def plotHistRatioAndFit(histos, ratio, fitfunc, outfname, graphics_attributes={}) :
can = r.TCanvas('can_'+outfname, outfname, 800, 600)
botPad, topPad = buildBotTopPads(can, splitFraction=0.35)
can.cd()
topPad.Draw()
topPad.cd()
pm = first(histos)
pm.Draw('axis')
xAx, yAx = pm.GetXaxis(), pm.GetYaxis()
xAx.SetTitle('')
xAx.SetLabelSize(0)
yAx.SetRangeUser(0.0, 1.2)
yAx.SetNdivisions(505) # for some reason some of the inputs are already ratios with -201
textScaleUp = 1.0/topPad.GetHNDC()
yAx.SetLabelSize(textScaleUp*0.04)
yAx.SetTitleSize(textScaleUp*0.04)
yAx.SetTitle(graphics_attributes['ytitle'])
yAx.SetTitleOffset(yAx.GetTitleOffset()/textScaleUp)
for k, h in histos.iteritems() :
h.SetMarkerStyle(graphics_attributes['markers'][k])
h.SetMarkerColor(graphics_attributes['colors'][k])
h.SetLineColor(graphics_attributes['colors'][k])
h.Draw('same')
labels = graphics_attributes['labels']
drawLegendWithDictKeys(topPad, dict([(labels[s], histos[s]) for s in histos.keys()]), legWidth=0.4)
can.cd()
botPad.Draw()
botPad.cd()
ratio.Draw('axis')
textScaleUp = 1.0/botPad.GetHNDC()
xAx, yAx = ratio.GetXaxis(), ratio.GetYaxis()
yAx.SetRangeUser(0.0, 2.0)
xAx.SetTitle(graphics_attributes['xtitle'])
yAx.SetNdivisions(-202)
yAx.SetTitle('Data/Sim')
yAx.CenterTitle()
for a in [xAx, yAx] :
if a.GetLabelSize()>0.04 : continue
a.SetLabelSize(a.GetLabelSize()*textScaleUp)
a.SetTitleSize(a.GetTitleSize()*textScaleUp)
a.SetTitleOffset(a.GetTitleOffset()/textScaleUp)
ratio.SetMarkerStyle(graphics_attributes['markers']['data'])
fitfunc.SetLineWidth(2)
fitfunc.SetLineStyle(2)
fitfunc.Draw('same')
ratio.Draw('same')
tex = r.TLatex()
tex.SetNDC(True)
p0, p0Err, chi2, ndf = fitResults(fitfunc)
fitParLabel = "Const. fit : %s #pm %s"%(pdgRound(p0, p0Err))
fitGoodLabel = "#chi^{2}/DOF : %.2f / %d"%(chi2, ndf)
tex.SetTextSize(yAx.GetTitleSize())
tex.SetTextFont(yAx.GetTitleFont())
tex.DrawLatex(0.15, 0.40, "#splitline{%s}{%s}"%(fitParLabel, fitGoodLabel))
can.Update()
outfname = outfname+'.png'
rmIfExists(outfname) # avoid root warnings
can.SaveAs(outfname)
def plot_emu_mue_with_ratio(canvas=None,
h_mue=None,
h_emu=None,
h_ratio=None,
filename='',
figformats=['png', 'eps']):
c = canvas
c.Clear()
c._graphics = []
c.cd()
botPad, topPad = buildBotTopPads(c, splitFraction=0.375)
topPad.Draw()
topPad.cd()
h_mue.SetStats(0)
h_emu.SetStats(0)
h_emu.SetLineColor(r.kBlue)
h_mue.SetLineColor(r.kRed)
h_mue.Draw()
h_emu.Draw('same')
pm = h_mue
yMin, yMax = getMinMax([h_mue, h_emu])
pm.SetMinimum(0.0)
pm.SetMaximum(1.1 * yMax)
xax = pm.GetXaxis()
yax = pm.GetYaxis()
xax.SetLabelSize(0)
xax.SetTitleSize(0)
leg = topLeftLegend(c, 0.275, 0.275, shift=0.05)
leg.SetBorderSize(0)
leg.SetHeader('non-prompt prediction')
leg.AddEntry(h_mue, '#mue', 'L')
leg.AddEntry(h_emu, 'e#mu', 'L')
leg.Draw()
c._graphics.append(leg)
c.cd()
botPad.Draw()
botPad.cd()
h_ratio.SetLineColor(r.kBlack)
h_ratio.SetTitle('')
xax = h_ratio.GetXaxis()
yax = h_ratio.GetYaxis()
yax.SetRangeUser(0.0, 2.0)
yax.SetTitle('e#mu / #mue ratio')
xA, yA = h_ratio.GetXaxis(), h_ratio.GetYaxis()
textScaleUp = 1.0 / botPad.GetHNDC()
yA.SetNdivisions(-104)
yA.CenterTitle()
yA.SetTitleOffset(yA.GetTitleOffset() / textScaleUp)
for a in [xA, yA]:
a.SetLabelSize(a.GetLabelSize() * textScaleUp)
a.SetTitleSize(a.GetTitleSize() * textScaleUp)
h_ratio.Draw()
refline = referenceLine(xA.GetXmin(), xA.GetXmax())
refline.Draw()
c._graphics.append(refline)
c.Update()
for ext in figformats:
c.SaveAs("{0}.{1}".format(filename, ext))
def plot_emu_mue_with_ratio(canvas=None, h_mue=None, h_emu=None, h_ratio=None,
filename='', figformats=['png', 'eps'], label=''):
c = canvas
c.Clear()
c._graphics = []
c.cd()
botPad, topPad = buildBotTopPads(c, splitFraction=0.375)
topPad.Draw()
topPad.cd()
h_mue.SetStats(0)
h_emu.SetStats(0)
h_emu.SetLineColor(r.kBlue)
h_mue.SetLineColor(r.kRed)
h_mue.Draw()
h_emu.Draw('same')
pm = h_mue
yMin, yMax = getMinMax([h_mue, h_emu])
pm.SetMinimum(0.0)
pm.SetMaximum(1.1 * yMax)
xax = pm.GetXaxis()
yax = pm.GetYaxis()
xax.SetLabelSize(0)
xax.SetTitleSize(0)
leg = topRightLegend(c, 0.275, 0.275, shift=0.05)
leg.SetBorderSize(0)
leg.SetHeader(label)
leg.AddEntry(h_mue, '#mue', 'L')
leg.AddEntry(h_emu, 'e#mu', 'L')
leg.Draw()
c._graphics.append(leg)
c.cd()
botPad.Draw()
botPad.cd()
h_ratio.SetLineColor(r.kBlack)
h_ratio.SetTitle('')
xax = h_ratio.GetXaxis()
yax = h_ratio.GetYaxis()
yax.SetRangeUser(0.0, 2.0)
yax.SetTitle('e#mu / #mue ratio')
xA, yA = h_ratio.GetXaxis(), h_ratio.GetYaxis()
textScaleUp = 1.0/botPad.GetHNDC()
yA.SetNdivisions(-104)
yA.CenterTitle()
yA.SetTitleOffset(yA.GetTitleOffset()/textScaleUp)
for a in [xA, yA] :
a.SetLabelSize(a.GetLabelSize()*textScaleUp)
a.SetTitleSize(a.GetTitleSize()*textScaleUp)
h_ratio.Draw()
refline = referenceLine(xA.GetXmin(), xA.GetXmax())
refline.Draw()
c._graphics.append(refline)
c.Update()
for ext in figformats:
c.SaveAs("{0}.{1}".format(filename, ext))
print "{0}.{1}".format(filename, ext)
def main() :
parser = optparse.OptionParser(usage=usage)
parser.add_option('-t', '--tag')
parser.add_option('-f', '--input_fake')
parser.add_option('-i', '--input_dir')
parser.add_option('-o', '--output_dir')
parser.add_option('-v','--verbose', action='store_true', default=False)
(opts, args) = parser.parse_args()
requiredOptions = ['tag', 'input_fake', 'input_dir', 'output_dir',]
otherOptions = ['verbose']
allOptions = requiredOptions + otherOptions
def optIsNotSpecified(o) : return not hasattr(opts, o) or getattr(opts,o) is None
if any(optIsNotSpecified(o) for o in requiredOptions) : parser.error('Missing required option')
tag = opts.tag.strip('_')
