def PyExec(self):
from IndirectCommon import convertToElasticQ
progress_tracker = Progress(self, start=0.05, end=0.95, nreports=2)
# Convert sample workspace to elastic Q
self._temporary_fit_name = '__fit_ws'
self._crop_workspace(self._sample_workspace, self._temporary_fit_name,
self._e_min, self._e_max)
self._convert_to_histogram(self._temporary_fit_name)
convertToElasticQ(self._temporary_fit_name)
# Perform fits
progress_tracker.report('Fitting 1 peak...')
self._fit('1L')
progress_tracker.report('Fitting 2 peaks...')
self._fit('2L')
self._delete_workspace(self._temporary_fit_name)
# Appends the chi workspaces and replaces the y labels
self._append_chi_squared_workspaces()
# Appends the result workspaces and replaces the y labels
self._append_result_workspaces()
def PyExec(self):
from IndirectCommon import (convertToElasticQ,
transposeFitParametersTable)
setup_prog = Progress(self, start=0.0, end=0.1, nreports=4)
setup_prog.report('generating output name')
output_workspace = self._fit_group_name
# check if the naming convention used is already correct
chopped_name = self._fit_group_name.split('_')
if 'WORKSPACE' in chopped_name[-1].upper():
output_workspace = '_'.join(chopped_name[:-1])
option = self._fit_type[:-2]
logger.information('Option: ' + option)
logger.information('Function: ' + str(self._function))
setup_prog.report('Cropping workspace')
# prepare input workspace for fitting
tmp_fit_workspace = "__Iqtfit_fit_ws"
if self._spec_max is None:
crop_alg = self.createChildAlgorithm("CropWorkspace", enableLogging=False)
crop_alg.setProperty("InputWorkspace", self._input_ws)
crop_alg.setProperty("OutputWorkspace", tmp_fit_workspace)
crop_alg.setProperty("XMin", self._start_x)
crop_alg.setProperty("XMax", self._end_x)
crop_alg.setProperty("StartWorkspaceIndex", self._spec_min)
crop_alg.execute()
else:
crop_alg = self.createChildAlgorithm("CropWorkspace", enableLogging=False)
crop_alg.setProperty("InputWorkspace", self._input_ws)
crop_alg.setProperty("OutputWorkspace", tmp_fit_workspace)
crop_alg.setProperty("XMin", self._start_x)
crop_alg.setProperty("XMax", self._end_x)
crop_alg.setProperty("StartWorkspaceIndex", self._spec_min)
crop_alg.setProperty("EndWorkspaceIndex", self._spec_max)
crop_alg.execute()
setup_prog.report('Converting to Histogram')
convert_to_hist_alg = self.createChildAlgorithm("ConvertToHistogram", enableLogging=False)
convert_to_hist_alg.setProperty("InputWorkspace", crop_alg.getProperty("OutputWorkspace").value)
convert_to_hist_alg.setProperty("OutputWorkspace", tmp_fit_workspace)
convert_to_hist_alg.execute()
mtd.addOrReplace(tmp_fit_workspace, convert_to_hist_alg.getProperty("OutputWorkspace").value)
setup_prog.report('Convert to Elastic Q')
convertToElasticQ(tmp_fit_workspace)
# fit multi-domain function to workspace
fit_prog = Progress(self, start=0.1, end=0.8, nreports=2)
multi_domain_func, kwargs = _create_multi_domain_func(self._function, tmp_fit_workspace)
fit_prog.report('Fitting...')
