Metadata-Version: 1.2
Name: persim
Version: 0.0.3
Summary: Python implementation persistent images representation of persistence diagrams.
Home-page: https://github.com/sauln/persim
Author: Nathaniel Saul
Author-email: nathaniel.saul@wsu.edu
License: MIT
Description: [![Build Status](https://travis-ci.org/sauln/persim.svg?branch=master)](https://travis-ci.org/sauln/persim)
        [![codecov](https://codecov.io/gh/sauln/persim/branch/master/graph/badge.svg)](https://codecov.io/gh/sauln/persim)
        [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
        
        # Persim <img align="right" width="40" height="40" src="https://imgur.com/8p6VwFm.jpg">
        
        Persim is a Python implementation of Persistence Images as first introduced in [https://arxiv.org/abs/1507.06217](https://arxiv.org/abs/1507.06217).
        
        It is designed to interface with [Ripser](https://github.com/sauln/ripser), though any persistence diagram should work fine.
        
        # Setup
        
        Currently, the only option is to install the library from source:
        
        ```
        pip install persim
        ```
        
        
        # Usage
        
        First, construct a diagram. In this example, we will use [Ripser](https://github.com/sauln/ripser).
        
        ``` Python
        import numpy as np
        from ripser import Rips
        
        data = np.random.random((100,2))
        rips = Rips()
        dgm = rips.fit_transform(data)
        diagram = dgm[1] # Just diagram for H1
        ```
        
        Then from this diagram, we construct the persistence image
        
        ``` Python
        from persim import PersImage
        
        pim = PersImage(diagram)
        img = pim.transform()
        pim.show(img)
        ```
        
        
        # TODO
        
        - The API needs a little work, not quite sklearn compliant. Please do offer any suggestions.
        - Implement more varieties of weighting and kernel functions.
        - Build tests.
        
        
Keywords: persistent homology,persistence images,persistence diagrams,topology data analysis,algebraic topology,unsupervised learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Intended Audience :: Healthcare Industry
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Requires-Python: >=2.7,!=3.1,!=3.2,!=3.3
