semvec/pypi-packages huggingface dataset:
triarray 0.2.1
Tools for working with symmetric matrices in non-redundant format
License: MIT
Requires Python: >=3.5
Keywords: numpy array matrix symmetric pairwise distance similarity

# triarray

[![CI](https://github.com/jlumpe/triarray/actions/workflows/ci.yml/badge.svg)](https://github.com/jlumpe/triarray/actions/workflows/ci.yml)
[![Documentation Status](https://readthedocs.org/projects/triarray/badge/?version=latest)](https://triarray.readthedocs.io/en/latest/?badge=latest)


Python package for working with symmetric matrices in non-redundant format.

See full documentation [here](http://triarray.readthedocs.io/en/latest/).


## Overview

The `triarray` package contains tools for working with large symmetric matrices while only storing the elements in the upper or lower triangle, thus halving memory requirements.

`triarray` uses [Numba](http://numba.pydata.org/)'s just-in-time compilation to generate high-performance C code that works with any data type and is easily extendable (including within a Jupyter notebook).


## Installation

The easiest way is to use pip:

    pip install triarray
    
or you can clone the repository and run the setup script:

    cd path/to/triarray
    pip install .


## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.