refactor: Major project cleanup and reorganization
## Removed Duplicate Directories - Deleted old `dashboard/` (replaced by atomizer-dashboard) - Deleted old `mcp_server/` Python tools (moved model_discovery to optimization_engine) - Deleted `tests/mcp_server/` (obsolete tests) - Deleted `launch_dashboard.bat` (old launcher) ## Consolidated Code - Moved `mcp_server/tools/model_discovery.py` to `optimization_engine/model_discovery/` - Updated import in `optimization_config_builder.py` - Deleted stub `extract_mass.py` (use extract_mass_from_bdf instead) - Deleted unused `intelligent_setup.py` and `hybrid_study_creator.py` - Archived `result_extractors/` to `archive/deprecated/` ## Documentation Cleanup - Deleted deprecated `docs/06_PROTOCOLS_DETAILED/` (14 files) - Archived dated dev docs to `docs/08_ARCHIVE/sessions/` - Archived old plans to `docs/08_ARCHIVE/plans/` - Updated `docs/protocols/README.md` with SYS_15 ## Skills Consolidation - Archived redundant study creation skills to `.claude/skills/archive/` - Kept `core/study-creation-core.md` as canonical ## Housekeeping - Updated `.gitignore` to prevent `nul` and `_dat_run*.dat` 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
Antoine
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docs/08_ARCHIVE/sessions/ATOMIZER_STATE_ASSESSMENT_NOV25.md
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# Atomizer State Assessment - November 25, 2025
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**Version**: Comprehensive Project Review
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**Author**: Claude Code Analysis
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**Date**: November 25, 2025
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---
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## Executive Summary
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Atomizer has evolved from a basic FEA optimization tool into a **production-ready, AI-accelerated structural optimization platform**. The core optimization loop is complete and battle-tested. Neural surrogate models provide **2,200x speedup** over traditional FEA. The system is ready for real engineering work but has clear opportunities for polish and expansion.
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### Key Metrics
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||||
| Metric | Value |
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||||
|--------|-------|
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| Total Python Code | 20,500+ lines |
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| Documentation Files | 80+ markdown files |
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| Active Studies | 4 fully configured |
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| Neural Speedup | 2,200x (4.5ms vs 10-30 min) |
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| Claude Code Skills | 7 production-ready |
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| Protocols Implemented | 10, 11, 13 |
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### Overall Status: **85% Complete for MVP**
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```
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Core Engine: [####################] 100%
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Neural Surrogates:[####################] 100%
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Dashboard Backend:[####################] 100%
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Dashboard Frontend:[##############------] 70%
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Documentation: [####################] 100%
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Testing: [###############-----] 75%
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Deployment: [######--------------] 30%
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```
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|
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---
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## Part 1: What's COMPLETE and Working
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### 1.1 Core Optimization Engine (100%)
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The heart of Atomizer is **production-ready**:
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```
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optimization_engine/
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├── runner.py # Main Optuna-based optimization loop
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├── config_manager.py # JSON schema validation
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├── logger.py # Structured logging (Phase 1.3)
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├── simulation_validator.py # Post-solve validation
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├── result_extractor.py # Modular FEA result extraction
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└── plugins/ # Lifecycle hook system
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```
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**Capabilities**:
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- Intelligent study creation with automated benchmarking
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- NX Nastran/UGRAF integration via Python journals
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- Multi-sampler support: TPE, CMA-ES, Random, Grid
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- Pruning with MedianPruner for early termination
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- Real-time trial tracking with incremental JSON history
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- Target-matching objective functions
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- Markdown report generation with embedded graphs
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|
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**Protocols Implemented**:
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| Protocol | Name | Status |
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|----------|------|--------|
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| 10 | IMSO (Intelligent Multi-Strategy) | Complete |
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| 11 | Multi-Objective Optimization | Complete |
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| 13 | Real-Time Dashboard Tracking | Complete |
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### 1.2 Neural Acceleration - AtomizerField (100%)
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The neural surrogate system is **the crown jewel** of Atomizer:
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```
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atomizer-field/
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├── neural_models/
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│ ├── parametric_predictor.py # Direct objective prediction (4.5ms!)
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│ ├── field_predictor.py # Full displacement/stress fields
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│ ├── physics_losses.py # Physics-informed training
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│ └── uncertainty.py # Ensemble-based confidence
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├── train.py # Field GNN training
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├── train_parametric.py # Parametric GNN training
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└── optimization_interface.py # Atomizer integration
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```
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**Performance Results**:
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```
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┌─────────────────┬────────────┬───────────────┐
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│ Model │ Inference │ Speedup │
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├─────────────────┼────────────┼───────────────┤
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│ Parametric GNN │ 4.5ms │ 2,200x │
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│ Field GNN │ 50ms │ 200x │
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│ Traditional FEA │ 10-30 min │ baseline │
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└─────────────────┴────────────┴───────────────┘
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```
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|
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**Hybrid Mode Intelligence**:
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- 97% predictions via neural network
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- 3% FEA validation on low-confidence cases
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- Automatic fallback when uncertainty > threshold
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- Physics-informed loss ensures equilibrium compliance
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|
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### 1.3 Dashboard Backend (100%)
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FastAPI backend is **complete and integrated**:
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|
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```python
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# atomizer-dashboard/backend/api/
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├── main.py # FastAPI app with CORS
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├── routes/
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│ ├── optimization.py # Study discovery, history, Pareto
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│ └── __init__.py
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└── websocket/
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└── optimization_stream.py # Real-time trial streaming
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```
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|
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**Endpoints**:
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- `GET /api/studies` - Discover all studies
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- `GET /api/studies/{name}/history` - Trial history with caching
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- `GET /api/studies/{name}/pareto` - Pareto front for multi-objective
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- `WS /ws/optimization/{name}` - Real-time WebSocket stream
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|
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### 1.4 Validation System (100%)
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|
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Four-tier validation ensures correctness:
|
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|
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```
|
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optimization_engine/validators/
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├── config_validator.py # JSON schema + semantic validation
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├── model_validator.py # NX file presence + naming
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├── results_validator.py # Trial quality + Pareto analysis
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└── study_validator.py # Complete health check
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```
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|
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**Usage**:
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```python
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from optimization_engine.validators import validate_study
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|
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result = validate_study("uav_arm_optimization")
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print(result) # Shows complete health check with actionable errors
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```
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|
||||
### 1.5 Claude Code Skills (100%)
|
||||
|
||||
Seven skills automate common workflows:
|
||||
|
||||
| Skill | Purpose |
|
||||
|-------|---------|
|
||||
| `create-study` | Interactive study creation from description |
|
||||
| `run-optimization` | Launch and monitor optimization |
|
||||
| `generate-report` | Create markdown reports with graphs |
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| `troubleshoot` | Diagnose and fix common issues |
|
||||
| `analyze-model` | Inspect NX model structure |
|
||||
| `analyze-workflow` | Verify workflow configurations |
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| `atomizer` | Comprehensive reference guide |
|
||||
|
||||
### 1.6 Documentation (100%)
|
||||
|
||||
Comprehensive documentation in organized structure:
|
||||
|
||||
```
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||||
docs/
|
||||
├── 00_INDEX.md # Navigation hub
|
||||
├── 01_PROTOCOLS.md # Master protocol specs
|
||||
├── 02_ARCHITECTURE.md # System architecture
|
||||
├── 03_GETTING_STARTED.md # Quick start guide
|
||||
├── 04_USER_GUIDES/ # 12 user guides
|
||||
├── 05_API_REFERENCE/ # 6 API docs
|
||||
├── 06_PROTOCOLS_DETAILED/ # 9 protocol deep-dives
|
||||
├── 07_DEVELOPMENT/ # 12 dev docs
|
||||
├── 08_ARCHIVE/ # Historical documents
|
||||
└── 09_DIAGRAMS/ # Mermaid architecture diagrams
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||||
```
|
||||
|
||||
---
|
||||
|
||||
## Part 2: What's IN-PROGRESS
|
||||
|
||||
### 2.1 Dashboard Frontend (70%)
|
||||
|
||||
React frontend exists but needs polish:
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||||
|
||||
**Implemented**:
|
||||
- Dashboard.tsx - Live optimization monitoring with charts
|
||||
- ParallelCoordinatesPlot.tsx - Multi-parameter visualization
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||||
- ParetoPlot.tsx - Multi-objective Pareto analysis
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- Basic UI components (Card, Badge, MetricCard)
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||||
|
||||
**Missing**:
|
||||
- LLM chat interface for study configuration
|
||||
- Study control panel (start/stop/pause)
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- Full Results Report Viewer
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||||
- Responsive mobile design
|
||||
- Dark mode
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||||
|
||||
### 2.2 Legacy Studies Migration
|
||||
|
||||
| Study | Modern Config | Status |
|
||||
|-------|--------------|--------|
|
||||
| uav_arm_optimization | Yes | Active |
|
||||
| drone_gimbal_arm_optimization | Yes | Active |
|
||||
| uav_arm_atomizerfield_test | Yes | Active |
|
||||
| bracket_stiffness_* (5 studies) | No | Legacy |
|
||||
|
||||
The bracket studies use an older configuration format and need migration to the new workflow-based system.