inputFakeFile = opts.input_fake
inputDirname = opts.input_dir
outputDir = opts.output_dir
outputDir = outputDir if outputDir.endswith('/') else outputDir+'/'
verbose = opts.verbose
if verbose : print '\nUsing the following options:\n'+'\n'.join("%s : %s"%(o, str(getattr(opts, o))) for o in allOptions)
inputFiles = getInputFiles(inputDirname, tag, verbose)
inputFiles[fakeSample()] = r.TFile.Open(inputFakeFile)
assert all(f for f in inputFiles.values()), ("missing inputs: \n%s"%'\n'.join(["%s : %s"%kv for kv in inputFiles.iteritems()]))
mkdirIfNeeded(outputDir)
for region in ['cr8lptee', 'cr8lptmm', 'cr9lpt', 'sr8', 'sr9', 'srSsEwk', 'crSsEwkLoose'] :
for channel in ['ee', 'em', 'mm'] :
for varname in ['l0_pt', 'l1_pt', 'll_M', 'metrel', 'met', 'njets', 'nbjets'] :
histo_basename = region+'_'+channel+'_'+varname
hists, err2s = buildHists(inputFiles, histo_basename)
if not hists[dataSample()].GetEntries() : continue
err_band = buildErrBandGraph(hists['sm'], err2s)
err_band_r = buildErrBandRatioGraph(err_band)
can = r.TCanvas('can_'+histo_basename, histo_basename, 800, 600)
botPad, topPad = buildBotTopPads(can)
can.cd()
topPad.Draw()
drawTop(topPad, hists, err_band, (channel, region))
can.cd()
botPad.Draw()
drawBot(botPad, hists[dataSample()], hists['sm'], err_band_r, xaxisLabel(varname))
can.Update()
outFilename = outputDir+histo_basename+'.png'
rmIfExists(outFilename) # avoid root warnings
can.SaveAs(outFilename)
if verbose : print "output saved to \n%s"%outputDir
def plotVar(bkgHistos, sigHistos, llnjvar, plotdir='./') :
def preferredSignal(signals):
pref = 'Herwigpp_sM_wA_noslep_notauhad_WH_2Lep_1'
return pref if pref in signals else first(sorted(signals))
signalSample = preferredSignal(sigHistos.keys())
allHistos = bkgHistos.values() + [sigHistos[signalSample],]
allHistosEmpty = all([h.GetEntries()==0 for h in allHistos])
if allHistosEmpty : return
can = r.TCanvas('can_'+llnjvar, llnjvar, 800, 800)
botPad, topPad = buildBotTopPads(can, splitFraction=0.75, squeezeMargins=False)
totBkg = summedHisto(bkgHistos.values())
totBkg.SetDirectory(0)
can._totBkg = totBkg
can._histos = [bkgHistos, sigHistos]
can.cd()
botPad.Draw()
drawBottom(botPad, totBkg, bkgHistos, sigHistos[signalSample], llnjvar)
can.cd()
topPad.Draw()
drawTop(topPad, totBkg, sigHistos[signalSample])
mkdirIfNeeded(plotdir)
outFilename = plotdir+'/'+llnjvar+'.png'
rmIfExists(outFilename) # avoid root warnings
can.SaveAs(outFilename)
def plotHistos(histoData=None, histoSignal=None, histoTotBkg=None, histosBkg={},
statErrBand=None, systErrBand=None, # these are TGraphAsymmErrors
canvasName='canvas', outdir='./', options=None,
stack_order=[],
topLabel='',
drawStatErr=False, drawSystErr=False,
drawYieldAndError=False, printYieldSummary=False) :
"Note: blinding can be required for only a subrange of the histo, so it is taken care of when filling"
verbose = options.verbose
formatAuxMaterial = options.format_aux
setAtlasStyle()
padMaster = histoData
if verbose : print "plotting ",padMaster.GetName(),' (',padMaster.GetEntries(),' entries)'
can = r.TCanvas(canvasName, padMaster.GetTitle(), 800, 800)
botPad, topPad = buildBotTopPads(can, squeezeMargins=False)
can.cd()
topPad.Draw()
# draw top
topPad.cd()
topPad._hists = [padMaster]
if formatAuxMaterial:
for ax in [padMaster.GetXaxis(), padMaster.GetYaxis()]:
ax.SetTitle(prettify(ax.GetTitle()))
padMaster.Draw('axis')
topPad.Update() # necessary to fool root's dumb object ownership of the stack
stack = r.THStack('stack_'+padMaster.GetName(), '')
r.SetOwnership(stack, False)
topPad._hists.append(stack)
leg = topRightLegend(can,
0.300 if formatAuxMaterial else 0.225,
0.400 if formatAuxMaterial else 0.325,
shiftX=-0.1 if formatAuxMaterial else 0.0,
shiftY=-0.1 if formatAuxMaterial else 0.0)
topPad._leg = leg
leg.SetBorderSize(0)
leg.SetTextFont(padMaster.GetTitleFont())
leg._reversedEntries = []
def integralWou(h):
"Integral with underflow and overflow"
return h.Integral(0, h.GetNbinsX()+1)
for group, histo in sortedAs(histosBkg, stack_order) :
histo.SetFillColor(getGroupColor(group))
histo.SetLineWidth(2)
histo.SetLineColor(r.kBlack)
stack.Add(histo)
topPad._hists.append(histo)
leg._reversedEntries.append((histo,
prettify(group) if formatAuxMaterial else
"{0}: {1:.2f}".format(group, integralWou(histo)), 'F'))
if not formatAuxMaterial:
leg._reversedEntries.append((dummyHisto(),
"{0}, {1:.2f}".format('bkg', sum([integralWou(h) for h in stack.GetHists()])), 'l'))
if statErrBand and drawStatErr :
statErrBand.SetFillStyle(3006)
leg._reversedEntries.append((statErrBand, 'stat', 'f'))
if systErrBand and drawSystErr :
systErrBand.SetFillStyle(3007)
leg._reversedEntries.append((systErrBand, 'syst', 'f'))
leg._reversedEntries.append((histoData,
"Data {0}".format(prettify(topLabel)) if formatAuxMaterial else
"{0}, {1:.2f}".format('data', integralWou(histoData)), 'p'))
for h, g, o in leg._reversedEntries[::-1] : leg.AddEntry(h, g, o) # stack goes b-t, legend goes t-b
stack.Draw('hist same')
histoData.SetMarkerStyle(r.kFullCircle)
histoData.SetLineWidth(2)
dataGraph = graphWithPoissonError(histoData)
dataGraph.Draw('same p')
isRightSignal = False
if histoSignal:
histoSignal.SetFillStyle(0)
histoSignal.SetFillColor(0)
histoSignal.SetLineColor(getGroupColor('signal'))
histoSignal.SetLineWidth(3)
isRightSignal = (all(s in histoSignal.GetName() for s in ['signaltaumu', 'sr_mue']) or
all(s in histoSignal.GetName() for s in ['signaltaue', 'sr_emu']))
if isRightSignal: histoSignal.Draw('histo same')
if statErrBand and drawStatErr :
statErrBand.SetFillStyle(3006)
statErrBand.Draw('E2 same')
leg.AddEntry(statErrBand, 'stat', 'f')
if systErrBand and drawSystErr :
