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Bkg_Fitter_enforce.py
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Bkg_Fitter_enforce.py
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#! /usr/bin/env python
###
### Macro used for fitting the background and saving the fits as a rooWorkspace for use by combine.
###
print "starting package import"
import global_paths
import os, sys, getopt, multiprocessing
import copy, math, pickle
from array import array
from ROOT import gROOT, gSystem, gStyle, gRandom
from ROOT import TMath, TFile, TChain, TTree, TCut, TH1F, TH2F, TF1, THStack, TGraph, TGraphErrors, TGaxis
from ROOT import TStyle, TCanvas, TPad, TLegend, TLatex, TText
# Import PDF library
from ROOT import RooFit, RooRealVar, RooDataHist, RooDataSet, RooAbsData, RooAbsReal, RooAbsPdf, RooPlot, RooBinning, RooCategory, RooSimultaneous, RooArgList, RooArgSet, RooWorkspace, RooMsgService
from ROOT import RooFormulaVar, RooGenericPdf, RooGaussian, RooExponential, RooPolynomial, RooChebychev, RooBreitWigner, RooCBShape, RooExtendPdf, RooAddPdf
from rooUtils import *
from samples import sample
from aliases import alias, aliasSM, working_points, dijet_bins
from aliases import additional_selections as SELECTIONS
#from selections import selection
#from utils import *
from utils import extend_binning
import optparse
print "packages imported"
usage = "usage: %prog [options]"
parser = optparse.OptionParser(usage)
parser.add_option("-d", "--test", action="store_true", default=False, dest="bias")
parser.add_option("-v", "--verbose", action="store_true", default=False, dest="verbose")
parser.add_option("-t", "--test_run", action="store_true", default=False, dest="test")
parser.add_option("-M", "--isMC", action="store_true", default=False, dest="isMC")
parser.add_option('-y', '--year', action='store', type='string', dest='year',default='2016')
parser.add_option("-c", "--category", action="store", type="string", dest="category", default="")
parser.add_option("-b", "--btagging", action="store", type="string", dest="btagging", default="medium")
parser.add_option("-u", "--unskimmed", action="store_true", default=False, dest="unskimmed")
parser.add_option("-s", "--selection", action="store", type="string", dest="selection", default="")
parser.add_option("-f", "--force", action="store", type="int", dest="force", default=0)
(options, args) = parser.parse_args()
gROOT.SetBatch(True) #suppress immediate graphic output
if options.test: print "performing test run on small QCD MC sample"
if options.test and not options.isMC:
print "There is no test sample on data. Select -M if you want to test on MC QCD 2016."
sys.exit()
if options.test and not options.unskimmed:
print "There is no skimmed test sample on data. Select -u if you want to test on the unskimmed MC QCD 2016."
sys.exit()
########## SETTINGS ##########
# Silent RooFit
RooMsgService.instance().setGlobalKillBelow(RooFit.FATAL)
#gStyle.SetOptStat(0)
gStyle.SetOptTitle(0)
gStyle.SetPadTopMargin(0.06)
gStyle.SetPadRightMargin(0.05)
gStyle.SetErrorX(0.)
BTAGGING = options.btagging
NTUPLEDIR = global_paths.SKIMMEDDIR
WORKDIR = "workspace/"+BTAGGING+"/"
RATIO = 4
SHOWERR = True
BLIND = False
VERBOSE = options.verbose
CUTCOUNT = False
VARBINS = True
BIAS = options.bias
YEAR = options.year
ISMC = options.isMC
ADDSELECTION= options.selection!=""
if options.force == 0:
FORCE_PARAMS=False
else:
if options.force not in [2,3,4,5]:
print "cannot enforce parameter number:", options.force, " --> Aborting!!"
sys.exit()
FORCE_PARAMS=True
print "Enforcing a parameter number of:", options.force
X_MIN = 1530.
X_MAX = 9067.
#X_MIN = 1800.
#X_MAX = 9000.
if YEAR=='2016':
LUMI=35920.
elif YEAR=='2017':
LUMI=41530.
elif YEAR=='2018':
LUMI=59740.
elif YEAR=='run2':
LUMI=137190.
else:
print "unknown year:",YEAR
sys.exit()
if BTAGGING not in ['tight', 'medium', 'loose', 'semimedium']:
print "unknown btagging requirement:", BTAGGING
sys.exit()
if ISMC:
DATA_TYPE = "MC_QCD_TTbar"
else:
DATA_TYPE = "data"
PLOTDIR = "plots/"+BTAGGING+"/{}_{}".format(DATA_TYPE, YEAR)
if options.test: PLOTDIR += "_test"
if options.unskimmed or options.test:
NTUPLEDIR=global_paths.WEIGHTEDDIR
if options.selection not in SELECTIONS.keys():
print "invalid selection!"