|
||||
|
||||
---
|
||||
|
||||
## Part 3: What's MISSING
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||||
|
||||
### 3.1 Critical Missing Pieces
|
||||
|
||||
#### Closed-Loop Neural Training
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||||
**The biggest gap**: No automated pipeline to:
|
||||
1. Run optimization study
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||||
2. Export training data automatically
|
||||
3. Train/retrain neural model
|
||||
4. Deploy updated model
|
||||
|
||||
**Current State**: Manual steps required
|
||||
```bash
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||||
# Manual process today:
|
||||
1. Run optimization with FEA
|
||||
2. python generate_training_data.py --study X
|
||||
3. python atomizer-field/train_parametric.py --train_dir X
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||||
4. Manually copy model checkpoint
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||||
5. Enable --enable-nn flag
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||||
```
|
||||
|
||||
**Needed**: Single command that handles all steps
|
||||
|
||||
#### Study Templates
|
||||
No quick-start templates for common problems:
|
||||
- Beam stiffness optimization
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||||
- Bracket stress minimization
|
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- Frequency tuning
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||||
- Multi-objective mass vs stiffness
|
||||
|
||||
#### Deployment Configuration
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||||
No Docker/container setup:
|
||||
```yaml
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||||
# Missing: docker-compose.yml
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||||
services:
|
||||
atomizer-api:
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||||
build: ./atomizer-dashboard/backend
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||||
atomizer-frontend:
|
||||
build: ./atomizer-dashboard/frontend
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||||
atomizer-worker:
|
||||
build: ./optimization_engine
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||||
```
|
||||
|
||||
### 3.2 Nice-to-Have Missing Features
|
||||
|
||||
| Feature | Priority | Effort |
|
||||
|---------|----------|--------|
|
||||
| Authentication/multi-user | Medium | High |
|
||||
| Parallel FEA evaluation | High | Very High |
|
||||
| Modal analysis (SOL 103) neural | Medium | High |
|
||||
| Study comparison view | Low | Medium |
|
||||
| Export to CAD | Low | Medium |
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||||
| Cloud deployment | Medium | High |
|
||||
|
||||
---
|
||||
|
||||
## Part 4: Closing the Neural Loop
|
||||
|
||||
### Current Neural Workflow (Manual)
|
||||
|
||||
```mermaid
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||||
graph TD
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||||
A[Run FEA Optimization] -->|Manual| B[Export Training Data]
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||||
B -->|Manual| C[Train Neural Model]
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||||
C -->|Manual| D[Deploy Model]
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||||
D --> E[Run Neural-Accelerated Optimization]
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||||
E -->|If drift detected| A
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||||
```
|
||||
|
||||
### Proposed Automated Pipeline
|
||||
|
||||
```mermaid
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||||
graph TD
|
||||
A[Define Study] --> B{Has Trained Model?}
|
||||
B -->|No| C[Run Initial FEA Exploration]
|
||||
C --> D[Auto-Export Training Data]
|
||||
D --> E[Auto-Train Neural Model]
|
||||
E --> F[Run Neural-Accelerated Optimization]
|
||||
B -->|Yes| F
|
||||
F --> G{Model Drift Detected?}
|
||||
G -->|Yes| H[Collect New FEA Points]
|
||||
H --> D
|
||||
G -->|No| I[Generate Report]
|
||||
```
|
||||
|
||||
### Implementation Plan
|
||||
|
||||
#### Phase 1: Training Data Auto-Export (2 hours)
|
||||
```python
|
||||
# Add to runner.py after each trial:
|
||||
def on_trial_complete(trial, objectives, parameters):
|
||||
if trial.number % 10 == 0: # Every 10 trials
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||||
export_training_point(trial, objectives, parameters)
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||||
```
|
||||
|
||||
#### Phase 2: Auto-Training Trigger (4 hours)
|
||||
```python
|
||||
# New module: optimization_engine/auto_trainer.py
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||||
class AutoTrainer:
|
||||
def __init__(self, study_name, min_points=50):
|
||||
self.study_name = study_name
|
||||
self.min_points = min_points
|
||||
|
||||
def should_train(self) -> bool:
|
||||
"""Check if enough new data for training."""
|
||||
return count_new_points() >= self.min_points
|
||||
|
||||
def train(self) -> Path:
|
||||
"""Launch training and return model path."""
|
||||
# Call atomizer-field training
|
||||
pass
|
||||
```
|
||||
|
||||
#### Phase 3: Model Drift Detection (4 hours)
|
||||
```python
|
||||
# In neural_surrogate.py
|
||||
def check_model_drift(predictions, actual_fea) -> bool:
|
||||
"""Detect when neural predictions drift from FEA."""
|
||||
error = abs(predictions - actual_fea) / actual_fea
|
||||
return error.mean() > 0.10 # 10% drift threshold
|
||||
```
|
||||
|
||||
#### Phase 4: One-Command Neural Study (2 hours)
|
||||
```bash
|
||||
# New CLI command
|
||||
python -m atomizer neural-optimize \
|
||||
--study my_study \
|
||||
--trials 500 \
|
||||
--auto-train \
|
||||
--retrain-every 50
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Part 5: Prioritized Next Steps
|
||||
|
||||
### Immediate (This Week)
|
||||
|
||||
| Task | Priority | Effort | Impact |
|
||||
|------|----------|--------|--------|
|
||||
| 1. Auto training data export on each trial | P0 | 2h | High |
|
||||
| 2. Create 3 study templates | P0 | 4h | High |
|
||||
| 3. Fix dashboard frontend styling | P1 | 4h | Medium |
|
||||
| 4. Add study reset/cleanup command | P1 | 1h | Medium |
|
||||
|
||||
### Short-Term (Next 2 Weeks)
|
||||
|
||||
| Task | Priority | Effort | Impact |
|
||||
|------|----------|--------|--------|
|
||||
| 5. Auto-training trigger system | P0 | 4h | Very High |
|
||||
| 6. Model drift detection | P0 | 4h | High |
|
||||
| 7. One-command neural workflow | P0 | 2h | Very High |
|
||||
| 8. Migrate bracket studies to modern config | P1 | 3h | Medium |
|
||||
| 9. Dashboard study control panel | P1 | 6h | Medium |
|
||||
|
||||
### Medium-Term (Month)
|
||||
|
||||
| Task | Priority | Effort | Impact |
|
||||
|------|----------|--------|--------|
|
||||
| 10. Docker deployment | P1 | 8h | High |
|
||||
| 11. End-to-end test suite | P1 | 8h | High |
|
||||
| 12. LLM chat interface | P2 | 16h | Medium |
|
||||
| 13. Parallel FEA evaluation | P2 | 24h | Very High |
|
||||
|
||||
---
|
||||
|
||||
## Part 6: Architecture Diagram
|
||||
|
||||
```
|
||||
┌─────────────────────────────────────────────────────────────────────┐
|
||||
│ ATOMIZER PLATFORM │
|
||||
├─────────────────────────────────────────────────────────────────────┤
|
||||
│ │
|
||||
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────────────────┐ │
|
||||
│ │ Claude │ │ Dashboard │ │ NX Nastran │ │
|
||||
│ │ Code │◄──►│ Frontend │ │ (FEA Solver) │ │
|
||||
│ │ Skills │ │ (React) │ └───────────┬─────────────┘ │
|
||||
│ └──────┬──────┘ └──────┬──────┘ │ │
|
||||
│ │ │ │ │
|
||||
│ ▼ ▼ ▼ │
|
||||
│ ┌──────────────────────────────────────────────────────────────┐ │
|
||||
│ │ OPTIMIZATION ENGINE │ │
|
||||
│ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────────┐ │ │
|
||||
│ │ │ Runner │ │ Validator│ │ Extractor│ │ Plugins │ │ │
|
||||
│ │ │ (Optuna) │ │ System │ │ Library │ │ (Hooks) │ │ │
|
||||
│ │ └────┬─────┘ └──────────┘ └──────────┘ └──────────────┘ │ │
|
||||
│ └───────┼──────────────────────────────────────────────────────┘ │
|
||||
│ │ │
|
||||
│ ▼ │
|
||||
│ ┌──────────────────────────────────────────────────────────────┐ │
|
||||
│ │ ATOMIZER-FIELD (Neural) │ │
|
||||
│ │ ┌──────────────┐ ┌──────────────┐ ┌────────────────────┐ │ │
|
||||
│ │ │ Parametric │ │ Field │ │ Physics-Informed │ │ │
|
||||
│ │ │ GNN │ │ Predictor GNN│ │ Training │ │ │
|
||||
│ │ │ (4.5ms) │ │ (50ms) │ │ │ │ │
|
||||
│ │ └──────────────┘ └──────────────┘ └────────────────────┘ │ │
|
||||
│ └──────────────────────────────────────────────────────────────┘ │
|
||||
│ │
|
||||
│ ┌──────────────────────────────────────────────────────────────┐ │
|
||||
│ │ DATA LAYER │ │
|
||||
│ │ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────────┐ │ │
|
||||
│ │ │ study.db │ │history. │ │ training │ │ model │ │ │
|
||||
│ │ │ (Optuna) │ │ json │ │ HDF5 │ │ checkpoints │ │ │
|
||||
│ │ └──────────┘ └──────────┘ └──────────┘ └──────────────┘ │ │
|
||||
│ └──────────────────────────────────────────────────────────────┘ │
|
||||
│ │
|
||||
└─────────────────────────────────────────────────────────────────────┘
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Part 7: Success Metrics
|
||||
|
||||
### Current Performance
|
||||
|
||||
| Metric | Current | Target |
|
||||
|--------|---------|--------|
|
||||
| FEA solve time | 10-30 min | N/A (baseline) |
|
||||
| Neural inference | 4.5ms | <10ms |
|
||||
| Hybrid accuracy | <5% error | <3% error |
|
||||
| Study setup time | 30 min manual | 5 min automated |
|
||||
| Dashboard load time | ~2s | <1s |
|
||||
|
||||
### Definition of "Done" for MVP
|
||||
|
||||
- [ ] One-command neural workflow (`atomizer neural-optimize`)
|
||||
- [ ] Auto training data export integrated in runner
|
||||
- [ ] 3 study templates (beam, bracket, frequency)
|
||||
- [ ] Dashboard frontend polish complete
|
||||
- [ ] Docker deployment working
|
||||
- [ ] 5 end-to-end integration tests passing
|
||||
|
||||
---
|
||||
|
||||
## Part 8: Risk Assessment
|
||||
|
||||
| Risk | Likelihood | Impact | Mitigation |
|
||||
|------|------------|--------|------------|
|
||||
| Neural drift undetected | Medium | High | Implement drift monitoring |
|
||||
| NX license bottleneck | High | Medium | Add license queueing |
|
||||
| Training data insufficient | Low | High | Min 100 points before training |
|
||||
| Dashboard performance | Low | Medium | Pagination + caching |
|
||||
| Config complexity | Medium | Medium | Templates + validation |
|
||||
|
||||
---
|
||||
|
||||
## Conclusion
|
||||
|
||||
Atomizer is **85% complete for production use**. The core optimization engine and neural acceleration are production-ready. The main gaps are:
|
||||
|
||||
1. **Automated neural training pipeline** - Currently manual
|
||||
2. **Dashboard frontend polish** - Functional but incomplete
|
||||
3. **Deployment infrastructure** - No containerization
|
||||
4. **Study templates** - Users start from scratch
|
||||
|
||||
The recommended focus for the next two weeks:
|
||||
1. Close the neural training loop with automation
|
||||
2. Create study templates for quick starts
|
||||
3. Polish the dashboard frontend
|
||||
4. Add Docker deployment
|
||||
|
||||
With these additions, Atomizer will be a complete, self-service structural optimization platform with AI acceleration.
