dynsight: an Open Python Platform for Simulation and Experimental Trajectory Data Analysis

Abstract: The study of complex many-body systems through the analysis of the trajectories of dynamically moving and interacting units is a non-trivial task. The workflow for extracting meaningful information from raw trajectory data typically involves several interconnected steps: (i) identifying and tracking objects, and resolving their trajectories (for example, in experimental systems where these are not automatically available as in molecular simulations); (ii) translating the trajectories into data that are easier to handle and analyze using suitable descriptors; and (iii) extracting meaningful information from these data. Each of these tasks often requires substantial programming skills, the use of different types of representations or methods, and the development of interfaces between them. Despite the progress made by new tools targeting individual steps, integrating them under a common framework would lower the barrier to use (especially for diverse communities of users), reduce fragmentation, and ultimately facilitate the development of new approaches in trajectory data analysis. To this end, we introduce dynsight, an open Python platform that streamlines the extraction and analysis of time-series data from simulation or experimentally resolved trajectories. Dynsight simplifies workflows, enhances accessibility, and supports the analysis of time-series and trajectory data to unravel the dynamic complexity of systems across different scales. Dynsight is open source (available at github.com/GMPavanLab/dynsight) and can be easily installed using pip.