class GaussianConstraint(object):
def __init__(self, name, expr, deps, mean, sigma):
self.name = name
self.expr = expr
self.deps = deps
self.mean = mean
self.sigma = sigma
# pdf of yield vs coupling modificators
dependentlist = RooArgList()
for dep in deps:
dependentlist.add(dep)
self.pdf_yield = RooGenericPdf(name, name, expr, dependentlist)
# observable (measurement)
obsname = name + 'Obs'
self.var_obs = RooRealVar(obsname, obsname, mean)
# width of Gaussian pdf
sname = name + 'Sigma'
self.var_sigma = RooRealVar(sname, sname, sigma)
# Gaussian pdf
gname = name + "Constraint"
self.pdf_constraint = RooGaussian(gname, gname, self.var_obs,
self.pdf_yield, self.var_sigma)
self.pulls = ROOT.TH1F('pulls_' + name, name, 1000, -10, 10)
def fill_pull(self):
pull = (self.pdf_yield.getVal() - 1) / self.var_sigma.getVal()
self.pulls.Fill(pull)
def info(self):
print self.name
self.pdf_yield.Print()
self.var_obs.Print()
self.var_sigma.Print()
self.pdf_constraint.Print()
def fit(self, save_to, signal_name=None, fix_p3=False, fit_range=[300., 1200.], fit_strategy=1):
# Run a RooFit fit
# Create background PDF
p1 = RooRealVar('p1','p1',args.p1,0.,100.)
p2 = RooRealVar('p2','p2',args.p2,0.,60.)
p3 = RooRealVar('p3','p3',args.p3,-10.,10.)
if args.fix_p3:
p3.setConstant()
background_pdf = RooGenericPdf('background_pdf','(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))'%(self.collision_energy,self.collision_energy,self.collision_energy),RooArgList(self.mjj_,p1,p2,p3))
background_pdf.Print()
data_integral = data_histogram.Integral(data_histogram.GetXaxis().FindBin(float(fit_range[0])),data_histogram.GetXaxis().FindBin(float(fit_range[1])))
background_norm = RooRealVar('background_norm','background_norm',data_integral,0.,1e+08)
background_norm.Print()
# Create signal PDF and fit model
if signal_name:
signal_pdf = RooHistPdf('signal_pdf', 'signal_pdf', RooArgSet(self.mjj_), self.signal_roohistograms_[signal_name])
signal_pdf.Print()
signal_norm = RooRealVar('signal_norm','signal_norm',0,-1e+05,1e+05)
signal_norm.Print()
model = RooAddPdf("model","s+b",RooArgList(background_pdf,signal_pdf),RooArgList(background_norm,signal_norm))
else:
model = RooAddPdf("model","b",RooArgList(background_pdf),RooArgList(background_norm))
# Run fit
res = model.fitTo(data_, RooFit.Save(kTRUE), RooFit.Strategy(fit_strategy))
# Save to workspace
self.workspace_ = RooWorkspace('w','workspace')
#getattr(w,'import')(background,ROOT.RooCmdArg())
getattr(self.workspace_,'import')(background_pdf,RooFit.Rename("background"))
getattr(self.workspace_,'import')(background_norm,ROOT.RooCmdArg())
getattr(self.workspace_,'import')(self.data_roohistogram_,RooFit.Rename("data_obs"))
getattr(self.workspace_, 'import')(model, RooFit.Rename("model"))
if signal_name:
getattr(self.workspace_,'import')(signal_roohistogram,RooFit.Rename("signal"))
getattr(self.workspace_,'import')(signal_pdf,RooFit.Rename("signal_pdf"))
getattr(self.workspace_,'import')(signal_norm,ROOT.RooCmdArg())
self.workspace_.Print()
self.workspace_.writeToFile(save_to)
if signal_name:
roofit_results[signal_name] = save_to
else:
roofit_results["background"] = save_to
class UniformConstraint(object):
def __init__(self, name, expr, deps):
self.name = name
self.expr = expr
self.deps = deps
dependentlist = RooArgList()
for dep in deps:
dependentlist.add(dep)
self.pdf_yield = RooGenericPdf(name, name, expr, dependentlist)
pname = name + "Constraint"
self.pdf_constraint = RooUniform(pname, pname,
RooArgSet(self.pdf_yield))
self.pulls = ROOT.TH1F('pulls_' + name, name, 1000, -10, 10)
def info(self):
print self.name
self.pdf_yield.Print()
self.pdf_constraint.Print()
def fill_pull(self):
pass
if bkgConst:
parsBkg.setAttribAll('Constant', True)
if histpdfBkg:
# -----------------------------------------
# hist pdf bkg
canBhist_name = 'can_Mjj' + str(mass)
canBhist = TCanvas(canSname, canSname, 900, 600)
gPad.SetLogy()
roohistBkg = RooDataHist('roohist', 'roohist', RooArgList(x), hBkg)
roohistBkg.Print()
background = RooHistPdf('background', 'background', RooArgSet(x),
roohistBkg)
background.Print()
norm_mc = hBkg.Integral()
background_norm = RooRealVar('background_norm', 'background_norm', norm_mc,
