| |
| """Validate ShapeNetCar mlcfd_data structure and readable arrays.""" |
|
|
| from __future__ import annotations |
|
|
| import argparse |
| import sys |
| from pathlib import Path |
|
|
| import numpy as np |
|
|
|
|
| STATS = { |
| "mean_in.npy": (7,), |
| "std_in.npy": (7,), |
| "mean_out.npy": (4,), |
| "std_out.npy": (4,), |
| } |
| TRAINING_FILES = ("Cd.npy", "I1.npy", "I2.npy", "Press.npy", "Velo.npy") |
| PREPROCESSED_FILES = ("x.npy", "y.npy", "pos.npy", "surf.npy", "edge_index.npy") |
|
|
|
|
| def fail(message: str) -> None: |
| print(f"[FAIL] {message}", file=sys.stderr) |
| raise SystemExit(1) |
|
|
|
|
| def load(path: Path) -> np.ndarray: |
| try: |
| return np.load(path, allow_pickle=False) |
| except Exception as exc: |
| fail(f"cannot read {path}: {exc}") |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--data-root", default="data/mlcfd_data") |
| args = parser.parse_args() |
| root = Path(args.data_root) |
|
|
| if not root.is_dir(): |
| fail(f"data root not found: {root}") |
|
|
| for subdir in ("training_data", "preprocessed_data", "stats"): |
| if not (root / subdir).is_dir(): |
| fail(f"missing directory: {root / subdir}") |
|
|
| for filename, shape in STATS.items(): |
| arr = load(root / "stats" / filename) |
| if arr.shape != shape: |
| fail(f"stats shape mismatch for {filename}: expected {shape}, got {arr.shape}") |
| if not np.issubdtype(arr.dtype, np.floating): |
| fail(f"stats dtype mismatch for {filename}: got {arr.dtype}") |
|
|
| train_param_dirs = sorted(p for p in (root / "training_data").glob("param*") if p.is_dir()) |
| if not train_param_dirs: |
| fail("no training_data/param* directories found") |
| for param_dir in train_param_dirs: |
| for filename in TRAINING_FILES: |
| path = param_dir / filename |
| if not path.is_file(): |
| fail(f"missing training file: {path}") |
| arr = load(path) |
| if arr.size == 0: |
| fail(f"empty training array: {path}") |
|
|
| sample_dirs = sorted(p for p in (root / "preprocessed_data").glob("param*/*") if p.is_dir()) |
| if not sample_dirs: |
| fail("no preprocessed sample directories found") |
| sample = sample_dirs[0] |
| arrays = {name: load(sample / name) for name in PREPROCESSED_FILES} |
| if arrays["x.npy"].ndim != 2 or arrays["x.npy"].shape[1] != 7: |
| fail(f"x.npy schema mismatch in {sample}: {arrays['x.npy'].shape}") |
| if arrays["y.npy"].ndim != 2 or arrays["y.npy"].shape[1] != 4: |
| fail(f"y.npy schema mismatch in {sample}: {arrays['y.npy'].shape}") |
| if arrays["pos.npy"].ndim != 2 or arrays["pos.npy"].shape[1] != 3: |
| fail(f"pos.npy schema mismatch in {sample}: {arrays['pos.npy'].shape}") |
| if arrays["surf.npy"].ndim != 1: |
| fail(f"surf.npy schema mismatch in {sample}: {arrays['surf.npy'].shape}") |
| if arrays["edge_index.npy"].ndim != 2 or arrays["edge_index.npy"].shape[0] != 2: |
| fail(f"edge_index.npy schema mismatch in {sample}: {arrays['edge_index.npy'].shape}") |
|
|
| node_count = arrays["x.npy"].shape[0] |
| if arrays["y.npy"].shape[0] != node_count or arrays["pos.npy"].shape[0] != node_count: |
| fail(f"node count mismatch in {sample}") |
| if arrays["surf.npy"].shape[0] != node_count: |
| fail(f"surface mask length mismatch in {sample}") |
|
|
| print("[OK] ShapeNetCar data validation passed") |
| print(f"[OK] training param dirs: {len(train_param_dirs)}") |
| print(f"[OK] preprocessed samples: {len(sample_dirs)}") |
| print(f"[OK] checked sample: {sample.relative_to(root)}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
