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 testClusterer(self):
""" test the mass clusterer """
from smodels.theory import lheReader, lheDecomposer, crossSection
from smodels.theory import clusterTools
from smodels.experiment.txnameObj import TxName, TxNameData
from smodels.experiment.infoObj import Info
from smodels.tools.physicsUnits import GeV, pb, fb
import copy
data = [[ [[ 674.99*GeV, 199.999*GeV], [ 674.99*GeV, 199.999*GeV] ],.03*fb ],
[ [[ 725.0001*GeV,200.*GeV], [ 725.0001*GeV,200.*GeV] ], .06*fb ] ,
[ [[ 750.*GeV,250.*GeV], [ 750.*GeV,250.*GeV] ], .03*fb ] ]
info = Info(os.path.join("./database/8TeV/ATLAS/ATLAS-SUSY-2013-05/data/","dataInfo.txt"))
globalInfo = Info(os.path.join("./database/8TeV/ATLAS/ATLAS-SUSY-2013-05/","globalInfo.txt"))
txnameData=TxNameData(data, "efficiencyMap", Id=1)
txname=TxName("./database/8TeV/ATLAS/ATLAS-SUSY-2013-05/data/T2bb.txt",globalInfo,info)
txname.txnameData = txnameData
filename = "./testFiles/lhe/simplyGluino.lhe"
reader = lheReader.LheReader(filename)
event = reader.next()
reader.close()
event_xsec=event.metainfo["totalxsec"]
self.assertTrue ( abs ( event_xsec - 0.262 * pb ) < .1 *pb )
xsecs = crossSection.getXsecFromLHEFile(filename)
element = lheDecomposer.elementFromEvent(event, xsecs )
element.txname=None
e0=copy.deepcopy(element) ## has a gluino with mass of 675 GeV
## make a second element with a slightly different gluino mass
e1=copy.deepcopy(element)
e1.branches[0].masses[0]=725*GeV
e1.branches[1].masses[0]=725*GeV
e0.txname = txname
e1.txname = txname
# lets now cluster the two different gluino masses.
newel=clusterTools.groupAll ( [e0,e1] )
newmasses=newel.getAvgMass()
self.assertTrue ( newmasses==None ) ## in the case of efficiency maps the avg mass is none
## since it makes no sense
txname.txnameData.dataTag = 'upperLimits'
newel=clusterTools.clusterElements ( [e0,e1], 5. )
## this example gives an avg cluster mass of 700 gev
self.assertTrue ( newel[0].getAvgMass()[0][0] == 700. * GeV )
newel=clusterTools.clusterElements ( [e0,e1], .5 )
#in this example the distance is not in maxdist, so we dont cluster
self.assertTrue ( len(newel)==2 )
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.dataInfo.dataType == 'efficiencyMap':
cluster = clusterTools.groupAll(elements)
clusters.append(cluster)
elif dataset.dataInfo.dataType == 'upperLimit':
txnames = list(set([el.txname for el in elements]))
for txname in txnames:
txnameEls = []
for element in elements:
if not element.txname == txname:
continue
else: txnameEls.append(element)
txnameClusters = clusterTools.clusterElements(txnameEls, maxDist)
clusters += txnameClusters
else:
logger.warning("Unkown data type: %s. Data will be ignored."
% dataset.dataInfo.dataType)
return clusters
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 = clusterTools.groupAll(elements)
clusters.append(cluster)
elif dataset.getType() == 'upperLimit':
txnames = list(set([el.txname for el in elements]))
for txname in txnames:
txnameEls = []
for element in elements:
if not element.txname == txname:
continue
else: txnameEls.append(element)
txnameClusters = clusterTools.clusterElements(txnameEls, maxDist)
clusters += txnameClusters
else:
logger.warning("Unkown data type: %s. Data will be ignored."
% dataset.getType())
return clusters
def _combineElements(elements, analysis, maxDist):
"""
Combine elements according to the analysis type.
If analysis == upper limit type, group elements into mass clusters. If
analysis == efficiency map type, group all elements into a single cluster.
