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+%global _empty_manifest_terminate_build 0
+Name:		python-angel-cd
+Version:	1.0.3
+Release:	1
+Summary:	Community Discovery algorithm
+License:	BSD-2-Clause
+URL:		https://github.com/GiulioRossetti/ANGEL
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/63/71/f09c98a662a385cd0e7c490f7c25962c95c8ca49dd6716dff94c5b517c84/angel_cd-1.0.3.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-future
+Requires:	python3-tqdm
+Requires:	python3-igraph
+Requires:	python3-networkx
+Requires:	python3-numpy
+
+%description
+# ANGEL
+[![Test and Coverage (Ubuntu)](https://github.com/GiulioRossetti/ANGEL/actions/workflows/test_ubuntu.yml/badge.svg)](https://github.com/GiulioRossetti/ANGEL/actions/workflows/test_ubuntu.yml)
+[![codecov](https://codecov.io/gh/GiulioRossetti/ANGEL/branch/master/graph/badge.svg?token=3YJOEVK02B)](https://codecov.io/gh/GiulioRossetti/ANGEL)
+[![PyPI download month](https://img.shields.io/pypi/dm/angel-cd.svg?color=blue&style=plastic)](https://pypi.python.org/pypi/angel-cd/)
+
+Community discovery in complex networks is an interesting problem with a number of applications, especially in the knowledge extraction task in social and information networks. 
+However, many large networks often lack a particular community organization at a global level. 
+In these cases, traditional graph partitioning algorithms fail to let the latent knowledge embedded in modular structure emerge, because they impose a top-down global view of a network. 
+We propose here a simple local-first approach to community discovery, namely **Angel**, able to unveil the modular organization of real complex networks. 
+This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, i.e. its ego neighborhood, using a label propagation algorithm; finally, the local communities are merged into a global collection. 
+
+Moreover, we provide also an evolution of Angel, namely **ArchAngel**, designed to extract community from evolving network topologies.
+
+**Note:** Angel has been integrated within [CDlib](http://cdlib.readthedocs.io) a python package dedicated to community detection algorithms, check it out!
+
+
+## Installation
+You can easily install the updated version of Angel (and Archangel) by using pip:
+
+```bash
+pip install angel-cd
+```
+
+or using conda
+
+```bash
+conda install -c giuliorossetti angel-cd
+```
+
+## Implementation details
+
+*Required input format(s)* 
+
+Angel:
+.ncol edgelist (nodes represented with integer ids).
+
+```
+node_id0    node_id1
+```
+
+ArchAngel:
+Extended .ncol edgelist (nodes represented with integer ids).
+
+```
+node_id0    node_id1	snapshot_id
+```
+
+# Execution
+Angel is written in python and requires the following package to run:
+- python 3.x
+- python-igraph
+- networkx
+- tqdm
+
+## Angel
+
+```python
+import angel as a
+an = a.Angel(filename, threshold=0.4, min_comsize=3, outfile_name="angel_communities.txt")
+an.execute()
+```
+
+Where:
+* filename: edgelist filename
+* threshold: merging threshold in [0,1]
+* min_com_size: minimum size for communities
+* out_filename: desired filename for the output 
+
+or alternatively
+
+```python
+import angel as a
+an = a.Angel(graph=g, threshold=0.4, min_com_size=3, out_filename="communities.txt")
+an.execute()
+```
+
+Where:
+* g: an igraph.Graph object
+
+## ArchAngel
+
+```python
+import angel as a
+aa = a.ArchAngel(filename, threshold=0.4, match_threshold=0.4, min_com_size=3, outfile_path="./")
+aa.execute()
+```
+
+Where:
+* filename: edgelist filename
+* threshold: merging threshold in [0,1]
+* match_threshold: cross-time community matching threshold in [0, 1]
+* min_com_size: minimum size for communities
+* outfile_path: path for algorithm output files 
+
+
+
+
+%package -n python3-angel-cd
+Summary:	Community Discovery algorithm
+Provides:	python-angel-cd
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-angel-cd
+# ANGEL
+[![Test and Coverage (Ubuntu)](https://github.com/GiulioRossetti/ANGEL/actions/workflows/test_ubuntu.yml/badge.svg)](https://github.com/GiulioRossetti/ANGEL/actions/workflows/test_ubuntu.yml)
+[![codecov](https://codecov.io/gh/GiulioRossetti/ANGEL/branch/master/graph/badge.svg?token=3YJOEVK02B)](https://codecov.io/gh/GiulioRossetti/ANGEL)
+[![PyPI download month](https://img.shields.io/pypi/dm/angel-cd.svg?color=blue&style=plastic)](https://pypi.python.org/pypi/angel-cd/)
+
+Community discovery in complex networks is an interesting problem with a number of applications, especially in the knowledge extraction task in social and information networks. 
+However, many large networks often lack a particular community organization at a global level. 
+In these cases, traditional graph partitioning algorithms fail to let the latent knowledge embedded in modular structure emerge, because they impose a top-down global view of a network. 
+We propose here a simple local-first approach to community discovery, namely **Angel**, able to unveil the modular organization of real complex networks. 
+This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, i.e. its ego neighborhood, using a label propagation algorithm; finally, the local communities are merged into a global collection. 
+
+Moreover, we provide also an evolution of Angel, namely **ArchAngel**, designed to extract community from evolving network topologies.
+
+**Note:** Angel has been integrated within [CDlib](http://cdlib.readthedocs.io) a python package dedicated to community detection algorithms, check it out!
