def testSelectors(self):
from databaseLoader import database
validated = database.getExpResults(analysisIDs=['*:8*TeV','CMS-PAS-SUS-15-002','CMS-PAS-SUS-16-024'],
useNonValidated = False )
nonval = database.getExpResults(analysisIDs=['*:8*TeV','CMS-PAS-SUS-15-002','CMS-PAS-SUS-16-024'],
useNonValidated = True )
# print ( "validated=",len(validated),map ( str, validated ) )
self.assertTrue(len(validated)==8)
self.assertTrue(len(nonval)==9)
def testCoordinateTransfInclusive(self):
""" test the transformation of data into coordinates, back into data """
#Test with an inclusive result:
expRes = database.getExpResults(analysisIDs=["CMS-EXO-13-006"],
datasetIDs=['c000'],
txnames=["THSCPM4"])
txname = expRes[0].datasets[0].txnameList[0]
initial = [[(300. * GeV, 1e-16 * GeV), 100. * GeV],
[(300. * GeV, 1e-16 * GeV), 100. * GeV]]
coords = txname.txnameData.dataToCoordinates(initial,
txname.txnameData._V,
txname.txnameData.delta_x)
data = txname.txnameData.coordinatesToData(coords,
txname.txnameData._V,
txname.txnameData.delta_x)
data = np.array(data, dtype=object)
newInitial = ['*', [300. * GeV, 100. * GeV]]
newInitial = np.array(newInitial, dtype=object)
self.assertEqual(data.shape, newInitial.shape)
dataFlat = np.array([
x.asNumber(GeV) if str(x) != '*' else -1
for x in flattenArray(data)
])
initialFlat = np.array([
x.asNumber(GeV) if str(x) != '*' else -1
for x in flattenArray(newInitial)
])
diff = np.linalg.norm(dataFlat - initialFlat)
self.assertAlmostEqual(diff, 0.)
#Test with another type of inclusive result:
expRes = database.getExpResults(analysisIDs=["CMS-EXO-13-006"],
datasetIDs=['c000'],
txnames=["THSCPM7"])
txname = expRes[0].datasets[0].txnameList[0]
initial = [[(300. * GeV, 1e-16 * GeV), 50. * GeV],
[(200. * GeV, 1e-18 * GeV), 100. * GeV, 1. * GeV]]
coords = txname.txnameData.dataToCoordinates(initial,
txname.txnameData._V,
txname.txnameData.delta_x)
data = txname.txnameData.coordinatesToData(coords,
txname.txnameData._V,
txname.txnameData.delta_x)
data = np.array(data, dtype=object)
newInitial = [[300. * GeV, 50. * GeV],
[200. * GeV, 100. * GeV, 1. * GeV]]
newInitial = np.array(newInitial, dtype=object)
self.assertEqual(data.shape, newInitial.shape)
dataFlat = np.array([x.asNumber(GeV) for x in flattenArray(data)])
initialFlat = np.array(
[x.asNumber(GeV) for x in flattenArray(newInitial)])
diff = np.linalg.norm(dataFlat - initialFlat)
self.assertAlmostEqual(diff, 0.)
def testExpectedFails(self):
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2013-05"],
datasetIDs=[None], txnames=["T2bb" ] )
txname=expRes[0].datasets[0].txnameList[0]
m = [[650.0*GeV, 50.0*GeV], [650.0*GeV, 50.0*GeV]]
expected = txname.getValueFor ( m, expected = True )
self.assertTrue(expected is None)
def checkPrediction(self, slhafile, expID, expectedValues, datasetID):
reducedModel = [
ptc for ptc in BSMList if abs(ptc.pdg) in [1000011, 1000012]
]
model = Model(reducedModel, SMList)
model.updateParticles(slhafile)
self.configureLogger()
smstoplist = decomposer.decompose(model,
0. * fb,
doCompress=True,
doInvisible=True,
minmassgap=5. * GeV)
expresults = database.getExpResults(analysisIDs=expID,
datasetIDs=datasetID)
for expresult in expresults:
theorypredictions = theoryPredictionsFor(expresult, smstoplist)
for pred in theorypredictions:
predval = pred.xsection.value
expval = expectedValues.pop()
delta = expval * 0.01
self.assertAlmostEqual(predval.asNumber(fb),
expval,
delta=delta)
self.assertTrue(len(expectedValues) == 0)
def testCombinedResult(self):
predXSecs, rvalues = {}, {}
for case in ["T1", "T5", "mixed"]:
filename = self.createSLHAFile(case=case)
deco = decompose(filename)
expRes = database.getExpResults(
analysisIDs=["CMS-SUS-16-050-agg"])[0]
# print ( "Experimental result: %s" % expRes )
tp = theoryPredictionsFor(expRes,
deco,
useBestDataset=False,
combinedResults=True)
for t in tp:
predXSecs[case] = t.xsection.value
rvalues[case] = t.getRValue(expected=True)
if True:
os.unlink(filename)
