'recreate')
nominal.Write()
if not vl.asimov:
asimov.Write()
for syst in sysList:
variations[syst].Write()
out.Close()
scaleval = 1 / vl.Lumi / 1000 if vl.doxsec else 1
nominal.Scale(scaleval)
for key in variations:
variations[key].Scale(scaleval)
nominal_withErrors = ep.propagateHistoAsym(nominal,
variations,
True,
doSym=vl.doSym)
plot = bp.beautifulUnfoldingPlots('{var}_folded'.format(var=varName))
plot.doRatio = True
plot.doFit = False
plot.plotspath = "results/"
plot.doPreliminary = vl.doPre
nominal.SetMarkerStyle(r.kFullCircle)
nominal.GetXaxis().SetNdivisions(505, True)
nominal_withErrors[0].SetFillColorAlpha(r.kBlue, 0.35)
nominal_withErrors[0].SetLineColor(0)
nominal_withErrors[0].SetFillStyle(1001)
if "legpos_fold" in vl.varList[varName]:
def doUnfoldingForAllNuis(self):
allHistos = {}
tauval = 0.
self.prepareAllHelpers()
if self.doRegularisation:
print '> Performing regularisation...'
self.doLCurveScan()
tauval = self.helpers[''].tunfolder.GetTau()
if verbose: print '> Unfolding nominal distribution'
self.helpers[''].tunfolder.DoUnfold(tauval)
nominal = copy.deepcopy(self.helpers[''].tunfolder.GetOutput(self.var))
for nuis in self.theoSysts:
if verbose:
print '> Unfolding distribution of {sys} systematic'.format(
sys=nuis)
self.helpers[nuis].tunfolder.DoUnfold(tauval)
allHistos[nuis] = self.helpers[nuis].tunfolder.GetOutput(self.var +
'_' +
nuis)
nominal_withErrors = ep.propagateHistoAsym(nominal, allHistos,
self.doColorEnvelope)
plot = bp.beautifulUnfoldingPlots(self.var)
plot.doRatio = True
plot.plotspath = self.plotspath
nominal.SetMarkerStyle(r.kFullCircle)
nominal_withErrors[0].SetFillColorAlpha(r.kBlue, 0.35)
nominal_withErrors[0].SetLineColor(r.kBlue)
nominal_withErrors[0].SetFillStyle(1001)
if self.doSanityCheck:
if not os.path.isfile(
'temp/{var}_/ClosureTest_{var}.root'.format(var=self.var)):
raise RuntimeError(
'The rootfile with the generated information does not exist'
)
tmptfile = r.TFile.Open(
'temp/{var}_/ClosureTest_{var}.root'.format(var=self.var))
tru = copy.deepcopy(tmptfile.Get('tW'))
tru.SetLineWidth(2)
tru.SetLineColor(bp.colorMap[0])
if not os.path.isfile(
'temp/{var}_/ClosureTest_aMCatNLO_{var}.root'.format(
var=self.var)):
raise RuntimeError(
'The rootfile with the generated information from an aMCatNLO sample does not exist'
)
tmptfile2 = r.TFile.Open(
'temp/{var}_/ClosureTest_aMCatNLO_{var}.root'.format(
var=self.var))
aMCatNLO = copy.deepcopy(tmptfile2.Get('tW'))
aMCatNLO.SetLineWidth(2)
aMCatNLO.SetLineColor(r.kAzure)
plot.addHisto(nominal_withErrors, 'hist', 'Total unc.', 'F', 'unc')
plot.addHisto(tru, 'L,same', 'tW Powheg', 'L', 'mc')
plot.addHisto(aMCatNLO, 'L,same', 'tW aMCatNLO', 'L', 'mc')
plot.addHisto(nominal, 'P,E,same', vl.labellegend, 'P', 'data')
plot.saveCanvas('TR')
tmptfile.Close()
tmptfile2.Close()
else:
plot.addHisto(nominal_withErrors, 'E2', 'Total unc.', 'F')
plot.addHisto(nominal, 'P,same', vl.labellegend, 'P', 'data')
plot.saveCanvas('TR')
del plot
plot2 = bp.beautifulUnfoldingPlots(self.var + 'uncertainties')
uncList = ep.getUncList(nominal, allHistos, self.doColorEnvelope)[:5]
incmax = []
for bin in range(1, nominal_withErrors[0].GetNbinsX() + 1):
if nominal_withErrors[1].GetBinError(
bin) > nominal_withErrors[0].GetBinContent(bin):
incmax.append(
max([
nominal_withErrors[0].GetBinError(bin),
nominal_withErrors[0].GetBinContent(bin)
]))
else:
incmax.append(
max([
nominal_withErrors[0].GetBinError(bin),
nominal_withErrors[1].GetBinError(bin)
]))
maxinctot = 0
hincmax = nominal_withErrors[0].Clone('hincmax')
for bin in range(1, nominal_withErrors[0].GetNbinsX() + 1):
hincmax.SetBinContent(bin,
incmax[bin - 1] / hincmax.GetBinContent(bin))
hincmax.SetBinError(bin, 0)
if (hincmax.GetBinContent(bin) > maxinctot):
maxinctot = hincmax.GetBinContent(bin)
hincmax.SetLineColor(r.kBlack)
hincmax.SetLineWidth(2)
hincmax.SetFillColorAlpha(r.kBlue, 0)
if (maxinctot >= 0.9):
if maxinctot >= 5:
uncList[0][1].GetYaxis().SetRangeUser(0, 5)
else:
uncList[0][1].GetYaxis().SetRangeUser(0, maxinctot + 0.1)
else:
uncList[0][1].GetYaxis().SetRangeUser(0, 0.9)
for i in range(5):
uncList[i][1].SetLineColor(vl.colorMap[uncList[i][0]])
uncList[i][1].SetLineWidth(2)
plot2.addHisto(uncList[i][1], 'H,same' if i else 'H',
uncList[i][0], 'L')
plot2.addHisto(hincmax, 'H,same', 'Total', 'L')
plot2.plotspath = self.plotspath
plot2.saveCanvas('TR')
out = r.TFile.Open('temp/{var}/cutOutput_{var}.root'.format(var = varName), 'recreate')
nominal.Write()
if not vl.asimov:
asimov.Write()
for syst in sysList:
variations[syst].Write()
out.Close()
scaleval = 1/vl.Lumi/1000 if vl.doxsec else 1
nominal.Scale(scaleval)
for key in variations:
variations[key].Scale(scaleval)
nominal_withErrors = ep.propagateHistoAsym(nominal, variations, True)
plot = bp.beautifulUnfoldingPlots('{var}_folded'.format(var = varName))
plot.doRatio = True
plot.doFit = False
plot.plotspath = "results/"
nominal.SetMarkerStyle(r.kFullCircle)
nominal.GetXaxis().SetNdivisions(505, True)
nominal_withErrors[0].SetFillColorAlpha(r.kBlue, 0.35)
nominal_withErrors[0].SetLineColor(r.kBlue)
nominal_withErrors[0].SetFillStyle(1001)
if "legpos_fold" in vl.varList[varName]: legloc = vl.varList[varName]["legpos_fold"]
else: legloc = "TR"
def doUnfoldingForAllNuis(self):
allHistos = {}
self.prepareAllHelpers()
if self.doRegularisation and self.tau == 0:
print '> Performing regularisation...'
