dynsight 2026.9.3

Overview

dynsight is an open Python platform built to support a wide range of tasks commonly encountered in the analysis of complex dynamical systems. The software contains and combines modules that allows resolving and handling trajectory data, computing single-particle descriptors obtaining time-series data that are easier to analyze, performing time-series data clustering, and extracting relevant information out of them. dynsight contains also various auxiliary tools useful to data analysis, including, e.g., modules (vision and track) for resolving individual-object trajectories for many-body systems - e.g., experimental ones - for which these are not readily available, denoising algorithms, and tools for assessing maximum information extraction from data.

The documentation is available at: dynsight.readthedocs.io

If you use dynsight, please to cite the associated scientific paper:

Installation

To get dynsight, you can install it with pip:

$ pip install dynsight

$ pip install cpctools

$ pip install ultralytics PyYAML

Tutorials and examples

We provide and continuously update a set of tutorials to help new users to get started with dynsight. They are available at the following link: https://dynsight.readthedocs.io/en/latest/tutorials_menu.html.

There are also examples throughout the documentation and available in the examples/ directory of this repository.

Related works and packages

dynsight uses many different open-source packages. Please cite them when appropriate:

• Most modules also use MDAnalysis, https://www.mdanalysis.org/pages/citations/

• If you use SOAP, please cite https://doi.org/10.1103/PhysRevB.87.184115 and DScribe https://singroup.github.io/dscribe/latest/citing.html

• If you use timeSOAP, please cite https://doi.org/10.1063/5.0147025

• If you use LENS, please cite: https://doi.org/10.1073/pnas.2300565120

• If you use onion-clustering, please cite: https://doi.org/10.1073/pnas.2403771121

• If you use tICA, please cite deeptime https://deeptime-ml.github.io/latest/index.html

• If you use dynsight.vision, please cite Ultralytics YOLO https://docs.ultralytics.com/it/models/yolo11/#usage-examples

• If you use dynsight.track, please cite Trackpy https://soft-matter.github.io/trackpy/dev/introduction.html

• Entropy calculations are based on infomeasure https://doi.org/10.1038/s41598-025-14053-5

Acknowledgements

dynsight is developed and mantained by the G. M. Pavan group at Politecnico di Torino, https://www.gmpavanlab.polito.it/. Many group members continuously provide, with their daily work, useful feedback that we gratefully acknowledge.