重构项目结构并更新README.md

1. 重构目录结构：
   - 创建src/visualization模块用于存放可视化相关功能
   - 移动script/visualize_csv_comparison.py到src/visualization/comparison.py
   - 创建src/visualization/__init__.py导出主要函数
   - 整理script目录，按功能分类存放脚本文件

2. 更新README.md：
   - 添加CSV文件比较可视化部分
   - 提供Python API和命令行使用方法说明
   - 描述功能特点和使用示例

3. 更新模块引用：
   - 修正comparison.py中的模块引用路径
   - 更新命令行帮助信息中的使用示例

script/data_processing/add_macrocycle_columns.py

@@ -0,0 +1,127 @@
+#!/usr/bin/env python3
+"""Add generated IDs and macrolactone ring size annotations to a CSV file."""
+
+import argparse
+import pathlib
+import warnings
+from dataclasses import dataclass, field
+from typing import Dict, Iterable, List, Optional, Set, Tuple
+
+import pandas as pd
+from rdkit import Chem
+
+
+@dataclass
+class MacrolactoneRingDetector:
+    """Detect macrolactone rings in SMILES strings via SMARTS patterns."""
+
+    min_size: int = 12
+    max_size: int = 20
+    patterns: Dict[int, Optional[Chem.Mol]] = field(init=False, default_factory=dict)
+
+    def __post_init__(self) -> None:
+        self.patterns = {
+            size: Chem.MolFromSmarts(f"[r{size}]([#8][#6](=[#8]))")
+            for size in range(self.min_size, self.max_size + 1)
+        }
+
+    def ring_sizes(self, smiles: str) -> List[int]:
+        """Return a sorted list of macrolactone ring sizes present in the SMILES."""
+        if not smiles:
+            return []
+
+        mol = Chem.MolFromSmiles(smiles)
+        if mol is None:
+            return []
+
+        ring_atoms = mol.GetRingInfo().AtomRings()
+        if not ring_atoms:
+            return []
+
+        matched_rings: Set[Tuple[int, ...]] = set()
+        matched_sizes: Set[int] = set()
+
+        for size, query in self.patterns.items():
+            if query is None:
+                continue
+
+            for match in mol.GetSubstructMatches(query, uniquify=True):
+                ring = self._pick_ring(match, ring_atoms, size)
+                if ring and ring not in matched_rings:
+                    matched_rings.add(ring)
+                    matched_sizes.add(size)
+
+        sizes = sorted(matched_sizes)
+
+        if len(sizes) > 1:
+            warnings.warn(
+                "Multiple macrolactone ring sizes detected",
+                RuntimeWarning,
+                stacklevel=2,
+            )
+            print(f"Multiple ring sizes {sizes} for SMILES: {smiles}")
+
+        return sizes
+
+    @staticmethod
+    def _pick_ring(
+        match: Tuple[int, ...], rings: Iterable[Tuple[int, ...]], expected_size: int
+    ) -> Optional[Tuple[int, ...]]:
+        ring_atom = match[0]
+        for ring in rings:
+            if len(ring) == expected_size and ring_atom in ring:
+                return tuple(sorted(ring))
+        return None
+
+
+def add_columns(df: pd.DataFrame, detector: MacrolactoneRingDetector) -> pd.DataFrame:
+    result = df.copy()
+    result["generated_id"] = [f"D{index:06d}" for index in range(1, len(result) + 1)]
+
+    smiles_series = result["smiles"].fillna("") if "smiles" in result.columns else pd.Series(
+        [""] * len(result), index=result.index
+    )
+
+    def format_sizes(smiles: str) -> str:
+        sizes = detector.ring_sizes(smiles)
+        return ";".join(str(size) for size in sizes) if sizes else ""
+
+    result["macrocycle_ring_sizes"] = smiles_series.apply(format_sizes)
+    return result
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("input_csv", type=pathlib.Path, help="Path to the source CSV file")
+    parser.add_argument(
+        "--output",
+        type=pathlib.Path,
+        default=None,
+        help="Destination for the augmented CSV (default: <input>_with_macrocycles.csv)",
+    )
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_args()
+
+    if not args.input_csv.exists():
+        raise FileNotFoundError(f"Input file not found: {args.input_csv}")
+
+    output_path = args.output or args.input_csv.with_name(
+        f"{args.input_csv.stem}_with_macrocycles.csv"
+    )
+
+    if args.input_csv.resolve() == output_path.resolve():
+        raise ValueError("Output path must differ from input path to avoid overwriting.")
+
+    df = pd.read_csv(args.input_csv)
+    detector = MacrolactoneRingDetector()
+    augmented = add_columns(df, detector)
+    augmented.to_csv(output_path, index=False)
+
+    print(f"Wrote augmented data with {len(augmented)} rows to {output_path}")
+
+
+if __name__ == "__main__":
+    main()