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 numericalInvertPlot(graph):
#pick up point by point pl
#loop on all data points
nPoints = graph.GetN()
newGraph = TGraphAsymmErrors(nPoints)
#x=array('f')
#y=array('f')
#ey=array('f')
for iPoint in xrange(0, nPoints):
#get info on data point
dataPointX = Double(0)
dataPointY = Double(0)
graph.GetPoint(iPoint, dataPointX, dataPointY)
dataErrorX = graph.GetErrorX(iPoint)
dataErrorY = graph.GetErrorY(iPoint)
#numerical-invert the X
newDataPointX = dataPointX * dataPointY
newErrorXLeft = fabs(newDataPointX - (dataPointX - dataErrorX))
newErrorXRight = fabs(newDataPointX - (dataPointX + dataErrorX))
#setting the new graph (with asymm errors this time)
newGraph.SetPoint(iPoint, newDataPointX, dataPointY)
newGraph.SetPointEXhigh(iPoint, newErrorXRight)
newGraph.SetPointEXlow(iPoint, newErrorXLeft)
newGraph.SetPointEYhigh(iPoint, dataErrorY)
newGraph.SetPointEYlow(iPoint, dataErrorY)
print dataPointY, newDataPointX
#check that the numerical-inverted X is still in the previous bin - otherwise, ERROR!
binRangeLow = dataPointX - dataErrorX
binRangeHigh = dataPointX + dataErrorX
if newDataPointX < binRangeLow or newDataPointX > binRangeHigh:
print "Warning! Data point should not be here!"
print "Old data point: ", dataPointX
print "New NI data point:", newDataPointX
print "XLow: ", binRangeLow
print "XHigh: ", binRangeHigh
newGraph.SetPoint(iPoint, dataPointX, 0.0000000000001)
newGraph.SetPointEXhigh(iPoint, dataErrorX)
newGraph.SetPointEXlow(iPoint, dataErrorX)
newGraph.SetPointEYhigh(iPoint, dataErrorY)
newGraph.SetPointEYlow(iPoint, dataErrorY)
return newGraph
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 convertToPoisson(h):
graph = TGraphAsymmErrors()
q = (1-0.6827)/2.
for i in range(1,h.GetNbinsX()+1):
x=h.GetXaxis().GetBinCenter(i)
xLow =h.GetXaxis().GetBinLowEdge(i)
xHigh =h.GetXaxis().GetBinUpEdge(i)
y=h.GetBinContent(i)
yLow=0
yHigh=0
if y !=0.0:
yLow = y-Math.chisquared_quantile_c(1-q,2*y)/2.
yHigh = Math.chisquared_quantile_c(q,2*(y+1))/2.-y
graph.SetPoint(i-1,x,y)
graph.SetPointEYlow(i-1,yLow)
graph.SetPointEYhigh(i-1,yHigh)
graph.SetPointEXlow(i-1,0.0)
graph.SetPointEXhigh(i-1,0.0)
graph.SetMarkerStyle(20)
graph.SetLineWidth(2)
graph.SetMarkerSize(1.)
graph.SetMarkerColor(kBlack)
return graph
def makeResidHist(data, bkg):
pulls = TGraphAsymmErrors(data.GetN())
pulls.SetName("Pulls")
pulls.SetLineWidth(data.GetLineWidth())
pulls.SetLineStyle(data.GetLineStyle())
pulls.SetLineColor(data.GetLineColor())
pulls.SetMarkerSize(data.GetMarkerSize())
pulls.SetMarkerStyle(data.GetMarkerStyle())
pulls.SetMarkerColor(data.GetMarkerColor())
pulls.SetFillStyle(data.GetFillStyle())
pulls.SetFillColor(data.GetFillColor())
# Add histograms, calculate Poisson confidence interval on sum value
for i in range(data.GetN()):
x = data.GetX()[i]
dyl = data.GetErrorYlow(i)
dyh = data.GetErrorYhigh(i)
yy = data.GetY()[i] - bkg.Interpolate(x) #bkg.GetBinContent(i+1)
norm = dyl if yy > 0. else dyh
if norm == 0.:
yy, dyh, dyl = 0., 0., 0.
else:
yy /= norm
dyh /= norm
dyl /= norm
pulls.SetPoint(i, x, yy)
pulls.SetPointEYhigh(i, dyh)
pulls.SetPointEYlow(i, dyl)
return pulls
def apply_centers(hPt, hCen):
#bin center points according to the data
cen = get_centers_from_toyMC(hCen)
gSig = TGraphAsymmErrors(hPt.GetNbinsX())
for i in xrange(hPt.GetNbinsX()):
#center point
#xcen = hPt.GetBinCenter(i+1)
xcen = cen[i]["val"]
#cross section value
gSig.SetPoint(i, xcen, hPt.GetBinContent(i + 1))
#vertical error
gSig.SetPointEYlow(i, hPt.GetBinErrorLow(i + 1))
gSig.SetPointEYhigh(i, hPt.GetBinErrorUp(i + 1))
#horizontal error
gSig.SetPointEXlow(i, cen[i]["err"])
gSig.SetPointEXhigh(i, cen[i]["err"])
return gSig
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 __init__( self, process_list, sf = None, df = None ):
self.__processes = process_list
if sf and df:
self.__sf = sf
self.__df = df
alpha = 1 - 0.6827
self.__sfCRGraph = TGraphAsymmErrors(self.__sf)
self.__dfCRGraph = TGraphAsymmErrors(self.__df)
for g in [self.__sfCRGraph, self.__dfCRGraph]:
for i in range(g.GetN()):
N = g.GetY()[i]
LowErr = 0 if N == 0 else Math.gamma_quantile(alpha/2, N, 1.)
UpErr = Math.gamma_quantile_c(alpha/2, N+1, 1.) # recommended by StatComm (1.8)
# Math.gamma_quantile_c(alpha, N+1, 1.) # gives 1.2, strictly 68% one-sided
g.SetPointEYlow(i, N-LowErr)
g.SetPointEYhigh(i, UpErr-N)
for p in self.__processes:
if p.name() in ['nonpromptSF', 'nonpromptDF']:
nom, low, high = [], [], []
for ib in range(p.nominal().GetXaxis().GetNbins()):
if p.name() in ['nonpromptDF']: hnp = self.__dfCRGraph
else: hnp = self.__sfCRGraph
npcr = hnp.GetY()[ib]
npcrErrLow = hnp.GetEYlow()[ib]
npcrErrHigh = hnp.GetEYhigh()[ib]
np = p.nominal().GetBinContent(ib+1)
npErr = p.nominal().GetBinError(ib+1)
alpha = np/npcr if npcr > 0 else npErr
nom.append(npcr*alpha)
low.append(npcrErrLow*alpha)
high.append(npcrErrHigh*alpha)
hnom = p.nominal().Clone()
for ib in range(hnom.GetXaxis().GetNbins()):
hnom.SetBinContent(ib+1, nom[ib])
npGraph = TGraphAsymmErrors(hnom)
for ib in range(hnom.GetXaxis().GetNbins()):
npGraph.SetPointEYlow(ib, low[ib])
npGraph.SetPointEYhigh(ib, high[ib])
if p.name() in ['nonpromptDF']:
self.__dfGraph = npGraph
else:
self.__sfGraph = npGraph
self.__uncertainties = set()
for p in self.__processes:
self.__uncertainties = self.__uncertainties.union( set( p.uncertaintySources() ) )
def normalizeGraph(graph, binwidth):
xsecSum = 0.
