Metadata-Version: 2.1
Name: ripser
Version: 0.2.4
Summary: A Lean Persistent Homology Library for Python
Home-page: https://github.com/ctralie/ripser
Author: Chris Tralie, Nathaniel Saul
Author-email: chris.tralie@gmail.com, nathaniel.saul@wsu.edu
License: LGPL
Description: [![PyPI version](https://badge.fury.io/py/ripser.svg)](https://badge.fury.io/py/ripser)
        [![Build Status](https://travis-ci.org/ctralie/ripser.svg?branch=master)](https://travis-ci.org/ctralie/ripser)
        [![Build status](https://ci.appveyor.com/api/projects/status/2owvb4vyh68ekcfp?svg=true)](https://ci.appveyor.com/project/ctralie/ripser)
        [![codecov](https://codecov.io/gh/ctralie/ripser/branch/master/graph/badge.svg)](https://codecov.io/gh/ctralie/ripser)
        [![License: LGPL v3](https://img.shields.io/badge/License-LGPL%20v3-blue.svg)](https://www.gnu.org/licenses/lgpl-3.0)
        
        # Ripser
        
        
        Ripser is now a Python class. It is easy to install, only requires that you have Cython installed first. It is even easier to use.
        
        For the C++ library, see [Ripser/ripser](https://github.com/Ripser/ripser/releases/latest).
        
        Details from the old readme can be found [here](docs/README.md).
        
        ## Setup
        
        Installation requires Cython, and currently must be installed from source. An example of how to install is
        ```
        pip install Cython
        pip install Ripser
        ```
        
        We use matplotlib for generating persistence diagrams
        
        
        ## Usage
        
        ```
        import numpy as np
        from ripser import ripser, plot_dgms
        
        data = np.random.random((100,2))
        diagrams = ripser(data)['dgms']
        plot_dgms(diagrams, show=True)
        ```
        
        
        Note that there is also a <i>Rips</i> object with the same functionality, which conforms to the Scikit-learn style
        
        ```
        import numpy as np
        from ripser import Rips
        r = Rips()
        
        data = np.random.random((100,2))
        diagram = r.fit_transform(data)
        r.plot(diagram, show=True)
        ```
        
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
