# Core method (training and inference)
# torch >= 1.13 is required, not 1.12: sodeep/sodeep.py calls torch.load with
# weights_only, which was introduced in 1.13.
torch>=1.13
numpy>=1.21
scipy>=1.7

# Used by the transformation banks, the corruption bank and the loaders
# PyWavelets provides pywt, used by BFT-classify/models/augment_trainset.py
PyWavelets>=1.3
scikit-learn>=1.0

# EEG Conformer and Deformer backbones (BFT-regression/models/)
einops>=0.6

# Training-time progress and logging
# tqdm: BFT-regression/utils/get_psd.py
# tensorboardX: sodeep/train.py, the mapping-module trainer
tqdm>=4.60
tensorboardX>=2.5

# Optional: dataset loading and preprocessing helpers used by some loaders
# mne>=1.0
