circularcoordinates 0.1.1

Package contains tools used to create and plot circular coordinates from persistent cohomology

Circular-Coordinates

Circular coordinates from persistent cohomology

Circular-Coordinates is an all in one class that facilitates creating and plotting circular coordinates from persistent cohomology functions hidden in your dataset.

Features

• takes input data in the form of numpy array or pandas dataframe
• utilizes the ripser library for fast persistent cohomology barcode calculation
• Provides multiple ways of plotting and visualizing the output.

Setup

You can install the library directly from github(will be published on Pypi soon). You will need PyQt5 before installing(only for visualizing plots in ide will be optional in future).

pip install PyQt5
pip install git+https://github.com/appliedtopology/circular-coordinates

Example usage

Circular coordinates can be calculated and visualized with only a few lines of code. The circular coordinates are outputted mapped between [0,1]. When visualising the coordinates are denoted by colors on the color wheel (with its values mapped to [0,1]). Thus forming a loop stretching from 0 to 1.

import pandas as pd
import circularcoordinates

df = pd.read_csv('malaria.csv')
features = df[['Weight', 'Glucose', 'Accuri', 'RBC']]

prime=11
circ=circularcoordinates.circular_coordinate(prime)
vertex_values=circ.fit_transform(features)
circ.plot_pca(features,vertex_values)

If we already have the ripser output dictionary we can directly compute the circular coordinates without recomputing the dictionary.

ripser_output=circ.rips
vertex_values=circ.circular_coordinate(ripser_output)

We can also plot the persistant homology barcodes

circ.plot_barcode(circ.rips['dgms'][1])

Circular coordinates can also be plottted against external data to see what patterns emerge.

circ.plot_eps(df['Day Post Infection'],vertex_values)

License

MIT