def doSuccessGeneratePrediction(self, _edPlugin=None):
EDVerbose.DEBUG(
"EDPluginControlGeneratePredictionv10.doSuccessGeneratePrediction")
self.retrieveSuccessMessages(
_edPlugin,
"EDPluginControlGeneratePredictionv10.doSuccessGeneratePrediction")
if (self.__xsDataGeneratePredictionResult is None):
self.__xsDataGeneratePredictionResult = XSDataGeneratePredictionResult(
)
xsDataMOSFLMOutputGeneratePrediction = _edPlugin.getDataOutput()
xsDataImage = xsDataMOSFLMOutputGeneratePrediction.getPredictionImage()
self.__xsDataGeneratePredictionResult.addPredictionImage(
XSDataImage.parseString(xsDataImage.marshal()))
def doSuccessGeneratePrediction(self, _edPlugin=None):
self.DEBUG("EDPluginControlGeneratePredictionv10.doSuccessGeneratePrediction")
self.retrieveSuccessMessages(_edPlugin, "EDPluginControlGeneratePredictionv10.doSuccessGeneratePrediction")
if (self.__xsDataGeneratePredictionResult is None):
self.__xsDataGeneratePredictionResult = XSDataGeneratePredictionResult()
xsDataMOSFLMOutputGeneratePrediction = _edPlugin.getDataOutput()
xsDataImage = xsDataMOSFLMOutputGeneratePrediction.getPredictionImage()
self.__xsDataGeneratePredictionResult.addPredictionImage(XSDataImage.parseString(xsDataImage.marshal()))
class EDPluginControlGeneratePredictionv10(EDPluginControl):
"""
The Plugin that control the plugin that generates the prediction.
If several images are available the execution of the plugin which generates
the prediction is performed in parallel.
"""
def __init__(self):
EDPluginControl.__init__(self)
self.__strPluginGeneratePredictionName = "EDPluginMOSFLMGeneratePredictionv10"
#self.m_strPluginGeneratePredictionName = "EDPluginMOSFLMv01GeneratePrediction"
self.__listPluginGeneratePrediction = []
self.__dNoSubwedges = None
self.__xsDataGeneratePredictionResult = None
self.setXSDataInputClass(XSDataGeneratePredictionInput)
def checkParameters(self):
"""
Checks the mandatory parameters
"""
EDVerbose.DEBUG("EDPluginControlGeneratePredictionv10.checkParameters")
self.checkMandatoryParameters(self.getDataInput(),
"Data Input is None")
self.checkMandatoryParameters(self.getDataInput().getDataCollection(),
"dataCollection")
self.checkMandatoryParameters(
self.getDataInput().getSelectedIndexingSolution(),
"selectedIndexingSolution")
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 process(self, _edObject=None):
"""
"""
EDPluginControl.process(self, _edObject)
EDVerbose.DEBUG("EDPluginControlGeneratePredictionv10.process")
# Prepare the action cluster
for edPluginGeneratePrediction in self.__listPluginGeneratePrediction:
edPluginGeneratePrediction.connectSUCCESS(
self.doSuccessGeneratePrediction)
edPluginGeneratePrediction.connectFAILURE(
self.doFailureGeneratePrediction)
self.addPluginToActionCluster(edPluginGeneratePrediction)
# Launch the cluster
self.executeActionCluster()
self.synchronizeActionCluster()
def doSuccessGeneratePrediction(self, _edPlugin=None):
EDVerbose.DEBUG(
"EDPluginControlGeneratePredictionv10.doSuccessGeneratePrediction")
self.retrieveSuccessMessages(
_edPlugin,
"EDPluginControlGeneratePredictionv10.doSuccessGeneratePrediction")
if (self.__xsDataGeneratePredictionResult is None):
self.__xsDataGeneratePredictionResult = XSDataGeneratePredictionResult(
)
xsDataMOSFLMOutputGeneratePrediction = _edPlugin.getDataOutput()
xsDataImage = xsDataMOSFLMOutputGeneratePrediction.getPredictionImage()
self.__xsDataGeneratePredictionResult.addPredictionImage(
XSDataImage.parseString(xsDataImage.marshal()))
def doFailureGeneratePrediction(self, _edPlugin=None):
EDVerbose.DEBUG(
"EDPluginControlGeneratePredictionv10.doFailureGeneratePrediction")
self.retrieveFailureMessages(
_edPlugin,
"EDPluginControlGeneratePredictionv10.doFailureGeneratePrediction")
def postProcess(self, _edPlugin=None):
EDVerbose.DEBUG("EDPluginControlGeneratePredictionv10.postProcess")
self.setDataOutput(self.__xsDataGeneratePredictionResult)
def generateExecutiveSummary(self, _edPlugin):
"""
Generates a summary of the execution of the plugin.
