def writeFullUnc(pred_file):
''' Update the input root file, add a hist with total prediction and full uncertainty. '''
f = rt.TFile(pred_file, 'UPDATE')
h = TGraphAsymmErrors(f.Get(pred_total_name).Clone('bkgtotal_unc_sr'))
h_syst = TGraphAsymmErrors(
f.Get(pred_total_name).Clone('bkgtotal_syst_unc_sr'))
h_pieces = {}
h_syst_pieces = {}
for hname, sample in zip(graph_names, all_samples):
h_pieces[sample] = TGraphAsymmErrors(
f.Get(hname).Clone(sample + '_unc_sr'))
h_syst_pieces[sample + "_up"] = TH1F(sample + '_syst_up',
sample + '_syst_up', 183, 0, 183)
h_syst_pieces[sample + "_dn"] = TH1F(sample + '_syst_dn',
sample + '_syst_dn', 183, 0, 183)
print "%30s %10s %16s" % ('bin', 'total pred', 'total unc.')
for ibin in xrange(0, h.GetN()):
bin = binlist[ibin]
val = h.GetY()[ibin]
e_low, e_up = fullUnc[bin]
h.SetPointEYlow(ibin, e_low)
h.SetPointEYhigh(ibin, e_up)
print "%30s %10.4f +%8.4f -%8.4f" % (bin, val, e_up, e_low)
allVals[bin] = {'bkg': (val, e_low, e_up)}
# Test for only syst histograms
val = h_syst.GetY()[ibin]
e_low, e_up = systUnc[bin]
h_syst.SetPointEYlow(ibin, e_low)
h_syst.SetPointEYhigh(ibin, e_up)
for sample in all_samples:
val = yields[bin][sample]
e_low, e_up = fullUnc_pieces[sample][bin]
#print "%11s %30s %10.4f +%8.4f -%8.4f" % (sample, bin, val, e_up, e_low)
h_pieces[sample].SetPointEYlow(ibin, e_low)
h_pieces[sample].SetPointEYhigh(ibin, e_up)
allVals[bin][sample] = (val, e_low, e_up)
# Test for only syst histograms
e_low, e_up = systUnc_rel_pieces[sample][bin]
#if sample in test_samp and bin in test_bin and type in test_type and debug:
#if 'TTZ' in sample and e_up > 8:
# print("%11s %30s %10.4f +%8.4f -%8.4f" % (sample, bin, val, e_up, e_low))
h_syst_pieces[sample + "_up"].SetBinContent(ibin + 1, e_up)
h_syst_pieces[sample + "_dn"].SetBinContent(ibin + 1, e_low)
h_syst_pieces[sample + "_up"].SetBinError(ibin + 1, 0)
h_syst_pieces[sample + "_dn"].SetBinError(ibin + 1, 0)
h.Write('bkgtotal_unc_sr', rt.TObject.kOverwrite)
h.Write('bkgtotal_syst_unc_sr', rt.TObject.kOverwrite)
for sample in all_samples:
h_pieces[sample].Write(sample + '_unc_sr', rt.TObject.kOverwrite)
h_syst_pieces[sample + "_up"].Write(sample + '_syst_up',
rt.TObject.kOverwrite)
h_syst_pieces[sample + "_dn"].Write(sample + '_syst_dn',
rt.TObject.kOverwrite)
f.Close()
def writeFullUnc(pred_file):
''' Update the input root file, add a hist with total prediction and full uncertainty. '''
f = rt.TFile(pred_file, 'UPDATE')
h = TGraphAsymmErrors(f.Get(pred_total_name).Clone('bkgtotal_unc_sr'))
h_pieces = {}
for hname, sample in zip(graph_names, all_samples):
h_pieces[sample] = TGraphAsymmErrors(
f.Get(hname).Clone(sample + '_unc_sr'))
print "%30s %10s %16s" % ('bin', 'total pred', 'total unc.')
