def loader(runs):
return load_files(runs,
ipf_filename,
minimum_spectrum,
maximum_spectrum,
load_logs=load_logs,
load_opts=load_opts)
def _load_and_scale_container(self, scale_factor, load_opts):
"""
Loads the container file if given
Applies the scale factor to the container if not 1.
"""
if self._container_data_files is not None:
self._container_workspace, _ = load_files(self._container_data_files,
self._ipf_filename,
self._spectra_range[0],
self._spectra_range[1],
sum_files=True,
load_logs=self._load_logs,
load_opts=load_opts)
self._container_workspace = self._container_workspace[0]
# Scale container if factor is given
if scale_factor != 1.0:
Scale(InputWorkspace=self._container_workspace,
OutputWorkspace=self._container_workspace,
Factor=scale_factor,
Operation='Multiply')
def _load_and_scale_container(self, scale_factor, load_opts):
"""
Loads the container file if given
Applies the scale factor to the container if not 1.
"""
if self._container_data_files is not None:
self._container_workspace, _ = load_files(self._container_data_files,
self._ipf_filename,
self._spectra_range[0],
self._spectra_range[1],
sum_files=True,
load_logs=self._load_logs,
load_opts=load_opts)
self._container_workspace = self._container_workspace[0]
# Scale container if factor is given
if scale_factor != 1.0:
Scale(InputWorkspace=self._container_workspace,
OutputWorkspace=self._container_workspace,
Factor=scale_factor,
Operation='Multiply')
def loader(runs):
return load_files(runs, ipf_filename, minimum_spectrum, maximum_spectrum,
load_logs=load_logs, load_opts=load_opts)
def PyExec(self):
from IndirectReductionCommon import (
load_files, get_multi_frame_rebin, identify_bad_detectors,
unwrap_monitor, process_monitor_efficiency, scale_monitor,
scale_detectors, rebin_reduction, group_spectra, fold_chopped,
rename_reduction)
self._setup()
load_prog = Progress(self, start=0.0, end=0.10, nreports=2)
load_prog.report('loading files')
self._workspace_names, self._chopped_data = load_files(
self._data_files, self._ipf_filename, self._spectra_range[0],
self._spectra_range[1], self._sum_files, self._load_logs)
load_prog.report('files loaded')
process_prog = Progress(self,
start=0.1,
end=0.9,
nreports=len(self._workspace_names))
for c_ws_name in self._workspace_names:
process_prog.report('processing workspace' + c_ws_name)
is_multi_frame = isinstance(mtd[c_ws_name], WorkspaceGroup)
# Get list of workspaces
if is_multi_frame:
workspaces = mtd[c_ws_name].getNames()
else:
workspaces = [c_ws_name]
# Process rebinning for framed data
rebin_string_2, num_bins = get_multi_frame_rebin(
c_ws_name, self._rebin_string)
masked_detectors = identify_bad_detectors(workspaces[0])
# Process workspaces
for ws_name in workspaces:
# Set Efixed if given to algorithm
if self._efixed != Property.EMPTY_DBL:
SetInstrumentParameter(Workspace=ws_name,
ComponentName=self._analyser,
ParameterName='Efixed',
ParameterType='Number',
Value=str(self._efixed))
monitor_ws_name = ws_name + '_mon'
# Process monitor
if not unwrap_monitor(ws_name):
ConvertUnits(InputWorkspace=monitor_ws_name,
OutputWorkspace=monitor_ws_name,
Target='Wavelength',
EMode='Elastic')
process_monitor_efficiency(ws_name)
scale_monitor(ws_name)
# Do background removal if a range was provided
if self._background_range is not None:
ConvertToDistribution(Workspace=ws_name)
CalculateFlatBackground(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
StartX=self._background_range[0],
EndX=self._background_range[1],
Mode='Mean')
ConvertFromDistribution(Workspace=ws_name)
# Divide by the calibration workspace if one was provided
if self._calibration_ws is not None:
index_min = self._calibration_ws.getIndexFromSpectrumNumber(
int(self._spectra_range[0]))
index_max = self._calibration_ws.getIndexFromSpectrumNumber(
int(self._spectra_range[1]))
CropWorkspace(InputWorkspace=self._calibration_ws,
OutputWorkspace=self._calibration_ws,
StartWorkspaceIndex=index_min,
EndWorkspaceIndex=index_max)
Divide(LHSWorkspace=ws_name,
RHSWorkspace=self._calibration_ws,
OutputWorkspace=ws_name)
# Scale detector data by monitor intensities
scale_detectors(ws_name, 'Indirect')
# Remove the no longer needed monitor workspace
DeleteWorkspace(monitor_ws_name)
# Convert to energy
ConvertUnits(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
Target='DeltaE',
EMode='Indirect')
CorrectKiKf(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
EMode='Indirect')
# Handle rebinning
rebin_reduction(ws_name, self._rebin_string, rebin_string_2,
num_bins)
# Detailed balance
if self._detailed_balance != Property.EMPTY_DBL:
corr_factor = 11.606 / (2 * self._detailed_balance)
ExponentialCorrection(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
C0=1.0,
C1=corr_factor,
Operation='Multiply')
# Scale
if self._scale_factor != 1.0:
Scale(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
Factor=self._scale_factor,
Operation='Multiply')
# Group spectra
group_spectra(ws_name,
masked_detectors=masked_detectors,
method=self._grouping_method,
group_file=self._grouping_map_file,
group_ws=self._grouping_ws)
if self._fold_multiple_frames and is_multi_frame:
fold_chopped(c_ws_name)
# Convert to output units if needed
if self._output_x_units != 'DeltaE':
ConvertUnits(InputWorkspace=c_ws_name,
OutputWorkspace=c_ws_name,
EMode='Indirect',
Target=self._output_x_units)
# Rename output workspaces
output_workspace_names = [
rename_reduction(ws_name, self._sum_files)
for ws_name in self._workspace_names
]
summary_prog = Progress(self, start=0.9, end=1.0, nreports=4)
# Group result workspaces
summary_prog.report('grouping workspaces')
GroupWorkspaces(InputWorkspaces=output_workspace_names,
OutputWorkspace=self._output_ws)
self.setProperty('OutputWorkspace', mtd[self._output_ws])
summary_prog.report('Algorithm complete')
def PyExec(self):
from IndirectReductionCommon import (
get_multi_frame_rebin, identify_bad_detectors, unwrap_monitor,
process_monitor_efficiency, scale_monitor, scale_detectors,
rebin_reduction, group_spectra, fold_chopped, rename_reduction)
self._setup()
load_opts = dict()
if self._instrument_name == 'VESUVIO':
load_opts['InstrumentParFile'] = self._par_filename
load_opts['Mode'] = 'FoilOut'
load_opts['LoadMonitors'] = True
self._workspace_names, self._chopped_data = load_file_ranges(
self._data_files,
self._ipf_filename,
self._spectra_range[0],
self._spectra_range[1],
sum_files=self._sum_files,
load_logs=self._load_logs,
load_opts=load_opts)
# applies the changes in the provided calibration file
self._apply_calibration()
# Load container if run is given
self._load_and_scale_container(self._container_scale_factor, load_opts)
# Load vanadium runs if given
if self._vanadium_runs:
self._vanadium_ws, _, _ = load_files(self._vanadium_runs,
self._ipf_filename,
self._spectra_range[0],
self._spectra_range[1],
load_logs=self._load_logs,
load_opts=load_opts)
if len(self._workspace_names) > len(self._vanadium_runs):
raise RuntimeError(
"There cannot be more sample runs than vanadium runs.")
