Results for “github” — Page 9

DOE OSTI2020

pnnl/building_sync_rails

BuildingSyncRails is a library for the Ruby on Rails [1] web application development framework that leverages the Isomorphic library for the Ruby programming language (IPID Submission #11610) in order to provide BuildingSync [2] XML capabilities to Ruby on Rails applications. BuildingSyncRails provides the following capabilities: Capability to download the most recent version of the BuildingSync XML schema document from GitHub and to automatically convert said document into Ruby code. Toolkit for constructing and manipulating Ruby objects that represent BuildingSync XML elements. BuildingSyncRails builds upon the capabilities of the Isomorphic library for the Ruby programming language; specializing the generic capabilities of the Isomorphic library for BuildingSync specifically, e.g., by providing a library of BuildingSyncfriendly methods to simplify the construction of BuildingSync XML documents.

Borkum, Mark↗

DOE OSTI2023

Livermore Computing User and System Scripts

LCUSS is a collection of scripts used to improve productivity on HPC systems for both administrators and general users. It will include general scripts for user management, scripts for helping users interact with LC resource management software (e.g. SLURM and Flux), and scripts to automate common user command-line tasks on LC and other HPC machines. These scripts are intended to be made available to all LC users. Hosting them on GitHub will allow LC staff, users, and collaborators to work on them together.

Long, Jeffery↗

DOE OSTI2024

Code for the manuscript "Mori-Zwanzig Modal Decomposition"

We would like to create an open source repository in LANL's github on code written in Julia, in which we implement and extend the data-driven Mori-Zwanzig method for extracting large-scale spatio-temporal structures from data, which we call MZMD. This method is an extension of Dynamic Mode Decomposition (DMD) in which Mori-Zwanzig memory kernels are included into the associated companion matrix. In the code we would like to release, we apply MZMD to a flow over a cylinder with Reynolds number 100 rather than the much larger data set used in the associated manuscript. DMD is used extensively in the fluid dynamics community mainly for extracting large scale spatio-temporal structures (patters) from flow data. This is useful for understanding the key mechanisms that generate certain complex dynamical process relevant in engineering design. In MZMD, we improve upon DMD by adding the Mori-Zwanzig memory kernels, and show this improvement is especially important in strongly nonlinear regions of the flow.

Woodward, Michael↗

DOE OSTI2024

Hyperparameter Studies for Vision Transformers Trained on High-Fidelity Simulations

This library is a collection of python modules that define, train, and analyze vision-transformer (ViT) machine learning models. The code implements, with mild modifications, ViT models that have been made publicly available through publication and GitHub code. The training data for these models is hydrodynamic simulation output in the form of numpy arrays. This library contains code to train these ViT models on the hydrodynamic simulation output with a variety of hyperparameters, and to compare the results of such models. Furthermore, the library contains definitions of simple convolutional neural network (CNN) machine learning architectures which can be trained on the same hydrodynamic simulation output. These are included as a reference point to compare the ViT models to. Additionally, the library includes trained ViT and CNN models and example input data for demonstration purposes. The code is based on the PyTorch python library.

Callis, Skylar↗

DOE OSTI2022

Repometer v.1.0

SAND2022-4256 O Online version control platforms offer insight into traffic and engagement with source code repositories, but only in limited ways and over short windows of time. Scientific software teams at Sandia National Laboratories and elsewhere want to collect and store engagement data to help tell their story and the impact their work has on the community. Repometer aims to supplement existing capabilities by collecting timely and insightful data from GitHub and GitLab repositories and passing it to a database for longer-term storage. 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.

Mundt, Miranda↗

DOE OSTI2024

Framework For Performing Time-dependent Pebble Bed Reactor Simulations

The present work details the creation of a high-fidelity Monte Carlo methodology for analyzing the run-in and subsequent approach to equilibrium for PBRs. The methodology entails a Python module wrapped around Serpent so as to perform neutronics calculations, move pebbles, refuel the core, and discharge pebbles, thereby modeling the explicit behavior of the PBR run-in. The code kugelpy is within the GitHub repository `pyrates`.

