Initial commit: NX OptiMaster project structure
- Set up Python package structure with pyproject.toml - Created MCP server, optimization engine, and NX journals modules - Added configuration templates - Implemented pluggable result extractor architecture - Comprehensive README with architecture overview - Project ready for GitHub push 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
82
.gitignore
vendored
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82
.gitignore
vendored
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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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.pytest_cache/
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.coverage
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htmlcov/
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*.cover
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.hypothesis/
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# Virtual Environment
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venv/
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ENV/
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env/
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.venv
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# IDEs
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.vscode/
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.idea/
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*.swp
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*.swo
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*~
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.DS_Store
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# NX/FEA Files
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*.op2
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*.f06
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*.f04
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*.xdb
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*.log
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*.diag
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*.pch
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*.master
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*.dball
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*.ldra
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*.sdb
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*.sim.bak
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*.prt.bak
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# Optimization Results
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optuna_study.db
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optuna_study.db-journal
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history.csv
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history.bak
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next.exp
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RMS_log.csv
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archives/
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temp/
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*.tmp
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# Node modules (for dashboard)
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node_modules/
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.npm
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.cache
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dist/
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build/
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# Environment variables
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.env
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.env.local
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# OS
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Thumbs.db
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desktop.ini
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12
LICENSE
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12
LICENSE
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Proprietary License
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Copyright (c) 2025 Atomaste
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All rights reserved.
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This software and associated documentation files (the "Software") are the proprietary
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property of Atomaste. Unauthorized copying, modification, distribution, or use of this
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Software, via any medium, is strictly prohibited without prior written permission from
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Atomaste.
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For licensing inquiries, please contact: contact@atomaste.com
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249
README.md
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README.md
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# NX OptiMaster
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> Advanced optimization platform for Siemens NX Simcenter with LLM-powered configuration
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[](https://www.python.org/downloads/)
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[](LICENSE)
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[](https://github.com)
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## Overview
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NX OptiMaster is a next-generation optimization framework for Siemens NX that combines:
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- **LLM-Driven Configuration**: Use natural language to set up complex optimizations
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- **Advanced Algorithms**: Optuna-powered TPE, Gaussian Process surrogates, multi-fidelity optimization
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- **Real-Time Monitoring**: Interactive dashboards with live updates
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- **Flexible Architecture**: Pluggable result extractors for any FEA analysis type
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- **MCP Integration**: Extensible via Model Context Protocol
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## Architecture
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```
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┌─────────────────────────────────────────────────────────┐
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│ UI Layer │
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│ Web Dashboard (React) + LLM Chat Interface (MCP) │
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└─────────────────────────────────────────────────────────┘
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↕
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┌─────────────────────────────────────────────────────────┐
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│ MCP Server │
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│ - Model Discovery - Config Builder │
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│ - Optimizer Control - Result Analyzer │
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└─────────────────────────────────────────────────────────┘
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↕
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┌─────────────────────────────────────────────────────────┐
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│ Execution Layer │
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│ NX Core (NXOpen) + Optuna Engine + Custom Scripts │
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└─────────────────────────────────────────────────────────┘
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```
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## Quick Start
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### Prerequisites
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- **Siemens NX 2306+** with NX Nastran solver
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- **Python 3.10+** (recommend Anaconda)
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- **Node.js 18+** (for dashboard frontend)
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### Installation
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1. **Clone the repository**:
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```bash
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git clone https://github.com/atomaste/nx-optimaster.git
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cd nx-optimaster
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```
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2. **Create Python environment**:
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```bash
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conda create -n nx-optimaster python=3.10
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conda activate nx-optimaster
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```
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3. **Install dependencies**:
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```bash
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pip install -e .
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# For development tools:
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pip install -e ".[dev]"
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# For MCP server:
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pip install -e ".[mcp]"
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```
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4. **Configure NX path** (edit `config/nx_config.json`):
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```json
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{
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"nx_executable": "C:/Program Files/Siemens/NX2306/NXBIN/ugraf.exe",
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"python_env": "C:/Users/YourName/anaconda3/envs/nx-optimaster/python.exe"
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}
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```
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### Basic Usage
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#### 1. Conversational Setup (via MCP)
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```
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You: My FEA is in C:\Projects\Bracket\analysis.sim, please import its features.