systErrBand.SetFillStyle(3007)
systErrBand.Draw('E2 same')
leg.AddEntry(systErrBand, 'syst', 'f')
totErrBand = systUtils.addErrorBandsInQuadrature(statErrBand, systErrBand)
if totErrBand :
totErrBand.Draw('E2 same')
totErrBand.SetFillStyle(3005)
leg.AddEntry(totErrBand, 'stat+syst', 'f')
if printYieldSummary:
print_total_error = False
print ("{}:".format(topLabel if topLabel else dataGraph.GetName())+
(" expected {:.2f}^{{+{:.2f}}}_{{{:.2f}}}".format(totErrBand.GetY()[0],
totErrBand.GetErrorYhigh(0),
totErrBand.GetErrorYlow(0))
if print_total_error else
" expected {:.2f}^{{+{:.2f}}}_{{{:.2f}}} (stat) ^{{+{:.2f}}}_{{{:.2f}}} (syst)"
.format(totErrBand.GetY()[0],
statErrBand.GetErrorYhigh(0) if statErrBand else 0.0,
statErrBand.GetErrorYlow(0) if statErrBand else 0.0,
systErrBand.GetErrorYhigh(0) if systErrBand else 0.0,
systErrBand.GetErrorYlow(0) if systErrBand else 0.0)
)+
" obs {:.2f}".format(dataGraph.GetY()[0]))
if histoSignal and isRightSignal:
hname = histoSignal.GetName()
label = ('H#rightarrow#mu#tau' if 'signaltaumu' in hname else
'H#rightarrowe#tau' if 'signaltaue' in hname else
'signal')
label = ("{}, BR=1%".format(label) if formatAuxMaterial else
"{}, BR=1%, {:.2f}".format(label, integralWou(histoSignal)))
leg.AddEntry(histoSignal, label, 'l')
leg.Draw('same')
topPad.Update()
tex = r.TLatex()
tex.SetTextSize(0.5 * tex.GetTextSize())
tex.SetNDC(True)
label = "%s tot bkg : "%(can.GetName())
label += "%.3f #pm %.3f (stat)"%(integralAndError(histoTotBkg))
if systErrBand :
sysUp, sysDo = systUtils.totalUpDownVariation(systErrBand)
label += "#pm #splitline{%.3f}{%.3f} (syst)"%(sysUp, sysDo)
if drawYieldAndError :
tex.DrawLatex(0.10, 0.95, label)
topPad.SetTopMargin(2.0*topPad.GetTopMargin())
atlasLabel = drawAtlasLabel(can, align=13, scale=0.75,
xpos=0.175 if formatAuxMaterial else 0.125,
ypos=0.945 if formatAuxMaterial else None,
public=True)
if topLabel and not formatAuxMaterial : topRightLabel(can, topLabel, ypos=1.0)
yMin, yMax = getMinMax([histoData, dataGraph, histoTotBkg, histoSignal, totErrBand])
padMaster.SetMinimum(0.0)
padMaster.SetMaximum(1.1 * yMax)
padMaster.GetXaxis().SetLabelSize(0)
padMaster.GetXaxis().SetTitleSize(0)
increaseAxisFont(padMaster.GetXaxis())
increaseAxisFont(padMaster.GetYaxis())
topPad.RedrawAxis()
topPad.Update() # force stack to create padMaster
# draw bot (ratio)
can.cd()
botPad.Draw()
botPad.cd()
ratio = systUtils.buildAsymmRatioGraph(dataGraph, totErrBand)
ratioPadMaster = padMaster.Clone(padMaster.GetName()+'_ratio')
ratioPadMaster.SetDirectory(0)
ratioPadMaster.Clear()
yMin, yMax = 0.0, 2.0
ratioPadMaster.SetMinimum(yMin)
ratioPadMaster.SetMaximum(yMax)
ratioPadMaster.SetStats(0)
ratioPadMaster.Draw('axis')
x_lo, x_hi = getXrange(ratioPadMaster)
refLines = [referenceLine(x_lo, x_hi, y, y) for y in [0.5, 1.0, 1.5]]
for l in refLines : l.Draw()
err_band_r = systUtils.buildErrBandRatioGraph(totErrBand)
err_band_r.Draw('E2 same')
ratio.Draw('ep')
xA, yA = ratioPadMaster.GetXaxis(), ratioPadMaster.GetYaxis()
textScaleUp = 0.75*1.0/botPad.GetHNDC()
# if xaxis_title : xA.SetTitle(xaxis_title)
yA.SetNdivisions(-104)
yA.SetTitle('Data/Bkg')
yA.CenterTitle()
yA.SetTitleOffset(yA.GetTitleOffset()/textScaleUp)
xA.SetTitleSize(yA.GetTitleSize()) # was set to 0 for padmaster, restore it
xA.SetLabelSize(yA.GetLabelSize())
for a in [xA, yA] :
a.SetLabelSize(a.GetLabelSize()*textScaleUp)
a.SetTitleSize(a.GetTitleSize()*textScaleUp)
botPad._graphical_objects = [ratio, ratioPadMaster, err_band_r] + refLines # avoid garbage collection
botPad.Update()
can.Update()
for ext in ['png','eps'] : can.SaveAs(outdir+'/'+can.GetName()+'.'+ext)
def plotHistos(
histoData=None,
histoSignal=None,
histoTotBkg=None,
histosBkg={},
statErrBand=None,
systErrBand=None, # these are TGraphAsymmErrors
canvasName='canvas',
outdir='./',
options=None,
stack_order=[],
topLabel='',
drawStatErr=False,
drawSystErr=False,
drawYieldAndError=False,
printYieldSummary=False):
"Note: blinding can be required for only a subrange of the histo, so it is taken care of when filling"
verbose = options.verbose
formatAuxMaterial = options.format_aux
setAtlasStyle()
padMaster = histoData
if verbose:
print "plotting ", padMaster.GetName(), ' (', padMaster.GetEntries(
), ' entries)'
can = r.TCanvas(canvasName, padMaster.GetTitle(), 800, 800)
botPad, topPad = buildBotTopPads(can, squeezeMargins=False)
can.cd()
topPad.Draw()
# draw top
topPad.cd()
topPad._hists = [padMaster]
if formatAuxMaterial:
for ax in [padMaster.GetXaxis(), padMaster.GetYaxis()]:
ax.SetTitle(prettify(ax.GetTitle()))
padMaster.Draw('axis')
topPad.Update(
) # necessary to fool root's dumb object ownership of the stack
stack = r.THStack('stack_' + padMaster.GetName(), '')
r.SetOwnership(stack, False)
topPad._hists.append(stack)
leg = topRightLegend(can,
0.300 if formatAuxMaterial else 0.225,
0.400 if formatAuxMaterial else 0.325,
shiftX=-0.1 if formatAuxMaterial else 0.0,
shiftY=-0.1 if formatAuxMaterial else 0.0)
topPad._leg = leg
leg.SetBorderSize(0)
leg.SetTextFont(padMaster.GetTitleFont())
leg._reversedEntries = []
def integralWou(h):
"Integral with underflow and overflow"
return h.Integral(0, h.GetNbinsX() + 1)
for group, histo in sortedAs(histosBkg, stack_order):
histo.SetFillColor(getGroupColor(group))
histo.SetLineWidth(2)
histo.SetLineColor(r.kBlack)
stack.Add(histo)
topPad._hists.append(histo)
leg._reversedEntries.append(
(histo, prettify(group) if formatAuxMaterial else
"{0}: {1:.2f}".format(group, integralWou(histo)), 'F'))
if not formatAuxMaterial:
leg._reversedEntries.append((dummyHisto(), "{0}, {1:.2f}".format(
