Requirements:

- Python 3.11
- Dependencies:
    numpy==2.3.3
    scipy==1.16.2
    PyWavelets==1.9.0
    matplotlib==3.10.6
    torch==2.8.0+cu129
    torchaudio==2.8.0+cu129
    torchvision==0.23.0+cu129
    torchmetrics==1.8.2
    torchsummary==1.5.1
    pandas==2.3.3
    typing_extensions==4.15.0

Instructions:

Event Detection:
    - Run training for event detection (indexing) by executing src/event_detection/n_event_det_train.py
        - model_name variable can be modifed to create new or overwrite exsisting model .pt files
        - Saves the .pt files required for inference in src/event_detection/models

    - Run inference for event detection (indexing) by executing src/event_detection/n_event_det_pipeline.py
        - Use model_name to configure the utilised .pt file
        - Saves the .pkl files containing the resultant list of inferred indices in src/event_detection/outputs


Classifcation:
    - Run training for classification by executing src/neuron_cls/n_cls_train.py
        - model_name variable can be modifed to create new or overwrite exsisting model .pt files
        - Saves the .pt files required for inference in src/neuron_cls/models

    - Run inference for classification by executing src/neuron_cls/n_cls_pipeline.py
        - Requires src/event_detection/outputs
        - Use model_name to configure the utilised .pt file
        - Saves the .pkl files containing the resultant list of inferred indices in src/neuron_cls/outputs


Full Sequencer and Export:
    - Run all infernce and export by executing src/sequencer/seq.py
        - Use model_name_evt and model_name_cls to configure the utilised .pt files
        - Can be run using existing .pkl files (if present). 
        - Trigger inference for both idx and cls by setting the argument run_inference.
        - Export to src/sequencer/outputs/sub

Repository Structure:

data/                            # CW specific dataset .mat files
│
src/
│
├── common/
│   ├── io.py                    # loading, saving, export utilities
│   └── paths.py                 # central directory resolver
│
├── event_detection/
│   ├── n_event_det_train.py     # training script
│   ├── n_event_det_pipeline.py  # inference script
│   ├── models/                  # saved .pt detectors
│   └── outputs/                 # saved index .pkl files
│
├── neuron_cls/
│   ├── n_cls_train.py           # classification training
│   ├── n_cls_pipeline.py        # classification inference
│   ├── models/                  # saved .pt classifiers
│   └── outputs/                 # saved classes .pkl files
│
└── sequencer/
    ├── seq.py                   # full workflow + export
    └── outputs/
        ├── vis/                 # waveform/class visual export
        └── sub/                 # submission-ready .mat files