Atomizer/docs/SESSION_SUMMARY_PHASE_2_9.md

Session Summary: Phase 2.9 - Post-Processing Hook Generator

Date: 2025-01-16 Phases Completed: Phase 2.9 ✅ Duration: Continued from Phase 2.8 session

What We Built Today

Phase 2.9: Post-Processing Hook Generator ✅

Files Created:

• optimization_engine/hook_generator.py - 760+ lines
• docs/SESSION_SUMMARY_PHASE_2_9.md - This document

Key Achievement: ✅ Auto-generates standalone Python hook scripts for post-processing operations ✅ Handles weighted objectives, custom formulas, constraint checks, and comparisons ✅ Complete I/O handling with JSON inputs/outputs ✅ Fully executable middleware scripts ready for optimization loops

Supported Hook Types:

1. Weighted Objective: Combine multiple metrics with custom weights
2. Custom Formula: Apply arbitrary formulas to inputs
3. Constraint Check: Validate constraints and calculate violations
4. Comparison: Calculate ratios, differences, percentage changes

Example Input → Output:

# LLM Phase 2.7 Output:
{
    "action": "weighted_objective",
    "description": "Combine normalized stress (70%) and displacement (30%)",
    "params": {
        "inputs": ["norm_stress", "norm_disp"],
        "weights": [0.7, 0.3],
        "objective": "minimize"
    }
}

# Phase 2.9 Generated Hook Script:
"""
Weighted Objective Function Hook
Auto-generated by Atomizer Phase 2.9

Combine normalized stress (70%) and displacement (30%)

Inputs: norm_stress, norm_disp
Weights: 0.7, 0.3
Formula: 0.7 * norm_stress + 0.3 * norm_disp
Objective: minimize
"""

import sys
import json
from pathlib import Path


def weighted_objective(norm_stress, norm_disp):
    """Calculate weighted objective from multiple inputs."""
    result = 0.7 * norm_stress + 0.3 * norm_disp
    return result


def main():
    """Main entry point for hook execution."""
    # Read inputs from JSON file
    input_file = Path(sys.argv[1])
    with open(input_file, 'r') as f:
        inputs = json.load(f)

    norm_stress = inputs.get("norm_stress")
    norm_disp = inputs.get("norm_disp")

    # Calculate weighted objective
    result = weighted_objective(norm_stress, norm_disp)

    # Write output
    output_file = input_file.parent / "weighted_objective_result.json"
    with open(output_file, 'w') as f:
        json.dump({
            "weighted_objective": result,
            "objective_type": "minimize",
            "inputs_used": {"norm_stress": norm_stress, "norm_disp": norm_disp},
            "formula": "0.7 * norm_stress + 0.3 * norm_disp"
        }, f, indent=2)

    print(f"Weighted objective calculated: {result:.6f}")
    return result


if __name__ == '__main__':
    main()

Test Results

Phase 2.9 Hook Generator:

Test Hook Generation:

1. Combine normalized stress (70%) and displacement (30%)
   Script: hook_weighted_objective_norm_stress_norm_disp.py
   Type: weighted_objective
   Inputs: norm_stress, norm_disp
   Outputs: weighted_objective
   ✅ PASS

2. Calculate safety factor
   Script: hook_custom_safety_factor.py
   Type: custom_formula
   Inputs: max_stress, yield_strength
   Outputs: safety_factor
   ✅ PASS

3. Compare min force to average
   Script: hook_compare_min_to_avg_ratio.py
   Type: comparison
   Inputs: min_force, avg_force
   Outputs: min_to_avg_ratio
   ✅ PASS

4. Check if stress is below yield
   Script: hook_constraint_yield_constraint.py
   Type: constraint_check
   Inputs: max_stress, yield_strength
   Outputs: yield_constraint, yield_constraint_satisfied, yield_constraint_violation
   ✅ PASS

Executable Test (Weighted Objective):

Input JSON:
{
  "norm_stress": 0.75,
  "norm_disp": 0.64
}

Execution:
$ python hook_weighted_objective_norm_stress_norm_disp.py test_input.json
Weighted objective calculated: 0.717000
Result saved to: weighted_objective_result.json

Output JSON:
{
  "weighted_objective": 0.717,
  "objective_type": "minimize",
  "inputs_used": {
    "norm_stress": 0.75,
    "norm_disp": 0.64
  },
  "formula": "0.7 * norm_stress + 0.3 * norm_disp"
}

Verification: 0.7 * 0.75 + 0.3 * 0.64 = 0.525 + 0.192 = 0.717 ✅

Architecture Evolution

Before Phase 2.9:

LLM detects: "weighted combination of stress and displacement"
    ↓
Manual hook script writing required ❌
    ↓
Write Python, handle I/O, test
    ↓
Integrate with optimization loop

After Phase 2.9:

LLM detects: "weighted combination of stress and displacement"
    ↓
Phase 2.9 Hook Generator ✅
    ↓
Complete Python script with I/O handling
    ↓
Ready to execute immediately!

