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MuonMaxent v1

MuonMaxent dialog.

Summary

See Also

PhaseQuad, FFT

Properties

Name	Direction	Type	Default	Description
InputWorkspace	Input	Workspace	Mandatory	Raw muon workspace to process
InputPhaseTable	Input	TableWorkspace		Phase table (initial guess)
InputDeadTimeTable	Input	TableWorkspace		Dead time table (initial)
GroupTable	Input	TableWorkspace		Group Table
GroupWorkspace	Input	Workspace		Group Workspace
FirstGoodTime	Input	number	0.1	First good data time
LastGoodTime	Input	number	33	Last good data time
Npts	Input	number	Mandatory	Number of frequency points to fit (should be power of 2). Allowed values: [‘256’, ‘512’, ‘1024’, ‘2048’, ‘4096’, ‘8192’, ‘16384’, ‘32768’, ‘65536’, ‘131072’, ‘262144’, ‘524288’, ‘1048576’]
MaxField	Input	number	1000	Maximum field for spectrum
FixPhases	Input	boolean	False	Fix phases to initial values
FitDeadTime	Input	boolean	True	Fit deadtimes
DoublePulse	Input	boolean	False	Double pulse data
OuterIterations	Input	number	10	Number of loops to optimise phase, amplitudes, backgrounds and dead times
InnerIterations	Input	number	10	Number of loops to optimise the spectrum
DefaultLevel	Input	number	0.1	Default Level
Factor	InOut	number	1.04	Used to control the value chi-squared converge to
OutputWorkspace	Output	Workspace	Mandatory	Output Spectrum (combined) versus field
OutputPhaseTable	Output	TableWorkspace		Output phase table (optional)
OutputDeadTimeTable	Output	TableWorkspace		Output dead time table (optional)
ReconstructedSpectra	Output	Workspace		Reconstructed time spectra (optional)
PhaseConvergenceTable	Output	Workspace		Convergence of phases (optional)

Description

This algorithm calculates a single frequency spectrum from the time domain spectra recorded by multiple groups/detectors.

If a group contains zero counts (i.e. the detectors are dead) then they are excluded from the frequency calculation. In the outputs these groups record the phase and asymmetry as zero and \(999\) respectively.

The time domain data \(D_k(t)\), where \(t\) is time and \(k\) is the spectrum number, has associated errors \(E_k(t)\). If the number of points chosen is greater than the number of time domain data points then extra points are added with infinite errors. The time domain data prior to FirstGoodTime also have their errors set to infinity. The algorithm will produce the frequency spectra \(f(\omega)\) and this is assumed to be real and positive. The upper limit of the frequency spectra is determined by MaxField. The maximum frequency, \(\omega_\mathrm{max}\) can be less than the Nyquist limit \(\frac{\pi}{\delta T}\) if the instrumental frequency response function for \(\omega>\omega_\mathrm{max}\) is approximatley zero. The initial estimate of the frequency spectrum is flat.

The algorithm calculates an estimate of each time domain spectra, \(g_k(t)\) by the equation

\[g_k(t)=(1+A_k \Re(\mathrm{IFFT}(f(\omega) R(\omega))\exp(-j\phi_k) ) ),\]

where \(\Re(z)\) is the real part of \(z\), \(\mathrm{IFFT}\) is the inverse fast Fourier transform (as defined by numpy), \(\phi_k\) is the phase and \(A_k\) is the asymmetry of the of the \(k^\mathrm{th}\) spectrum. The asymmetry is normalised such that \(\sum_k A_k = 1\). The instrumental frequency response function, \(R(\omega)\), is is in general complex (due to a non-symmetric pulse shape) and is the same for all spectra. The values of the phases and asymmetries are fitted in the outer loop of the algorithm.

The \(\chi^2\) value is calculated via the equation

\[\chi^2 = F\frac{\sum_{k,t} (D_k(t)-g_k(t))^2 }{E_k(t)^2},\]

where \(F\) is the Factor and is of order 1.0 (but can be adjusted by the user at the start of the algorithm for a better fit). The entropy is given by

\[S = - \sum_\omega f(\omega) \log\left(\frac{f(\omega)}{A}\right),\]

where \(A\) is the DefaultLevel; it is a parameter of the entropy function. It has a number of names in the literature, one of which is default-value since the maximum entropy solution with no data is \(f(\omega)=A\) for all \(\omega\). The algorithm maximises \(S-\chi^2\) and it is seen from the definition of Factor above that this algorithm property acts a Lagrange multiplier, i.e. controlling the value \(\chi^2\) converges to.

Usage

# load data
Load(Filename='MUSR00022725.nxs', OutputWorkspace='MUSR00022725')
# estimate phases
CalMuonDetectorPhases(InputWorkspace='MUSR00022725', FirstGoodData=0.10000000000000001, LastGoodData=16, DetectorTable='phases', DataFitted='fitted', ForwardSpectra='9-16,57-64', BackwardSpectra='25-32,41-48')
MuonMaxent(InputWorkspace='MUSR00022725', InputPhaseTable='phases', Npts='16384', OuterIterations='9', InnerIterations='12', DefaultLevel=0.11, Factor=1.03, OutputWorkspace='freq', OutputPhaseTable='phasesOut', ReconstructedSpectra='time')
# get data
freq = AnalysisDataService.retrieve("freq")
print('frequency values {:.3f} {:.3f} {:.3f} {:.3f} {:.3f}'.format(freq.readY(0)[5], freq.readY(0)[690],freq.readY(0)[700], freq.readY(0)[710],freq.readY(0)[900]))

Output

frequency values 0.110 0.789 0.871 0.821 0.105

Categories: AlgorithmIndex | Muon | Arithmetic\FFT

Source

Python: MuonMaxent.py