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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

hkl-projects/ioc-hkl

HKL-IOC is an open source EPICS IOC that performs real‑time crystallographic HKL calculations for diffractometers and scattering instruments. It integrates the Python hkl library with EPICS through PyDevice, exposing HKL calculations and diffractometer geometry transformations as standard EPICS process variables. This allows beamline and laboratory control systems to convert between motor positions and reciprocal‑space coordinates, configure diffractometer geometries, and drive scans directly in HKL space. The software is written in Python and designed to run alongside existing EPICS deployments without requiring changes to core EPICS components. It is intended for use at synchrotron and neutron scattering facilities, as well as laboratory X‑ray diffractometers, where reliable and reproducible HKL calculations are needed for experiment control, data collection, and automation. HKL-IOC is distributed under the GNU General Public License v3.0 (GPL‑3.0) and contributions and extensions for additional geometries and beamlines are welcomed.

Baekey, Alex↗
DOE OSTI2021

nsCamera ImageJ Plugin

ImageJ plugin used to run hCMOS via the Python nsCamera library. The Python nsCamera has already been open-sourced under CP02612

Hill, Jeremy↗
DOE OSTI2020

Damage Pseudo Cross Section Generator

This software is a python script for generating group damage pseudo-cross-sections for specific elements, and tabulating them in forms useful to python or to office-open workbooks (xlsx). The damage pseudo-cross-section is needed for calculating how much neutron radiation damage a structural material receives in a reactor measured in displacements per atom (DPA). The process of generating these pseudo-cross-sections is laborious as it requires running the nuclear data processing code, NJOY, for hundreds of isotopes, and then post-processing the data. This also allows many opportunities for human error to be involved. This script eliminates these problems by fully automating the process.

Gale, MicahD.↗
DOE OSTI2026

py-boomer v0.1.0

Py-BOOMER (Python Bayesian OWL Ontology MErgER in Python) is a probabilistic reasoning system for knowledge representation and ontological reasoning with uncertainty. Itnables reasoning over probabilistic facts and taxonomic relationships, finding the most likely consistent interpretation of potentially conflicting assertions. It uses a combination of graph-based reasoning and Bayesian probabilistic inference. Key features: Represent probabilistic ontological statements Reason over class subsumption hierarchies Evaluate class equivalence relationships Detect and resolve logical inconsistencies Calculate posterior probabilities for each assertion

Mungall, Chris [Lawrence Berkeley National Laborat↗
DOE OSTI2026

buhito

buhito is a Python library for graph analysis and machine learning. Graphs can represent networks with objects as nodes and their relationships as edges. buhito focuses on graphlet methods that study graphs through enumerating their component subgraphs to enable interpretable and fast models of complex systems. The package provides tools for different algorithmic designs for computing, analyzing, and applying graphlets to research problems such as machine learning, data compression, and anomaly detection in graph-structured data. A central feature is performing decomposition data analysis on graphs for machine learning models. Implemented in Python and built upon open-source scientific libraries such as NetworkX, NumPy, and SciPy, buhito provides high-performance methods for researchers exploring the mathematical and computational foundations of graphlet analysis applicable to systems of different sizes.

Pimonova, Yulia↗
DOE OSTI2026

Rhythm

Rhythm is a small Python framework that automates Cadence Spectre simulations from the command line. Instead of creating an ADE testbench, you'll write a Rhythm Recipe, a short, readable Python script that contains each step needed to test your circuit from setting up libraries and stimulus waveforms to setting up analyses and reading the results (in Rhythm, these steps are called stages). Need to run more than one simulation? Use a Rhythm Campaign to easily sweep across corners and test conditions with a live dashboard and multithreading support.

Quinn, Adam [Fermi National Accelerator Laboratory↗
DOE OSTI2026

pyndev v0.2.0

Pyndev is development wrapper over a proprietary network automation framework name Cisco NSO. NSO consists of developer created and maintained packages that are flat and fragile. Pyndev translates these artifacts to pure python packages that can then be managed with modern Python tooling.

Stephens, William [Lawrence Berkeley National Labo↗
DOE OSTI2026

Generalized Tensor-on-Tensor Regression (GToTR)

SAND2026-23069O Generalized Tensor-on-Tensor Regression (GToTR) is a Python-based tool for conducting generalized tensor-on-tensor regression. It provides Canonical Polyadic (CP)-based generalized tensor regression models, support for generalized linear model-like families and links, alternating-optimization model fitting methods, and a standard statistics software interface. The tool supports tensor-valued responses and covariates using the open-source Python Tensor Toolbox (pyttb) software package. 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.

