elif opt == '-n':
configMgr.nTOYs = int(arg)
elif opt == '-c':
configMgr.calculatorType = int(arg)
elif opt == '-i':
runInterpreter = True
elif opt == '-v':
configMgr.setVerbose( int(arg) )
elif opt == '-l':
printLimits = True
elif opt == '-p':
doHypoTests = True
elif opt == '-s':
configMgr.toySeedSet = True
configMgr.toySeed = int(arg)
RooRandom.randomGenerator().SetSeed(int(arg))
elif opt == '-a':
configMgr.useAsimovSet = True
elif opt == '-g':
sigSamples = arg.split(',')
elif opt == '-x':
fixSigXSec = True
pass
gROOT.SetBatch(not runInterpreter)
if configMgr.calculatorType == 2:
configMgr.nTOYs = -1
)
# safe settings for numerical integration (if needed)
RooAbsReal.defaultIntegratorConfig().setEpsAbs(1e-9)
RooAbsReal.defaultIntegratorConfig().setEpsRel(1e-9)
RooAbsReal.defaultIntegratorConfig().getConfigSection(
'RooAdaptiveGaussKronrodIntegrator1D').setCatLabel('method','15Points')
RooAbsReal.defaultIntegratorConfig().getConfigSection(
'RooAdaptiveGaussKronrodIntegrator1D').setRealValue('maxSeg', 1000)
RooAbsReal.defaultIntegratorConfig().method1D().setLabel(
'RooAdaptiveGaussKronrodIntegrator1D')
RooAbsReal.defaultIntegratorConfig().method1DOpen().setLabel(
'RooAdaptiveGaussKronrodIntegrator1D')
# seed the Random number generator
rndm = TRandom3(SEED + 1)
RooRandom.randomGenerator().SetSeed(int(rndm.Uniform(4294967295)))
del rndm
# start building the fit
from B2DXFitters.WS import WS
ws = RooWorkspace('ws')
one = WS(ws, RooConstVar('one', '1', 1.0))
zero = WS(ws, RooConstVar('zero', '0', 0.0))
# start by defining observables
time = WS(ws, RooRealVar('time', 'time [ps]', 0.2, 15.0))
qf = WS(ws, RooCategory('qf', 'final state charge'))
qf.defineType('h+', +1)
qf.defineType('h-', -1)
qt = WS(ws, RooCategory('qt', 'tagging decision'))
print "\nr0=GenerateFitAndPlot(configMgr.topLvls[0])"
print "r1=GenerateFitAndPlot(configMgr.topLvls[1])"
print "r2=GenerateFitAndPlot(configMgr.topLvls[2])"
pass
if printLimits:
configMgr.cppMgr.doUpperLimitAll()
#for tl in configMgr.topLvls:
# GetLimits(tl,f)
# pass
pass
if doHypoTests:
configMgr.cppMgr.doHypoTestAll()
pass
if configMgr.nTOYs>0 and doHypoTests==False and printLimits==False and runFit==False:
RooRandom.randomGenerator().SetSeed( configMgr.toySeed )
configMgr.cppMgr.runToysAll()
pass
if runInterpreter:
from code import InteractiveConsole
from ROOT import Util
cons = InteractiveConsole(locals())
cons.interact("Continuing interactive session... press Ctrl+d to exit")
pass
print "Leaving HistFitter... Bye!"
def GenerateFitAndPlot(tl):
from ROOT import Util
from ROOT import RooExpandedFitResult
print "\n***GenerateFitAndPlot for TopLevelXML %s***\n"%tl.name
w = Util.GetWorkspaceFromFile(tl.wsFileName,"combined")
Util.SaveInitialSnapshot(w)
# Util.ReadWorkspace(w, tl.wsFileName,"combined")
plotChannels = ""
for reg in tl.validationChannels:
if len(plotChannels)>0:
plotChannels+=","
pass
plotChannels+=reg
plotChannels = "ALL"
fitChannels = ""
for reg in tl.bkgConstrainChannels:
if len(fitChannels)>0:
fitChannels+=","
pass
fitChannels+=reg
pass
fitChannelsCR = fitChannels
for reg in tl.signalChannels:
if len(fitChannels)>0:
fitChannels+=","
pass
fitChannels+=reg
#fitChannels = "ALL"
lumiConst = not tl.signalSample
# fit toy MC if specified. When left None, data is fit by default
toyMC = None
if configMgr.toySeedSet and not configMgr.useAsimovSet: # generate a toy dataset
print "INFO : generating toy MC set for fitting and plotting. Seed = %i" % configMgr.toySeed
RooRandom.randomGenerator().SetSeed( configMgr.toySeed )
toyMC = Util.GetToyMC() # this generates one toy dataset
pass
elif configMgr.useAsimovSet and not configMgr.toySeedSet: #
print "INFO : using Asimov set for fitting and plotting."
toyMC = Util.GetAsimovSet(w) # this returns the asimov set
pass
else:
print "INFO : using data for fitting and plotting."