## first test: the theory prediction of the mixed scenario should be 25% of the sum
## 25%, because the total cross section is a fixed quantity, and half of the mixed scenario
## goes into asymmetric branches which we miss out on.
self.assertAlmostEqual(
(predXSecs["T1"] + predXSecs["T5"]).asNumber(fb),
(4 * predXSecs["mixed"]).asNumber(fb), 2)
## second test: the r value of the mixed scenario * 2 must be between the r values
## of the pure scenarios. The factor of two comes from the fact, that we loose 50%
## to asymmetric branches
self.assertTrue(rvalues["T5"] < 2 * rvalues["mixed"] < rvalues["T1"])
def testDatabaseInt(self):
"""Test the database with intermediate states."""
#Create test elements
c1 = mssm.c1
n1 = mssm.n1
g = mssm.gluino
n1.mass = 100*GeV
g.mass = 1000*GeV
c1.mass = 500*GeV
el1 = Element(info="[[[q,q]],[[q,q]]]", model=finalStates)
el2 = Element(info="[[[q,q]],[[q,q]]]", model=finalStates)
el1.branches[0].oddParticles = [g,n1]
el1.branches[1].oddParticles = [g,n1]
el2.branches[0].oddParticles = [c1,n1]
el2.branches[1].oddParticles = [c1,n1]
#Test result without intermediate states:
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2016-081"],
datasetIDs=[None], txnames=["T5Disp" ] )
txname=expRes[0].datasets[0].txnameList[0]
#el1 should match, since txname requires gluinos
newEl = txname.hasElementAs(el1)
self.assertEqual(newEl,el1)
self.assertNotEqual(newEl,el2)
#el2 should not match, since txname requires gluinos
newEl = txname.hasElementAs(el2)
self.assertEqual(False,newEl)
def runPrinterMain(self, slhafile, mprinter, addTopList=False):
"""
Main program. Displays basic use case.
"""
runtime.modelFile = 'mssm'
reload(particlesLoader)
#Set main options for decomposition:
sigmacut = 0.03 * fb
mingap = 5. * GeV
""" Decompose model """
model = Model(BSMList, SMList)
model.updateParticles(slhafile)
smstoplist = decomposer.decompose(model,
sigmacut,
doCompress=True,
doInvisible=True,
minmassgap=mingap)
#Add the decomposition result to the printers
if addTopList:
mprinter.addObj(smstoplist)
listOfExpRes = database.getExpResults(analysisIDs=[
'*:8*TeV', 'CMS-PAS-SUS-15-002', 'CMS-PAS-SUS-16-024'
])
# Compute the theory predictions for each analysis
allPredictions = []
for expResult in listOfExpRes:
predictions = theoryPredictionsFor(expResult, smstoplist)
if not predictions:
continue
allPredictions += predictions._theoryPredictions
for theoPred in allPredictions:
if theoPred.dataType() == 'efficiencyMap' and hasattr(
theoPred,
'expectedUL') and not theoPred.expectedUL is None:
theoPred.computeStatistics()
maxcond = 0.2
theoryPredictions = TheoryPredictionList(allPredictions, maxcond)
mprinter.addObj(theoryPredictions)
#Add coverage information:
coverageInfo = coverage.Uncovered(smstoplist)
mprinter.addObj(coverageInfo)
#Add additional information:
databaseVersion = database.databaseVersion
outputStatus = ioObjects.OutputStatus(
[1, 'Input file ok'], slhafile, {
'sigmacut': sigmacut.asNumber(fb),
'minmassgap': mingap.asNumber(GeV),
'maxcond': maxcond
}, databaseVersion)
outputStatus.status = 1
mprinter.addObj(outputStatus)
mprinter.flush()
def testGoodFile(self):
filename = "./testFiles/slha/lightEWinos.slha"
topolist = slhaDecomposer.decompose(filename,doCompress=True, doInvisible=True, minmassgap = 5*GeV)
analyses = database.getExpResults (txnames=["TChiWZoff"])
theoryPrediction = theoryPredictionsFor(analyses[0], topolist)[0]
conditionViolation = theoryPrediction.conditions
self.assertEqual(conditionViolation['Cgtr([[[mu+,mu-]],[[l,nu]]],[[[e+,e-]],[[l,nu]]])'],0.)
