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MANtag_Signal_Fitter.py
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MANtag_Signal_Fitter.py
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#!/usr/bin/env python
###
### Macro used for fitting the MC signal and saving the fits as a rooWorkspace for use by combine.
###
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, THStack, TGraph, TGaxis
from ROOT import TStyle, TCanvas, TPad, TLegend, TLatex, TText, TColor
from ROOT import TH1, TF1, TGraph, TGraphErrors, TGraphAsymmErrors, TVirtualFitter
gSystem.Load("PDFs/HWWLVJRooPdfs_cxx.so") #needed to get the DoubleCrytalBall pdf
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, RooDoubleCrystalBall
#from alpha import drawPlot
from rooUtils import *
from samples import sample
from aliases import alias, aliasSM, working_points, dijet_bins
from aliases import additional_selections as SELECTIONS
from utils import extend_binning
import optparse
usage = "usage: %prog [options]"
parser = optparse.OptionParser(usage)
parser.add_option("-v", "--verbose", action="store_true", default=False, dest="verbose")
parser.add_option("-y", "--year", action="store", type="string", dest="year",default="2017")
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")
(options, args) = parser.parse_args()
gROOT.SetBatch(True)
colour = [
TColor(1001, 0., 0., 0., "black", 1.),
TColor(1002, 230./255, 159./255, 0., "orange", 1.),
TColor(1003, 86./255, 180./255, 233./255, "skyblue", 1.),
TColor(1004, 0., 158./255, 115./255, "bluishgreen", 1.),
TColor(1005, 0., 114./255, 178./255, "blue", 1.),
TColor(1006, 213./255, 94./255, 0., "vermillion", 1.),
TColor(1007, 204./255, 121./255, 167./255, "reddishpurple", 1.),
]
########## SETTINGS ##########
channel = 'bb'
stype = 'Zprime'
genPoints = [1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 7000, 8000]
massPoints = [x for x in range(1600, 8000+1, 100)]
# Silent RooFit
RooMsgService.instance().setGlobalKillBelow(RooFit.FATAL)
gStyle.SetOptTitle(0)
gStyle.SetPadTopMargin(0.06)
gStyle.SetPadRightMargin(0.05)
gStyle.SetPadLeftMargin(0.12)
gStyle.SetErrorX(0.)
BTAGGING = options.btagging
NTUPLEDIR = global_paths.SKIMMEDDIR+"MANtag/"
PLOTDIR = "plots/MANtag_study/"+BTAGGING+"/"
WORKDIR = "workspace/MANtag_study/"+BTAGGING+"/"
RATIO = 4
YEAR = options.year
VERBOSE = options.verbose
READTREE = True
VARBINS = False #FIXME
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()
channelList = ['bb']
signalList = ['Zprbb']
color = {'bb' : 4, 'bq': 2, 'qq': 8, 'mumu': 6}
channel = 'bb'
categories = ['bb', 'bq', 'mumu']
stype = 'Zprime'
signalType = 'Zprime'
jobs = []
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)
narrow_bins = extend_binning(10, bins)
abins = array( 'd', narrow_bins )
print "dijet bins:", bins
print "narrow bins:", narrow_bins
else:
X_min = X_MIN-X_MIN%10
X_max = X_MAX-(X_MAX-X_min)%100
print "X_min =",X_min
print "X_max =",X_max
def signal(category):
interPar = True
n = len(genPoints)
cColor = color[category] if category in color else 4
nBtag = category.count('b')
isAH = False #relict from using Alberto's more complex script
if not os.path.exists(PLOTDIR+"MC_signal_"+YEAR): os.makedirs(PLOTDIR+"MC_signal_"+YEAR)
#*******************************************************#
# #
# Variables and selections #
# #
#*******************************************************#
X_mass = RooRealVar ( "jj_mass_widejet", "m_{jj}", X_min, X_max, "GeV")
weight = RooRealVar( "MANtag_weight", "", -1.e9, 1.e9 )
# Define the RooArgSet which will include all the variables defined before
# there is a maximum of 9 variables in the declaration, so the others need to be added with 'add'
variables = RooArgSet(X_mass)
variables.add(RooArgSet(weight))
X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_integration_range", X_mass.getMin(), X_mass.getMax())
if VARBINS:
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
