%global _empty_manifest_terminate_build 0 Name: python-graspologic Version: 3.0.0 Release: 1 Summary: A set of python modules for graph statistics License: MIT URL: https://github.com/microsoft/graspologic Source0: https://mirrors.nju.edu.cn/pypi/web/packages/95/0a/ffc73e04cc189ed10b9b97a4cbce1064c7eb1606394e9bba5afd276e051f/graspologic-3.0.0.tar.gz BuildArch: noarch %description # graspologic [![Paper shield](https://img.shields.io/badge/JMLR-Paper-red)](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) [![PyPI version](https://img.shields.io/pypi/v/graspologic.svg)](https://pypi.org/project/graspologic/) [![Downloads shield](https://pepy.tech/badge/graspologic)](https://pepy.tech/project/graspologic) ![graspologic CI](https://github.com/microsoft/graspologic/workflows/graspologic%20CI/badge.svg) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) ## `graspologic` is a package for graph statistical algorithms. - [Overview](#overview) - [Documentation](#documentation) - [System Requirements](#system-requirements) - [Installation Guide](#installation-guide) - [Contributing](#contributing) - [License](#license) - [Issues](#issues) - [Citing `graspologic`](#citing-graspologic) # Overview A graph, or network, provides a mathematically intuitive representation of data with some sort of relationship between items. For example, a social network can be represented as a graph by considering all participants in the social network as nodes, with connections representing whether each pair of individuals in the network are friends with one another. Naively, one might apply traditional statistical techniques to a graph, which neglects the spatial arrangement of nodes within the network and is not utilizing all of the information present in the graph. In this package, we provide utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms. # Documentation The official documentation with usage is at https://microsoft.github.io/graspologic/latest Please visit the [tutorial section](https://microsoft.github.io/graspologic/latest/tutorials/index.html) in the official website for more in depth usage. # System Requirements ## Hardware requirements `graspologic` package requires only a standard computer with enough RAM to support the in-memory operations. ## Software requirements ### OS Requirements `graspologic` is tested on the following OSes: - Linux x64 - macOS x64 - Windows 10 x64 And across the following **x86_64** versions of Python: - 3.8 - 3.9 - 3.10 If you try to use `graspologic` for a different platform than the ones listed and notice any unexpected behavior, please feel free to [raise an issue](https://github.com/microsoft/graspologic/issues/new). It's better for ourselves and our users if we have concrete examples of things not working! # Installation Guide ## Install from pip ``` pip install graspologic ``` ## Install from Github ``` git clone https://github.com/microsoft/graspologic cd graspologic python3 -m venv venv source venv/bin/activate python3 setup.py install ``` # Contributing We welcome contributions from anyone. Please see our [contribution guidelines](https://github.com/microsoft/graspologic/blob/dev/CONTRIBUTING.md) before making a pull request. Our [issues](https://github.com/microsoft/graspologic/issues) page is full of places we could use help! If you have an idea for an improvement not listed there, please [make an issue](https://github.com/microsoft/graspologic/issues/new) first so you can discuss with the developers. # License This project is covered under the MIT License. # Issues We appreciate detailed bug reports and feature requests (though we appreciate pull requests even more!). Please visit our [issues](https://github.com/microsoft/graspologic/issues) page if you have questions or ideas. # Citing `graspologic` If you find `graspologic` useful in your work, please cite the package via the [GraSPy paper](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) > Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7. %package -n python3-graspologic Summary: A set of python modules for graph statistics Provides: python-graspologic BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-graspologic # graspologic [![Paper shield](https://img.shields.io/badge/JMLR-Paper-red)](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) [![PyPI version](https://img.shields.io/pypi/v/graspologic.svg)](https://pypi.org/project/graspologic/) [![Downloads shield](https://pepy.tech/badge/graspologic)](https://pepy.tech/project/graspologic) ![graspologic CI](https://github.com/microsoft/graspologic/workflows/graspologic%20CI/badge.svg) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) ## `graspologic` is a package for graph statistical algorithms. - [Overview](#overview) - [Documentation](#documentation) - [System Requirements](#system-requirements) - [Installation Guide](#installation-guide) - [Contributing](#contributing) - [License](#license) - [Issues](#issues) - [Citing `graspologic`](#citing-graspologic) # Overview A graph, or network, provides a mathematically intuitive representation of data with some sort of relationship between items. For example, a social network can be represented as a graph by considering all participants in the social network as nodes, with connections representing whether each pair of individuals in the network are friends with one another. Naively, one might apply traditional statistical techniques to a graph, which neglects the spatial arrangement of nodes within the network and is not utilizing all of the information present in the graph. In this package, we provide utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms. # Documentation The official documentation with usage is at https://microsoft.github.io/graspologic/latest Please visit the [tutorial section](https://microsoft.github.io/graspologic/latest/tutorials/index.html) in the official