%global _empty_manifest_terminate_build 0
Name:		python-ASLPAw
Version:	2.2.0
Release:	1
Summary:	Adaptive overlapping community discovery algorithm package in python.
License:	GNU Affero General Public License v3
URL:		https://github.com/fsssosei/ASLPAw
Source0:	https://mirrors.aliyun.com/pypi/web/packages/aa/0b/83d1289c05d7c299d556c527d9ab7455db1a016a84510063bc5b49329c38/ASLPAw-2.2.0.tar.gz
BuildArch:	noarch

Requires:	python3-networkx
Requires:	python3-multivalued-dict
Requires:	python3-shuffle-graph
Requires:	python3-count-dict
Requires:	python3-similarity-index-of-label-graph
Requires:	python3-scikit-learn

%description
# ASLPAw

![PyPI](https://img.shields.io/pypi/v/ASLPAw?color=red)
![PyPI - Status](https://img.shields.io/pypi/status/ASLPAw)
![GitHub Release Date](https://img.shields.io/github/release-date/fsssosei/ASLPAw)
[![Build Status](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/badges/build.png?b=master)](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/build-status/master)
[![Code Intelligence Status](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/badges/code-intelligence.svg?b=master)](https://scrutinizer-ci.com/code-intelligence)
[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/fsssosei/ASLPAw.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/fsssosei/ASLPAw/context:python)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/e85d538645c44b9e87bf16448a9ac6f1)](https://www.codacy.com/manual/fsssosei/ASLPAw?utm_source=github.com&utm_medium=referral&utm_content=fsssosei/ASLPAw&utm_campaign=Badge_Grade)
[![Scrutinizer Code Quality](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/badges/quality-score.png?b=master)](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/?branch=master)
![PyPI - Downloads](https://img.shields.io/pypi/dw/ASLPAw?label=PyPI%20-%20Downloads)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ASLPAw)
![PyPI - License](https://img.shields.io/pypi/l/ASLPAw)

*Adaptive overlapping community discovery algorithm package in python.*

ASLPAw can be used for disjoint and overlapping community detection and works on weighted/unweighted and directed/undirected networks.
ASLPAw is adaptive with virtually no configuration parameters.

This is an easy-to-understand reference implementation that is not optimized for efficiency, but is robust. The underlying NetworkX package is inherently inefficient and unsuitable for use on large networks.
The next release will extend support for multiple productivity packages, such as SNAP, graph-tool, and igraph.

## Installation

Installation can be done through pip. You must have python version >= 3.8

	pip install ASLPAw

## Usage

The statement to import the package:

	from ASLPAw_package import ASLPAw

Example:

	>>> from networkx.generators.community import relaxed_caveman_graph

	>>> #Set seed to make the results repeatable.
	>>> data_graph = relaxed_caveman_graph(3, 6, 0.22, seed = 65535)
	>>> ASLPAw(data_graph, seed=65535).adj
	AdjacencyView({0: {2: {'weight': 0.9}}, 2: {2: {'weight': 0.9333333333333333}}, 1: {6: {'weight': 0.6}}, 6: {6: {'weight': 1.0}}, 3: {2: {'weight': 0.6}}, 4: {2: {'weight': 0.8666666666666667}}, 5: {2: {'weight': 0.9333333333333333}}, 7: {6: {'weight': 1.0}}, 8: {6: {'weight': 0.9666666666666667}}, 9: {6: {'weight': 0.9333333333333333}}, 10: {6: {'weight': 0.8666666666666667}}, 11: {6: {'weight': 0.9666666666666667}}, 12: {12: {'weight': 1.0333333333333334}}, 13: {12: {'weight': 0.9666666666666667}}, 14: {12: {'weight': 1.0}}, 15: {12: {'weight': 1.0}}, 16: {12: {'weight': 1.0}}, 17: {12: {'weight': 1.0}}})

	>>> data_graph = relaxed_caveman_graph(3, 6, 0.39, seed = 65535)
	>>> ASLPAw(data_graph, seed=65535).adj
	AdjacencyView({0: {1: {'weight': 0.9333333333333333}}, 1: {1: {'weight': 1.0}}, 2: {1: {'weight': 1.0}}, 3: {1: {'weight': 0.9666666666666667}}, 4: {1: {'weight': 1.0}}, 5: {1: {'weight': 0.9666666666666667}}, 6: {}, 7: {7: {'weight': 0.7666666666666667}}, 8: {}, 9: {13: {'weight': 0.4}, 6: {'weight': 0.26666666666666666}}, 13: {13: {'weight': 0.6333333333333333}}, 10: {1: {'weight': 0.5666666666666667}}, 11: {7: {'weight': 0.6333333333333333}}, 12: {12: {'weight': 0.4666666666666667}, 13: {'weight': 0.4}}, 14: {13: {'weight': 0.5666666666666667}}, 15: {13: {'weight': 0.5333333333333333}, 12: {'weight': 0.3333333333333333}}, 16: {13: {'weight': 0.43333333333333335}}, 17: {13: {'weight': 0.43333333333333335}, 12: {'weight': 0.4}}})