for pt in range(1, graph.GetN()):
xsecSum += graph.GetY()[pt] * binwidth[pt]
normGraph = TGraphAsymmErrors(graph.GetN() - 1)
for pt in range(0, normGraph.GetN()):
normGraph.SetPoint(pt,
graph.GetX()[pt + 1],
graph.GetY()[pt + 1] / xsecSum)
normGraph.SetPointEYhigh(pt, graph.GetErrorYhigh(pt + 1) / xsecSum)
normGraph.SetPointEYlow(pt, graph.GetErrorYlow(pt + 1) / xsecSum)
return normGraph
def histToGraph(hist, name='', keepErrors=True, poissonErrors=True):
## Helper method to convert a histogram to a corresponding graph
# @hist TH1 object
# @name name of the graph (default is name of histogram)
# @keepErrors decide if the y-errors should be propagated to the graph
# @poissonErrors decide if the y-errors should be calculated as Poisson errors
# @return graph
if not name:
name = 'g%s' % (hist.GetName())
from ROOT import TGraphAsymmErrors
nBins = hist.GetNbinsX()
graph = TGraphAsymmErrors(nBins)
graph.SetNameTitle(name, hist.GetTitle())
xAxis = hist.GetXaxis()
for i in xrange(nBins):
xVal = xAxis.GetBinCenter(i + 1)
yVal = hist.GetBinContent(i + 1)
graph.SetPoint(i, xVal, yVal)
graph.SetPointEXlow(i, abs(xVal - xAxis.GetBinLowEdge(i + 1)))
graph.SetPointEXhigh(i, abs(xVal - xAxis.GetBinUpEdge(i + 1)))
if keepErrors:
if poissonErrors:
lo, hi = calculatePoissonErrors(yVal)
graph.SetPointEYlow(i, lo)
graph.SetPointEYhigh(i, hi)
else:
graph.SetPointEYlow(i, hist.GetBinErrorLow(i + 1))
graph.SetPointEYhigh(i, hist.GetBinErrorUp(i + 1))
# copy the style
graph.SetMarkerStyle(hist.GetMarkerStyle())
graph.SetMarkerColor(hist.GetMarkerColor())
graph.SetMarkerSize(hist.GetMarkerSize())
graph.SetLineStyle(hist.GetLineStyle())
graph.SetLineColor(hist.GetLineColor())
graph.SetLineWidth(hist.GetLineWidth())
graph.SetFillColor(hist.GetFillColor())
graph.SetFillStyle(hist.GetFillStyle())
return graph
def diffGraph(self, g, h, opt):
gdiff = TGraphAsymmErrors(g)
gdiff.SetName(g.GetName() + h.GetName() + '_diff')
for i in range(0, gdiff.GetN()):
x = Double(0.)
y = Double(0.)
g.GetPoint(i, x, y)
gdiff.SetPoint(i, x, y - h.GetBinContent(i + 1))
if opt == 1: # keep bin errors
pass
elif opt == 2:
gdiff.SetPointEYhigh(
i,
math.sqrt(g.GetErrorYhigh(i)**2 + h.GetBinError(i + 1)**2))
return gdiff
def createTGraphPoisson(self, h, w=1.):
self.tfile.cd()
g = TGraphAsymmErrors(h)
for i in range(0, g.GetN()):
y = h.GetBinContent(i + 1)
binWidth = h.GetBinWidth(i + 1)
# From https://twiki.cern.ch/twiki/bin/view/CMS/PoissonErrorBars
alpha = 1. - 0.6827
n = int(
round(y * binWidth /
w)) # Round is necessary due to, well, rounding errors
l = Math.gamma_quantile(alpha / 2., n, 1.) if n != 0. else 0.
u = Math.gamma_quantile_c(alpha, n + 1, 1)
# print y*binWidth/w, n, y, u, l
g.SetPointEYlow(i, (n - l) / binWidth * w)
g.SetPointEYhigh(i, (u - n) / binWidth * w)
return g
def fixed_centers(hPt):
#fixed bin centers
gSig = TGraphAsymmErrors(hPt.GetNbinsX())
for i in xrange(hPt.GetNbinsX()):
#cross section value
gSig.SetPoint(i, hPt.GetBinCenter(i + 1), hPt.GetBinContent(i + 1))
#vertical error
gSig.SetPointEYlow(i, hPt.GetBinErrorLow(i + 1))
gSig.SetPointEYhigh(i, hPt.GetBinErrorUp(i + 1))
#horizontal error
gSig.SetPointEXlow(i, hPt.GetBinWidth(i + 1) / 2.)
gSig.SetPointEXhigh(i, hPt.GetBinWidth(i + 1) / 2.)
return gSig
def totalBkgWithStatErrors( self ):
total_bkg_hist = None
low, high = None, None
for p in self.__processes:
if p.isSignal():
continue
nbins = p.nominal().GetXaxis().GetNbins()
if low is None: low = [0]*nbins
if high is None: high = [0]*nbins
if total_bkg_hist is None:
total_bkg_hist = p.nominal().Clone()
else:
total_bkg_hist.Add( p.nominal() )
gr = None
if p.name() in ['nonpromptDF']: gr = self.__dfGraph
elif p.name() in ['nonpromptSF']: gr = self.__sfGraph
if gr:
for ib in range(nbins):
low[ib] += math.pow(gr.GetEYlow()[ib], 2)
high[ib] += math.pow(gr.GetEYhigh()[ib], 2)
else:
for ib in range(nbins):
low[ib] += math.pow(p.nominal().GetBinError(ib), 2)
high[ib] += math.pow(p.nominal().GetBinError(ib), 2)
for ib in range(len(low)):
low[ib] = math.sqrt(low[ib])
high[ib] = math.sqrt(high[ib])
total_bkg_graph = TGraphAsymmErrors(total_bkg_hist)
for ib in range(total_bkg_graph.GetN()):
total_bkg_graph.SetPointEYlow(ib, low[ib])
total_bkg_graph.SetPointEYhigh(ib, high[ib])
return [total_bkg_hist, total_bkg_graph]
alpha = 1. - 0.6827
nevents = 0
for b in range(h14G.GetN()):
if unfoldedData:
N = origh14.GetBinContent(b + 1)
else:
N = 1. / pow(
h14.GetBinError(b + 1) / h14.GetBinContent(b + 1), 2)
print N
nevents += N
L = 0
if N > 0:
L = ROOT.Math.gamma_quantile(alpha / 2., N, 1.)
U = ROOT.Math.gamma_quantile_c(alpha / 2., N + 1, 1.)
h14G.SetPointEYlow(b, (N - L) / N * h14.GetBinContent(b + 1))
h14G.SetPointEYhigh(b, (U - N) / N * h14.GetBinContent(b + 1))
print "data events:", nevents
h14Gsys = h14G.Clone(histname + "sys")
new_hists += [h14Gsys]
h14Gsysstat = h14G.Clone(histname + "sysstat")
new_hists += [h14Gsysstat]
filename = "datacard_shapelimit13TeV_QCD_chi2016_backup.root"
print filename
fsys = TFile.Open(filename)
new_hists += [fsys]
uncertaintynames = ["jer", "jes", "pdf", "scale"]
#uncertaintynames=["jer","jes","scale"]
uncertainties = []
for u in uncertaintynames:
def make_plots_ROOT(histograms, category, save_path, histname, channel):
global variable, translateOptions, k_value, b_tag_bin, maximum
ROOT.TH1.SetDefaultSumw2(False)
ROOT.gROOT.SetBatch(True)
ROOT.gROOT.ProcessLine('gErrorIgnoreLevel = 1001;')
plotting.setStyle()
gStyle.SetTitleYOffset(2.)