ms.Fit(Function=multi_domain_func,
InputWorkspace=tmp_fit_workspace,
WorkspaceIndex=0,
Output=output_workspace,
CreateOutput=True,
Minimizer=self._minimizer,
MaxIterations=self._max_iterations,
OutputCompositeMembers=self._do_extract_members,
**kwargs)
fit_prog.report('Fitting complete')
conclusion_prog = Progress(self, start=0.8, end=1.0, nreports=5)
conclusion_prog.report('Renaming workspaces')
# rename workspaces to match user input
rename_alg = self.createChildAlgorithm("RenameWorkspace", enableLogging=False)
if output_workspace + "_Workspaces" != self._fit_group_name:
rename_alg.setProperty("InputWorkspace", output_workspace + "_Workspaces")
rename_alg.setProperty("OutputWorkspace", self._fit_group_name)
rename_alg.execute()
if output_workspace + "_Parameters" != self._parameter_name:
rename_alg.setProperty("InputWorkspace", output_workspace + "_Parameters")
rename_alg.setProperty("OutputWorkspace", self._parameter_name)
rename_alg.execute()
conclusion_prog.report('Transposing parameter table')
transposeFitParametersTable(self._parameter_name)
# set first column of parameter table to be axis values
x_axis = mtd[tmp_fit_workspace].getAxis(1)
axis_values = x_axis.extractValues()
for i, value in enumerate(axis_values):
mtd[self._parameter_name].setCell('axis-1', i, value)
# convert parameters to matrix workspace
parameter_names = 'A0,Height,Lifetime,Stretching'
conclusion_prog.report('Processing indirect fit parameters')
pifp_alg = self.createChildAlgorithm("ProcessIndirectFitParameters")
pifp_alg.setProperty("InputWorkspace", self._parameter_name)
pifp_alg.setProperty("ColumnX", "axis-1")
pifp_alg.setProperty("XAxisUnit", "MomentumTransfer")
pifp_alg.setProperty("ParameterNames", parameter_names)
pifp_alg.setProperty("OutputWorkspace", self._result_name)
pifp_alg.execute()
result_workspace = pifp_alg.getProperty("OutputWorkspace").value
mtd.addOrReplace(self._result_name, result_workspace)
# create and add sample logs
sample_logs = {'start_x': self._start_x, 'end_x': self._end_x, 'fit_type': self._fit_type[:-2],
'intensities_constrained': self._intensities_constrained, 'beta_constrained': True}
conclusion_prog.report('Copying sample logs')
copy_log_alg = self.createChildAlgorithm("CopyLogs", enableLogging=False)
copy_log_alg.setProperty("InputWorkspace", self._input_ws)
copy_log_alg.setProperty("OutputWorkspace", result_workspace)
copy_log_alg.execute()
copy_log_alg.setProperty("InputWorkspace", self._input_ws)
copy_log_alg.setProperty("OutputWorkspace", self._fit_group_name)
copy_log_alg.execute()
log_names = [item for item in sample_logs]
log_values = [sample_logs[item] for item in sample_logs]
conclusion_prog.report('Adding sample logs')
add_sample_log_multi = self.createChildAlgorithm("AddSampleLogMultiple", enableLogging=False)
add_sample_log_multi.setProperty("Workspace", result_workspace.name())
add_sample_log_multi.setProperty("LogNames", log_names)
add_sample_log_multi.setProperty("LogValues", log_values)
add_sample_log_multi.execute()
add_sample_log_multi.setProperty("Workspace", self._fit_group_name)
add_sample_log_multi.setProperty("LogNames", log_names)
add_sample_log_multi.setProperty("LogValues", log_values)
add_sample_log_multi.execute()
delete_alg = self.createChildAlgorithm("DeleteWorkspace", enableLogging=False)
delete_alg.setProperty("Workspace", tmp_fit_workspace)
delete_alg.execute()
if self._do_extract_members:
ms.ExtractQENSMembers(InputWorkspace=self._input_ws,
ResultWorkspace=self._fit_group_name,
OutputWorkspace=self._fit_group_name.rsplit('_', 1)[0] + "_Members")
self.setProperty('OutputResultWorkspace', result_workspace)
self.setProperty('OutputParameterWorkspace', self._parameter_name)
self.setProperty('OutputWorkspaceGroup', self._fit_group_name)
conclusion_prog.report('Algorithm complete')
def PyExec(self):
from IndirectCommon import (convertToElasticQ,
transposeFitParametersTable)
setup_prog = Progress(self, start=0.0, end=0.1, nreports=4)
setup_prog.report('generating output name')
output_workspace = self._fit_group_name
# check if the naming convention used is alreay correct
chopped_name = self._fit_group_name.split('_')
if 'WORKSPACE' in chopped_name[-1].upper():
output_workspace = ('_').join(chopped_name[:-1])
option = self._fit_type[:-2]
logger.information('Option: ' + option)
logger.information('Function: ' + self._function)
setup_prog.report('Cropping workspace')
#prepare input workspace for fitting
tmp_fit_workspace = "__Iqtfit_fit_ws"
if self._spec_max is None:
CropWorkspace(InputWorkspace=self._input_ws,
OutputWorkspace=tmp_fit_workspace,
XMin=self._start_x,
XMax=self._end_x,
StartWorkspaceIndex=self._spec_min)
else:
CropWorkspace(InputWorkspace=self._input_ws,
OutputWorkspace=tmp_fit_workspace,
XMin=self._start_x,
XMax=self._end_x,
StartWorkspaceIndex=self._spec_min,
EndWorkspaceIndex=self._spec_max)
setup_prog.report('Converting to Histogram')
ConvertToHistogram(tmp_fit_workspace,
OutputWorkspace=tmp_fit_workspace)
setup_prog.report('Convert to Elastic Q')
convertToElasticQ(tmp_fit_workspace)
#fit multi-domian functino to workspace
fit_prog = Progress(self, start=0.1, end=0.8, nreports=2)
multi_domain_func, kwargs = self._create_mutli_domain_func(
self._function, tmp_fit_workspace)
fit_prog.report('Fitting...')
