def PyExec(self):
raw_ws = self.getProperty('InputWorkspace').value
sample_geometry = self.getPropertyValue('SampleGeometry')
sample_material = self.getPropertyValue('SampleMaterial')
cal_file_name = self.getPropertyValue('CalFileName')
SetSample(InputWorkspace=raw_ws,
Geometry=sample_geometry,
Material=sample_material)
# find the closest monitor to the sample for incident spectrum
raw_spec_info = raw_ws.spectrumInfo()
incident_index = None
for i in range(raw_spec_info.size()):
if raw_spec_info.isMonitor(i):
l2 = raw_spec_info.position(i)[2]
if not incident_index:
incident_index = i
else:
if raw_spec_info.position(incident_index)[2] < l2 < 0:
incident_index = i
monitor = ExtractSpectra(InputWorkspace=raw_ws,
WorkspaceIndexList=[incident_index])
monitor = ConvertUnits(InputWorkspace=monitor, Target="Wavelength")
x_data = monitor.dataX(0)
min_x = np.min(x_data)
max_x = np.max(x_data)
width_x = (max_x - min_x) / x_data.size
fit_spectra = FitIncidentSpectrum(
InputWorkspace=monitor,
BinningForCalc=[min_x, 1 * width_x, max_x],
BinningForFit=[min_x, 10 * width_x, max_x],
FitSpectrumWith="CubicSpline")
self_scattering_correction = CalculatePlaczekSelfScattering(
InputWorkspace=raw_ws, IncidentSpecta=fit_spectra)
cal_workspace = LoadCalFile(InputWorkspace=self_scattering_correction,
CalFileName=cal_file_name,
Workspacename='cal_workspace',
MakeOffsetsWorkspace=False,
MakeMaskWorkspace=False)
self_scattering_correction = DiffractionFocussing(
InputWorkspace=self_scattering_correction,
GroupingFilename=cal_file_name)
n_pixel = np.zeros(self_scattering_correction.getNumberHistograms())
for i in range(cal_workspace.getNumberHistograms()):
grouping = cal_workspace.dataY(i)
if grouping[0] > 0:
n_pixel[int(grouping[0] - 1)] += 1
correction_ws = CreateWorkspace(
DataY=n_pixel,
DataX=[0, 1],
NSpec=self_scattering_correction.getNumberHistograms())
self_scattering_correction = Divide(
LHSWorkspace=self_scattering_correction,
RHSWorkspace=correction_ws)
ConvertToDistribution(Workspace=self_scattering_correction)
self_scattering_correction = ConvertUnits(
InputWorkspace=self_scattering_correction,
Target="MomentumTransfer",
EMode='Elastic')
DeleteWorkspace('cal_workspace_group')
DeleteWorkspace(correction_ws)
DeleteWorkspace(fit_spectra)
DeleteWorkspace(monitor)
DeleteWorkspace(raw_ws)
self.setProperty('OutputWorkspace', self_scattering_correction)
def PyExec(self):
raw_ws = self.getProperty('InputWorkspace').value
sample_geometry = self.getPropertyValue('SampleGeometry')
sample_material = self.getPropertyValue('SampleMaterial')
cal_file_name = self.getPropertyValue('CalFileName')
SetSample(InputWorkspace=raw_ws,
Geometry=sample_geometry,
Material=sample_material)
# find the closest monitor to the sample for incident spectrum
raw_spec_info = raw_ws.spectrumInfo()
incident_index = None
for i in range(raw_spec_info.size()):
if raw_spec_info.isMonitor(i):
l2 = raw_spec_info.position(i)[2]
if not incident_index:
incident_index = i
else:
if raw_spec_info.position(incident_index)[2] < l2 < 0:
incident_index = i
monitor = ExtractSpectra(InputWorkspace=raw_ws, WorkspaceIndexList=[incident_index])
monitor = ConvertUnits(InputWorkspace=monitor, Target="Wavelength")
x_data = monitor.dataX(0)
min_x = np.min(x_data)
max_x = np.max(x_data)
width_x = (max_x - min_x) / x_data.size
fit_spectra = FitIncidentSpectrum(InputWorkspace=monitor,
BinningForCalc=[min_x, 1 * width_x, max_x],
BinningForFit=[min_x, 10 * width_x, max_x],
FitSpectrumWith="CubicSpline")
self_scattering_correction = CalculatePlaczekSelfScattering(InputWorkspace=raw_ws,
IncidentSpectra=fit_spectra,
ScaleByPackingFraction=False,
Version=1)
# Convert to Q
self_scattering_correction = ConvertUnits(InputWorkspace=self_scattering_correction,
Target="MomentumTransfer", EMode='Elastic')
cal_workspace = LoadCalFile(InputWorkspace=self_scattering_correction,
CalFileName=cal_file_name,
Workspacename='cal_workspace',
MakeOffsetsWorkspace=False,
MakeMaskWorkspace=False,
MakeGroupingWorkspace=True)
ssc_min_x, ssc_max_x = float('inf'), float('-inf')
for index in range(self_scattering_correction.getNumberHistograms()):
spec_info = self_scattering_correction.spectrumInfo()
if not spec_info.isMasked(index) and not spec_info.isMonitor(index):
ssc_x_data = np.ma.masked_invalid(self_scattering_correction.dataX(index))
if np.min(ssc_x_data) < ssc_min_x:
ssc_min_x = np.min(ssc_x_data)
if np.max(ssc_x_data) > ssc_max_x:
ssc_max_x = np.max(ssc_x_data)
ssc_width_x = (ssc_max_x - ssc_min_x) / ssc_x_data.size
# TO DO: calculate rebin parameters per group
# and run GroupDetectors on each separately
self_scattering_correction = Rebin(InputWorkspace=self_scattering_correction,
Params=[ssc_min_x, ssc_width_x, ssc_max_x],
IgnoreBinErrors=True)
self_scattering_correction = GroupDetectors(InputWorkspace=self_scattering_correction,
CopyGroupingFromWorkspace='cal_workspace_group')
n_pixel = np.zeros(self_scattering_correction.getNumberHistograms())
for i in range(cal_workspace.getNumberHistograms()):
grouping = cal_workspace.dataY(i)
if grouping[0] > 0:
n_pixel[int(grouping[0] - 1)] += 1
correction_ws = CreateWorkspace(DataY=n_pixel, DataX=[0, 1],
NSpec=self_scattering_correction.getNumberHistograms())
self_scattering_correction = Divide(LHSWorkspace=self_scattering_correction, RHSWorkspace=correction_ws)
DeleteWorkspace('cal_workspace_group')
DeleteWorkspace(correction_ws)
DeleteWorkspace(fit_spectra)
DeleteWorkspace(monitor)
DeleteWorkspace(raw_ws)
self.setProperty('OutputWorkspace', self_scattering_correction)