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.
@article{arxiv.2409.10217,
title = {UADAPy: An Uncertainty-Aware Visualization and Analysis Toolbox},
author = {Patrick Paetzold and David Hägele and Marina Evers and Daniel Weiskopf and Oliver Deussen},
journal= {arXiv preprint arXiv:2409.10217},
year = {2024}
}