self.doLCurveScan()
tauval = 0
if self.doRegularisation: tauval = self.taulist['']
if verbose: print '> Unfolding nominal distribution'
self.helpers[''].tunfolder.DoUnfold(tauval)
nominal = copy.deepcopy(self.helpers[''].tunfolder.GetOutput(self.var))
covnom = copy.deepcopy(
self.helpers[''].tunfolder.GetEmatrixTotal("CovMat"))
for bin in range(1, nominal.GetNbinsX() + 1):
nominal.SetBinError(bin, math.sqrt(covnom.GetBinContent(bin, bin)))
for nuis in self.sysList:
if verbose:
print '> Unfolding distribution of {sys} systematic'.format(
sys=nuis)
self.helpers[nuis].tunfolder.DoUnfold(tauval)
allHistos[nuis] = self.helpers[nuis].tunfolder.GetOutput(self.var +
'_' +
nuis)
scaleval = 1 / vl.Lumi / 1000 if vl.doxsec else 1
nominal.Scale(scaleval)
for key in allHistos:
allHistos[key].Scale(scaleval)
if not self.wearedoingasimov:
if not os.path.isfile(
'temp/{var}_/ClosureTest_{var}.root'.format(var=self.var)):
raise RuntimeError(
'The rootfile with the generated information does not exist'
)
tmptfile = r.TFile.Open(
'temp/{var}_/ClosureTest_{var}.root'.format(var=self.var))
#tmptfile2 = r.TFile.Open('temp/{var}_/ClosureTest_recobinning_{var}.root'.format(var = self.var))
tru = copy.deepcopy(tmptfile.Get('tW'))
#tru2 = copy.deepcopy(tmptfile2.Get('tW'))
#tru2.Scale(vl.Lumi*1000)
for bin in range(1, allHistos['asimov'].GetNbinsX() + 1):
#print "\nasimov:", allHistos['asimov'].GetBinContent(bin)
#print "verdad:", tru.GetBinContent(bin)
allHistos['asimov'].SetBinError(
bin,
abs(allHistos['asimov'].GetBinContent(bin) -
tru.GetBinContent(bin)))
#allHistos['asimov'].SetBinContent(bin, nominal.GetBinContent(bin))
#print "errorin:", allHistos['asimov'].GetBinError(bin)
#print "nominal:", nominal.GetBinContent(bin)
#print "relativo", round(allHistos['asimov'].GetBinError(bin)/allHistos['asimov'].GetBinContent(bin)*100, 1)
#print "ratio:", allHistos['asimov'].GetBinContent(bin)/tru.GetBinContent(bin)
#for bin in range(1, tru2.GetNbinsX() + 1):
#print "verdad2:", tru2.GetBinContent(bin)
#tmptfile2.Close()
tmptfile.Close()
del tru
if not self.wearedoingasimov:
savetfile = r.TFile(
"temp/{var}_/unfOutput_{var}.root".format(var=self.var),
"recreate")
nominal.Write()
for key in allHistos:
allHistos[key].Write()
covnom.Scale(scaleval**2)
covnom.Write()
#covitwo = copy.deepcopy(self.helpers[''].tunfolder.GetEmatrixInput("CovMatInput"))
#covitwo.Scale(scaleval**2)
#covitwo.Write()
#covithree = copy.deepcopy(self.helpers[''].tunfolder.GetEmatrixSysUncorr("CovMatResponse"))
#covithree.Scale(scaleval**2)
#covithree.Write()
savetfile.Close()
if not self.wearedoingasimov:
nominal_withErrors = ep.propagateHistoAsym(nominal, allHistos,
self.doColorEnvelope,
True)
else:
nominal_withErrors = ep.propagateHistoAsym(nominal, allHistos,
self.doColorEnvelope)
plot = bp.beautifulUnfoldingPlots(
self.var + "_asimov" if self.wearedoingasimov else self.var)
plot.doRatio = True
plot.doFit = False
plot.plotspath = self.plotspath
nominal.SetMarkerStyle(r.kFullCircle)
nominal.SetLineColor(r.kBlack)
nominal_withErrors[0].SetFillColorAlpha(r.kBlue, 0.35)
nominal_withErrors[0].SetLineColor(r.kBlue)
nominal_withErrors[0].SetFillStyle(1001)
if not self.wearedoingasimov:
savetfile2 = r.TFile(
"temp/{var}_/unfOutput_{var}.root".format(var=self.var),
"update")
nom0 = copy.deepcopy(nominal_withErrors[0].Clone("nom0"))
nom1 = copy.deepcopy(nominal_withErrors[1].Clone("nom1"))
nom0.Write()
nom1.Write()
savetfile2.Close()
del nom0, nom1
#############################
print "\nLOS RESULTAOS - {uno} - {dos}".format(
uno="ASIMOV" if self.wearedoingasimov else "DATOS", dos=self.var)
for bin in range(1, nominal_withErrors[0].GetNbinsX() + 1):
print "Bin", bin, "(abs.): (", round(
nominal.GetBinContent(bin), 4), "+", round(
nominal_withErrors[0].GetBinError(bin),
4), "-", round(nominal_withErrors[1].GetBinError(bin),
4), ") pb"
print "Bin", bin, "(rel.): (", round(
nominal.GetBinContent(bin), 4), "+", round(
nominal_withErrors[0].GetBinError(bin) /
nominal.GetBinContent(bin) * 100, 4), "-", round(
nominal_withErrors[1].GetBinError(bin) /
nominal.GetBinContent(bin) * 100, 4), ") pb\n"
