automatic import of python-imap

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+/imap-1.0.0.tar.gz
diff --git a/python-imap.spec b/python-imap.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-imap
+Version:	1.0.0
+Release:	1
+Summary:	The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch removal framework, called iMAP, based on two state-of-art deep generative models – autoencoders and generative adversarial networks.
+License:	MIT Licence
+URL:		https://github.com/Svvord/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/8f/39/7c78f15ed87edf30277c474a2823dcdf79e8416c044b8ba46e5204c9a6fc/imap-1.0.0.tar.gz
+BuildArch:	noarch
+
+
+%description
+# iMAP - Integration of multiple single-cell datasets by adversarial paired transfer networks
+
+### Installation
+
+#### 1. Prerequisites
+
+<ul>
+    <li>Install Python >= 3.6. Typically, you should use the Linux system and install a newest version of <a href='https://www.anaconda.com/'>Anaconda</a> or <a href = 'https://docs.conda.io/en/latest/miniconda.html'> Miniconda </a>.</li>
+    <li>Install pytorch >= 1.1.0. To obtain the optimal performance of deep learning-based models, you should have a Nivdia GPU and install the appropriate version of CUDA. (We tested with CUDA = 9.0)</li>
+    <li> Install scanpy >= 1.5.1 for pre-processing. </li>
+    <li>(Optional) Install <a href='https://github.com/slundberg/shap'>SHAP</a> for interpretation.</li>
+</ul>
+
+#### 2. Installation
+
+The iMAP python package is available for pip install(`pip install imap`). The functions required for the stage I and II of iMAP could be imported from “imap.stage1” and “imap.stage2”, respectively.
+
+### Tutorials
+
+Tutorials and API reference are available in the <a href='tutorials'>tutorials directory</a>.
+
+%package -n python3-imap
+Summary:	The integration of single-cell RNA-sequencing datasets from multiple sources is critical for deciphering cell-cell heterogeneities and interactions in complex biological systems. We present a novel unsupervised batch removal framework, called iMAP, based on two state-of-art deep generative models – autoencoders and generative adversarial networks.
+Provides:	python-imap
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-imap
+# iMAP - Integration of multiple single-cell datasets by adversarial paired transfer networks
+
+### Installation
+
+#### 1. Prerequisites
+
+<ul>
+    <li>Install Python >= 3.6. Typically, you should use the Linux system and install a newest version of <a href='https://www.anaconda.com/'>Anaconda</a> or <a href = 'https://docs.conda.io/en/latest/miniconda.html'> Miniconda </a>.</li>
+    <li>Install pytorch >= 1.1.0. To obtain the optimal performance of deep learning-based models, you should have a Nivdia GPU and install the appropriate version of CUDA. (We tested with CUDA = 9.0)</li>
+    <li> Install scanpy >= 1.5.1 for pre-processing. </li>
+    <li>(Optional) Install <a href='https://github.com/slundberg/shap'>SHAP</a> for interpretation.</li>
+</ul>
+
+#### 2. Installation
+
+The iMAP python package is available for pip install(`pip install imap`). The functions required for the stage I and II of iMAP could be imported from “imap.stage1” and “imap.stage2”, respectively.
+
+### Tutorials
+
+Tutorials and API reference are available in the <a href='tutorials'>tutorials directory</a>.
+
+%package help
+Summary:	Development documents and examples for imap
+Provides:	python3-imap-doc
+%description help
+# iMAP - Integration of multiple single-cell datasets by adversarial paired transfer networks
+
+### Installation
+
+#### 1. Prerequisites
+
+<ul>
+    <li>Install Python >= 3.6. Typically, you should use the Linux system and install a newest version of <a href='https://www.anaconda.com/'>Anaconda</a> or <a href = 'https://docs.conda.io/en/latest/miniconda.html'> Miniconda </a>.</li>
+    <li>Install pytorch >= 1.1.0. To obtain the optimal performance of deep learning-based models, you should have a Nivdia GPU and install the appropriate version of CUDA. (We tested with CUDA = 9.0)</li>
+    <li> Install scanpy >= 1.5.1 for pre-processing. </li>
+    <li>(Optional) Install <a href='https://github.com/slundberg/shap'>SHAP</a> for interpretation.</li>
+</ul>
+
+#### 2. Installation
+
+The iMAP python package is available for pip install(`pip install imap`). The functions required for the stage I and II of iMAP could be imported from “imap.stage1” and “imap.stage2”, respectively.
+
+### Tutorials
+
+Tutorials and API reference are available in the <a href='tutorials'>tutorials directory</a>.
+
+%prep
+%autosetup -n imap-1.0.0
+
+%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-imap -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
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+++ b/sources
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+fec0ee9c815cf02259f4d618f1e5a423  imap-1.0.0.tar.gz