def addDataToRootFile(data, name):
global flag
global h_buf
#f = open('workfile_tmp.txt', 'a')
#f.write(data)
lines = data.split('\n')
# print lines[0]
x = TVector(len(lines)-1)
y = TVector(len(lines)-1)
h = TH1F("h","h",BINS,MIN_BIN,MAX_BIN)
for i in range(0,len(lines)-1):
if(i > 4):
pair = lines[i].split(' ')
#
h.SetBinContent(i,float(pair[1]));
#x[i] = float(pair[0])
#y[i] = float(pair[1])
# print 'x='+pair[0]+" y="+pair[1]
#g = TGraph(x,y)
if (flag == 0):
h_buf = h
flag = 1
else:
c = TCanvas()
c.cd()
h.Draw()
h_buf.Draw("same")
flag = 0
c.Write()
#h.Write(str(name))
print 'write graph'
def make_spectral_plot(image_name,
stack,
legend):
"""
:param image_name: name of output image (ext. ".png" will be added)
:type image_name: str
:param stack: stack of histograms to plot
:type stack: THStack
:param legend: legend to accompany histograms in stack
:type legend: TLegend
"""
ROOT.gROOT.SetStyle("Plain")
gStyle.SetCanvasBorderMode(0)
gStyle.SetPadBorderMode(0)
gStyle.SetPadColor(0)
gStyle.SetCanvasColor(0)
gStyle.SetOptTitle(0)
gStyle.SetLabelSize(0.06, "xyz")
gStyle.SetTitleSize(0.06, "xyz")
gStyle.SetOptStat("")
c1 = TCanvas("canvas", "canvas", 1200, 900)
c1.cd()
p1 = TPad("p1", "Region of Interest", 0.0, 0.5, 0.7, 1.0, 0)
p1.SetBottomMargin(0.15)
p1.SetLeftMargin(0.15)
p1.Draw()
p2 = TPad("p2", "Key", 0.7, 0.5, 1.0, 1.0, 0)
p2.SetBottomMargin(0.15)
p2.SetTopMargin(0.15)
p2.SetLeftMargin(0.05)
p2.SetRightMargin(0.05)
p2.Draw()
p3 = TPad("p3", "Full Spectrum", 0.0, 0.0, 1.0, 0.5, 0)
p3.SetBottomMargin(0.15)
p3.SetLeftMargin(0.15)
p3.Draw()
p3.cd()
gStyle.SetOptStat(0)
p3.SetLogy()
stack.Draw("nostack")
stack.GetXaxis().SetTitle("E (MeV)")
stack.GetYaxis().SetTitle("Events/(0.02 MeV)")
stack.SetMinimum(0.1)
p1.cd()
gStyle.SetOptStat(0)
p1.SetLogy()
stack_zoomed = stack.Clone()
x_axis = stack_zoomed.GetXaxis()
bin_width = x_axis.GetBinWidth(0) # assume uniform
e_lo = 2.0 # MeV
e_hi = 3.5 # MeV
x_axis.SetRange(int(e_lo/bin_width), int(e_hi/bin_width))
stack_zoomed.SetMaximum(5e3)
stack_zoomed.Draw("nostack")
p2.cd()
legend.Draw()
c1.Draw()
image_path = image_name + ".png"
c1.Print(image_path)
raw_input("RETURN to exit")
def plotAverageEnergyAroundL1(self):
canvas = TCanvas('canvasAverageEnergy','Average energy',900,800)
canvas.cd().SetLogz()
canvas.cd().SetRightMargin(.15)
hSum = self.fileHandler.getHistogram('averageEnergy/averageEnergyAroundPoint' + self.key + '_SummedEnergy')
hCounter = self.fileHandler.getHistogram('averageEnergy/averageEnergyAroundPoint' + self.key + '_Counter')
for i in range(0,hSum.GetNbinsX()):
for j in range(0,hSum.GetNbinsY()):
if hCounter.GetBinContent(hCounter.GetBin(i,j)) != 0:
hSum.SetBinContent(hSum.GetBin(i,j),hSum.GetBinContent(hSum.GetBin(i,j))/hCounter.GetBinContent(hCounter.GetBin(i,j)))
pass
hSum.GetXaxis().SetRangeUser(-0.6,0.6)
hSum.GetYaxis().SetRangeUser(-0.6,0.6)
hSum.GetZaxis().SetTitle('Reconstructed Energy / GeV')
hSum.SetTitle(';#Delta#eta;#Delta#phi;Reconstructed Energy / GeV')#'Average Energy in HO tiles around L1 direction
hSum.Draw('colz')
canvas.Update()
#Setup plot style
setupAxes(hSum)
setStatBoxOptions(hSum,1100)
setStatBoxPosition(hSum,x1=.65,x2=.85)
setupPalette(hSum,x2ndc=.87)
canvas.Update()
self.storeCanvas(canvas,'averageEnergy',marginRight=.15)
return canvas,hSum,hCounter,drawHoBoxes(canvas)
def plotHoDigiMatchesPerDetId(self):
canvas = TCanvas('canvasDigiMatchesMultiplicity')
digiMatches = self.fileHandler.getHistogram('hoDigiMatchesPerDetId_Multiplicity')
setupAxes(digiMatches)
digiMatches.SetTitle('Number of matches between RecHit and Digi for a given DetId')
digiMatches.GetXaxis().SetRangeUser(0,5)
digiMatches.GetXaxis().SetTitle('Number of matches per DetId')
digiMatches.GetYaxis().SetTitle('#')
canvas.cd().SetLogy()
digiMatches.SetLineWidth(3)
digiMatches.SetLineColor(colorRwthDarkBlue)
digiMatches.Draw()
label = drawLabelCmsPrivateSimulation()
canvas.Update()
stats = digiMatches.GetListOfFunctions().FindObject("stats")
stats.SetX1NDC(.7)
stats.SetX2NDC(.9)
stats.SetY1NDC(.75)
stats.SetY2NDC(.9)
canvas.Update()
self.storeCanvas(canvas,"digiMatchesPerDetId")
return canvas,digiMatches,label
def plot1DEnergyAroundL1(self):
'''
eta[P,M][2,1,0]phi[P,M][2,1,0]_averageEnergyAroundPoint
Central tile is central
'''
histList = []
fitList = []
labelList = []
canvas = TCanvas('canvas1DEnergy','1D energy',1200,1200)
for p in reversed(range(-2,3)):
for e in range(-2,3):
if e == 0 and p == 0:
histList.append(self.fileHandler.getHistogram('etaPhi/energy1D/central_averageEnergyAroundPoint' + self.key))
else:
histName = ('etaPhi/energy1D/eta%s%dPhi%s%d_averageEnergyAroundPoint' + self.key) % ('P' if e >= 0 else 'M',abs(e),'P' if p >= 0 else 'M',abs(p))
histList.append(self.fileHandler.getHistogram(histName))
canvas.Divide(5,5)
for i,hist in enumerate(histList):
canvas.cd(i+1).SetLogy()
hist.GetXaxis().SetRangeUser(-0.5,4)
hist.SetLineWidth(3)
setupAxes(hist)
hist.Draw()
fit = TF1('fit%d' % (i),'landau',0.5,2)
hist.Fit(fit,'RQ')
label = TPaveText(0.6,0.7,0.9,0.9,"NDC")
label.AddText('MPV: %5.2f' % (fit.GetParameter(1)))
label.Draw()
labelList.append(label)
fitList.append(fit)
canvas.Update()
self.storeCanvas(canvas, '1DPlots')
return histList,canvas,fitList,labelList
def main():
gROOT.SetStyle("DStyle")
c1 = TCanvas("c1","c1")
module = TH2D("mod",";x #rightarrow;y #rightarrow",64,0,64,4,0,4)
pins_to_pixels = pins("pins.csv")
pixels_to_fibers = mapit()
pins_to_count = struck("vics_list.txt",sys.argv[1])
rows = rowmaker()
module = fillhisto(module,
pins_to_pixels,
pixels_to_fibers,
pins_to_count,
rows)
c1.cd()
title=GetTitle("Raw FEB Data")
module.GetYaxis().SetNdivisions(4)
module.GetXaxis().SetNdivisions(8)
module.GetYaxis().CenterTitle()
module.GetXaxis().CenterTitle()
gStyle.SetOptStat(0)
module.Draw("COLZ")
title.Draw("SAMES")
c1.Update()
c1.Modified()
sys.stdin.readline()
def __TDrawAll2D(self):
"""
Root implementation of 2D
"""
self.mxz.SetMarkerColor(TColor.kRed)
self.hxz.SetMarkerColor(TColor.kBlue)
self.myz.SetMarkerColor(TColor.kRed)
self.hyz.SetMarkerColor(TColor.kBlue)
self.mxz.SetMarkerStyle(6)
#self.mxz.SetMarkerSize(1)
self.hxz.SetMarkerStyle(7)
#self.hxz.SetMarkerSize(2)
self.myz.SetMarkerStyle(6)
self.hyz.SetMarkerStyle(7)
#self.hyz.SetMarkerSize(2)
c1 = TCanvas( 'c1', 'Meas & Hits 2D', 200, 10, 600, 800 )
c1.Divide(1,2)
c1.cd(1)
self.mxz.Draw("")
self.hxz.Draw("same")
c1.cd(2)
self.myz.Draw("")
self.hyz.Draw("same")
s=raw_input("return to continue")
def doPlotEtaPtOfSuccessfulMatches():
file = TFile.Open('L1MuonHistogram.root')
#Prepare canvas
canvas = TCanvas("canvasPtEtaHoMatch","PtEtaHoMatch",1200,1200)
canvas.cd().Draw()
#prepare histogram
hist = file.Get("hoMuonAnalyzer/etaPhi/3D/NoTrgTdmiAboveThr_EtaPhiPt")
stack = THStack(hist,"zx","2dStack","",-1,-1,-1,-1,"zx","")
#Create new histogram and add the histograms from the stack
histNew = TH2D("histPtEtaHoMatch","p_{T} vs. #eta distribution;#eta;p_{T} / 5 GeV;#",40,-1.6,1.6,40,0,200)
histNew.GetYaxis().SetTitleOffset(1.2)
for i in stack.GetHists():
histNew.Add(i)
gStyle.SetPalette(1)
histNew.SetStats(0)
histNew.Draw('COLZ')
canvas.Update()
palette = histNew.FindObject("palette")
palette.SetX1NDC(0.9)
palette.SetX2NDC(0.92)
#add label
label = PlotStyle.getLabelCmsPrivateSimulation()
label.Draw()
canvas.Update()
canvas.SaveAs('plots/NoL1HoMatchPtEta.pdf')
canvas.SaveAs('plots/NoL1HoMatchPtEta.png')
return canvas,hist,stack,histNew,label,palette,file
def plotMatchedAndNotMatchedPerWheel(self):
resHo = self.plotEnergyPerWheel('horeco')
resHoMatched = self.plotEnergyPerWheel('L1MuonWithHoMatchAboveThr')
cTogether = TCanvas('cTogether','Matched and not Matched',1800,500)
cTogether.Divide(4,1)
plotTitles = [
'Wheel -1',
'Wheel 0 (-)',
'Wheel 0 (+)',
'Wheel +1',
]
for i in range(0,4):
cTogether.cd(i+1).SetLogy()
resHo[i].SetTitle(plotTitles[i] + ';E_{Rec} / GeV;fraction of MIP peak')
#resHo[i].GetXaxis().SetRangeUser(-1,6)
maxBin = resHoMatched[i].GetMaximumBin()#resHo[i].FindBin()
resHo[i].Scale(1/resHo[i].GetBinContent(maxBin))
resHo[i].SetStats(0)
resHo[i].Draw()
setupAxes(resHo[i])
#resHo[i].GetYaxis().SetRangeUser(4e-3,2)
resHoMatched[i].Scale(1/resHoMatched[i].GetBinContent(maxBin))
resHoMatched[i].Draw('same')
cTogether.Update()
cTogether.SaveAs('plots/energyPerWheelTogether.root')
return cTogether,resHo, resHoMatched
def plotGroomed( obs="grthrust", filenames=[ "sjm136_test.root" ], ecm="136", logy=1, canv=None ):
thplotoptions= { "xmin": 0.0, "xmax": 0.5, "ymin": 0.005, "ymax": 50.0, "markerStyle": 20, "markerSize": 0.5, "title": "groomed Thrust", "xlabel": "1-T_{gr}", "ylabel": "1/\sigma d\sigma/d(1-T_{gr})", "logy":logy }
cpplotoptions= { "xmin": 0.0, "xmax": 1.0, "ymin": 0.03, "ymax": 30.0, "markerStyle": 20, "markerSize": 0.5, "title": "groomed C-parameter", "xlabel": "C_{gr}", "ylabel": "1/\sigma d\sigma/d(C_{gr})", "logy":logy }
plotopts= { "grthrust": thplotoptions, "grcpar": cpplotoptions }
if canv == None:
canv= TCanvas( "canv", obs+" "+ecm, 1200, 800 )
icanv= 0
for beta in [ "0.0", "1.0" ]:
for zcut in [ "0.05", "0.10", "0.15" ]:
icanv= icanv+1
canv.cd( icanv )
gPad.SetLeftMargin( 0.15 )
gPad.SetRightMargin( 0.025 )
key= obs + "_" + beta + "_" + zcut
print key
aogr= createCombineAnalysisObservables( filenames, key )
aogr.plot( plotopts[obs] )
tl= TLegend( 0.4, 0.8, 0.85, 0.85 )
tl.SetTextSize( 0.05 )
tl.SetBorderSize( 0 )
tl.AddEntry( key, "OPAL "+ecm+" GeV", "ep" )
tl.Draw( "same" )
txt= TLatex( 0.6, 0.7, "#beta="+beta+ " z_{cut}="+zcut )
txt.SetNDC( True )
txt.SetTextSize( 0.035 )
txt.Draw()
return
def plotMM():
NMM=4
hmms=[[],[]]
for idt in range(DTYPS):
# hmms[idt].append(f[idt].top.hmmppippimVw.ProjectionY("0",0,-1,"e"))
# hmms[idt].append(f[idt].top.hmmppimVw.ProjectionY("1",0,-1,"e"))
# hmms[idt].append(f[idt].top.hmmppipVw.ProjectionY("2",0,-1,"e"))
# hmms[idt].append(f[idt].top.hmmpippimVw.ProjectionY("3",0,-1,"e"))
# hmms[idt].append(f[idt].Get("/top/hmmppippimVw"))#.ProjectionY())
# hmms[idt].append(f[idt].Get("/top/hmmppimVw"))#.ProjectionY())
# hmms[idt].append(f[idt].Get("/top/hmmppipVw"))#.ProjectionY())
# hmms[idt].append(f[idt].Get("/top/hmmpippimVw"))#.ProjectionY())
hmms[idt].append(f[idt].Get("/top/hmmppippimVw").ProjectionY('hmmppippim_%s'%DTYPS_NAME[idt]))
hmms[idt].append(f[idt].Get("/top/hmmppimVw").ProjectionY('hmmppim_%s'%DTYPS_NAME[idt]))
hmms[idt].append(f[idt].Get("/top/hmmppipVw").ProjectionY('hmmppip_%s'%DTYPS_NAME[idt]))
hmms[idt].append(f[idt].Get("/top/hmmpippimVw").ProjectionY('hmmpippim_%s'%DTYPS_NAME[idt]))
cmm = TCanvas()
cmm.Divide(2,2)
for imm in range(NMM):
cmm.cd(imm+1)
# hmms[EXP][imm].SetLineColor(gROOT.ProcessLine("kBlue"))
# hmms[EXP][imm].DrawNormalized("",10000)
hmms[SIM][imm].SetLineColor(gROOT.ProcessLine("kRed"))
hmms[SIM][imm].DrawNormalized("",10000)
hmms[EXP][imm].SetLineColor(gROOT.ProcessLine("kBlue"))
hmms[EXP][imm].DrawNormalized("sames",10000)
def compareShapes(histograms=[], histogramlables=[], styles=[], maxfactor = 1.3):
leg = TLegend(0.6, 0.7, 0.94, 0.92);
leg.SetBorderSize(0);
leg.SetLineStyle(0);
leg.SetTextFont(42);
leg.SetFillStyle(0);
AddLegendEntry = leg.AddEntry
c = TCanvas("compareShapes", 'compareShapes', defaultCanvasWidth, defaultCanvasHeight)
c.cd()
for hist, label, style in zip(histograms,histogramlables,styles):
hist.Sumw2()
hist = normalise(hist)
hist.SetLineColor(style['color'])
hist.SetLineWidth(4)
AddLegendEntry(hist, label, "f")
index = 0
maximum = getMax(histograms)
for hist in histograms:
if index == 0:
hist.GetYaxis().SetRangeUser(0, maximum*maxfactor)
hist.Draw('histe')
else:
hist.Draw('histe same')
index += 1
return c, leg
def GetContour(self, histo):
ctemp = TCanvas('temp' , 'tmp canvas' , 800 , 600 )
ctemp.cd()
histo.Draw('CONT Z LIST')
ctemp.Update()
conts = gROOT.GetListOfSpecials().FindObject("contours")
ret = TMultiGraph()
if conts.GetSize() == 0:
ctemp.Close()
return ret
if conts.At(0).GetSize() == 0:
ctemp.Close()
return ret
for ggid in range(0,conts.At(0).GetSize()) :
graph = conts.At(0).At(ggid).Clone( histo.GetName() + "_Con1Graph_%d" % (ggid) )
graph.SetTitle( histo.GetTitle() )
graph.SetLineWidth( histo.GetLineWidth() )
graph.SetLineColor( histo.GetLineColor() )
graph.SetLineStyle( histo.GetLineStyle() )
graph.Write()
ret.Add(graph)
ctemp.Close()
return ret
class MyHistograms:
def __init__(self, name, title, bins = 100, min = 0, max = 1000):
self.name = name
self.h1 = TH1F('h1_'+name, title, bins,min,max)
self.h2 = TH1F('h2_'+name, title, bins,min,max)
self.h2 = TH1F('h3_'+name, title, bins,min,max)
self.canMet = TCanvas('canMet'+name, 'canMet'+name)
self.canEff = TCanvas('canEff'+name, 'canEff'+name)
self.canMet.SetLogy()
self.canEff.SetLogy()
self.h1.SetLineWidth(2)
def computeEff(self):
self.eff = self.h2.Clone( 'eff_'+ self.name)
self.eff.GetYaxis().SetTitle('efficiency')
self.eff.Divide( self.h1 )
return self.eff
def setUpLegend(self, caption):
self.legend = TLegend(0.5,0.5,0.85,0.8)
self.legend.AddEntry(histos.h1,'all events')
self.legend.AddEntry(histos.h2,caption)
def draw(self):
self.canMet.cd()
self.h1.Draw()
self.h2.Draw('same')
if self.legend != None:
self.legend.Draw()
self.canMet.SaveAs(self.canMet.GetName()+'.png')
self.canEff.cd()
self.eff.Draw()
self.canEff.SaveAs(self.canEff.GetName()+'.png')
def makeTwoScalesGraph(file):
print(file)
ch1 = OPData.fromPath('../data/part3/%s' % file, 1)
ch2 = OPData.fromPath('../data/part3/%s' % file, 2)
print('make canvas + pad')
c = TCanvas('c-%s' % file, '', 1280, 720)
pad = TPad('pad-%s' % file, '', 0, 0, 1, 1)
pad.Draw()
pad.cd()
print('frame')
frame = pad.DrawFrame(0, min(ch1.getY()) * 1.1, 0.051, max(ch1.getY()) * 1.1)
frame.SetXTitle('Zeit t / s')
frame.GetXaxis().CenterTitle()
frame.SetYTitle('Spannung Spule 2: U_{2} / V')
frame.GetYaxis().CenterTitle()
frame.GetYaxis().SetLabelColor(4)
frame.GetYaxis().SetTitleColor(4)
print('g1')
g1 = ch1.makeGraph('g1-%s' % file)
prepareGraph(g1)
g1.Draw('PX')
print('overlay')
c.cd()
overlay = TPad('overlay-%s' % file, '', 0, 0, 1, 1)
overlay.SetFillStyle(4000) # transparent
overlay.SetFillColor(0) # white
overlay.SetFrameFillStyle(4000) # transparent
overlay.Draw()
overlay.cd()
print('g2')
g2 = ch2.makeGraph('g2-%s' % file)
prepareGraph(g2, 2)
g2ymin = min(ch2.getY())
xmin = pad.GetUxmin()
xmax = pad.GetUxmax()
ymin = 1.1 * g2ymin
ymax = abs(ymin)
if file == '07.tab': # same scale like 06.tab
ymin, ymax = -0.07128, 0.07128
oframe = overlay.DrawFrame(xmin, ymin, xmax, ymax)
oframe.GetXaxis().SetLabelOffset(99)
oframe.GetYaxis().SetLabelOffset(99)
oframe.GetYaxis().SetTickLength(0)
g2.Draw('PX')
print('axis')
axis = TGaxis(xmax, ymin, xmax, ymax, ymin, ymax, 510, "+L")
axis.SetTitle('Spannung Photodiode: U_{ph} / V')
axis.CenterTitle()
axis.SetTitleOffset(1.2)
axis.Draw()
print('print')
c.Update()
c.Print('../img/part3/%s.pdf' % file[:-4], 'pdf')
def plotHistNorm2(self,fname,name1,name2,logy=False):
cnv = TCanvas(name1,"",600,600)
cnv.Draw()
cnv.cd()
if(logy): cnv.SetLogy()
self.histos[name1].SetMinimum(0)
self.histos[name2].SetMinimum(0)
int1 = self.histos[name1].Integral()
max1 = self.histos[name1].GetMaximum()
int2 = self.histos[name2].Integral()
max2 = self.histos[name2].GetMaximum()
if(max2/int2>max1/int1):
self.histos[name2].DrawNormalized()
self.histos[name1].DrawNormalized("same")
else:
self.histos[name1].DrawNormalized()
self.histos[name2].DrawNormalized("same")
leg = TLegend(0.5,0.60,0.87,0.9,"","brNDC");
leg.SetFillStyle(4000); # will be transparent
leg.SetFillColor(0);
leg.SetTextFont(42);
leg.SetBorderSize(0);
leg.AddEntry(0, "MadGraph+Pythia8 (OTF)", "");
leg.AddEntry(0, "#it{pp}#rightarrow#it{t}#bar{#it{t}}#rightarrow#mu+jets", "");
leg.AddEntry(0, "Resolved selection", "");
leg.AddEntry(self.histos[name1],"gg production","ple");
leg.AddEntry(self.histos[name2],"q#bar{q} production","ple");
leg.Draw("same")
cnv.Update()
cnv.RedrawAxis()
cnv.SaveAs(fname)
def plotHist2(self,fname,name1,name2,logy=False):
cnv = TCanvas(name1,"",600,600)
cnv.Draw()
cnv.cd()
if(logy): cnv.SetLogy()
self.histos[name1].SetMinimum(0)
self.histos[name2].SetMinimum(0)