X_mass.setBinning(plot_binning, "PLOT")
massArg = RooArgSet(X_mass)
SRcut = ""
#*******************************************************#
# #
# Signal fits #
# #
#*******************************************************#
treeSign = {}
setSignal = {}
vmean = {}
vsigma = {}
valpha1 = {}
vslope1 = {}
valpha2 = {}
vslope2 = {}
smean = {}
ssigma = {}
salpha1 = {}
sslope1 = {}
salpha2 = {}
sslope2 = {}
sbrwig = {}
signal = {}
signalExt = {}
signalYield = {}
signalIntegral = {}
signalNorm = {}
signalXS = {}
frSignal = {}
frSignal1 = {}
frSignal2 = {}
frSignal3 = {}
# Signal shape uncertainties (common amongst all mass points)
xmean_jes = RooRealVar("CMS"+YEAR+"_sig_"+category+"_p1_scale_jes", "Variation of the resonance position with the jet energy scale", 0.02, -1., 1.) #0.001
smean_jes = RooRealVar("CMS"+YEAR+"_sig_"+category+"_p1_jes", "Change of the resonance position with the jet energy scale", 0., -10, 10)
xsigma_jer = RooRealVar("CMS"+YEAR+"_sig_"+category+"_p2_scale_jer", "Variation of the resonance width with the jet energy resolution", 0.10, -1., 1.)
ssigma_jer = RooRealVar("CMS"+YEAR+"_sig_"+category+"_p2_jer", "Change of the resonance width with the jet energy resolution", 0., -10, 10)
xmean_jes.setConstant(True)
smean_jes.setConstant(True)
xsigma_jer.setConstant(True)
ssigma_jer.setConstant(True)
for m in massPoints:
signalMass = "%s_M%d" % (stype, m)
signalName = "ZpBB_{}_{}_M{}".format(YEAR, category, m)
sampleName = "ZpBB_M{}".format(m)
signalColor = sample[sampleName]['linecolor'] if signalName in sample else 1
# define the signal PDF
vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m, m*0.96, m*1.05)
smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)", RooArgList(vmean[m], xmean_jes, smean_jes))
vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma", m*0.0222, m*0.020, m*0.023)
ssigma[m] = RooFormulaVar(signalName + "_sigma", "@0*(1+@1*@2)", RooArgList(vsigma[m], xsigma_jer, ssigma_jer))
valpha1[m] = RooRealVar(signalName + "_valpha1", "Crystal Ball alpha 1", 0.2, 0.05, 0.28) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0", RooArgList(valpha1[m]))
#vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope 1", 10., 0.1, 20.) # slope of the power tail
vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope 1", 10., 2., 15.) # slope of the power tail
sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0", RooArgList(vslope1[m]))
valpha2[m] = RooRealVar(signalName + "_valpha2", "Crystal Ball alpha 2", 1.)
valpha2[m].setConstant(True)
salpha2[m] = RooFormulaVar(signalName + "_alpha2", "@0", RooArgList(valpha2[m]))
#vslope2[m] = RooRealVar(signalName + "_vslope2", "Crystal Ball slope 2", 6., 2.5, 15.) # slope of the higher power tail
## FIXME test FIXME
vslope2_estimation = -5.88111436852 + m*0.00728809389442 + m*m*(-1.65059568762e-06) + m*m*m*(1.25128996309e-10)
vslope2[m] = RooRealVar(signalName + "_vslope2", "Crystal Ball slope 2", vslope2_estimation, vslope2_estimation*0.9, vslope2_estimation*1.1) # slope of the higher power tail
## FIXME end FIXME
sslope2[m] = RooFormulaVar(signalName + "_slope2", "@0", RooArgList(vslope2[m])) # slope of the higher power tail
signal[m] = RooDoubleCrystalBall(signalName, "m_{%s'} = %d GeV" % ('X', m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m], salpha2[m], sslope2[m])
# extend the PDF with the yield to perform an extended likelihood fit
signalYield[m] = RooRealVar(signalName+"_yield", "signalYield", 50, 0., 1.e15)
signalNorm[m] = RooRealVar(signalName+"_norm", "signalNorm", 1., 0., 1.e15)
signalXS[m] = RooRealVar(signalName+"_xs", "signalXS", 1., 0., 1.e15)
signalExt[m] = RooExtendPdf(signalName+"_ext", "extended p.d.f", signal[m], signalYield[m])
# ---------- if there is no simulated signal, skip this mass point ----------
if m in genPoints:
if VERBOSE: print " - Mass point", m
# define the dataset for the signal applying the SR cuts
treeSign[m] = TChain("tree")
