automatic import of python-mlreflectopeneuler20.03

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+/mlreflect-0.21.1.tar.gz
diff --git a/python-mlreflect.spec b/python-mlreflect.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-mlreflect
+Version:	0.21.1
+Release:	1
+Summary:	mlreflect is a Python package for training and using artificial neural networks to analyze specular X-ray and neutron reflectivity data. The training and usage of the neural network models is done via Keras as an API for TensorFlow.
+License:	MIT
+URL:		https://pypi.org/project/mlreflect/
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/75/2e/85600533b4fbf0e35513daf5607040c35c4f415410ac5eacfdc6f5f09fd5/mlreflect-0.21.1.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-tensorflow
+Requires:	python3-scipy
+Requires:	python3-pandas
+Requires:	python3-tqdm
+Requires:	python3-h5py
+Requires:	python3-numpy
+Requires:	python3-typing
+Requires:	python3-matplotlib
+Requires:	python3-packaging
+Requires:	python3-fabio
+
+%description
+# mlreflect
+
+_mlreflect_ is a Python package for training and using artificial neural networks to analyze specular X-ray and 
+neutron reflectivity data. The training and usage of the neural network models is done via Keras as an API for TensorFlow. 
+
+## Installation
+The mlreflect package can be installed directly from the command line using the python package manager pip:
+
+`pip install mlreflect`
+
+In case the newest version is not available on PyPI, the package can also be installed locally. Download the package, unzip it and navigate to the folder containing the downloaded mlreflect folder. Then use:
+
+`pip install mlreflect/`
+
+## Online documentation
+
+Documentation and API reference can be found online on https://mlreflect.readthedocs.io/en/latest/
+
+## Usage example
+The package can then be imported in python using
+
+`import mlreflect`
+
+or
+
+`from mlreflect import <module>`
+
+An example of how to generate training data, train the model and test it on experimental data is shown in the 
+_example/notebooks/training_example.ipynb_ Jupyter notebook.
+
+An example of how to use the default pre-trained model for single layers on Si/SiOx substrates to fit data directly 
+from a SPEC file is shown in _examples/notebooks/spec_usage_example.ipynb_ Jupyter notebook.
+
+A detailed explanation as well as API info can be found in the documentation.
+
+## Authors
+#### Main author
+- Alessandro Greco <alessandro.greco@uni-tuebingen.de> (Institut für Angewandte Physik, University of Tübingen)
+
+#### Contributors
+- Vladimir Starostin (Institut für Angewandte Physik, University of Tübingen)
+- Christos Karapanagiotis (Institut für Physik, Humboldt Universität zu Berlin)
+- Alexander Hinderhofer (Institut für Angewandte Physik, University of Tübingen)
+- Alexander Gerlach (Institut für Angewandte Physik, University of Tübingen)
+- Linus Pithan (ESRF The European Synchrotron)
+- Sascha Liehr (Bundesanstalt für Materialforschung und -prüfung (BAM))
+- Frank Schreiber (Institut für Angewandte Physik, University of Tübingen)
+- Stefan Kowarik (Bundesanstalt für Materialforschung und -prüfung (BAM) and Institut für Physik, Humboldt Universität zu Berlin)
+
+
+
+
+%package -n python3-mlreflect
+Summary:	mlreflect is a Python package for training and using artificial neural networks to analyze specular X-ray and neutron reflectivity data. The training and usage of the neural network models is done via Keras as an API for TensorFlow.
+Provides:	python-mlreflect
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-mlreflect
+# mlreflect
+
+_mlreflect_ is a Python package for training and using artificial neural networks to analyze specular X-ray and 
+neutron reflectivity data. The training and usage of the neural network models is done via Keras as an API for TensorFlow. 
+
+## Installation
+The mlreflect package can be installed directly from the command line using the python package manager pip:
+
+`pip install mlreflect`
+
+In case the newest version is not available on PyPI, the package can also be installed locally. Download the package, unzip it and navigate to the folder containing the downloaded mlreflect folder. Then use:
+
+`pip install mlreflect/`
+
+## Online documentation
+
+Documentation and API reference can be found online on https://mlreflect.readthedocs.io/en/latest/
+
+## Usage example
+The package can then be imported in python using
+
+`import mlreflect`
+
+or
+
+`from mlreflect import <module>`
+
+An example of how to generate training data, train the model and test it on experimental data is shown in the 
+_example/notebooks/training_example.ipynb_ Jupyter notebook.