if(self.histos[name2].GetMaximum()>self.histos[name1].GetMaximum()): self.histos[name1].SetMaximum(self.histos[name2].GetMaximum()*1.2)
else: self.histos[name1].SetMaximum(self.histos[name1].GetMaximum()*1.2)
self.histos[name1].Draw()
self.histos[name2].Draw("same")
leg = TLegend(0.5,0.60,0.87,0.9,"","brNDC");
leg.SetFillStyle(4000); # will be transparent
leg.SetFillColor(0);
leg.SetTextFont(42);
leg.SetBorderSize(0);
leg.AddEntry(0, "MadGraph+Pythia8 (OTF)", "");
leg.AddEntry(0, "#it{gg}#rightarrow#it{t}#bar{#it{t}}#rightarrow#mu+jets", "");
leg.AddEntry(0, "Resolved selection", "");
leg.AddEntry(self.histos[name2],"Hard Process tops","ple");
leg.AddEntry(self.histos[name1],"Reconstructed tops","ple");
leg.Draw("same")
cnv.Update()
cnv.RedrawAxis()
cnv.SaveAs(fname)
def makePlot(tasks):
da = TDatime(2011,01,01,12,00,00);
gStyle.SetTimeOffset(da.Convert());
ct = TCanvas("ct","Time on axis");
ndays = 390
toodledo = TH1F("toodledo","toodledo",ndays,0.,ndays*86400);
for i in range(1,toodledo.GetNbinsX()+1):
try:
num = tasks[i]
except:
num = 0
toodledo.SetBinContent(i,num);
ct.cd(2);
toodledo.SetStats(0)
toodledo.GetXaxis().SetLabelSize(0.04);
toodledo.GetXaxis().SetTimeDisplay(1);
toodledo.GetXaxis().SetNdivisions(5);
toodledo.GetXaxis().SetTimeFormat("%d\/%m\/%y");
toodledo.Draw();
ct.Modified()
ct.Update()
return ct, toodledo
class PadService:
def __init__(self, name, title, nPlots):
if nPlots == 1:
self.nPadsX = 1
self.nPadsY = 1
self.width = 600
self.height = 1.0 * self.width
elif nPlots == 2:
self.nPadsX = 1
self.nPadsY = 2
self.height = 600
self.width = 0.55*self.height;
elif nPlots > 2 and nPlots < 5:
self.nPadsX = 2
self.nPadsY = 2
self.width = 600
self.height = 0.8 * self.width;
elif nPlots > 4 and nPlots < 7:
self.nPadsX = 2
self.nPadsY = 3
self.height = 800
self.width = 0.7 * self.height;
elif nPlots > 6 and nPlots < 9:
self.nPadsX = 2
self.nPadsY = 4
self.height = 800
self.width = 0.6 * self.height;
else:
self.nPadsX = 3
self.nPadsY = 4
self.height = 800
self.width = 0.8 * self.height;
self.last = None
self.index = 0
self.count = 0
self.name = name
self.title = title
def Next(self):
if not self.last:
self.count += 1
name = '%s_%d' % (self.name, self.count)
self.last = TCanvas(name.__str__(), self.title.__str__(), int(self.width), int(self.height))
self.last.SetBorderSize(2);
self.last.SetFrameFillColor(0);
self.last.SetHighLightColor(0);
self.last.Divide(self.nPadsX, self.nPadsY);
self.index = 0;
self.count+=1
self.index += 1
self.last.cd(self.index);
#prevent override
if self.index > self.nPadsX * self.nPadsY:
print "EERER"
self.last = None
else:
pad = self.last.GetPad(self.index)
return pad
def plotAverageEMaxAroundL1(self):
canvas = TCanvas('canvasAverageEMax','Average EMax',1200,1200)
canvas.cd().SetLogz()
hSum = self.fileHandler.getHistogram('hoMuonAnalyzer/deltaEtaDeltaPhiEnergy/averageEMaxAroundPoint' + self.key + '_2dSummedWeights')
hCounter = self.fileHandler.getHistogram('hoMuonAnalyzer/deltaEtaDeltaPhiEnergy/averageEMaxAroundPoint' + self.key + '_2dCounter')
hSum = setupEAvplot(hSum, hCounter,same=True,limitForAll=0.3)
hSum.SetTitle('Mean E_{Max} in HO tiles around L1 direction')
hSum.SetMaximum(2)
hSum.Draw('colz')
setupEAvplot(hCounter,same=True,limitForAll=0.3).Draw('same,text')
label = getLabelCmsPrivateSimulation()
label.Draw()
canvas.Update()
setupPalette(hSum)
canvas.Update()
self.storeCanvas(canvas, 'averageEmax')
hCounter.SaveAs('histogramEMaxCounter.root')
return canvas,hSum,label,hCounter
def make_page(drawables, name="plots", title="plots", landscape=False, dpi=96, margin=2, split=(2,3), format="pdf", log=False):
inch_per_cm = 0.3937
wx, wy = (21 - 2*margin)*inch_per_cm*dpi, (29.7 - 2*margin)*inch_per_cm*dpi
sx, sy = split
if landscape:
wx, wy = wy, wx
sx, sy = sy, sx
wx, wy = int(wx), int(wy)
saver = []
n_per_page = sx*sy
pages = int(ceil(len(drawables)*1.0/n_per_page))
for page in range(1, pages+1):
on_page = drawables[(page-1)*n_per_page:page*n_per_page]
c = TCanvas("%s_p%i" % (name, page), "%s (Page %i/%i)" % (title, page, pages), wx, wy)
c.SetRightMargin(wx/2.0)
c.Divide(sx, sy)
for i in range(len(on_page)):
c.cd(i+1)
if log:
c.SetLogy(i+1)
saver.append(on_page[i]())
if log:
gPad.SetLogy(True)
c.SetLogy(i+1)
c.cd()
c.SaveAs("%s_%i.%s" % (name, page, format))
def draw_hist(BCALhist):
c1 = TCanvas('c1','c1',600,900) # Creates the canvas to draw the histogram to.
c1.Divide(1,2) # separates the canvas into two for two plots
c1.SetGrid() # Adds grid lines to canvas.
#hithist.LabelsDeflate("X") # Trim the number of bins to match the number of active labels - Took ages to find this, didn't work without.
c1.cd(1) # selects first canvas
BCALhist.Draw("COLZ") # Draws the histogram to the canvas, sets draw style.
BCALhist.SetStats(0) # removes stats legend
BCALhist.SetXTitle("X") # X axis label
BCALhist.SetYTitle("Y") # Y axis label
c1.Update() # Makes the canvas show the histogram.
c1.cd(2) # selects second canvas
BCALhist.Draw("SURF1") # Draws the histogram to the canvas.
BCALhist.SetStats(0)
BCALhist.SetXTitle("X")
BCALhist.SetYTitle("Y")
c1.Update() # Makes the canvas show the histogram.
img = ROOT.TImage.Create() # creates image
img.FromPad(c1) # takes it from canvas
img.WriteImage("BCALsurf-z.png") # Saves it as png file with this name the the input file directory.
return c1 # returns the canvas with histograms drawn to it
def drawGraph(self,g,figDir="",SetLogx=False,SetLogy=False,option='APL'):
'''
output graph to file
'''
title = g.GetTitle()
name = g.GetName()
if SetLogx: name += '_logx'
if SetLogy: name += '_logy'
pdf = os.path.join( figDir, name + '.pdf')
xsize,ysize = 1100,850 # landscape style
noPopUp = True
if noPopUp : gROOT.ProcessLine("gROOT->SetBatch()")
canvas = TCanvas(pdf,title,xsize,ysize)
g.Draw(option)
if SetLogy: canvas.SetLogy(1)
if SetLogx: canvas.SetLogx(1)
canvas.Draw()
canvas.SetGrid(1)
canvas.SetTicks(1)
canvas.cd()
canvas.Modified()
canvas.Update()
canvas.Print(pdf,'pdf')
return
def drawFit(self,h,figdir='',SetLogy=False,SetLogx=False,extraName=None):
'''
draw histogram with fit parameters
'''
name = h.GetName()
if extraName is not None: name += '_' + extraName
title = h.GetTitle()
if SetLogx: name += '_logx'
if SetLogy: name += '_logy'
pdf = os.path.join( figdir , name + '.pdf' )
ps = os.path.join( figdir , name + '.ps' )
xsize,ysize = 1100,850 # landscape style
noPopUp = True
if noPopUp : gROOT.ProcessLine("gROOT->SetBatch()")
canvas = TCanvas(pdf,title,xsize,ysize)
gStyle.SetOptFit(1111)
h.Draw()
if SetLogy: canvas.SetLogy(1)
if SetLogx: canvas.SetLogx(1)
canvas.Draw()
canvas.SetGrid(1)
canvas.SetTicks(1)
canvas.cd()
canvas.Modified()
canvas.Update()
canvas.Print(ps,'Landscape')
os.system('ps2pdf ' + ps + ' ' + pdf)
if os.path.exists(pdf): os.system('rm ' + ps)
return
def plotPtAndPhiOfWrongBxId(self):
#Prepare canvas
canvas = TCanvas("canvasPtPhiBxWrong","PtPhiBxWrong",1200,1200)
canvas.cd().Draw()
#prepare histogram
hist = self.fileHandler.getHistogram("etaPhi/3D/BxWrongGen_EtaPhiPt")
stack = THStack(hist,"zy","2dStack","",-1,-1,-1,-1,"zy","")
#Create new histogram and add the histograms from the stack
histNew = TH2D("histPtPhiBxWrong","p_{T} vs. #phi distribution for wrong BX ID;#phi;p_{T} / 5 GeV;#",80,-3.2,3.2,40,0,200)
histNew.GetYaxis().SetTitleOffset(1.2)
for i in stack.GetHists():
histNew.Add(i)
gStyle.SetPalette(1)
histNew.SetStats(0)
setupAxes(histNew)
histNew.Draw('COLZ')
canvas.Update()
palette = histNew.FindObject("palette")
palette.SetX1NDC(0.9)
palette.SetX2NDC(0.92)
#add label
label = getLabelCmsPrivateSimulation()
label.Draw()
canvas.Update()
self.storeCanvas(canvas,"bxWrongPtPhi")
return canvas,hist,stack,histNew,label
def drawMCMass( tree, list ):
'''
draws different simulated mass distributions
list: list containing integers from 0-18
returns: void ( histgram will be saved )
'''
masses = weightToMass()
from ROOT import TCanvas,TLegend
can = TCanvas()
can.cd()
can.SetCanvasSize(1400, 800)
leg = TLegend(.8,.6,.95,.9)
leg.SetBorderSize(0)
leg.SetFillColor(0)
leg.SetShadowColor(0)
histlist = []
niceColors = [1,2,4,6,9,12,46]
for colorindex, massindex in enumerate( list ):
histo = createHistoFromTree(tree, "mc_w_m", "weight[{}]".format(massindex), 100, 75, 85)
histo.SetTitle(";M_{W} [GeV];Eintr#ddot{a}ge")
histo.SetLineColor( niceColors[colorindex] )
histo.SetLineWidth(3)
if histlist == []:
histo.Draw("hist")
else:
histo.Draw("hist,same")
histlist.append( histo )
leg.AddEntry(histo, "{0} GeV".format(masses[massindex]), "l")
leg.Draw()
can.SaveAs('diffenentMcMasses.pdf')
can.Close()
def splot( pdf, sdata ) :
# switch off all yields, except current one
from contextlib import contextmanager
@contextmanager
def __select_component( i, yields ):
orig = dict( (j,j.getVal()) for j in yields )
[ j.setVal(0) for j in orig.iterkeys() if j!=i ]
try : yield
finally : [ j.setVal(v) for (j,v) in orig.iteritems() ]
from ROOT import TCanvas, kDashed, kRed, kGreen, kBlue, kBlack
canvas = TCanvas(pdf.GetName() + '_splot')
obs = [ o for o in pdf.Observables() if hasattr(o,'frame') and o not in sdata.usedObservables() ]
for (p,o) in zip( canvas.pads(len(obs)), obs ) :
# select yields
_yields = [ y for y in pdf.Parameters() if y.getAttribute('Yield') ]
# loop over components
for (pp,i) in zip( p.pads(1,len(_yields)), _yields ) :
# switch off all yields, except current one
with __select_component( i, _yields ) :
# plot both weighed data and PDF
# TODO: add the same color coding as above...
c_name = i.GetName()[2:]
c_opts = { 'signal' : dict( LineColor = kGreen )
, 'psi_background' : dict( LineColor = kRed )
, 'cmb_background' : dict( LineColor = kBlue )
}
from P2VV.Utilities.Plotting import plot
plot( pp, o, sdata.data( c_name ), pdf, pdfOpts = c_opts[c_name] if c_name in c_opts else {})
return canvas
def plotHitTruthPositions(topPlotPath,topPlotTitle,bottomPlotPath,bottomPlotTitle) :
#Create a fresh canvas
c = TCanvas("c","",1200,800)
c.Divide(1,2)
#First hit plot
h_primaryPositronHits = rh.getFromFile(rootFile,topPlotPath)
c.cd(1)
h_primaryPositronHits.SetTitle(topPlotTitle)
#h_primaryPositronHits.GetYaxis().SetTitleOffset(1.5)
h_primaryPositronHits.SetStats(False)
h_primaryPositronHits.GetYaxis().SetRangeUser(-200.,400.)
h_primaryPositronHits.Draw("COLZ")
#Second hit plot
h_secondaryHits = rh.getFromFile(rootFile,bottomPlotPath)
c.cd(2)
h_secondaryHits.SetTitle(bottomPlotTitle)
#h_secondaryHits.GetYaxis().SetTitleOffset(1.5)
h_secondaryHits.SetStats(False)
h_secondaryHits.GetYaxis().SetRangeUser(-200.,400.)
h_secondaryHits.Draw("COLZ")
raw_input("Press Enter to continue...")
def plotEMaxCounts(self):
canvas = TCanvas('canvasEmaxcounts','E max counts',1200,1200)
canvas.cd().SetLogz()
hCounter = self.fileHandler.getHistogram('hoMuonAnalyzer/deltaEtaDeltaPhiEnergy/averageEMaxAroundPointL1MuonPresent_2dCounter')
#hSum = setupEAvplot(hSum, hCounter,same=True,limitForAll=0.3)
hCounter.SetTitle('# of E_{Max} in HO tiles around L1 direction;#Delta#eta;#Delta#phi;# Entries')
hCounter.SetStats(0)
hCounter.GetXaxis().SetRangeUser(-.5,.5)
hCounter.GetYaxis().SetRangeUser(-.5,.5)
hCounter.Draw('colz')
label = self.drawLabel()
canvas.Update()
setupPalette(hCounter)
canvas.Update()
#boxes = drawHoBoxes(canvas)
self.storeCanvas(canvas,'eMaxCounts')
#Calculate fraction in 3x3 grid
integralCentral = hCounter.Integral(hCounter.GetXaxis().FindBin(-.0435),hCounter.GetXaxis().FindBin(.0435),
hCounter.GetYaxis().FindBin(-.0435),hCounter.GetYaxis().FindBin(.0435))
integral3x3 = hCounter.Integral(hCounter.GetXaxis().FindBin(-.1305),hCounter.GetXaxis().FindBin(.1305),
hCounter.GetYaxis().FindBin(-.1305),hCounter.GetYaxis().FindBin(.1305))
integralTotal = hCounter.Integral()
self.output(80*'#')
self.output('%20s:%5.2f%% +/- %5.2f%%' % ('Central Fraction',calcPercent(integralCentral,integralTotal),
calcSigma(integralCentral,integralTotal)*100))
self.output('%20s:%5.2f%% +/- %5.2f%%' % ('3x3 Fraction',calcPercent(integral3x3,integralTotal),
calcSigma(integral3x3,integralTotal)*100))
self.output(80*'#')
return canvas,label,hCounter
def plotQualityCodesStacked(gridSize):
gridString = gridSizeDict.get(gridSize)
c = TCanvas('cQualityCodes' + gridString + 'Stacked','Stacked QC ' + gridString,600,0,800,600)
c.SetLogy()
c.cd().SetBottomMargin(0.15)
c.cd().SetRightMargin(0.20)
qualityCodes = fileHandler.getHistogram('hoMuonAnalyzer/multiplicity/L1MuonQualityCodes' + gridString + '_Multiplicity')
qualityCodesFail = fileHandler.getHistogram('hoMuonAnalyzer/multiplicity/L1MuonQualityCodes' + gridString + 'Fail_Multiplicity')
countQualityCodes = fileHandler.getHistogram('hoMuonAnalyzer/multiplicity/L1MuonAllQualityCodes_Multiplicity')
countQualityCodesTruth = fileHandler.getHistogram('hoMuonAnalyzer/multiplicity/L1MuonTruthAllQualityCodes_Multiplicity')
print
cli.output('Sanity check for quality code counts')
for i in range(1,8):
nTotalHistogram = countQualityCodes.GetBinContent(countQualityCodes.FindBin(i))
nFail = qualityCodesFail.GetBinContent(qualityCodesFail.FindBin(i))
nPass = qualityCodes.GetBinContent(qualityCodes.FindBin(i))
nSummed = nFail + nPass
print
cli.output('NTotal: %d\t\tNSummed: %d' % (nTotalHistogram,nSummed))
cli.output('Sanity check: %s'% (CliColors.OKBLUE + 'OK' + CliColors.ENDC if nTotalHistogram == nSummed else CliColors.FAIL + 'FAIL' + CliColors.ENDC) )
print
if nTotalHistogram:
qualityCodes.SetBinContent(qualityCodes.FindBin(i),nPass/float(nTotalHistogram))
qualityCodesFail.SetBinContent(qualityCodesFail.FindBin(i),nFail/float(nTotalHistogram))
stack = THStack("hstack","Quality Codes in matching to HO (" + gridString + ");;rel. fraction")
qualityCodes.SetLineColor(colorRwthDarkBlue)
qualityCodes.SetFillColor(colorRwthDarkBlue)
qualityCodes.SetFillStyle(3002)
qualityCodesFail.SetFillColor(colorRwthMagenta)
qualityCodesFail.SetLineColor(colorRwthMagenta)
qualityCodesFail.SetFillStyle(3002)
stack.Add(qualityCodes)
stack.Add(qualityCodesFail)
stack.Draw()
stack.GetXaxis().SetRangeUser(0,8)
#Label the bins with the meaning of the quality code
for i in range(1,8):
stack.GetXaxis().SetBinLabel(stack.GetXaxis().FindBin(i),qualityCodeDict.get(i))
legend = TLegend(0.82,0.75,0.99,0.9)
legend.AddEntry(qualityCodes,"Passed","f")
legend.AddEntry(qualityCodesFail,"Failed","f")
legend.Draw()
label = drawLabelCmsPrivateSimulation(x1ndc=0.5,y1ndc=0.9,x2ndc=0.8,y2ndc=0.93)
setupAxes(stack)
c.Update()
c.SaveAs('plots/efficiency/qualityCodesStacked' + gridString + '.pdf')
return stack,c,qualityCodes,qualityCodesFail,legend,label
h_El_dataSS_a = ScaleHisto(h_El_dataSS_a, 1)
h_q2_dataSS_a = ScaleHisto(h_q2_dataSS_a, 1)
h_mmiss_dataSS_a = ScaleHisto(h_mmiss_dataSS_a, 1)
h_LbM_dataSS_a = ScaleHisto(h_LbM_dataSS_a, 1)
h_El_dataSS_b = ScaleHisto(h_El_dataSS_b, 1)
h_q2_dataSS_b = ScaleHisto(h_q2_dataSS_b, 1)
h_mmiss_dataSS_b = ScaleHisto(h_mmiss_dataSS_b, 1)
h_El_dataSS_f = ScaleHisto(h_El_dataSS_f, 1)
h_q2_dataSS_f = ScaleHisto(h_q2_dataSS_f, 1)
h_mmiss_dataSS_f = ScaleHisto(h_mmiss_dataSS_f, 1)
h_LbM_dataSS_f = ScaleHisto(h_LbM_dataSS_f, 1)
#plot the full range kinematical templates superimposed
c0 = TCanvas('c0', 'full range', 1500, 500)
c0.Divide(3, 1)
c0.cd(1)
h_El_data_f.SetMarkerStyle(20)
h_El_data_f.SetMarkerSize(1)
h_El_data_f.Draw('PE0')
h_El_dataSS_f.SetLineColor(r.kRed)
h_El_dataSS_f.Draw('hist sames')
legend = r.TLegend(0.6, 0.75, 0.9, 0.9)
legend.AddEntry(h_El_dataSS_f, 'Data SS', "l")
legend.AddEntry(h_El_data_f, 'Data OS', "pl")
legend.Draw()
c0.cd(2)
h_q2_data_f.SetMarkerStyle(20)
h_q2_data_f.SetMarkerSize(1)
h_q2_data_f.Draw('PE0')
def SignificanceOpt(Lumi_scalefactor = 1.00, plot = "n",speak="") :
if speak!='mute': print("------- Significance Optimization -------")
# Prepare histograms
if speak!='mute': print(" Info: Mass distribution in the 4 lepton channel")
h_sig = GetMassDistribution(125, Lumi_scalefactor)
h_bgr = GetMassDistribution(1, Lumi_scalefactor)
h_data = GetMassDistribution(2, Lumi_scalefactor)
# Compute significance for various mass windows
# Define histograms for mass windows to try
h_masswindow = TH1D("h_masswindow", "", 250, 0.,25.) #full width \in [0,25]
h_masswindow_exp = TH1D("h_masswindow_expected", "", 250, 0.,25.)
