def sobWeightedPlot(self,
fileName,
datasetName,
channel,
cat,
log,
mass,
tanb,
blind,
sob=False):
# print 'yuta', channel, cat
c = TCanvas(fileName, '', 600, 600)
c.cd()
if log: c.SetLogy(1)
f = self.openTFile('Plot_' + fileName + '.root')
isEMSM = fileName.find('SM') != -1 and fileName.find('em') != -1
isETSM = fileName.find('SM') != -1 and fileName.find('et') != -1
samples = ['ggH', 'Ztt', 'signal', 'data_obs', 'ttbar', 'EWK', 'Fakes']
if isEMSM:
samples.append('ggH_hww')
if isETSM:
samples.append('Zee')
dataGraph = self.tfileGet(f, 'Graph_from_data_obs')
histDict = {}
for sample in samples:
histDict[sample] = self.tfileGet(f, sample)
# print 'check :', sample, histDict[sample].GetSumOfWeights()
if not histDict[sample]:
print 'Missing histogram', sample, 'in file', 'Plot_' + fileName + '.root'
# original for plots
xminInset = 60 # 0
xmaxInset = 179 # 340 (for full range)
# all range
# xminInset = 0 # 0
# xmaxInset = 350 # 340 (for full range)
# xminInset = 40 # 0
# xmaxInset = 200 # 340 (for full range)
# xminInset = 120 # 0
# xmaxInset = 251 # 340 (for full range)
if tanb > 0:
xminInset = mass - 100
xmaxInset = mass + 100
if sob:
xminInset = 0.4
xmaxInset = 0.7
ztt = histDict['Ztt']
ggH = histDict['ggH']
data = histDict['data_obs']
# This is to fix a weird plotting bug
if sob:
new_data = TH1F('new_data', '', ggH.GetNbinsX(), 0., 0.7)
for i in range(1, new_data.GetNbinsX() + 1):
new_data.SetBinContent(i, data.GetBinContent(i))
# print data.GetBinContent(i)
data = new_data
ggH_hww = 0
zee = 0
signal = histDict['signal']
if isEMSM:
ggH_hww = histDict['ggH_hww']
if isETSM:
print 'retrieve Zee'
zee = histDict['Zee']
tt = histDict['ttbar']
ewk = histDict['EWK']
fakes = histDict['Fakes']
ztt.GetYaxis().SetRangeUser(0., 1.3 * self.findMaxY(data, 0))
if log:
ztt.GetYaxis().SetRangeUser(0.001, 50. * self.findMaxY(data, 0))
ztt.GetXaxis().SetTitle('#bf{m_{#tau#tau} [GeV]}')
ztt.GetYaxis().SetTitle('#bf{S/B Weighted dN/dm_{#tau#tau} [1/GeV]}')
if tanb > 0. and not log:
ztt.GetXaxis().SetRangeUser(0., mass + 200.)
if sob:
ztt.GetXaxis().SetTitle('S/(S+B)')
ztt.GetYaxis().SetTitle('Events')
ztt.SetTitleOffset(1.3, 'Y')
ztt.SetTitleOffset(1., 'X')
ztt.GetYaxis().SetNdivisions(505)
ztt.SetNdivisions(505)
for b in range(0, signal.GetNbinsX() + 2):
if signal.GetBinCenter(
b) < xminInset or xmaxInset < signal.GetBinCenter(b):
signal.SetBinContent(b, 0.)
signal.SetBinError(b, 0.)
signal.SetName('sig')
signal.SetFillStyle(3353) # 1001=solid , 3004,3005=diagonal
signal.SetFillColor(2)
signal.SetLineColor(2)
signal.SetLineStyle(1)
signal.SetLineWidth(0)
ggH.SetBinContent(0, 0) # remove red line on top of y axis in plot
ggH.SetBinContent(ggH.GetNbinsX() + 1, 0)
ggH.SetBinError(0, 0)
ggH.SetBinError(ggH.GetNbinsX() + 1, 0)
ggH.SetName('ggH')
ggH.SetFillStyle(3353) # 1001=solid , 3004,3005=diagonal
ggH.SetFillColor(2)
ggH.SetLineColor(2)
ggH.SetLineStyle(1)
ggH.SetLineWidth(0)
if isEMSM:
errorBand = TH1F(ggH_hww)
else:
errorBand = TH1F(ztt)
errorBand.SetName("errorBand")
errorBand.SetMarkerSize(0)
errorBand.SetFillColor(1)
errorBand.SetFillStyle(3013)
errorBand.SetLineWidth(1)
legend = TLegend()
mssmLabel = ''
if tanb > 0:
mssmLabel = "tan#beta={tanb}".format(tanb=tanb)
higgsLabel = "H(125 GeV)#rightarrow#tau#tau"
if tanb > 0:
higgsLabel = "H(125 GeV)#rightarrow#tau#tau"
legend.SetFillStyle(0)
legend.SetFillColor(0)
legend.SetBorderSize(0)
legend.AddEntry(ggH, higgsLabel, "F")
if tanb > 0:
legend.AddEntry(TObject(0), mssmLabel, "")
legend.AddEntry(data, "observed", "LP")
if isEMSM:
legend.AddEntry(ggH_hww, "H(125 GeV)#rightarrowWW", "F")
legend.AddEntry(ztt, "Z#rightarrow#tau#tau", "F")
legend.AddEntry(tt, "t#bar{t}", "F")
if isETSM:
legend.AddEntry(zee, "Z#rightarrowee", "F")
legend.AddEntry(ewk, "electroweak", "F")
legend.AddEntry(fakes, "QCD", "F")
legend.SetX1NDC(0.63)
legend.SetX2NDC(1.05)
legend.SetY1NDC(0.25)
legend.SetY2NDC(0.46)
if log:
legend.SetX1NDC(0.18)
legend.SetX2NDC(0.60)
legend.SetY1NDC(0.17)
legend.SetY2NDC(0.38)
legend.SetTextSize(.028)
legend.SetTextAlign(12)
if isEMSM:
dataDiff = self.diffPlot(data, ggH_hww, 1)
dataDiffGraph = self.diffGraph(dataGraph, ggH_hww, 1)
errBand = self.getErrorBand(ggH_hww)
else:
dataDiff = self.diffPlot(data, ztt, 1)
dataDiffGraph = self.diffGraph(dataGraph, ztt, 1)
errBand = self.getErrorBand(ztt)
errBand.SetFillStyle(
3013
) # 1001=solid , 3004,3005=diagonal, 3013=hatched official for H.tau tau
errBand.SetFillColor(1)
errBand.SetLineStyle(1)
errBand.SetLineColor(1)
errBand.SetLineWidth(1)
errBandFrame = TH1F(
'errBandFrame', '',
int((xmaxInset - xminInset) / dataDiff.GetBinWidth(1)), xminInset,
xmaxInset)
errBandFrame.GetYaxis().SetTitle("")
errBandFrame.GetYaxis().SetRangeUser(
-1.1 * self.findMinY(dataDiff, blind, 0, xminInset, xmaxInset),
2.0 * self.findMaxY(dataDiff, blind, 0, xminInset, xmaxInset))
# errBandFrame.GetYaxis().SetRangeUser(-1.*self.findMinY(dataDiff,blind,0,xminInset,xmaxInset),1.1*self.findMaxY(dataDiff,blind,0,xminInset,xmaxInset)) # good !
# errBandFrame.GetYaxis().SetRangeUser(-0.2*self.findMinY(dataDiff,blind,0,xminInset,xmaxInset),0.5*self.findMaxY(dataDiff,blind,0,xminInset,xmaxInset))
print 'Yuta', channel, cat
if (channel == 'e#tau_{h}' and cat == 'vbf') or (channel == 'e#tau_{h}'
and cat == ''):
print 'enter'
errBandFrame.GetYaxis().SetRangeUser(
-2. * self.findMinY(dataDiff, blind, 0, xminInset, xmaxInset),
2.0 * self.findMaxY(dataDiff, blind, 0, xminInset, xmaxInset))
errBandFrame.GetYaxis().SetNdivisions(5)
errBandFrame.GetYaxis().SetLabelSize(0.06)
errBandFrame.GetXaxis().SetTitle("#bf{m_{#tau#tau} [GeV]} ")
errBandFrame.GetXaxis().SetTitleColor(kBlack)
errBandFrame.GetXaxis().SetTitleSize(0.07)
errBandFrame.GetXaxis().SetTitleOffset(0.85)
errBandFrame.GetXaxis().SetLabelSize(0.06)
errBandFrame.GetXaxis().SetNdivisions(506)
# errBandFrame.SetNdivisions(505)
legendDiff = TLegend()
legendDiff.SetFillStyle(0)
legendDiff.SetFillColor(0)
legendDiff.SetBorderSize(0)
legendDiff.AddEntry(signal, higgsLabel, "F")
if tanb > 0:
legendDiff.AddEntry(TObject(0), mssmLabel,
'') # That might not work in python
# legendDiff.AddEntry(dataDiff,"Data - Background","LP")
legendDiff.AddEntry(dataDiffGraph, "Data - Background", "LEP")
legendDiff.AddEntry(errBand, "Bkg. Uncertainty", "F")
legendDiff.SetX1NDC(0.45)
legendDiff.SetX2NDC(0.88)
legendDiff.SetY1NDC(0.67)
legendDiff.SetY2NDC(0.88)
if dataDiff.GetBinContent(dataDiff.FindBin(mass)) < 0.:
legendDiff.SetX1NDC(0.45)
legendDiff.SetX2NDC(0.88)
legendDiff.SetY1NDC(0.24)
legendDiff.SetY2NDC(0.45)
legendDiff.SetTextSize(.045)
legendDiff.SetTextAlign(12)
padBack = TPad(
"padBack", "padBack", 0.57, 0.58, 0.975,
0.956) # TPad must be created after TCanvas otherwise ROOT crashes
padBack.SetFillColor(0)
pad = TPad(
"diff", "diff", 0.45, 0.5, 0.9765,
0.957) # TPad must be created after TCanvas otherwise ROOT crashes
pad.cd()
pad.SetFillColor(0)
pad.SetFillStyle(0)
errBandFrame.Draw()
errBand.Draw("e2lsame")
signal.Draw("histsame")
line = TLine()
line.DrawLine(xminInset, 0, xmaxInset, 0)
# dataDiff.Draw("pe same")
if blind == False:
self.HideBin(dataDiffGraph)
dataDiffGraph.SetMarkerSize(0.5)
dataDiffGraph.Draw('pe same')
for ibin in range(0, dataDiffGraph.GetN()):
x = Double(0.)
y = Double(0.)
dataDiffGraph.GetPoint(ibin, x, y)
print 'Yuta_bin=', ibin, 'x=', x, 'y=', y, dataDiffGraph.GetErrorYhigh(
ibin), dataDiffGraph.GetErrorYlow(ibin)
print 'Yuta_error=', ibin, errBand.GetXaxis().GetBinCenter(
ibin + 1), errBand.GetBinError(ibin + 1)
legendDiff.Draw()
pad.RedrawAxis()
c.cd()
ztt.Draw("hist")
ggH.Draw("hist same")
if isEMSM:
ggH_hww.Draw("hist same")
ztt.Draw("hist same")
errorBand.Draw("e2 same")
tt.Draw("hist same")
if isETSM:
zee.Draw("hist same")
ewk.Draw("hist same")
fakes.Draw("hist same")
# data.Draw("pe same")
dataGraph.SetMarkerSize(1)
dataGraph.Draw('PE same')
if blind == False:
self.HideBin(dataGraph)
print '#####', data.Integral(), data.GetBinContent(1)
legend.Draw()
c.RedrawAxis()
padBack.Draw() # clear the background axe
pad.Draw()
if log: c.SetLogy(1)
self.CMSPrelim(c, datasetName, channel, cat)
savepath = 'figure/Plot_' + fileName
if cat == '':
savepath = savepath + '.pdf'
else:
savepath = savepath + '_' + cat + '.pdf'
# c.Print('figure/Plot_'+fileName+".eps")
# c.Print('figure/Plot_'+fileName+".png")
# c.Print('figure/Plot_'+fileName+".pdf")
c.Print(savepath)
c.Close()
def saveRatio(self, legend, isData, log, lumi, hdata, hMC, r_ymin=0, r_ymax=2, label ="Data/Prediction", outputDir = 'plots/'):
self.myCanvas.cd()
pad1 = TPad("pad1", "pad1", 0, 0.25, 1, 1.0)
pad1.SetBottomMargin(0.12)
pad1.Draw()
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.25)
pad2.SetTopMargin(0.1);
pad2.SetBottomMargin(0.3);
pad2.Draw();
pad1.cd()
if(log):
pad1.SetLogy(1)
for i in range(0, len(self.histos)):
if(self.ToDraw[i] != 0):
#self.histos[i].SetMinimum(0.00001)
self.histos[i].SetMinimum(0.1)
#self.histos[i].SetMinimum(0.0001)
#self.histos[i].SetMaximum(0.5)
self.histos[i].Draw(self.options[i])
if(legend):
self.makeLegend()
self.myLegend.SetTextSize(0.035) # Modify the legend size
self.myLegend.Draw()
for band in self.bands:
band.Draw('f')
for line in self.lines:
line.Draw()
for arrow in self.arrows:
arrow.Draw()
for latex in self.latexs:
lat = TLatex()
lat.SetNDC()
lat.SetTextSize(latex[-1])
lat.SetTextFont(latex[-2])
lat.DrawLatex(latex[0], latex[1], latex[2])
if type(hMC) != list:
hMClist = [hMC]
else: hMClist = hMC
ratios = []
for tmp_hMC in hMClist:
ind = hMClist.index(tmp_hMC)
tmp_ratio = hdata.Clone(tmp_hMC.GetName()+'_ratio')
tmp_ratio.Divide(tmp_hMC)
tmp_ratio.SetTitle("")
tmp_ratio.GetYaxis().SetRangeUser(r_ymin, r_ymax);
tmp_ratio.GetYaxis().SetTitle(label);
tmp_ratio.GetYaxis().CenterTitle();
tmp_ratio.GetYaxis().SetLabelSize(0.12);
tmp_ratio.GetXaxis().SetLabelSize(0.12);
tmp_ratio.GetYaxis().SetTitleOffset(0.3);
tmp_ratio.GetYaxis().SetNdivisions(4);
tmp_ratio.GetYaxis().SetTitleSize(0.12);
tmp_ratio.GetXaxis().SetTitleSize(0.12);
tmp_ratio.GetXaxis().SetLabelOffset(0.08);
tmp_ratio.GetXaxis().SetTitle('');
tmp_ratio.SetMarkerStyle(tmp_hMC.GetMarkerStyle());
tmp_ratio.SetFillColorAlpha(r.kBlue-3,0.9)
tmp_ratio.SetFillStyle(3017)
tmp_ratio.SetMarkerColor(r.kBlack);
tmp_ratio.SetMarkerSize(0.6);
#tmp_ratio.SetMarkerColor(r.kBlack if len(hMClist) == 1 else tmp_hMC.GetMarkerColor());
#tmp_ratio.SetLineColor (r.kBlack if len(hMClist) == 1 else tmp_hMC.GetLineColor ());
tmp_ratio.SetLineColor (tmp_hMC.GetLineColor());
tmp_ratio.SetLineStyle(tmp_hMC.GetLineStyle())
