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40 results found

Optimal Bayesian Experimental Design Version 1.2.0

Data provided by  National Institute of Standards and Technology

Python module 'optbayesexpt' uses optimal Bayesian experimental design methods to control measurement settings in order to efficiently determine model parameters. Given an parametric model - analogous to a fitting function - Bayesian inference uses each measurement 'data point' to refine model parameters. Using this information, the software suggests measurement settings that are likely to efficiently reduce uncertainties. A TCP socket interface allows the software to be used from experimental control software written in other programming languages.

Tags: GitHub pages template,experimental design,Bayesian,optbayesexpt,python,adaptive measurement,

Modified: 2024-02-22

Views: 0

Simantha: Simulation for Manufacturing

Data provided by  National Institute of Standards and Technology

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.

Tags: discrete-event simulation,manufacturing,production,maintenance,python,

Modified: 2024-02-22

Views: 0

Python tools for measuring filament defects in embedded 3D printing

Data provided by  National Institute of Standards and Technology

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.

Tags: python,digital image analysis,computer vision,3D printing,additive manufacturing,openCV,

Modified: 2024-02-22

Views: 0

Entropy Source Validation Client

Data provided by  National Institute of Standards and Technology

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

Tags: python,Entropy Source Validation Test System,NIST,CMVP,ESV Client,

Modified: 2024-02-22

Views: 0

Thermodynamic Data from Unpublished Sources to Support the New Reference Equation of State for Carbon Dioxide

Data provided by  National Institute of Standards and Technology

During work on the new reference equation of state for carbon dioxide [A.H. Harvey, S.A. Tashkun, R. Hellmann, and E.W. Lemmon, J. Phys. Chem. Ref. Data, in preparation], we obtained unpublished data from several sources. These represent numerical values for data only presented graphically in a publication, or in some cases data not present in the publication at all. With the permission of the authors, we document and deposit these data here so they will be available for future workers.

Tags: CO2,carbon dioxide,thermodynamics,melting,heat capacity,sound speed,vapor pressure,virial coefficients,equation of state,

Modified: 2024-02-22

Views: 0

The effects of advanced spectral line shapes on atmospheric carbon dioxide retrievals

Data provided by  National Institute of Standards and Technology

This is the data presented in the figures of the paper "The effects of advanced spectral line shapes on atmospheric carbon dioxide retrievals" published in J. Quant. Spectrosc. Radiat. Transfer at https://doi.org/10.1016/j.jqsrt.2022.108324

Tags: greenhouse gases,carbon dioxide,remote sensing,

Modified: 2024-02-22

Views: 0

Sim-PROCESD: Simulated-Production Resource for Operations and Conditions Evaluation to Support Decision-making

Data provided by  National Institute of Standards and Technology

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.

Tags: discrete-event simulation,manufacturing,production,maintenance,python,

Modified: 2024-02-22

Views: 0

FCpy: Feldman-Cousins Confidence Interval Calculator

Data provided by  National Institute of Standards and Technology

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.

Tags: python,SIMS,statistics,mass spectrometry,Confidence Interval,CI,Feldman,Cousins,Poisson,Gaussian,

Modified: 2024-02-22

Views: 0

Software and Data associated with "Binding, Brightness, or Noise? Extracting Temperature-dependent Properties of Dye Bound to DNA"

Data provided by  National Institute of Standards and Technology

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.

Tags: fluorescence,numerical-optimization,mathematical-modeling,fluorescence-data,total-least-squares,python,intercalating dyes,noise-removal,

Modified: 2024-02-22

Views: 0

tmmc-lnpy: A python package to analyze Transition Matrix Monte Carlo lnPi data.

Data provided by  National Institute of Standards and Technology

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.

Tags: python,molecular simulation,data analysis,Transition Matrix Monte Carlo,statistical mechanics,

Modified: 2024-02-22

Views: 0