h_masswindow_obs = TH1D("h_masswindow_observed", "", 250, 0.,25.)
# Loop over various mass windows
for i in range( 0,h_masswindow.GetNbinsX() ):
masswindow_fullwidth = h_masswindow.GetBinCenter(i)
if speak!='mute': print(" Trying as mass window: ",masswindow_fullwidth," GeV\n")
# determine events in the mass window for each event type
Ndata = h_data.Integral( h_data.FindBin(125-0.5*masswindow_fullwidth),h_data.FindBin(125+0.5*masswindow_fullwidth) )
Nbgr = h_bgr.Integral( h_bgr.FindBin(125-0.5*masswindow_fullwidth),h_bgr.FindBin(125+0.5*masswindow_fullwidth) )
Nsig = h_sig.Integral( h_sig.FindBin(125-0.5*masswindow_fullwidth),h_sig.FindBin(125+0.5*masswindow_fullwidth) )
if speak!='mute': print("Number of events: ", Nsig, "\n")
if( (Nbgr+Nsig)<1 ): continue
# Computing EXPECTED significance
pvalue_exp = IntegratePoissonFromRight(Nbgr, Nsig+Nbgr)
significance_exp = ROOT.Math.gaussian_quantile_c(pvalue_exp,1)
h_masswindow_exp.SetBinContent(i, significance_exp)
# Computing OBSERVED significance
pvalue_obs = IntegratePoissonFromRight(Nbgr, Ndata)
significance_obs = ROOT.Math.gaussian_quantile_c(pvalue_obs,1)
h_masswindow_obs.SetBinContent(i, significance_obs)
if(pvalue_obs<0): continue
# Getting maximum
MaxBin_exp = h_masswindow_exp.GetMaximumBin()
OptimumSign_exp = h_masswindow_exp.GetBinContent(MaxBin_exp)
OptMassWindow_exp = h_masswindow_exp.GetXaxis().GetBinCenter(MaxBin_exp)
if speak!='mute':
print("Luminosity factor: ", Lumi_scalefactor,"\n")
print("EXPECTED significance - Optimal mass window\n")
print(" Expected significance: ",OptimumSign_exp,"\n")
print(" Mass window: ", OptMassWindow_exp, "\n")
if( abs(Lumi_scalefactor-1.0)<0.01 ):
MaxBin_obs = h_masswindow_obs.GetMaximumBin()
OptimumSign_obs = h_masswindow_obs.GetBinContent(MaxBin_obs)
OptMassWindow_obs = h_masswindow_obs.GetXaxis().GetBinCenter(MaxBin_obs)
if speak!='mute':
print("Luminosity factor: ", Lumi_scalefactor,"\n")
print("OBSERVED significance - Optimal mass window\n")
print(" Observed significance: ",OptimumSign_obs,"\n")
print(" Mass window: ", OptMassWindow_obs, "\n")
# plot and save histograms
if(plot == "y"):
canvas1 = TCanvas( "canvas1","Standard Canvas",600,400);
canvas1.SetLeftMargin(0.125);
canvas1.SetBottomMargin(0.125);
canvas1.cd();
h_masswindow_exp.SetLineColor(1);
h_masswindow_exp.SetLineWidth(2);
h_masswindow_obs.SetLineColor(4);
h_masswindow_obs.SetLineWidth(2);
h_masswindow_exp.SetAxisRange(-1.,6.,"Y");
h_masswindow_exp.SetStats(kFALSE)
h_masswindow_exp.SetTitle("OPTIMAL MASS WINDOW")
h_masswindow_exp.GetXaxis().SetTitle("mass window [GeV]")
h_masswindow_exp.GetYaxis().SetTitle("significance Z")
leg1 = TLegend(0.70,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0); #leg1.SetO
leg1a = leg1.AddEntry(h_masswindow_exp, "expected", "l"); leg1a.SetTextSize(0.04);
h_masswindow_exp.Draw("l");
if( abs(Lumi_scalefactor-1.00)<0.01 ):
h_masswindow_obs.Draw("l same");
leg1b = leg1.AddEntry(h_masswindow_obs, "observed", "l"); leg1b.SetTextSize(0.04);
leg1.Draw()
canvas1.Print("Plots/Significance_Optimization_lumiscalefactor"+str(Lumi_scalefactor)+".pdf");
return OptimumSign_exp;
inputReconFile = TFile(str(reconFiles[i])) #tuple files after cuts
inputTruthFile = TFile(str(reconFiles[i + nMass])) #truth files
inputReconFile.Get("cut").Draw(
"triEndZ>>histoRecon({0},{1},{2})".format(nBins, targZ, maxZ),
"triP>0.8*{0}".format(eBeam))
histoRecon = ROOT.gROOT.FindObject("histoRecon")
inputTruthFile.Get("ntuple").Draw(
"triEndZ>>histoTruth({0},{1},{2})".format(nBins, targZ, maxZ),
"triP>0.8*{0}".format(eBeam))
histoTruth = ROOT.gROOT.FindObject("histoTruth")
#Write the efficiency for a given mass (row) as function of z
for j in range(nBins):
if (histoTruth.GetBinContent(j + 1) == 0): textfile.write(str(0))
else:
textfile.write(
str(
histoRecon.GetBinContent(j + 1) /
histoTruth.GetBinContent(j + 1)) + " ")
textfile.write("\n")
textfile.close()
#Make test plots if desired
if (makeTestPlots):
c = TCanvas("c", "c", 1200, 900)
c.Print(outfile + ".pdf[")
for i in range(1, nMass - 1):
plotTest(i, outfile + ".eff", outfile, targZ, maxZ)
c.Print(outfile + ".pdf]")
def PoissonError(nObs, ErrorType, plot=""):
# Prepare histograms
LambdaMin = 0.
LambdaMax = nObs + 6*np.sqrt(nObs)
Nsteps = 1000
h_likelihood = TH1D( "h_likelihood","",Nsteps,LambdaMin,LambdaMax )
h_2loglik = TH1D( "h_2loglik","",Nsteps,LambdaMin,LambdaMax )
h_pdf_full = TH1D( "h_pdf_full","",Nsteps,LambdaMin,LambdaMax )
IntFraction =ROOT.Math.gaussian_cdf(-1,1,0)
# loop over possible Lambda values
for iBin in range(1,Nsteps+1):
Lambda=h_likelihood.GetBinCenter(iBin)
PoissonProb = TMath.Poisson(nObs,Lambda)
LogLikelihood = -2.*TMath.Log(PoissonProb)
h_likelihood.Fill( Lambda,PoissonProb )
h_2loglik.Fill( Lambda, LogLikelihood )
h_pdf_full.Fill( Lambda, PoissonProb*1. )
# get characteristic values
bin_central = h_2loglik.GetMinimumBin()
LoglikMin = h_2loglik.GetBinContent(bin_central)
Lambda_central = h_2loglik.GetBinCenter(bin_central)
LambdaLow = -1.
LambdaUp = -1.
if ErrorType=="ClassicalCentral": # Frequentist
NobsMax = nObs+100
for iBin in range(1,h_pdf_full.GetNbinsX()+1):
Lambda = h_pdf_full.GetBinCenter(iBin)
PoissonSumLow = 0.
PoissonSumUp = 0.
for i in range(nObs,NobsMax+1):
PoissonSumLow += TMath.Poisson(i,Lambda)
if( PoissonSumLow>IntFraction and LambdaLow<0 ):
LambdaLow = Lambda
for i in range(0,nObs+1):
PoissonSumUp += TMath.Poisson(i,Lambda)
if( PoissonSumUp<IntFraction and LambdaUp<0 ):
LambdaUp = Lambda
cvs = TCanvas("Standard Canvas","",600,600)
cvs.Divide(1,2)
cvs.cd(1)
hLow = TH1D("hLow","CLASSICAL CENTRAL",1000,0,17)
for i in range(1,hLow.GetNbinsX()+1):
hLow.SetBinContent(i, TMath.Poisson( hLow.GetBinCenter(i),LambdaLow ))
hLow.SetLineColor(1)
hLow.GetXaxis().SetTitle(r"#mu")
h_conf = hLow.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)<=nObs): h_conf.SetBinContent(i, 0)
h_conf.SetFillColorAlpha(9,0.5)
h_conf.SetLineWidth(1)
h_conf.SetLineColor(1)
LowLine = TLine(LambdaLow,0,LambdaLow,hLow.GetBinContent(hLow.FindBin(LambdaLow)))
hLow.SetStats(kFALSE)
hLow.Draw("l")
h_conf.Draw("l same")
LowLine.Draw()
text = TLatex()
text.SetTextSize(0.5)
text.SetTextColor(1)
text.DrawLatex(0.5,0.8, "LOL")
cvs.cd(2)
hUp = TH1D("hUp","",1000,0,17)
for i in range(1,hUp.GetNbinsX()+1):
hUp.SetBinContent(i, TMath.Poisson( hLow.GetBinCenter(i),LambdaUp ))
hUp.SetLineColor(1)
hUp.GetXaxis().SetTitle(r"#mu")
h_conf1 = hUp.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)>=nObs): h_conf1.SetBinContent(i, 0)
h_conf1.SetFillColorAlpha(9,0.5)
h_conf1.SetLineWidth(1)
h_conf1.SetLineColor(1)
UpLine = TLine(LambdaUp,0,LambdaUp,hUp.GetBinContent(hUp.FindBin(LambdaUp)))
hUp.SetStats(kFALSE)
hUp.Draw("l")
h_conf1.Draw("l same")
UpLine.Draw()
cvs.cd()
leg1 = TLegend(0.35,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
h_conf.SetMarkerColor(0)
leg1a = leg1.AddEntry(h_conf, r"(#mu_{low}, #mu_{up}) = ("+str(round(LambdaLow,2))+","+str(round(LambdaUp,2))+")","p"); leg1a.SetTextSize(0.04);
leg1.Draw()
cvs.Print("Plots/Errors_"+ErrorType+".eps")
if ErrorType=="LikelihoodRatio":
for i in range(1,h_2loglik.GetNbinsX()+1):
if (h_2loglik.GetBinCenter(i)<LoglikMin and h_2loglik.GetBinContent(i)-LoglikMin>=1.): LambdaLow=h_2loglik.GetBinCenter(i)
if (h_2loglik.GetBinCenter(i)>LoglikMin and h_2loglik.GetBinContent(i)-LoglikMin<=1.): LambdaUp=h_2loglik.GetBinCenter(i)
cvs = TCanvas("Standard Canvas","",600,400)
cvs.cd()
LowLine = TLine(LambdaLow,2,LambdaLow,h_2loglik.GetBinContent(h_2loglik.FindBin(LambdaLow))); LowLine.SetLineWidth(1)
UpLine = TLine(LambdaUp,2,LambdaUp,h_2loglik.GetBinContent(h_2loglik.FindBin(LambdaUp))); UpLine.SetLineWidth(1)
ObsLine = TLine(nObs,2,nObs,h_2loglik.GetBinContent(h_2loglik.FindBin(nObs))); ObsLine.SetLineWidth(1); ObsLine.SetLineColor(2); ObsLine.SetLineStyle(7)
h_2loglik.SetFillColor(0)
h_2loglik.SetStats(kFALSE)
h_2loglik.SetTitle("LIKELIHOOD RATIO")
h_2loglik.SetAxisRange(0.8,8,"X")
h_2loglik.SetAxisRange(2,7,"Y")
h_2loglik.Draw("hist lp")
h_2loglik.GetXaxis().SetTitle(r"#mu")
h_conf = h_2loglik.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)>LambdaLow and h_conf.GetBinCenter(i)<LambdaUp): h_conf.SetBinContent(i,0)
h_conf.SetFillColor(9)
h_conf.Draw("hist same")
leg1 = TLegend(0.55,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
h_conf.SetMarkerColor(0)
leg1a = leg1.AddEntry(h_conf, r"(#mu_{low}, #mu_{up}) = ("+str(round(LambdaLow,2))+","+str(round(LambdaUp,2))+")","p"); leg1a.SetTextSize(0.04);
leg1.Draw()
LowLine.Draw()
UpLine.Draw()
ObsLine.Draw()
cvs.Print("Plots/Errors_"+ErrorType+".eps")
if ErrorType=="BayesCentral":
# Work on likelihood as PDF
Integral = h_likelihood.Integral()
for i in range(1,h_likelihood.GetNbinsX()+1):
h_likelihood.SetBinContent(i,h_likelihood.GetBinContent(i)/Integral)
# Sum over bins until reached CL
Sum_low = 0.
Sum_up = 0.
for i in range(1,bin_central+1):
if Sum_low<=IntFraction:
Sum_low += h_likelihood.GetBinContent(i)
LambdaLow = h_likelihood.GetBinCenter(i)
for i in range(h_likelihood.GetNbinsX(),bin_central,-1):
if Sum_up<=IntFraction:
Sum_up += h_likelihood.GetBinContent(i)
LambdaUp = h_likelihood.GetBinCenter(i)
cvs = TCanvas("Standard Canvas","",600,400)
cvs.cd()
LowLine = TLine(LambdaLow,0,LambdaLow,h_likelihood.GetBinContent(h_likelihood.FindBin(LambdaLow))); LowLine.SetLineWidth(1)
UpLine = TLine(LambdaUp,0,LambdaUp,h_likelihood.GetBinContent(h_likelihood.FindBin(LambdaUp))); UpLine.SetLineWidth(1)
ObsLine = TLine(nObs,0,nObs,h_likelihood.GetBinContent(h_2loglik.FindBin(nObs))); ObsLine.SetLineWidth(1); ObsLine.SetLineColor(2); ObsLine.SetLineStyle(7)
h_likelihood.SetFillColor(0)
h_likelihood.SetStats(kFALSE)
h_likelihood.SetTitle("BAYES CENTRAL")
#h_likelihood.SetAxisRange(0.8,8,"X")
#h_likelihood.SetAxisRange(2,7,"Y")
h_likelihood.Draw("hist lp")
h_likelihood.GetXaxis().SetTitle(r"#mu")
h_conf = h_likelihood.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)>LambdaLow and h_conf.GetBinCenter(i)<LambdaUp): h_conf.SetBinContent(i,0)
h_conf.SetFillColor(9)
h_conf.Draw("hist same")
leg1 = TLegend(0.55,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
h_conf.SetMarkerColor(0)
leg1a = leg1.AddEntry(h_conf, r"(#mu_{low}, #mu_{up}) = ("+str(round(LambdaLow,2))+","+str(round(LambdaUp,2))+")","p"); leg1a.SetTextSize(0.04);
leg1.Draw()
LowLine.Draw()
UpLine.Draw()
ObsLine.Draw()
cvs.Print("Plots/Errors_"+ErrorType+".eps")
if ErrorType=="BayesHDI":
# Work on likelihood as PDF
Integral = h_likelihood.Integral()
for i in range(1,h_likelihood.GetNbinsX()+1):
h_likelihood.SetBinContent(i,h_likelihood.GetBinContent(i)/Integral)
Area = 0.