if YEAR=='run2':
pd = sample[sampleName]['files']
if len(pd)>3:
print "multiple files given than years for a single masspoint:",pd
sys.exit()
for ss in pd:
if not '2016' in ss and not '2017' in ss and not '2018' in ss:
print "unknown year given in:", ss
sys.exit()
else:
pd = [x for x in sample[sampleName]['files'] if YEAR in x]
if len(pd)>1:
print "multiple files given for a single masspoint/year:",pd
sys.exit()
for ss in pd:
#if os.path.exists(NTUPLEDIR + ss + "_"+BTAGGING+ ".root"):
# treeSign[m].Add(NTUPLEDIR + ss + "_"+BTAGGING+ ".root")
if os.path.exists(NTUPLEDIR + ss + "_"+BTAGGING+ "_" + category + ".root"):
treeSign[m].Add(NTUPLEDIR + ss + "_"+BTAGGING+ "_" + category + ".root")
else:
print "found no file for sample:", ss
if treeSign[m].GetEntries() <= 0.:
print " - 0 events available for mass", m, "skipping mass point..."
signalNorm[m].setVal(-1)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
salpha1[m].setConstant(True)
sslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
continue
setSignal[m] = RooDataSet("setSignal_"+signalName, "setSignal", variables, RooFit.Cut(SRcut), RooFit.WeightVar(weight), RooFit.Import(treeSign[m]))
if VERBOSE: print " - Dataset with", setSignal[m].sumEntries(), "events loaded"
# FIT
entries = setSignal[m].sumEntries()
if entries < 0. or entries != entries: entries = 0
signalYield[m].setVal(entries)
# Instead of eventWeightLumi
#signalYield[m].setVal(entries * LUMI / (300000 if YEAR=='run2' else 100000) )
if treeSign[m].GetEntries(SRcut) > 5:
if VERBOSE: print " - Running fit"
frSignal[m] = signalExt[m].fitTo(setSignal[m], RooFit.Save(1), RooFit.Extended(True), RooFit.SumW2Error(True), RooFit.PrintLevel(-1))
if VERBOSE: print "********** Fit result [", m, "] **", category, "*"*40, "\n", frSignal[m].Print(), "\n", "*"*80
if VERBOSE: frSignal[m].correlationMatrix().Print()
drawPlot(signalMass+"_"+category, stype+category, X_mass, signal[m], setSignal[m], frSignal[m])
else:
print " WARNING: signal", stype, "and mass point", m, "in category", category, "has 0 entries or does not exist"
# Remove HVT cross sections
#xs = getCrossSection(stype, channel, m)
xs = 1.
signalXS[m].setVal(xs * 1000.)
signalIntegral[m] = signalExt[m].createIntegral(massArg, RooFit.NormSet(massArg), RooFit.Range("X_integration_range"))
boundaryFactor = signalIntegral[m].getVal()
if boundaryFactor < 0. or boundaryFactor != boundaryFactor: boundaryFactor = 0
if VERBOSE: print " - Fit normalization vs integral:", signalYield[m].getVal(), "/", boundaryFactor, "events"
signalNorm[m].setVal( boundaryFactor * signalYield[m].getVal() / signalXS[m].getVal()) # here normalize to sigma(X) x Br = 1 [fb]
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
valpha1[m].setConstant(True)
vslope1[m].setConstant(True)
valpha2[m].setConstant(True)
vslope2[m].setConstant(True)
signalNorm[m].setConstant(True)
signalXS[m].setConstant(True)
#*******************************************************#
# #
# Signal interpolation #
# #
#*******************************************************#
# ====== CONTROL PLOT ======
c_signal = TCanvas("c_signal", "c_signal", 800, 600)
c_signal.cd()
frame_signal = X_mass.frame()
for j, m in enumerate(genPoints):
if m in signalExt.keys():
#print "color:",(j%9)+1
#print "signalNorm[m].getVal() =", signalNorm[m].getVal()
#print "RooAbsReal.NumEvent =", RooAbsReal.NumEvent
signal[m].plotOn(frame_signal, RooFit.LineColor((j%9)+1), RooFit.Normalization(signalNorm[m].getVal(), RooAbsReal.NumEvent), RooFit.Range("X_reasonable_range"))
frame_signal.GetXaxis().SetRangeUser(0, 10000)
frame_signal.Draw()
#drawCMS(-1, "Simulation Preliminary", year=YEAR)
drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c_signal.SaveAs(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_Signal.pdf")
c_signal.SaveAs(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_Signal.png")
#if VERBOSE: raw_input("Press Enter to continue...")
