def legend4Plot(plot, left = False):
if left:
theLeg = TLegend(0.2, 0.62, 0.55, 0.92, "", "NDC")
else:
theLeg = TLegend(0.60, 0.62, 0.92, 0.92, "", "NDC")
theLeg.SetName('theLegend')
theLeg.SetBorderSize(0)
theLeg.SetLineColor(0)
theLeg.SetFillColor(0)
theLeg.SetFillStyle(0)
theLeg.SetLineWidth(0)
theLeg.SetLineStyle(0)
theLeg.SetTextFont(42)
theLeg.SetTextSize(.045)
entryCnt = 0
for obj in range(0, int(plot.numItems())):
objName = plot.nameOf(obj)
if (not plot.getInvisible(objName)):
theObj = plot.getObject(obj)
objTitle = theObj.GetTitle()
if len(objTitle) < 1:
objTitle = objName
dopts = plot.getDrawOptions(objName).Data()
# print 'obj:',theObj,'title:',objTitle,'opts:',dopts,'type:',type(dopts)
if theObj.IsA().InheritsFrom('TNamed'):
theLeg.AddEntry(theObj, objTitle, dopts)
entryCnt += 1
theLeg.SetY1NDC(0.9 - 0.05*entryCnt - 0.005)
theLeg.SetY1(theLeg.GetY1NDC())
return theLeg
def makePlot(variable):
data_var = np.array(data[variable])
data[
"totalWeight"] = data.evtWeight * data.lep1_frWeight * data.lep2_frWeight * data.lep3_frWeight * data.tau_frWeight
data_weights = np.array(data['totalWeight'])
data_tight_var = np.array(data_tight[variable])
data_tight[
"totalWeight"] = data_tight.evtWeight * data_tight.lep1_frWeight * data_tight.lep2_frWeight * data_tight.lep3_frWeight
data_tight_weights = np.array(data_tight['totalWeight'])
c1 = TCanvas()
c1.SetFillColor(10)
c1.SetBorderSize(2)
c1.SetLeftMargin(0.12)
c1.SetBottomMargin(0.12)
c1.SetRightMargin(0.05)
c1.SetLogy()
histogram_base = TH1F("histogram_base", "", 100, np.nanmin(data_var),
np.nanmax(data_var))
histogram_base.SetTitle("")
histogram_base.SetStats(False)
histogram_base.SetMinimum(0.001)
histogram_base.SetMaximum(10.0)
histogram_base.GetXaxis().SetTitle(variable)
histogram_base.GetYaxis().SetTitle("Events")
histogram_base.Draw("hist")
hist_loose = TH1F("hist_loose", "", 100, np.nanmin(data_var),
np.nanmax(data_var))
hist_tight = TH1F("hist_tight", "", 100, np.nanmin(data_var),
np.nanmax(data_var))
root_numpy.fill_hist(hist_loose, data_var, weights=data_weights)
root_numpy.fill_hist(hist_tight,
data_tight_var,
weights=data_tight_weights)
hist_loose.SetLineColor(2)
hist_tight.SetLineColor(4)
hist_loose.SetFillColor(2)
hist_tight.SetFillColor(4)
hist_loose.SetFillStyle(3004)
hist_tight.SetFillStyle(3005)
leg = TLegend(0.2, 0.65, 0.5, 0.9)
leg.SetBorderSize(0)
leg.SetFillColor(10)
leg.SetLineColor(0)
leg.SetFillStyle(0)
leg.SetTextSize(0.04)
leg.SetTextFont(42)
leg.AddEntry(hist_loose, "loose", "F")
leg.AddEntry(hist_tight, "loose_genTau_matched", "F")
hist_loose.DrawNormalized("histsame")
hist_tight.DrawNormalized("histsame")
leg.Draw()
c1.SaveAs("plots/" + variable + "_" + process + "_loose_vs_genTau.png")
def makeNumPro(method):
c = Canvas('c')
c.cd()
c.SetTicks(1,1)
c.SetLogy()
h_16 = getHist(method,['2016'],'h_num_pro')
h_17 = getHist(method,['2017'],'h_num_pro')
h_18 = getHist(method,['2018'],'h_num_pro')
h_18.SetTitle(''), h_18.GetXaxis().SetTitle('Number Of Reconstructed Protons'), h_18.GetYaxis().SetTitle('Events')
h_18.SetMarkerStyle(20), h_17.SetMarkerStyle(20), h_16.SetMarkerStyle(20)
h_18.SetMarkerColor(ROOT.darkLime), h_17.SetMarkerColor(ROOT.turquois), h_16.SetMarkerColor(ROOT.orangeSoda)
h_18.SetMarkerSize(1.5), h_17.SetMarkerSize(1.5), h_16.SetMarkerSize(1.5)
h_18.SetLineColor(ROOT.darkLime), h_17.SetLineColor(ROOT.turquois), h_16.SetLineColor(ROOT.orangeSoda)
h_18.SetLineWidth(2), h_17.SetLineWidth(2), h_16.SetLineWidth(2)
h_18.SetMinimum(10000.0)
h_18.Draw('p')
h_17.Draw('p same')
h_16.Draw('p same')
c.SetGrid(0,1)
legend = TLegend(0.6,0.7,0.8,0.78)
legend.SetTextSize(0.03)
legend.SetLineColor( 0 )
legend.SetFillColor( 0 )
legend.AddEntry(h_18,"2018",'lp')
legend.AddEntry(h_17,"2017",'lp')
legend.AddEntry(h_16,"2016",'lp')
legend.Draw()
pLabel = prelimLabel('top',True,h_18.GetMaximum())
pLabel.Draw()
c.SaveAs('plots/combined/h_num_pro_%s.png' % (selection))
def produceStackedPlots(processes, selections, variables, colors, intLumi, pdir, delphesVersion, log, stacksig, hfile):
print ''
print 'Preparing distribution plots ...'
myStyle()
gROOT.SetBatch(True)
intLumiab = intLumi/1e+06
yl = "Events"
rt = "RECO: Delphes-{}".format(delphesVersion)
lt = "#sqrt{{s}} = 100 TeV, L = {} ab^{{-1}}".format(intLumiab)
ff = "png"
logstr = ''
if log:
logstr = 'log'
else:
logstr = 'lin'
stackstr = ''
if stacksig:
stackstr = 'stack'
else:
stackstr = 'nostack'
hfile.cd()
nsel = 0
for s in selections:
selstr = 'sel{}'.format(int(nsel))
nsel += 1
for v in variables.keys() :
histos = []
i = 0
filename = '{}_{}_{}_{}'.format(v, selstr, stackstr, logstr)
leg = TLegend(0.70,0.65,0.95,0.88)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetLineColor(0)
cols = []
for p in processes:
hname = '{}_{}_{}'.format(p, selstr, v)
h = hfile.Get(hname)
hh = TH1D.Clone(h)
hh.Scale(intLumi)
histos.append(hh)
cols.append(colors[p])
if i > 0:
leg.AddEntry(hh,p,"f")
else:
leg.AddEntry(hh,p,"l")
i+=1
drawStack(filename, yl, leg, lt, rt, ff, pdir, log, stacksig, histos, cols)
print 'DONE.'
