Anto01
2b3573ec42
Major additions: - Training data export system for AtomizerField neural network training - Bracket stiffness optimization study with 50+ training samples - Intelligent NX model discovery (auto-detect solutions, expressions, mesh) - Result extractors module for displacement, stress, frequency, mass - User-generated NX journals for advanced workflows - Archive structure for legacy scripts and test outputs - Protocol documentation and dashboard launcher 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
140 lines
3.7 KiB
Python
140 lines
3.7 KiB
Python
"""
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Test script for training data export functionality.
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Creates a simple beam optimization study with training data export enabled
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to verify end-to-end functionality of AtomizerField training data collection.
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"""
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import json
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import shutil
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from pathlib import Path
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# Configuration for test study with training data export
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test_config = {
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"study_name": "training_data_export_test",
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"sim_file": "examples/Models/Circular Plate/Circular_Plate.sim",
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"fem_file": "examples/Models/Circular Plate/Circular_Plate_fem1.fem",
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"design_variables": [
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{
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"name": "thickness",
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"expression_name": "thickness",
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"min": 2.0,
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"max": 8.0
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},
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{
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"name": "radius",
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"expression_name": "radius",
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"min": 80.0,
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"max": 120.0
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}
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],
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"objectives": [
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{
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"name": "max_stress",
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"type": "minimize",
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"extractor": {
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"type": "result_parameter",
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"parameter_name": "Max Von Mises Stress"
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}
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},
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{
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"name": "mass",
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"type": "minimize",
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"extractor": {
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"type": "expression",
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"expression_name": "mass"
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}
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}
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],
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"constraints": [
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{
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"name": "stress_limit",
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"type": "less_than",
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"value": 300.0,
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"extractor": {
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"type": "result_parameter",
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"parameter_name": "Max Von Mises Stress"
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}
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}
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],
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"optimization": {
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"algorithm": "NSGA-II",
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"n_trials": 10,
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"population_size": 4
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},
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# Enable training data export
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"training_data_export": {
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"enabled": True,
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"export_dir": "atomizer_field_training_data/test_study_001"
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},
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"version": "1.0"
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}
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def main():
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"""Run test study with training data export."""
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# Create study directory
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study_dir = Path("studies/training_data_export_test")
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study_dir.mkdir(parents=True, exist_ok=True)
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setup_dir = study_dir / "1_setup"
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setup_dir.mkdir(exist_ok=True)
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results_dir = study_dir / "2_results"
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results_dir.mkdir(exist_ok=True)
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# Save workflow config
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config_path = setup_dir / "workflow_config.json"
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with open(config_path, 'w') as f:
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json.dump(test_config, f, indent=2)
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print("=" * 80)
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print("TRAINING DATA EXPORT TEST STUDY")
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print("=" * 80)
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print(f"\nStudy created: {study_dir}")
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print(f"Config saved: {config_path}")
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print(f"\nTraining data will be exported to:")
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print(f" {test_config['training_data_export']['export_dir']}")
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print(f"\nNumber of trials: {test_config['optimization']['n_trials']}")
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print("\n" + "=" * 80)
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print("To run the test:")
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print(f" cd {study_dir}")
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print(" python run_optimization.py")
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print("=" * 80)
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# Create run_optimization.py in study directory
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run_script = study_dir / "run_optimization.py"
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run_script_content = '''"""Run optimization for training data export test."""
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import sys
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from pathlib import Path
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# Add parent directory to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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from optimization_engine.runner import OptimizationRunner
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def main():
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"""Run the optimization."""
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config_path = Path(__file__).parent / "1_setup" / "workflow_config.json"
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runner = OptimizationRunner(config_path)
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runner.run()
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if __name__ == "__main__":
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main()
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'''
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with open(run_script, 'w') as f:
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f.write(run_script_content)
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print(f"\nRun script created: {run_script}")
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print("\nTest study setup complete!")
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if __name__ == "__main__":
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main()
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