def getEff(name, den_input, num_input, rebin=0, xtitle='', ytitle=''):
c = TCanvas(name + '_Canvas')
legend = TLegend(0.8, 0.1, 0.999, 0.6)
legend.SetFillColor(kWhite)
den = den_input.Clone()
num = num_input.Clone()
if rebin != 0:
den.Rebin(rebin)
num.Rebin(rebin)
error_bars = TGraphAsymmErrors()
error_bars.Divide(num, den, "cl=0.683 b(1,1) mode")
error_bars.SetLineWidth(3)
if xtitle == '':
error_bars.GetXaxis().SetTitle(num.GetXaxis().GetTitle())
else:
error_bars.GetXaxis().SetTitle(xtitle)
if ytitle == '':
error_bars.GetYaxis().SetTitle(num.GetYaxis().GetTitle())
else:
error_bars.GetYaxis().SetTitle(ytitle)
error_bars.GetXaxis().SetRangeUser(400, 2000)
error_bars.SetLineColor(kBlack)
error_bars.SetMaximum(1.01)
error_bars.SetMinimum(0.)
if ytitle == '':
error_bars.GetYaxis().SetTitle("Trigger rate")
else:
error_bars.GetYaxis().SetTitle(ytitle)
error_bars.SetTitle('')
error_bars.Draw('AP')
c.SaveAs('pdf/' + name + '.pdf')
c.Write(name + '_Canvas')
error_bars.Write(name)
def createAssymSFFile(filename, sftable, name):
"""Create histogram from table."""
print(">>> Creating '%s'..." % filename)
file = TFile(filename, 'RECREATE')
file.cd()
x = [float(i) + 0.5 for i in range(len(sftable.keys()))]
ex = [0.0] * len(sftable.keys())
x_names = sorted(sftable.keys())
y = [sftable[k]['val'] for k in x_names]
eyl = [sftable[k]['down'] for k in x_names]
eyh = [sftable[k]['up'] for k in x_names]
g = TGraphAsymmErrors(len(x), np.array(x), np.array(y), np.array(ex),
np.array(ex), np.array(eyl), np.array(eyh))
g.GetXaxis().SetNdivisions(len(x) * 2)
g.GetXaxis().ChangeLabel(0, -1, 0, -1, -1, -1, "")
for i in x:
print(1 + int(i), x_names[int(i)])
g.GetXaxis().ChangeLabel(2 + int(i) * 2, -1, 0)
g.GetXaxis().ChangeLabel(1 + int(i) * 2, -1, -1, -1, -1, -1,
x_names[int(i)])
g.Write(name)
# file.ls() ; g.Draw("A*") ; raw_input()
file.Close()
return file
def ratioplot():
# create required parts
leg = getLegend()
latex = getLatex()
c = TCanvas()
c.SetLogy()
#Draw input histograms
hists = ['h_frac_recoil_', 'h_full_recoil_']
label = ['recoil with MET triggers', 'recoil without MET triggers']
combineHist(hists, label, leg)
#leg.Draw()
#c.SaveAs("Combinehists_D.pdf")
ratio = []
h1 = f.Get('h_frac_recoil_')
#h1=setHistStyle(h1,bins)
h2 = f.Get('h_full_recoil_')
#h2=setHistStyle(h2,bins)
h3 = createRatio(h1, h2)
gr = TGraphAsymmErrors(h1, h2)
gr.GetXaxis().SetRangeUser(0, 1500)
gr.GetYaxis().SetRangeUser(0, 1.2)
gr.SetMarkerStyle(20)
gr.SetMarkerSize(0.5)
gr.SetLineColor(1)
gr.GetYaxis().SetTitle("Trigger Efficiency")
gr.GetXaxis().SetTitle("Recoil [GeV]")
gr.SetTitle("")
# print ("ratio",ratio )
# c, pad1, pad2 = createCanvasPads()
#
# # draw everything
# pad1.cd()
# h1.Draw()
# h2.Draw("same")
# to avoid clipping the bottom zero, redraw a small axis
# h1.GetYaxis().SetLabelSize(0.0)
# axis = TGaxis(-5, 20, -5, 220, 20, 220, 510, "")
# axis.SetLabelFont(43)
# axis.SetLabelSize(15)
# axis.Draw()
# pad2.cd()
gr.Draw()
latex.DrawLatex(0.41, 0.93, "Trigger Efficincy in MET Run2017E")
xmin = 0.0
line = TLine(max(xmin, gr.GetXaxis().GetXmin()), 1, 1500, 1)
line.SetLineColor(1)
line.SetLineWidth(1)
line.SetLineStyle(7)
line.Draw()
#h3.Draw('pl')
c.SaveAs("test.pdf")
def eff(wrps, option='cl=0.683 b(1,1) mode'):
"""
Applies to HistoWrappers only. Returns GraphWrapper. Takes lumi from first.
>>> from ROOT import TH1I
>>> h1 = TH1I("h1", "", 2, .5, 2.5)
>>> h1.Fill(1)
1
>>> h1.Fill(1)
1
>>> w1 = wrappers.HistoWrapper(h1, lumi=2)
>>> h2 = TH1I("h2", "", 2, .5, 2.5)
>>> h2.Sumw2()
>>> h2.Fill(1)
1
>>> h2.Fill(1)
1
>>> h2.Fill(1)
1
>>> h2.Fill(2)
2
>>> w2 = wrappers.HistoWrapper(h2, lumi=3)
>>> w3 = eff([w1, w2])
>>> w3.graph.GetN()
2
>>> hi = w3.graph.GetErrorYhigh(0)
>>> lo = w3.graph.GetErrorYlow(0)
>>> abs(hi - 0.189572034007) < 1e-10
True
>>> abs(lo - 0.236650832868) < 1e-10
True
"""
wrps = iterableize(wrps)
wrps = iter(wrps)
try:
nominator = next(wrps)
denominator = next(wrps)
except StopIteration:
raise TooFewWrpsError("eff needs exactly two Wrappers.")
try:
wrps.next()
raise TooManyWrpsError("eff needs exactly two Wrappers.")
except StopIteration:
pass
if not isinstance(nominator, wrappers.HistoWrapper):
raise WrongInputError(
"eff needs nominator to be of type HistoWrapper. nominator: "
+ str(nominator)
)
if not isinstance(denominator, wrappers.HistoWrapper):
raise WrongInputError(
"eff needs denominator to be of type HistoWrapper. denominator: "
+ str(denominator)
)
graph = TGraphAsymmErrors(nominator.histo, denominator.histo, option)
graph.GetXaxis().SetTitle(nominator.histo.GetXaxis().GetTitle())
graph.GetYaxis().SetTitle('efficiency')
info = nominator.all_info()
return wrappers.GraphWrapper(graph, **info)
def readDataIntoGraph():
f = open('HESSj1745_290.dat', "r")
lines = f.readlines()
x, y, yHigh, yLow = array('d'), array('d'), array('d'), array('d')
# yScale = 10e12
yScale = 1
xScale = 1000
for iLine in lines:
if iLine.isspace() == True or iLine[0] == '#':
continue
tmpList = iLine.split()
tmpX = xScale * float(tmpList[0]) * float(tmpList[0])
x.append(xScale * float(tmpList[0]))
y.append(yScale * float(tmpList[1]) * tmpX)
yLow.append(yScale * (float(tmpList[1]) - float(tmpList[2])) * tmpX)
yHigh.append(yScale * (float(tmpList[3]) - float(tmpList[1])) * tmpX)
f.close()
listOfZeros = array("d", [0] * len(x))
gr = TGraphAsymmErrors(len(x), x, y, listOfZeros, listOfZeros, yLow, yHigh)
# gr.SetLineColor( color )
gr.SetLineWidth(2)
# gr.SetMarkerColor( color )
gr.SetMarkerStyle(21)
gr.GetXaxis().SetTitle('Energy [GeV]')
gr.GetYaxis().SetTitle('E^{2} x Flux [GeV cm^{-2}s^{-1}]')
gr.SetTitle('')
return gr
def HistToAsymmErrs(name,hist) :
X = []
EXlow = []
EXhigh = []
Y = []
EYlow = []
EYhigh = []
for i in range(hist.GetNbinsX()) :
X .append(hist.GetBinCenter (i+1))
EXlow .append(hist.GetBinWidth (i+1)/2.)
EXhigh.append(hist.GetBinWidth (i+1)/2.)
