def draw_limits_per_category(nchannels, xmin, xmax, obs, expect, upper1sig,
lower1sig, upper2sig, lower2sig):
channel = np.array(
[3. * nchannels - 1.5 - 3. * i for i in range(0, nchannels)])
ey = np.array([0.8 for i in range(0, nchannels)])
zero = np.zeros(nchannels)
gexpect1sig = TGraphAsymmErrors(nchannels, expect, channel, lower1sig,
upper1sig, ey, ey)
gexpect1sig.SetFillColor(kGreen)
gexpect1sig.SetLineWidth(2)
gexpect1sig.SetLineStyle(2)
gexpect2sig = TGraphAsymmErrors(nchannels, expect, channel, lower2sig,
upper2sig, ey, ey)
gexpect2sig.SetFillColor(kYellow)
gexpect2sig.SetLineWidth(2)
gexpect2sig.SetLineStyle(2)
gexpect2sig.Draw("2")
gexpect1sig.Draw("2")
gobs = TGraphErrors(nchannels, obs, channel, zero, ey)
gobs.SetMarkerStyle(21)
gobs.SetMarkerSize(1.5)
gobs.SetLineWidth(2)
gobs.Draw("pz")
# dashed line at median expected limits
l = TLine()
l.SetLineStyle(2)
l.SetLineWidth(2)
for bin in range(nchannels):
l.DrawLine(expect[bin], channel[bin] - ey[bin], expect[bin],
channel[bin] + ey[bin])
# line to separate individual and combined limits
l.SetLineStyle(1)
l.SetLineWidth(1)
l.DrawLine(xmin, 0, xmax, 0)
# legend
x1 = gStyle.GetPadLeftMargin() + 0.01
y2 = 1 - gStyle.GetPadTopMargin() - 0.01
leg = TLegend(x1, y2 - 0.17, x1 + 0.25, y2)
leg.SetFillColor(4000)
leg.AddEntry(gexpect1sig, "Expected #pm1#sigma", "FL")
leg.AddEntry(gexpect2sig, "Expected #pm2#sigma", "FL")
leg.AddEntry(gobs, "Observed", "pl")
leg.Draw()
return gobs, gexpect1sig, gexpect2sig, leg
def draw_limits_per_category(nchannels, xmin, xmax, obs, expect, upper1sig,
lower1sig, upper2sig, lower2sig):
channel = np.array(
[nchannels - 1.5 - float(i) for i in range(0, nchannels)])
ey = np.full(nchannels, 0.494)
zero = np.zeros(nchannels)
gexpect1sig = TGraphAsymmErrors(nchannels, expect, channel, lower1sig,
upper1sig, ey, ey)
gexpect1sig.SetFillColor(kGreen)
gexpect1sig.SetLineWidth(2)
gexpect1sig.SetLineStyle(2)
gexpect2sig = TGraphAsymmErrors(nchannels, expect, channel, lower2sig,
upper2sig, ey, ey)
gexpect2sig.SetFillColor(kYellow)
gexpect2sig.SetLineWidth(2)
gexpect2sig.SetLineStyle(2)
gexpect2sig.Draw("2")
gexpect1sig.Draw("2")
gobs = TGraphErrors(nchannels, obs, channel, zero, ey)
gobs.SetMarkerStyle(21)
gobs.SetMarkerSize(1.5)
gobs.SetLineWidth(2)
#gobs.Draw("pz")
# dashed line at median expected limits
l = TLine()
l.SetLineStyle(2)
l.SetLineWidth(2)
for bin in range(nchannels):
l.DrawLine(expect[bin], channel[bin] - ey[bin], expect[bin],
channel[bin] + ey[bin])
# line to separate individual and combined limits
l.SetLineStyle(1)
l.SetLineWidth(1)
l.DrawLine(xmin, 0, xmax, 0)
# legend
leg = TLegend(0.75, 0.75, 0.95, 0.9)
leg.SetFillColor(4000)
leg.AddEntry(gexpect1sig, "Expected #pm1#sigma", "FL")
leg.AddEntry(gexpect2sig, "Expected #pm2#sigma", "FL")
#leg.AddEntry( gobs, "Observed", "pl" )
leg.Draw()
return gobs, gexpect1sig, gexpect2sig, leg
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 bestfit():
xmin = -10
xmax = 6
c, h = draw_canvas_histo(
xmin, xmax, "Best fit #mu = #sigma/#sigma_{SM} at m_{H} = 125 GeV")
# line at SM expectation of mu = 1
l = TLine()
l.SetLineStyle(2)
l.DrawLine(1.0, -1.0, 1.0, -1.0 + nchannels)
mu = np.array([-0.4, -4.734, -2.014])
upper = np.array([2.12, 3.688, 1.80])
lower = np.array([2.08, 3.754, 1.8224])
channel = np.array([1.5, 0.5, -0.5])
zero = np.zeros(nchannels)
gmu = TGraphAsymmErrors(nchannels, mu, channel, lower, upper, zero, zero)
gmu.SetMarkerStyle(21)
gmu.SetMarkerSize(1.5)
gmu.SetLineColor(2)
gmu.SetLineWidth(2)
gmu.Draw("pz")
#draw_disclaimer()
c.RedrawAxis()
c.Modified()
c.Update()
c.SaveAs("bestfit.pdf")
def makeEffPlotsVars(tree,
varx,
vary,
sel,
nbinx,
xmin,
xmax,
nbiny,
ymin,
ymax,
xtitle,
ytitle,
leglabel=None,
header='',
addon='',
option='pt',
marker=20):
binning = [20, 30, 40, 50, 60, 70, 80, 100, 150, 200]
c = TCanvas()
if option == 'pt':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(binning) - 1, array('d', binning))
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(binning) - 1, array('d', binning))
elif option == 'eta':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon, nbinx, xmin, xmax)
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon, nbinx, xmin,
xmax)
elif option == 'nvtx':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(vbinning) - 1, array('d', vbinning))
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(vbinning) - 1, array('d', vbinning))
tree.Draw(varx + ' >> ' + _hist_.GetName(), sel)
tree.Draw(varx + ' >> ' + _ahist_.GetName(), sel + ' && ' + vary)
g_efficiency = TGraphAsymmErrors()
g_efficiency.BayesDivide(_ahist_, _hist_)
g_efficiency.GetXaxis().SetTitle(xtitle)
g_efficiency.GetYaxis().SetTitle('efficiency')
g_efficiency.GetYaxis().SetNdivisions(507)
g_efficiency.SetLineWidth(3)
g_efficiency.SetName(header)
g_efficiency.SetMinimum(0.)
g_efficiency.GetYaxis().SetTitleOffset(1.3)
g_efficiency.SetMarkerStyle(marker)
g_efficiency.SetMarkerSize(1)
g_efficiency.Draw('ap')
# save(c, 'plots/' + addon)
return copy.deepcopy(g_efficiency)
def ratioplot():
# create required parts
leg = getLegend()
latex = getLatex()
c = TCanvas()
c.SetLogy()
#Draw input histograms
hists = ['h_frac_recoil_', 'h_full_recoil_']
label = ['recoil with MET triggers', 'recoil without MET triggers']
combineHist(hists, label, leg)
#leg.Draw()
#c.SaveAs("Combinehists_D.pdf")
ratio = []
h1 = f.Get('h_frac_recoil_')
#h1=setHistStyle(h1,bins)
h2 = f.Get('h_full_recoil_')
#h2=setHistStyle(h2,bins)
h3 = createRatio(h1, h2)
gr = TGraphAsymmErrors(h1, h2)
gr.GetXaxis().SetRangeUser(0, 1500)
gr.GetYaxis().SetRangeUser(0, 1.2)
gr.SetMarkerStyle(20)
gr.SetMarkerSize(0.5)
gr.SetLineColor(1)
gr.GetYaxis().SetTitle("Trigger Efficiency")
gr.GetXaxis().SetTitle("Recoil [GeV]")
gr.SetTitle("")
# print ("ratio",ratio )
# c, pad1, pad2 = createCanvasPads()
#
# # draw everything
# pad1.cd()
# h1.Draw()
# h2.Draw("same")
# to avoid clipping the bottom zero, redraw a small axis
# h1.GetYaxis().SetLabelSize(0.0)
# axis = TGaxis(-5, 20, -5, 220, 20, 220, 510, "")
# axis.SetLabelFont(43)
# axis.SetLabelSize(15)
# axis.Draw()
# pad2.cd()
gr.Draw()
latex.DrawLatex(0.41, 0.93, "Trigger Efficincy in MET Run2017E")
xmin = 0.0
line = TLine(max(xmin, gr.GetXaxis().GetXmin()), 1, 1500, 1)
line.SetLineColor(1)
line.SetLineWidth(1)
line.SetLineStyle(7)
line.Draw()
#h3.Draw('pl')
c.SaveAs("test.pdf")
def makeEffPlotsVars(tree,
varx,
vary,
sel,
nbinx,
xmin,
xmax,
nbiny,
ymin,
ymax,
xtitle,
ytitle,
leglabel=None,
header='',
addon='',
option='pt',
marker=20,
col=1):
binning = [20, 200] if args.onebin else [
20, 30, 40, 50, 60, 70, 80, 100, 150, 200
]
if option == 'pt':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(binning) - 1, array('d', binning))
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(binning) - 1, array('d', binning))
elif option == 'eta':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon, nbinx, xmin, xmax)
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon, nbinx, xmin,
xmax)
tree.Draw(varx + ' >> ' + _hist_.GetName(), sel)
tree.Draw(varx + ' >> ' + _ahist_.GetName(), sel + ' && ' + vary)
g_efficiency = TGraphAsymmErrors()
g_efficiency.Divide(_ahist_, _hist_, "cl=0.683 b(1,1) mode")
g_efficiency.GetXaxis().SetTitle(xtitle)
g_efficiency.GetYaxis().SetTitle('efficiency')
g_efficiency.GetYaxis().SetNdivisions(507)
g_efficiency.SetLineWidth(3)
g_efficiency.SetName(header)
g_efficiency.SetMinimum(0.)
g_efficiency.GetYaxis().SetTitleOffset(1.3)
g_efficiency.SetMarkerStyle(marker)
g_efficiency.SetMarkerSize(1)
g_efficiency.SetMarkerColor(col)
g_efficiency.SetLineColor(col)
g_efficiency.Draw('ap')
# save(c, 'plots/' + addon)
return g_efficiency
def make_eff_pt2():
#efficiency vs. pT^2
ptbin = 0.005
ptmin = 0.
ptmax = 0.12 # 0.3
mmin = 2.8
mmax = 3.2
strsel = "jRecM>{0:.3f} && jRecM<{1:.3f}".format(mmin, mmax)
can = ut.box_canvas()
nbins, ptmax = ut.get_nbins(ptbin, ptmin, ptmax)
hPtRec = TH1D("hPtRec", "hPtRec", nbins, ptmin, ptmax)
hPtGen = TH1D("hPtGen", "hPtGen", nbins, ptmin, ptmax)
#hPtRec = ut.prepare_TH1D("hPtRec", ptbin, ptmin, ptmax)
#hPtGen = ut.prepare_TH1D("hPtGen", ptbin, ptmin, ptmax)
hPtRec.Sumw2()
hPtGen.Sumw2()
ut.set_margin_lbtr(gPad, 0.11, 0.09, 0.01, 0.02)
#generated trees
tree_coh_gen = inp_coh.Get("jGenTree")
tree_incoh_gen = inp_incoh.Get("jGenTree")
tree_coh.Draw("jGenPt*jGenPt >> hPtRec", strsel)
tree_coh_gen.Draw("jGenPt*jGenPt >> hPtGen")
#tree_incoh.Draw("jGenPt*jGenPt >>+ hPtRec", strsel)
#tree_incoh_gen.Draw("jGenPt*jGenPt >>+ hPtGen")
#tree_incoh.Draw("jGenPt*jGenPt >> hPtRec", strsel)
#tree_incoh_gen.Draw("jGenPt*jGenPt >> hPtGen")
#calculate the efficiency
hEff = TGraphAsymmErrors(hPtRec, hPtGen)
#hPtRec.Divide(hPtGen)
#hPtRec.Draw()
#hPtGen.Draw("same")
hEff.Draw()
ut.invert_col(rt.gPad)
can.SaveAs("01fig.pdf")
def plotTriggerEfficiency(self, passesHist, totalHist, label=""):
if self._fout and self._canvas:
passesHist = passesHist.Rebin(self._rebinFactor, "passesHist")
totalHist = totalHist.Rebin(self._rebinFactor, "totalHist")
self.makePassesConsistentWithTotal(passesHist, totalHist)
metGraph = TGraphAsymmErrors(passesHist, totalHist)
metGraph.SetEditable(0)
pt = TPaveText(0.409774, 0.843023, 0.809524, 0.890827, "brNDC")
pt.SetBorderSize(0)
pt.SetFillStyle(0)
pt.SetTextFont(42)
pt.SetTextSize(0.0387597)
pt.AddText(str(self._plotLabel))
cmsLabel = TPaveText(0.134085, 0.937984, 0.418546, 0.984496,
"brNDC")
cmsLabel.SetBorderSize(0)
cmsLabel.SetFillStyle(0)
cmsLabel.SetTextFont(62)
cmsLabel.SetTextSize(0.0387597)
cmsLabel.AddText("CMS Preliminary")
lumiLabel = TPaveText(0.575188, 0.937339, 0.874687, 0.992894,
"brNDC")
lumiLabel.SetBorderSize(0)
lumiLabel.SetFillStyle(0)
lumiLabel.SetTextFont(42)
lumiLabel.SetTextSize(0.0387597)
lumiLabel.AddText(str(self._luminosityLabel))
setStyle(metGraph)
self._canvas.cd()
metGraph.Draw("ap")
setAxisStyle(metGraph, "E_{T}^{miss, no #mu} [GeV]",
label + " trigger efficiency", (0.0, 500.0),
(0.0, 1.4))
pt.Draw("same")
cmsLabel.Draw("same")
lumiLabel.Draw("same")
self._fout.cd()
self._canvas.Write("triggerEfficiency_" + label)
else:
print "A TFile and TCanvas must be added. Not making plots..."