print "\n"
#############################
if "legpos_unf" in vl.varList[self.var] and not self.wearedoingasimov:
legloc = vl.varList[self.var]["legpos_unf"]
elif "legpos_unfas" in vl.varList[self.var] and self.wearedoingasimov:
legloc = vl.varList[self.var]["legpos_unfas"]
else:
legloc = "TR"
if self.doSanityCheck:
if not os.path.isfile(
'temp/{var}_/ClosureTest_{var}.root'.format(var=self.var)):
raise RuntimeError(
'The rootfile with the generated information does not exist.'
)
tmptfile = r.TFile.Open(
'temp/{var}_/ClosureTest_{var}.root'.format(var=self.var))
tru = copy.deepcopy(tmptfile.Get('tW').Clone('tru'))
tru.SetLineWidth(2)
tru.SetLineColor(bp.colorMap[0])
if not os.path.isfile(
'temp/{var}_/ClosureTest_aMCatNLO_{var}.root'.format(
var=self.var)):
raise RuntimeError(
'The rootfile with the generated information from an aMCatNLO sample does not exist.'
)
tmptfile2 = r.TFile.Open(
'temp/{var}_/ClosureTest_aMCatNLO_{var}.root'.format(
var=self.var))
aMCatNLO = copy.deepcopy(tmptfile2.Get('tW').Clone('aMCatNLO'))
aMCatNLO.SetLineWidth(2)
aMCatNLO.SetLineColor(r.kAzure)
if not os.path.isfile(
'temp/{var}_/ClosureTest_DS_{var}.root'.format(
var=self.var)):
raise RuntimeError(
'The rootfile with the generated information with the DS variation does not exist.'
)
tmptfile3 = r.TFile.Open(
'temp/{var}_/ClosureTest_DS_{var}.root'.format(var=self.var))
hDS = copy.deepcopy(tmptfile3.Get('tW').Clone('hDS'))
hDS.SetLineWidth(2)
hDS.SetLineColor(r.kGreen)
plot.addHisto(nominal_withErrors, 'hist', 'Total unc.', 'F', 'unc')
plot.addHisto(tru, 'L,same', 'tW Powheg', 'L', 'mc')
plot.addHisto(aMCatNLO, 'L,same', 'tW aMCatNLO', 'L', 'mc')
plot.addHisto(hDS, 'L,same', 'tW DS', 'L', 'mc')
if self.wearedoingasimov:
plot.addHisto(nominal, 'P,E,same', "Pseudodata", 'P', 'data')
else:
plot.addHisto(nominal, 'P,E,same', vl.labellegend, 'P', 'data')
plot.saveCanvas(legloc)
tmptfile.Close()
tmptfile2.Close()
tmptfile3.Close()
else:
plot.addHisto(nominal_withErrors, 'E2', 'Total unc.', 'F')
if self.wearedoingasimov:
plot.addHisto(nominal, 'P,same', "Pseudodata", 'P', 'data')
else:
plot.addHisto(nominal, 'P,same', vl.labellegend, 'P', 'data')
plot.saveCanvas(legloc)
del plot
plot2 = bp.beautifulUnfoldingPlots(
self.var +
'uncertainties_asimov' if self.wearedoingasimov else self.var +
'uncertainties')
plot2.doFit = False
if not self.wearedoingasimov:
uncList = ep.getUncList(nominal, allHistos, self.doColorEnvelope,
False, True)
else:
uncList = ep.getUncList(nominal, allHistos, self.doColorEnvelope,
False)
print 'Full uncertainties list (ordered by impact):', uncList
uncList = uncList[:vl.nuncs]
incmax = []
for bin in range(1, nominal_withErrors[0].GetNbinsX() + 1):
if nominal_withErrors[1].GetBinError(
bin) > nominal_withErrors[0].GetBinContent(bin):
incmax.append(
max([
nominal_withErrors[0].GetBinError(bin),
nominal_withErrors[0].GetBinContent(bin)
]))
else:
incmax.append(
max([
nominal_withErrors[0].GetBinError(bin),
nominal_withErrors[1].GetBinError(bin)
]))
incsyst = []
for bin in range(1, nominal_withErrors[0].GetNbinsX() + 1):
if math.sqrt(nominal_withErrors[1].GetBinError(bin)**2 -
nominal.GetBinError(bin)**2
) > nominal_withErrors[0].GetBinContent(bin):
incsyst.append(
max([
math.sqrt(nominal_withErrors[0].GetBinError(bin)**2 -
nominal.GetBinError(bin)**2),
nominal_withErrors[0].GetBinContent(bin)
]))
else:
incsyst.append(
max([
math.sqrt(nominal_withErrors[0].GetBinError(bin)**2 -
nominal.GetBinError(bin)**2),
math.sqrt(nominal_withErrors[1].GetBinError(bin)**2 -
nominal.GetBinError(bin)**2)
]))
maxinctot = 0
hincmax = nominal_withErrors[0].Clone('hincmax')
for bin in range(1, nominal_withErrors[0].GetNbinsX() + 1):
hincmax.SetBinContent(bin,
incmax[bin - 1] / hincmax.GetBinContent(bin))
hincmax.SetBinError(bin, 0)
if (hincmax.GetBinContent(bin) > maxinctot):
maxinctot = hincmax.GetBinContent(bin)
hincsyst = copy.deepcopy(nominal.Clone('hincsyst'))
for bin in range(1, nominal_withErrors[0].GetNbinsX() + 1):
hincsyst.SetBinContent(
bin, incsyst[bin - 1] / hincsyst.GetBinContent(bin))
hincsyst.SetBinError(bin, 0.)