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AI: I've analyzed your model:
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- Solution: Static Analysis (NX Nastran)
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- Expressions: wall_thickness (5mm), hole_diameter (10mm)
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- Mesh: 8234 nodes, 4521 elements
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Which parameters would you like to optimize?
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You: Optimize wall_thickness and hole_diameter to minimize max stress while keeping mass low.
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AI: Configuration created! Ready to start optimization with 100 iterations.
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Would you like to review the config or start now?
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You: Start it!
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AI: Optimization launched! 🚀
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Dashboard: http://localhost:8080/dashboard
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```
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#### 2. Manual Configuration (JSON)
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Create `optimization_config.json`:
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```json
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{
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"design_variables": {
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"wall_thickness": {
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"low": 3.0,
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"high": 8.0,
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"enabled": true
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}
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},
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"objectives": {
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"metrics": {
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"max_stress": {
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"weight": 10,
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"target": 200,
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"extractor": "nastran_stress"
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}
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}
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},
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"nx_settings": {
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"sim_path": "C:/Projects/Bracket/analysis.sim",
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"solution_name": "Solution 1"
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}
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}
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```
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Run optimization:
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```bash
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python -m optimization_engine.run_optimizer --config optimization_config.json
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```
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## Features
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### ✨ Core Capabilities
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- **Multi-Objective Optimization**: Weighted sum, Pareto front analysis
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- **Smart Sampling**: TPE, Latin Hypercube, Gaussian Process surrogates
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- **Result Extraction**: Nastran (OP2/F06), NX Mass Properties, custom parsers
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- **Crash Recovery**: Automatic resume from interruptions
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- **Parallel Evaluation**: Multi-core FEA solving (coming soon)
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### 📊 Visualization
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- **Real-time progress monitoring**
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- **3D Pareto front plots** (Plotly)
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- **Parameter importance charts**
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- **Convergence history**
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- **FEA result overlays**
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### 🔧 Extensibility
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- **Pluggable result extractors**: Add custom metrics easily
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- **Custom post-processing scripts**: Python integration
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- **MCP tools**: Extend via protocol
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- **NXOpen API access**: Full NX automation
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## Project Structure
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```
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nx-optimaster/
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├── mcp_server/ # MCP server implementation
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│ ├── tools/ # MCP tool definitions
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│ ├── schemas/ # JSON schemas for validation
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│ └── prompts/ # LLM system prompts
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├── optimization_engine/ # Core optimization logic
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│ ├── result_extractors/ # Pluggable metric extractors
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│ ├── multi_optimizer.py # Optuna integration
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│ ├── config_loader.py # Configuration parser
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│ └── history_manager.py # CSV/SQLite persistence
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├── nx_journals/ # NXOpen Python scripts
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│ ├── update_and_solve.py # CAD update + solver
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│ ├── post_process.py # Result extraction
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│ └── utils/ # Helper functions
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├── dashboard/ # Web UI
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│ ├── frontend/ # React app
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│ └── backend/ # FastAPI server
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├── tests/ # Unit tests
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├── examples/ # Example projects
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└── docs/ # Documentation
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```
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## Configuration Schema
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See [docs/configuration.md](docs/configuration.md) for full schema documentation.
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**Key sections**:
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- `design_variables`: Parameters to optimize
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- `objectives`: Metrics to minimize/maximize
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- `nx_settings`: NX/FEA solver configuration
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- `optimization`: Optuna sampler settings
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- `post_processing`: Result extraction pipelines
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## Development
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### Running Tests
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```bash
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pytest
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```
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### Code Formatting
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||||
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```bash
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black .
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||||
ruff check .