## MB : turn on all JES bins. Some are turned off by HistFactory by default
if True:
if w.var("gamma_J3_bin_0")!=None: w.var("gamma_J3_bin_0").setConstant(False)
if w.var("gamma_J3_bin_1")!=None: w.var("gamma_J3_bin_1").setConstant(False)
if w.var("gamma_J3_bin_2")!=None: w.var("gamma_J3_bin_2").setConstant(False)
if w.var("gamma_J3_bin_3")!=None: w.var("gamma_J3_bin_3").setConstant(False)
if w.var("gamma_J3_bin_4")!=None: w.var("gamma_J3_bin_4").setConstant(False)
if w.var("gamma_J3_bin_5")!=None: w.var("gamma_J3_bin_5").setConstant(False)
#if w.var("gamma_J4_bin_0")!=None: w.var("gamma_J4_bin_0").setConstant(False)
#if w.var("gamma_J4_bin_1")!=None: w.var("gamma_J4_bin_1").setConstant(False)
if w.var("gamma_J4_bin_2")!=None: w.var("gamma_J4_bin_2").setConstant(False)
if w.var("gamma_J4_bin_3")!=None: w.var("gamma_J4_bin_3").setConstant(False)
if w.var("gamma_J4_bin_4")!=None: w.var("gamma_J4_bin_4").setConstant(False)
if w.var("gamma_J4_bin_5")!=None: w.var("gamma_J4_bin_5").setConstant(False)
if w.var("gamma_JC_bin_0")!=None: w.var("gamma_JC_bin_0").setConstant(False)
if w.var("gamma_JC_bin_1")!=None: w.var("gamma_JC_bin_1").setConstant(False)
if w.var("gamma_JC_bin_2")!=None: w.var("gamma_JC_bin_2").setConstant(False)
if w.var("gamma_JC_bin_3")!=None: w.var("gamma_JC_bin_3").setConstant(False)
if w.var("gamma_JC_bin_4")!=None: w.var("gamma_JC_bin_4").setConstant(False)
if w.var("gamma_JC_bin_5")!=None: w.var("gamma_JC_bin_5").setConstant(False)
if w.var("gamma_JC_bin_6")!=None: w.var("gamma_JC_bin_6").setConstant(False)
# Soft lepton
# if w.var("gamma_JSS_bin_0")!=None: w.var("gamma_JSS_bin_0").setConstant(False)
# set Errors of all parameters to 'natural' values before plotting/fitting
Util.resetAllErrors(w)
mu_Top = w.var("mu_Top")
print "mu_Top: "
print mu_Top
if mu_Top:
mu_Top.setError(0.001)
else:
mu_Top = w.var("mu_Top_Np0")
if mu_Top:
mu_Top.setError(0.001)
mu_Top = w.var("mu_Top_Np1")
if mu_Top:
mu_Top.setError(0.001)
mu_Top = w.var("mu_Top_Np2")
if mu_Top:
mu_Top.setError(0.001)
mu_Top = w.var("mu_Top_Np3")
if mu_Top:
mu_Top.setError(0.001)
mu_Top = w.var("mu_Top_Np4")
if mu_Top:
mu_Top.setError(0.001)
mu_Top = w.var("mu_Top_Np5")
if mu_Top:
mu_Top.setError(0.001)
mu_WZ = w.var("mu_WZ")
mu_WZpT0GeV = w.var("mu_WZpT0GeV")
if mu_WZ:
mu_WZ.setError(0.001)
elif mu_WZpT0GeV:
mu_WZpT0GeV = w.var("mu_WZpT0GeV")
mu_WZpT0GeV.setError(0.001)
mu_WZpT0GeV = w.var("mu_WZpT50GeV")
mu_WZpT0GeV.setError(0.001)
mu_WZpT0GeV = w.var("mu_WZpT100GeV")
mu_WZpT0GeV.setError(0.001)
mu_WZpT0GeV = w.var("mu_WZpT150GeV")
mu_WZpT0GeV.setError(0.001)
mu_WZpT0GeV = w.var("mu_WZpT200GeV")
mu_WZpT0GeV.setError(0.001)
mu_WZpT0GeV = w.var("mu_WZpT250GeV")
mu_WZpT0GeV.setError(0.001)
else:
mu_WZ = w.var("mu_WZ_Np0")
mu_WZ.setError(0.001)
mu_WZ = w.var("mu_WZ_Np1")
mu_WZ.setError(0.001)
mu_WZ = w.var("mu_WZ_Np2")
mu_WZ.setError(0.001)
mu_WZ = w.var("mu_WZ_Np3")
mu_WZ.setError(0.001)
mu_WZ = w.var("mu_WZ_Np4")
mu_WZ.setError(0.001)
mu_WZ = w.var("mu_WZ_Np5")
mu_WZ.setError(0.001)
# set the flag for plotting ratio or pull distribution under the plot
plotRatio = True # plotRatio = False means that a pull distribution will be drawn
# get a list of all floating parameters for all regions
simPdf = w.pdf("simPdf");
mc = Util.GetModelConfig(w)
obsSet = mc.GetObservables()
