Update filtering/.#FilterTime.py filtering/FilterTime.py...

Update filtering/.#FilterTime.py filtering/FilterTime.py filtering/PowerReduction_temp_filter.py filtering/PowerReduction_ttime_filter.py filtering/Temp_ROI.py

filtering/.#FilterTime.py

+rwp@diffuse.12279:1532959676
\ No newline at end of file

filtering/FilterTime.py

+from mantid.simpleapi import *
+
+name = 'output'
+IPTS = 19834
+run = 122687
+vanadium = 101567 # Run number or `None`
+vanadium_IPTS = 7776
+normaliseBy='Monitor' # One on (None, Monitor, Time)
+units = 'Theta' # One of (Theta, ElasticQ, ElasticDSpacing)
+Binning = '3,123,1200' # Min,Max,Number_of_bins
+
+# Time filter options
+TimeInterval=3600
+StartTime=None
+StopTime=None
+
+###############################################################################
+
+# Load vandium if not already loaded.
+vanadium_ws = 'HB2C_{}'.format(vanadium)
+
+if vanadium_ws not in mtd:
+     cal = LoadWAND(Filename=van_iptsdir+'nexus/HB2C_{}.nxs.h5'.format(vanadium))
+
+# Load data if not already loaded
+ws = 'HB2C_{}'.format(vanadium)
+
+if ws not in mtd:
+    LoadEventNexus(Filename='/HFIR/HB2C/IPTS-{}/nexus/HB2C_{}.nxs.h5'.format(IPTS, run), OutputWorkspace=ws,LoadMonitors=True)
+
+# Filter events
+
+GenerateEventsFilter(InputWorkspace=ws, OutputWorkspace='filter', InformationWorkspace='info', TimeInterval='3600',StartTime='0',StopTime='84000')
+FilterEvents(InputWorkspace=ws, SplitterWorkspace='filter', OutputWorkspaceBaseName=ws+'_filtered', InformationWorkspace='info',
+             GroupWorkspaces=True,FilterByPulseTime=True,OutputWorkspaceIndexedFrom1=True)
+FilterEvents(InputWorkspace=ws+'_monitors', SplitterWorkspace='filter', OutputWorkspaceBaseName=ws+'_filtered_mon', InformationWorkspace='info',
+             GroupWorkspaces=True,FilterByPulseTime=True,SpectrumWithoutDetector='Skip only if TOF correction',OutputWorkspaceIndexedFrom1=True)
+
+for n in range(mtd['out'].getNumberOfEntries()):
+    AddSampleLog(mtd['out'].getItem(n), LogName="gd_prtn_chrg", LogType='Number', NumberType='Double',LogText=str(mtd['mon'].getItem(n).getNumberEvents()))
+
+MaskBTP('out', Bank='8', Tube='475-480')
+MaskBTP('out', Pixel='1,2,511,512')
+
+xmin, xmax, bins = Binning.split(',')
+group_list=[]
+for n in range(mtd['out'].getNumberOfEntries()):
+    nameT = name+'_'+mtd['out'][n].name()
+    group_list.append(nameT)
+    WANDPowderReduction(mtd['out'][n], OutputWorkspace=nameT, cal=cal, target=units, XMin=xmin, XMax=xmax, NumberBins=bins, normaliseBy=normaliseBy)
+
+GroupWorkspaces(group_list,OutputWorkspace=name)

