def getEff(name, den_input, num_input, rebin=0, xtitle='', ytitle=''):
c = TCanvas(name + '_Canvas')
legend = TLegend(0.8, 0.1, 0.999, 0.6)
legend.SetFillColor(kWhite)
den = den_input.Clone()
num = num_input.Clone()
if rebin != 0:
den.Rebin(rebin)
num.Rebin(rebin)
error_bars = TGraphAsymmErrors()
error_bars.Divide(num, den, "cl=0.683 b(1,1) mode")
error_bars.SetLineWidth(3)
if xtitle == '':
error_bars.GetXaxis().SetTitle(num.GetXaxis().GetTitle())
else:
error_bars.GetXaxis().SetTitle(xtitle)
if ytitle == '':
error_bars.GetYaxis().SetTitle(num.GetYaxis().GetTitle())
else:
error_bars.GetYaxis().SetTitle(ytitle)
error_bars.GetXaxis().SetRangeUser(400, 2000)
error_bars.SetLineColor(kBlack)
error_bars.SetMaximum(1.01)
error_bars.SetMinimum(0.)
if ytitle == '':
error_bars.GetYaxis().SetTitle("Trigger rate")
else:
error_bars.GetYaxis().SetTitle(ytitle)
error_bars.SetTitle('')
error_bars.Draw('AP')
c.SaveAs('pdf/' + name + '.pdf')
c.Write(name + '_Canvas')
error_bars.Write(name)
def makePlot(finname,foutname,plottitle='',masstitle='',scale=False):
xsecs = resonantXsecs if 'resonant' in finname else fcncXsecs
points = {}
if BLIND:
cls = [2.5, 16, 50, 84, 97.5]
else:
cls = [2.5, 16, 50, 84, 97.5,'Observed']
xaxis = []
for cl in cls:
points[cl] = []
xsec=1
for l in open(finname):
try:
if l.strip()[0]=='#':
continue
if 'MASS' in l:
if scale:
xsec = xsecs[int(l.split()[1])]
if VERBOSE:
print ''
stdout.write('$%6s$ & $%7.3g$'%(l.split()[1],xsec/(0.667)))
xaxis.append(float(l.split()[1]))
else:
cl,val = parseLine(l)
points[cl].append(val/xsec)
if VERBOSE and (cl==50 or cl=='Observed'):
stdout.write(' & $%10.4g$'%(val/xsec))
except:
pass
if VERBOSE:
print ''
N = len(xaxis)
up1Sigma=[]; up2Sigma=[]
down1Sigma=[]; down2Sigma=[]
for iM in xrange(N):
up1Sigma.append(points[84][iM]-points[50][iM])
up2Sigma.append(points[97.5][iM]-points[50][iM])
down1Sigma.append(-points[16][iM]+points[50][iM])
down2Sigma.append(-points[2.5][iM]+points[50][iM])
up1Sigma = array('f',up1Sigma)
up2Sigma = array('f',up2Sigma)
down1Sigma = array('f',down1Sigma)
down2Sigma = array('f',down2Sigma)
cent = array('f',points[50])
if not BLIND:
obs = array('f',points['Observed'])
xarray = array('f',xaxis)
xsecarray = array('f',[xsecs[xx] for xx in xaxis])
xsecarrayLow = array('f',[0.0625*xsecs[xx] for xx in xaxis])
onearray = array('f',[1 for xx in xaxis])
graphXsec = TGraph(N,xarray,xsecarray)
graphXsecLow = TGraph(N,xarray,xsecarrayLow)
graphOne = TGraph(N,xarray,onearray)
zeros = array('f',[0 for i in xrange(N)])
graphCent = TGraph(N,xarray,cent)
if not BLIND:
graphObs = TGraph(N,xarray,obs)
graph1Sigma = TGraphAsymmErrors(N,xarray,cent,zeros,zeros,down1Sigma,up1Sigma)
graph2Sigma = TGraphAsymmErrors(N,xarray,cent,zeros,zeros,down2Sigma,up2Sigma)
c = TCanvas('c','c',700,600)
c.SetLogy()
c.SetLeftMargin(.15)
graph2Sigma.GetXaxis().SetTitle(masstitle+' [GeV]')
if scale:
graph2Sigma.GetYaxis().SetTitle('Upper limit [#sigma/#sigma_{theory}]')
else:
graph2Sigma.GetYaxis().SetTitle("Upper limit [#sigma] [pb]")