Fit(Function=multi_domain_func,
InputWorkspace=tmp_fit_workspace,
WorkspaceIndex=0,
Output=output_workspace,
CreateOutput=True,
Minimizer=self._minimizer,
MaxIterations=self._max_iterations,
**kwargs)
fit_prog.report('Fitting complete')
conclusion_prog = Progress(self, start=0.8, end=1.0, nreports=5)
conclusion_prog.report('Renaming workspaces')
# rename workspaces to match user input
if output_workspace + "_Workspaces" != self._fit_group_name:
RenameWorkspace(InputWorkspace=output_workspace + "_Workspaces",
OutputWorkspace=self._fit_group_name)
if output_workspace + "_Parameters" != self._parameter_name:
RenameWorkspace(InputWorkspace=output_workspace + "_Parameters",
OutputWorkspace=self._parameter_name)
conclusion_prog.report('Tansposing parameter table')
transposeFitParametersTable(self._parameter_name)
#set first column of parameter table to be axis values
x_axis = mtd[tmp_fit_workspace].getAxis(1)
axis_values = x_axis.extractValues()
for i, value in enumerate(axis_values):
mtd[self._parameter_name].setCell('axis-1', i, value)
#convert parameters to matrix workspace
parameter_names = 'A0,Intensity,Tau,Beta'
conclusion_prog.report('Processing indirect fit parameters')
result_workspace = ProcessIndirectFitParameters(
InputWorkspace=self._parameter_name,
ColumnX="axis-1",
XAxisUnit="MomentumTransfer",
ParameterNames=parameter_names,
OutputWorkspace=self._result_name)
# create and add sample logs
sample_logs = {
'start_x': self._start_x,
'end_x': self._end_x,
'fit_type': self._fit_type[:-2],
'intensities_constrained': self._intensities_constrained,
'beta_constrained': True
}
conclusion_prog.report('Copying sample logs')
CopyLogs(InputWorkspace=self._input_ws,
OutputWorkspace=result_workspace)
CopyLogs(InputWorkspace=self._input_ws,
OutputWorkspace=self._fit_group_name)
log_names = [item for item in sample_logs]
log_values = [sample_logs[item] for item in sample_logs]
conclusion_prog.report('Adding sample logs')
AddSampleLogMultiple(Workspace=result_workspace,
LogNames=log_names,
LogValues=log_values)
AddSampleLogMultiple(Workspace=self._fit_group_name,
LogNames=log_names,
LogValues=log_values)
DeleteWorkspace(tmp_fit_workspace)
self.setProperty('OutputResultWorkspace', result_workspace)
self.setProperty('OutputParameterWorkspace', self._parameter_name)
self.setProperty('OutputWorkspaceGroup', self._fit_group_name)
conclusion_prog.report('Algorithm complete')
def PyExec(self):
from IndirectCommon import (getWSprefix, convertToElasticQ)
setup_prog = Progress(self, start=0.0, end=0.1, nreports=4)
self._fit_type = self._fit_type[:-2]
logger.information('Option: ' + self._fit_type)
logger.information(self._function)
setup_prog.report('Cropping workspace')
tmp_fit_name = "__IqtFit_ws"
crop_alg = self.createChildAlgorithm("CropWorkspace")
crop_alg.setProperty("InputWorkspace", self._input_ws)
crop_alg.setProperty("OutputWorkspace", tmp_fit_name)
crop_alg.setProperty("XMin", self._start_x)
crop_alg.setProperty("XMax", self._end_x)
crop_alg.execute()
num_hist = self._input_ws.getNumberHistograms()
if self._spec_max is None:
self._spec_max = num_hist - 1
# Name stem for generated workspace
output_workspace = '%sIqtFit_%s_s%d_to_%d' % (getWSprefix(
self._input_ws.getName()), self._fit_type, self._spec_min,
self._spec_max)
setup_prog.report('Converting to Histogram')
convert_to_hist_alg = self.createChildAlgorithm("ConvertToHistogram")
convert_to_hist_alg.setProperty(
"InputWorkspace",
crop_alg.getProperty("OutputWorkspace").value)
convert_to_hist_alg.setProperty("OutputWorkspace", tmp_fit_name)
convert_to_hist_alg.execute()
mtd.addOrReplace(
tmp_fit_name,
convert_to_hist_alg.getProperty("OutputWorkspace").value)
setup_prog.report('Convert to Elastic Q')
convertToElasticQ(tmp_fit_name)
# Build input string for PlotPeakByLogValue
input_str = [
tmp_fit_name + ',i%d' % i
for i in range(self._spec_min, self._spec_max + 1)
]
input_str = ';'.join(input_str)
fit_prog = Progress(self, start=0.1, end=0.8, nreports=2)
fit_prog.report('Fitting...')
