diff --git a/.claude/skills/analyze-model.md b/.claude/skills/analyze-model.md new file mode 100644 index 00000000..91887182 --- /dev/null +++ b/.claude/skills/analyze-model.md @@ -0,0 +1,269 @@ +# Analyze Model Skill + +**Last Updated**: November 25, 2025 +**Version**: 1.0 - Model Analysis and Feature Extraction + +You are helping the user understand their NX model's structure and identify optimization opportunities. + +## Purpose + +Extract and present information about an NX model to help the user: +1. Identify available parametric expressions (potential design variables) +2. Understand the simulation setup (analysis types, boundary conditions) +3. Discover material properties +4. Recommend optimization strategies based on model characteristics + +## Triggers + +- "analyze this model" +- "what can I optimize" +- "show me the expressions" +- "look at my NX model" +- "what parameters are available" + +## Prerequisites + +- User must provide path to NX model files (.prt, .sim, .fem) +- NX must be available on the system (configured in config.py) +- Model files must be valid NX format + +## Information Gathering + +Ask these questions if not already provided: + +1. **Model Location**: + - "Where is your NX model? (path to .prt file)" + - Default: Look in `studies/*/1_setup/model/` + +2. **Analysis Interest**: + - "What type of optimization are you considering?" (optional) + - This helps focus the analysis on relevant aspects + +## Execution Steps + +### Step 1: Validate Model Files + +Check that required files exist: + +```python +from pathlib import Path + +def validate_model_files(model_path: Path) -> dict: + """Validate NX model files exist.""" + prt_file = model_path + base_name = prt_file.stem + parent_dir = prt_file.parent + + result = { + 'prt_exists': prt_file.exists(), + 'sim_file': None, + 'fem_file': None, + 'errors': [] + } + + # Look for simulation file + sim_patterns = [ + f"{base_name}_sim1.sim", + f"{base_name}_sim.sim", + f"{base_name}.sim" + ] + for pattern in sim_patterns: + sim_path = parent_dir / pattern + if sim_path.exists(): + result['sim_file'] = sim_path + break + + # Look for FEM file + fem_patterns = [ + f"{base_name}_fem1.fem", + f"{base_name}_fem.fem", + f"{base_name}.fem" + ] + for pattern in fem_patterns: + fem_path = parent_dir / pattern + if fem_path.exists(): + result['fem_file'] = fem_path + break + + if not result['prt_exists']: + result['errors'].append(f"Part file not found: {prt_file}") + if not result['sim_file']: + result['errors'].append("No simulation file (.sim) found") + if not result['fem_file']: + result['errors'].append("No FEM file (.fem) found - will be created on first solve") + + return result +``` + +### Step 2: Extract Expressions + +Use NX Python API to extract all parametric expressions: + +```python +# This requires running a journal inside NX +# Use the expression extractor from optimization_engine + +from optimization_engine.extractors.expression_extractor import extract_all_expressions + +expressions = extract_all_expressions(prt_file) +# Returns: [{'name': 'thickness', 'value': 2.0, 'unit': 'mm', 'formula': None}, ...] +``` + +**Manual Extraction Method** (if NX API not available): +1. Read the .prt file header for expression metadata +2. Look for common parameter naming patterns +3. Ask user to provide expression names from NX + +### Step 3: Classify Expressions + +Categorize expressions by likely purpose: + +| Pattern | Category | Optimization Relevance | +|---------|----------|----------------------| +| `*_thickness`, `*_t`, `wall_*` | Structural | High - affects mass & stiffness | +| `*_diameter`, `*_d`, `hole_*` | Geometric | High - affects mass & stress | +| `*_radius`, `*_r`, `fillet_*` | Geometric | Medium - affects stress concentration | +| `*_count`, `n_*`, `num_*` | Discrete | Medium - affects mass & complexity | +| `*_length`, `*_l`, `span_*` | Dimensional | Context-dependent | +| `*_angle`, `*_deg` | Orientation | Low-Medium | +| `material_*`, `mat_*` | Material | Special handling required | + +### Step 4: Analyze Simulation Setup + +If .sim file exists, analyze: + +1. **Solution Types**: + - SOL 101 (Static) → Displacement, Stress + - SOL 103 (Modal) → Natural Frequencies + - SOL 105 (Buckling) → Buckling Load Factor + - SOL 106 (Nonlinear Static) → Large deformation + - SOL 112 (Transient) → Dynamic response + +2. **Load Cases**: Identify applied loads and boundary conditions + +3. **Material Properties**: Extract material definitions + +### Step 5: Generate Recommendations + +Based on analysis, recommend: + +1. **Design Variables**: Which expressions are good candidates +2. **Objectives**: What can be optimized +3. **Constraints**: What should be bounded +4. **Protocol**: Which optimization protocol fits best + +## Output Format + +Present analysis in structured format: + +``` +MODEL ANALYSIS REPORT +===================== + +Model: {model_name} +Location: {model_path} + +FILES FOUND +----------- +✓ Part file: {prt_file} +✓ Simulation: {sim_file} +✓ FEM mesh: {fem_file} + +PARAMETRIC EXPRESSIONS +---------------------- +| Name | Current Value | Unit | Category | Optimization Candidate | +|------|---------------|------|----------|----------------------| +| thickness | 2.0 | mm | Structural | ✓ High | +| hole_diameter | 10.0 | mm | Geometric | ✓ High | +| fillet_radius | 3.0 | mm | Geometric | ✓ Medium | +| length | 100.0 | mm | Dimensional | ? Check constraints | + +SIMULATION SETUP +---------------- +Analysis Types: Static (SOL 101), Modal (SOL 103) +Material: Aluminum 6061-T6 (E=68.9 GPa, ρ=2700 kg/m³) +Loads: + - Force: 500 N at tip + - Constraint: Fixed at root + +RECOMMENDATIONS +--------------- +Suggested Objectives: + - Minimize mass (extract from p173 expression or FEM) + - Maximize first natural frequency + +Suggested Constraints: + - Max von Mises stress < 276 MPa (Al 6061 yield) + - Max displacement < {user to specify} + +Recommended Protocol: Protocol 11 (Multi-Objective NSGA-II) + - Reason: Multiple competing objectives (mass vs frequency) + +Ready to create optimization study? Say "create study" to proceed. +``` + +## Error Handling + +| Error | Recovery | +|-------|----------| +| File not found | Ask user for correct path | +| Invalid NX format | Suggest checking NX version compatibility | +| No expressions found | Guide user to create expressions in NX | +| Simulation incomplete | List missing components | + +## Integration with Other Skills + +After analysis, can chain to: +- `/create-study` - Create optimization based on findings +- `/configure-surrogate` - Setup NN if large-scale optimization needed + +## Example Conversation + +``` +USER: Look at my NX model at studies/uav_arm_optimization/1_setup/model/Beam.prt + +CLAUDE: I'll analyze your UAV arm model. Let me examine its structure... + +[Runs analysis] + +MODEL ANALYSIS REPORT +===================== +Model: Beam +Location: studies/uav_arm_optimization/1_setup/model/ + +PARAMETRIC EXPRESSIONS +---------------------- +Found 4 controllable parameters: +| Name | Value | Bounds Suggestion | +|------|-------|------------------| +| beam_half_core_thickness | 15.0 mm | [5, 25] | +| beam_face_thickness | 2.0 mm | [1, 5] | +| holes_diameter | 30.0 mm | [10, 50] | +| hole_count | 8 | [4, 12] | + +SIMULATION SETUP +---------------- +✓ Static Analysis (SOL 101) - for stress/displacement +✓ Modal Analysis (SOL 103) - for natural frequencies +Material: Aluminum sandwich panel + +RECOMMENDATION +-------------- +This model is well-suited for multi-objective optimization: +- Objective 1: Minimize mass +- Objective 2: Maximize first natural frequency +- Constraint: Keep stress below yield strength + +Would you like me to create an optimization study with these settings? + +USER: Yes, but add a displacement constraint of 5mm max + +CLAUDE: [Invokes create-study skill with analyzed parameters + user constraint] +``` + +## Notes + +- Expression extraction requires NX to be running or using file parsing +- Some expressions may be formula-driven (not directly editable) +- Material properties may need separate handling +- Consider mesh quality when recommending optimization bounds diff --git a/.claude/skills/create-study.md b/.claude/skills/create-study.md index 9ac66b16..9940d9e6 100644 --- a/.claude/skills/create-study.md +++ b/.claude/skills/create-study.md @@ -1,7 +1,7 @@ # Create Optimization Study Skill -**Last Updated**: November 24, 2025 -**Version**: 1.0 - Complete Study Scaffolding +**Last Updated**: November 25, 2025 +**Version**: 1.1 - Complete Study Scaffolding with Validator Integration You are helping the user create a complete Atomizer optimization study from a natural language description. @@ -18,6 +18,8 @@ Guide the user through an interactive conversation to: A complete Atomizer study has this structure: +**CRITICAL**: All study files, including README.md and results, MUST be located within the study directory. NEVER create study documentation at the project root. + ``` studies/{study_name}/ ├── 1_setup/ @@ -33,7 +35,7 @@ studies/{study_name}/ │ └── [various analysis files] ├── run_optimization.py # YOU GENERATE THIS ├── reset_study.py # YOU GENERATE THIS -├── README.md # YOU GENERATE THIS +├── README.md # YOU GENERATE THIS (INSIDE study directory!) └── NX_FILE_MODIFICATIONS_REQUIRED.md # YOU GENERATE THIS (if needed) ``` @@ -275,6 +277,10 @@ except Exception as e: ### 5. README.md +**CRITICAL: ALWAYS place README.md INSIDE the study directory at `studies/{study_name}/README.md`** + +Never create study documentation at the project root. All study-specific documentation must be centralized within the study directory structure. + Comprehensive documentation including: - Engineering scenario and context - Problem statement with real-world constraints @@ -289,6 +295,7 @@ Comprehensive documentation including: - Comparison with other studies - Technical notes +**Location**: `studies/{study_name}/README.md` (NOT at project root) **Template**: Use [studies/drone_gimbal_arm_optimization/README.md](../studies/drone_gimbal_arm_optimization/README.md:1) as reference ### 6. NX_FILE_MODIFICATIONS_REQUIRED.md (if needed) @@ -441,6 +448,91 @@ Extractors: extract_solid_stress, extract_displacement Multi-Solution: No (static only) ``` +## Validation Integration + +After generating files, always validate the study setup using the validator system: + +### Config Validation + +```python +from optimization_engine.validators import validate_config_file + +result = validate_config_file("studies/{study_name}/1_setup/optimization_config.json") +if result.is_valid: + print("[OK] Configuration is valid!") +else: + for error in result.errors: + print(f"[ERROR] {error}") +``` + +### Model Validation + +```python +from optimization_engine.validators import validate_study_model + +result = validate_study_model("{study_name}") +if result.is_valid: + print(f"[OK] Model files valid!") + print(f" Part: {result.prt_file.name}") + print(f" Simulation: {result.sim_file.name}") +else: + for error in result.errors: + print(f"[ERROR] {error}") +``` + +### Complete Study Validation + +```python +from optimization_engine.validators import validate_study + +result = validate_study("{study_name}") +print(result) # Shows complete health check +``` + +### Validation Checklist for Generated Studies + +Before declaring a study complete, ensure: + +1. **Config Validation Passes**: + - All design variables have valid bounds (min < max) + - All objectives have proper extraction methods + - All constraints have thresholds defined + - Protocol matches objective count + +2. **Model Files Ready** (user must provide): + - Part file (.prt) exists in model directory + - Simulation file (.sim) exists + - FEM file (.fem) will be auto-generated + +3. **Run Script Works**: + - Test with `python run_optimization.py --trials 1` + - Verify imports resolve correctly + - Verify NX solver can be reached + +### Automated Pre-Flight Check + +Add this to run_optimization.py: + +```python +def preflight_check(): + """Validate study setup before running.""" + from optimization_engine.validators import validate_study + + result = validate_study(STUDY_NAME) + + if not result.is_ready_to_run: + print("[X] Study validation failed!") + print(result) + sys.exit(1) + + print("[OK] Pre-flight check passed!") + return True + +if __name__ == "__main__": + preflight_check() + # ... rest of optimization +``` + ## Critical Reminders ### Multi-Objective Return Format diff --git a/.claude/skills/generate-report.md b/.claude/skills/generate-report.md new file mode 100644 index 00000000..c4c43749 --- /dev/null +++ b/.claude/skills/generate-report.md @@ -0,0 +1,399 @@ +# Generate Report Skill + +**Last Updated**: November 25, 2025 +**Version**: 1.0 - Optimization Results Analysis and Reporting + +You are helping the user understand and communicate optimization results. + +## Purpose + +Generate comprehensive reports from completed optimization studies: +1. Pareto front analysis and visualization +2. Design space exploration insights +3. Constraint satisfaction analysis +4. Engineering recommendations +5. Export-ready summaries + +## Triggers + +- "generate report" +- "show results" +- "analyze the optimization" +- "what did we find?" +- "summarize the study" +- "export results" + +## Prerequisites + +- Completed optimization study with `study.db` +- At least 10 completed trials (for meaningful analysis) +- Optional: `optimization_config.json` for context + +## Report Types + +### 1. Quick Summary +Fast overview of key findings (default). + +### 2. Full Technical Report +Comprehensive analysis with all visualizations. + +### 3. Executive Summary +High-level findings for stakeholders. + +### 4. Export Package +Data files for external analysis. + +## Execution Steps + +### Step 1: Load Study Data + +```python +import optuna +import json +from pathlib import Path + +def load_study_data(study_name: str): + """Load all optimization data for analysis.""" + study_dir = Path(f"studies/{study_name}") + + # Load Optuna study + storage = f"sqlite:///{study_dir / '2_results' / 'study.db'}" + study = optuna.load_study(study_name=study_name, storage=storage) + + # Load config for context + config_path = study_dir / "1_setup" / "optimization_config.json" + config = json.loads(config_path.read_text()) if config_path.exists() else {} + + return { + 'study': study, + 'config': config, + 'n_trials': len(study.trials), + 'n_completed': len([t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE]), + 'is_multi_objective': len(study.directions) > 1 + } +``` + +### Step 2: Analyze Results + +#### For Multi-Objective (Pareto Analysis) + +```python +def analyze_pareto_front(study): + """Extract and analyze Pareto-optimal solutions.""" + pareto_trials = study.best_trials + + results = { + 'n_pareto': len(pareto_trials), + 'designs': [], + 'ranges': {}, + 'trade_offs': [] + } + + for trial in pareto_trials: + design = { + 'trial_number': trial.number, + 'objectives': trial.values, + 'parameters': trial.params, + 'user_attrs': trial.user_attrs + } + results['designs'].append(design) + + # Calculate objective ranges on Pareto front + if pareto_trials: + obj_names = ['mass', 'frequency'] # From config + for i, name in enumerate(obj_names): + values = [t.values[i] for t in pareto_trials] + results['ranges'][name] = { + 'min': min(values), + 'max': max(values), + 'spread': max(values) - min(values) + } + + return results +``` + +#### For Single-Objective + +```python +def analyze_single_objective(study): + """Analyze single-objective optimization results.""" + best = study.best_trial + + return { + 'best_value': best.value, + 'best_params': best.params, + 'convergence': get_convergence_history(study), + 'improvement_rate': calculate_improvement_rate(study) + } +``` + +### Step 3: Constraint Analysis + +```python +def analyze_constraints(study, config): + """Analyze constraint satisfaction across trials.""" + constraints = config.get('constraints', []) + + results = { + 'total_trials': len(study.trials), + 'feasible': 0, + 'infeasible': 0, + 'violations': {} + } + + for constraint in constraints: + results['violations'][constraint['name']] = 0 + + for trial in study.trials: + if trial.state != optuna.trial.TrialState.COMPLETE: + continue + + is_feasible = trial.user_attrs.get('feasible', True) + if is_feasible: + results['feasible'] += 1 + else: + results['infeasible'] += 1 + # Track which constraints were violated + for constraint in constraints: + if constraint['name'] in trial.user_attrs.get('violated_constraints', []): + results['violations'][constraint['name']] += 1 + + results['feasibility_rate'] = results['feasible'] / results['total_trials'] * 100 + + return results +``` + +### Step 4: Generate Visualizations + +Describe what plots to generate (actual generation done via Python scripts): + +1. **Pareto Front Plot**: Mass vs Frequency with feasibility coloring +2. **Parallel Coordinates**: All design variables with objective coloring +3. **Parameter Importance**: Which variables most affect objectives +4. **Convergence History**: Best value over trials +5. **Design Space Coverage**: Parameter distributions + +### Step 5: Generate Report Text + +## Output Format: Quick Summary + +``` +OPTIMIZATION RESULTS: {study_name} +=================================== + +Study Info: + Protocol: Protocol 11 (Multi-Objective NSGA-II) + Trials: 30 completed + Duration: 14m 23s + Feasibility Rate: 80% + +PARETO FRONT (8 optimal designs) +-------------------------------- + +| # | Mass (g) | Freq (Hz) | Feasible | Notes | +|---|----------|-----------|----------|-------| +| 1 | 231 | 118 | Yes | Lightest | +| 2 | 248 | 128 | Yes | | +| 3 | 265 | 138 | Yes | Balanced | +| 4 | 282 | 148 | Yes | | +| 5 | 298 | 156 | Yes | Stiffest | + +Trade-off Summary: + - Mass range: 231g - 298g (67g spread) + - Frequency range: 118Hz - 156Hz (38Hz spread) + - Trade-off rate: ~1.7g per Hz + +BEST DESIGNS BY OBJECTIVE +------------------------- + +Lightest Design (#1): + Mass: 231g, Frequency: 118Hz + Parameters: + beam_half_core_thickness: 8.2mm + beam_face_thickness: 1.1mm + holes_diameter: 42mm + hole_count: 10 + +Stiffest Design (#5): + Mass: 298g, Frequency: 156Hz + Parameters: + beam_half_core_thickness: 18.5mm + beam_face_thickness: 2.8mm + holes_diameter: 22mm + hole_count: 6 + +CONSTRAINT ANALYSIS +------------------- + +Constraints checked: + - max_displacement < 5mm: 24/30 satisfied (80%) + - max_stress < 200 MPa: 28/30 satisfied (93%) + +Most common violation: Displacement (6 trials) + Occurred when: holes_diameter > 40mm AND face_thickness < 1.5mm + +PARAMETER INSIGHTS +------------------ + +Most influential on mass: + 1. holes_diameter (negative correlation) + 2. beam_face_thickness (positive) + 3. beam_half_core_thickness (positive) + +Most influential on frequency: + 1. beam_half_core_thickness (positive) + 2. holes_diameter (negative) + 3. beam_face_thickness (moderate positive) + +RECOMMENDATIONS +--------------- + +1. For lightweight priority: + - Use larger holes (40-45mm) + - Minimize face thickness (1.0-1.5mm) + - Accept frequency around 120Hz + +2. For stiffness priority: + - Use smaller holes (20-25mm) + - Increase core thickness (18-20mm) + - Accept mass around 300g + +3. Balanced recommendation: + - Design #3 offers good compromise + - 265g mass with 138Hz frequency + - 13% lighter than stiffest, 17% stiffer than lightest + +NEXT STEPS +---------- + +1. Export selected designs: python export_pareto.py --designs 1,3,5 +2. Validate in NX: Open model with exported parameters +3. Continue optimization: python run_optimization.py --trials 50 --resume +4. Enable NN acceleration: Add --enable-nn flag for faster exploration +``` + +## Output Format: Full Technical Report + +Generate markdown file with: + +```markdown +# {study_name} Optimization Report + +Generated: {timestamp} +Protocol: {protocol} + +## Executive Summary +[2-3 paragraph overview] + +## Study Configuration +### Design Variables +[Table of variables, bounds, types] + +### Objectives +[Table of objectives, goals, targets] + +### Constraints +[Table of constraints, thresholds] + +## Results Analysis + +### Pareto Front +[Detailed table of all Pareto-optimal designs] + +![Pareto Front](pareto_front.png) + +### Design Space Exploration +[Parallel coordinates plot] + +![Parallel Coordinates](parallel_coords.png) + +### Convergence History +[Plot of best values over trials] + +### Parameter Importance +[SHAP or importance analysis] + +## Constraint Satisfaction +[Detailed constraint analysis] + +## Engineering Recommendations +[Specific design recommendations] + +## Appendix +### All Trial Data +[Exportable CSV reference] + +### Methodology Notes +[Protocol details, sampler settings] +``` + +## Export Package + +Generate files: +``` +{study_name}_export/ +├── pareto_designs.csv # All Pareto-optimal designs +├── all_trials.csv # Complete trial history +├── optimization_summary.json # Machine-readable summary +├── pareto_front.png # Visualization +├── parallel_coords.png # Visualization +└── README.md # Export documentation +``` + +## Error Handling + +| Error | Recovery | +|-------|----------| +| No completed trials | Suggest running more trials | +| No feasible designs | Analyze constraint violations | +| Study not found | List available studies | +| Incomplete data | Note missing fields | + +## Integration with Other Skills + +- After report → `/export-results` for specific designs +- If poor results → `/troubleshoot` for analysis +- To continue → `/run-optimization` with --resume + +## Example Conversation + +``` +USER: Show me the results for uav_arm_optimization + +CLAUDE: I'll generate a report for the UAV arm optimization study... + +[Loads study data] + +OPTIMIZATION RESULTS: uav_arm_optimization +========================================== + +Found 30 completed trials with 8 Pareto-optimal designs. + +PARETO FRONT SUMMARY +-------------------- +| Design | Mass (g) | Freq (Hz) | Trade-off | +|--------|----------|-----------|-----------| +| #1 | 231 | 118 | Lightest | +| #3 | 265 | 138 | Balanced | +| #5 | 298 | 156 | Stiffest | + +Key Finding: You can save 67g (23%) by accepting 38Hz (24%) +lower frequency. + +RECOMMENDATION: Design #3 (265g, 138Hz) offers the best balance +for a camera gimbal application where both vibration and weight +matter. + +Would you like me to: +1. Generate a full technical report with visualizations? +2. Export the top designs for validation in NX? +3. Continue optimization to explore more designs? +``` + +## Notes + +- Reports should be actionable, not just data dumps +- Always provide engineering context and recommendations +- Consider the user's stated goals when highlighting results +- Visualizations should be generated via Python scripts +- Export formats should be compatible with common tools (Excel, etc.) diff --git