|
||||
|
||||
---
|
||||
|
||||
*Document generated by Claude Code analysis on November 25, 2025*
|
||||
334
docs/08_ARCHIVE/sessions/Phase_1_2_Implementation_Plan.md
Normal file
334
docs/08_ARCHIVE/sessions/Phase_1_2_Implementation_Plan.md
Normal file
@@ -0,0 +1,334 @@
|
||||
# Phase 1.2: Configuration Management Overhaul - Implementation Plan
|
||||
|
||||
**Status**: In Progress
|
||||
**Started**: January 2025
|
||||
**Target Completion**: 2 days
|
||||
|
||||
---
|
||||
|
||||
## ✅ Completed (January 24, 2025)
|
||||
|
||||
### 1. Configuration Inventory
|
||||
- Found 4 `optimization_config.json` files
|
||||
- Found 5 `workflow_config.json` files
|
||||
- Analyzed bracket_V3 (old format) vs drone_gimbal (new format)
|
||||
|
||||
### 2. Schema Analysis
|
||||
Documented critical inconsistencies:
|
||||
- Objectives: `"goal"` (new) vs `"type"` (old)
|
||||
- Design vars: `"parameter"` + `"bounds": [min, max]` (new) vs `"name"` + `"min"/"max"` (old)
|
||||
- Constraints: `"threshold"` (new) vs `"value"` (old)
|
||||
- Location: `1_setup/` (correct) vs root directory (incorrect)
|
||||
|
||||
### 3. JSON Schema Design
|
||||
Created [`optimization_engine/schemas/optimization_config_schema.json`](../../optimization_engine/schemas/optimization_config_schema.json):
|
||||
- Based on drone_gimbal format (cleaner, matches create-study skill)
|
||||
- Validates all required fields
|
||||
- Supports Protocol 10 (single-objective) and Protocol 11 (multi-objective)
|
||||
- Includes extraction spec validation
|
||||
|
||||
---
|
||||
|
||||
## 🔨 Remaining Implementation Tasks
|
||||
|
||||
### Task 1: Implement ConfigManager Class
|
||||
**Priority**: HIGH
|
||||
**File**: `optimization_engine/config_manager.py`
|
||||
|
||||
```python
|
||||
"""Configuration validation and management for Atomizer studies."""
|
||||
|
||||
import json
|
||||
from pathlib import Path
|
||||
from typing import Dict, List, Any, Optional
|
||||
import jsonschema
|
||||
|
||||
class ConfigValidationError(Exception):
|
||||
"""Raised when configuration validation fails."""
|
||||
pass
|
||||
|
||||
class ConfigManager:
|
||||
"""Manages and validates optimization configuration files."""
|
||||
|
||||
def __init__(self, config_path: Path):
|
||||
"""
|
||||
Initialize ConfigManager with path to optimization_config.json.
|
||||
|
||||
Args:
|
||||
config_path: Path to optimization_config.json file
|
||||
"""
|
||||
self.config_path = Path(config_path)
|
||||
self.schema_path = Path(__file__).parent / "schemas" / "optimization_config_schema.json"
|
||||
self.config: Optional[Dict[str, Any]] = None
|
||||
self.validation_errors: List[str] = []
|
||||
|
||||
def load_schema(self) -> Dict[str, Any]:
|
||||
"""Load JSON schema for validation."""
|
||||
with open(self.schema_path, 'r') as f:
|
||||
return json.load(f)
|
||||
|
||||
def load_config(self) -> Dict[str, Any]:
|
||||
"""Load configuration file."""
|
||||
if not self.config_path.exists():
|
||||
raise FileNotFoundError(f"Config file not found: {self.config_path}")
|
||||
|
||||
with open(self.config_path, 'r') as f:
|
||||
self.config = json.load(f)
|
||||
return self.config
|
||||
|
||||
def validate(self) -> bool:
|
||||
"""
|
||||
Validate configuration against schema.
|
||||
|
||||
Returns:
|
||||
True if valid, False otherwise
|
||||
"""
|
||||
if self.config is None:
|
||||
self.load_config()
|
||||
|
||||
schema = self.load_schema()
|
||||
self.validation_errors = []
|
||||
|
||||
try:
|
||||
jsonschema.validate(instance=self.config, schema=schema)
|
||||
# Additional custom validations
|
||||
self._validate_design_variable_bounds()
|
||||
self._validate_multi_objective_consistency()
|
||||
self._validate_file_locations()
|
||||
return True
|
||||
except jsonschema.ValidationError as e:
|
||||
self.validation_errors.append(str(e))
|
||||
return False
|
||||
|
||||
def _validate_design_variable_bounds(self):
|
||||
"""Ensure bounds are valid (min < max)."""
|
||||
for dv in self.config.get("design_variables", []):
|
||||
bounds = dv.get("bounds", [])
|
||||
if len(bounds) == 2 and bounds[0] >= bounds[1]:
|
||||
self.validation_errors.append(
|
||||
f"Design variable '{dv['parameter']}': min ({bounds[0]}) must be < max ({bounds[1]})"
|
||||
)
|
||||
|
||||
def _validate_multi_objective_consistency(self):
|
||||
"""Validate multi-objective settings consistency."""
|
||||
n_objectives = len(self.config.get("objectives", []))
|
||||
protocol = self.config.get("optimization_settings", {}).get("protocol")
|
||||
sampler = self.config.get("optimization_settings", {}).get("sampler")
|
||||
|
||||
if n_objectives > 1:
|
||||
# Multi-objective must use protocol_11 and NSGA-II
|
||||
if protocol != "protocol_11_multi_objective":
|
||||
self.validation_errors.append(
|
||||
f"Multi-objective optimization ({n_objectives} objectives) requires protocol_11_multi_objective"
|
||||
)
|
||||
if sampler != "NSGAIISampler":
|
||||
self.validation_errors.append(
|
||||
f"Multi-objective optimization requires NSGAIISampler (got {sampler})"
|
||||
)
|
||||
|
||||
def _validate_file_locations(self):
|
||||
"""Check if config is in correct location (1_setup/)."""
|
||||
if "1_setup" not in str(self.config_path.parent):
|
||||
self.validation_errors.append(
|
||||
f"Config should be in '1_setup/' directory, found in {self.config_path.parent}"
|
||||
)
|
||||
|
||||
def get_validation_report(self) -> str:
|
||||
"""Get human-readable validation report."""
|
||||
if not self.validation_errors:
|
||||
return "✓ Configuration is valid"
|
||||
|
||||
report = "✗ Configuration validation failed:\n"
|
||||
for i, error in enumerate(self.validation_errors, 1):
|
||||
report += f" {i}. {error}\n"
|
||||
return report
|
||||
|
||||
# Type-safe accessor methods
|
||||
|
||||
def get_design_variables(self) -> List[Dict[str, Any]]:
|
||||
"""Get design variables with validated structure."""
|
||||
if self.config is None:
|
||||
self.load_config()
|
||||
return self.config.get("design_variables", [])
|
||||
|
||||
def get_objectives(self) -> List[Dict[str, Any]]:
|
||||
"""Get objectives with validated structure."""
|
||||
if self.config is None:
|
||||
self.load_config()
|
||||
return self.config.get("objectives", [])
|
||||
|
||||
def get_constraints(self) -> List[Dict[str, Any]]:
|
||||
"""Get constraints with validated structure."""
|
||||
if self.config is None:
|
||||
self.load_config()
|
||||
return self.config.get("constraints", [])
|
||||
|
||||
def get_simulation_settings(self) -> Dict[str, Any]:
|
||||
"""Get simulation settings."""
|
||||
if self.config is None:
|
||||
self.load_config()
|
||||
return self.config.get("simulation", {})
|
||||
|
||||
|
||||
# CLI tool for validation
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
|
||||
if len(sys.argv) < 2:
|
||||
print("Usage: python config_manager.py <path_to_optimization_config.json>")
|
||||
sys.exit(1)
|
||||
|
||||
config_path = Path(sys.argv[1])
|
||||
manager = ConfigManager(config_path)
|
||||
|
||||
try:
|
||||
manager.load_config()
|
||||
is_valid = manager.validate()
|
||||
print(manager.get_validation_report())
|
||||
sys.exit(0 if is_valid else 1)
|
||||
except Exception as e:
|
||||
print(f"Error: {e}")
|
||||
sys.exit(1)
|
||||
```
|
||||
|
||||
**Dependencies**: Add to requirements.txt:
|
||||
```
|
||||
jsonschema>=4.17.0
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Task 2: Create Configuration Migration Tool
|
||||
**Priority**: MEDIUM
|
||||
**File**: `optimization_engine/config_migrator.py`
|
||||
|
||||
Tool to automatically migrate old-format configs to new format:
|
||||
- Convert `"type"` → `"goal"` in objectives
|
||||
- Convert `"min"/"max"` → `"bounds": [min, max]` in design variables
|
||||
- Convert `"name"` → `"parameter"` in design variables
|
||||
- Convert `"value"` → `"threshold"` in constraints
|
||||
- Move config files to `1_setup/` if in wrong location
|
||||
|
||||
---
|
||||
|
||||
### Task 3: Integration with run_optimization.py
|
||||
**Priority**: HIGH
|
||||
|
||||
Add validation to optimization runners:
|
||||
```python
|
||||
# At start of run_optimization.py
|
||||
from optimization_engine.config_manager import ConfigManager
|
||||
|
||||
# Load and validate config
|
||||
config_manager = ConfigManager(Path(__file__).parent / "1_setup" / "optimization_config.json")
|
||||
config_manager.load_config()
|
||||
|
||||
if not config_manager.validate():
|
||||
print(config_manager.get_validation_report())
|
||||
sys.exit(1)
|
||||
|
||||
print("✓ Configuration validated successfully")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
### Task 4: Update create-study Claude Skill
|
||||
**Priority**: HIGH
|
||||
**File**: `.claude/skills/create-study.md`
|
||||
|
||||
Update skill to reference the JSON schema:
|
||||
- Add link to schema documentation
|
||||
- Emphasize validation after generation
|
||||
- Include validation command in "Next Steps"
|
||||
|
||||
---
|
||||
|
||||
### Task 5: Create Configuration Documentation
|
||||
**Priority**: HIGH
|
||||
**File**: `docs/CONFIGURATION_GUIDE.md`
|
||||
|
||||
Comprehensive documentation covering:
|
||||
1. Standard configuration format (with drone_gimbal example)
|
||||
2. Field-by-field descriptions
|
||||
3. Validation rules and how to run validation
|
||||
4. Common validation errors and fixes
|
||||
5. Migration guide for old configs
|
||||
6. Protocol selection (10 vs 11)
|
||||
7. Extractor mapping table
|
||||
|
||||
---
|
||||
|
||||
### Task 6: Validate All Existing Studies
|
||||
**Priority**: MEDIUM
|
||||
|
||||
Run validation on all existing studies:
|
||||
```bash
|
||||
# Test validation tool
|
||||
python optimization_engine/config_manager.py studies/drone_gimbal_arm_optimization/1_setup/optimization_config.json
|
||||
python optimization_engine/config_manager.py studies/bracket_stiffness_optimization_V3/optimization_config.json
|
||||
|
||||
# Expected: drone passes, bracket_V3 fails with specific errors
|
||||
```
|
||||
|
||||
Create migration plan for failing configs.