def produceNormalizedPlots(processes, selections, variables, colors, intLumi, pdir, lt, rt, log, hfile):
print ''
print 'Preparing normalized plots ...'
gROOT.SetBatch(True)
intLumiab = intLumi/1e+06
yl = "Normalized Event Rate"
ff = "eps"
logstr = ''
if log:
logstr = 'log'
else:
logstr = 'lin'
stackstr = ''
hfile.cd()
nsel = 0
for s in selections:
selstr = 'sel{}'.format(int(nsel))
nsel += 1
for v, dic in variables.iteritems() :
histos = []
i = 0
filename = '{}_{}_{}'.format(v, selstr, logstr)
leg = TLegend(0.60,0.65,0.90,0.88)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetLineColor(0)
leg.SetShadowColor(10)
leg.SetTextSize(0.035)
leg.SetTextFont(42)
cols = []
for p in processes:
hname = '{}_{}_{}'.format(p, selstr, v)
h = hfile.Get(hname)
hh = TH1D.Clone(h)
# rebin if needed
hh.Rebin(int(hh.GetNbinsX()/dic['bin']))
if hh.Integral(0, hh.GetNbinsX()+1) > 0:
hh.Scale(1./hh.Integral(0, hh.GetNbinsX()+1))
histos.append(hh)
cols.append(colors[p])
leg.AddEntry(hh,p,"l")
i+=1
drawNormalized(filename, yl, leg, lt, rt, ff, pdir, log, histos, cols)
print 'DONE.'
def draw_error_hist(histogram, hist_file, text):
leg_text = text.replace('_', ' ')
leg_text = leg_text.replace('Chi2', '')
leg_text = leg_text.replace('Background', '')
leg_text = leg_text.replace('Mu', ' #mu channel ')
leg_text = leg_text.replace('Ele', ' e channel ')
leg_text = leg_text.replace('BT', 'b-t')
leg_text = leg_text.replace('WT', 'W-t')
leg_text = leg_text.replace('TopT', 't-t')
leg_text = leg_text.replace('Anti', '0 ')
can = ROOT.gROOT.MakeDefCanvas()
root_style()
histogram.SetAxisRange(0.0, 1.79, "Y")
histogram.GetXaxis().SetTitle("m_{reco} [GeV]")
histogram.GetYaxis().SetTitle("SR/CR ratio")
leg = TLegend(0.5, 0.7, 0.8, 0.8)
leg.SetFillColor(0)
leg.SetLineColor(1)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.SetTextSize(.05)
leg.SetFillStyle(0)
leg.AddEntry(histogram, leg_text)
histogram.Draw()
leg.Draw()
can.Print(hist_file)
def plot_data_vs_refold(args, regularisation_settings, tau):
'''
Plot the differences between the unfolded and refolded distributions
TODO Include also with best tau - redo unfolding with best tau then come here
'''
from ROOT import gStyle
variable = regularisation_settings.variable
channel = regularisation_settings.channel
plot_outpath = regularisation_settings.outpath.replace('tables/', 'plots/')+'tauscan/taus/'
make_folder_if_not_exists(plot_outpath)
# tau as string name for output
tau = str(tau).replace('.', 'p')
outfile = plot_outpath+'data_vs_refold_'+channel+'_'+variable+'_tau_'+tau+'.pdf'
if args.run_measured_as_data:
outfile = plot_outpath+'measured_vs_refold_'+channel+'_'+variable+'_tau_'+tau+'.pdf'
if args.run_smeared_measured_as_data:
outfile = plot_outpath+'smeared_vs_refold_'+channel+'_'+variable+'_tau_'+tau+'.pdf'
if args.unfolded_binning:
outfile = outfile.replace('.pdf', '_unf_binning.pdf')
c = TCanvas('c1','c1',1000,800)
gStyle.SetOptStat(0)
p1 = TPad("pad1", "p1",0.0,0.2,1.0,1.0,21)
p1.SetFillColor(0);
p1.Draw()
p2 = TPad("pad2", "p2",0.0,0.0,1.0,0.2,22)
p2.SetFillColor(0);
p2.Draw()
p1.cd()
regularisation_settings.h_data.SetTitle("Data vs Refolded Data;;NEvents")
regularisation_settings.h_data.Draw()
regularisation_settings.h_refolded.SetLineColor(2)
regularisation_settings.h_refolded.Draw("same")
leg1 = TLegend(0.7, 0.8, 0.9, 0.9)
leg1.SetLineColor(0)
leg1.SetFillColor(0)
leg1.AddEntry(regularisation_settings.h_data, "Data")
leg1.AddEntry(regularisation_settings.h_refolded, "Refolded Data")
leg1.Draw()
p2.cd()
h_ratio = regularisation_settings.h_data.Clone()
h_ratio.Divide(regularisation_settings.h_refolded)
h_ratio.SetTitle(";"+variable+";")
h_ratio.SetLineColor(1);
h_ratio.Draw()
c.SaveAs(outfile)
c.Delete()
print "Written plots to {outfile}".format(outfile = outfile)
return
def draw_nice_legend(x=[0.7, 0.92], y=[0.7, 0.92], tit="", columns=1):
global nice_legends
leg = TLegend(x[0], y[0], x[1], y[1], tit)
leg.SetLineColor(0)
leg.SetNColumns(columns)
nice_legends.append(leg)
leg.Draw()
return leg
def produceNormalizedPlots(processes, selections, variables, colors, intLumi,
pdir, delphesVersion, log, hfile):
print ''
print 'Preparing normalized plots ...'
myStyle()
gROOT.SetBatch(True)
intLumiab = intLumi / 1e+06
yl = "Normalized Event Rate"
rt = "RECO: Delphes-{}".format(delphesVersion)
lt = "#sqrt{{s}} = 100 TeV, L = {} ab^{{-1}}".format(intLumiab)
ff = "png"
logstr = ''
if log:
logstr = 'log'
else:
logstr = 'lin'
stackstr = ''
hfile.cd()
nsel = 0
for s in selections:
selstr = 'sel{}'.format(int(nsel))
nsel += 1
for v in variables.keys():
histos = []
i = 0
filename = '{}_{}_{}'.format(v, selstr, logstr)
leg = TLegend(0.60, 0.65, 0.90, 0.88)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetLineColor(0)
cols = []
for p in processes:
hname = '{}_{}_{}'.format(p, selstr, v)
h = hfile.Get(hname)
hh = TH1D.Clone(h)
if hh.Integral(0, hh.GetNbinsX() + 1) > 0:
hh.Scale(1. / hh.Integral(0, hh.GetNbinsX() + 1))
histos.append(hh)
cols.append(colors[p])
leg.AddEntry(hh, p, "l")
i += 1
drawNormalized(filename, yl, leg, lt, rt, ff, pdir, log, histos,
cols)
print 'DONE.'
