tad-py 1.0.3

They All Die (TAD)

TAD

TAD ("They All Die") is a Python package for handling and analyzing neural recordings, with a focus on spike rasters, burst detection, synchrony, firing rates, avalanches, and event-triggered analyses.

TAD is developed jointly at:

• IMS laboratory, University of Bordeaux Florian Kolbl, Bioelectronics group: https://www.ims-bordeaux.fr/research-groups/bioelectronics/
• Universidade Federal do Parana (UFPR) Jaderson Polli, Department of Physics: http://fisica.ufpr.br/pagina_ppgf_english/

Scientific context

At this stage, TAD is primarily grounded in the analysis workflows described in:

1. Pasquale, V., Massobrio, P., Bologna, L. L., Chiappalone, M., & Martinoia, S. (2008). Self-organization and neuronal avalanches in networks of dissociated cortical neurons. Neuroscience, 153(4), 1354-1369.
2. Bologna, L. L., Pasquale, V., Garofalo, M., Gandolfo, M., Baljon, P. L., Maccione, A., ... & Chiappalone, M. (2010). Investigating neuronal activity by SPYCODE multi-channel data analyzer. Neural Networks, 23(6), 685-697.

Features

• Flexible raster creation, manipulation, and serialization
• Spike metrics such as counts, rates, and inter-spike intervals
• Burst detection and burst-related visualizations
• Avalanche analysis and synchrony metrics
• Trigger and event-window handling
• Support for Multi Channel Systems recordings

Installation

For a standard user installation, install TAD from PyPI:

pip install tad-py

If you want to work on TAD locally in development mode:

git clone https://github.com/Neuro-Interface-Lab/TAD.git
cd TAD
pip install -e ".[dev]"

If you prefer a conda-based environment, you can also use:

conda env create -f environment.yml
conda activate tad-env

Quick start

import numpy as np
from tad import Raster

raster = Raster.empty(channels=range(10))

for ch in range(10):
    raster.insert_timestamparray(
        ch,
        np.random.uniform(0, 10, 100),
    )

raster.save("my_raster.h5", h5=True)

Documentation

• Installation guide: https://tad.readthedocs.io/en/latest/installation.html
• Tutorials: https://tad.readthedocs.io/en/latest/tutorials.html
• API reference: https://tad.readthedocs.io/en/latest/api/index.html

Citation

The repository now includes a CITATION.cff file for GitHub and a .zenodo.json file for Zenodo metadata.

Once Zenodo is connected to the GitHub repository and the first GitHub release is archived, TAD will receive a DOI that can be added here as a badge and used in future scientific publications.

License

TAD is distributed under the GPL-3.0 license.