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All sources NASA NTRS DOE OSTI

NASA NTRS2018

System and Method for Providing a Climate Data Analytic Services Application Programming Interface

A system, method and computer-readable storage devices for providing a climate data analytic services application programming interface. The system includes a programming library that enables client device software to invoke the capabilities of a climate data analytics system through requests to various services supported by the climate data analytics system, and also includes a client-side communications interface that enables the programming library's methods to interact with a climate data analytics system's server interface to obtain access to the capabilities of the system. In one implementation, the programming library is implemented in the Python programming language. The programming library can include basic utilities that call a single, server-side method implemented by one of the various services supported by the climate data analytics system, and extended utilities that call a series of basic utilities and/or other extended utilities that have been placed under programmatic control in order to create client-side convenience methods and workflows.

Schnase, John L.↗

DOE OSTI2023

Generating MCNP Input Files for Unstructured Mesh Geometries

The Los Alamos National Laboratory’s (LANL) Monte Carlo N-Particle (MCNP)1 transport code version 6.3 (also known as MCNP6.3) has the capability for tracking particles on unstructured mesh (UM) geometry models embedded into constructive solid geometry (CSG) cells. This feature has been developed for performing calculations of complex geometry models because manually creating CSG models is time-consuming and error-prone as the complexities of geometries increase. A UM geometry model is a collection of finite elements representing a solid geometry. The first step of the MCNP UM calculation is using other software packages to create a finite element mesh representation of a solid 3D geometry because the MCNP code cannot be used to generate a UM model. Computer-aided design (CAD) software is typically used to create a solid geometry model, which is later imported into mesh generation software to create a UM model. Some mesh generation software packages may also be used to create solid geometries and thus CAD files are not needed. The MCNP UM feature was originally designed for models generated by the Abaqus/CAE software suite. The MCNP code version 6.0 and later can process UM models formatted as Abaqus input files. Starting with a 6.3 version, the MCNP code can process HDF5 mesh input files. We only focus on the UM models formatted as Abaqus input files in this report since currently no external software can be used to generate HDF5 mesh input files for MCNP UM calculations. The MCNP code version 6.3 can be used to convert the Abaqus mesh input files into the HDF5 mesh input files, but this option is typically used by the MCNP code development team to test the HDF5 mesh input file feature. Several software packages (such as Abaqus, Attila4MC, or Cubit) can be used to create the Abaqus input files for MCNP UM calculations. An MCNP UM calculation using an Abaqus model requires two input file types: MCNP and Abaqus input files. The Abaqus input files needed for MCNP UM calcu lations must have the correct Abaqus syntax and meet the additional requirements by the MCNP code. The MCNP code can process only Abaqus input files that make use of part and assembly definitions, where elements in each part must be grouped into one or more element sets (i.e., elset) using *Elset keyword lines with specified naming formats. The MCNP and Abaqus input files required for MCNP UM simulations must be related; pseudo-cells in an MCNP input file must be constructed from mesh model data from an Abaqus input file. For large complex UM models, it is tedious to manually create MCNP UM input files. The um pre op (unstructured mesh pre operations) program with the -m option can be used to create a skeleton MCNP input file from an Abaqus input file [6]. Since the um pre op program was written in Fortran and was not written for optimized performance, this program is a deprecated feature in the MCNP code version 6.3 and may be removed in the next release of the code. To improve calculation flow of multiphysics calculations, a Python3 code called write mcnp um input has been developed to generate an MCNP input file instead of using the um_pre_op -m option. This Python code was initially released to the public in 2020. We have updated this Python code for MCNP6.3 and it was used to generate the MCNP input files used to verify the MCNP6.3 code. The write_mcnp_um_input code is included with the MCNP6.3 code package which will be released to the public through the Radiation Safety Information Computational Center (RSICC) at Oak Ridge National Laboratory. This report is a revision of LA-UR-20-27139 report.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

pdas-experiments

SAND2025-04589O pdas-experiments automates computational experiments of fluid flow simulations. It uses the pressio-demoapps-schwarz package as a basis to break down complex simulations into smaller, manageable parts. This application is an extension of the Sandia Pressio software which uses domain decomposition to work with complex simulations more efficiently. Users can test different simulation setups, while keeping a detailed record of their experiments so they can be reproduced later. The software includes a C++ program that runs individual experiments based on user-defined settings in a YAML file, as well as a Python script that can manage multiple simulations at once. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Tezaur, Irina [Sandia National Lab. (SNL-CA), Live↗

