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NormaliseByPeakArea v1

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Summary

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

See Also

MonitorEfficiencyCorUser, Divide

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

Source

C++ header: NormaliseByPeakArea.h

C++ source: NormaliseByPeakArea.cpp