0, 100000000)
frame = x.frame()
roohistBkg.plotOn(frame, RooFit.Binning(166))
background.plotOn(frame, RooFit.Binning(166), RooFit.LineColor(ROOT.kRed),
RooFit.LineWidth(2), RooFit.LineStyle(ROOT.kDashed))
#frame.GetXaxis().SetRangeUser(1118,6099)
#frame.GetXaxis().SetRangeUser(1500,6000)
frame.GetXaxis().SetRangeUser(minX_mass, maxX_mass)
frame.GetXaxis().SetTitle('m_{jj} (GeV)')
frame.Draw()
filetest = TFile(
outdir_datacards + "test_bkg_hist_" + str(mass) + "_" + name + ".root",
def main():
# usage description
usage = "Example: ./scripts/createDatacards.py --inputData inputs/rawhistV7_Run2015D_scoutingPFHT_UNBLINDED_649_838_JEC_HLTplusV7_Mjj_cor_smooth.root --dataHistname mjj_mjjcor_gev --inputSig inputs/ResonanceShapes_gg_13TeV_Scouting_Spring15.root -f gg -o datacards -l 1866 --lumiUnc 0.027 --massrange 1000 1500 50 --runFit --p1 5 --p2 7 --p3 0.4 --massMin 838 --massMax 2037 --fitStrategy 2"
# input parameters
parser = ArgumentParser(
description=
'Script that creates combine datacards and corresponding RooFit workspaces',
epilog=usage)
parser.add_argument("--inputData",
dest="inputData",
required=True,
help="Input data spectrum",
metavar="INPUT_DATA")
parser.add_argument("--dataHistname",
dest="dataHistname",
required=True,
help="Data histogram name",
metavar="DATA_HISTNAME")
parser.add_argument("--inputSig",
dest="inputSig",
required=True,
help="Input signal shapes",
metavar="INPUT_SIGNAL")
parser.add_argument("-f",
"--final_state",
dest="final_state",
required=True,
help="Final state (e.g. qq, qg, gg)",
metavar="FINAL_STATE")
parser.add_argument("-f2",
"--type",
dest="atype",
required=True,
help="Type (e.g. hG, lG, hR, lR)")
parser.add_argument(
"-o",
"--output_path",
dest="output_path",
required=True,
help="Output path where datacards and workspaces will be stored",
metavar="OUTPUT_PATH")
parser.add_argument(
"-l",
"--lumi",
dest="lumi",
required=True,
default=1000.,
type=float,
help="Integrated luminosity in pb-1 (default: %(default).1f)",
metavar="LUMI")
parser.add_argument(
"--massMin",
dest="massMin",
default=500,
type=int,
help=
"Lower bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MIN")
parser.add_argument(
"--massMax",
dest="massMax",
default=1200,
type=int,
help=
"Upper bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MAX")
parser.add_argument(
"--p1",
dest="p1",
default=5.0000e-03,
type=float,
help="Fit function p1 parameter (default: %(default)e)",
metavar="P1")
parser.add_argument(
"--p2",
dest="p2",
default=9.1000e+00,
type=float,
help="Fit function p2 parameter (default: %(default)e)",
metavar="P2")
parser.add_argument(
"--p3",
dest="p3",
default=5.0000e-01,
type=float,
help="Fit function p3 parameter (default: %(default)e)",
metavar="P3")
parser.add_argument(
"--lumiUnc",
dest="lumiUnc",
required=True,
type=float,
help="Relative uncertainty in the integrated luminosity",
metavar="LUMI_UNC")
parser.add_argument("--jesUnc",
dest="jesUnc",
type=float,
help="Relative uncertainty in the jet energy scale",
metavar="JES_UNC")
parser.add_argument(
"--jerUnc",
dest="jerUnc",
type=float,
help="Relative uncertainty in the jet energy resolution",
metavar="JER_UNC")
parser.add_argument(
"--sqrtS",
dest="sqrtS",
default=13000.,
type=float,
help="Collision center-of-mass energy (default: %(default).1f)",
metavar="SQRTS")
parser.add_argument("--fixP3",
dest="fixP3",
default=False,
action="store_true",
help="Fix the fit function p3 parameter")
parser.add_argument("--runFit",
dest="runFit",
default=False,
action="store_true",
help="Run the fit")
parser.add_argument("--fitBonly",
dest="fitBonly",
default=False,
action="store_true",
help="Run B-only fit")
parser.add_argument("--fixBkg",
dest="fixBkg",
default=False,
action="store_true",
help="Fix all background parameters")
parser.add_argument("--decoBkg",
dest="decoBkg",
default=False,
action="store_true",
help="Decorrelate background parameters")
parser.add_argument("--fitStrategy",
dest="fitStrategy",
type=int,
default=1,
help="Fit strategy (default: %(default).1f)")
parser.add_argument("--debug",
dest="debug",
default=False,
action="store_true",
help="Debug printout")
parser.add_argument(