def makeEffPlotsVars(tree,
varx,
numeratorAddSelection,
baseSelection,
binning,
xtitle='',
header='',
addon='',
marker=20,
col=1):
_denomHist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(binning) - 1, binning)
_nominatorHist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(binning) - 1, binning)
tree.Draw(varx + ' >> ' + _denomHist_.GetName(), baseSelection)
tree.Draw(varx + ' >> ' + _nominatorHist_.GetName(),
baseSelection + ' && ' + numeratorAddSelection)
g_eff = TGraphAsymmErrors()
g_eff.Divide(_nominatorHist_, _denomHist_, "cl=0.683 b(1,1) mode")
g_eff.GetXaxis().SetTitle(xtitle)
g_eff.GetYaxis().SetTitle('efficiency')
g_eff.GetYaxis().SetNdivisions(507)
g_eff.SetLineWidth(3)
g_eff.SetName(header)
g_eff.SetMinimum(0.)
g_eff.GetYaxis().SetTitleOffset(1.3)
g_eff.SetMarkerStyle(marker)
g_eff.SetMarkerSize(1)
g_eff.SetMarkerColor(col)
g_eff.SetLineColor(col)
g_eff.Draw('ap')
return g_eff
def makeLimitPlot(output,
obs,
exp,
chan,
printStats=False,
obs2="",
ratioLabel=""):
#fileForHEPData = TFile("plots/"+output+"_forHEPData.root","RECREATE")
fileObs = open(obs, 'r')
fileExp = open(exp, 'r')
observedx = []
observedy = []
obsLimits = {}
for entry in fileObs:
massPoint = float(entry.split()[0])
limitEntry = float(entry.split()[1])
if massPoint not in obsLimits: obsLimits[massPoint] = []
obsLimits[massPoint].append(limitEntry)
if printStats: print "len obsLimits:", len(obsLimits)
for massPoint in sorted(obsLimits):
observedx.append(massPoint)
observedy.append(numpy.mean(obsLimits[massPoint]))
if (numpy.std(obsLimits[massPoint]) / numpy.mean(obsLimits[massPoint])
> 0.05):
print massPoint, " mean: ", numpy.mean(
obsLimits[massPoint]), " std dev: ", numpy.std(
obsLimits[massPoint]), " from: ", obsLimits[massPoint]
if not obs2 == "":
fileObs2 = open(obs2, 'r')
observedx2 = []
observedy2 = []
obsLimits2 = {}
for entry in fileObs2:
massPoint = float(entry.split()[0])
limitEntry = float(entry.split()[1])
if massPoint not in obsLimits2: obsLimits2[massPoint] = []
obsLimits2[massPoint].append(limitEntry)
if printStats: print "len obsLimits:", len(obsLimits2)
for massPoint in sorted(obsLimits2):
observedx2.append(massPoint)
observedy2.append(numpy.mean(obsLimits2[massPoint]))
if (numpy.std(obsLimits2[massPoint]) /
numpy.mean(obsLimits2[massPoint]) > 0.05):
print massPoint, " mean: ", numpy.mean(
obsLimits2[massPoint]), " std dev: ", numpy.std(
obsLimits2[massPoint]
), " from: ", obsLimits2[massPoint]
limits = {}
expectedx = []
expectedy = []
expected1SigLow = []
expected1SigHigh = []
expected2SigLow = []
expected2SigHigh = []
for entry in fileExp:
massPoint = float(entry.split()[0])
limitEntry = float(entry.split()[1])
if massPoint not in limits: limits[massPoint] = []
limits[massPoint].append(limitEntry)
if printStats: print "len limits:", len(limits)
for massPoint in sorted(limits):
limits[massPoint].sort()
numLimits = len(limits[massPoint])
nrExpts = len(limits[massPoint])
medianNr = int(nrExpts * 0.5)
#get indexes:
upper1Sig = int(nrExpts * (1 - (1 - 0.68) * 0.5))
lower1Sig = int(nrExpts * (1 - 0.68) * 0.5)
upper2Sig = int(nrExpts * (1 - (1 - 0.95) * 0.5))
lower2Sig = int(nrExpts * (1 - 0.95) * 0.5)
if printStats:
print massPoint, ":", limits[massPoint][lower2Sig], limits[
massPoint][lower1Sig], limits[massPoint][medianNr], limits[
massPoint][upper1Sig], limits[massPoint][upper2Sig]
#fill lists:
expectedx.append(massPoint)
print massPoint, limits[massPoint][medianNr]
expectedy.append(limits[massPoint][medianNr])
expected1SigLow.append(limits[massPoint][lower1Sig])
expected1SigHigh.append(limits[massPoint][upper1Sig])
expected2SigLow.append(limits[massPoint][lower2Sig])
expected2SigHigh.append(limits[massPoint][upper2Sig])
expX = numpy.array(expectedx)
expY = numpy.array(expectedy)
values2 = []
xPointsForValues2 = []
values = []
xPointsForValues = []
xPointsForErrors = []
if printStats: print "length of expectedx: ", len(expectedx)
if printStats: print "length of expected1SigLow: ", len(expected1SigLow)
if printStats: print "length of expected1SigHigh: ", len(expected1SigHigh)
#Here is some Voodoo via Sam:
for x in range(0, len(expectedx)):
values2.append(expected2SigLow[x])
xPointsForValues2.append(expectedx[x])
xPointsForErrors.append(0)
for x in range(len(expectedx) - 1, 0 - 1, -1):
values2.append(expected2SigHigh[x])
xPointsForValues2.append(expectedx[x])
if printStats: print "length of values2: ", len(values2)
for x in range(0, len(expectedx)):
values.append(expected1SigLow[x])
xPointsForValues.append(expectedx[x])
for x in range(len(expectedx) - 1, 0 - 1, -1):
values.append(expected1SigHigh[x])
xPointsForValues.append(expectedx[x])
if printStats: print "length of values: ", len(values)
exp2Sig = numpy.array(values2)
xPoints2 = numpy.array(xPointsForValues2)
exp1Sig = numpy.array(values)
xPoints = numpy.array(xPointsForValues)
xPointsErrors = numpy.array(xPointsForErrors)
if printStats: print "xPoints2: ", xPoints2
if printStats: print "exp2Sig: ", exp2Sig
if printStats: print "xPoints: ", xPoints
if printStats: print "exp1Sig: ", exp1Sig
GraphErr2SigForHEPData = TGraphAsymmErrors(len(expX), expX, expY,
numpy.array(xPointsErrors),
numpy.array(xPointsErrors),
numpy.array(expected2SigLow),
numpy.array(expected2SigHigh))
GraphErr1SigForHEPData = TGraphAsymmErrors(len(expX), expX, expY,
numpy.array(xPointsErrors),
numpy.array(xPointsErrors),
numpy.array(expected1SigLow),
numpy.array(expected1SigHigh))
GraphErr2Sig = TGraphAsymmErrors(len(xPoints), xPoints2, exp2Sig)
GraphErr2Sig.SetFillColor(ROOT.kOrange)
GraphErr1Sig = TGraphAsymmErrors(len(xPoints), xPoints, exp1Sig)
GraphErr1Sig.SetFillColor(ROOT.kGreen + 1)
#cCL=TCanvas("cCL", "cCL",0,0,567,384)
cCL = TCanvas("cCL", "cCL", 0, 0, 600, 450)
gStyle.SetOptStat(0)
gStyle.SetPadRightMargin(0.063)
gStyle.SetPadLeftMargin(0.14)
gStyle.SetPadBottomMargin(0.12)
if not obs2 == "":
plotPad = ROOT.TPad("plotPad", "plotPad", 0, 0.3, 1, 1)
ratioPad = ROOT.TPad("ratioPad", "ratioPad", 0, 0., 1, 0.3)
plotPad.Draw()
ratioPad.Draw()
plotPad.cd()
else:
plotPad = ROOT.TPad("plotPad", "plotPad", 0, 0, 1, 1)
plotPad.Draw()
plotPad.cd()
expX = numpy.array(expectedx)
expY = numpy.array(expectedy)
GraphExp = TGraph(len(expX), expX, expY)
GraphExp.SetLineWidth(3)
GraphExp.SetLineStyle(2)
GraphExp.SetLineColor(ROOT.kBlue)
obsX = numpy.array(observedx)
obsY = numpy.array(observedy)
if printStats: print "obsX: ", obsX
if printStats: print "obsY: ", obsY
if SMOOTH:
smooth_obs = TGraphSmooth("normal")
GraphObs_nonSmooth = TGraph(len(obsX), obsX, obsY)
GraphObs = smooth_obs.SmoothSuper(GraphObs_nonSmooth, "linear", 0,
0.005)
else:
GraphObs = TGraph(len(obsX), obsX, obsY)
GraphObs.SetLineWidth(3)
if not obs2 == "":
ratio = []
ratiox = []
for index, val in enumerate(observedy):
mass = observedx[index]
foundIndex = -1
for index2, mass2 in enumerate(observedx2):
if mass == mass2:
foundIndex = index2
if foundIndex > 0:
ratio.append(observedy2[foundIndex] / val)
ratiox.append(mass)
ratioA = numpy.array(ratio)
ratioX = numpy.array(ratiox)
obsX2 = numpy.array(observedx2)
obsY2 = numpy.array(observedy2)
ratioGraph = TGraph(len(ratioX), ratioX, ratioA)
if printStats: print "obsX2: ", obsX2
if printStats: print "obsY2: ", obsY2
if SMOOTH:
smooth_obs2 = TGraphSmooth("normal")
GraphObs2_nonSmooth = TGraph(len(obsX2), obsX2, obsY2)
GraphObs2 = smooth_obs2.SmoothSuper(GraphObs2_nonSmooth, "linear",
0, 0.005)
else:
GraphObs2 = TGraph(len(obsX2), obsX2, obsY2)
GraphObs2.SetLineWidth(3)
if SPIN2:
signals = ["RS_kMpl01", "RS_kMpl005", "RS_kMpl001"]
elif GUT:
signals = ["ssm", "psi", "kai", "eta", "I", "S", "N"]
else:
signals = ["ssm", "psi"]
xSecCurves = []
for signal in signals:
xSecCurves.append(getXSecCurve(signal, kFacs[signal]))
#xSecCurves.append(getXSecCurve(signal,kFacs[signal],massDependent=True))
#Draw the graphs:
plotPad.SetLogy()
DummyGraph = TH1F("DummyGraph", "", 100, 200, 5500)
DummyGraph.GetXaxis().SetTitle("M [GeV]")
if SPIN2:
DummyGraph.GetYaxis().SetTitle(
"[#sigma#upoint#font[12]{B}] G_{KK} / [#sigma#upoint#font[12]{B}] Z"
)
else:
DummyGraph.GetYaxis().SetTitle(
"[#sigma#upoint#font[12]{B}] Z' / [#sigma#upoint#font[12]{B}] Z")
# if SPIN2:
# if chan=="mumu":
# DummyGraph.GetYaxis().SetTitle("#sigma(pp#rightarrowG_{KK}+X#rightarrow#mu^{+}#mu^{-}+X) / #sigma(pp#rightarrowZ+X#rightarrow#mu^{+}#mu^{-}+X)")
# elif chan=="elel":
# DummyGraph.GetYaxis().SetTitle("#sigma(pp#rightarrowG_{KK}+X#rightarrowee+X) / #sigma(pp#rightarrowZ+X#rightarrowee+X)")
# elif chan=="elmu":
# DummyGraph.GetYaxis().SetTitle("#sigma(pp#rightarrowG_{KK}+X#rightarrow#font[12]{ll}+X) / #sigma(pp#rightarrowZ+X#rightarrow#font[12]{ll}+X)")
# else:
# if chan=="mumu":
# DummyGraph.GetYaxis().SetTitle("#sigma(pp#rightarrowZ'+X#rightarrow#mu^{+}#mu^{-}+X) / #sigma(pp#rightarrowZ+X#rightarrow#mu^{+}#mu^{-}+X)")
# elif chan=="elel":
# DummyGraph.GetYaxis().SetTitle("#sigma(pp#rightarrowZ'+X#rightarrowee+X) / #sigma(pp#rightarrowZ+X#rightarrowee+X)")
# elif chan=="elmu":
# DummyGraph.GetYaxis().SetTitle("#sigma(pp#rightarrowZ'+X#rightarrow#font[12]{ll}+X) / #sigma(pp#rightarrowZ+X#rightarrow#font[12]{ll}+X)")
gStyle.SetOptStat(0)
