6.3

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NormaliseByPeakArea v1

../_images/NormaliseByPeakArea-v1_dlg.png

NormaliseByPeakArea dialog.

Summary

Normalises the input data by the area of of peak defined by the input mass value.

Properties

Name Direction Type Default Description
InputWorkspace Input MatrixWorkspace Mandatory An input workspace.
Mass Input number Mandatory The mass, in AMU, defining the recoil peak to fit
Sum Input boolean True If true all spectra on the Y-space, fitted & symmetrised workspaces are summed in quadrature to produce the final result
OutputWorkspace Output MatrixWorkspace Mandatory Input workspace normalised by the fitted peak area
YSpaceDataWorkspace Output MatrixWorkspace Mandatory Input workspace converted to units of Y-space
FittedWorkspace Output MatrixWorkspace Mandatory Output from fit of the single mass peakin y-space. The output units are in momentum (A^-1)
SymmetrisedWorkspace Output MatrixWorkspace Mandatory The input data symmetrised about Y=0. The output units are in momentum (A^-1)

Description

Takes an input TOF spectrum from LoadVesuvio and converts it to Y-space using the ConvertToYSpace algorithm. The result is then fitted using the ComptonPeakProfile function and the given mass to produce an estimate of the peak area. The input data is normalised by this value.

The input workspace is required to be a point data workspace, see ConvertToPointData, and each detector is required to have an instrument parameter named t0 that specifies the detector delay time in \(\mu s\), see SetInstrumentParameter.

The algorithm has 4 outputs:

  • the input data normalised by the fitted peak area;
  • the input data (without normalisation) converted Y-space;
  • the fitted peak in Y-space;
  • the input data converted to Y and then symmetrised about Y=0.

If the sum option is requested then all input spectra are rebinned, in steps of 0.5 \(\AA^{-1}\), to a common Y grid and then summed to give a single spectrum.

Usage

Example - Normalise without summation:

###### Simulates LoadVesuvio #################
tof_ws = CreateSimulationWorkspace(Instrument='Vesuvio',BinParams=[50,0.5,562],UnitX='TOF')
tof_ws = CropWorkspace(tof_ws,StartWorkspaceIndex=0,EndWorkspaceIndex=4) # index one less than spectrum number
tof_ws = ConvertToPointData(tof_ws)
SetInstrumentParameter(tof_ws, ParameterName='t0',ParameterType='Number',Value='0.5')
SetInstrumentParameter(tof_ws, ParameterName='sigma_l1', ParameterType='Number', Value='0.021')
SetInstrumentParameter(tof_ws, ParameterName='sigma_l2', ParameterType='Number', Value='0.023')
SetInstrumentParameter(tof_ws, ParameterName='sigma_tof', ParameterType='Number', Value='0.3')
SetInstrumentParameter(tof_ws, ParameterName='sigma_theta', ParameterType='Number', Value='0.028')
SetInstrumentParameter(tof_ws, ParameterName='hwhm_lorentz', ParameterType='Number', Value='24.0')
SetInstrumentParameter(tof_ws, ParameterName='sigma_gauss', ParameterType='Number', Value='73.0')
##############################################

normalised, yspace, fitted, symmetrised = \
  NormaliseByPeakArea(InputWorkspace=tof_ws, Mass=1.0079,Sum=False)

print("Number of normalised spectra is: {}".format(normalised.getNumberHistograms()))
print("Number of Y-space spectra is: {}".format(yspace.getNumberHistograms()))
print("Number of fitted spectra is: {}".format(fitted.getNumberHistograms()))
print("Number of symmetrised spectra is: {}".format(symmetrised.getNumberHistograms()))

Number of normalised spectra is: 5
Number of Y-space spectra is: 5
Number of fitted spectra is: 5
Number of symmetrised spectra is: 5

Example - Normalise with summation:

###### Simulates LoadVesuvio ################
tof_ws = CreateSimulationWorkspace(Instrument='Vesuvio',BinParams=[50,0.5,562],UnitX='TOF')
tof_ws = CropWorkspace(tof_ws,StartWorkspaceIndex=0,EndWorkspaceIndex=4) # index one less than spectrum number
tof_ws = ConvertToPointData(tof_ws)
SetInstrumentParameter(tof_ws, ParameterName='t0',ParameterType='Number',Value='0.5')
SetInstrumentParameter(tof_ws, ParameterName='sigma_l1', ParameterType='Number', Value='0.021')
SetInstrumentParameter(tof_ws, ParameterName='sigma_l2', ParameterType='Number', Value='0.023')
SetInstrumentParameter(tof_ws, ParameterName='sigma_tof', ParameterType='Number', Value='0.3')
SetInstrumentParameter(tof_ws, ParameterName='sigma_theta', ParameterType='Number', Value='0.028')
SetInstrumentParameter(tof_ws, ParameterName='hwhm_lorentz', ParameterType='Number', Value='24.0')
SetInstrumentParameter(tof_ws, ParameterName='sigma_gauss', ParameterType='Number', Value='73.0')
##############################################

normalised, yspace, fitted, symmetrised = \
  NormaliseByPeakArea(InputWorkspace=tof_ws, Mass=1.0079,Sum=True)

print("Number of normalised spectra is: {}".format(normalised.getNumberHistograms()))
print("Number of Y-space spectra is: {}".format(yspace.getNumberHistograms()))
print("Number of fitted spectra is: {}".format(fitted.getNumberHistograms()))
print("Number of symmetrised spectra is: {}".format(symmetrised.getNumberHistograms()))

Number of normalised spectra is: 5
Number of Y-space spectra is: 1
Number of fitted spectra is: 1
Number of symmetrised spectra is: 1

Categories: AlgorithmIndex | CorrectionFunctions\NormalisationCorrections