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EstimateMuonAsymmetryFromCounts v1

Summary

This algorithm gives an estimate for the asymmetry.

See Also

ConvertFitFunctionForMuonTFAsymmetry, CalculateMuonAsymmetry

Properties

Name	Direction	Type	Default	Description
InputWorkspace	Input	MatrixWorkspace	Mandatory	The name of the input 2D workspace.
WorkspaceName	Input	string		The name used in the normalization table. If this is blank the InputWorkspace’s name will be used.
OutputWorkspace	Output	MatrixWorkspace	Mandatory	The name of the output 2D workspace.
OutputUnNormData	Input	boolean	False	If to output the data with just the exponential decay removed.
OutputUnNormWorkspace	Output	MatrixWorkspace	unNormalisedData	The name of the output unnormalized workspace.
Spectra	Input	int list		The workspace indices to remove the exponential decay from.
StartX	Input	number	0.1	The lower limit for calculating the asymmetry (an X value).
EndX	Input	number	15	The upper limit for calculating the asymmetry (an X value).
NormalizationIn	Input	number	0	If this value is non-zero then this is used for the normalization, instead of being estimated.
NormalizationTable	InOut	TableWorkspace		Name of the table containing the normalizations for the asymmetries.

If the NormalizationIn property is greater than zero, the algorithm will apply the user defined normalization to the data instead of calculating an estimate.

Description

This algorithm estimates the asymmetry from the specified muon spectra. By default, all of the spectra in a workspace will be corrected.

The formula for estimating the asymmetry is given by:

\[\textrm{NewData} = (\textrm{OldData}\times e^\frac{t}{\tau})/(F N_0) - 1.0,\]

where \(\tau\) is the muon lifetime (2.1969811e-6 seconds), \(F\) is the number of good frames and \(N_0\) is a fitted normalization constant. The normalization is given by

\[N_0= \frac{\Delta t\sum_j(\textrm{OldData}_j)}{\tau F \left( \exp(-\frac{t_0}{\tau})-\exp(-\frac{t_N}{\tau})\right) },\]

where the summation only includes the data with times bewtween \(t_0\) and \(t_N\) and \(\Delta t\) is the time step.

Usage

Example - Removing exponential decay:

import math
import numpy as np

tab = CreateEmptyTableWorkspace()
tab.addColumn('double', 'norm')
tab.addColumn('str', 'name')
tab.addColumn('str', 'method')

y = [100, 150, 50, 10, 5]
x = [1,2,3,4,5,6]
input = CreateWorkspace(x,y)
run = input.getRun()
run.addProperty("goodfrm","10","None",True)
output,unnorm=EstimateMuonAsymmetryFromCounts(InputWorkspace=input,spectra=0,NormalizationTable=tab,StartX=1,EndX=5,OutputUnNormData=True)
print("Asymmetry   :  {}".format(['{0:.2f}'.format(value) for value in output.readY(0)]))
print("Unnormalized:  {}".format(['{0:.2f}'.format(value) for value in unnorm.readY(0)]))
print("Normalization constant: {0:.2f}".format(tab.column(0)[0]))

Output:

Asymmetry   :  ['-0.31', '0.64', '-0.14', '-0.73', '-0.79']
Unnormalized:  ['19.79', '46.80', '24.59', '7.75', '6.11']
Normalization constant: 28.56

Example - Setting the normalization:

import math
import numpy as np

tab = CreateEmptyTableWorkspace()
tab.addColumn('double', 'norm')
tab.addColumn('str', 'name')
tab.addColumn('str', 'method')

y = [100, 150, 50, 10, 5]
x = [1,2,3,4,5,6]
input = CreateWorkspace(x,y)
run = input.getRun()
run.addProperty("goodfrm","10","None",True)

output=EstimateMuonAsymmetryFromCounts(InputWorkspace=input,spectra=0,NormalizationTable=tab,StartX=1,EndX=5,NormalizationIn=20.0)

print("Asymmetry:  {}".format(['{0:.2f}'.format(value) for value in output.readY(0)]))
print("Normalization constant: {0:.2f}".format(tab.column(0)[0]))

Output:

Asymmetry:  ['-0.01', '1.34', '0.23', '-0.61', '-0.69']
Normalization constant: 20.00

Categories: AlgorithmIndex | Muon

Source

C++ header: EstimateMuonAsymmetryFromCounts.h

C++ source: EstimateMuonAsymmetryFromCounts.cpp