"""
.. module:: theoryPrediction
:synopsis: Provides a class to encapsulate the results of the computation of
reference cross sections and related functions.
.. moduleauthor:: Andre Lessa <lessa.a.p@gmail.com>
.. autofunction:: _getElementsFrom
"""
from smodels.theory import clusterTools, crossSection, element
from smodels.theory.auxiliaryFunctions import cSim, cGtr, elementsInStr, average
from smodels.tools.physicsUnits import TeV, fb
from smodels.theory.exceptions import SModelSTheoryError as SModelSError
from smodels.experiment.datasetObj import CombinedDataSet
from smodels.tools.smodelsLogging import logger
from smodels.tools.statistics import likelihoodFromLimits, chi2FromLimits
from smodels.tools.combinations import computeCombinedStatistics, getCombinedUpperLimitFor,\
computeCombinedLikelihood
import itertools
[docs]class TheoryPrediction(object):
"""
An instance of this class represents the results of the theory prediction
for an analysis.
"""
def __init__(self):
self.analysis = None
self.xsection = None
self.conditions = None
self.mass = None
self.totalwidth = None
[docs] def dataId(self):
"""
Return ID of dataset
"""
return self.dataset.getID()
[docs] def analysisId(self):
"""
Return experimental analysis ID
"""
return self.dataset.globalInfo.id
[docs] def dataType(self, short=False ):
"""
Return the type of dataset
:param: short, if True, return abbreviation (ul,em,comb)
"""
if short:
t = self.dataset.getType()
D = { "upperLimit": "ul", "efficiencyMap": "em",
"combined": "comb" }
if t in D.keys():
return D[t]
return "??"
return self.dataset.getType()
[docs] def getUpperLimit(self, expected=False, deltas_rel=0.2):
"""
Get the upper limit on sigma*eff.
For UL-type results, use the UL map. For EM-Type returns
the corresponding dataset (signal region) upper limit.
For combined results, returns the upper limit on the
total sigma*eff (for all signal regions/datasets).
:param expected: return expected Upper Limit, instead of observed.
:param deltas_rel: relative uncertainty in signal (float). Default value is 20%.
:return: upper limit (Unum object)
"""
#First check if the upper-limit and expected upper-limit have already been computed.
#If not, compute it and store them.
if not hasattr(self, 'expectedUL') or not hasattr(self, 'upperLimit'):
if self.dataType() == 'efficiencyMap':
self.expectedUL = self.dataset.getSRUpperLimit(expected=True,deltas_rel=deltas_rel)
self.upperLimit = self.dataset.getSRUpperLimit(expected=False,deltas_rel=deltas_rel)
if self.dataType() == 'upperLimit':
self.expectedUL = self.dataset.getUpperLimitFor(element=self.avgElement,
txnames=self.txnames,
expected=True)
self.upperLimit = self.dataset.getUpperLimitFor(element=self.avgElement,
txnames=self.txnames,
expected=False)
if self.dataType() == 'combined':
#Create a list of signal events in each dataset/SR sorted according to datasetOrder
# lumi = self.dataset.getLumi()
if hasattr(self.dataset.globalInfo, "covariance"):
srNsigDict = dict([[pred.dataset.getID(),(pred.xsection.value*pred.dataset.getLumi() ).asNumber()] for pred in self.datasetPredictions])
srNsigs = [srNsigDict[dataID] if dataID in srNsigDict else 0. for dataID in self.dataset.globalInfo.datasetOrder]
elif hasattr(self.dataset.globalInfo, "jsonFiles"):
srNsigDict = dict([[pred.dataset.getID(),(pred.xsection.value*pred.dataset.getLumi() ).asNumber()] for pred in self.datasetPredictions])
srNsigs = [srNsigDict[ds.getID()] if ds.getID() in srNsigDict else 0. for ds in self.dataset._datasets]
self.expectedUL = getCombinedUpperLimitFor(self.dataset, srNsigs,expected=True,deltas_rel=deltas_rel)
self.upperLimit = getCombinedUpperLimitFor(self.dataset, srNsigs,expected=False,deltas_rel=deltas_rel)
#Return the expected or observed UL:
if expected:
return self.expectedUL
else:
return self.upperLimit
[docs] def getRValue(self, expected = False):
"""
Get the r value = theory prediction / experimental upper limit
"""
upperLimit = self.getUpperLimit(expected)
if upperLimit is None or upperLimit.asNumber(fb)==0.:
return None
else:
return (self.xsection.value/upperLimit).asNumber()
[docs] def likelihoodFromLimits( self, mu=1., marginalize=False, deltas_rel=.2,
expected=False, chi2also=False, corr = 0.6 ):
""" compute the likelihood from expected and observed upper limits.