ROOT.gROOT.ForceStyle()
canvas = Canvas(width=700, height=500)
canvas.SetLeftMargin(0.18)
canvas.SetBottomMargin(0.15)
canvas.SetTopMargin(0.05)
canvas.SetRightMargin(0.05)
legend = plotting.create_legend(x0=0.6, y1=0.5)
hist_data = histograms['unfolded']
hist_data.GetXaxis().SetTitle(translate_options[variable] + ' [GeV]')
hist_data.GetYaxis().SetTitle('#frac{1}{#sigma} #frac{d#sigma}{d' +
translate_options[variable] + '} [GeV^{-1}]')
hist_data.GetXaxis().SetTitleSize(0.05)
hist_data.GetYaxis().SetTitleSize(0.05)
hist_data.SetMinimum(0)
hist_data.SetMaximum(maximum[variable])
hist_data.SetMarkerSize(1)
hist_data.SetMarkerStyle(8)
plotAsym = TGraphAsymmErrors(hist_data)
plotStatErr = TGraphAsymmErrors(hist_data)
xsections = read_unfolded_xsections(channel)
bins = variable_bins_ROOT[variable]
assert (len(bins) == len(xsections['central']))
for bin_i in range(len(bins)):
scale = 1 # / width
centralresult = xsections['central'][bin_i]
fit_error = centralresult[1]
uncertainty = calculateTotalUncertainty(xsections, bin_i)
uncertainty_total_plus = uncertainty['Total+'][0]
uncertainty_total_minus = uncertainty['Total-'][0]
uncertainty_total_plus, uncertainty_total_minus = symmetriseErrors(
uncertainty_total_plus, uncertainty_total_minus)
error_up = sqrt(fit_error**2 + uncertainty_total_plus**2) * scale
error_down = sqrt(fit_error**2 + uncertainty_total_minus**2) * scale
plotStatErr.SetPointEYhigh(bin_i, fit_error * scale)
plotStatErr.SetPointEYlow(bin_i, fit_error * scale)
plotAsym.SetPointEYhigh(bin_i, error_up)
plotAsym.SetPointEYlow(bin_i, error_down)
gStyle.SetEndErrorSize(20)
plotAsym.SetLineWidth(2)
plotStatErr.SetLineWidth(2)
hist_data.Draw('P')
plotStatErr.Draw('same P')
plotAsym.Draw('same P Z')
legend.AddEntry(hist_data, 'unfolded', 'P')
hist_measured = histograms['measured']
hist_measured.SetMarkerSize(1)
hist_measured.SetMarkerStyle(20)
hist_measured.SetMarkerColor(2)
#hist_measured.Draw('same P')
#legend.AddEntry(hist_measured, 'measured', 'P')
for key, hist in sorted(histograms.iteritems()):
if not 'unfolded' in key and not 'measured' in key:
hist.SetLineStyle(7)
hist.SetLineWidth(2)
# setting colours
if 'POWHEG' in key or 'matchingdown' in key:
hist.SetLineColor(kBlue)
elif 'MADGRAPH' in key or 'matchingup' in key:
hist.SetLineColor(kRed + 1)
elif 'MCATNLO' in key or 'scaleup' in key:
hist.SetLineColor(kGreen - 3)
elif 'scaledown' in key:
hist.SetLineColor(kMagenta + 3)
hist.Draw('hist same')
legend.AddEntry(hist, translate_options[key], 'l')
legend.Draw()
mytext = TPaveText(0.5, 0.97, 1, 1.01, "NDC")
channelLabel = TPaveText(0.18, 0.97, 0.5, 1.01, "NDC")
if 'electron' in histname:
channelLabel.AddText(
"e, %s, %s, k = %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
elif 'muon' in histname:
channelLabel.AddText(
"#mu, %s, %s, k = %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
else:
channelLabel.AddText(
"combined, %s, %s, k = %s" %
("#geq 4 jets", b_tag_bins_latex[b_tag_bin], k_value))
mytext.AddText("CMS Preliminary, L = %.1f fb^{-1} at #sqrt{s} = 8 TeV" %
(5.8))
mytext.SetFillStyle(0)
mytext.SetBorderSize(0)
mytext.SetTextFont(42)
mytext.SetTextAlign(13)
channelLabel.SetFillStyle(0)
channelLabel.SetBorderSize(0)
channelLabel.SetTextFont(42)
channelLabel.SetTextAlign(13)
mytext.Draw()
channelLabel.Draw()
canvas.Modified()
canvas.Update()
path = save_path + '/' + variable + '/' + category
make_folder_if_not_exists(path)
canvas.SaveAs(path + '/' + histname + '_kv' + str(k_value) + '.png')
canvas.SaveAs(path + '/' + histname + '_kv' + str(k_value) + '.pdf')
def uncertainty_band(process, region, calculation, uncertainty, estimation):
"""Calculates an uncertainty band (TGraphAsymmErrors) for a specific
uncertainty.
Any uncertainties (except statistical) that have a shape component will
have the shape component converted to an overall component (considered in
addition to any existing overall component).
NOTE: CURRENTLY ROOT'S STATISTICAL ERRORS ARE USED AND THIS METHOD IS
DISABLED
NOTE: The statistical error calculated is the statistical error on the
estimate given the poisson statistics of the unweighted samples. This is
calculated per-bin as:
uncertainty = sqrt(unweighted) * weighted / unweighted
= weighted / sqrt(unweighted)
For samples such as data, where there is no weighting applied, this just
reduces to sqrt(weighted) = sqrt(unweighted), which is the usual
uncertainty applied by ROOT.
NOTE: The y-component of the uncertainty band will NOT be set to bin
content because this leads to errors in usage when combining the bands
and is only really necessary when computing the final combined band, so
instead simply pass a base for the error band when calling
combined_uncertainty_band.
Args:
process: The process to consider
region: The region to consider
calculation: The calculation, which should return a histogram
uncertainty: The Uncertainty subclass to consider, or None to compute
statistical uncertainty of Monte Carlo samples
estimation: The Estimation subclass to consider
Returns:
A TGraphAsymmErrors representing the error band.
"""
# Compute the nominal histogram
nominal = estimation(calculation)(process, region)
# Compute and unpack variations
if uncertainty is not None:
# Perform the uncertainty estimation
variations = estimation(uncertainty(calculation))(process, region)
# Unpack variations
overall_up, overall_down, shape_up, shape_down = variations
# Convert any shape variations to overall
if shape_up is None:
shape_overall_up = None
else:
shape_overall_up = to_overall(shape_up, nominal)
shape_up = None
if shape_down is None:
shape_overall_down = None
else:
shape_overall_down = to_overall(shape_down, nominal)
shape_down = None
else:
# We're computing statistical variation, so we don't need these
overall_up = overall_down = None
shape_up = shape_down = None
shape_overall_up = shape_overall_down = None
# TODO: What's the idea here? This doesn't work as it is.
# For computing the uncertainty of weighted MC samples, we need the
# unweighted histogram
#unweighted = estimation(calculation)(
#process,
#region.weighted(False),
#weighted_combination = False
#)
# Create the error band. We pass it the nominal histogram just to get
# the binning correct. The graph will also extract values and errors
# from the histogram, but that's okay because we'll overwrite them
# below.
band = TGraphAsymmErrors(nominal)
# Get the number of bins in the histogram
bins = nominal.GetNbinsX()
# Go through each point in the graph and 0-out the Y-value and Y-error.
# Unfortunately we can't set the Y-value individually (which would have
# been great since the X-values would already be at bin centers).
# Anyway, no big deal, X-values are easy to set. The X-error will have
# already bin set to bin width.
for bin in xrange(0, bins):
band.SetPoint(bin, band.GetX()[bin], 0)
band.SetPointEYhigh(bin, 0.0)
band.SetPointEYlow(bin, 0.0)
# Loop over all bins and compute uncertainties
for bin, point in zip(range(1, bins + 1), range(0, bins)):
# Get the bin content
content = nominal.GetBinContent(bin)
# If the content is 0, there are no uncertainties, because we only
# consider overall and statistical uncertainties
if content == 0.0:
band.SetPointEYhigh(point, 0.0)
band.SetPointEYlow(point, 0.0)
continue
# Create a list of fractional variations for this bin. These lists
# will hold FRACTIONAL variations, i.e. variations normalized to bin
# content, and will be converted to absolute variations below when they
# are set as errors.
up_variations = []
down_variations = []
# Add any overall variations
if overall_up is not None:
up_variations.append(overall_up - 1.0)
if overall_down is not None:
down_variations.append(1.0 - overall_down)
if shape_overall_up is not None:
up_variations.append(shape_overall_up - 1.0)
if shape_overall_down is not None:
down_variations.append(1.0 - shape_overall_down)
