def setcanvas(self, **kwargs):
"""Make canvas and pads for ratio plots."""
square = kwargs.get('square', False)
double = kwargs.get('ratio', False) # include lower panel
width = kwargs.get('width', 900 if square else 800 if double else 800)
height = kwargs.get('height',
900 if square else 750 if double else 600)
lmargin = kwargs.get('lmargin', 1.)
rmargin = kwargs.get('rmargin', 1.)
tmargin = kwargs.get('tmargin', 1.)
bmargin = kwargs.get('bmargin', 1.)
pads = kwargs.get('pads', []) # pass list as reference
#if not CMSStyle.lumi_13TeV:
# tmargin *= 0.7
if square:
lmargin *= 1.15
tmargin *= 0.90
#rmargin *= 3.6
#CMSStyle.relPosX = 0.15
canvas = TCanvas('canvas', 'canvas', 100, 100, width, height)
canvas.SetFillColor(0)
#canvas.SetFillStyle(0)
canvas.SetBorderMode(0)
canvas.SetFrameBorderMode(0)
if double:
canvas.SetMargin(0.0, 0.0, 0.0, 0.0) # LRBT
canvas.Divide(2)
canvas.cd(1)
gPad.SetPad('pad1', 'pad1', 0.0, 0.33, 1.0, 1.0)
gPad.SetMargin(0.145 * lmargin, 0.04 * rmargin, 0.029,
0.075 * tmargin)
gPad.SetFillColor(0)
gPad.SetFillStyle(4000) # transparant (for pdf)
#gPad.SetFillStyle(0)
gPad.SetBorderMode(0)
gPad.Draw()
canvas.cd(2)
gPad.SetPad('pad2', 'pad2', 0.0, 0.0, 1.0, 0.33)
gPad.SetMargin(0.145 * lmargin, 0.04 * rmargin, 0.355 * bmargin,
0.04)
gPad.SetFillColor(0) #gPad.SetFillColorAlpha(0,0.0)
gPad.SetFillStyle(4000) # transparant (for pdf)
gPad.SetBorderMode(0)
gPad.Draw()
canvas.cd(1)
else:
canvas.SetMargin(0.145 * lmargin, 0.05 * rmargin, 0.145 * bmargin,
0.06 * tmargin)
return canvas
def PlotPosteriors(ListOfPosteriors, outputname = ""):
RepeatIteration = False
if (outputname != "" ):
outputname = "_"+outputname
c = TCanvas("Posteriors"+outputname,"Posteriors"+outputname,0,0,1600, 1600)
c.Divide(math.ceil(pow(len(ListOfPosteriors),0.5)),math.ceil(pow(len(ListOfPosteriors),0.5)))
legends = []
for i in range(len(ListOfPosteriors)):
ListOfPosteriors[i].SetMaximum(ListOfPosteriors[i].GetMaximum()*2.0)
#ListOfPosteriors[i].Fit("gaus")
fit = ListOfPosteriors[i].GetFunction("gaus")
chi2 = fit.GetChisquare()
p1 = fit.GetParameter(1)
#p0 = fit.GetParameter(0)
p2 = fit.GetParameter(2)
FitIntegral = fit.Integral(p1-10*p2,p1+10*p2) # fit integral , get mean plus minus 10 sigma
PriorIntegral = ListOfPosteriors[i].Integral("width")
Percentage = round((100*PriorIntegral/FitIntegral),0)
c.cd(i+1)
gPad.SetFrameFillColor(10)
if (Percentage < 90):
gPad.SetFillColor(kRed-4)
RepeatIteration = True
else:
gPad.SetFillColor(8)
leg = TLegend(0.1,0.5,0.85,0.9)
leg.SetFillStyle(0)