ratios.append(tmp_ratio)
xmin = tmp_ratio.GetBinLowEdge(1)
xmax = tmp_ratio.GetBinLowEdge(tmp_ratio.GetNbinsX()+1)
#tmp_ratio.Draw("E,SAME");
pad2.cd();
for rat in ratios:
rat.Draw('PE2,same');
line = TLine(xmin, 1, xmax, 1)
line.SetLineColor(r.kGray+2);
line.Draw('');
pad1.cd()
self.banner2(isData, lumi)
if not outputDir[-1] == '/': dirName = outputDir + '/'
else: dirName = outputDir
for i,plotName in enumerate(self.plotNames):
pad1.cd()
pad1.SetLogy(0)
path = dirName+plotName
pathlog = dirName+self.plotNamesLog[i]
self.ensurePath(path)
self.myCanvas.SaveAs(path)
if not '.root' in pathlog:
pad1.cd()
pad1.SetLogy()
self.myCanvas.SaveAs(pathlog)
pad1.IsA().Destructor(pad1)
pad2.IsA().Destructor(pad2)
self.myLegend.IsA().Destructor(self.myLegend)
self.myCanvas.IsA().Destructor(self.myCanvas)
def drawLine(x1, y1, x2, y2):
line = TLine(x1, y1, x2, y2)
line.SetLineStyle(2)
line.SetLineWidth(2)
line.Draw()
return line
def drawHline(yval, col):
lines.append(TLine(-0.2, yval, 1, yval))
lines[-1].SetLineColor(col)
lines[-1].SetLineWidth(2)
lines[-1].Draw()
kkTot.paramOn(kkFrame, RooFit.Layout(0.57, 0.99, 0.65))
kkFrame.Draw()
pullpad.cd()
hpull = kkFrame.pullHist("Data", "Pdf")
pullframe = mmtt_mass.frame(Title("Pull Distribution"),
Range(fitphimin, fitphimax))
pullframe.SetAxisRange(-5.0, 5.0, "Y")
#pullframe.GetXaxis().SetTitleSize(0.04)
#pullframe.GetYaxis().SetTitleSize(0.03)
gStyle.SetTitleFontSize(0.07)
pullframe.addPlotable(hpull, "P")
pullframe.Draw()
lineup = TLine(fitphimin, 4.0, fitphimax, 4.0)
lineup.SetLineColor(kRed)
lineup.SetLineStyle(kDashed)
lineup.Draw()
linedw = TLine(fitphimin, -4.0, fitphimax, -4.0)
linedw.SetLineColor(kRed)
linedw.SetLineStyle(kDashed)
linedw.Draw()
kcanvas.SaveAs(region + "/kk_Phi_fit_" + region + ".png")
kcanvas.SaveAs(region + "/kk_Phi_fit_" + region + ".root")
kcanvas.Clear()
signalhist.SetFillColor(kBlue)
signalhist.SetName("#mu#muKK Candidates Mass - " + region)
def plot_fits(canvas,
histos,
x_range,
x_bin_step,
title_formatter,
save_name,
label,
y_fit_range,
y_range=None,
title=None,
xtitle=None,
ytitle=None,
hline=None):
canvas.Clear()
canvas.Divide(2, 3)
root_is_dumb = []
for i in range(1, 7):
canvas.cd(i)
title = title_formatter.format(i)
graph, slices, fits = fit_slices(histos.get(title, default_histo2d),
x_range,
x_bin_step,
y_fit_range=y_fit_range)
graph.SetMarkerStyle(8)
graph.SetMarkerSize(1)
if y_range:
graph.GetHistogram().SetMinimum(y_range[0])
graph.GetHistogram().SetMaximum(y_range[1])
graph.Draw('AP')
root_is_dumb.append(graph)
else:
graph.Draw('AP')
root_is_dumb.append(graph)
if hline:
line = TLine(x_range[0], hline, x_range[1], hline)
line.SetLineStyle(8)
line.SetLineWidth(1)
line.Draw()
root_is_dumb.append(line)
if title:
label.DrawLatex(0.1, 0.925, title)
if xtitle:
label.DrawLatex(0.5, 0.015, xtitle)
if ytitle:
label.SetTextAngle(90)
label.DrawLatex(0.035, 0.5, ytitle)
label.SetTextAngle(0)
canvas.Print(save_name)
# For the slices
slice_can = TCanvas('slice_can', 'slice_can', 1200, 1600)
slice_pdfname = title_formatter.split('_{}')[0] + '_slices.pdf'
slice_can.Print(slice_pdfname + '[')
for i in range(1, 7):
title = title_formatter.format(i)
graph, slices, fits = fit_slices(histos.get(title, default_histo2d),
x_range, x_bin_step, y_fit_range)
# Size of slices page
nrows = 5
ncols = int(np.ceil(len(slices) / nrows) + 1)
slice_can.Clear()
slice_can.Divide(ncols, nrows)
for j, (s, f) in enumerate(zip(slices, fits)):
slice_can.cd(j + 1)
s.Draw()
lab.DrawLatex(
0.15, 0.88, '#mu = {0:6.4f}, #sigma = {1:6.4f}'.format(
f.GetParameter(1), f.GetParameter(2)))
slice_can.Print(slice_pdfname)
slice_can.Print(slice_pdfname + ']')
tnt.SetTextAlign(22)
tnt.SetTextSize(0.030)
tree.AddText('fScanField')
tree.AddText('fMaxEventLoop')
tree.AddText('fMaxVirtualSize')
tree.AddText('fEntries')
tree.AddText('fDimension')
tree.AddText('fSelectedRows')
tree.Draw()
farm = TPavesText(.01, 1.02, .15, 1.1, 9, 'tr')
tfarm = farm.AddText('CHAIN')
tfarm.SetTextSize(0.024)
farm.AddText('Collection')
farm.AddText('of Trees')
farm.Draw()
link = TLine(.15, .92, .80, .92)
link.SetLineWidth(2)
link.SetLineColor(1)
link.Draw()
link.DrawLine(.21, .87, .21, .275)
link.DrawLine(.23, .87, .23, .375)
link.DrawLine(.25, .87, .25, .775)
link.DrawLine(.41, .25, .41, -.025)
link.DrawLine(.43, .25, .43, .075)
link.DrawLine(.45, .25, .45, .175)
branch0 = TPaveLabel(.20, .87, .35, .97, 'Branch 0')
branch0.SetTextSize(0.35)
branch0.SetFillColor(branchcolor)
branch0.Draw()
branch1 = TPaveLabel(.40, .87, .55, .97, 'Branch 1')
branch1.SetTextSize(0.35)
def doMCFit(inputfile_name,
mass_chib,
cuts,
output_name='ChiB',
plotTitle="#Chi_{b}",
fittedVariable='qValue',
returnOnlyFitResult=False,
useOtherSignalParametrization=False,
drawPulls=False,
legendOnPlot=True):
mass_error = 0.15
print "Creating DataSet from file " + str(inputfile_name)
dataSet = makeRooDataset(inputfile_name)
if (fittedVariable == 'refittedMass'):
x_var = 'rf1S_chib_mass'
output_suffix = '_refit'
x_axis_label = 'm_{#mu^{+} #mu^{-} #gamma} [GeV]'
else:
x_var = 'invm1S'
output_suffix = '_qValue'
x_axis_label = 'm_{#gamma #mu^{+} #mu^{-}} - m_{#mu^{+} #mu^{-}} + m^{PDG}_{#Upsilon} [GeV]'
cuts_str = str(cuts)
#cuts_str = quality_cut +"photon_pt > 0.5 && abs(photon_eta) < 1.0 && abs(dimuon_rapidity) < 1.0 && dimuon_pt>5.0 && pi0_abs_mass > 0.025 && fabs(dz) < 0.1 "#&& numPrimaryVertices < 16"
data = dataSet.reduce(RooFit.Cut(cuts_str))
print 'Creating pdf'
x = RooRealVar(x_var, 'm(#mu #mu #gamma) - m(#mu #mu) + m_{#Upsilon}', 9.8,
9.96) #9.7,10.1,'GeV')
numBins = 32 # define here so that if I change it also the ndof change accordingly
x.setBins(numBins)
# Double sided Crystal Ball
mean = RooRealVar("#mu", "mean ChiB", mass_chib, mass_chib - mass_error,
mass_chib + mass_error, "GeV")
sigma = RooRealVar("#sigma", "sigma ChiB", 0.006, 0, 0.2, 'GeV')
a1 = RooRealVar('#alpha1', '#alpha1', 0.75, 0, 3)
a2 = RooRealVar('#alpha2', '#alpha2', 1.6, 0, 3)
n1 = RooRealVar('n1', 'n1', 2.8) #, 1.0,4.0) # 2 per 2S
n2 = RooRealVar('n2', 'n2', 3) #, 1.,4.0) # 1 per 2S
parameters = RooArgSet(mean, sigma, a1, a2, n1, n2)
cb_pdf = My_double_CB('chib', 'chib', x, mean, sigma, a1, n1, a2, n2)
#cb_pdf = RooCBShape('chib', 'chib', x, mean, sigma, a1, n1)
# ndof
floatPars = parameters.selectByAttrib("Constant", ROOT.kFALSE)
ndof = numBins - floatPars.getSize() - 1
if useOtherSignalParametrization: # In this case I redefine cb_pdf
n1 = RooRealVar('n1', 'n1', 2)
cb = RooCBShape('cb1', 'cb1', x, mean, sigma, a1, n1)
# I use a2 as the sigma of my second CB
a2 = RooRealVar('#alpha2', '#alpha2', 0.5, 0, 3)
gauss = RooCBShape('cb2', 'cb2', x, mean, a2, a1, n1)
# I use n2 as the ratio of cb1 with respect to cb2
n2 = RooRealVar('n2', 'n2', 0., 1.)
cb_pdf = RooAddPdf('chib', 'chib', RooArgList(cb, gauss),
RooArgList(n2))
#cb_pdf = cb
print 'Fitting to data'
fit_region = x.setRange("fit_region", 9.8, 9.96)
result = cb_pdf.fitTo(data, RooFit.Save(), RooFit.Range("fit_region"))
# a1_val = a1.getVal()
# a2_val = a2.getVal()
# a1 = RooRealVar('#alpha1', '#alpha1', a1_val)
# a2 = RooRealVar('#alpha2', '#alpha2', a2_val)
# n1 = RooRealVar('n1', 'n1', 1.7,1.0,5.)
# n2 = RooRealVar('n2', 'n2', 1.7,0.,4.)
# cb_pdf = My_double_CB('chib', 'chib', x, mean, sigma, a1, n1, a2, n2)
# result = cb_pdf.fitTo(data, RooFit.Save())
if returnOnlyFitResult:
return result
# define frame
frame = x.frame()
frame.SetNameTitle("fit_resonance", "Fit Resonanace")
frame.GetXaxis().SetTitle(x_axis_label)
frame.GetYaxis().SetTitle("Events/5 MeV ")
frame.GetXaxis().SetTitleSize(0.04)
frame.GetYaxis().SetTitleSize(0.04)
frame.GetXaxis().SetTitleOffset(1.1)
frame.GetXaxis().SetLabelSize(0.04)
frame.GetYaxis().SetLabelSize(0.04)
frame.SetLineWidth(1)
frame.SetTitle(plotTitle)
# plot things on frame
data.plotOn(frame, RooFit.MarkerSize(0.7))
cb_pdf.plotOn(frame, RooFit.LineWidth(2))
# chiSquare legend
chi2 = frame.chiSquare()
probChi2 = TMath.Prob(chi2 * ndof, ndof)
chi2 = round(chi2, 2)
probChi2 = round(probChi2, 2)
leg = TLegend(0.3, 0, .10, .10)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
cb_pdf.paramOn(frame, RooFit.Layout(0.17, 0.56, 0.93))
leg.AddEntry(0, '#chi^{2} =' + str(chi2), '')
leg.AddEntry(0, 'Prob #chi^{2} = ' + str(probChi2), '')
leg.SetTextSize(0.04)
frame.addObject(leg)
if legendOnPlot:
legend = TLegend(.08, .5, .55, .7)
#legend.SetTextSize(0.04)
legend.SetFillStyle(0)
legend.SetBorderSize(0)
#legend.AddEntry(0,'CMS','')
legend.AddEntry(
0,
str(cuts.upsilon_pt_lcut) + ' GeV < p_{T}(#Upsilon) < ' +
str(cuts.upsilon_pt_hcut) + ' GeV', '')
#legend.AddEntry(0,'p_{T}(#Upsilon)<'+str(cuts.upsilon_pt_hcut),'')
frame.addObject(legend)
title = TLegend(.8, .75, .9, .93)
title.SetTextSize(0.15)
title.SetFillStyle(0)
title.SetBorderSize(0)
title.AddEntry(0, plotTitle, '')
frame.addObject(title)
# Canvas
c1 = TCanvas(output_name + output_suffix, output_name + output_suffix)
frame.Draw()
if drawPulls:
#c1=TCanvas(output_name+output_suffix,output_name+output_suffix,700, 625)
hpull = frame.pullHist()
framePulls = x.frame()
framePulls.SetTitle(';;Pulls')
framePulls.GetYaxis().SetLabelSize(0.18)
framePulls.GetYaxis().SetTitle('Pulls')
framePulls.GetYaxis().SetTitleSize(0.18)
framePulls.GetYaxis().SetTitleOffset(0.15)
framePulls.GetYaxis().SetNdivisions(005)
framePulls.GetXaxis().SetLabelSize(0.16)
framePulls.GetXaxis().SetTitle('')
line0 = TLine(9.8, 0, 9.96, 0)
line0.SetLineColor(ROOT.kBlue)
line0.SetLineWidth(2)
framePulls.addObject(line0)
framePulls.addPlotable(hpull, "P")
framePulls.SetMaximum(5)
framePulls.SetMinimum(-5)
pad1 = TPad("pad1", "The pad 80% of the height", 0.0, 0.2, 1.0, 1.0)
pad1.cd()
frame.Draw()
pad2 = TPad("pad2", "The pad 20% of the height", 0.0, 0.01, 1.0, 0.2)
pad2.cd()
framePulls.Draw()
c1.cd()
pad1.Draw()
pad2.Draw()
#c1.SaveAs(output_name+output_suffix+'.png')
print 'Chi2 = ' + str(frame.chiSquare())
pdf_parameters = CB_parameters(mean=mean.getVal(),
sigma=sigma.getVal(),
n1=n1.getVal(),
n2=n2.getVal(),
a1=a1.getVal(),
a2=a2.getVal(),
s_mean=mean.getError(),
s_sigma=sigma.getError(),
s_n1=n1.getError(),
s_n2=n2.getError(),
s_a1=a1.getError(),
s_a2=a2.getError(),
chiSquare=frame.chiSquare())
#pdf_parameters.saveToFile("CB_"+output_name+output_suffix+".txt")
return pdf_parameters, c1
def stackHistograms(t_plots, t_processList, l_histoList, l_legendList, l_textList, l_axisList, saveAs,l_ratioList, saveString, tagText):
'''
PURPOSE:
Function to create stacked plots and save them to the local disk.
