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
Warning
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 to install it on your machine.
References
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
Note
This project has been set up using PyScaffold 4.1.3. For details and usage information on PyScaffold see https://pyscaffold.org/.
1 maintainer
Metadata
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Release files for gratin 0.1.11
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