scripts/prepare_data.py

"""End-to-end data preparation: extract discs -> build cohort -> preprocess.

Usage:
    python scripts/prepare_data.py --extract      # extract all discs (idempotent)
    python scripts/prepare_data.py --cohort       # build subjects_clean.csv + stats
    python scripts/prepare_data.py --preprocess   # 3D volumes + 2.5D slices
    python scripts/prepare_data.py --splits       # repeated stratified folds
    python scripts/prepare_data.py --all          # everything in order
"""
from __future__ import annotations

import argparse
import subprocess
import sys
from pathlib import Path

ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT / "src"))

RAW = ROOT / "data" / "raw"
META = ROOT / "data" / "metadata"


def extract_discs() -> None:
    """Extract every disc tarball into data/raw/ (skips already-extracted)."""
    discs = sorted(RAW.glob("oasis_cross-sectional_disc*.tar.gz"))
    if not discs:
        sys.exit("no disc tarballs found in data/raw/")
    for tgz in discs:
        n = tgz.name.split("disc")[1].split(".")[0]
        marker = RAW / f"disc{n}"
        if marker.exists():
            print(f"disc{n}: already extracted")
            continue
        print(f"disc{n}: verifying gzip ...", flush=True)
        r = subprocess.run(["gzip", "-t", str(tgz)], capture_output=True)
        if r.returncode != 0:
            print(f"  disc{n} CORRUPT: {r.stderr.decode()[:200]}", flush=True)
            continue
        print(f"disc{n}: extracting ...", flush=True)
        subprocess.run(["tar", "xzf", str(tgz), "-C", str(RAW)], check=True)
    print("extraction done")


def build_cohort() -> None:
    from trifuse.data.cohort import build_cohort as _bc
    _bc(RAW, META)


def preprocess() -> None:
    from trifuse.data.preprocess_3d import run as run3d
    from trifuse.data.preprocess_2d import run as run2d
    csv = META / "subjects_clean.csv"
    if not csv.exists():
        sys.exit("run --cohort first (subjects_clean.csv missing)")
    run3d(csv, ROOT / "data" / "processed_3d")
    run2d(csv, ROOT / "data" / "processed_2d")


def make_splits() -> None:
    import pandas as pd
    from trifuse.data.splits import make_folds
    csv = META / "subjects_clean.csv"
    df = pd.read_csv(csv)
    folds = make_folds(df)
    out = META / "folds.csv"
    folds.to_csv(out, index=False)
    print(f"wrote {out} ({len(folds)} rows = {folds['seed'].nunique()} seeds x {len(df)} subjects)")


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--extract", action="store_true")
    ap.add_argument("--cohort", action="store_true")
    ap.add_argument("--preprocess", action="store_true")
    ap.add_argument("--splits", action="store_true")
    ap.add_argument("--all", action="store_true")
    a = ap.parse_args()
    if a.all or a.extract:
        extract_discs()
    if a.all or a.cohort:
        build_cohort()
    if a.all or a.preprocess:
        preprocess()
    if a.all or a.splits:
        make_splits()
    if not any([a.extract, a.cohort, a.preprocess, a.splits, a.all]):
        ap.print_help()


if __name__ == "__main__":
    main()