Skip to content

Commit

Permalink
Fixed issue with data tilted when using geometry correction. This refs …
Browse files Browse the repository at this point in the history 
…#4303
  • Loading branch information
@JeanBilheux
JeanBilheux committed Mar 1, 2012
1 parent 713a7b3 commit 7493b58
Show file tree
Hide file tree
Showing 2 changed files with 142 additions and 89 deletions.
12 changes: 2 additions & 10 deletions Code/Mantid/Framework/PythonAPI/PythonAlgorithms/RefLReduction.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -416,7 +416,7 @@ def PyExec(self):
ws_data_scaled = wks_utility.applySF(ws_data,
slitsValuePrecision,
sfCalculatorFile=sfCalculator)

if dMD is not None and theta is not None:

_tof_axis = mtd[ws_histo_data].readX(0)
Expand All @@ -440,7 +440,7 @@ def PyExec(self):
source_to_detector=dMD,
sample_to_detector=dSD,
theta=theta,
geo_correction=False,
geo_correction=True,
q_binning=[q_min,q_step,q_max])

mt = mtd[ws_data_Q]
Expand Down Expand Up @@ -481,14 +481,6 @@ def PyExec(self):
Nspec=1,
UnitX="MomentumTransfer")

# if (list_of_rows_to_remove != []):
# list_of_rows_to_remove = ','.join(list_of_rows_to_remove)
# Transpose(InputWorkspace=output_ws,
# OutputWorkspace=output_ws)
# DeleteTableRows(TableWorkspace=output_ws, Rows=list_of_rows_to_remove)
# Transpose(InputWorkspace=output_ws,
# OutputWorkspace=output_ws)

self.setProperty("OutputWorkspace", mtd[output_ws])

mtd.registerPyAlgorithm(RefLReduction())
219 changes: 140 additions & 79 deletions Code/Mantid/scripts/reduction/instruments/reflectometer/wks_utility.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -217,99 +217,159 @@ def convertWorkspaceToQ(ws_data,
y_size = toYpixel - fromYpixel + 1
y_range = arange(y_size) + fromYpixel

_y_axis = zeros((maxY, len(_tof_axis) - 1))
_y_error_axis = zeros((maxY, len(_tof_axis) - 1))

for y in y_range:
_y_axis[int(y), :] = mt1.readY(int(y))[:]
_y_error_axis[int(y), :] = mt1.readE(int(y))[:]