Integration with Existing Phases

Phase 2.7 (LLM Analyzer) → Phase 2.9 (Hook Generator)

# Phase 2.7 Output:
analysis = {
    "post_processing_hooks": [
        {
            "action": "weighted_objective",
            "description": "Combine stress (70%) and displacement (30%)",
            "params": {
                "inputs": ["norm_stress", "norm_disp"],
                "weights": [0.7, 0.3],
                "objective": "minimize"
            }
        }
    ]
}

# Phase 2.9 Processing:
from optimization_engine.hook_generator import HookGenerator

generator = HookGenerator()
hooks = generator.generate_batch(analysis['post_processing_hooks'])

# Save hooks to optimization study
for hook in hooks:
    script_path = generator.save_hook_to_file(hook, "studies/my_study/hooks/")

# Result: Executable hook scripts ready for optimization loop!

Key Design Decisions

1. Standalone Executable Scripts

Each hook is a complete, self-contained Python script:

• No dependencies on Atomizer core
• Can be executed independently for testing
• Easy to debug and validate

2. JSON-Based I/O

All inputs and outputs use JSON:

• Easy to serialize/deserialize
• Compatible with any language/tool
• Human-readable for debugging

3. Error Handling

Generated hooks validate all inputs:

norm_stress = inputs.get("norm_stress")
if norm_stress is None:
    print(f"Error: Required input 'norm_stress' not found")
    sys.exit(1)

4. Hook Registry

Automatically generates a registry documenting all hooks:

{
  "hooks": [
    {
      "name": "hook_weighted_objective_norm_stress_norm_disp.py",
      "type": "weighted_objective",
      "description": "Combine normalized stress (70%) and displacement (30%)",
      "inputs": ["norm_stress", "norm_disp"],
      "outputs": ["weighted_objective"]
    }
  ]
}

Hook Types in Detail

1. Weighted Objective Hooks

Purpose: Combine multiple objectives with custom weights

Example Use Case: "I want to minimize a combination of 70% stress and 30% displacement"

Generated Code Features:

• Dynamic weight application
• Multiple input handling
• Objective type tracking (minimize/maximize)

2. Custom Formula Hooks

Purpose: Apply arbitrary mathematical formulas

Example Use Case: "Calculate safety factor as yield_strength / max_stress"

Generated Code Features:

• Custom formula evaluation
• Variable name inference
• Output naming based on formula

3. Constraint Check Hooks

Purpose: Validate engineering constraints

Example Use Case: "Ensure stress is below yield strength"

Generated Code Features:

• Boolean satisfaction flag
• Violation magnitude calculation
• Threshold comparison

4. Comparison Hooks

Purpose: Calculate ratios, differences, percentages

Example Use Case: "Compare minimum force to average force"

Generated Code Features:

• Multiple comparison operations (ratio, difference, percent)
• Automatic operation detection
• Clean output naming

Files Modified/Created

New Files:

• optimization_engine/hook_generator.py (760+ lines)
• docs/SESSION_SUMMARY_PHASE_2_9.md
• generated_hooks/ directory with 4 test hooks + registry

Generated Test Hooks:

• hook_weighted_objective_norm_stress_norm_disp.py
• hook_custom_safety_factor.py
• hook_compare_min_to_avg_ratio.py
• hook_constraint_yield_constraint.py
• hook_registry.json

Success Metrics

Phase 2.9 Success Criteria:

• ✅ Auto-generates functional hook scripts
• ✅ Correct I/O handling with JSON
• ✅ Integrates seamlessly with Phase 2.7 output
• ✅ Generates executable, standalone scripts
• ✅ Multiple hook types supported

Code Quality:

• ✅ Clean, readable generated code
• ✅ Proper error handling
• ✅ Complete documentation in docstrings
• ✅ Self-contained (no external dependencies)

Real-World Example: CBAR Optimization

User Request:

"Extract element forces in Z direction from CBAR elements, calculate average, find minimum, then create an objective that minimizes the ratio of min to average. Use genetic algorithm to optimize CBAR stiffness in X direction."

