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ConvolutionFitSequential v1¶
Summary¶
Performs a sequential fit for a convolution workspace
See Also¶
Properties¶
Name 
Direction 
Type 
Default 
Description 

InputWorkspace 
Input 
The input workspace for the fit. This property will be ignored if ‘Input’ is provided. 

SpecMin 
Input 
number 
0 
The first spectrum to be used in the fit. Spectra values can not be negative. This property will be ignored if ‘Input’ is provided. 
SpecMax 
Input 
number 
0 
The final spectrum to be used in the fit. Spectra values can not be negative. This property will be ignored if ‘Input’ is provided. 
Input 
Input 
string 
A list of sources of data to fit. Sources can be either workspace names or file names followed optionally by a list of spectra/workspaceindices or values using the notation described in the description section of the help page. 

ResultXAxisUnit 
Input 
string 
MomentumTransfer 
The unit to assign to the X Axis of the result workspace, defaults to MomentumTransfer. Allowed values: [‘AtomicDistance’, ‘Degrees’, ‘DeltaE’, ‘DeltaE_inFrequency’, ‘DeltaE_inWavenumber’, ‘dSpacing’, ‘dSpacingPerpendicular’, ‘Empty’, ‘Energy’, ‘Energy_inWavenumber’, ‘Label’, ‘Momentum’, ‘MomentumTransfer’, ‘Phi’, ‘QSquared’, ‘SpinEchoLength’, ‘SpinEchoTime’, ‘Temperature’, ‘Time’, ‘TOF’, ‘Wavelength’, ‘’] 
OutputWorkspace 
Output 
WorkspaceGroup 
Mandatory 
The output result workspace(s) 
OutputParameterWorkspace 
Output 
The output parameter workspace 

OutputWorkspaceGroup 
Output 
WorkspaceGroup 
The output group workspace 

Function 
InOut 
Function 
Mandatory 
The fitting function, common for all workspaces in the input. 
LogName 
Input 
string 
axis1 
Name of the log value to plot the parameters against. Default: use spectra numbers. 
StartX 
Input 
dbl list 
A value of x in, or on the low x boundary of, the first bin to include in the fit (default lowest value of x) 

EndX 
Input 
dbl list 
A value in, or on the high x boundary of, the last bin the fitting range (default the highest value of x) 

PassWSIndexToFunction 
Input 
boolean 
False 
For each spectrum in Input pass its workspace index to all functions thathave attribute WorkspaceIndex. 
Minimizer 
Input 
string 
LevenbergMarquardt 
Minimizer to use for fitting. Minimizers available are ‘LevenbergMarquardt’, ‘Simplex’, ‘FABADA’, ‘Conjugate gradient (FletcherReeves imp.)’, ‘Conjugate gradient (PolakRibiere imp.)’ and ‘BFGS’ 
CostFunction 
InOut 
string 
Least squares 
Cost functions to use for fitting. Cost functions available are ‘Least squares’ and ‘Ignore positive peaks’. Allowed values: [‘Least squares’, ‘Poisson’, ‘Rwp’, ‘Unweighted least squares’] 
MaxIterations 
Input 
number 
500 
Stop after this number of iterations if a good fit is not found 
PeakRadius 
Input 
number 
0 
A value of the peak radius the peak functions should use. A peak radius defines an interval on the x axis around the centre of the peak where its values are calculated. Values outside the interval are not calculated and assumed zeros.Numerically the radius is a whole number of peak widths (FWHM) that fit into the interval on each side from the centre. The default value of 0 means the whole x axis. 
ExtractMembers 
Input 
boolean 
False 
If true, then each member of the fit will be extracted, into their own workspace. These workspaces will have a histogram for each spectrum (Qvalue) and will be grouped. 
OutputCompositeMembers 
Input 
boolean 
False 
If true and CreateOutput is true then the value of each member of a Composite Function is also output. 
ConvolveMembers 
Input 
boolean 
False 
If true and OutputCompositeMembers is true members of any Convolution are output convolved with corresponding resolution 
EvaluationType 
Input 
string 
CentrePoint 
The way the function is evaluated: CentrePoint or Histogram. Allowed values: [‘CentrePoint’, ‘Histogram’] 
FitType 
Input 
string 
Sequential 
Defines the way of setting initial values. If set to Sequential every next fit starts with parameters returned by the previous fit. If set to Individual each fit starts with the same initial values defined in the Function property. Allowed values: [Sequential, Individual]. Allowed values: [‘Sequential’, ‘Individual’] 
Exclude 
Input 
dbl list 
A list of pairs of real numbers, defining the regions to exclude from the fit. 

ExcludeMultiple 
Input 
str list 
A list of Exclusion ranges, defining the regions to exclude from the fit for each spectra. Must have the same number of sets as the number of the spectra. 

IgnoreInvalidData 
Input 
boolean 
False 
Flag to ignore infinities, NaNs and data with zero errors. 
OutputFitStatus 
Input 
boolean 
False 
Flag to output fit status information, which consists of the fit OutputStatus and the OutputChiSquared 
Description¶
Performs a sequential fit involving a convolution with a defined resolution. This algorithm is a specialcase of QENSFitSequential, which calculates the elastic incoherent scattering factor when a delta function is provided in the fitting model.
Workflow¶
Usage¶
Example  ConvolutionFitSequential
# Load sample and resolution files
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=resolution,WorkspaceIndex=0;
name=Lorentzian,Amplitude=1,PeakCentre=0,FWHM=0.0175)"""
startX = 0.547608
endX = 0.543217
specMin = 0
specMax = sample.getNumberHistograms()  1
convolve = True # Convolve the fitted model components with the resolution
minimizer = "LevenbergMarquardt"
maxIt = 500
# Run algorithm
result_ws, _, _ = ConvolutionFitSequential(InputWorkspace=sample,
Function=function,
PassWSIndexToFunction=True,
StartX=startX, EndX=endX,
SpecMin=specMin, SpecMax=specMax,
ConvolveMembers=convolve,
Minimizer=minimizer, MaxIterations=maxIt)
result_ws = result_ws[0]
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 3 Spectra
Amplitude 0: 4.314
Amplitude 1: 4.213
Amplitude 2: 4.555
X axis at 0: 0.52531
X axis at 1: 0.72917
X axis at 2: 0.92340
Amplitude Err 0: 0.00460
Amplitude Err 1: 0.00468
Amplitude Err 2: 0.00577
Categories: AlgorithmIndex  Workflow\MIDAS
Source¶
C++ header: ConvolutionFit.h
C++ source: ConvolutionFit.cpp