for index, c_ws_name in enumerate(self._workspace_names):
is_multi_frame = isinstance(mtd[c_ws_name], WorkspaceGroup)
# Get list of workspaces
if is_multi_frame:
workspaces = mtd[c_ws_name].getNames()
else:
workspaces = [c_ws_name]
# Process rebinning for framed data
rebin_string_2, num_bins = get_multi_frame_rebin(
c_ws_name, self._rebin_string)
masked_detectors = identify_bad_detectors(workspaces[0])
# Process workspaces
for ws_name in workspaces:
monitor_ws_name = ws_name + '_mon'
# Subtract empty container if there is one
if self._container_workspace is not None:
Minus(LHSWorkspace=ws_name,
RHSWorkspace=self._container_workspace,
OutputWorkspace=ws_name)
if self._vanadium_ws:
van_ws_name = self._vanadium_ws[index]
van_ws = mtd[van_ws_name]
if self._container_workspace is not None:
cont_ws = mtd[self._container_workspace]
if van_ws.blocksize() > cont_ws.blocksize():
RebinToWorkspace(
WorkspaceToRebin=van_ws_name,
WorkspaceToMatch=self._container_workspace,
OutputWorkspace=van_ws_name)
elif cont_ws.blocksize() > van_ws.blocksize():
RebinToWorkspace(
WorkspaceToRebin=self._container_workspace,
WorkspaceToMatch=van_ws_name,
OutputWorkspace=self._container_workspace)
Minus(LHSWorkspace=van_ws_name,
RHSWorkspace=self._container_workspace,
OutputWorkspace=van_ws_name)
if mtd[ws_name].blocksize() > van_ws.blocksize():
RebinToWorkspace(WorkspaceToRebin=ws_name,
WorkspaceToMatch=van_ws_name,
OutputWorkspace=ws_name)
elif van_ws.blocksize() > mtd[ws_name].blocksize():
RebinToWorkspace(WorkspaceToRebin=van_ws_name,
WorkspaceToMatch=ws_name,
OutputWorkspace=van_ws_name)
replacement_value = 0.1 * find_minimum_non_zero_y_in_workspace(
van_ws)
logger.information(
'Replacing zeros in {0} with {1}.'.format(
van_ws_name, replacement_value))
ReplaceSpecialValues(
InputWorkspace=van_ws_name,
SmallNumberThreshold=0.0000001,
SmallNumberValue=replacement_value,
OutputWorkspace=self._replace_zeros_name)
Divide(LHSWorkspace=ws_name,
RHSWorkspace=self._replace_zeros_name,
OutputWorkspace=ws_name,
AllowDifferentNumberSpectra=True)
DeleteWorkspace(self._replace_zeros_name)
# Process monitor
if not unwrap_monitor(ws_name):
ConvertUnits(InputWorkspace=monitor_ws_name,
OutputWorkspace=monitor_ws_name,
Target='Wavelength',
EMode='Elastic')
process_monitor_efficiency(ws_name)
scale_monitor(ws_name)
# Scale detector data by monitor intensities
scale_detectors(ws_name, 'Elastic')
# Remove the no longer needed monitor workspace
DeleteWorkspace(monitor_ws_name)
# Convert to dSpacing
ConvertUnits(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
Target='dSpacing',
EMode='Elastic')
# Handle rebinning
rebin_reduction(ws_name, self._rebin_string, rebin_string_2,
num_bins)
# Group spectra
group_spectra(ws_name,
masked_detectors=masked_detectors,
method=self._grouping_method,
group_ws=self._grouping_workspace)
if is_multi_frame:
fold_chopped(c_ws_name)
# Remove the container workspaces
if self._container_workspace is not None:
self._delete_all([self._container_workspace])
# Remove the vanadium workspaces
if self._vanadium_ws:
self._delete_all(self._vanadium_ws)
# Rename output workspaces
output_workspace_names = [
rename_reduction(ws_name, self._sum_files)
for ws_name in self._workspace_names
]
# Group result workspaces
GroupWorkspaces(InputWorkspaces=output_workspace_names,
OutputWorkspace=self._output_ws)
self.setProperty('OutputWorkspace', self._output_ws)
def PyExec(self):
from IndirectReductionCommon import (
get_multi_frame_rebin, identify_bad_detectors, unwrap_monitor,
process_monitor_efficiency, scale_monitor, scale_detectors,
rebin_reduction, group_spectra, fold_chopped, rename_reduction)
self._setup()
load_opts = dict()
if self._instrument_name == 'VESUVIO':
load_opts['Mode'] = 'FoilOut'
self._workspace_names, self._chopped_data = load_files(
self._data_files,
self._ipf_filename,
self._spectra_range[0],
self._spectra_range[1],
sum_files=self._sum_files,
load_logs=self._load_logs,
load_opts=load_opts)
# applies the changes in the provided calibration file
self._apply_calibration()
# Load container if run is given
self._load_and_scale_container(self._container_scale_factor, load_opts)
for c_ws_name in self._workspace_names:
is_multi_frame = isinstance(mtd[c_ws_name], WorkspaceGroup)
# Get list of workspaces
if is_multi_frame:
workspaces = mtd[c_ws_name].getNames()
else:
workspaces = [c_ws_name]
# Process rebinning for framed data
rebin_string_2, num_bins = get_multi_frame_rebin(
c_ws_name, self._rebin_string)
masked_detectors = identify_bad_detectors(workspaces[0])
# Process workspaces
for ws_name in workspaces:
# Subtract empty container if there is one
if self._container_workspace is not None:
Minus(LHSWorkspace=ws_name,
RHSWorkspace=self._container_workspace,
OutputWorkspace=ws_name)
monitor_ws_name = ws_name + '_mon'
# Process monitor
if not unwrap_monitor(ws_name):
ConvertUnits(InputWorkspace=monitor_ws_name,
OutputWorkspace=monitor_ws_name,
Target='Wavelength',
EMode='Elastic')
process_monitor_efficiency(ws_name)
scale_monitor(ws_name)
# Scale detector data by monitor intensities
scale_detectors(ws_name, 'Elastic')
# Remove the no longer needed monitor workspace
DeleteWorkspace(monitor_ws_name)
# Convert to dSpacing
ConvertUnits(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
Target='dSpacing',
EMode='Elastic')
