def crystalBall(mean, sigma_, alpha_, n_, tagged_mass, w, fn, bin, rangeAlpha):
sigmaCB = RooRealVar ("#sigma_{%s}^{%s}"%(fn, bin) , "sigmaCB_%s"%fn , sigma_ , 0, 1 )
alpha = RooRealVar ("#alpha_{%s}^{%s}"%(fn, bin) , "#alpha_{%s}^{%s}"%(fn, bin) , alpha_ , rangeAlpha[0], rangeAlpha[1] ) # was 0 - 5
n = RooRealVar ("n_{%s}^{%s}"%(fn, bin) , "n_%s"%fn , n_ , 0.001, 100 )
cbshape = RooCBShape ("cbshape_%s_%s"%(fn,bin) , "cbshape_%s_%s"%(fn, bin) , tagged_mass, mean, sigmaCB, alpha, n)
_import(w,cbshape)
def RooFitSig(mbbarray, bdtarray, weightarray, TC_mass, binstart, binend):
fitstart = 40
fitend = 150
mbbarray = range(200)
bdtarray = range(200)
weightarray = range(200)
mass = RooRealVar("X", "m(bb)[GeV]", fitstart, fitend)
BDT = RooRealVar("BDT", "BDT", -1, 100)
weight = RooRealVar("weight", "weight", -100, 200)
branchnames = ["X", "BDT", "weight"]
dtype = np.dtype([(branchnames[idx], np.float64)
for idx in range(len(branchnames))])
treearray = np.array([(mbbarray[idx], bdtarray[idx], weightarray[idx])
for idx in range(len(mbbarray))], dtype)
tree = rnp.array2tree(treearray)
m0 = RooRealVar("m0", "m0", TC_mass * 1., TC_mass * 1. - 60.,
TC_mass * 1. + 60.)
m02 = RooRealVar("m02", "m02", TC_mass * 1., TC_mass * 1. - 60.,
TC_mass * 1. + 60.)
alpha = RooRealVar("alpha", "alpha", 1.295, 1.0, 1.6)
sigma2 = RooRealVar("sigma2", "sigma2", 35, 8., 100)
n = RooRealVar("n", "n", 5, 1, 35)
mean = RooRealVar("mean", "mean of gaussian", 750, 0, 6000)
sigma = RooRealVar("sigma", "width of gaussian", 90, 38, 300)
gauss = RooGaussian("gauss", "gaussian PDF", mass, m0, sigma)
gauss2 = RooGaussian("gauss2", "gaussian PDF", mass, m02, sigma2)
CBshape = RooCBShape("CBshape", "Crystal Ball PDF", mass, m0, sigma2,
alpha, n)
##PDF normalization
num1 = RooRealVar("num1", "number of events", 400, 0, 5000)
##relative weight of 2 PDFs
f = RooRealVar("f", "f", 0.95, 0.6, 1)
sigPdf = RooAddPdf("sigPdf", "Signal PDF", RooArgList(CBshape, gauss),
RooArgList(f))
extPdf = RooExtendPdf("extPdf", "extPdf", sigPdf, num1)
data = RooDataSet("data", "data", tree, RooArgSet(mass, BDT, weight),
"BDT>0", "weight")
xframe = mass.frame()
mass.setBins(20)
data.plotOn(xframe)
extPdf.plotOn(
xframe) #,Normalization(1.0,RooAbsReal.RelativeExpected),LineColor(1))
hist = extPdf.createHistogram("X", fitend - fitstart)
hist.SetAxisRange(binstart, binend)
return deepcopy(hist)
def DoRooFit(histo, title):
can = makeCMSCanvas(str(random.random()),"Fit result ",900,700)
#Varible
if "ele" in title:
x1 = RooRealVar("x1","m_{e^{+}e^{-}}",80,100)
if "mu" in title:
x1 = RooRealVar("x1","m_{#mu^{+}#mu^{-}}",80,100)
#Define CB function
m = RooRealVar("mean_{CB}","mean of gaussian",60,120)
s = RooRealVar("#sigma_{CB}","width of gaussian",0,3)
a = RooRealVar("#alpha_{CB}","mean of gaussian",0,100)
n = RooRealVar("n_{CB}","width of gaussian",0,5)
CB = RooCBShape("CB","CB PDF",x1, m, s, a, n)
m.setConstant(kFALSE)
s.setConstant(kFALSE)
a.setConstant(kFALSE)
n.setConstant(kFALSE)
#Define Gaussian function
mean1 = RooRealVar("mean_{G}","mean of gaussian",-60,60)
sigma1 = RooRealVar("#sigma_{G}","width of gaussian",0,10)
gauss1 = RooGaussian("gauss1","gaussian PDF",x1,mean1,sigma1)
mean1.setConstant(kFALSE)
sigma1.setConstant(kFALSE)
#Starting values of the parameters
mean1.setVal(1.0)
sigma1.setVal(1.0)
m.setVal(90.0)
s.setVal(1.0)
a.setVal(10.0)
n.setVal(2.0)
# Construct CB (x) gauss
x1.setBins(10000, "cache")
CBxG = RooFFTConvPdf("CBxG", "CB (X) gauss", x1, CB, gauss1)
can.cd()
d = RooDataHist("d","d",RooArgList(x1),RooFit.Import(histo))
CBxG.fitTo(d, RooLinkedList())
# Plot PDF and toy data overlaid
xframe2 = x1.frame(RooFit.Name("xframe"),RooFit.Title("")) # RooPlot
d.plotOn(xframe2, RooLinkedList() )
CBxG.paramOn(xframe2, RooFit.Layout(0.65,0.99,0.9))
xframe2.getAttText().SetTextSize(0.03)
CBxG.plotOn(xframe2)
xframe2.Draw()
can.SaveAs("DataVsMC/FitResults/"+title+"_Roofit.pdf")
can.SaveAs("DataVsMC/FitResults/"+title+"_Roofit.png")
return;
def CBintialization(self):
round_energy = round(float(self.energy),-1)
if round_energy ==240 : round_energy = 250
self.x = RooRealVar("signal_%s_%dGeV"%(self.crystal,round_energy),"signal_%s_%dGeV"%(self.crystal,round_energy),max(0.,self.peak_position*(1-self.xaxis_scale)),self.peak_position*(1+self.xaxis_scale))
self.roohist = RooDataHist("roohist_fit_%s_%s"%(self.crystal,self.energy),"roohist_fit_%s_%s"%(self.crystal,self.energy),RooArgList(self.x),self.hist)
self.m = RooRealVar("mean_%s_%s"%(self.crystal,self.energy),"mean_%s_%s"%(self.crystal,self.energy),self.peak_position,max(0.,self.peak_position*(1-self.xaxis_scale)),self.peak_position*(1+self.xaxis_scale))
self.s = RooRealVar("sigma_%s_%s"%(self.crystal,self.energy),"sigma_%s_%s"%(self.crystal,self.energy),self.s_initial,0.001,1.) #500.
self.a = RooRealVar("alpha_%s_%s"%(self.crystal,self.energy),"alpha_%s_%s"%(self.crystal,self.energy),self.a_initial,-10.,10)
self.n = RooRealVar("exp_%s_%s"%(self.crystal,self.energy),"exp_%s_%s"%(self.crystal,self.energy),self.n_initial,0.,30)
self.sig = RooCBShape("signal_%s_%s"%(self.crystal,self.energy),"signal_%s_%s"%(self.crystal,self.energy),self.x,self.m,self.s,self.a,self.n)
def bwcb(mean_, width_, sigma_, alpha_, n_, fn, tagged_mass, w):
## Breit-Wigner
meanBW = RooRealVar ("massBW_%s"%fn , "massBW_%s"%fn , mean_ , 3, 7, "GeV")
widthBW = RooRealVar ("widthBW_%s"%fn , "widthBW_%s"%fn , width_ , 0, 10 )
bwshape = RooBreitWigner ("bwshape_%s"%fn , "bwshape_%s"%fn , tagged_mass, meanBW, widthBW)
meanCB = RooRealVar ("massBW_%s"%fn , "massBW_%s"%fn , 0.)
sigmabwCB = RooRealVar ("#sigma_{bwCB}_%s"%fn , "sigmabwCB_%s"%fn , sigma_ , 0, 1 )
alphabw = RooRealVar ("#alphabw_%s"%fn , "alphabw_%s"%fn , alpha_ , 0, 10 ) # was 0 - 5
nbw = RooRealVar ("nbw_%s"%fn , "nbw_%s"%fn , n_ , 0, 25 )
cbshape = RooCBShape ("cbshapebw_%s"%fn , "cbshapebw_%s"%fn , tagged_mass, meanCB, sigmabwCB, alphabw, nbw)
cbbw = RooFFTConvPdf( "cbbw_%s"%fn, "cbbw_%s"%fn, tagged_mass, bwshape, cbshape);
_import(w,cbbw)
def __init__(self, adc, name):
#input ADC values
self.adc = adc
#2+n reversed Crystal Ball
#mean
mean_nam = "mean_2n_" + name
self.mean_2n = RooRealVar(mean_nam, mean_nam, 50., 250.) # 200. 250. min 150
#sigma
sigma_nam = "sigma_2n_" + name
self.sigma_2n = RooRealVar(sigma_nam, sigma_nam, 0., 100.)
#alpha
alpha_nam = "alpha_2xn_" + name
self.alpha_2xn = RooRealVar(alpha_nam, alpha_nam, -10., 0.)
#n
n_nam = "n_2xn_" + name
self.n_2xn = RooRealVar(n_nam, n_nam, 0., 20.)
#CrystalBall
cb_name = "cb_2xn_" + name
self.cb_2xn = RooCBShape(cb_name, cb_name, self.adc, self.mean_2n, self.sigma_2n, self.alpha_2xn, self.n_2xn)
def dofit(roodataset, hname):
x = RooRealVar("ups_mass", "m_{#mu #mu}", 8.5, 11.0)
# choose here binning of mass plot
x.setBins(250)
# model signal
# one CB for each Y(nS) or sum of two CB for each Y(nS)
# CB parameters
mass1S = RooRealVar('mass1S', 'mass1S', 9.4603, 9.400, 9.500)
mass2S = RooRealVar('mass2S', 'mass2S', 10.022, 10.000, 10.040)
mass3S = RooRealVar('mass3S', 'mass3S', 10.3552, 10.300, 10.370)
sigma1S_1 = RooRealVar('sigma1S_1', 'sigma1S_1', 0.080, 0.010, 0.100)
sigma1S_2 = RooRealVar('sigma1S_2', 'sigma1S_2', 0.085, 0.010, 0.100)
sigma2S_1 = RooRealVar('sigma2S_1', 'sigma2S_1', 0.085, 0.020, 0.100)
sigma2S_2 = RooRealVar('sigma2S_2', 'sigma2S_2', 0.090, 0.020, 0.100)
sigma3S_1 = RooRealVar('sigma3S_1', 'sigma3S_1', 0.090, 0.020, 0.100)
sigma3S_2 = RooRealVar('sigma3S_2', 'sigma3S_2', 0.095, 0.020, 0.100)
alpha = RooRealVar('alpha', 'alpha', 0.5, 0, 5)
n = RooRealVar('n', 'n', 0.5, 0, 5) # fix n
#signal model
cb1S_1 = RooCBShape('y1S_1', 'y1S_1', x, mass1S, sigma1S_1, alpha, n)
cb1S_2 = RooCBShape('y1S_2', 'y1S_2', x, mass1S, sigma1S_2, alpha, n)
cb2S_1 = RooCBShape('y2S_1', 'y2S_1', x, mass2S, sigma2S_1, alpha, n)
cb2S_2 = RooCBShape('y2S_2', 'y2S_2', x, mass2S, sigma2S_2, alpha, n)
cb3S_1 = RooCBShape('y3S_1', 'y3S_1', x, mass3S, sigma3S_1, alpha, n)
cb3S_2 = RooCBShape('y3S_2', 'y3S_2', x, mass3S, sigma3S_2, alpha, n)
cb1frac1S = RooRealVar("cb1frac1S", "cc", 0.1, 0., 1.)
cb1frac2S = RooRealVar("cb1frac2S", "cc", 0.1, 0., 1.)
cb1frac3S = RooRealVar("cb1frac3S", "cc", 0.1, 0., 1.)
# sum of two CB
sig1S = RooAddPdf("sig1S", "Signal1S", RooArgList(cb1S_1, cb1S_2),
RooArgList(cb1frac1S))
sig2S = RooAddPdf("sig2S", "Signal2S", RooArgList(cb2S_1, cb2S_2),
RooArgList(cb1frac2S))
sig3S = RooAddPdf("sig3S", "Signal3S", RooArgList(cb3S_1, cb3S_2),
RooArgList(cb1frac3S))
#background model
c1 = RooRealVar('c1', 'c1', 200, 0, 3000000)
c2 = RooRealVar('c2', 'c2', -1, -50, 50)
background = RooPolynomial('bkg', 'bkg', x, RooArgList(c1, c2))
# complete model
n1S = RooRealVar('n1s', '', 100000, 0, 10000000)
n2S = RooRealVar('n2s', '', 100000, 0, 10000000)
n3S = RooRealVar('n3s', '', 100000, 0, 10000000)
nbck = RooRealVar('nbck', '', 100000, 0, 10000000)
# sum of two CB for each Y(nS)
modelPdf = RooAddPdf('model', 'model',
RooArgList(sig1S, sig2S, sig3S, background),
RooArgList(n1S, n2S, n3S, nbck))
# or one CB for each Y(nS)
#modelPdf = RooAddPdf('model','model',RooArgList(cb1S_1,cb2S_1,cb3S_1,background),RooArgList(n1S,n2S,n3S,nbck))
rcut = x.setRange('rcut', 8.5, 11.0)
result = modelPdf.fitTo(roodataset, RooFit.Save(), RooFit.Range('rcut'))
frame = x.frame(RooFit.Title('mass'))
roodataset.plotOn(frame, RooFit.MarkerSize(0.7))
modelPdf.plotOn(frame, RooFit.LineWidth(2))
#plotting
canvas = TCanvas('fit', "", 1400, 700)
canvas.Divide(1)
canvas.cd(1)
gPad.SetRightMargin(0.3)
gPad.SetFillColor(10)
modelPdf.paramOn(frame, RooFit.Layout(0.725, 0.9875, 0.9))
frame.Draw()
canvas.SaveAs(str(hname) + '.png')
def bw_fit(ecm, infile, outdir, reconstruction):
"""Breit-Wigner fit of the Mw distribution"""
file_ = TFile(infile, "r")
file_.cd()
mass_ = 'h_mW2'
h_mass = gDirectory.Get(mass_)
scale = h_mass.GetXaxis().GetBinWidth(1) / (h_mass.Integral("width"))
h_mass.Scale(scale)
mass_min = 40
mass_max = 120
mass = RooRealVar("Dijet mass", "Dijet mass", mass_min, mass_max, "GeV")
# parameters for gaussian function
gaus_sig = RooRealVar("#sigma_{G}", "Core Width", 1., 0.5, 10., "GeV")
# gaus_sig.setConstant()
# parameters for Crystall Ball distribution
m_ = RooRealVar("#Delta m", "Bias", 0., -3., 3., "GeV")
sigma = RooRealVar("#sigma", "Width", 1.7, 0., 10., "GeV")
alpha = RooRealVar("#alpha", "Cut", -0.15, -5., 0.)
n = RooRealVar("n", "Power", 2.4, 0.5, 10.)
alpha.setConstant()
n.setConstant()
# Parameters for Breit-Wigner distribution
m_res = RooRealVar("M_{W}", "W boson mass", 80.385, 80.0, 81.0, "GeV")
width = RooRealVar("#Gamma", "W width", 2.085, 1.5, 2.5, "GeV")
m_res.setConstant()
width.setConstant()
# Cristall-Ball lineshape
resG = RooGaussian("resG", "Gaussian distribution", mass, m_, gaus_sig)
resCB = RooCBShape("resCB", "Crystal Ball distribution", mass, m_, sigma,
alpha, n)
fracG = RooRealVar("f_{G}", "Gaussian Fraction", 0., 0., 1.)
res = RooAddPdf("res", "Resolution Model", resG, resCB, fracG)
# Breit-wigner lineshape
bw = RooBreitWigner("bw", "Breit-Wigner distribution", mass, m_res, width)
# Convolution
bw_CB_conv = RooFFTConvPdf("bw_CB_conv", "Convolution", mass, bw, res)
# Background p.d.f
bgtau = RooRealVar("a_{BG}", "Backgroung Shape", -0.15, -1.0, 0.0,
"1/GeV/c^{2}")
bg = RooExponential("bg", "Background distribution", mass, bgtau)
# Fit model
nentries = h_mass.GetEntries()
nsigmin = 0.5 * nentries
nsigmean = 1.0 * nentries
nsigmax = 1.05 * nentries
nbkgmean = 0.01 * nentries
nbkgmax = 0.1 * nentries
nsig = RooRealVar("N_S", "#signal events", nsigmean, nsigmin, nsigmax)
nbkg = RooRealVar("N_B", "#background events", nbkgmean, 0, nbkgmax)
model = RooAddPdf("model", "W mass fit", RooArgList(bw_CB_conv, bg),
RooArgList(nsig, nbkg))
###### FIT
c_name = "c_ " + mass_ + "_" + str(ecm) + "_" + reconstruction + "_fit"
c_mass_fit = TCanvas(
c_name,
'Fit of the reconstructed mass distribution with a convolution of Breit-Wigner and Crystal-Ball',
700, 500)
c_mass_fit.cd()
data = RooDataHist("data", "data", RooArgList(mass), h_mass)
frame = mass.frame()
data.plotOn(frame)
model.fitTo(data, RooFit.Optimize(0))
model.plotOn(frame)
model.paramOn(frame, RooFit.Layout(0.6, 0.90, 0.85))
frame.Draw()
# norm = h_mass.Integral()/bw_CB_conv.createIntegral(RooArgSet(mass)).getValV()
# m = m_.getValV()*norm
# s = sigma.getValV()*norm
m = m_.getVal()
s = sigma.getVal()
print("\n\n----------------------------------------------")
print(" Fit results :")
print(" Bias to apply on mW : {} GeV".format(m))
print(" Mw = {} +/- {} GeV".format((m + 80.385), s))
print("--------------------------------------------------")
raw_input("")
c_mass_fit.Print("{}fit/fit_{}_{}_{}.pdf".format(outdir, mass_, ecm,
reconstruction))
# write into an output file and close the file
outfilename = "{}fit/fit_{}_{}.root".format(outdir, mass_, ecm)
outfile = TFile(outfilename, "UPDATE")
c_mass_fit.Write("", TObject.kOverwrite)
outfile.Write()
outfile.Close()
crystal_mean = RooRealVar("crystal_mean", "mean of crystal ball", 1.77, 1.5,
1.9)
crystal_sigma = RooRealVar("crystal_sigma", "width of crystal ball", 0.02, 0.0,
0.1)
n = RooRealVar("n", "power parameter in the crystal ball", 1, 0, 20)
alpha = RooRealVar("alpha", "boundry in the crystal ball", 1.75, 1.6, 1.9)
crystal_constant = RooRealVar("crystal_constant", "constant", 7417.0, 1769.0,
8011.0)
#Parameters of gaussian fit
gaus_mean = RooRealVar("gaus_mean", "mean of the gaussian", 1.77, 1.5, 1.9)
gaus_sigma = RooRealVar("gaus_sigma", "width of the gaussian", 0.01, 0.0, 0.1)
gaus_constant = RooRealVar("gau_constant", "constant of the gaussian", 100, 0,
1000)
# Build crystall p.d.f in terms of x,mean and sigma
crystalball = RooCBShape("CBShape", "Cystal Ball Function", x, crystal_mean,
crystal_sigma, alpha, n)
gaus = RooGaussian("GaussShape", "Gaussian Function", x, gaus_mean, gaus_sigma)
model = RooAddPdf("model", "model", RooArgList(crystalball, gaus),
RooArgList(crystal_constant, gaus_constant))
# Construct plot frame in 'x'
#xframe = x.frame(Title("Crystalball p.d.f."))
