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.
CITATION STYLE
Eerland, W., Box, S., Fangohr, H., & Sóbester, A. (2017). Teetool -- a probabilistic trajectory analysis tool. Journal of Open Research Software, 5(1), 14. https://doi.org/10.5334/jors.163
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