def doSuccessEvaluationIndexingMOSFLM(self, _edPlugin=None):
EDVerbose.DEBUG("EDPluginControlCharacterisationv1_2.doSuccessEvaluationIndexingMOSFLM")
self.retrieveSuccessMessages(_edPlugin, "EDPluginControlCharacterisationv1_2.doSuccessEvaluationIndexing")
# Retrieve status messages (if any)
if self.__edPluginExecEvaluationIndexingMOSFLM.hasDataOutput("statusMessageImageQualityIndicators"):
self.addStatusMessage(self.__edPluginExecEvaluationIndexingMOSFLM.getDataOutput("statusMessageImageQualityIndicators")[0].getValue())
if self.__edPluginExecEvaluationIndexingMOSFLM.hasDataOutput("statusMessageIndexing"):
self.addStatusMessage("MOSFLM: " + self.__edPluginExecEvaluationIndexingMOSFLM.getDataOutput("statusMessageIndexing")[0].getValue())
# Check if indexing was successful
bIndexWithLabelit = False
bIndexingSuccess = self.__edPluginExecEvaluationIndexingMOSFLM.getDataOutput("indexingSuccess")[0].getValue()
if bIndexingSuccess:
xsDataIndexingResult = self.__edPluginExecEvaluationIndexingMOSFLM.getDataOutput("indexingResult")[0]
self.__xsDataIndexingResultMOSFLM = xsDataIndexingResult
# Check if space group is P1 - if yes run Labelit indexing
xsDataIndexingSolutionSelected = xsDataIndexingResult.getSelectedSolution()
xsDataCrystal = xsDataIndexingSolutionSelected.getCrystal()
xsDataSpaceGroup = xsDataCrystal.getSpaceGroup()
strSpaceGroupName = xsDataSpaceGroup.getName().getValue().upper()
# Check if MOSFLM has indexed in P1
if strSpaceGroupName == "P1":
# Check if the user maybe asked for P1!
bIndexWithLabelit = True
if self.__xsDataCollection.getDiffractionPlan() is not None:
if self.__xsDataCollection.getDiffractionPlan().getForcedSpaceGroup() is not None:
if self.__xsDataCollection.getDiffractionPlan().getForcedSpaceGroup().getValue().upper() == "P1":
EDVerbose.screen("P1 space forced by diffraction plan")
bIndexWithLabelit = False
if bIndexWithLabelit:
EDVerbose.screen("P1 space group choosed - reindexing with Labelit")
else:
EDVerbose.screen("MOSFLM indexing successful!")
if self.__edPluginControlIndexingIndicators.hasDataOutput("indexingShortSummary"):
self.__strCharacterisationShortSummary += self.__edPluginControlIndexingIndicators.getDataOutput("indexingShortSummary")[0].getValue()
# Generate prediction images
xsDataCollection = self.__xsDataResultCharacterisation.getDataCollection()
self.__xsDataResultCharacterisation.setIndexingResult(xsDataIndexingResult)
xsDataGeneratePredictionInput = XSDataGeneratePredictionInput()
xsDataGeneratePredictionInput.setDataCollection(XSDataCollection.parseString(xsDataCollection.marshal()))
xsDataGeneratePredictionInput.setSelectedIndexingSolution(XSDataIndexingSolutionSelected.parseString(xsDataIndexingResult.getSelectedSolution().marshal()))
self.__edPluginControlGeneratePrediction.setDataInput(xsDataGeneratePredictionInput)
# Start the generation of prediction images - we synchronize in the post-process
self.__edPluginControlGeneratePrediction.execute()
# Then start the integration of the reference images
self.indexingToIntegration()
else:
EDVerbose.screen("Indexing with MOSFLM failed!")
bIndexWithLabelit = True
if bIndexWithLabelit:
# Execute Labelit indexing
EDVerbose.screen("Now trying to index with Labelit - please be patient...")
