automatic import of python-mhcflurry

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diff --git a/.gitignore b/.gitignore
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--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/mhcflurry-2.0.6.tar.gz
diff --git a/python-mhcflurry.spec b/python-mhcflurry.spec
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index 0000000..ea14e20
--- /dev/null
+++ b/python-mhcflurry.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-mhcflurry
+Version:	2.0.6
+Release:	1
+Summary:	MHC Binding Predictor
+License:	http://www.apache.org/licenses/LICENSE-2.0.html
+URL:		https://github.com/openvax/mhcflurry
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/b6/8c/449b1b81d3731cd668df54ff0b14bf9083378717a09deaef8bf884be6cf2/mhcflurry-2.0.6.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-six
+Requires:	python3-pandas
+Requires:	python3-appdirs
+Requires:	python3-scikit-learn
+Requires:	python3-mhcgnomes
+Requires:	python3-pyyaml
+Requires:	python3-tqdm
+Requires:	python3-np-utils
+
+%description
+[![Build Status](https://app.travis-ci.com/openvax/mhcflurry.svg?branch=master)](https://app.travis-ci.com/openvax/mhcflurry)
+
+# mhcflurry
+[MHC I](https://en.wikipedia.org/wiki/MHC_class_I) ligand
+prediction package with competitive accuracy and a fast and 
+[documented](http://openvax.github.io/mhcflurry/) implementation.
+
+MHCflurry implements class I peptide/MHC binding affinity prediction. 
+The current version provides pan-MHC I predictors supporting any MHC
+allele of known sequence. MHCflurry runs on Python 3.4+ using the
+[tensorflow](https://www.tensorflow.org/) neural network library.
+It exposes [command-line](http://openvax.github.io/mhcflurry/commandline_tutorial.html)
+and [Python library](http://openvax.github.io/mhcflurry/python_tutorial.html)
+interfaces.
+
+Starting in version 1.6.0, MHCflurry also includes two expermental predictors,
+an "antigen processing" predictor that attempts to model MHC allele-independent
+effects such as proteosomal cleavage and a "presentation" predictor that
+integrates processing predictions with binding affinity predictions to give a
+composite "presentation score." Both models are trained on mass spec-identified
+MHC ligands. These models were updated to incorporate minor improvements
+for the MHCflurry 2.0 release.
+
+If you find MHCflurry useful in your research please cite:
+
+> T. O'Donnell, A. Rubinsteyn, U. Laserson. "MHCflurry 2.0: Improved pan-allele prediction of MHC I-presented peptides by incorporating antigen processing," *Cell Systems*, 2020. https://doi.org/10.1016/j.cels.2020.06.010
+
+> T. O’Donnell, A. Rubinsteyn, M. Bonsack, A. B. Riemer, U. Laserson, and J. Hammerbacher, "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction," *Cell Systems*, 2018. https://doi.org/10.1016/j.cels.2018.05.014
+
+Please file an issue if you have questions or encounter problems.
+
+Have a bugfix or other contribution? We would love your help. See our [contributing guidelines](CONTRIBUTING.md).
+
+## Installation (pip)
+
+Install the package:
+
+```
+$ pip install mhcflurry
+```
+
+If you don't already have it, you will also need to install tensorflow version 2.2.0 or later. On most platforms you can do this with:
+
+```
+$ pip install tensorflow
+```
+
+If you are on Apple silicon (M1 processor), then you'll need to run `pip install tensorflow-macos` instead. See these [instructions](https://caffeinedev.medium.com/how-to-install-tensorflow-on-m1-mac-8e9b91d93706) for more info.
+
+Next download our datasets and trained models:
+
+```
+$ mhcflurry-downloads fetch
+```
+
+You can now generate predictions:
+
+```
+$ mhcflurry-predict \
+       --alleles HLA-A0201 HLA-A0301 \
+       --peptides SIINFEKL SIINFEKD SIINFEKQ \
+       --out /tmp/predictions.csv
+       
+Wrote: /tmp/predictions.csv
+```
+
+Or scan protein sequences for potential epitopes:
+
+```
+$ mhcflurry-predict-scan \
+        --sequences MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHS \
+        --alleles HLA-A*02:01 \
+        --out /tmp/predictions.csv
+        
+Wrote: /tmp/predictions.csv  
+```
+
+
+See the [documentation](http://openvax.github.io/mhcflurry/) for more details.
