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IndirectTwoPeakFit v1¶
Summary¶
Performs a convolution fit for 1 and 2 Lorentzians.
Properties¶
Name |
Direction |
Type |
Default |
Description |
---|---|---|---|---|
SampleWorkspace |
Input |
Mandatory |
Name for the sample workspace. |
|
EnergyMin |
Input |
number |
-0.5 |
Minimum energy for fit. Default=-0.5 |
EnergyMax |
Input |
number |
0.5 |
Maximum energy for fit. Default=0.5 |
Minimizer |
Input |
string |
Levenberg-Marquardt |
Type of minimizer. Allowed values: [‘Levenberg-Marquardt’, ‘FABADA’] |
MaxIterations |
Input |
long |
500 |
Max iterations. Default=500 |
OutputName |
Input |
string |
Output workspace base name |
Description¶
This performs a one lorentzian convolution fit, and then performs a two lorentzian convolution fit on the sample workspace.
Usage¶
Example - IndirectTwoPeakFit
# Produce the reduced file used in the two peak fit
EnergyWindowScan(InputFiles='92762', Instrument='OSIRIS', Analyser='graphite', Reflection='002',
SpectraRange='963,980', ElasticRange='-0.02,0.02', InelasticRange='0.4,0.5',
TotalRange='-0.5,0.5', ReducedWorkspace='__reduced_group', ScanWorkspace='__scan_workspace')
# Perform a two peak fit
IndirectTwoPeakFit(SampleWorkspace='osiris92762_graphite002_red', EnergyMin=-0.5,
EnergyMax=0.5, OutputName='osiris92762_graphite002_two_peak_fit')
Categories: AlgorithmIndex | Workflow\Inelastic | Workflow\MIDAS
Source¶
Python: IndirectTwoPeakFit.py