Metadata-Version: 2.1
Name: hetmatpy
Version: 0.0.0
Summary: Matrix implementations for hetnets and path-count-based measures
Home-page: https://github.com/hetio/hetmatpy
License: BSD-2-Clause Plus Patent License
Project-URL: Source, https://github.com/hetio/hetmatpy
Project-URL: Documentation, https://hetio.github.io/hetmatpy
Project-URL: Tracker, https://github.com/hetio/hetmatpy/issues
Project-URL: Homepage, https://het.io/software/
Project-URL: Publication, https://greenelab.github.io/connectivity-search-manuscript/
Platform: UNKNOWN
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: hetnetpy (>=0.3.0)
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scipy
Provides-Extra: dev
Requires-Dist: black ; extra == 'dev'
Requires-Dist: flake8 ; extra == 'dev'
Requires-Dist: portray ; extra == 'dev'
Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: xarray ; extra == 'dev'

# hetmatpy: a Python 3 package for matrix operations of hetnets

[![Documentation](https://img.shields.io/badge/-Documentation-purple?logo=read-the-docs&style=for-the-badge)](https://hetio.github.io/hetmatpy/)
[![PyPI](https://img.shields.io/pypi/v/hetmatpy.svg?logo=PyPI&style=for-the-badge)](https://pypi.org/project/hetmatpy/)
[![GitHub Actions CI Tests Status](https://img.shields.io/github/workflow/status/hetio/hetmatpy/Tests?label=actions&logo=github&style=for-the-badge)](https://github.com/hetio/hetmatpy/actions)
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This codebase enables identifying the relevant network connections between a set of query nodes.
The method is designed to operate on hetnets (networks with multiple node or relationship types).

This project is still under development.
Use with caution.

## Environment

Install via pip from GitHub using:

```shell
# install the latest release from PyPI
pip install hetmatpy

# install latest version on GitHub
pip install git+https://github.com/hetio/hetmatpy

# for local development, run the following inside the development environment:
pip install --editable .
```


## Acknowledgments

This work is supported through a research collaboration with [Pfizer Worldwide Research and Development](https://www.pfizer.com/partners/research-and-development).
This work is funded in part by the Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative through Grants [GBMF4552](https://www.moore.org/grant-detail?grantId=GBMF4552) to Casey Greene and [GBMF4560](https://www.moore.org/grant-detail?grantId=GBMF4560) to Blair Sullivan.


