Teetool is a Python package which models and visualises motion patterns found in two-and three-dimensional trajectory data. It models the trajectories as a Gaussian process and uses the mean and covariance of the trajectory data to produce a confidence region, an area (or volume) through which a given percentage of trajectories travel. The confidence region is useful in obtaining an understanding of, or quantifying, dispersion in trajectory data. Furthermore, by modelling the trajectories as a Gaussian process, missing data can be recovered and noisy measurements can be corrected. Teetool is available as a Python package on GitHub, and includes Jupyter Notebooks, showing examples for two-and three-dimensional trajectory data.
Eerland, W., Box, S., Fangohr, H., & Sóbester, A. (2017). Teetool – A Probabilistic Trajectory Analysis Tool. Journal of Open Research Software, 5. https://doi.org/10.5334/jors.163