def _read_ws(number, instrument):
cycle_information = instrument._get_cycle_information(run_number=number)
input_dir = instrument._generate_raw_data_cycle_dir(
cycle_information["cycle"])
read_in_ws = _load_raw_files(run_number=number,
instrument=instrument,
input_dir=input_dir)
# TODO move this into instrument specific
read_ws = mantid.ConvertUnits(InputWorkspace=read_in_ws,
Target="Wavelength")
remove_intermediate_workspace(read_in_ws)
_read_pearl_monitor = instrument._get_monitor(run_number=number,
input_dir=input_dir,
spline_terms=20)
read_ws = mantid.NormaliseToMonitor(InputWorkspace=read_ws,
MonitorWorkspace=_read_pearl_monitor,
IntegrationRangeMin=0.6,
IntegrationRangeMax=5.0)
output_name = "read_ws_output-" + str(g_ads_workaround["read_pearl_ws"])
g_ads_workaround["read_pearl_ws"] += 1
output_ws = mantid.ConvertUnits(InputWorkspace=read_ws,
OutputWorkspace=output_name,
Target="TOF")
remove_intermediate_workspace(_read_pearl_monitor)
remove_intermediate_workspace(read_ws)
return output_ws
def normalise_ws_current(ws_to_correct, monitor_ws, spline_coeff, lambda_values, integration_range, ex_regions):
processed_monitor_ws = mantid.ConvertUnits(InputWorkspace=monitor_ws, Target="Wavelength")
processed_monitor_ws = mantid.CropWorkspace(InputWorkspace=processed_monitor_ws,
XMin=lambda_values[0], XMax=lambda_values[-1])
for reg in range(0, 4):
processed_monitor_ws = mantid.MaskBins(InputWorkspace=processed_monitor_ws, XMin=ex_regions[0, reg],
XMax=ex_regions[1, reg])
splined_monitor_ws = mantid.SplineBackground(InputWorkspace=processed_monitor_ws,
WorkspaceIndex=0, NCoeff=spline_coeff)
normalised_ws = mantid.ConvertUnits(InputWorkspace=ws_to_correct, Target="Wavelength",
OutputWorkspace=ws_to_correct)
normalised_ws = mantid.NormaliseToMonitor(InputWorkspace=normalised_ws, MonitorWorkspace=splined_monitor_ws,
IntegrationRangeMin=integration_range[0],
IntegrationRangeMax=integration_range[-1],
OutputWorkspace=normalised_ws)
normalised_ws = mantid.ConvertUnits(InputWorkspace=normalised_ws, Target="TOF", OutputWorkspace=normalised_ws)
common.remove_intermediate_workspace(processed_monitor_ws)
common.remove_intermediate_workspace(splined_monitor_ws)
return normalised_ws
def normalise_ws_current(ws_to_correct, monitor_ws, spline_coeff,
lambda_values, integration_range):
processed_monitor_ws = mantid.ConvertUnits(InputWorkspace=monitor_ws,
Target="Wavelength")
processed_monitor_ws = mantid.CropWorkspace(
InputWorkspace=processed_monitor_ws,
XMin=lambda_values[0],
XMax=lambda_values[-1])
# TODO move these masks to the adv. config file
ex_regions = numpy.zeros((2, 4))
ex_regions[:, 0] = [3.45, 3.7]
ex_regions[:, 1] = [2.96, 3.2]
ex_regions[:, 2] = [2.1, 2.26]
ex_regions[:, 3] = [1.73, 1.98]
for reg in range(0, 4):
processed_monitor_ws = mantid.MaskBins(
InputWorkspace=processed_monitor_ws,
XMin=ex_regions[0, reg],
XMax=ex_regions[1, reg])
splined_monitor_ws = mantid.SplineBackground(
InputWorkspace=processed_monitor_ws,
WorkspaceIndex=0,
NCoeff=spline_coeff)
normalised_ws = mantid.ConvertUnits(InputWorkspace=ws_to_correct,
Target="Wavelength",