# Handle rebinning
rebin_reduction(ws_name, self._rebin_string, rebin_string_2,
num_bins)
# Group spectra
group_spectra(ws_name, masked_detectors, self._grouping_method)
if is_multi_frame:
fold_chopped(c_ws_name)
# Remove the container workspaces
if self._container_workspace is not None:
DeleteWorkspace(self._container_workspace)
DeleteWorkspace(self._container_workspace + '_mon')
# Rename output workspaces
output_workspace_names = [
rename_reduction(ws_name, self._sum_files)
for ws_name in self._workspace_names
]
# Group result workspaces
GroupWorkspaces(InputWorkspaces=output_workspace_names,
OutputWorkspace=self._output_ws)
self.setProperty('OutputWorkspace', self._output_ws)
def PyExec(self):
"""
Execute the algorithm in diffraction-only mode
"""
# Load all sample, vanadium files
ipf_file_name = 'OSIRIS_diffraction_diffonly_Parameters.xml'
sample_ws_names, _ = load_files(self._sample_runs,
ipf_file_name,
self._spec_min,
self._spec_max,
load_logs=self._load_logs)
vanadium_ws_names, _ = load_files(self._vanadium_runs,
ipf_file_name,
self._spec_min,
self._spec_max,
load_logs=self._load_logs)
container_ws_names = []
# Load the container run
if self._container_files:
container_ws_names, _ = load_files(self._container_files,
ipf_file_name,
self._spec_min,
self._spec_max,
load_logs=self._load_logs)
for container in container_ws_names:
# Scale the container run if required
if self._container_scale_factor != 1.0:
Scale(InputWorkspace=container,
OutputWorkspace=container,
Factor=self._container_scale_factor,
Operation='Multiply')
# Add the sample workspaces to the dRange to sample map
self._sam_ws_map = DRangeToWorkspaceMap()
for idx in range(len(sample_ws_names)):
sample = sample_ws_names[idx]
if container_ws_names:
container = container_ws_names[idx]
RebinToWorkspace(WorkspaceToRebin=container,
WorkspaceToMatch=sample,
OutputWorkspace=container)
Minus(LHSWorkspace=sample,
RHSWorkspace=container,
OutputWorkspace=sample)
self._sam_ws_map.addWs(sample)
# Add the vanadium workspaces to the dRange to vanadium map
self._van_ws_map = DRangeToWorkspaceMap()
for van in vanadium_ws_names:
self._van_ws_map.addWs(van)
# Finished with container now so delete it
for container in container_ws_names:
DeleteWorkspace(container)
DeleteWorkspace(container + "_mon")
# Check to make sure that there are corresponding vanadium files with the same DRange for each sample file.
for d_range in self._sam_ws_map.getMap():
if d_range not in self._van_ws_map.getMap():
raise RuntimeError("There is no van file that covers the " +
str(d_range) + " DRange.")
# Average together any sample workspaces with the same DRange.
# This will mean our map of DRanges to list of workspaces becomes a map
# of DRanges, each to a *single* workspace.
temp_sam_map = DRangeToWorkspaceMap()
for d_range, ws_list in self._sam_ws_map.getMap().items():
temp_sam_map.setItem(d_range, average_ws_list(ws_list))
self._sam_ws_map = temp_sam_map
# Now do the same to the vanadium workspaces.
temp_van_map = DRangeToWorkspaceMap()
for d_range, ws_list in self._van_ws_map.getMap().items():
temp_van_map.setItem(d_range, average_ws_list(ws_list))
self._van_ws_map = temp_van_map
# Run necessary algorithms on BOTH the Vanadium and Sample workspaces.
for d_range, wrksp in list(self._sam_ws_map.getMap().items()) + list(
self._van_ws_map.getMap().items()):
self.log().information('Wrksp:' + str(wrksp) + ' Cal:' +
str(self._cal))
NormaliseByCurrent(InputWorkspace=wrksp, OutputWorkspace=wrksp)
AlignDetectors(InputWorkspace=wrksp,
OutputWorkspace=wrksp,
CalibrationFile=self._cal)
DiffractionFocussing(InputWorkspace=wrksp,
OutputWorkspace=wrksp,
GroupingFileName=self._cal)
CropWorkspace(InputWorkspace=wrksp,
OutputWorkspace=wrksp,
XMin=d_range[0],
XMax=d_range[1])
# Divide all sample files by the corresponding vanadium files.
for sam_ws, van_ws in zip(self._sam_ws_map.getMap().values(),
self._van_ws_map.getMap().values()):
sam_ws, van_ws = self._rebin_to_smallest(sam_ws, van_ws)
Divide(LHSWorkspace=sam_ws,
RHSWorkspace=van_ws,
OutputWorkspace=sam_ws)
ReplaceSpecialValues(InputWorkspace=sam_ws,
OutputWorkspace=sam_ws,
NaNValue=0.0,
InfinityValue=0.0)
# Create a list of sample workspace NAMES, since we need this for MergeRuns.
samWsNamesList = list(self._sam_ws_map.getMap().values())
if len(samWsNamesList) > 1:
# Merge the sample files into one.
MergeRuns(InputWorkspaces=samWsNamesList,
OutputWorkspace=self._output_ws_name)
for name in samWsNamesList:
DeleteWorkspace(Workspace=name)
DeleteWorkspace(Workspace=name + "_mon")
else:
RenameWorkspace(InputWorkspace=samWsNamesList[0],
OutputWorkspace=self._output_ws_name)
result = mtd[self._output_ws_name]
# Create scalar data to cope with where merge has combined overlapping data.
intersections = get_intersection_of_ranges(
list(self._sam_ws_map.getMap().keys()))
dataX = result.dataX(0)
dataY = []
dataE = []
for i in range(0, len(dataX) - 1):
x_val = (dataX[i] + dataX[i + 1]) / 2.0
if is_in_ranges(intersections, x_val):
dataY.append(2)
dataE.append(2)
else:
dataY.append(1)
dataE.append(1)
# apply scalar data to result workspace
for i in range(0, result.getNumberHistograms()):
resultY = result.dataY(i)
resultE = result.dataE(i)
resultY = resultY / dataY
resultE = resultE / dataE
result.setY(i, resultY)
result.setE(i, resultE)