Y .append(hist.GetBinContent(i+1))
EYlow .append(hist.GetBinError (i+1))
EYhigh.append(hist.GetBinError (i+1))
a_X = array('d',X )
a_EXlow = array('d',EXlow )
a_EXhigh = array('d',EXhigh )
a_Y = array('d',Y )
a_EYlow = array('d',EYlow )
a_EYhigh = array('d',EYhigh )
asymm = TGraphAsymmErrors(len(a_X),a_X,a_Y,a_EXlow,a_EXhigh,a_EYlow,a_EYhigh)
asymm.GetXaxis().SetTitle(hist.GetXaxis().GetTitle())
asymm.GetYaxis().SetTitle(hist.GetYaxis().GetTitle())
asymm.SetLineWidth(2)
asymm.SetName(name)
return asymm
def QuadratureUpDown(asymlist,
hists=[],
doFlatUnc=False,
FlatUp=0.0,
FlatDown=0.0): # These are TGraphAsymmErrors
total_unc_up_sq = []
total_unc_dn_sq = []
for i in range(asymlist[0].GetN()):
total_unc_up_sq.append(0)
total_unc_dn_sq.append(0)
#
# asym err hists
#
for i in range(asymlist[0].GetN()):
for j in range(len(asymlist)):
E_up = asymlist[j].GetEYhigh()[i]
E_dn = asymlist[j].GetEYlow()[i]
total_unc_up_sq[i] += math.pow(E_up, 2)
total_unc_dn_sq[i] += math.pow(E_dn, 2)
#
# hists
#
for i in range(len(total_unc_up_sq)):
for j in range(len(hists)):
E_up = hists[j].GetBinError(i + 1)
E_dn = hists[j].GetBinError(i + 1)
total_unc_up_sq[i] += math.pow(E_up, 2)
total_unc_dn_sq[i] += math.pow(E_dn, 2)
#
# flat uncertainty
#
if doFlatUnc:
for i in range(len(total_unc_up_sq)):
total_unc_up_sq[i] += math.pow(FlatUp * asymlist[0].GetY()[i], 2)
total_unc_dn_sq[i] += math.pow(FlatDown * asymlist[0].GetY()[i], 2)
#
# sqrt
#
for i in range(len(total_unc_up_sq)):
total_unc_up_sq[i] = math.sqrt(total_unc_up_sq[i])
total_unc_dn_sq[i] = math.sqrt(total_unc_dn_sq[i])
#print 'Setting up unc to ',total_unc_up_sq[i]
#print 'Setting dn unc to ',total_unc_dn_sq[i]
#print total_unc_up_sq
#print total_unc_dn_sq
yup = array('d', total_unc_up_sq)
ydn = array('d', total_unc_dn_sq)
#print yup
#print ydn
result = TGraphAsymmErrors(asymlist[0].GetN(), asymlist[0].GetX(),
asymlist[0].GetY(), asymlist[0].GetEXlow(),
asymlist[0].GetEXhigh(), ydn, yup)
result.GetXaxis().SetTitle(asymlist[0].GetXaxis().GetTitle())
result.GetYaxis().SetTitle(asymlist[0].GetYaxis().GetTitle())
result.SetLineWidth(2)
result.SetName(asymlist[0].GetName() + ' Total Error')
return result
def envelope(thedict, thelist, nominal):
#
# Takes the nominal and makes an envelope around it
#
diff_up = nominal.Clone()
key = 'diff_up'
diff_up.SetNameTitle(key, key)
diff_dn = nominal.Clone()
key = 'diff_dn'
diff_dn.SetNameTitle(key, key)
for i in range(diff_up.GetNbinsX()):
diff_up.SetBinContent(i + 1, 0)
diff_dn.SetBinContent(i + 1, 0)
#
for var in thelist:
if var == 'dummy': continue
for i in range(nominal.GetNbinsX()):
curr_diff_up = diff_up.GetBinContent(i + 1)
curr_diff_dn = diff_dn.GetBinContent(i + 1)
nominal_bc = nominal.GetBinContent(i + 1)
thisvar_bc = thedict[var].GetBinContent(i + 1)
diff_up.SetBinContent(i + 1,
max(curr_diff_up, thisvar_bc - nominal_bc))
diff_dn.SetBinContent(i + 1,
min(curr_diff_dn, thisvar_bc - nominal_bc))
nbins = nominal.GetNbinsX()
x = array(
'd',
list(nominal.GetBinCenter(a + 1) for a in range(nominal.GetNbinsX())))
xup = array(
'd',
list(
nominal.GetBinWidth(a + 1) / 2.
for a in range(nominal.GetNbinsX())))
xdn = array(
'd',
list(
nominal.GetBinWidth(a + 1) / 2.
for a in range(nominal.GetNbinsX())))
y = array(
'd',
list(nominal.GetBinContent(a + 1) for a in range(nominal.GetNbinsX())))
yup = array(
'd',
list(diff_up.GetBinContent(a + 1) for a in range(nominal.GetNbinsX())))
ydn = array(
'd',
list(
diff_dn.GetBinContent(a + 1) * -1.
for a in range(nominal.GetNbinsX())))
result = TGraphAsymmErrors(nbins, x, y, xdn, xup, ydn, yup)
result.GetXaxis().SetTitle(nominal.GetXaxis().GetTitle())
result.GetYaxis().SetTitle(nominal.GetYaxis().GetTitle())
result.SetLineWidth(2)
result.SetName(nominal.GetName() + ' Envelope')
return result
def makeEffPlotsVars(tree,
varx,
vary,
sel,
nbinx,
xmin,
xmax,
nbiny,
ymin,
ymax,
xtitle,
ytitle,
leglabel=None,
header='',
addon='',
option='pt',
marker=20):
binning = [20, 30, 40, 50, 60, 70, 80, 100, 150, 200]
c = TCanvas()
if option == 'pt':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(binning) - 1, array('d', binning))
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(binning) - 1, array('d', binning))
elif option == 'eta':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon, nbinx, xmin, xmax)
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon, nbinx, xmin,
xmax)
elif option == 'nvtx':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(vbinning) - 1, array('d', vbinning))
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(vbinning) - 1, array('d', vbinning))
tree.Draw(varx + ' >> ' + _hist_.GetName(), sel)
tree.Draw(varx + ' >> ' + _ahist_.GetName(), sel + ' && ' + vary)
g_efficiency = TGraphAsymmErrors()
g_efficiency.BayesDivide(_ahist_, _hist_)
g_efficiency.GetXaxis().SetTitle(xtitle)
g_efficiency.GetYaxis().SetTitle('efficiency')
g_efficiency.GetYaxis().SetNdivisions(507)
g_efficiency.SetLineWidth(3)
g_efficiency.SetName(header)
g_efficiency.SetMinimum(0.)
g_efficiency.GetYaxis().SetTitleOffset(1.3)
g_efficiency.SetMarkerStyle(marker)
g_efficiency.SetMarkerSize(1)
g_efficiency.Draw('ap')
# save(c, 'plots/' + addon)
return copy.deepcopy(g_efficiency)
def QuadratureUpDown(name,asymlist=[],hists=[],AddHists=False) : # These are TGraphAsymmErrors
asymlist1 = []
for i in asymlist :
asymlist1.append(i)
for i in range(len(hists)) :
asymlist1.append(HistToAsymmErrs(hists[i].GetName()+' QuadratureUpDown Asymm',hists[i]))
nbins = asymlist1[0].GetN()
total_unc_up_sq = []
total_unc_dn_sq = []
for i in range(nbins) :
total_unc_up_sq.append(0)
total_unc_dn_sq.append(0)
#
# asym err hists
#
for i in range(nbins) :
for j in range(len(asymlist1)) :
E_up = asymlist1[j].GetEYhigh()[i]
E_dn = asymlist1[j].GetEYlow()[i]
total_unc_up_sq[i] += math.pow(E_up,2)
total_unc_dn_sq[i] += math.pow(E_dn,2)
# #
# # hists
# #
# for i in range(nbins) :
# for j in range(len(hists)) :
# E_up = hists[j].GetBinError(i+1)
# E_dn = hists[j].GetBinError(i+1)
# total_unc_up_sq[i] += math.pow(E_up,2)
# total_unc_dn_sq[i] += math.pow(E_dn,2)
#
# sqrt
#
for i in range(nbins) :
total_unc_up_sq[i] = math.sqrt(total_unc_up_sq[i])
total_unc_dn_sq[i] = math.sqrt(total_unc_dn_sq[i])
y = []
for i in range(nbins) :
y.append(asymlist1[0].GetY()[i])
if AddHists :
for j in range(1,len(asymlist1)) :
y[-1] += asymlist1[j].GetY()[i]
ybincontent = array('d',y)
yup = array('d',total_unc_up_sq)
ydn = array('d',total_unc_dn_sq)
result = TGraphAsymmErrors(asymlist1[0].GetN(),asymlist1[0].GetX(),ybincontent
,asymlist1[0].GetEXlow(),asymlist1[0].GetEXhigh()
,ydn,yup)
result.GetXaxis().SetTitle(asymlist1[0].GetXaxis().GetTitle())
result.GetYaxis().SetTitle(asymlist1[0].GetYaxis().GetTitle())
result.SetLineWidth(2)
result.SetName(name)
return result
def makeEffPlotsVars(tree,
varx,
vary,
sel,
nbinx,
xmin,
xmax,
nbiny,
ymin,
ymax,
xtitle,
ytitle,
leglabel=None,
header='',
addon='',
option='pt',
marker=20,
col=1):
binning = [20, 200] if args.onebin else [
20, 30, 40, 50, 60, 70, 80, 100, 150, 200
]
if option == 'pt':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(binning) - 1, array('d', binning))
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(binning) - 1, array('d', binning))
elif option == 'eta':
_hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon, nbinx, xmin, xmax)
_ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon, nbinx, xmin,
xmax)
tree.Draw(varx + ' >> ' + _hist_.GetName(), sel)
tree.Draw(varx + ' >> ' + _ahist_.GetName(), sel + ' && ' + vary)
g_efficiency = TGraphAsymmErrors()
g_efficiency.Divide(_ahist_, _hist_, "cl=0.683 b(1,1) mode")
g_efficiency.GetXaxis().SetTitle(xtitle)
g_efficiency.GetYaxis().SetTitle('efficiency')
g_efficiency.GetYaxis().SetNdivisions(507)
g_efficiency.SetLineWidth(3)
g_efficiency.SetName(header)
g_efficiency.SetMinimum(0.)