RightIndex=bin_central
while Area<=(1-2*IntFraction):
RightIndex+=1
# find corresponding bin on left side
for i in range(1,bin_central):
if h_likelihood.GetBinContent(i)<=h_likelihood.GetBinContent(RightIndex):
LeftIndex=i
Area = h_likelihood.Integral( LeftIndex,RightIndex )
LambdaLow = h_likelihood.GetBinCenter(LeftIndex)
LambdaUp = h_likelihood.GetBinCenter(RightIndex)
cvs = TCanvas("Standard Canvas","",600,400)
cvs.cd()
LowLine = TLine(LambdaLow,0,LambdaLow,h_likelihood.GetBinContent(h_likelihood.FindBin(LambdaLow))); LowLine.SetLineWidth(1)
UpLine = TLine(LambdaUp,0,LambdaUp,h_likelihood.GetBinContent(h_likelihood.FindBin(LambdaUp))); UpLine.SetLineWidth(1)
ObsLine = TLine(nObs,0,nObs,h_likelihood.GetBinContent(h_2loglik.FindBin(nObs))); ObsLine.SetLineWidth(1); ObsLine.SetLineColor(2); ObsLine.SetLineStyle(7)
h_likelihood.SetFillColor(0)
h_likelihood.SetStats(kFALSE)
h_likelihood.SetTitle("BAYES SHORTEST")
h_likelihood.Draw("hist lp")
h_likelihood.GetXaxis().SetTitle(r"#mu")
h_conf = h_likelihood.Clone("h_conf")
for i in range(1,h_conf.GetNbinsX()+1):
if(h_conf.GetBinCenter(i)>LambdaLow and h_conf.GetBinCenter(i)<LambdaUp): h_conf.SetBinContent(i,0)
h_conf.SetFillColor(9)
h_conf.Draw("hist same")
leg1 = TLegend(0.55,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
h_conf.SetMarkerColor(0)
leg1a = leg1.AddEntry(h_conf, r"(#mu_{low}, #mu_{up}) = ("+str(round(LambdaLow,2))+","+str(round(LambdaUp,2))+")","p"); leg1a.SetTextSize(0.04);
leg1.Draw()
LowLine.Draw()
UpLine.Draw()
ObsLine.Draw()
cvs.Print("Plots/Errors_"+ErrorType+".eps")
return [LambdaLow, LambdaUp]
return
def MuFit(Nbins,irebin=1.):
# Get histrograms
h_bgr = GetMassDistribution(1)
h_data = GetMassDistribution(2)
h_sig = GetMassDistribution(125)
h_bgr.Rebin(irebin)
h_data.Rebin(irebin)
h_sig.Rebin(irebin)
h_sf = TH2D("scalefactor","title",Nbins,0.5,2.,Nbins,0.,5.)
for i in range(1,h_sf.GetNbinsX()+1):
for j in range(1,h_sf.GetNbinsY()+1):
sf_bgr = h_sf.GetXaxis().GetBinCenter(i)
sf_sig = h_sf.GetYaxis().GetBinCenter(j)
# Loop over bins, compute likelihood
loglik = 0.
for iDataBin in range(1,h_data.GetNbinsX()+1):
m4lepBin = h_data.GetBinCenter(iDataBin)
NObsBin = h_data.GetBinContent(iDataBin)
MeanBin = sf_bgr*h_bgr.GetBinContent(iDataBin) + sf_sig*h_sig.GetBinContent(iDataBin)
if (MeanBin>0): loglik += TMath.Log( TMath.Poisson(NObsBin,MeanBin) )
h_sf.SetBinContent(i,j,-2.*loglik)
# Get best SF parameters
x=ctypes.c_int(0)
y=ctypes.c_int(0)
z=ctypes.c_int(0)
h_sf.GetBinXYZ(h_sf.GetMinimumBin(),x,y,z)
Minimum = h_sf.GetBinContent(x.value,y.value)
best_alpha = h_sf.GetXaxis().GetBinCenter(x.value)
best_mu = h_sf.GetYaxis().GetBinCenter(y.value)
print(Minimum,best_alpha,best_mu)
# Rescale histogram
for i in range(1,h_sf.GetNbinsX()+1):
for j in range(1,h_sf.GetNbinsY()+1):
h_sf.SetBinContent( i,j, h_sf.GetBinContent(i,j)-Minimum )
canvas = TCanvas("canvas","Standard Canvas",600,400)
canvas.cd()
Min = TMarker(best_alpha,best_mu,29)
Min.SetMarkerSize(2)
h_sf.SetStats(kFALSE)
h_sf.SetTitle("HIGGS PRODUCTION CROSS SECTION - PARAMETERS")
h_sf.GetXaxis().SetTitle(r"#alpha")
h_sf.GetYaxis().SetTitle(r"#mu")
h_sf.Draw("COLZ")
h_sigma = h_sf.Clone("h_sigma")
h_sigma.Reset()
for i in range(1,h_sigma.GetNbinsX()+1):
for j in range(1,h_sigma.GetNbinsY()+1):
if( h_sf.GetBinContent(i,j)<=1.): h_sigma.SetBinContent(i,j, 1.)
h_sigma.SetMarkerColorAlpha(kRed,0.40)
h_sigma.SetMarkerSize(10)
h_sigma.Draw("same L")
Min.Draw()
leg1 = TLegend(0.65,0.85,0.85,0.75)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
leg1a = leg1.AddEntry(Min, r"optimal (#alpha,#mu)", "p"); leg1a.SetTextSize(0.04);
leg1.Draw()
canvas.Print("Plots/MuFit.pdf")
def MassPlot(Irebin):
gROOT.Clear()
gROOT.Delete()
# Prepare canvas
c1 = TCanvas("c1","Standard Canvas",600,400)
c1.SetLeftMargin(0.125)
c1.SetBottomMargin(0.125)
#c1.SetLogy()
c1.cd()
# Prepare histograms
# get from file and rebin
h_sig = GetMassDistribution(125)
h_sig.Rebin(Irebin)
h_bgr = GetMassDistribution(1)
h_bgr.Rebin(Irebin)
h_data = GetMassDistribution(2)
h_data.Rebin(Irebin)
# cumulative S+B
h_SB = h_bgr.Clone("h_SB")
h_SB.Reset()
for i in range(1,h_bgr.GetNbinsX()+1):
h_SB.SetBinContent(i, h_sig.GetBinContent(i)+h_bgr.GetBinContent(i) )
print(" REBINNED HISTOGRAM: bin", i, "Ndata = ",h_data.GetBinContent(i),"\n")
h_fit = h_SB.Clone("h_fit")
h_fit.Reset()
for i in range( 0,h_bgr.GetNbinsX() ):
if( h_SB.GetBinCenter(i)>=150 and h_fit.GetBinCenter(i)>=400 ): h_fit.SetBinContent(i, h_SB.GetBinContent(i) )
# prepare and plot histograms
Data_max = h_data.GetBinContent(h_data.GetMaximumBin())
Ymax_plot = 1.20*(Data_max + 0.1*(Data_max))
h_SB.SetFillColor(7)
h_SB.SetAxisRange(1,Ymax_plot,"Y")
h_SB.SetAxisRange(0,400.,"X")
h_bgr.SetFillColor(2)
h_SB.SetTitle("4-LEPTON INVARIANT MASS DISTRIBUTION")
h_SB.GetXaxis().SetTitle("m [GeV]")
h_SB.SetTitleSize(0.05,"X")
h_SB.SetTitleOffset(0.9,"X")
h_SB.GetYaxis().SetTitle("counts")
h_SB.SetTitleSize(0.05,"Y")
h_SB.SetTitleOffset(0.7,"Y")
h_SB.Draw("hist")
h_bgr.Draw("same")
h_bgr.Draw("axis same")
h_data.Draw("e same")
# image format
#AddText(0.900,0.035,"4-lepton invariant mass [GeV]",0.060,0.,"right")
#AddText(0.040,0.900, "Number of events / "+str(h_bgr.GetBinWidth(1))+" GeV", 0.060, 90., "right")
leg1 = TLegend(0.70,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0); #leg1.SetO
leg1a = leg1.AddEntry(h_bgr, "SM(ZZ)", "f"); leg1a.SetTextSize(0.04);
leg1b = leg1.AddEntry(h_SB, "Higgs", "f"); leg1b.SetTextSize(0.04);
leg1.Draw()
# save image
c1.Print("Plots/MassPlot_rebin"+str(Irebin)+".pdf")
h_fit = h_SB.Clone("h_fit")
h_fit.Reset()
for i in range( 0,h_bgr.GetNbinsX() ):
if( h_SB.GetBinCenter(i)>=150 and h_fit.GetBinCenter(i)<=400 ): h_fit.SetBinContent(i, h_SB.GetBinContent(i) )
# printing plot for sidebandfit
c2 = TCanvas("c2","Standard Canvas",600,400)
c2.SetLeftMargin(0.125)
c2.SetBottomMargin(0.125)
c2.cd()
h_SB.SetAxisRange(1,20,"Y")
h_fit.SetAxisRange(0,400.,"X")
h_fit.SetFillColor(8);
h_SB.SetFillColor(0); h_SB.SetLineColor(9); h_SB.SetTitle("SIDE-BAND FIT REGION")
h_bgr.SetFillColor(0); h_bgr.SetLineColor(1)
h_SB.Draw("hist")
h_bgr.Draw("hist same")
h_fit.Draw("hist same")
h_data.Draw("e same")
leg1.Draw()
c2.Print("Plots/Sideband_region.pdf")
return
def Pvalues(signal=1.0):
h_data = GetMassDistribution(2)
h_bgr = GetMassDistribution(1)
h_sig = GetMassDistribution(125)
dir = ROOT.gDirectory
file = ROOT.TFile("toy_"+str(signal)+".root","READ")
dir.cd()
h_test_bgr = file.Get("test_bgr").Clone("h_test_bgr")
h_test_sb = file.Get("test_sb").Clone("h_test_sb")
TestStat_data = GetTestStatistics(h_data,h_bgr,h_sig)
print("--------------------")
print("Test Statistics value for real data: ",TestStat_data)
print("--------------------")
# Computing p-values
# DISCOVERY - CLb (b-only hypothesis)
Nbins = h_test_bgr.GetNbinsX()
# Median b-only
Median_bgr = Quantiles(h_test_bgr)[2]
CLb_bgr = h_test_bgr.Integral(h_test_bgr.FindBin(Median_bgr),Nbins)/h_test_bgr.Integral()
# Median s+b
Median_sb = Quantiles(h_test_sb)[2]
CLb_sb = h_test_bgr.Integral(h_test_bgr.FindBin(Median_sb),Nbins)/h_test_bgr.Integral()
# Data
CLb_data = h_test_bgr.Integral(h_test_bgr.FindBin(TestStat_data),h_test_bgr.GetNbinsX())/h_test_bgr.Integral()
# Print a log
print("==============================")
print("b-only hypothesis")
print("-----------------------------------------")
print(" CLb pvalue significance ")
print("bgr: ",CLb_bgr," ",1-CLb_bgr," ",ROOT.Math.gaussian_quantile_c(1-CLb_bgr,1))
print("S+B: ",CLb_sb," ",1-CLb_sb," ",ROOT.Math.gaussian_quantile_c(1-CLb_sb,1))
print("Data: ",CLb_data," ",1-CLb_data," ",ROOT.Math.gaussian_quantile_c(1-CLb_data,1))
print("-----------------------------------------")
# CCL_{s+b} (S+B hypothesis)
CLsb_bgr = h_test_sb.Integral(h_test_bgr.FindBin(Median_bgr),h_test_sb.GetNbinsX())/h_test_sb.Integral()
# # Median s+b
CLsb_sb = h_test_sb.Integral(h_test_sb.FindBin(Median_sb),h_test_sb.GetNbinsX())/h_test_sb.Integral()
# # Data
CLsb_data = h_test_sb.Integral(h_test_sb.FindBin(TestStat_data),h_test_sb.GetNbinsX())/h_test_sb.Integral()
print("\ns+b hypothesis")
print("-----------------------------------------")
print(" CL_sb pvalue CL_s ")
print("bgr: ",CLsb_bgr," ",1-CLsb_bgr," ",CLsb_bgr/CLb_bgr )
print("S+B: ",CLsb_sb," ",1-CLsb_sb," ",CLsb_sb/CLb_sb )
print("Data: ",CLsb_data," ",1-CLsb_data," ",CLsb_data/CLb_data )
print("-----------------------------------------")
if signal!=1.:
cvs = TCanvas("cvs","Standard Canvas",600,400)
Data_max = h_test_bgr.GetBinContent(h_test_bgr.GetMaximumBin())
Ymax_plot = Data_max + np.sqrt(Data_max)
h_test_bgr.SetAxisRange(0.,Ymax_plot,"Y")
line = TLine(TestStat_data,0,TestStat_data,Ymax_plot)
cvs.cd()
h_test_bgr.SetTitle("TEST STATISTIC DISTRIBUTION")
h_test_bgr.SetLineColor(9)
h_test_sb.SetLineColor(8)
line.SetLineColor(2)
h_test_bgr.GetXaxis().SetTitle("test statistic values")
h_test_bgr.GetYaxis().SetTitle("# toy experiments")
h_test_bgr.SetStats(kFALSE)
h_test_bgr.Draw("L")
h_test_sb.Draw("same L")
line.Draw("same")
leg1 = TLegend(0.70,0.70,0.90,0.85)
leg1.SetBorderSize(1); leg1.SetFillColor(0);
leg1a = leg1.AddEntry(h_test_bgr, "b- only", "l"); leg1a.SetTextSize(0.04);
leg1b = leg1.AddEntry(h_test_sb, "s+b", "l"); leg1b.SetTextSize(0.04);
leg1.Draw()
cvs.Print("Plots/TestStat"+str(signal)+".pdf")
file.Close()
return CLsb_data, CLsb_data/CLb_data
def SideBandFit(irebin=1):
print("\n Side-band fit \n\n")
# prepare histograms
h_bgr = GetMassDistribution(1)
h_data = GetMassDistribution(2)
# rebin
h_bgr.Rebin(irebin)
h_data.Rebin(irebin)
print("INFO: Rebinning histograms with factor",irebin,". Binwidth: ",h_data.GetBinWidth(1))
# Loop over scalefactor (alpha_bgr)
h_sf_bgr = TH1D("h_sf_bgr","",100,0.8,1.5)
for i in range(1,h_sf_bgr.GetNbinsX()+1):
sf_bgr = h_sf_bgr.GetBinCenter(i)
# Loop over bins, compute loglikelihood, save in histogram
loglik = 0.
for j in range(1,h_data.GetNbinsX()+1):
# Signal-free region
m4lepBin = h_data.GetBinCenter(j)
NObsBin = h_data.GetBinContent(j)
if ( m4lepBin>=150. and m4lepBin<=400.):
MeanBgdBin = sf_bgr * h_bgr.GetBinContent(j)
if MeanBgdBin>0.: loglik += TMath.Log( TMath.Poisson(NObsBin,MeanBgdBin) )
h_sf_bgr.SetBinContent(i,-2.*loglik)
# Interpret the likelihood distribution
h_sf_bgr_rescaled = h_sf_bgr.Clone("h_sf_bgr_rescaled")
# find minimum
MinBin = h_sf_bgr.GetMinimumBin()
Minimum = h_sf_bgr.GetBinContent(MinBin)
BestSF_bgd = h_sf_bgr.GetBinCenter(MinBin)
# Rescale and find \pm 1\sigma errors
for i in range(1,h_sf_bgr.GetNbinsX()+1):
h_sf_bgr_rescaled.SetBinContent(i,Minimum-h_sf_bgr.GetBinContent(i))
LeftLim = h_sf_bgr.GetBinCenter( h_sf_bgr_rescaled.FindFirstBinAbove(-1)-1 )
RightLim = h_sf_bgr.GetBinCenter( h_sf_bgr_rescaled.FindLastBinAbove(-1)+1 )
LeftError = BestSF_bgd - LeftLim
RightError = RightLim - BestSF_bgd
# Print summary
print(" ----------\n"," Result fit: \n"," ----------","Background scale factor from sideband fit: ",BestSF_bgd," - ",LeftError," + ",RightError)
# Plot histogram
canvas1 = TCanvas("canvas1","Standard Canvas",600,400)
canvas1.SetLeftMargin(0.175)
canvas1.SetBottomMargin(0.125)
canvas1.cd()
h_sf_bgr_rescaled.SetStats(kFALSE)
h_sf_bgr_rescaled.SetTitle("Background scale factor")
h_sf_bgr_rescaled.GetXaxis().SetTitle("scale factor")
h_sf_bgr_rescaled.GetYaxis().SetTitle("2 log L")
#h_sf_bgr_rescaled.SetAxisRange(1,-1 ,"Y")
h_sf_bgr_rescaled.Draw("C")
CenterLine = TLine(BestSF_bgd,0,BestSF_bgd,-31)
CenterLine.SetLineColor(2)
LeftLine = TLine(LeftLim,Minimum-h_sf_bgr.GetBinContent( h_sf_bgr_rescaled.FindFirstBinAbove(-1)-1 ),LeftLim,-31)
LeftLine.SetLineColor(40)
RightLine = TLine(RightLim,Minimum-h_sf_bgr.GetBinContent( h_sf_bgr_rescaled.FindLastBinAbove(-1)+1 ),RightLim,-31)
RightLine.SetLineColor(40)
CenterLine.Draw("same axis")
LeftLine.Draw("same axis")
RightLine.Draw("same axis")
canvas1.Print("Plots/SideBandFit.pdf")
# Find expected background
bgr = h_bgr.Integral(h_bgr.FindBin(125.-0.5*7.15),h_bgr.FindBin(125+0.5*7.15))
sig = GetMassDistribution(125).Integral(h_bgr.FindBin(125.-0.5*7.15),h_bgr.FindBin(125+0.5*7.15))
obs = h_data.Integral(h_bgr.FindBin(125.-0.5*7.15),h_bgr.FindBin(125+0.5*7.15))
print("BACKGROUND - without rescaling : ", bgr)
print("Best scalefactor: ",BestSF_bgd)
print("BACKGROUND - with rescaling : ", BestSF_bgd*bgr," - ",LeftError*bgr,' + ',RightError*bgr)
print("SIGNAL EVENTS: ",sig)
print("OBSERVED EVENTS: ",obs)
return
def compare(name,file_list,name_list,legend_list,normalize=False,drawoption='hE',xtitle='',ytitle='',minx=0,maxx=0,rebin=1,miny=0,maxy=0,textsizefactor=1,logy=False,fit='',fitlow=0,fithigh=0,colors=[]):
c=TCanvas(name,'',600,600)
# c.SetLeftMargin(0.15)#
# c.SetRightMargin(0.05)#
# # c.SetTopMargin(0.05)#
# c.SetBottomMargin(0.10)
# # if not useOutfile:
# # legend=TLegend(0.7,0.7,0.95,0.95)
# # else:
# c.SetTopMargin(0.15)
# legend=TLegend(0.0,0.85,0.99,0.99)
# #legend=TLegend(0.35,0.2,0.85,0.5)
c.SetLeftMargin(0.15)#
c.SetRightMargin(0.05)#
c.SetBottomMargin(0.11)
c.SetTopMargin(0.25)
legend=TLegend(0.0,0.76,0.99,1.04)
legend.SetHeader('')
#legend.SetTextSize(0.03)
legend.SetBorderSize(0)
legend.SetTextFont(42)
legend.SetLineColor(1)
legend.SetLineStyle(1)
legend.SetLineWidth(1)
legend.SetFillColor(0)
legend.SetFillStyle(0)
histo_list=[]
# tfile_list=[]
the_maxy=0
widths=[]
for i in range(len(name_list)):
# tfile_list.append(TFile(file_list[i],'READ'))
histo_list.append(file_list[i].Get(name_list[i]).Clone())
print(legend_list[i],histo_list[-1].Integral())
if normalize:
histo_list[-1].Scale(1.0/(histo_list[-1].Integral()+0.00000001))
if not histo_list[-1].ClassName()=='TGraphAsymmErrors':
histo_list[-1].SetStats(0)
histo_list[-1].SetLineWidth(3)
if colors==[]:
histo_list[-1].SetLineColor(i+1)
if i>6:
histo_list[-1].SetLineColor(i+4)
else:
histo_list[-1].SetLineColor(colors[i])
histo_list[-1].SetTitle('')
if rebin!=1:
histo_list[-1].Rebin(rebin)
if not histo_list[-1].ClassName()=='TGraphAsymmErrors':
the_maxy=max(the_maxy,histo_list[-1].GetBinContent(histo_list[-1].GetMaximumBin())*1.05)
if fit!='':
if fitlow==0 and fithigh==0:
histo_list[-1].Fit(fit)
else:
histo_list[-1].Fit(fit,'','',fitlow[i],fithigh[i])
histo_list[-1].GetFunction("gaus").SetLineColor(i+1)
gaus=histo_list[-1].GetFunction("gaus")
print(i,gaus.GetParameter(0),gaus.GetParameter(1),gaus.GetParameter(2))
widths.append(gaus.GetParameter(2))
if fit=='':
legend.AddEntry(histo_list[-1],legend_list[i],'l')
else:
legend.AddEntry(histo_list[-1],legend_list[i]+' FWHM='+ "%.2f [GeV]" % (2.354*widths[i]),'l')
for i in range(len(name_list)):
if i==0:
if not histo_list[-1].ClassName()=='TGraphAsymmErrors':
if miny!=0 or maxy!=0:
histo_list[i].SetMaximum(maxy)
histo_list[i].SetMinimum(miny)
else:
histo_list[i].SetMaximum(the_maxy)
histo_list[i].SetMinimum(0.0001)
else:
histo_list[i].SetMaximum(1.05)
histo_list[i].SetMinimum(0.0001)
histo_list[i].Draw(drawoption)
charsize=0.05*textsizefactor
histo_list[i].GetYaxis().SetLabelSize(charsize)
histo_list[i].GetYaxis().SetTitleSize(charsize)
histo_list[i].GetYaxis().SetTitleOffset(1.6)
histo_list[i].GetXaxis().SetLabelSize(charsize)
histo_list[i].GetXaxis().SetTitleSize(charsize)
histo_list[i].GetXaxis().SetTitleOffset(0.95)
# if useOutfile:
if xtitle!='':
histo_list[i].GetXaxis().SetTitle(xtitle)
if ytitle!='':
histo_list[i].GetYaxis().SetTitle(ytitle)
if maxx!=0 or minx!=0:
histo_list[i].GetXaxis().SetRangeUser(minx,maxx)
# histo_list[i].GetYaxis().SetTitle('Efficiency')
else:
if histo_list[-1].ClassName()=='TGraphAsymmErrors':
drawoption= drawoption.replace("A", "")
histo_list[i].Draw(drawoption+'SAME')
if logy:
c.SetLogy()
legend.Draw()
# outfile.cd()
c.Write(name)
c.SaveAs('pdf/'+name+'.pdf')
#!/usr/bin/env python
import ROOT
from ROOT import TH1I, TChain, TCanvas
chGabriel = TChain("evt2l")
chEGamma = TChain("ZeeCandidate")
c = TCanvas("c", "Canvas", 1)
hGabriel = TH1I("hGabriel", "Compare", 10, 0, 10)
hGabriel.Fill(1)
hGabriel.Draw()
c.Print("try.pdf")
elif opt == '-s':
signal_strength = float(arg)
elif opt == '-j':
junk_strength = float(arg)
elif opt == '-h':
print_usage()
sys.exit(0)
if (len(remainder) != 1):
print_usage()
sys.exit()
print "{0} events, signal fraction {1}, junk fraction {2}".format(
n, signal_strength, junk_strength)
