.. algorithm:: .. summary:: .. relatedalgorithms:: .. properties:: Description ----------- Extracts the fit members from a QENS fit and stores each of them in a group workspace. Usage ----- **Example** .. testcode:: ExExtractQENSMembers # 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, OutputCompositeMembers=convolve, 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()) .. testcleanup:: ExExtractQENSMembers DeleteWorkspace(output_ws_name + "_Workspaces") DeleteWorkspace(output_ws_name + "_Parameters") DeleteWorkspace(output_ws_name + "_Members") DeleteWorkspace(output_ws_name) DeleteWorkspace(sample) DeleteWorkspace(resolution) Output: .. testoutput:: ExExtractQENSMembers 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 .. testcode:: ExExtractQENSMembersProperty # 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, OutputCompositeMembers=convolve, 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()) .. testcleanup:: ExExtractQENSMembersProperty DeleteWorkspace(output_ws_name + "_Workspaces") DeleteWorkspace(output_ws_name + "_Parameters") DeleteWorkspace(output_ws_name + "_Members") DeleteWorkspace(output_ws_name) DeleteWorkspace(sample) DeleteWorkspace(resolution) .. testoutput:: ExExtractQENSMembersProperty 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:: .. sourcelink::