IndirectTwoPeakFit dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| SampleWorkspace | Input | MatrixWorkspace | 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 | number | 500 | Max iterations. Default=500 |
| OutputName | Input | string | Output workspace base name |
This performs a one lorentzian convolution fit, and then performs a two lorentzian convolution fit on the sample workspace.
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 | PythonAlgorithms | Workflow\MIDAS
Python: IndirectTwoPeakFit.py (last modified: 2019-09-23)