"""
.. 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.statsTools import StatsComputer
from typing import Union, Text
import itertools
import numpy as np
__all__ = [ "TheoryPrediction", "theoryPredictionsFor", "TheoryPredictionsCombiner" ]
class TheoryPrediction(object):
"""
An instance of this class represents the results of the theory prediction
for an analysis.
"""
def __init__(self, deltas_rel=None):
"""a theory prediction. deltas_rel is meant to be a constant
:param deltas_rel: relative uncertainty in signal (float).
Default value is 20%.
"""
self.analysis = None
self.xsection = None
self.conditions = None
self.mass = None
self.totalwidth = None
if deltas_rel is None:
from smodels.tools.runtime import _deltas_rel_default
deltas_rel = _deltas_rel_default
self.deltas_rel = deltas_rel
self.cachedObjs = {False: {}, True: {}, "posteriori": {}}
self.cachedNlls = {False: {}, True: {}, "posteriori": {}}
self._statsComputer = None
def __str__(self):
ret = "%s:%s" % (self.analysisId(), self.totalXsection())
return ret
def dataId(self):
"""
Return ID of dataset
"""
return self.dataset.getID()
def analysisId(self):
"""
Return experimental analysis ID
"""
return self.dataset.globalInfo.id
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()
def totalXsection(self):
return self.xsection.value
def getmaxCondition(self):
"""
Returns the maximum xsection from the list conditions
:returns: maximum condition xsection (float)
"""
if not self.conditions:
return 0.0
# maxcond = 0.
values = [0.0]
for value in self.conditions.values():
if value == "N/A" or value is None:
continue
# print ( "value=",value,type(value),float(value) )
# maxcond = max(maxcond,float(value))
values.append(float(value))
return max(values)
# return maxcond
@property
def statsComputer(self):
if self._statsComputer is None:
self.setStatsComputer()
return self._statsComputer
def setStatsComputer(self):
"""
Creates and instance of StatsComputer depending on the
type of TheoryPrediction/dataset. In case it is not possible
to define a statistical computer (upper limit result or no expected
upper limits), set the computer to 'N/A'.
"""
if self.dataType() == "upperLimit":
from smodels.tools.runtime import experimentalFeatures
if not experimentalFeatures():
computer = 'N/A'
else:
computer = StatsComputer.forTruncatedGaussian(self)
if computer is None: # No expected UL available
computer = 'N/A'
elif self.dataType() == "efficiencyMap":
nsig = (self.xsection.value * self.dataset.getLumi()).asNumber()
computer = StatsComputer.forSingleBin(dataset=self.dataset,
nsig=nsig,
deltas_rel=self.deltas_rel )
elif self.dataType() == "combined":
# Get dictionary with dataset IDs and signal yields
srNsigDict = {pred.dataset.getID() :
(pred.xsection.value*pred.dataset.getLumi()).asNumber()
for pred in self.datasetPredictions}
# Get ordered list of datasets:
if hasattr(self.dataset.globalInfo, "covariance"):
datasetList = self.dataset.globalInfo.datasetOrder[:]
# Get list of signal yields corresponding to the dataset order:
srNsigs = [srNsigDict[dataID] if dataID in srNsigDict else 0.0
for dataID in datasetList]
# Get computer
computer = StatsComputer.forMultiBinSL(dataset=self.dataset,
nsig=srNsigs,
deltas_rel = self.deltas_rel)
elif hasattr(self.dataset.globalInfo, "jsonFiles"):
datasetList = [ds.getID() for ds in self.dataset.origdatasets]
# Get list of signal yields corresponding to the dataset order:
srNsigs = [srNsigDict[dataID] if dataID in srNsigDict else 0.0
for dataID in datasetList]
# Get computer
computer = StatsComputer.forPyhf(dataset=self.dataset,
nsig=srNsigs,
deltas_rel = self.deltas_rel)
self._statsComputer = computer
def getUpperLimit(self, expected=False):
"""
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.