%package -n python3-ASLPAw
Summary:	Adaptive overlapping community discovery algorithm package in python.
Provides:	python-ASLPAw
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-ASLPAw
# ASLPAw

![PyPI](https://img.shields.io/pypi/v/ASLPAw?color=red)
![PyPI - Status](https://img.shields.io/pypi/status/ASLPAw)
![GitHub Release Date](https://img.shields.io/github/release-date/fsssosei/ASLPAw)
[![Build Status](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/badges/build.png?b=master)](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/build-status/master)
[![Code Intelligence Status](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/badges/code-intelligence.svg?b=master)](https://scrutinizer-ci.com/code-intelligence)
[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/fsssosei/ASLPAw.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/fsssosei/ASLPAw/context:python)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/e85d538645c44b9e87bf16448a9ac6f1)](https://www.codacy.com/manual/fsssosei/ASLPAw?utm_source=github.com&utm_medium=referral&utm_content=fsssosei/ASLPAw&utm_campaign=Badge_Grade)
[![Scrutinizer Code Quality](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/badges/quality-score.png?b=master)](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/?branch=master)
![PyPI - Downloads](https://img.shields.io/pypi/dw/ASLPAw?label=PyPI%20-%20Downloads)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ASLPAw)
![PyPI - License](https://img.shields.io/pypi/l/ASLPAw)

*Adaptive overlapping community discovery algorithm package in python.*

ASLPAw can be used for disjoint and overlapping community detection and works on weighted/unweighted and directed/undirected networks.
ASLPAw is adaptive with virtually no configuration parameters.

This is an easy-to-understand reference implementation that is not optimized for efficiency, but is robust. The underlying NetworkX package is inherently inefficient and unsuitable for use on large networks.
The next release will extend support for multiple productivity packages, such as SNAP, graph-tool, and igraph.

## Installation

Installation can be done through pip. You must have python version >= 3.8

	pip install ASLPAw

## Usage

The statement to import the package:

	from ASLPAw_package import ASLPAw

Example:

	>>> from networkx.generators.community import relaxed_caveman_graph

	>>> #Set seed to make the results repeatable.
	>>> data_graph = relaxed_caveman_graph(3, 6, 0.22, seed = 65535)
	>>> ASLPAw(data_graph, seed=65535).adj
	AdjacencyView({0: {2: {'weight': 0.9}}, 2: {2: {'weight': 0.9333333333333333}}, 1: {6: {'weight': 0.6}}, 6: {6: {'weight': 1.0}}, 3: {2: {'weight': 0.6}}, 4: {2: {'weight': 0.8666666666666667}}, 5: {2: {'weight': 0.9333333333333333}}, 7: {6: {'weight': 1.0}}, 8: {6: {'weight': 0.9666666666666667}}, 9: {6: {'weight': 0.9333333333333333}}, 10: {6: {'weight': 0.8666666666666667}}, 11: {6: {'weight': 0.9666666666666667}}, 12: {12: {'weight': 1.0333333333333334}}, 13: {12: {'weight': 0.9666666666666667}}, 14: {12: {'weight': 1.0}}, 15: {12: {'weight': 1.0}}, 16: {12: {'weight': 1.0}}, 17: {12: {'weight': 1.0}}})

	>>> data_graph = relaxed_caveman_graph(3, 6, 0.39, seed = 65535)
	>>> ASLPAw(data_graph, seed=65535).adj
	AdjacencyView({0: {1: {'weight': 0.9333333333333333}}, 1: {1: {'weight': 1.0}}, 2: {1: {'weight': 1.0}}, 3: {1: {'weight': 0.9666666666666667}}, 4: {1: {'weight': 1.0}}, 5: {1: {'weight': 0.9666666666666667}}, 6: {}, 7: {7: {'weight': 0.7666666666666667}}, 8: {}, 9: {13: {'weight': 0.4}, 6: {'weight': 0.26666666666666666}}, 13: {13: {'weight': 0.6333333333333333}}, 10: {1: {'weight': 0.5666666666666667}}, 11: {7: {'weight': 0.6333333333333333}}, 12: {12: {'weight': 0.4666666666666667}, 13: {'weight': 0.4}}, 14: {13: {'weight': 0.5666666666666667}}, 15: {13: {'weight': 0.5333333333333333}, 12: {'weight': 0.3333333333333333}}, 16: {13: {'weight': 0.43333333333333335}}, 17: {13: {'weight': 0.43333333333333335}, 12: {'weight': 0.4}}})



