NormaliseToUnity v1

../_images/NormaliseToUnity-v1_dlg.png

NormaliseToUnity dialog.

Summary

NormaliseToUnity takes a 2D workspace or an EventWorkspace as input and normalises it to 1. Optionally, the range summed can be restricted in either dimension.

Properties

Name Direction Type Default Description
InputWorkspace Input MatrixWorkspace Mandatory The name of the input workspace.
OutputWorkspace Output MatrixWorkspace Mandatory The name with which to store the output workspace in the [[Analysis Data Service]]
RangeLower Input number Optional The X (frame) value to integrate from
RangeUpper Input number Optional The X (frame) value to integrate to
StartWorkspaceIndex Input number 0 The lowest workspace index of the specta that will be integrated
EndWorkspaceIndex Input number Optional The highest workspace index of the spectra that will be integrated
IncludePartialBins Input boolean False If true then partial bins from the beginning and end of the input range are also included in the integration.
IncludeMonitors Input boolean True Whether to include monitor spectra in the sum (default: yes)

Description

NormaliseToUnity uses Integration v1 to sum up all the X bins, then sums up the resulting spectra using SumSpectra v1. Each bin of the input workspace is then divided by the total sum, regardless of whether a bin was included in the sum or not. It is thus possible to normalize a workspace so that a range of X bins and spectra sums to 1. In that case the sum of the whole workspace will likely not be equal to 1.

Usage

Example - Normalise for 4 spectra each with 5 values

# Create a workspace with 4 spectra each with 5 values
ws = CreateSampleWorkspace("Histogram",  NumBanks=1, BankPixelWidth=2, BinWidth=10, Xmax=50)

# Run algorithm
wsNorm = NormaliseToUnity (ws)

print "Normalised Workspace"
for i in range(4):
   print "[ %.4f,%.4f,%.4f, %.4f, %.4f ]" % (wsNorm.readY(i)[0], wsNorm.readY(i)[1],
   wsNorm.readY(i)[2], wsNorm.readY(i)[3], wsNorm.readY(i)[4],)

Output:

Normalised Workspace
[ 0.2239,0.0065,0.0065, 0.0065, 0.0065 ]
[ 0.2239,0.0065,0.0065, 0.0065, 0.0065 ]
[ 0.2239,0.0065,0.0065, 0.0065, 0.0065 ]
[ 0.2239,0.0065,0.0065, 0.0065, 0.0065 ]

Categories: Algorithms | CorrectionFunctions\NormalisationCorrections