# ====== CONTROL PLOT ======
# Normalization
gnorm = TGraphErrors()
gnorm.SetTitle(";m_{X} (GeV);integral (GeV)")
gnorm.SetMarkerStyle(20)
gnorm.SetMarkerColor(1)
gnorm.SetMaximum(0)
inorm = TGraphErrors()
inorm.SetMarkerStyle(24)
fnorm = TF1("fnorm", "pol9", 700, 3000)
fnorm.SetLineColor(920)
fnorm.SetLineStyle(7)
fnorm.SetFillColor(2)
fnorm.SetLineColor(cColor)
# Mean
gmean = TGraphErrors()
gmean.SetTitle(";m_{X} (GeV);gaussian mean (GeV)")
gmean.SetMarkerStyle(20)
gmean.SetMarkerColor(cColor)
gmean.SetLineColor(cColor)
imean = TGraphErrors()
imean.SetMarkerStyle(24)
fmean = TF1("fmean", "pol1", 0, 10000)
fmean.SetLineColor(2)
fmean.SetFillColor(2)
# Width
gsigma = TGraphErrors()
gsigma.SetTitle(";m_{X} (GeV);gaussian width (GeV)")
gsigma.SetMarkerStyle(20)
gsigma.SetMarkerColor(cColor)
gsigma.SetLineColor(cColor)
isigma = TGraphErrors()
isigma.SetMarkerStyle(24)
fsigma = TF1("fsigma", "pol1", 0, 10000)
fsigma.SetLineColor(2)
fsigma.SetFillColor(2)
# Alpha1
galpha1 = TGraphErrors()
galpha1.SetTitle(";m_{X} (GeV);crystal ball lower alpha")
galpha1.SetMarkerStyle(20)
galpha1.SetMarkerColor(cColor)
galpha1.SetLineColor(cColor)
ialpha1 = TGraphErrors()
ialpha1.SetMarkerStyle(24)
falpha1 = TF1("falpha", "pol1", 0, 10000) #pol0
falpha1.SetLineColor(2)
falpha1.SetFillColor(2)
# Slope1
gslope1 = TGraphErrors()
gslope1.SetTitle(";m_{X} (GeV);exponential lower slope (1/Gev)")
gslope1.SetMarkerStyle(20)
gslope1.SetMarkerColor(cColor)
gslope1.SetLineColor(cColor)
islope1 = TGraphErrors()
islope1.SetMarkerStyle(24)
fslope1 = TF1("fslope", "pol1", 0, 10000) #pol0
fslope1.SetLineColor(2)
fslope1.SetFillColor(2)
# Alpha2
galpha2 = TGraphErrors()
galpha2.SetTitle(";m_{X} (GeV);crystal ball upper alpha")
galpha2.SetMarkerStyle(20)
galpha2.SetMarkerColor(cColor)
galpha2.SetLineColor(cColor)
ialpha2 = TGraphErrors()
ialpha2.SetMarkerStyle(24)
falpha2 = TF1("falpha", "pol1", 0, 10000) #pol0
falpha2.SetLineColor(2)
falpha2.SetFillColor(2)
# Slope2
gslope2 = TGraphErrors()
gslope2.SetTitle(";m_{X} (GeV);exponential upper slope (1/Gev)")
gslope2.SetMarkerStyle(20)
gslope2.SetMarkerColor(cColor)
gslope2.SetLineColor(cColor)
islope2 = TGraphErrors()
islope2.SetMarkerStyle(24)
fslope2 = TF1("fslope", "pol1", 0, 10000) #pol0
fslope2.SetLineColor(2)
fslope2.SetFillColor(2)
n = 0
for i, m in enumerate(genPoints):
if not m in signalNorm.keys(): continue
if signalNorm[m].getVal() < 1.e-6: continue
if gnorm.GetMaximum() < signalNorm[m].getVal(): gnorm.SetMaximum(signalNorm[m].getVal())
gnorm.SetPoint(n, m, signalNorm[m].getVal())
#gnorm.SetPointError(i, 0, signalNorm[m].getVal()/math.sqrt(treeSign[m].GetEntriesFast()))
gmean.SetPoint(n, m, vmean[m].getVal())
gmean.SetPointError(n, 0, min(vmean[m].getError(), vmean[m].getVal()*0.02))
gsigma.SetPoint(n, m, vsigma[m].getVal())
gsigma.SetPointError(n, 0, min(vsigma[m].getError(), vsigma[m].getVal()*0.05))
galpha1.SetPoint(n, m, valpha1[m].getVal())
galpha1.SetPointError(n, 0, min(valpha1[m].getError(), valpha1[m].getVal()*0.10))