a/.claude/skills/run-optimization.md b/.claude/skills/run-optimization.md new file mode 100644 index 00000000..28d07629 --- /dev/null +++ b/.claude/skills/run-optimization.md @@ -0,0 +1,371 @@ +# Run Optimization Skill + +**Last Updated**: November 25, 2025 +**Version**: 1.0 - Optimization Execution and Monitoring + +You are helping the user run and monitor Atomizer optimization studies. + +## Purpose + +Execute optimization studies with proper: +1. Pre-flight validation +2. Resource management +3. Progress monitoring +4. Error recovery +5. Dashboard integration + +## Triggers + +- "run optimization" +- "start the study" +- "run {study_name}" +- "execute optimization" +- "begin the optimization" + +## Prerequisites + +- Study must exist in `studies/{study_name}/` +- `optimization_config.json` must be present and valid +- `run_optimization.py` must exist +- NX model files must be in place + +## Pre-Flight Checklist + +Before running, verify: + +### 1. Study Structure +``` +studies/{study_name}/ +├── 1_setup/ +│ ├── model/ +│ │ ├── {Model}.prt ✓ Required +│ │ ├── {Model}_sim1.sim ✓ Required +│ │ └── {Model}_fem1.fem ? Optional (created by NX) +│ ├── optimization_config.json ✓ Required +│ └── workflow_config.json ? Optional +├── 2_results/ ? Created automatically +└── run_optimization.py ✓ Required +``` + +### 2. Configuration Validation +```python +from optimization_engine.validators.config_validator import validate_config + +result = validate_config(study_dir / "1_setup" / "optimization_config.json") +if result.errors: + # STOP - fix errors first + for error in result.errors: + print(f"ERROR: {error}") +if result.warnings: + # WARN but can continue + for warning in result.warnings: + print(f"WARNING: {warning}") +``` + +### 3. NX Environment +- Verify NX is installed (check `config.py` for `NX_VERSION`) +- Verify Nastran solver is available +- Check for any running NX processes that might conflict + +## Execution Modes + +### Mode 1: Quick Test (3-5 trials) +```bash +cd studies/{study_name} +python run_optimization.py --trials 3 +``` +**Use when**: First time running, testing configuration + +### Mode 2: Standard Run +```bash +cd studies/{study_name} +python run_optimization.py --trials 30 +``` +**Use when**: Production optimization with FEA only + +### Mode 3: Extended with NN Surrogate +```bash +cd studies/{study_name} +python run_optimization.py --trials 200 --enable-nn +``` +**Use when**: Large-scale optimization with trained surrogate + +### Mode 4: Resume Interrupted +```bash +cd studies/{study_name} +python run_optimization.py --trials 30 --resume +``` +**Use when**: Optimization was interrupted + +## Execution Steps + +### Step 1: Validate Study + +Run pre-flight checks and present status: + +``` +PRE-FLIGHT CHECK: {study_name} +=============================== + +Configuration: + ✓ optimization_config.json valid + ✓ 4 design variables defined + ✓ 2 objectives (multi-objective) + ✓ 2 constraints configured + +Model Files: + ✓ Beam.prt exists (3.2 MB) + ✓ Beam_sim1.sim exists (1.1 MB) + ✓ Beam_fem1.fem exists (0.8 MB) + +Environment: + ✓ NX 2412 detected + ✓ Nastran solver available + ? No NX processes running (clean slate) + +Estimated Runtime: + - 30 trials × ~30s/trial = ~15 minutes + - With NN: 200 trials × ~2s/trial = ~7 minutes (after training) + +Ready to run. Proceed? (Y/n) +``` + +### Step 2: Start Optimization + +```python +import subprocess +import sys +from pathlib import Path + +def run_optimization(study_name: str, trials: int = 30, + enable_nn: bool = False, resume: bool = False): + """Start optimization as background process.""" + study_dir = Path(f"studies/{study_name}") + + cmd = [ + sys.executable, + str(study_dir / "run_optimization.py"), + "--trials", str(trials) + ] + + if enable_nn: + cmd.append("--enable-nn") + if resume: + cmd.append("--resume") + + # Run in background + process = subprocess.Popen( + cmd, + cwd=study_dir, + stdout=subprocess.PIPE, + stderr=subprocess.STDOUT + ) + + return process.pid +``` + +### Step 3: Monitor Progress + +Provide real-time updates: + +``` +OPTIMIZATION RUNNING: {study_name} +================================== + +Progress: [████████░░░░░░░░░░░░] 12/30 trials (40%) +Elapsed: 5m 32s +ETA: ~8 minutes + +Current Trial #12: + Parameters: thickness=2.3, diameter=15.2, ... + Status: Running FEA... + +Best So Far (Pareto Front): + #1: mass=245g, freq=125Hz (feasible) + #2: mass=280g, freq=142Hz (feasible) + #3: mass=310g, freq=158Hz (feasible) + +Constraint Violations: 3/12 trials (25%) + Common: max_stress exceeded + +Dashboard: http://localhost:3003 +Optuna Dashboard: http://localhost:8081 +``` + +### Step 4: Handle Completion/Errors + +**On Success**: +``` +OPTIMIZATION COMPLETE +===================== + +Study: {study_name} +Duration: 14m 23s +Trials: 30/30 + +Results: + Feasible designs: 24/30 (80%) + Pareto-optimal: 8 designs + +Best Designs: + #1: mass=231g, freq=118Hz ← Lightest + #2: mass=298g, freq=156Hz ← Stiffest + #3: mass=265g, freq=138Hz ← Balanced + +Next Steps: + 1. View results: /generate-report {study_name} + 2. Continue optimization: python run_optimization.py --trials 50 --resume + 3. Export designs: python export_pareto.py +``` + +**On Error**: +``` +OPTIMIZATION ERROR +================== + +Trial #15 failed with: + Error: NX simulation timeout after 600s + Design: thickness=1.2, diameter=45, hole_count=12 + +Possible causes: + 1. Mesh quality issues with extreme parameters + 2. Convergence problems in solver + 3. NX process locked/crashed + +Recovery options: + 1. [Recommended] Resume with --resume flag (skips failed) + 2. Narrow design variable bounds + 3. Check NX manually with these parameters + +Resume command: + python run_optimization.py --trials 30 --resume +``` + +## Dashboard Integration + +Always inform user about monitoring options: + +``` +MONITORING OPTIONS +================== + +1. Atomizer Dashboard (recommended): + cd atomizer-dashboard/backend && python -m uvicorn api.main:app --port 8000 + cd atomizer-dashboard/frontend && npm run dev + → http://localhost:3003 + +2. Optuna Dashboard: + python -c "import optuna; from optuna_dashboard import run_server; ..." + → http://localhost:8081 + +3. Command Line: + Watch study.db for updates + Tail the log file +``` + +## Resource Management + +### Before Running +- Check available RAM (recommend 8GB+ free) +- Check disk space (OP2 files can be large) +- Close unnecessary NX sessions + +### During Run +- Monitor CPU usage (Nastran is CPU-intensive) +- Watch for disk space issues +- Handle NX license timeouts + +### Cleanup After +- Option to remove intermediate OP2 files +- Compress results for archival +- Clean worker directories + +## Error Handling + +| Error | Cause | Recovery | +|-------|-------|----------| +| NX not found | NX not installed/configured | Check config.py settings | +| Simulation timeout | Complex mesh/convergence | Increase timeout or simplify | +| License error | NX license unavailable | Wait for license or use queue | +| Database locked | Multiple processes | Stop conflicting processes | +| Out of memory | Large mesh | Reduce mesh density | +| OP2 parse error | Corrupted output | Re-run trial | + +## Parallel Execution (Advanced) + +For large studies, can run parallel workers: + +```python +# Warning: Requires careful NX session management +# Each worker needs isolated model directory + +python run_training_fea.py --study {study_name} --workers 2 +``` + +## Output Format + +After starting optimization: + +``` +OPTIMIZATION STARTED +==================== + +Study: {study_name} +Trials: 30 +Mode: Multi-objective (NSGA-II) +Started: 2025-11-25 15:30:00 + +Process ID: 12345 +Log File: studies/{study_name}/2_results/optimization.log + +Monitoring: + Dashboard: http://localhost:3003 + Database: studies/{study_name}/2_results/study.db + +Commands: + Check progress: "how's the optimization going?" + Stop: Ctrl+C in terminal or kill process 12345 + Resume: python run_optimization.py --resume +``` + +## Integration with Other Skills + +- After completion → `/generate-report` +- On error → `/troubleshoot` +- For more trials → `/configure-surrogate` (enable NN) + +## Example Conversation + +``` +USER: Run the uav_arm optimization + +CLAUDE: I'll start the UAV arm optimization. Let me run pre-flight checks... + +PRE-FLIGHT CHECK: uav_arm_optimization +====================================== +✓ Configuration valid (Protocol 11, 30 trials) +✓ Model files present +✓ NX 2412 available + +Ready to start. This will take approximately 15 minutes. +Starting now... + +OPTIMIZATION STARTED +==================== +Process running in background. +Monitor at: http://localhost:3003 + +I'll let you know when it completes. You can ask "how's it going?" anytime. + +[... 15 minutes later ...] + +OPTIMIZATION COMPLETE +===================== +30/30 trials completed successfully. +Found 8 Pareto-optimal designs. + +Best lightweight: 231g @ 118Hz +Best stiff: 298g @ 156Hz + +Would you like me to generate a detailed report? +``` diff --git a/.claude/skills/troubleshoot.md b/.claude/skills/troubleshoot.md new file mode 100644 index 00000000..2e316f31 --- /dev/null +++ b/.claude/skills/troubleshoot.md @@ -0,0 +1,358 @@ +# Troubleshoot Skill + +**Last Updated**: November 25, 2025 +**Version**: 1.0 - Debug Common Issues and Error Recovery + +You are helping the user diagnose and fix problems with Atomizer optimization studies. + +## Purpose + +Diagnose and resolve common issues: +1. Configuration validation failures +2. Model file problems +3. NX solver errors +4. Optuna/database issues +5. Result extraction failures +6. Constraint violation patterns + +## Triggers + +- "troubleshoot" +- "debug" +- "error" +- "failed" +- "not working" +- "what's wrong" +- "fix" + +## Prerequisites + +- Study must exist in `studies/{study_name}/` +- User should describe the error or symptom + +## Diagnostic Process + +### Step 1: Run Full Validation + +```python +from optimization_engine.validators import validate_study + +result = validate_study("{study_name}") +print(result) +``` + +This provides a complete health check covering: +- Configuration validity +- Model file presence +- Results integrity (if any) + +### Step 2: Identify Error Category + +Classify the issue into one of these categories: + +| Category | Symptoms | First Check | +|----------|----------|-------------| +| Config | "Invalid config", validation errors | `validate_config_file()` | +| Model | "File not found", NX errors | `validate_study_model()` | +| Solver | "Simulation failed", timeout | NX logs, OP2 files | +| Database | "Study not found", lock errors | `study.db` file, Optuna | +| Extraction | "Cannot extract", NaN values | OP2 file validity | +| Constraints | All trials infeasible | Constraint thresholds | + +## Common Issues & Solutions + +### Issue 1: Configuration Validation Fails + +**Symptoms**: +``` +[ERROR] [DESIGN_VAR_BOUNDS] beam_thickness: min (5) >= max (3) +``` + +**Diagnosis**: +```python +from optimization_engine.validators import validate_config_file + +result = validate_config_file("studies/{study_name}/1_setup/optimization_config.json") +for error in result.errors: + print(error) +``` + +**Solutions**: +| Error Code | Cause | Fix | +|------------|-------|-----| +| DESIGN_VAR_BOUNDS | Bounds inverted | Swap min/max values | +| MISSING_OBJECTIVES | No objectives defined | Add objectives array | +| INVALID_DIRECTION | Wrong goal value | Use "minimize" or "maximize" | +| PROTOCOL_MISMATCH | Wrong protocol for objectives | Match protocol to # objectives | + +### Issue 2: Model Files Missing + +**Symptoms**: +``` +[ERROR] No part file (.prt) found in model directory +``` + +**Diagnosis**: +```python +from optimization_engine.validators import validate_study_model + +result = validate_study_model("{study_name}") +print(f"Part: {result.prt_file}") +print(f"Sim: {result.sim_file}") +print(f"FEM: {result.fem_file}") +``` + +**Solutions**: +1. Ensure files are in `studies/{study_name}/1_setup/model/` +2. Check file naming convention (e.g., `Beam.prt`, `Beam_sim1.sim`) +3. FEM file auto-generates on first solve (not required initially) + +### Issue 3: NX Solver Fails + +**Symptoms**: +``` +[NX SOLVER] Error: Simulation timeout after 600s +[NX SOLVER] Error: Unable to open simulation file +``` + +**Diagnosis**: +1. Check NX is installed and configured: + ```python + from config import NX_VERSION, NX_INSTALL_PATH + print(f"NX Version: {NX_VERSION}") + print(f"Path: {NX_INSTALL_PATH}") + ``` + +2. Check for running NX processes: + ```bash + tasklist | findstr "ugraf" + ``` + +3. Read NX journal output: + ``` + studies/{study_name}/1_setup/model/_temp_solve_journal.py + ``` + +**Solutions**: +| Error | Cause | Fix | +|-------|-------|-----| +| Timeout | Complex mesh or bad parameters | Increase timeout or simplify design | +| License error | NX license unavailable | Wait or check license server | +| File locked | Another NX process has file open | Close NX and retry | +| Expression not found | NX expression name mismatch | Verify expression names in NX | + +### Issue 4: Database Errors + +**Symptoms**: +``` +[ERROR] Study 'my_study' not found in storage +[ERROR] database is locked +``` + +**Diagnosis**: +```python +import optuna +storage = "sqlite:///studies/{study_name}/2_results/study.db" +studies = optuna.study.get_all_study_summaries(storage) +print([s.study_name for s in studies]) +``` + +**Solutions**: +| Error | Cause | Fix | +|-------|-------|-----| +| Study not found | Wrong study name | Check exact name in database | +| Database locked | Multiple processes | Kill other optimization processes | +| Corrupted DB | Interrupted write | Delete and restart (backup first) | + +### Issue 5: Result Extraction Fails + +**Symptoms**: +``` +[ERROR] Cannot extract displacement from OP2 +[ERROR] NaN values in objectives +``` + +**Diagnosis**: +1. Check OP2 file exists: + ```bash + dir studies\{study_name}\1_setup\model\*.op2 + ``` + +2. Validate OP2 contents: + ```python + from pyNastran.op2.op2 import OP2 + op2 = OP2() + op2.read_op2("path/to/file.op2") + print(op2.get_result_table_names()) + ``` + +3. Check extraction config matches OP2: + ```json + { + "extraction": { + "params": { + "subcase": 1, + "result_type": "displacement" + } + } + } + ``` + +**Solutions**: +| Error | Cause | Fix | +|-------|-------|-----| +| No OP2 file | Solve didn't run | Check NX solver output | +| Wrong subcase | Subcase ID mismatch | Match subcase to solution | +| Missing result | Result not requested | Enable output in NX | + +### Issue 6: All Trials Infeasible + +**Symptoms**: +``` +Feasibility rate: 0% +All trials violating constraints +``` + +**Diagnosis**: +```python +from optimization_engine.validators import validate_results + +result = validate_results("studies/{study_name}/2_results/study.db") +print(f"Feasibility: {result.info.feasibility_rate}%") +``` + +Check constraint violations in Optuna dashboard or: +```python +import optuna +study = optuna.load_study(...) +for trial in study.trials: + if trial.user_attrs.get('feasible') == False: + print(f"Trial {trial.number}: {trial.user_attrs.get('violated_constraints')}") +``` + +**Solutions**: +| Issue | Cause | Fix | +|-------|-------|-----| +| All trials fail constraint | Threshold too tight | Relax constraint threshold | +| Single constraint always fails | Wrong extraction | Check constraint extraction | +| Bounds cause violations | Design space infeasible | Expand design variable bounds | + +## Quick Diagnostic Commands + +### Validate Everything +```bash +python -m optimization_engine.validators.study_validator {study_name} +``` + +### Check Results +```bash +python -m optimization_engine.validators.results_validator {study_name} +``` + +### List All Studies +```bash +python -m optimization_engine.validators.study_validator +``` + +### Check Optuna Database +```python +import optuna +storage = "sqlite:///studies/{study_name}/2_results/study.db" +study = optuna.load_study(study_name="{study_name}", storage=storage) +print(f"Trials: {len(study.trials)}") +print(f"Completed: {len([t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE])}") +print(f"Failed: {len([t for t in study.trials if t.state == optuna.trial.TrialState.FAIL])}") +``` + +## Recovery Actions + +### Reset Study Database +```python +import optuna +storage = "sqlite:///studies/{study_name}/2_results/study.db" +optuna.delete_study(study_name="{study_name}", storage=storage) +``` + +Or use the reset script: +```bash +python studies/{study_name}/reset_study.py +``` + +### Resume Interrupted Study +```bash +python studies/{study_name}/run_optimization.py --trials 30 --resume +``` + +### Clean Worker Directories +```bash +# Remove temp files from worker dirs +del /S /Q studies\{study_name}\1_setup\worker_* +``` + +### Backup and Restore Database +```bash +# Backup +copy studies\{study_name}\2_results\study.db studies\{study_name}\2_results\study_backup.db + +# Restore +copy studies\{study_name}\2_results\study_backup.db studies\{study_name}\2_results\study.db +``` + +## Error Message Reference + +| Error Message | Category | Quick Fix | +|---------------|----------|-----------| +| "min >= max" | Config | Swap bounds | +| "No part file found" | Model | Add .prt file | +| "Simulation timeout" | Solver | Increase timeout or check NX | +| "database is locked" | Database | Kill other processes | +| "Cannot extract" | Extraction | Check OP2 and extraction config | +| "All trials infeasible" | Constraints | Relax thresholds | + +## Integration with Other Skills + +- After fixing config -> `/run-optimization` to restart +- After debugging results -> `/generate-report` to analyze +- If model needs changes -> Check `NX_FILE_MODIFICATIONS_REQUIRED.md` + +## Example Conversation + +``` +USER: My optimization is failing with "Cannot extract displacement" + +CLAUDE: Let me diagnose the displacement extraction issue. + +First, I'll validate the study: + +[Runs validate_study()] + +The validation shows model files are present. Let me check if the OP2 file exists +and contains displacement data... + +[Checks OP2 file] + +I found the issue: Your extraction config specifies subcase=1, but the displacement +results are in subcase=2 (the static analysis solution). + +Fix: Update optimization_config.json: +```json +{ + "extraction": { + "params": { + "subcase": 2 // Was 1, should be 2 + } + } +} +``` + +After this change, run again with: + python run_optimization.py --trials 30 --resume +``` + +## Notes + +- Always run full validation before investigating specific issues +- Most problems are configuration or file path issues +- NX solver errors often require checking NX directly +- Keep backups before modifying study.db +- Use --resume to continue after fixing issues diff --git a/CLAUDE.md b/CLAUDE.md new file mode 100644 index 00000000..d83b1726 --- /dev/null +++ b/CLAUDE.md @@ -0,0 +1,171 @@ +# Atomizer - Claude Code System Instructions + +You are the AI orchestrator for **Atomizer**, an LLM-first FEA optimization framework. Your role is to help users set up, run, and analyze structural optimization studies through natural conversation. + +## Core Philosophy + +**Talk, don't click.** Users describe what they want in plain language. You interpret, configure, execute, and explain. The dashboard is for monitoring - you handle the setup. + +## What Atomizer Does + +Atomizer automates parametric FEA optimization using NX Nastran: +- User describes optimization goals in natural language +- You create configurations, scripts, and study structure +- NX Nastran runs FEA simulations +- Optuna optimizes design parameters +- Neural networks accelerate repeated evaluations +- Dashboard visualizes results in real-time + +## Your Capabilities + +### 1. Create Optimization Studies +When user wants to optimize something: +- Gather requirements through conversation +- Read `.claude/skills/create-study.md` for the full protocol +- Generate all configuration files +- Validate setup before running + +### 2. Analyze NX Models +When user provides NX files: +- Extract expressions (design parameters) +- Identify simulation setup +- Suggest optimization targets +- Check for multi-solution requirements + +### 3. Run & Monitor Optimizations +- Start optimization runs +- Check progress in databases +- Interpret results +- Generate reports + +### 4. Configure Neural Network Surrogates +When optimization needs >50 trials: +- Generate space-filling training data +- Run parallel FEA for training +- Train and validate surrogates +- Enable accelerated optimization + +### 5. Troubleshoot Issues +- Parse error logs +- Identify common problems +- Suggest fixes +- Recover from failures + +## Key Files & Locations + +``` +Atomizer/ +├── .claude/ +│ ├── skills/ # Skill instructions (READ THESE) +│ │ ├── create-study.md # Main study creation skill +│ │ └── analyze-workflow.md +│ └── settings.local.json +├── docs/ +│ ├── 01_PROTOCOLS.md # Quick protocol reference +│ ├── 06_PROTOCOLS_DETAILED/ # Full protocol docs +│ └── 07_DEVELOPMENT/ # Development plans +├── optimization_engine/ # Core Python modules +│ ├── runner.py # Main optimizer +│ ├── nx_solver.py # NX interface +│ ├── extractors/ # Result extraction +│ └── validators/ # Config validation +├── studies/ # User studies live here +│ └── {study_name}/ +│ ├── 1_setup/ # Config & model files +│ ├── 2_results/ # Optuna DB & outputs +│ └── run_optimization.py +└── atomizer-dashboard/ # React dashboard +``` + +## Conversation Patterns + +### User: "I want to optimize this bracket" +1. Ask about model location, goals, constraints +2. Load skill: `.claude/skills/create-study.md` +3. Follow the interactive discovery process +4. Generate files, validate, confirm + +### User: "Run 200 trials with neural network" +1. Check if surrogate_settings needed +2. Modify config to enable NN +3. Explain the hybrid workflow stages +4. Start run, show monitoring options + +### User: "What's the status?" +1. Query database for trial counts +2. Check for running background processes +3. Summarize progress and best results +4. Suggest next steps + +### User: "The optimization failed" +1. Read error logs +2. Check common failure modes +3. Suggest fixes +4. Offer to retry + +## Protocols Reference + +| Protocol | Use Case | Sampler | +|----------|----------|---------| +| Protocol 10 | Single objective + constraints | TPE/CMA-ES | +| Protocol 11 | Multi-objective (2-3 goals) | NSGA-II | +| Protocol 12 | Hybrid FEA/NN acceleration | NSGA-II + surrogate | + +## Result Extraction + +Use centralized extractors from `optimization_engine/extractors/`: + +| Need | Extractor | Example | +|------|-----------|---------| +| Displacement | `extract_displacement` | Max tip deflection | +| Stress | `extract_solid_stress` | Max von Mises | +| Frequency | `extract_frequency` | 1st natural freq | +| Mass | `extract_mass_from_expression` | CAD mass property | + +## Multi-Solution Detection + +If user needs BOTH: +- Static results (stress, displacement) +- Modal results (frequency) + +Then set `solution_name=None` to solve ALL solutions. + +## Validation Before Action + +Always validate before: +- Starting optimization (config validator) +- Generating files (check paths exist) +- Running FEA (check NX files present) + +## Dashboard Integration + +- Setup/Config: **You handle it** +- Real-time monitoring: **Dashboard at localhost:3000** +- Results analysis: **Both (you interpret, dashboard visualizes)** + +## Key Principles + +1. **Conversation first** - Don't ask user to edit JSON manually +2. **Validate everything** - Catch errors before they cause failures +3. **Explain decisions** - Say why you chose a sampler/protocol +4. **Sensible defaults** - User only specifies what they care about +5. **Progressive disclosure** - Start simple, add complexity when needed + +## Current State Awareness + +Check these before suggesting actions: +- Running background processes: `/tasks` command +- Study databases: `studies/*/2_results/study.db` +- Model files: `studies/*/1_setup/model/` +- Dashboard status: Check if servers running + +## When Uncertain + +1. Read the relevant skill file +2. Check docs/06_PROTOCOLS_DETAILED/ +3. Look at existing similar studies +4. Ask user for clarification + +--- + +*Atomizer: Where engineers talk, AI optimizes.