'bkg', sum([integralWou(h) for h in stack.GetHists()])), 'l'))
if statErrBand and drawStatErr:
statErrBand.SetFillStyle(3006)
leg._reversedEntries.append((statErrBand, 'stat', 'f'))
if systErrBand and drawSystErr:
systErrBand.SetFillStyle(3007)
leg._reversedEntries.append((systErrBand, 'syst', 'f'))
leg._reversedEntries.append(
(histoData, "Data {0}".format(prettify(topLabel)) if formatAuxMaterial
else "{0}, {1:.2f}".format('data', integralWou(histoData)), 'p'))
for h, g, o in leg._reversedEntries[::-1]:
leg.AddEntry(h, g, o) # stack goes b-t, legend goes t-b
stack.Draw('hist same')
histoData.SetMarkerStyle(r.kFullCircle)
histoData.SetLineWidth(2)
dataGraph = graphWithPoissonError(histoData)
dataGraph.Draw('same p')
isRightSignal = False
if histoSignal:
histoSignal.SetFillStyle(0)
histoSignal.SetFillColor(0)
histoSignal.SetLineColor(getGroupColor('signal'))
histoSignal.SetLineWidth(3)
isRightSignal = (all(s in histoSignal.GetName()
for s in ['signaltaumu', 'sr_mue'])
or all(s in histoSignal.GetName()
for s in ['signaltaue', 'sr_emu']))
if isRightSignal: histoSignal.Draw('histo same')
if statErrBand and drawStatErr:
statErrBand.SetFillStyle(3006)
statErrBand.Draw('E2 same')
leg.AddEntry(statErrBand, 'stat', 'f')
if systErrBand and drawSystErr:
systErrBand.SetFillStyle(3007)
systErrBand.Draw('E2 same')
leg.AddEntry(systErrBand, 'syst', 'f')
totErrBand = systUtils.addErrorBandsInQuadrature(statErrBand, systErrBand)
if totErrBand:
totErrBand.Draw('E2 same')
totErrBand.SetFillStyle(3005)
leg.AddEntry(totErrBand, 'stat+syst', 'f')
if printYieldSummary:
print_total_error = False
print(
"{}:".format(topLabel if topLabel else dataGraph.GetName()) +
(" expected {:.2f}^{{+{:.2f}}}_{{{:.2f}}}".format(
totErrBand.GetY()[0], totErrBand.GetErrorYhigh(0),
totErrBand.GetErrorYlow(0)) if print_total_error else
" expected {:.2f}^{{+{:.2f}}}_{{{:.2f}}} (stat) ^{{+{:.2f}}}_{{{:.2f}}} (syst)"
.format(totErrBand.GetY()[0],
statErrBand.GetErrorYhigh(0) if statErrBand else 0.0,
statErrBand.GetErrorYlow(0) if statErrBand else 0.0,
systErrBand.GetErrorYhigh(0) if systErrBand else 0.0,
systErrBand.GetErrorYlow(0) if systErrBand else 0.0))
+ " obs {:.2f}".format(dataGraph.GetY()[0]))
if histoSignal and isRightSignal:
hname = histoSignal.GetName()
label = ('H#rightarrow#mu#tau' if 'signaltaumu' in hname else
'H#rightarrowe#tau' if 'signaltaue' in hname else 'signal')
label = ("{}, BR=1%".format(label) if formatAuxMaterial else
"{}, BR=1%, {:.2f}".format(label, integralWou(histoSignal)))
leg.AddEntry(histoSignal, label, 'l')
leg.Draw('same')
topPad.Update()
tex = r.TLatex()
tex.SetTextSize(0.5 * tex.GetTextSize())
tex.SetNDC(True)
label = "%s tot bkg : " % (can.GetName())
label += "%.3f #pm %.3f (stat)" % (integralAndError(histoTotBkg))
if systErrBand:
sysUp, sysDo = systUtils.totalUpDownVariation(systErrBand)
label += "#pm #splitline{%.3f}{%.3f} (syst)" % (sysUp, sysDo)
if drawYieldAndError:
tex.DrawLatex(0.10, 0.95, label)
topPad.SetTopMargin(2.0 * topPad.GetTopMargin())
atlasLabel = drawAtlasLabel(can,
align=13,
scale=0.75,
xpos=0.175 if formatAuxMaterial else 0.125,
ypos=0.945 if formatAuxMaterial else None,
public=True)
if topLabel and not formatAuxMaterial:
topRightLabel(can, topLabel, ypos=1.0)
yMin, yMax = getMinMax(
[histoData, dataGraph, histoTotBkg, histoSignal, totErrBand])
padMaster.SetMinimum(0.0)
padMaster.SetMaximum(1.1 * yMax)
padMaster.GetXaxis().SetLabelSize(0)
padMaster.GetXaxis().SetTitleSize(0)
increaseAxisFont(padMaster.GetXaxis())
increaseAxisFont(padMaster.GetYaxis())
topPad.RedrawAxis()
topPad.Update() # force stack to create padMaster
# draw bot (ratio)
can.cd()
botPad.Draw()
botPad.cd()
ratio = systUtils.buildAsymmRatioGraph(dataGraph, totErrBand)
ratioPadMaster = padMaster.Clone(padMaster.GetName() + '_ratio')
ratioPadMaster.SetDirectory(0)
ratioPadMaster.Clear()
yMin, yMax = 0.0, 2.0
ratioPadMaster.SetMinimum(yMin)
ratioPadMaster.SetMaximum(yMax)
ratioPadMaster.SetStats(0)
ratioPadMaster.Draw('axis')
x_lo, x_hi = getXrange(ratioPadMaster)
refLines = [referenceLine(x_lo, x_hi, y, y) for y in [0.5, 1.0, 1.5]]
for l in refLines:
l.Draw()
err_band_r = systUtils.buildErrBandRatioGraph(totErrBand)
err_band_r.Draw('E2 same')
ratio.Draw('ep')
xA, yA = ratioPadMaster.GetXaxis(), ratioPadMaster.GetYaxis()
textScaleUp = 0.75 * 1.0 / botPad.GetHNDC()
# if xaxis_title : xA.SetTitle(xaxis_title)
yA.SetNdivisions(-104)
yA.SetTitle('Data/Bkg')
yA.CenterTitle()
yA.SetTitleOffset(yA.GetTitleOffset() / textScaleUp)
xA.SetTitleSize(
yA.GetTitleSize()) # was set to 0 for padmaster, restore it
xA.SetLabelSize(yA.GetLabelSize())
for a in [xA, yA]:
a.SetLabelSize(a.GetLabelSize() * textScaleUp)
a.SetTitleSize(a.GetTitleSize() * textScaleUp)
botPad._graphical_objects = [ratio, ratioPadMaster, err_band_r
] + refLines # avoid garbage collection
botPad.Update()
can.Update()
for ext in ['png', 'eps']:
can.SaveAs(outdir + '/' + can.GetName() + '.' + ext)
def subtractRealAndComputeScaleFactor(histosPerGroup={}, variable='', outRatiohistoname='',outDataeffhistoname='',
outputDir='./', region='', subtractReal=True, verbose=False):
"efficiency scale factor"
groups = histosPerGroup.keys()
mkdirIfNeeded(outputDir)
histosPerType = dict([(lt,
dict([(g,
histosPerGroup[g][variable][lt])
for g in groups]))
for lt in leptonTypes])
for lt in leptonTypes :
histosPerType[lt]['totSimBkg'] = summedHisto([histo for group,histo in histosPerType[lt].iteritems()
if group not in ['data', 'signal']])
simuTight = histosPerType['fake_tight']['totSimBkg']
simuLoose = histosPerType['fake_loose']['totSimBkg']
dataTight = histosPerType['tight' ]['data' ]
dataLoose = histosPerType['loose' ]['data' ]
