Anto01
38abb0d8d2
Phase 3 implements automated OP2 extraction code generation using pyNastran documentation research. This completes the zero-manual-coding pipeline for FEA optimization workflows. Key Features: - PyNastranResearchAgent for automated OP2 code generation - Documentation research via WebFetch integration - 3 core extraction patterns (displacement, stress, force) - Knowledge base architecture for learned patterns - Successfully tested on real OP2 files Phase 2.9 Integration: - Updated HookGenerator with lifecycle hook generation - Added POST_CALCULATION hook point to hooks.py - Created post_calculation/ plugin directory - Generated hooks integrate seamlessly with HookManager New Files: - optimization_engine/pynastran_research_agent.py (600+ lines) - optimization_engine/hook_generator.py (800+ lines) - optimization_engine/inline_code_generator.py - optimization_engine/plugins/post_calculation/ - tests/test_lifecycle_hook_integration.py - docs/SESSION_SUMMARY_PHASE_3.md - docs/SESSION_SUMMARY_PHASE_2_9.md - docs/SESSION_SUMMARY_PHASE_2_8.md - docs/HOOK_ARCHITECTURE.md Modified Files: - README.md - Added Phase 3 completion status - optimization_engine/plugins/hooks.py - Added POST_CALCULATION hook Test Results: - Phase 3 research agent: PASSED - Real OP2 extraction: PASSED (max_disp=0.362mm) - Lifecycle hook integration: PASSED Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
"""
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Combine normalized stress (70%) and displacement (30%)
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Auto-generated lifecycle hook by Atomizer Phase 2.9
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Hook Point: post_calculation
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Inputs: norm_stress, norm_disp
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Outputs: weighted_objective
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"""
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import logging
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from typing import Dict, Any, Optional
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logger = logging.getLogger(__name__)
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def weighted_objective_hook(context: Dict[str, Any]) -> Optional[Dict[str, Any]]:
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"""
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Combine normalized stress (70%) and displacement (30%)
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Args:
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context: Hook context containing:
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- trial_number: Current optimization trial
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- results: Dictionary with extracted FEA results
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- calculations: Dictionary with inline calculation results
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Returns:
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Dictionary with calculated values to add to context
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"""
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logger.info(f"Executing weighted_objective_hook for trial {context.get('trial_number', 'unknown')}")
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# Extract inputs from context
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results = context.get('results', {})
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calculations = context.get('calculations', {})
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norm_stress = calculations.get('norm_stress') or results.get('norm_stress')
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if norm_stress is None:
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logger.error(f"Required input 'norm_stress' not found in context")
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raise ValueError(f"Missing required input: norm_stress")
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norm_disp = calculations.get('norm_disp') or results.get('norm_disp')
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if norm_disp is None:
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logger.error(f"Required input 'norm_disp' not found in context")
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raise ValueError(f"Missing required input: norm_disp")
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# Calculate weighted objective
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result = 0.7 * norm_stress + 0.3 * norm_disp
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logger.info(f"Weighted objective calculated: {result:.6f}")
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return {
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'weighted_objective': result,
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'weighted_objective': result
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}
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def register_hooks(hook_manager):
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"""
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Register this hook with the HookManager.
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This function is called automatically when the plugin is loaded.
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Args:
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hook_manager: The HookManager instance
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"""
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hook_manager.register_hook(
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hook_point='post_calculation',
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function=weighted_objective_hook,
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description="Combine normalized stress (70%) and displacement (30%)",
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name="weighted_objective_hook",
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priority=100,
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enabled=True
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)
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logger.info(f"Registered weighted_objective_hook at post_calculation")
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