____            _   _     _  ___ __   __
        / ___| ___ _ __ | \ | |   | |/ _ \ \ / /
       | |  _ / _ \ '_ \|  \| |_  | | | | \ V / 
       | |_| |  __/ | | | |\  | |_| | |_| || |  
        \____|\___|_| |_|_| \_|\___/ \___/ |_|  

GenNJOY is an advanced software framework written in Python designed to automate complex nuclear data processing workflows. The system seamlessly bridges raw nuclear data libraries (ENDF) with the NJOY processing code, and subsequently converts the outputs into HDF5 format ready for OpenMC Monte Carlo simulations. It also supports exact perturbations and covariance matrix generation for sensitivity and uncertainty analysis.

• 📦 Library Management: Automated fetching and organization of raw nuclear data libraries (ENDF/B-VIII.0) for both Incident Neutrons and Thermal Scattering Laws (TSL).
• ⚙️ Input Generation: Intelligent generation of NJOY input decks for isotopes and thermal scattering materials, handling complex parameter definitions automatically.
• ⚡ Parallel Processing: Full utilization of multi-core CPU architectures to accelerate NJOY execution batches.
• 🔄 Automated Conversion: Seamless conversion of generated ACE files into OpenMC-compatible HDF5 libraries.
• 🎯 Exact Perturbation [NEW]: Create exact perturbed HDF5 cross-section libraries for sensitivity analysis.
• 📊 Covariance Processing [NEW]: Automated generation of multi-group covariance matrices (.coverx) and visual correlation plots (PDF/PS) using ERRORR and COVR modules.
• 🗂️ xsdir Management: Automatic merging and updating of xsdir files to ensure data consistency across the pipeline.
• 🛠️ Interactive CLI: A user-friendly Command Line Interface (CLI) that eliminates the need for manual script handling.

Before installing GenNJOY, ensure the following requirements are met on your system:

1. Python 3.8+
2. NJOY2016:
   • The NJOY2016 source code must be compiled and installed.
   • Ensure the njoy executable is accessible in your system PATH, or be ready to provide its path during runtime.
3. OpenMC:
   • Required for converting ACE to HDF5 and for executing exact perturbations.
   • OpenMC Installation Guide
4. Ghostscript (ps2pdf) [Optional but Recommended]:
   • Required by the Covariance module to automatically convert NJOY Viewr PostScript plots into readable PDFs.
   • Ubuntu/Debian: sudo apt install ghostscript
   • macOS: brew install ghostscript

1. Clone the Repository:

   git clone https://github.com/laidastro7/GenNJOY
   cd GenNJOY

2. Install the Package:

   pip install -e .

   This command installs the package along with all dependencies listed in pyproject.toml and setup.py.

Once installed, you can run the tool from anywhere in your terminal using the command:

gennjoy

Typical Workflow:

• Option [1]: Download raw nuclear data libraries (ENDF). Data will be stored in the internal data/ directory.
• Option [2] & [3]: Generate NJOY input decks based on the downloaded files.
• Option [4] & [5]: Execute NJOY processing (Generates ACE files and updates xsdir).
• Option [6]: Convert the generated ACE libraries into an HDF5 library for OpenMC.
• Option [7]: Create Perturbed HDF5 Libraries by manually specifying isotopes, reaction MTs, and perturbation percentages.
• Option [8]: Generate Covariance Matrices and cross-section correlation plots.

GenNJOY/
├── gennjoy/                         # Package Source Code
│   ├── __init__.py                  # Package Initialization & Versioning
│   ├── cli.py                       # Main Entry Point (CLI) and Menu System
│   ├── compile_openmc_library.py    # Converts generated ACE files to OpenMC HDF5 format
│   ├── fetch_endf_library.py        # Automates downloading and organizing ENDF libraries
│   ├── generate_neutron_input.py    # Generates NJOY input decks for incident neutron data
│   ├── generate_tsl_input.py        # Generates NJOY input decks for thermal scattering data
│   ├── njoy_execution_engine.py     # Core engine wrapper for executing NJOY commands
│   ├── perturb_hdf5.py              # Exact HDF5 cross-section perturbation module
│   ├── run_covariance_processing.py # Multi-core covariance matrix processing & plotting
│   ├── run_neutron_processing.py    # Orchestrates incident neutron data processing
│   ├── run_tsl_processing.py        # Orchestrates thermal scattering processing
│   ├── temperature_index.json       # Database for TSL temperature mappings
│   ├── xsdir_mcnp5                  # MCNP5 xsdir Template used for merging
│   ├── data/                        # Processed Data Storage
│   │   ├── incident_neutron_ace/
│   │   ├── incident_neutron_endf/
│   │   ├── thermal_scattering_ace/
│   │   ├── thermal_scattering_endf/
│   │   ├── hdf5_library/
│   │   ├── hdf5_perturbed_library/  # Output for exact perturbations
│   │   ├── covariance_matrices/     # Generated .coverx files
│   │   ├── covariance_plots/        # Generated PDF/PS correlation plots
│   │   └── njoy_input_decks/
│   └── inputs/                      # Control files (groups.i, flux.i, inventories)
├── pyproject.toml                   # Modern Build Configuration
├── setup.py                         # Setup Script for package definition
├── requirements.txt                 # List of Python Dependencies
├── MANIFEST.in                      # Package Data Configuration
├── .gitignore                       # Git Ignore Rules
├── LICENSE                          # Project License (MIT)
└── README.md                        # Project Documentation

Contributions are welcome! If you have suggestions for improvements or new features:

• Fork the Project.
• Create your Feature Branch (git checkout -b feature/AmazingFeature).
• Commit your Changes (git commit -m 'Add some AmazingFeature').
• Push to the Branch (git push origin feature/AmazingFeature).
• Open a Pull Request.

Dr. Mohamed Laid YAHIAOUI - Lead Developer - GitHub Profile

📧 Email: [email protected]

This project is licensed under the MIT License - see the LICENSE file for details.