+
+An example of how to use the default pre-trained model for single layers on Si/SiOx substrates to fit data directly 
+from a SPEC file is shown in _examples/notebooks/spec_usage_example.ipynb_ Jupyter notebook.
+
+A detailed explanation as well as API info can be found in the documentation.
+
+## Authors
+#### Main author
+- Alessandro Greco <alessandro.greco@uni-tuebingen.de> (Institut für Angewandte Physik, University of Tübingen)
+
+#### Contributors
+- Vladimir Starostin (Institut für Angewandte Physik, University of Tübingen)
+- Christos Karapanagiotis (Institut für Physik, Humboldt Universität zu Berlin)
+- Alexander Hinderhofer (Institut für Angewandte Physik, University of Tübingen)
+- Alexander Gerlach (Institut für Angewandte Physik, University of Tübingen)
+- Linus Pithan (ESRF The European Synchrotron)
+- Sascha Liehr (Bundesanstalt für Materialforschung und -prüfung (BAM))
+- Frank Schreiber (Institut für Angewandte Physik, University of Tübingen)
+- Stefan Kowarik (Bundesanstalt für Materialforschung und -prüfung (BAM) and Institut für Physik, Humboldt Universität zu Berlin)
+
+
+
+
+%package help
+Summary:	Development documents and examples for mlreflect
+Provides:	python3-mlreflect-doc
+%description help
+# mlreflect
+
+_mlreflect_ is a Python package for training and using artificial neural networks to analyze specular X-ray and 
+neutron reflectivity data. The training and usage of the neural network models is done via Keras as an API for TensorFlow. 
+
+## Installation
+The mlreflect package can be installed directly from the command line using the python package manager pip:
+
+`pip install mlreflect`
+
+In case the newest version is not available on PyPI, the package can also be installed locally. Download the package, unzip it and navigate to the folder containing the downloaded mlreflect folder. Then use:
+
+`pip install mlreflect/`
+
+## Online documentation
+
+Documentation and API reference can be found online on https://mlreflect.readthedocs.io/en/latest/
+
+## Usage example
+The package can then be imported in python using
+
+`import mlreflect`
+
+or
+
+`from mlreflect import <module>`
+
+An example of how to generate training data, train the model and test it on experimental data is shown in the 
+_example/notebooks/training_example.ipynb_ Jupyter notebook.
+
+An example of how to use the default pre-trained model for single layers on Si/SiOx substrates to fit data directly 
+from a SPEC file is shown in _examples/notebooks/spec_usage_example.ipynb_ Jupyter notebook.
+
+A detailed explanation as well as API info can be found in the documentation.
+
+## Authors
+#### Main author
+- Alessandro Greco <alessandro.greco@uni-tuebingen.de> (Institut für Angewandte Physik, University of Tübingen)
+
+#### Contributors
+- Vladimir Starostin (Institut für Angewandte Physik, University of Tübingen)
+- Christos Karapanagiotis (Institut für Physik, Humboldt Universität zu Berlin)
+- Alexander Hinderhofer (Institut für Angewandte Physik, University of Tübingen)
+- Alexander Gerlach (Institut für Angewandte Physik, University of Tübingen)
+- Linus Pithan (ESRF The European Synchrotron)
+- Sascha Liehr (Bundesanstalt für Materialforschung und -prüfung (BAM))
+- Frank Schreiber (Institut für Angewandte Physik, University of Tübingen)
+- Stefan Kowarik (Bundesanstalt für Materialforschung und -prüfung (BAM) and Institut für Physik, Humboldt Universität zu Berlin)
+
+
+
+
+%prep
+%autosetup -n mlreflect-0.21.1
+
+%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-mlreflect -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.21.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..e80faf4
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+daa099d0507e131fea0a241d92c70f46  mlreflect-0.21.1.tar.gz