# subtract real contribution from data
# _Note to self_: currently estimating the real contr from MC; in
# the past also used iterative corr, which might be more
# appropriate in cases like here, where the normalization is
# so-so. Todo: investigate the normalization.
dataSubTight = dataTight.Clone(dataTight.GetName().replace('data_tight','data_minus_prompt_tight'))
dataSubLoose = dataLoose.Clone(dataLoose.GetName().replace('data_loose','data_minus_prompt_loose'))
dataSubTight.SetDirectory(0)
dataSubLoose.SetDirectory(0)
dataSubTight.Add(histosPerType['real_tight']['totSimBkg'], -1.0 if subtractReal else 0.0)
dataSubLoose.Add(histosPerType['real_loose']['totSimBkg'], -1.0 if subtractReal else 0.0)
effData = dataSubTight.Clone(outDataeffhistoname)
effData.SetDirectory(0)
effData.Divide(dataSubLoose)
effSimu = simuTight.Clone(simuTight.GetName().replace('fake_tight','fake_eff'))
effSimu.SetDirectory(0)
effSimu.Divide(simuLoose)
print "eff(T|L) vs. ",variable
def formatFloat(floats): return ["%.4f"%f for f in floats]
print "efficiency data : ",formatFloat(getBinContents(effData))
print "efficiency simu : ",formatFloat(getBinContents(effSimu))
ratio = effData.Clone(outRatiohistoname)
ratio.SetDirectory(0)
ratio.Divide(effSimu)
print "sf data/simu : ",formatFloat(getBinContents(ratio))
print " +/- : ",formatFloat(getBinErrors(ratio))
can = r.TCanvas('c_'+outRatiohistoname, outRatiohistoname, 800, 600)
botPad, topPad = rootUtils.buildBotTopPads(can)
can.cd()
topPad.Draw()
topPad.cd()
pm = effData
pm.SetStats(0)
pm.Draw('axis')
xAx, yAx = pm.GetXaxis(), pm.GetYaxis()
xAx.SetTitle('')
xAx.SetLabelSize(0)
yAx.SetRangeUser(0.0, 0.25)
textScaleUp = 1.0/topPad.GetHNDC()
yAx.SetLabelSize(textScaleUp*0.04)
yAx.SetTitleSize(textScaleUp*0.04)
yAx.SetTitle('#epsilon(T|L)')
yAx.SetTitleOffset(yAx.GetTitleOffset()/textScaleUp)
effSimu.SetLineColor(r.kRed)
effSimu.SetMarkerStyle(r.kOpenCross)
effSimu.SetMarkerColor(effSimu.GetLineColor())
effData.Draw('same')
effSimu.Draw('same')
leg = drawLegendWithDictKeys(topPad, {'data':effData, 'simulation':simuTight}, legWidth=0.4)
leg.SetHeader('scale factor '+region+' '+('electron' if '_el_'in outRatiohistoname else
'muon' if '_mu_' in outRatiohistoname else ''))
can.cd()
botPad.Draw()
botPad.cd()
ratio.SetStats(0)
ratio.Draw()
textScaleUp = 1.0/botPad.GetHNDC()
xAx, yAx = ratio.GetXaxis(), ratio.GetYaxis()
yAx.SetRangeUser(0.0, 2.0)
xAx.SetTitle({'pt1':'p_{T}', 'eta1':'|#eta|', 'pt1_eta1':'p_{T}'}[variable])
yAx.SetNdivisions(-202)
yAx.SetTitle('Data/Sim')
yAx.CenterTitle()
xAx.SetLabelSize(textScaleUp*0.04)
xAx.SetTitleSize(textScaleUp*0.04)
yAx.SetLabelSize(textScaleUp*0.04)
yAx.SetTitleSize(textScaleUp*0.04)
refLine = rootUtils.referenceLine(xAx.GetXmin(), xAx.GetXmax())
refLine.Draw()
can.Update()
outFname = os.path.join(outputDir, region+'_'+outRatiohistoname)
for ext in ['.eps','.png']:
utils.rmIfExists(outFname+ext)
can.SaveAs(outFname+ext)
return {outRatiohistoname : ratio,
outDataeffhistoname : effData,
outDataeffhistoname.replace('_fake_rate_data_', '_tight_data_minus_prompt') : dataSubTight,
outDataeffhistoname.replace('_fake_rate_data_', '_loose_data_minus_prompt') : dataSubLoose
}
def main():
parser = argparse.ArgumentParser(
description=description,
epilog=epilog,
formatter_class=argparse.RawDescriptionHelpFormatter)
add_arg = parser.add_argument
add_arg('-o', '--output-dir', default="./")
add_arg('-p', '--process', help='one physics process, eg. ttw')
add_arg('-s', '--systematic', help='one of the systematic variations')
add_arg('-v', '--verbose', action='store_true')
add_arg('-d', '--debug', action='store_true')
args = parser.parse_args()
set_log(args.verbose, args.debug)
outdir = args.output_dir
process = args.process
systematic = args.systematic
available_processes = get_input_samples().keys()
if process not in available_processes:
raise StandardError("invalid process %s, should be one of %s" %
(process, str(available_processes)))
available_systematics = get_input_samples()[process].keys()
if systematic not in available_systematics:
raise StandardError("invalid systematic %s, should be one of %s" %
(systematic, str(available_systematics)))
file_label = process + '_sys_' + systematic
plot_label = process + ' sys. ' + systematic
normalize_to_unity = False # True
luminosity = 1.0
combiner = HistogramCombiner()
combiner.build_samples(process=process, systematic=systematic)
histogram_names = [
'h_meff',
'h_jetN',
# 'h_electronPt', 'h_muonPt',
# 'h_bjetEmulN', 'h_bjetN',
# 'h_bjetEmulN_sr3b', 'h_bjetN_sr3b',
# 'h_bjetEmulN_sr1b', 'h_bjetN_sr1b',
# 'h_bjetEmulN_cr2bttV', 'h_bjetN_cr2bttV',
# 'h_meff_sr3b', 'h_jetN_sr3b', 'h_met_sr3b',
# 'h_meff_sr1b', 'h_jetN_sr1b', 'h_met_sr1b',
# 'h_meff_sr0b5j', 'h_jetN_sr0b5j', 'h_met_sr0b5j',
# 'h_meff_sr0b4j', 'h_jetN_sr0b4j', 'h_met_sr0b4j',
# 'h_meff_sr0b3j', 'h_jetN_sr0b3j', 'h_met_sr0b3j',
# 'h_meff_cr2bttV', 'h_jetN_cr2bttV', 'h_met_cr2bttV'
]
combiner.compute_normalization_factors()
output_pdf_name = outdir + '/' + file_label + '.pdf'
c_summary = R.TCanvas('c_summary', 'plotExplicitSamples sampes summary ')
combiner.print_sample_summary_to_pdf(
c_summary,
label="%s: nominal vs. %s systematic" % (process, systematic))
c_summary.SaveAs(output_pdf_name + '(')
for histogram_name in histogram_names:
rebin = 'meff' in histogram_name # and '_sr' in histogram_name # non-inclusive histos: low stats
rebin_factor = (2 if 'meff' in histogram_name else
2 if 'jetN' in histogram_name else 1) if rebin else 1
histograms = combiner.get_histograms(histogram_name=histogram_name)
h_nom = histograms['nominal']
h_up = histograms['up']
h_dn = histograms['down']
if 'h_jetFlavorMultiplicity' in histogram_name:
h_nom = emulate_btag_multiplicity_from_truth_flavor(h_nom, 'nom')
h_up = emulate_btag_multiplicity_from_truth_flavor(h_up, 'up')
h_dn = emulate_btag_multiplicity_from_truth_flavor(h_dn, 'dn')
histos = [h_nom, h_up, h_dn]
for h in set(histos): # set: avoid rebinning twice when up==down
h.Rebin(rebin_factor)
h_nom.SetLineWidth(2 * h_nom.GetLineWidth())
h_nom.SetLineColor(R.kBlack)
