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MSDFit v1¶
Summary¶
Fits Intensity vs Q for 3 models to obtain the mean squared displacement.
Properties¶
| Name | Direction | Type | Default | Description | 
|---|---|---|---|---|
| InputWorkspace | Input | 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 | long | 0 | Start of spectra range to be fit | 
| SpecMax | Input | long | 0 | End of spectra range to be fit | 
| OutputWorkspace | Output | Mandatory | Output mean squared displacement | |
| ParameterWorkspace | Output | 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’.
Workflow¶
 
diagram generation was disabled¶
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: [ ...]
  A1: [ ...]
  Using Yi Model
  A0: [ ...]
  A1: [ ...]
Categories: AlgorithmIndex | Workflow\MIDAS
Source¶
Python: MSDFit.py