ConvolutionFitSequential v1

../_images/ConvolutionFitSequential-v1_dlg.png

ConvolutionFitSequential dialog.

Summary

Performs a sequential fit for a convolution workspace

Properties

Name Direction Type Default Description
InputWorkspace Input MatrixWorkspace Mandatory The input workspace for the fit.
Function Input string Mandatory The function that describes the parameters of the fit.
BackgroundType Input string Fixed Flat The Type of background used in the fitting. Allowed values: [‘Fixed Flat’, ‘Fit Flat’, ‘Fit Linear’]
StartX Input number Mandatory The start of the range for the fit function.
EndX Input number Mandatory The end of the range for the fit function.
SpecMin Input number 0 The first spectrum to be used in the fit. Spectra values can not be negative
SpecMax Input number 0 The final spectrum to be used in the fit. Spectra values can not be negative
Convolve Input boolean True If true, the fit is treated as a convolution workspace.
Minimizer Input string Levenberg-Marquardt Minimizer to use for fitting. Minimizers available are ‘Levenberg-Marquardt’, ‘Simplex’, ‘FABADA’, ‘Conjugate gradient (Fletcher-Reeves imp.)’, ‘Conjugate gradient (Polak-Ribiere imp.)’ and ‘BFGS’
MaxIterations Input number 500 The maximum number of iterations permitted
OutputWorkspace Output MatrixWorkspace Mandatory The OutputWorkspace containing the results of the fit.

Description

An algorithm designed mainly as a sequential call to PlotPeakByLogValue but used within the ConvFit tab within the Indirect Analysis interface to fit Convolution Functions.

Usage

Example - ConvolutionFitSequential

# Create a host workspace
sample = Load('irs26176_graphite002_red.nxs')
resolution = Load('irs26173_graphite002_red.nxs')

# Set up algorithm parameters
function = "name=LinearBackground,A0=0,A1=0,ties=(A0=0.000000,A1=0.0);(composite=Convolution,FixResolution=true,NumDeriv=true;name=Resolution,Workspace=__ConvFit_Resolution,WorkspaceIndex=0;((composite=ProductFunction,NumDeriv=false;name=Lorentzian,Amplitude=1,PeakCentre=0,FWHM=0.0175)))"
bgType = "Fixed Flat"
startX = -0.547608
endX = 0.543217
specMin = 0
specMax = sample.getNumberHistograms() - 1
convolve = True
minimizer = "Levenberg-Marquardt"
maxIt = 500

# Build resolution workspace (normally done by the Convfit tab when files load)
AppendSpectra(InputWorkspace1=resolution.getName(), InputWorkspace2=resolution.getName(), OutputWorkspace="__ConvFit_Resolution")
for i in range(1, sample.getNumberHistograms()):
  AppendSpectra(InputWorkspace1="__ConvFit_Resolution", InputWorkspace2=resolution.getName(), OutputWorkspace="__ConvFit_Resolution")

# Run algorithm
result_ws = ConvolutionFitSequential(InputWorkspace=sample, Function=function ,BackgroundType=bgType, StartX=startX, EndX=endX, SpecMin=specMin, SpecMax=specMax, Convolve=convolve, Minimizer=minimizer, MaxIterations=maxIt)

print "Result has %i Spectra" %result_ws.getNumberHistograms()

print "Amplitude 0: %.3f" %(result_ws.readY(0)[0])
print "Amplitude 1: %.3f" %(result_ws.readY(0)[1])
print "Amplitude 2: %.3f" %(result_ws.readY(0)[2])

print "X axis at 0: %.5f" %(result_ws.readX(0)[0])
print "X axis at 1: %.5f" %(result_ws.readX(0)[1])
print "X axis at 2: %.5f" %(result_ws.readX(0)[2])

print "Amplitude Err 0: %.5f" %(result_ws.readE(0)[0])
print "Amplitude Err 1: %.5f" %(result_ws.readE(0)[1])
print "Amplitude Err 2: %.5f" %(result_ws.readE(0)[2])

Output:

Result has 2 Spectra

Amplitude 0: 4.293
Amplitude 1: 4.179
Amplitude 2: 3.979

X axis at 0: 0.52531
X axis at 1: 0.72917
X axis at 2: 0.92340

Amplitude Err 0: 0.00465
Amplitude Err 1: 0.00464
Amplitude Err 2: 0.00504

Categories: Algorithms | Workflow\MIDAS