for ibin in xrange(0, h.GetN()):
bin = binlist[ibin]
val = h.GetY()[ibin]
e_low, e_up = fullUnc[bin]
h.SetPointEYlow(ibin, e_low)
h.SetPointEYhigh(ibin, e_up)
print "%30s %10.2f +%8.2f -%8.2f" % (bin, val, e_up, e_low)
allVals[bin] = {'bkg': (val, e_low, e_up)}
for sample in all_samples:
val = yields[bin][sample]
e_low, e_up = fullUnc_pieces[sample][bin]
h_pieces[sample].SetPointEYlow(ibin, e_low)
h_pieces[sample].SetPointEYhigh(ibin, e_up)
allVals[bin][sample] = (val, e_low, e_up)
h.Write('bkgtotal_unc_sr', rt.TObject.kOverwrite)
for sample in all_samples:
h_pieces[sample].Write(sample + '_unc_sr', rt.TObject.kOverwrite)
f.Close()
def obsOverPredWithDataStat(data_hist, bkg_total):
obs_over_pred = TGraphAsymmErrors(data_hist)
for b in range(1, data_hist.GetNbinsX() + 1):
#divide data by background
bkg_bin = bkg_total.GetBinContent(b)
if abs(bkg_bin) > 1e-8:
obs_over_pred.GetY()[b - 1] /= bkg_bin
obs_over_pred.SetPointError(b - 1, 0., 0.,
data_hist.GetBinErrorLow(b) / bkg_bin,
data_hist.GetBinErrorUp(b) / bkg_bin)
else:
obs_over_pred.GetY()[b - 1] = 0
obs_over_pred.SetPointError(b - 1, 0., 0., 0, 0)
return obs_over_pred
def calculate(self, histo):
from ROOT import TGraphAsymmErrors
frac = self.__getWeightOneHisto(histo, self.__name)
total = self.__getWeightOneHisto( histo, self.__norm)
if(frac.GetEntries() > total.GetEntries()):
raise StandardError," comparing '%s' to '%s' in '%s' makes no sense eff > 1!"%(self.__name, self.__norm, histo.GetName())
eff = TGraphAsymmErrors(1)
eff.BayesDivide(frac, total)
if eff.GetN() < 1:
raise StandardError,"Efficiency cannot be calculated '%s' in '%s'"%(self.__name, histo.GetName())
return ( eff.GetY()[0], (eff.GetEYlow()[0],eff.GetEYhigh()[0]) )
def obsOverPredWithDataStat(data_hist, bkg_total):
obs_over_pred = TGraphAsymmErrors(data_hist)
for b in range(1, data_hist.GetNbinsX() + 1):
#divide data by background
bkg_bin = bkg_total.GetBinContent(b)
if abs(bkg_bin) > 1e-8:
obs_over_pred.GetY()[b - 1] /= bkg_bin
obs_over_pred.SetPointError(b - 1, 0., 0.,
data_hist.GetBinErrorLow(b) / bkg_bin,
data_hist.GetBinErrorUp(b) / bkg_bin)
else:
obs_over_pred.GetY()[b - 1] = 0
obs_over_pred.SetPointError(b - 1, 0., 0., 0, 0)
#in case we plot just the expected distribution data will be 0
#in this case we don't want to plot the data points in the ratio
if data_hist.GetSumOfWeights() <= 0:
for b in range(1, data_hist.GetNbinsX() + 1):
obs_over_pred.GetY()[b - 1] += 1e30
return obs_over_pred
def fixData(hist, useGarwood=True, cutGrass=False, maxPoisson=False):
if hist == None: return
varBins = False
data = TGraphAsymmErrors()
alpha = 1 - 0.6827
for i in list(reversed(range(0, hist.GetNbinsX()))):
#print "bin", i, "x:", hist.GetX()[i], "y:", hist.GetY()[i]
# X error bars to 0 - do not move this, otherwise the first bin will disappear, thanks Wouter and Rene!
N = max(hist.GetBinContent(i + 1), 0.) # Avoid unphysical bins
data.SetPoint(i, hist.GetXaxis().GetBinCenter(i + 1), N)
if not varBins:
data.SetPointEXlow(i, 0)
data.SetPointEXhigh(i, 0)
# Garwood confidence intervals
if (useGarwood):
L = ROOT.Math.gamma_quantile(alpha / 2, N, 1.) if N > 0 else 0.