website for more in depth usage. # System Requirements ## Hardware requirements `graspologic` package requires only a standard computer with enough RAM to support the in-memory operations. ## Software requirements ### OS Requirements `graspologic` is tested on the following OSes: - Linux x64 - macOS x64 - Windows 10 x64 And across the following **x86_64** versions of Python: - 3.8 - 3.9 - 3.10 If you try to use `graspologic` for a different platform than the ones listed and notice any unexpected behavior, please feel free to [raise an issue](https://github.com/microsoft/graspologic/issues/new). It's better for ourselves and our users if we have concrete examples of things not working! # Installation Guide ## Install from pip ``` pip install graspologic ``` ## Install from Github ``` git clone https://github.com/microsoft/graspologic cd graspologic python3 -m venv venv source venv/bin/activate python3 setup.py install ``` # Contributing We welcome contributions from anyone. Please see our [contribution guidelines](https://github.com/microsoft/graspologic/blob/dev/CONTRIBUTING.md) before making a pull request. Our [issues](https://github.com/microsoft/graspologic/issues) page is full of places we could use help! If you have an idea for an improvement not listed there, please [make an issue](https://github.com/microsoft/graspologic/issues/new) first so you can discuss with the developers. # License This project is covered under the MIT License. # Issues We appreciate detailed bug reports and feature requests (though we appreciate pull requests even more!). Please visit our [issues](https://github.com/microsoft/graspologic/issues) page if you have questions or ideas. # Citing `graspologic` If you find `graspologic` useful in your work, please cite the package via the [GraSPy paper](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) > Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7. %package help Summary: Development documents and examples for graspologic Provides: python3-graspologic-doc %description help # graspologic [![Paper shield](https://img.shields.io/badge/JMLR-Paper-red)](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) [![PyPI version](https://img.shields.io/pypi/v/graspologic.svg)](https://pypi.org/project/graspologic/) [![Downloads shield](https://pepy.tech/badge/graspologic)](https://pepy.tech/project/graspologic) ![graspologic CI](https://github.com/microsoft/graspologic/workflows/graspologic%20CI/badge.svg) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) ## `graspologic` is a package for graph statistical algorithms. - [Overview](#overview) - [Documentation](#documentation) - [System Requirements](#system-requirements) - [Installation Guide](#installation-guide) - [Contributing](#contributing) - [License](#license) - [Issues](#issues) - [Citing `graspologic`](#citing-graspologic) # Overview A graph, or network, provides a mathematically intuitive representation of data with some sort of relationship between items. For example, a social network can be represented as a graph by considering all participants in the social network as nodes, with connections representing whether each pair of individuals in the network are friends with one another. Naively, one might apply traditional statistical techniques to a graph, which neglects the spatial arrangement of nodes within the network and is not utilizing all of the information present in the graph. In this package, we provide utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms. # Documentation The official documentation with usage is at https://microsoft.github.io/graspologic/latest Please visit the [tutorial section](https://microsoft.github.io/graspologic/latest/tutorials/index.html) in the official website for more in depth usage. # System Requirements ## Hardware requirements `graspologic` package requires only a standard computer with enough RAM to support the in-memory operations. ## Software requirements ### OS Requirements `graspologic` is tested on the following OSes: - Linux x64 - macOS x64 - Windows 10 x64 And across the following **x86_64** versions of Python: - 3.8 - 3.9 - 3.10 If you try to use `graspologic` for a different platform than the ones listed and notice any unexpected behavior, please feel free to [raise an issue](https://github.com/microsoft/graspologic/issues/new). It's better for ourselves and our users if we have concrete examples of things not working! # Installation Guide ## Install from pip ``` pip install graspologic ``` ## Install from Github ``` git clone https://github.com/microsoft/graspologic cd graspologic python3 -m venv venv source venv/bin/activate python3 setup.py install ``` # Contributing We welcome contributions from anyone. Please see our [contribution guidelines](https://github.com/microsoft/graspologic/blob/dev/CONTRIBUTING.md) before making a pull request. Our [issues](https://github.com/microsoft/graspologic/issues) page is full of places we could use help! If you have an idea for an improvement not listed there, please [make an issue](https://github.com/microsoft/graspologic/issues/new) first so you can discuss with the developers. # License This project is covered under the MIT License. # Issues We appreciate detailed bug reports and feature requests (though we appreciate pull requests even more!). Please visit our [issues](https://github.com/microsoft/graspologic/issues) page if you have questions or ideas. # Citing `graspologic` If you find `graspologic` useful in your work, please cite the package via the [GraSPy paper](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) > Chung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7. %prep %autosetup -n graspologic-3.0.0 %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-graspologic -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Fri May 05 2023 Python_Bot - 3.0.0-1 - Package Spec generated