andi-datasets 2.1.13
Generate, manage and analyze anomalous diffusion trajectories.
Requires Python: >=3.10
Keywords: anomalous diffusion

The anomalous diffusion library
================

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<img width="250" src="figures/logos_banners/logo.png">
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<h3 align="center">
Generate, manage and analyze anomalous diffusion trajectories
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<p align="center">
<a href="https://doi.org/10.5281/zenodo.4775311"><img src="https://zenodo.org/badge/DOI/10.5281/zenodo.4775311.svg" alt="PyPI version"></a>
<a href="https://badge.fury.io/py/andi-datasets"><img src="https://badge.fury.io/py/andi-datasets.svg" alt="PyPI version"></a>
<a href="https://badge.fury.io/py/andi-datasets"><img src="https://img.shields.io/badge/python-3.10-red" alt="Python version"></a>
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<p align="center">
<a href="https://andichallenge.github.io/andi_datasets/">Get started</a>
\|
<a href="https://andichallenge.github.io/andi_datasets/lib_nbs/index_docs.html">Documentation</a>
\|
<a href="https://andichallenge.github.io/andi_datasets/tutorials/index_tutorials.html">Tutorials</a>
\| <a href="#cite-us">Cite us</a>
</p>

This library has been created in the framework of the [**Anomalous
Diffusion (AnDi) Challenge**](http://andi-challenge.org/) and allows to
create trajectories and datasets from various anomalous diffusion
models. You can install the package using:

``` python
pip install andi-datasets
```

You can then import the package in a Python3 environment using:

``` python
import andi_datasets
```

## Library organization

The `andi_datasets` class 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:

![](figures/scheme_v1.svg)

### Theoretical models

The library allows to generate trajectories from various anomalous
diffusion models: [continuous-time random walk
(CTRW)](https://journals.aps.org/prb/abstract/10.1103/PhysRevB.12.2455),
[fractional Brownian motion (FBM)](https://doi.org/10.1137%2F1010093),
[Lévy walks (LW)](https://doi.org/10.1103%2FPhysRevE.49.4873), [annealed
transit time model
(ATTM)](https://doi.org/10.1103%2FPhysRevLett.112.150603) and [scaled
Brownian motion (SBM)](https://doi.org/10.1103%2FPhysRevE.66.021114).
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 in [this
tutorial](tutorials/challenge_one_datasets.ipynb).

### Phenomenological models

We 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 in [this
tutorial](tutorials/challenge_two_datasets.ipynb).

## The AnDi Challenges

### 1st AnDi Challenge (2020)

![](figures/logos_banners/experiments_andi1.svg)

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 Commun **12**, 6253
(2021)](https://doi.org/10.1038/s41467-021-26320-w).

If you want to reproduce the datasets used during the challenge, please
check [this tutorial](tutorials/challenge_one_submission.ipynb). You can
then test your predictions and compare them with the those of challenge
participants in this [online interactive
tool](http://andi-challenge.org/interactive-tool/).

### 2nd AnDi Challenge (2023 / 2024)

The second AnDi challenge is
[LIVE](https://andi-challenge.org/challenge-2024/). Follow the previous
link to keep updated on all news. If you want to learn more about the
data we will use, you can check [this
tutorial](tutorials/challenge_two_datasets).

We have officialy released the [Benchark dataset of the 2nd AnDi
Challenge](https://doi.org/10.5281/zenodo.14281478). You can find all
details on we created it in [this
tutorial](tutorials/challenge_two_benchmark.ipynb).

## Version control

Details on each release are presented [here](changes_andi_v2.ipynb).

## Contributing

The 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 us

If you found this package useful and used it in your projects, you can
use the following to directly cite the package:

``` python
Muñoz-Gil, G., Requena B., Volpe G., Garcia-March M.A. and Manzo C.
AnDiChallenge/ANDI_datasets: Challenge 2020 release (v.1.0). Zenodo (2021). 
https://doi.org/10.5281/zenodo.4775311
```

Or you can cite the paper this package was developed for:

**- AnDi Challenge 1**

``` python
G. Muñoz-Gil, G. Volpe ... C. Manzo 
Objective comparison of methods to decode anomalous diffusion. 
Nat Commun 12, 6253 (2021). 
https://doi.org/10.1038/s41467-021-26320-w
```

**- AnDi Challenge 2**

``` python
G. Muñoz-Gil, H. Bachimanchi ...  C. Manzo
In-principle accepted at Nature Communications (Registered Report Phase 1)
arXiv:2311.18100
https://doi.org/10.48550/arXiv.2311.18100
```