|
||||
|
||||
---
|
||||
|
||||
### Task 7: Migrate Legacy Configs
|
||||
**Priority**: LOW (can defer to Phase 1.3)
|
||||
|
||||
Migrate all legacy configs to new format:
|
||||
- bracket_stiffness_optimization_V3
|
||||
- bracket_stiffness_optimization_V2
|
||||
- bracket_stiffness_optimization
|
||||
|
||||
Keep old versions in `archive/` for reference.
|
||||
|
||||
---
|
||||
|
||||
## Success Criteria
|
||||
|
||||
Phase 1.2 is complete when:
|
||||
- [x] JSON schema created and comprehensive
|
||||
- [ ] ConfigManager class implemented with all validation methods
|
||||
- [ ] Validation integrated into at least 1 study (drone_gimbal)
|
||||
- [ ] Configuration documentation written
|
||||
- [ ] create-study skill updated with schema reference
|
||||
- [ ] Migration tool created (basic version)
|
||||
- [ ] All tests pass on drone_gimbal study
|
||||
- [ ] Phase 1.2 changes committed with clear message
|
||||
|
||||
---
|
||||
|
||||
## Testing Plan
|
||||
|
||||
1. **Schema Validation Test**:
|
||||
- Valid config passes ✓
|
||||
- Invalid configs fail with clear errors ✓
|
||||
|
||||
2. **ConfigManager Test**:
|
||||
- Load valid config
|
||||
- Validate and get clean report
|
||||
- Load invalid config
|
||||
- Validate and get error details
|
||||
|
||||
3. **Integration Test**:
|
||||
- Run drone_gimbal study with validation enabled
|
||||
- Verify no performance impact
|
||||
- Check validation messages appear correctly
|
||||
|
||||
4. **Migration Test**:
|
||||
- Migrate bracket_V3 config
|
||||
- Validate migrated config
|
||||
- Compare before/after
|
||||
|
||||
---
|
||||
|
||||
## Next Phase Preview
|
||||
|
||||
**Phase 1.3: Error Handling & Logging** will build on this by:
|
||||
- Adding structured logging with configuration context
|
||||
- Error recovery using validated configurations
|
||||
- Checkpoint system that validates config before saving
|
||||
|
||||
The clean configuration management from Phase 1.2 enables reliable error handling in Phase 1.3.
|
||||
312
docs/08_ARCHIVE/sessions/Phase_1_3_Implementation_Plan.md
Normal file
312
docs/08_ARCHIVE/sessions/Phase_1_3_Implementation_Plan.md
Normal file
@@ -0,0 +1,312 @@
|
||||
# Phase 1.3: Error Handling & Logging - Implementation Plan
|
||||
|
||||
**Goal**: Implement production-ready logging and error handling system for MVP stability.
|
||||
|
||||
**Status**: MVP Complete (2025-11-24)
|
||||
|
||||
## Overview
|
||||
|
||||
Phase 1.3 establishes a consistent, professional logging system across all Atomizer optimization studies. This replaces ad-hoc `print()` statements with structured logging that supports:
|
||||
|
||||
- File and console output
|
||||
- Color-coded log levels (Windows 10+ and Unix)
|
||||
- Trial-specific logging methods
|
||||
- Automatic log rotation
|
||||
- Zero external dependencies (stdlib only)
|
||||
|
||||
## Problem Analysis
|
||||
|
||||
### Current State (Before Phase 1.3)
|
||||
|
||||
Analyzed the codebase and found:
|
||||
- **1416 occurrences** of logging/print across 79 files (mostly ad-hoc `print()` statements)
|
||||
- **411 occurrences** of `try:/except/raise` across 59 files
|
||||
- Mixed error handling approaches:
|
||||
- Some studies use traceback.print_exc()
|
||||
- Some use simple print() for errors
|
||||
- No consistent logging format
|
||||
- No file logging in most studies
|
||||
- Some studies have `--resume` capability, but implementation varies
|
||||
|
||||
### Requirements
|
||||
|
||||
1. **Drop-in Replacement**: Minimal code changes to adopt
|
||||
2. **Production-Ready**: File logging with rotation, timestamps, proper levels
|
||||
3. **Dashboard-Friendly**: Structured trial logging for future integration
|
||||
4. **Windows-Compatible**: ANSI color support on Windows 10+
|
||||
5. **No Dependencies**: Use only Python stdlib
|
||||
|
||||
---
|
||||
|
||||
## ✅ Phase 1.3 MVP - Completed (2025-11-24)
|
||||
|
||||
### Task 1: Structured Logging System ✅ DONE
|
||||
|
||||
**File Created**: `optimization_engine/logger.py` (330 lines)
|
||||
|
||||
**Features Implemented**:
|
||||
|
||||
1. **AtomizerLogger Class** - Extended logger with trial-specific methods:
|
||||
```python
|
||||
logger.trial_start(trial_number=5, design_vars={"thickness": 2.5})
|
||||
logger.trial_complete(trial_number=5, objectives={"mass": 120})
|
||||
logger.trial_failed(trial_number=5, error="Simulation failed")
|
||||
logger.study_start(study_name="test", n_trials=30, sampler="TPESampler")
|
||||
logger.study_complete(study_name="test", n_trials=30, n_successful=28)
|
||||
```
|
||||
|
||||
2. **Color-Coded Console Output** - ANSI colors for Windows and Unix:
|
||||
- DEBUG: Cyan
|
||||
- INFO: Green
|
||||
- WARNING: Yellow
|
||||
- ERROR: Red
|
||||
- CRITICAL: Magenta
|
||||
|
||||
3. **File Logging with Rotation**:
|
||||
- Automatically creates `{study_dir}/optimization.log`
|
||||
- 50MB max file size
|
||||
- 3 backup files (optimization.log.1, .2, .3)
|
||||
- UTF-8 encoding
|
||||
- Detailed format: `timestamp | level | module | message`
|
||||
|
||||
4. **Simple API**:
|
||||
```python
|
||||
# Basic logger
|
||||
from optimization_engine.logger import get_logger
|
||||
logger = get_logger(__name__)
|
||||
logger.info("Starting optimization...")
|
||||
|
||||
# Study logger with file output
|
||||
logger = get_logger(
|
||||
"drone_gimbal_arm",
|
||||
study_dir=Path("studies/drone_gimbal_arm/2_results")
|
||||
)
|
||||
```
|
||||
|
||||
**Testing**: Successfully tested on Windows with color output and file logging.
|
||||
|
||||
### Task 2: Documentation ✅ DONE
|
||||
|
||||
**File Created**: This implementation plan
|
||||
|
||||
**Docstrings**: Comprehensive docstrings in `logger.py` with usage examples
|
||||
|
||||
---
|
||||
|
||||
## 🔨 Remaining Tasks (Phase 1.3.1+)
|
||||
|
||||
### Phase 1.3.1: Integration with Existing Studies
|
||||
|
||||
**Priority**: HIGH | **Effort**: 1-2 days
|
||||
|
||||
1. **Update drone_gimbal_arm_optimization study** (Reference implementation)
|
||||
- Replace print() statements with logger calls
|
||||
- Add file logging to 2_results/
|
||||
- Use trial-specific logging methods
|
||||
- Test to ensure colors work, logs rotate
|
||||
|
||||
2. **Create Migration Guide**
|
||||
- Document how to convert existing studies
|
||||
- Provide before/after examples
|
||||
- Add to DEVELOPMENT.md
|
||||
|
||||
3. **Update create-study Claude Skill**
|
||||
- Include logger setup in generated run_optimization.py
|
||||
- Add logging best practices
|
||||
|
||||
### Phase 1.3.2: Enhanced Error Recovery
|
||||
|
||||
**Priority**: MEDIUM | **Effort**: 2-3 days
|
||||
|
||||
1. **Study Checkpoint Manager**
|
||||
- Automatic checkpointing every N trials
|
||||
- Save study state to `2_results/checkpoint.json`
|
||||
- Resume from last checkpoint on crash
|
||||
- Clean up old checkpoints
|
||||
|
||||
2. **Enhanced Error Context**
|
||||
- Capture design variables on failure
|
||||
- Log simulation command that failed
|
||||
- Include FEA solver output in error log
|
||||
- Structured error reporting for dashboard
|
||||
|
||||
3. **Graceful Degradation**
|
||||
- Fallback when file logging fails
|
||||
- Handle disk full scenarios
|
||||
- Continue optimization if dashboard unreachable
|
||||
|
||||
### Phase 1.3.3: Notification System (Future)
|
||||
|
||||
**Priority**: LOW | **Effort**: 1-2 days
|
||||
|
||||
1. **Study Completion Notifications**
|
||||
- Optional email notification when study completes
|
||||
- Configurable via environment variables
|
||||
- Include summary (best trial, success rate, etc.)
