automatic import of python-congas

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diff --git a/.gitignore b/.gitignore
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+/congas-0.0.75.tar.gz
diff --git a/python-congas.spec b/python-congas.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-congas
+Version:	0.0.75
+Release:	1
+Summary:	Copy Number genotyping from single cell RNA sequencing
+License:	GPL-3.0
+URL:		https://github.com/Militeee/congas
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/0b/81/40cafaca7406d60c112f93d1ea0bc0208cfa06667f5b1ef0510bd26eb7fc/congas-0.0.75.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-matplotlib
+Requires:	python3-pandas
+Requires:	python3-pyro-ppl
+Requires:	python3-numpy
+Requires:	python3-scikit-learn
+
+%description
+# Copy number genotyping from scRNA sequencing
+
+
+[![Build Status](https://travis-ci.org/Militeee/anneal.svg?branch=master)](https://travis-ci.org/Militeee/congas)
+[![codecov](https://codecov.io/gh/Militeee/anneal/branch/master/graph/badge.svg)](https://codecov.io/gh/Militeee/congas)
+
+
+A set of Pyro models and functions to infer CNA from scRNA-seq data. 
+It comes with a companion R package (hlink) that works as an interface and provides preprocessing, simulation and visualization routines.
+
+
+Currently providing:
+
+- A mixture model on segments where CNV are modelled as LogNormal random variable (MixtureGaussian) 
+- Same as above but the number of cluster is learned (MixtureGaussianDMP)
+- A model where CNVs are modelled as outcome from Categorical distributions, clusters share the same parameters (MixtureDirichlet)
+- A simple Hmm where CNVs are again categorical, but there is no clustering (SimpleHmm)
+- The version of MixtureDirichlet but with temporal dependency  (HmmMixtureRNA)
+
+Coming soon:
+- A linear model in the emission that can account for known covariates
+- The equivalent of MixtureGaussian but with CNVs as Categorical random variable
+- A model on genes (all the other models assume a division in segments)
+
+To install:
+
+`$ pip install congas`
+
+To run a simple analysis on the example data
+
+```python
+import congas as cn
+from congas.models import MixtureGaussian
+data_dict = cn.simulation_data
+params, loss = cn.run_analysis(data_dict,MixtureGaussian, steps=200, lr=0.05)
+```
+
+
+[Full Documentation](https://annealpyro.readthedocs.io/en/latest/)
+
+
+
+
+%package -n python3-congas
+Summary:	Copy Number genotyping from single cell RNA sequencing
+Provides:	python-congas
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-congas
+# Copy number genotyping from scRNA sequencing
+
+
+[![Build Status](https://travis-ci.org/Militeee/anneal.svg?branch=master)](https://travis-ci.org/Militeee/congas)
+[![codecov](https://codecov.io/gh/Militeee/anneal/branch/master/graph/badge.svg)](https://codecov.io/gh/Militeee/congas)
+
+
+A set of Pyro models and functions to infer CNA from scRNA-seq data. 
+It comes with a companion R package (hlink) that works as an interface and provides preprocessing, simulation and visualization routines.
+
+
+Currently providing:
+
+- A mixture model on segments where CNV are modelled as LogNormal random variable (MixtureGaussian) 
+- Same as above but the number of cluster is learned (MixtureGaussianDMP)
+- A model where CNVs are modelled as outcome from Categorical distributions, clusters share the same parameters (MixtureDirichlet)
+- A simple Hmm where CNVs are again categorical, but there is no clustering (SimpleHmm)
+- The version of MixtureDirichlet but with temporal dependency  (HmmMixtureRNA)
+
+Coming soon:
+- A linear model in the emission that can account for known covariates
+- The equivalent of MixtureGaussian but with CNVs as Categorical random variable
+- A model on genes (all the other models assume a division in segments)
+
+To install:
+
+`$ pip install congas`
+
+To run a simple analysis on the example data
+
+```python
+import congas as cn
+from congas.models import MixtureGaussian
+data_dict = cn.simulation_data
+params, loss = cn.run_analysis(data_dict,MixtureGaussian, steps=200, lr=0.05)
+```
+
+
+[Full Documentation](https://annealpyro.readthedocs.io/en/latest/)
+
+
+
+
+%package help
+Summary:	Development documents and examples for congas
+Provides:	python3-congas-doc
+%description help
+# Copy number genotyping from scRNA sequencing
+
+
+[![Build Status](https://travis-ci.org/Militeee/anneal.svg?branch=master)](https://travis-ci.org/Militeee/congas)
+[![codecov](https://codecov.io/gh/Militeee/anneal/branch/master/graph/badge.svg)](https://codecov.io/gh/Militeee/congas)
+
+
+A set of Pyro models and functions to infer CNA from scRNA-seq data. 
+It comes with a companion R package (hlink) that works as an interface and provides preprocessing, simulation and visualization routines.
+
+
+Currently providing:
+
+- A mixture model on segments where CNV are modelled as LogNormal random variable (MixtureGaussian) 
+- Same as above but the number of cluster is learned (MixtureGaussianDMP)
+- A model where CNVs are modelled as outcome from Categorical distributions, clusters share the same parameters (MixtureDirichlet)
+- A simple Hmm where CNVs are again categorical, but there is no clustering (SimpleHmm)
+- The version of MixtureDirichlet but with temporal dependency  (HmmMixtureRNA)
+
+Coming soon:
+- A linear model in the emission that can account for known covariates
+- The equivalent of MixtureGaussian but with CNVs as Categorical random variable
+- A model on genes (all the other models assume a division in segments)
+
+To install:
+
+`$ pip install congas`
+
+To run a simple analysis on the example data
+
+```python
+import congas as cn
+from congas.models import MixtureGaussian
+data_dict = cn.simulation_data
+params, loss = cn.run_analysis(data_dict,MixtureGaussian, steps=200, lr=0.05)
+```
+
+
+[Full Documentation](https://annealpyro.readthedocs.io/en/latest/)
+
+
+
+
+%prep
+%autosetup -n congas-0.0.75
+
+%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-congas -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.75-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..4d799e8
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+106c7e4d0cd356aabda9b331fce878b9  congas-0.0.75.tar.gz