Anto01
b4c0831230
Critical bug fix for LLM mode optimization: **Problem**: - NXParameterUpdater.update_expressions() uses NX journal to import expressions (default use_nx_import=True) - The NX journal directly updates the PRT file on disk and saves it - But then run_optimization.py was calling updater.save() afterwards - save() writes self.content (loaded at initialization) back to file - This overwrote the NX journal changes with stale binary content! **Result**: All optimization trials produced identical FEM results because the model was never actually updated. **Fixes**: 1. Removed updater.save() call from model_updater closure in run_optimization.py 2. Added theSession.Parts.CloseAll() in import_expressions.py to ensure changes are flushed and file is released 3. Fixed test_phase_3_2_e2e.py variable name (best_trial_file → results_file) **Testing**: Verified expressions persist to disk correctly with standalone test. Next step: Address remaining issue where FEM results are still identical (likely solve journal not reloading updated PRT).
106 lines
3.1 KiB
Python
106 lines
3.1 KiB
Python
"""
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Setup Beam Optimization Study
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This script configures the optimization study based on benchmarking results:
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- Design variables: beam_half_core_thickness, beam_face_thickness, holes_diameter, hole_count
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- Objectives: Minimize stress AND minimize weight (p173 mass)
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- Constraint: max_displacement < 10mm
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"""
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import sys
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import json
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from optimization_engine.study_creator import StudyCreator
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def main():
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print()
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print("="*80)
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print("SIMPLE BEAM OPTIMIZATION - SETUP")
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print("="*80)
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print()
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# Load configuration
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config_file = Path('studies/simple_beam_optimization/beam_optimization_config.json')
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if not config_file.exists():
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print(f"[ERROR] Configuration file not found: {config_file}")
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return False
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with open(config_file, 'r') as f:
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config = json.load(f)
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print("Configuration loaded:")
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print(f" Study: {config['study_name']}")
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print(f" Substudy: {config['substudy_name']}")
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print()
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print("Design Variables:")
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for name, info in config['design_variables'].items():
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print(f" - {name}: [{info['min']}, {info['max']}] {info['units']} (baseline: {info['baseline']})")
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print()
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print("Objectives:")
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for obj in config['objectives']:
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print(f" - {obj['name']}: {obj['goal']} (weight: {obj['weight']})")
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print()
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print("Constraints:")
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for const in config['constraints']:
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print(f" - {const['name']}: {const['extractor']} {const['type']} {const['value']} {const['units']}")
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print()
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# Create substudy
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creator = StudyCreator()
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study_dir = Path('studies') / config['study_name']
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print(f"Creating substudy '{config['substudy_name']}'...")
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try:
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substudy_dir = creator.create_substudy(
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study_dir=study_dir,
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substudy_name=config['substudy_name'],
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config=config
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)
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print(f"[SUCCESS] Substudy created: {substudy_dir}")
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print()
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# Save configuration to substudy
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substudy_config_file = substudy_dir / "optimization_config.json"
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with open(substudy_config_file, 'w', encoding='utf-8') as f:
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json.dump(config, f, indent=2)
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print(f"[OK] Configuration saved to: {substudy_config_file}")
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print()
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print("="*80)
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print("NEXT STEPS")
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print("="*80)
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print()
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print("1. Review configuration:")
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print(f" {substudy_config_file}")
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print()
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print("2. Run initial trials to validate pipeline:")
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print(f" python tests/run_initial_trials.py")
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print()
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print("3. If validation passes, run full optimization:")
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print(f" python run_optimization.py studies/{config['study_name']} --substudy {config['substudy_name']}")
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print()
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return True
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except Exception as e:
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print(f"[ERROR] Failed to create substudy: {e}")
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import traceback
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traceback.print_exc()
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return False
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if __name__ == '__main__':
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success = main()
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sys.exit(0 if success else 1)
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