def ts10(wspace=None,
data=None,
snapSb=None,
snapB=None,
snapfHat=None,
verbose=False):
wspace.loadSnapshot(snapSb)
results = utils.rooFitResults(common.pdf(wspace), data)
if verbose:
print "S+B"
print "---"
results.Print("v")
sbLl = -results.minNll()
utils.delete(results)
wspace.loadSnapshot(snapB)
results = utils.rooFitResults(common.pdf(wspace), data)
if verbose:
print " B "
print "---"
results.Print("v")
bLl = -results.minNll()
utils.delete(results)
out = -2.0 * (sbLl - bLl)
if verbose: print "TS:", out
return out
def ntupleOfFitToys(wspace=None,
data=None,
nToys=None,
cutVar=("", ""),
cutFunc=None,
toyNumberMod=5):
results = utils.rooFitResults(common.pdf(wspace), data)
wspace.saveSnapshot("snap", wspace.allVars())
obs = collect(wspace, results, extraStructure=True)
toys = []
for i, dataSet in enumerate(common.pseudoData(wspace, nToys)):
if not (i % toyNumberMod): print "iToy = %d" % i
wspace.loadSnapshot("snap")
#dataSet.Print("v")
results = utils.rooFitResults(common.pdf(wspace), dataSet)
wspace.allVars().assignValueOnly(
dataSet.get()
) #store this toy's observations, needed for (a) computing chi2 in collect(); (b) making "snapA"
if all(cutVar) and cutFunc and cutFunc(
getattr(wspace, cutVar[0])(cutVar[1]).getVal()):
wspace.saveSnapshot("snapA", wspace.allVars())
return obs, results, i
toys.append(collect(wspace, results))
utils.delete(results)
return obs, toys
def pValue(wspace, data, nToys = 100, note = "", plots = True) :
def lMax(results) :
return math.exp(-results.minNll())
def indexFraction(item, l) :
totalList = sorted(l+[item])
assert totalList.count(item)==1
return totalList.index(item)/(0.0+len(totalList))
results = utils.rooFitResults(pdf(wspace), data) #fit to data
#wspace.saveSnapshot("snap", wspace.allVars(), False)
#results.Print()
lMaxData = lMax(results)
dataset = pdf(wspace).generate(wspace.set("obs"), nToys) #make pseudo experiments with final parameter values
graph = r.TGraph()
lMaxs = []
for i in range(int(dataset.sumEntries())) :
argSet = dataset.get(i)
pseudoData = r.RooDataSet("pseudoData%d"%i, "title", argSet)
data.reset()
data.add(argSet)
#data.Print("v")
#wspace.loadSnapshot("snap")
#wspace.var("A").setVal(initialA())
#wspace.var("k").setVal(initialk())
results = utils.rooFitResults(pdf(wspace), data)
lMaxs.append(lMax(results))
graph.SetPoint(i, i, indexFraction(lMaxData, lMaxs))
#utils.delete(results)
out = indexFraction(lMaxData, lMaxs)
if plots : plotting.pValuePlots(pValue = out, lMaxData = lMaxData, lMaxs = lMaxs, graph = graph, note = note)
return out
def expectedLimit(dataset, modelConfig, wspace, smOnly, cl, nToys, plusMinus, note = "", makePlots = False) :
assert not smOnly
#fit to SM-only
wspace.var("f").setVal(0.0)
wspace.var("f").setConstant(True)
results = utils.rooFitResults(common.pdf(wspace), dataset)
#generate toys
toys = common.pseudoData(wspace, nToys)
#restore signal model
wspace.var("f").setVal(1.0)
wspace.var("f").setConstant(False)
#save snapshot
snapName = "snap"
wspace.saveSnapshot(snapName, wspace.allVars())
#fit toys
l = limits(wspace, snapName, modelConfig, smOnly, cl, toys)
q,hist = quantiles(l, plusMinus, histoName = "upperLimit", histoTitle = ";upper limit on XS factor;toys / bin", histoBins = (50, 1, -1), cutZero = True) #enable auto-range
nSuccesses = hist.GetEntries()
obsLimit = limits(wspace, snapName, modelConfig, smOnly, cl, [dataset])[0]
if makePlots : plotting.expectedLimitPlots(quantiles = q, hist = hist, obsLimit = obsLimit, note = note)
return q,nSuccesses
