3.5

MSDFit v1

../_images/MSDFit-v1_dlg.png

MSDFit dialog.

Summary

Fits log(intensity) vs Q-squared to obtain the mean squared displacement.

Properties

Name Direction Type Default Description
InputWorkspace Input MatrixWorkspace Mandatory Sample input workspace
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
Plot Input boolean False Plots results after fit
OutputWorkspace Output WorkspaceGroup Mandatory Output mean squared displacement
ParameterWorkspace Output TableWorkspace   Output fit parameters table
FitWorkspaces Output WorkspaceGroup   Output fitted workspaces

Description

Fits log(intensity) vs Q^{2} with a straight line for each run to obtain the mean square displacement for a given range of runs.

This algorithm operates on the QSquared workspace (_q2) generated by the ElasticWindowMultiple algorithm.

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='QSquared', XMin=0.0,
                XMax=5.0, BinWidth=0.1)

msd, param, fit = MSDFit(InputWorkspace=sample,
                         XStart=0.0, XEnd=5.0,
                         SpecMin=0, SpecMax=0)

print ', '.join(msd.getNames())
print 'A0: ' + str(msd.getItem(0).readY(0))
print 'A1: ' + str(msd.getItem(1).readY(0))

Output:

msd_A0, msd_A1
A0: [ 0.95908058]
A1: [ 0.11014908]

Categories: Algorithms | Workflow | MIDAS | PythonAlgorithms