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

TL;DR

Yupi is a comprehensive Python library designed for trajectory data handling, offering tools for tracking, generation, analysis, and pre-processing across various disciplines without assuming specific trajectory types.

Contribution

It introduces a generic, multi-purpose framework for trajectory data management, integrating tracking, stochastic generation, and analysis tools in a single library.

Findings

Successfully reproduces key environmental modeling results

Provides a versatile, assumption-free toolkit for trajectory analysis

Facilitates cross-disciplinary trajectory data handling

Abstract

The study of trajectories is often a core task in several research fields. In environmental 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 current approaches to handle trajectory data. Within this scenario, challenges extend from the extraction of a trajectory from raw sensor data to the application of mathematical tools for modeling or making inferences about populations and their environments. This work introduces a generic framework that addresses the problem as a whole, i.e., a software library to handle trajectory data. It contains a robust tracking module aiming at making data acquisition handy, artificial generation of trajectories powered by different stochastic models to aid comparisons among experimental and theoretical data, a statistical kit for analyzing patterns in groups of trajectories and other resources to speed up pre-processing of trajectory data. It is worth emphasizing that this library does not make assumptions about the nature of trajectories (e.g., those from GPS), which facilitates its usage across different disciplines. We validate the software by reproducing key results when modelling dynamical systems related to environmental modelling applications. An example script to facilitate reproduction is presented for each case.