U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1)
# maximum between Poisson and Sumw2 error bars
EL = N - L if not maxPoisson else max(N -
L, hist.GetBinErrorLow(i))
EU = U - N if not maxPoisson else max(U -
N, hist.GetBinErrorHigh(i))
data.SetPointEYlow(i, EL)
data.SetPointEYhigh(i, EU)
else:
data.SetPointEYlow(i, math.sqrt(N))
data.SetPointEYhigh(i, math.sqrt(N))
# Cut grass
if cutGrass and data.GetY()[i] > 0.: cutGrass = False
# Treatment for 0 bins
# if abs(hist.GetY()[i])<=1.e-6:
# if cutGrass: hist.SetPointError(i, hist.GetErrorXlow(i), hist.GetErrorXhigh(i), 1.e-6, 1.e-6, )
# if (hist.GetX()[i]>65 and hist.GetX()[i]<135 and hist.GetY()[i]==0): hist.SetPointError(i, hist.GetErrorXlow(i), hist.GetErrorXhigh(i), 1.e-6, 1.e-6, )
# hist.SetPoint(i, hist.GetX()[i], -1.e-4)
# X error bars
#if hist.GetErrorXlow(i)<1.e-4:
# binwidth = hist.GetX()[1]-hist.GetX()[0]
# hist.SetPointEXlow(i, binwidth/2.)
# hist.SetPointEXhigh(i, binwidth/2.)
data.SetMarkerColor(hist.GetMarkerColor())
data.SetMarkerStyle(hist.GetMarkerStyle())
data.SetMarkerSize(hist.GetMarkerSize())
#data.SetLineSize(hist.GetLineSize())
return data
def prepareData(data):
#set poison errors
data.SetBinErrorOption(ROOT.TH1.kPoisson)
#build TGraphAsymmErrors for plotting
data_graph = TGraphAsymmErrors(data)
for b in range(1, data.GetNbinsX() + 1):
bin_content = data.GetBinContent(b)
data_graph.SetPointError(
b - 1, 0, 0,
data.GetBinErrorLow(b) if bin_content >= 0. else 0.,
data.GetBinErrorUp(b) if bin_content >= 0. else 0.)
#hack for not displaying points at zero
maximum = (data.GetBinContent(data.GetMaximumBin()) +
data.GetBinErrorUp(data.GetMaximumBin()))
for b in range(1, data.GetNbinsX() + 1):
if data.GetBinContent(b) < 1e-8:
data_graph.GetY()[b - 1] += (maximum * 1e8)
return data, data_graph
def plotDataOverMCEff(hist_mc_tight,
hist_mc_loose,
hist_data_tight,
hist_data_loose,
plot_name='fakerate.pdf'):
g = TGraphAsymmErrors(hist_mc_tight)
g.Divide(hist_mc_tight, hist_mc_loose)
g.GetYaxis().SetTitle('Fake rate')
g.GetXaxis().SetTitle(hist_mc_tight.GetXaxis().GetTitle())
g.GetYaxis().SetTitleOffset(1.2)
g.GetYaxis().SetTitleOffset(1.3)
g.SetLineColor(2)
g.SetMarkerColor(2)
g_data = TGraphAsymmErrors(hist_data_tight)
g_data.Divide(hist_data_tight, hist_data_loose)
g_data.GetYaxis().SetTitle('Fake rate')
g_data.GetXaxis().SetTitle(hist_data_tight.GetXaxis().GetTitle())
g_data.GetYaxis().SetTitleOffset(1.2)
g_data.GetYaxis().SetTitleOffset(1.3)
g_data.SetMarkerColor(1)
g_vals = g.GetY()
g_data_vals = g_data.GetY()
g_ratio = g_data.Clone('ratio')
for i in xrange(g_data.GetN()):
ratio = g_data_vals[i] / g_vals[i] if g_vals[i] else 0.
g_ratio.SetPoint(i, g.GetX()[i], ratio)
rel_y_low = math.sqrt((g_data.GetErrorYlow(i) / g_data_vals[i])**2 + (
g.GetErrorYlow(i) /
g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYlow(i, rel_y_low * ratio)
rel_y_high = math.sqrt(
(g_data.GetErrorYhigh(i) / g_data_vals[i])**2 +
(g.GetErrorYhigh(i) /
g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYhigh(i, rel_y_high * ratio)
# Gymnastics to get same label sizes etc in ratio and main plot
ytp_ratio = 2.
xtp_ratio = 2.
