dynsight 2026.2.16

Simplifies analysis of simulation and experimental trajectories.

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:

S. Martino, M. Becchi, A. Tarzia, D. Rapetti, C. Lionello & G. M. Pavan

“dynsight: an open Python platform for simulation and experimental trajectory data analysis”

J. Chem. Phys. (2026), DOI: 10.1063/5.0309974

Installation

To get dynsight, you can install it with pip:

$ pip install dynsight

Optional Dependancies

Old versions dynsight used cpctools for SOAP calculations, if you are using Python 3.10 and below, you can use cpctools to access SOAPify and hd5er using

$ pip install cpctools

If you want to use the dynsight.vision and dynsight.track modules you will need to install a series of packages. This can be done with with pip:

$ pip install ultralytics PyYAML

How to get started

We suggest you give a read to the dynsight.trajectory module documentation, which offers a compact and easy way of using most of the dynsight tools. Also, the documentation offers some copiable Recipes and Examples for the most common analyses.

How to contribute

If you make changes or improvements to the codebase, please open a pull request on our GitHub repository. This allows us to review, discuss, and integrate contributions in a transparent and collaborative manner. Make sure to include a clear description of the changes and link any related issues if applicable.

Developer Setup

1. Install just.

2. In a new virtual environment run:

   $ just dev

3. Run code checks:

   $ just check

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.