Phase 2.7 LLM Analysis:

{
  "engineering_features": [
    {
      "action": "extract_1d_element_forces",
      "domain": "result_extraction",
      "params": {"element_types": ["CBAR"], "direction": "Z"}
    },
    {
      "action": "update_cbar_stiffness",
      "domain": "fea_properties",
      "params": {"property": "stiffness_x"}
    }
  ],
  "inline_calculations": [
    {"action": "calculate_average", "params": {"input": "forces_z"}},
    {"action": "find_minimum", "params": {"input": "forces_z"}}
  ],
  "post_processing_hooks": [
    {
      "action": "comparison",
      "description": "Calculate min/avg ratio",
      "params": {
        "inputs": ["min_force", "avg_force"],
        "operation": "ratio",
        "output_name": "min_to_avg_ratio"
      }
    }
  ]
}

Phase 2.8 Generated Code (Inline):

# Calculate average of extracted forces
avg_forces_z = sum(forces_z) / len(forces_z)

# Find minimum force value
min_forces_z = min(forces_z)

Phase 2.9 Generated Hook Script:

# hook_compare_min_to_avg_ratio.py
def compare_ratio(min_force, avg_force):
    """Compare values using ratio."""
    result = min_force / avg_force
    return result

# (Full I/O handling, error checking, JSON serialization included)

Complete Workflow:

1. Extract CBAR forces from OP2 → forces_z = [10.5, 12.3, 8.9, 11.2, 9.8]
2. Phase 2.8 inline: Calculate avg and min → avg = 10.54, min = 8.9
3. Phase 2.9 hook: Calculate ratio → min_to_avg_ratio = 0.844
4. Optimization uses ratio as objective to minimize

All code auto-generated! No manual scripting required!

Integration with Optimization Loop

Typical Workflow:

Optimization Trial N
    ↓
1. Update FEA parameters (NX journal)
    ↓
2. Run FEA solve (NX Nastran)
    ↓
3. Extract results (OP2 reader)
    ↓
4. **Phase 2.8: Inline calculations**
   avg_stress = sum(stresses) / len(stresses)
   norm_stress = avg_stress / 200.0
    ↓
5. **Phase 2.9: Post-processing hook**
   python hook_weighted_objective.py trial_N_results.json
   → weighted_objective = 0.717
    ↓
6. Report objective to Optuna
    ↓
7. Optuna suggests next trial parameters
    ↓
Repeat

Next Steps

Immediate (Next Session):

1. ⏳ Phase 3: pyNastran Documentation Integration

   • Use WebFetch to access pyNastran docs
   • Build automated research for OP2 extraction
   • Create pattern library for result extraction operations

2. ⏳ Phase 3.5: NXOpen Pattern Library

   • Implement journal learning system
   • Extract patterns from recorded NX journals
   • Store in knowledge base for reuse

Short Term:

1. Integrate Phase 2.8 + 2.9 with optimization runner
2. Test end-to-end workflow with real FEA cases
3. Build knowledge base for common FEA operations
4. Implement Python introspection for NXOpen

Medium Term (Phase 4-6):

1. Code generation for complex FEA features (Phase 4)
2. Analysis & decision support (Phase 5)
3. Automated reporting (Phase 6)

Conclusion

Phase 2.9 delivers on the promise of zero manual scripting for post-processing operations:

1. ✅ LLM understands the request (Phase 2.7)
2. ✅ Identifies post-processing needs (Phase 2.7)
3. ✅ Auto-generates complete hook scripts (Phase 2.9)
4. ✅ Ready to execute in optimization loop

Combined with Phase 2.8:

• Inline calculations: Auto-generated ✅
• Post-processing hooks: Auto-generated ✅
• Custom objectives: Auto-generated ✅
• Constraints: Auto-generated ✅

The system now writes middleware code autonomously!

🚀 Phases 2.8-2.9 Complete: Full code generation for simple operations and custom workflows!

Environment

• Python Environment: test_env (c:/Users/antoi/anaconda3/envs/test_env)
• Testing: All Phase 2.9 tests passing ✅
• Generated Hooks: 4 hook scripts + registry
• Execution Test: Weighted objective hook verified working (0.7 * 0.75 + 0.3 * 0.64 = 0.717) ✅