DummyGraph.GetXaxis().SetRangeUser(200, 5500)
DummyGraph.SetMinimum(1e-8)
DummyGraph.SetMaximum(1e-4)
DummyGraph.GetXaxis().SetLabelSize(0.055)
DummyGraph.GetXaxis().SetTitleSize(0.055)
DummyGraph.GetXaxis().SetTitleOffset(1.05)
DummyGraph.GetYaxis().SetLabelSize(0.055)
DummyGraph.GetYaxis().SetTitleSize(0.055)
DummyGraph.GetYaxis().SetTitleOffset(1.3)
DummyGraph.Draw()
if (FULL):
GraphErr2Sig.Draw("F")
GraphErr1Sig.Draw("F")
GraphExp.Draw("lpsame")
else:
if obs2 == "":
GraphExp.Draw("lp")
if not EXPONLY:
GraphObs.Draw("plsame")
if not obs2 == "":
GraphObs2.SetLineColor(ROOT.kRed)
GraphObs2.SetLineStyle(ROOT.kDashed)
GraphObs2.Draw("plsame")
for curve in xSecCurves:
curve.Draw("lsame")
plCMS = TPaveLabel(.16, .81, .27, .88, "CMS", "NBNDC")
#plCMS.SetTextSize(0.8)
plCMS.SetTextAlign(12)
plCMS.SetTextFont(62)
plCMS.SetFillColor(0)
plCMS.SetFillStyle(0)
plCMS.SetBorderSize(0)
plCMS.Draw()
plPrelim = TPaveLabel(.16, .76, .27, .82, "Preliminary", "NBNDC")
plPrelim.SetTextSize(0.6)
plPrelim.SetTextAlign(12)
plPrelim.SetTextFont(52)
plPrelim.SetFillColor(0)
plPrelim.SetFillStyle(0)
plPrelim.SetBorderSize(0)
if "2017" in output or "Combination" in output:
plPrelim.Draw()
cCL.SetTickx(1)
cCL.SetTicky(1)
cCL.RedrawAxis()
cCL.Update()
#leg=TLegend(0.65,0.65,0.87,0.87,"","brNDC")
#leg=TLegend(0.540517,0.623051,0.834885,0.878644,"","brNDC") Default
leg = TLegend(0.5, 0.58, 0.834885, 0.878644, "", "brNDC")
if SPIN2:
leg = TLegend(0.5, 0.58, 0.834885, 0.878644, "", "brNDC")
# leg=TLegend(0.55,0.55,0.87,0.87,"","brNDC")
leg.SetTextSize(0.0425)
if not obs2 == "":
if ratioLabel == "":
ratioLabel = "Variant/Default"
ratioLabels = ratioLabel.split("/")
print ratioLabels
leg.AddEntry(GraphObs, "%s Obs. 95%% CL limit" % ratioLabels[1], "l")
leg.AddEntry(GraphObs2, "%s Obs. 95%% CL limit" % ratioLabels[0], "l")
else:
if not EXPONLY:
leg.AddEntry(GraphObs, "Obs. 95% CL limit", "l")
leg.AddEntry(GraphExp, "Exp. 95% CL limit, median", "l")
if (FULL):
leg.AddEntry(GraphErr1Sig, "Exp. (68%)", "f")
leg.AddEntry(GraphErr2Sig, "Exp. (95%)", "f")
leg1 = TLegend(0.7, 0.4, 0.9, 0.55, "", "brNDC")
leg1.SetTextSize(0.05)
if GUT:
leg1 = TLegend(0.6, 0.35, 0.75, 0.623051, "", "brNDC")
if SPIN2:
leg1 = TLegend(0.7, 0.35, 0.9, 0.58, "G_{KK} (LO x 1.6)", "brNDC")
leg1.SetTextSize(0.045)
for index, signal in enumerate(signals):
xSecCurves[index].SetName(labels[signal])
xSecCurves[index].Write(labels[signal])
leg1.AddEntry(xSecCurves[index], labels[signal], "l")
leg1.SetBorderSize(0)
leg.SetLineWidth(0)
leg.SetLineStyle(0)
leg.SetFillStyle(0)
leg.SetLineColor(0)
leg.Draw("hist")
leg1.SetLineWidth(0)
leg1.SetLineStyle(0)
leg1.SetFillStyle(0)
leg1.SetLineColor(0)
leg1.Draw("hist")
if "Moriond" in output:
if (chan == "mumu"):
plLumi = TPaveLabel(.65, .885, .9, .99,
"36.3 fb^{-1} (13 TeV, #mu^{+}#mu^{-})",
"NBNDC")
elif (chan == "elel"):
plLumi = TPaveLabel(.65, .885, .9, .99,
"35.9 fb^{-1} (13 TeV, ee)", "NBNDC")
elif (chan == "elmu"):
plLumi = TPaveLabel(
.27, .885, .9, .99,
"35.9 fb^{-1} (13 TeV, ee) + 36.3 fb^{-1} (13 TeV, #mu^{+}#mu^{-})",
"NBNDC")
elif "2017" in output or "Combination" in output:
if (chan == "mumu"):
plLumi = TPaveLabel(.65, .885, .9, .99,
"42.4 fb^{-1} (13 TeV, #mu^{+}#mu^{-})",
"NBNDC")
elif (chan == "elel"):
plLumi = TPaveLabel(.65, .885, .9, .99,
"41.4 fb^{-1} (13 TeV, ee)", "NBNDC")
elif (chan == "elmu"):
plLumi = TPaveLabel(
.27, .885, .9, .99,
"77.3 fb^{-1} (13 TeV, ee) + 78.7 fb^{-1} (13 TeV, #mu^{+}#mu^{-})",
"NBNDC")
else:
if (chan == "mumu"):
plLumi = TPaveLabel(.65, .905, .9, .99,
"13.0 fb^{-1} (13 TeV, #mu#mu)", "NBNDC")
elif (chan == "elel"):
plLumi = TPaveLabel(.65, .905, .9, .99, "2.7 fb^{-1} (13 TeV, ee)",
"NBNDC")
elif (chan == "elmu"):
plLumi = TPaveLabel(
.4, .905, .9, .99,
"12.4 fb^{-1} (13 TeV, ee) + 13.0 fb^{-1} (13 TeV, #mu#mu)",
"NBNDC")
plLumi.SetTextSize(0.5)
plLumi.SetTextFont(42)
plLumi.SetFillColor(0)
plLumi.SetBorderSize(0)
plLumi.Draw()
plotPad.SetTicks(1, 1)
plotPad.RedrawAxis()
if not obs2 == "":
ratioPad.cd()
line = ROOT.TLine(200, 1, 5500, 1)
line.SetLineStyle(ROOT.kDashed)
ROOT.gStyle.SetTitleSize(0.12, "Y")
ROOT.gStyle.SetTitleYOffset(0.35)
ROOT.gStyle.SetNdivisions(000, "Y")
ROOT.gStyle.SetNdivisions(408, "Y")
ratioPad.DrawFrame(200, 0.8, 5500, 1.2, "; ; %s" % ratioLabel)
line.Draw("same")
ratioGraph.Draw("sameP")
#GraphErr2SigForHEPData.SetName("graph2Sig")
#GraphErr2SigForHEPData.Write("graph2Sig")
#GraphErr1SigForHEPData.SetName("graph1Sig")
#GraphErr1SigForHEPData.Write("graph1Sig")
#GraphExp.SetName("graphExp")
#GraphExp.Write("graphExp")
#GraphObs.SetName("graphObs")
#GraphObs.Write("graphObs")
#fileForHEPData.Write()
#fileForHEPData.Close()
cCL.Update()
printPlots(cCL, output)
def draw(hist,
channel,
data,
back,
sign,
snorm=1,
lumi=-1,
ratio=0,
log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
# Set Poisson error bars
#if len(data) > 0: hist['data_obs'].SetBinErrorOption(1) # doesn't work
alpha = 1 - 0.6827
hist['data_obs'].SetBinErrorOption(TH1.kPoisson)
data_graph = TGraphAsymmErrors(hist['data_obs'].GetNbinsX())
data_graph.SetMarkerStyle(hist['data_obs'].GetMarkerStyle())
data_graph.SetMarkerSize(hist['data_obs'].GetMarkerSize())
res_graph = data_graph.Clone()
for i in range(hist['data_obs'].GetNbinsX()):
N = hist['data_obs'].GetBinContent(i + 1)
B = hist['BkgSum'].GetBinContent(i + 1)
L = 0 if N == 0 else ROOT.Math.gamma_quantile(alpha / 2, N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
data_graph.SetPoint(i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N if not N == 0 else -1.e99)
data_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2., N - L, U - N)
res_graph.SetPoint(i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N / B if not B == 0 and not N == 0 else -1.e99)
res_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
(N - L) / B if not B == 0 else -1.e99,
(U - N) / B if not B == 0 else -1.e99)
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if sample[x]['plot']])
leg = TLegend(0.7, 0.9 - 0.05 * n, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pl")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s],
sample[s]['label'].replace("m_{#Chi}=1 GeV",
""), "fl")
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if ratio else 600)
if ratio:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), ratio)
setBotPad(c1.GetPad(2), ratio)
c1.cd(1)
c1.GetPad(bool(ratio)).SetTopMargin(0.06)
c1.GetPad(bool(ratio)).SetRightMargin(0.05)
c1.GetPad(bool(ratio)).SetTicks(1, 1)
if log:
c1.GetPad(bool(ratio)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
#if len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
data_graph.Draw("SAME, PE")
for i, s in enumerate(sign):
if sample[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
bkg.SetMaximum((2. if log else 1.2) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
#if log: bkg.SetMinimum(1)
leg.Draw()
drawCMS(lumi, "Preliminary")
drawRegion(channel)
drawAnalysis(channel)
#if nm1 and not cutValue is None: drawCut(cutValue, bkg.GetMinimum(), bkg.GetMaximum()) #FIXME
if len(sign) > 0:
if channel.startswith('X') and len(sign) > 0:
drawNorm(0.9 - 0.04 * (n + 1),
"#sigma(X) #times B(X #rightarrow Vh) = %.1f pb" % snorm)
#elif "SR" in channel: drawNorm(0.9-0.04*(n+1), "DM+bb/tt, scaled by %.0f" % snorm, "m_{#chi}=1 GeV, scalar mediator")
elif "SR" in channel:
drawNorm(0.9 - 0.04 * (n + 1), "DM+bb/tt, m_{#chi}=1 GeV",
"scalar mediator")
setHistStyle(bkg, 1.2 if ratio else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if ratio else 1.1)
if ratio:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / Bkg")
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist['data_obs'].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if len(data) > 0:
#res.Draw("SAME, PE0")
res_graph.Draw("SAME, PE0")
# if len(err.GetXaxis().GetBinLabel(1))==0: # Bin labels: not a ordinary plot
# drawRatio(hist['data_obs'], hist['BkgSum'])
# drawKolmogorov(hist['data_obs'], hist['BkgSum'])
c1.Update()
# return list of objects created by the draw() function
return [c1, bkg, leg, err, errLine, res, data_graph, res_graph]
file = TFile(output + ".root")
runPileup = None
ipoint = 0
for key in file.GetListOfKeys():
kname = key.GetName()
if (kname.find('pileup_') < 0): continue
runnb = int(kname.split('_')[1])
h = file.Get(kname)
avgpileup = h.GetMean()
rmspileup = h.GetRMS()
evol.SetPoint(ipoint, runnb, avgpileup)
evol.SetPointError(ipoint, 0, 0, rmspileup, rmspileup)
ipoint = ipoint + 1
if (runPileup is None):
runPileup = h.Clone('runPileup')
formatPlot(runPileup, 1, 1, 1, 20, 0, True, True, 1, 1, 1)
file.Close()
setStyle()
c = getNewCanvas("evolc", "evolc", False)
c.SetWindowSize(1200, 600)
c.Divide(2, 1)
c.cd(1)
evol.Draw('ap')
c.cd(2)
runPileup.DrawNormalized('hist')
c.SaveAs(output + "_summary.C")