"--postfix",
dest="postfix",
default='',
help="Postfix for the output file names (default: %(default)s)")
parser.add_argument("--pyes",
dest="pyes",
default=False,
action="store_true",
help="Make files for plots")
parser.add_argument("--jyes",
dest="jyes",
default=False,
action="store_true",
help="Make files for JES/JER plots")
parser.add_argument(
"--pdir",
dest="pdir",
default='testarea',
help="Name a directory for the plots (default: %(default)s)")
parser.add_argument("--chi2",
dest="chi2",
default=False,
action="store_true",
help="Compute chi squared")
parser.add_argument("--widefit",
dest="widefit",
default=False,
action="store_true",
help="Fit with wide bin hist")
mass_group = parser.add_mutually_exclusive_group(required=True)
mass_group.add_argument(
"--mass",
type=int,
nargs='*',
default=1000,
help=
"Mass can be specified as a single value or a whitespace separated list (default: %(default)i)"
)
mass_group.add_argument(
"--massrange",
type=int,
nargs=3,
help="Define a range of masses to be produced. Format: min max step",
metavar=('MIN', 'MAX', 'STEP'))
mass_group.add_argument("--masslist",
help="List containing mass information")
args = parser.parse_args()
if args.atype == 'hG':
fstr = "bbhGGBB"
in2 = 'bcorrbin/binmodh.root'
elif args.atype == 'hR':
fstr = "bbhRS"
in2 = 'bcorrbin/binmodh.root'
elif args.atype == 'lG':
fstr = "bblGGBB"
in2 = 'bcorrbin/binmodl.root'
else:
fstr = "bblRS"
in2 = 'bcorrbin/binmodl.root'
# check if the output directory exists
if not os.path.isdir(os.path.join(os.getcwd(), args.output_path)):
os.mkdir(os.path.join(os.getcwd(), args.output_path))
# mass points for which resonance shapes will be produced
masses = []
if args.massrange != None:
MIN, MAX, STEP = args.massrange
masses = range(MIN, MAX + STEP, STEP)
elif args.masslist != None:
# A mass list was provided
print "Will create mass list according to", args.masslist
masslist = __import__(args.masslist.replace(".py", ""))
masses = masslist.masses
else:
masses = args.mass
# sort masses
masses.sort()
# import ROOT stuff
from ROOT import gStyle, TFile, TH1F, TH1D, TGraph, kTRUE, kFALSE, TCanvas, TLegend, TPad, TLine
from ROOT import RooHist, RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf, RooExtendPdf
if not args.debug:
RooMsgService.instance().setSilentMode(kTRUE)
RooMsgService.instance().setStreamStatus(0, kFALSE)
RooMsgService.instance().setStreamStatus(1, kFALSE)
# input data file
inputData = TFile(args.inputData)
# input data histogram
hData = inputData.Get(args.dataHistname)
inData2 = TFile(in2)
hData2 = inData2.Get('h_data')
# input sig file
inputSig = TFile(args.inputSig)
sqrtS = args.sqrtS
# mass variable
mjj = RooRealVar('mjj', 'mjj', float(args.massMin), float(args.massMax))
# integrated luminosity and signal cross section
lumi = args.lumi
signalCrossSection = 1. # set to 1. so that the limit on r can be interpreted as a limit on the signal cross section
for mass in masses:
print ">> Creating datacard and workspace for %s resonance with m = %i GeV..." % (
args.final_state, int(mass))
# get signal shape
hSig = inputSig.Get("h_" + args.final_state + "_" + str(int(mass)))
# normalize signal shape to the expected event yield (works even if input shapes are not normalized to unity)
hSig.Scale(
signalCrossSection * lumi / hSig.Integral()
) # divide by a number that provides roughly an r value of 1-10
rooSigHist = RooDataHist('rooSigHist', 'rooSigHist', RooArgList(mjj),
hSig)
print 'Signal acceptance:', (rooSigHist.sumEntries() / hSig.Integral())
signal = RooHistPdf('signal', 'signal', RooArgSet(mjj), rooSigHist)
signal_norm = RooRealVar('signal_norm', 'signal_norm', 0, -1e+05,
1e+05)
signal_norm2 = RooRealVar('signal_norm2', 'signal_norm2', 0, -1e+05,
1e+05)
signal_norm3 = RooRealVar('signal_norm3', 'signal_norm3', 0, -1e+05,
1e+05)
signal_norm4 = RooRealVar('signal_norm4', 'signal_norm4', 0, -1e+05,
1e+05)
signal_norm5 = RooRealVar('signal_norm5', 'signal_norm5', 0, -1e+05,
1e+05)
if args.fitBonly:
signal_norm.setConstant()
signal_norm2.setConstant()
signal_norm3.setConstant()
signal_norm4.setConstant()
signal_norm5.setConstant()
p1 = RooRealVar('p1', 'p1', args.p1, 0., 100.)
