feat(toolkit): add classification and migration

Implement the standard/non-standard/not-macrolactone classification layer
and integrate it into analyzer, fragmenter, and CLI outputs.

Port the remaining legacy package capabilities into new visualization and
workflow modules, restore batch/statistics/SDF scripts on top of the flat
CSV workflow, and update active docs to the new package API.

scripts/analyze_fragments.py

@@ -1,10 +1,74 @@
 from __future__ import annotations
 
-import sys
+import argparse
+from pathlib import Path
 
-from macro_lactone_toolkit.cli import main
+import matplotlib
+
+matplotlib.use("Agg")
+
+import matplotlib.pyplot as plt
+import pandas as pd
+
+
+def build_parser() -> argparse.ArgumentParser:
+    parser = argparse.ArgumentParser(description="Analyze flat fragment CSV output and generate reports.")
+    parser.add_argument("--input", required=True)
+    parser.add_argument("--output-dir", required=True)
+    return parser
+
+
+def main(argv: list[str] | None = None) -> None:
+    args = build_parser().parse_args(argv)
+    dataframe = pd.read_csv(args.input)
+    output_dir = Path(args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    position_stats = (
+        dataframe.groupby("cleavage_position")
+        .agg(
+            total_count=("fragment_id", "size"),
+            unique_fragments=("fragment_smiles_plain", "nunique"),
+            mean_atom_count=("atom_count", "mean"),
+            mean_molecular_weight=("molecular_weight", "mean"),
+        )
+        .reset_index()
+        .sort_values("cleavage_position")
+    )
+    position_stats.to_csv(output_dir / "position_statistics.csv", index=False)
+
+    property_summary = pd.DataFrame(
+        [
+            {
+                "unique_parents": dataframe["parent_id"].nunique(),
+                "total_fragments": len(dataframe),
+                "unique_fragments": dataframe["fragment_smiles_plain"].nunique(),
+                "mean_atom_count": dataframe["atom_count"].mean(),
+                "mean_molecular_weight": dataframe["molecular_weight"].mean(),
+            }
+        ]
+    )
+    property_summary.to_csv(output_dir / "fragment_property_summary.csv", index=False)
+
+    figure, axis = plt.subplots(figsize=(10, 6))
+    axis.bar(position_stats["cleavage_position"], position_stats["total_count"], color="steelblue")
+    axis.set_xlabel("Cleavage Position")
+    axis.set_ylabel("Fragment Count")
+    axis.set_title("Fragment Frequency by Cleavage Position")
+    axis.grid(axis="y", alpha=0.3)
+    figure.tight_layout()
+    figure.savefig(output_dir / "position_frequencies.png", dpi=300, bbox_inches="tight")
+    plt.close(figure)
+
+    summary_lines = [
+        f"Input file: {args.input}",
+        f"Rows: {len(dataframe)}",
+        f"Unique parent molecules: {dataframe['parent_id'].nunique()}",
+        f"Unique fragments: {dataframe['fragment_smiles_plain'].nunique()}",
+        f"Most frequent cleavage position: {int(position_stats.sort_values('total_count', ascending=False).iloc[0]['cleavage_position'])}",
+    ]
+    (output_dir / "analysis_summary.txt").write_text("\n".join(summary_lines) + "\n", encoding="utf-8")
 
 
 if __name__ == "__main__":
-    sys.argv = ["macro-lactone-toolkit", "analyze", *sys.argv[1:]]
     main()