gslope1.SetPoint(n, m, vslope1[m].getVal())
gslope1.SetPointError(n, 0, min(vslope1[m].getError(), vslope1[m].getVal()*0.10))
galpha2.SetPoint(n, m, salpha2[m].getVal())
galpha2.SetPointError(n, 0, min(valpha2[m].getError(), valpha2[m].getVal()*0.10))
gslope2.SetPoint(n, m, sslope2[m].getVal())
gslope2.SetPointError(n, 0, min(vslope2[m].getError(), vslope2[m].getVal()*0.10))
#tmpVar = w.var(var+"_"+signalString)
#print m, tmpVar.getVal(), tmpVar.getError()
n = n + 1
gmean.Fit(fmean, "Q0", "SAME")
gsigma.Fit(fsigma, "Q0", "SAME")
galpha1.Fit(falpha1, "Q0", "SAME")
gslope1.Fit(fslope1, "Q0", "SAME")
galpha2.Fit(falpha2, "Q0", "SAME")
gslope2.Fit(fslope2, "Q0", "SAME")
# gnorm.Fit(fnorm, "Q0", "", 700, 5000)
#for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S"))
#gnorm.Fit(fnorm, "Q", "SAME", 700, 6000)
gnorm.Fit(fnorm, "Q", "SAME", 1800, 8000) ## adjusted recently
for m in massPoints:
if vsigma[m].getVal() < 10.: vsigma[m].setVal(10.)
# Interpolation method
syield = gnorm.Eval(m)
spline = gnorm.Eval(m, 0, "S")
sfunct = fnorm.Eval(m)
#delta = min(abs(1.-spline/sfunct), abs(1.-spline/syield))
delta = abs(1.-spline/sfunct) if sfunct > 0 else 0
syield = spline
if interPar:
#jmean = gmean.Eval(m)
#jsigma = gsigma.Eval(m)
#jalpha1 = galpha1.Eval(m)
#jslope1 = gslope1.Eval(m)
#jalpha2 = galpha2.Eval(m)
#jslope2 = gslope2.Eval(m)
jmean = gmean.Eval(m, 0, "S") ## checking if a spline yields nicer fits FIXME
jsigma = gsigma.Eval(m, 0, "S")
jalpha1 = galpha1.Eval(m, 0, "S")
jslope1 = gslope1.Eval(m, 0, "S")
jalpha2 = galpha2.Eval(m, 0, "S")
jslope2 = gslope2.Eval(m, 0, "S")
else:
jmean = fmean.GetParameter(0) + fmean.GetParameter(1)*m + fmean.GetParameter(2)*m*m
jsigma = fsigma.GetParameter(0) + fsigma.GetParameter(1)*m + fsigma.GetParameter(2)*m*m
jalpha1 = falpha1.GetParameter(0) + falpha1.GetParameter(1)*m + falpha1.GetParameter(2)*m*m
jslope1 = fslope1.GetParameter(0) + fslope1.GetParameter(1)*m + fslope1.GetParameter(2)*m*m
jalpha2 = falpha2.GetParameter(0) + falpha2.GetParameter(1)*m + falpha2.GetParameter(2)*m*m
jslope2 = fslope2.GetParameter(0) + fslope2.GetParameter(1)*m + fslope2.GetParameter(2)*m*m
inorm.SetPoint(inorm.GetN(), m, syield)
signalNorm[m].setVal(max(0., syield))
imean.SetPoint(imean.GetN(), m, jmean)
if jmean > 0: vmean[m].setVal(jmean)
isigma.SetPoint(isigma.GetN(), m, jsigma)
if jsigma > 0: vsigma[m].setVal(jsigma)
ialpha1.SetPoint(ialpha1.GetN(), m, jalpha1)
if not jalpha1==0: valpha1[m].setVal(jalpha1)
islope1.SetPoint(islope1.GetN(), m, jslope1)
if jslope1 > 0: vslope1[m].setVal(jslope1)
ialpha2.SetPoint(ialpha2.GetN(), m, jalpha2)
if not jalpha2==0: valpha2[m].setVal(jalpha2)
islope2.SetPoint(islope2.GetN(), m, jslope2)
if jslope2 > 0: vslope2[m].setVal(jslope2)
#### newly introduced, not yet sure if helpful:
vmean[m].removeError()
vsigma[m].removeError()
valpha1[m].removeError()
valpha2[m].removeError()
vslope1[m].removeError()
vslope2[m].removeError()
#signalNorm[m].setConstant(False) ## newly put here to ensure it's freely floating in the combine fit
