def initialize(self, runInfo, oriInputFiles):
"""
Method to initialize the run of a new step
@ In, runInfo, dict, dictionary of the info in the <RunInfo> XML block
@ In, oriInputFiles, list, list of the original input files
@ Out, None
"""
import RAVENparser
index = self.__findInputFile(oriInputFiles)
parser = RAVENparser.RAVENparser(oriInputFiles[index].getAbsFile())
# get the OutStreams names
self.outStreamsNamesAndType, self.outDatabases = parser.returnOutputs()
# check if the linked DataObjects are among the Outstreams
if self.linkedDataObjectOutStreamsNames:
pointSetNumber, historySetNumber = 0, 0
for outstream, dataObj in self.outStreamsNamesAndType.items():
if outstream in self.linkedDataObjectOutStreamsNames:
if dataObj[1].strip() == 'PointSet':
pointSetNumber += 1
else:
historySetNumber += 1
if pointSetNumber > 1 or historySetNumber > 1:
raise IOError(
self.printTag +
' ERROR: Only one OutStream for PointSet and/or one for HistorySet can be linked as output export!'
)
if pointSetNumber == 0 and historySetNumber == 0:
raise IOError(
self.printTag +
' ERROR: No one of the OutStreams linked to this interface have been found in the SLAVE RAVEN!'
+ ' Expected: "' +
' '.join(self.linkedDataObjectOutStreamsNames) +
'" but found "' +
' '.join(self.outStreamsNamesAndType.keys()) + '"!')
else: # self.linkedDatabaseName
for dbName, dbXml in self.outDatabases.items():
if dbName == self.linkedDatabaseName:
break
else:
# the one we want wasn't found!
raise IOError(
f'{self.printTag} ERROR: The Database named "{self.linkedDatabaseName}" listed '
+
'in <outputDatabase> was not found among the written Databases in active Steps in the inner RAVEN! '
+ f'Found: {list(self.outDatabases.keys())}')
# get variable groups
varGroupNames = parser.returnVarGroups()
## store globally
self.variableGroups = varGroupNames
# get inner working dir
self.innerWorkingDir = parser.workingDir
# check operating system and define prefix if needed
if platform.startswith("win") and utils.which("bash.exe") is not None:
self.preCommand = 'bash.exe'
def initialize(self, runInfo, oriInputFiles):
"""
Method to initialize the run of a new step
@ In, runInfo, dict, dictionary of the info in the <RunInfo> XML block
@ In, oriInputFiles, list, list of the original input files
@ Out, None
"""
import RAVENparser
index = self.__findInputFile(oriInputFiles)
parser = RAVENparser.RAVENparser(oriInputFiles[index].getAbsFile())
# get the OutStreams names
self.outStreamsNamesAndType = parser.returnOutstreamsNamesAnType()
# check if the linked DataObjects are among the Outstreams
pointSetNumber, historySetNumber = 0, 0
for outstream, dataObj in self.outStreamsNamesAndType.items():
if outstream in self.linkedDataObjectOutStreamsNames:
if dataObj[1].strip() == 'PointSet':
pointSetNumber += 1
else:
historySetNumber += 1
if pointSetNumber > 1 or historySetNumber > 1:
raise IOError(
self.printTag +
' ERROR: Only one OutStream for PointSet and/or one for HistorySet can be linked as output export!'
)
if pointSetNumber == 0 and historySetNumber == 0:
raise IOError(
self.printTag +
' ERROR: No one of the OutStreams linked to this interface have been found in the SLAVE RAVEN!'
+ ' Expected: "' +
' '.join(self.linkedDataObjectOutStreamsNames) +
'" but found "' +
' '.join(self.outStreamsNamesAndType.keys()) + '"!')