# hr.GetYaxis().SetNdivisions(4)
g_ratio.GetYaxis().SetTitleSize(g.GetYaxis().GetTitleSize() * xtp_ratio)
g_ratio.GetXaxis().SetTitleSize(g.GetXaxis().GetTitleSize() * ytp_ratio)
g_ratio.GetYaxis().SetTitleOffset(g.GetYaxis().GetTitleOffset() /
xtp_ratio)
g_ratio.GetXaxis().SetTitleOffset(
g.GetXaxis().GetTitleOffset()) # / ytp_ratio)
g_ratio.GetYaxis().SetLabelSize(g.GetYaxis().GetLabelSize() * xtp_ratio)
g_ratio.GetXaxis().SetLabelSize(g.GetXaxis().GetLabelSize() * ytp_ratio)
g_data.GetXaxis().SetLabelColor(0)
g_data.GetXaxis().SetLabelSize(0)
g.GetXaxis().SetLabelColor(0)
g.GetXaxis().SetLabelSize(0)
g_ratio.GetXaxis().SetTitle(g.GetXaxis().GetTitle())
# maxy = 1.1 * min(g.GetMaximum(), g_data.GetMaximum(), 0.2)
g.GetYaxis().SetRangeUser(0.001, 0.2)
cv, pad, padr = HistDrawer.buildCanvas()
pad.cd()
g.Draw('AP')
g_data.Draw('P')
legend = TLegend(0.23, 0.73, 0.43, 0.91)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetLineColor(0)
legend.SetLineWidth(0)
legend.AddEntry(g.GetName(), 'MC', 'lep')
legend.AddEntry(g_data.GetName(), 'Observed', 'lep')
legend.Draw()
padr.cd()
g_ratio.GetYaxis().SetRangeUser(0.51, 1.49)
g_ratio.GetYaxis().SetTitle('Obs/MC')
g_ratio.Draw('AP')
drawRatioLines(g_ratio)
cv.Print(plot_name)
',', "_").replace(' ', "") + "_rebin1_" + u + "Down"
print histname2
histname3 = 'QCD#chi' + str(massbins[massbin]).strip("()").replace(
',', "_").replace(' ', "") + "_rebin1"
print histname3
up = fsys.Get(histname1)
down = fsys.Get(histname2)
central = fsys.Get(histname3)
uncertainties += [[up, down, central]]
h2new = h14.Clone("down" + str(massbins[massbin]))
h3new = h14.Clone("up" + str(massbins[massbin]))
chi2 = 0
for b in range(h14.GetXaxis().GetNbins()):
if b == 0: # or b==h14G.GetXaxis().GetNbins()-1:
print massbins[massbin], b, "stat", h14G.GetErrorYlow(
b) / h14G.GetY()[b], h14G.GetErrorYhigh(b) / h14G.GetY()[b]
exp_sumdown = 0
exp_sumup = 0
theory_sumdown = 0
theory_sumup = 0
for up, down, central in uncertainties:
if b == 0: # or b==h14G.GetXaxis().GetNbins()-1:
print massbins[massbin], b, uncertaintynames[
uncertainties.index([up, down, central])], abs(
up.GetBinContent(b + 1) - central.GetBinContent(
b + 1)) / central.GetBinContent(b + 1), abs(
down.GetBinContent(b + 1) -
central.GetBinContent(b + 1)
) / central.GetBinContent(b + 1)
addup = pow(
max(
def plotDataOverMCEff(hist_mc_tight, hist_mc_loose, hist_data_tight, hist_data_loose, plot_name='fakerate.pdf', mc_leg='MC', obs_leg='Observed', ratio_leg='Obs/MC'):
g = TGraphAsymmErrors(hist_mc_tight)
g.Divide(hist_mc_tight, hist_mc_loose)