filtering/PowerReduction_temp_filter.py

+from mantid.simpleapi import *
+
+name = 'La1p3Sr2p3FeO3_1'
+IPTS = 20325
+run = 26563
+vanadium = 7553 # Run number of `None`
+vanadium_IPTS = 20325
+normaliseBy='Monitor' # One on (None, Monitor, Time)
+units = 'Theta' # One of (Theta, ElasticQ, ElasticDSpacing)
+Binning = '2.5,122,2500' # Min,Max,Number_of_bins
+use_autoreduced = False
+use_autoreduced_van = True
+
+###############################################################################
+
+def reduceToPowder(ws, OutputWorkspace, cal=None, target='Theta', XMin=10, XMax=135, NumberBins=2500, normaliseBy='Monitor'):
+    ConvertSpectrumAxis(InputWorkspace=ws, Target=target, OutputWorkspace=OutputWorkspace)
+    Transpose(InputWorkspace=OutputWorkspace, OutputWorkspace=OutputWorkspace)
+    ResampleX(InputWorkspace=OutputWorkspace, OutputWorkspace=OutputWorkspace, XMin=XMin, XMax=XMax, NumberBins=NumberBins)
+    if cal is not None:
+        CopyInstrumentParameters(ws, cal)
+        ConvertSpectrumAxis(InputWorkspace=cal, Target=target, OutputWorkspace='__cal')
+        Transpose(InputWorkspace='__cal', OutputWorkspace='__cal')
+        ResampleX(InputWorkspace='__cal', OutputWorkspace='__cal', XMin=XMin, XMax=XMax, NumberBins=NumberBins)
+        Divide(LHSWorkspace=OutputWorkspace, RHSWorkspace='__cal', OutputWorkspace=OutputWorkspace)
+        DeleteWorkspace('__cal')
+    if normaliseBy == "Monitor":
+        ws_monitor = mtd[str(ws)].run().getProtonCharge()
+        cal_monitor = mtd[str(cal)].run().getProtonCharge()        
+        Scale(InputWorkspace=OutputWorkspace, OutputWorkspace=OutputWorkspace, Factor=cal_monitor/ws_monitor)
+    elif normaliseBy == "Time":
+        ws_duration = mtd[str(ws)].run().getLogData('duration').value
+        cal_duration = mtd[str(cal)].run().getLogData('duration').value
+        Scale(InputWorkspace=OutputWorkspace, OutputWorkspace=OutputWorkspace, Factor=cal_duration/ws_duration)
+    return OutputWorkspace
+
+###############################################################################
+
+iptsdir = '/HFIR/HB2C/IPTS-{}/'.format(IPTS)
+van_iptsdir = '/HFIR/HB2C/IPTS-{}/'.format(vanadium_IPTS)
+
+LoadEventNexus(Filename=iptsdir+'nexus/HB2C_{}.nxs.h5'.format(run), OutputWorkspace='ws',LoadMonitors=True)
+
+GenerateEventsFilter(InputWorkspace='ws', OutputWorkspace='filter', InformationWorkspace='info', LogName='HB2C:SE:SampleTemp', LogValueInterval=1,MinimumLogValue=190,MaximumLogValue=210)
+FilterEvents(InputWorkspace='ws', SplitterWorkspace='filter', OutputWorkspaceBaseName='out', InformationWorkspace='info',GroupWorkspaces=True,FilterByPulseTime=True,OutputWorkspaceIndexedFrom1=True)
+FilterEvents(InputWorkspace='ws_monitors', SplitterWorkspace='filter', OutputWorkspaceBaseName='mon', InformationWorkspace='info',GroupWorkspaces=True,FilterByPulseTime=True,SpectrumWithoutDetector='Skip only if TOF correction',OutputWorkspaceIndexedFrom1=True)
+
+for n in range(mtd['out'].getNumberOfEntries()):
+    AddSampleLog(mtd['out'].getItem(n), LogName="gd_prtn_chrg", LogType='Number', NumberType='Double',LogText=str(mtd['mon'].getItem(n).getNumberEvents()))
+
+
+MaskBTP('out', Bank='8', Tube='475-480')
+MaskBTP('out', Pixel='1,2,511,512')
+
+
+if vanadium is not None:
+    if 'cal' in mtd:
+        cal = mtd['cal']
+    else:
+        if use_autoreduced_van:
+            cal = LoadNexus(Filename=van_iptsdir+'shared/autoreduce/HB2C_{}.nxs'.format(vanadium))
+        else:
+            cal = LoadWAND(Filename=van_iptsdir+'shared/autoreduce/HB2C_{}.nxs.h5'.format(vanadium))
+else:
+    cal = None
+
+xmin, xmax, bins = Binning.split(',')
+group_list=[]
+for n in range(mtd['out'].getNumberOfEntries()):
+    nameT = name+'_{:05.1f}K'.format(mtd['out'][n].run().getLogData("HB2C:SE:SampleTemp").timeAverageValue())
+    group_list.append(nameT)
+    reduceToPowder(mtd['out'][n], OutputWorkspace=nameT, cal=cal, target=units, XMin=xmin, XMax=xmax, NumberBins=bins, normaliseBy=normaliseBy)
+
+GroupWorkspaces(group_list,OutputWorkspace=name)
+labels=','.join(['{:.1f}'.format(w.run().getLogData("HB2C:SE:SampleTemp").timeAverageValue()) for w in mtd['out']])
+print(labels)

