import zipfile from pathlib import Path import numpy as np EXPECTED_OOD_KEYS = { "chaplin1", "chaplin2", "mononoke1", "mononoke2", "passepartout1", "passepartout2", "planetearth1", "planetearth2", "pulpfiction1", "pulpfiction2", "wot1", "wot2", } EXPECTED_SUBJECTS = {"sub-01", "sub-02", "sub-03", "sub-05"} def load_fmri_num_samples(datasets_root: Path, test_set_name: str) -> dict[str, dict[str, int]]: """Load per-subject, per-episode fMRI sample counts for the test set.""" if test_set_name == "friends-s7": file_pattern = "friends-s7" elif test_set_name == "ood": file_pattern = "ood" else: raise ValueError(f"Unknown test set: {test_set_name}") fmri_dir = datasets_root / "algonauts_2025.competitors" / "fmri" sample_paths = sorted(fmri_dir.rglob(f"*_{file_pattern}_fmri_samples.npy")) if not sample_paths: raise FileNotFoundError( f"No fmri_samples files found for {test_set_name} in {fmri_dir}. " f"Expected pattern: *_{file_pattern}_fmri_samples.npy" ) fmri_num_samples = {} for path in sample_paths: sub = path.parents[1].name fmri_num_samples[sub] = np.load(path, allow_pickle=True).item() print(f" Loaded fmri_num_samples for {list(fmri_num_samples.keys())}") return fmri_num_samples def print_summary(predictions: dict[str, dict[str, np.ndarray]]): """Print a summary of the generated predictions.""" for subject, episodes_dict in predictions.items(): print(f" {subject}: {len(episodes_dict)} episodes") for episode, pred in episodes_dict.items(): print(f" {episode}: {pred.shape} {pred.dtype}") def save_predictions( predictions: dict[str, dict[str, np.ndarray]], test_set_name: str, out_dir: Path, ): """Save predictions as .npy and .zip files.""" file_name = f"fmri_predictions_{test_set_name.replace('-', '_')}" npy_path = out_dir / f"{file_name}.npy" np.save(npy_path, predictions) print(f" Saved: {npy_path}") zip_path = out_dir / f"{file_name}.zip" with zipfile.ZipFile(zip_path, "w") as zipf: zipf.write(npy_path, npy_path.name) print(f" Saved: {zip_path}")