# #start of old code
# _y_axis = zeros((maxY, len(_tof_axis) - 1))
# _y_error_axis = zeros((maxY, len(_tof_axis) - 1))
#
# for y in y_range:
# _y_axis[int(y), :] = mt1.readY(int(y))[:]
# _y_error_axis[int(y), :] = mt1.readE(int(y))[:]
#
# for _q_index in range(y_size):
#
# _tmp_q_axis = _q_axis[_q_index]
# q_axis = _tmp_q_axis[::-1] #reverse the axis (now increasing order)
#
# _y_axis_tmp = _y_axis[yrange[_q_index],:]
# _y_axis_tmp = _y_axis_tmp.flatten()
#
# _y_error_axis_tmp = _y_error_axis[yrange[_q_index],:]
## _y_error_axis_tmp = numpy.sqrt(_y_error_axis_tmp)
# _y_error_axis_tmp = _y_error_axis_tmp.flatten()
#
# #keep only the overlap region of Qs
# _q_min = _q_axis_min_max_index[_q_index,0]
# if (_q_min != 0):
# _y_axis_tmp[0:_q_min] = 0
# _y_error_axis_tmp[0:_q_min] = 0
#
# _q_max = _q_axis_min_max_index[_q_index,1]
# if (_q_max != shape(_y_axis_tmp)[0]):
# _y_axis_tmp[_q_max:-1] = 0
# _y_error_axis_tmp[_q_max:-1] = 0
#
# _y_axis_tmp = _y_axis_tmp[::-1]
# _y_error_axis_tmp = _y_error_axis_tmp[::-1]
#
# _outputWorkspace = 'tmpOWks_' + str(_q_index)
# CreateWorkspace(OutputWorkspace=_outputWorkspace,
# DataX=q_axis,
# DataY=_y_axis_tmp,
# DataE=_y_error_axis_tmp,
# Nspec=1,
# UnitX="MomentumTransfer")
#
# if _q_index == 0:
# mt_tmp = mtd[_outputWorkspace]
#
# _outputWorkspace_rebin = 'tmpOWks_' + str(_q_index)
#
# Rebin(InputWorkspace=_outputWorkspace,
# OutputWorkspace=_outputWorkspace_rebin,
# Params=str(_q_bin_width))
#
# if _q_index == 0:
# mt_tmp = mtd[_outputWorkspace_rebin]
#
# _mt = mtd['tmpOWks_0']
# _x_array = _mt.readX(0)[:]
#
# #create big y_array of the all the pixel of interest (yrange)
# big_y_array = zeros((maxY, len(_x_array)))
# big_y_error_array = zeros((maxY, len(_x_array)))
## big_y_array = zeros((y_size, len(_x_array)))
## big_y_error_array = zeros((y_size, len(_x_array)))
#
# for _q_index in range(y_size):
#
# _wks = 'tmpOWks_' + str(_q_index)
# _mt = mtd[_wks]
# _tmp_y = _mt.readY(0)[:]
# _tmp_y_error = _mt.readE(0)[:]
#
# _index = y_range[_q_index]
#
# big_y_array[_index,:] = _tmp_y
# big_y_error_array[_index,:] = _tmp_y_error
#
## big_y_array[int(_q_index),:] = _tmp_y
## big_y_error_array[int(_q_index),:] = _tmp_y_error
#
## mtd.deleteWorkspace(_wks)
#
# _x_axis = _x_array.flatten()
# _y_axis = big_y_array.flatten()
# _y_error_axis = big_y_error_array.flatten()
#
# nbr_pixel_in_peak = y_size
#
# print 'in with geometry correction'
# print 'shape(_x_axis):'
# print shape(_x_axis)
# print 'shape(_y_axis):'
# print shape(_y_axis)
# print 'nbr_pixel_in_peak: '
# print nbr_pixel_in_peak
#
# CreateWorkspace(OutputWorkspace=outputWorkspace,
# DataX=_x_axis,
# DataY=_y_axis,
# DataE=_y_error_axis,
## Nspec=nbr_pixel_in_peak,
# Nspec=maxY,
# UnitX="MomentumTransfer",
# ParentWorkspace=mt1)
#
# #end of old code

_y_axis = zeros((y_size, len(_tof_axis) - 1))
_y_error_axis = zeros((y_size, len(_tof_axis) - 1))

#now determine the y_axis
for _q_index in range(y_size):

_tmp_q_axis = _q_axis[_q_index]
q_axis = _tmp_q_axis[::-1]

_outputWorkspace = 'tmpOWks_' + str(_q_index)
q_axis = _tmp_q_axis[::-1] #reverse the axis (now increasing order)

_y_axis_tmp = _y_axis[yrange[_q_index],:]
_y_axis_tmp = _y_axis_tmp.flatten()

_y_error_axis_tmp = _y_error_axis[yrange[_q_index],:]
# _y_error_axis_tmp = numpy.sqrt(_y_error_axis_tmp)
_y_error_axis_tmp = _y_error_axis_tmp.flatten()
_a = yrange[_q_index]
_y_axis_tmp = list(mt1.readY(int(_a))[:])
_y_error_axis_tmp = list(mt1.readE(int(_a))[:])

#keep only the overlap region of Qs
_q_min = _q_axis_min_max_index[_q_index,0]
if (_q_min != 0):
_y_axis_tmp[0:_q_min] = 0
_y_error_axis_tmp[0:_q_min] = 0

_q_max = _q_axis_min_max_index[_q_index,1]
if (_q_max != shape(_y_axis_tmp)[0]):
_y_axis_tmp[_q_max:-1] = 0
_y_error_axis_tmp[_q_max:-1] = 0

