automatic import of python-quickgraph

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+/quickgraph-0.45.tar.gz
diff --git a/python-quickgraph.spec b/python-quickgraph.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-quickgraph
+Version:	0.45
+Release:	1
+Summary:	A Python package to view the skeleton of a social graph quickly.
+License:	MIT License
+URL:		https://gongqingyuan.wordpress.com/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/bd/cc/099d303ced7dc3e87a3f9a82d8bfb0f7cb2e7ca05ced62dfe91f2a50a8fc/quickgraph-0.45.tar.gz
+BuildArch:	noarch
+
+
+%description
+
+
+## Introduction 
+
+QuickGraph library can help you get a quick overview of a social graph in an extremely convenient way. QuickGraph will show the basic information of a graph, plot the CDF of selected metrics, characterize the largest connected component (LCC).
+
+## Overview
+
+QuickGraph library can help you get a quick overview of a social graph in an extremely convenient way.
+Show the basic information of a graph, plot the CDF of selected metrics, characterize the largest connected component (LCC), compute representative structural hole related indexes.  
+Copyright (C) <2021-2026> by Qingyuan Gong, Fudan University (gongqingyuan@fudan.edu.cn)
+
+## Before Installation
+
+Please upgrade to Python 3.5
+
+## System Requirements
+
+We have tested QuickGraph on both MacOSX (version 11.5.1) and Ubuntu (Version: 20.04 LTS). This library have not been tested on other platforms.
+
+## Usage
+
+Please run the following commond and install the dependent libiraires:
+
+Run 
+`conda config --add channels conda-forge`
+
+`conda update –all`
+to make the libraries fit to the operation system
+
+Run
+`pip install python-igraph` 
+to install the iGraph library
+
+Run `pip install leidenalg` 
+to help the modularity related analysis 
+
+Note: Please change to `pip3 install` if you are using Apple M1 Chip
+
+## Functions
+quickgraph.info(G) returns the the basic information of a graph and plots the CDF of selected metrics. 
+
+quickgraph.LCC_analysis(G) characterizes the largest connected component (LCC) of the input graph G on selected metrics. 
+
+## Example
+We utilize the SCHOLAT Social Network dataset as one example. 
+https://www.scholat.com/research/opendata/#social_network
+
+```python
+>>> import quickgraph
+>>> quickgraph.demo()
+Number of Nodes: 16007, Number of Edges: 202248
+Avg. degree: 25.2699, Avg. clustering coefficient: 0.5486
+Modularity (Leidenalg): 0.8651, Modularity (Label_Propagation): 0.8372
+Number of connected components: 5423, Number of nodes in LCC: 9583 ( 59.8676 %)
+Time (G_info): 4.675
+LCC: Avg. degree = 40.023, Avg. clustering coefficient = 0.625, Modularity (Leidenalg): 0.8551, Modularity (Label_Propagation): 0.8209
+(rough) shortest path length = 1 : 1 ( 0.1 %), 2 : 26 ( 2.6 %), 3 : 98 ( 9.8 %), 4 : 162 ( 16.2 %), 5 : 133 ( 13.3 %), 6 : 65 ( 6.5 %), 7 : 12 ( 1.2 %), 8 : 3 ( 0.3 %), Avg. shortest path length = 4.316
+Time (LCC): 1.907
+```
+
+# License
+
+See the LICENSE file for license rights and limitations (MIT).
+
+
+
+
+
+%package -n python3-quickgraph
+Summary:	A Python package to view the skeleton of a social graph quickly.
+Provides:	python-quickgraph
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-quickgraph
+
+
+## Introduction 
+
+QuickGraph library can help you get a quick overview of a social graph in an extremely convenient way. QuickGraph will show the basic information of a graph, plot the CDF of selected metrics, characterize the largest connected component (LCC).
+
+## Overview
+
+QuickGraph library can help you get a quick overview of a social graph in an extremely convenient way.
+Show the basic information of a graph, plot the CDF of selected metrics, characterize the largest connected component (LCC), compute representative structural hole related indexes.  
+Copyright (C) <2021-2026> by Qingyuan Gong, Fudan University (gongqingyuan@fudan.edu.cn)
+
+## Before Installation
+
+Please upgrade to Python 3.5
+
+## System Requirements
+
+We have tested QuickGraph on both MacOSX (version 11.5.1) and Ubuntu (Version: 20.04 LTS). This library have not been tested on other platforms.
+
+## Usage
+
+Please run the following commond and install the dependent libiraires:
+
+Run 
+`conda config --add channels conda-forge`
+
+`conda update –all`
+to make the libraries fit to the operation system
+
+Run
+`pip install python-igraph` 
+to install the iGraph library
+
+Run `pip install leidenalg` 
+to help the modularity related analysis 
+
+Note: Please change to `pip3 install` if you are using Apple M1 Chip
+
+## Functions
+quickgraph.info(G) returns the the basic information of a graph and plots the CDF of selected metrics. 
