feat: Complete Phase 3 - pyNastran Documentation Integration

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>

optimization_engine/plugins/hooks.py

@@ -20,6 +20,7 @@ class HookPoint(Enum):
     PRE_SOLVE = "pre_solve"         # Before solver execution
     POST_SOLVE = "post_solve"       # After solve, before extraction
     POST_EXTRACTION = "post_extraction"  # After result extraction
+    POST_CALCULATION = "post_calculation"  # After inline calculations (Phase 2.8), before reporting
     CUSTOM_OBJECTIVE = "custom_objective"  # Custom objective functions
 
 

optimization_engine/plugins/post_calculation/min_to_avg_ratio_hook.py

@@ -0,0 +1,72 @@
+"""
+Compare min force to average
+Auto-generated lifecycle hook by Atomizer Phase 2.9
+
+Hook Point: post_calculation
+Inputs: min_force, avg_force
+Outputs: min_to_avg_ratio
+"""
+
+import logging
+from typing import Dict, Any, Optional
+
+logger = logging.getLogger(__name__)
+
+
+def ratio_hook(context: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+    """
+    Compare min force to average
+
+    Args:
+        context: Hook context containing:
+            - trial_number: Current optimization trial
+            - results: Dictionary with extracted FEA results
+            - calculations: Dictionary with inline calculation results
+
+    Returns:
+        Dictionary with calculated values to add to context
+    """
+    logger.info(f"Executing ratio_hook for trial {context.get('trial_number', 'unknown')}")
+
+    # Extract inputs from context
+    results = context.get('results', {})
+    calculations = context.get('calculations', {})
+
+    min_force = calculations.get('min_force') or results.get('min_force')
+    if min_force is None:
+        logger.error(f"Required input 'min_force' not found in context")
+        raise ValueError(f"Missing required input: min_force")
+
+    avg_force = calculations.get('avg_force') or results.get('avg_force')
+    if avg_force is None:
+        logger.error(f"Required input 'avg_force' not found in context")
+        raise ValueError(f"Missing required input: avg_force")
+
+    # Calculate comparison
+    result = min_force / avg_force
+
+    logger.info(f"min_to_avg_ratio = {result:.6f}")
+
+    return {
+        'min_to_avg_ratio': result
+    }
+
+
+def register_hooks(hook_manager):
+    """
+    Register this hook with the HookManager.
+
+    This function is called automatically when the plugin is loaded.
+
+    Args:
+        hook_manager: The HookManager instance
+    """
+    hook_manager.register_hook(
+        hook_point='post_calculation',
+        function=ratio_hook,
+        description="Compare min force to average",
+        name="ratio_hook",
+        priority=100,
+        enabled=True
+    )
+    logger.info(f"Registered ratio_hook at post_calculation")

optimization_engine/plugins/post_calculation/safety_factor_hook.py

@@ -0,0 +1,72 @@
+"""
+Calculate safety factor
+Auto-generated lifecycle hook by Atomizer Phase 2.9
+
+Hook Point: post_calculation
+Inputs: max_stress, yield_strength
+Outputs: safety_factor
+"""
+
+import logging
+from typing import Dict, Any, Optional
+
+logger = logging.getLogger(__name__)
+
+
+def safety_factor_hook(context: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+    """
+    Calculate safety factor
+
+    Args:
+        context: Hook context containing:
+            - trial_number: Current optimization trial
+            - results: Dictionary with extracted FEA results
+            - calculations: Dictionary with inline calculation results
+
+    Returns:
+        Dictionary with calculated values to add to context
+    """
+    logger.info(f"Executing safety_factor_hook for trial {context.get('trial_number', 'unknown')}")
+
+    # Extract inputs from context
+    results = context.get('results', {})
+    calculations = context.get('calculations', {})
+
+    max_stress = calculations.get('max_stress') or results.get('max_stress')
+    if max_stress is None:
+        logger.error(f"Required input 'max_stress' not found in context")
+        raise ValueError(f"Missing required input: max_stress")
+
+    yield_strength = calculations.get('yield_strength') or results.get('yield_strength')
+    if yield_strength is None:
+        logger.error(f"Required input 'yield_strength' not found in context")
+        raise ValueError(f"Missing required input: yield_strength")
+
+    # Calculate using custom formula
+    safety_factor = yield_strength / max_stress
+
+    logger.info(f"safety_factor = {safety_factor:.6f}")
+
+    return {
+        'safety_factor': safety_factor
+    }
+
+
+def register_hooks(hook_manager):
+    """
+    Register this hook with the HookManager.
+
+    This function is called automatically when the plugin is loaded.
+
+    Args:
+        hook_manager: The HookManager instance
+    """
+    hook_manager.register_hook(
+        hook_point='post_calculation',
+        function=safety_factor_hook,
+        description="Calculate safety factor",
+        name="safety_factor_hook",
+        priority=100,
+        enabled=True
+    )
+    logger.info(f"Registered safety_factor_hook at post_calculation")

optimization_engine/plugins/post_calculation/weighted_objective_test.py

@@ -0,0 +1,73 @@
+"""
+Combine normalized stress (70%) and displacement (30%)
+Auto-generated lifecycle hook by Atomizer Phase 2.9
+
+Hook Point: post_calculation
+Inputs: norm_stress, norm_disp
+Outputs: weighted_objective
+"""
+
+import logging
+from typing import Dict, Any, Optional
+
+logger = logging.getLogger(__name__)
+
+
+def weighted_objective_hook(context: Dict[str, Any]) -> Optional[Dict[str, Any]]:
+    """
+    Combine normalized stress (70%) and displacement (30%)
+
+    Args:
+        context: Hook context containing:
+            - trial_number: Current optimization trial
+            - results: Dictionary with extracted FEA results
+            - calculations: Dictionary with inline calculation results
+
+    Returns:
+        Dictionary with calculated values to add to context
+    """
+    logger.info(f"Executing weighted_objective_hook for trial {context.get('trial_number', 'unknown')}")
+
+    # Extract inputs from context
+    results = context.get('results', {})
+    calculations = context.get('calculations', {})
+
+    norm_stress = calculations.get('norm_stress') or results.get('norm_stress')
+    if norm_stress is None:
+        logger.error(f"Required input 'norm_stress' not found in context")
+        raise ValueError(f"Missing required input: norm_stress")
+
+    norm_disp = calculations.get('norm_disp') or results.get('norm_disp')
+    if norm_disp is None:
+        logger.error(f"Required input 'norm_disp' not found in context")
+        raise ValueError(f"Missing required input: norm_disp")
+
+    # Calculate weighted objective
+    result = 0.7 * norm_stress + 0.3 * norm_disp
+
+    logger.info(f"Weighted objective calculated: {result:.6f}")
+
+    return {
+        'weighted_objective': result,
+        'weighted_objective': result
+    }
+
+
+def register_hooks(hook_manager):
+    """
+    Register this hook with the HookManager.
+
+    This function is called automatically when the plugin is loaded.
+
+    Args:
+        hook_manager: The HookManager instance
+    """
+    hook_manager.register_hook(
+        hook_point='post_calculation',
+        function=weighted_objective_hook,
+        description="Combine normalized stress (70%) and displacement (30%)",
+        name="weighted_objective_hook",
+        priority=100,
+        enabled=True
+    )
+    logger.info(f"Registered weighted_objective_hook at post_calculation")