# Delete all workspaces we've created, except the result.
for wrksp in self._van_ws_map.getMap().values():
DeleteWorkspace(Workspace=wrksp)
DeleteWorkspace(Workspace=wrksp + "_mon")
self.setProperty("OutputWorkspace", result)
def PyExec(self):
from IndirectReductionCommon import (load_files,
get_multi_frame_rebin,
identify_bad_detectors,
unwrap_monitor,
process_monitor_efficiency,
scale_monitor,
scale_detectors,
rebin_reduction,
group_spectra,
fold_chopped,
rename_reduction)
self._setup()
load_opts = dict()
load_opts['Mode'] = 'FoilOut'
load_opts['InstrumentParFile'] = self._par_filename
prog_reporter = Progress(self, start=0.0, end=1.0, nreports=1)
prog_reporter.report("Loading Files")
self._workspace_names, self._chopped_data = load_files(self._data_files,
ipf_filename=self._ipf_filename,
spec_min=self._spectra_range[0],
spec_max=self._spectra_range[1],
sum_files=self._sum_files,
load_opts=load_opts)
prog_reporter.resetNumSteps(self._workspace_names.__len__(), 0.0, 1.0)
for c_ws_name in self._workspace_names:
is_multi_frame = isinstance(mtd[c_ws_name], WorkspaceGroup)
# Get list of workspaces
if is_multi_frame:
workspaces = mtd[c_ws_name].getNames()
else:
workspaces = [c_ws_name]
# Process rebinning for framed data
rebin_string_2, num_bins = get_multi_frame_rebin(c_ws_name,
self._rebin_string)
masked_detectors = identify_bad_detectors(workspaces[0])
# Process workspaces
for ws_name in workspaces:
monitor_ws_name = ws_name + '_mon'
# Process monitor
if not unwrap_monitor(ws_name):
ConvertUnits(InputWorkspace=monitor_ws_name,
OutputWorkspace=monitor_ws_name,
Target='Wavelength',
EMode='Elastic')
process_monitor_efficiency(ws_name)
scale_monitor(ws_name)
# Scale detector data by monitor intensities
scale_detectors(ws_name, 'Elastic')
# Remove the no longer needed monitor workspace
DeleteWorkspace(monitor_ws_name)
# Convert to dSpacing
ConvertUnits(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
Target='dSpacing',
EMode='Elastic')
# Handle rebinning
rebin_reduction(ws_name,
self._rebin_string,
rebin_string_2,
num_bins)
# Group spectra
group_spectra(ws_name,
masked_detectors,
self._grouping_method)
if is_multi_frame:
fold_chopped(c_ws_name)
prog_reporter.report()
# Rename output workspaces
output_workspace_names = [rename_reduction(ws_name, self._sum_files) for ws_name in self._workspace_names]
# Group result workspaces
GroupWorkspaces(InputWorkspaces=output_workspace_names,
OutputWorkspace=self._output_ws)
self.setProperty('OutputWorkspace', self._output_ws)
def PyExec(self):
from IndirectReductionCommon import (load_files,
get_multi_frame_rebin,
identify_bad_detectors,
unwrap_monitor,
process_monitor_efficiency,
scale_monitor,
scale_detectors,
rebin_reduction,
group_spectra,
fold_chopped,
rename_reduction,
save_reduction,
plot_reduction)
self._setup()
load_prog = Progress(self, start=0.0, end=0.10, nreports=2)
load_prog.report('loading files')
self._workspace_names, self._chopped_data = load_files(self._data_files,
self._ipf_filename,
self._spectra_range[0],
self._spectra_range[1],
self._sum_files,
self._load_logs)
load_prog.report('files loaded')
process_prog = Progress(self, start=0.1, end=0.9, nreports=len(self._workspace_names))
for c_ws_name in self._workspace_names:
process_prog.report('processing workspace' + c_ws_name)
is_multi_frame = isinstance(mtd[c_ws_name], WorkspaceGroup)
# Get list of workspaces
if is_multi_frame:
workspaces = mtd[c_ws_name].getNames()
else:
workspaces = [c_ws_name]
# Process rebinning for framed data
rebin_string_2, num_bins = get_multi_frame_rebin(c_ws_name,
self._rebin_string)
masked_detectors = identify_bad_detectors(workspaces[0])
# Process workspaces
for ws_name in workspaces:
# Set Efixed if given to algorithm
if self._efixed != Property.EMPTY_DBL:
SetInstrumentParameter(Workspace=ws_name,
ComponentName=self._analyser,
ParameterName='Efixed',
ParameterType='Number',
Value=str(self._efixed))
monitor_ws_name = ws_name + '_mon'
# Process monitor
if not unwrap_monitor(ws_name):
ConvertUnits(InputWorkspace=monitor_ws_name,
OutputWorkspace=monitor_ws_name,
Target='Wavelength',
EMode='Elastic')
process_monitor_efficiency(ws_name)
scale_monitor(ws_name)
# Do background removal if a range was provided
if self._background_range is not None:
ConvertToDistribution(Workspace=ws_name)
CalculateFlatBackground(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
StartX=self._background_range[0],
EndX=self._background_range[1],
Mode='Mean')
ConvertFromDistribution(Workspace=ws_name)
# Divide by the calibration workspace if one was provided
if self._calibration_ws is not None:
index_min = self._calibration_ws.getIndexFromSpectrumNumber(int(self._spectra_range[0]))
index_max = self._calibration_ws.getIndexFromSpectrumNumber(int(self._spectra_range[1]))
CropWorkspace(InputWorkspace=self._calibration_ws,
OutputWorkspace=self._calibration_ws,
StartWorkspaceIndex=index_min,
EndWorkspaceIndex=index_max)
Divide(LHSWorkspace=ws_name,
RHSWorkspace=self._calibration_ws,
OutputWorkspace=ws_name)
# Scale detector data by monitor intensities
scale_detectors(ws_name, 'Indirect')
# Remove the no longer needed monitor workspace
DeleteWorkspace(monitor_ws_name)
# Convert to energy
ConvertUnits(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
Target='DeltaE',
EMode='Indirect')
CorrectKiKf(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
EMode='Indirect')
# Handle rebinning
rebin_reduction(ws_name,
self._rebin_string,
rebin_string_2,
num_bins)
# Detailed balance
if self._detailed_balance != Property.EMPTY_DBL:
corr_factor = 11.606 / (2 * self._detailed_balance)
ExponentialCorrection(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
C0=1.0,
C1=corr_factor,
Operation='Multiply')
# Scale
if self._scale_factor != 1.0:
Scale(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
Factor=self._scale_factor,
Operation='Multiply')
# Group spectra
group_spectra(ws_name,
masked_detectors=masked_detectors,
method=self._grouping_method,
group_file=self._grouping_map_file,
group_ws=self._grouping_ws)
if self._fold_multiple_frames and is_multi_frame:
fold_chopped(c_ws_name)
# Convert to output units if needed
if self._output_x_units != 'DeltaE':
ConvertUnits(InputWorkspace=c_ws_name,
OutputWorkspace=c_ws_name,
EMode='Indirect',
Target=self._output_x_units)
# Rename output workspaces
output_workspace_names = [rename_reduction(ws_name, self._sum_files) for ws_name in self._workspace_names]
summary_prog = Progress(self, start=0.9, end=1.0, nreports=4)
# Save result workspaces
if self._save_formats is not None:
summary_prog.report('saving')
save_reduction(output_workspace_names,
self._save_formats,
self._output_x_units)
# Group result workspaces
summary_prog.report('grouping workspaces')
GroupWorkspaces(InputWorkspaces=output_workspace_names,
OutputWorkspace=self._output_ws)
self.setProperty('OutputWorkspace', mtd[self._output_ws])
# Plot result workspaces
if self._plot_type != 'None':
summary_prog.report('Plotting')
for ws_name in mtd[self._output_ws].getNames():
plot_reduction(ws_name, self._plot_type)
summary_prog.report('Algorithm complete')
def PyExec(self):
from IndirectReductionCommon import (get_multi_frame_rebin,
identify_bad_detectors,
unwrap_monitor,
process_monitor_efficiency,
scale_monitor,
scale_detectors,
rebin_reduction,
group_spectra,
fold_chopped,
rename_reduction)
self._setup()
load_opts = dict()
if self._instrument_name == 'VESUVIO':
load_opts['InstrumentParFile'] = self._par_filename
load_opts['Mode'] = 'FoilOut'
load_opts['LoadMonitors'] = True
self._workspace_names, self._chopped_data = load_file_ranges(self._data_files,
self._ipf_filename,
self._spectra_range[0],
self._spectra_range[1],
sum_files=self._sum_files,
load_logs=self._load_logs,
load_opts=load_opts)
# applies the changes in the provided calibration file
self._apply_calibration()
# Load container if run is given
self._load_and_scale_container(self._container_scale_factor, load_opts)
# Load vanadium runs if given
if self._vanadium_runs:
self._vanadium_ws, _ = load_files(self._vanadium_runs,
self._ipf_filename,
self._spectra_range[0],
self._spectra_range[1],
load_logs=self._load_logs,
load_opts=load_opts)
if len(self._workspace_names) > len(self._vanadium_runs):
raise RuntimeError("There cannot be more sample runs than vanadium runs.")
