def correct_for_multiple_scattering(ws_name,first_spectrum,last_spectrum, sample_properties, transmission_guess,
multiple_scattering_order, number_of_events, g_log, masses, mean_intensity_ratios):
g_log.debug( "Evaluating the Multiple Scattering Correction.")
dens, trans = sapi.VesuvioThickness(Masses=masses, Amplitudes=mean_intensity_ratios, TransmissionGuess=transmission_guess,Thickness=0.1)
_TotScattering, _MulScattering = sapi.VesuvioCalculateMS(ws_name, NoOfMasses=len(masses), SampleDensity=dens.cell(9,1),
AtomicProperties=sample_properties, BeamRadius=2.5,
NumScatters=multiple_scattering_order,
NumEventsPerRun=int(number_of_events))
data_normalisation = sapi.Integration(ws_name)
simulation_normalisation = sapi.Integration("_TotScattering")
for workspace in ("_MulScattering","_TotScattering"):
ws = sapi.mtd[workspace]
for j in range(ws.getNumberHistograms()):
for k in range(ws.blocksize()):
ws.dataE(j)[
k] =0. # set the errors from the MonteCarlo simulation to zero - no propagation of such uncertainties
#- Use high number of events for final corrections!!!
sapi.Divide(LHSWorkspace = workspace, RHSWorkspace = simulation_normalisation, OutputWorkspace = workspace)
sapi.Multiply(LHSWorkspace = workspace, RHSWorkspace = data_normalisation, OutputWorkspace = workspace)
sapi.RenameWorkspace(InputWorkspace = workspace, OutputWorkspace = str(ws_name)+workspace)
safe_delete_ws(data_normalisation)
safe_delete_ws(simulation_normalisation)
safe_delete_ws(trans)
safe_delete_ws(dens)
return
def _ms_correction(self):
"""
Calculates the contributions from multiple scattering
on the input data from the set of given options
"""
params_dict = TableWorkspaceDictionaryFacade(
self.getProperty("FitParameters").value)
atom_props = list()
intensities = list()
contains_hydrogen = False
i = 0
for idx, mass in enumerate(self._masses):
if str(idx) in self._index_to_symbol_map:
symbol = self._index_to_symbol_map[str(idx)].value
else:
symbol = None
if symbol == 'H' and self._back_scattering:
contains_hydrogen = True
continue
intensity_prop = 'f%d.Intensity' % i
c0_prop = 'f%d.C_0' % i
if intensity_prop in params_dict:
intensity = params_dict[intensity_prop]
elif c0_prop in params_dict:
intensity = params_dict[c0_prop]
else:
i = i + 1
continue
# The program DINSMS_BATCH uses those sample parameters together with the sigma divided
# by the sum absolute of scattering intensities for each detector (detector bank),
# sigma/int_sum
# Thus:
# intensity = intensity/intensity_sum
# In the thin sample limit, 1-exp(-n*dens*sigma) ~ n*dens*sigma, effectively the same
# scattering power (ratio of double to single scatt.) is obtained either by using
# relative intensities ( sigma/int_sum ) or density divided by the total intensity
# However, in the realistic case of thick sample, the SampleDensity, dens, must be
# obtained by iterative numerical solution of the Eq:
# 1-exp(-n*dens*sigma) = measured scattering power of the sample.
# For this, a program like THICK must be used.
# The program THICK also uses sigma/int_sum to be consistent with the prgram
# DINSMS_BATCH
width_prop = 'f%d.Width' % i
sigma_x_prop = 'f%d.SigmaX' % i
sigma_y_prop = 'f%d.SigmaY' % i
sigma_z_prop = 'f%d.SigmaZ' % i
if width_prop in params_dict:
width = params_dict['f%d.Width' % i]
elif sigma_x_prop in params_dict:
sigma_x = float(params_dict[sigma_x_prop])
sigma_y = float(params_dict[sigma_y_prop])
sigma_z = float(params_dict[sigma_z_prop])
width = math.sqrt((sigma_x**2 + sigma_y**2 + sigma_z**2) / 3.0)
else:
i = i + 1
continue
atom_props.append(mass)
atom_props.append(intensity)
atom_props.append(width)
intensities.append(intensity)
