FindTheGap

This package provides tools for geometric data analysis, targeted at finding gaps and valleys in data distribution. It provides a (twice-differentiable) density estimator (Quartic Kernel Density Estimator) relying on pytorch for auto-differentaition, and methods to estimate critical points in the density as well as various statistics to identify and trace `gaps' and valleys in the distribution.

This package can be installed through pip (https://pypi.org/project/findthegap/):

pip install findthegap 

Dependencies:

• numpy >= 1.19.5

• torch >= 1.10.1

• scipy >= 1.5.4

Notebook requirements: sklearn, matplotlib

The folder 'examples' contains a notebook showcasing how to use those tools on 2D data (available in the folder data).

Disclaimer: this code is work in progress and might go through some changes especially for higher (>2!) dimension...

Contributors: Gabriella Contardo (CCA at Simons Foundation), David W. Hogg(CCA/NYU/MPIA), Jason S.A. Hunt (CCA)

You can find more information about the methods in the paper "The emptiness inside: Finding gaps, valleys, and lacunae with geometric data analysis" https://arxiv.org/abs/2201.10674