UADAPy: An Uncertainty-Aware Visualization and Analysis Toolbox
Patrick Paetzold, David H\"agele, Marina Evers, Daniel Weiskopf,, Oliver Deussen

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.
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Taxonomy
TopicsSimulation Techniques and Applications · Time Series Analysis and Forecasting · Anomaly Detection Techniques and Applications
