BayesStretch v1¶
Summary¶
This is a variation of the stretched exponential option of Quasi.
Properties¶
Name |
Direction |
Type |
Default |
Description |
---|---|---|---|---|
SampleWorkspace |
Input |
Mandatory |
Name of the Sample input Workspace |
|
ResolutionWorkspace |
Input |
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
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
# 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