
+%autosetup -n root-numpy-4.8.0
+
+%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-root-numpy -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 10 2023 Python_Bot <Python_Bot@openeuler.org> - 4.8.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..6e5c51d
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+a8744cf13f868ddc2762f84bfc0e23fb  root_numpy-4.8.0.tar.gz