for index, c_ws_name in enumerate(self._workspace_names):
is_multi_frame = isinstance(mtd[c_ws_name], WorkspaceGroup)
# Get list of workspaces
if is_multi_frame:
workspaces = mtd[c_ws_name].getNames()
else:
workspaces = [c_ws_name]
# Process rebinning for framed data
rebin_string_2, num_bins = get_multi_frame_rebin(c_ws_name,
self._rebin_string)
masked_detectors = identify_bad_detectors(workspaces[0])
# Process workspaces
for ws_name in workspaces:
monitor_ws_name = ws_name + '_mon'
# Subtract empty container if there is one
if self._container_workspace is not None:
Minus(LHSWorkspace=ws_name,
RHSWorkspace=self._container_workspace,
OutputWorkspace=ws_name)
if self._vanadium_ws:
van_ws_name = self._vanadium_ws[index]
van_ws = mtd[van_ws_name]
if self._container_workspace is not None:
cont_ws = mtd[self._container_workspace]
if van_ws.blocksize() > cont_ws.blocksize():
RebinToWorkspace(WorkspaceToRebin=van_ws_name,
WorkspaceToMatch=self._container_workspace,
OutputWorkspace=van_ws_name)
elif cont_ws.blocksize() > van_ws.blocksize():
RebinToWorkspace(WorkspaceToRebin=self._container_workspace,
WorkspaceToMatch=van_ws_name,
OutputWorkspace=self._container_workspace)
Minus(LHSWorkspace=van_ws_name,
RHSWorkspace=self._container_workspace,
OutputWorkspace=van_ws_name)
if mtd[ws_name].blocksize() > van_ws.blocksize():
RebinToWorkspace(WorkspaceToRebin=ws_name,
WorkspaceToMatch=van_ws_name,
OutputWorkspace=ws_name)
elif van_ws.blocksize() > mtd[ws_name].blocksize():
RebinToWorkspace(WorkspaceToRebin=van_ws_name,
WorkspaceToMatch=ws_name,
OutputWorkspace=van_ws_name)
Divide(LHSWorkspace=ws_name,
RHSWorkspace=van_ws_name,
OutputWorkspace=ws_name,
AllowDifferentNumberSpectra=True)
# Process monitor
if not unwrap_monitor(ws_name):
ConvertUnits(InputWorkspace=monitor_ws_name,
OutputWorkspace=monitor_ws_name,
Target='Wavelength',
EMode='Elastic')
process_monitor_efficiency(ws_name)
scale_monitor(ws_name)
# Scale detector data by monitor intensities
scale_detectors(ws_name, 'Elastic')
# Remove the no longer needed monitor workspace
DeleteWorkspace(monitor_ws_name)
# Convert to dSpacing
ConvertUnits(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
Target='dSpacing',
EMode='Elastic')
# Handle rebinning
rebin_reduction(ws_name,
self._rebin_string,
rebin_string_2,
num_bins)
# Group spectra
group_spectra(ws_name,
masked_detectors=masked_detectors,
method=self._grouping_method,
group_ws=self._grouping_workspace)
if is_multi_frame:
fold_chopped(c_ws_name)
# Remove the container workspaces
if self._container_workspace is not None:
self._delete_all([self._container_workspace])
# Remove the vanadium workspaces
if self._vanadium_ws:
self._delete_all(self._vanadium_ws)
# Rename output workspaces
output_workspace_names = [rename_reduction(ws_name, self._sum_files) for ws_name in self._workspace_names]
# Group result workspaces
GroupWorkspaces(InputWorkspaces=output_workspace_names,
OutputWorkspace=self._output_ws)
self.setProperty('OutputWorkspace', self._output_ws)
def PyExec(self):
from IndirectReductionCommon import (load_files,
get_multi_frame_rebin,
identify_bad_detectors,
unwrap_monitor,
process_monitor_efficiency,
scale_monitor,
scale_detectors,
rebin_reduction,
group_spectra,
fold_chopped,
rename_reduction,
save_reduction,
plot_reduction)
self._setup()
self._workspace_names, self._chopped_data = load_files(self._data_files,
self._ipf_filename,
self._spectra_range[0],
self._spectra_range[1],
self._sum_files,
self._load_logs)
for c_ws_name in self._workspace_names:
is_multi_frame = isinstance(mtd[c_ws_name], WorkspaceGroup)
# Get list of workspaces
if is_multi_frame:
workspaces = mtd[c_ws_name].getNames()
else:
workspaces = [c_ws_name]
# Process rebinning for framed data
rebin_string_2, num_bins = get_multi_frame_rebin(c_ws_name,
self._rebin_string)
masked_detectors = identify_bad_detectors(workspaces[0])
# Process workspaces
for ws_name in workspaces:
monitor_ws_name = ws_name + '_mon'
# Process monitor
if not unwrap_monitor(ws_name):
ConvertUnits(InputWorkspace=monitor_ws_name,
OutputWorkspace=monitor_ws_name,
Target='Wavelength',
EMode='Elastic')
process_monitor_efficiency(ws_name)
scale_monitor(ws_name)
# Do background removal if a range was provided
if self._background_range is not None:
ConvertToDistribution(Workspace=ws_name)
CalculateFlatBackground(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
StartX=self._background_range[0],
EndX=self._background_range[1],
Mode='Mean')
ConvertFromDistribution(Workspace=ws_name)
# Divide by the calibration workspace if one was provided
if self._calibration_ws is not None:
Divide(LHSWorkspace=ws_name,
RHSWorkspace=self._calibration_ws,
OutputWorkspace=ws_name)
# Scale detector data by monitor intensities
scale_detectors(ws_name, 'Indirect')
# Remove the no longer needed monitor workspace
DeleteWorkspace(monitor_ws_name)
# Convert to energy
ConvertUnits(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
Target='DeltaE',
EMode='Indirect')
CorrectKiKf(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
EMode='Indirect')
# Handle rebinning
rebin_reduction(ws_name,
self._rebin_string,
rebin_string_2,
num_bins)
# Detailed balance
if self._detailed_balance is not None:
corr_factor = 11.606 / (2 * self._detailed_balance)
ExponentialCorrection(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
C0=1.0,
C1=corr_factor,
Operation='Multiply')
# Scale
if self._scale_factor != 1.0:
Scale(InputWorkspaces=ws_name,
OutputWorkspace=ws_name,
Factor=self._scale_factor,
Operation='Multiply')
# Group spectra
group_spectra(ws_name,
masked_detectors,
self._grouping_method,
self._grouping_map_file,
self._grouping_ws)
if self._fold_multiple_frames and is_multi_frame:
fold_chopped(c_ws_name)
# Convert to output units if needed
if self._output_x_units != 'DeltaE':
ConvertUnits(InputWorkspace=c_ws_name,
OutputWorkspace=c_ws_name,
EMode='Indirect',
Target=self._output_x_units)
# Rename output workspaces
output_workspace_names = [rename_reduction(ws_name, self._sum_files) for ws_name in self._workspace_names]
# Save result workspaces
if self._save_formats is not None:
save_reduction(output_workspace_names,
self._save_formats,
self._output_x_units)
# Group result workspaces
GroupWorkspaces(InputWorkspaces=output_workspace_names,
OutputWorkspace=self._output_ws)