floatPars = Util.getFloatParList(simPdf, obsSet)
# create an RooExpandedFitResult encompassing all the regions/parameters & save it to workspace
expResultBefore = RooExpandedFitResult(floatPars)
# expResultBefore.Print()
Util.ImportInWorkspace(w,expResultBefore,"RooExpandedFitResult_beforeFit")
# plot before fit
#Util.PlotPdfWithComponents(w,tl.name,plotChannels,"beforeFit_ORIGINAL",None,toyMC)
Util.PlotPdfWithComponents(w,tl.name,plotChannels,"beforeFit",expResultBefore,toyMC,plotRatio)
#return
# fit of CRs only
# resultCR = Util.FitPdf(w,fitChannelsCR,lumiConst,toyMC)
# load original snapshot
# w.loadSnapshot('snapshot_paramsVals_initial')
# fit of all regions
result = Util.FitPdf(w,fitChannels,lumiConst,toyMC)
# create an RooExpandedFitResult encompassing all the regions/parameters with the result & save it to workspace
expResultAfter = RooExpandedFitResult(result, floatPars)
Util.ImportInWorkspace(w,expResultAfter,"RooExpandedFitResult_afterFit")
# plot after fit
#Util.PlotPdfWithComponents(w,tl.name,plotChannels,"afterFit_ORIGINAL",result,toyMC)
Util.PlotPdfWithComponents(w,tl.name,plotChannels,"afterFit",expResultAfter,toyMC,plotRatio)
# plot each component of each region separately with propagated error after fit (interesting for debugging)
# Util.PlotSeparateComponents(tl.name,plotChannels,"afterFit",result,toyMC)
# plot correlation matrix for result
#Util.PlotCorrelationMatrix(result)
# Util.GetCorrelations(result, 0.85)
# plotPLL = False
# Util.PlotNLL(w, result, plotPLL, "", toyMC)
if toyMC:
Util.WriteWorkspace(w, tl.wsFileName,toyMC.GetName())
else:
Util.WriteWorkspace(w, tl.wsFileName)
try:
if not result == None:
result.Print()
return result
except:
pass
return
RooDataHist, RooMsgService, TStopwatch, RooAbsPdf, TBox, kBlack, kRed, \
kBlue, kOrange, kDashed, RooAbsCollection, RooArgSet, RooStats, Double, \
RooAbsReal, RooAbsData, TH1F
import pulls
timer = TStopwatch()
timer.Start()
#RooAbsPdf.defaultIntegratorConfig().setEpsRel(1e-9)
#RooAbsPdf.defaultIntegratorConfig().setEpsAbs(1e-9)
if not opts.debug:
RooMsgService.instance().setGlobalKillBelow(RooFit.WARNING)
if hasattr(opts, "seed") and (opts.seed >= 0):
print "random seed:", opts.seed
RooRandom.randomGenerator().SetSeed(opts.seed)
mvaCutOverride = None
if hasattr(opts, "mvaCut"):
mvaCutOverride = opts.mvaCut
mjjArgs = []
sideArgs = []
mWWArgs = []
for arg in args:
if arg[-8:] == '_mjj.txt':
mjjArgs.append(arg)
elif arg[-8:] == '_mWW.txt':
mWWArgs.append(arg)
else:
mWWArgs.append(arg)
for channel in channels:
base_dir = os.path.join(analysis_path,channel)
study_inputs[channel] = \
[prepend + os.path.join(base_dir,sample) for sample in input_mc_samples]
import ROOT
from ROOT import RooWorkspace, RooMCStudy, RooFit, TFile, gDirectory,\
RooDataSet, RooFormulaVar, RooConstVar, RooArgList, RooArgSet,\
RooMinuit, RooMinimizer, RooRandom
ROOT.gSystem.Load("libUWHiggshzg.so")
pwd = gDirectory.GetPath()
RooRandom.randomGenerator().SetSeed(int(time.clock()*1e9))
int_lumi = 19.6*1000 #19.6/fb in /pb
def calculate_median(var,dataset):
vals = []
for i in xrange(dataset.numEntries()):
set = dataset.get(i)
vals.append(set.getRealValue(var.GetName()))
del set
vals.sort()
middle = len(vals)/2
return vals[middle]
def prepare_truth_models(ws,cat,mass,channel,turnon,truth):
if channel in study_inputs:
def run(self, **kwargs):