: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 "UL" not in self.cachedObjs[expected]:
ul = None
if self.dataType() == "efficiencyMap":
ul = self.dataset.getSRUpperLimit(expected=expected)
if self.dataType() == "upperLimit":
ul = self.dataset.getUpperLimitFor(
element=self.avgElement, txnames=self.txnames, expected=expected
)
if self.dataType() == "combined":
ul = self.statsComputer.poi_upper_limit(expected = expected,
limit_on_xsec = True)
self.cachedObjs[expected]["UL"] = ul
return self.cachedObjs[expected]["UL"]
def getUpperLimitOnMu(self, expected=False):
"""
Get upper limit on signal strength multiplier, using the
theory prediction value and the corresponding upper limit
(i.e. mu_UL = upper limit/theory xsec)
:param expected: if True, compute expected upper limit, else observed
:returns: upper limit on signal strength multiplier mu
"""
upperLimit = self.getUpperLimit(expected=expected)
xsec = self.totalXsection()
if xsec is None or upperLimit is None:
return None
muUL = (upperLimit/xsec).asNumber()
return muUL
def getRValue(self, expected=False):
"""
Get the r value = theory prediction / experimental upper limit
"""
if "r" not in self.cachedObjs[expected]:
upperLimit = self.getUpperLimit(expected)
if upperLimit is None or upperLimit.asNumber(fb) == 0.0:
r = None
self.cachedObjs[expected]["r"] = r
return r
else:
r = (self.totalXsection() / upperLimit).asNumber()
self.cachedObjs[expected]["r"] = r
return r
return self.cachedObjs[expected]["r"]
def whenDefined(function):
"""
Returns the function whenever the statistical
calculation is possible (i.e. when it is possible to define
self.StatsComputer)
"""
def wrapper(self, *args, **kwargs):
if self.statsComputer == 'N/A':
return None
else:
return function(self, *args, **kwargs)
return wrapper
@whenDefined
def lsm(self, expected=False, return_nll : bool = False ):
"""likelihood at SM point, same as .def likelihood( ( mu = 0. )"""
if "nll_sm" not in self.cachedObjs[expected]:
self.computeStatistics(expected)
if "nll_sm" not in self.cachedObjs[expected]:
self.cachedObjs[expected]["lsm"] = None
return self.nllToLikelihood ( self.cachedObjs[expected]["nll_sm"],
return_nll )
@whenDefined
def lmax(self, expected=False, return_nll : bool = False ):
"""likelihood at mu_hat"""
if not "nllmax" in self.cachedObjs[expected]:
self.computeStatistics(expected)
return self.nllToLikelihood ( self.cachedObjs[expected]["nllmax"],
return_nll )
@whenDefined
def CLs(self, mu : float = 1., expected : Union[Text,bool] = False ) -> \
Union[float,None]:
""" obtain the CLs value of the combination for a given poi value "mu" """
if not "CLs" in self.cachedObjs[expected]:
self.cachedObjs[expected]["CLs"] = {}
if mu in self.cachedObjs[expected]["CLs"]:
return self.cachedObjs[expected]["CLs"][mu]
cls = self.statsComputer.CLs ( poi_test = mu, expected = expected )
self.cachedObjs[expected]["CLs"][mu] = cls
return cls
@whenDefined
def sigma_mu(self, expected=False):
"""sigma_mu of mu_hat"""
if not "sigma_mu" in self.cachedObjs[expected]:
self.computeStatistics(expected)
return self.cachedObjs[expected]["sigma_mu"]
@whenDefined
def muhat(self, expected=False):
"""position of maximum likelihood"""
if not "muhat" in self.cachedObjs[expected]:
self.computeStatistics(expected)
return self.cachedObjs[expected]["muhat"]
@whenDefined
def likelihood(self, mu=1.0, expected=False, return_nll=False, useCached=True):
"""
get the likelihood for a signal strength modifier mu
:param expected: compute expected, not observed likelihood. if "posteriori",
compute expected posteriori.
:param return_nll: if True, return negative log likelihood, else likelihood
:param useCached: if True, will return the cached value, if available
"""
if useCached and mu in self.cachedNlls[expected]:
nll = self.cachedNlls[expected][mu]
return self.nllToLikelihood ( nll, return_nll )
if useCached:
if "nll" in self.cachedObjs[expected] and abs(mu - 1.0) < 1e-5:
nll = self.cachedObjs[expected]["nll"]
return self.nllToLikelihood ( nll, return_nll )
if "nll_sm" in self.cachedObjs[expected] and abs(mu) < 1e-5:
nllsm = self.cachedObjs[expected]["nll_sm"]
return self.nllToLikelihood ( nllsm, return_nll )
# for truncated gaussians the fits only work with negative signals!
nll = self.statsComputer.likelihood(poi_test = mu,
expected = expected,
return_nll = True )
self.cachedNlls[expected][mu] = nll
return self.nllToLikelihood ( nll, return_nll )
def nllToLikelihood ( self, nll : Union[None,float], return_nll : bool ):
""" if not return_nll, then compute likelihood from nll """
if return_nll:
return nll
return np.exp ( - nll ) if nll is not None else None
@whenDefined
def computeStatistics(self, expected=False):
"""
Compute the likelihoods, and upper limit for this theory prediction.