filtering/PowerReduction_ttime_filter.py

+from mantid.simpleapi import *
+
+name = 'AmV6a_time'
+IPTS = 19834
+run = 122687
+vanadium = 101567 # Run number of `None`
+vanadium_IPTS = 7776
+normaliseBy='Monitor' # One on (None, Monitor, Time)
+units = 'Theta' # One of (Theta, ElasticQ, ElasticDSpacing)
+Binning = '3,123,1200' # Min,Max,Number_of_bins
+use_autoreduced = False
+use_autoreduced_van = False
+
+###############################################################################
+
+def reduceToPowder(ws, OutputWorkspace, cal=None, target='Theta', XMin=10, XMax=135, NumberBins=2500, normaliseBy='Monitor'):
+    ConvertSpectrumAxis(InputWorkspace=ws, Target=target, OutputWorkspace=OutputWorkspace)
+    Transpose(InputWorkspace=OutputWorkspace, OutputWorkspace=OutputWorkspace)
+    ResampleX(InputWorkspace=OutputWorkspace, OutputWorkspace=OutputWorkspace, XMin=XMin, XMax=XMax, NumberBins=NumberBins)
+    if cal is not None:
+        CopyInstrumentParameters(ws, cal)
+        ConvertSpectrumAxis(InputWorkspace=cal, Target=target, OutputWorkspace='__cal')
+        Transpose(InputWorkspace='__cal', OutputWorkspace='__cal')
+        ResampleX(InputWorkspace='__cal', OutputWorkspace='__cal', XMin=XMin, XMax=XMax, NumberBins=NumberBins)
+        Divide(LHSWorkspace=OutputWorkspace, RHSWorkspace='__cal', OutputWorkspace=OutputWorkspace)
+        DeleteWorkspace('__cal')
+    if normaliseBy == "Monitor":
+        ws_monitor = mtd[str(ws)].run().getProtonCharge()
+        cal_monitor = mtd[str(cal)].run().getProtonCharge()        
+        Scale(InputWorkspace=OutputWorkspace, OutputWorkspace=OutputWorkspace, Factor=cal_monitor/ws_monitor)
+    elif normaliseBy == "Time":
+        ws_duration = mtd[str(ws)].run().getLogData('duration').value
+        cal_duration = mtd[str(cal)].run().getLogData('duration').value
+        Scale(InputWorkspace=OutputWorkspace, OutputWorkspace=OutputWorkspace, Factor=cal_duration/ws_duration)
+    return OutputWorkspace
+
+###############################################################################
+
+iptsdir = '/HFIR/HB2C/IPTS-{}/'.format(IPTS)
+van_iptsdir = '/HFIR/HB2C/IPTS-{}/'.format(vanadium_IPTS)
+
+LoadEventNexus(Filename=iptsdir+'nexus/HB2C_{}.nxs.h5'.format(run), OutputWorkspace='ws',LoadMonitors=True)
+
+GenerateEventsFilter(InputWorkspace='ws', OutputWorkspace='filter', InformationWorkspace='info', TimeInterval='3600',StartTime='0',StopTime='84000')
+FilterEvents(InputWorkspace='ws', SplitterWorkspace='filter', OutputWorkspaceBaseName='out', InformationWorkspace='info',GroupWorkspaces=True,FilterByPulseTime=True,OutputWorkspaceIndexedFrom1=True)
+FilterEvents(InputWorkspace='ws_monitors', SplitterWorkspace='filter', OutputWorkspaceBaseName='mon', InformationWorkspace='info',GroupWorkspaces=True,FilterByPulseTime=True,SpectrumWithoutDetector='Skip only if TOF correction',OutputWorkspaceIndexedFrom1=True)
+
+for n in range(mtd['out'].getNumberOfEntries()):
+    AddSampleLog(mtd['out'].getItem(n), LogName="gd_prtn_chrg", LogType='Number', NumberType='Double',LogText=str(mtd['mon'].getItem(n).getNumberEvents()))
+
+MaskBTP('out', Bank='8', Tube='475-480')
+MaskBTP('out', Pixel='1,2,511,512')
+
+
+if vanadium is not None:
+    if 'cal' in mtd:
+        cal = mtd['cal']
+    else:
+        if use_autoreduced_van:
+            cal = LoadNexus(Filename=van_iptsdir+'shared/autoreduce/HB2C_{}.nxs'.format(vanadium))
+        else:
+            cal = LoadWAND(Filename=van_iptsdir+'nexus/HB2C_{}.nxs.h5'.format(vanadium))
+else:
+    cal = None
+
+xmin, xmax, bins = Binning.split(',')
+group_list=[]
+for n in range(mtd['out'].getNumberOfEntries()):
+    nameT = name+'_'+mtd['out'][n].name()
+    group_list.append(nameT)
+    reduceToPowder(mtd['out'][n], OutputWorkspace=nameT, cal=cal, target=units, XMin=xmin, XMax=xmax, NumberBins=bins, normaliseBy=normaliseBy)
+
+GroupWorkspaces(group_list,OutputWorkspace=name)

