Anto01
eabcc4c3ca
BREAKING CHANGE: Module paths have been reorganized for better maintainability. Backwards compatibility aliases with deprecation warnings are provided. New Structure: - core/ - Optimization runners (runner, intelligent_optimizer, etc.) - processors/ - Data processing - surrogates/ - Neural network surrogates - nx/ - NX/Nastran integration (solver, updater, session_manager) - study/ - Study management (creator, wizard, state, reset) - reporting/ - Reports and analysis (visualizer, report_generator) - config/ - Configuration management (manager, builder) - utils/ - Utilities (logger, auto_doc, etc.) - future/ - Research/experimental code Migration: - ~200 import changes across 125 files - All __init__.py files use lazy loading to avoid circular imports - Backwards compatibility layer supports old import paths with warnings - All existing functionality preserved To migrate existing code: OLD: from optimization_engine.nx_solver import NXSolver NEW: from optimization_engine.nx.solver import NXSolver OLD: from optimization_engine.runner import OptimizationRunner NEW: from optimization_engine.core.runner import OptimizationRunner 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
36 lines
1.2 KiB
Python
36 lines
1.2 KiB
Python
"""Test neural surrogate integration"""
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import sys
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from pathlib import Path
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# Add project paths
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project_root = Path(__file__).parent
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sys.path.insert(0, str(project_root))
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sys.path.insert(0, str(project_root / 'atomizer-field'))
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from optimization_engine.processors.surrogates.neural_surrogate import create_parametric_surrogate_for_study
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# Create surrogate
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print("Creating parametric surrogate...")
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surrogate = create_parametric_surrogate_for_study(project_root=project_root)
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if surrogate:
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print('Surrogate created successfully!')
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print(f'Design vars: {surrogate.design_var_names}')
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print(f'Number of nodes: {surrogate.num_nodes}')
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# Test prediction with example params
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test_params = {name: 2.0 for name in surrogate.design_var_names}
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print(f'\nTest params: {test_params}')
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results = surrogate.predict(test_params)
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print(f'\nTest prediction:')
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print(f' Mass: {results["mass"]:.2f}')
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print(f' Frequency: {results["frequency"]:.2f}')
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print(f' Max Displacement: {results["max_displacement"]:.6f}')
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print(f' Max Stress: {results["max_stress"]:.2f}')
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print(f' Inference time: {results["inference_time_ms"]:.2f} ms')
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print('\nSurrogate is ready for use in optimization!')
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else:
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print('Failed to create surrogate')
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