diff --git a/.claude/settings.local.json b/.claude/settings.local.json index f514e1af..01ef08cf 100644 --- a/.claude/settings.local.json +++ b/.claude/settings.local.json @@ -1,15 +1,30 @@ { "permissions": { "allow": [ - "Bash(\"c:/Users/antoi/anaconda3/envs/test_env/python.exe\" optimization_engine/visualizer.py \"studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials\" png pdf)", - "Bash(\"c:/Users/antoi/anaconda3/envs/test_env/python.exe\" optimization_engine/model_cleanup.py \"studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials\" --keep-top-n 3 --dry-run)", - "Bash(\"c:/Users/antoi/anaconda3/envs/test_env/python.exe\":*)", - "Bash(\"c:/Users/antoi/anaconda3/envs/test_env/python.exe\" tests/test_task_1_2_integration.py)", - "Bash(\"c:/Users/antoi/anaconda3/envs/test_env/python.exe\" tests/test_phase_3_2_e2e.py)", - "Bash(cmd /c \"set ANTHROPIC_API_KEY=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 && c:/Users/antoi/anaconda3/envs/test_env/python.exe tests/test_phase_3_2_e2e.py\")", - "Bash(cmd /c \"set ANTHROPIC_API_KEY=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 && c:/Users/antoi/anaconda3/envs/test_env/python.exe -c \"\"import os; print(''API Key set:'', ''ANTHROPIC_API_KEY'' in os.environ)\"\"\")", - "Bash(run_e2e_test.bat)", - "Bash(\"c:/Users/antoi/anaconda3/envs/test_env/python.exe\" run_e2e_with_env.py)" + "Bash(powershell -Command \"Get-ChildItem -Filter ''*SESSION*.md'' | Move-Item -Destination ''08_ARCHIVE/session_summaries/''\")", + "Bash(powershell -Command \"Get-ChildItem -Filter ''PHASE*.md'' | Move-Item -Destination ''08_ARCHIVE/phase_documents/''\")", + "Bash(powershell -Command \"Get-ChildItem -Filter ''TODAY*.md'' | Move-Item -Destination ''08_ARCHIVE/historical/''; Get-ChildItem -Filter ''GOOD*.md'' | Move-Item -Destination ''08_ARCHIVE/historical/''; Get-ChildItem -Filter ''LESSONS*.md'' | Move-Item -Destination ''08_ARCHIVE/historical/''\")", + "Bash(if exist \"studies\\bracket_stiffness_optimization_V3\\2_results\\study.db\" del /Q \"studies\\bracket_stiffness_optimization_V3\\2_results\\study.db\")", + "Bash(if exist \"studies\\bracket_stiffness_optimization_V3\\2_results\\intelligent_optimizer\" rd /S /Q \"studies\\bracket_stiffness_optimization_V3\\2_results\\intelligent_optimizer\")", + "Bash(timeout /t 30 /nobreak)", + "Bash(del studiesdrone_gimbal_arm_optimizationrun_optimization.py)", + "Bash(if exist \"studies\\drone_gimbal_arm_optimization\\2_results\\study.db\" del /Q \"studies\\drone_gimbal_arm_optimization\\2_results\\study.db\")", + "Bash(del /S /Q \"optimization_engine\\extractors\\__pycache__\" 2)", + "Bash(del /S /Q \"optimization_engine\\__pycache__\" 2)", + "Bash(if exist \"2_results\\study.db\" del /Q \"2_results\\study.db\")", + "Bash(sqlite3:*)", + "Bash(if exist \"studies\\drone_gimbal_arm_optimization\\2_results\\intelligent_optimizer\" rd /S /Q \"studies\\drone_gimbal_arm_optimization\\2_results\\intelligent_optimizer\")", + "Bash(if exist \"atomizer-dashboard\\frontend\\src\\components\\Card.tsx\" del /Q \"atomizer-dashboard\\frontend\\src\\components\\Card.tsx\")", + "Bash(del \"studies\\drone_gimbal_arm_optimization\\2_results\\study.db\" 2)", + "Bash(powershell -Command \"Get-ChildItem studies\\drone_gimbal_arm_optimization | Select-Object Name\")", + "Bash(powershell -Command:*)", + "Bash(git mv:*)", + "Bash(move optimization_enginellm_optimization_runner.py optimization_enginefuture )", + "Bash(move optimization_enginellm_workflow_analyzer.py optimization_enginefuture )", + "Bash(move optimization_engineinline_code_generator.py optimization_enginefuture )", + "Bash(move optimization_enginehook_generator.py optimization_enginefuture )", + "Bash(move optimization_enginereport_generator.py optimization_enginefuture )", + "Bash(move optimization_engineextractor_orchestrator.py optimization_enginefuture)" ], "deny": [], "ask": [] diff --git a/DEVELOPMENT.md b/DEVELOPMENT.md index fb38639d..8af36f3e 100644 --- a/DEVELOPMENT.md +++ b/DEVELOPMENT.md @@ -1,14 +1,13 @@ -# Atomizer Development Status +# Atomizer Development Guide -> Tactical development tracking - What's done, what's next, what needs work +**Last Updated**: 2025-11-21 +**Current Phase**: Phase 3.2 - Integration Sprint + Documentation +**Status**: 🟢 Core Complete (100%) | ✅ Protocols 10/11/13 Active (100%) | 🎯 Dashboard Live (95%) | 📚 Documentation Reorganized -**Last Updated**: 2025-11-17 -**Current Phase**: Phase 3.2 - Integration Sprint -**Status**: 🟢 Phase 1 Complete | ✅ Phases 2.5-3.1 Built (85%) | 🎯 Phase 3.2 Integration TOP PRIORITY - -📘 **Strategic Direction**: See [DEVELOPMENT_GUIDANCE.md](DEVELOPMENT_GUIDANCE.md) for comprehensive status, priorities, and development strategy. - -📘 **Long-Term Vision**: See [DEVELOPMENT_ROADMAP.md](DEVELOPMENT_ROADMAP.md) for the complete roadmap. +📘 **Quick Links**: +- [Protocol Specifications](docs/PROTOCOLS.md) - All active protocols consolidated +- [Documentation Index](docs/00_INDEX.md) - Complete documentation navigation +- [README](README.md) - Project overview and quick start --- @@ -131,6 +130,85 @@ ## Completed Features +### ✅ Live Dashboard System (Completed 2025-11-21) + +#### Backend (FastAPI + WebSocket) +- [x] **FastAPI Backend** ([atomizer-dashboard/backend/](atomizer-dashboard/backend/)) + - REST API endpoints for study management + - WebSocket streaming with file watching (Watchdog) + - Real-time updates (<100ms latency) + - CORS configured for local development + +- [x] **REST API Endpoints** ([backend/api/routes/optimization.py](atomizer-dashboard/backend/api/routes/optimization.py)) + - `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 + +- [x] **WebSocket Streaming** ([backend/api/websocket/optimization_stream.py](atomizer-dashboard/backend/api/websocket/optimization_stream.py)) + - File watching on `optimization_history_incremental.json` + - Real-time trial updates via WebSocket + - Pruning alerts and progress updates + - Automatic observer lifecycle management + +#### Frontend (HTML + Chart.js) +- [x] **Enhanced Live Dashboard** ([atomizer-dashboard/dashboard-enhanced.html](atomizer-dashboard/dashboard-enhanced.html)) + - Real-time WebSocket updates + - Interactive convergence chart (Chart.js) + - Parameter space scatter plot + - Pruning alerts (toast notifications) + - Data export (JSON/CSV) + - Study auto-discovery and selection + - Metric dashboard (trials, best value, pruned count) + +#### React Frontend (In Progress) +- [x] **Project Configuration** ([atomizer-dashboard/frontend/](atomizer-dashboard/frontend/)) + - React 18 + Vite 5 + TypeScript 5.2 + - TailwindCSS 3.3 for styling + - Recharts 2.10 for charts + - Complete build configuration + +- [x] **TypeScript Types** ([frontend/src/types/](atomizer-dashboard/frontend/src/types/)) + - Complete type definitions for API data + - WebSocket message types + - Chart data structures + +- [x] **Custom Hooks** ([frontend/src/hooks/useWebSocket.ts](atomizer-dashboard/frontend/src/hooks/useWebSocket.ts)) + - WebSocket connection management + - Auto-reconnection with exponential backoff + - Type-safe message routing + +- [x] **Reusable Components** ([frontend/src/components/](atomizer-dashboard/frontend/src/components/)) + - Card, MetricCard, Badge, StudyCard components + - TailwindCSS styling with dark theme + +- [ ] **Dashboard Page** (Pending manual completion) + - Need to run `npm install` + - Create main.tsx, App.tsx, Dashboard.tsx + - Integrate Recharts for charts + - Test end-to-end + +#### Documentation +- [x] **Dashboard Master Plan** ([docs/DASHBOARD_MASTER_PLAN.md](docs/DASHBOARD_MASTER_PLAN.md)) + - Complete 3-page architecture (Configurator, Live Dashboard, Results Viewer) + - Tech stack recommendations + - Implementation phases + +- [x] **Implementation Status** ([docs/DASHBOARD_IMPLEMENTATION_STATUS.md](docs/DASHBOARD_IMPLEMENTATION_STATUS.md)) + - Current progress tracking + - Testing instructions + - Next steps + +- [x] **React Implementation Guide** ([docs/DASHBOARD_REACT_IMPLEMENTATION.md](docs/DASHBOARD_REACT_IMPLEMENTATION.md)) + - Complete templates for remaining components + - Recharts integration examples + - Troubleshooting guide + +- [x] **Session Summary** ([docs/DASHBOARD_SESSION_SUMMARY.md](docs/DASHBOARD_SESSION_SUMMARY.md)) + - Features demonstrated + - How to use the dashboard + - Architecture explanation + ### ✅ Phase 1: Plugin System & Infrastructure (Completed 2025-01-16) #### Core Architecture @@ -206,7 +284,23 @@ ## Active Development -### In Progress +### In Progress - Dashboard (High Priority) +- [x] Backend API complete (FastAPI + WebSocket) +- [x] HTML dashboard with Chart.js complete +- [x] React project structure and configuration complete +- [ ] **Complete React frontend** (Awaiting manual npm install) + - [ ] Run `npm install` in frontend directory + - [ ] Create main.tsx and App.tsx + - [ ] Create Dashboard.tsx with Recharts + - [ ] Test end-to-end with live optimization + +### Up Next - Dashboard (Next Session) +- [ ] Study Configurator page (React) +- [ ] Results Report Viewer page (React) +- [ ] LLM chat interface integration (future) +- [ ] Docker deployment configuration + +### In Progress - Phase 3.2 Integration - [ ] Feature registry creation (Phase 2, Week 1) - [ ] Claude skill definition (Phase 2, Week 1) @@ -256,15 +350,19 @@ - ✅ Log file generation in correct locations - ✅ Hook execution at all lifecycle points - ✅ Path resolution across different script locations +- ✅ **Dashboard backend** - REST API and WebSocket tested successfully +- ✅ **HTML dashboard** - Live updates working with Chart.js +- ⏳ **React dashboard** - Pending npm install and completion - ⏳ Resume functionality with config validation -- ⏳ Dashboard integration with new plugin system ### Test Coverage - Hook manager: ~80% (core functionality tested) - Logging plugins: 100% (tested via integration tests) - Path resolution: 100% (tested in all scripts) - Result extractors: ~70% (basic tests exist) -- Overall: ~60% estimated +- **Dashboard backend**: ~90% (REST endpoints and WebSocket tested) +- **Dashboard frontend**: ~60% (HTML version tested, React pending) +- Overall: ~65% estimated --- @@ -401,6 +499,21 @@ ## Development Commands +### Running Dashboard +```bash +# Start backend server +cd atomizer-dashboard/backend +python -m uvicorn api.main:app --reload --host 0.0.0.0 --port 8000 + +# Access HTML dashboard (current) +# Open browser: http://localhost:8000 + +# Start React frontend (when ready) +cd atomizer-dashboard/frontend +npm install # First time only +npm run dev # Starts on http://localhost:3000 +``` + ### Running Tests ```bash # Hook validation (3 trials, fast) @@ -450,8 +563,18 @@ git push origin main - [studies/README.md](studies/README.md) - Studies folder organization - [CHANGELOG.md](CHANGELOG.md) - Version history +### Dashboard Documentation +- [docs/DASHBOARD_MASTER_PLAN.md](docs/DASHBOARD_MASTER_PLAN.md) - Complete architecture blueprint +- [docs/DASHBOARD_IMPLEMENTATION_STATUS.md](docs/DASHBOARD_IMPLEMENTATION_STATUS.md) - Current progress +- [docs/DASHBOARD_REACT_IMPLEMENTATION.md](docs/DASHBOARD_REACT_IMPLEMENTATION.md) - React implementation guide +- [docs/DASHBOARD_SESSION_SUMMARY.md](docs/DASHBOARD_SESSION_SUMMARY.md) - Session summary +- [atomizer-dashboard/README.md](atomizer-dashboard/README.md) - Dashboard quick start +- [atomizer-dashboard/backend/README.md](atomizer-dashboard/backend/README.md) - Backend API docs +- [atomizer-dashboard/frontend/README.md](atomizer-dashboard/frontend/README.md) - Frontend setup guide + ### For Users - [README.md](README.md) - Project overview and quick start +- [docs/INDEX.md](docs/INDEX.md) - Complete documentation index - [docs/](docs/) - Additional documentation --- @@ -475,6 +598,22 @@ git push origin main --- -**Last Updated**: 2025-01-16 +**Last Updated**: 2025-11-21 **Maintained by**: Antoine Polvé (antoine@atomaste.com) **Repository**: [GitHub - Atomizer](https://github.com/yourusername/Atomizer) + +--- + +## Recent Updates (November 21, 2025) + +### Dashboard System Implementation ✅ +- **Backend**: FastAPI + WebSocket with real-time file watching complete +- **HTML Dashboard**: Functional dashboard with Chart.js, data export, pruning alerts +- **React Setup**: Complete project configuration, types, hooks, components +- **Documentation**: 5 comprehensive markdown documents covering architecture, implementation, and usage + +### Next Immediate Steps +1. Run `npm install` in `atomizer-dashboard/frontend` +2. Create `main.tsx`, `App.tsx`, and `Dashboard.tsx` using provided templates +3. Test React dashboard with live optimization +4. Build Study Configurator page (next major feature) diff --git a/DEVELOPMENT_GUIDANCE.md b/DEVELOPMENT_GUIDANCE.md deleted file mode 100644 index 362a78e7..00000000 --- a/DEVELOPMENT_GUIDANCE.md +++ /dev/null @@ -1,1239 +0,0 @@ -# Atomizer Development Guidance - -> **Living Document**: Strategic direction, current status, and development priorities for Atomizer -> -> **Last Updated**: 2025-11-17 (Evening - Phase 3.2 Integration Planning Complete) -> -> **Status**: Alpha Development - 80-90% Complete, Integration Phase -> -> 🎯 **NOW IN PROGRESS**: Phase 3.2 Integration Sprint - [Integration Plan](docs/PHASE_3_2_INTEGRATION_PLAN.md) - ---- - -## Table of Contents - -1. [Executive Summary](#executive-summary) -2. [Comprehensive Status Report](#comprehensive-status-report) -3. [Development Strategy](#development-strategy) -4. [Priority Initiatives](#priority-initiatives) -5. [Foundation for Future](#foundation-for-future) -6. [Technical Roadmap](#technical-roadmap) -7. [Development Standards](#development-standards) -8. [Key Principles](#key-principles) - ---- - -## Executive Summary - -### Current State - -**Status**: Alpha Development - Significant Progress Made ✅ -**Readiness**: Foundation solid, LLM features partially implemented, ready for integration phase -**Direction**: ✅ Aligned with roadmap vision - moving toward LLM-native optimization platform - -### Quick Stats - -- **110+ Python files** (~10,000+ lines in core engine) -- **23 test files** covering major components -- **Phase 1 (Plugin System)**: ✅ 100% Complete & Production Ready -- **Phases 2.5-3.1 (LLM Intelligence)**: ✅ 85% Complete - Components Built, Integration Needed -- **Phase 3.3 (Visualization & Cleanup)**: ✅ 100% Complete & Production Ready -- **Study Organization v2.0**: ✅ 100% Complete with Templates -- **Working Example Study**: simple_beam_optimization (4 substudies, 56 trials, full documentation) - -### Key Insight - -**You've built more than the documentation suggests!** The roadmap says "Phase 2: 0% Complete" but you've actually built sophisticated LLM components through Phase 3.1 (85% complete). The challenge now is **integration**, not development. - ---- - -## Comprehensive Status Report - -### 🎯 What's Actually Working (Production Ready) - -#### ✅ Core Optimization Engine -**Status**: FULLY FUNCTIONAL - -The foundation is rock solid: - -- **Optuna Integration**: TPE, CMA-ES, GP samplers operational -- **NX Solver Integration**: Journal-based parameter updates and simulation execution -- **OP2 Result Extraction**: Stress and displacement extractors tested on real files -- **Study Management**: Complete folder structure with resume capability -- **Precision Control**: 4-decimal rounding for engineering units - -**Evidence**: -- `studies/simple_beam_optimization/` - Complete 4D optimization study - - 4 substudies (01-04) with numbered organization - - 56 total trials across all substudies - - 4 design variables (beam thickness, face thickness, hole diameter, hole count) - - 3 objectives (displacement, stress, mass) + 1 constraint - - Full documentation with substudy READMEs -- `studies/bracket_displacement_maximizing/` - Earlier study (20 trials) - -#### ✅ Plugin System (Phase 1) -**Status**: PRODUCTION READY - -This is exemplary architecture: - -- **Hook Manager**: Priority-based execution at 7 lifecycle points - - `pre_solve`, `post_solve`, `post_extraction`, `post_calculation`, etc. -- **Auto-discovery**: Plugins load automatically from directories -- **Context Passing**: Full trial data available to hooks -- **Logging Infrastructure**: - - Per-trial detailed logs (`trial_logs/`) - - High-level optimization log (`optimization.log`) - - Clean, parseable format - -**Evidence**: Hook system tested in `test_hooks_with_bracket.py` - all passing ✅ - -#### ✅ Substudy System -**Status**: WORKING & ELEGANT - -NX-like hierarchical studies: - -- **Shared models**, independent configurations -- **Continuation support** (fine-tuning builds on coarse exploration) -- **Live incremental history** tracking -- **Clean separation** of concerns - -**File**: `studies/simple_beam_optimization/run_optimization.py` - -#### ✅ Phase 3.3: Visualization & Model Cleanup -**Status**: PRODUCTION READY - -Automated post-processing system for optimization results: - -- **6 Plot Types**: - - Convergence (objective vs trial with running best) - - Design space evolution (parameter changes over time) - - Parallel coordinates (high-dimensional visualization) - - Sensitivity heatmap (parameter correlation analysis) - - Constraint violations tracking - - Multi-objective breakdown -- **Output Formats**: PNG (300 DPI) + PDF (vector graphics) -- **Model Cleanup**: Selective deletion of large CAD/FEM files - - Keeps top-N best trials (default: 10) - - Preserves all results.json files - - 50-90% disk space savings typical -- **Configuration**: JSON-based `post_processing` section - -**Evidence**: -- Tested on 50-trial beam optimization -- Generated 12 plot files (6 types × 2 formats) -- Plots saved to `studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/plots/` -- Documentation: `docs/PHASE_3_3_VISUALIZATION_AND_CLEANUP.md` - -**Integration**: Runs automatically after optimization completes (if enabled in config) - -#### ✅ Study Organization System v2.0 -**Status**: PRODUCTION READY - -Standardized directory structure for all optimization studies: - -**Structure**: -``` -studies/[study_name]/ -├── 1_setup/ # Pre-optimization (model, benchmarking) -├── 2_substudies/ # Numbered runs (01_, 02_, 03_...) -└── 3_reports/ # Study-level analysis -``` - -**Features**: -- **Numbered Substudies**: Chronological ordering (01, 02, 03...) -- **Self-Documenting**: Each substudy has README.md with purpose/results -- **Metadata Tracking**: study_metadata.json with complete substudy registry -- **Templates**: Complete templates for new studies and substudies -- **Migration Tool**: reorganize_study.py for existing studies - -**Evidence**: -- Applied to simple_beam_optimization study -- 4 substudy READMEs documenting progression -- Complete template system in `templates/` -- How-to guide: `templates/HOW_TO_CREATE_A_STUDY.md` -- Documentation: `docs/STUDY_ORGANIZATION.md` - -**File**: `studies/simple_beam_optimization/study_metadata.json` - -### 🚧 What's Built But Not Yet Integrated - -#### 🟡 Phase 2.5-3.1: LLM Intelligence Components -**Status**: 85% Complete - Individual Modules Working, Integration Pending - -These are sophisticated, well-designed modules that are 90% ready but not yet connected to the main optimization loop: - -##### ✅ Built & Tested: - -1. **LLM Workflow Analyzer** (`llm_workflow_analyzer.py` - 14.5KB) - - Uses Claude API to analyze natural language optimization requests - - Outputs structured JSON with engineering_features, inline_calculations, post_processing_hooks - - Status: Fully functional standalone - -2. **Extractor Orchestrator** (`extractor_orchestrator.py` - 12.7KB) - - Processes LLM output and generates OP2 extractors - - Dynamic loading and execution - - Test: `test_phase_3_1_integration.py` - PASSING ✅ - - Evidence: Generated 3 working extractors in `result_extractors/generated/` - -3. **pyNastran Research Agent** (`pynastran_research_agent.py` - 13.3KB) - - Uses WebFetch to learn pyNastran API patterns - - Knowledge base system stores learned patterns - - 3 core extraction patterns: displacement, stress, force - - Test: `test_complete_research_workflow.py` - PASSING ✅ - -4. **Hook Generator** (`hook_generator.py` - 27.8KB) - - Auto-generates post-processing hook scripts - - Weighted objectives, custom formulas, constraints, comparisons - - Complete JSON I/O handling - - Evidence: 4 working hooks in `plugins/post_calculation/` - -5. **Inline Code Generator** (`inline_code_generator.py` - 17KB) - - Generates Python code for simple math operations - - Normalization, averaging, min/max calculations - -6. **Codebase Analyzer & Capability Matcher** (Phase 2.5) - - Scans existing code to detect gaps before requesting examples - - 80-90% accuracy on complex optimization requests - - Test: `test_phase_2_5_intelligent_gap_detection.py` - PASSING ✅ - -##### 🟡 What's Missing: - -**Integration into main runner!** The components exist but aren't connected to `runner.py`: - -```python -# Current runner.py (Line 29-76): -class OptimizationRunner: - def __init__(self, config_path, model_updater, simulation_runner, result_extractors): - # Uses MANUAL config.json - # Uses MANUAL result_extractors dict - # No LLM workflow integration ❌ -``` - -New `LLMOptimizationRunner` exists (`llm_optimization_runner.py`) but: -- Not used in any production study -- Not tested end-to-end with real NX solves -- Missing integration with `run_optimization.py` scripts - -### 📊 Architecture Assessment - -#### 🟢 Strengths - -1. **Clean Separation of Concerns** - - Each phase is a self-contained module - - Dependencies flow in one direction (no circular imports) - - Easy to test components independently - -2. **Excellent Documentation** - - Session summaries for each phase (`docs/SESSION_SUMMARY_PHASE_*.md`) - - Comprehensive roadmap (`DEVELOPMENT_ROADMAP.md`) - - Inline docstrings with examples - -3. **Feature Registry** (`feature_registry.json` - 35KB) - - Well-structured capability catalog - - Each feature has: implementation, interface, usage examples, metadata - - Perfect foundation for LLM navigation - -4. **Knowledge Base System** - - Research sessions stored with rationale - - 9 markdown files documenting learned patterns - - Enables "learn once, use forever" approach - -5. **Test Coverage** - - 23 test files covering major components - - Tests for individual phases (2.5, 2.9, 3.1) - - Integration tests passing - -#### 🟡 Areas for Improvement - -1. **Integration Gap** - - **Critical**: LLM components not connected to main runner - - Two parallel runners exist (`runner.py` vs `llm_optimization_runner.py`) - - Production studies still use manual JSON config - -2. **Documentation Drift** - - `README.md` says "Phase 2" is next priority - - But Phases 2.5-3.1 are actually 85% complete - - `DEVELOPMENT.md` shows "Phase 2: 0% Complete" - **INCORRECT** - -3. **Test vs Production Gap** - - LLM features tested in isolation - - No end-to-end test: Natural language → LLM → Generated code → Real NX solve → Results - - `test_bracket_llm_runner.py` exists but may not cover full pipeline - -4. **User Experience** - - No simple way to run LLM-enhanced optimization yet - - User must manually edit JSON configs (old workflow) - - Natural language interface exists but not exposed - -5. **Code Duplication Risk** - - `runner.py` and `llm_optimization_runner.py` share similar structure - - Could consolidate into single runner with "LLM mode" flag - -### 🎯 Phase 3.2 Integration Sprint - ACTIVE NOW - -**Status**: 🟢 **IN PROGRESS** (2025-11-17) - -**Goal**: Connect LLM components to production workflow - make LLM mode accessible - -**Detailed Plan**: See [docs/PHASE_3_2_INTEGRATION_PLAN.md](docs/PHASE_3_2_INTEGRATION_PLAN.md) - -#### What's Being Built (4-Week Sprint) - -**Week 1: Make LLM Mode Accessible** (16 hours) -- Create unified entry point with `--llm` flag -- Wire LLMOptimizationRunner to production -- Create minimal working example -- End-to-end integration test - -**Week 2: Robustness & Safety** (16 hours) -- Code validation pipeline (syntax, security, test execution) -- Graceful fallback mechanisms -- LLM audit trail for transparency -- Failure scenario testing - -**Week 3: Learning System** (12 hours) -- Knowledge base implementation -- Template extraction and reuse -- ResearchAgent integration - -**Week 4: Documentation & Discoverability** (8 hours) -- Update README with LLM capabilities -- Create docs/LLM_MODE.md -- Demo video/GIF -- Update all planning docs - -#### Success Metrics - -- [ ] Natural language request → Optimization results (single command) -- [ ] Generated code validated before execution (no crashes) -- [ ] Successful workflows saved and reused (learning system operational) -- [ ] Documentation shows LLM mode prominently (users discover it) - -#### Impact - -Once complete: -- **100 lines of JSON config** → **3 lines of natural language** -- Users describe goals → LLM generates code automatically -- System learns from successful workflows → gets faster over time -- Complete audit trail for all LLM decisions - ---- - -### 🎯 Gap Analysis: What's Missing for Complete Vision - -#### Critical Gaps (Being Addressed in Phase 3.2) - -1. **Phase 3.2: Runner Integration** ✅ **IN PROGRESS** - - Connect `LLMOptimizationRunner` to production workflows - - Update `run_optimization.py` to support both manual and LLM modes - - End-to-end test: Natural language → Actual NX solve → Results - - **Timeline**: Week 1 of Phase 3.2 (2025-11-17 onwards) - -2. **User-Facing Interface** ✅ **IN PROGRESS** - - CLI command: `python run_optimization.py --llm --request "minimize stress"` - - Dual-mode: LLM or traditional JSON config - - **Timeline**: Week 1 of Phase 3.2 - -3. **Error Handling & Recovery** ✅ **IN PROGRESS** - - Code validation before execution - - Graceful fallback to manual mode - - Complete audit trail - - **Timeline**: Week 2 of Phase 3.2 - -#### Important Gaps (Should-Have) - -1. **Dashboard Integration** - - Dashboard exists (`dashboard/`) but may not show LLM-generated components - - No visualization of generated code - - No "LLM mode" toggle in UI - -2. **Performance Optimization** - - LLM calls in optimization loop could be slow - - Caching for repeated patterns? - - Batch code generation before optimization starts? - -3. **Validation & Safety** - - Generated code execution sandboxing? - - Code review before running? - - Unit tests for generated extractors? - -#### Nice-to-Have Gaps - -1. **Phase 4: Advanced Code Generation** - - Complex FEA features (topology optimization, multi-physics) - - NXOpen journal script generation - -2. **Phase 5: Analysis & Decision Support** - - Surrogate quality assessment (R², CV scores) - - Sensitivity analysis - - Engineering recommendations - -3. **Phase 6: Automated Reporting** - - HTML/PDF report generation - - LLM-written narrative insights - -### 🔍 Code Quality Assessment - -**Excellent**: -- Modularity: Each component is self-contained (can be imported independently) -- Type Hints: Extensive use of `Dict[str, Any]`, `Path`, `Optional[...]` -- Error Messages: Clear, actionable error messages -- Logging: Comprehensive logging at appropriate levels - -**Good**: -- Naming: Clear, descriptive function/variable names -- Documentation: Most functions have docstrings with examples -- Testing: Core components have tests - -**Could Improve**: -- Consolidation: Some code duplication between runners -- Configuration Validation: Some JSON configs lack schema validation -- Async Operations: No async/await for potential concurrency -- Type Checking: Not using mypy or similar (no `mypy.ini` found) - ---- - -## Development Strategy - -### Current Approach: Claude Code + Manual Development - -**Strategic Decision**: We are NOT integrating LLM API calls into Atomizer right now for development purposes. - -#### Why This Makes Sense: - -1. **Use What Works**: Claude Code (your subscription) is already providing LLM assistance for development -2. **Avoid Premature Optimization**: Don't block on LLM API integration when you can develop without it -3. **Focus on Foundation**: Build the architecture first, add LLM API later -4. **Keep Options Open**: Architecture supports LLM API, but doesn't require it for development - -#### Future LLM Integration Strategy: - -- **Near-term**: Maybe test simple use cases to validate API integration works -- **Medium-term**: Integrate LLM API for production user features (not dev workflow) -- **Long-term**: Fully LLM-native optimization workflow for end users - -**Bottom Line**: Continue using Claude Code for Atomizer development. LLM API integration is a "later" feature, not a blocker. - ---- - -## Priority Initiatives - -### ✅ Phase 3.2 Integration - Framework Complete (2025-11-17) - -**Status**: ✅ 75% Complete - Framework implemented, API integration pending - -**What's Done**: -- ✅ Generic `run_optimization.py` CLI with `--llm` flag support -- ✅ Integration with `LLMOptimizationRunner` for automated extractor/hook generation -- ✅ Argument parsing and validation -- ✅ Comprehensive help message and examples -- ✅ Test suite verifying framework functionality -- ✅ Documentation of hybrid approach (Claude Code → JSON → LLMOptimizationRunner) - -**Current Limitation**: -- ⚠️ `LLMWorkflowAnalyzer` requires Anthropic API key for natural language parsing -- `--llm` mode works but needs `--api-key` argument -- Without API key, use hybrid approach (pre-generated workflow JSON) - -**Working Approaches**: -1. **With API Key**: `--llm "request" --api-key "sk-ant-..."` -2. **Hybrid (Recommended)**: Claude Code → workflow JSON → `LLMOptimizationRunner` -3. **Study-Specific**: Hardcoded workflow (see bracket study example) - -**Files**: -- [optimization_engine/run_optimization.py](../optimization_engine/run_optimization.py) - Generic CLI runner -- [docs/PHASE_3_2_INTEGRATION_STATUS.md](../docs/PHASE_3_2_INTEGRATION_STATUS.md) - Complete status report -- [tests/test_phase_3_2_llm_mode.py](../tests/test_phase_3_2_llm_mode.py) - Integration tests - -**Next Steps** (When API integration becomes priority): -- Implement true Claude Code integration in `LLMWorkflowAnalyzer` -- OR defer until Anthropic API integration is prioritized -- OR continue with hybrid approach (90% of value, 10% of complexity) - -**Recommendation**: ✅ Framework Complete - Proceed to other priorities (NXOpen docs, Engineering pipeline) - -### 🔬 HIGH PRIORITY: NXOpen Documentation Access - -**Goal**: Enable LLM to reference NXOpen documentation when developing Atomizer features and generating NXOpen code - -#### Options to Investigate: - -1. **Authenticated Web Fetching** - - Can we login to Siemens documentation portal? - - Can WebFetch tool use authenticated sessions? - - Explore Siemens PLM API access - -2. **Documentation Scraping** - - Ethical/legal considerations - - Caching locally for offline use - - Structured extraction of API signatures - -3. **Official API Access** - - Does Siemens provide API documentation in structured format? - - JSON/XML schema files? - - OpenAPI/Swagger specs? - -4. **Community Resources** - - TheScriptingEngineer blog content - - NXOpen examples repository - - Community-contributed documentation - -#### Research Tasks: - -- [ ] Investigate Siemens documentation portal login mechanism -- [ ] Test WebFetch with authentication headers -- [ ] Explore Siemens PLM API documentation access -- [ ] Review legal/ethical considerations for documentation access -- [ ] Create proof-of-concept: LLM + NXOpen docs → Generated code - -**Success Criteria**: LLM can fetch NXOpen documentation on-demand when writing code - -### 🔧 MEDIUM PRIORITY: NXOpen Intellisense Integration - -**Goal**: Investigate if NXOpen Python stub files can improve Atomizer development workflow - -#### Background: - -From NX2406 onwards, Siemens provides stub files for Python intellisense: -- **Location**: `UGII_BASE_DIR\ugopen\pythonStubs` -- **Purpose**: Enable code completion, parameter info, member lists for NXOpen objects -- **Integration**: Works with VSCode Pylance extension - -#### TheScriptingEngineer's Configuration: - -```json -// settings.json -"python.analysis.typeCheckingMode": "basic", -"python.analysis.stubPath": "path_to_NX/ugopen/pythonStubs/Release2023/" -``` - -#### Questions to Answer: - -1. **Development Workflow**: - - Does this improve Atomizer development speed? - - Can Claude Code leverage intellisense information? - - Does it reduce NXOpen API lookup time? - -2. **Code Generation**: - - Can generated code use these stubs for validation? - - Can we type-check generated NXOpen scripts before execution? - - Does it catch errors earlier? - -3. **Integration Points**: - - Should this be part of Atomizer setup process? - - Can we distribute stubs with Atomizer? - - Legal considerations for redistribution? - -#### Implementation Plan: - -- [ ] Locate stub files in NX2412 installation -- [ ] Configure VSCode with stub path -- [ ] Test intellisense with sample NXOpen code -- [ ] Evaluate impact on development workflow -- [ ] Document setup process for contributors -- [ ] Decide: Include in Atomizer or document as optional enhancement? - -**Success Criteria**: Developers have working intellisense for NXOpen APIs - ---- - -## Foundation for Future - -### 🏗️ Engineering Feature Documentation Pipeline - -**Purpose**: Establish rigorous validation process for LLM-generated engineering features - -**Important**: This is NOT for current software development. This is the foundation for future user-generated features. - -#### Vision: - -When a user asks Atomizer to create a new FEA feature (e.g., "calculate buckling safety factor"), the system should: - -1. **Generate Code**: LLM creates the implementation -2. **Generate Documentation**: Auto-create comprehensive markdown explaining the feature -3. **Human Review**: Engineer reviews and approves before integration -4. **Version Control**: Documentation and code committed together - -This ensures **scientific rigor** and **traceability** for production use. - -#### Auto-Generated Documentation Format: - -Each engineering feature should produce a markdown file with these sections: - -```markdown -# Feature Name: [e.g., Buckling Safety Factor Calculator] - -## Goal -What problem does this feature solve? -- Engineering context -- Use cases -- Expected outcomes - -## Engineering Rationale -Why this approach? -- Design decisions -- Alternative approaches considered -- Why this method was chosen - -## Mathematical Foundation - -### Equations -\``` -σ_buckling = (π² × E × I) / (K × L)² -Safety Factor = σ_buckling / σ_applied -\``` - -### Sources -- Euler Buckling Theory (1744) -- AISC Steel Construction Manual, 15th Edition, Chapter E -- Timoshenko & Gere, "Theory of Elastic Stability" (1961) - -### Assumptions & Limitations -- Elastic buckling only -- Slender columns (L/r > 100) -- Perfect geometry assumed -- Material isotropy - -## Implementation - -### Code Structure -\```python -def calculate_buckling_safety_factor( - youngs_modulus: float, - moment_of_inertia: float, - effective_length: float, - applied_stress: float, - k_factor: float = 1.0 -) -> float: - """ - Calculate buckling safety factor using Euler formula. - - Parameters: - ... - """ -\``` - -### Input Validation -- Positive values required -- Units: Pa, m⁴, m, Pa -- K-factor range: 0.5 to 2.0 - -### Error Handling -- Division by zero checks -- Physical validity checks -- Numerical stability considerations - -## Testing & Validation - -### Unit Tests -\```python -def test_euler_buckling_simple_case(): - # Steel column: E=200GPa, I=1e-6m⁴, L=3m, σ=100MPa - sf = calculate_buckling_safety_factor(200e9, 1e-6, 3.0, 100e6) - assert 2.0 < sf < 2.5 # Expected range -\``` - -### Validation Cases -1. **Benchmark Case 1**: AISC Manual Example 3.1 (page 45) - - Input: [values] - - Expected: [result] - - Actual: [result] - - Error: [%] - -2. **Benchmark Case 2**: Timoshenko Example 2.3 - - ... - -### Edge Cases Tested -- Very short columns (L/r < 50) - should warn/fail -- Very long columns - numerical stability -- Zero/negative inputs - should error gracefully - -## Approval - -- **Author**: [LLM Generated | Engineer Name] -- **Reviewer**: [Engineer Name] -- **Date Reviewed**: [YYYY-MM-DD] -- **Status**: [Pending | Approved | Rejected] -- **Notes**: [Reviewer comments] - -## References - -1. Euler, L. (1744). "Methodus inveniendi lineas curvas maximi minimive proprietate gaudentes" -2. American Institute of Steel Construction (2016). *Steel Construction Manual*, 15th Edition -3. Timoshenko, S.P. & Gere, J.M. (1961). *Theory of Elastic Stability*, 2nd Edition, McGraw-Hill - -## Change Log - -- **v1.0** (2025-11-17): Initial implementation -- **v1.1** (2025-11-20): Added K-factor validation per reviewer feedback -``` - -#### Implementation Requirements: - -1. **Template System**: - - Markdown template for each feature type - - Auto-fill sections where possible - - Highlight sections requiring human input - -2. **Generation Pipeline**: - ``` - User Request → LLM Analysis → Code Generation → Documentation Generation → Human Review → Approval → Integration - ``` - -3. **Storage Structure**: - ``` - atomizer/ - ├── engineering_features/ - │ ├── approved/ - │ │ ├── buckling_safety_factor/ - │ │ │ ├── implementation.py - │ │ │ ├── tests.py - │ │ │ └── FEATURE_DOCS.md - │ │ └── ... - │ └── pending_review/ - │ └── ... - ``` - -4. **Validation Checklist**: - - [ ] Equations match cited sources - - [ ] Units are documented and validated - - [ ] Edge cases are tested - - [ ] Physical validity checks exist - - [ ] Benchmarks pass within tolerance - - [ ] Code matches documentation - - [ ] References are credible and accessible - -#### Who Uses This: - -- **NOT YOU (current development)**: You're building Atomizer's software foundation - different process -- **FUTURE USERS**: When users ask Atomizer to create custom FEA features -- **PRODUCTION DEPLOYMENTS**: Where engineering rigor and traceability matter - -#### Development Now vs Foundation for Future: - -| Aspect | Development Now | Foundation for Future | -|--------|----------------|----------------------| -| **Scope** | Building Atomizer software | User-generated FEA features | -| **Process** | Agile, iterate fast | Rigorous validation pipeline | -| **Documentation** | Code comments, dev docs | Full engineering documentation | -| **Review** | You approve | Human engineer approves | -| **Testing** | Unit tests, integration tests | Benchmark validation required | -| **Speed** | Move fast | Move carefully | - -**Bottom Line**: Build the framework now, but don't use it yourself yet. It's for future credibility and production use. - -### 🔐 Validation Pipeline Framework - -**Goal**: Define the structure for rigorous validation of LLM-generated scientific tools - -#### Pipeline Stages: - -```mermaid -graph LR - A[User Request] --> B[LLM Analysis] - B --> C[Code Generation] - C --> D[Documentation Generation] - D --> E[Automated Tests] - E --> F{Tests Pass?} - F -->|No| G[Feedback Loop] - G --> C - F -->|Yes| H[Human Review Queue] - H --> I{Approved?} - I -->|No| J[Reject with Feedback] - J --> G - I -->|Yes| K[Integration] - K --> L[Production Ready] -``` - -#### Components to Build: - -1. **Request Parser**: - - Natural language → Structured requirements - - Identify required equations/standards - - Classify feature type (stress, displacement, buckling, etc.) - -2. **Code Generator with Documentation**: - - Generate implementation code - - Generate test cases - - Generate markdown documentation - - Link code ↔ docs bidirectionally - -3. **Automated Validation**: - - Run unit tests - - Check benchmark cases - - Validate equation implementations - - Verify units consistency - -4. **Review Queue System**: - - Pending features awaiting approval - - Review interface (CLI or web) - - Approval/rejection workflow - - Feedback mechanism to LLM - -5. **Integration Manager**: - - Move approved features to production - - Update feature registry - - Generate release notes - - Version control integration - -#### Current Status: - -- [ ] Request parser - Not started -- [ ] Code generator with docs - Partially exists (hook_generator, extractor_orchestrator) -- [ ] Automated validation - Basic tests exist, need benchmark framework -- [ ] Review queue - Not started -- [ ] Integration manager - Not started - -**Priority**: Build the structure and interfaces now, implement validation logic later. - -#### Example Workflow (Future): - -```bash -# User creates custom feature -$ atomizer create-feature --request "Calculate von Mises stress safety factor using Tresca criterion" - -[LLM Analysis] -✓ Identified: Stress-based safety factor -✓ Standards: Tresca yield criterion -✓ Required inputs: stress_tensor, yield_strength -✓ Generating code... - -[Code Generation] -✓ Created: engineering_features/pending_review/tresca_safety_factor/ - - implementation.py - - tests.py - - FEATURE_DOCS.md - -[Automated Tests] -✓ Unit tests: 5/5 passed -✓ Benchmark cases: 3/3 passed -✓ Edge cases: 4/4 passed - -[Status] -🟡 Pending human review -📋 Review with: atomizer review tresca_safety_factor - -# Engineer reviews -$ atomizer review tresca_safety_factor - -[Review Interface] -Feature: Tresca Safety Factor Calculator -Status: Automated tests PASSED - -Documentation Preview: -[shows FEATURE_DOCS.md] - -Code Preview: -[shows implementation.py] - -Test Results: -[shows test output] - -Approve? [y/N]: y -Review Notes: Looks good, equations match standard - -[Approval] -✓ Feature approved -✓ Integrated into feature registry -✓ Available for use - -# Now users can use it -$ atomizer optimize --objective "maximize displacement" --constraint "tresca_sf > 2.0" -``` - -**This is the vision**. Build the foundation now for future implementation. - ---- - -## Technical Roadmap - -### Revised Phase Timeline - -| Phase | Status | Description | Priority | -|-------|--------|-------------|----------| -| **Phase 1** | ✅ 100% | Plugin System | Complete | -| **Phase 2.5** | ✅ 85% | Intelligent Gap Detection | Built, needs integration | -| **Phase 2.6** | ✅ 85% | Workflow Decomposition | Built, needs integration | -| **Phase 2.7** | ✅ 85% | Step Classification | Built, needs integration | -| **Phase 2.9** | ✅ 85% | Hook Generation | Built, tested | -| **Phase 3.0** | ✅ 85% | Research Agent | Built, tested | -| **Phase 3.1** | ✅ 85% | Extractor Orchestration | Built, tested | -| **Phase 3.2** | ✅ 75% | **Runner Integration** | Framework complete, API integration pending | -| **Phase 3.3** | 🟡 50% | Optimization Setup Wizard | Partially built | -| **Phase 3.4** | 🔵 0% | NXOpen Documentation Integration | Research phase | -| **Phase 3.5** | 🔵 0% | Engineering Feature Pipeline | Foundation design | -| **Phase 4+** | 🔵 0% | Advanced Features | Paused until 3.2 complete | - -### Immediate Next Steps (Next 2 Weeks) - -#### Week 1: Integration & Testing - -**Monday-Tuesday**: Runner Integration -- [ ] Add `--llm` flag to `run_optimization.py` -- [ ] Connect `LLMOptimizationRunner` to production workflow -- [ ] Implement fallback to manual mode -- [ ] Test with bracket study - -**Wednesday-Thursday**: End-to-End Testing -- [ ] Run complete LLM workflow: Request → Code → Solve → Results -- [ ] Compare LLM-generated vs manual extractors -- [ ] Performance profiling -- [ ] Fix any integration bugs - -**Friday**: Polish & Documentation -- [ ] Improve error messages -- [ ] Add progress indicators -- [ ] Create example script -- [ ] Update inline documentation - -#### Week 2: NXOpen Documentation Research - -**Monday-Tuesday**: Investigation -- [ ] Research Siemens documentation portal -- [ ] Test authenticated WebFetch -- [ ] Explore PLM API access -- [ ] Review legal considerations - -**Wednesday**: Intellisense Setup -- [ ] Locate NX2412 stub files -- [ ] Configure VSCode with Pylance -- [ ] Test intellisense with NXOpen code -- [ ] Document setup process - -**Thursday-Friday**: Documentation Updates -- [ ] Update `README.md` with LLM capabilities -- [ ] Update `DEVELOPMENT.md` with accurate status -- [ ] Create `NXOPEN_INTEGRATION.md` guide -- [ ] Update this guidance document - -### Medium-Term Goals (1-3 Months) - -1. **Phase 3.4: NXOpen Documentation Integration** - - Implement authenticated documentation access - - Create NXOpen knowledge base - - Test LLM code generation with docs - -2. **Phase 3.5: Engineering Feature Pipeline** - - Build documentation template system - - Create review queue interface - - Implement validation framework - -3. **Dashboard Enhancement** - - Add LLM mode toggle - - Visualize generated code - - Show approval workflow - -4. **Performance Optimization** - - LLM response caching - - Batch code generation - - Async operations - -### Long-Term Vision (3-12 Months) - -1. **Phase 4: Advanced Code Generation** - - Complex FEA feature generation - - Multi-physics setup automation - - Topology optimization support - -2. **Phase 5: Intelligent Analysis** - - Surrogate quality assessment - - Sensitivity analysis - - Pareto front optimization - -3. **Phase 6: Automated Reporting** - - HTML/PDF generation - - LLM-written insights - - Executive summaries - -4. **Production Hardening** - - Security audits - - Performance optimization - - Enterprise features - ---- - -## Development Standards - -### Reference Hierarchy for Feature Implementation - -When implementing new features or capabilities in Atomizer, follow this **prioritized order** for consulting documentation and APIs: - -#### Tier 1: Primary References (ALWAYS CHECK FIRST) - -These are the authoritative sources that define the actual APIs and behaviors we work with: - -1. **NXOpen Python Stub Files** (`C:\Program Files\Siemens\NX2412\UGOPEN\pythonStubs`) - - **Why**: Exact method signatures, parameter types, return values for all NXOpen APIs - - **When**: Writing NX journal scripts, updating part parameters, CAE operations - - **Access**: VSCode Pylance intellisense (configured in `.vscode/settings.json`) - - **Accuracy**: ~95% - this is the actual API definition - - **Example**: For updating expressions, check `NXOpen/Part.pyi` → `ExpressionCollection` class → see `FindObject()` and `EditExpressionWithUnits()` methods - -2. **Existing Atomizer Journals** (`optimization_engine/*.py`, `studies/*/`) - - **Why**: Working, tested code that already solves similar problems - - **When**: Before writing new NX integration code - - **Files to Check**: - - `optimization_engine/solve_simulation.py` - NX journal for running simulations - - `optimization_engine/nx_updater.py` - Parameter update patterns - - Any study-specific journals in `studies/*/` - - **Pattern**: Search for similar functionality first, adapt existing code - -3. **NXOpen API Patterns in Codebase** (`optimization_engine/`, `result_extractors/`) - - **Why**: Established patterns for NX API usage in Atomizer - - **When**: Implementing new NX operations - - **What to Look For**: - - Session management patterns - - Part update workflows - - Expression handling - - Save/load patterns - -#### Tier 2: Specialized References (USE FOR SPECIFIC TASKS) - -These are secondary sources for specialized tasks - use **ONLY** for their specific domains: - -1. **pyNastran** (`knowledge_base/`, online docs) - - **ONLY FOR**: OP2/F06 file post-processing (reading Nastran output files) - - **NOT FOR**: NXOpen guidance, simulation setup, parameter updates - - **Why Limited**: pyNastran is for reading results, not for NX API integration - - **When to Use**: Creating result extractors, reading stress/displacement from OP2 files - - **Example Valid Use**: `result_extractors/stress_extractor.py` - reads OP2 stress data - - **Example INVALID Use**: ❌ Don't use pyNastran docs to learn how to update NX part expressions - -2. **TheScriptingEngineer Blog** (https://thescriptingengineer.com) - - **When**: Need working examples of NXOpen usage patterns - - **Why**: High-quality, practical examples with explanations - - **Best For**: Learning NXOpen workflow patterns, discovering API usage - - **Limitation**: Blog may use different NX versions, verify against stub files - -#### Tier 3: Last Resort References (USE SPARINGLY) - -Use these only when Tier 1 and Tier 2 don't provide answers: - -1. **Web Search / External Documentation** - - **When**: Researching new concepts not covered by existing code - - **Caution**: Verify information against stub files and existing code - - **Best For**: Conceptual understanding, theory, background research - -2. **Siemens Official Documentation Portal** (https://plm.sw.siemens.com) - - **When**: Need detailed API documentation beyond stub files - - **Status**: Authenticated access under investigation (see NXOpen Integration initiative) - - **Future**: May become Tier 1 once integration is complete - -### Reference Hierarchy Decision Tree - -``` -Need to implement NXOpen functionality? -│ -├─> Check NXOpen stub files (.pyi) - Do exact methods exist? -│ ├─> YES: Use those method signatures ✅ -│ └─> NO: Continue ↓ -│ -├─> Search existing Atomizer journals - Has this been done before? -│ ├─> YES: Adapt existing code ✅ -│ └─> NO: Continue ↓ -│ -├─> Check TheScriptingEngineer - Are there examples? -│ ├─> YES: Adapt pattern, verify against stub files ✅ -│ └─> NO: Continue ↓ -│ -└─> Web search for concept - Understand theory, then implement using stub files - └─> ALWAYS verify final code against stub files before using ✅ - -Need to extract results from OP2/F06? -│ -└─> Use pyNastran ✅ - └─> Check knowledge_base/ for existing patterns first - -Need to understand FEA theory/equations? -│ -└─> Web search / textbooks ✅ - └─> Document sources in feature documentation -``` - -### Why This Hierarchy Matters - -**Before** (guessing/hallucinating): -```python -# ❌ Guessed API - might not exist or have wrong signature -work_part.Expressions.Edit("tip_thickness", "5.0") # Wrong method name! -``` - -**After** (checking stub files): -```python -# ✅ Verified against NXOpen/Part.pyi stub file -expr = work_part.Expressions.FindObject("tip_thickness") # Correct! -work_part.Expressions.EditExpressionWithUnits(expr, unit, "5.0") # Correct! -``` - -**Improvement**: ~60% accuracy (guessing) → ~95% accuracy (stub files) - -### NXOpen Integration Status - -✅ **Completed** (2025-11-17): -- NXOpen stub files located and configured in VSCode -- Python 3.11 environment setup for NXOpen compatibility -- NXOpen module import enabled via `.pth` file -- Intellisense working for all NXOpen APIs -- Documentation: [NXOPEN_INTELLISENSE_SETUP.md](docs/NXOPEN_INTELLISENSE_SETUP.md) - -🔜 **Future Work**: -- Authenticated Siemens documentation access (research phase) -- Documentation scraping for LLM knowledge base -- LLM-generated journal scripts with validation - ---- - -## Key Principles - -### Development Philosophy - -1. **Ship Before Perfecting**: Integration is more valuable than new features -2. **User Value First**: Every feature must solve a real user problem -3. **Scientific Rigor**: Engineering features require validation and documentation -4. **Progressive Enhancement**: System works without LLM, better with LLM -5. **Learn and Improve**: Knowledge base grows with every use - -### Decision Framework - -When prioritizing work, ask: - -1. **Does this unlock user value?** If yes, prioritize -2. **Does this require other work first?** If yes, do dependencies first -3. **Can we test this independently?** If no, split into testable pieces -4. **Will this create technical debt?** If yes, document and plan to address -5. **Does this align with long-term vision?** If no, reconsider - -### Quality Standards - -**For Software Development (Atomizer itself)**: -- Unit tests for core components -- Integration tests for workflows -- Code review by you (main developer) -- Documentation for contributors -- Move fast, iterate - -**For Engineering Features (User-generated FEA)**: -- Comprehensive mathematical documentation -- Benchmark validation required -- Human engineer approval mandatory -- Traceability to standards/papers -- Move carefully, validate thoroughly - ---- - -## Success Metrics - -### Phase 3.2 Success Criteria - -- [ ] Users can run: `python run_optimization.py --llm "maximize displacement"` -- [ ] End-to-end test passes: Natural language → NX solve → Results -- [ ] LLM-generated extractors produce same results as manual extractors -- [ ] Error handling works gracefully (fallback to manual mode) -- [ ] Documentation updated to reflect LLM capabilities -- [ ] Example workflow created and tested - -### NXOpen Integration Success Criteria - -- [ ] LLM can fetch NXOpen documentation on-demand -- [ ] Generated code references correct NXOpen API methods -- [ ] Intellisense working in VSCode for NXOpen development -- [ ] Setup documented for contributors -- [ ] Legal/ethical review completed - -### Engineering Feature Pipeline Success Criteria - -- [ ] Documentation template system implemented -- [ ] Example feature with full documentation created -- [ ] Review workflow interface built (CLI or web) -- [ ] Validation framework structure defined -- [ ] At least one feature goes through full pipeline (demo) - ---- - -## Communication & Collaboration - -### Stakeholders - -- **Antoine Letarte**: Main developer, architect, decision maker -- **Claude Code**: Development assistant for Atomizer software -- **Future Contributors**: Will follow established patterns and documentation -- **Future Users**: Will use LLM features for optimization workflows - -### Documentation Strategy - -1. **DEVELOPMENT_GUIDANCE.md** (this doc): Strategic direction, priorities, status -2. **README.md**: User-facing introduction, quick start, features -3. **DEVELOPMENT.md**: Detailed development status, todos, completed work -4. **DEVELOPMENT_ROADMAP.md**: Long-term vision, phases, future work -5. **Session summaries**: Detailed records of development sessions - -Keep all documents synchronized and consistent. - -### Review Cadence - -- **Weekly**: Review progress against priorities -- **Monthly**: Update roadmap and adjust course if needed -- **Quarterly**: Major strategic reviews and planning - ---- - -## Appendix: Quick Reference - -### File Locations - -**Core Engine**: -- `optimization_engine/runner.py` - Current production runner -- `optimization_engine/llm_optimization_runner.py` - LLM-enhanced runner (needs integration) -- `optimization_engine/nx_solver.py` - NX Simcenter integration -- `optimization_engine/nx_updater.py` - Parameter update system - -**LLM Components**: -- `optimization_engine/llm_workflow_analyzer.py` - Natural language parser -- `optimization_engine/extractor_orchestrator.py` - Extractor generation -- `optimization_engine/pynastran_research_agent.py` - Documentation learning -- `optimization_engine/hook_generator.py` - Hook code generation - -**Studies**: -- `studies/bracket_displacement_maximizing/` - Working example with substudies -- `studies/bracket_displacement_maximizing/run_substudy.py` - Substudy runner -- `studies/bracket_displacement_maximizing/SUBSTUDIES_README.md` - Substudy guide - -**Tests**: -- `tests/test_phase_2_5_intelligent_gap_detection.py` - Gap detection tests -- `tests/test_phase_3_1_integration.py` - Extractor orchestration tests -- `tests/test_complete_research_workflow.py` - Research agent tests - -**Documentation**: -- `docs/SESSION_SUMMARY_PHASE_*.md` - Development session records -- `knowledge_base/` - Learned patterns and research sessions -- `feature_registry.json` - Complete capability catalog - -### Common Commands - -```bash -# Run optimization (current manual mode) -cd studies/bracket_displacement_maximizing -python run_optimization.py - -# Run substudy -python run_substudy.py coarse_exploration - -# Run tests -python -m pytest tests/test_phase_3_1_integration.py -v - -# Start dashboard -python dashboard/start_dashboard.py -``` - -### Key Contacts & Resources - -- **Siemens NX Documentation**: [PLM Portal](https://plm.sw.siemens.com) -- **TheScriptingEngineer**: [Blog](https://thescriptingengineer.com) -- **pyNastran Docs**: [GitHub](https://github.com/SteveDoyle2/pyNastran) -- **Optuna Docs**: [optuna.org](https://optuna.org) - ---- - -**Document Maintained By**: Antoine Letarte (Main Developer) -**Last Review**: 2025-11-17 -**Next Review**: 2025-11-24 diff --git a/DEVELOPMENT_ROADMAP.md b/DEVELOPMENT_ROADMAP.md deleted file mode 100644 index fea28529..00000000 --- a/DEVELOPMENT_ROADMAP.md +++ /dev/null @@ -1,787 +0,0 @@ -# Atomizer Development Roadmap - -> Vision: Transform Atomizer into an LLM-native engineering assistant for optimization - -**Last Updated**: 2025-01-16 - ---- - -## Vision Statement - -Atomizer will become an **LLM-driven optimization framework** where AI acts as a scientist/programmer/coworker that can: - -- Understand natural language optimization requests -- Configure studies autonomously -- Write custom Python functions on-the-fly during optimization -- Navigate and extend its own codebase -- Make engineering decisions based on data analysis -- Generate comprehensive optimization reports -- Continuously expand its own capabilities through learning - ---- - -## Architecture Philosophy - -### LLM-First Design Principles - -1. **Discoverability**: Every feature must be discoverable and usable by LLM via feature registry -2. **Extensibility**: Easy to add new capabilities without modifying core engine -3. **Safety**: Validate all generated code, sandbox execution, rollback on errors -4. **Transparency**: Log all LLM decisions and generated code for auditability -5. **Human-in-the-loop**: Confirm critical decisions (e.g., deleting studies, pushing results) -6. **Documentation as Code**: Auto-generate docs from code with semantic metadata - ---- - -## Development Phases - -### Phase 1: Foundation - Plugin & Extension System ✅ -**Timeline**: 2 weeks -**Status**: ✅ **COMPLETED** (2025-01-16) -**Goal**: Make Atomizer extensible and LLM-navigable - -#### Deliverables - -1. **Plugin Architecture** ✅ - - [x] Hook system for optimization lifecycle - - [x] `pre_solve`: Execute before solver launch - - [x] `post_solve`: Execute after solve, before extraction - - [x] `post_extraction`: Execute after result extraction - - [x] Python script execution at optimization stages - - [x] Plugin auto-discovery and registration - - [x] Hook manager with priority-based execution - -2. **Logging Infrastructure** ✅ - - [x] Detailed per-trial logs (`trial_logs/`) - - Complete iteration trace - - Design variables, config, timeline - - Extracted results and constraint evaluations - - [x] High-level optimization log (`optimization.log`) - - Configuration summary - - Trial progress (START/COMPLETE entries) - - Compact one-line-per-trial format - - [x] Context passing system for hooks - - `output_dir` passed from runner to all hooks - - Trial number, design variables, results - -3. **Project Organization** ✅ - - [x] Studies folder structure with templates - - [x] Comprehensive studies documentation ([studies/README.md](studies/README.md)) - - [x] Model file organization (`model/` folder) - - [x] Intelligent path resolution (`atomizer_paths.py`) - - [x] Test suite for hook system - -**Files Created**: -``` -optimization_engine/ -├── plugins/ -│ ├── __init__.py -│ ├── hook_manager.py # Hook registration and execution ✅ -│ ├── pre_solve/ -│ │ ├── detailed_logger.py # Per-trial detailed logs ✅ -│ │ └── optimization_logger.py # High-level optimization.log ✅ -│ ├── post_solve/ -│ │ └── log_solve_complete.py # Append solve completion ✅ -│ └── post_extraction/ -│ ├── log_results.py # Append extracted results ✅ -│ └── optimization_logger_results.py # Append to optimization.log ✅ - -studies/ -├── README.md # Comprehensive guide ✅ -└── bracket_stress_minimization/ - ├── README.md # Study documentation ✅ - ├── model/ # FEA files folder ✅ - │ ├── Bracket.prt - │ ├── Bracket_sim1.sim - │ └── Bracket_fem1.fem - └── optimization_results/ # Auto-generated ✅ - ├── optimization.log - └── trial_logs/ - -tests/ -├── test_hooks_with_bracket.py # Hook validation test ✅ -├── run_5trial_test.py # Quick integration test ✅ -└── test_journal_optimization.py # Full optimization test ✅ - -atomizer_paths.py # Intelligent path resolution ✅ -``` - ---- - -### Phase 2: Research & Learning System -**Timeline**: 2 weeks -**Status**: 🟡 **NEXT PRIORITY** -**Goal**: Enable autonomous research and feature generation when encountering unknown domains - -#### Philosophy - -When the LLM encounters a request it cannot fulfill with existing features (e.g., "Create NX materials XML"), it should: -1. **Detect the knowledge gap** by searching the feature registry -2. **Plan research strategy** prioritizing: user examples → NX MCP → web documentation -3. **Execute interactive research** asking the user first for examples -4. **Learn patterns and schemas** from gathered information -5. **Generate new features** following learned patterns -6. **Test and validate** with user confirmation -7. **Document and integrate** into knowledge base and feature registry - -This creates a **self-extending system** that grows more capable with each research session. - -#### Key Deliverables - -**Week 1: Interactive Research Foundation** - -1. **Knowledge Base Structure** - - [x] Create `knowledge_base/` folder hierarchy - - [x] `nx_research/` - NX-specific learned patterns - - [x] `research_sessions/[date]_[topic]/` - Session logs with rationale - - [x] `templates/` - Reusable code patterns learned from research - -2. **ResearchAgent Class** (`optimization_engine/research_agent.py`) - - [ ] `identify_knowledge_gap(user_request)` - Search registry, identify missing features - - [ ] `create_research_plan(knowledge_gap)` - Prioritize sources (user > MCP > web) - - [ ] `execute_interactive_research(plan)` - Ask user for examples first - - [ ] `synthesize_knowledge(findings)` - Extract patterns, schemas, best practices - - [ ] `design_feature(synthesized_knowledge)` - Create feature spec from learned patterns - - [ ] `validate_with_user(feature_spec)` - Confirm implementation meets needs - -3. **Interactive Research Workflow** - - [ ] Prompt templates for asking users for examples - - [ ] Example parser (extract structure from XML, Python, journal scripts) - - [ ] Pattern recognition (identify reusable templates) - - [ ] Confidence tracking (how reliable is this knowledge?) - -**Week 2: Web Integration & Feature Generation** - -4. **Web Research Integration** - - [ ] WebSearch integration for NXOpen documentation - - [ ] NXOpenTSE scraping for code examples - - [ ] Siemens official docs search and parsing - - [ ] Multi-source synthesis (combine user examples + web docs) - -5. **Feature Generation Pipeline** - - [ ] Code generator using learned templates - - [ ] Feature registry auto-update - - [ ] Documentation auto-generation (following FEATURE_REGISTRY_ARCHITECTURE.md format) - - [ ] Unit test scaffolding from examples - -6. **Knowledge Base Management** - - [ ] Research session logging (questions, sources, findings, decisions) - - [ ] Confidence score tracking (user-validated > MCP > web docs) - - [ ] Knowledge retrieval (search past research before starting new) - - [ ] Template library growth (extract reusable patterns from generated code) - -#### Success Criteria - -- [ ] **Materials XML Example**: LLM asks for example XML → learns schema → generates new material XMLs -- [ ] **Knowledge Persistence**: Research session saved with rationale, retrievable for future requests -- [ ] **Multi-Source Synthesis**: Combines user example + NXOpenTSE + official docs into one coherent feature -- [ ] **Template Reuse**: Second request for similar feature uses learned template (faster generation) -- [ ] **User Validation Loop**: Generated feature tested, user confirms it works, feedback integrated - -#### Example Workflow - -``` -User: "Please create a new material XML for NX with titanium Ti-6Al-4V properties" - -LLM (detects gap): - "I don't have a feature for generating NX material XMLs yet. Let me research this. - - Do you have an example .xml material file I can learn from?" - -User: [uploads steel_material.xml] - -LLM (analyzes example): - "Thanks! I've learned the NX material XML schema: - - Root element: - - Required fields: Name, Density, YoungModulus, PoissonRatio, ThermalExpansion - - Units specified via attributes - - Let me create titanium Ti-6Al-4V with these properties: - - Density: 4430 kg/m³ - - Young's Modulus: 113.8 GPa - - Poisson's Ratio: 0.342 - - Thermal Expansion: 8.6e-6 /K - - [Generates XML] - - Does this look correct?" - -User: "Yes, perfect!" - -LLM (documents): - "Great! I've saved: - 1. NX material XML schema to knowledge_base/nx_research/material_xml_schema.md - 2. Template generator to optimization_engine/custom_functions/nx_material_generator.py - 3. Research session log to knowledge_base/research_sessions/2025-01-16_nx_materials/ - - Next time you request a material, I can generate it instantly using this template!" -``` - -#### Files to Create - -``` -knowledge_base/ -├── nx_research/ -│ ├── material_xml_schema.md # Learned from user example -│ ├── journal_script_patterns.md # Common NXOpen patterns -│ └── best_practices.md # Engineering guidelines -├── research_sessions/ -│ └── 2025-01-16_nx_materials/ -│ ├── user_question.txt # Original request -│ ├── sources_consulted.txt # User example, NXOpenTSE, etc. -│ ├── findings.md # What was learned -│ └── decision_rationale.md # Why this implementation -└── templates/ - ├── xml_generation_template.py # Learned from research - └── journal_script_template.py - -optimization_engine/ -├── research_agent.py # Main ResearchAgent class -└── custom_functions/ - └── nx_material_generator.py # Generated from learned template -``` - ---- - -### Phase 3: LLM Integration Layer -**Timeline**: 2 weeks -**Status**: 🔵 Not Started -**Goal**: Enable natural language control of Atomizer - -#### Key Deliverables - -1. **Feature Registry** - Centralized catalog of all Atomizer capabilities -2. **Claude Skill** - LLM can navigate codebase and understand architecture -3. **Natural Language Parser** - Intent recognition and entity extraction -4. **Conversational Workflow** - Multi-turn conversations with context preservation - -#### Success Vision - -``` -User: "Create a stress minimization study for my bracket" -LLM: "I'll set up a new study. Please drop your .sim file in the study folder." - -User: "Done. Vary wall_thickness from 3-8mm" -LLM: "Perfect! I've configured: - - Objective: Minimize max von Mises stress - - Design variable: wall_thickness (3.0-8.0mm) - - Sampler: TPE with 50 trials - Ready to start?" - -User: "Yes!" -LLM: "Optimization running! View progress at http://localhost:8080" -``` - ---- - -### Phase 4: Dynamic Code Generation -**Timeline**: 3 weeks -**Status**: 🔵 Not Started -**Goal**: LLM writes and integrates custom code during optimization - -#### Deliverables - -1. **Custom Function Generator** - - [ ] Template system for common patterns: - - RSS (Root Sum Square) of multiple metrics - - Weighted objectives - - Custom constraints (e.g., stress/yield_strength < 1) - - Conditional objectives (if-then logic) - - [ ] Code validation pipeline (syntax check, safety scan) - - [ ] Unit test auto-generation - - [ ] Auto-registration in feature registry - - [ ] Persistent storage in `optimization_engine/custom_functions/` - -2. **Journal Script Generator** - - [ ] Generate NX journal scripts from natural language - - [ ] Library of common operations: - - Modify geometry (fillets, chamfers, thickness) - - Apply loads and boundary conditions - - Extract custom data (centroid, inertia, custom expressions) - - [ ] Validation against NXOpen API - - [ ] Dry-run mode for testing - -3. **Safe Execution Environment** - - [ ] Sandboxed Python execution (RestrictedPython or similar) - - [ ] Whitelist of allowed imports - - [ ] Error handling with detailed logs - - [ ] Rollback mechanism on failure - - [ ] Logging of all generated code to audit trail - -**Files to Create**: -``` -optimization_engine/ -├── custom_functions/ -│ ├── __init__.py -│ ├── templates/ -│ │ ├── rss_template.py -│ │ ├── weighted_sum_template.py -│ │ └── constraint_template.py -│ ├── generator.py # Code generation engine -│ ├── validator.py # Safety validation -│ └── sandbox.py # Sandboxed execution -├── code_generation/ -│ ├── __init__.py -│ ├── journal_generator.py # NX journal script generation -│ └── function_templates.py # Jinja2 templates -``` - ---- - -### Phase 5: Intelligent Analysis & Decision Support -**Timeline**: 3 weeks -**Status**: 🔵 Not Started -**Goal**: LLM analyzes results and guides engineering decisions - -#### Deliverables - -1. **Result Analyzer** - - [ ] Statistical analysis module - - Convergence detection (plateau in objective) - - Pareto front identification (multi-objective) - - Sensitivity analysis (which params matter most) - - Outlier detection - - [ ] Trend analysis (monotonic relationships, inflection points) - - [ ] Recommendations engine (refine mesh, adjust bounds, add constraints) - -2. **Surrogate Model Manager** - - [ ] Quality metrics calculation - - R² (coefficient of determination) - - CV score (cross-validation) - - Prediction error distribution - - Confidence intervals - - [ ] Surrogate fitness assessment - - "Ready to use" threshold (e.g., R² > 0.9) - - Warning if predictions unreliable - - [ ] Active learning suggestions (which points to sample next) - -3. **Decision Assistant** - - [ ] Trade-off interpreter (explain Pareto fronts) - - [ ] "What-if" analysis (predict outcome of parameter change) - - [ ] Constraint violation diagnosis - - [ ] Next-step recommendations - -**Example**: -``` -User: "Summarize optimization results" -→ LLM: - Analyzes 50 trials, identifies best design at trial #34: - - wall_thickness = 3.2mm (converged from initial 5mm) - - max_stress = 187 MPa (target: 200 MPa ✓) - - mass = 0.45 kg (15% lighter than baseline) - - Issues detected: - - Stress constraint violated in 20% of trials (trials 5,12,18...) - - Displacement shows high sensitivity to thickness (Sobol index: 0.78) - - Recommendations: - 1. Relax stress limit to 210 MPa OR - 2. Add fillet radius as design variable (currently fixed at 2mm) - 3. Consider thickness > 3mm for robustness -``` - -**Files to Create**: -``` -optimization_engine/ -├── analysis/ -│ ├── __init__.py -│ ├── statistical_analyzer.py # Convergence, sensitivity -│ ├── surrogate_quality.py # R², CV, confidence intervals -│ ├── decision_engine.py # Recommendations -│ └── visualizers.py # Plot generators -``` - ---- - -### Phase 6: Automated Reporting -**Timeline**: 2 weeks -**Status**: 🔵 Not Started -**Goal**: Generate comprehensive HTML/PDF optimization reports - -#### Deliverables - -1. **Report Generator** - - [ ] Template system (Jinja2) - - Executive summary (1-page overview) - - Detailed analysis (convergence plots, sensitivity charts) - - Appendices (all trial data, config files) - - [ ] Auto-generated plots (Chart.js for web, Matplotlib for PDF) - - [ ] Embedded data tables (sortable, filterable) - - [ ] LLM-written narrative explanations - -2. **Multi-Format Export** - - [ ] HTML (interactive, shareable via link) - - [ ] PDF (static, for archival/print) - - [ ] Markdown (for version control, GitHub) - - [ ] JSON (machine-readable, for post-processing) - -3. **Smart Narrative Generation** - - [ ] LLM analyzes data and writes insights in natural language - - [ ] Explains why certain designs performed better - - [ ] Highlights unexpected findings (e.g., "Counter-intuitively, reducing thickness improved stress") - - [ ] Includes engineering recommendations - -**Files to Create**: -``` -optimization_engine/ -├── reporting/ -│ ├── __init__.py -│ ├── templates/ -│ │ ├── executive_summary.html.j2 -│ │ ├── detailed_analysis.html.j2 -│ │ └── markdown_report.md.j2 -│ ├── report_generator.py # Main report engine -│ ├── narrative_writer.py # LLM-driven text generation -│ └── exporters/ -│ ├── html_exporter.py -│ ├── pdf_exporter.py # Using WeasyPrint or similar -│ └── markdown_exporter.py -``` - ---- - -### Phase 7: NX MCP Enhancement -**Timeline**: 4 weeks -**Status**: 🔵 Not Started -**Goal**: Deep NX integration via Model Context Protocol - -#### Deliverables - -1. **NX Documentation MCP Server** - - [ ] Index full Siemens NX API documentation - - [ ] Semantic search across NX docs (embeddings + vector DB) - - [ ] Code examples from official documentation - - [ ] Auto-suggest relevant API calls based on task - -2. **Advanced NX Operations** - - [ ] Geometry manipulation library - - Parametric CAD automation (change sketches, features) - - Assembly management (add/remove components) - - Advanced meshing controls (refinement zones, element types) - - [ ] Multi-physics setup - - Thermal-structural coupling - - Modal analysis - - Fatigue analysis setup - -3. **Feature Bank Expansion** - - [ ] Library of 50+ pre-built NX operations - - [ ] Topology optimization integration - - [ ] Generative design workflows - - [ ] Each feature documented in registry with examples - -**Files to Create**: -``` -mcp/ -├── nx_documentation/ -│ ├── __init__.py -│ ├── server.py # MCP server implementation -│ ├── indexer.py # NX docs indexing -│ ├── embeddings.py # Vector embeddings for search -│ └── vector_db.py # Chroma/Pinecone integration -├── nx_features/ -│ ├── geometry/ -│ │ ├── fillets.py -│ │ ├── chamfers.py -│ │ └── thickness_modifier.py -│ ├── analysis/ -│ │ ├── thermal_structural.py -│ │ ├── modal_analysis.py -│ │ └── fatigue_setup.py -│ └── feature_registry.json # NX feature catalog -``` - ---- - -### Phase 8: Self-Improving System -**Timeline**: 4 weeks -**Status**: 🔵 Not Started -**Goal**: Atomizer learns from usage and expands itself - -#### Deliverables - -1. **Feature Learning System** - - [ ] When LLM creates custom function, prompt user to save to library - - [ ] User provides name + description - - [ ] Auto-update feature registry with new capability - - [ ] Version control for user-contributed features - -2. **Best Practices Database** - - [ ] Store successful optimization strategies - - [ ] Pattern recognition (e.g., "Adding fillets always reduces stress by 10-20%") - - [ ] Similarity search (find similar past optimizations) - - [ ] Recommend strategies for new problems - -3. **Continuous Documentation** - - [ ] Auto-generate docs when new features added - - [ ] Keep examples updated with latest API - - [ ] Version control for all generated code - - [ ] Changelog auto-generation - -**Files to Create**: -``` -optimization_engine/ -├── learning/ -│ ├── __init__.py -│ ├── feature_learner.py # Capture and save new features -│ ├── pattern_recognizer.py # Identify successful patterns -│ ├── similarity_search.py # Find similar optimizations -│ └── best_practices_db.json # Pattern library -├── auto_documentation/ -│ ├── __init__.py -│ ├── doc_generator.py # Auto-generate markdown docs -│ ├── changelog_builder.py # Track feature additions -│ └── example_extractor.py # Extract examples from code -``` - ---- - -## Final Architecture - -``` -Atomizer/ -├── optimization_engine/ -│ ├── core/ # Existing optimization loop -│ ├── plugins/ # NEW: Hook system (Phase 1) ✅ -│ │ ├── hook_manager.py -│ │ ├── pre_solve/ -│ │ ├── post_solve/ -│ │ └── post_extraction/ -│ ├── research_agent.py # NEW: Research & Learning (Phase 2) -│ ├── custom_functions/ # NEW: User/LLM generated code (Phase 4) -│ ├── llm_interface/ # NEW: Natural language control (Phase 3) -│ ├── analysis/ # NEW: Result analysis (Phase 5) -│ ├── reporting/ # NEW: Report generation (Phase 6) -│ ├── learning/ # NEW: Self-improvement (Phase 8) -│ └── feature_registry.json # NEW: Capability catalog (Phase 1) ✅ -├── knowledge_base/ # NEW: Learned knowledge (Phase 2) -│ ├── nx_research/ # NX-specific patterns and schemas -│ ├── research_sessions/ # Session logs with rationale -│ └── templates/ # Reusable code patterns -├── .claude/ -│ └── skills/ -│ └── atomizer.md # NEW: Claude skill (Phase 1) ✅ -├── mcp/ -│ ├── nx_documentation/ # NEW: NX docs MCP server (Phase 7) -│ └── nx_features/ # NEW: NX feature bank (Phase 7) -├── docs/ -│ ├── FEATURE_REGISTRY_ARCHITECTURE.md # NEW: Registry design (Phase 1) ✅ -│ └── llm/ # NEW: LLM-readable docs (Phase 1) -│ ├── capabilities.md -│ ├── examples.md -│ └── api_reference.md -├── dashboard/ # Existing web UI -└── examples/ # Example projects -``` - ---- - -## Example Use Cases - -### Use Case 1: Natural Language Optimization Setup -``` -User: "Let's make a new optimization" - -LLM: "I'll help you set up a new optimization study. What would you like to name it?" - -User: "bracket_weight_reduction" - -LLM: "Study created! Please drop your .sim and .prt files into: - C:\Atomizer\optimization_results\bracket_weight_reduction\sim\" - -User: "Done. I want to minimize displacement, play with wall_thickness (3-8mm) - and fillet_radius (1-5mm)" - -LLM: "Got it! Let me configure: - - Objective: Minimize max displacement - - Design variables: - * wall_thickness: 3.0 - 8.0 mm - * fillet_radius: 1.0 - 5.0 mm - - How many iterations would you like? (Recommend 50 for 2 variables)" - -User: "50 sounds good" - -LLM: "Perfect! Configuration saved. Ready to start optimization? - This will run 50 FEA simulations (estimated time: 2-3 hours)" - -User: "Start it!" - -LLM: "Optimization launched! 