Tonic is a tool to facilitate the download, manipulation and loading of event-based/spike-based data. It's like PyTorch Vision but for neuromorphic data!

🚀 Stable version 1 out now! Check out the release notes here.

Documentation

You can find the full documentation on Tonic on this site.

• Never worked with events? Start here.
• A first example to get a feeling for how Tonic works.
• Run tutorials in your browser quick and easy.
• List of datasets.
• List of transformations.
• About this project.
• Release notes on version changes.

Install

pip install tonic

or (thanks to @Tobias-Fischer)

conda install -c conda-forge tonic

For the latest pre-release on the develop branch that passed the tests:

pip install tonic --pre

This package has been tested on:

Linux
Windows

Quickstart

If you're looking for a minimal example to run, this is it!

import tonic
import tonic.transforms as transforms

sensor_size = tonic.datasets.NMNIST.sensor_size
transform = transforms.Compose([transforms.Denoise(filter_time=10000),
                                transforms.ToFrame(sensor_size=sensor_size, n_time_bins=3),])

testset = tonic.datasets.NMNIST(save_to='./data',
                                train=False,
                                transform=transform)

from torch.utils.data import DataLoader
testloader = DataLoader(testset, shuffle=True)

events, target = next(iter(testloader))

Discussion and questions

Have a question about how something works? Ideas for improvement? Feature request? Please get in touch on the #tonic Discord channel or alternatively here on GitHub via the Discussions page!

Contributing

Please check out the contributions page for details.

Citation

If you find this package helpful, please consider citing it:

@software{lenz_gregor_2021_5079802,
  author       = {Lenz, Gregor and
                  Chaney, Kenneth and
                  Shrestha, Sumit Bam and
                  Oubari, Omar and
                  Picaud, Serge and
                  Zarrella, Guido},
  title        = {Tonic: event-based datasets and transformations.},
  month        = jul,
  year         = 2021,
  note         = {{Documentation available under 
                   https://tonic.readthedocs.io}},
  publisher    = {Zenodo},
  version      = {0.4.0},
  doi          = {10.5281/zenodo.5079802},
  url          = {https://doi.org/10.5281/zenodo.5079802}
}