xsDataIndexingInput = XSDataIndexingInput()
xsDataSubWedgeList = self.__xsDataCollection.getSubWedge()
xsDataExperimentalCondition = xsDataSubWedgeList[0].getExperimentalCondition()
xsDataIndexingInput.setDataCollection(self.__xsDataCollection)
xsDataIndexingInput.setExperimentalCondition(xsDataExperimentalCondition)
if self.__xsDataCrystal != None:
xsDataIndexingInput.setCrystal(self.__xsDataCrystal)
self.__edPluginControlIndexingLabelit.setDataInput(xsDataIndexingInput)
self.__edPluginControlIndexingLabelit.executeSynchronous()
def doSuccessEvaluationIndexingMOSFLM(self, _edPlugin=None):
self.DEBUG("EDPluginControlCharacterisationv1_2.doSuccessEvaluationIndexingMOSFLM")
self.retrieveSuccessMessages(_edPlugin, "EDPluginControlCharacterisationv1_2.doSuccessEvaluationIndexing")
# Retrieve status messages (if any)
if self.__edPluginExecEvaluationIndexingMOSFLM.hasDataOutput("statusMessageImageQualityIndicators"):
self.addStatusMessage(self.__edPluginExecEvaluationIndexingMOSFLM.getDataOutput("statusMessageImageQualityIndicators")[0].getValue())
if self.__edPluginExecEvaluationIndexingMOSFLM.hasDataOutput("statusMessageIndexing"):
self.addStatusMessage("MOSFLM: " + self.__edPluginExecEvaluationIndexingMOSFLM.getDataOutput("statusMessageIndexing")[0].getValue())
# Check if indexing was successful
bIndexWithLabelit = False
bIndexingSuccess = self.__edPluginExecEvaluationIndexingMOSFLM.getDataOutput("indexingSuccess")[0].getValue()
if bIndexingSuccess:
xsDataIndexingResult = self.__edPluginExecEvaluationIndexingMOSFLM.getDataOutput("indexingResult")[0]
self.__xsDataIndexingResultMOSFLM = xsDataIndexingResult
# Check if space group is P1 - if yes run Labelit indexing
xsDataIndexingSolutionSelected = xsDataIndexingResult.getSelectedSolution()
xsDataCrystal = xsDataIndexingSolutionSelected.getCrystal()
xsDataSpaceGroup = xsDataCrystal.getSpaceGroup()
strSpaceGroupName = xsDataSpaceGroup.getName().getValue().upper()
# Check if MOSFLM has indexed in P1
if strSpaceGroupName == "P1":
# Check if the user maybe asked for P1!
bIndexWithLabelit = True
if self.__xsDataCollection.getDiffractionPlan() is not None:
if self.__xsDataCollection.getDiffractionPlan().getForcedSpaceGroup() is not None:
if self.__xsDataCollection.getDiffractionPlan().getForcedSpaceGroup().getValue().upper() == "P1":
self.screen("P1 space forced by diffraction plan")
bIndexWithLabelit = False
if bIndexWithLabelit:
self.screen("P1 space group choosed - reindexing with Labelit")
else:
self.screen("MOSFLM indexing successful!")
if self.__edPluginControlIndexingIndicators.hasDataOutput("indexingShortSummary"):
self.__strCharacterisationShortSummary += self.__edPluginControlIndexingIndicators.getDataOutput("indexingShortSummary")[0].getValue()
# Generate prediction images
xsDataCollection = self.__xsDataResultCharacterisation.getDataCollection()
self.__xsDataResultCharacterisation.setIndexingResult(xsDataIndexingResult)
xsDataGeneratePredictionInput = XSDataGeneratePredictionInput()
xsDataGeneratePredictionInput.setDataCollection(XSDataCollection.parseString(xsDataCollection.marshal()))
xsDataGeneratePredictionInput.setSelectedIndexingSolution(XSDataIndexingSolutionSelected.parseString(xsDataIndexingResult.getSelectedSolution().marshal()))
self.__edPluginControlGeneratePrediction.setDataInput(xsDataGeneratePredictionInput)
# Start the generation of prediction images - we synchronize in the post-process
self.__edPluginControlGeneratePrediction.execute()
# Then start the integration of the reference images
self.indexingToIntegration()
else:
self.screen("Indexing with MOSFLM failed!")
bIndexWithLabelit = True
if bIndexWithLabelit:
# Execute Labelit indexing
self.screen("Now trying to index with Labelit - please be patient...")