OutputWorkspace=ws_to_correct)
normalised_ws = mantid.NormaliseToMonitor(
InputWorkspace=normalised_ws,
MonitorWorkspace=splined_monitor_ws,
IntegrationRangeMin=integration_range[0],
IntegrationRangeMax=integration_range[-1],
OutputWorkspace=normalised_ws)
normalised_ws = mantid.ConvertUnits(InputWorkspace=normalised_ws,
Target="TOF",
OutputWorkspace=normalised_ws)
common.remove_intermediate_workspace(processed_monitor_ws)
common.remove_intermediate_workspace(splined_monitor_ws)
return normalised_ws
def read(self, number, panel, extension):
if type(number) is int:
filename = self.datafile
logger.notice("will be reading filename...{}".format(filename))
spectra_min, spectra_max = self.return_panel_van.get(panel) if self.is_vanadium else \
self.return_panel.get(panel)
if panel != 0:
output = "w{0}-{1}".format(number, panel)
else:
output = "w{}".format(number)
shared_load_files(extension, filename, output, spectra_max,
spectra_min, False)
if extension == "nxs_event":
simple.LoadEventNexus(Filename=filename,
OutputWorkspace=output,
LoadMonitors='1')
self.read_event_nexus(number, output, panel)
if extension[:10] == "nxs_event_":
label, tmin, tmax = split_string_event(extension)
output = output + "_" + label
if tmax == "end":
simple.LoadEventNexus(Filename=filename,
OutputWorkspace=output,
FilterByTimeStart=tmin,
LoadMonitors='1',
MonitorsAsEvents='1',
FilterMonByTimeStart=tmin)
else:
simple.LoadEventNexus(Filename=filename,
OutputWorkspace=output,
FilterByTimeStart=tmin,
FilterByTimeStop=tmax,
LoadMonitors='1',
MonitorsAsEvents='1',
FilterMonByTimeStart=tmin,
FilterMonByTimeStop=tmax)
self.read_event_nexus(number, output, panel)
else:
num_1, num_2 = split_run_string(number)
output = "w{0}_{1}-{2}".format(num_1, num_2, panel)
output1 = self.load_multi_run_part(extension, num_1, panel)
output2 = self.load_multi_run_part(extension, num_2, panel)
simple.MergeRuns(output1 + "," + output2, output)
simple.DeleteWorkspace(output1)
simple.DeleteWorkspace(output2)
simple.ConvertUnits(InputWorkspace=output,
OutputWorkspace=output,
Target="Wavelength",
Emode="Elastic")
lmin, lmax = Wish.LAMBDA_RANGE
simple.CropWorkspace(InputWorkspace=output,
OutputWorkspace=output,
XMin=lmin,
XMax=lmax)
monitor_run = "monitor{}".format(number)
if monitor_run not in simple.mtd:
monitor = self.process_incidentmon(number,
extension,
spline_terms=70)
else:
monitor = simple.mtd[monitor_run]
simple.NormaliseToMonitor(InputWorkspace=output,
OutputWorkspace=output + "norm1",
MonitorWorkspace=monitor)
simple.NormaliseToMonitor(InputWorkspace=output + "norm1",
OutputWorkspace=output + "norm2",
MonitorWorkspace=monitor,
IntegrationRangeMin=0.7,
IntegrationRangeMax=10.35)
simple.DeleteWorkspace(output)
simple.DeleteWorkspace(output + "norm1")
simple.RenameWorkspace(InputWorkspace=output + "norm2",
OutputWorkspace=output)
simple.ConvertUnits(InputWorkspace=output,
OutputWorkspace=output,
Target="TOF",
EMode="Elastic")
simple.ReplaceSpecialValues(InputWorkspace=output,
OutputWorkspace=output,
NaNValue=0.0,
NaNError=0.0,
InfinityValue=0.0,
InfinityError=0.0)
return output