from ROOT import RooArgSet
__check_req_kw__("Observables", kwargs)
__check_req_kw__("Pdf", kwargs)
__check_req_kw__("Sigmat", kwargs)
__check_req_kw__("Time", kwargs)
__check_req_kw__("SigmaGen", kwargs)
sigma_gen = kwargs.pop("SigmaGen")
observables = kwargs.pop("Observables")
obs_set = RooArgSet(*observables)
pdf = kwargs.pop("Pdf")
sigmat = kwargs.pop("Sigmat")
time = kwargs.pop("Time")
gen_obs_set = RooArgSet(*observables)
# Make another ArgSet to put the fit results in
result_params = RooArgSet("result_params")
from P2VV.RooFitWrappers import RealVar
da = RealVar("da", Observable=True, MinMax=(0.01, 1.1))
dft = RealVar("dft", Observable=True, MinMax=(0.01, 1.1))
result_params.add(da._target_())
result_params.add(dft._target_())
transform = self.transform()
if transform:
trans_params = transform.gen_params(gen_obs_set)
self._gen_params.extend(trans_params)
for p in trans_params:
result_params.add(p)
# Some extra numbers of interest
from ROOT import RooRealVar
seed = RooRealVar("seed", "random seed", 0.0)
result_params.add(seed)
# The dataset to store the results
from ROOT import RooDataSet
self._data = RooDataSet("result_data", "result_data", result_params)
data_params = self._data.get()
from ROOT import RooRandom
import struct, os
# Reset pdf parameters to initial values. Note: this does not reset the estimated errors...
args = dict(NumEvents=self.options().nevents)
if "ProtoData" in kwargs:
args["ProtoData"] = kwargs.pop("ProtoData")
spec = pdf.prepareMultiGen(obs_set, **args)
while self._data.numEntries() < self.options().ntoys:
# Get a good random seed, set it and store it
s = struct.unpack("I", os.urandom(4))[0]
RooRandom.randomGenerator().SetSeed(s)
seed.setVal(s)
data = pdf.generate(spec)
if self.transform():
old_data = data
data = self.transform()(old_data)
if not data:
transform.set_params(data_params)
self._data.add(data_params)
continue
from P2VV import Dilution
d_ft = Dilution.dilution_ft(data, time, t_range=2, quiet=True)
d_a = Dilution.signal_dilution_dg(data, sigmat, *sigma_gen)
da.setVal(d_a[0])
da.setError(d_a[1] if d_a[1] != None else 0.0)
dft.setVal(d_ft[0])
dft.setError(d_ft[1] if d_ft[1] != None else 0.0)
if transform:
transform.set_params(data_params)
self._data.add(result_params)
return self.data()
study_inputs = {}
for channel in channels:
base_dir = os.path.join(analysis_path, channel)
study_inputs[channel] = \
[prepend + os.path.join(base_dir,sample) for sample in input_mc_samples]
import ROOT
from ROOT import RooWorkspace, RooMCStudy, RooFit, TFile, gDirectory,\
RooDataSet, RooFormulaVar, RooConstVar, RooArgList, RooArgSet,\
RooMinuit, RooMinimizer, RooRandom
ROOT.gSystem.Load("libUWHiggshzg.so")
pwd = gDirectory.GetPath()
RooRandom.randomGenerator().SetSeed(int(time.clock() * 1e9))
int_lumi = 19.6 * 1000 #19.6/fb in /pb
def calculate_median(var, dataset):
vals = []
for i in xrange(dataset.numEntries()):
set = dataset.get(i)
vals.append(set.getRealValue(var.GetName()))
del set
vals.sort()
middle = len(vals) / 2
return vals[middle]
pipiUnbPruneDataSet = RooDataSet("pipiUnbPruneDataSet", "pipiUnbPruneDataSet", pipiUnbDataSet, w.set("argsBasic"), "D0_Mass>1826 && D0_Mass<1920")
pipiUnbPruneDataSet.Print()
elif config['norm'] is 'kpi':
pipiFile = TFile(config['kpiFile'],"OPEN")
normTree = pipiFile.Get("subTree")
