Atomizer/tools/analysis/analyze_v11.py

"""Analyze V11 optimization results."""
import sqlite3
from pathlib import Path
import json
import sys
sys.path.insert(0, '.')

db_path = Path('studies/M1_Mirror/m1_mirror_cost_reduction_V11/3_results/study.db')

conn = sqlite3.connect(db_path)
c = conn.cursor()

# Get all completed trials with their objectives
c.execute('''
    SELECT t.trial_id
    FROM trials t
    WHERE t.state = 'COMPLETE'
    ORDER BY t.trial_id
''')
completed_ids = [row[0] for row in c.fetchall()]
print(f'Completed trials: {len(completed_ids)}')

# Build trial data
trials = []
for tid in completed_ids:
    c.execute('''
        SELECT key, value_json
        FROM trial_user_attributes
        WHERE trial_id = ?
    ''', (tid,))
    attrs = {row[0]: json.loads(row[1]) for row in c.fetchall()}

    if 'rel_filtered_rms_40_vs_20' in attrs:
        trials.append({
            'id': tid,
            'rms_40': attrs.get('rel_filtered_rms_40_vs_20', 999),
            'rms_60': attrs.get('rel_filtered_rms_60_vs_20', 999),
            'mfg_90': attrs.get('mfg_90_optician_workload', 999),
            'ws': attrs.get('weighted_sum', 999),
            'lateral': attrs.get('lateral_rms_um', 0),
        })

# Sort by weighted sum or calculate it
for t in trials:
    if t['ws'] == 999:
        # Calculate weighted sum
        w40 = 100 / 4.0    # Target 4 nm
        w60 = 50 / 10.0    # Target 10 nm
        w90 = 20 / 20.0    # Target 20 nm
        t['ws'] = w40 * t['rms_40'] + w60 * t['rms_60'] + w90 * t['mfg_90']

# Find best
trials.sort(key=lambda x: x['ws'])
best = trials[0] if trials else None

print('\nV11 Optimization Summary')
print('=' * 70)
print(f'Completed trials: {len(trials)}')

if trials:
    rms40 = [t['rms_40'] for t in trials]
    rms60 = [t['rms_60'] for t in trials]
    mfg90 = [t['mfg_90'] for t in trials]

    print(f'\n40-20 RMS range: {min(rms40):.2f} - {max(rms40):.2f} nm (target: 4.0 nm)')
    print(f'60-20 RMS range: {min(rms60):.2f} - {max(rms60):.2f} nm (target: 10.0 nm)')
    print(f'90 MFG range:    {min(mfg90):.2f} - {max(mfg90):.2f} nm (target: 20.0 nm)')

    print(f'\nBest Trial (#{best["id"]}):')
    print(f'  40-20 RMS: {best["rms_40"]:.2f} nm')
    print(f'  60-20 RMS: {best["rms_60"]:.2f} nm')
    print(f'  MFG 90:    {best["mfg_90"]:.2f} nm')
    print(f'  Lateral:   {best["lateral"]:.3f} um')
    print(f'  WS:        {best["ws"]:.1f}')

    # Check if values make sense (not too good to be true)
    print('\nSanity Check:')
    if best['rms_40'] < 3.0:
        print('  WARNING: 40-20 RMS suspiciously low!')
    else:
        print(f'  40-20 RMS {best["rms_40"]:.2f} nm - looks reasonable (expected ~6-7nm)')

    if best['rms_60'] < 8.0:
        print('  WARNING: 60-20 RMS suspiciously low!')
    else:
        print(f'  60-20 RMS {best["rms_60"]:.2f} nm - looks reasonable (expected ~13-15nm)')

    # Compare to V7 baseline and V10 buggy values
    print('\n' + '=' * 70)
    print('Comparison to Known Values:')
    print('=' * 70)
    print('\nV7 Baseline (original Zernike, correct):')
    print('  40-20 RMS: 6.05 nm')
    print('  60-20 RMS: 13.03 nm')
    print('  MFG 90:    26.34 nm')

    print('\nV10 BUGGY (abs(RMS_target - RMS_ref) - WRONG):')
    print('  40-20 RMS: ~1.99 nm  <- WAY TOO LOW')
    print('  60-20 RMS: ~6.82 nm  <- WAY TOO LOW')

    print('\nV11 Best (extract_relative() - CORRECT):')
    print(f'  40-20 RMS: {best["rms_40"]:.2f} nm')
    print(f'  60-20 RMS: {best["rms_60"]:.2f} nm')
    print(f'  MFG 90:    {best["mfg_90"]:.2f} nm')

    # Verdict
    print('\n' + '=' * 70)
    print('VERDICT:')
    print('=' * 70)
    if best['rms_40'] > 5.0 and best['rms_40'] < 10.0:
        print('  40-20: CORRECT - matches expected V7 range')
    else:
        print(f'  40-20: INVESTIGATE - {best["rms_40"]:.2f} nm is outside expected range')

    if best['rms_60'] > 10.0 and best['rms_60'] < 20.0:
        print('  60-20: CORRECT - matches expected V7 range')
    else:
        print(f'  60-20: INVESTIGATE - {best["rms_60"]:.2f} nm is outside expected range')

conn.close()

# Now generate Zernike dashboard for visual verification
print('\n' + '=' * 70)
print('Generating Zernike Dashboard for Best Iteration...')
print('=' * 70)

from optimization_engine.insights import ZernikeDashboardInsight, InsightConfig

study_path = Path('studies/M1_Mirror/m1_mirror_cost_reduction_V11')

# Find the iteration with the best design
best_archive = study_path / '3_results' / 'best_design_archive'
if best_archive.exists():
    op2_files = list(best_archive.glob('*.op2'))
    if op2_files:
        print(f'Found best design archive: {op2_files[0].name}')

insight = ZernikeDashboardInsight(study_path)
if insight.can_generate():
    print('Generating dashboard...')
    result = insight.generate(InsightConfig())
    if result.success:
        print(f'\nDashboard generated: {result.html_path}')
        print(f'\nDashboard Summary:')
        for key, value in result.summary.items():
            print(f'  {key}: {value}')
    else:
        print(f'Error: {result.error}')
else:
    print('Cannot generate insight - no OP2 files found')