def drawAppMVAOutputPlots(region, method, isMC):
MCstr = ""
if isMC:
MCstr = "BsMC12"
Region = region[:1].upper() + region[
1:] #capitalize first letter for retrieving file names
HistName = "ApplicationOutput" + MCstr + Region + method
CvsName = "canvasMVA_" + MCstr + method + region
canvas = TCanvas(CvsName, CvsName)
stackBDT = THStack(HistName, HistName)
histos = []
appFiles = []
canvas.Draw()
# note: TMVApp file name structure: "TMVApp"+Region+method+sample+".root"
# "BsMC12TMVApp"+Region+method+sample+".root"
appFile = TFile(MCstr + "TMVApp" + Region + method + ".root")
histo = appFile.Get("MVA_" + method).Clone(HistName)
histo.Scale(1 / histo.GetEntries())
stackBDT.Add(histo)
for sample in range(3):
appFiles.append(
TFile(MCstr + "TMVApp" + Region + method + str(sample) + ".root"))
histos.append(appFiles[sample].Get("MVA_" + method).Clone("HistName" +
str(sample)))
histos[sample].SetLineColor(2 * sample)
histos[sample].Scale(1 / histos[sample].GetEntries())
stackBDT.Add(histos[sample])
stackBDT.Draw("nostack")
if isMC:
applicationBDTLegend = TLegend(0.2, 0.7, 0.5, 0.9, "", "brNDC")
#applicationBDTLegend.SetHeader("Bs MC "+region.split("BsMC")[1])
applicationBDTLegend.SetHeader("Bs MC " + Region)
else:
applicationBDTLegend = TLegend(0.55, 0.7, 0.85, 0.9, "", "brNDC")
applicationBDTLegend.SetHeader(Region)
applicationBDTLegend.AddEntry(histo, "Full sample", "l")
applicationBDTLegend.AddEntry(histos[0],
"Trained on 0, tested on 1, applied on 2",
"l")
applicationBDTLegend.AddEntry(histos[1],
"Trained on 1, tested on 2, applied on 0",
"l")
applicationBDTLegend.AddEntry(histos[2],
"Trained on 2, tested on 0, applied on 1",
"l")
applicationBDTLegend.Draw("same")
applicationBDTLegend.SetFillColor(0)
applicationBDTLegend.SetLineColor(0)
canvas.SaveAs(figuresDir + "Application" + method + "Output_" + MCstr +
region + ".pdf")
def getLegend():
legend = TLegend(0.55010112, 0.7183362, 0.70202143, 0.919833)
legend.SetTextSize(0.032)
legend.SetLineColor(0)
legend.SetShadowColor(0)
legend.SetLineStyle(1)
legend.SetLineWidth(1)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetMargin(0.35)
return legend
def getLegend(x1=0.70010112,y1=0.123362,x2=0.90202143,y2=0.279833): legend = TLegend(x1,y1,x2,y2) legend.SetTextSize(0.032) legend.SetLineColor(0) legend.SetShadowColor(0) legend.SetLineStyle(1) legend.SetLineWidth(1) legend.SetFillColor(0) legend.SetFillStyle(0) legend.SetMargin(0.35) return legend
def legend(title=''):
lg = TLegend(0.65, 0.55, 0.95, 0.90, title)
lg.SetFillColor(0)
lg.SetFillStyle(0)
lg.SetLineColor(0)
lg.SetLineStyle(0)
lg.SetBorderSize(0)
lg.SetShadowColor(0)
lg.SetTextFont(42)
lg.SetTextSize(0.06)
return lg
def myLegend(coordinate=[0.48, 0.55, 0.97, 0.87], ncol=1):
co = coordinate
leg = TLegend(co[0], co[1], co[2], co[3])
leg.SetNColumns(ncol)
leg.SetBorderSize(0)
leg.SetLineColor(1)
leg.SetLineStyle(1)
leg.SetLineWidth(1)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextFont(42)
leg.SetTextSize(0.035)
return leg
def SetLegend(coordinate_=[.15, .7, .47, .87], ncol=1):
c_ = coordinate_
legend = TLegend(c_[0], c_[1], c_[2], c_[3])
legend.SetBorderSize(0)
legend.SetNColumns(ncol)
legend.SetLineColor(1)
legend.SetLineStyle(1)
legend.SetLineWidth(1)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetTextFont(42)
legend.SetTextSize(0.05)
return legend
def main(progname, file_name, number):
myFile1 = TFile.Open("%s" % (file_name), "READ")
hist1 = myFile1.Get("Data_s%s" % (number))
myFile2 = TFile.Open("%s" % (file_name), "READ")
hist2 = myFile2.Get("Model_cat1_s%s" % (number))
c1 = TCanvas("c1", "fit", 1000, 4000)
hist1.Sumw2()
hist1.SetLineColor(ROOT.kOrange + 10)
hist1.SetMarkerStyle(20)
hist1.SetLineWidth(1)
hist1.SetMarkerSize(0.7)
hist1.SetMarkerColorAlpha(ROOT.kOrange + 10, 1)
# hist1.SetFillColorAlpha(ROOT.kOrange + 10, 0.4)
hist1.SetStats(False)
hist2.SetLineColor(ROOT.kViolet + 10)
hist2.SetLineStyle(1)
hist2.Sumw2()
hist2.SetMarkerStyle(20)
hist2.SetMarkerSize(0.7)
hist2.SetLineWidth(1)
hist2.SetMarkerColorAlpha(ROOT.kViolet + 10, 1)
hist2.SetStats(False)
#Create pad1
pad1 = TPad("pad1", "", 0, 0, 1, 1)
pad1.SetLeftMargin(0.1)
pad1.SetRightMargin(0.1)
pad1.SetTopMargin(0.1)
pad1.SetBottomMargin(0.1)
pad1.Draw()
pad1.cd()
hist1.Draw("E1")
hist2.Draw("E1 same")
#Add the legend
legend = TLegend(0.65, 0.75, 0.89, 0.89)
legend.AddEntry(hist1, "LHCb data", "lpe")
legend.AddEntry(hist2, "AmpGen fitter", "lpe")
legend.SetTextFont(133)
legend.SetTextSize(30)
legend.SetLineColor(0)
legend.Draw()
c1.SaveAs("compare%s.png" % (number))
def makeXiComp(era, sector, log):
pf = TFile("output_reconstructionPlotter_2017" + era + ".root")
c = Canvas('c')
c.cd()
c.SetTicks(1, 1)
if log: c.SetLogy()
#c.SetGrid(1,1)
h_near = ROOT.TH1F('h_near', '', 100, 0.0, 0.3)
h_far = ROOT.TH1F('h_far', '', 100, 0.0, 0.3)
h_multi = ROOT.TH1F('h_multi', '', 100, 0.0, 0.3)
# Add hists
if sector == '45':
