Package: andi-datasets
The anomalous diffusion libraryGenerate, manage and analyze anomalous diffusion trajectoriesGet started|Documentation|Tutorials|Cite usThis library has been created in the framework of theAnomalous
Diffusion (AnDi) Challengeand allows to
create trajectories and datasets from various anomalous diffusion
models. You can install the package using:pipinstallandi-datasetsYou can then import the package in a Python3 environment using:importandi_datasetsLibrary organizationTheandi_datasetsclass allows to generate, transform, analyse, save
and load diffusion trajectories from a plethora of diffusion models and
experimental generated with various diffusion models. The library is
structured in two main blocks, containing either theoretical or
phenomenological models. Here is a scheme of the library’s content:Theoretical modelsThe library allows to generate trajectories from various anomalous
diffusion models:continuous-time random walk
(CTRW),fractional Brownian motion (FBM),Lévy walks (LW),annealed
transit time model
(ATTM)andscaled
Brownian motion (SBM).
You can generate trajectories with the desired anomalous exponent in
either one, two or three dimensions.Examples of their use and properties can be found inthis
tutorial.Phenomenological modelsWe have also included models specifically developed to simulate
realistic physical systems, in which random events alter the diffusion
behaviour of the particle. The sources of these changes can be very
broad, from the presence of heterogeneities either in space or time, the
possibility of creating dimers and condensates or the presence of
immobile traps in the environment.Examples of their use and properties can be found inthis
tutorial.The AnDi Challenges1st AnDi Challenge (2020)The first AnDi challenge was held between March and November 2020 and
focused on the characterization of trajectories arising from different
theoretical diffusion models under various experimental conditions. The
results of the challenge are published in this article:Muñoz-Gil et
al., Nat Commun12, 6253
(2021).If you want to reproduce the datasets used during the challenge, please
checkthis
tutorial.
You can then test your predictions and compare them with the those of
challenge participants in thisonline interactive
tool.2nd AnDi Challenge (2023)The second AnDi challenge isLIVE. Follow the previous link to keep updated on all news. If you want to learn more about the data we will use, you can checkthis tutorial.Version controlDetails on each release are presentedhere.ContributingThe AnDi challenge is a community effort, hence any contribution to this
library is more than welcome. If you think we should include a new model
to the library, you can contact us in this mail:andi.challenge@gmail.com. You can also perform pull-requests and open
issues with any feedback or comments you may have.Cite usIf you found this package useful and used it in your projects, you can
use the following to directly cite the package:Muñoz-Gil,G.,RequenaB.,VolpeG.,Garcia-MarchM.A.andManzoC.AnDiChallenge/ANDI_datasets:Challenge2020release(v.1.0).Zenodo(2021).https://doi.org/10.5281/zenodo.4775311Or you can cite the paper this package was developed for:- AnDi Challenge 1G.Muñoz-Gil,G.Volpe...C.ManzoObjectivecomparisonofmethodstodecodeanomalousdiffusion.NatCommun12,6253(2021).https://doi.org/10.1038/s41467-021-26320-w- AnDi Challenge 2G.Muñoz-Gil,H.Bachimanchi...C.ManzoIn-principleacceptedatNatureCommunications(RegisteredReportPhase1)arXiv:2311.18100https://doi.org/10.48550/arXiv.2311.18100