def testExpectedFails(self):
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2013-05"],
datasetIDs=[None],
txnames=["T2bb"])
txname = expRes[0].datasets[0].txnameList[0]
m = [[650.0 * GeV, 50.0 * GeV], [650.0 * GeV, 50.0 * GeV]]
expected = txname.getValueFor(m, expected=True)
self.assertTrue(expected is None)
def testCoordinateTransf(self):
""" test the transformation of data into coordinates, back into data """
#Test with a regular mass array:
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2013-05"],
datasetIDs=[None],
txnames=["T2bb"])
txname = expRes[0].datasets[0].txnameList[0] # T2bb
initial = [[300. * GeV, 100. * GeV], [300. * GeV, 100. * GeV]]
coords = txname.txnameData.dataToCoordinates(initial,
txname.txnameData._V,
txname.txnameData.delta_x)
data = txname.txnameData.coordinatesToData(coords,
txname.txnameData._V,
txname.txnameData.delta_x)
data = np.array(data)
initial = np.array(initial)
self.assertEqual(data.shape, initial.shape)
dataFlat = np.array([x.asNumber(GeV) for x in flattenArray(data)])
initialFlat = np.array(
[x.asNumber(GeV) for x in flattenArray(initial)])
diff = np.linalg.norm(dataFlat - initialFlat)
self.assertAlmostEqual(diff, 0.)
#Test with with a mass array containing tuples:
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2016-08"],
datasetIDs=[None],
txnames=["T5Disp"])
txname = expRes[0].datasets[0].txnameList[0]
initial = [[(300. * GeV, 1e-16 * GeV), 100. * GeV],
[(300. * GeV, 1e-16 * GeV), 100. * GeV]]
coords = txname.txnameData.dataToCoordinates(initial,
txname.txnameData._V,
txname.txnameData.delta_x)
data = txname.txnameData.coordinatesToData(coords,
txname.txnameData._V,
txname.txnameData.delta_x)
data = np.array(data, dtype=object)
initial = np.array(initial, dtype=object)
self.assertEqual(data.shape, initial.shape)
dataFlat = np.array([x.asNumber(GeV) for x in flattenArray(data)])
initialFlat = np.array(
[x.asNumber(GeV) for x in flattenArray(initial)])
diff = np.linalg.norm(dataFlat - initialFlat)
self.assertAlmostEqual(diff, 0.)
def testOutofBounds(self):
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2013-05"],
datasetIDs=[None],
txnames=["T6bbWW"])
ul = expRes[0].getUpperLimitFor(
txname="T6bbWW",
mass=[[400 * GeV, 250 * GeV, 100 * GeV],
[400 * GeV, 250 * GeV, 100 * GeV]])
self.assertTrue(ul == None)
def testCascadeDecay(self):
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2013-05"],
datasetIDs=[None],
txnames=["T6bbWW"])
ul = expRes[0].getUpperLimitFor(
txname="T6bbWW",
mass=[[150. * GeV, 140. * GeV, 135. * GeV],
[150. * GeV, 140. * GeV, 135. * GeV]]).asNumber(pb)
self.assertAlmostEqual(ul, 324.682)
def testExpected(self):
expRes = database.getExpResults(analysisIDs=["CMS-PAS-SUS-12-026"],
datasetIDs=[None], txnames=[ "T1tttt" ] )
txname=expRes[0].datasets[0].txnameList[0]
m = [[650.0*GeV, 50.0*GeV], [650.0*GeV, 50.0*GeV]]
observed = txname.getValueFor ( m, expected = False )
expected = txname.getValueFor ( m, expected = True )
delta = abs ( ( ( observed - expected ) / observed ).asNumber() )
self.assertTrue ( delta > .55 and delta < .60 )
def testExpected(self):
expRes = database.getExpResults(analysisIDs=["CMS-PAS-SUS-12-026"],
datasetIDs=[None],
txnames=["T1tttt"])
txname = expRes[0].datasets[0].txnameList[0]
m = [[650.0 * GeV, 50.0 * GeV], [650.0 * GeV, 50.0 * GeV]]
observed = txname.getValueFor(m, expected=False)
expected = txname.getValueFor(m, expected=True)
delta = abs(((observed - expected) / observed).asNumber())
self.assertTrue(delta > .55 and delta < .60)
def testDirectDecay(self):
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2013-05"],
datasetIDs=[None],
txnames=["T2bb"])
ul = expRes[0].getUpperLimitFor(txname="T2bb",
mass=[[400 * GeV, 100 * GeV],
[400 * GeV,
100 * GeV]]).asNumber(pb)
self.assertAlmostEqual(ul, 0.0608693)
def runPrinterMain(self, slhafile, mprinter, addTopList=False):
"""
Main program. Displays basic use case.