g.GetYaxis().SetTitle('Misidentification rate')
g.GetXaxis().SetTitle(hist_mc_tight.GetXaxis().GetTitle())
g.GetYaxis().SetTitleOffset(1.2)
g.GetYaxis().SetTitleOffset(1.3)
g.SetLineColor(2)
g.SetMarkerColor(2)
g_data = TGraphAsymmErrors(hist_data_tight)
g_data.Divide(hist_data_tight, hist_data_loose)
# if g_data.GetN() != hist_data_tight.GetNbinsX():
# import pdb; pdb.set_trace()
g_data.GetYaxis().SetTitle('Misidentification rate')
g_data.GetXaxis().SetTitle(hist_data_tight.GetXaxis().GetTitle())
g_data.GetYaxis().SetTitleOffset(1.2)
g_data.GetYaxis().SetTitleOffset(1.3)
g_data.SetMarkerColor(1)
g_vals = g.GetY()
g_data_vals = g_data.GetY()
g_ratio = g_data.Clone('ratio')
for i in xrange(g_data.GetN()):
ratio = g_data_vals[i]/g_vals[i] if g_vals[i] else 0.
g_ratio.SetPoint(i, g.GetX()[i], ratio)
rel_y_low = math.sqrt((g_data.GetErrorYlow(i)/g_data_vals[i])**2 + (g.GetErrorYlow(i)/g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYlow(i, rel_y_low * ratio)
rel_y_high = math.sqrt((g_data.GetErrorYhigh(i)/g_data_vals[i])**2 + (g.GetErrorYhigh(i)/g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYhigh(i, rel_y_high * ratio)
# Gymnastics to get same label sizes etc in ratio and main plot
ytp_ratio = 2.
xtp_ratio = 2.
# hr.GetYaxis().SetNdivisions(4)
g_ratio.GetYaxis().SetTitleSize(g.GetYaxis().GetTitleSize() * xtp_ratio)
g_ratio.GetXaxis().SetTitleSize(g.GetXaxis().GetTitleSize() * ytp_ratio)
g_ratio.GetYaxis().SetTitleOffset(g.GetYaxis().GetTitleOffset() / xtp_ratio)
g_ratio.GetXaxis().SetTitleOffset(g.GetXaxis().GetTitleOffset()) # / ytp_ratio)
g_ratio.GetYaxis().SetLabelSize(g.GetYaxis().GetLabelSize() * xtp_ratio)
g_ratio.GetXaxis().SetLabelSize(g.GetXaxis().GetLabelSize() * ytp_ratio)
g_data.GetXaxis().SetLabelColor(0)
g_data.GetXaxis().SetLabelSize(0)
g.GetXaxis().SetLabelColor(0)
g.GetXaxis().SetLabelSize(0)
g_ratio.GetXaxis().SetTitle(g.GetXaxis().GetTitle())
maxy = 1.3 * max(g.GetMaximum(), g_data.GetMaximum(), 0.05)
g.GetYaxis().SetRangeUser(0.0011, maxy)
cv, pad, padr = HistDrawer.buildCanvas()
pad.cd()
g.Draw('AP')
g_data.Draw('P')
legend = TLegend(0.23, 0.73, 0.43, 0.91)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetLineColor(0)
legend.SetLineWidth(0)
legend.AddEntry(g.GetName(), mc_leg, 'lep')
legend.AddEntry(g_data.GetName(), obs_leg, 'lep')
legend.Draw()
padr.cd()
g_ratio.GetYaxis().SetRangeUser(0.01, 1.99)
g_ratio.GetYaxis().SetTitle(ratio_leg)
g_ratio.Draw('AP')
drawRatioLines(g_ratio)
cv.Print(plot_name)
g.GetYaxis().SetRangeUser(0.0001, 1)
pad.SetLogy(True)
cv.Print(plot_name.replace('.', '_log.'))