leg.AddEntry(None,"MeanHist = "+str(round(ListOfPosteriors[i].GetMean(),0))+", RMShist = "+str(round(ListOfPosteriors[i].GetRMS(),0))+", MeanFit = "+str(round(p1,0))+", #sigma_{fit} = "+str(round(p2,0)),"")
leg.AddEntry(ListOfPosteriors[i],"Posterior ","l")
leg.AddEntry(fit,"Fit ","l")
leg.AddEntry(None,"#chi^{2}/NDF = "+str(round(chi2/len(ListOfPosteriors),2)),"")
leg.AddEntry(None,"Integral Prior/Fit = "+str(round(Percentage,0))+" %","")
leg.SetBorderSize(0)
legends.append(leg)
ListOfPosteriors[i].SetTitle("Posterior in bin "+str(i+1))
gStyle.SetOptStat(0)
ListOfPosteriors[i].Draw()
legends[i].Draw("same")
if (round(chi2/len(ListOfPosteriors),2)) > 20.0:
RepeatIteration = True
#c.Update()
c.cd(1)
gStyle.SetOptStat(0)
#gPad.Modified()
#c.Update()
PrintCan(c,c.GetName()+"_posteriors")
return RepeatIteration
def setPadStyle():
gPad.SetLeftMargin(0.05)
gPad.SetRightMargin(0.08)
gPad.SetTopMargin(0.10)
gPad.SetBottomMargin(0.10)
gPad.SetLogz(plots[plot].zLog)
gPad.SetFillColor(kWhite)
gPad.SetBorderMode(0)
def setpad(left, right, top, bottom):
gPad.SetFillColor(10)
gPad.SetBorderMode(0)
gPad.SetBorderSize(0)
gPad.SetFrameFillColor(10)
gPad.SetFrameBorderMode(0)
gPad.SetFrameBorderSize(0)
gPad.SetLeftMargin(left)
gPad.SetRightMargin(right)
gPad.SetTopMargin(top)
gPad.SetBottomMargin(bottom)
gPad.SetGridx(0)
gPad.SetGridy(0)
gStyle.SetOptStat(0)
def fitChicSpectrum(dataset, binname):
""" Fit chic spectrum"""
x = RooRealVar('Qvalue', 'Q', 9.7, 10.1)
x.setBins(80)
mean_1 = RooRealVar("mean_1", "mean ChiB1", 9.892, 9, 10, "GeV")
sigma_1 = RooRealVar("sigma_1", "sigma ChiB1", 0.0058, 'GeV')
a1_1 = RooRealVar('#alpha1_1', '#alpha1_1', 0.748)
n1_1 = RooRealVar('n1_1', 'n1_1', 2.8)
a2_1 = RooRealVar('#alpha2_1', '#alpha2_1', 1.739)
n2_1 = RooRealVar('n2_1', 'n2_1', 3.0)
deltam = RooRealVar('deltam', 'deltam', 0.01943)
mean_2 = RooFormulaVar("mean_2", "@0+@1", RooArgList(mean_1, deltam))
sigma_2 = RooRealVar("sigma_2", "sigma ChiB2", 0.0059, 'GeV')
a1_2 = RooRealVar('#alpha1_2', '#alpha1_2', 0.738)
n1_2 = RooRealVar('n1_2', 'n1_2', 2.8)
a2_2 = RooRealVar('#alpha2_2', '#alpha2_2', 1.699)
n2_2 = RooRealVar('n2_2', 'n2_2', 3.0)
parameters = RooArgSet()
parameters.add(RooArgSet(sigma_1, sigma_2))
parameters = RooArgSet(a1_1, a2_1, n1_1, n2_1)
parameters.add(RooArgSet(a1_2, a2_2, n1_2, n2_2))
chib1_pdf = My_double_CB('chib1', 'chib1', x, mean_1, sigma_1, a1_1, n1_1,
a2_1, n2_1)
chib2_pdf = My_double_CB('chib2', 'chib2', x, mean_2, sigma_2, a1_2, n1_2,
a2_2, n2_2)
#background
q01S_Start = 9.5
alpha = RooRealVar("#alpha", "#alpha", 1.5, -1, 3.5) #0.2 anziche' 1
beta = RooRealVar("#beta", "#beta", -2.5, -7., 0.)