"""
runtime.modelFile = 'mssm'
reload(particlesLoader)
#Set main options for decomposition:
sigmacut = 0.03 * fb
mingap = 5. * GeV
""" Decompose model (use slhaDecomposer for SLHA input or lheDecomposer
for LHE input) """
smstoplist = slhaDecomposer.decompose(slhafile,
sigmacut,
doCompress=True,
doInvisible=True,
minmassgap=mingap)
#Add the decomposition result to the printers
if addTopList:
mprinter.addObj(smstoplist)
listOfExpRes = database.getExpResults(analysisIDs=[
'*:8*TeV', 'CMS-PAS-SUS-15-002', 'CMS-PAS-SUS-16-024'
])
# Compute the theory predictions for each analysis
allPredictions = []
for expResult in listOfExpRes:
predictions = theoryPredictionsFor(expResult, smstoplist)
if not predictions:
continue
allPredictions += predictions._theoryPredictions
maxcond = 0.2
results = ioObjects.ResultList(allPredictions, maxcond)
mprinter.addObj(results)
#Add coverage information:
coverageInfo = coverage.Uncovered(smstoplist)
mprinter.addObj(coverageInfo)
#Add additional information:
databaseVersion = database.databaseVersion
outputStatus = ioObjects.OutputStatus(
[1, 'Input file ok'], slhafile, {
'sigmacut': sigmacut.asNumber(fb),
'minmassgap': mingap.asNumber(GeV),
'maxcond': maxcond
}, databaseVersion)
outputStatus.status = 1
mprinter.addObj(outputStatus)
mprinter.flush()
def testInterpolation(self):
# print database
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2013-05"],
datasetIDs=[None], txnames=["T2bb" ] )
#expRes=listOfExpRes[0] # ATLAS-SUSY-2013-05
txname=expRes[0].datasets[0].txnameList[0] # T2bb
result=txname.txnameData.getValueFor(
[[ 300.*GeV,100.*GeV], [ 300.*GeV,100.*GeV] ])
self.assertAlmostEqual( result.asNumber(pb),0.162457 )
result=txname.txnameData.getValueFor(
[[ 300.*GeV,125.*GeV], [ 300.*GeV,125.*GeV] ])
self.assertAlmostEqual( result.asNumber(pb),0.237745 )
def testIntegration(self):
slhafile = './testFiles/slha/simplyGluino.slha'
self.configureLogger()
smstoplist = slhaDecomposer.decompose(slhafile, .1*fb, doCompress=True,
doInvisible=True, minmassgap=5.*GeV)
listofanalyses = database.getExpResults(
analysisIDs= [ "ATLAS-SUSY-2013-02", "CMS-SUS-13-012" ],
txnames = [ "T1" ] )
if type(listofanalyses) != list:
listofanalyses= [ listofanalyses]
for analysis in listofanalyses:
self.checkAnalysis(analysis,smstoplist)
def testInterpolation(self):
# print database
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2013-05"],
datasetIDs=[None],
txnames=["T2bb"])
#expRes=listOfExpRes[0] # ATLAS-SUSY-2013-05
txname = expRes[0].datasets[0].txnameList[0] # T2bb
result = txname.txnameData.getValueFor([[300. * GeV, 100. * GeV],
[300. * GeV, 100. * GeV]])
self.assertAlmostEqual(result.asNumber(pb), 0.162457)
result = txname.txnameData.getValueFor([[300. * GeV, 125. * GeV],
[300. * GeV, 125. * GeV]])
self.assertAlmostEqual(result.asNumber(pb), 0.237745)
def testGoodFile(self):
listOfIDs = {'ATLAS-CONF-2013-037': [31, 32, 33, 34, 27, 28, 29, 30],
'ATLAS-SUSY-2013-05' : [26]}
filename = "./testFiles/slha/higgsinoStop.slha"
topoList = slhaDecomposer.decompose(filename,doCompress = True, doInvisible=True, minmassgap = 5*GeV)
resultlist = database.getExpResults(analysisIDs=['*:8*TeV','CMS-PAS-SUS-15-002','CMS-PAS-SUS-16-024'])
for res in resultlist:
theorypredictions = theoryPredictionsFor(res, topoList)
if not theorypredictions: continue
self.assertEqual(len(theorypredictions),1)
tpIDs = theorypredictions[0].IDs
self.assertEqual(sorted(tpIDs),sorted(listOfIDs[res.globalInfo.id]))
def testGoodFile(self):
filename = "./testFiles/slha/lightEWinos.slha"
topolist = slhaDecomposer.decompose(filename,
doCompress=True,
doInvisible=True,
minmassgap=5 * GeV)
analyses = database.getExpResults(txnames=["TChiWZoff"])
theoryPrediction = theoryPredictionsFor(analyses[0], topolist)[0]
conditionViolation = theoryPrediction.conditions
self.assertEqual(
conditionViolation[
'Cgtr([[[mu+,mu-]],[[l,nu]]],[[[e+,e-]],[[l,nu]]])'], 0.)
def testTxnameDataReweight(self):
c1 = mssm.c1.copy()
c1.totalwidth = 1e-17 * GeV
c1.mass = 100 * GeV
gluino = mssm.gluino.copy()
gluino.totalwidth = 1e-15 * GeV
gluino.mass = 500 * GeV
branch1 = Branch()
branch1.oddParticles = [gluino, c1]
branch2 = Branch()
branch2.oddParticles = [gluino, c1]
el1 = Element([branch1, branch2])
listofanalyses = database.getExpResults(analysisIDs=["CMS-EXO-13-006"],
dataTypes=['efficiencyMap'])
exp = listofanalyses[0]
ds = [d for d in exp.datasets if d.dataInfo.dataId == 'c000'][0]
tx = [t for t in ds.txnameList if str(t) == 'THSCPM3'][0]
effDefault = tx.getEfficiencyFor(el1)
self.assertAlmostEqual(tx.txnameData.Leff_inner, 0.007)
self.assertAlmostEqual(tx.txnameData.Leff_outer, 7.0)
self.assertAlmostEqual(effDefault / 1e-5, 5.223348, places=3)
ds = [d for d in exp.datasets if d.dataInfo.dataId == 'c000track'][0]
tx = [t for t in ds.txnameList if str(t) == 'THSCPM3'][0]
effNewSize = tx.getEfficiencyFor(el1)
self.assertAlmostEqual(tx.txnameData.Leff_inner, 0.007)
self.assertAlmostEqual(tx.txnameData.Leff_outer, 3.0)
self.assertAlmostEqual(effNewSize / 1e-5, 7.83466, places=3)
branch1 = Branch()
branch1.oddParticles = [c1]
branch2 = Branch()
branch2.oddParticles = [c1]
el2 = Element([branch1, branch2])
ds = [d for d in exp.datasets if d.dataInfo.dataId == 'c000'][0]
tx = [t for t in ds.txnameList if str(t) == 'THSCPM1'][0]
effDefault = tx.getEfficiencyFor(el2)
self.assertAlmostEqual(tx.txnameData.Leff_inner, 0.007)
self.assertAlmostEqual(tx.txnameData.Leff_outer, 7.0)
self.assertAlmostEqual(effDefault, 0.073292924, places=3)
ds = [d for d in exp.datasets if d.dataInfo.dataId == 'c000track'][0]
tx = [t for t in ds.txnameList if str(t) == 'THSCPM1'][0]
effNewSize = tx.getEfficiencyFor(el2)
self.assertAlmostEqual(tx.txnameData.Leff_inner, 0.1)
self.assertAlmostEqual(tx.txnameData.Leff_outer, 5.0)
self.assertAlmostEqual(effNewSize, 0.0897630, places=3)
def checkPrediction(self,slhafile,expID,expectedValues):
self.configureLogger()
smstoplist = slhaDecomposer.decompose(slhafile, 0.*fb, doCompress=True,
doInvisible=True, minmassgap=5.*GeV)
expresults = database.getExpResults(analysisIDs= expID)
for expresult in expresults:
theorypredictions = theoryPredictionsFor(expresult, smstoplist)
for pred in theorypredictions:
predval=pred.xsection.value
expval = expectedValues.pop()
delta = expval*0.01
self.assertAlmostEqual(predval.asNumber(fb), expval,delta=delta)
self.assertTrue(len(expectedValues) == 0)
def runPrinterMain(self, slhafile, mprinter,addTopList=False):
"""
Main program. Displays basic use case.