The resulting values are stored as the likelihood, lmax, and lsm
attributes.
:param expected: computed expected quantities, not observed
"""
if not "lmax" in self.cachedObjs[expected]:
self.cachedObjs[expected]["lmax"] = {}
self.cachedObjs[expected]["muhat"] = {}
self.cachedObjs[expected]["sigma_mu"] = {}
# Compute likelihoods and related parameters:
llhdDict = self.statsComputer.get_five_values(expected = expected,
return_nll = True )
self.cachedObjs[expected]["nll"] = llhdDict["lbsm"]
self.cachedObjs[expected]["nll_sm"] = llhdDict["lsm"]
self.cachedObjs[expected]["nllmax"] = llhdDict["lmax"]
self.cachedObjs[expected]["muhat"] = llhdDict["muhat"]
self.cachedObjs[expected]["sigma_mu"] = llhdDict["sigma_mu"]
class TheoryPredictionsCombiner(TheoryPrediction):
"""
Facility used to combine theory predictions from different analyes.
If a list with a single TheoryPrediction is given, return the TheoryPrediction
object.
"""
def __new__(cls,theoryPredictions: list, slhafile=None, deltas_rel=None):
"""
If called with a list containing a single TheoryPrediction, return the TheoryPrediction object.
Otherwise, create a TheoryPredictionsCombiner object.
"""
if len(theoryPredictions) == 1:
return theoryPredictions[0]
else:
tpCombiner = super(TheoryPredictionsCombiner, cls).__new__(cls)
return tpCombiner
def __init__(self, theoryPredictions: list, slhafile=None, deltas_rel=None):
"""
Constructor.
:param theoryPredictions: the List of theory predictions
:param slhafile: optionally, the slhafile can be given, for debugging
:param deltas_rel: relative uncertainty in signal (float).
Default value is 20%.
"""
if len(theoryPredictions) == 0:
raise SModelSError("asking to combine zero theory predictions")
self.theoryPredictions = theoryPredictions
self.slhafile = slhafile
if deltas_rel is None:
from smodels.tools.runtime import _deltas_rel_default
deltas_rel = _deltas_rel_default
self.deltas_rel = deltas_rel
self.cachedObjs = {False: {}, True: {}, "posteriori": {}}
self.cachedNlls = {False: {}, True: {}, "posteriori": {}}
self._statsComputer = None
@classmethod
def selectResultsFrom(cls, theoryPredictions, anaIDs):
"""
Select the results from theoryPrediction list which match one
of the IDs in anaIDs. If there are multiple predictions for the
same ID for which a likelihood is available, it gives priority
to the ones with largest expected r-values.
:param theoryPredictions: list of TheoryPrediction objects
:param anaIDs: list with the analyses IDs (in string format) to be combined
:return: a TheoryPredictionsCombiner object for the selected predictions.
If no theory prediction was selected, return None.
"""
# First select the theory predictions which correspond to the analyses to be combined
filteredTPs = [tp for tp in theoryPredictions if tp.analysisId() in anaIDs]
filteredIDs = set([tp.analysisId() for tp in filteredTPs])
# Now remove results with no likelihood available
selectedTPs = [tp for tp in filteredTPs if tp.likelihood() is not None]
selectedIDs = set([tp.analysisId() for tp in selectedTPs])
# Warn the user concerning results with no likelihoods:
for anaID in filteredIDs.difference(selectedIDs):
logger.info(
"No likelihood available for %s. This analysis will not be used in analysis combination."
% anaID
)
# If no results are available, return None
if len(selectedTPs) == 0:
return None
# Define a hierarchy for the results:
priority = {"combined": 2, "efficiencyMap": 1, "upperLimit": 0}
# Now sort by highest priority and then by highest expected r-value:
selectedTPs = sorted(
selectedTPs, key=lambda tp: (priority[tp.dataType()], tp.getRValue(expected=True))
)
# Now get a single TP for each result
# (the highest ranking analyses come last and are kept in the dict)
uniqueTPs = {tp.analysisId(): tp for tp in selectedTPs}
uniqueTPs = list(uniqueTPs.values())
combiner = cls(uniqueTPs)
return combiner
def dataId(self):
"""
Return a string with the IDs of all the datasets used in the combination.
"""
ids = [str(tp.dataset.getID()) for tp in self.theoryPredictions]
ret = ",".join(ids)
return ret
def analysisId(self):
"""
Return a string with the IDs of all the experimental results used in the combination.
