trackmol 1.0.1

<img src=”logo_trackmol.png” alt=”Logo of trackmol” width=”600”>

The trackmol package offers a set of tools for manipulating, analyzing, and visualizing molecular structures. It is divided into several modules to cover different needs: data analysis, clustering, image processing using computer vision techniques, molecular trajectory generation, and various tools to facilitate the research and development workflow in computational chemistry.

## Table of Contents

• [Installation](#installation)

• [Usage](#usage)

• [Main Modules](#main-modules) - [analysis](#analysis) - [clustering](#clustering) - [computer_vision](#computer_vision) - [generation_walks](#generation_walks) - [gratin](#gratin) - [tools](#tools)

• [Examples](#examples)

• [Contribution](#contribution)

• [License](#license)

## Installation

You can install trackmol from the source repository. Make sure you have Python 3.6 or a later version.

`sh # Clone the repository git clone https://your-repository.git `

The package is structured in the directory [src/trackmol](src/trackmol).

## Usage

Refer to the documentation of each module for more details on the available functions and classes.

## Main Modules ### analysis

Enables analysis of random walk trajectories (MSD…).

### clustering

Allows clustering in latent space and links between latent space and the physical properties of the environment in which the random walks take place.

### computer_vision

Using computer vision techniques, experimental trajectories can be determined from experimentally collected videos.

### generation_walks

Enables random walk generation both statistically and from a position in latent space by denoising diffusion.

### gratin

Module developed by Institut Pasteur and H. Verdier, which uses graph-based neural networks to classify different random walk models and estimate key walk parameters.

## Exemples

Une série d’exemples illustrant l’utilisation des différents modules se trouve dans le répertoire [src/trackmol/examples](src/trackmol/examples).

## Contribution

Les contributions sont les bienvenues ! Veuillez lire le [CONTRIBUTING.rst](CONTRIBUTING.rst) ainsi que le [docs/contributing.rst](docs/contributing.rst) pour les instructions et les bonnes pratiques de contribution.

Avant de soumettre une pull request, assurez-vous que tous les tests passent et que le code respecte les normes du projet.

## Licence

Ce projet est sous licence [LICENSE](LICENSE). Consultez le fichier pour connaître les détails de la licence.

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Pour toute question ou contribution, merci de soumettre une issue ou de contacter l’équipe de développement.