graph2Sigma.SetLineColor(5)
graph1Sigma.SetLineColor(3)
graph2Sigma.SetFillColor(5)
graph1Sigma.SetFillColor(3)
graph2Sigma.SetMinimum(0.5*min(points[2.5]))
if scale:
graph2Sigma.SetMaximum(10*max(max(points[97.5]),max(xsecarray),4))
else:
graph2Sigma.SetMaximum(10*max(max(points[97.5]),max(xsecarray)))
graphCent.SetLineWidth(2)
graphCent.SetLineStyle(2)
if not BLIND:
graphObs.SetLineColor(1)
graphObs.SetLineWidth(3)
graph1Sigma.SetLineStyle(0)
graph2Sigma.SetLineStyle(0)
leg = TLegend(0.55,0.7,0.9,0.9)
leg.AddEntry(graphCent,'Expected','L')
if not BLIND:
leg.AddEntry(graphObs,'Observed','L')
leg.AddEntry(graph1Sigma,'1 #sigma','F')
leg.AddEntry(graph2Sigma,'2 #sigma','F')
leg.SetFillStyle(0)
leg.SetBorderSize(0)
graph2Sigma.Draw('A3')
graph1Sigma.Draw('3 same')
graphCent.Draw('same L')
if not BLIND:
graphObs.Draw('same L')
if scale:
graphOne.SetLineColor(2)
graphOne.SetLineWidth(2)
graphOne.SetLineStyle(2)
graphOne.Draw('same L')
else:
graphXsec.SetLineColor(2)
graphXsecLow.SetLineColor(4)
subscript = 'SR' if 'Resonant' in plottitle else 'FC'
if 'Resonant' in plottitle:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=0.1}{m_{#chi}=100 GeV}'%(subscript,subscript),'l')
else:
leg.AddEntry(graphXsec,'Theory a_{%s}=b_{%s}=0.1'%(subscript,subscript),'l')
# leg.AddEntry(graphXsecLow,'Theory a_{%s}=b_{%s}=0.025'%(subscript,subscript),'l')
for g in [graphXsec]:
g.SetLineWidth(2)
g.SetLineStyle(2)
g.Draw('same L')
leg.Draw()
label = TLatex()
label.SetNDC()
label.SetTextFont(62)
label.SetTextAlign(11)
label.DrawLatex(0.19,0.85,"CMS")
label.SetTextFont(52)
label.DrawLatex(0.28,0.85,"Preliminary")
label.SetTextFont(42)
label.SetTextSize(0.6*c.GetTopMargin())
label.DrawLatex(0.19,0.77,plottitle)
if scale:
if 'Resonant' in plottitle:
label.DrawLatex(0.19,0.7,"a_{SR} = b_{SR} = 0.1")
label.DrawLatex(0.19,0.64,"m_{#chi}=100 GeV")
else:
label.DrawLatex(0.19,0.7,"a_{FC} = b_{FC} = 0.1")
label.SetTextSize(0.5*c.GetTopMargin())
label.SetTextFont(42)
label.SetTextAlign(31) # align right
label.DrawLatex(0.9, 0.94,"%.1f fb^{-1} (13 TeV)"%(plotConfig.lumi))
c.SaveAs(foutname+'.pdf')
c.SaveAs(foutname+'.png')
def makePlot1D(filepath, foutname, plottitle='', masstitle=''):
br = 1 if 'Resonant' in plottitle else 0.68
limits = parseLimitFiles2D(filepath, br)
xaxis = []
xseclist = []
xsecerr = []
cent = []
obs = []
up1 = []
up2 = []
down1 = []
down2 = []
maxval = 0
minval = 999
for m in sorted(limits):
l = limits[m]
xaxis.append(m)
xseclist.append(l.xsec)
xsecerr.append(l.xsec * .2)
cent.append(l.cent)
up1.append(l.up1 - l.cent)
up2.append(l.up2 - l.cent)
down1.append(l.cent - l.down1)
down2.append(l.cent - l.down2)
obs.append(l.obs)
maxval = max(maxval, l.up2)
minval = min(minval, l.down2)
N = len(xaxis)
up1Sigma = array('f', up1)
up2Sigma = array('f', up2)
down1Sigma = array('f', down1)
down2Sigma = array('f', down2)
cent = array('f', cent)
obs = array('f', obs)
xarray = array('f', xaxis)
xsecarray = array('f', xseclist)
xsecerrarray = array('f', xsecerr)
zeros = array('f', [0 for i in xrange(N)])