This function will:
* Create a stacked plot
* Create a ratio plot
* Allow for Blinding
* Save the canvas as requested
'''
# Release the variables from the lists
h_st, location_suf, name, c_x, c_y, v_rebin, xMin, xMax, doBlinding, b_low, b_high = l_histoList
l_st, l_x1, l_y1, l_x2, l_y2, l_textSize = l_legendList
t_st, plotText, t_textSize, t_alignment, t_start, t_gap, t_x = l_textList
a_st, xLabel, yLabel, xOffset, yOffset, a_labelSize, a_textSize, y_scale = l_axisList
r_st, doRatio, r_yLabel, r_xOffset, r_yOffset, r_labelSize, r_textSize = l_ratioList
len_plots = len(t_plots)
plotText = plotText + tagText
l_overlay = []
l_data = []
sh = THStack('sh', '')
t_legend = []
listMC = TList()
for i in xrange(len_plots):
process, location_pre, v_colour, v_scale, ovack, sigrouta, l_marker, sel = t_processList[i]
histogram = rebin(t_plots[i][0], v_rebin)
histogram.Scale(float(v_scale))
histogram.SetLineWidth(1)
histogram.SetLineColor(int(v_colour))
l_legendLine = [t_plots[i][0], process, l_marker]
t_legend.append(l_legendLine)
if sigrouta == "DATA" and int(doBlinding):
histogram = blindingPlots(histogram, b_low, b_high)
if ovack == "STACK":
if sigrouta == "BACKGROUND":
listMC.Add(histogram)
histogram.SetFillColor(int(v_colour))
sh.Add(histogram)
elif ovack == "OVERLAY" and sigrouta != "DATA":
l_overlay.append(histogram)
elif ovack == "OVERLAY" and sigrouta == "DATA":
histogram.SetMarkerStyle(8)
histogram.SetMarkerColor(1)
l_data.append(histogram)
else:
print "\n%s or %s are not a valid options" % (sigrouta, ovack)
sys.exit(1)
mergeMC = histogram.Clone('mergeMC')
mergeMC.Reset()
mergeMC.Merge(listMC)
gROOT.SetBatch(1)
c = TCanvas('c', 'c', int(c_x), int(c_y))
gStyle.SetOptStat(0)
if int(doRatio):
m_Pad = TPad('m_pad', 'm_pad', 0, 0.30, 1, 1)
r_Pad = TPad('r_pad', 'r_pad', 0, 0, 1, 0.30)
m_Pad.Draw()
r_Pad.Draw()
m_Pad.SetTopMargin(0.05)
m_Pad.SetBottomMargin(0.01)
r_Pad.SetTopMargin(0)
r_Pad.SetBottomMargin(0.3)
m_Pad.cd()
m_Pad.SetTicks(1)
m_Pad.Update()
else:
c.SetTicks(1)
c.Update
# Draw our plots we defined earlier. First the stacked plot, then any overlaid plots and finally data
sh.Draw()
if l_overlay:
for i in xrange(len(l_overlay)):
l_overlay[i].Draw('hist same')
h_MCerr = getStatUncert(mergeMC)
h_MCerr.SetMarkerSize(0)
h_MCerr.SetFillColor(921)
h_MCerr.SetFillStyle(3344)
h_MCerr.Draw('e2s same')
t_legend.append([h_MCerr, 'Stat Uncert.', 'f'])
if l_data:
h_data = mergeData(l_data, doBlinding, b_low, b_high)
h_data.Draw('epsames')
## Next up is to add the styling:
## 1, Axis labels
## 2. Text on the plot
## 3. Legend
if not int(doRatio):
setupAxes(sh, xLabel, yLabel, xOffset, 0.8, a_textSize, xMin, xMax, y_scale)
else:
sh.SetMinimum(0.001)
setupAxes(sh, "", yLabel, -999, yOffset, a_textSize, xMin, xMax, y_scale)
setupLegend(t_legend, l_x1, l_y1, l_x2, l_y2, l_textSize)
setupTextOnPlot(plotText, t_textSize, t_alignment, t_x, t_start, t_gap)
if int(doRatio):
r_Pad.cd()
r_Pad.SetTicks(1)
r_Pad.Update()
l_mc = TList()
l_data = TList()
for i in xrange(len_plots):
l_histogram = rebin(t_plots[i][0], v_rebin)
process, location_pre, v_colour, v_scale, ovack, sigrouta, l_marker, tag = t_processList[i]
if sigrouta == "SIGNAL":
l_mc.Add(l_histogram)
elif sigrouta == "BACKGROUND":
l_mc.Add(l_histogram)
elif sigrouta == "DATA":
l_data.Add(l_histogram)
elif sigrouta == "OVERLAY":
pass
else:
print "\nOption %s not available" % (sigrouta)
mh_MC = l_histogram.Clone('mh_MC')
mh_MC.Merge(l_mc)
h_statMC = getRelError(mh_MC)
h_statMC.SetMarkerSize(0)
h_statMC.SetFillColor(921)
h_statMC.SetFillStyle(3344)
h_statMC.Draw('e2s')
setupAxes(h_statMC, xLabel, r_yLabel, r_xOffset, r_yOffset, r_textSize, xMin, xMax, 1)
h_statMC.GetYaxis().SetRangeUser(-0.38, 0.38)
mh_data = l_histogram.Clone('mh_data')
mh_data.Merge(l_data)
h_dataMC = getRatio(mh_data, mh_MC, doBlinding, b_low, b_high)
h_dataMC.Draw('epsames')
h_line = TLine(float(xMin), 0, float(xMax), 0)
h_line.SetLineWidth(2)
h_line.SetLineColor(1)
h_line.SetLineStyle(2)
h_line.Draw('same')
savePlots(c, saveString, saveAs)
neupt_nomad.Fill(neu_nomad.Pt(),weight_nomad)
neueta_nomad.Fill(neu_nomad.Eta(),weight_nomad)
lphi_nomad.Scale(1./lphi_nomad.Integral())
lpt_nomad.Scale(1./lpt_nomad.Integral())
leta_nomad.Scale(1./leta_nomad.Integral())
neuphi_nomad.Scale(1./neuphi_nomad.Integral())
neupt_nomad.Scale(1./neupt_nomad.Integral())
neueta_nomad.Scale(1./neueta_nomad.Integral())
lphi_nomad.SetMaximum(1.4*lphi_nomad.GetMaximum())
lpt_nomad.SetMaximum(1.4*lpt_nomad.GetMaximum())
leta_nomad.SetMaximum(1.4*leta_nomad.GetMaximum())
neuphi_nomad.SetMaximum(1.4*neuphi_nomad.GetMaximum())
neupt_nomad.SetMaximum(1.4*neupt_nomad.GetMaximum())
neueta_nomad.SetMaximum(1.4*neueta_nomad.GetMaximum())
tline1 = TLine(-3,1,3,1)
tline1.SetLineColor(kRed)
tline2 = TLine(0,1,200,1)
tline2.SetLineColor(kRed)
lphi_ratio = lphi_nomad.Clone()
lphi_ratio.Divide(lphi_mad)
lphi_ratio.SetMaximum(1.5)
lphi_ratio.SetMinimum(0.5)
lphi_ratio.GetYaxis().SetNdivisions(2,kFALSE);
lphi_ratio.GetXaxis().SetTitle('lep phi')
lphi_ratio.GetXaxis().SetTitleSize(0.15);
lphi_ratio.GetXaxis().SetTitleOffset(0.75);
lphi_ratio.GetXaxis().SetLabelSize(0.13);
metTriggerEfficiency_ff_H_WW_enuenu_1000events.Draw("AP")
metTriggerEfficiency_ff_H_WW_enuenu_1000events.GetXaxis().SetTitle("MET [GeV]")
metTriggerEfficiency_ff_H_WW_enuenu_1000events.GetXaxis().SetRangeUser(0, 150)
metTriggerEfficiency_ff_H_WW_enuenu_1000events.GetYaxis().SetTitle("% accepted events")
metTriggerEfficiency_ff_H_WW_enuenu_1000events.GetYaxis().SetTitleOffset(1.2)
metTriggerEfficiency_ff_H_WW_enuenu_1000events.GetYaxis().SetRangeUser(0, 1.1)
text.Draw()
canvas.Update()
canvas.Draw()
canvas.Print("metTriggerEfficiency_ff_H_WW_enuenu_1000events.svg", "svg")
canvas.Print("metTriggerEfficiency_ff_H_WW_enuenu_1000events.png", "png")
canvas.Print("metTriggerEfficiency_ff_H_WW_enuenu_1000events.pdf", "pdf")
label_200kHz = TLatex(220000, 0.9, "110 GeV - 200 kHz")
label_200kHz.SetTextSize(0.03)
line_200kHz = TLine(200000, 0, 200000, 1.1)
line_200kHz.SetLineColor(2)
line_200kHz.SetLineStyle(2)
canvas.SetLogx(1)
canvas.SetLogy(0)
text.SetX1NDC(0.12)
text.SetX2NDC(0.52)
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events = TGraphErrors(14)
for x in xrange(1, 15):
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetPoint(x - 1, jetToMETTriggerRate.GetY()[x], metTriggerEfficiency_ff_H_WW_enuenu_1000events.GetY()[x])
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetPointError(x - 1, jetToMETTriggerRate.GetEY()[x], metTriggerEfficiency_ff_H_WW_enuenu_1000events.GetEY()[x])
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetTitle("ff_H_WW_enuenu_1000events")
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.SetName("efficiencyPlotWithRate_ff_H_WW_enuenu_1000events")
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.GetYaxis().SetTitle("% accepted events")
efficiencyPlotWithRate_ff_H_WW_enuenu_1000events.GetYaxis().SetTitleOffset(1.2)
def plotSysts():
inputfile = TFile(filename, "read")
if inputfile.IsZombie():
print("inputfile is Zombie")
sys.exit()
# loop over samples
file = open(
"%s%s_isNorm%s_wtStat%s.txt" %
(outputdir, plotname, normalization, showStats), "w")
# loop over features
for feature, values in features.items():
# get nominal histograms
hist_nom_name = "TT_mcFakes_" + feature + "_" + region + "Fakes"
if not inputfile.GetListOfKeys().Contains(hist_nom_name):
print("%s doesn't have histogram %s" % (filename, hist_nom_name))
continue
hist_nom = inputfile.Get(hist_nom_name)
hist_nom.SetFillColor(0)
hist_nom.SetLineColor(Color["nominal"])
hist_nom.SetLineWidth(2)
hist_nom.SetMarkerColor(Color["nominal"])
#h_ratio = createRatio(hist_nom, hist_nom, values["xlabel"], Color["nominal"],normalization)
h_ratio = createRatio(hist_nom, hist_nom, values["xlabel"],
normalization)
# loop over variations
for syst in systematics:
if syst == "nominal": continue
file.write(" sample TT mcFakes, feature %s, nominal yield %f\n" %
(feature, hist_nom.Integral()))
# set up legend
legend = TLegend(0.5, 0.7, 0.8, 0.88)
#legend.SetHeader("CMS preliminary %i %s"%(year,region))
#legend.SetNColumns(3)
legend.SetBorderSize(0)
legend.AddEntry(hist_nom, "TT_mcFakes_%s" % region, "l")
c, pad1, pad2 = createCanvasPads()
hist_vars = []
hist_ratio_vars = []
for var in upDown:
hist_name = "TT_ddFakes_" + feature + "_" + syst + "_" + region + var
if not inputfile.GetListOfKeys().Contains(hist_name):
print("%s doesn't have histogram %s" %
(filename, hist_name))
continue
hist_var = inputfile.Get(hist_name)
hist_var.SetFillColor(0)
hist_var.SetLineColor(Color[var])
hist_var.SetLineWidth(2)
hist_var.SetMarkerColor(Color[var])
hist_vars.append(hist_var)
var_yield = hist_var.Integral()
delta = (100. * (var_yield / hist_nom.Integral() - 1.))
file.write(
" sample TT ddFakes, feature {}, {}{} yield {} diff {}% \n"
.format(feature, syst, var, var_yield, delta))
h_ratio_var = createRatio(hist_var, hist_nom, values["xlabel"],
normalization)
hist_ratio_vars.append(h_ratio_var)
legend.AddEntry(h_ratio_var, "FR_%s_%s" % (var, region), "l")
# draw everything
pad1.cd()
#pad1.SetGridx()
#pad1.SetGridy()
# set bounds
maximum = 0
for hist in hist_vars:
if normalization:
hist.Scale(1. / hist.Integral())
if hist.GetMaximum() > maximum: maximum = hist.GetMaximum()
upperbound = 1.8 * maximum
lowerbound = -maximum / 40.