filtering/Temp_ROI.py

+ws=LoadEventNexus('/HFIR/HB2C/IPTS-20148/nexus/HB2C_55562.nxs.h5',LoadMonitors=True)
+
+GenerateEventsFilter(InputWorkspace='ws', OutputWorkspace='filter', InformationWorkspace='info', LogName='HB2C:SE:SampleTemp', LogValueInterval=2,MinimumLogValue=150,MaximumLogValue=300)
+FilterEvents(InputWorkspace='ws', SplitterWorkspace='filter', OutputWorkspaceBaseName='out', InformationWorkspace='info',GroupWorkspaces=True,FilterByPulseTime=True,OutputWorkspaceIndexedFrom1=True)
+FilterEvents(InputWorkspace='ws_monitors', SplitterWorkspace='filter', OutputWorkspaceBaseName='mon', InformationWorkspace='info',GroupWorkspaces=True,FilterByPulseTime=True,SpectrumWithoutDetector='Skip only if TOF correction',OutputWorkspaceIndexedFrom1=True)
+
+Ei=UnitConversion.run('Wavelength', 'Energy', 1.488, 0, 0, 0, Elastic, 0)
+
+for n in range(mtd['out'].getNumberOfEntries()):
+    AddSampleLog(mtd['out'].getItem(n), LogName="gd_prtn_chrg", LogType='Number', NumberType='Double',LogText=str(mtd['mon'].getItem(n).getNumberEvents()))
+    AddSampleLog(mtd['out'], LogName="Ei", LogType='Number', NumberType='Double',LogText=str(Ei))
+
+MaskBTP('out', Bank='8', Tube='475-480')
+MaskBTP('out', Pixel='1,2,511,512')
+
+name = 'LSCO_150to300'
+IPTS = 20148
+first_run = 55562
+vanadium = 26613
+vanadium_IPTS = 20148
+normaliseBy='Monitor' # One on (None, Monitor, Time)
+units = 'ElasticDSpacing' # One of (Theta, ElasticQ, ElasticDSpacing)
+Binning = '1.5,2,201' # Min,Max,Number_of_bins
+use_autoreduced = False
+use_autoreduced_van = False
+
+###############################################################################
+
+def reduceToPowder(ws, OutputWorkspace, cal=None, target='Theta', XMin=10, XMax=135, NumberBins=2500, normaliseBy='Monitor'):
+    ConvertSpectrumAxis(InputWorkspace=ws, Target=target, OutputWorkspace=OutputWorkspace)
+    Transpose(InputWorkspace=OutputWorkspace, OutputWorkspace=OutputWorkspace)
+    ResampleX(InputWorkspace=OutputWorkspace, OutputWorkspace=OutputWorkspace, XMin=XMin, XMax=XMax, NumberBins=NumberBins)
+    if cal is not None:
+        CopyInstrumentParameters(ws, cal)
+        ConvertSpectrumAxis(InputWorkspace=cal, Target=target, OutputWorkspace='__cal')
+        Transpose(InputWorkspace='__cal', OutputWorkspace='__cal')
+        ResampleX(InputWorkspace='__cal', OutputWorkspace='__cal', XMin=XMin, XMax=XMax, NumberBins=NumberBins)
+        Divide(LHSWorkspace=OutputWorkspace, RHSWorkspace='__cal', OutputWorkspace=OutputWorkspace)
+        DeleteWorkspace('__cal')
+    if normaliseBy == "Monitor":
+        ws_monitor = mtd[str(ws)].run().getProtonCharge()
+        cal_monitor = mtd[str(cal)].run().getProtonCharge()        
+        Scale(InputWorkspace=OutputWorkspace, OutputWorkspace=OutputWorkspace, Factor=cal_monitor/ws_monitor)
+    elif normaliseBy == "Time":
+        ws_duration = mtd[str(ws)].run().getLogData('duration').value
+        cal_duration = mtd[str(cal)].run().getLogData('duration').value
+        Scale(InputWorkspace=OutputWorkspace, OutputWorkspace=OutputWorkspace, Factor=cal_duration/ws_duration)