"""
runtime.modelFile = 'mssm'
reload(particlesLoader)
#Set main options for decomposition:
sigmacut = 0.03 * fb
mingap = 5. * GeV
""" Decompose model (use slhaDecomposer for SLHA input or lheDecomposer
for LHE input) """
smstoplist = slhaDecomposer.decompose(slhafile, sigmacut,
doCompress=True, doInvisible=True, minmassgap=mingap )
#Add the decomposition result to the printers
if addTopList:
mprinter.addObj(smstoplist)
listOfExpRes = database.getExpResults(analysisIDs=['*:8*TeV','CMS-PAS-SUS-15-002','CMS-PAS-SUS-16-024'])
# Compute the theory predictions for each analysis
allPredictions = []
for expResult in listOfExpRes:
predictions = theoryPredictionsFor(expResult, smstoplist)
if not predictions:
continue
allPredictions += predictions._theoryPredictions
maxcond = 0.2
results = ioObjects.ResultList(allPredictions,maxcond)
mprinter.addObj(results)
#Add coverage information:
coverageInfo = coverage.Uncovered(smstoplist)
mprinter.addObj(coverageInfo)
#Add additional information:
databaseVersion = database.databaseVersion
outputStatus = ioObjects.OutputStatus([1,'Input file ok'], slhafile,
{'sigmacut' : sigmacut.asNumber(fb),
'minmassgap' : mingap.asNumber(GeV),
'maxcond': maxcond },
databaseVersion)
outputStatus.status = 1
mprinter.addObj(outputStatus)
mprinter.flush()
def testOutsidePlane(self):
expRes = database.getExpResults( analysisIDs=["ATLAS-SUSY-2013-05"],
txnames=["T2bb" ] )
# expRes=listOfExpRes[0] # ATLAS-SUSY-2013-05
txname=expRes[0].datasets[0].txnameList[0] # T6bbWW
result=txname.txnameData.getValueFor(
[[ 300.*GeV,127.*GeV], [ 300.*GeV,127.5*GeV] ])
self.assertAlmostEqual( result.asNumber(pb),0.24452092 )
result=txname.txnameData.getValueFor(
[[ 600.*GeV,120.*GeV], [ 600.*GeV,130.*GeV] ])
self.assertAlmostEqual( result.asNumber(pb),0.0197154 )
result=txname.txnameData.getValueFor(
[[ 300.*GeV,120.*GeV], [ 300.*GeV,130.*GeV] ])
self.assertTrue ( result == None )
def testOutsidePlane(self):
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2013-05"],
txnames=["T2bb"])
# expRes=listOfExpRes[0] # ATLAS-SUSY-2013-05
txname = expRes[0].datasets[0].txnameList[0] # T6bbWW
result = txname.txnameData.getValueFor([[300. * GeV, 127. * GeV],
[300. * GeV, 127.5 * GeV]])
self.assertAlmostEqual(result.asNumber(pb), 0.24452092)
result = txname.txnameData.getValueFor([[600. * GeV, 120. * GeV],
[600. * GeV, 130. * GeV]])
self.assertAlmostEqual(result.asNumber(pb), 0.0197154)
result = txname.txnameData.getValueFor([[300. * GeV, 120. * GeV],
[300. * GeV, 130. * GeV]])
self.assertTrue(result == None)
def test6D(self):
# print database
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2013-05"],
txnames=[ "T6bbWW" ] )
# expRes=listOfExpRes[0] # ATLAS-SUSY-2013-05
txname=expRes[0].datasets[0].txnameList[0] # T6bbWW
result=txname.txnameData.getValueFor(
[[ 300.*GeV,105.*GeV,100.*GeV], [ 300.*GeV,105.*GeV,100.*GeV] ])
self.assertAlmostEqual( result.asNumber(pb),0.176266 )
result=txname.txnameData.getValueFor(
[[ 300.*GeV,270.*GeV,200.*GeV], [ 300.*GeV,270.*GeV,200.*GeV] ])
self.assertAlmostEqual( result.asNumber(pb), 87.0403 )
result=txname.txnameData.getValueFor(
[[ 300.*GeV,270.*GeV,200.*GeV], [ 300.*GeV,271.*GeV,200.*GeV] ])
self.assertAlmostEqual( result.asNumber(pb), 88.6505675 )