Y_name = "Events"
if normalization:
hist_nom.Scale(1. / hist_nom.Integral())
Y_name = " Unit "
if showStats:
hist_nom.SetStats(1)
else:
hist_nom.SetStats(0)
hist_nom.SetMaximum(upperbound)
hist_nom.SetMinimum(lowerbound)
# Adjust y-axis settings
y = hist_nom.GetYaxis()
y.SetTitleSize(30)
y.SetTitleFont(43)
y.SetTitleOffset(1.3)
y.SetLabelFont(43)
y.SetLabelSize(20)
y.SetTitle(Y_name)
# Adjust x-axis settings
x = hist_nom.GetXaxis()
x.SetTitleSize(25)
x.SetTitleFont(43)
x.SetTitleOffset(1.55)
x.SetLabelFont(43)
x.SetLabelSize(20)
x.SetTitle(values["xlabel"])
hist_nom.SetTitle("#scale[1.0]{%i at %.2f fb^{-1}(13TeV)}" %
(year, luminosity[year]))
hist_nom.GetXaxis().SetRangeUser(values["min"], values["max"])
hist_nom.Draw("EP")
for hist in hist_vars:
hist.Draw("EPsame")
legend.Draw("same")
pad2.cd()
#pad2.SetGridx(2)
#pad2.SetGridy(2)
bins = h_ratio.GetNbinsX()
LowEdge = h_ratio.GetBinLowEdge(1)
HighEdge = h_ratio.GetBinLowEdge(bins + 1)
#LowEdge = h_ratio.GetBinLowEdge(0)
#HighEdge = h_ratio.GetBinLowEdge(bins+2)
line = TLine(LowEdge, 1, HighEdge, 1)
line.SetLineColor(kBlack)
h_ratio.GetXaxis().SetRangeUser(values["min"], values["max"])
h_ratio.SetFillColor(kGray + 3)
h_ratio.SetFillStyle(3001)
h_ratio.Draw("E2")
h_ratio.SetMinimum(0.0)
h_ratio.SetMaximum(2.0)
for i in range(len(hist_ratio_vars)):
hist_ratio_vars[i].Draw("EPsame")
line.Draw("same")
c.SaveAs("%s%s_%s_SF%s_isNorm%s_wtStat%s.png" %
(outputdir, plotname, hist_nom_name, syst, normalization,
showStats))
file.close()
leg.Draw()
hFrame = cFracs.cd(2).DrawFrame(
ptMin,
0.,
ptMax,
0.2,
';#it{p}_{T} (GeV/#it{c});#it{f}_{non-prompt}'
)
hFrame.GetYaxis().SetDecimals()
hFrame.GetYaxis().SetNdivisions(505)
for histo, graph in zip(hFracNonPrompt, gFracNonPrompt):
graph.Draw('2')
histo.DrawCopy('same')
line = TLine(ptMin, 1., ptMax, 1.)
line.SetLineWidth(2)
line.SetLineColor(GetROOTColor('kGrey+2'))
line.SetLineStyle(9)
hFrame = cFracs.cd(3).DrawFrame(
ptMin,
0.9,
ptMax,
1.1,
f';#it{{p}}_{{T}} (GeV/#it{{c}});#it{{f}}_{{prompt}} / #it{{f}}_{{prompt}}^{{{ titles[0]}}}'
)
hFrame.GetYaxis().SetDecimals()
line.Draw()
for histo in hRatioFracPrompt:
if histo:
def pulls(fileName):
with open(fileName) as f:
content = f.readlines()
content_nostat = [x for x in content if not 'CMS_stat' in x]
nbins, off = len(content_nostat), 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_nostat):
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(2)
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(2)
b_pulls.GetYaxis().SetRangeUser(-2.5, 2.5)
c1 = TCanvas("c1", "Pulls", 1600, 800)
c1.cd()
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetBottomMargin(0.15)
c1.GetPad(0).SetTicks(1, 1)
# box = TBox(950., 105., 2000., 200.)
# box.SetFillStyle(3354)
# #box.SetFillStyle(0)
# box.SetFillColor(1)
# box.SetLineWidth(2)
# box.SetLineStyle(2)
# box.SetLineColor(1)
# box.Draw()
# Draw
b_pulls.Draw("PE1")
#b_pulls.Draw("B")
s_pulls.Draw("SAME, PE1")
leg = TLegend(0.25, 0.95, 0.75, 0.995)
leg.SetBorderSize(0)
leg.SetFillStyle(0)
leg.SetFillColor(0)
leg.SetNColumns(2)
leg.AddEntry(b_pulls, "background-only fit", "flp")
leg.AddEntry(s_pulls, "signal+background fit", "lp")
line = TLine()
line.DrawLine(0., 0., nbins, 0.)
line.SetLineStyle(7)
line.SetLineWidth(2)
line.SetLineColor(417)
line.DrawLine(0., 1., nbins, 1.)
line.DrawLine(0., -1., nbins, -1.)
line.SetLineColor(800)
line.DrawLine(0., 2., nbins, 2.)
line.DrawLine(0., -2., nbins, -2.)
leg.Draw()
# drawCMS(LUMI, "Simulation")
# drawAnalysis("DM")
# drawRegion(channel)
# c1.Print(outName+".jpg")
c1.Print(outName + ".png")
c1.Print(outName + ".pdf")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
def addLine(theta, centerx, centery):
line = TLine(centerx, centery, centerx + 200 * np.cos(theta),
centery + 200 * np.sin(theta))
line.SetLineColor(2)
return line
def printSystematic(self):
fakeRate3, fakeRate3Error, ratio3, ratio3Error = self.printFakeRateRatio(
3)
fakeRate4, fakeRate4Error, ratio4, ratio4Error = self.printFakeRateRatio(
4)
fakeRate5, fakeRate5Error, ratio5, ratio5Error = self.printFakeRateRatio(
5)
fakeRate6, fakeRate6Error, ratio6, ratio6Error = self.printFakeRateRatio(
6)
print "systematic uncertainty: " + str(
(abs(ratio5 - 1.0) + ratio5Error) * 100.0) + "%"
basicFakeRate = TH1D("basicFakeRate", ";number of hits", 400, 2.5, 6.5)
zToMuMuFakeRate = TH1D("zToMuMuFakeRate", ";number of hits", 400, 2.5,
6.5)
ratio = TH1D("ratio", ";number of hits", 400, 2.5, 6.5)
basicFakeRate.SetBinContent(basicFakeRate.FindBin(3.0), fakeRate3[0])
basicFakeRate.SetBinError(basicFakeRate.FindBin(3.0),
fakeRate3Error[0])
basicFakeRate.SetBinContent(basicFakeRate.FindBin(4.0), fakeRate4[0])
basicFakeRate.SetBinError(basicFakeRate.FindBin(4.0),
fakeRate4Error[0])
basicFakeRate.SetBinContent(basicFakeRate.FindBin(5.0), fakeRate5[0])
basicFakeRate.SetBinError(basicFakeRate.FindBin(5.0),
fakeRate5Error[0])
basicFakeRate.SetBinContent(basicFakeRate.FindBin(6.0), fakeRate6[0])
basicFakeRate.SetBinError(basicFakeRate.FindBin(6.0),
fakeRate6Error[0])
zToMuMuFakeRate.SetBinContent(zToMuMuFakeRate.FindBin(3.0),
fakeRate3[1])
zToMuMuFakeRate.SetBinError(zToMuMuFakeRate.FindBin(3.0),
fakeRate3Error[1])
zToMuMuFakeRate.SetBinContent(zToMuMuFakeRate.FindBin(4.0),
fakeRate4[1])
zToMuMuFakeRate.SetBinError(zToMuMuFakeRate.FindBin(4.0),
fakeRate4Error[1])
zToMuMuFakeRate.SetBinContent(zToMuMuFakeRate.FindBin(5.0),
fakeRate5[1])
zToMuMuFakeRate.SetBinError(zToMuMuFakeRate.FindBin(5.0),
fakeRate5Error[1])
zToMuMuFakeRate.SetBinContent(zToMuMuFakeRate.FindBin(6.0),
fakeRate6[1])
zToMuMuFakeRate.SetBinError(zToMuMuFakeRate.FindBin(6.0),
fakeRate6Error[1])
ratio.SetBinContent(ratio.FindBin(3.0), ratio3)
ratio.SetBinError(ratio.FindBin(3.0), ratio3Error)
ratio.SetBinContent(ratio.FindBin(4.0), ratio4)
ratio.SetBinError(ratio.FindBin(4.0), ratio4Error)
ratio.SetBinContent(ratio.FindBin(5.0), ratio5)
ratio.SetBinError(ratio.FindBin(5.0), ratio5Error)
ratio.SetBinContent(ratio.FindBin(6.0), ratio6)
ratio.SetBinError(ratio.FindBin(6.0), ratio6Error)
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(0.66416, 0.937339, 0.962406, 0.992894, "brNDC")
lumiLabel.SetBorderSize(0)
lumiLabel.SetFillStyle(0)
lumiLabel.SetTextFont(42)
lumiLabel.SetTextSize(0.0387597)
lumiLabel.AddText(str(self._luminosityLabel))
leg = TLegend(0.7550251, 0.7759067, 0.9459799, 0.8795337)
leg.SetBorderSize(0)
leg.SetTextSize(0.0388601)
leg.SetTextFont(42)
leg.SetLineColor(1)
leg.SetLineStyle(1)
leg.SetLineWidth(1)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.AddEntry(zToMuMuFakeRate, "Z#rightarrow#mu#mu", "p")
leg.AddEntry(basicFakeRate, "basic", "p")
setStyle(basicFakeRate, 632)
setStyle(zToMuMuFakeRate, 600)
setAxisStyle(basicFakeRate, yTitle="P_{fake}")
setAxisStyle(zToMuMuFakeRate, yTitle="P_{fake}")
self._canvas.cd()
self._canvas.SetLogy()
zToMuMuFakeRate.Draw()
basicFakeRate.Draw("same")
leg.Draw("same")
cmsLabel.Draw("same")
lumiLabel.Draw("same")
self._fout.cd()
self._canvas.Write("fakeRate")
line = TLine(2.5, 1.0, 6.5, 1.0)
line.SetLineStyle(2)
line.SetLineWidth(2)
line.SetLineColor(1)
setStyle(ratio)
setAxisStyle(
ratio,
yTitle=("P_{fake}^{Z#rightarrow#mu#mu} / P_{fake}^{basic}"
if not self._plotDiff else
"P_{fake}^{Z#rightarrow#mu#mu} - P_{fake}^{basic}"))
self._canvas.cd()
self._canvas.SetLogy(False)
ratio.Draw()
line.Draw("same")
cmsLabel.Draw("same")
lumiLabel.Draw("same")
self._fout.cd()
self._canvas.Write("fakeRateRatio")
pxlVsThr.append([item[ipix] for item in hitArray])
# Generate graphs for each pixel
graphs = []
for ipixScan in pxlVsThr:
graphs.append(
TGraph(len(thrArray), array('d', thrArray), array('d', ipixScan)))
# Draw graphs on canvas
c1 = TCanvas("graph", "graph", 1024, 768)
c1.cd()
for i, graph in enumerate(graphs):
if i == 0:
graph.SetTitle('Threshold Scan;Threshold (DAC);Number of Hits')
graph.GetYaxis().SetRangeUser(0, max(eventArray) * 1.1)
graph.Draw("APL")
else:
graph.Draw("PL")
# Add line for nMax
line = TLine(0, max(eventArray), graphs[0].GetXaxis().GetXmax(),
max(eventArray))
line.SetLineColor(2)
line.Draw()
print "Max number of events during scan: %s" % max(eventArray)
raw_input("Press any key to exit")
def saveSigmaDev(self,
legend,
isData,
log,
lumi,
hdata,
hMC,
r_ymin=-8,
r_ymax=8,
label="(Data - MC)/#sigma(MC)",
outputDir='plots/',
xlog=False):
self.myCanvas.cd()
pad1 = TPad("pad1", "pad1", 0, 0.25, 1, 1.0)
pad1.SetBottomMargin(0.12)
pad1.Draw()
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.25)
pad2.SetTopMargin(0.1)
pad2.SetBottomMargin(0.3)
pad2.Draw()
pad1.cd()
if (log):
pad1.SetLogy(1)
if (xlog):
pad1.SetLogx(1)
pad2.SetLogx(1)
for i in range(0, len(self.histos)):
if (self.ToDraw[i] != 0):
if str(type(self.histos[i])) == "<class 'ROOT.TEfficiency'>":
self.makeRate(self.histos[i], self.options[i])
else:
self.histos[i].Draw(self.options[i])
if (legend):
self.makeLegend()
self.myLegend.SetTextSize(0.035) # Modify the legend size
self.myLegend.Draw()
for band in self.bands:
band.Draw('f')
for line in self.lines:
line.Draw()
for arrow in self.arrows:
arrow.Draw()
for latex in self.latexs:
lat = TLatex()
lat.SetNDC()
lat.SetTextAlign(latex[-1])
lat.SetTextSize(latex[-2])
lat.SetTextFont(latex[-3])
lat.DrawLatex(latex[0], latex[1], latex[2])
if type(hMC) != list:
hMClist = [hMC]
else:
hMClist = hMC
devs = []
for tmp_hMC in hMClist:
ind = hMClist.index(tmp_hMC)
tmp_dev = hdata.Clone(tmp_hMC.GetName() + '_ratio')
for n in range(1, tmp_dev.GetNbinsX() + 1):
data_value = hdata.GetBinContent(n)
MC_value = tmp_hMC.GetBinContent(n)
MC_err = tmp_hMC.GetBinError(n)
tmp_dev.SetBinContent(n, (data_value - MC_value) / MC_err)
tmp_dev.SetTitle("")
tmp_dev.GetYaxis().SetRangeUser(r_ymin, r_ymax)
tmp_dev.GetYaxis().SetTitle(label)
tmp_dev.GetYaxis().CenterTitle()
tmp_dev.GetYaxis().SetLabelSize(0.12)
tmp_dev.GetXaxis().SetLabelSize(0.12)
tmp_dev.GetYaxis().SetTitleOffset(0.3)
tmp_dev.GetYaxis().SetNdivisions(4)
tmp_dev.GetYaxis().SetTitleSize(0.12)
tmp_dev.GetXaxis().SetTitleSize(0.12)
tmp_dev.GetXaxis().SetLabelOffset(0.08)
tmp_dev.GetXaxis().SetTitle('')
tmp_dev.SetFillColor(r.kRed + 1)
tmp_dev.SetLineColor(r.kBlack)
devs.append(tmp_dev)
xmin = tmp_dev.GetBinLowEdge(1)
xmax = tmp_dev.GetBinLowEdge(tmp_dev.GetNbinsX() + 1)
#tmp_ratio.Draw("E,SAME");
pad2.cd()
for dev in devs:
dev.Draw('hist,same')
line = TLine(xmin, 0, xmax, 0)
line.SetLineColor(r.kGray + 2)
line.Draw('')
pad1.cd()
self.banner2(isData, lumi)
if not outputDir[-1] == '/': dirName = outputDir + '/'
else: dirName = outputDir
for i, plotName in enumerate(self.plotNames):
pad1.cd()
pad1.SetLogy(0)
path = dirName + plotName
pathlog = dirName + self.plotNamesLog[i]
self.ensurePath(path)
self.myCanvas.SaveAs(path)
"""
if not '.root' in pathlog:
pad1.cd()
pad1.SetLogy()
self.myCanvas.SaveAs(pathlog)
"""
pad1.IsA().Destructor(pad1)
pad2.IsA().Destructor(pad2)
self.myLegend.IsA().Destructor(self.myLegend)
self.myCanvas.IsA().Destructor(self.myCanvas)
def plot_sector_page(canvas,
histos,
title_formatter,
label,
save_name,
xtitle=None,
ytitle=None,
title=None,
log=False,
y_fit_range=None,
landscape=False,
x_range=None,
vline=None,
hline=None):
root_garbage_can = []
canvas.Clear()
if landscape:
canvas.Divide(3, 2)
else:
canvas.Divide(2, 3)
for i in range(1, 7):
canvas.cd(i)
#if isinstance(histos[title_formatter.format(i)], TH1F):
if isinstance(histos.get(title_formatter.format(i), default_histo),
TH1F):
if y_fit_range:
fit = TF1(title_formatter.format(i) + '_fit', 'gaus')
histos.get(title_formatter.format(i),
default_histo).Fit(fit, '', 'R', y_fit_range[0],
y_fit_range[1])
if x_range:
histos.get(title_formatter.format(i),
