ExtractQENSMembers dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| InputWorkspace | Input | MatrixWorkspace | The input workspace used in the fit. Ignored if ‘InputWorkspaces’ property is provided. | |
| InputWorkspaces | Input | str list | List of the workspaces used in the fit. | |
| ResultWorkspace | Input | WorkspaceGroup | Mandatory | The result group workspace produced in a QENS fit. |
| RenameConvolvedMembers | Input | boolean | False | If true, renames the n-th ‘Convolution’ member, to the n-th supplied name in the ConvolvedMembers property. |
| ConvolvedMembers | Input | str list | A list of the names of the members which were convolved before being output by the fit routine. These must be provided in the same order as originally provided to the fit. | |
| OutputWorkspace | Output | WorkspaceGroup | Mandatory | The output workspace group, containing the fit members. |
Extracts the fit members from a QENS fit and stores each of them in a group workspace.
Example
from __future__ import print_function
# Load sample and resolution files
sample = Load('irs26176_graphite002_red.nxs', OutputWorkspace='irs26176_graphite002_red')
resolution = Load('irs26173_graphite002_red.nxs')
# Set up fit 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 = "Levenberg-Marquardt"
maxIt = 500
output_ws_name = "irs26176_graphite002_conv_1LFixF_s0_to_9"
# Run ConvolutionFitSequential algorithm
ConvolutionFitSequential(InputWorkspace=sample, Function=function,
PassWSIndexToFunction=True, StartX=startX, EndX=endX,
SpecMin=specMin, SpecMax=specMax, ConvolveMembers=convolve,
Minimizer=minimizer, MaxIterations=maxIt,
OutputWorkspace=output_ws_name)
# Extract members from the output of the ConvolutionFitSequential algorithm
members_ws = ExtractQENSMembers(InputWorkspace=sample, ResultWorkspace=output_ws_name+"_Workspaces",
RenameConvolvedMembers=True, ConvolvedMembers=["Lorentzian"],
OutputWorkspace=output_ws_name+"_Members")
for member_ws in members_ws:
print(member_ws.getName())
Output:
irs26176_graphite002_conv_1LFixF_s0_to_9_Members_Data
irs26176_graphite002_conv_1LFixF_s0_to_9_Members_Calc
irs26176_graphite002_conv_1LFixF_s0_to_9_Members_Diff
irs26176_graphite002_conv_1LFixF_s0_to_9_Members_LinearBackground
irs26176_graphite002_conv_1LFixF_s0_to_9_Members_Lorentzian
from __future__ import print_function
# Load sample and resolution files
sample = Load('irs26176_graphite002_red.nxs', OutputWorkspace='irs26176_graphite002_red')
resolution = Load('irs26173_graphite002_red.nxs')
# Set up fit 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 = "Levenberg-Marquardt"
maxIt = 500
output_ws_name = "irs26176_graphite002_conv_1LFixF_s0_to_9"
# Run ConvolutionFitSequential algorithm with ExtractMembers property
ConvolutionFitSequential(InputWorkspace=sample, Function=function, PassWSIndexToFunction=True,
StartX=startX, EndX=endX, SpecMin=specMin, SpecMax=specMax,
ConvolveMembers=convolve, Minimizer=minimizer, MaxIterations=maxIt,
ExtractMembers=True, OutputWorkspace=output_ws_name)
members_ws = mtd[output_ws_name + "_Members"]
for member_ws in members_ws:
print(member_ws.getName())
irs26176_graphite002_conv_1LFixF_s0_to_9_Members_Data
irs26176_graphite002_conv_1LFixF_s0_to_9_Members_Calc
irs26176_graphite002_conv_1LFixF_s0_to_9_Members_Diff
irs26176_graphite002_conv_1LFixF_s0_to_9_Members_LinearBackground
irs26176_graphite002_conv_1LFixF_s0_to_9_Members_Lorentzian
Categories: AlgorithmIndex | Workflow\MIDAS
C++ source: ExtractQENSMembers.cpp (last modified: 2019-06-17)
C++ header: ExtractQENSMembers.h (last modified: 2018-10-05)