|
||||
|
||||
2. **Error Alerts**
|
||||
- Optional notifications on critical failures
|
||||
- Threshold-based (e.g., >50% trials failing)
|
||||
|
||||
---
|
||||
|
||||
## Migration Strategy
|
||||
|
||||
### Priority 1: New Studies (Immediate)
|
||||
|
||||
All new studies created via create-study skill should use the new logging system by default.
|
||||
|
||||
**Action**: Update `.claude/skills/create-study.md` to generate run_optimization.py with logger.
|
||||
|
||||
### Priority 2: Reference Study (Phase 1.3.1)
|
||||
|
||||
Update `drone_gimbal_arm_optimization` as the reference implementation.
|
||||
|
||||
**Before**:
|
||||
```python
|
||||
print(f"Trial #{trial.number}")
|
||||
print(f"Design Variables:")
|
||||
for name, value in design_vars.items():
|
||||
print(f" {name}: {value:.3f}")
|
||||
```
|
||||
|
||||
**After**:
|
||||
```python
|
||||
logger.trial_start(trial.number, design_vars)
|
||||
```
|
||||
|
||||
### Priority 3: Other Studies (Phase 1.3.2)
|
||||
|
||||
Migrate remaining studies (bracket_stiffness, simple_beam, etc.) gradually.
|
||||
|
||||
**Timeline**: After drone_gimbal reference implementation is validated.
|
||||
|
||||
---
|
||||
|
||||
## API Reference
|
||||
|
||||
### Basic Usage
|
||||
|
||||
```python
|
||||
from optimization_engine.logger import get_logger
|
||||
|
||||
# Module logger
|
||||
logger = get_logger(__name__)
|
||||
logger.info("Starting optimization")
|
||||
logger.warning("Design variable out of range")
|
||||
logger.error("Simulation failed", exc_info=True)
|
||||
```
|
||||
|
||||
### Study Logger
|
||||
|
||||
```python
|
||||
from optimization_engine.logger import get_logger
|
||||
from pathlib import Path
|
||||
|
||||
# Create study logger with file logging
|
||||
logger = get_logger(
|
||||
name="drone_gimbal_arm",
|
||||
study_dir=Path("studies/drone_gimbal_arm/2_results")
|
||||
)
|
||||
|
||||
# Study lifecycle
|
||||
logger.study_start("drone_gimbal_arm", n_trials=30, sampler="NSGAIISampler")
|
||||
|
||||
# Trial logging
|
||||
logger.trial_start(1, {"thickness": 2.5, "width": 10.0})
|
||||
logger.info("Running FEA simulation...")
|
||||
logger.trial_complete(
|
||||
1,
|
||||
objectives={"mass": 120, "stiffness": 1500},
|
||||
constraints={"max_stress": 85},
|
||||
feasible=True
|
||||
)
|
||||
|
||||
# Error handling
|
||||
try:
|
||||
result = run_simulation()
|
||||
except Exception as e:
|
||||
logger.trial_failed(trial_number=2, error=str(e))
|
||||
logger.error("Full traceback:", exc_info=True)
|
||||
raise
|
||||
|
||||
logger.study_complete("drone_gimbal_arm", n_trials=30, n_successful=28)
|
||||
```
|
||||
|
||||
### Log Levels
|
||||
|
||||
```python
|
||||
import logging
|
||||
|
||||
# Set logger level
|
||||
logger = get_logger(__name__, level=logging.DEBUG)
|
||||
|
||||
logger.debug("Detailed debugging information")
|
||||
logger.info("General information")
|
||||
logger.warning("Warning message")
|
||||
logger.error("Error occurred")
|
||||
logger.critical("Critical failure")
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## File Structure
|
||||
|
||||
```
|
||||
optimization_engine/
|
||||
├── logger.py # ✅ NEW - Structured logging system
|
||||
└── config_manager.py # Phase 1.2
|
||||
|
||||
docs/07_DEVELOPMENT/
|
||||
├── Phase_1_2_Implementation_Plan.md # Phase 1.2
|
||||
└── Phase_1_3_Implementation_Plan.md # ✅ NEW - This file
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Testing Checklist
|
||||
|
||||
- [x] Logger creates file at correct location
|
||||
- [x] Color output works on Windows 10
|
||||
- [x] Log rotation works (max 50MB, 3 backups)
|
||||
- [x] Trial-specific methods format correctly
|
||||
- [x] UTF-8 encoding handles special characters
|
||||
- [ ] Integration test with real optimization study
|
||||
- [ ] Verify dashboard can parse structured logs
|
||||
- [ ] Test error scenarios (disk full, permission denied)
|
||||
|
||||
---
|
||||
|
||||
## Success Metrics
|
||||
|
||||
**Phase 1.3 MVP** (Complete):
|
||||
- [x] Structured logging system implemented
|
||||
- [x] Zero external dependencies
|
||||
- [x] Works on Windows and Unix
|
||||
- [x] File + console logging
|
||||
- [x] Trial-specific methods
|
||||
|
||||
**Phase 1.3.1** (Next):
|
||||
- [ ] At least one study uses new logging
|
||||
- [ ] Migration guide written
|
||||
- [ ] create-study skill updated
|
||||
|
||||
**Phase 1.3.2** (Later):
|
||||
- [ ] Checkpoint/resume system
|
||||
- [ ] Enhanced error reporting
|
||||
- [ ] All studies migrated
|
||||
|
||||
---
|
||||
|
||||
## References
|
||||
|
||||
- **Phase 1.2**: [Configuration Management](./Phase_1_2_Implementation_Plan.md)
|
||||
- **MVP Plan**: [12-Week Development Plan](./Today_Todo.md)
|
||||
- **Python Logging**: https://docs.python.org/3/library/logging.html
|
||||
- **Log Rotation**: https://docs.python.org/3/library/logging.handlers.html#rotatingfilehandler
|
||||
|
||||
---
|
||||
|
||||
## Questions?
|
||||
|
||||
For MVP development questions, refer to [DEVELOPMENT.md](../../DEVELOPMENT.md) or the main plan in `docs/07_DEVELOPMENT/Today_Todo.md`.
|
||||
752
docs/08_ARCHIVE/sessions/Today_Todo.md
Normal file
752
docs/08_ARCHIVE/sessions/Today_Todo.md
Normal file
@@ -0,0 +1,752 @@
|
||||
# Atomizer MVP Development Plan
|
||||
|
||||
> **Objective**: Create a robust, production-ready Atomizer MVP with professional dashboard and solid foundation for future extensions
|
||||
>
|
||||
> **Timeline**: 8-12 weeks to complete MVP
|
||||
>
|
||||
> **Mode**: Claude Code assistance (no LLM API integration for now)
|
||||
>
|
||||
> **Last Updated**: January 2025
|
||||
|
||||
---
|
||||
|
||||
## 📋 Executive Summary
|
||||
|
||||
### Current State
|
||||
- **Core Engine**: 95% complete, needs polish
|
||||
- **Plugin System**: 100% complete, needs documentation
|
||||
- **Dashboard**: 40% complete, needs major overhaul
|
||||
- **LLM Components**: Built but not integrated (defer to post-MVP)
|
||||
- **Documentation**: Scattered, needs consolidation
|
||||
|
||||
### MVP Goal
|
||||
A **production-ready optimization tool** that:
|
||||
- Runs reliable FEA optimizations via manual configuration
|
||||
- Provides professional dashboard for monitoring and analysis
|
||||
- Has clear documentation and examples
|
||||
- Is extensible for future LLM/AtomizerField integration
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Phase 1: Core Stabilization (Week 1-2)
|
||||
|
||||
### 1.1 Code Cleanup & Organization
|
||||
**Priority**: HIGH | **Effort**: 3 days
|
||||
|
||||
#### Tasks
|
||||
```markdown
|
||||
[ ] Consolidate duplicate runner code
|
||||
- Merge runner.py and llm_optimization_runner.py logic
|
||||
- Create single OptimizationRunner with mode flag
|
||||
- Remove redundant workflow implementations
|
||||
|
||||
[ ] Standardize naming conventions
|
||||
- Convert all to snake_case
|
||||
- Rename protocol files with consistent pattern
|
||||
- Update imports across codebase
|
||||
|
||||
[ ] Clean up project structure
|
||||
- Archive old/experimental files to `archive/`
|
||||
- Remove unused imports and dead code
|
||||
- Organize tests into proper test suite
|
||||
```
|
||||
|
||||
#### File Structure After Cleanup
|
||||
```
|
||||
Atomizer/
|
||||
├── optimization_engine/
|
||||
│ ├── core/
|
||||
│ │ ├── runner.py # Single unified runner
|
||||
│ │ ├── nx_interface.py # All NX interactions
|
||||
│ │ └── config_manager.py # Configuration with validation
|
||||
│ ├── extractors/
|
||||
│ │ ├── base.py # Base extractor class
|
||||
│ │ ├── stress.py # Stress extractor
|
||||
│ │ ├── displacement.py # Displacement extractor
|
||||
│ │ └── registry.py # Extractor registry
|
||||
│ ├── plugins/
|
||||
│ │ └── [existing structure]
|
||||
│ └── future/ # LLM components (not used in MVP)
|
||||
│ ├── llm_analyzer.py
|
||||
│ └── research_agent.py
|
||||
```
|
||||
|
||||
### 1.2 Configuration Management Overhaul
|
||||
**Priority**: HIGH | **Effort**: 2 days
|
||||
|
||||
#### Tasks
|
||||
```markdown
|
||||
[ ] Implement JSON Schema validation
|
||||
- Create schemas/ directory
|
||||
- Define optimization_config_schema.json
|
||||
- Add validation on config load
|
||||
|
||||
[ ] Add configuration builder class
|
||||
- Type checking for all parameters
|
||||
- Bounds validation for design variables
|
||||
- Automatic unit conversion
|
||||
|
||||
[ ] Environment auto-detection
|
||||
- Auto-find NX installation
|
||||
- Detect Python environments
|
||||
- Create setup wizard for first run
|
||||
```
|
||||
|
||||
#### New Configuration System
|
||||
```python
|
||||
# optimization_engine/core/config_manager.py
|
||||
class ConfigManager:
|