sys.exit()
signalList = ['Zprime_to_bb']
categories = ['bb', 'bq', 'mumu']
if VARBINS:
bins = [x for x in dijet_bins if x>=X_MIN and x<=X_MAX]
X_min = min(bins)
X_max = max(bins)
abins = array( 'd', bins )
narrow_bins = extend_binning(10, bins)
abins_narrow = array('d', narrow_bins)
else:
X_min = X_MIN-X_MIN%10
X_max = X_MAX-(X_MAX-X_min)%100
data = ["data_obs"]
back = ["QCD", "TTbar"]
########## ######## ##########
def dijet(category):
channel = 'bb'
stype = channel
isSB = True # relict from using Alberto's more complex script
isData = not ISMC
nTupleDir = NTUPLEDIR
samples = data if isData else back
pd = []
if options.test:
if ISMC and YEAR == '2016':
pd.append("MC_QCD_"+YEAR)
nTupleDir = NTUPLEDIR.replace("weighted/","test_for_fit/")
else:
print "No test sample for real data was implemented. Select '-M' if you want to test on a small MC QCD sample."
sys.exit()
else:
for sample_name in samples:
if YEAR=='run2':
pd += sample[sample_name]['files']
else:
pd += [x for x in sample[sample_name]['files'] if YEAR in x]
print "datasets:", pd
if not os.path.exists(PLOTDIR): os.makedirs(PLOTDIR)
if BIAS: print "Running in BIAS mode"
order = 0
RSS = {}
X_mass = RooRealVar( "jj_mass_widejet", "m_{jj}", X_min, X_max, "GeV")
j1_pt = RooRealVar( "jpt_1", "jet1 pt", 0., 13000., "GeV")
jbtag_WP_1 = RooRealVar("jbtag_WP_1", "", -1., 4. )
jbtag_WP_2 = RooRealVar("jbtag_WP_2", "", -1., 4. )
fatjetmass_1 = RooRealVar("fatjetmass_1", "", -1., 2500. )
fatjetmass_2 = RooRealVar("fatjetmass_2", "", -1., 2500. )
jnmuons_1 = RooRealVar("jnmuons_1", "j1 n_{#mu}", -1., 8. )
jnmuons_2 = RooRealVar("jnmuons_2", "j2 n_{#mu}", -1., 8. )
jmuonpt_1 = RooRealVar( "jmuonpt_1", "j1 muon pt", 0., 13000.)
jmuonpt_2 = RooRealVar( "jmuonpt_2", "j2 muon pt", 0., 13000.)
jid_1 = RooRealVar("jid_1", "j1 ID", -1., 8. )
jid_2 = RooRealVar("jid_2", "j2 ID", -1., 8. )
nmuons = RooRealVar( "nmuons", "n_{#mu}", -1., 10. )
nelectrons = RooRealVar("nelectrons", "n_{e}", -1., 10. )
jj_deltaEta = RooRealVar( "jj_deltaEta_widejet", "", 0., 5.)
HLT_AK8PFJet500 = RooRealVar("HLT_AK8PFJet500" , "", -1., 1. )
HLT_PFJet500 = RooRealVar("HLT_PFJet500" , "" , -1., 1. )
HLT_CaloJet500_NoJetID = RooRealVar("HLT_CaloJet500_NoJetID" , "" , -1., 1. )
HLT_PFHT900 = RooRealVar("HLT_PFHT900" , "" , -1., 1. )
HLT_AK8PFJet550 = RooRealVar("HLT_AK8PFJet550" , "", -1., 1. )
HLT_PFJet550 = RooRealVar("HLT_PFJet550" , "" , -1., 1. )
HLT_CaloJet550_NoJetID = RooRealVar("HLT_CaloJet550_NoJetID" , "" , -1., 1. )
HLT_PFHT1050 = RooRealVar("HLT_PFHT1050" , "" , -1., 1. )
HLT_DoublePFJets100_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets100_CaloBTagDeepCSV_p71" , "", -1., 1. )
HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1. )
HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71", "", -1., 1. )
HLT_DoublePFJets200_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets200_CaloBTagDeepCSV_p71" , "", -1., 1. )
HLT_DoublePFJets350_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets350_CaloBTagDeepCSV_p71" , "", -1., 1. )
HLT_DoublePFJets40_CaloBTagDeepCSV_p71 =RooRealVar("HLT_DoublePFJets40_CaloBTagDeepCSV_p71" , "", -1., 1. )
weight = RooRealVar( "eventWeightLumi", "", -1.e9, 1.e9 )
variables = RooArgSet(X_mass)
variables.add(RooArgSet(jbtag_WP_1, jbtag_WP_2, fatjetmass_1, fatjetmass_2, jnmuons_1, jnmuons_2, nmuons, nelectrons, weight))
variables.add(RooArgSet(j1_pt, jj_deltaEta, jid_1, jid_2, jmuonpt_1, jmuonpt_2))
variables.add(RooArgSet(HLT_AK8PFJet500, HLT_PFJet500, HLT_CaloJet500_NoJetID, HLT_PFHT900, HLT_AK8PFJet550, HLT_PFJet550, HLT_CaloJet550_NoJetID, HLT_PFHT1050))