h_near.Add(pf.Get('singleRPPlots/rp3/h_xi'))
h_far.Add(pf.Get('singleRPPlots/rp23/h_xi'))
h_multi.Add(pf.Get('multiRPPlots/arm0/h_xi'))
elif sector == '56':
h_near.Add(pf.Get('singleRPPlots/rp103/h_xi'))
h_far.Add(pf.Get('singleRPPlots/rp123/h_xi'))
h_multi.Add(pf.Get('multiRPPlots/arm1/h_xi'))
h_far.SetLineColor(210)
h_near.SetLineColor(62)
h_multi.SetLineColor(207)
h_far.GetYaxis().SetTitle('Events')
h_far.GetYaxis().SetTitleOffset(1.5)
h_far.GetXaxis().SetTitle('#xi - sector' + sector)
if log: h_far.SetMaximum(h_far.GetMaximum() * 10)
else: h_far.SetMaximum(h_far.GetMaximum() * 1.2)
h_far.Draw('HIST')
h_near.Draw('HIST same')
h_multi.Draw('HIST same')
legend = TLegend(0.6, 0.7, 0.8, 0.8)
legend.SetTextSize(0.03)
legend.SetLineColor(0)
legend.SetFillColor(0)
legend.AddEntry(h_near, "singleRP near", 'l')
legend.AddEntry(h_far, "singleRP far", 'l')
legend.AddEntry(h_multi, "multiRP", 'l')
legend.Draw()
pLabel, lLabel = prelimLabel('top', log, h_far.GetMaximum()), lumiLabel(
False, ['2017'])
pLabel.Draw(), lLabel.Draw()
c.SaveAs('h_xi_comp_' + sector + '_Run2017' + era + '.pdf')
def plot_GOF(hist, obs, critical, p_value, alpha="0.05"):
# canvas
canv = TCanvas("canv", "canv", 600, 600)
canv.SetBottomMargin(0.10)
canv.SetLeftMargin(0.15)
canv.SetRightMargin(0.12)
# histograms
hist.SetMarkerColor(0)
hist.SetTitle("GOF test - toys {}".format(int(hist.GetEntries())))
hist.GetXaxis().SetTitleFont(43)
hist.GetYaxis().SetTitleFont(43)
hist.GetXaxis().SetTitleSize(20)
hist.GetYaxis().SetTitleSize(20)
hist.GetXaxis().SetTitleOffset(0.8)
hist.GetYaxis().SetTitleOffset(1.5)
hist.GetXaxis().SetTitle("Statistics")
hist.GetYaxis().SetTitle("Probability")
hist.Draw("hist")
up = hist.GetMaximum()
hist.GetYaxis().SetRangeUser(0, 1.2 * up)
# lines
c_line = TLine(critical, 0, critical, 1.2 * up)
c_line.SetLineColor(2)
c_line.SetLineWidth(4)
c_line.SetLineStyle(2)
c_line.Draw("L same")
obs_line = TLine(obs, 0, obs, 1.2 * up)
obs_line.SetLineColor(1)
obs_line.SetLineWidth(4)
obs_line.Draw("L same")
# legend
leg = TLegend(0.6, 0.65, .95, .95)
leg.SetFillColor(0)
leg.SetShadowColor(0)
leg.SetLineColor(0)
leg.SetTextFont(43)
leg.SetTextSize(18)
leg.AddEntry(c_line, "alpha = {}".format(alpha), 'L')
leg.AddEntry(obs_line, "data (p-value={0:.2f})".format(p_value), 'L')
leg.AddEntry(hist, "toys", "L")
leg.Draw("same")
# save canvas
canv.SaveAs("{}.pdf".format(saveout))
canv.SaveAs("{}.png".format(saveout))
canv.SaveAs("{}.root".format(saveout))
return canv
def plot_ROC_curve(ROC_curve, file_name_portion):
gStyle.SetOptStat(0)
gStyle.SetLabelSize(0.03, "xy")
gStyle.SetTitleSize(0.03, "xy")
gStyle.SetTitleOffset(1.2, "xy")
gStyle.SetPadLeftMargin(0.15)
gStyle.SetPadRightMargin(0.1)
gStyle.SetPadTopMargin(0.1)
gStyle.SetPadBottomMargin(0.15)
canvas = TCanvas("c1", "c1", 800, 800)
canvas.SetTickx()
canvas.SetTicky()
ROC_curve.GetYaxis().SetTitle('Background rejection')
ROC_curve.GetXaxis().SetTitle('Signal efficiency')
ROC_curve.SetLineColor(TColor.kAzure)
ROC_curve.SetFillColor(TColor.kAzure - 2)
ROC_curve.SetLineWidth(2)
ROC_curve.SetFillStyle(0)
from array import array
x, y = array('d'), array('d')
x.append(EFF_SIGNAL_DIRECT_CUT)
y.append(1 - EFF_BACKGROUND_DIRECT_CUT)
dot = TGraph(1, x, y)
dot.SetMarkerColor(8)
dot.SetMarkerSize(3)
dot.SetMarkerStyle(21)
legend = TLegend(0.2, 0.2, 0.4, 0.4)
legend.SetTextSize(0.04)
legend.SetTextFont(42)
legend.SetFillColor(10)
legend.SetLineColor(10)
legend.SetBorderSize(0)
legend.AddEntry(ROC_curve, "BDT 500 trees", "l")
legend.AddEntry(dot, "m_{jj} > 500 GeV and |#Delta#eta_{jj}| > 3 cut", "P")
ROC_curve.Draw("HIST")
legend.Draw("same")
dot.Draw("LPsame")
canvas.RedrawAxis()
canvas.Print(file_name_portion + "_ROC" + ".png")
def makeXiComp(sector,log):
sec = 'p' if sector == '45' else 'm'
c = Canvas('c')
c.cd()
c.SetTicks(1,1)
if log: c.SetLogy()
#c.SetGrid(1,1)
h_near = ROOT.TH1F('h_near', '', 100, 0.0, 0.2)
h_far = ROOT.TH1F('h_far', '', 100, 0.0, 0.2)
h_multi = ROOT.TH1F('h_multi', '', 100, 0.0, 0.2)
# Add hists
for year in years:
for pf in protonFiles:
if year == pf[0]: pf_single, pf_multi = pf[1], pf[2]
h_near.Add( pf_single.Get('plots/h_pro_xi_'+sector+'n') )
h_far.Add( pf_single.Get('plots/h_pro_xi_'+sector+'f') )
h_multi.Add( pf_multi.Get('plots/h_pro_xi'+sec) )
h_far.SetLineColor(210)
#h_far.Rebin(2)
h_near.SetLineColor(62)
#h_near.Rebin(2)
h_multi.SetLineColor(207)
#h_multi.Rebin(2)
h_far.GetYaxis().SetTitle('Events')
h_far.GetYaxis().SetTitleOffset(1.5)
h_far.GetXaxis().SetTitle('#xi - sector'+sector)
if log: h_far.SetMaximum( h_far.GetMaximum()*10 )
else: h_far.SetMaximum( h_far.GetMaximum()*1.2 )
h_far.Draw('HIST')
h_near.Draw('HIST same')
h_multi.Draw('HIST same')
legend = TLegend(0.6,0.7,0.8,0.8)
legend.SetTextSize(0.03)
legend.SetLineColor( 0 )
legend.SetFillColor( 0 )
legend.AddEntry(h_near,"singleRP near",'l')
legend.AddEntry(h_far,"singleRP far",'l')