c1 = TCanvas("c1", "Crystal Ball", 1200, 1200) #if not isAH else 1200
c1.Divide(2, 3)
c1.cd(1)
gmean.SetMinimum(0.)
gmean.Draw("APL")
imean.Draw("P, SAME")
drawRegion(category)
c1.cd(2)
gsigma.SetMinimum(0.)
gsigma.Draw("APL")
isigma.Draw("P, SAME")
drawRegion(category)
c1.cd(3)
galpha1.Draw("APL")
ialpha1.Draw("P, SAME")
drawRegion(category)
galpha1.GetYaxis().SetRangeUser(0., 1.1) #adjusted upper limit from 5 to 2
c1.cd(4)
gslope1.Draw("APL")
islope1.Draw("P, SAME")
drawRegion(category)
gslope1.GetYaxis().SetRangeUser(0., 150.) #adjusted upper limit from 125 to 60
if True: #isAH:
c1.cd(5)
galpha2.Draw("APL")
ialpha2.Draw("P, SAME")
drawRegion(category)
galpha2.GetYaxis().SetRangeUser(0., 2.)
c1.cd(6)
gslope2.Draw("APL")
islope2.Draw("P, SAME")
drawRegion(category)
gslope2.GetYaxis().SetRangeUser(0., 20.)
c1.Print(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_SignalShape.pdf")
c1.Print(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_SignalShape.png")
c2 = TCanvas("c2", "Signal Efficiency", 800, 600)
c2.cd(1)
gnorm.SetMarkerColor(cColor)
gnorm.SetMarkerStyle(20)
gnorm.SetLineColor(cColor)
gnorm.SetLineWidth(2)
gnorm.Draw("APL")
inorm.Draw("P, SAME")
gnorm.GetXaxis().SetRangeUser(genPoints[0]-100, genPoints[-1]+100)
gnorm.GetYaxis().SetRangeUser(0., gnorm.GetMaximum()*1.25)
#drawCMS(-1, "Simulation Preliminary", year=YEAR)
drawCMS(-1, "Work in Progress", year=YEAR, suppressCMS=True)
drawAnalysis(category)
drawRegion(category)
c2.Print(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_SignalNorm.pdf")
c2.Print(PLOTDIR+"MC_signal_"+YEAR+"/"+stype+"_"+category+"_SignalNorm.png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
# create workspace
w = RooWorkspace("Zprime_"+YEAR, "workspace")
for m in massPoints:
getattr(w, "import")(signal[m], RooFit.Rename(signal[m].GetName()))
getattr(w, "import")(signalNorm[m], RooFit.Rename(signalNorm[m].GetName()))
getattr(w, "import")(signalXS[m], RooFit.Rename(signalXS[m].GetName()))
w.writeToFile(WORKDIR+"MC_signal_%s_%s.root" % (YEAR, category), True)
print "Workspace", WORKDIR+"MC_signal_%s_%s.root" % (YEAR, category), "saved successfully"
def drawPlot(name, channel, variable, model, dataset, fitRes=[], norm=-1, reg=None, cat="", alt=None, anorm=-1, signal=None, snorm=-1):
isData = norm>0
isMass = "Mass" in name
isSignal = '_M' in name
mass = name[8:12]
isCategory = reg is not None
#isBottomPanel = not isSignal
isBottomPanel = True
postfix = "Mass" if isMass else ('SR' if 'SR' in name else ('SB' if 'SB' in name else ""))
cut = "reg==reg::"+cat if reg is not None else ""
normRange = "h_extended_reasonable_range" if isMass else "X_reasonable_range"
dataRange = "LSBrange,HSBrange" if isMass and isData else normRange
cmsLabel = "Preliminary" if isData else "Simulation Preliminary"
#if not type(fitRes) is list: cmsLabel = "Preliminary"
if 'paper' in name: cmsLabel = ""
pullRange = 5
dataMin, dataMax = array('d', [0.]), array('d', [0.])