pipiUnbDataSet = RooDataSet("pipiUnbDataSet", "pipiUnbDataSet", normTree, w.set("argsPreCutKPi"), "Del_Mass>139.4 && RAND < %f" % (config["normScale"]))
pipiUnbDataSet.Print()
pipiUnbPruneDataSet = RooDataSet("pipiUnbPruneDataSet", "pipiUnbPruneDataSet", pipiUnbDataSet, w.set("argsBasic"), "D0_Mass>1800 && D0_Mass<1920")
pipiUnbPruneDataSet.Print()
if config['mode'] is "toy":
print "Setting random seed to 1"
RooRandom.randomGenerator().SetSeed(1)
print "Generating dataset 1 ..."
bdt1UnbToyDataSet = w.obj("BDT1_Comb").generate(w.set("argsBasic"),int(round(toyRatio*bdt1UnbPruneDataSet.numEntries())))
print "Generating dataset 2 ..."
bdt2UnbToyDataSet = w.obj("BDT2_Comb").generate(w.set("argsBasic"),int(round(toyRatio*bdt2UnbPruneDataSet.numEntries())))
print "Generating dataset 3 ..."
bdt3UnbToyDataSet = w.obj("BDT3_Comb").generate(w.set("argsBasic"),int(round(toyRatio*bdt3UnbPruneDataSet.numEntries())))
fullUnbDataSet = RooDataSet("fullUnbDataSet",
"fullUnbDataSet",
w.set("argsBasic"),
RooFit.Index(w.cat("DataSet")),
RooFit.Import("BDT1",bdt1UnbToyDataSet),
RooFit.Import("BDT2",bdt2UnbToyDataSet),
RooFit.Import("BDT3",bdt3UnbToyDataSet),
RooFit.Import("Norm",pipiUnbPruneDataSet))
def run(self, **kwargs):
from ROOT import RooArgSet
__check_req_kw__("Observables", kwargs)
__check_req_kw__("Pdf", kwargs)
observables = kwargs.pop("Observables")
obs_set = RooArgSet(*observables)
pdf = kwargs.pop("Pdf")
genPdf = kwargs.pop("GenPdf", pdf)
gen_obs_set = RooArgSet()
for o in list(observables) + list(genPdf.ConditionalObservables()):
gen_obs_set.add(o._target_())
gen_pdf_params = genPdf.getParameters(gen_obs_set).snapshot(True)
genPdf = genPdf.clone(genPdf.GetName() + "_toy_clone")
genPdf.recursiveRedirectServers(gen_pdf_params)
fit_obs_set = RooArgSet()
for o in list(observables) + list(pdf.ConditionalObservables()):
fit_obs_set.add(o._target_())
params = pdf.getParameters(fit_obs_set)
pdf_params = RooArgSet()
for p in params:
if p.isConstant():
continue
pdf_params.add(p)
## for param in pdf_params:
## if param.GetName() not in ['Gamma', 'dGamma']:
## param.setConstant()
self._gen_params = pdf_params.snapshot(True)
# Make another ArgSet to put the fit results in
result_params = RooArgSet(pdf_params, "result_params")
transform = self.transform()
if transform:
trans_params = transform.gen_params(gen_obs_set)
for p in trans_params:
result_params.add(p)
# Some extra numbers of interest
from ROOT import RooRealVar
NLL = RooRealVar("NLL", "-log(Likelihood)", 1.0)
ngen = RooRealVar("ngen", "number of generated events", self.options().nevents)
seed = RooRealVar("seed", "random seed", 0.0)
from ROOT import RooCategory
status = RooCategory("status", "fit status")
status.defineType("success", 0)
status.defineType("one", 1)
status.defineType("two", 2)
status.defineType("three", 3)
status.defineType("other", 4)
result_params.add(status)
result_params.add(NLL)
result_params.add(ngen)
result_params.add(seed)
# The dataset to store the results
from ROOT import RooDataSet
self._data = RooDataSet("result_data", "result_data", result_params)
data_params = self._data.get()
from ROOT import RooRandom
import struct, os
while self._data.numEntries() < self.options().ntoys:
# Get a good random seed, set it and store it
s = struct.unpack("I", os.urandom(4))[0]
RooRandom.randomGenerator().SetSeed(s)
seed.setVal(s)