🚀 - - Live dashboard: http://localhost:8080/dashboard - - Study: bracket_weight_reduction - - Status: Trial 1/50 running..." -``` - -### Use Case 2: Custom Metric via LLM -``` -User: "Let's implement a function to calculate the RSS of stress and displacement" - -LLM: "I'll create a Root Sum Square (RSS) function for you. Let me confirm: - - RSS = sqrt(stress² + displacement²) - - Should I normalize the metrics first? (Recommended for different units)" - -User: "Yes, normalize to their targets" - -LLM: "Creating custom objective function... Done! - - File: optimization_engine/custom_functions/rss_stress_displacement.py - - def rss_objective(stress, displacement, stress_target=200, disp_target=1.0): - norm_stress = stress / stress_target - norm_disp = displacement / disp_target - return sqrt(norm_stress**2 + norm_disp**2) - - Added to feature registry. You can now use 'rss_stress_displacement' - as an objective in your optimization config." -``` - -### Use Case 3: Surrogate Model Usage -``` -User: "Can you use the surrogate model to predict stress for these parameters: - wall_thickness = [3.5, 4.0, 4.5, 5.0, 5.5]" - -LLM: "Checking surrogate model quality... - - Surrogate Model Status: - - Training data: 50 trials - - R² score: 0.94 (excellent) - - CV score: 0.91 (good generalization) - - Confidence: HIGH ✓ - - Predictions: - | Thickness | Predicted Stress | Confidence Interval | - |-----------|------------------|---------------------| - | 3.5 mm | 245.2 MPa | ±12.3 MPa | - | 4.0 mm | 198.7 MPa | ±8.1 MPa | - | 4.5 mm | 165.4 MPa | ±7.2 MPa | - | 5.0 mm | 142.1 MPa | ±9.5 MPa | - | 5.5 mm | 126.8 MPa | ±11.8 MPa | - - Note: 4.0mm appears optimal (stress closest to 200 MPa target)" -``` - -### Use Case 4: Optimization Report -``` -User: "Make an optimization report for bracket_weight_reduction" - -LLM: "Generating comprehensive optimization report... - - Analyzing 50 completed trials... - Writing narrative insights... - Generating plots... - - Report complete! - - 📄 HTML: optimization_results/bracket_weight_reduction/report.html - 📄 PDF: optimization_results/bracket_weight_reduction/report.pdf - - Key Findings: - - Best design: Trial #34 - * wall_thickness = 4.2 mm - * fillet_radius = 3.1 mm - * Displacement = 0.78 mm (22% below target) - * Mass = 0.51 kg (18% lighter than baseline) - - - Sensitivity: Thickness has 3x more impact than fillet radius - - Recommendation: Lock thickness at 4.2mm, explore other variables - - Open report? [Y/n]" -``` - ---- - -## Success Metrics - -### Phase 1 Success ✅ -- [x] Hook system operational with 5 plugins created and tested -- [x] Plugin auto-discovery and registration working -- [x] Comprehensive logging system (trial logs + optimization log) -- [x] Studies folder structure established with documentation -- [x] Path resolution system working across all test scripts -- [x] Integration tests passing (hook validation test) - -### Phase 2 Success (Research Agent) -- [ ] LLM detects knowledge gaps by searching feature registry -- [ ] Interactive research workflow (ask user for examples first) -- [ ] Successfully learns NX material XML schema from single user example -- [ ] Knowledge persisted across sessions (research session logs retrievable) -- [ ] Template library grows with each research session -- [ ] Second similar request uses learned template (instant generation) - -### Phase 3 Success (LLM Integration) -- [ ] LLM can create optimization from natural language in <5 turns -- [ ] 90% of user requests understood correctly -- [ ] Zero manual JSON editing required - -### Phase 4 Success (Code Generation) -- [ ] LLM generates 10+ custom functions with zero errors -- [ ] All generated code passes safety validation -- [ ] Users save 50% time vs. manual coding - -### Phase 5 Success (Analysis & Decision Support) -- [ ] Surrogate quality detection 95% accurate -- [ ] Recommendations lead to 30% faster convergence -- [ ] Users report higher confidence in results - -### Phase 6 Success (Automated Reporting) -- [ ] Reports generated in <30 seconds -- [ ] Narrative quality rated 4/5 by engineers -- [ ] 80% of reports used without manual editing - -### Phase 7 Success (NX MCP Enhancement) -- [ ] NX MCP answers 95% of API questions correctly -- [ ] Feature bank covers 80% of common workflows -- [ ] Users write 50% less manual journal code - -### Phase 8 Success (Self-Improving System) -- [ ] 20+ user-contributed features in library -- [ ] Pattern recognition identifies 10+ best practices -- [ ] Documentation auto-updates with zero manual effort - ---- - -## Risk Mitigation - -### Risk: LLM generates unsafe code -**Mitigation**: -- Sandbox all execution -- Whitelist allowed imports -- Code review by static analysis tools -- Rollback on any error - -### Risk: Feature registry becomes stale -**Mitigation**: -- Auto-update on code changes (pre-commit hook) -- CI/CD checks for registry sync -- Weekly audit of documented vs. actual features - -### Risk: NX API changes break features -**Mitigation**: -- Version pinning for NX (currently 2412) -- Automated tests against NX API -- Migration guides for version upgrades - -### Risk: User overwhelmed by LLM autonomy -**Mitigation**: -- Confirm before executing destructive actions -- "Explain mode" that shows what LLM plans to do -- Undo/rollback for all operations - ---- - -**Last Updated**: 2025-01-16 -**Maintainer**: Antoine Polvé (antoine@atomaste.com) -**Status**: 🟢 Phase 1 Complete | 🟡 Phase 2 (Research Agent) - NEXT PRIORITY - ---- - -## For Developers - -**Active development tracking**: See [DEVELOPMENT.md](DEVELOPMENT.md) for: -- Detailed todos for current phase -- Completed features list -- Known issues and bug tracking -- Testing status and coverage -- Development commands and workflows diff --git a/atomizer-dashboard/backend/api/routes/optimization.py b/atomizer-dashboard/backend/api/routes/optimization.py index 24655b8e..6df98583 100644 --- a/atomizer-dashboard/backend/api/routes/optimization.py +++ b/atomizer-dashboard/backend/api/routes/optimization.py @@ -3,12 +3,15 @@ Optimization API endpoints Handles study status, history retrieval, and control operations """ -from fastapi import APIRouter, HTTPException +from fastapi import APIRouter, HTTPException, UploadFile, File, Form +from fastapi.responses import JSONResponse, FileResponse from pathlib import Path from typing import List, Dict, Optional import json import sys import sqlite3 +import shutil +from datetime import datetime # Add project root to path sys.path.append(str(Path(__file__).parent.parent.parent.parent.parent)) @@ -307,12 +310,40 @@ async def get_optimization_history(study_id: str, limit: Optional[int] = None): except (ValueError, TypeError): params[param_name] = param_value + # Get user attributes (extracted results: mass, frequency, stress, displacement, etc.) + cursor.execute(""" + SELECT key, value_json + FROM trial_user_attributes + WHERE trial_id = ? + """, (trial_id,)) + user_attrs = {} + for key, value_json in cursor.fetchall(): + try: + user_attrs[key] = json.loads(value_json) + except (ValueError, TypeError): + user_attrs[key] = value_json + + # Extract relevant metrics for results (mass, frequency, stress, displacement, etc.) + results = {} + if "mass" in user_attrs: + results["mass"] = user_attrs["mass"] + if "frequency" in user_attrs: + results["frequency"] = user_attrs["frequency"] + if "max_stress" in user_attrs: + results["max_stress"] = user_attrs["max_stress"] + if "max_displacement" in user_attrs: + results["max_displacement"] = user_attrs["max_displacement"] + # Fallback to first frequency from objectives if available + if not results and len(values) > 0: + results["first_frequency"] = values[0] + 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, - "results": {"first_frequency": values[0]} if len(values) > 0 else {}, + "results": results, + "user_attrs": user_attrs, # Include all user attributes "start_time": start_time, "end_time": end_time }) @@ -488,3 +519,268 @@ async def get_pareto_front(study_id: str): 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 Pareto front: {str(e)}") + +@router.post("/studies") +async def create_study( + config: str = Form(...), + prt_file: Optional[UploadFile] = File(None), + sim_file: Optional[UploadFile] = File(None), + fem_file: Optional[UploadFile] = File(None) +): + """ + Create a new optimization study + Accepts: + - config: JSON string with study configuration + - prt_file: NX part file (optional if using existing study) + - sim_file: NX simulation file (optional) + - fem_file: NX FEM file (optional) + """ + try: + # Parse config + config_data = json.loads(config) + study_name = config_data.get("name") # Changed from study_name to name to match frontend + + if not study_name: + raise HTTPException(status_code=400, detail="name is required in config") + + # Create study directory structure + study_dir = STUDIES_DIR / study_name + if study_dir.exists(): + raise HTTPException(status_code=400, detail=f"Study {study_name} already exists") + + setup_dir = study_dir / "1_setup" + model_dir = setup_dir / "model" + results_dir = study_dir / "2_results" + + setup_dir.mkdir(parents=True, exist_ok=True) + model_dir.mkdir(parents=True, exist_ok=True) + results_dir.mkdir(parents=True, exist_ok=True) + + # Save config file + config_file = setup_dir / "optimization_config.json" + with open(config_file, 'w') as f: + json.dump(config_data, f, indent=2) + + # Save uploaded files + files_saved = {} + if prt_file: + prt_path = model_dir / prt_file.filename + with open(prt_path, 'wb') as f: + content = await prt_file.read() + f.write(content) + files_saved['prt_file'] = str(prt_path) + + if sim_file: + sim_path = model_dir / sim_file.filename + with open(sim_path, 'wb') as f: + content = await sim_file.read() + f.write(content) + files_saved['sim_file'] = str(sim_path) + + if fem_file: + fem_path = model_dir / fem_file.filename + with open(fem_path, 'wb') as f: + content = await fem_file.read() + f.write(content) + files_saved['fem_file'] = str(fem_path) + + return JSONResponse( + status_code=201, + content={ + "status": "created", + "study_id": study_name, + "study_path": str(study_dir), + "config_path": str(config_file), + "files_saved": files_saved, + "message": f"Study {study_name} created successfully. Ready to run optimization." + } + ) + + except json.JSONDecodeError as e: + raise HTTPException(status_code=400, detail=f"Invalid JSON in config: {str(e)}") + except Exception as e: + # Clean up on error + if 'study_dir' in locals() and study_dir.exists(): + shutil.rmtree(study_dir) + raise HTTPException(status_code=500, detail=f"Failed to create study: {str(e)}") + +@router.post("/studies/{study_id}/convert-mesh") +async def convert_study_mesh(study_id: str): + """ + Convert study mesh to GLTF for 3D visualization + Creates a web-viewable 3D model with FEA results as vertex colors + """ + try: + study_dir = STUDIES_DIR / study_id + if not study_dir.exists(): + raise HTTPException(status_code=404, detail=f"Study {study_id} not found") + + # Import mesh converter + sys.path.append(str(Path(__file__).parent.parent.parent.parent.parent)) + from optimization_engine.mesh_converter import convert_study_mesh + + # Convert mesh + output_path = convert_study_mesh(study_dir) + + if output_path and output_path.exists(): + return { + "status": "success", + "gltf_path": str(output_path), + "gltf_url": f"/api/optimization/studies/{study_id}/mesh/model.gltf", + "metadata_url": f"/api/optimization/studies/{study_id}/mesh/model.json", + "message": "Mesh converted successfully" + } + else: + raise HTTPException(status_code=500, detail="Mesh conversion failed") + + 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 convert mesh: {str(e)}") + +@router.get("/studies/{study_id}/mesh/{filename}") +async def get_mesh_file(study_id: str, filename: str): + """ + Serve GLTF mesh files and metadata + Supports .gltf, .bin, and .json files + """ + try: + # Validate filename to prevent directory traversal + if '..' in filename or '/' in filename or '\\' in filename: + raise HTTPException(status_code=400, detail="Invalid filename") + + study_dir = STUDIES_DIR / study_id + visualization_dir = study_dir / "3_visualization" + + file_path = visualization_dir / filename + + if not file_path.exists(): + raise HTTPException(status_code=404, detail=f"File {filename} not found") + + # Determine content type + suffix = file_path.suffix.lower() + content_types = { + '.gltf': 'model/gltf+json', + '.bin': 'application/octet-stream', + '.json': 'application/json', + '.glb': 'model/gltf-binary' + } + + content_type = content_types.get(suffix, 'application/octet-stream') + + return FileResponse( + path=str(file_path), + media_type=content_type, + filename=filename + ) + + except FileNotFoundError: + raise HTTPException(status_code=404, detail=f"File not found") + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to serve mesh file: {str(e)}") + +@router.post("/studies/{study_id}/generate-report") +async def generate_report( + study_id: str, + format: str = "markdown", + include_llm_summary: bool = False +): + """ + Generate an optimization report in the specified format + + Args: + study_id: Study identifier + format: Report format ('markdown', 'html', or 'pdf') + include_llm_summary: Whether to include LLM-generated executive summary + + Returns: + Information about the generated report including download URL + """ + try: + study_dir = STUDIES_DIR / study_id + if not study_dir.exists(): + raise HTTPException(status_code=404, detail=f"Study {study_id} not found") + + # Validate format + valid_formats = ['markdown', 'md', 'html', 'pdf'] + if format.lower() not in valid_formats: + raise HTTPException(status_code=400, detail=f"Invalid format. Must be one of: {', '.join(valid_formats)}") + + # Import report generator + sys.path.append(str(Path(__file__).parent.parent.parent.parent.parent)) + from optimization_engine.report_generator import generate_study_report + + # Generate report + output_path = generate_study_report( + study_dir=study_dir, + output_format=format.lower(), + include_llm_summary=include_llm_summary + ) + + if output_path and output_path.exists(): + # Get relative path for URL + rel_path = output_path.relative_to(study_dir) + + return { + "status": "success", + "format": format, + "file_path": str(output_path), + "download_url": f"/api/optimization/studies/{study_id}/reports/{output_path.name}", + "file_size": output_path.stat().st_size, + "message": f"Report generated successfully in {format} format" + } + else: + raise HTTPException(status_code=500, detail="Report generation failed") + + 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 generate report: {str(e)}") + +@router.get("/studies/{study_id}/reports/{filename}") +async def download_report(study_id: str, filename: str): + """ + Download a generated report file + + Args: + study_id: Study identifier + filename: Report filename + + Returns: + Report file for download + """ + try: + # Validate filename to prevent directory traversal + if '..' in filename or '/' in filename or '\\' in filename: + raise HTTPException(status_code=400, detail="Invalid filename") + + study_dir = STUDIES_DIR / study_id + results_dir = study_dir / "2_results" + + file_path = results_dir / filename + + if not file_path.exists(): + raise HTTPException(status_code=404, detail=f"Report file {filename} not found") + + # Determine content type + suffix = file_path.suffix.lower() + content_types = { + '.md': 'text/markdown', + '.html': 'text/html', + '.pdf': 'application/pdf', + '.json': 'application/json' + } + + content_type = content_types.get(suffix, 'application/octet-stream') + + return FileResponse( + path=str(file_path), + media_type=content_type, + filename=filename, + headers={"Content-Disposition": f"attachment; filename={filename}"} + ) + + except FileNotFoundError: + raise HTTPException(status_code=404, detail=f"Report file not found") + except Exception as e: + raise HTTPException(status_code=500, detail=f"Failed to download report: {str(e)}") diff --git a/atomizer-dashboard/frontend/src/pages/Dashboard.tsx b/atomizer-dashboard/frontend/src/pages/Dashboard.tsx index 1cf7585f..15d47f69 100644 --- a/atomizer-dashboard/frontend/src/pages/Dashboard.tsx +++ b/atomizer-dashboard/frontend/src/pages/Dashboard.tsx @@ -3,33 +3,53 @@ import { LineChart, Line, ScatterChart, Scatter, XAxis, YAxis, CartesianGrid, Tooltip, Legend, ResponsiveContainer, Cell } from 'recharts'; -import { useWebSocket } from '../hooks/useWebSocket'; -import { Card } from '../components/Card'; -import { MetricCard } from '../components/MetricCard'; -import { StudyCard } from '../components/StudyCard'; +import { useOptimizationWebSocket } from '../hooks/useWebSocket'; +import { apiClient } from '../api/client'; +import { Card } from '../components/common/Card'; +import { MetricCard } from '../components/dashboard/MetricCard'; +import { StudyCard } from '../components/dashboard/StudyCard'; import { OptimizerPanel } from '../components/OptimizerPanel'; import { ParetoPlot } from '../components/ParetoPlot'; import { ParallelCoordinatesPlot } from '../components/ParallelCoordinatesPlot'; import type { Study, Trial, ConvergenceDataPoint, ParameterSpaceDataPoint } from '../types'; -interface DashboardProps { - studies: Study[]; - selectedStudyId: string | null; - onStudySelect: (studyId: string) => void; -} - -export default function Dashboard({ studies, selectedStudyId, onStudySelect }: DashboardProps) { - const [trials, setTrials] = useState([]); +export default function Dashboard() { + const [studies, setStudies] = useState([]); + const [selectedStudyId, setSelectedStudyId] = useState(null); const [allTrials, setAllTrials] = useState([]); + const [displayedTrials, setDisplayedTrials] = useState([]); const [bestValue, setBestValue] = useState(Infinity); const [prunedCount, setPrunedCount] = useState(0); const [alerts, setAlerts] = useState>([]); const [alertIdCounter, setAlertIdCounter] = useState(0); + const [expandedTrials, setExpandedTrials] = useState>(new Set()); + const [sortBy, setSortBy] = useState<'performance' | 'chronological'>('performance'); // Protocol 13: New state for metadata and Pareto front const [studyMetadata, setStudyMetadata] = useState(null); const [paretoFront, setParetoFront] = useState([]); + // Load studies on mount + useEffect(() => { + apiClient.getStudies() + .then(data => { + setStudies(data.studies); + if (data.studies.length > 0) { + // Check LocalStorage for last selected study + const savedStudyId = localStorage.getItem('lastSelectedStudyId'); + const studyExists = data.studies.find(s => s.id === savedStudyId); + + if (savedStudyId && studyExists) { + setSelectedStudyId(savedStudyId); + } else { + const running = data.studies.find(s => s.status === 'running'); + setSelectedStudyId(running?.id || data.studies[0].id); + } + } + }) + .catch(console.error); + }, []); + const showAlert = (type: 'success' | 'warning', message: string) => { const id = alertIdCounter; setAlertIdCounter(prev => prev + 1); @@ -40,54 +60,50 @@ export default function Dashboard({ studies, selectedStudyId, onStudySelect }: D }; // WebSocket connection - const { isConnected } = useWebSocket({ + const { connectionStatus } = useOptimizationWebSocket({ studyId: selectedStudyId, - onTrialCompleted: (trial) => { - setTrials(prev => [trial, ...prev].slice(0, 20)); - setAllTrials(prev => [...prev, trial]); - if (trial.objective < bestValue) { - setBestValue(trial.objective); - showAlert('success', `New best: ${trial.objective.toFixed(4)} (Trial #${trial.trial_number})`); + onMessage: (msg) => { + if (msg.type === 'trial_completed') { + const trial = msg.data as Trial; + setAllTrials(prev => [...prev, trial]); + if (trial.objective !== null && trial.objective !== undefined && trial.objective < bestValue) { + setBestValue(trial.objective); + showAlert('success', `New best: ${trial.objective.toFixed(4)} (Trial #${trial.trial_number})`); + } + } else if (msg.type === 'trial_pruned') { + setPrunedCount(prev => prev + 1); + showAlert('warning', `Trial pruned: ${msg.data.pruning_cause}`); } - }, - onNewBest: (trial) => { - console.log('New best trial:', trial); - }, - onTrialPruned: (pruned) => { - setPrunedCount(prev => prev + 1); - showAlert('warning', `Trial #${pruned.trial_number} pruned: ${pruned.pruning_cause}`); - }, + } }); // Load initial trial history when study changes useEffect(() => { if (selectedStudyId) { - setTrials([]); setAllTrials([]); setBestValue(Infinity); setPrunedCount(0); + setExpandedTrials(new Set()); - // Fetch full history - fetch(`/api/optimization/studies/${selectedStudyId}/history`) - .then(res => res.json()) + // Save to LocalStorage + localStorage.setItem('lastSelectedStudyId', selectedStudyId); + + apiClient.getStudyHistory(selectedStudyId) .then(data => { - const sortedTrials = data.trials.sort((a: Trial, b: Trial) => a.trial_number - b.trial_number); - setAllTrials(sortedTrials); - setTrials(sortedTrials.slice(-20).reverse()); - if (sortedTrials.length > 0) { - const minObj = Math.min(...sortedTrials.map((t: Trial) => t.objective)); + const validTrials = data.trials.filter(t => t.objective !== null && t.objective !== undefined); + setAllTrials(validTrials); + if (validTrials.length > 0) { + const minObj = Math.min(...validTrials.map(t => t.objective)); setBestValue(minObj); } }) - .catch(err => console.error('Failed to load history:', err)); + .catch(console.error); - // Fetch pruning count - fetch(`/api/optimization/studies/${selectedStudyId}/pruning`) - .then(res => res.json()) + apiClient.getStudyPruning(selectedStudyId) .then(data => { setPrunedCount(data.pruned_trials?.length || 0); }) - .catch(err => console.error('Failed to load pruning data:', err)); + .catch(console.error); // Protocol 13: Fetch metadata fetch(`/api/optimization/studies/${selectedStudyId}/metadata`) @@ -97,12 +113,12 @@ export default function Dashboard({ studies, selectedStudyId, onStudySelect }: D }) .catch(err => console.error('Failed to load metadata:', err)); - // Protocol 13: Fetch Pareto front + // Protocol 13: Fetch Pareto front (raw format for Protocol 13 components) fetch(`/api/optimization/studies/${selectedStudyId}/pareto-front`) .then(res => res.json()) - .then(data => { - if (data.is_multi_objective) { - setParetoFront(data.pareto_front); + .then(paretoData => { + if (paretoData.is_multi_objective && paretoData.pareto_front) { + setParetoFront(paretoData.pareto_front); } else { setParetoFront([]); } @@ -111,42 +127,92 @@ export default function Dashboard({ studies, selectedStudyId, onStudySelect }: D } }, [selectedStudyId]); - // Prepare chart data - const convergenceData: ConvergenceDataPoint[] = allTrials.map((trial, idx) => ({ - trial_number: trial.trial_number, - objective: trial.objective, - best_so_far: Math.min(...allTrials.slice(0, idx + 1).map(t => t.objective)), - })); + // Sort trials based on selected sort order + useEffect(() => { + let sorted = [...allTrials]; + if (sortBy === 'performance') { + // Sort by objective (best first) + sorted.sort((a, b) => { + const aObj = a.objective ?? Infinity; + const bObj = b.objective ?? Infinity; + return aObj - bObj; + }); + } else { + // Chronological (newest first) + sorted.sort((a, b) => b.trial_number - a.trial_number); + } + setDisplayedTrials(sorted); + }, [allTrials, sortBy]); - const parameterSpaceData: ParameterSpaceDataPoint[] = allTrials.map(trial => { - const params = Object.values(trial.design_variables); - return { - trial_number: trial.trial_number, - x: params[0] || 0, - y: params[1] || 0, - objective: trial.objective, - isBest: trial.objective === bestValue, - }; - }); + // Auto-refresh polling (every 3 seconds) for trial history + useEffect(() => { + if (!selectedStudyId) return; + + const refreshInterval = setInterval(() => { + apiClient.getStudyHistory(selectedStudyId) + .then(data => { + const validTrials = data.trials.filter(t => t.objective !== null && t.objective !== undefined); + setAllTrials(validTrials); + if (validTrials.length > 0) { + const minObj = Math.min(...validTrials.map(t => t.objective)); + setBestValue(minObj); + } + }) + .catch(err => console.error('Auto-refresh failed:', err)); + }, 3000); // Poll every 3 seconds + + return () => clearInterval(refreshInterval); + }, [selectedStudyId]); + + // Prepare chart data with proper null/undefined handling + const convergenceData: ConvergenceDataPoint[] = allTrials + .filter(t => t.objective !== null && t.objective !== undefined) + .sort((a, b) => a.trial_number - b.trial_number) + .map((trial, idx, arr) => { + const previousTrials = arr.slice(0, idx + 1); + const validObjectives = previousTrials.map(t => t.objective).filter(o => o !== null && o !== undefined); + return { + trial_number: trial.trial_number, + objective: trial.objective, + best_so_far: validObjectives.length > 0 ? Math.min(...validObjectives) : trial.objective, + }; + }); + + const parameterSpaceData: ParameterSpaceDataPoint[] = allTrials + .filter(t => t.objective !== null && t.objective !== undefined && t.design_variables) + .map(trial => { + const params = Object.values(trial.design_variables); + return { + trial_number: trial.trial_number, + x: params[0] || 0, + y: params[1] || 0, + objective: trial.objective, + isBest: trial.objective === bestValue, + }; + }); // Calculate average objective - const avgObjective = allTrials.length > 0 - ? allTrials.reduce((sum, t) => sum + t.objective, 0) / allTrials.length + const validObjectives = allTrials.filter(t => t.objective !== null && t.objective !== undefined).map(t => t.objective); + const avgObjective = validObjectives.length > 0 + ? validObjectives.reduce((sum, obj) => sum + obj, 0) / validObjectives.length : 0; // Get parameter names - const paramNames = allTrials.length > 0 ? Object.keys(allTrials[0].design_variables) : []; + const paramNames = allTrials.length > 0 && allTrials[0].design_variables + ? Object.keys(allTrials[0].design_variables) + : []; - // Helper: Format parameter label with unit from metadata - const getParamLabel = (paramName: string, index: number): string => { - if (!studyMetadata?.design_variables) { - return paramName || `Parameter ${index + 1}`; - } - const dv = studyMetadata.design_variables.find((v: any) => v.name === paramName); - if (dv && dv.unit) { - return `${paramName} (${dv.unit})`; - } - return paramName || `Parameter ${index + 1}`; + // Toggle trial expansion + const toggleTrialExpansion = (trialNumber: number) => { + setExpandedTrials(prev => { + const newSet = new Set(prev); + if (newSet.has(trialNumber)) { + newSet.delete(trialNumber); + } else { + newSet.add(trialNumber); + } + return newSet; + }); }; // Export functions @@ -169,7 +235,7 @@ export default function Dashboard({ studies, selectedStudyId, onStudySelect }: D const rows = allTrials.map(t => [ t.trial_number, t.objective, - ...paramNames.map(k => t.design_variables[k]) + ...paramNames.map(k => t.design_variables?.[k] ?? '') ].join(',')); const csv = [headers, ...rows].join('\n'); const blob = new Blob([csv], { type: 'text/csv' }); @@ -183,7 +249,7 @@ export default function Dashboard({ studies, selectedStudyId, onStudySelect }: D }; return ( -
+
{/* Alerts */}
{alerts.map(alert => ( @@ -201,12 +267,24 @@ export default function Dashboard({ studies, selectedStudyId, onStudySelect }: D
{/* Header */} -
+
-

Atomizer Dashboard

-

Real-time optimization monitoring

+

Live Dashboard

+

Real-time optimization monitoring

+ @@ -226,7 +304,7 @@ export default function Dashboard({ studies, selectedStudyId, onStudySelect }: D key={study.id} study={study} isActive={study.id === selectedStudyId} - onClick={() => onStudySelect(study.id)} + onClick={() => setSelectedStudyId(study.id)} /> ))}
@@ -248,14 +326,6 @@ export default function Dashboard({ studies, selectedStudyId, onStudySelect }: D value={avgObjective > 0 ? avgObjective.toFixed(4) : '-'} valueColor="text-blue-400" /> - -
- -
{/* Protocol 13: Intelligent Optimizer & Pareto Front */} - {selectedStudyId && ( + {selectedStudyId && paretoFront.length > 0 && studyMetadata && studyMetadata.objectives && (
- - {paretoFront.length > 0 && studyMetadata && ( - - )} + +
+
+ Algorithm: {studyMetadata.sampler || 'NSGA-II'} +
+
+ Type: Multi-objective +
+
+ Objectives: {studyMetadata.objectives?.length || 2} +
+
+ Design Variables: {studyMetadata.design_variables?.length || 0} +
+
+ +
)} {/* Parallel Coordinates (full width for multi-objective) */} - {paretoFront.length > 0 && studyMetadata && ( + {paretoFront.length > 0 && studyMetadata && studyMetadata.objectives && studyMetadata.design_variables && (
)} @@ -344,14 +427,14 @@ export default function Dashboard({ studies, selectedStudyId, onStudySelect }: D dataKey="x" stroke="#94a3b8" name={paramNames[0] || 'X'} - label={{ value: getParamLabel(paramNames[0], 0), position: 'insideBottom', offset: -5, fill: '#94a3b8' }} + label={{ value: paramNames[0] || 'Parameter 1', position: 'insideBottom', offset: -5, fill: '#94a3b8' }} />
- {/* Trial Feed */} - -
- {trials.length > 0 ? ( - trials.map(trial => ( -
+ Trial History ({displayedTrials.length} trials) +
+ + +
+
+ } + > +
+ {displayedTrials.length > 0 ? ( + displayedTrials.map(trial => { + const isExpanded = expandedTrials.has(trial.trial_number); + const isBest = trial.objective === bestValue; + + return ( +
toggleTrialExpansion(trial.trial_number)} + > + {/* Collapsed View */} +
+
+ + Trial #{trial.trial_number} + {isBest && BEST} + +
+ + {trial.objective !== null && trial.objective !== undefined + ? trial.objective.toFixed(4) + : 'N/A'} + + + {isExpanded ? '▼' : '▶'} + +
+
+ + {/* Quick Preview */} + {!isExpanded && trial.results && Object.keys(trial.results).length > 0 && ( +
+ {trial.results.mass && ( + Mass: {trial.results.mass.toFixed(2)}g + )} + {trial.results.frequency && ( + Freq: {trial.results.frequency.toFixed(2)}Hz + )} +
+ )} +
+ + {/* Expanded View */} + {isExpanded && ( +
+ {/* Design Variables */} + {trial.design_variables && Object.keys(trial.design_variables).length > 0 && ( +
+

Design Variables

+
+ {Object.entries(trial.design_variables).map(([key, val]) => ( +
+ {key}: + {val.toFixed(4)} +
+ ))} +
+
+ )} + + {/* Results */} + {trial.results && Object.keys(trial.results).length > 0 && ( +
+

Extracted Results

+
+ {Object.entries(trial.results).map(([key, val]) => ( +
+ {key}: + + {typeof val === 'number' ? val.toFixed(4) : String(val)} + +
+ ))} +
+
+ )} + + {/* All User Attributes */} + {trial.user_attrs && Object.keys(trial.user_attrs).length > 0 && ( +
+

All Attributes

+
+ 
+                                  {JSON.stringify(trial.user_attrs, null, 2)}
+
+
+
+ )} + + {/* Timestamps */} + {trial.start_time && trial.end_time && ( +
+
+ Duration: + + {((new Date(trial.end_time).getTime() - new Date(trial.start_time).getTime()) / 1000).toFixed(1)}s + +
+
+ )} +
+ )}
-
- {Object.entries(trial.design_variables).map(([key, val]) => ( - - {key}: {val.toFixed(3)} - - ))} -
-
- )) + ); + }) ) : (
No trials yet. Waiting for optimization to start... diff --git a/docs/ARCHITECTURE_REFACTOR_NOV17.md b/docs/ARCHITECTURE_REFACTOR_NOV17.md deleted file mode 100644 index 7fe1b665..00000000 --- a/docs/ARCHITECTURE_REFACTOR_NOV17.md +++ /dev/null @@ -1,284 +0,0 @@ -# Architecture Refactor: Centralized Library System -**Date**: November 17, 2025 -**Phase**: 3.2 Architecture Cleanup -**Author**: Claude Code (with Antoine's direction) - -## Problem Statement - -You identified a critical architectural flaw: - -> "ok, now, quick thing, why do very basic hooks get recreated and stored in the substudies? those should be just core accessed hooked right? is it only because its a test? -> -> What I need in studies is the config, files, setup, report, results etc not core hooks, those should go in atomizer hooks library with their doc etc no? I mean, applied only info = studies, and reusdable and core functions = atomizer foundation. -> -> My study folder is a mess, why? I want some order and real structure to develop an insanely good engineering software that evolve with time." - -### Old Architecture (BAD): -``` -studies/ - simple_beam_optimization/ - 2_substudies/ - test_e2e_3trials_XXX/ - generated_extractors/ ❌ Code pollution! - extract_displacement.py - extract_von_mises_stress.py - extract_mass.py - generated_hooks/ ❌ Code pollution! - custom_hook.py - llm_workflow_config.json - optimization_results.json -``` - -**Problems**: -- Every substudy duplicates extractor code -- Study folders polluted with reusable code -- No code reuse across studies -- Mess! Not production-grade engineering software - -### New Architecture (GOOD): -``` -optimization_engine/ - extractors/ ✓ Core reusable library - extract_displacement.py - extract_stress.py - extract_mass.py - catalog.json ✓ Tracks all extractors - - hooks/ ✓ Core reusable library - (future implementation) - -studies/ - simple_beam_optimization/ - 2_substudies/ - my_optimization/ - extractors_manifest.json ✓ Just references! - llm_workflow_config.json ✓ Study config - optimization_results.json ✓ Results - optimization_history.json ✓ History -``` - -**Benefits**: -- ✅ Clean study folders (only metadata) -- ✅ Reusable core libraries -- ✅ Deduplication (same extractor = single file) -- ✅ Production-grade architecture -- ✅ Evolves with time (library grows, studies stay clean) - -## Implementation - -### 1. Extractor Library Manager (`extractor_library.py`) - -New smart library system with: -- **Signature-based deduplication**: Two extractors with same functionality = one file -- **Catalog tracking**: `catalog.json` tracks all library extractors -- **Study manifests**: Studies just reference which extractors they used - -```python -class ExtractorLibrary: - def get_or_create(self, llm_feature, extractor_code): - """Add to library or reuse existing.""" - signature = self._compute_signature(llm_feature) - - if signature in self.catalog: - # Reuse existing! - return self.library_dir / self.catalog[signature]['filename'] - else: - # Add new to library - self.catalog[signature] = {...} - return extractor_file -``` - -### 2. Updated Components - -**ExtractorOrchestrator** (`extractor_orchestrator.py`): -- Now uses `ExtractorLibrary` instead of per-study generation -- Creates `extractors_manifest.json` instead of copying code -- Backward compatible (legacy mode available) - -**LLMOptimizationRunner** (`llm_optimization_runner.py`): -- Removed per-study `generated_extractors/` directory creation -- Removed per-study `generated_hooks/` directory creation -- Uses core library exclusively - -**Test Suite** (`test_phase_3_2_e2e.py`): -- Updated to check for `extractors_manifest.json` instead of `generated_extractors/` -- Verifies clean study folder structure - -## Results - -### Before Refactor: -``` -test_e2e_3trials_XXX/ -├── generated_extractors/ ❌ 3 Python files -│ ├── extract_displacement.py -│ ├── extract_von_mises_stress.py -│ └── extract_mass.py -├── generated_hooks/ ❌ Hook files -├── llm_workflow_config.json -└── optimization_results.json -``` - -### After Refactor: -``` -test_e2e_3trials_XXX/ -├── extractors_manifest.json ✅ Just references! -├── llm_workflow_config.json ✅ Study config -├── optimization_results.json ✅ Results -└── optimization_history.json ✅ History - -optimization_engine/extractors/ ✅ Core library -├── extract_displacement.py -├── extract_von_mises_stress.py -├── extract_mass.py -└── catalog.json -``` - -## Testing - -E2E test now passes with clean folder structure: -- ✅ `extractors_manifest.json` created -- ✅ Core library populated with 3 extractors -- ✅ NO `generated_extractors/` pollution -- ✅ Study folder clean and professional - -Test output: -``` -Verifying outputs... - [OK] Output directory created - [OK] History file created - [OK] Results file created - [OK] Extractors manifest (references core library) - -Checks passed: 18/18 -[SUCCESS] END-TO-END TEST PASSED! -``` - -## Migration Guide - -### For Future Studies: - -**What changed**: -- Extractors are now in `optimization_engine/extractors/` (core library) -- Study folders only contain `extractors_manifest.json` (not code) - -**No action required**: -- System automatically uses new architecture -- Backward compatible (legacy mode available with `use_core_library=False`) - -### For Developers: - -**To add new extractors**: -1. LLM generates extractor code -2. `ExtractorLibrary.get_or_create()` checks if already exists -3. If new: adds to `optimization_engine/extractors/` -4. If exists: reuses existing file -5. Study gets manifest reference, not copy of code - -**To view library**: -```python -from optimization_engine.extractor_library import ExtractorLibrary - -library = ExtractorLibrary() -print(library.get_library_summary()) -``` - -## Next Steps (Future Work) - -1. **Hook Library System**: Implement same architecture for hooks - - Currently: Hooks still use legacy per-study generation - - Future: `optimization_engine/hooks/` library like extractors - -2. **Library Documentation**: Auto-generate docs for each extractor - - Extract docstrings from library extractors - - Create browsable documentation - -3. **Versioning**: Track extractor versions for reproducibility - - Tag extractors with creation date/version - - Allow studies to pin specific versions - -4. **CLI Tool**: View and manage library - - `python -m optimization_engine.extractors list` - - `python -m optimization_engine.extractors info ` - -## Files Modified - -1. **New Files**: - - `optimization_engine/extractor_library.py` - Core library manager - - `optimization_engine/extractors/__init__.py` - Package init - - `optimization_engine/extractors/catalog.json` - Library catalog - - `docs/ARCHITECTURE_REFACTOR_NOV17.md` - This document - -2. **Modified Files**: - - `optimization_engine/extractor_orchestrator.py` - Use library instead of per-study - - `optimization_engine/llm_optimization_runner.py` - Remove per-study directories - - `tests/test_phase_3_2_e2e.py` - Check for manifest instead of directories - -## Commit Message - -``` -refactor: Implement centralized extractor library to eliminate code duplication - -MAJOR ARCHITECTURE REFACTOR - Clean Study Folders - -Problem: -- Every substudy was generating duplicate extractor code -- Study folders polluted with reusable library code -- No code reuse across studies -- Not production-grade architecture - -Solution: -Implemented centralized library system: -- Core extractors in optimization_engine/extractors/ -- Signature-based deduplication -- Studies only store metadata (extractors_manifest.json) -- Clean separation: studies = data, core = code - -Changes: -1. Created ExtractorLibrary with smart deduplication -2. Updated ExtractorOrchestrator to use core library -3. Updated LLMOptimizationRunner to stop creating per-study directories -4. Updated tests to verify clean study folder structure - -Results: -BEFORE: study folder with generated_extractors/ directory (code pollution) -AFTER: study folder with extractors_manifest.json (just references) - -Core library: optimization_engine/extractors/ -- extract_displacement.py -- extract_von_mises_stress.py -- extract_mass.py -- catalog.json (tracks all extractors) - -Study folders NOW ONLY contain: -- extractors_manifest.json (references to core library) -- llm_workflow_config.json (study configuration) -- optimization_results.json (results) -- optimization_history.json (trial history) - -Production-grade architecture for "insanely good engineering software that evolves with time" - -🤖 Generated with [Claude Code](https://claude.com/claude-code) - -Co-Authored-By: Claude -``` - -## Summary for Morning - -**What was done**: -1. ✅ Created centralized extractor library system -2. ✅ Eliminated per-study code duplication -3. ✅ Clean study folder architecture -4. ✅ E2E tests pass with new structure -5. ✅ Comprehensive documentation - -**What you'll see**: -- Studies now only contain metadata (no code!) -- Core library in `optimization_engine/extractors/` -- Professional, production-grade architecture - -**Ready for**: -- Continue Phase 3.2 development -- Same approach for hooks library (next iteration) -- Building "insanely good engineering software" - -Have a good night! ✨ diff --git a/docs/FEATURE_REGISTRY_ARCHITECTURE.md b/docs/FEATURE_REGISTRY_ARCHITECTURE.md deleted file mode 100644 index bb4a72cf..00000000 --- a/docs/FEATURE_REGISTRY_ARCHITECTURE.md +++ /dev/null @@ -1,843 +0,0 @@ -# Feature Registry Architecture - -> Comprehensive guide to Atomizer's LLM-instructed feature database system - -**Last Updated**: 2025-01-16 -**Status**: Phase 2 - Design Document - ---- - -## Table of Contents - -1. [Vision and Goals](#vision-and-goals) -2. [Feature Categorization System](#feature-categorization-system) -3. [Feature Registry Structure](#feature-registry-structure) -4. [LLM Instruction Format](#llm-instruction-format) -5. [Feature Documentation Strategy](#feature-documentation-strategy) -6. [Dynamic Tool Building](#dynamic-tool-building) -7. [Examples](#examples) -8. [Implementation Plan](#implementation-plan) - ---- - -## Vision and Goals - -### Core Philosophy - -Atomizer's feature registry is not just a catalog - it's an **LLM instruction system** that enables: - -1. **Self-Documentation**: Features describe themselves to the LLM -2. **Intelligent Composition**: LLM can combine features into workflows -3. **Autonomous Proposals**: LLM suggests new features based on user needs -4. **Structured Customization**: Users customize the tool through natural language -5. **Continuous Evolution**: Feature database grows as users add capabilities - -### Key Principles - -- **Feature Types Are First-Class**: Engineering, software, UI, and analysis features are equally important -- **Location-Aware**: Features know where their code lives and how to use it -- **Metadata-Rich**: Each feature has enough context for LLM to understand and use it -- **Composable**: Features can be combined into higher-level workflows -- **Extensible**: New feature types can be added without breaking the system - ---- - -## Feature Categorization System - -### Primary Feature Dimensions - -Features are organized along **three dimensions**: - -#### Dimension 1: Domain (WHAT it does) -- **Engineering**: Physics-based operations (stress, thermal, modal, etc.) -- **Software**: Core algorithms and infrastructure (optimization, hooks, path resolution) -- **UI**: User-facing components (dashboard, reports, visualization) -- **Analysis**: Post-processing and decision support (sensitivity, Pareto, surrogate quality) - -#### Dimension 2: Lifecycle Stage (WHEN it runs) -- **Pre-Mesh**: Before meshing (geometry operations) -- **Pre-Solve**: Before FEA solve (parameter updates, logging) -- **Solve**: During FEA execution (solver control) -- **Post-Solve**: After solve, before extraction (file validation) -- **Post-Extraction**: After result extraction (logging, analysis) -- **Post-Optimization**: After optimization completes (reporting, visualization) - -#### Dimension 3: Abstraction Level (HOW it's used) -- **Primitive**: Low-level functions (extract_stress, update_expression) -- **Composite**: Mid-level workflows (RSS_metric, weighted_objective) -- **Workflow**: High-level operations (run_optimization, generate_report) - -### Feature Type Classification - -``` -┌─────────────────────────────────────────────────────────────┐ -│ FEATURE UNIVERSE │ -└─────────────────────────────────────────────────────────────┘ - │ - ┌─────────────────────┼─────────────────────┐ - │ │ │ - ENGINEERING SOFTWARE UI - │ │ │ - ┌───┴───┐ ┌────┴────┐ ┌─────┴─────┐ - │ │ │ │ │ │ -Extractors Metrics Optimization Hooks Dashboard Reports - │ │ │ │ │ │ - Stress RSS Optuna Pre-Solve Widgets HTML - Thermal SCF TPE Post-Solve Controls PDF - Modal FOS Sampler Post-Extract Charts Markdown -``` - ---- - -## Feature Registry Structure - -### JSON Schema - -```json -{ - "feature_registry": { - "version": "0.2.0", - "last_updated": "2025-01-16", - "categories": { - "engineering": { ... }, - "software": { ... }, - "ui": { ... }, - "analysis": { ... } - } - } -} -``` - -### Feature Entry Schema - -Each feature has: - -```json -{ - "feature_id": "unique_identifier", - "name": "Human-Readable Name", - "description": "What this feature does (for LLM understanding)", - "category": "engineering|software|ui|analysis", - "subcategory": "extractors|metrics|optimization|hooks|...", - "lifecycle_stage": "pre_solve|post_solve|post_extraction|...", - "abstraction_level": "primitive|composite|workflow", - "implementation": { - "file_path": "relative/path/to/implementation.py", - "function_name": "function_or_class_name", - "entry_point": "how to invoke this feature" - }, - "interface": { - "inputs": [ - { - "name": "parameter_name", - "type": "str|int|float|dict|list", - "required": true, - "description": "What this parameter does", - "units": "mm|MPa|Hz|none", - "example": "example_value" - } - ], - "outputs": [ - { - "name": "output_name", - "type": "float|dict|list", - "description": "What this output represents", - "units": "mm|MPa|Hz|none" - } - ] - }, - "dependencies": { - "features": ["feature_id_1", "feature_id_2"], - "libraries": ["optuna", "pyNastran"], - "nx_version": "2412" - }, - "usage_examples": [ - { - "description": "Example scenario", - "code": "example_code_snippet", - "natural_language": "How user would request this" - } - ], - "composition_hints": { - "combines_with": ["feature_id_3", "feature_id_4"], - "typical_workflows": ["workflow_name_1"], - "prerequisites": ["feature that must run before this"] - }, - "metadata": { - "author": "Antoine Polvé", - "created": "2025-01-16", - "status": "stable|experimental|deprecated", - "tested": true, - "documentation_url": "docs/features/feature_name.md" - } -} -``` - ---- - -## LLM Instruction Format - -### How LLM Uses the Registry - -The feature registry serves as a **structured instruction manual** for the LLM: - -#### 1. Discovery Phase -``` -User: "I want to minimize stress on my bracket" - -LLM reads registry: - → Finds category="engineering", subcategory="extractors" - → Discovers "stress_extractor" feature - → Reads: "Extracts von Mises stress from OP2 files" - → Checks composition_hints: combines_with=["optimization_runner"] - -LLM response: "I'll use the stress_extractor feature to minimize stress. - This requires an OP2 file from NX solve." -``` - -#### 2. Composition Phase -``` -User: "Add a custom RSS metric combining stress and displacement" - -LLM reads registry: - → Finds abstraction_level="composite" examples - → Discovers "rss_metric" template feature - → Reads interface: inputs=[stress_value, displacement_value] - → Checks composition_hints: combines_with=["stress_extractor", "displacement_extractor"] - -LLM generates new composite feature following the pattern -``` - -#### 3. Proposal Phase -``` -User: "What features could help me analyze fatigue life?" - -LLM reads registry: - → Searches category="engineering", subcategory="extractors" - → Finds: stress_extractor, displacement_extractor (exist) - → Doesn't find: fatigue_extractor (missing) - → Reads composition_hints for similar features - -LLM proposes: "I can create a fatigue_life_extractor that: - 1. Extracts stress history from OP2 - 2. Applies rainflow counting algorithm - 3. Uses S-N curve to estimate fatigue life - - This would be similar to stress_extractor but with - time-series analysis. Should I implement it?" -``` - -#### 4. Execution Phase -``` -User: "Run the optimization" - -LLM reads registry: - → Finds abstraction_level="workflow", feature_id="run_optimization" - → Reads implementation.entry_point - → Checks dependencies: ["optuna", "nx_solver", "stress_extractor"] - → Reads lifecycle_stage to understand execution order - -LLM executes: python optimization_engine/runner.py -``` - -### Natural Language Mapping - -Each feature includes `natural_language` examples showing how users might request it: - -```json -"usage_examples": [ - { - "natural_language": [ - "minimize stress", - "reduce von Mises stress", - "find lowest stress configuration", - "optimize for minimum stress" - ], - "maps_to": { - "feature": "stress_extractor", - "objective": "minimize", - "metric": "max_von_mises" - } - } -] -``` - -This enables LLM to understand user intent and select correct features. - ---- - -## Feature Documentation Strategy - -### Multi-Location Documentation - -Features are documented in **three places**, each serving different purposes: - -#### 1. Feature Registry (feature_registry.json) -**Purpose**: LLM instruction and discovery -**Location**: `optimization_engine/feature_registry.json` -**Content**: -- Structured metadata -- Interface definitions -- Composition hints -- Usage examples - -**Example**: -```json -{ - "feature_id": "stress_extractor", - "name": "Stress Extractor", - "description": "Extracts von Mises stress from OP2 files", - "category": "engineering", - "subcategory": "extractors" -} -``` - -#### 2. Code Implementation (*.py files) -**Purpose**: Actual functionality -**Location**: Codebase (e.g., `optimization_engine/result_extractors/extractors.py`) -**Content**: -- Python code with docstrings -- Type hints -- Implementation details - -**Example**: -```python -def extract_stress_from_op2(op2_file: Path) -> dict: - """ - Extracts von Mises stress from OP2 file. - - Args: - op2_file: Path to OP2 file - - Returns: - dict with max_von_mises, min_von_mises, avg_von_mises - """ - # Implementation... -``` - -#### 3. Feature Documentation (docs/features/*.md) -**Purpose**: Human-readable guides and tutorials -**Location**: `docs/features/` -**Content**: -- Detailed explanations -- Extended examples -- Best practices -- Troubleshooting - -**Example**: `docs/features/stress_extractor.md` -```markdown -# Stress Extractor - -## Overview -Extracts von Mises stress from NX Nastran OP2 files. - -## When to Use -- Structural optimization where stress is the objective -- Constraint checking (yield stress limits) -- Multi-objective with stress as one objective - -## Example Workflows -[detailed examples...] -``` - -### Documentation Flow - -``` -User Request - ↓ -LLM reads feature_registry.json (discovers feature) - ↓ -LLM reads code docstrings (understands interface) - ↓ -LLM reads docs/features/*.md (if complex usage needed) - ↓ -LLM composes workflow using features -``` - ---- - -## Dynamic Tool Building - -### How LLM Builds New Features - -The registry enables **autonomous feature creation** through templates and patterns: - -#### Step 1: Pattern Recognition -``` -User: "I need thermal stress extraction" - -LLM: -1. Reads existing feature: stress_extractor -2. Identifies pattern: OP2 parsing → result extraction → return dict -3. Finds similar features: displacement_extractor -4. Recognizes template: engineering.extractors -``` - -#### Step 2: Feature Generation -``` -LLM generates new feature following pattern: -{ - "feature_id": "thermal_stress_extractor", - "name": "Thermal Stress Extractor", - "description": "Extracts thermal stress from OP2 files (steady-state heat transfer analysis)", - "category": "engineering", - "subcategory": "extractors", - "lifecycle_stage": "post_extraction", - "abstraction_level": "primitive", - "implementation": { - "file_path": "optimization_engine/result_extractors/thermal_extractors.py", - "function_name": "extract_thermal_stress_from_op2", - "entry_point": "from optimization_engine.result_extractors.thermal_extractors import extract_thermal_stress_from_op2" - }, - # ... rest of schema -} -``` - -#### Step 3: Code Generation -```python -# LLM writes implementation following stress_extractor pattern -def extract_thermal_stress_from_op2(op2_file: Path) -> dict: - """ - Extracts thermal stress from OP2 file. - - Args: - op2_file: Path to OP2 file from thermal analysis - - Returns: - dict with max_thermal_stress, temperature_at_max_stress - """ - from pyNastran.op2.op2 import OP2 - - op2 = OP2() - op2.read_op2(op2_file) - - # Extract thermal stress (element type depends on analysis) - thermal_stress = op2.thermal_stress_data - - return { - 'max_thermal_stress': thermal_stress.max(), - 'temperature_at_max_stress': # ... - } -``` - -#### Step 4: Registration -``` -LLM adds to feature_registry.json -LLM creates docs/features/thermal_stress_extractor.md -LLM updates CHANGELOG.md with new feature -LLM runs tests to validate implementation -``` - -### Feature Composition Examples - -#### Example 1: RSS Metric (Composite Feature) -``` -User: "Create RSS metric combining stress and displacement" - -LLM composes from primitives: - stress_extractor + displacement_extractor → rss_metric - -Generated feature: -{ - "feature_id": "rss_stress_displacement", - "abstraction_level": "composite", - "dependencies": { - "features": ["stress_extractor", "displacement_extractor"] - }, - "composition_hints": { - "composed_from": ["stress_extractor", "displacement_extractor"], - "composition_type": "root_sum_square" - } -} -``` - -#### Example 2: Complete Workflow -``` -User: "Run bracket optimization minimizing stress" - -LLM composes workflow from features: - 1. study_manager (create study folder) - 2. nx_updater (update wall_thickness parameter) - 3. nx_solver (run FEA) - 4. stress_extractor (extract results) - 5. optimization_runner (Optuna TPE loop) - 6. report_generator (create HTML report) - -Each step uses a feature from registry with proper sequencing -based on lifecycle_stage metadata. -``` - ---- - -## Examples - -### Example 1: Engineering Feature (Stress Extractor) - -```json -{ - "feature_id": "stress_extractor", - "name": "Stress Extractor", - "description": "Extracts von Mises stress from NX Nastran OP2 files", - "category": "engineering", - "subcategory": "extractors", - "lifecycle_stage": "post_extraction", - "abstraction_level": "primitive", - "implementation": { - "file_path": "optimization_engine/result_extractors/extractors.py", - "function_name": "extract_stress_from_op2", - "entry_point": "from optimization_engine.result_extractors.extractors import extract_stress_from_op2" - }, - "interface": { - "inputs": [ - { - "name": "op2_file", - "type": "Path", - "required": true, - "description": "Path to OP2 file from NX solve", - "example": "bracket_sim1-solution_1.op2" - } - ], - "outputs": [ - { - "name": "max_von_mises", - "type": "float", - "description": "Maximum von Mises stress across all elements", - "units": "MPa" - }, - { - "name": "element_id_at_max", - "type": "int", - "description": "Element ID where max stress occurs" - } - ] - }, - "dependencies": { - "features": [], - "libraries": ["pyNastran"], - "nx_version": "2412" - }, - "usage_examples": [ - { - "description": "Minimize stress in bracket optimization", - "code": "result = extract_stress_from_op2(Path('bracket.op2'))\nmax_stress = result['max_von_mises']", - "natural_language": [ - "minimize stress", - "reduce von Mises stress", - "find lowest stress configuration" - ] - } - ], - "composition_hints": { - "combines_with": ["displacement_extractor", "mass_extractor"], - "typical_workflows": ["structural_optimization", "stress_minimization"], - "prerequisites": ["nx_solver"] - }, - "metadata": { - "author": "Antoine Polvé", - "created": "2025-01-10", - "status": "stable", - "tested": true, - "documentation_url": "docs/features/stress_extractor.md" - } -} -``` - -### Example 2: Software Feature (Hook Manager) - -```json -{ - "feature_id": "hook_manager", - "name": "Hook Manager", - "description": "Manages plugin lifecycle hooks for optimization workflow", - "category": "software", - "subcategory": "infrastructure", - "lifecycle_stage": "all", - "abstraction_level": "composite", - "implementation": { - "file_path": "optimization_engine/plugins/hook_manager.py", - "function_name": "HookManager", - "entry_point": "from optimization_engine.plugins.hook_manager import HookManager" - }, - "interface": { - "inputs": [ - { - "name": "hook_type", - "type": "str", - "required": true, - "description": "Lifecycle point: pre_solve, post_solve, post_extraction", - "example": "pre_solve" - }, - { - "name": "context", - "type": "dict", - "required": true, - "description": "Context data passed to hooks (trial_number, design_variables, etc.)" - } - ], - "outputs": [ - { - "name": "execution_history", - "type": "list", - "description": "List of hooks executed with timestamps and success status" - } - ] - }, - "dependencies": { - "features": [], - "libraries": [], - "nx_version": null - }, - "usage_examples": [ - { - "description": "Execute pre-solve hooks before FEA", - "code": "hook_manager.execute_hooks('pre_solve', context={'trial': 1})", - "natural_language": [ - "run pre-solve plugins", - "execute hooks before solving" - ] - } - ], - "composition_hints": { - "combines_with": ["detailed_logger", "optimization_logger"], - "typical_workflows": ["optimization_runner"], - "prerequisites": [] - }, - "metadata": { - "author": "Antoine Polvé", - "created": "2025-01-16", - "status": "stable", - "tested": true, - "documentation_url": "docs/features/hook_manager.md" - } -} -``` - -### Example 3: UI Feature (Dashboard Widget) - -```json -{ - "feature_id": "optimization_progress_chart", - "name": "Optimization Progress Chart", - "description": "Real-time chart showing optimization convergence", - "category": "ui", - "subcategory": "dashboard_widgets", - "lifecycle_stage": "post_optimization", - "abstraction_level": "composite", - "implementation": { - "file_path": "dashboard/frontend/components/ProgressChart.js", - "function_name": "OptimizationProgressChart", - "entry_point": "new OptimizationProgressChart(containerId)" - }, - "interface": { - "inputs": [ - { - "name": "trial_data", - "type": "list[dict]", - "required": true, - "description": "List of trial results with objective values", - "example": "[{trial: 1, value: 45.3}, {trial: 2, value: 42.1}]" - } - ], - "outputs": [ - { - "name": "chart_element", - "type": "HTMLElement", - "description": "Rendered chart DOM element" - } - ] - }, - "dependencies": { - "features": [], - "libraries": ["Chart.js"], - "nx_version": null - }, - "usage_examples": [ - { - "description": "Display optimization progress in dashboard", - "code": "chart = new OptimizationProgressChart('chart-container')\nchart.update(trial_data)", - "natural_language": [ - "show optimization progress", - "display convergence chart", - "visualize trial results" - ] - } - ], - "composition_hints": { - "combines_with": ["trial_history_table", "best_parameters_display"], - "typical_workflows": ["dashboard_view", "result_monitoring"], - "prerequisites": ["optimization_runner"] - }, - "metadata": { - "author": "Antoine Polvé", - "created": "2025-01-10", - "status": "stable", - "tested": true, - "documentation_url": "docs/features/dashboard_widgets.md" - } -} -``` - -### Example 4: Analysis Feature (Surrogate Quality Checker) - -```json -{ - "feature_id": "surrogate_quality_checker", - "name": "Surrogate Quality Checker", - "description": "Evaluates surrogate model quality using R², CV score, and confidence intervals", - "category": "analysis", - "subcategory": "decision_support", - "lifecycle_stage": "post_optimization", - "abstraction_level": "composite", - "implementation": { - "file_path": "optimization_engine/analysis/surrogate_quality.py", - "function_name": "check_surrogate_quality", - "entry_point": "from optimization_engine.analysis.surrogate_quality import check_surrogate_quality" - }, - "interface": { - "inputs": [ - { - "name": "trial_data", - "type": "list[dict]", - "required": true, - "description": "Trial history with design variables and objectives" - }, - { - "name": "min_r_squared", - "type": "float", - "required": false, - "description": "Minimum acceptable R² threshold", - "example": "0.9" - } - ], - "outputs": [ - { - "name": "r_squared", - "type": "float", - "description": "Coefficient of determination", - "units": "none" - }, - { - "name": "cv_score", - "type": "float", - "description": "Cross-validation score", - "units": "none" - }, - { - "name": "quality_verdict", - "type": "str", - "description": "EXCELLENT|GOOD|POOR based on metrics" - } - ] - }, - "dependencies": { - "features": ["optimization_runner"], - "libraries": ["sklearn", "numpy"], - "nx_version": null - }, - "usage_examples": [ - { - "description": "Check if surrogate is reliable for predictions", - "code": "quality = check_surrogate_quality(trial_data)\nif quality['r_squared'] > 0.9:\n print('Surrogate is reliable')", - "natural_language": [ - "check surrogate quality", - "is surrogate reliable", - "can I trust the surrogate model" - ] - } - ], - "composition_hints": { - "combines_with": ["sensitivity_analysis", "pareto_front_analyzer"], - "typical_workflows": ["post_optimization_analysis", "decision_support"], - "prerequisites": ["optimization_runner"] - }, - "metadata": { - "author": "Antoine Polvé", - "created": "2025-01-16", - "status": "experimental", - "tested": false, - "documentation_url": "docs/features/surrogate_quality_checker.md" - } -} -``` - ---- - -## Implementation Plan - -### Phase 2 Week 1: Foundation - -#### Day 1-2: Create Initial Registry -- [ ] Create `optimization_engine/feature_registry.json` -- [ ] Document 15-20 existing features across all categories -- [ ] Add engineering features (stress_extractor, displacement_extractor) -- [ ] Add software features (hook_manager, optimization_runner, nx_solver) -- [ ] Add UI features (dashboard widgets) - -#### Day 3-4: LLM Skill Setup -- [ ] Create `.claude/skills/atomizer.md` -- [ ] Define how LLM should read and use feature_registry.json -- [ ] Add feature discovery examples -- [ ] Add feature composition examples -- [ ] Test LLM's ability to navigate registry - -#### Day 5: Documentation -- [ ] Create `docs/features/` directory -- [ ] Write feature guides for key features -- [ ] Link registry entries to documentation -- [ ] Update DEVELOPMENT.md with registry usage - -### Phase 2 Week 2: LLM Integration - -#### Natural Language Parser -- [ ] Intent classification using registry metadata -- [ ] Entity extraction for design variables, objectives -- [ ] Feature selection based on user request -- [ ] Workflow composition from features - -### Future Phases: Feature Expansion - -#### Phase 3: Code Generation -- [ ] Template features for common patterns -- [ ] Validation rules for generated code -- [ ] Auto-registration of new features - -#### Phase 4-7: Continuous Evolution -- [ ] User-contributed features -- [ ] Pattern learning from usage -- [ ] Best practices extraction -- [ ] Self-documentation updates - ---- - -## Benefits of This Architecture - -### For Users -- **Natural language control**: "minimize stress" → LLM selects stress_extractor -- **Intelligent suggestions**: LLM proposes features based on context -- **No configuration files**: LLM generates config from conversation - -### For Developers -- **Clear structure**: Features organized by domain, lifecycle, abstraction -- **Easy extension**: Add new features following templates -- **Self-documenting**: Registry serves as API documentation - -### For LLM -- **Comprehensive context**: All capabilities in one place -- **Composition guidance**: Knows how features combine -- **Natural language mapping**: Understands user intent -- **Pattern recognition**: Can generate new features from templates - ---- - -## Next Steps - -1. **Create initial feature_registry.json** with 15-20 existing features -2. **Test LLM navigation** with Claude skill -3. **Validate registry structure** with real user requests -4. **Iterate on metadata** based on LLM's needs -5. **Build out documentation** in docs/features/ - ---- - -**Maintained by**: Antoine Polvé (antoine@atomaste.com) -**Repository**: [GitHub - Atomizer](https://github.com/yourusername/Atomizer) diff --git a/docs/FIX_VALIDATOR_PRUNING.md b/docs/FIX_VALIDATOR_PRUNING.md deleted file mode 100644 index b6514bac..00000000 --- a/docs/FIX_VALIDATOR_PRUNING.md +++ /dev/null @@ -1,113 +0,0 @@ -# Validator Pruning Investigation - November 20, 2025 - -## DEPRECATED - This document is retained for historical reference only. - -**Status**: Investigation completed. Aspect ratio validation approach was abandoned. - ---- - -## Original Problem - -The v2.1 and v2.2 tests showed 18-20% pruning rate. Investigation revealed two separate issues: - -### Issue 1: Validator Not Enforcing Rules (FIXED, then REMOVED) - -The `_validate_circular_plate_aspect_ratio()` method initially returned only **warnings**, not **rejections**. - -**Fix Applied**: Changed to return hard rejections for aspect ratio violations. - -**Result**: All pruned trials in v2.2 still had VALID aspect ratios (5.0-50.0 range). - -**Conclusion**: Aspect ratio violations were NOT the cause of pruning. - -### Issue 2: pyNastran False Positives (ROOT CAUSE) - -All pruned trials failed due to pyNastran FATAL flag sensitivity: -- ✅ Nastran simulations succeeded (F06 files have no errors) -- ⚠️ FATAL flag in OP2 header (benign warning) -- ❌ pyNastran throws exception when reading OP2 -- ❌ Valid trials incorrectly marked as failed - -**Evidence**: All 9 pruned trials in v2.2 had: -- `is_pynastran_fatal_flag: true` -- `f06_has_fatal_errors: false` -- Valid aspect ratios within bounds - ---- - -## Final Solution (Post-v2.3) - -### Aspect Ratio Validation REMOVED - -After deploying v2.3 with aspect ratio validation, user feedback revealed: - -**User Requirement**: "I never asked for this check, where does that come from?" - -**Issue**: Arbitrary aspect ratio limits (5.0-50.0) without: -- User approval -- Physical justification for circular plate modal analysis -- Visibility in optimization_config.json - -**Fix Applied**: -- Removed ALL aspect ratio validation from circular_plate model type -- Validator now returns empty rules `{}` -- Relies solely on Optuna parameter bounds (50-150mm diameter, 2-10mm thickness) - -**User Requirements Established**: -1. **No arbitrary checks** - validation rules must be proposed, not automatic -2. **Configurable validation** - rules should be visible in optimization_config.json -3. **Parameter bounds suffice** - ranges already define feasibility -4. **Physical justification required** - any constraint needs clear reasoning - -### Real Solution: Robust OP2 Extraction - -**Module**: [optimization_engine/op2_extractor.py](../optimization_engine/op2_extractor.py) - -Multi-strategy extraction that handles pyNastran issues: -1. Standard OP2 read -2. Lenient read (debug=False, skip benign flags) -3. F06 fallback parsing - -See [PRUNING_DIAGNOSTICS.md](PRUNING_DIAGNOSTICS.md) for details. - ---- - -## Lessons Learned - -1. **Validator is for simulation failures, not arbitrary physics assumptions** - - Parameter bounds already define feasible ranges - - Don't add validation rules without user approval - -2. **18% pruning was pyNastran false positives, not validation issues** - - All pruned trials had valid parameters - - Robust extraction eliminates these false positives - -3. **Transparency is critical** - - Validation rules must be visible in optimization_config.json - - Arbitrary constraints confuse users and reject valid designs - ---- - -## Current State - -**File**: [simulation_validator.py](../optimization_engine/simulation_validator.py:41-45) - -```python -if model_type == 'circular_plate': - # NOTE: Only use parameter bounds for validation - # No arbitrary aspect ratio checks - let Optuna explore the full parameter space - # Modal analysis is robust and doesn't need strict aspect ratio limits - return {} -``` - -**Impact**: Clean separation of concerns -- **Parameter bounds** = Feasibility (user-defined ranges) -- **Validator** = Genuine simulation failures (e.g., mesh errors, solver crashes) - ---- - -## References - -- [SESSION_SUMMARY_NOV20.md](SESSION_SUMMARY_NOV20.md) - Complete session documentation -- [PRUNING_DIAGNOSTICS.md](PRUNING_DIAGNOSTICS.md) - Robust extraction solution -- [optimization_engine/simulation_validator.py](../optimization_engine/simulation_validator.py) - Current validator implementation diff --git a/docs/HOOK_ARCHITECTURE.md b/docs/HOOK_ARCHITECTURE.md deleted file mode 100644 index bc7ec857..00000000 --- a/docs/HOOK_ARCHITECTURE.md +++ /dev/null @@ -1,463 +0,0 @@ -# Hook Architecture - Unified Lifecycle System - -## Overview - -Atomizer uses a **unified lifecycle hook system** where all hooks - whether system plugins or auto-generated post-processing scripts - integrate seamlessly through the `HookManager`. - -## Hook Types - -### 1. Lifecycle Hooks (Phase 1 - System Plugins) - -Located in: `optimization_engine/plugins//` - -**Purpose**: Plugin system for FEA workflow automation - -**Hook Points**: -``` -pre_mesh → Before meshing -post_mesh → After meshing, before solve -pre_solve → Before FEA solver execution -post_solve → After solve, before extraction -post_extraction → After result extraction -post_calculation → After inline calculations (NEW in Phase 2.9) -custom_objective → Custom objective functions -``` - -**Example**: System logging, state management, file operations - -### 2. Generated Post-Processing Hooks (Phase 2.9) - -Located in: `optimization_engine/plugins/post_calculation/` (by default) - -**Purpose**: Auto-generated custom calculations on extracted data - -**Can be placed at ANY hook point** for maximum flexibility! - -**Types**: -- Weighted objectives -- Custom formulas -- Constraint checks -- Comparisons (ratios, differences, percentages) - -## Complete Optimization Workflow - -``` -Optimization Trial N - ↓ -┌─────────────────────────────────────┐ -│ PRE-SOLVE HOOKS │ -│ - Log trial parameters │ -│ - Validate design variables │ -│ - Backup model files │ -└─────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────┐ -│ RUN NX NASTRAN SOLVE │ -└─────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────┐ -│ POST-SOLVE HOOKS │ -│ - Check solution convergence │ -│ - Log solve completion │ -└─────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────┐ -│ EXTRACT RESULTS (OP2/F06) │ -│ - Read stress, displacement, etc. │ -└─────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────┐ -│ POST-EXTRACTION HOOKS │ -│ - Log extracted values │ -│ - Validate result ranges │ -└─────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────┐ -│ INLINE CALCULATIONS (Phase 2.8) │ -│ - avg_stress = sum(stresses) / len │ -│ - norm_stress = avg_stress / 200 │ -│ - norm_disp = max_disp / 5 │ -└─────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────┐ -│ POST-CALCULATION HOOKS (Phase 2.9) │ -│ - weighted_objective() │ -│ - safety_factor() │ -│ - constraint_check() │ -└─────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────┐ -│ REPORT TO OPTUNA │ -│ - Return objective value(s) │ -└─────────────────────────────────────┘ - ↓ -Next Trial -``` - -## Directory Structure - -``` -optimization_engine/plugins/ -├── hooks.py # HookPoint enum, Hook dataclass -├── hook_manager.py # HookManager class -├── pre_mesh/ # Pre-meshing hooks -├── post_mesh/ # Post-meshing hooks -├── pre_solve/ # Pre-solve hooks -│ ├── detailed_logger.py -│ └── optimization_logger.py -├── post_solve/ # Post-solve hooks -│ └── log_solve_complete.py -├── post_extraction/ # Post-extraction hooks -│ ├── log_results.py -│ └── optimization_logger_results.py -└── post_calculation/ # Post-calculation hooks (NEW!) - ├── weighted_objective_test.py # Generated by Phase 2.9 - ├── safety_factor_hook.py # Generated by Phase 2.9 - └── min_to_avg_ratio_hook.py # Generated by Phase 2.9 -``` - -## Hook Format - -All hooks follow the same interface: - -```python -def my_hook(context: Dict[str, Any]) -> Optional[Dict[str, Any]]: - """ - Hook function. - - Args: - context: Dictionary containing relevant data: - - trial_number: Current optimization trial - - design_variables: Current design variable values - - results: Extracted FEA results (post-extraction) - - calculations: Inline calculation results (post-calculation) - - Returns: - Optional dictionary with results to add to context - """ - # Hook logic here - return {'my_result': value} - - -def register_hooks(hook_manager): - """Register this hook with the HookManager.""" - hook_manager.register_hook( - hook_point='post_calculation', # or any other HookPoint - function=my_hook, - description="My custom hook", - name="my_hook", - priority=100, - enabled=True - ) -``` - -## Hook Generation (Phase 2.9) - -### Standalone Scripts (Original) - -Generated as independent Python scripts with JSON I/O: - -```python -from optimization_engine.hook_generator import HookGenerator - -generator = HookGenerator() - -hook_spec = { - "action": "weighted_objective", - "description": "Combine stress and displacement", - "params": { - "inputs": ["norm_stress", "norm_disp"], - "weights": [0.7, 0.3] - } -} - -# Generate standalone script -hook = generator.generate_from_llm_output(hook_spec) -generator.save_hook_to_file(hook, "generated_hooks/") -``` - -**Use case**: Independent execution, debugging, external tools - -### Lifecycle Hooks (Integrated) - -Generated as lifecycle-compatible plugins: - -```python -from optimization_engine.hook_generator import HookGenerator - -generator = HookGenerator() - -hook_spec = { - "action": "weighted_objective", - "description": "Combine stress and displacement", - "params": { - "inputs": ["norm_stress", "norm_disp"], - "weights": [0.7, 0.3] - } -} - -# Generate lifecycle hook -hook_content = generator.generate_lifecycle_hook( - hook_spec, - hook_point='post_calculation' # or pre_solve, post_extraction, etc. -) - -# Save to plugins directory -output_file = Path("optimization_engine/plugins/post_calculation/weighted_objective.py") -with open(output_file, 'w') as f: - f.write(hook_content) - -# HookManager automatically discovers and loads it! -``` - -**Use case**: Integration with optimization workflow, automatic execution - -## Flexibility: Hooks Can Be Placed Anywhere! - -The beauty of the lifecycle system is that **generated hooks can be placed at ANY hook point**: - -### Example 1: Pre-Solve Validation - -```python -# Generate a constraint check to run BEFORE solving -constraint_spec = { - "action": "constraint_check", - "description": "Ensure wall thickness is reasonable", - "params": { - "inputs": ["wall_thickness", "max_thickness"], - "condition": "wall_thickness / max_thickness", - "threshold": 1.0, - "constraint_name": "thickness_check" - } -} - -hook_content = generator.generate_lifecycle_hook( - constraint_spec, - hook_point='pre_solve' # Run BEFORE solve! -) -``` - -###Example 2: Post-Extraction Safety Factor - -```python -# Generate safety factor calculation right after extraction -safety_spec = { - "action": "custom_formula", - "description": "Calculate safety factor from extracted stress", - "params": { - "inputs": ["max_stress", "yield_strength"], - "formula": "yield_strength / max_stress", - "output_name": "safety_factor" - } -} - -hook_content = generator.generate_lifecycle_hook( - safety_spec, - hook_point='post_extraction' # Run right after extraction! -) -``` - -### Example 3: Pre-Mesh Parameter Validation - -```python -# Generate parameter check before meshing -validation_spec = { - "action": "comparison", - "description": "Check if thickness exceeds maximum", - "params": { - "inputs": ["requested_thickness", "max_allowed"], - "operation": "ratio", - "output_name": "thickness_ratio" - } -} - -hook_content = generator.generate_lifecycle_hook( - validation_spec, - hook_point='pre_mesh' # Run before meshing! -) -``` - -## Hook Manager Usage - -```python -from optimization_engine.plugins.hook_manager import HookManager - -# Create manager -hook_manager = HookManager() - -# Auto-load all plugins from directory structure -hook_manager.load_plugins_from_directory( - Path("optimization_engine/plugins") -) - -# Execute hooks at specific point -context = { - 'trial_number': 42, - 'results': {'max_stress': 150.5}, - 'calculations': {'norm_stress': 0.75, 'norm_disp': 0.64} -} - -results = hook_manager.execute_hooks('post_calculation', context) - -# Get summary -summary = hook_manager.get_summary() -print(f"Total hooks: {summary['total_hooks']}") -print(f"Hooks at post_calculation: {summary['by_hook_point']['post_calculation']}") -``` - -## Integration with Optimization Runner - -The optimization runner will be updated to call hooks at appropriate lifecycle points: - -```python -# In optimization_engine/runner.py - -def run_trial(self, trial_number, design_variables): - # Create context - context = { - 'trial_number': trial_number, - 'design_variables': design_variables, - 'working_dir': self.working_dir - } - - # Pre-solve hooks - self.hook_manager.execute_hooks('pre_solve', context) - - # Run solve - self.nx_solver.run(...) - - # Post-solve hooks - self.hook_manager.execute_hooks('post_solve', context) - - # Extract results - results = self.extractor.extract(...) - context['results'] = results - - # Post-extraction hooks - self.hook_manager.execute_hooks('post_extraction', context) - - # Inline calculations (Phase 2.8) - calculations = self.inline_calculator.calculate(...) - context['calculations'] = calculations - - # Post-calculation hooks (Phase 2.9) - hook_results = self.hook_manager.execute_hooks('post_calculation', context) - - # Merge hook results into context - for result in hook_results: - if result: - context.update(result) - - # Return final objective - return context.get('weighted_objective') or results['stress'] -``` - -## Benefits of Unified System - -1. **Consistency**: All hooks use same interface, same registration, same execution -2. **Flexibility**: Generated hooks can be placed at any lifecycle point -3. **Discoverability**: HookManager auto-loads from directory structure -4. **Extensibility**: Easy to add new hook points or new hook types -5. **Debugging**: All hooks have logging, history tracking, enable/disable -6. **Priority Control**: Hooks execute in priority order -7. **Error Handling**: Configurable fail-fast or continue-on-error - -## Example: Complete CBAR Optimization - -**User Request:** -> "Extract CBAR element forces in Z direction, calculate average and minimum, create objective that minimizes min/avg ratio, optimize CBAR stiffness X with genetic algorithm" - -**Phase 2.7 LLM Analysis:** -```json -{ - "engineering_features": [ - {"action": "extract_1d_element_forces", "domain": "result_extraction"}, - {"action": "update_cbar_stiffness", "domain": "fea_properties"} - ], - "inline_calculations": [ - {"action": "calculate_average", "params": {"input": "forces_z"}}, - {"action": "find_minimum", "params": {"input": "forces_z"}} - ], - "post_processing_hooks": [ - { - "action": "comparison", - "params": { - "inputs": ["min_force", "avg_force"], - "operation": "ratio", - "output_name": "min_to_avg_ratio" - } - } - ] -} -``` - -**Phase 2.8 Generated (Inline):** -```python -avg_forces_z = sum(forces_z) / len(forces_z) -min_forces_z = min(forces_z) -``` - -**Phase 2.9 Generated (Lifecycle Hook):** -```python -# optimization_engine/plugins/post_calculation/min_to_avg_ratio_hook.py - -def min_to_avg_ratio_hook(context): - calculations = context.get('calculations', {}) - - min_force = calculations.get('min_forces_z') - avg_force = calculations.get('avg_forces_z') - - result = min_force / avg_force - - return {'min_to_avg_ratio': result, 'objective': result} - -def register_hooks(hook_manager): - hook_manager.register_hook( - hook_point='post_calculation', - function=min_to_avg_ratio_hook, - description="Compare min force to average", - name="min_to_avg_ratio_hook" - ) -``` - -**Execution:** -``` -Trial 1: - pre_solve hooks → log trial - solve → NX Nastran - post_solve hooks → check convergence - post_extraction hooks → validate results - - Extract: forces_z = [10.5, 12.3, 8.9, 11.2, 9.8] - - Inline calculations: - avg_forces_z = 10.54 - min_forces_z = 8.9 - - post_calculation hooks → min_to_avg_ratio_hook - min_to_avg_ratio = 8.9 / 10.54 = 0.844 - - Report to Optuna: objective = 0.844 -``` - -**All code auto-generated! Zero manual scripting!** 🚀 - -## Future Enhancements - -1. **Hook Dependencies**: Hooks can declare dependencies on other hooks -2. **Conditional Execution**: Hooks can have conditions (e.g., only run if stress > threshold) -3. **Hook Composition**: Combine multiple hooks into pipelines -4. **Study-Specific Hooks**: Hooks stored in `studies//plugins/` -5. **Hook Marketplace**: Share hooks between projects/users - -## Summary - -The unified lifecycle hook system provides: -- ✅ Single consistent interface for all hooks -- ✅ Generated hooks integrate seamlessly with system hooks -- ✅ Hooks can be placed at ANY lifecycle point -- ✅ Auto-discovery and loading -- ✅ Priority control and error handling -- ✅ Maximum flexibility for optimization workflows - -**Phase 2.9 hooks are now true lifecycle hooks, usable anywhere in the FEA workflow!** diff --git a/docs/NXOPEN_DOCUMENTATION_INTEGRATION_STRATEGY.md b/docs/NXOPEN_DOCUMENTATION_INTEGRATION_STRATEGY.md deleted file mode 100644 index 831e6535..00000000 --- a/docs/NXOPEN_DOCUMENTATION_INTEGRATION_STRATEGY.md +++ /dev/null @@ -1,431 +0,0 @@ -# NXOpen Documentation Integration Strategy - -## Overview - -This document outlines the strategy for integrating NXOpen Python documentation into Atomizer's AI-powered code generation system. - -**Target Documentation**: https://docs.sw.siemens.com/en-US/doc/209349590/PL20190529153447339.nxopen_python_ref - -**Goal**: Enable Atomizer to automatically research NXOpen APIs and generate correct code without manual documentation lookup. - -## Current State (Phase 2.7 Complete) - -✅ **Intelligent Workflow Analysis**: LLM detects engineering features needing research -✅ **Capability Matching**: System knows what's already implemented -✅ **Gap Identification**: Identifies missing FEA/CAE operations - -❌ **Auto-Research**: No automated documentation lookup -❌ **Code Generation**: Manual implementation still required - -## Documentation Access Challenges - -### Challenge 1: Authentication Required -- Siemens documentation requires login -- Not accessible via direct WebFetch -- Cannot be scraped programmatically - -### Challenge 2: Dynamic Content -- Documentation is JavaScript-rendered -- Not available as static HTML -- Requires browser automation or API access - -## Integration Strategies - -### Strategy 1: MCP Server (RECOMMENDED) 🚀 - -**Concept**: Build a Model Context Protocol (MCP) server for NXOpen documentation - -**How it Works**: -``` -Atomizer (Phase 2.5-2.7) - ↓ -Detects: "Need to modify PCOMP ply thickness" - ↓ -MCP Server Query: "How to modify PCOMP in NXOpen?" - ↓ -MCP Server → Local Documentation Cache or Live Lookup - ↓ -Returns: Code examples + API reference - ↓ -Phase 2.8-2.9: Auto-generate code -``` - -**Implementation**: -1. **Local Documentation Cache** - - Download key NXOpen docs pages locally (one-time setup) - - Store as markdown/JSON in `knowledge_base/nxopen/` - - Index by module/class/method - -2. **MCP Server** - - Runs locally on `localhost:3000` - - Provides search/query API - - Returns relevant code snippets + documentation - -3. **Integration with Atomizer** - - `research_agent.py` calls MCP server - - Gets documentation for missing capabilities - - Generates code based on examples - -**Advantages**: -- ✅ No API consumption costs (runs locally) -- ✅ Fast lookups (local cache) -- ✅ Works offline after initial setup -- ✅ Can be extended to pyNastran docs later - -**Disadvantages**: -- Requires one-time manual documentation download -- Needs periodic updates for new NX versions - -### Strategy 2: NX Journal Recording (USER-DRIVEN LEARNING) 🎯 **RECOMMENDED!** - -**Concept**: User records NX journals while performing operations, system learns from recorded Python code - -**How it Works**: -1. User needs to learn how to "merge FEM nodes" -2. User starts journal recording in NX (Tools → Journal → Record) -3. User performs the operation manually in NX GUI -4. NX automatically generates Python journal showing exact API calls -5. User shares journal file with Atomizer -6. Atomizer extracts pattern and stores in knowledge base - -**Example Workflow**: -``` -User Action: Merge duplicate FEM nodes in NX - ↓ -NX Records: journal_merge_nodes.py - ↓ -Contains: session.FemPart().MergeNodes(tolerance=0.001, ...) - ↓ -Atomizer learns: "To merge nodes, use FemPart().MergeNodes()" - ↓ -Pattern saved to: knowledge_base/nxopen_patterns/fem/merge_nodes.md - ↓ -Future requests: Auto-generate code using this pattern! -``` - -**Real Recorded Journal Example**: -```python -# User records: "Renumber elements starting from 1000" -import NXOpen - -def main(): - session = NXOpen.Session.GetSession() - fem_part = session.Parts.Work.BasePart.FemPart - - # NX generates this automatically! - fem_part.RenumberElements( - startingNumber=1000, - increment=1, - applyToAll=True - ) -``` - -**Advantages**: -- ✅ **User-driven**: Learn exactly what you need, when you need it -- ✅ **Accurate**: Code comes directly from NX (can't be wrong!) -- ✅ **Comprehensive**: Captures full API signature and parameters -- ✅ **No documentation hunting**: NX generates the code for you -- ✅ **Builds knowledge base organically**: Grows with actual usage -- ✅ **Handles edge cases**: Records exactly how you solved the problem - -**Use Cases Perfect for Journal Recording**: -- Merge/renumber FEM nodes -- Node/element renumbering -- Mesh quality checks -- Geometry modifications -- Property assignments -- Solver setup configurations -- Any complex operation hard to find in docs - -**Integration with Atomizer**: -```python -# User provides recorded journal -atomizer.learn_from_journal("journal_merge_nodes.py") - -# System analyzes: -# - Identifies API calls (FemPart().MergeNodes) -# - Extracts parameters (tolerance, node_ids, etc.) -# - Creates reusable pattern -# - Stores in knowledge_base with description - -# Future requests automatically use this pattern! -``` - -### Strategy 3: Python Introspection - -**Concept**: Use Python's introspection to explore NXOpen modules at runtime - -**How it Works**: -```python -import NXOpen - -# Discover all classes -for name in dir(NXOpen): - cls = getattr(NXOpen, name) - print(f"{name}: {cls.__doc__}") - -# Discover methods -for method in dir(NXOpen.Part): - print(f"{method}: {getattr(NXOpen.Part, method).__doc__}") -``` - -**Advantages**: -- ✅ No external dependencies -- ✅ Always up-to-date with installed NX version -- ✅ Includes method signatures automatically - -**Disadvantages**: -- ❌ Limited documentation (docstrings often minimal) -- ❌ No usage examples -- ❌ Requires NX to be running - -### Strategy 4: Hybrid Approach (BEST COMBINATION) 🏆 - -**Combine all strategies for maximum effectiveness**: - -**Phase 1 (Immediate)**: Journal Recording + pyNastran -1. **For NXOpen**: - - User records journals for needed operations - - Atomizer learns from recorded code - - Builds knowledge base organically - -2. **For Result Extraction**: - - Use pyNastran docs (publicly accessible!) - - WebFetch documentation as needed - - Auto-generate OP2 extraction code - -**Phase 2 (Short Term)**: Pattern Library + Introspection -1. **Knowledge Base Growth**: - - Store learned patterns from journals - - Categorize by domain (FEM, geometry, properties, etc.) - - Add examples and parameter descriptions - -2. **Python Introspection**: - - Supplement journal learning with introspection - - Discover available methods automatically - - Validate generated code against signatures - -**Phase 3 (Future)**: MCP Server + Full Automation -1. **MCP Integration**: - - Build MCP server for documentation lookup - - Index knowledge base for fast retrieval - - Integrate with NXOpen TSE resources - -2. **Full Automation**: - - Auto-generate code for any request - - Self-learn from successful executions - - Continuous improvement through usage - -**This is the winning strategy!** - -## Recommended Immediate Implementation - -### Step 1: Python Introspection Module - -Create `optimization_engine/nxopen_introspector.py`: -```python -class NXOpenIntrospector: - def get_module_docs(self, module_path: str) -> Dict[str, Any]: - """Get all classes/methods from NXOpen module""" - - def find_methods_for_task(self, task_description: str) -> List[str]: - """Use LLM to match task to NXOpen methods""" - - def generate_code_skeleton(self, method_name: str) -> str: - """Generate code template from method signature""" -``` - -### Step 2: Knowledge Base Structure - -``` -knowledge_base/ -├── nxopen_patterns/ -│ ├── geometry/ -│ │ ├── create_part.md -│ │ ├── modify_expression.md -│ │ └── update_parameter.md -│ ├── fea_properties/ -│ │ ├── modify_pcomp.md -│ │ ├── modify_cbar.md -│ │ └── modify_cbush.md -│ ├── materials/ -│ │ └── create_material.md -│ └── simulation/ -│ ├── run_solve.md -│ └── check_solution.md -└── pynastran_patterns/ - ├── op2_extraction/ - │ ├── stress_extraction.md - │ ├── displacement_extraction.md - │ └── element_forces.md - └── bdf_modification/ - └── property_updates.md -``` - -### Step 3: Integration with Research Agent - -Update `research_agent.py`: -```python -def research_engineering_feature(self, feature_name: str, domain: str): - # 1. Check knowledge base first - kb_result = self.search_knowledge_base(feature_name) - - # 2. If not found, use introspection - if not kb_result: - introspection_result = self.introspector.find_methods_for_task(feature_name) - - # 3. Generate code skeleton - code = self.introspector.generate_code_skeleton(method) - - # 4. Use LLM to complete implementation - full_implementation = self.llm_generate_implementation(code, feature_name) - - # 5. Save to knowledge base for future use - self.save_to_knowledge_base(feature_name, full_implementation) -``` - -## Implementation Phases - -### Phase 2.8: Inline Code Generator (CURRENT PRIORITY) -**Timeline**: Next 1-2 sessions -**Scope**: Auto-generate simple math operations - -**What to Build**: -- `optimization_engine/inline_code_generator.py` -- Takes inline_calculations from Phase 2.7 LLM output -- Generates Python code directly -- No documentation needed (it's just math!) - -**Example**: -```python -Input: { - "action": "normalize_stress", - "params": {"input": "max_stress", "divisor": 200.0} -} - -Output: -norm_stress = max_stress / 200.0 -``` - -### Phase 2.9: Post-Processing Hook Generator -**Timeline**: Following Phase 2.8 -**Scope**: Generate middleware scripts - -**What to Build**: -- `optimization_engine/hook_generator.py` -- Takes post_processing_hooks from Phase 2.7 LLM output -- Generates standalone Python scripts -- Handles I/O between FEA steps - -**Example**: -```python -Input: { - "action": "weighted_objective", - "params": { - "inputs": ["norm_stress", "norm_disp"], - "weights": [0.7, 0.3], - "formula": "0.7 * norm_stress + 0.3 * norm_disp" - } -} - -Output: hook script that reads inputs, calculates, writes output -``` - -### Phase 3: MCP Integration for Documentation -**Timeline**: After Phase 2.9 -**Scope**: Automated NXOpen/pyNastran research - -**What to Build**: -1. Local documentation cache system -2. MCP server for doc lookup -3. Integration with research_agent.py -4. Automated code generation from docs - -## Alternative: Community Resources & pyNastran (RECOMMENDED STARTING POINT) - -### pyNastran Documentation (START HERE!) 🚀 - -**URL**: https://pynastran-git.readthedocs.io/en/latest/index.html - -**Why Start with pyNastran**: -- ✅ Fully open and publicly accessible -- ✅ Comprehensive API documentation -- ✅ Code examples for every operation -- ✅ Already used extensively in Atomizer -- ✅ Can WebFetch directly - no authentication needed -- ✅ Covers 80% of FEA result extraction needs - -**What pyNastran Handles**: -- OP2 file reading (displacement, stress, strain, element forces) -- F06 file parsing -- BDF/Nastran deck modification -- Result post-processing -- Nodal/Element data extraction - -**Strategy**: Use pyNastran as the primary documentation source for result extraction, and NXOpen only when modifying geometry/properties in NX. - -### NXOpen Community Resources - -1. **NXOpen TSE** (The Scripting Engineer) - - https://nxopentsedocumentation.thescriptingengineer.com/ - - Extensive examples and tutorials - - Can be scraped/cached legally - -2. **GitHub NXOpen Examples** - - Search GitHub for "NXOpen" + specific functionality - - Real-world code examples - - Community-vetted patterns - -## Next Steps - -### Immediate (This Session): -1. ✅ Create this strategy document -2. ✅ Implement Phase 2.8: Inline Code Generator -3. ✅ Test inline code generation (all tests passing!) -4. ⏳ Implement Phase 2.9: Post-Processing Hook Generator -5. ⏳ Integrate pyNastran documentation lookup via WebFetch - -### Short Term (Next 2-3 Sessions): -1. Implement Phase 2.9: Hook Generator -2. Build NXOpenIntrospector module -3. Start curating knowledge_base/nxopen_patterns/ -4. Test with real optimization scenarios - -### Medium Term (Phase 3): -1. Build local documentation cache -2. Implement MCP server -3. Integrate automated research -4. Full end-to-end code generation - -## Success Metrics - -**Phase 2.8 Success**: -- ✅ Auto-generates 100% of inline calculations -- ✅ Correct Python syntax every time -- ✅ Properly handles variable naming - -**Phase 2.9 Success**: -- ✅ Auto-generates functional hook scripts -- ✅ Correct I/O handling -- ✅ Integrates with optimization loop - -**Phase 3 Success**: -- ✅ Automatically finds correct NXOpen methods -- ✅ Generates working code 80%+ of the time -- ✅ Self-learns from successful patterns - -## Conclusion - -**Recommended Path Forward**: -1. Focus on Phase 2.8-2.9 first (inline + hooks) -2. Build knowledge base organically as we encounter patterns -3. Use Python introspection for discovery -4. Build MCP server once we have critical mass of patterns - -This approach: -- ✅ Delivers value incrementally -- ✅ No external dependencies initially -- ✅ Builds towards full automation -- ✅ Leverages both LLM intelligence and structured knowledge - -**The documentation will come to us through usage, not upfront scraping!** diff --git a/docs/NXOPEN_INTELLISENSE_SETUP.md b/docs/NXOPEN_INTELLISENSE_SETUP.md deleted file mode 100644 index 2ed881e5..00000000 --- a/docs/NXOPEN_INTELLISENSE_SETUP.md +++ /dev/null @@ -1,306 +0,0 @@ -# NXOpen Python Intellisense Setup - -> **Status**: ✅ Implemented (2025-11-17) -> -> Enable intelligent code completion for NXOpen Python API using Siemens-provided stub files - ---- - -## Overview - -Siemens NX 2412 includes Python stub files (`.pyi`) that provide full type hints for the NXOpen API. These enable: - -- **Autocomplete**: Suggestions for classes, methods, and properties -- **Type Hints**: Parameter types and return values -- **Documentation**: Inline docstrings and API descriptions -- **Error Detection**: Type checking catches errors before runtime - -This dramatically improves development speed and reduces NXOpen API lookup time. - ---- - -## Prerequisites - -- **Siemens NX 2412** (or later) installed with Programming Tools -- **VSCode** with **Pylance extension** (usually installed with Python extension) -- **Python 3.11** environment (required for NXOpen module compatibility) - ---- - -## Setup Instructions - -### Step 0: Ensure Python 3.11 Environment - -NXOpen modules are compiled for Python 3.11. **You must use Python 3.11**: - -```bash -# Check your Python version -python --version # Should show: Python 3.11.x - -# If using conda, upgrade atomizer environment: -conda install -n atomizer python=3.11 -y -``` - -### Step 1: Add NXOpen to Python Path - -Create a `.pth` file in your Python environment's site-packages to enable NXOpen imports: - -```bash -# For atomizer environment: -# Create file: C:\Users\\anaconda3\envs\atomizer\Lib\site-packages\nxopen.pth -# Contents: -C:\Program Files\Siemens\NX2412\NXBIN\python -``` - -This allows `import NXOpen` to work in your Python scripts! - -### Step 2: Verify Stub Files Exist - -Check that stub files are installed: - -```bash -# Windows path: -dir "C:\Program Files\Siemens\NX2412\UGOPEN\pythonStubs\NXOpen" - -# Should show: __init__.pyi and many module folders (CAE, Assemblies, etc.) -``` - -**If missing**: Reinstall NX 2412 and ensure "Programming Tools" is checked during installation. - -### Step 3: Configure VSCode - -Update `.vscode/settings.json` in your Atomizer project: - -```json -{ - "python.analysis.typeCheckingMode": "basic", - "python.analysis.stubPath": "C:\\Program Files\\Siemens\\NX2412\\UGOPEN\\pythonStubs" -} -``` - -**Note**: Use double backslashes (`\\`) in Windows paths for JSON. - -### Step 4: Restart VSCode - -Close and reopen VSCode to load the new stub files. - -### Step 5: Verify NXOpen Import and Intellisense Works - -First, test that NXOpen can be imported: - -```python -python ->>> import NXOpen ->>> print("Success! NXOpen is available") ->>> exit() -``` - -Then test intellisense: - -Open `tests/test_nxopen_intellisense.py` and verify: - -1. **Import Autocomplete**: - - Type `import NXOpen.` → Should suggest: Part, Session, CAE, Assemblies, etc. - -2. **Method Autocomplete**: - - Type `session.` → Should suggest: GetSession(), Parts, etc. - - Type `expressions.` → Should suggest: FindObject, CreateExpression, etc. - -3. **Parameter Hints**: - - Hover over `CreateExpression()` → Shows parameter types and documentation - -4. **Documentation Tooltips**: - - Hover over any NXOpen class/method → Shows docstring - -**If working**: ✅ Intellisense is configured correctly! - ---- - -## What You Get - -### Before Intellisense: -```python -import NXOpen -session = NXOpen.Session.GetSession() -# ❌ No suggestions when typing "session." -# ❌ No parameter hints for methods -# ❌ Must look up API in documentation -``` - -### After Intellisense: -```python -import NXOpen -session = NXOpen.Session.GetSession() -# ✅ Type "session." → See: Parts, ListingWindow, LogFile, etc. -# ✅ Type "session.Parts." → See: Work, Display, FindObject, etc. -# ✅ Hover over methods → See parameter types and documentation -# ✅ Catch type errors before running code -``` - ---- - -## Available Modules - -The stub files cover **all** NXOpen modules: - -**Core Modules**: -- `NXOpen.Session` - NX session management -- `NXOpen.Part` - Part objects and operations -- `NXOpen.Assemblies` - Assembly operations - -**CAE Modules**: -- `NXOpen.CAE` - Finite element analysis -- `NXOpen.CAE.FemPart` - FEM models -- `NXOpen.CAE.SimSolution` - Solutions and solver control - -**Design Modules**: -- `NXOpen.Features` - Parametric features -- `NXOpen.Sketches` - Sketch operations -- `NXOpen.Modeling` - Modeling operations - -**And many more**: Drafting, Display, Motion, Optimization, etc. - ---- - -## Example: Using Intellisense During Development - -### Scenario: Update Part Expression - -**Without Intellisense** (manual lookup required): -```python -# 1. Google: "NXOpen get expression" -# 2. Find documentation -# 3. Copy method signature -# 4. Hope you got it right -work_part.Expressions.FindObject("tip_thickness") -``` - -**With Intellisense** (guided development): -```python -# 1. Type "work_part.Exp" → Autocomplete suggests "Expressions" -# 2. Type "work_part.Expressions." → See all methods -# 3. Select "FindObject" → See parameter types -# 4. Hover for documentation -work_part.Expressions.FindObject("tip_thickness") # ✅ Correct! -``` - ---- - -## Benefits for Atomizer Development - -### 1. **Faster Development** -- No context switching to documentation -- Discover APIs as you type -- Reduce typos and API misuse - -### 2. **Better Code Quality** -- Type checking catches errors early -- Method signatures documented inline -- Parameter validation before runtime - -### 3. **LLM-Assisted Coding** -When using Claude Code to develop Atomizer: -- Claude can "see" NXOpen API structure via stub files -- Better code generation suggestions -- Reduced hallucination of API methods - -### 4. **Onboarding** -- New contributors learn NXOpen API faster -- Inline documentation reduces learning curve -- Explore API without leaving IDE - ---- - -## Integration with Atomizer Workflow - -### Journal Script Development - -When writing NX journal scripts (`optimization_engine/solve_simulation.py`): - -```python -import NXOpen - -theSession = NXOpen.Session.GetSession() -workPart = theSession.Parts.Work - -# Intellisense shows: -# - workPart.Expressions.FindObject(...) -# - workPart.Expressions.EditWithUnits(...) -# - workPart.Update() -# - workPart.Save(...) -``` - -### LLM Code Generation - -When LLM generates NXOpen code, stub files help: -- Validate generated code against actual API -- Suggest corrections for API misuse -- Provide parameter type hints - -### Future: NXOpen Documentation Integration - -This is **Step 1** of NXOpen integration. Future work: - -1. ✅ **Stub files for intellisense** (current) -2. 🔜 **Documentation scraping** for LLM knowledge base -3. 🔜 **Authenticated docs access** for latest API references -4. 🔜 **LLM-generated journal scripts** with validation - ---- - -## Troubleshooting - -### Intellisense Not Working - -**Problem**: No autocomplete suggestions appear - -**Solutions**: -1. **Check Pylance Extension**: VSCode → Extensions → Search "Pylance" → Ensure installed -2. **Verify Settings**: `.vscode/settings.json` has correct stub path -3. **Check Python Interpreter**: VSCode bottom-left → Select correct Python environment -4. **Restart VSCode**: Close all windows and reopen -5. **Check Stub Path**: Ensure path exists and contains `NXOpen` folder - -### Wrong Suggestions - -**Problem**: Autocomplete shows incorrect or outdated methods - -**Solution**: Ensure stub files match your NX version: -- NX 2412 → Use `NX2412\ugopen\pythonStubs` -- Different NX version → Update stub path in settings - -### Type Errors Shown - -**Problem**: Pylance shows type errors for valid code - -**Solutions**: -1. Set `"python.analysis.typeCheckingMode": "basic"` (not "strict") -2. Add `# type: ignore` for false positives -3. Update stub files if using newer NX version - ---- - -## References - -- **Siemens NX Documentation**: [PLM Portal](https://plm.sw.siemens.com) -- **TheScriptingEngineer**: [Blog with NXOpen examples](https://thescriptingengineer.com) -- **Pylance Documentation**: [VSCode Python](https://code.visualstudio.com/docs/python/editing) - ---- - -## Next Steps - -Now that intellisense is configured: - -1. **Try It**: Open `tests/test_nxopen_intellisense.py` and explore -2. **Develop Faster**: Use autocomplete when writing journal scripts -3. **Contribute**: Help improve Atomizer's NXOpen integration - -**See**: [DEVELOPMENT_GUIDANCE.md](../DEVELOPMENT_GUIDANCE.md) for strategic roadmap including NXOpen documentation access. - ---- - -**Implemented By**: Antoine Letarte -**Date**: 2025-11-17 -**NX Version**: 2412 -**Status**: Production Ready diff --git a/docs/NXOPEN_RESOURCES.md b/docs/NXOPEN_RESOURCES.md deleted file mode 100644 index dc27974b..00000000 --- a/docs/NXOPEN_RESOURCES.md +++ /dev/null @@ -1,335 +0,0 @@ -# NXOpen Resources and References - -## Overview - -This document lists valuable resources for NXOpen development that can inform our implementation without direct code copying. - ---- - -## Primary Resources - -### 1. **Official Siemens NXOpen API Documentation** - -**URL**: https://docs.sw.siemens.com/en-US/doc/209349590/PL20231101866122454.custom_api.nxopen_net - -**Usage**: -- Primary reference for API syntax and methods -- Official namespace documentation -- Method signatures and return types -- Parameter descriptions - -**Integration Strategy**: -- MCP tool `search_nxopen_docs` will fetch pages on-demand -- Cache frequently-used API snippets locally -- LLM can reference documentation when generating NXOpen code - ---- - -### 2. **NXOpenTSE by The Scripting Engineer** - -**GitHub**: https://github.com/theScriptingEngineer/nxopentse/tree/main -**Documentation**: https://nxopentsedocumentation.thescriptingengineer.com/ - -#### About NXOpenTSE - -NXOpenTSE is an open-source Python library that provides: -- **High-level wrappers** around NXOpen API -- **Utility functions** for common NX operations -- **Well-documented examples** of NX automation patterns -- **Best practices** for NX scripting - -**License**: MIT (as of last check - verify before use) - -#### Why NXOpenTSE is Valuable for Atomizer - -1. **Reference for Design Patterns**: - - How to structure NXOpen scripts - - Error handling approaches - - Session management patterns - - Part loading/unloading workflows - -2. **Understanding API Usage**: - - See real-world examples of API calls - - Learn parameter combinations that work - - Understand method call sequences - -3. **Avoiding Common Pitfalls**: - - See solutions to typical problems - - Learn about NX-specific gotchas - - Understand threading/transaction requirements - -4. **Inspiration for Features**: - - Discover what's possible with NXOpen - - See advanced techniques - - Learn about lesser-known APIs - -#### Integration Strategy for Atomizer - -**Approach**: Reference, don't copy - -``` -✅ DO: -- Study NXOpenTSE documentation for understanding NX concepts -- Reference example patterns when writing our own code -- Learn from error handling approaches -- Use as inspiration for our API wrapper design -- Link to NXOpenTSE docs in our MCP system prompts -- Ask LLM to "check NXOpenTSE documentation for similar examples" - -❌ DON'T: -- Copy code verbatim without attribution -- Replicate their library structure -- Import NXOpenTSE as a dependency (we build our own) -- Reuse their code without understanding it -``` - -#### Specific Areas to Reference - -| Our Component | NXOpenTSE Reference Area | What to Learn | -|--------------|-------------------------|---------------| -| `nx_journals/update_and_solve.py` | Expression handling, part updates | How to safely modify expressions | -| `nx_journals/api_dispatcher.py` | Session management | Best practices for NX session handling | -| `mcp_server/tools/model_discovery.py` | Part file reading | How to extract model information | -| `optimization_engine/result_extractors/` | CAE result reading | Methods for accessing simulation results | - ---- - -## Using NXOpenTSE Documentation with MCP - -### MCP System Prompt Extension - -When generating NXOpen code, the LLM can be instructed to: - -```markdown -When writing NXOpen code: -1. First check official Siemens NXOpen API docs for method signatures -2. Reference NXOpenTSE documentation (https://nxopentsedocumentation.thescriptingengineer.com/) - for real-world usage patterns and best practices -3. Adapt patterns to our specific use case rather than copying directly -4. Always include error handling and validation -5. Add comments explaining NX-specific requirements -``` - -### Example LLM Workflow - -**User**: "I need to read all expressions from a .sim file" - -**LLM Reasoning**: -1. Check official API: `NXOpen.Part.Expressions` -2. Look at NXOpenTSE example: https://nxopentsedocumentation.thescriptingengineer.com/expressions.html -3. See pattern: Loop through `work_part.Expressions`, handle edge cases -4. Adapt to our needs: Add filtering, type checking, JSON serialization - -**Generated Code** (our implementation, inspired by reference): -```python -def get_expressions(sim_file_path: Path) -> dict: - """Extract all expressions from a .sim file.""" - import NXOpen - - session = NXOpen.Session.GetSession() - - # Load sim file - part, status = session.Parts.OpenActiveDisplay(str(sim_file_path)) - - expressions = {} - try: - for expr in part.Expressions: - # Filter out system expressions (start with underscore) - if not expr.Name.startswith("_"): - expressions[expr.Name] = { - "value": expr.Value, - "units": expr.Units if expr.Units else "", - "type": str(expr.Type) - } - finally: - # Clean up - session.Parts.SetWork(None) - - return expressions -``` - ---- - -## Additional Resources - -### 3. **Eng-Tips NXOpen Forum** - -**URL**: https://www.eng-tips.com/threadminder.cfm?pid=561 - -- Community Q&A -- Troubleshooting help -- User-contributed examples - -### 4. **Stack Overflow - NXOpen Tag** - -**URL**: https://stackoverflow.com/questions/tagged/nxopen - -- Specific problem solutions -- Code snippets for common tasks - -### 5. **Siemens PLM Community Forums** - -**URL**: https://community.sw.siemens.com/ - -- Official support -- Product announcements -- Beta access information - ---- - -## Best Practices Learned from NXOpenTSE - -### 1. **Session Management** - -```python -# Always get session at the start -session = NXOpen.Session.GetSession() - -# Always check if part is loaded -if session.Parts.Work is None: - raise ValueError("No work part loaded") -``` - -### 2. **Error Handling** - -```python -# Wrap NX operations in try-finally for cleanup -try: - # NX operations here - result = do_something() -finally: - # Always clean up, even on error - if temp_part: - session.Parts.CloseAll(NXOpen.BasePart.CloseWholeTree.True) -``` - -### 3. **Expression Updates** - -```python -# Use Edit method for updating expressions -expr = part.Expressions.FindObject("parameter_name") -if expr: - expr.Edit(new_value) -else: - # Create if doesn't exist - unit = part.UnitCollection.FindObject("MilliMeter") - part.Expressions.CreateExpression(unit, "parameter_name", str(new_value)) -``` - -### 4. **Simulation Solution Access** - -```python -# Access simulation objects safely -sim_simulation = sim_part.Simulation -if sim_simulation: - solutions = sim_simulation.Solutions - for solution in solutions: - if solution.Name == target_name: - # Found our solution - pass -``` - ---- - -## Attribution and Licensing - -### When Using Ideas from NXOpenTSE - -1. **Add attribution in comments**: - ```python - # Approach inspired by NXOpenTSE expression handling - # See: https://nxopentsedocumentation.thescriptingengineer.com/expressions.html - ``` - -2. **Link in documentation**: - - Acknowledge inspiration in our docs - - Link to relevant NXOpenTSE pages - - Credit The Scripting Engineer for educational resources - -3. **Respect MIT License** (verify current license): - - Give credit to original authors - - Don't claim their work as ours - - Contribute back to community if we find improvements - ---- - -## Contributing to NXOpenTSE - -If we discover useful patterns or fixes while building Atomizer: -- Consider contributing examples back to NXOpenTSE -- Report issues if we find documentation errors -- Share knowledge with the NX scripting community - ---- - -## Integration with Atomizer MCP - -### MCP Tool: `search_nxopen_resources` - -```python -{ - "name": "search_nxopen_resources", - "description": "Search NXOpen documentation and reference materials", - "inputSchema": { - "query": "How to update expressions in NX", - "sources": ["official", "nxopentse", "community"], - "return_examples": true - } -} -``` - -**Output**: -```json -{ - "official_docs": "https://docs.sw.siemens.com/.../Expressions", - "nxopentse_example": "https://nxopentsedocumentation.thescriptingengineer.com/expressions.html", - "code_pattern": "Use part.Expressions.CreateExpression() or FindObject().Edit()", - "community_threads": [...] -} -``` - -### System Prompt Reference Section - -```markdown -## NXOpen Development Resources - -When implementing NXOpen functionality: - -1. **Official API**: Consult Siemens NXOpen .NET documentation for authoritative API reference -2. **NXOpenTSE**: Reference https://nxopentsedocumentation.thescriptingengineer.com/ for: - - Practical usage patterns - - Common parameter combinations - - Error handling approaches - - Real-world examples -3. **Adaptation**: Always adapt patterns to Atomizer's specific architecture rather than copying - -Remember: NXOpenTSE is a reference for learning, not a dependency to import. -``` - ---- - -## Summary - -**NXOpenTSE is invaluable** for accelerating Atomizer development by: -- ✅ Showing proven patterns -- ✅ Teaching NX best practices -- ✅ Providing working examples to learn from -- ✅ Documenting edge cases and gotchas - -**We will use it as**: -- 📚 Educational reference -- 🎯 Design pattern inspiration -- 🔍 Problem-solving resource -- 🧭 Navigation aid through complex NXOpen API - -**Not as**: -- ❌ Code to copy-paste -- ❌ Dependency to import -- ❌ Replacement for understanding - -This approach allows us to learn from the community while building something unique and tailored to Atomizer's specific optimization use case. - ---- - -**Last Updated**: 2025-11-15 -**Maintainer**: Atomaster Development Team diff --git a/docs/NX_EXPRESSION_IMPORT_SYSTEM.md b/docs/NX_EXPRESSION_IMPORT_SYSTEM.md deleted file mode 100644 index 3ff37f9b..00000000 --- a/docs/NX_EXPRESSION_IMPORT_SYSTEM.md +++ /dev/null @@ -1,374 +0,0 @@ -# NX Expression Import System - -> **Feature**: Robust NX part expression update via .exp file import -> -> **Status**: ✅ Production Ready (2025-11-17) -> -> **Impact**: Enables updating ALL NX expressions including those not stored in text format in binary .prt files - ---- - -## Overview - -The NX Expression Import System provides a robust method for updating NX part expressions by leveraging NX's native .exp file import functionality through journal scripts. - -### Problem Solved - -Some NX expressions (like `hole_count` in parametric features) are stored in binary .prt file formats that cannot be reliably parsed or updated through text-based regex operations. Traditional binary .prt editing fails for expressions that: -- Are used inside feature parameters -- Are stored in non-text binary sections -- Are linked to parametric pattern features - -### Solution - -Instead of binary .prt editing, use NX's native expression import/export: -1. Export all expressions to .exp file format (text-based) -2. Create .exp file containing only study design variables with new values -3. Import .exp file using NX journal script -4. NX updates all expressions natively, including binary-stored ones - ---- - -## Architecture - -### Components - -1. **NXParameterUpdater** ([optimization_engine/nx_updater.py](../optimization_engine/nx_updater.py)) - - Main class handling expression updates - - Provides both legacy (binary edit) and new (NX import) methods - - Automatic method selection based on expression type - -2. **import_expressions.py** ([optimization_engine/import_expressions.py](../optimization_engine/import_expressions.py)) - - NX journal script for importing .exp files - - Handles part loading, expression import, model update, and save - - Robust error handling and status reporting - -3. **.exp File Format** - - Plain text format for NX expressions - - Format: `[Units]name=value` or `name=value` (unitless) - - Human-readable and LLM-friendly - -### Workflow - -``` -┌─────────────────────────────────────────────────────────┐ -│ 1. Export ALL expressions to .exp format │ -│ (NX journal: export_expressions.py) │ -│ Purpose: Determine units for each expression │ -└─────────────────────────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────────────────────────┐ -│ 2. Create .exp file with ONLY study variables │ -│ [MilliMeter]beam_face_thickness=22.0 │ -│ [MilliMeter]beam_half_core_thickness=25.0 │ -│ [MilliMeter]holes_diameter=280.0 │ -│ hole_count=12 │ -└─────────────────────────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────────────────────────┐ -│ 3. Run NX journal to import expressions │ -│ (NX journal: import_expressions.py) │ -│ - Opens .prt file │ -│ - Imports .exp using Replace mode │ -│ - Updates model geometry │ -│ - Saves .prt file │ -└─────────────────────────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────────────────────────┐ -│ 4. Verify updates │ -│ - Re-export expressions │ -│ - Confirm all values updated │ -└─────────────────────────────────────────────────────────┘ -``` - ---- - -## Usage - -### Basic Usage - -```python -from pathlib import Path -from optimization_engine.nx_updater import NXParameterUpdater - -# Create updater -prt_file = Path("studies/simple_beam_optimization/model/Beam.prt") -updater = NXParameterUpdater(prt_file) - -# Define design variables to update -design_vars = { - "beam_half_core_thickness": 25.0, # mm - "beam_face_thickness": 22.0, # mm - "holes_diameter": 280.0, # mm - "hole_count": 12 # unitless -} - -# Update expressions using NX import (default method) -updater.update_expressions(design_vars) - -# Verify updates -expressions = updater.get_all_expressions() -for name, value in design_vars.items(): - actual = expressions[name]["value"] - print(f"{name}: expected={value}, actual={actual}, match={abs(actual - value) < 0.001}") -``` - -### Integration in Optimization Loop - -The system is automatically used in optimization workflows: - -```python -# In OptimizationRunner -for trial in range(n_trials): - # Optuna suggests new design variable values - design_vars = { - "beam_half_core_thickness": trial.suggest_float("beam_half_core_thickness", 10, 40), - "holes_diameter": trial.suggest_float("holes_diameter", 150, 450), - "hole_count": trial.suggest_int("hole_count", 5, 15), - # ... other variables - } - - # Update NX model (automatically uses .exp import) - updater.update_expressions(design_vars) - - # Run FEM simulation - solver.solve(sim_file) - - # Extract results - results = extractor.extract(op2_file) -``` - ---- - -## File Format: .exp - -### Format Specification - -``` -[UnitSystem]expression_name=value -expression_name=value # For unitless expressions -``` - -### Example .exp File - -``` -[MilliMeter]beam_face_thickness=20.0 -[MilliMeter]beam_half_core_thickness=20.0 -[MilliMeter]holes_diameter=400.0 -hole_count=10 -``` - -### Supported Units - -NX units are specified in square brackets: -- `[MilliMeter]` - Length in mm -- `[Meter]` - Length in m -- `[Newton]` - Force in N -- `[Kilogram]` - Mass in kg -- `[Pascal]` - Pressure/stress in Pa -- `[Degree]` - Angle in degrees -- No brackets - Unitless values - ---- - -## Implementation Details - -### NXParameterUpdater.update_expressions_via_import() - -**Location**: [optimization_engine/nx_updater.py](../optimization_engine/nx_updater.py) - -**Purpose**: Update expressions by creating and importing .exp file - -**Algorithm**: -1. Export ALL expressions from .prt to get units information -2. Create .exp file with ONLY study variables: - - Use units from full export - - Format: `[units]name=value` or `name=value` -3. Run NX journal script to import .exp file -4. Delete temporary .exp file -5. Return success/failure status - -**Key Code**: -```python -def update_expressions_via_import(self, updates: Dict[str, float]): - # Get all expressions to determine units - all_expressions = self.get_all_expressions(use_exp_export=True) - - # Create .exp file with ONLY study variables - exp_file = self.prt_path.parent / f"{self.prt_path.stem}_study_variables.exp" - - with open(exp_file, 'w', encoding='utf-8') as f: - for name, value in updates.items(): - units = all_expressions[name].get('units', '') - if units: - f.write(f"[{units}]{name}={value}\n") - else: - f.write(f"{name}={value}\n") - - # Run NX journal to import - journal_script = Path(__file__).parent / "import_expressions.py" - cmd_str = f'"{self.nx_run_journal_path}" "{journal_script}" -args "{self.prt_path}" "{exp_file}"' - result = subprocess.run(cmd_str, capture_output=True, text=True, shell=True) - - # Clean up - exp_file.unlink() - - return result.returncode == 0 -``` - -### import_expressions.py Journal - -**Location**: [optimization_engine/import_expressions.py](../optimization_engine/import_expressions.py) - -**Purpose**: NX journal script to import .exp file into .prt file - -**NXOpen API Usage**: -```python -# Open part file -workPart, partLoadStatus1 = theSession.Parts.OpenActiveDisplay( - prt_file, - NXOpen.DisplayPartOption.AllowAdditional -) - -# Import expressions (Replace mode overwrites existing values) -expModified, errorMessages = workPart.Expressions.ImportFromFile( - exp_file, - NXOpen.ExpressionCollection.ImportMode.Replace -) - -# Update geometry with new expression values -markId = theSession.SetUndoMark(NXOpen.Session.MarkVisibility.Invisible, "NX update") -nErrs = theSession.UpdateManager.DoUpdate(markId) - -# Save part -partSaveStatus = workPart.Save( - NXOpen.BasePart.SaveComponents.TrueValue, - NXOpen.BasePart.CloseAfterSave.FalseValue -) -``` - ---- - -## Validation Results - -### Test Case: 4D Beam Optimization - -**Study**: `studies/simple_beam_optimization/` - -**Design Variables**: -- `beam_half_core_thickness`: 10-40 mm -- `beam_face_thickness`: 10-40 mm -- `holes_diameter`: 150-450 mm -- `hole_count`: 5-15 (integer, unitless) - -**Problem**: `hole_count` was not updating with binary .prt editing - -**Solution**: Implemented .exp import system - -**Results**: -``` -✅ Trial 0: hole_count=6 (successfully updated from baseline=10) -✅ Trial 1: hole_count=15 (successfully updated) -✅ Trial 2: hole_count=11 (successfully updated) - -Mesh adaptation confirmed: -- Trial 0: 5373 CQUAD4 elements (6 holes) -- Trial 1: 5158 CQUAD4 + 1 CTRIA3 (15 holes) -- Trial 2: 5318 CQUAD4 (11 holes) - -All 3 trials: ALL 4 variables updated successfully -``` - ---- - -## Advantages - -### Robustness -- Works for ALL expression types, not just text-parseable ones -- Native NX functionality - no binary file hacks -- Handles units automatically -- No regex pattern failures - -### Simplicity -- .exp format is human-readable -- Easy to debug (just open .exp file) -- LLM-friendly format - -### Reliability -- NX validates expressions during import -- Automatic model update after import -- Error messages from NX if import fails - -### Performance -- Fast: .exp file creation + journal execution < 1 second -- No need to parse large .prt files -- Minimal I/O operations - ---- - -## Comparison: Binary Edit vs .exp Import - -| Aspect | Binary .prt Edit | .exp Import (New) | -|--------|------------------|-------------------| -| **Expression Coverage** | ~60-80% (text-parseable only) | ✅ 100% (all expressions) | -| **Reliability** | Fragile (regex failures) | ✅ Robust (native NX) | -| **Units Handling** | Manual regex parsing | ✅ Automatic via .exp format | -| **Model Update** | Requires separate step | ✅ Integrated in journal | -| **Debugging** | Hard (binary file) | ✅ Easy (.exp is text) | -| **Performance** | Fast (direct edit) | Fast (journal execution) | -| **Error Handling** | Limited | ✅ Full NX validation | -| **Feature Parameters** | ❌ Fails for linked expressions | ✅ Works for all | - -**Recommendation**: Use .exp import by default. Binary edit only for legacy/special cases. - ---- - -## Future Enhancements - -### Batch Updates -Currently creates one .exp file per update operation. Could optimize: -- Cache .exp file across multiple trials -- Only recreate if design variables change - -### Validation -Add pre-import validation: -- Check expression names exist -- Validate value ranges -- Warn about unit mismatches - -### Rollback -Implement undo capability: -- Save original .exp before updates -- Restore from backup if import fails - -### Performance Profiling -Measure and optimize: -- .exp export time -- Journal execution time -- Model update time - ---- - -## References - -### NXOpen Documentation -- `NXOpen.ExpressionCollection.ImportFromFile()` - Import expressions from .exp file -- `NXOpen.ExpressionCollection.ExportMode.Replace` - Overwrite existing expression values -- `NXOpen.Session.UpdateManager.DoUpdate()` - Update model after expression changes - -### Files -- [nx_updater.py](../optimization_engine/nx_updater.py) - Main implementation -- [import_expressions.py](../optimization_engine/import_expressions.py) - NX journal script -- [NXOPEN_INTELLISENSE_SETUP.md](NXOPEN_INTELLISENSE_SETUP.md) - NXOpen development setup - -### Related Features -- [OPTIMIZATION_WORKFLOW.md](OPTIMIZATION_WORKFLOW.md) - Overall optimization pipeline -- [DEVELOPMENT_GUIDANCE.md](../DEVELOPMENT_GUIDANCE.md) - Development standards -- [NX_SOLVER_INTEGRATION.md](archive/NX_SOLVER_INTEGRATION.md) - NX Simcenter integration - ---- - -**Author**: Antoine Letarte -**Date**: 2025-11-17 -**Status**: ✅ Production Ready -**Version**: 1.0 diff --git a/docs/OPTIMIZATION_WORKFLOW.md b/docs/OPTIMIZATION_WORKFLOW.md deleted file mode 100644 index c9b71784..00000000 Binary files a/docs/OPTIMIZATION_WORKFLOW.md and /dev/null differ diff --git a/docs/OPTUNA_DASHBOARD.md b/docs/OPTUNA_DASHBOARD.md deleted file mode 100644 index 44d6669a..00000000 --- a/docs/OPTUNA_DASHBOARD.md +++ /dev/null @@ -1,227 +0,0 @@ -# Optuna Dashboard Integration - -Atomizer leverages Optuna's built-in dashboard for advanced real-time optimization visualization. - -## Quick Start - -### 1. Install Optuna Dashboard - -```bash -# Using atomizer environment -conda activate atomizer -pip install optuna-dashboard -``` - -### 2. Launch Dashboard for a Study - -```bash -# Navigate to your substudy directory -cd studies/simple_beam_optimization/substudies/full_optimization_50trials - -# Launch dashboard pointing to the Optuna study database -optuna-dashboard sqlite:///optuna_study.db -``` - -The dashboard will start at http://localhost:8080 - -### 3. View During Active Optimization - -```bash -# Start optimization in one terminal -python studies/simple_beam_optimization/run_optimization.py - -# In another terminal, launch dashboard -cd studies/simple_beam_optimization/substudies/full_optimization_50trials -optuna-dashboard sqlite:///optuna_study.db -``` - -The dashboard updates in real-time as new trials complete! - ---- - -## Dashboard Features - -### **1. Optimization History** -- Interactive plot of objective value vs trial number -- Hover to see parameter values for each trial -- Zoom and pan for detailed analysis - -### **2. Parallel Coordinate Plot** -- Multi-dimensional visualization of parameter space -- Each line = one trial, colored by objective value -- Instantly see parameter correlations - -### **3. Parameter Importances** -- Identifies which parameters most influence the objective -- Based on fANOVA (functional ANOVA) analysis -- Helps focus optimization efforts - -### **4. Slice Plot** -- Shows objective value vs individual parameters -- One plot per design variable -- Useful for understanding parameter sensitivity - -### **5. Contour Plot** -- 2D contour plots of objective surface -- Select any two parameters to visualize -- Reveals parameter interactions - -### **6. Intermediate Values** -- Track metrics during trial execution (if using pruning) -- Useful for early stopping of poor trials - ---- - -## Advanced Usage - -### Custom Port - -```bash -optuna-dashboard sqlite:///optuna_study.db --port 8888 -``` - -### Multiple Studies - -```bash -# Compare multiple optimization runs -optuna-dashboard sqlite:///substudy1/optuna_study.db sqlite:///substudy2/optuna_study.db -``` - -### Remote Access - -```bash -# Allow connections from other machines -optuna-dashboard sqlite:///optuna_study.db --host 0.0.0.0 -``` - ---- - -## Integration with Atomizer Workflow - -### Study Organization - -Each Atomizer substudy has its own Optuna database: - -``` -studies/simple_beam_optimization/ -├── substudies/ -│ ├── full_optimization_50trials/ -│ │ ├── optuna_study.db # ← Optuna database (SQLite) -│ │ ├── optuna_study.pkl # ← Optuna study object (pickle) -│ │ ├── history.json # ← Atomizer history -│ │ └── plots/ # ← Matplotlib plots -│ └── validation_3trials/ -│ └── optuna_study.db -``` - -### Visualization Comparison - -**Optuna Dashboard** (Interactive, Web-based): -- ✅ Real-time updates during optimization -- ✅ Interactive plots (zoom, hover, filter) -- ✅ Parameter importance analysis -- ✅ Multiple study comparison -- ❌ Requires web browser -- ❌ Not embeddable in reports - -**Atomizer Matplotlib Plots** (Static, High-quality): -- ✅ Publication-quality PNG/PDF exports -- ✅ Customizable styling and annotations -- ✅ Embeddable in reports and papers -- ✅ Offline viewing -- ❌ Not interactive -- ❌ Not real-time - -**Recommendation**: Use **both**! -- Monitor optimization in real-time with Optuna Dashboard -- Generate final plots with Atomizer visualizer for reports - ---- - -## Troubleshooting - -### "No studies found" - -Make sure you're pointing to the correct database file: - -```bash -# Check if optuna_study.db exists -ls studies/*/substudies/*/optuna_study.db - -# Use absolute path if needed -optuna-dashboard sqlite:///C:/Users/antoi/Documents/Atomaste/Atomizer/studies/simple_beam_optimization/substudies/full_optimization_50trials/optuna_study.db -``` - -### Database Locked - -If optimization is actively writing to the database: - -```bash -# Use read-only mode -optuna-dashboard sqlite:///optuna_study.db?mode=ro -``` - -### Port Already in Use - -```bash -# Use different port -optuna-dashboard sqlite:///optuna_study.db --port 8888 -``` - ---- - -## Example Workflow - -```bash -# 1. Start optimization -python studies/simple_beam_optimization/run_optimization.py - -# 2. In another terminal, launch Optuna dashboard -cd studies/simple_beam_optimization/substudies/full_optimization_50trials -optuna-dashboard sqlite:///optuna_study.db - -# 3. Open browser to http://localhost:8080 and watch optimization live - -# 4. After optimization completes, generate static plots -python -m optimization_engine.visualizer studies/simple_beam_optimization/substudies/full_optimization_50trials png pdf - -# 5. View final plots -explorer studies/simple_beam_optimization/substudies/full_optimization_50trials/plots -``` - ---- - -## Optuna Dashboard Screenshots - -### Optimization History -![Optuna History](https://optuna.readthedocs.io/en/stable/_images/dashboard_history.png) - -### Parallel Coordinate Plot -![Optuna Parallel Coords](https://optuna.readthedocs.io/en/stable/_images/dashboard_parallel_coordinate.png) - -### Parameter Importance -![Optuna Importance](https://optuna.readthedocs.io/en/stable/_images/dashboard_param_importances.png) - ---- - -## Further Reading - -- [Optuna Dashboard Documentation](https://optuna-dashboard.readthedocs.io/) -- [Optuna Visualization Module](https://optuna.readthedocs.io/en/stable/reference/visualization/index.html) -- [fANOVA Parameter Importance](https://optuna.readthedocs.io/en/stable/reference/generated/optuna.importance.FanovaImportanceEvaluator.html) - ---- - -## Summary - -| Feature | Optuna Dashboard | Atomizer Matplotlib | -|---------|-----------------|-------------------| -| Real-time updates | ✅ Yes | ❌ No | -| Interactive | ✅ Yes | ❌ No | -| Parameter importance | ✅ Yes | ⚠️ Manual | -| Publication quality | ⚠️ Web only | ✅ PNG/PDF | -| Embeddable in docs | ❌ No | ✅ Yes | -| Offline viewing | ❌ Needs server | ✅ Yes | -| Multi-study comparison | ✅ Yes | ⚠️ Manual | - -**Best Practice**: Use Optuna Dashboard for monitoring and exploration, Atomizer visualizer for final reporting. diff --git a/docs/PHASE_2_5_INTELLIGENT_GAP_DETECTION.md b/docs/PHASE_2_5_INTELLIGENT_GAP_DETECTION.md deleted file mode 100644 index 73d8b27c..00000000 --- a/docs/PHASE_2_5_INTELLIGENT_GAP_DETECTION.md +++ /dev/null @@ -1,253 +0,0 @@ -# Phase 2.5: Intelligent Codebase-Aware Gap Detection - -## Problem Statement - -The current Research Agent uses dumb keyword matching and doesn't understand what already exists in the Atomizer codebase. When a user asks: - -> "I want to evaluate strain on a part with sol101 and optimize this (minimize) using iterations and optuna to lower it varying all my geometry parameters that contains v_ in its expression" - -**Current (Wrong) Behavior:** -- Detects keyword "geometry" -- Asks user for geometry examples -- Completely misses the actual request - -**Expected (Correct) Behavior:** -``` -Analyzing your optimization request... - -Workflow Components Identified: ---------------------------------- -1. Run SOL101 analysis [KNOWN - nx_solver.py] -2. Extract geometry parameters (v_ prefix) [KNOWN - expression system] -3. Update parameter values [KNOWN - parameter updater] -4. Optuna optimization loop [KNOWN - optimization engine] -5. Extract strain from OP2 [MISSING - not implemented] -6. Minimize strain objective [SIMPLE - max(strain values)] - -Knowledge Gap Analysis: ------------------------ -HAVE: - OP2 displacement extraction (op2_extractor_example.py) -HAVE: - OP2 stress extraction (op2_extractor_example.py) -MISSING: - OP2 strain extraction - -Research Needed: ----------------- -Only need to learn: How to extract strain data from Nastran OP2 files using pyNastran - -Would you like me to: -1. Search pyNastran documentation for strain extraction -2. Look for strain extraction examples in op2_extractor_example.py pattern -3. Ask you for an example of strain extraction code -``` - -## Solution Architecture - -### 1. Codebase Capability Analyzer - -Scan Atomizer to build capability index: - -```python -class CodebaseCapabilityAnalyzer: - """Analyzes what Atomizer can already do.""" - - def analyze_codebase(self) -> Dict[str, Any]: - """ - Returns: - { - 'optimization': { - 'optuna_integration': True, - 'parameter_updating': True, - 'expression_parsing': True - }, - 'simulation': { - 'nx_solver': True, - 'sol101': True, - 'sol103': False - }, - 'result_extraction': { - 'displacement': True, - 'stress': True, - 'strain': False, # <-- THE GAP! - 'modal': False - } - } - """ -``` - -### 2. Workflow Decomposer - -Break user request into atomic steps: - -```python -class WorkflowDecomposer: - """Breaks complex requests into atomic workflow steps.""" - - def decompose(self, user_request: str) -> List[WorkflowStep]: - """ - Input: "minimize strain using SOL101 and optuna varying v_ params" - - Output: - [ - WorkflowStep("identify_parameters", domain="geometry", params={"filter": "v_"}), - WorkflowStep("update_parameters", domain="geometry", params={"values": "from_optuna"}), - WorkflowStep("run_analysis", domain="simulation", params={"solver": "SOL101"}), - WorkflowStep("extract_strain", domain="results", params={"metric": "max_strain"}), - WorkflowStep("optimize", domain="optimization", params={"objective": "minimize", "algorithm": "optuna"}) - ] - """ -``` - -### 3. Capability Matcher - -Match workflow steps to existing capabilities: - -```python -class CapabilityMatcher: - """Matches required workflow steps to existing capabilities.""" - - def match(self, workflow_steps, capabilities) -> CapabilityMatch: - """ - Returns: - { - 'known_steps': [ - {'step': 'identify_parameters', 'implementation': 'expression_parser.py'}, - {'step': 'update_parameters', 'implementation': 'parameter_updater.py'}, - {'step': 'run_analysis', 'implementation': 'nx_solver.py'}, - {'step': 'optimize', 'implementation': 'optuna_optimizer.py'} - ], - 'unknown_steps': [ - {'step': 'extract_strain', 'similar_to': 'extract_stress', 'gap': 'strain_from_op2'} - ], - 'confidence': 0.80 # 4/5 steps known - } - """ -``` - -### 4. Targeted Research Planner - -Create research plan ONLY for missing pieces: - -```python -class TargetedResearchPlanner: - """Creates research plan focused on actual gaps.""" - - def plan(self, unknown_steps) -> ResearchPlan: - """ - For gap='strain_from_op2', similar_to='stress_from_op2': - - Research Plan: - 1. Read existing op2_extractor_example.py to understand pattern - 2. Search pyNastran docs for strain extraction API - 3. If not found, ask user for strain extraction example - 4. Generate extract_strain() function following same pattern as extract_stress() - """ -``` - -## Implementation Plan - -### Week 1: Capability Analysis -- [X] Map existing Atomizer capabilities -- [X] Build capability index from code -- [X] Create capability query system - -### Week 2: Workflow Decomposition -- [X] Build workflow step extractor -- [X] Create domain classifier -- [X] Implement step-to-capability matcher - -### Week 3: Intelligent Gap Detection -- [X] Integrate all components -- [X] Test with strain optimization request -- [X] Verify correct gap identification - -## Success Criteria - -**Test Input:** -"minimize strain using SOL101 and optuna varying v_ parameters" - -**Expected Output:** -``` -Request Analysis Complete -------------------------- - -Known Capabilities (80%): -- Parameter identification (v_ prefix filter) -- Parameter updating -- SOL101 simulation execution -- Optuna optimization loop - -Missing Capability (20%): -- Strain extraction from OP2 files - -Recommendation: -The only missing piece is extracting strain data from Nastran OP2 output files. -I found a similar implementation for stress extraction in op2_extractor_example.py. - -Would you like me to: -1. Research pyNastran strain extraction API -2. Generate extract_max_strain() function following the stress extraction pattern -3. Integrate into your optimization workflow - -Research needed: Minimal (1 function, ~50 lines of code) -``` - -## Benefits - -1. **Accurate Gap Detection**: Only identifies actual missing capabilities -2. **Minimal Research**: Focuses effort on real unknowns -3. **Leverages Existing Code**: Understands what you already have -4. **Better UX**: Clear explanation of what's known vs unknown -5. **Faster Iterations**: Doesn't waste time on known capabilities - -## Current Status - -- [X] Problem identified -- [X] Solution architecture designed -- [X] Implementation completed -- [X] All tests passing - -## Implementation Summary - -Phase 2.5 has been successfully implemented with 4 core components: - -1. **CodebaseCapabilityAnalyzer** ([codebase_analyzer.py](../optimization_engine/codebase_analyzer.py)) - - Scans Atomizer codebase for existing capabilities - - Identifies what's implemented vs missing - - Finds similar capabilities for pattern reuse - -2. **WorkflowDecomposer** ([workflow_decomposer.py](../optimization_engine/workflow_decomposer.py)) - - Breaks user requests into atomic workflow steps - - Extracts parameters from natural language - - Classifies steps by domain - -3. **CapabilityMatcher** ([capability_matcher.py](../optimization_engine/capability_matcher.py)) - - Matches workflow steps to existing code - - Identifies actual knowledge gaps - - Calculates confidence based on pattern similarity - -4. **TargetedResearchPlanner** ([targeted_research_planner.py](../optimization_engine/targeted_research_planner.py)) - - Creates focused research plans - - Leverages similar capabilities when available - - Prioritizes research sources - -## Test Results - -Run the comprehensive test: -```bash -python tests/test_phase_2_5_intelligent_gap_detection.py -``` - -**Test Output (strain optimization request):** -- Workflow: 5 steps identified -- Known: 4/5 steps (80% coverage) -- Missing: Only strain extraction -- Similar: Can adapt from displacement/stress -- Overall confidence: 90% -- Research plan: 4 focused steps - -## Next Steps - -1. Integrate Phase 2.5 with existing Research Agent -2. Update interactive session to use new gap detection -3. Test with diverse optimization requests -4. Build MCP integration for documentation search diff --git a/docs/PHASE_2_7_LLM_INTEGRATION.md b/docs/PHASE_2_7_LLM_INTEGRATION.md deleted file mode 100644 index 2e05baef..00000000 --- a/docs/PHASE_2_7_LLM_INTEGRATION.md +++ /dev/null @@ -1,245 +0,0 @@ -# Phase 2.7: LLM-Powered Workflow Intelligence - -## Problem: Static Regex vs. Dynamic Intelligence - -**Previous Approach (Phase 2.5-2.6):** -- ❌ Dumb regex patterns to extract workflow steps -- ❌ Static rules for step classification -- ❌ Missed intermediate calculations -- ❌ Couldn't understand nuance (CBUSH vs CBAR, element forces vs reaction forces) - -**New Approach (Phase 2.7):** -- ✅ **Use Claude LLM to analyze user requests** -- ✅ **Understand engineering context dynamically** -- ✅ **Detect ALL intermediate steps intelligently** -- ✅ **Distinguish subtle differences (element types, directions, metrics)** - -## Architecture - -``` -User Request - ↓ -LLM Analyzer (Claude) - ↓ -Structured JSON Analysis - ↓ -┌────────────────────────────────────┐ -│ Engineering Features (FEA) │ -│ Inline Calculations (Math) │ -│ Post-Processing Hooks (Custom) │ -│ Optimization Config │ -└────────────────────────────────────┘ - ↓ -Phase 2.5 Capability Matching - ↓ -Research Plan / Code Generation -``` - -## Example: CBAR Optimization Request - -**User Input:** -``` -I want to extract forces in direction Z of all the 1D elements and find the average of it, -then find the minimum value and compare it to the average, then assign it to a objective -metric that needs to be minimized. - -I want to iterate on the FEA properties of the Cbar element stiffness in X to make the -objective function minimized. - -I want to use genetic algorithm to iterate and optimize this -``` - -**LLM Analysis Output:** -```json -{ - "engineering_features": [ - { - "action": "extract_1d_element_forces", - "domain": "result_extraction", - "description": "Extract element forces from CBAR in Z direction from OP2", - "params": { - "element_types": ["CBAR"], - "result_type": "element_force", - "direction": "Z" - } - }, - { - "action": "update_cbar_stiffness", - "domain": "fea_properties", - "description": "Modify CBAR stiffness in X direction", - "params": { - "element_type": "CBAR", - "property": "stiffness_x" - } - } - ], - "inline_calculations": [ - { - "action": "calculate_average", - "params": {"input": "forces_z", "operation": "mean"}, - "code_hint": "avg = sum(forces_z) / len(forces_z)" - }, - { - "action": "find_minimum", - "params": {"input": "forces_z", "operation": "min"}, - "code_hint": "min_val = min(forces_z)" - } - ], - "post_processing_hooks": [ - { - "action": "custom_objective_metric", - "description": "Compare min to average", - "params": { - "inputs": ["min_force", "avg_force"], - "formula": "min_force / avg_force", - "objective": "minimize" - } - } - ], - "optimization": { - "algorithm": "genetic_algorithm", - "design_variables": [ - {"parameter": "cbar_stiffness_x", "type": "FEA_property"} - ] - } -} -``` - -## Key Intelligence Improvements - -### 1. Detects Intermediate Steps -**Old (Regex):** -- ❌ Only saw "extract forces" and "optimize" -- ❌ Missed average, minimum, comparison - -**New (LLM):** -- ✅ Identifies: extract → average → min → compare → optimize -- ✅ Classifies each as engineering vs. simple math - -### 2. Understands Engineering Context -**Old (Regex):** -- ❌ "forces" → generic "reaction_force" extraction -- ❌ Didn't distinguish CBUSH from CBAR - -**New (LLM):** -- ✅ "1D element forces" → element forces (not reaction forces) -- ✅ "CBAR stiffness in X" → specific property in specific direction -- ✅ Understands these come from different sources (OP2 vs property cards) - -### 3. Smart Classification -**Old (Regex):** -```python -if 'average' in text: - return 'simple_calculation' # Dumb! -``` - -**New (LLM):** -```python -# LLM reasoning: -# - "average of forces" → simple Python (sum/len) -# - "extract forces from OP2" → engineering (pyNastran) -# - "compare min to avg for objective" → hook (custom logic) -``` - -### 4. Generates Actionable Code Hints -**Old:** Just action names like "calculate_average" - -**New:** Includes code hints for auto-generation: -```json -{ - "action": "calculate_average", - "code_hint": "avg = sum(forces_z) / len(forces_z)" -} -``` - -## Integration with Existing Phases - -### Phase 2.5 (Capability Matching) -LLM output feeds directly into existing capability matcher: -- Engineering features → check if implemented -- If missing → create research plan -- If similar → adapt existing code - -### Phase 2.6 (Step Classification) -Now **replaced by LLM** for better accuracy: -- No more static rules -- Context-aware classification -- Understands subtle differences - -## Implementation - -**File:** `optimization_engine/llm_workflow_analyzer.py` - -**Key Function:** -```python -analyzer = LLMWorkflowAnalyzer(api_key=os.getenv('ANTHROPIC_API_KEY')) -analysis = analyzer.analyze_request(user_request) - -# Returns structured JSON with: -# - engineering_features -# - inline_calculations -# - post_processing_hooks -# - optimization config -``` - -## Benefits - -1. **Accurate**: Understands engineering nuance -2. **Complete**: Detects ALL steps, including intermediate ones -3. **Dynamic**: No hardcoded patterns to maintain -4. **Extensible**: Automatically handles new request types -5. **Actionable**: Provides code hints for auto-generation - -## LLM Integration Modes - -### Development Mode (Recommended) -For development within Claude Code: -- Use Claude Code directly for interactive workflow analysis -- No API consumption or costs -- Real-time feedback and iteration -- Perfect for testing and refinement - -### Production Mode (Future) -For standalone Atomizer execution: -- Optional Anthropic API integration -- Set `ANTHROPIC_API_KEY` environment variable -- Falls back to heuristics if no key provided -- Useful for automated batch processing - -**Current Status**: llm_workflow_analyzer.py supports both modes. For development, continue using Claude Code interactively. - -## Next Steps - -1. ✅ Install anthropic package -2. ✅ Create LLM analyzer module -3. ✅ Document integration modes -4. ⏳ Integrate with Phase 2.5 capability matcher -5. ⏳ Test with diverse optimization requests via Claude Code -6. ⏳ Build code generator for inline calculations -7. ⏳ Build hook generator for post-processing - -## Success Criteria - -**Input:** -"Extract 1D forces, find average, find minimum, compare to average, optimize CBAR stiffness" - -**Output:** -``` -Engineering Features: 2 (need research) - - extract_1d_element_forces - - update_cbar_stiffness - -Inline Calculations: 2 (auto-generate) - - calculate_average - - find_minimum - -Post-Processing: 1 (generate hook) - - custom_objective_metric (min/avg ratio) - -Optimization: 1 - - genetic_algorithm - -✅ All steps detected -✅ Correctly classified -✅ Ready for implementation -``` diff --git a/docs/PHASE_3_2_INTEGRATION_PLAN.md b/docs/PHASE_3_2_INTEGRATION_PLAN.md deleted file mode 100644 index cbafc93e..00000000 --- a/docs/PHASE_3_2_INTEGRATION_PLAN.md +++ /dev/null @@ -1,699 +0,0 @@ -# Phase 3.2: LLM Integration Roadmap - -**Status**: ✅ **WEEK 1 COMPLETE** - 🎯 **Week 2 IN PROGRESS** -**Timeline**: 2-4 weeks -**Last Updated**: 2025-11-17 -**Current Progress**: 25% (Week 1/4 Complete) - ---- - -## Executive Summary - -### The Problem -We've built 85% of an LLM-native optimization system, but **it's not integrated into production**. The components exist but are disconnected islands: - -- ✅ **LLMWorkflowAnalyzer** - Parses natural language → workflow (Phase 2.7) -- ✅ **ExtractorOrchestrator** - Auto-generates result extractors (Phase 3.1) -- ✅ **InlineCodeGenerator** - Creates custom calculations (Phase 2.8) -- ✅ **HookGenerator** - Generates post-processing hooks (Phase 2.9) -- ✅ **LLMOptimizationRunner** - Orchestrates LLM workflow (Phase 3.2) -- ⚠️ **ResearchAgent** - Learns from examples (Phase 2, partially complete) - -**Reality**: Users still write 100+ lines of JSON config manually instead of using 3 lines of natural language. - -### The Solution -**Phase 3.2 Integration Sprint**: Wire LLM components into production workflow with a single `--llm` flag. - ---- - -## Strategic Roadmap - -### Week 1: Make LLM Mode Accessible (16 hours) - -**Goal**: Users can invoke LLM mode with a single command - -#### Tasks - -**1.1 Create Unified Entry Point** (4 hours) ✅ COMPLETE -- [x] Create `optimization_engine/run_optimization.py` as unified CLI -- [x] Add `--llm` flag for natural language mode -- [x] Add `--request` parameter for natural language input -- [x] Preserve existing `--config` for traditional JSON mode -- [x] Support both modes in parallel (no breaking changes) - -**Files**: -- `optimization_engine/run_optimization.py` (NEW) - -**Success Metric**: -```bash -python optimization_engine/run_optimization.py --llm \ - --request "Minimize stress for bracket. Vary wall thickness 3-8mm" \ - --prt studies/bracket/model/Bracket.prt \ - --sim studies/bracket/model/Bracket_sim1.sim -``` - ---- - -**1.2 Wire LLMOptimizationRunner to Production** (8 hours) ✅ COMPLETE -- [x] Connect LLMWorkflowAnalyzer to entry point -- [x] Bridge LLMOptimizationRunner → OptimizationRunner for execution -- [x] Pass model updater and simulation runner callables -- [x] Integrate with existing hook system -- [x] Preserve all logging (detailed logs, optimization.log) -- [x] Add workflow validation and error handling -- [x] Create comprehensive integration test suite (5/5 tests passing) - -**Files Modified**: -- `optimization_engine/run_optimization.py` -- `optimization_engine/llm_optimization_runner.py` (integration points) - -**Success Metric**: LLM workflow generates extractors → runs FEA → logs results - ---- - -**1.3 Create Minimal Example** (2 hours) ✅ COMPLETE -- [x] Create `examples/llm_mode_simple_example.py` -- [x] Show: Natural language request → Optimization results -- [x] Compare: Traditional mode (100 lines JSON) vs LLM mode (3 lines) -- [x] Include troubleshooting tips - -**Files Created**: -- `examples/llm_mode_simple_example.py` - -**Success Metric**: Example runs successfully, demonstrates value ✅ - ---- - -**1.4 End-to-End Integration Test** (2 hours) ✅ COMPLETE -- [x] Test with simple_beam_optimization study -- [x] Natural language → JSON workflow → NX solve → Results -- [x] Verify all extractors generated correctly -- [x] Check logs created properly -- [x] Validate output matches manual mode -- [x] Test graceful failure without API key -- [x] Comprehensive verification of all output files - -**Files Created**: -- `tests/test_phase_3_2_e2e.py` - -**Success Metric**: LLM mode completes beam optimization without errors ✅ - ---- - -### Week 2: Robustness & Safety (16 hours) - -**Goal**: LLM mode handles failures gracefully, never crashes - -#### Tasks - -**2.1 Code Validation Pipeline** (6 hours) -- [ ] Create `optimization_engine/code_validator.py` -- [ ] Implement syntax validation (ast.parse) -- [ ] Implement security scanning (whitelist imports) -- [ ] Implement test execution on example OP2 -- [ ] Implement output schema validation -- [ ] Add retry with LLM feedback on validation failure - -**Files Created**: -- `optimization_engine/code_validator.py` - -**Integration Points**: -- `optimization_engine/extractor_orchestrator.py` (validate before saving) -- `optimization_engine/inline_code_generator.py` (validate calculations) - -**Success Metric**: Generated code passes validation, or LLM fixes based on feedback - ---- - -**2.2 Graceful Fallback Mechanisms** (4 hours) -- [ ] Wrap all LLM calls in try/except -- [ ] Provide clear error messages -- [ ] Offer fallback to manual mode -- [ ] Log failures to audit trail -- [ ] Never crash on LLM failure - -**Files Modified**: -- `optimization_engine/run_optimization.py` -- `optimization_engine/llm_workflow_analyzer.py` -- `optimization_engine/llm_optimization_runner.py` - -**Success Metric**: LLM failures degrade gracefully to manual mode - ---- - -**2.3 LLM Audit Trail** (3 hours) -- [ ] Create `optimization_engine/llm_audit.py` -- [ ] Log all LLM requests and responses -- [ ] Log generated code with prompts -- [ ] Log validation results -- [ ] Create `llm_audit.json` in study output directory - -**Files Created**: -- `optimization_engine/llm_audit.py` - -**Integration Points**: -- All LLM components log to audit trail - -**Success Metric**: Full LLM decision trace available for debugging - ---- - -**2.4 Failure Scenario Testing** (3 hours) -- [ ] Test: Invalid natural language request -- [ ] Test: LLM unavailable (API down) -- [ ] Test: Generated code has syntax error -- [ ] Test: Generated code fails validation -- [ ] Test: OP2 file format unexpected -- [ ] Verify all fail gracefully - -**Files Created**: -- `tests/test_llm_failure_modes.py` - -**Success Metric**: All failure scenarios handled without crashes - ---- - -### Week 3: Learning System (12 hours) - -**Goal**: System learns from successful workflows and reuses patterns - -#### Tasks - -**3.1 Knowledge Base Implementation** (4 hours) -- [ ] Create `optimization_engine/knowledge_base.py` -- [ ] Implement `save_session()` - Save successful workflows -- [ ] Implement `search_templates()` - Find similar past workflows -- [ ] Implement `get_template()` - Retrieve reusable pattern -- [ ] Add confidence scoring (user-validated > LLM-generated) - -**Files Created**: -- `optimization_engine/knowledge_base.py` -- `knowledge_base/sessions/` (directory for session logs) -- `knowledge_base/templates/` (directory for reusable patterns) - -**Success Metric**: Successful workflows saved with metadata - ---- - -**3.2 Template Extraction** (4 hours) -- [ ] Analyze generated extractor code to identify patterns -- [ ] Extract reusable template structure -- [ ] Parameterize variable parts -- [ ] Save template with usage examples -- [ ] Implement template application to new requests - -**Files Modified**: -- `optimization_engine/extractor_orchestrator.py` - -**Integration**: -```python -# After successful generation: -template = extract_template(generated_code) -knowledge_base.save_template(feature_name, template, confidence='medium') - -# On next request: -existing_template = knowledge_base.search_templates(feature_name) -if existing_template and existing_template.confidence > 0.7: - code = existing_template.apply(new_params) # Reuse! -``` - -**Success Metric**: Second identical request reuses template (faster) - ---- - -**3.3 ResearchAgent Integration** (4 hours) -- [ ] Complete ResearchAgent implementation -- [ ] Integrate into ExtractorOrchestrator error handling -- [ ] Add user example collection workflow -- [ ] Implement pattern learning from examples -- [ ] Save learned knowledge to knowledge base - -**Files Modified**: -- `optimization_engine/research_agent.py` (complete implementation) -- `optimization_engine/llm_optimization_runner.py` (integrate ResearchAgent) - -**Workflow**: -``` -Unknown feature requested - → ResearchAgent asks user for example - → Learns pattern from example - → Generates feature using pattern - → Saves to knowledge base - → Retry with new feature -``` - -**Success Metric**: Unknown feature request triggers learning loop successfully - ---- - -### Week 4: Documentation & Discoverability (8 hours) - -**Goal**: Users discover and understand LLM capabilities - -#### Tasks - -**4.1 Update README** (2 hours) -- [ ] Add "🤖 LLM-Powered Mode" section to README.md -- [ ] Show example command with natural language -- [ ] Explain what LLM mode can do -- [ ] Link to detailed docs - -**Files Modified**: -- `README.md` - -**Success Metric**: README clearly shows LLM capabilities upfront - ---- - -**4.2 Create LLM Mode Documentation** (3 hours) -- [ ] Create `docs/LLM_MODE.md` -- [ ] Explain how LLM mode works -- [ ] Provide usage examples -- [ ] Document when to use LLM vs manual mode -- [ ] Add troubleshooting guide -- [ ] Explain learning system - -**Files Created**: -- `docs/LLM_MODE.md` - -**Contents**: -- How it works (architecture diagram) -- Getting started (first LLM optimization) -- Natural language patterns that work well -- Troubleshooting common issues -- How learning system improves over time - -**Success Metric**: Users understand LLM mode from docs - ---- - -**4.3 Create Demo Video/GIF** (1 hour) -- [ ] Record terminal session: Natural language → Results -- [ ] Show before/after (100 lines JSON vs 3 lines) -- [ ] Create animated GIF for README -- [ ] Add to documentation - -**Files Created**: -- `docs/demo/llm_mode_demo.gif` - -**Success Metric**: Visual demo shows value proposition clearly - ---- - -**4.4 Update All Planning Docs** (2 hours) -- [ ] Update DEVELOPMENT.md with Phase 3.2 completion status -- [ ] Update DEVELOPMENT_GUIDANCE.md progress (80-90% → 90-95%) -- [ ] Update DEVELOPMENT_ROADMAP.md Phase 3 status -- [ ] Mark Phase 3.2 as ✅ Complete - -**Files Modified**: -- `DEVELOPMENT.md` -- `DEVELOPMENT_GUIDANCE.md` -- `DEVELOPMENT_ROADMAP.md` - -**Success Metric**: All docs reflect completed Phase 3.2 - ---- - -## Implementation Details - -### Entry Point Architecture - -```python -# optimization_engine/run_optimization.py (NEW) - -import argparse -from pathlib import Path - -def main(): - parser = argparse.ArgumentParser( - description="Atomizer Optimization Engine - Manual or LLM-powered mode" - ) - - # Mode selection - mode_group = parser.add_mutually_exclusive_group(required=True) - mode_group.add_argument('--llm', action='store_true', - help='Use LLM-assisted workflow (natural language mode)') - mode_group.add_argument('--config', type=Path, - help='JSON config file (traditional mode)') - - # LLM mode parameters - parser.add_argument('--request', type=str, - help='Natural language optimization request (required with --llm)') - - # Common parameters - parser.add_argument('--prt', type=Path, required=True, - help='Path to .prt file') - parser.add_argument('--sim', type=Path, required=True, - help='Path to .sim file') - parser.add_argument('--output', type=Path, - help='Output directory (default: auto-generated)') - parser.add_argument('--trials', type=int, default=50, - help='Number of optimization trials') - - args = parser.parse_args() - - if args.llm: - run_llm_mode(args) - else: - run_traditional_mode(args) - - -def run_llm_mode(args): - """LLM-powered natural language mode.""" - from optimization_engine.llm_workflow_analyzer import LLMWorkflowAnalyzer - from optimization_engine.llm_optimization_runner import LLMOptimizationRunner - from optimization_engine.nx_updater import NXParameterUpdater - from optimization_engine.nx_solver import NXSolver - from optimization_engine.llm_audit import LLMAuditLogger - - if not args.request: - raise ValueError("--request required with --llm mode") - - print(f"🤖 LLM Mode: Analyzing request...") - print(f" Request: {args.request}") - - # Initialize audit logger - audit_logger = LLMAuditLogger(args.output / "llm_audit.json") - - # Analyze natural language request - analyzer = LLMWorkflowAnalyzer(use_claude_code=True) - - try: - workflow = analyzer.analyze_request(args.request) - audit_logger.log_analysis(args.request, workflow, - reasoning=workflow.get('llm_reasoning', '')) - - print(f"✓ Workflow created:") - print(f" - Design variables: {len(workflow['design_variables'])}") - print(f" - Objectives: {len(workflow['objectives'])}") - print(f" - Extractors: {len(workflow['engineering_features'])}") - - except Exception as e: - print(f"✗ LLM analysis failed: {e}") - print(" Falling back to manual mode. Please provide --config instead.") - return - - # Create model updater and solver callables - updater = NXParameterUpdater(args.prt) - solver = NXSolver() - - def model_updater(design_vars): - updater.update_expressions(design_vars) - - def simulation_runner(): - result = solver.run_simulation(args.sim) - return result['op2_file'] - - # Run LLM-powered optimization - runner = LLMOptimizationRunner( - llm_workflow=workflow, - model_updater=model_updater, - simulation_runner=simulation_runner, - study_name=args.output.name if args.output else "llm_optimization", - output_dir=args.output - ) - - study = runner.run(n_trials=args.trials) - - print(f"\n✓ Optimization complete!") - print(f" Best trial: {study.best_trial.number}") - print(f" Best value: {study.best_value:.6f}") - print(f" Results: {args.output}") - - -def run_traditional_mode(args): - """Traditional JSON configuration mode.""" - from optimization_engine.runner import OptimizationRunner - import json - - print(f"📄 Traditional Mode: Loading config...") - - with open(args.config) as f: - config = json.load(f) - - runner = OptimizationRunner( - config_file=args.config, - prt_file=args.prt, - sim_file=args.sim, - output_dir=args.output - ) - - study = runner.run(n_trials=args.trials) - - print(f"\n✓ Optimization complete!") - print(f" Results: {args.output}") - - -if __name__ == '__main__': - main() -``` - ---- - -### Validation Pipeline - -```python -# optimization_engine/code_validator.py (NEW) - -import ast -import subprocess -import tempfile -from pathlib import Path -from typing import Dict, Any, List - -class CodeValidator: - """ - Validates LLM-generated code before execution. - - Checks: - 1. Syntax (ast.parse) - 2. Security (whitelist imports) - 3. Test execution on example data - 4. Output schema validation - """ - - ALLOWED_IMPORTS = { - 'pyNastran', 'numpy', 'pathlib', 'typing', 'dataclasses', - 'json', 'sys', 'os', 'math', 'collections' - } - - FORBIDDEN_CALLS = { - 'eval', 'exec', 'compile', '__import__', 'open', - 'subprocess', 'os.system', 'os.popen' - } - - def validate_extractor(self, code: str, test_op2_file: Path) -> Dict[str, Any]: - """ - Validate generated extractor code. - - Args: - code: Generated Python code - test_op2_file: Example OP2 file for testing - - Returns: - { - 'valid': bool, - 'error': str (if invalid), - 'test_result': dict (if valid) - } - """ - # 1. Syntax check - try: - tree = ast.parse(code) - except SyntaxError as e: - return { - 'valid': False, - 'error': f'Syntax error: {e}', - 'stage': 'syntax' - } - - # 2. Security scan - security_result = self._check_security(tree) - if not security_result['safe']: - return { - 'valid': False, - 'error': security_result['error'], - 'stage': 'security' - } - - # 3. Test execution - try: - test_result = self._test_execution(code, test_op2_file) - except Exception as e: - return { - 'valid': False, - 'error': f'Runtime error: {e}', - 'stage': 'execution' - } - - # 4. Output schema validation - schema_result = self._validate_output_schema(test_result) - if not schema_result['valid']: - return { - 'valid': False, - 'error': schema_result['error'], - 'stage': 'schema' - } - - return { - 'valid': True, - 'test_result': test_result - } - - def _check_security(self, tree: ast.AST) -> Dict[str, Any]: - """Check for dangerous imports and function calls.""" - for node in ast.walk(tree): - # Check imports - if isinstance(node, ast.Import): - for alias in node.names: - module = alias.name.split('.')[0] - if module not in self.ALLOWED_IMPORTS: - return { - 'safe': False, - 'error': f'Disallowed import: {alias.name}' - } - - # Check function calls - if isinstance(node, ast.Call): - if isinstance(node.func, ast.Name): - if node.func.id in self.FORBIDDEN_CALLS: - return { - 'safe': False, - 'error': f'Forbidden function call: {node.func.id}' - } - - return {'safe': True} - - def _test_execution(self, code: str, test_file: Path) -> Dict[str, Any]: - """Execute code in sandboxed environment with test data.""" - # Write code to temp file - with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f: - f.write(code) - temp_code_file = Path(f.name) - - try: - # Execute in subprocess (sandboxed) - result = subprocess.run( - ['python', str(temp_code_file), str(test_file)], - capture_output=True, - text=True, - timeout=30 - ) - - if result.returncode != 0: - raise RuntimeError(f"Execution failed: {result.stderr}") - - # Parse JSON output - import json - output = json.loads(result.stdout) - return output - - finally: - temp_code_file.unlink() - - def _validate_output_schema(self, output: Dict[str, Any]) -> Dict[str, Any]: - """Validate output matches expected extractor schema.""" - # All extractors must return dict with numeric values - if not isinstance(output, dict): - return { - 'valid': False, - 'error': 'Output must be a dictionary' - } - - # Check for at least one result value - if not any(key for key in output if not key.startswith('_')): - return { - 'valid': False, - 'error': 'No result values found in output' - } - - # All values must be numeric - for key, value in output.items(): - if not key.startswith('_'): # Skip metadata - if not isinstance(value, (int, float)): - return { - 'valid': False, - 'error': f'Non-numeric value for {key}: {type(value)}' - } - - return {'valid': True} -``` - ---- - -## Success Metrics - -### Week 1 Success -- [ ] LLM mode accessible via `--llm` flag -- [ ] Natural language request → Workflow generation works -- [ ] End-to-end test passes (simple_beam_optimization) -- [ ] Example demonstrates value (100 lines → 3 lines) - -### Week 2 Success -- [ ] Generated code validated before execution -- [ ] All failure scenarios degrade gracefully (no crashes) -- [ ] Complete LLM audit trail in `llm_audit.json` -- [ ] Test suite covers failure modes - -### Week 3 Success -- [ ] Successful workflows saved to knowledge base -- [ ] Second identical request reuses template (faster) -- [ ] Unknown features trigger ResearchAgent learning loop -- [ ] Knowledge base grows over time - -### Week 4 Success -- [ ] README shows LLM mode prominently -- [ ] docs/LLM_MODE.md complete and clear -- [ ] Demo video/GIF shows value proposition -- [ ] All planning docs updated - ---- - -## Risk Mitigation - -### Risk: LLM generates unsafe code -**Mitigation**: Multi-stage validation pipeline (syntax, security, test, schema) - -### Risk: LLM unavailable (API down) -**Mitigation**: Graceful fallback to manual mode with clear error message - -### Risk: Generated code fails at runtime -**Mitigation**: Sandboxed test execution before saving, retry with LLM feedback - -### Risk: Users don't discover LLM mode -**Mitigation**: Prominent README section, demo video, clear examples - -### Risk: Learning system fills disk with templates -**Mitigation**: Confidence-based pruning, max template limit, user confirmation for saves - ---- - -## Next Steps After Phase 3.2 - -Once integration is complete: - -1. **Validate with Real Studies** - - Run simple_beam_optimization in LLM mode - - Create new study using only natural language - - Compare results manual vs LLM mode - -2. **Fix atomizer Conda Environment** - - Rebuild clean environment - - Test visualization in atomizer env - -3. **NXOpen Documentation Integration** (Phase 2, remaining tasks) - - Research Siemens docs portal access - - Integrate NXOpen stub files for intellisense - - Enable LLM to reference NXOpen API - -4. **Phase 4: Dynamic Code Generation** (Roadmap) - - Journal script generator - - Custom function templates - - Safe execution sandbox - ---- - -**Last Updated**: 2025-11-17 -**Owner**: Antoine Polvé -**Status**: Ready to begin Week 1 implementation diff --git a/docs/PHASE_3_2_INTEGRATION_STATUS.md b/docs/PHASE_3_2_INTEGRATION_STATUS.md deleted file mode 100644 index 64278416..00000000 --- a/docs/PHASE_3_2_INTEGRATION_STATUS.md +++ /dev/null @@ -1,346 +0,0 @@ -# Phase 3.2 Integration Status - -> **Date**: 2025-11-17 -> **Status**: Partially Complete - Framework Ready, API Integration Pending - ---- - -## Overview - -Phase 3.2 aims to integrate the LLM components (Phases 2.5-3.1) into the production optimization workflow, enabling users to run optimizations using natural language requests. - -**Goal**: Enable users to run: -```bash -python run_optimization.py --llm "maximize displacement, ensure safety factor > 4" -``` - ---- - -## What's Been Completed ✅ - -### 1. Generic Optimization Runner (`optimization_engine/run_optimization.py`) - -**Created**: 2025-11-17 - -A flexible, command-line driven optimization runner supporting both LLM and manual modes: - -```bash -# LLM Mode (Natural Language) -python optimization_engine/run_optimization.py \ - --llm "maximize displacement, ensure safety factor > 4" \ - --prt model/Bracket.prt \ - --sim model/Bracket_sim1.sim \ - --trials 20 - -# Manual Mode (JSON Config) -python optimization_engine/run_optimization.py \ - --config config.json \ - --prt model/Bracket.prt \ - --sim model/Bracket_sim1.sim \ - --trials 50 -``` - -**Features**: -- ✅ Command-line argument parsing (`--llm`, `--config`, `--prt`, `--sim`, etc.) -- ✅ Integration with `LLMWorkflowAnalyzer` for natural language parsing -- ✅ Integration with `LLMOptimizationRunner` for automated extractor/hook generation -- ✅ Proper error handling and user feedback -- ✅ Comprehensive help message with examples -- ✅ Flexible output directory and study naming - -**Files**: -- [optimization_engine/run_optimization.py](../optimization_engine/run_optimization.py) - Generic runner -- [tests/test_phase_3_2_llm_mode.py](../tests/test_phase_3_2_llm_mode.py) - Integration tests - -### 2. Test Suite - -**Test Results**: ✅ All tests passing - -Tests verify: -- Argument parsing works correctly -- Help message displays `--llm` flag -- Framework is ready for LLM integration - ---- - -## Current Limitation ⚠️ - -### LLM Workflow Analysis Requires API Key - -The `LLMWorkflowAnalyzer` currently requires an Anthropic API key to actually parse natural language requests. The `use_claude_code` flag exists but **doesn't implement actual integration** with Claude Code's AI capabilities. - -**Current Behavior**: -- `--llm` mode is implemented in the CLI -- But `LLMWorkflowAnalyzer.analyze_request()` returns empty workflow when `use_claude_code=True` and no API key provided -- Actual LLM analysis requires `--api-key` argument - -**Workaround Options**: - -#### Option 1: Use Anthropic API Key -```bash -python run_optimization.py \ - --llm "maximize displacement" \ - --prt model/part.prt \ - --sim model/sim.sim \ - --api-key "sk-ant-..." -``` - -#### Option 2: Pre-Generate Workflow JSON (Hybrid Approach) -1. Use Claude Code to help create workflow JSON manually -2. Save as `llm_workflow.json` -3. Load and use with `LLMOptimizationRunner` - -Example: -```python -# In your study's run_optimization.py -from optimization_engine.llm_optimization_runner import LLMOptimizationRunner -import json - -# Load pre-generated workflow (created with Claude Code assistance) -with open('llm_workflow.json', 'r') as f: - llm_workflow = json.load(f) - -# Run optimization with LLM runner -runner = LLMOptimizationRunner( - llm_workflow=llm_workflow, - model_updater=model_updater, - simulation_runner=simulation_runner, - study_name='my_study' -) - -results = runner.run_optimization(n_trials=20) -``` - -#### Option 3: Use Existing Study Scripts -The bracket study's `run_optimization.py` already demonstrates the complete workflow with hardcoded configuration - this works perfectly! - ---- - -## Architecture - -### LLM Mode Flow (When API Key Provided) - -``` -User Natural Language Request - ↓ -LLMWorkflowAnalyzer (Phase 2.7) - ├─> Claude API call - └─> Parse to structured workflow JSON - ↓ -LLMOptimizationRunner (Phase 3.2) - ├─> ExtractorOrchestrator (Phase 3.1) → Auto-generate extractors - ├─> InlineCodeGenerator (Phase 2.8) → Auto-generate calculations - ├─> HookGenerator (Phase 2.9) → Auto-generate hooks - └─> Run Optuna optimization with generated code - ↓ -Results -``` - -### Manual Mode Flow (Current Working Approach) - -``` -Hardcoded Workflow JSON (or manually created) - ↓ -LLMOptimizationRunner (Phase 3.2) - ├─> ExtractorOrchestrator → Auto-generate extractors - ├─> InlineCodeGenerator → Auto-generate calculations - ├─> HookGenerator → Auto-generate hooks - └─> Run Optuna optimization - ↓ -Results -``` - ---- - -## What Works Right Now - -### ✅ **LLM Components are Functional** - -All individual components work and are tested: - -1. **Phase 2.5**: Intelligent Gap Detection ✅ -2. **Phase 2.7**: LLM Workflow Analysis (requires API key) ✅ -3. **Phase 2.8**: Inline Code Generator ✅ -4. **Phase 2.9**: Hook Generator ✅ -5. **Phase 3.0**: pyNastran Research Agent ✅ -6. **Phase 3.1**: Extractor Orchestrator ✅ -7. **Phase 3.2**: LLM Optimization Runner ✅ - -### ✅ **Generic CLI Runner** - -The new `run_optimization.py` provides: -- Clean command-line interface -- Argument validation -- Error handling -- Comprehensive help - -### ✅ **Bracket Study Demonstrates End-to-End Workflow** - -[studies/bracket_displacement_maximizing/run_optimization.py](../studies/bracket_displacement_maximizing/run_optimization.py) shows the complete integration: -- Wizard-based setup (Phase 3.3) -- LLMOptimizationRunner with hardcoded workflow -- Auto-generated extractors and hooks -- Real NX simulations -- Complete results with reports - ---- - -## Next Steps to Complete Phase 3.2 - -### Short Term (Can Do Now) - -1. **Document Hybrid Approach** ✅ (This document!) - - Show how to use Claude Code to create workflow JSON - - Example workflow JSON templates for common use cases - -2. **Create Example Workflow JSONs** - - `examples/llm_workflows/maximize_displacement.json` - - `examples/llm_workflows/minimize_stress.json` - - `examples/llm_workflows/multi_objective.json` - -3. **Update DEVELOPMENT_GUIDANCE.md** - - Mark Phase 3.2 as "Partially Complete" - - Document the API key requirement - - Provide hybrid approach guidance - -### Medium Term (Requires Decision) - -**Option A: Implement True Claude Code Integration** -- Modify `LLMWorkflowAnalyzer` to actually interface with Claude Code -- Would require understanding Claude Code's internal API/skill system -- Most aligned with "Development Strategy" (use Claude Code, defer API integration) - -**Option B: Defer Until API Integration is Priority** -- Document current state as "Framework Ready" -- Focus on other high-priority items (NXOpen docs, Engineering pipeline) -- Return to full LLM integration when ready to integrate Anthropic API - -**Option C: Hybrid Approach (Recommended for Now)** -- Keep generic CLI runner as-is -- Document how to use Claude Code to manually create workflow JSONs -- Use `LLMOptimizationRunner` with pre-generated workflows -- Provides 90% of the value with 10% of the complexity - ---- - -## Recommendation - -**For now, adopt Option C (Hybrid Approach)**: - -### Why: -1. **Development Strategy Alignment**: We're using Claude Code for development, not integrating API yet -2. **Provides Value**: All automation components (extractors, hooks, calculations) work perfectly -3. **No Blocker**: Users can still leverage LLM components via pre-generated workflows -4. **Flexible**: Can add full API integration later without changing architecture -5. **Focus**: Allows us to prioritize Phase 3.3+ items (NXOpen docs, Engineering pipeline) - -### What This Means: -- ✅ Phase 3.2 is "Framework Complete" -- ⚠️ Full natural language CLI requires API key (documented limitation) -- ✅ Hybrid approach (Claude Code → JSON → LLMOptimizationRunner) works today -- 🎯 Can return to full integration when API integration becomes priority - ---- - -## Example: Using Hybrid Approach - -### Step 1: Create Workflow JSON (with Claude Code assistance) - -```json -{ - "engineering_features": [ - { - "action": "extract_displacement", - "domain": "result_extraction", - "description": "Extract displacement results from OP2 file", - "params": {"result_type": "displacement"} - }, - { - "action": "extract_solid_stress", - "domain": "result_extraction", - "description": "Extract von Mises stress from CTETRA elements", - "params": { - "result_type": "stress", - "element_type": "ctetra" - } - } - ], - "inline_calculations": [ - { - "action": "calculate_safety_factor", - "params": { - "input": "max_von_mises", - "yield_strength": 276.0, - "operation": "divide" - }, - "code_hint": "safety_factor = 276.0 / max_von_mises" - } - ], - "post_processing_hooks": [], - "optimization": { - "algorithm": "TPE", - "direction": "minimize", - "design_variables": [ - { - "parameter": "thickness", - "min": 3.0, - "max": 10.0, - "units": "mm" - } - ] - } -} -``` - -### Step 2: Use in Python Script - -```python -import json -from pathlib import Path -from optimization_engine.llm_optimization_runner import LLMOptimizationRunner -from optimization_engine.nx_updater import NXParameterUpdater -from optimization_engine.nx_solver import NXSolver - -# Load pre-generated workflow -with open('llm_workflow.json', 'r') as f: - workflow = json.load(f) - -# Setup model updater -updater = NXParameterUpdater(prt_file_path=Path("model/part.prt")) -def model_updater(design_vars): - updater.update_expressions(design_vars) - updater.save() - -# Setup simulation runner -solver = NXSolver(nastran_version='2412', use_journal=True) -def simulation_runner(design_vars) -> Path: - result = solver.run_simulation(Path("model/sim.sim"), expression_updates=design_vars) - return result['op2_file'] - -# Run optimization -runner = LLMOptimizationRunner( - llm_workflow=workflow, - model_updater=model_updater, - simulation_runner=simulation_runner, - study_name='my_optimization' -) - -results = runner.run_optimization(n_trials=20) -print(f"Best design: {results['best_params']}") -``` - ---- - -## References - -- [DEVELOPMENT_GUIDANCE.md](../DEVELOPMENT_GUIDANCE.md) - Strategic direction -- [optimization_engine/run_optimization.py](../optimization_engine/run_optimization.py) - Generic CLI runner -- [optimization_engine/llm_optimization_runner.py](../optimization_engine/llm_optimization_runner.py) - LLM runner -- [optimization_engine/llm_workflow_analyzer.py](../optimization_engine/llm_workflow_analyzer.py) - Workflow analyzer -- [studies/bracket_displacement_maximizing/run_optimization.py](../studies/bracket_displacement_maximizing/run_optimization.py) - Complete example - ---- - -**Document Maintained By**: Antoine Letarte -**Last Updated**: 2025-11-17 -**Status**: Framework Complete, API Integration Pending diff --git a/docs/PHASE_3_2_NEXT_STEPS.md b/docs/PHASE_3_2_NEXT_STEPS.md deleted file mode 100644 index 3571401c..00000000 --- a/docs/PHASE_3_2_NEXT_STEPS.md +++ /dev/null @@ -1,617 +0,0 @@ -# Phase 3.2 Integration - Next Steps - -**Status**: Week 1 Complete (Task 1.2 Verified) -**Date**: 2025-11-17 -**Author**: Antoine Letarte - -## Week 1 Summary - COMPLETE ✅ - -### Task 1.2: Wire LLMOptimizationRunner to Production ✅ - -**Deliverables Completed**: -- ✅ Interface contracts verified (`model_updater`, `simulation_runner`) -- ✅ LLM workflow validation in `run_optimization.py` -- ✅ Error handling for initialization failures -- ✅ Comprehensive integration test suite (5/5 tests passing) -- ✅ Example walkthrough (`examples/llm_mode_simple_example.py`) -- ✅ Documentation updated (README, DEVELOPMENT, DEVELOPMENT_GUIDANCE) - -**Commit**: `7767fc6` - feat: Phase 3.2 Task 1.2 - Wire LLMOptimizationRunner to production - -**Key Achievement**: Natural language optimization is now wired to production infrastructure. Users can describe optimization problems in plain English, and the system will auto-generate extractors, hooks, and run optimization. - ---- - -## Immediate Next Steps (Week 1 Completion) - -### Task 1.3: Create Minimal Working Example ✅ (Already Done) - -**Status**: COMPLETE - Created in Task 1.2 commit - -**Deliverable**: `examples/llm_mode_simple_example.py` - -**What it demonstrates**: -```python -request = """ -Minimize displacement and mass while keeping stress below 200 MPa. - -Design variables: -- beam_half_core_thickness: 15 to 30 mm -- beam_face_thickness: 15 to 30 mm - -Run 5 trials using TPE sampler. -""" -``` - -**Usage**: -```bash -python examples/llm_mode_simple_example.py -``` - ---- - -### Task 1.4: End-to-End Integration Test ✅ COMPLETE - -**Priority**: HIGH ✅ DONE -**Effort**: 2 hours (completed) -**Objective**: Verify complete LLM mode workflow works with real FEM solver ✅ - -**Deliverable**: `tests/test_phase_3_2_e2e.py` ✅ - -**Test Coverage** (All Implemented): -1. ✅ Natural language request parsing -2. ✅ LLM workflow generation (with API key or Claude Code) -3. ✅ Extractor auto-generation -4. ✅ Hook auto-generation -5. ✅ Model update (NX expressions) -6. ✅ Simulation run (actual FEM solve) -7. ✅ Result extraction -8. ✅ Optimization loop (3 trials minimum) -9. ✅ Results saved to output directory -10. ✅ Graceful failure without API key - -**Acceptance Criteria**: ALL MET ✅ -- [x] Test runs without errors -- [x] 3 trials complete successfully (verified with API key mode) -- [x] Best design found and saved -- [x] Generated extractors work correctly -- [x] Generated hooks execute without errors -- [x] Optimization history written to JSON -- [x] Graceful skip when no API key (provides clear instructions) - -**Implementation Plan**: -```python -def test_e2e_llm_mode(): - """End-to-end test of LLM mode with real FEM solver.""" - - # 1. Natural language request - request = """ - Minimize mass while keeping displacement below 5mm. - Design variables: beam_half_core_thickness (20-30mm), - beam_face_thickness (18-25mm) - Run 3 trials with TPE sampler. - """ - - # 2. Setup test environment - study_dir = Path("studies/simple_beam_optimization") - prt_file = study_dir / "1_setup/model/Beam.prt" - sim_file = study_dir / "1_setup/model/Beam_sim1.sim" - output_dir = study_dir / "2_substudies/test_e2e_3trials" - - # 3. Run via subprocess (simulates real usage) - cmd = [ - "c:/Users/antoi/anaconda3/envs/test_env/python.exe", - "optimization_engine/run_optimization.py", - "--llm", request, - "--prt", str(prt_file), - "--sim", str(sim_file), - "--output", str(output_dir.parent), - "--study-name", "test_e2e_3trials", - "--trials", "3" - ] - - result = subprocess.run(cmd, capture_output=True, text=True) - - # 4. Verify outputs - assert result.returncode == 0 - assert (output_dir / "history.json").exists() - assert (output_dir / "best_trial.json").exists() - assert (output_dir / "generated_extractors").exists() - - # 5. Verify results are valid - with open(output_dir / "history.json") as f: - history = json.load(f) - - assert len(history) == 3 # 3 trials completed - assert all("objective" in trial for trial in history) - assert all("design_variables" in trial for trial in history) -``` - -**Known Issue to Address**: -- LLMWorkflowAnalyzer Claude Code integration returns empty workflow -- **Options**: - 1. Use Anthropic API key for testing (preferred for now) - 2. Implement Claude Code integration in Phase 2.7 first - 3. Mock the LLM response for testing purposes - -**Recommendation**: Use API key for E2E test, document Claude Code gap separately - ---- - -## Week 2: Robustness & Safety (16 hours) 🎯 - -**Objective**: Make LLM mode production-ready with validation, fallbacks, and safety - -### Task 2.1: Code Validation System (6 hours) - -**Deliverable**: `optimization_engine/code_validator.py` - -**Features**: -1. **Syntax Validation**: - - Run `ast.parse()` on generated Python code - - Catch syntax errors before execution - - Return detailed error messages with line numbers - -2. **Security Validation**: - - Check for dangerous imports (`os.system`, `subprocess`, `eval`, etc.) - - Whitelist-based approach (only allow: numpy, pandas, pathlib, json, etc.) - - Reject code with file system modifications outside working directory - -3. **Schema Validation**: - - Verify extractor returns `Dict[str, float]` - - Verify hook has correct signature - - Validate optimization config structure - -**Example**: -```python -class CodeValidator: - """Validates generated code before execution.""" - - DANGEROUS_IMPORTS = [ - 'os.system', 'subprocess', 'eval', 'exec', - 'compile', '__import__', 'open' # open needs special handling - ] - - ALLOWED_IMPORTS = [ - 'numpy', 'pandas', 'pathlib', 'json', 'math', - 'pyNastran', 'NXOpen', 'typing' - ] - - def validate_syntax(self, code: str) -> ValidationResult: - """Check if code has valid Python syntax.""" - try: - ast.parse(code) - return ValidationResult(valid=True) - except SyntaxError as e: - return ValidationResult( - valid=False, - error=f"Syntax error at line {e.lineno}: {e.msg}" - ) - - def validate_security(self, code: str) -> ValidationResult: - """Check for dangerous operations.""" - tree = ast.parse(code) - - for node in ast.walk(tree): - # Check imports - if isinstance(node, ast.Import): - for alias in node.names: - if alias.name not in self.ALLOWED_IMPORTS: - return ValidationResult( - valid=False, - error=f"Disallowed import: {alias.name}" - ) - - # Check function calls - if isinstance(node, ast.Call): - if hasattr(node.func, 'id'): - if node.func.id in self.DANGEROUS_IMPORTS: - return ValidationResult( - valid=False, - error=f"Dangerous function call: {node.func.id}" - ) - - return ValidationResult(valid=True) - - def validate_extractor_schema(self, code: str) -> ValidationResult: - """Verify extractor returns Dict[str, float].""" - # Check for return type annotation - tree = ast.parse(code) - - for node in ast.walk(tree): - if isinstance(node, ast.FunctionDef): - if node.name.startswith('extract_'): - # Verify has return annotation - if node.returns is None: - return ValidationResult( - valid=False, - error=f"Extractor {node.name} missing return type annotation" - ) - - return ValidationResult(valid=True) -``` - ---- - -### Task 2.2: Fallback Mechanisms (4 hours) - -**Deliverable**: Enhanced error handling in `run_optimization.py` and `llm_optimization_runner.py` - -**Scenarios to Handle**: - -1. **LLM Analysis Fails**: - ```python - try: - llm_workflow = analyzer.analyze_request(request) - except Exception as e: - logger.error(f"LLM analysis failed: {e}") - logger.info("Falling back to manual mode...") - logger.info("Please provide a JSON config file or try:") - logger.info(" - Simplifying your request") - logger.info(" - Checking API key is valid") - logger.info(" - Using Claude Code mode (no API key)") - sys.exit(1) - ``` - -2. **Extractor Generation Fails**: - ```python - try: - extractors = extractor_orchestrator.generate_all() - except Exception as e: - logger.error(f"Extractor generation failed: {e}") - logger.info("Attempting to use fallback extractors...") - - # Use pre-built generic extractors - extractors = { - 'displacement': GenericDisplacementExtractor(), - 'stress': GenericStressExtractor(), - 'mass': GenericMassExtractor() - } - logger.info("Using generic extractors - results may be less specific") - ``` - -3. **Hook Generation Fails**: - ```python - try: - hook_manager.generate_hooks(llm_workflow['post_processing_hooks']) - except Exception as e: - logger.warning(f"Hook generation failed: {e}") - logger.info("Continuing without custom hooks...") - # Optimization continues without hooks (reduced functionality but not fatal) - ``` - -4. **Single Trial Failure**: - ```python - def _objective(self, trial): - try: - # ... run trial - return objective_value - except Exception as e: - logger.error(f"Trial {trial.number} failed: {e}") - # Return worst-case value instead of crashing - return float('inf') if self.direction == 'minimize' else float('-inf') - ``` - ---- - -### Task 2.3: Comprehensive Test Suite (4 hours) - -**Deliverable**: Extended test coverage in `tests/` - -**New Tests**: - -1. **tests/test_code_validator.py**: - - Test syntax validation catches errors - - Test security validation blocks dangerous code - - Test schema validation enforces correct signatures - - Test allowed imports pass validation - -2. **tests/test_fallback_mechanisms.py**: - - Test LLM failure falls back gracefully - - Test extractor generation failure uses generic extractors - - Test hook generation failure continues optimization - - Test single trial failure doesn't crash optimization - -3. **tests/test_llm_mode_error_cases.py**: - - Test empty natural language request - - Test request with missing design variables - - Test request with conflicting objectives - - Test request with invalid parameter ranges - -4. **tests/test_integration_robustness.py**: - - Test optimization with intermittent FEM failures - - Test optimization with corrupted OP2 files - - Test optimization with missing NX expressions - - Test optimization with invalid design variable values - ---- - -### Task 2.4: Audit Trail System (2 hours) - -**Deliverable**: `optimization_engine/audit_trail.py` - -**Features**: -- Log all LLM-generated code to timestamped files -- Save validation results -- Track which extractors/hooks were used -- Record any fallbacks or errors - -**Example**: -```python -class AuditTrail: - """Records all LLM-generated code and validation results.""" - - def __init__(self, output_dir: Path): - self.output_dir = output_dir / "audit_trail" - self.output_dir.mkdir(exist_ok=True) - - self.log_file = self.output_dir / f"audit_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - self.entries = [] - - def log_generated_code(self, code_type: str, code: str, validation_result: ValidationResult): - """Log generated code and validation result.""" - entry = { - "timestamp": datetime.now().isoformat(), - "type": code_type, - "code": code, - "validation": { - "valid": validation_result.valid, - "error": validation_result.error - } - } - self.entries.append(entry) - - # Save to file immediately - with open(self.log_file, 'w') as f: - json.dump(self.entries, f, indent=2) - - def log_fallback(self, component: str, reason: str, fallback_action: str): - """Log when a fallback mechanism is used.""" - entry = { - "timestamp": datetime.now().isoformat(), - "type": "fallback", - "component": component, - "reason": reason, - "fallback_action": fallback_action - } - self.entries.append(entry) - - with open(self.log_file, 'w') as f: - json.dump(self.entries, f, indent=2) -``` - -**Integration**: -```python -# In LLMOptimizationRunner.__init__ -self.audit_trail = AuditTrail(output_dir) - -# When generating extractors -for feature in engineering_features: - code = generator.generate_extractor(feature) - validation = validator.validate(code) - self.audit_trail.log_generated_code("extractor", code, validation) - - if not validation.valid: - self.audit_trail.log_fallback( - component="extractor", - reason=validation.error, - fallback_action="using generic extractor" - ) -``` - ---- - -## Week 3: Learning System (20 hours) - -**Objective**: Build intelligence that learns from successful generations - -### Task 3.1: Template Library (8 hours) - -**Deliverable**: `optimization_engine/template_library/` - -**Structure**: -``` -template_library/ -├── extractors/ -│ ├── displacement_templates.py -│ ├── stress_templates.py -│ ├── mass_templates.py -│ └── thermal_templates.py -├── calculations/ -│ ├── safety_factor_templates.py -│ ├── objective_templates.py -│ └── constraint_templates.py -├── hooks/ -│ ├── plotting_templates.py -│ ├── logging_templates.py -│ └── reporting_templates.py -└── registry.py -``` - -**Features**: -- Pre-validated code templates for common operations -- Success rate tracking for each template -- Automatic template selection based on context -- Template versioning and deprecation - ---- - -### Task 3.2: Knowledge Base Integration (8 hours) - -**Deliverable**: Enhanced ResearchAgent with optimization-specific knowledge - -**Knowledge Sources**: -1. pyNastran documentation (already integrated in Phase 3) -2. NXOpen API documentation (NXOpen intellisense - already set up) -3. Optimization best practices -4. Common FEA pitfalls and solutions - -**Features**: -- Query knowledge base during code generation -- Suggest best practices for extractor design -- Warn about common mistakes (unit mismatches, etc.) - ---- - -### Task 3.3: Success Metrics & Learning (4 hours) - -**Deliverable**: `optimization_engine/learning_system.py` - -**Features**: -- Track which LLM-generated code succeeds vs fails -- Store successful patterns to knowledge base -- Suggest improvements based on past failures -- Auto-tune LLM prompts based on success rate - ---- - -## Week 4: Documentation & Polish (12 hours) - -### Task 4.1: User Guide (4 hours) - -**Deliverable**: `docs/LLM_MODE_USER_GUIDE.md` - -**Contents**: -- Getting started with LLM mode -- Natural language request formatting tips -- Common patterns and examples -- Troubleshooting guide -- FAQ - ---- - -### Task 4.2: Architecture Documentation (4 hours) - -**Deliverable**: `docs/ARCHITECTURE.md` - -**Contents**: -- System architecture diagram -- Component interaction flows -- LLM integration points -- Extractor/hook generation pipeline -- Data flow diagrams - ---- - -### Task 4.3: Demo Video & Presentation (4 hours) - -**Deliverable**: -- `docs/demo_video.mp4` -- `docs/PHASE_3_2_PRESENTATION.pdf` - -**Contents**: -- 5-minute demo video showing LLM mode in action -- Presentation slides explaining the integration -- Before/after comparison (manual JSON vs LLM mode) - ---- - -## Success Criteria for Phase 3.2 - -At the end of 4 weeks, we should have: - -- [x] Week 1: LLM mode wired to production (Task 1.2 COMPLETE) -- [ ] Week 1: End-to-end test passing (Task 1.4) -- [ ] Week 2: Code validation preventing unsafe executions -- [ ] Week 2: Fallback mechanisms for all failure modes -- [ ] Week 2: Test coverage > 80% -- [ ] Week 2: Audit trail for all generated code -- [ ] Week 3: Template library with 20+ validated templates -- [ ] Week 3: Knowledge base integration working -- [ ] Week 3: Learning system tracking success metrics -- [ ] Week 4: Complete user documentation -- [ ] Week 4: Architecture documentation -- [ ] Week 4: Demo video completed - ---- - -## Priority Order - -**Immediate (This Week)**: -1. Task 1.4: End-to-end integration test (2-4 hours) -2. Address LLMWorkflowAnalyzer Claude Code gap (or use API key) - -**Week 2 Priorities**: -1. Code validation system (CRITICAL for safety) -2. Fallback mechanisms (CRITICAL for robustness) -3. Comprehensive test suite -4. Audit trail system - -**Week 3 Priorities**: -1. Template library (HIGH value - improves reliability) -2. Knowledge base integration -3. Learning system - -**Week 4 Priorities**: -1. User guide (CRITICAL for adoption) -2. Architecture documentation -3. Demo video - ---- - -## Known Gaps & Risks - -### Gap 1: LLMWorkflowAnalyzer Claude Code Integration -**Status**: Empty workflow returned when `use_claude_code=True` -**Impact**: HIGH - LLM mode doesn't work without API key -**Options**: -1. Implement Claude Code integration in Phase 2.7 -2. Use API key for now (temporary solution) -3. Mock LLM responses for testing - -**Recommendation**: Use API key for testing, implement Claude Code integration as Phase 2.7 task - ---- - -### Gap 2: Manual Mode Not Yet Integrated -**Status**: `--config` flag not fully implemented -**Impact**: MEDIUM - Users must use study-specific scripts -**Timeline**: Week 2-3 (lower priority than robustness) - ---- - -### Risk 1: LLM-Generated Code Failures -**Mitigation**: Code validation system (Week 2, Task 2.1) -**Severity**: HIGH if not addressed -**Status**: Planned for Week 2 - ---- - -### Risk 2: FEM Solver Failures -**Mitigation**: Fallback mechanisms (Week 2, Task 2.2) -**Severity**: MEDIUM -**Status**: Planned for Week 2 - ---- - -## Recommendations - -1. **Complete Task 1.4 this week**: Verify E2E workflow works before moving to Week 2 - -2. **Use API key for testing**: Don't block on Claude Code integration - it's a Phase 2.7 component issue - -3. **Prioritize safety over features**: Week 2 validation is CRITICAL before any production use - -4. **Build template library early**: Week 3 templates will significantly improve reliability - -5. **Document as you go**: Don't leave all documentation to Week 4 - ---- - -## Conclusion - -**Phase 3.2 Week 1 Status**: ✅ COMPLETE - -**Task 1.2 Achievement**: Natural language optimization is now wired to production infrastructure with comprehensive testing and validation. - -**Next Immediate Step**: Complete Task 1.4 (E2E integration test) to verify the complete workflow before moving to Week 2 robustness work. - -**Overall Progress**: 25% of Phase 3.2 complete (1 week / 4 weeks) - -**Timeline on Track**: YES - Week 1 completed on schedule - ---- - -**Author**: Claude Code -**Last Updated**: 2025-11-17 -**Next Review**: After Task 1.4 completion diff --git a/docs/PHASE_3_3_VISUALIZATION_AND_CLEANUP.md b/docs/PHASE_3_3_VISUALIZATION_AND_CLEANUP.md deleted file mode 100644 index b940dead..00000000 --- a/docs/PHASE_3_3_VISUALIZATION_AND_CLEANUP.md +++ /dev/null @@ -1,419 +0,0 @@ -# Phase 3.3: Visualization & Model Cleanup System - -**Status**: ✅ Complete -**Date**: 2025-11-17 - -## Overview - -Phase 3.3 adds automated post-processing capabilities to Atomizer, including publication-quality visualization and intelligent model cleanup to manage disk space. - ---- - -## Features Implemented - -### 1. Automated Visualization System - -**File**: `optimization_engine/visualizer.py` - -**Capabilities**: -- **Convergence Plots**: Objective value vs trial number with running best -- **Design Space Exploration**: Parameter evolution colored by performance -- **Parallel Coordinate Plots**: High-dimensional visualization -- **Sensitivity Heatmaps**: Parameter correlation analysis -- **Constraint Violations**: Track constraint satisfaction over trials -- **Multi-Objective Breakdown**: Individual objective contributions - -**Output Formats**: -- PNG (high-resolution, 300 DPI) -- PDF (vector graphics, publication-ready) -- Customizable via configuration - -**Example Usage**: -```bash -# Standalone visualization -python optimization_engine/visualizer.py studies/beam/substudies/opt1 png pdf - -# Automatic during optimization (configured in JSON) -``` - -### 2. Model Cleanup System - -**File**: `optimization_engine/model_cleanup.py` - -**Purpose**: Reduce disk usage by deleting large CAD/FEM files from non-optimal trials - -**Strategy**: -- Keep top-N best trials (configurable) -- Delete large files: `.prt`, `.sim`, `.fem`, `.op2`, `.f06` -- Preserve ALL `results.json` (small, critical data) -- Dry-run mode for safety - -**Example Usage**: -```bash -# Standalone cleanup -python optimization_engine/model_cleanup.py studies/beam/substudies/opt1 --keep-top-n 10 - -# Dry run (preview without deleting) -python optimization_engine/model_cleanup.py studies/beam/substudies/opt1 --dry-run - -# Automatic during optimization (configured in JSON) -``` - -### 3. Optuna Dashboard Integration - -**File**: `docs/OPTUNA_DASHBOARD.md` - -**Capabilities**: -- Real-time monitoring during optimization -- Interactive parallel coordinate plots -- Parameter importance analysis (fANOVA) -- Multi-study comparison - -**Usage**: -```bash -# Launch dashboard for a study -cd studies/beam/substudies/opt1 -optuna-dashboard sqlite:///optuna_study.db - -# Access at http://localhost:8080 -``` - ---- - -## Configuration - -### JSON Configuration Format - -Add `post_processing` section to optimization config: - -```json -{ - "study_name": "my_optimization", - "design_variables": { ... }, - "objectives": [ ... ], - "optimization_settings": { - "n_trials": 50, - ... - }, - "post_processing": { - "generate_plots": true, - "plot_formats": ["png", "pdf"], - "cleanup_models": true, - "keep_top_n_models": 10, - "cleanup_dry_run": false - } -} -``` - -### Configuration Options - -#### Visualization Settings - -| Parameter | Type | Default | Description | -|-----------|------|---------|-------------| -| `generate_plots` | boolean | `false` | Enable automatic plot generation | -| `plot_formats` | list | `["png", "pdf"]` | Output formats for plots | - -#### Cleanup Settings - -| Parameter | Type | Default | Description | -|-----------|------|---------|-------------| -| `cleanup_models` | boolean | `false` | Enable model cleanup | -| `keep_top_n_models` | integer | `10` | Number of best trials to keep models for | -| `cleanup_dry_run` | boolean | `false` | Preview cleanup without deleting | - ---- - -## Workflow Integration - -### Automatic Post-Processing - -When configured, post-processing runs automatically after optimization completes: - -``` -OPTIMIZATION COMPLETE -=========================================================== -... - -POST-PROCESSING -=========================================================== - -Generating visualization plots... - - Generating convergence plot... - - Generating design space exploration... - - Generating parallel coordinate plot... - - Generating sensitivity heatmap... - Plots generated: 2 format(s) - Improvement: 23.1% - Location: studies/beam/substudies/opt1/plots - -Cleaning up trial models... - Deleted 320 files from 40 trials - Space freed: 1542.3 MB - Kept top 10 trial models -=========================================================== -``` - -### Directory Structure After Post-Processing - -``` -studies/my_optimization/ -├── substudies/ -│ └── opt1/ -│ ├── trial_000/ # Top performer - KEPT -│ │ ├── Beam.prt # CAD files kept -│ │ ├── Beam_sim1.sim -│ │ └── results.json -│ ├── trial_001/ # Poor performer - CLEANED -│ │ └── results.json # Only results kept -│ ├── ... -│ ├── plots/ # NEW: Auto-generated -│ │ ├── convergence.png -│ │ ├── convergence.pdf -│ │ ├── design_space_evolution.png -│ │ ├── design_space_evolution.pdf -│ │ ├── parallel_coordinates.png -│ │ ├── parallel_coordinates.pdf -│ │ └── plot_summary.json -│ ├── history.json -│ ├── best_trial.json -│ ├── cleanup_log.json # NEW: Cleanup statistics -│ └── optuna_study.pkl -``` - ---- - -## Plot Types - -### 1. Convergence Plot - -**File**: `convergence.png/pdf` - -**Shows**: -- Individual trial objectives (scatter) -- Running best (line) -- Best trial highlighted (gold star) -- Improvement percentage annotation - -**Use Case**: Assess optimization convergence and identify best trial - -### 2. Design Space Exploration - -**File**: `design_space_evolution.png/pdf` - -**Shows**: -- Each design variable evolution over trials -- Color-coded by objective value (darker = better) -- Best trial highlighted -- Units displayed on y-axis - -**Use Case**: Understand how parameters changed during optimization - -### 3. Parallel Coordinate Plot - -**File**: `parallel_coordinates.png/pdf` - -**Shows**: -- High-dimensional view of design space -- Each line = one trial -- Color-coded by objective -- Best trial highlighted - -**Use Case**: Visualize relationships between multiple design variables - -### 4. Sensitivity Heatmap - -**File**: `sensitivity_heatmap.png/pdf` - -**Shows**: -- Correlation matrix: design variables vs objectives -- Values: -1 (negative correlation) to +1 (positive) -- Color-coded: red (negative), blue (positive) - -**Use Case**: Identify which parameters most influence objectives - -### 5. Constraint Violations - -**File**: `constraint_violations.png/pdf` (if constraints exist) - -**Shows**: -- Constraint values over trials -- Feasibility threshold (red line at y=0) -- Trend of constraint satisfaction - -**Use Case**: Verify constraint satisfaction throughout optimization - -### 6. Objective Breakdown - -**File**: `objective_breakdown.png/pdf` (if multi-objective) - -**Shows**: -- Stacked area plot of individual objectives -- Total objective overlay -- Contribution of each objective over trials - -**Use Case**: Understand multi-objective trade-offs - ---- - -## Benefits - -### Visualization - -✅ **Publication-Ready**: High-DPI PNG and vector PDF exports -✅ **Automated**: No manual post-processing required -✅ **Comprehensive**: 6 plot types cover all optimization aspects -✅ **Customizable**: Configurable formats and styling -✅ **Portable**: Plots embedded in reports, papers, presentations - -### Model Cleanup - -✅ **Disk Space Savings**: 50-90% reduction typical (depends on model size) -✅ **Selective**: Keeps best trials for validation/reproduction -✅ **Safe**: Preserves all critical data (results.json) -✅ **Traceable**: Cleanup log documents what was deleted -✅ **Reversible**: Dry-run mode previews before deletion - -### Optuna Dashboard - -✅ **Real-Time**: Monitor optimization while it runs -✅ **Interactive**: Zoom, filter, explore data dynamically -✅ **Advanced**: Parameter importance, contour plots -✅ **Comparative**: Multi-study comparison support - ---- - -## Example: Beam Optimization - -**Configuration**: -```json -{ - "study_name": "simple_beam_optimization", - "optimization_settings": { - "n_trials": 50 - }, - "post_processing": { - "generate_plots": true, - "plot_formats": ["png", "pdf"], - "cleanup_models": true, - "keep_top_n_models": 10 - } -} -``` - -**Results**: -- 50 trials completed -- 6 plots generated (× 2 formats = 12 files) -- 40 trials cleaned up -- 1.2 GB disk space freed -- Top 10 trial models retained for validation - -**Files Generated**: -- `plots/convergence.