def getEfficiency(passes, passesError, total, totalError):
passesHist = TH1D("passes", "", 1, 0.0, 1.0)
totalHist = TH1D("total", "", 1, 0.0, 1.0)
passesHist.SetBinContent(1, passes)
passesHist.SetBinError(1, passesError)
totalHist.SetBinContent(1, total)
totalHist.SetBinError(1, totalError)
g = TGraphAsymmErrors(passesHist, totalHist)
x = Double(0.0)
y = Double(0.0)
g.GetPoint(0, x, y)
return (y, g.GetErrorYlow(0), g.GetErrorYhigh(0))
def getEfficiency(nominatorHisto, denominatorHisto, cutString):
eff = TGraphAsymmErrors(nominatorHisto, denominatorHisto, "cp")
effValue = ROOT.Double(0.)
blubb = ROOT.Double(0.)
intttt = eff.GetPoint(0, blubb, effValue)
n = {
"Nominator": nominatorHisto.Integral(),
"Denominator": denominatorHisto.Integral(),
"Efficiency": nominatorHisto.Integral() / denominatorHisto.Integral(),
"UncertaintyUp": eff.GetErrorYhigh(0),
"UncertaintyDown": eff.GetErrorYlow(0),
}
# print cut, n
n["cut"] = cutString
return n
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)
def limit(method, channel):
particle = channel[1:2]
particleP = particle + "'" if channel.startswith('X') else channel[0]
THEORY = ['A1', 'B3'] if channel.startswith('X') else []
suffix = ""
if method == "hvt": suffix = "_HVT"
if method == "cls": suffix = "_CLs"
if method == "monoH": suffix = "_monoH"
filename = "./combine/" + method + "/" + channel + "_M%d.txt"
mass, val = fillValues(filename)
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])
Exp0s.SetPoint(n, m, val[m][3])
Exp1s.SetPoint(n, m, val[m][3])
Exp1s.SetPointError(n, 0., 0., val[m][3] - val[m][2],
val[m][4] - val[m][3])
Exp2s.SetPoint(n, m, val[m][3])
Exp2s.SetPointError(n, 0., 0., val[m][3] - val[m][1],
val[m][5] - val[m][3])
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()
for m in sorted(HVT[t]['Z']['XS'].keys()):
if m < mass[0] or m > mass[-1]: continue
XsW, XsW_Up, XsW_Down, XsZ, XsZ_Up, XsZ_Down = 0., 0., 0., 0., 0., 0.
XsZ = 1000. * HVT[t]['Z']['XS'][m] * HVT[t]['Z']['BR'][m]
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, XsW + XsZ)
Theory[t].SetPointError(n, 0., 0.,
(XsW - XsW_Down) + (XsZ - XsZ_Down),
(XsW_Up - XsW) + (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 +
"h) (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(0.01, 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))
drawAnalysis(channel)
drawRegion(channel, True)
drawCMS(LUMI, YEAR, "Preliminary") #Preliminary
# legend
top = 0.9
nitems = 4 + len(THEORY)
leg = TLegend(0.55, 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()
"""
if True and channel.endswith('sl'):
mass, val = fillValues("./combine/alpha/X"+particle+"Hbonly_M%d.txt")
Exp, Obs = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(mass):
if not m in val: continue
Exp.SetPoint(Exp.GetN(), m, val[m][3])
Obs.SetPoint(Obs.GetN(), m, val[m][0])
Exp.SetLineWidth(3)
Exp.SetLineColor(602) #602
Exp.SetLineStyle(5)
Obs.SetLineWidth(3)
Obs.SetLineColor(602)
Exp.Draw("SAME, L")
Obs.Draw("SAME, L")
mass15, val15 = fillValues("./combine/Vh_2015/X"+particle+"h_M%d.txt")
Exp15, Obs15 = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(mass15):
if not m in val: continue
Exp15.SetPoint(Exp15.GetN(), m, val15[m][3]*multF*Theory['B3'].GetY()[i]*(0.625 if particle=='V' and m>3000 else 1.))
Obs15.SetPoint(Obs15.GetN(), m, val15[m][0]*multF*Theory['B3'].GetY()[i]*(0.625 if particle=='V' and m>3000 else 1.))