c = TCanvas("c", "c", 800, 600)
c.Print(remainder[0] + ".pdf[")
w = ROOT.RooWorkspace("w")
w.factory("Exponential::bkg(x[0,100],{0})".format(-1.0 / bkg_lambda))
#w.factory("Gaussian::bkg(x[-50,50],0,3)")
w.factory("Exponential::junk(x,{0})".format(-1.0 / junk_lambda))
w.factory("Exponential::signal(x,{0})".format(-1.0 / sig_lambda))
#w.factory("EXPR::signal_step('x>0?1:0',x)")
#w.factory("Gaussian::signal_res(x,0,3)")
#w.factory("PROD::signal(signal_decay,signal_step)")
#w.factory("FCONV::signal(x,signal_truth,signal_res)")
w.factory("SUM::source(strength[{0},0,0.005]*signal,{1}*junk,bkg)".format(
signal_strength, junk_strength))
w.factory("SUM::model(strength*signal,bkg)")
#!/usr/bin/env python
from ROOT import TTree, TFile, gROOT, gStyle, TCanvas, TGraph, TLegend
from array import array
f = TFile('mleak_chk.root')
tr = f.Get('tr')
gROOT.SetStyle('Plain')
c = TCanvas('c', 'c', 640, 480)
c.SetTopMargin(0.05)
c.SetRightMargin(0.05)
gStyle.SetGridStyle(1)
gStyle.SetGridColor(16)
c.SetGrid()
h = c.DrawFrame(0, 0, 8000, 1200)
N = tr.GetEntries()
tr.Draw('committed_AS/1000:Entry$', '', 'goff')
g1 = TGraph(tr.GetSelectedRows(), tr.GetV2(), tr.GetV1())
g1.SetLineWidth(2)
g1.Draw('')
tr.Draw('memFree/1000:Entry$', '', 'goff')
g2 = TGraph(tr.GetSelectedRows(), tr.GetV2(), tr.GetV1())
g2.SetLineWidth(2)
g2.SetLineStyle(2)
g2.Draw('')
tr.Draw('swapFree/1000:Entry$', '', 'goff')
cut_name_index].Clone(histogram_name + '_' + cut_name +
'_DoverMC_Add')
added_histo_DoverMC.Divide(added_histo)
cut_sublist.append(added_histo)
cut_sublist_signal.append(added_histo_signal)
cut_sublist_background.append(added_histo_background)
cut_sublist_SoverB.append(added_histo_SoverB)
cut_sublist_DoverMC.append(added_histo_DoverMC)
if histogram_name == 'Nevts' and cut_name in [
"heptoptag_masscut_hists", "NOheptoptag_NOmasscut_hists",
"NOheptoptag_masscut_hists", "heptoptag_NOmasscut_hists",
"heptoptag_NOmasscut__NOheptoptag_masscut_hists"
]:
added_histo_SoverB.Write()
added_histo_DoverMC.Write()
canvas = TCanvas(histogram_name + '_' + cut_name + '_Info')
SoverBvalue = added_histo_SoverB.GetBinContent(1)
SoverBerror = added_histo_SoverB.GetBinError(1)
DoverMCvalue = added_histo_DoverMC.GetBinContent(1)
DoverMCerror = added_histo_DoverMC.GetBinError(1)
latex = TLatex()
#latex.DrawLatex(0.1,0.8,"#frac{S}{B}="+('%.3f' % SoverBvalue)+'#pm'+('%.3f' % SoverBerror))
#latex.DrawLatex(0.1,0.6,"#frac{DATA}{MC}="+('%.3f' % DoverMCvalue)+'#pm'+('%.3f' % DoverMCerror))
latex.DrawLatex(
0.1, 0.8,
"#frac{S}{B}=" + str(SoverBvalue) + '#pm' + str(SoverBerror))
latex.DrawLatex(
0.1, 0.6, "#frac{DATA}{MC}=" + str(DoverMCvalue) + '#pm' +
str(DoverMCerror))
latex.Draw()
canvas.Write()
def make_ratioplot(name, ttbar_file=0, qcd_file=0, signal_files=[], histo=0,rebin=1,minx=0,maxx=0,miny=0,maxy=0,logy=False,
xtitle='',ytitle='',textsizefactor=1,signal_legend=[],outfile=0,signal_colors=[], signal_zoom=1, qcd_zoom=1, ttbar_zoom=1,
ttbar_legend='t#bar{t}',qcd_legend='QCD from MC',dosys=False,docms=True,legendtitle=''):
###canvas setting up
canvas=0
canvas=TCanvas(name,'',0,0,600,600)
canvas.SetLeftMargin(0.15)
canvas.SetRightMargin(0.05)
canvas.SetTopMargin(0.10)
canvas.SetBottomMargin(0.10)
charsize=0.04
offset=1.9
###latex label
latex=0
latex=TLatex(0.6,0.7,'13 TeV, 2.69 fb^{-1}')
latex.SetTextSize(charsize)
latex.SetNDC(1)
latex.SetTextFont(42)
###legend setting up
#legend=TLegend(0.0,0.75,0.99,1.04)
legend=TLegend(0.4,0.6,0.94,0.95)
legend.SetNColumns(2)
legend.SetHeader('')
legend.SetFillStyle(0)
legend.SetBorderSize(0)
###mc stack
stack=THStack(name+'_stack','')
qcd_histo=qcd_file.Get(histo).Clone(name+'_make_plot')
qcd_histo.Rebin(rebin)
ttbar_histo=ttbar_file.Get(histo).Clone()
ttbar_histo.Rebin(rebin)
ttbar_histo.SetFillColor(kRed-9)
ttbar_histo.SetLineColor(kRed-9)
ttbar_histo.SetMarkerColor(kRed-9)
if ttbar_zoom!=1:
ttbar_histo.Scale(ttbar_zoom)
legend.AddEntry(ttbar_histo,ttbar_legend,'f')
qcd_histo.SetFillColor(kOrange-5)
qcd_histo.SetLineColor(kOrange-5)
qcd_histo.SetMarkerColor(kOrange-5)
if qcd_zoom!=1:
qcd_histo.Scale(qcd_zoom)
legend.AddEntry(qcd_histo,qcd_legend,'f')
sum_mc=qcd_histo.Clone(histo+'tmp')
sum_mc.Add(ttbar_histo)
stack.Add(ttbar_histo)
stack.Add(qcd_histo)
sum_mc.SetLineColor(kBlack)
sum_mc.SetFillStyle(0)
err=TGraphAsymmErrors(sum_mc)
legend.AddEntry(err,'Total uncertainty','f')
if legendtitle=='':
legend.AddEntry(0,"",'')
legend.AddEntry(0,"g_{RS} #rightarrow t#bar{t} (2pb)",'')
else:
legend.AddEntry(0,"",'')
legend.AddEntry(0,legendtitle,'')
###signal setting up
signal_histos=[]
colors=[kBlack,kRed,kOrange,kBlue,kGreen+3,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60]
styles=[1,3,5,7,7,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1]
if signal_colors!=[]:
colors=signal_colors
for i in range(len(signal_files)):
signal_histos.append(signal_files[i].Get(histo).Clone())
signal_histos[i].SetLineWidth(3)
signal_histos[i].SetLineStyle(styles[i])
signal_histos[i].SetLineColor(colors[i])
signal_histos[i].SetMarkerColor(colors[i])
signal_histos[i].Rebin(rebin)
if signal_zoom!=1:
signal_histos[i].Scale(signal_zoom)
legend.AddEntry(signal_histos[i],signal_legend[i],'l')
###mc shape line
ttbar_line=0
ttbar_line=ttbar_histo.Clone()
ttbar_line.SetLineColor(kBlack)
ttbar_line.SetFillStyle(0)
###mc errors
if dosys:
sys_diff_qcd=[]
sys_diff_ttbar=[]
for imtt in range(1,ttbar_histo.GetNbinsX()+1):
sys_diff_qcd.append([])
sys_diff_ttbar.append([])
#adding stat uncertainties <--removed
# for imtt in range(1,ttbar_histo.GetNbinsX()+1):
# sys_diff_ttbar[imtt-1].append(ttbar_histo.GetBinError(imtt))
# sys_diff_ttbar[imtt-1].append(-ttbar_histo.GetBinError(imtt))
# sys_diff_qcd[imtt-1].append(qcd_histo.GetBinError(imtt))
# sys_diff_qcd[imtt-1].append(-qcd_histo.GetBinError(imtt))
#adding flat uncertainties
for imtt in range(1,ttbar_histo.GetNbinsX()+1):
#ttbar
for i in [2.4,#pdf
10.0,#mu
3.0,#xsec
6.0,#toppt
1.0,#lumi
3.5,#jec
3.0,#jer
10.0,#btag
#3.0,#trig
10.0,#toptag
3.0]:#pileup
sys_diff_ttbar[imtt-1].append(i/100.0*ttbar_histo.GetBinContent(imtt))
sys_diff_ttbar[imtt-1].append(-i/100.0*ttbar_histo.GetBinContent(imtt))
closureunc=5.0
# if '1b' in histo:
# closureunc=5.0
# elif '2b' in histo:
# closureunc=10.0
for i in [2.0,#modmass
closureunc]:#closure
sys_diff_qcd[imtt-1].append(i/100.0*qcd_histo.GetBinContent(imtt))
sys_diff_qcd[imtt-1].append(-i/100.0*qcd_histo.GetBinContent(imtt))
# #3% trigger
# sys_diff_ttbar[imtt-1].append(0.03*ttbar_histo.GetBinContent(imtt))
# sys_diff_ttbar[imtt-1].append(-0.03*ttbar_histo.GetBinContent(imtt))
# #2.7% lumi
# sys_diff_ttbar[imtt-1].append(0.023*ttbar_histo.GetBinContent(imtt))
# sys_diff_ttbar[imtt-1].append(-0.023*ttbar_histo.GetBinContent(imtt))
# #15% ttbar
# #sys_diff_ttbar[imtt-1].append(0.15*ttbar_histo.GetBinContent(imtt))
# #sys_diff_ttbar[imtt-1].append(-0.15*ttbar_histo.GetBinContent(imtt))
# #2.8% QCD
# sys_diff_qcd[imtt-1].append(0.028*qcd_histo.GetBinContent(imtt))
# sys_diff_qcd[imtt-1].append(-0.028*qcd_histo.GetBinContent(imtt))
#combining uncertainties
sys_tot_ttbar=[]
sys_tot_qcd=[]
sys_tot=[]
sys_global_ttbar=[0.0,0.0]
sys_global_qcd=[0.0,0.0]
nevt_global=[0.0,0.0,0.0]
for imtt in range(1,ttbar_histo.GetNbinsX()+1):
uperr_qcd=0
downerr_qcd=0
uperr_ttbar=0
downerr_ttbar=0
for error in sys_diff_ttbar[imtt-1]:
if error<0:
downerr_ttbar=downerr_ttbar+error*error
else:
uperr_ttbar=uperr_ttbar+error*error
for error in sys_diff_qcd[imtt-1]:
if error<0:
downerr_qcd=downerr_qcd+error*error
else:
uperr_qcd=uperr_qcd+error*error
sys_tot_ttbar.append([math.sqrt(downerr_ttbar),math.sqrt(uperr_ttbar)])
sys_tot_qcd.append([math.sqrt(downerr_qcd),math.sqrt(uperr_qcd)])
sys_tot.append([math.sqrt(downerr_qcd+downerr_ttbar),math.sqrt(uperr_qcd+uperr_ttbar)])
sys_global_qcd[0]=sys_global_qcd[0]+downerr_qcd
sys_global_qcd[1]=sys_global_qcd[1]+uperr_qcd
sys_global_ttbar[0]=sys_global_ttbar[0]+downerr_ttbar
sys_global_ttbar[1]=sys_global_ttbar[1]+uperr_ttbar
# nevt_global[0]=nevt_global[0]+data_histo.GetBinContent(imtt)
nevt_global[1]=nevt_global[1]+qcd_histo.GetBinContent(imtt)
nevt_global[2]=nevt_global[2]+ttbar_histo.GetBinContent(imtt)
#print 'ttbar+qcd',math.sqrt(uperr_qcd+uperr_ttbar),math.sqrt(downerr_qcd+downerr_ttbar)
#print 'qcd',math.sqrt(uperr_qcd),math.sqrt(downerr_qcd)
#print 'ttbar',math.sqrt(uperr_ttbar),math.sqrt(downerr_ttbar)
err.SetPointEYhigh(imtt-1,math.sqrt(uperr_qcd+uperr_ttbar))
err.SetPointEYlow(imtt-1,math.sqrt(downerr_qcd+downerr_ttbar))
sys_global=[0.0,0.0]
sys_global[0]=math.sqrt(sys_global_qcd[0]+sys_global_ttbar[0])
sys_global[1]=math.sqrt(sys_global_qcd[1]+sys_global_ttbar[1])
sys_global_qcd[0]=math.sqrt(sys_global_qcd[0])
sys_global_qcd[1]=math.sqrt(sys_global_qcd[1])
sys_global_ttbar[0]=math.sqrt(sys_global_ttbar[0])
sys_global_ttbar[1]=math.sqrt(sys_global_ttbar[1])
# print name
# print "\hline"
# print "Multijet QCD & $%.0f^{+%.0f}_{-%.0f}$ \\\\" % (nevt_global[1],sys_global_qcd[1],sys_global_qcd[0])
# print "SM ttbar & $%.0f^{+%.0f}_{-%.0f}$ \\\\" % (nevt_global[2],sys_global_ttbar[1],sys_global_ttbar[0])
# print "\hline"
# print "Total background & $%.0f^{+%.0f}_{-%.0f}$ \\\\" % (nevt_global[1]+nevt_global[2],sys_global[1],sys_global[0])
# print 'DATA & %.0f' %nevt_global[0]
err.SetFillStyle(3145)
err.SetFillColor(kGray+1)
###drawing top
canvas.cd()
stack.Draw('hist')
stack.GetXaxis().SetTitle(ttbar_histo.GetXaxis().GetTitle())
stack.GetYaxis().SetTitle(ttbar_histo.GetYaxis().GetTitle())
stack.GetXaxis().SetLabelSize(charsize)
stack.GetXaxis().SetTitleSize(charsize)
stack.GetYaxis().SetLabelSize(charsize)
stack.GetYaxis().SetTitleSize(charsize)
stack.GetYaxis().SetTitleOffset(offset)
if minx!=0 or maxx!=0:
stack.GetXaxis().SetRangeUser(minx,maxx)
#else:
# stack.GetXaxis().SetRangeUser(0,4000)
if miny!=0 or maxy!=0:
stack.SetMaximum(maxy)
stack.SetMinimum(miny)
else:
if logy:
stack.SetMaximum(stack.GetMaximum()*10)
stack.SetMinimum(0.2)
else:
stack.SetMaximum(stack.GetMaximum()*2.0)
stack.SetMinimum(0.001)
err.Draw('2')
sum_mc.Draw('samehist')
if ttbar_file!=0:
ttbar_line.Draw('samehist')
for i in signal_histos:
i.Draw('samehist')
if logy:
canvas.SetLogy()
legend.Draw()
latex2text=''
if 'ldy_0b' in name:
latex2text='#Deltay < 1; 0 b tag'
elif 'ldy_1b' in name:
latex2text='#Deltay < 1; 1 b tag'
elif 'ldy_2b' in name:
latex2text='#Deltay < 1; 2 b tag'
elif 'hdy_0b' in name:
latex2text='#Deltay > 1; 0 b tag'
elif 'hdy_1b' in name:
latex2text='#Deltay > 1; 1 b tag'
elif 'hdy_2b' in name:
latex2text='#Deltay > 1; 2 b tag'
latex2=TLatex(0.19,0.7,latex2text)
latex2.SetTextSize(0.03)
latex2.SetNDC(1)
latex2.SetTextFont(42)
latex2.Draw()
if docms:
if '3000' in name:
CMS_lumi.CMS_lumi(canvas, 3, 11)
elif '1000' in name:
CMS_lumi.CMS_lumi(canvas, 2, 11)
elif '300' in name:
CMS_lumi.CMS_lumi(canvas, 1, 11)
elif '36' in name:
CMS_lumi.CMS_lumi(canvas, 0, 11)
###saving
canvas.SaveAs('pdf/'+name+'.pdf')
if outfile!=0:
canvas.Write()
def plot(var, cut, year, norm=False, nm1=False):
from root_numpy import root2array, fill_hist, array2root
import numpy.lib.recfunctions as rfn
### Preliminary Operations ###
treeRead = not cut in [
"nnqq", "en", "enqq", "mn", "mnqq", "ee", "eeqq", "mm", "mmqq", "em",
"emqq", "qqqq"
] # Read from tree
channel = cut
unit = ''
if "GeV" in variable[var]['title']: unit = ' GeV'
isBlind = BLIND and 'SR' in channel
isAH = False #'qqqq' in channel or 'hp' in channel or 'lp' in channel
showSignal = False if 'SB' in cut or 'TR' in cut else True #'SR' in channel or channel=='qqqq'#or len(channel)==5
stype = "HVT model B"
if len(sign) > 0 and 'AZh' in sign[0]: stype = "2HDM"
elif len(sign) > 0 and 'monoH' in sign[0]: stype = "Z'-2HDM m_{A}=300 GeV"
if treeRead:
for k in sorted(alias.keys(), key=len, reverse=True):
if BTAGGING == 'semimedium':
if k in cut:
cut = cut.replace(k, aliasSM[k])
else:
if k in cut:
cut = cut.replace(
k, alias[k].format(WP=working_points[BTAGGING]))
print "Plotting from", ("tree" if treeRead else
"file"), var, "in", channel, "channel with:"
print " cut :", cut
if var == 'jj_deltaEta_widejet':
if "jj_deltaEta_widejet<1.1 && " in cut:
print
print "omitting jj_deltaEta_widejet<1.1 cut to draw the deltaEta distribution"
print
cut = cut.replace("jj_deltaEta_widejet<1.1 && ", "")
else:
print
print "no 'jj_deltaEta_widejet<1.1 && ' in the cut string detected, so it cannot be ommited explicitly"
print
### Create and fill MC histograms ###
# Create dict
file = {}
tree = {}
hist = {}
### Create and fill MC histograms ###
for i, s in enumerate(back + sign):
if True: #FIXME
if variable[var]['nbins'] > 0:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events / ( " + str(
(variable[var]['max'] - variable[var]['min']) /
variable[var]['nbins']) + unit + " );" +
('log' if variable[var]['log'] else ''),
variable[var]['nbins'], variable[var]['min'],
variable[var]['max'])
else:
hist[s] = TH1F(
s, ";" + variable[var]['title'] + ";Events" +
('log' if variable[var]['log'] else ''),
len(variable[var]['bins']) - 1,
array('f', variable[var]['bins']))
hist[s].Sumw2()
for j, ss in enumerate(sample[s]['files']):
if not 'data' in s:
if year == "run2" or year in ss:
arr = root2array(
NTUPLEDIR + ss + ".root",
branches=[
var, "jpt_1", "jpt_2", "eventWeightLumi",
"TMath::Abs(jflavour_1)==5 && TMath::Abs(jflavour_2)==5",
"TMath::Abs(jflavour_1)==5 && TMath::Abs(jflavour_2)!=5",
"TMath::Abs(jflavour_1)!=5 && TMath::Abs(jflavour_2)==5",
"TMath::Abs(jflavour_1)!=5 && TMath::Abs(jflavour_2)!=5"
],
selection=cut if len(cut) > 0 else "")
print "imported " + NTUPLEDIR + ss + ".root"
arr.dtype.names = [
var, "jpt_1", "jpt_2", "eventWeightLumi", "bb",
"bq", "qb", "qq"
]
MANtag_eff1 = np.array(map(MANtag_eff, arr["jpt_1"]))
MANtag_eff2 = np.array(map(MANtag_eff, arr["jpt_2"]))
MANtag_mis1 = np.array(map(MANtag_mis, arr["jpt_1"]))
MANtag_mis2 = np.array(map(MANtag_mis, arr["jpt_2"]))
MANtag_weight = np.multiply(
arr["eventWeightLumi"],
np.multiply(arr['bb'],
np.multiply(MANtag_eff1, MANtag_eff2))
+ np.multiply(
arr['bq'], np.multiply(MANtag_eff1,
MANtag_mis2)) +
np.multiply(arr['qb'],
np.multiply(MANtag_mis1,
MANtag_eff2)) +
np.multiply(arr['qq'],
np.multiply(MANtag_mis1, MANtag_mis2)))
fill_hist(hist[s], arr[var], weights=MANtag_weight)
deepCSV_eff1 = np.array(map(deepCSV_eff, arr["jpt_1"]))
deepCSV_eff2 = np.array(map(deepCSV_eff, arr["jpt_2"]))
deepCSV_mis1 = np.array(map(deepCSV_mis, arr["jpt_1"]))
deepCSV_mis2 = np.array(map(deepCSV_mis, arr["jpt_2"]))
deepCSV_weight = np.multiply(
arr["eventWeightLumi"],
np.multiply(
arr['bb'],
np.multiply(deepCSV_eff1, deepCSV_eff2)) +
np.multiply(
arr['bq'],
np.multiply(deepCSV_eff1, deepCSV_mis2)) +
np.multiply(
arr['qb'],
np.multiply(deepCSV_mis1, deepCSV_eff2)) +
np.multiply(
arr['qq'],
np.multiply(deepCSV_mis1, deepCSV_mis2)))
if var == "jj_mass_widejet" and options.save and not "data" in ss:
arr = rfn.append_fields(arr,
"MANtag_weight",
MANtag_weight,
usemask=False)
arr = rfn.append_fields(arr,
"deepCSV_weight",
deepCSV_weight,
usemask=False)
array2root(arr,
NTUPLEDIR + "MANtag/" + ss + "_" +
BTAGGING + ".root",
treename="tree",
mode='recreate')
print "saved as", NTUPLEDIR + "MANtag/" + ss + "_" + BTAGGING + ".root"
arr = None
#tree[s] = TChain("tree")
#for j, ss in enumerate(sample[s]['files']):
# if not 'data' in s or ('data' in s and ss in pd):
# if year=="run2" or year in ss:
# tree[s].Add(NTUPLEDIR + ss + ".root")
#if variable[var]['nbins']>0: hist[s] = TH1F(s, ";"+variable[var]['title']+";Events / ( "+str((variable[var]['max']-variable[var]['min'])/variable[var]['nbins'])+unit+" );"+('log' if variable[var]['log'] else ''), variable[var]['nbins'], variable[var]['min'], variable[var]['max'])
#else: hist[s] = TH1F(s, ";"+variable[var]['title']+";Events"+('log' if variable[var]['log'] else ''), len(variable[var]['bins'])-1, array('f', variable[var]['bins']))
#hist[s].Sumw2()
#cutstring = "(eventWeightLumi)" + ("*("+cut+")" if len(cut)>0 else "")
#tree[s].Project(s, var, cutstring)
#if not tree[s].GetTree()==None: hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast())
#else: # Histogram written to file