ms.PlotPeakByLogValue(Input=input_str,
OutputWorkspace=output_workspace,
Function=self._function,
Minimizer=self._minimizer,
MaxIterations=self._max_iterations,
StartX=self._start_x,
EndX=self._end_x,
FitType='Sequential',
CreateOutput=True)
fit_prog.report('Fitting complete')
conclusion_prog = Progress(self, start=0.8, end=1.0, nreports=5)
# Remove unsused workspaces
delete_alg = self.createChildAlgorithm("DeleteWorkspace")
delete_alg.setProperty(
"Workspace", output_workspace + '_NormalisedCovarianceMatrices')
delete_alg.execute()
delete_alg.setProperty("Workspace", output_workspace + '_Parameters')
delete_alg.execute()
delete_alg.setProperty("Workspace", tmp_fit_name)
delete_alg.execute()
conclusion_prog.report('Renaming workspaces')
# rename workspaces to match user input
rename_alg = self.createChildAlgorithm("RenameWorkspace")
if output_workspace + "_Workspaces" != self._fit_group_name:
rename_alg.setProperty("InputWorkspace",
output_workspace + "_Workspaces")
rename_alg.setProperty("OutputWorkspace", self._fit_group_name)
rename_alg.execute()
if output_workspace != self._parameter_name:
rename_alg.setProperty("InputWorkspace", output_workspace)
rename_alg.setProperty("OutputWorkspace", self._parameter_name)
rename_alg.execute()
# Create *_Result workspace
parameter_names = 'A0,Intensity,Tau,Beta'
conclusion_prog.report('Processing indirect fit parameters')
pifp_alg = self.createChildAlgorithm("ProcessIndirectFitParameters")
pifp_alg.setProperty("InputWorkspace", self._parameter_name)
pifp_alg.setProperty("ColumnX", "axis-1")
pifp_alg.setProperty("XAxisUnit", "MomentumTransfer")
pifp_alg.setProperty("ParameterNames", parameter_names)
pifp_alg.setProperty("OutputWorkspace", self._result_name)
pifp_alg.execute()
self._result_ws = pifp_alg.getProperty("OutputWorkspace").value
mtd.addOrReplace(self._result_name, self._result_ws)
# Process generated workspaces
wsnames = mtd[self._fit_group_name].getNames()
for i, workspace in enumerate(wsnames):
output_ws = output_workspace + '_Workspace_%d' % i
rename_alg.setProperty("InputWorkspace", workspace)
rename_alg.setProperty("OutputWorkspace", output_ws)
rename_alg.execute()
conclusion_prog.report('Copying and transfering sample logs')
self._transfer_sample_logs()
self.setProperty('OutputParameterWorkspace', self._parameter_name)
self.setProperty('OutputWorkspaceGroup', self._fit_group_name)
self.setProperty('OutputResultWorkspace', self._result_ws)
conclusion_prog.report('Algorithm complete')
def PyExec(self):
from IndirectCommon import (getWSprefix, convertToElasticQ)
setup_prog = Progress(self, start=0.0, end=0.1, nreports=4)
self._fit_type = self._fit_type[:-2]
logger.information('Option: ' + self._fit_type)
logger.information(self._function)
setup_prog.report('Cropping workspace')
tmp_fit_name = "__IqtFit_ws"
crop_alg = self.createChildAlgorithm("CropWorkspace", enableLogging=False)
crop_alg.setProperty("InputWorkspace", self._input_ws)
crop_alg.setProperty("OutputWorkspace", tmp_fit_name)
crop_alg.setProperty("XMin", self._start_x)
crop_alg.setProperty("XMax", self._end_x)
crop_alg.execute()
num_hist = self._input_ws.getNumberHistograms()
if self._spec_max is None:
self._spec_max = num_hist - 1
# Name stem for generated workspace
output_workspace = '%sIqtFit_%s_s%d_to_%d' % (getWSprefix(self._input_ws.name()),
self._fit_type, self._spec_min,
self._spec_max)
setup_prog.report('Converting to Histogram')
convert_to_hist_alg = self.createChildAlgorithm("ConvertToHistogram", enableLogging=False)
