def PyExec(self):
# Read the state
state_property_manager = self.getProperty("SANSState").value
state = create_deserialized_sans_state_from_property_manager(state_property_manager)
progress = Progress(self, start=0.0, end=1.0, nreports=3)
input_workspace = self.getProperty("InputWorkspace").value
data_type_as_string = self.getProperty("DataType").value
data_type = DataType.from_string(data_type_as_string)
slicer = SliceEventFactory.create_slicer(state, input_workspace, data_type)
slice_info = state.slice
# Perform the slicing
progress.report("Starting to slice the workspace.")
sliced_workspace, slice_factor = slicer.create_slice(input_workspace, slice_info)
# Scale the monitor accordingly
progress.report("Scaling the monitors.")
self.scale_monitors(slice_factor)
# Set the outputs
append_to_sans_file_tag(sliced_workspace, "_sliced")
self.setProperty("OutputWorkspace", sliced_workspace)
self.setProperty("SliceEventFactor", slice_factor)
progress.report("Finished slicing.")
def PyExec(self):
# Read the state
state_property_manager = self.getProperty("SANSState").value
state = create_deserialized_sans_state_from_property_manager(state_property_manager)
progress = Progress(self, start=0.0, end=1.0, nreports=3)
input_workspace = self.getProperty("InputWorkspace").value
data_type_as_string = self.getProperty("DataType").value
data_type = DataType.from_string(data_type_as_string)
slicer = SliceEventFactory.create_slicer(state, input_workspace, data_type)
slice_info = state.slice
# Perform the slicing
progress.report("Starting to slice the workspace.")
sliced_workspace, slice_factor = slicer.create_slice(input_workspace, slice_info)
# Scale the monitor accordingly
progress.report("Scaling the monitors.")
self.scale_monitors(slice_factor)
# Set the outputs
append_to_sans_file_tag(sliced_workspace, "_sliced")
self.setProperty("OutputWorkspace", sliced_workspace)
self.setProperty("SliceEventFactor", slice_factor)
progress.report("Finished slicing.")
def PyExec(self):
# Read the state
state_property_manager = self.getProperty("SANSState").value
state = create_deserialized_sans_state_from_property_manager(
state_property_manager)
calculate_transmission_state = state.adjustment.calculate_transmission
# The calculation of the transmission has the following steps:
# 1. Get all spectrum numbers which take part in the transmission calculation
# 2. Clean up the transmission and direct workspaces, ie peak prompt correction, flat background calculation,
# wavelength conversion and rebinning of the data.
# 3. Run the CalculateTransmission algorithm
transmission_workspace = self.getProperty(
"TransmissionWorkspace").value
direct_workspace = self.getProperty("DirectWorkspace").value
incident_monitor_spectrum_number = calculate_transmission_state.incident_monitor
if incident_monitor_spectrum_number is None:
incident_monitor_spectrum_number = calculate_transmission_state.default_incident_monitor
# 1. Get relevant spectra
detector_id_incident_monitor = get_detector_id_for_spectrum_number(
transmission_workspace, incident_monitor_spectrum_number)
detector_ids_roi, detector_id_transmission_monitor, detector_id_default_transmission_monitor = \
self._get_detector_ids_for_transmission_calculation(transmission_workspace, calculate_transmission_state)
all_detector_ids = [detector_id_incident_monitor]
if len(detector_ids_roi) > 0:
all_detector_ids.extend(detector_ids_roi)
elif detector_id_transmission_monitor is not None:
all_detector_ids.append(detector_id_transmission_monitor)
elif detector_id_default_transmission_monitor is not None:
all_detector_ids.append(detector_id_default_transmission_monitor)
else:
raise RuntimeError(
"SANSCalculateTransmission: No region of interest or transmission monitor selected."
)
# 2. Clean transmission data
data_type_string = self.getProperty("DataType").value
data_type = DataType.from_string(data_type_string)
transmission_workspace = self._get_corrected_wavelength_workspace(
transmission_workspace, all_detector_ids,
calculate_transmission_state)
direct_workspace = self._get_corrected_wavelength_workspace(
direct_workspace, all_detector_ids, calculate_transmission_state)
# 3. Fit
output_workspace, unfitted_transmission_workspace = \
self._perform_fit(transmission_workspace, direct_workspace, detector_ids_roi,
detector_id_transmission_monitor, detector_id_default_transmission_monitor,
detector_id_incident_monitor, calculate_transmission_state, data_type)
self.setProperty("OutputWorkspace", output_workspace)
if unfitted_transmission_workspace:
self.setProperty("UnfittedData", unfitted_transmission_workspace)
def validateInputs(self):
errors = dict()
# Check that the input can be converted into the right state object
state_property_manager = self.getProperty("SANSState").value
try:
state = create_deserialized_sans_state_from_property_manager(
state_property_manager)
state.property_manager = state_property_manager
state.validate()
except ValueError as err:
errors.update({"SANSCalculateTransmission": str(err)})
state = None
if state is not None:
transmission_workspace = self.getProperty(
"TransmissionWorkspace").value
calculate_transmission_state = state.adjustment.calculate_transmission
try:
incident_monitor = calculate_transmission_state.incident_monitor
if incident_monitor is None:
incident_monitor = calculate_transmission_state.default_incident_monitor
transmission_workspace.getIndexFromSpectrumNumber(
incident_monitor)
except RuntimeError:
errors.update({
"IncidentMonitorSpectrumNumber":
"The spectrum number for the incident monitor spectrum "
"does not seem to exist for the transmission"
" workspace."