DOE OSTI2021

Lowest-dimensional portals to SU( N ) exotics

New matter fields charged under the strong nuclear force would have dramatic phenomenological implications. In this work, we systematically explore how these new states, which we postulate belong to some representation of SU(3) of quantum chromo-dynamics, could interact with standard model fields: We analyze all lowest-dimensional “portal” operators for any SU(3) representation, and motivated by grand unification, we extend our results to SU( N ) for N > 3. We provide a publicly available python code, “tessellation,” that automatically constructs said lowest-dimensional portal operators for any new exotic matter field charged under SU( N ) for any N .

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2025

Computerized Assignment of Reviewers to Applicants for the Truman Fellowship

This report presents a user-friendly tool for assigning reviewers to the President Harry S. Truman Postdoctoral Fellowship applicants, replacing a previous program that required technical skills. Built with Python libraries, it organizes reviewer preferences and balances workloads, outputting five solutions as Excel files. Validation confirms its effectiveness, making it suitable for broader applications while ensuring efficient assignments without technical expertise.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

FLARE: field line analysis and reconstruction for 3D boundary plasma modeling

The FLARE code is a magnetic mesh generator that is integrated within a suite of tools for the analysis of the magnetic geometry in toroidal fusion devices. A magnetic mesh is constructed from field line segments and permits fast reconstruction of field lines in 3D boundary plasma codes such as EMC3-EIRENE. Both intrinsically non-axisymmetric configurations (stellarators) and those with symmetry breaking perturbations of an axisymmetric equilibrium (tokamaks) are supported. The code itself is written in Modern Fortran with MPI support for parallel computing, and it incorporates object-oriented programming for the definition of the magnetic field and the material surface geometry. Extended derived types for a number of different magnetohydrodynamic equilibrium and plasma response models are implemented. The core element of FLARE is a field line tracer with adaptive step-size control, and this is integrated into tools for the construction of Poincaré maps and invariant manifolds of X-points. A collection of high-level procedures that generate output files for visualization is build on top of that. The analysis modules are build with Python frontends that facilitate customization of tasks and/or scripting of parameter scans.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Choppy Lite

The existing methodologies for gathering and computing zonal statistics from the combination of a raster image and a shapefile either require advanced programming knowledge in at least one language or the use of proprietary software such as ArcGIS. The software “Choppy Lite” offers several solutions for acquiring zonal statistics that can be used without proprietary software or programming knowledge. This software provides a Docker-based ecosystem, allowing users of any experience level to simply run a single line command in a terminal prompt independent of the operating system. Intermediate to advanced users can import the class provided by the software package into their own Python scripts.

Grant, JoshuaN.↗

DOE OSTI2020

OpenCGRA: An Open-Source Unified Framework for Modeling,Testing, and Evaluating CGRAs