xframe = x.frame()
# ---------------------------------------
# Plot model and change parameter values
# ---------------------------------------
# Change the values of the parameters
crystal_mean.setVal(1.776)
def fitChicSpectrum(dataset, binname):
""" Fit chic spectrum"""
x = RooRealVar('s', 's', -2, 2)
x.setBins(200)
#signal model
q_chi1 = RooRealVar('qchi1', 'q_{#chi 1}', 0.414, 0.2, 0.6)
q_chi2 = RooRealVar('qchi2', 'q_{#chi 2}', 0.430, 0.2, 0.6)
delta_chi10 = RooRealVar('delta_chi10', 'delta_chi10', 0.09591)
q_chi0 = RooFormulaVar('q_chi0', '@0 - @1',
RooArgList(q_chi1, delta_chi10))
alphacb_chi1 = RooRealVar('alphacb_chi1', '#alpha^{CB}_{#chi 1}', 0.6, 0,
2)
alphacb_chi2 = RooRealVar('alphacb_chi2', '#alpha^{CB}_{#chi 2}', 0.4, 0,
2)
sigmacb_chi1 = RooRealVar('sigmacb_chi1', '#sigma^{CB}_{#chi 1}', 0.005, 0,
1)
sigmacb_chi2 = RooRealVar('sigmacb_chi2', '#sigma^{CB}_{#chi 2}', 0.005, 0,
1)
n_cb = RooRealVar('ncb', 'n^{CB}', 3.0, 0., 5.)
gamma_chi0 = RooRealVar('gamma_chi0', 'gamma_chi0', 0.0104)
sigmacb_chi0 = RooRealVar('sigmacb_chi0', '#sigma^{CB}_{#chi 0}', 0.005)
chi0_sig = RooVoigtian('chi0sig', 'chi0sig,', x, q_chi0, sigmacb_chi0,
gamma_chi0)
chi1_sig = RooCBShape('chi1sig', 'chi1sig', x, q_chi1, sigmacb_chi1,
alphacb_chi1, n_cb)
chi2_sig = RooCBShape('chi2sig', 'chi2sig', x, q_chi2, sigmacb_chi2,
alphacb_chi2, n_cb)
fchi0 = RooRealVar('fchi0', 'f_{#chi 0}', 0.01, 0, 1)
fchi1 = RooRealVar('fchi1', 'f_{#chi 1}', 0.5, 0, 1)
fchi2 = RooFormulaVar('fchi2', '1-@0-@1', RooArgList(fchi0, fchi1))
fbck = RooRealVar('fbck', 'f_{bck}', 0.2, 0, 1)
sigmodel = RooAddPdf('sigm', 'sigm',
RooArgList(chi0_sig, chi1_sig, chi2_sig),
RooArgList(fchi0, fchi1, fchi2))
#background model
q0Start = 0.0
a_bck = RooRealVar('a_bck', 'a_{bck}', 0.5, -5, 5)
b_bck = RooRealVar('b_bck', 'b_{bck}', -2.5, -7., 0.)
q0 = RooRealVar('q0', 'q0', q0Start)
delta = RooFormulaVar('delta', 'TMath::Abs(@0-@1)', RooArgList(x, q0))
bfun = RooFormulaVar('bfun', '@0*(@1-@2)', RooArgList(b_bck, x, q0))
signum = RooFormulaVar('signum', '( TMath::Sign( -1.,@0-@1 )+1 )/2.',
RooArgList(x, q0))
background = RooGenericPdf('background', 'Background',
'signum*pow(delta,a_bck)*exp(bfun)',
RooArgList(signum, delta, a_bck, bfun))
modelPdf = RooAddPdf('chicmodel', 'chicmodel',
RooArgList(sigmodel, background), RooArgList(fbck))
frame = x.frame(RooFit.Title('Q'))
range = x.setRange('range', 0, 2)
# result = modelPdf.fitTo(dataset,RooFit.Save(),RooFit.Range('range'))
dataset.plotOn(frame, RooFit.MarkerSize(0.7))
modelPdf.plotOn(frame, RooFit.LineWidth(2))
#plotting
canvas = TCanvas('fit', "", 1400, 700)
canvas.Divide(1)
canvas.cd(1)
gPad.SetRightMargin(0.3)
gPad.SetFillColor(10)
modelPdf.paramOn(frame, RooFit.Layout(0.725, 0.9875, 0.9))
frame.Draw()
canvas.SaveAs('out-' + binname + '.png')
canvas.SaveAs('out-' + binname + '.root')
def fit_mbc():
from tools import normalizedRooFitIntegral, RooFitIntegral
from ROOT import RooRealVar, RooDataSet, RooArgList, RooArgSet, \
RooGaussian, RooArgusBG, RooCBShape, RooAddPdf, RooPolynomial, \
RooDataHist, RooFit, kTRUE, kFALSE
signal_margins = [1.86, 1.87]
sb_margins = [1.84, 1.85]
# Right here we compute background yield
mbc = RooRealVar('mbc', 'mbc', 1.83, 1.89, 'GeV')
arg_cutoff = RooRealVar('arg_cutoff', 'Argus cutoff', 1.8869, 1.885, 1.888, 'GeV')
arg_slope = RooRealVar('arg_slope', 'Argus slope', -13, -100, -1)
mbc_d0 = RooRealVar('mbc_d0', 'D0 Mbc', 1.8647, 'GeV')
mbc_dp = RooRealVar('mbc_dp', 'D+ Mbc', 1.8694, 'GeV')
mbc_float = RooRealVar('mbc_float', 'Floating D mass', 1.869, 1.855, 1.875, 'GeV')
sigma = RooRealVar('sigma', 'D width', 0.00145, 0.0001, 0.0025, 'GeV')
sigma2 = RooRealVar('sigma2', 'CB width', 0.00145, 0.0001, 0.005,
'GeV')
alpha = RooRealVar('alpha', 'CB shape cutoff', -1.515, -2., 2)
n = RooRealVar('n', 'CB tail parameter', 6, 0, 20)
gauss_d0 = RooGaussian('gauss_d0', 'D0 gaussian', mbc, mbc_d0, sigma2)
gauss_dp = RooGaussian('gauss_dp', 'D+ gaussian', mbc, mbc_dp, sigma2)
gauss_float = RooGaussian('gauss_float', 'Floating gaussian',
mbc, mbc_float, sigma2)
cb_d0 = RooCBShape('cb_d0', 'D0 Crystal Barrel', mbc,
mbc_d0, sigma, alpha, n)
cb_dp = RooCBShape('cb_dp', 'D+ Crystal Barrel', mbc,
mbc_dp, sigma, alpha, n)
cb_float = RooCBShape('cb_float', 'Floating Crystal Barrel', mbc,
mbc_float, sigma, alpha, n)
argus = RooArgusBG('argus', 'Argus BG', mbc, arg_cutoff, arg_slope)
yld = RooRealVar('yield', 'D yield', 25700, 0, 100000)
yld2 = RooRealVar('yield2', '2nd yield', 100, 0, 2000)
bkg = RooRealVar('bkg', 'Background', 1300, 0, 40000)
a = RooRealVar('a', 'Norm', 1)
poly = RooPolynomial('poly', 'poly PDF', mbc, RooArgList(a), 0)
sumpdf_d0 = RooAddPdf('sumpdf_d0', 'D0 sum pdf',
RooArgList(cb_d0, argus),
RooArgList(yld, bkg))
sumpdf_dp = RooAddPdf('sumpdf_dp', 'Dp sum pdf',
RooArgList(cb_dp, argus),
RooArgList(yld, bkg))
sumpdf_float = RooAddPdf('sumpdf_float', 'Generic D sum pdf',
RooArgList(cb_float, argus),
RooArgList(yld, bkg))
width_modes = { '0': 0.00150, '1': 0.001831, '3': 0.001426, '200': 0.001387,
'202': 0.001407 }
n_modes = { '0': 2.68, '1': 4.06, '3': 4.34, '200': 4.05, '202': 5.26 }
alpha_modes = { '0': -1.6145, '1': -1.4562, '3': -1.5834, '200': -1.6538,
'202': -1.5598 }
pdf = sumpdf_float
sigma.setVal(width_modes['1']) # sigma.setConstant()
n.setVal(n_modes['1']) #n.setConstant()
alpha.setVal(alpha_modes['1']) #alpha.setConstant()
#sigma.setConstant()
#arg_cutoff.setVal(1.8865); #arg_cutoff.setConstant()
c1.Divide(1,2)
c1.cd(1)
dset = RooDataHist('dsetmc', 'title', RooArgList(mbc), h_mbc['mc'])
#pdf.fitTo(dset, 'eq')
Extended = RooFit.Extended(kTRUE) # e
Verbose = RooFit.Verbose(kFALSE) #q
pdf.fitTo(dset, Extended, Verbose)
# xframe = mbc.frame()
# dset.plotOn(xframe)
# pdf.plotOn(xframe)
# pdf.paramOn(xframe,dset)
# xframe.SetTitle('Fake type 1, MC')
# xframe.Draw()
c1.cd(2)
dset = RooDataHist('dsetdata', 'title', RooArgList(mbc), h_mbc['data'])
#pdf.fitTo(dset, 'eq')
pdf.fitTo(dset, Extended, Verbose)
# xframe = mbc.frame()
# dset.plotOn(xframe)
# pdf.plotOn(xframe)
# pdf.paramOn(xframe,dset)
# xframe.SetTitle('Fake type 1, data')
# xframe.Draw()
sb_scale = (normalizedRooFitIntegral(argus, mbc, signal_margins[0],
signal_margins[1])/
normalizedRooFitIntegral(argus, mbc, sb_margins[0], sb_margins[1]))
def PeakFit_likelihood(Likelihood_cut: pd.DataFrame,
mass_energy: pd.DataFrame,
cutval,
plots=True,
constant_mean=True,
constant_width=True,
classifier_name='Likelihood',
CB=True,
Gauss=False,
bkg_comb=True,
bkg_exp=False,
bkg_cheb=False):
print('Starting fit...')
matplotlib.use('Agg')
# Check if we have mass in MeV or GeV
if np.mean(mass_energy) > 1000:
normalization_mass = 1000
else:
normalization_mass = 1
sns.set_style("whitegrid") # White background on plot
prediction = Likelihood_cut # rename to prediction
# Set range
mZmin = 60.0
mZmax = 130.0
# Number of bins
NbinsZmass = 100
#Initiate the mass variable
m_ee = ROOT.RooRealVar("m_ee", "Invariant mass (GeV/c^{2})", mZmin, mZmax)
m_ee.setRange("MC_mZfit_range", mZmin, mZmax)
# =============================================================================
# fit signal
# =============================================================================
# Make a mask in the signal range. Prediction is 0 or 1, so above 0.5 is signal
mask_mass = (mass_energy / normalization_mass > mZmin) & (
mass_energy / normalization_mass < mZmax) & (prediction > 0.5)
Z_mass_signal = np.array(mass_energy[mask_mass] / normalization_mass)
#Make np.array
# Initiate 1D histogram
h_mZ_all = ROOT.TH1D("h_mZ_all", "Histogram of Z mass", NbinsZmass, mZmin,
mZmax)
for isample in range(Z_mass_signal.shape[0]):
score = Z_mass_signal[isample]
h_mZ_all.Fill(score)
# Constructs histogram with m_ee as argument from the 1d histogram h_mZ_all
mc_Zee_mZ = ROOT.RooDataHist("mc_Zee_mZ", "Dataset with Zee m_ee",
RooArgList(m_ee), h_mZ_all)