q0 = RooRealVar("q0", "q0", q01S_Start) #,9.5,9.7)
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))
parameters.add(RooArgSet(alpha, beta, q0))
#together
chibs = RooArgList(chib1_pdf, chib2_pdf, background)
n_chib = RooRealVar("n_chib", "n_chib", 2075, 0, 100000)
ratio_21 = RooRealVar("ratio_21", "ratio_21", 0.5, 0, 1)
n_chib1 = RooFormulaVar("n_chib1", "@0/(1+@1)",
RooArgList(n_chib, ratio_21))
n_chib2 = RooFormulaVar("n_chib2", "@0/(1+1/@1)",
RooArgList(n_chib, ratio_21))
n_background = RooRealVar('n_background', 'n_background', 4550, 0, 50000)
ratio_list = RooArgList(n_chib1, n_chib2, n_background)
modelPdf = RooAddPdf('ModelPdf', 'ModelPdf', chibs, ratio_list)
frame = x.frame(RooFit.Title('m'))
range = x.setRange('range', 9.7, 10.1)
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')
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 drawHistsWithRatio(hists, name, **kwargs):
"""Draw histograms with ratios."""
title = kwargs.get('title', "")
xtitle = kwargs.get('xtitle', "")
ytitle = kwargs.get('ytitle', "")
rtitle = kwargs.get('rtitle', "Ratio")
xmin = kwargs.get('xmin', hists[0].GetXaxis().GetXmin())
xmax = kwargs.get('xmax', hists[0].GetXaxis().GetXmax())
ymin = kwargs.get('ymin', None)
ymax = kwargs.get('ymax', None)
rmin = kwargs.get('rmin', 0.45)
rmax = kwargs.get('rmax', 1.55)
logx = kwargs.get('logx', False)
logy = kwargs.get('logy', False)
denom = kwargs.get('denom', 1) - 1 # denominator for ratio
textsize = kwargs.get('textsize', 0.045)
texts = kwargs.get('text', [])
#textheight = kwargs.get('textheight', 1.09 )
#ctext = kwargs.get('ctext', [ ] ) # corner text
#cposition = kwargs.get('cposition', 'topleft' ).lower() # cornertext
#ctextsize = kwargs.get('ctextsize', 1.4*legendtextsize )
colors = kwargs.get('colors', linecolors)
if not isinstance(texts, list) or isinstance(texts, tuple):
texts = [texts]
if ymax == None:
ymax = 1.12 * max(h.GetMaximum() for h in hists)
# MAIN plot
canvas = TCanvas('canvas', 'canvas', 100, 100, 800, 800)
canvas.SetFillColor(0)
canvas.SetBorderMode(0)
canvas.SetFrameBorderMode(0)
canvas.Divide(2)
canvas.SetMargin(0.0, 0.0, 0.0, 0.0)
canvas.cd(1)
gPad.SetPad('pad1', 'pad1', 0, 0.33, 1, 1)
gPad.SetMargin(0.12, 0.04, 0.02, 0.08)
gPad.SetFillColor(0)
gPad.SetBorderMode(0)
gPad.SetTickx(0)
gPad.SetTicky(0)
gPad.SetGrid()
gPad.Draw()
canvas.cd(2)
gPad.SetPad('pad2', 'pad2', 0, 0, 1, 0.33)
gPad.SetMargin(0.12, 0.04, 0.30, 0.03)
gPad.SetFillColor(0)
gPad.SetFillStyle(4000)
gPad.SetFrameFillStyle(0)
gPad.SetBorderMode(0)
gPad.Draw()
# MAIN plot
canvas.cd(1)
for i, hist in enumerate(hists):
color = colors[i % len(colors)]
hist.SetLineColor(color)
hist.SetLineWidth(2)
hist.Draw('HIST SAME')
frame = hists[0]
frame.GetYaxis().SetTitleSize(0.060)
frame.GetXaxis().SetTitleSize(0)
frame.GetXaxis().SetLabelSize(0)
frame.GetYaxis().SetLabelSize(0.052)
frame.GetXaxis().SetLabelOffset(0.010)
frame.GetXaxis().SetTitleOffset(0.98)
frame.GetYaxis().SetTitleOffset(1.05)
frame.GetXaxis().SetNdivisions(508)
frame.GetYaxis().SetTitle(ytitle)
frame.GetXaxis().SetTitle(xtitle)
if logx:
gPad.Update()
gPad.SetLogx()
if logy:
gPad.Update()
gPad.SetLogy()
if ymin: frame.SetMinimum(ymin)
if ymax: frame.SetMaximum(ymax)
width = 0.25
height = 1.1 * textsize * len([l for l in texts + hists if l])
x1, y1 = 0.65, 0.88
x2, y2 = x1 + width, y1 - height
legend = TLegend(x1, y1, x2, y2)
legend.SetTextSize(textsize)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
legend.SetFillColor(0)
legend.SetMargin(0.05 / width)
if title:
legend.SetTextFont(62)
legend.SetHeader(title)
legend.SetTextFont(42)
for hist in hists:
legend.AddEntry(hist, hist.GetTitle(), 'l')
for text in texts:
legend.AddEntry(0, text, '')
legend.Draw()
gPad.SetTicks(1, 1)
gPad.Modified()
frame.Draw('AXIS SAME')
CMS_style.CMS_lumi(gPad, 13, 0)
# RATIO plot
canvas.cd(2)
ratios = []
for i, hist in enumerate(hists):
if i == denom: continue
ratio = hist.Clone(hist.GetName() + "_ratio")
ratio.Divide(hists[denom])
ratio.Draw('HIST SAME')
ratios.append(ratio)
frame_ratio = ratios[0]
frame_ratio.GetYaxis().SetRangeUser(rmin, rmax)
frame_ratio.GetYaxis().CenterTitle()
frame_ratio.GetYaxis().SetTitleSize(0.13)
frame_ratio.GetXaxis().SetTitleSize(0.13)
frame_ratio.GetXaxis().SetLabelSize(0.12)
frame_ratio.GetYaxis().SetLabelSize(0.11)
frame_ratio.GetXaxis().SetLabelOffset(0.012)
frame_ratio.GetXaxis().SetTitleOffset(1.02)
frame_ratio.GetYaxis().SetTitleOffset(0.48)
frame_ratio.GetXaxis().SetNdivisions(508)
frame_ratio.GetYaxis().CenterTitle(True)
frame_ratio.GetYaxis().SetTitle(rtitle)
frame_ratio.GetXaxis().SetTitle(xtitle)
frame_ratio.GetYaxis().SetNdivisions(505)
if logx:
gPad.Update()
gPad.SetLogx()
line = TLine(xmin, 1., xmax, 1.)
line.SetLineColor(hists[denom].GetLineColor())
line.SetLineWidth(hists[denom].GetLineWidth())
line.SetLineStyle(1)
line.Draw('SAME')
gPad.SetTicks(1, 1)
gPad.Update()
gPad.SetGrid()
gPad.Modified()
frame_ratio.Draw('SAME AXIS')
canvas.SaveAs(name + ".png")
canvas.SaveAs(name + ".pdf")
canvas.Close()
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 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 smear(mu, sigma0, smearfactor, N=100000, lcut=None, ucut=None, nbins=80):
print ">>> smearing for N(%s,%s) with a factor of %s" % (mu, sigma0,
smearfactor)
# HISTS
xmin, xmax = mu - sigma0 * 5, mu + sigma0 * 4
sigma1 = sigma0 * (1 + smearfactor)
histname0 = "unsmeared"
histname1 = "smeared"
histtitle0 = "unsmeared, #sigma_{0} = %s" % (sigma0)
histtitle1 = "smeared, #sigma_{new} = %s" % (sigma1)
hist0 = TH1F(histname0, histtitle0, nbins, xmin, xmax)
hist1 = TH1F(histname1, histtitle1, nbins, xmin, xmax)
# FIT FUNCTIONS
xminf = xmin if lcut == None else lcut
xmaxf = xmax if ucut == None else ucut
gaus0 = TF1("gaus0", "gaus", xminf, xmaxf)
gaus1 = TF1("gaus1", "gaus", xminf, xmaxf)
gaus0.SetTitle("%s fit" % histname0)
gaus1.SetTitle("%s fit" % histname1)
gaus0.SetParameters(N, mu, sigma0)
gaus1.SetParameters(N, mu, sigma1)
#gaus0.SetParLimits(2,sigma0*0.9,sigma0*1.1)
#gaus1.SetParLimits(2,sigma1*0.9,sigma1*1.1)
hists = [(hist0, gaus0), (hist1, gaus1)]
# SMEAR & FILL
#sigma1 = smearfactor
#sigma1 = (smearfactor**2)/2.