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||||
```
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### Building Documentation
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```bash
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cd docs
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mkdocs build
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```
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## Roadmap
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- [x] MCP server foundation
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- [x] Basic optimization engine
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- [ ] NXOpen integration
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- [ ] Web dashboard
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- [ ] Multi-fidelity optimization
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- [ ] Parallel evaluations
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- [ ] Sensitivity analysis tools
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- [ ] Export to engineering reports
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||||
## Contributing
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||||
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This is a private repository. Contact [contact@atomaste.com](mailto:contact@atomaste.com) for access.
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||||
## License
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||||
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Proprietary - Atomaste © 2025
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## Support
|
||||
|
||||
- **Documentation**: [docs/](docs/)
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||||
- **Examples**: [examples/](examples/)
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- **Issues**: GitHub Issues (private repository)
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- **Email**: support@atomaste.com
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---
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**Built with ❤️ by Atomaste** | Powered by Optuna, NXOpen, and Claude
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8
config/nx_config.json.template
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8
config/nx_config.json.template
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{
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"nx_executable": "C:/Program Files/Siemens/NX2306/NXBIN/ugraf.exe",
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"python_env": "C:/Users/YourName/anaconda3/envs/nx-optimaster/python.exe",
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"journals_dir": "./nx_journals",
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"temp_dir": "./temp",
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"default_timeout": 300,
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"max_parallel_solves": 1
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}
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76
config/optimization_config_template.json
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76
config/optimization_config_template.json
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{
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"optimization": {
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"max_iterations": 100,
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"seed": 42,
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"evaluate_baseline_first": true,
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"sampler": {
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"type": "TPE",
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"n_startup_trials": 15,
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"n_ei_candidates": 150,
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"multivariate": true,
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"gamma": 0.25,
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"prior_weight": 1.0
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||||
}
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},
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"baseline": {
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"parameter1": 10.0,
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"parameter2": 20.0
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},
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"design_variables": {
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"parameter1": {
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||||
"type": "float",
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||||
"low": 5.0,
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"high": 15.0,
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"description": "First design parameter",
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"enabled": true
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},
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"parameter2": {
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"type": "float",
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"low": 10.0,
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"high": 30.0,
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||||
"description": "Second design parameter",
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||||
"enabled": true
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||||
}
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||||
},
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||||
"objectives": {
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"primary_strategy": "weighted_sum",
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"direction": "minimize",
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"metrics": {
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"max_stress": {
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"weight": 10,
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||||
"target": 200.0,
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"description": "Maximum von Mises stress",
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"units": "MPa",
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||||
"enabled": true,
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"extractor": "nastran_stress",
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"extractor_params": {
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"subcase": 101,
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"stress_type": "von_mises"
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||||
}
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||||
},
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||||
"mass": {
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"weight": 1,
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"target": 0.5,
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"description": "Total mass",
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||||
"units": "kg",
|
||||
"enabled": true,
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||||
"extractor": "nx_mass",
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"extractor_params": {
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||||
"bodies": "all"
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||||
}
|
||||
}
|
||||
}
|
||||
},
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||||
"nx_settings": {
|
||||
"sim_path": "path/to/model.sim",
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||||
"solution_name": "Solution 1",
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||||
"post_solve_delay_s": 5,
|
||||
"op2_timeout_s": 1800,
|
||||
"op2_stable_s": 4
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||||
},
|
||||
"post_processing": {
|
||||
"archive_results": true,
|
||||
"export_expressions": true,
|
||||
"custom_scripts": []
|
||||
}
|
||||
}
|
||||
8
mcp_server/__init__.py
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8
mcp_server/__init__.py
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|
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"""
|
||||
NX OptiMaster MCP Server
|
||||
|
||||
Model Context Protocol server for LLM-driven NX optimization configuration.