# Define variables for the fits.
# BW: Breit-Wigner. CB: Crystal-Ball
meanBW = ROOT.RooRealVar("meanBW", "meanBW", 91.1876, 60.0, 120.0)
#91.1876
meanBW.setConstant(True)
# this is a theoretical constant
sigmaBW = ROOT.RooRealVar("sigmaBW", "sigmaBW", 2.4952, 2.0, 20.0)
#2.4952
sigmaBW.setConstant(True)
# this is a theoretical constant
# if constant_mean:
func_BW = ROOT.RooBreitWigner("func_BW", "Breit-Wigner", m_ee, meanBW,
sigmaBW)
# Make the function from the constants
# Crystal ball
if CB:
meanCB = RooRealVar("meanCB", "meanCB", -0.0716, -10.0, 10.0)
# meanCB.setConstant(True) #if commented out, it can float between the minimum and maximum
sigmaCB = RooRealVar("sigmaCB", "sigmaCB", 0.193, 0, 15)
# sigmaCB.setConstant(True)
alphaCB = RooRealVar("alphaCB", "alphaCB", 1.58, 0.0, 10.0)
# alphaCB.setConstant(True)
nCB = RooRealVar("nCB", "nCB", 0.886, -10, 50.0)
# nCB.setConstant(True)
func_sig_CB = RooCBShape("func_CB", "Crystal Ball", m_ee, meanCB,
sigmaCB, alphaCB, nCB)
# Define Crystal-Ball function
# Gaussian
elif Gauss: # Use Gaussian if True in function call
meanGA = RooRealVar("meanGA", "meanGA", 10.0, -10.0, 10.0)
sigmaGA = RooRealVar("sigmaGA", "sigmaGA", 3.0, 0.01, 10.0)
if constant_width:
sigmaGA.setConstant(True)
nGA = RooRealVar("nGA", "nGA", 1.5, 0.0, 20.0)
func_GA = RooGaussian("func_GA", "Gaussian", m_ee, meanGA, sigmaGA)
#, nGA);
if CB: # Convolute Breit-Wigner and Crystal-Ball
print("Convoluting a Crystal-Ball and Breit-Wigner for signal")
func_BWxCB_unextended = RooFFTConvPdf("func_BWxCB_unextended",
"Breit-Wigner (X) Crystal Ball",
m_ee, func_BW, func_sig_CB)
elif Gauss: # Convolute Breit-Wigner and Gauss
print("Convoluting a Gauss and Breit-Wigner for signal")
func_BWxCB_unextended = RooFFTConvPdf("func_BWxCB_unextended",
"Breit-Wigner (X) Gaussian",
m_ee, func_BW, func_GA)
else: # only Breit-Wigner fit on the signal
print("Fitting only with Breit-Wigner for signal")
func_BWxCB_unextended = func_BW
m_ee.setRange("MC_mZfit_range", 85, 97)
# Set the fit range for the signal
nsig = RooRealVar("ntotal", "ntotal", 1000, 0, 10e6)
# Define the variable for the number of signal
func_BWxCB = ROOT.RooExtendPdf("signal_func_Zee", "signal_func_Zee",
func_BWxCB_unextended, nsig)
# Adding the nsig term to the pdf
func_BWxCB.fitTo(mc_Zee_mZ, RooFit.Range("MC_mZfit_range"))
# Fit the signal
if plots: # Plots the signal using the function "root_plot" defined above
mc_Zee_signal = root_plot(m_ee=m_ee,
distribution=mc_Zee_mZ,
fit=func_BWxCB,
mZmin=mZmin,
mZmax=mZmax,
title=f'signal for cut {cutval}')
#cut {cutval}
# =============================================================================
# background
# =============================================================================
nbkg = RooRealVar("nbkg", "nbkg", 1000, 0, 10e6)
# Define the variable for the number of background
#if True:
m_ee.setRange("MC_mZfit_range", mZmin, mZmax)
# Set range for fit as defined in the beginning
c_bkg_mZ = ROOT.TCanvas("c_bkg_mZ", "", 0, 0, 1000, 500)
# Make the canvas for plotting
Z_mass_background = np.array(mass_energy[mask_mass] / normalization_mass)
# Mask for background
h_mZWenu_all = ROOT.TH1D("h_mZ_all", "Histogram of Z mass", NbinsZmass,
mZmin, mZmax)
# Initiate 1D histogram
for isample in range(Z_mass_background.shape[0]):
score = Z_mass_background[isample]
h_mZWenu_all.Fill(score)
# Create the lin + exponential fit
lam = RooRealVar("lambda", "Exponent", -0.04, -5.0, 0.0)
func_expo = ROOT.RooExponential("func_expo", "Exponential PDF", m_ee, lam)
#coef_pol1 = RooRealVar("coef_pol1", "Slope of background", 0.0, -10.0, 10.0);
#func_pol1 = ROOT.RooPolynomial("func_pol1", "Linear PDF", m_ee, RooArgList(coef_pol1));
# Create Chebychev polymonial
a0 = RooRealVar("a0", "a0", -0.4, -5.0, 5.0)
a1 = RooRealVar("a1", "a1", -0.03, -5.0, 5.0)
a2 = RooRealVar("a2", "a2", 0.02, -5.0, 5.0)
a3 = RooRealVar("a3", "a3", 0.02, -5.0, 5.0)
# Polynomials with different order
func_Cpol1 = RooChebychev("func_Cpol1",
"Chebychev polynomial of 1st order", m_ee,
RooArgList(a0, a1))
func_Cpol2 = RooChebychev("func_Cpol2",
"Chebychev polynomial of 2nd order", m_ee,
RooArgList(a0, a1, a2))
func_Cpol3 = RooChebychev("func_Cpol3",
"Chebychev polynomial of 3rd order", m_ee,
RooArgList(a0, a1, a2, a3))
f_exp_mZ = RooRealVar("N_lin_mZ", "CLinear fraction", 0.50, 0, 1)
m_ee.setRange("low", 60, 70)
m_ee.setRange("high", 110, 130)
# Adding exponential and Chebychev if comb:
if bkg_comb:
func_ExpLin_mZ_unextended = ROOT.RooAddPdf(
"func_ExpLin_mZ_unextended", "Exponential and Linear PDF",
RooArgList(func_Cpol3, func_expo), RooArgList(f_exp_mZ))
elif bkg_exp:
func_ExpLin_mZ_unextended = func_expo
elif bkg_cheb:
func_ExpLin_mZ_unextended = func_Cpol3
else:
print("No background fit called. Exiting")
return None
func_ExpLin_mZ = ROOT.RooExtendPdf("func_ExpLin_mZ", "func_ExpLin_mZ",
func_ExpLin_mZ_unextended, nbkg)
# Adding the nbkg term to the pdf
# Constructs histogram with m_ee as argument from the 1d histogram h_mZ_all
mc_Wenu_mZ = ROOT.RooDataHist("mc_Zee_mZ", "Dataset with Zee m_ee",
RooArgList(m_ee), h_mZWenu_all)
func_ExpLin_mZ.fitTo(mc_Wenu_mZ, RooFit.Range("MC_mZfit_range"))
#ROOT.RooFit.Range("low,high")); # Fits background
#Plotting background
residue = root_plot(m_ee=m_ee,
distribution=mc_Wenu_mZ,
fit=func_ExpLin_mZ,
mZmin=mZmin,
mZmax=mZmax,
title=f'Background for cut {cutval}')
#
# =============================================================================
# Combining signal and background
# =============================================================================
m_ee.setRange("MC_mZfit_range", mZmin, mZmax)
Z_mass = np.array(mass_energy[mask_mass] / normalization_mass)
h_mZWenu = ROOT.TH1D("h_mZ_all", "Histogram of Z mass", NbinsZmass, mZmin,
mZmax)
for isample in range(Z_mass.shape[0]):
score = Z_mass[isample]
h_mZWenu.Fill(score)
# Constructs histogram with m_ee as argument from the 1d hist ogram h_mZ_all
mc_ZeeWenu_mZ = ROOT.RooDataHist("mc_Zee_mZ", "Dataset with Zee m_ee",
RooArgList(m_ee), h_mZWenu)
## Fits the data and returns the fraction of background
f_bkg_mZ = RooRealVar("f_bkg_mZ", "Signal fraction",
nbkg.getVal() / nsig.getVal(), 0.0, 1)
## Combining the signal and background fits
func_SigBkg_mZ_unextended = ROOT.RooAddPdf(
"func_SigBkg_mZ", "Signal and Background PDF",
RooArgList(func_ExpLin_mZ_unextended, func_BWxCB_unextended),
RooArgList(f_bkg_mZ))
# func_SigBkg_mZ_unextended = func_BWxCB_unextended;#ROOT.RooAddPdf("func_SigBkg_mZ", "Signal and Background PDF", RooArgList(func_BWxCB_unextended, func_BWxCB_unextended), RooArgList(f_bkg_mZ));
ntotal = RooRealVar("ntotal", "ntotal", 10000, 0, 10e6)
func_SigBkg_mZ = ROOT.RooExtendPdf("func_ExpLin_mZ", "func_ExpLin_mZ",
func_SigBkg_mZ_unextended, ntotal)
func_SigBkg_mZ.fitTo(mc_ZeeWenu_mZ)
# Fits the full data set
if plots:
mc_ZeeWenu_mZ_resid = root_plot(m_ee=m_ee,
distribution=mc_ZeeWenu_mZ,
fit=func_SigBkg_mZ,
mZmin=mZmin,
mZmax=mZmax,
title=f'Bkg+Sig for cut {cutval}')
# Baseline ntotal = 41231 (Data)
# fraction 0.9333
# Baseline ntotal = 74747 (MC)
# fraction 0.4427
# Malte script len(Z_mass)
bkg = len(Z_mass) * f_bkg_mZ.getVal()
sig = len(Z_mass) * (1 - f_bkg_mZ.getVal())
print(f_bkg_mZ.getVal())
#DATA
#BL_sig = 41231*(1-0.9333) # BL = baseline, the number is the fraction of bkg in baseline
#BL_bkg = 41231*0.9333 # BL = baseline
# DATA OS
# BL_sig = 22276 * (1-0.853) # BL = baseline, the number is the fraction of bkg in baseline
# BL_bkg = 22276 * 0.853 # BL = baseline
# DATA SS
# BL_sig = 18925 * (1-0.993552)#74747 * (1-0.4427)#41054
# BL_bkg = 18925 - BL_sig
#MC OS
# exp
BL_sig = 46547 * (1 - 0.0350) #74747 * (1-0.4427)#41054
BL_bkg = 46547 * 0.0350
#comb
#BL_sig = 74747*(1-0.4427) # BL = baseline, the number is the fraction of bkg in baseline
#BL_bkg = 74747*0.4427 # BL = baseline
bkg_ratio = bkg / BL_bkg
sig_ratio = sig / BL_sig
max_residue = max(abs(mc_ZeeWenu_mZ_resid.getYAxisMax()),
abs(mc_ZeeWenu_mZ_resid.getYAxisMin()))
print(max_residue)
print(bkg_ratio)
print(sig_ratio)
if (bkg_ratio < 1.009) & (sig_ratio < 1.009) & (abs(
mc_ZeeWenu_mZ_resid.getYAxisMin()) < 4) & (abs(
mc_ZeeWenu_mZ_resid.getYAxisMax()) < 4):
# input('....')
return BL_sig, BL_bkg, sig_ratio, bkg_ratio #max_residue, ntotal.getVal(), nsig.getVal(), nbkg.getVal()return sigmaCB if CB else sigmaGA #sig_ratio, sigma_sig, bkg_ratio, sigma_bkg
else:
return 0, 0, 0, 0
def doMCFit(inputfile_name,
mass_chib,
cuts,
output_name='ChiB',
plotTitle="#Chi_{b}",
fittedVariable='qValue',
returnOnlyFitResult=False,
useOtherSignalParametrization=False,
drawPulls=False,
legendOnPlot=True):
mass_error = 0.15
print "Creating DataSet from file " + str(inputfile_name)
dataSet = makeRooDataset(inputfile_name)
if (fittedVariable == 'refittedMass'):
x_var = 'rf1S_chib_mass'
output_suffix = '_refit'
x_axis_label = 'm_{#mu^{+} #mu^{-} #gamma} [GeV]'
else:
x_var = 'invm1S'
output_suffix = '_qValue'
x_axis_label = 'm_{#gamma #mu^{+} #mu^{-}} - m_{#mu^{+} #mu^{-}} + m^{PDG}_{#Upsilon} [GeV]'
cuts_str = str(cuts)
#cuts_str = quality_cut +"photon_pt > 0.5 && abs(photon_eta) < 1.0 && abs(dimuon_rapidity) < 1.0 && dimuon_pt>5.0 && pi0_abs_mass > 0.025 && fabs(dz) < 0.1 "#&& numPrimaryVertices < 16"
data = dataSet.reduce(RooFit.Cut(cuts_str))
print 'Creating pdf'
x = RooRealVar(x_var, 'm(#mu #mu #gamma) - m(#mu #mu) + m_{#Upsilon}', 9.8,
9.96) #9.7,10.1,'GeV')
numBins = 32 # define here so that if I change it also the ndof change accordingly
x.setBins(numBins)
# Double sided Crystal Ball
mean = RooRealVar("#mu", "mean ChiB", mass_chib, mass_chib - mass_error,
mass_chib + mass_error, "GeV")
sigma = RooRealVar("#sigma", "sigma ChiB", 0.006, 0, 0.2, 'GeV')
a1 = RooRealVar('#alpha1', '#alpha1', 0.75, 0, 3)
a2 = RooRealVar('#alpha2', '#alpha2', 1.6, 0, 3)
n1 = RooRealVar('n1', 'n1', 2.8) #, 1.0,4.0) # 2 per 2S
n2 = RooRealVar('n2', 'n2', 3) #, 1.,4.0) # 1 per 2S
parameters = RooArgSet(mean, sigma, a1, a2, n1, n2)
cb_pdf = My_double_CB('chib', 'chib', x, mean, sigma, a1, n1, a2, n2)
#cb_pdf = RooCBShape('chib', 'chib', x, mean, sigma, a1, n1)
# ndof
floatPars = parameters.selectByAttrib("Constant", ROOT.kFALSE)
ndof = numBins - floatPars.getSize() - 1
if useOtherSignalParametrization: # In this case I redefine cb_pdf
n1 = RooRealVar('n1', 'n1', 2)
cb = RooCBShape('cb1', 'cb1', x, mean, sigma, a1, n1)
# I use a2 as the sigma of my second CB
a2 = RooRealVar('#alpha2', '#alpha2', 0.5, 0, 3)
gauss = RooCBShape('cb2', 'cb2', x, mean, a2, a1, n1)
# I use n2 as the ratio of cb1 with respect to cb2
n2 = RooRealVar('n2', 'n2', 0., 1.)
cb_pdf = RooAddPdf('chib', 'chib', RooArgList(cb, gauss),
RooArgList(n2))
#cb_pdf = cb
print 'Fitting to data'
fit_region = x.setRange("fit_region", 9.8, 9.96)
result = cb_pdf.fitTo(data, RooFit.Save(), RooFit.Range("fit_region"))
# a1_val = a1.getVal()
# a2_val = a2.getVal()
# a1 = RooRealVar('#alpha1', '#alpha1', a1_val)
# a2 = RooRealVar('#alpha2', '#alpha2', a2_val)
# n1 = RooRealVar('n1', 'n1', 1.7,1.0,5.)
# n2 = RooRealVar('n2', 'n2', 1.7,0.,4.)
# cb_pdf = My_double_CB('chib', 'chib', x, mean, sigma, a1, n1, a2, n2)
# result = cb_pdf.fitTo(data, RooFit.Save())
if returnOnlyFitResult:
return result
# define frame
frame = x.frame()
frame.SetNameTitle("fit_resonance", "Fit Resonanace")
frame.GetXaxis().SetTitle(x_axis_label)
frame.GetYaxis().SetTitle("Events/5 MeV ")
frame.GetXaxis().SetTitleSize(0.04)
frame.GetYaxis().SetTitleSize(0.04)
frame.GetXaxis().SetTitleOffset(1.1)
frame.GetXaxis().SetLabelSize(0.04)
frame.GetYaxis().SetLabelSize(0.04)
frame.SetLineWidth(1)
frame.SetTitle(plotTitle)
# plot things on frame
data.plotOn(frame, RooFit.MarkerSize(0.7))
cb_pdf.plotOn(frame, RooFit.LineWidth(2))
# chiSquare legend
chi2 = frame.chiSquare()
probChi2 = TMath.Prob(chi2 * ndof, ndof)
chi2 = round(chi2, 2)
probChi2 = round(probChi2, 2)
leg = TLegend(0.3, 0, .10, .10)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
cb_pdf.paramOn(frame, RooFit.Layout(0.17, 0.56, 0.93))
leg.AddEntry(0, '#chi^{2} =' + str(chi2), '')
leg.AddEntry(0, 'Prob #chi^{2} = ' + str(probChi2), '')
leg.SetTextSize(0.04)
frame.addObject(leg)
if legendOnPlot:
legend = TLegend(.08, .5, .55, .7)
#legend.SetTextSize(0.04)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
#legend.AddEntry(0,'CMS','')
legend.AddEntry(
0,
str(cuts.upsilon_pt_lcut) + ' GeV < p_{T}(#Upsilon) < ' +
str(cuts.upsilon_pt_hcut) + ' GeV', '')
#legend.AddEntry(0,'p_{T}(#Upsilon)<'+str(cuts.upsilon_pt_hcut),'')
frame.addObject(legend)
title = TLegend(.8, .75, .9, .93)
title.SetTextSize(0.15)
title.SetFillStyle(0)
title.SetBorderSize(0)
title.AddEntry(0, plotTitle, '')
frame.addObject(title)
# Canvas
c1 = TCanvas(output_name + output_suffix, output_name + output_suffix)
frame.Draw()
if drawPulls:
#c1=TCanvas(output_name+output_suffix,output_name+output_suffix,700, 625)
hpull = frame.pullHist()
framePulls = x.frame()
framePulls.SetTitle(';;Pulls')
framePulls.GetYaxis().SetLabelSize(0.18)
framePulls.GetYaxis().SetTitle('Pulls')
framePulls.GetYaxis().SetTitleSize(0.18)
framePulls.GetYaxis().SetTitleOffset(0.15)
framePulls.GetYaxis().SetNdivisions(005)
framePulls.GetXaxis().SetLabelSize(0.16)
framePulls.GetXaxis().SetTitle('')
line0 = TLine(9.8, 0, 9.96, 0)
line0.SetLineColor(ROOT.kBlue)
line0.SetLineWidth(2)
framePulls.addObject(line0)
framePulls.addPlotable(hpull, "P")
framePulls.SetMaximum(5)
framePulls.SetMinimum(-5)
pad1 = TPad("pad1", "The pad 80% of the height", 0.0, 0.2, 1.0, 1.0)
pad1.cd()
frame.Draw()
pad2 = TPad("pad2", "The pad 20% of the height", 0.0, 0.01, 1.0, 0.2)
pad2.cd()
framePulls.Draw()
c1.cd()
pad1.Draw()
pad2.Draw()
#c1.SaveAs(output_name+output_suffix+'.png')
print 'Chi2 = ' + str(frame.chiSquare())
pdf_parameters = CB_parameters(mean=mean.getVal(),
sigma=sigma.getVal(),
n1=n1.getVal(),
n2=n2.getVal(),
a1=a1.getVal(),
a2=a2.getVal(),
s_mean=mean.getError(),
s_sigma=sigma.getError(),
s_n1=n1.getError(),
s_n2=n2.getError(),
s_a1=a1.getError(),
s_a2=a2.getError(),
chiSquare=frame.chiSquare())
#pdf_parameters.saveToFile("CB_"+output_name+output_suffix+".txt")
return pdf_parameters, c1
def plot_rec_minus_gen_pt2():
#reconstructed pT^2 vs. generated pT^2 for resolution
#distribution range
ptbin = 0.001
ptmin = -0.1
ptmax = 0.15
#generated pT^2 selection to input data
ptlo = 0.04
pthi = 0.1
#mass selection
mmin = 2.8
mmax = 3.2
fitran = [-0.003, 0.05]
#fitran = [-0.003, 0.003]
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
strsel += " && (jGenPt*jGenPt)>{0:.3f}".format(ptlo)
strsel += " && (jGenPt*jGenPt)<{0:.3f}".format(pthi)
print strsel
nbins, ptmax = ut.get_nbins(ptbin, ptmin, ptmax)
hPt2 = ut.prepare_TH1D("hPt2", ptbin, ptmin, ptmax)
ytit = "Events / ({0:.3f}".format(ptbin) + " GeV^{2})"
xtit = "#it{p}_{T, reconstructed}^{2} - #it{p}_{T, generated}^{2} (GeV^{2})"
ut.put_yx_tit(hPt2, ytit, xtit)
draw = "(jRecPt*jRecPt)-(jGenPt*jGenPt)"
mctree.Draw(draw + " >> hPt2", strsel)
#roofit binned data
x = RooRealVar("x", "x", -1, 1)
dataH = RooDataHist("dataH", "dataH", RooArgList(x), hPt2)
x.setRange("fitran", fitran[0], fitran[1])
#reversed Crystal Ball
mean = RooRealVar("mean", "mean", 0., -0.1, 0.1)
sigma = RooRealVar("sigma", "sigma", 0.0011, 0., 0.1)
alpha = RooRealVar("alpha", "alpha", -1.046, -10., 0.)
n = RooRealVar("n", "n", 1.403, 0., 20.)