variables.add(RooArgSet(HLT_DoublePFJets100_CaloBTagDeepCSV_p71, HLT_DoublePFJets116MaxDeta1p6_DoubleCaloBTagDeepCSV_p71, HLT_DoublePFJets128MaxDeta1p6_DoubleCaloBTagDeepCSV_p71, HLT_DoublePFJets200_CaloBTagDeepCSV_p71, HLT_DoublePFJets350_CaloBTagDeepCSV_p71, HLT_DoublePFJets40_CaloBTagDeepCSV_p71))
if VARBINS:
binsXmass = RooBinning(len(abins)-1, abins)
X_mass.setBinning(RooBinning(len(abins_narrow)-1, abins_narrow))
#binsXmass = RooBinning(len(abins)-1, abins)
#X_mass.setBinning(binsXmass)
plot_binning = RooBinning(int((X_mass.getMax()-X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax())
else:
X_mass.setBins(int((X_mass.getMax()-X_mass.getMin())/10))
binsXmass = RooBinning(int((X_mass.getMax()-X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax())
plot_binning = binsXmass
if BTAGGING=='semimedium':
baseCut = aliasSM[category]
else:
baseCut = alias[category].format(WP=working_points[BTAGGING])
if ADDSELECTION: baseCut += SELECTIONS[options.selection]
print stype, "|", baseCut
print " - Reading from Tree"
treeBkg = TChain("tree")
if options.unskimmed or options.test:
for i, ss in enumerate(pd):
j = 0
while True:
if os.path.exists(nTupleDir + ss + "/" + ss + "_flatTuple_{}.root".format(j)):
treeBkg.Add(nTupleDir + ss + "/" + ss + "_flatTuple_{}.root".format(j))
j += 1
else:
print "found {} files for sample:".format(j), ss
break
else:
for ss in pd:
if os.path.exists(nTupleDir + ss + ".root"):
treeBkg.Add(nTupleDir + ss + ".root")
else:
print "found no file for sample:", ss
#setData = RooDataSet("setData", "Data" if isData else "Data (QCD MC)", variables, RooFit.Cut(baseCut), RooFit.WeightVar(weight), RooFit.Import(treeBkg))
if isData or options.test:
setData = RooDataSet("setData", "Data", variables, RooFit.Cut(baseCut), RooFit.Import(treeBkg))
else:
setData = RooDataSet("setData", "Data (QCD+TTbar MC)", variables, RooFit.Cut(baseCut), RooFit.WeightVar(weight), RooFit.Import(treeBkg))
nevents = setData.sumEntries()
dataMin, dataMax = array('d', [0.]), array('d', [0.])
setData.getRange(X_mass, dataMin, dataMax)
xmin, xmax = dataMin[0], dataMax[0]
lastBin = X_mass.getMax()
if VARBINS:
for b in narrow_bins: # switched to narrow bins here
if b > xmax:
lastBin = b
break
print "Imported", ("data" if isData else "MC"), "RooDataSet with", nevents, "events between [%.1f, %.1f]" % (xmin, xmax)
#xmax = xmax+binsXmass.averageBinWidth() # start form next bin
SKIP = False
# 1 parameter
print "fitting 1 parameter model"
p1_1 = RooRealVar("CMS"+YEAR+"_"+category+"_p1_1", "p1", 7.0, 0., 2000.)
modelBkg1 = RooGenericPdf("Bkg1", "Bkg. fit (2 par.)", "1./pow(@0/13000, @1)", RooArgList(X_mass, p1_1))
normzBkg1 = RooRealVar(modelBkg1.GetName()+"_norm", "Number of background events", nevents, 0., 5.*nevents) #range dependent of actual number of events!
modelExt1 = RooExtendPdf(modelBkg1.GetName()+"_ext", modelBkg1.GetTitle(), modelBkg1, normzBkg1)
fitRes1 = modelExt1.fitTo(setData, RooFit.Extended(True), RooFit.Save(1), RooFit.SumW2Error(not isData), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes1.Print()
RSS[1] = drawFit("Bkg1", category, X_mass, modelBkg1, setData, binsXmass, [fitRes1], normzBkg1.getVal())
normzBkg1.setConstant(True)
if options.force==2: SKIP=True
# 2 parameters
if not SKIP:
print "fitting 2 parameter model"
p2_1 = RooRealVar("CMS"+YEAR+"_"+category+"_p2_1", "p1", 0., -100., 1000.)
p2_2 = RooRealVar("CMS"+YEAR+"_"+category+"_p2_2", "p2", p1_1.getVal(), -100., 600.)
