UADAPy: An Uncertainty-Aware Visualization and Analysis Toolbox

TL;DR

UADAPy addresses the gap that existing visualization frameworks lack uncertainty-aware data transformations. It centers on a distribution abstraction to propagate uncertainty through data modeling, transformations, and visualization, integrating with SciPy and Matplotlib. The package provides uncertainty-aware dimensionality reduction (UAPCA, UAMDS) and visualization methods for both univariate and multivariate distributions, along with example datasets. The toolbox aims to lower the barrier for researchers and practitioners to apply uncertainty-aware methods and to serve as a foundation for future extensions, including time series analysis and additional plotting backends.

Abstract

Current research provides methods to communicate uncertainty and adapts classical algorithms of the visualization pipeline to take the uncertainty into account. Various existing visualization frameworks include methods to present uncertain data but do not offer transformation techniques tailored to uncertain data. Therefore, we propose a software package for uncertainty-aware data analysis in Python (UADAPy) offering methods for uncertain data along the visualization pipeline. We aim to provide a platform that is the foundation for further integration of uncertainty algorithms and visualizations. It provides common utility functionality to support research in uncertainty-aware visualization algorithms and makes state-of-the-art research results accessible to the end user. The project is available at https://github.com/UniStuttgart-VISUS/uadapy.

Figures (1)

• Figure 1: The distribution forms the center of our library. It can be created in different ways, transformed into other distributions by uncertainty-aware algorithms, and visualized where the choices for visual encodings depend on the dimensionality of the distribution.