+    return OutputWorkspace
+
+###############################################################################
+
+van_iptsdir = '/HFIR/HB2C/IPTS-{}/'.format(vanadium_IPTS)
+
+if vanadium is not None:
+    if 'cal' in mtd:
+        cal = mtd['cal']
+    else:
+        if use_autoreduced_van:
+            cal = LoadNexus(Filename=van_iptsdir+'shared/autoreduce/HB2C_{}.nxs'.format(vanadium))
+        else:
+            cal = LoadWAND(Filename=van_iptsdir+'shared/HB2C_{}.nxs.h5'.format(vanadium))
+else:
+    cal = None
+
+
+xmin, xmax, bins = Binning.split(',')
+group_list=[]
+for n in range(mtd['out'].getNumberOfEntries()):
+    nameT = name+'_{:05.1f}K'.format(mtd['out'][n].run().getLogData("HB2C:SE:SampleTemp").timeAverageValue())
+    group_list.append(nameT)
+    reduceToPowder(mtd['out'][n], OutputWorkspace=nameT, cal=cal, target=units, XMin=xmin, XMax=xmax, NumberBins=bins, normaliseBy=normaliseBy)
+
+GroupWorkspaces(group_list,OutputWorkspace=name)
+labels=','.join(['{:.1f}'.format(w.run().getLogData("HB2C:SE:SampleTemp").timeAverageValue()) for w in mtd['out']])
+print(labels)
+
+# ROI  1
+
+ROI_workspace='MaskWorkspace'
+roi_name = '_ROI1'
+CloneWorkspace('out',OutputWorkspace='out'+roi_name)
+CloneWorkspace('cal',OutputWorkspace='cal'+roi_name)
+MaskDetectors('out'+roi_name,MaskedWorkspace=ROI_workspace)
+MaskDetectors('cal'+roi_name,MaskedWorkspace=ROI_workspace)
+
+xmin, xmax, bins = Binning.split(',')
+group_list=[]
+for n in range(mtd['out'+roi_name].getNumberOfEntries()):
+    nameT = name+roi_name+'_{:05.1f}K'.format(mtd['out'+roi_name][n].run().getLogData("HB2C:SE:SampleTemp").timeAverageValue())
+    group_list.append(nameT)
+    reduceToPowder(mtd['out'+roi_name][n], OutputWorkspace=nameT, cal=cal, target=units, XMin=xmin, XMax=xmax, NumberBins=bins, normaliseBy=normaliseBy)
+GroupWorkspaces(group_list,OutputWorkspace=name+roi_name)
+
+# ROI  2
+
+ROI_workspace='MaskWorkspace_2'
+roi_name = '_ROI2'
+CloneWorkspace('out',OutputWorkspace='out'+roi_name)
+CloneWorkspace('cal',OutputWorkspace='cal'+roi_name)
+MaskDetectors('out'+roi_name,MaskedWorkspace=ROI_workspace)
+MaskDetectors('cal'+roi_name,MaskedWorkspace=ROI_workspace)
+
+xmin, xmax, bins = Binning.split(',')
+group_list=[]
+for n in range(mtd['out'+roi_name].getNumberOfEntries()):
+    nameT = name+roi_name+'_{:05.1f}K'.format(mtd['out'+roi_name][n].run().getLogData("HB2C:SE:SampleTemp").timeAverageValue())
+    group_list.append(nameT)
+    reduceToPowder(mtd['out'+roi_name][n], OutputWorkspace=nameT, cal=cal, target=units, XMin=xmin, XMax=xmax, NumberBins=bins, normaliseBy=normaliseBy)
+GroupWorkspaces(group_list,OutputWorkspace=name+roi_name)
+
+DeleteWorkspace('out'+roi_name)
+DeleteWorkspace('cal'+roi_name)
+
+# ROI  3
+
+ROI_workspace='MaskWorkspace_3'
+roi_name = '_ROI3'
+CloneWorkspace('out',OutputWorkspace='out'+roi_name)
+CloneWorkspace('cal',OutputWorkspace='cal'+roi_name)
+MaskDetectors('out'+roi_name,MaskedWorkspace=ROI_workspace)
+MaskDetectors('cal'+roi_name,MaskedWorkspace=ROI_workspace)
+
+xmin, xmax, bins = Binning.split(',')
