%global _empty_manifest_terminate_build 0
Name: python-tonic
Version: 1.2.6
Release: 1
Summary: Neuromorphic datasets and transformations.
License: GNU GPLv3
URL: https://pypi.org/project/tonic/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/4c/db/31e879521e3a1f1f1f0f91e7a316a17488994ce1697b6c1fa915dbb00e7d/tonic-1.2.6.tar.gz
BuildArch: noarch
Requires: python3-numpy
Requires: python3-h5py
Requires: python3-importRosbag
Requires: python3-scipy
Requires: python3-tqdm
Requires: python3-typing-extensions
Requires: python3-librosa
Requires: python3-pbr
Requires: python3-expelliarmus
%description
[](https://pypi.org/project/tonic/)
[](https://codecov.io/gh/neuromorphs/tonic)
[](https://tonic.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/neuromorphs/tonic/pulse)
[](https://mybinder.org/v2/gh/neuromorphs/tonic/main?labpath=docs%2Ftutorials)
[](https://doi.org/10.5281/zenodo.5079802)
[](https://discord.gg/V6FHBZURkg)
**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](https://tonic.readthedocs.io/en/latest/index.html).
* [A first example](https://tonic.readthedocs.io/en/latest/getting_started/nmnist.html) to get a feeling for how Tonic works.
* [Run tutorials in your browser](https://mybinder.org/v2/gh/neuromorphs/tonic/main?labpath=docs%2Ftutorials) quick and easy.
* [List of datasets](https://tonic.readthedocs.io/en/main/datasets.html).
* [List of transformations](https://tonic.readthedocs.io/en/main/auto_examples/index.html).
* [About](https://tonic.readthedocs.io/en/latest/about/info.html) this project.
* [Release notes](https://tonic.readthedocs.io/en/latest/about/release_notes.html) on version changes.
## Install
```bash
pip install tonic
```
or (thanks to [@Tobias-Fischer](https://github.com/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 | [](https://github.com/neuromorphs/tonic)|
|----------|-------------------------------------------------------------------------------------------------------------------------------------------|
| **Windows** | [](https://github.com/neuromorphs/tonic) |
## Quickstart
If you're looking for a minimal example to run, this is it!
```python
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](https://discord.gg/V6FHBZURkg)
or alternatively here on GitHub via the [Discussions](https://github.com/neuromorphs/tonic/discussions) page!
## Contributing
Please check out the [contributions](https://tonic.readthedocs.io/en/latest/about/contribute.html) page for details.
## Sponsoring
The development of this library is supported by
 |
## Citation
If you find this package helpful, please consider citing it:
```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}
}
```
%package -n python3-tonic
Summary: Neuromorphic datasets and transformations.
Provides: python-tonic
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-tonic
[](https://pypi.org/project/tonic/)
[](https://codecov.io/gh/neuromorphs/tonic)
[](https://tonic.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/neuromorphs/tonic/pulse)
[](https://mybinder.org/v2/gh/neuromorphs/tonic/main?labpath=docs%2Ftutorials)
[](https://doi.org/10.5281/zenodo.5079802)
[](https://discord.gg/V6FHBZURkg)
**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](https://tonic.readthedocs.io/en/latest/index.html).
* [A first example](https://tonic.readthedocs.io/en/latest/getting_started/nmnist.html) to get a feeling for how Tonic works.
* [Run tutorials in your browser](https://mybinder.org/v2/gh/neuromorphs/tonic/main?labpath=docs%2Ftutorials) quick and easy.
* [List of datasets](https://tonic.readthedocs.io/en/main/datasets.html).
* [List of transformations](https://tonic.readthedocs.io/en/main/auto_examples/index.html).
* [About](https://tonic.readthedocs.io/en/latest/about/info.html) this project.
* [Release notes](https://tonic.readthedocs.io/en/latest/about/release_notes.html) on version changes.
## Install
```bash
pip install tonic
```
or (thanks to [@Tobias-Fischer](https://github.com/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 | [](https://github.com/neuromorphs/tonic)|
|----------|-------------------------------------------------------------------------------------------------------------------------------------------|
| **Windows** | [](https://github.com/neuromorphs/tonic) |
## Quickstart
If you're looking for a minimal example to run, this is it!
```python
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](https://discord.gg/V6FHBZURkg)
or alternatively here on GitHub via the [Discussions](https://github.com/neuromorphs/tonic/discussions) page!
## Contributing
Please check out the [contributions](https://tonic.readthedocs.io/en/latest/about/contribute.html) page for details.
## Sponsoring
The development of this library is supported by
|
|
## Citation
If you find this package helpful, please consider citing it:
```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}
}
```
%package help
Summary: Development documents and examples for tonic
Provides: python3-tonic-doc
%description help
[](https://pypi.org/project/tonic/)
[](https://codecov.io/gh/neuromorphs/tonic)
[](https://tonic.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/neuromorphs/tonic/pulse)
[](https://mybinder.org/v2/gh/neuromorphs/tonic/main?labpath=docs%2Ftutorials)
[](https://doi.org/10.5281/zenodo.5079802)
[](https://discord.gg/V6FHBZURkg)
**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](https://tonic.readthedocs.io/en/latest/index.html).
* [A first example](https://tonic.readthedocs.io/en/latest/getting_started/nmnist.html) to get a feeling for how Tonic works.
* [Run tutorials in your browser](https://mybinder.org/v2/gh/neuromorphs/tonic/main?labpath=docs%2Ftutorials) quick and easy.
* [List of datasets](https://tonic.readthedocs.io/en/main/datasets.html).
* [List of transformations](https://tonic.readthedocs.io/en/main/auto_examples/index.html).
* [About](https://tonic.readthedocs.io/en/latest/about/info.html) this project.
* [Release notes](https://tonic.readthedocs.io/en/latest/about/release_notes.html) on version changes.
## Install
```bash
pip install tonic
```
or (thanks to [@Tobias-Fischer](https://github.com/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 | [](https://github.com/neuromorphs/tonic)|
|----------|-------------------------------------------------------------------------------------------------------------------------------------------|
| **Windows** | [](https://github.com/neuromorphs/tonic) |
## Quickstart
If you're looking for a minimal example to run, this is it!
```python
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](https://discord.gg/V6FHBZURkg)
or alternatively here on GitHub via the [Discussions](https://github.com/neuromorphs/tonic/discussions) page!
## Contributing
Please check out the [contributions](https://tonic.readthedocs.io/en/latest/about/contribute.html) page for details.
## Sponsoring
The development of this library is supported by
|
|
## Citation
If you find this package helpful, please consider citing it:
```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}
}
```
%prep
%autosetup -n tonic-1.2.6
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-tonic -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot - 1.2.6-1
- Package Spec generated
|