#sigma1 = sqrt(2.*(smearfactor))
#sigma1 = sqrt(-2.*log(smearfactor))
#sigma1 = 1./(2*smearfactor**2)
sigma2 = sqrt(sigma1**2 - sigma0**2)
print ">>> sigma0 = %.3f, sigma1 = %.3f, sigma2 = %.3f" % (
sigma0, sigma1, sigma2)
print ">>> generating %s events..." % (N)
for i in xrange(N):
xval0 = gRandom.Gaus(mu, sigma0)
if lcut != None and xval0 < lcut: continue
if ucut != None and xval0 > ucut: continue
#rand = gRandom.Gaus(1,smearfactor)
#xval1 = xval0 * rand
#rand = gRandom.Gaus(0,1+smearfactor)
#xval1 = xval0 + rand
rand = gRandom.Gaus(0, 1)
xval1 = xval0 + sigma2 * rand
hist0.Fill(xval0)
if lcut != None and xval1 < lcut: continue
if ucut != None and xval1 > ucut: continue
hist1.Fill(xval1)
# PLOT SETTINGS
xtitle = "x variable"
ytitle = "events"
title = "Gauss(%s,%s)" % (mu, "#sigma")
canvasname = "smear_%.1f-%.1f_by_%.2f" % (mu, sigma0, smearfactor)
if lcut != None: canvasname += "_gt%.1f" % (lcut)
if ucut != None: canvasname += "_lt%.1f" % (ucut)
canvasname = canvasname.replace('.', 'p')
ymin, ymax = 0, 1.14 * max(hist0.GetMaximum(), hist1.GetMaximum())
rmin, rmax = 0.60, 1.40
lmargin, rmargin = 0.14, 0.04
# CANVAS
print ">>> plotting..."
canvas = TCanvas("canvas", "canvas", 100, 100, 800, 800)
canvas.Divide(2)
# MAIN plot
canvas.cd(1)
gPad.SetPad("pad1", "pad1", 0, 0.33, 1, 1, 0, -1, 0)
gPad.SetFillColor(0)
gPad.SetBorderMode(0)
gPad.SetFrameFillStyle(0)
gPad.SetFrameBorderMode(0)
gPad.SetTopMargin(0.06)
gPad.SetBottomMargin(0.02)
gPad.SetLeftMargin(lmargin)
gPad.SetRightMargin(rmargin)
gPad.SetGrid()
gPad.cd()
textsize = 0.050
x1, width = 0.18, 0.25
y1, height = 0.89, textsize * 1.08 * 5
legend = TLegend(x1, y1, x1 + width, y1 - height)
legend.SetTextSize(textsize)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
legend.SetFillColor(0)
legend.SetTextFont(62)
legend.SetHeader(title)
legend.SetTextFont(42)
# FRAME
frame = gPad.DrawFrame(xmin, ymin, xmax, ymax)
frame.GetYaxis().SetTitleSize(0.070)
frame.GetXaxis().SetTitleSize(0.070)
frame.GetXaxis().SetLabelSize(0.000)
frame.GetYaxis().SetLabelSize(0.060)
frame.GetXaxis().SetTitleOffset(1.00)
frame.GetYaxis().SetTitleOffset(1.06)
frame.GetXaxis().SetNdivisions(508)
frame.GetXaxis().SetTitle(xtitle)
frame.GetYaxis().SetTitle(ytitle)
# DRAW & FIT
print ">>> fitting..."