# TODO: What's the point of using the unweighted distribution? It
# makes no sense to me.
# Add the statistical variation if uncertainty is None. Note that we
# compute this for the statistics of the unweighted Monte Carlo and not
# the weighted bin count.
#if uncertainty is None:
## Get the unweighted content
#unweighted_content = unweighted.GetBinContent(bin)
## Calculate error if possible
#if content > 0.0 and unweighted_content > 0.0:
## The extra factor of 1/content is just because we normalize
## everything to content for combining together. It has nothing
## to do with the derivation of the uncertainty, and it is
## multipled out below.
#statistical_variation = (
#content / sqrt(unweighted_content)
#) / content
#up_variations.append(statistical_variation)
#down_variations.append(statistical_variation)
# Statistical error; use the error from the distribution directly
if uncertainty is None:
error = nominal.GetBinError(bin)
band.SetPointEYhigh(point, error)
band.SetPointEYlow(point, error)
else:
# Set the point and error. Note that, since we sum things in
# quadrature, it really doesn't matter how we compute the
# differences above.
band.SetPointEYhigh(point, sum_quadrature(up_variations) * content)
band.SetPointEYlow(point,
sum_quadrature(down_variations) * content)
# All done
return band
def make_ratioplot(name,
ttbar_file=0,
qcd_file=0,
signal_files=[],
histo=0,
rebin=1,
minx=0,
maxx=0,
miny=0,
maxy=0,
logy=False,
xtitle='',
ytitle='',
textsizefactor=1,
signal_legend=[],
outfile=0,
signal_colors=[],
signal_zoom=1,
qcd_zoom=1,
ttbar_zoom=1,
ttbar_legend='t#bar{t}',
qcd_legend='QCD from MC',
dosys=False,
docms=True,
legendtitle=''):
###canvas setting up
canvas = 0
canvas = TCanvas(name, '', 0, 0, 600, 600)
canvas.SetLeftMargin(0.15)
canvas.SetRightMargin(0.05)
canvas.SetTopMargin(0.10)
canvas.SetBottomMargin(0.10)
charsize = 0.04
offset = 1.9
###latex label
latex = 0
latex = TLatex(0.6, 0.7, '13 TeV, 2.69 fb^{-1}')
latex.SetTextSize(charsize)
latex.SetNDC(1)
latex.SetTextFont(42)
###legend setting up
#legend=TLegend(0.0,0.75,0.99,1.04)
legend = TLegend(0.4, 0.6, 0.94, 0.95)
legend.SetNColumns(2)
legend.SetHeader('')
legend.SetFillStyle(0)
legend.SetBorderSize(0)
###mc stack
stack = THStack(name + '_stack', '')
qcd_histo = qcd_file.Get(histo).Clone(name + '_make_plot')
qcd_histo.Rebin(rebin)
ttbar_histo = ttbar_file.Get(histo).Clone()
ttbar_histo.Rebin(rebin)
ttbar_histo.SetFillColor(kRed - 9)
ttbar_histo.SetLineColor(kRed - 9)
ttbar_histo.SetMarkerColor(kRed - 9)
if ttbar_zoom != 1:
ttbar_histo.Scale(ttbar_zoom)
legend.AddEntry(ttbar_histo, ttbar_legend, 'f')
qcd_histo.SetFillColor(kOrange - 5)
qcd_histo.SetLineColor(kOrange - 5)
qcd_histo.SetMarkerColor(kOrange - 5)
if qcd_zoom != 1:
qcd_histo.Scale(qcd_zoom)
legend.AddEntry(qcd_histo, qcd_legend, 'f')
sum_mc = qcd_histo.Clone(histo + 'tmp')
sum_mc.Add(ttbar_histo)
stack.Add(ttbar_histo)
stack.Add(qcd_histo)
sum_mc.SetLineColor(kBlack)
sum_mc.SetFillStyle(0)
err = TGraphAsymmErrors(sum_mc)
legend.AddEntry(err, 'Total uncertainty', 'f')
if legendtitle == '':
legend.AddEntry(0, "", '')
legend.AddEntry(0, "g_{RS} #rightarrow t#bar{t} (2pb)", '')
else:
legend.AddEntry(0, "", '')
legend.AddEntry(0, legendtitle, '')
###signal setting up
signal_histos = []
colors = [
kBlack, kRed, kOrange, kBlue, kGreen + 3, 44, 45, 46, 47, 48, 49, 50,
51, 52, 53, 54, 55, 56, 57, 58, 59, 60
]
styles = [
1, 3, 5, 7, 7, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1
]
if signal_colors != []:
colors = signal_colors
for i in range(len(signal_files)):
signal_histos.append(signal_files[i].Get(histo).Clone())
signal_histos[i].SetLineWidth(3)
signal_histos[i].SetLineStyle(styles[i])
signal_histos[i].SetLineColor(colors[i])
signal_histos[i].SetMarkerColor(colors[i])
signal_histos[i].Rebin(rebin)
if signal_zoom != 1:
signal_histos[i].Scale(signal_zoom)
legend.AddEntry(signal_histos[i], signal_legend[i], 'l')
###mc shape line
ttbar_line = 0
ttbar_line = ttbar_histo.Clone()
ttbar_line.SetLineColor(kBlack)
ttbar_line.SetFillStyle(0)
###mc errors
if dosys:
sys_diff_qcd = []
sys_diff_ttbar = []
for imtt in range(1, ttbar_histo.GetNbinsX() + 1):
sys_diff_qcd.append([])
sys_diff_ttbar.append([])
#adding stat uncertainties <--removed
# for imtt in range(1,ttbar_histo.GetNbinsX()+1):
# sys_diff_ttbar[imtt-1].append(ttbar_histo.GetBinError(imtt))
# sys_diff_ttbar[imtt-1].append(-ttbar_histo.GetBinError(imtt))
# sys_diff_qcd[imtt-1].append(qcd_histo.GetBinError(imtt))
# sys_diff_qcd[imtt-1].append(-qcd_histo.GetBinError(imtt))
#adding flat uncertainties
for imtt in range(1, ttbar_histo.GetNbinsX() + 1):
#ttbar
for i in [
2.4, #pdf
10.0, #mu
3.0, #xsec
6.0, #toppt
1.0, #lumi
3.5, #jec
3.0, #jer
10.0, #btag
#3.0,#trig
10.0, #toptag
3.0
]: #pileup
sys_diff_ttbar[imtt - 1].append(
i / 100.0 * ttbar_histo.GetBinContent(imtt))
sys_diff_ttbar[imtt - 1].append(
-i / 100.0 * ttbar_histo.GetBinContent(imtt))
closureunc = 5.0
# if '1b' in histo:
# closureunc=5.0
# elif '2b' in histo:
# closureunc=10.0
for i in [
2.0, #modmass
closureunc
]: #closure
sys_diff_qcd[imtt - 1].append(i / 100.0 *