self.setProperty('OutputWorkspace', self._output_ws)
# Plot result workspaces
if self._plot_type != 'None':
for ws_name in mtd[self._output_ws].getNames():
plot_reduction(ws_name, self._plot_type)
def PyExec(self):
from IndirectReductionCommon import (load_files,
get_multi_frame_rebin,
identify_bad_detectors,
unwrap_monitor,
process_monitor_efficiency,
scale_monitor,
scale_detectors,
rebin_reduction,
group_spectra,
fold_chopped,
rename_reduction)
self._setup()
load_opts = dict()
if self._instrument_name == 'VESUVIO':
load_opts['Mode'] = 'FoilOut'
self._workspace_names, self._chopped_data = load_files(self._data_files,
self._ipf_filename,
self._spectra_range[0],
self._spectra_range[1],
sum_files=self._sum_files,
load_logs=self._load_logs,
load_opts=load_opts)
# Load container if run is given
if self._container_data_files is not None:
self._container_workspace, _ = load_files(self._container_data_files,
self._ipf_filename,
self._spectra_range[0],
self._spectra_range[1],
sum_files=True,
load_logs=self._load_logs,
load_opts=load_opts)
self._container_workspace = self._container_workspace[0]
# Scale container if factor is given
if self._container_scale_factor != 1.0:
Scale(InputWorkspace=self._container_workspace,
OutputWorkspace=self._container_workspace,
Factor=self._container_scale_factor,
Operation='Multiply')
for c_ws_name in self._workspace_names:
is_multi_frame = isinstance(mtd[c_ws_name], WorkspaceGroup)
# Get list of workspaces
if is_multi_frame:
workspaces = mtd[c_ws_name].getNames()
else:
workspaces = [c_ws_name]
# Process rebinning for framed data
rebin_string_2, num_bins = get_multi_frame_rebin(c_ws_name,
self._rebin_string)
masked_detectors = identify_bad_detectors(workspaces[0])
# Process workspaces
for ws_name in workspaces:
# Subtract empty container if there is one
if self._container_workspace is not None:
Minus(LHSWorkspace=ws_name,
RHSWorkspace=self._container_workspace,
OutputWorkspace=ws_name)
monitor_ws_name = ws_name + '_mon'
# Process monitor
if not unwrap_monitor(ws_name):
ConvertUnits(InputWorkspace=monitor_ws_name,
OutputWorkspace=monitor_ws_name,
Target='Wavelength',
EMode='Elastic')
process_monitor_efficiency(ws_name)
scale_monitor(ws_name)
# Scale detector data by monitor intensities
scale_detectors(ws_name, 'Elastic')
# Remove the no longer needed monitor workspace
DeleteWorkspace(monitor_ws_name)
# Convert to dSpacing
ConvertUnits(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
Target='dSpacing',
EMode='Elastic')
# Handle rebinning
rebin_reduction(ws_name,
self._rebin_string,
rebin_string_2,
num_bins)
# Group spectra
group_spectra(ws_name,
masked_detectors,
self._grouping_method)
if is_multi_frame:
fold_chopped(c_ws_name)
# Remove the container workspaces
if self._container_workspace is not None:
DeleteWorkspace(self._container_workspace)
DeleteWorkspace(self._container_workspace + '_mon')
# Rename output workspaces
output_workspace_names = [rename_reduction(ws_name, self._sum_files) for ws_name in self._workspace_names]
# Group result workspaces
GroupWorkspaces(InputWorkspaces=output_workspace_names,
OutputWorkspace=self._output_ws)
self.setProperty('OutputWorkspace', self._output_ws)
def PyExec(self):
warnings.warn("This algorithm is depreciated (April-2017). Please use ISISIndirectDiffractionReduction")
from IndirectReductionCommon import (load_files,
get_multi_frame_rebin,
identify_bad_detectors,
unwrap_monitor,
process_monitor_efficiency,
scale_monitor,
scale_detectors,
rebin_reduction,
group_spectra,
fold_chopped,
rename_reduction)
self._setup()
load_opts = dict()
load_opts['Mode'] = 'FoilOut'
load_opts['InstrumentParFile'] = self._par_filename
# Tell LoadVesuvio to load the monitors and keep them in the output
load_opts['LoadMonitors'] = True
prog_reporter = Progress(self, start=0.0, end=1.0, nreports=1)
prog_reporter.report("Loading Files")
self._workspace_names, self._chopped_data = load_files(self._data_files,
ipf_filename=self._ipf_filename,
spec_min=self._spectra_range[0],
spec_max=self._spectra_range[1],
sum_files=self._sum_files,
load_opts=load_opts)
prog_reporter.resetNumSteps(len(self._workspace_names), 0.0, 1.0)
for c_ws_name in self._workspace_names:
is_multi_frame = isinstance(mtd[c_ws_name], WorkspaceGroup)
# Get list of workspaces
if is_multi_frame:
workspaces = mtd[c_ws_name].getNames()
else:
workspaces = [c_ws_name]
# Process rebinning for framed data
rebin_string_2, num_bins = get_multi_frame_rebin(c_ws_name,
self._rebin_string)
masked_detectors = identify_bad_detectors(workspaces[0])
# Process workspaces
for ws_name in workspaces:
monitor_ws_name = ws_name + '_mon'
# Process monitor
if not unwrap_monitor(ws_name):
ConvertUnits(InputWorkspace=monitor_ws_name,
OutputWorkspace=monitor_ws_name,
Target='Wavelength',
EMode='Elastic')
process_monitor_efficiency(ws_name)
scale_monitor(ws_name)
# Scale detector data by monitor intensities
scale_detectors(ws_name, 'Elastic')
# Remove the no longer needed monitor workspace
DeleteWorkspace(monitor_ws_name)
# Convert to dSpacing
ConvertUnits(InputWorkspace=ws_name,
OutputWorkspace=ws_name,
Target='dSpacing',
EMode='Elastic')
# Handle rebinning
rebin_reduction(ws_name,
self._rebin_string,
rebin_string_2,
num_bins)
# Group spectra
group_spectra(ws_name,
masked_detectors,
self._grouping_method)
if is_multi_frame:
fold_chopped(c_ws_name)
prog_reporter.report()
# Rename output workspaces
output_workspace_names = [rename_reduction(ws_name, self._sum_files) for ws_name in self._workspace_names]
# Group result workspaces
GroupWorkspaces(InputWorkspaces=output_workspace_names,
OutputWorkspace=self._output_ws)
self.setProperty('OutputWorkspace', self._output_ws)
def PyExec(self):
"""
Execute the algorithm in diffraction-only mode
"""
# Load all sample, vanadium files
ipf_file_name = 'OSIRIS_diffraction_diffonly_Parameters.xml'
load_opts = {"DeleteMonitors": True}
sample_ws_names, _ = load_files(self._sample_runs,
ipf_file_name,
self._spec_min,
self._spec_max,
load_logs=self._load_logs,
load_opts=load_opts)
# Add the sample workspaces to the sample d-range map
self._sam_ws_map.add_workspaces(
[mtd[sample_ws_name] for sample_ws_name in sample_ws_names],
rebin_and_average)
vanadium_ws_names, _ = load_files(self._vanadium_runs,
ipf_file_name,
self._spec_min,
