The NIST scan framework is a framework that greatly simplifies the process of writing and maintaining scans of experimental parameters using the ARTIQ control system and language. The framework adopts the philosophy of convention over configuration where datasets are stored for analysis and plotting in a standard directory structure. The framework provides a number of useful features such as automatic calculation of statistics, fitting, validation of fits, and plotting that do not need to be performed by the user.

The library multicomplex is an implementation of multicomplex algebra in C++ to allow for higher-order derivatives of numerical functions. Many (though not all) mathematical functions are implemented, allowing for calculation of derivatives (straight and mixed) to approximately numerical precision, which is difficult or impossible to achieve in conventional double precision

Simantha is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines with finite buffers. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Classes for five basic manufacturing objects are included: source, machine, buffer, sink, and maintainer. These objects can be defined by the user and configured in different ways to model various real-world manufacturing systems.

In embedded 3D printing, a nozzle is embedded into a support bath and extrudes filaments or droplets into the bath. This repository includes Python code for analyzing and managing images and videos of the printing process during extrusion of single filaments. The zip file contains the state of the code when the associated paper was submitted. The link to the GitHub page goes to version 1.0.0, which is the same as the code attached here. From there, you can also access the current state of the code.Associated with: L. Friedrich, R. Gunther, J.

This tool is a Python client that can interact with the NIST Entropy Source Validation Test System.

Sim-PROCESD is a discrete event simulation package written in Python that is designed to model the behavior of discrete manufacturing systems. Specifically, it focuses on asynchronous production lines. It also provides functionality for modeling the degradation and maintenance of machines in these systems. Sim-PROCESD provides class definitions for manufacturing devices/components that can be configured by the user to model various real-world manufacturing systems.

Python scripts and Python+Qt graphical user interface for calculating Feldman-Cousins confidence intervals for low-count Poisson processes in the presence of a known background and for Gaussian processes with a physical lower limit of 0.

The purpose of this software and data is to enable reproduction and facilitate extension of the computational results associated with the following referenceDeJaco, R. F.; Majikes, J. M.; Liddle, J. A.; Kearsley, A. J. Binding, Brightness, or Noise? Extracting Temperature-dependent Properties of Dye Bound to DNA. Biophysical Journal, 2023, https://doi.org/10.1016/j.bpj.2023.03.002.The software and data can also be found at https://github.com/usnistgov/dye_dna_plates.

A python package to analyze ``lnPi`` data from Transition Matrix Monte Carlo(TMMC) simulation. The main output from TMMC simulations, ``lnPi``, provides a means to calculate a host of thermodynamicproperties. Moreover, if ``lnPi`` is calculated at a specific chemical potential, it can be reweighted to providethermodynamic information at a different chemical potential. The python package``tmmc-lnpy`` provides a wide array of routines to analyze ``lnPi`` data.

Polarized Resonant Soft X-ray scattering (P-RSoXS) has emerged as a powerful synchrotron-based tool to measure structure in complex, chemically heterogeneous systems. P-RSoXS combines principles of X-ray scattering and X-ray spectroscopy; this combination provides unique sensitivity to molecular orientation and chemical heterogeneity in soft materials such as polymers and biomaterials.