# for j, ss in enumerate(sample[s]['files']):
# if not 'data' in s or ('data' in s and ss in pd):
# file[ss] = TFile(NTUPLEDIR + ss + ".root", "R")
# if file[ss].IsZombie():
# print "WARNING: file", NTUPLEDIR + ss + ".root", "does not exist"
# continue
# tmphist = file[ss].Get(cut+"/"+var)
# if tmphist==None: continue
# if not s in hist.keys(): hist[s] = tmphist
# else: hist[s].Add(tmphist)
hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0)
hist[s].SetFillColor(sample[s]['fillcolor'])
hist[s].SetFillStyle(sample[s]['fillstyle'])
hist[s].SetLineColor(sample[s]['linecolor'])
hist[s].SetLineStyle(sample[s]['linestyle'])
if channel.endswith('TR') and channel.replace('TR', '') in topSF:
hist['TTbarSL'].Scale(topSF[channel.replace('TR', '')][0])
hist['ST'].Scale(topSF[channel.replace('TR', '')][0])
hist['BkgSum'] = hist['data_obs'].Clone(
"BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum")
hist['BkgSum'].Reset("MICES")
hist['BkgSum'].SetFillStyle(3003)
hist['BkgSum'].SetFillColor(1)
for i, s in enumerate(back):
hist['BkgSum'].Add(hist[s])
#if options.norm:
# for i, s in enumerate(back + ['BkgSum']): hist[s].Scale(hist[data[0]].Integral()/hist['BkgSum'].Integral())
# Create data and Bkg sum histograms
if options.blind or 'SR' in channel:
hist['data_obs'] = hist['BkgSum'].Clone("data_obs")
hist['data_obs'].Reset("MICES")
# Set histogram style
hist['data_obs'].SetMarkerStyle(20)
hist['data_obs'].SetMarkerSize(1.25)
for i, s in enumerate(back + sign + ['BkgSum']):
addOverflow(hist[s], False) # Add overflow
for i, s in enumerate(sign):
hist[s].SetLineWidth(3)
for i, s in enumerate(sign):
sample[s][
'plot'] = True #sample[s]['plot'] and s.startswith(channel[:2])
if isAH:
for i, s in enumerate(back):
hist[s].SetFillStyle(3005)
hist[s].SetLineWidth(2)
#for i, s in enumerate(sign):
# hist[s].SetFillStyle(0)
if not var == "Events":
sfnorm = hist[data[0]].Integral() / hist['BkgSum'].Integral()
print "Applying SF:", sfnorm
for i, s in enumerate(back + ['BkgSum']):
hist[s].Scale(sfnorm)
if BLIND and var.endswith("Mass"):
for i, s in enumerate(data + back + ['BkgSum']):
first, last = hist[s].FindBin(65), hist[s].FindBin(135)
for j in range(first, last):
hist[s].SetBinContent(j, -1.e-4)
if BLIND and var.endswith("Tau21"):
for i, s in enumerate(data):
first, last = hist[s].FindBin(0), hist[s].FindBin(0.6)
for j in range(first, last):
hist[s].SetBinContent(j, -1.e-4)
# Create stack
if variable[var]['nbins'] > 0:
bkg = THStack(
"Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events / ( " + str(
(variable[var]['max'] - variable[var]['min']) /
variable[var]['nbins']) + unit + " )")
else:
bkg = THStack("Bkg",
";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events; ")
for i, s in enumerate(back):
bkg.Add(hist[s])
# Legend
leg = TLegend(0.65, 0.6, 0.95, 0.9)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
if len(data) > 0:
leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe")
for i, s in reversed(list(enumerate(['BkgSum'] + back))):
leg.AddEntry(hist[s], sample[s]['label'], "f")
if showSignal:
for i, s in enumerate(sign):
if sample[s]['plot']:
leg.AddEntry(hist[s], sample[s]['label'], "fl")
leg.SetY1(0.9 - leg.GetNRows() * 0.05)
# --- Display ---
c1 = TCanvas("c1",
hist.values()[0].GetXaxis().GetTitle(), 800,
800 if RATIO else 600)
if RATIO:
c1.Divide(1, 2)
setTopPad(c1.GetPad(1), RATIO)
setBotPad(c1.GetPad(2), RATIO)
c1.cd(1)
c1.GetPad(bool(RATIO)).SetTopMargin(0.06)
c1.GetPad(bool(RATIO)).SetRightMargin(0.05)
c1.GetPad(bool(RATIO)).SetTicks(1, 1)
log = variable[var]['log'] #"log" in hist['BkgSum'].GetZaxis().GetTitle()
if log: c1.GetPad(bool(RATIO)).SetLogy()
# Draw
bkg.Draw("HIST") # stack
hist['BkgSum'].Draw("SAME, E2") # sum of bkg
if not isBlind and len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data
if 'sync' in hist: hist['sync'].Draw("SAME, PE")
#data_graph.Draw("SAME, PE")
if showSignal:
smagn = 1. #if treeRead else 1.e2 #if log else 1.e2
for i, s in enumerate(sign):
# if sample[s]['plot']:
hist[s].Scale(smagn)
hist[s].Draw(
"SAME, HIST"
) # signals Normalized, hist[s].Integral()*sample[s]['weight']
textS = drawText(0.80, 0.9 - leg.GetNRows() * 0.05 - 0.02,
stype + " (x%d)" % smagn, True)
#bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset()*1.075)
bkg.GetYaxis().SetTitleOffset(0.9)
#bkg.GetYaxis().SetTitleOffset(2.)
bkg.SetMaximum((5. if log else 1.25) * max(
bkg.GetMaximum(),
hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) +
hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin())))
#if bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum(max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum())*1.25)
bkg.SetMinimum(
max(
min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin(
)), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.)
if log:
bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4)
#bkg.GetYaxis().SetMoreLogLabels(True)
bkg.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
#if log: bkg.SetMinimum(1)
leg.Draw()
#drawCMS(LUMI[year], "Preliminary")
drawCMS(LUMI[year], "Work in Progress", suppressCMS=True)
drawRegion('XVH' + channel, True)
drawAnalysis(channel)
setHistStyle(bkg, 1.2 if RATIO else 1.1)
setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1)
if RATIO:
c1.cd(2)
err = hist['BkgSum'].Clone("BkgErr;")
err.SetTitle("")
err.GetYaxis().SetTitle("Data / MC")
err.GetYaxis().SetTitleOffset(0.9)
err.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max'])
for i in range(1, err.GetNbinsX() + 1):
err.SetBinContent(i, 1)
if hist['BkgSum'].GetBinContent(i) > 0:
err.SetBinError(
i, hist['BkgSum'].GetBinError(i) /
hist['BkgSum'].GetBinContent(i))
setBotStyle(err)
errLine = err.Clone("errLine")
errLine.SetLineWidth(1)
errLine.SetFillStyle(0)
res = hist['data_obs'].Clone("Residues")
for i in range(0, res.GetNbinsX() + 1):
if hist['BkgSum'].GetBinContent(i) > 0:
res.SetBinContent(
i,
res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i))
res.SetBinError(
i,
res.GetBinError(i) / hist['BkgSum'].GetBinContent(i))
if 'sync' in hist:
res.SetMarkerColor(2)
res.SetMarkerStyle(31)
res.Reset()
for i in range(0, res.GetNbinsX() + 1):
x = hist['data_obs'].GetXaxis().GetBinCenter(i)
if hist['sync'].GetBinContent(hist['sync'].FindBin(x)) > 0:
res.SetBinContent(
i, hist['data_obs'].GetBinContent(
hist['data_obs'].FindBin(x)) /
hist['sync'].GetBinContent(hist['sync'].FindBin(x)))
res.SetBinError(
i, hist['data_obs'].GetBinError(
hist['data_obs'].FindBin(x)) /
hist['sync'].GetBinContent(hist['sync'].FindBin(x)))
setBotStyle(res)
#err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5)
#err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2)
err.Draw("E2")
errLine.Draw("SAME, HIST")
if not isBlind and len(data) > 0:
res.Draw("SAME, PE0")
#res_graph.Draw("SAME, PE0")
if len(err.GetXaxis().GetBinLabel(
1)) == 0: # Bin labels: not a ordinary plot
drawRatio(hist['data_obs'], hist['BkgSum'])
drawStat(hist['data_obs'], hist['BkgSum'])
c1.Update()
if gROOT.IsBatch():
if channel == "": channel = "nocut"
varname = var.replace('.', '_').replace('()', '')
if not os.path.exists("plots/" + channel):
os.makedirs("plots/" + channel)
suffix = ''
if "b" in channel or 'mu' in channel: suffix += "_" + BTAGGING
c1.Print("plots/MANtag_study/" + channel + "/" + varname + "_" + year +
suffix + ".png")
c1.Print("plots/MANtag_study/" + channel + "/" + varname + "_" + year +
suffix + ".pdf")
# Print table
printTable(hist, sign)
# if True:
# sFile = TFile("sync/data_2016.root", "RECREATE")
# sFile.cd()
# hist['data_obs'].
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
def DrawOverlap(fileVec,
histVec,
titleVec,
legendtext,
pngname,
logstatus=[0, 0],
xRange=[-99999, 99999, 1]):
gStyle.SetOptTitle(0)
gStyle.SetOptStat(0)
gStyle.SetTitleOffset(1.1, "Y")
gStyle.SetTitleOffset(0.9, "X")
gStyle.SetLineWidth(3)
gStyle.SetFrameLineWidth(3)
i = 0
histList_ = []
histList = []
histList1 = []
maximum = []
## Legend
leg = TLegend(0.1, 0.70, 0.89, 0.89) #,NULL,"brNDC");
leg.SetBorderSize(0)
leg.SetNColumns(3)
leg.SetLineColor(1)
leg.SetLineStyle(1)
leg.SetLineWidth(1)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetTextFont(22)
leg.SetTextSize(0.035)
c = TCanvas("c1", "c1", 0, 0, 500, 500)
#c.SetBottomMargin(0.15)
#c.SetLeftMargin(0.15)
#c.SetLogy(0)
#c.SetLogx(0)
c1_2 = TPad("c1_2", "newpad", 0.04, 0.13, 1, 0.994)
c1_2.Draw()
print("you have provided " + str(len(fileVec)) + " files and " +
str(len(histVec)) + " histograms to make a overlapping plot")
print "opening rootfiles"
c.cd()
c1_2.SetBottomMargin(0.013)
c1_2.SetLogy(logstatus[1])
c1_2.SetLogx(logstatus[0])
c1_2.cd()
ii = 0
inputfile = {}
print str(fileVec[(len(fileVec) - 1)])
for ifile_ in range(len(fileVec)):
print("opening file " + fileVec[ifile_])
inputfile[ifile_] = TFile(fileVec[ifile_])
print "fetching histograms"
for ihisto_ in range(len(histVec)):
print("printing histo " + str(histVec[ihisto_]))
histo = inputfile[ifile_].Get(histVec[ihisto_])
#status_ = type(histo) is TGraphAsymmErrors
histList.append(histo)
# for ratio plot as they should nt be normalize
histList1.append(histo)
#print histList[ii].Integral()
#histList[ii].Rebin(xRange[2])
histList[ii].Scale(1.0 / histList[ii].Integral())
maximum.append(histList[ii].GetMaximum())
maximum.sort()
ii = ii + 1
print histList
for ih in range(len(histList)):
tt = type(histList[ih])
if logstatus[1] is 1:
histList[ih].SetMaximum(100) #1.4 for log
histList[ih].SetMinimum(0.01) #1.4 for log
if logstatus[1] is 0:
histList[ih].SetMaximum(1.4) #1.4 for log
histList[ih].SetMinimum(0.001) #1.4 for log
# print "graph_status =" ,(tt is TGraphAsymmErrors)
# print "hist status =", (tt is TH1D) or (tt is TH1F)
if ih == 0:
if tt is TGraphAsymmErrors:
histList[ih].Draw("APL")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].Draw("hist")
if ih > 0:
#histList[ih].SetLineWidth(2)
if tt is TGraphAsymmErrors:
histList[ih].Draw("PL same")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].Draw("hist same")
if tt is TGraphAsymmErrors:
histList[ih].SetMaximum(100)
histList[ih].SetMarkerColor(colors[ih])
histList[ih].SetLineColor(colors[ih])
histList[ih].SetMarkerStyle(markerStyle[ih])
histList[ih].SetMarkerSize(1)
leg.AddEntry(histList[ih], legendtext[ih], "PL")
if (tt is TH1D) or (tt is TH1F) or (tt is TH1) or (tt is TH1I):
histList[ih].SetLineStyle(linestyle[ih])
histList[ih].SetLineColor(colors[ih])
histList[ih].SetLineWidth(3)
leg.AddEntry(histList[ih], legendtext[ih], "L")
histList[ih].GetYaxis().SetTitle(titleVec[1])
histList[ih].GetYaxis().SetTitleSize(0.052)
histList[ih].GetYaxis().SetTitleOffset(0.88)
histList[ih].GetYaxis().SetTitleFont(22)
histList[ih].GetYaxis().SetLabelFont(22)
histList[ih].GetYaxis().SetLabelSize(.052)
histList[ih].GetXaxis().SetRangeUser(xRange[0], xRange[1])
histList[ih].GetXaxis().SetLabelSize(0.0000)
histList[ih].GetXaxis().SetTitle(titleVec[0])
histList[ih].GetXaxis().SetLabelSize(0.052)
histList[ih].GetXaxis().SetTitleSize(0.052)
histList[ih].GetXaxis().SetTitleOffset(1.04)
histList[ih].GetXaxis().SetTitleFont(22)
histList[ih].GetXaxis().SetTickLength(0.07)
histList[ih].GetXaxis().SetLabelFont(22)
histList[ih].GetYaxis().SetLabelFont(22)
histList[ih].GetXaxis().SetNdivisions(508)
#histList[ih].GetXaxis().SetMoreLogLabels();
#histList[ih].GetXaxis().SetNoExponent();
#histList[ih].GetTGaxis().SetMaxDigits(3);
i = i + 1
pt = TPaveText(0.0877181, 0.9, 0.9580537, 0.96, "brNDC")
pt.SetBorderSize(0)
pt.SetTextAlign(12)
pt.SetFillStyle(0)
pt.SetTextFont(22)
pt.SetTextSize(0.046)
text = pt.AddText(0.2, 0.5, "CMS Internal")
text = pt.AddText(0.65, 0.5, "#sqrt{s} = 13 TeV(2016) ")
pt.Draw()
# t2a = TPaveText(0.0877181,0.81,0.9580537,0.89,"brNDC")
# t2a.SetBorderSize(0)
# t2a.SetFillStyle(0)
# t2a.SetTextSize(0.040)
# t2a.SetTextAlign(12)
# t2a.SetTextFont(62)
# histolabel1= str(fileVec[(len(fileVec)-1)])
# text1 = t2a.AddText(0.06,0.5,"CMS Internal")
# t2a.Draw()
leg.Draw()
#
# c.cd()
outputdirname = 'plots/'
histname = outputdirname + pngname
c.SaveAs(histname + '.png')
c.SaveAs(histname + '.pdf')
[0.6, 0.15, 0.85, 0.4], [0.6, 0.15, 0.85, 0.4]]
outRootDir = "out/"
if not os.path.exists(outRootDir):
os.makedirs(outRootDir)
outRootFile = ROOT.TFile.Open(outRootDir + "outFile.root", "RECREATE")
# for jj in range(1,2):
# for ii in {0,3,4,5,6,7,8,9,10}:
for jj in range(0, 1):
for ii in {0, 1, 2, 10}:
# for ii in range(0,9):
graphs = createGraphs(
globalArr, ii + 2
) # +2 is needed since two first elements in the array correspond to the truth information
c1 = TCanvas('c1', 'A Simple Graph Example', 0, 0, 800, 600)
c1.cd()
leg = TLegend(legendPosition[ii][0], legendPosition[ii][1],
legendPosition[ii][2], legendPosition[ii][3])
leg.SetBorderSize(0)
leg.SetTextSize(0.03)
for kk in range(0, len(graphs[jj])):
iGr = graphs[jj][kk]
styleGraph(iGr, kk + 1)
leg.AddEntry(iGr, iGr.GetTitle(), "p")
if kk == 0:
iGr.SetTitle("")
iGr.GetYaxis().SetTitle(yAxisTitle[ii])
iGr.GetXaxis().SetTitle(xAxisTitle[jj])
iGr.GetYaxis().SetTitleOffset(1.3)
def efficiencytracking(var):
# plots the efficiency vs pT, eta and phi for all the species(it extracts the
# Efficiency from qa - tracking - efficiency if you have ran with-- make - eff)
hadron_list = ["pion", "proton", "kaon", "electron", "muon"]
color_list = [1, 2, 4, 6, 8]
marker_list = [20, 21, 22, 34, 45]
fileo2 = TFile("../codeHF/AnalysisResults_O2.root")
c1 = TCanvas("c1", "Efficiency")
gStyle.SetOptStat(0)
gStyle.SetErrorX(0)
gStyle.SetFrameLineWidth(2)
gStyle.SetTitleSize(0.045, "x")
gStyle.SetTitleSize(0.045, "y")
gStyle.SetMarkerSize(1)
gStyle.SetLabelOffset(0.015, "x")
gStyle.SetLabelOffset(0.02, "y")
gStyle.SetTickLength(-0.02, "x")
gStyle.SetTickLength(-0.02, "y")
gStyle.SetTitleOffset(1.1, "x")
gStyle.SetTitleOffset(1.0, "y")
c1.SetCanvasSize(800, 600)
c1.cd()
c1.SetGridy()
c1.SetGridx()
eff_list = []
if var == "Pt":
hempty = TH1F("hempty", ";Transverse Momentum(GeV/c);Efficiency", 100,
0.05, 10)
gPad.SetLogx()
elif var == "Eta":
hempty = TH1F("hempty", ";Pseudorapidity;Efficiency", 100, -4.0, 4.0)
elif var == "Phi":
hempty = TH1F("hempty", ";Azimuthal angle(rad);Efficiency", 100, 0.0,
6.0)
hempty.GetYaxis().CenterTitle()
hempty.GetXaxis().CenterTitle()
hempty.GetXaxis().SetNoExponent()
hempty.GetXaxis().SetMoreLogLabels(1)
hempty.Draw()
leg = TLegend(0.55, 0.15, 0.89, 0.35, "P")
leg.SetNColumns(2)
leg.SetHeader("Minimum bias KrKr #sqrt{s} = 6.46TeV", "C")
leg.SetFillColor(0)
for i, had in enumerate(hadron_list):
leff = fileo2.Get("qa-tracking-efficiency-%s/Efficiency" % had)
if var == "Pt":
eff = leff.At(0)
elif var == "Eta":
eff = leff.At(1)
elif var == "Phi":
eff = leff.At(2)
gPad.Update()
eff.Paint("p")
gr = eff.GetPaintedGraph().Clone()
for j in range(0, gr.GetN()):
gr.GetEXlow()[j] = 0
gr.GetEXhigh()[j] = 0
gr.SetLineColor(color_list[i])
gr.SetMarkerColor(color_list[i])
gr.SetMarkerStyle(marker_list[i])
eff_list.append(gr)
gr.Draw(" same p")
leg.AddEntry(eff_list[i], had, "p")
leg.Draw()
saveCanvas(c1, "efficiency_tracking_%s" % var)
"module_L6t_halfmodule_axial_hole_sensor0 - Occupancy")
L6t_axial_slot_nom = inFile2.Get(
"module_L6t_halfmodule_axial_slot_sensor0 - Occupancy")
L6t_stereo_hole_nom = inFile2.Get(
"module_L6t_halfmodule_stereo_hole_sensor0 - Occupancy")
L6t_stereo_slot_nom = inFile2.Get(
"module_L6t_halfmodule_stereo_slot_sensor0 - Occupancy")
def plot(histo, outName):
histo.Draw("")
histo.SetTitle(histo.GetTitle())
c.Print(outName + ".pdf", "Title:histoTitle")
c = TCanvas("c", "c", 800, 600)
outnameL0 = outname + "L0"
outnamenom = outname + "nom"
c.Print(outnameL0 + ".pdf[", "Title:histoTitle")
plot(L0b_axial_hole_L0, outnameL0)
plot(L0b_axial_slot_L0, outnameL0)
plot(L0b_stereo_hole_L0, outnameL0)
plot(L0b_stereo_slot_L0, outnameL0)
plot(L0t_axial_hole_L0, outnameL0)
plot(L0t_axial_slot_L0, outnameL0)
plot(L0t_stereo_hole_L0, outnameL0)
plot(L0t_stereo_slot_L0, outnameL0)
ratiograph.SetMinimum(0.976)
ratiograph.SetMaximum(1.024)
ratiograph.GetXaxis().SetLimits(0.0, 155.0)
ratiograph.SetTitle("")
ratiograph.GetXaxis().SetLabelSize(.105)
ratiograph.GetYaxis().SetLabelSize(0.105)
ratiograph.GetXaxis().SetNdivisions(520)
ratiograph.GetYaxis().SetNdivisions(504)
lingraph.SetTitle("")
lingraph.SetMinimum(0.0)
lingraph.SetMaximum(155.0)
lingraph.GetXaxis().SetLimits(0.0, 155.0)
lingraph.GetXaxis().SetLabelSize(0.)