Exp15.SetLineWidth(3)
Exp15.SetLineColor(856) #602
Exp15.SetLineStyle(6)
Obs15.SetLineWidth(3)
Obs15.SetLineColor(856)
Exp15.Draw("SAME, L")
#Obs15.Draw("SAME, L")
leg2.AddEntry(Exp, "1+2 b-tag", "l")
"""
if True and channel == 'AZh':
massLL, valLL = fillValues("./combine/AZh/AZhll_M%d.txt")
ExpLL, ObsLL = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(massLL):
if not m in val: continue
ExpLL.SetPoint(ExpLL.GetN(), m, valLL[m][3] * multF)
ObsLL.SetPoint(ObsLL.GetN(), m, valLL[m][0] * multF)
ExpLL.SetLineWidth(3)
ExpLL.SetLineColor(833) #602
ExpLL.SetLineStyle(5)
ObsLL.SetLineWidth(3)
ObsLL.SetLineColor(833)
ExpLL.Draw("SAME, L")
#ObsLL.Draw("SAME, L")
massNN, valNN = fillValues("./combine/AZh/AZhnn_M%d.txt")
ExpNN, ObsNN = TGraphAsymmErrors(), TGraphAsymmErrors()
for i, m in enumerate(massNN):
if not m in val: continue
ExpNN.SetPoint(ExpNN.GetN(), m, valNN[m][3] * multF)
ObsNN.SetPoint(ObsNN.GetN(), m, valNN[m][0] * multF)
ExpNN.SetLineWidth(3)
ExpNN.SetLineColor(855) #602
ExpNN.SetLineStyle(6)
ObsNN.SetLineWidth(3)
ObsNN.SetLineColor(855)
ExpNN.Draw("SAME, L")
#ObsNN.Draw("SAME, L")
leg2.AddEntry(ExpLL,
"Expected, A #rightarrow Zh #rightarrow llb#bar{b}", "l")
leg2.AddEntry(ExpNN,
"Expected, A #rightarrow Zh #rightarrow #nu#nub#bar{b}",
"l")
if method == 'combo':
massAH, valAH = fillValues("./combine/dijet/X" + particle +
"Hah_M%d.txt")
ExpAH = TGraphAsymmErrors()
for i, m in enumerate(massAH):
if not m in val: continue
ExpAH.SetPoint(ExpAH.GetN(), m, valAH[m][3] * multF)
ExpAH.SetLineWidth(2)
ExpAH.SetLineColor(602) #602
ExpAH.SetLineStyle(4)
ExpAH.Draw("SAME, L")
massSL, valSL = fillValues("./combine/alpha/X" + particle +
"Hsl_M%d.txt")
ExpSL = TGraphAsymmErrors()
for i, m in enumerate(massSL):
if not m in val: continue
ExpSL.SetPoint(ExpSL.GetN(), m, valSL[m][3] * multF)
ExpSL.SetLineWidth(3)
ExpSL.SetLineColor(860 - 9) #602
ExpSL.SetLineStyle(7)
ExpSL.Draw("SAME, L")
leg2.AddEntry(ExpAH, "B2G-17-002", "l")
leg2.AddEntry(ExpSL, "B2G-17-004", "l")
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("plotsLimit/Exclusion/" + channel + suffix + ".png")
c1.Print("plotsLimit/Exclusion/" + channel + suffix + ".pdf")
if 'ah' in channel or 'sl' in channel:
c1.Print("plotsLimit/Exclusion/" + channel + suffix + ".C")
c1.Print("plotsLimit/Exclusion/" + channel + 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 ""
print "p1s[",
for i in range(Exp0s.GetN()):
print Exp0s.GetY()[i] + Exp1s.GetErrorYhigh(i), ",",
print "],"
print "m1s[",
for i in range(Exp0s.GetN()):
print Exp0s.GetY()[i] - Exp1s.GetErrorYlow(i), ",",
print "],"
print "[",
for i in range(Exp0s.GetN()):
print Exp0s.GetY()[i], ",",
print "]"
#if not 'ah' in channel and not 'sl' in channel: return
# ---------- Significance ----------
c2 = TCanvas("c2", "Significance", 800, 600)
c2.cd()
c2.GetPad(0).SetTopMargin(0.06)
c2.GetPad(0).SetRightMargin(0.05)
c2.GetPad(0).SetTicks(1, 1)
c2.GetPad(0).SetGridx()
c2.GetPad(0).SetGridy()
Sign.GetYaxis().SetRangeUser(0., 5.)