:parameter elements: list of elements (Element objects)
:parameter analysis: analysis to be considered (ULanalysis or EManalysis object)
:returns: list of element clusters (ElementCluster objects)
"""
if type(analysis) == type(EManalysis()):
clusters = [clusterTools.groupAll(elements)]
elif type(analysis) == type(ULanalysis()):
clusters = clusterTools.clusterElements(elements, analysis, maxDist)
return clusters
def testClusteringEM(self):
slhafile = 'testFiles/slha/lightEWinos.slha'
model = Model(BSMparticles=BSMList, SMparticles=SMList)
model.updateParticles(slhafile)
sigmacut = 5. * fb
mingap = 5. * GeV
toplist = decomposer.decompose(model,
sigmacut,
doCompress=True,
doInvisible=True,
minmassgap=mingap)
#Test clustering for EM results
dataset = database.getExpResults(
analysisIDs='CMS-SUS-13-012',
datasetIDs='3NJet6_800HT1000_300MHT450')[0].getDataset(
'3NJet6_800HT1000_300MHT450')
el1 = toplist[0].elementList[0].copy()
el2 = toplist[0].elementList[1].copy()
el3 = toplist[2].elementList[1].copy()
el1.eff = 1. #(Used in clustering)
el2.eff = 1. #(Used in clustering)
el3.eff = 1. #(Used in clustering)
#All elements have the same UL (for EM results)
el1._upperLimit = el2._upperLimit = el3._upperLimit = 1. * fb
#Clustering should not depend on the mass, width or txname:
el1.txname = el2.txname = el3.txname = None
el1.mass = [[1000. * GeV, 10 * GeV]] * 2
el2.mass = [[1500. * GeV, 10 * GeV]] * 2
el3.mass = [[200. * GeV, 100 * GeV, 90. * GeV]] * 2
el3.totalwidth = [[1e-10 * GeV, 1e-15 * GeV, 0. * GeV]] * 2
clusters = clusterElements([el1, el2, el3],
maxDist=0.2,
dataset=dataset)
self.assertEqual(len(clusters), 1)
self.assertEqual(sorted(clusters[0].elements), sorted([el1, el2, el3]))
self.assertEqual(clusters[0].averageElement().mass, None)
self.assertEqual(clusters[0].averageElement().totalwidth, None)
def testClusterer(self):
""" test the mass clusterer """
from smodels.theory import lheReader, lheDecomposer, crossSection
from smodels.theory import clusterTools
from smodels.experiment.txnameObj import TxName, TxNameData
from smodels.experiment.infoObj import Info
from smodels.tools.physicsUnits import GeV, pb, fb
import copy
data = [[[[674.99 * GeV, 199.999 * GeV], [674.99 * GeV,
199.999 * GeV]], .03 * fb],
[[[725.0001 * GeV, 200. * GeV], [725.0001 * GeV, 200. * GeV]],
.06 * fb],
[[[750. * GeV, 250. * GeV], [750. * GeV, 250. * GeV]],
.03 * fb]]
info = Info(
os.path.join("./database/8TeV/ATLAS/ATLAS-SUSY-2013-05/data/",
"dataInfo.txt"))
globalInfo = Info(
os.path.join("./database/8TeV/ATLAS/ATLAS-SUSY-2013-05/",
"globalInfo.txt"))
txnameData = TxNameData(data, "efficiencyMap", Id=1)
txname = TxName(
"./database/8TeV/ATLAS/ATLAS-SUSY-2013-05/data/T2bb.txt",
globalInfo, info)
txname.txnameData = txnameData
filename = "./testFiles/lhe/simplyGluino.lhe"
reader = lheReader.LheReader(filename)
event = reader.next()
reader.close()
event_xsec = event.metainfo["totalxsec"]
self.assertTrue(abs(event_xsec - 0.262 * pb) < .1 * pb)
xsecs = crossSection.getXsecFromLHEFile(filename)
element = lheDecomposer.elementFromEvent(event, xsecs)
element.txname = None
e0 = copy.deepcopy(element) ## has a gluino with mass of 675 GeV
## make a second element with a slightly different gluino mass
e1 = copy.deepcopy(element)
