Metadata-Version: 2.1
Name: tonic
Version: 0.4.2
Summary: Event-based datasets and transformations based on pyTorch vision.
Home-page: https://github.com/neuromorphs/tonic
Author: The Neuromorphs of Telluride
Author-email: lenz.gregor@gmail.com
License: UNKNOWN
Description: ![tonic](tonic-logo-padded.png)
        [![PyPI](https://img.shields.io/pypi/v/tonic)](https://pypi.org/project/tonic/)
        [![Travis Build Status](https://travis-ci.com/neuromorphs/tonic.svg?branch=master)](https://travis-ci.com/neuromorphs/tonic)
        [![Documentation Status](https://readthedocs.org/projects/tonic/badge/?version=latest)](https://tonic.readthedocs.io/en/latest/?badge=latest)
        [![contributors](https://img.shields.io/github/contributors-anon/neuromorphs/tonic)](https://github.com/neuromorphs/tonic/pulse)
        [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5079802.svg)](https://doi.org/10.5281/zenodo.5079802)
        
        
        Battling with all the different file formats of publicly available neuromorphic datasets? No more!
        **Tonic** is a tool to facilitate the download, manipulation and loading of event-based/spike-based data. Have a look at the list of [supported datasets](https://tonic.readthedocs.io/en/latest/datasets.html) and [transformations](https://tonic.readthedocs.io/en/latest/transformations.html)!
        It's somewhat modeled after PyTorch Vision for an intuitive interface, so that you spend less time worrying about how to read files and more time on things that matter.
        
        ## Install
        ```bash
        pip install tonic
        ```
        If you prefer conda, please check out the [forge repository](https://github.com/conda-forge/tonic-feedstock).
        
        ## Getting started
        Have a look at our [introduction](https://tonic.readthedocs.io/en/latest/getting_started.html) page to see how some of the moving parts work. There are some more short examples available [here](https://tonic.readthedocs.io/en/latest/examples.html).
        
        ## Quickstart
        If you're looking for a minimal example to run, this is it!
        
        ```python
        import tonic
        import tonic.transforms as transforms
        
        transform = transforms.Compose([transforms.Denoise(filter_time=10000),
                                        transforms.TimeJitter(std=10),])
        
        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
        Have a question about how something works? Ideas for improvement? Feature request? Please get in touch here on GitHub via the [Discussions](https://github.com/neuromorphs/tonic/discussions) page!
        
        ## Documentation
        You can find the full documentation on Tonic [on this site](https://tonic.readthedocs.io/en/latest/index.html).
        
        ## Citation
        If you find this package helpful, please use the following citation:
        
        ```BibTex
        @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}
        }
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Description-Content-Type: text/markdown
