def ProcessVana(rnum, cycle):
# Preparation of the V/Nd sphere run for SX normalization
mantid.LoadRaw(Filename='/archive/NDXWISH/Instrument/data/cycle_' + cycle + '/WISH000' + str(rnum) + '.raw',
OutputWorkspace='Vana', LoadMonitors='Separate')
mantid.CropWorkspace(InputWorkspace='Vana', OutputWorkspace='Vana', XMin=6000, XMax=99000)
mantid.NormaliseByCurrent(InputWorkspace='Vana', OutputWorkspace='Vana')
mantid.ConvertUnits(InputWorkspace='Vana', OutputWorkspace='Vana', Target='Wavelength')
# create Abs Correction for V
shape = '''<sphere id="V-sphere">
<centre x="0.0" y="0.0" z="0.0" />
<radius val="0.0025"/>
</sphere>'''
mantid.CreateSampleShape('Vana', shape)
mantid.SetSampleMaterial('Vana', SampleNumberDensity=0.0719, ScatteringXSection=5.197, AttenuationXSection=4.739,
ChemicalFormula='V0.95 Nb0.05')
mantid.AbsorptionCorrection(InputWorkspace='Vana', OutputWorkspace='Abs_corr', ElementSize=0.5)
# SphericalAbsorption(InputWorkspace='WISH00038428', OutputWorkspace='Abs_corr_sphere', SphericalSampleRadius=0.25)
# correct Vanadium run for absorption
mantid.Divide(LHSWorkspace='Vana', RHSWorkspace='Abs_corr', OutputWorkspace='Vana_Abs')
mantid.DeleteWorkspace('Vana')
mantid.DeleteWorkspace('Abs_corr')
# Smoot data with redius 3
mantid.SmoothNeighbours(InputWorkspace='Vana_Abs', OutputWorkspace='Vana_smoot', Radius=3)
mantid.DeleteWorkspace('Vana_Abs')
# SmoothData38428
mantid.SmoothData(InputWorkspace='Vana_smoot', OutputWorkspace='Vana_smoot1', NPoints=300)
mantid.DeleteWorkspace('Vana_smoot')
def create_slab_geometry(ws_name,vertical_width, horizontal_width, thickness):
half_height, half_width, half_thick = 0.5*vertical_width, 0.5*horizontal_width, 0.5*thickness
xml_str = \
" <cuboid id=\"sample-shape\"> " \
+ "<left-front-bottom-point x=\"%f\" y=\"%f\" z=\"%f\" /> " % (half_width,-half_height,half_thick) \
+ "<left-front-top-point x=\"%f\" y=\"%f\" z=\"%f\" /> " % (half_width, half_height, half_thick) \
+ "<left-back-bottom-point x=\"%f\" y=\"%f\" z=\"%f\" /> " % (half_width, -half_height, -half_thick) \
+ "<right-front-bottom-point x=\"%f\" y=\"%f\" z=\"%f\" /> " % (-half_width, -half_height, half_thick) \
+ "</cuboid>"
sapi.CreateSampleShape(ws_name, xml_str)
return
def generate_vanadium_absorb_corrections(van_ws, output_filename):
shape_ws = mantid.CloneWorkspace(InputWorkspace=van_ws)
shape_ws = mantid.ConvertUnits(InputWorkspace=shape_ws, OutputWorkspace=shape_ws, Target="Wavelength")
mantid.CreateSampleShape(InputWorkspace=shape_ws, ShapeXML='<sphere id="sphere_1"> <centre x="0" y="0" z= "0" />\
<radius val="0.005" /> </sphere>')
absorb_ws = \
mantid.AbsorptionCorrection(InputWorkspace=shape_ws, AttenuationXSection="5.08",
ScatteringXSection="5.1", SampleNumberDensity="0.072",
NumberOfWavelengthPoints="25", ElementSize="0.05")
mantid.SaveNexus(Filename=output_filename,
InputWorkspace=absorb_ws, Append=False)
common.remove_intermediate_workspace(shape_ws)
return absorb_ws
def generate_vanadium_absorb_corrections(van_ws):
raise NotImplementedError("Generating absorption corrections needs to be implemented correctly")
# TODO are these values applicable to all instruments
shape_ws = mantid.CloneWorkspace(InputWorkspace=van_ws)
mantid.CreateSampleShape(InputWorkspace=shape_ws, ShapeXML='<sphere id="sphere_1"> <centre x="0" y="0" z= "0" />\
<radius val="0.005" /> </sphere>')
calibration_full_paths = None
absorb_ws = \
mantid.AbsorptionCorrection(InputWorkspace=shape_ws, AttenuationXSection="5.08",
ScatteringXSection="5.1", SampleNumberDensity="0.072",
NumberOfWavelengthPoints="25", ElementSize="0.05")
mantid.SaveNexus(Filename=calibration_full_paths["vanadium_absorption"],
InputWorkspace=absorb_ws, Append=False)
common.remove_intermediate_workspace(shape_ws)
return absorb_ws
def _generate_vanadium_absorb_corrections(calibration_full_paths, ws_to_match):
# TODO are these values applicable to all instruments
shape_ws = mantid.CloneWorkspace(InputWorkspace=ws_to_match)
mantid.CreateSampleShape(
InputWorkspace=shape_ws,
ShapeXML='<sphere id="sphere_1"> <centre x="0" y="0" z= "0" />\
<radius val="0.005" /> </sphere>'
)
absorb_ws = \
mantid.AbsorptionCorrection(InputWorkspace=shape_ws, AttenuationXSection="5.08",
ScatteringXSection="5.1", SampleNumberDensity="0.072",
NumberOfWavelengthPoints="25", ElementSize="0.05")
mantid.SaveNexus(Filename=calibration_full_paths["vanadium_absorption"],
InputWorkspace=absorb_ws,
Append=False)
remove_intermediate_workspace(shape_ws)
return absorb_ws
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