p2 = RooRealVar('p2', 'p2', args.p2, 0., 60.)
p3 = RooRealVar('p3', 'p3', args.p3, -10., 10.)
p4 = RooRealVar('p4', 'p4', 5.6, -50., 50.)
p5 = RooRealVar('p5', 'p5', 10., -50., 50.)
p6 = RooRealVar('p6', 'p6', .016, -50., 50.)
p7 = RooRealVar('p7', 'p7', 8., -50., 50.)
p8 = RooRealVar('p8', 'p8', .22, -50., 50.)
p9 = RooRealVar('p9', 'p9', 14.1, -50., 50.)
p10 = RooRealVar('p10', 'p10', 8., -50., 50.)
p11 = RooRealVar('p11', 'p11', 4.8, -50., 50.)
p12 = RooRealVar('p12', 'p12', 7., -50., 50.)
p13 = RooRealVar('p13', 'p13', 7., -50., 50.)
p14 = RooRealVar('p14', 'p14', 7., -50., 50.)
p15 = RooRealVar('p15', 'p15', 1., -50., 50.)
p16 = RooRealVar('p16', 'p16', 9., -50., 50.)
p17 = RooRealVar('p17', 'p17', 0.6, -50., 50.)
if args.fixP3: p3.setConstant()
background = RooGenericPdf(
'background',
'(pow(1-@0/%.1f,@1)/pow(@0/%.1f,@2+@3*log(@0/%.1f)))' %
(sqrtS, sqrtS, sqrtS), RooArgList(mjj, p1, p2, p3))
dataInt = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background_norm = RooRealVar('background_norm', 'background_norm',
dataInt, 0., 1e+08)
background2 = RooGenericPdf(
'background2',
'(pow(@0/%.1f,-@1)*pow(1-@0/%.1f,@2))' % (sqrtS, sqrtS),
RooArgList(mjj, p4, p5))
dataInt2 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background2_norm = RooRealVar('background2_norm', 'background2_norm',
dataInt2, 0., 1e+08)
background3 = RooGenericPdf('background3',
'(1/pow(@1+@0/%.1f,@2))' % (sqrtS),
RooArgList(mjj, p6, p7))
dataInt3 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background3_norm = RooRealVar('background3_norm', 'background3_norm',
dataInt3, 0., 1e+08)
background4 = RooGenericPdf(
'background4',
'(1/pow(@1+@2*@0/%.1f+pow(@0/%.1f,2),@3))' % (sqrtS, sqrtS),
RooArgList(mjj, p8, p9, p10))
dataInt4 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background4_norm = RooRealVar('background4_norm', 'background4_norm',
dataInt4, 0., 1e+08)
background5 = RooGenericPdf(
'background5',
'(pow(@0/%.1f,-@1)*pow(1-pow(@0/%.1f,1/3),@2))' % (sqrtS, sqrtS),
RooArgList(mjj, p11, p12))
dataInt5 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background5_norm = RooRealVar('background5_norm', 'background5_norm',
dataInt5, 0., 1e+08)
background6 = RooGenericPdf(
'background6', '(pow(@0/%.1f,2)+@1*@0/%.1f+@2)' % (sqrtS, sqrtS),
RooArgList(mjj, p13, p14))
dataInt6 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background_norm6 = RooRealVar('background_norm6', 'background_norm6',
dataInt6, 0., 1e+08)
background7 = RooGenericPdf(
'background7',
'((-1+@1*@0/%.1f)*pow(@0/%.1f,@2+@3*log(@0/%.1f)))' %
(sqrtS, sqrtS, sqrtS), RooArgList(mjj, p15, p16, p17))
dataInt7 = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background_norm7 = RooRealVar('background_norm7', 'background_norm7',
dataInt7, 0., 1e+08)
#Extend PDFs
exts = RooExtendPdf('extsignal', 'Extended Signal Pdf', signal,
signal_norm)
extb = RooExtendPdf('extbackground', 'Extended Background Pdf',
background, background_norm)
exts2 = RooExtendPdf('extsignal2', 'Extended Signal Pdf2', signal,
signal_norm2)
extb2 = RooExtendPdf('extbackground2', 'Extended Background Pdf2',
background2, background2_norm)
exts3 = RooExtendPdf('extsignal3', 'Extended Signal Pdf3', signal,
signal_norm3)
extb3 = RooExtendPdf('extbackground3', 'Extended Background Pdf3',
background3, background3_norm)
exts4 = RooExtendPdf('extsignal4', 'Extended Signal Pdf4', signal,
signal_norm4)
extb4 = RooExtendPdf('extbackground4', 'Extended Background Pdf4',
background4, background4_norm)
exts5 = RooExtendPdf('extsignal5', 'Extended Signal Pdf5', signal,
signal_norm5)
extb5 = RooExtendPdf('extbackground5', 'Extended Background Pdf5',
background5, background5_norm)
# S+B model
model = RooAddPdf("model", "s+b", RooArgList(extb, exts))
model2 = RooAddPdf("model2", "s+b2", RooArgList(extb2, exts2))
model3 = RooAddPdf("model3", "s+b3", RooArgList(extb3, exts3))
model4 = RooAddPdf("model4", "s+b4", RooArgList(extb4, exts4))
model5 = RooAddPdf("model5", "s+b5", RooArgList(extb5, exts5))