Coarse-grained reconfigurable arrays (CGRAs),loosely defined as arrays of functional units (e.g, adder, sub-tractor, multiplier, divider, or larger multi-operation units, butsmaller than a general-purpose core) interconnected through aNetwork-on-Chip, provide higher flexibility than domain-specificASIC accelerators while offering increased hardware efficiencywith respect to fine-grained reconfigurable devices, such as FieldProgrammable Gate Arrays (FPGAs). The fast evolving fieldsof machine learning and edge computing, which are seeing acontinuous flow of novel algorithms and larger models, makeCGRAs ideal target architectures to allow domain specializationwithout loosing too much generality. They also generally offerquicker and more effective reconfigurability than FPGAs, po-tentially allowing adaptation during actual algorithm execution,and implement a dataflow programming paradigm that adaptswell to these emerging workloads. Designing and generating aCGRA, however, still requires to define the type and number ofthe specific functional units, implement their interconnect andthe network topology, and perform its simulation and validation,given a variety of workloads of interest.In this paper, we propose OpenCGRA, a Python-based unifiedframework that integrates generation, modeling, testing and eval-uation for CGRAs. OpenCGRA is the first open-source integratedframework able to support the full top-to-bottom design flow forspecializing and implementing CGRAs: modeling at different ab-straction levels (functional level, cycle level, register-transfer level),generation, simulation, testing at different granularities (unit test-ing, integration testing, property-based testing), and characteriza-tion (area, power, and timing). OpenCGRAs will be made availableon GitHub.

CGRA, synthesis↗

DOE OSTI2020

The MolSSI QCArchive project: An open-source platform to compute, organize, and share quantum chemistry data

The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python-based software infrastructure that powers automated computation and storage of quantum chemistry (QC) results. The MolSSI-hosted central server provides the computational molecular sciences community a location to freely access tens of millions of QC computations for machine learning, methodology assessment, force-field fitting, and more through a Python interface. Facile, user-friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at the website. The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of QC computations for individuals or groups of researchers at any scale. The QCArchiveInfrastructure is open-source (BSD-3C), code repositories can be found at github, and releases can be downloaded via PyPI and Conda. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Quantum Chemistry Data Science > Computer Algorithms and Programming

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

A Python Tool for Reconstructing MCNP6 Particle Histories from an HDF5 PTRAC File [Slides]

A Python tool for converting the MCNP6 HDF5 PTRAC file to a list of Python trees is presented. The particle trees store MCNP6 simulated events for each history using parent-child relationships, which ensures that branching processes are accurately reproduced. A variety of post-processing scripts are presented and used in conjunction with the Python particle trees to make special tallies that are currently not available in the MCNP6 software and visualize the particle tracks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

SimpleMass (Version 2.0.1 Report)

SimpleMass v2 is an updated GUI version of the SimpleMass Excel spreadsheet. It was developed using Python 3.7 and wxPython, a cross-platform GUI toolkit. The program was developed on Windows 10 and tested on macOS Catalina. In theory, the program should work on Linux based operating systems such as Ubuntu, but this has not been tested. Executable versions of the code were built using PyInstaller. The version described in this report is an alpha version that is still in development. It has only been tested by the developer.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Jas4pp — A data-analysis framework for physics and detector studies

This paper describes the Jas4pp framework for exploring physics cases and for detector-performance studies of future particle collision experiments. Jas4pp is a multi-platform Java program for numeric calculations, scientific visualization in 2D and 3D, storing data in various file formats and displaying collision events and detector geometries. It also includes complex data-analysis algorithms for function minimization, regression analysis, event reconstruction (such as jet reconstruction), limit settings and other libraries widely used in particle physics. The framework can be used with several scripting languages, such as Python/Jython, Groovy and JShell. Several benchmark tests discussed in the paper illustrate significant improvements in the performance of the Groovy and JShell scripting languages compared to the standard Python implementation in C. Furthermore, the improvements for numeric computations in Java are attributed to recent enhancements in the Java Virtual Machine.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

The ARM Data-oriented Metrics and Diagnostics Package for Climate Models - A New Tool for Evaluating Climate Models with Field Data

The U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Inter-comparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2007

Computational Workbench for Multibody Dynamics

PyCraft is a computer program that provides an interactive, workbenchlike computing environment for developing and testing algorithms for multibody dynamics. Examples of multibody dynamic systems amenable to analysis with the help of PyCraft include land vehicles, spacecraft, robots, and molecular models. PyCraft is based on the Spatial-Operator- Algebra (SOA) formulation for multibody dynamics. The SOA operators enable construction of simple and compact representations of complex multibody dynamical equations. Within the Py-Craft computational workbench, users can, essentially, use the high-level SOA operator notation to represent the variety of dynamical quantities and algorithms and to perform computations interactively. PyCraft provides a Python-language interface to underlying C++ code. Working with SOA concepts, a user can create and manipulate Python-level operator classes in order to implement and evaluate new dynamical quantities and algorithms. During use of PyCraft, virtually all SOA-based algorithms are available for computational experiments.