e1.branches[0].masses[0] = 725 * GeV
e1.branches[1].masses[0] = 725 * GeV
e0.txname = txname
e1.txname = txname
# lets now cluster the two different gluino masses.
newel = clusterTools.groupAll([e0, e1])
newmasses = newel.getAvgMass()
self.assertTrue(
newmasses ==
None) ## in the case of efficiency maps the avg mass is none
## since it makes no sense
txname.txnameData.dataTag = 'upperLimits'
newel = clusterTools.clusterElements([e0, e1], 5.)
## this example gives an avg cluster mass of 700 gev
self.assertTrue(newel[0].getAvgMass()[0][0] == 700. * GeV)
newel = clusterTools.clusterElements([e0, e1], .5)
#in this example the distance is not in maxdist, so we dont cluster
self.assertTrue(len(newel) == 2)
def testSimpleCluster(self):
""" test the mass clusterer """
data = [[[[674.99 * GeV, 199.999 * GeV], [674.99 * GeV,
199.999 * GeV]], .03 * fb],
[[[725.0001 * GeV, 200. * GeV], [725.0001 * GeV, 200. * GeV]],
.06 * fb],
[[[750. * GeV, 250. * GeV], [750. * GeV, 250. * GeV]],
.03 * fb]]
info = Info(
"./database/8TeV/ATLAS/ATLAS-SUSY-2013-05/data/dataInfo.txt")
globalInfo = Info(
"./database/8TeV/ATLAS/ATLAS-SUSY-2013-05/globalInfo.txt")
txnameData = TxNameData(data, "upperLimit", Id=1)
txname = TxName(
"./database/8TeV/ATLAS/ATLAS-SUSY-2013-05/data/T2bb.txt",
globalInfo, info, finalStates)
txname.txnameData = txnameData
dataset = DataSet(info=globalInfo, createInfo=False)
dataset.dataInfo = info
dataset.txnameList = [txname]
u = SMparticles.u
gluino = mssm.gluino.copy()
gluino.__setattr__("mass", 675. * GeV)
gluino.__setattr__('totalwidth', float('inf') * GeV)
n1 = mssm.n1.copy()
n1.__setattr__("mass", 200. * GeV)
n1.__setattr__('totalwidth', 0. * GeV)
w1 = XSectionList()
w1.xSections.append(XSection())
w1.xSections[0].info = XSectionInfo()
w1.xSections[0].info.sqrts = 8. * TeV
w1.xSections[0].info.label = '8 TeV'
w1.xSections[0].info.order = 0
w1.xSections[0].value = 10. * fb
b1 = Branch()
b1.evenParticles = [[u, u]]
b1.oddParticles = [gluino, n1]
b2 = b1.copy()
el1 = Element()
el1.branches = [b1, b2]
el1.weight = w1
el1.txname = txname
el1.eff = 1. #(Used in clustering)
## make a second element with a slightly different gluino mass
el2 = el1.copy()
el2.motherElements = [el2] #Enforce el2 and el1 not to be related
el2.txname = txname
el2.branches[0].oddParticles = [
ptc.copy() for ptc in el1.branches[0].oddParticles
]
el2.branches[1].oddParticles = [
ptc.copy() for ptc in el1.branches[1].oddParticles
]
el2.branches[0].oddParticles[0].__setattr__("mass", 725. * GeV)
el2.branches[1].oddParticles[0].__setattr__("mass", 725. * GeV)
el2.eff = 1. #(Used in clustering)
#Cluster for upper limits (all elements close in upper limit should be clustered together)
maxDist = 5. #Cluster all elements
newel = clusterTools.clusterElements([el1, el2], maxDist, dataset)[0]
newmasses = newel.averageElement().mass
self.assertEqual(newmasses, [[700. * GeV, 200. * GeV]] * 2)
maxDist = 0.5 #Elements differ and should not be clustered
newel = clusterTools.clusterElements([el1, el2], maxDist, dataset)