:param expected: compute expected, not observed likelihood
:param mu: signal strength multiplier, applied to theory prediction. If None,
then find muhat
:param chi2also: if true, return also chi2
:param corr: correction factor:
ULexp_mod = ULexp / (1. - corr*((ULobs-ULexp)/(ULobs+ULexp)))
a factor of corr = 0.6 is proposed.
:returns: likelihood; none if no expected upper limit is defined.
"""
## marked as experimental feature
from smodels.tools.runtime import experimentalFeatures
if not experimentalFeatures():
if chi2also:
return ( None, None )
return None
if not hasattr ( self, "avgElement" ):
logger.error ( "theory prediction %s has no average element! why??" % self.analysisId() )
if chi2also:
return ( None, None )
return None
eul = self.dataset.getUpperLimitFor(element=self.avgElement,
txnames=self.txnames,
expected=True)
if type(eul) == type(None):
if chi2also:
return ( None, None )
return None
ul = self.dataset.getUpperLimitFor(element=self.avgElement,
txnames=self.txnames,
expected=False )
lumi = self.dataset.getLumi()
ulN = float(ul * lumi) ## upper limit on yield
eulN = float(eul * lumi) ## upper limit on yield
nsig = None
if mu != None:
nsig = mu*(self.xsection.value*lumi).asNumber()
llhd = likelihoodFromLimits ( ulN, eulN, nsig,
allowNegativeMuhat = True, corr = corr )
if chi2also:
return ( llhd, chi2FromLimits ( llhd, ulN, eulN, corr = corr ) )
return llhd
[docs] def getLikelihood(self,mu=1.,marginalize=False,deltas_rel=.2,expected=False):
"""
get the likelihood for a signal strength modifier mu
:param expected: compute expected, not observed likelihood
"""
self.computeStatistics ( marginalize, deltas_rel )
if hasattr ( self, "likelihood" ) and abs ( mu - 1. ) < 1e-5:
return self.likelihood
lumi = self.dataset.getLumi()
if self.dataType() == 'combined':
srNsigDict = dict([[pred.dataset.getID(),(pred.xsection.value*lumi).asNumber()] for \
pred in self.datasetPredictions])
srNsigs = [mu*srNsigDict[ds.getID()] if ds.getID() in srNsigDict else 0. \
for ds in self.dataset._datasets]
llhd = computeCombinedLikelihood ( self.dataset, srNsigs, marginalize,
deltas_rel )
return llhd
if self.dataType() == 'efficiencyMap':
nsig = (mu*self.xsection.value*lumi).asNumber()
llhd = self.dataset.likelihood(nsig,marginalize=marginalize,deltas_rel=deltas_rel)
return llhd
if self.dataType() == 'upperLimit':
llhd, chi2 = self.likelihoodFromLimits ( mu, marginalize, deltas_rel, chi2also=True )
return llhd
return None
[docs] def computeStatistics(self,marginalize=False,deltas_rel=0.2):
"""
Compute the likelihoods, chi2 and expected upper limit for this theory prediction.
The resulting values are stored as the likelihood, lmax, lsm and chi2
attributes (chi2 being phased out).
:param marginalize: if true, marginalize nuisances. Else, profile them.
:param deltas_rel: relative uncertainty in signal (float). Default value is 20%.
