6.4

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MSDFit v1

../_images/MSDFit-v1_dlg.png

MSDFit dialog.

Summary

Fits Intensity vs Q for 3 models to obtain the mean squared displacement.

Properties

Name Direction Type Default Description
InputWorkspace Input MatrixWorkspace Mandatory Sample input workspace
Model Input string Gauss Model options : Gauss, Peters, Yi. Allowed values: [‘Gauss’, ‘Peters’, ‘Yi’]
XStart Input number 0 Start of fitting range
XEnd Input number 0 End of fitting range
SpecMin Input number 0 Start of spectra range to be fit
SpecMax Input number 0 End of spectra range to be fit
OutputWorkspace Output MatrixWorkspace Mandatory Output mean squared displacement
ParameterWorkspace Output TableWorkspace   Output fit parameters table
FitWorkspaces Output WorkspaceGroup   Output fitted workspaces

Description

Fits \(intensity\) vs \(Q\) with a straight line for each run to obtain the mean square displacement for a given range of runs.

This algorithm operates on the \(Q\) workspace (_eq) generated by the ElasticWindowMultiple algorithm.

The model used for obtaining the mean squared displacement can be selected. These models include ‘Gaussian’, ‘Peters’, ‘Yi’.

Usage

Example - Performing MSDFit on simulated data.

# Create some data that is similar to the output of ElasticWindowMultiple
sample = CreateSampleWorkspace(Function='User Defined',
                UserDefinedFunction='name=ExpDecay,Height=1,Lifetime=6',
                NumBanks=1, BankPixelWidth=1, XUnit='momentum', XMin=0.0,
                XMax=5.0, BinWidth=0.1)

g_msd, g_param, g_fit = MSDFit(InputWorkspace=sample,
                               Model="Gauss",
                               XStart=0.0, XEnd=5.0,
                               SpecMin=0, SpecMax=0)

y_msd, y_param, y_fit = MSDFit(InputWorkspace=sample,
                               Model="Yi",
                               XStart=0.0, XEnd=5.0,
                               SpecMin=0, SpecMax=0)

print('Using Gauss Model')
print('A0: ' + str(g_msd.readY(0)))
print('A1: ' + str(g_msd.readY(1)))
print('Using Yi Model')
print('A0: ' + str(y_msd.readY(0)))
print('A1: ' + str(y_msd.readY(1)))

Output (the numbers on your machine my not match exactly):

  Using Gauss Model
  A0: [ 0.87...]
  A1: [ 0.19...]
  Using Yi Model
  A0: [ 0.95...]
  A1: [ 0.58...]

Categories: AlgorithmIndex | Workflow\MIDAS