persim


Namepersim JSON
Version 0.3.5 PyPI version JSON
download
home_pagehttps://persim.scikit-tda.org
SummaryDistances and representations of persistence diagrams
upload_time2024-03-06 13:37:20
maintainer
docs_urlNone
authorNathaniel Saul, Chris Tralie, Francis Motta, Michael Catanzaro, Gabrielle Angeloro, Calder Sheagren
requires_python>=3.6
licenseMIT
keywords persistent homology persistence images persistence diagrams topological data analysis algebraic topology unsupervised learning supervised learning machine learning sliced wasserstein distance bottleneck distance
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            [![PyPI version](https://badge.fury.io/py/persim.svg)](https://badge.fury.io/py/persim)
![PyPI - Downloads](https://img.shields.io/pypi/dm/persim)
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/persim.svg)](https://anaconda.org/conda-forge/persim)
[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/persim.svg)](https://anaconda.org/conda-forge/persim)
[![codecov](https://codecov.io/gh/scikit-tda/persim/branch/master/graph/badge.svg)](https://codecov.io/gh/scikit-tda/persim)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)


<img align="right" width="40" height="40" src="https://imgur.com/8p6VwFm.jpg">

Persim is a Python package for many tools used in analyzing Persistence Diagrams.  It currently houses implementations of 

- Persistence Images
- Persistence Landscapes
- Bottleneck distance
- Modified Gromov&ndash;Hausdorff distance
- Sliced Wasserstein Kernel
- Heat Kernel
- Diagram plotting


## Setup

The latest version of persim can be found on Pypi and installed with pip:

```
pip install persim
```

## Documentation and Usage

Documentation about the library, it's API, and examples of how to use it can be found at [persim.scikit-tda.org](http://persim.scikit-tda.org).

## Contributions

We welcome contributions of all shapes and sizes. There are lots of opportunities for potential projects, so please get in touch if you would like to help out. Everything from an implementation of your favorite distance, notebooks, examples, and documentation are all equally valuable so please don't feel you can't contribute. 

To contribute please fork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.



            

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