c.SaveAs(output + "_summary.root")
print "Average pileup: " + str(runPileup.GetMean()) + ' +/- ' + str(
runPileup.GetMeanError())
gxs.SetLineStyle(5)
gxs.SetLineWidth(3)
gxs.SetLineColor(ROOT.kRed)
canvasName = 'Limits'
if useSub:
canvasName = 'Limits_Sub'
can = TCanvas(canvasName,canvasName,900,600)
gPad.SetLogy()
g2.GetXaxis().SetTitle(' qg resonance Mass (TeV)')
g2.GetYaxis().SetTitle('#sigma #times A #times BR (q*#rightarrow jj) (pb)')
#g2.GetYaxis().SetTitle('#sigma #times BR (q*#rightarrow jj) (pb)')
g2.GetYaxis().CenterTitle(ROOT.kTRUE)
g2.GetYaxis().SetNdivisions(510)
g2.GetYaxis().SetRangeUser(1e-3,10)
g2.Draw('AE3')
g1.Draw('sameE3')
gExp.Draw('sameL')
gObs.Draw('sameLP')
gxs.Draw('sameL')
if superimpose:
gExpDinko.Draw('sameL')
leg = TLegend(0.65,0.65,0.9,0.9)
if useSub:
leg.SetHeader('Substructure Selection')
else:
leg.SetHeader('Simple Dijet Selection')
leg.AddEntry(gObs,'Observed','LP')
leg.AddEntry(gExp,'Expected','L')
leg.AddEntry(g1,'Expected #pm 1 #sigma','F')
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()
latex2.SetNDC()
def ratioplot():
# create required parts
leg = getLegend()
latex = getLatex()
c = SetCanvas()
#c.SetLogy()
#c = TCanvas()
#c.SetLogy()
h1 = f.Get('h_num_calo_') #'calo',pf
h1 = setHistStyle(h1, bins)
h2 = f.Get('h_den_calo_')
h2 = setHistStyle(h2, bins)
h11 = f2.Get('h_num_calo_')
h11 = setHistStyle(h11, bins)
h21 = f2.Get('h_den_calo_')
h21 = setHistStyle(h21, bins)
gr = TGraphAsymmErrors(30)
#gr.Divide(h1,h2)
gr = TGraphAsymmErrors(h1, h2)
gr2 = TGraphAsymmErrors(h11, h21)
gr2.SetMarkerStyle(20)
gr2.GetXaxis().SetRangeUser(0, 1000)
gr2.SetMarkerSize(1.5)
gr2.SetLineColor(2)
gr2.SetLineWidth(1)
gr2.SetMarkerColor(2)
gr.GetXaxis().SetRangeUser(0, 1000)
# gr.GetYaxis().SetRangeUser(0.0001,1.2)
gr.SetMarkerStyle(20)
gr.SetMarkerSize(1.5)
gr.SetLineColor(1)
gr.SetLineWidth(1)
gr.SetMarkerColor(1)
gr.GetYaxis().SetTitle("Trigger Efficiency")
gr.GetXaxis().SetTitle("MET [GeV]")
gr.SetTitle("")
#base histogram
histogram_base = TH1F("histogram_base", "", 1000, 0, 1000.)
histogram_base.SetTitle("")
histogram_base.SetStats(0)
histogram_base.SetMarkerSize(2)
#histogram_base.SetMinimum(0.0)
histogram_base.SetMaximum(1.2)
histogram_base.GetXaxis().SetTitle("Online E_{T}^{miss} (GeV)")
histogram_base.GetYaxis().SetTitle("Efficiency")
histogram_base = setHistStyle(histogram_base, bins)
histogram_base.Draw("HIST")
# c.SaveAs()
gr.Draw('P same')
gr2.Draw('P same')
latex.DrawLatex(0.49, 0.93, " EGamma Run2018C, 13 TeV")
xmin = 0.0
line = TLine(max(xmin, gr.GetXaxis().GetXmin()), 1, 1000, 1)
line.SetLineColor(1)
line.SetLineWidth(1)
line.SetLineStyle(7)
line.Draw()
leg.AddEntry(gr, 'With HBHENoise filter', 'P')
leg.AddEntry(gr2, 'Without HBHENoise filter', 'P')
leg.Draw()
txt = 'Path: HLT_PFMETTypeOne200_HBHE_BeamHaloCleaned'
texcms = AddText(txt)
texcms.Draw("same")
c.SaveAs('testTurnOn_EGamma.png')
def pullsVertical(fileName):
content = filterPullFile(fileName)
nbins, off = len(content), 0.10
b_pulls = TH1F("b_pulls", ";;Pulls", nbins, 0. - off, nbins - off)
s_pulls = TH1F("s_pulls", ";;Pulls", nbins, 0. + off, nbins + off) #
for i, s in enumerate(content):
l = s.split()
b_pulls.GetXaxis().SetBinLabel(i + 1, l[0])
s_pulls.GetXaxis().SetBinLabel(i + 1, l[0])
b_pulls.SetBinContent(i + 1, float(l[1]))
b_pulls.SetBinError(i + 1, float(l[2]))
s_pulls.SetBinContent(i + 1, float(l[3]))
s_pulls.SetBinError(i + 1, float(l[4]))
b_pulls.SetFillStyle(3005)
b_pulls.SetFillColor(923)
b_pulls.SetLineColor(923)
b_pulls.SetLineWidth(1)
b_pulls.SetMarkerStyle(20)
b_pulls.SetMarkerSize(1.25)
s_pulls.SetLineColor(602)
s_pulls.SetMarkerColor(602)
s_pulls.SetMarkerStyle(24) #24
s_pulls.SetLineWidth(1)
b_pulls.GetYaxis().SetRangeUser(-2.5, 2.5)
# Graphs
h_pulls = TH2F("pulls", "", 6, -3., 3., nbins, 0, nbins)
B_pulls = TGraphAsymmErrors(nbins)
S_pulls = TGraphAsymmErrors(nbins)
boxes = []
canvas = TCanvas("canvas", "Pulls", 600, 150 + nbins * 10) #nbins*20)
canvas.cd()
canvas.SetGrid(0, 1)
canvas.GetPad(0).SetTopMargin(0.01)
canvas.GetPad(0).SetRightMargin(0.01)
canvas.GetPad(0).SetBottomMargin(0.05)
canvas.GetPad(0).SetLeftMargin(0.25) #(0.25)#(0.065)
canvas.GetPad(0).SetTicks(1, 1)
for i, s in enumerate(content):
l = s.split()
if "1034h" in l[0]: l[0] = "CMS_PDF_13TeV"
h_pulls.GetYaxis().SetBinLabel(i + 1, l[0].replace('CMS2016_', '')) #C
#y1 = gStyle.GetPadBottomMargin()
#y2 = 1. - gStyle.GetPadTopMargin()
#h = (y2 - y1) / float(nbins)
#y1 = y1 + float(i) * h
#y2 = y1 + h
#box = TPaveText(0, y1, 1, y2, 'NDC')
#box.SetFillColor(0)
#box.SetTextSize(0.02)
#box.SetBorderSize(0)
#box.SetTextAlign(12)
#box.SetMargin(0.005)
#if i % 2 == 0:
# box.SetFillColor(18)
#box.Draw()
#boxes.append(box)
B_pulls.SetPoint(i + 1, float(l[1]), float(i + 1) - 0.3) #C
B_pulls.SetPointError(i + 1, float(l[2]), float(l[2]), 0., 0.) #C
for i, s in enumerate(content):
l = s.split()
S_pulls.SetPoint(i + 1, float(l[3]), float(i + 1) - 0.7) #C
S_pulls.SetPointError(i + 1, float(l[4]), float(l[4]), 0., 0.) #C
h_pulls.GetXaxis().SetTitle("(#hat{#theta} - #theta_{0}) / #Delta#theta")
h_pulls.GetXaxis().SetLabelOffset(-0.01)
h_pulls.GetXaxis().SetTitleOffset(.6)
h_pulls.GetYaxis().SetNdivisions(nbins, 0, 0)
B_pulls.SetFillColor(1)
B_pulls.SetLineColor(1)
B_pulls.SetLineStyle(1)
B_pulls.SetLineWidth(2)
B_pulls.SetMarkerColor(1)
B_pulls.SetMarkerStyle(20)
B_pulls.SetMarkerSize(1) #(0.75)
S_pulls.SetFillColor(629)
S_pulls.SetLineColor(629)
S_pulls.SetMarkerColor(629)
S_pulls.SetLineWidth(2)
S_pulls.SetMarkerStyle(20)
S_pulls.SetMarkerSize(1)
box1 = TBox(-1., 0., 1., nbins)
box1.SetFillStyle(3001)
#box1.SetFillStyle(0)
box1.SetFillColor(417)
box1.SetLineWidth(2)
box1.SetLineStyle(2)
box1.SetLineColor(417)
box2 = TBox(-2., 0., 2., nbins)
box2.SetFillStyle(3001)
#box2.SetFillStyle(0)
box2.SetFillColor(800)
box2.SetLineWidth(2)
box2.SetLineStyle(2)
box2.SetLineColor(800)
leg = TLegend(0.1, -0.05, 0.7, 0.08)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetFillColor(0)
leg.SetNColumns(2)
leg.AddEntry(B_pulls, "B-only fit", "lp")
leg.AddEntry(S_pulls, "S+B fit", "lp")
if text: leg.AddEntry(0, text, "")
h_pulls.Draw("")
box2.Draw()
box1.Draw()
B_pulls.Draw("P6SAME")
S_pulls.Draw("P6SAME")
leg.Draw()
# drawCMS(35867, "Preliminary")
# drawAnalysis("VH")
# drawRegion(outName)
canvas.Print(outName + ".png")
canvas.Print(outName + ".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
selden2 = 'cmgMuonSelVec.size()>1 && cmgMuonSel[1].getSelection(\"cuts_vbtfmuon\")'
LPcanvas.cd(1)
cmg.Draw('cmgMuonSel[0].pt()>>den1', selden1)
LPcanvas.cd(2)
cmg.Draw('cmgMuonSel[1].pt()>>den2', selden2)
LPcanvas.cd(3)
cmg.Draw('cmgMuonSel[0].pt()>>num1', selnum1)
LPcanvas.cd(4)
cmg.Draw('cmgMuonSel[1].pt()>>num2', selnum2)
eff1 = TGraphAsymmErrors(num1, den1)
eff2 = TGraphAsymmErrors(num2, den2)
LPcanvas.cd(5)
eff1.Draw('AP')
sRedPoints.formatHisto(eff2)
eff2.Draw('P')
#LPnocut = TH1F('LPnocut', 'LPnocut', 20, -0.5, 1.5)
LPplus = TH1F('LPplus', 'LPplus', 20, -0.5, 1.5)
LPplus.Sumw2()
LPminus = TH1F('LPminus', 'LPminus', 20, -0.5, 1.5)
LPminus.Sumw2()
sBlack.formatHisto(LPplus)
sRedPoints.formatHisto(LPminus)
LPcanvas.cd(6)
#cmg.Draw('LPWMuNu[0].lp()>>LPnocut', 'LPWMuNuVec.size()>0')
selection = 'LPWMuNuVec.size()>0 && LPWMuNu.getSelection(\"cuts_ptLoose\") && LPPFJetSelVec.size()>1 && LPMHTPFJet30LooseSelVec.size()>0'
cmg.Draw(
def doit():
frame = h.ProfileX(hname + "_frame", 1, -1, "s")
gr1 = TGraphAsymmErrors(h.GetNbinsX())
gr2 = TGraphAsymmErrors(h.GetNbinsX())
gr1_aspt = TGraphAsymmErrors(h.GetNbinsX())
gr2_aspt = TGraphAsymmErrors(h.GetNbinsX())
# Apply gaussian fits
for i in xrange(h.GetNbinsX()):
h_py = h.ProjectionY("_py", i + 1, i + 1)
if 50 <= i <= 60: # high pT, not enough entries (300 bins -> 150)
h_py.Rebin(2)
elif i >= 78: # low pT, resolution affected by finite bin width
h_py = h.ProjectionY("_py", i + 1,
i + 2) # merge i & (i+1) entries
if i == 82: # even lower pT, resolution affected by finite bin width
h_py = h.ProjectionY("_py", i + 1,
i + 4) # merge i & (i+4) entries
elif i >= 82:
continue
if h_py.Integral() < 20: continue
r = h_py.Fit("gaus", "SNQ", "", -1, 1.2)
#r = h_py.Fit("gaus", "SNQ", "", h_py.GetMean() - 0.04*5, h_py.GetMean() + 0.04*5)
mean, sigma, meanErr, sigmaErr = r.Parameter(1), r.Parameter(
2), r.ParError(1), r.ParError(2)
gr1.SetPoint(i, h.GetXaxis().GetBinCenter(i + 1), mean)
gr1.SetPointError(i, 0, 0, sigma, sigma)
gr2.SetPoint(i, h.GetXaxis().GetBinCenter(i + 1), sigma)
gr2.SetPointError(i, 0, 0, sigmaErr, sigmaErr)
gr1_aspt.SetPoint(i, 1.0 / h.GetXaxis().GetBinCenter(i + 1), mean)
gr1_aspt.SetPointError(i, 0, 0, sigma, sigma)
gr2_aspt.SetPoint(i, 1.0 / h.GetXaxis().GetBinCenter(i + 1), sigma)
gr2_aspt.SetPointError(i, 0, 0, sigmaErr, sigmaErr)
# Draw
h.Draw("COLZ")
gPad.SetLogx(0)
#draw_cms_lumi()
#gPad.Print("figures_perf/" + hname + "_omtf" + ".png")
#gPad.Print("figures_perf/" + hname + "_omtf" + ".pdf")
#
frame.Reset()
frame.SetBins(50, 0, 50)