graphXsec = TGraphErrors(N, xarray, xsecarray, zeros, xsecerrarray)
graphCent = TGraph(N, xarray, cent)
graphObs = TGraph(N, xarray, obs)
graph1Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down1Sigma,
up1Sigma)
graph2Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down2Sigma,
up2Sigma)
c = TCanvas('c', 'c', 700, 600)
c.SetLogy()
c.SetLeftMargin(.15)
graph2Sigma.GetXaxis().SetTitle(masstitle + ' [GeV]')
graph2Sigma.GetYaxis().SetTitle(
'95% C.L. upper limit [#sigma/#sigma_{theory}]')
c2 = root.kOrange
c1 = root.kGreen + 1
graph2Sigma.SetLineColor(c2)
graph1Sigma.SetLineColor(c1)
graph2Sigma.SetFillColor(c2)
graph1Sigma.SetFillColor(c1)
graph2Sigma.SetMinimum(0.5 * minval)
graph2Sigma.SetMaximum(10 * maxval)
graphCent.SetLineWidth(2)
graphCent.SetLineStyle(2)
graphObs.SetLineColor(1)
graphObs.SetLineWidth(3)
graphObs.SetMarkerStyle(20)
graphObs.SetMarkerSize(1)
graphObs.SetMarkerColor(1)
graph1Sigma.SetLineStyle(0)
graph2Sigma.SetLineStyle(0)
leg = TLegend(0.55, 0.7, 0.9, 0.9)
leg.AddEntry(graphCent, 'Expected', 'L')
if not BLIND:
leg.AddEntry(graphObs, 'Observed', 'Lp')
leg.AddEntry(graph1Sigma, '1 std. dev.', 'F')
leg.AddEntry(graph2Sigma, '2 std. dev.', 'F')
leg.SetFillStyle(0)
leg.SetBorderSize(0)
graph2Sigma.Draw('A3')
graph1Sigma.Draw('3 same')
graphCent.Draw('same L')
if not BLIND:
graphObs.Draw('same Lp')
subscript = 'SR' if 'Resonant' in plottitle else 'FC'
coupling = '0.1' if 'Resonant' in plottitle else '0.25'
graphXsec.SetLineColor(2)
graphXsec.SetLineWidth(2)
graphXsec.SetLineStyle(2)
graphXsec.SetFillColor(2)
graphXsec.SetFillStyle(3005)
graphXsec.Draw('same L3')
'''
if not scale:
if 'Resonant' in plottitle:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=100 GeV}'%(subscript,subscript,coupling),'l')
else:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=10 GeV}'%(subscript,subscript,coupling),'l')
'''
if not BLIND:
findIntersect1D(graphObs, graphXsec, xaxis[0], xaxis[-1])
findIntersect1D(graphCent, graphXsec, xaxis[0], xaxis[-1])
leg.Draw()
label = TLatex()
label.SetNDC()
label.SetTextSize(0.8 * c.GetTopMargin())
label.SetTextFont(62)
label.SetTextAlign(11)
label.DrawLatex(0.15, 0.94, "CMS")
label.SetTextFont(52)
label.SetTextSize(0.6 * c.GetTopMargin())
# label.DrawLatex(0.25,0.94,"Preliminary")
label.SetTextFont(42)
label.SetTextSize(0.7 * c.GetTopMargin())
label.DrawLatex(0.19, 0.83, plottitle)
if 'Resonant' in plottitle:
label.DrawLatex(0.19, 0.75, "a_{SR} = b_{SR} = %s" % coupling)
label.DrawLatex(0.19, 0.68, "m_{#chi}=100 GeV")
else:
label.DrawLatex(0.19, 0.75, "g_{DM}^{V}=1,g_{q}^{V}=0.25")
label.DrawLatex(0.19, 0.68, "m_{#chi}=1 GeV")
label.SetTextSize(0.6 * c.GetTopMargin())
label.SetTextFont(42)
label.SetTextAlign(31) # align right
label.DrawLatex(0.95, 0.94, "%.1f fb^{-1} (13 TeV)" % (plotConfig.lumi))
c.SaveAs(foutname + '.pdf')
c.SaveAs(foutname + '.png')
expected68 = TGraphAsymmErrors(massv, expv, masserrv, masserrv, exp68Lv,
exp68Hv)
expected68.SetFillColor(ROOT.kGreen)
