diff --git a/Code/Mantid/Framework/PythonInterface/plugins/algorithms/RefLReduction.py b/Code/Mantid/Framework/PythonInterface/plugins/algorithms/RefLReduction.py
index 3f2140113243ab525b14a21b7698dcd43677e575..8123bdf230eda1c0779724dc313c0910cf6b975f 100644
--- a/Code/Mantid/Framework/PythonInterface/plugins/algorithms/RefLReduction.py
+++ b/Code/Mantid/Framework/PythonInterface/plugins/algorithms/RefLReduction.py
@@ -177,7 +177,10 @@ class RefLReduction(PythonAlgorithm):
[dMD, dSD] = wks_utility.getDistances(ws_event_data)
# get theta
theta = wks_utility.getTheta(ws_event_data, angleOffsetDeg)
-
+ # get proton charge
+ pc = wks_utility.getProtonCharge(ws_event_data)
+ error_0 = 1. / pc
+
# rebin data
ws_histo_data = wks_utility.rebinNeXus(ws_event_data,
[binTOFrange[0], binTOFsteps, binTOFrange[1]],
@@ -194,7 +197,7 @@ class RefLReduction(PythonAlgorithm):
TOFrange[0],
TOFrange[1],
'data')
-
+
# normalize by current proton charge
ws_histo_data = wks_utility.normalizeNeXus(ws_histo_data, 'data')
@@ -203,6 +206,12 @@ class RefLReduction(PythonAlgorithm):
dataLowResRange,
'data')
+# #DEBUG ONLY
+# wks_utility.ouput_big_ascii_file('/mnt/hgfs/j35/Matlab/DebugMantid/Strange0ValuesToData/data_file_after_low_resolution_integration.txt',
+# data_tof_axis,
+# data_y_axis,
+# data_y_error_axis)
+
tof_axis = data_tof_axis[0:-1].copy()
tof_axis_full = data_tof_axis.copy()
@@ -216,13 +225,23 @@ class RefLReduction(PythonAlgorithm):
dataPeakRange,
dataBackFlag,
dataBackRange,
+ error_0,
'data')
-
+# #DEBUG ONLY
+# wks_utility.ouput_big_ascii_file('/mnt/hgfs/j35/Matlab/DebugMantid/Strange0ValuesToData/data_file_back_sub_not_integrated.txt',
+# data_tof_axis,
+# data_y_axis,
+# data_y_error_axis)
+
# work with normalization
# load normalization
ws_event_norm = wks_utility.loadNeXus(int(normalizationRunNumber), 'normalization')
+ # get proton charge
+ pc = wks_utility.getProtonCharge(ws_event_norm)
+ error_0 = 1. / pc
+
# rebin normalization
ws_histo_norm = wks_utility.rebinNeXus(ws_event_norm,
[binTOFrange[0], binTOFsteps, binTOFrange[1]],
@@ -249,16 +268,29 @@ class RefLReduction(PythonAlgorithm):
normPeakRange,
normBackFlag,
normBackRange,
+ error_0,
'normalization')
[av_norm, av_norm_error] = wks_utility.fullSumWithError(norm_y_axis,
norm_y_error_axis)
+# ## DEBUGGING ONLY
+# wks_utility.ouput_ascii_file('/mnt/hgfs/j35/Matlab/DebugMantid/Strange0ValuesToData/norm_file_back_sub_not_integrated.txt',
+# norm_tof_axis,
+# av_norm,
+# av_norm_error)
+
[final_data_y_axis, final_data_y_error_axis] = wks_utility.divideDataByNormalization(data_y_axis,
data_y_error_axis,
av_norm,
av_norm_error)
+# #DEBUG ONLY
+# wks_utility.ouput_big_ascii_file('/mnt/hgfs/j35/Matlab/DebugMantid/Strange0ValuesToData/data_divided_by_norm_not_integrated.txt',
+# data_tof_axis,
+# final_data_y_axis,
+# final_data_y_error_axis)
+
# apply Scaling factor
[tof_axis_full, y_axis, y_error_axis] = wks_utility.applyScalingFactor(tof_axis_full,
final_data_y_axis,
@@ -268,6 +300,11 @@ class RefLReduction(PythonAlgorithm):
slitsValuePrecision,
slitsWidthFlag)
+# #DEBUG ONLY
+# wks_utility.ouput_big_ascii_file('/mnt/hgfs/j35/Matlab/DebugMantid/Strange0ValuesToData/after_applying_scaling_factor.txt',
+# data_tof_axis,
+# y_axis,
+# y_error_axis)
if geometryCorrectionFlag: # convert To Q with correction
[q_axis, y_axis, y_error_axis] = wks_utility.convertToQ(tof_axis_full,
@@ -294,6 +331,11 @@ class RefLReduction(PythonAlgorithm):
first_slit_size = first_slit_size,
last_slit_size = last_slit_size)
+
+# wks_utility.ouput_big_Q_ascii_file('/mnt/hgfs/j35/Matlab/DebugMantid/Strange0ValuesToData/after_conversion_to_q.txt',
+# q_axis,
+# y_axis,
+# y_error_axis)
sz = q_axis.shape
nbr_pixel = sz[0]
diff --git a/Code/Mantid/scripts/reduction/instruments/reflectometer/wks_utility.py b/Code/Mantid/scripts/reduction/instruments/reflectometer/wks_utility.py
index d54d9b0d2e7f79eb0630aa125c418a300171683b..f64e2399942d2d5616fc404b1790cc4746ce12f6 100644