+
+quickgraph.LCC_analysis(G) characterizes the largest connected component (LCC) of the input graph G on selected metrics. 
+
+## Example
+We utilize the SCHOLAT Social Network dataset as one example. 
+https://www.scholat.com/research/opendata/#social_network
+
+```python
+>>> import quickgraph
+>>> quickgraph.demo()
+Number of Nodes: 16007, Number of Edges: 202248
+Avg. degree: 25.2699, Avg. clustering coefficient: 0.5486
+Modularity (Leidenalg): 0.8651, Modularity (Label_Propagation): 0.8372
+Number of connected components: 5423, Number of nodes in LCC: 9583 ( 59.8676 %)
+Time (G_info): 4.675
+LCC: Avg. degree = 40.023, Avg. clustering coefficient = 0.625, Modularity (Leidenalg): 0.8551, Modularity (Label_Propagation): 0.8209
+(rough) shortest path length = 1 : 1 ( 0.1 %), 2 : 26 ( 2.6 %), 3 : 98 ( 9.8 %), 4 : 162 ( 16.2 %), 5 : 133 ( 13.3 %), 6 : 65 ( 6.5 %), 7 : 12 ( 1.2 %), 8 : 3 ( 0.3 %), Avg. shortest path length = 4.316
+Time (LCC): 1.907
+```
+
+# License
+
+See the LICENSE file for license rights and limitations (MIT).
+
+
+
+
+
+%package help
+Summary:	Development documents and examples for quickgraph
+Provides:	python3-quickgraph-doc
+%description help
+
+
+## Introduction 
+
+QuickGraph library can help you get a quick overview of a social graph in an extremely convenient way. QuickGraph will show the basic information of a graph, plot the CDF of selected metrics, characterize the largest connected component (LCC).
+
+## Overview
+
+QuickGraph library can help you get a quick overview of a social graph in an extremely convenient way.
+Show the basic information of a graph, plot the CDF of selected metrics, characterize the largest connected component (LCC), compute representative structural hole related indexes.  
+Copyright (C) <2021-2026> by Qingyuan Gong, Fudan University (gongqingyuan@fudan.edu.cn)
+
+## Before Installation
+
+Please upgrade to Python 3.5
+
+## System Requirements
+
+We have tested QuickGraph on both MacOSX (version 11.5.1) and Ubuntu (Version: 20.04 LTS). This library have not been tested on other platforms.
+
+## Usage
+
+Please run the following commond and install the dependent libiraires:
+
+Run 
+`conda config --add channels conda-forge`
+
+`conda update –all`
+to make the libraries fit to the operation system
+
+Run
+`pip install python-igraph` 
+to install the iGraph library
+
+Run `pip install leidenalg` 
+to help the modularity related analysis 
+
+Note: Please change to `pip3 install` if you are using Apple M1 Chip
+
+## Functions
+quickgraph.info(G) returns the the basic information of a graph and plots the CDF of selected metrics. 
+
+quickgraph.LCC_analysis(G) characterizes the largest connected component (LCC) of the input graph G on selected metrics. 
+
+## Example
+We utilize the SCHOLAT Social Network dataset as one example. 
+https://www.scholat.com/research/opendata/#social_network
+
+```python
+>>> import quickgraph
+>>> quickgraph.demo()
+Number of Nodes: 16007, Number of Edges: 202248
+Avg. degree: 25.2699, Avg. clustering coefficient: 0.5486
+Modularity (Leidenalg): 0.8651, Modularity (Label_Propagation): 0.8372
+Number of connected components: 5423, Number of nodes in LCC: 9583 ( 59.8676 %)
+Time (G_info): 4.675
+LCC: Avg. degree = 40.023, Avg. clustering coefficient = 0.625, Modularity (Leidenalg): 0.8551, Modularity (Label_Propagation): 0.8209
+(rough) shortest path length = 1 : 1 ( 0.1 %), 2 : 26 ( 2.6 %), 3 : 98 ( 9.8 %), 4 : 162 ( 16.2 %), 5 : 133 ( 13.3 %), 6 : 65 ( 6.5 %), 7 : 12 ( 1.2 %), 8 : 3 ( 0.3 %), Avg. shortest path length = 4.316
+Time (LCC): 1.907
+```
+
+# License
+
+See the LICENSE file for license rights and limitations (MIT).
+
+
+
+
+
+%prep
+%autosetup -n quickgraph-0.45
+
+%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-quickgraph -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 31 2023 Python_Bot <Python_Bot@openeuler.org> - 0.45-1
+- Package Spec generated
diff --git a/sources b/sources
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+d82d8434d6a9039aa9b1be2cdcf53f29  quickgraph-0.45.tar.gz