def plotHists(hists, files, xtitle, ytitle, title, pdfname, ly=False):
c = ROOT.TCanvas()
start = 0
label = Legend(p_ops.legendname)
multi = ROOT.TMultiGraph()
for i in range(
len(hists)
): #This loops over the files. nhists(f1,f2,f3) is the format of hists.
temp = files[i].GetName()
if (p_ops.Normalized): hists[i] = NormalizeGraph(hists[i])
hists[i].SetLineColor(color[i])
hists[i].SetMarkerColor(color[i])
hists[i].SetMarkerStyle(style[i])
hists[i].SetMarkerSize(1)
hists[i].SetLineWidth(2)
hists[i].Rebin()
# print 'i ' , i , ' MINIMUM: ' , hists[i].GetHistogram().GetMinimum() , ' MAXIMUM : ' , hists[i].GetHistogram().GetMaximum(),
label.Add(hists[i], tag[i], "L")
multi.Add(hists[i])
cluslabel = Legend("15 GeV < p_{T_{trigger}} < 20 GeV")
#multi.SetMaximum(15)
#multi.SetMinimum(5)
multi.Draw("ALP")
ROOT.gStyle.SetErrorX(0.0001)
multi.SetMaximum(1.02 * multi.GetHistogram().GetMaximum())
multi.SetTitle(title)
multi.GetXaxis().SetTitle(xtitle)
multi.GetYaxis().SetTitle(ytitle)
#multi.GetXaxis().SetTitle('x_{obs}^{Pb}:= #frac{p_{T}^{#gamma}e^{-#eta^{#gamma}} + p_{T}^{jet}e^{-#eta^{jet}}}{2E_{Pb}}')
multi.GetXaxis().SetTitleSize(0.04)
multi.GetXaxis().SetTitleOffset(1.6)
#multi.GetYaxis().SetTitle('#frac{1}{N_{trig}}#frac{dN}{dx_{obs}^{Pb}}')
#multi.GetXaxis().SetTitle('#Delta #phi (rads)')
multi.GetXaxis().SetTitle(p_ops.xaxislabel)
#multi.GetYaxis().SetTitle('#frac{1}{N_{trig}}#frac{dN}{d#Delta #phi}}')
#multi.GetXaxis().SetRangeUser(0.005, 0.020)
multi.GetXaxis().SetNdivisions(10)
label.Draw(0.6, .96)
cluslabel.Draw(0.55, 0.65)
if ly == True:
ROOT.gPad.SetLogy(1)
latex = TLatex()
latex.SetNDC()
c.SaveAs(pdfname)
c.Clear()
print "END OF PLOTTING FUNCTION "
def plotHists(hists, files, xtitle, ytitle, title, pdfname, ly=False):
c = ROOT.TCanvas("c", "c", 900, 600)
pad1 = ROOT.TPad("pad1", "This is pad1", 0.0, 0.0, 0.9, 1.0, 0)
pad2 = ROOT.TPad("pad2", "This is pad2", 0.85, 0.0, 1.0, 1.0, 0)
pad1.Draw()
pad2.Draw()
pad1.cd()
start = 0
label = Legend("")
multi = ROOT.TMultiGraph()
for i in range(
len(hists)
): #This loops over the files. nhists(f1,f2,f3) is the format of hists.
temp = files[i].GetName()
if (p_ops.Normalized): hists[i] = NormalizeGraph(hists[i])
hists[i].SetLineColorAlpha(color[i], alphas[i])
hists[i].SetMarkerColorAlpha(color[i], alphas[i])
hists[i].SetMarkerStyle(style[i])
hists[i].SetMarkerSize(1)
hists[i].SetLineWidth(2)
# print 'i ' , i , ' MINIMUM: ' , hists[i].GetHistogram().GetMinimum() , ' MAXIMUM : ' , hists[i].GetHistogram().GetMaximum(),
label.Add(hists[i], tag[i], "P")
multi.Add(hists[i])
multi.Draw("AP")
ROOT.gStyle.SetErrorX(0.0001)
multi.SetMaximum(1.02 * multi.GetHistogram().GetMaximum())
multi.SetTitle(title)
multi.GetXaxis().SetTitle(xtitle)
multi.GetYaxis().SetTitle(ytitle)
multi.GetXaxis().SetNdivisions(8)
multi.GetYaxis().SetNdivisions(8)
multi.GetXaxis().CenterTitle()
multi.GetYaxis().CenterTitle()
multi.SetMinimum(0)
#if ly==True:
#multi.SetMinimum(multi.GetHistogram().GetMinimum()+1e-4)
if ly:
multi.SetMinimum(multi.GetHistogram().GetMinimum() + 1e-4)
ROOT.gPad.SetLogy(1)
pad2.cd()
label.Draw(0.0, .7)
latex = TLatex()
latex.SetNDC()
c.SaveAs(pdfname)
c.Clear()
print "END OF PLOTTING FUNCTION "
def plotHists(hists, files, xtitle, ytitle, title, pdfname, ly=False):
c = ROOT.TCanvas()
start = 0
label = Legend("")
multi = ROOT.TMultiGraph()
for i in range(
len(hists)
): #This loops over the files. nhists(f1,f2,f3) is the format of hists.