"""
EDVerbose.DEBUG(
"EDPluginControlGeneratePredictionv10.generateExecutiveSummary")
self.addExecutiveSummaryLine(
"Summary of generation of image(s) with prediction:")
self.addErrorWarningMessagesToExecutiveSummary(
"Generation of prediction failure! Error messages: ")
xsDataGeneratePredictionResult = self.getDataOutput()
if (xsDataGeneratePredictionResult is not None):
for predictionImage in xsDataGeneratePredictionResult.getPredictionImage(
):
self.addExecutiveSummaryLine("Path to prediction image number %d : %s" % \
(predictionImage.getNumber().getValue(), predictionImage.getPath().getValue()))
class EDPluginControlGeneratePredictionv10(EDPluginControl):
"""
The Plugin that control the plugin that generates the prediction.
If several images are available the execution of the plugin which generates
the prediction is performed in parallel.
"""
def __init__ (self):
EDPluginControl.__init__(self)
self.__strPluginGeneratePredictionName = "EDPluginMOSFLMGeneratePredictionv10"
#self.m_strPluginGeneratePredictionName = "EDPluginMOSFLMv01GeneratePrediction"
self.__listPluginGeneratePrediction = []
self.__dNoSubwedges = None
self.__xsDataGeneratePredictionResult = None
self.setXSDataInputClass(XSDataGeneratePredictionInput)
def checkParameters(self):
"""
Checks the mandatory parameters
"""
self.DEBUG("EDPluginControlGeneratePredictionv10.checkParameters")
self.checkMandatoryParameters(self.getDataInput(), "Data Input is None")
self.checkMandatoryParameters(self.getDataInput().getDataCollection(), "dataCollection")
self.checkMandatoryParameters(self.getDataInput().getSelectedIndexingSolution(), "selectedIndexingSolution")
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 process(self, _edObject=None):
"""
"""
EDPluginControl.process(self, _edObject)
self.DEBUG("EDPluginControlGeneratePredictionv10.process")
# Prepare the action cluster
for edPluginGeneratePrediction in self.__listPluginGeneratePrediction:
edPluginGeneratePrediction.connectSUCCESS(self.doSuccessGeneratePrediction)
edPluginGeneratePrediction.connectFAILURE(self.doFailureGeneratePrediction)
self.addPluginToActionCluster(edPluginGeneratePrediction)
# Launch the cluster
self.executeActionCluster()
self.synchronizeActionCluster()
def doSuccessGeneratePrediction(self, _edPlugin=None):
self.DEBUG("EDPluginControlGeneratePredictionv10.doSuccessGeneratePrediction")
self.retrieveSuccessMessages(_edPlugin, "EDPluginControlGeneratePredictionv10.doSuccessGeneratePrediction")
if (self.__xsDataGeneratePredictionResult is None):
self.__xsDataGeneratePredictionResult = XSDataGeneratePredictionResult()
xsDataMOSFLMOutputGeneratePrediction = _edPlugin.getDataOutput()
xsDataImage = xsDataMOSFLMOutputGeneratePrediction.getPredictionImage()
self.__xsDataGeneratePredictionResult.addPredictionImage(XSDataImage.parseString(xsDataImage.marshal()))
def doFailureGeneratePrediction(self, _edPlugin=None):
self.DEBUG("EDPluginControlGeneratePredictionv10.doFailureGeneratePrediction")
self.retrieveFailureMessages(_edPlugin, "EDPluginControlGeneratePredictionv10.doFailureGeneratePrediction")
def postProcess(self, _edPlugin=None):
self.DEBUG("EDPluginControlGeneratePredictionv10.postProcess")
self.setDataOutput(self.__xsDataGeneratePredictionResult)
def generateExecutiveSummary(self, _edPlugin):
"""
Generates a summary of the execution of the plugin.
"""
self.DEBUG("EDPluginControlGeneratePredictionv10.generateExecutiveSummary")
self.addExecutiveSummaryLine("Summary of generation of image(s) with prediction:")
self.addErrorWarningMessagesToExecutiveSummary("Generation of prediction failure! Error messages: ")
xsDataGeneratePredictionResult = self.getDataOutput()
if (xsDataGeneratePredictionResult is not None):
for predictionImage in xsDataGeneratePredictionResult.getPredictionImage():
self.addExecutiveSummaryLine("Path to prediction image number %d : %s" % \
(predictionImage.getNumber().getValue(), predictionImage.getPath().getValue()))