# Check for NoneType is necessary as hydrogen constraints are
# stored in a C++ PropertyManager object, not a dict; call to
# __contains__ must match the C++ signature.
if self._back_scattering and symbol is not None and symbol in self._hydrogen_constraints:
self._hydrogen_constraints[symbol].value[
'intensity'] = intensity
i = i + 1
if self._back_scattering and contains_hydrogen:
material_builder = MaterialBuilder()
hydrogen = material_builder.setFormula('H').build()
hydrogen_intensity = \
self._calculate_hydrogen_intensity(hydrogen, self._hydrogen_constraints)
hydrogen_width = 5
atom_props.append(hydrogen.relativeMolecularMass())
atom_props.append(hydrogen_intensity)
atom_props.append(hydrogen_width)
intensities.append(hydrogen_intensity)
intensity_sum = sum(intensities)
# Create the sample shape
# Input dimensions are expected in CM
ms.CreateSampleShape(InputWorkspace=self._output_ws,
ShapeXML=create_cuboid_xml(
self.getProperty("SampleHeight").value / 100.,
self.getProperty("SampleWidth").value / 100.,
self.getProperty("SampleDepth").value / 100.))
# Massage options into how algorithm expects them
total_scatter_correction = str(
self._correction_wsg) + "_TotalScattering"
multi_scatter_correction = str(
self._correction_wsg) + "_MultipleScattering"
# Calculation
# In the thin sample limit, 1-exp(-n*dens*sigma) ~ n*dens*sigma, effectively the same
# scattering power(ratio of double to single scatt.) is obtained either by using relative
# intensities ( sigma/int_sum )or density divided by the total intensity.
# However, in the realistic case of thick sample, the SampleDensity, dens, must be
# obtained by iterative numerical solution of the Eq:
# 1-exp(-n*dens*sigma) = measured scattering power of the sample.
# For this, a program like THICK must be used.
# The program THICK also uses sigma/int_sum to be consistent with the prgram DINSMS_BATCH
# The algorithm VesuvioCalculateMs called by the algorithm VesuvioCorrections takes the
# parameter AtomicProperties with the absolute intensities, contraty to DINSMS_BATCH which
# takes in relative intensities.
# To compensate for this, the thickness parameter, dens (SampleDensity), is divided in by
# the sum of absolute intensities in VesuvioCorrections before being passed to
# VesuvioCalculateMs.
# Then, for the modified VesuvioCorrection algorithm one can use the thickenss parameter is
# as is from the THICK command, i.e. 43.20552
# This works, however, only in the thin sample limit, contrary to the THICK program. Thus,
# for some detectors (detector banks) the SampleDensiy parameter may be over(under)
# estimated.
ms.VesuvioCalculateMS(
InputWorkspace=self._output_ws,
NoOfMasses=int(len(atom_props) / 3),
SampleDensity=self.getProperty("SampleDensity").value /
intensity_sum,
AtomicProperties=atom_props,
BeamRadius=self.getProperty("BeamRadius").value,
NumEventsPerRun=self.getProperty("NumEvents").value,
TotalScatteringWS=total_scatter_correction,
MultipleScatteringWS=multi_scatter_correction)
# Smooth the output
smooth_neighbours = self.getProperty("SmoothNeighbours").value
ms.SmoothData(InputWorkspace=total_scatter_correction,
OutputWorkspace=total_scatter_correction,
NPoints=smooth_neighbours)
ms.SmoothData(InputWorkspace=multi_scatter_correction,
OutputWorkspace=multi_scatter_correction,
NPoints=smooth_neighbours)
return total_scatter_correction, multi_scatter_correction
def _ms_correction(self):
"""
Calculates the contributions from multiple scattering
on the input data from the set of given options
"""
masses = self.getProperty("Masses").value
params_ws_name = self.getPropertyValue("FitParameters")
params_dict = TableWorkspaceDictionaryFacade(mtd[params_ws_name])
atom_props = list()
intensities = list()
for i, mass in enumerate(masses):
intentisty_prop = 'f%d.Intensity' % i
c0_prop = 'f%d.C_0' % i
if intentisty_prop in params_dict:
intentisy = params_dict[intentisty_prop]
elif c0_prop in params_dict:
intentisy = params_dict[c0_prop]
else:
continue
# The program DINSMS_BATCH uses those sample parameters together with the sigma divided
# by the sum absolute of scattering intensities for each detector (detector bank),
# sigma/int_sum
# Thus:
# intensity = intensity/intensity_sum
# In the thin sample limit, 1-exp(-n*dens*sigma) ~ n*dens*sigma, effectively the same
# scattering power (ratio of double to single scatt.) is obtained either by using
# relative intensities ( sigma/int_sum ) or density divided by the total intensity
# However, in the realistic case of thick sample, the SampleDensity, dens, must be
# obtained by iterative numerical solution of the Eq:
# 1-exp(-n*dens*sigma) = measured scattering power of the sample.
# For this, a program like THICK must be used.
# The program THICK also uses sigma/int_sum to be consistent with the prgram
# DINSMS_BATCH
width_prop = 'f%d.Width' % i
sigma_x_prop = 'f%d.SigmaX' % i
sigma_y_prop = 'f%d.SigmaY' % i
sigma_z_prop = 'f%d.SigmaZ' % i
if width_prop in params_dict:
width = params_dict['f%d.Width' % i]
elif sigma_x_prop in params_dict:
sigma_x = float(params_dict[sigma_x_prop])
sigma_y = float(params_dict[sigma_y_prop])
sigma_z = float(params_dict[sigma_z_prop])
width = math.sqrt((sigma_x**2 + sigma_y**2 + sigma_z**2) / 3.0)
else:
continue
atom_props.append(mass)
atom_props.append(intentisy)
atom_props.append(width)
intensities.append(intentisy)
intensity_sum = sum(intensities)
# Create the sample shape
# Input dimensions are expected in CM
ms.CreateSampleShape(InputWorkspace=self._output_ws,
ShapeXML=create_cuboid_xml(
self.getProperty("SampleHeight").value / 100.,
self.getProperty("SampleWidth").value / 100.,
self.getProperty("SampleDepth").value / 100.))
# Massage options into how algorithm expects them
total_scatter_correction = str(
self._correction_wsg) + "_TotalScattering"
multi_scatter_correction = str(
self._correction_wsg) + "_MultipleScattering"
# Calculation
# In the thin sample limit, 1-exp(-n*dens*sigma) ~ n*dens*sigma, effectively the same
# scattering power(ratio of double to single scatt.) is obtained either by using relative
# intensities ( sigma/int_sum )or density divided by the total intensity.
# However, in the realistic case of thick sample, the SampleDensity, dens, must be
# obtained by iterative numerical solution of the Eq:
# 1-exp(-n*dens*sigma) = measured scattering power of the sample.
# For this, a program like THICK must be used.
# The program THICK also uses sigma/int_sum to be consistent with the prgram DINSMS_BATCH
# The algorithm VesuvioCalculateMs called by the algorithm VesuvioCorrections takes the
# parameter AtomicProperties with the absolute intensities, contraty to DINSMS_BATCH which
# takes in relative intensities.
# To compensate for this, the thickness parameter, dens (SampleDensity), is divided in by
# the sum of absolute intensities in VesuvioCorrections before being passed to
# VesuvioCalculateMs.
# Then, for the modified VesuvioCorrection algorithm one can use the thickenss parameter is
# as is from the THICK command, i.e. 43.20552
# This works, however, only in the thin sample limit, contrary to the THICK program. Thus,
# for some detectors (detector banks) the SampleDensiy parameter may be over(under)
# estimated.
ms.VesuvioCalculateMS(
InputWorkspace=self._output_ws,
NoOfMasses=len(atom_props) / 3,
SampleDensity=self.getProperty("SampleDensity").value /
intensity_sum,
AtomicProperties=atom_props,
BeamRadius=self.getProperty("BeamRadius").value,
NumEventsPerRun=self.getProperty("NumEvents").value,
TotalScatteringWS=total_scatter_correction,
MultipleScatteringWS=multi_scatter_correction)
# Smooth the output
smooth_neighbours = self.getProperty("SmoothNeighbours").value
ms.SmoothData(InputWorkspace=total_scatter_correction,
OutputWorkspace=total_scatter_correction,
NPoints=smooth_neighbours)
ms.SmoothData(InputWorkspace=multi_scatter_correction,
OutputWorkspace=multi_scatter_correction,
NPoints=smooth_neighbours)
return total_scatter_correction, multi_scatter_correction