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Thermal Conductivity of Binary Mixtures of 1,1,1,2-Tetrafluoroethane(R-134a), 2,3,3,3-Tetrafluoropropene (R-1234yf), and trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants
Data provided by National Institute of Standards and Technology
Workflow: The data that falls into this category are gathered from a measurement program and are published in the archival literature.
Tags: Advanced Materials,Energy,Environment and Climate,Physical Infrastructure,Safety,Security and Forensics,
Modified: 2024-02-22
Views: 0
Speed of Sound Measurements of Binary Mixtures of Difluoromethane (R-32) with 2,3,3,3-Tetrafluoropropene (R-1234yf) or trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants
Data provided by National Institute of Standards and Technology
Speed of Sound Measurements of Binary Mixtures of Difluoromethane (R-32) with 2,3,3,3-Tetrafluoropropene (R-1234yf) or trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants
Tags: Advanced Materials,Energy,Environment and Climate,Physical Infrastructure,Safety,Security and Forensics,
Modified: 2024-02-22
Views: 0
Imppy3d: Image processing in python for 3D image stacks
Data provided by National Institute of Standards and Technology
Image Processing in Python for 3D image stacks, or imppy3d, is a softwarerepository comprising mostly Python scripts that simplify post-processing and3D shape characterization of grayscale image stacks, otherwise known asvolume-based images, 3D images, or voxel models. imppy3d was originally createdfor post-processing image stacks generated from X-ray computed tomographymeasurements. However, imppy3d also contains a functions to aid inpost-processing general 2D/3D images.Python was chosen for this library because of it is a productive, easy-to-uselanguage.
Tags: python,image processing,3d,x-ray,tomography,image stack,
Modified: 2024-02-22
Views: 0
"pyproject2conda": A script to convert `pyproject.toml` dependencies to `environemnt.yaml` files.
Data provided by National Institute of Standards and Technology
The main goal of `pyproject2conda` is to provide a means to keep all basicdependency information, for both `pip` based and `conda` based environments, in`pyproject.toml`. I often use a mix of pip and conda when developing packages,and in my everyday workflow. Some packages just aren't available on both. The application provides a simple comment based syntax to add information to dependencies when creating `environment.yaml`. This package is actively used by the author, but is still very much a work inprogress.
Tags: python,devoloper tool,python packaging,
Modified: 2024-02-22
Views: 0
The NIST Scan Framework for ARTIQ
Data provided by National Institute of Standards and Technology
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.
Tags: ARTIQ,python,Scans,
Modified: 2024-02-22
Views: 0
multicomplex: C++ and Python code for multicomplex arithmetic
Data provided by National Institute of Standards and Technology
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
Tags: mathematics,multicomplex,derivatives,C++,python,
Modified: 2024-02-22
Views: 0
Optimal Bayesian Experimental Design
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 a 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,measurement,
Modified: 2024-02-22
Views: 0
SEDCORR: An Algorithm for Correcting Systematic Energy Deficits in the Atom Probe Mass Spectra
Data provided by National Institute of Standards and Technology
SEDCORR is an open-source Python module designed to correct for the systematic energy deficits in atom probe mass spectra of electrically insulating samples. The assumption of the algorithm is that the mass spectrum for a dataset is conserved throughout the dataset and that any changes to the peak positions arise from an unknown slowly-fluctuating accelerating voltage. For computational speed, the unknown accelerating voltage is determined using a template matching FFT-based cross correlation method.
Tags: atom probe microscopy,insulator,mass spectra,energy deficit correction,python,FFT,
Modified: 2024-02-22
Views: 0
pySCATMECH: A Python interface to the SCATMECH C++ library of polarized light scattering codes
Data provided by National Institute of Standards and Technology
SCATMECH is a library of object-oriented C++ computer codes originally developed for disseminating models for polarized light scattering from surfaces and aerosols and for diffraction from gratings. The pySCATMECH package has been developed as an interface to the SCATMECH library, simplifying use of the codes and allowing for more rapid development of software for these applications.
Tags: aerosol,bidirectional reflectance,BRDF,diffuse,gratings,Mie scattering,modeling,Mueller matrix,polarization,python,roughness,scatter,surface,
Modified: 2024-02-22
Views: 0
Optimal Bayesian Experimental Design Version 1.0.1
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,measurement,
Modified: 2024-02-22
Views: 0