xsDataIndexingInput = XSDataIndexingInput()
xsDataSubWedgeList = self.__xsDataCollection.getSubWedge()
xsDataExperimentalCondition = xsDataSubWedgeList[0].getExperimentalCondition()
xsDataIndexingInput.setDataCollection(self.__xsDataCollection)
xsDataIndexingInput.setExperimentalCondition(xsDataExperimentalCondition)
if self.__xsDataCrystal != None:
xsDataIndexingInput.setCrystal(self.__xsDataCrystal)
self.__edPluginControlIndexingLabelit.setDataInput(xsDataIndexingInput)
self.__edPluginControlIndexingLabelit.executeSynchronous()
def doFailureIndexingLabelit(self, _edPlugin=None):
EDVerbose.DEBUG("EDPluginControlCharacterisationv1_2.doFailureIndexingLabelit")
self.addStatusMessage("Labelit: Indexing FAILURE.")
if self.__xsDataResultCharacterisation is not None:
self.setDataOutput(self.__xsDataResultCharacterisation)
if self.__xsDataIndexingResultMOSFLM == None:
strErrorMessage = "Execution of indexing with both MOSFLM and Labelit failed. Execution of characterisation aborted."
EDVerbose.ERROR(strErrorMessage)
self.addErrorMessage(strErrorMessage)
self.generateExecutiveSummary(self)
self.setFailure()
if self.__strStatusMessage != None:
self.setDataOutput(XSDataString(self.__strStatusMessage), "statusMessage")
self.writeDataOutput()
else:
# Use the MOSFLM indexing results - even if it's P1
self.__xsDataResultCharacterisation.setIndexingResult(self.__xsDataIndexingResultMOSFLM)
xsDataCollection = self.__xsDataResultCharacterisation.getDataCollection()
xsDataGeneratePredictionInput = XSDataGeneratePredictionInput()
xsDataGeneratePredictionInput.setDataCollection(XSDataCollection.parseString(xsDataCollection.marshal()))
xsDataGeneratePredictionInput.setSelectedIndexingSolution(XSDataIndexingSolutionSelected.parseString(self.__xsDataIndexingResultMOSFLM.getSelectedSolution().marshal()))
self.__edPluginControlGeneratePrediction.setDataInput(xsDataGeneratePredictionInput)
if self.__edPluginControlIndexingIndicators.hasDataOutput("indexingShortSummary"):
self.__strCharacterisationShortSummary += self.__edPluginControlIndexingIndicators.getDataOutput("indexingShortSummary")[0].getValue()
# Start the generation of prediction images - we synchronize in the post-process
self.__edPluginControlGeneratePrediction.execute()
# Then start the integration of the reference images
self.indexingToIntegration()
def doSuccessEvaluationIndexingMOSFLM(self, _edPlugin=None):
self.DEBUG("EDPluginControlCharacterisationv1_5.doSuccessEvaluationIndexingMOSFLM")
self.retrieveSuccessMessages(_edPlugin, "EDPluginControlCharacterisationv1_5.doSuccessEvaluationIndexingMOSFLM")
# Retrieve status messages (if any)
if self._edPluginExecEvaluationIndexingMOSFLM.hasDataOutput("statusMessageIndexing"):
self.addStatusMessage("MOSFLM: " + self._edPluginExecEvaluationIndexingMOSFLM.getDataOutput("statusMessageIndexing")[0].getValue())
# Check if indexing was successful
bIndexingSuccess = self._edPluginExecEvaluationIndexingMOSFLM.getDataOutput("indexingSuccess")[0].getValue()
if bIndexingSuccess:
xsDataIndexingResult = self._edPluginExecEvaluationIndexingMOSFLM.getDataOutput("indexingResult")[0]
self._xsDataResultCharacterisation.setIndexingResult(xsDataIndexingResult)
self._strCharacterisationShortSummary += self.generateIndexingShortSummary(xsDataIndexingResult)
xsDataCollection = self._xsDataResultCharacterisation.getDataCollection()
xsDataGeneratePredictionInput = XSDataGeneratePredictionInput()
xsDataGeneratePredictionInput.setDataCollection(XSDataCollection.parseString(xsDataCollection.marshal()))
xsDataGeneratePredictionInput.setSelectedIndexingSolution(XSDataIndexingSolutionSelected.parseString(xsDataIndexingResult.getSelectedSolution().marshal()))
self._edPluginControlGeneratePrediction.setDataInput(xsDataGeneratePredictionInput)
if self._edPluginControlIndexingIndicators.hasDataOutput("indexingShortSummary"):
self._strCharacterisationShortSummary += self._edPluginControlIndexingIndicators.getDataOutput("indexingShortSummary")[0].getValue()
# Start the generation of prediction images - we synchronize in the post-process
self._edPluginControlGeneratePrediction.execute()
# Then start the integration of the reference images
self.indexingToIntegration()
else:
strErrorMessage = "Execution of indexing with MOSFLM failed."
