Metadata-Version: 2.1
Name: neurocode
Version: 0.1.0
Summary: 🧠 EEG/MEG self-supervised learning toolbox.
Home-page: https://github.com/neurocode-ai/neurocode
Author: Wilhelm Ågren
Author-email: Wilhelm Ågren <wilhelmagren98@gmail.com>
Project-URL: Homepage, https://github.com/neurocode-ai/neurocode
Project-URL: Bug tracker, https://github.com/neurocode-ai/neurocode/issues
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE

<div align="center">
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<div align="left">
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<p align="center">
<a href="https://github.com/neurocode-ai/neurocode">
<img align="center" width=40% src="https://github.com/neurocode-ai/neurocode/blob/main/docs/images/neurocode_logo.webp"></img>
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[![PyPi version](https://img.shields.io/pypi/v/neurocode.svg)](https://pypi.org/project/neurocode/)
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</div>

## 🔎 Overview
A minimalistic Python library for EEG/MEG deep learning research, primarely focused on self-supervised learning. 

## 🚀 Example usage
Below you can see an example adapted for a SSL training workflow using the SimCLR framework.

```python
import torch

from pytorch_metric_learning import losses
from neurocode.datasets import SimulatedDataset, RecordingDataset
from neurocode.samplers import SignalSampler
from neurocode.models import SignalNet
from neurocode.training import SimCLR
from neurocode.datautil import manifold_plot, history_plot

sample_data = SimulatedDataset("sample", seed=7815891891337)
sample_data.read_from_file("MEG/sample/sample_audvis_raw.fif")

# create random extrapolated data from the raw MEG recording,
# you need to provide a location to a forward solution (source space) to use
sample_data.simulate("MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif")

dataset = RecordingDataset(sample_data.data(), sample_data.labels(), sfreq=200)
train, valid = dataset.train_valid_split(split=0.75)

samplers = {
  'train': SignalSampler(train.data(), train.labels(), train.info(), ...),
  'valid': SignalSampler(valid.data(), valid.labels(), valid.info(), ...),
}

device = "cuda" if torch.cuda.is_available() else "cpu"
model = SignalNet(...)
optimizer = torch.optim.Adam(model.parameters(), ...)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, ...)
criterion = losses.NTXentLoss()

# train the neural network using the self-supervised learning SimCLR framework,
# save or plot the history to see training loss evolution
simclr = SimCLR(model, device, ...)
history = simclr.fit(samplers, save_model=True)

```

## 📋 License
All code is to be held under a general MIT license, please see [LICENSE](https://github.com/neurocode-ai/neurocode/blob/main/LICENSE) for specific information.
