MaRGE (MaRCoS Graphical Environment)

🚀 Version 1.0.0 coming soon — stay tuned!

MaRGE is a Python-based graphical environment for interacting with the MaRCoS MRI research system. It provides a user-friendly GUI for running MRI sequences, configuring parameters, and managing experiments without needing to work directly with low-level control code.

MaRGE is designed for MRI researchers, developers, and students working with the MaRCoS platform.

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Index

1. Preview
2. Features
3. General requirements
4. Installation with pip
5. Installation from source (For developers)
6. Full Installation (MaRGE + MaRCoS)
7. Documentation
8. Additional notes

1. Preview

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2. Features

🧲 Graphical interface for MaRCoS MRI experiments

⚙️ Sequence configuration and execution

🧪 Research-oriented workflow

🧩 Extensible sequence architecture

🐍 Fully Python-based

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3. General requirements

Before installing MaRGE, make sure your system meets the following minimum requirements. The software is primarily tested and supported on Ubuntu, but other platforms may work with some limitations.

1. Ubuntu 22.04.5 LTS with Python 3.10
2. Windows 10 with Python 3.13
3. Windows 11 with Python 3.13
4. Internet connection

Without a configured MaRCoS + Red Pitaya setup, MaRGE can still be launched, but only Trial Mode will be available (no hardware acquisition).

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4. Installation with pip

Install MaRGE with pip if you only need to run the GUI and do not plan to modify the source code or add custom sequences.

Note: Tyger capabilities are not supported from pip installation.

1. Go to your project folder.Create and activate a virtual environment:

• Ubuntu

  python3 -m venv venv
  source venv/bin/activate

• Windows

  python -m venv venv
  source venv/Scripts/activate

2. Install marge-mri

   pip install marge-mri==1.0.0b1

3. Launch the GUI from terminal:

     marge-mri

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5. Installation from source (Developer mode)

Use this method if you want to modify the codebase or include your own sequences.

1. Go to the folder where you want to create the project and clone the repo

   git clone https://github.com/josalggui/MaRGE.git

2. Go into the created MaRGE folder. Create and activate a virtual environment. Then, add the current folder (MaRGE) to Python's module search path:

• Ubuntu

  cd MaRGE
  python3 -m venv venv
  source venv/bin/activate
  export PYTHONPATH=$(pwd)

• Windows

  cd MaRGE
  python -m venv venv
  source venv/Scripts/activate
  export PYTHONPATH=$(pwd)

3. Install requirements

   pip install -r requirements.txt

4. Go into MaRGE/marge folder and run the main.py.

• Ubuntu

  cd marge
  python main.py

• Windows

  cd marge
  python3 main.py

Notes

When you execute main.py additional folders are created to save results, configurations, or calibrations. These folders are created in the directory where you run main.py. It is highly recommended to run main.py from MaRGE/marge folder.

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6. Full Installation (MaRGE + MaRCoS Setup)

For a complete installation including MaRCoS configuration, hardware setup, and developer options, please follow the detailed step-by-step guide in the Wiki:

👉 Full Installation Guide

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7. Documentation

📖 Wiki: https://github.com/josalggui/MaRGE/wiki (under development)

📚 Documentation site: https://josalggui.github.io/MaRGE/ (under development)

📦 PyPI package: https://pypi.org/project/marge-mri/

8. Additional notes

1. cupy-cuda12x module related error

During the installation of the requirements, the following error may appear:

ERROR: No matching distribution found for cupy-cuda12x

This usually means your Python environment is not compatible with the prebuilt CuPy CUDA 12 wheels. It is not typically caused by a missing CUDA Toolkit installation.

Common causes and fixes:

• Unsupported Python version — CuPy wheels are only published for specific Python versions. Check your version:

  python --version

If you are using Python 3.14 (or a newer unsupported version), install Python 3.13.0 and recreate your virtual environment.

2. CuPy / CUDA dependency and Tyger capability

CuPy is used in the postprocessing toolbox to accelerate the Algebraic Reconstruction Technique (ART) with GPU computation. The code attempts to import CuPy at runtime; if the import fails, ART automatically falls back to a CPU implementation.

In the current version, with the introduction of the Tyger capability, the ART-based postprocessing workflow is generally no longer needed. As a result:

• CuPy is effectively optional
• CUDA Toolkit is not required for normal Tyger-based workflows
• Failing to install cupy-cuda12x will not break the pipeline
• The code will continue to run using CPU paths (or Tyger paths) instead

You may safely skip CuPy/CUDA installation unless you explicitly plan to use the legacy ART postprocessing toolbox.