UADAPy: An Uncertainty-Aware Visualization and Analysis Toolbox

TL;DR

UADAPy is a Python toolbox designed to facilitate uncertainty-aware data analysis and visualization, integrating methods for uncertain data throughout the visualization pipeline to support research and practical applications.

Contribution

It introduces a comprehensive software package that offers utility functions and a foundation for integrating uncertainty algorithms into visualization workflows.

Findings

Provides a unified platform for uncertainty-aware visualization

Supports integration of state-of-the-art uncertainty algorithms

Enhances accessibility of uncertainty visualization research

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.