* diff --git a/atomizer-dashboard/README.md b/atomizer-dashboard/README.md new file mode 100644 index 00000000..2a0bff27 --- /dev/null +++ b/atomizer-dashboard/README.md @@ -0,0 +1,236 @@ +# Atomizer Dashboard + +Real-time optimization monitoring and control dashboard for the Atomizer optimization engine. + +## Features + +### ✅ Live Dashboard (Current) +- **Real-time WebSocket streaming** - Instant updates on new trials +- **Interactive charts** - Convergence plots and parameter space visualization +- **Pruning alerts** - Toast notifications for failed trials +- **Data export** - Download trial history as JSON or CSV +- **Study discovery** - Automatically detects all active studies +- **Connection monitoring** - WebSocket status indicator + +### 🔮 Future Features +- React + TypeScript frontend +- Study Configurator page +- Results Report Viewer +- LLM chat interface for configuration +- Study control (start/stop/pause) + +--- + +## Quick Start + +### 1. Install Backend Dependencies +```bash +cd atomizer-dashboard/backend +pip install -r requirements.txt +``` + +### 2. Start the Backend +```bash +# From backend directory +python -m uvicorn api.main:app --reload --host 0.0.0.0 --port 8000 +``` + +### 3. Access the Dashboard +Open your browser: **http://localhost:8000** + +### 4. Monitor an Optimization +```bash +# In a separate terminal +cd ../.. +python studies/circular_plate_frequency_tuning/run_optimization.py +``` + +The dashboard will automatically detect the running study and stream updates in real-time! + +--- + +## Architecture + +### Backend Stack +- **FastAPI** - Modern async Python web framework +- **Uvicorn** - ASGI server +- **Watchdog** - File system event monitoring +- **WebSockets** - Bidirectional real-time communication + +### Current Frontend +- **HTML/CSS/JavaScript** - Single-page application +- **Chart.js** - Interactive charts +- **WebSocket API** - Real-time data streaming + +### Planned Frontend +- **React 18** + **Vite** + **TypeScript** +- **TailwindCSS** - Utility-first CSS +- **Recharts** - React charting library +- **React Query** - Server state management + +--- + +## File Structure + +``` +atomizer-dashboard/ +├── backend/ ✅ COMPLETE +│ ├── api/ +│ │ ├── main.py # FastAPI app entry +│ │ ├── routes/ +│ │ │ └── optimization.py # REST endpoints +│ │ └── websocket/ +│ │ └── optimization_stream.py # WebSocket + file watching +│ ├── requirements.txt +│ └── README.md # Backend API docs +│ +├── dashboard-test.html ✅ Basic live dashboard +├── dashboard-enhanced.html ✅ Enhanced with charts & export +└── README.md (this file) +``` + +--- + +## API Documentation + +### REST Endpoints +- `GET /api/optimization/studies` - List all studies +- `GET /api/optimization/studies/{id}/status` - Get study status +- `GET /api/optimization/studies/{id}/history` - Get trial history +- `GET /api/optimization/studies/{id}/pruning` - Get pruning diagnostics + +### WebSocket Endpoint +- `ws://localhost:8000/api/ws/optimization/{study_id}` - Real-time trial stream + +**Message Types**: +- `connected` - Initial connection confirmation +- `trial_completed` - New trial finished +- `new_best` - New best trial found +- `progress` - Progress update (X/Y trials) +- `trial_pruned` - Trial pruned with diagnostics + +--- + +## Dashboard Features + +### Convergence Chart +Line chart showing: +- **Objective value** progression over trials +- **Best so far** trajectory +- Real-time updates without animation lag + +### Parameter Space Chart +Scatter plot showing: +- 2D visualization of first two design variables +- Points colored by objective value +- Best trial highlighted in green + +### Pruning Alerts +- Toast notifications for pruned trials +- Auto-dismiss after 5 seconds +- Warning styling (orange) with pruning cause + +### Data Export +- **Export JSON** - Download complete trial history +- **Export CSV** - Export as spreadsheet-compatible format +- Success alerts on export + +### Metrics Dashboard +- **Total Trials** - Number of completed trials +- **Best Value** - Best objective value found +- **Avg Objective** - Average objective value +- **Pruned** - Number of failed trials + +--- + +## Testing + +### Verify Backend is Running +```bash +curl http://localhost:8000/health +# Should return: {"status":"healthy"} + +curl http://localhost:8000/api/optimization/studies +# Should return: {"studies":[...]} +``` + +### Test WebSocket Connection +```bash +# Using wscat (npm install -g wscat) +wscat -c ws://localhost:8000/api/ws/optimization/circular_plate_frequency_tuning + +# Or using Python +python -c " +import asyncio +import websockets +import json + +async def test(): + uri = 'ws://localhost:8000/api/ws/optimization/circular_plate_frequency_tuning' + async with websockets.connect(uri) as ws: + while True: + msg = await ws.recv() + print(json.loads(msg)) + +asyncio.run(test()) +" +``` + +--- + +## Documentation + +- [Master Plan](../docs/DASHBOARD_MASTER_PLAN.md) - Complete architecture roadmap +- [Implementation Status](../docs/DASHBOARD_IMPLEMENTATION_STATUS.md) - Current progress +- [Session Summary](../docs/DASHBOARD_SESSION_SUMMARY.md) - Implementation notes +- [Backend API](backend/README.md) - Detailed API documentation + +--- + +## Next Steps + +### Short Term +1. Build full React + Vite + TypeScript frontend +2. Migrate to Recharts for React-compatible charts +3. Add parameter importance visualization +4. Polish UI/UX with TailwindCSS + +### Medium Term +5. Build Study Configurator page +6. Build Results Report Viewer page +7. Add study control (start/stop/pause) +8. Implement authentication + +### Long Term +9. Add LLM chat interface for configuration +10. Deploy with Docker +11. Add user management +12. Implement study templates + +--- + +## Troubleshooting + +### Dashboard shows "Failed to fetch" +- Ensure backend is running: `http://localhost:8000/health` +- Check CORS settings in `backend/api/main.py` +- Access dashboard via `http://localhost:8000` (not `file://`) + +### WebSocket not connecting +- Verify backend is running on port 8000 +- Check firewall settings +- Look for errors in browser console (F12) + +### No studies appearing +- Ensure studies directory exists: `studies/` +- Check study has `1_setup/optimization_config.json` +- Verify `2_results/optimization_history_incremental.json` exists + +### Charts not updating +- Check WebSocket connection status in dashboard +- Verify file watcher is running (check backend console) +- Ensure optimization is actually running and creating trials + +--- + +**Status**: ✅ Live dashboard functional and ready for use! diff --git a/atomizer-dashboard/backend/api/__init__.py b/atomizer-dashboard/backend/api/__init__.py new file mode 100644 index 00000000..10060414 --- /dev/null +++ b/atomizer-dashboard/backend/api/__init__.py @@ -0,0 +1 @@ +# Atomizer Dashboard API diff --git a/atomizer-dashboard/backend/api/main.py b/atomizer-dashboard/backend/api/main.py new file mode 100644 index 00000000..625b53fc --- /dev/null +++ b/atomizer-dashboard/backend/api/main.py @@ -0,0 +1,57 @@ +""" +Atomizer Dashboard - FastAPI Backend +Real-time optimization monitoring and control +""" + +from fastapi import FastAPI +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import FileResponse +from pathlib import Path +import sys + +# Add parent directory to path to import optimization_engine +sys.path.append(str(Path(__file__).parent.parent.parent.parent)) + +from api.routes import optimization +from api.websocket import optimization_stream + +# Create FastAPI app +app = FastAPI( + title="Atomizer Dashboard API", + description="Real-time optimization monitoring and control", + version="1.0.0" +) + +# Configure CORS for local development +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # Allow all origins for local development + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# Include routers +app.include_router(optimization.router, prefix="/api/optimization", tags=["optimization"]) +app.include_router(optimization_stream.router, prefix="/api/ws", tags=["websocket"]) + +@app.get("/") +async def root(): + """Serve the enhanced dashboard HTML""" + dashboard_path = Path(__file__).parent.parent.parent / "dashboard-enhanced.html" + return FileResponse(dashboard_path) + +@app.get("/health") +async def health_check(): + """Health check endpoint""" + return {"status": "healthy"} + +if __name__ == "__main__": + import uvicorn + uvicorn.run( + "main:app", + host="0.0.0.0", + port=8000, + reload=True, + log_level="info" + ) diff --git a/atomizer-dashboard/backend/api/routes/__init__.py b/atomizer-dashboard/backend/api/routes/__init__.py new file mode 100644 index 00000000..0154438c --- /dev/null +++ b/atomizer-dashboard/backend/api/routes/__init__.py @@ -0,0 +1 @@ +# API Routes diff --git a/atomizer-dashboard/backend/api/routes/optimization.py b/atomizer-dashboard/backend/api/routes/optimization.py index 6df98583..f0fe06fa 100644 --- a/atomizer-dashboard/backend/api/routes/optimization.py +++ b/atomizer-dashboard/backend/api/routes/optimization.py @@ -337,11 +337,18 @@ async def get_optimization_history(study_id: str, limit: Optional[int] = None): if not results and len(values) > 0: results["first_frequency"] = values[0] + # CRITICAL: Extract design_vars from user_attrs if stored there + # The optimization code does: trial.set_user_attr("design_vars", design_vars) + design_vars_from_attrs = user_attrs.get("design_vars", {}) + + # Merge with params (prefer user_attrs design_vars if available) + final_design_vars = {**params, **design_vars_from_attrs} if design_vars_from_attrs else params + trials.append({ "trial_number": trial_num, "objective": values[0] if len(values) > 0 else None, # Primary objective "objectives": values if len(values) > 1 else None, # All objectives for multi-objective - "design_variables": params, + "design_variables": final_design_vars, # Use merged design vars "results": results, "user_attrs": user_attrs, # Include all user attributes "start_time": start_time, @@ -679,6 +686,53 @@ async def get_mesh_file(study_id: str, filename: str): except Exception as e: raise HTTPException(status_code=500, detail=f"Failed to serve mesh file: {str(e)}") +@router.get("/studies/{study_id}/optuna-url") +async def get_optuna_dashboard_url(study_id: str): + """ + Get the Optuna dashboard URL for a specific study. + Returns the URL to access the study in Optuna dashboard. + + The Optuna dashboard should be started with a relative path from the Atomizer root: + sqlite:///studies/{study_id}/2_results/study.db + """ + try: + study_dir = STUDIES_DIR / study_id + if not study_dir.exists(): + raise HTTPException(status_code=404, detail=f"Study {study_id} not found") + + results_dir = study_dir / "2_results" + study_db = results_dir / "study.db" + + if not study_db.exists(): + raise HTTPException(status_code=404, detail=f"No Optuna database found for study {study_id}") + + # Get the study name from the database (may differ from folder name) + import optuna + storage = optuna.storages.RDBStorage(f"sqlite:///{study_db}") + studies = storage.get_all_studies() + + if not studies: + raise HTTPException(status_code=404, detail=f"No Optuna study found in database for {study_id}") + + # Use the actual study name from the database + optuna_study_name = studies[0].study_name + + # Return URL info for the frontend + # The dashboard should be running on port 8081 with the correct database + return { + "study_id": study_id, + "optuna_study_name": optuna_study_name, + "database_path": f"studies/{study_id}/2_results/study.db", + "dashboard_url": f"http://localhost:8081/dashboard/studies/{studies[0]._study_id}", + "dashboard_base": "http://localhost:8081", + "note": "Optuna dashboard must be started with: sqlite:///studies/{study_id}/2_results/study.db" + } + + except FileNotFoundError: + raise HTTPException(status_code=404, detail=f"Study {study_id} not found") + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to get Optuna URL: {str(e)}") + @router.post("/studies/{study_id}/generate-report") async def generate_report( study_id: str, diff --git a/atomizer-dashboard/backend/api/websocket/__init__.py b/atomizer-dashboard/backend/api/websocket/__init__.py new file mode 100644 index 00000000..cd024dfc --- /dev/null +++ b/atomizer-dashboard/backend/api/websocket/__init__.py @@ -0,0 +1 @@ +# Init file for websocket package diff --git a/atomizer-dashboard/backend/api/websocket/optimization_stream.py b/atomizer-dashboard/backend/api/websocket/optimization_stream.py new file mode 100644 index 00000000..4d53fb2e --- /dev/null +++ b/atomizer-dashboard/backend/api/websocket/optimization_stream.py @@ -0,0 +1,196 @@ +import asyncio +import json +from pathlib import Path +from typing import Dict, Set +from fastapi import APIRouter, WebSocket, WebSocketDisconnect +from watchdog.observers import Observer +from watchdog.events import FileSystemEventHandler +import aiofiles + +router = APIRouter() + +# Base studies directory +STUDIES_DIR = Path(__file__).parent.parent.parent.parent.parent / "studies" + +class OptimizationFileHandler(FileSystemEventHandler): + def __init__(self, study_id: str, callback): + self.study_id = study_id + self.callback = callback + self.last_trial_count = 0 + self.last_pruned_count = 0 + self.last_pareto_count = 0 + self.last_state_timestamp = "" + + def on_modified(self, event): + if event.src_path.endswith("optimization_history_incremental.json"): + asyncio.run(self.process_history_update(event.src_path)) + elif event.src_path.endswith("pruning_history.json"): + asyncio.run(self.process_pruning_update(event.src_path)) + elif event.src_path.endswith("study.db"): # Watch for Optuna DB changes (Pareto front) + asyncio.run(self.process_pareto_update(event.src_path)) + elif event.src_path.endswith("optimizer_state.json"): + asyncio.run(self.process_state_update(event.src_path)) + + async def process_history_update(self, file_path): + try: + async with aiofiles.open(file_path, mode='r') as f: + content = await f.read() + history = json.loads(content) + + current_count = len(history) + if current_count > self.last_trial_count: + # New trials added + new_trials = history[self.last_trial_count:] + for trial in new_trials: + await self.callback({ + "type": "trial_completed", + "data": trial + }) + self.last_trial_count = current_count + except Exception as e: + print(f"Error processing history update: {e}") + + async def process_pruning_update(self, file_path): + try: + async with aiofiles.open(file_path, mode='r') as f: + content = await f.read() + history = json.loads(content) + + current_count = len(history) + if current_count > self.last_pruned_count: + # New pruned trials + new_pruned = history[self.last_pruned_count:] + for trial in new_pruned: + await self.callback({ + "type": "trial_pruned", + "data": trial + }) + self.last_pruned_count = current_count + except Exception as e: + print(f"Error processing pruning update: {e}") + + async def process_pareto_update(self, file_path): + # This is tricky because study.db is binary. + # Instead of reading it directly, we'll trigger a re-fetch of the Pareto front via Optuna + # We debounce this to avoid excessive reads + try: + # Import here to avoid circular imports or heavy load at startup + import optuna + + # Connect to DB + storage = optuna.storages.RDBStorage(f"sqlite:///{file_path}") + study = optuna.load_study(study_name=self.study_id, storage=storage) + + # Check if multi-objective + if len(study.directions) > 1: + pareto_trials = study.best_trials + + # Only broadcast if count changed (simple heuristic) + # In a real app, we might check content hash + if len(pareto_trials) != self.last_pareto_count: + pareto_data = [ + { + "trial_number": t.number, + "values": t.values, + "params": t.params, + "user_attrs": dict(t.user_attrs), + "constraint_satisfied": t.user_attrs.get("constraint_satisfied", True) + } + for t in pareto_trials + ] + + await self.callback({ + "type": "pareto_front", + "data": { + "pareto_front": pareto_data, + "count": len(pareto_trials) + } + }) + self.last_pareto_count = len(pareto_trials) + + except Exception as e: + # DB might be locked, ignore transient errors + pass + + async def process_state_update(self, file_path): + try: + async with aiofiles.open(file_path, mode='r') as f: + content = await f.read() + state = json.loads(content) + + # Check timestamp to avoid duplicate broadcasts + if state.get("timestamp") != self.last_state_timestamp: + await self.callback({ + "type": "optimizer_state", + "data": state + }) + self.last_state_timestamp = state.get("timestamp") + except Exception as e: + print(f"Error processing state update: {e}") + +class ConnectionManager: + def __init__(self): + self.active_connections: Dict[str, Set[WebSocket]] = {} + self.observers: Dict[str, Observer] = {} + + async def connect(self, websocket: WebSocket, study_id: str): + await websocket.accept() + if study_id not in self.active_connections: + self.active_connections[study_id] = set() + self.start_watching(study_id) + self.active_connections[study_id].add(websocket) + + def disconnect(self, websocket: WebSocket, study_id: str): + if study_id in self.active_connections: + self.active_connections[study_id].remove(websocket) + if not self.active_connections[study_id]: + del self.active_connections[study_id] + self.stop_watching(study_id) + + async def broadcast(self, message: dict, study_id: str): + if study_id in self.active_connections: + for connection in self.active_connections[study_id]: + try: + await connection.send_json(message) + except Exception as e: + print(f"Error broadcasting to client: {e}") + + def start_watching(self, study_id: str): + study_dir = STUDIES_DIR / study_id / "2_results" + if not study_dir.exists(): + return + + async def callback(message): + await self.broadcast(message, study_id) + + event_handler = OptimizationFileHandler(study_id, callback) + observer = Observer() + observer.schedule(event_handler, str(study_dir), recursive=True) + observer.start() + self.observers[study_id] = observer + + def stop_watching(self, study_id: str): + if study_id in self.observers: + self.observers[study_id].stop() + self.observers[study_id].join() + del self.observers[study_id] + +manager = ConnectionManager() + +@router.websocket("/optimization/{study_id}") +async def optimization_stream(websocket: WebSocket, study_id: str): + await manager.connect(websocket, study_id) + try: + await websocket.send_json({ + "type": "connected", + "data": {"message": f"Connected to stream for study {study_id}"} + }) + while True: + # Keep connection alive and handle incoming messages if needed + data = await websocket.receive_text() + # We could handle client commands here (e.g., "pause", "stop") + except WebSocketDisconnect: + manager.disconnect(websocket, study_id) + except Exception as e: + print(f"WebSocket error: {e}") + manager.disconnect(websocket, study_id) diff --git a/atomizer-dashboard/backend/requirements.txt b/atomizer-dashboard/backend/requirements.txt new file mode 100644 index 00000000..091de0a9 --- /dev/null +++ b/atomizer-dashboard/backend/requirements.txt @@ -0,0 +1,31 @@ +# Atomizer Dashboard Backend Dependencies + +# FastAPI and ASGI server +fastapi>=0.104.0 +uvicorn[standard]>=0.24.0 +python-multipart>=0.0.6 + +# WebSocket support +websockets>=12.0 + +# File watching +watchdog>=3.0.0 +aiofiles>=23.2.1 + +# Data validation +pydantic>=2.5.0 + +# Optimization & Analysis +optuna>=3.4.0 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+1,42 @@ +{ + "name": "atomizer-dashboard-frontend", + "private": true, + "version": "1.0.0", + "type": "module", + "scripts": { + "dev": "vite", + "build": "tsc && vite build", + "lint": "eslint . --ext ts,tsx --report-unused-disable-directives --max-warnings 0", + "preview": "vite preview" + }, + "dependencies": { + "@react-three/drei": "^10.7.7", + "@react-three/fiber": "^9.4.0", + "@tanstack/react-query": "^5.90.10", + "@types/three": "^0.181.0", + "clsx": "^2.1.1", + "lucide-react": "^0.554.0", + "react": "^18.2.0", + "react-dom": "^18.2.0", + "react-router-dom": "^6.20.0", + "react-use-websocket": "^4.13.0", + "recharts": "^2.10.3", + "tailwind-merge": "^3.4.0", + "three": "^0.181.2" + }, + "devDependencies": { + "@types/react": "^18.2.43", + "@types/react-dom": "^18.2.17", + "@typescript-eslint/eslint-plugin": "^6.14.0", + "@typescript-eslint/parser": "^6.14.0", + "@vitejs/plugin-react": "^4.2.1", + "autoprefixer": "^10.4.16", + "eslint": "^8.55.0", + "eslint-plugin-react-hooks": "^4.6.0", + "eslint-plugin-react-refresh": "^0.4.5", + "postcss": "^8.4.32", + "tailwindcss": "^3.3.6", + "typescript": "^5.2.2", + "vite": "^5.0.8" + } +} diff --git a/atomizer-dashboard/frontend/postcss.config.js b/atomizer-dashboard/frontend/postcss.config.js new file mode 100644 index 00000000..2e7af2b7 --- /dev/null +++ b/atomizer-dashboard/frontend/postcss.config.js @@ -0,0 +1,6 @@ +export default { + plugins: { + tailwindcss: {}, + autoprefixer: {}, + }, +} diff --git a/atomizer-dashboard/frontend/src/App.tsx b/atomizer-dashboard/frontend/src/App.tsx new file mode 100644 index 00000000..a6eeaa86 --- /dev/null +++ b/atomizer-dashboard/frontend/src/App.tsx @@ -0,0 +1,27 @@ +import { BrowserRouter, Routes, Route, Navigate } from 'react-router-dom'; +import { QueryClient, QueryClientProvider } from '@tanstack/react-query'; +import { MainLayout } from './components/layout/MainLayout'; +import Dashboard from './pages/Dashboard'; +import Configurator