def clsCustom(wspace, data, nToys = 100, smOnly = None, testStatType = None, note = "", plots = True) :
assert not smOnly
toys = {}
for label,f in {"b":0.0, "sb":1.0, "fHat":None}.iteritems() :
if f!=None :
wspace.var("f").setVal(f)
wspace.var("f").setConstant()
else :
wspace.var("f").setVal(1.0)
wspace.var("f").setConstant(False)
results = utils.rooFitResults(common.pdf(wspace), data)
wspace.saveSnapshot("snap_%s"%label, wspace.allVars())
toys[label] = common.pseudoData(wspace, nToys)
utils.delete(results)
args = {"wspace": wspace, "testStatType": testStatType, "snapSb": "snap_sb", "snapB": "snap_b", "snapfHat": "snap_fHat"}
obs = ts(data = data, **args)
out = {}
values = {}
for label in ["b", "sb"] :
values[label] = []
for toy in toys[label] :
values[label].append(ts(data = toy, **args))
out["CL%s"%label] = 1.0-utils.indexFraction(obs, values[label])
if plots : plotting.clsCustomPlots(obs = obs, valuesDict = values, note = "TS%d_%s"%(testStatType, note))
out["CLs"] = out["CLsb"]/out["CLb"] if out["CLb"] else 9.9
return out
def ntupleOfFitToys(wspace = None, data = None, nToys = None, cutVar = ("",""), cutFunc = None ) :
results = utils.rooFitResults(pdf(wspace), data)
wspace.saveSnapshot("snap", wspace.allVars())
obs = collect(wspace, results, extraStructure = True)
toys = []
for i,dataSet in enumerate(pseudoData(wspace, nToys)) :
wspace.loadSnapshot("snap")
#dataSet.Print("v")
results = utils.rooFitResults(pdf(wspace), dataSet)
if all(cutVar) and cutFunc and cutFunc(getattr(wspace,cutVar[0])(cutVar[1]).getVal()) :
wspace.allVars().assignValueOnly(dataSet.get())
wspace.saveSnapshot("snapA", wspace.allVars())
return obs,results,i
toys.append( collect(wspace, results) )
utils.delete(results)
return obs,toys
def ts40(wspace = None, data = None, snapSb = None, snapB = None, snapfHat = None, verbose = False) :
wspace.loadSnapshot(snapB)
results = utils.rooFitResults(common.pdf(wspace), data)
if verbose :
print " B "
print "---"
results.Print("v")
out = -results.minNll()
utils.delete(results)
if verbose : print "TS:",out
return out
def ntupleOfFitToys(wspace = None, data = None, nToys = None, cutVar = ("",""), cutFunc = None, toyNumberMod = 5) :
results = utils.rooFitResults(common.pdf(wspace), data)
wspace.saveSnapshot("snap", wspace.allVars())
obs = collect(wspace, results, extraStructure = True)
toys = []
for i,dataSet in enumerate(common.pseudoData(wspace, nToys)) :
if not (i%toyNumberMod) : print "iToy = %d"%i
wspace.loadSnapshot("snap")
#dataSet.Print("v")
results = utils.rooFitResults(common.pdf(wspace), dataSet)
wspace.allVars().assignValueOnly(dataSet.get()) #store this toy's observations, needed for (a) computing chi2 in collect(); (b) making "snapA"
if all(cutVar) and cutFunc and cutFunc(getattr(wspace,cutVar[0])(cutVar[1]).getVal()) :
wspace.saveSnapshot("snapA", wspace.allVars())
return obs,results,i
toys.append( collect(wspace, results) )
utils.delete(results)
return obs,toys
def ts10(wspace = None, data = None, snapSb = None, snapB = None, snapfHat = None, verbose = False) :
wspace.loadSnapshot(snapSb)
results = utils.rooFitResults(pdf(wspace), data)
if verbose :
print "S+B"
print "---"
results.Print("v")
sbLl = -results.minNll()
utils.delete(results)
wspace.loadSnapshot(snapB)
results = utils.rooFitResults(pdf(wspace), data)
if verbose :
print " B "
print "---"
results.Print("v")
bLl = -results.minNll()
utils.delete(results)
out = -2.0*(sbLl-bLl)
if verbose : print "TS:",out
return out
def bestFit(self, printPages = False) :
args = {"wspace": self.wspace, "results": utils.rooFitResults(pdf(self.wspace), self.data),