{png,pdf}` -- `plots/design_space_evolution.{png,pdf}` -- `plots/parallel_coordinates.{png,pdf}` -- `plots/plot_summary.json` -- `cleanup_log.json` - ---- - -## Future Enhancements - -### Potential Additions - -1. **Interactive HTML Plots**: Plotly-based interactive visualizations -2. **Automated Report Generation**: Markdown → PDF with embedded plots -3. **Video Animation**: Design evolution as animated GIF/MP4 -4. **3D Scatter Plots**: For high-dimensional design spaces -5. **Statistical Analysis**: Confidence intervals, significance tests -6. **Comparison Reports**: Side-by-side substudy comparison - -### Configuration Expansion - -```json -"post_processing": { - "generate_plots": true, - "plot_formats": ["png", "pdf", "html"], // Add interactive - "plot_style": "publication", // Predefined styles - "generate_report": true, // Auto-generate PDF report - "report_template": "default", // Custom templates - "cleanup_models": true, - "keep_top_n_models": 10, - "archive_cleaned_trials": false // Compress instead of delete -} -``` - ---- - -## Troubleshooting - -### Matplotlib Import Error - -**Problem**: `ImportError: No module named 'matplotlib'` - -**Solution**: Install visualization dependencies -```bash -conda install -n atomizer matplotlib pandas "numpy<2" -y -``` - -### Unicode Display Error - -**Problem**: Checkmark character displays incorrectly in Windows console - -**Status**: Fixed (replaced Unicode with "SUCCESS:") - -### Missing history.json - -**Problem**: Older substudies don't have `history.json` - -**Solution**: Generate from trial results -```bash -python optimization_engine/generate_history_from_trials.py studies/beam/substudies/opt1 -``` - -### Cleanup Deleted Wrong Files - -**Prevention**: ALWAYS use dry-run first! -```bash -python optimization_engine/model_cleanup.py --dry-run -``` - ---- - -## Technical Details - -### Dependencies - -**Required**: -- `matplotlib >= 3.10` -- `numpy < 2.0` (pyNastran compatibility) -- `pandas >= 2.3` -- `optuna >= 3.0` (for dashboard) - -**Optional**: -- `optuna-dashboard` (for real-time monitoring) - -### Performance - -**Visualization**: -- 50 trials: ~5-10 seconds -- 100 trials: ~10-15 seconds -- 500 trials: ~30-40 seconds - -**Cleanup**: -- Depends on file count and sizes -- Typically < 1 minute for 100 trials - ---- - -## Summary - -Phase 3.3 completes Atomizer's post-processing capabilities with: - -✅ Automated publication-quality visualization -✅ Intelligent model cleanup for disk space management -✅ Optuna dashboard integration for real-time monitoring -✅ Comprehensive configuration options -✅ Full integration with optimization workflow - -**Next Phase**: Phase 3.4 - Report Generation & Statistical Analysis diff --git a/docs/PROTOCOL_13_DASHBOARD.md b/docs/PROTOCOL_13_DASHBOARD.md deleted file mode 100644 index 2c210d3e..00000000 --- a/docs/PROTOCOL_13_DASHBOARD.md +++ /dev/null @@ -1,333 +0,0 @@ -# Protocol 13: Real-Time Dashboard Tracking - -**Status**: ✅ COMPLETED -**Date**: November 21, 2025 -**Priority**: P1 (Critical) - -## Overview - -Protocol 13 implements a comprehensive real-time web dashboard for monitoring multi-objective optimization studies. It provides live visualization of optimizer state, Pareto fronts, parallel coordinates, and trial history. - -## Architecture - -### Backend Components - -#### 1. Real-Time Tracking System -**File**: `optimization_engine/realtime_tracking.py` - -- **Per-Trial JSON Writes**: Writes `optimizer_state.json` after every trial completion -- **Optimizer State Tracking**: Captures current phase, strategy, trial progress -- **Multi-Objective Support**: Tracks study directions and Pareto front status - -```python -def create_realtime_callback(tracking_dir, optimizer_ref, verbose=False): - """Creates Optuna callback for per-trial JSON writes""" - # Writes to: {study_dir}/2_results/intelligent_optimizer/optimizer_state.json -``` - -**Data Structure**: -```json -{ - "timestamp": "2025-11-21T15:27:28.828930", - "trial_number": 29, - "total_trials": 50, - "current_phase": "adaptive_optimization", - "current_strategy": "GP_UCB", - "is_multi_objective": true, - "study_directions": ["maximize", "minimize"] -} -``` - -#### 2. REST API Endpoints -**File**: `atomizer-dashboard/backend/api/routes/optimization.py` - -**New Protocol 13 Endpoints**: - -1. **GET `/api/optimization/studies/{study_id}/metadata`** - - Returns objectives, design variables, constraints with units - - Implements unit inference from descriptions - - Supports Protocol 11 multi-objective format - -2. **GET `/api/optimization/studies/{study_id}/optimizer-state`** - - Returns real-time optimizer state from JSON - - Shows current phase and strategy - - Updates every trial - -3. **GET `/api/optimization/studies/{study_id}/pareto-front`** - - Returns Pareto-optimal solutions for multi-objective studies - - Uses Optuna's `study.best_trials` API - - Includes constraint satisfaction status - -**Unit Inference Function**: -```python -def _infer_objective_unit(objective: Dict) -> str: - """Infer unit from objective name and description""" - # Pattern matching: frequency→Hz, stiffness→N/mm, mass→kg - # Regex extraction: "(N/mm)" from description -``` - -### Frontend Components - -#### 1. OptimizerPanel Component -**File**: `atomizer-dashboard/frontend/src/components/OptimizerPanel.tsx` - -**Features**: -- Real-time phase display (Characterization, Exploration, Exploitation, Adaptive) -- Current strategy indicator (TPE, GP, NSGA-II, etc.) -- Progress bar with trial count -- Multi-objective study detection -- Auto-refresh every 2 seconds - -**Visual Design**: -``` -┌─────────────────────────────────┐ -│ Intelligent Optimizer Status │ -├─────────────────────────────────┤ -│ Phase: [Adaptive Optimization] │ -│ Strategy: [GP_UCB] │ -│ Progress: [████████░░] 29/50 │ -│ Multi-Objective: ✓ │ -└─────────────────────────────────┘ -``` - -#### 2. ParetoPlot Component -**File**: `atomizer-dashboard/frontend/src/components/ParetoPlot.tsx` - -**Features**: -- Scatter plot of Pareto-optimal solutions -- Pareto front line connecting optimal points -- **3 Normalization Modes**: - - **Raw**: Original engineering values - - **Min-Max**: Scales to [0, 1] for equal comparison - - **Z-Score**: Standardizes to mean=0, std=1 -- Tooltip shows raw values regardless of normalization -- Color-coded feasibility (green=feasible, red=infeasible) -- Dynamic axis labels with units - -**Normalization Math**: -```typescript -// Min-Max: (x - min) / (max - min) → [0, 1] -// Z-Score: (x - mean) / std → standardized -``` - -#### 3. ParallelCoordinatesPlot Component -**File**: `atomizer-dashboard/frontend/src/components/ParallelCoordinatesPlot.tsx` - -**Features**: -- High-dimensional visualization (objectives + design variables) -- Interactive trial selection (click to toggle, hover to highlight) -- Normalized [0, 1] axes for all dimensions -- Color coding: green (feasible), red (infeasible), yellow (selected) -- Opacity management: non-selected fade to 10% when selection active -- Clear selection button - -**Visualization Structure**: -``` -Stiffness Mass support_angle tip_thickness - | | | | - | ╱─────╲ ╱ | - | ╱ ╲─────────╱ | - | ╱ ╲ | -``` - -#### 4. Dashboard Integration -**File**: `atomizer-dashboard/frontend/src/pages/Dashboard.tsx` - -**Layout Structure**: -``` -┌──────────────────────────────────────────────────┐ -│ Study Selection │ -├──────────────────────────────────────────────────┤ -│ Metrics Grid (Best, Avg, Trials, Pruned) │ -├──────────────────────────────────────────────────┤ -│ [OptimizerPanel] [ParetoPlot] │ -├──────────────────────────────────────────────────┤ -│ [ParallelCoordinatesPlot - Full Width] │ -├──────────────────────────────────────────────────┤ -│ [Convergence] [Parameter Space] │ -├──────────────────────────────────────────────────┤ -│ [Recent Trials Table] │ -└──────────────────────────────────────────────────┘ -``` - -**Dynamic Units**: -- `getParamLabel()` helper function looks up units from metadata -- Applied to Parameter Space chart axes -- Format: `"support_angle (degrees)"`, `"tip_thickness (mm)"` - -## Integration with Existing Protocols - -### Protocol 10: Intelligent Optimizer -- Real-time callback integrated into `IntelligentOptimizer.optimize()` -- Tracks phase transitions (characterization → adaptive optimization) -- Reports strategy changes -- Location: `optimization_engine/intelligent_optimizer.py:117-121` - -### Protocol 11: Multi-Objective Support -- Pareto front endpoint checks `len(study.directions) > 1` -- Dashboard conditionally renders Pareto plots -- Handles both single and multi-objective studies gracefully -- Uses Optuna's `study.best_trials` for Pareto front - -### Protocol 12: Unified Extraction Library -- Extractors provide objective values for dashboard visualization -- Units defined in extractor classes flow to dashboard -- Consistent data format across all studies - -## Data Flow - -``` -Trial Completion (Optuna) - ↓ -Realtime Callback (optimization_engine/realtime_tracking.py) - ↓ -Write optimizer_state.json - ↓ -Backend API /optimizer-state endpoint - ↓ -Frontend OptimizerPanel (2s polling) - ↓ -User sees live updates -``` - -## Testing - -### Tested With -- **Study**: `bracket_stiffness_optimization_V2` -- **Trials**: 50 (30 completed in testing) -- **Objectives**: 2 (stiffness maximize, mass minimize) -- **Design Variables**: 2 (support_angle, tip_thickness) -- **Pareto Solutions**: 20 identified -- **Dashboard Port**: 3001 (frontend) + 8000 (backend) - -### Verified Features -✅ Real-time optimizer state updates -✅ Pareto front visualization with line -✅ Normalization toggle (Raw, Min-Max, Z-Score) -✅ Parallel coordinates with selection -✅ Dynamic units from config -✅ Multi-objective detection -✅ Constraint satisfaction coloring - -## File Structure - -``` -atomizer-dashboard/ -├── backend/ -│ └── api/ -│ └── routes/ -│ └── optimization.py (Protocol 13 endpoints) -└── frontend/ - └── src/ - ├── components/ - │ ├── OptimizerPanel.tsx (NEW) - │ ├── ParetoPlot.tsx (NEW) - │ └── ParallelCoordinatesPlot.tsx (NEW) - └── pages/ - └── Dashboard.tsx (updated with Protocol 13) - -optimization_engine/ -├── realtime_tracking.py (NEW - per-trial JSON writes) -└── intelligent_optimizer.py (updated with realtime callback) - -studies/ -└── {study_name}/ - └── 2_results/ - └── intelligent_optimizer/ - └── optimizer_state.json (written every trial) -``` - -## Configuration - -### Backend Setup -```bash -cd atomizer-dashboard/backend -python -m uvicorn api.main:app --reload --port 8000 -``` - -### Frontend Setup -```bash -cd atomizer-dashboard/frontend -npm run dev # Runs on port 3001 -``` - -### Study Requirements -- Must use Protocol 10 (IntelligentOptimizer) -- Must have `optimization_config.json` with objectives and design_variables -- Real-time tracking enabled by default in IntelligentOptimizer - -## Usage - -1. **Start Dashboard**: - ```bash - # Terminal 1: Backend - cd atomizer-dashboard/backend - python -m uvicorn api.main:app --reload --port 8000 - - # Terminal 2: Frontend - cd atomizer-dashboard/frontend - npm run dev - ``` - -2. **Start Optimization**: - ```bash - cd studies/my_study - python run_optimization.py --trials 50 - ``` - -3. **View Dashboard**: - - Open browser to `http://localhost:3001` - - Select study from dropdown - - Watch real-time updates every trial - -4. **Interact with Plots**: - - Toggle normalization on Pareto plot - - Click lines in parallel coordinates to select trials - - Hover for detailed trial information - -## Performance - -- **Backend**: ~10ms per endpoint (SQLite queries cached) -- **Frontend**: 2s polling interval (configurable) -- **Real-time writes**: <5ms per trial (JSON serialization) -- **Dashboard load time**: <500ms initial render - -## Future Enhancements (P3) - -- [ ] WebSocket support for instant updates (currently polling) -- [ ] Export Pareto front as CSV/JSON -- [ ] 3D Pareto plot for 3+ objectives -- [ ] Strategy performance comparison charts -- [ ] Historical phase duration analysis -- [ ] Mobile-responsive design -- [ ] Dark/light theme toggle - -## Troubleshooting - -### Dashboard shows "No Pareto front data yet" -- Study must have multiple objectives -- At least 2 trials must complete -- Check `/api/optimization/studies/{id}/pareto-front` endpoint - -### OptimizerPanel shows "Not available" -- Study must use IntelligentOptimizer (Protocol 10) -- Check `2_results/intelligent_optimizer/optimizer_state.json` exists -- Verify realtime_callback is registered in optimize() call - -### Units not showing -- Add `unit` field to objectives in `optimization_config.json` -- Or ensure description contains unit pattern: "(N/mm)", "Hz", etc. -- Backend will infer from common patterns - -## Related Documentation - -- [Protocol 10: Intelligent Optimizer](PROTOCOL_10_V2_IMPLEMENTATION.md) -- [Protocol 11: Multi-Objective Support](PROTOCOL_10_IMSO.md) -- [Protocol 12: Unified Extraction](HOW_TO_EXTEND_OPTIMIZATION.md) -- [Dashboard React Implementation](DASHBOARD_REACT_IMPLEMENTATION.md) - ---- - -**Implementation Complete**: All P1 and P2 features delivered -**Ready for Production**: Yes -**Tested**: Yes (50-trial multi-objective study) diff --git a/docs/PRUNING_DIAGNOSTICS.md b/docs/PRUNING_DIAGNOSTICS.md deleted file mode 100644 index 524c6bc7..00000000 --- a/docs/PRUNING_DIAGNOSTICS.md +++ /dev/null @@ -1,367 +0,0 @@ -# Pruning Diagnostics - Comprehensive Trial Failure Tracking - -**Created**: November 20, 2025 -**Status**: ✅ Production Ready - ---- - -## Overview - -The pruning diagnostics system provides detailed logging and analysis of failed optimization trials. It helps identify: -- **Why trials are failing** (validation, simulation, or extraction) -- **Which parameters cause failures** -- **False positives** from pyNastran OP2 reader -- **Patterns** that can improve validation rules - ---- - -## Components - -### 1. Pruning Logger -**Module**: [optimization_engine/pruning_logger.py](../optimization_engine/pruning_logger.py) - -Logs every pruned trial with full details: -- Parameters that failed -- Failure cause (validation, simulation, OP2 extraction) -- Error messages and stack traces -- F06 file analysis (for OP2 failures) - -### 2. Robust OP2 Extractor -**Module**: [optimization_engine/op2_extractor.py](../optimization_engine/op2_extractor.py) - -Handles pyNastran issues gracefully: -- Tries multiple extraction strategies -- Ignores benign FATAL flags -- Falls back to F06 parsing -- Prevents false positive failures - ---- - -## Usage in Optimization Scripts - -### Basic Integration - -```python -from pathlib import Path -from optimization_engine.pruning_logger import PruningLogger -from optimization_engine.op2_extractor import robust_extract_first_frequency -from optimization_engine.simulation_validator import SimulationValidator - -# Initialize pruning logger -results_dir = Path("studies/my_study/2_results") -pruning_logger = PruningLogger(results_dir, verbose=True) - -# Initialize validator -validator = SimulationValidator(model_type='circular_plate', verbose=True) - -def objective(trial): - """Objective function with comprehensive pruning logging.""" - - # Sample parameters - params = { - 'inner_diameter': trial.suggest_float('inner_diameter', 50, 150), - 'plate_thickness': trial.suggest_float('plate_thickness', 2, 10) - } - - # VALIDATION - is_valid, warnings = validator.validate(params) - if not is_valid: - # Log validation failure - pruning_logger.log_validation_failure( - trial_number=trial.number, - design_variables=params, - validation_warnings=warnings - ) - raise optuna.TrialPruned() - - # Update CAD and run simulation - updater.update_expressions(params) - result = solver.run_simulation(str(sim_file), solution_name="Solution_Normal_Modes") - - # SIMULATION FAILURE - if not result['success']: - pruning_logger.log_simulation_failure( - trial_number=trial.number, - design_variables=params, - error_message=result.get('error', 'Unknown error'), - return_code=result.get('return_code'), - solver_errors=result.get('errors') - ) - raise optuna.TrialPruned() - - # OP2 EXTRACTION (robust method) - op2_file = result['op2_file'] - f06_file = result.get('f06_file') - - try: - frequency = robust_extract_first_frequency( - op2_file=op2_file, - mode_number=1, - f06_file=f06_file, - verbose=True - ) - except Exception as e: - # Log OP2 extraction failure - pruning_logger.log_op2_extraction_failure( - trial_number=trial.number, - design_variables=params, - exception=e, - op2_file=op2_file, - f06_file=f06_file - ) - raise optuna.TrialPruned() - - # Success - calculate objective - return abs(frequency - 115.0) - -# After optimization completes -pruning_logger.save_summary() -``` - ---- - -## Output Files - -### Pruning History (Detailed Log) -**File**: `2_results/pruning_history.json` - -Contains every pruned trial with full details: - -```json -[ - { - "trial_number": 0, - "timestamp": "2025-11-20T19:09:45.123456", - "pruning_cause": "op2_extraction_failure", - "design_variables": { - "inner_diameter": 126.56, - "plate_thickness": 9.17 - }, - "exception_type": "ValueError", - "exception_message": "There was a Nastran FATAL Error. Check the F06.", - "stack_trace": "Traceback (most recent call last)...", - "details": { - "op2_file": "studies/.../circular_plate_sim1-solution_normal_modes.op2", - "op2_exists": true, - "op2_size_bytes": 245760, - "f06_file": "studies/.../circular_plate_sim1-solution_normal_modes.f06", - "is_pynastran_fatal_flag": true, - "f06_has_fatal_errors": false, - "f06_errors": [] - } - }, - { - "trial_number": 5, - "timestamp": "2025-11-20T19:11:23.456789", - "pruning_cause": "simulation_failure", - "design_variables": { - "inner_diameter": 95.2, - "plate_thickness": 3.8 - }, - "error_message": "Mesh generation failed - element quality below threshold", - "details": { - "return_code": 1, - "solver_errors": ["FATAL: Mesh quality check failed"] - } - } -] -``` - -### Pruning Summary (Analysis Report) -**File**: `2_results/pruning_summary.json` - -Statistical analysis and recommendations: - -```json -{ - "generated": "2025-11-20T19:15:30.123456", - "total_pruned_trials": 9, - "breakdown": { - "validation_failures": 2, - "simulation_failures": 1, - "op2_extraction_failures": 6 - }, - "validation_failure_reasons": {}, - "simulation_failure_types": { - "Mesh generation failed": 1 - }, - "op2_extraction_analysis": { - "total_op2_failures": 6, - "likely_false_positives": 6, - "description": "False positives are OP2 extraction failures where pyNastran detected FATAL flag but F06 has no errors" - }, - "recommendations": [ - "CRITICAL: 6 trials failed due to pyNastran OP2 reader being overly strict. Use robust_extract_first_frequency() to ignore benign FATAL flags and extract valid results." - ] -} -``` - ---- - -## Robust OP2 Extraction - -### Problem: pyNastran False Positives - -pyNastran's OP2 reader can be overly strict - it throws exceptions when it sees a FATAL flag in the OP2 header, even if: -- The F06 file shows **no errors** -- The simulation **completed successfully** -- The eigenvalue data **is valid and extractable** - -### Solution: Multi-Strategy Extraction - -The `robust_extract_first_frequency()` function tries multiple strategies: - -```python -from optimization_engine.op2_extractor import robust_extract_first_frequency - -frequency = robust_extract_first_frequency( - op2_file=Path("results.op2"), - mode_number=1, - f06_file=Path("results.f06"), # Optional fallback - verbose=True -) -``` - -**Strategies** (in order): -1. **Standard OP2 read** - Normal pyNastran reading -2. **Lenient OP2 read** - `debug=False`, `skip_undefined_matrices=True` -3. **F06 fallback** - Parse text file if OP2 fails - -**Output** (verbose mode): -``` -[OP2 EXTRACT] Attempting standard read: circular_plate_sim1-solution_normal_modes.op2 -[OP2 EXTRACT] ✗ Standard read failed: There was a Nastran FATAL Error -[OP2 EXTRACT] Detected pyNastran FATAL flag issue -[OP2 EXTRACT] Attempting partial extraction... -[OP2 EXTRACT] ✓ Success (lenient mode): 125.1234 Hz -[OP2 EXTRACT] Note: pyNastran reported FATAL but data is valid! -``` - ---- - -## Analyzing Pruning Patterns - -### View Summary - -```python -import json -from pathlib import Path - -# Load pruning summary -with open('studies/my_study/2_results/pruning_summary.json') as f: - summary = json.load(f) - -print(f"Total pruned: {summary['total_pruned_trials']}") -print(f"False positives: {summary['op2_extraction_analysis']['likely_false_positives']}") -print("\nRecommendations:") -for rec in summary['recommendations']: - print(f" - {rec}") -``` - -### Find Specific Failures - -```python -import json - -# Load detailed history -with open('studies/my_study/2_results/pruning_history.json') as f: - history = json.load(f) - -# Find all OP2 false positives -false_positives = [ - event for event in history - if event['pruning_cause'] == 'op2_extraction_failure' - and event['details']['is_pynastran_fatal_flag'] - and not event['details']['f06_has_fatal_errors'] -] - -print(f"Found {len(false_positives)} false positives:") -for fp in false_positives: - params = fp['design_variables'] - print(f" Trial #{fp['trial_number']}: {params}") -``` - -### Parameter Analysis - -```python -# Find which parameter ranges cause failures -import numpy as np - -validation_failures = [e for e in history if e['pruning_cause'] == 'validation_failure'] - -diameters = [e['design_variables']['inner_diameter'] for e in validation_failures] -thicknesses = [e['design_variables']['plate_thickness'] for e in validation_failures] - -print(f"Validation failures occur at:") -print(f" Diameter range: {min(diameters):.1f} - {max(diameters):.1f} mm") -print(f" Thickness range: {min(thicknesses):.1f} - {max(thicknesses):.1f} mm") -``` - ---- - -## Expected Impact - -### Before Robust Extraction -- **Pruning rate**: 18-20% -- **False positives**: ~6-10 per 50 trials -- **Wasted time**: ~5 minutes per study - -### After Robust Extraction -- **Pruning rate**: <2% (only genuine failures) -- **False positives**: 0 -- **Time saved**: ~4-5 minutes per study -- **Better optimization**: More valid trials = better convergence - ---- - -## Testing - -Test the robust extractor on a known "failed" OP2 file: - -```bash -python -c " -from pathlib import Path -from optimization_engine.op2_extractor import robust_extract_first_frequency - -# Use an OP2 file that pyNastran rejects -op2_file = Path('studies/circular_plate_protocol10_v2_2_test/1_setup/model/circular_plate_sim1-solution_normal_modes.op2') -f06_file = op2_file.with_suffix('.f06') - -try: - freq = robust_extract_first_frequency(op2_file, f06_file=f06_file, verbose=True) - print(f'\n✓ Successfully extracted: {freq:.6f} Hz') -except Exception as e: - print(f'\n✗ Extraction failed: {e}') -" -``` - -Expected output: -``` -[OP2 EXTRACT] Attempting standard read: circular_plate_sim1-solution_normal_modes.op2 -[OP2 EXTRACT] ✗ Standard read failed: There was a Nastran FATAL Error -[OP2 EXTRACT] Detected pyNastran FATAL flag issue -[OP2 EXTRACT] Attempting partial extraction... -[OP2 EXTRACT] ✓ Success (lenient mode): 115.0442 Hz -[OP2 EXTRACT] Note: pyNastran reported FATAL but data is valid! - -✓ Successfully extracted: 115.044200 Hz -``` - ---- - -## Summary - -| Feature | Description | File | -|---------|-------------|------| -| **Pruning Logger** | Comprehensive failure tracking | [pruning_logger.py](../optimization_engine/pruning_logger.py) | -| **Robust OP2 Extractor** | Handles pyNastran issues | [op2_extractor.py](../optimization_engine/op2_extractor.py) | -| **Pruning History** | Detailed JSON log | `2_results/pruning_history.json` | -| **Pruning Summary** | Analysis and recommendations | `2_results/pruning_summary.json` | - -**Status**: ✅ Ready for production use - -**Benefits**: -- Zero false positive failures -- Detailed diagnostics for genuine failures -- Pattern analysis for validation improvements -- ~5 minutes saved per 50-trial study diff --git a/docs/QUICK_CONFIG_REFERENCE.md b/docs/QUICK_CONFIG_REFERENCE.md deleted file mode 100644 index 810b1a9e..00000000 --- a/docs/QUICK_CONFIG_REFERENCE.md +++ /dev/null @@ -1,81 +0,0 @@ -# Quick Configuration Reference - -## Change NX Version (e.g., when NX 2506 is released) - -**Edit ONE file**: [`config.py`](../config.py) - -```python -# Line 14-15 -NX_VERSION = "2506" # ← Change this -NX_INSTALLATION_DIR = Path(f"C:/Program Files/Siemens/NX{NX_VERSION}") -``` - -**That's it!** All modules automatically use new paths. - ---- - -## Change Python Environment - -**Edit ONE file**: [`config.py`](../config.py) - -```python -# Line 49 -PYTHON_ENV_NAME = "my_new_env" # ← Change this -``` - ---- - -## Verify Configuration - -```bash -python config.py -``` - -Output shows all paths and validates they exist. - ---- - -## Using Config in Your Code - -```python -from config import ( - NX_RUN_JOURNAL, # Path to run_journal.exe - NX_MATERIAL_LIBRARY, # Path to material library XML - PYTHON_ENV_NAME, # Current environment name - get_nx_journal_command, # Helper function -) - -# Generate journal command -cmd = get_nx_journal_command( - journal_script, - arg1, - arg2 -) -``` - ---- - -## What Changed? - -**OLD** (hardcoded paths in multiple files): -- `optimization_engine/nx_updater.py`: Line 66 -- `dashboard/api/app.py`: Line 598 -- `README.md`: Line 92 -- `docs/NXOPEN_INTELLISENSE_SETUP.md`: Line 269 -- ...and more - -**NEW** (all use `config.py`): -- Edit `config.py` once -- All files automatically updated - ---- - -## Files Using Config - -- ✅ `optimization_engine/nx_updater.py` -- ✅ `dashboard/api/app.py` -- Future: All NX-related modules will use config - ---- - -**See also**: [SYSTEM_CONFIGURATION.md](SYSTEM_CONFIGURATION.md) for full documentation diff --git a/docs/SESSION_SUMMARY_NOV20.md b/docs/SESSION_SUMMARY_NOV20.md deleted file mode 100644 index e96cb178..00000000 --- a/docs/SESSION_SUMMARY_NOV20.md +++ /dev/null @@ -1,230 +0,0 @@ -# Session Summary - November 20, 2025 - -## Mission Accomplished! 🎯 - -Today we solved the mysterious 18-20% pruning rate in Protocol 10 optimization studies. - ---- - -## The Problem - -Protocol 10 v2.1 and v2.2 tests showed: -- **18-20% pruning rate** (9-10 out of 50 trials failing) --Validator wasn't catching failures -- All pruned trials had **valid aspect ratios** (5.0-50.0 range) -- For a simple 2D circular plate, this shouldn't happen! - ---- - -## The Investigation - -### Discovery 1: Validator Was Too Lenient -- Validator returned only warnings, not rejections -- Fixed by making aspect ratio violations **hard rejections** -- **Result**: Validator now works, but didn't reduce pruning - -### Discovery 2: The Real Culprit - pyNastran False Positives -Analyzed the actual failures and found: -- ✅ **Nastran simulations succeeded** (F06 files show no errors) -- ⚠️ **FATAL flag in OP2 header** (probably benign warning) -- ❌ **pyNastran throws exception** when reading OP2 -- ❌ **Trials marked as failed** (but data is actually valid!) - -**Proof**: Successfully extracted 116.044 Hz from a "failed" OP2 file using our new robust extractor. - ---- - -## The Solution - -### 1. Pruning Logger -**File**: [optimization_engine/pruning_logger.py](../optimization_engine/pruning_logger.py) - -Comprehensive tracking of every pruned trial: -- **What failed**: Validation, simulation, or OP2 extraction -- **Why it failed**: Full error messages and stack traces -- **Parameters**: Exact design variable values -- **F06 analysis**: Detects false positives vs. real errors - -**Output Files**: -- `2_results/pruning_history.json` - Detailed log -- `2_results/pruning_summary.json` - Statistical analysis - -### 2. Robust OP2 Extractor -**File**: [optimization_engine/op2_extractor.py](../optimization_engine/op2_extractor.py) - -Multi-strategy extraction that handles pyNastran issues: -1. **Standard OP2 read** - Try normal pyNastran -2. **Lenient read** - `debug=False`, ignore benign flags -3. **F06 fallback** - Parse text file if OP2 fails - -**Key Function**: -```python -from optimization_engine.op2_extractor import robust_extract_first_frequency - -frequency = robust_extract_first_frequency( - op2_file=Path("results.op2"), - mode_number=1, - f06_file=Path("results.f06"), - verbose=True -) -``` - -### 3. Study Continuation API -**File**: [optimization_engine/study_continuation.py](../optimization_engine/study_continuation.py) - -Standardized continuation feature (not improvised): -```python -from optimization_engine.study_continuation import continue_study - -results = continue_study( - study_dir=Path("studies/my_study"), - additional_trials=50, - objective_function=my_objective -) -``` - ---- - -## Impact - -### Before -- **Pruning rate**: 18-20% (9-10 failures per 50 trials) -- **False positives**: ~6-9 per study -- **Wasted time**: ~5 minutes per study -- **Optimization quality**: Reduced by noisy data - -### After (Expected) -- **Pruning rate**: <2% (only genuine failures) -- **False positives**: 0 -- **Time saved**: ~4-5 minutes per study -- **Optimization quality**: All trials contribute valid data - ---- - -## Files Created - -### Core Modules -1. [optimization_engine/pruning_logger.py](../optimization_engine/pruning_logger.py) - Pruning diagnostics -2. [optimization_engine/op2_extractor.py](../optimization_engine/op2_extractor.py) - Robust extraction -3. [optimization_engine/study_continuation.py](../optimization_engine/study_continuation.py) - Already existed, documented - -### Documentation -1. [docs/PRUNING_DIAGNOSTICS.md](PRUNING_DIAGNOSTICS.md) - Complete guide -2. [docs/STUDY_CONTINUATION_STANDARD.md](STUDY_CONTINUATION_STANDARD.md) - API docs -3. [docs/FIX_VALIDATOR_PRUNING.md](FIX_VALIDATOR_PRUNING.md) - Validator fix notes - -### Test Studies -1. `studies/circular_plate_protocol10_v2_2_test/` - Protocol 10 v2.2 test - ---- - -## Key Insights - -### Why Pruning Happened -The 18% pruning was **NOT real simulation failures**. It was: -1. Nastran successfully solving -2. Writing a benign FATAL flag in OP2 header -3. pyNastran being overly strict -4. Valid results being rejected - -### The Fix -Use `robust_extract_first_frequency()` which: -- Tries multiple extraction strategies -- Validates against F06 to detect false positives -- Extracts valid data even if FATAL flag exists - ---- - -## Next Steps (Optional) - -1. **Integrate into Protocol 11**: Use robust extractor + pruning logger by default -2. **Re-test v2.2**: Run with robust extractor to confirm 0% false positive rate -3. **Dashboard integration**: Add pruning diagnostics view -4. **Pattern analysis**: Use pruning logs to improve validation rules - ---- - -## Testing - -Verified the robust extractor works: -```bash -python -c " -from pathlib import Path -from optimization_engine.op2_extractor import robust_extract_first_frequency - -op2_file = Path('studies/circular_plate_protocol10_v2_2_test/1_setup/model/circular_plate_sim1-solution_normal_modes.op2') -f06_file = op2_file.with_suffix('.f06') - -freq = robust_extract_first_frequency(op2_file, f06_file=f06_file, verbose=True) -print(f'SUCCESS: {freq:.6f} Hz') -" -``` - -**Result**: ✅ Extracted 116.044227 Hz from previously "failed" file - ---- - -## Validator Fix Status - -### What We Fixed -- ✅ Validator now hard-rejects bad aspect ratios -- ✅ Returns `(is_valid, warnings)` tuple -- ✅ Properly tested on v2.1 pruned trials - -### What We Learned -- Aspect ratio violations were NOT the cause of pruning -- All 9 pruned trials in v2.2 had valid aspect ratios -- The failures were pyNastran false positives - ---- - -## Summary - -**Problem**: 18-20% false positive pruning -**Root Cause**: pyNastran FATAL flag sensitivity -**Solution**: Robust OP2 extractor + comprehensive logging -**Impact**: Near-zero false positive rate expected -**Status**: ✅ Production ready - -**Tools Created**: -- Pruning diagnostics system -- Robust OP2 extraction -- Comprehensive documentation - -All tools are tested, documented, and ready for integration into future protocols. - ---- - -## Validation Fix (Post-v2.3) - -### Issue Discovered -After deploying v2.3 test, user identified that I had added **arbitrary aspect ratio validation** without approval: -- Hard limit: aspect_ratio < 50.0 -- Rejected trial #2 with aspect ratio 53.6 (valid for modal analysis) -- No physical justification for this constraint - -### User Requirements -1. **No arbitrary checks** - validation rules must be proposed, not automatic -2. **Configurable validation** - rules should be visible in optimization_config.json -3. **Parameter bounds suffice** - ranges already define feasibility -4. **Physical justification required** - any constraint needs clear reasoning - -### Fix Applied -**File**: [simulation_validator.py](../optimization_engine/simulation_validator.py) - -**Removed**: -- Aspect ratio hard limits (min: 5.0, max: 50.0) -- All circular_plate validation rules -- Aspect ratio checking function call - -**Result**: Validator now returns empty rules for circular_plate - relies only on Optuna parameter bounds. - -**Impact**: -- No more false rejections due to arbitrary physics assumptions -- Clean separation: parameter bounds = feasibility, validator = genuine simulation issues -- User maintains full control over constraint definition - ---- - -**Session Date**: November 20, 2025 -**Status**: ✅ Complete (with validation fix applied) diff --git a/docs/SESSION_SUMMARY_PHASE_2_5_TO_2_7.md b/docs/SESSION_SUMMARY_PHASE_2_5_TO_2_7.md deleted file mode 100644 index 2ed405fa..00000000 --- a/docs/SESSION_SUMMARY_PHASE_2_5_TO_2_7.md +++ /dev/null @@ -1,251 +0,0 @@ -# Session Summary: Phase 2.5 → 2.7 Implementation - -## What We Built Today - -### Phase 2.5: Intelligent Codebase-Aware Gap Detection ✅ -**Files Created:** -- [optimization_engine/codebase_analyzer.py](../optimization_engine/codebase_analyzer.py) - Scans codebase for existing capabilities -- [optimization_engine/workflow_decomposer.py](../optimization_engine/workflow_decomposer.py) - Breaks requests into workflow steps (v0.2.0) -- [optimization_engine/capability_matcher.py](../optimization_engine/capability_matcher.py) - Matches steps to existing code -- [optimization_engine/targeted_research_planner.py](../optimization_engine/targeted_research_planner.py) - Creates focused research plans - -**Key Achievement:** -✅ System now understands what already exists before asking for examples -✅ Identifies ONLY actual knowledge gaps -✅ 80-90% confidence on complex requests -✅ Fixed expression reading misclassification (geometry vs result_extraction) - -**Test Results:** -- Strain optimization: 80% coverage, 90% confidence -- Multi-objective mass: 83% coverage, 93% confidence - -### Phase 2.6: Intelligent Step Classification ✅ -**Files Created:** -- [optimization_engine/step_classifier.py](../optimization_engine/step_classifier.py) - Classifies steps into 3 types - -**Classification Types:** -1. **Engineering Features** - Complex FEA/CAE needing research -2. **Inline Calculations** - Simple math to auto-generate -3. **Post-Processing Hooks** - Middleware between FEA steps - -**Key Achievement:** -✅ Distinguishes "needs feature" from "just generate Python" -✅ Identifies FEA operations vs simple math -✅ Foundation for smart code generation - -**Problem Identified:** -❌ Still too static - using regex patterns instead of LLM intelligence -❌ Misses intermediate calculation steps -❌ Can't understand nuance (CBUSH vs CBAR, element forces vs reactions) - -### Phase 2.7: LLM-Powered Workflow Intelligence ✅ -**Files Created:** -- [optimization_engine/llm_workflow_analyzer.py](../optimization_engine/llm_workflow_analyzer.py) - Uses Claude API -- [.claude/skills/analyze-workflow.md](../.claude/skills/analyze-workflow.md) - Skill template for LLM integration -- [docs/PHASE_2_7_LLM_INTEGRATION.md](PHASE_2_7_LLM_INTEGRATION.md) - Architecture documentation - -**Key Breakthrough:** -🚀 **Replaced static regex with LLM intelligence** -- Calls Claude API to analyze requests -- Understands engineering context dynamically -- Detects ALL intermediate steps -- Distinguishes subtle differences (CBUSH vs CBAR, X vs Z, min vs max) - -**Example LLM Output:** -```json -{ - "engineering_features": [ - {"action": "extract_1d_element_forces", "domain": "result_extraction"}, - {"action": "update_cbar_stiffness", "domain": "fea_properties"} - ], - "inline_calculations": [ - {"action": "calculate_average", "code_hint": "avg = sum(forces_z) / len(forces_z)"}, - {"action": "find_minimum", "code_hint": "min_val = min(forces_z)"} - ], - "post_processing_hooks": [ - {"action": "custom_objective_metric", "formula": "min_force / avg_force"} - ], - "optimization": { - "algorithm": "genetic_algorithm", - "design_variables": [{"parameter": "cbar_stiffness_x"}] - } -} -``` - -## Critical Fixes Made - -### 1. Expression Reading Misclassification -**Problem:** System classified "read mass from .prt expression" as result_extraction (OP2) -**Fix:** -- Updated `codebase_analyzer.py` to detect `find_expressions()` in nx_updater.py -- Updated `workflow_decomposer.py` to classify custom expressions as geometry domain -- Updated `capability_matcher.py` to map `read_expression` action - -**Result:** ✅ 83% coverage, 93% confidence on complex multi-objective request - -### 2. Environment Setup -**Fixed:** All references now use `atomizer` environment instead of `test_env` -**Installed:** anthropic package for LLM integration - -## Test Files Created - -1. **test_phase_2_5_intelligent_gap_detection.py** - Comprehensive Phase 2.5 test -2. **test_complex_multiobj_request.py** - Multi-objective optimization test -3. **test_cbush_optimization.py** - CBUSH stiffness optimization -4. **test_cbar_genetic_algorithm.py** - CBAR with genetic algorithm -5. **test_step_classifier.py** - Step classification test - -## Architecture Evolution - -### Before (Static & Dumb): -``` -User Request - ↓ -Regex Pattern Matching ❌ - ↓ -Hardcoded Rules ❌ - ↓ -Missed Steps ❌ -``` - -### After (LLM-Powered & Intelligent): -``` -User Request - ↓ -Claude LLM Analysis ✅ - ↓ -Structured JSON ✅ - ↓ -┌─────────────────────────────┐ -│ Engineering (research) │ -│ Inline (auto-generate) │ -│ Hooks (middleware) │ -│ Optimization (config) │ -└─────────────────────────────┘ - ↓ -Phase 2.5 Capability Matching ✅ - ↓ -Code Generation / Research ✅ -``` - -## Key Learnings - -### What Worked: -1. ✅ Phase 2.5 architecture is solid - understanding existing capabilities first -2. ✅ Breaking requests into atomic steps is correct approach -3. ✅ Distinguishing FEA operations from simple math is crucial -4. ✅ LLM integration is the RIGHT solution (not static patterns) - -### What Didn't Work: -1. ❌ Regex patterns for workflow decomposition - too static -2. ❌ Static rules for step classification - can't handle nuance -3. ❌ Hardcoded result type mappings - always incomplete - -### The Realization: -> "We have an LLM! Why are we writing dumb static patterns??" - -This led to Phase 2.7 - using Claude's intelligence for what it's good at. - -## Next Steps - -### Immediate (Ready to Implement): -1. ⏳ Set `ANTHROPIC_API_KEY` environment variable -2. ⏳ Test LLM analyzer with live API calls -3. ⏳ Integrate LLM output with Phase 2.5 capability matcher -4. ⏳ Build inline code generator (simple math → Python) -5. ⏳ Build hook generator (post-processing scripts) - -### Phase 3 (MCP Integration): -1. ⏳ Connect to NX documentation MCP server -2. ⏳ Connect to pyNastran docs MCP server -3. ⏳ Automated research from documentation -4. ⏳ Self-learning from examples - -## Files Modified - -**Core Engine:** -- `optimization_engine/codebase_analyzer.py` - Enhanced pattern detection -- `optimization_engine/workflow_decomposer.py` - Complete rewrite v0.2.0 -- `optimization_engine/capability_matcher.py` - Added read_expression mapping - -**Tests:** -- Created 5 comprehensive test files -- All tests passing ✅ - -**Documentation:** -- `docs/PHASE_2_5_INTELLIGENT_GAP_DETECTION.md` - Complete -- `docs/PHASE_2_7_LLM_INTEGRATION.md` - Complete - -## Success Metrics - -### Coverage Improvements: -- **Before:** 0% (dumb keyword matching) -- **Phase 2.5:** 80-83% (smart capability matching) -- **Phase 2.7 (LLM):** Expected 95%+ with all intermediate steps - -### Confidence Improvements: -- **Before:** <50% (guessing) -- **Phase 2.5:** 87-93% (pattern matching) -- **Phase 2.7 (LLM):** Expected >95% (true understanding) - -### User Experience: -**Before:** -``` -User: "Optimize CBAR with genetic algorithm..." -Atomizer: "I see geometry keyword. Give me geometry examples." -User: 😡 (that's not what I asked!) -``` - -**After (Phase 2.7):** -``` -User: "Optimize CBAR with genetic algorithm..." -Atomizer: "Analyzing your request... - -Engineering Features (need research): 2 - - extract_1d_element_forces (OP2 extraction) - - update_cbar_stiffness (FEA property) - -Auto-Generated (inline Python): 2 - - calculate_average - - find_minimum - -Post-Processing Hook: 1 - - custom_objective_metric (min/avg ratio) - -Research needed: Only 2 FEA operations -Ready to implement!" - -User: 😊 (exactly what I wanted!) -``` - -## Conclusion - -We've successfully transformed Atomizer from a **dumb pattern matcher** to an **intelligent AI-powered engineering assistant**: - -1. ✅ **Understands** existing capabilities (Phase 2.5) -2. ✅ **Identifies** only actual gaps (Phase 2.5) -3. ✅ **Classifies** steps intelligently (Phase 2.6) -4. ✅ **Analyzes** with LLM intelligence (Phase 2.7) - -**The foundation is now in place for true AI-assisted structural optimization!** 🚀 - -## Environment -- **Python Environment:** `atomizer` (c:/Users/antoi/anaconda3/envs/atomizer) -- **Required Package:** anthropic (installed ✅) - -## LLM Integration Notes - -For Phase 2.7, we have two integration approaches: - -### Development Phase (Current): -- Use **Claude Code** directly for workflow analysis -- No API consumption or costs -- Interactive analysis through Claude Code interface -- Perfect for development and testing - -### Production Phase (Future): -- Optional Anthropic API integration for standalone execution -- Set `ANTHROPIC_API_KEY` environment variable if needed -- Fallback to heuristics if no API key provided - -**Recommendation**: Keep using Claude Code for development to avoid API costs. The architecture supports both modes seamlessly. diff --git a/docs/SESSION_SUMMARY_PHASE_2_8.md b/docs/SESSION_SUMMARY_PHASE_2_8.md deleted file mode 100644 index a4efd417..00000000 --- a/docs/SESSION_SUMMARY_PHASE_2_8.md +++ /dev/null @@ -1,313 +0,0 @@ -# Session Summary: Phase 2.8 - Inline Code Generation & Documentation Strategy - -**Date**: 2025-01-16 -**Phases Completed**: Phase 2.8 ✅ -**Duration**: Continued from Phase 2.5-2.7 session - -## What We Built Today - -### Phase 2.8: Inline Code Generator ✅ - -**Files Created:** -- [optimization_engine/inline_code_generator.py](../optimization_engine/inline_code_generator.py) - 450+ lines -- [docs/NXOPEN_DOCUMENTATION_INTEGRATION_STRATEGY.md](NXOPEN_DOCUMENTATION_INTEGRATION_STRATEGY.md) - Comprehensive integration strategy - -**Key Achievement:** -✅ Auto-generates Python code for simple mathematical operations -✅ Zero manual coding required for trivial calculations -✅ Direct integration with Phase 2.7 LLM output -✅ All test cases passing - -**Supported Operations:** -1. **Statistical**: Average, Min, Max, Sum -2. **Normalization**: Divide by constant -3. **Percentage**: Percentage change, percentage calculations -4. **Ratios**: Division of two values - -**Example Input → Output:** -```python -# LLM Phase 2.7 Output: -{ - "action": "normalize_stress", - "description": "Normalize stress by 200 MPa", - "params": { - "input": "max_stress", - "divisor": 200.0 - } -} - -# Phase 2.8 Generated Code: -norm_max_stress = max_stress / 200.0 -``` - -### Documentation Integration Strategy - -**Critical Decision**: Use pyNastran as primary documentation source - -**Why pyNastran First:** -- ✅ Fully open and publicly accessible -- ✅ Comprehensive API documentation at https://pynastran-git.readthedocs.io/en/latest/index.html -- ✅ No authentication required - can WebFetch directly -- ✅ Already extensively used in Atomizer -- ✅ Covers 80% of FEA result extraction needs - -**What pyNastran Handles:** -- OP2 file reading (displacement, stress, strain, element forces) -- F06 file parsing -- BDF/Nastran deck modification -- Result post-processing -- Nodal/Element data extraction - -**NXOpen Strategy:** -- Use Python introspection (`inspect` module) for immediate needs -- Curate knowledge base organically as patterns emerge -- Leverage community resources (NXOpen TSE) -- Build MCP server later when we have critical mass - -## Test Results - -**Phase 2.8 Inline Code Generator:** -``` -Test Calculations: - -1. Normalize stress by 200 MPa - Generated Code: norm_max_stress = max_stress / 200.0 - ✅ PASS - -2. Normalize displacement by 5 mm - Generated Code: norm_max_disp_y = max_disp_y / 5.0 - ✅ PASS - -3. Calculate mass increase percentage vs baseline - Generated Code: mass_increase_pct = ((panel_total_mass - baseline_mass) / baseline_mass) * 100.0 - ✅ PASS - -4. Calculate average of extracted forces - Generated Code: avg_forces_z = sum(forces_z) / len(forces_z) - ✅ PASS - -5. Find minimum force value - Generated Code: min_forces_z = min(forces_z) - ✅ PASS -``` - -**Complete Executable Script Generated:** -```python -""" -Auto-generated inline calculations -Generated by Atomizer Phase 2.8 Inline Code Generator -""" - -# Input values -max_stress = 150.5 -max_disp_y = 3.2 -panel_total_mass = 2.8 -baseline_mass = 2.5 -forces_z = [10.5, 12.3, 8.9, 11.2, 9.8] - -# Inline calculations -# Normalize stress by 200 MPa -norm_max_stress = max_stress / 200.0 - -# Normalize displacement by 5 mm -norm_max_disp_y = max_disp_y / 5.0 - -# Calculate mass increase percentage vs baseline -mass_increase_pct = ((panel_total_mass - baseline_mass) / baseline_mass) * 100.0 - -# Calculate average of extracted forces -avg_forces_z = sum(forces_z) / len(forces_z) - -# Find minimum force value -min_forces_z = min(forces_z) -``` - -## Architecture Evolution - -### Before Phase 2.8: -``` -LLM detects: "calculate average of forces" - ↓ -Manual implementation required ❌ - ↓ -Write Python code by hand - ↓ -Test and debug -``` - -### After Phase 2.8: -``` -LLM detects: "calculate average of forces" - ↓ -Phase 2.8 Inline Generator ✅ - ↓ -avg_forces = sum(forces) / len(forces) - ↓ -Ready to execute immediately! -``` - -## Integration with Existing Phases - -**Phase 2.7 (LLM Analyzer) → Phase 2.8 (Code Generator)** - -```python -# Phase 2.7 Output: -analysis = { - "inline_calculations": [ - { - "action": "calculate_average", - "params": {"input": "forces_z", "operation": "mean"} - }, - { - "action": "find_minimum", - "params": {"input": "forces_z", "operation": "min"} - } - ] -} - -# Phase 2.8 Processing: -from optimization_engine.inline_code_generator import InlineCodeGenerator - -generator = InlineCodeGenerator() -generated_code = generator.generate_batch(analysis['inline_calculations']) - -# Result: Executable Python code for all calculations! -``` - -## Key Design Decisions - -### 1. Variable Naming Intelligence - -The generator automatically infers meaningful variable names: -- Input: `max_stress` → Output: `norm_max_stress` -- Input: `forces_z` → Output: `avg_forces_z` -- Mass calculations → `mass_increase_pct` - -### 2. LLM Code Hints - -If Phase 2.7 LLM provides a `code_hint`, the generator: -1. Validates the hint -2. Extracts variable dependencies -3. Checks for required imports -4. Uses the hint directly if valid - -### 3. Fallback Mechanisms - -Generator handles unknown operations gracefully: -```python -# Unknown operation generates TODO: -result = value # TODO: Implement calculate_custom_metric -``` - -## Files Modified/Created - -**New Files:** -- `optimization_engine/inline_code_generator.py` (450+ lines) -- `docs/NXOPEN_DOCUMENTATION_INTEGRATION_STRATEGY.md` (295+ lines) - -**Updated Files:** -- `README.md` - Added Phase 2.8 completion status -- `docs/NXOPEN_DOCUMENTATION_INTEGRATION_STRATEGY.md` - Updated with pyNastran priority - -## Success Metrics - -**Phase 2.8 Success Criteria:** -- ✅ Auto-generates 100% of inline calculations -- ✅ Correct Python syntax every time -- ✅ Properly handles variable naming -- ✅ Integrates seamlessly with Phase 2.7 output -- ✅ Generates executable scripts - -**Code Quality:** -- ✅ Clean, readable generated code -- ✅ Meaningful variable names -- ✅ Proper descriptions as comments -- ✅ No external dependencies for simple math - -## Next Steps - -### Immediate (Next Session): -1. ⏳ **Phase 2.9**: Post-Processing Hook Generator - - Generate middleware scripts for custom objectives - - Handle I/O between FEA steps - - Support weighted combinations and custom formulas - -2. ⏳ **pyNastran Documentation Integration** - - Use WebFetch to access pyNastran docs - - Build automated research for OP2 extraction - - Create pattern library for common operations - -### Short Term: -1. Build NXOpen introspector using Python `inspect` module -2. Start curating `knowledge_base/nxopen_patterns/` -3. Create first automated FEA feature (stress extraction) -4. Test end-to-end workflow: LLM → Code Gen → Execution - -### Medium Term (Phase 3): -1. Build MCP server for documentation lookup -2. Automated code generation from documentation examples -3. Self-learning system that improves from usage patterns - -## Real-World Example - -**User Request:** -> "I want to optimize a composite panel. Extract stress and displacement, normalize them by 200 MPa and 5 mm, then minimize a weighted combination (70% stress, 30% displacement)." - -**Phase 2.7 LLM Analysis:** -```json -{ - "inline_calculations": [ - {"action": "normalize_stress", "params": {"input": "max_stress", "divisor": 200.0}}, - {"action": "normalize_displacement", "params": {"input": "max_disp_y", "divisor": 5.0}} - ], - "post_processing_hooks": [ - { - "action": "weighted_objective", - "params": { - "inputs": ["norm_stress", "norm_disp"], - "weights": [0.7, 0.3], - "formula": "0.7 * norm_stress + 0.3 * norm_disp" - } - } - ] -} -``` - -**Phase 2.8 Generated Code:** -```python -# Inline calculations (auto-generated) -norm_max_stress = max_stress / 200.0 -norm_max_disp_y = max_disp_y / 5.0 -``` - -**Phase 2.9 Will Generate:** -```python -# Post-processing hook script -def weighted_objective_hook(norm_stress, norm_disp): - """Weighted combination: 70% stress + 30% displacement""" - objective = 0.7 * norm_stress + 0.3 * norm_disp - return objective -``` - -## Conclusion - -Phase 2.8 delivers on the promise of **zero manual coding for trivial operations**: - -1. ✅ **LLM understands** the request (Phase 2.7) -2. ✅ **Identifies** inline calculations vs engineering features (Phase 2.7) -3. ✅ **Auto-generates** clean Python code (Phase 2.8) -4. ✅ **Ready to execute** immediately - -**The system is now capable of writing its own code for simple operations!** - -Combined with the pyNastran documentation strategy, we have a clear path to: -- Automated FEA result extraction -- Self-generating optimization workflows -- True AI-assisted structural analysis - -🚀 **The foundation for autonomous code generation is complete!** - -## Environment -- **Python Environment:** `atomizer` (c:/Users/antoi/anaconda3/envs/atomizer) -- **pyNastran Docs:** https://pynastran-git.readthedocs.io/en/latest/index.html (publicly accessible!) -- **Testing:** All Phase 2.8 tests passing ✅ diff --git a/docs/SESSION_SUMMARY_PHASE_2_9.md b/docs/SESSION_SUMMARY_PHASE_2_9.md deleted file mode 100644 index 43963338..00000000 --- a/docs/SESSION_SUMMARY_PHASE_2_9.md +++ /dev/null @@ -1,477 +0,0 @@ -# Session Summary: Phase 2.9 - Post-Processing Hook Generator - -**Date**: 2025-01-16 -**Phases Completed**: Phase 2.9 ✅ -**Duration**: Continued from Phase 2.8 session - -## What We Built Today - -### Phase 2.9: Post-Processing Hook Generator ✅ - -**Files Created:** -- [optimization_engine/hook_generator.py](../optimization_engine/hook_generator.py) - 760+ lines -- [docs/SESSION_SUMMARY_PHASE_2_9.md](SESSION_SUMMARY_PHASE_2_9.md) - This document - -**Key Achievement:** -✅ Auto-generates standalone Python hook scripts for post-processing operations -✅ Handles weighted objectives, custom formulas, constraint checks, and comparisons -✅ Complete I/O handling with JSON inputs/outputs -✅ Fully executable middleware scripts ready for optimization loops - -**Supported Hook Types:** -1. **Weighted Objective**: Combine multiple metrics with custom weights -2. **Custom Formula**: Apply arbitrary formulas to inputs -3. **Constraint Check**: Validate constraints and calculate violations -4. **Comparison**: Calculate ratios, differences, percentage changes - -**Example Input → Output:** -```python -# LLM Phase 2.7 Output: -{ - "action": "weighted_objective", - "description": "Combine normalized stress (70%) and displacement (30%)", - "params": { - "inputs": ["norm_stress", "norm_disp"], - "weights": [0.7, 0.3], - "objective": "minimize" - } -} - -# Phase 2.9 Generated Hook Script: -""" -Weighted Objective Function Hook -Auto-generated by Atomizer Phase 2.9 - -Combine normalized stress (70%) and displacement (30%) - -Inputs: norm_stress, norm_disp -Weights: 0.7, 0.3 -Formula: 0.7 * norm_stress + 0.3 * norm_disp -Objective: minimize -""" - -import sys -import json -from pathlib import Path - - -def weighted_objective(norm_stress, norm_disp): - """Calculate weighted objective from multiple inputs.""" - result = 0.7 * norm_stress + 0.3 * norm_disp - return result - - -def main(): - """Main entry point for hook execution.""" - # Read inputs from JSON file - input_file = Path(sys.argv[1]) - with open(input_file, 'r') as f: - inputs = json.load(f) - - norm_stress = inputs.get("norm_stress") - norm_disp = inputs.get("norm_disp") - - # Calculate weighted objective - result = weighted_objective(norm_stress, norm_disp) - - # Write output - output_file = input_file.parent / "weighted_objective_result.json" - with open(output_file, 'w') as f: - json.dump({ - "weighted_objective": result, - "objective_type": "minimize", - "inputs_used": {"norm_stress": norm_stress, "norm_disp": norm_disp}, - "formula": "0.7 * norm_stress + 0.3 * norm_disp" - }, f, indent=2) - - print(f"Weighted objective calculated: {result:.6f}") - return result - - -if __name__ == '__main__': - main() -``` - -## Test Results - -**Phase 2.9 Hook Generator:** -``` -Test Hook Generation: - -1. Combine normalized stress (70%) and displacement (30%) - Script: hook_weighted_objective_norm_stress_norm_disp.py - Type: weighted_objective - Inputs: norm_stress, norm_disp - Outputs: weighted_objective - ✅ PASS - -2. Calculate safety factor - Script: hook_custom_safety_factor.py - Type: custom_formula - Inputs: max_stress, yield_strength - Outputs: safety_factor - ✅ PASS - -3. Compare min force to average - Script: hook_compare_min_to_avg_ratio.py - Type: comparison - Inputs: min_force, avg_force - Outputs: min_to_avg_ratio - ✅ PASS - -4. Check if stress is below yield - Script: hook_constraint_yield_constraint.py - Type: constraint_check - Inputs: max_stress, yield_strength - Outputs: yield_constraint, yield_constraint_satisfied, yield_constraint_violation - ✅ PASS -``` - -**Executable Test (Weighted Objective):** -```bash -Input JSON: -{ - "norm_stress": 0.75, - "norm_disp": 0.64 -} - -Execution: -$ python hook_weighted_objective_norm_stress_norm_disp.py test_input.json -Weighted objective calculated: 0.717000 -Result saved to: weighted_objective_result.json - -Output JSON: -{ - "weighted_objective": 0.717, - "objective_type": "minimize", - "inputs_used": { - "norm_stress": 0.75, - "norm_disp": 0.64 - }, - "formula": "0.7 * norm_stress + 0.3 * norm_disp" -} - -Verification: 0.7 * 0.75 + 0.3 * 0.64 = 0.525 + 0.192 = 0.717 ✅ -``` - -## Architecture Evolution - -### Before Phase 2.9: -``` -LLM detects: "weighted combination of stress and displacement" - ↓ -Manual hook script writing required ❌ - ↓ -Write Python, handle I/O, test - ↓ -Integrate with optimization loop -``` - -### After Phase 2.9: -``` -LLM detects: "weighted combination of stress and displacement" - ↓ -Phase 2.9 Hook Generator ✅ - ↓ -Complete Python script with I/O handling - ↓ -Ready to execute immediately! -``` - -## Integration with Existing Phases - -**Phase 2.7 (LLM Analyzer) → Phase 2.9 (Hook Generator)** - -```python -# Phase 2.7 Output: -analysis = { - "post_processing_hooks": [ - { - "action": "weighted_objective", - "description": "Combine stress (70%) and displacement (30%)", - "params": { - "inputs": ["norm_stress", "norm_disp"], - "weights": [0.7, 0.3], - "objective": "minimize" - } - } - ] -} - -# Phase 2.9 Processing: -from optimization_engine.hook_generator import HookGenerator - -generator = HookGenerator() -hooks = generator.generate_batch(analysis['post_processing_hooks']) - -# Save hooks to optimization study -for hook in hooks: - script_path = generator.save_hook_to_file(hook, "studies/my_study/hooks/") - -# Result: Executable hook scripts ready for optimization loop! -``` - -## Key Design Decisions - -### 1. Standalone Executable Scripts - -Each hook is a complete, self-contained Python script: -- No dependencies on Atomizer core -- Can be executed independently for testing -- Easy to debug and validate - -### 2. JSON-Based I/O - -All inputs and outputs use JSON: -- Easy to serialize/deserialize -- Compatible with any language/tool -- Human-readable for debugging - -### 3. Error Handling - -Generated hooks validate all inputs: -```python -norm_stress = inputs.get("norm_stress") -if norm_stress is None: - print(f"Error: Required input 'norm_stress' not found") - sys.exit(1) -``` - -### 4. Hook Registry - -Automatically generates a registry documenting all hooks: -```json -{ - "hooks": [ - { - "name": "hook_weighted_objective_norm_stress_norm_disp.py", - "type": "weighted_objective", - "description": "Combine normalized stress (70%) and displacement (30%)", - "inputs": ["norm_stress", "norm_disp"], - "outputs": ["weighted_objective"] - } - ] -} -``` - -## Hook Types in Detail - -### 1. Weighted Objective Hooks - -**Purpose**: Combine multiple objectives with custom weights - -**Example Use Case**: -"I want to minimize a combination of 70% stress and 30% displacement" - -**Generated Code Features**: -- Dynamic weight application -- Multiple input handling -- Objective type tracking (minimize/maximize) - -### 2. Custom Formula Hooks - -**Purpose**: Apply arbitrary mathematical formulas - -**Example Use Case**: -"Calculate safety factor as yield_strength / max_stress" - -**Generated Code Features**: -- Custom formula evaluation -- Variable name inference -- Output naming based on formula - -### 3. Constraint Check Hooks - -**Purpose**: Validate engineering constraints - -**Example Use Case**: -"Ensure stress is below yield strength" - -**Generated Code Features**: -- Boolean satisfaction flag -- Violation magnitude calculation -- Threshold comparison - -### 4. Comparison Hooks - -**Purpose**: Calculate ratios, differences, percentages - -**Example Use Case**: -"Compare minimum force to average force" - -**Generated Code Features**: -- Multiple comparison operations (ratio, difference, percent) -- Automatic operation detection -- Clean output naming - -## Files Modified/Created - -**New Files:** -- `optimization_engine/hook_generator.py` (760+ lines) -- `docs/SESSION_SUMMARY_PHASE_2_9.md` -- `generated_hooks/` directory with 4 test hooks + registry - -**Generated Test Hooks:** -- `hook_weighted_objective_norm_stress_norm_disp.py` -- `hook_custom_safety_factor.py` -- `hook_compare_min_to_avg_ratio.py` -- `hook_constraint_yield_constraint.py` -- `hook_registry.json` - -## Success Metrics - -**Phase 2.9 Success Criteria:** -- ✅ Auto-generates functional hook scripts -- ✅ Correct I/O handling with JSON -- ✅ Integrates seamlessly with Phase 2.7 output -- ✅ Generates executable, standalone scripts -- ✅ Multiple hook types supported - -**Code Quality:** -- ✅ Clean, readable generated code -- ✅ Proper error handling -- ✅ Complete documentation in docstrings -- ✅ Self-contained (no external dependencies) - -## Real-World Example: CBAR Optimization - -**User Request:** -> "Extract element forces in Z direction from CBAR elements, calculate average, find minimum, then create an objective that minimizes the ratio of min to average. Use genetic algorithm to optimize CBAR stiffness in X direction." - -**Phase 2.7 LLM Analysis:** -```json -{ - "engineering_features": [ - { - "action": "extract_1d_element_forces", - "domain": "result_extraction", - "params": {"element_types": ["CBAR"], "direction": "Z"} - }, - { - "action": "update_cbar_stiffness", - "domain": "fea_properties", - "params": {"property": "stiffness_x"} - } - ], - "inline_calculations": [ - {"action": "calculate_average", "params": {"input": "forces_z"}}, - {"action": "find_minimum", "params": {"input": "forces_z"}} - ], - "post_processing_hooks": [ - { - "action": "comparison", - "description": "Calculate min/avg ratio", - "params": { - "inputs": ["min_force", "avg_force"], - "operation": "ratio", - "output_name": "min_to_avg_ratio" - } - } - ] -} -``` - -**Phase 2.8 Generated Code (Inline):** -```python -# Calculate average of extracted forces -avg_forces_z = sum(forces_z) / len(forces_z) - -# Find minimum force value -min_forces_z = min(forces_z) -``` - -**Phase 2.9 Generated Hook Script:** -```python -# hook_compare_min_to_avg_ratio.py -def compare_ratio(min_force, avg_force): - """Compare values using ratio.""" - result = min_force / avg_force - return result - -# (Full I/O handling, error checking, JSON serialization included) -``` - -**Complete Workflow:** -1. Extract CBAR forces from OP2 → `forces_z = [10.5, 12.3, 8.9, 11.2, 9.8]` -2. Phase 2.8 inline: Calculate avg and min → `avg = 10.54, min = 8.9` -3. Phase 2.9 hook: Calculate ratio → `min_to_avg_ratio = 0.844` -4. Optimization uses ratio as objective to minimize - -**All code auto-generated! No manual scripting required!** - -## Integration with Optimization Loop - -### Typical Workflow: - -``` -Optimization Trial N - ↓ -1. Update FEA parameters (NX journal) - ↓ -2. Run FEA solve (NX Nastran) - ↓ -3. Extract results (OP2 reader) - ↓ -4. **Phase 2.8: Inline calculations** - avg_stress = sum(stresses) / len(stresses) - norm_stress = avg_stress / 200.0 - ↓ -5. **Phase 2.9: Post-processing hook** - python hook_weighted_objective.py trial_N_results.json - → weighted_objective = 0.717 - ↓ -6. Report objective to Optuna - ↓ -7. Optuna suggests next trial parameters - ↓ -Repeat -``` - -## Next Steps - -### Immediate (Next Session): -1. ⏳ **Phase 3**: pyNastran Documentation Integration - - Use WebFetch to access pyNastran docs - - Build automated research for OP2 extraction - - Create pattern library for result extraction operations - -2. ⏳ **Phase 3.5**: NXOpen Pattern Library - - Implement journal learning system - - Extract patterns from recorded NX journals - - Store in knowledge base for reuse - -### Short Term: -1. Integrate Phase 2.8 + 2.9 with optimization runner -2. Test end-to-end workflow with real FEA cases -3. Build knowledge base for common FEA operations -4. Implement Python introspection for NXOpen - -### Medium Term (Phase 4-6): -1. Code generation for complex FEA features (Phase 4) -2. Analysis & decision support (Phase 5) -3. Automated reporting (Phase 6) - -## Conclusion - -Phase 2.9 delivers on the promise of **zero manual scripting for post-processing operations**: - -1. ✅ **LLM understands** the request (Phase 2.7) -2. ✅ **Identifies** post-processing needs (Phase 2.7) -3. ✅ **Auto-generates** complete hook scripts (Phase 2.9) -4. ✅ **Ready to execute** in optimization loop - -**Combined with Phase 2.8:** -- Inline calculations: Auto-generated ✅ -- Post-processing hooks: Auto-generated ✅ -- Custom objectives: Auto-generated ✅ -- Constraints: Auto-generated ✅ - -**The system now writes middleware code autonomously!** - -🚀 **Phases 2.8-2.9 Complete: Full code generation for simple operations and custom workflows!** - -## Environment -- **Python Environment:** `test_env` (c:/Users/antoi/anaconda3/envs/test_env) -- **Testing:** All Phase 2.9 tests passing ✅ -- **Generated Hooks:** 4 hook scripts + registry -- **Execution Test:** Weighted objective hook verified working (0.7 * 0.75 + 0.3 * 0.64 = 0.717) ✅ diff --git a/docs/SESSION_SUMMARY_PHASE_3.md b/docs/SESSION_SUMMARY_PHASE_3.md deleted file mode 100644 index 867d4d42..00000000 --- a/docs/SESSION_SUMMARY_PHASE_3.md +++ /dev/null @@ -1,499 +0,0 @@ -# Session Summary: Phase 3 - pyNastran Documentation Integration - -**Date**: 2025-01-16 -**Phase**: 3.0 - Automated OP2 Extraction Code Generation -**Status**: ✅ Complete - -## Overview - -Phase 3 implements **LLM-enhanced research and code generation** for OP2 result extraction using pyNastran. The system can: -1. Research pyNastran documentation to find appropriate APIs -2. Generate complete, executable Python extraction code -3. Store learned patterns in a knowledge base -4. Auto-generate extractors from Phase 2.7 LLM output - -This enables **LLM-enhanced optimization workflows**: Users can describe goals in natural language and optionally have the system generate code automatically, or write custom extractors manually as needed. - -## Objectives Achieved - -### ✅ Core Capabilities - -1. **Documentation Research** - - WebFetch integration to access pyNastran docs - - Pattern extraction from documentation - - API path discovery (e.g., `model.cbar_force[subcase]`) - - Data structure learning (e.g., `data[ntimes, nelements, 8]`) - -2. **Code Generation** - - Complete Python modules with imports, functions, docstrings - - Error handling and validation - - Executable standalone scripts - - Integration-ready extractors - -3. **Knowledge Base** - - ExtractionPattern dataclass for storing learned patterns - - JSON persistence for patterns - - Pattern matching from LLM requests - - Expandable pattern library - -4. **Real-World Testing** - - Successfully tested on bracket OP2 file - - Extracted displacement results: max_disp=0.362mm at node 91 - - Validated against actual FEA output - -## Architecture - -### PyNastranResearchAgent - -Core module: [optimization_engine/pynastran_research_agent.py](../optimization_engine/pynastran_research_agent.py) - -```python -@dataclass -class ExtractionPattern: - """Represents a learned pattern for OP2 extraction.""" - name: str - description: str - element_type: Optional[str] # e.g., 'CBAR', 'CQUAD4' - result_type: str # 'force', 'stress', 'displacement', 'strain' - code_template: str - api_path: str # e.g., 'model.cbar_force[subcase]' - data_structure: str - examples: List[str] - -class PyNastranResearchAgent: - def __init__(self, knowledge_base_path: Optional[Path] = None): - """Initialize with knowledge base for learned patterns.""" - - def research_extraction(self, request: Dict[str, Any]) -> ExtractionPattern: - """Find or generate extraction pattern for a request.""" - - def generate_extractor_code(self, request: Dict[str, Any]) -> str: - """Generate complete extractor code.""" - - def save_pattern(self, pattern: ExtractionPattern): - """Save pattern to knowledge base.""" - - def load_pattern(self, name: str) -> Optional[ExtractionPattern]: - """Load pattern from knowledge base.""" -``` - -### Core Extraction Patterns - -The agent comes pre-loaded with 3 core patterns learned from pyNastran documentation: - -#### 1. Displacement Extraction - -**API**: `model.displacements[subcase]` -**Data Structure**: `data[itime, :, :6]` where `:6=[tx, ty, tz, rx, ry, rz]` - -```python -def extract_displacement(op2_file: Path, subcase: int = 1): - """Extract displacement results from OP2 file.""" - model = OP2() - model.read_op2(str(op2_file)) - - disp = model.displacements[subcase] - itime = 0 # static case - - # Extract translation components - txyz = disp.data[itime, :, :3] - total_disp = np.linalg.norm(txyz, axis=1) - max_disp = np.max(total_disp) - - node_ids = [nid for (nid, grid_type) in disp.node_gridtype] - max_disp_node = node_ids[np.argmax(total_disp)] - - return { - 'max_displacement': float(max_disp), - 'max_disp_node': int(max_disp_node), - 'max_disp_x': float(np.max(np.abs(txyz[:, 0]))), - 'max_disp_y': float(np.max(np.abs(txyz[:, 1]))), - 'max_disp_z': float(np.max(np.abs(txyz[:, 2]))) - } -``` - -#### 2. Solid Element Stress Extraction - -**API**: `model.ctetra_stress[subcase]` or `model.chexa_stress[subcase]` -**Data Structure**: `data[itime, :, 10]` where `column 9=von_mises` - -```python -def extract_solid_stress(op2_file: Path, subcase: int = 1, element_type: str = 'ctetra'): - """Extract stress from solid elements (CTETRA, CHEXA).""" - model = OP2() - model.read_op2(str(op2_file)) - - stress_attr = f"{element_type}_stress" - stress = getattr(model, stress_attr)[subcase] - itime = 0 - - if stress.is_von_mises(): - von_mises = stress.data[itime, :, 9] # Column 9 is von Mises - max_stress = float(np.max(von_mises)) - - element_ids = [eid for (eid, node) in stress.element_node] - max_stress_elem = element_ids[np.argmax(von_mises)] - - return { - 'max_von_mises': max_stress, - 'max_stress_element': int(max_stress_elem) - } -``` - -#### 3. CBAR Force Extraction - -**API**: `model.cbar_force[subcase]` -**Data Structure**: `data[ntimes, nelements, 8]` -**Columns**: `[bm_a1, bm_a2, bm_b1, bm_b2, shear1, shear2, axial, torque]` - -```python -def extract_cbar_force(op2_file: Path, subcase: int = 1, direction: str = 'Z'): - """Extract forces from CBAR elements.""" - model = OP2() - model.read_op2(str(op2_file)) - - force = model.cbar_force[subcase] - itime = 0 - - direction_map = { - 'shear1': 4, 'shear2': 5, 'axial': 6, - 'Z': 6, # Commonly axial is Z direction - 'torque': 7 - } - - col_idx = direction_map.get(direction, 6) - forces = force.data[itime, :, col_idx] - - return { - f'max_{direction}_force': float(np.max(np.abs(forces))), - f'avg_{direction}_force': float(np.mean(np.abs(forces))), - f'min_{direction}_force': float(np.min(np.abs(forces))), - 'forces_array': forces.tolist() - } -``` - -## Workflow Integration - -### End-to-End Flow - -``` -User Natural Language Request - ↓ -Phase 2.7 LLM Analysis - ↓ -{ - "engineering_features": [ - { - "action": "extract_1d_element_forces", - "domain": "result_extraction", - "params": { - "element_types": ["CBAR"], - "result_type": "element_force", - "direction": "Z" - } - } - ] -} - ↓ -Phase 3 Research Agent - ↓ -1. Match request to CBAR force pattern -2. Generate extractor code -3. Save to optimization_engine/result_extractors/ - ↓ -Auto-Generated Extractor - ↓ -def extract_cbar_force(op2_file, subcase=1, direction='Z'): - # Complete working code - return {'max_Z_force': ..., 'avg_Z_force': ...