gratin 0.1.11
Random walk analysis tool using graph neural networks
License: mit
Requires Python: >=3.8

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Gratin *(Graphs on Trajectories for Inference)*
===============================================

Gratin is a tool to characterize trajectories of random walks, i.e. motion driven by random fluctuations. This type of motion is observed at various scales and in a wide diversity of systems. 
While this package was developed for the purpose of analysing experimental data coming from photo-activated localization microscopy (PALM) experiments, nothing prevents it from being used on random walk recordings coming from other experimental setups and other domains !

To extract *summary statistics* describing trajectories, Gratin mixes two ingredients :

* an original neural network architecture using graph neural networks (GNN)
* a simulation-based inference framework

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Warning
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Gratin requires the ``pytorch-geometric`` package, whose installation depends on you CUDA version. 
Note however that you **do not need CUDA** to run Gratin, it works on CPU, it's only a bit slower. 
See `here <https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html>`_ to install it on your machine.

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References
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* Hippolyte Verdier, Maxime Duval, François Laurent, Alhassan Cassé,  Christian Vestergaard, et al.. 
  Learning physical properties of anomalous random walks using graph neural networks. 2021. : https://arxiv.org/abs/2103.11738

* Hippolyte Verdier, François Laurent, Alhassan Cassé, Christian L. Vestergaard, Christian G. Specht, Jean-Baptiste Masson
  A maximum mean discrepancy approach reveals subtle changes in α-synuclein dynamics. 2022 : https://doi.org/10.1101/2022.04.11.487825


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Note
====

This project has been set up using PyScaffold 4.1.3. For details and usage
information on PyScaffold see https://pyscaffold.org/.