hincmax.SetLineColor(r.kBlack)
hincmax.SetLineWidth(2)
hincmax.SetFillColorAlpha(r.kBlue, 0)
hincsyst.SetLineColor(r.kBlack)
hincsyst.SetLineWidth(2)
hincsyst.SetLineStyle(3)
hincsyst.SetFillColorAlpha(r.kBlue, 0.)
#if (maxinctot >= 0.9):
#if maxinctot >= 5:
#uncList[0][1].GetYaxis().SetRangeUser(0, 5)
#else:
#uncList[0][1].GetYaxis().SetRangeUser(0, maxinctot + 0.1)
#else:
#uncList[0][1].GetYaxis().SetRangeUser(0, 0.9)
uncList[0][1].GetYaxis().SetRangeUser(0, 2)
for i in range(vl.nuncs):
uncList[i][1].SetLineColor(vl.NewColorMap[uncList[i][0]])
uncList[i][1].SetLineWidth(2)
if "Stat" in uncList[i][0]:
uncList[i][1].SetLineColor(r.kBlack)
uncList[i][1].SetLineStyle(2)
elif "Lumi" in uncList[i][0]:
uncList[i][1].SetLineColor(r.kBlack)
uncList[i][1].SetLineStyle(4)
plot2.addHisto(uncList[i][1], 'H,same' if i else 'H',
uncList[i][0], 'L')
plot2.addHisto(hincsyst, 'hist,same', 'Syst.', 'L')
plot2.addHisto(hincmax, 'hist,same', 'Total', 'L')
plot2.plotspath = self.plotspath
if "uncleg_unf" in vl.varList[self.var] and not self.wearedoingasimov:
unclegpos = vl.varList[self.var]["uncleg_unf"]
elif "uncleg_unfas" in vl.varList[self.var]:
unclegpos = vl.varList[self.var]["uncleg_unfas"]
else:
unclegpos = "TR"
plot2.saveCanvas(unclegpos)
del plot2
return
def doUnfoldingForAllNuis(self):
allHistos = {}
self.prepareAllHelpers()
if self.doRegularisation and self.tau == 0:
print '> Performing regularisation...'
self.doLCurveScan()
tauval = 0
if self.doRegularisation: tauval = self.taulist['']
if verbose: print '\n> Unfolding nominal distribution'
self.helpers[''].tunfolder.DoUnfold(tauval)
nominal = copy.deepcopy(self.helpers[''].tunfolder.GetOutput(self.var))
if "Fiducial" in self.var:
print "\n"
#wr.warn("WARNING: you are calculating the fiducial cross section for the Asimov dataset. For unknown reasons, the obtention of the full covariance matrix after the unfolding gives an error. As a by-pass of this situation, the statistical uncertainty that the nominal results will carry will be ONLY the ones introduced as input, NOT the ones related with the response matrix nor the signal efficiency / fiducial region efficiency detection. TAKE THIS INTO ACCOUNT.")
mat = self.helpers[''].response.GetBinContent(1, 1)
incmat = self.helpers[''].response.GetBinError(1, 1)
ns = self.helpers[''].unfInput.GetBinContent(1)
incns = self.helpers[''].unfInput.GetBinError(1)
nf = self.helpers[''].bkg.GetBinContent(1)
incnf = self.helpers[''].bkg.GetBinError(1)
valor = (ns - nf) / mat
inc = incns / mat + incnf / mat + (ns - nf) / mat**2 * incmat
print "ns", ns, "nf", nf, "mat", mat, "ns-nf", ns - nf
print "valor nom.", valor / vl.Lumi / 1000, "inc. nom.", inc / vl.Lumi / 1000, "\n"
#sys.exit()
nominal.SetBinContent(1, valor)
nominal.SetBinError(1, inc)
covnom = r.TH2F("CovMat", "", 1,
vl.varList[self.var]['genbinning'][0],
vl.varList[self.var]['genbinning'][-1], 1,
vl.varList[self.var]['genbinning'][0],
vl.varList[self.var]['genbinning'][-1])
covnom.SetBinContent(1, 1, inc)
else:
if verbose:
print '> Obtaining covariance matrix of all the statistical components that take part here.'