"""
if self.dataType() == 'upperLimit':
llhd, chi2 = self.likelihoodFromLimits ( 1., marginalize, deltas_rel, chi2also=True )
self.likelihood = llhd
self.chi2 = chi2
self.lsm = self.likelihoodFromLimits ( 0., marginalize, deltas_rel, False )
self.lmax = self.likelihoodFromLimits ( None, marginalize, deltas_rel, False )
elif self.dataType() == 'efficiencyMap':
lumi = self.dataset.getLumi()
nsig = (self.xsection.value*lumi).asNumber()
llhd = self.dataset.likelihood(nsig,marginalize=marginalize,deltas_rel=deltas_rel)
llhd_sm = self.dataset.likelihood(nsig=0.,marginalize=marginalize,deltas_rel=deltas_rel)
llhd_max = self.dataset.lmax(marginalize=marginalize,deltas_rel=deltas_rel,\
allowNegativeSignals = False )
self.likelihood = llhd
self.lmax = llhd_max
self.lsm = llhd_sm
from math import log
chi2 = None
if llhd == 0. and llhd_max == 0.:
chi2 = 0.
if llhd == 0. and llhd_max > 0.:
chi2 = float("inf")
if llhd > 0.:
chi2 = -2 * log ( llhd / llhd_max )
self.chi2 = chi2
elif self.dataType() == 'combined':
lumi = self.dataset.getLumi()
#Create a list of signal events in each dataset/SR sorted according to datasetOrder
srNsigDict = dict([[pred.dataset.getID(),(pred.xsection.value*lumi).asNumber()] for pred in self.datasetPredictions])
srNsigs = [srNsigDict[ds.getID()] if ds.getID() in srNsigDict else 0. for ds in self.dataset._datasets]
# srNsigs = [srNsigDict[dataID] if dataID in srNsigDict else 0. for dataID in self.dataset.globalInfo.datasetOrder]
llhd,lmax,lsm = computeCombinedStatistics ( self.dataset, srNsigs, marginalize,
deltas_rel )
self.likelihood = llhd
self.lmax = lmax
self.lsm = lsm
[docs] def getmaxCondition(self):
"""
Returns the maximum xsection from the list conditions
:returns: maximum condition xsection (float)
"""
if not self.conditions: return 0.
# maxcond = 0.
values = [ 0. ]
for value in self.conditions.values():
if value == 'N/A': return value
if value == None: continue
#print ( "value=",value,type(value),float(value) )
#maxcond = max(maxcond,float(value))
values.append ( float(value) )
return max(values)
# return maxcond
def __str__(self):
ret = "%s:%s" % ( self.analysisId(), self.xsection )
#self.computeStatistics()
#ret += ", llhd=%f." % self.likelihood
return ret
[docs] def describe ( self ):
if not hasattr ( self, "chi2" ):
self.computeStatistics()
# return a lengthy description
ret = "[theoryPrediction] analysis: %s\n" % self.analysisId()
ret += " prediction: %s\n" % self.xsection
# ret += " prediction (sigma): %s\n" % ( self.xsection.value / self.effectiveEff )
# ret += " effective efficiency: %s\n" % self.effectiveEff
ds = "None"
if type (self.dataset) == list:
ds = "multiple (%d combined)" % len(self.dataset)
else:
dataId = self.dataset.getID()
fname="" ## folder name
if dataId != None and not "combined" in dataId:
fname=" (%s)" % self.dataset.folderName()
ds = "%s%s" % ( dataId, fname )
ret += " datasets: %s\n" % ds
ret += " obs limit: %s\n" % self.getUpperLimit()
ret += " exp limit: %s\n" % self.getUpperLimit( expected=True )
ret += " obs r: %f\n" % ( self.getRValue(expected=False) )
expr = self.getRValue(expected=True)
sexpr = "%s" % expr
if expr != None:
sexpr= "%f" % expr
ret += " exp r: %s\n" % ( sexpr )
ret += " chi2: %s\n" % ( self.chi2 )
return ret
[docs]class TheoryPredictionList(object):
"""
An instance of this class represents a collection of theory prediction
objects.
"""
def __init__(self, theoryPredictions=None, maxCond=None):
"""
Initializes the list.