from './pages/Configurator'; +import Results from './pages/Results'; + +const queryClient = new QueryClient(); + +function App() { + return ( + + + + }> + } /> + } /> + } /> + } /> + + + + + ); +} + +export default App; diff --git a/atomizer-dashboard/frontend/src/api/client.ts b/atomizer-dashboard/frontend/src/api/client.ts new file mode 100644 index 00000000..67ffd428 --- /dev/null +++ b/atomizer-dashboard/frontend/src/api/client.ts @@ -0,0 +1,58 @@ +import { Study, StudyListResponse, HistoryResponse, PruningResponse, StudyStatus } from '../types'; + +const API_BASE = '/api'; + +class ApiClient { + async getStudies(): Promise { + const response = await fetch(`${API_BASE}/optimization/studies`); + if (!response.ok) throw new Error('Failed to fetch studies'); + return response.json(); + } + + async getStudyStatus(studyId: string): Promise { + const response = await fetch(`${API_BASE}/optimization/studies/${studyId}/status`); + if (!response.ok) throw new Error('Failed to fetch study status'); + return response.json(); + } + + async getStudyHistory(studyId: string): Promise { + const response = await fetch(`${API_BASE}/optimization/studies/${studyId}/history`); + if (!response.ok) throw new Error('Failed to fetch study history'); + return response.json(); + } + + async getStudyPruning(studyId: string): Promise { + const response = await fetch(`${API_BASE}/optimization/studies/${studyId}/pruning`); + if (!response.ok) throw new Error('Failed to fetch pruning data'); + return response.json(); + } + + async createStudy(config: any): Promise<{ study_id: string }> { + const response = await fetch(`${API_BASE}/optimization/studies`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify(config), + }); + if (!response.ok) throw new Error('Failed to create study'); + return response.json(); + } + + async getStudyReport(studyId: string): Promise<{ content: string }> { + const response = await fetch(`${API_BASE}/optimization/studies/${studyId}/report`); + if (!response.ok) throw new Error('Failed to fetch report'); + return response.json(); + } + + // Future endpoints for control + async startOptimization(studyId: string): Promise { + const response = await fetch(`${API_BASE}/optimization/studies/${studyId}/start`, { method: 'POST' }); + if (!response.ok) throw new Error('Failed to start optimization'); + } + + async stopOptimization(studyId: string): Promise { + const response = await fetch(`${API_BASE}/optimization/studies/${studyId}/stop`, { method: 'POST' }); + if (!response.ok) throw new Error('Failed to stop optimization'); + } +} + +export const apiClient = new ApiClient(); \ No newline at end of file diff --git a/atomizer-dashboard/frontend/src/components/Badge.tsx b/atomizer-dashboard/frontend/src/components/Badge.tsx new file mode 100644 index 00000000..aec68811 --- /dev/null +++ b/atomizer-dashboard/frontend/src/components/Badge.tsx @@ -0,0 +1,24 @@ +import React from 'react'; + +type BadgeVariant = 'success' | 'warning' | 'error' | 'info'; + +interface BadgeProps { + children: React.ReactNode; + variant?: BadgeVariant; + className?: string; +} + +const variantClasses: Record = { + success: 'badge-success', + warning: 'badge-warning', + error: 'badge-error', + info: 'badge-info', +}; + +export function Badge({ children, variant = 'info', className = '' }: BadgeProps) { + return ( + + {children} + + ); +} diff --git a/atomizer-dashboard/frontend/src/components/Card.tsx b/atomizer-dashboard/frontend/src/components/Card.tsx new file mode 100644 index 00000000..180b317f --- /dev/null +++ b/atomizer-dashboard/frontend/src/components/Card.tsx @@ -0,0 +1,16 @@ +import React from 'react'; + +interface CardProps { + children: React.ReactNode; + className?: string; + title?: string | React.ReactNode; +} + +export function Card({ children, className = '', title }: CardProps) { + return ( +
+ {title &&

{title}

} + {children} +
+ ); +} diff --git a/atomizer-dashboard/frontend/src/components/MetricCard.tsx b/atomizer-dashboard/frontend/src/components/MetricCard.tsx new file mode 100644 index 00000000..ec82d6fe --- /dev/null +++ b/atomizer-dashboard/frontend/src/components/MetricCard.tsx @@ -0,0 +1,17 @@ +import React from 'react'; + +interface MetricCardProps { + label: string; + value: string | number; + className?: string; + valueColor?: string; +} + +export function MetricCard({ label, value, className = '', valueColor = 'text-primary-400' }: MetricCardProps) { + return ( +
+
{label}
+
{value}
+
+ ); +} diff --git a/atomizer-dashboard/frontend/src/components/ParallelCoordinatesPlot.tsx b/atomizer-dashboard/frontend/src/components/ParallelCoordinatesPlot.tsx index 60ebfa72..76c75c49 100644 --- a/atomizer-dashboard/frontend/src/components/ParallelCoordinatesPlot.tsx +++ b/atomizer-dashboard/frontend/src/components/ParallelCoordinatesPlot.tsx @@ -39,17 +39,22 @@ interface ParallelCoordinatesPlotProps { objectives: Objective[]; designVariables: DesignVariable[]; constraints?: Constraint[]; + paretoFront?: ParetoTrial[]; } export function ParallelCoordinatesPlot({ paretoData, objectives, designVariables, - constraints = [] + constraints = [], + paretoFront = [] }: ParallelCoordinatesPlotProps) { const [hoveredTrial, setHoveredTrial] = useState(null); const [selectedTrials, setSelectedTrials] = useState>(new Set()); + // Create set of Pareto front trial numbers for easy lookup + const paretoTrialNumbers = new Set(paretoFront.map(t => t.trial_number)); + // Safety checks if (!paretoData || paretoData.length === 0) { return ( @@ -83,9 +88,10 @@ export function ParallelCoordinatesPlot({ // Add design variables designVariables.forEach(dv => { + const paramName = dv.parameter || dv.name; // Support both formats axes.push({ - name: dv.name, - label: dv.unit ? `${dv.name}\n(${dv.unit})` : dv.name, + name: paramName, + label: dv.unit ? `${paramName}\n(${dv.unit})` : paramName, type: 'design_var', unit: dv.unit }); @@ -134,9 +140,10 @@ export function ParallelCoordinatesPlot({ const trialData = paretoData.map(trial => { const values: number[] = []; - // Design variables + // Design variables - use .parameter field from metadata designVariables.forEach(dv => { - values.push(trial.params[dv.name] ?? 0); + const paramName = dv.parameter || dv.name; // Support both formats + values.push(trial.params[paramName] ?? 0); }); // Objectives @@ -152,10 +159,32 @@ export function ParallelCoordinatesPlot({ return { trial_number: trial.trial_number, values, - feasible: trial.constraint_satisfied !== false + feasible: trial.constraint_satisfied !== false, + objectiveValues: trial.values || [] }; }); + // Rank trials by their first objective (for multi-objective, this is just one metric) + // For proper multi-objective ranking, we use Pareto dominance + const rankedTrials = [...trialData].sort((a, b) => { + // Primary: Pareto front members come first + const aIsPareto = paretoTrialNumbers.has(a.trial_number); + const bIsPareto = paretoTrialNumbers.has(b.trial_number); + if (aIsPareto && !bIsPareto) return -1; + if (!aIsPareto && bIsPareto) return 1; + + // Secondary: Sort by first objective value (minimize assumed) + const aObj = a.objectiveValues[0] ?? Infinity; + const bObj = b.objectiveValues[0] ?? Infinity; + return aObj - bObj; + }); + + // Create ranking map: trial_number -> rank (0-indexed) + const trialRanks = new Map(); + rankedTrials.forEach((trial, index) => { + trialRanks.set(trial.trial_number, index); + }); + // Calculate min/max for normalization const ranges = axes.map((_, axisIdx) => { const values = trialData.map(d => d.values[axisIdx]); @@ -192,12 +221,26 @@ export function ParallelCoordinatesPlot({ setSelectedTrials(newSelected); }; - // Color scheme - highly visible + // Color scheme - gradient grayscale for top 10, light gray for rest const getLineColor = (trial: typeof trialData[0], isHovered: boolean, isSelected: boolean) => { if (isSelected) return '#FF6B00'; // Bright orange for selected - if (!trial.feasible) return '#DC2626'; // Red for infeasible if (isHovered) return '#2563EB'; // Blue for hover - return '#10B981'; // Green for feasible + if (!trial.feasible) return '#DC2626'; // Red for infeasible + + const rank = trialRanks.get(trial.trial_number) ?? 999; + + // Top 10: Gradient from dark gray (#374151) to light gray (#9CA3AF) + if (rank < 10) { + // Interpolate: rank 0 = darkest, rank 9 = lighter + const t = rank / 9; // 0 to 1 + const r = Math.round(55 + t * (156 - 55)); // 55 to 156 + const g = Math.round(65 + t * (163 - 65)); // 65 to 163 + const b = Math.round(81 + t * (175 - 81)); // 81 to 175 + return `rgb(${r}, ${g}, ${b})`; + } + + // Remaining trials: Very light gray + return '#D1D5DB'; // Very light gray }; return ( @@ -371,8 +414,12 @@ export function ParallelCoordinatesPlot({ {/* Legend */}
-
- Feasible +
+ Top 10 (gradient) +
+
+
+ Others
diff --git a/atomizer-dashboard/frontend/src/components/ParetoPlot.tsx b/atomizer-dashboard/frontend/src/components/ParetoPlot.tsx index ddcb4e65..a865ad35 100644 --- a/atomizer-dashboard/frontend/src/components/ParetoPlot.tsx +++ b/atomizer-dashboard/frontend/src/components/ParetoPlot.tsx @@ -22,14 +22,18 @@ interface Objective { interface ParetoPlotProps { paretoData: ParetoTrial[]; objectives: Objective[]; + allTrials?: ParetoTrial[]; // All trials including non-Pareto } type NormalizationMode = 'raw' | 'minmax' | 'zscore'; -export function ParetoPlot({ paretoData, objectives }: ParetoPlotProps) { +export function ParetoPlot({ paretoData, objectives, allTrials }: ParetoPlotProps) { const [normMode, setNormMode] = useState('raw'); - if (paretoData.length === 0) { + // Use allTrials if provided, otherwise fall back to paretoData + const trialsToShow = allTrials && allTrials.length > 0 ? allTrials : paretoData; + + if (trialsToShow.length === 0) { return (

Pareto Front

@@ -40,12 +44,16 @@ export function ParetoPlot({ paretoData, objectives }: ParetoPlotProps) { ); } - // Extract raw values - const rawData = paretoData.map(trial => ({ + // Create set of Pareto front trial numbers for easy lookup + const paretoTrialNumbers = new Set(paretoData.map(t => t.trial_number)); + + // Extract raw values for ALL trials + const rawData = trialsToShow.map(trial => ({ x: trial.values[0], y: trial.values[1], trial_number: trial.trial_number, - feasible: trial.constraint_satisfied !== false + feasible: trial.constraint_satisfied !== false, + isPareto: paretoTrialNumbers.has(trial.trial_number) })); // Calculate statistics for normalization @@ -89,11 +97,12 @@ export function ParetoPlot({ paretoData, objectives }: ParetoPlotProps) { rawX: d.x, rawY: d.y, trial_number: d.trial_number, - feasible: d.feasible + feasible: d.feasible, + isPareto: d.isPareto })); - // Sort data by x-coordinate for Pareto front line - const sortedData = [...data].sort((a, b) => a.x - b.x); + // Sort ONLY Pareto front data by x-coordinate for line + const paretoOnlyData = data.filter(d => d.isPareto).sort((a, b) => a.x - b.x); // Get objective labels with normalization indicator const normSuffix = normMode === 'minmax' ? ' [0-1]' : normMode === 'zscore' ? ' [z-score]' : ''; @@ -219,24 +228,29 @@ export function ParetoPlot({ paretoData, objectives }: ParetoPlotProps) {