temp = files[i].GetName()
#print 'printing Temp ' , temp
temp = temp.split('_MinBias')[0]
if temp in tag:
temp = temp + " , version %s " % (i + 1)
hists[i].SetLineColor(color[i])
hists[i].SetMarkerColor(color[i])
hists[i].SetMarkerStyle(style[i])
hists[i].SetMarkerSize(1)
hists[i].SetLineWidth(2)
# print 'i ' , i , ' MINIMUM: ' , hists[i].GetHistogram().GetMinimum() , ' MAXIMUM : ' , hists[i].GetHistogram().GetMaximum(),
label.Add(hists[i], tag[i], "L")
multi.Add(hists[i])
multi.Draw("AP")
ROOT.gStyle.SetErrorX(0.0001)
#multi.SetMaximum(1.02*multi.GetHistogram().GetMaximum())
multi.SetMaximum(0.25)
multi.SetMinimum(0.0)
multi.SetTitle(title)
multi.GetXaxis().SetTitle(xtitle)
multi.GetYaxis().SetTitle(ytitle)
multi.GetXaxis().SetNdivisions(10)
label.Draw(0.2, .95)
if ly == True:
ROOT.gPad.SetLogy(1)
latex = TLatex()
latex.SetNDC()
#latex.DrawLatex(0.2,0.90,"#font[62]{#scale[0.9]{lucidEvtOr}}")
c.SaveAs(pdfname)
c.Clear()
print "END OF PLOTTING FUNCTION "
def plotHists(hists, files, xtitle, ytitle, title, pdfname, ly=False):
c = ROOT.TCanvas()
start = 0
label = Legend("")
multi = ROOT.TMultiGraph()
for i in range(
len(hists)
): #This loops over the files. nhists(f1,f2,f3) is the format of hists.
temp = files[i].GetName()
if (p_ops.Normalized): hists[i] = NormalizeGraph(hists[i])
hists[i].SetLineColor(color[i])
hists[i].SetMarkerColor(color[i])
hists[i].SetMarkerStyle(style[i])
hists[i].SetMarkerSize(1)
hists[i].SetLineWidth(2)
# print 'i ' , i , ' MINIMUM: ' , hists[i].GetHistogram().GetMinimum() , ' MAXIMUM : ' , hists[i].GetHistogram().GetMaximum(),
label.Add(hists[i], tag[i], "L")
multi.Add(hists[i])
#multi.SetMaximum(15)
#multi.SetMinimum(5)
multi.Draw("ALP")
ROOT.gStyle.SetErrorX(0.0001)
multi.SetMaximum(1.02 * multi.GetHistogram().GetMaximum())
multi.SetTitle(title)
multi.GetXaxis().SetTitle(xtitle)
multi.GetYaxis().SetTitle(ytitle)
multi.GetXaxis().SetTitle('Median density #rho [GeV]')
multi.GetYaxis().SetTitle('normalized counts')
multi.GetXaxis().SetNdivisions(10)
label.Draw(0.6, .95)
if ly == True:
ROOT.gPad.SetLogy(1)
latex = TLatex()
latex.SetNDC()
c.SaveAs(pdfname)
c.Clear()
print "END OF PLOTTING FUNCTION "
def Plotting(histos, purity, variable, rebin=1, title='', ymax=None):
hSR, hBR, hBR_scaled, hSub, hMC, hweights = histos
#hSR, hBR ,hMC = GetHistos(variable,rebin)
hBR.SetTitle(title)
#hBR.SetMinimum(0)
hBR.Draw()
hBR.SetMaximum(hBR.GetMaximum() * 1.2)
hBR.GetYaxis().SetNdivisions(22)
hBR.GetXaxis().SetNdivisions(9)
hBR_scaled.Draw('samehist')
#hBR_scaled.Draw('samehist')
hSR.Draw('same')
label = Legend(labeltitle)
label.Add(hSR, "Signal Region", "P")
label.Add(hBR, "Bkg Region", "P")
label.Add(hBR_scaled, "Bkg Region, scaled by purity", "L")
label.Draw(0.45, 0.90)
plotname = variable + '_step1_purity%2.3f' % purity['nominal']
canvas.SaveAs('IntermediateStep_' + plotname +