pdf = RooCBShape("pdf", "pdf", x, mean, sigma, alpha, n)
#gaus = RooGaussian("gaus", "gaus", x, mean, sigma)
#make the fit
#res = pdf.fitTo(dataH, rf.Range("fitran"), rf.Save())
#res = gaus.fitTo(dataH, rf.Range("fitran"), rf.Save())
can = ut.box_canvas()
ut.set_margin_lbtr(gPad, 0.12, 0.1, 0.015, 0.03)
frame = x.frame(rf.Bins(nbins), rf.Title(""))
frame.SetTitle("")
frame.SetYTitle(ytit)
frame.SetXTitle(xtit)
frame.GetXaxis().SetTitleOffset(1.2)
frame.GetYaxis().SetTitleOffset(1.7)
dataH.plotOn(frame, rf.Name("data"))
pdf.plotOn(frame, rf.Precision(1e-6), rf.Name("pdf"))
#gaus.plotOn(frame, rf.Precision(1e-6), rf.Name("gaus"))
frame.Draw()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def RooFitHist(inputhist,title='title',path='.'):
# RooFit.gErrorIgnoreLevel = RooFit.kInfo # <6.02
# RooFit.SetSilentMode()# >6.02
# RooMsgService().instance().SetSilentMode(kTRUE)
fitbinning=array('d')
binwidth=200
#NBins=(14000/binwidth) - ( (1040/binwidth) + 1 ) Mjj
NBins=(8000/binwidth) - ( (200/binwidth)+1 )#pT
for i in range(NBins+1):
fitbinning.append(210+i*binwidth)# Mjj 1050
# print(fitbinning)
#import numpy as np
#fitbinning=np.linspace(0,8000,81)
hist=inputhist.Rebin(NBins,"fit parameter",fitbinning)
#hist=inputhist.Rebin(80,"fit parameter", fitbinning)
#meanstart=hist.GetBinCenter(2000)
meanstart=hist.GetBinCenter(hist.GetMaximumBin())#maximum
sigmastart=hist.GetRMS()
#sigmastart=3000
print('meanstart:',meanstart,'sigmastart:',sigmastart)
# inputhist.Draw()
# hist.Draw()
# hold=raw_input('press enter to exit.')
gStyle.SetOptFit(1111)
gStyle.SetOptTitle(0)
RooFit.SumW2Error(kTRUE)
RooFit.Extended(kTRUE)
# RooDataHist::adjustBinning(dh): fit range of variable mjj expanded to nearest bin boundaries: [1050,13850] --> [1050,13850]
mjj=RooRealVar('mjj','P_{T-AK8}',fitbinning[0],fitbinning[len(fitbinning)-1],'GeV')
mjjral=RooArgList(mjj)
dh=RooDataHist('dh','dh',mjjral,RooFit.Import(hist))
#shape.fitTo(dh,RooFit.Range("FitRange"),RooFit.Extended(True),RooFit.SumW2Error(False))
shapes={}
#Gaussian not really
# 3rd, 4th and 5th arguments are: (starting value, minimum possible value, maximum possible value) -- not goot
gaussmean = RooRealVar('#mu_{gauss}','mass mean value',meanstart,0,2*meanstart)
gausssigma= RooRealVar('#sigma_{gauss}','mass resolution',sigmastart,0,2*meanstart)
gauss=RooGaussian('gauss','gauss',mjj,gaussmean,gausssigma)
shapes.update({'Gauss':gauss})
#poisson
poissonmean = RooRealVar('#mu_{poisson}', 'pT mean value', meanstart,0,2*meanstart)
poissonsigma = RooRealVar('#sigma_{poisson]', 'pT resolution', sigmastart, 0, 2*sigmastart)
poisson = RooPoisson('poisson', 'poisson', mjj, poissonmean, False)
shapes.update({'Poisson': poisson})
#Landau -- not good
landaumean=RooRealVar('#mu_{landau}','mean landau',meanstart,0,2*meanstart)
landausigma= RooRealVar('#sigma_{landau}','mass resolution',sigmastart,0,2*sigmastart)#bzw8
landau=RooLandau('landau','landau',mjj,landaumean,landausigma)
shapes.update({'Landau':landau})
#CrystalBall -> this is close to be good but matrix error :(
mean = RooRealVar('#mu','mean',meanstart,0,2*meanstart)
sigma= RooRealVar('#sigma','sigma',sigmastart,0,2*sigmastart)
alpha=RooRealVar('#alpha','Gaussian tail',-1000,0)
n=RooRealVar('n','Normalization',-1000,1000)
cbshape=RooCBShape('cbshape','crystalball PDF',mjj,mean,sigma,alpha,n)
shapes.update({'CrystalBall':cbshape})
#Voigt ---pff
voigtmean = RooRealVar('#mu','mass mean value',meanstart,0,2*meanstart)
voigtwidth = RooRealVar('#gamma','width of voigt',0,100)
voigtsigma= RooRealVar('#sigma','mass resolution',sigmastart,0,150)
voigt=RooVoigtian('voigt','voigt',mjj,voigtmean,voigtwidth,voigtsigma)
shapes.update({'Voigt':voigt})
#BreitWigner--worst
bwmean = RooRealVar('#mu','mass mean value',meanstart,0,2*meanstart)
bwwidth = RooRealVar('#sigma','width of bw',sigmastart,100, 150)
bw=RooBreitWigner('bw','bw',mjj,bwmean,bwwidth)
shapes.update({'BreitWigner':bw})
#Logistics
#logisticsmean=RooRealVar('#mu_{logistics}','mean logistics',meanstart,0,2*meanstart)
#logisticssigma= RooRealVar('#sigma_{logistics}','mass resolution',sigmastart,0,2*sigmastart)
#logistics=RooLogistics('logistics','logistics',mjj,logisticsmean,logisticssigma)
#shapes.update({'Logistics':logistics})
#ExpAndGauss
expgaussmean=RooRealVar('#mu_{expgauss}','mean expgauss',meanstart,490,900)
expgausssigma= RooRealVar('#sigma_{expgauss}','mass resolution',sigmastart,20,3*sigmastart)
expgausstrans= RooRealVar('trans','trans',0,1000)
ExpAndGauss=RooExpAndGauss('expgauss','expgauss',mjj,expgaussmean,expgausssigma,expgausstrans)
shapes.update({'ExpAndGauss':ExpAndGauss})
#Exp
#expmean=RooRealVar('')
#BifurGauss -bad
BifurGaussmean=RooRealVar('#mu_{BifurGauss}','mean BifurGauss',meanstart,0,2*meanstart)
BifurGausslsigma= RooRealVar('#sigma_{left}','mass resolution',sigmastart,200,2*sigmastart)#2*sigmastart
BifurGaussrsigma= RooRealVar('#sigma_{right}','mass resolution',sigmastart,200,2*sigmastart)
BifurGauss=RooBifurGauss('BifurGauss','BifurGauss',mjj,BifurGaussmean,BifurGausslsigma,BifurGaussrsigma)
shapes.update({'BifurGauss':BifurGauss})
#Chebychev -nope
Chebychev1=RooRealVar('c0','Chebychev0',-1000,1000)
Chebychev2= RooRealVar('c1','Chebychev1',-1000,1000)
Chebychev3= RooRealVar('c2','Chebychev2',2,-1000,1000)
Chebychev=RooChebychev('Chebychev','Chebychev',mjj,RooArgList(Chebychev1,Chebychev2,Chebychev3))
shapes.update({'Chebychev':Chebychev})
#Polynomial -nope
Polynomial1=RooRealVar('Polynomial1','Polynomial1',100,0,1000)
Polynomial2= RooRealVar('Polynomial2','Polynomial2',100,0,1000)
Polynomial=RooPolynomial('Polynomial','Polynomial',mjj,RooArgList(Polynomial1,Polynomial2))
shapes.update({'Polynomial':Polynomial})
#pareto
#___________________________________________________________We will see
#Convolutions
#-> use bin > 1000 for better accuracy -----cyclic trouble
#-> Convolution depends on order!!! RooFFTConvPdf the first p.d.f. is the theory model and that the second p.d.f. is the resolution model
#
#LandauGauss Convolution - really bad
landaugauss=RooFFTConvPdf('landaugauss','landau x gauss',mjj,landau, gauss)
#landaugauss.setBufferFraction(126)
shapes.update({'LandauGauss':landaugauss})
#GaussLandau Convolution -> Better but NOT posdef. ...but for 100 it is
gausslandau=RooFFTConvPdf('gausslandau','gauss x landau ',mjj,gauss,landau)
#gausslandau.setBufferFraction(126)
shapes.update({'GaussLandau':gausslandau})
#CrystalBallLandau Convolution cbshape x landau looks better -> status failed
crystlandau=RooFFTConvPdf('crystallandau','cbshape x landau', mjj, landau, cbshape)
crystlandau.setBufferFraction(39)
shapes.update({'CrystLandau': crystlandau})
#BifurGaussLandau Convolution -> Better in look, NO matrix error for Binwidth 200
BifurGaussLandau=RooFFTConvPdf('bifurgausslandau','landau x bifurgauss',mjj,landau, BifurGauss)
BifurGaussLandau.setBufferFraction(39) #against over cycling
shapes.update({'BifurGaussLandau':BifurGaussLandau})
#CrystalGauss Convolution looks better -> status failed
crystgauss=RooFFTConvPdf('crystalgauss','cbshape x gauss', mjj, cbshape, gauss)
#crystgauss.setBufferFraction(39)
shapes.update({'CrystGauss': crystgauss})
#BreitWignerLandau Convolution (Breitwigner = Lorentz)-> status OK....
BreitWignerLandau=RooFFTConvPdf('breitwignerlandau','breitwigner x landau',mjj,landau,bw,3)
#BreitWignerLandau.setBufferFraction(48) #setBufferFraction(fraction of the sampling array size) ->cyclic behaviour fix
#crystgauss.setShift(0,0)
#s1 and s2 are the amounts by which the sampling ranges for pdf's are shifted respectively
#(0,-(xmin+xmax)/2) replicates the default behavior
#(0,0) disables the shifting feature altogether
shapes.update({'BreitWignerLandau':BreitWignerLandau})
for fname in ['ExpAndGauss']:
plottitle='%s Fit of %s'%(fname,title)
shape=shapes[fname]
# shape.fitTo(dh,RooFit.Range("FitRange"),RooFit.SumW2Error(True))
#shape.fitTo(dh,RooFit.Extended(True),RooFit.SumW2Error(True))
mjj.setRange("FitRange",500,4000)#tried
shape.fitTo(dh,RooFit.Range("FitRange"),RooFit.Extended(False),RooFit.SumW2Error(True))
frame=mjj.frame(RooFit.Title(plottitle))
#frame.GetYaxis().SetTitleOffset(2)
dh.plotOn(frame,RooFit.MarkerStyle(4))
shape.plotOn(frame,RooFit.LineColor(2))
ndof=dh.numEntries()-3
print ('ndof', ndof)
#chiSquare legend
chi2 = frame.chiSquare()#there are 2 chiSquare. the 2cond returns chi2/ndf /// \return \f$ \chi^2 / \mathrm{ndf} \f$
print ('chi2', chi2)
probChi2 = TMath.Prob(chi2*ndof, ndof)# why chi2*ndof ?! makes no sense to me
#Double_t Prob(Double_t chi2, Int_t ndf)
#Computation of the probability for a certain Chi-squared (chi2)
#and number of degrees of freedom (ndf).
#P(a,x) represents the probability that the observed Chi-squared
#for a correct model should be less than the value chi2.
#The returned probability corresponds to 1-P(a,x), !!!!
#which denotes the probability that an observed Chi-squared exceeds
#the value chi2 by chance, even for a correct model.
#--- NvE 14-nov-1998 UU-SAP Utrecht
#probChi2=TMath.Prob(chi2, ndof)
chi2 = round(chi2,2)
#probChi2 = round(probChi2,2)
leg = TLegend(0.5,0.5,0.5,0.65)#0.9
leg.SetBorderSize(0)
leg.SetFillStyle(0)
shape.paramOn(frame, RooFit.Layout(0.5,0.9,0.9))
leg.AddEntry(0,'#chi^{2} ='+str(chi2),'')
leg.AddEntry(0,'Prob #chi^{2} = '+str(probChi2),'')
leg.SetTextSize(0.04)
frame.addObject(leg)
canv=TCanvas(plottitle,plottitle,700,700)
canv.SetLogy()
canv.SetLeftMargin(0.20)
canv.cd()
frame.SetMaximum(10**(1))
frame.SetMinimum(10**(-11))#from -3 -> -6
frame.Draw()
#canv.Print(path+'/%s__%sS2MoreLessY.pdf'%(title,fname))
raw_input('press enter to continue')
return chi2
def plot_rec_minus_gen_pt():
#reconstructed pT vs. generated pT for resolution
#distribution range
ptbin = 0.005
ptmin = -0.2
ptmax = 0.4
#generated pT selection to input data
ptlo = 0
pthi = 0.1
#mass selection
mmin = 2.8
mmax = 3.2
#range for the fit
fitran = [-0.02, 0.2]
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
strsel += " && jGenPt>{0:.3f}".format(ptlo)
strsel += " && jGenPt<{0:.3f}".format(pthi)
nbins, ptmax = ut.get_nbins(ptbin, ptmin, ptmax)
hPtDiff = ut.prepare_TH1D("hPtDiff", ptbin, ptmin, ptmax)
ytit = "Events / ({0:.3f}".format(ptbin) + " GeV)"
xtit = "#it{p}_{T, reconstructed} - #it{p}_{T, generated} (GeV)"
mctree.Draw("jRecPt-jGenPt >> hPtDiff", strsel)
#roofit binned data
x = RooRealVar("x", "x", -1, 1)
x.setRange("fitran", fitran[0], fitran[1])
rfPt = RooDataHist("rfPt", "rfPt", RooArgList(x), hPtDiff)
#reversed Crystal Ball
mean = RooRealVar("mean", "mean", 0., -0.1, 0.1)
sigma = RooRealVar("sigma", "sigma", 0.01, 0., 0.1)
alpha = RooRealVar("alpha", "alpha", -1.046, -10., 0.)
n = RooRealVar("n", "n", 1.403, 0., 20.)
pdf = RooCBShape("pdf", "pdf", x, mean, sigma, alpha, n)
#make the fit
res = pdf.fitTo(rfPt, rf.Range("fitran"), rf.Save())
can = ut.box_canvas()
ut.set_margin_lbtr(gPad, 0.12, 0.1, 0.05, 0.03)
frame = x.frame(rf.Bins(nbins), rf.Title(""))
ut.put_frame_yx_tit(frame, ytit, xtit)
rfPt.plotOn(frame, rf.Name("data"))
pdf.plotOn(frame, rf.Precision(1e-6), rf.Name("pdf"))
frame.Draw()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_rec_gen_pt_relative():
# relative dielectron pT resolution as ( pT_rec - pT_gen )/pT_gen
ptbin = 0.01
ptmin = -1.2
ptmax = 4
#generated pT selection to input data
ptlo = 0.2
pthi = 1.
fitran = [-0.1, 3]
mmin = 2.8
mmax = 3.2
#output log file
out = open("out.txt", "w")
#log fit parameters
loglist1 = [(x, eval(x)) for x in ["infile_mc", "ptbin", "ptmin", "ptmax"]]
loglist2 = [(x, eval(x))
for x in ["ptlo", "pthi", "fitran", "mmin", "mmax"]]
strlog = ut.make_log_string(loglist1, loglist2)
ut.log_results(out, strlog + "\n")
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
strsel += " && jGenPt>{0:.3f}".format(ptlo)
strsel += " && jGenPt<{0:.3f}".format(pthi)
nbins, ptmax = ut.get_nbins(ptbin, ptmin, ptmax)
hPtRel = ut.prepare_TH1D("hPtRel", ptbin, ptmin, ptmax)
ytit = "Events / ({0:.3f})".format(ptbin)
xtit = "(#it{p}_{T, rec} - #it{p}_{T, gen})/#it{p}_{T, gen}"
mctree.Draw("(jRecPt-jGenPt)/jGenPt >> hPtRel", strsel)
x = RooRealVar("x", "x", ptmin, ptmax)
x.setRange("fitran", fitran[0], fitran[1])
rfPtRel = RooDataHist("rfPtRel", "rfPtRel", RooArgList(x), hPtRel)
#reversed Crystal Ball
mean = RooRealVar("mean", "mean", 0., -0.1, 0.1)
sigma = RooRealVar("sigma", "sigma", 0.2, 0., 0.9)
alpha = RooRealVar("alpha", "alpha", -1.2, -10., 0.)
n = RooRealVar("n", "n", 1.3, 0., 20.)
cbpdf = RooCBShape("cbpdf", "cbpdf", x, mean, sigma, alpha, n)
res = cbpdf.fitTo(rfPtRel, rf.Range("fitran"), rf.Save())
#log fit results
ut.log_results(out, ut.log_fit_result(res))
can = ut.box_canvas()
ut.set_margin_lbtr(gPad, 0.12, 0.1, 0.05, 0.03)
frame = x.frame(rf.Bins(nbins), rf.Title(""))
ut.put_frame_yx_tit(frame, ytit, xtit)
rfPtRel.plotOn(frame, rf.Name("data"))
cbpdf.plotOn(frame, rf.Precision(1e-6), rf.Name("cbpdf"))
frame.Draw()
desc = pdesc(frame, 0.65, 0.8, 0.057)
#x, y, sep
desc.set_text_size(0.03)
desc.itemD("#chi^{2}/ndf", frame.chiSquare("cbpdf", "data", 4), -1,
rt.kBlue)
desc.prec = 5
desc.itemR("mean", mean, rt.kBlue)
desc.prec = 4
desc.itemR("#sigma", sigma, rt.kBlue)
desc.itemR("#alpha", alpha, rt.kBlue)
desc.prec = 3
desc.itemR("#it{n}", n, rt.kBlue)
desc.draw()
leg = ut.prepare_leg(0.6, 0.82, 0.21, 0.12, 0.03) # x, y, dx, dy, tsiz
leg.SetMargin(0.05)
leg.AddEntry(0, "#bf{%.1f < #it{p}_{T}^{pair} < %.1f GeV}" % (ptlo, pthi),
"")
leg.Draw("same")
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plot_rec_gen_track_pt():
#track pT resolution as ( pT_track_rec - pT_track_gen )/pT_track_gen
ptbin = 0.001
ptmin = -0.3
ptmax = 0.1
#generated dielectron pT selection to input data
ptlo = 0.2
pthi = 1
fitran = [-0.15, 0.018]
mmin = 2.8
mmax = 3.2
ccb = rt.kBlue
#output log file
out = open("out.txt", "w")
#log fit parameters
loglist1 = [(x, eval(x)) for x in ["infile_mc", "ptbin", "ptmin", "ptmax"]]
loglist2 = [(x, eval(x))
for x in ["ptlo", "pthi", "fitran", "mmin", "mmax"]]
strlog = ut.make_log_string(loglist1, loglist2)
ut.log_results(out, strlog + "\n")
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
strsel += " && jGenPt>{0:.3f}".format(ptlo)
strsel += " && jGenPt<{0:.3f}".format(pthi)
#strsel = ""
nbins, ptmax = ut.get_nbins(ptbin, ptmin, ptmax)
hPtTrackRel = ut.prepare_TH1D_n("hPtTrackRel", nbins, ptmin, ptmax)
ytit = "Events / ({0:.3f})".format(ptbin)
xtit = "(#it{p}_{T, rec}^{track} - #it{p}_{T, gen}^{track})/#it{p}_{T, gen}^{track}"
mctree.Draw("(jT0pT-jGenP0pT)/jGenP0pT >> hPtTrackRel",
strsel) # positive charge
mctree.Draw("(jT1pT-jGenP1pT)/jGenP1pT >>+hPtTrackRel",
strsel) # add negative charge
x = RooRealVar("x", "x", ptmin, ptmax)
x.setRange("fitran", fitran[0], fitran[1])
rfPtTrackRel = RooDataHist("rfPtTrackRel", "rfPtTrackRel", RooArgList(x),
hPtTrackRel)
#standard Crystal Ball
mean = RooRealVar("mean", "mean", -0.003, -0.1, 0.1)
sigma = RooRealVar("sigma", "sigma", 0.01, 0., 0.9)
alpha = RooRealVar("alpha", "alpha", 1.2, 0., 10.)