default_histo).GetXaxis().SetRangeUser(
x_range[0], x_range[1])
histos.get(title_formatter.format(i),
default_histo).SetFillColorAlpha(55, 0.65)
histos.get(title_formatter.format(i), default_histo).Draw()
if y_fit_range:
label.DrawLatex(
0.15, 0.86, '#mu = {0:6.4f}, #sigma = {1:6.4f}'.format(
fit.GetParameter(1), fit.GetParameter(2)))
#elif isinstance(histos[title_formatter.format(i)], TH2F):
elif isinstance(histos.get(title_formatter.format(i), default_histo),
TH2F):
# histos[title_formatter.format(i)].Draw('colz')
histos.get(title_formatter.format(i), default_histo).Draw('colz')
if log:
gPad.SetLogz()
else:
#raise NotImplementedException('plot_sector_page only supports TH1F, TH2F')
pass
if vline:
line = TLine(
vline,
histos.get(title_formatter.format(i),
default_histo).GetYaxis().GetXmin(), vline,
histos.get(title_formatter.format(i),
default_histo).GetYaxis().GetXmax())
line.SetLineColor(1)
line.SetLineStyle(1)
line.Draw('same')
root_garbage_can.append(line)
if hline:
xmin = histos.get(title_formatter.format(i)).GetXaxis().GetXmin()
xmax = histos.get(title_formatter.format(i)).GetXaxis().GetXmax()
line = TLine(xmin, hline, xmax, hline)
line.SetLineColor(1)
line.SetLineStyle(1)
line.Draw('same')
root_garbage_can.append(line)
if title:
label.DrawLatex(0.1, 0.925, title)
if xtitle:
label.DrawLatex(0.5, 0.015, xtitle)
if ytitle:
label.SetTextAngle(90)
label.DrawLatex(0.04, 0.5, ytitle)
label.SetTextAngle(0)
canvas.Print(save_name)
sig1box7 = TPolyLine(4, sigboxX7, sig1boxY7)
sig1box8 = TPolyLine(4, sigboxX8, sig1boxY8)
sig1box = [
sig1box1, sig1box2, sig1box3, sig1box4, sig1box5, sig1box6, sig1box7,
sig1box8
]
for box1 in sig1box:
box1.SetLineColor(3)
box1.SetFillColor(3)
box1.Draw('f same')
#################################
observed = [0.0777, 0.1223, 0.0791, 0.0597, 0.1496, 0.1103, 0.0890, 0.0726]
expected = [0.1362, 0.0977, 0.1187, 0.1001, 0.1460, 0.1118, 0.1396, 0.1177]
obs_hct_keras = TLine(0, xsec * observed[0], 1, xsec * observed[0])
obs_hct_bdt = TLine(1, xsec * observed[1], 2, xsec * observed[1])
obs_hct_keras_mat = TLine(2, xsec * observed[2], 3, xsec * observed[2])
obs_hct_bdt_mat = TLine(3, xsec * observed[3], 4, xsec * observed[3])
obs_hut_keras = TLine(4, xsec * observed[4], 5, xsec * observed[4])
obs_hut_bdt = TLine(5, xsec * observed[5], 6, xsec * observed[5])
obs_hut_keras_mat = TLine(6, xsec * observed[6], 7, xsec * observed[6])
obs_hut_bdt_mat = TLine(7, xsec * observed[7], 8, xsec * observed[7])
obs = [
obs_hct_keras, obs_hct_bdt, obs_hct_keras_mat, obs_hct_bdt_mat,
obs_hut_keras, obs_hut_bdt, obs_hut_keras_mat, obs_hut_bdt_mat
]
for lines in obs:
lines.SetLineColor(2)
lines.SetLineWidth(2)
lines.Draw('same')
def main(): # pylint: disable=too-many-locals, too-many-statements, too-many-branches
"""
Main plotting function
"""
gROOT.SetBatch(True)
# pylint: disable=unused-variable
parser = argparse.ArgumentParser()
parser.add_argument("--database-analysis",
"-d",
dest="database_analysis",
help="analysis database to be used",
required=True)
parser.add_argument("--analysis",
"-a",
dest="type_ana",
help="choose type of analysis",
required=True)
parser.add_argument("--input",
"-i",
dest="input_file",
help="results input file",
required=True)
args = parser.parse_args()
typean = args.type_ana
shape = typean[len("jet_"):]
print("Shape:", shape)
file_in = args.input_file
with open(args.database_analysis, "r") as file_db:
data_param = yaml.safe_load(file_db)
case = list(data_param.keys())[0]
datap = data_param[case]
logger = get_logger()
i_cut = file_in.rfind("/")
rootpath = file_in[:i_cut]
# plotting
# LaTeX string
p_latexnhadron = datap["analysis"][typean]["latexnamehadron"]
p_latexbin2var = datap["analysis"][typean]["latexbin2var"]
v_varshape_latex = datap["analysis"][typean]["var_shape_latex"]
# first variable (hadron pt)
lpt_finbinmin = datap["analysis"][typean]["sel_an_binmin"]
lpt_finbinmax = datap["analysis"][typean]["sel_an_binmax"]
var1ranges = lpt_finbinmin.copy()
var1ranges.append(lpt_finbinmax[-1])
# second variable (jet pt)
v_var2_binning = datap["analysis"][typean]["var_binning2"] # name
lvar2_binmin_reco = datap["analysis"][typean].get("sel_binmin2_reco", None)
lvar2_binmax_reco = datap["analysis"][typean].get("sel_binmax2_reco", None)
p_nbin2_reco = len(lvar2_binmin_reco) # number of reco bins
lvar2_binmin_gen = datap["analysis"][typean].get("sel_binmin2_gen", None)
lvar2_binmax_gen = datap["analysis"][typean].get("sel_binmax2_gen", None)
p_nbin2_gen = len(lvar2_binmin_gen) # number of gen bins
var2ranges_reco = lvar2_binmin_reco.copy()
var2ranges_reco.append(lvar2_binmax_reco[-1])
var2binarray_reco = array(
"d",
var2ranges_reco) # array of bin edges to use in histogram constructors
var2ranges_gen = lvar2_binmin_gen.copy()
var2ranges_gen.append(lvar2_binmax_gen[-1])
var2binarray_gen = array(
"d",
var2ranges_gen) # array of bin edges to use in histogram constructors
# observable (z, shape,...)
v_varshape_binning = datap["analysis"][typean][
"var_binningshape"] # name (reco)
v_varshape_binning_gen = datap["analysis"][typean][
"var_binningshape_gen"] # name (gen)
lvarshape_binmin_reco = \
datap["analysis"][typean].get("sel_binminshape_reco", None)
lvarshape_binmax_reco = \
datap["analysis"][typean].get("sel_binmaxshape_reco", None)
p_nbinshape_reco = len(lvarshape_binmin_reco) # number of reco bins
lvarshape_binmin_gen = \
datap["analysis"][typean].get("sel_binminshape_gen", None)
lvarshape_binmax_gen = \
datap["analysis"][typean].get("sel_binmaxshape_gen", None)
p_nbinshape_gen = len(lvarshape_binmin_gen) # number of gen bins
varshaperanges_reco = lvarshape_binmin_reco.copy()
varshaperanges_reco.append(lvarshape_binmax_reco[-1])
varshapebinarray_reco = array(
"d", varshaperanges_reco
) # array of bin edges to use in histogram constructors
varshaperanges_gen = lvarshape_binmin_gen.copy()
varshaperanges_gen.append(lvarshape_binmax_gen[-1])
varshapebinarray_gen = array(
"d", varshaperanges_gen
) # array of bin edges to use in histogram constructors
file_results = TFile.Open(file_in)
if not file_results:
logger.fatal(make_message_notfound(file_in))
ibin2 = 1
suffix = "%s_%g_%g" % (v_var2_binning, lvar2_binmin_gen[ibin2],
lvar2_binmax_gen[ibin2])
# HF data
nameobj = "%s_hf_data_%d_stat" % (shape, ibin2)
hf_data_stat = file_results.Get(nameobj)
if not hf_data_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_hf_data_%d_syst" % (shape, ibin2)
hf_data_syst = file_results.Get(nameobj)
if not hf_data_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# HF PYTHIA
nameobj = "%s_hf_pythia_%d_stat" % (shape, ibin2)
hf_pythia_stat = file_results.Get(nameobj)
if not hf_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
# HF ratio
nameobj = "%s_hf_ratio_%d_stat" % (shape, ibin2)
hf_ratio_stat = file_results.Get(nameobj)
if not hf_ratio_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_hf_ratio_%d_syst" % (shape, ibin2)
hf_ratio_syst = file_results.Get(nameobj)
if not hf_ratio_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# inclusive data
nameobj = "%s_incl_data_%d_stat" % (shape, ibin2)
incl_data_stat = file_results.Get(nameobj)
if not incl_data_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_incl_data_%d_syst" % (shape, ibin2)
incl_data_syst = file_results.Get(nameobj)
if not incl_data_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# inclusive PYTHIA
nameobj = "%s_incl_pythia_%d_stat" % (shape, ibin2)
incl_pythia_stat = file_results.Get(nameobj)
if not incl_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_incl_pythia_%d_syst" % (shape, ibin2)
incl_pythia_syst = file_results.Get(nameobj)
if not incl_pythia_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# inclusive ratio
nameobj = "%s_incl_ratio_%d_stat" % (shape, ibin2)
incl_ratio_stat = file_results.Get(nameobj)
if not incl_ratio_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_incl_ratio_%d_syst" % (shape, ibin2)
incl_ratio_syst = file_results.Get(nameobj)
if not incl_ratio_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# quark PYTHIA
nameobj = "%s_quark_pythia_%d_stat" % (shape, ibin2)
quark_pythia_stat = file_results.Get(nameobj)
if not quark_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_quark_pythia_%d_syst" % (shape, ibin2)
quark_pythia_syst = file_results.Get(nameobj)
if not quark_pythia_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# gluon PYTHIA
nameobj = "%s_gluon_pythia_%d_stat" % (shape, ibin2)
gluon_pythia_stat = file_results.Get(nameobj)
if not gluon_pythia_stat:
logger.fatal(make_message_notfound(nameobj, file_in))
nameobj = "%s_gluon_pythia_%d_syst" % (shape, ibin2)
gluon_pythia_syst = file_results.Get(nameobj)
if not gluon_pythia_syst:
logger.fatal(make_message_notfound(nameobj, file_in))
# plot the results with systematic uncertainties and models
size_can = [800, 800]
offsets_axes = [0.8, 1.1]
margins_can = [0.1, 0.13, 0.1, 0.03]
size_thg = 0.05
offset_thg = 0.85
gStyle.SetErrorX(0) # do not plot horizontal error bars of histograms
fontsize = 0.035
opt_leg_g = "FP"
opt_plot_g = "2"
list_new = [] # list to avoid loosing objects created in loops
# labels
x_latex = 0.16
y_latex_top = 0.83
y_step = 0.055
title_x = v_varshape_latex
title_y = "(1/#it{N}_{jet}) d#it{N}/d%s" % v_varshape_latex
title_full = ";%s;%s" % (title_x, title_y)
title_full_ratio = ";%s;data/MC: ratio of %s" % (title_x, title_y)
text_alice = "#bf{ALICE} Preliminary, pp, #sqrt{#it{s}} = 13 TeV"
text_alice_sim = "#bf{ALICE} Simulation, pp, #sqrt{#it{s}} = 13 TeV"
text_pythia = "PYTHIA 8 (Monash)"
text_pythia_split = "#splitline{PYTHIA 8}{(Monash)}"
text_jets = "charged jets, anti-#it{k}_{T}, #it{R} = 0.4"
text_ptjet = "%g #leq %s < %g GeV/#it{c}, #left|#it{#eta}_{jet}#right| #leq 0.5" % (
lvar2_binmin_reco[ibin2], p_latexbin2var, lvar2_binmax_reco[ibin2])
text_pth = "%g #leq #it{p}_{T}^{%s} < %g GeV/#it{c}, #left|#it{y}_{%s}#right| #leq 0.8" % (
lpt_finbinmin[0], p_latexnhadron,
min(lpt_finbinmax[-1], lvar2_binmax_reco[ibin2]), p_latexnhadron)
text_ptcut = "#it{p}_{T, incl. ch. jet}^{leading track} #geq 5.33 GeV/#it{c}"
text_ptcut_sim = "#it{p}_{T, incl. ch. jet}^{leading h^{#pm}} #geq 5.33 GeV/#it{c} (varied)"
text_sd = "Soft Drop (#it{z}_{cut} = 0.1, #it{#beta} = 0)"
title_thetag = "#it{#theta}_{g} = #it{R}_{g}/#it{R}"
radius_jet = 0.4
# colour and marker indeces
c_hf_data = 0
c_incl_data = 1
c_hf_mc = 2
c_incl_mc = 6
c_quark_mc = 5
c_gluon_mc = 0
# markers
m_hf_data = get_marker(0)
m_incl_data = get_marker(1)
m_hf_mc = get_marker(0, 2)
m_incl_mc = get_marker(1, 2)
m_quark_mc = get_marker(2)
m_gluon_mc = get_marker(3)
# make the horizontal error bars smaller
if shape == "nsd":
for gr in [
hf_data_syst, incl_data_syst, hf_ratio_syst, incl_ratio_syst,
incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst
]:
for i in range(gr.GetN()):
gr.SetPointEXlow(i, 0.1)
gr.SetPointEXhigh(i, 0.1)
# data, HF and inclusive
hf_data_syst_cl = hf_data_syst.Clone()
leg_pos = [.72, .75, .85, .85]
list_obj = [hf_data_syst, incl_data_syst, hf_data_stat, incl_data_stat]
labels_obj = ["%s-tagged" % p_latexnhadron, "inclusive", "", ""]
colours = [
get_colour(i, j) for i, j in zip((c_hf_data, c_incl_data, c_hf_data,
c_incl_data), (2, 2, 1, 1))
]
markers = [m_hf_data, m_incl_data, m_hf_data, m_incl_data]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_data, list_obj_data_new = make_plot("cshape_data_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip((hf_data_syst, incl_data_syst), (c_hf_data, c_incl_data)):
gr.SetMarkerColor(get_colour(c))
list_obj_data_new[0].SetTextSize(fontsize)
if shape == "nsd":
hf_data_syst.GetXaxis().SetNdivisions(5)