modelBkg2 = RooGenericPdf("Bkg2", "Bkg. fit (3 par.)", "pow(1-@0/13000, @1) / pow(@0/13000, @2)", RooArgList(X_mass, p2_1, p2_2))
normzBkg2 = RooRealVar(modelBkg2.GetName()+"_norm", "Number of background events", nevents, 0., 5.*nevents)
modelExt2 = RooExtendPdf(modelBkg2.GetName()+"_ext", modelBkg2.GetTitle(), modelBkg2, normzBkg2)
fitRes2 = modelExt2.fitTo(setData, RooFit.Extended(True), RooFit.Save(1), RooFit.SumW2Error(not isData), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes2.Print()
RSS[2] = drawFit("Bkg2", category, X_mass, modelBkg2, setData, binsXmass, [fitRes2], normzBkg2.getVal())
normzBkg2.setConstant(True)
if options.force==3: SKIP=True
# 3 parameters
if not SKIP:
print "fitting 3 parameter model"
p3_1 = RooRealVar("CMS"+YEAR+"_"+category+"_p3_1", "p1", p2_1.getVal(), -2000., 2000.)
p3_2 = RooRealVar("CMS"+YEAR+"_"+category+"_p3_2", "p2", p2_2.getVal(), -400., 2000.)
p3_3 = RooRealVar("CMS"+YEAR+"_"+category+"_p3_3", "p3", -2.5, -500., 500.)
modelBkg3 = RooGenericPdf("Bkg3", "Bkg. fit (4 par.)", "pow(1-@0/13000, @1) / pow(@0/13000, @2+@3*log(@0/13000))", RooArgList(X_mass, p3_1, p3_2, p3_3))
normzBkg3 = RooRealVar(modelBkg3.GetName()+"_norm", "Number of background events", nevents, 0., 5.*nevents)
modelExt3 = RooExtendPdf(modelBkg3.GetName()+"_ext", modelBkg3.GetTitle(), modelBkg3, normzBkg3)
fitRes3 = modelExt3.fitTo(setData, RooFit.Extended(True), RooFit.Save(1), RooFit.SumW2Error(not isData), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes3.Print()
RSS[3] = drawFit("Bkg3", category, X_mass, modelBkg3, setData, binsXmass, [fitRes3], normzBkg3.getVal())
normzBkg3.setConstant(True)
if options.force==4: SKIP=True
# 4 parameters
if not SKIP:
print "fitting 4 parameter model"
p4_1 = RooRealVar("CMS"+YEAR+"_"+category+"_p4_1", "p1", p3_1.getVal(), -2000., 2000.)
p4_2 = RooRealVar("CMS"+YEAR+"_"+category+"_p4_2", "p2", p3_2.getVal(), -2000., 2000.)
p4_3 = RooRealVar("CMS"+YEAR+"_"+category+"_p4_3", "p3", p3_3.getVal(), -50., 50.)
p4_4 = RooRealVar("CMS"+YEAR+"_"+category+"_p4_4", "p4", 0.1, -50., 50.)
modelBkg4 = RooGenericPdf("Bkg4", "Bkg. fit (5 par.)", "pow(1 - @0/13000, @1) / pow(@0/13000, @2+@3*log(@0/13000)+@4*pow(log(@0/13000), 2))", RooArgList(X_mass, p4_1, p4_2, p4_3, p4_4))
normzBkg4 = RooRealVar(modelBkg4.GetName()+"_norm", "Number of background events", nevents, 0., 5.*nevents)
modelExt4 = RooExtendPdf(modelBkg4.GetName()+"_ext", modelBkg4.GetTitle(), modelBkg4, normzBkg4)
fitRes4 = modelExt4.fitTo(setData, RooFit.Extended(True), RooFit.Save(1), RooFit.SumW2Error(not isData), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.PrintLevel(1 if VERBOSE else -1))
fitRes4.Print()
RSS[4] = drawFit("Bkg4", category, X_mass, modelBkg4, setData, binsXmass, [fitRes4], normzBkg4.getVal())
normzBkg4.setConstant(True)
if options.force==5: SKIP=True
# Normalization parameters are should be set constant, but shape ones should not
# if BIAS:
# p1_1.setConstant(True)
# p2_1.setConstant(True)
# p2_2.setConstant(True)
# p3_1.setConstant(True)
# p3_2.setConstant(True)
# p3_3.setConstant(True)
# p4_1.setConstant(True)
# p4_2.setConstant(True)
# p4_3.setConstant(True)
# p4_4.setConstant(True)
#*******************************************************#
# #
# Fisher #
# #
#*******************************************************#
if not SKIP:
# Fisher test
with open(PLOTDIR+"/Fisher_"+category+".tex", 'w') as fout:
fout.write(r"\begin{tabular}{c|c|c|c|c}")
fout.write("\n")
fout.write(r"function & $\chi^2$ & RSS & ndof & F-test \\")
fout.write("\n")
fout.write("\hline")
fout.write("\n")
CL_high = False
for o1 in range(1, 5):
o2 = min(o1 + 1, 5)
if o2 > len(RSS):
fout.write( "%d par & %.2f & %.2f & %d & " % (o1+1, RSS[o1]["chi2"], RSS[o1][
"rss"], RSS[o1]["nbins"]-RSS[o1]["npar"]))
fout.write(r"\\")
fout.write("\n")
continue #order==0 and
CL = fisherTest(RSS[o1]['rss'], RSS[o2]['rss'], o1+1., o2+1., RSS[o1]["nbins"])