#model6 = RooAddPdf("model6","s+b6",RooArgList(background6,signal),RooArgList(background_norm6,signal_norm))
#model7 = RooAddPdf("model7","s+b7",RooArgList(background7,signal),RooArgList(background_norm7,signal_norm))
rooDataHist = RooDataHist('rooDatahist', 'rooDathist', RooArgList(mjj),
hData)
if args.runFit:
mframe = mjj.frame()
rooDataHist.plotOn(mframe, ROOT.RooFit.Name("setonedata"))
res = model.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
model.plotOn(mframe, ROOT.RooFit.Name("model1"),
ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kRed))
res2 = model2.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
# model2.plotOn(mframe, ROOT.RooFit.Name("model2"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kOrange))
res3 = model3.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
# model3.plotOn(mframe, ROOT.RooFit.Name("model3"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
res4 = model4.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
# model4.plotOn(mframe, ROOT.RooFit.Name("model4"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
res5 = model5.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy))
# model5.plotOn(mframe, ROOT.RooFit.Name("model5"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kViolet))
# res6 = model6.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
# model6.plotOn(mframe, ROOT.RooFit.Name("model6"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kPink))
# res7 = model7.fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Strategy(args.fitStrategy))
# model7.plotOn(mframe, ROOT.RooFit.Name("model7"), ROOT.RooFit.LineStyle(1), ROOT.RooFit.LineWidth(1), ROOT.RooFit.LineColor(ROOT.EColor.kAzure))
rooDataHist2 = RooDataHist('rooDatahist2', 'rooDathist2',
RooArgList(mjj), hData2)
# rooDataHist2.plotOn(mframe, ROOT.RooFit.Name("data"))
if args.pyes:
c = TCanvas("c", "c", 800, 800)
mframe.SetAxisRange(300., 1300.)
c.SetLogy()
# mframe.SetMaximum(10)
# mframe.SetMinimum(1)
mframe.Draw()
fitname = args.pdir + '/5funcfit_m' + str(mass) + fstr + '.pdf'
c.SaveAs(fitname)
# cpull = TCanvas("cpull","cpull",800,800)
# pulls = mframe.pullHist("data","model1")
# pulls.Draw("ABX")
# pullname = args.pdir+'/pull_m'+str(mass)+fstr+'.pdf'
# cpull.SaveAs(pullname)
# cpull2 = TCanvas("cpull2","cpull2",800,800)
# pulls2 = mframe.pullHist("setonedata","model1")
# pulls2.Draw("ABX")
# pull2name = args.pdir+'/pull2_m'+str(mass)+fstr+'.pdf'
# cpull2.SaveAs(pull2name)
if args.widefit:
mframew = mjj.frame()
rooDataHist2.plotOn(mframew, ROOT.RooFit.Name("data"))
res6 = model.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model.plotOn(mframew, ROOT.RooFit.Name("model1"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kRed))
res7 = model2.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model2.plotOn(mframew, ROOT.RooFit.Name("model2"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kOrange))
res8 = model3.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model3.plotOn(mframew, ROOT.RooFit.Name("model3"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
res9 = model4.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model4.plotOn(mframew, ROOT.RooFit.Name("model4"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
res10 = model5.fitTo(rooDataHist2, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
model5.plotOn(mframew, ROOT.RooFit.Name("model5"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.LineWidth(1),
ROOT.RooFit.LineColor(ROOT.EColor.kViolet))
if args.pyes:
c = TCanvas("c", "c", 800, 800)
mframew.SetAxisRange(300., 1300.)
c.SetLogy()
# mframew.SetMaximum(10)
# mframew.SetMinimum(1)
mframew.Draw()
fitname = args.pdir + '/5funcfittowide_m' + str(
mass) + fstr + '.pdf'
c.SaveAs(fitname)
cpull = TCanvas("cpull", "cpull", 800, 800)
pulls = mframew.pullHist("data", "model1")
pulls.Draw("ABX")
pullname = args.pdir + '/pullwidefit_m' + str(
mass) + fstr + '.pdf'
cpull.SaveAs(pullname)
if args.chi2:
fullInt = model.createIntegral(RooArgSet(mjj))
norm = dataInt / fullInt.getVal()
chi1 = 0.