g_efficiency.GetYaxis().SetTitleOffset(1.3)
g_efficiency.SetMarkerStyle(marker)
g_efficiency.SetMarkerSize(1)
g_efficiency.SetMarkerColor(col)
g_efficiency.SetLineColor(col)
g_efficiency.Draw('ap')
# save(c, 'plots/' + addon)
return g_efficiency
def FlatErrorToAsymmErrs(name,nomhist,up_frac=0.,dn_frac=0.) :
nom_2_asymm = HistToAsymmErrs(nomhist.GetName()+' FlatErrorToAsymmErrs Asymm',nomhist)
EYlow = []
EYhigh = []
for i in range(nom_2_asymm.GetN()) :
EYlow.append(nom_2_asymm.GetY()[i]*dn_frac)
EYhigh.append(nom_2_asymm.GetY()[i]*up_frac)
a_EYlow = array('d',EYlow )
a_EYhigh = array('d',EYhigh )
result = TGraphAsymmErrors(nom_2_asymm.GetN(),nom_2_asymm.GetX(),nom_2_asymm.GetY()
,nom_2_asymm.GetEXlow(),nom_2_asymm.GetEXhigh()
,a_EYlow,a_EYhigh)
result.GetXaxis().SetTitle(nom_2_asymm.GetXaxis().GetTitle())
result.GetYaxis().SetTitle(nom_2_asymm.GetYaxis().GetTitle())
result.SetLineWidth(2)
result.SetName(name)
return result
def GetTGraphAsymmErrors(histo, offset):
graph_in = TGraphAsymmErrors(histo)
graph_in.SetName(histo.GetName() + '_tmp')
graph_out = TGraphAsymmErrors(histo)
graph_out.SetName(histo.GetName() + '_offset')
for i in range(graph_in.GetN()):
graph_out.GetX()[i] = graph_in.GetX()[i] + offset
graph_out.GetEXlow()[i] = graph_in.GetEXlow()[i] + offset
graph_out.GetEXhigh()[i] = graph_in.GetEXhigh()[i] - offset
x_min = graph_out.GetX()[0] - graph_out.GetEXlow()[0]
if (x_min > 0): x_min = 0
x_max = graph_out.GetX()[graph_out.GetN() -
1] + graph_out.GetEXhigh()[graph_out.GetN() - 1]
graph_out.GetXaxis().SetLimits(x_min, x_max)
return graph_out
def LinearUpDown(name,asymlist=[],hists=[],AddHists=False) :
for i in range(len(hists)) :
asymlist.append(HistToAsymmErrs(hists[i].GetName()+' LinearUpDown Asymm',hists[i]))
nbins = asymlist[0].GetN()
total_unc_up = []
total_unc_dn = []
for i in range(nbins) :
total_unc_up.append(0)
total_unc_dn.append(0)
#
# asym err hists
#
for i in range(nbins) :
for j in range(len(asymlist)) :
total_unc_up[i] += asymlist[j].GetEYhigh()[i]
total_unc_dn[i] += asymlist[j].GetEYlow()[i]
y = []
for i in range(nbins) :
y.append(asymlist[0].GetY()[i])
if AddHists :
for j in range(1,len(asymlist)) :
y[-1] += asymlist[j].GetY()[i]
ybincontent = array('d',y)
yup = array('d',total_unc_up)
ydn = array('d',total_unc_dn)
result = TGraphAsymmErrors(asymlist[0].GetN(),asymlist[0].GetX(),ybincontent
,asymlist[0].GetEXlow(),asymlist[0].GetEXhigh()
,ydn,yup)
result.GetXaxis().SetTitle(asymlist[0].GetXaxis().GetTitle())
result.GetYaxis().SetTitle(asymlist[0].GetYaxis().GetTitle())
result.SetLineWidth(2)
result.SetName(name)
return result
return
def makeEffPlotsVars(tree,
varx,
numeratorAddSelection,
baseSelection,
binning,
xtitle='',
header='',
addon='',
marker=20,
col=1):
_denomHist_ = TH1F('h_effp_' + addon, 'h_effp' + addon,
len(binning) - 1, binning)
_nominatorHist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon,
len(binning) - 1, binning)
tree.Draw(varx + ' >> ' + _denomHist_.GetName(), baseSelection)
tree.Draw(varx + ' >> ' + _nominatorHist_.GetName(),
baseSelection + ' && ' + numeratorAddSelection)
g_eff = TGraphAsymmErrors()
g_eff.Divide(_nominatorHist_, _denomHist_, "cl=0.683 b(1,1) mode")
g_eff.GetXaxis().SetTitle(xtitle)
g_eff.GetYaxis().SetTitle('efficiency')
g_eff.GetYaxis().SetNdivisions(507)
g_eff.SetLineWidth(3)
g_eff.SetName(header)
g_eff.SetMinimum(0.)
g_eff.GetYaxis().SetTitleOffset(1.3)
g_eff.SetMarkerStyle(marker)
g_eff.SetMarkerSize(1)
g_eff.SetMarkerColor(col)
g_eff.SetLineColor(col)
g_eff.Draw('ap')
return g_eff
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)
grRatio.GetYaxis().SetTitleSize(0.04)
grRatio.GetYaxis().SetTitleOffset(1.5)
# grRatio.GetYaxis().SetRangeUser(ylo, yhi)
grRatio.Draw("ALP")
fOutput = TFile('durp.root', 'recreate')
grRatio.Write('durp')
canvas.Write('canv')
fOutput.Close()
gr_LC = GAE(1, x_c, y_c, exl_c, exh_c, eyl_c, eyh_c)
gr_c = gr.Clone()
gr_sc = gr_s.Clone()
gr.SetTitle('ROC(zoomed in)')
#gr.SetMarkerColor(8)
#gr.SetMarkerStyle(21)
gr.SetFillColor(632 - 9)
gr_s.SetTitle('ROC')
gr_s.SetFillColor(632 - 9)
gr_sc.SetLineColor(4)
c1.cd(1)
gr.GetXaxis().SetRangeUser(0, 0.5)
gr.GetYaxis().SetRangeUser(0.00001, 0.01)
gr_c.GetXaxis().SetRangeUser(0, 0.5)
gr_c.GetXaxis().SetRangeUser(0.00001, 0.01)
gr_LC.GetXaxis().SetRangeUser(0, 0.5)
gr_LC.GetYaxis().SetRangeUser(0.00001, 0.01)
gr_c.SetLineColor(4)
gr.Draw('SAME 3A')
gr_c.Draw('SAME XLP')
gr_LC.Draw('SAME A')
c1.cd(2)
def makePlot(finname,foutname,plottitle='',masstitle='',scale=False):
xsecs = resonantXsecs if 'resonant' in finname else fcncXsecs
points = {}
if BLIND:
cls = [2.5, 16, 50, 84, 97.5]
else:
cls = [2.5, 16, 50, 84, 97.5,'Observed']
xaxis = []
for cl in cls:
points[cl] = []
xsec=1
for l in open(finname):
try:
if l.strip()[0]=='#':
continue
if 'MASS' in l:
if scale:
xsec = xsecs[int(l.split()[1])]
if VERBOSE:
print ''
stdout.write('$%6s$ & $%7.3g$'%(l.split()[1],xsec/(0.667)))
xaxis.append(float(l.split()[1]))
else:
cl,val = parseLine(l)
points[cl].append(val/xsec)
if VERBOSE and (cl==50 or cl=='Observed'):
stdout.write(' & $%10.4g$'%(val/xsec))
except:
pass
if VERBOSE:
print ''
N = len(xaxis)
up1Sigma=[]; up2Sigma=[]
down1Sigma=[]; down2Sigma=[]
for iM in xrange(N):
up1Sigma.append(points[84][iM]-points[50][iM])
