def doPreProRefine(self,taskParamMap):
# no preprocessing
if not taskParamMap.has_key('preproSpec'):
return None,taskParamMap
# already preprocessed
if self.taskSpec.checkPreProcessed():
# get replaced task params
tmpStat,tmpJsonStr = self.taskBufferIF.getPreprocessMetadata_JEDI(self.taskSpec.jediTaskID)
try:
# replace placeholders
replaceParams = RefinerUtils.decodeJSON(tmpJsonStr)
self.tmpLog.debug("replace placeholders with "+str(replaceParams))
for tmpKey,tmpVal in replaceParams.iteritems():
self.replacePlaceHolders(taskParamMap,tmpKey,tmpVal)
except:
errtype,errvalue = sys.exc_info()[:2]
self.tmpLog.error('{0} failed to get additional task params with {1}:{2}'.format(self.__class__.__name__,
errtype.__name__,errvalue))
return False,taskParamMap
# succeeded
self.updatedTaskParams = taskParamMap
return None,taskParamMap
# make dummy dataset to keep track of preprocessing
datasetSpec = JediDatasetSpec()
datasetSpec.datasetName = 'panda.pp.in.{0}.{1}'.format(uuid.uuid4(),self.taskSpec.jediTaskID)
datasetSpec.jediTaskID = self.taskSpec.jediTaskID
datasetSpec.type = 'pp_input'
datasetSpec.vo = self.taskSpec.vo
datasetSpec.nFiles = 1
datasetSpec.nFilesUsed = 0
datasetSpec.nFilesToBeUsed = 1
datasetSpec.nFilesFinished = 0
datasetSpec.nFilesFailed = 0
datasetSpec.nFilesOnHold = 0
datasetSpec.status = 'ready'
self.inMasterDatasetSpec.append(datasetSpec)
# make file
fileSpec = JediFileSpec()
fileSpec.jediTaskID = datasetSpec.jediTaskID
fileSpec.type = datasetSpec.type
fileSpec.status = 'ready'
fileSpec.lfn = 'pseudo_lfn'
fileSpec.attemptNr = 0
fileSpec.maxAttempt = 3
fileSpec.keepTrack = 1
datasetSpec.addFile(fileSpec)
# make log dataset
logDatasetSpec = JediDatasetSpec()
logDatasetSpec.datasetName = 'panda.pp.log.{0}.{1}'.format(uuid.uuid4(),self.taskSpec.jediTaskID)
logDatasetSpec.jediTaskID = self.taskSpec.jediTaskID
logDatasetSpec.type = 'tmpl_pp_log'
logDatasetSpec.streamName = 'PP_LOG'
logDatasetSpec.vo = self.taskSpec.vo
logDatasetSpec.nFiles = 0
logDatasetSpec.nFilesUsed = 0
logDatasetSpec.nFilesToBeUsed = 0
logDatasetSpec.nFilesFinished = 0
logDatasetSpec.nFilesFailed = 0
logDatasetSpec.nFilesOnHold = 0
logDatasetSpec.status = 'defined'
self.outDatasetSpecList.append(logDatasetSpec)
# make output template for log
outTemplateMap = {'jediTaskID' : self.taskSpec.jediTaskID,
'serialNr' : 1,
'streamName' : logDatasetSpec.streamName,
'filenameTemplate' : "{0}._${{SN}}.log.tgz".format(logDatasetSpec.datasetName),
'outtype' : re.sub('^tmpl_','',logDatasetSpec.type),
}
self.outputTemplateMap[logDatasetSpec.outputMapKey()] = [outTemplateMap]
# set split rule to use preprocessing
self.taskSpec.setPrePro()
# set task status
self.taskSpec.status = 'topreprocess'
# return
return True,taskParamMap
def doPreProRefine(self, taskParamMap):
# no preprocessing
if not taskParamMap.has_key('preproSpec'):
return None, taskParamMap
# already preprocessed
if self.taskSpec.checkPreProcessed():
# get replaced task params
tmpStat, tmpJsonStr = self.taskBufferIF.getPreprocessMetadata_JEDI(
self.taskSpec.jediTaskID)
try:
# replace placeholders
replaceParams = RefinerUtils.decodeJSON(tmpJsonStr)
self.tmpLog.debug("replace placeholders with " +
str(replaceParams))
for tmpKey, tmpVal in replaceParams.iteritems():
self.replacePlaceHolders(taskParamMap, tmpKey, tmpVal)
except:
errtype, errvalue = sys.exc_info()[:2]
self.tmpLog.error(
'{0} failed to get additional task params with {1}:{2}'.
