def execute(self, reducer, workspace):
# Perform standard sensitivity correction
# If the sensitivity correction workspace exists, just apply it. Otherwise create it.
output_str = " Using data set: %s" % extract_workspace_name(self._flood_data)
if self._efficiency_ws is None:
self._compute_efficiency(reducer, workspace)
# Modify for wavelength dependency of the efficiency of the detector tubes
EQSANSSensitivityCorrection(InputWorkspace=workspace, EfficiencyWorkspace=self._efficiency_ws,
Factor=0.95661, Error=0.005, OutputWorkspace=workspace,
OutputEfficiencyWorkspace="__wl_efficiency")
# Copy over the efficiency's masked pixels to the reduced workspace
masked_detectors = GetMaskedDetectors(self._efficiency_ws)
MaskDetectors(workspace, None, masked_detectors.getPropertyValue("DetectorList"))
return "Wavelength-dependent sensitivity correction applied\n%s\n" % output_str
def _mask_plot_clicked(self):
self.mask_ws = "__mask_%s" % extract_workspace_name(str(self._summary.mask_edit.text()))
self.show_instrument(self._summary.mask_edit.text, workspace=self.mask_ws, tab=2, reload=self.mask_reload, mask=self._masked_detectors)
self._masked_detectors = []
self.mask_reload = False
def _compute_scaling_factor(self, reducer):
"""
Compute the scaling factor
"""
# Sanity check
if self._data_file is None:
raise RuntimeError, "AbsoluteScale called with no defined direct beam file"
# Load data file
filepath = find_data(self._data_file, instrument=reducer.instrument.name())
data_file_ws = "__abs_scale_" + extract_workspace_name(filepath)
loader = reducer._data_loader.clone(data_file=filepath)
loader.set_beam_center(reducer.get_beam_center())
loader.execute(reducer, data_file_ws)
# Get counting time
if reducer._normalizer is None:
# Note: this option shouldn't really be allowed
monitor_id = reducer.NORMALIZATION_MONITOR
else:
monitor_id = -1
if hasattr(reducer._normalizer, "get_normalization_spectrum"):
monitor_id = reducer._normalizer.get_normalization_spectrum()
monitor = mtd[data_file_ws].dataY(monitor_id)[0]
# HFIR-specific: If we count for monitor we need to multiply by 1e8
# Need to be consistent with the Normalization step
if monitor_id == reducer.NORMALIZATION_MONITOR:
monitor /= 1.0e8
if mtd[data_file_ws].getRun().hasProperty("sample_detector_distance"):
sdd = mtd[data_file_ws].getRun().getProperty("sample_detector_distance").value
else:
raise RuntimeError, "AbsoluteScale could not read the sample-detector-distance"
if self._beamstop_diameter is not None:
beam_diameter = self._beamstop_diameter
else:
if mtd[data_file_ws].getRun().hasProperty("beam-diameter"):
beam_diameter = mtd[data_file_ws].getRun().getProperty("beam-diameter").value
else:
raise RuntimeError, "AbsoluteScale could not read the beam radius and none was provided"
# Apply sensitivity correction
if self._apply_sensitivity and reducer.get_sensitivity_correcter() is not None:
reducer.get_sensitivity_correcter().execute(data_file_ws)
det_count = 1
cylXML = (
'<infinite-cylinder id="asbsolute_scale">'
+ '<centre x="0.0" y="0.0" z="0.0" />'
+ '<axis x="0.0" y="0.0" z="1.0" />'
+ '<radius val="%12.10f" />' % (beam_diameter / 2000.0)
+ "</infinite-cylinder>\n"
)
det_finder = FindDetectorsInShape(Workspace=data_file_ws, ShapeXML=cylXML)
det_list = det_finder.getPropertyValue("DetectorList")
det_count_ws = "__absolute_scale"
GroupDetectors(
InputWorkspace=data_file_ws, OutputWorkspace=det_count_ws, DetectorList=det_list, KeepUngroupedSpectra="0"
)
det_count = mtd[det_count_ws].dataY(0)[0]
# Pixel size, in mm
pixel_size_param = mtd[data_file_ws].getInstrument().getNumberParameter("x-pixel-size")
if pixel_size_param is not None:
pixel_size = pixel_size_param[0]
else:
raise RuntimeError, "AbsoluteScale could not read the pixel size"
# (detector count rate)/(attenuator transmission)/(monitor rate)*(pixel size/SDD)**2
self._scaling_factor = 1.0 / (
det_count / self._attenuator_trans / (monitor) * (pixel_size / sdd) * (pixel_size / sdd)
)
mtd.deleteWorkspace(data_file_ws)
mtd.deleteWorkspace(det_count_ws)