||||
def __init__(self, config_path: Path):
|
||||
self.schema = self.load_schema()
|
||||
self.config = self.load_and_validate(config_path)
|
||||
|
||||
def validate(self) -> List[str]:
|
||||
"""Return list of validation errors"""
|
||||
|
||||
def get_design_variables(self) -> List[DesignVariable]:
|
||||
"""Type-safe design variable access"""
|
||||
|
||||
def get_objectives(self) -> List[Objective]:
|
||||
"""Type-safe objective access"""
|
||||
```
|
||||
|
||||
### 1.3 Error Handling & Logging
|
||||
**Priority**: HIGH | **Effort**: 2 days
|
||||
|
||||
#### Tasks
|
||||
```markdown
|
||||
[ ] Implement comprehensive logging system
|
||||
- Structured logging with levels
|
||||
- Separate logs for engine, extractors, plugins
|
||||
- Rotating log files with size limits
|
||||
|
||||
[ ] Add error recovery mechanisms
|
||||
- Checkpoint saves every N trials
|
||||
- Automatic resume on crash
|
||||
- Graceful degradation on plugin failure
|
||||
|
||||
[ ] Create notification system
|
||||
- Email alerts for completion/failure
|
||||
- Slack/Teams integration (optional)
|
||||
- Dashboard notifications
|
||||
```
|
||||
|
||||
#### Logging Architecture
|
||||
```python
|
||||
# optimization_engine/core/logging_config.py
|
||||
LOGGING_CONFIG = {
|
||||
'version': 1,
|
||||
'handlers': {
|
||||
'console': {...},
|
||||
'file': {
|
||||
'class': 'logging.handlers.RotatingFileHandler',
|
||||
'maxBytes': 10485760, # 10MB
|
||||
'backupCount': 5
|
||||
},
|
||||
'error_file': {...}
|
||||
},
|
||||
'loggers': {
|
||||
'optimization_engine': {'level': 'INFO'},
|
||||
'extractors': {'level': 'DEBUG'},
|
||||
'plugins': {'level': 'INFO'}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🖥️ Phase 2: Dashboard Professional Overhaul (Week 3-5)
|
||||
|
||||
### 2.1 Frontend Architecture Redesign
|
||||
**Priority**: CRITICAL | **Effort**: 5 days
|
||||
|
||||
#### Current Problems
|
||||
- Vanilla JavaScript (hard to maintain)
|
||||
- No state management
|
||||
- Poor component organization
|
||||
- Limited error handling
|
||||
- No responsive design
|
||||
|
||||
#### New Architecture
|
||||
```markdown
|
||||
[ ] Migrate to modern React with TypeScript
|
||||
- Set up Vite build system
|
||||
- Configure TypeScript strictly
|
||||
- Add ESLint and Prettier
|
||||
|
||||
[ ] Implement proper state management
|
||||
- Use Zustand for global state
|
||||
- React Query for API calls
|
||||
- Optimistic updates
|
||||
|
||||
[ ] Create component library
|
||||
- Consistent design system
|
||||
- Reusable components
|
||||
- Storybook for documentation
|
||||
```
|
||||
|
||||
#### New Frontend Structure
|
||||
```
|
||||
dashboard/frontend/
|
||||
├── src/
|
||||
│ ├── components/
|
||||
│ │ ├── common/ # Buttons, Cards, Modals
|
||||
│ │ ├── charts/ # Chart components
|
||||
│ │ ├── optimization/ # Optimization-specific
|
||||
│ │ └── layout/ # Header, Sidebar, Footer
|
||||
│ ├── pages/
|
||||
│ │ ├── Dashboard.tsx # Main dashboard
|
||||
│ │ ├── StudyDetail.tsx # Single study view
|
||||
│ │ ├── NewStudy.tsx # Study creation wizard
|
||||
│ │ └── Settings.tsx # Configuration
|
||||
│ ├── services/
|
||||
│ │ ├── api.ts # API client
|
||||
│ │ ├── websocket.ts # Real-time updates
|
||||
│ │ └── storage.ts # Local storage
|
||||
│ ├── hooks/ # Custom React hooks
|
||||
│ ├── utils/ # Utilities
|
||||
│ └── types/ # TypeScript types
|
||||
```
|
||||
|
||||
### 2.2 UI/UX Improvements
|
||||
**Priority**: HIGH | **Effort**: 3 days
|
||||
|
||||
#### Design System
|
||||
```markdown
|
||||
[ ] Create consistent design language
|
||||
- Color palette with semantic meaning
|
||||
- Typography scale
|
||||
- Spacing system (4px grid)
|
||||
- Shadow and elevation system
|
||||
|
||||
[ ] Implement dark/light theme
|
||||
- System preference detection
|
||||
- Manual toggle
|
||||
- Persistent preference
|
||||
|
||||
[ ] Add responsive design
|
||||
- Mobile-first approach
|
||||
- Breakpoints: 640px, 768px, 1024px, 1280px
|
||||
- Touch-friendly interactions
|
||||
```
|
||||
|
||||
#### Key UI Components to Build
|
||||
```markdown
|
||||
[ ] Study Card Component
|
||||
- Status indicator (running/complete/failed)
|
||||
- Progress bar with ETA
|
||||
- Key metrics display
|
||||
- Quick actions menu
|
||||
|
||||
[ ] Interactive Charts
|
||||
- Zoomable convergence plot
|
||||
- 3D Pareto front (for 3+ objectives)
|
||||
- Parallel coordinates with filtering
|
||||
- Parameter importance plot
|
||||
|
||||
[ ] Study Creation Wizard
|
||||
- Step-by-step guided process
|
||||
- File drag-and-drop with validation
|
||||
- Visual parameter bounds editor
|
||||
- Configuration preview
|
||||
|
||||
[ ] Results Analysis View
|
||||
- Best trials table with sorting
|
||||
- Parameter correlation matrix
|
||||
- Constraint satisfaction overview
|
||||
- Export options (CSV, PDF, Python)
|
||||
```
|
||||
|
||||
### 2.3 Backend API Improvements
|
||||
**Priority**: HIGH | **Effort**: 3 days
|
||||
|
||||
#### Tasks
|
||||
```markdown
|
||||
[ ] Migrate from Flask to FastAPI completely
|
||||
- OpenAPI documentation
|
||||
- Automatic validation
|
||||
- Async support
|
||||
|
||||
[ ] Implement proper database
|
||||
- SQLite for study metadata
|
||||
- Efficient trial data queries
|
||||
- Study comparison features
|
||||
|
||||
[ ] Add caching layer
|
||||
- Redis for real-time data
|
||||
- Response caching
|
||||
- WebSocket message queuing
|
||||
```
|
||||
|
||||
#### New API Structure
|
||||
```python
|
||||
# dashboard/backend/api/routes.py
|
||||
@router.get("/studies", response_model=List[StudySummary])
|
||||
async def list_studies(
|
||||
status: Optional[StudyStatus] = None,
|
||||
limit: int = Query(100, le=1000),
|
||||
offset: int = 0
|
||||
):
|
||||
"""List all studies with filtering and pagination"""
|
||||
|
||||
@router.post("/studies", response_model=StudyResponse)
|
||||
async def create_study(
|
||||
study: StudyCreate,
|
||||
background_tasks: BackgroundTasks
|
||||
):
|
||||
"""Create new study and start optimization"""
|
||||
|
||||
@router.websocket("/ws/{study_id}")
|
||||
async def websocket_endpoint(
|
||||
websocket: WebSocket,
|
||||
study_id: int
|
||||
):
|
||||
"""Real-time study updates"""
|
||||
```
|
||||
|
||||
### 2.4 Dashboard Features
|
||||
**Priority**: HIGH | **Effort**: 4 days
|
||||
|
||||
#### Essential Features
|
||||
```markdown
|
||||
[ ] Live optimization monitoring
|
||||
- Real-time trial updates
|
||||
- Resource usage (CPU, memory)
|
||||
- Estimated time remaining
|
||||
- Pause/resume capability
|
||||
|
||||
[ ] Advanced filtering and search
|
||||
- Filter by status, date, objective
|
||||
- Search by study name, config
|
||||
- Tag system for organization
|
||||
|
||||
[ ] Batch operations
|
||||
- Compare multiple studies
|
||||
- Bulk export results
|
||||
- Archive old studies
|
||||
- Clone study configuration
|
||||
|
||||
[ ] Analysis tools
|
||||
- Sensitivity analysis
|
||||
- Parameter importance (SHAP-like)
|
||||
- Convergence diagnostics
|
||||
- Optimization health metrics
|
||||
```
|
||||
|
||||
#### Nice-to-Have Features
|
||||
```markdown
|
||||
[ ] Collaboration features
|
||||
- Share study via link
|
||||
- Comments on trials
|
||||
- Study annotations
|
||||
|
||||
[ ] Advanced visualizations
|
||||
- Animation of optimization progress
|
||||
- Interactive 3D scatter plots
|
||||
- Heatmaps for parameter interactions
|
||||
|
||||
[ ] Integration features
|
||||
- Jupyter notebook export
|
||||
- MATLAB export
|
||||
- Excel report generation
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🔧 Phase 3: Extractor & Plugin Enhancement (Week 6-7)
|
||||
|
||||
### 3.1 Extractor Library Expansion
|
||||
**Priority**: MEDIUM | **Effort**: 3 days
|
||||
|
||||
#### New Extractors to Implement
|
||||
```markdown
|
||||
[ ] Modal Analysis Extractor
|
||||
- Natural frequencies
|
||||
- Mode shapes
|
||||
- Modal mass participation
|
||||
|
||||
[ ] Thermal Analysis Extractor
|
||||
- Temperature distribution
|
||||
- Heat flux
|
||||
- Thermal gradients
|
||||
|
||||
[ ] Fatigue Analysis Extractor
|
||||
- Life cycles
|
||||
- Damage accumulation
|
||||
- Safety factors
|
||||
|
||||
[ ] Composite Analysis Extractor
|
||||
- Layer stresses
|
||||
- Failure indices
|
||||
- Interlaminar stresses
|
||||
```
|
||||
|
||||
#### Extractor Template
|
||||
```python
|
||||
# optimization_engine/extractors/template.py
|
||||
from typing import Dict, Any, Optional
|
||||
from pathlib import Path
|
||||
from .base import BaseExtractor
|
||||
|
||||
class CustomExtractor(BaseExtractor):
|
||||
"""Extract [specific] results from FEA output files."""