self._spec_max,
load_logs=self._load_logs,
load_opts=load_opts)
# Add the vanadium workspaces to the vanadium drange map
self._van_ws_map.add_workspaces(
[mtd[vanadium_ws_name] for vanadium_ws_name in vanadium_ws_names],
rebin_and_average)
# Load the container run
if self._container_files:
container_ws_names, _ = load_files(self._container_files,
ipf_file_name,
self._spec_min,
self._spec_max,
load_logs=self._load_logs,
load_opts=load_opts)
# Scale the container run if required
if self._container_scale_factor != 1.0:
self._con_ws_map.add_workspaces([
mtd[container_ws_name] * self._container_scale_factor
for container_ws_name in container_ws_names
], rebin_and_average)
else:
self._con_ws_map.add_workspaces([
mtd[container_ws_name]
for container_ws_name in container_ws_names
], rebin_and_average)
result_map = self._sam_ws_map.combine(self._con_ws_map,
rebin_and_subtract)
self._delete_workspaces(container_ws_names)
else:
result_map = self._sam_ws_map
# Run necessary algorithms on the Sample workspaces.
self._calibrate_runs_in_map(result_map)
# Run necessary algorithms on the Vanadium workspaces.
self._calibrate_runs_in_map(self._van_ws_map)
# Divide all sample files by the corresponding vanadium files.
result_map = result_map.combine(self._van_ws_map, divide_workspace)
# Workspaces in vanadium map are no longer in the ADS - can safely delete
# vanadium workspaces in ADS.
self._delete_workspaces(vanadium_ws_names)
# Workspaces in sample map are no longer in the ADS - can safely delete
# sample workspaces in the ADS.
self._delete_workspaces(sample_ws_names)
if len(result_map) > 1:
# Workspaces must be added to the ADS, as there does not yet exist
# a workspace list property (must be passed to merge runs by name).
for sample_ws_name, sample_ws in zip(sample_ws_names,
result_map.values()):
mtd.addOrReplace(sample_ws_name, sample_ws)
# Merge the sample files into one.
output_ws = MergeRuns(InputWorkspaces=sample_ws_names,
OutputWorkspace="merged_sample_runs",
StoreInADS=False,
EnableLogging=False)
self._delete_workspaces(sample_ws_names)
elif len(result_map) == 1:
output_ws = list(result_map.values())[0]
else:
logger.error("D-Ranges found in runs have no overlap:\n" +
"Found Sample D-Ranges: " +
", ".join(map(str, self._sam_ws_map.keys())) + "\n" +
"Found Container D-Ranges: " +
", ".join(map(str, self._con_ws_map.keys())) + "\n" +
"Found Vanadium D-Ranges: " +
", ".join(map(str, self._van_ws_map.keys())))
return
if self._output_ws_name:
mtd.addOrReplace(self._output_ws_name, output_ws)
d_ranges = result_map.keys()
AddSampleLog(Workspace=output_ws,
LogName="D-Ranges",
LogText="D-Ranges used for reduction: " +
", ".join(map(str, d_ranges)))
# Create scalar data to cope with where merge has combined overlapping data.
intersections = get_intersection_of_ranges(d_ranges)
data_x = output_ws.dataX(0)
data_y = []
data_e = []
for i in range(0, len(data_x) - 1):
x_val = (data_x[i] + data_x[i + 1]) / 2.0
if is_in_ranges(intersections, x_val):
data_y.append(2)
data_e.append(2)
else:
data_y.append(1)
data_e.append(1)
# apply scalar data to result workspace
for i in range(0, output_ws.getNumberHistograms()):
result_y = output_ws.dataY(i)
result_e = output_ws.dataE(i)
result_y = result_y / data_y
result_e = result_e / data_e
output_ws.setY(i, result_y)
output_ws.setE(i, result_e)
self.setProperty("OutputWorkspace", output_ws)
def PyExec(self):
"""
Execute the algorithm in diffraction-only mode
"""
# Load all sample, vanadium files
ipf_file_name = 'OSIRIS_diffraction_diffonly_Parameters.xml'
load_opts = {"DeleteMonitors": True}
sample_ws_names, _ = load_files(self._sample_runs,
ipf_file_name,
self._spec_min,
self._spec_max,
load_logs=self._load_logs,
load_opts=load_opts)
vanadium_ws_names, _ = load_files(self._vanadium_runs,
ipf_file_name,
self._spec_min,
self._spec_max,
load_logs=self._load_logs,
load_opts=load_opts)
container_ws_names = []
container_workspaces = []
# Load the container run
if self._container_files:
container_ws_names, _ = load_files(self._container_files,
ipf_file_name,
self._spec_min,
self._spec_max,
load_logs=self._load_logs,
load_opts=load_opts)
# Scale the container run if required
if self._container_scale_factor != 1.0:
# Retrieve function pointers in advance to avoid expensive hash
# function on each loop iteration (improves performance)
scale_get_property, scale_set_property, scale_exec = self._init_child_algorithm(
"Scale")
scale_set_property("Operation", "Multiply")
# Scale every container workspace
for container_ws_name in container_ws_names:
scale_set_property("InputWorkspace", container_ws_name)
scale_set_property("Factor", self._container_scale_factor)
scale_exec()
container_workspaces.append(
scale_get_property("OutputWorkspace").value)
else:
container_workspaces = container_ws_names
# Initialize rebin algorithm and retrieve function pointers to improve performance
rebin_get_property, rebin_set_property, rebin_exec \
= self._init_child_algorithm("RebinToWorkspace")
# Initialize minus algorithm and retrieve function pointers to improve performance
minus_get_property, minus_set_property, minus_exec \
= self._init_child_algorithm("Minus")
# Add the sample workspaces to the dRange to sample map
for idx, sample_ws_name in enumerate(sample_ws_names):
if container_workspaces:
rebin_set_property("WorkspaceToRebin",
container_workspaces[idx])
rebin_set_property("WorkspaceToMatch", sample_ws_name)
rebin_exec()
minus_set_property("LHSWorkspace", sample_ws_name)
minus_set_property("RHSWorkspace",
rebin_get_property("OutputWorkspace").value)
minus_exec()
sample_ws = minus_get_property("OutputWorkspace").value
else:
sample_ws = mtd[sample_ws_name]
if self._man_d_range is not None and idx < len(self._man_d_range):
self._sam_ws_map.add_ws(sample_ws, self._man_d_range[idx])
else:
self._sam_ws_map.add_ws(sample_ws)
# Initialize delete workspace algorithm and retrieve function pointers
# to improve performance
_, delete_set_property, delete_exec \
= self._init_child_algorithm("DeleteWorkspace")
# Finished with container workspaces, so delete them
for container_ws_name in container_ws_names:
delete_set_property("Workspace", container_ws_name)
delete_exec()
# Add the vanadium workspaces to the vanadium drange map
self._add_to_drange_map(vanadium_ws_names, self._van_ws_map)