)} /> - {/* Pareto front line */} + {/* Pareto front line - only connects Pareto front points */} - + {/* All trials as scatter points */} + {data.map((entry, index) => ( ))} diff --git a/atomizer-dashboard/frontend/src/components/StudyCard.tsx b/atomizer-dashboard/frontend/src/components/StudyCard.tsx new file mode 100644 index 00000000..2dc04259 --- /dev/null +++ b/atomizer-dashboard/frontend/src/components/StudyCard.tsx @@ -0,0 +1,53 @@ +import React from 'react'; +import type { Study } from '../types'; +import { Badge } from './Badge'; + +interface StudyCardProps { + study: Study; + isActive: boolean; + onClick: () => void; +} + +export function StudyCard({ study, isActive, onClick }: StudyCardProps) { + const percentage = study.progress.total > 0 + ? (study.progress.current / study.progress.total) * 100 + : 0; + + const statusVariant = study.status === 'completed' + ? 'success' + : study.status === 'running' + ? 'info' + : 'warning'; + + return ( +
+
+

{study.name}

+ + {study.status} + +
+ +
+ {study.progress.current} / {study.progress.total} trials + {study.best_value !== null && ( + • Best: {study.best_value.toFixed(4)} + )} +
+ +
+
+
+
+ ); +} diff --git a/atomizer-dashboard/frontend/src/components/common/Button.tsx b/atomizer-dashboard/frontend/src/components/common/Button.tsx new file mode 100644 index 00000000..66c189e4 --- /dev/null +++ b/atomizer-dashboard/frontend/src/components/common/Button.tsx @@ -0,0 +1,51 @@ +import { ButtonHTMLAttributes, ReactNode } from 'react'; +import clsx from 'clsx'; +import { Loader2 } from 'lucide-react'; + +interface ButtonProps extends ButtonHTMLAttributes { + variant?: 'primary' | 'secondary' | 'danger' | 'ghost'; + size?: 'sm' | 'md' | 'lg'; + isLoading?: boolean; + icon?: ReactNode; +} + +export const Button = ({ + children, + className, + variant = 'primary', + size = 'md', + isLoading = false, + icon, + disabled, + ...props +}: ButtonProps) => { + const variants = { + primary: 'bg-primary-600 hover:bg-primary-700 text-white shadow-sm', + secondary: 'bg-dark-700 hover:bg-dark-600 text-dark-100 border border-dark-600', + danger: 'bg-red-600 hover:bg-red-700 text-white', + ghost: 'hover:bg-dark-700 text-dark-300 hover:text-white', + }; + + const sizes = { + sm: 'px-3 py-1.5 text-sm', + md: 'px-4 py-2', + lg: 'px-6 py-3 text-lg', + }; + + return ( + + ); +}; \ No newline at end of file diff --git a/atomizer-dashboard/frontend/src/components/common/Card.tsx b/atomizer-dashboard/frontend/src/components/common/Card.tsx new file mode 100644 index 00000000..5df4d794 --- /dev/null +++ b/atomizer-dashboard/frontend/src/components/common/Card.tsx @@ -0,0 +1,21 @@ +import { ReactNode } from 'react'; +import clsx from 'clsx'; + +interface CardProps { + title?: string; + children: ReactNode; + className?: string; +} + +export const Card = ({ title, children, className }: CardProps) => { + return ( +
+ {title && ( +
+

{title}

+
+ )} +
{children}
+
+ ); +}; \ No newline at end of file diff --git a/atomizer-dashboard/frontend/src/components/common/Input.tsx b/atomizer-dashboard/frontend/src/components/common/Input.tsx new file mode 100644 index 00000000..6ddb62d1 --- /dev/null +++ b/atomizer-dashboard/frontend/src/components/common/Input.tsx @@ -0,0 +1,39 @@ +import { InputHTMLAttributes, forwardRef } from 'react'; +import clsx from 'clsx'; + +interface InputProps extends InputHTMLAttributes { + label?: string; + error?: string; + helperText?: string; +} + +export const Input = forwardRef( + ({ className, label, error, helperText, ...props }, ref) => { + return ( +
+ {label && ( + + )} + + {error &&

{error}

} + {helperText && !error && ( +

{helperText}

+ )} +
+ ); + } +); + +Input.displayName = 'Input'; \ No newline at end of file diff --git a/atomizer-dashboard/frontend/src/components/dashboard/MeshViewer.tsx b/atomizer-dashboard/frontend/src/components/dashboard/MeshViewer.tsx new file mode 100644 index 00000000..82fccc30 --- /dev/null +++ b/atomizer-dashboard/frontend/src/components/dashboard/MeshViewer.tsx @@ -0,0 +1,79 @@ +import { useRef, useState, Suspense } from 'react'; +import { Canvas, useFrame } from '@react-three/fiber'; +import { OrbitControls, Stage, useGLTF } from '@react-three/drei'; +import { Card } from '../common/Card'; +import { Button } from '../common/Button'; +import { Maximize2, RotateCcw } from 'lucide-react'; + +// Placeholder component for the mesh +// In a real implementation, this would load the GLTF/OBJ file converted from Nastran +const Model = ({ path }: { path?: string }) => { + // For now, we'll render a simple box to demonstrate the viewer + const meshRef = useRef(); + + useFrame((state, delta) => { + if (meshRef.current) { + meshRef.current.rotation.y += delta * 0.2; + } + }); + + return ( + + + + + ); +}; + +interface MeshViewerProps { + modelPath?: string; + resultField?: string; +} + +export const MeshViewer = ({ modelPath, resultField }: MeshViewerProps) => { + const [autoRotate, setAutoRotate] = useState(true); + + return ( + +
+ + + + + + + + + + {/* Controls Overlay */} +
+ + +
+ + {/* Legend Overlay */} +
+
Displacement (mm)
+
+
+ 0.0 + 5.2 +
+
+
+ + ); +}; \ No newline at end of file diff --git a/atomizer-dashboard/frontend/src/components/dashboard/MetricCard.tsx b/atomizer-dashboard/frontend/src/components/dashboard/MetricCard.tsx new file mode 100644 index 00000000..ddb8acb1 --- /dev/null +++ b/atomizer-dashboard/frontend/src/components/dashboard/MetricCard.tsx @@ -0,0 +1,23 @@ +import { Card } from '../common/Card'; +import clsx from 'clsx'; + +interface MetricCardProps { + label: string; + value: string | number; + valueColor?: string; + subtext?: string; +} + +export const MetricCard = ({ label, value, valueColor = 'text-white', subtext }: MetricCardProps) => { + return ( + +
+ {label} +
+ {value} + {subtext &&

{subtext}

} +
+
+
+ ); +}; \ No newline at end of file diff --git a/atomizer-dashboard/frontend/src/components/dashboard/ParallelCoordinatesPlot.tsx b/atomizer-dashboard/frontend/src/components/dashboard/ParallelCoordinatesPlot.tsx new file mode 100644 index 00000000..5161b4a8 --- /dev/null +++ b/atomizer-dashboard/frontend/src/components/dashboard/ParallelCoordinatesPlot.tsx @@ -0,0 +1,138 @@ +import { Card } from '../common/Card'; + +interface ParallelCoordinatesPlotProps { + data: any[]; + dimensions: string[]; + colorBy?: string; +} + +export const ParallelCoordinatesPlot = ({ data, dimensions }: ParallelCoordinatesPlotProps) => { + // Filter out null/undefined data points + const validData = data.filter(d => d && dimensions.every(dim => d[dim] !== null && d[dim] !== undefined)); + + if (validData.length === 0 || dimensions.length === 0) { + return ( + +
+ No data available for parallel coordinates +
+
+ ); + } + + // Calculate min/max for each dimension for normalization + const ranges = dimensions.map(dim => { + const values = validData.map(d => d[dim]); + return { + min: Math.min(...values), + max: Math.max(...values) + }; + }); + + // Normalize function + const normalize = (value: number, dimIdx: number): number => { + const range = ranges[dimIdx]; + if (range.max === range.min) return 0.5; + return (value - range.min) / (range.max - range.min); + }; + + // Chart dimensions + const width = 800; + const height = 400; + const margin = { top: 80, right: 20, bottom: 40, left: 20 }; + const plotWidth = width - margin.left - margin.right; + const plotHeight = height - margin.top - margin.bottom; + + const axisSpacing = plotWidth / (dimensions.length - 1); + + return ( + + + + {/* Draw axes */} + {dimensions.map((dim, i) => { + const x = i * axisSpacing; + return ( + + {/* Axis line */} + + + {/* Axis label */} + + {dim} + + + {/* Min/max labels */} + + {ranges[i].min.toFixed(2)} + + + {ranges[i].max.toFixed(2)} + + + ); + })} + + {/* Draw lines for each trial */} + {validData.map((trial, trialIdx) => { + // Build path + const pathData = dimensions.map((dim, i) => { + const x = i * axisSpacing; + const normalizedY = normalize(trial[dim], i); + const y = plotHeight * (1 - normalizedY); + return i === 0 ? `M ${x} ${y}` : `L ${x} ${y}`; + }).join(' '); + + return ( + + ); + })} + + + + {/* Legend */} +
+
+
+ Pareto Front +
+
+
+ Other Solutions +
+
+ + ); +}; diff --git a/atomizer-dashboard/frontend/src/components/dashboard/ParetoPlot.tsx b/atomizer-dashboard/frontend/src/components/dashboard/ParetoPlot.tsx new file mode 100644 index 00000000..4b213ec6 --- /dev/null +++ b/atomizer-dashboard/frontend/src/components/dashboard/ParetoPlot.tsx @@ -0,0 +1,85 @@ +import { ResponsiveContainer, ScatterChart, Scatter, XAxis, YAxis, ZAxis, Tooltip, Cell, CartesianGrid, Line } from 'recharts'; +import { Card } from '../common/Card'; + +interface ParetoPlotProps { + data: any[]; + xKey: string; + yKey: string; + zKey?: string; +} + +export const ParetoPlot = ({ data, xKey, yKey, zKey }: ParetoPlotProps) => { + // Filter out null/undefined data points + const validData = data.filter(d => + d && + d[xKey] !== null && d[xKey] !== undefined && + d[yKey] !== null && d[yKey] !== undefined + ); + + if (validData.length === 0) { + return ( + +
+ No Pareto front data yet +
+
+ ); + } + + // Sort data by x-coordinate for Pareto front line + const sortedData = [...validData].sort((a, b) => a[xKey] - b[xKey]); + + return ( + +
+ + + + + + {zKey && } + { + if (typeof value === 'number') { + return value.toFixed(2); + } + return value; + }} + /> + {/* Pareto front line */} + + + {validData.map((entry, index) => ( + + ))} + + + +
+
+ ); +}; \ No newline at end of file diff --git a/atomizer-dashboard/frontend/src/components/dashboard/ReportBuilder.tsx b/atomizer-dashboard/frontend/src/components/dashboard/ReportBuilder.tsx new file mode 100644 index 00000000..1d64a3b7 --- /dev/null +++ b/atomizer-dashboard/frontend/src/components/dashboard/ReportBuilder.tsx @@ -0,0 +1,120 @@ +import { useState } from 'react'; +import { Card } from '../common/Card'; +import { Button } from '../common/Button'; +import { Input } from '../common/Input'; +import { FileText, Download, Plus, Trash2, MoveUp, MoveDown } from 'lucide-react'; + +interface ReportSection { + id: string; + type: 'text' | 'chart' | 'table' | 'image'; + title: string; + content: string; +} + +export const ReportBuilder = () => { + const [sections, setSections] = useState([ + { id: '1', type: 'text', title: 'Executive Summary', content: 'The optimization study successfully converged...' }, + { id: '2', type: 'chart', title: 'Convergence Plot', content: 'convergence_plot' }, + { id: '3', type: 'table', title: 'Top 10 Designs', content: 'top_designs_table' }, + ]); + + const addSection = (type: ReportSection['type']) => { + setSections([ + ...sections, + { id: Date.now().toString(), type, title: 'New Section', content: '' } + ]); + }; + + const removeSection = (id: string) => { + setSections(sections.filter(s => s.id !== id)); + }; + + const moveSection = (index: number, direction: 'up' | 'down') => { + if (direction === 'up' && index === 0) return; + if (direction === 'down' && index === sections.length - 1) return; + + const newSections = [...sections]; + const targetIndex = direction === 'up' ? index - 1 : index + 1; + [newSections[index], newSections[targetIndex]] = [newSections[targetIndex], newSections[index]]; + setSections(newSections); + }; + + const updateSection = (id: string, field: keyof ReportSection, value: string) => { + setSections(sections.map(s => s.id === id ? { ...s, [field]: value } : s)); + }; + + return ( +
+ {/* Editor Sidebar */} +
+ +
+ {sections.map((section, index) => ( +
+
+ {section.type} +
+ + + +
+
+ updateSection(section.id, 'title', e.target.value)} + className="mb-2 text-sm" + /> + {section.type === 'text' && ( +