ConvolutionFitSequential dialog.
Table of Contents
| 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. |
| PassWSIndexToFunction | Input | boolean | False | For each spectrum in Input pass its workspace index to all functions that have attribute WorkspaceIndex. |
| 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, output fitted model components will be convolved with the resolution. |
| ExtractMembers | Input | boolean | False | If true, then each member of the convolution fit will be extracted, into their own workspace. These workspaces will have a histogram for each spectrum (Q-value) and will be grouped. |
| 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 |
| 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. |
| OutputWorkspace | Output | MatrixWorkspace | Mandatory | The OutputWorkspace containing the results of the fit. |
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.
Example - ConvolutionFitSequential
from __future__ import print_function
# 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)"""
bgType = "Fixed Flat"
startX = -0.547608
endX = 0.543217
specMin = 0
specMax = sample.getNumberHistograms() - 1
convolve = True # Convolve the fitted model components with the resolution
minimizer = "Levenberg-Marquardt"
maxIt = 500
# Run algorithm
result_ws = ConvolutionFitSequential(InputWorkspace=sample,
Function=function, PassWSIndexToFunction=True, 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: 4.314
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: Algorithms | Workflow\MIDAS