_y_axis_tmp = _y_axis_tmp[::-1]
_y_error_axis_tmp = _y_error_axis_tmp[::-1]

CreateWorkspace(OutputWorkspace=_outputWorkspace,
DataX=q_axis,
DataY=_y_axis_tmp,
DataE=_y_error_axis_tmp,
Nspec=1,
UnitX="MomentumTransfer")

if _q_index == 0:
mt_tmp = mtd[_outputWorkspace]

_outputWorkspace_rebin = 'tmpOWks_' + str(_q_index)

Rebin(InputWorkspace=_outputWorkspace,
OutputWorkspace=_outputWorkspace_rebin,
Params=q_binning)

if _q_index == 0:
mt_tmp = mtd[_outputWorkspace_rebin]

_mt = mtd['tmpOWks_0']
_x_array = _mt.readX(0)[:]
_q_max = int(_q_axis_min_max_index[_q_index,1])
sz = shape(_y_axis_tmp)[0]
if (_q_max != sz):
_index_q_max_range = arange(sz-_q_max)+_q_max
for i in _index_q_max_range:
_y_axis_tmp[i] = 0
_y_error_axis_tmp[i] = 0

#create big y_array of the all the pixel of interest (yrange)
#big_y_array = zeros((maxY, len(_x_array)))
#big_y_error_array = zeros((maxY, len(_x_array)))
big_y_array = zeros((y_size, len(_x_array)))
big_y_error_array = zeros((y_size, len(_x_array)))

for _q_index in range(y_size):

_wks = 'tmpOWks_' + str(_q_index)
_mt = mtd[_wks]
_tmp_y = _mt.readY(0)[:]
_tmp_y_error = _mt.readE(0)[:]
_y_axis[_q_index,:] = _y_axis_tmp[::-1]
_y_error_axis[_q_index,:] = _y_error_axis_tmp[::-1]

# big_y_array[int(yrange[int(_q_index)]),:] = _tmp_y
# print '.....'
# big_y_error_array[int(yrange[int(_q_index)]),:] = _tmp_y_error
# print '......'
x_axis = q_axis.flatten()
y_axis = _y_axis.flatten()
y_error_axis = _y_error_axis.flatten()

big_y_array[int(_q_index),:] = _tmp_y
big_y_error_array[int(_q_index),:] = _tmp_y_error

mtd.deleteWorkspace(_wks)

_x_axis = _x_array.flatten()
_y_axis = big_y_array.flatten()
_y_error_axis = big_y_error_array.flatten()

nbr_pixel_in_peak = y_size

CreateWorkspace(OutputWorkspace=outputWorkspace,
DataX=_x_axis,
DataY=_y_axis,
DataE=_y_error_axis,
Nspec=nbr_pixel_in_peak,
DataX=x_axis,
DataY=y_axis,
DataE=y_error_axis,
Nspec=y_size,
UnitX="MomentumTransfer",
ParentWorkspace=mt1)

mtd[outputWorkspace].setDistribution(True)

Rebin(InputWorkspace=outputWorkspace,
OutputWorkspace=outputWorkspace,
Params=q_binning)

else:

Expand Down Expand Up @@ -534,12 +594,13 @@ def convertToRvsQWithCorrection(mt, dMD=-1, theta=-1,tof=None, yrange=None, cpix
_theta = theta + dangle
else:
_theta = theta

for t in range(sz_tof-1):
tof1 = tof[t]
tof2 = tof[t+1]
tofm = (tof1+tof2)/2.
_Q = _const * math.sin(_theta) / (tofm*1e-6)
# tof2 = tof[t+1]
# tofm = (tof1+tof2)/2.
# _Q = _const * math.sin(_theta) / (tofm*1e-6)
_Q = _const * math.sin(_theta) / (tof1*1e-6)
q_array[y,t] = _Q*1e-10

return q_array
Expand Down

0 comments on commit 7493b58

Please sign in to comment.