# Draw a line through the points.
if shape == "nsd":
for h in (hf_data_stat, incl_data_stat):
h_line = h.Clone(h.GetName() + "_line")
h_line.SetLineStyle(2)
h_line.Draw("l hist same")
list_new.append(h_line)
cshape_data.Update()
if shape == "rg":
# plot the theta_g axis
gr_frame = hf_data_syst
axis_rg = gr_frame.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_data.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_data.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_data = []
for text_latex in [
text_alice, text_jets, text_ptjet, text_pth, text_ptcut, text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_data.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_data.Update()
cshape_data.SaveAs("%s/%s_data_%s.pdf" % (rootpath, shape, suffix))
# data and PYTHIA, HF
leg_pos = [.72, .65, .85, .85]
list_obj = [hf_data_syst_cl, hf_data_stat, hf_pythia_stat]
labels_obj = ["data", "", text_pythia_split]
colours = [
get_colour(i, j)
for i, j in zip((c_hf_data, c_hf_data, c_hf_mc), (2, 1, 1))
]
markers = [m_hf_data, m_hf_data, m_hf_mc]
y_margin_up = 0.4
y_margin_down = 0.05
cshape_data_mc_hf, list_obj_data_mc_hf_new = make_plot("cshape_data_mc_hf_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip([hf_data_syst_cl], [c_hf_data]):
gr.SetMarkerColor(get_colour(c))
leg_data_mc_hf = list_obj_data_mc_hf_new[0]
leg_data_mc_hf.SetHeader("%s-tagged" % p_latexnhadron)
leg_data_mc_hf.SetTextSize(fontsize)
if shape == "nsd":
hf_data_syst_cl.GetXaxis().SetNdivisions(5)
#axis_nsd = hf_data_syst_cl.GetHistogram().GetXaxis()
#x1 = axis_nsd.GetBinLowEdge(1)
#x2 = axis_nsd.GetBinUpEdge(axis_nsd.GetNbins())
#axis_nsd.Set(5, x1, x2)
#for ibin in range(axis_nsd.GetNbins()):
# axis_nsd.SetBinLabel(ibin + 1, "%d" % ibin)
#axis_nsd.SetNdivisions(5)
cshape_data_mc_hf.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_data_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_data_mc_hf.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_data_mc_hf.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_data_mc_hf = []
for text_latex in [text_alice, text_jets, text_ptjet, text_pth, text_sd]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_data_mc_hf.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_data_mc_hf.Update()
cshape_data_mc_hf.SaveAs("%s/%s_data_mc_hf_%s.pdf" %
(rootpath, shape, suffix))
# data and PYTHIA, inclusive
#leg_pos = [.68, .65, .85, .85]
list_obj = [
incl_data_syst, incl_pythia_syst, incl_data_stat, incl_pythia_stat
]
labels_obj = ["data", text_pythia_split]
colours = [
get_colour(i, j) for i, j in zip((c_incl_data, c_incl_mc, c_incl_data,
c_incl_mc), (2, 2, 1, 1))
]
markers = [m_incl_data, m_incl_mc, m_incl_data, m_incl_mc]
y_margin_up = 0.4
y_margin_down = 0.05
cshape_data_mc_incl, list_obj_data_mc_incl_new = make_plot("cshape_data_mc_incl_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip([incl_data_syst, incl_pythia_syst],
[c_incl_data, c_incl_mc]):
gr.SetMarkerColor(get_colour(c))
leg_data_mc_incl = list_obj_data_mc_incl_new[0]
leg_data_mc_incl.SetHeader("inclusive")
leg_data_mc_incl.SetTextSize(fontsize)
if shape == "nsd":
incl_data_syst.GetXaxis().SetNdivisions(5)
cshape_data_mc_incl.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = incl_data_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_data_mc_incl.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_data_mc_incl.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_data_mc_incl = []
for text_latex in [text_alice, text_jets, text_ptjet, text_ptcut, text_sd]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_data_mc_incl.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_data_mc_incl.Update()
cshape_data_mc_incl.SaveAs("%s/%s_data_mc_incl_%s.pdf" %
(rootpath, shape, suffix))
# Ratios data/MC, HF and inclusive
line_1 = TLine(lvarshape_binmin_reco[0], 1, lvarshape_binmax_reco[-1], 1)
line_1.SetLineStyle(9)
line_1.SetLineColor(1)
line_1.SetLineWidth(3)
#leg_pos = [.72, .7, .85, .85] # with header
leg_pos = [.72, .75, .85, .85] # without header
list_obj = [
hf_ratio_syst, line_1, incl_ratio_syst, hf_ratio_stat, incl_ratio_stat
]
labels_obj = ["%s-tagged" % p_latexnhadron, "inclusive"]
colours = [
get_colour(i, j) for i, j in zip((c_hf_data, c_incl_data, c_hf_data,
c_incl_data), (2, 2, 1, 1))
]
markers = [m_hf_data, m_incl_data, m_hf_data, m_incl_data]
y_margin_up = 0.52
y_margin_down = 0.05
if shape == "nsd":
y_margin_up = 0.22
cshape_ratio, list_obj_ratio_new = make_plot("cshape_ratio_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full_ratio)
cshape_ratio.Update()
for gr, c in zip((hf_ratio_syst, incl_ratio_syst),
(c_hf_data, c_incl_data)):
gr.SetMarkerColor(get_colour(c))
leg_ratio = list_obj_ratio_new[0]
leg_ratio.SetTextSize(fontsize)
#leg_ratio.SetHeader("data/MC")
if shape == "nsd":
hf_ratio_syst.GetXaxis().SetNdivisions(5)
cshape_ratio.Update()
if shape == "rg":
# plot the theta_g axis
gr_frame = hf_ratio_syst
axis_rg = gr_frame.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_ratio.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_ratio.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_ratio = []
for text_latex in [
text_alice, text_jets, text_ptjet, text_pth, text_ptcut, text_sd,
text_pythia
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_ratio.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_ratio.Update()
cshape_ratio.SaveAs("%s/%s_ratio_%s.pdf" % (rootpath, shape, suffix))
# PYTHIA, HF, inclusive, quark, gluon
incl_pythia_syst_cl = incl_pythia_syst.Clone()
y_min_h, y_max_h = get_y_window_his([
hf_pythia_stat, incl_pythia_stat, quark_pythia_stat, gluon_pythia_stat
])
y_min_g, y_max_g = get_y_window_gr(
[incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst])
y_min = min(y_min_h, y_min_g)
y_max = max(y_max_h, y_max_g)
y_margin_up = 0.46
y_margin_down = 0.05
y_min_plot, y_max_plot = get_plot_range(y_min, y_max, y_margin_down,
y_margin_up)
#leg_pos = [.6, .65, .75, .85]
leg_pos = [.72, .55, .85, .85]
list_obj = [
incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst, hf_pythia_stat,
incl_pythia_stat, quark_pythia_stat, gluon_pythia_stat
]
labels_obj = ["inclusive", "quark", "gluon", "%s-tagged" % p_latexnhadron]
colours = [
get_colour(i, j)
for i, j in zip((c_incl_mc, c_quark_mc, c_gluon_mc, c_hf_mc, c_incl_mc,
c_quark_mc, c_gluon_mc), (2, 2, 2, 1, 1, 1, 1))
]
markers = [
m_incl_mc, m_quark_mc, m_gluon_mc, m_hf_mc, m_incl_mc, m_quark_mc,
m_gluon_mc
]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, range_y=[y_min_plot, y_max_plot], margins_c=margins_can, \
title=title_full)
cshape_mc.Update()
for gr, c in zip((incl_pythia_syst, quark_pythia_syst, gluon_pythia_syst),
(c_incl_mc, c_quark_mc, c_gluon_mc)):
gr.SetMarkerColor(get_colour(c))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
leg_mc.SetHeader(text_pythia_split)
if shape == "nsd":
incl_pythia_syst.GetXaxis().SetNdivisions(5)
cshape_mc.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_pythia_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_mc.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_mc.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_mc = []
for text_latex in [
text_alice_sim, text_jets, text_ptjet, text_pth, text_ptcut_sim,
text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_mc.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_mc_%s.pdf" % (rootpath, shape, suffix))
# PYTHIA, HF, quark, gluon
#leg_pos = [.6, .65, .75, .85]
leg_pos = [.72, .61, .85, .85]
list_obj = [
quark_pythia_syst, gluon_pythia_syst, hf_pythia_stat,
quark_pythia_stat, gluon_pythia_stat
]
labels_obj = ["quark", "gluon", "%s-tagged" % p_latexnhadron]
colours = [
get_colour(i, j)
for i, j in zip((c_quark_mc, c_gluon_mc, c_hf_mc, c_quark_mc,
c_gluon_mc), (2, 2, 1, 1, 1))
]
markers = [m_quark_mc, m_gluon_mc, m_hf_mc, m_quark_mc, m_gluon_mc]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_qgd_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, range_y=[y_min_plot, y_max_plot], margins_c=margins_can, \
title=title_full)
cshape_mc.Update()
for gr, c in zip((quark_pythia_syst, gluon_pythia_syst),
(c_quark_mc, c_gluon_mc)):
gr.SetMarkerColor(get_colour(c))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
leg_mc.SetHeader(text_pythia_split)
if shape == "nsd":
quark_pythia_syst.GetXaxis().SetNdivisions(5)
cshape_mc.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_pythia_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_mc.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_mc.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_mc = []
for text_latex in [
text_alice_sim, text_jets, text_ptjet, text_pth, text_ptcut_sim,
text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_mc.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_mc_qgd_%s.pdf" % (rootpath, shape, suffix))
# PYTHIA, HF, inclusive
#leg_pos = [.6, .65, .75, .85]
leg_pos = [.72, .67, .85, .85]
list_obj = [incl_pythia_syst_cl, incl_pythia_stat, hf_pythia_stat]
labels_obj = ["inclusive", "", "%s-tagged" % p_latexnhadron]
colours = [
get_colour(i, j)
for i, j in zip((c_incl_mc, c_incl_mc, c_hf_mc), (2, 1, 1))
]
markers = [m_incl_mc, m_incl_mc, m_hf_mc]
y_margin_up = 0.46
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_id_" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, range_y=[y_min_plot, y_max_plot], margins_c=margins_can, \
title=title_full)
# Draw a line through the points.
if shape == "nsd":
for h in (incl_pythia_stat, hf_pythia_stat):
h_line = h.Clone(h.GetName() + "_line")
h_line.SetLineStyle(2)
h_line.Draw("l hist same")
list_new.append(h_line)
cshape_mc.Update()
incl_pythia_syst_cl.SetMarkerColor(get_colour(c_incl_mc))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
leg_mc.SetHeader(text_pythia_split)
if shape == "nsd":
incl_pythia_syst_cl.GetXaxis().SetNdivisions(5)
cshape_mc.Update()
if shape == "rg":
# plot the theta_g axis
axis_rg = hf_pythia_stat.GetXaxis()
rg_min = axis_rg.GetBinLowEdge(axis_rg.GetFirst())
rg_max = axis_rg.GetBinUpEdge(axis_rg.GetLast())
thetag_min = rg_min / radius_jet
thetag_max = rg_max / radius_jet
y_axis = cshape_mc.GetUymax()
axis_thetag = TGaxis(rg_min, y_axis, rg_max, y_axis, thetag_min,
thetag_max, 510, "-")
axis_thetag.SetTitle(title_thetag)
axis_thetag.SetTitleSize(size_thg)
axis_thetag.SetLabelSize(0.036)
axis_thetag.SetTitleFont(42)
axis_thetag.SetLabelFont(42)
axis_thetag.SetLabelOffset(0)
axis_thetag.SetTitleOffset(offset_thg)
cshape_mc.SetTickx(0)
axis_thetag.Draw("same")
# Draw LaTeX
y_latex = y_latex_top
list_latex_mc = []
for text_latex in [
text_alice_sim, text_jets, text_ptjet, text_pth, text_ptcut_sim,
text_sd
]:
latex = TLatex(x_latex, y_latex, text_latex)
list_latex_mc.append(latex)
draw_latex(latex, textsize=fontsize)
y_latex -= y_step
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_mc_id_%s.pdf" % (rootpath, shape, suffix))
# data inclusive vs PYTHIA, quark, gluon
#leg_pos = [.6, .65, .75, .85]
#leg_pos = [.72, .55, .85, .85]
leg_pos = [.6, .7, .85, .85]
list_obj = [
incl_data_syst, quark_pythia_syst, gluon_pythia_syst, incl_data_stat,
quark_pythia_stat, gluon_pythia_stat
]
labels_obj = ["inclusive (data)", "quark (PYTHIA 8)", "gluon (PYTHIA 8)"]
colours = [
get_colour(i, j)
for i, j in zip((c_incl_data, c_quark_mc, c_gluon_mc, c_incl_data,
c_quark_mc, c_gluon_mc), (2, 2, 2, 1, 1, 1))
]
markers = [
m_incl_data, m_quark_mc, m_gluon_mc, m_incl_data, m_quark_mc,
m_gluon_mc
]
y_margin_up = 0.3
y_margin_down = 0.05
cshape_mc, list_obj_mc_new = make_plot("cshape_mc_data_iqg" + suffix, size=size_can, \
list_obj=list_obj, labels_obj=labels_obj, opt_leg_g=opt_leg_g, opt_plot_g=opt_plot_g, offsets_xy=offsets_axes, \
colours=colours, markers=markers, leg_pos=leg_pos, margins_y=[y_margin_down, y_margin_up], margins_c=margins_can, \
title=title_full)
for gr, c in zip((incl_data_syst, quark_pythia_syst, gluon_pythia_syst),
(c_incl_data, c_quark_mc, c_gluon_mc)):
gr.SetMarkerColor(get_colour(c))
leg_mc = list_obj_mc_new[0]
leg_mc.SetTextSize(fontsize)
cshape_mc.Update()
cshape_mc.SaveAs("%s/%s_data_i_mc_qg_%s.pdf" % (rootpath, shape, suffix))
def plot_2(var,cuts):
for s in attr:
c1 = TCanvas("c1", "Signals", 1200, 800)
if log_y == 1:
c1.SetLogy()
c1.SetTopMargin(0.08)#0.12
c1.SetBottomMargin(0.11)#0.12
c1.SetLeftMargin(0.14)
c1.SetRightMargin(0.14)#0.24
c1.cd()
#c1.SetGrid()
gStyle.SetTitleFontSize(0.04)
if ct_dep == 0:
if s in ('elf','muf','cm','nm','chm'):
c1.SetLogx()
for cc in channel:
#hist[cc][s].SetMaximum(0.44)
if 'combind' in cc:
fc = 30
#hist[cc][s].SetFillStyle()#3005)
elif 'VBF' in cc:
fc = 38
hist[cc][s].SetFillStyle(3444)
elif 'HT50' in cc:
fc = 7
hist[cc][s].SetFillStyle(3001)
elif 'HT100' in cc:
fc = 4
hist[cc][s].SetFillStyle(3002)
elif 'HT200' in cc:
fc = 6
hist[cc][s].SetFillStyle(3003)
elif 'HT300' in cc:
fc = 9
hist[cc][s].SetFillStyle(3004)
if histFillColOn == 1:
pass
#hist[cc][s].SetFillColor(fc)
hist[cc][s].Draw(histStyl)
#legend = TLegend(0.76, 0.56, 0.99, 0.88)
legend = TLegend(0.60, 0.9-0.04*2, 0.85, 0.9) #x_left y_bottom x_right y_top
legend.SetBorderSize(0)
legend.SetFillStyle(0)#1001
legend.SetFillColor(0)
#legend.SetHeader( entry['entries'] )
for cc in channel:
legend.AddEntry(hist[cc][s],cc)
legend.Draw()
for ct in cut_text:
cut_text[ct].Draw()
#>>>>>>>>>>>>>>>>>>>>>>>>>>>>>Line for critical value
if s == 'chm':
l = TLine(11.4,0.0,11.4,0.084)
elif s == 'cHadEFrac':
l = TLine(0.38,0.0,0.38,0.027)
elif s == 'FracCal':
l = TLine(30,0.0,30,0.44)
#l.SetLineColor(2)
#l.SetLineWidth(3)
if critical_line == 1:
l.SetLineColor(46)#4,2
l.SetLineWidth(3)
l.Draw('same')
c1.Print(path1 + s + var + cuts.replace('(','_').replace(')','_').replace('&&','_').replace('>','LG').replace('<','LS').replace('=','EQ').replace('.','P').replace('-','N').replace('Jet','J').replace('GenBquark','GBQ') + ".pdf")
elif ct_dep == 1:
eac0 = str( entries_after_cut['ct0'][s] )
c1.SetLogx()
#gr = TGraph( len_of_lt , x , yy['sgn'][s] )
gr = TGraphErrors( len_of_lt , x , yy['sgn'][s] , ex , ey['sgn'][s] )
gr.SetMarkerSize(1.5)
gr.SetMarkerStyle(1)
gr.GetYaxis().SetTitleOffset(1.6)
gr.SetLineColor(4)
gr.SetLineWidth(4)
gr.SetTitle('mean ' + s )
gr.GetXaxis().SetTitle('decaying length (mm)')
gr.GetYaxis().SetTitle('mean normalized number of events')
gr.GetXaxis().SetTitleOffset(1.4)
gr.SetMaximum( plotrange[s] * 1.12 )
gr.SetName('sgn')
gr.Draw('ACP') # '' sets up the scattering style
gr1 = TGraphErrors( len_of_lt , x , yy['QCD'][s] , ex , ey['QCD'][s] )
gr1.SetMarkerSize(1.0)
gr1.SetMarkerStyle(1)
gr.GetYaxis().SetTitleOffset(1.6)
gr1.SetLineColor(2)
gr1.SetLineWidth(2)
gr1.SetName('QCD')
#gr1.SetTitle('averaged ' + s)
#gr1.GetXaxis().SetTitle('decaying length (mm)')
#gr1.GetYaxis().SetTitle('mean frequency')
gr1.Draw('CP') # '' sets up the scattering style
legend = TLegend(0.76, 0.56, 0.99, 0.88)
legend.SetHeader( 'Entries: ' + eac0 )
legend.AddEntry('QCD', legendb, 'l')
legend.AddEntry('sgn', legends, 'l')
legend.Draw()
for ct in cut_text:
cut_text[ct].Draw()
c1.Print(path1 + 'mean_' + s + var + cuts.replace('(','_').replace(')','_').replace('&&','_').replace('>','LG').replace('<','LS').replace('=','EQ').replace('.','P').replace('-','N').replace('Jet','J').replace('GenBquark','GBQ') + ".pdf")
c1.Update()
c1.Close()
print('|||||||||||||||||||||||||||||||||||||||||||||||||||')
def plotDeltaTime(self):
hDeltaTAllHo = self.fileHandler.getHistogram('L1MuonPresentHoMatch_DeltaTime')
hDeltaTCleanHo = self.fileHandler.getHistogram('timingSupport_UnmatchedDtHo_DeltaTime')
c = TCanvas("c","Delta Time",1200,1200)
c.SetLogy()
hDeltaTAllHo.SetLineColor(colorRwthDarkBlue)
hDeltaTAllHo.SetLineWidth(3)
hDeltaTAllHo.SetFillColor(colorRwthDarkBlue)
hDeltaTAllHo.SetFillStyle(3017)
hDeltaTAllHo.SetTitle("#Delta time")
hDeltaTAllHo.SetStats(0)
hDeltaTCleanHo.SetLineColor(8)
hDeltaTCleanHo.SetFillColor(8)
hDeltaTCleanHo.SetLineWidth(3)
hDeltaTCleanHo.SetFillStyle(3002)
#hDeltaTAllHo.Scale(1/hDeltaTAllHo.Integral())
#hDeltaTCleanHo.Scale(1/hDeltaTCleanHo.Integral())
print hDeltaTCleanHo.Integral(),hDeltaTAllHo.Integral()
fitFirstMin = TF1("fitFirstMin","[0]+x*[1]+[2]*x**2")
fitSecondMin = TF1("fitsecondMin","[0]+x*[1]+[2]*x**2",10,20)
hDeltaTCleanHo.Fit(fitFirstMin,"+q","",-20,-10)
hDeltaTCleanHo.Fit(fitSecondMin,"R+q","")
hDeltaTAllHo.Draw()
legend = TLegend(0.6,0.75,0.9,0.9)
legend.AddEntry(hDeltaTAllHo,"L1Muon matched to any HO","le")
legend.Draw()
label = getLabelCmsPrivateSimulation()
label.Draw()
c.Update()
self.storeCanvas(c,"deltaTimeAllHo")
hDeltaTCleanHo.Draw('same')
fitFirstMin.SetRange(-50,50)
fitSecondMin.SetRange(-50,50)
#fitFirstMin.Draw('lSame')
#fitSecondMin.Draw('lSame')
lineFirstMin = TLine(fitFirstMin.GetMinimumX(-20,-10),hDeltaTAllHo.GetMinimum(),fitFirstMin.GetMinimumX(-20,-10),hDeltaTAllHo.GetMaximum())
lineFirstMin.SetLineWidth(3)
lineFirstMin.SetLineColor(colorRwthRot)
lineFirstMin.Draw()
lineSecondMin = TLine(fitSecondMin.GetMinimumX(10,20),hDeltaTAllHo.GetMinimum(),fitSecondMin.GetMinimumX(10,20),hDeltaTAllHo.GetMaximum())
lineSecondMin.SetLineWidth(3)
lineSecondMin.SetLineColor(colorRwthRot)
lineSecondMin.Draw()
legend.AddEntry(hDeltaTCleanHo,"L1Muon matched to HO > 0.2 GeV","le")
legend.AddEntry(lineFirstMin,"Integral boundaries","e")
legend.Draw()
integralCenter = hDeltaTCleanHo.Integral(hDeltaTCleanHo.FindBin(fitFirstMin.GetMinimumX(-20,-10)),hDeltaTCleanHo.FindBin(fitSecondMin.GetMinimumX(10,20)))
integralCenterAll = hDeltaTAllHo.Integral(hDeltaTAllHo.FindBin(fitFirstMin.GetMinimumX(-20,-10)),hDeltaTAllHo.FindBin(fitSecondMin.GetMinimumX(10,20)))
self.debug(80*'#')
self.debug('Integral of center area in clean histogram :%d' % integralCenter)
self.debug('==> %.2f%% +/- %.2f%%' % (integralCenter/hDeltaTCleanHo.Integral()*100
,calcSigma(integralCenter, hDeltaTCleanHo.Integral())*100))
self.debug('Integral of center area in all matched HO events:%d' % integralCenterAll)
self.debug('==> %.2f%% +/- %.2f%%' % (integralCenterAll/hDeltaTAllHo.Integral()*100
,calcSigma(integralCenterAll, hDeltaTAllHo.Integral())*100))
self.debug(80*'#')
paveText = TPaveText(0.6,0.7,0.9,0.75,'NDC')
paveText.AddText('%s' % ('Central peak contains (filtered hist.)'))
paveText.AddText('%.2f%% +/- %.2f%%' % (integralCenter/hDeltaTCleanHo.Integral()*100,calcSigma(integralCenter, hDeltaTCleanHo.Integral())*100))
paveText.SetBorderSize(1)
paveText.Draw()
label = getLabelCmsPrivateSimulation()
label.Draw()
c.Update()
self.storeCanvas(c,"deltaTime")
return c, hDeltaTCleanHo
def Plot2DCorrection(hist2D,
graph,
f1,
etBin=None,
etaBin=None,
outname=None,
xlabel='N_{vtx}',
runLabel=None,
legends=None,
extraText1=None,
legendX1=.5,
etidx=None,
etaidx=None,
etlist=None,
etalist=None):
import array as ar
from ROOT import TCanvas, gStyle, TLegend, kRed, kBlue, kBlack, TLine, kBird, kOrange
from ROOT import TGraphErrors, TF1, TColor
pileup_max = hist2D.GetYaxis().GetXmax()
pileup_min = hist2D.GetYaxis().GetXmin()
# Retrieve some usefull information
gStyle.SetPalette(kBird)
canvas = TCanvas('canvas', 'canvas', 500, 500)
#FormatCanvasAxes(canvas, XLabelSize=18, YLabelSize=18, XTitleOffset=0.87, YTitleOffset=1.5)
canvas.SetRightMargin(0.12)
canvas.SetLeftMargin(0.10)
hist2D.Draw('colz')
hist2D.GetZaxis().SetTitle("Entries")
canvas.SetLogz()
AddTopLabels(canvas,
legends,
runLabel=runLabel,
legOpt='p',
etlist=etlist,
etalist=etalist,
etidx=etidx,
etaidx=etaidx)
FormatCanvasAxes(canvas,
XLabelSize=12,
YLabelSize=12,
XTitleOffset=0.87,
ZLabelSize=12,
ZTitleSize=14,
YTitleOffset=0.87,
ZTitleOffset=0.67)
SetAxisLabels(canvas, 'Neural Network output (Discriminant)', xlabel)
# Invert graph
import array
x = graph.GetX()
x.SetSize(graph.GetN())
ex = graph.GetEX()
ex.SetSize(graph.GetN())
y = graph.GetY()
y.SetSize(graph.GetN())
ey = graph.GetEY()
ey.SetSize(graph.GetN())
nvtx_points = array.array('d', x)
nvtx_error_points = array.array('d', ex)
discr_points = array.array('d', y)
discr_error_points = array.array('d', ey)
g1 = TGraphErrors(len(discr_points), discr_points, nvtx_points,
discr_error_points, nvtx_error_points)
g1.SetLineWidth(1)
#g1.SetLineColor(kBlack)
g1.SetMarkerColor(kBlue + 1)
g1.SetMarkerSize(.6)
g1.Draw("P same")
tobject_collector.append(g1)
#l2 = TLine(eff_uncorr.thres,miny,eff_uncorr.thres,maxy)
#l2.SetLineColor(kRed)
#l2.SetLineWidth(2)
#l2.Draw("l,same")
#tobject_collector.append(l2)
#f1 = eff.f1
if type(f1) is not list: f1 = [f1]
for idx, f in enumerate(f1):
l3 = TLine(f.Eval(pileup_min), pileup_min, f.Eval(pileup_max),
pileup_max)
if idx > 0: l3.SetLineColor(GetTransparent(kRed))
else:
l3.SetLineColor(kBlack)
l3.SetLineWidth(2)
l3.Draw("l,same")
tobject_collector.append(l3)
if outname: canvas.SaveAs(outname)
def outputErrors(self):
# Output all kinds of info to a file
rootFileName = "%s/CertificationSummaries.root" % (self.saveDirectory)
rootFile = TFile(rootFileName, "RECREATE")
print "\nWriting summary root file %s" % (rootFileName)
sortedRuns = sorted(self.run_trig_ls)
try:
fileName = "CertificationSummary_run" + str(
sortedRuns[0]) + "_run" + str(sortedRuns[-1]) + ".txt"
file = open(
self.saveDirectory + "/" + fileName,
'w') # come up with a name based on something about the runs
print "\nCertification Summary txt file: %s/%s \n" % (
self.saveDirectory, fileName)
except:
print "Error writing certification summary ."