# get variable groups
varGroupNames = parser.returnVarGroups()
## store globally
self.variableGroups = varGroupNames
# get inner working dir
self.innerWorkingDir = parser.workingDir
def createNewInput(self, currentInputFiles, oriInputFiles, samplerType,
**Kwargs):
"""
this generates a new input file depending on which sampler has been chosen
@ In, currentInputFiles, list, list of current input files (input files from last this method call)
@ In, oriInputFiles, list, list of the original input files
@ In, samplerType, string, Sampler type (e.g. MonteCarlo, Adaptive, etc. see manual Samplers section)
@ In, Kwargs, dictionary, kwarded dictionary of parameters. In this dictionary there is another dictionary called "SampledVars"
where RAVEN stores the variables that got sampled (e.g. Kwargs['SampledVars'] => {'var1':10,'var2':40})
@ Out, newInputFiles, list, list of newer input files, list of the new input files (modified and not)
"""
import RAVENparser
if 'dynamiceventtree' in str(samplerType).strip().lower():
raise IOError(
self.printTag +
' ERROR: DynamicEventTree-based sampling not supported!')
index = self.__findInputFile(currentInputFiles)
parser = RAVENparser.RAVENparser(currentInputFiles[index].getAbsFile())
# get sampled variables
modifDict = Kwargs['SampledVars']
# apply conversion scripts
for source, convDict in self.conversionDict.items():
module = utils.importFromPath(source)
varVals = dict((var, np.asarray(modifDict[var]))
for var in convDict['variables'])
# modify vector+ variables that need to be flattened
if convDict['noScalar']:
# call conversion
newVars = module.convertNotScalarSampledVariables(varVals)
# check type
if type(newVars).__name__ != 'dict':
raise IOError(
self.printTag +
' ERROR: convertNotScalarSampledVariables in "{}" must return a dictionary!'
.format(source))
# apply new and/or updated values
modifDict.update(newVars)
# modify scalar variables
if convDict['scalar']:
# call conversion, value changes happen in-place
module.manipulateScalarSampledVariables(modifDict)
# we work on batchSizes here
newBatchSize = Kwargs['NumMPI']
internalParallel = Kwargs.get('internalParallel', False)
if int(Kwargs['numberNodes']) > 0:
# we are in a distributed memory machine => we allocate a node file
nodeFileToUse = os.path.join(Kwargs['BASE_WORKING_DIR'],
"node_" + str(Kwargs['INDEX']))
if os.path.exists(nodeFileToUse):
modifDict['RunInfo|mode'] = 'mpi'
modifDict['RunInfo|mode|nodefile'] = nodeFileToUse
else:
raise IOError(self.printTag + ' ERROR: The nodefile "' +
str(nodeFileToUse) + '" does not exist!')
if internalParallel or newBatchSize > 1:
# either we have an internal parallel or NumMPI > 1
modifDict['RunInfo|batchSize'] = newBatchSize
#modifDict['RunInfo|internalParallel'] = internalParallel
# make tree
modifiedRoot = parser.modifyOrAdd(modifDict, save=True, allowAdd=True)
# modify tree
if self.inputManipulationModule is not None:
module = utils.importFromPath(self.inputManipulationModule)
modifiedRoot = module.modifyInput(modifiedRoot, modifDict)
# write input file
parser.printInput(modifiedRoot, currentInputFiles[index].getAbsFile())
# copy slave files
parser.copySlaveFiles(currentInputFiles[index].getPath())
return currentInputFiles
def createNewInput(self,currentInputFiles,oriInputFiles,samplerType,**Kwargs):
"""
this generates a new input file depending on which sampler has been chosen
@ In, currentInputFiles, list, list of current input files (input files from last this method call)
@ In, oriInputFiles, list, list of the original input files
@ In, samplerType, string, Sampler type (e.g. MonteCarlo, Adaptive, etc. see manual Samplers section)
@ In, Kwargs, dictionary, kwarded dictionary of parameters. In this dictionary there is another dictionary called "SampledVars"
where RAVEN stores the variables that got sampled (e.g. Kwargs['SampledVars'] => {'var1':10,'var2':40})
@ Out, newInputFiles, list, list of newer input files, list of the new input files (modified and not)
"""
import RAVENparser
if 'dynamiceventtree' in str(samplerType).strip().lower():
raise IOError(self.printTag+' ERROR: DynamicEventTree-based sampling not supported!')