covnom = copy.deepcopy(
self.helpers[''].tunfolder.GetEmatrixTotal("CovMat"))
for bin in range(1, nominal.GetNbinsX() + 1):
nominal.SetBinError(bin,
math.sqrt(covnom.GetBinContent(bin, bin)))
for nuis in self.sysList:
if verbose:
print '> Unfolding distribution of {sys} systematic'.format(
sys=nuis)
self.helpers[nuis].tunfolder.DoUnfold(tauval)
allHistos[nuis] = self.helpers[nuis].tunfolder.GetOutput(self.var +
'_' +
nuis)
if "Fiducial" in self.var:
mat = self.helpers[nuis].response.GetBinContent(1, 1)
incmat = self.helpers[nuis].response.GetBinError(1, 1)
ns = self.helpers[nuis].unfInput.GetBinContent(1)
incns = self.helpers[nuis].unfInput.GetBinError(1)
nf = self.helpers[nuis].bkg.GetBinContent(1)
incnf = self.helpers[nuis].bkg.GetBinError(1)
valor = (ns - nf) / mat
inc = incns / mat + incnf / mat + (ns - nf) / mat**2 * incmat
allHistos[nuis].SetBinContent(1, 1, valor)
allHistos[nuis].SetBinError(1, 1, inc)
scaleval = 1 / vl.Lumi / 1000 if vl.doxsec else 1
nominal.Scale(scaleval)
for key in allHistos:
allHistos[key].Scale(scaleval)
if not self.wearedoingasimov:
if not os.path.isfile(
'temp/{var}_/ClosureTest_{var}.root'.format(var=self.var)):
raise RuntimeError(
'The rootfile with the generated information does not exist'
)
tmptfile = r.TFile.Open(
'temp/{var}_/ClosureTest_{var}.root'.format(var=self.var))
#tmptfile2 = r.TFile.Open('temp/{var}_/ClosureTest_recobinning_{var}.root'.format(var = self.var))
tru = copy.deepcopy(tmptfile.Get('tW'))
#tru2 = copy.deepcopy(tmptfile2.Get('tW'))
#tru2.Scale(vl.Lumi*1000)
for bin in range(1, allHistos['asimov'].GetNbinsX() + 1):
#print "\nasimov:", allHistos['asimov'].GetBinContent(bin)
#print "verdad:", tru.GetBinContent(bin)
allHistos['asimov'].SetBinError(
bin,
abs(allHistos['asimov'].GetBinContent(bin) -
tru.GetBinContent(bin)))
#allHistos['asimov'].SetBinContent(bin, nominal.GetBinContent(bin))
#print "errorin:", allHistos['asimov'].GetBinError(bin)
#print "nominal:", nominal.GetBinContent(bin)
#print "relativo", round(allHistos['asimov'].GetBinError(bin)/allHistos['asimov'].GetBinContent(bin)*100, 1)
#print "ratio:", allHistos['asimov'].GetBinContent(bin)/tru.GetBinContent(bin)
#for bin in range(1, tru2.GetNbinsX() + 1):
#print "verdad2:", tru2.GetBinContent(bin)
#tmptfile2.Close()
tmptfile.Close()
del tru
if not self.wearedoingasimov:
savetfile = r.TFile(
"temp/{var}_/unfOutput_{var}.root".format(var=self.var),
"recreate")
nominal.Write()
for key in allHistos:
allHistos[key].Write()
covnom.Scale(scaleval**2)
covnom.Write()
#covitwo = copy.deepcopy(self.helpers[''].tunfolder.GetEmatrixInput("CovMatInput"))
#covitwo.Scale(scaleval**2)
#covitwo.Write()
#covithree = copy.deepcopy(self.helpers[''].tunfolder.GetEmatrixSysUncorr("CovMatResponse"))
#covithree.Scale(scaleval**2)
#covithree.Write()
savetfile.Close()
if not self.wearedoingasimov:
nominal_withErrors = ep.propagateHistoAsym(nominal,
allHistos,
self.doColorEnvelope,
True,
doSym=vl.doSym)
else:
nominal_withErrors = ep.propagateHistoAsym(nominal,
allHistos,
self.doColorEnvelope,
doSym=vl.doSym)
plot = bp.beautifulUnfoldingPlots(
self.var + "_asimov" if self.wearedoingasimov else self.var)
plot.doRatio = True
plot.doFit = False
plot.doPreliminary = vl.doPre
plot.doSupplementary = False
plot.plotspath = self.plotspath
nominal.SetMarkerStyle(r.kFullCircle)
nominal.SetLineColor(r.kBlack)
nominal.SetMarkerSize(1)
nominal_withErrors[0].SetFillColorAlpha(r.kBlue, 0.35)
nominal_withErrors[0].SetLineColor(0)
nominal_withErrors[0].SetFillStyle(1001)
if "yaxisuplimitunf" in vl.varList[self.var]:
plot.yaxisuplimit = vl.varList[self.var]["yaxisuplimitunf"]
if not self.wearedoingasimov:
savetfile2 = r.TFile(
"temp/{var}_/unfOutput_{var}.root".format(var=self.var),
"update")
nom0 = copy.deepcopy(nominal_withErrors[0].Clone("nom0"))
nom1 = copy.deepcopy(nominal_withErrors[1].Clone("nom1"))
nom0.Write()
nom1.Write()
savetfile2.Close()
del nom0, nom1
#############################
print "\nLOS RESULTAOS - {uno} - {dos}".format(
uno="ASIMOV" if self.wearedoingasimov else "DATOS", dos=self.var)
for bin in range(1, nominal_withErrors[0].GetNbinsX() + 1):
print "Bin", bin, "(abs.): (", round(
nominal.GetBinContent(bin), 4), "+", round(
nominal_withErrors[0].GetBinError(bin),
4), "-", round(nominal_withErrors[1].GetBinError(bin),
4), ") pb"
print "Bin", bin, "(rel.): (", round(
nominal.GetBinContent(bin), 4), "+", round(
nominal_withErrors[0].GetBinError(bin) /
nominal.GetBinContent(bin) * 100, 4), "-", round(
nominal_withErrors[1].GetBinError(bin) /
nominal.GetBinContent(bin) * 100, 4), ") pb\n"
print "\n"
#############################
if not self.wearedoingasimov and varName != "Fiducial":
tex.saveLaTeXfromhisto(nominal,
varName,
path=vl.tablespath,
errhisto=nominal_withErrors[0],
ty="unfolded")
if "legpos_unf" in vl.varList[self.var] and not self.wearedoingasimov:
legloc = vl.varList[self.var]["legpos_unf"]
elif "legpos_unfas" in vl.varList[self.var] and self.wearedoingasimov:
legloc = vl.varList[self.var]["legpos_unfas"]
else:
legloc = "TR"
if self.doSanityCheck:
if not os.path.isfile(
'temp/{var}_/ClosureTest_{var}.root'.format(var=self.var)):
raise RuntimeError(
'The rootfile with the generated information does not exist.'
