Metadata-Version: 2.1
Name: andi-datasets
Version: 2.0.0
Summary: Generate, manage and analyze anomalous diffusion trajectories.
Home-page: https://github.com/gorka.munoz/andi_datasets
Author: Gorka Munoz-Gil
Author-email: munoz.gil.gorka@gmail.com
License: Apache Software License 2.0
Keywords: anomalous diffusion
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: Apache Software License
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE

The Anomalous Diffusion (AnDi) dataset library
================

<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

<div>

[![](https://zenodo.org/badge/DOI/10.5281/zenodo.4775311.svg)](https://doi.org/10.5281/zenodo.4775311)

</div>

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
```

### 1st AnDi Challenge 2020

![](figures/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](https://github.com/AnDiChallenge/andi_datasets/blob/master/tutorials/challenge2021_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 2022

We are currently preparing the second edition of the AnDi Challenge.
Stay tuned, more info will be announced soon in
[Twitter](https://twitter.com/AndiChallenge). If you want to start
playing with the new *phenomenological* diffusion models on which the
challenge will be based, you can check [this
tutorial](https://github.com/AnDiChallenge/andi_datasets/blob/master/tutorials/challenge2022_datasets.ipynb).

## 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](https://github.com/AnDiChallenge/andi_datasets/blob/master/tutorials/challenge2020_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 or bigger clusters or condensates or the
presence of immobile traps in the environment.

Examples of their use and properties can be found in [this
tutorial](https://github.com/AnDiChallenge/andi_datasets/blob/master/tutorials/challenge2022_datasets.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.

## Requirements

All current requirements are declared in the file `setting.ini`.

Further details can be found at the [PYPI package
webpage](https://pypi.org/project/andi-datasets/).