return
for runNumber in sortedRuns:
file.write("Run Number: %s\n" % (runNumber))
file.write("\n")
file.write(" TRIGGERS: BAD LUMIECTION(S) \n")
file.write("\n")
totalErrs = 0
badLumiList = {}
badLumiListSorted = {}
for triggerName in sorted(self.run_trig_ls[runNumber]):
file.write(" %s: " % (triggerName))
list = formatJSON(
sorted(self.run_trig_ls[runNumber][triggerName]))
file.write(list + "\n")
for LS in sorted(self.run_trig_ls[runNumber][triggerName]):
if badLumiList.has_key(LS): badLumiList[LS] += 1
else: badLumiList[LS] = 1
file.write("\n")
totalLumis = len(
self.run_allLs[runNumber][self.run_allLs[runNumber].keys()[0]])
minLumi = min(
self.run_allLs[runNumber][self.run_allLs[runNumber].keys()[0]])
maxLumi = max(
self.run_allLs[runNumber][self.run_allLs[runNumber].keys()[0]])
# maxNumBadPaths = sorted(badLumiListSorted.keys(), reverse =True)[0]
maxNumBadPaths = len(self.run_allLs[runNumber])
canvas = TCanvas("can", "can", 1000, 600)
canvas.SetName("Certification Summary of Run %s" % runNumber)
canvas.SetGridx(1)
canvas.SetGridy(1)
summaryHist = TH1D("Certification_Summary_of_Run%s" % (runNumber),
"Run %s" % (runNumber), (totalLumis + 2),
(minLumi - 1), (maxLumi + 1))
summaryHist.GetXaxis().SetTitle("LS")
summaryHist.GetYaxis().SetTitle("Number of bad paths")
summaryHist.SetMaximum(1.2 * maxNumBadPaths)
#sort the dict so the number of bad triggers is now the key
for LS in sorted(badLumiList.keys(),
key=badLumiList.__getitem__,
reverse=True):
summaryHist.Fill(LS, badLumiList[LS])
if badLumiListSorted.has_key(badLumiList[LS]):
badLumiListSorted[badLumiList[LS]].append(LS)
else:
badLumiListSorted[badLumiList[LS]] = [LS]
testVariable = 0
file.write(" # OF BAD PATHS : LUMISECTION(S)\n")
file.write("\n")
for numBadTrigs in sorted(badLumiListSorted.keys(), reverse=True):
testVariable += numBadTrigs * (len(
sorted(badLumiListSorted[numBadTrigs])))
file.write(
" %s : %s\n" %
(numBadTrigs, sorted(badLumiListSorted[numBadTrigs])))
totalErrs += len(sorted(badLumiListSorted[numBadTrigs]))
maxLine = TLine(minLumi - 1, maxNumBadPaths, maxLumi + 1,
maxNumBadPaths)
maxLine.SetLineStyle(9)
maxLine.SetLineColor(2)
maxLine.SetLineWidth(2)
summaryHist.Draw("hist")
summaryHist.SetLineColor(4)
summaryHist.SetFillColor(4)
summaryHist.SetFillStyle(3004)
canvas.Update()
latex = TLatex()
latex.SetNDC()
latex.SetTextColor(1)
latex.SetTextAlign(11)
latex.SetTextFont(62)
latex.SetTextSize(0.05)
latex.DrawLatex(0.15, 0.84, "CMS")
latex.SetTextSize(0.035)
latex.SetTextFont(52)
latex.DrawLatex(0.15, 0.80, "Rate Monitoring")
canvas.Update()
maxLine.Draw("same")
canvas.Update()
canvas.Modified()
canvas.Write()
canvas.Print(
"%s/CertificationSummary_run%s.png" %
(self.saveDirectory, runNumber), "png")
fractionBadLumis = 100. * float(totalErrs) / float(totalLumis)
fractionBadRun = 100. * summaryHist.Integral() / float(
(totalLumis + 2) * maxNumBadPaths)
file.write("\n")
file.write("BAD LS SUMMARY: \n")
file.write(
"\n---- Total bad LS: %s ( bad LS: >= 1 trigger(s) deviating more than 3 sigma from prediction )\n"
% (totalErrs))
file.write("---- Total LS: %s\n" % (totalLumis))
file.write("---- Fraction bad LS: %s\n" % (fractionBadLumis))
fractionBadpaths = (100. * (float(testVariable) /
(float(totalLumis * maxNumBadPaths))))
totalPossPaths = float(totalLumis * maxNumBadPaths)
totalBadPaths = float(testVariable)
file.write("\n")
file.write("BAD PATH SUMMARY: \n")
file.write("\n")
file.write("---- Total Bad Paths: %.1f\n" % (totalBadPaths))
file.write("---- Total Possible Paths: %.1f\n" % (totalPossPaths))
file.write("---- Fraction that are Bad Paths: %.1f\n" %
(float(fractionBadpaths)))
file.write("\n")
file.write(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n"
)
file.close()
rootFile.Close()
# h_pionplus_long_trans.GetYaxis().SetTitleFont(12)
h_pionplus_long_trans.GetXaxis().SetRangeUser(0, 130)
h_pionplus_long_trans.GetYaxis().SetRangeUser(0, 5)
h_pionplus_long_trans.GetXaxis().SetLabelSize(0.05)
h_pionplus_long_trans.GetXaxis().SetTitleSize(0.05)
h_pionplus_long_trans.GetYaxis().SetLabelSize(0.05)
h_pionplus_long_trans.GetYaxis().SetTitleSize(0.05)
cpionplus_long_trans.SetLeftMargin(0.15)
cpionplus_long_trans.SetBottomMargin(0.12)
cpionplus_long_trans.SetRightMargin(0.14)
gStyle.SetTitleH(0.07)
h_pionplus_long_trans.Draw("colz")
# Seun's K/pi measurements (MIPP, thick target, so with tptype particle) [FERMILAB-THESIS-2007-61]
line1 = TLine(20, 0, 20, 1)
line2 = TLine(20, 1, 24, 1)
line3 = TLine(24, 1, 24, 1.2)
line4 = TLine(24, 1.2, 31, 1.2)
line5 = TLine(31, 1.2, 31, 1.55)
line6 = TLine(31, 1.55, 42, 1.55)
line7 = TLine(42, 1.55, 42, 2)
line8 = TLine(42, 2, 60, 2)
line9 = TLine(60, 2, 90, 2)
line10 = TLine(90, 2, 90, 0)
line11 = TLine(90, 0, 20, 0)
lineVector = [
line1, line2, line3, line4, line5, line6, line7, line8, line9, line10,
line11
]
# ______________________________________________________________________________
if __name__ == '__main__':
from ROOT import gROOT, gPad, gStyle, TFile, TCanvas, TH1F, TH2F, TPolyLine, TLatex, TColor, TEfficiency, TLine, TLatex, TGraph, TGraphAsymmErrors
# ____________________________________________________________________________
# Setup basic drawer
gROOT.LoadMacro("tdrstyle.C")
gROOT.ProcessLine("setTDRStyle();")
#gROOT.SetBatch(True)
gStyle.SetPadGridX(True)
gStyle.SetPadGridY(True)
gStyle.SetMarkerStyle(1)
gStyle.SetEndErrorSize(0)
gROOT.ForceStyle()
tline = TLine()
tline.SetLineColor(920 + 2) # kGray+2
tline.SetLineStyle(2)
tlatexCMS1 = TLatex()
tlatexCMS1.SetNDC()
tlatexCMS1.SetTextFont(61)
tlatexCMS1.SetTextSize(0.75 * 0.05)
tlatexCMS2 = TLatex()
tlatexCMS2.SetNDC()
tlatexCMS2.SetTextFont(52)
tlatexCMS2.SetTextSize(0.60 * 0.05)
tlatexCMS3 = TLatex()
tlatexCMS3.SetNDC()
gr1.GetXaxis().SetLimits(math.pow(10, 10 + 0 * 0.5),
math.pow(10, 10 + 16 * 0.5))
leg1 = TLegend(0.2, 0.7, 0.5, 0.9)
leg1.SetFillColor(0)
leg1.SetFillStyle(0)
leg1.SetTextSize(0.05)
leg1.SetBorderSize(0)
leg1.SetTextFont(22)
leg1.AddEntry("", "GeIA group", "")
leg1.AddEntry("", "Avalanche region:", "")
leg1.AddEntry(gr, str(Avalanche_region) + "cm(Square)", "lp")
leg1.AddEntry(gr1, str(Avalanche_region1) + "cm(Circle)", "lp")
line = TLine(
math.pow(10, 15), 0, math.pow(10, 15),
Number_of_Atom(Avalanche_region, math.pow(math.pow(10, 15), 0.33)))
line1 = TLine(
math.pow(10, 16), 0, math.pow(10, 16),
Number_of_Atom(Avalanche_region, math.pow(math.pow(10, 16), 0.33)))
line3 = TLine(math.pow(10, 15), 0, math.pow(10, 16), 0)
line3.SetLineWidth(10)
gr1.Draw("ALP")
gr.Draw("LPsame")
line.Draw("Lsame")
line1.Draw("Lsame")
c.SetLogx()
#c.SetLogy()
c.Draw()
leg1.Draw()
def drawCut(cut, ymin, ymax):
line = TLine()
line.SetLineWidth(2)
line.SetLineStyle(7)
line.SetLineColor(1)
line.PaintLineNDC(cut, ymin, cut, ymax)
def __applyThresholdCorrection( self, refValue, sgn_hist2D, bkg_hist2D, partition_name, output_name, **kwargs):
legend_position = retrieve_kw( kwargs, 'legend_position', (0.36,0.20,0.66,0.40))
useNoActivationFunctionInTheLastLayer=retrieve_kw(kwargs,'useNoActivationFunctionInTheLastLayer',False)
xname = retrieve_kw( kwargs, 'xname', 'n_{vtx}' )
draw = retrieve_kw( kwargs, 'draw', False)
limits = retrieve_kw( kwargs, 'limits', [0,10,20])
dovertical = retrieve_kw( kwargs, 'dovertical' , False)
mumin = limits[0]; mumax=limits[-1]
mubins = mumax-mumin
from TrigEgammaDevelopments.helper.util import *
sgn_hist2D = copy2DRegion(sgn_hist2D,1000,-12,7,mubins,mumin,mumax)
bkg_hist2D = copy2DRegion(bkg_hist2D,1000,-12,7,mubins,mumin,mumax)
from copy import deepcopy
refValue_requested = refValue
false_alarm = 1.0
false_alarm_limit = 0.20
while false_alarm > false_alarm_limit:
# Calculate the original threshold
b0, error = find_threshold(sgn_hist2D.ProjectionX(), refValue )
# Take eff points using uncorrection threshold
discr_points, nvtx_points, error_points = calculate_dependent_discr_points(sgn_hist2D , refValue )
# Calculate eff without correction
sgn_histNum, sgn_histDen, sgn_histEff, det0 = calculate_efficiency(sgn_hist2D, refValue, b0, 0, doCorrection=False)
# Generate correction parameters and produce fixed plots
sgn_histNum_corr, sgn_histDen_corr, sgn_histEff_corr, detection ,b, a = calculate_efficiency( sgn_hist2D,
refValue, b0, 0, limits = limits, doCorretion=True)
# Calculate eff without correction
bkg_histNum, bkg_histDen, bkg_histEff, _ = calculate_efficiency(bkg_hist2D, refValue, b0, 0, doCorrection=False)
# Calculate eff using the correction from signal
bkg_histNum_corr, bkg_histDen_corr, bkg_histEff_corr, false_alarm = calculate_efficiency(bkg_hist2D, refValue, b, a, doCorrection=False)
if false_alarm > false_alarm_limit:
refValue-=0.025
# To np.array
discr_points = np.array(discr_points)
# Plot correction
if draw:
# Retrieve some usefull information
y_max = sgn_hist2D.GetYaxis().GetXmax()
y_min = sgn_hist2D.GetYaxis().GetXmin()
x_min = y_min; x_max = y_max
from ROOT import TCanvas, gStyle, TLegend, kRed, kBlue, kBlack,TLine
from ROOT import TGraphErrors,TF1
gStyle.SetPalette(107)
if dovertical:
canvas = TCanvas('canvas','canvas',1600,2000)
canvas.Divide(2,3)
else:
canvas = TCanvas('canvas','canvas',2800,1600)
canvas.Divide(3,2)
pad1= canvas.cd(1)
sgn_histEff.SetTitle('Signal Efficiency in: '+partition_name)
sgn_histEff.SetLineColor(kRed)
sgn_histEff.SetMarkerColor(kRed)
sgn_histEff.GetYaxis().SetTitle('#epsilon('+xname+')')
sgn_histEff.GetXaxis().SetTitle(xname)
sgn_histEff.GetYaxis().SetRangeUser( 0.6, 1.1 )
sgn_histEff.Draw()
sgn_histEff_corr.SetLineColor(kBlack)
sgn_histEff_corr.SetMarkerColor(kBlack)
sgn_histEff_corr.Draw('sames')
l0 = TLine(x_min,refValue_requested,x_max,refValue_requested)
l0.SetLineColor(kBlack)
l0.Draw()
l1 = TLine(x_min,refValue,x_max,refValue)
l1.SetLineColor(kBlack)
l1.SetLineStyle(9)
l1.Draw()
leg1 = TLegend(legend_position[0],legend_position[1], legend_position[2],legend_position[3])
setLegend1(leg1)
leg1.SetHeader('Signal efficiency in '+partition_name)
leg1.AddEntry(sgn_histEff,('Old: d = %1.3f')%(b0),'p' )
lg1 = leg1.AddEntry(sgn_histEff_corr,('New: d = %1.3f + %s %1.3f')%(b,xname,a),'p' )
lg1.SetTextColor(kRed)
leg1.AddEntry(l1,('Reference: %1.3f')%(refValue) ,'l')
leg1.SetTextSize(0.03)
leg1.SetBorderSize(0)
leg1.Draw()
atlas_template(pad1,**kwargs)
pad2 = canvas.cd(2) if dovertical else canvas.cd(4)
bkg_histEff.SetTitle('Background rejection in: '+partition_name)
bkg_histEff.SetLineColor(kRed)
bkg_histEff.SetMarkerColor(kRed)
bkg_histEff.GetYaxis().SetTitle('#epsilon('+xname+')')
bkg_histEff.GetXaxis().SetTitle(xname)
bkg_histEff.Draw()
bkg_histEff_corr.SetLineColor(kBlack)
bkg_histEff_corr.SetMarkerColor(kBlack)
bkg_histEff_corr.Draw('sames')
l0.Draw()
l1.Draw()
leg2 = TLegend(legend_position[0],legend_position[1]+0.4, legend_position[2],legend_position[3]+0.4)
setLegend1(leg2)
leg2.SetHeader('Background rejection in '+partition_name)
leg2.AddEntry(sgn_histEff,('Old: d = %1.3f')%(b0),'p' )
lg2 = leg2.AddEntry(sgn_histEff_corr,('New: d = %1.3f + %s %1.3f')%(b,xname,a),'p' )
lg2.SetTextColor(kRed)
leg2.SetTextSize(0.03)
leg2.SetBorderSize(0)
leg2.Draw()
atlas_template(pad2,**kwargs)
pad3 = canvas.cd(3) if dovertical else canvas.cd(2)
sgn_hist2D.SetTitle('Neural Network output as a function fo nvtx, '+partition_name)
sgn_hist2D.GetXaxis().SetTitle('Neural Network output (Discriminant)')
sgn_hist2D.GetYaxis().SetTitle(xname)
sgn_hist2D.GetZaxis().SetTitle('')
if not useNoActivationFunctionInTheLastLayer: sgn_hist2D.SetAxisRange(-1,1, 'X' )
sgn_hist2D.Draw('colz')
pad3.SetLogz()
# Add points
g1 = TGraphErrors(len(discr_points), discr_points, np.array(nvtx_points)+limits[0], np.array(error_points), np.zeros(discr_points.shape))
g1.SetLineWidth(1)
g1.SetLineColor(kBlue)
g1.SetMarkerColor(kBlue)
g1.Draw("P same")
# Old threshold line
l2 = TLine(b0,y_min,b0,y_max)
l2.SetLineColor(kRed)
l2.SetLineWidth(2)
l2.Draw()
# New threshold line
l3 = TLine(b,y_min, a*y_max+b, y_max)
l3.SetLineColor(kBlack)
l3.SetLineWidth(2)
l3.Draw()
atlas_template(pad3,**kwargs)
pad4 = canvas.cd(4) if dovertical else canvas.cd(5)
bkg_hist2D.SetTitle('Neural Network output as a function fo nvtx, '+partition_name)
bkg_hist2D.GetXaxis().SetTitle('Neural Network output (Discriminant)')
bkg_hist2D.GetYaxis().SetTitle(xname)
bkg_hist2D.GetZaxis().SetTitle('')
if not useNoActivationFunctionInTheLastLayer: bkg_hist2D.SetAxisRange(-1,1, 'X' )
#sgn_hist2D.SetAxisRange(b+y_max*a-0.2,1, 'X' )
bkg_hist2D.Draw('colz')
pad4.SetLogz()
# Add points
g2 = TGraphErrors(len(discr_points), discr_points, np.array(nvtx_points)+limits[0], np.array(error_points), np.zeros(discr_points.shape))
g2.SetLineWidth(1)
g2.SetLineColor(kBlue)
g2.SetMarkerColor(kBlue)
g2.Draw("P same")
# Old threshold line
l4 = TLine(b0,y_min,b0,y_max)
l4.SetLineColor(kRed)
l4.SetLineWidth(2)
l4.Draw()
# New threshold line
l5 = TLine(b,y_min, a*y_max+b, y_max)
l5.SetLineColor(kBlack)
l5.SetLineWidth(2)
l5.Draw()
atlas_template(pad4,**kwargs)
from ROOT import TH1D, kAzure
from TrigEgammaDevelopments.plots import AutoFixAxes
pad5 = canvas.cd(5) if dovertical else canvas.cd(3)
#pad5.SetLogy()
h5 = TH1D(sgn_hist2D.ProjectionX())
h6 = TH1D(bkg_hist2D.ProjectionX())
if max(h5.GetMaximum(),h6.GetMaximum()) > 10*(min(h5.GetMaximum(),h6.GetMaximum())):
pad5.SetLogy()
h5.Rebin(10)
h6.Rebin(10)
h5.SetLineColor(kAzure+6)
h5.SetFillColor(kAzure+6)
h6.SetLineColor(kRed-7)
h5.Draw()
h6.Draw('sames')
AutoFixAxes(pad5,False,False,1.5)
atlas_template(pad5,**kwargs)
pad6 = canvas.cd(6)
#pad6.SetLogy()
h7 = TH1D(bkg_hist2D.ProjectionX())
h8 = TH1D(sgn_hist2D.ProjectionX())
if max(h7.GetMaximum(),h8.GetMaximum()) > 10*(min(h7.GetMaximum(),h8.GetMaximum())):
pad6.SetLogy()
h7.Rebin(10)
h8.Rebin(10)
h7.SetLineColor(kRed-7)
h7.SetFillColor(kRed-7)
h8.SetLineColor(kAzure+6)
h7.Draw()
h8.Draw('sames')
AutoFixAxes(pad6,False,False,1.5)
atlas_template(pad6,**kwargs)
canvas.SaveAs(output_name+'.pdf')
return b,a,b0