Automotive Measurement Analysis VS Code Extension

An AI-powered VS Code extension for analyzing automotive measurement data (MDF/BLF files) with collaborative plotting and requirement validation, specifically designed for steering system engineers.

Features

Epic 1: Core Infrastructure & File Handling ✅

• File Detection: Automatic detection of .mdf, .mf4, .blf, .asc files with custom icons
• File Metadata: Hover preview showing file size, duration, signal count, and recording date
• Multi-Backend Architecture: 🐳 Docker, ⚡ UV, 🐍 Python backends with intelligent auto-selection
• REST API: Endpoints for file info, signal listing, and data retrieval
• Memory Efficient: Data chunking for handling 1GB+ files

Planned Features (Epics 2-6)

• Epic 2: Signal management and Bokeh visualization
• Epic 3: AI integration with natural language commands
• Epic 4: Advanced analysis (filtering, FFT, anomaly detection)
• Epic 5: Requirements validation and reporting
• Epic 6: Steering-specific templates and workflows

Architecture

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   VS Code       │    │   Multi-Backend │    │   Measurement   │
│   Extension     │◄──►│   Manager       │◄──►│   Files         │
│   (TypeScript)  │    │   (Auto-Select) │    │   (MDF/BLF)     │
└─────────────────┘    └─────────────────┘    └─────────────────┘
                                │
                ┌───────────────┼───────────────┐
                │               │               │
        ┌───────▼──────┐ ┌──────▼──────┐ ┌─────▼──────┐
        │   🐳 Docker  │ │   ⚡ UV      │ │ 🐍 Python  │
        │   Backend    │ │   Backend   │ │  Backend   │
        │ (Reliable)   │ │ (Fast)      │ │ (Compat)   │
        └──────────────┘ └─────────────┘ └────────────┘

Backend Options

• 🐳 Docker Backend: Containerized, most reliable, zero Python setup
• ⚡ UV Backend: Ultra-fast Python package manager, rapid installation
• 🐍 Python Backend: Traditional approach, maximum compatibility
• 🤖 Auto-Selection: Automatically chooses the best available backend

Installation

Prerequisites

• VS Code 1.85.0 or later
• Optional: Docker Desktop (recommended for best experience)
• Optional: Python 3.11+ (for traditional backend)

Quick Setup

Linux/macOS:

git clone <repository-url>
cd mesaic
chmod +x install.sh
./install.sh

Windows:

git clone <repository-url>
cd mesaic
install.bat

This automatically:

• Installs Node.js dependencies
• Compiles the extension
• Packages and installs it in VS Code
• Sets up the optimal backend for your system

Manual Setup

1. Clone the repository

   git clone <repository-url>
   cd mesaic

2. Install and compile

   npm install
   npm run compile
   npx vsce package --no-dependencies
   code --install-extension automotive-analysis-0.1.0.vsix

3. Restart VS Code - The extension will auto-select the best backend

Usage

Basic Workflow

1. Open a measurement file

   • Navigate to an .mdf, .mf4, .blf, or .asc file in VS Code
   • Hover over the file to see metadata preview
   • Right-click and select "Open in Analysis View"

2. Backend management (automatic)

   • Extension tries Docker → UV → Python automatically
   • Use "Switch Backend Type" to manually select
   • Check "Show Backend Status" for current state

3. View file information

   • File metadata panel shows size, duration, signal count
   • Available signals are listed with units and sample rates

4. Browse signals

   • Use "Show Available Signals" command
   • Search and filter signals by name
   • Click signals to add them to plots (Epic 2)

Commands

Access via Ctrl+Shift+P (or Cmd+Shift+P on Mac):

• Start Backend - Start the optimal backend
• Stop Backend - Stop the current backend
• Switch Backend Type - Choose Docker/UV/Python manually
• Show Backend Status - Check current backend info
• Troubleshooting Guide - Built-in help and solutions

Configuration

The extension can be configured through VS Code settings:

{
  "automotiveAnalysis.backendType": "auto",
  "automotiveAnalysis.backendPort": 8000,
  "automotiveAnalysis.autoStartBackend": true,
  "automotiveAnalysis.maxFileSize": 1073741824,
  "automotiveAnalysis.dockerImage": "automotive-analysis-backend"
}

Development

Project Structure

mesaic/
├── src/                    # TypeScript extension code
│   ├── extension.ts        # Main entry point
│   ├── backendManager.ts   # Multi-backend orchestration
│   ├── dockerService.ts    # Docker backend service
│   ├── uvService.ts        # UV backend service
│   ├── pythonService.ts    # Traditional Python backend
│   ├── fileHandler.ts      # File detection and metadata
│   └── utils/
│       └── logger.ts       # Logging utilities
├── python/                 # FastAPI backend
│   ├── server.py           # Main server
│   ├── parsers/            # File parsers
│   ├── models/             # Data models
│   └── utils/              # Utilities
├── Dockerfile             # Docker backend image
├── pyproject.toml         # UV/modern Python config
├── scripts/               # Build and deployment scripts
├── install.sh/.bat        # Installation scripts
└── package.json           # Extension manifest

Development Commands

# Compile TypeScript
npm run compile

# Watch for changes
npm run watch

# Run tests
npm test

# Build Docker image
docker build -t automotive-analysis-backend .

# Start Python backend manually
cd python
python server.py

API Endpoints

The backend provides these REST endpoints (all backends):

• GET /health - Health check
• POST /files/info - Get file metadata
• POST /signals - Get available signals
• POST /data - Get signal data for time range

Testing

# Run Python tests
cd python
pytest

# Run extension tests
npm test

Performance

Targets

• File Parsing: 1GB+ MDF files within 5 seconds
• API Response: <500ms for typical queries
• Memory Usage: Efficient chunking for large datasets
• Cross-Platform: Windows, macOS, Linux support

Backend Performance

• 🐳 Docker: Most reliable, consistent performance across platforms
• ⚡ UV: Fastest installation, modern Python package management
• 🐍 Python: Traditional approach, maximum compatibility

Supported File Formats

MDF/MF4 Files

• Format: MDF3 and MDF4 measurement data files
• Parser: asammdf library
• Features: Full signal extraction, metadata, time series data

BLF/ASC Files

• Format: CAN bus log files (Binary Logging Format, ASCII)
• Parser: python-can library
• Features: CAN message extraction, ID-based signal grouping

Troubleshooting

Quick Help

Use the built-in troubleshooting guide:

1. Press Ctrl+Shift+P
2. Run "Automotive Analysis: Troubleshooting Guide"
3. Follow the step-by-step instructions

Backend-Specific Issues

Docker Backend:

• Install Docker Desktop
• Ensure Docker daemon is running
• Check Docker is in PATH

UV Backend:

• UV auto-installs when needed
• Manual install: curl -LsSf https://astral.sh/uv/install.sh | sh

Python Backend:

• Use Python 3.11 or 3.12
• Run: python setup.py for manual dependency installation

Common Issues

1. Backend won't start

   • Use "Switch Backend Type" to try different options
   • Check "Show Backend Status" for error details
   • Run "Troubleshooting Guide" for specific solutions

2. Port conflicts

   • Change automotiveAnalysis.backendPort in settings
   • Kill existing processes: lsof -ti:8000 | xargs kill

3. File parsing errors

   • Ensure file is not corrupted
   • Check file permissions
   • Verify file format is supported

Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes: Follow the coding standards
4. Add tests: Ensure your changes are tested
5. Submit a pull request: Describe your changes clearly

License

This project is licensed under the MIT License.

---

Made with ❤️ for automotive engineers

Now with intelligent multi-backend support for the best possible user experience!