f = ROOT.TFile(plot_name.replace('.', '_log.').replace('.pdf', '.root'), 'RECREATE')
g.Write()
g_data.Write()
cv.Write()
f.Close()
def fillAcceptance(self):
'''
This is to calculate the acceptance of the sample
'''
if debug: print('Info in Sample.fillAcceptance()')
#f = TFile.Open(self.infileName)
#t = f.Get(self.treeName)
#if not t:
# raise RuntimeError( 'ERROR: no tree in file %s' % self.infileName)
chain = TChain(self.treeName)
for fn in self.infileNames:
chain.Add(fn)
self.effnum = ROOT.TH1F('effnum', 'effnum', 1, 0, 13000)
self.effden = ROOT.TH1F('effden', 'effden', 1, 0, 13000)
self.effnumB = dict([(b, ROOT.TH1F('effnum_%s' % b, 'effnum_%s' % b, 1, 0, 13000)) for b in self.Bspecies])
self.effdenB = dict([(b, ROOT.TH1F('effden_%s' % b, 'effden_%s' % b, 1, 0, 13000)) for b in self.Bspecies])
#if doInclusive:
cutsnum = '(l0_pt>{mp} && abs(l0_eta)<1.5'.format(mp=muTrigPt)
#else:
#cutsnum = '(l0_pt>{mp} && abs(l0_eta)<1.5 && k_pt>1 && abs(k_eta)<2.5 && pi_pt>1 && abs(pi_eta)<2.5'.format(mp=muTrigPt)
if doSkipDispl:
cutsnum += ''
else:
cutsnum += '&& Lxy < 500'
if not doDisplZ:
pass
else:
cutsnum += '&& Lz < 20'
if doSkipHNLptEta:
cutsnum += ''
else:
cutsnum += '&& l1_pt>3 && abs(l1_eta)<2.5 && pi1_pt>0.8 && abs(pi1_eta)<2.5'
cutsnum += ')'
cutsden = '(l0_pt>{mp} && abs(l0_eta)<1.5)'.format(mp=muTrigPt)
###### fill the total acceptance
chain.Draw('hnl_pt>>effnum', cutsnum+'*'+self.evt_w, 'goff')
chain.Draw('hnl_pt>>effden', cutsden, 'goff')
if TEfficiency.CheckConsistency(self.effnum,self.effden):
peff = TEfficiency(self.effnum,self.effden)
# check usage of TGraphAsymmetricErrors
self.acc = peff.GetEfficiency(1)
self.acc_errup = peff.GetEfficiencyErrorUp(1)
self.acc_errdn = peff.GetEfficiencyErrorLow(1)
tgra = TGraphAsymmErrors()
tgra.BayesDivide(self.effnum, self.effden)
self.acc_tg = tgra.GetY()[0]
self.acc_errup_tg = tgra.GetErrorYhigh(0)
self.acc_errdn_tg = tgra.GetErrorYlow(0)
# for debugging purposes
self.num = self.effnum.GetEntries()
if self.num==0: print('**** 0 entries for mass={}'.format(self.mass))
self.den = self.effden.GetEntries()
###### fill the partial acceptances
for ib,b in enumerate(self.Bspecies):
bsel = '(abs(b_pdgid)=={bid})'.format(bid=self.BpdgIds[ib])
selnum = '(' + cutsnum + '&&' + bsel + ')'
selden = '(' + cutsden + '&&' + bsel + ')'
chain.Draw('hnl_pt>>effnum_{b}'.format(b=b), selnum + '*' + self.evt_w, 'goff')
chain.Draw('hnl_pt>>effden_{b}'.format(b=b), selden, 'goff')
if TEfficiency.CheckConsistency(self.effnumB[b],self.effdenB[b]):
peff = TEfficiency(self.effnumB[b],self.effdenB[b])
self.accB[b] = peff.GetEfficiency(1)
self.accB_errup[b] = peff.GetEfficiencyErrorUp(1)
self.accB_errdn[b] = peff.GetEfficiencyErrorLow(1)
else:
self.accB[b] = 0
self.accB_errup[b] = 0
self.accB_errdn[b] = 0
