| |
| """ |
| Generate precomputed 8x8 correlation previews as JSON for the preview split. |
| |
| Usage: |
| # From a JSONL manifest listing parcellation/subject/corr_path |
| python scripts/make_preview.py --manifest manifests/preview.jsonl --size 8 |
| |
| Notes: |
| - Attempts to load correlation .mat using scipy.io.loadmat, falling back to mat73 if needed. |
| - Variable name fallbacks: correlation_matrix | corr | A |
| - Writes files under preview/<parc>__<subject>__corr8x8.json |
| """ |
| import argparse |
| import json |
| import os |
| from pathlib import Path |
| from typing import Dict, Optional |
|
|
|
|
| def try_import(): |
| try: |
| import scipy.io as sio |
| except Exception: |
| sio = None |
| try: |
| import mat73 |
| except Exception: |
| mat73 = None |
| return sio, mat73 |
|
|
|
|
| def load_mat(path: Path) -> Optional[Dict]: |
| sio, mat73 = try_import() |
| if sio is not None: |
| try: |
| return sio.loadmat(str(path), squeeze_me=True, simplify_cells=True) |
| except Exception: |
| pass |
| if mat73 is not None: |
| try: |
| return mat73.loadmat(str(path)) |
| except Exception: |
| pass |
| return None |
|
|
|
|
| def top_left_8x8(arr, size=8): |
| import numpy as np |
| a = np.asarray(arr) |
| if a.ndim < 2: |
| return None |
| n = min(size, a.shape[-1]) |
| return a[:n, :n].astype("float32").tolist() |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--manifest", type=Path, required=True) |
| ap.add_argument("--size", type=int, default=8) |
| args = ap.parse_args() |
|
|
| repo = Path(__file__).resolve().parents[1] |
| out_dir = repo / "preview" |
| out_dir.mkdir(parents=True, exist_ok=True) |
|
|
| with args.manifest.open("r", encoding="utf-8") as f: |
| rows = [json.loads(line) for line in f if line.strip()] |
|
|
| wrote = 0 |
| for r in rows: |
| parc = r.get("parcellation") |
| subject = r.get("subject") |
| corr_path = r.get("corr_path") |
| if not (parc and subject and corr_path): |
| continue |
| dst = out_dir / f"{parc}__{subject}__corr8x8.json" |
| if dst.exists(): |
| continue |
| src = repo / corr_path |
| if not src.exists(): |
| print("skip missing:", src) |
| continue |
| data = load_mat(src) |
| if not isinstance(data, dict): |
| print("cannot load .mat (no scipy/mat73?)", src) |
| continue |
| for k in ("correlation_matrix", "corr", "A"): |
| if k in data: |
| sl = top_left_8x8(data[k], size=args.size) |
| if sl is not None: |
| with dst.open("w", encoding="utf-8") as g: |
| json.dump(sl, g) |
| wrote += 1 |
| break |
|
|
| print(f"Wrote {wrote} preview tiles to {out_dir}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
|
|