%package help
Summary:	Development documents and examples for ASLPAw
Provides:	python3-ASLPAw-doc
%description help
# ASLPAw

![PyPI](https://img.shields.io/pypi/v/ASLPAw?color=red)
![PyPI - Status](https://img.shields.io/pypi/status/ASLPAw)
![GitHub Release Date](https://img.shields.io/github/release-date/fsssosei/ASLPAw)
[![Build Status](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/badges/build.png?b=master)](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/build-status/master)
[![Code Intelligence Status](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/badges/code-intelligence.svg?b=master)](https://scrutinizer-ci.com/code-intelligence)
[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/fsssosei/ASLPAw.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/fsssosei/ASLPAw/context:python)
[![Codacy Badge](https://api.codacy.com/project/badge/Grade/e85d538645c44b9e87bf16448a9ac6f1)](https://www.codacy.com/manual/fsssosei/ASLPAw?utm_source=github.com&utm_medium=referral&utm_content=fsssosei/ASLPAw&utm_campaign=Badge_Grade)
[![Scrutinizer Code Quality](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/badges/quality-score.png?b=master)](https://scrutinizer-ci.com/g/fsssosei/ASLPAw/?branch=master)
![PyPI - Downloads](https://img.shields.io/pypi/dw/ASLPAw?label=PyPI%20-%20Downloads)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/ASLPAw)
![PyPI - License](https://img.shields.io/pypi/l/ASLPAw)

*Adaptive overlapping community discovery algorithm package in python.*

ASLPAw can be used for disjoint and overlapping community detection and works on weighted/unweighted and directed/undirected networks.
ASLPAw is adaptive with virtually no configuration parameters.

This is an easy-to-understand reference implementation that is not optimized for efficiency, but is robust. The underlying NetworkX package is inherently inefficient and unsuitable for use on large networks.
The next release will extend support for multiple productivity packages, such as SNAP, graph-tool, and igraph.

## Installation

Installation can be done through pip. You must have python version >= 3.8

	pip install ASLPAw

## Usage

The statement to import the package:

	from ASLPAw_package import ASLPAw

Example:

	>>> from networkx.generators.community import relaxed_caveman_graph

	>>> #Set seed to make the results repeatable.
	>>> data_graph = relaxed_caveman_graph(3, 6, 0.22, seed = 65535)
	>>> ASLPAw(data_graph, seed=65535).adj
	AdjacencyView({0: {2: {'weight': 0.9}}, 2: {2: {'weight': 0.9333333333333333}}, 1: {6: {'weight': 0.6}}, 6: {6: {'weight': 1.0}}, 3: {2: {'weight': 0.6}}, 4: {2: {'weight': 0.8666666666666667}}, 5: {2: {'weight': 0.9333333333333333}}, 7: {6: {'weight': 1.0}}, 8: {6: {'weight': 0.9666666666666667}}, 9: {6: {'weight': 0.9333333333333333}}, 10: {6: {'weight': 0.8666666666666667}}, 11: {6: {'weight': 0.9666666666666667}}, 12: {12: {'weight': 1.0333333333333334}}, 13: {12: {'weight': 0.9666666666666667}}, 14: {12: {'weight': 1.0}}, 15: {12: {'weight': 1.0}}, 16: {12: {'weight': 1.0}}, 17: {12: {'weight': 1.0}}})

	>>> data_graph = relaxed_caveman_graph(3, 6, 0.39, seed = 65535)
	>>> ASLPAw(data_graph, seed=65535).adj
	AdjacencyView({0: {1: {'weight': 0.9333333333333333}}, 1: {1: {'weight': 1.0}}, 2: {1: {'weight': 1.0}}, 3: {1: {'weight': 0.9666666666666667}}, 4: {1: {'weight': 1.0}}, 5: {1: {'weight': 0.9666666666666667}}, 6: {}, 7: {7: {'weight': 0.7666666666666667}}, 8: {}, 9: {13: {'weight': 0.4}, 6: {'weight': 0.26666666666666666}}, 13: {13: {'weight': 0.6333333333333333}}, 10: {1: {'weight': 0.5666666666666667}}, 11: {7: {'weight': 0.6333333333333333}}, 12: {12: {'weight': 0.4666666666666667}, 13: {'weight': 0.4}}, 14: {13: {'weight': 0.5666666666666667}}, 15: {13: {'weight': 0.5333333333333333}, 12: {'weight': 0.3333333333333333}}, 16: {13: {'weight': 0.43333333333333335}}, 17: {13: {'weight': 0.43333333333333335}, 12: {'weight': 0.4}}})



%prep
%autosetup -n ASLPAw-2.2.0

%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-ASLPAw -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 2.2.0-1
- Package Spec generated