fits = []
for i, (hist, gaus) in enumerate(hists):
color = colors[i % len(colors)]
hist.SetLineColor(color)
hist.SetLineWidth(2)
hist.SetLineStyle(1)
gaus.SetLineColor(color + 1)
gaus.SetLineWidth(2)
gaus.SetLineStyle(2)
hist.Fit(gaus.GetName(), '0', '', xminf, xmaxf)
hist.Draw('SAME')
gaus.Draw('SAME')
gtitle = "#sigma_{fit} = %.3f" % (gaus.GetParameter(2)
) #gaus.GetTitle()
legend.AddEntry(hist, hist.GetTitle(), 'l')
legend.AddEntry(gaus, gtitle, 'l')
print ">>> real unsmeared sigma: %5.3f" % (sigma0)
print ">>> fitted unsmeared sigma: %5.3f" % (gaus0.GetParameter(2))
print ">>> real smear factor: %5.3f" % (smearfactor)
print ">>> fitted smeared sigma: %5.3f" % (gaus1.GetParameter(2))
legend.Draw()
frame.Draw('SAMEAXIS')
gPad.Modified()
# RATIO plot
canvas.cd(2)
gPad.SetPad("pad2", "pad2", 0, 0, 1, 0.32, 0, -1, 0)
gPad.SetTopMargin(0.04)
gPad.SetBottomMargin(0.29)
gPad.SetLeftMargin(lmargin)
gPad.SetRightMargin(rmargin)
# RATIO FRAME
rframe = gPad.DrawFrame(xmin, rmin, xmax, rmax)
rframe.GetYaxis().CenterTitle()
rframe.GetXaxis().SetTitleSize(0.144)
rframe.GetYaxis().SetTitleSize(0.140)
rframe.GetXaxis().SetLabelSize(0.130)
rframe.GetYaxis().SetLabelSize(0.120)
rframe.GetXaxis().SetLabelOffset(0.012)
rframe.GetXaxis().SetTitleOffset(0.85)
rframe.GetYaxis().SetTitleOffset(0.53)
rframe.GetXaxis().SetNdivisions(508)
rframe.GetYaxis().CenterTitle(True)
rframe.GetYaxis().SetTitle("ratio")
rframe.GetXaxis().SetTitle(xtitle)
rframe.GetYaxis().SetNdivisions(5)
# RATIO
ratios = []
for i, (hist, gaus) in enumerate(hists):
#if i==0: continue
#ratio = hist.Clone(hist.GetName()+"_ratio")
#ratio.Divide(hist0)
ratio = divideHists(hist, hist0, name=hist.GetName() + "_ratio")
ratio.Draw('HISTSAME')
ratios.append(ratio)
#ratiof = createTH1FromTF1(gaus,nbins,xmin,xmax)
#ratiof.Divide(hist0)
ratiof = divideTF1ByTH1(gaus, hist0, name=gaus.GetName() + "_ratio")
ratiof.Draw('HISTSAME')
ratios.append(ratiof)
print ratiof
#line = TLine(xmin,1.,xmax,1.)
#line.SetLineColor(hist0.GetLineColor())
#line.SetLineWidth(hist0.GetLineWidth())
#line.SetLineStyle(1)
#line.Draw('SAME')
gPad.SetGrid()
gPad.Modified()
rframe.Draw('sameaxis')
canvas.SaveAs(canvasname + ".png")
canvas.SaveAs(canvasname + ".pdf")
canvas.Close()
print ">>> "