frame.GetXaxis().SetTitle("gen p_{T} [GeV]")
frame.GetYaxis().SetTitle("#Delta(p_{T})/p_{T} bias")
frame.SetMaximum(0.5)
frame.SetMinimum(-0.5)
frame.SetStats(0)
frame.Draw()
gr1_aspt.SetLineColor(col)
gr1_aspt.SetMarkerColor(col)
gr1_aspt.Draw("p")
gPad.SetLogx()
draw_cms_lumi()
gPad.Print("figures_perf/" + hname + "_bias" + "_omtf" + ".png")
gPad.Print("figures_perf/" + hname + "_bias" + "_omtf" + ".pdf")
#
frame.GetXaxis().SetTitle("gen p_{T} [GeV]")
frame.GetYaxis().SetTitle("#Delta(p_{T})/p_{T} resolution")
frame.SetMaximum(0.6)
frame.SetMinimum(0.0)
frame.SetStats(0)
frame.Draw()
gr2_aspt.SetLineColor(col)
gr2_aspt.SetMarkerColor(col)
gr2_aspt.Draw("p")
#gr2_aspt.Fit("pol1", "", "", 10, 40)
gPad.SetLogx()
draw_cms_lumi()
gPad.Print("figures_perf/" + hname + "_res" + "_omtf" + ".png")
gPad.Print("figures_perf/" + hname + "_res" + "_omtf" + ".pdf")
#
h.cache = [frame, gr1, gr2, gr1_aspt, gr2_aspt]
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 limit():
method = ''
channel = "bb"
particleP = "Z'"
particle = channel
multF = ZPTOBB
THEORY = ['A1', 'B3']
suffix = "_" + BTAGGING
if ISMC: suffix += "_MC"
if SY: suffix += "_comb"
#if method=="cls": suffix="_CLs"
if SY:
filename = "./combine/limits/MANtag_study/" + BTAGGING + "/combined_run2/" + YEAR + "_M%d.txt"
else:
filename = "./combine/limits/MANtag_study/" + BTAGGING + "/" + YEAR + "_M%d.txt"
if CATEGORY != "":
filename = filename.replace(
BTAGGING + "/", BTAGGING + "/single_category/" + CATEGORY + "_")
suffix += "_" + CATEGORY
if ISMC: filename = filename.replace(".txt", "_MC.txt")
mass, val = fillValues(filename)
#print "mass =",mass
#print "val =", val
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
Sign = TGraph()
pVal = TGraph()
Best = TGraphAsymmErrors()
Theory = {}
for i, m in enumerate(mass):
if not m in val:
print "Key Error:", m, "not in value map"
continue
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0] * multF)
Exp0s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][2] * multF,
val[m][4] * multF - val[m][3] * multF)
Exp2s.SetPoint(n, m, val[m][3] * multF)
Exp2s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][1] * multF,
val[m][5] * multF - val[m][3] * multF)
if len(val[m]) > 6: Sign.SetPoint(n, m, val[m][6])
if len(val[m]) > 7: pVal.SetPoint(n, m, val[m][7])
if len(val[m]) > 8: Best.SetPoint(n, m, val[m][8])
if len(val[m]) > 10:
Best.SetPointError(n, 0., 0., abs(val[m][9]), val[m][10])
for t in THEORY:
Theory[t] = TGraphAsymmErrors()
addXZH = True
for m in sorted(HVT[t]['W']['XS'].keys()):
if m < mass[0] or m > mass[-1]: continue
if m > 4500:
continue ## for now because I don't have the higher mass xs FIXME
XsZ, XsZ_Up, XsZ_Down = 0., 0., 0.
if addXZH:
XsZ = 1000. * HVT[t]['Z']['XS'][
m] * 0.12 #temporary BR value set to 0.12 FIXME
XsZ_Up = XsZ * (1. + math.hypot(HVT[t]['Z']['QCD'][m][0] - 1.,
HVT[t]['Z']['PDF'][m][0] - 1.))
XsZ_Down = XsZ * (1. -
math.hypot(1. - HVT[t]['Z']['QCD'][m][0],
1. - HVT[t]['Z']['PDF'][m][0]))
n = Theory[t].GetN()
Theory[t].SetPoint(n, m, XsZ)
Theory[t].SetPointError(n, 0., 0., (XsZ - XsZ_Down),
(XsZ_Up - XsZ))
Theory[t].SetLineColor(theoryLineColor[t])
Theory[t].SetFillColor(theoryFillColor[t])
Theory[t].SetFillStyle(theoryFillStyle[t])
Theory[t].SetLineWidth(2)
#Theory[t].SetLineStyle(7)
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp1s.SetFillColor(417) #kGreen+1
Exp1s.SetLineColor(417) #kGreen+1
Exp2s.SetFillColor(800) #kOrange
Exp2s.SetLineColor(800) #kOrange
Exp2s.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle("#sigma(" + particleP + ") #bf{#it{#Beta}}(" +
particleP + " #rightarrow " + particle +
") (fb)")
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Sign.SetLineWidth(2)
Sign.SetLineColor(629)
Sign.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Sign.GetXaxis().SetTitleSize(Sign.GetXaxis().GetTitleSize() * 1.1)
Sign.GetYaxis().SetTitle("Significance")
pVal.SetLineWidth(2)
pVal.SetLineColor(629)
pVal.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
pVal.GetXaxis().SetTitleSize(pVal.GetXaxis().GetTitleSize() * 1.1)
pVal.GetYaxis().SetTitle("local p-Value")
Best.SetLineWidth(2)
Best.SetLineColor(629)
Best.SetFillColor(629)
Best.SetFillStyle(3003)
Best.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Best.GetXaxis().SetTitleSize(Best.GetXaxis().GetTitleSize() * 1.1)
Best.GetYaxis().SetTitle("Best Fit (pb)")
c1 = TCanvas("c1", "Exclusion Limits", 800, 600)
c1.cd()
#SetPad(c1.GetPad(0))
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetLeftMargin(0.12)
c1.GetPad(0).SetTicks(1, 1)
#c1.GetPad(0).SetGridx()
#c1.GetPad(0).SetGridy()
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
Exp0s.Draw("SAME, L")
if not options.blind: Obs0s.Draw("SAME, L")
#setHistStyle(Exp2s)
Exp2s.GetXaxis().SetTitleSize(0.050)
Exp2s.GetYaxis().SetTitleSize(0.050)
Exp2s.GetXaxis().SetLabelSize(0.045)
Exp2s.GetYaxis().SetLabelSize(0.045)
Exp2s.GetXaxis().SetTitleOffset(0.90)
Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetRangeUser(0.1, 5.e3)
#else: Exp2s.GetYaxis().SetRangeUser(0.1, 1.e2)
#Exp2s.GetXaxis().SetRangeUser(mass[0], min(mass[-1], MAXIMUM[channel] if channel in MAXIMUM else 1.e6))
Exp2s.GetXaxis().SetRangeUser(SIGNALS[0], SIGNALS[-1])
#drawAnalysis(channel)
drawAnalysis("")
#drawRegion(channel, True)
drawRegion("", True)
#drawCMS(LUMI, "Simulation Preliminary") #Preliminary
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
# legend
top = 0.9
nitems = 4 + len(THEORY)
leg = TLegend(0.55, top - nitems * 0.3 / 5., 0.98, top)
#leg = TLegend(0.45, top-nitems*0.3/5., 0.98, top)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL upper limits")
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected", "l")
leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f")
leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f")
for t in THEORY:
leg.AddEntry(Theory[t], theoryLabel[t], "fl")
leg.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.045)
latex.SetTextFont(42)
#latex.DrawLatex(0.66, leg.GetY1()-0.045, particleP+" #rightarrow "+particle+"h")
leg2 = TLegend(0.12, 0.225 - 2 * 0.25 / 5., 0.65, 0.225)
leg2.SetBorderSize(0)
leg2.SetFillStyle(0) #1001
leg2.SetFillColor(0)
c1.GetPad(0).RedrawAxis()
leg2.Draw()
if not options.blind: Obs0s.Draw("SAME, L")
c1.GetPad(0).Update()
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR +
suffix + ".png")
c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR +
suffix + ".pdf")
if 'ah' in channel or 'sl' in channel:
c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR +
suffix + ".C")
c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR +
suffix + ".root")
for t in THEORY:
print "Model", t, ":",
for m in range(mass[0], mass[-1], 1):
if not (Theory[t].Eval(m) > Obs0s.Eval(m)) == (
Theory[t].Eval(m + 1) > Obs0s.Eval(m + 1)):
print m,
print ""
return
def limit2HDM():
global signals
signals = range(800, 2000 + 1, 50)
multF = HTOBB
THEORY = ['T1', 'T2']
mass, val = fillValues("./combine/AZh/AZh_M%d.txt")
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
massB, valB = fillValues("./combine/BBAZh/BBAZh_M%d.txt")
Obs0sB = TGraph()
Exp0sB = TGraph()
Exp1sB = TGraphAsymmErrors()
Exp2sB = TGraphAsymmErrors()
for i, m in enumerate(mass):
if not m in val:
print "Key Error:", m, "not in value map"
continue
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0] * multF)
Exp0s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][2] * multF,
val[m][4] * multF - val[m][3] * multF)
Exp2s.SetPoint(n, m, val[m][3] * multF)
Exp2s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][1] * multF,
val[m][5] * multF - val[m][3] * multF)
Obs0sB.SetPoint(n, m, valB[m][0] * multF)
Exp0sB.SetPoint(n, m, valB[m][3] * multF)
Exp1sB.SetPoint(n, m, valB[m][3] * multF)
Exp1sB.SetPointError(n, 0., 0.,
valB[m][3] * multF - valB[m][2] * multF,
valB[m][4] * multF - valB[m][3] * multF)
Exp2sB.SetPoint(n, m, valB[m][3] * multF)
Exp2sB.SetPointError(n, 0., 0.,
valB[m][3] * multF - valB[m][1] * multF,
valB[m][5] * multF - valB[m][3] * multF)
col = 629
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp0s.SetLineColor(1)
# Exp1s.SetFillColorAlpha(col, 0.4) #kGreen+1
# Exp1s.SetLineColorAlpha(col, 0.4)
# Exp2s.SetFillColorAlpha(col, 0.2) #kOrange
# Exp2s.SetLineColorAlpha(col, 0.2)
Exp1s.SetFillColor(417)
Exp1s.SetLineColor(417)
Exp2s.SetFillColor(800)
Exp2s.SetLineColor(800)
colB = 922
Exp2sB.SetLineWidth(2)
Obs0sB.SetLineStyle(9)
Obs0sB.SetLineWidth(3)
Obs0sB.SetMarkerStyle(0)
Obs0sB.SetLineColor(colB)
Exp0sB.SetLineStyle(8)
Exp0sB.SetLineWidth(3)
Exp0sB.SetLineColor(colB)
Exp1sB.SetFillColorAlpha(colB, 0.4) #kGreen+1
Exp1sB.SetLineColorAlpha(colB, 0.4)
Exp2sB.SetFillColorAlpha(colB, 0.2) #kOrange
Exp2sB.SetLineColorAlpha(colB, 0.2)
Exp2s.GetXaxis().SetTitle("m_{A} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle(
"#sigma(A) #bf{#it{#Beta}}(A #rightarrow Zh) #bf{#it{#Beta}}(h #rightarrow bb) (fb)"
)
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Theory = {}
#for t in THEORY:
# Theory[t] = TGraphAsymmErrors()
# for m in sorted(THDM[t]['ggA'].keys()):
# if m < mass[0] or m > mass[-1]: continue
# Xs, Xs_Up, Xs_Down = 0., 0., 0.
# Xs = THDM[t]['ggA'][m]
# Xs_Up = Xs*(1.+math.sqrt((THDM['PDF']['ggA'][m][0]-1.)**2 + (THDM['QCD']['ggA'][m][0]-1.)**2))
# Xs_Down = Xs*(1.-math.sqrt((1.-THDM['PDF']['ggA'][m][1])**2 + (1.-THDM['QCD']['ggA'][m][1])**2))
# n = Theory[t].GetN()
# Theory[t].SetPoint(n, m, Xs)
# Theory[t].SetPointError(n, 0., 0., (Xs-Xs_Down), (Xs_Up-Xs))
# Theory[t].SetLineColor(theoryLineColor[t])
# Theory[t].SetFillColor(theoryFillColor[t])
# Theory[t].SetFillStyle(theoryFillStyle[t])
# Theory[t].SetLineWidth(2)
# #Theory[t].SetLineStyle(7)
c1 = TCanvas("c1", "Exclusion Limits", 800, 600)
c1.cd()
#SetPad(c1.GetPad(0))
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetLeftMargin(0.12)
c1.GetPad(0).SetTicks(1, 1)
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
Exp0s.Draw("SAME, L")
# Exp2sB.Draw("SAME, 3")
# Exp1sB.Draw("SAME, 3")
Exp0sB.Draw("SAME, L")
if not options.blind:
Obs0s.Draw("SAME, L")
Obs0sB.Draw("SAME, L")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
#setHistStyle(Exp2s)
# Exp2s.GetXaxis().SetTitleSize(0.045)
# Exp2s.GetYaxis().SetTitleSize(0.04)
# Exp2s.GetXaxis().SetLabelSize(0.04)
# Exp2s.GetYaxis().SetLabelSize(0.04)
# Exp2s.GetXaxis().SetTitleOffset(1)
# Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetXaxis().SetTitleSize(0.050)
Exp2s.GetYaxis().SetTitleSize(0.050)
Exp2s.GetXaxis().SetLabelSize(0.045)
Exp2s.GetYaxis().SetLabelSize(0.045)
Exp2s.GetXaxis().SetTitleOffset(0.90)
Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetRangeUser(0.5, 1.e3)
Exp2s.GetXaxis().SetRangeUser(mass[0], mass[-1])
drawAnalysis('AZh')
drawRegion('AZHsl', True)
drawCMS(LUMI, "") #Preliminary
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
# legend
leg = TLegend(0.6, 0.90, 0.99, 0.90)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL upper limits")
leg.AddEntry(None, "gg #rightarrow A #rightarrow Zh",
"") #"95% CL upper limits"
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected", "l")
leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f")
leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f")
leg.AddEntry(None, "", "")
leg.AddEntry(None, "bbA #rightarrow Zh", "")
leg.AddEntry(Obs0sB, "Observed", "l")
leg.AddEntry(Exp0sB, "Expected", "l")
leg.SetY1(leg.GetY2() - leg.GetNRows() * 0.045)
leg.Draw()
# latex = TLatex()
# latex.SetNDC()
# latex.SetTextSize(0.040)
# latex.SetTextFont(42)
# latex.DrawLatex(0.65, leg.GetY1()-0.045, "cos(#beta-#alpha)=0.25, tan(#beta)=1")
# legB = TLegend(0.12, 0.4-4*0.3/5., 0.65, 0.4)
legB = TLegend(0.15, 0.27, 0.68, 0.27)
legB.SetBorderSize(0)
legB.SetFillStyle(0) #1001
legB.SetFillColor(0)
for t in THEORY:
legB.AddEntry(Theory[t], theoryLabel[t], "fl")
legB.AddEntry(None, "cos(#beta-#alpha)=0.25, tan(#beta)=1", "")
legB.SetY1(legB.GetY2() - legB.GetNRows() * 0.045)
legB.Draw()
c1.GetPad(0).RedrawAxis()
leg.Draw()
c1.Update()
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Print("plotsLimit/Exclusion/THDM.png")
c1.Print("plotsLimit/Exclusion/THDM.pdf")
gDplusOverD0.Draw('p')
gDplusOverD0.Fit('fDplusOverD0', 'rq')
hFrameDstar = cRatios.cd(2).DrawFrame(
0., 0., 50., 1.5, ';#it{p}_{T} (GeV/#it{c}); D*^{+} / D^{0}')
hFrameDstar.GetYaxis().SetDecimals()
gDstarOverD0.Draw('p')
gDstarOverD0.Fit('fDstarOverD0', 'rq')
hFrameDs = cRatios.cd(3).DrawFrame(
0., 0., 50., 1.5, ';#it{p}_{T} (GeV/#it{c}); D_{s}^{+} / D^{0}')
hFrameDs.GetYaxis().SetDecimals()
gDsOverD0.Draw('p')
gDsOverD0.Fit('fDsOverD0', 'rq')
hFrameLc = cRatios.cd(4).DrawFrame(
0., 0., 50., 1.5, ';#it{p}_{T} (GeV/#it{c}); #Lambda_{c}^{+} / D^{0}')
hFrameLc.GetYaxis().SetDecimals()
gLcOverD0.Draw('p')
gLcOverD0.Fit('fLcOverD0', 'r')
dfDplus = dfD0FONLL.copy()
for col in dfDplus.columns:
if col not in ['ptmin', 'ptmax']:
dfDplus[col] = dfDplus.apply(lambda x: x[col] * gDplusOverD0.Eval(
(x['ptmax'] + x['ptmin']) / 2),
axis=1)
dfDs = dfD0FONLL.copy()
for col in dfDs.columns:
if col not in ['ptmin', 'ptmax']:
dfDs[col] = dfDs.apply(lambda x: x[col] * gDsOverD0.Eval(
(x['ptmax'] + x['ptmin']) / 2),
axis=1)
def significanceSB(cutlist, labellist):
basecut = labellist[0]
dim = len(cutlist)
significance = [0] * (dim + 1)
file = {}
tree = {}
effs = {}
hist = {}
GrAsym = {}
yErrorUp = {}
yErrorDown = {}
totEve = 0
GrAsym = TGraphAsymmErrors()
cuts = ""
for j, c in enumerate(cutlist):
s = 0.
b = 0.
cuts += cutlist[0] if j == 0 else " && " + cutlist[j]
print "cuts = ", cuts
for num1, v in enumerate(signals):
#print "Signal = ", v
for num2, filename in enumerate(samples[v]['files']):
#print "Signal rootfile read = ", filename
file[filename] = TFile(NTUPLESIG + filename + ".root",
"READ") # Read TFile
tree[filename] = file[filename].Get("Events") # Read TTree
nevents = float(sample[filename]['nevents'])
xs = float(sample[filename]['xsec']) * float(
sample[filename]['kfactor'])
LumiMC = nevents / xs
Weight = float(LUMI) / float(LumiMC)
sig_entries = tree[filename].GetEntries(cuts)
#print "s = ", float(sig_entries) * float(Weight)
s += float(sig_entries) * float(Weight)
print "TOT SIG = ", s
for num1, k in enumerate(back):
#print "backgrounds = ", k
for num2, filename in enumerate(samples[k]['files']):
#print "backgrounds rootfile read = ", filename
file[filename] = TFile(NTUPLEDIR + filename + ".root",
"READ") # Read TFile
tree[filename] = file[filename].Get("Events") # Read TTree
nevents = float(sample[filename]['nevents'])
xs = float(sample[filename]['xsec']) * float(
sample[filename]['kfactor'])
LumiMC = nevents / xs
Weight = float(LUMI) / float(LumiMC)
bkg_entries = tree[filename].GetEntries(cuts)
#print "b = ", float(bkg_entries) * float(Weight)
b += float(bkg_entries) * float(Weight)
print "TOT BKG = ", b
##End of cutlist
#COMPUTE
#print "s = ", s
#print "b = ", b
#print "sqrt(b) = ", math.sqrt(b)
#print "significance = ", float(s/math.sqrt(b))
significance[j] = float(s / math.sqrt(b))
yErrorUp[j] = float(
TEfficiency.ClopperPearson(math.sqrt(b), s, 0.68, True) -
significance[j])
yErrorDown[j] = float(
significance[j] -
TEfficiency.ClopperPearson(math.sqrt(b), s, 0.68, False))
GrAsym.SetPoint(j, j + 0.5, significance[j])
GrAsym.SetPointError(j, 0, 0, yErrorUp[j], yErrorDown[j])
for k, cs in enumerate(labellist):
GrAsym.GetHistogram().GetXaxis().Set(dim, 0, dim)
GrAsym.GetHistogram().GetXaxis().SetBinLabel(k + 1,
"%s" % labellist[k])
GrAsym.SetLineColor(2)
GrAsym.SetLineWidth(3)
GrAsym.SetMarkerStyle(8)
GrAsym.SetMarkerColor(2)
c1 = TCanvas("c1", "Signals Acceptance", 800, 600)
c1.cd()
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetTicks(1, 1)
gStyle.SetOptStat(0)
#GrAsym.SetMaximum(1.3)
#GrAsym.SetMinimum(0.)
GrAsym.GetHistogram().GetXaxis().SetTitle("")
GrAsym.GetHistogram().GetYaxis().SetTitle("Significance (S/#sqrt{B})")
GrAsym.Draw("pa")
drawCMS(LUMI, "Work In Progress")
drawRegion(basecut)
if not os.path.exists('plots/Signal/Significance/'):
os.system('mkdir -p plots/Signal/Significance/')
c1.Print("plots/Signal/Significance/Sigf_SB_" + basecut + ".png")
c1.Print("plots/Signal/Significance/Sigf_SB_" + basecut + ".pdf")
#if not options.runBash: raw_input("Press Enter to continue...")
pass
gSignifCatania.Write('gSignifCatania')
hSignifCatania.Write('hSignifCatania')
cSignif.Write()
cSignif.SaveAs('%s_Significance.pdf' % outFileName)
cBackground = TCanvas('cBackground', '', 500, 500)
cBackground.SetLogy()
hBackground.Draw()
hBackground.Write('hBackground')
cBackground.SaveAs('%s_Background.pdf' % outFileName)
cFprompt = TCanvas('cFprompt', '', 500, 500)
cFprompt.DrawFrame(cutVars['Pt']['min'][0], 0.,
cutVars['Pt']['max'][len(cutVars['Pt']['max']) - 1], 1.,
';#it{p}_{T} (GeV/#it{c});#it{f}_{prompt} (#it{f}_{c})')
gFprompt.Draw('p')
gFprompt.Write('gFprompt')
cFprompt.SaveAs('%s_Fprompt.pdf' % outFileName)
cEff = TCanvas('cEff', '', 500, 500)
cEff.DrawFrame(
cutVars['Pt']['min'][0], 1.e-4,
cutVars['Pt']['max'][len(cutVars['Pt']['max']) - 1], 1.,
';#it{p}_{T} (GeV/#it{c});(Acc #times #epsilon) #times 2#it{y}_{fid}')
cEff.SetLogy()
hAccEffPrompt.Draw('same')
hAccEffFD.Draw('same')
hAccEffPrompt.Write()
hAccEffFD.Write()
cEff.Write()
cEff.SaveAs('%s_Efficiency.pdf' % outFileName)
gr_s.SetTitle('ROC')
gr_s.SetFillColor(632 - 9)
gr_sc.SetLineColor(4)
c1.cd(1)
gr.GetXaxis().SetRangeUser(0, 0.5)
gr.GetYaxis().SetRangeUser(0.00001, 0.01)
gr_c.GetXaxis().SetRangeUser(0, 0.5)
gr_c.GetXaxis().SetRangeUser(0.00001, 0.01)
gr_LC.GetXaxis().SetRangeUser(0, 0.5)
gr_LC.GetYaxis().SetRangeUser(0.00001, 0.01)
gr_c.SetLineColor(4)
gr.Draw('SAME 3A')
gr_c.Draw('SAME XLP')
gr_LC.Draw('SAME A')
c1.cd(2)
gr_s.Draw('SAME 3A')
gr_sc.Draw('SAME XLP')
slp(2)
c1.Print('roc.png')
c1.Update()
###########
# #
# BackUps #
def pullsVertical_noBonly(fileName):
content = filterPullFile(fileName)
nbins, off = len(content), 0.10
# Graphs
h_pulls = TH2F("pulls", "", 6, -3., 3., nbins, 0, nbins)
S_pulls = TGraphAsymmErrors(nbins)
boxes = []
canvas = TCanvas("canvas", "Pulls", 720, 300 + nbins * 18) #nbins*20)
canvas.cd()
canvas.SetGrid(0, 1)
canvas.SetTopMargin(0.01)
canvas.SetRightMargin(0.01)
canvas.SetBottomMargin(0.10)
canvas.SetLeftMargin(0.40)
canvas.SetTicks(1, 1)
for i, s in enumerate(content):
l = s.split()
h_pulls.GetYaxis().SetBinLabel(i + 1, l[0])
S_pulls.SetPoint(i, float(l[3]), float(i + 1) - 0.5)
S_pulls.SetPointError(i, float(l[4]), float(l[4]), 0., 0.)