self.ERROR(strErrorMessage)
self.sendMessageToMXCuBE(strErrorMessage, "error")
self.addErrorMessage(strErrorMessage)
self.setFailure()
self.generateExecutiveSummary(self)
if self._strStatusMessage != None:
self.setDataOutput(XSDataString(self._strStatusMessage), "statusMessage")
self.writeDataOutput()
def doFailureIndexingLabelit(self, _edPlugin=None):
self.DEBUG("EDPluginControlCharacterisationv1_2.doFailureIndexingLabelit")
self.addStatusMessage("Labelit: Indexing FAILURE.")
if self.__xsDataResultCharacterisation is not None:
self.setDataOutput(self.__xsDataResultCharacterisation)
if self.__xsDataIndexingResultMOSFLM == None:
strErrorMessage = "Execution of indexing with both MOSFLM and Labelit failed. Execution of characterisation aborted."
self.ERROR(strErrorMessage)
self.addErrorMessage(strErrorMessage)
self.generateExecutiveSummary(self)
self.setFailure()
if self.__strStatusMessage != None:
self.setDataOutput(XSDataString(self.__strStatusMessage), "statusMessage")
self.writeDataOutput()
else:
# Use the MOSFLM indexing results - even if it's P1
self.__xsDataResultCharacterisation.setIndexingResult(self.__xsDataIndexingResultMOSFLM)
xsDataCollection = self.__xsDataResultCharacterisation.getDataCollection()
xsDataGeneratePredictionInput = XSDataGeneratePredictionInput()
xsDataGeneratePredictionInput.setDataCollection(XSDataCollection.parseString(xsDataCollection.marshal()))
xsDataGeneratePredictionInput.setSelectedIndexingSolution(XSDataIndexingSolutionSelected.parseString(self.__xsDataIndexingResultMOSFLM.getSelectedSolution().marshal()))
self.__edPluginControlGeneratePrediction.setDataInput(xsDataGeneratePredictionInput)
if self.__edPluginControlIndexingIndicators.hasDataOutput("indexingShortSummary"):
self.__strCharacterisationShortSummary += self.__edPluginControlIndexingIndicators.getDataOutput("indexingShortSummary")[0].getValue()
# Start the generation of prediction images - we synchronize in the post-process
self.__edPluginControlGeneratePrediction.execute()
# Then start the integration of the reference images
self.indexingToIntegration()
def generatePredictionImageList(self, _edPluginGeneratePrediction, _xsDataCollection, _xsDataIndexingResult):
"""
Generate a list containing the prediction images
"""
self.verboseDebug("EDPluginControlIndexingv10.generatePredictionImageList")
xsDataGeneratePredictionInput = XSDataGeneratePredictionInput()
xsDataGeneratePredictionInput.setDataCollection(XSDataCollection.parseString(_xsDataCollection.marshal()))
xsDataGeneratePredictionInput.setSelectedIndexingSolution(XSDataIndexingSolutionSelected.parseString(_xsDataIndexingResult.getSelectedSolution().marshal()))
_edPluginGeneratePrediction.setDataInput(xsDataGeneratePredictionInput)
_edPluginGeneratePrediction.executeSynchronous()
def preProcess(self, _edObject=None):
"""
Gets the Configuration Parameters, if found, overrides default parameters
"""
EDPluginControl.preProcess(self, _edObject)
self.DEBUG("EDPluginControlGeneratePredictionv10.preProcess...")
