dynsight：一个用于模拟和实验轨迹数据分析的开放Python平台

摘要(中文译文)

通过分析在复杂多体系统中动态运动和相互作用的单元的轨迹来研究此类系统是一项非平凡的任务。从原始轨迹数据中提取有意义信息的工作流程通常由一系列相互关联的步骤组成，例如：(i) 识别和追踪构成对象/粒子，解析其轨迹（例如在实验情况下，这些轨迹不像典型分子模拟中那样自动可用）；(ii) 通过使用合适的描述符将轨迹转换为更易于处理/分析的数据；(iii) 从此类数据中提取有意义的信息。这些不同的任务通常需要不可忽视的编程技能、使用各种类型的表示或方法，以及它们之间接口的可用性/开发。尽管新工具为这些单独步骤中的每一个都做出了相当大的贡献，但将它们整合在一个共同框架下将降低使用门槛（尤其是对于不同的用户群体），避免碎片化，并最终促进数据分析新方法的发展。为此，我们在此介绍 dynsight，一个开放的 Python 平台，可简化从模拟或实验解析轨迹中提取和分析时间序列数据的流程。dynsight 简化了工作流程，增强了可访问性，并促进了时间序列和轨迹数据分析，为揭示不同尺度下各种系统（或信号）的动态复杂性提供了一个有用的工具。dynsight 是开源的（github.com/GMPavanLab/dynsight），可通过 pip 轻松安装。

Abstract

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.

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