:parameter theoryPredictions: list of TheoryPrediction objects
:parameter maxCond: maximum relative violation of conditions for valid
results. If defined, it will keep only the theory predictions with
condition violation < maxCond.
"""
self._theoryPredictions = []
if theoryPredictions and isinstance(theoryPredictions,list):
if not maxCond:
self._theoryPredictions = theoryPredictions
else:
newPredictions = []
for theoPred in theoryPredictions:
mCond = theoPred.getmaxCondition()
if mCond == 'N/A' or round(mCond/maxCond,2) > 1.0:
continue
else: newPredictions.append(theoPred)
self._theoryPredictions = newPredictions
[docs] def append(self,theoryPred):
self._theoryPredictions.append(theoryPred)
def __str__(self):
if len ( self._theoryPredictions ) == 0:
return "no predictions."
ret = "%d predictions: " % len ( self._theoryPredictions )
ret += ", ".join ( [ str(s) for s in self._theoryPredictions ] )
return ret
def __iter__(self):
for theoryPrediction in self._theoryPredictions:
yield theoryPrediction
def __getitem__(self, index):
return self._theoryPredictions[index]
def __len__(self):
return len(self._theoryPredictions)
def __add__(self,theoPredList):
if isinstance(theoPredList,TheoryPredictionList):
res = TheoryPredictionList()
res._theoryPredictions = self._theoryPredictions + theoPredList._theoryPredictions
return res
else:
return None
def __radd__(self, theoPredList):
if theoPredList == 0:
return self
else:
return self.__add__(theoPredList)
[docs] def sortTheoryPredictions(self):
"""
Reverse sort theoryPredictions by R value.
Used for printer.
"""
self._theoryPredictions = sorted(self._theoryPredictions, key=lambda theoPred: ( theoPred.getRValue() is not None, theoPred.getRValue()), reverse=True)
[docs]def theoryPredictionsFor(expResult, smsTopList, maxMassDist=0.2,
useBestDataset=True, combinedResults=True,
marginalize=False,deltas_rel=0.2):
"""
Compute theory predictions for the given experimental result, using the list of
elements in smsTopList.
For each Txname appearing in expResult, it collects the elements and
efficiencies, combine the masses (if needed) and compute the conditions
(if exist).
:parameter expResult: expResult to be considered (ExpResult object)
:parameter smsTopList: list of topologies containing elements
(TopologyList object)
:parameter maxMassDist: maximum mass distance for clustering elements (float)
:parameter useBestDataset: If True, uses only the best dataset (signal region).
If False, returns predictions for all datasets (if combinedResults is False),
or only the combinedResults (if combinedResults is True).
:parameter combinedResults: add theory predictions that result from
combining datasets.
:parameter marginalize: If true, marginalize nuisances. If false, profile them.
:parameter deltas_rel: relative uncertainty in signal (float). Default value is 20%.
:returns: a TheoryPredictionList object containing a list of TheoryPrediction
objects
"""
dataSetResults = []
#Compute predictions for each data set (for UL analyses there is one single set)
for dataset in expResult.datasets:
predList = _getDataSetPredictions(dataset,smsTopList,maxMassDist)
if predList:
dataSetResults.append(predList)
if not dataSetResults: ## no results at all?
return None
elif len(dataSetResults) == 1: ## only a single dataset? Easy case.
result = dataSetResults[0]
for theoPred in result:
theoPred.expResult = expResult
theoPred.upperLimit = theoPred.getUpperLimit(deltas_rel=deltas_rel)
return result
#For results with more than one dataset, return all dataset predictions
#if useBestDataSet=False and combinedResults=False:
if not useBestDataset and not combinedResults:
allResults = sum(dataSetResults)
for theoPred in allResults:
theoPred.expResult = expResult
theoPred.upperLimit = theoPred.getUpperLimit(deltas_rel=deltas_rel)
return allResults
#Else include best signal region results, if asked for.
bestResults = TheoryPredictionList()
if useBestDataset:
bestResults.append(_getBestResult(dataSetResults))
#If combinedResults = True, also include the combined result (when available):
if combinedResults and len(dataSetResults) > 1:
combinedDataSetResult = _getCombinedResultFor(dataSetResults,
expResult,marginalize)
if combinedDataSetResult:
bestResults.append(combinedDataSetResult)
for theoPred in bestResults:
theoPred.expResult = expResult
theoPred.upperLimit = theoPred.getUpperLimit(deltas_rel=deltas_rel)
return bestResults
def _getCombinedResultFor(dataSetResults,expResult,marginalize=False):
"""
Compute the combined result for all datasets, if covariance
matrices are available. Return a TheoryPrediction object
with the signal cross-section summed over all the signal regions
and the respective upper limit.
