The study of complex many-body systems via analysis of the trajectories of the units that dynamically move and interact within them is a non-trivial task. The workflow for extracting meaningful information from the raw trajectory data is often composed of a series of interconnected steps, such as (i) identifying and tracking the constitutive objects/particles, resolving their trajectories (e.g., in experimental cases, where these are not automatically available as in typical molecular simulations); (ii) translating the trajectories into data that are easier to handle/analyze by using well-suited descriptors; and (iii) extracting meaningful information from such data. Each of these different tasks often requires non-negligible programming skills, the use of various types of representations or methods, and the availability/development of an interface between them. Despite the considerable potential that new tools contributed to each of these individual steps, their integration under a common framework would decrease the barrier to usage (especially by diverse communities of users), avoid fragmentation, and ultimately facilitate the development of new approaches in data analysis. To this end, here 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 facilitates time-series and trajectories data analysis, offering a useful tool for unraveling the dynamic complexity of a variety of systems (or signals) across different scales. dynsight is open source (github.com/GMPavanLab/dynsight) and can be easily installed using pip.
Martino et al. (Tue,) studied this question.