Dunlavy, Daniel [Sandia National Lab. (SNL-CA), Li↗
DOE OSTI2026

infrastore [SWR-26-077]

Infrastore is time-series storage for energy-systems simulations, backed by HDF5 + SQLite, with Rust, Python, Julia, gRPC, and CLI bindings. It is a Rust library for managing time-series data in power-systems and energy simulations. Numerical arrays are persisted in HDF5, and the metadata associating each array with its owning component lives in SQLite. Identical arrays are stored once and shared through content addressing. It ships native Rust, Python (PyO3), and Julia (C ABI) interfaces, the infrastore command-line tool, and a read-only gRPC server with a Rust client. Documentation: https://natlabrockies.github.io/infrastore/latest/ — start with the Quick Start or the Architecture.

Thom, Daniel [National Laboratory of the Rockies (↗
DOE OSTI2026

Pyctos

Pyctos is a concolic testing framework for standard, dynamically-typed Python. Pyctos is able to generate exhaustive test inputs reaching 100% coverage for a subset of pure Python in the absence of type annotations, even where modern fuzzers would fail. Pyctos's underlying reasoning engine is the CVC5 SMT solver, though Z3 is also supported. Pyctos also supports a growing subset of the standard library and some third-party libraries, such as NumPy.

Washbourne, ErickN [Lawrence Livermore National La↗
DOE OSTI2020

Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know

Remote sensing instruments are heavily used to provide observations for both the operational and research communities. These sensors do not provide direct observations of the desired atmospheric variables, but instead, retrieval algorithms are necessary to convert the indirect observations into the variable of interest. It is critical to be aware of the underlying assumptions made by many retrieval algorithms, including that the retrieval problem is often ill posed and that there are various sources of uncertainty that need to be treated properly. In short, the retrieval challenge is to invert a set of noisy observations to obtain estimates of atmospheric quantities. The problem is often complicated by imperfect forward models, by imperfect prior knowledge, and by the existence of nonunique solutions. Optimal estimation (OE) is a widely used physical retrieval method that combines measurements, prior information, and the corresponding uncertainties based on Bayes’s theorem to find an optimal solution for the atmospheric state. Furthermore, OE also allows the relative contributions of the different sources of error to the uncertainty in the final retrieved atmospheric state to be understood. Here, we provide a novel Python library to illustrate the use of OE for inverse problems in the atmospheric sciences. We introduce two example problems: how to retrieve drop size distribution parameters from radar observations and how to retrieve the temperature profile from ground-based microwave sensors. Using these examples, we discuss common pitfalls, how the various error sources impact the retrieval, and how the quality of the retrieval results can be quantified.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Integrating ytopt and libEnsemble to autotune OpenMC

Ytopt is a Python machine-learning-based autotuning software package developed within the ECP PROTEAS-TUNE project. The ytopt software adopts an asynchronous search framework that consists of sampling a small number of input parameter configurations and progressively fitting a surrogate model over the input-output space until exhausting the user-defined maximum number of evaluations or the wall-clock time. libEnsemble is a Python toolkit for coordinating workflows of asynchronous and dynamic ensembles of calculations across massively parallel resources developed within the ECP PETSc/TAO project. libEnsemble helps users take advantage of massively parallel resources to solve design, decision, and inference problems and expands the class of problems that can benefit from increased parallelism. In this paper we present our methodology and framework to integrate ytopt and libEnsemble to take advantage of massively parallel resources to accelerate the autotuning process. Specifically, we focus on using the proposed framework to autotune the ECP ExaSMR application OpenMC, an open source Monte Carlo particle transport code. OpenMC has seven tunable parameters some of which have large ranges such as the number of particles in-flight, which is in the range of 100,000 to 8 million, with its default setting of 1 million. Setting the proper combination of these parameter values to achieve the best performance is extremely time-consuming. Therefore, we apply the proposed framework to autotune the MPI/OpenMP offload version of OpenMC based on a user-defined metric such as the figure of merit (FoM) (particles/s) or energy efficiency energy-delay product (EDP) on Crusher at Oak Ridge Leadership Computing Facility. In conclusion, the experimental results show that we achieve the improvement up to 29.49% in FoM and up to 30.44% in EDP.