})
if state is not None:
calculate_transmission_state = state.adjustment.calculate_transmission
fit = calculate_transmission_state.fit
data_type_string = self.getProperty("DataType").value
data_type = DataType.from_string(data_type_string)
sample = fit[DataType.to_string(DataType.Sample)]
can = fit[DataType.to_string(DataType.Can)]
if data_type is DataType.Sample and sample.fit_type is None:
errors.update({
"DataType":
"There does not seem to be a fit type set for the selected data type"
})
if data_type is DataType.Can and can.fit_type is None:
errors.update({
"DataType":
"There does not seem to be a fit type set for the selected data type"
})
return errors
def PyExec(self):
# Read the state
state_property_manager = self.getProperty("SANSState").value
state = create_deserialized_sans_state_from_property_manager(state_property_manager)
calculate_transmission_state = state.adjustment.calculate_transmission
# The calculation of the transmission has the following steps:
# 1. Get all spectrum numbers which take part in the transmission calculation
# 2. Clean up the transmission and direct workspaces, ie peak prompt correction, flat background calculation,
# wavelength conversion and rebinning of the data.
# 3. Run the CalculateTransmission algorithm
transmission_workspace = self.getProperty("TransmissionWorkspace").value
direct_workspace = self.getProperty("DirectWorkspace").value
incident_monitor_spectrum_number = calculate_transmission_state.incident_monitor
if incident_monitor_spectrum_number is None:
incident_monitor_spectrum_number = calculate_transmission_state.default_incident_monitor
# 1. Get relevant spectra
detector_id_incident_monitor = get_detector_id_for_spectrum_number(transmission_workspace,
incident_monitor_spectrum_number)
detector_ids_roi, detector_id_transmission_monitor, detector_id_default_transmission_monitor = \
self._get_detector_ids_for_transmission_calculation(transmission_workspace, calculate_transmission_state)
all_detector_ids = [detector_id_incident_monitor]
if len(detector_ids_roi) > 0:
all_detector_ids.extend(detector_ids_roi)
elif detector_id_transmission_monitor is not None:
all_detector_ids.append(detector_id_transmission_monitor)
elif detector_id_default_transmission_monitor is not None:
all_detector_ids.append(detector_id_default_transmission_monitor)
else:
raise RuntimeError("SANSCalculateTransmission: No region of interest or transmission monitor selected.")
# 2. Clean transmission data
data_type_string = self.getProperty("DataType").value
data_type = DataType.from_string(data_type_string)
transmission_workspace = self._get_corrected_wavelength_workspace(transmission_workspace, all_detector_ids,
calculate_transmission_state)
direct_workspace = self._get_corrected_wavelength_workspace(direct_workspace, all_detector_ids,
calculate_transmission_state)
# 3. Fit
output_workspace, unfitted_transmission_workspace = \
self._perform_fit(transmission_workspace, direct_workspace, detector_ids_roi,
detector_id_transmission_monitor, detector_id_default_transmission_monitor,
detector_id_incident_monitor, calculate_transmission_state, data_type)
self.setProperty("OutputWorkspace", output_workspace)
if unfitted_transmission_workspace:
self.setProperty("UnfittedData", unfitted_transmission_workspace)
def validateInputs(self):
errors = dict()
# Check that the input can be converted into the right state object
state_property_manager = self.getProperty("SANSState").value
try:
state = create_deserialized_sans_state_from_property_manager(state_property_manager)
state.property_manager = state_property_manager
state.validate()
except ValueError as err:
errors.update({"SANSCalculateTransmission": str(err)})
state = None
if state is not None:
transmission_workspace = self.getProperty("TransmissionWorkspace").value
calculate_transmission_state = state.adjustment.calculate_transmission
try:
incident_monitor = calculate_transmission_state.incident_monitor
if incident_monitor is None:
incident_monitor = calculate_transmission_state.default_incident_monitor
transmission_workspace.getIndexFromSpectrumNumber(incident_monitor)
except RuntimeError:
errors.update({"IncidentMonitorSpectrumNumber": "The spectrum number for the incident monitor spectrum "
"does not seem to exist for the transmission"
" workspace."})
if state is not None:
calculate_transmission_state = state.adjustment.calculate_transmission
fit = calculate_transmission_state.fit
data_type_string = self.getProperty("DataType").value
data_type = DataType.from_string(data_type_string)
sample = fit[DataType.to_string(DataType.Sample)]
can = fit[DataType.to_string(DataType.Can)]
if data_type is DataType.Sample and sample.fit_type is None:
errors.update({"DataType": "There does not seem to be a fit type set for the selected data type"})
if data_type is DataType.Can and can.fit_type is None:
errors.update({"DataType": "There does not seem to be a fit type set for the selected data type"})
return errors