lingraph.GetXaxis().SetNdivisions(520)
lingraph.GetYaxis().SetNdivisions(520)
c = TCanvas("c", "canvas", 800, 800)
# Upper histogram plot is pad1
pad1 = TPad("pad1", "pad1", 0, 0.3, 1, 1.0)
pad1.SetBottomMargin(0.02) # joins upper and lower plot
pad1.SetLeftMargin(0.1)
pad1.SetRightMargin(0.1)
pad1.Draw()
# Lower ratio plot is pad2
c.cd() # returns to main canvas before defining pad2
pad2 = TPad("pad2", "pad2", 0, 0.05, 1, 0.3)
pad2.SetTopMargin(0.05) # joins upper and lower plot
pad2.SetBottomMargin(0.3)
pad2.SetLeftMargin(0.1)
pad2.SetRightMargin(0.1)
pad2.Draw()
pad1.cd()
def fitSloChannelsFast(dsNum):
# Just get the 95% upper value from the peak.
# NOTE: the slowness model might not be good at the 238 peak (S/N too high ??)
# Ben is shaking his head in disapproval.
ds = dsNum
chCal = {}
chCal[0] = [640, 644, 646, 656, 662, 664, 674, 688, 690, 692, 696, 576, 592, 594, 598, 600, 608, 610, 624, 626]
chCal[1] = [672, 640, 610, 580, 578, 582, 648, 600, 626, 592, 664, 594, 692, 598, 690, 632, 608]
chCal[3] = [608, 624, 610, 580, 614, 582, 648, 664, 694, 578, 678, 592, 600, 626, 692, 598, 690, 632, 640, 672]
chCal[4] = [1232, 1236, 1332, 1298, 1330, 1170, 1296, 1136, 1176, 1106, 1204, 1174, 1144, 1172]
chCal[5] = [640, 1302, 648, 1170, 1172, 662, 1176, 672, 678, 680, 1330, 688, 690, 1332, 694, 1208, 1204, 1110, 1174, 608, 584, 1298, 592, 1106, 598, 1120, 610, 1124, 614, 1128, 658, 624, 626, 628, 632, 660]
f1 = TFile("~/project/lat/latSkimDS%d_0_0.root"%ds)
theCut = f1.Get("theCut").GetTitle()
calib = TChain("skimTree")
calib.Add("~/project/cal-lat/latSkimDS%d*.root"%ds)
c = TCanvas("c","c",1600,600)
c.Divide(3,1,0.00001,0.00001)
# ch = 626 # the golden detector
for ch in chCal[ds]:
var = "fitSlo"
eb, elo, ehi = 10,237,240
vb, vlo, vhi = 200,0,30
d2Draw = "fitSlo:trapENFCal>>b(%d,%d,%d,%d,%d,%d)"%(eb,elo,ehi,vb,vlo,vhi)
d2Cut = theCut + " && channel==%d" % ch
d1Cut = theCut + " && trapENFCal > %d && trapENFCal < %d && channel==%d" % (elo,ehi,ch)
c.cd(2)
gPad.SetLogy(0)
h1 = TH1D("h1","h1",vb,vlo,vhi)
calib.Project("h1",var,d1Cut)
h1.Scale(1/h1.Integral())
h1.Draw("hist")
c.cd(1)
gPad.SetLogy(0)
calib.Draw(d2Draw,d2Cut)
pk99,pk95,pk01,pk05 = GetIntegralPoints(h1)
line99 = TLine()
line99.SetLineColor(ROOT.kGreen)
line95 = TLine()
line95.SetLineColor(ROOT.kRed)
line99.DrawLine(elo, pk99, ehi, pk99)
line95.DrawLine(elo, pk95, ehi, pk95)
line95.DrawLine(elo, pk05, ehi, pk05)
line99.DrawLine(elo, pk01, ehi, pk01)
c.cd(3)
x_h1, y_h1 = npTH1D(h1)
int_h1 = integFunc(y_h1)
g2 = TGraph(len(x_h1), x_h1, int_h1)
g2.Draw("ACP")
line99.DrawLine(pk99, 0, pk99, 1)
line95.DrawLine(pk95, 0, pk95, 1)
line99.DrawLine(pk01, 0, pk01, 1)
line95.DrawLine(pk05, 0, pk05, 1)
c.Print("./plots/fitSlo/fast_ds%d_ch%d.png" % (ds,ch))
print "DS-%d Ch %d fitSlo95 %.2f " % (ds,ch,pk95)
def provideQuadCanvas(title="Same Sign Dilepton SUSY analysis"):
# window settings
theCanvasSizeX = 1400
theCanvasSizeY = 920
theScreenSizeX = 1680
theScreenSizeY = 1050
theOffsetX = -80
theOffsetY = -60
# canvas
posX = (theScreenSizeX - theCanvasSizeX) / 2 + theOffsetX
posY = (theScreenSizeY - theCanvasSizeY) / 2 + theOffsetY
c1 = TCanvas('c1', title, posX, posY, theCanvasSizeX, theCanvasSizeY)
c1.Divide(2, 2)
c1.cd(1)
pad1 = TPad('pad1', 'Pad for top left histogram', 0.03, 0.03, 0.97, 0.97,
0)
pad1.Draw()
c1.cd(2)
pad2 = TPad('pad2', 'Pad for top right histogram', 0.03, 0.03, 0.97, 0.97,
0)
pad2.Draw()
c1.cd(3)
pad3 = TPad('pad1', 'Pad for bottom left histogram', 0.03, 0.03, 0.97,
0.97, 0)
pad3.Draw()
c1.cd(4)
pad4 = TPad('pad2', 'Pad for bottom right histogram', 0.03, 0.03, 0.97,
0.97, 0)
pad4.Draw()
return [c1, pad1, pad2, pad3, pad4]
def fitArb10(tree,theCut):
# fit an arbitrary low-energy cut
print theCut
theCut += " && !isEnr"
checkCut = ""
thisVar = "bandTime"
fileName = "./plots/lat-max.pdf"
parLim = [0,20000]
# scatter plot
h0 = wl.H2D(tree,"h0",100,0,20,500,parLim[0],parLim[1],thisVar+":trapENFCal",theCut)
# -- 10 peak --
fitModel10 = "[0] + [1]*x + [2]*x**2 + [3] * exp(-1.0 * ((x-[4])**2 / (2 * [5]**2)) )"
h1 = wl.H1D(tree,"h1",20,9.5,11.5,"trapENFCal",theCut)
h2 = wl.H1D(tree,"h2",20,9.5,11.5,"trapENFCal",theCut+checkCut)
f0 = TF1("f0","gaus",10,11)
h1.Fit("f0","Lq")
[norm,mu,sig] = wl.GetPars(f0)
f1 = TF1("f1", fitModel10, 9.5,11)
wl.SetPars(f1,[-209, 41, -2, norm, mu, sig])
f1.SetLineColor(4)
h1.Fit("f1","Lq")
[p0,p1,p2,norm1,mu,sig] = wl.GetPars(f1)
f2 = TF1("f2", fitModel10, 9.5,11)
wl.SetPars(f2,[-209, 41, -2, norm1, mu, sig])
f2.SetLineColor(2)
h2.Fit("f2","Lq")
[p0,p1,p2,norm2,mu,sig] = wl.GetPars(f2)
retention = 100*norm2/norm1
print "Retention:",retention," Cut used:",checkCut
# -- Energy Spectrum --
h3 = wl.H1D(tree,"h3",250,0,50,"trapENFCal",theCut)
h4 = wl.H1D(tree,"h4",250,0,50,"trapENFCal",theCut+checkCut)
cts3 = h3.Integral( *wl.Get1DBins(h3,0,20))
cts4 = h4.Integral( *wl.Get1DBins(h4,0,20))
cts5 = h3.Integral( *wl.Get1DBins(h3,20,40))
cts6 = h4.Integral( *wl.Get1DBins(h4,20,40))
print "Cts. 0-20 %d before %d after. 20-40 %d before %d after" % (cts3,cts4,cts5,cts6)
h3.GetXaxis().SetRangeUser(0,20)
h4.GetXaxis().SetRangeUser(0,20)
# -- Plots --
c1 = TCanvas("c1","Bob Ross's Canvas",1800,600)
c1.Divide(3,1,0.00001) # TPad::Divide
c1.cd(1)
gPad.SetLogz()
h0.SetMinimum(1)
h0.Draw("COLZ")
c1.cd(2)
gPad.SetLogy()
h3.SetLineColor(4)
h3.Draw()
h4.SetLineColor(2)
h4.Draw("same")
l1 = TLegend(0.4,0.7,0.87,0.92)
l1.AddEntry(h1,"basic","l")
l1.AddEntry(h2,thisVar,"l")
l1.Draw("same")
c1.cd(3)
h1.SetLineColor(4) # blue
h1.Draw()
h2.SetLineColor(2) # red
h2.Draw("same")
l2 = TLegend(0.6,0.6,0.87,0.92)
l2.AddEntry(h1,"basic","l")
l2.AddEntry(h2,thisVar,"l")
l2.AddEntry(h2,"Retention: %.3f" % retention,"")
l2.Draw("same")
c1.Print(fileName)
def fitSlo238(tree,theCut):
# for calibration data, keep 99% of the fast events in the 238 peak.
# this is practice for doing this with the 10 kev peak in natural bg data.
# scatter plot
h1 = wl.H2D(tree,"h1",500,0,250,500,0,200,"fitSlo:trapENFCal",theCut)
cts1 = h1.Integral( *wl.Get2DBins(h1,0,250,0,200) )
# -- 238 peak --
h2 = wl.H1D(tree,"h2",50,237,240,"trapENFCal",theCut)
cts2 = h2.Integral( *wl.Get1DBins(h2,237,240) )
h2.SetLineColor(4)
f0 = TF1("f0","gaus",237,240)
h2.Fit("f0","Lq") # use to set initial guesses
f1 = TF1("f1", "[0] * x + [1] * exp(-1.0 * (TMath::Power((x-[2]),2) / (2 * TMath::Power([3],2)) ))", 237, 240)
wl.SetPars(f1,[1,418,238,0.381])
f1.SetLineColor(4)
h2.Fit("f1","q")
[flat, norm1, mu, sig] = wl.GetPars(f1)
h3 = wl.H1D(tree,"h3",50,237,240,"trapENFCal",theCut+" && fitSlo < 20")
cts3 = h3.Integral( *wl.Get1DBins(h3,237,240) )
f2 = TF1("f2", "[0] * x + [1] * exp(-1.0 * (TMath::Power((x-[2]),2) / (2 * TMath::Power([3],2)) ))", 237, 240)
wl.SetPars(f2,[1,418,238,0.381])
f2.SetLineColor(2)
h3.Fit("f2","q")
[flat, norm2, mu, sig] = wl.GetPars(f2)
retention = 100*norm2/norm1
print "norm1 %.2f norm2 %.2f retention: %.2f" % (norm1, norm2, retention)
# -- Plots --
c1 = TCanvas("c1","Bob Ross's Canvas",1200,600)
c1.Divide(2,1) # x, y
c1.cd(1)
gPad.SetLogz()
h1.Draw("COLZ")
c1.cd(2)
h2.Draw()
h3.SetLineColor(2) # red
h3.Draw("same")
l1 = TLegend(0.6,0.7,0.87,0.92)
l1.AddEntry(h2,"basic","l")
l1.AddEntry(f1,"basic fit:","l")
# l1.AddEntry(f1,"flat %.2f norm %.2f" % (flat,norm1) ,"")
# l1.AddEntry(f1,"mu %.2f sig %.2f" % (mu,sig) ,"")
l1.AddEntry(h3,"+fitSlo < 20","l")
l1.AddEntry(h3,"Retention: %.3f" % retention,"")
l1.Draw("same")
c1.Update()
c1.Print("./plots/cal-fitSlo.pdf")
def PlotProfiles(hists,
xlabel,
these_colors,
these_transcolors,
these_markers,
drawopt='pE1',
rlabel='Trigger/Ref.',
ylabel='Trigger Efficiency',
doRatioCanvas=False,
y_axes_maximum=1.1,
y_axes_minimum=0.0,
pad_top_y_axes_maximum=1.1,
pad_top_y_axes_minimum=0.0,
pad_bot_y_axes_maximum=1.1,
pad_bot_y_axes_minimum=0.9):
from ROOT import TCanvas
from Gaugi.monet import RatioCanvas, AddHistogram, FormatCanvasAxes, SetAxisLabels
doRatio = True if (doRatioCanvas and (len(hists) > 1)) else False
collect = []
canvas = RatioCanvas('canvas', "", 700, 500) if doRatio else TCanvas(
'canvas', "", 700, 500)
if doRatio:
pad_top = canvas.GetPrimitive('pad_top')
pad_bot = canvas.GetPrimitive('pad_bot')
refHist = hists[0]
for idx, hist in enumerate(hists):
if doRatio:
if idx < 1:
refHist.SetLineColor(these_colors[idx])
refHist.SetMarkerColor(these_colors[idx])
refHist.SetMaximum(pad_top_y_axes_maximum)
refHist.SetMinimum(pad_top_y_axes_minimum)
AddHistogram(pad_top,
refHist,
drawopt=drawopt,
markerStyle=these_markers[idx])
else:
h_hist = hist.Clone()
h_hist.Divide(h_hist, refHist, 1., 1., 'B')
h_hist.SetMinimum(pad_bot_y_axes_minimum)
h_hist.SetMaximum(pad_bot_y_axes_maximum)
collect.append(h_hist)
hist.SetLineColor(these_colors[idx])
hist.SetMarkerColor(these_colors[idx])
hist.SetMaximum(pad_top_y_axes_maximum)
AddHistogram(pad_top,
hist,
drawopt,
markerStyle=these_markers[idx])
AddHistogram(pad_bot,
h_hist,
'P',
markerStyle=these_markers[idx])
else:
hist.SetLineColor(these_colors[idx])
hist.SetMarkerColor(these_colors[idx])
hist.SetMaximum(y_axes_maximum)
hist.SetMinimum(y_axes_minimum)
AddHistogram(canvas,
hist,
drawopt=drawopt,
markerStyle=these_markers[idx])
FormatCanvasAxes(canvas,
XLabelSize=18,
YLabelSize=18,
XTitleOffset=0.87,
YTitleOffset=1.5)
SetAxisLabels(canvas, xlabel, ylabel, rlabel)
return canvas
def fitSloChannels():
ds = 1
f1 = TFile("~/project/lat/latSkimDS%d_0_0.root"%ds)
theCut = f1.Get("theCut").GetTitle()
calib = TChain("skimTree")
calib.Add("~/project/cal-lat/latSkimDS%d*.root"%ds)
c = TCanvas("c","c",1600,1200)
c.Divide(3,2,0.00001,0.00001)
# ch = 626 # the golden detector
for ch in chCalDS1:
# 10-200 spectrum
var = "fitSlo"
eb, elo, ehi = 10,10,200
vb, vlo, vhi = 200,0,30
d2Draw = "fitSlo:trapENFCal>>a(%d,%d,%d,%d,%d,%d)"%(eb,elo,ehi,vb,vlo,vhi)
d2Cut = theCut + " && channel==%d" % ch
d1Cut = theCut + " && trapENFCal > 5 && channel==%d" % ch
c.cd(2)
gPad.SetLogy(0)
h0 = TH1D("h0","h0",vb,vlo,vhi)
calib.Project("h0",var,d1Cut)
h0.Scale(1/h0.Integral())
h0.Draw("hist")
c.cd(1)
gPad.SetLogy(1)
calib.Draw(d2Draw,d2Cut)
sp99,sp95,sp01,sp05 = GetIntegralPoints(h0)
line99 = TLine()
line99.SetLineColor(ROOT.kGreen)
line95 = TLine()
line95.SetLineColor(ROOT.kRed)
line99.DrawLine(elo, sp99, ehi, sp99)
line95.DrawLine(elo, sp95, ehi, sp95)
line95.DrawLine(elo, sp05, ehi, sp05)
line99.DrawLine(elo, sp01, ehi, sp01)
c.cd(3)
x_h0, y_h0 = npTH1D(h0)
int_h0 = integFunc(y_h0)
g1 = TGraph(len(x_h0), x_h0, int_h0)
g1.Draw("ACP")
line99.DrawLine(sp99, 0, sp99, 1)
line95.DrawLine(sp95, 0, sp95, 1)
line99.DrawLine(sp01, 0, sp01, 1)
line95.DrawLine(sp05, 0, sp05, 1)
# 238 peak spectrum
eb, elo, ehi = 10,237,240
d2Draw = "fitSlo:trapENFCal>>b(%d,%d,%d,%d,%d,%d)"%(eb,elo,ehi,vb,vlo,vhi)
d2Cut = theCut + " && channel==%d" % ch
d1Cut = theCut + " && trapENFCal > %d && trapENFCal < %d && channel==%d" % (elo,ehi,ch)
c.cd(5)
gPad.SetLogy(0)
h1 = TH1D("h1","h1",vb,vlo,vhi)
calib.Project("h1",var,d1Cut)
h1.Scale(1/h1.Integral())
h1.Draw("hist")
c.cd(4)
gPad.SetLogy(0)
calib.Draw(d2Draw,d2Cut)
pk99,pk95,pk01,pk05 = GetIntegralPoints(h1)
line99 = TLine()
line99.SetLineColor(ROOT.kGreen)
line95 = TLine()
line95.SetLineColor(ROOT.kRed)
line99.DrawLine(elo, pk99, ehi, pk99)
line95.DrawLine(elo, pk95, ehi, pk95)
line95.DrawLine(elo, pk05, ehi, pk05)
line99.DrawLine(elo, pk01, ehi, pk01)
c.cd(6)
x_h1, y_h1 = npTH1D(h1)
int_h1 = integFunc(y_h1)
g2 = TGraph(len(x_h1), x_h1, int_h1)
g2.Draw("ACP")
line99.DrawLine(pk99, 0, pk99, 1)
line95.DrawLine(pk95, 0, pk95, 1)
line99.DrawLine(pk01, 0, pk01, 1)
line95.DrawLine(pk05, 0, pk05, 1)
c.Print("./plots/fitSlo/ds%d_ch%d.png" % (ds,ch))
print "DS-%d Channel %d:" % (ds,ch)
print " Peak - 99: %.2f 95: %.2f 5: %.2f 1: %.2f" % (sp99,sp95,sp05,sp01)
print " Spec - 99: %.2f 95: %.2f 5: %.2f 1: %.2f" % (pk99,pk95,pk05,pk01)
def report():