# Reset pdf parameters to initial values. Note: this does not reset the estimated errors...
pdf_params.assignValueOnly(self.gen_params())
args = dict(NumEvents=self.options().nevents)
if "ProtoData" in kwargs:
args["ProtoData"] = kwargs.pop("ProtoData")
genPdf.getParameters(obs_set).assignValueOnly(gen_pdf_params)
data = genPdf.generate(obs_set, **args)
if transform:
data = transform(data)
if not data:
# Transform has failed
transform.set_params(data_params)
self._data.add(data_params)
continue
if data.isWeighted() and "SumW2Error" not in self.fit_opts():
self.fit_opts()["SumW2Error"] = False
j = 0
while j < 4:
fit_result = pdf.fitTo(data, NumCPU=self.options().ncpu, **(self.fit_opts()))
if fit_result.status() == 0:
fit_result.Print()
break
j += 1
if fit_result.status() != 0:
print "Fit result status = %s" % fit_result.status()
NLL.setVal(fit_result.minNll())
if fit_result.status() < 4:
status.setIndex(fit_result.status())
else:
status.setIndex(4)
for result_param in result_params:
data_param = data_params.find(result_param.GetName())
if isinstance(result_param, RooCategory):
data_param.setIndex(result_param.getIndex())
else:
data_param.setVal(result_param.getVal())
# This sets a symmetric error, but since we don't run Minos, that's ok
data_param.setError(result_param.getError())
if transform:
transform.set_params(data_params)
self._data.add(data_params)
return self.data()
elif opt == '-n':
configMgr.nTOYs = int(arg)
elif opt == '-c':
configMgr.calculatorType = int(arg)
elif opt == '-i':
runInterpreter = True
elif opt == '-v':
configMgr.setVerbose(int(arg))
elif opt == '-l':
printLimits = True
elif opt == '-p':
doHypoTests = True
elif opt == '-s':
configMgr.toySeedSet = True
configMgr.toySeed = int(arg)
RooRandom.randomGenerator().SetSeed(int(arg))
elif opt == '-a':
configMgr.useAsimovSet = True
elif opt == '-g':
sigSamples = arg.split(',')
elif opt == '-x':
fixSigXSec = True
pass
gROOT.SetBatch(not runInterpreter)
if configMgr.calculatorType == 2:
configMgr.nTOYs = -1
print "------------------------ Now running hypo test :"
print "calculatorType : ", configMgr.calculatorType
# print parameters
print 120 * '='
print 'genBs2JpsiKKSignal: observables in PDF:'
pdfObs.Print('v')
print 'genBs2JpsiKKSignal: parameters in PDF:'
pdfPars.Print('v')
###########################################################################################################################################
## generate data ##
###################
# set seed of RooFit random-number generator
from ROOT import RooRandom
RooRandom.randomGenerator().SetSeed(args.jobID)
# get observables to generate and create prototype dataset
genObsSet = [ ws[var] for var in [ 'time', 'helcosthetaK', 'helcosthetaL', 'helphi' ] ]
condObsSet = [ var for var in inputData.get() if not any( var.GetName() == genVar.GetName() for genVar in genObsSet ) ]
if condObsSet :
protoData = inputData.reduce( ArgSet = condObsSet )
#protoData = protoData.reduce( Cut = 'hlt1_excl_biased==1' )
print 'genBs2JpsiKKSignal: proto-data set:'
protoData.Print()
else :
protoData = inputData
# set maximum value of PDF function
from P2VV.RooFitWrappers import SimultaneousPdf
if isinstance( pdf, SimultaneousPdf ) :
momsW.initCovariances()
# initialize variables for calculation of covariances
covs = dict( [ ( name1, dict( [ ( name2, 0. ) for name2 in funcNames ] ) ) for name1 in funcNames ] )
