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The study of trajectories is often a core task in several research fields. Inenvironmental modelling, trajectories are crucial to study fluid pollution,animal migrations, oil slick patterns or land movements. In this contribution,we address the lack of standardization and integration existing in currentapproaches to handle trajectory data. Within this scenario, challenges extendfrom the extraction of a trajectory from raw sensor data to the application ofmathematical tools for modeling or making inferences about populations andtheir environments. This work introduces a generic framework that addresses theproblem as a whole, i.e., a software library to handle trajectory data. Itcontains a robust tracking module aiming at making data acquisition handy,artificial generation of trajectories powered by different stochastic models toaid comparisons among experimental and theoretical data, a statistical kit foranalyzing patterns in groups of trajectories and other resources to speed uppre-processing of trajectory data. It is worth emphasizing that this librarydoes not make assumptions about the nature of trajectories (e.g., those fromGPS), which facilitates its usage across different disciplines. We validate thesoftware by reproducing key results when modelling dynamical systems related toenvironmental modelling applications. An example script to facilitatereproduction is presented for each case.

yupi: Generation, Tracking and Analysis of Trajectory data in Python