|
||||
|
||||
def __init__(self, config: Optional[Dict[str, Any]] = None):
|
||||
super().__init__(config)
|
||||
self.supported_formats = ['.op2', '.f06', '.pch']
|
||||
|
||||
def extract(self, file_path: Path) -> Dict[str, Any]:
|
||||
"""Extract results from file."""
|
||||
self.validate_file(file_path)
|
||||
|
||||
# Implementation specific to result type
|
||||
results = self._parse_file(file_path)
|
||||
|
||||
return {
|
||||
'max_value': results.max(),
|
||||
'min_value': results.min(),
|
||||
'average': results.mean(),
|
||||
'location_max': results.location_of_max(),
|
||||
'metadata': self._get_metadata(file_path)
|
||||
}
|
||||
|
||||
def validate(self, results: Dict[str, Any]) -> bool:
|
||||
"""Validate extracted results."""
|
||||
required_keys = ['max_value', 'min_value', 'average']
|
||||
return all(key in results for key in required_keys)
|
||||
```
|
||||
|
||||
### 3.2 Plugin System Documentation
|
||||
**Priority**: MEDIUM | **Effort**: 2 days
|
||||
|
||||
#### Tasks
|
||||
```markdown
|
||||
[ ] Create plugin developer guide
|
||||
- Hook lifecycle documentation
|
||||
- Context object specification
|
||||
- Example plugins with comments
|
||||
|
||||
[ ] Build plugin testing framework
|
||||
- Mock trial data generator
|
||||
- Plugin validation suite
|
||||
- Performance benchmarks
|
||||
|
||||
[ ] Add plugin marketplace concept
|
||||
- Plugin registry/catalog
|
||||
- Version management
|
||||
- Dependency handling
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📚 Phase 4: Documentation & Examples (Week 8)
|
||||
|
||||
### 4.1 User Documentation
|
||||
**Priority**: HIGH | **Effort**: 3 days
|
||||
|
||||
#### Documentation Structure
|
||||
```markdown
|
||||
docs/
|
||||
├── user-guide/
|
||||
│ ├── getting-started.md
|
||||
│ ├── installation.md
|
||||
│ ├── first-optimization.md
|
||||
│ ├── configuration-guide.md
|
||||
│ └── troubleshooting.md
|
||||
├── tutorials/
|
||||
│ ├── bracket-optimization/
|
||||
│ ├── heat-sink-design/
|
||||
│ └── composite-layup/
|
||||
├── api-reference/
|
||||
│ ├── extractors.md
|
||||
│ ├── plugins.md
|
||||
│ └── configuration.md
|
||||
└── developer-guide/
|
||||
├── architecture.md
|
||||
├── contributing.md
|
||||
└── extending-atomizer.md
|
||||
```
|
||||
|
||||
### 4.2 Example Studies
|
||||
**Priority**: HIGH | **Effort**: 2 days
|
||||
|
||||
#### Complete Example Studies to Create
|
||||
```markdown
|
||||
[ ] Simple Beam Optimization
|
||||
- Single objective (minimize stress)
|
||||
- 2 design variables
|
||||
- Full documentation
|
||||
|
||||
[ ] Multi-Objective Bracket
|
||||
- Minimize mass and stress
|
||||
- 5 design variables
|
||||
- Constraint handling
|
||||
|
||||
[ ] Thermal-Structural Coupling
|
||||
- Temperature-dependent properties
|
||||
- Multi-physics extraction
|
||||
- Complex constraints
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🚀 Phase 5: Testing & Deployment (Week 9-10)
|
||||
|
||||
### 5.1 Comprehensive Testing
|
||||
**Priority**: CRITICAL | **Effort**: 4 days
|
||||
|
||||
#### Test Coverage Goals
|
||||
```markdown
|
||||
[ ] Unit tests: >80% coverage
|
||||
- All extractors
|
||||
- Configuration validation
|
||||
- Plugin system
|
||||
|
||||
[ ] Integration tests
|
||||
- Full optimization workflow
|
||||
- Dashboard API endpoints
|
||||
- WebSocket communications
|
||||
|
||||
[ ] End-to-end tests
|
||||
- Study creation to completion
|
||||
- Error recovery scenarios
|
||||
- Multi-study management
|
||||
|
||||
[ ] Performance tests
|
||||
- 100+ trial optimizations
|
||||
- Concurrent study execution
|
||||
- Dashboard with 1000+ studies
|
||||
```
|
||||
|
||||
### 5.2 Deployment Preparation
|
||||
**Priority**: MEDIUM | **Effort**: 3 days
|
||||
|
||||
#### Tasks
|
||||
```markdown
|
||||
[ ] Create Docker containers
|
||||
- Backend service
|
||||
- Frontend service
|
||||
- Database service
|
||||
|
||||
[ ] Write deployment guide
|
||||
- Local installation
|
||||
- Server deployment
|
||||
- Cloud deployment (AWS/Azure)
|
||||
|
||||
[ ] Create installer package
|
||||
- Windows MSI installer
|
||||
- Linux DEB/RPM packages
|
||||
- macOS DMG
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🔮 Phase 6: Future Preparation (Week 11-12)
|
||||
|
||||
### 6.1 AtomizerField Integration Preparation
|
||||
**Priority**: LOW | **Effort**: 2 days
|
||||
|
||||
#### Documentation Only (No Implementation)
|
||||
```markdown
|
||||
[ ] Create integration specification
|
||||
- Data flow between Atomizer and AtomizerField
|
||||
- API contracts
|
||||
- Performance requirements
|
||||
|
||||
[ ] Design surrogate model interface
|
||||
- Abstract base class for surrogates
|
||||
- Neural field surrogate implementation plan
|
||||
- Gaussian Process comparison
|
||||
|
||||
[ ] Plan training data generation
|
||||
- Automated study creation for training
|
||||
- Data format specification
|
||||
- Storage and versioning strategy
|
||||
```
|
||||
|
||||
#### Integration Architecture Document
|
||||
```markdown
|
||||
# atomizer-field-integration.md
|
||||
|
||||
## Overview
|
||||
AtomizerField will integrate as a surrogate model provider
|
||||
|
||||
## Integration Points
|
||||
1. Training data generation via Atomizer studies
|
||||
2. Surrogate model predictions in optimization loop
|
||||
3. Field visualization in dashboard
|
||||
4. Uncertainty quantification display
|
||||
|
||||
## API Design
|
||||
```python
|
||||
class NeuralFieldSurrogate(BaseSurrogate):
|
||||
def predict(self, params: Dict) -> Tuple[float, float]:
|
||||
"""Returns (mean, uncertainty)"""
|
||||
|
||||
def update(self, new_data: Trial) -> None:
|
||||
"""Online learning with new trials"""
|
||||
```
|
||||
|
||||
## Data Pipeline
|
||||
Atomizer → Training Data → AtomizerField → Predictions → Optimizer
|
||||
```
|
||||
|
||||
### 6.2 LLM Integration Preparation
|
||||
**Priority**: LOW | **Effort**: 2 days
|
||||
|
||||
#### Documentation Only
|
||||
```markdown
|
||||
[ ] Document LLM integration points
|
||||
- Where LLM will hook into system
|
||||
- Required APIs
|
||||
- Security considerations
|
||||
|
||||
[ ] Create prompting strategy
|
||||
- System prompts for different tasks
|
||||
- Few-shot examples
|
||||
- Error handling patterns
|
||||
|
||||
[ ] Plan gradual rollout
|
||||
- Feature flags for LLM features
|
||||
- A/B testing framework
|
||||
- Fallback mechanisms
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📊 Success Metrics
|
||||
|
||||
### MVP Success Criteria
|
||||
```markdown
|
||||
✓ Run 100-trial optimization without crashes
|
||||
✓ Dashboard loads in <2 seconds
|
||||
✓ All core extractors working (stress, displacement, modal)
|
||||
✓ Plugin system documented with 3+ examples
|
||||
✓ 80%+ test coverage
|
||||
✓ Complete user documentation
|
||||
✓ 3 full example studies
|
||||
✓ Docker deployment working
|
||||
```
|
||||
|
||||
### Quality Metrics
|
||||
```markdown
|
||||
- Code complexity: Cyclomatic complexity <10
|
||||
- Performance: <100ms API response time
|
||||
- Reliability: >99% uptime in 24-hour test
|
||||
- Usability: New user can run optimization in <30 minutes
|
||||
- Maintainability: Clean code analysis score >8/10
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 🛠️ Development Workflow
|
||||
|
||||
### Daily Development Process
|
||||
```markdown
|
||||
1. Review this plan document
|
||||
2. Pick highest priority unchecked task
|
||||
3. Create feature branch
|
||||
4. Implement with Claude Code assistance
|
||||
5. Write tests
|
||||
6. Update documentation
|
||||
7. Commit with conventional commits
|
||||
8. Update task status in this document
|
||||
```
|
||||
|
||||
### Weekly Review Process
|
||||
```markdown
|
||||
Every Friday:
|
||||
1. Review completed tasks
|
||||
2. Update percentage complete for each phase
|
||||
3. Adjust priorities based on blockers
|
||||
4. Plan next week's focus
|
||||
5. Update timeline if needed
|
||||
```
|
||||
|
||||
### Using Claude Code Effectively
|
||||
```markdown
|
||||
Best practices for Claude Code assistance:
|
||||
|
||||
1. Provide clear context:
|
||||
"I'm working on Phase 2.1, migrating dashboard to React TypeScript"
|
||||
|
||||
2. Share relevant files:
|
||||
- Current implementation
|
||||
- Target architecture
|
||||
- Specific requirements
|
||||
|
||||
3. Ask for complete implementations:
|
||||
"Create the complete StudyCard component with TypeScript"
|
||||
|
||||
4. Request tests alongside code:
|
||||
"Also create unit tests for this component"
|
||||
|
||||
5. Get documentation:
|
||||
"Write the API documentation for this endpoint"
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 📅 Timeline Summary
|
||||
|
||||
| Phase | Duration | Start | End | Status |
|
||||
|-------|----------|-------|-----|--------|
|
||||
