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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