"lumi": self.inputData.lumi(), "htBinLowerEdges": self.inputData.htBinLowerEdges(),
"htMaxForPlot": self.inputData.htMaxForPlot(), "smOnly": self.smOnly(), "note": self.note(),
"signalExampleToStack": self.signalExampleToStack, "printPages": printPages}
for item in ["REwk", "RQcd"] :
args[item] = self.likelihoodSpec[item]
args["mumuTerms"] = False #temporary
args["hadControlLabels"] = [] #temporary
plotter = plotting.validationPlotter(args)
plotter.inputData = self.inputData
plotter.go()
def expectedLimit(dataset,
modelConfig,
wspace,
smOnly,
cl,
nToys,
plusMinus,
note="",
makePlots=False):
assert not smOnly
#fit to SM-only
wspace.var("f").setVal(0.0)
wspace.var("f").setConstant(True)
results = utils.rooFitResults(common.pdf(wspace), dataset)
#generate toys
toys = common.pseudoData(wspace, nToys)
#restore signal model
wspace.var("f").setVal(1.0)
wspace.var("f").setConstant(False)
#save snapshot
snapName = "snap"
wspace.saveSnapshot(snapName, wspace.allVars())
#fit toys
l = limits(wspace, snapName, modelConfig, smOnly, cl, toys)
q, hist = quantiles(l,
plusMinus,
histoName="upperLimit",
histoTitle=";upper limit on XS factor;toys / bin",
histoBins=(50, 1, -1),
cutZero=True) #enable auto-range
nSuccesses = hist.GetEntries()
obsLimit = limits(wspace, snapName, modelConfig, smOnly, cl, [dataset])[0]
if makePlots:
plotting.expectedLimitPlots(quantiles=q,
hist=hist,
obsLimit=obsLimit,
note=note)
return q, nSuccesses
def clsCustom(wspace,
data,
nToys=100,
smOnly=None,
testStatType=None,
note="",
plots=True):
assert not smOnly
toys = {}
for label, f in {"b": 0.0, "sb": 1.0, "fHat": None}.iteritems():
if f != None:
wspace.var("f").setVal(f)
wspace.var("f").setConstant()
else:
wspace.var("f").setVal(1.0)
wspace.var("f").setConstant(False)
results = utils.rooFitResults(common.pdf(wspace), data)
wspace.saveSnapshot("snap_%s" % label, wspace.allVars())
toys[label] = common.pseudoData(wspace, nToys)
utils.delete(results)
args = {
"wspace": wspace,
"testStatType": testStatType,
"snapSb": "snap_sb",
"snapB": "snap_b",
"snapfHat": "snap_fHat"
}
obs = ts(data=data, **args)
out = {}
values = {}
for label in ["b", "sb"]:
values[label] = []
for toy in toys[label]:
values[label].append(ts(data=toy, **args))
out["CL%s" % label] = 1.0 - utils.indexFraction(obs, values[label])
if plots:
plotting.clsCustomPlots(obs=obs,
valuesDict=values,
note="TS%d_%s" % (testStatType, note))
out["CLs"] = out["CLsb"] / out["CLb"] if out["CLb"] else 9.9
return out
def debug(self) :
self.wspace.Print("v")
plotting.writeGraphVizTree(self.wspace)
#pars = utils.rooFitResults(pdf(wspace), data).floatParsFinal(); pars.Print("v")
utils.rooFitResults(pdf(self.wspace), self.data).Print("v")
def qcdPlot(self):
plotting.errorsPlot(
self.wspace,
utils.rooFitResults(workspace.pdf(self.wspace), self.data),
)
def rooFitResults(self):
return utils.rooFitResults(workspace.pdf(self.wspace), self.data)
def bestFit(self) :
plotting.validationPlots(self.wspace, utils.rooFitResults(pdf(self.wspace), self.data), self.inputData, self.REwk, self.RQcd, self.smOnly())
def qcdPlot(self):
plotting.errorsPlot(self.wspace,
utils.rooFitResults(workspace.pdf(self.wspace), self.data),
)
def rooFitResults(self):
return utils.rooFitResults(workspace.pdf(self.wspace), self.data)
def debug(self) :
self.wspace.Print("v")
plotting.writeGraphVizTree(self.wspace)
#pars = utils.rooFitResults(pdf(wspace), data).floatParsFinal(); pars.Print("v")
utils.rooFitResults(pdf(self.wspace), self.data).Print("v")