} - ↓ -Optimization Runner Integration - ↓ -Trial N → Solve → Extract using generated code → Return results -``` - -### Example: Complete Automation - -**User Request**: -> "Extract CBAR element forces in Z direction, calculate average and minimum, create objective that minimizes min/avg ratio" - -**Phase 2.7 Output**: -```json -{ - "engineering_features": [ - { - "action": "extract_1d_element_forces", - "domain": "result_extraction", - "params": { - "element_types": ["CBAR"], - "result_type": "element_force", - "direction": "Z" - } - } - ], - "inline_calculations": [ - {"action": "calculate_average", "params": {"input": "forces_z"}}, - {"action": "find_minimum", "params": {"input": "forces_z"}} - ], - "post_processing_hooks": [ - { - "action": "comparison", - "params": { - "inputs": ["min_force", "avg_force"], - "operation": "ratio", - "output_name": "min_to_avg_ratio" - } - } - ] -} -``` - -**Phase 3 Generation**: -```python -# Auto-generated: optimization_engine/result_extractors/cbar_force_extractor.py - -def extract_cbar_force(op2_file: Path, subcase: int = 1, direction: str = 'Z'): - """ - Extract forces from CBAR elements. - Auto-generated by Atomizer Phase 3 - """ - model = OP2() - model.read_op2(str(op2_file)) - force = model.cbar_force[subcase] - # ... (complete implementation) - return { - 'max_Z_force': float(np.max(np.abs(forces))), - 'avg_Z_force': float(np.mean(np.abs(forces))), - 'min_Z_force': float(np.min(np.abs(forces))), - 'forces_array': forces.tolist() - } -``` - -**Phase 2.8 Inline Calculations**: -```python -avg_forces_z = sum(forces_z) / len(forces_z) -min_forces_z = min(forces_z) -``` - -**Phase 2.9 Hook**: -```python -# optimization_engine/plugins/post_calculation/min_to_avg_ratio_hook.py - -def min_to_avg_ratio_hook(context): - calculations = context.get('calculations', {}) - min_force = calculations.get('min_forces_z') - avg_force = calculations.get('avg_forces_z') - result = min_force / avg_force - return {'min_to_avg_ratio': result, 'objective': result} -``` - -**Result**: LLM-enhanced optimization setup from natural language with flexible automation! 🚀 - -## Testing - -### Test Results - -**Test File**: [tests/test_pynastran_research_agent.py](../optimization_engine/pynastran_research_agent.py) - -``` -================================================================================ -Phase 3: pyNastran Research Agent Test -================================================================================ - -Test Request: - Action: extract_1d_element_forces - Description: Extract element forces from CBAR in Z direction from OP2 - -1. Researching extraction pattern... - Found pattern: cbar_force - API path: model.cbar_force[subcase] - -2. Generating extractor code... - -================================================================================ -Generated Extractor Code: -================================================================================ -[70 lines of complete, executable Python code] - -[OK] Saved to: generated_extractors/cbar_force_extractor.py -``` - -**Real-World Test**: Bracket OP2 File - -``` -================================================================================ -Testing Phase 3 pyNastran Research Agent on Real OP2 File -================================================================================ - -1. Generating displacement extractor... - [OK] Saved to: generated_extractors/test_displacement_extractor.py - -2. Executing on real OP2 file... - [OK] Extraction successful! - -Results: - max_displacement: 0.36178338527679443 - max_disp_node: 91 - max_disp_x: 0.0029173935763537884 - max_disp_y: 0.07424411177635193 - max_disp_z: 0.3540833592414856 - -================================================================================ -Phase 3 Test: PASSED! -================================================================================ -``` - -## Knowledge Base Structure - -``` -knowledge_base/ -└── pynastran_patterns/ - ├── displacement.json - ├── solid_stress.json - ├── cbar_force.json - ├── cquad4_stress.json (future) - ├── cbar_stress.json (future) - └── eigenvector.json (future) -``` - -Each pattern file contains: -```json -{ - "name": "cbar_force", - "description": "Extract forces from CBAR elements", - "element_type": "CBAR", - "result_type": "force", - "code_template": "def extract_cbar_force(...):\n ...", - "api_path": "model.cbar_force[subcase]", - "data_structure": "data[ntimes, nelements, 8] where 8=[bm_a1, ...]", - "examples": ["forces = extract_cbar_force(Path('results.op2'), direction='Z')"] -} -``` - -## pyNastran Documentation Research - -### Documentation Sources - -The research agent learned patterns from these pyNastran documentation pages: - -1. **OP2 Overview** - - URL: https://pynastran-git.readthedocs.io/en/latest/reference/op2/index.html - - Key Learnings: Basic OP2 reading, result object structure - -2. **Displacement Results** - - URL: https://pynastran-git.readthedocs.io/en/latest/reference/op2/results/displacement.html - - Key Learnings: `model.displacements[subcase]`, data array structure - -3. **Stress Results** - - URL: https://pynastran-git.readthedocs.io/en/latest/reference/op2/results/stress.html - - Key Learnings: Element-specific stress objects, von Mises column indices - -4. **Element Forces** - - URL: https://pynastran-git.readthedocs.io/en/latest/reference/op2/results/force.html - - Key Learnings: CBAR force structure, column mapping for different force types - -### Learned Patterns - -| Element Type | Result Type | API Path | Data Columns | -|-------------|-------------|----------|--------------| -| General | Displacement | `model.displacements[subcase]` | `[tx, ty, tz, rx, ry, rz]` | -| CTETRA/CHEXA | Stress | `model.ctetra_stress[subcase]` | Column 9: von Mises | -| CBAR | Force | `model.cbar_force[subcase]` | `[bm_a1, bm_a2, bm_b1, bm_b2, shear1, shear2, axial, torque]` | - -## Next Steps (Phase 3.1+) - -### Immediate Integration Tasks - -1. **Connect Phase 3 to Phase 2.7 LLM** - - Parse `engineering_features` from LLM output - - Map to research agent requests - - Auto-generate extractors - -2. **Dynamic Extractor Loading** - - Create `optimization_engine/result_extractors/` directory - - Dynamic import of generated extractors - - Extractor registry for runtime lookup - -3. **Optimization Runner Integration** - - Update runner to use generated extractors - - Context passing between extractor → inline calc → hooks - - Error handling for missing results - -### Future Enhancements - -1. **Expand Pattern Library** - - CQUAD4/CTRIA3 stress patterns - - CBAR stress patterns - - Eigenvectors/eigenvalues - - Strain results - - Composite stress - -2. **Advanced Research Capabilities** - - Real-time WebFetch for unknown patterns - - LLM-assisted code generation for complex cases - - Pattern learning from user corrections - -3. **Multi-File Results** - - Combine OP2 + F06 extraction - - XDB result extraction - - Result validation across formats - -4. **Performance Optimization** - - Cached OP2 reading (don't re-read for multiple extractions) - - Parallel extraction for multiple result types - - Memory-efficient large file handling - -## Files Created/Modified - -### New Files - -1. **optimization_engine/pynastran_research_agent.py** (600+ lines) - - PyNastranResearchAgent class - - ExtractionPattern dataclass - - 3 core extraction patterns - - Pattern persistence methods - - Code generation logic - -2. **generated_extractors/cbar_force_extractor.py** - - Auto-generated test output - - Complete CBAR force extraction - -3. **generated_extractors/test_displacement_extractor.py** - - Auto-generated from real-world test - - Successfully extracted displacement from bracket OP2 - -4. **docs/SESSION_SUMMARY_PHASE_3.md** (this file) - - Complete Phase 3 documentation - -### Modified Files - -1. **docs/HOOK_ARCHITECTURE.md** - - Updated with Phase 2.9 integration details - - Added lifecycle hook examples - - Documented flexibility of hook placement - -## Summary - -Phase 3 successfully implements **automated OP2 extraction code generation** using pyNastran documentation research. Key achievements: - -- ✅ Documentation research via WebFetch -- ✅ Pattern extraction and storage -- ✅ Complete code generation from LLM requests -- ✅ Real-world validation on bracket OP2 file -- ✅ Knowledge base architecture -- ✅ 3 core extraction patterns (displacement, stress, force) - -This enables the **LLM-enhanced automation pipeline**: -- Phase 2.7: LLM analyzes natural language → engineering features -- Phase 2.8: Inline calculation code generation (optional) -- Phase 2.9: Post-processing hook generation (optional) -- **Phase 3: OP2 extraction code generation (optional)** - -Users can describe optimization goals in natural language and choose to leverage automated code generation, manual coding, or a hybrid approach! 🎉 - -## Related Documentation - -- [HOOK_ARCHITECTURE.md](HOOK_ARCHITECTURE.md) - Unified lifecycle hook system -- [SESSION_SUMMARY_PHASE_2_9.md](SESSION_SUMMARY_PHASE_2_9.md) - Hook generator -- [PHASE_2_7_LLM_INTEGRATION.md](PHASE_2_7_LLM_INTEGRATION.md) - LLM analysis -- [SESSION_SUMMARY_PHASE_2_8.md](SESSION_SUMMARY_PHASE_2_8.md) - Inline calculations diff --git a/docs/SESSION_SUMMARY_PHASE_3_1.md b/docs/SESSION_SUMMARY_PHASE_3_1.md deleted file mode 100644 index eb5956c0..00000000 --- a/docs/SESSION_SUMMARY_PHASE_3_1.md +++ /dev/null @@ -1,614 +0,0 @@ -# Session Summary: Phase 3.1 - Extractor Orchestration & Integration - -**Date**: 2025-01-16 -**Phase**: 3.1 - Complete End-to-End Automation Pipeline -**Status**: ✅ Complete - -## Overview - -Phase 3.1 completes the **LLM-enhanced automation pipeline** by integrating: -- **Phase 2.7**: LLM workflow analysis -- **Phase 3.0**: pyNastran research agent -- **Phase 2.8**: Inline code generation -- **Phase 2.9**: Post-processing hook generation - -The result: Users can describe optimization goals in natural language and choose to leverage automatic code generation, manual coding, or a hybrid approach! - -## Objectives Achieved - -### ✅ LLM-Enhanced Automation Pipeline - -**From User Request to Execution - Flexible LLM-Assisted Workflow:** - -``` -User Natural Language Request - ↓ -Phase 2.7 LLM Analysis - ↓ -Structured Engineering Features - ↓ -Phase 3.1 Extractor Orchestrator - ↓ -Phase 3.0 Research Agent (auto OP2 code generation) - ↓ -Generated Extractor Modules - ↓ -Dynamic Loading & Execution on OP2 - ↓ -Phase 2.8 Inline Calculations - ↓ -Phase 2.9 Post-Processing Hooks - ↓ -Final Objective Value → Optuna -``` - -### ✅ Core Capabilities - -1. **Extractor Orchestrator** - - Takes Phase 2.7 LLM output - - Generates extractors using Phase 3 research agent - - Manages extractor registry - - Provides dynamic loading and execution - -2. **Dynamic Code Generation** - - Automatic extractor generation from LLM requests - - Saved to `result_extractors/generated/` - - Smart parameter filtering per pattern type - - Executable on real OP2 files - -3. **Multi-Extractor Support** - - Generate multiple extractors in one workflow - - Mix displacement, stress, force extractors - - Each extractor gets appropriate pattern - -4. **End-to-End Testing** - - Successfully tested on real bracket OP2 file - - Extracted displacement: 0.361783mm - - Calculated normalized objective: 0.072357 - - Complete pipeline verified! - -## Architecture - -### ExtractorOrchestrator - -Core module: [optimization_engine/extractor_orchestrator.py](../optimization_engine/extractor_orchestrator.py) - -```python -class ExtractorOrchestrator: - """ - Orchestrates automatic extractor generation from LLM workflow analysis. - - Bridges Phase 2.7 (LLM analysis) and Phase 3 (pyNastran research) - to create complete end-to-end automation pipeline. - """ - - def __init__(self, extractors_dir=None, knowledge_base_path=None): - """Initialize with Phase 3 research agent.""" - self.research_agent = PyNastranResearchAgent(knowledge_base_path) - self.extractors: Dict[str, GeneratedExtractor] = {} - - def process_llm_workflow(self, llm_output: Dict) -> List[GeneratedExtractor]: - """ - Process Phase 2.7 LLM output and generate all required extractors. - - Args: - llm_output: Dict with engineering_features, inline_calculations, etc. - - Returns: - List of GeneratedExtractor objects - """ - # Process each extraction feature - # Generate extractor code using Phase 3 agent - # Save to files - # Register in session - - def load_extractor(self, extractor_name: str) -> Callable: - """Dynamically load a generated extractor module.""" - # Dynamic import using importlib - # Return the extractor function - - def execute_extractor(self, extractor_name: str, op2_file: Path, **kwargs) -> Dict: - """Load and execute an extractor on OP2 file.""" - # Load extractor function - # Filter parameters by pattern type - # Execute and return results -``` - -### GeneratedExtractor Dataclass - -```python -@dataclass -class GeneratedExtractor: - """Represents a generated extractor module.""" - name: str # Action name from LLM - file_path: Path # Where code is saved - function_name: str # Extracted from generated code - extraction_pattern: ExtractionPattern # From Phase 3 research agent - params: Dict[str, Any] # Parameters from LLM -``` - -### Directory Structure - -``` -optimization_engine/ -├── extractor_orchestrator.py # Phase 3.1: NEW -├── pynastran_research_agent.py # Phase 3.0 -├── hook_generator.py # Phase 2.9 -├── inline_code_generator.py # Phase 2.8 -└── result_extractors/ - ├── extractors.py # Manual extractors (legacy) - └── generated/ # Auto-generated extractors (NEW!) - ├── extract_displacement.py - ├── extract_1d_element_forces.py - └── extract_solid_stress.py -``` - -## Complete Workflow Example - -### User Request (Natural Language) - -> "Extract displacement from OP2, normalize by 5mm maximum allowed, and minimize" - -### Phase 2.7: LLM Analysis - -```json -{ - "engineering_features": [ - { - "action": "extract_displacement", - "domain": "result_extraction", - "description": "Extract displacement results from OP2 file", - "params": { - "result_type": "displacement" - } - } - ], - "inline_calculations": [ - { - "action": "find_maximum", - "params": {"input": "max_displacement"} - }, - { - "action": "normalize", - "params": { - "input": "max_displacement", - "reference": "max_allowed_disp", - "value": 5.0 - } - } - ], - "post_processing_hooks": [ - { - "action": "weighted_objective", - "params": { - "inputs": ["norm_disp"], - "weights": [1.0], - "objective": "minimize" - } - } - ] -} -``` - -### Phase 3.1: Orchestrator Processing - -```python -# Initialize orchestrator -orchestrator = ExtractorOrchestrator() - -# Process LLM output -extractors = orchestrator.process_llm_workflow(llm_output) - -# Result: extract_displacement.py generated -``` - -### Phase 3.0: Generated Extractor Code - -**File**: `result_extractors/generated/extract_displacement.py` - -```python -""" -Extract displacement results from OP2 file -Auto-generated by Atomizer Phase 3 - pyNastran Research Agent - -Pattern: displacement -Result Type: displacement -API: model.displacements[subcase] -""" - -from pathlib import Path -from typing import Dict, Any -import numpy as np -from pyNastran.op2.op2 import OP2 - - -def extract_displacement(op2_file: Path, subcase: int = 1): - """Extract displacement results from OP2 file.""" - model = OP2() - model.read_op2(str(op2_file)) - - disp = model.displacements[subcase] - itime = 0 # static case - - # Extract translation components - txyz = disp.data[itime, :, :3] - total_disp = np.linalg.norm(txyz, axis=1) - max_disp = np.max(total_disp) - - node_ids = [nid for (nid, grid_type) in disp.node_gridtype] - max_disp_node = node_ids[np.argmax(total_disp)] - - return { - 'max_displacement': float(max_disp), - 'max_disp_node': int(max_disp_node), - 'max_disp_x': float(np.max(np.abs(txyz[:, 0]))), - 'max_disp_y': float(np.max(np.abs(txyz[:, 1]))), - 'max_disp_z': float(np.max(np.abs(txyz[:, 2]))) - } -``` - -### Execution on Real OP2 - -```python -# Execute on bracket OP2 -result = orchestrator.execute_extractor( - 'extract_displacement', - Path('tests/bracket_sim1-solution_1.op2'), - subcase=1 -) - -# Result: -# { -# 'max_displacement': 0.361783, -# 'max_disp_node': 91, -# 'max_disp_x': 0.002917, -# 'max_disp_y': 0.074244, -# 'max_disp_z': 0.354083 -# } -``` - -### Phase 2.8: Inline Calculations (Auto-Generated) - -```python -# Auto-generated by Phase 2.8 -max_disp = result['max_displacement'] # 0.361783 -max_allowed_disp = 5.0 -norm_disp = max_disp / max_allowed_disp # 0.072357 -``` - -### Phase 2.9: Post-Processing Hook (Auto-Generated) - -```python -# Auto-generated hook in plugins/post_calculation/ -def weighted_objective_hook(context): - calculations = context.get('calculations', {}) - norm_disp = calculations.get('norm_disp') - - objective = 1.0 * norm_disp - - return {'weighted_objective': objective} - -# Result: weighted_objective = 0.072357 -``` - -### Final Result → Optuna - -``` -Trial N completed -Objective value: 0.072357 -``` - -**LLM-enhanced workflow with optional automation from user request to Optuna trial!** 🚀 - -## Key Integration Points - -### 1. LLM → Orchestrator - -**Input** (Phase 2.7 output): -```json -{ - "engineering_features": [ - { - "action": "extract_1d_element_forces", - "domain": "result_extraction", - "params": { - "element_types": ["CBAR"], - "direction": "Z" - } - } - ] -} -``` - -**Processing**: -```python -for feature in llm_output['engineering_features']: - if feature['domain'] == 'result_extraction': - extractor = orchestrator.generate_extractor_from_feature(feature) -``` - -### 2. Orchestrator → Research Agent - -**Request to Phase 3**: -```python -research_request = { - 'action': 'extract_1d_element_forces', - 'domain': 'result_extraction', - 'description': 'Extract element forces from CBAR in Z direction', - 'params': { - 'element_types': ['CBAR'], - 'direction': 'Z' - } -} - -pattern = research_agent.research_extraction(research_request) -code = research_agent.generate_extractor_code(research_request) -``` - -**Response**: -- `pattern`: ExtractionPattern(name='cbar_force', ...) -- `code`: Complete Python module string - -### 3. Generated Code → Execution - -**Dynamic Loading**: -```python -# Import the generated module -spec = importlib.util.spec_from_file_location(name, file_path) -module = importlib.util.module_from_spec(spec) -spec.loader.exec_module(module) - -# Get the function -extractor_func = getattr(module, function_name) - -# Execute -result = extractor_func(op2_file, **params) -``` - -### 4. Smart Parameter Filtering - -Different extraction patterns need different parameters: - -```python -if pattern_name == 'displacement': - # Only pass subcase (no direction, element_type, etc.) - params = {k: v for k, v in kwargs.items() if k in ['subcase']} - -elif pattern_name == 'cbar_force': - # Pass direction and subcase - params = {k: v for k, v in kwargs.items() if k in ['direction', 'subcase']} - -elif pattern_name == 'solid_stress': - # Pass element_type and subcase - params = {k: v for k, v in kwargs.items() if k in ['element_type', 'subcase']} -``` - -This prevents errors from passing irrelevant parameters! - -## Testing - -### Test File: [tests/test_phase_3_1_integration.py](../tests/test_phase_3_1_integration.py) - -**Test 1: End-to-End Workflow** - -``` -STEP 1: Phase 2.7 LLM Analysis - - 1 engineering feature - - 2 inline calculations - - 1 post-processing hook - -STEP 2: Phase 3.1 Orchestrator - - Generated 1 extractor (extract_displacement) - -STEP 3: Execution on Real OP2 - - OP2 File: bracket_sim1-solution_1.op2 - - Result: max_displacement = 0.361783mm at node 91 - -STEP 4: Inline Calculations - - norm_disp = 0.361783 / 5.0 = 0.072357 - -STEP 5: Post-Processing Hook - - weighted_objective = 0.072357 - -Result: PASSED! -``` - -**Test 2: Multiple Extractors** - -``` -LLM Output: - - extract_displacement - - extract_solid_stress - -Result: Generated 2 extractors - - extract_displacement (displacement pattern) - - extract_solid_stress (solid_stress pattern) - -Result: PASSED! -``` - -## Benefits - -### 1. LLM-Enhanced Flexibility - -**Traditional Manual Workflow**: -``` -1. User describes optimization -2. Engineer manually writes OP2 extractor -3. Engineer manually writes calculations -4. Engineer manually writes objective function -5. Engineer integrates with optimization runner -Time: Hours to days -``` - -**LLM-Enhanced Workflow**: -``` -1. User describes optimization in natural language -2. System offers to generate code automatically OR user writes custom code -3. Hybrid approach: mix automated and manual components as needed -Time: Seconds to minutes (user choice) -``` - -### 2. Reduced Learning Curve - -LLM assistance helps users who are unfamiliar with: -- pyNastran API (can still write custom extractors if desired) -- OP2 file structure (LLM provides templates) -- Python coding best practices (LLM generates examples) -- Optimization framework patterns (LLM suggests approaches) - -Users can describe goals in natural language and choose their preferred level of automation! - -### 3. Quality LLM-Generated Code - -When using automated generation, code uses: -- ✅ Proven extraction patterns from research agent -- ✅ Correct API paths from documentation -- ✅ Proper data structure access -- ✅ Error handling and validation - -Users can review, modify, or replace generated code as needed! - -### 4. Extensible - -Adding new extraction patterns: -1. Research agent learns from pyNastran docs -2. Stores pattern in knowledge base -3. Available immediately for all future requests - -## Future Enhancements - -### Phase 3.2: Optimization Runner Integration - -**Next Step**: Integrate orchestrator with optimization runner for complete automation: - -```python -class OptimizationRunner: - def __init__(self, llm_output: Dict): - # Process LLM output - self.orchestrator = ExtractorOrchestrator() - self.extractors = self.orchestrator.process_llm_workflow(llm_output) - - # Generate inline calculations (Phase 2.8) - self.calculator = InlineCodeGenerator() - self.calculations = self.calculator.generate(llm_output) - - # Generate hooks (Phase 2.9) - self.hook_gen = HookGenerator() - self.hooks = self.hook_gen.generate_lifecycle_hooks(llm_output) - - def run_trial(self, trial_number, design_variables): - # Run NX solve - op2_file = self.nx_solver.run(...) - - # Extract results using generated extractors - results = {} - for extractor_name in self.extractors: - results.update( - self.orchestrator.execute_extractor(extractor_name, op2_file) - ) - - # Execute inline calculations - calculations = self.calculator.execute(results) - - # Execute hooks - hook_results = self.hook_manager.execute_hooks('post_calculation', { - 'results': results, - 'calculations': calculations - }) - - # Return objective - return hook_results.get('objective') -``` - -### Phase 3.3: Error Recovery - -- Detect extraction failures -- Attempt pattern variations -- Fallback to generic extractors -- Log failures for pattern learning - -### Phase 3.4: Performance Optimization - -- Cache OP2 reading for multiple extractions -- Parallel extraction for multiple result types -- Reuse loaded models across trials - -### Phase 3.5: Pattern Expansion - -- Learn patterns for more element types -- Composite stress/strain -- Eigenvectors/eigenvalues -- F06 result extraction -- XDB database extraction - -## Files Created/Modified - -### New Files - -1. **optimization_engine/extractor_orchestrator.py** (380+ lines) - - ExtractorOrchestrator class - - GeneratedExtractor dataclass - - Dynamic loading and execution - - Parameter filtering logic - -2. **tests/test_phase_3_1_integration.py** (200+ lines) - - End-to-end workflow test - - Multiple extractors test - - Complete pipeline validation - -3. **optimization_engine/result_extractors/generated/** (directory) - - extract_displacement.py (auto-generated) - - extract_1d_element_forces.py (auto-generated) - - extract_solid_stress.py (auto-generated) - -4. **docs/SESSION_SUMMARY_PHASE_3_1.md** (this file) - - Complete Phase 3.1 documentation - -### Modified Files - -None - Phase 3.1 is purely additive! - -## Summary - -Phase 3.1 successfully completes the **LLM-enhanced automation pipeline**: - -- ✅ Orchestrator integrates Phase 2.7 + Phase 3.0 -- ✅ Optional automatic extractor generation from LLM output -- ✅ Dynamic loading and execution on real OP2 files -- ✅ Smart parameter filtering per pattern type -- ✅ Multi-extractor support -- ✅ Complete end-to-end test passed -- ✅ Extraction successful: max_disp=0.361783mm -- ✅ Normalized objective calculated: 0.072357 - -**LLM-Enhanced Workflow Verified:** -``` -Natural Language Request - ↓ -Phase 2.7 LLM → Engineering Features - ↓ -Phase 3.1 Orchestrator → Generated Extractors (or manual extractors) - ↓ -Phase 3.0 Research Agent → OP2 Extraction Code (optional) - ↓ -Execution on Real OP2 → Results - ↓ -Phase 2.8 Inline Calc → Calculations (optional) - ↓ -Phase 2.9 Hooks → Objective Value (optional) - ↓ -Optuna Trial Complete - -LLM-ENHANCED WITH USER FLEXIBILITY! 🚀 -``` - -Users can describe optimization goals in natural language and choose to leverage automated code generation, write custom code, or use a hybrid approach as needed! - -## Related Documentation - -- [SESSION_SUMMARY_PHASE_3.md](SESSION_SUMMARY_PHASE_3.md) - Phase 3.0 pyNastran research -- [SESSION_SUMMARY_PHASE_2_9.md](SESSION_SUMMARY_PHASE_2_9.md) - Hook generation -- [SESSION_SUMMARY_PHASE_2_8.md](SESSION_SUMMARY_PHASE_2_8.md) - Inline calculations -- [PHASE_2_7_LLM_INTEGRATION.md](PHASE_2_7_LLM_INTEGRATION.md) - LLM workflow analysis -- [HOOK_ARCHITECTURE.md](HOOK_ARCHITECTURE.md) - Unified lifecycle hooks diff --git a/docs/STUDY_CONTINUATION_STANDARD.md b/docs/STUDY_CONTINUATION_STANDARD.md deleted file mode 100644 index c8f78614..00000000 --- a/docs/STUDY_CONTINUATION_STANDARD.md +++ /dev/null @@ -1,414 +0,0 @@ -# Study Continuation - Atomizer Standard Feature - -**Date**: November 20, 2025 -**Status**: ✅ Implemented as Standard Feature - ---- - -## Overview - -Study continuation is now a **standardized Atomizer feature** for dashboard integration. It provides a clean API for continuing existing optimization studies with additional trials. - -Previously, continuation was improvised on-demand. Now it's a first-class feature alongside "Start New Optimization". - ---- - -## Module - -[optimization_engine/study_continuation.py](../optimization_engine/study_continuation.py) - ---- - -## API - -### Main Function: `continue_study()` - -```python -from optimization_engine.study_continuation import continue_study - -results = continue_study( - study_dir=Path("studies/my_study"), - additional_trials=50, - objective_function=my_objective, - design_variables={'param1': (0, 10), 'param2': (0, 100)}, - target_value=115.0, - tolerance=0.1, - verbose=True -) -``` - -**Returns**: -```python -{ - 'study': optuna.Study, # The study object - 'total_trials': 100, # Total after continuation - 'successful_trials': 95, # Completed trials - 'pruned_trials': 5, # Failed trials - 'best_value': 0.05, # Best objective value - 'best_params': {...}, # Best parameters - 'target_achieved': True # If target specified -} -``` - -### Utility Functions - -#### `can_continue_study()` - -Check if a study is ready for continuation: - -```python -from optimization_engine.study_continuation import can_continue_study - -can_continue, message = can_continue_study(Path("studies/my_study")) - -if can_continue: - print(f"Ready: {message}") - # message: "Study 'my_study' ready (current trials: 50)" -else: - print(f"Cannot continue: {message}") - # message: "No study.db found. Run initial optimization first." -``` - -#### `get_study_status()` - -Get current study information: - -```python -from optimization_engine.study_continuation import get_study_status - -status = get_study_status(Path("studies/my_study")) - -if status: - print(f"Study: {status['study_name']}") - print(f"Trials: {status['total_trials']}") - print(f"Success rate: {status['successful_trials']/status['total_trials']*100:.1f}%") - print(f"Best: {status['best_value']}") -else: - print("Study not found or invalid") -``` - -**Returns**: -```python -{ - 'study_name': 'my_study', - 'total_trials': 50, - 'successful_trials': 47, - 'pruned_trials': 3, - 'pruning_rate': 0.06, - 'best_value': 0.42, - 'best_params': {'param1': 5.2, 'param2': 78.3} -} -``` - ---- - -## Dashboard Integration - -### UI Workflow - -When user selects a study in the dashboard: - -``` -1. User clicks on study → Dashboard calls get_study_status() - -2. Dashboard shows study info card: - ┌──────────────────────────────────────┐ - │ Study: circular_plate_test │ - │ Current Trials: 50 │ - │ Success Rate: 94% │ - │ Best Result: 0.42 Hz error │ - │ │ - │ [Continue Study] [View Results] │ - └──────────────────────────────────────┘ - -3. User clicks "Continue Study" → Shows form: - ┌──────────────────────────────────────┐ - │ Continue Optimization │ - │ │ - │ Additional Trials: [50] │ - │ Target Value (optional): [115.0] │ - │ Tolerance (optional): [0.1] │ - │ │ - │ [Cancel] [Start] │ - └──────────────────────────────────────┘ - -4. User clicks "Start" → Dashboard calls continue_study() - -5. Progress shown in real-time (like initial optimization) -``` - -### Example Dashboard Code - -```python -from pathlib import Path -from optimization_engine.study_continuation import ( - get_study_status, - can_continue_study, - continue_study -) - -def show_study_panel(study_dir: Path): - """Display study panel with continuation option.""" - - # Get current status - status = get_study_status(study_dir) - - if not status: - print("Study not found or incomplete") - return - - # Show study info - print(f"Study: {status['study_name']}") - print(f"Current Trials: {status['total_trials']}") - print(f"Best Result: {status['best_value']:.4f}") - - # Check if can continue - can_continue, message = can_continue_study(study_dir) - - if can_continue: - # Enable "Continue" button - print("✓ Ready to continue") - else: - # Disable "Continue" button, show reason - print(f"✗ Cannot continue: {message}") - - -def handle_continue_button_click(study_dir: Path, additional_trials: int): - """Handle user clicking 'Continue Study' button.""" - - # Load the objective function for this study - # (Dashboard needs to reconstruct this from study config) - from studies.my_study.run_optimization import objective - - # Continue the study - results = continue_study( - study_dir=study_dir, - additional_trials=additional_trials, - objective_function=objective, - verbose=True # Stream output to dashboard - ) - - # Show completion notification - if results.get('target_achieved'): - notify_user(f"Target achieved! Best: {results['best_value']:.4f}") - else: - notify_user(f"Completed {additional_trials} trials. Best: {results['best_value']:.4f}") -``` - ---- - -## Comparison: Old vs New - -### Before (Improvised) - -Each study needed a custom `continue_optimization.py`: - -``` -studies/my_study/ -├── run_optimization.py # Standard (from protocol) -├── continue_optimization.py # Improvised (custom for each study) -└── 2_results/ - └── study.db -``` - -**Problems**: -- Not standardized across studies -- Manual creation required -- No dashboard integration possible -- Inconsistent behavior - -### After (Standardized) - -All studies use the same continuation API: - -``` -studies/my_study/ -├── run_optimization.py # Standard (from protocol) -└── 2_results/ - └── study.db - -# No continue_optimization.py needed! -# Just call continue_study() from anywhere -``` - -**Benefits**: -- ✅ Standardized behavior -- ✅ Dashboard-ready API -- ✅ Consistent across all studies -- ✅ No per-study custom code - ---- - -## Usage Examples - -### Example 1: Simple Continuation - -```python -from pathlib import Path -from optimization_engine.study_continuation import continue_study -from studies.my_study.run_optimization import objective - -# Continue with 50 more trials -results = continue_study( - study_dir=Path("studies/my_study"), - additional_trials=50, - objective_function=objective -) - -print(f"New best: {results['best_value']}") -``` - -### Example 2: With Target Checking - -```python -# Continue until target is met or 100 additional trials -results = continue_study( - study_dir=Path("studies/circular_plate_test"), - additional_trials=100, - objective_function=objective, - target_value=115.0, - tolerance=0.1 -) - -if results['target_achieved']: - print(f"Success! Achieved in {results['total_trials']} total trials") -else: - print(f"Target not reached. Best: {results['best_value']}") -``` - -### Example 3: Dashboard Batch Processing - -```python -from pathlib import Path -from optimization_engine.study_continuation import get_study_status - -# Find all studies that can be continued -studies_dir = Path("studies") - -for study_dir in studies_dir.iterdir(): - if not study_dir.is_dir(): - continue - - status = get_study_status(study_dir) - - if status and status['pruning_rate'] > 0.10: - print(f"⚠️ {status['study_name']}: High pruning rate ({status['pruning_rate']*100:.1f}%)") - print(f" Consider investigating before continuing") - elif status: - print(f"✓ {status['study_name']}: {status['total_trials']} trials, best={status['best_value']:.4f}") -``` - ---- - -## File Structure - -### Standard Study Directory - -``` -studies/my_study/ -├── 1_setup/ -│ ├── model/ # FEA model files -│ ├── workflow_config.json # Contains study_name -│ └── optimization_config.json -├── 2_results/ -│ ├── study.db # Optuna database (required for continuation) -│ ├── optimization_history_incremental.json -│ └── intelligent_optimizer/ -└── 3_reports/ - └── OPTIMIZATION_REPORT.md -``` - -**Required for Continuation**: -- `1_setup/workflow_config.json` (contains study_name) -- `2_results/study.db` (Optuna database with trial data) - ---- - -## Error Handling - -The API provides clear error messages: - -```python -# Study doesn't exist -can_continue_study(Path("studies/nonexistent")) -# Returns: (False, "No workflow_config.json found in studies/nonexistent/1_setup") - -# Study exists but not run yet -can_continue_study(Path("studies/new_study")) -# Returns: (False, "No study.db found. Run initial optimization first.") - -# Study database corrupted -can_continue_study(Path("studies/bad_study")) -# Returns: (False, "Study 'bad_study' not found in database") - -# Study has no trials -can_continue_study(Path("studies/empty_study")) -# Returns: (False, "Study exists but has no trials yet") -``` - ---- - -## Dashboard Buttons - -### Two Standard Actions - -Every study in the dashboard should have: - -1. **"Start New Optimization"** → Calls `run_optimization.py` - - Requires: Study setup complete - - Creates: Fresh study database - - Use when: Starting from scratch - -2. **"Continue Study"** → Calls `continue_study()` - - Requires: Existing study.db with trials - - Preserves: All existing trial data - - Use when: Adding more iterations - -Both are now **standardized Atomizer features**. - ---- - -## Testing - -Test the continuation API: - -```bash -# Test status check -python -c " -from pathlib import Path -from optimization_engine.study_continuation import get_study_status - -status = get_study_status(Path('studies/circular_plate_protocol10_v2_1_test')) -if status: - print(f\"Study: {status['study_name']}\") - print(f\"Trials: {status['total_trials']}\") - print(f\"Best: {status['best_value']}\") -" - -# Test continuation check -python -c " -from pathlib import Path -from optimization_engine.study_continuation import can_continue_study - -can_continue, msg = can_continue_study(Path('studies/circular_plate_protocol10_v2_1_test')) -print(f\"Can continue: {can_continue}\") -print(f\"Message: {msg}\") -" -``` - ---- - -## Summary - -| Feature | Before | After | -|---------|--------|-------| -| Implementation | Improvised per study | Standardized module | -| Dashboard integration | Not possible | Full API support | -| Consistency | Varies by study | Uniform behavior | -| Error handling | Manual | Built-in with messages | -| Study status | Manual queries | `get_study_status()` | -| Continuation check | Manual | `can_continue_study()` | - -**Status**: ✅ Ready for dashboard integration - -**Module**: [optimization_engine/study_continuation.py](../optimization_engine/study_continuation.py) diff --git a/docs/STUDY_ORGANIZATION.md b/docs/STUDY_ORGANIZATION.md deleted file mode 100644 index b168667e..00000000 --- a/docs/STUDY_ORGANIZATION.md +++ /dev/null @@ -1,518 +0,0 @@ -# Study Organization Guide - -**Date**: 2025-11-17 -**Purpose**: Document recommended study directory structure and organization principles - ---- - -## Current Organization Analysis - -### Study Directory: `studies/simple_beam_optimization/` - -**Current Structure**: -``` -studies/simple_beam_optimization/ -├── model/ # Base CAD/FEM model (reference) -│ ├── Beam.prt -│ ├── Beam_sim1.sim -│ ├── beam_sim1-solution_1.op2 -│ ├── beam_sim1-solution_1.f06 -│ └── comprehensive_results_analysis.json -│ -├── substudies/ # All optimization runs -│ ├── benchmarking/ -│ │ ├── benchmark_results.json -│ │ └── BENCHMARK_REPORT.md -│ ├── initial_exploration/ -│ │ ├── config.json -│ │ └── optimization_config.json -│ ├── validation_3trials/ -│ │ ├── trial_000/ -│ │ ├── trial_001/ -│ │ ├── trial_002/ -│ │ ├── best_trial.json -│ │ └── optuna_study.pkl -│ ├── validation_4d_3trials/ -│ │ └── [similar structure] -│ └── full_optimization_50trials/ -│ ├── trial_000/ -│ ├── ... trial_049/ -│ ├── plots/ # NEW: Auto-generated plots -│ ├── history.json -│ ├── best_trial.json -│ └── optuna_study.pkl -│ -├── README.md # Study overview -├── study_metadata.json # Study metadata -├── beam_optimization_config.json # Main configuration -├── baseline_validation.json # Baseline results -├── COMPREHENSIVE_BENCHMARK_RESULTS.md -├── OPTIMIZATION_RESULTS_50TRIALS.md -└── run_optimization.py # Study-specific runner - -``` - ---- - -## Assessment - -### ✅ What's Working Well - -1. **Substudy Isolation**: Each optimization run (substudy) is self-contained with its own trial directories, making it easy to compare different optimization strategies. - -2. **Centralized Model**: The `model/` directory serves as a reference CAD/FEM model, which all substudies copy from. - -3. **Configuration at Study Level**: `beam_optimization_config.json` provides the main configuration that substudies inherit from. - -4. **Study-Level Documentation**: `README.md` and results markdown files at the study level provide high-level overviews. - -5. **Clear Hierarchy**: - - Study = Overall project (e.g., "optimize this beam") - - Substudy = Specific optimization run (e.g., "50 trials with TPE sampler") - - Trial = Individual design evaluation - -### ⚠️ Issues Found - -1. **Documentation Scattered**: Results documentation is at the study level (`OPTIMIZATION_RESULTS_50TRIALS.md`) but describes a specific substudy (`full_optimization_50trials`). - -2. **Benchmarking Placement**: `substudies/benchmarking/` is not really a "substudy" - it's a validation step that should happen before optimization. - -3. **Missing Substudy Metadata**: Some substudies lack their own README or summary files to explain what they tested. - -4. **Inconsistent Naming**: `validation_3trials` vs `validation_4d_3trials` - unclear what distinguishes them without investigation. - -5. **Study Metadata Incomplete**: `study_metadata.json` lists only "initial_exploration" substudy, but there are 5 substudies present. - ---- - -## Recommended Organization - -### Proposed Structure - -``` -studies/simple_beam_optimization/ -│ -├── 1_setup/ # NEW: Pre-optimization setup -│ ├── model/ # Reference CAD/FEM model -│ │ ├── Beam.prt -│ │ ├── Beam_sim1.sim -│ │ └── ... -│ ├── benchmarking/ # Baseline validation -│ │ ├── benchmark_results.json -│ │ └── BENCHMARK_REPORT.md -│ └── baseline_validation.json -│ -├── 2_substudies/ # Optimization runs -│ ├── 01_initial_exploration/ -│ │ ├── README.md # What was tested, why -│ │ ├── config.json -│ │ ├── trial_000/ -│ │ ├── ... -│ │ └── results_summary.md # Substudy-specific results -│ ├── 02_validation_3d_3trials/ -│ │ └── [similar structure] -│ ├── 03_validation_4d_3trials/ -│ │ └── [similar structure] -│ └── 04_full_optimization_50trials/ -│ ├── README.md -│ ├── trial_000/ -│ ├── ... trial_049/ -│ ├── plots/ -│ ├── history.json -│ ├── best_trial.json -│ ├── OPTIMIZATION_RESULTS.md # Moved from study level -│ └── cleanup_log.json -│ -├── 3_reports/ # NEW: Study-level analysis -│ ├── COMPREHENSIVE_BENCHMARK_RESULTS.md -│ ├── COMPARISON_ALL_SUBSTUDIES.md # NEW: Compare substudies -│ └── final_recommendations.md # NEW: Engineering insights -│ -├── README.md # Study overview -├── study_metadata.json # Updated with all substudies -├── beam_optimization_config.json # Main configuration -└── run_optimization.py # Study-specific runner -``` - -### Key Changes - -1. **Numbered Directories**: Indicate workflow sequence (setup → substudies → reports) - -2. **Numbered Substudies**: Chronological naming (01_, 02_, 03_) makes progression clear - -3. **Moved Benchmarking**: From `substudies/` to `1_setup/` (it's pre-optimization) - -4. **Substudy-Level Documentation**: Each substudy has: - - `README.md` - What was tested, parameters, hypothesis - - `OPTIMIZATION_RESULTS.md` - Results and analysis - -5. **Centralized Reports**: All comparative analysis and final recommendations in `3_reports/` - -6. **Updated Metadata**: `study_metadata.json` tracks all substudies with status - ---- - -## Comparison: Current vs Proposed - -| Aspect | Current | Proposed | Benefit | -|--------|---------|----------|---------| -| **Substudy naming** | Descriptive only | Numbered + descriptive | Chronological clarity | -| **Documentation** | Mixed levels | Clear hierarchy | Easier to find results | -| **Benchmarking** | In substudies/ | In 1_setup/ | Reflects true purpose | -| **Model location** | study root | 1_setup/model/ | Grouped with setup | -| **Reports** | Study root | 3_reports/ | Centralized analysis | -| **Substudy docs** | Minimal | README + results | Self-documenting | -| **Metadata** | Incomplete | All substudies tracked | Accurate status | - ---- - -## Migration Guide - -### Option 1: Reorganize Existing Study (Recommended) - -**Steps**: -1. Create new directory structure -2. Move files to new locations -3. Update `study_metadata.json` -4. Update file references in documentation -5. Create missing substudy READMEs - -**Commands**: -```bash -# Create new structure -mkdir -p studies/simple_beam_optimization/1_setup/model -mkdir -p studies/simple_beam_optimization/1_setup/benchmarking -mkdir -p studies/simple_beam_optimization/2_substudies -mkdir -p studies/simple_beam_optimization/3_reports - -# Move model -mv studies/simple_beam_optimization/model/* studies/simple_beam_optimization/1_setup/model/ - -# Move benchmarking -mv studies/simple_beam_optimization/substudies/benchmarking/* studies/simple_beam_optimization/1_setup/benchmarking/ - -# Rename and move substudies -mv studies/simple_beam_optimization/substudies/initial_exploration studies/simple_beam_optimization/2_substudies/01_initial_exploration -mv studies/simple_beam_optimization/substudies/validation_3trials studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials -mv studies/simple_beam_optimization/substudies/validation_4d_3trials studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials -mv studies/simple_beam_optimization/substudies/full_optimization_50trials studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials - -# Move reports -mv studies/simple_beam_optimization/COMPREHENSIVE_BENCHMARK_RESULTS.md studies/simple_beam_optimization/3_reports/ -mv studies/simple_beam_optimization/OPTIMIZATION_RESULTS_50TRIALS.md studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/ - -# Clean up -rm -rf studies/simple_beam_optimization/substudies/ -rm -rf studies/simple_beam_optimization/model/ -``` - -### Option 2: Apply to Future Studies Only - -Keep existing study as-is, apply new organization to future studies. - -**When to Use**: -- Current study is complete and well-understood -- Reorganization would break existing scripts/references -- Want to test new organization before migrating - ---- - -## Best Practices - -### Study-Level Files - -**Required**: -- `README.md` - High-level overview, purpose, design variables, objectives -- `study_metadata.json` - Metadata, status, substudy registry -- `beam_optimization_config.json` - Main configuration (inheritable) -- `run_optimization.py` - Study-specific runner script - -**Optional**: -- `CHANGELOG.md` - Track configuration changes across substudies -- `LESSONS_LEARNED.md` - Engineering insights, dead ends avoided - -### Substudy-Level Files - -**Required** (Generated by Runner): -- `trial_XXX/` - Trial directories with CAD/FEM files and results.json -- `history.json` - Full optimization history -- `best_trial.json` - Best trial metadata -- `optuna_study.pkl` - Optuna study object -- `config.json` - Substudy-specific configuration - -**Required** (User-Created): -- `README.md` - Purpose, hypothesis, parameter choices - -**Optional** (Auto-Generated): -- `plots/` - Visualization plots (if post_processing.generate_plots = true) -- `cleanup_log.json` - Model cleanup statistics (if post_processing.cleanup_models = true) - -**Optional** (User-Created): -- `OPTIMIZATION_RESULTS.md` - Detailed analysis and interpretation - -### Trial-Level Files - -**Always Kept** (Small, Critical): -- `results.json` - Extracted objectives, constraints, design variables - -**Kept for Top-N Trials** (Large, Useful): -- `Beam.prt` - CAD model -- `Beam_sim1.sim` - Simulation setup -- `beam_sim1-solution_1.op2` - FEA results (binary) -- `beam_sim1-solution_1.f06` - FEA results (text) - -**Cleaned for Poor Trials** (Large, Less Useful): -- All `.prt`, `.sim`, `.fem`, `.op2`, `.f06` files deleted -- Only `results.json` preserved - ---- - -## Naming Conventions - -### Substudy Names - -**Format**: `NN_descriptive_name` - -**Examples**: -- `01_initial_exploration` - First exploration of design space -- `02_validation_3d_3trials` - Validate 3 design variables work -- `03_validation_4d_3trials` - Validate 4 design variables work -- `04_full_optimization_50trials` - Full optimization run -- `05_refined_search_30trials` - Refined search in promising region -- `06_sensitivity_analysis` - Parameter sensitivity study - -**Guidelines**: -- Start with two-digit number (01, 02, ..., 99) -- Use underscores for spaces -- Be concise but descriptive -- Include trial count if relevant - -### Study Names - -**Format**: `descriptive_name` (no numbering) - -**Examples**: -- `simple_beam_optimization` - Optimize simple beam -- `bracket_displacement_maximizing` - Maximize bracket displacement -- `engine_mount_fatigue` - Engine mount fatigue optimization - -**Guidelines**: -- Use underscores for spaces -- Include part name and optimization goal -- Avoid dates (use substudy numbering for chronology) - ---- - -## Metadata Format - -### study_metadata.json - -**Recommended Format**: -```json -{ - "study_name": "simple_beam_optimization", - "description": "Minimize displacement and weight of beam with existing loadcases", - "created": "2025-11-17T10:24:09.613688", - "status": "active", - "design_variables": ["beam_half_core_thickness", "beam_face_thickness", "holes_diameter", "hole_count"], - "objectives": ["minimize_displacement", "minimize_stress", "minimize_mass"], - "constraints": ["displacement_limit"], - "substudies": [ - { - "name": "01_initial_exploration", - "created": "2025-11-17T10:30:00", - "status": "completed", - "trials": 10, - "purpose": "Explore design space boundaries" - }, - { - "name": "02_validation_3d_3trials", - "created": "2025-11-17T11:00:00", - "status": "completed", - "trials": 3, - "purpose": "Validate 3D parameter updates (without hole_count)" - }, - { - "name": "03_validation_4d_3trials", - "created": "2025-11-17T12:00:00", - "status": "completed", - "trials": 3, - "purpose": "Validate 4D parameter updates (with hole_count)" - }, - { - "name": "04_full_optimization_50trials", - "created": "2025-11-17T13:00:00", - "status": "completed", - "trials": 50, - "purpose": "Full optimization with all 4 design variables" - } - ], - "last_modified": "2025-11-17T15:30:00" -} -``` - -### Substudy README.md Template - -```markdown -# [Substudy Name] - -**Date**: YYYY-MM-DD -**Status**: [planned | running | completed | failed] -**Trials**: N - -## Purpose - -[Why this substudy was created, what hypothesis is being tested] - -## Configuration Changes - -[Compared to previous substudy or baseline config, what changed?] - -- Design variable bounds: [if changed] -- Objective weights: [if changed] -- Sampler settings: [if changed] - -## Expected Outcome - -[What do you hope to learn or achieve?] - -## Actual Results - -[Fill in after completion] - -- Best objective: X.XX -- Feasible designs: N / N_total -- Key findings: [summary] - -## Next Steps - -[What substudy should follow based on these results?] -``` - ---- - -## Workflow Integration - -### Creating a New Substudy - -**Steps**: -1. Determine substudy number (next in sequence) -2. Create substudy README.md with purpose and changes -3. Update configuration if needed -4. Run optimization: - ```bash - python run_optimization.py --substudy-name "05_refined_search_30trials" - ``` -5. After completion: - - Review results - - Update substudy README.md with findings - - Create OPTIMIZATION_RESULTS.md if significant - - Update study_metadata.json - -### Comparing Substudies - -**Create Comparison Report**: -```markdown -# Substudy Comparison - -| Substudy | Trials | Best Obj | Feasible | Key Finding | -|----------|--------|----------|----------|-------------| -| 01_initial_exploration | 10 | 1250.3 | 0/10 | Design space too large | -| 02_validation_3d_3trials | 3 | 1180.5 | 0/3 | 3D updates work | -| 03_validation_4d_3trials | 3 | 1120.2 | 0/3 | hole_count updates work | -| 04_full_optimization_50trials | 50 | 842.6 | 0/50 | No feasible designs found | - -**Conclusion**: Constraint appears infeasible. Recommend relaxing displacement limit. -``` - ---- - -## Benefits of Proposed Organization - -### For Users - -1. **Clarity**: Numbered substudies show chronological progression -2. **Self-Documenting**: Each substudy explains its purpose -3. **Easy Comparison**: All results in one place (3_reports/) -4. **Less Clutter**: Study root only has essential files - -### For Developers - -1. **Predictable Structure**: Scripts can rely on consistent paths -2. **Automated Discovery**: Easy to find all substudies programmatically -3. **Version Control**: Clear history through numbered substudies -4. **Scalability**: Works for 5 substudies or 50 - -### For Collaboration - -1. **Onboarding**: New team members can understand study progression quickly -2. **Documentation**: Substudy READMEs explain decisions made -3. **Reproducibility**: Clear configuration history -4. **Communication**: Easy to reference specific substudies in discussions - ---- - -## FAQ - -### Q: Should I reorganize my existing study? - -**A**: Only if: -- Study is still active (more substudies planned) -- Current organization is causing confusion -- You have time to update documentation references - -Otherwise, apply to future studies only. - -### Q: What if my substudy doesn't have a fixed trial count? - -**A**: Use descriptive name instead: -- `05_refined_search_until_feasible` -- `06_sensitivity_sweep` -- `07_validation_run` - -### Q: Can I delete old substudies? - -**A**: Generally no. Keep for: -- Historical record -- Lessons learned -- Reproducibility - -If disk space is critical: -- Use model cleanup to delete CAD/FEM files -- Archive old substudies to external storage -- Keep metadata and results.json files - -### Q: Should benchmarking be a substudy? - -**A**: No. Benchmarking validates the baseline model before optimization. It belongs in `1_setup/benchmarking/`. - -### Q: How do I handle multi-stage optimizations? - -**A**: Create separate substudies: -- `05_stage1_meet_constraint_20trials` -- `06_stage2_minimize_mass_30trials` - -Document the relationship in substudy READMEs. - ---- - -## Summary - -**Current Organization**: Functional but has room for improvement -- ✅ Substudy isolation works well -- ⚠️ Documentation scattered across levels -- ⚠️ Chronology unclear from names alone - -**Proposed Organization**: Clearer hierarchy and progression -- 📁 `1_setup/` - Pre-optimization (model, benchmarking) -- 📁 `2_substudies/` - Numbered optimization runs -- 📁 `3_reports/` - Comparative analysis - -**Next Steps**: -1. Decide: Reorganize existing study or apply to future only -2. If reorganizing: Follow migration guide -3. Update `study_metadata.json` with all substudies -4. Create substudy README templates -5. Document lessons learned in study-level docs - -**Bottom Line**: The proposed organization makes it easier to understand what was done, why it was done, and what was learned. diff --git a/docs/SYSTEM_CONFIGURATION.md b/docs/SYSTEM_CONFIGURATION.md deleted file mode 100644 index 4319f2d0..00000000 --- a/docs/SYSTEM_CONFIGURATION.md +++ /dev/null @@ -1,144 +0,0 @@ -# System Configuration - -> **Critical**: These are the ONLY paths and environments to be used unless explicitly reconfigured by the user. - ---- - -## Python Environment - -**Environment Name**: `atomizer` - -**Path**: `c:/Users/antoi/anaconda3/envs/atomizer/python.exe` - -**Usage**: ALL Python scripts and commands MUST use this environment. - -### Examples: -```bash -# Correct -"c:/Users/antoi/anaconda3/envs/atomizer/python.exe" script.py - -# WRONG - Never use test_env -"c:/Users/antoi/anaconda3/envs/test_env/python.exe" script.py -``` - ---- - -## NX/Simcenter Installation - -**Active Installation**: NX 2412 - -**Base Path**: `C:\Program Files\Siemens\NX2412` - -**Key Directories**: -- NX Binaries: `C:\Program Files\Siemens\NX2412\NXBIN` -- Material Library: `C:\Program Files\Siemens\NX2412\UGII\materials` -- Python Stubs: `C:\Program Files\Siemens\NX2412\ugopen\pythonStubs` - -### Critical Files: -- **run_journal.exe**: `C:\Program Files\Siemens\NX2412\NXBIN\run_journal.exe` -- **Material Library**: `C:\Program Files\Siemens\NX2412\UGII\materials\physicalmateriallibrary.xml` - -### PROHIBITED Paths: -- ❌ `C:\Program Files\Siemens\Simcenter3D_2412` - DO NOT USE -- ❌ Any path containing "Simcenter3D" - DO NOT USE - -**Reason**: NX2412 is the primary CAD/CAE environment. Simcenter3D_2412 is a separate installation and should not be accessed unless explicitly configured by the user. - ---- - -## NX Journal Execution - -**Command Template**: -```bash -"C:/Program Files/Siemens/NX2412/NXBIN/run_journal.exe" -args -``` - -**Example**: -```bash -"C:/Program Files/Siemens/NX2412/NXBIN/run_journal.exe" "optimization_engine/import_expressions.py" -args "studies/beam/model/Beam.prt" "studies/beam/model/Beam_study_variables.exp" -``` - ---- - -## NXOpen Python Stubs (for Intellisense) - -**Path**: `C:\Program Files\Siemens\NX2412\ugopen\pythonStubs` - -**VSCode Configuration** (`.vscode/settings.json`): -```json -{ - "python.analysis.extraPaths": [ - "C:\\Program Files\\Siemens\\NX2412\\ugopen\\pythonStubs" - ], - "python.analysis.typeCheckingMode": "basic" -} -``` - ---- - -## Material Library Access - -**Library File**: `C:\Program Files\Siemens\NX2412\UGII\materials\physicalmateriallibrary.xml` - -**Format**: MatML XML format - -**Properties Available**: -- `Mass_Density__RHO__6` (kg/mm³) -- `Youngs_Modulus_E__31` (Pa) -- `PoissonsRatio` (dimensionless) -- `Yield_Strength_32` (Pa) -- `Thermal_Expansion_A__34` (1/°C) -- `Thermal_Conductivity__K__35` (mW/mm/°C) -- `Specific_Heat_CP__23` (mJ/kg/°C) - -**Common Materials**: -- AISI_Steel_1005 (E=200 GPa, ρ=7872 kg/m³, ν=0.25) -- AISI_Steel_4340 (E=193 GPa, ρ=7850 kg/m³, ν=0.284) -- Aluminum_6061-T6 (E=69 GPa, ρ=2700 kg/m³, ν=0.33) -- Titanium_Ti-6Al-4V (E=114 GPa, ρ=4430 kg/m³, ν=0.34) - ---- - -## Nastran Solver - -**Solver Path**: Embedded in NX2412 installation - -**Input Files**: `.dat` (Nastran bulk data) - -**Output Files**: -- `.op2` (binary results - use pyNastran) -- `.f06` (text results - human readable) - -**Material Units in .dat files**: -- Young's Modulus: Pa (Pascals) -- Density: kg/mm³ -- Poisson's Ratio: dimensionless - ---- - -## Future Expansion - -If using a different NX or Simcenter version, the user will explicitly configure: - -1. Update this file with new paths -2. Update `nx_updater.py` configuration -3. Update `.vscode/settings.json` for new stub paths - -**Until then**: ALWAYS use NX2412 paths as documented above. - ---- - -## Validation Checklist - -Before running any NX-related operation, verify: - -- ✅ Python command uses `atomizer` environment -- ✅ NX paths point to `NX2412` (NOT Simcenter3D_2412) -- ✅ Material library accessed from `NX2412\UGII\materials` -- ✅ Journal script uses `NX2412\NXBIN\run_journal.exe` - ---- - -**Last Updated**: 2025-11-17 -**Maintained By**: Antoine Letarte -**Critical Importance**: HIGH - Incorrect paths will cause system failures diff --git a/docs/archive/DEVELOPMENT.md b/docs/archive/DEVELOPMENT.md deleted file mode 100644 index bb5295f3..00000000 --- a/docs/archive/DEVELOPMENT.md +++ /dev/null @@ -1,262 +0,0 @@ -# Development Guide - -## Project Setup Complete! ✅ - -Your Atomizer project has been initialized with the following structure: - -``` -C:\Users\antoi\Documents\Atomaste\Atomizer\ -├── .git/ # Git repository -├── .gitignore # Ignore patterns -├── LICENSE # Proprietary license -├── README.md # Main documentation -├── GITHUB_SETUP.md # GitHub push instructions -├── DEVELOPMENT.md # This file -├── pyproject.toml # Python package configuration -├── requirements.txt # Pip dependencies -│ -├── config/ # Configuration templates -│ ├── nx_config.json.template -│ └── optimization_config_template.json -│ -├── mcp_server/ # MCP Server (Phase 1) -│ ├── __init__.py -│ ├── tools/ # MCP tool implementations -│ │ └── __init__.py -│ ├── schemas/ # JSON schemas for validation -│ └── prompts/ # LLM system prompts -│ └── examples/ # Few-shot examples -│ -├── optimization_engine/ # Core optimization (Phase 4) -│ ├── __init__.py -│ └── result_extractors/ # Pluggable metric extractors -│ └── __init__.py # Base classes + registry -│ -├── nx_journals/ # NXOpen scripts (Phase 3) -│ ├── __init__.py -│ └── utils/ # Helper functions -│ -├── dashboard/ # Web UI (Phase 2) -│ ├── frontend/ # React app -│ └── backend/ # FastAPI server -│ -├── tests/ # Unit tests -├── docs/ # Documentation -└── examples/ # Example projects -``` - -## Current Status: Phase 0 - Foundation ✅ - -- [x] Project structure created -- [x] Git repository initialized -- [x] Python package configuration -- [x] License and documentation -- [x] Initial commit ready - -## Next Development Phases - -### 🎯 Immediate Next Steps (Choose One) - -#### Option A: Start with MCP Server (Recommended) -**Goal**: Get conversational FEA model discovery working - -1. **Implement `discover_fea_model` tool**: - ```bash - # Create the tool - touch mcp_server/tools/model_discovery.py - ``` - - - Parse .sim files to extract solutions, expressions, FEM info - - Use existing Atomizer patterns from your P04 project - - Return structured JSON for LLM consumption - -2. **Set up MCP server skeleton**: - ```bash - # Install MCP SDK - pip install mcp - - # Create server entry point - touch mcp_server/server.py - ``` - -3. **Test with a real .sim file**: - - Point it to one of your existing models - - Verify it extracts expressions correctly - -#### Option B: Port Atomizer Optimization Engine -**Goal**: Get core optimization working independently - -1. **Copy Atomizer modules**: - ```bash - # From your P04/Atomizer project, copy: - cp ../Projects/P04/Atomizer/code/multi_optimizer.py optimization_engine/ - cp ../Projects/P04/Atomizer/code/config_loader.py optimization_engine/ - cp ../Projects/P04/Atomizer/code/surrogate_optimizer.py optimization_engine/ - ``` - -2. **Adapt for general use**: - - Remove Zernike-specific code - - Generalize result extraction to use plugin system - - Update import paths - -3. **Create a simple test**: - ```python - # tests/test_optimizer.py - def test_basic_optimization(): - config = load_config("config/optimization_config_template.json") - optimizer = MultiParameterOptimizer(config) - # ... - ``` - -#### Option C: Build Dashboard First -**Goal**: Get real-time monitoring UI working - -1. **Set up React frontend**: - ```bash - cd dashboard/frontend - npx create-react-app . --template typescript - npm install plotly.js recharts - ``` - -2. **Set up FastAPI backend**: - ```bash - cd dashboard/backend - touch server.py - # Implement WebSocket endpoint for live updates - ``` - -3. **Create mock data endpoint**: - - Serve fake optimization history - - Test plots and visualizations - -## Recommended Workflow: Iterative Development - -### Week 1: MCP + Model Discovery -- Implement `discover_fea_model` tool -- Test with real .sim files -- Get LLM integration working - -### Week 2: Optimization Engine Port -- Copy and adapt Atomizer core modules -- Create pluggable result extractors -- Test with simple optimization - -### Week 3: NXOpen Bridge -- Build file-based communication -- Create generic journal dispatcher -- Test expression updates - -### Week 4: Dashboard MVP -- React frontend skeleton -- FastAPI backend with WebSocket -- Real-time iteration monitoring - -## Development Commands - -### Python Environment - -```bash -# Create conda environment -conda create -n atomizer python=3.10 -conda activate atomizer - -# Install in development mode -pip install -e . - -# Install dev dependencies -pip install -e ".[dev]" -``` - -### Testing - -```bash -# Run all tests -pytest - -# With coverage -pytest --cov=mcp_server --cov=optimization_engine - -# Run specific test -pytest tests/test_model_discovery.py -v -``` - -### Code Quality - -```bash -# Format code -black . - -# Lint -ruff check . - -# Type checking -mypy mcp_server optimization_engine -``` - -### Git Workflow - -```bash -# Create feature branch -git checkout -b feature/model-discovery - -# Make changes, test, commit -git add . -git commit -m "feat: implement FEA model discovery tool" - -# Push to GitHub -git push -u origin feature/model-discovery - -# Merge to main -git checkout main -git merge feature/model-discovery -git push origin main -``` - -## Integration with Existing Atomizer - -Your existing Atomizer project is at: -``` -C:\Users\antoi\Documents\Atomaste\Projects\P04\Atomizer\ -``` - -You can reference and copy modules from there as needed. Key files to adapt: - -| Atomizer File | New Location | Adaptation Needed | -|--------------|--------------|-------------------| -| `code/multi_optimizer.py` | `optimization_engine/multi_optimizer.py` | Minimal - works as-is | -| `code/config_loader.py` | `optimization_engine/config_loader.py` | Extend schema for extractors | -| `code/zernike_Post_Script_NX.py` | `optimization_engine/result_extractors/zernike.py` | Convert to plugin class | -| `code/journal_NX_Update_and_Solve.py` | `nx_journals/update_and_solve.py` | Generalize for any .sim | -| `code/nx_post_each_iter.py` | `nx_journals/post_process.py` | Use extractor registry | - -## Useful Resources - -- **Optuna Docs**: https://optuna.readthedocs.io/ -- **NXOpen API**: https://docs.sw.siemens.com/en-US/doc/209349590/ -- **MCP Protocol**: https://modelcontextprotocol.io/ -- **FastAPI**: https://fastapi.tiangolo.com/ -- **React + TypeScript**: https://react-typescript-cheatsheet.netlify.app/ - -## Questions to Consider - -Before starting development, decide on: - -1. **Which phase to tackle first?** (MCP, Engine, Dashboard, or NXOpen) -2. **Target NX version?** (NX 2412 | Future: multi-version support, or multi-version support) -3. **Deployment strategy?** (Local only or client-server architecture) -4. **Testing approach?** (Unit tests only or integration tests with real NX) -5. **Documentation format?** (Markdown, Sphinx, MkDocs) - -## Getting Help - -When you're ready to start coding: - -1. Choose a phase from the options above -2. Tell me which component you want to build first -3. I'll create the detailed implementation with working code -4. We'll test it with your existing .sim files -5. Iterate and expand - ---- - -**You're all set!** The foundation is ready. Choose your starting point and let's build! 🚀 diff --git a/docs/archive/FEM_REGENERATION_STATUS.md b/docs/archive/FEM_REGENERATION_STATUS.md deleted file mode 100644 index 3c671877..00000000 --- a/docs/archive/FEM_REGENERATION_STATUS.md +++ /dev/null @@ -1,133 +0,0 @@ -# FEM Regeneration Status - -## Current Status: EXPRESSIONS NOT LINKED TO GEOMETRY - -The optimization loop infrastructure is **FULLY FUNCTIONAL**, but the stress results are not changing because the parametric model is not properly configured. - -### ✅ Working Components -1. **Parameter updates** - Expressions in Bracket.prt ARE being updated (verified via binary edit) -2. **NX solver** - Journal connects to NX GUI and runs solves successfully (~4s per solve) -3. **Result extraction** - Stress and displacement ARE being read from .op2 files -4. **History tracking** - All trials ARE being saved to history.json/csv -5. **Optimization** - Optuna IS exploring the parameter space -6. **FEM regeneration workflow** - Journal IS executing all required steps: - - Opens .sim file ✅ - - Switches to Bracket.prt ✅ - - Calls `UpdateManager.DoUpdate()` to rebuild geometry ✅ - - Switches to Bracket_fem1.fem ✅ - - Calls `UpdateFemodel()` to regenerate FEM ✅ - - Solves and saves ✅ - -### ❌ Root Cause: Expressions Not Linked to Geometry Features - -All 3 trials return the SAME stress (197.89159375 MPa) because: - -1. Expressions (`tip_thickness=20`, `support_angle=36`) exist in Bracket.prt ✅ -2. Binary updates correctly modify these expression values ✅ -3. Journal calls `UpdateManager.DoUpdate()` to rebuild geometry ✅ -4. **BUT: No geometry features reference these expressions** ❌ -5. Therefore the CAD geometry doesn't change ❌ -6. Therefore the FEM doesn't see geometry changes ❌ -7. So the mesh stays the same, and stress doesn't change ❌ - -## Evidence - -### Journal Output (From Test Run) -``` -[JOURNAL] Opening simulation: C:\...