up2Sigma.append(points[97.5][iM]-points[50][iM])
down1Sigma.append(-points[16][iM]+points[50][iM])
down2Sigma.append(-points[2.5][iM]+points[50][iM])
up1Sigma = array('f',up1Sigma)
up2Sigma = array('f',up2Sigma)
down1Sigma = array('f',down1Sigma)
down2Sigma = array('f',down2Sigma)
cent = array('f',points[50])
if not BLIND:
obs = array('f',points['Observed'])
xarray = array('f',xaxis)
xsecarray = array('f',[xsecs[xx] for xx in xaxis])
xsecarrayLow = array('f',[0.0625*xsecs[xx] for xx in xaxis])
onearray = array('f',[1 for xx in xaxis])
graphXsec = TGraph(N,xarray,xsecarray)
graphXsecLow = TGraph(N,xarray,xsecarrayLow)
graphOne = TGraph(N,xarray,onearray)
zeros = array('f',[0 for i in xrange(N)])
graphCent = TGraph(N,xarray,cent)
if not BLIND:
graphObs = TGraph(N,xarray,obs)
graph1Sigma = TGraphAsymmErrors(N,xarray,cent,zeros,zeros,down1Sigma,up1Sigma)
graph2Sigma = TGraphAsymmErrors(N,xarray,cent,zeros,zeros,down2Sigma,up2Sigma)
c = TCanvas('c','c',700,600)
c.SetLogy()
c.SetLeftMargin(.15)
graph2Sigma.GetXaxis().SetTitle(masstitle+' [GeV]')
if scale:
graph2Sigma.GetYaxis().SetTitle('Upper limit [#sigma/#sigma_{theory}]')
else:
graph2Sigma.GetYaxis().SetTitle("Upper limit [#sigma] [pb]")
graph2Sigma.SetLineColor(5)
graph1Sigma.SetLineColor(3)
graph2Sigma.SetFillColor(5)
graph1Sigma.SetFillColor(3)
graph2Sigma.SetMinimum(0.5*min(points[2.5]))
if scale:
graph2Sigma.SetMaximum(10*max(max(points[97.5]),max(xsecarray),4))
else:
graph2Sigma.SetMaximum(10*max(max(points[97.5]),max(xsecarray)))
graphCent.SetLineWidth(2)
graphCent.SetLineStyle(2)
if not BLIND:
graphObs.SetLineColor(1)
graphObs.SetLineWidth(3)
graph1Sigma.SetLineStyle(0)
graph2Sigma.SetLineStyle(0)
leg = TLegend(0.55,0.7,0.9,0.9)
leg.AddEntry(graphCent,'Expected','L')
if not BLIND:
leg.AddEntry(graphObs,'Observed','L')
leg.AddEntry(graph1Sigma,'1 #sigma','F')
leg.AddEntry(graph2Sigma,'2 #sigma','F')
leg.SetFillStyle(0)
leg.SetBorderSize(0)
graph2Sigma.Draw('A3')
graph1Sigma.Draw('3 same')
graphCent.Draw('same L')
if not BLIND:
graphObs.Draw('same L')
if scale:
graphOne.SetLineColor(2)
graphOne.SetLineWidth(2)
graphOne.SetLineStyle(2)
graphOne.Draw('same L')
else:
graphXsec.SetLineColor(2)
graphXsecLow.SetLineColor(4)
subscript = 'SR' if 'Resonant' in plottitle else 'FC'
if 'Resonant' in plottitle:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=0.1}{m_{#chi}=100 GeV}'%(subscript,subscript),'l')
else:
leg.AddEntry(graphXsec,'Theory a_{%s}=b_{%s}=0.1'%(subscript,subscript),'l')
# leg.AddEntry(graphXsecLow,'Theory a_{%s}=b_{%s}=0.025'%(subscript,subscript),'l')
for g in [graphXsec]:
g.SetLineWidth(2)
g.SetLineStyle(2)
g.Draw('same L')
leg.Draw()
label = TLatex()
label.SetNDC()
label.SetTextFont(62)
label.SetTextAlign(11)
label.DrawLatex(0.19,0.85,"CMS")
label.SetTextFont(52)
label.DrawLatex(0.28,0.85,"Preliminary")
label.SetTextFont(42)
label.SetTextSize(0.6*c.GetTopMargin())
label.DrawLatex(0.19,0.77,plottitle)
if scale:
if 'Resonant' in plottitle:
label.DrawLatex(0.19,0.7,"a_{SR} = b_{SR} = 0.1")
label.DrawLatex(0.19,0.64,"m_{#chi}=100 GeV")
else:
label.DrawLatex(0.19,0.7,"a_{FC} = b_{FC} = 0.1")
label.SetTextSize(0.5*c.GetTopMargin())
label.SetTextFont(42)
label.SetTextAlign(31) # align right
label.DrawLatex(0.9, 0.94,"%.1f fb^{-1} (13 TeV)"%(plotConfig.lumi))
c.SaveAs(foutname+'.pdf')
c.SaveAs(foutname+'.png')
def limit():
method = ''
channel = "bb"
particleP = "Z'"
particle = channel
multF = ZPTOBB
THEORY = ['A1', 'B3']
suffix = "_" + BTAGGING
if ISMC: suffix += "_MC"
if SY: suffix += "_comb"
#if method=="cls": suffix="_CLs"
if SY:
filename = "./combine/limits/MANtag_study/" + BTAGGING + "/combined_run2/" + YEAR + "_M%d.txt"
else:
filename = "./combine/limits/MANtag_study/" + BTAGGING + "/" + YEAR + "_M%d.txt"
if CATEGORY != "":
filename = filename.replace(
BTAGGING + "/", BTAGGING + "/single_category/" + CATEGORY + "_")
suffix += "_" + CATEGORY
if ISMC: filename = filename.replace(".txt", "_MC.txt")
mass, val = fillValues(filename)
#print "mass =",mass
#print "val =", val
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
Sign = TGraph()
pVal = TGraph()
Best = TGraphAsymmErrors()
Theory = {}
for i, m in enumerate(mass):
if not m in val:
print "Key Error:", m, "not in value map"
continue
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0] * multF)
Exp0s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][2] * multF,
val[m][4] * multF - val[m][3] * multF)
Exp2s.SetPoint(n, m, val[m][3] * multF)
Exp2s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][1] * multF,
val[m][5] * multF - val[m][3] * multF)
if len(val[m]) > 6: Sign.SetPoint(n, m, val[m][6])
if len(val[m]) > 7: pVal.SetPoint(n, m, val[m][7])
if len(val[m]) > 8: Best.SetPoint(n, m, val[m][8])
if len(val[m]) > 10:
Best.SetPointError(n, 0., 0., abs(val[m][9]), val[m][10])
for t in THEORY:
Theory[t] = TGraphAsymmErrors()
addXZH = True
for m in sorted(HVT[t]['W']['XS'].keys()):
if m < mass[0] or m > mass[-1]: continue
if m > 4500:
continue ## for now because I don't have the higher mass xs FIXME
XsZ, XsZ_Up, XsZ_Down = 0., 0., 0.
if addXZH:
XsZ = 1000. * HVT[t]['Z']['XS'][
m] * 0.12 #temporary BR value set to 0.12 FIXME
XsZ_Up = XsZ * (1. + math.hypot(HVT[t]['Z']['QCD'][m][0] - 1.,
HVT[t]['Z']['PDF'][m][0] - 1.))