+
+
+## Docker
+You can also try the latest (GitHub master) version of MHCflurry using the Docker
+image hosted on [Dockerhub](https://hub.docker.com/r/openvax/mhcflurry) by
+running:
+
+```
+$ docker run -p 9999:9999 --rm openvax/mhcflurry:latest
+``` 
+
+This will start a [jupyter](https://jupyter.org/) notebook server in an
+environment that has MHCflurry installed. Go to `http://localhost:9999` in a
+browser to use it.
+
+To build the Docker image yourself, from a checkout run:
+
+```
+$ docker build -t mhcflurry:latest .
+$ docker run -p 9999:9999 --rm mhcflurry:latest
+```
+## Predicted sequence motifs
+Sequence logos for the binding motifs learned by MHCflurry BA are available [here](https://openvax.github.io/mhcflurry-motifs/).
+
+## Common issues and fixes
+
+### Problems downloading data and models
+Some users have reported HTTP connection issues when using `mhcflurry-downloads fetch`. As a workaround, you can download the data manually (e.g. using `wget`) and then use `mhcflurry-downloads` just to copy the data to the right place.
+
+To do this, first get the URL(s) of the downloads you need using `mhcflurry-downloads url`:
+
+```
+$ mhcflurry-downloads url models_class1_presentation
+https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```
+```
+
+Then make a directory and download the needed files to this directory:
+
+```
+$ mkdir downloads
+$ wget  --directory-prefix downloads https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```
+
+HTTP request sent, awaiting response... 200 OK
+Length: 72616448 (69M) [application/octet-stream]
+Saving to: 'downloads/models_class1_presentation.20200205.tar.bz2'
+```
+
+Now call `mhcflurry-downloads fetch` with the `--already-downloaded-dir` option to indicate that the downloads should be retrived from the specified directory:
+
+```
+$ mhcflurry-downloads fetch models_class1_presentation --already-downloaded-dir downloads
+```
+
+
+
+
+%package -n python3-mhcflurry
+Summary:	MHC Binding Predictor
+Provides:	python-mhcflurry
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-mhcflurry
+[![Build Status](https://app.travis-ci.com/openvax/mhcflurry.svg?branch=master)](https://app.travis-ci.com/openvax/mhcflurry)
+
+# mhcflurry
+[MHC I](https://en.wikipedia.org/wiki/MHC_class_I) ligand
+prediction package with competitive accuracy and a fast and 
+[documented](http://openvax.github.io/mhcflurry/) implementation.
+
+MHCflurry implements class I peptide/MHC binding affinity prediction. 
+The current version provides pan-MHC I predictors supporting any MHC
+allele of known sequence. MHCflurry runs on Python 3.4+ using the
+[tensorflow](https://www.tensorflow.org/) neural network library.
+It exposes [command-line](http://openvax.github.io/mhcflurry/commandline_tutorial.html)
+and [Python library](http://openvax.github.io/mhcflurry/python_tutorial.html)
+interfaces.
+
+Starting in version 1.6.0, MHCflurry also includes two expermental predictors,
+an "antigen processing" predictor that attempts to model MHC allele-independent
+effects such as proteosomal cleavage and a "presentation" predictor that
+integrates processing predictions with binding affinity predictions to give a
+composite "presentation score." Both models are trained on mass spec-identified
+MHC ligands. These models were updated to incorporate minor improvements
+for the MHCflurry 2.0 release.
+
+If you find MHCflurry useful in your research please cite:
+
+> T. O'Donnell, A. Rubinsteyn, U. Laserson. "MHCflurry 2.0: Improved pan-allele prediction of MHC I-presented peptides by incorporating antigen processing," *Cell Systems*, 2020. https://doi.org/10.1016/j.cels.2020.06.010
+
+> T. O’Donnell, A. Rubinsteyn, M. Bonsack, A. B. Riemer, U. Laserson, and J. Hammerbacher, "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction," *Cell Systems*, 2018. https://doi.org/10.1016/j.cels.2018.05.014
+
+Please file an issue if you have questions or encounter problems.
+
+Have a bugfix or other contribution? We would love your help. See our [contributing guidelines](CONTRIBUTING.md).