qcd_histo.GetBinContent(imtt))
sys_diff_qcd[imtt - 1].append(-i / 100.0 *
qcd_histo.GetBinContent(imtt))
# #3% trigger
# sys_diff_ttbar[imtt-1].append(0.03*ttbar_histo.GetBinContent(imtt))
# sys_diff_ttbar[imtt-1].append(-0.03*ttbar_histo.GetBinContent(imtt))
# #2.7% lumi
# sys_diff_ttbar[imtt-1].append(0.023*ttbar_histo.GetBinContent(imtt))
# sys_diff_ttbar[imtt-1].append(-0.023*ttbar_histo.GetBinContent(imtt))
# #15% ttbar
# #sys_diff_ttbar[imtt-1].append(0.15*ttbar_histo.GetBinContent(imtt))
# #sys_diff_ttbar[imtt-1].append(-0.15*ttbar_histo.GetBinContent(imtt))
# #2.8% QCD
# sys_diff_qcd[imtt-1].append(0.028*qcd_histo.GetBinContent(imtt))
# sys_diff_qcd[imtt-1].append(-0.028*qcd_histo.GetBinContent(imtt))
#combining uncertainties
sys_tot_ttbar = []
sys_tot_qcd = []
sys_tot = []
sys_global_ttbar = [0.0, 0.0]
sys_global_qcd = [0.0, 0.0]
nevt_global = [0.0, 0.0, 0.0]
for imtt in range(1, ttbar_histo.GetNbinsX() + 1):
uperr_qcd = 0
downerr_qcd = 0
uperr_ttbar = 0
downerr_ttbar = 0
for error in sys_diff_ttbar[imtt - 1]:
if error < 0:
downerr_ttbar = downerr_ttbar + error * error
else:
uperr_ttbar = uperr_ttbar + error * error
for error in sys_diff_qcd[imtt - 1]:
if error < 0:
downerr_qcd = downerr_qcd + error * error
else:
uperr_qcd = uperr_qcd + error * error
sys_tot_ttbar.append(
[math.sqrt(downerr_ttbar),
math.sqrt(uperr_ttbar)])
sys_tot_qcd.append([math.sqrt(downerr_qcd), math.sqrt(uperr_qcd)])
sys_tot.append([
math.sqrt(downerr_qcd + downerr_ttbar),
math.sqrt(uperr_qcd + uperr_ttbar)
])
sys_global_qcd[0] = sys_global_qcd[0] + downerr_qcd
sys_global_qcd[1] = sys_global_qcd[1] + uperr_qcd
sys_global_ttbar[0] = sys_global_ttbar[0] + downerr_ttbar
sys_global_ttbar[1] = sys_global_ttbar[1] + uperr_ttbar
# nevt_global[0]=nevt_global[0]+data_histo.GetBinContent(imtt)
nevt_global[1] = nevt_global[1] + qcd_histo.GetBinContent(imtt)
nevt_global[2] = nevt_global[2] + ttbar_histo.GetBinContent(imtt)
#print 'ttbar+qcd',math.sqrt(uperr_qcd+uperr_ttbar),math.sqrt(downerr_qcd+downerr_ttbar)
#print 'qcd',math.sqrt(uperr_qcd),math.sqrt(downerr_qcd)
#print 'ttbar',math.sqrt(uperr_ttbar),math.sqrt(downerr_ttbar)
err.SetPointEYhigh(imtt - 1, math.sqrt(uperr_qcd + uperr_ttbar))
err.SetPointEYlow(imtt - 1, math.sqrt(downerr_qcd + downerr_ttbar))
sys_global = [0.0, 0.0]
sys_global[0] = math.sqrt(sys_global_qcd[0] + sys_global_ttbar[0])
sys_global[1] = math.sqrt(sys_global_qcd[1] + sys_global_ttbar[1])
sys_global_qcd[0] = math.sqrt(sys_global_qcd[0])
sys_global_qcd[1] = math.sqrt(sys_global_qcd[1])
sys_global_ttbar[0] = math.sqrt(sys_global_ttbar[0])
sys_global_ttbar[1] = math.sqrt(sys_global_ttbar[1])
# print name
# print "\hline"
# print "Multijet QCD & $%.0f^{+%.0f}_{-%.0f}$ \\\\" % (nevt_global[1],sys_global_qcd[1],sys_global_qcd[0])
# print "SM ttbar & $%.0f^{+%.0f}_{-%.0f}$ \\\\" % (nevt_global[2],sys_global_ttbar[1],sys_global_ttbar[0])
# print "\hline"
# print "Total background & $%.0f^{+%.0f}_{-%.0f}$ \\\\" % (nevt_global[1]+nevt_global[2],sys_global[1],sys_global[0])
# print 'DATA & %.0f' %nevt_global[0]
err.SetFillStyle(3145)
err.SetFillColor(kGray + 1)
###drawing top
canvas.cd()
stack.Draw('hist')
stack.GetXaxis().SetTitle(ttbar_histo.GetXaxis().GetTitle())
stack.GetYaxis().SetTitle(ttbar_histo.GetYaxis().GetTitle())
stack.GetXaxis().SetLabelSize(charsize)
stack.GetXaxis().SetTitleSize(charsize)
stack.GetYaxis().SetLabelSize(charsize)
stack.GetYaxis().SetTitleSize(charsize)
stack.GetYaxis().SetTitleOffset(offset)
if minx != 0 or maxx != 0:
stack.GetXaxis().SetRangeUser(minx, maxx)
#else:
# stack.GetXaxis().SetRangeUser(0,4000)
if miny != 0 or maxy != 0:
stack.SetMaximum(maxy)
stack.SetMinimum(miny)
else:
if logy:
stack.SetMaximum(stack.GetMaximum() * 10)
stack.SetMinimum(0.2)
else:
stack.SetMaximum(stack.GetMaximum() * 2.0)
stack.SetMinimum(0.001)
err.Draw('2')
sum_mc.Draw('samehist')
if ttbar_file != 0:
ttbar_line.Draw('samehist')
for i in signal_histos:
i.Draw('samehist')
if logy:
canvas.SetLogy()
legend.Draw()
latex2text = ''
if 'ldy_0b' in name:
latex2text = '#Deltay < 1; 0 b tag'
elif 'ldy_1b' in name:
latex2text = '#Deltay < 1; 1 b tag'
elif 'ldy_2b' in name:
latex2text = '#Deltay < 1; 2 b tag'
elif 'hdy_0b' in name:
latex2text = '#Deltay > 1; 0 b tag'
elif 'hdy_1b' in name:
latex2text = '#Deltay > 1; 1 b tag'
elif 'hdy_2b' in name:
latex2text = '#Deltay > 1; 2 b tag'
latex2 = TLatex(0.19, 0.7, latex2text)
latex2.SetTextSize(0.03)
latex2.SetNDC(1)
latex2.SetTextFont(42)
latex2.Draw()
if docms:
if '3000' in name:
CMS_lumi.CMS_lumi(canvas, 3, 11)
elif '1000' in name:
CMS_lumi.CMS_lumi(canvas, 2, 11)
elif '300' in name:
CMS_lumi.CMS_lumi(canvas, 1, 11)
elif '36' in name:
CMS_lumi.CMS_lumi(canvas, 0, 11)
###saving
canvas.SaveAs('pdf/' + name + '.pdf')
if outfile != 0:
canvas.Write()
def nuisance_plot(input_file_, orien_, sticker_, oname_):
pars = get_parameters_from_file(input_file_)
#adjust_parameters(pars)
N = len(pars)
g68 = TGraph(2 * N + 7)
g95 = TGraph(2 * N + 7)
gPR = TGraphAsymmErrors(N)
for i in range(0, N):
x = pars[i].value
y = i + 1.5
xerr = pars[i].error
yerr = 0.