XsZ_Down = XsZ * (1. -
math.hypot(1. - HVT[t]['Z']['QCD'][m][0],
1. - HVT[t]['Z']['PDF'][m][0]))
n = Theory[t].GetN()
Theory[t].SetPoint(n, m, XsZ)
Theory[t].SetPointError(n, 0., 0., (XsZ - XsZ_Down),
(XsZ_Up - XsZ))
Theory[t].SetLineColor(theoryLineColor[t])
Theory[t].SetFillColor(theoryFillColor[t])
Theory[t].SetFillStyle(theoryFillStyle[t])
Theory[t].SetLineWidth(2)
#Theory[t].SetLineStyle(7)
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp1s.SetFillColor(417) #kGreen+1
Exp1s.SetLineColor(417) #kGreen+1
Exp2s.SetFillColor(800) #kOrange
Exp2s.SetLineColor(800) #kOrange
Exp2s.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle("#sigma(" + particleP + ") #bf{#it{#Beta}}(" +
particleP + " #rightarrow " + particle +
") (fb)")
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Sign.SetLineWidth(2)
Sign.SetLineColor(629)
Sign.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Sign.GetXaxis().SetTitleSize(Sign.GetXaxis().GetTitleSize() * 1.1)
Sign.GetYaxis().SetTitle("Significance")
pVal.SetLineWidth(2)
pVal.SetLineColor(629)
pVal.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
pVal.GetXaxis().SetTitleSize(pVal.GetXaxis().GetTitleSize() * 1.1)
pVal.GetYaxis().SetTitle("local p-Value")
Best.SetLineWidth(2)
Best.SetLineColor(629)
Best.SetFillColor(629)
Best.SetFillStyle(3003)
Best.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)")
Best.GetXaxis().SetTitleSize(Best.GetXaxis().GetTitleSize() * 1.1)
Best.GetYaxis().SetTitle("Best Fit (pb)")
c1 = TCanvas("c1", "Exclusion Limits", 800, 600)
c1.cd()
#SetPad(c1.GetPad(0))
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetLeftMargin(0.12)
c1.GetPad(0).SetTicks(1, 1)
#c1.GetPad(0).SetGridx()
#c1.GetPad(0).SetGridy()
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
Exp0s.Draw("SAME, L")
if not options.blind: Obs0s.Draw("SAME, L")
#setHistStyle(Exp2s)
Exp2s.GetXaxis().SetTitleSize(0.050)
Exp2s.GetYaxis().SetTitleSize(0.050)
Exp2s.GetXaxis().SetLabelSize(0.045)
Exp2s.GetYaxis().SetLabelSize(0.045)
Exp2s.GetXaxis().SetTitleOffset(0.90)
Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetRangeUser(0.1, 5.e3)
#else: Exp2s.GetYaxis().SetRangeUser(0.1, 1.e2)
#Exp2s.GetXaxis().SetRangeUser(mass[0], min(mass[-1], MAXIMUM[channel] if channel in MAXIMUM else 1.e6))
Exp2s.GetXaxis().SetRangeUser(SIGNALS[0], SIGNALS[-1])
#drawAnalysis(channel)
drawAnalysis("")
#drawRegion(channel, True)
drawRegion("", True)
#drawCMS(LUMI, "Simulation Preliminary") #Preliminary
drawCMS(LUMI, "Work in Progress", suppressCMS=True)
# legend
top = 0.9
nitems = 4 + len(THEORY)
leg = TLegend(0.55, top - nitems * 0.3 / 5., 0.98, top)
#leg = TLegend(0.45, top-nitems*0.3/5., 0.98, top)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL upper limits")
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected", "l")
leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f")
leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f")
for t in THEORY:
leg.AddEntry(Theory[t], theoryLabel[t], "fl")
leg.Draw()
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.045)
latex.SetTextFont(42)
#latex.DrawLatex(0.66, leg.GetY1()-0.045, particleP+" #rightarrow "+particle+"h")
leg2 = TLegend(0.12, 0.225 - 2 * 0.25 / 5., 0.65, 0.225)
leg2.SetBorderSize(0)
leg2.SetFillStyle(0) #1001
leg2.SetFillColor(0)
c1.GetPad(0).RedrawAxis()
leg2.Draw()
if not options.blind: Obs0s.Draw("SAME, L")
c1.GetPad(0).Update()
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR +
suffix + ".png")
c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR +
suffix + ".pdf")
if 'ah' in channel or 'sl' in channel:
c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR +
suffix + ".C")
c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR +
suffix + ".root")
for t in THEORY:
print "Model", t, ":",
for m in range(mass[0], mass[-1], 1):
if not (Theory[t].Eval(m) > Obs0s.Eval(m)) == (
Theory[t].Eval(m + 1) > Obs0s.Eval(m + 1)):
print m,
print ""
return
n=0
for a in sorted(threshold_scan):
#for a in sorted(run_parameters):
##Fill the TGraph with threshold (x-axis) and rate (y-axis)
#######graph.SetPoint(n,int(run_parameters[a]['VTHR']),float(run_parameters[a]['RATE_SL1_L1']))
graph.SetPoint(n,int(a),float(threshold_scan[a]))
n = n+1
graph.SetMarkerSize(1.)
graph.SetMarkerStyle(21)
graph.SetMarkerColor(862)
graph.SetFillColor(868)
graph.SetFillStyle(3844)
graph.SetLineColor(868)
graph.SetLineWidth(2)
graph.SetLineStyle(2)
graph.GetXaxis().SetTitle("threshold [mV]")
graph.GetYaxis().SetTitleOffset(1.2)
graph.GetYaxis().SetTitle("rate [kHz]")
graph.Draw("APL")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextColor(1)
latex.SetTextFont(42)
latex.SetTextAlign(33)
latex.SetTextSize(0.04)
latex.SetTextFont(62)
latex.DrawLatex(0.30, 0.96, "GIF++")
etichetta = TLatex()
etichetta.SetNDC()
etichetta.SetTextSize(0.04)
def RootifyCrossBR(self):
for processname in self.processnames:
outfilename_cross = self.crosssecfolder+'/Crosssections_%s%s.root' % (processname, self.tag)
cross_module = importlib.import_module('crosssections.ChiPsi.Crosssections_%s' % (processname))
crosssections = cross_module.crosssection
if self.submit:
outfile = TFile(outfilename_cross, 'RECREATE')
else:
print yellow('--> Would have created outfile %s' % (outfilename_cross))
print green('--> Now at sample %s' % (processname))
for lamb in self.lambdas:
if not lamb in crosssections: continue
print green(' --> Now at lambda: %s' % (get_lambdastring(lamb)))
xsecs_per_mref = crosssections[lamb]
graph2d = TGraph2D()
npoints2d=0
set_points ={}
all_combinations = get_all_combinations(preferred_configurations=preferred_configurations)
for mlq in all_combinations:
set_points[mlq] = {}
for mch in all_combinations[mlq]:
set_points[mlq][mch] = False
for mref in xsecs_per_mref:
xsecs = xsecs_per_mref[mref]
final_xsecs = []
mdeps = array('d')
sigmas = array('d')
tot_los = array('d')
tot_his = array('d')
mdeps_lo = array('d')
mdeps_hi = array('d')
for tuple in xsecs:
mdep, sigma, q_lo, q_hi, pdf = tuple
tot_lo = XsecTotErr(sigma, q_lo, pdf)
tot_hi = XsecTotErr(sigma, q_hi, pdf)
final_xsecs.append((mdep, sigma, tot_lo, tot_hi))
mdeps.append(mdep)
sigmas.append(sigma)
tot_los.append(tot_lo)
tot_his.append(tot_hi)
mdeps_lo.append(0.)
mdeps_hi.append(0.)
if 'LQLQ' in processname or 'LQTChannel' in processname:
graph2d.SetPoint(npoints2d, mdep, mref, sigma)
set_points[mdep][mref] = True
elif 'PsiPsi' in processname:
graph2d.SetPoint(npoints2d, mref, mdep, sigma)
set_points[mref][mdep] = True
else:
raise ValueError('processname does not contain \'LQLQ\' or \'PsiPsi\', what kind of process are we looking at here?')
npoints2d += 1
# make TGraph out of it
graph = TGraphAsymmErrors(len(mdeps), mdeps, sigmas, mdeps_lo, mdeps_hi, tot_los, tot_his)
xaxistitle = 'M_{LQ} [GeV]' if ('LQLQ' in processname or 'LQTChannel' in processname) else 'M_{#chi_{1}} [GeV]'
graph.GetXaxis().SetTitle('M_{LQ} [GeV]')
graph.GetYaxis().SetTitle('#sigma [pb]')
graphname = processname
if 'LQLQ' in processname or 'LQTChannel' in processname:
graphname += '_MC1%i' % (mref)
elif 'PsiPsi' in processname:
graphname += '_MLQ%i' % (mref)
else:
raise ValueError('processname does not contain \'LQLQ\' or \'PsiPsi\', what kind of process are we looking at here?')
graphname += '_L%s' % (get_lambdastring(lamb))
graph.SetName(graphname)
# print 'graphname: %s' % (graphname)
graphtitle = processname
if 'LQLQ' in processname or 'LQTChannel' in processname:
graphtitle += ', M_{#chi_{1}} = %i GeV' % (mref)
elif 'PsiPsi' in processname:
graphtitle += ', M_{LQ} = %i GeV' % (mref)
graphtitle += ', #lambda = %s' % (get_lambdastring(lamb).replace('p', '.'))