n = RooRealVar("n", "n", 1.3, 0., 20.)
cbpdf = RooCBShape("cbpdf", "cbpdf", x, mean, sigma, alpha, n)
res = cbpdf.fitTo(rfPtTrackRel, rf.Range("fitran"), rf.Save())
#log fit results
ut.log_results(out, ut.log_fit_result(res))
#generate new distribution according to the fit
gROOT.LoadMacro("cb_gen.h")
#Crystal Ball generator, min, max, mean, sigma, alpha, n
#cbgen = rt.cb_gen(-0.18, 0.05, -0.00226, 0.00908, 1.40165, 1.114) # -0.18, 0.05 ptmin, ptmax
cbgen = rt.cb_gen(-0.5, 0.05, -0.00226, 0.00908, 0.2,
2.) # -0.18, 0.05 ptmin, ptmax
hRelGen = ut.prepare_TH1D_n("hRelGen", nbins, ptmin, ptmax)
ut.set_H1D_col(hRelGen, rt.kBlue)
#rt.cb_generate_n(cbgen, hRelGen, int(hPtTrackRel.GetEntries()))
rfRelGen = RooDataHist("rfRelGen", "rfRelGen", RooArgList(x), hRelGen)
#generate distribution with additional smearing applied
hRelSmear = ut.prepare_TH1D_n("hRelSmear", nbins, ptmin, ptmax)
ut.set_H1D_col(hRelSmear, rt.kOrange)
#tcopy = mctree.CopyTree(strsel)
#rt.cb_apply_smear(cbgen, mctree, hRelSmear)
can = ut.box_canvas()
ut.set_margin_lbtr(gPad, 0.12, 0.1, 0.05, 0.03)
frame = x.frame(rf.Bins(nbins), rf.Title(""))
ut.put_frame_yx_tit(frame, ytit, xtit)
rfPtTrackRel.plotOn(frame, rf.Name("data"))
#rfRelGen.plotOn(frame, rf.Name("data"))
cbpdf.plotOn(frame, rf.Precision(1e-6), rf.Name("cbpdf"),
rf.LineColor(ccb))
frame.Draw()
#hRelGen.Draw("e1same")
#hRelSmear.Draw("e1same")
desc = pdesc(frame, 0.2, 0.8, 0.057)
#x, y, sep
desc.set_text_size(0.03)
desc.itemD("#chi^{2}/ndf", frame.chiSquare("cbpdf", "data", 4), -1, ccb)
desc.prec = 5
desc.itemR("mean", mean, ccb)
desc.itemR("#sigma", sigma, ccb)
desc.itemR("#alpha", alpha, ccb)
desc.prec = 3
desc.itemR("#it{n}", n, ccb)
desc.draw()
leg = ut.prepare_leg(0.2, 0.82, 0.21, 0.12, 0.03) # x, y, dx, dy, tsiz
leg.SetMargin(0.05)
leg.AddEntry(0, "#bf{%.1f < #it{p}_{T}^{pair} < %.1f GeV}" % (ptlo, pthi),
"")
leg.Draw("same")
#ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
alpha_1S = RooRealVar("alpha_1S","#alpha(3P)1S", 0.6)
alpha_2S = RooRealVar("alpha_2S","#alpha(3P)2S", 0.6)
n_1S = RooRealVar("n_1S","n(3P1)1S", 2.5)
n_2S = RooRealVar("n_2S","n(3P1)2S", 2.5)
rawmass = RooRealVar('rm','rm',10.5,10.4,10.6)
mass3P_1S2 =RooFormulaVar("m1","(@0+@1)",RooArgList(rawmass,deltaM_v3s))
mass3P_2S2 =RooFormulaVar("m2","(@0+@1)",RooArgList(rawmass,deltaM_v3s))
mass3P_3S2 =RooFormulaVar("m3","(@0+@1)",RooArgList(rawmass,deltaM_v3s))
signal1S_1 = RooCBShape('signal1S1','s1S1',x,rawmass,sigma_1S,alpha_1S,n_1S)
signal1S_2 = RooCBShape('signal1S2','s1S2',x,mass3P_1S2,sigma_1S,alpha_1S,n_1S)
signal2S_1 = RooCBShape('signal2S1','s2S1',x,rawmass,sigma_2S,alpha_2S,n_2S)
signal2S_2 = RooCBShape('signal2S2','s2S1',x,mass3P_2S2,sigma_2S,alpha_2S,n_2S)
#3S parameters
# the following numbers are from an old 3P gun simulation
# that needs to be re-done
sigma13s = 0.003#0.0031
sigma23s = 0.003#0.0035
def RooFitHist(inputhist, title='title', path='.'):
# RooFit.gErrorIgnoreLevel = RooFit.kInfo # <6.02
# RooFit.SetSilentMode()# >6.02
# RooMsgService().instance().SetSilentMode(kTRUE)
fitbinning = array('d')
binwidth = 200
#NBins=(14000/binwidth) - ( (1040/binwidth) + 1 )
NBins = (14000 / binwidth) - ((1040 / binwidth) + 1)
for i in range(NBins + 1):
fitbinning.append(1050 + i * binwidth)
# print(fitbinning)
hist = inputhist.Rebin(NBins, "fit parameter", fitbinning)
meanstart = hist.GetBinCenter(hist.GetMaximumBin())
sigmastart = hist.GetRMS()
print('meanstart:', meanstart, 'sigmastart:', sigmastart)
# inputhist.Draw()
# hist.Draw()
# hold=raw_input('press enter to exit.')
gStyle.SetOptFit(1100)
gStyle.SetOptTitle(0)
RooFit.SumW2Error(kTRUE)
mjj = RooRealVar('mjj', 'M_{jj-AK8}', fitbinning[0],
fitbinning[len(fitbinning) - 1], 'GeV')
mjjral = RooArgList(mjj)
dh = RooDataHist('dh', 'dh', mjjral, RooFit.Import(hist))
shapes = {}
#Gaussian
gaussmean = RooRealVar('#mu_{gauss}', 'mass mean value', meanstart, 0,
2 * meanstart)
gausssigma = RooRealVar('#sigma_{gauss}', 'mass resolution', sigmastart, 0,
2 * sigmastart)
gauss = RooGaussian('gauss', 'gauss', mjj, gaussmean, gausssigma)
shapes.update({'Gauss': gauss})
#CrystalBall
mean = RooRealVar('#mu', 'mean', meanstart, 0, 2 * meanstart)
sigma = RooRealVar('#sigma', 'sigma', sigmastart, 0, 2 * sigmastart)
alpha = RooRealVar('#alpha', 'Gaussian tail', -1000, 0)
n = RooRealVar('n', 'Normalization', -1000, 1000)
cbshape = RooCBShape('cbshape', 'crystalball PDF', mjj, mean, sigma, alpha,
n)
shapes.update({'CrystalBall': cbshape})
#Voigt
voigtmean = RooRealVar('#mu', 'mass mean value', meanstart, 0,
2 * meanstart)
voigtwidth = RooRealVar('#gamma', 'width of voigt', 0, 5000)
voigtsigma = RooRealVar('#sigma', 'mass resolution', sigmastart, 0,
2 * sigmastart)
voigt = RooVoigtian('voigt', 'voigt', mjj, voigtmean, voigtwidth,
voigtsigma)
shapes.update({'Voigt': voigt})
#BreitWigner
bwmean = RooRealVar('#mu', 'mass mean value', meanstart, 0, 2 * meanstart)
bwwidth = RooRealVar('#sigma', 'width of bw', sigmastart, 0,
2 * sigmastart)
bw = RooBreitWigner('bw', 'bw', mjj, bwmean, bwwidth)
shapes.update({'BreitWigner': bw})
#Landau
landaumean = RooRealVar('#mu_{landau}', 'mean landau', meanstart, 0,
2 * meanstart)
landausigma = RooRealVar('#sigma_{landau}', 'mass resolution', sigmastart,
0, 2 * sigmastart)
landau = RooLandau('landau', 'landau', mjj, landaumean, landausigma)
shapes.update({'Landau': landau})
#LandauGauss Convolution
landaugauss = RooFFTConvPdf('landaugauss', 'landau x gauss', mjj, landau,
gauss)
shapes.update({'LandauGauss': landaugauss})
#Logistics
# logisticsmean=RooRealVar('#mu_{logistics}','mean logistics',meanstart,0,2*meanstart)
# logisticssigma= RooRealVar('#sigma_{logistics}','mass resolution',sigmastart,0,2*sigmastart)
# logistics=RooLogistics('logistics','logistics',mjj,logisticsmean,logisticssigma)
# shapes.update({'Logistics':logistics})
#ExpAndGauss
# expgaussmean=RooRealVar('#mu_{expgauss}','mean expgauss',meanstart,0,2*meanstart)
# expgausssigma= RooRealVar('#sigma_{expgauss}','mass resolution',sigmastart,0,2*sigmastart)
# expgausstrans= RooRealVar('trans','trans',0,100)
# expgauss=RooExpAndGauss('expgauss','expgauss',mjj,expgaussmean,expgausssigma,expgausstrans)
# shapes.update({'ExpAndGauss':expgauss})
#BifurGauss
BifurGaussmean = RooRealVar('#mu_{BifurGauss}', 'mean BifurGauss',
meanstart, 0, 2 * meanstart)
BifurGausslsigma = RooRealVar('#sigma_{left}', 'mass resolution',
sigmastart, 0, 2 * sigmastart)
BifurGaussrsigma = RooRealVar('#sigma_{right}', 'mass resolution',
sigmastart, 0, 2 * sigmastart)
BifurGauss = RooBifurGauss('BifurGauss', 'BifurGauss', mjj, BifurGaussmean,
BifurGausslsigma, BifurGaussrsigma)
shapes.update({'BifurGauss': BifurGauss})
#Chebychev
Chebychev1 = RooRealVar('c0', 'Chebychev0', -1000, 1000)
Chebychev2 = RooRealVar('c1', 'Chebychev1', -1000, 1000)
Chebychev3 = RooRealVar('c2', 'Chebychev2', 2, -1000, 1000)
Chebychev = RooChebychev('Chebychev', 'Chebychev', mjj,
RooArgList(Chebychev1, Chebychev2, Chebychev3))
shapes.update({'Chebychev': Chebychev})
#Polynomial
Polynomial1 = RooRealVar('Polynomial1', 'Polynomial1', 100, 0, 1000)
Polynomial2 = RooRealVar('Polynomial2', 'Polynomial2', 100, 0, 1000)
Polynomial = RooPolynomial('Polynomial', 'Polynomial', mjj,
RooArgList(Polynomial1, Polynomial2))
shapes.update({'Polynomial': Polynomial})
# mjj.setRange("FitRange",1050.,14000.)
# for fname in ['Gauss','Logistics','BifurGauss']:
for fname in ['BifurGauss']:
plottitle = '%s Fit of %s' % (fname, title)
shape = shapes[fname]
# shape.fitTo(dh,RooFit.Range("FitRange"),RooFit.SumW2Error(True))
shape.fitTo(dh, RooFit.SumW2Error(True))
frame = mjj.frame(RooFit.Title(plottitle))
frame.GetYaxis().SetTitleOffset(2)
dh.plotOn(frame, RooFit.MarkerStyle(4))
shape.plotOn(frame, RooFit.LineColor(2))
ndof = dh.numEntries() - 3
#chiSquare legend
chi2 = frame.chiSquare()
probChi2 = TMath.Prob(chi2 * ndof, ndof)
chi2 = round(chi2, 2)
probChi2 = round(probChi2, 2)
leg = TLegend(0.5, 0.5, 0.9, 0.65)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
shape.paramOn(frame, RooFit.Layout(0.5, 0.9, 0.9))
leg.AddEntry(0, '#chi^{2} =' + str(chi2), '')
leg.AddEntry(0, 'Prob #chi^{2} = ' + str(probChi2), '')
leg.SetTextSize(0.04)
frame.addObject(leg)
canv = TCanvas(plottitle, plottitle, 700, 700)
canv.SetLogy()
canv.SetLeftMargin(0.20)
canv.cd()
frame.SetMinimum(10**(-3))
frame.Draw()
canv.Print(path + '/%s__%s.eps' % (title, fname))
return chi2
# x = RooRealVar("x", "x variable", 4800, 6000)
# TODO: export somewhere? does not need to be defined inside...
mean = RooRealVar("mean", "Mean of Double CB PDF", 5280, 5270,
5290) #, 5300, 5500)
sigma = RooRealVar("sigma", "Sigma of Double CB PDF", 40, 0, 45)
alpha_0 = RooRealVar("alpha_0", "alpha_0 of one side", 40, 30, 50)
alpha_1 = RooRealVar("alpha_1", "alpha_1 of other side", -40, -50, -30.)
lambda_0 = RooRealVar("lambda_0", "Exponent of one side", 40, 30, 50)
lambda_1 = RooRealVar("lambda_1", "Exponent of other side", 40, 30, 50)
# TODO: export somewhere? pdf construction
frac = RooRealVar("frac", "Fraction of crystal ball pdfs", 0.479, 0.01,
0.99)
crystalball1 = RooCBShape("crystallball1", "First CrystalBall PDF", x,
mean, sigma, alpha_0, lambda_0)
crystalball2 = RooCBShape("crystallball2", "Second CrystalBall PDF", x,
mean, sigma, alpha_1, lambda_1)
doubleCB = RooAddPdf("doubleCB", "Double CrystalBall PDF", crystalball1,
crystalball2, frac)
# create signal pdf END
# create bkg-pdf BEGIN
lambda_exp = RooRealVar("lambda_exp", "lambda exp pdf bkg", -0.002, -10.,
-0.000001)
bkg_pdf = RooExponential("bkg_pdf", "Background PDF exp", x, lambda_exp)
# create bkg-pdf END
n_sig = 2500
data = pd.DataFrame(np.random.normal(loc=5280, scale=37, size=(n_sig, 3)),
#test -- restrict mjj range
#x = RooRealVar('mjj','mjj',1500,6000)
x = RooRealVar('mjj', 'mjj', minX_mass, maxX_mass)
dataHist_data = RooDataHist("RooDataHist", "RooDataHist", RooArgList(x), hDat)
if fitSig:
# define parameters for signal fit
m = RooRealVar('mean', 'mean', float(mass),
float(mass) - 200,
float(mass) + 200)
s = RooRealVar('sigma', 'sigma', 0.1 * float(mass), 0, 10000)
a = RooRealVar('alpha', 'alpha', 1, -10, 10)
n = RooRealVar('n', 'n', 1, 0, 100)
sig = RooCBShape('sig', 'sig', x, m, s, a, n)
p = RooRealVar('p', 'p', 1, 0, 5)
x0 = RooRealVar('x0', 'x0', 1000, 100, 5000)
bkg = RooGenericPdf('bkg', '1/(exp(pow(@0/@1,@2))+1)',
RooArgList(x, x0, p))
fsig = RooRealVar('fsig', 'fsig', 0.5, 0., 1.)
signal = RooAddPdf('signal', 'signal', sig, bkg, fsig)
# -----------------------------------------
# fit signal
canSname = 'can_Mjj' + str(mass)
canS = TCanvas(canSname, canSname, 900, 600)
gPad.SetLogy()
alpha_initial = 0.5
n_initial = 7
if crystal != 'C3':
alpha_initial = -1.
n_initial = 0.5
a.append(
RooRealVar("alpha_%s_%s" % (crystal, energy),
"alpha_%s_%s" % (crystal, energy), alpha_initial,
-10., 10))
n.append(
RooRealVar("exp_%s_%s" % (crystal, energy),
"exp_%s_%s" % (crystal, energy), n_initial, 0.,
150))
sig.append(
RooCBShape("signal_%s_%s" % (crystal, energy),
"signal_%s_%s" % (crystal, energy), x[file_num],
m[file_num], s[file_num], a[file_num], n[file_num]))
res.append(sig[file_num].fitTo(roohist[file_num])) #RooFit.Save())
#res.Print()
frame.append(x[file_num].frame())
roohist[file_num].plotOn(
frame[file_num],
RooFit.Name("roohist_%s_%s" % (crystal, energy)))
sig[file_num].plotOn(
frame[file_num],
RooFit.Name("signal_%s_%s" % (crystal, energy)))
hists_fits.append(hist_ampl)
chi2s.append(frame[file_num].chiSquare(
"signal_%s_%s" % (crystal, energy),
theBMassfunc = RooFormulaVar("theBMass", "theBMass", "@0", RooArgList(bMass))
theBMass = fulldata.addColumn(theBMassfunc)
theBMass.setRange(lowRange, highRange)
thevars.add(theBMass)
cut = ''
print cut
data = fulldata.reduce(thevars, cut)
## mass model
mean = RooRealVar("mass", "mean", B0Mass_, 5, 5.5, "GeV")
sigma = RooRealVar("sigma", "sigma", 9.0e-2, 1.0e-4, 1.0, "GeV")
alpha = RooRealVar("alpha", "alpha", 1.0, 0.0, 1.0e+4)
n = RooRealVar("n", "n", 5, 1, 100)
signalCB = RooCBShape("signalCB", "signal cb", theBMass, mean, sigma, alpha, n)
sigma2 = RooRealVar("sigma2", "sigma2", 9.0e-3, 1.0e-5, 1.0, "GeV")
alpha2 = RooRealVar("alpha2", "alpha2", 1.0, 0.0, 1.0e+4)
n2 = RooRealVar("n2", "n2", 5, 1, 100)
signalCB2 = RooCBShape("signalCB2", "signal cb 2", theBMass, mean, sigma2,
alpha2, n2)
f1 = RooRealVar("f1", "f1", 0.5, 0., 1.)