legend.AddEntry(h_multi,"multiRP",'l')
legend.Draw()
pLabel, lLabel = prelimLabel('top',log,h_far.GetMaximum()), lumiLabel(False,years)
pLabel.Draw(), lLabel.Draw()
c.SaveAs('plots/%s/h_xi_comp_%s_%s.pdf' % (s_years,s_years,sector))
def makeProtonSide(log):
c = Canvas('c')
c.cd()
c.SetLeftMargin(0.15)
if log: c.SetLogy()
c.SetTicks(1,1)
h_single = ROOT.TH1F('h_single', '', 4, 0, 4)
h_multi = ROOT.TH1F('h_multi', '', 4, 0, 4)
for year in years:
for pf in protonFiles:
if year == pf[0]: pf_single, pf_multi = pf[1], pf[2]
h_single.Add( pf_single.Get('plots/h_proton_side') )
h_multi.Add( pf_multi.Get('plots/h_proton_side') )
denom = h_single.GetEntries()
h_multi.Scale(1.0/denom)
h_multi.GetYaxis().SetTitle('Fraction Of Events')
h_multi.GetXaxis().SetBinLabel(1,'No protons'), h_multi.GetXaxis().SetBinLabel(2,'sector45 only'), h_multi.GetXaxis().SetBinLabel(3,'sector56 only'), h_multi.GetXaxis().SetBinLabel(4,'Both')
h_multi.Draw('p')
h_single.Scale(1.0/denom)
h_single.Draw('p same')
h_single.SetMarkerColor(62), h_multi.SetMarkerColor(207)
h_single.SetLineColor(62), h_multi.SetLineColor(207)
h_single.SetLineWidth(2), h_multi.SetLineWidth(2)
h_single.SetMarkerStyle(20), h_multi.SetMarkerStyle(20)
h_multi.SetMinimum(0.0), h_multi.SetMaximum( h_multi.GetMaximum()*1.5 )
h_single.SetMarkerSize(0.9), h_multi.SetMarkerSize(0.9)
c.SetGrid(0,1)
legend = TLegend(0.6,0.7,0.8,0.78)
legend.SetTextSize(0.03)
legend.SetLineColor( 0 )
legend.SetFillColor( 0 )
legend.AddEntry(h_single,"singleRP",'lp')
legend.AddEntry(h_multi,"multiRP",'lp')
legend.Draw()
pLabel, lLabel = prelimLabel('top',log,h_single.GetMaximum()), lumiLabel(False,years)
pLabel.SetMargin(0.49)
pLabel.Draw(), lLabel.Draw()
c.SaveAs('plots/%s/h_proton_side_%s_%s.pdf' % (s_years,selection,s_years))
for ilayer, layer in enumerate(layers):
for iplot, plot in enumerate(plots):
print "\nPLOT %i / %i" % (
iplot
+ ilayer * len(plots)
+ 1
, len(layers) * len(plots))
# Do TH2 histos
plotname = 'h_%s_%s' % (plot, layer)
if 'TH2' in plots[plot]['class']:
c2.cd()
legend = TLegend(0.25, 0.72, 0.80, 0.93, "")
legend.SetNColumns(2)
legend.SetFillColor(ROOT.kWhite)
legend.SetLineColor(ROOT.kWhite)
legend.SetShadowColor(ROOT.kWhite)
for irun, run in enumerate(runs):
for ilumisection, lumisection in enumerate(lumisections):
for ibx, bx in enumerate(bxs):
# for ibx, bx in enumerate(bx_list):
for k in keyList:
if plots[plot]['histname'] not in k:
continue
if layer not in k or str(run) not in k:
continue
# if 'LS_%i-%i_%s' % (lumisection[0], lumisection[1], layer) not in k:
if '_%i-%i_%s' % (lumisection[0], lumisection[1], layer) not in k:
continue
if bx not in k:
continue
zll_color = ROOT.TColor.GetColor(0.537, 0.494, 0.580)
wjets_color = ROOT.TColor.GetColor(0.717, 0.815, 0.749)
zvv_color = (kAzure + 3)
syst_color = ROOT.TColor.GetColor(0.960, 0.925, 0.6)
color = [
qcd_color, vh_color, diboson_color, singletop_color, ttbar_color,
zll_color, wjets_color, zvv_color
]
print color
legend = TLegend(0.65, 0.7, 0.92, 0.89)
legend.SetTextSize(0.046)
legend.SetBorderSize(0)
legend.SetLineColor(1)
legend.SetLineStyle(1)
legend.SetLineWidth(1)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetTextFont(42)
legend.SetNColumns(2)
i = 0
for iprocess in processes:
hist_met_ = fin.Get('h_summary_' + iprocess)
hist_met_.Sumw2()
hist_met_.SetDirectory(0)
TH1.AddDirectory(0)
# gStyle.SetPalette(53)
hist_met_.SetFillColor(color[i])
#hist_met_.SetLineColor(color[i])
hnames = datasamples[datasamples.keys()[mode]]["hname"][i].split("_")
string0 = "%s \n" % hnames
fNevt.write(string0)
#print string0
printHistName = "NJet"
##if hnames[1] == printHistName :
#print hnames[1], " ", hnames[2], " ", hnames[3]
hs = THStack()
#l = TLegend(0.30, 0.99 - 0.8 * N_bkgsamples / 20., 0.89, 0.85)
l = TLegend(0.15, 0.71, 0.89, 0.87)
l.SetNColumns(4)
l.SetTextSize(0.05)
l.SetLineColor(0)
l.SetFillColor(0)
h_data = datasamples[datasamples.keys()[mode]]["file"].Get(
datasamples[datasamples.keys()[mode]]["hname"][i])
h_data.Add(datasamples[datasamples.keys()[mode + 1]]["file"].Get(
datasamples[datasamples.keys()[mode + 1]]["hname"][j]))
nbins = h_data.GetNbinsX()
h_data.AddBinContent(nbins, h_data.GetBinContent(nbins + 1)) #overflow
h_sub = h_data.Clone("h_sub")
if QCDestimate:
h_sub.SetName(hnames[0] + "_" + hnames[1] + "_" + hnames[2] + "_" +
hnames[3] + "_qcd")
ntotalbkg = 0
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)
format = "png"
outputDirectory = "plots"
if not os.path.exists(outputDirectory) :
os.system("mkdir "+outputDirectory)
for var in Vars.keys() :
dict_histoS[var].SetLineWidth(3)
dict_histoS[var].SetLineWidth(3)
dict_histoB[var].SetLineColor(ROOT.kRed)
try :
dict_histoS[var].Scale(1./float(dict_histoS[var].Integral()))
dict_histoB[var].Scale(1./float(dict_histoB[var].Integral()))
except ZeroDivisionError :
print "Can not renormalize because of integral = 0."