index = self.__findInputFile(currentInputFiles)
parser = RAVENparser.RAVENparser(currentInputFiles[index].getAbsFile())
# get the OutStreams names
self.outStreamsNamesAndType = parser.returnOutstreamsNamesAnType()
# check if the linked DataObjects are among the Outstreams
pointSetNumber, historySetNumber = 0, 0
for outstream, dataObj in self.outStreamsNamesAndType.items():
if outstream in self.linkedDataObjectOutStreamsNames:
if dataObj[1].strip() == 'PointSet':
pointSetNumber+=1
else:
historySetNumber+=1
if pointSetNumber > 1 or historySetNumber > 1:
raise IOError(self.printTag+' ERROR: Only one OutStream for PointSet and/or one for HistorySet can be linked as output export!')
if pointSetNumber == 0 and historySetNumber == 0:
raise IOError(self.printTag+' ERROR: No one of the OutStreams linked to this interface have been found in the SLAVE RAVEN!'
+' Expected: "'+' '.join(self.linkedDataObjectOutStreamsNames)+'" but found "'
+' '.join(self.outStreamsNamesAndType.keys())+'"!')
# get variable groups
varGroupNames = parser.returnVarGroups()
if len(varGroupNames) > 0:
# check if they are not present in the linked outstreams
for outstream in self.linkedDataObjectOutStreamsNames:
inputNode = self.outStreamsNamesAndType[outstream][2].find("Input")
outputNode = self.outStreamsNamesAndType[outstream][2].find("Output")
inputVariables = inputNode.text.split(",") if inputNode is not None else []
outputVariables = outputNode.text.split(",") if outputNode is not None else []
if any (varGroupName in inputVariables+outputVariables for varGroupName in varGroupNames):
raise IOError(self.printTag+' ERROR: The VariableGroup system is not supported in the current ' +
'implementation of the interface for the DataObjects specified in the '+
'<outputExportOutStreams> XML node!')
# get inner working dir
self.innerWorkingDir = parser.workingDir
# get sampled variables
modifDict = Kwargs['SampledVars']
# apply conversion scripts
for source,convDict in self.conversionDict.items():
module = utils.importFromPath(source)
varVals = dict((var,np.asarray(modifDict[var])) for var in convDict['variables'])
# modify vector+ variables that need to be flattened
if convDict['noScalar']:
# call conversion
newVars = module.convertNotScalarSampledVariables(varVals)
# check type
if type(newVars).__name__ != 'dict':
raise IOError(self.printTag+' ERROR: convertNotScalarSampledVariables in "{}" must return a dictionary!'.format(source))
# apply new and/or updated values
modifDict.update(newVars)
# modify scalar variables
if convDict['scalar']:
# call conversion, value changes happen in-place
module.manipulateScalarSampledVariables(modifDict)
# we work on batchSizes here
newBatchSize = Kwargs['NumMPI']
internalParallel = Kwargs.get('internalParallel',False)
if int(Kwargs['numberNodes']) > 0:
# we are in a distributed memory machine => we allocate a node file
nodeFileToUse = os.path.join(Kwargs['BASE_WORKING_DIR'],"node_" +str(Kwargs['INDEX']))
if os.path.exists(nodeFileToUse):
modifDict['RunInfo|mode' ] = 'mpi'
modifDict['RunInfo|mode|nodefile' ] = nodeFileToUse
else:
raise IOError(self.printTag+' ERROR: The nodefile "'+str(nodeFileToUse)+'" does not exist!')
if internalParallel or newBatchSize > 1:
# either we have an internal parallel or NumMPI > 1
modifDict['RunInfo|batchSize' ] = newBatchSize
#modifDict['RunInfo|internalParallel'] = internalParallel
#make tree
modifiedRoot = parser.modifyOrAdd(modifDict,save=True,allowAdd = True)
#make input
parser.printInput(modifiedRoot,currentInputFiles[index].getAbsFile())
# copy slave files
parser.copySlaveFiles(currentInputFiles[index].getPath())
return currentInputFiles