+
+
+## Installation
+You can easily install the updated version of Angel (and Archangel) by using pip:
+
+```bash
+pip install angel-cd
+```
+
+or using conda
+
+```bash
+conda install -c giuliorossetti angel-cd
+```
+
+## Implementation details
+
+*Required input format(s)* 
+
+Angel:
+.ncol edgelist (nodes represented with integer ids).
+
+```
+node_id0    node_id1
+```
+
+ArchAngel:
+Extended .ncol edgelist (nodes represented with integer ids).
+
+```
+node_id0    node_id1	snapshot_id
+```
+
+# Execution
+Angel is written in python and requires the following package to run:
+- python 3.x
+- python-igraph
+- networkx
+- tqdm
+
+## Angel
+
+```python
+import angel as a
+an = a.Angel(filename, threshold=0.4, min_comsize=3, outfile_name="angel_communities.txt")
+an.execute()
+```
+
+Where:
+* filename: edgelist filename
+* threshold: merging threshold in [0,1]
+* min_com_size: minimum size for communities
+* out_filename: desired filename for the output 
+
+or alternatively
+
+```python
+import angel as a
+an = a.Angel(graph=g, threshold=0.4, min_com_size=3, out_filename="communities.txt")
+an.execute()
+```
+
+Where:
+* g: an igraph.Graph object
+
+## ArchAngel
+
+```python
+import angel as a
+aa = a.ArchAngel(filename, threshold=0.4, match_threshold=0.4, min_com_size=3, outfile_path="./")
+aa.execute()
+```
+
+Where:
+* filename: edgelist filename
+* threshold: merging threshold in [0,1]
+* match_threshold: cross-time community matching threshold in [0, 1]
+* min_com_size: minimum size for communities
+* outfile_path: path for algorithm output files 
+
+
+
+
+%package help
+Summary:	Development documents and examples for angel-cd
+Provides:	python3-angel-cd-doc
+%description help
+# ANGEL
+[![Test and Coverage (Ubuntu)](https://github.com/GiulioRossetti/ANGEL/actions/workflows/test_ubuntu.yml/badge.svg)](https://github.com/GiulioRossetti/ANGEL/actions/workflows/test_ubuntu.yml)
+[![codecov](https://codecov.io/gh/GiulioRossetti/ANGEL/branch/master/graph/badge.svg?token=3YJOEVK02B)](https://codecov.io/gh/GiulioRossetti/ANGEL)
+[![PyPI download month](https://img.shields.io/pypi/dm/angel-cd.svg?color=blue&style=plastic)](https://pypi.python.org/pypi/angel-cd/)
+
+Community discovery in complex networks is an interesting problem with a number of applications, especially in the knowledge extraction task in social and information networks. 
+However, many large networks often lack a particular community organization at a global level. 
+In these cases, traditional graph partitioning algorithms fail to let the latent knowledge embedded in modular structure emerge, because they impose a top-down global view of a network. 
+We propose here a simple local-first approach to community discovery, namely **Angel**, able to unveil the modular organization of real complex networks. 
+This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, i.e. its ego neighborhood, using a label propagation algorithm; finally, the local communities are merged into a global collection. 
+
+Moreover, we provide also an evolution of Angel, namely **ArchAngel**, designed to extract community from evolving network topologies.
+
+**Note:** Angel has been integrated within [CDlib](http://cdlib.readthedocs.io) a python package dedicated to community detection algorithms, check it out!
+
+
+## Installation
+You can easily install the updated version of Angel (and Archangel) by using pip:
+
+```bash
+pip install angel-cd
+```
+
+or using conda
+
+```bash
+conda install -c giuliorossetti angel-cd
+```
+
+## Implementation details
+
+*Required input format(s)* 
+
+Angel:
+.ncol edgelist (nodes represented with integer ids).
+
+```
+node_id0    node_id1
+```
+
+ArchAngel:
+Extended .ncol edgelist (nodes represented with integer ids).
+
+```
+node_id0    node_id1	snapshot_id
+```
+
+# Execution
+Angel is written in python and requires the following package to run:
+- python 3.x
+- python-igraph
+- networkx
+- tqdm
+
+## Angel
+
+```python
+import angel as a
+an = a.Angel(filename, threshold=0.4, min_comsize=3, outfile_name="angel_communities.txt")
+an.execute()
+```
+
+Where:
+* filename: edgelist filename
+* threshold: merging threshold in [0,1]
+* min_com_size: minimum size for communities
+* out_filename: desired filename for the output 
+
+or alternatively
+
+```python
+import angel as a
+an = a.Angel(graph=g, threshold=0.4, min_com_size=3, out_filename="communities.txt")
+an.execute()
+```
+
+Where:
+* g: an igraph.Graph object
+
+## ArchAngel
+
+```python
+import angel as a
+aa = a.ArchAngel(filename, threshold=0.4, match_threshold=0.4, min_com_size=3, outfile_path="./")
+aa.execute()
+```
+
+Where:
+* filename: edgelist filename
+* threshold: merging threshold in [0,1]
+* match_threshold: cross-time community matching threshold in [0, 1]
+* min_com_size: minimum size for communities
+* outfile_path: path for algorithm output files 
+
+
+
+
+%prep
+%autosetup -n angel-cd-1.0.3
+
+%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-angel-cd -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Wed May 17 2023 Python_Bot <Python_Bot@openeuler.org> - 1.0.3-1
+- Package Spec generated