Excel to DBC to C Toolchain 🚗💻

An advanced, GUI-driven toolchain designed to streamline the workflow for automotive embedded software engineers. It effortlessly converts CAN matrix definitions from Excel spreadsheets into .dbc (CAN Database) files, and subsequently generates corresponding C code structures and headers.

Additionally, it supports reverse-engineering existing .dbc files back into human-readable Excel formats, all wrapped in a premium PyQt6-based graphical interface inspired by the AUTOSAR Authoring Tool.

✨ Features

• Excel to DBC Conversion: Robustly transforms standard CAN matrix Excel grids into .dbc files.
• DBC to C Code Generation: Automatically generates production-ready C data structures and headers based on DBC inputs.
• DBC to Excel Reverse Engineering: Accurately unpacks existing .dbc databases into easily editable Excel sheets.
• Premium User Interface: A modern, interactive PyQt6 UI featuring custom themes, animated progress bars, and a multi-tab workflow.
• CAN Frame Visualizer: Real-time visualization of CAN frame layouts directly within the application.
• Intelligent Input Handling: Quality-of-life enhancements such as auto-capslock for CAN IDs and dynamic UI validation.
• Headless Pipeline Mode: Includes a fully-automated CLI pipeline for CI/CD integration.

📦 Requirements

• Python 3.8+
• The tool uses the following pivotal libraries:
  • pandas
  • cantools
  • openpyxl
  • PyQt6

🚀 Installation

1. Clone the repository:

   git clone https://github.com/your-username/Excel_to_DBC_C_Toolchain.git
   cd Excel_to_DBC_C_Toolchain

2. Install the dependencies: It is recommended to use a virtual environment.

   pip install -r requirements.txt

3. Verify directory structure: Ensure the following directories exist (they should be present from the repository):

   • input/ (Place your sample CAN matrices here)
   • output/ (Generated files will appear here)

🛠️ Usage

This toolchain can be operated via the graphical interface or in headless mode using the pipeline script.

Using the GUI (Recommended)

Launch the graphical interface by running:

python run_ui.py

Workflow Steps:

1. Use the Excel Population tab to load and configure your CAN matrix.
2. Advance to the DBC Review / CAN Frame Visualizer tabs to ensure the data is mapped correctly.
3. Finally, use the C/H File Review to generate and inspect the resulting embedded code.

Using the CLI Pipeline

For batch operations or automation, modify the config.json with your desired paths:

{
    "excel_file": "input/CAN_Matrix.xlsx",
    "dbc_output": "output/dbc/generated.dbc",
    "c_output_dir": "output/c_code",
    "bus_name": "CAN1"
}

Then execute:

python run_pipeline.py

📁 Directory Structure

Excel_to_DBC_C_Toolchain/
├── config.json               # Headless pipeline configuration
├── input/                    # Target directory for input Excel tables and DBC files
├── output/                   # Destination for generated .dbc and .c/.h files
├── requirements.txt          # Python dependencies
├── run_pipeline.py           # CLI automated pipeline entrypoint 
├── run_ui.py                 # PyQt6 GUI application entrypoint
├── scripts/                  # Core Python algorithms
│   ├── dbc_to_excel.py       # DBC to Excel logic
│   ├── excel_to_dbc.py       # Excel to DBC logic
│   └── generate_code.py      # C code generation logic
└── ui/                       # Frontend application modules

🤝 Contributing

Contributions are welcome! Please feel free to open an issue or submit a pull request if you find bugs or have feature suggestions.