# 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](../optimization_engine/hook_generator.py) - 760+ lines
- [docs/SESSION_SUMMARY_PHASE_2_9.md](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:**
```python
# 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):**
```bash
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)**

```python
# 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:
```python
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:
```json
{
  "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:**
```json
{
  "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):**
```python
# 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:**
```python
# 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) ✅