)
tmptfile = r.TFile.Open(
'temp/{var}_/ClosureTest_{var}.root'.format(var=self.var))
tru = copy.deepcopy(tmptfile.Get('tW').Clone('tru'))
tru.SetLineWidth(2)
tru.SetLineColor(bp.colorMap[0])
#print "tW DR", tru.GetBinContent(1)
if not os.path.isfile(
'temp/{var}_/ClosureTest_aMCatNLO_{var}.root'.format(
var=self.var)):
raise RuntimeError(
'The rootfile with the generated information from an aMCatNLO sample does not exist.'
)
tmptfile2 = r.TFile.Open(
'temp/{var}_/ClosureTest_aMCatNLO_{var}.root'.format(
var=self.var))
aMCatNLO = copy.deepcopy(tmptfile2.Get('tW').Clone('aMCatNLO'))
aMCatNLO.SetLineWidth(2)
aMCatNLO.SetLineColor(r.kAzure)
aMCatNLO.SetLineStyle(2)
#print "tW aMCatNLO DR", aMCatNLO.GetBinContent(1)
if not os.path.isfile(
'temp/{var}_/ClosureTest_DS_{var}.root'.format(
var=self.var)):
raise RuntimeError(
'The rootfile with the generated information with the DS variation does not exist.'
)
tmptfile3 = r.TFile.Open(
'temp/{var}_/ClosureTest_DS_{var}.root'.format(var=self.var))
hDS = copy.deepcopy(tmptfile3.Get('tW').Clone('hDS'))
hDS.SetLineWidth(2)
hDS.SetLineColor(r.kGreen)
#print "tW DS", hDS.GetBinContent(1)
#sys.exit()
plot.addHisto(nominal_withErrors, 'hist', 'Uncertainty', 'F',
'unc')
plot.addHisto(tru, 'L,same', 'tW Powheg DR + Pythia8', 'L', 'mc')
plot.addHisto(hDS, 'L,same', 'tW Powheg DS + Pythia8', 'L', 'mc')
plot.addHisto(aMCatNLO, 'L,same', 'tW aMC@NLO DR + Pythia8', 'L',
'mc')
if self.wearedoingasimov:
plot.addHisto(
nominal, 'P,E,same{s}'.format(
s=",X0" if "equalbinsunf" in
vl.varList[self.var.replace('_folded', '').replace(
'_asimov', '').replace("_fiducial", "").
replace('norm', '')] else ""), "Pseudodata",
'PE{s}'.format(s="L" if not "equalbinsunf" in vl.varList[
self.var.replace('_folded', '').replace('_asimov', '').
replace("_fiducial", "").replace('norm', '')] else ""),
'data')
else:
plot.addHisto(
nominal, 'P,E,same{s}'.format(
s=",X0" if "equalbinsunf" in
vl.varList[self.var.replace('_folded', '').replace(
'_asimov', '').replace("_fiducial", "").
replace('norm', '')] else ""),
vl.labellegend,
'PE{s}'.format(s="L" if not "equalbinsunf" in vl.varList[
self.var.replace('_folded', '').replace('_asimov', '').
replace("_fiducial", "").replace('norm', '')] else ""),
'data')
plot.saveCanvas(legloc)
tmptfile.Close()
tmptfile2.Close()
tmptfile3.Close()
else:
plot.addHisto(nominal_withErrors, 'E2', 'Uncertainty', 'F')
if self.wearedoingasimov:
plot.addHisto(
nominal,
'P,same{s}'.format(s=",X0" if "equalbinsunf" in vl.varList[
self.var.replace('_folded', '').replace('_asimov', '').
replace("_fiducial", "").replace('norm', '')] else ""),
"Pseudodata",
'PE{s}'.format(s="L" if not "equalbinsunf" in vl.varList[
self.var.replace('_folded', '').replace('_asimov', '').
replace("_fiducial", "").replace('norm', '')] else ""),
'data')
else:
plot.addHisto(
nominal,
'P,same{s}'.format(s=",X0" if "equalbinsunf" in vl.varList[
self.var.replace('_folded', '').replace('_asimov', '').
replace("_fiducial", "").replace('norm', '')] else ""),
vl.labellegend,
'PE{s}'.format(s="L" if not "equalbinsunf" in vl.varList[
self.var.replace('_folded', '').replace('_asimov', '').