"""
ret = ",".join(sorted([tp.analysisId() for tp in self.theoryPredictions]))
return ret
def dataType(self, short=False):
"""
Return its type (combined)
:param: short, if True, return abbreviation (anacomb)
"""
if short:
return "comb"
else:
return "combined"
def totalXsection(self):
ret = 0.0 * fb
if self.theoryPredictions is not None:
for tp in self.theoryPredictions:
ret += tp.xsection.value
return ret
def getmaxCondition(self):
"""
Returns the maximum xsection from the list conditions
:returns: maximum condition xsection (float)
"""
conditions = [tp.getmaxCondition() for tp in self.theoryPredictions]
return max(conditions)
def setStatsComputer(self):
"""
Creates and instance of StatsComputer depending on the
type of TheoryPrediction/dataset. In case it is not possible
to define a statistical computer (upper limit result or no expected
upper limits), set the computer to 'N/A'.
"""
# First make sure all theory predictions in the combiner
# have well-defined stats models
if any(tp.statsComputer == 'N/A' for tp in self.theoryPredictions):
computer = 'N/A'
else:
computer = StatsComputer.forAnalysesComb(self.theoryPredictions, self.deltas_rel)
self._statsComputer = computer
def getLlhds(self,muvals,expected=False,normalize=True):
"""
Facility to access the likelihoods for the individual analyses and the combined
likelihood.
Returns a dictionary with the analysis IDs as keys and the likelihood values as values.
Mostly used for plotting the likelihoods.
:param muvals: List with values for the signal strenth for which the likelihoods must
be evaluated.
:param expected: If True returns the expected likelihood values.
:param normalize: If True normalizes the likelihood by its integral over muvals.
"""
return self.statsComputer.likelihoodComputer.getLlhds(muvals,expected,normalize)
def describe(self):
"""returns a string containing a list of all analysisId and dataIds"""
ids = []
for tp in self.theoryPredictions:
ids.append(f"{tp.analysisId()}:{tp.dataId()}")
return f"SRs: {', '.join(ids)}"
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" and round(mCond / maxCond, 2) > 1.0:
continue
else:
newPredictions.append(theoPred)
self._theoryPredictions = newPredictions
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)
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,
)
def theoryPredictionsFor(
expResult,
smsTopList,
maxMassDist=0.2,
useBestDataset=True,
combinedResults=True,
deltas_rel=None,
):
"""
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), if list of ExpResults is given, produce theory predictions for all
: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 deltas_rel: relative uncertainty in signal (float). Default value is 20%.
:returns: a TheoryPredictionList object containing a list of TheoryPrediction
objects
"""
if deltas_rel is None:
from smodels.tools.runtime import _deltas_rel_default
deltas_rel = _deltas_rel_default
if type(expResult) in [list, tuple]:
ret = []
for er in expResult:
tpreds = theoryPredictionsFor(
er,
smsTopList,
maxMassDist,
useBestDataset,
combinedResults,
deltas_rel,
)
if tpreds:
for tp in tpreds:
ret.append(tp)
return TheoryPredictionList(ret)
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.deltas_rel = deltas_rel
theoPred.upperLimit = theoPred.getUpperLimit()
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.deltas_rel = deltas_rel
theoPred.upperLimit = theoPred.getUpperLimit()
return allResults
elif combinedResults: # Try to combine results
bestResults = TheoryPredictionList()
combinedRes = _getCombinedResultFor(dataSetResults, expResult )
if combinedRes is None: # Can not combine, use best dataset:
combinedRes = _getBestResult(dataSetResults)
bestResults.append(combinedRes)
else: # Use best dataset:
bestResults = TheoryPredictionList()
bestResults.append(_getBestResult(dataSetResults))
for theoPred in bestResults:
theoPred.expResult = expResult
theoPred.deltas_rel = deltas_rel
theoPred.upperLimit = theoPred.getUpperLimit()
return bestResults
def _getCombinedResultFor(dataSetResults, expResult ):
"""
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
: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)
# 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 = 0.0
bestXsec = 0.0 * 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(expected=True)).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, deltas_rel=None):
"""
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
"""
if deltas_rel is None:
from smodels.tools.runtime import _deltas_rel_default
deltas_rel = _deltas_rel_default
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( deltas_rel)
theoryPrediction.dataset = dataset
theoryPrediction.txnames = cluster.txnames
theoryPrediction.xsection = _evalConstraint(cluster)
# Skip results with too small (invisible) cross-sections
if theoryPrediction.xsection.value < 1e-6*fb:
continue
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
[docs]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