LStein: A new approach to visualizing sparse 2.5-dimensional data

TL;DR

LStein is a Python visualization method designed to effectively display sparse 2.5D datasets, inspired by astrophysics, applicable across various scientific fields, and available on GitHub.

Contribution

The paper introduces LStein, a novel visualization approach for sparse 2.5D data, inspired by astrophysical applications, with broad applicability beyond astronomy.

Findings

LStein preserves information effectively in 2.5D visualizations.

Compared to traditional methods, LStein offers minimal information loss.

The approach is versatile across multiple scientific domains.

Abstract

Visualization of high-dimensional data is crucial to retrieve all the knowledge that is contained within a dataset. Effective and informative presentation of three-dimensional data via a two-dimensional medium is challenging, especially if the dataset more closely resembles a 2.5-dimensional (2.5D) entity due to sparse sampling. We present LStein (Linking Series to envision information neatly), a novel visualisation approach implemented in Python, in an attempt to solve this challenge. Inspired by the astrophysical application of displaying photometric timeseries in multiple passbands with minimal loss of information, we compare our method to traditional approaches. While astronomy -- specifically multi-passband visualisation for lightcurves obtained with the Rubin Observatory -- serves as the principal driver for the design, we demonstrate that LStein can be used in any context with 2.5D datasets from radio astronomy to machine learning hyperparameter search visualization. LStein can be installed from GitHub (https://github.com/TheRedElement/LStein).