xsDataGeneratePredictionInput = self.getDataInput()
xsDataSelectedIndexingSolution = xsDataGeneratePredictionInput.getSelectedIndexingSolution()
xsDataExperimentalConditionRefined = xsDataSelectedIndexingSolution.getExperimentalConditionRefined()
xsDataCollection = xsDataGeneratePredictionInput.getDataCollection()
xsDataSubWedgeList = xsDataCollection.getSubWedge()
# List containing instances of all the generate prediction plugins
self.__listPluginGeneratePrediction = []
# Loop through all subwedges
iIndex = 0
for xsDataSubWedge in xsDataSubWedgeList:
xsDataImageList = xsDataSubWedge.getImage()
# First find the lowest image number
iLowestImageNumber = None
for xsDataImage in xsDataImageList:
iImageNumber = xsDataImage.getNumber().getValue()
if (iLowestImageNumber is None):
iLowestImageNumber = iImageNumber
elif (iImageNumber < iLowestImageNumber):
iLowestImageNumber = iImageNumber
# Then loop through all images in a sub wedge
for xsDataImage in xsDataImageList:
iIndex += 1
edPluginGeneratePrediction = self.loadPlugin(self.__strPluginGeneratePredictionName,
"%s-%02d" % (self.__strPluginGeneratePredictionName, iIndex))
xsDataGeneratePredictionInput = XSDataGeneratePredictionInput()
xsDataGeneratePredictionInput.setSelectedIndexingSolution(XSDataIndexingSolutionSelected.parseString(xsDataSelectedIndexingSolution.marshal()))
xsDataCollectionNew = XSDataCollection()
xsDataSubWedgeNew = XSDataSubWedge()
xsDataSubWedgeNew.addImage(XSDataImage.parseString(xsDataImage.marshal()))
xsDataSubWedgeNew.setExperimentalCondition(XSDataExperimentalCondition.parseString(xsDataSubWedge.getExperimentalCondition().marshal()))
# We must modify the rotationOscillationStart for the new subwedge
xsDataGoniostatNew = xsDataSubWedgeNew.getExperimentalCondition().getGoniostat()
fGoniostatRotationAxisStart = xsDataGoniostatNew.getRotationAxisStart().getValue()
fGonioStatOscillationRange = xsDataGoniostatNew.getOscillationWidth().getValue()
iImageNumber = xsDataImage.getNumber().getValue()
fGoniostatRotationAxisStartNew = fGoniostatRotationAxisStart + (iImageNumber - iLowestImageNumber) * fGonioStatOscillationRange
xsDataGoniostatNew.setRotationAxisStart(XSDataAngle(fGoniostatRotationAxisStartNew))
#
xsDataCollectionNew.addSubWedge(xsDataSubWedgeNew)
xsDataGeneratePredictionInput.setDataCollection(xsDataCollectionNew)
from EDHandlerXSDataMOSFLMv10 import EDHandlerXSDataMOSFLMv10
xsDataMOSFLMInputGeneratePrediction = EDHandlerXSDataMOSFLMv10.generateXSDataMOSFLMInputGeneratePrediction(xsDataGeneratePredictionInput)
edPluginGeneratePrediction.setDataInput(xsDataMOSFLMInputGeneratePrediction)
self.__listPluginGeneratePrediction.append(edPluginGeneratePrediction)
def doSuccessEvaluationIndexingMOSFLM(self, _edPlugin=None):
self.DEBUG(
"EDPluginControlCharacterisationv1_4.doSuccessEvaluationIndexingMOSFLM"
)
self.retrieveSuccessMessages(
_edPlugin,
"EDPluginControlCharacterisationv1_4.doSuccessEvaluationIndexingMOSFLM"
)
# Retrieve status messages (if any)
if self._edPluginExecEvaluationIndexingMOSFLM.hasDataOutput(
"statusMessageIndexing"):
self.addStatusMessage(
"MOSFLM: " +
self._edPluginExecEvaluationIndexingMOSFLM.getDataOutput(
"statusMessageIndexing")[0].getValue())
# Check if indexing was successful
bIndexingSuccess = self._edPluginExecEvaluationIndexingMOSFLM.getDataOutput(
"indexingSuccess")[0].getValue()
if bIndexingSuccess:
xsDataIndexingResult = self._edPluginExecEvaluationIndexingMOSFLM.getDataOutput(
"indexingResult")[0]
self._xsDataResultCharacterisation.setIndexingResult(
xsDataIndexingResult)
self._strCharacterisationShortSummary += self.generateIndexingShortSummary(
xsDataIndexingResult)
xsDataCollection = self._xsDataResultCharacterisation.getDataCollection(
)
xsDataGeneratePredictionInput = XSDataGeneratePredictionInput()
xsDataGeneratePredictionInput.setDataCollection(
XSDataCollection.parseString(xsDataCollection.marshal()))
xsDataGeneratePredictionInput.setSelectedIndexingSolution(
XSDataIndexingSolutionSelected.parseString(
xsDataIndexingResult.getSelectedSolution().marshal()))
self._edPluginControlGeneratePrediction.setDataInput(
xsDataGeneratePredictionInput)
if self._edPluginControlIndexingIndicators.hasDataOutput(
"indexingShortSummary"):
self._strCharacterisationShortSummary += self._edPluginControlIndexingIndicators.getDataOutput(
"indexingShortSummary")[0].getValue()
# Start the generation of prediction images - we synchronize in the post-process
self._edPluginControlGeneratePrediction.execute()
# Then start the integration of the reference images
self.indexingToIntegration()
else:
strErrorMessage = "Execution of indexing with MOSFLM failed."
