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40 results found

CyRSoXS: A GPU-accelerated virtual instrument for Polarized Resonant Soft X-ray Scattering (P-RSoXS)

Data provided by  National Institute of Standards and Technology

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.

Tags: polymer,python,C++,CUDA,polymer nanocomposite,polymer solution,X-ray scattering,software,tool,computation,

Modified: 2024-02-22

Views: 0

analphipy: A python package to analyze pair-potential metrics.

Data provided by  National Institute of Standards and Technology

`analphipy` is a python package to calculate metrics for classical models for pair potentials. It provides a simple and extendable api for pair potentials creation. Several routines to calculate metrics are included in the package. The main features of `analphipy` are 1) Pre-defined spherically symmetric potentials. 2) Simple interface to extended to user defined pair potentials. 3) Routines to calculate Noro-Frenkel effective parameters. 4) Routines to calculate Jensen-Shannon divergence.

Tags: python,statistical mechanics,

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

In Situ Carbon Dioxide, Methane, and Carbon Monoxide Mole Fractions from the Los Angeles Megacity Carbon Project

Data provided by  National Institute of Standards and Technology

Hourly observations of carbon dioxide (CO2) and methane (CH4) mole fractions in dry air from tower- and rooftop-based sites in the Los Angeles Megacity Carbon Project network (currently 11 stations, with a 12th to be added in 2022). Carbon monoxide (CO) observations exist at several stations in this network but are not included in this data release pending additional calibration verification. Please contact the authors for higher frequency data, which are available on request. Data files are comma delimited (CSV).

Tags: greenhouse gases,GHG Measurements,Urban Emissions Modeling,carbon dioxide,methane,

Modified: 2024-02-22

Views: 0

Air-broadening in near-infrared carbon dioxide line shapes: quantifying contributions from O2, N2, and Ar

Data provided by  National Institute of Standards and Technology

Dataset for generation of figures in Air-broadening in near-infrared carbon dioxide line shapes: quantifying contributions from O2, N2, and Ar (https://doi.org/10.1016/j.jqsrt.2021.107669)

Tags: spectroscopy,higher order lineshapes,carbon dioxide,

Modified: 2024-02-22

Views: 0

High accuracy spectroscopic parameters of the 1.27 um band of O2 measured with comb-referenced, cavity ring-down spectroscopy

Data provided by  National Institute of Standards and Technology

Data corresponding to Figs. 2,3,5,6,11,12,13,14 for Fleurbaey et al J. Quant Spectrosc. Radiat. Transf. vol 270, 107684 (2021). doi.org.10.1016/j.jqsrt.2021.107684

Tags: greenhouse gases,carbon dioxide,oceans,ph,marine mammals,remote sensing,seabirds,Environment and Climate,

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