if CL > 0.10: # The function with less parameters is enough
if not CL_high:
order = o1
fout.write( "\\rowcolor{MarkerColor}\n")
CL_high=True
else:
#fout.write( "%d par are needed " % (o2+1))
if not CL_high:
order = o2
fout.write( "%d par & %.2f & %.2f & %d & " % (o1+1, RSS[o1]["chi2"], RSS[o1]["rss"], RSS[o1]["nbins"]-RSS[o1]["npar"]))
fout.write("CL=%.3f " % (CL))
fout.write(r"\\")
fout.write("\n")
fout.write("\hline")
fout.write("\n")
fout.write(r"\end{tabular}")
print "saved F-test table as", PLOTDIR+"/Fisher_"+category+".tex"
#print "-"*25
#print "function & $\\chi^2$ & RSS & ndof & F-test & result \\\\"
#print "\\multicolumn{6}{c}{", "Zprime_to_bb", "} \\\\"
#print "\\hline"
#CL_high = False
#for o1 in range(1, 5):
# o2 = min(o1 + 1, 5)
# print "%d par & %.2f & %.2f & %d & " % (o1+1, RSS[o1]["chi2"], RSS[o1]["rss"], RSS[o1]["nbins"]-RSS[o1]["npar"]),
# if o2 > len(RSS):
# print "\\\\"
# continue #order==0 and
# CL = fisherTest(RSS[o1]['rss'], RSS[o2]['rss'], o1+1., o2+1., RSS[o1]["nbins"])
# print "%d par vs %d par CL=%f & " % (o1+1, o2+1, CL),
# if CL > 0.10: # The function with less parameters is enough
# if not CL_high:
# order = o1
# print "%d par are sufficient" % (o1+1),
# CL_high=True
# else:
# print "%d par are needed" % (o2+1),
# if not CL_high:
# order = o2
# print "\\\\"
#print "\\hline"
#print "-"*25
#print "@ Order is", order, "("+category+")"
#order = min(3, order)
#order = 2
if FORCE_PARAMS: order=options.force-1
if order==1:
modelBkg = modelBkg1#.Clone("Bkg")
if not SKIP: modelAlt = modelBkg2#.Clone("BkgAlt")
normzBkg = normzBkg1#.Clone("Bkg_norm")
fitRes = fitRes1
elif order==2:
modelBkg = modelBkg2#.Clone("Bkg")
if not SKIP: modelAlt = modelBkg3#.Clone("BkgAlt")
normzBkg = normzBkg2#.Clone("Bkg_norm")
fitRes = fitRes2
elif order==3:
modelBkg = modelBkg3#.Clone("Bkg")
if not SKIP: modelAlt = modelBkg4#.Clone("BkgAlt")
normzBkg = normzBkg3#.Clone("Bkg_norm")
fitRes = fitRes3
elif order==4:
modelBkg = modelBkg4#.Clone("Bkg")
if not SKIP: modelAlt = modelBkg3#.Clone("BkgAlt")
normzBkg = normzBkg4#.Clone("Bkg_norm")
fitRes = fitRes4
else:
print "Functions with", order+1, "or more parameters are needed to fit the background"
exit()
modelBkg.SetName("Bkg_"+YEAR+"_"+category)
if not SKIP: modelAlt.SetName("Alt_"+YEAR+"_"+category)
normzBkg.SetName("Bkg_"+YEAR+"_"+category+"_norm")
print "-"*25
# Generate pseudo data
setToys = RooDataSet()
setToys.SetName("data_toys")
setToys.SetTitle("Data (toys)")
if not isData:
print " - Generating", nevents, "events for toy data"
setToys = modelBkg.generate(RooArgSet(X_mass), nevents)
#setToys = modelAlt.generate(RooArgSet(X_mass), nevents)
print "toy data generated"
if VERBOSE: raw_input("Press Enter to continue...")
#*******************************************************#
# #
# Plot #
# #
#*******************************************************#
if not SKIP:
print "starting to plot"
c = TCanvas("c_"+category, category, 800, 800)
c.Divide(1, 2)
setTopPad(c.GetPad(1), RATIO)
setBotPad(c.GetPad(2), RATIO)
c.cd(1)
frame = X_mass.frame()
setPadStyle(frame, 1.25, True)
if VARBINS: frame.GetXaxis().SetRangeUser(X_mass.getMin(), lastBin)
signal = getSignal(category, stype, 2000) #replacing Alberto's getSignal by own dummy function
graphData = setData.plotOn(frame, RooFit.Binning(plot_binning), RooFit.Scaling(False), RooFit.Invisible())
modelBkg.plotOn(frame, RooFit.VisualizeError(fitRes, 1, False), RooFit.LineColor(602), RooFit.FillColor(590), RooFit.FillStyle(1001), RooFit.DrawOption("FL"), RooFit.Name("1sigma"))
modelBkg.plotOn(frame, RooFit.LineColor(602), RooFit.FillColor(590), RooFit.FillStyle(1001), RooFit.DrawOption("L"), RooFit.Name(modelBkg.GetName()))
modelAlt.plotOn(frame, RooFit.LineStyle(7), RooFit.LineColor(613), RooFit.FillColor(609), RooFit.FillStyle(1001), RooFit.DrawOption("L"), RooFit.Name(modelAlt.GetName()))
if not isSB and signal[0] is not None: # FIXME remove /(2./3.)