+group_list=[]
+for n in range(mtd['out'+roi_name].getNumberOfEntries()):
+    nameT = name+roi_name+'_{:05.1f}K'.format(mtd['out'+roi_name][n].run().getLogData("HB2C:SE:SampleTemp").timeAverageValue())
+    group_list.append(nameT)
+    reduceToPowder(mtd['out'+roi_name][n], OutputWorkspace=nameT, cal=cal, target=units, XMin=xmin, XMax=xmax, NumberBins=bins, normaliseBy=normaliseBy)
+GroupWorkspaces(group_list,OutputWorkspace=name+roi_name)
+
+DeleteWorkspace('out'+roi_name)
+DeleteWorkspace('cal'+roi_name)
+
+# ROI  4
+
+ROI_workspace='MaskWorkspace_4'
+roi_name = '_ROI4'
+CloneWorkspace('out',OutputWorkspace='out'+roi_name)
+CloneWorkspace('cal',OutputWorkspace='cal'+roi_name)
+MaskDetectors('out'+roi_name,MaskedWorkspace=ROI_workspace)
+MaskDetectors('cal'+roi_name,MaskedWorkspace=ROI_workspace)
+
+xmin, xmax, bins = Binning.split(',')
+group_list=[]
+for n in range(mtd['out'+roi_name].getNumberOfEntries()):
+    nameT = name+roi_name+'_{:05.1f}K'.format(mtd['out'+roi_name][n].run().getLogData("HB2C:SE:SampleTemp").timeAverageValue())
+    group_list.append(nameT)
+    reduceToPowder(mtd['out'+roi_name][n], OutputWorkspace=nameT, cal=cal, target=units, XMin=xmin, XMax=xmax, NumberBins=bins, normaliseBy=normaliseBy)
+GroupWorkspaces(group_list,OutputWorkspace=name+roi_name)
+
+DeleteWorkspace('out'+roi_name)
+DeleteWorkspace('cal'+roi_name)
+
+# ROI  5
+
+ROI_workspace='MaskWorkspace_5'
+roi_name = '_ROI5'
+CloneWorkspace('out',OutputWorkspace='out'+roi_name)
+CloneWorkspace('cal',OutputWorkspace='cal'+roi_name)
+MaskDetectors('out'+roi_name,MaskedWorkspace=ROI_workspace)
+MaskDetectors('cal'+roi_name,MaskedWorkspace=ROI_workspace)
+
+xmin, xmax, bins = Binning.split(',')
+group_list=[]
+for n in range(mtd['out'+roi_name].getNumberOfEntries()):
+    nameT = name+roi_name+'_{:05.1f}K'.format(mtd['out'+roi_name][n].run().getLogData("HB2C:SE:SampleTemp").timeAverageValue())
+    group_list.append(nameT)
+    reduceToPowder(mtd['out'+roi_name][n], OutputWorkspace=nameT, cal=cal, target=units, XMin=xmin, XMax=xmax, NumberBins=bins, normaliseBy=normaliseBy)
+GroupWorkspaces(group_list,OutputWorkspace=name+roi_name)
+
+DeleteWorkspace('out'+roi_name)
+DeleteWorkspace('cal'+roi_name)
+
+# ROI  6
+
+ROI_workspace='MaskWorkspace_6'
+roi_name = '_ROI6'
+CloneWorkspace('out',OutputWorkspace='out'+roi_name)
+CloneWorkspace('cal',OutputWorkspace='cal'+roi_name)
+MaskDetectors('out'+roi_name,MaskedWorkspace=ROI_workspace)
+MaskDetectors('cal'+roi_name,MaskedWorkspace=ROI_workspace)
+
+xmin, xmax, bins = Binning.split(',')
+group_list=[]
+for n in range(mtd['out'+roi_name].getNumberOfEntries()):
+    nameT = name+roi_name+'_{:05.1f}K'.format(mtd['out'+roi_name][n].run().getLogData("HB2C:SE:SampleTemp").timeAverageValue())
+    group_list.append(nameT)
+    reduceToPowder(mtd['out'+roi_name][n], OutputWorkspace=nameT, cal=cal, target=units, XMin=xmin, XMax=xmax, NumberBins=bins, normaliseBy=normaliseBy)
+GroupWorkspaces(group_list,OutputWorkspace=name+roi_name)
+
+DeleteWorkspace('out'+roi_name)
+DeleteWorkspace('cal'+roi_name)