def testCache(self):
Cache.n_stored = 10
Cache.reset()
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2013-05"],
datasetIDs=[None], txnames=["T2bb" ] )
txname=expRes[0].datasets[0].txnameList[0] # T2bb
massesvec = []
for i in range(170,290,10):
massesvec.append ( [ i*GeV, 100*GeV ] )
for masses in massesvec:
result=txname.txnameData.getValueFor( [ masses, masses ])
# print masses,result,Cache.size()
self.assertEqual(Cache.size(), 7)
m = [ [ 270*GeV, 100*GeV], [ 270*GeV, 100*GeV ] ]
result=txname.txnameData.getValueFor( m )
self.assertAlmostEqual(result.asNumber ( fb ) , 459.658)
def testGoodFile(self):
filename = "./testFiles/slha/lightEWinos.slha"
model = Model(BSMList, SMList)
model.updateParticles(filename)
topolist = decomposer.decompose(model,
sigmacut=0.1 * fb,
doCompress=True,
doInvisible=True,
minmassgap=5 * GeV)
analyses = database.getExpResults(txnames=["TChiWZoff"],
analysisIDs='ATLAS-SUSY-2013-12')
theoryPrediction = theoryPredictionsFor(analyses[0], topolist)[0]
conditionViolation = theoryPrediction.conditions
self.assertEqual(
conditionViolation[
'Cgtr([[[mu+,mu-]],[[l,nu]]],[[[e+,e-]],[[l,nu]]])'], 0.)
def test6D(self):
# print database
expRes = database.getExpResults(analysisIDs=["ATLAS-SUSY-2013-05"],
txnames=["T6bbWW"])
# expRes=listOfExpRes[0] # ATLAS-SUSY-2013-05
txname = expRes[0].datasets[0].txnameList[0] # T6bbWW
result = txname.txnameData.getValueFor(
[[300. * GeV, 105. * GeV, 100. * GeV],
[300. * GeV, 105. * GeV, 100. * GeV]])
self.assertAlmostEqual(result.asNumber(pb), 0.176266)
result = txname.txnameData.getValueFor(
[[300. * GeV, 270. * GeV, 200. * GeV],
[300. * GeV, 270. * GeV, 200. * GeV]])
self.assertAlmostEqual(result.asNumber(pb), 87.0403)
result = txname.txnameData.getValueFor(
[[300. * GeV, 270. * GeV, 200. * GeV],
[300. * GeV, 271. * GeV, 200. * GeV]])
self.assertAlmostEqual(result.asNumber(pb), 88.6505675)
def testIntegration(self):
slhafile = '../inputFiles/slha/simplyGluino.slha'
model = Model(BSMList, SMList)
model.updateParticles(slhafile)
self.configureLogger()
smstoplist = decomposer.decompose(model,
.1 * fb,
doCompress=True,
doInvisible=True,
minmassgap=5. * GeV)
listofanalyses = database.getExpResults(
analysisIDs=["ATLAS-SUSY-2013-02", "CMS-SUS-13-012"],
txnames=["T1"])
if type(listofanalyses) != list:
listofanalyses = [listofanalyses]
for analysis in listofanalyses:
self.checkAnalysis(analysis, smstoplist)
def testPredictionInterface(self):
""" A simple test to see that the interface in datasetObj
and TheoryPrediction to the statistics tools is working correctly
"""
expRes = database.getExpResults( analysisIDs=['CMS-SUS-13-012'] )[0]
slhafile= "./testFiles/slha/simplyGluino.slha"
smstoplist = slhaDecomposer.decompose( slhafile )
prediction = theoryPredictionsFor(expRes, smstoplist,deltas_rel=0.)[0]
pred_signal_strength = prediction.xsection.value
prediction.computeStatistics()
ill = math.log(prediction.likelihood)
ichi2 = prediction.chi2
nsig = (pred_signal_strength*expRes.globalInfo.lumi).asNumber()
m = Data(4, 2.2, 1.1**2, None, nsignal=nsig,deltas_rel=0.2)
computer = LikelihoodComputer(m)
dll = math.log(computer.likelihood(nsig, marginalize=False ) )