Stewart, RyanH. [Idaho National Laboratory (INL), ↗

DOE OSTI2023

STPPHAWKES

SAND2024-08541O STTPHAWKES software solution is a Bayesian estimation approach to a Hawkes process model that treats missing data as a latent variable in the estimation. The open-source software will be released to the R CRAN repository and Sandia National Labs’ Github. 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.

Rowe, Stephen↗

DOE OSTI2020

BioSTEAMDevelopmentGroup/biosteam

BioSTEAM is a fast and flexible package for the design, simulation, and techno-economic analysis of biorefineries under uncertainty. BioSTEAM’s framework is built to streamline and automate early-stage technology evaluations and to enable rigorous sensitivity and uncertainty analyses. Complete biorefinery configurations are available at the Bioindustrial-Park GitHub repository, BioSTEAM’s premier repository for biorefinery models and results. The long-term growth and maintenance of BioSTEAM is supported through both community-led development and the research institutions invested in BioSTEAM. Through the open-source and community-lead platform, BioSTEAM aims to foster communication and transparency within the biorefinery research community for an integrated effort to expedite the evaluation of candidate biofuels and bioproducts. Additionally, an agile life cycle assessment (LCA) platform has been designed to interface with BioSTEAM, BioSTEAM-LCA. This open-source, installable package allows users to perform streamlined LCAs of biorefineries. The focus of BioSTEAM-LCA is to streamline and automate early-stage environmental impact analyses of processes and technologies, and to enable rigorous sensitivity and uncertainty analyses linking process design, performance, economics, and environmental impacts.

Cortes-Peña, Yoel↗

DOE OSTI2020

BioSTEAMDevelopmentGroup/thermosteam

BioSTEAM is a fast and flexible package for the design, simulation, and techno-economic analysis of biorefineries under uncertainty. BioSTEAM’s framework is built to streamline and automate early-stage technology evaluations and to enable rigorous sensitivity and uncertainty analyses. Complete biorefinery configurations are available at the Bioindustrial-Park GitHub repository, BioSTEAM’s premier repository for biorefinery models and results. The long-term growth and maintenance of BioSTEAM is supported through both community-led development and the research institutions invested in BioSTEAM. Through the open-source and community-lead platform, BioSTEAM aims to foster communication and transparency within the biorefinery research community for an integrated effort to expedite the evaluation of candidate biofuels and bioproducts. Additionally, an agile life cycle assessment (LCA) platform has been designed to interface with BioSTEAM, BioSTEAM-LCA. This open-source, installable package allows users to perform streamlined LCAs of biorefineries. The focus of BioSTEAM-LCA is to streamline and automate early-stage environmental impact analyses of processes and technologies, and to enable rigorous sensitivity and uncertainty analyses linking process design, performance, economics, and environmental impacts. ThermoSTEAM is a standalone thermodynamic engine capable of estimating mixture properties, solving thermodynamic phase equilibria, and modeling stoichiometric reactions. ThermoSTEAM builds upon chemicals, the chemical properties component of the Chemical Engineering Design Library, with a robust and flexible framework that facilitates the creation of property packages. The Biorefinery Simulation and Techno-Economic Analysis Modules (BioSTEAM) is dependent on ThermoSTEAM for the simulation of unit operations.

Cortes-Peña, Yoel↗

DOE OSTI2024

ScholarGuard

The ScholarGuard framework aims to address the gap in archiving and preservation efforts for scholarly artifacts beyond traditional research papers, such as software source code, datasets, presentation slides, workflows, protocols, videos, and more. It introduces a prototype system designed to automatically track researchers' outputs across various scholarly productivity portals on the open web, including platforms like GitHub, Slideshare, Figshare, and Wikipedia. The system detects the availability of new scholarly artifacts and applies modern web archiving technology to create a durable archival record, including high-level metadata for each artifact. This metadata is displayed within the system, linking both to the live version and the archived version of the resource, ensuring long-term accessibility and preservation of diverse research outputs. The software serves as a critical tool for preserving the broader spectrum of scholarly contributions, facilitating visibility, searchability, and long-term access to research artifacts beyond the traditional scope of journal publications.