signal[0].plotOn(frame, RooFit.Normalization(signal[1]*signal[2], RooAbsReal.NumEvent), RooFit.LineStyle(3), RooFit.LineWidth(6), RooFit.LineColor(629), RooFit.DrawOption("L"), RooFit.Name("Signal"))
graphData = setData.plotOn(frame, RooFit.Binning(plot_binning), RooFit.Scaling(False), RooFit.XErrorSize(0 if not VARBINS else 1), RooFit.DataError(RooAbsData.Poisson if isData else RooAbsData.SumW2), RooFit.DrawOption("PE0"), RooFit.Name(setData.GetName()))
fixData(graphData.getHist(), True, True, not isData)
pulls = frame.pullHist(setData.GetName(), modelBkg.GetName(), True)
chi = frame.chiSquare(setData.GetName(), modelBkg.GetName(), True)
#setToys.plotOn(frame, RooFit.DataError(RooAbsData.Poisson), RooFit.DrawOption("PE0"), RooFit.MarkerColor(2))
frame.GetYaxis().SetTitle("Events / ( 100 GeV )")
frame.GetYaxis().SetTitleOffset(1.05)
frame.Draw()
#print "frame drawn"
# Get Chi2
# chi2[1] = frame.chiSquare(modelBkg1.GetName(), setData.GetName())
# chi2[2] = frame.chiSquare(modelBkg2.GetName(), setData.GetName())
# chi2[3] = frame.chiSquare(modelBkg3.GetName(), setData.GetName())
# chi2[4] = frame.chiSquare(modelBkg4.GetName(), setData.GetName())
frame.SetMaximum(frame.GetMaximum()*10)
frame.SetMinimum(max(frame.GetMinimum(), 1.e-1))
c.GetPad(1).SetLogy()
drawAnalysis(category)
drawRegion(category, True)
#drawCMS(LUMI, "Simulation Preliminary")
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawCMS(LUMI, "", suppressCMS=True)
leg = TLegend(0.575, 0.6, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.AddEntry(setData.GetName(), setData.GetTitle()+" (%d events)" % nevents, "PEL")
leg.AddEntry(modelBkg.GetName(), modelBkg.GetTitle(), "FL")#.SetTextColor(629)
leg.AddEntry(modelAlt.GetName(), modelAlt.GetTitle(), "L")
if not isSB and signal[0] is not None: leg.AddEntry("Signal", signal[0].GetTitle(), "L")
leg.SetY1(0.9-leg.GetNRows()*0.05)
leg.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextFont(42)
if not isSB: latex.DrawLatex(leg.GetX1()*1.16, leg.GetY1()-0.04, "HVT model B (g_{V}=3)")
# latex.DrawLatex(0.67, leg.GetY1()-0.045, "#sigma_{X} = 1.0 pb")
c.cd(2)
frame_res = X_mass.frame()
setPadStyle(frame_res, 1.25)
frame_res.addPlotable(pulls, "P")
setBotStyle(frame_res, RATIO, False)
if VARBINS: frame_res.GetXaxis().SetRangeUser(X_mass.getMin(), lastBin)
frame_res.GetYaxis().SetRangeUser(-5, 5)
frame_res.GetYaxis().SetTitle("pulls(#sigma)")
frame_res.GetYaxis().SetTitleOffset(0.3)
frame_res.Draw()
fixData(pulls, False, True, False)
drawChi2(RSS[order]["chi2"], RSS[order]["nbins"]-(order+1), True)
line = drawLine(X_mass.getMin(), 0, lastBin, 0)
if VARBINS:
c.SaveAs(PLOTDIR+"/BkgSR_"+category+".pdf")
c.SaveAs(PLOTDIR+"/BkgSR_"+category+".png")
else:
c.SaveAs(PLOTDIR+"/BkgSR_"+category+".pdf")
c.SaveAs(PLOTDIR+"/BkgSR_"+category+".png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
if BIAS:
gSystem.Load("libHiggsAnalysisCombinedLimit.so")
from ROOT import RooMultiPdf
cat = RooCategory("pdf_index", "Index of Pdf which is active");
pdfs = RooArgList(modelBkg, modelAlt)
roomultipdf = RooMultiPdf("roomultipdf", "All Pdfs", cat, pdfs)
normulti = RooRealVar("roomultipdf_norm", "Number of background events", nevents, 0., 1.e6)
normzBkg.setConstant(False) ## ensure it's freely floating in the combine fit
# create workspace
w = RooWorkspace("Zprime_"+YEAR, "workspace")
# Dataset
if isData: getattr(w, "import")(setData, RooFit.Rename("data_obs"))
else: getattr(w, "import")(setToys, RooFit.Rename("data_obs"))
#getattr(w, "import")(setData, RooFit.Rename("data_obs"))
if BIAS:
getattr(w, "import")(cat, RooFit.Rename(cat.GetName()))
getattr(w, "import")(normulti, RooFit.Rename(normulti.GetName()))
getattr(w, "import")(roomultipdf, RooFit.Rename(roomultipdf.GetName()))
getattr(w, "import")(modelBkg, RooFit.Rename(modelBkg.GetName()))
if not SKIP: getattr(w, "import")(modelAlt, RooFit.Rename(modelAlt.GetName()))
getattr(w, "import")(normzBkg, RooFit.Rename(normzBkg.GetName()))
w.writeToFile(WORKDIR+"%s_%s%s.root" % (DATA_TYPE+"_"+YEAR, category, "_test" if options.test else ""), True)
print "Workspace", WORKDIR+"%s_%s%s.root" % (DATA_TYPE+"_"+YEAR, category, "_test" if options.test else ""), "saved successfully"
if VERBOSE: raw_input("Press Enter to continue...")