h_pulls.GetXaxis().SetTitle("(#hat{#theta} - #theta_{0}) / #Delta#theta")
h_pulls.GetXaxis().SetLabelOffset(0.0)
h_pulls.GetXaxis().SetTitleOffset(0.8)
h_pulls.GetXaxis().SetLabelSize(0.045)
h_pulls.GetXaxis().SetTitleSize(0.050)
h_pulls.GetYaxis().SetLabelSize(0.046)
h_pulls.GetYaxis().SetNdivisions(nbins, 0, 0)
S_pulls.SetFillColor(kBlack)
S_pulls.SetLineColor(kBlack)
S_pulls.SetMarkerColor(kBlack)
S_pulls.SetLineWidth(2)
S_pulls.SetMarkerStyle(20)
S_pulls.SetMarkerSize(1)
box1 = TBox(-1., 0., 1., nbins)
#box1.SetFillStyle(3001) # 3001 checkered
#box1.SetFillStyle(0)
box1.SetFillColor(kGreen + 1) # 417
box1.SetLineWidth(2)
box1.SetLineStyle(2)
box1.SetLineColor(kGreen + 1) # 417
box2 = TBox(-2., 0., 2., nbins)
#box2.SetFillStyle(3001) # 3001 checkered
#box2.SetFillStyle(0)
box2.SetFillColor(kOrange) # 800
box2.SetLineWidth(2)
box2.SetLineStyle(2)
box2.SetLineColor(kOrange) # 800
leg = TLegend(0.01, 0.01, 0.3, 0.15)
leg.SetTextSize(0.05)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetFillColor(0)
#leg.SetNColumns(2)
leg.AddEntry(S_pulls, "S+B fit", "lp")
if text: leg.AddEntry(0, text, "")
h_pulls.Draw("")
box2.Draw()
box1.Draw()
S_pulls.Draw("P6SAME")
leg.Draw()
canvas.RedrawAxis()
canvas.Print(outName + ".png")
canvas.Print(outName + ".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
def draw(hist, data, back, sign, snorm=1, ratio=0, poisson=False, log=False):
# If not present, create BkgSum
if not 'BkgSum' in hist.keys():
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
hist['BkgSum'].SetMarkerStyle(0)
# Some style
for i, s in enumerate(data):
hist[s].SetMarkerStyle(21)
hist[s].SetMarkerSize(1.25)
for i, s in enumerate(sign):
hist[s].SetLineWidth(3)
for i, s in enumerate(data + back + sign + ['BkgSum']):
addOverflow(hist[s], False) # Add overflow
# Set Poisson error bars
#if len(data) > 0: hist['data_obs'].SetBinErrorOption(1) # doesn't work
# Poisson error bars for data
if poisson:
alpha = 1 - 0.6827
hist['data_obs'].SetBinErrorOption(TH1.kPoisson)
data_graph = TGraphAsymmErrors(hist['data_obs'].GetNbinsX())
data_graph.SetMarkerStyle(hist['data_obs'].GetMarkerStyle())
data_graph.SetMarkerSize(hist['data_obs'].GetMarkerSize())
res_graph = data_graph.Clone()
for i in range(hist['data_obs'].GetNbinsX()):
N = hist['data_obs'].GetBinContent(i + 1)
B = hist['BkgSum'].GetBinContent(i + 1)
L = 0 if N == 0 else ROOT.Math.gamma_quantile(alpha / 2, N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
data_graph.SetPoint(
i, hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N if not N == 0 else -1.e99)
data_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2., N - L,
U - N)
res_graph.SetPoint(i,
hist['data_obs'].GetXaxis().GetBinCenter(i + 1),
N / B if not B == 0 and not N == 0 else -1.e99)
res_graph.SetPointError(
i, hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
hist['data_obs'].GetXaxis().GetBinWidth(i + 1) / 2.,
(N - L) / B if not B == 0 else -1.e99,
(U - N) / B if not B == 0 else -1.e99)
# Create stack
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
n = len([x for x in data + back + ['BkgSum'] + sign if samples[x]['plot']])
for i, s in enumerate(sign):
if 'sublabel' in samples[s]: n += 1
if 'subsublabel' in samples[s]: n += 1
#leg = TLegend(0.68, 0.9-0.05*n, 0.93, 0.9)
leg = TLegend(0.68 - 0.05, 0.9 - 0.05 * n, 0.93, 0.9) #DCMS
leg.SetTextSize(0.03) #DCMS
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("Signal x-sec=%.0f pb" % (1 * snorm))
if len(data) > 0:
leg.AddEntry(hist[data[0]], samples[data[0]]['label'], "ple1")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], samples[s]['label'], "f")
for i, s in enumerate(sign):
leg.AddEntry(hist[s], samples[s]['label'], "f")
# --- Display ---
c1 = TCanvas("c1",
hist.values()[-1].GetXaxis().GetTitle(), 1000,
800 if ratio else 700)
if ratio:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), ratio)
setBotPad(c1.GetPad(2), ratio)
c1.cd(1)
c1.GetPad(bool(ratio)).SetTopMargin(0.06)
c1.GetPad(bool(ratio)).SetRightMargin(0.05)
c1.GetPad(bool(ratio)).SetTicks(1, 1)
if log:
c1.GetPad(bool(ratio)).SetLogy()
#c1.GetPad(bool(ratio)).SetLogx()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if poisson: data_graph.Draw("SAME, PE")
elif len(data) > 0: hist['data_obs'].Draw("SAME, PE")
for i, s in enumerate(sign):
if samples[s]['plot']:
hist[s].DrawNormalized("SAME, HIST",
hist[s].Integral() * snorm) # signals
bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075)
# Determine range
if 'data_obs' in hist:
bkg.SetMaximum((2.5 if log else 1.2) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
bkg.SetMinimum(
max(
min(
hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
else:
bkg.SetMaximum(bkg.GetMaximum() * (2.5 if log else 1.2))
bkg.SetMinimum(5.e-1 if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
bkg.GetYaxis().SetMoreLogLabels(True)
leg.Draw()
#drawCMS(LUMI, "Preliminary")
#drawRegion(channel)
#drawAnalysis("LL")
setHistStyle(bkg, 1.2 if ratio else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if ratio else 1.1)
if ratio:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / Bkg")
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(2)
errLine.SetFillStyle(0)
errLine.SetLineColor(2) #L#
errLine.SetLineStyle(2) #L#
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if 'data_obs' in hist:
res = hist['data_obs'].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
setBotStyle(res)
if poisson: res_graph.Draw("SAME, PE0")
else: res.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawKolmogorov(hist['data_obs'], hist['BkgSum'])
else:
res = None
c1.Update()
# return list of objects created by the draw() function
return [
c1, bkg, leg, err if ratio else None, errLine if ratio else None,
res if ratio else None, data_graph if poisson else None,
res_graph if poisson else None
]
def makeLimitPlot(output,
obs,
exp,
chan,
printStats=False,
obs2="",
ratioLabel=""):
fileObs = open(obs, 'r')
fileExp = open(exp, 'r')
observedx = []
observedy = []
obsLimits = {}
for entry in fileObs:
massPoint = float(entry.split()[0])
limitEntry = float(entry.split()[1])
if massPoint not in obsLimits: obsLimits[massPoint] = []
obsLimits[massPoint].append(limitEntry)
if printStats: print "len obsLimits:", len(obsLimits)
for massPoint in sorted(obsLimits):
observedx.append(massPoint)
observedy.append(numpy.mean(obsLimits[massPoint]))
if (numpy.std(obsLimits[massPoint]) / numpy.mean(obsLimits[massPoint])
> 0.05):
print massPoint, " mean: ", numpy.mean(
obsLimits[massPoint]), " std dev: ", numpy.std(
obsLimits[massPoint]), " from: ", obsLimits[massPoint]
if not obs2 == "":
fileObs2 = open(obs2, 'r')
observedx2 = []
observedy2 = []
obsLimits2 = {}
for entry in fileObs2:
massPoint = float(entry.split()[0])
limitEntry = float(entry.split()[1])
if massPoint not in obsLimits2: obsLimits2[massPoint] = []
obsLimits2[massPoint].append(limitEntry)
if printStats: print "len obsLimits:", len(obsLimits2)
for massPoint in sorted(obsLimits2):
observedx2.append(massPoint)
observedy2.append(numpy.mean(obsLimits2[massPoint]))
if (numpy.std(obsLimits2[massPoint]) /
numpy.mean(obsLimits2[massPoint]) > 0.05):
print massPoint, " mean: ", numpy.mean(
obsLimits2[massPoint]), " std dev: ", numpy.std(
obsLimits2[massPoint]
), " from: ", obsLimits2[massPoint]
limits = {}
expectedx = []
expectedy = []
expected1SigLow = []
expected1SigHigh = []
expected2SigLow = []
expected2SigHigh = []
for entry in fileExp:
massPoint = float(entry.split()[0])
limitEntry = float(entry.split()[1])
if massPoint not in limits: limits[massPoint] = []
limits[massPoint].append(limitEntry)
if printStats: print "len limits:", len(limits)
for massPoint in sorted(limits):
limits[massPoint].sort()
numLimits = len(limits[massPoint])
nrExpts = len(limits[massPoint])
medianNr = int(nrExpts * 0.5)
#get indexes:
upper1Sig = int(nrExpts * (1 - (1 - 0.68) * 0.5))
lower1Sig = int(nrExpts * (1 - 0.68) * 0.5)
upper2Sig = int(nrExpts * (1 - (1 - 0.95) * 0.5))
lower2Sig = int(nrExpts * (1 - 0.95) * 0.5)
if printStats:
print massPoint, ":", limits[massPoint][lower2Sig], limits[
massPoint][lower1Sig], limits[massPoint][medianNr], limits[
massPoint][upper1Sig], limits[massPoint][upper2Sig]
#fill lists:
expectedx.append(massPoint)
expectedy.append(limits[massPoint][medianNr])
expected1SigLow.append(limits[massPoint][lower1Sig])
expected1SigHigh.append(limits[massPoint][upper1Sig])
expected2SigLow.append(limits[massPoint][lower2Sig])
expected2SigHigh.append(limits[massPoint][upper2Sig])
expX = numpy.array(expectedx)
expY = numpy.array(expectedy)
values2 = []
xPointsForValues2 = []
values = []
xPointsForValues = []
if printStats: print "length of expectedx: ", len(expectedx)
