3.8

BayesStretch v1

../_images/BayesStretch-v1_dlg.png

BayesStretch dialog.

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 number 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 number 50 Number of sigma values
NumberBeta Input number 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: Algorithms | Workflow\MIDAS