Sign.Draw("AL3")
drawCMS(LUMI, YEAR, "Preliminary")
drawAnalysis(channel[1:3])
c2.Print("plotsLimit/Significance/" + channel + suffix + ".png")
c2.Print("plotsLimit/Significance/" + channel + suffix + ".pdf")
# c2.Print("plotsLimit/Significance/"+channel+suffix+".root")
# c2.Print("plotsLimit/Significance/"+channel+suffix+".C")
# ---------- p-Value ----------
c3 = TCanvas("c3", "p-Value", 800, 600)
c3.cd()
c3.GetPad(0).SetTopMargin(0.06)
c3.GetPad(0).SetRightMargin(0.05)
c3.GetPad(0).SetTicks(1, 1)
c3.GetPad(0).SetGridx()
c3.GetPad(0).SetGridy()
c3.GetPad(0).SetLogy()
pVal.Draw("AL3")
pVal.GetYaxis().SetRangeUser(2.e-7, 0.5)
ci = [
1., 0.317310508, 0.045500264, 0.002699796, 0.00006334, 0.000000573303,
0.000000001973
]
line = TLine()
line.SetLineColor(922)
line.SetLineStyle(7)
text = TLatex()
text.SetTextColor(922)
text.SetTextSize(0.025)
text.SetTextAlign(12)
for i in range(1, len(ci) - 1):
line.DrawLine(pVal.GetXaxis().GetXmin(), ci[i] / 2,
pVal.GetXaxis().GetXmax(), ci[i] / 2)
text.DrawLatex(pVal.GetXaxis().GetXmax() * 1.01, ci[i] / 2,
"%d #sigma" % i)
drawCMS(LUMI, YEAR, "Preliminary")
drawAnalysis(channel[1:3])
c3.Print("plotsLimit/pValue/" + channel + suffix + ".png")
c3.Print("plotsLimit/pValue/" + channel + suffix + ".pdf")
# c3.Print("plotsLimit/pValue/"+channel+suffix+".root")
# c3.Print("plotsLimit/pValue/"+channel+suffix+".C")
# --------- Best Fit ----------
c4 = TCanvas("c4", "Best Fit", 800, 600)
c4.cd()
c4.GetPad(0).SetTopMargin(0.06)
c4.GetPad(0).SetRightMargin(0.05)
c4.GetPad(0).SetTicks(1, 1)
c4.GetPad(0).SetGridx()
c4.GetPad(0).SetGridy()
Best.Draw("AL3")
drawCMS(LUMI, YEAR, "Preliminary")
drawAnalysis(channel[1:3])
c4.Print("plotsLimit/BestFit/" + channel + suffix + ".png")
c4.Print("plotsLimit/BestFit/" + channel + suffix + ".pdf")
# c4.Print("plotsLimit/BestFit/"+channel+suffix+".root")
# c4.Print("plotsLimit/BestFit/"+channel+suffix+".C")
if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
if 'ah' in channel:
outFile = TFile("bands.root", "RECREATE")
outFile.cd()
pVal.Write("graph")
Best.Write("best")
outFile.Close()
',', "_").replace(' ', "") + "_rebin1_" + u + "Down"
print histname2
histname3 = 'QCD#chi' + str(massbins[massbin]).strip("()").replace(
',', "_").replace(' ', "") + "_rebin1"
print histname3
up = fsys.Get(histname1)
down = fsys.Get(histname2)
central = fsys.Get(histname3)
uncertainties += [[up, down, central]]
h2new = h14.Clone("down" + str(massbins[massbin]))
h3new = h14.Clone("up" + str(massbins[massbin]))
chi2 = 0
for b in range(h14.GetXaxis().GetNbins()):
if b == 0: # or b==h14G.GetXaxis().GetNbins()-1:
print massbins[massbin], b, "stat", h14G.GetErrorYlow(
b) / h14G.GetY()[b], h14G.GetErrorYhigh(b) / h14G.GetY()[b]
exp_sumdown = 0
exp_sumup = 0
theory_sumdown = 0
theory_sumup = 0
for up, down, central in uncertainties:
if b == 0: # or b==h14G.GetXaxis().GetNbins()-1:
print massbins[massbin], b, uncertaintynames[
uncertainties.index([up, down, central])], abs(
up.GetBinContent(b + 1) - central.GetBinContent(
b + 1)) / central.GetBinContent(b + 1), abs(
down.GetBinContent(b + 1) -
central.GetBinContent(b + 1)
) / central.GetBinContent(b + 1)
addup = pow(
max(
class HLTRate:
_denominator = 0
_crossSectionInPb = 0.0
_nFiles = 0
_iFile = 0
_label = ""
_denominatorHist = None
_numeratorHist = None
_rateGraph = None
def addFile (self, fileName):
if not fileName:
return
if not os.path.isfile (fileName):
print "\"" + fileName + "\" does not exist or is not a file!"