expected95 = TGraphAsymmErrors(massv, expv, masserrv, masserrv, exp95Lv,
exp95Hv)
expected95.SetFillColor(ROOT.kYellow)
c4 = TCanvas("c4", "Diphoton c = 01 Limits", 1000, 800)
c4.SetBottomMargin(0.15)
c4.SetRightMargin(0.06)
#c4.SetLogy(1)
#expected95.SetMinimum(0.0001);
expected95.SetMaximum(0.0057)
expected95.Draw("a3")
expected95.GetXaxis().SetTitle("Diphoton Mass [GeV/c^{2}]")
expected95.GetYaxis().SetTitle("#sigma(G_{RS} #rightarrow #gamma #gamma)[pb]")
expected68.Draw("3same")
expected_p.Draw("csame")
observed_p.Draw("cpsame")
theory.Draw("same")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextAlign(31) # align right
latex.DrawLatex(0.45, 0.95, "CMS Preliminary")
latex2 = TLatex()
exp2sigma = TGraphAsymmErrors(3, x, y_exp, zeros, zeros,
y_err2down, y_err2up)
explim = TGraph(3, x, y_exp)
obslim = TGraph(3, x, y_obs)
obslim.SetLineWidth(3)
explim.SetLineWidth(3)
explim.SetLineStyle(2)
explim.SetTitle('')
obslim.SetTitle('')
exp2sigma.SetTitle('')
exp1sigma.SetTitle('')
#obslim.SetMinimum(0.001);
#duesigma=TGraphAsymmErrors(3,)
exp1sigma.SetFillColor(kGreen + 1)
exp2sigma.SetFillColor(kOrange)
exp2sigma.SetMaximum(150)
exp2sigma.SetMinimum(0.07)
exp2sigma.Draw('a3lp')
exp2sigma.GetXaxis().SetTitle("Z' mass [TeV]")
exp2sigma.GetXaxis().SetRangeUser(1.4, 2.6)
exp2sigma.GetYaxis().SetTitle("Upper cross section limit [pb]")
sizefactor = 1.6
exp2sigma.GetXaxis().SetTitleSize(
sizefactor * exp2sigma.GetXaxis().GetTitleSize())
exp2sigma.GetYaxis().SetTitleSize(
sizefactor * exp2sigma.GetYaxis().GetTitleSize())
exp2sigma.GetXaxis().SetLabelSize(
sizefactor * exp2sigma.GetXaxis().GetLabelSize())
exp2sigma.GetYaxis().SetLabelSize(
sizefactor * exp2sigma.GetYaxis().GetLabelSize())
expected68 = TGraphAsymmErrors(massv, expv, masserrv, masserrv, exp68Lv,
exp68Hv)
expected68.SetFillColor(ROOT.kGreen)
expected95 = TGraphAsymmErrors(massv, expv, masserrv, masserrv, exp95Lv,
exp95Hv)
expected95.SetFillColor(ROOT.kYellow)
c4 = TCanvas("c4", "Diphoton c = 01 Limits", 1000, 800)
c4.SetBottomMargin(0.15)
c4.SetRightMargin(0.06)
c4.SetLogy(1)
#expected95.SetMinimum(0.0001);
expected95.SetMaximum(0.01)
expected95.Draw("a3")
expected95.GetXaxis().SetTitle("Diphoton Mass [GeV/c^{2}]")
expected95.GetYaxis().SetTitle("#sigma(G_{RS} #rightarrow #gamma #gamma)[pb]")
expected68.Draw("3same")
expected_p.Draw("csame")
observed_p.Draw("cpsame")
theory.Draw("same")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextAlign(31) # align right
latex.DrawLatex(0.45, 0.95, "CMS Preliminary")
latex2 = TLatex()
def trigger_efficiency(year, separate=False):
from root_numpy import root2array, fill_hist
from aliases import triggers, triggers_PFHT, triggers_Jet, triggers_BTag
import numpy as np
#spec_triggers = {"PFHT": triggers_PFHT, "Jet": triggers_Jet, "BTag": triggers_BTag}
spec_triggers = {
"HT/Jet": "(" + triggers_PFHT + " || " + triggers_Jet + ")",
"BTag": triggers_BTag
}
spec_triggers_colors = {
"PFHT": 418,
"Jet": 4,
"BTag": 6,
"HT/Jet": 4,
"total": 2
}
hist_pass = TH1F("pass", "pass", 100, 0., 10000.)