# Check to make sure that there are corresponding vanadium files with the same DRange for each sample file.
for d_range in self._sam_ws_map:
if d_range not in self._van_ws_map:
raise RuntimeError("There is no van file that covers the " +
str(d_range) + " DRange.")
# Average together any sample workspaces with the same DRange.
# This will mean our map of DRanges to list of workspaces becomes a map
# of DRanges, each to a *single* workspace.
self._sam_ws_map.average_across_dranges()
# Now do the same to the vanadium workspaces
self._van_ws_map.average_across_dranges()
# Create NormaliseByCurrent algorithm and retrieve function pointers to improve performance
normalise_get_property, normalise_set_property, normalise_exec \
= self._init_child_algorithm("NormaliseByCurrent")
# Create AlignDetectors algorithm and retrieve function pointers to improve performance
align_get_property, align_set_property, align_exec \
= self._init_child_algorithm("AlignDetectors")
# Create DiffractionFocussing algorithm and retrieve function pointers to improve performance
diff_focus_get_property, diff_focus_set_property, diff_focus_exec \
= self._init_child_algorithm("DiffractionFocussing")
# Create CropWorkspace algorithm and retrieve function pointers to improve performance
crop_get_property, crop_set_property, crop_exec \
= self._init_child_algorithm("CropWorkspace")
# Run necessary algorithms on the Sample workspaces.
for d_range, wrksp in self._sam_ws_map.items():
normalise_set_property("InputWorkspace", wrksp)
normalise_exec()
align_set_property("InputWorkspace",
normalise_get_property("OutputWorkspace").value)
align_set_property("CalibrationFile", self._cal)
align_exec()
diff_focus_set_property(
"InputWorkspace",
align_get_property("OutputWorkspace").value)
diff_focus_set_property("GroupingFileName", self._cal)
diff_focus_exec()
crop_set_property("InputWorkspace",
diff_focus_get_property("OutputWorkspace").value)
crop_set_property("XMin", d_range[0])
crop_set_property("XMax", d_range[1])
crop_exec()
self._sam_ws_map[d_range] = crop_get_property(
"OutputWorkspace").value
# Run necessary algorithms on the Vanadium workspaces.
for d_range, wrksp in self._van_ws_map.items():
normalise_set_property("InputWorkspace", wrksp)
normalise_exec()
align_set_property("InputWorkspace",
normalise_get_property("OutputWorkspace").value)
align_set_property("CalibrationFile", self._cal)
align_exec()
diff_focus_set_property(
"InputWorkspace",
align_get_property("OutputWorkspace").value)
diff_focus_set_property("GroupingFileName", self._cal)
diff_focus_exec()
crop_set_property("InputWorkspace",
diff_focus_get_property("OutputWorkspace").value)
crop_set_property("XMin", d_range[0])
crop_set_property("XMax", d_range[1])
crop_exec()
self._van_ws_map[d_range] = crop_get_property(
"OutputWorkspace").value
# Workspaces in vanadium map are no longer in the ADS - can safely delete
# vanadium workspaces in ADS.
self._delete_workspaces(vanadium_ws_names, delete_set_property,
delete_exec)
# Divide all sample files by the corresponding vanadium files.
divided = self._divide_all_by(self._sam_ws_map.values(),
self._van_ws_map.values())
# Workspaces must be added to the ADS, as there does not yet exist
# a workspace list property (must be passed to merge runs by name).
for sample_ws_name, sample_ws in zip(sample_ws_names, divided):
mtd.addOrReplace(sample_ws_name, sample_ws)
if len(divided) > 1:
# Merge the sample files into one.
merge_runs_alg = self.createChildAlgorithm("MergeRuns",
enableLogging=False)
merge_runs_alg.setProperty("InputWorkspaces", sample_ws_names)
merge_runs_alg.execute()
output_ws = merge_runs_alg.getProperty("OutputWorkspace").value
else:
output_ws = divided[0]
# Sample workspaces are now finished with and can be deleted
# safely from the ADS.
self._delete_workspaces(sample_ws_names, delete_set_property,
delete_exec)
mtd.addOrReplace(self._output_ws_name, output_ws)
add_log_alg = self.createChildAlgorithm("AddSampleLog",
enableLogging=False)
add_log_alg.setProperty("Workspace", output_ws)
add_log_alg.setProperty("LogName", "D-Ranges")
add_log_alg.setProperty(
"LogText",
"D-Ranges used for reduction: " + self.getPropertyValue("Drange"))
result = mtd[self._output_ws_name]
# Create scalar data to cope with where merge has combined overlapping data.
intersections = get_intersection_of_ranges(self._sam_ws_map.keys())
data_x = result.dataX(0)
data_y = []
data_e = []
for i in range(0, len(data_x) - 1):
x_val = (data_x[i] + data_x[i + 1]) / 2.0
if is_in_ranges(intersections, x_val):
data_y.append(2)
data_e.append(2)
else:
data_y.append(1)
data_e.append(1)
# apply scalar data to result workspace
for i in range(0, result.getNumberHistograms()):
result_y = result.dataY(i)
result_e = result.dataE(i)
result_y = result_y / data_y
result_e = result_e / data_e
result.setY(i, result_y)
result.setE(i, result_e)
self.setProperty("OutputWorkspace", result)