def set_detector_names(state, ipf_path, invalid_detector_types=None):
"""
Sets the detectors names on a State object which has a `detector` map entry, e.g. StateMask
:param state: the state object
:param ipf_path: the path to the Instrument Parameter File
:param invalid_detector_types: a list of invalid detector types which don't exist for the instrument
"""
if invalid_detector_types is None:
invalid_detector_types = []
lab_keyword = DetectorType.to_string(DetectorType.LAB)
hab_keyword = DetectorType.to_string(DetectorType.HAB)
detector_names = {
lab_keyword: "low-angle-detector-name",
hab_keyword: "high-angle-detector-name"
}
detector_names_short = {
lab_keyword: "low-angle-detector-short-name",
hab_keyword: "high-angle-detector-short-name"
}
names_to_search = []
names_to_search.extend(list(detector_names.values()))
names_to_search.extend(list(detector_names_short.values()))
found_detector_names = get_named_elements_from_ipf_file(
ipf_path, names_to_search, str)
for detector_type in state.detectors:
try:
if DetectorType.from_string(
detector_type) in invalid_detector_types:
continue
detector_name_tag = detector_names[detector_type]
detector_name_short_tag = detector_names_short[detector_type]
detector_name = found_detector_names[detector_name_tag]
detector_name_short = found_detector_names[detector_name_short_tag]
except KeyError:
continue
state.detectors[detector_type].detector_name = detector_name
state.detectors[
detector_type].detector_name_short = detector_name_short
def set_detector_names(state, ipf_path, invalid_detector_types=None):
"""
Sets the detectors names on a State object which has a `detector` map entry, e.g. StateMask
:param state: the state object
:param ipf_path: the path to the Instrument Parameter File
:param invalid_detector_types: a list of invalid detector types which don't exist for the instrument
"""
if invalid_detector_types is None:
invalid_detector_types = []
lab_keyword = DetectorType.to_string(DetectorType.LAB)
hab_keyword = DetectorType.to_string(DetectorType.HAB)
detector_names = {lab_keyword: "low-angle-detector-name",
hab_keyword: "high-angle-detector-name"}
detector_names_short = {lab_keyword: "low-angle-detector-short-name",
hab_keyword: "high-angle-detector-short-name"}
names_to_search = []
names_to_search.extend(list(detector_names.values()))
names_to_search.extend(list(detector_names_short.values()))
found_detector_names = get_named_elements_from_ipf_file(ipf_path, names_to_search, str)
for detector_type in state.detectors:
try:
if DetectorType.from_string(detector_type) in invalid_detector_types:
continue
detector_name_tag = detector_names[detector_type]
detector_name_short_tag = detector_names_short[detector_type]
detector_name = found_detector_names[detector_name_tag]
detector_name_short = found_detector_names[detector_name_short_tag]
except KeyError:
continue
state.detectors[detector_type].detector_name = detector_name
state.detectors[detector_type].detector_name_short = detector_name_short
def _get_component(self, workspace):
component_as_string = self.getProperty("Component").value
component = DetectorType.from_string(component_as_string)
return get_component_name(workspace, component)
def _get_component(self):
component_as_string = self.getProperty("Component").value
return DetectorType.from_string(component_as_string)
def _get_component(self):
component_as_string = self.getProperty("Component").value
return DetectorType.from_string(component_as_string)
def _get_component(self, workspace):
component_as_string = self.getProperty("Component").value
component = DetectorType.from_string(component_as_string)
return get_component_name(workspace, component)
def PyExec(self):
# Get the input
state = self._get_state()
state_serialized = state.property_manager
component_as_string = self.getProperty("Component").value
progress = self._get_progress()
# --------------------------------------------------------------------------------------------------------------
# 1. Crop workspace by detector name
# This will create a reduced copy of the original workspace with only those spectra which are relevant
# for this particular reduction.
# --------------------------------------------------------------------------------------------------------------
progress.report("Cropping ...")