if orien_ == 'h':
x, y = y, x
xerr, yerr = yerr, xerr
gPR.SetPoint(i, x, y)
gPR.SetPointEXlow(i, xerr)
gPR.SetPointEYlow(i, yerr)
gPR.SetPointEXhigh(i, xerr)
gPR.SetPointEYhigh(i, yerr)
for a in range(0, N + 3):
if orien_ == 'h':
g68.SetPoint(a, a, -1)
g95.SetPoint(a, a, -2)
g68.SetPoint(a + 1 + N + 2, N + 2 - a, 1)
g95.SetPoint(a + 1 + N + 2, N + 2 - a, 2)
else:
g68.SetPoint(a, -1, a)
g95.SetPoint(a, -2, a)
g68.SetPoint(a + 1 + N + 2, 1, N + 2 - a)
g95.SetPoint(a + 1 + N + 2, 2, N + 2 - a)
gPR.SetLineStyle(1)
gPR.SetLineWidth(1)
gPR.SetLineColor(1)
gPR.SetMarkerStyle(21)
gPR.SetMarkerSize(1.25)
g68.SetFillColor(kGreen)
g95.SetFillColor(kYellow)
if orien_ == 'h':
text_TL = TPaveText(0.100, 0.925, 0.440, 0.995, 'NDC')
text_TR = TPaveText(0.587, 0.925, 0.999, 0.993, 'NDC')
else:
text_TL = TPaveText(0.370, 0.945, 0.590, 0.995, 'NDC')
text_TR = TPaveText(0.600, 0.945, 0.995, 0.993, 'NDC')
text_TL.AddText(label_TL)
text_TL.SetFillColor(0)
text_TL.SetTextAlign(12)
text_TL.SetTextSize(0.06)
text_TL.SetTextFont(42)
text_TR.AddText(sticker_)
text_TR.SetFillColor(0)
text_TR.SetTextAlign(32)
text_TR.SetTextSize(0.05)
text_TR.SetTextFont(42)
if orien_ == 'h':
c = TCanvas('c', 'c', 1000, 700)
c.SetTopMargin(0.08)
c.SetRightMargin(0.02)
c.SetBottomMargin(0.16)
c.SetLeftMargin(0.11)
else:
c = TCanvas('c', 'c', 700, 1000)
c.SetTopMargin(0.06)
c.SetRightMargin(0.02)
c.SetBottomMargin(0.12)
c.SetLeftMargin(0.35 * 700 / 650)
c.SetTickx()
c.SetTicky()
c.Update()
g95.Draw('AF')
g68.Draw('F')
gPR.Draw('P')
text_TL.Draw('same')
text_TR.Draw('same')
ax_1 = g95.GetHistogram().GetYaxis()
ax_2 = g95.GetHistogram().GetXaxis()
if orien_ == 'h': ax_1, ax_2 = ax_2, ax_1
ax_1.Set(N + 2, 0, N + 2)
ax_1.SetNdivisions(-414)
for i in range(0, N):
ax_1.SetBinLabel(i + 2, pars[i].name)
g95.SetTitle('')
ax_2.SetTitle('post-fit nuisance parameters values')
#ax_2.SetTitle('deviation in units of #sigma')
ax_1.SetTitleSize(0.050)
ax_2.SetTitleSize(0.050)
ax_1.SetTitleOffset(1.4)
ax_2.SetTitleOffset(1.0)
ax_1.SetLabelSize(0.05)
#ax_2.SetLabelSize(0.05)
ax_1.SetRangeUser(0, N + 2)
ax_2.SetRangeUser(-2.4, 2.4)
g95.GetHistogram().Draw('axis,same')
c.SaveAs(oname_ + '.pdf')
c.Close()
def makeplot_single(
h1_sig=None,
h1_bkg=None,
h1_data=None,
sig_legends_=None,
bkg_legends_=None,
sig_colors_=None,
bkg_colors_=None,
hist_name_=None,
sig_scale_=1.0,
dir_name_="plots",
output_name_=None,
extraoptions=None
):
if h1_sig == None or h1_bkg == None:
print("nothing to plot...")
return
os.system("mkdir -p "+dir_name_)
os.system("cp index.php "+dir_name_)
s_color = [632, 617, 839, 800, 1]
b_color = [920, 2007, 2005, 2003, 2001, 2011]
if sig_colors_:
s_color = sig_colors_
if bkg_colors_:
b_color = bkg_colors_
for idx in range(len(h1_sig)):
h1_sig[idx].SetLineWidth(3)
h1_sig[idx].SetLineColor(s_color[idx])
for idx in range(len(h1_bkg)):
h1_bkg[idx].SetLineWidth(2)
h1_bkg[idx].SetLineColor(b_color[idx])
h1_bkg[idx].SetFillColorAlpha(b_color[idx], 1)
if h1_data:
h1_data.SetBinErrorOption(1)
h1_data.SetLineColor(1)
h1_data.SetLineWidth(2)
h1_data.SetMarkerColor(1)
h1_data.SetMarkerStyle(20)
myC = r.TCanvas("myC","myC", 600, 600)
myC.SetTicky(1)
pad1 = r.TPad("pad1","pad1", 0.05, 0.33,0.95, 0.97)
pad1.SetBottomMargin(0.027)
pad1.SetRightMargin( rightMargin )
pad1.SetLeftMargin( leftMargin )
pad2 = r.TPad("pad2","pad2", 0.05, 0.04, 0.95, 0.31)
pad2.SetBottomMargin(0.4)
pad2.SetTopMargin(0.05)
pad2.SetRightMargin( rightMargin )
pad2.SetLeftMargin( leftMargin )
pad2.Draw()
pad1.Draw()
pad1.cd()
for idx in range(len(h1_sig)):
print("before signal scaling",h1_sig[idx].Integral())
h1_sig[idx].Scale(sig_scale_)
print("after signal scaling",h1_sig[idx].Integral())
stack = r.THStack("stack", "stack")
nS = np.zeros(h1_bkg[0].GetNbinsX())
eS = np.zeros(h1_bkg[0].GetNbinsX())
#hist_all is used to make the data/mc ratio. remove signal for the moment due to signal is scaled right now
hist_all = h1_sig[0].Clone("hist_all")
hist_all.Scale(0.0)
hist_s = h1_sig[0].Clone("hist_s")
hist_b = h1_bkg[0].Clone("hist_b")
for idx in range(len(h1_bkg)):
stack.Add(h1_bkg[idx])
for ib in range(h1_bkg[0].GetNbinsX()):
nS[ib] += h1_bkg[idx].GetBinContent(ib+1)
eS[ib] = math.sqrt(eS[ib]*eS[ib] + h1_bkg[idx].GetBinError(ib+1)*h1_bkg[idx].GetBinError(ib+1))
hist_all.Add(h1_bkg[idx])
if idx > 0:
hist_b.Add(h1_bkg[idx])
for idx in range(len(h1_sig)):
print("ggH signal yield: ", hist_s.Integral())
if idx > 0:
hist_temp = h1_sig[idx].Clone(h1_sig[idx].GetName()+"_temp")
#hist_all.Add(hist_temp)
hist_s.Add(h1_sig[idx])
print("all signal yield: ", hist_s.Integral())
stack.SetTitle("")
maxY = 0.0
if "stack_signal" in extraoptions and extraoptions["stack_signal"]:
for idx in range(len(h1_sig)):
h1_sig[idx].SetFillColorAlpha(s_color[idx], 1)
stack.Add(h1_sig[idx])
for ib in range(h1_bkg[0].GetNbinsX()):
nS[ib] += h1_sig[idx].GetBinContent(ib+1)
eS[ib] = math.sqrt(eS[ib]*eS[ib] + h1_sig[idx].GetBinError(ib+1)*h1_sig[idx].GetBinError(ib+1))
if stack.GetMaximum() > maxY:
maxY = stack.GetMaximum()
#if "SR" in h.GetTitle():
stack.Draw("hist")
else:
stack.Draw("hist")
if stack.GetMaximum() > maxY:
maxY = stack.GetMaximum()
for idx in range(len(h1_sig)):
if h1_sig[idx].GetMaximum() > maxY:
maxY = h1_sig[idx].GetMaximum()
if "SR" in h1_bkg[0].GetTitle():
#h1_sig[idx].Draw("samehist")
hist_s.Draw("samehist")
##draw stack total unc on top of total histogram
box = r.TBox(0,0,1,1,)
box.SetFillStyle(3002)
box.SetLineWidth(0)
box.SetFillColor(r.kBlack)
for idx in range(h1_bkg[0].GetNbinsX()):
box.DrawBox(h1_bkg[0].GetBinCenter(idx+1)-0.5*h1_bkg[0].GetBinWidth(idx+1), nS[idx]-eS[idx], h1_bkg[0].GetBinCenter(idx+1)+0.5*h1_bkg[0].GetBinWidth(idx+1), nS[idx]+eS[idx])
if h1_data:
if h1_data.GetMaximum() > maxY:
maxY = h1_data.GetMaximum()+np.sqrt(h1_data.GetMaximum())
#if not "SR" in h1_data.GetTitle() or "fail" in h1_data.GetTitle():
if True:
#print("debug h1_data.GetName()",h1_data.GetName(), h1_data.GetTitle())
TGraph_data = TGraphAsymmErrors(h1_data)
for i in range(TGraph_data.GetN()):
#data point
var_x, var_y = Double(0.), Double(0.)