self.ERROR(strErrorMessage)
self.sendMessageToMXCuBE(strErrorMessage, "error")
self.addErrorMessage(strErrorMessage)
self.setFailure()
self.generateExecutiveSummary(self)
if self._strStatusMessage != None:
self.setDataOutput(XSDataString(self._strStatusMessage),
"statusMessage")
self.writeDataOutput()
def doSuccessEvaluationIndexingLABELIT(self, _edPlugin=None):
self.DEBUG("EDPluginControlCharacterisationv1_5.doSuccessEvaluationIndexingLABELIT")
self.retrieveSuccessMessages(_edPlugin, "EDPluginControlCharacterisationv1_5.doSuccessEvaluationIndexingLABELIT")
# Retrieve status messages (if any)
if self._edPluginExecEvaluationIndexingLABELIT.hasDataOutput("statusMessageIndexing"):
self.addStatusMessage("Labelit: " + self._edPluginExecEvaluationIndexingLABELIT.getDataOutput("statusMessageIndexing")[0].getValue())
# Check if indexing was successful
bIndexingSuccess = self._edPluginExecEvaluationIndexingLABELIT.getDataOutput("indexingSuccess")[0].getValue()
if bIndexingSuccess:
xsDataIndexingResult = self._edPluginExecEvaluationIndexingLABELIT.getDataOutput("indexingResult")[0]
self._xsDataResultCharacterisation.setIndexingResult(xsDataIndexingResult)
if self._edPluginControlIndexingIndicators.hasDataOutput("indexingShortSummary"):
self._strCharacterisationShortSummary += self._edPluginControlIndexingIndicators.getDataOutput("indexingShortSummary")[0].getValue()
xsDataCollection = self._xsDataResultCharacterisation.getDataCollection()
xsDataGeneratePredictionInput = XSDataGeneratePredictionInput()
xsDataGeneratePredictionInput.setDataCollection(XSDataCollection.parseString(xsDataCollection.marshal()))
xsDataGeneratePredictionInput.setSelectedIndexingSolution(XSDataIndexingSolutionSelected.parseString(xsDataIndexingResult.getSelectedSolution().marshal()))
self._edPluginControlGeneratePrediction.setDataInput(xsDataGeneratePredictionInput)
if self._edPluginControlIndexingIndicators.hasDataOutput("indexingShortSummary"):
indexingShortSummary = self._edPluginControlIndexingIndicators.getDataOutput("indexingShortSummary")[0].getValue()
self._strCharacterisationShortSummary += indexingShortSummary
self.sendMessageToMXCuBE(indexingShortSummary)
# Start the generation of prediction images - we synchronize in the post-process
self._edPluginControlGeneratePrediction.execute()
# Then start the integration of the reference images
self.indexingToIntegration()
else:
if self._iNoImagesWithDozorScore > 0:
strWarningMessage = "Execution of Indexing and Indicators plugin failed - trying to index with MOSFLM."
self.WARNING(strWarningMessage)
self.sendMessageToMXCuBE(strWarningMessage, "warning")
self.addWarningMessage(strWarningMessage)
xsDataIndexingInput = XSDataIndexingInput()
xsDataIndexingInput.dataCollection = self._xsDataCollection
xsDataIndexingInput.experimentalCondition = self._xsDataCollection.subWedge[0].experimentalCondition
xsDataIndexingInput.crystal = self._xsDataCrystal
self._edPluginControlIndexingMOSFLM.setDataInput(xsDataIndexingInput)
self.executePluginSynchronous(self._edPluginControlIndexingMOSFLM)
else:
strErrorMessage = "Execution of indexing with Labelit failed."