h_up.SetLineColor(R.kBlue)
h_dn.SetLineColor(R.kRed)
pad_master = h_nom
pad_master.SetMaximum(1.1 * max([h.GetMaximum() for h in histos]))
pad_master.SetMinimum(1.0 *
min([0.0] + [h.GetMinimum() for h in histos]))
pad_master.GetYaxis().SetTitle('Arbitrary Units')
pad_master.SetStats(0)
can = R.TCanvas('c_ttV_syst_' + histogram_name,
'ttV explicit variations ' + pad_master.GetTitle(),
700, 700)
botPad, topPad = ru.buildBotTopPads(can, squeezeMargins=False)
# top
can.cd()
topPad.Draw()
topPad.cd()
topPad._po = [pad_master] # persistent objects
pad_master.GetXaxis().SetTitleSize(0)
pad_master.GetXaxis().SetLabelSize(0)
pad_master.Draw('axis')
# ru.topRightLabel(topPad, pad_master.GetTitle(), xpos=0.5)
ru.topRightLabel(topPad,
"#bf{#it{ATLAS}} Simulation Preliminary",
xpos=0.85,
ypos=0.9)
ru.topRightLabel(topPad, "#sqrt{s} = 13 TeV", xpos=0.85, ypos=0.8)
leg = ru.topRightLegend(can,
legWidth=0.225,
legHeight=0.300,
hShift=-0.10,
vShift=-0.25)
leg.SetBorderSize(0)
# leg.SetHeader(plot_label+ ("(norm=1)" if normalize_to_unity else "(lumi %.1f)"%luminosity))
topPad._po.append(leg)
def format_legend_label(h, l):
return "{0}: {1:.2E} ({2:.0f})".format(l, h.Integral(),
h.GetEntries())
def pretty_scale_legend_label(h, l):
return ("nominal"
if l is 'nom' else "#mu = 2.0 #mu_{0}" if l is 'up' else
"#mu = 0.5 #mu_{0}" if l is 'dn' else 'unknown')
for h, l in [(h_nom, 'nom'), (h_up, 'up'), (h_dn, 'dn')]:
h.Draw('hist same')
leg.AddEntry(h, pretty_scale_legend_label(h, l), 'l')
topPad._po.append(h)
leg.Draw('same')
def integral_and_error(h):
error = R.Double(0.0)
integral = h.IntegralAndError(1, h.GetNbinsX() + 1, error)
return integral, error
def ratio_and_error(ave=(1.0, 0.01), bve=(2.0, 0.001)):
a, sa = ave
b, sb = bve
if a and b:
r = a / b
e = r * sqrt((sa / a) * (sa / a) + (sb / b) * (sb / b))
return r, e
else:
return 0.0, 0.0
print_normalization_summary = histogram_name.startswith('h_meff')
if print_normalization_summary:
nom_int = h_nom.Integral()
up_int = h_up.Integral()
dn_int = h_dn.Integral()
nom_int, nom_err = integral_and_error(h_nom)
up_int, up_err = integral_and_error(h_up)
dn_int, dn_err = integral_and_error(h_dn)
rup, rupe = ratio_and_error((up_int, up_err), (nom_int, nom_err))
rdn, rdne = ratio_and_error((dn_int, dn_err), (nom_int, nom_err))
# print ("normalization change: "
# +"{} up {:.1%} down {:.1%} (nom {:.1f}, up {:.1f}, do {:.1f})".format(h_nom.GetName(),
# 1.0-up_int/nom_int if nom_int else 1.0,
# 1.0-dn_int/nom_int if nom_int else 1.0,
# nom_int,
# up_int,
# dn_int))
print(
"normalization change: " +
"{} up {:.1%} +/- {:.1%} down {:.1%} +/- {:.1%} ".format(
h_nom.GetName(), 1.0 - rup, rupe, 1.0 - rdn, rdne) +
"(integral: " +
"nom {:.2E} +/- {:.2E}, up {:.2E} +/- {:.2E}, do {:.2E} +/- {:.2E})"
.format(nom_int, nom_err, up_int, up_err, dn_int, dn_err) +
" (entries nom {:.2E} up {:.2E} do {:.2E}".format(
h_nom.GetEntries(), h_up.GetEntries(), h_dn.GetEntries()))
print(
"tex normalization change: " +
"{} up ${:.1%} \pm {:.1%}$ down ${:.1%} \pm {:.1%}$ ".format(
h_nom.GetName(), 1.0 - rup, rupe, 1.0 - rdn, rdne))
def bc(h):
return [
h.GetBinContent(i) for i in range(1, 1 + h.GetNbinsX())
]
def max_frac_variation(h1, h2):
"maximum bin-by-bin fractional variation; h1 is denominator, empty bins skipped"
bc1 = bc(h1)
bc2 = bc(h2)
return max(
[abs(b2 / b1) for b1, b2 in zip(bc1, bc2) if b1 and b2])
def max_frac_variation_within10(h1, h2):
"""maximum bin-by-bin fractional variation; h1 is denominator.
Bins with <0.1*peak are skipped"""
bc1 = bc(h1)
bc2 = bc(h2)
m1 = max(bc1)
m2 = max(bc2)
return max([
abs(b2 / b1) for b1, b2 in zip(bc1, bc2)
if b1 > 0.1 * m1 and b2 > 0.1 * m2
])
# print ("shape change: "
# +"{} up {:.1%} down {:.1%} ".format(h_nom.GetName(),
# 1.0-max_frac_variation_within10(h_up, h_nom),
# 1.0-max_frac_variation_within10(h_dn, h_nom)))
topPad.Update()
# bottom
can.cd()
botPad.SetTopMargin(1.25 * botPad.GetTopMargin())
botPad.Draw()
botPad.cd()
ratio_up = ru.buildRatioHistogram(h_up, h_nom)
ratio_dn = ru.buildRatioHistogram(h_dn, h_nom)
yMin, yMax = 0.5, 1.5
ratioPadMaster = pad_master.Clone(pad_master.GetName() + '_ratio')
ratioPadMaster.SetMinimum(yMin)
ratioPadMaster.SetMaximum(yMax)
ratioPadMaster.SetStats(0)
ratioPadMaster.Draw('axis')
x_lo, x_hi = ru.getXrange(ratioPadMaster)
refLines = [
ru.referenceLine(x_lo, x_hi, y, y) for y in [0.5, 1.0, 1.5]
if y > yMin and y < yMax
]
for l in refLines:
l.Draw()
ratio_up.Draw('same')
ratio_dn.Draw('same')
xA, yA = ratioPadMaster.GetXaxis(), ratioPadMaster.GetYaxis()
textScaleUp = 0.75 * 1.0 / botPad.GetHNDC()
yA.SetNdivisions(-102)
yA.SetTitle('Ratio')
yA.CenterTitle()
yA.SetTitleOffset(yA.GetTitleOffset() / textScaleUp)
xA.SetTitleSize(
yA.GetTitleSize()) # x- was set to 0 for padmaster, restore it
xA.SetLabelSize(yA.GetLabelSize())
xA.SetTitle(prettify_title(xA.GetTitle()))
for a in [xA, yA]:
a.SetLabelSize(a.GetLabelSize() * textScaleUp)
a.SetTitleSize(a.GetTitleSize() * textScaleUp)
botPad._graphical_objects = [ratio_up, ratio_dn, ratioPadMaster
] + refLines # avoid garbage collection
botPad.Update()
can.Update()
first_histo = histogram_name is histogram_names[0]
last_histo = histogram_name is histogram_names[-1]
can.SaveAs(outdir + '/' + can.GetName() + '.png')
can.SaveAs(outdir + '/' + can.GetName() + '.eps')
can.SaveAs(output_pdf_name + (')' if last_histo else ''))
def subtractRealAndComputeScaleFactor(histosPerGroup={},
variable='',
outRatiohistoname='',
outDataeffhistoname='',
outputDir='./',
region='',
subtractReal=True,
verbose=False):
"efficiency scale factor"
groups = histosPerGroup.keys()
mkdirIfNeeded(outputDir)
histosPerType = dict([(lt,
dict([(g, histosPerGroup[g][variable][lt])
for g in groups])) for lt in leptonTypes])
for lt in leptonTypes:
histosPerType[lt]['totSimBkg'] = summedHisto([
histo for group, histo in histosPerType[lt].iteritems()
if group not in ['data', 'signal']
])
simuTight = histosPerType['fake_tight']['totSimBkg']
simuLoose = histosPerType['fake_loose']['totSimBkg']
dataTight = histosPerType['tight']['data']
dataLoose = histosPerType['loose']['data']