TGraph_data.GetPoint(i,var_x,var_y)
if np.fabs(var_y) < 1e-5:
TGraph_data.SetPoint(i,var_x,-1.0)
TGraph_data.SetPointEYlow(i,-1)
TGraph_data.SetPointEYhigh(i,-1)
#print("zero bins in the data TGraph: bin",i+1)
else:
TGraph_data.SetPoint(i,var_x,var_y)
err_low = var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y))
TGraph_data.SetPointEYlow(i, var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y)))
TGraph_data.SetPointEYhigh(i, (0.5*TMath.ChisquareQuantile(1.-0.1586555,2.*(var_y+1))) - var_y)
TGraph_data.SetMarkerColor(1)
TGraph_data.SetMarkerSize(1)
TGraph_data.SetMarkerStyle(20)
TGraph_data.Draw("same P")
stack.GetYaxis().SetTitle("Events")
stack.GetYaxis().SetTitleOffset(1.05)
stack.GetYaxis().SetTitleSize(0.08)
stack.GetYaxis().SetLabelSize(0.06)
#stack.GetYaxis().CenterTitle()
stack.GetXaxis().SetLabelSize(0.)
#stack.GetXaxis().SetLabelOffset(0.013)
#if "xaxis_range" in extraoptions:
# stack.GetXaxis().SetRangeUser(float(extraoptions["xaxis_range"][0]),float(extraoptions["xaxis_range"][1]))
leg = r.TLegend(0.2, 0.60, 0.9, 0.88)
leg.SetNColumns(3)
leg.SetFillStyle(0)
leg.SetBorderSize(0)
leg.SetTextFont(42)
leg.SetTextSize(0.05)
for idx in range(len(h1_bkg)):
leg.AddEntry(h1_bkg[idx], bkg_legends_[idx], "F")
if "SR" in hist_s.GetTitle():
leg.AddEntry(hist_s, 'HH #times {:1.2}'.format(sig_scale_), "L")
leg.AddEntry(box, "Total unc", "F")
if h1_data:
leg.AddEntry(h1_data, "Data", "ep")
leg.Draw()
pad2.cd()
pad2.SetGridy(1)
ratio = None
ratio_Low = 0.0
ratio_High = 4
if h1_data:
ratio = TGraphAsymmErrors(h1_data)
for i in range(ratio.GetN()):
#bkg prediction
imc = Double(hist_all.GetBinContent(i+1))
#data point
var_x, var_y = Double(0.), Double(0.)
if not ("SR" in h1_data.GetTitle() and (i>5 and i<9)):
ratio.GetPoint(i,var_x,var_y)
if var_y == 0.:
ratio.SetPoint(i,var_x,-1.0)
ratio.SetPointEYlow(i,-1)
ratio.SetPointEYhigh(i,-1)
continue
ratio.SetPoint(i,var_x,var_y/imc)
err_low = (var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y)))/imc
err_high = ((0.5*TMath.ChisquareQuantile(1.-0.1586555,2.*(var_y+1))) - var_y)/imc
ratio.SetPointEYlow(i, err_low)
ratio.SetPointEYhigh(i, err_high)
ratio.SetMarkerColor(1)
ratio.SetMarkerSize(1)
ratio.SetMarkerStyle(20)
ratio.GetXaxis().SetTitle("j_{2} regressed mass [GeV]")
#myC.Update()
if "ratio_range" in extraoptions:
ratio_Low = extraoptions["ratio_range"][0]
ratio_High = extraoptions["ratio_range"][1]
ratio.GetYaxis().SetTitle("data/mc")
ratio.GetYaxis().SetRangeUser(ratio_Low, ratio_High)
ratio.GetXaxis().SetRangeUser(50, 220)
ratio.SetTitle("")
ratio.Draw("same AP")
pad2.Update()
print(ratio.GetTitle(),ratio.GetName(),"debug")
else:
ratio = h1_sig[0].Clone("ratio")
ratio_High = 0.0
for ibin in range(1,ratio.GetNbinsX()+1):
s = hist_s.GetBinContent(ibin)
b = hist_b.GetBinContent(ibin)
L = 0.0
if b > 0.0:
L = s/math.sqrt(b)
if L > ratio_High:
ratio_High = L
ratio.SetBinContent(ibin, L)
if ratio_High > 1.0:
ratio_High = 1.0
ratio.GetYaxis().SetRangeUser(ratio_Low, ratio_High*1.2)
ratio.GetYaxis().SetTitle("S/#sqrt{B}")
ratio.Draw("samehist")
ratio.SetLineColor(1)
ratio.SetLineWidth(2)
ratio.SetMarkerStyle(20)
ratio.SetMarkerColor(1)
ratio.SetFillColorAlpha(1, 0)
ratio.GetXaxis().SetTitleOffset(0.94)
ratio.GetXaxis().SetTitleSize(0.18)
ratio.GetXaxis().SetLabelSize(0.12)
ratio.GetXaxis().SetLabelOffset(0.013)
ratio.GetYaxis().SetTitleOffset(0.40)
ratio.GetYaxis().SetTitleSize(0.17)
ratio.GetYaxis().SetLabelSize(0.13)
ratio.GetYaxis().SetTickLength(0.01)
ratio.GetYaxis().SetNdivisions(505)
#if "xaxis_range" in extraoptions:
# ratio.GetXaxis().SetRangeUser(float(extraoptions["xaxis_range"][0]),float(extraoptions["xaxis_range"][1]))
#draw stack total unc on the ratio plot to present the background uncertainty
box_ratio = r.TBox(0,0,1,1,)
box_ratio.SetFillStyle(3002)
box_ratio.SetLineWidth(0)
box_ratio.SetFillColor(r.kBlack)
for idx in range(h1_bkg[0].GetNbinsX()):
if np.fabs(nS[idx])> 1e-06:
box_ratio.DrawBox(h1_bkg[0].GetBinCenter(idx+1)-0.5*h1_bkg[0].GetBinWidth(idx+1), (nS[idx]-eS[idx])/nS[idx], h1_bkg[0].GetBinCenter(idx+1)+0.5*h1_bkg[0].GetBinWidth(idx+1), (nS[idx]+eS[idx])/nS[idx])
else:
print("blinded Higgs peak region")
if "xaxis_label" in extraoptions and extraoptions["xaxis_label"] != None:
x_title = extraoptions["xaxis_label"]
ratio.GetXaxis().SetTitle(x_title)
ratio.GetYaxis().CenterTitle()
##########draw CMS preliminary
pad1.cd()
tex1 = r.TLatex(leftMargin, 0.91, "CMS")
tex1.SetNDC()
tex1.SetTextFont(61)
tex1.SetTextSize(0.070)
tex1.SetLineWidth(2)
tex1.Draw()
tex2 = r.TLatex(leftMargin+0.12,0.912,"Internal")
tex2.SetNDC()
tex2.SetTextFont(52)
tex2.SetTextSize(0.055)
tex2.SetLineWidth(2)
tex2.Draw()
lumi_value = 137
if "lumi_value" in extraoptions:
lumi_value = extraoptions["lumi_value"]
tex3 = r.TLatex(0.72,0.912,"%d"%lumi_value+" fb^{-1} (13 TeV)")
tex3.SetNDC()
tex3.SetTextFont(42)
tex3.SetTextSize(0.055)
tex3.SetLineWidth(2)
tex3.Draw()
outFile = dir_name_
if output_name_:
outFile = outFile + "/" +output_name_
else:
outFile = outFile + "/" + hist_name_
#print("maxY = "+str(maxY))
stack.SetMaximum(maxY*1.7)