if separate:
hist_pass_spec = {}
for trig in spec_triggers.keys():
hist_pass_spec[trig] = TH1F("pass_" + trig, "pass_" + trig, 100,
0., 10000.)
hist_all = TH1F("all", "all", 100, 0., 10000.)
file_list = []
data_2016_letters = ["B", "C", "D", "E", "F", "G", "H"]
data_2017_letters = ["B", "C", "D", "E", "F"]
data_2018_letters = ["A", "B", "C", "D"]
sample_names = []
if year == '2016':
letters = data_2016_letters
elif year == '2017':
letters = data_2017_letters
elif year == '2018':
letters = data_2018_letters
else:
print "unknown year"
sys.exit()
for letter in letters:
#dir_content = os.listdir(TRIGGERDIR+"/SingleMuon_{}_{}/".format(year, letter)) ## intended to run directly on ntuples
#for entry in dir_content:
# if "_flatTuple" in entry: file_list.append(TRIGGERDIR+"/SingleMuon_{}_{}/".format(year, letter)+entry)
file_list.append(TRIGGERDIR +
"/SingleMuon_{}_{}.root".format(year, letter))
print "appending:", TRIGGERDIR + "/SingleMuon_{}_{}.root".format(
year, letter)
for file_name in file_list:
temp_array = root2array(file_name,
treename='tree',
branches='jj_mass_widejet',
selection="jj_deltaEta_widejet<1.1")
fill_hist(hist_all, temp_array)
temp_array = root2array(file_name,
treename='tree',
branches='jj_mass_widejet',
selection="jj_deltaEta_widejet<1.1 && " +
triggers)
fill_hist(hist_pass, temp_array)
if separate:
for trig in spec_triggers.keys():
temp_array = root2array(
file_name,
treename='tree',
branches='jj_mass_widejet',
selection="jj_deltaEta_widejet<1.1 && " +
spec_triggers[trig])
fill_hist(hist_pass_spec[trig], temp_array)
temp_array = None
import array
from aliases import dijet_bins
binning = []
for num in dijet_bins:
if num <= 10000: binning.append(num)
#binning = range(0,1500,100)+range(1500,2000,100)+range(2000,3100,150)+range(3100,10000,300)
binning_ = array.array('d', binning)
hist_pass2 = hist_pass.Rebin(
len(binning_) - 1, "hist_pass_rebinned", binning_)
hist_all2 = hist_all.Rebin(
len(binning_) - 1, "hist_all_rebinned", binning_)
if separate:
hist_pass_spec2 = {}
for trig in spec_triggers.keys():
hist_pass_spec2[trig] = hist_pass_spec[trig].Rebin(
len(binning_) - 1, "hist_pass_" + trig + "_rebinned", binning_)
hist_pass2.Sumw2()
hist_all2.Sumw2()
eff = TGraphAsymmErrors()
eff.Divide(hist_pass2, hist_all2)
eff.SetMarkerColor(spec_triggers_colors["total"])
if separate:
eff.SetMarkerStyle(5)
else:
eff.SetMarkerStyle(1)
eff.SetLineColor(spec_triggers_colors["total"])
eff.SetLineWidth(2)
if separate:
eff_spec = {}
for trig in spec_triggers.keys():
hist_pass_spec2[trig].Sumw2()
eff_spec[trig] = TGraphAsymmErrors()
eff_spec[trig].Divide(hist_pass_spec2[trig], hist_all2)
eff_spec[trig].SetMarkerColor(spec_triggers_colors[trig])
eff_spec[trig].SetMarkerStyle(1)
eff_spec[trig].SetLineColor(spec_triggers_colors[trig])
eff_spec[trig].SetLineWidth(2)
one_line = TGraph()
one_line.SetPoint(0, 0., 1.)
one_line.SetPoint(1, 10000., 1.)
one_line.SetLineStyle(2)
c1 = TCanvas("c1", "Trigger Efficiency", 800, 800)
c1.cd(1)
eff.Draw("AP")
one_line.Draw("L")
eff.SetTitle(";m_{jj} (GeV);trigger efficiency")
eff.SetMinimum(0.)