Balakireva, Lyudmila↗

DOE OSTI2025

aiida-flux-scheduler

AiiDA is a workflow management software that is capable of accelerating simulations on HPC machines. Currently, there is no scheduler plugin for flux. The current code that is being submitted to be released is the initial alpha version. The code will be hosted on the external LLNL github group.

Keilbart, Nathan [Lawrence Livermore National Labo↗

DOE OSTI2024

MODAQ 2.0 (Modular Ocean Data AcQuisition System v2.0) [SWR-24-138]

MODAQ 2.0 is the continuation of the Modular Ocean/Offshore Data AcQuisition system which offers new benefits of being based on generally available hardware and open-source development tools. The MODAQ 2.0 project is the amalgamation of ROS packages, tools, and guidance for developing data acquisition and control applications for marine energy devices using a ROS2 based architecture. The MODAQ 2.0 Reference Design includes several packages that are targeted for the marine energy sector and allow most developers with a basic background in programming to spin up a high-quality DAQ and control system. It is comprised of multiple software repositories which can be found at the MODAQ2 GitHub Organization: https://github.com/NREL-MODAQ2

Nichols, Casey↗

DOE OSTI2025

fife-utils

This is a collection of scripts related to the FIFE project at Fermilab, including utiltities for performing bulk oprations with our SAM and MetaCat (github)data handling systems. The most heavily used is the fife_launch/fife_wrap script pair, which is used to convert physics analysis executables into distributed grid jobs.

Mengel, Marc [Fermi National Accelerator Laborator↗

DOE OSTI2025

Responsive Assistant For Navigating And Guiding Engineering With Rigor (ranger)

The bot uses the GitHub API to fetch discussions from a MOOSE repository and store the data in a vector database. When a new discussion is initiated, the algorithm compares the discussion title with the content of all previous discussions (title + discussions) in the database and provides the most relevant posts to the user. The database is updated regularly to include all new posts, potentially on a monthly basis.

Li, Mengnan [Idaho National Laboratory (INL), Idah↗

DOE OSTI2024

Metric DBSCAN

SAND2025-11725O Metric DBSCAN is an implementation of the popular DBSCAN clustering algorithm that works in general metric spaces. DBSCAN is a clustering algorithm, a fundamental building block in machine learning. It takes a set of objects and, given some notion of distance, identifies coherent groups of objects. With Metric DBSCAN, users can provide an arbitrary function to compute distance. Nearly all existing implementations of DBSCAN restrict distance to one of a few formulations. Metric DBScan accomplishes this cleanly and efficiently. The Python source code is on Github. 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.

Dalbey, Keith↗

DOE OSTI2024

CHMMPY: A python package for constrained Hidden Markov Models

SAND2025-11909O chmmpy software analyzes multivariate timeseries data to detect patterns. It uses a Hidden Markov Model (HMM) and application-specific constraints that reflect known relationships among hidden states to accomplish this. The chmmpy software provides a generic framework for expressing application-specific constraints and supporting constrained HMM inference using optimization solvers. chmmpy is available on GitHub. 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.

Hart, William↗

DOE OSTI2025

BLDAP Intro to Python/Data Science Curriculum v1

The Github repository contains the Jupyter notebooks for the intro to Python / Data Science course for Berkeley Lab Director's Apprenticeship Program (BLDAP). This course is designed for students with little to no experience in coding to learn skills in Python necessary for data science. Students utilize Jupyter notebooks throughout the course. The overall goal is for students to learn how to use Python to clean, analyze, and visualize large data sets in order to communicate effectively their conclusions about the data set. Students apply the skills they learned on actual data sets provided by researchers in Berkeley Lab.

Hales, Laurel [Lawrence Berkeley National Laborato↗

DOE OSTI2024

mrizwanriaz/lcm-dataset

Laser capture microdissection (LCM) derived stem cell-type transcriptome (Jie et al.) How to run on your system Install R on your system. Install the required packages shiny, shinyjs, ggplot2, dplyr, formattable, rcartocolor Download the RShiny app code (ui.R and server.R) and RDS files from the GitHub repository to the same directory. Usage: Run the R code file (ui.R) in the RStudio. The app will open in the default web browser.

Riaz, Muhammad Rizwan↗