This release is a pre-release and may not be stable for production use.
# graspologic [](http://www.jmlr.org/papers/volume20/19-490/19-490.pdf) [](https://pypi.org/project/graspologic/) [](https://pepy.tech/project/graspologic) [](https://graspologic.readthedocs.io/)  [](https://codecov.io/gh/microsoft/graspologic) [](https://zenodo.org/badge/latestdoi/147768493) [](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)
# Notice: graspologic is the merger project of GraSPy and topologic We’re actively merging these projects into one, but you may see some references to graspy or topologic from time to time in documentation and issues. If you notice anything in the documentation referencing either graspy or topologic, please raise an issue (if one does not already exist) noting the missed titles so we can address all of them.
# 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://graspologic.readthedocs.io/en/latest/
Please visit the [tutorial section](https://graspologic.readthedocs.io/en/latest/tutorial.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 versions of Python: - 3.6 (x64) - 3.7 (x64) - 3.8 (x64)
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!
### Python Dependencies graspologic has the following direct dependencies: ` hyppo matplotlib networkx numpy POT seaborn scikit-learn scipy umap-learn `
Developers of graspologic will also have the following dependencies: ` black ipykernel ipython myp nbsphinx numpydoc pandoc pytest pytest-cov sphinx sphinxcontrib-rawfiles sphinx-rtd-theme `
Please note that pandoc will also [need to be installed for your system](https://pandoc.org/installing.html).
# 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.
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Release files for graspologic 0.1.0.dev472387129
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