longitudinal/AGENTS.md

AGENTS.md

Environment

This project uses a conda environment named longitudinal (Python 3.14).

Activate with:

source /opt/conda/etc/profile.d/conda.sh && conda activate longitudinal

Key packages: torch 2.14 (+cu126), torchvision, monai 1.6 (pip, Pipeline C), numpy, scipy, pandas, scikit-learn, scikit-image, matplotlib, nibabel, SimpleITK.

Project

Longitudinal (repeated-measures) analysis of medical imaging data. Repo is at an early stage — see README.md for any project notes.

All path config lives in config/paths.json — the external data roots the code reads or writes (data, lee, m6, ntuh_register_inv). Code resolves them through src.common (PATHS dict, path(name) lookup, DATA/d() for data/... paths). To move any dataset, only edit config/paths.json; the repo root is derived from the code location (override with LONGITUDINAL_ROOT if needed).

Conventions

• All Python commands must run inside the longitudinal conda environment.
• GPU is available (CUDA 12.6); use torch.device('cuda') when appropriate. Multi-GPU stages of Pipelines A and C run via torchrun --standalone --nproc_per_node N (rank = GPU).
• No lint/test tooling is configured yet; run scripts directly with python <script> from the repo root.