convert_to_hist_alg.setProperty("InputWorkspace", crop_alg.getProperty("OutputWorkspace").value)
convert_to_hist_alg.setProperty("OutputWorkspace", tmp_fit_name)
convert_to_hist_alg.execute()
mtd.addOrReplace(tmp_fit_name, convert_to_hist_alg.getProperty("OutputWorkspace").value)
setup_prog.report('Convert to Elastic Q')
convertToElasticQ(tmp_fit_name)
# Build input string for PlotPeakByLogValue
input_str = [tmp_fit_name + ',i%d' % i for i in range(self._spec_min, self._spec_max + 1)]
input_str = ';'.join(input_str)
fit_prog = Progress(self, start=0.1, end=0.8, nreports=2)
fit_prog.report('Fitting...')
ms.PlotPeakByLogValue(Input=input_str,
OutputWorkspace=output_workspace,
Function=self._function,
Minimizer=self._minimizer,
MaxIterations=self._max_iterations,
StartX=self._start_x,
EndX=self._end_x,
FitType='Sequential',
CreateOutput=True)
fit_prog.report('Fitting complete')
conclusion_prog = Progress(self, start=0.8, end=1.0, nreports=5)
# Remove unused workspaces
delete_alg = self.createChildAlgorithm("DeleteWorkspace", enableLogging=False)
delete_alg.setProperty("Workspace", output_workspace + '_NormalisedCovarianceMatrices')
delete_alg.execute()
delete_alg.setProperty("Workspace", output_workspace + '_Parameters')
delete_alg.execute()
delete_alg.setProperty("Workspace", tmp_fit_name)
delete_alg.execute()
conclusion_prog.report('Renaming workspaces')
# rename workspaces to match user input
rename_alg = self.createChildAlgorithm("RenameWorkspace", enableLogging=False)
if output_workspace + "_Workspaces" != self._fit_group_name:
rename_alg.setProperty("InputWorkspace", output_workspace + "_Workspaces")
rename_alg.setProperty("OutputWorkspace", self._fit_group_name)
rename_alg.execute()
if output_workspace != self._parameter_name:
rename_alg.setProperty("InputWorkspace", output_workspace)
rename_alg.setProperty("OutputWorkspace", self._parameter_name)
rename_alg.execute()
# Create *_Result workspace
parameter_names = 'A0,Height,Lifetime,Stretching'
conclusion_prog.report('Processing indirect fit parameters')
pifp_alg = self.createChildAlgorithm("ProcessIndirectFitParameters")
pifp_alg.setProperty("InputWorkspace", self._parameter_name)
pifp_alg.setProperty("ColumnX", "axis-1")
pifp_alg.setProperty("XAxisUnit", "MomentumTransfer")
pifp_alg.setProperty("ParameterNames", parameter_names)
pifp_alg.setProperty("OutputWorkspace", self._result_name)
pifp_alg.execute()
self._result_ws = pifp_alg.getProperty("OutputWorkspace").value
mtd.addOrReplace(self._result_name, self._result_ws)
# Process generated workspaces
wsnames = mtd[self._fit_group_name].getNames()
for i, workspace in enumerate(wsnames):
output_ws = output_workspace + '_Workspace_%d' % i
rename_alg.setProperty("InputWorkspace", workspace)
rename_alg.setProperty("OutputWorkspace", output_ws)
rename_alg.execute()
conclusion_prog.report('Copying and transferring sample logs')
self._transfer_sample_logs()
self.setProperty('OutputParameterWorkspace', self._parameter_name)
self.setProperty('OutputWorkspaceGroup', self._fit_group_name)
self.setProperty('OutputResultWorkspace', self._result_ws)
conclusion_prog.report('Algorithm complete')
def PyExec(self):
from IndirectCommon import (convertToElasticQ,
transposeFitParametersTable)
setup_prog = Progress(self, start=0.0, end=0.1, nreports=4)
setup_prog.report('generating output name')
output_workspace = self._fit_group_name
# check if the naming convention used is alreay correct
chopped_name = self._fit_group_name.split('_')