+
+## Installation (pip)
+
+Install the package:
+
+```
+$ pip install mhcflurry
+```
+
+If you don't already have it, you will also need to install tensorflow version 2.2.0 or later. On most platforms you can do this with:
+
+```
+$ pip install tensorflow
+```
+
+If you are on Apple silicon (M1 processor), then you'll need to run `pip install tensorflow-macos` instead. See these [instructions](https://caffeinedev.medium.com/how-to-install-tensorflow-on-m1-mac-8e9b91d93706) for more info.
+
+Next download our datasets and trained models:
+
+```
+$ mhcflurry-downloads fetch
+```
+
+You can now generate predictions:
+
+```
+$ mhcflurry-predict \
+       --alleles HLA-A0201 HLA-A0301 \
+       --peptides SIINFEKL SIINFEKD SIINFEKQ \
+       --out /tmp/predictions.csv
+       
+Wrote: /tmp/predictions.csv
+```
+
+Or scan protein sequences for potential epitopes:
+
+```
+$ mhcflurry-predict-scan \
+        --sequences MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHS \
+        --alleles HLA-A*02:01 \
+        --out /tmp/predictions.csv
+        
+Wrote: /tmp/predictions.csv  
+```
+
+
+See the [documentation](http://openvax.github.io/mhcflurry/) for more details.
+
+
+## Docker
+You can also try the latest (GitHub master) version of MHCflurry using the Docker
+image hosted on [Dockerhub](https://hub.docker.com/r/openvax/mhcflurry) by
+running:
+
+```
+$ docker run -p 9999:9999 --rm openvax/mhcflurry:latest
+``` 
+
+This will start a [jupyter](https://jupyter.org/) notebook server in an
+environment that has MHCflurry installed. Go to `http://localhost:9999` in a
+browser to use it.
+
+To build the Docker image yourself, from a checkout run:
+
+```
+$ docker build -t mhcflurry:latest .
+$ docker run -p 9999:9999 --rm mhcflurry:latest
+```
+## Predicted sequence motifs
+Sequence logos for the binding motifs learned by MHCflurry BA are available [here](https://openvax.github.io/mhcflurry-motifs/).
+
+## Common issues and fixes
+
+### Problems downloading data and models
+Some users have reported HTTP connection issues when using `mhcflurry-downloads fetch`. As a workaround, you can download the data manually (e.g. using `wget`) and then use `mhcflurry-downloads` just to copy the data to the right place.
+
+To do this, first get the URL(s) of the downloads you need using `mhcflurry-downloads url`:
+
+```
+$ mhcflurry-downloads url models_class1_presentation
+https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```
+```
+
+Then make a directory and download the needed files to this directory:
+
+```
+$ mkdir downloads
+$ wget  --directory-prefix downloads https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```
+
+HTTP request sent, awaiting response... 200 OK
+Length: 72616448 (69M) [application/octet-stream]
+Saving to: 'downloads/models_class1_presentation.20200205.tar.bz2'
+```
+
+Now call `mhcflurry-downloads fetch` with the `--already-downloaded-dir` option to indicate that the downloads should be retrived from the specified directory:
+
+```
+$ mhcflurry-downloads fetch models_class1_presentation --already-downloaded-dir downloads
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for mhcflurry
+Provides:	python3-mhcflurry-doc
+%description help
+[![Build Status](https://app.travis-ci.com/openvax/mhcflurry.svg?branch=master)](https://app.travis-ci.com/openvax/mhcflurry)
+
+# mhcflurry
+[MHC I](https://en.wikipedia.org/wiki/MHC_class_I) ligand
+prediction package with competitive accuracy and a fast and 
+[documented](http://openvax.github.io/mhcflurry/) implementation.
+
+MHCflurry implements class I peptide/MHC binding affinity prediction. 
+The current version provides pan-MHC I predictors supporting any MHC
+allele of known sequence. MHCflurry runs on Python 3.4+ using the
+[tensorflow](https://www.tensorflow.org/) neural network library.
+It exposes [command-line](http://openvax.github.io/mhcflurry/commandline_tutorial.html)
+and [Python library](http://openvax.github.io/mhcflurry/python_tutorial.html)
+interfaces.
+
+Starting in version 1.6.0, MHCflurry also includes two expermental predictors,
+an "antigen processing" predictor that attempts to model MHC allele-independent
+effects such as proteosomal cleavage and a "presentation" predictor that
+integrates processing predictions with binding affinity predictions to give a
+composite "presentation score." Both models are trained on mass spec-identified
+MHC ligands. These models were updated to incorporate minor improvements
+for the MHCflurry 2.0 release.