#in this example the distance is not in maxdist, so we dont cluster
self.assertTrue(len(newel) == 2)
info = Info(
"./database/8TeV/CMS/CMS-SUS-13-012-eff/6NJet8_1000HT1250_200MHT300/dataInfo.txt"
)
globalInfo = Info(
"./database/8TeV/CMS/CMS-SUS-13-012-eff/globalInfo.txt")
txnameData = TxNameData(data, "efficiencyMap", Id=1)
txname = TxName(
"./database/8TeV/CMS/CMS-SUS-13-012-eff/6NJet8_1000HT1250_200MHT300/T2.txt",
globalInfo, info, finalStates)
txname.txnameData = txnameData
dataset = DataSet(info=globalInfo, createInfo=False)
dataset.dataInfo = info
dataset.txnameList = [txname]
#Cluster for efficiency maps (all elements should be clustered together independent of maxDist)
maxDist = 0.001
newel = clusterTools.clusterElements([el1, el2], maxDist, dataset)[0]
newmasses = newel.averageElement().mass
self.assertEqual(newmasses, [[700. * GeV, 200. * GeV]] * 2)
def testClustererLifeTimes(self):
""" test the clustering with distinct lifetimes"""
data = [[[[674.99 * GeV, 199.999 * GeV], [674.99 * GeV,
199.999 * GeV]], .03 * fb],
[[[725.0001 * GeV, 200. * GeV], [725.0001 * GeV, 200. * GeV]],
.06 * fb],
[[[750. * GeV, 250. * GeV], [750. * GeV, 250. * GeV]],
.03 * fb]]
info = Info(
"./database/8TeV/ATLAS/ATLAS-SUSY-2013-05/data/dataInfo.txt")
globalInfo = Info(
"./database/8TeV/ATLAS/ATLAS-SUSY-2013-05/globalInfo.txt")
txnameData = TxNameData(data, "upperLimit", Id=1)
txname = TxName(
"./database/8TeV/ATLAS/ATLAS-SUSY-2013-05/data/T2bb.txt",
globalInfo, info, finalStates)
txname.txnameData = txnameData
dataset = DataSet(info=globalInfo, createInfo=False)
dataset.dataInfo = info
dataset.txnameList = [txname]
u = SMparticles.u
gluino = mssm.gluino.copy()
gluino.__setattr__("mass", 675. * GeV)
gluino.__setattr__('totalwidth', 1e-15 * GeV)
n1 = mssm.n1.copy()
n1.__setattr__("mass", 200. * GeV)
n1.__setattr__('totalwidth', 0. * GeV)
w1 = XSectionList()
w1.xSections.append(XSection())
w1.xSections[0].info = XSectionInfo()
w1.xSections[0].info.sqrts = 8. * TeV
w1.xSections[0].info.label = '8 TeV'
w1.xSections[0].info.order = 0
w1.xSections[0].value = 10. * fb
b1 = Branch()
b1.evenParticles = [ParticleList([u, u])]
b1.oddParticles = [gluino, n1]
b2 = b1.copy()
el1 = Element()
el1.branches = [b1, b2]
el1.weight = w1
el1.txname = txname
el1.eff = 1. #(Used in clustering)
## make a second element with a slightly different gluino width
el2 = el1.copy()
el2.motherElements = [el2] #Enforce el2 and el1 not to be related
el2.txname = txname
el2.branches[0].oddParticles = [
ptc.copy() for ptc in el1.branches[0].oddParticles
]
el2.branches[1].oddParticles = [
ptc.copy() for ptc in el1.branches[1].oddParticles
]
el2.eff = 1. #(Used in clustering)
el2.branches[0].oddParticles[0].__setattr__("mass", 675. * GeV)
el2.branches[1].oddParticles[0].__setattr__("mass", 675. * GeV)
el2.branches[0].oddParticles[0].__setattr__("totalwidth",
0.9e-15 * GeV)
el2.branches[1].oddParticles[0].__setattr__("totalwidth",
0.9e-15 * GeV)
newel = clusterTools.clusterElements([el1, el2], 5., dataset)