if 'WORKSPACE' in chopped_name[-1].upper():
output_workspace = ('_').join(chopped_name[:-1])
option = self._fit_type[:-2]
logger.information('Option: '+ option)
logger.information('Function: '+ self._function)
setup_prog.report('Cropping workspace')
#prepare input workspace for fitting
tmp_fit_workspace = "__Iqtfit_fit_ws"
if self._spec_max is None:
CropWorkspace(InputWorkspace=self._input_ws, OutputWorkspace=tmp_fit_workspace,
XMin=self._start_x, XMax=self._end_x,
StartWorkspaceIndex=self._spec_min)
else:
CropWorkspace(InputWorkspace=self._input_ws, OutputWorkspace=tmp_fit_workspace,
XMin=self._start_x, XMax=self._end_x,
StartWorkspaceIndex=self._spec_min, EndWorkspaceIndex=self._spec_max)
setup_prog.report('Converting to Histogram')
ConvertToHistogram(tmp_fit_workspace, OutputWorkspace=tmp_fit_workspace)
setup_prog.report('Convert to Elastic Q')
convertToElasticQ(tmp_fit_workspace)
#fit multi-domian functino to workspace
fit_prog = Progress(self, start=0.1, end=0.8, nreports=2)
multi_domain_func, kwargs = self._create_mutli_domain_func(self._function, tmp_fit_workspace)
fit_prog.report('Fitting...')
Fit(Function=multi_domain_func,
InputWorkspace=tmp_fit_workspace,
WorkspaceIndex=0,
Output=output_workspace,
CreateOutput=True,
Minimizer=self._minimizer,
MaxIterations=self._max_iterations,
**kwargs)
fit_prog.report('Fitting complete')
conclusion_prog = Progress(self, start=0.8, end=1.0, nreports=5)
conclusion_prog.report('Renaming workspaces')
# rename workspaces to match user input
if output_workspace + "_Workspaces" != self._fit_group_name:
RenameWorkspace(InputWorkspace=output_workspace + "_Workspaces",
OutputWorkspace=self._fit_group_name)
if output_workspace + "_Parameters" != self._parameter_name:
RenameWorkspace(InputWorkspace=output_workspace + "_Parameters",
OutputWorkspace=self._parameter_name)
conclusion_prog.report('Tansposing parameter table')
transposeFitParametersTable(self._parameter_name)
#set first column of parameter table to be axis values
x_axis = mtd[tmp_fit_workspace].getAxis(1)
axis_values = x_axis.extractValues()
for i, value in enumerate(axis_values):
mtd[self._parameter_name].setCell('axis-1', i, value)
#convert parameters to matrix workspace
parameter_names = 'A0,Intensity,Tau,Beta'
conclusion_prog.report('Processing indirect fit parameters')
result_workspace = ProcessIndirectFitParameters(InputWorkspace=self._parameter_name,
ColumnX="axis-1", XAxisUnit="MomentumTransfer",
ParameterNames=parameter_names,
OutputWorkspace=self._result_name)
# create and add sample logs
sample_logs = {'start_x': self._start_x, 'end_x': self._end_x, 'fit_type': self._fit_type[:-2],
'intensities_constrained': self._intensities_constrained, 'beta_constrained': True}
conclusion_prog.report('Copying sample logs')
CopyLogs(InputWorkspace=self._input_ws, OutputWorkspace=result_workspace)
CopyLogs(InputWorkspace=self._input_ws, OutputWorkspace=self._fit_group_name)
log_names = [item for item in sample_logs]
log_values = [sample_logs[item] for item in sample_logs]
conclusion_prog.report('Adding sample logs')
AddSampleLogMultiple(Workspace=result_workspace, LogNames=log_names, LogValues=log_values)
AddSampleLogMultiple(Workspace=self._fit_group_name, LogNames=log_names, LogValues=log_values)
DeleteWorkspace(tmp_fit_workspace)
self.setProperty('OutputResultWorkspace', result_workspace)
self.setProperty('OutputParameterWorkspace', self._parameter_name)
self.setProperty('OutputWorkspaceGroup', self._fit_group_name)
conclusion_prog.report('Algorithm complete')
