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data/util/prepare_mosaec_partial_structure.py

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+#!/usr/bin/env python3
+from __future__ import annotations
+
+import argparse
+import shutil
+from pathlib import Path
+
+import pandas as pd
+
+DATASET_DIRS = ("charged", "neutral")
+
+COLUMN_MAP = {
+    "density_g/cm3": "density_g_cm3",
+    "lcd_ang_H2": "lcd_ang_H2",
+    "pld_ang_H2": "pld_ang_H2",
+    "asa_m2/cm3_H2": "asa_m2_cm3_H2",
+    "asa_m2/g_H2": "asa_m2_g_H2",
+    "void_fraction_H2": "void_fraction_H2",
+    "av_ang3_H2": "av_ang3_H2",
+    "av_cm3/g_H2": "av_cm3_g_H2",
+    "void_fraction_probe-occupiable_H2": "void_fraction_probe-occupiable_H2",
+    "av_probe-occupiable_ang3_H2": "av_probe-occupiable_ang3_H2",
+    "av_probe-occupiable_cm3/g_H2": "av_probe-occupiable_cm3_g_H2",
+}
+
+TARGET_COLUMNS = [
+    "cif",
+    "density_g_cm3",
+    "lcd_ang_H2",
+    "pld_ang_H2",
+    "asa_m2_cm3_H2",
+    "asa_m2_g_H2",
+    "void_fraction_H2",
+    "av_ang3_H2",
+    "av_cm3_g_H2",
+    "void_fraction_probe-occupiable_H2",
+    "av_probe-occupiable_ang3_H2",
+    "av_probe-occupiable_cm3_g_H2",
+]
+
+
+def normalize_cif(series: pd.Series) -> pd.Series:
+    cif = series.astype(str).str.strip()
+    return cif.str.replace(r"\\.cif$", "", regex=True)
+
+
+def load_properties_table(csv_path: Path) -> tuple[pd.DataFrame, set[str]]:
+    required = ["cif", *COLUMN_MAP.keys()]
+    df = pd.read_csv(csv_path)
+
+    missing = [c for c in required if c not in df.columns]
+    if missing:
+        raise ValueError(f"Missing required columns in CSV: {', '.join(missing)}")
+
+    df["cif"] = normalize_cif(df["cif"])
+
+    duplicated = df["cif"].duplicated(keep=False)
+    if duplicated.any():
+        dup_count = int(duplicated.sum())
+        print(
+            f"[WARN] Found {dup_count} duplicated cif rows in CSV. "
+            "Keeping the first occurrence for each cif."
+        )
+
+    props = df[["cif", *COLUMN_MAP.keys()]].copy()
+    props = props.rename(columns=COLUMN_MAP)
+    props = props.drop_duplicates(subset=["cif"], keep="first")
+    props = props[TARGET_COLUMNS]
+
+    return props, set(props["cif"])
+
+
+def process_subset(subset_dir: Path, valid_cif: set[str], props: pd.DataFrame) -> dict[str, int]:
+    raw_dir = subset_dir / "raw"
+    raw_dir.mkdir(exist_ok=True)
+
+    moved = 0
+    deleted = 0
+
+    # Move/delete CIFs from subset root
+    for cif_path in subset_dir.glob("*.cif"):
+        if cif_path.stem in valid_cif:
+            destination = raw_dir / cif_path.name
+            if destination.exists():
+                cif_path.unlink()
+            else:
+                shutil.move(str(cif_path), str(destination))
+                moved += 1
+        else:
+            cif_path.unlink()
+            deleted += 1
+
+    # Clean invalid CIFs inside raw/
+    for cif_path in raw_dir.glob("*.cif"):
+        if cif_path.stem not in valid_cif:
+            cif_path.unlink()
+            deleted += 1
+
+    raw_cif = sorted(p.stem for p in raw_dir.glob("*.cif") if p.stem in valid_cif)
+
+    props_indexed = props.set_index("cif")
+    available = [c for c in raw_cif if c in props_indexed.index]
+    missing_in_csv = [c for c in raw_cif if c not in props_indexed.index]
+
+    if missing_in_csv:
+        print(
+            f"[WARN] {subset_dir.name}: {len(missing_in_csv)} files in raw/ "
+            "not found in CSV; skipped in id_prop.csv"
+        )
+
+    id_prop = props_indexed.loc[available].reset_index()
+    id_prop_path = subset_dir / "id_prop.csv"
+    id_prop.to_csv(id_prop_path, index=False)
+
+    return {
+        "moved": moved,
+        "deleted": deleted,
+        "raw_count": len(raw_cif),
+        "id_prop_rows": len(id_prop),
+    }
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Prepare MOSAEC-DB full/charged and full/neutral datasets: "
+            "move valid .cif files into raw/ and create id_prop.csv"
+        )
+    )
+    parser.add_argument(
+        "--csv",
+        default="mosaec-db.csv",
+        help="Path to source CSV (default: mosaec-db.csv)",
+    )
+    parser.add_argument(
+        "--root",
+        default=".",
+        help="Root directory containing charged and neutral folders (default: current directory)",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+    root = Path(args.root).resolve()
+    csv_path = (root / args.csv).resolve() if not Path(args.csv).is_absolute() else Path(args.csv)
+
+    if not csv_path.exists():
+        raise FileNotFoundError(f"CSV file not found: {csv_path}")
+
+    props, valid_cif = load_properties_table(csv_path)
+
+    print(f"Loaded {len(valid_cif)} unique cif values from: {csv_path}")
+    print()
+
+    for subset_name in DATASET_DIRS:
+        subset_dir = root / subset_name
+        if not subset_dir.exists() or not subset_dir.is_dir():
+            print(f"[WARN] Skip {subset_name}: folder not found at {subset_dir}")
+            continue
+
+        stats = process_subset(subset_dir, valid_cif, props)
+        print(
+            f"{subset_name}: moved={stats['moved']}, deleted={stats['deleted']}, "
+            f"raw_files={stats['raw_count']}, id_prop_rows={stats['id_prop_rows']}"
+        )
+
+
+if __name__ == "__main__":
+    main()