format(self.__class__.__name__, errtype.__name__,
errvalue))
return False, taskParamMap
# succeeded
self.updatedTaskParams = taskParamMap
return None, taskParamMap
# make dummy dataset to keep track of preprocessing
datasetSpec = JediDatasetSpec()
datasetSpec.datasetName = 'panda.pp.in.{0}.{1}'.format(
uuid.uuid4(), self.taskSpec.jediTaskID)
datasetSpec.jediTaskID = self.taskSpec.jediTaskID
datasetSpec.type = 'pp_input'
datasetSpec.vo = self.taskSpec.vo
datasetSpec.nFiles = 1
datasetSpec.nFilesUsed = 0
datasetSpec.nFilesToBeUsed = 1
datasetSpec.nFilesFinished = 0
datasetSpec.nFilesFailed = 0
datasetSpec.nFilesOnHold = 0
datasetSpec.status = 'ready'
self.inMasterDatasetSpec.append(datasetSpec)
# make file
fileSpec = JediFileSpec()
fileSpec.jediTaskID = datasetSpec.jediTaskID
fileSpec.type = datasetSpec.type
fileSpec.status = 'ready'
fileSpec.lfn = 'pseudo_lfn'
fileSpec.attemptNr = 0
fileSpec.maxAttempt = 3
fileSpec.keepTrack = 1
datasetSpec.addFile(fileSpec)
# make log dataset
logDatasetSpec = JediDatasetSpec()
logDatasetSpec.datasetName = 'panda.pp.log.{0}.{1}'.format(
uuid.uuid4(), self.taskSpec.jediTaskID)
logDatasetSpec.jediTaskID = self.taskSpec.jediTaskID
logDatasetSpec.type = 'tmpl_pp_log'
logDatasetSpec.streamName = 'PP_LOG'
logDatasetSpec.vo = self.taskSpec.vo
logDatasetSpec.nFiles = 0
logDatasetSpec.nFilesUsed = 0
logDatasetSpec.nFilesToBeUsed = 0
logDatasetSpec.nFilesFinished = 0
logDatasetSpec.nFilesFailed = 0
logDatasetSpec.nFilesOnHold = 0
logDatasetSpec.status = 'defined'
self.outDatasetSpecList.append(logDatasetSpec)
# make output template for log
outTemplateMap = {
'jediTaskID':
self.taskSpec.jediTaskID,
'serialNr':
1,
'streamName':
logDatasetSpec.streamName,
'filenameTemplate':
"{0}._${{SN}}.log.tgz".format(logDatasetSpec.datasetName),
'outtype':
re.sub('^tmpl_', '', logDatasetSpec.type),
}
self.outputTemplateMap[logDatasetSpec.outputMapKey()] = [
outTemplateMap
]
# set split rule to use preprocessing
self.taskSpec.setPrePro()
# set task status
self.taskSpec.status = 'topreprocess'
# return
return True, taskParamMap
def doBasicRefine(self,taskParamMap):
# get input/output/log dataset specs
nIn = 0
nOutMap = {}
if isinstance(taskParamMap['log'],dict):
itemList = taskParamMap['jobParameters'] + [taskParamMap['log']]
else:
itemList = taskParamMap['jobParameters'] + taskParamMap['log']
# pseudo input
if taskParamMap.has_key('noInput') and taskParamMap['noInput'] == True:
tmpItem = {}
tmpItem['type'] = 'template'
tmpItem['value'] = ''
tmpItem['dataset'] = 'pseudo_dataset'
tmpItem['param_type'] = 'pseudo_input'
itemList = [tmpItem] + itemList
# random seed
if RefinerUtils.useRandomSeed(taskParamMap):
tmpItem = {}
tmpItem['type'] = 'template'
tmpItem['value'] = ''
tmpItem['dataset'] = 'RNDMSEED'
tmpItem['param_type'] = 'random_seed'
itemList.append(tmpItem)
# loop over all items
allDsList = []
for tmpItem in itemList:
# look for datasets
if tmpItem['type'] == 'template' and tmpItem.has_key('dataset'):
# avoid duplication
if not tmpItem['dataset'] in allDsList:
allDsList.append(tmpItem['dataset'])
else:
continue
datasetSpec = JediDatasetSpec()
datasetSpec.datasetName = tmpItem['dataset']
datasetSpec.jediTaskID = self.taskSpec.jediTaskID
datasetSpec.type = tmpItem['param_type']
if tmpItem.has_key('container'):
datasetSpec.containerName = tmpItem['container']
if tmpItem.has_key('token'):
datasetSpec.storageToken = tmpItem['token']
if tmpItem.has_key('destination'):
datasetSpec.destination = tmpItem['destination']
if tmpItem.has_key('attributes'):
datasetSpec.setDatasetAttribute(tmpItem['attributes'])
if tmpItem.has_key('ratio'):
datasetSpec.setDatasetAttribute('ratio={0}'.format(tmpItem['ratio']))
if tmpItem.has_key('check'):
datasetSpec.setDatasetAttribute('cc')
if tmpItem.has_key('usedup'):
datasetSpec.setDatasetAttribute('ud')
if tmpItem.has_key('random'):
datasetSpec.setDatasetAttribute('rd')
if tmpItem.has_key('reusable'):
datasetSpec.setDatasetAttribute('ru')
if tmpItem.has_key('offset'):
datasetSpec.setOffset(tmpItem['offset'])
if tmpItem.has_key('allowNoOutput'):
datasetSpec.allowNoOutput()
if tmpItem.has_key('nFilesPerJob'):
datasetSpec.setNumFilesPerJob(tmpItem['nFilesPerJob'])
if tmpItem.has_key('num_records'):
datasetSpec.setNumRecords(tmpItem['num_records'])
if 'transient' in tmpItem:
datasetSpec.setTransient(tmpItem['transient'])
datasetSpec.vo = self.taskSpec.vo
datasetSpec.nFiles = 0
datasetSpec.nFilesUsed = 0
datasetSpec.nFilesFinished = 0
datasetSpec.nFilesFailed = 0
datasetSpec.nFilesOnHold = 0
datasetSpec.nEvents = 0
datasetSpec.nEventsUsed = 0
datasetSpec.nEventsToBeUsed = 0
datasetSpec.status = 'defined'
if datasetSpec.type in JediDatasetSpec.getInputTypes() + ['random_seed']:
datasetSpec.streamName = RefinerUtils.extractStreamName(tmpItem['value'])
if not tmpItem.has_key('expandedList'):
tmpItem['expandedList'] = []
# dataset names could be comma-concatenated
datasetNameList = datasetSpec.datasetName.split(',')
# datasets could be added by incexec
incexecDS = 'dsFor{0}'.format(datasetSpec.streamName)
# remove /XYZ
incexecDS = incexecDS.split('/')[0]
if taskParamMap.has_key(incexecDS):
for tmpDatasetName in taskParamMap[incexecDS].split(','):
if not tmpDatasetName in datasetNameList:
datasetNameList.append(tmpDatasetName)
# loop over all dataset names
inDatasetSpecList = []
for datasetName in datasetNameList:
# skip empty
if datasetName == '':
continue
# expand
if datasetSpec.isPseudo() or datasetSpec.type in ['random_seed'] or datasetName == 'DBR_LATEST':
# pseudo input
tmpDatasetNameList = [datasetName]
elif tmpItem.has_key('expand') and tmpItem['expand'] == True:
# expand dataset container
tmpDatasetNameList = self.ddmIF.getInterface(self.taskSpec.vo).expandContainer(datasetName)
else:
# normal dataset name
tmpDatasetNameList = self.ddmIF.getInterface(self.taskSpec.vo).listDatasets(datasetName)
for elementDatasetName in tmpDatasetNameList:
if not elementDatasetName in tmpItem['expandedList']:
tmpItem['expandedList'].append(elementDatasetName)
inDatasetSpec = copy.copy(datasetSpec)