# print 'graphtitle: %s' % (graphtitle)
graph.SetTitle(graphtitle)
if self.submit:
outfile.cd()
graph.Write()
else:
print yellow(' --> Would have written graph %s to outfile' % (graphname))
# fill remaining points in 2d graph with zeros
for mlq in set_points:
for mch in set_points[mlq]:
if not set_points[mlq][mch]:
graph2d.SetPoint(npoints2d, mlq, mch, 0.)
npoints2d += 1
graph2d.SetName(processname + '_L%s' % (get_lambdastring(lamb)))
graph2d.GetXaxis().SetTitle('M_{LQ} [GeV]')
graph2d.GetYaxis().SetTitle('M_{#chi_{1}} = %i [GeV]')
graph2d.GetZaxis().SetTitle('#sigma [pb]')
graph2d.SetTitle(processname + ', #lambda = %s' % (get_lambdastring(lamb).replace('p', '.')))
if self.submit:
graph2d.Write()
else:
print yellow(' --> Would have written 2d-graph to outfile')
if self.submit:
outfile.Close()
# also rootify BRs if we are looking at the LQLQ process without decays (just for fun, could also be any other LQLQ process)
if processname == 'LQLQ':
outfilename_br = self.crosssecfolder+'/Branchingratios_%s%s.root' % (processname, self.tag)
if self.submit:
outfile = TFile(outfilename_br, 'RECREATE')
else:
print yellow('--> Would have created outfile %s' % (outfilename_br))
br_module = importlib.import_module('crosssections.ChiPsi.Branchingratios_%s' % (processname))
allbrs = br_module.branchingratio
for lamb in allbrs.keys():
brs = allbrs[lamb]
brs2d = {}
npoints2d = {}
set_points ={}
for mlq in all_combinations:
set_points[mlq] = {}
for mch in all_combinations[mlq]:
set_points[mlq][mch] = {}
for decaymode in decaymode_dict.keys():
set_points[mlq][mch][decaymode] = False
decaymodes_present = []
for mlq in sorted(brs):
mchs_per_decaymode = {}
brs_per_decaymode = {}
for mch in sorted(brs[mlq]):
for decaymode in brs[mlq][mch]:
if not decaymode in decaymodes_present:
decaymodes_present.append(decaymode)
if not decaymode in mchs_per_decaymode.keys(): mchs_per_decaymode[decaymode] = array('d')
if not decaymode in brs_per_decaymode.keys(): brs_per_decaymode[decaymode] = array('d')
# if not decaymode in set_points[mlq][mch].keys():
# # print mlq, mch, decaymode
# set_points[mlq][mch][decaymode] = False
if not decaymode in brs2d.keys():
graphname2d = processname + ('_L%s_%i_%i' % (get_lambdastring(lamb), abs(decaymode[0]), abs(decaymode[1])))
# print graphname2d
npoints2d[decaymode] = 0
brs2d[decaymode] = TGraph2D()
brs2d[decaymode].SetName(graphname2d)
brs2d[decaymode].GetXaxis().SetTitle('M_{LQ} [GeV]')
brs2d[decaymode].GetYaxis().SetTitle('M_{#chi_{1}} = %i [GeV]')
brs2d[decaymode].GetZaxis().SetTitle('BR (LQLQ#rightarrow%s)' % (decaymode_dict[decaymode]))
brs2d[decaymode].SetTitle(processname + ', %s' % (decaymode_dict[decaymode]))
mchs_per_decaymode[decaymode].append(mch)
brs_per_decaymode[decaymode].append(brs[mlq][mch][decaymode][0])
brs2d[decaymode].SetPoint(npoints2d[decaymode], mlq, mch, brs[mlq][mch][decaymode][0])
set_points[mlq][mch][decaymode] = True
npoints2d[decaymode] += 1
for decaymode in mchs_per_decaymode.keys():
graph = TGraph(len(mchs_per_decaymode[decaymode]), mchs_per_decaymode[decaymode], brs_per_decaymode[decaymode])
graphname = processname + ('_MLQ%i_L%s_%i_%i' % (mlq, get_lambdastring(lamb), abs(decaymode[0]), abs(decaymode[1])))
graph.SetName(graphname)
graph.GetXaxis().SetTitle('M_{#chi_{1}} [GeV]')
graph.GetYaxis().SetTitle('BR (LQLQ#rightarrow%s)' % (decaymode_dict[decaymode]))
graph.SetTitle(processname + ', %s' % (decaymode_dict[decaymode]))
if self.submit:
graph.Write()
else:
print yellow(' --> Would have written graph %s to outfile' % (graphname))
# fill remaining points in 2d graph with zeros
for mlq in set_points:
for mch in set_points[mlq]:
for decaymode in set_points[mlq][mch]:
if not set_points[mlq][mch][decaymode] and decaymode in decaymodes_present:
# print decaymode
# print brs2d.keys()
# print npoints2d.keys()
# print 'Setting BR for MLQ=%i, MCH=%i, decay=(%i, %i) to 0' % (mlq, mch, decaymode[0], decaymode[1])
brs2d[decaymode].SetPoint(npoints2d[decaymode], mlq, mch, 0)
npoints2d[decaymode] += 1
if self.submit:
for decaymode in brs2d:
brs2d[decaymode].Write()
else:
print yellow(' --> Would have written 2d-graphs to outfile')
outfile.Close()
expected68.SetFillColor(ROOT.kGreen)
expected95 = TGraphAsymmErrors(massv, expv, masserrv, masserrv, exp95Lv,
exp95Hv)
expected95.SetFillColor(ROOT.kYellow)
c4 = TCanvas("c4", "Diphoton c = 01 Limits", 1000, 800)
c4.SetBottomMargin(0.15)
c4.SetRightMargin(0.06)
#c4.SetLogy(1)
#expected95.SetMinimum(0.0001);
expected95.SetMaximum(0.0057)
expected95.Draw("a3")
expected95.GetXaxis().SetTitle("Diphoton Mass [GeV/c^{2}]")
expected95.GetYaxis().SetTitle("#sigma(G_{RS} #rightarrow #gamma #gamma)[pb]")
expected68.Draw("3same")
expected_p.Draw("csame")
observed_p.Draw("cpsame")
theory.Draw("same")
latex = TLatex()
latex.SetNDC()
latex.SetTextSize(0.04)
latex.SetTextAlign(31) # align right
latex.DrawLatex(0.45, 0.95, "CMS Preliminary")
latex2 = TLatex()
latex2.SetNDC()
latex2.SetTextSize(0.04)
def limit2HDM():
global signals
signals = range(800, 2000 + 1, 50)
multF = HTOBB
THEORY = ['T1', 'T2']
mass, val = fillValues("./combine/AZh/AZh_M%d.txt")
Obs0s = TGraph()
Exp0s = TGraph()
Exp1s = TGraphAsymmErrors()
Exp2s = TGraphAsymmErrors()
massB, valB = fillValues("./combine/BBAZh/BBAZh_M%d.txt")
Obs0sB = TGraph()
Exp0sB = TGraph()
Exp1sB = TGraphAsymmErrors()
Exp2sB = TGraphAsymmErrors()
for i, m in enumerate(mass):
if not m in val:
print "Key Error:", m, "not in value map"
continue
n = Exp0s.GetN()
Obs0s.SetPoint(n, m, val[m][0] * multF)
Exp0s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPoint(n, m, val[m][3] * multF)
Exp1s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][2] * multF,
val[m][4] * multF - val[m][3] * multF)
Exp2s.SetPoint(n, m, val[m][3] * multF)
Exp2s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][1] * multF,
val[m][5] * multF - val[m][3] * multF)
Obs0sB.SetPoint(n, m, valB[m][0] * multF)
Exp0sB.SetPoint(n, m, valB[m][3] * multF)
Exp1sB.SetPoint(n, m, valB[m][3] * multF)
Exp1sB.SetPointError(n, 0., 0.,
valB[m][3] * multF - valB[m][2] * multF,
valB[m][4] * multF - valB[m][3] * multF)
Exp2sB.SetPoint(n, m, valB[m][3] * multF)
Exp2sB.SetPointError(n, 0., 0.,
valB[m][3] * multF - valB[m][1] * multF,
valB[m][5] * multF - valB[m][3] * multF)
col = 629
Exp2s.SetLineWidth(2)
Exp2s.SetLineStyle(1)
Obs0s.SetLineWidth(3)
Obs0s.SetMarkerStyle(0)
Obs0s.SetLineColor(1)
Exp0s.SetLineStyle(2)
Exp0s.SetLineWidth(3)
Exp0s.SetLineColor(1)
# Exp1s.SetFillColorAlpha(col, 0.4) #kGreen+1
# Exp1s.SetLineColorAlpha(col, 0.4)
# Exp2s.SetFillColorAlpha(col, 0.2) #kOrange
# Exp2s.SetLineColorAlpha(col, 0.2)
Exp1s.SetFillColor(417)
Exp1s.SetLineColor(417)
Exp2s.SetFillColor(800)
Exp2s.SetLineColor(800)
colB = 922
Exp2sB.SetLineWidth(2)
Obs0sB.SetLineStyle(9)
Obs0sB.SetLineWidth(3)
Obs0sB.SetMarkerStyle(0)
Obs0sB.SetLineColor(colB)
Exp0sB.SetLineStyle(8)
Exp0sB.SetLineWidth(3)
Exp0sB.SetLineColor(colB)
Exp1sB.SetFillColorAlpha(colB, 0.4) #kGreen+1
Exp1sB.SetLineColorAlpha(colB, 0.4)
Exp2sB.SetFillColorAlpha(colB, 0.2) #kOrange
Exp2sB.SetLineColorAlpha(colB, 0.2)
Exp2s.GetXaxis().SetTitle("m_{A} (GeV)")
Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25)
Exp2s.GetXaxis().SetNoExponent(True)
Exp2s.GetXaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetTitle(
"#sigma(A) #bf{#it{#Beta}}(A #rightarrow Zh) #bf{#it{#Beta}}(h #rightarrow bb) (fb)"
)
Exp2s.GetYaxis().SetTitleOffset(1.5)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetMoreLogLabels()
Theory = {}
#for t in THEORY:
# Theory[t] = TGraphAsymmErrors()
# for m in sorted(THDM[t]['ggA'].keys()):
# if m < mass[0] or m > mass[-1]: continue
# Xs, Xs_Up, Xs_Down = 0., 0., 0.
# Xs = THDM[t]['ggA'][m]
# Xs_Up = Xs*(1.+math.sqrt((THDM['PDF']['ggA'][m][0]-1.)**2 + (THDM['QCD']['ggA'][m][0]-1.)**2))
# Xs_Down = Xs*(1.-math.sqrt((1.-THDM['PDF']['ggA'][m][1])**2 + (1.-THDM['QCD']['ggA'][m][1])**2))
# n = Theory[t].GetN()
# Theory[t].SetPoint(n, m, Xs)
# Theory[t].SetPointError(n, 0., 0., (Xs-Xs_Down), (Xs_Up-Xs))
# Theory[t].SetLineColor(theoryLineColor[t])
# Theory[t].SetFillColor(theoryFillColor[t])
# Theory[t].SetFillStyle(theoryFillStyle[t])
# Theory[t].SetLineWidth(2)
# #Theory[t].SetLineStyle(7)
c1 = TCanvas("c1", "Exclusion Limits", 800, 600)
c1.cd()
#SetPad(c1.GetPad(0))
c1.GetPad(0).SetTopMargin(0.06)
c1.GetPad(0).SetRightMargin(0.05)
c1.GetPad(0).SetLeftMargin(0.12)
c1.GetPad(0).SetTicks(1, 1)
c1.GetPad(0).SetLogy()
Exp2s.Draw("A3")
Exp1s.Draw("SAME, 3")
Exp0s.Draw("SAME, L")
# Exp2sB.Draw("SAME, 3")
# Exp1sB.Draw("SAME, 3")
Exp0sB.Draw("SAME, L")
if not options.blind:
Obs0s.Draw("SAME, L")
Obs0sB.Draw("SAME, L")
for t in THEORY:
Theory[t].Draw("SAME, L3")
Theory[t].Draw("SAME, L3X0Y0")
#setHistStyle(Exp2s)
# Exp2s.GetXaxis().SetTitleSize(0.045)
# Exp2s.GetYaxis().SetTitleSize(0.04)
# Exp2s.GetXaxis().SetLabelSize(0.04)
# Exp2s.GetYaxis().SetLabelSize(0.04)
# Exp2s.GetXaxis().SetTitleOffset(1)
# Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetXaxis().SetTitleSize(0.050)
Exp2s.GetYaxis().SetTitleSize(0.050)
Exp2s.GetXaxis().SetLabelSize(0.045)
Exp2s.GetYaxis().SetLabelSize(0.045)
Exp2s.GetXaxis().SetTitleOffset(0.90)
Exp2s.GetYaxis().SetTitleOffset(1.25)
Exp2s.GetYaxis().SetMoreLogLabels(True)
Exp2s.GetYaxis().SetNoExponent(True)
Exp2s.GetYaxis().SetRangeUser(0.5, 1.e3)
Exp2s.GetXaxis().SetRangeUser(mass[0], mass[-1])
drawAnalysis('AZh')
drawRegion('AZHsl', True)
drawCMS(LUMI, "") #Preliminary
#drawCMS(LUMI, "Work in Progress", suppressCMS=True)
# legend
leg = TLegend(0.6, 0.90, 0.99, 0.90)
leg.SetBorderSize(0)
leg.SetFillStyle(0) #1001
leg.SetFillColor(0)
leg.SetHeader("95% CL upper limits")
leg.AddEntry(None, "gg #rightarrow A #rightarrow Zh",
"") #"95% CL upper limits"
leg.AddEntry(Obs0s, "Observed", "l")
leg.AddEntry(Exp0s, "Expected", "l")
leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f")
leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f")
leg.AddEntry(None, "", "")
leg.AddEntry(None, "bbA #rightarrow Zh", "")
leg.AddEntry(Obs0sB, "Observed", "l")
leg.AddEntry(Exp0sB, "Expected", "l")
leg.SetY1(leg.GetY2() - leg.GetNRows() * 0.045)
leg.Draw()
# latex = TLatex()
# latex.SetNDC()
# latex.SetTextSize(0.040)
# latex.SetTextFont(42)
# latex.DrawLatex(0.65, leg.GetY1()-0.045, "cos(#beta-#alpha)=0.25, tan(#beta)=1")
# legB = TLegend(0.12, 0.4-4*0.3/5., 0.65, 0.4)
legB = TLegend(0.15, 0.27, 0.68, 0.27)
legB.SetBorderSize(0)
legB.SetFillStyle(0) #1001
legB.SetFillColor(0)
for t in THEORY:
legB.AddEntry(Theory[t], theoryLabel[t], "fl")
legB.AddEntry(None, "cos(#beta-#alpha)=0.25, tan(#beta)=1", "")
legB.SetY1(legB.GetY2() - legB.GetNRows() * 0.045)
legB.Draw()
c1.GetPad(0).RedrawAxis()
leg.Draw()
c1.Update()
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
c1.Print("plotsLimit/Exclusion/THDM.png")
c1.Print("plotsLimit/Exclusion/THDM.pdf")
gObs.SetLineWidth(2)
gObs.SetLineStyle(1)
gObs.SetLineColor(ROOT.kBlue+1)
gObs.SetMarkerColor(ROOT.kBlue+1)
gObs.SetMarkerStyle(21)
gObs.SetMarkerSize(1.5)
gxs.SetLineStyle(5)
gxs.SetLineWidth(3)
gxs.SetLineColor(ROOT.kRed)
canvasName = 'Limits'
if useSub:
canvasName = 'Limits_Sub'
can = TCanvas(canvasName,canvasName,900,600)
gPad.SetLogy()
g2.GetXaxis().SetTitle(' qg resonance Mass (TeV)')
g2.GetYaxis().SetTitle('#sigma #times A #times BR (q*#rightarrow jj) (pb)')
#g2.GetYaxis().SetTitle('#sigma #times BR (q*#rightarrow jj) (pb)')
g2.GetYaxis().CenterTitle(ROOT.kTRUE)
g2.GetYaxis().SetNdivisions(510)
g2.GetYaxis().SetRangeUser(1e-3,10)
g2.Draw('AE3')
g1.Draw('sameE3')
gExp.Draw('sameL')
gObs.Draw('sameLP')
gxs.Draw('sameL')
if superimpose:
gExpDinko.Draw('sameL')
leg = TLegend(0.65,0.65,0.9,0.9)
if useSub:
C = TCanvas()
C.Print("PUWeights.pdf[")
# Loop over json content #
for name, path in dict_files.items():
print("Looking at %s" % name)
# Load json content #
with open(path, "r") as handle:
data = json.load(handle)
# Get binning content #
bins = data['data'] # List of dict (1 dict per bin)
g = TGraphAsymmErrors(len(bins))
# Loop over bins #
for i, b in enumerate(bins):
val = b['value']
up = b['error_high']
down = b['error_low']
bincenter = (b['bin'][0] + b['bin'][1]) / 2
g.SetPoint(i, bincenter, val)
g.SetPointError(i, 0, 0, up, down)
g.GetHistogram().SetMinimum(0.)
g.GetHistogram().SetMaximum(2.)