:param datasetPredictions: List of TheoryPrediction objects for each signal region
:param expResult: ExpResult object corresponding to the experimental result
:parameter marginalize: If true, marginalize nuisances. If false, profile them.
:return: TheoryPrediction object
"""
if len(dataSetResults) == 1:
return dataSetResults[0]
elif not expResult.hasCovarianceMatrix() and not expResult.hasJsonFile():
return None
txnameList = []
elementList = []
totalXsec = None
massList = []
widthList = []
PIDList = []
datasetPredictions = []
weights = []
for predList in dataSetResults:
if len(predList) != 1:
raise SModelSError("Results with multiple datasets should have a single theory prediction (EM-type)!")
pred = predList[0]
datasetPredictions.append(pred)
txnameList += pred.txnames
elementList += pred.elements
if not totalXsec:
totalXsec = pred.xsection
else:
totalXsec += pred.xsection
massList.append(pred.mass)
widthList.append(pred.totalwidth)
weights.append(pred.xsection.value.asNumber(fb))
PIDList += pred.PIDs
txnameList = list(set(txnameList))
if None in massList:
mass = None
totalwidth = None
else:
mass = average(massList,weights=weights)
totalwidth = average(widthList,weights=weights)
#Create a combinedDataSet object:
combinedDataset = CombinedDataSet(expResult)
combinedDataset._marginalize = marginalize
#Create a theory preidction object for the combined datasets:
theoryPrediction = TheoryPrediction()
theoryPrediction.dataset = combinedDataset
theoryPrediction.txnames = txnameList
theoryPrediction.xsection = totalXsec
theoryPrediction.datasetPredictions = datasetPredictions
theoryPrediction.conditions = None
theoryPrediction.elements = elementList
theoryPrediction.mass = mass
theoryPrediction.totalwidth = totalwidth
theoryPrediction.PIDs = [pdg for pdg,_ in itertools.groupby(PIDList)] #Remove duplicates
return theoryPrediction
def _getBestResult(dataSetResults):
"""
Returns the best result according to the expected upper limit
:param datasetPredictions: list of TheoryPredictionList objects
:return: best result (TheoryPrediction object)
"""
#In the case of UL analyses or efficiency-maps with a single signal region
#return the single result:
if len(dataSetResults) == 1:
return dataSetResults[0]
#For efficiency-map analyses with multipler signal regions,
#select the best one according to the expected upper limit:
bestExpectedR = 0.
bestXsec = 0.*fb
for predList in dataSetResults:
if len(predList) != 1:
logger.error("Multiple clusters should only exist for upper limit results!")
raise SModelSError()
dataset = predList[0].dataset
if dataset.getType() != 'efficiencyMap':
txt = "Multiple data sets should only exist for efficiency map results, but we have them for %s?" % (predList[0].analysisId())
logger.error( txt )
raise SModelSError( txt )
pred = predList[0]
xsec = pred.xsection
expectedR = (xsec.value/dataset.getSRUpperLimit(0.05,True,False) ).asNumber()
if expectedR > bestExpectedR or (expectedR == bestExpectedR and xsec.value > bestXsec):
bestExpectedR = expectedR
bestPred = pred
bestXsec = xsec.value
return bestPred
def _getDataSetPredictions(dataset,smsTopList,maxMassDist):
"""
Compute theory predictions for a given data set.
For upper-limit results returns the list of theory predictions for the
experimental result. For efficiency-map results returns the list of theory
predictions for the signal region. Uses the list of elements in
smsTopList.