CB = RooAddPdf("CB", "CB1+CB2", RooArgList(signalCB, signalCB2),
RooArgList(f1))
## make bkg model
exp_alpha = RooRealVar("exp_alpha", "exp_alpha", -1.0, -100, 0)
bkg_exp = RooExponential("bkg_exp", "bkg_exp", theBMass, exp_alpha)
def studyVqqResolution(rootFile):
#get all from file
histos = {}
inF = TFile.Open(rootFile)
keys = inF.GetListOfKeys()
for k in keys:
obj = inF.Get(k.GetName())
obj.SetDirectory(0)
histos[k.GetName()] = obj
inF.Close()
#plot
gROOT.SetBatch()
gROOT.SetStyle('Plain')
gStyle.SetOptStat(0)
gStyle.SetOptFit(1111)
gStyle.SetOptTitle(0)
gStyle.SetStatFont(42)
kin = ['', '30to40', '40to50', '50to75', '75to100', '100toInf']
for k in kin:
c = TCanvas('c', 'c', 600, 600)
c.cd()
c.SetCanvasSize(1000, 500)
c.SetWindowSize(1000, 500)
c.Divide(2, 1)
c.cd(1)
histos['deta' + k + 'barrel'].SetLineWidth(2)
histos['deta' + k + 'barrel'].SetTitle('barrel')
histos['deta' + k + 'barrel'].Draw('hist')
histos['deta' + k + 'endcap'].SetLineWidth(2)
histos['deta' + k + 'endcap'].SetLineStyle(7)
histos['deta' + k + 'endcap'].SetTitle('endcap')
histos['deta' + k + 'endcap'].Draw('histsame')
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['deta' + k + 'barrel'], 'barrel', 'f')
leg.AddEntry(histos['deta' + k + 'endcap'], 'endcap', 'f')
leg.SetNColumns(2)
leg.Draw()
drawHeader()
c.cd(2)
histos['dphi' + k + 'barrel'].SetLineWidth(2)
histos['dphi' + k + 'barrel'].SetTitle('barrel')
histos['dphi' + k + 'barrel'].Draw('hist')
histos['dphi' + k + 'endcap'].SetLineWidth(2)
histos['dphi' + k + 'endcap'].SetLineStyle(7)
histos['dphi' + k + 'endcap'].SetTitle('endcap')
histos['dphi' + k + 'endcap'].Draw('histsame')
c.Modified()
c.Update()
c.SaveAs('dr_%s.png' % k)
labels = []
responseVars = ['dpt', 'den', 'dphi', 'deta', 'dr']
for r in responseVars:
barrelResponse = TGraphErrors()
barrelResponse.SetName(r + 'barrelresponse')
barrelResponse.SetLineWidth(2)
barrelResponse.SetFillStyle(0)
barrelResponse.SetMarkerStyle(20)
barrelCoreResponse = barrelResponse.Clone(r + 'barrelcoreresponse')
endcapResponse = TGraphErrors()
endcapResponse.SetName(r + 'endcapresponse')
endcapResponse.SetLineWidth(2)
endcapResponse.SetFillStyle(0)
endcapResponse.SetMarkerStyle(24)
endcapCoreResponse = endcapResponse.Clone(r + 'endcapresponse')
for k in kin:
c.cd()
c.Clear()
c.SetWindowSize(1000, 500)
c.Divide(2, 1)
for i in [1, 2]:
c.cd(i)
reg = 'barrel'
if i == 2: reg = 'endcap'
h = histos[r + k + reg]
x = RooRealVar("x",
h.GetXaxis().GetTitle(),
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
data = RooDataHist("data", "dataset with x", RooArgList(x), h)
frame = x.frame()
RooAbsData.plotOn(data, frame,
RooFit.DataError(RooAbsData.SumW2))
mean1 = RooRealVar("mean1", "mean1", 0, -0.5, 0.5)
sigma1 = RooRealVar("sigma1", "sigma1", 0.1, 0.01, 1.0)
gauss1 = RooGaussian("g1", "g", x, mean1, sigma1)
if r == 'dpt' or r == 'den':
mean2 = RooRealVar("mean2", "mean2", 0, -0.5, 0.5)
sigma2 = RooRealVar("sigma2", "sigma2", 0.1, 0.01, 1.0)
alphacb = RooRealVar("alphacb", "alphacb", 1, 0.1, 3)
ncb = RooRealVar("ncb", "ncb", 4, 1, 100)
gauss2 = RooCBShape("cb2", "cb", x, mean2, sigma2, alphacb,
ncb)
else:
mean1.setRange(0, 0.5)
mean2 = RooRealVar("mean2", "mean", 0, 0, 1)
sigma2 = RooRealVar("sigma2", "sigma", 0.1, 0.01, 1.0)
gauss2 = RooGaussian("g2", "g", x, mean2, sigma2)
frac = RooRealVar("frac", "fraction", 0.9, 0.0, 1.0)
if data.sumEntries() < 100:
frac.setVal(1.0)
frac.setConstant(True)
model = RooAddPdf("sum", "g1+g2", RooArgList(gauss1, gauss2),
RooArgList(frac))
status = model.fitTo(data, RooFit.Save()).status()
if status != 0: continue
model_cdf = model.createCdf(RooArgSet(x))
cl = 0.90
ul = 0.5 * (1.0 + cl)
closestToCL = 1.0
closestToUL = -1
closestToMedianCL = 1.0
closestToMedian = -1
for ibin in xrange(1, h.GetXaxis().GetNbins() * 10):
xval = h.GetXaxis().GetXmin() + (
ibin - 1) * h.GetXaxis().GetBinWidth(ibin) / 10.
x.setVal(xval)
cdfValToCL = math.fabs(model_cdf.getVal() - ul)
if cdfValToCL < closestToCL:
closestToCL = cdfValToCL
closestToUL = xval
cdfValToCL = math.fabs(model_cdf.getVal() - 0.5)
if cdfValToCL < closestToMedianCL:
closestToMedianCL = cdfValToCL
closestToMedian = xval
RooAbsPdf.plotOn(model, frame)
frame.Draw()
if i == 1: drawHeader()
labels.append(TPaveText(0.6, 0.92, 0.9, 0.98, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'txt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
kinReg = k.replace('to', '-')
kinReg = kinReg.replace('Inf', '#infty')
labels[ilab].AddText('[' + reg + '] ' + kinReg)
labels[ilab].Draw()
resolutionVal = math.fabs(closestToUL - closestToMedian)
responseGr = barrelResponse
responseCoreGr = barrelCoreResponse
coreResolutionVal = sigma1.getVal()
coreResolutionErr = sigma1.getError()
if frac.getVal() < 0.7 and (sigma2.getVal() < sigma1.getVal()):
coreResolutionVal = sigma2.getVal()
coreResolutionErr = sigma2.getError()
if i == 2:
responseGr = endcapResponse
responseCoreGr = endcapCoreResponse
if k != '':
nrespPts = responseGr.GetN()
kinAvg = 150
kinWidth = 50
if k == '30to40':
kinAvg = 35
kinWidth = 5
if k == '40to50':
kinAvg = 45
kinWidth = 5
if k == '50to75':
kinAvg = 62.5
kinWidth = 12.5
elif k == '75to100':
kinAvg = 87.5
kinWidth = 12.5
responseGr.SetPoint(nrespPts, kinAvg, resolutionVal)
responseCoreGr.SetPoint(nrespPts, kinAvg,
coreResolutionVal)
responseCoreGr.SetPointError(nrespPts, kinWidth,
coreResolutionErr)
labels.append(TPaveText(0.15, 0.7, 0.4, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'fitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)' % frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean1.getVal(), mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma1.getVal(), sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)' %
(1 - frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean2.getVal(), mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma2.getVal(), sigma2.getError()))
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs(r + 'res_' + k + '.png')
frame = TGraphErrors()
frame.SetPoint(0, 0, 0)
frame.SetPoint(1, 200, 0.3)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('frame')
cresp = TCanvas('cresp', 'cresp', 500, 500)
cresp.cd()
frame.Draw('ap')
barrelResponse.Draw('pl')
endcapResponse.Draw('pl')
frame.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Resolution %3.2f C.L." % cl)
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelResponse, 'barrel', 'fp')
leg.AddEntry(endcapResponse, 'endcap', 'fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r + 'res_evol.png')
frameCore = frame.Clone('framecore')
cresp.Clear()
frameCore.Draw('ap')
barrelCoreResponse.Draw('pl')
endcapCoreResponse.Draw('pl')
frameCore.GetXaxis().SetTitle("Quark transverse momentum [GeV]")
frameCore.GetYaxis().SetTitle("Core resolution")
frameCore.GetYaxis().SetTitleOffset(1.4)
frameCore.GetYaxis().SetNdivisions(10)
frameCore.GetYaxis().SetRangeUser(0, 0.2)
drawHeader()
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(barrelCoreResponse, 'barrel', 'fp')
leg.AddEntry(endcapCoreResponse, 'endcap', 'fp')
leg.SetNColumns(2)
leg.Draw()
cresp.Modified()
cresp.Update()
cresp.SaveAs(r + 'rescore_evol.png')
bosons = ['h', 'z', 'w']
kin = ['', '50', '100']
region = ['', 'bb', 'eb', 'ee']
for k in kin:
for r in region:
c = TCanvas('c', 'c', 600, 600)
c.cd()
histos['mjj' + k + r].Rebin()
histos['mjj' + k + r].Draw()
ic = 1
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.AddEntry(histos['mjj' + k + r], 'inclusive', 'f')
for b in bosons:
if histos[b + 'mjj' + k + r].Integral() <= 0: continue
ic = ic + 1
histos[b + 'mjj' + k + r].Rebin()
histos[b + 'mjj' + k + r].SetLineColor(ic)
histos[b + 'mjj' + k + r].SetLineWidth(2)
histos[b + 'mjj' + k + r].SetMarkerColor(ic)
histos[b + 'mjj' + k + r].SetMarkerStyle(1)
histos[b + 'mjj' + k + r].SetFillStyle(3000 + ic)
histos[b + 'mjj' + k + r].SetFillColor(ic)
histos[b + 'mjj' + k + r].Draw('histsame')
leg.AddEntry(histos[b + 'mjj' + k + r], b, "f")
leg.SetNColumns(ic)
leg.Draw()
drawHeader()
labels.append(TPaveText(0.65, 0.8, 0.9, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(k + r + 'mjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle = "inclusive"
if r == 'bb': regionTitle = 'barrel-barrel'
if r == 'eb': regionTitle = 'endcap-barrel'
if r == 'ee': regionTitle = 'endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold = 30
if k != '': ptthreshold = float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV' % ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('mjj' + k + r + '.png')
massResolutionGrs = []
for r in region:
massResolution = TGraphErrors()
massResolution.SetName(r + 'dm')
massResolution.SetLineWidth(2)
massResolution.SetFillStyle(0)
massResolution.SetMarkerStyle(20 + len(massResolutionGrs))
massResolution.SetMarkerColor(1 + len(massResolutionGrs))
massResolution.SetLineColor(1 + len(massResolutionGrs))
massResolution.SetFillColor(1 + len(massResolutionGrs))
massResolutionGrs.append(massResolution)
for k in kin:
c = TCanvas('c', 'c', 600, 600)
c.cd()
h = histos['dmjj' + k + r]
x = RooRealVar("x",
h.GetXaxis().GetTitle(),
h.GetXaxis().GetXmin(),
h.GetXaxis().GetXmax())
data = RooDataHist("data", "dataset with x", RooArgList(x), h)
frame = x.frame()
RooAbsData.plotOn(data, frame, RooFit.DataError(RooAbsData.SumW2))
mean1 = RooRealVar("mean1", "mean1", 0, -0.5, 0.5)
sigma1 = RooRealVar("sigma1", "sigma1", 0.1, 0.01, 1.0)
gauss1 = RooGaussian("g1", "g", x, mean1, sigma1)
mean2 = RooRealVar("mean2", "mean2", 0, -0.5, 0.5)
sigma2 = RooRealVar("sigma2", "sigma2", 0.1, 0.01, 1.0)
alphacb = RooRealVar("alphacb", "alphacb", 1, 0.1, 3)
ncb = RooRealVar("ncb", "ncb", 4, 1, 100)
gauss2 = RooCBShape("cb2", "cb", x, mean2, sigma2, alphacb, ncb)
frac = RooRealVar("frac", "fraction", 0.9, 0.0, 1.0)
model = RooAddPdf("sum", "g1+g2", RooArgList(gauss1, gauss2),
RooArgList(frac))
status = model.fitTo(data, RooFit.Save()).status()
if status != 0: continue
RooAbsPdf.plotOn(model, frame)
frame.Draw()
labels.append(TPaveText(0.6, 0.65, 0.85, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(r + k + 'dmfitrestxt')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
labels[ilab].AddText('Gaussian #1 (f=%3.3f)' % frac.getVal())
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean1.getVal(), mean1.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma1.getVal(), sigma1.getError()))
labels[ilab].AddText('Gaussian #2 (f=%3.3f)' % (1 - frac.getVal()))
labels[ilab].AddText('#mu=%3.3f#pm%3.3f' %
(mean2.getVal(), mean2.getError()))
labels[ilab].AddText('#sigma=%3.3f#pm%3.3f' %
(sigma2.getVal(), sigma2.getError()))
labels[ilab].Draw()
drawHeader()
labels.append(TPaveText(0.15, 0.8, 0.4, 0.9, 'brNDC'))
ilab = len(labels) - 1
labels[ilab].SetName(k + r + 'dmjj')
labels[ilab].SetBorderSize(0)
labels[ilab].SetFillStyle(0)
labels[ilab].SetTextFont(42)
labels[ilab].SetTextAlign(12)
regionTitle = "inclusive"
if r == 'bb': regionTitle = 'barrel-barrel'
if r == 'eb': regionTitle = 'endcap-barrel'
if r == 'ee': regionTitle = 'endcap-endcap'
labels[ilab].AddText(regionTitle)
ptthreshold = 30
if k != '': ptthreshold = float(k)
labels[ilab].AddText('p_{T}>%3.0f GeV' % ptthreshold)
labels[ilab].Draw()
c.Modified()
c.Update()
c.SaveAs('dmjj' + k + r + '.png')
massResolution.SetTitle(regionTitle)
ip = massResolution.GetN()
x = 40
xerr = 10
if k == '50':
x = 75
xerr = 25
elif k == '100':
x = 150
xerr = 50
y = sigma1.getVal()
yerr = sigma1.getError()
if frac.getVal() < 0.8:
if sigma2.getVal() < sigma1.getVal():
y = sigma2.getVal()
ey = sigma2.getError()
massResolution.SetPoint(ip, x, y)
massResolution.SetPointError(ip, xerr, yerr)
frame = TGraphErrors()
frame.SetPoint(0, 0, 0)
frame.SetPoint(1, 200, 0.2)
frame.SetMarkerStyle(1)
frame.SetFillStyle(0)
frame.SetName('dmframe')
cdmevol = TCanvas('cdmevol', 'cdmevol', 500, 500)
cdmevol.cd()
frame.Draw('ap')
leg = TLegend(0.6, 0.92, 0.9, 0.98)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
for dmGr in massResolutionGrs:
dmGr.Draw('pl')
leg.AddEntry(dmGr, dmGr.GetTitle(), 'fp')
frame.GetXaxis().SetTitle("Leading quark transverse momentum [GeV]")
frame.GetYaxis().SetTitle("Core resolution")
frame.GetYaxis().SetTitleOffset(1.4)
frame.GetYaxis().SetNdivisions(10)
drawHeader()
leg.SetNColumns(2)
leg.Draw()
cdmevol.Modified()
cdmevol.Update()
cdmevol.SaveAs('dm_evol.png')
c = TCanvas('c', 'c', 600, 600)
c.cd()
histos['sel'].Draw('histtext')
drawHeader()
c.Modified()
c.Update()
c.SaveAs('selection.png')
return
def dofit(roodataset, hname):
mass_chib = 10.5103 # from PES uncorrected mass measurement
deltaM = 0.0105 # MeV theoretical expectations
ratio21 = 0.45 # same as chic2/chic1 and chib2/chib1
# the following numbers are from an old 3P gun simulation
# that needs to be re-done
sigma1 = 0.003 #0.0031
sigma2 = 0.003 #0.0035
alpha1 = 0.95
alpha2 = 1.12
n = 2.5
mass1_v = RooRealVar('mchi1', 'm_{#chi1}', mass_chib)
deltaM_v = RooRealVar('deltaM', '#Delta_{m}', deltaM, 0.005, 0.015)
mass2_v = RooFormulaVar('mchi2', '@0+@1', RooArgList(mass1_v, deltaM_v))
sigma1_v = RooRealVar('sigma1', '#sigma_1', sigma1)
sigma2_v = RooRealVar('sigma2', '#sigma_2', sigma2)
alpha1_v = RooRealVar('alpha1', '#alpha_1', alpha1)
alpha2_v = RooRealVar('alpha2', '#alpha_2', alpha2)
n_v = RooRealVar('n', 'n', n)
ratio21_v = RooRealVar('ratio21', 'r_{21}', ratio21)
x = RooRealVar("invm3S", "#chi_{b} Data", 10.4, 10.7)
# choose here binning of mass plot
x.setBins(150)
#signal pdf
chib1 = RooCBShape('chib1', 'chib1', x, mass1_v, sigma1_v, alpha1_v, n_v)
chib2 = RooCBShape('chib2', 'chib2', x, mass2_v, sigma2_v, alpha2_v, n_v)
# define background
q01S_Start = 10.4
alpha = RooRealVar("#alpha", "#alpha", 1.5, 0.2, 3.5)
beta = RooRealVar("#beta", "#beta", -2.5, -7., 0.)