g.SetTitle("Sample : %s" % name)
g.GetXaxis().SetTitle("nTrueInt")
g.GetYaxis().SetTitle("PU weight")
C.Clear()
g.Draw()
C.Print("PUWeights.pdf", "Title:%s" % name)
C.Print("PUWeights.pdf]")
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()
import math
from ROOT import TFile, TCanvas, TGraph, TGraphAsymmErrors
import array
Binning_PT = array.array("d", [0, 30, 35, 40, 50, 60, 75, 95, 120, 150, 200])
OutFile = TFile("OutPutFR.root")
HistoNum = OutFile.Get("histoLooseNumerator")
HistoNum = HistoNum.Rebin(len(Binning_PT) - 1, "", Binning_PT)
HistoDeNum = OutFile.Get("histoDenominator")
HistoDeNum = HistoDeNum.Rebin(len(Binning_PT) - 1, "", Binning_PT)
fakeRate = TGraphAsymmErrors(HistoNum, HistoDeNum, "")
canv = TCanvas("canv", "histograms", 0, 0, 600, 600)
canv.SetLogy()
fakeRate.GetXaxis().SetRangeUser(0, 200)
fakeRate.GetXaxis().SetTitle("#tau p_{T} [GeV]")
fakeRate.GetYaxis().SetRangeUser(0.01, 0.5)
fakeRate.SetTitle('Jet to Tau Fake Rate')
fakeRate.SetMarkerStyle(20)
fakeRate.Draw()
canv.SaveAs("jetToTauFR.pdf")
def makePlot1D(filepath, foutname, plottitle='', masstitle=''):
br = 1 if 'Resonant' in plottitle else 0.68
limits = parseLimitFiles2D(filepath, br)
xaxis = []
xseclist = []
xsecerr = []
cent = []
obs = []
up1 = []
up2 = []
down1 = []
down2 = []
maxval = 0
minval = 999
for m in sorted(limits):
l = limits[m]
xaxis.append(m)
xseclist.append(l.xsec)
xsecerr.append(l.xsec * .2)
cent.append(l.cent)
up1.append(l.up1 - l.cent)
up2.append(l.up2 - l.cent)
down1.append(l.cent - l.down1)
down2.append(l.cent - l.down2)
obs.append(l.obs)
maxval = max(maxval, l.up2)
minval = min(minval, l.down2)
N = len(xaxis)
up1Sigma = array('f', up1)
up2Sigma = array('f', up2)
down1Sigma = array('f', down1)
down2Sigma = array('f', down2)
cent = array('f', cent)
obs = array('f', obs)
xarray = array('f', xaxis)
xsecarray = array('f', xseclist)
xsecerrarray = array('f', xsecerr)
zeros = array('f', [0 for i in xrange(N)])
graphXsec = TGraphErrors(N, xarray, xsecarray, zeros, xsecerrarray)
graphCent = TGraph(N, xarray, cent)
graphObs = TGraph(N, xarray, obs)
graph1Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down1Sigma,
up1Sigma)
graph2Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down2Sigma,
up2Sigma)
c = TCanvas('c', 'c', 700, 600)
c.SetLogy()
c.SetLeftMargin(.15)
graph2Sigma.GetXaxis().SetTitle(masstitle + ' [GeV]')
graph2Sigma.GetYaxis().SetTitle(
'95% C.L. upper limit [#sigma/#sigma_{theory}]')
c2 = root.kOrange
c1 = root.kGreen + 1
graph2Sigma.SetLineColor(c2)
graph1Sigma.SetLineColor(c1)
graph2Sigma.SetFillColor(c2)
graph1Sigma.SetFillColor(c1)
graph2Sigma.SetMinimum(0.5 * minval)
graph2Sigma.SetMaximum(10 * maxval)
graphCent.SetLineWidth(2)
graphCent.SetLineStyle(2)
graphObs.SetLineColor(1)
graphObs.SetLineWidth(3)
graphObs.SetMarkerStyle(20)
graphObs.SetMarkerSize(1)
graphObs.SetMarkerColor(1)
graph1Sigma.SetLineStyle(0)
graph2Sigma.SetLineStyle(0)
leg = TLegend(0.55, 0.7, 0.9, 0.9)
leg.AddEntry(graphCent, 'Expected', 'L')
if not BLIND:
leg.AddEntry(graphObs, 'Observed', 'Lp')
leg.AddEntry(graph1Sigma, '1 std. dev.', 'F')
leg.AddEntry(graph2Sigma, '2 std. dev.', 'F')
leg.SetFillStyle(0)
leg.SetBorderSize(0)
graph2Sigma.Draw('A3')
graph1Sigma.Draw('3 same')
graphCent.Draw('same L')
if not BLIND:
graphObs.Draw('same Lp')
subscript = 'SR' if 'Resonant' in plottitle else 'FC'
coupling = '0.1' if 'Resonant' in plottitle else '0.25'
graphXsec.SetLineColor(2)
graphXsec.SetLineWidth(2)
graphXsec.SetLineStyle(2)
graphXsec.SetFillColor(2)
graphXsec.SetFillStyle(3005)
graphXsec.Draw('same L3')
'''
if not scale:
if 'Resonant' in plottitle:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=100 GeV}'%(subscript,subscript,coupling),'l')
else:
leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=10 GeV}'%(subscript,subscript,coupling),'l')
'''
if not BLIND:
findIntersect1D(graphObs, graphXsec, xaxis[0], xaxis[-1])
findIntersect1D(graphCent, graphXsec, xaxis[0], xaxis[-1])
leg.Draw()
label = TLatex()
label.SetNDC()
label.SetTextSize(0.8 * c.GetTopMargin())
label.SetTextFont(62)
label.SetTextAlign(11)
label.DrawLatex(0.15, 0.94, "CMS")
label.SetTextFont(52)
label.SetTextSize(0.6 * c.GetTopMargin())
# label.DrawLatex(0.25,0.94,"Preliminary")
label.SetTextFont(42)
label.SetTextSize(0.7 * c.GetTopMargin())
label.DrawLatex(0.19, 0.83, plottitle)
if 'Resonant' in plottitle:
label.DrawLatex(0.19, 0.75, "a_{SR} = b_{SR} = %s" % coupling)
label.DrawLatex(0.19, 0.68, "m_{#chi}=100 GeV")
else:
label.DrawLatex(0.19, 0.75, "g_{DM}^{V}=1,g_{q}^{V}=0.25")
label.DrawLatex(0.19, 0.68, "m_{#chi}=1 GeV")
label.SetTextSize(0.6 * c.GetTopMargin())
label.SetTextFont(42)
label.SetTextAlign(31) # align right
label.DrawLatex(0.95, 0.94, "%.1f fb^{-1} (13 TeV)" % (plotConfig.lumi))
c.SaveAs(foutname + '.pdf')
c.SaveAs(foutname + '.png')
def ratioplot():
# create required parts
leg = getLegend()
latex = getLatex()
c = SetCanvas()
#c.SetLogy()
#c = TCanvas()
#c.SetLogy()
h1 = f.Get('h_num_calo_') #'calo',pf
h1 = setHistStyle(h1, bins)
h2 = f.Get('h_den_calo_')
h2 = setHistStyle(h2, bins)
h11 = f2.Get('h_num_calo_')
h11 = setHistStyle(h11, bins)
h21 = f2.Get('h_den_calo_')
h21 = setHistStyle(h21, bins)
gr = TGraphAsymmErrors(30)
#gr.Divide(h1,h2)
gr = TGraphAsymmErrors(h1, h2)
gr2 = TGraphAsymmErrors(h11, h21)
gr2.SetMarkerStyle(20)
gr2.GetXaxis().SetRangeUser(0, 1000)
gr2.SetMarkerSize(1.5)
gr2.SetLineColor(2)
gr2.SetLineWidth(1)
gr2.SetMarkerColor(2)
gr.GetXaxis().SetRangeUser(0, 1000)
# gr.GetYaxis().SetRangeUser(0.0001,1.2)
gr.SetMarkerStyle(20)
gr.SetMarkerSize(1.5)
gr.SetLineColor(1)
gr.SetLineWidth(1)
gr.SetMarkerColor(1)
gr.GetYaxis().SetTitle("Trigger Efficiency")
gr.GetXaxis().SetTitle("MET [GeV]")
gr.SetTitle("")
#base histogram
histogram_base = TH1F("histogram_base", "", 1000, 0, 1000.)
histogram_base.SetTitle("")
histogram_base.SetStats(0)
histogram_base.SetMarkerSize(2)
#histogram_base.SetMinimum(0.0)
histogram_base.SetMaximum(1.2)
histogram_base.GetXaxis().SetTitle("Online E_{T}^{miss} (GeV)")
histogram_base.GetYaxis().SetTitle("Efficiency")
histogram_base = setHistStyle(histogram_base, bins)
histogram_base.Draw("HIST")
# c.SaveAs()
gr.Draw('P same')
gr2.Draw('P same')
latex.DrawLatex(0.49, 0.93, " EGamma Run2018C, 13 TeV")
xmin = 0.0
line = TLine(max(xmin, gr.GetXaxis().GetXmin()), 1, 1000, 1)
line.SetLineColor(1)
line.SetLineWidth(1)
line.SetLineStyle(7)
line.Draw()
leg.AddEntry(gr, 'With HBHENoise filter', 'P')
leg.AddEntry(gr2, 'Without HBHENoise filter', 'P')
leg.Draw()
txt = 'Path: HLT_PFMETTypeOne200_HBHE_BeamHaloCleaned'
texcms = AddText(txt)
texcms.Draw("same")
c.SaveAs('testTurnOn_EGamma.png')
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)