replace("_fiducial", "").replace('norm', '')] else ""),
'data')
plot.saveCanvas(legloc)
del plot
plot2 = bp.beautifulUnfoldingPlots(
self.var +
'uncertainties_asimov' if self.wearedoingasimov else self.var +
'uncertainties')
plot2.doFit = False
plot2.doPreliminary = vl.doPre
plot2.doSupplementary = False
if not self.wearedoingasimov:
uncListorig = ep.getUncList(nominal, allHistos,
self.doColorEnvelope, False, True)
else:
uncListorig = ep.getUncList(nominal, allHistos,
self.doColorEnvelope, False)
print 'Full uncertainties list (ordered by impact):', uncListorig
uncList = uncListorig[:vl.nuncs]
incmax = []
for bin in range(1, nominal_withErrors[0].GetNbinsX() + 1):
if nominal_withErrors[1].GetBinError(
bin) > nominal_withErrors[0].GetBinContent(bin):
incmax.append(
max([
nominal_withErrors[0].GetBinError(bin),
nominal_withErrors[0].GetBinContent(bin)
]))
else:
incmax.append(
max([
nominal_withErrors[0].GetBinError(bin),
nominal_withErrors[1].GetBinError(bin)
]))
incsyst = []
for bin in range(1, nominal_withErrors[0].GetNbinsX() + 1):
if math.sqrt(nominal_withErrors[1].GetBinError(bin)**2 -
nominal.GetBinError(bin)**2
) > nominal_withErrors[0].GetBinContent(bin):
incsyst.append(
max([
math.sqrt(nominal_withErrors[0].GetBinError(bin)**2 -
nominal.GetBinError(bin)**2),
nominal_withErrors[0].GetBinContent(bin)
]))
else:
incsyst.append(
max([
math.sqrt(nominal_withErrors[0].GetBinError(bin)**2 -
nominal.GetBinError(bin)**2),
math.sqrt(nominal_withErrors[1].GetBinError(bin)**2 -
nominal.GetBinError(bin)**2)
]))
maxinctot = 0
hincmax = nominal_withErrors[0].Clone('hincmax')
for bin in range(1, nominal_withErrors[0].GetNbinsX() + 1):
hincmax.SetBinContent(bin,
incmax[bin - 1] / hincmax.GetBinContent(bin))
hincmax.SetBinError(bin, 0)
if (hincmax.GetBinContent(bin) > maxinctot):
maxinctot = hincmax.GetBinContent(bin)
hincsyst = copy.deepcopy(nominal.Clone('hincsyst'))
for bin in range(1, nominal_withErrors[0].GetNbinsX() + 1):
hincsyst.SetBinContent(
bin, incsyst[bin - 1] / hincsyst.GetBinContent(bin))
hincsyst.SetBinError(bin, 0.)
hincmax.SetLineColor(r.kBlack)
hincmax.SetLineWidth(2)
hincmax.SetFillColorAlpha(r.kBlue, 0)
hincsyst.SetLineColor(r.kBlack)
hincsyst.SetLineWidth(2)
hincsyst.SetLineStyle(3)
hincsyst.SetFillColorAlpha(r.kBlue, 0.)
#if (maxinctot >= 0.9):
#if maxinctot >= 5:
#uncList[0][1].GetYaxis().SetRangeUser(0, 5)
#else:
#uncList[0][1].GetYaxis().SetRangeUser(0, maxinctot + 0.1)
#else:
#uncList[0][1].GetYaxis().SetRangeUser(0, 0.9)
yaxismax_unc = 2
if "yaxismax_unf" in vl.varList[self.var]:
yaxismax_unc = vl.varList[self.var]["yaxismax_unf"]
hincmax.GetYaxis().SetRangeUser(0, yaxismax_unc)
plot2.addHisto(hincmax, 'hist', 'Total', 'L')
plot2.addHisto(hincsyst, 'hist,same', 'Systematic', 'L')
actualindex = 0
isstat = False
for i in range(vl.nuncs):
#if "Stat" in uncListorig[i][0]:
#uncListorig[actualindex][1].SetLineColor(r.kBlack)
#uncListorig[actualindex][1].SetLineStyle( 2 )
#if "Stat" in uncListorig[actualindex][0]:
#actualindex += 1
uncListorig[actualindex][1].SetLineColor(
vl.NewColorMap[uncListorig[actualindex][0]])
uncListorig[actualindex][1].SetLineWidth(2)
if "Lumi" in uncListorig[actualindex][0]:
uncListorig[actualindex][1].SetLineColor(r.kBlack)
uncListorig[actualindex][1].SetLineStyle(4)
if "Stat" in uncListorig[actualindex][0]:
isstat = True
uncListorig[actualindex][1].SetLineColor(r.kBlack)
uncListorig[actualindex][1].SetLineStyle(2)
plot2.addHisto(
uncListorig[actualindex][1], 'hist,same',
vl.SysNameTranslator[uncListorig[actualindex][0]], 'L')
actualindex += 1
elif (not isstat and actualindex == vl.nuncs - 1):
isstat = True
for j in range(len(uncListorig)):
if "Stat" in uncListorig[j][0]:
uncListorig[j][1].SetLineColor(r.kBlack)
uncListorig[j][1].SetLineStyle(2)
plot2.addHisto(uncListorig[j][1], 'hist,same',
vl.SysNameTranslator[uncListorig[j][0]],
'L')
else:
plot2.addHisto(
uncListorig[actualindex][1], 'H,same',
vl.SysNameTranslator[uncListorig[actualindex][0]], 'L')
actualindex += 1
#for i in range(len(uncListorig)):
#if "Stat" in uncListorig[i][0]:
#uncListorig[i][1].SetLineColor(r.kBlack)
#uncListorig[i][1].SetLineStyle( 2 )
#plot2.addHisto(uncListorig[i][1], 'hist,same', 'Statistical', 'L')
plot2.plotspath = self.plotspath
if "uncleg_unf" in vl.varList[self.var] and not self.wearedoingasimov:
unclegpos = vl.varList[self.var]["uncleg_unf"]
elif "uncleg_unfas" in vl.varList[self.var]:
unclegpos = vl.varList[self.var]["uncleg_unfas"]
else:
unclegpos = "TR"
plot2.saveCanvas(unclegpos)
del plot2
return
nominal = copy.deepcopy(dirfinal[varName])
allHistos = {key.replace(varName + "_", ""): dirfinal[key] for key in dirfinal}
del allHistos[varName]
savetfile = r.TFile("temp/{var}_/fidOutput_{var}.root".format(var=varName),
"recreate")
nominal.Write()
for key in allHistos:
allHistos[key].Write()
savetfile.Close()
del savetfile
nominal_withErrors = ep.propagateHistoAsym(nominal,
allHistos,
True,
False,
doSym=vl.doSym)
plot = bp.beautifulUnfoldingPlots(varName + "_fiducial")
plot.doRatio = True
plot.doFit = False
plot.plotspath = 'results/'
plot.doPreliminary = vl.doPre
nominal.SetMarkerStyle(r.kFullCircle)
nominal.SetLineColor(r.kBlack)
nominal_withErrors[0].SetFillColorAlpha(r.kBlue, 0.35)
nominal_withErrors[0].SetLineColor(0)
nominal_withErrors[0].SetFillStyle(1001)
savetfile2 = r.TFile("temp/{var}_/fidOutput_{var}.root".format(var=varName),
errors = copy.deepcopy(fitinfo.Get('hFitResult_forPlotting_%s_' % varName))
allHistos = {}
for nuis in [i for i in vl.systMap
] + ['pdfUp', 'pdfDown', 'ttbarMEUp', 'ttbarMEDown']:
if 'hFitResult_%s_%s' % (varName, nuis) not in listofkeys:
raise RuntimeError('Variations of the variable ' + varName +
' with the systematic ' + nuis + ' were not found.')