if printStats: print "length of expected1SigLow: ", len(expected1SigLow)
if printStats: print "length of expected1SigHigh: ", len(expected1SigHigh)
#Here is some Voodoo via Sam:
for x in range(0, len(expectedx)):
values2.append(expected2SigLow[x])
xPointsForValues2.append(expectedx[x])
for x in range(len(expectedx) - 1, 0 - 1, -1):
values2.append(expected2SigHigh[x])
xPointsForValues2.append(expectedx[x])
if printStats: print "length of values2: ", len(values2)
for x in range(0, len(expectedx)):
values.append(expected1SigLow[x])
xPointsForValues.append(expectedx[x])
for x in range(len(expectedx) - 1, 0 - 1, -1):
values.append(expected1SigHigh[x])
xPointsForValues.append(expectedx[x])
if printStats: print "length of values: ", len(values)
exp2Sig = numpy.array(values2)
xPoints2 = numpy.array(xPointsForValues2)
exp1Sig = numpy.array(values)
xPoints = numpy.array(xPointsForValues)
if printStats: print "xPoints2: ", xPoints2
if printStats: print "exp2Sig: ", exp2Sig
if printStats: print "xPoints: ", xPoints
if printStats: print "exp1Sig: ", exp1Sig
GraphErr2Sig = TGraphAsymmErrors(len(xPoints), xPoints2, exp2Sig)
GraphErr2Sig.SetFillColor(ROOT.kYellow + 1)
GraphErr1Sig = TGraphAsymmErrors(len(xPoints), xPoints, exp1Sig)
GraphErr1Sig.SetFillColor(ROOT.kGreen)
cCL = TCanvas("cCL", "cCL", 0, 0, 800, 500)
gStyle.SetOptStat(0)
if not obs2 == "":
plotPad = ROOT.TPad("plotPad", "plotPad", 0, 0.3, 1, 1)
ratioPad = ROOT.TPad("ratioPad", "ratioPad", 0, 0., 1, 0.3)
plotPad.Draw()
ratioPad.Draw()
plotPad.cd()
else:
plotPad = ROOT.TPad("plotPad", "plotPad", 0, 0, 1, 1)
plotPad.Draw()
plotPad.cd()
expX = numpy.array(expectedx)
expY = numpy.array(expectedy)
GraphExp = TGraph(len(expX), expX, expY)
GraphExp.SetLineWidth(3)
GraphExp.SetLineStyle(2)
GraphExp.SetLineColor(ROOT.kBlue)
obsX = numpy.array(observedx)
obsY = numpy.array(observedy)
if printStats: print "obsX: ", obsX
if printStats: print "obsY: ", obsY
if SMOOTH:
smooth_obs = TGraphSmooth("normal")
GraphObs_nonSmooth = TGraph(len(obsX), obsX, obsY)
GraphObs = smooth_obs.SmoothSuper(GraphObs_nonSmooth, "linear", 0,
0.005)
else:
GraphObs = TGraph(len(obsX), obsX, obsY)
GraphObs.SetLineWidth(3)
if not obs2 == "":
ratio = []
ratiox = []
for index, val in enumerate(observedy):
mass = observedx[index]
foundIndex = -1
for index2, mass2 in enumerate(observedx2):
if mass == mass2:
foundIndex = index2
if foundIndex > 0:
ratio.append(observedy2[foundIndex] / val)
ratiox.append(mass)
ratioA = numpy.array(ratio)
ratioX = numpy.array(ratiox)
obsX2 = numpy.array(observedx2)
obsY2 = numpy.array(observedy2)
ratioGraph = TGraph(len(ratioX), ratioX, ratioA)
if printStats: print "obsX2: ", obsX2
if printStats: print "obsY2: ", obsY2
if SMOOTH:
smooth_obs2 = TGraphSmooth("normal")
GraphObs2_nonSmooth = TGraph(len(obsX2), obsX2, obsY2)
GraphObs2 = smooth_obs2.SmoothSuper(GraphObs2_nonSmooth, "linear",
0, 0.005)
else:
GraphObs2 = TGraph(len(obsX2), obsX2, obsY2)
GraphObs2.SetLineWidth(3)
smoother = TGraphSmooth("normal")
smoother2 = TGraphSmooth("normal")
zprimeX = []
zprimeY = []
fileZPrime = open('tools/xsec_SSM.txt', 'r')
for entries in fileZPrime:
entry = entries.split()
zprimeX.append(float(entry[0]))
zprimeY.append(float(entry[1]) * 1.3 / 1928)
zpX = numpy.array(zprimeX)
zpY = numpy.array(zprimeY)
GraphZPrime = TGraph(481, zpX, zpY)
GraphZPrimeSmooth = smoother2.SmoothSuper(GraphZPrime, "linear")
GraphZPrimeSmooth.SetLineWidth(3)
GraphZPrimeSmooth.SetLineColor(ROOT.kGreen + 3)
GraphZPrimeSmooth.SetLineStyle(2)
zprimePsiX = []
zprimePsiY = []
fileZPrimePsi = open('tools/xsec_PSI.txt', 'r')
for entries in fileZPrimePsi:
entry = entries.split()
zprimePsiX.append(float(entry[0]))
zprimePsiY.append(float(entry[1]) * 1.3 / 1928)
zpPsiX = numpy.array(zprimePsiX)
zpPsiY = numpy.array(zprimePsiY)
GraphZPrimePsi = TGraph(481, zpPsiX, zpPsiY)
GraphZPrimePsiSmooth = smoother.SmoothSuper(GraphZPrimePsi, "linear")
GraphZPrimePsiSmooth.SetLineWidth(3)
GraphZPrimePsiSmooth.SetLineColor(ROOT.kBlue)
#Draw the graphs:
plotPad.SetLogy()
if "Moriond" in output:
DummyGraph = TH1F("DummyGraph", "", 100, 120, 4500)
else:
DummyGraph = TH1F("DummyGraph", "", 100, 400, 4500)
DummyGraph.GetXaxis().SetTitle("M [GeV]")
if chan == "mumu":
DummyGraph.GetYaxis().SetTitle(
"#sigma(pp#rightarrowZ'+X#rightarrow#mu#mu+X) / #sigma(pp#rightarrowZ+X#rightarrow#mu#mu+X)"
)
elif chan == "elel":
DummyGraph.GetYaxis().SetTitle(
"#sigma(pp#rightarrowZ'+X#rightarrowee+X) / #sigma(pp#rightarrowZ+X#rightarrowee+X)"
)
elif chan == "elmu":
DummyGraph.GetYaxis().SetTitle(
"#sigma(pp#rightarrowZ'+X#rightarrow#font[12]{ll}+X) / #sigma(pp#rightarrowZ+X#rightarrow#font[12]{ll}+X)"
)
gStyle.SetOptStat(0)
if "Moriond" in output:
DummyGraph.GetXaxis().SetRangeUser(120, 4500)
else:
DummyGraph.GetXaxis().SetRangeUser(400, 4500)
DummyGraph.SetMinimum(1e-8)
DummyGraph.SetMaximum(4e-4)
DummyGraph.GetXaxis().SetLabelSize(0.04)
DummyGraph.GetXaxis().SetTitleSize(0.045)
DummyGraph.GetXaxis().SetTitleOffset(1.)
DummyGraph.GetYaxis().SetLabelSize(0.04)
DummyGraph.GetYaxis().SetTitleSize(0.045)
DummyGraph.GetYaxis().SetTitleOffset(1.)
DummyGraph.Draw()
if (FULL):
GraphErr2Sig.Draw("F")
GraphErr1Sig.Draw("F")
GraphExp.Draw("lpsame")
else:
if obs2 == "":
GraphExp.Draw("lp")
GraphObs.Draw("plsame")
if not obs2 == "":
GraphObs2.SetLineColor(ROOT.kRed)
GraphObs2.SetLineStyle(ROOT.kDashed)
GraphObs2.Draw("plsame")
if not SPIN2:
GraphZPrimeSmooth.Draw("lsame")
GraphZPrimePsiSmooth.Draw("lsame")
cCL.SetTickx(1)
cCL.SetTicky(1)
cCL.RedrawAxis()
cCL.Update()
#leg=TLegend(0.65,0.65,0.87,0.87,"","brNDC")
leg = TLegend(0.540517, 0.623051, 0.834885, 0.878644, "", "brNDC")
# leg=TLegend(0.55,0.55,0.87,0.87,"","brNDC")
leg.SetTextSize(0.032)
if not obs2 == "":
if ratioLabel == "":
ratioLabel = "Variant/Default"
ratioLabels = ratioLabel.split("/")
leg.AddEntry(GraphObs, "% Observed 95% CL limit" % ratioLabel[1], "l")
leg.AddEntry(GraphObs2, "%s Observed 95% CL limit" % ratioLabel[0],
"l")
else:
leg.AddEntry(GraphObs, "Observed 95% CL limit", "l")
leg.AddEntry(GraphExp, "Expected 95% CL limit, median", "l")
if (FULL):
leg.AddEntry(GraphErr1Sig, "Expected 95% CL limit, 1 s.d.", "f")
leg.AddEntry(GraphErr2Sig, "Expected 95% CL limit, 2 s.d.", "f")
leg1 = TLegend(0.665517, 0.483051, 0.834885, 0.623051, "", "brNDC")
leg1.SetTextSize(0.032)
if not SPIN2:
leg1.AddEntry(GraphZPrimePsiSmooth, "Z'_{#Psi} (LOx1.3)", "l")
leg1.AddEntry(GraphZPrimeSmooth, "Z'_{SSM} (LOx1.3)", "l")
leg.SetLineWidth(0)
leg.SetLineStyle(0)
leg.SetLineColor(0)
leg.Draw("hist")
leg1.SetLineWidth(0)
leg1.SetLineStyle(0)
leg1.SetLineColor(0)
leg1.Draw("hist")
if "Moriond" in output:
if (chan == "mumu"):
plLumi = TPaveLabel(.65, .905, .9, .99,
"36.3 fb^{-1} (13 TeV, #mu#mu)", "NBNDC")
elif (chan == "elel"):
plLumi = TPaveLabel(.65, .905, .9, .99, "2.7 fb^{-1} (13 TeV, ee)",
"NBNDC")
elif (chan == "elmu"):
plLumi = TPaveLabel(
.4, .905, .9, .99,
"12.4 fb^{-1} (13 TeV, ee) + 13.0 fb^{-1} (13 TeV, #mu#mu)",
"NBNDC")
else:
if (chan == "mumu"):
plLumi = TPaveLabel(.65, .905, .9, .99,
"13.0 fb^{-1} (13 TeV, #mu#mu)", "NBNDC")
elif (chan == "elel"):
plLumi = TPaveLabel(.65, .905, .9, .99, "2.7 fb^{-1} (13 TeV, ee)",
"NBNDC")
elif (chan == "elmu"):
plLumi = TPaveLabel(
.4, .905, .9, .99,
"12.4 fb^{-1} (13 TeV, ee) + 13.0 fb^{-1} (13 TeV, #mu#mu)",
"NBNDC")
plLumi.SetTextSize(0.5)
plLumi.SetTextFont(42)
plLumi.SetFillColor(0)
plLumi.SetBorderSize(0)
plLumi.Draw()
plCMS = TPaveLabel(.12, .81, .22, .88, "CMS", "NBNDC")
#plCMS.SetTextSize(0.8)
plCMS.SetTextAlign(12)
plCMS.SetTextFont(62)
plCMS.SetFillColor(0)
plCMS.SetBorderSize(0)
plCMS.Draw()
plPrelim = TPaveLabel(.12, .76, .25, .82, "Preliminary", "NBNDC")
plPrelim.SetTextSize(0.6)
plPrelim.SetTextAlign(12)
plPrelim.SetTextFont(52)
plPrelim.SetFillColor(0)
plPrelim.SetBorderSize(0)
plPrelim.Draw()
if not obs2 == "":
ratioPad.cd()
line = ROOT.TLine(400, 1, 4500, 1)
line.SetLineStyle(ROOT.kDashed)
ROOT.gStyle.SetTitleSize(0.12, "Y")
ROOT.gStyle.SetTitleYOffset(0.35)
ROOT.gStyle.SetNdivisions(000, "Y")
ROOT.gStyle.SetNdivisions(408, "Y")
ratioPad.DrawFrame(400, 0.9, 4500, 1.1, "; ; %s" % ratioLabel)
line.Draw("same")
ratioGraph.Draw("sameP")
cCL.Update()
printPlots(cCL, output)
gr_c = gr.Clone()
gr_sc = gr_s.Clone()
gr.SetTitle('ROC(zoomed in)')
#gr.SetMarkerColor(8)
#gr.SetMarkerStyle(21)
gr.SetFillColor(632 - 9)
gr_s.SetTitle('ROC')
gr_s.SetFillColor(632 - 9)
gr_sc.SetLineColor(4)
c1.cd(1)
gr.GetXaxis().SetRangeUser(0, 0.5)
gr.GetYaxis().SetRangeUser(0.00001, 0.01)
gr_c.GetXaxis().SetRangeUser(0, 0.5)
gr_c.GetXaxis().SetRangeUser(0.00001, 0.01)
gr_c.SetLineColor(4)
gr.Draw('SAME 3A')
gr_c.Draw('SAME XLP')
c1.cd(2)
gr_s.Draw('SAME 3A')
gr_sc.Draw('SAME XLP')
slp(12)
c1.Print('roc.png')
c1.Update()