\Bracket_sim1.sim -[JOURNAL] Checking for open parts... -[JOURNAL] Opening simulation fresh from disk... -[JOURNAL] STEP 1: Updating Bracket.prt geometry... -[JOURNAL] Bracket geometry updated (0 errors) ← UpdateManager.DoUpdate() ran successfully -[JOURNAL] STEP 2: Opening Bracket_fem1.fem... -[JOURNAL] Updating FE Model... -[JOURNAL] FE Model updated with new geometry! ← UpdateFemodel() ran successfully -[JOURNAL] STEP 3: Switching back to sim part... -[JOURNAL] Switched back to sim part -[JOURNAL] Starting solve... -[JOURNAL] Solve submitted! -[JOURNAL] Save complete! -``` - -### Optimization Results (3 Trials with Different Parameters) -``` -Trial 0: tip_thickness=23.48, support_angle=37.21 → stress=197.89 MPa -Trial 1: tip_thickness=20.08, support_angle=20.32 → stress=197.89 MPa (SAME!) -Trial 2: tip_thickness=18.19, support_angle=35.23 → stress=197.89 MPa (SAME!) -``` - -### Feature Dependency Check -Ran journal to check if any features reference the expressions: -``` -============================================================ -CHECKING FEATURE DEPENDENCIES: -============================================================ -(empty - no features found that reference tip_thickness or support_angle) -``` - -## Solution Required - -**The Bracket.prt parametric model needs to be fixed in NX:** - -1. Open Bracket.prt in NX -2. Find the sketches/features that define the bracket geometry -3. Link the sketch dimensions to the expressions: - - Find the dimension that should control tip thickness - - Edit it to reference the expression: `=tip_thickness` - - Find the dimension that should control support angle - - Edit it to reference the expression: `=support_angle` -4. Update the part to verify the links work -5. Save Bracket.prt - -### How to Verify the Fix - -After linking the expressions to geometry features: - -1. In NX, manually change `tip_thickness` from 20 to 24 -2. Update the part (Ctrl+U) -3. **The 3D geometry should visibly change** -4. If the geometry changes, the parametric model is now working! - -## Test to Verify Optimization Works - -After fixing the .prt file, run: -```bash -cd "C:\Users\antoi\Documents\Atomaste\Atomizer" -echo yes | python examples/test_journal_optimization.py -``` - -You should see **different stress values** for different parameters: -- tip_thickness=20, support_angle=25 → stress = ??? MPa (unique value!) -- tip_thickness=24, support_angle=35 → stress = ??? MPa (different from above!) - -## What's Been Implemented - -The solve_simulation.py journal now includes the COMPLETE regeneration workflow from the user's working journal (journal_with_regenerate.py): - -```python -# STEP 1: Switch to Bracket.prt and update geometry -bracketPart = theSession.Parts.FindObject("Bracket") -theSession.Parts.SetActiveDisplay(bracketPart, ...) -markId_update = theSession.SetUndoMark(...) -nErrs = theSession.UpdateManager.DoUpdate(markId_update) # Rebuild geometry - -# STEP 2: Switch to Bracket_fem1 and update FE model -femPart1 = theSession.Parts.FindObject("Bracket_fem1") -theSession.Parts.SetActiveDisplay(femPart1, ...) -fEModel1 = workFemPart.FindObject("FEModel") -fEModel1.UpdateFemodel() # Regenerate FEM with new geometry - -# STEP 3: Switch back to sim and solve -theSession.Parts.SetActiveDisplay(simPart1, ...) -# ... solve and save -``` - -This workflow is CORRECT and WORKING - verified by journal output showing all steps execute successfully. - -## Conclusion - -**The optimization infrastructure is complete and functional.** - -The code is ready - it's just waiting for the Bracket.prt file to have its expressions properly linked to the geometry features. Once that's done in NX, the optimization will work perfectly with varying stress results based on the design parameters. - -**Status: Ready for parametric model fix in NX** diff --git a/docs/archive/GITHUB_SETUP.md b/docs/archive/GITHUB_SETUP.md deleted file mode 100644 index 0077a948..00000000 --- a/docs/archive/GITHUB_SETUP.md +++ /dev/null @@ -1,102 +0,0 @@ -# GitHub Setup Guide - -## Creating the Private Repository - -1. **Go to GitHub**: https://github.com/new - -2. **Repository Settings**: - - **Owner**: `Anto01` - - **Repository name**: `Atomizer` - - **Description**: "Advanced optimization platform for Siemens NX Simcenter with LLM integration" - - **Visibility**: ✅ **Private** - - **DO NOT** initialize with README, .gitignore, or license (we already have these) - -3. **Click "Create repository"** - -## Pushing to GitHub - -After creating the repository on GitHub, run these commands: - -```bash -cd /c/Users/antoi/Documents/Atomaste/Atomizer - -# Add the remote repository -git remote add origin https://github.com/Anto01/Atomizer.git - -# OR if using SSH: -# git remote add origin git@github.com:Anto01/Atomizer.git - -# Push the initial commit -git branch -M main -git push -u origin main -``` - -## Verify Push - -Visit your repository at: `https://github.com/Anto01/Atomizer` - -You should see: -- README.md displayed on the main page -- All files committed -- Private repository badge - -## Next Steps - -### Set Up GitHub Actions (Optional) - -Create `.github/workflows/tests.yml` for automated testing: - -```yaml -name: Tests - -on: [push, pull_request] - -jobs: - test: - runs-on: windows-latest - steps: - - uses: actions/checkout@v3 - - uses: actions/setup-python@v4 - with: - python-version: '3.10' - - run: pip install -r requirements.txt - - run: pytest -``` - -### Protect Main Branch - -Settings → Branches → Add rule: -- Branch name pattern: `main` -- ✅ Require pull request reviews before merging -- ✅ Require status checks to pass - -### Add Collaborators - -Settings → Collaborators → Add people - -## Clone on Another Machine - -```bash -git clone https://github.com/Anto01/Atomizer.git -cd Atomizer -pip install -r requirements.txt -``` - -## Useful Git Commands - -```bash -# Check status -git status - -# View commit history -git log --oneline - -# Create a new branch -git checkout -b feature/new-feature - -# Push branch to GitHub -git push -u origin feature/new-feature - -# Pull latest changes -git pull origin main -``` diff --git a/docs/archive/NX_SOLVER_INTEGRATION.md b/docs/archive/NX_SOLVER_INTEGRATION.md deleted file mode 100644 index 5056b0fa..00000000 --- a/docs/archive/NX_SOLVER_INTEGRATION.md +++ /dev/null @@ -1,294 +0,0 @@ -# NX Solver Integration Guide - -## Overview - -The NX solver integration allows Atomizer to automatically run Siemens NX Nastran simulations in batch mode during optimization loops. - -## Architecture - -``` -Optimization Loop: -1. Update parameters in .prt file → nx_updater.py -2. Run NX solver in batch mode → nx_solver.py ← NEW! -3. Extract results from OP2 → op2_extractor_example.py -4. Evaluate objectives/constraints → runner.py -5. Optuna suggests next parameters → repeat -``` - -## Quick Start - -### Test 1: Verify Solver Integration - -```bash -conda activate test_env -python examples/test_nx_solver.py -``` - -This tests: -- NX installation detection -- Batch solver execution -- OP2 file generation -- Error handling - -**Expected**: Solver runs and produces .op2 file in ~1-2 minutes - -### Test 2: Run Optimization with Real Solver - -```bash -conda activate test_env -python examples/test_optimization_with_solver.py -``` - -This runs 3 optimization trials with REAL simulations! - -**Expected time**: ~5-10 minutes (depends on model complexity) - -## Usage in Your Code - -### Simple Usage (Convenience Function) - -```python -from optimization_engine.nx_solver import run_nx_simulation -from pathlib import Path - -sim_file = Path("path/to/model.sim") -op2_file = run_nx_simulation( - sim_file=sim_file, - nastran_version="2412", - timeout=600, # 10 minutes - cleanup=True # Remove temp files -) - -# op2_file now contains path to results -``` - -### Advanced Usage (Full Control) - -```python -from optimization_engine.nx_solver import NXSolver - -solver = NXSolver( - nastran_version="2412", - timeout=600 -) - -result = solver.run_simulation( - sim_file=sim_file, - working_dir=None, # Defaults to sim file directory - cleanup=True -) - -if result['success']: - print(f"OP2: {result['op2_file']}") - print(f"Time: {result['elapsed_time']:.1f}s") -else: - print(f"Errors: {result['errors']}") -``` - -### Integration with Optimization Runner - -```python -from optimization_engine.nx_solver import run_nx_simulation - -def my_simulation_runner() -> Path: - """Simulation runner for optimization.""" - sim_file = Path("my_model.sim") - - # Run solver - op2_file = run_nx_simulation( - sim_file=sim_file, - nastran_version="2412", - timeout=600, - cleanup=True - ) - - return op2_file - -# Use in OptimizationRunner -runner = OptimizationRunner( - config_path=config_path, - model_updater=my_model_updater, - simulation_runner=my_simulation_runner, # Uses real solver! - result_extractors=extractors -) -``` - -## Configuration - -### Auto-Detection - -By default, NXSolver auto-detects NX installation: - -```python -solver = NXSolver(nastran_version="2412") -# Searches: -# - C:/Program Files/Siemens/NX2412 -# - C:/Program Files/Siemens/NX2412 -# - C:/Program Files (x86)/Siemens/NX2412 -``` - -### Manual Configuration - -```python -from pathlib import Path - -solver = NXSolver( - nx_install_dir=Path("C:/Program Files/Siemens/NX2412"), - nastran_version="2412", - timeout=1200 # 20 minutes -) -``` - -## Solver Output Files - -### Files Created -- `model.op2` - Binary results (kept) -- `model.f06` - Text output (kept) -- `model.log` - Solver log (kept) -- `model.f04` - Intermediate (cleaned up) -- `model.dat` - Intermediate (cleaned up) -- `model.diag` - Diagnostic (cleaned up) - -### Cleanup Behavior - -With `cleanup=True` (recommended): -- Keeps: .op2, .f06, .log -- Removes: .f04, .dat, .diag, .master, .dball, plots - -With `cleanup=False`: -- Keeps all files for debugging - -## Error Handling - -### Common Issues - -**Issue**: `FileNotFoundError: NX Nastran solver not found` - -**Solution**: -- Check NX is installed at standard location -- Specify `nx_install_dir` manually -- Verify nastran.exe exists in NXNASTRAN/bin/ - -**Issue**: `RuntimeError: NX simulation failed` - -**Solution**: -- Check .f06 file for error messages -- Verify .sim file is valid -- Check NX license is available -- Ensure model can solve in NX GUI first - -**Issue**: `TimeoutExpired` - -**Solution**: -- Increase `timeout` parameter -- Simplify model (fewer elements, linear analysis) -- Check solver isn't stuck (memory issues) - -### Checking Solver Success - -The solver checks for completion by: -1. Looking for "NORMAL TERMINATION" in .f06 -2. Checking for "FATAL MESSAGE" errors -3. Verifying .op2 file was created recently - -## Performance Tips - -### Speed Up Optimization - -1. **Reduce Model Complexity** - - Use coarser mesh for initial exploration - - Simplify geometry in non-critical areas - - Use linear analysis if possible - -2. **Parallel Trials (Future)** - - Run multiple trials simultaneously - - Requires separate working directories - - Use Optuna's parallelization features - -3. **Smart Sampling** - - Use TPE sampler (default) for efficiency - - Increase `n_startup_trials` for better initial sampling - - Use constraints to avoid infeasible regions - -4. **Cleanup Strategy** - - Use `cleanup=True` to save disk space - - Only keep .op2 and .log files - - Archive results after optimization - -### Typical Solve Times - -| Model Size | Analysis Type | Time per Trial | -|------------|---------------|----------------| -| Small (<10k nodes) | Linear Static | 30-60s | -| Medium (10-50k) | Linear Static | 1-3 min | -| Large (>50k) | Linear Static | 3-10 min | -| Any | Nonlinear | 5-30 min | - -## Batch Processing - -For running many optimizations: - -```python -# Save solver instance to reuse -solver = NXSolver(nastran_version="2412", timeout=600) - -for trial_params in parameter_sets: - # Update model - update_nx_model(prt_file, trial_params) - - # Solve - result = solver.run_simulation(sim_file, cleanup=True) - - if result['success']: - # Extract and analyze - results = extract_all_results(result['op2_file']) -``` - -## Troubleshooting - -### Enable Debug Output - -```python -solver = NXSolver(nastran_version="2412") - -result = solver.run_simulation( - sim_file=sim_file, - cleanup=False # Keep all files -) - -# Check detailed output -print(result['errors']) - -# Manually inspect files -# - Check .f06 for solver messages -# - Check .log for execution details -# - Check .f04 for input deck -``` - -### Verify NX Installation - -```python -from optimization_engine.nx_solver import NXSolver - -solver = NXSolver(nastran_version="2412") -print(f"NX Dir: {solver.nx_install_dir}") -print(f"Solver: {solver.solver_exe}") -print(f"Exists: {solver.solver_exe.exists()}") -``` - -## Next Steps - -1. **Test solver integration**: Run `test_nx_solver.py` -2. **Test optimization loop**: Run `test_optimization_with_solver.py` -3. **Customize for your model**: Modify simulation_runner function -4. **Run real optimization**: Increase n_trials to 50-150 -5. **Analyze results**: Use history.csv to understand parameter sensitivity - -## Support - -For issues: -1. Check this guide -2. Verify NX installation -3. Test .sim file in NX GUI first -4. Check solver logs (.f06, .log files) -5. Review error messages in result['errors'] diff --git a/docs/archive/PROJECT_SUMMARY.md b/docs/archive/PROJECT_SUMMARY.md deleted file mode 100644 index efcb3b4a..00000000 --- a/docs/archive/PROJECT_SUMMARY.md +++ /dev/null @@ -1,474 +0,0 @@ -# Atomizer - Project Summary - -**Last Updated**: 2025-11-15 -**Repository**: https://github.com/Anto01/Atomizer (Private) -**Branch**: main (5 commits) - ---- - -## 🎯 Project Vision - -Atomizer is an advanced optimization platform for Siemens NX Simcenter that combines: -- **LLM-driven configuration** via conversational interface (MCP) -- **Superior optimization** using Optuna (TPE, Gaussian Process surrogates) -- **Real-time monitoring** with interactive dashboards -- **Flexible architecture** with pluggable result extractors -- **NXOpen integration** for seamless NX automation - -**Goal**: Create a general-purpose FEA optimization tool more powerful and flexible than NX's built-in optimizer. - ---- - -## 📂 Repository Structure - -``` -C:\Users\antoi\Documents\Atomaste\Atomizer\ -├── .git/ # Git repository -├── .gitignore # Python, NX, optimization files -├── LICENSE # Proprietary license -├── README.md # Main documentation -├── GITHUB_SETUP.md # GitHub push instructions -├── DEVELOPMENT.md # Development workflow guide -├── PROJECT_SUMMARY.md # This file -├── pyproject.toml # Python package config -├── requirements.txt # Pip dependencies -│ -├── config/ # Configuration templates -│ ├── nx_config.json.template # NX paths (NX 2412) -│ └── optimization_config_template.json # Optimization setup -│ -├── mcp_server/ # MCP Server (LLM interface) -│ ├── __init__.py -│ ├── tools/ # MCP tool implementations -│ │ └── __init__.py # Tool registry -│ ├── schemas/ # JSON validation schemas -│ └── prompts/ -│ ├── system_prompt.md # LLM instructions (complete) -│ └── examples/ # Few-shot examples -│ -├── optimization_engine/ # Core optimization logic -│ ├── __init__.py -│ └── result_extractors/ # Pluggable metric extractors -│ └── __init__.py # Base class + registry -│ -├── nx_journals/ # NXOpen scripts -│ ├── __init__.py -│ └── utils/ # Helper functions -│ -├── dashboard/ # Web UI (planned) -│ ├── frontend/ # React app -│ └── backend/ # FastAPI server -│ -├── docs/ -│ ├── NXOPEN_RESOURCES.md # NXOpen reference guide -│ └── configuration.md # (planned) -│ -├── tests/ # Unit tests -└── examples/ # Example projects -``` - ---- - -## 🔑 Key Technologies - -### Core Stack -- **Python 3.10** (conda environment: `atomizer`) -- **Siemens NX 2412** with NX Nastran solver -- **Optuna 3.5+** for optimization -- **pyNastran 1.4+** for OP2/F06 parsing -- **Pandas 2.0+** for data management - -### MCP & Dashboard -- **MCP Protocol** for LLM integration -- **FastAPI** for backend server -- **WebSockets** for real-time updates -- **React + TypeScript** for frontend (planned) -- **Plotly.js** for 3D visualizations - -### Development Tools -- **pytest** for testing -- **black** for code formatting -- **ruff** for linting -- **Git** for version control - ---- - -## 🏗️ Architecture Overview - -### Three-Tier Design - -``` -┌─────────────────────────────────────────────────────────┐ -│ TIER 1: UI Layer │ -│ ┌─────────────────────┐ ┌──────────────────────────┐ │ -│ │ Web Dashboard │ │ LLM Chat Interface │ │ -│ │ (React + Plotly) │ │ (MCP Client) │ │ -│ └─────────────────────┘ └──────────────────────────┘ │ -└─────────────────────────────────────────────────────────┘ - ↕ HTTP/WebSocket -┌─────────────────────────────────────────────────────────┐ -│ TIER 2: MCP Server │ -│ ┌──────────────────────────────────────────────────┐ │ -│ │ • Model Discovery (parse .sim files) │ │ -│ │ • Config Builder (generate optimization.json) │ │ -│ │ • Optimizer Control (start/stop/monitor) │ │ -│ │ • Result Analyzer (extract metrics) │ │ -│ │ • NXOpen API Wrapper (file-based bridge) │ │ -│ └──────────────────────────────────────────────────┘ │ -└─────────────────────────────────────────────────────────┘ - ↕ File I/O + Subprocess -┌─────────────────────────────────────────────────────────┐ -│ TIER 3: Execution Layer │ -│ ┌──────────┐ ┌──────────┐ ┌────────────────────┐ │ -│ │ NX Core │ │ Optuna │ │ Custom Scripts │ │ -│ │ (NXOpen) │ │ Engine │ │ (Parsing/Analysis) │ │ -│ └──────────┘ └──────────┘ └────────────────────┘ │ -└─────────────────────────────────────────────────────────┘ -``` - -### File-Based Communication Pattern -- **MCP Server** ↔ **NX Journals**: JSON request/response files -- **Optimization Engine** ↔ **NX**: Expression files (.exp) -- **Results**: CSV (history.csv) + SQLite (optuna_study.db) - ---- - -## 🧩 Reusable Components from P04/Atomizer - -The following modules from your existing Atomizer project can be adapted: - -| P04 Module | New Location | Adaptation Needed | -|-----------|--------------|-------------------| -| `code/multi_optimizer.py` | `optimization_engine/multi_optimizer.py` | Minimal - Optuna logic is general | -| `code/config_loader.py` | `optimization_engine/config_loader.py` | Extend for extractor plugins | -| `code/surrogate_optimizer.py` | `optimization_engine/surrogate_optimizer.py` | Direct copy | -| `code/journal_NX_Update_and_Solve.py` | `nx_journals/update_and_solve.py` | Generalize for any .sim file | -| `code/zernike_Post_Script_NX.py` | `optimization_engine/result_extractors/zernike.py` | Convert to plugin class | -| `code/nx_post_each_iter.py` | `nx_journals/post_process.py` | Use extractor registry | - -**Key Insight**: The optimization engine core (Optuna, history management, CSV/DB) works as-is. Main changes are in result extraction (pluggable) and NX interaction (generalized). - ---- - -## 🛠️ MCP Tools (Planned) - -### Model Discovery -- **`discover_fea_model`**: Parse .sim files → extract solutions, expressions, FEM info -- **`search_nxopen_docs`**: Fetch Siemens NXOpen API documentation - -### Optimization Control -- **`build_optimization_config`**: Natural language → optimization_config.json -- **`start_optimization`**: Launch optimization run -- **`query_optimization_status`**: Get current iteration metrics - -### Result Analysis -- **`extract_results`**: Parse OP2/F06/XDB → stress, displacement, mass, etc. -- **`run_nx_journal`**: Execute custom NXOpen scripts - ---- - -## 📚 NXOpen Development Strategy - -### Resource Hierarchy -1. **Official Siemens NXOpen API** - Authoritative reference -2. **NXOpenTSE** - Patterns & best practices (reference only, not dependency) -3. **Atomizer-specific conventions** - Our implementation - -### NXOpenTSE Integration -- **GitHub**: https://github.com/theScriptingEngineer/nxopentse -- **Docs**: https://nxopentsedocumentation.thescriptingengineer.com/ -- **Usage**: Reference for learning, NOT for copying code -- **Benefits**: - - See proven design patterns - - Learn error handling approaches - - Understand NX-specific gotchas - - Discover lesser-known APIs - -### Code Generation Workflow (via MCP) -1. Check official API for method signatures -2. Reference NXOpenTSE for usage patterns -3. Adapt to Atomizer's architecture -4. Add error handling and attribution comments - -**See**: `docs/NXOPEN_RESOURCES.md` for complete guide - ---- - -## 🔄 Development Phases (Recommended Order) - -### ✅ Phase 0: Foundation (COMPLETE) -- [x] Project structure created -- [x] Git repository initialized -- [x] GitHub remote configured (Anto01/Atomizer) -- [x] Documentation framework -- [x] NXOpen resources documented -- [x] MCP system prompt complete - -### 🎯 Phase 1: MCP Server Foundation (NEXT) -- [ ] Implement `discover_fea_model` tool -- [ ] Create .sim file parser -- [ ] Build expression extractor -- [ ] Test with real .sim files -- [ ] Validate JSON schema - -### 🔧 Phase 2: Optimization Engine Port -- [ ] Copy Atomizer's `multi_optimizer.py` -- [ ] Adapt `config_loader.py` for extractors -- [ ] Create pluggable result extractor system -- [ ] Implement Nastran OP2 extractor -- [ ] Implement NX mass properties extractor - -### 🔌 Phase 3: NXOpen Bridge -- [ ] Build file-based NXOpen API wrapper -- [ ] Create generic journal dispatcher -- [ ] Implement expression update journal -- [ ] Implement solve simulation journal -- [ ] Test with NX 2412 - -### 🎨 Phase 4: Dashboard UI -- [ ] React frontend skeleton -- [ ] FastAPI backend with WebSocket -- [ ] Real-time iteration monitoring -- [ ] Plotly visualizations -- [ ] Config editor UI - -### 🚀 Phase 5: Integration & Testing -- [ ] End-to-end workflow testing -- [ ] LLM prompt refinement -- [ ] Error handling improvements -- [ ] Performance optimization -- [ ] User documentation - ---- - -## 📝 Current Git Status - -**Repository**: https://github.com/Anto01/Atomizer -**Visibility**: Private -**Branch**: main -**Commits**: 5 - -### Commit History -``` -f359d4e - chore: Update NX version to 2412 -14d2b67 - docs: Add NXOpen resources guide and MCP system prompt -d1cbeb7 - Rebrand project from nx-optimaster to Atomizer -2201aee - docs: Add GitHub setup and development guides -aa3dafb - Initial commit: NX OptiMaster project structure -``` - -### Files Tracked (15) -- `.gitignore` -- `LICENSE` -- `README.md` -- `GITHUB_SETUP.md` -- `DEVELOPMENT.md` -- `PROJECT_SUMMARY.md` (this file) -- `pyproject.toml` -- `requirements.txt` -- `config/nx_config.json.template` -- `config/optimization_config_template.json` -- `mcp_server/__init__.py` -- `mcp_server/tools/__init__.py` -- `optimization_engine/__init__.py` -- `optimization_engine/result_extractors/__init__.py` -- `nx_journals/__init__.py` -- `mcp_server/prompts/system_prompt.md` -- `docs/NXOPEN_RESOURCES.md` - ---- - -## 🚀 Quick Start Commands - -### Clone Repository (Different Machine) -```bash -git clone https://github.com/Anto01/Atomizer.git -cd Atomizer -``` - -### Set Up Python Environment -```bash -# Create conda environment -conda create -n atomizer python=3.10 -conda activate atomizer - -# Install dependencies -pip install -e . - -# Install dev tools (optional) -pip install -e ".[dev]" - -# Install MCP (when ready) -pip install -e ".[mcp]" -``` - -### Configure NX Path -```bash -# Copy template -cp config/nx_config.json.template config/nx_config.json - -# Edit config/nx_config.json: -# - Set nx_executable to your NX 2412 path -# - Set python_env to your atomizer conda environment -``` - -### Git Workflow -```bash -# Create feature branch -git checkout -b feature/model-discovery - -# Make changes, commit -git add . -git commit -m "feat: implement FEA model discovery tool" - -# Push to GitHub -git push -u origin feature/model-discovery - -# Merge to main (after review) -git checkout main -git merge feature/model-discovery -git push origin main -``` - ---- - -## 🎯 Next Immediate Steps - -**When you're ready to start coding**, choose one of these entry points: - -### Option A: MCP Model Discovery (Recommended First) -**Goal**: Get LLM to parse .sim files and extract expressions - -**Tasks**: -1. Create `mcp_server/tools/model_discovery.py` -2. Implement .sim file parsing logic -3. Extract expression names and values -4. Return structured JSON for LLM -5. Test with your P04 .sim files - -**Benefit**: Establishes MCP pattern, immediately useful - -### Option B: Port Optimization Engine -**Goal**: Get core optimization working independently - -**Tasks**: -1. Copy `multi_optimizer.py`, `config_loader.py` from P04 -2. Adapt for general use (remove Zernike-specific code) -3. Update import paths -4. Create simple test case -5. Verify Optuna integration works - -**Benefit**: Core functionality ready, can test optimization without MCP - -### Option C: NXOpen Bridge -**Goal**: Get NX automation working via file-based communication - -**Tasks**: -1. Create `nx_journals/api_dispatcher.py` -2. Implement JSON request/response pattern -3. Test expression updates -4. Test .sim file loading -5. Document NXOpen patterns - -**Benefit**: NX integration ready for optimization loop - ---- - -## 📊 Key Design Decisions - -### 1. **Why File-Based Communication?** -- NXOpen requires NX GUI process -- MCP server runs in separate Python environment -- Files are robust, inspectable, version-controllable -- Proven pattern from P04 Atomizer - -### 2. **Why Pluggable Result Extractors?** -- Different FEA problems need different metrics -- Zernike analysis is specific to optical surfaces -- General tool needs stress, thermal, modal, etc. -- Easy to add new extractors without changing core - -### 3. **Why MCP vs. Direct UI?** -- Natural language is faster than JSON editing -- LLM can suggest reasonable parameter bounds -- Conversational debugging ("why did this fail?") -- Future: voice commands, multi-modal input - -### 4. **Why Dual Persistence (CSV + SQLite)?** -- CSV: Human-readable, Excel-compatible, git-friendly -- SQLite: Fast queries, Optuna requirement -- Sync on each iteration for crash recovery - ---- - -## 🔗 Important Links - -### GitHub -- **Repository**: https://github.com/Anto01/Atomizer -- **Issues**: GitHub Issues (private repository) - -### Documentation -- **Official NXOpen API**: https://docs.sw.siemens.com/en-US/doc/209349590/ -- **NXOpenTSE**: https://nxopentsedocumentation.thescriptingengineer.com/ -- **Optuna**: https://optuna.readthedocs.io/ -- **pyNastran**: https://github.com/SteveDoyle2/pyNastran - -### Local Documentation -- **NXOpen Resources**: `docs/NXOPEN_RESOURCES.md` -- **MCP System Prompt**: `mcp_server/prompts/system_prompt.md` -- **Development Guide**: `DEVELOPMENT.md` -- **GitHub Setup**: `GITHUB_SETUP.md` - ---- - -## 💡 Key Insights for Success - -1. **Start Small**: Implement one MCP tool at a time, test thoroughly -2. **Reference P04**: Your existing Atomizer is 80% of the solution -3. **Use NXOpenTSE**: Learn patterns, don't copy code -4. **Test Early**: Use real .sim files from day one -5. **Document as You Go**: Future you will thank present you -6. **Commit Often**: Small, focused commits are easier to debug - ---- - -## 🏁 Success Criteria - -**Phase 1 Complete When**: -- LLM can parse a .sim file and list expressions -- User can ask "what parameters can I optimize?" -- System responds with structured list - -**Phase 2 Complete When**: -- Can run optimization without LLM (manual config.json) -- Optuna suggests parameters -- Results saved to history.csv - -**Phase 3 Complete When**: -- NX journals can update expressions via file commands -- Solver runs automatically -- Results extracted to CSV - -**Phase 4 Complete When**: -- Dashboard shows real-time iteration updates -- User can monitor without opening NX -- Plots update automatically - -**Full System Complete When**: -- User says: "Optimize bracket.sim to reduce stress" -- LLM configures optimization -- NX runs iterations automatically -- Dashboard shows progress -- User gets optimized design - ---- - -**Project Status**: Foundation complete, ready for development -**Next Action**: Choose entry point (A, B, or C above) and start coding -**Estimated Timeline**: 12-16 weeks for full system (part-time) - ---- - -**Last Updated**: 2025-11-15 -**Maintained By**: Antoine (Anto01) -**Built With**: Claude Code 🤖 diff --git a/docs/archive/README_OLD.md b/docs/archive/README_OLD.md deleted file mode 100644 index 259ac198..00000000 --- a/docs/archive/README_OLD.md +++ /dev/null @@ -1,260 +0,0 @@ -# Atomizer - -> Advanced optimization platform for Siemens NX Simcenter with LLM-powered configuration - -[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/) -[![License](https://img.shields.io/badge/license-Proprietary-red.svg)](LICENSE) -[![Status](https://img.shields.io/badge/status-alpha-yellow.svg)](https://github.com) - -## Overview - -Atomizer is a next-generation optimization framework for Siemens NX that combines: - -- **LLM-Driven Configuration**: Use natural language to set up complex optimizations -- **Advanced Algorithms**: Optuna-powered TPE, Gaussian Process surrogates, multi-fidelity optimization -- **Real-Time Monitoring**: Interactive dashboards with live updates -- **Flexible Architecture**: Pluggable result extractors for any FEA analysis type -- **MCP Integration**: Extensible via Model Context Protocol - -## Architecture - -``` -┌─────────────────────────────────────────────────────────┐ -│ UI Layer │ -│ Web Dashboard (React) + LLM Chat Interface (MCP) │ -└─────────────────────────────────────────────────────────┘ - ↕ -┌─────────────────────────────────────────────────────────┐ -│ MCP Server │ -│ - Model Discovery - Config Builder │ -│ - Optimizer Control - Result Analyzer │ -└─────────────────────────────────────────────────────────┘ - ↕ -┌─────────────────────────────────────────────────────────┐ -│ Execution Layer │ -│ NX Core (NXOpen) + Optuna Engine + Custom Scripts │ -└─────────────────────────────────────────────────────────┘ -``` - -## Quick Start - -### Prerequisites - -- **Siemens NX 2412** with NX Nastran solver -- **Python 3.10+** (recommend Anaconda) -- **Node.js 18+** (for dashboard frontend) - -### Installation - -1. **Clone the repository**: - ```bash - git clone https://github.com/Anto01/Atomizer.git - cd Atomizer - ``` - -2. **Create Python environment**: - ```bash - conda create -n atomizer python=3.10 - conda activate atomizer - ``` - -3. **Install dependencies**: - ```bash - pip install -e . - # For development tools: - pip install -e ".[dev]" - # For MCP server: - pip install -e ".[mcp]" - ``` - -4. **Configure NX path** (edit `config/nx_config.json`): - ```json - { - "nx_executable": "C:/Program Files/Siemens/NX2412/NXBIN/ugraf.exe", - "python_env": "C:/Users/YourName/anaconda3/envs/atomizer/python.exe" - } - ``` - -### Basic Usage - -#### 1. Conversational Setup (via MCP) - -``` -You: My FEA is in C:\Projects\Bracket\analysis.sim, please import its features. - -AI: I've analyzed your model: - - Solution: Static Analysis (NX Nastran) - - Expressions: wall_thickness (5mm), hole_diameter (10mm) - - Mesh: 8234 nodes, 4521 elements - - Which parameters would you like to optimize? - -You: Optimize wall_thickness and hole_diameter to minimize max stress while keeping mass low. - -AI: Configuration created! Ready to start optimization with 100 iterations. - Would you like to review the config or start now? - -You: Start it! - -AI: Optimization launched! 🚀 - Dashboard: http://localhost:8080/dashboard -``` - -#### 2. Manual Configuration (JSON) - -Create `optimization_config.json`: - -```json -{ - "design_variables": { - "wall_thickness": { - "low": 3.0, - "high": 8.0, - "enabled": true - } - }, - "objectives": { - "metrics": { - "max_stress": { - "weight": 10, - "target": 200, - "extractor": "nastran_stress" - } - } - }, - "nx_settings": { - "sim_path": "C:/Projects/Bracket/analysis.sim", - "solution_name": "Solution 1" - } -} -``` - -Run optimization: -```bash -python -m optimization_engine.run_optimizer --config optimization_config.json -``` - -## Features - -### ✨ Core Capabilities - -- **Multi-Objective Optimization**: Weighted sum, Pareto front analysis -- **Smart Sampling**: TPE, Latin Hypercube, Gaussian Process surrogates -- **Result Extraction**: Nastran (OP2/F06), NX Mass Properties, custom parsers -- **Crash Recovery**: Automatic resume from interruptions -- **Parallel Evaluation**: Multi-core FEA solving (coming soon) - -### 📊 Visualization - -- **Real-time progress monitoring** -- **3D Pareto front plots** (Plotly) -- **Parameter importance charts** -- **Convergence history** -- **FEA result overlays** - -### 🔧 Extensibility - -- **Pluggable result extractors**: Add custom metrics easily -- **Custom post-processing scripts**: Python integration -- **MCP tools**: Extend via protocol -- **NXOpen API access**: Full NX automation - -## Project Structure - -``` -Atomizer/ -├── mcp_server/ # MCP server implementation -│ ├── tools/ # MCP tool definitions -│ ├── schemas/ # JSON schemas for validation -│ └── prompts/ # LLM system prompts -├── optimization_engine/ # Core optimization logic -│ ├── result_extractors/ # Pluggable metric extractors -│ ├── multi_optimizer.py # Optuna integration -│ ├── config_loader.py # Configuration parser -│ └── history_manager.py # CSV/SQLite persistence -├── nx_journals/ # NXOpen Python scripts -│ ├── update_and_solve.py # CAD update + solver -│ ├── post_process.py # Result extraction -│ └── utils/ # Helper functions -├── dashboard/ # Web UI -│ ├── frontend/ # React app -│ └── backend/ # FastAPI server -├── tests/ # Unit tests -├── examples/ # Example projects -└── docs/ # Documentation - -``` - -## Configuration Schema - -See [docs/configuration.md](docs/configuration.md) for full schema documentation. - -**Key sections**: -- `design_variables`: Parameters to optimize -- `objectives`: Metrics to minimize/maximize -- `nx_settings`: NX/FEA solver configuration -- `optimization`: Optuna sampler settings -- `post_processing`: Result extraction pipelines - -## Development - -### Running Tests - -```bash -pytest -``` - -### Code Formatting - -```bash -black . -ruff check . -``` - -### Building Documentation - -```bash -cd docs -mkdocs build -``` - -## Roadmap - -- [x] MCP server foundation -- [x] Basic optimization engine -- [ ] NXOpen integration -- [ ] Web dashboard -- [ ] Multi-fidelity optimization -- [ ] Parallel evaluations -- [ ] Sensitivity analysis tools -- [ ] Export to engineering reports - -## Contributing - -This is a private repository. Contact [contact@atomaste.com](mailto:contact@atomaste.com) for access. - -## License - -Proprietary - Atomaste © 2025 - -## Support - -- **Documentation**: [docs/](docs/) -- **Examples**: [examples/](examples/) -- **Issues**: GitHub Issues (private repository) -- **Email**: support@atomaste.com - -## Resources - -### NXOpen References -- **Official API Docs**: [Siemens NXOpen .NET Documentation](https://docs.sw.siemens.com/en-US/doc/209349590/) -- **NXOpenTSE**: [The Scripting Engineer's Documentation](https://nxopentsedocumentation.thescriptingengineer.com/) (reference for patterns and best practices) -- **Our Guide**: [NXOpen Resources](docs/NXOPEN_RESOURCES.md) - -### Optimization -- **Optuna Documentation**: [optuna.readthedocs.io](https://optuna.readthedocs.io/) -- **pyNastran**: [github.com/SteveDoyle2/pyNastran](https://github.com/SteveDoyle2/pyNastran) - ---- - -**Built with ❤️ by Atomaste** | Powered by Optuna, NXOpen, and Claude diff --git a/docs/archive/STRESS_EXTRACTION_FIXED.md b/docs/archive/STRESS_EXTRACTION_FIXED.md deleted file mode 100644 index d1049589..00000000 --- a/docs/archive/STRESS_EXTRACTION_FIXED.md +++ /dev/null @@ -1,130 +0,0 @@ -# Stress Extraction Fix - Complete ✅ - -## Problem Summary -Stress extraction from NX Nastran OP2 files was returning **0.0 MPa** instead of expected values (~113 MPa). - -## Root Causes Identified - -### 1. pyNastran API Structure (Primary Issue) -**Problem**: The OP2 object uses dotted attribute names like `'stress.chexa_stress'` (not `op2.stress.chexa_stress`) - -**Solution**: Check for dotted attribute names using `hasattr(op2, 'stress.chexa_stress')` - -### 2. Von Mises Stress Index -**Problem**: Originally tried to use last column for all elements - -**Solution**: -- Solid elements (CHEXA, CTETRA, CPENTA): Use **index 9** -- Shell elements (CQUAD4, CTRIA3): Use **last column (-1)** - -### 3. Units Conversion (Critical!) -**Problem**: NX Nastran outputs stress in **kPa** (kiloPascals), not MPa - -**Solution**: Divide by 1000 to convert kPa → MPa - -## Code Changes - -### File: [op2_extractor_example.py](optimization_engine/result_extractors/op2_extractor_example.py) - -#### Change 1: API Access Pattern (Lines 97-107) -```python -# Try format 1: Attribute name with dot (e.g., 'stress.chexa_stress') -dotted_name = f'stress.{table_name}' -if hasattr(op2, dotted_name): - stress_table = getattr(op2, dotted_name) -# Try format 2: Nested attribute op2.stress.chexa_stress -elif hasattr(op2, 'stress') and hasattr(op2.stress, table_name): - stress_table = getattr(op2.stress, table_name) -# Try format 3: Direct attribute op2.chexa_stress (older pyNastran) -elif hasattr(op2, table_name): - stress_table = getattr(op2, table_name) -``` - -#### Change 2: Correct Index for Solid Elements (Lines 120-126) -```python -if table_name in ['chexa_stress', 'ctetra_stress', 'cpenta_stress']: - # Solid elements: data shape is [itime, nnodes, 10] - # Index 9 is von_mises [oxx, oyy, ozz, txy, tyz, txz, o1, o2, o3, von_mises] - stresses = stress_data.data[0, :, 9] -else: - # Shell elements: von Mises is last column - stresses = stress_data.data[0, :, -1] -``` - -#### Change 3: Units Conversion (Lines 141-143) -```python -# CRITICAL: NX Nastran outputs stress in kPa (mN/mm²), convert to MPa -# 1 kPa = 0.001 MPa -max_stress_overall_mpa = max_stress_overall / 1000.0 -``` - -## Test Results - -### Before Fix -``` -Max von Mises: 0.00 MPa -Element ID: None -``` - -### After Fix -``` -Max von Mises: 113.09 MPa -Element ID: 83 -Element type: chexa -``` - -## How to Test - -```bash -# In test_env environment -conda activate test_env -python examples/test_stress_direct.py -``` - -**Expected output:** -- Max stress: ~113.09 MPa -- Element: 83 (CHEXA) -- Status: SUCCESS! - -## Technical Details - -### pyNastran Data Structure -``` -OP2 Object Attributes (NX 2412.5): -├── 'stress.chexa_stress' (dotted attribute name) -├── 'stress.cpenta_stress' -└── [other element types...] - -stress_data structure: -├── data[itime, nnodes, 10] for solid elements -│ └── [oxx, oyy, ozz, txy, tyz, txz, o1, o2, o3, von_mises] -│ 0 1 2 3 4 5 6 7 8 9 -└── element_node[:, 0] = element IDs -``` - -### Units in NX Nastran OP2 -- Stress units: **kPa** (kilopascals) = mN/mm² -- To convert to MPa: divide by 1000 -- Example: 113094.73 kPa = 113.09 MPa - -## Files Modified -- [optimization_engine/result_extractors/op2_extractor_example.py](optimization_engine/result_extractors/op2_extractor_example.py) - Main extraction logic - -## Files Created for Testing -- [examples/test_stress_direct.py](examples/test_stress_direct.py) - Direct stress extraction test -- [examples/test_stress_fix.py](examples/test_stress_fix.py) - Verification script -- [examples/debug_op2_stress.py](examples/debug_op2_stress.py) - Deep OP2 diagnostic - -## Next Steps -1. ✅ Stress extraction working -2. ✅ Units conversion applied -3. ✅ Compatible with multiple pyNastran versions -4. ⏭️ Test complete optimization pipeline -5. ⏭️ Integrate with NX solver execution - -## Compatibility -- ✅ NX Nastran 2412.5 -- ✅ pyNastran (latest version with dotted attribute names) -- ✅ Older pyNastran versions (fallback to direct attributes) -- ✅ CHEXA, CPENTA, CTETRA solid elements -- ✅ CQUAD4, CTRIA3 shell elements diff --git a/docs/archive/TESTING_STRESS_FIX.md b/docs/archive/TESTING_STRESS_FIX.md deleted file mode 100644 index d849c177..00000000 --- a/docs/archive/TESTING_STRESS_FIX.md +++ /dev/null @@ -1,87 +0,0 @@ -# Testing the Stress Extraction Fix - -## Issue Fixed -Previously, stress extraction was returning **0.0 MPa** instead of the expected **~122.91 MPa**. - -**Root Cause**: For solid elements (CHEXA, CTETRA, CPENTA), von Mises stress is at **index 9**, not the last column. - -**Fix Applied**: Modified [op2_extractor_example.py](optimization_engine/result_extractors/op2_extractor_example.py#L106-L109) to check element type and use correct index. - -## How to Test - -### 1. Activate your test environment -```bash -conda activate test_env -``` - -### 2. Run the verification script -```bash -python examples/test_stress_fix.py -``` - -### Expected Output -``` -============================================================ -STRESS EXTRACTION FIX VERIFICATION -============================================================ - ---- Displacement (baseline test) --- -Max displacement: 0.315xxx mm -Node ID: xxx -OK Displacement extractor working - ---- Stress (FIXED - should show ~122.91 MPa) --- -Max von Mises: 122.91 MPa -Element ID: 79 -Element type: chexa - -SUCCESS! Stress extraction fixed! -Expected: ~122.91 MPa -Got: 122.91 MPa -============================================================ -``` - -## Alternative: Test All Extractors -```bash -python optimization_engine/result_extractors/extractors.py examples/bracket/bracket_sim1-solution_1.op2 -``` - -## If Successful, Commit the Fix -```bash -git add optimization_engine/result_extractors/op2_extractor_example.py -git commit -m "fix: Correct von Mises stress extraction for solid elements (CHEXA) - -- Use index 9 for solid elements (CHEXA, CTETRA, CPENTA) -- Keep last column for shell elements (CQUAD4, CTRIA3) -- Fixes stress extraction returning 0.0 instead of actual values (122.91 MPa)" - -git push origin main -``` - -## Technical Details - -### pyNastran OP2 Data Structure for Solid Elements -- Shape: `[itime, nnodes, 10]` -- The 10 values are: - ``` - [oxx, oyy, ozz, txy, tyz, txz, o1, o2, o3, von_mises] - 0 1 2 3 4 5 6 7 8 9 - ``` -- **Von Mises is at index 9** - -### Code Change -```python -# BEFORE (WRONG): -stresses = stress_data.data[0, :, -1] # Last column - WRONG for CHEXA! - -# AFTER (CORRECT): -if table_name in ['chexa_stress', 'ctetra_stress', 'cpenta_stress']: - # Solid elements: von Mises at index 9 - stresses = stress_data.data[0, :, 9] -else: - # Shell elements: von Mises at last column - stresses = stress_data.data[0, :, -1] -``` - -## Files Modified -- [optimization_engine/result_extractors/op2_extractor_example.py](optimization_engine/result_extractors/op2_extractor_example.py) - Lines 103-112 diff --git a/docs/archive/mesh_update_issue.txt b/docs/archive/mesh_update_issue.txt deleted file mode 100644 index 6c520144..00000000 --- a/docs/archive/mesh_update_issue.txt +++ /dev/null @@ -1,23 +0,0 @@ - -The optimization loop is working, but the results show that: - -1. ✅ Parameter updates are happening in Bracket.prt -2. ✅ NX solver is running successfully -3. ✅ Results are being extracted from .op2 files -4. ✅ Optimization loop completes -5. ❌ BUT: All trials return the SAME stress/displacement values - -This indicates that the bracket geometry is NOT actually changing when we update -the tip_thickness and support_angle parameters. - -The issue is that these expressions exist in Bracket.prt, but they may not be -linked to any geometric features (sketches, extrudes, etc.) that define the -actual bracket shape. - -To fix this, the Bracket.prt file needs to be set up so that: -- The 'tip_thickness' expression controls an actual dimension -- The 'support_angle' expression controls an actual angle -- These dimensions are used in sketches/features to define the geometry - -Without this, changing the expressions has no effect on the mesh or the analysis results. - diff --git a/examples/Models/Circular Plate/Circular_Plate.prt b/examples/Models/Circular Plate/Circular_Plate.prt index 8cbf7948..6e87098d 100644 Binary files a/examples/Models/Circular Plate/Circular_Plate.prt and b/examples/Models/Circular Plate/Circular_Plate.prt differ diff --git a/examples/Models/Circular Plate/Circular_Plate_fem1_i.prt b/examples/Models/Circular Plate/Circular_Plate_fem1_i.prt index 8afd71f3..0386c374 100644 Binary files a/examples/Models/Circular Plate/Circular_Plate_fem1_i.prt and b/examples/Models/Circular Plate/Circular_Plate_fem1_i.prt differ diff --git a/examples/Models/Circular Plate/Circular_Plate_sim1.sim b/examples/Models/Circular Plate/Circular_Plate_sim1.sim index eee33535..0eee3fc9 100644 Binary files a/examples/Models/Circular Plate/Circular_Plate_sim1.sim and b/examples/Models/Circular Plate/Circular_Plate_sim1.sim differ diff --git a/optimization_engine/future/README.md b/optimization_engine/future/README.md new file mode 100644 index 00000000..b448ea35 --- /dev/null +++ b/optimization_engine/future/README.md @@ -0,0 +1,105 @@ +# Experimental LLM Features (Archived) + +**Status**: Archived for post-MVP development +**Date Archived**: November 24, 2025 + +## Purpose + +This directory contains experimental LLM integration code that was explored during early development phases. These features are archived (not deleted) for potential future use after the MVP is stable and shipped. + +## MVP LLM Integration Strategy + +For the **MVP**, LLM integration is achieved through: +- **Claude Code Development Assistant**: Interactive development-time assistance +- **Claude Skills** (`.claude/skills/`): + - `create-study.md` - Interactive study scaffolding + - `analyze-workflow.md` - Workflow classification and analysis + +This approach provides LLM assistance **without adding runtime dependencies** or complexity to the core optimization engine. + +## Archived Experimental Files + +### 1. `llm_optimization_runner.py` +Experimental runner that makes runtime LLM API calls during optimization. This attempted to automate: +- Extractor generation +- Inline calculations +- Post-processing hooks + +**Why Archived**: Adds runtime dependencies, API costs, and complexity. The centralized extractor library (`optimization_engine/extractors/`) provides better maintainability. + +### 2. `llm_workflow_analyzer.py` +LLM-based workflow analysis for automated study setup. + +**Why Archived**: The `analyze-workflow` Claude skill provides the same functionality through development-time assistance, without runtime overhead. + +### 3. `inline_code_generator.py` +Auto-generates inline Python calculations from natural language. + +**Why Archived**: Manual calculation definition in `optimization_config.json` is clearer and more maintainable for MVP. + +### 4. `hook_generator.py` +Auto-generates post-processing hooks from natural language descriptions. + +**Why Archived**: The plugin system (`optimization_engine/plugins/`) with manual hook definition is more robust and debuggable. + +### 5. `report_generator.py` +LLM-based report generation from optimization results. + +**Why Archived**: Dashboard provides rich visualizations. LLM summaries can be added post-MVP if needed. + +### 6. `extractor_orchestrator.py` +Orchestrates LLM-based extractor generation and management. + +**Why Archived**: Centralized extractor library (`optimization_engine/extractors/`) is the production approach. No code generation needed at runtime. + +## When to Revisit + +Consider reviving these experimental features **after MVP** if: +1. ✅ MVP is stable and well-tested +2. ✅ Users request more automation +3. ✅ Core architecture is mature enough to support optional LLM features +4. ✅ Clear ROI on LLM API costs vs manual configuration time + +## Production Architecture (MVP) + +For reference, the **stable production** components are: + +``` +optimization_engine/ +├── runner.py # Production optimization runner +├── extractors/ # Centralized extractor library +│ ├── __init__.py +│ ├── base.py +│ ├── displacement.py +│ ├── stress.py +│ ├── frequency.py +│ └── mass.py +├── plugins/ # Plugin system (hooks) +│ ├── __init__.py +│ └── hook_manager.py +├── nx_solver.py # NX simulation interface +├── nx_updater.py # NX expression updates +└── visualizer.py # Result plotting + +.claude/skills/ # Claude Code skills +├── create-study.md # Interactive study creation +└── analyze-workflow.md # Workflow analysis +``` + +## Migration Notes + +If you need to use any of these experimental files: +1. They are functional but not maintained +2. Update imports to `optimization_engine.future.{module_name}` +3. Install any additional dependencies (LLM client libraries) +4. Be aware of API costs for LLM calls + +## Related Documents + +- [`docs/07_DEVELOPMENT/Today_Todo.md`](../../docs/07_DEVELOPMENT/Today_Todo.md) - MVP Development Plan +- [`DEVELOPMENT.md`](../../DEVELOPMENT.md) - Development guide +- [`.claude/skills/create-study.md`](../../.claude/skills/create-study.md) - Study creation skill + +## Questions? + +For MVP development questions, refer to the [DEVELOPMENT.md](../../DEVELOPMENT.md) guide or the MVP plan in `docs/07_DEVELOPMENT/Today_Todo.md`. diff --git a/optimization_engine/extractor_orchestrator.py b/optimization_engine/future/extractor_orchestrator.py similarity index 100% rename from optimization_engine/extractor_orchestrator.py rename to optimization_engine/future/extractor_orchestrator.py diff --git a/optimization_engine/hook_generator.py b/optimization_engine/future/hook_generator.py similarity index 100% rename from optimization_engine/hook_generator.py rename to optimization_engine/future/hook_generator.py diff --git a/optimization_engine/inline_code_generator.py b/optimization_engine/future/inline_code_generator.py similarity index 100% rename from optimization_engine/inline_code_generator.py rename to optimization_engine/future/inline_code_generator.py diff --git a/optimization_engine/llm_optimization_runner.py b/optimization_engine/future/llm_optimization_runner.py similarity index 100% rename from optimization_engine/llm_optimization_runner.py rename to optimization_engine/future/llm_optimization_runner.py diff --git a/optimization_engine/llm_workflow_analyzer.py b/optimization_engine/future/llm_workflow_analyzer.py similarity index 100% rename from optimization_engine/llm_workflow_analyzer.py rename to optimization_engine/future/llm_workflow_analyzer.py diff --git a/optimization_engine/future/report_generator.py b/optimization_engine/future/report_generator.py new file mode 100644 index 00000000..7d0bed82 --- /dev/null +++ b/optimization_engine/future/report_generator.py @@ -0,0 +1,134 @@ +""" +Report Generator Utility +Generates Markdown/HTML/PDF reports for optimization studies +""" + +import json +from pathlib import Path +from typing import Optional +import markdown +from datetime import datetime + +def generate_study_report( + study_dir: Path, + output_format: str = "markdown", + include_llm_summary: bool = False +) -> Optional[Path]: + """ + Generate a report for the study. + + Args: + study_dir: Path to the study directory + output_format: 'markdown', 'html', or 'pdf' + include_llm_summary: Whether to include AI-generated summary + + Returns: + Path to the generated report file + """ + try: + # Load data + config_path = study_dir / "1_setup" / "optimization_config.json" + history_path = study_dir / "2_results" / "optimization_history_incremental.json" + + if not config_path.exists() or not history_path.exists(): + return None + + with open(config_path) as f: + config = json.load(f) + + with open(history_path) as f: + history = json.load(f) + + # Find best trial + best_trial = None + if history: + best_trial = min(history, key=lambda x: x['objective']) + + # Generate Markdown content + md_content = f"""# Optimization Report: {config.get('study_name', study_dir.name)} + +**Date**: {datetime.now().strftime('%Y-%m-%d %H:%M')} +**Status**: {'Completed' if len(history) >= config.get('optimization_settings', {}).get('n_trials', 50) else 'In Progress'} + +## Executive Summary +{_generate_summary(history, best_trial, include_llm_summary)} + +## Study Configuration +- **Objectives**: {', '.join([o['name'] for o in config.get('objectives', [])])} +- **Design Variables**: {len(config.get('design_variables', []))} variables +- **Total Trials**: {len(history)} + +## Best Result (Trial #{best_trial['trial_number'] if best_trial else 'N/A'}) +- **Objective Value**: {best_trial['objective'] if best_trial else 'N/A'} +- **Parameters**: +""" + + if best_trial: + for k, v in best_trial['design_variables'].items(): + md_content += f" - **{k}**: {v:.4f}\n" + + md_content += "\n## Optimization Progress\n" + md_content += "The optimization process showed convergence towards the optimal solution.\n" + + # Save report based on format + timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') + output_dir = study_dir / "2_results" + + if output_format in ['markdown', 'md']: + output_path = output_dir / f"optimization_report_{timestamp}.md" + with open(output_path, 'w') as f: + f.write(md_content) + + elif output_format == 'html': + output_path = output_dir / f"optimization_report_{timestamp}.html" + html_content = markdown.markdown(md_content) + # Add basic styling + styled_html = f""" + + + + + + {html_content} + + + """ + with open(output_path, 'w') as f: + f.write(styled_html) + + elif output_format == 'pdf': + # Requires weasyprint + try: + from weasyprint import HTML + output_path = output_dir / f"optimization_report_{timestamp}.pdf" + html_content = markdown.markdown(md_content) + HTML(string=html_content).write_pdf(str(output_path)) + except ImportError: + print("WeasyPrint not installed, falling back to HTML") + return generate_study_report(study_dir, 'html', include_llm_summary) + + return output_path + + except Exception as e: + print(f"Report generation error: {e}") + return None + +def _generate_summary(history, best_trial, use_llm): + if use_llm: + return "[AI Summary Placeholder] The optimization successfully identified a design that minimizes mass while satisfying all constraints." + + if not history: + return "No trials completed yet." + + improvement = 0 + if len(history) > 1: + first = history[0]['objective'] + best = best_trial['objective'] + improvement = ((first - best) / first) * 100 + + return f"The optimization run completed {len(history)} trials. The best design found (Trial #{best_trial['trial_number']}) achieved an objective value of {best_trial['objective']:.4f}, representing a {improvement:.1f}% improvement over the initial design." diff --git a/optimization_engine/nx_solver.py b/optimization_engine/nx_solver.py index 75096080..eeb02ef0 100644 --- a/optimization_engine/nx_solver.py +++ b/optimization_engine/nx_solver.py @@ -2,6 +2,7 @@ NX Nastran Solver Integration Executes NX Nastran solver in batch mode for optimization loops. +Includes session management to prevent conflicts with concurrent optimizations. """ from pathlib import Path @@ -10,6 +11,7 @@ import subprocess import time import shutil import os +from optimization_engine.nx_session_manager import NXSessionManager class NXSolver: @@ -28,7 +30,9 @@ class NXSolver: nx_install_dir: Optional[Path] = None, nastran_version: str = "2412", timeout: int = 600, - use_journal: bool = True + use_journal: bool = True, + enable_session_management: bool = True, + study_name: str = "default_study" ): """ Initialize NX Solver. @@ -38,10 +42,20 @@ class NXSolver: nastran_version: NX version (e.g., "2412", "2506") timeout: Maximum solver time in seconds (default: 10 minutes) use_journal: Use NX journal for solving (recommended for licensing) + enable_session_management: Enable session conflict prevention (default: True) + study_name: Name of the study (used for session tracking) """ self.nastran_version = nastran_version self.timeout = timeout self.use_journal = use_journal + self.study_name = study_name + + # Initialize session manager + self.session_manager = None + if enable_session_management: + self.session_manager = NXSessionManager(verbose=True) + # Clean up any stale locks from crashed processes + self.session_manager.cleanup_stale_locks() # Auto-detect NX installation if nx_install_dir is None: @@ -128,7 +142,8 @@ class NXSolver: sim_file: Path, working_dir: Optional[Path] = None, cleanup: bool = True, - expression_updates: Optional[Dict[str, float]] = None + expression_updates: Optional[Dict[str, float]] = None, + solution_name: Optional[str] = None ) -> Dict[str, Any]: """ Run NX Nastran simulation. @@ -140,6 +155,8 @@ class NXSolver: expression_updates: Dict of expression name -> value to update (only used in journal mode) e.g., {'tip_thickness': 22.5, 'support_angle': 35.0} + solution_name: Specific solution to solve (e.g., "Solution_Normal_Modes") + If None, solves all solutions. Only used in journal mode. Returns: Dictionary with: @@ -148,6 +165,7 @@ class NXSolver: - log_file: Path to .log file - elapsed_time: Solve time in seconds - errors: List of error messages (if any) + - solution_name: Name of the solution that was solved """ sim_file = Path(sim_file) if not sim_file.exists(): @@ -174,13 +192,20 @@ class NXSolver: sim_file = dat_file # Prepare output file names - # When using journal mode with .sim files, output is named: -solution_1.op2 + # When using journal mode with .sim files, output is named: -solution_name.op2 # When using direct mode with .dat files, output is named: .op2 base_name = sim_file.stem if self.use_journal and sim_file.suffix == '.sim': - # Journal mode: look for -solution_1 pattern - output_base = f"{base_name.lower()}-solution_1" + # Journal mode: determine solution-specific output name + if solution_name: + # Convert solution name to lowercase and replace spaces with underscores + # E.g., "Solution_Normal_Modes" -> "solution_normal_modes" + solution_suffix = solution_name.lower().replace(' ', '_') + output_base = f"{base_name.lower()}-{solution_suffix}" + else: + # Default to solution_1 + output_base = f"{base_name.lower()}-solution_1" else: # Direct mode or .dat file output_base = base_name @@ -216,17 +241,21 @@ class NXSolver: with open(journal_template, 'r') as f: journal_content = f.read() - # Create a custom journal that passes the sim file path and expression values + # Create a custom journal that passes the sim file path, solution name, and expression values # Build argv list with expression updates argv_list = [f"r'{sim_file.absolute()}'"] + # Add solution name if provided (passed as second argument) + if solution_name: + argv_list.append(f"'{solution_name}'") + else: + argv_list.append("None") + # Add expression values if provided + # Pass all expressions as key=value pairs if expression_updates: - # For bracket example, we expect: tip_thickness, support_angle - if 'tip_thickness' in expression_updates: - argv_list.append(str(expression_updates['tip_thickness'])) - if 'support_angle' in expression_updates: - argv_list.append(str(expression_updates['support_angle'])) + for expr_name, expr_value in expression_updates.items(): + argv_list.append(f"'{expr_name}={expr_value}'") argv_str = ', '.join(argv_list) @@ -372,7 +401,8 @@ sys.argv = ['', {argv_str}] # Set argv for the main function 'f06_file': f06_file if f06_file.exists() else None, 'elapsed_time': elapsed_time, 'errors': errors, - 'return_code': result.returncode + 'return_code': result.returncode, + 'solution_name': solution_name } except subprocess.TimeoutExpired: @@ -384,7 +414,8 @@ sys.argv = ['', {argv_str}] # Set argv for the main function 'log_file': log_file if log_file.exists() else None, 'elapsed_time': elapsed_time, 'errors': [f'Solver timeout after {self.timeout}s'], - 'return_code': -1 + 'return_code': -1, + 'solution_name': solution_name } except Exception as e: @@ -396,7 +427,8 @@ sys.argv = ['', {argv_str}] # Set argv for the main function 'log_file': None, 'elapsed_time': elapsed_time, 'errors': [str(e)], - 'return_code': -1 + 'return_code': -1, + 'solution_name': solution_name } def _check_solution_success(self, f06_file: Path, log_file: Path) -> bool: @@ -476,7 +508,8 @@ def run_nx_simulation( timeout: int = 600, cleanup: bool = True, use_journal: bool = True, - expression_updates: Optional[Dict[str, float]] = None + expression_updates: Optional[Dict[str, float]] = None, + solution_name: Optional[str] = None ) -> Path: """ Convenience function to run NX simulation and return OP2 file path. @@ -488,6 +521,7 @@ def run_nx_simulation( cleanup: Remove temp files use_journal: Use NX journal for solving (recommended for licensing) expression_updates: Dict of expression name -> value to update in journal + solution_name: Specific solution to solve (e.g., "Solution_Normal_Modes") Returns: Path to output .op2 file @@ -496,7 +530,12 @@ def run_nx_simulation( RuntimeError: If simulation fails """ solver = NXSolver(nastran_version=nastran_version, timeout=timeout, use_journal=use_journal) - result = solver.run_simulation(sim_file, cleanup=cleanup, expression_updates=expression_updates) + result = solver.run_simulation( + sim_file, + cleanup=cleanup, + expression_updates=expression_updates, + solution_name=solution_name + ) if not result['success']: error_msg = '\n'.join(result['errors']) if result['errors'] else 'Unknown error' diff --git a/optimization_engine/realtime_tracking.py b/optimization_engine/realtime_tracking.py index 0f84710b..c743ace0 100644 --- a/optimization_engine/realtime_tracking.py +++ b/optimization_engine/realtime_tracking.py @@ -98,13 +98,25 @@ class RealtimeTrackingCallback: def _write_optimizer_state(self, study: optuna.Study, trial: optuna.trial.FrozenTrial): """Write current optimizer state.""" + # [Protocol 11] For multi-objective, strategy is always NSGA-II + is_multi_objective = len(study.directions) > 1 + + if is_multi_objective: + # Multi-objective studies use NSGA-II, skip adaptive characterization + current_strategy = "NSGA-II" + current_phase = "multi_objective_optimization" + else: + # Single-objective uses intelligent strategy selection + current_strategy = getattr(self.optimizer, 'current_strategy', 'unknown') + current_phase = getattr(self.optimizer, 'current_phase', 'unknown') + state = { "timestamp": datetime.now().isoformat(), "trial_number": trial.number, "total_trials": len(study.trials), - "current_phase": getattr(self.optimizer, 'current_phase', 'unknown'), - "current_strategy": getattr(self.optimizer, 'current_strategy', 'unknown'), - "is_multi_objective": len(study.directions) > 1, + "current_phase": current_phase, + "current_strategy": current_strategy, + "is_multi_objective": is_multi_objective, "study_directions": [str(d) for d in study.directions], } @@ -132,18 +144,27 @@ class RealtimeTrackingCallback: else: log = [] + # [Protocol 11] Handle both single and multi-objective + is_multi_objective = len(study.directions) > 1 + # Append new trial trial_entry = { "trial_number": trial.number, "timestamp": datetime.now().isoformat(), "state": str(trial.state), "params": trial.params, - "value": trial.value if trial.value is not None else None, - "values": trial.values if hasattr(trial, 'values') and trial.values is not None else None, "duration_seconds": (trial.datetime_complete - trial.datetime_start).total_seconds() if trial.datetime_complete else None, "user_attrs": dict(trial.user_attrs) if trial.user_attrs else {} } + # Add objectives (Protocol 11 compliant) + if is_multi_objective: + trial_entry["values"] = trial.values if trial.values is not None else None + trial_entry["value"] = None # Not available + else: + trial_entry["value"] = trial.value if trial.value is not None else None + trial_entry["values"] = None + log.append(trial_entry) self._atomic_write(trial_log_file, log) diff --git a/optimization_engine/solve_simulation.py b/optimization_engine/solve_simulation.py index 6dd95343..5ad36891 100644 --- a/optimization_engine/solve_simulation.py +++ b/optimization_engine/solve_simulation.py @@ -20,31 +20,41 @@ def main(args): Args: args: Command line arguments args[0]: .sim file path - args[1]: tip_thickness value (optional) - args[2]: support_angle value (optional) + args[1]: solution_name (optional, e.g., "Solution_Normal_Modes" or None for default) + args[2+]: expression updates as "name=value" pairs """ if len(args) < 1: print("ERROR: No .sim file path provided") - print("Usage: run_journal.exe solve_simulation.py [tip_thickness] [support_angle]") + print("Usage: run_journal.exe solve_simulation.py [solution_name] [expr1=val1] [expr2=val2] ...") return False sim_file_path = args[0] + # Parse solution name if provided (args[1]) + solution_name = args[1] if len(args) > 1 and args[1] != 'None' else None + # Extract base name from sim file (e.g., "Beam_sim1.sim" -> "Beam") import os sim_filename = os.path.basename(sim_file_path) part_base_name = sim_filename.split('_sim')[0] if '_sim' in sim_filename else sim_filename.split('.sim')[0] - # Parse expression values if provided - tip_thickness = float(args[1]) if len(args) > 1 else None - support_angle = float(args[2]) if len(args) > 2 else None + # Parse expression updates from args[2+] as "name=value" pairs + expression_updates = {} + for arg in args[2:]: + if '=' in arg: + name, value = arg.split('=', 1) + expression_updates[name] = float(value) print(f"[JOURNAL] Opening simulation: {sim_file_path}") print(f"[JOURNAL] Detected part base name: {part_base_name}") - if tip_thickness is not None: - print(f"[JOURNAL] Will update tip_thickness = {tip_thickness}") - if support_angle is not None: - print(f"[JOURNAL] Will update support_angle = {support_angle}") + if solution_name: + print(f"[JOURNAL] Will solve specific solution: {solution_name}") + else: + print(f"[JOURNAL] Will solve default solution (Solution 1)") + if expression_updates: + print(f"[JOURNAL] Will update expressions:") + for name, value in expression_updates.items(): + print(f"[JOURNAL] {name} = {value}") try: theSession = NXOpen.Session.GetSession() @@ -134,27 +144,21 @@ def main(args): # CRITICAL: Apply expression changes BEFORE updating geometry expressions_updated = [] - if tip_thickness is not None: - print(f"[JOURNAL] Applying tip_thickness = {tip_thickness}") - expr_tip = workPart.Expressions.FindObject("tip_thickness") - if expr_tip: - unit_mm = workPart.UnitCollection.FindObject("MilliMeter") - workPart.Expressions.EditExpressionWithUnits(expr_tip, unit_mm, str(tip_thickness)) - expressions_updated.append(expr_tip) - print(f"[JOURNAL] tip_thickness updated") - else: - print(f"[JOURNAL] WARNING: tip_thickness expression not found!") - - if support_angle is not None: - print(f"[JOURNAL] Applying support_angle = {support_angle}") - expr_angle = workPart.Expressions.FindObject("support_angle") - if expr_angle: - unit_deg = workPart.UnitCollection.FindObject("Degrees") - workPart.Expressions.EditExpressionWithUnits(expr_angle, unit_deg, str(support_angle)) - expressions_updated.append(expr_angle) - print(f"[JOURNAL] support_angle updated") - else: - print(f"[JOURNAL] WARNING: support_angle expression not found!") + # Apply all expression updates dynamically + for expr_name, expr_value in expression_updates.items(): + print(f"[JOURNAL] Applying {expr_name} = {expr_value}") + try: + expr_obj = workPart.Expressions.FindObject(expr_name) + if expr_obj: + # Use millimeters as default unit for geometric parameters + unit_mm = workPart.UnitCollection.FindObject("MilliMeter") + workPart.Expressions.EditExpressionWithUnits(expr_obj, unit_mm, str(expr_value)) + expressions_updated.append(expr_obj) + print(f"[JOURNAL] {expr_name} updated successfully") + else: + print(f"[JOURNAL] WARNING: {expr_name} expression not found!") + except Exception as e: + print(f"[JOURNAL] ERROR updating {expr_name}: {e}") # Make expressions up to date if expressions_updated: @@ -171,6 +175,20 @@ def main(args): nErrs = theSession.UpdateManager.DoUpdate(markId_update) theSession.DeleteUndoMark(markId_update, "NX update") print(f"[JOURNAL] {part_base_name} geometry updated ({nErrs} errors)") + + # Extract mass from expression p173 if it exists and write to temp file + try: + mass_expr = workPart.Expressions.FindObject("p173") + if mass_expr: + mass_kg = mass_expr.Value + mass_output_file = os.path.join(working_dir, "_temp_mass.txt") + with open(mass_output_file, 'w') as f: + f.write(str(mass_kg)) + print(f"[JOURNAL] Mass from p173: {mass_kg:.6f} kg ({mass_kg * 1000:.2f} g)") + print(f"[JOURNAL] Mass written to: {mass_output_file}") + except: + pass # Expression p173 might not exist in all models + geometry_updated = True else: print(f"[JOURNAL] {part_base_name} part not found - may be embedded in sim file") @@ -247,31 +265,45 @@ def main(args): theCAESimSolveManager = NXOpen.CAE.SimSolveManager.GetSimSolveManager(theSession) - # Get the first solution from the simulation + # Get the simulation object simSimulation1 = workSimPart.FindObject("Simulation") - simSolution1 = simSimulation1.FindObject("Solution[Solution 1]") - psolutions1 = [simSolution1] + # Get the solution(s) to solve - either specific or all + if solution_name: + # Solve specific solution in background mode + solution_obj_name = f"Solution[{solution_name}]" + print(f"[JOURNAL] Looking for solution: {solution_obj_name}") + simSolution1 = simSimulation1.FindObject(solution_obj_name) + psolutions1 = [simSolution1] - # Solve in background mode - numsolutionssolved1, numsolutionsfailed1, numsolutionsskipped1 = theCAESimSolveManager.SolveChainOfSolutions( - psolutions1, - NXOpen.CAE.SimSolution.SolveOption.Solve, - NXOpen.CAE.SimSolution.SetupCheckOption.CompleteDeepCheckAndOutputErrors, - NXOpen.CAE.SimSolution.SolveMode.Background - ) + numsolutionssolved1, numsolutionsfailed1, numsolutionsskipped1 = theCAESimSolveManager.SolveChainOfSolutions( + psolutions1, + NXOpen.CAE.SimSolution.SolveOption.Solve, + NXOpen.CAE.SimSolution.SetupCheckOption.CompleteDeepCheckAndOutputErrors, + NXOpen.CAE.SimSolution.SolveMode.Background + ) + else: + # Solve ALL solutions using SolveAllSolutions API (Foreground mode) + # This ensures all solutions (static + modal, etc.) complete before returning + print(f"[JOURNAL] Solving all solutions using SolveAllSolutions API (Foreground mode)...") + + numsolutionssolved1, numsolutionsfailed1, numsolutionsskipped1 = theCAESimSolveManager.SolveAllSolutions( + NXOpen.CAE.SimSolution.SolveOption.Solve, + NXOpen.CAE.SimSolution.SetupCheckOption.CompleteCheckAndOutputErrors, + NXOpen.CAE.SimSolution.SolveMode.Foreground, + False + ) theSession.DeleteUndoMark(markId5, None) theSession.SetUndoMarkName(markId3, "Solve") - print(f"[JOURNAL] Solve submitted!") + print(f"[JOURNAL] Solve completed!") print(f"[JOURNAL] Solutions solved: {numsolutionssolved1}") print(f"[JOURNAL] Solutions failed: {numsolutionsfailed1}") print(f"[JOURNAL] Solutions skipped: {numsolutionsskipped1}") - # NOTE: In Background mode, these values may not be accurate since the solve - # runs asynchronously. The solve will continue after this journal finishes. - # We rely on the Save operation and file existence checks to verify success. + # NOTE: When solution_name=None, we use Foreground mode to ensure all solutions + # complete before returning. When solution_name is specified, Background mode is used. # Save the simulation to write all output files print("[JOURNAL] Saving simulation to ensure output files are written...") diff --git a/studies/bracket_displacement_maximizing/model/Bracket.prt b/studies/bracket_displacement_maximizing/model/Bracket.prt index 939c3513..894f6489 100644 Binary files a/studies/bracket_displacement_maximizing/model/Bracket.prt and b/studies/bracket_displacement_maximizing/model/Bracket.prt differ diff --git a/studies/bracket_displacement_maximizing/model/Bracket_fem1_i.prt b/studies/bracket_displacement_maximizing/model/Bracket_fem1_i.prt index eab4bb4b..7d361f4d 100644 Binary files a/studies/bracket_displacement_maximizing/model/Bracket_fem1_i.prt and b/studies/bracket_displacement_maximizing/model/Bracket_fem1_i.prt differ diff --git a/studies/simple_beam_optimization/1_setup/model/Beam.prt b/studies/simple_beam_optimization/1_setup/model/Beam.prt index bee74343..2ca409c8 100644 Binary files a/studies/simple_beam_optimization/1_setup/model/Beam.prt and b/studies/simple_beam_optimization/1_setup/model/Beam.prt differ diff --git a/studies/simple_beam_optimization/1_setup/model/Beam_fem1.fem b/studies/simple_beam_optimization/1_setup/model/Beam_fem1.fem index e63322b0..21caa162 100644 Binary files a/studies/simple_beam_optimization/1_setup/model/Beam_fem1.fem and b/studies/simple_beam_optimization/1_setup/model/Beam_fem1.fem differ diff --git a/studies/simple_beam_optimization/1_setup/model/Beam_fem1_i.prt b/studies/simple_beam_optimization/1_setup/model/Beam_fem1_i.prt index 4a9dde87..7020fde8 100644 Binary files a/studies/simple_beam_optimization/1_setup/model/Beam_fem1_i.prt and b/studies/simple_beam_optimization/1_setup/model/Beam_fem1_i.prt differ diff --git a/studies/simple_beam_optimization/1_setup/model/Beam_sim1.sim b/studies/simple_beam_optimization/1_setup/model/Beam_sim1.sim index c9f79251..5b141c7c 100644 Binary files a/studies/simple_beam_optimization/1_setup/model/Beam_sim1.sim and b/studies/simple_beam_optimization/1_setup/model/Beam_sim1.sim differ diff --git a/studies/simple_beam_optimization/2_substudies/01_initial_exploration/README.md b/studies/simple_beam_optimization/2_substudies/01_initial_exploration/README.md deleted file mode 100644 index d365d533..00000000 --- a/studies/simple_beam_optimization/2_substudies/01_initial_exploration/README.md +++ /dev/null @@ -1,43 +0,0 @@ -# Substudy 01: Initial Exploration - -**Date**: 2025-11-17 -**Status**: Completed -**Trials**: 10 - -## Purpose - -Initial exploration of the 4D design space to understand parameter ranges and baseline behavior. - -## Configuration - -**Design Variables**: -- `beam_half_core_thickness`: 10-40 mm -- `beam_face_thickness`: 10-40 mm -- `holes_diameter`: 150-450 mm -- `hole_count`: 5-15 (integer) - -**Objectives** (equal weights): -- Minimize displacement -- Minimize stress -- Minimize mass - -**Sampler**: TPE (Tree-structured Parzen Estimator) - -## Expected Outcome - -- Explore full design space -- Identify promising regions -- Validate optimization workflow - -## Actual Results - -**Status**: Early exploration run - baseline for subsequent substudies - -**Key Findings**: -- Established that optimization workflow is functional -- Provided initial data for parameter importance analysis -- Informed subsequent validation runs - -## Next Steps - -→ Substudy 02: Validate 3D parameter updates (without hole_count) diff --git a/studies/simple_beam_optimization/2_substudies/01_initial_exploration/config.json b/studies/simple_beam_optimization/2_substudies/01_initial_exploration/config.json deleted file mode 100644 index e980bd6a..00000000 --- a/studies/simple_beam_optimization/2_substudies/01_initial_exploration/config.json +++ /dev/null @@ -1,100 +0,0 @@ -{ - "study_name": "simple_beam_optimization", - "description": "Minimize displacement and weight of beam with stress constraint", - "substudy_name": "initial_exploration", - "design_variables": { - "beam_half_core_thickness": { - "type": "continuous", - "min": 10.0, - "max": 40.0, - "baseline": 20.0, - "units": "mm", - "description": "Half thickness of beam core" - }, - "beam_face_thickness": { - "type": "continuous", - "min": 10.0, - "max": 40.0, - "baseline": 20.0, - "units": "mm", - "description": "Thickness of beam face sheets" - }, - "holes_diameter": { - "type": "continuous", - "min": 150.0, - "max": 450.0, - "baseline": 300.0, - "units": "mm", - "description": "Diameter of lightening holes" - }, - "hole_count": { - "type": "integer", - "min": 5, - "max": 20, - "baseline": 10, - "units": "unitless", - "description": "Number of lightening holes" - } - }, - "extractors": [ - { - "name": "max_displacement", - "action": "extract_displacement", - "description": "Extract maximum displacement from OP2", - "parameters": { - "metric": "max" - } - }, - { - "name": "max_von_mises", - "action": "extract_solid_stress", - "description": "Extract maximum von Mises stress from OP2", - "parameters": { - "stress_type": "von_mises", - "metric": "max" - } - }, - { - "name": "mass", - "action": "extract_expression", - "description": "Extract mass from p173 expression", - "parameters": { - "expression_name": "p173" - } - } - ], - "objectives": [ - { - "name": "minimize_stress", - "extractor": "max_von_mises", - "goal": "minimize", - "weight": 0.5, - "description": "Minimize maximum von Mises stress for structural safety" - }, - { - "name": "minimize_weight", - "extractor": "mass", - "goal": "minimize", - "weight": 0.5, - "description": "Minimize beam mass (p173 in kg)" - } - ], - "constraints": [ - { - "name": "displacement_limit", - "extractor": "max_displacement", - "type": "less_than", - "value": 10.0, - "units": "mm", - "description": "Maximum displacement must be less than 10mm across entire beam" - } - ], - "optimization_settings": { - "algorithm": "optuna", - "n_trials": 50, - "sampler": "TPE", - "pruner": "HyperbandPruner", - "direction": "minimize", - "timeout_per_trial": 600 - } -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/01_initial_exploration/optimization_config.json b/studies/simple_beam_optimization/2_substudies/01_initial_exploration/optimization_config.json deleted file mode 100644 index e980bd6a..00000000 --- a/studies/simple_beam_optimization/2_substudies/01_initial_exploration/optimization_config.json +++ /dev/null @@ -1,100 +0,0 @@ -{ - "study_name": "simple_beam_optimization", - "description": "Minimize displacement and weight of beam with stress constraint", - "substudy_name": "initial_exploration", - "design_variables": { - "beam_half_core_thickness": { - "type": "continuous", - "min": 10.0, - "max": 40.0, - "baseline": 20.0, - "units": "mm", - "description": "Half thickness of beam core" - }, - "beam_face_thickness": { - "type": "continuous", - "min": 10.0, - "max": 40.0, - "baseline": 20.0, - "units": "mm", - "description": "Thickness of beam face sheets" - }, - "holes_diameter": { - "type": "continuous", - "min": 150.0, - "max": 450.0, - "baseline": 300.0, - "units": "mm", - "description": "Diameter of lightening holes" - }, - "hole_count": { - "type": "integer", - "min": 5, - "max": 20, - "baseline": 10, - "units": "unitless", - "description": "Number of lightening holes" - } - }, - "extractors": [ - { - "name": "max_displacement", - "action": "extract_displacement", - "description": "Extract maximum displacement from OP2", - "parameters": { - "metric": "max" - } - }, - { - "name": "max_von_mises", - "action": "extract_solid_stress", - "description": "Extract maximum von Mises stress from OP2", - "parameters": { - "stress_type": "von_mises", - "metric": "max" - } - }, - { - "name": "mass", - "action": "extract_expression", - "description": "Extract mass from p173 expression", - "parameters": { - "expression_name": "p173" - } - } - ], - "objectives": [ - { - "name": "minimize_stress", - "extractor": "max_von_mises", - "goal": "minimize", - "weight": 0.5, - "description": "Minimize maximum von Mises stress for structural safety" - }, - { - "name": "minimize_weight", - "extractor": "mass", - "goal": "minimize", - "weight": 0.5, - "description": "Minimize beam mass (p173 in kg)" - } - ], - "constraints": [ - { - "name": "displacement_limit", - "extractor": "max_displacement", - "type": "less_than", - "value": 10.0, - "units": "mm", - "description": "Maximum displacement must be less than 10mm across entire beam" - } - ], - "optimization_settings": { - "algorithm": "optuna", - "n_trials": 50, - "sampler": "TPE", - "pruner": "HyperbandPruner", - "direction": "minimize", - "timeout_per_trial": 600 - } -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/README.md b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/README.md deleted file mode 100644 index ae3190ab..00000000 --- a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/README.md +++ /dev/null @@ -1,44 +0,0 @@ -# Substudy 02: Validation - 3D Parameter Updates - -**Date**: 2025-11-17 -**Status**: Completed -**Trials**: 3 - -## Purpose - -Validate that 3 design variables (beam_half_core_thickness, beam_face_thickness, holes_diameter) update correctly in the CAD model and propagate through to FEA results. - -## Configuration Changes - -**From Substudy 01**: -- Reduced to 3 trials (validation run) -- Testing parameter update mechanism without hole_count - -**Design Variables** (3D): -- `beam_half_core_thickness`: 10-40 mm ✓ -- `beam_face_thickness`: 10-40 mm ✓ -- `holes_diameter`: 150-450 mm ✓ -- `hole_count`: FIXED (not varied) - -## Expected Outcome - -- All 3 continuous variables update correctly -- NX parameter update system works -- FEA results reflect design changes - -## Actual Results - -**Status**: ✅ SUCCESS - -**Key Findings**: -- All 3 continuous design variables updated correctly -- NX .exp export/import method validated -- FEA mesh and results properly reflect parameter changes - -**Validation Method**: -- Verified expression values in updated .prt files -- Compared FEA results across trials to confirm variation - -## Next Steps - -→ Substudy 03: Validate 4D parameter updates (ADD hole_count) diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/best_trial.json b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/best_trial.json deleted file mode 100644 index 4c4dc292..00000000 --- a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/best_trial.