feat(validation): archive key result assets

Keep key validation outputs and analysis tables tracked directly,
package analysis plot PNGs into a small tar.gz backup, and add
analysis scripts plus tests so the stored results remain
reproducible without flooding git with large image trees.

scripts/analyze_fragment_atom_counts.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python3
+"""
+分析 fragment_library.csv 中碎片的原子数分布。
+"""
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from rdkit import Chem
+import os
+import sys
+
+
+def count_atoms(smiles):
+    """计算 SMILES 中的原子数（不包括 dummy atom）"""
+    try:
+        mol = Chem.MolFromSmiles(smiles)
+        if mol is None:
+            return None
+        # 计算所有原子，但排除 dummy atom（原子序数为 0）
+        atom_count = sum(1 for atom in mol.GetAtoms() if atom.GetAtomicNum() != 0)
+        return atom_count
+    except:
+        return None
+
+
+def main():
+    # 读取 CSV 文件
+    csv_path = "validation_output/fragment_library.csv"
+    if not os.path.exists(csv_path):
+        print(f"文件不存在: {csv_path}")
+        sys.exit(1)
+
+    df = pd.read_csv(csv_path)
+    print(f"总碎片数: {len(df)}")
+
+    # 计算原子数
+    df["atom_count"] = df["fragment_smiles_plain"].apply(count_atoms)
+
+    # 检查是否有无效的 SMILES
+    invalid_count = df["atom_count"].isna().sum()
+    if invalid_count > 0:
+        print(f"无效 SMILES 数量: {invalid_count}")
+
+    # 过滤掉无效的 SMILES
+    df_valid = df.dropna(subset=["atom_count"])
+    print(f"有效碎片数: {len(df_valid)}")
+
+    # 统计描述
+    print("\n原子数分布统计:")
+    print(df_valid["atom_count"].describe())
+
+    # 绘制分布图
+    plt.figure(figsize=(10, 6))
+    sns.histplot(
+        df_valid["atom_count"],
+        bins=range(0, int(df_valid["atom_count"].max()) + 2),
+        kde=False,
+    )
+    plt.title("Fragment Atom Count Distribution")
+    plt.xlabel("Atom Count")
+    plt.ylabel("Frequency")
+    plt.grid(True, alpha=0.3)
+
+    # 保存图片
+    output_dir = "validation_output"
+    os.makedirs(output_dir, exist_ok=True)
+    plot_path = os.path.join(output_dir, "fragment_atom_count_distribution.png")
+    plt.savefig(plot_path, dpi=300, bbox_inches="tight")
+    print(f"\n分布图已保存至: {plot_path}")
+
+    # 显示图片（如果在交互式环境中）
+    try:
+        plt.show()
+    except:
+        pass
+
+    # 分析不同原子数的碎片数量
+    atom_count_stats = df_valid["atom_count"].value_counts().sort_index()
+    print("\n不同原子数的碎片数量:")
+    for atom_count, count in atom_count_stats.items():
+        print(f"  原子数 {atom_count}: {count} 个碎片")
+
+    # 计算累积百分比
+    total_fragments = len(df_valid)
+    cumulative = 0
+    print("\n累积分布:")
+    for atom_count, count in atom_count_stats.items():
+        cumulative += count
+        percentage = (cumulative / total_fragments) * 100
+        print(f"  原子数 <= {atom_count}: {cumulative} 个碎片 ({percentage:.2f}%)")
+
+    # 建议筛选标准
+    print("\n建议筛选标准:")
+    # 例如，过滤掉原子数小于 3 的碎片
+    min_atom_count = 3
+    filtered_count = len(df_valid[df_valid["atom_count"] >= min_atom_count])
+    filtered_percentage = (filtered_count / total_fragments) * 100
+    print(
+        f"  如果过滤掉原子数 < {min_atom_count} 的碎片，剩余 {filtered_count} 个碎片 ({filtered_percentage:.2f}%)"
+    )
+
+
+if __name__ == "__main__":
+    main()