+
+If you find MHCflurry useful in your research please cite:
+
+> T. O'Donnell, A. Rubinsteyn, U. Laserson. "MHCflurry 2.0: Improved pan-allele prediction of MHC I-presented peptides by incorporating antigen processing," *Cell Systems*, 2020. https://doi.org/10.1016/j.cels.2020.06.010
+
+> T. O’Donnell, A. Rubinsteyn, M. Bonsack, A. B. Riemer, U. Laserson, and J. Hammerbacher, "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction," *Cell Systems*, 2018. https://doi.org/10.1016/j.cels.2018.05.014
+
+Please file an issue if you have questions or encounter problems.
+
+Have a bugfix or other contribution? We would love your help. See our [contributing guidelines](CONTRIBUTING.md).
+
+## Installation (pip)
+
+Install the package:
+
+```
+$ pip install mhcflurry
+```
+
+If you don't already have it, you will also need to install tensorflow version 2.2.0 or later. On most platforms you can do this with:
+
+```
+$ pip install tensorflow
+```
+
+If you are on Apple silicon (M1 processor), then you'll need to run `pip install tensorflow-macos` instead. See these [instructions](https://caffeinedev.medium.com/how-to-install-tensorflow-on-m1-mac-8e9b91d93706) for more info.
+
+Next download our datasets and trained models:
+
+```
+$ mhcflurry-downloads fetch
+```
+
+You can now generate predictions:
+
+```
+$ mhcflurry-predict \
+       --alleles HLA-A0201 HLA-A0301 \
+       --peptides SIINFEKL SIINFEKD SIINFEKQ \
+       --out /tmp/predictions.csv
+       
+Wrote: /tmp/predictions.csv
+```
+
+Or scan protein sequences for potential epitopes:
+
+```
+$ mhcflurry-predict-scan \
+        --sequences MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHS \
+        --alleles HLA-A*02:01 \
+        --out /tmp/predictions.csv
+        
+Wrote: /tmp/predictions.csv  
+```
+
+
+See the [documentation](http://openvax.github.io/mhcflurry/) for more details.
+
+
+## Docker
+You can also try the latest (GitHub master) version of MHCflurry using the Docker
+image hosted on [Dockerhub](https://hub.docker.com/r/openvax/mhcflurry) by
+running:
+
+```
+$ docker run -p 9999:9999 --rm openvax/mhcflurry:latest
+``` 
+
+This will start a [jupyter](https://jupyter.org/) notebook server in an
+environment that has MHCflurry installed. Go to `http://localhost:9999` in a
+browser to use it.
+
+To build the Docker image yourself, from a checkout run:
+
+```
+$ docker build -t mhcflurry:latest .
+$ docker run -p 9999:9999 --rm mhcflurry:latest
+```
+## Predicted sequence motifs
+Sequence logos for the binding motifs learned by MHCflurry BA are available [here](https://openvax.github.io/mhcflurry-motifs/).
+
+## Common issues and fixes
+
+### Problems downloading data and models
+Some users have reported HTTP connection issues when using `mhcflurry-downloads fetch`. As a workaround, you can download the data manually (e.g. using `wget`) and then use `mhcflurry-downloads` just to copy the data to the right place.
+
+To do this, first get the URL(s) of the downloads you need using `mhcflurry-downloads url`:
+
+```
+$ mhcflurry-downloads url models_class1_presentation
+https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```
+```
+
+Then make a directory and download the needed files to this directory:
+
+```
+$ mkdir downloads
+$ wget  --directory-prefix downloads https://github.com/openvax/mhcflurry/releases/download/1.6.0/models_class1_presentation.20200205.tar.bz2```
+
+HTTP request sent, awaiting response... 200 OK
+Length: 72616448 (69M) [application/octet-stream]
+Saving to: 'downloads/models_class1_presentation.20200205.tar.bz2'
+```
+
+Now call `mhcflurry-downloads fetch` with the `--already-downloaded-dir` option to indicate that the downloads should be retrived from the specified directory:
+
+```
+$ mhcflurry-downloads fetch models_class1_presentation --already-downloaded-dir downloads
+```
+
+
+
+
+%prep
+%autosetup -n mhcflurry-2.0.6
+
+%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-mhcflurry -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 2.0.6-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..3eef3f8
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+61d3cd5ecaa0aaac013b7ec0802e7923  mhcflurry-2.0.6.tar.gz