return
if not re.match (r".*\/cmsRun_.*\.log\.tar\.gz", fileName):
print "\"" + fileName + "\" does not look like a CRAB log file!"
return
print " (" + str (self._iFile) + " / " + str (self._nFiles) + ") Processing file \"" + re.sub (r".*\/([^/]+)$", r"\1", fileName) + "\"..."
n = re.sub (r".*\/cmsRun_(.*)\.log\.tar\.gz", r"\1", fileName)
print " Extracting log file..."
tin = tarfile.open (fileName)
fin = tin.extractfile ("cmsRun-stdout-" + n + ".log")
print " Parsing log file..."
for line in fin:
line = line.rstrip ()
if not re.match (r"HLT-Report.*", line):
continue
if re.match (r".*Events total = .* wasrun = .* passed = .* errors = .*", line):
self._denominator += int (re.sub (r".*Events total = (.*) wasrun = .* passed = .* errors = .*", r"\1", line))
if re.match (r".*ETM[^_]*_MET75_IsoTrk50.*", line):
seed = re.sub (r".*ETM([^_]*)_MET75_IsoTrk50.*", r"\1", line)
if seed not in numerator[self._label]:
numerator[self._label][seed] = 0
numerator[self._label][seed] += int (re.sub (r"HLT-Report *[^ ]* *[^ ]* *[^ ]* *[^ ]* *([^ ]*) *[^ ]* *[^ ]* *[^ ]* *[^ ]* *[^ ]*", r"\1", line))
fin.close ()
tin.close ()
def addDir (self, dirName):
if not dirName:
return
if not os.path.isdir (dirName):
print "\"" + dirName + "\" does not exist or is not a directory!"
return
print "Processing directory \"" + dirName + "\"..."
logFiles = glob.glob (dirName + "/cmsRun_*.log.tar.gz")
self._nFiles = len (logFiles)
self._iFile = 0
for logFile in logFiles:
self._iFile += 1
self.addFile (logFile)
def setCrossSectionInPb (self, crossSectionInPb):
self._crossSectionInPb = crossSectionInPb
def setLabel (self, label):
self._label = label
numerator[self._label] = {}
def __init__ (self, label = "", dirName = "", crossSectionInPb = 0.0):
self.setLabel (label)
self.addDir (dirName)
self.setCrossSectionInPb (crossSectionInPb)
def getRateInPb (self, seed):
if seed not in numerator[self._label]:
print "ETM" + seed + " not found in results!"