inDatasetSpec.datasetName = elementDatasetName
inDatasetSpec.containerName = datasetName
inDatasetSpecList.append(inDatasetSpec)
# empty input
if inDatasetSpecList == [] and self.oldTaskStatus != 'rerefine':
errStr = 'doBasicRefine : unknown input dataset "{0}"'.format(datasetSpec.datasetName)
self.taskSpec.setErrDiag(errStr)
if not datasetSpec.datasetName in self.unknownDatasetList:
self.unknownDatasetList.append(datasetSpec.datasetName)
raise JediException.UnknownDatasetError,errStr
# set master flag
for inDatasetSpec in inDatasetSpecList:
if nIn == 0:
# master
self.inMasterDatasetSpec.append(inDatasetSpec)
else:
# secondary
self.inSecDatasetSpecList.append(inDatasetSpec)
nIn += 1
continue
if datasetSpec.type in ['output','log']:
if not nOutMap.has_key(datasetSpec.type):
nOutMap[datasetSpec.type] = 0
# make stream name
datasetSpec.streamName = "{0}{1}".format(datasetSpec.type.upper(),nOutMap[datasetSpec.type])
nOutMap[datasetSpec.type] += 1
# set attribute for event service
if self.taskSpec.useEventService() and taskParamMap.has_key('objectStore') and datasetSpec.type in ['output']:
datasetSpec.setObjectStore(taskParamMap['objectStore'])
# extract output filename template and change the value field
outFileTemplate,tmpItem['value'] = RefinerUtils.extractReplaceOutFileTemplate(tmpItem['value'],
datasetSpec.streamName)
# make output template
if outFileTemplate != None:
if tmpItem.has_key('offset'):
offsetVal = 1 + tmpItem['offset']
else:
offsetVal = 1
outTemplateMap = {'jediTaskID' : self.taskSpec.jediTaskID,
'serialNr' : offsetVal,
'streamName' : datasetSpec.streamName,
'filenameTemplate' : outFileTemplate,
'outtype' : datasetSpec.type,
}
if self.outputTemplateMap.has_key(datasetSpec.outputMapKey()):
# multiple files are associated to the same output datasets
self.outputTemplateMap[datasetSpec.outputMapKey()].append(outTemplateMap)
# don't insert the same output dataset
continue
self.outputTemplateMap[datasetSpec.outputMapKey()] = [outTemplateMap]
# append
self.outDatasetSpecList.append(datasetSpec)
# make unmerged dataset
if taskParamMap.has_key('mergeOutput') and taskParamMap['mergeOutput'] == True:
umDatasetSpec = JediDatasetSpec()
umDatasetSpec.datasetName = 'panda.um.' + datasetSpec.datasetName
umDatasetSpec.jediTaskID = self.taskSpec.jediTaskID
umDatasetSpec.storageToken = 'TOMERGE'
umDatasetSpec.vo = datasetSpec.vo
umDatasetSpec.type = "tmpl_trn_" + datasetSpec.type
umDatasetSpec.nFiles = 0
umDatasetSpec.nFilesUsed = 0
umDatasetSpec.nFilesToBeUsed = 0
umDatasetSpec.nFilesFinished = 0
umDatasetSpec.nFilesFailed = 0
umDatasetSpec.nFilesOnHold = 0
umDatasetSpec.status = 'defined'
umDatasetSpec.streamName = datasetSpec.streamName
if datasetSpec.isAllowedNoOutput():
umDatasetSpec.allowNoOutput()
# make unmerged output template
if outFileTemplate != None:
umOutTemplateMap = {'jediTaskID' : self.taskSpec.jediTaskID,
'serialNr' : 1,
'streamName' : umDatasetSpec.streamName,
'outtype' : datasetSpec.type,
}
# append temporary name
if taskParamMap.has_key('umNameAtEnd') and taskParamMap['umNameAtEnd'] == True:
# append temporary name at the end
umOutTemplateMap['filenameTemplate'] = outFileTemplate + '.panda.um'
else:
umOutTemplateMap['filenameTemplate'] = 'panda.um.' + outFileTemplate
if self.outputTemplateMap.has_key(umDatasetSpec.outputMapKey()):
# multiple files are associated to the same output datasets
self.outputTemplateMap[umDatasetSpec.outputMapKey()].append(umOutTemplateMap)
# don't insert the same output dataset
continue
self.outputTemplateMap[umDatasetSpec.outputMapKey()] = [umOutTemplateMap]
# use log as master for merging
if datasetSpec.type == 'log':
self.unmergeMasterDatasetSpec[datasetSpec.outputMapKey()] = umDatasetSpec
else:
# append
self.unmergeDatasetSpecMap[datasetSpec.outputMapKey()] = umDatasetSpec
# set attributes for merging
if taskParamMap.has_key('mergeOutput') and taskParamMap['mergeOutput'] == True:
self.setSplitRule(None,1,JediTaskSpec.splitRuleToken['mergeOutput'])
# make job parameters
rndmSeedOffset = None
firstEventOffset = None
jobParameters = ''
for tmpItem in taskParamMap['jobParameters']:
if tmpItem.has_key('value'):
# hidden parameter
if tmpItem.has_key('hidden') and tmpItem['hidden'] == True:
continue
# add tags for ES-only parameters
esOnly = False
if 'es_only' in tmpItem and tmpItem['es_only'] == True:
esOnly = True
if esOnly:
jobParameters += '<PANDA_ES_ONLY>'
jobParameters += '{0}'.format(tmpItem['value'])
if esOnly:
jobParameters += '</PANDA_ES_ONLY>'
# padding
if tmpItem.has_key('padding') and tmpItem['padding'] == False:
pass
else:
jobParameters += ' '
# get offset for random seed and first event
if tmpItem['type'] == 'template' and tmpItem['param_type'] == 'number':
if '${RNDMSEED}' in tmpItem['value']:
if tmpItem.has_key('offset'):
rndmSeedOffset = tmpItem['offset']
else:
rndmSeedOffset = 0
elif '${FIRSTEVENT}' in tmpItem['value']:
if tmpItem.has_key('offset'):
firstEventOffset = tmpItem['offset']
jobParameters = jobParameters[:-1]
# append parameters for event service merging if necessary
esmergeParams = self.getParamsForEventServiceMerging(taskParamMap)
if esmergeParams != None:
jobParameters += esmergeParams
self.setJobParamsTemplate(jobParameters)
# set random seed offset
if rndmSeedOffset != None:
self.setSplitRule(None,rndmSeedOffset,JediTaskSpec.splitRuleToken['randomSeed'])
if firstEventOffset != None:
self.setSplitRule(None,firstEventOffset,JediTaskSpec.splitRuleToken['firstEvent'])
# return
return
def doBasicRefine(self, taskParamMap):
# get input/output/log dataset specs
nIn = 0
nOutMap = {}
if isinstance(taskParamMap['log'], dict):
itemList = taskParamMap['jobParameters'] + [taskParamMap['log']]
else:
itemList = taskParamMap['jobParameters'] + taskParamMap['log']
# pseudo input
if taskParamMap.has_key('noInput') and taskParamMap['noInput'] == True:
tmpItem = {}
tmpItem['type'] = 'template'
tmpItem['value'] = ''
tmpItem['dataset'] = 'pseudo_dataset'
tmpItem['param_type'] = 'pseudo_input'
itemList = [tmpItem] + itemList
# random seed
if RefinerUtils.useRandomSeed(taskParamMap):
tmpItem = {}