covsW = dict( [ ( name1, dict( [ ( name2, 0. ) for name2 in funcNames ] ) ) for name1 in funcNames ] )
# generate data and compute moments in loop
import sys
from ROOT import RooRandom
for it in range(nIters) :
if it % 100 == 0 :
print 'iteration %d' % it
sys.stdout.flush()
# generate data with (1 + x) / 2
RooRandom.randomGenerator().SetSeed( 100000 + it )
noWeightData.reset()
weightData.reset()
for evIt in range(nEvents) :
weight = RooRandom.uniform()
obs.setVal( 2. * weight - 1. )
if RooRandom.uniform() <= weight :
noWeightData.add(obsSet)
weightData.add( obsSet, weight )
# compute moments of functions
moms.compute( noWeightData, ResetFirst = True, Verbose = False )
momsW.compute( weightData, ResetFirst = True, Verbose = False )
# update covariance sums
momCoefs = moms.coefficients()
log.info("Python commands executed: %s" % HistFitterArgs.cmd)
exec(HistFitterArgs.cmd) ## python execute
gROOT.SetBatch(not runInterpreter)
"""
mandatory user-defined configuration file
"""
execfile(HistFitterArgs.configFile[0]) #[0] since any extra arguments (sys.argv[-1], etc.) are caught here
"""
standard execution from now on
"""
configMgr.initialize()
RooRandom.randomGenerator().SetSeed(configMgr.toySeed)
ReduceCorrMatrix = configMgr.ReduceCorrMatrix
"""
runs Trees->histos and/or histos->workspace according to specifications
"""
if configMgr.readFromTree or configMgr.executeHistFactory:
if doCodeProfiling:
import cProfile
cProfile.run('configMgr.executeAll()')
else:
configMgr.executeAll()
"""
shows systematics
)
# safe settings for numerical integration (if needed)
RooAbsReal.defaultIntegratorConfig().setEpsAbs(1e-9)
RooAbsReal.defaultIntegratorConfig().setEpsRel(1e-9)
RooAbsReal.defaultIntegratorConfig().getConfigSection(
'RooAdaptiveGaussKronrodIntegrator1D').setCatLabel('method','15Points')
RooAbsReal.defaultIntegratorConfig().getConfigSection(
'RooAdaptiveGaussKronrodIntegrator1D').setRealValue('maxSeg', 1000)
RooAbsReal.defaultIntegratorConfig().method1D().setLabel(
'RooAdaptiveGaussKronrodIntegrator1D')
RooAbsReal.defaultIntegratorConfig().method1DOpen().setLabel(
'RooAdaptiveGaussKronrodIntegrator1D')
# seed the Random number generator
rndm = TRandom3(SEED + 1)
RooRandom.randomGenerator().SetSeed(int(rndm.Uniform(4294967295)))
del rndm
# as things become more complicated, it's useful to have "build-it-all"
# routine, which works for both generation and fitting; this also allows for
# easier setups of scenarios where you build with one pdf and fit with a
# different one (e.g. per-event mistag in generation, average in fit to see
# the gain in sensitivity from the per-event mistag)
def buildTimePdf(config):
"""
build time pdf, return pdf and associated data in dictionary
"""
from B2DXFitters.WS import WS
print 'CONFIGURATION'
for k in sorted(config.keys()):
print ' %32s: %32s' % (k, config[k])
config = __import__(opts.modeConfig)
import RooWjj2DFitter
from ROOT import TCanvas, RooFit, RooLinkedListIter, TMath, RooRandom, TFile, \
RooDataHist, RooMsgService, TStopwatch
import pulls
timer = TStopwatch()
timer.Start()
if not opts.debug:
RooMsgService.instance().setGlobalKillBelow(RooFit.WARNING)
if hasattr(opts, "seed") and (opts.seed >= 0):
print "random seed:", opts.seed
RooRandom.randomGenerator().SetSeed(opts.seed)