# ====== END PLOT ======
def fisherTest(RSS1, RSS2, o1, o2, N):
#print "Testing functions with parameters", o1, "and", o2, "with RSS", RSS1, "and", RSS2
#if (RSS1-RSS2)/(RSS2) < 0.125: return True
#return (RSS1-RSS2)/RSS1 < (o2-o1)/o1
dof1 = N - o1
dof2 = N - o2
n1 = N - dof1 - 1
n2 = N - dof2 - 1
F = ((RSS1-RSS2)/(n2-n1)) / (RSS2/(N-n2))
#F_dist = TF1("F_distr", "TMath::Sqrt( (TMath::Power([0]*x,[0]) * TMath::Power([1],[1])) / (TMath::Power([0]*x + [1],[0]+[1])) ) / (x*TMath::Beta([0]/2,[1]/2))", 0, 1000)
#F_dist.SetParameter(0, n2-n1)
#F_dist.SetParameter(1, N-n2)
#CL = 1 - F_dist.Integral(0.00000001, F)
CL = 1.-TMath.FDistI(F, n2-n1, N-n2)
#print F, N, n2-n1, N-n2, TMath.FDistI(F, n2-n1, N-n2)
#print "F-test:", o1+1, "par vs", o2, "par & : F =", F, ", CL = %.4f" % CL
return CL
def drawFit(name, category, variable, model, dataset, binning, fitRes=[], norm=-1):
isData = (not 'MC' in dataset.GetTitle())
order = int(name[-1])
npar = fitRes[0].floatParsFinal().getSize() if len(fitRes)>0 else 0
varArg = RooArgSet(variable)
c = TCanvas("c_"+category, category, 800, 800)
c.Divide(1, 2)
setTopPad(c.GetPad(1), RATIO)
setBotPad(c.GetPad(2), RATIO)
c.cd(1)
frame = variable.frame()
setPadStyle(frame, 1.25, True)
dataset.plotOn(frame, RooFit.Binning(binning), RooFit.Invisible())
if len(fitRes) > 0:
model.plotOn(frame, RooFit.VisualizeError(fitRes[0], 1, False), RooFit.Normalization(norm if norm>0 else dataset.sumEntries(), RooAbsReal.NumEvent), RooFit.LineColor(getColor(order, category)[0]), RooFit.FillColor(getColor(order, category)[1]), RooFit.FillStyle(1001), RooFit.DrawOption("FL"))
model.plotOn(frame, RooFit.Normalization(norm if norm>0 else dataset.sumEntries(), RooAbsReal.NumEvent), RooFit.LineColor(getColor(order, category)[0]), RooFit.FillColor(getColor(order, category)[1]), RooFit.FillStyle(1001), RooFit.DrawOption("L"), RooFit.Name(model.GetName()))
model.paramOn(frame, RooFit.Label(model.GetTitle()), RooFit.Layout(0.45, 0.95, 0.94), RooFit.Format("NEAU"))
graphData = dataset.plotOn(frame, RooFit.Binning(binning), RooFit.DataError(RooAbsData.Poisson if isData else RooAbsData.SumW2), RooFit.DrawOption("PE0"), RooFit.Name(dataset.GetName()))
fixData(graphData.getHist(), True, True, not isData)
pulls = frame.pullHist(dataset.GetName(), model.GetName(), True)
residuals = frame.residHist(dataset.GetName(), model.GetName(), False, True) # this is y_i - f(x_i)
roochi2 = frame.chiSquare()#dataset.GetName(), model.GetName()) #model.GetName(), dataset.GetName()
frame.SetMaximum(frame.GetMaximum()*10)
frame.SetMinimum(max(frame.GetMinimum(), 1.e-2))
c.GetPad(1).SetLogy()
frame.Draw()
drawAnalysis(category)
drawRegion(category, True)
#drawCMS(LUMI, "Simulation Preliminary")
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawCMS(LUMI, "", suppressCMS=True)
c.cd(2)
frame_res = variable.frame()
setPadStyle(frame_res, 1.25)
frame_res.addPlotable(pulls, "P")
setBotStyle(frame_res, RATIO, False)
frame_res.GetYaxis().SetRangeUser(-5, 5)
frame_res.GetYaxis().SetTitle("pulls(#sigma)")
frame_res.GetYaxis().SetTitleOffset(0.4)
frame_res.Draw()
fixData(pulls, False, True, False)
# calculate RSS
nbins, res, rss, chi1, chi2 = 0, 0., 0., 0., 0.