--- a/Code/Mantid/scripts/reduction/instruments/reflectometer/wks_utility.py
+++ b/Code/Mantid/scripts/reduction/instruments/reflectometer/wks_utility.py
@@ -1,4 +1,4 @@
-from numpy import zeros, arctan2, arange, shape, sqrt, fliplr, asfarray, where, mean, sum
+from numpy import zeros, ones, arctan2, arange, shape, sqrt, fliplr, asfarray, where, mean, sum
from mantid.simpleapi import *
# from MantidFramework import *
import math
@@ -40,8 +40,8 @@ def getProtonCharge(st=None):
if st is not None:
mt_run = st.getRun()
proton_charge_mtd_unit = mt_run.getProperty('gd_prtn_chrg').value
- proton_charge = proton_charge_mtd_unit / 2.77777778e-10
- return proton_charge
+# proton_charge = proton_charge_mtd_unit / 2.77777778e-10
+ return proton_charge_mtd_unit
return None
def getIndex(value, array):
@@ -1048,7 +1048,7 @@ def integrateOverLowResRange(mt1,
def substractBackground(tof_axis, y_axis, y_error_axis,
peakRange, backFlag, backRange,
- type):
+ error_0, type):
"""
shape of y_axis : [256, nbr_tof]
This routine will calculate the background, remove it from the peak
@@ -1072,11 +1072,7 @@ def substractBackground(tof_axis, y_axis, y_error_axis,
# retrieve data
_tofAxis = tof_axis
nbrTof = len(_tofAxis)
-
- # by default, no space for background subtraction below and above peak
- bMinBack = False
- bMaxBack = False
-
+
# size peak
szPeak = peakMax - peakMin + 1
@@ -1094,19 +1090,22 @@ def substractBackground(tof_axis, y_axis, y_error_axis,
for t in range(nbrTof):
+ # by default, no space for background subtraction below and above peak
+ bMinBack = False
+ bMaxBack = False
+
if backMin < (peakMin):
bMinBack = True
_backMinArray = y_axis[backMin:peakMin, t]
_backMinErrorArray = y_error_axis[backMin:peakMin, t]
[_backMin, _backMinError] = weightedMean(_backMinArray,
- _backMinErrorArray)
+ _backMinErrorArray, error_0)
if (peakMax) < backMax:
bMaxBack = True
_backMaxArray = y_axis[peakMax+1:backMax+1, t]
-
_backMaxErrorArray = y_error_axis[peakMax+1:backMax+1, t]
- [_backMax, _backMaxError] = weightedMean(_backMaxArray, _backMaxErrorArray)
+ [_backMax, _backMaxError] = weightedMean(_backMaxArray, _backMaxErrorArray, error_0)
# if no max background use min background
if not bMaxBack:
@@ -1119,7 +1118,7 @@ def substractBackground(tof_axis, y_axis, y_error_axis,
background_error = _backMaxError
if bMinBack and bMaxBack:
- [background, background_error] = weightedMean([_backMin, _backMax], [_backMinError, _backMaxError])
+ [background, background_error] = weightedMean([_backMin, _backMax], [_backMinError, _backMaxError], error_0)
# remove background for each pixel of the peak
for x in range(szPeak):
@@ -1131,32 +1130,35 @@ def substractBackground(tof_axis, y_axis, y_error_axis,
return [final_y_axis, final_y_error_axis]
-def weightedMean(data_array, error_array):
+def weightedMean(data_array, error_array, error_0):
sz = len(data_array)
# calculate the numerator of mean
dataNum = 0;
for i in range(sz):
- if not (data_array[i] == 0):
- tmpFactor = float(data_array[i]) / float((pow(error_array[i],2)))
- dataNum += tmpFactor
+ if (error_array[i] == 0):
+ error_array[i] = error_0
+
+ tmpFactor = float(data_array[i]) / float((pow(error_array[i],2)))
+ dataNum += tmpFactor
# calculate denominator
dataDen = 0;
for i in range(sz):
- if not (error_array[i] == 0):
- tmpFactor = 1./float((pow(error_array[i],2)))
- dataDen += tmpFactor
+ if (error_array[i] == 0):
+ error_array[i] = error_0
+ tmpFactor = 1./float((pow(error_array[i],2)))
+ dataDen += tmpFactor
if dataDen == 0:
- mean = 0
+ data_mean = 0
mean_error = 0
else:
- mean = float(dataNum) / float(dataDen)
+ data_mean = float(dataNum) / float(dataDen)
mean_error = math.sqrt(1/dataDen)
- return [mean, mean_error]
+ return [data_mean, mean_error]
def weightedMeanOfRange(norm_y_axis, norm_y_error_axis):
"""
@@ -1325,11 +1327,6 @@ def ouput_big_Q_ascii_file(file_name,
y_axis,
y_error_axis):
- print x_axis.shape
- print y_axis.shape
- print y_error_axis.shape
-
-
f=open(file_name,'w')
sz = y_axis.shape # (nbr_pixel, nbr_tof)