fullInt2 = model2.createIntegral(RooArgSet(mjj))
norm2 = dataInt2 / fullInt2.getVal()
chi2 = 0.
fullInt3 = model3.createIntegral(RooArgSet(mjj))
norm3 = dataInt3 / fullInt3.getVal()
chi3 = 0.
fullInt4 = model4.createIntegral(RooArgSet(mjj))
norm4 = dataInt4 / fullInt4.getVal()
chi4 = 0.
fullInt5 = model5.createIntegral(RooArgSet(mjj))
norm5 = dataInt5 / fullInt5.getVal()
chi5 = 0.
for i in range(args.massMin, args.massMax):
new = 0
new2 = 0
new3 = 0
new4 = 0
new5 = 0
height = hData.GetBinContent(i)
xLow = hData.GetXaxis().GetBinLowEdge(i)
xUp = hData.GetXaxis().GetBinLowEdge(i + 1)
obs = height * (xUp - xLow)
mjj.setRange("intrange", xLow, xUp)
integ = model.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp = integ.getVal() * norm
new = pow(exp - obs, 2) / exp
chi1 = chi1 + new
integ2 = model2.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp2 = integ2.getVal() * norm2
new2 = pow(exp2 - obs, 2) / exp2
chi2 = chi2 + new2
integ3 = model3.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp3 = integ3.getVal() * norm3
new3 = pow(exp3 - obs, 2) / exp3
chi3 = chi3 + new3
integ4 = model4.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp4 = integ4.getVal() * norm4
if exp4 != 0:
new4 = pow(exp4 - obs, 2) / exp4
else:
new4 = 0
chi4 = chi4 + new4
integ5 = model5.createIntegral(
RooArgSet(mjj), ROOT.RooFit.NormSet(RooArgSet(mjj)),
ROOT.RooFit.Range("intrange"))
exp5 = integ5.getVal() * norm5
new5 = pow(exp5 - obs, 2) / exp5
chi5 = chi5 + new5
print "chi1 %d " % (chi1)
print "chi2 %d " % (chi2)
print "chi3 %d " % (chi3)
print "chi4 %d " % (chi4)
print "chi5 %d " % (chi5)
if not args.decoBkg:
print " "
res.Print()
res2.Print()
res3.Print()
res4.Print()
res5.Print()
# res6.Print()
# res7.Print()
# decorrelated background parameters for Bayesian limits
if args.decoBkg:
signal_norm.setConstant()
res = model.fitTo(rooDataHist, RooFit.Save(kTRUE),
RooFit.Strategy(args.fitStrategy))
res.Print()
## temp workspace for the PDF diagonalizer
w_tmp = RooWorkspace("w_tmp")
deco = PdfDiagonalizer("deco", w_tmp, res)
# here diagonalizing only the shape parameters since the overall normalization is already decorrelated
background_deco = deco.diagonalize(background)
print "##################### workspace for decorrelation"
w_tmp.Print("v")
print "##################### original parameters"
background.getParameters(rooDataHist).Print("v")
print "##################### decorrelated parameters"
# needed if want to evaluate limits without background systematics
if args.fixBkg:
w_tmp.var("deco_eig1").setConstant()
w_tmp.var("deco_eig2").setConstant()
if not args.fixP3: w_tmp.var("deco_eig3").setConstant()
background_deco.getParameters(rooDataHist).Print("v")
print "##################### original pdf"
background.Print()
print "##################### decorrelated pdf"
background_deco.Print()
# release signal normalization
signal_norm.setConstant(kFALSE)
# set the background normalization range to +/- 5 sigma
bkg_val = background_norm.getVal()
bkg_error = background_norm.getError()
background_norm.setMin(bkg_val - 5 * bkg_error)
background_norm.setMax(bkg_val + 5 * bkg_error)
background_norm.Print()
# change background PDF names
background.SetName("background_old")
background_deco.SetName("background")
# needed if want to evaluate limits without background systematics
if args.fixBkg:
background_norm.setConstant()
p1.setConstant()
p2.setConstant()
p3.setConstant()
# -----------------------------------------
# dictionaries holding systematic variations of the signal shape
hSig_Syst = {}
hSig_Syst_DataHist = {}
sigCDF = TGraph(hSig.GetNbinsX() + 1)
# JES and JER uncertainties
if args.jesUnc != None or args.jerUnc != None:
sigCDF.SetPoint(0, 0., 0.)
integral = 0.
