With the improvements in hardware, sample environment, and new data packets/storage of events we require new software for the production, distribution, caching, saving, histograming, reducing, fitting, monitoring, and feedback to users/NICE.
CHRNS Computing resources available via ORCiD login within NIST
DAVE and Mslice for MACS
SASView for VSANS
REF1D for CANDOR
Developing necessary web-based tools to perform all other tasks
Web-based tools, fitting, and experimental optimizations
Refl1D/CANDOR data fitting
Enabled remote (web-based) access to a 'live' fitting server for interactive fitting sessions, or static fit results for batch fits for the Refl1D analysis software, which is used for analysis of data from the CHRNS CANDOR instrument.
The interface is detachable and re-attachable to the fitting session at any time
Parameter error bars are determined with MCMC, correlations, fit history etc. visually represented
Necessary for Autonomous Experiments
Low-code fitting development:
Simple web app lets users build physical models
Compare model to data directly in-app
Code is generated from the visual model representation and piped to Refl1D
Experimental optimization of the neutron instrument, measurement, and sample to greatly reduce measurement times and increase data quality.
Remote Execution of CANDOR experimental optimizations
Jupyter Notebook data fitting using Refl1D
Feature-complete experimental optimization for bio materials
C++ classes wrapped in Python for speed and compatibility
Declarative model langue for use in Refl1D
See section on CANDOR Fluids handling robot for 1) ‘immediate’ feedback to the user on the information content in a measurement 2) a mechanism for feeding results from these routines back into the NICE control software to be used in intelligent, automated determinations of how to efficiently complete the measurements.
SASView/VSANS data fitting and modeling
decoupled GUI from backend computations
GPU/CPU multiprocessor support for fitting
Intelligent experiment-design and experiment-planning tools using predictions of information content based on user-supplied models (bio/soft materials initially)
Remote Execution of SANS experimental optimizations
Jupyter Notebooks data fitting using SASVIEW
Feature-complete experimental optimization
lipid vesicle molecular model
Any SASView model
Monte Carlo Markov Chain fitting of SANS data
also needed to classifying SANS patterns with machine learning (in collaboration with nSOFT)
While instrument optimization isn't too helpful on VSANS, the ‘immediate’ feedback to the user on the information content in a measurement and statistical error analysis for a given model(s) can be used to reduce the amount of overcounting on SANS. We will implement this first on CANDOR and use that understanding with the above infrastructure to provide feedback to users/the NICE control software to be used in intelligent, automated determinations of how to efficiently complete the measurements.
Preliminary web-interface for data retrieval/histograming of VSANS events
Scripts for all data files from an experiment
Graphical view of detector counts vs. time (1 sec default for now)
Click-drag to histogram a range
Automatic file naming
DAVE and Mslice
Used for reducing MACS data and has been adapted to visualize time-slice data, taking special care of the error bar due to low count rate of the event mode.
AI error monitoring
AI/ML routines providing real-time reporting of unexpected experimental conditions, such as significant statistical data fluctuations or sample environment deviations, will be developed. User alerts and automated instrument responses can be added