leg = TLegend(0.50,0.76,0.89,0.89)
leg.AddEntry(dict_histoS[var],"p p #rightarrow H #rightarrow 4l","l")
leg.AddEntry(dict_histoB[var],"p p #rightarrow ZZ / Z #gamma^{*} #rightarrow 4l","l")
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetLineColor(0)
drawDoublehisto(dict_histoS[var],dict_histoB[var],var,yAxisLabel,leg,leftText,rightText,format,outputDirectory,0)
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)
hs.GetYaxis().SetTitleOffset(1.8)
hs.GetYaxis().SetLabelFont(43)
hs.GetYaxis().SetLabelSize(15)
hs.GetYaxis().SetTitle("Events")
gStyle.SetOptStat(0)
pad1.SetLogy()
# legend
legend = TLegend(0.1, 0.75, 0.3, 0.9) #left alignment
#legend = TLegend(0.7,0.75,0.9,0.9) #right alignment
legend.AddEntry(LepBDTDATA_list[i], "Data", "p")
legend.AddEntry(LepBDTMCDY_list[i], "DY MC", "f")
legend.AddEntry(LepBDTMCTTbar_list[i], "t#bar{t} MC", "f")
legend.SetFillColor(kWhite)
legend.SetLineColor(kBlack)
legend.SetTextFont(43)
legend.SetTextSize(20)
legend.Draw()
canvas.Update()
#lower plot pad
canvas.cd()
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2.SetGridy()
pad2.Draw()
pad2.cd() #pad2 becomes the current pad
#define ratio plot
rp = TH1F(LepBDTDATA_list[i].Clone("rp"))
def DrawOverlap(fileVec,
histVec,
titleVec,
legendtext,
pngname,
logstatus=[0, 0],
xRange=[-99999, 99999, 1],
text_="",
x_=0.5,
y_=0.5,
legendloc=[0.53, 0.13, 0.93, 0.39]):
gStyle.SetOptTitle(0)
gStyle.SetOptStat(0)
#gStyle.SetTitleOffset(1.1,"Y");
#gStyle.SetTitleOffset(0.9,"X");
gStyle.SetLineWidth(3)
gStyle.SetFrameLineWidth(3)
i = 0
histList_ = []
histList = []
histList1 = []
maximum = []
## Legend
legpos = legendloc
leg = TLegend(legpos[0], legpos[1], legpos[2], legpos[3])
leg.SetBorderSize(0)
leg.SetNColumns(2)
leg.SetLineColor(1)
leg.SetLineStyle(1)
leg.SetLineWidth(1)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextFont(42)
leg.SetTextSize(0.049)
from PlotTemplates import myCanvas1D
c = myCanvas1D()
c.SetLogy(logstatus[1])
c.SetLogx(logstatus[0])
#c.SetBottomMargin(0.15)
#c.SetLeftMargin(0.15)
#c1_2 = TPad("c1_2","newpad",0.04,0.13,1,0.994)
#c1_2.Draw()
print("you have provided " + str(len(fileVec)) + " files and " +
str(len(histVec)) + " histograms to make a overlapping plot")
print "opening rootfiles"
c.cd()
#c1_2.SetBottomMargin(0.013)
#c1_2.SetLogy(logstatus[1])
#c1_2.SetLogx(logstatus[0])
#c1_2.cd()
ii = 0
inputfile = {}
print str(fileVec[(len(fileVec) - 1)])
for ifile_ in range(len(fileVec)):
print("opening file " + fileVec[ifile_])
inputfile[ifile_] = TFile(fileVec[ifile_])
print "fetching histograms"
for ihisto_ in range(len(histVec)):
print("printing histo " + str(histVec[ihisto_]))
histo = inputfile[ifile_].Get(histVec[ihisto_])
#status_ = type(histo) is TGraphAsymmErrors
histList.append(histo)
# for ratio plot as they should nt be normalize
histList1.append(histo)
#print histList[ii].Integral()
#histList[ii].Rebin(xRange[2])
#histList[ii].Scale(1.0/histList[ii].Integral())
maximum.append(histList[ii].GetMaximum())
maximum.sort()
ii = ii + 1
print histList
for ih in range(len(histList)):
tt = type(histList[ih])
if logstatus[1] is 1:
histList[ih].SetMaximum(100) #1.4 for log
histList[ih].SetMinimum(0.00001) #1.4 for log
if logstatus[1] is 0:
histList[ih].SetMaximum(1.4) #1.4 for log
histList[ih].SetMinimum(0.001) #1.4 for log
# print "graph_status =" ,(tt is TGraphAsymmErrors)
# print "hist status =", (tt is TH1D) or (tt is TH1F)
if ih == 0:
if tt is TGraphAsymmErrors:
histList[ih].Draw("APL")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].Draw("hist")
if ih > 0:
#histList[ih].SetLineWidth(2)
if tt is TGraphAsymmErrors:
histList[ih].Draw("PL same")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].Draw("hist same")
if tt is TGraphAsymmErrors:
histList[ih].SetMaximum(10)
histList[ih].SetMarkerColor(colors[ih])
histList[ih].SetLineColor(colors[ih])
histList[ih].SetMarkerStyle(markerStyle[ih])
histList[ih].SetMarkerSize(1)
leg.AddEntry(histList[ih], legendtext[ih], "PL")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].SetLineStyle(linestyle[ih])
histList[ih].SetLineColor(colors[ih])
histList[ih].SetLineWidth(3)
leg.AddEntry(histList[ih], legendtext[ih], "L")
histList[ih].GetYaxis().SetTitle(titleVec[1])
histList[ih].GetYaxis().SetTitleSize(0.052)
histList[ih].GetYaxis().SetTitleOffset(1.08)
histList[ih].GetYaxis().SetLabelSize(.052)
histList[ih].GetXaxis().SetRangeUser(xRange[0], xRange[1])
histList[ih].GetXaxis().SetLabelSize(0.052)
histList[ih].GetXaxis().SetTitle(titleVec[0])
histList[ih].GetXaxis().SetLabelSize(0.052)
histList[ih].GetXaxis().SetTitleSize(0.052)
histList[ih].GetXaxis().SetTitleOffset(1.14)
#histList[ih].GetXaxis().SetTickLength(0.07)
histList[ih].GetXaxis().SetNdivisions(508)
from PlotTemplates import SetCMSAxis
histList[ih] = SetCMSAxis(histList[ih], 1.0, 1.15)
#histList[ih].GetXaxis().SetMoreLogLabels();
#histList[ih].GetXaxis().SetNoExponent();
#histList[ih].GetTGaxis().SetMaxDigits(3);
i = i + 1
'''
pt = TPaveText(0.0877181,0.9,0.9580537,0.96,"brNDC")
pt.SetBorderSize(0)
pt.SetTextAlign(12)
pt.SetFillStyle(0)
pt.SetTextFont(22)
pt.SetTextSize(0.046)
text = pt.AddText(0.2,0.5,"CMS Internal")
'''
from PlotTemplates import drawenergy1D
pt_ = drawenergy1D(False)
for ipt in pt_:
ipt.Draw()
'''
if len(text_) >0:
ltx = TLatex()
ltx.SetTextFont(42)
ltx.SetTextSize(0.049)
#text_f = "#font[42]{Phase Scan}"
ltx.DrawTextNDC(x_,y_,text_)
'''
from PlotTemplates import ExtraText
text_ex = ExtraText(text_, x_, y_)
text_ex.Draw()
ExtraText("ECAL Endcaps", 0.4, 0.85)
ltx_ = TLatex()
ltx_.SetTextFont(42)
ltx_.SetTextSize(0.049)
ltx_.DrawLatexNDC(0.32, 0.81, "L1 e-#gamma object p_{T} > 10 GeV")
#text_ex.Draw()
#pt = TPaveText(0.0877181,0.9,0.9580537,0.96,"brNDC")
#text = pt.AddText(0.65,0.5,"Phase Scan data #sqrt{s} = 13 TeV (2018)")
#pt.Draw()
# t2a = TPaveText(0.0877181,0.81,0.9580537,0.89,"brNDC")
# t2a.SetBorderSize(0)
# t2a.SetFillStyle(0)
# t2a.SetTextSize(0.040)
# t2a.SetTextAlign(12)
# t2a.SetTextFont(62)
# histolabel1= str(fileVec[(len(fileVec)-1)])
# text1 = t2a.AddText(0.06,0.5,"CMS Internal")
# t2a.Draw()
leg.SetHeader("Matched TP energy")
leg.Draw()
#
# c.cd()
outputdirname = './'
histname = outputdirname + pngname
c.SaveAs(histname + '.png')
c.SaveAs(histname + '.pdf')
class DataMCPlot(object):
'''Handles a Data vs MC plot.
