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Move normalization code inside its own if statement to prepare option…
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…al normalization flag. This refs #4303
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@JeanBilheux
JeanBilheux committed Feb 8, 2012
1 parent 820f53a commit b829a59
Showing 1 changed file with 109 additions and 105 deletions.
214 changes: 109 additions & 105 deletions Code/Mantid/Framework/PythonAPI/PythonAlgorithms/RefLReduction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -268,131 +268,135 @@ def PyExec(self):
ConvertToHistogram(InputWorkspace=ws_integrated_data,
OutputWorkspace=ws_data)

# Work on Normalization file #########################################

s_normalization_run = str(normalization_run).strip()
s_normalization_run = '' #REMOVE_ME
if (s_normalization_run != ''):

# Work on Normalization file #########################################
# Find full path to event NeXus data file
f = FileFinder.findRuns("REF_L%d" %normalization_run)
if len(f)>0 and os.path.isfile(f[0]):
norm_file = f[0]
else:
msg = "RefLReduction: could not find run %d\n" % run_numbers[0]
msg += "Add your data folder to your User Data Directories in the File menu"
raise RuntimeError(msg)
# Find full path to event NeXus data file
f = FileFinder.findRuns("REF_L%d" %normalization_run)
if len(f)>0 and os.path.isfile(f[0]):
norm_file = f[0]
else:
msg = "RefLReduction: could not find run %d\n" % run_numbers[0]
msg += "Add your data folder to your User Data Directories in the File menu"
raise RuntimeError(msg)

#load normalization file
ws_name = "__normalization_refl%d" % run_numbers[0]
ws_norm_event_data = ws_name+"_evt"
ws_norm_histo_data = ws_name+"_histo"
#load normalization file
ws_name = "__normalization_refl%d" % run_numbers[0]
ws_norm_event_data = ws_name+"_evt"
ws_norm_histo_data = ws_name+"_histo"

if not mtd.workspaceExists(ws_norm_event_data):
LoadEventNexus(Filename=norm_file, OutputWorkspace=ws_norm_event_data)
if not mtd.workspaceExists(ws_norm_event_data):
LoadEventNexus(Filename=norm_file, OutputWorkspace=ws_norm_event_data)

# Rebin data
Rebin(InputWorkspace=ws_norm_event_data, OutputWorkspace=ws_norm_histo_data, Params=[TOFrange[0], TOFsteps, TOFrange[1]])
# Rebin data
Rebin(InputWorkspace=ws_norm_event_data, OutputWorkspace=ws_norm_histo_data, Params=[TOFrange[0], TOFsteps, TOFrange[1]])

# Keep only range of TOF of interest
CropWorkspace(ws_norm_histo_data, ws_norm_histo_data, XMin=TOFrange[0], XMax=TOFrange[1])
# Keep only range of TOF of interest
CropWorkspace(ws_norm_histo_data, ws_norm_histo_data, XMin=TOFrange[0], XMax=TOFrange[1])

# Normalized by Current (proton charge)
NormaliseByCurrent(InputWorkspace=ws_norm_histo_data, OutputWorkspace=ws_norm_histo_data)
# Normalized by Current (proton charge)
NormaliseByCurrent(InputWorkspace=ws_norm_histo_data, OutputWorkspace=ws_norm_histo_data)

##Background subtraction

#Create a new event workspace of only the range of pixel of interest
#background range (along the y-axis) and of only the pixel
#of interest along the x-axis (to avoid the frame effect)
ws_integrated_data = "__IntegratedNormWks"
wks_utility.createIntegratedWorkspace(mtd[ws_norm_histo_data],
ws_integrated_data,
fromXpixel=Xrange[0],
toXpixel=Xrange[1],
fromYpixel=BackfromYpixel,
toYpixel=BacktoYpixel,
maxX=maxX,
maxY=maxY,
cpix=data_cpix,
source_to_detector=dMD,
sample_to_detector=dSD,
theta=theta,
geo_correction=False)

ws_data_bck = "__NormBckWks"
if subtract_norm_bck:
Transpose(InputWorkspace=ws_integrated_data,
OutputWorkspace=ws_transposed)
##Background subtraction

#Create a new event workspace of only the range of pixel of interest
#background range (along the y-axis) and of only the pixel
#of interest along the x-axis (to avoid the frame effect)
ws_integrated_data = "__IntegratedNormWks"
wks_utility.createIntegratedWorkspace(mtd[ws_norm_histo_data],
ws_integrated_data,
fromXpixel=Xrange[0],
toXpixel=Xrange[1],
fromYpixel=BackfromYpixel,
toYpixel=BacktoYpixel,
maxX=maxX,
maxY=maxY,
cpix=data_cpix,
source_to_detector=dMD,
sample_to_detector=dSD,
theta=theta,
geo_correction=False)

ws_data_bck = "__NormBckWks"
if subtract_norm_bck:
Transpose(InputWorkspace=ws_integrated_data,
OutputWorkspace=ws_transposed)

ConvertToHistogram(InputWorkspace=ws_transposed,
OutputWorkspace=ws_transposed)
ConvertToHistogram(InputWorkspace=ws_transposed,
OutputWorkspace=ws_transposed)

BackfromYpixel = norm_back[0]
BacktoYpixel = norm_back[1]

FlatBackground(InputWorkspace=ws_transposed,
OutputWorkspace=ws_transposed_1,
StartX=BackfromYpixel,
Mode='Mean',
EndX=norm_peak[0],
OutputMode="Return Background")
BackfromYpixel = norm_back[0]
BacktoYpixel = norm_back[1]