hist = graphData.getHist()
xmin, xmax = array('d', [0.]), array('d', [0.])
dataset.getRange(variable, xmin, xmax)
#print "hist.GetN() =", hist.GetN()
for i in range(0, hist.GetN()):
if hist.GetX()[i] - hist.GetErrorXlow(i) > xmax[0] and hist.GetX()[i] + hist.GetErrorXhigh(i) > xmax[0]: continue# and abs(pulls.GetY()[i]) < 5:
if hist.GetY()[i] <= 0.: continue
#print "i =", i
#print "residuals.GetY() =", residuals.GetY()
#print "residuals.GetY()[i] =", residuals.GetY()[i]
res += residuals.GetY()[i]
rss += residuals.GetY()[i]**2
#print i, pulls.GetY()[i]
chi1 += abs(pulls.GetY()[i])
chi2 += pulls.GetY()[i]**2
nbins = nbins + 1
#data = hist.GetY()[i]
#bkg = norm * (model.createIntegral(varArg, RooFit.NormSet(varArg), RooFit.Range(bins[i], bins[i+1]))).getVal() / (bins[i+1] - bins[i])
#bkg2 = model.Eval( (bins[i+1] - bins[i])/2. )
#print i, bins[i], bins[i+1], data, bkg#, bkg2
rss = math.sqrt(rss)
out = {"chi2" : chi2, "chi1" : chi1, "rss" : rss, "res" : res, "nbins" : nbins, "npar" : npar}
drawChi2(chi2, binning.numBins() - npar)
line = drawLine(variable.getMin(), 0, variable.getMax(), 0)
if len(name) > 0 and len(category) > 0:
c.SaveAs(PLOTDIR+"/"+name+"_"+category+".pdf")
c.SaveAs(PLOTDIR+"/"+name+"_"+category+".png")
# if( hMassNEW.GetXaxis().GetBinLowEdge(bin+1)>=fFitXmin and hMassNEW.GetXaxis().GetBinUpEdge(bin-1)<=fFitXmax ):
# NumberOfVarBins += 1
# data = hMassNEW.GetBinContent(bin)
# # data = g.Integral(hMassNEW.GetXaxis().GetBinLowEdge(bin) , hMassNEW.GetXaxis().GetBinUpEdge(bin) )
# err_data_low = g.GetErrorYlow(bin-1)
# err_data_high= g.GetErrorYhigh(bin-1)
# fit = BKGfit.Integral(hMassNEW.GetXaxis().GetBinLowEdge(bin) , hMassNEW.GetXaxis().GetBinUpEdge(bin) )
# fit = fit / ( hMassNEW.GetBinWidth(bin) )
# # fit = BKGfit.Eval(hMassNEW.GetBinCenter(bin)) #yields same results
# if(fit > data):
# err_tot = err_data_high
# else:
# err_tot = err_data_low
# fit_residual = (data - fit) / err_tot
# err_fit_residual = 1
#
# if (hMassNEW.GetBinContent(bin)>0):
# NumberOfObservations_VarBin+=1
# chi2_VarBin += pow( (data - fit) , 2 ) / pow( err_tot , 2 )
#chi2_VarBin_notNorm += pow( (data - fit) , 2 )
#print " Integral:", integral, "-", norm
#print " RSS:", rss
#print " Chi2:", chi2, " - ", roochi2
return out
def getSignal(cat, sig, mass):
#try:
# sample = "" # FIXME
# file = TFile(NTUPLEDIR+sample+"/"+sample+"_flatTuple_0.root", "READ")
# w = file.Get("VH_2016")
# signal = w.pdf("%s%s_M%d" % (sig, cat, mass))
# norm = w.var("%s%s_M%d_norm" % (sig, cat, mass))
# xs = w.var("%s%s_M%d_xs" % (sig, cat, mass))
# return [signal, norm.getVal(), xs.getVal()]
#except:
# print "WARNING: failed to get signal pdf"
# return [None, 0., 0.]
return [None, 0., 0. ]
if __name__ == "__main__":
if options.category!='':
dijet(options.category)
else:
jobs=[]
for c in categories:
p = multiprocessing.Process(target=dijet, args=(c,))
jobs.append(p)
p.start()