Speaker
Description
Across the nuclear data pipeline, it is important to drive improvements in validation and verification. One powerful tool in this process, for evaluation through applications, is simply visualizing the data. The ability to visualize nuclear data exists in many forms already, ranging from the legacy but robust tools in NJOY [1] (PLOTR and VIEWR) to the NEA’s Java-based Nuclear Information System (JANIS) [2] to transport-code-provided visualization like the MCNP© plotter [3]. While every one of these tools is still useful, a more modern approach that enables consistent visualization from evaluated data and experimental data sets all the way through application data is warranted.
The NJOY21 project [4] has prioritized the development of open source and robust tools to read and write nuclear data in a wide number of formats. ENDFtk provides an interface to ENDF formatted data, ACEtk to ACE data, and GNDStk to GNDS data. With ENDFtk nearing completion and ACEtk and GNDStk undergoing rapid development, each provides a Python interface to extract data. With a Python interface to the data, a plotting tool simply needs hooks to the widely used plotting package Matplotlib [5] to create flexible and professional-quality visualizations.
A new nuclear data visualization package is thus underway at the Los Alamos National Laboratory, with the capability to plot evaluated data from ENDF and GNDS files, interface files such as GENDF and PENDF, and application files including ACE and NDI. This tool also has the flexibility to plot user-provided data in a variety of simple formats, used to display, for instance, a new proposed evaluation not yet formatted or experimental datasets. There is functionality to plot integrated cross sections, angular distributions, and energy spectra. There is also a convenient interface for creating comparisons between two curves, displaying both the curves themselves and the difference between them. This visualization package has both a Python API and a YML input file, allowing flexibility for differing workflows and environments. Simply plotting a variety of datasets from a variety of data sources, such as shown in Figure 1, has proven to be a very useful tool for nuclear data creators and users at LANL.
Figure 1
Figure 1. Plot of integrated cross sections from the ENDF/B-VIII.0 evaluation of U-235 taken directly from a variety of file types.
References
1. Macfarlane, Robert, Muir, Douglas W., Boicourt, R. M., Kahler, III, Albert Comstock, & Conlin, Jeremy Lloyd, The NJOY Nuclear Data Processing System, Version 2016. https://doi.org/10.2172/1338791
2. N. Soppera, M. Bossant, E. Dupont, JANIS 4: An Improved Version of the NEA Java-based Nuclear Data Information System, Nuclear Data Sheets, 120, 294-296, (2014).
3. T. Goorley, M. James, T. Booth, F. Brown, J. Bull, L. J. Cox, J. Durkee, J. Elson, M. Fensin, R. A. Forster, J. Hendricks, H. G. Hughes, R. Johns, B. Kiedrowski, R. Martz, S. Mashnik, G. McKinney, D. Pelowitz, R. Prael, J. Sweezy, L. Waters, T. Wilcox & T. Zukaitis, Initial MCNP6 Release Overview, Nuclear Technology, 180:3, 298-315, (2012).
4. Jeremy Lloyd Conlin, A.C. Kahler, Austin P. McCartney and Daniel A. Rehn, NJOY21: Next generation nuclear data processing capabilities, EPJ Web Conf., 146, 09040, (2017).
5. J. D. Hunter, Matplotlib: A 2D Graphics Environment, Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, (2007).
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