return
self._denominatorHist = TH1D ("total", "", 1, -0.5, 0.5)
self._numeratorHist = TH1D ("pass", "", 1, -0.5, 0.5)
self._denominatorHist.SetBinContent (1, self._denominator)
self._denominatorHist.SetBinError (1, math.sqrt (self._denominator))
self._numeratorHist.SetBinContent (1, numerator[self._label][seed])
self._numeratorHist.SetBinError (1, math.sqrt (numerator[self._label][seed]))
self._rateGraph = TGraphAsymmErrors (self._numeratorHist, self._denominatorHist)
x = Double (0.0)
y = Double (0.0)
self._rateGraph.GetPoint (0, x, y)
eLow = self._rateGraph.GetErrorYlow (0)
eHigh = self._rateGraph.GetErrorYhigh (0)
y *= self._crossSectionInPb
eLow *= self._crossSectionInPb
eHigh *= self._crossSectionInPb
return (y, eLow, eHigh)
def getSeeds (self):
return map (str, sorted (map (int, numerator[self._label].keys ())))
def plotDataOverMCEff(hist_mc_tight, hist_mc_loose, hist_data_tight, hist_data_loose, plot_name='fakerate.pdf', mc_leg='MC', obs_leg='Observed', ratio_leg='Obs/MC'):
g = TGraphAsymmErrors(hist_mc_tight)
g.Divide(hist_mc_tight, hist_mc_loose)
g.GetYaxis().SetTitle('Misidentification rate')
g.GetXaxis().SetTitle(hist_mc_tight.GetXaxis().GetTitle())
g.GetYaxis().SetTitleOffset(1.2)
g.GetYaxis().SetTitleOffset(1.3)
g.SetLineColor(2)
g.SetMarkerColor(2)
g_data = TGraphAsymmErrors(hist_data_tight)
g_data.Divide(hist_data_tight, hist_data_loose)
# if g_data.GetN() != hist_data_tight.GetNbinsX():
# import pdb; pdb.set_trace()
g_data.GetYaxis().SetTitle('Misidentification rate')
g_data.GetXaxis().SetTitle(hist_data_tight.GetXaxis().GetTitle())
g_data.GetYaxis().SetTitleOffset(1.2)
g_data.GetYaxis().SetTitleOffset(1.3)
g_data.SetMarkerColor(1)
g_vals = g.GetY()
g_data_vals = g_data.GetY()
g_ratio = g_data.Clone('ratio')
for i in xrange(g_data.GetN()):
ratio = g_data_vals[i]/g_vals[i] if g_vals[i] else 0.
g_ratio.SetPoint(i, g.GetX()[i], ratio)
rel_y_low = math.sqrt((g_data.GetErrorYlow(i)/g_data_vals[i])**2 + (g.GetErrorYlow(i)/g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYlow(i, rel_y_low * ratio)
rel_y_high = math.sqrt((g_data.GetErrorYhigh(i)/g_data_vals[i])**2 + (g.GetErrorYhigh(i)/g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0.
g_ratio.SetPointEYhigh(i, rel_y_high * ratio)
# Gymnastics to get same label sizes etc in ratio and main plot
ytp_ratio = 2.
xtp_ratio = 2.
# hr.GetYaxis().SetNdivisions(4)
g_ratio.GetYaxis().SetTitleSize(g.GetYaxis().GetTitleSize() * xtp_ratio)
g_ratio.GetXaxis().SetTitleSize(g.GetXaxis().GetTitleSize() * ytp_ratio)
g_ratio.GetYaxis().SetTitleOffset(g.GetYaxis().GetTitleOffset() / xtp_ratio)
g_ratio.GetXaxis().SetTitleOffset(g.GetXaxis().GetTitleOffset()) # / ytp_ratio)
g_ratio.GetYaxis().SetLabelSize(g.GetYaxis().GetLabelSize() * xtp_ratio)
g_ratio.GetXaxis().SetLabelSize(g.GetXaxis().GetLabelSize() * ytp_ratio)
g_data.GetXaxis().SetLabelColor(0)
g_data.GetXaxis().SetLabelSize(0)
g.GetXaxis().SetLabelColor(0)
g.GetXaxis().SetLabelSize(0)
g_ratio.GetXaxis().SetTitle(g.GetXaxis().GetTitle())
maxy = 1.3 * max(g.GetMaximum(), g_data.GetMaximum(), 0.05)
g.GetYaxis().SetRangeUser(0.0011, maxy)
cv, pad, padr = HistDrawer.buildCanvas()
pad.cd()
g.Draw('AP')
g_data.Draw('P')
legend = TLegend(0.23, 0.73, 0.43, 0.91)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetLineColor(0)
legend.SetLineWidth(0)
legend.AddEntry(g.GetName(), mc_leg, 'lep')
legend.AddEntry(g_data.GetName(), obs_leg, 'lep')
legend.Draw()
padr.cd()
g_ratio.GetYaxis().SetRangeUser(0.01, 1.99)
g_ratio.GetYaxis().SetTitle(ratio_leg)
g_ratio.Draw('AP')
drawRatioLines(g_ratio)
cv.Print(plot_name)
g.GetYaxis().SetRangeUser(0.0001, 1)
pad.SetLogy(True)
cv.Print(plot_name.replace('.', '_log.'))
