%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.aliyun.com/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
* Fri Jun 09 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.0-1
- Package Spec generated