dataset.getRange(variable, dataMin, dataMax)
xmin, xmax = dataMin[0], dataMax[0]
lastBin = variable.getMax()
if not isMass and not isSignal:
if 'nn' in channel or 'll' in channel or 'ee' in channel or 'mm' in channel: lastBin = 3500.
else: lastBin = 4500.
# ====== CONTROL PLOT ======
c = TCanvas("c_"+name, "Fitting "+name, 800, 800 if isBottomPanel else 600)
if isBottomPanel:
c.Divide(1, 2)
setTopPad(c.GetPad(1), RATIO)
setBotPad(c.GetPad(2), RATIO)
else: setPad(c.GetPad(0))
c.cd(1)
frame = variable.frame()
if isBottomPanel: setPadStyle(frame, 1.25, True)
# Plot Data
data, res = None, None
if dataset is not None: data = dataset.plotOn(frame, RooFit.Cut(cut), RooFit.Binning(variable.getBinning("PLOT")), RooFit.DataError(RooAbsData.Poisson if isData else RooAbsData.SumW2), RooFit.Range(dataRange), RooFit.DrawOption("PE0"), RooFit.Name("data_obs"))
if data is not None and isData: fixData(data.getHist(), True)
# Simple fit
if isCategory:
if type(fitRes) is list:
for f in fitRes:
if f is not None:
model.plotOn(frame, RooFit.Slice(reg, cat), RooFit.ProjWData(RooArgSet(reg), dataset), RooFit.VisualizeError(f, 1, False), RooFit.SumW2Error(True), RooFit.FillColor(1), RooFit.FillStyle(3002))
if VERBOSE: model.plotOn(frame, RooFit.Slice(reg, cat), RooFit.ProjWData(RooArgSet(reg), dataset), RooFit.VisualizeError(f), RooFit.SumW2Error(True), RooFit.FillColor(2), RooFit.FillStyle(3004))
elif fitRes is not None: frame.addObject(fitRes, "E3")
model.plotOn(frame, RooFit.Slice(reg, cat), RooFit.ProjWData(RooArgSet(reg), dataset), RooFit.LineColor(getColor(name, channel)))
res = frame.pullHist()
if alt is not None: alt.plotOn(frame, RooFit.Normalization(anorm, RooAbsReal.NumEvent), RooFit.LineStyle(7), RooFit.LineColor(922), RooFit.Name("Alternate"))
else:
if type(fitRes) is list:
for f in fitRes:
if f is not None:
model.plotOn(frame, RooFit.VisualizeError(f, 1, False), RooFit.Normalization(norm if norm>0 or dataset is None else dataset.sumEntries(), RooAbsReal.NumEvent), RooFit.SumW2Error(True), RooFit.Range(normRange), RooFit.FillColor(1), RooFit.FillStyle(3002), RooFit.DrawOption("F"))
if VERBOSE: model.plotOn(frame, RooFit.VisualizeError(f), RooFit.Normalization(norm if norm>0 or dataset is None else dataset.sumEntries(), RooAbsReal.NumEvent), RooFit.SumW2Error(True), RooFit.Range(normRange), RooFit.FillColor(2), RooFit.FillStyle(3004), RooFit.DrawOption("F"))
model.paramOn(frame, RooFit.Label(model.GetTitle()), RooFit.Layout(0.5, 0.95, 0.94), RooFit.Format("NEAU"))
elif fitRes is not None: frame.addObject(fitRes, "E3")
model.plotOn(frame, RooFit.LineColor(getColor(name, channel)), RooFit.Range(normRange), RooFit.Normalization(norm if norm>0 or dataset is None else dataset.sumEntries(), RooAbsReal.NumEvent)) #RooFit.Normalization(norm if norm>0 or dataset is None else dataset.sumEntries(), RooAbsReal.NumEvent)
