feat: Add post-optimization tools and mandatory best design archiving
New Tools (tools/): - analyze_study.py: Generate comprehensive optimization reports - find_best_iteration.py: Find best iteration folder, optionally copy it - archive_best_design.py: Archive best design to 3_results/best_design_archive/<timestamp>/ Protocol Updates: - OP_02_RUN_OPTIMIZATION.md v1.1: Add mandatory archive_best_design step in Post-Run Actions. This MUST be done after every optimization run. V14 Updates: - run_optimization.py: Auto-archive best design at end of optimization - optimization_config.json: Expand bounds for V14 continuation - lateral_outer_angle: min 13->11 deg (was at 4.7%) - lateral_inner_pivot: min 7->5 mm (was at 8.1%) - lateral_middle_pivot: max 23->27 mm (was at 99.4%) - whiffle_min: max 60->72 mm (was at 96.3%) Usage: python tools/analyze_study.py m1_mirror_adaptive_V14 python tools/find_best_iteration.py m1_mirror_adaptive_V14 python tools/archive_best_design.py m1_mirror_adaptive_V14 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Antoine
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tools/find_best_iteration.py
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tools/find_best_iteration.py
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#!/usr/bin/env python
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"""
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Atomizer Best Iteration Finder
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Finds the iteration folder containing the best design for a study.
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Useful for extracting the best model files after optimization.
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Usage:
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python tools/find_best_iteration.py <study_name>
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python tools/find_best_iteration.py m1_mirror_adaptive_V14
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python tools/find_best_iteration.py m1_mirror_adaptive_V14 --copy-to ./best_design
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Author: Atomizer
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Created: 2025-12-12
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"""
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import argparse
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import json
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import shutil
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import sqlite3
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from pathlib import Path
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import sys
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def find_study_path(study_name: str) -> Path:
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"""Find study directory by name."""
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studies_dir = Path(__file__).parent.parent / "studies"
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study_path = studies_dir / study_name
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if not study_path.exists():
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raise FileNotFoundError(f"Study not found: {study_path}")
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return study_path
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def get_best_trial(study_path: Path) -> dict:
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"""Get the best trial from the database."""
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db_path = study_path / "3_results" / "study.db"
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if not db_path.exists():
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raise FileNotFoundError(f"Database not found: {db_path}")
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conn = sqlite3.connect(str(db_path))
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cursor = conn.cursor()
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# Get trial with best weighted_sum (or lowest objective 0 value)
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cursor.execute("""
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SELECT t.number, tua_ws.value_json, tua_src.value_json
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FROM trials t
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LEFT JOIN trial_user_attributes tua_ws ON t.trial_id = tua_ws.trial_id AND tua_ws.key = 'weighted_sum'
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LEFT JOIN trial_user_attributes tua_src ON t.trial_id = tua_src.trial_id AND tua_src.key = 'source'
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WHERE t.state = 'COMPLETE'
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AND CAST(tua_ws.value_json AS REAL) < 1000
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ORDER BY CAST(tua_ws.value_json AS REAL) ASC
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LIMIT 1
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""")
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result = cursor.fetchone()
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if not result:
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# Fallback to trial_values
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cursor.execute("""
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SELECT t.number, tv.value
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FROM trials t
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JOIN trial_values tv ON t.trial_id = tv.trial_id AND tv.objective = 0
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WHERE t.state = 'COMPLETE'
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AND tv.value < 1000
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ORDER BY tv.value ASC
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LIMIT 1
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""")
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result = cursor.fetchone()
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if result:
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conn.close()
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return {
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"number": result[0],
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"weighted_sum": result[1],
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"source": "unknown"
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}
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raise ValueError("No valid trials found")
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conn.close()
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return {
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"number": result[0],
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"weighted_sum": float(result[1]) if result[1] else None,
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"source": json.loads(result[2]) if result[2] else "unknown"
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}
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def trial_to_iteration(study_path: Path, trial_number: int) -> int:
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"""Convert trial number to iteration number."""
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db_path = study_path / "3_results" / "study.db"
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conn = sqlite3.connect(str(db_path))
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cursor = conn.cursor()
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# Get all FEA trial numbers in order
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cursor.execute("""
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SELECT t.number
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FROM trials t
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JOIN trial_user_attributes tua ON t.trial_id = tua.trial_id
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WHERE t.state = 'COMPLETE' AND tua.key = 'source' AND tua.value_json = '"FEA"'
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ORDER BY t.number
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""")
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fea_trials = [r[0] for r in cursor.fetchall()]
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conn.close()
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if trial_number not in fea_trials:
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raise ValueError(f"Trial {trial_number} is not an FEA trial (seeded data has no iteration folder)")
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return fea_trials.index(trial_number) + 1
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def find_best_iteration(study_name: str) -> dict:
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"""Find the best iteration folder for a study."""
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study_path = find_study_path(study_name)
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best_trial = get_best_trial(study_path)
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result = {
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"study_name": study_name,
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"trial_number": best_trial["number"],
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"weighted_sum": best_trial["weighted_sum"],
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"source": best_trial["source"],
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"iteration_folder": None,
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"iteration_path": None
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}
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# Only FEA trials have iteration folders
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if best_trial["source"] == "FEA":
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iter_num = trial_to_iteration(study_path, best_trial["number"])
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iter_folder = f"iter{iter_num}"
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iter_path = study_path / "2_iterations" / iter_folder
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if iter_path.exists():
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result["iteration_folder"] = iter_folder
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result["iteration_path"] = str(iter_path)
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return result
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def copy_best_iteration(study_name: str, dest_path: str):
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"""Copy the best iteration folder to a destination."""
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result = find_best_iteration(study_name)
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if not result["iteration_path"]:
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raise ValueError(f"Best trial ({result['trial_number']}) is from seeded data, no iteration folder")
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src = Path(result["iteration_path"])
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dst = Path(dest_path)
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if dst.exists():
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print(f"Removing existing destination: {dst}")
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shutil.rmtree(dst)
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print(f"Copying {src} to {dst}")
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shutil.copytree(src, dst)
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return result
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def main():
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parser = argparse.ArgumentParser(description="Find best iteration folder for Atomizer study")
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parser.add_argument("study_name", help="Name of the study")
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parser.add_argument("--copy-to", "-c", help="Copy best iteration folder to this path")
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parser.add_argument("--json", "-j", action="store_true", help="Output as JSON")
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args = parser.parse_args()
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try:
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if args.copy_to:
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result = copy_best_iteration(args.study_name, args.copy_to)
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print(f"\nCopied best iteration to: {args.copy_to}")
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else:
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result = find_best_iteration(args.study_name)
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if args.json:
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print(json.dumps(result, indent=2))
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else:
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print(f"\n{'='*60}")
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print(f"BEST ITERATION FOR: {result['study_name']}")
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print(f"{'='*60}")
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print(f" Trial Number: {result['trial_number']}")
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print(f" Weighted Sum: {result['weighted_sum']:.2f}" if result['weighted_sum'] else " Weighted Sum: N/A")
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print(f" Source: {result['source']}")
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print(f" Iteration Folder: {result['iteration_folder'] or 'N/A (seeded data)'}")
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if result['iteration_path']:
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print(f" Full Path: {result['iteration_path']}")
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print(f"{'='*60}")
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except Exception as e:
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print(f"Error: {e}")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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