## this example gives an avg cluster mass of 700 gev
self.assertEqual(newel[0].averageElement().mass[0][0], 675. * GeV)
self.assertAlmostEqual(
newel[0].averageElement().totalwidth[0][0].asNumber(GeV) * 1e15,
0.95)
newel = clusterTools.clusterElements([el1, el2], .5, dataset)
#in this example the distance is in maxdist, so we cluster
self.assertTrue(len(newel) == 1)
newel = clusterTools.clusterElements([el1, el2], .1, dataset)
#in this example the distance is not in maxdist, so we dont cluster
self.assertTrue(len(newel) == 2)
def testClusteringUL(self):
slhafile = 'testFiles/slha/lightEWinos.slha'
model = Model(BSMparticles=BSMList, SMparticles=SMList)
model.updateParticles(slhafile)
sigmacut = 5. * fb
mingap = 5. * GeV
toplist = decomposer.decompose(model,
sigmacut,
doCompress=True,
doInvisible=True,
minmassgap=mingap)
#Test clustering for UL results
dataset = database.getExpResults(analysisIDs='ATLAS-SUSY-2013-02',
datasetIDs=None)[0].getDataset(None)
el1 = toplist[1].elementList[0]
el2 = toplist[1].elementList[1]
el3 = toplist[1].elementList[2]
weights = [
el.weight.getMaxXsec().asNumber(fb) for el in [el1, el2, el3]
]
#Overwrite masses and txname label
el1.mass = [[1000. * GeV, 100. * GeV]] * 2
el2.mass = [[1020. * GeV, 100. * GeV]] * 2
el3.mass = [[500. * GeV, 100. * GeV]] * 2
el1.txname = el2.txname = el3.txname = 'T1'
el1.eff = 1. #(Used in clustering)
el2.eff = 1. #(Used in clustering)
el3.eff = 1. #(Used in clustering)
#Check clustering with distinct elements
clusters = clusterElements([el1, el2, el3],
maxDist=0.2,
dataset=dataset)
self.assertEqual(len(clusters), 2)
averageMasses = [
[[(1000. * GeV * weights[0] + 1020. * GeV * weights[1]) /
(weights[0] + weights[1]), 100. * GeV]] * 2, el3.mass
]
elClusters = [[el1, el2], [el3]]
for ic, cluster in enumerate(clusters):
avgEl = cluster.averageElement()
self.assertEqual(sorted(cluster.elements), sorted(elClusters[ic]))
for ibr, br in enumerate(avgEl.mass):
for im, m in enumerate(br):
self.assertAlmostEqual(
m.asNumber(GeV),
averageMasses[ic][ibr][im].asNumber(GeV))
self.assertEqual(avgEl.totalwidth, elClusters[ic][0].totalwidth)
#Check clustering with distinct elements but no maxDist limit
clusters = clusterElements([el1, el2, el3],
maxDist=10.,
dataset=dataset)
self.assertEqual(len(clusters), 1)
cluster = clusters[0]
avgEl = cluster.averageElement()
averageMass = [[(1000. * GeV * weights[0] + 1020. * GeV * weights[1] +
500. * GeV * weights[2]) / sum(weights), 100. * GeV]
] * 2
self.assertEqual(sorted(cluster.elements), sorted([el1, el2, el3]))
for ibr, br in enumerate(avgEl.mass):
for im, m in enumerate(br):
self.assertAlmostEqual(m.asNumber(GeV),
averageMass[ibr][im].asNumber(GeV))
self.assertEqual(avgEl.totalwidth, el1.totalwidth)
#Check clustering where elements have same upper limits, but not the average element:
el1._upperLimit = 1. * fb
el2._upperLimit = 1. * fb
el3._upperLimit = 1. * fb
clusters = clusterElements([el1, el2, el3],
maxDist=0.1,
dataset=dataset)
self.assertEqual(len(clusters), 2)