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BayesStretch v1

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Summary

This is a variation of the stretched exponential option of Quasi.

Properties

Name	Direction	Type	Default	Description
SampleWorkspace	Input	MatrixWorkspace	Mandatory	Name of the Sample input Workspace
ResolutionWorkspace	Input	MatrixWorkspace	Mandatory	Name of the resolution input Workspace
EMin	Input	number	-0.2	The start of the fitting range
EMax	Input	number	0.2	The end of the fitting range
SampleBins	Input	long	1	The number of sample bins
Elastic	Input	boolean	True	Fit option for using the elastic peak
Background	Input	string	Flat	Fit option for the type of background. Allowed values: [‘Sloping’, ‘Flat’, ‘Zero’]
NumberSigma	Input	long	50	Number of sigma values
NumberBeta	Input	long	30	Number of beta values
Loop	Input	boolean	True	Switch Sequential fit On/Off
OutputWorkspaceFit	Output	WorkspaceGroup	Mandatory	The name of the fit output workspaces
OutputWorkspaceContour	Output	WorkspaceGroup	Mandatory	The name of the contour output workspaces

Description

This is a variation of the stretched exponential option of Quasi. For each spectrum a fit is performed for a grid of \(\beta\) and \(\sigma\) values. The distribution of goodness of fit values is plotted.

This routine was originally part of the MODES package. Note that this algorithm uses F2Py and is currently only supported on Windows.

Usage

Example - BayesStretch

# Check OS support for F2Py (Windows only)
from IndirectImport import is_supported_f2py_platform
if is_supported_f2py_platform():
    # Load in test data
    sample_ws = Load('irs26176_graphite002_red.nxs')
    resolution_ws = Load('irs26173_graphite002_red.nxs')

    # Run BayesStretch algorithm
    fit_group, contour_group = BayesStretch(SampleWorkspace=sample_ws, ResolutionWorkspace=resolution_ws,
                                            EMin=-0.2, EMax=0.2, Background='Sloping', Loop=True)

Categories: AlgorithmIndex | Workflow\MIDAS

Source

Python: BayesStretch.py