| Phase 1: Core Stabilization | 2 weeks | Week 1 | Week 2 | 🔴 Not Started |
|
||||
| Phase 2: Dashboard Overhaul | 3 weeks | Week 3 | Week 5 | 🔴 Not Started |
|
||||
| Phase 3: Extractors & Plugins | 2 weeks | Week 6 | Week 7 | 🔴 Not Started |
|
||||
| Phase 4: Documentation | 1 week | Week 8 | Week 8 | 🔴 Not Started |
|
||||
| Phase 5: Testing & Deployment | 2 weeks | Week 9 | Week 10 | 🔴 Not Started |
|
||||
| Phase 6: Future Preparation | 2 weeks | Week 11 | Week 12 | 🔴 Not Started |
|
||||
|
||||
**Total Duration**: 12 weeks to production-ready MVP
|
||||
|
||||
---
|
||||
|
||||
## 🎯 Quick Start Actions
|
||||
|
||||
### Today
|
||||
1. [ ] Review this entire plan
|
||||
2. [ ] Set up development environment
|
||||
3. [ ] Create project board with all tasks
|
||||
4. [ ] Start Phase 1.1 code cleanup
|
||||
|
||||
### This Week
|
||||
1. [ ] Complete Phase 1.1 code cleanup
|
||||
2. [ ] Begin Phase 1.2 configuration management
|
||||
3. [ ] Set up testing framework
|
||||
|
||||
### This Month
|
||||
1. [ ] Complete Phase 1 entirely
|
||||
2. [ ] Complete Phase 2 dashboard frontend
|
||||
3. [ ] Have working MVP demo
|
||||
|
||||
---
|
||||
|
||||
## 📝 Notes
|
||||
|
||||
### Development Principles
|
||||
1. **Stability First**: Make existing features rock-solid before adding new ones
|
||||
2. **User Experience**: Every feature should make the tool easier to use
|
||||
3. **Documentation**: Document as you build, not after
|
||||
4. **Testing**: Write tests before marking anything complete
|
||||
5. **Modularity**: Keep components loosely coupled for future extensions
|
||||
|
||||
### Risk Mitigation
|
||||
- **Dashboard complexity**: Start with essential features, add advanced later
|
||||
- **NX compatibility**: Test with multiple NX versions early
|
||||
- **Performance**: Profile and optimize before issues arise
|
||||
- **User adoption**: Create video tutorials alongside written docs
|
||||
|
||||
### Future Vision (Post-MVP)
|
||||
- LLM integration for natural language control
|
||||
- AtomizerField for 1000x speedup
|
||||
- Cloud deployment with team features
|
||||
- Plugin marketplace
|
||||
- SaaS offering
|
||||
|
||||
---
|
||||
|
||||
**Document Maintained By**: Development Team
|
||||
**Last Updated**: January 2025
|
||||
**Next Review**: End of Week 1
|
||||
**Location**: Project root directory
|
||||
154
docs/08_ARCHIVE/sessions/dashboard_initial_prompt.md
Normal file
154
docs/08_ARCHIVE/sessions/dashboard_initial_prompt.md
Normal file
@@ -0,0 +1,154 @@
|
||||
MASTER PROMPT FOR CLAUDE CODE: ADVANCED NX OPTIMIZATION DASHBOARD
|
||||
PROJECT CONTEXT
|
||||
I need you to build an advanced optimization dashboard for my atomizer project that manages Nastran structural optimizations. The dashboard should be professional, scientific (dark theme, no emojis), and integrate with my existing backend/frontend architecture.
|
||||
CORE REQUIREMENTS
|
||||
1. CONFIGURATION PAGE
|
||||
|
||||
Load NX optimization files via Windows file explorer
|
||||
Display optimization parameters that LLM created (ranges, objectives, constraints)
|
||||
Allow real-time editing and fine-tuning of optimization setup
|
||||
Generate and display optimization configuration report (markdown/PDF)
|
||||
Parameters the LLM might have missed or gotten wrong should be adjustable
|
||||
|
||||
2. MONITORING PAGE (Real-time Optimization Tracking)
|
||||
|
||||
Live optimization progress with pause/stop controls
|
||||
State-of-the-art visualization suite:
|
||||
|
||||
Convergence plots (objective values over iterations)
|
||||
Parallel coordinates plot (all parameters and objectives)
|
||||
Hypervolume evolution
|
||||
Surrogate model accuracy plots
|
||||
Pareto front evolution
|
||||
Parameter correlation matrices
|
||||
Cross-correlation heatmaps
|
||||
Diversity metrics
|
||||
|
||||
|
||||
WebSocket connection for real-time updates
|
||||
Display optimizer thinking/decisions
|
||||
|
||||
3. ITERATIONS VIEWER PAGE
|
||||
|
||||
Table view of all iterations with parameters and objective values
|
||||
3D mesh visualization using Three.js:
|
||||
|
||||
Show deformation and stress from .op2/.dat files
|
||||
Use pyNastran to extract mesh and results
|
||||
Interactive rotation/zoom
|
||||
Color-mapped stress/displacement results
|
||||
|
||||
|
||||
Compare iterations side-by-side
|
||||
Filter and sort by any parameter/objective
|
||||
|
||||
4. REPORT PAGE
|
||||
|
||||
Comprehensive optimization report sections:
|
||||
|
||||
Executive summary
|
||||
Problem definition
|
||||
Objectives and constraints
|
||||
Optimization methodology
|
||||
Convergence analysis
|
||||
Results and recommendations
|
||||
All plots and visualizations
|
||||
|
||||
|
||||
Interactive editing with LLM assistance
|
||||
"Clean up report with my notes" functionality
|
||||
Export to PDF/Markdown
|
||||
|
||||
TECHNICAL SPECIFICATIONS
|
||||
Architecture Requirements
|
||||
|
||||
Frontend: React + TypeScript with Plotly.js, D3.js, Three.js
|
||||
Backend: FastAPI with WebSocket support
|
||||
Data: pyNastran for OP2/BDF processing
|
||||
Real-time: WebSocket for live updates
|
||||
Storage: Study folders with iteration data
|
||||
|
||||
Visual Design
|
||||
|
||||
Dark theme (#0a0a0a background)
|
||||
Scientific color palette (no bright colors)
|
||||
Clean, professional typography
|
||||
No emojis or decorative elements
|
||||
Focus on data density and clarity
|
||||
|
||||
Integration Points
|
||||
|
||||
File selection through Windows Explorer
|
||||
Claude Code integration for optimization setup
|
||||
Existing optimizer callbacks for real-time data
|
||||
pyNastran for mesh/results extraction
|
||||
|
||||
IMPLEMENTATION PLAN
|
||||
Phase 1: Foundation
|
||||
|
||||
Setup project structure with proper separation of concerns
|
||||
Create dark theme scientific UI framework
|
||||
Implement WebSocket infrastructure for real-time updates
|
||||
Setup pyNastran integration for OP2/BDF processing
|
||||
|
||||
Phase 2: Configuration System
|
||||
|
||||
Build file loader for NX optimization files
|
||||
Create parameter/objective/constraint editors
|
||||
Implement LLM configuration parser and display
|
||||
Add configuration validation and adjustment tools
|
||||
Generate configuration reports
|
||||
|
||||
Phase 3: Monitoring Dashboard
|
||||
|
||||
Implement real-time WebSocket data streaming
|
||||
Create convergence plot component
|
||||
Build parallel coordinates visualization
|
||||
Add hypervolume and diversity trackers
|
||||
Implement surrogate model visualization
|
||||
Create pause/stop optimization controls
|
||||
|
||||
Phase 4: Iteration Analysis
|
||||
|
||||
Build iteration data table with filtering/sorting
|
||||
Implement 3D mesh viewer with Three.js
|
||||
Add pyNastran mesh/results extraction pipeline
|
||||
Create stress/displacement overlay system
|
||||
Build iteration comparison tools
|
||||
|
||||
Phase 5: Report Generation
|
||||
|
||||
Design report structure and sections
|
||||
Implement automated report generation
|
||||
Add interactive editing capabilities
|
||||
Integrate LLM assistance for report modification
|
||||
Create PDF/Markdown export functionality
|
||||
|
||||
Phase 6: Integration & Polish
|
||||
|
||||
Connect all pages with proper navigation
|
||||
Implement state management across pages
|
||||
Add error handling and recovery
|
||||
Performance optimization
|
||||
Testing and refinement
|
||||
|
||||
KEY FEATURES TO RESEARCH AND IMPLEMENT
|
||||
|
||||
Convergence Visualization: Research best practices from Optuna, pymoo, scikit-optimize
|
||||
Parallel Coordinates: Implement brushing, highlighting, and filtering capabilities
|
||||
3D Mesh Rendering: Use pyNastran's mesh extraction with Three.js WebGL rendering
|
||||
Surrogate Models: Visualize Gaussian Process or Neural Network approximations
|
||||
Hypervolume Calculation: Implement proper reference point selection and normalization
|
||||
|
||||
SUCCESS CRITERIA
|
||||
|
||||
Dashboard can load and configure optimizations without manual file editing
|
||||
Real-time monitoring shows all critical optimization metrics
|
||||
3D visualization clearly shows design changes between iterations
|
||||
Reports are publication-ready and comprehensive
|
||||
System maintains scientific rigor and professional appearance
|
||||
All interactions are smooth and responsive
|
||||
|
||||
START IMPLEMENTATION
|
||||
Begin by creating the project structure, then implement the Configuration Page with file loading and parameter display. Focus on getting the data flow working before adding advanced visualizations. Use pyNastran from the start for mesh/results handling.
|
||||
Remember: Keep it scientific, professional, and data-focused. No unnecessary UI elements or decorations.
|
||||
Reference in New Issue
Block a user