The Materials Compendium package facilitates the parsing of essential material composition data from the "Compendium of Material Composition Data for Radiation Transport Modeling," a comprehensive resource provided by the esteemed Pacific Northwest National Laboratory (PNNL). This package equips radiation transport modelers with the necessary tools to access material properties crucial for accurate simulation within various radiation transport codes.
To integrate the Materials Compendium package seamlessly into your workflow, you can use the Python Package Index using pip command:
pip install materials-compendiumAlternatively, if you prefer to work directly from the repository, follow these steps:
git clone https://github.com/pyne/materials-compendium.git
cd materials-compendium
pip install .For running tests, use:
pip install .[test]For comprehensive guidance on leveraging the capabilities of the materials-compendium package and an exhaustive API reference, kindly refer to our online documentation (working).
- The material composition data enclosed within the JSON file are meticulously curated and aligned with Revision 2 of the Compendium. These data are thoughtfully annotated with references for user assurance. It's imperative to acknowledge the potential variances in composition or densities for certain materials, and we've diligently included ranges in references whenever feasible.
- For materials not cataloged in the provided references, users may find it necessary to supply application-specific impurity information.
- We stress the importance of meticulously aligning simulation parameters, such as reaction cross sections, within your selected radiation transport code to uphold the integrity of simulation outcomes.
- While this script is meticulously tailored to JSON file parsing for radiation transport modeling, its adaptability for other applications is an avenue worth exploring.
We extend a warm invitation to contribute to the Materials Compendium. We believe that fostering an environment of collaboration is paramount. Should you wish to contribute, the process is as straightforward as forking our repository on GitHub, implementing your modifications, and subsequently initiating a pull request. Should queries arise or assistance be required during this process, please don't hesitate to engage with us through the PyNE mailing list (https://groups.google.com/forum/#!forum/pyne-dev, [email protected]). Your involvement will undoubtedly enrich the package and its utility.