eff.SetMaximum(1.4) #0.65
## new
dijet_bin_centers = []
for b, lthr in enumerate(dijet_bins[:-1]):
if lthr < 1200 or lthr > 2500: continue
dijet_bin_centers.append(0.5 * (dijet_bins[b] + dijet_bins[b + 1]))
print "total trigger efficiency:"
for cval in dijet_bin_centers:
print cval, ":", eff.Eval(cval)
## end new
if separate:
leg = TLegend(0.65, 0.75, 0.9, 0.95)
leg.AddEntry(eff, "total")
for trig in spec_triggers.keys():
leg.AddEntry(eff_spec[trig], trig + "-based")
eff_spec[trig].Draw("P SAME")
## new
print trig, "trigger efficiency"
for cval in dijet_bin_centers:
print cval, ":", eff_spec[trig].Eval(cval)
## end new
leg.Draw()
eff.GetXaxis().SetTitleSize(0.045)
eff.GetYaxis().SetTitleSize(0.045)
eff.GetYaxis().SetTitleOffset(1.1)
eff.GetXaxis().SetTitleOffset(1.05)
eff.GetXaxis().SetLimits(700., 5000.)
c1.SetTopMargin(0.05)
#drawCMS(-1, "Preliminary", year=year) #Preliminary
#drawCMS(-1, "Work in Progress", year=year, suppressCMS=True)
drawCMS(-1, "", year=year, suppressCMS=True)
drawAnalysis("")
suffix = ""
if separate: suffix = "_sep"
c1.Print("plots/Efficiency/trigger_" + year + suffix + ".pdf")
c1.Print("plots/Efficiency/trigger_" + year + suffix + ".png")
c = TCanvas()
c.SetLeftMargin(0.14)
c.SetBottomMargin(0.14)
c.SetRightMargin(0.04)
c.SetTopMargin(0.08)
g_th_162.GetXaxis().SetTitle('m_{t#bar{t}} [GeV]')
g_th_162.GetYaxis().SetTitle('d#sigma_{t#bar{t}} / dm_{t#bar{t}} #Deltam_{t#bar{t}} [pb]')
g_th_162.GetYaxis().SetTitleSize(0.05)
g_th_162.GetYaxis().SetTitleOffset(1.2)
g_th_162.GetXaxis().SetTitleSize(0.06)
g_th_162.GetXaxis().SetTitleOffset(1)
g_th_162.GetYaxis().SetLabelSize(0.05)
g_th_162.GetXaxis().SetLabelSize(0.05)
g_th_162.GetXaxis().SetRangeUser(bins[0],bins[len(bins)-1])
g_th_162.GetXaxis().SetLimits(137,2162)
g_th_162.SetMaximum(360)
g_th_162.Draw('ap')
g_th_162.Draw('psame')
g_exp.Draw('psame')
g_th_164.Draw('psame')
g_th_166.Draw('psame')
g_exp.Draw('psame')
leg1.Draw('same')
leg2.Draw('same')
if cms: latexLabel1.DrawLatex(0.16, 0.94, "CMS")
latexLabel2.DrawLatex(0.76, 0.94, "35.9 fb^{-1} (13 TeV)")
if preliminary: latexLabel3.DrawLatex(0.215, 0.92 , "#it{Preliminary}")
explim = TGraph(3, x, y_exp)
obslim = TGraph(3, x, y_obs)
obslim.SetLineWidth(2)
explim.SetLineWidth(2)
explim.SetLineStyle(2)
explim.SetTitle('')
obslim.SetTitle('')
exp2sigma.SetTitle('')
exp1sigma.SetTitle('')
#obslim.SetMinimum(0.001);
#duesigma=TGraphAsymmErrors(3,)
exp1sigma.SetFillColor(kGreen + 1)
exp2sigma.SetFillColor(kOrange)
# exp2sigma.SetMaximum(150)
# exp2sigma.SetMinimum(0.07)
exp2sigma.SetMaximum(10000)
if triplet[0] == 0.0:
exp2sigma.SetMinimum(0.0007)
else:
exp2sigma.SetMinimum(0.007)
exp2sigma.Draw('a3lp')
if triplet[0] == 0.0:
exp2sigma.GetXaxis().SetTitle("#rho^{0}_{L} mass [TeV]")
else:
exp2sigma.GetXaxis().SetTitle("G* mass [TeV]")
exp2sigma.GetXaxis().SetRangeUser(1.4, 2.6)
exp2sigma.GetYaxis().SetTitle("Upper cross section limit [pb]")
sizefactor = 1.6
exp2sigma.GetXaxis().SetTitleSize(
sizefactor * exp2sigma.GetXaxis().GetTitleSize())