FlatBackground(InputWorkspace=ws_transposed,
OutputWorkspace=ws_transposed_1,
StartX=BackfromYpixel,
Mode='Mean',
EndX=norm_peak[0],
OutputMode="Return Background")

Transpose(InputWorkspace=ws_transposed,
OutputWorkspace=ws_data_bck_1)

FlatBackground(InputWorkspace=ws_transposed,
OutputWorkspace=ws_transposed_2,
StartX=norm_peak[1],
Mode='Mean',
EndX=BacktoYpixel,
OutputMode="Return Background")

Transpose(InputWorkspace=ws_transposed,
OutputWorkspace=ws_data_bck_2)
Transpose(InputWorkspace=ws_transposed,
OutputWorkspace=ws_data_bck_1)

FlatBackground(InputWorkspace=ws_transposed,
OutputWorkspace=ws_transposed_2,
StartX=norm_peak[1],
Mode='Mean',
EndX=BacktoYpixel,
OutputMode="Return Background")

Transpose(InputWorkspace=ws_transposed,
OutputWorkspace=ws_data_bck_2)

ConvertToHistogram(ws_data_bck_1, OutputWorkspace=ws_data_bck_1)
ConvertToHistogram(ws_data_bck_2, OutputWorkspace=ws_data_bck_2)
ConvertToHistogram(ws_data_bck_1, OutputWorkspace=ws_data_bck_1)
ConvertToHistogram(ws_data_bck_2, OutputWorkspace=ws_data_bck_2)

RebinToWorkspace(WorkspaceToRebin=ws_data_bck_1,
WorkspaceToMatch=ws_integrated_data,
OutputWorkspace=ws_data_bck_1)
RebinToWorkspace(WorkspaceToRebin=ws_data_bck_1,
WorkspaceToMatch=ws_integrated_data,
OutputWorkspace=ws_data_bck_1)

RebinToWorkspace(WorkspaceToRebin=ws_data_bck_2,
WorkspaceToMatch=ws_integrated_data,
OutputWorkspace=ws_data_bck_2)
RebinToWorkspace(WorkspaceToRebin=ws_data_bck_2,
WorkspaceToMatch=ws_integrated_data,
OutputWorkspace=ws_data_bck_2)

WeightedMean(ws_data_bck_1, ws_data_bck_2, ws_data_bck)
WeightedMean(ws_data_bck_1, ws_data_bck_2, ws_data_bck)

ws_norm = "__NormWks"
Minus(ws_integrated_data, ws_data_bck, OutputWorkspace=ws_norm)
ws_norm = "__NormWks"
Minus(ws_integrated_data, ws_data_bck, OutputWorkspace=ws_norm)

# Clean up intermediary workspaces
mtd.deleteWorkspace(ws_data_bck)
mtd.deleteWorkspace(ws_integrated_data)
mtd.deleteWorkspace(ws_transposed)
# Clean up intermediary workspaces
mtd.deleteWorkspace(ws_data_bck)
mtd.deleteWorkspace(ws_integrated_data)
mtd.deleteWorkspace(ws_transposed)

ws_norm_rebinned = "__NormRebinnedWks"
RebinToWorkspace(WorkspaceToRebin=ws_norm,
WorkspaceToMatch=ws_data,
OutputWorkspace=ws_norm_rebinned)

ws_norm_rebinned = "__NormRebinnedWks"
RebinToWorkspace(WorkspaceToRebin=ws_norm,
WorkspaceToMatch=ws_data,
OutputWorkspace=ws_norm_rebinned)

# else:
# else:
#
# ws_norm_rebinned = "__NormRebinnedWks"
# RebinToWorkspace(WorkspaceToRebin=ws_norm,
# WorkspaceToMatch=ws_data,
# OutputWorkspace=ws_norm_rebinned)
# ws_norm_rebinned = "__NormRebinnedWks"
# RebinToWorkspace(WorkspaceToRebin=ws_norm,
# WorkspaceToMatch=ws_data,
# OutputWorkspace=ws_norm_rebinned)

#perform the integration myself
mt_temp = mtd[ws_norm_rebinned]
x_axis = mt_temp.readX(0)[:] #[9100,9300,.... 23500] (73,1)
NormPeakRange = numpy.arange(to_peak-from_peak+1) + from_peak
counts_vs_tof = numpy.zeros(len(x_axis))

# Normalization
SumSpectra(InputWorkspace=ws_norm_rebinned, OutputWorkspace=ws_norm_rebinned)
#perform the integration myself
mt_temp = mtd[ws_norm_rebinned]
x_axis = mt_temp.readX(0)[:] #[9100,9300,.... 23500] (73,1)
NormPeakRange = numpy.arange(to_peak-from_peak+1) + from_peak
counts_vs_tof = numpy.zeros(len(x_axis))

#Normalization
SumSpectra(InputWorkspace=ws_norm_rebinned, OutputWorkspace=ws_norm_rebinned)

#### divide data by normalize histo workspace
Divide(LHSWorkspace=ws_data,
RHSWorkspace=ws_norm_rebinned,
OutputWorkspace=ws_data)
#### divide data by normalize histo workspace
Divide(LHSWorkspace=ws_data,
RHSWorkspace=ws_norm_rebinned,
OutputWorkspace=ws_data)

ReplaceSpecialValues(InputWorkspace=ws_data, NaNValue=0, NaNError=0, InfinityValue=0, InfinityError=0, OutputWorkspace=ws_data)

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