#q0 = RooRealVar("q0","q0",q01S_Start,q01S_Start-0.05,q01S_Start+0.05)
q0 = RooRealVar("q0", "q0", q01S_Start)
delta = RooFormulaVar("delta", "TMath::Abs(@0-@1)", RooArgList(x, q0))
b1 = RooFormulaVar("b1", "@0*(@1-@2)", RooArgList(beta, x, q0))
signum1 = RooFormulaVar("signum1", "( TMath::Sign( -1.,@0-@1 )+1 )/2.",
RooArgList(x, q0))
background = RooGenericPdf("background", "Background",
"signum1*pow(delta,#alpha)*exp(b1)",
RooArgList(signum1, delta, alpha, b1))
n_evts_1 = RooRealVar('N_{3P_{1}}', 'N_{3P_{1}}', 50, 30, 1000)
n_evts_2 = RooFormulaVar('N_{3P_{2}}', '@0*@1',
RooArgList(n_evts_1, ratio21_v))
n_bck = RooRealVar('nbkg', 'n_{bkg}', 500, 0, 100000)
#build final pdf
modelPdf = RooAddPdf('ModelPdf', 'ModelPdf',
RooArgList(chib1, chib2, background),
RooArgList(n_evts_1, n_evts_2, n_bck))
# fit
low_cut = x.setRange("low_cut", 10.4, 10.7)
result = modelPdf.fitTo(roodataset, RooFit.Save(), RooFit.Range("low_cut"))
frame = x.frame(RooFit.Title("m(#chi_{b}(3P))"))
roodataset.plotOn(frame, RooFit.MarkerSize(0.7))
modelPdf.plotOn(frame, RooFit.LineWidth(1))
modelPdf.plotOn(frame, RooFit.LineWidth(2))
frame.GetXaxis().SetTitle(
'm_{#gamma #mu^{+} #mu^{-}} - m_{#mu^{+} #mu^{-}} + m^{PDG}_{#Upsilon(3S)} [GeV/c^{2}]'
)
#frame.GetYaxis().SetTitle( "Events/15.0 MeV " )
frame.GetXaxis().SetTitleSize(0.04)
frame.GetYaxis().SetTitleSize(0.04)
frame.GetXaxis().SetTitleOffset(1.1)
frame.GetXaxis().SetLabelSize(0.04)
frame.GetYaxis().SetLabelSize(0.04)
frame.SetLineWidth(1)
frame.SetName("fit_resonance")
chi2 = frame.chiSquare()
chi2 = round(chi2, 2)
leg = TLegend(0.50, 0.7, 0.60, 0.8)
leg.AddEntry(0, '#chi^{2} =' + str(chi2), '')
leg.SetBorderSize(0)
leg.SetFillColor(0)
leg.SetTextSize(0.06)
gROOT.SetStyle("Plain")
frame.SaveAs(str(hname) + '.root')
# param_set = RooArgSet(n_evts_Roo4, m_chib[1][3],alpha, beta, q0)
canvas = TCanvas('fit', "", 1400, 700)
canvas.Divide(1)
canvas.cd(1)
gPad.SetRightMargin(0.3)
gPad.SetFillColor(10)
# modelPdf.paramOn(frame, RooFit.Layout(0.725,0.9875,0.9), RooFit.Parameters(param_set))
modelPdf.paramOn(frame, RooFit.Layout(0.725, 0.9875, 0.9))
frame.Draw()
leg.Draw("same")
canvas.SaveAs(str(hname) + '.png')
def fitMass(rangem=[0.4, 2.0], iflag=1, iopt_bkgshape=0, CBpar=[0., 0., 0.]):
global myc, fitres
m0 = sum(rangem) / 2
#w0=(rangem[1]-rangem[0])/10
w0 = 0.004
mass = RooRealVar("ee_ivm", "ee_ivm", rangem[0], rangem[1])
if iflag == 1:
###Construct signal pdf with gaus
mean = RooRealVar("mean", "mean", m0)
sigma = RooRealVar("sigma", "sigma", w0)
signal = RooGaussian("signal", "signal", mass, mean, sigma)
elif iflag == 2 or iflag == 3:
## Construct signal pdf with CB function
##print "outinfo",x,CBpar[0],CBpar[1],CBpar[2],CBpar[3]
cbmean = RooRealVar("cbmean", "cbmean", m0)
cbsigma = RooRealVar("cbsigma", "cbsigma", CBpar[0])
n1 = RooRealVar("n1", "", CBpar[1])
alpha = RooRealVar("alpha", "", CBpar[2])
cbsigma.setConstant(ROOT.kTRUE)
n1.setConstant(ROOT.kTRUE)
alpha.setConstant(ROOT.kTRUE)
signal = RooCBShape("cball", "crystal ball1", mass, cbmean, cbsigma,
alpha, n1)
# elif iflag ==3:
# pass
else:
print "ERROR, please specify signal shape for fitting!!"
sys.exit()
# Construct background pdf
a0 = RooRealVar("a0", "a0", 0.1, -1, 1)
a1 = RooRealVar("a1", "a1", 0.004, -1, 1)
a2 = RooRealVar("a2", "a2", 0.001, -1, 1)
if iopt_bkgshape == 0:
background = RooChebychev("background", "background", mass,
RooArgList(a0, a1))
else:
background = RooChebychev("background", "background", mass,
RooArgList(a0, a1, a2))
# Construct composite pdf
if iflag == 1:
up_nsig = 40
else:
up_nsig = 60
nsig = RooRealVar("nsig", "nsig", 5, 0.0, up_nsig)
nbkg = RooRealVar("nbkg", "nbkg", 800, 0, 3000)
#frac = RooRealVar("frac", "frac", 0.001, 0.0001, 0.1)
model = RooAddPdf("model", "model", RooArgList(signal, background),
RooArgList(nsig, nbkg))
#model = RooAddPdf("model", "model", RooArgList(signal, background), RooArgList(frac))
mcdata = RooDataSet(
"ds", "ds", RooArgSet(mass), RooFit.Import(data),
RooFit.Cut("ee_ivm<" + str(rangem[1]) + "&&ee_ivm>" + str(rangem[0])))
if optp == 1:
ipr = 1
verbose = 0
elif optp == 2:
ipr = 1
verbose = 1
else:
ipr = -1
verbose = 0
fitres=model.fitTo(mcdata,RooFit.Save(),RooFit.Minos(1), RooFit.Strategy(2),\
RooFit.PrintLevel(ipr), RooFit.Verbose(verbose))
nll = RooNLLVar("nll", "nll", model, mcdata,
RooFit.Range(rangem[0], rangem[1]))
pll = nll.createProfile(RooArgSet(nsig))
Profile = RooProfileLL("Profile", "Profile", nll, RooArgSet(nsig))
llhoodP = RooFormulaVar("llhoodP", "exp(-0.5*Profile)",
RooArgList(Profile))
xframe2 = nsig.frame(RooFit.Title("number of signal"))
nllplot = nll.plotOn(xframe2, RooFit.ShiftToZero())
themin = RooConstVar("themin", "themin", nllplot.GetMinimum())
llhood = RooFormulaVar("llhood", "exp(-0.5*(nll-themin*0.95))",
RooArgList(nll, themin))
if optp:
xframe = mass.frame(RooFit.Title("mass of ee pair"))
xframe3 = nsig.frame(RooFit.Title("number of signal"))
xframe3.SetYTitle("Likelihood")
mcdata.plotOn(xframe)
model.plotOn(xframe)
model.plotOn(xframe, RooFit.Components("background"),
RooFit.LineStyle(ROOT.kDashed),
RooFit.LineColor(ROOT.kRed))
model.plotOn(xframe, RooFit.Components("cball"),
RooFit.LineStyle(ROOT.kDashed),
RooFit.LineColor(ROOT.kGreen))
myc.cd(1)
xframe.Draw()
#pll.plotOn(xframe2,RooFit.LineColor(ROOT.kRed))
if optp: print "***** archmin ", themin.Print()
#llhoodP.plotOn(xframe3, RooFit.LineColor(ROOT.kRed))
llhood.plotOn(xframe3)
myc.cd(2)
xframe2.SetMinimum(0)
xframe2.Draw()
myc.cd(3)
xframe3.Draw()
myc.Update()
raw_input()
nsig.setRange("IntRange1", 0, 1000.)
Int1 = llhood.createIntegral(RooArgSet(nsig),
ROOT.RooFit.Range('IntRange1'))
Int1Val = Int1.getVal()
i = 0
hit = False
while not (hit):
i = i + 1
nsig.setRange("IntRange2", 0, float(i))
Int2 = llhood.createIntegral(RooArgSet(nsig),
ROOT.RooFit.Range('IntRange2'))
if Int2.getVal() >= Int1Val * 0.9:
if optp: print "&&& ", i
hit = True
return i
else:
fun = dx_funs[tag]
fun.SetLineColor(kBlue)
dx_results[tag] = g.Fit(fun, 'S')
g.Draw('AP')
from ROOT import RooCBShape
cb_mean = RooRealVar('cb_mean', 'cb_mean', 0, -100, 100)
cb_s1 = RooRealVar('cb_s1', 'cb_s1', 100, 0, 500)
cb_sf = RooRealVar('cb_sf', 'cb_sf', 100, 0.1, 500)
cb_s2 = RooFormulaVar('cb_s2', 'cb_s2', "@0 * @1", RooArgList(cb_sf, cb_s1))
cb_frac = RooRealVar('cb_f', 'f' + tag, 0.2, 0.001, 0.99)
cb_n = RooRealVar('cb_n', 'n', 1)
cb_a1 = RooRealVar('cb_a', 'alpha', 20, 0.001, 200)
cb_a2 = RooFormulaVar('cb_a2', 'cb_a2', "@0 * @1", RooArgList(cb_sf, cb_a1))
cb1 = RooCBShape("cb1", "cb1", match_res_x, cb_mean, cb_s1, cb_a1, cb_n)
cb2 = RooCBShape("cb2", "cb2", match_res_x, cb_mean, cb_s2, cb_a2, cb_n)
dcb = RooAddModel("dcb", "double_cb", RooArgList(cb1, cb2),
RooArgList(cb_frac))
cor_xy = defaultdict(list)
sigmas_m = []
err_m = []
x_m = []
for i, (vname, j) in enumerate(product(['x', 'y'], range(1, 5))):
x_m += [i + 1]
r = results[(j, vname)]
# if (vname, j) == ('x', 2) or vname == 'y':
# par = r.floatParsFinal().find('s1_' + vname)
# else:
par = r.floatParsFinal().find('sm_' + vname)
def signal(channel, stype):
if 'VBF' in channel:
stype = 'XZHVBF'
else:
stype = 'XZH'
# HVT model
if stype.startswith('X'):
signalType = 'HVT'
genPoints = [800, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000]
massPoints = [x for x in range(800, 5000+1, 100)]
interPar = True
else:
print "Signal type", stype, "not recognized"
return
n = len(genPoints)
category = channel
cColor = color[category] if category in color else 1
nElec = channel.count('e')
nMuon = channel.count('m')
nLept = nElec + nMuon
nBtag = channel.count('b')
if '0b' in channel:
nBtag = 0
X_name = "VH_mass"
if not os.path.exists(PLOTDIR+stype+category): os.makedirs(PLOTDIR+stype+category)
#*******************************************************#
# #
# Variables and selections #
# #
#*******************************************************#
X_mass = RooRealVar( "X_mass", "m_{ZH}", XBINMIN, XBINMAX, "GeV")
J_mass = RooRealVar( "H_mass", "jet mass", LOWMIN, HIGMAX, "GeV")
V_mass = RooRealVar( "V_mass", "V jet mass", -9., 1.e6, "GeV")
CSV1 = RooRealVar( "H_csv1", "", -999., 2. )
CSV2 = RooRealVar( "H_csv2", "", -999., 2. )
DeepCSV1= RooRealVar( "H_deepcsv1", "", -999., 2. )
DeepCSV2= RooRealVar( "H_deepcsv2", "", -999., 2. )
H_ntag = RooRealVar( "H_ntag", "", -9., 9. )
H_dbt = RooRealVar( "H_dbt", "", -2., 2. )
H_tau21 = RooRealVar( "H_tau21", "", -9., 2. )
H_eta = RooRealVar( "H_eta", "", -9., 9. )
H_tau21_ddt = RooRealVar( "H_ddt", "", -9., 2. )
MaxBTag = RooRealVar( "MaxBTag", "", -10., 2. )
H_chf = RooRealVar( "H_chf", "", -1., 2. )
MinDPhi = RooRealVar( "MinDPhi", "", -1., 99. )
DPhi = RooRealVar( "DPhi", "", -1., 99. )
DEta = RooRealVar( "DEta", "", -1., 99. )
Mu1_relIso = RooRealVar( "Mu1_relIso", "", -1., 99. )
Mu2_relIso = RooRealVar( "Mu2_relIso", "", -1., 99. )
nTaus = RooRealVar( "nTaus", "", -1., 99. )
Vpt = RooRealVar( "V.Pt()", "", -1., 1.e6 )
V_pt = RooRealVar( "V_pt", "", -1., 1.e6 )
H_pt = RooRealVar( "H_pt", "", -1., 1.e6 )
VH_deltaR=RooRealVar( "VH_deltaR", "", -1., 99. )
isZtoNN = RooRealVar( "isZtoNN", "", 0., 2. )
isZtoEE = RooRealVar( "isZtoEE", "", 0., 2. )
isZtoMM = RooRealVar( "isZtoMM", "", 0., 2. )
isHtobb = RooRealVar( "isHtobb", "", 0., 2. )
isVBF = RooRealVar( "isVBF", "", 0., 2. )
isMaxBTag_loose = RooRealVar( "isMaxBTag_loose", "", 0., 2. )
weight = RooRealVar( "eventWeightLumi", "", -1.e9, 1.e9 )
Xmin = XBINMIN
Xmax = XBINMAX
# 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, J_mass, V_mass, CSV1, CSV2, H_ntag, H_dbt, H_tau21)
variables.add(RooArgSet(DEta, DPhi, MaxBTag, MinDPhi, nTaus, Vpt))
variables.add(RooArgSet(DeepCSV1, DeepCSV2,VH_deltaR, H_tau21_ddt))
variables.add(RooArgSet(isZtoNN, isZtoEE, isZtoMM, isHtobb, isMaxBTag_loose, weight))
variables.add(RooArgSet(isVBF, Mu1_relIso, Mu2_relIso, H_chf, H_pt, V_pt,H_eta))
#X_mass.setRange("X_extended_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_reasonable_range", X_mass.getMin(), X_mass.getMax())
X_mass.setRange("X_integration_range", Xmin, Xmax)
X_mass.setBins(int((X_mass.getMax() - X_mass.getMin())/100))
binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin())/100), X_mass.getMin(), X_mass.getMax())
X_mass.setBinning(binsXmass, "PLOT")
massArg = RooArgSet(X_mass)
# Cuts
SRcut = selection[category]+selection['SR']
print " Cut:\t", SRcut
#*******************************************************#
# #
# Signal fits #
# #
#*******************************************************#
treeSign = {}
setSignal = {}
vmean = {}
vsigma = {}
valpha1 = {}
vslope1 = {}
smean = {}
ssigma = {}
salpha1 = {}
sslope1 = {}
salpha2 = {}
sslope2 = {}
a1 = {}
a2 = {}
sbrwig = {}
signal = {}
signalExt = {}
signalYield = {}
signalIntegral = {}
signalNorm = {}
signalXS = {}
frSignal = {}
frSignal1 = {}
frSignal2 = {}
frSignal3 = {}
# Signal shape uncertainties (common amongst all mass points)
xmean_fit = RooRealVar("sig_p1_fit", "Variation of the resonance position with the fit uncertainty", 0.005, -1., 1.)
smean_fit = RooRealVar("CMSRunII_sig_p1_fit", "Change of the resonance position with the fit uncertainty", 0., -10, 10)
xmean_jes = RooRealVar("sig_p1_scale_jes", "Variation of the resonance position with the jet energy scale", 0.010, -1., 1.) #0.001
smean_jes = RooRealVar("CMSRunII_sig_p1_jes", "Change of the resonance position with the jet energy scale", 0., -10, 10)
xmean_e = RooRealVar("sig_p1_scale_e", "Variation of the resonance position with the electron energy scale", 0.001, -1., 1.)
smean_e = RooRealVar("CMSRunII_sig_p1_scale_e", "Change of the resonance position with the electron energy scale", 0., -10, 10)
xmean_m = RooRealVar("sig_p1_scale_m", "Variation of the resonance position with the muon energy scale", 0.001, -1., 1.)
smean_m = RooRealVar("CMSRunII_sig_p1_scale_m", "Change of the resonance position with the muon energy scale", 0., -10, 10)
xsigma_fit = RooRealVar("sig_p2_fit", "Variation of the resonance width with the fit uncertainty", 0.02, -1., 1.)
ssigma_fit = RooRealVar("CMSRunII_sig_p2_fit", "Change of the resonance width with the fit uncertainty", 0., -10, 10)
xsigma_jes = RooRealVar("sig_p2_scale_jes", "Variation of the resonance width with the jet energy scale", 0.010, -1., 1.) #0.001
ssigma_jes = RooRealVar("CMSRunII_sig_p2_jes", "Change of the resonance width with the jet energy scale", 0., -10, 10)
xsigma_jer = RooRealVar("sig_p2_scale_jer", "Variation of the resonance width with the jet energy resolution", 0.020, -1., 1.)
ssigma_jer = RooRealVar("CMSRunII_sig_p2_jer", "Change of the resonance width with the jet energy resolution", 0., -10, 10)
xsigma_e = RooRealVar("sig_p2_scale_e", "Variation of the resonance width with the electron energy scale", 0.001, -1., 1.)
ssigma_e = RooRealVar("CMSRunII_sig_p2_scale_e", "Change of the resonance width with the electron energy scale", 0., -10, 10)
xsigma_m = RooRealVar("sig_p2_scale_m", "Variation of the resonance width with the muon energy scale", 0.040, -1., 1.)
