def _can_merge(self):
"""
checks whether it is possible to merge the given list of workspaces
"""
# list of workspaces must not be empty
if not self.workspace_names:
message = "No workspace names has been specified! Nothing to merge."
self.log().error(message)
raise RuntimeError(message)
# workspaces must exist
mlzutils.ws_exist(self.workspace_names, self.log())
# all workspaces must be either normalized or not normalized, but not mixed
self._are_normalized()
# if data are not normalized, normalization workspaces must exist
if not self.is_normalized:
wslist = [wsname + '_NORM' for wsname in self.workspace_names]
mlzutils.ws_exist(wslist, self.log())
# they must have the same dimensions
mlzutils.same_dimensions(self.workspace_names)
# and the same wavelength
self._same_wavelength()
# algorithm must warn if some properties_to_compare are different
ws1 = api.AnalysisDataService.retrieve(self.workspace_names[0])
run1 = ws1.getRun()
for wsname in self.workspace_names[1:]:
wks = api.AnalysisDataService.retrieve(wsname)
run = wks.getRun()
mlzutils.compare_properties(run1, run, self.properties_to_compare, self.log())
return True
def PyExec(self):
# Input
self.dataws = api.mtd[self.getPropertyValue("InputWorkspace")]
self.outws_name = self.getPropertyValue("OutputWorkspace")
self.vanaws = api.mtd[self.getPropertyValue("VanaWorkspace")]
self.bkgws = api.mtd[self.getPropertyValue("BkgWorkspace")]
self.vana_mean_name = self.getPropertyValue("VanadiumMean")
# check dimensions
wslist = [self.dataws.getName(), self.vanaws.getName(), self.bkgws.getName()]
mlzutils.same_dimensions(wslist)
# check if the _NORM workspaces exist
wslist = [self.vanaws.getName() + '_NORM', self.bkgws.getName() + '_NORM']
mlzutils.ws_exist(wslist, self.log())
# check sample logs, produce warnings if different
drun = self.dataws.getRun()
vrun = self.vanaws.getRun()
brun = self.bkgws.getRun()
mlzutils.compare_properties(drun, vrun, self.properties_to_compare, self.log())
mlzutils.compare_properties(vrun, brun, self.properties_to_compare, self.log())
# apply correction
outws = self._vana_correct()
if not outws:
raise RuntimeError("Correction failed. Invalid VanadiumMean workspace dimensions.")
# copy sample logs from data workspace to the output workspace
api.CopyLogs(InputWorkspace=self.dataws.getName(), OutputWorkspace=outws.getName(),
MergeStrategy='MergeReplaceExisting')
self.setProperty("OutputWorkspace", outws)
# clone the normalization workspace for data if it exists
if api.mtd.doesExist(self.dataws.getName()+'_NORM'):
api.CloneWorkspace(InputWorkspace=self.dataws.getName()+'_NORM', OutputWorkspace=self.outws_name+'_NORM')
self.log().debug('DNSDetEffCorrVana: OK. The result has been saved to ' + self.outws_name)
return
def _can_correct(self):
"""
checks whether FR correction is possible with the given list of workspaces
"""
# list of workspaces must not be empty
if not self.input_workspaces:
message = "No workspace names has been specified! Nothing for FR correction."
self.log().error(message)
raise RuntimeError(message)
# workspaces and normalization workspaces must exist
wslist = self.input_workspaces.values()
# for data workspaces normalization is not mandatory
dataws_names = [self.input_workspaces['SF_Data'], self.input_workspaces['NSF_Data']]
for wsname in self.input_workspaces.values():
if wsname not in dataws_names:
wslist.append(wsname + '_NORM')
mlzutils.ws_exist(wslist, self.log())
# they must have the same dimensions
mlzutils.same_dimensions(self.input_workspaces.values())
# and the same polarisation
self._same_polarisation()
# check validity of flipper status
self._flipper_valid()
# algorithm must warn if some properties_to_compare are different
ws1 = api.AnalysisDataService.retrieve(self.input_workspaces.values()[0])
run1 = ws1.getRun()
for wsname in self.input_workspaces.values()[1:]:
wks = api.AnalysisDataService.retrieve(wsname)
run = wks.getRun()
mlzutils.compare_properties(run1, run, self.properties_to_compare, self.log())
return True