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "best_trial_number": 0, - "best_params": { - "beam_half_core_thickness": 29.337408537581144, - "beam_face_thickness": 30.46892531252702, - "holes_diameter": 355.50168387567, - "hole_count": 9 - }, - "best_value": 1593.7016555239895, - "timestamp": "2025-11-17T12:07:15.761846" -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/optuna_study.pkl b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/optuna_study.pkl deleted file mode 100644 index a33f02ea..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/optuna_study.pkl and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/Beam.prt b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/Beam.prt deleted file mode 100644 index d97f996a..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/Beam.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/Beam_fem1.fem b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/Beam_fem1.fem deleted file mode 100644 index 51063cc4..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/Beam_fem1.fem and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/Beam_fem1_i.prt b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/Beam_fem1_i.prt deleted file mode 100644 index de340333..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/Beam_fem1_i.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/Beam_sim1.sim b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/Beam_sim1.sim deleted file mode 100644 index 5159cbd7..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/Beam_sim1.sim and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/results.json b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/results.json deleted file mode 100644 index 5e96931d..00000000 --- a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_000/results.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "trial_number": 0, - "design_variables": { - "beam_half_core_thickness": 29.337408537581144, - "beam_face_thickness": 30.46892531252702, - "holes_diameter": 355.50168387567, - "hole_count": 9 - }, - "results": { - "max_displacement": 22.118558883666992, - "max_stress": 131.5071875, - "mass": 973.968443678471 - }, - "objective": 381.8457671572903, - "penalty": 1211.8558883666992, - "total_objective": 1593.7016555239895, - "timestamp": "2025-11-17T12:07:06.957242" -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_001/Beam.prt b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_001/Beam.prt deleted file mode 100644 index d97f996a..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_001/Beam.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_001/Beam_fem1.fem b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_001/Beam_fem1.fem deleted file mode 100644 index 51063cc4..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_001/Beam_fem1.fem and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_001/Beam_fem1_i.prt b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_001/Beam_fem1_i.prt deleted file mode 100644 index de340333..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_001/Beam_fem1_i.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_001/Beam_sim1.sim b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_001/Beam_sim1.sim deleted file mode 100644 index 91429506..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_001/Beam_sim1.sim and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_002/Beam.prt b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_002/Beam.prt deleted file mode 100644 index d97f996a..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_002/Beam.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_002/Beam_fem1.fem b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_002/Beam_fem1.fem deleted file mode 100644 index 51063cc4..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_002/Beam_fem1.fem and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_002/Beam_fem1_i.prt b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_002/Beam_fem1_i.prt deleted file mode 100644 index de340333..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_002/Beam_fem1_i.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_002/Beam_sim1.sim b/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_002/Beam_sim1.sim deleted file mode 100644 index a8b4c81f..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/02_validation_3d_3trials/trial_002/Beam_sim1.sim and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/README.md b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/README.md deleted file mode 100644 index aa336cfb..00000000 --- a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/README.md +++ /dev/null @@ -1,49 +0,0 @@ -# Substudy 03: Validation - 4D Parameter Updates (with hole_count) - -**Date**: 2025-11-17 -**Status**: Completed -**Trials**: 3 - -## Purpose - -Validate that ALL 4 design variables update correctly, including the integer variable `hole_count` which was previously failing. - -## Configuration Changes - -**From Substudy 02**: -- Added hole_count as a variable (integer type) -- Still only 3 trials (validation run) - -**Design Variables** (4D): -- `beam_half_core_thickness`: 10-40 mm ✓ -- `beam_face_thickness`: 10-40 mm ✓ -- `holes_diameter`: 150-450 mm ✓ -- `hole_count`: 5-15 (integer) ✓ **NEW** - -## Expected Outcome - -- hole_count expression updates correctly via .exp import -- Pattern feature regenerates with new hole count -- Mesh element count changes to reflect geometry changes - -## Actual Results - -**Status**: ✅ SUCCESS - -**Key Findings**: -- **hole_count now updates correctly!** (previously was failing) -- .exp export/import method works for integer expressions -- Mesh element counts varied across trials, confirming geometry changes -- All 4 design variables validated for full optimization - -**Validation Method**: -- Verified hole_count expression values in .prt files -- Checked mesh element counts (different counts = hole pattern changed) -- Compared trial results to confirm parameter variation - -**Technical Note**: -The .exp-based import method (vs. direct expression editing) was critical for successfully updating integer-typed pattern expressions like hole_count. - -## Next Steps - -→ Substudy 04: Full optimization with all 4 validated design variables (50 trials) diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/best_trial.json b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/best_trial.json deleted file mode 100644 index d14bd4bd..00000000 --- a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/best_trial.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "best_trial_number": 1, - "best_params": { - "beam_half_core_thickness": 13.335138090779976, - "beam_face_thickness": 36.82522985402573, - "holes_diameter": 415.43387770285864, - "hole_count": 15 - }, - "best_value": 1143.4527894999778, - "timestamp": "2025-11-17T12:29:37.481988" -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/optuna_study.pkl b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/optuna_study.pkl deleted file mode 100644 index a2344ccf..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/optuna_study.pkl and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/Beam.prt b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/Beam.prt deleted file mode 100644 index 3b5b1619..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/Beam.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/Beam_fem1.fem b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/Beam_fem1.fem deleted file mode 100644 index 3676ff70..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/Beam_fem1.fem and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/Beam_fem1_i.prt b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/Beam_fem1_i.prt deleted file mode 100644 index 7c0ca99c..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/Beam_fem1_i.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/Beam_sim1.sim b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/Beam_sim1.sim deleted file mode 100644 index a01bad2e..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/Beam_sim1.sim and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/results.json b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/results.json deleted file mode 100644 index 731e849d..00000000 --- a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_000/results.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "trial_number": 0, - "design_variables": { - "beam_half_core_thickness": 26.634771334983725, - "beam_face_thickness": 23.041706900371068, - "holes_diameter": 157.22022765320852, - "hole_count": 6 - }, - "results": { - "max_displacement": 16.740266799926758, - "max_stress": 104.73846875, - "mass": 1447.02973874444 - }, - "objective": 532.0780939045854, - "penalty": 674.0266799926758, - "total_objective": 1206.104773897261, - "timestamp": "2025-11-17T12:28:44.775388" -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/Beam.prt b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/Beam.prt deleted file mode 100644 index 9f0f7169..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/Beam.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/Beam_fem1.fem b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/Beam_fem1.fem deleted file mode 100644 index 9a294cc7..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/Beam_fem1.fem and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/Beam_fem1_i.prt b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/Beam_fem1_i.prt deleted file mode 100644 index 012f0697..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/Beam_fem1_i.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/Beam_sim1.sim b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/Beam_sim1.sim deleted file mode 100644 index 45c2d184..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/Beam_sim1.sim and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/results.json b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/results.json deleted file mode 100644 index a447a6a6..00000000 --- a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_001/results.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "trial_number": 1, - "design_variables": { - "beam_half_core_thickness": 13.335138090779976, - "beam_face_thickness": 36.82522985402573, - "holes_diameter": 415.43387770285864, - "hole_count": 15 - }, - "results": { - "max_displacement": 16.610559463500977, - "max_stress": 164.141953125, - "mass": 1243.37798234022 - }, - "objective": 482.3968431498801, - "penalty": 661.0559463500977, - "total_objective": 1143.4527894999778, - "timestamp": "2025-11-17T12:29:11.287235" -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/Beam.prt b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/Beam.prt deleted file mode 100644 index 56fee3eb..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/Beam.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/Beam_fem1.fem b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/Beam_fem1.fem deleted file mode 100644 index c8f66921..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/Beam_fem1.fem and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/Beam_fem1_i.prt b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/Beam_fem1_i.prt deleted file mode 100644 index 9ba51ec1..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/Beam_fem1_i.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/Beam_sim1.sim b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/Beam_sim1.sim deleted file mode 100644 index 8c342795..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/Beam_sim1.sim and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/results.json b/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/results.json deleted file mode 100644 index d08ead34..00000000 --- a/studies/simple_beam_optimization/2_substudies/03_validation_4d_3trials/trial_002/results.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "trial_number": 2, - "design_variables": { - "beam_half_core_thickness": 19.64544476046235, - "beam_face_thickness": 24.671288535930103, - "holes_diameter": 305.1411636455331, - "hole_count": 11 - }, - "results": { - "max_displacement": 20.071578979492188, - "max_stress": 119.826984375, - "mass": 1053.38667475693 - }, - "objective": 404.31799532433865, - "penalty": 1007.1578979492189, - "total_objective": 1411.4758932735576, - "timestamp": "2025-11-17T12:29:37.479981" -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/OPTIMIZATION_RESULTS_50TRIALS.md b/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/OPTIMIZATION_RESULTS_50TRIALS.md deleted file mode 100644 index c164cef8..00000000 --- a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/OPTIMIZATION_RESULTS_50TRIALS.md +++ /dev/null @@ -1,274 +0,0 @@ -# Simple Beam Optimization - 50 Trials Results - -**Date**: 2025-11-17 -**Study**: simple_beam_optimization -**Substudy**: full_optimization_50trials -**Total Runtime**: ~21 minutes - ---- - -## Executive Summary - -The 50-trial optimization successfully explored the 4D design space but **did not find a feasible design** that meets the displacement constraint (< 10mm). The best design achieved 11.399 mm displacement, which is **14% over the limit**. - -### Key Findings - -- **Total Trials**: 50 -- **Feasible Designs**: 0 (0%) -- **Best Design**: Trial 43 - - Displacement: 11.399 mm (1.399 mm over limit) - - Stress: 70.263 MPa - - Mass: 1987.556 kg - - Objective: 702.717 - -### Design Variables (Best Trial 43) - -``` -beam_half_core_thickness: 39.836 mm (upper bound: 40 mm) ✓ -beam_face_thickness: 39.976 mm (upper bound: 40 mm) ✓ -holes_diameter: 235.738 mm (mid-range) -hole_count: 11 (mid-range) -``` - -**Observation**: The optimizer pushed beam thickness to the **maximum allowed values**, suggesting that the constraint might not be achievable within the current design variable bounds. - ---- - -## Detailed Analysis - -### Performance Statistics - -| Metric | Minimum | Maximum | Range | -|--------|---------|---------|-------| -| Displacement (mm) | 11.399 | 37.075 | 25.676 | -| Stress (MPa) | 70.263 | 418.652 | 348.389 | -| Mass (kg) | 645.90 | 1987.56 | 1341.66 | - -### Constraint Violation Analysis - -- **Minimum Violation**: 1.399 mm (Trial 43) - **Closest to meeting constraint** -- **Maximum Violation**: 27.075 mm (Trial 1) -- **Average Violation**: 5.135 mm across all 50 trials - -### Top 5 Trials (Closest to Feasibility) - -| Trial | Displacement (mm) | Violation (mm) | Stress (MPa) | Mass (kg) | Objective | -|-------|------------------|----------------|--------------|-----------|-----------| -| 43 | 11.399 | 1.399 | 70.263 | 1987.56 | 842.59 | -| 49 | 11.578 | 1.578 | 73.339 | 1974.84 | 857.25 | -| 42 | 11.614 | 1.614 | 71.674 | 1951.52 | 852.44 | -| 47 | 11.643 | 1.643 | 73.596 | 1966.00 | 860.82 | -| 32 | 11.682 | 1.682 | 71.887 | 1930.16 | 852.06 | - -**Pattern**: All top designs cluster around 11.4-11.7 mm displacement with masses near 2000 kg, suggesting this is the **practical limit** for the current design space. - ---- - -## Physical Interpretation - -### Why No Feasible Design Was Found - -1. **Beam Thickness Maxed Out**: Both beam_half_core_thickness (39.836mm) and beam_face_thickness (39.976mm) are at or very near the upper bound (40mm), indicating that **thicker beams are needed** to meet the constraint. - -2. **Moderate Hole Configuration**: hole_count=11 and holes_diameter=235.738mm suggest a balance between: - - Weight reduction (more/larger holes) - - Stiffness maintenance (fewer/smaller holes) - -3. **Trade-off Tension**: The multi-objective formulation (minimize displacement, stress, AND mass) creates competing goals: - - Reducing displacement requires thicker beams → **increases mass** - - Reducing mass requires thinner beams → **increases displacement** - -### Engineering Insights - -The best design (Trial 43) achieved: -- **Low stress**: 70.263 MPa (well within typical aluminum limits ~200-300 MPa) -- **High stiffness**: Displacement only 14% over limit -- **Heavy**: 1987.56 kg (high mass due to thick beams) - -This suggests the design is **structurally sound** but **overweight** for the displacement target. - ---- - -## Recommendations - -### Option 1: Relax Displacement Constraint (Quick Win) - -Change displacement limit from 10mm to **12.5mm** (10% margin above best achieved). - -**Why**: Trial 43 is very close (11.399mm). A slightly relaxed constraint would immediately yield 5+ feasible designs. - -**Implementation**: -```json -// In beam_optimization_config.json -"constraints": [ - { - "name": "displacement_limit", - "type": "less_than", - "value": 12.5, // Changed from 10.0 - "units": "mm" - } -] -``` - -**Expected Outcome**: Feasible designs with good mass/stiffness trade-off. - ---- - -### Option 2: Expand Design Variable Ranges (Engineering Solution) - -Allow thicker beams to meet the original constraint. - -**Why**: The optimizer is already at the upper bounds, indicating it needs more thickness to achieve <10mm displacement. - -**Implementation**: -```json -// In beam_optimization_config.json -"design_variables": { - "beam_half_core_thickness": { - "min": 10.0, - "max": 60.0, // Increased from 40.0 - ... - }, - "beam_face_thickness": { - "min": 10.0, - "max": 60.0, // Increased from 40.0 - ... - } -} -``` - -**Trade-off**: Heavier beams (mass will increase significantly). - ---- - -### Option 3: Adjust Objective Weights (Prioritize Stiffness) - -Give more weight to displacement reduction. - -**Current Weights**: -- minimize_displacement: 33% -- minimize_stress: 33% -- minimize_mass: 34% - -**Recommended Weights**: -```json -"objectives": [ - { - "name": "minimize_displacement", - "weight": 0.50, // Increased from 0.33 - ... - }, - { - "name": "minimize_stress", - "weight": 0.25, // Decreased from 0.33 - ... - }, - { - "name": "minimize_mass", - "weight": 0.25 // Decreased from 0.34 - ... - } -] -``` - -**Expected Outcome**: Optimizer will prioritize meeting displacement constraint even at the cost of higher mass. - ---- - -### Option 4: Run Refined Optimization in Promising Region - -Focus search around the best trial's design space. - -**Strategy**: -1. Use Trial 43 design as baseline -2. Narrow variable ranges around these values: - - beam_half_core_thickness: 35-40 mm (Trial 43: 39.836) - - beam_face_thickness: 35-40 mm (Trial 43: 39.976) - - holes_diameter: 200-270 mm (Trial 43: 235.738) - - hole_count: 9-13 (Trial 43: 11) - -3. Run 30-50 additional trials with tighter bounds - -**Why**: TPE sampler may find feasible designs by exploiting local gradients near Trial 43. - ---- - -### Option 5: Multi-Stage Optimization (Advanced) - -**Stage 1**: Focus solely on meeting displacement constraint -- Objective: minimize displacement only -- Constraint: displacement < 10mm -- Run 20 trials - -**Stage 2**: Optimize mass while maintaining feasibility -- Use Stage 1 best design as starting point -- Objective: minimize mass -- Constraint: displacement < 10mm -- Run 30 trials - -**Why**: Decoupling objectives can help find feasible designs first, then optimize them. - ---- - -## Validation of 4D Expression Updates - -All 50 trials successfully updated all 4 design variables using the new .exp import system: - -- ✅ beam_half_core_thickness: Updated correctly in all trials -- ✅ beam_face_thickness: Updated correctly in all trials -- ✅ holes_diameter: Updated correctly in all trials -- ✅ **hole_count**: Updated correctly in all trials (previously failing!) - -**Verification**: Mesh element counts varied across trials (e.g., Trial 43: 5665 nodes), confirming that hole_count changes are affecting geometry. - ---- - -## Next Steps - -### Immediate Actions - -1. **Choose a strategy** from the 5 options above based on project priorities: - - Need quick results? → Option 1 (relax constraint) - - Engineering rigor? → Option 2 (expand bounds) - - Balanced approach? → Option 3 (adjust weights) - -2. **Update configuration** accordingly - -3. **Run refined optimization** (30-50 trials should suffice) - -### Long-Term Enhancements - -1. **Pareto Front Analysis**: Since this is multi-objective, generate Pareto front to visualize displacement-mass-stress trade-offs - -2. **Sensitivity Analysis**: Identify which design variables have the most impact on displacement - -3. **Constraint Reformulation**: Instead of hard constraint, use soft penalty with higher weight - ---- - -## Conclusion - -The 50-trial optimization was **successful from a technical standpoint**: -- All 4 design variables updated correctly (validation of .exp import system) -- Optimization converged to a consistent region (11.4-11.7mm displacement) -- Multiple trials explored the full design space - -However, the **displacement constraint appears infeasible** with the current design variable bounds. The optimizer is telling us: "To meet <10mm displacement, I need thicker beams than you're allowing me to use." - -**Recommended Action**: Start with **Option 1** (relax constraint to 12.5mm) to validate the workflow, then decide if achieving <10mm is worth the mass penalty of thicker beams (Options 2-5). - ---- - -## Files - -- **Configuration**: [beam_optimization_config.json](beam_optimization_config.json) -- **Best Trial**: [substudies/full_optimization_50trials/best_trial.json](substudies/full_optimization_50trials/best_trial.json) -- **Full Log**: [../../beam_optimization_50trials.log](../../beam_optimization_50trials.log) -- **Analysis Script**: [../../analyze_beam_results.py](../../analyze_beam_results.py) -- **Summary Data**: [../../beam_optimization_summary.json](../../beam_optimization_summary.json) - ---- - -**Generated**: 2025-11-17 -**Analyst**: Claude Code -**Atomizer Version**: Phase 3.2 (NX Expression Import System) diff --git a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/README.md b/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/README.md deleted file mode 100644 index 9b23232f..00000000 --- a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/README.md +++ /dev/null @@ -1,106 +0,0 @@ -# Substudy 04: Full Optimization (50 Trials) - -**Date**: 2025-11-17 -**Status**: Completed -**Trials**: 50 - -## Purpose - -Full-scale optimization with all 4 validated design variables to find optimal beam design that minimizes displacement, stress, and mass while meeting displacement constraint. - -## Configuration Changes - -**From Substudy 03**: -- Increased from 3 trials to 50 trials -- Full TPE sampler optimization -- All 4 design variables active - -**Design Variables** (4D): -- `beam_half_core_thickness`: 10-40 mm -- `beam_face_thickness`: 10-40 mm -- `holes_diameter`: 150-450 mm -- `hole_count`: 5-15 (integer) - -**Objectives** (weighted sum): -- Minimize displacement: 33% weight -- Minimize stress: 33% weight -- Minimize mass: 34% weight - -**Constraint**: -- Maximum displacement < 10.0 mm - -## Expected Outcome - -- Find feasible designs meeting displacement constraint -- Optimize trade-off between stiffness and weight -- Identify optimal parameter ranges - -## Actual Results - -**Status**: ⚠️ **NO FEASIBLE DESIGNS FOUND** - -**Best Trial**: #43 -- **Displacement**: 11.399 mm (1.399 mm over limit) -- **Stress**: 70.263 MPa -- **Mass**: 1987.556 kg -- **Objective**: 842.59 - -**Key Findings**: - -1. **Constraint Appears Infeasible**: All 50 trials violated displacement constraint - - Minimum violation: 1.399 mm (Trial 43) - - Maximum violation: 27.075 mm (Trial 1) - - Average violation: 5.135 mm - -2. **Optimizer Pushed to Bounds**: Best designs maximized beam thickness - - beam_half_core_thickness: 39.836 mm (at upper bound of 40 mm) - - beam_face_thickness: 39.976 mm (at upper bound of 40 mm) - - This indicates thicker beams are needed to meet <10mm displacement - -3. **Convergence Achieved**: Optimizer converged to consistent region - - Top 5 trials all cluster around 11.4-11.7 mm displacement - - Mass around 1950-2000 kg - - Indicates this is practical limit for current bounds - -**Validation of 4D Updates**: -✅ All 4 design variables updated correctly across all 50 trials -✅ hole_count parameter update system working reliably -✅ Mesh element counts varied, confirming hole pattern changes - -## Engineering Interpretation - -The displacement constraint (<10mm) is **not achievable within the current design variable bounds**. The optimizer is telling us: "To meet <10mm displacement, I need thicker beams than you're allowing me to use." - -**Physical Reasoning**: -- Stiffer beam → thicker faces and core → higher mass -- Lighter beam → thinner sections → higher displacement -- Current bounds create a hard trade-off that can't meet the constraint - -## Recommendations - -See detailed analysis in [OPTIMIZATION_RESULTS.md](OPTIMIZATION_RESULTS.md) for 5 potential strategies: - -1. **Option 1 (Quick)**: Relax constraint to 12.5mm -2. **Option 2 (Engineering)**: Increase beam thickness bounds to 60mm -3. **Option 3 (Reweight)**: Prioritize displacement (50% weight instead of 33%) -4. **Option 4 (Refined)**: Run 30-50 more trials in promising region -5. **Option 5 (Multi-Stage)**: Two-stage optimization (feasibility first, then mass) - -## Visualization - -Plots generated automatically via Phase 3.3 post-processing: -- [convergence.pdf](plots/convergence.pdf) - Objective vs trial number -- [design_space_evolution.pdf](plots/design_space_evolution.pdf) - Parameter evolution -- [parallel_coordinates.pdf](plots/parallel_coordinates.pdf) - High-dimensional view - -## Next Steps - -**Immediate**: -- Decide on strategy (recommend Option 1 or 2) -- Update configuration accordingly -- Run refined optimization (30-50 trials) - -**Long-term**: -- Consider multi-objective Pareto front analysis -- Perform parameter sensitivity analysis -- Investigate alternative design concepts (e.g., different hole patterns) diff --git a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/best_trial.json b/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/best_trial.json deleted file mode 100644 index 8528ca68..00000000 --- a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/best_trial.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "best_trial_number": 43, - "best_params": { - "beam_half_core_thickness": 39.835977148950434, - "beam_face_thickness": 39.97606330808705, - "holes_diameter": 235.73841184921832, - "hole_count": 11 - }, - "best_value": 842.5871322101043, - "timestamp": "2025-11-17T12:56:49.443658" -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/optuna_study.pkl b/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/optuna_study.pkl deleted file mode 100644 index bba5c361..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/optuna_study.pkl and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/plots/convergence.pdf b/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/plots/convergence.pdf deleted file mode 100644 index ae4b9d21..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/plots/convergence.pdf and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/plots/design_space_evolution.pdf b/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/plots/design_space_evolution.pdf deleted file mode 100644 index 0b0ac447..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/plots/design_space_evolution.pdf and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/plots/parallel_coordinates.pdf b/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/plots/parallel_coordinates.pdf deleted file mode 100644 index bab9dd14..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/plots/parallel_coordinates.pdf and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/plots/plot_summary.json b/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/plots/plot_summary.json deleted file mode 100644 index 1eda0799..00000000 --- a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/plots/plot_summary.json +++ /dev/null @@ -1,36 +0,0 @@ -{ - 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"trial_number": 48, - "design_variables": { - "beam_half_core_thickness": 36.492800645744865, - "beam_face_thickness": 39.82758070370754, - "holes_diameter": 221.86713778262373, - "hole_count": 13 - }, - "results": { - "max_displacement": 11.724766731262207, - "max_stress": 72.4340234375, - "mass": 1925.97299370716 - }, - "objective": 682.6032186161259, - "penalty": 172.4766731262207, - "total_objective": 855.0798917423466, - "timestamp": "2025-11-17T12:56:23.436440" -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/Beam.prt b/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/Beam.prt deleted file mode 100644 index 3969fe8b..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/Beam.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/Beam_fem1.fem b/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/Beam_fem1.fem deleted file mode 100644 index 7bb6e99c..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/Beam_fem1.fem and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/Beam_fem1_i.prt b/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/Beam_fem1_i.prt deleted file mode 100644 index 0d9f5a2b..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/Beam_fem1_i.prt and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/Beam_sim1.sim b/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/Beam_sim1.sim deleted file mode 100644 index 89f478b6..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/Beam_sim1.sim and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/results.json b/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/results.json deleted file mode 100644 index 5849bb22..00000000 --- a/studies/simple_beam_optimization/2_substudies/04_full_optimization_50trials/trial_049/results.json +++ /dev/null @@ -1,18 +0,0 @@ -{ - "trial_number": 49, - "design_variables": { - "beam_half_core_thickness": 34.900827947904915, - "beam_face_thickness": 39.89789174684708, - "holes_diameter": 168.0380849411955, - "hole_count": 13 - }, - "results": { - "max_displacement": 11.57779598236084, - "max_stress": 73.3386875, - "mass": 1974.83622116564 - }, - "objective": 699.4667547454967, - "penalty": 157.77959823608398, - "total_objective": 857.2463529815807, - "timestamp": "2025-11-17T12:56:49.436502" -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/generated_extractors/extract_displacement.py b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/generated_extractors/extract_displacement.py deleted file mode 100644 index bc7dd284..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/generated_extractors/extract_displacement.py +++ /dev/null @@ -1,56 +0,0 @@ -""" -Extract maximum displacement from structural analysis -Auto-generated by Atomizer Phase 3 - pyNastran Research Agent - -Pattern: displacement -Element Type: General -Result Type: displacement -API: model.displacements[subcase] -""" - -from pathlib import Path -from typing import Dict, Any -import numpy as np -from pyNastran.op2.op2 import OP2 - - -def extract_displacement(op2_file: Path, subcase: int = 1): - """Extract displacement results from OP2 file.""" - from pyNastran.op2.op2 import OP2 - import numpy as np - - model = OP2() - model.read_op2(str(op2_file)) - - disp = model.displacements[subcase] - itime = 0 # static case - - # Extract translation components - txyz = disp.data[itime, :, :3] # [tx, ty, tz] - - # Calculate total displacement - total_disp = np.linalg.norm(txyz, axis=1) - max_disp = np.max(total_disp) - - # Get node info - node_ids = [nid for (nid, grid_type) in disp.node_gridtype] - max_disp_node = node_ids[np.argmax(total_disp)] - - return { - 'max_displacement': float(max_disp), - 'max_disp_node': int(max_disp_node), - 'max_disp_x': float(np.max(np.abs(txyz[:, 0]))), - 'max_disp_y': float(np.max(np.abs(txyz[:, 1]))), - 'max_disp_z': float(np.max(np.abs(txyz[:, 2]))) - } - - -if __name__ == '__main__': - # Example usage - import sys - if len(sys.argv) > 1: - op2_file = Path(sys.argv[1]) - result = extract_displacement(op2_file) - print(f"Extraction result: {result}") - else: - print("Usage: python {sys.argv[0]} ") diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/generated_extractors/extract_mass.py b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/generated_extractors/extract_mass.py deleted file mode 100644 index 2ee2f0b2..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/generated_extractors/extract_mass.py +++ /dev/null @@ -1,39 +0,0 @@ -""" -Extract total structural mass -Auto-generated by Atomizer Phase 3 - pyNastran Research Agent - -Pattern: generic_extraction -Element Type: General -Result Type: unknown -API: model.[subcase] -""" - -from pathlib import Path -from typing import Dict, Any -import numpy as np -from pyNastran.op2.op2 import OP2 - - -def extract_generic(op2_file: Path): - """Generic OP2 extraction - needs customization.""" - from pyNastran.op2.op2 import OP2 - - model = OP2() - model.read_op2(str(op2_file)) - - # TODO: Customize extraction based on requirements - # Available: model.displacements, model.ctetra_stress, etc. - # Use model.get_op2_stats() to see available results - - return {'result': None} - - -if __name__ == '__main__': - # Example usage - import sys - if len(sys.argv) > 1: - op2_file = Path(sys.argv[1]) - result = extract_generic(op2_file) - print(f"Extraction result: {result}") - else: - print("Usage: python {sys.argv[0]} ") diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/generated_extractors/extract_von_mises_stress.py b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/generated_extractors/extract_von_mises_stress.py deleted file mode 100644 index 13db6227..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/generated_extractors/extract_von_mises_stress.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -Extract maximum von Mises stress from structural analysis -Auto-generated by Atomizer Phase 3 - pyNastran Research Agent - -Pattern: solid_stress -Element Type: CTETRA -Result Type: stress -API: model.ctetra_stress[subcase] or model.chexa_stress[subcase] -""" - -from pathlib import Path -from typing import Dict, Any -import numpy as np -from pyNastran.op2.op2 import OP2 - - -def extract_solid_stress(op2_file: Path, subcase: int = 1, element_type: str = 'ctetra'): - """Extract stress from solid elements.""" - from pyNastran.op2.op2 import OP2 - import numpy as np - - model = OP2() - model.read_op2(str(op2_file)) - - # Get stress object for element type - # In pyNastran, stress is stored in model.op2_results.stress - stress_attr = f"{element_type}_stress" - - if not hasattr(model, 'op2_results') or not hasattr(model.op2_results, 'stress'): - raise ValueError(f"No stress results in OP2") - - stress_obj = model.op2_results.stress - if not hasattr(stress_obj, stress_attr): - raise ValueError(f"No {element_type} stress results in OP2") - - stress = getattr(stress_obj, stress_attr)[subcase] - itime = 0 - - # Extract von Mises if available - if stress.is_von_mises: # Property, not method - von_mises = stress.data[itime, :, 9] # Column 9 is von Mises - max_stress = float(np.max(von_mises)) - - # Get element info - element_ids = [eid for (eid, node) in stress.element_node] - max_stress_elem = element_ids[np.argmax(von_mises)] - - return { - 'max_von_mises': max_stress, - 'max_stress_element': int(max_stress_elem) - } - else: - raise ValueError("von Mises stress not available") - - -if __name__ == '__main__': - # Example usage - import sys - if len(sys.argv) > 1: - op2_file = Path(sys.argv[1]) - result = extract_solid_stress(op2_file) - print(f"Extraction result: {result}") - else: - print("Usage: python {sys.argv[0]} ") diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/optimization_history_incremental.json b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/optimization_history_incremental.json deleted file mode 100644 index a8e9dd09..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/optimization_history_incremental.json +++ /dev/null @@ -1,50 +0,0 @@ -[ - { - "trial_number": 0, - "design_variables": { - "beam_half_core_thickness": 0.5904337021534598, - "beam_face_thickness": 0.5106113578754768 - }, - "results": { - "max_displacement": 22.118558883666992, - "max_disp_node": 5186.0, - "max_disp_x": 1.4659312963485718, - "max_disp_y": 0.021927518770098686, - "max_disp_z": 22.07024574279785 - }, - "calculations": {}, - "objective": 22.118558883666992 - }, - { - "trial_number": 1, - "design_variables": { - "beam_half_core_thickness": 0.3831811850567225, - "beam_face_thickness": 0.7664443193648687 - }, - "results": { - "max_displacement": 22.118558883666992, - "max_disp_node": 5186.0, - "max_disp_x": 1.4659312963485718, - "max_disp_y": 0.021927518770098686, - "max_disp_z": 22.07024574279785 - }, - "calculations": {}, - "objective": 22.118558883666992 - }, - { - "trial_number": 2, - "design_variables": { - "beam_half_core_thickness": 0.9474745640068595, - "beam_face_thickness": 0.2903563103499731 - }, - "results": { - "max_displacement": 22.118558883666992, - "max_disp_node": 5186.0, - "max_disp_x": 1.4659312963485718, - "max_disp_y": 0.021927518770098686, - "max_disp_z": 22.07024574279785 - }, - "calculations": {}, - "objective": 22.118558883666992 - } -] \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/optimization_results.json b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/optimization_results.json deleted file mode 100644 index 5a69594c..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/optimization_results.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "best_params": { - "beam_half_core_thickness": 0.5904337021534598, - "beam_face_thickness": 0.5106113578754768 - }, - "best_value": 22.118558883666992, - "best_trial_number": 0, - "timestamp": "2025-11-17T21:09:52.773223", - "study_name": "test_e2e_3trials_20251117_210654", - "n_trials": 3 -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/test_e2e_3trials_20251117_210654.db b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/test_e2e_3trials_20251117_210654.db deleted file mode 100644 index d3b824fc..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_210654/test_e2e_3trials_20251117_210654.db and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/generated_extractors/extract_displacement.py b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/generated_extractors/extract_displacement.py deleted file mode 100644 index 0375b735..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/generated_extractors/extract_displacement.py +++ /dev/null @@ -1,56 +0,0 @@ -""" -Extract maximum displacement from FEA results -Auto-generated by Atomizer Phase 3 - pyNastran Research Agent - -Pattern: displacement -Element Type: General -Result Type: displacement -API: model.displacements[subcase] -""" - -from pathlib import Path -from typing import Dict, Any -import numpy as np -from pyNastran.op2.op2 import OP2 - - -def extract_displacement(op2_file: Path, subcase: int = 1): - """Extract displacement results from OP2 file.""" - from pyNastran.op2.op2 import OP2 - import numpy as np - - model = OP2() - model.read_op2(str(op2_file)) - - disp = model.displacements[subcase] - itime = 0 # static case - - # Extract translation components - txyz = disp.data[itime, :, :3] # [tx, ty, tz] - - # Calculate total displacement - total_disp = np.linalg.norm(txyz, axis=1) - max_disp = np.max(total_disp) - - # Get node info - node_ids = [nid for (nid, grid_type) in disp.node_gridtype] - max_disp_node = node_ids[np.argmax(total_disp)] - - return { - 'max_displacement': float(max_disp), - 'max_disp_node': int(max_disp_node), - 'max_disp_x': float(np.max(np.abs(txyz[:, 0]))), - 'max_disp_y': float(np.max(np.abs(txyz[:, 1]))), - 'max_disp_z': float(np.max(np.abs(txyz[:, 2]))) - } - - -if __name__ == '__main__': - # Example usage - import sys - if len(sys.argv) > 1: - op2_file = Path(sys.argv[1]) - result = extract_displacement(op2_file) - print(f"Extraction result: {result}") - else: - print("Usage: python {sys.argv[0]} ") diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/generated_extractors/extract_mass.py b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/generated_extractors/extract_mass.py deleted file mode 100644 index 5fd2923d..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/generated_extractors/extract_mass.py +++ /dev/null @@ -1,39 +0,0 @@ -""" -Extract total mass from FEA model -Auto-generated by Atomizer Phase 3 - pyNastran Research Agent - -Pattern: generic_extraction -Element Type: General -Result Type: unknown -API: model.[subcase] -""" - -from pathlib import Path -from typing import Dict, Any -import numpy as np -from pyNastran.op2.op2 import OP2 - - -def extract_generic(op2_file: Path): - """Generic OP2 extraction - needs customization.""" - from pyNastran.op2.op2 import OP2 - - model = OP2() - model.read_op2(str(op2_file)) - - # TODO: Customize extraction based on requirements - # Available: model.displacements, model.ctetra_stress, etc. - # Use model.get_op2_stats() to see available results - - return {'result': None} - - -if __name__ == '__main__': - # Example usage - import sys - if len(sys.argv) > 1: - op2_file = Path(sys.argv[1]) - result = extract_generic(op2_file) - print(f"Extraction result: {result}") - else: - print("Usage: python {sys.argv[0]} ") diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/generated_extractors/extract_von_mises_stress.py b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/generated_extractors/extract_von_mises_stress.py deleted file mode 100644 index d7deee10..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/generated_extractors/extract_von_mises_stress.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -Extract maximum von Mises stress from FEA results -Auto-generated by Atomizer Phase 3 - pyNastran Research Agent - -Pattern: solid_stress -Element Type: CTETRA -Result Type: stress -API: model.ctetra_stress[subcase] or model.chexa_stress[subcase] -""" - -from pathlib import Path -from typing import Dict, Any -import numpy as np -from pyNastran.op2.op2 import OP2 - - -def extract_solid_stress(op2_file: Path, subcase: int = 1, element_type: str = 'ctetra'): - """Extract stress from solid elements.""" - from pyNastran.op2.op2 import OP2 - import numpy as np - - model = OP2() - model.read_op2(str(op2_file)) - - # Get stress object for element type - # In pyNastran, stress is stored in model.op2_results.stress - stress_attr = f"{element_type}_stress" - - if not hasattr(model, 'op2_results') or not hasattr(model.op2_results, 'stress'): - raise ValueError(f"No stress results in OP2") - - stress_obj = model.op2_results.stress - if not hasattr(stress_obj, stress_attr): - raise ValueError(f"No {element_type} stress results in OP2") - - stress = getattr(stress_obj, stress_attr)[subcase] - itime = 0 - - # Extract von Mises if available - if stress.is_von_mises: # Property, not method - von_mises = stress.data[itime, :, 9] # Column 9 is von Mises - max_stress = float(np.max(von_mises)) - - # Get element info - element_ids = [eid for (eid, node) in stress.element_node] - max_stress_elem = element_ids[np.argmax(von_mises)] - - return { - 'max_von_mises': max_stress, - 'max_stress_element': int(max_stress_elem) - } - else: - raise ValueError("von Mises stress not available") - - -if __name__ == '__main__': - # Example usage - import sys - if len(sys.argv) > 1: - op2_file = Path(sys.argv[1]) - result = extract_solid_stress(op2_file) - print(f"Extraction result: {result}") - else: - print("Usage: python {sys.argv[0]} ") diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/generated_hooks/constraint_evaluation.py b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/generated_hooks/constraint_evaluation.py deleted file mode 100644 index 1756d158..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/generated_hooks/constraint_evaluation.py +++ /dev/null @@ -1,77 +0,0 @@ -""" -Evaluate displacement and stress constraints -Auto-generated lifecycle hook by Atomizer Phase 2.9 - -Hook Point: post_calculation -Inputs: max_displacement, max_von_mises_stress -Outputs: constraint, constraint_satisfied, constraint_violation -""" - -import logging -from typing import Dict, Any, Optional - -logger = logging.getLogger(__name__) - - -def constraint_hook(context: Dict[str, Any]) -> Optional[Dict[str, Any]]: - """ - Evaluate displacement and stress constraints - - Args: - context: Hook context containing: - - trial_number: Current optimization trial - - results: Dictionary with extracted FEA results - - calculations: Dictionary with inline calculation results - - Returns: - Dictionary with calculated values to add to context - """ - logger.info(f"Executing constraint_hook for trial {context.get('trial_number', 'unknown')}") - - # Extract inputs from context - results = context.get('results', {}) - calculations = context.get('calculations', {}) - - max_displacement = calculations.get('max_displacement') or results.get('max_displacement') - if max_displacement is None: - logger.error(f"Required input 'max_displacement' not found in context") - raise ValueError(f"Missing required input: max_displacement") - - max_von_mises_stress = calculations.get('max_von_mises_stress') or results.get('max_von_mises_stress') - if max_von_mises_stress is None: - logger.error(f"Required input 'max_von_mises_stress' not found in context") - raise ValueError(f"Missing required input: max_von_mises_stress") - - # Check constraint - value = max_displacement / 1.0 - satisfied = value <= 1.0 - violation = max(0.0, value - 1.0) - - status = "SATISFIED" if satisfied else "VIOLATED" - logger.info(f"Constraint {status}: {value:.6f} (threshold: 1.0)") - - return { - 'constraint': value, - 'constraint_satisfied': satisfied, - 'constraint_violation': violation - } - - -def register_hooks(hook_manager): - """ - Register this hook with the HookManager. - - This function is called automatically when the plugin is loaded. - - Args: - hook_manager: The HookManager instance - """ - hook_manager.register_hook( - hook_point='post_calculation', - function=constraint_hook, - description="Evaluate displacement and stress constraints", - name="constraint_hook", - priority=100, - enabled=True - ) - logger.info(f"Registered constraint_hook at post_calculation") diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/optimization_history_incremental.json b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/optimization_history_incremental.json deleted file mode 100644 index ae62de09..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/optimization_history_incremental.json +++ /dev/null @@ -1,50 +0,0 @@ -[ - { - "trial_number": 0, - "design_variables": { - "beam_half_core_thickness": 0.8957771821976109, - "beam_face_thickness": 0.13207406457923798 - }, - "results": { - "max_displacement": 76444.203125, - "max_disp_node": 5244.0, - "max_disp_x": 94.31665802001953, - "max_disp_y": 1.4079378843307495, - "max_disp_z": 76444.1484375 - }, - "calculations": {}, - "objective": 76444.203125 - }, - { - "trial_number": 1, - "design_variables": { - "beam_half_core_thickness": 0.6456489367890321, - "beam_face_thickness": 0.5479701381678562 - }, - "results": { - "max_displacement": 76444.203125, - "max_disp_node": 5244.0, - "max_disp_x": 94.31665802001953, - "max_disp_y": 1.4079378843307495, - "max_disp_z": 76444.1484375 - }, - "calculations": {}, - "objective": 76444.203125 - }, - { - "trial_number": 2, - "design_variables": { - "beam_half_core_thickness": 0.9166324033378569, - "beam_face_thickness": 0.45399835794681165 - }, - "results": { - "max_displacement": 76444.203125, - "max_disp_node": 5244.0, - "max_disp_x": 94.31665802001953, - "max_disp_y": 1.4079378843307495, - "max_disp_z": 76444.1484375 - }, - "calculations": {}, - "objective": 76444.203125 - } -] \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/optimization_results.json b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/optimization_results.json deleted file mode 100644 index 9f944780..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/optimization_results.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "best_params": { - "beam_half_core_thickness": 0.8957771821976109, - "beam_face_thickness": 0.13207406457923798 - }, - "best_value": 76444.203125, - "best_trial_number": 0, - "timestamp": "2025-11-17T21:21:45.970676", - "study_name": "test_e2e_3trials_20251117_211856", - "n_trials": 3 -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/test_e2e_3trials_20251117_211856.db b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/test_e2e_3trials_20251117_211856.db deleted file mode 100644 index b0ae2c72..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_211856/test_e2e_3trials_20251117_211856.db and /dev/null differ diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/generated_extractors/extract_displacement.py b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/generated_extractors/extract_displacement.py deleted file mode 100644 index bc7dd284..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/generated_extractors/extract_displacement.py +++ /dev/null @@ -1,56 +0,0 @@ -""" -Extract maximum displacement from structural analysis -Auto-generated by Atomizer Phase 3 - pyNastran Research Agent - -Pattern: displacement -Element Type: General -Result Type: displacement -API: model.displacements[subcase] -""" - -from pathlib import Path -from typing import Dict, Any -import numpy as np -from pyNastran.op2.op2 import OP2 - - -def extract_displacement(op2_file: Path, subcase: int = 1): - """Extract displacement results from OP2 file.""" - from pyNastran.op2.op2 import OP2 - import numpy as np - - model = OP2() - model.read_op2(str(op2_file)) - - disp = model.displacements[subcase] - itime = 0 # static case - - # Extract translation components - txyz = disp.data[itime, :, :3] # [tx, ty, tz] - - # Calculate total displacement - total_disp = np.linalg.norm(txyz, axis=1) - max_disp = np.max(total_disp) - - # Get node info - node_ids = [nid for (nid, grid_type) in disp.node_gridtype] - max_disp_node = node_ids[np.argmax(total_disp)] - - return { - 'max_displacement': float(max_disp), - 'max_disp_node': int(max_disp_node), - 'max_disp_x': float(np.max(np.abs(txyz[:, 0]))), - 'max_disp_y': float(np.max(np.abs(txyz[:, 1]))), - 'max_disp_z': float(np.max(np.abs(txyz[:, 2]))) - } - - -if __name__ == '__main__': - # Example usage - import sys - if len(sys.argv) > 1: - op2_file = Path(sys.argv[1]) - result = extract_displacement(op2_file) - print(f"Extraction result: {result}") - else: - print("Usage: python {sys.argv[0]} ") diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/generated_extractors/extract_mass.py b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/generated_extractors/extract_mass.py deleted file mode 100644 index d8fe0ad9..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/generated_extractors/extract_mass.py +++ /dev/null @@ -1,39 +0,0 @@ -""" -Extract total mass of the structure -Auto-generated by Atomizer Phase 3 - pyNastran Research Agent - -Pattern: generic_extraction -Element Type: General -Result Type: unknown -API: model.[subcase] -""" - -from pathlib import Path -from typing import Dict, Any -import numpy as np -from pyNastran.op2.op2 import OP2 - - -def extract_generic(op2_file: Path): - """Generic OP2 extraction - needs customization.""" - from pyNastran.op2.op2 import OP2 - - model = OP2() - model.read_op2(str(op2_file)) - - # TODO: Customize extraction based on requirements - # Available: model.displacements, model.ctetra_stress, etc. - # Use model.get_op2_stats() to see available results - - return {'result': None} - - -if __name__ == '__main__': - # Example usage - import sys - if len(sys.argv) > 1: - op2_file = Path(sys.argv[1]) - result = extract_generic(op2_file) - print(f"Extraction result: {result}") - else: - print("Usage: python {sys.argv[0]} ") diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/generated_extractors/extract_von_mises_stress.py b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/generated_extractors/extract_von_mises_stress.py deleted file mode 100644 index 13db6227..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/generated_extractors/extract_von_mises_stress.py +++ /dev/null @@ -1,64 +0,0 @@ -""" -Extract maximum von Mises stress from structural analysis -Auto-generated by Atomizer Phase 3 - pyNastran Research Agent - -Pattern: solid_stress -Element Type: CTETRA -Result Type: stress -API: model.ctetra_stress[subcase] or model.chexa_stress[subcase] -""" - -from pathlib import Path -from typing import Dict, Any -import numpy as np -from pyNastran.op2.op2 import OP2 - - -def extract_solid_stress(op2_file: Path, subcase: int = 1, element_type: str = 'ctetra'): - """Extract stress from solid elements.""" - from pyNastran.op2.op2 import OP2 - import numpy as np - - model = OP2() - model.read_op2(str(op2_file)) - - # Get stress object for element type - # In pyNastran, stress is stored in model.op2_results.stress - stress_attr = f"{element_type}_stress" - - if not hasattr(model, 'op2_results') or not hasattr(model.op2_results, 'stress'): - raise ValueError(f"No stress results in OP2") - - stress_obj = model.op2_results.stress - if not hasattr(stress_obj, stress_attr): - raise ValueError(f"No {element_type} stress results in OP2") - - stress = getattr(stress_obj, stress_attr)[subcase] - itime = 0 - - # Extract von Mises if available - if stress.is_von_mises: # Property, not method - von_mises = stress.data[itime, :, 9] # Column 9 is von Mises - max_stress = float(np.max(von_mises)) - - # Get element info - element_ids = [eid for (eid, node) in stress.element_node] - max_stress_elem = element_ids[np.argmax(von_mises)] - - return { - 'max_von_mises': max_stress, - 'max_stress_element': int(max_stress_elem) - } - else: - raise ValueError("von Mises stress not available") - - -if __name__ == '__main__': - # Example usage - import sys - if len(sys.argv) > 1: - op2_file = Path(sys.argv[1]) - result = extract_solid_stress(op2_file) - print(f"Extraction result: {result}") - else: - print("Usage: python {sys.argv[0]} ") diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/generated_hooks/constraint_penalty.py b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/generated_hooks/constraint_penalty.py deleted file mode 100644 index e77cc654..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/generated_hooks/constraint_penalty.py +++ /dev/null @@ -1,82 +0,0 @@ -""" -Apply penalty to objective function if constraints are violated -Auto-generated lifecycle hook by Atomizer Phase 2.9 - -Hook Point: post_calculation -Inputs: max_displacement, max_von_mises_stress, total_mass -Outputs: constraint, constraint_satisfied, constraint_violation -""" - -import logging -from typing import Dict, Any, Optional - -logger = logging.getLogger(__name__) - - -def constraint_hook(context: Dict[str, Any]) -> Optional[Dict[str, Any]]: - """ - Apply penalty to objective function if constraints are violated - - Args: - context: Hook context containing: - - trial_number: Current optimization trial - - results: Dictionary with extracted FEA results - - calculations: Dictionary with inline calculation results - - Returns: - Dictionary with calculated values to add to context - """ - logger.info(f"Executing constraint_hook for trial {context.get('trial_number', 'unknown')}") - - # Extract inputs from context - results = context.get('results', {}) - calculations = context.get('calculations', {}) - - max_displacement = calculations.get('max_displacement') or results.get('max_displacement') - if max_displacement is None: - logger.error(f"Required input 'max_displacement' not found in context") - raise ValueError(f"Missing required input: max_displacement") - - max_von_mises_stress = calculations.get('max_von_mises_stress') or results.get('max_von_mises_stress') - if max_von_mises_stress is None: - logger.error(f"Required input 'max_von_mises_stress' not found in context") - raise ValueError(f"Missing required input: max_von_mises_stress") - - total_mass = calculations.get('total_mass') or results.get('total_mass') - if total_mass is None: - logger.error(f"Required input 'total_mass' not found in context") - raise ValueError(f"Missing required input: total_mass") - - # Check constraint - value = max_displacement / 1.0 - satisfied = value <= 1.0 - violation = max(0.0, value - 1.0) - - status = "SATISFIED" if satisfied else "VIOLATED" - logger.info(f"Constraint {status}: {value:.6f} (threshold: 1.0)") - - return { - 'constraint': value, - 'constraint_satisfied': satisfied, - 'constraint_violation': violation - } - - -def register_hooks(hook_manager): - """ - Register this hook with the HookManager. - - This function is called automatically when the plugin is loaded. - - Args: - hook_manager: The HookManager instance - """ - hook_manager.register_hook( - hook_point='post_calculation', - function=constraint_hook, - description="Apply penalty to objective function if constraints are violated", - name="constraint_hook", - priority=100, - enabled=True - ) - logger.info(f"Registered constraint_hook at post_calculation") diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/optimization_history_incremental.json b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/optimization_history_incremental.json deleted file mode 100644 index 256ba4b8..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/optimization_history_incremental.json +++ /dev/null @@ -1,56 +0,0 @@ -[ - { - "trial_number": 0, - "design_variables": { - "beam_half_core_thickness": 0.3934195274712988, - "beam_face_thickness": 0.07146930128208218 - }, - "results": { - "max_displacement": 7315679.0, - "max_disp_node": 5225.0, - "max_disp_x": 156.94375610351562, - "max_disp_y": 2.3414955139160156, - "max_disp_z": 7315679.0 - }, - "calculations": { - "check_displacement_constraint_result": 7315679.0 - }, - "objective": 7315679.0 - }, - { - "trial_number": 1, - "design_variables": { - "beam_half_core_thickness": 0.9471234007929267, - "beam_face_thickness": 0.6764087446304129 - }, - "results": { - "max_displacement": 9158.6748046875, - "max_disp_node": 5204.0, - "max_disp_x": 36.21176528930664, - "max_disp_y": 0.5410690903663635, - "max_disp_z": 9158.603515625 - }, - "calculations": { - "check_displacement_constraint_result": 9158.6748046875 - }, - "objective": 9158.6748046875 - }, - { - "trial_number": 2, - "design_variables": { - "beam_half_core_thickness": 0.2679911361372036, - "beam_face_thickness": 0.7283091311059705 - }, - "results": { - "max_displacement": 7655.2783203125, - "max_disp_node": 5224.0, - "max_disp_x": 47.8192024230957, - "max_disp_y": 0.7131573557853699, - "max_disp_z": 7655.1298828125 - }, - "calculations": { - "check_displacement_constraint_result": 7655.2783203125 - }, - "objective": 7655.2783203125 - } -] \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/optimization_results.json b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/optimization_results.json deleted file mode 100644 index 6e173135..00000000 --- a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/optimization_results.json +++ /dev/null @@ -1,11 +0,0 @@ -{ - "best_params": { - "beam_half_core_thickness": 0.2679911361372036, - "beam_face_thickness": 0.7283091311059705 - }, - "best_value": 7655.2783203125, - "best_trial_number": 2, - "timestamp": "2025-11-17T21:28:43.477179", - "study_name": "test_e2e_3trials_20251117_212730", - "n_trials": 3 -} \ No newline at end of file diff --git a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/test_e2e_3trials_20251117_212730.db b/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/test_e2e_3trials_20251117_212730.db deleted file mode 100644 index fee8b847..00000000 Binary files a/studies/simple_beam_optimization/2_substudies/test_e2e_3trials_20251117_212730/test_e2e_3trials_20251117_212730.db and /dev/null differ