self.assertAlmostEqual(ill, dll, places=2)
dchi2 = computer.chi2( nsig, marginalize=False )
# print ( "dchi2,ichi2",dchi2,ichi2)
self.assertAlmostEqual(ichi2, dchi2, places=2)
def testPredictionInterface(self):
""" A simple test to see that the interface in datasetObj
and TheoryPrediction to the statistics tools is working correctly
"""
expRes = database.getExpResults(analysisIDs=['CMS-SUS-13-012'])[0]
slhafile = "./testFiles/slha/simplyGluino.slha"
smstoplist = slhaDecomposer.decompose(slhafile)
prediction = theoryPredictionsFor(expRes, smstoplist, deltas_rel=0.)[0]
pred_signal_strength = prediction.xsection.value
prediction.computeStatistics()
ill = math.log(prediction.likelihood)
ichi2 = prediction.chi2
nsig = (pred_signal_strength * expRes.globalInfo.lumi).asNumber()
m = Data(4, 2.2, 1.1**2, None, nsignal=nsig, deltas_rel=0.2)
computer = LikelihoodComputer(m)
dll = math.log(computer.likelihood(nsig, marginalize=False))
self.assertAlmostEqual(ill, dll, places=2)
dchi2 = computer.chi2(nsig, marginalize=False)
# print ( "dchi2,ichi2",dchi2,ichi2)
self.assertAlmostEqual(ichi2, dchi2, places=2)
def testApproxGaussian(self):
## turn experimental features on
from smodels.tools import runtime
runtime._experimental = True
expRes = database.getExpResults(analysisIDs=["CMS-PAS-SUS-12-026"])
self.assertTrue(len(expRes), 1)
filename = "./testFiles/slha/T1tttt.slha"
model = Model(BSMList, SMList)
model.updateParticles(filename)
smstoplist = decomposer.decompose(model, sigmacut=0)
prediction = theoryPredictionsFor(expRes[0], smstoplist)[0]
prediction.computeStatistics()
import numpy
tot = 0.
for i in numpy.arange(0., .2, .02):
tot += prediction.getLikelihood(i)
c = 0.
for i in numpy.arange(0., .2, .02):
l = prediction.getLikelihood(i)
c += l
self.assertAlmostEqual(prediction.likelihood, 1.563288e-35, 3)
def testGoodFile(self):
listOfIDs = {
'ATLAS-CONF-2013-037': [31, 32, 33, 34, 27, 28, 29, 30],
'ATLAS-SUSY-2013-05': [26]
}
filename = "./testFiles/slha/higgsinoStop.slha"
topoList = slhaDecomposer.decompose(filename,
doCompress=True,
doInvisible=True,
minmassgap=5 * GeV)
resultlist = database.getExpResults(analysisIDs=[
'*:8*TeV', 'CMS-PAS-SUS-15-002', 'CMS-PAS-SUS-16-024'
])
for res in resultlist:
theorypredictions = theoryPredictionsFor(res, topoList)
if not theorypredictions: continue
self.assertEqual(len(theorypredictions), 1)
tpIDs = theorypredictions[0].IDs
self.assertEqual(sorted(tpIDs),
sorted(listOfIDs[res.globalInfo.id]))
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 testComplexCluster(self):
""" test the mass clusterer """
slhafile = 'testFiles/slha/416126634.slha'
model = Model(BSMparticles=BSMList, SMparticles=SMList)
model.updateParticles(slhafile)
sigmacut = 0.03 * fb
mingap = 5. * GeV
toplist = decomposer.decompose(model,
sigmacut,
doCompress=True,
doInvisible=True,
minmassgap=mingap)
#Test clustering for UL results
expResult = database.getExpResults(analysisIDs='CMS-SUS-16-039',
dataTypes='upperLimit')[0]
predictions = theoryPredictionsFor(expResult,
toplist,
combinedResults=False,
marginalize=False)
clusterSizes = sorted([len(p.elements) for p in predictions])
self.assertEqual(clusterSizes, [1, 16, 24])
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)
