tokeye 0.9.0

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TokEye

TokEye is a open-source Python-based application for automatic classification and localization of fluctuating signals. It is designed to be used in the context of plasma physics, but can be used for any type of fluctuating signal.

Check out this poster from APS DPP 2025 for more information.

Example Demonstration

Expected processing time:

• A100: < 0.5 seconds on any size spectrogram after warmup.
• CPU: not yet tested.

Verified Datatypes

• DIII-D Fast Magnetics (cite)
• DIII-D CO2 Interferometer (cite)
• DIII-D Electron Cyclotron Emission (cite)
• DIII-D Beam Emission Spectroscopy (cite)

Evaluation

Recall Scores:

• TJII2021: 0.8254
• DCLDE2011 (Delphinus capensis): 0.7708
• DCLDE2011 (Delphinus delphis): 0.7953

With more data, comes better models. Please contribute to the project!

Installation

uv (recommended)

git clone [e-mail]:PlasmaControl/TokEye.git
cd TokEye
uv sync

pip (from source)

git clone [e-mail]:PlasmaControl/TokEye.git
cd TokEye
python3 -m venv .venv
source venv/bin/activate
pip install uv
uv sync

pip (from PyPI)

pip install tokeye

Coming soon.

Containerized installation (Docker) Coming soon.

Usage

python -m TokEye.app

This will start a web app on http://localhost:8888.

If you are on a remote server, you can use SSH port forwarding to access the web app on your local machine:

ssh -L 8888:localhost:7860 user@remote_server

Then open your web browser and navigate to http://localhost:8888.

Models

Pre-trained models are available at this link. Copy them into the models/ directory after downloading them.

• big_mode_v1.pt: Original training regime (window = 1024, hop = 128)
• big_mode_v2.pt: Trained on multiscale (multiwindow, multihop) spectrograms

Input should be a tensor that has shape (B, 1, H, W) where B, H, and W can vary Output will be a tensor of shape (B, 2, H, W)

Best performance when spectrograms are oriented so that when they are plotted with matplotlib, the lowest frequency bin is oriented with the bottom when origin='lower'. Spectrograms should be standardized (mean = 0, std = 1). If baseline activity is very strong, clipping the input may help, but is generally not needed.

The first channel of the output will return preferential measurements of coherent activity (useful for most tasks) THe second channel of the output will return preferential measurements of transient activity

Data

Right now, keep all data as 1d numpy float arrays. No need to normalize or preprocess them. Copy them into the data/ directory.

Citation

If you use this code in your research, please cite:

@article{NaN,
  title={Paper not yet published},
  author={Nathaniel Chen},
  year={2025}
}

Contact

Please check back for updates or reach out to Nathaniel Chen at [e-mail].