for i in range(1, hSig.GetNbinsX() + 1):
x = hSig.GetXaxis().GetBinLowEdge(i + 1)
integral = integral + hSig.GetBinContent(i)
sigCDF.SetPoint(i, x, integral)
if args.jesUnc != None:
hSig_Syst['JESUp'] = copy.deepcopy(hSig)
hSig_Syst['JESDown'] = copy.deepcopy(hSig)
if args.jerUnc != None:
hSig_Syst['JERUp'] = copy.deepcopy(hSig)
hSig_Syst['JERDown'] = copy.deepcopy(hSig)
# reset signal histograms
for key in hSig_Syst.keys():
hSig_Syst[key].Reset()
hSig_Syst[key].SetName(hSig_Syst[key].GetName() + '_' + key)
# produce JES signal shapes
if args.jesUnc != None:
for i in range(1, hSig.GetNbinsX() + 1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i + 1)
jes = 1. - args.jesUnc
xLowPrime = jes * xLow
xUpPrime = jes * xUp
hSig_Syst['JESUp'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jes = 1. + args.jesUnc
xLowPrime = jes * xLow
xUpPrime = jes * xUp
hSig_Syst['JESDown'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JESUp'] = RooDataHist('hSig_JESUp',
'hSig_JESUp',
RooArgList(mjj),
hSig_Syst['JESUp'])
hSig_Syst_DataHist['JESDown'] = RooDataHist(
'hSig_JESDown', 'hSig_JESDown', RooArgList(mjj),
hSig_Syst['JESDown'])
if args.jyes:
c2 = TCanvas("c2", "c2", 800, 800)
mframe2 = mjj.frame(ROOT.RooFit.Title("JES One Sigma Shifts"))
mframe2.SetAxisRange(args.massMin, args.massMax)
hSig_Syst_DataHist['JESUp'].plotOn(
mframe2, ROOT.RooFit.Name("JESUP"),
ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kRed),
ROOT.RooFit.LineColor(ROOT.EColor.kRed))
hSig_Syst_DataHist['JESDown'].plotOn(
mframe2, ROOT.RooFit.Name("JESDOWN"),
ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kBlue),
ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
rooSigHist.plotOn(mframe2, ROOT.RooFit.DataError(2),
ROOT.RooFit.Name("SIG"),
ROOT.RooFit.DrawOption("L"),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kGreen),
ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
mframe2.Draw()
mframe2.GetXaxis().SetTitle("Dijet Mass (GeV)")
leg = TLegend(0.7, 0.8, 0.9, 0.9)
leg.SetFillColor(0)
leg.AddEntry(mframe2.findObject("SIG"), "Signal Model", "l")
leg.AddEntry(mframe2.findObject("JESUP"), "+1 Sigma", "l")
leg.AddEntry(mframe2.findObject("JESDOWN"), "-1 Sigma", "l")
leg.Draw()
jesname = args.pdir + '/jes_m' + str(mass) + fstr + '.pdf'
c2.SaveAs(jesname)
# produce JER signal shapes
if args.jesUnc != None:
for i in range(1, hSig.GetNbinsX() + 1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i + 1)
jer = 1. - args.jerUnc
xLowPrime = jer * (xLow - float(mass)) + float(mass)
xUpPrime = jer * (xUp - float(mass)) + float(mass)
hSig_Syst['JERUp'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jer = 1. + args.jerUnc
xLowPrime = jer * (xLow - float(mass)) + float(mass)
xUpPrime = jer * (xUp - float(mass)) + float(mass)
hSig_Syst['JERDown'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JERUp'] = RooDataHist('hSig_JERUp',
'hSig_JERUp',
RooArgList(mjj),
hSig_Syst['JERUp'])
hSig_Syst_DataHist['JERDown'] = RooDataHist(
'hSig_JERDown', 'hSig_JERDown', RooArgList(mjj),
hSig_Syst['JERDown'])
if args.jyes:
c3 = TCanvas("c3", "c3", 800, 800)
mframe3 = mjj.frame(ROOT.RooFit.Title("JER One Sigma Shifts"))
mframe3.SetAxisRange(args.massMin, args.massMax)
hSig_Syst_DataHist['JERUp'].plotOn(
mframe3, ROOT.RooFit.Name("JERUP"),
ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kRed),
ROOT.RooFit.LineColor(ROOT.EColor.kRed))
hSig_Syst_DataHist['JERDown'].plotOn(
mframe3, ROOT.RooFit.Name("JERDOWN"),
ROOT.RooFit.DrawOption("L"), ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kBlue),
ROOT.RooFit.LineColor(ROOT.EColor.kBlue))
rooSigHist.plotOn(mframe3, ROOT.RooFit.DrawOption("L"),
ROOT.RooFit.Name("SIG"),
ROOT.RooFit.DataError(2),
ROOT.RooFit.LineStyle(1),
ROOT.RooFit.MarkerColor(ROOT.EColor.kGreen),
ROOT.RooFit.LineColor(ROOT.EColor.kGreen))
mframe3.Draw()
mframe3.GetXaxis().SetTitle("Dijet Mass (GeV)")