# subtract real contribution from data
# _Note to self_: currently estimating the real contr from MC; in
# the past also used iterative corr, which might be more
# appropriate in cases like here, where the normalization is
# so-so. Todo: investigate the normalization.
dataSubTight = dataTight.Clone(dataTight.GetName().replace(
'data_tight', 'data_minus_prompt_tight'))
dataSubLoose = dataLoose.Clone(dataLoose.GetName().replace(
'data_loose', 'data_minus_prompt_loose'))
dataSubTight.SetDirectory(0)
dataSubLoose.SetDirectory(0)
dataSubTight.Add(histosPerType['real_tight']['totSimBkg'],
-1.0 if subtractReal else 0.0)
dataSubLoose.Add(histosPerType['real_loose']['totSimBkg'],
-1.0 if subtractReal else 0.0)
effData = dataSubTight.Clone(outDataeffhistoname)
effData.SetDirectory(0)
effData.Divide(dataSubLoose)
effSimu = simuTight.Clone(simuTight.GetName().replace(
'fake_tight', 'fake_eff'))
effSimu.SetDirectory(0)
effSimu.Divide(simuLoose)
print "eff(T|L) vs. ", variable
def formatFloat(floats):
return ["%.4f" % f for f in floats]
print "efficiency data : ", formatFloat(getBinContents(effData))
print "efficiency simu : ", formatFloat(getBinContents(effSimu))
ratio = effData.Clone(outRatiohistoname)
ratio.SetDirectory(0)
ratio.Divide(effSimu)
print "sf data/simu : ", formatFloat(getBinContents(ratio))
print " +/- : ", formatFloat(getBinErrors(ratio))
can = r.TCanvas('c_' + outRatiohistoname, outRatiohistoname, 800, 600)
botPad, topPad = rootUtils.buildBotTopPads(can)
can.cd()
topPad.Draw()
topPad.cd()
pm = effData
pm.SetStats(0)
pm.Draw('axis')
xAx, yAx = pm.GetXaxis(), pm.GetYaxis()
xAx.SetTitle('')
xAx.SetLabelSize(0)
yAx.SetRangeUser(0.0, 0.25)
textScaleUp = 1.0 / topPad.GetHNDC()
yAx.SetLabelSize(textScaleUp * 0.04)
yAx.SetTitleSize(textScaleUp * 0.04)
yAx.SetTitle('#epsilon(T|L)')
yAx.SetTitleOffset(yAx.GetTitleOffset() / textScaleUp)
effSimu.SetLineColor(r.kRed)
effSimu.SetMarkerStyle(r.kOpenCross)
effSimu.SetMarkerColor(effSimu.GetLineColor())
effData.Draw('same')
effSimu.Draw('same')
leg = drawLegendWithDictKeys(topPad, {
'data': effData,
'simulation': simuTight
},
legWidth=0.4)
leg.SetHeader('scale factor ' + region + ' ' +
('electron' if '_el_' in outRatiohistoname else
'muon' if '_mu_' in outRatiohistoname else ''))
can.cd()
botPad.Draw()
botPad.cd()
ratio.SetStats(0)
ratio.Draw()
textScaleUp = 1.0 / botPad.GetHNDC()
xAx, yAx = ratio.GetXaxis(), ratio.GetYaxis()
yAx.SetRangeUser(0.0, 2.0)
xAx.SetTitle({
'pt1': 'p_{T}',
'eta1': '|#eta|',
'pt1_eta1': 'p_{T}'
}[variable])
yAx.SetNdivisions(-202)
yAx.SetTitle('Data/Sim')
yAx.CenterTitle()
xAx.SetLabelSize(textScaleUp * 0.04)
xAx.SetTitleSize(textScaleUp * 0.04)
yAx.SetLabelSize(textScaleUp * 0.04)
yAx.SetTitleSize(textScaleUp * 0.04)
refLine = rootUtils.referenceLine(xAx.GetXmin(), xAx.GetXmax())
refLine.Draw()
can.Update()
outFname = os.path.join(outputDir, region + '_' + outRatiohistoname)
for ext in ['.eps', '.png']:
utils.rmIfExists(outFname + ext)
can.SaveAs(outFname + ext)
return {
outRatiohistoname:
ratio,
outDataeffhistoname:
effData,
outDataeffhistoname.replace('_fake_rate_data_', '_tight_data_minus_prompt'):
dataSubTight,
outDataeffhistoname.replace('_fake_rate_data_', '_loose_data_minus_prompt'):
dataSubLoose
}
def plotHistRatioAndFit(histos,
ratio,
fitfunc,
outfname,
graphics_attributes={}):
can = r.TCanvas('can_' + outfname, outfname, 800, 600)
botPad, topPad = buildBotTopPads(can, splitFraction=0.35)
can.cd()
topPad.Draw()
topPad.cd()
pm = first(histos)
pm.Draw('axis')
xAx, yAx = pm.GetXaxis(), pm.GetYaxis()
xAx.SetTitle('')
xAx.SetLabelSize(0)
yAx.SetRangeUser(0.0, 1.2)
yAx.SetNdivisions(
505) # for some reason some of the inputs are already ratios with -201
textScaleUp = 1.0 / topPad.GetHNDC()
yAx.SetLabelSize(textScaleUp * 0.04)
yAx.SetTitleSize(textScaleUp * 0.04)
yAx.SetTitle(graphics_attributes['ytitle'])
yAx.SetTitleOffset(yAx.GetTitleOffset() / textScaleUp)
for k, h in histos.iteritems():
h.SetMarkerStyle(graphics_attributes['markers'][k])
h.SetMarkerColor(graphics_attributes['colors'][k])
h.SetLineColor(graphics_attributes['colors'][k])
h.Draw('same')
labels = graphics_attributes['labels']
drawLegendWithDictKeys(topPad,
dict([(labels[s], histos[s])
for s in histos.keys()]),
legWidth=0.4)
can.cd()
botPad.Draw()
botPad.cd()
ratio.Draw('axis')
textScaleUp = 1.0 / botPad.GetHNDC()
xAx, yAx = ratio.GetXaxis(), ratio.GetYaxis()
yAx.SetRangeUser(0.0, 2.0)
xAx.SetTitle(graphics_attributes['xtitle'])
yAx.SetNdivisions(-202)
yAx.SetTitle('Data/Sim')
yAx.CenterTitle()
for a in [xAx, yAx]:
if a.GetLabelSize() > 0.04: continue
a.SetLabelSize(a.GetLabelSize() * textScaleUp)
a.SetTitleSize(a.GetTitleSize() * textScaleUp)
a.SetTitleOffset(a.GetTitleOffset() / textScaleUp)
ratio.SetMarkerStyle(graphics_attributes['markers']['data'])
fitfunc.SetLineWidth(2)
fitfunc.SetLineStyle(2)
fitfunc.Draw('same')
ratio.Draw('same')
tex = r.TLatex()
tex.SetNDC(True)
p0, p0Err, chi2, ndf = fitResults(fitfunc)
fitParLabel = "Const. fit : %s #pm %s" % (pdgRound(p0, p0Err))
fitGoodLabel = "#chi^{2}/DOF : %.2f / %d" % (chi2, ndf)
tex.SetTextSize(yAx.GetTitleSize())
tex.SetTextFont(yAx.GetTitleFont())
tex.DrawLatex(0.15, 0.40,
"#splitline{%s}{%s}" % (fitParLabel, fitGoodLabel))
can.Update()
outfname = outfname + '.png'
rmIfExists(outfname) # avoid root warnings
can.SaveAs(outfname)
def main() :
parser = argparse.ArgumentParser(description=description,
epilog=epilog,
formatter_class=argparse.RawDescriptionHelpFormatter)
add_arg = parser.add_argument
add_arg('-o', '--output-dir', default="./")
add_arg('-p', '--process', help='one physics process, eg. ttw')
add_arg('-s', '--systematic', help='one of the systematic variations')
add_arg('-v', '--verbose', action='store_true')
add_arg('-d', '--debug', action='store_true')
args = parser.parse_args()
set_log(args.verbose, args.debug)
outdir = args.output_dir
process = args.process
systematic = args.systematic
available_processes = get_input_samples().keys()
if process not in available_processes:
raise StandardError("invalid process %s, should be one of %s"%(process, str(available_processes)))
available_systematics = get_input_samples()[process].keys()
if systematic not in available_systematics:
raise StandardError("invalid systematic %s, should be one of %s"%(systematic, str(available_systematics)))
file_label = process+'_sys_'+systematic
plot_label = process+' sys. '+systematic
normalize_to_unity = False # True
luminosity = 1.0
combiner = HistogramCombiner()
combiner.build_samples(process=process, systematic=systematic)
histogram_names = ['h_meff', 'h_jetN',