For each Txname appearing in dataset, it collects the elements and efficiencies,
combine the masses (if needed) and compute the conditions (if existing).
:parameter dataset: Data Set to be considered (DataSet object)
:parameter smsTopList: list of topologies containing elements
(TopologyList object)
:parameter maxMassDist: maximum mass distance for clustering elements (float)
:returns: a TheoryPredictionList object containing a list of TheoryPrediction
objects
"""
predictionList = TheoryPredictionList()
# Select elements belonging to expResult and apply efficiencies
elements = _getElementsFrom(smsTopList, dataset)
#Check dataset sqrts format:
if (dataset.globalInfo.sqrts/TeV).normalize()._unit:
ID = dataset.globalInfo.id
logger.error( "Sqrt(s) defined with wrong units for %s" % (ID) )
return False
#Remove unwanted cross sections
newelements = []
for el in elements:
el.weight = el.weight.getXsecsFor(dataset.globalInfo.sqrts)
if not el.weight: continue
newelements.append(el)
elements = newelements
if len(elements) == 0:
return None
# Combine elements according to their respective constraints and masses
# (For efficiencyMap analysis group all elements)
clusters = _combineElements(elements, dataset, maxDist=maxMassDist)
# Collect results and evaluate conditions
for cluster in clusters:
theoryPrediction = TheoryPrediction()
theoryPrediction.dataset = dataset
theoryPrediction.txnames = cluster.txnames
theoryPrediction.xsection = _evalConstraint(cluster)
theoryPrediction.conditions = _evalConditions(cluster)
theoryPrediction.elements = cluster.elements
theoryPrediction.avgElement = cluster.averageElement()
theoryPrediction.mass = theoryPrediction.avgElement.mass
theoryPrediction.totalwidth = theoryPrediction.avgElement.totalwidth
PIDs = [el.pdg for el in cluster.elements]
theoryPrediction.PIDs = [pdg for pdg,_ in itertools.groupby(PIDs)] #Remove duplicates
predictionList._theoryPredictions.append(theoryPrediction)
if len(predictionList) == 0:
return None
else:
return predictionList
def _getElementsFrom(smsTopList, dataset):
"""
Get elements that belong to any of the TxNames in dataset
(appear in any of constraints in the result).
Loop over all elements in smsTopList and returns a copy of the elements belonging
to any of the constraints (i.e. have efficiency != 0). The copied elements
have their weights multiplied by their respective efficiencies.
:parameter dataset: Data Set to be considered (DataSet object)
:parameter smsTopList: list of topologies containing elements
(TopologyList object)
:returns: list of elements (Element objects)
"""
elements = []
for txname in dataset.txnameList:
for top in smsTopList:
itop = txname._topologyList.index(top) #Check if the topology appear in txname
if itop is None: continue
for el in top.getElements():
newEl = txname.hasElementAs(el) #Check if element appears in txname
if not newEl:
continue
el.setCoveredBy(dataset.globalInfo.type)
eff = txname.getEfficiencyFor(newEl)
if eff == None or abs(eff)<1e-14:
continue
el.setTestedBy(dataset.globalInfo.type)
newEl.eff = eff
newEl.weight *= eff
newEl.txname = txname
elements.append(newEl) #Save element with correct branch ordering
return elements
def _combineElements(elements, dataset, maxDist):
"""
Combine elements according to the data set type.
If expResult == upper limit type, first group elements with different TxNames
and then into mass clusters.
If expResult == efficiency map type, group all elements into a single cluster.
:parameter elements: list of elements (Element objects)
:parameter expResult: Data Set to be considered (DataSet object)
:returns: list of element clusters (ElementCluster objects)
"""
clusters = []
if dataset.getType() == 'efficiencyMap': #cluster all elements
clusters += clusterTools.clusterElements(elements,maxDist,dataset)
elif dataset.getType() == 'upperLimit': #Cluster each txname individually
txnames = list(set([el.txname for el in elements]))
for txname in txnames:
txnameEls = [el for el in elements if el.txname == txname]
clusters += clusterTools.clusterElements(txnameEls, maxDist, dataset)
else:
logger.warning("Unkown data type: %s. Data will be ignored."