leg = TLegend(0.7, 0.8, 0.9, 0.9)
leg.SetFillColor(0)
leg.AddEntry(mframe3.findObject("SIG"), "Signal Model", "l")
leg.AddEntry(mframe3.findObject("JERUP"), "+1 Sigma", "l")
leg.AddEntry(mframe3.findObject("JERDOWN"), "-1 Sigma", "l")
leg.Draw()
jername = args.pdir + '/jer_m' + str(mass) + fstr + '.pdf'
c3.SaveAs(jername)
# -----------------------------------------
# create a datacard and corresponding workspace
postfix = (('_' + args.postfix) if args.postfix != '' else '')
dcName = 'datacard_' + args.final_state + '_m' + str(
mass) + postfix + '.txt'
wsName = 'workspace_' + args.final_state + '_m' + str(
mass) + postfix + '.root'
w = RooWorkspace('w', 'workspace')
getattr(w, 'import')(rooSigHist, RooFit.Rename("signal"))
if args.jesUnc != None:
getattr(w, 'import')(hSig_Syst_DataHist['JESUp'],
RooFit.Rename("signal__JESUp"))
getattr(w, 'import')(hSig_Syst_DataHist['JESDown'],
RooFit.Rename("signal__JESDown"))
if args.jerUnc != None:
getattr(w, 'import')(hSig_Syst_DataHist['JERUp'],
RooFit.Rename("signal__JERUp"))
getattr(w, 'import')(hSig_Syst_DataHist['JERDown'],
RooFit.Rename("signal__JERDown"))
if args.decoBkg:
getattr(w, 'import')(background_deco, ROOT.RooCmdArg())
else:
getattr(w, 'import')(background, ROOT.RooCmdArg(),
RooFit.Rename("background"))
getattr(w, 'import')(background2, ROOT.RooCmdArg(),
RooFit.Rename("background2"))
getattr(w, 'import')(background3, ROOT.RooCmdArg(),
RooFit.Rename("background3"))
getattr(w, 'import')(background4, ROOT.RooCmdArg(),
RooFit.Rename("background4"))
getattr(w, 'import')(background5, ROOT.RooCmdArg(),
RooFit.Rename("background5"))
getattr(w, 'import')(background_norm, ROOT.RooCmdArg(),
RooFit.Rename("background_norm"))
getattr(w, 'import')(background2_norm, ROOT.RooCmdArg(),
RooFit.Rename("background2_norm"))
getattr(w, 'import')(background3_norm, ROOT.RooCmdArg(),
RooFit.Rename("background3_norm"))
getattr(w, 'import')(background4_norm, ROOT.RooCmdArg(),
RooFit.Rename("background4_norm"))
getattr(w, 'import')(background5_norm, ROOT.RooCmdArg(),
RooFit.Rename("background5_norm"))
getattr(w, 'import')(res)
getattr(w, 'import')(res2)
getattr(w, 'import')(res3)
getattr(w, 'import')(res4)
getattr(w, 'import')(res5)
getattr(w, 'import')(background_norm, ROOT.RooCmdArg())
getattr(w, 'import')(signal_norm, ROOT.RooCmdArg())
getattr(w, 'import')(rooDataHist, RooFit.Rename("data_obs"))
w.Print()
w.writeToFile(os.path.join(args.output_path, wsName))
beffUnc = 0.3
boffUnc = 0.06
datacard = open(os.path.join(args.output_path, dcName), 'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if args.jesUnc != None or args.jerUnc != None:
datacard.write('shapes * * ' + wsName +
' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
datacard.write('shapes * * ' + wsName + ' w:$PROCESS\n')
datacard.write('---------------\n')
datacard.write('bin 1\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin 1 1\n')
datacard.write('process signal background\n')
datacard.write('process 0 1\n')
datacard.write('rate -1 1\n')
datacard.write('------------------------------\n')
datacard.write('lumi lnN %f -\n' % (1. + args.lumiUnc))
datacard.write('beff lnN %f -\n' % (1. + beffUnc))
datacard.write('boff lnN %f -\n' % (1. + boffUnc))
datacard.write('bkg lnN - 1.03\n')
if args.jesUnc != None:
datacard.write('JES shape 1 -\n')
if args.jerUnc != None:
datacard.write('JER shape 1 -\n')
# flat parameters --- flat prior
datacard.write('background_norm flatParam\n')
if args.decoBkg:
datacard.write('deco_eig1 flatParam\n')
datacard.write('deco_eig2 flatParam\n')
if not args.fixP3: datacard.write('deco_eig3 flatParam\n')
else:
datacard.write('p1 flatParam\n')
datacard.write('p2 flatParam\n')
if not args.fixP3: datacard.write('p3 flatParam\n')
datacard.close()
print '>> Datacards and workspaces created and stored in %s/' % (
os.path.join(os.getcwd(), args.output_path))