global freed_file
print ' HeapMonReport.report(): heapmon_file=', freed_file
#findStaticHoles()
tfile = TFile(freed_file, "READ")
print " root compression factor = ", tfile.GetCompressionFactor()
mem_canvas = TCanvas("HeapMon_report", "Memory Holes Statistics", 10, 10,
800, 1034)
mem_canvas.SetFillColor(17)
mem_canvas.cd()
pad1 = TPad("pad1", "pad1", 0.01, 0.57, 0.50, 0.93, 33)
pad2 = TPad("pad2", "pad2", 0.51, 0.57, 0.99, 0.93, 33)
pad3 = TPad("pad3", "pad3", 0.01, 0.01, 0.99, 0.50, 33)
pad3.SetPhi(210)
pad3.SetTheta(25)
pad1.Draw()
pad2.Draw()
pad3.Draw()
memTree = tfile.Get("holeTree")
infoTree = tfile.Get("infoTree")
mallocTree = tfile.Get("mallocTree")
#holesm_th1i = TH1I('holesm_th1i', 'Holes size evolution', fcount, 0, fcount)
#holesn_th1i = TH1I('holesn_th1i', 'Holes number evolution', fcount, 0, fcount)
#total_th1i = TH1I('total_th1i', 'Total memory size evolution', fcount, 0, fcount)
max_hole_size = memTree.GetMaximum("hole_size")
print " max_hole_size=", max_hole_size, " min_hole_size", memTree.GetMinimum(
"hole_size")
max_scan_number = memTree.GetMaximum("scan_number")
print " max_scan_number=", max_scan_number
memHist1 = TH2I("mem2d", "Hole-sizes distribution evolution", 128, -0.5,
max_hole_size - 0.5, 50, 0, max_scan_number)
memTree.Project("mem2d", "scan_number:hole_size")
multiGraph1 = TMultiGraph()
multiGraph2 = TMultiGraph()
print " memHist.GetMaximum() = ", memHist1.GetMaximum()
# Working on a Report
gStyle.SetOptStat(0)
gStyle.SetPalette(1)
gStyle.SetCanvasColor(33)
gStyle.SetFrameFillColor(18)
memHist1.SetFillColor(30)
memHist1.SetFillStyle(0)
memHist1.GetXaxis().SetTitle("Size of holes, kb")
memHist1.GetXaxis().SetLabelOffset(0.02)
memHist1.GetXaxis().SetLabelSize(0.02)
memHist1.GetXaxis().SetTitleSize(0.04)
memHist1.GetXaxis().SetTitleColor(2)
memHist1.GetYaxis().SetTitle("Event number")
memHist1.GetYaxis().SetLabelSize(0.04)
memHist1.GetXaxis().SetLabelOffset(0.04)
memHist1.GetYaxis().SetTitleSize(0.04)
memHist1.GetYaxis().SetTitleColor(2)
memHist1.GetZaxis().SetTitle("Number of holes")
memHist1.GetZaxis().SetLabelSize(0.02)
memHist1.GetZaxis().SetTitleSize(0.04)
memHist1.GetZaxis().SetTitleColor(2)
title = TPaveLabel(0.1, 0.95, 0.9, 0.99, "Job Memory Usage Plots")
title.SetFillColor(42)
title.SetTextFont(42)
title.Draw()
text_box = TPaveText(0.1, 0.51, 0.9, 0.54)
text_box.AddText("Malloc freed ('marked') Heap Memory Profile")
text_box.SetFillColor(42)
text_box.SetTextAlign(12)
text_box.SetTextFont(42)
text_box.Draw()
x1 = 0.2
y1 = 0.91
x2 = 0.8
y2 = 0.98
#Drawing upper-left corner Pad1 of the report
pad1.cd()
pad1.SetGridx()
pad1.SetGridy()
infoTree.Draw("total_maps_memory:scan_number", "", "goff")
mapsGraph = TGraph(int(infoTree.GetSelectedRows()), infoTree.GetV2(),
infoTree.GetV1())
mapsGraph.SetLineColor(1)
mapsGraph.SetLineWidth(1)
#mapsGraph.SetFillStyle(3001); mapsGraph.SetFillColor(2);
mapsGraph.SetName("total_maps_memory")
#VmSize, VmLck, VmRSS, VmData, VmStk, VmExe, VmLib;
infoTree.Draw("VmSize:scan_number", "", "goff")
vmsizeGraph = TGraph(int(infoTree.GetSelectedRows()), infoTree.GetV2(),
infoTree.GetV1())
vmsizeGraph.SetLineColor(2)
vmsizeGraph.SetLineWidth(1)
#vmsizeGraph.SetFillStyle(3002); vmsizeGraph.SetFillColor(3);
vmsizeGraph.SetName("VmSize")
infoTree.Draw("VmRSS:scan_number", "", "goff")
vmrssGraph = TGraph(infoTree.GetSelectedRows(), infoTree.GetV2(),
infoTree.GetV1())
vmrssGraph.SetLineColor(3)
vmrssGraph.SetLineWidth(1)
#vmrssGraph.SetFillStyle(3003); vmrssGraph.SetFillColor(4);
vmrssGraph.SetName("VmRSS")
infoTree.Draw("VmData:scan_number", "", "goff")
vmdataGraph = TGraph(infoTree.GetSelectedRows(), infoTree.GetV2(),
infoTree.GetV1())
vmdataGraph.SetLineColor(4)
vmdataGraph.SetLineWidth(1)
#vmdataGraph.SetFillStyle(3004); vmdataGraph.SetFillColor(4);
vmdataGraph.SetName("VmData")
infoTree.Draw("VmStk:scan_number", "", "goff")
vmstkGraph = TGraph(infoTree.GetSelectedRows(), infoTree.GetV2(),
infoTree.GetV1())
vmstkGraph.SetLineColor(5)
vmstkGraph.SetLineWidth(1)
#vmstkGraph.SetFillStyle(3005); vmstkGraph.SetFillColor(4);
vmstkGraph.SetName("VmStk")
infoTree.Draw("VmExe:scan_number", "", "goff")
vmexeGraph = TGraph(infoTree.GetSelectedRows(), infoTree.GetV2(),
infoTree.GetV1())
vmexeGraph.SetLineColor(6)
vmexeGraph.SetLineWidth(1)
#vmexeGraph.SetFillStyle(3003); vmexeGraph.SetFillColor(4);
vmexeGraph.SetName("VmExe")
infoTree.Draw("VmLib:scan_number", "", "goff")
vmlibGraph = TGraph(infoTree.GetSelectedRows(), infoTree.GetV2(),
infoTree.GetV1())
vmlibGraph.SetLineColor(7)
vmlibGraph.SetLineWidth(1)
#vmlibGraph.SetFillStyle(3003); vmlibGraph.SetFillColor(4);
vmlibGraph.SetName("VmLib")
infoTree.Draw("VmLck:scan_number", "", "goff")
vmlckGraph = TGraph(infoTree.GetSelectedRows(), infoTree.GetV2(),
infoTree.GetV1())
vmlckGraph.SetLineColor(8)
vmlckGraph.SetLineWidth(1)
#vmlckGraph.SetFillStyle(3003); vmlckGraph.SetFillColor(4);
vmlckGraph.SetName("VmLck")
infoTree.Draw("total_hole_memory:scan_number", "", "goff")
holeGraph = TGraph(infoTree.GetSelectedRows(), infoTree.GetV2(),
infoTree.GetV1())
holeGraph.SetLineColor(9)
holeGraph.SetLineWidth(1)
#holeGraph.SetFillStyle(3004); holeGraph.SetFillColor(5);
holeGraph.SetName("HolesSize")
mallocTree.Draw("malloc_free:scan_number", "", "goff")
freeGraph = TGraph(mallocTree.GetSelectedRows(), mallocTree.GetV2(),
mallocTree.GetV1())
freeGraph.SetLineColor(11)
freeGraph.SetLineWidth(1)
freeGraph.SetFillStyle(3003)
freeGraph.SetFillColor(4)
freeGraph.SetName("malloc_free")
mallocTree.Draw("malloc_inuse:scan_number", "", "goff")
inuseGraph = TGraph(mallocTree.GetSelectedRows(), mallocTree.GetV2(),
mallocTree.GetV1())
inuseGraph.SetLineColor(21)
inuseGraph.SetLineWidth(1)
inuseGraph.SetFillStyle(3003)
inuseGraph.SetFillColor(4)
inuseGraph.SetName("malloc_inuse")
mallocTree.Draw("malloc_sbrk:scan_number", "", "goff")
sbrkGraph = TGraph(mallocTree.GetSelectedRows(), mallocTree.GetV2(),
mallocTree.GetV1())
sbrkGraph.SetLineColor(31)
sbrkGraph.SetLineWidth(1)
sbrkGraph.SetFillStyle(3003)
sbrkGraph.SetFillColor(4)
sbrkGraph.SetName("malloc_sbrk")
mallocTree.Draw("malloc_mmap:scan_number", "", "goff")
mmapGraph = TGraph(mallocTree.GetSelectedRows(), mallocTree.GetV2(),
mallocTree.GetV1())
mmapGraph.SetLineColor(41)
mmapGraph.SetLineWidth(1)
mmapGraph.SetFillStyle(3003)
mmapGraph.SetFillColor(4)
mmapGraph.SetName("malloc_mmap")
pad1.cd()
multiGraph1.Add(mapsGraph)
multiGraph1.Add(vmsizeGraph)
multiGraph1.Add(vmrssGraph)
multiGraph1.Add(vmdataGraph)
multiGraph1.Add(vmlckGraph)
multiGraph1.Add(vmexeGraph)
multiGraph1.Add(vmstkGraph)
#multiGraph1.Add(vmlibGraph);
multiGraph1.Add(inuseGraph)
multiGraph1.Add(sbrkGraph)
multiGraph1.Add(mmapGraph)
multiGraph1.Add(holeGraph)
multiGraph1.Add(freeGraph)
#multiGraph1.SetTitle("PROCESS VM and Malloc MEMORY USAGE");
title.DrawPaveLabel(x1, y1, x2, y2, "PROCESS VM and Malloc MEMORY USAGE",
"brNDC")
multiGraph1.Draw("ALg")
hist = multiGraph1.GetHistogram()
hist.SetXTitle("Event Number")
hist.SetYTitle("Memory, kb")
legend1 = TLegend(0.84, 0.20, 0.99, 0.90)
legend1.AddEntry(mapsGraph, "Maps", "l")
legend1.AddEntry(vmsizeGraph, "VmSize", "l")
legend1.AddEntry(vmrssGraph, "VmRSS", "l")
legend1.AddEntry(vmdataGraph, "VmData", "l")
legend1.AddEntry(sbrkGraph, "MallocSbrk", "l")
legend1.AddEntry(inuseGraph, "MallocInuse", "l")
#legend1.AddEntry(vmlibGraph, "VmLib", "l");
legend1.AddEntry(mmapGraph, "MallocMmap", "l")
legend1.AddEntry(freeGraph, "MallocFree", "l")
legend1.AddEntry(holeGraph, "Freed-Holes", "l")
legend1.AddEntry(vmstkGraph, "VmStk", "l")
legend1.AddEntry(vmexeGraph, "VmExe", "l")
legend1.AddEntry(vmlckGraph, "VmLck", "l")
legend1.Draw()
#multiGraph1.Draw("ALg")
#title.DrawPaveLabel(x1,y1,x2,y2,"Process Memory Usage Charts","brNDC");
#Drawing upper-left corner Pad1 of the report
pad2.cd()
pad2.SetGridx()
pad2.SetGridy()
infoTree.Draw("total_hole_memory:scan_number", "", "goff")
holeGraph = TGraph(infoTree.GetSelectedRows(), infoTree.GetV2(),
infoTree.GetV1())
holeGraph.SetLineColor(9)
holeGraph.SetLineWidth(1)
holeGraph.SetFillStyle(3004)
holeGraph.SetFillColor(5)
holeGraph.SetName("total_hole_memory")
mallocTree.Draw("malloc_free:scan_number", "", "goff")
freeGraph = TGraph(mallocTree.GetSelectedRows(), mallocTree.GetV2(),
mallocTree.GetV1())
freeGraph.SetLineColor(11)
freeGraph.SetLineWidth(1)
freeGraph.SetFillStyle(3004)
freeGraph.SetFillColor(5)
freeGraph.SetName("malloc_free")
pad2.cd()
multiGraph2.Add(holeGraph)
multiGraph2.Add(freeGraph)
#multiGraph2.Add(sbrkGraph);
#multiGraph2.Add(mmapGraph);
#multiGraph2.SetTitle("Free and Marked Holes Memory Graph");
title.DrawPaveLabel(x1, y1, x2, y2, "Malloc Free and Marked Holes Memory",
"brNDC")
multiGraph2.Draw("ALg")
hist = multiGraph2.GetHistogram()
hist.SetXTitle("Event Number")
hist.SetYTitle("Memory, kb")
legend2 = TLegend(0.9, 0.30, 0.99, 0.90)
legend2.AddEntry(freeGraph, "Free", "l")
legend2.AddEntry(holeGraph, "Holes", "l")
#legend2.AddEntry(inuseGraph, "Inuse", "l");
#legend2.AddEntry(mmapGraph, "Mmap", "l");
#legend2.AddEntry(sbrkGraph, "Sbrk", "l");
legend2.Draw()
#multiGraph2.Draw("ALg")
#title.DrawPaveLabel(x1,y1,x2,y2,"Malloc Memory Usage and Deallocation Charts","brNDC");
#PAD3
pad3.cd()
pad3.SetLogz()
memHist1.Draw("lego2")
#title.DrawPaveLabel(x1,y1,x2,y2,"TH2I-LEGO2","brNDC");
mem_canvas.SetBorderSize(1)
#mem_canvas.Modified()
mem_canvas.Update()
mem_canvas.Print(".pdf")
mem_canvas.Print(".C")
def fitSlo10(tree,theCut):
theCut += " && !isEnr"
# scatter plot
h0 = wl.H2D(tree,"h0",100,0,20,500,0,200,"fitSlo:trapENFCal",theCut)
# -- 10 peak --
fitModel10 = "[0] + [1]*x + [2]*x**2 + [3] * exp(-1.0 * ((x-[4])**2 / (2 * [5]**2)) )"
h1 = wl.H1D(tree,"h1",20,9.5,11.5,"trapENFCal",theCut)
h1.SetLineColor(4)
# f1 = TF1("f1","gaus",10,11)
# h1.Fit("f1","L") # use to set initial guesses
f1 = TF1("f1", fitModel10, 9.5,11)
wl.SetPars(f1,[-209, 41, -2, 28, 10.3, 0.135])
f1.SetLineColor(4)
h1.Fit("f1","Lq")
[p0,p1,p2,norm1,mu,sig] = wl.GetPars(f1)
# print wl.GetPars(f1)
h2 = wl.H1D(tree,"h2",20,9.5,11.5,"trapENFCal",theCut+" && fitSlo < 18")
f2 = TF1("f2", fitModel10, 9.5,11)
wl.SetPars(f2,[-209, 41, -2, 28, 10.3, 0.135])
f2.SetLineColor(2)
h2.Fit("f2","Lq")
[p0,p1,p2,norm2,mu,sig] = wl.GetPars(f2)
retention1 = 100*norm2/norm1
# -- 6.54 (Fe55) peak --
# TODO: This doesn't work well. wait till you're using RooFit.
fitModel6 = "[0] * x + [1] * exp( -1.0 * ((x - [2])**2 / (2 * [3]**2)) )"
# fitModel6 = "[0] * exp( -1.0 * ((x - [1])**2 / (2 * [2]**2)) )"
h3 = wl.H1D(tree,"h3",20,5.5,7.5,"trapENFCal",theCut)
# f3 = TF1("f3","gaus",6,7)
# h3.Fit("f3","L") # use to set initial guesses
# f3 = TF1("f3", fitModel6, 6,7)
# wl.SetPars(f3,[-1, 19.4, 6.47, 0.631])
# f3.SetLineColor(4)
# h3.Fit("f3","Lq")
# [lin,norm3,mu,sig] = wl.GetPars(f3)
#
# h4 = wl.H1D(tree,"h4",20,5.5,7.5,"trapENFCal",theCut+" && fitSlo < 18")
# f4 = TF1("f4", fitModel6, 6,7)
# wl.SetPars(f4,[-1, 19.4, 6.47, 0.631])
# f4.SetLineColor(2)
# h4.Fit("f4","Lq")
# [lin,norm4,mu,sig] = wl.GetPars(f4)
# retention2 = 100*norm4/norm3
# print "norm3 %.2f norm4 %.2f retention2: %.2f" % (norm3, norm4, retention2)
# -- Plots --
c1 = TCanvas("c1","Bob Ross's Canvas",1200,600)
# c1.Divide(3,1,0.00001) # TPad::Divide
c1.Divide(2,1,0.00001)
c1.cd(1)
gPad.SetLogz()
h0.SetMinimum(1)
h0.Draw("COLZ")
c1.cd(2)
h1.SetLineColor(4) # blue
h1.Draw()
h2.SetLineColor(2) # red
h2.Draw("same")
l1 = TLegend(0.6,0.6,0.87,0.92)
l1.AddEntry(h1,"basic","l")
l1.AddEntry(h2,"+fitSlo < 20","l")
l1.AddEntry(h2,"Retention: %.3f" % retention1,"")
l1.Draw("same")
# c1.cd(3)
# h3.Draw()
# h4.SetLineColor(2) # red
# h4.Draw("same")
#
# l2 = TLegend(0.6,0.6,0.87,0.92)
# l2.AddEntry(h3,"basic","l")
# l2.AddEntry(h4,"+fitSlo < 20","l")
# l2.AddEntry(h4,"Retention: %.3f" % retention2,"")
# l2.Draw("same")
c1.Print("./plots/ds1-fitSlo.pdf")