|
||||
"""
|
||||
|
||||
__version__ = "0.1.0"
|
||||
__author__ = "Atomaste"
|
||||
23
mcp_server/tools/__init__.py
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23
mcp_server/tools/__init__.py
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|
||||
"""
|
||||
MCP Tools for NX OptiMaster
|
||||
|
||||
Available tools:
|
||||
- discover_fea_model: Analyze .sim files to extract configurable elements
|
||||
- build_optimization_config: Generate optimization config from LLM instructions
|
||||
- start_optimization: Launch optimization run
|
||||
- query_optimization_status: Get current iteration status
|
||||
- extract_results: Parse FEA result files
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||||
- run_nx_journal: Execute NXOpen scripts
|
||||
- search_nxopen_docs: Search NXOpen API documentation
|
||||
"""
|
||||
|
||||
from typing import Dict, Any
|
||||
|
||||
__all__ = [
|
||||
"discover_fea_model",
|
||||
"build_optimization_config",
|
||||
"start_optimization",
|
||||
"query_optimization_status",
|
||||
"extract_results",
|
||||
"run_nx_journal",
|
||||
]
|
||||
8
nx_journals/__init__.py
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8
nx_journals/__init__.py
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|
||||
"""
|
||||
NXOpen Journal Scripts
|
||||
|
||||
Python scripts that execute within the NX environment using NXOpen API.
|
||||
These scripts are called via subprocess from the MCP server.
|
||||
"""
|
||||
|
||||
__version__ = "0.1.0"
|
||||
7
optimization_engine/__init__.py
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7
optimization_engine/__init__.py
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@@ -0,0 +1,7 @@
|
||||
"""
|
||||
NX OptiMaster Optimization Engine
|
||||
|
||||
Core optimization logic with Optuna integration, reused and enhanced from Atomizer.
|
||||
"""
|
||||
|
||||
__version__ = "0.1.0"
|
||||
66
optimization_engine/result_extractors/__init__.py
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66
optimization_engine/result_extractors/__init__.py
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|
||||
"""
|
||||
Pluggable Result Extractor System
|
||||
|
||||
Base classes and implementations for extracting metrics from FEA results.
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import Dict, Any, Optional
|
||||
from pathlib import Path
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||||
|
||||
|
||||
class ResultExtractor(ABC):
|
||||
"""Base class for all result extractors."""
|
||||
|
||||
@abstractmethod
|
||||
def extract(self, result_files: Dict[str, Path], config: Dict[str, Any]) -> Dict[str, float]:
|
||||
"""
|
||||
Extract metrics from FEA results.
|
||||
|
||||
Args:
|
||||
result_files: Dictionary mapping file types to paths (e.g., {'op2': Path(...), 'f06': Path(...)})
|
||||
config: Extractor-specific configuration parameters
|
||||
|
||||
Returns:
|
||||
Dictionary mapping metric names to values
|
||||
"""
|
||||
pass
|
||||
|
||||
@property
|
||||
@abstractmethod
|
||||
def required_files(self) -> list[str]:
|
||||
"""List of required file types (e.g., ['op2'], ['f06'], etc.)."""
|
||||
pass
|
||||
|
||||
@property
|
||||
def name(self) -> str:
|
||||
"""Extractor name for registration."""
|
||||
return self.__class__.__name__.replace("Extractor", "").lower()
|
||||
|
||||
|
||||
# Registry of available extractors
|
||||
_EXTRACTOR_REGISTRY: Dict[str, type[ResultExtractor]] = {}
|
||||
|
||||
|
||||
def register_extractor(extractor_class: type[ResultExtractor]) -> type[ResultExtractor]:
|
||||
"""Decorator to register an extractor."""
|
||||
_EXTRACTOR_REGISTRY[extractor_class().name] = extractor_class
|
||||
return extractor_class
|
||||
|
||||
|
||||
def get_extractor(name: str) -> Optional[type[ResultExtractor]]:
|
||||
"""Get extractor class by name."""
|
||||
return _EXTRACTOR_REGISTRY.get(name)
|
||||
|
||||
|
||||
def list_extractors() -> list[str]:
|
||||
"""List all registered extractor names."""
