def do_reduction(self, reduction_alg, reduction_setting_bundles, use_optimizations, progress):
"""
Perform the main reduction.
:param reduction_alg: SANSReductionCore algorithm
:param reduction_setting_bundles: a list of lists containing workspaces to be reduced.
The outer list is for compatibility with version 2
and only contains one inner list
:param use_optimizations: bool. If true, use can optimizations
:param progress: a progress bar
:return: output_bundles: a list containing a single list of output workspaces
output_parts_bundles: a list containing a single list of output workspaces
output_transmission_bundles: a list containing transmission workspaces
"""
reduction_setting_bundles = reduction_setting_bundles[0]
output_bundles = []
output_parts_bundles = []
output_transmission_bundles = []
for reduction_setting_bundle in reduction_setting_bundles:
progress.report("Running a single reduction ...")
# We want to make use of optimizations here. If a can workspace has already been reduced with the same can
# settings and is stored in the ADS, then we should use it (provided the user has optimizations enabled).
if use_optimizations and reduction_setting_bundle.data_type is DataType.CAN:
output_bundle, output_parts_bundle, \
output_transmission_bundle = run_optimized_for_can(reduction_alg, reduction_setting_bundle)
else:
output_bundle, output_parts_bundle, \
output_transmission_bundle = run_core_reduction(reduction_alg, reduction_setting_bundle)
output_bundles.append(output_bundle)
output_parts_bundles.append(output_parts_bundle)
output_transmission_bundles.append(output_transmission_bundle)
return [output_bundles], [output_parts_bundles], output_transmission_bundles
def do_reduction(self, reduction_alg, reduction_setting_bundles,
use_optimizations, progress):
"""
Perform the main reduction
:param reduction_alg: SANSReductionCoreEventSlice algorithm
:param reduction_setting_bundles: a list of list containing workspaces to be reduced.
:param use_optimizations: bool. If true, use can optimizations in reduction
:param progress: a progress bar
:return: output_bundles: a list of list containing output workspaces
output_parts_bundles: a list of lists containing output workspaces
output_transmission_bundles: a list containing transmission workspaces
"""
output_bundles = []
output_parts_bundles = []
output_transmission_bundles = []
for event_slice_bundles in reduction_setting_bundles:
# Output bundles and parts bundles need to be separated by a event slices, but grouped by component
# e.g. [[workspaces for slice1], [workspaces for slice2]]
slice_bundles = []
slice_parts_bundles = []
for slice_bundle in event_slice_bundles:
progress.report("Running a single reduction ...")
# We want to make use of optimizations here.
# If a can workspace has already been reduced with the same can
# settings and is stored in the ADS, then we should use it
# (provided the user has optimizations enabled).
if use_optimizations and slice_bundle.data_type is DataType.Can:
output_bundle, output_parts_bundle, \
output_transmission_bundle = run_optimized_for_can(reduction_alg,
slice_bundle,
event_slice_optimisation=True)
else:
output_bundle, output_parts_bundle, \
output_transmission_bundle = run_core_event_slice_reduction(reduction_alg, slice_bundle)
slice_bundles.append(output_bundle)
slice_parts_bundles.append(output_parts_bundle)
output_transmission_bundles.append(output_transmission_bundle)
output_bundles.append(slice_bundles)
output_parts_bundles.append(slice_parts_bundles)
return output_bundles, output_parts_bundles, output_transmission_bundles
def PyExec(self):
# Get state
state = self._get_state()
# Get reduction mode
overall_reduction_mode = self._get_reduction_mode(state)
# Decide which core reduction information to run, i.e. HAB, LAB, ALL, MERGED. In the case of ALL and MERGED,
# the required simple reduction modes need to be run. Normally this is HAB and LAB, future implementations
# might have more detectors though (or different types)
reduction_setting_bundles = self._get_reduction_setting_bundles(
state, overall_reduction_mode)
# Run core reductions
use_optimizations = self.getProperty("UseOptimizations").value
# Create the reduction core algorithm
reduction_name = "SANSReductionCore"
reduction_options = {}
reduction_alg = create_child_algorithm(self, reduction_name,
**reduction_options)
# Set up progress
progress = self._get_progress(len(reduction_setting_bundles),
overall_reduction_mode)
# --------------------------------------------------------------------------------------------------------------
# Reduction
# --------------------------------------------------------------------------------------------------------------
output_bundles = []
output_parts_bundles = []
for reduction_setting_bundle in reduction_setting_bundles:
progress.report("Running a single reduction ...")
# We want to make use of optimizations here. If a can workspace has already been reduced with the same can
# settings and is stored in the ADS, then we should use it (provided the user has optimizations enabled).
if use_optimizations and reduction_setting_bundle.data_type is DataType.Can:
output_bundle, output_parts_bundle = run_optimized_for_can(
reduction_alg, reduction_setting_bundle)
else:
output_bundle, output_parts_bundle = run_core_reduction(
reduction_alg, reduction_setting_bundle)
output_bundles.append(output_bundle)
output_parts_bundles.append(output_parts_bundle)
# --------------------------------------------------------------------------------------------------------------
# Deal with merging
# --------------------------------------------------------------------------------------------------------------
reduction_mode_vs_output_workspaces = {}
# Merge if required with stitching etc.
if overall_reduction_mode is ReductionMode.Merged:
progress.report("Merging reductions ...")