tmpItem['type'] = 'template'
tmpItem['value'] = ''
tmpItem['dataset'] = 'RNDMSEED'
tmpItem['param_type'] = 'random_seed'
itemList.append(tmpItem)
# loop over all items
allDsList = []
for tmpItem in itemList:
# look for datasets
if tmpItem['type'] == 'template' and tmpItem.has_key('dataset'):
# avoid duplication
if not tmpItem['dataset'] in allDsList:
allDsList.append(tmpItem['dataset'])
else:
continue
datasetSpec = JediDatasetSpec()
datasetSpec.datasetName = tmpItem['dataset']
datasetSpec.jediTaskID = self.taskSpec.jediTaskID
datasetSpec.type = tmpItem['param_type']
if tmpItem.has_key('container'):
datasetSpec.containerName = tmpItem['container']
if tmpItem.has_key('token'):
datasetSpec.storageToken = tmpItem['token']
if tmpItem.has_key('destination'):
datasetSpec.destination = tmpItem['destination']
if tmpItem.has_key('attributes'):
datasetSpec.setDatasetAttribute(tmpItem['attributes'])
if tmpItem.has_key('ratio'):
datasetSpec.setDatasetAttribute('ratio={0}'.format(
tmpItem['ratio']))
if tmpItem.has_key('eventRatio'):
datasetSpec.setEventRatio(tmpItem['eventRatio'])
if tmpItem.has_key('check'):
datasetSpec.setDatasetAttribute('cc')
if tmpItem.has_key('usedup'):
datasetSpec.setDatasetAttribute('ud')
if tmpItem.has_key('random'):
datasetSpec.setDatasetAttribute('rd')
if tmpItem.has_key('reusable'):
datasetSpec.setDatasetAttribute('ru')
if tmpItem.has_key('offset'):
datasetSpec.setOffset(tmpItem['offset'])
if tmpItem.has_key('allowNoOutput'):
datasetSpec.allowNoOutput()
if tmpItem.has_key('nFilesPerJob'):
datasetSpec.setNumFilesPerJob(tmpItem['nFilesPerJob'])
if tmpItem.has_key('num_records'):
datasetSpec.setNumRecords(tmpItem['num_records'])
if 'transient' in tmpItem:
datasetSpec.setTransient(tmpItem['transient'])
datasetSpec.vo = self.taskSpec.vo
datasetSpec.nFiles = 0
datasetSpec.nFilesUsed = 0
datasetSpec.nFilesFinished = 0
datasetSpec.nFilesFailed = 0
datasetSpec.nFilesOnHold = 0
datasetSpec.nEvents = 0
datasetSpec.nEventsUsed = 0
datasetSpec.nEventsToBeUsed = 0
datasetSpec.status = 'defined'
if datasetSpec.type in JediDatasetSpec.getInputTypes() + [
'random_seed'
]:
datasetSpec.streamName = RefinerUtils.extractStreamName(
tmpItem['value'])
if not tmpItem.has_key('expandedList'):
tmpItem['expandedList'] = []
# dataset names could be comma-concatenated
datasetNameList = datasetSpec.datasetName.split(',')
# datasets could be added by incexec
incexecDS = 'dsFor{0}'.format(datasetSpec.streamName)
# remove /XYZ
incexecDS = incexecDS.split('/')[0]
if taskParamMap.has_key(incexecDS):
for tmpDatasetName in taskParamMap[incexecDS].split(
','):
if not tmpDatasetName in datasetNameList:
datasetNameList.append(tmpDatasetName)
# loop over all dataset names
inDatasetSpecList = []
for datasetName in datasetNameList:
# skip empty
if datasetName == '':
continue
# expand
if datasetSpec.isPseudo() or datasetSpec.type in [
'random_seed'
] or datasetName == 'DBR_LATEST':
# pseudo input
tmpDatasetNameList = [datasetName]
elif tmpItem.has_key(
'expand') and tmpItem['expand'] == True:
# expand dataset container
tmpDatasetNameList = self.ddmIF.getInterface(
self.taskSpec.vo).expandContainer(datasetName)
else:
# normal dataset name
tmpDatasetNameList = self.ddmIF.getInterface(
self.taskSpec.vo).listDatasets(datasetName)
for elementDatasetName in tmpDatasetNameList:
if nIn > 0 or not elementDatasetName in tmpItem[
'expandedList']:
tmpItem['expandedList'].append(
elementDatasetName)
inDatasetSpec = copy.copy(datasetSpec)
inDatasetSpec.datasetName = elementDatasetName
inDatasetSpec.containerName = datasetName
inDatasetSpecList.append(inDatasetSpec)
# empty input
if inDatasetSpecList == [] and self.oldTaskStatus != 'rerefine':
errStr = 'doBasicRefine : unknown input dataset "{0}"'.format(
datasetSpec.datasetName)
self.taskSpec.setErrDiag(errStr)
if not datasetSpec.datasetName in self.unknownDatasetList:
self.unknownDatasetList.append(
datasetSpec.datasetName)
raise JediException.UnknownDatasetError, errStr
# set master flag
for inDatasetSpec in inDatasetSpecList:
if nIn == 0:
# master
self.inMasterDatasetSpec.append(inDatasetSpec)
else:
# secondary
self.inSecDatasetSpecList.append(inDatasetSpec)
nIn += 1
continue
if datasetSpec.type in ['output', 'log']:
if not nOutMap.has_key(datasetSpec.type):
nOutMap[datasetSpec.type] = 0
# make stream name
datasetSpec.streamName = "{0}{1}".format(
datasetSpec.type.upper(), nOutMap[datasetSpec.type])
nOutMap[datasetSpec.type] += 1
# set attribute for event service
if self.taskSpec.useEventService(
) and taskParamMap.has_key(
'objectStore') and datasetSpec.type in ['output']:
datasetSpec.setObjectStore(taskParamMap['objectStore'])
# extract output filename template and change the value field
outFileTemplate, tmpItem[
'value'] = RefinerUtils.extractReplaceOutFileTemplate(
tmpItem['value'], datasetSpec.streamName)
# make output template
if outFileTemplate != None:
if tmpItem.has_key('offset'):
offsetVal = 1 + tmpItem['offset']
else:
offsetVal = 1
outTemplateMap = {
'jediTaskID': self.taskSpec.jediTaskID,
'serialNr': offsetVal,
'streamName': datasetSpec.streamName,
'filenameTemplate': outFileTemplate,
'outtype': datasetSpec.type,
}
if self.outputTemplateMap.has_key(
datasetSpec.outputMapKey()):
# multiple files are associated to the same output datasets
self.outputTemplateMap[datasetSpec.outputMapKey(
)].append(outTemplateMap)
# don't insert the same output dataset
continue
self.outputTemplateMap[datasetSpec.outputMapKey()] = [
outTemplateMap
]
# append
self.outDatasetSpecList.append(datasetSpec)
# make unmerged dataset
if taskParamMap.has_key('mergeOutput') and taskParamMap[
'mergeOutput'] == True:
umDatasetSpec = JediDatasetSpec()
umDatasetSpec.datasetName = 'panda.um.' + datasetSpec.datasetName
umDatasetSpec.jediTaskID = self.taskSpec.jediTaskID
umDatasetSpec.storageToken = 'TOMERGE'
umDatasetSpec.vo = datasetSpec.vo
umDatasetSpec.type = "tmpl_trn_" + datasetSpec.type
umDatasetSpec.nFiles = 0
umDatasetSpec.nFilesUsed = 0
umDatasetSpec.nFilesToBeUsed = 0
umDatasetSpec.nFilesFinished = 0
umDatasetSpec.nFilesFailed = 0
umDatasetSpec.nFilesOnHold = 0