#print everything into txt file
text_file = open(outFile+"_linY.txt", "w")
text_file.write("bin | x ")
for idx in range(len(h1_bkg)):
text_file.write(" | %21s"%bkg_legends_[idx])
text_file.write(" | %21s"%("total B"))
for idx in range(len(sig_legends_)):
text_file.write(" | %25s"%sig_legends_[idx])
if h1_data:
text_file.write(" | data | data/mc")
text_file.write("\n-------------")
for idx in range(24*(len(h1_bkg) + 1)+ 29*len(sig_legends_)):
text_file.write("-")
if h1_data:
text_file.write("-------")
text_file.write("\n")
for ibin in range(0,h1_sig[0].GetNbinsX()+1):
text_file.write("%3d"%ibin+" ")
text_file.write(" | %6.3f"%h1_data.GetBinCenter(ibin)+" ")
for idx in range(len(h1_bkg)):
text_file.write(" | %7.3f "%h1_bkg[idx].GetBinContent(ibin)+"$\\pm$"+ " %7.3f"%h1_bkg[idx].GetBinError(ibin))
text_file.write(" | %7.3f "%hist_b.GetBinContent(ibin)+"$\\pm$"+ " %7.3f"%hist_b.GetBinError(ibin))
for idx in range(len(sig_legends_)):
text_file.write(" | %9.3f "%h1_sig[idx].GetBinContent(ibin)+"$\\pm$"+ " %9.3f"%h1_sig[idx].GetBinError(ibin))
if h1_data:
text_file.write(" | %d"%h1_data.GetBinContent(ibin) + " | %7.3f "%h1_data.GetBinContent(ibin) +"$\\pm$"+ " %7.3f"%h1_data.GetBinError(ibin))
text_file.write("\n\n")
#print yield table for AN
text_file.write("print yield table for AN\n")
bkg_all = 0
bkg_all_errsq = 0
for idx in range(len(h1_bkg)):
bkg_tmp = h1_bkg[idx].GetBinContent(7)+h1_bkg[idx].GetBinContent(8)+h1_bkg[idx].GetBinContent(9)
bkg_errsq_tmp = h1_bkg[idx].GetBinError(7)*h1_bkg[idx].GetBinError(7)+h1_bkg[idx].GetBinError(8)*h1_bkg[idx].GetBinError(8)+h1_bkg[idx].GetBinError(9)*h1_bkg[idx].GetBinError(9)
bkg_all += bkg_tmp
bkg_all_errsq += bkg_errsq_tmp
text_file.write("%s"%(bkg_legends_[idx])+"& %7.2f"%(bkg_tmp)+"$\\pm$"+ "%7.2f"%np.sqrt(bkg_errsq_tmp)+"\n")
text_file.write("total background & %7.2f"%(bkg_all)+"$\\pm$"+ "%7.2f"%np.sqrt(bkg_all_errsq)+"\n")
text_file.write("\ggHH SM ($\kapl=1$) & %7.2f"%((h1_sig[0].GetBinContent(7)+h1_sig[0].GetBinContent(8)+h1_sig[0].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.1f"%(sig_scale_*np.sqrt(h1_sig[0].GetBinError(7)*h1_sig[0].GetBinError(7)+h1_sig[0].GetBinError(8)*h1_sig[0].GetBinError(8)+h1_sig[0].GetBinError(9)*h1_sig[0].GetBinError(9)))+"\n")
text_file.write("\VBFHH SM ($\kapl=1$) & %7.2f"%((h1_sig[1].GetBinContent(7)+h1_sig[1].GetBinContent(8)+h1_sig[1].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.1f"%(sig_scale_*np.sqrt(h1_sig[1].GetBinError(7)*h1_sig[1].GetBinError(7)+h1_sig[1].GetBinError(8)*h1_sig[1].GetBinError(8)+h1_sig[1].GetBinError(9)*h1_sig[1].GetBinError(9)))+"\n")
text_file.write("HH bin 8 value %s"%h1_sig[0].GetBinContent(8)+"\n")
text_file.write("HH bin 9 value %s"%h1_sig[0].GetBinContent(9)+"\n")
text_file.write("HH bin 7 value %s"%h1_sig[0].GetBinContent(7)+"\n")
text_file.write("HH bin 8 error %s"%h1_sig[0].GetBinError(8)+"\n")
text_file.write("HH bin 9 error %s"%h1_sig[0].GetBinError(9)+"\n")
text_file.write("HH bin 7 error %s"%h1_sig[0].GetBinError(7)+"\n")
text_file.write("total & %7.2f"%(bkg_all+(h1_sig[0].GetBinContent(7)+h1_sig[0].GetBinContent(8)+h1_sig[0].GetBinContent(9)+h1_sig[1].GetBinContent(7)+h1_sig[1].GetBinContent(8)+h1_sig[1].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.2f"%(np.sqrt((h1_sig[0].GetBinError(7)*h1_sig[0].GetBinError(7)+h1_sig[0].GetBinError(8)*h1_sig[0].GetBinError(8)+h1_sig[0].GetBinError(9)*h1_sig[0].GetBinError(9))/(sig_scale_*sig_scale_)+(h1_sig[1].GetBinError(7)*h1_sig[1].GetBinError(7)+h1_sig[1].GetBinError(8)*h1_sig[1].GetBinError(8)+h1_sig[1].GetBinError(9)*h1_sig[1].GetBinError(9))/(sig_scale_*sig_scale_)+bkg_all_errsq))+"\n")
text_file.close()
os.system("cp "+outFile+"_linY.txt "+outFile+"_logY.txt")
pad1.RedrawAxis()
myC.SaveAs(outFile+"_linY.png")
myC.SaveAs(outFile+"_linY.pdf")
myC.SaveAs(outFile+"_linY.C")
pad1.cd()
stack.SetMaximum(maxY*100.0)
stack.SetMinimum(0.5)
pad1.SetLogy()
pad1.RedrawAxis()
myC.SaveAs(outFile+"_logY.png")
myC.SaveAs(outFile+"_logY.pdf")
myC.SaveAs(outFile+"_logY.C")
#save histogram and ratio to root file
outFile_root = r.TFile(outFile+".root", "recreate")
outFile_root.cd()
for idx in range(len(h1_bkg)):
h1_bkg[idx].Write()
for idx in range(len(sig_legends_)):
h1_sig[idx].Write()
if h1_data:
h1_data.Write()
ratio.Write()
#outFile_root.Write()
outFile_root.Close()
ey_lo_ratio = 0.0
else:
ey_lo_ratio = y_ratio * math.hypot(ey_lo_mu / y_mu, ey_lo_ele / y_ele)
ey_hi_mu = grMu.GetErrorYhigh(i)
ey_hi_ele = grEle.GetErrorYhigh(i)
if y_mu < 1.e-9 or y_ele < 1.e-9:
ey_hi_ratio = 0.0
else:
ey_hi_ratio = y_ratio * math.hypot(ey_hi_mu / y_mu, ey_hi_ele / y_ele)
grRatio.SetPoint(i, x, y_ratio)
grRatio.SetPointEXlow(i, ex_lo)
grRatio.SetPointEXhigh(i, ex_hi)
grRatio.SetPointEYlow(i, ey_lo_ratio)
grRatio.SetPointEYhigh(i, ey_hi_ratio)
if True:
grRatio.SetMarkerStyle(20)
grRatio.SetMarkerSize(1.5)
grRatio.SetMarkerColor(1)
grRatio.SetLineStyle(1)
grRatio.SetLineColor(1)
grRatio.SetLineWidth(1)
grRatio.GetXaxis().SetLabelSize(0.04)
grRatio.GetXaxis().SetTitleSize(0.04)
grRatio.GetXaxis().SetTitleOffset(1.25)
grRatio.GetYaxis().SetLabelSize(0.04)