# 'h_electronPt', 'h_muonPt',
# 'h_bjetEmulN', 'h_bjetN',
# 'h_bjetEmulN_sr3b', 'h_bjetN_sr3b',
# 'h_bjetEmulN_sr1b', 'h_bjetN_sr1b',
# 'h_bjetEmulN_cr2bttV', 'h_bjetN_cr2bttV',
# 'h_meff_sr3b', 'h_jetN_sr3b', 'h_met_sr3b',
# 'h_meff_sr1b', 'h_jetN_sr1b', 'h_met_sr1b',
# 'h_meff_sr0b5j', 'h_jetN_sr0b5j', 'h_met_sr0b5j',
# 'h_meff_sr0b4j', 'h_jetN_sr0b4j', 'h_met_sr0b4j',
# 'h_meff_sr0b3j', 'h_jetN_sr0b3j', 'h_met_sr0b3j',
# 'h_meff_cr2bttV', 'h_jetN_cr2bttV', 'h_met_cr2bttV'
]
combiner.compute_normalization_factors()
output_pdf_name = outdir+'/'+file_label+'.pdf'
c_summary = R.TCanvas('c_summary', 'plotExplicitSamples sampes summary ')
combiner.print_sample_summary_to_pdf(c_summary, label="%s: nominal vs. %s systematic"%(process, systematic))
c_summary.SaveAs(output_pdf_name+'(')
for histogram_name in histogram_names:
rebin = 'meff' in histogram_name # and '_sr' in histogram_name # non-inclusive histos: low stats
rebin_factor = (2 if 'meff' in histogram_name else 2 if 'jetN' in histogram_name else 1) if rebin else 1
histograms = combiner.get_histograms(histogram_name=histogram_name)
h_nom = histograms['nominal']
h_up = histograms['up']
h_dn = histograms['down']
if 'h_jetFlavorMultiplicity' in histogram_name:
h_nom = emulate_btag_multiplicity_from_truth_flavor(h_nom, 'nom')
h_up = emulate_btag_multiplicity_from_truth_flavor(h_up, 'up')
h_dn = emulate_btag_multiplicity_from_truth_flavor(h_dn, 'dn')
histos = [h_nom, h_up, h_dn]
for h in set(histos): # set: avoid rebinning twice when up==down
h.Rebin(rebin_factor)
h_nom.SetLineWidth(2*h_nom.GetLineWidth())
h_nom.SetLineColor(R.kBlack)
h_up.SetLineColor(R.kBlue)
h_dn.SetLineColor(R.kRed)
pad_master = h_nom
pad_master.SetMaximum(1.1*max([h.GetMaximum() for h in histos]))
pad_master.SetMinimum(1.0*min([0.0]+[h.GetMinimum() for h in histos]))
pad_master.GetYaxis().SetTitle('Arbitrary Units')
pad_master.SetStats(0)
can = R.TCanvas('c_ttV_syst_'+histogram_name, 'ttV explicit variations '+pad_master.GetTitle(), 700, 700)
botPad, topPad = ru.buildBotTopPads(can, squeezeMargins=False)
# top
can.cd()
topPad.Draw()
topPad.cd()
topPad._po = [pad_master] # persistent objects
pad_master.GetXaxis().SetTitleSize(0)
pad_master.GetXaxis().SetLabelSize(0)
pad_master.Draw('axis')
# ru.topRightLabel(topPad, pad_master.GetTitle(), xpos=0.5)
ru.topRightLabel(topPad, "#bf{#it{ATLAS}} Simulation Preliminary", xpos=0.85, ypos=0.9)
ru.topRightLabel(topPad, "#sqrt{s} = 13 TeV", xpos=0.85, ypos=0.8)
leg = ru.topRightLegend(can, legWidth=0.225, legHeight=0.300, hShift=-0.10, vShift=-0.25)
leg.SetBorderSize(0)
# leg.SetHeader(plot_label+ ("(norm=1)" if normalize_to_unity else "(lumi %.1f)"%luminosity))
topPad._po.append(leg)
def format_legend_label(h, l):
return "{0}: {1:.2E} ({2:.0f})".format(l, h.Integral(), h.GetEntries())
def pretty_scale_legend_label(h, l):
return ("nominal" if l is 'nom' else
"#mu = 2.0 #mu_{0}" if l is 'up' else
"#mu = 0.5 #mu_{0}" if l is 'dn' else
'unknown')
for h,l in [(h_nom, 'nom'), (h_up, 'up'), (h_dn, 'dn')]:
h.Draw('hist same')
leg.AddEntry(h, pretty_scale_legend_label(h, l), 'l')
topPad._po.append(h)
leg.Draw('same')
def integral_and_error(h):
error = R.Double(0.0)
integral = h.IntegralAndError(1, h.GetNbinsX()+1, error)
return integral, error
def ratio_and_error(ave=(1.0, 0.01), bve=(2.0, 0.001)):
a, sa = ave
b, sb = bve
if a and b:
r = a/b
e = r * sqrt((sa/a)*(sa/a)+(sb/b)*(sb/b))
return r, e
else:
return 0.0, 0.0
print_normalization_summary = histogram_name.startswith('h_meff')
if print_normalization_summary:
nom_int = h_nom.Integral()
up_int = h_up.Integral()
dn_int = h_dn.Integral()
nom_int, nom_err = integral_and_error(h_nom)
up_int, up_err = integral_and_error(h_up)
dn_int, dn_err = integral_and_error(h_dn)
rup, rupe = ratio_and_error((up_int, up_err), (nom_int, nom_err))
rdn, rdne = ratio_and_error((dn_int, dn_err), (nom_int, nom_err))
# print ("normalization change: "
# +"{} up {:.1%} down {:.1%} (nom {:.1f}, up {:.1f}, do {:.1f})".format(h_nom.GetName(),
# 1.0-up_int/nom_int if nom_int else 1.0,
# 1.0-dn_int/nom_int if nom_int else 1.0,
# nom_int,
# up_int,
# dn_int))
print ("normalization change: "
+"{} up {:.1%} +/- {:.1%} down {:.1%} +/- {:.1%} ".format(h_nom.GetName(), 1.0-rup, rupe, 1.0-rdn, rdne)
+"(integral: "
+"nom {:.2E} +/- {:.2E}, up {:.2E} +/- {:.2E}, do {:.2E} +/- {:.2E})".format(nom_int, nom_err,
up_int, up_err,
dn_int, dn_err)
+" (entries nom {:.2E} up {:.2E} do {:.2E}".format(h_nom.GetEntries(), h_up.GetEntries(), h_dn.GetEntries()))
print ("tex normalization change: "
+"{} up ${:.1%} \pm {:.1%}$ down ${:.1%} \pm {:.1%}$ ".format(h_nom.GetName(), 1.0-rup, rupe, 1.0-rdn, rdne)
)
def bc(h): return [h.GetBinContent(i) for i in range(1,1+h.GetNbinsX())]
def max_frac_variation(h1, h2):
"maximum bin-by-bin fractional variation; h1 is denominator, empty bins skipped"
bc1 = bc(h1)
bc2 = bc(h2)
return max([abs(b2/b1) for b1, b2 in zip(bc1, bc2) if b1 and b2])
def max_frac_variation_within10(h1, h2):
"""maximum bin-by-bin fractional variation; h1 is denominator.
Bins with <0.1*peak are skipped"""
bc1 = bc(h1)
bc2 = bc(h2)
m1 = max(bc1)
m2 = max(bc2)
return max([abs(b2/b1) for b1, b2 in zip(bc1, bc2) if b1>0.1*m1 and b2>0.1*m2])
# print ("shape change: "
# +"{} up {:.1%} down {:.1%} ".format(h_nom.GetName(),
# 1.0-max_frac_variation_within10(h_up, h_nom),
# 1.0-max_frac_variation_within10(h_dn, h_nom)))
topPad.Update()
# bottom
can.cd()
botPad.SetTopMargin(1.25*botPad.GetTopMargin())
botPad.Draw()
botPad.cd()
ratio_up = ru.buildRatioHistogram(h_up, h_nom)
ratio_dn = ru.buildRatioHistogram(h_dn, h_nom)
yMin, yMax = 0.5, 1.5
ratioPadMaster = pad_master.Clone(pad_master.GetName()+'_ratio')
ratioPadMaster.SetMinimum(yMin)
ratioPadMaster.SetMaximum(yMax)
ratioPadMaster.SetStats(0)
ratioPadMaster.Draw('axis')
x_lo, x_hi = ru.getXrange(ratioPadMaster)
refLines = [ru.referenceLine(x_lo, x_hi, y, y) for y in [0.5, 1.0, 1.5] if y>yMin and y<yMax]
for l in refLines : l.Draw()
ratio_up.Draw('same')
ratio_dn.Draw('same')
xA, yA = ratioPadMaster.GetXaxis(), ratioPadMaster.GetYaxis()
textScaleUp = 0.75*1.0/botPad.GetHNDC()
yA.SetNdivisions(-102)
yA.SetTitle('Ratio')
yA.CenterTitle()
yA.SetTitleOffset(yA.GetTitleOffset()/textScaleUp)
xA.SetTitleSize(yA.GetTitleSize()) # x- was set to 0 for padmaster, restore it
xA.SetLabelSize(yA.GetLabelSize())
xA.SetTitle(prettify_title(xA.GetTitle()))
for a in [xA, yA] :
a.SetLabelSize(a.GetLabelSize()*textScaleUp)
a.SetTitleSize(a.GetTitleSize()*textScaleUp)
botPad._graphical_objects = [ratio_up, ratio_dn, ratioPadMaster] + refLines # avoid garbage collection
botPad.Update()
can.Update()
first_histo = histogram_name is histogram_names[0]
last_histo = histogram_name is histogram_names[-1]
can.SaveAs(outdir+'/'+can.GetName()+'.png')
can.SaveAs(outdir+'/'+can.GetName()+'.eps')
can.SaveAs(output_pdf_name+ (')' if last_histo else ''))