Edmonds, Karina↗

DOE OSTI2020

FLIMJ: An open-source ImageJ toolkit for fluorescence lifetime image data analysis

In the field of fluorescence microscopy, there is continued demand for dynamic technologies that can exploit the complete information from every pixel of an image. One imaging technique with proven ability for yielding additional information from fluorescence imaging is Fluorescence Lifetime Imaging Microscopy (FLIM). FLIM allows for the measurement of how long a fluorophore stays in an excited energy state, and this measurement is affected by changes in its chemical microenvironment, such as proximity to other fluorophores, pH, and hydrophobic regions. This ability to provide information about the microenvironment has made FLIM a powerful tool for cellular imaging studies ranging from metabolic measurement to measuring distances between proteins. The increased use of FLIM has necessitated the development of computational tools for integrating FLIM analysis with image and data processing. To address this need, we have created FLIMJ, an ImageJ plugin and toolkit that allows for easy use and development of extensible image analysis workflows with FLIM data. Built on the FLIMLib decay curve fitting library and the ImageJ Ops framework, FLIMJ offers FLIM fitting routines with seamless integration with many other ImageJ components, and the ability to be extended to create complex FLIM analysis workflows. Building on ImageJ Ops also enables FLIMJ’s routines to be used with Jupyter notebooks and integrate naturally with science-friendly programming in, e.g., Python and Groovy. We show the extensibility of FLIMJ in two analysis scenarios: lifetime-based image segmentation and image colocalization. We also validate the fitting routines by comparing them against industry FLIM analysis standards.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

ENDFtk: A robust tool for reading and writing ENDF-formatted nuclear data

ENDFtk is a recently developed C++ and Python interface to interact with ENDF-6 formatted nuclear data files. It provides a robust and complete interface, allowing the reading and writing of all formats currently part of the ENDF-6 formats manual, as well as some non-ENDF formats used by the NJOY processing code. It provides an interface that mimics the names in the ENDF-6 formats manual as well as an equivalent interface using human-readable attribute names. It is robust and powerful enogh for nuclear data experts to develop complex applications, while also simple enough to be used non-experts to retrieve and manipulate evaluated nuclear data. ENDFtk offers the ability to easily interrogate and manipulate data either in large-scale code projects or in simple Python scripts. Here, in this paper, a brief overview of the interface is given, as well as more substantial examples demonstrating plotting simple data, interacting with more complex data, and writing new data to files. ENDFtk is open source and available for download via GitHub (https://github.com/njoy/ENDFtk).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

The iRage Cookbook [Slides]

iRage is a family of Zsh and Python 3 scripts designed to accelerate the process of submitting calculations for the novice xRage user. iRage reduces the time to write a new input deck and submit an xRage problem to the production queue to about 15 minutes, assuming the user has defined the initial geometry using a program like Osito or linked to problem geometries generated by codes such as Abaqus, Flag, or Pagosa. iRage runs on Linux and Mac systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

COTS Data Analytics Software User Manual: Version 1.0

Large volumes of data are being collected by Sandia National Laboratories as part of an active commercial-off-the-shelf (COTS) part testing and surveillance program. This user manual documents Python-based COTS Data Analytics software that has been developed for standardizing, displaying, visualizing, and analyzing the resulting COTS part testing and surveillance data. It is the objective of these software tools to streamline the analysis of COTS testing and surveillance data and improve the efficiency with which test engineers and data analytics experts can pinpoint possible performance and reliability problems in COTS parts.

42 ENGINEERING↗