|
||||
return list(_EXTRACTOR_REGISTRY.keys())
|
||||
|
||||
|
||||
__all__ = [
|
||||
"ResultExtractor",
|
||||
"register_extractor",
|
||||
"get_extractor",
|
||||
"list_extractors",
|
||||
]
|
||||
76
pyproject.toml
Normal file
76
pyproject.toml
Normal file
@@ -0,0 +1,76 @@
|
||||
[project]
|
||||
name = "nx-optimaster"
|
||||
version = "0.1.0"
|
||||
description = "Advanced optimization platform for Siemens NX Simcenter with MCP integration"
|
||||
authors = [
|
||||
{name = "Atomaste", email = "contact@atomaste.com"}
|
||||
]
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
license = {text = "Proprietary"}
|
||||
|
||||
dependencies = [
|
||||
"optuna>=3.5.0",
|
||||
"pandas>=2.0.0",
|
||||
"numpy>=1.24.0",
|
||||
"scipy>=1.10.0",
|
||||
"scikit-learn>=1.3.0",
|
||||
"pyNastran>=1.4.0",
|
||||
"plotly>=5.18.0",
|
||||
"fastapi>=0.109.0",
|
||||
"uvicorn>=0.27.0",
|
||||
"websockets>=12.0",
|
||||
"pydantic>=2.5.0",
|
||||
"python-multipart>=0.0.6",
|
||||
"jinja2>=3.1.3",
|
||||
]
|
||||
|
||||
[project.optional-dependencies]
|
||||
dev = [
|
||||
"pytest>=7.4.0",
|
||||
"pytest-cov>=4.1.0",
|
||||
"black>=23.12.0",
|
||||
"ruff>=0.1.0",
|
||||
"mypy>=1.8.0",
|
||||
"pre-commit>=3.6.0",
|
||||
]
|
||||
|
||||
mcp = [
|
||||
"mcp>=0.1.0",
|
||||
]
|
||||
|
||||
dashboard = [
|
||||
"dash>=2.14.0",
|
||||
"dash-bootstrap-components>=1.5.0",
|
||||
]
|
||||
|
||||
[build-system]
|
||||
requires = ["setuptools>=68.0", "wheel"]
|
||||
build-backend = "setuptools.build_meta"
|
||||
|
||||
[tool.setuptools.packages.find]
|
||||
where = ["."]
|
||||
include = ["mcp_server*", "optimization_engine*", "nx_journals*"]
|
||||
|
||||
[tool.black]
|
||||
line-length = 100
|
||||
target-version = ['py310']
|
||||
include = '\.pyi?$'
|
||||
|
||||
[tool.ruff]
|
||||
line-length = 100
|
||||
select = ["E", "F", "I", "N", "W"]
|
||||
ignore = ["E501"]
|
||||
|
||||
[tool.mypy]
|
||||
python_version = "3.10"
|
||||
warn_return_any = true
|
||||
warn_unused_configs = true
|
||||
disallow_untyped_defs = false
|
||||
|
||||
[tool.pytest.ini_options]
|
||||
testpaths = ["tests"]
|
||||
python_files = ["test_*.py"]
|
||||
python_classes = ["Test*"]
|
||||
python_functions = ["test_*"]
|
||||
addopts = "-v --cov=mcp_server --cov=optimization_engine --cov-report=html --cov-report=term"
|
||||
23
requirements.txt
Normal file
23
requirements.txt
Normal file
@@ -0,0 +1,23 @@
|
||||
# Core Dependencies
|
||||
optuna>=3.5.0
|
||||
pandas>=2.0.0
|
||||
numpy>=1.24.0
|
||||
scipy>=1.10.0
|
||||
scikit-learn>=1.3.0
|
||||
pyNastran>=1.4.0
|
||||
plotly>=5.18.0
|
||||
|
||||
# Web Framework
|
||||
fastapi>=0.109.0
|
||||
uvicorn>=0.27.0
|
||||
websockets>=12.0
|
||||
pydantic>=2.5.0
|
||||
python-multipart>=0.0.6
|
||||
jinja2>=3.1.3
|
||||
|
||||
# Development Tools (optional)
|
||||
pytest>=7.4.0
|
||||
pytest-cov>=4.1.0
|
||||
black>=23.12.0
|
||||
ruff>=0.1.0
|
||||
mypy>=1.8.0
|
||||
Reference in New Issue
Block a user