self.ERROR(strErrorMessage)
self.sendMessageToMXCuBE(strErrorMessage, "error")
self.addErrorMessage(strErrorMessage)
self.setFailure()
self.generateExecutiveSummary(self)
if self._strStatusMessage != None:
self.setDataOutput(XSDataString(self._strStatusMessage), "statusMessage")
self.writeDataOutput()
def doSuccessActionIndexing(self, _edPlugin=None):
self.verboseDebug("EDPluginControlIndexingv10.doSuccessActionIndexing")
self.retrieveSuccessMessages(_edPlugin, "EDPluginControlIndexingv10.doSuccessActionIndexing")
# Retrieve the output from the plugin
self.xsDataIndexingResult = self.getDataIndexingResult(_edPlugin)
self.generateShortSummary()
# Add the list of images to the results
xsDataListImage = self.generateImageList(self.xsDataCollection)
self.xsDataIndexingResult.setImage(xsDataListImage)
if (self.bGeneratePredictionImage):
# Generate prediction images
xsDataGeneratePredictionInput = XSDataGeneratePredictionInput()
xsDataGeneratePredictionInput.setDataCollection(XSDataCollection.parseString(self.xsDataCollection.marshal()))
xsDataGeneratePredictionInput.setSelectedIndexingSolution(XSDataIndexingSolutionSelected.parseString(self.xsDataIndexingResult.getSelectedSolution().marshal()))
self.edPluginGeneratePrediction.setDataInput(xsDataGeneratePredictionInput)
self.edPluginGeneratePrediction.executeSynchronous()
def preProcess(self, _edObject=None):
"""
Gets the Configuration Parameters, if found, overrides default parameters
"""
EDPluginControl.preProcess(self, _edObject)
EDVerbose.DEBUG("EDPluginControlGeneratePredictionv10.preProcess...")
xsDataGeneratePredictionInput = self.getDataInput()
xsDataSelectedIndexingSolution = xsDataGeneratePredictionInput.getSelectedIndexingSolution(
)
xsDataExperimentalConditionRefined = xsDataSelectedIndexingSolution.getExperimentalConditionRefined(
)
xsDataCollection = xsDataGeneratePredictionInput.getDataCollection()
xsDataSubWedgeList = xsDataCollection.getSubWedge()
# List containing instances of all the generate prediction plugins
self.__listPluginGeneratePrediction = []
# Loop through all subwedges
iIndex = 0
for xsDataSubWedge in xsDataSubWedgeList:
xsDataImageList = xsDataSubWedge.getImage()
# First find the lowest image number
iLowestImageNumber = None
for xsDataImage in xsDataImageList:
iImageNumber = xsDataImage.getNumber().getValue()
if (iLowestImageNumber is None):
iLowestImageNumber = iImageNumber
elif (iImageNumber < iLowestImageNumber):
iLowestImageNumber = iImageNumber
# Then loop through all images in a sub wedge
for xsDataImage in xsDataImageList:
iIndex += 1
edPluginGeneratePrediction = self.loadPlugin(
self.__strPluginGeneratePredictionName, "%s-%02d" %
(self.__strPluginGeneratePredictionName, iIndex))
xsDataGeneratePredictionInput = XSDataGeneratePredictionInput()
xsDataGeneratePredictionInput.setSelectedIndexingSolution(
XSDataIndexingSolutionSelected.parseString(
xsDataSelectedIndexingSolution.marshal()))
xsDataCollectionNew = XSDataCollection()
xsDataSubWedgeNew = XSDataSubWedge()
xsDataSubWedgeNew.addImage(
XSDataImage.parseString(xsDataImage.marshal()))
xsDataSubWedgeNew.setExperimentalCondition(
XSDataExperimentalCondition.parseString(
xsDataSubWedge.getExperimentalCondition().marshal()))
# We must modify the rotationOscillationStart for the new subwedge
xsDataGoniostatNew = xsDataSubWedgeNew.getExperimentalCondition(
).getGoniostat()
fGoniostatRotationAxisStart = xsDataGoniostatNew.getRotationAxisStart(
).getValue()
fGonioStatOscillationRange = xsDataGoniostatNew.getOscillationWidth(
).getValue()
iImageNumber = xsDataImage.getNumber().getValue()
fGoniostatRotationAxisStartNew = fGoniostatRotationAxisStart + (