% dataset.getType())
return clusters
def _evalConstraint(cluster):
"""
Evaluate the constraint inside an element cluster.
If the cluster refers to a specific TxName, sum all the elements' weights
according to the analysis constraint.
For efficiency map results, sum all the elements' weights.
:parameter cluster: cluster of elements (ElementCluster object)
:returns: cluster cross section
"""
if cluster.getDataType() == 'efficiencyMap':
return cluster.getTotalXSec()
elif cluster.getDataType() == 'upperLimit':
if len(cluster.txnames) != 1:
logger.error("An upper limit cluster should never contain more than one TxName")
raise SModelSError()
txname = cluster.txnames[0]
if not txname.constraint or txname.constraint == "not yet assigned":
return txname.constraint
exprvalue = _evalExpression(txname.constraint,cluster)
return exprvalue
else:
logger.error("Unknown data type %s" %(str(cluster.getDataType())))
raise SModelSError()
def _evalConditions(cluster):
"""
Evaluate the conditions (if any) inside an element cluster.
:parameter cluster: cluster of elements (ElementCluster object)
:returns: list of condition values (floats) if analysis type == upper limit. None, otherwise.
"""
conditionVals = {}
for txname in cluster.txnames:
if not txname.condition or txname.condition == "not yet assigned":
continue
#Make sure conditions is always a list
if isinstance(txname.condition,str):
conditions = [txname.condition]
elif isinstance(txname.condition,list):
conditions = txname.condition
else:
logger.error("Conditions should be a list or a string")
raise SModelSError()
# Loop over conditions
for cond in conditions:
exprvalue = _evalExpression(cond,cluster)
if isinstance(exprvalue,crossSection.XSection):
conditionVals[cond] = exprvalue.value
else:
conditionVals[cond] = exprvalue
if not conditionVals:
return None
else:
return conditionVals
def _evalExpression(stringExpr,cluster):
"""
Auxiliary method to evaluate a string expression using the weights of the elements in the cluster.
Replaces the elements in stringExpr (in bracket notation) by their weights and evaluate the
expression.
e.g. computes the total weight of string expressions such as "[[[e^+]],[[e^-]]]+[[[mu^+]],[[mu^-]]]"
or ratios of weights of string expressions such as "[[[e^+]],[[e^-]]]/[[[mu^+]],[[mu^-]]]"
and so on...
:parameter stringExpr: string containing the expression to be evaluated
:parameter cluster: cluster of elements (ElementCluster object)
:returns: xsection for the expression. Can be a XSection object, a float or not numerical (None,string,...)
"""
#Get model for final state particles (database particles):
model = cluster.dataset.globalInfo._databaseParticles
#Get txname final state:
if not hasattr(cluster.txnames[0],'finalState'):
finalState = ['MET','MET']
else:
finalState = cluster.txnames[0].finalState
if not hasattr(cluster.txnames[0],'intermediateState'):
intermediateState = None
else:
intermediateState = cluster.txnames[0].intermediateState
#Get cross section info from cluster (to generate zero cross section values):
infoList = cluster.elements[0].weight.getInfo()
#Get weights for elements appearing in stringExpr
weightsDict = {}
evalExpr = stringExpr.replace("'","").replace(" ","")
for i,elStr in enumerate(elementsInStr(evalExpr)):
el = element.Element(elStr,intermediateState=intermediateState,
finalState=finalState,model=model)
weightsDict['w%i'%i] = crossSection.XSectionList(infoList)
for el1 in cluster.elements:
if el1 == el:
weightsDict['w%i'%i] += el1.weight
evalExpr = evalExpr.replace(elStr,'w%i'%i)
weightsDict.update({"Cgtr" : cGtr, "cGtr" : cGtr, "cSim" : cSim, "Csim" : cSim})
exprvalue = eval(evalExpr, weightsDict)
if type(exprvalue) == type(crossSection.XSectionList()):
if len(exprvalue) != 1:
logger.error("Evaluation of expression "+evalExpr+" returned multiple values.")
return exprvalue[0] #Return XSection object
return exprvalue