tonic

To help you get started, we've collected the most common ways that tonic is being used within popular public projects.

UW-Hydro / VIC / tests / run_tests.py View on Github

See Also
    --------
    run_unit_tests
    run_examples
    run_system
    run_release
    '''

    # Print test set welcome
    print('\n-'.ljust(OUTPUT_WIDTH + 1, '-'))
    print('Running Science Tests')
    print('-'.ljust(OUTPUT_WIDTH, '-'))

    # Get setup
    config = read_config(config_file)

    # drop invalid driver tests
    config = drop_tests(config, driver)

    test_results = OrderedDict()

    # Run individual tests
    for i, (testname, test_dict) in enumerate(config.items()):

        # print out status info
        print('Running test {0}/{1}: {2}'.format(i + 1, len(config.items()),
                                                 testname))

        # Setup directories for test
        dirs = setup_test_dirs(testname, out_dir,
                               mkdirs=['results', 'state', 'logs', 'plots'])

View more ways to use tonic

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!

Documentation

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

• 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, time_window=3000),
    ]
)

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

from torch.utils.data import DataLoader

testloader = DataLoader(
    testset,
    batch_size=10,
    collate_fn=tonic.collation.PadTensors(batch_first=True),
)

frames, targets = 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.

Sponsoring

The development of this library is supported by

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}
}