allHistos[nuis] = copy.deepcopy(
fitinfo.Get('hFitResult_%s_%s' % (varName, nuis)))
fitinfo.Close()
for bin in range(1, errors.GetNbinsX() + 1):
nominal.SetBinError(bin, errors.GetBinContent(bin))
nominal_withErrors = ep.propagateHistoAsym(nominal, allHistos, True)
plot = bp.beautifulUnfoldingPlots(varName + '_folded')
plot.doRatio = True
plot.plotspath = "results/"
nominal.SetMarkerStyle(r.kFullCircle)
nominal_withErrors[0].SetFillColorAlpha(r.kBlue, 0.35)
nominal_withErrors[0].SetLineColor(r.kBlue)
nominal_withErrors[0].SetFillStyle(1001)
if doSanityCheck:
print '> Adding generated distribution with used software and others.'
if not os.path.isfile(
'temp/{var}_/ClosureTest_recobinning_{var}.root'.format(
var=varName)):
raise RuntimeError(
def doUnfoldingForAllNuis(self, asym=False):
self.prepareAllHelpers()
allHistos = {}
nominal = copy.deepcopy(self.helpers[''].tunfolder.GetOutput(self.var))
for nuis in self.sysList:
self.helpers[nuis].tunfolder.DoUnfold(
self.helpers[''].tunfolder.GetTau())
allHistos[nuis] = self.helpers[nuis].tunfolder.GetOutput(self.var +
'_' +
nuis)
if asym:
nominal_withErrors = errorPropagator.propagateHistoAsym(
nominal, allHistos, self.doColorEnvelope)
else:
nominal_withErrors = errorPropagator.propagateHisto(
nominal, allHistos, self.doColorEnvelope)
plot = beautifulUnfoldingPlots.beautifulUnfoldingPlots(self.var)
plot.plotspath = self.plotspath
nominal.SetMarkerStyle(r.kFullCircle)
nominal.GetXaxis().SetNdivisions(505, True)
if asym:
nominal_withErrors[0].SetFillColorAlpha(r.kBlue, 0.35)
nominal_withErrors[0].SetLineColor(r.kBlue)
nominal_withErrors[0].SetFillStyle(1001)
else:
nominal_withErrors.SetFillColorAlpha(r.kBlue, 0.35)
nominal_withErrors.SetLineColor(r.kBlue)
nominal_withErrors.SetFillStyle(1001)
if self.doSanityCheck:
#tmptfile = r.TFile.Open('/nfs/fanae/user/sscruz/TW_differential/AnalysisPAF/plotter/./Datacards/closuretest_TGenLeadingJet_TGen{var}.root'.format(var=self.var))
if not os.path.isfile(
'temp/ClosureTest_{var}.root'.format(var=self.var)):
raise RuntimeError(
'The rootfile with the generated information does not exist'
)
tmptfile = r.TFile.Open(
'temp/ClosureTest_{var}.root'.format(var=self.var))
tru = copy.deepcopy(tmptfile.Get('tW'))
tru.SetLineWidth(2)
tru.SetLineColor(beautifulUnfoldingPlots.colorMap[0])
if asym:
plot.addHisto(nominal_withErrors, 'hist', 'Syst. unc.', 'F')
else:
plot.addHisto(nominal_withErrors, 'E2', 'Syst. unc.', 'F')
plot.addHisto(nominal, 'P,same', 'Pseudodata', 'P')
plot.addHisto(tru, 'L,same', 'Truth', 'L')
plot.saveCanvas('TR')
tmptfile.Close()
else:
if asym:
plot.addHisto(nominal_withErrors, 'hist', 'Syst. unc.', 'F')
else:
plot.addHisto(nominal_withErrors, 'E2', 'Syst. unc.', 'F')
plot.addHisto(nominal, 'P,same', 'Pseudodata', 'P')
plot.saveCanvas('TR')
plot2 = beautifulUnfoldingPlots.beautifulUnfoldingPlots(
self.var + 'uncertainties')
uncList = errorPropagator.getUncList(nominal, allHistos,
self.doColorEnvelope)[:5]
uncList[0][1].Draw()
if uncList[0][1].GetMaximum() < 0.5:
uncList[0][1].GetYaxis().SetRangeUser(0, 0.5)
else:
uncList[0][1].GetYaxis().SetRangeUser(0, 0.75)
for i in range(5):
uncList[i][1].SetLineColor(beautifulUnfoldingPlots.colorMap[i])
uncList[i][1].SetLineWidth(2)
plot2.addHisto(uncList[i][1], 'H,same' if i else 'H',
uncList[i][0], 'L')
plot2.plotspath = self.plotspath
plot2.saveCanvas('TR')