ssigma_m = RooRealVar("CMSRunII_sig_p2_scale_m", "Change of the resonance width with the muon energy scale", 0., -10, 10)
xalpha1_fit = RooRealVar("sig_p3_fit", "Variation of the resonance alpha with the fit uncertainty", 0.03, -1., 1.)
salpha1_fit = RooRealVar("CMSRunII_sig_p3_fit", "Change of the resonance alpha with the fit uncertainty", 0., -10, 10)
xslope1_fit = RooRealVar("sig_p4_fit", "Variation of the resonance slope with the fit uncertainty", 0.10, -1., 1.)
sslope1_fit = RooRealVar("CMSRunII_sig_p4_fit", "Change of the resonance slope with the fit uncertainty", 0., -10, 10)
xmean_fit.setConstant(True)
smean_fit.setConstant(True)
xmean_jes.setConstant(True)
smean_jes.setConstant(True)
xmean_e.setConstant(True)
smean_e.setConstant(True)
xmean_m.setConstant(True)
smean_m.setConstant(True)
xsigma_fit.setConstant(True)
ssigma_fit.setConstant(True)
xsigma_jes.setConstant(True)
ssigma_jes.setConstant(True)
xsigma_jer.setConstant(True)
ssigma_jer.setConstant(True)
xsigma_e.setConstant(True)
ssigma_e.setConstant(True)
xsigma_m.setConstant(True)
ssigma_m.setConstant(True)
xalpha1_fit.setConstant(True)
salpha1_fit.setConstant(True)
xslope1_fit.setConstant(True)
sslope1_fit.setConstant(True)
# the alpha method is now done.
for m in massPoints:
signalString = "M%d" % m
signalMass = "%s_M%d" % (stype, m)
signalName = "%s%s_M%d" % (stype, category, m)
signalColor = sample[signalMass]['linecolor'] if signalName in sample else 1
# define the signal PDF
vmean[m] = RooRealVar(signalName + "_vmean", "Crystal Ball mean", m, m*0.5, m*1.25)
smean[m] = RooFormulaVar(signalName + "_mean", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)", RooArgList(vmean[m], xmean_e, smean_e, xmean_m, smean_m, xmean_jes, smean_jes, xmean_fit, smean_fit))
vsigma[m] = RooRealVar(signalName + "_vsigma", "Crystal Ball sigma", m*0.035, m*0.01, m*0.4)
sigmaList = RooArgList(vsigma[m], xsigma_e, ssigma_e, xsigma_m, ssigma_m, xsigma_jes, ssigma_jes, xsigma_jer, ssigma_jer)
sigmaList.add(RooArgList(xsigma_fit, ssigma_fit))
ssigma[m] = RooFormulaVar(signalName + "_sigma", "@0*(1+@1*@2)*(1+@3*@4)*(1+@5*@6)*(1+@7*@8)*(1+@9*@10)", sigmaList)
valpha1[m] = RooRealVar(signalName + "_valpha1", "Crystal Ball alpha", 1., 0., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
salpha1[m] = RooFormulaVar(signalName + "_alpha1", "@0*(1+@1*@2)", RooArgList(valpha1[m], xalpha1_fit, salpha1_fit))
vslope1[m] = RooRealVar(signalName + "_vslope1", "Crystal Ball slope", 10., 1., 60.) # slope of the power tail #10 1 60
sslope1[m] = RooFormulaVar(signalName + "_slope1", "@0*(1+@1*@2)", RooArgList(vslope1[m], xslope1_fit, sslope1_fit))
salpha2[m] = RooRealVar(signalName + "_alpha2", "Crystal Ball alpha", 2, 1., 5.) # number of sigmas where the exp is attached to the gaussian core. >0 left, <0 right
sslope2[m] = RooRealVar(signalName + "_slope2", "Crystal Ball slope", 10, 1.e-1, 115.) # slope of the power tail
#define polynomial
#a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", m, 0.5*m, 2*m)
a1[m] = RooRealVar(signalName + "_a1", "par 1 for polynomial", 0.001*m, 0.0005*m, 0.01*m)
a2[m] = RooRealVar(signalName + "_a2", "par 2 for polynomial", 0.05, -1.,1.)
#if channel=='nnbbVBF' or channel=='nn0bVBF':
# signal[m] = RooPolynomial(signalName,"m_{%s'} = %d GeV" % (stype[1], m) , X_mass, RooArgList(a1[m],a2[m]))
#else:
# signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel
signal[m] = RooCBShape(signalName, "m_{%s'} = %d GeV" % (stype[1], m), X_mass, smean[m], ssigma[m], salpha1[m], sslope1[m]) # Signal name does not have the channel
# extend the PDF with the yield to perform an extended likelihood fit
signalYield[m] = RooRealVar(signalName+"_yield", "signalYield", 100, 0., 1.e6)
signalNorm[m] = RooRealVar(signalName+"_norm", "signalNorm", 1., 0., 1.e6)
signalXS[m] = RooRealVar(signalName+"_xs", "signalXS", 1., 0., 1.e6)
signalExt[m] = RooExtendPdf(signalName+"_ext", "extended p.d.f", signal[m], signalYield[m])
vslope1[m].setMax(50.)
vslope1[m].setVal(20.)
#valpha1[m].setVal(1.0)
#valpha1[m].setConstant(True)
if 'bb' in channel and 'VBF' not in channel:
if 'nn' in channel:
valpha1[m].setVal(0.5)
elif '0b' in channel and 'VBF' not in channel:
if 'nn' in channel:
if m==800:
valpha1[m].setVal(2.)
vsigma[m].setVal(m*0.04)
elif 'ee' in channel:
valpha1[m].setVal(0.8)
if m==800:
#valpha1[m].setVal(1.2)
valpha1[m].setVal(2.5)
vslope1[m].setVal(50.)
elif 'mm' in channel:
if m==800:
valpha1[m].setVal(2.)
vsigma[m].setVal(m*0.03)
else:
vmean[m].setVal(m*0.9)
vsigma[m].setVal(m*0.08)
elif 'bb' in channel and 'VBF' in channel:
if 'nn' in channel:
if m!=1800:
vmean[m].setVal(m*0.8)
vsigma[m].setVal(m*0.08)
valpha1[m].setMin(1.)
elif 'ee' in channel:
valpha1[m].setVal(0.7)
elif 'mm' in channel:
if m==800:
vslope1[m].setVal(50.)
valpha1[m].setVal(0.7)
elif '0b' in channel and 'VBF' in channel:
if 'nn' in channel:
valpha1[m].setVal(3.)
vmean[m].setVal(m*0.8)
vsigma[m].setVal(m*0.08)
valpha1[m].setMin(1.)
elif 'ee' in channel:
if m<2500:
valpha1[m].setVal(2.)
if m==800:
vsigma[m].setVal(m*0.05)
elif m==1000:
vsigma[m].setVal(m*0.03)
elif m>1000 and m<1800:
vsigma[m].setVal(m*0.04)
elif 'mm' in channel:
if m<2000:
valpha1[m].setVal(2.)
if m==1000 or m==1800:
vsigma[m].setVal(m*0.03)
elif m==1200 or m==1600:
vsigma[m].setVal(m*0.04)
#if m < 1000: vsigma[m].setVal(m*0.06)
# If it's not the proper channel, make it a gaussian
#if nLept==0 and 'VBF' in channel:
# valpha1[m].setVal(5)
# valpha1[m].setConstant(True)
# vslope1[m].setConstant(True)
# salpha2[m].setConstant(True)
# sslope2[m].setConstant(True)
# ---------- 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")
for j, ss in enumerate(sample[signalMass]['files']):
treeSign[m].Add(NTUPLEDIR + ss + ".root")
if treeSign[m].GetEntries() <= 0.:
if VERBOSE: 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
signalYield[m].setVal(setSignal[m].sumEntries())
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, stype+channel, X_mass, signal[m], setSignal[m], frSignal[m])
else:
print " WARNING: signal", stype, "and mass point", m, "in channel", channel, "has 0 entries or does not exist"
# Remove HVT cross section (which is the same for Zlep and Zinv)
if stype == "XZHVBF":
sample_name = 'Zprime_VBF_Zh_Zlephinc_narrow_M-%d' % m
else:
sample_name = 'ZprimeToZHToZlepHinc_narrow_M%d' % m
xs = xsection[sample_name]['xsec']
signalXS[m].setVal(xs * 1000.)
signalIntegral[m] = signalExt[m].createIntegral(massArg, RooFit.NormSet(massArg), RooFit.Range("X_integration_range"))
boundaryFactor = signalIntegral[m].getVal()
if VERBOSE:
print " - Fit normalization vs integral:", signalYield[m].getVal(), "/", boundaryFactor, "events"
if channel=='nnbb' and m==5000:
signalNorm[m].setVal(2.5)
elif channel=='nn0b' and m==5000:
signalNorm[m].setVal(6.7)
else:
signalNorm[m].setVal( boundaryFactor * signalYield[m].getVal() / signalXS[m].getVal()) # here normalize to sigma(X) x Br(X->VH) = 1 [fb]
a1[m].setConstant(True)
a2[m].setConstant(True)
vmean[m].setConstant(True)
vsigma[m].setConstant(True)
valpha1[m].setConstant(True)
vslope1[m].setConstant(True)
salpha2[m].setConstant(True)
sslope2[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 m in genPoints[:-2]:
if m in signalExt.keys():
signal[m].plotOn(frame_signal, RooFit.LineColor(sample["%s_M%d" % (stype, m)]['linecolor']), RooFit.Normalization(signalNorm[m].getVal(), RooAbsReal.NumEvent), RooFit.Range("X_reasonable_range"))
frame_signal.GetXaxis().SetRangeUser(0, 6500)
frame_signal.Draw()
drawCMS(-1, YEAR, "Simulation")
drawAnalysis(channel)
drawRegion(channel)
c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_Signal.pdf")
c_signal.SaveAs(PLOTDIR+"/"+stype+category+"/"+stype+"_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", 800, 5000) #"pol5" if not channel=="XZHnnbb" else "pol6" #pol5*TMath::Floor(x-1800) + ([5]*x + [6]*x*x)*(1-TMath::Floor(x-1800))
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, 5000)
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, 5000)
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", "pol0", 0, 5000)
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", "pol0", 0, 5000)
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", "pol0", 0, 5000)
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", "pol0", 0, 5000)
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
signalString = "M%d" % m
signalName = "%s_M%d" % (stype, m)
if gnorm.GetMaximum() < signalNorm[m].getVal(): gnorm.SetMaximum(signalNorm[m].getVal())
gnorm.SetPoint(n, m, signalNorm[m].getVal())
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(salpha2[m].getError(), salpha2[m].getVal()*0.10))
gslope2.SetPoint(n, m, sslope2[m].getVal())
gslope2.SetPointError(n, 0, min(sslope2[m].getError(), sslope2[m].getVal()*0.10))
n = n + 1
print "fit on gmean:"
gmean.Fit(fmean, "Q0", "SAME")
print "fit on gsigma:"
gsigma.Fit(fsigma, "Q0", "SAME")
print "fit on galpha:"
galpha1.Fit(falpha1, "Q0", "SAME")
print "fit on gslope:"
gslope1.Fit(fslope1, "Q0", "SAME")
galpha2.Fit(falpha2, "Q0", "SAME")
gslope2.Fit(fslope2, "Q0", "SAME")
#for m in [5000, 5500]: gnorm.SetPoint(gnorm.GetN(), m, gnorm.Eval(m, 0, "S"))
gnorm.Fit(fnorm, "Q", "SAME", 700, 5000)
for m in massPoints:
signalName = "%s_M%d" % (stype, m)
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)
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
inorm.SetPoint(inorm.GetN(), m, syield)
signalNorm[m].setVal(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)
c1 = TCanvas("c1", "Crystal Ball", 1200, 800)
c1.Divide(2, 2)
c1.cd(1)
gmean.SetMinimum(0.)
gmean.Draw("APL")
imean.Draw("P, SAME")
drawRegion(channel)
c1.cd(2)
gsigma.SetMinimum(0.)
gsigma.Draw("APL")
isigma.Draw("P, SAME")
drawRegion(channel)
c1.cd(3)
galpha1.Draw("APL")
ialpha1.Draw("P, SAME")
drawRegion(channel)
galpha1.GetYaxis().SetRangeUser(0., 5.)
c1.cd(4)
gslope1.Draw("APL")
islope1.Draw("P, SAME")
drawRegion(channel)
gslope1.GetYaxis().SetRangeUser(0., 125.)
if False:
c1.cd(5)
galpha2.Draw("APL")
ialpha2.Draw("P, SAME")
drawRegion(channel)
c1.cd(6)
gslope2.Draw("APL")
islope2.Draw("P, SAME")
drawRegion(channel)
gslope2.GetYaxis().SetRangeUser(0., 10.)
c1.Print(PLOTDIR+stype+category+"/"+stype+"_SignalShape.pdf")
c1.Print(PLOTDIR+stype+category+"/"+stype+"_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,YEAR , "Simulation")
drawAnalysis(channel)
drawRegion(channel)
c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.pdf")
c2.Print(PLOTDIR+stype+category+"/"+stype+"_SignalNorm.png")
#*******************************************************#
# #
# Generate workspace #
# #
#*******************************************************#
# create workspace
w = RooWorkspace("ZH_RunII", "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("%s%s.root" % (WORKDIR, stype+channel), True)
print "Workspace", "%s%s.root" % (WORKDIR, stype+channel), "saved successfully"
sys.exit()
def fitWPeak():
# Get histograms from file
mcHist = 0
with root_open(inputFile, 'read') as file:
gROOT.cd()
mcHist = file.Get(inputHistogram).Clone('histToFit')
# mcHist = file.Get(inputHistogram).GetStack().Last().Clone('histToFit')
# Data hist gone out of scope?
print mcHist
# Import the data to fit
# Declare variables x,mean,sigma with associated name, title, initial value and allowed range
x = RooRealVar("M(jj)", "M(jj)", 40, 180)
histToFit = RooDataHist('histToFit', 'histToFit', RooArgList(x),
RooFit.Import(mcHist))
frame = x.frame(RooFit.Title("E Plus Jets"))
histToFit.plotOn(frame)
# Setup fit function
fitFunction = None
print fitFunction
if whatToFit == 'realLife':
# Stuff for gauss
mg = RooRealVar("mean", "mean of gaussian", 86, 50, 120)
sg = RooRealVar("sigma", "width of gaussian", 10, 0, 50)
gauss = RooGaussian("gauss", "gaussian PDF", x, mg, sg)
CBmean = RooRealVar("CBmean", "CBmean", 110, 60, 200)
CBsigma = RooRealVar("CBsigma", "CBsigma", 40, 20, 100)
CBalpha = RooRealVar("CBalpha", "CBalpha", -0.5, -20., 0.)
# CBalpha = RooRealVar("CBalpha", "CBalpha", 10, 0., 20.)
CBn = RooRealVar("CBn", "CBn", 1., 0., 20.)
crystalBall = RooCBShape("crystalBall",
"Crystal Ball resolution model", x, CBmean,
CBsigma, CBalpha, CBn)
fracGauss = RooRealVar("fracGauss", "fracGauss", 0.4, 0, 1)
gaussPlusCrystalBall = RooAddPdf("gaussPlusCrystalBall",
"Gauss plus Crystal Ball",
RooArgList(gauss, crystalBall),
RooArgList(fracGauss))
fitFunction = gaussPlusCrystalBall
elif whatToFit == 'partons':
mg = RooRealVar("mean", "mean of gaussian", 86, 50, 120)
sg = RooRealVar("sigma", "width of gaussian", 10, 0, 50)
breitWigner = RooBreitWigner('bw', 'bw', x, mg, sg)
fitFunction = breitWigner
elif whatToFit == 'genJetsFromPartons':
# mg = RooRealVar("mean","mean of gaussian",86,50,120)
# sg = RooRealVar("sigma","width of gaussian",10,0,50)
# breitWigner = RooBreitWigner('bw', 'bw', x, mg, sg)
# fitFunction = breitWigner
# mg = RooRealVar("mean","mean of gaussian",86,50,120)
# sg = RooRealVar("sigma","width of gaussian",1,0,20)
# width = RooRealVar("width","width of gaussian",5,0,50)
# voigtian = RooVoigtian("voigt","voigt",x,mg,sg,width);
# fitFunction = voigtian
mg = RooRealVar("mean", "mean of gaussian", 86, 50, 120)
sg = RooRealVar("sigma", "width of gaussian", 10, 0, 50)
gauss = RooGaussian("gauss", "gaussian PDF", x, mg, sg)
fitFunction = gauss
print fitFunction
# # Fit pdf to data
fitFunction.fitTo(histToFit)
# Plot histogram being fitted
histToFit.plotOn(frame)
# Plot fit functions and components
fitFunction.plotOn(frame, RooFit.LineColor(kRed))
print 'Chi square with 7 : ', frame.chiSquare(7)
print frame.chiSquare()
print fitFunction.getParameters(histToFit).selectByAttrib(
'Constant', kFALSE).getSize()
toPlot = RooArgSet(crystalBall)
fitFunction.plotOn(frame, RooFit.Components(toPlot),
RooFit.LineStyle(kDashed), RooFit.LineColor(kRed))
toPlot = RooArgSet(gauss)
fitFunction.plotOn(frame, RooFit.Components(toPlot),
RooFit.LineStyle(kDashed), RooFit.LineColor(kBlue))
fitFunction.paramOn(frame, RooFit.Layout(0.55, 0.9, 0.9))
# Print values of mean and sigma (that now reflect fitted values and errors)
mg.Print()
sg.Print()
# CBmean.Print()
# CBsigma.Print()
# CBalpha.Print()
# CBn.Print()
# fracGauss.Print()
# # Draw frame on a canvas
c = TCanvas("WMass", "WMass", 800, 400)
gPad.SetLeftMargin(0.15)
frame.GetYaxis().SetTitleOffset(1.6)
frame.Draw()
gPad.Update()
c.Print('EPlusJets_mjj_fit.pdf')
raw_input()