merge_bundle = get_merge_bundle_for_merge_request(
output_parts_bundles, self)
self.set_shift_and_scale_output(merge_bundle)
reduction_mode_vs_output_workspaces.update(
{ReductionMode.Merged: merge_bundle.merged_workspace})
# --------------------------------------------------------------------------------------------------------------
# Deal with non-merged
# Note that we have non-merged workspaces even in the case of a merged reduction, ie LAB and HAB results
# --------------------------------------------------------------------------------------------------------------
progress.report("Final clean up...")
output_workspaces_non_merged = get_final_output_workspaces(
output_bundles, self)
reduction_mode_vs_output_workspaces.update(
output_workspaces_non_merged)
# --------------------------------------------------------------------------------------------------------------
# Set the output workspaces
# --------------------------------------------------------------------------------------------------------------
# Set sample logs
# Todo: Set sample log -> Userfile and unfitted transmission workspace. Should probably set on
# higher level (SANSBatch)
# Set the output workspaces
self.set_output_workspaces(reduction_mode_vs_output_workspaces)
# --------------------------------------------------------------------------------------------------------------
# Set the reduced can workspaces on the output if optimizations are
# enabled. This will allow SANSBatchReduction to add them to the ADS.
# --------------------------------------------------------------------------------------------------------------
if use_optimizations:
self.set_reduced_can_workspace_on_output(output_bundles,
output_parts_bundles)
def PyExec(self):
# Get state
state = self._get_state()
# Get reduction mode
overall_reduction_mode = self._get_reduction_mode(state)
# Decide which core reduction information to run, i.e. HAB, LAB, ALL, MERGED. In the case of ALL and MERGED,
# the required simple reduction modes need to be run. Normally this is HAB and LAB, future implementations
# might have more detectors though (or different types)
reduction_setting_bundles = self._get_reduction_setting_bundles(state, overall_reduction_mode)
# Run core reductions
use_optimizations = self.getProperty("UseOptimizations").value
save_can = self.getProperty("SaveCan").value
# Create the reduction core algorithm
reduction_name = "SANSReductionCore"
reduction_options = {}
reduction_alg = create_child_algorithm(self, reduction_name, **reduction_options)
# Set up progress
progress = self._get_progress(len(reduction_setting_bundles), overall_reduction_mode)
# --------------------------------------------------------------------------------------------------------------
# Reduction
# --------------------------------------------------------------------------------------------------------------
output_bundles = []
output_parts_bundles = []
output_transmission_bundles = []
for reduction_setting_bundle in reduction_setting_bundles:
progress.report("Running a single reduction ...")
# We want to make use of optimizations here. If a can workspace has already been reduced with the same can
# settings and is stored in the ADS, then we should use it (provided the user has optimizations enabled).
if use_optimizations and reduction_setting_bundle.data_type is DataType.Can:
output_bundle, output_parts_bundle, output_transmission_bundle = run_optimized_for_can(reduction_alg,
reduction_setting_bundle)
else:
output_bundle, output_parts_bundle, output_transmission_bundle = run_core_reduction(reduction_alg,
reduction_setting_bundle)
output_bundles.append(output_bundle)
output_parts_bundles.append(output_parts_bundle)
output_transmission_bundles.append(output_transmission_bundle)
reduction_mode_vs_output_workspaces = {}
# --------------------------------------------------------------------------------------------------------------
# Deal with non-merged
# Note that we have non-merged workspaces even in the case of a merged reduction, ie LAB and HAB results
# --------------------------------------------------------------------------------------------------------------
progress.report("Final clean up...")
output_workspaces_non_merged = get_final_output_workspaces(output_bundles, self)
reduction_mode_vs_output_workspaces.update(output_workspaces_non_merged)
# --------------------------------------------------------------------------------------------------------------
# Deal with merging
# --------------------------------------------------------------------------------------------------------------
# Merge if required with stitching etc.
if overall_reduction_mode is ReductionMode.Merged:
progress.report("Merging reductions ...")
merge_bundle = get_merge_bundle_for_merge_request(output_parts_bundles, self)
self.set_shift_and_scale_output(merge_bundle)
reduction_mode_vs_output_workspaces.update({ReductionMode.Merged: merge_bundle.merged_workspace})
scaled_HAB = strip_end_nans(merge_bundle.scaled_hab_workspace, self)
reduction_mode_vs_output_workspaces.update({ISISReductionMode.HAB: scaled_HAB})
# --------------------------------------------------------------------------------------------------------------
# Set the output workspaces
# --------------------------------------------------------------------------------------------------------------
# Set sample logs
# Todo: Set sample log -> Userfile and unfitted transmission workspace. Should probably set on
# higher level (SANSBatch)
# Set the output workspaces
self.set_output_workspaces(reduction_mode_vs_output_workspaces)
# --------------------------------------------------------------------------------------------------------------
# Set the reduced can workspaces on the output if optimizations are
# enabled. This will allow SANSBatchReduction to add them to the ADS.
# --------------------------------------------------------------------------------------------------------------
if use_optimizations:
self.set_reduced_can_workspace_on_output(output_bundles, output_parts_bundles)
if save_can:
self.set_can_and_sam_on_output(output_bundles)
self.set_transmission_workspaces_on_output(output_transmission_bundles,
state.adjustment.calculate_transmission.fit)