Features a list of histograms (some of them being stacked),
and several Drawing functions.
'''
def __init__(self, name):
self.histosDict = {}
self.histos = []
self.supportHist = None
self.name = name
self.stack = None
self.legendOn = True
self.legend = None
self.legendBorders = 0.17, 0.46, 0.44, 0.89
# self.lastDraw = None
# self.lastDrawArgs = None
self.stack = None
self.nostack = None
self.blindminx = None
self.blindmaxx = None
self.groups = {}
self.axisWasSet = False
def Blind(self, minx, maxx, blindStack):
self.blindminx = minx
self.blindmaxx = maxx
if self.stack and blindStack:
self.stack.Blind(minx, maxx)
if self.nostack:
for hist in self.nostack:
hist.Blind(minx, maxx)
def AddHistogram(self, name, histo, layer=0, legendLine=None):
'''Add a ROOT histogram, with a given name.
Histograms will be drawn by increasing layer.'''
tmp = Histogram(name, histo, layer, legendLine)
self.histos.append(tmp)
self.histosDict[name] = tmp
# tmp.AddEntry( self.legend, legendLine)
def Group(self, groupName, namesToGroup, layer=None, style=None):
'''Group all histos with names in namesToGroup into a single
histo with name groupName. All histogram properties are taken
from the first histogram in namesToGroup.
See UnGroup as well
'''
groupHist = None
realNames = []
actualNamesInGroup = []
for name in namesToGroup:
hist = self.histosDict.get(name, None)
if hist is None:
print 'warning, no histo with name', name
continue
if groupHist is None:
groupHist = hist.Clone(groupName)
self.histos.append(groupHist)
self.histosDict[groupName] = groupHist
else:
groupHist.Add(hist)
actualNamesInGroup.append(name)
realNames.append(hist.realName)
hist.on = False
if groupHist:
self.groups[groupName] = actualNamesInGroup
groupHist.realName = ','.join(realNames)
def UnGroup(self, groupName):
'''Ungroup groupName, recover the histograms in the group'''
group = self.groups.get(groupName, None)
if group is None:
print groupName, 'is not a group in this plot.'
return
for name in group:
self.histosDict[name].on = True
self.histosDict[groupName].on = False
def Replace(self, name, pyhist):
'''Not very elegant... should have a clone function in Histogram...'''
oldh = self.histosDict.get(name, None)
pythist = copy.deepcopy(pyhist)
pyhist.layer = oldh.layer
pyhist.stack = oldh.stack
pyhist.name = oldh.name
pyhist.legendLine = oldh.legendLine
pyhist.SetStyle(oldh.style)
pyhist.weighted.SetFillStyle(oldh.weighted.GetFillStyle())
if oldh is None:
print 'histogram', name, 'does not exist, cannot replace it.'
return
else:
index = self.histos.index(oldh)
self.histosDict[name] = pyhist
self.histos[index] = pyhist
def _SortedHistograms(self, reverse=False):
'''Returns the histogram dictionary, sorted by increasing layer,
excluding histograms which are not "on".
This function is used in all the Draw functions.'''
byLayer = sorted(self.histos, key=attrgetter('layer'))
byLayerOn = [hist for hist in byLayer if (hist.on is True)]
if reverse:
byLayerOn.reverse()
return byLayerOn
def Hist(self, histName):
'''Returns an histogram.
Print the DataMCPlot object to see which histograms are available.'''
return self.histosDict[histName]
def DrawNormalized(self, opt=''):
'''All histograms are drawn as PDFs, even the stacked ones'''
same = ''
for hist in self._SortedHistograms():
hist.obj.DrawNormalized(same + opt)
if same == '':
same = 'same'
self.DrawLegend()
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'DrawNormalized'
# self.lastDrawArgs = [ opt ]
def Draw(self, opt=''):
'''All histograms are drawn.'''
same = ''
self.supportHist = None
for hist in self._SortedHistograms():
if self.supportHist is None:
self.supportHist = hist
hist.Draw(same + opt)
if same == '':
same = 'same'
yaxis = self.supportHist.GetYaxis()
yaxis.SetRangeUser(0.01, ymax(self._SortedHistograms()))
self.DrawLegend()
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'Draw'
# self.lastDrawArgs = [ opt ]
def CreateLegend(self, ratio=False):
if self.legend is None:
self.legend = TLegend(*self.legendBorders)
self.legend.SetFillColor(0)
self.legend.SetFillStyle(0)
self.legend.SetLineColor(0)
else:
self.legend.Clear()
hists = self._SortedHistograms(reverse=True)
if ratio:
hists = hists[:-1] # removing the last histo.
for index, hist in enumerate(hists):
hist.AddEntry(self.legend)
def DrawLegend(self, ratio=False):
'''Draw the legend.'''
if self.legendOn:
self.CreateLegend(ratio)
self.legend.Draw('same')
def DrawRatio(self, opt=''):
'''Draw ratios : h_i / h_0.
h_0 is the histogram with the smallest layer, and h_i, i>0 are the other histograms.
if the DataMCPlot object contains N histograms, N-1 ratio plots will be drawn.