f = ROOT.TFile(plot_name.replace('.', '_log.').replace('.pdf', '.root'), 'RECREATE')
g.Write()
g_data.Write()
cv.Write()
f.Close()
def fillAcceptance(self):
'''
This is to calculate the acceptance of the sample
'''
if debug: print('Info in Sample.fillAcceptance()')
#f = TFile.Open(self.infileName)
#t = f.Get(self.treeName)
#if not t:
# raise RuntimeError( 'ERROR: no tree in file %s' % self.infileName)
chain = TChain(self.treeName)
for fn in self.infileNames:
chain.Add(fn)
self.effnum = ROOT.TH1F('effnum', 'effnum', 1, 0, 13000)
self.effden = ROOT.TH1F('effden', 'effden', 1, 0, 13000)
self.effnumB = dict([(b, ROOT.TH1F('effnum_%s' % b, 'effnum_%s' % b, 1, 0, 13000)) for b in self.Bspecies])
self.effdenB = dict([(b, ROOT.TH1F('effden_%s' % b, 'effden_%s' % b, 1, 0, 13000)) for b in self.Bspecies])
#if doInclusive:
cutsnum = '(l0_pt>{mp} && abs(l0_eta)<1.5'.format(mp=muTrigPt)
#else:
#cutsnum = '(l0_pt>{mp} && abs(l0_eta)<1.5 && k_pt>1 && abs(k_eta)<2.5 && pi_pt>1 && abs(pi_eta)<2.5'.format(mp=muTrigPt)
if doSkipDispl:
cutsnum += ''
else:
cutsnum += '&& Lxy < 500'
if not doDisplZ:
pass
else:
cutsnum += '&& Lz < 20'
if doSkipHNLptEta:
cutsnum += ''
else:
cutsnum += '&& l1_pt>3 && abs(l1_eta)<2.5 && pi1_pt>0.8 && abs(pi1_eta)<2.5'
cutsnum += ')'
cutsden = '(l0_pt>{mp} && abs(l0_eta)<1.5)'.format(mp=muTrigPt)
###### fill the total acceptance
chain.Draw('hnl_pt>>effnum', cutsnum+'*'+self.evt_w, 'goff')
chain.Draw('hnl_pt>>effden', cutsden, 'goff')
if TEfficiency.CheckConsistency(self.effnum,self.effden):
peff = TEfficiency(self.effnum,self.effden)
# check usage of TGraphAsymmetricErrors
self.acc = peff.GetEfficiency(1)
self.acc_errup = peff.GetEfficiencyErrorUp(1)
self.acc_errdn = peff.GetEfficiencyErrorLow(1)
tgra = TGraphAsymmErrors()
tgra.BayesDivide(self.effnum, self.effden)
self.acc_tg = tgra.GetY()[0]
self.acc_errup_tg = tgra.GetErrorYhigh(0)
self.acc_errdn_tg = tgra.GetErrorYlow(0)
# for debugging purposes
self.num = self.effnum.GetEntries()
if self.num==0: print('**** 0 entries for mass={}'.format(self.mass))
self.den = self.effden.GetEntries()
###### fill the partial acceptances
for ib,b in enumerate(self.Bspecies):
bsel = '(abs(b_pdgid)=={bid})'.format(bid=self.BpdgIds[ib])
selnum = '(' + cutsnum + '&&' + bsel + ')'
selden = '(' + cutsden + '&&' + bsel + ')'
chain.Draw('hnl_pt>>effnum_{b}'.format(b=b), selnum + '*' + self.evt_w, 'goff')
chain.Draw('hnl_pt>>effden_{b}'.format(b=b), selden, 'goff')
if TEfficiency.CheckConsistency(self.effnumB[b],self.effdenB[b]):
peff = TEfficiency(self.effnumB[b],self.effdenB[b])
self.accB[b] = peff.GetEfficiency(1)
self.accB_errup[b] = peff.GetEfficiencyErrorUp(1)
self.accB_errdn[b] = peff.GetEfficiencyErrorLow(1)
else:
self.accB[b] = 0
self.accB_errup[b] = 0
self.accB_errdn[b] = 0