Autotuning↗
DOE OSTI2020

ATLAS: a Snakemake workflow for assembly, annotation, and genomic binning of metagenome sequence data

Background: Metagenomics and metatranscriptomics studies provide valuable insight into the composition and function of microbial populations from diverse environments, however the data processing pipelines that rely on mapping reads to gene catalogs or genome databases for cultured strains yield results that underrepresent the genes and functional potential of uncultured microbes. Recent improvements in sequence assembly methods have eased the reliance on genome databases, thereby allowing the recovery of genomes from uncultured microbes. However, configuring these tools, linking them with advanced binning and annotation tools, and maintaining provenance of the processing continues to be challenging for researchers. Results: Here we present ATLAS, a software package for customizable data processing from raw sequence reads to functional and taxonomic annotations using state-of-the-art tools to assemble, annotate, quantify, and bin metagenome and metatranscriptome data. Genome-centric resolution and abundance estimates are provided for each sample in a dataset. ATLAS is written in Python and the workflow implemented in Snakemake; it operates in a Linux environment, and is compatible with Python 3.5+ and Anaconda 3+ versions. The source code for ATLAS is freely available, distributed under a BSD-3 license. Conclusions: ATLAS provides a user-friendly, modular and customizable Snakemake workflow for metagenome and metatranscriptome data processing; it is easily installable with conda and maintained as open-source on GitHub at https://github.com/metagenome-atlas/atlas.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Conceptual Spacer Design for the ATR GEN I Target for Pu-238 Production in the Advanced Test Reactor at Idaho National Laboratory

The initial target design used for Pu-238 production at Idaho National Laboratory was designed by Oak Ridge National Laboratory to optimize the production of Pu-238 in the High Flux Isotope Reactor (HFIR) and are referred to as HFIR GEN II targets. To take advantage of the Advanced Test Reactor’s (ATR) taller active core region a redesign of the HFIR GEN II targets was needed. It was proposed to stack two HFIR GEN II targets nose to nose about the core center line; however, this resulted in excessive neutron and photon heating in the pellets located in the center. This peak heating was not desirable so three alternative designs were investigated for the ATR GEN I targets. The python-based code, MCNP to ORIGEN2 in Python (MOPY), was used to calculate the heating rates after 40 days of irradiation to capture the effects of each configuration. The purpose of this paper is to document the details of these conceptual design calculations and comparisons for the ATR GEN I targets.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2020

Towards scalable dataframe systems

Dataframes are a popular abstraction to represent, prepare, and analyze data. Despite the remarkable success of dataframe libraries in R and Python, dataframes face performance issues even on moderately large datasets. Moreover, there is significant ambiguity regarding dataframe semantics. In this paper we lay out a vision and roadmap for scalable dataframe systems. To demonstrate the potential in this area, we report on our experience building Modin, a scaled-up implementation of the most widely-used and complex dataframe API today, Python's pandas. With pandas as a reference, we propose a simple data model and algebra for dataframes to ground discussion in the field. Given this foundation, we lay out an agenda of open research opportunities where the distinct features of dataframes will require extending the state of the art in many dimensions of data management. We discuss the implications of signature dataframe features including flexible schemas, ordering, row/column equivalence, and data/metadata fluidity, as well as the piecemeal, trial-and-error-based approach to interacting with dataframes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Tidal Resource Data from Sequim Bay Inlet, WA, August 2020

Data from a Nortek Signature1000 deployed on a lander for 14 days in Aug 2020 in the entrance to Sequim Bay, WA. Raw data were processed using the DOLfYN python package and standardized using the ME Data Pipeline python package, tsdat version 0.2.12. Processed data were partitioned into 24 hour increments and saved in the NETCDF file format.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Advanced Terrestrial Simulator (ATS) evaluation dataset at 7 catchments across the continental United States

This dataset comprises of the input files and other files required for Advanced Terrestrial Simulator (ATS) simulations at 7 catchments across the continental United States. ATS is an integrated surface-subsurface hydrology model. We include Jupyter notebooks (within scripts folder) for individual catchments showing information (including data sources, river network, soil, geology, landuse types etc.) on preparing the machine readable input files. ATS observation output files are provided in the output folder. Figures and analyses (.xlsx sheets) are also provided. The catchments include, Taylor River Upstream (Colorado); (b) Cossatot River (Arkansas); (c) Panther Creek (Alabama); (d) Little Tennessee River (North Carolina and Georgia); (e) Mayo River (Virginia); (f) Flat Brook (New Jersey); (g) Neversink River headwaters (New York). Readme files are provided inside the directories providing more details. Files types include: .xml, .h5, .xlsx, .png, .ipynb, .py, .nc, .txt. All of the files types can be accessed by open source software, details on software requirements are following: .xml (any text editors including notepad and textedit), .h5 (in python using hdf libraries), .xlsx (WPS Office Spreadsheets, OpenOffice Calc, LibreOffice Calc, Microsoft Office etc.), .png (any image viewer), .ipynb (Jupyter notebook), .py (any text editors including notepad and textedit), .nc (using python or other open source software).

54 ENVIRONMENTAL SCIENCES↗