workspace = self._get_cropped_workspace(component_as_string)
# --------------------------------------------------------------------------------------------
# 2. Perform dark run subtraction
# This will subtract a dark background from the scatter workspace. Note that dark background subtraction
# will also affect the transmission calculation later on.
# --------------------------------------------------------------------------------------------------------------
# --------------------------------------------------------------------------------------------------------------
# 3. Create event slice
# If we are dealing with an event workspace as input, this will cut out a time-based (user-defined) slice.
# In case of a histogram workspace, nothing happens.
# --------------------------------------------------------------------------------------------------------------
progress.report("Event slicing ...")
data_type_as_string = self.getProperty("DataType").value
monitor_workspace = self._get_monitor_workspace()
workspace, monitor_workspace, slice_event_factor = self._slice(state_serialized, workspace, monitor_workspace,
data_type_as_string)
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# COMPATIBILITY
# The old reduction workflow converted the workspace to a histogram at this point.
# A more recent workflow keeps the workspaces as Events for longer, to make use of cheap rebinning for
# EventWorkspaces, and to optimise for event slicing.
# However, in the new workflow ("non-compatibility mode") it is necessary to keep a workspace as a histogram
# to keep track of the bin masking. These masks are lifted from the dummy workspace to the actual workspace
# near the end of the reduction.
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
workspace, dummy_mask_workspace, \
use_dummy_workspace = self._check_compatibility_mode(workspace, monitor_workspace, state.compatibility)
# ------------------------------------------------------------
# 4. Move the workspace into the correct position
# The detectors in the workspaces are set such that the beam centre is at (0,0). The position is
# a user-specified value which can be obtained with the help of the beam centre finder.
# ------------------------------------------------------------
progress.report("Moving ...")
workspace = self._move(state_serialized, workspace, component_as_string)
monitor_workspace = self._move(state_serialized, monitor_workspace, component_as_string)
# --------------------------------------------------------------------------------------------------------------
# 5. Apply masking (pixel masking and time masking)
# --------------------------------------------------------------------------------------------------------------
progress.report("Masking ...")
workspace = self._mask(state_serialized, workspace, component_as_string)
# --------------------------------------------------------------------------------------------------------------
# 6. Convert to Wavelength
# --------------------------------------------------------------------------------------------------------------
progress.report("Converting to wavelength ...")
workspace = self._convert_to_wavelength(state_serialized, workspace)
# Convert and rebin the dummy workspace to get correct bin flags
if use_dummy_workspace:
dummy_mask_workspace = mask_bins(state.mask, dummy_mask_workspace, DetectorType.from_string(component_as_string))
dummy_mask_workspace = self._convert_to_wavelength(state_serialized, dummy_mask_workspace)
# --------------------------------------------------------------------------------------------------------------
# 7. Multiply by volume and absolute scale
# --------------------------------------------------------------------------------------------------------------
progress.report("Multiplying by volume and absolute scale ...")
workspace = self._scale(state_serialized, workspace)
# --------------------------------------------------------------------------------------------------------------
# 8. Create adjustment workspaces, those are
# 1. pixel-based adjustments
# 2. wavelength-based adjustments
# 3. pixel-and-wavelength-based adjustments
# Note that steps 4 to 7 could run in parallel if we don't use wide angle correction. If we do then the
# creation of the adjustment workspaces requires the sample workspace itself and we have to run it sequentially.
# We could consider to have a serial and a parallel strategy here, depending on the wide angle correction
# settings. On the other hand it is not clear that this would be an advantage with the GIL.
# --------------------------------------------------------------------------------------------------------------
progress.report("Creating adjustment workspaces ...")
wavelength_adjustment_workspace, pixel_adjustment_workspace, wavelength_and_pixel_adjustment_workspace, \
calculated_transmission_workspace, unfitted_transmission_workspace = \
self._adjustment(state_serialized, workspace, monitor_workspace, component_as_string, data_type_as_string)
# ----------------------------------------------------------------
# 9. Convert event workspaces to histogram workspaces, and re-mask
# ----------------------------------------------------------------
progress.report("Converting to histogram mode ...")
workspace = self._convert_to_histogram(workspace)
if use_dummy_workspace:
workspace = self._copy_bin_masks(workspace, dummy_mask_workspace)
# ------------------------------------------------------------
# 10. Convert to Q
# -----------------------------------------------------------
progress.report("Converting to q ...")
workspace, sum_of_counts, sum_of_norms = self._convert_to_q(state_serialized,
workspace,
wavelength_adjustment_workspace,
pixel_adjustment_workspace,
wavelength_and_pixel_adjustment_workspace)
progress.report("Completed SANSReductionCore ...")