iImageNumber -
iLowestImageNumber) * fGonioStatOscillationRange
xsDataGoniostatNew.setRotationAxisStart(
XSDataAngle(fGoniostatRotationAxisStartNew))
#
xsDataCollectionNew.addSubWedge(xsDataSubWedgeNew)
xsDataGeneratePredictionInput.setDataCollection(
xsDataCollectionNew)
from EDHandlerXSDataMOSFLMv10 import EDHandlerXSDataMOSFLMv10
xsDataMOSFLMInputGeneratePrediction = EDHandlerXSDataMOSFLMv10.generateXSDataMOSFLMInputGeneratePrediction(
xsDataGeneratePredictionInput)
edPluginGeneratePrediction.setDataInput(
xsDataMOSFLMInputGeneratePrediction)
self.__listPluginGeneratePrediction.append(
edPluginGeneratePrediction)
def doSuccessEvaluationIndexingLABELIT(self, _edPlugin=None):
self.DEBUG(
"EDPluginControlCharacterisationv1_4.doSuccessEvaluationIndexingLABELIT"
)
self.retrieveSuccessMessages(
_edPlugin,
"EDPluginControlCharacterisationv1_4.doSuccessEvaluationIndexingLABELIT"
)
# Retrieve status messages (if any)
if self._edPluginExecEvaluationIndexingLABELIT.hasDataOutput(
"statusMessageIndexing"):
self.addStatusMessage(
"Labelit: " +
self._edPluginExecEvaluationIndexingLABELIT.getDataOutput(
"statusMessageIndexing")[0].getValue())
# Check if indexing was successful
bIndexingSuccess = self._edPluginExecEvaluationIndexingLABELIT.getDataOutput(
"indexingSuccess")[0].getValue()
if bIndexingSuccess:
xsDataIndexingResult = self._edPluginExecEvaluationIndexingLABELIT.getDataOutput(
"indexingResult")[0]
self._xsDataResultCharacterisation.setIndexingResult(
xsDataIndexingResult)
xsDataCollection = self._xsDataResultCharacterisation.getDataCollection(
)
xsDataGeneratePredictionInput = XSDataGeneratePredictionInput()
xsDataGeneratePredictionInput.setDataCollection(
XSDataCollection.parseString(xsDataCollection.marshal()))
xsDataGeneratePredictionInput.setSelectedIndexingSolution(
XSDataIndexingSolutionSelected.parseString(
xsDataIndexingResult.getSelectedSolution().marshal()))
self._edPluginControlGeneratePrediction.setDataInput(
xsDataGeneratePredictionInput)
if self._edPluginControlIndexingIndicators.hasDataOutput(
"indexingShortSummary"):
indexingShortSummary = self._edPluginControlIndexingIndicators.getDataOutput(
"indexingShortSummary")[0].getValue()
self._strCharacterisationShortSummary += indexingShortSummary
self.sendMessageToMXCuBE(indexingShortSummary)
# Start the generation of prediction images - we synchronize in the post-process
self._edPluginControlGeneratePrediction.execute()
# Then start the integration of the reference images
self.indexingToIntegration()
else:
if self._iNoImagesWithDozorScore > 0:
if not self._bDoOnlyMoslmfIndexing:
strWarningMessage = "Execution of Indexing and Indicators plugin failed - trying to index with MOSFLM."
self.WARNING(strWarningMessage)
self.sendMessageToMXCuBE(strWarningMessage, "warning")
self.addWarningMessage(strWarningMessage)
xsDataIndexingInput = XSDataIndexingInput()
xsDataIndexingInput.dataCollection = self._xsDataCollection
xsDataIndexingInput.experimentalCondition = self._xsDataCollection.subWedge[
0].experimentalCondition
xsDataIndexingInput.crystal = self._xsDataCrystal
self._edPluginControlIndexingMOSFLM.setDataInput(
xsDataIndexingInput)
self.executePluginSynchronous(
self._edPluginControlIndexingMOSFLM)
else:
strErrorMessage = "Execution of indexing with Labelit failed."
self.ERROR(strErrorMessage)
self.sendMessageToMXCuBE(strErrorMessage, "error")
self.addErrorMessage(strErrorMessage)
self.setFailure()
self.generateExecutiveSummary(self)
if self._strStatusMessage != None:
self.setDataOutput(XSDataString(self._strStatusMessage),
"statusMessage")
self.writeDataOutput()