"_DATANAME_%s.pdf" % dataname)
canvas.Clear()
########################
hMC.SetFillColorAlpha(ROOT.kOrange, 0.70)
#hMC.Draw('hist')
hs = ROOT.THStack()
hs.Add(hSub)
hs.Add(hBR)
#hs.Add(hBR_scaled,'hist')
hs.Draw('nostack')
if (ymax > 0):
hs.SetMaximum(ymax)
else:
hs.SetMaximum(hBR.GetMaximum() * 1.2)
hs.SetTitle(title)
hs.GetYaxis().SetNdivisions(12)
hs.GetXaxis().SetNdivisions(10)
#hSys = ObtainSystematicVariation(purity, rebin, variable)
#hs.Add(hSys)
#hSys.SetFillColorAlpha(ROOT.kGray,0.5)
#hSys.SetFillStyle(3144)
#hSys.Draw("E2same")
#hSub.Draw("same")
#hSys.Draw("E2same")
#hMC.Draw("samehist")
label = Legend(labeltitle)
label.Add(hSub, "#gamma-jet", "P")
#label.Add(hMC, "Pythia LO #gamma-jet","L")
label.Add(hBR, "#pi^{0}-jet ", "P")
label.Draw(.2, .90)
latex = TLatex()
latex.SetNDC()
latex.SetTextColor(1)
latex.DrawLatex(0.65, 0.88, "15 < p_{T}^{trig} < 20 GeV")
latex.DrawLatex(0.65, 0.81, "p_{T}^{jet} > 5 GeV")
if (variable != 'dPhi'): latex.DrawLatex(0.65, 0.74, "#Delta#phi > #pi/2")
# myText(0.6,0.82,ROOT.kBlack, "12 < p_{T}^{trig} < 16 GeV")
#myText(0.6,0.76, kBlack, "p_{T}^{jet} > 5 GeV")
#myText(0.6,0.71,kBlack, "#Delta #phi > #pi/2")
canvas.SaveAs('Final_' + plotname + "_DATANAME_%s.pdf" % dataname)
canvas.Clear()
mc_truth.SetLineColorAlpha(2, 0.8)
mc_reco.SetLineColorAlpha(4, 0.8)
##draw mc truth, mc reco, response matrix
c = ROOT.TCanvas('c', 'c', 1600, 600)
c.Divide(3)
c.cd(1)
responseMatrix.Draw('colz')
responseMatrix.SetTitle('; x^{true}_{J} ; x^{reco}_{J} ')
ROOT.gPad.SetLogz()
c.cd(2)
label = Legend("")
label.Add(mc_reco, 'MC Reco', 'L')
label.Add(mc_truth, 'MC True', 'L')
hs_mc = ROOT.THStack()
hs_mc.Add(mc_reco)
hs_mc.Add(mc_truth)
hs_mc.Draw('nostack')
hs_mc.SetTitle('; x^{reco}_{J}; counts')
label.Draw(0.5, .87)
c.cd(3)
ratio_mc = mc_truth.Clone()
ratio_mc.SetMinimum(0)
ratio_mc.SetLineColor(1)
ratio_mc.Divide(mc_reco)
ratio_mc.Draw()
#fdata = ROOT.TFile("fout_195783.root")
fdata = ROOT.TFile("fout_DATA.root")
DeepPionSpectra = fdata.Get("DeepPionSpectra")
MergedPionLambdaSpectra = fdata.Get("MergedPionLambdaSpectra")
MergedPionLambdaSpectra.Print()
c = ROOT.TCanvas("c", "c")
l = Legend("")
purity_NN.SetLineColorAlpha(2, 0.85)
purity_Lambda.SetLineColorAlpha(4, 0.85)
purity_NN.SetMarkerColorAlpha(2, 0.85)
purity_Lambda.SetMarkerColorAlpha(4, 0.85)
l.Add(purity_NN, "NN>0.85", "L")
l.Add(purity_Lambda, "#lambda_{0}^{2} > 0.27", "L")
purity_NN.SetTitle("; p_{T} cluster [GeV]; purity")
purity_NN.GetXaxis().SetRangeUser(8.0, 25.0)
purity_NN.SetMaximum(1.0)
purity_NN.SetMinimum(0.0)
purity_NN.Draw()
purity_Lambda.Draw("same")
purity_NN.SetLineColorAlpha(2, 0.85)
purity_Lambda.SetLineColorAlpha(4, 0.85)
l.Draw(.2, .35)
c.SaveAs("purity.pdf")
c.Clear()
closure_NN.SetTitle("; p_{T} cluster [GeV]; closure test")
closure_NN.SetLineColorAlpha(2, 0.65)
