automatic import of python-csbdeepopeneuler20.03

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diff --git a/.gitignore b/.gitignore
index e69de29..3c9af6c 100644
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+/csbdeep-0.7.3.tar.gz
diff --git a/python-csbdeep.spec b/python-csbdeep.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-csbdeep
+Version:	0.7.3
+Release:	1
+Summary:	CSBDeep - a toolbox for Content-aware Image Restoration (CARE)
+License:	BSD 3-Clause License
+URL:		http://csbdeep.bioimagecomputing.com/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/ce/51/46132795844348e19a77ce4fb0201e4339d0e14956666ff22f6b82e0d6f6/csbdeep-0.7.3.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-scipy
+Requires:	python3-matplotlib
+Requires:	python3-six
+Requires:	python3-tifffile
+Requires:	python3-tqdm
+Requires:	python3-packaging
+Requires:	python3-sphinx
+Requires:	python3-sphinx-rtd-theme
+Requires:	python3-pytest
+Requires:	python3-keras
+Requires:	python3-protobuf
+
+%description
+[![PyPI version](https://badge.fury.io/py/csbdeep.svg)](https://pypi.org/project/csbdeep)
+[![Anaconda-Server Badge](https://anaconda.org/conda-forge/csbdeep/badges/version.svg)](https://anaconda.org/conda-forge/csbdeep)
+[![Test](https://github.com/CSBDeep/CSBDeep/workflows/Test/badge.svg)](https://github.com/CSBDeep/CSBDeep/actions?query=workflow%3ATest)
+[![Test (PyPI)](https://github.com/CSBDeep/CSBDeep/workflows/Test%20(PyPI)/badge.svg)](https://github.com/CSBDeep/CSBDeep/actions?query=workflow%3A%22Test+%28PyPI%29%22)
+[![Test (Legacy)](https://github.com/CSBDeep/CSBDeep/workflows/Test%20(Legacy)/badge.svg)](https://github.com/CSBDeep/CSBDeep/actions?query=workflow%3A%22Test+%28Legacy%29%22)
+
+# CSBDeep – a toolbox for CARE
+
+This is the CSBDeep Python package, which provides a toolbox for content-aware restoration of fluorescence microscopy images (CARE), based on deep learning via Keras and TensorFlow.
+
+Please see the documentation at http://csbdeep.bioimagecomputing.com/doc/.
+
+
+
+
+%package -n python3-csbdeep
+Summary:	CSBDeep - a toolbox for Content-aware Image Restoration (CARE)
+Provides:	python-csbdeep
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-csbdeep
+[![PyPI version](https://badge.fury.io/py/csbdeep.svg)](https://pypi.org/project/csbdeep)
+[![Anaconda-Server Badge](https://anaconda.org/conda-forge/csbdeep/badges/version.svg)](https://anaconda.org/conda-forge/csbdeep)
+[![Test](https://github.com/CSBDeep/CSBDeep/workflows/Test/badge.svg)](https://github.com/CSBDeep/CSBDeep/actions?query=workflow%3ATest)
+[![Test (PyPI)](https://github.com/CSBDeep/CSBDeep/workflows/Test%20(PyPI)/badge.svg)](https://github.com/CSBDeep/CSBDeep/actions?query=workflow%3A%22Test+%28PyPI%29%22)
+[![Test (Legacy)](https://github.com/CSBDeep/CSBDeep/workflows/Test%20(Legacy)/badge.svg)](https://github.com/CSBDeep/CSBDeep/actions?query=workflow%3A%22Test+%28Legacy%29%22)
+
+# CSBDeep – a toolbox for CARE
+
+This is the CSBDeep Python package, which provides a toolbox for content-aware restoration of fluorescence microscopy images (CARE), based on deep learning via Keras and TensorFlow.
+
+Please see the documentation at http://csbdeep.bioimagecomputing.com/doc/.
+
+
+
+
+%package help
+Summary:	Development documents and examples for csbdeep
+Provides:	python3-csbdeep-doc
+%description help
+[![PyPI version](https://badge.fury.io/py/csbdeep.svg)](https://pypi.org/project/csbdeep)
+[![Anaconda-Server Badge](https://anaconda.org/conda-forge/csbdeep/badges/version.svg)](https://anaconda.org/conda-forge/csbdeep)
+[![Test](https://github.com/CSBDeep/CSBDeep/workflows/Test/badge.svg)](https://github.com/CSBDeep/CSBDeep/actions?query=workflow%3ATest)
+[![Test (PyPI)](https://github.com/CSBDeep/CSBDeep/workflows/Test%20(PyPI)/badge.svg)](https://github.com/CSBDeep/CSBDeep/actions?query=workflow%3A%22Test+%28PyPI%29%22)
+[![Test (Legacy)](https://github.com/CSBDeep/CSBDeep/workflows/Test%20(Legacy)/badge.svg)](https://github.com/CSBDeep/CSBDeep/actions?query=workflow%3A%22Test+%28Legacy%29%22)
+
+# CSBDeep – a toolbox for CARE
+
+This is the CSBDeep Python package, which provides a toolbox for content-aware restoration of fluorescence microscopy images (CARE), based on deep learning via Keras and TensorFlow.
+
+Please see the documentation at http://csbdeep.bioimagecomputing.com/doc/.
+
+
+
+
+%prep
+%autosetup -n csbdeep-0.7.3
+
+%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-csbdeep -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.3-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..2cd7aa0
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+474054554be85d926b375262864cc26d  csbdeep-0.7.3.tar.gz