# How to Create a New Study

This guide shows you how to set up a new optimization study using Atomizer's standardized directory structure.

---

## Quick Start

### 1. Copy the Study Template

```bash
cp -r templates/study_template studies/your_study_name
cd studies/your_study_name
```

### 2. Add Your CAD/FEM Model

Place your reference model files in `1_setup/model/`:

```
1_setup/model/
├── YourPart.prt          # NX CAD model
├── YourPart_sim1.sim     # NX simulation file
└── [baseline results]    # Optional: baseline FEA results
```

### 3. Run Benchmarking

Validate the baseline model before optimization:

```bash
cd ../..  # Back to Atomizer root
python optimization_engine/benchmarking.py \
    --prt "studies/your_study_name/1_setup/model/YourPart.prt" \
    --sim "studies/your_study_name/1_setup/model/YourPart_sim1.sim" \
    --output "studies/your_study_name/1_setup/benchmarking"
```

This will:
- Extract all NX expressions
- Run baseline FEA
- Extract all results (displacement, stress, etc.)
- Save benchmark data

### 4. Create Configuration File

Copy and modify the beam optimization config as a starting point:

```bash
cp studies/simple_beam_optimization/beam_optimization_config.json \
   studies/your_study_name/your_config.json
```

Edit `your_config.json`:

```json
{
  "study_name": "your_study_name",
  "description": "Describe what you're optimizing",
  "substudy_name": "01_initial_exploration",

  "design_variables": {
    "your_param_1": {
      "type": "continuous",
      "min": 10.0,
      "max": 50.0,
      "baseline": 30.0,
      "units": "mm"
    },
    "your_param_2": {
      "type": "integer",
      "min": 5,
      "max": 15,
      "baseline": 10,
      "units": "unitless"
    }
  },

  "extractors": [
    {
      "name": "max_displacement",
      "action": "extract_displacement",
      "parameters": {"metric": "max"}
    }
  ],

  "objectives": [
    {
      "name": "minimize_displacement",
      "extractor": "max_displacement",
      "goal": "minimize",
      "weight": 1.0
    }
  ],

  "optimization_settings": {
    "n_trials": 10,
    "sampler": "TPE"
  },

  "post_processing": {
    "generate_plots": true,
    "plot_formats": ["png", "pdf"],
    "cleanup_models": false,
    "keep_top_n_models": 10
  }
}
```

### 5. Create Runner Script

Create `run_optimization.py` in the study directory:

```python
"""
Runner script for your_study_name optimization.
"""
from pathlib import Path
import sys

# Add optimization_engine to path
sys.path.insert(0, str(Path(__file__).parent.parent.parent))

from optimization_engine.runner import OptimizationRunner

if __name__ == '__main__':
    study_dir = Path(__file__).parent

    # Paths
    config_file = study_dir / "your_config.json"
    prt_file = study_dir / "1_setup" / "model" / "YourPart.prt"
    sim_file = study_dir / "1_setup" / "model" / "YourPart_sim1.sim"
    output_dir = study_dir / "2_substudies" / "01_initial_exploration"

    # Run optimization
    runner = OptimizationRunner(
        config_file=config_file,
        prt_file=prt_file,
        sim_file=sim_file,
        output_dir=output_dir
    )

    study = runner.run()

    print("\nOptimization complete!")
    print(f"Results saved to: {output_dir}")
```

### 6. Update Study Metadata

Edit `study_metadata.json`:

```json
{
  "study_name": "your_study_name",
  "description": "Brief description",
  "created": "2025-11-17T19:00:00",
  "status": "active",
  "design_variables": ["your_param_1", "your_param_2"],
  "objectives": ["minimize_displacement"],
  "constraints": [],
  "substudies": [],
  "organization_version": "2.0"
}
```

### 7. Run First Substudy

```bash
python studies/your_study_name/run_optimization.py
```

This will:
- Create `2_substudies/01_initial_exploration/`
- Run N trials (as specified in config)
- Generate plots (if enabled)
- Save results

### 8. Document Your Substudy

Create `2_substudies/01_initial_exploration/README.md` using the template:

```bash
cp templates/substudy_README_template.md \
   studies/your_study_name/2_substudies/01_initial_exploration/README.md
```

Fill in:
- Purpose
- Configuration
- Expected outcome
- Actual results (after run completes)

### 9. Update Study Metadata

After the substudy completes, add it to `study_metadata.json`:

```json
{
  "substudies": [
    {
      "name": "01_initial_exploration",
      "created": "2025-11-17T19:00:00",
      "status": "completed",
      "trials": 10,
      "purpose": "Initial design space exploration",
      "notes": "Completed successfully"
    }
  ]
}
```

---

## Substudy Workflow

### Creating a New Substudy

When you want to run a new optimization (e.g., with different settings):

**1. Choose a Number**
- Next in sequence (02, 03, 04, etc.)

**2. Choose a Name**
- Descriptive of what changes: `02_validation_5trials`, `03_refined_search_30trials`

**3. Update Configuration**
- Modify `your_config.json` with new settings
- Update `substudy_name` field

**4. Create Substudy README**
```bash
cp templates/substudy_README_template.md \
   studies/your_study_name/2_substudies/02_your_substudy/README.md
```