To take another histogram as the denominator, change the layer of this histogram by doing:
dataMCPlot.Hist("histName").layer = -99 '''
same = ''
denom = None
self.ratios = []
for hist in self._SortedHistograms():
if denom == None:
denom = hist
continue
ratio = copy.deepcopy(hist)
ratio.obj.Divide(denom.obj)
ratio.obj.Draw(same)
self.ratios.append(ratio)
if same == '':
same = 'same'
self.DrawLegend(ratio=True)
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'DrawRatio'
# self.lastDrawArgs = [ opt ]
def DrawDataOverMCMinus1(self, ymin=-0.5, ymax=0.5):
stackedHists = []
dataHist = None
for hist in self._SortedHistograms():
if hist.stack is False:
dataHist = hist
continue
stackedHists.append(hist)
self._BuildStack(stackedHists, ytitle='Data/MC')
mcHist = self.stack.totalHist
self.dataOverMCHist = copy.deepcopy(dataHist)
self.dataOverMCHist.Add(mcHist, -1)
self.dataOverMCHist.Divide(mcHist)
self.dataOverMCHist.Draw()
yaxis = self.dataOverMCHist.GetYaxis()
yaxis.SetRangeUser(ymin, ymax)
yaxis.SetTitle('Data/MC - 1')
yaxis.SetNdivisions(5)
fraclines = 0.2
if ymax <= 0.2 or ymin >= -0.2:
fraclines = 0.1
self.DrawRatioLines(self.dataOverMCHist, fraclines, 0.)
if TPad.Pad():
TPad.Pad().Update()
def DrawRatioStack(self,
opt='',
xmin=None,
xmax=None,
ymin=None,
ymax=None):
'''Draw ratios.
The stack is considered as a single histogram.'''
denom = None
# import pdb; pdb.set_trace()
histForRatios = []
denom = None
for hist in self._SortedHistograms():
if hist.stack is False:
# if several unstacked histograms, the highest layer is used
denom = hist
continue
histForRatios.append(hist)
self._BuildStack(histForRatios, ytitle='MC/Data')
self.stack.Divide(denom.obj)
if self.blindminx and self.blindmaxx:
self.stack.Blind(self.blindminx, self.blindmaxx)
self.stack.Draw(opt, xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax)
self.ratios = []
for hist in self.nostack:
if hist is denom: continue
ratio = copy.deepcopy(hist)
ratio.obj.Divide(denom.obj)
ratio.obj.Draw('same')
self.ratios.append(ratio)
self.DrawLegend(ratio=True)
self.DrawRatioLines(denom, 0.2, 1)
if TPad.Pad():
TPad.Pad().Update()
def DrawNormalizedRatioStack(self,
opt='',
xmin=None,
xmax=None,
ymin=None,
ymax=None):
'''Draw ratios.
The stack is considered as a single histogram.
All histograms are normalized before computing the ratio'''
denom = None
histForRatios = []
for hist in self._SortedHistograms():
# taking the first histogram (lowest layer)
# as the denominator histogram.
if denom == None:
denom = copy.deepcopy(hist)
continue
# other histograms will be divided by the denominator
histForRatios.append(hist)
self._BuildStack(histForRatios, ytitle='MC p.d.f. / Data p.d.f.')
self.stack.Normalize()
denom.Normalize()
self.stack.Divide(denom.weighted)
self.stack.Draw(opt, xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax)
self.ratios = []
for hist in self.nostack:
# print 'nostack ', hist
ratio = copy.deepcopy(hist)
ratio.Normalize()
ratio.obj.Divide(denom.weighted)
ratio.obj.Draw('same')
self.ratios.append(ratio)
self.DrawLegend(ratio=True)
self.DrawRatioLines(denom, 0.2, 1)
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'DrawNormalizedRatioStack'
# self.lastDrawArgs = [ opt ]
def DrawRatioLines(self, hist, frac=0.2, y0=1.):
'''Draw a line at y = 1, at 1+frac, and at 1-frac.
hist is used to get the x axis range.'''
xmin = hist.obj.GetXaxis().GetXmin()
xmax = hist.obj.GetXaxis().GetXmax()
line = TLine()
line.DrawLine(xmin, y0, xmax, y0)
line.DrawLine(xmin, y0 + frac, xmax, y0 + frac)
line.DrawLine(xmin, y0 - frac, xmax, y0 - frac)
def DrawStack(self, opt='', xmin=None, xmax=None, ymin=None, ymax=None):
'''Draw all histograms, some of them in a stack.
if Histogram.stack is True, the histogram is put in the stack.'''
self._BuildStack(self._SortedHistograms(), ytitle='Events')
same = 'same'
if len(self.nostack) == 0:
same = ''
self.supportHist = None
for hist in self.nostack:
hist.Draw()
if not self.supportHist:
self.supportHist = hist
self.stack.Draw(opt + same, xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax)
if self.supportHist is None:
self.supportHist = self.stack.totalHist
if not self.axisWasSet:
mxsup = self.supportHist.weighted.GetBinContent(
self.supportHist.weighted.GetMaximumBin())
mxstack = self.stack.totalHist.weighted.GetBinContent(
self.stack.totalHist.weighted.GetMaximumBin())
mx = max(mxsup, mxstack)
if ymin is None: ymin = 0.01
if ymax is None: ymax = mx * 1.3
self.supportHist.GetYaxis().SetRangeUser(ymin, ymax)
self.axisWasSet = True
for hist in self.nostack:
if self.blindminx:
hist.Blind(self.blindminx, self.blindmaxx)
hist.Draw('same')
self.DrawLegend()
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'DrawStack'
# self.lastDrawArgs = [ opt ]
def DrawNormalizedStack(self,
opt='',
xmin=None,
xmax=None,
ymin=0.001,
ymax=None):
'''Draw all histograms, some of them in a stack.
if Histogram.stack is True, the histogram is put in the stack.
all histograms out of the stack, and the stack itself, are shown as PDFs.'''
self._BuildStack(self._SortedHistograms(), ytitle='p.d.f.')
self.stack.DrawNormalized(opt,
xmin=xmin,
xmax=xmax,
ymin=ymin,
ymax=ymax)
for hist in self.nostack:
hist.obj.DrawNormalized('same')
self.DrawLegend()
if TPad.Pad():
TPad.Pad().Update()
# self.lastDraw = 'DrawNormalizedStack'
# self.lastDrawArgs = [ opt ]
def Rebin(self, factor):
'''Rebin, and redraw.'''
# the dispatching technique is not too pretty,
# but keeping a self.lastDraw function initialized to one of the Draw functions
# when calling it creates a problem in deepcopy.
for hist in self.histos:
hist.Rebin(factor)
self.axisWasSet = False
def NormalizeToBinWidth(self):
'''Normalize each Histograms bin to the bin width.'''
for hist in self.histos:
hist.NormalizeToBinWidth()
def _BuildStack(self, hists, ytitle=None):
'''build a stack from a list of Histograms.
The histograms for which Histogram.stack is False are put in self.nostack'''
self.stack = None
self.stack = Stack(self.name + '_stack', ytitle=ytitle)
self.nostack = []
for hist in hists:
if hist.stack:
self.stack.Add(hist)
else:
self.nostack.append(hist)
def __str__(self):
if self.stack is None:
self._BuildStack(self._SortedHistograms(), ytitle='Events')
tmp = [' '.join(['DataMCPlot: ', self.name])]
tmp.append('Histograms:')
for hist in self._SortedHistograms(reverse=True):
tmp.append(' '.join(['\t', str(hist)]))
tmp.append(
'Stack yield = {integ:7.1f}'.format(integ=self.stack.integral))
return '\n'.join(tmp)