res = frame.pullHist() #if not isSignal else frame.residHist()
# plot components
for comp in ["baseTop", "gausW", "gausT", "baseVV", "gausVW", "gausVZ", "gausVH"]: model.plotOn(frame, RooFit.LineColor(getColor(name, channel)), RooFit.Range(normRange), RooFit.LineStyle(2), RooFit.Components(comp), RooFit.Normalization(norm if norm>0 or dataset is None else dataset.sumEntries(), RooAbsReal.NumEvent))
if alt is not None: alt.plotOn(frame, RooFit.Range(normRange), RooFit.LineStyle(7), RooFit.LineColor(922), RooFit.Name("Alternate"))
# Replot data
if dataset is not None: data = dataset.plotOn(frame, RooFit.Cut(cut), RooFit.Binning(variable.getBinning("PLOT")), RooFit.DataError(RooAbsData.Poisson if isData else RooAbsData.SumW2), RooFit.Range(dataRange), RooFit.DrawOption("PE0"), RooFit.Name("data_obs"))
if not isMass and not isSignal: # Log scale
frame.SetMaximum(frame.GetMaximum()*10)
frame.SetMinimum(max(frame.SetMinimum(), 8.e-2 if isData else 1.e-4))
c.GetPad(1).SetLogy()
else:
frame.GetYaxis().SetRangeUser(0, frame.GetMaximum())
frame.SetMaximum(frame.GetMaximum()*1.25)
frame.SetMinimum(0)
#frame.GetYaxis().SetTitleOffset(frame.GetYaxis().GetTitleOffset()*0.8)
frame.GetYaxis().SetTitleOffset(1.4)
frame.Draw()
#drawCMS(LUMI, cmsLabel)
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
drawAnalysis(channel)
drawRegion(channel + ("" if isData and not isCategory else ('SR' if 'SR' in name else ('SB' if 'SB' in name else ""))), True)
if isSignal: drawMass("M_{Z'} = "+mass+" GeV")
if isBottomPanel:
c.cd(2)
frame_res = variable.frame()
setPadStyle(frame_res, 1.25)
#res = frame.residHist()
if res is not None and isData: fixData(res)
if dataset is not None: frame_res.addPlotable(res, "P")
setBotStyle(frame_res, RATIO, False)
frame_res.GetYaxis().SetRangeUser(-pullRange, pullRange)
#frame_res.GetYaxis().SetTitleOffset(frame_res.GetYaxis().GetTitleOffset()*0.4)
frame_res.GetYaxis().SetTitle("pulls (#sigma)")
frame_res.GetYaxis().SetTitleOffset(0.4)
frame_res.Draw()
chi2, nbins, npar = 0., 0, 0
if not res==None:
for i in range(0, res.GetN()):
if data.getHist().GetY()[i] > 1.e-3:
nbins = nbins + 1
chi2 += res.GetY()[i]**2
#if isData and not isMass:
frame.GetXaxis().SetRangeUser(variable.getMin(), lastBin)
frame_res.GetXaxis().SetRangeUser(variable.getMin(), lastBin)
line_res = drawLine(frame_res.GetXaxis().GetXmin(), 0, lastBin, 0)
c.SaveAs(PLOTDIR+"MC_signal_"+YEAR+"/"+name+".pdf")
c.SaveAs(PLOTDIR+"MC_signal_"+YEAR+"/"+name+".png")
#if VERBOSE: raw_input("Press Enter to continue...")
# ====== END PLOT ======
if __name__ == "__main__":
if options.category != "":
signal(options.category)
else:
for c in categories:
p = multiprocessing.Process(target=signal, args=(c,))
jobs.append(p)
p.start()