# ------------------------------------------------------------
# Populate the output
# ------------------------------------------------------------
self.setProperty("OutputWorkspace", workspace)
# ------------------------------------------------------------
# Diagnostic output
# ------------------------------------------------------------
if sum_of_counts:
self.setProperty("SumOfCounts", sum_of_counts)
if sum_of_norms:
self.setProperty("SumOfNormFactors", sum_of_norms)
self.setProperty("CalculatedTransmissionWorkspace", calculated_transmission_workspace)
self.setProperty("UnfittedTransmissionWorkspace", unfitted_transmission_workspace)
def PyExec(self):
# Get the input
state = self._get_state()
state_serialized = state.property_manager
component_as_string = self.getProperty("Component").value
progress = self._get_progress()
# --------------------------------------------------------------------------------------------------------------
# 1. Crop workspace by detector name
# This will create a reduced copy of the original workspace with only those spectra which are relevant
# for this particular reduction.
# --------------------------------------------------------------------------------------------------------------
progress.report("Cropping ...")
workspace = self._get_cropped_workspace(component_as_string)
# --------------------------------------------------------------------------------------------
# 2. Perform dark run subtraction
# This will subtract a dark background from the scatter workspace. Note that dark background subtraction
# will also affect the transmission calculation later on.
# --------------------------------------------------------------------------------------------------------------
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
# COMPATIBILITY BEGIN
# IMPORTANT: This section of the code should only be temporary. It allows us to convert to histogram
# early on and hence compare the new reduction results with the output of the new reduction chain.
# Once the new reduction chain is established, we should remove the compatibility feature.
# !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
monitor_workspace = self._get_monitor_workspace()
workspace, dummy_mask_workspace, \
use_dummy_workspace = self._check_compatibility_mode(workspace, monitor_workspace, state.compatibility)
# ------------------------------------------------------------
# 3. Move the workspace into the correct position
# The detectors in the workspaces are set such that the beam centre is at (0,0). The position is
# a user-specified value which can be obtained with the help of the beam centre finder.
# ------------------------------------------------------------
progress.report("Moving ...")
workspace = self._move(state_serialized, workspace,
component_as_string)
monitor_workspace = self._move(state_serialized, monitor_workspace,
component_as_string)
# --------------------------------------------------------------------------------------------------------------
# 4. Apply masking (pixel masking and time masking)
# --------------------------------------------------------------------------------------------------------------
progress.report("Masking ...")
workspace = self._mask(state_serialized, workspace,
component_as_string)
# --------------------------------------------------------------------------------------------------------------
# 5. Convert to Wavelength
# --------------------------------------------------------------------------------------------------------------
progress.report("Converting to wavelength ...")
workspace = self._convert_to_wavelength(state_serialized, workspace)
# Convert and rebin the dummy workspace to get correct bin flags
if use_dummy_workspace:
dummy_mask_workspace = mask_bins(
state.mask, dummy_mask_workspace,
DetectorType.from_string(component_as_string))
dummy_mask_workspace = self._convert_to_wavelength(
state_serialized, dummy_mask_workspace)
# --------------------------------------------------------------------------------------------------------------
# 6. Multiply by volume and absolute scale
# --------------------------------------------------------------------------------------------------------------
progress.report("Multiplying by volume and absolute scale ...")
workspace = self._scale(state_serialized, workspace)
progress.report("Completed SANSReductionCorePreprocess ...")
# ------------------------------------------------------------
# Populate the output
# ------------------------------------------------------------
self.setProperty("OutputWorkspace", workspace)
if use_dummy_workspace:
self.setProperty("DummyMaskWorkspace", dummy_mask_workspace)
self.setProperty("OutputMonitorWorkspace", monitor_workspace)
