def _update_function_from_params(self, function_str, params):
params_dict = TableWorkspaceDictionaryFacade(params)
functions = function_str.split(';')
new_functions = [functions[0]]
idx = 0;
for func in functions[1:]:
new_params = []
for param in func.split(','):
if param.split('=')[0] == 'name':
new_params.append(param)
param_prefix = 'f{0}.'.format(idx)
for name, value in params_dict.items():
if param_prefix in name:
new_params.append('{0}={1}'.format(name, value))
new_functions.append(','.join(new_params))
idx += 1
new_function_str = ';'.join(new_functions)
return new_function_str
def _get_correction_scale_factor(self, correction_name, corrections,
params_ws):
index = self._get_correction_workspace(correction_name, corrections)[0]
if index is None:
raise RuntimeError('No workspace for given correction')
params_dict = TableWorkspaceDictionaryFacade(params_ws)
scale_param_name = 'f%d.Scaling' % index
return params_dict[scale_param_name]
def _run_fit_impl(self,
data_ws,
workspace_index,
fit_options,
simulation=False):
"""
Run the Fit algorithm with the given options on the input data
@param data_ws :: The workspace containing the data to fit too
@param workspace_index :: The spectra to fit against
@param fit_options :: An object of type FittingOptions containing
the parameters
@param simulation :: If true then only a single iteration is run
"""
if simulation:
raise RuntimeError("Simulation not implemented yet")
else:
# Run fitting first time using constraints matrix to reduce active parameter set
function_str = fit_options.create_function_str()
constraints = fit_options.create_constraints_str()
ties = fit_options.create_ties_str()
user_ties = self.getProperty('Ties').value
if user_ties != "":
ties = ties + ',' + user_ties
_, params, fitted_data = self._do_fit(
function_str,
data_ws,
workspace_index,
constraints,
ties,
max_iter=self.getProperty("MaxIterations").value)
# Run second time using standard CompositeFunction & no constraints matrix to
# calculate correct reduced chisq
param_values = TableWorkspaceDictionaryFacade(params)
function_str = fit_options.create_function_str(param_values)
max_iter = 0 if simulation else 1
reduced_chi_square, _, _ = self._do_fit(function_str,
data_ws,
workspace_index,
constraints,
ties,
max_iter=max_iter)
return reduced_chi_square, params, fitted_data
def _gamma_correction(self):
correction_background_ws = str(
self._correction_wsg) + "_GammaBackground"
fit_opts = parse_fit_options(
mass_values=self._masses,
profile_strs=self.getProperty("MassProfiles").value,
constraints_str=self.getProperty("IntensityConstraints").value)
params_dict = TableWorkspaceDictionaryFacade(
self.getProperty("FitParameters").value)
func_str = fit_opts.create_function_str(params_dict)
ms.VesuvioCalculateGammaBackground(
InputWorkspace=self._output_ws,
ComptonFunction=func_str,
BackgroundWorkspace=correction_background_ws,
CorrectedWorkspace='__corrected_dummy')
ms.DeleteWorkspace('__corrected_dummy')
return correction_background_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