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Re #4303 starting to clean up
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@mdoucet
mdoucet committed Jan 18, 2012
1 parent a700252 commit da7df62
Showing 1 changed file with 47 additions and 42 deletions.
89 changes: 47 additions & 42 deletions Code/Mantid/Framework/PythonAPI/PythonAlgorithms/RefLReduction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,6 @@
import numpy
from reduction.instruments.reflectometer import wks_utility

COMPRESS_TOL_TOF = .01

class RefLReduction(PythonAlgorithm):

def category(self):
Expand Down Expand Up @@ -91,7 +89,8 @@ def PyExec(self):
raise RuntimeError("Failed to specify the binning")

run_numbers = self.getProperty("RunNumbers")
if len(run_numbers)>1:
allow_multiple = False
if len(run_numbers)>1 and not allow_multiple:
raise RuntimeError("Not ready for multiple runs yet, please specify only one run number")

normalization_run = self.getProperty("NormalizationRunNumber")
Expand All @@ -113,7 +112,6 @@ def PyExec(self):
maxX = 304
maxY = 256


#nexus_path = '/mnt/hgfs/j35/results/'
nexus_path = '/home/m2d/data/ref_l/'
pre = 'REF_L_'
Expand All @@ -122,66 +120,74 @@ def PyExec(self):
BackfromYpixel = norm_back[0]
BacktoYpixel = norm_back[1]

norm_peak = self.getProperty("NormPeakPixelRange")
from_peak = norm_peak[0]
to_peak = norm_peak[1]

########################################################################
########################################################################



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

##load the data into its workspace
LoadEventNexus(Filename=data_file, OutputWorkspace='EventData')
mt = mtd['EventData']
# Pick a good workspace name
ws_name = "refl%d" % run_numbers[0]
ws_event_data = ws_name+"_evt"
# Load the data into its workspace
LoadEventNexus(Filename=data_file, OutputWorkspace=ws_event_data)

#Get metadata
mt_run = mt.getRun()
# Get metadata
mt_run = mtd[ws_event_data].getRun()
##get angles value
ths_value = mt_run.getProperty('ths').value[0]
ths_units = mt_run.getProperty('ths').units
tthd_value = mt_run.getProperty('tthd').value[0]
tthd_units = mt_run.getProperty('tthd').units
ths_rad = wks_utility.angleUnitConversion(value=ths_value,
from_units=ths_units,
to_units='rad')
from_units=ths_units,
to_units='rad')
tthd_rad = wks_utility.angleUnitConversion(value=tthd_value,
from_units=tthd_units,
to_units='rad')
##retrieve geometry of instrument
# distance Sample detector
sample = mt.getInstrument().getSample()
source = mt.getInstrument().getSource()
from_units=tthd_units,
to_units='rad')
# Retrieve geometry of instrument
# Sample-to-detector distance
sample = mtd[ws_event_data].getInstrument().getSample()
source = mtd[ws_event_data].getInstrument().getSource()
dSM = sample.getDistance(source)
#create array of distances pixel->sample
# Create array of distances pixel->sample
dPS_array = numpy.zeros((maxY, maxX))
for x in range(maxX):
for y in range(maxY):
_index = maxY * x + y
detector = mt.getDetector(_index)
detector = mtd[ws_event_data].getDetector(_index)
dPS_array[y, x] = sample.getDistance(detector)
#array of distances pixel->source
# Array of distances pixel->source
dMP_array = dPS_array + dSM
#distance sample->center of detector
# Distance sample->center of detector
dSD = dPS_array[maxY / 2, maxX / 2]
#distance source->center of detector
# Distance source->center of detector
dMD = dSD + dSM


##rebin data (x-axis is in TOF)
rebin(InputWorkspace='EventData', OutputWorkspace='HistoData', Params=self._binning)
# Rebin data (x-axis is in TOF)
ws_histo_data = ws_name+"_histo"
Rebin(InputWorkspace=ws_event_data, OutputWorkspace=ws_histo_data, Params=self._binning)

##keep only range of TOF of interest
CropWorkspace('HistoData','CropHistoData',XMin=TOFrange[0], XMax=TOFrange[1])
# Keep only range of TOF of interest
CropWorkspace(ws_histo_data,ws_histo_data,XMin=TOFrange[0], XMax=TOFrange[1])

##Normalized by Current (proton charge)
NormaliseByCurrent(InputWorkspace='CropHistoData', OutputWorkspace='HistoData')
# Normalized by Current (proton charge)
NormaliseByCurrent(InputWorkspace=ws_histo_data, OutputWorkspace=ws_histo_data)
mt = mtd['HistoData']

##Calculation of the central pixel (using weighted average)
pixelXtof_data = wks_utility.getPixelXTOF(mt, maxX=maxX, maxY=maxY)
# Calculation of the central pixel (using weighted average)
pixelXtof_data = wks_utility.getPixelXTOF(mtd[ws_histo_data], maxX=maxX, maxY=maxY)
pixelXtof_1d = pixelXtof_data.sum(axis=1)
#keep only range of pixels
# Keep only range of pixels
pixelXtof_roi = pixelXtof_1d[data_peak[0]:data_peak[1]]
sz = pixelXtof_roi.size
_num = 0
Expand All @@ -193,14 +199,15 @@ def PyExec(self):
_den += pixelXtof_roi[i]
data_cpix = _num / _den

##Background subtraction
# Background subtraction
BackfromYpixel = data_back[0]
BacktoYpixel = data_back[1]

#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)
mt2 = wks_utility.createIntegratedWorkspace(mt, "IntegratedDataWks",
mt2 = wks_utility.createIntegratedWorkspace(mtd[ws_histo_data],
"IntegratedDataWks",
fromXpixel=Xrange[0],
toXpixel=Xrange[1],
fromYpixel=BackfromYpixel,
Expand Down Expand Up @@ -279,7 +286,7 @@ def PyExec(self):
OutputWorkspace='TransposedFlatID',
StartX=BackfromYpixel,
Mode='Mean',
EndX=list_norm_peak[0][0])
EndX=norm_peak[0])

Transpose(InputWorkspace='TransposedFlatID',
OutputWorkspace='NormWks')
Expand All @@ -289,8 +296,6 @@ def PyExec(self):
#perform the integration myself
mt_temp = mtd['NormWks']
x_axis = mt_temp.readX(0)[:] #[9100,9300,.... 23500] (73,1)
from_peak = list_norm_peak[0][0]
to_peak = list_norm_peak[0][1]
NormPeakRange = numpy.arange(to_peak-from_peak+1) + from_peak
counts_vs_tof = numpy.zeros(len(x_axis))

Expand Down

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