umDatasetSpec.status = 'defined'
umDatasetSpec.streamName = datasetSpec.streamName
if datasetSpec.isAllowedNoOutput():
umDatasetSpec.allowNoOutput()
# ratio
if datasetSpec.getRatioToMaster() > 1:
umDatasetSpec.setDatasetAttribute(
'ratio={0}'.format(
datasetSpec.getRatioToMaster()))
# make unmerged output template
if outFileTemplate != None:
umOutTemplateMap = {
'jediTaskID': self.taskSpec.jediTaskID,
'serialNr': 1,
'streamName': umDatasetSpec.streamName,
'outtype': datasetSpec.type,
}
# append temporary name
if taskParamMap.has_key(
'umNameAtEnd'
) and taskParamMap['umNameAtEnd'] == True:
# append temporary name at the end
umOutTemplateMap[
'filenameTemplate'] = outFileTemplate + '.panda.um'
else:
umOutTemplateMap[
'filenameTemplate'] = 'panda.um.' + outFileTemplate
if self.outputTemplateMap.has_key(
umDatasetSpec.outputMapKey()):
# multiple files are associated to the same output datasets
self.outputTemplateMap[
umDatasetSpec.outputMapKey()].append(
umOutTemplateMap)
# don't insert the same output dataset
continue
self.outputTemplateMap[
umDatasetSpec.outputMapKey()] = [
umOutTemplateMap
]
# use log as master for merging
if datasetSpec.type == 'log':
self.unmergeMasterDatasetSpec[
datasetSpec.outputMapKey()] = umDatasetSpec
else:
# append
self.unmergeDatasetSpecMap[
datasetSpec.outputMapKey()] = umDatasetSpec
# set attributes for merging
if taskParamMap.has_key(
'mergeOutput') and taskParamMap['mergeOutput'] == True:
self.setSplitRule(None, 1,
JediTaskSpec.splitRuleToken['mergeOutput'])
# make job parameters
rndmSeedOffset = None
firstEventOffset = None
jobParameters = ''
for tmpItem in taskParamMap['jobParameters']:
if tmpItem.has_key('value'):
# hidden parameter
if tmpItem.has_key('hidden') and tmpItem['hidden'] == True:
continue
# add tags for ES-only parameters
esOnly = False
if 'es_only' in tmpItem and tmpItem['es_only'] == True:
esOnly = True
if esOnly:
jobParameters += '<PANDA_ES_ONLY>'
jobParameters += '{0}'.format(tmpItem['value'])
if esOnly:
jobParameters += '</PANDA_ES_ONLY>'
# padding
if tmpItem.has_key('padding') and tmpItem['padding'] == False:
pass
else:
jobParameters += ' '
# get offset for random seed and first event
if tmpItem['type'] == 'template' and tmpItem[
'param_type'] == 'number':
if '${RNDMSEED}' in tmpItem['value']:
if tmpItem.has_key('offset'):
rndmSeedOffset = tmpItem['offset']
else:
rndmSeedOffset = 0
elif '${FIRSTEVENT}' in tmpItem['value']:
if tmpItem.has_key('offset'):
firstEventOffset = tmpItem['offset']
jobParameters = jobParameters[:-1]
# append parameters for event service merging if necessary
esmergeParams = self.getParamsForEventServiceMerging(taskParamMap)
if esmergeParams != None:
jobParameters += esmergeParams
self.setJobParamsTemplate(jobParameters)
# set random seed offset
if rndmSeedOffset != None:
self.setSplitRule(None, rndmSeedOffset,
JediTaskSpec.splitRuleToken['randomSeed'])
if firstEventOffset != None:
self.setSplitRule(None, firstEventOffset,
JediTaskSpec.splitRuleToken['firstEvent'])
# return
return
