# Separate PDB files based on train/validation/test splits # - Build an index of all *_model_*.pdb files once, then copy by split. # - Report counts in terms of number of .pdb files (not just unique PDB IDs). # Use '04_run_dataset_split.sh' to run this script with appropriate arguments. import os import re import glob import shutil import argparse import pandas as pd PDBID_PREFIX_RE = re.compile(r"^([0-9a-zA-Z]{4}).*\.pdb$", re.IGNORECASE) def read_split_pdb_ids(csv_path: str, pdb_col: str = "PDB_ID") -> list[str]: df = pd.read_csv(csv_path, dtype={pdb_col: "string"}) if pdb_col not in df.columns: raise ValueError(f"Column '{pdb_col}' not found in {csv_path}. Columns: {list(df.columns)}") pdb_ids = ( df[pdb_col] .dropna() .astype("string") .str.strip() .str.lower() .unique() .tolist() ) return pdb_ids def build_pdb_index(src_dirs: list[str]) -> dict[str, list[str]]: pdb_to_files: dict[str, list[str]] = {} all_files: list[str] = [] for sd in src_dirs: all_files.extend(glob.glob(os.path.join(sd, "*.pdb"))) print("Found *.pdb files in source folders:", len(all_files)) kept_files = 0 skipped_badname = 0 for fpath in all_files: fname = os.path.basename(fpath) m = PDBID_PREFIX_RE.match(fname) if not m: skipped_badname += 1 continue pid = m.group(1).lower() pdb_to_files.setdefault(pid, []).append(fpath) kept_files += 1 print("Indexed .pdb files (matched prefix rule):", kept_files) print("Skipped files (could not parse PDB_ID from filename):", skipped_badname) print("Unique PDB_IDs with at least one .pdb file:", len(pdb_to_files)) return pdb_to_files def copy_by_split( split_to_pdbids: dict[str, list[str]], pdb_to_files: dict[str, list[str]], out_dirs: dict[str, str], ) -> tuple[dict[str, int], dict[str, int], dict[str, list[str]], list[str]]: report_files_copied: dict[str, int] = {} report_expected_files: dict[str, int] = {} missing_ids: dict[str, list[str]] = {} duplicate_name_collisions: list[str] = [] for split_name, pdb_ids in split_to_pdbids.items(): out_dir = out_dirs[split_name] copied_files = 0 expected_files = 0 missing: list[str] = [] for pid in pdb_ids: files = pdb_to_files.get(pid, []) if not files: missing.append(pid) continue expected_files += len(files) for fpath in files: dst = os.path.join(out_dir, os.path.basename(fpath)) # Avoid overwrite if same filename already exists (e.g., duplicates across src dirs) if os.path.exists(dst): duplicate_name_collisions.append(dst) continue shutil.copy2(fpath, dst) copied_files += 1 report_files_copied[split_name] = copied_files report_expected_files[split_name] = expected_files missing_ids[split_name] = missing return report_files_copied, report_expected_files, missing_ids, duplicate_name_collisions def save_missing_reports(missing_ids: dict[str, list[str]], out_dir: str): os.makedirs(out_dir, exist_ok=True) for split_name, ids in missing_ids.items(): out_csv = os.path.join(out_dir, f"missing_{split_name}_pdb_ids.csv") # quoting=1 => csv.QUOTE_ALL (prevents 12e8-like auto parsing in Excel/Sheets) pd.DataFrame({"missing_pdb_id": pd.Series(ids, dtype="string")}).to_csv( out_csv, index=False, quoting=1 ) print("saved:", out_csv) def main(): ap = argparse.ArgumentParser( description="Separate PDB files into train/validation/test using split CSVs (PDB-level split)." ) ap.add_argument( "--splits_dir", required=True, help="Directory containing train.csv, validation.csv, test.csv", ) ap.add_argument( "--src_dirs", required=True, nargs="+", help="One or more directories containing .pdb files (e.g., processed_pdb_models_..., processed_NoLongerMissing)", ) ap.add_argument( "--out_root", required=True, help="Output root directory. Creates PDB/{train,validation,test} inside.", ) ap.add_argument( "--pdb_col", default="PDB_ID", help="Column name in split CSVs that contains PDB IDs (default: PDB_ID).", ) args = ap.parse_args() # Output dirs out_root = os.path.join(args.out_root, "PDB") out_dirs = { "train": os.path.join(out_root, "train"), "validation": os.path.join(out_root, "validation"), "test": os.path.join(out_root, "test"), } os.makedirs(out_root, exist_ok=True) for d in out_dirs.values(): os.makedirs(d, exist_ok=True) # Read split PDB IDs split_to_pdbids = { "train": read_split_pdb_ids(os.path.join(args.splits_dir, "train.csv"), pdb_col=args.pdb_col), "validation": read_split_pdb_ids(os.path.join(args.splits_dir, "validation.csv"), pdb_col=args.pdb_col), "test": read_split_pdb_ids(os.path.join(args.splits_dir, "test.csv"), pdb_col=args.pdb_col), } # Build index once pdb_to_files = build_pdb_index(args.src_dirs) # Copy by split report_files_copied, report_expected_files, missing_ids, duplicate_name_collisions = copy_by_split( split_to_pdbids, pdb_to_files, out_dirs ) print("\n=== Copy summary (number of .pdb files) ===") for split_name in ["train", "validation", "test"]: print( f"{split_name}: copied={report_files_copied.get(split_name, 0)} " f"(expected={report_expected_files.get(split_name, 0)}), " f"missing_PDB_IDs={len(missing_ids.get(split_name, []))}" ) print("\n=== Output folder counts (actual *.pdb files on disk) ===") for split_name, out_dir in out_dirs.items(): n_disk = len(glob.glob(os.path.join(out_dir, "*.pdb"))) print(f"{split_name}: {n_disk}") print("\n=== Missing PDB_IDs (no .pdb found in either source folder) ===") for split_name, ids in missing_ids.items(): print(f"{split_name}: {len(ids)}") if ids: print(" examples:", ids[:20]) if duplicate_name_collisions: print("\n[Note] Some destination filenames already existed (possible duplicates across source dirs).") print("Examples:", duplicate_name_collisions[:20]) print("Count:", len(duplicate_name_collisions)) # Save missing lists missing_dir = os.path.join(args.out_root, "PDB_missing_reports") save_missing_reports(missing_ids, missing_dir) if __name__ == "__main__": main()