**5. Run Optimization**
```bash
python studies/your_study_name/run_optimization.py
```

**6. Document Results**
- Fill in README.md with actual results
- Update `study_metadata.json`

---

## Directory Structure Reference

```
studies/your_study_name/
│
├── 1_setup/                           # Pre-optimization
│   ├── model/                         # Reference CAD/FEM
│   │   ├── YourPart.prt
│   │   └── YourPart_sim1.sim
│   └── benchmarking/                  # Baseline validation
│       ├── benchmark_results.json
│       └── BENCHMARK_REPORT.md
│
├── 2_substudies/                      # Optimization runs
│   ├── 01_initial_exploration/
│   │   ├── README.md                  # Purpose, findings
│   │   ├── trial_000/
│   │   ├── trial_001/
│   │   ├── plots/                     # Auto-generated
│   │   ├── history.json
│   │   └── best_trial.json
│   ├── 02_validation_5trials/
│   └── 03_refined_search_30trials/
│
├── 3_reports/                         # Study-level analysis
│   ├── SUBSTUDY_COMPARISON.md
│   └── FINAL_RECOMMENDATIONS.md
│
├── README.md                          # Study overview
├── study_metadata.json                # Metadata & substudy registry
├── your_config.json                   # Main configuration
└── run_optimization.py                # Runner script
```

---

## Best Practices

### Naming Conventions

**Studies**: `lowercase_with_underscores`
- `simple_beam_optimization`
- `bracket_displacement_maximizing`
- `engine_mount_fatigue`

**Substudies**: `NN_descriptive_name_Ntrials`
- `01_initial_exploration`
- `02_validation_3trials`
- `03_full_optimization_50trials`
- `04_refined_search_promising_region`

### Substudy Numbering

- Start at 01, increment by 1
- Use two digits (01, 02, ..., 99)
- Chronological order = number order

### Documentation

**Always Create**:
- Study README.md (overview, current status)
- Substudy README.md (purpose, results)
- study_metadata.json (registry of substudies)

**Optional**:
- Detailed result analysis (OPTIMIZATION_RESULTS.md)
- Study-level comparisons (in 3_reports/)
- Lessons learned document

### Configuration Management

- Keep one main config file per study
- Modify `substudy_name` for each new substudy
- Document config changes in substudy README
- Consider version control for config changes

### Post-Processing

Enable in config for automatic plots and cleanup:

```json
"post_processing": {
  "generate_plots": true,
  "plot_formats": ["png", "pdf"],
  "cleanup_models": true,
  "keep_top_n_models": 10,
  "cleanup_dry_run": false
}
```

**Recommended**:
- `generate_plots: true` - Always generate plots
- `cleanup_models: false` initially, `true` after validation
- `keep_top_n_models: 10` for most studies
- Use `cleanup_dry_run: true` first to preview deletion

---

## Troubleshooting

### Model Files Not Updating

**Symptom**: Design variables don't change between trials

**Solutions**:
1. Check expression names match config exactly
2. Verify .exp export works: `NX_updater.get_all_expressions(use_exp_export=True)`
3. Check NX version compatibility

### Optimization Not Converging

**Symptom**: No improvement over many trials

**Solutions**:
1. Check objective scaling (are values similar magnitude?)
2. Verify design variable bounds are reasonable
3. Try different sampler (TPE → Random for wide exploration)
4. Increase trial count

### No Feasible Designs Found

**Symptom**: All trials violate constraints

**Solutions**:
1. Relax constraints
2. Expand design variable bounds
3. Adjust objective weights (prioritize meeting constraints)
4. Consider multi-stage optimization (feasibility first, then optimize)

### Plots Not Generating

**Symptom**: No plots/ directory created

**Solutions**:
1. Check matplotlib installation: `conda install matplotlib pandas "numpy<2"`
2. Verify `post_processing.generate_plots: true` in config
3. Check history.json exists (use generate_history_from_trials.py if needed)
4. Look for errors in post-processing output

---

## Examples

See existing studies for reference:
- [studies/simple_beam_optimization/](../studies/simple_beam_optimization/) - Full 4D optimization with substudies
- [templates/study_template/](study_template/) - Clean template to copy

---

## Summary

**Study Creation Checklist**:
- [ ] Copy study_template
- [ ] Add CAD/FEM model to 1_setup/model/
- [ ] Run benchmarking
- [ ] Create configuration file
- [ ] Create runner script
- [ ] Update study_metadata.json
- [ ] Run first substudy (01_initial_exploration)
- [ ] Create substudy README
- [ ] Document results in study README

**For Each New Substudy**:
- [ ] Choose number and name (02_, 03_, etc.)
- [ ] Update configuration
- [ ] Create substudy README (from template)
- [ ] Run optimization
- [ ] Fill in actual results in README
- [ ] Update study_metadata.json
- [ ] Review plots and best trial

**When Study Complete**:
- [ ] Create comparison report in 3_reports/
- [ ] Write final recommendations
- [ ] Update study README with final status
- [ ] Archive or cleanup if needed