pars = config.theConfig(Nj=opts.Nj,
mH=opts.mH,
isElectron=opts.isElectron,
initFile=args,
includeSignal=opts.includeSignal)
fitter = RooWjj2DFitter.Wjj2DFitter(pars)
totalPdf = fitter.makeFitter()
if opts.ws:
fitter.loadWorkspaceFromFile(opts.ws, getFloatPars=opts.reuse)
#fitter.loadData()
fitter.readParametersFromFile()
def makeTree(outFile_name, cuts, fittedVariable):
rootupla_Chib1_Sel = '../../store/ChiB_1P_1_Upsilon2SPt.root'
rootupla_Chib2_Sel = '../../store/ChiB_1P_2_Upsilon2SPt.root'
rootupla_2012Data = "../../store/2012_AllData.root"
mass_chib1_1P = 9.89278
mass_chib2_1P = 9.91221
rootFile = TFile(outFile_name, 'recreate')
tree = TTree('numChib', 'tree with number of chibs')
numChib1 = array('i', [0])
numChib2 = array('i', [0])
ratio = array('d', [0])
mean1 = array('d', [0])
sigma1 = array('d', [0])
a1_1 = array('d', [0])
a2_1 = array('d', [0])
mean2 = array('d', [0])
sigma2 = array('d', [0])
a1_2 = array('d', [0])
a2_2 = array('d', [0])
reducedChi2 = array('d', [0])
tree.Branch('numChib1', numChib1, 'numChib1/I')
tree.Branch('numChib2', numChib2, 'numChib2/I')
tree.Branch('ratio', ratio, 'ratio/D')
tree.Branch('mean1', mean1, 'mean1/D')
tree.Branch('sigma1', sigma1, 'sigma1/D')
tree.Branch('a1_1', a1_1, 'a1_1/D')
tree.Branch('a2_1', a2_1, 'a2_1/D')
tree.Branch('mean2', mean2, 'mean2/D')
tree.Branch('sigma2', sigma2, 'sigma2/D')
tree.Branch('a1_2', a1_2, 'a1_2/D')
tree.Branch('a2_2', a2_2, 'a2_2/D')
tree.Branch('reducedChi2', reducedChi2, 'reducedChi2/D')
Chib1_fitResult = doMCFit(inputfile_name=rootupla_Chib1_Sel, mass_chib=mass_chib1_1P, cuts=cuts, fittedVariable=fittedVariable, returnOnlyFitResult = True)
Chib2_fitResult = doMCFit(inputfile_name=rootupla_Chib2_Sel, mass_chib=mass_chib2_1P, cuts=cuts, fittedVariable=fittedVariable, returnOnlyFitResult = True)
# make dataset outside the loop only once to speed up the process
print "Creating DataSet from file "+str(rootupla_2012Data)
dataSet = makeRooDataset(rootupla_2012Data)
RooRandom.randomGenerator().SetSeed(int(time.time()*256))
for i in range(100):
print '\nstarting data fit number ' + str(i) + '\n'
Chib1_listParams = Chib1_fitResult.randomizePars()
Chib2_listParams = Chib2_fitResult.randomizePars()
Chib1_parameters = CB_parameters(a1 = Chib1_listParams[0].getVal(),
a2 = Chib1_listParams[1].getVal(),
mean = Chib1_listParams[2].getVal(),
n1 = 2.5,
n2 = 3,
sigma = Chib1_listParams[3].getVal(),
s_sigma = Chib1_listParams[3].getError())
Chib2_parameters = CB_parameters(a1 = Chib2_listParams[0].getVal(),
a2 = Chib2_listParams[1].getVal(),
mean = Chib2_listParams[2].getVal(),
n1 = 2.5,
n2 = 3,
sigma = Chib2_listParams[3].getVal(),
s_sigma = Chib2_listParams[3].getError())
numChib, chi2r = doDataFit(RooDataSet = dataSet,Chib1_parameters=Chib1_parameters,Chib2_parameters=Chib2_parameters, cuts=cuts, fittedVariable=fittedVariable, noPlots = True)
numChib1[0] = int(numChib.numChib1)
numChib2[0] = int(numChib.numChib2)
ratio[0] = numChib.calcRatio()
a1_1[0] = Chib1_listParams[0].getVal()
a2_1[0] = Chib1_listParams[1].getVal()
mean1[0] = Chib1_listParams[2].getVal()
sigma1[0] = Chib1_listParams[3].getVal()
a1_2[0] = Chib2_listParams[0].getVal()
a2_2[0] = Chib2_listParams[1].getVal()
mean2[0] = Chib2_listParams[2].getVal()
sigma2[0] = Chib2_listParams[3].getVal()
reducedChi2[0] = chi2r
tree.Fill()
rootFile.Write()
rootFile.Close()
