def __init__(self, all, passed, dataset):
self.all = all
self.passed = passed
if all == 0:
self.eff = 0
else:
self.eff = passed/all
self.eff_up = ROOT.TEfficiency.ClopperPearson(int(all), int(passed), 0.95, True)
self.eff_down = ROOT.TEfficiency.ClopperPearson(int(all), int(passed), 0.95, False)
self.eff_up = self.eff_up - self.eff
self.eff_down = self.eff - self.eff_down
a = ROOT.TH1F("hall_"+dataset.getName(), "all", 1, 0, 1)
a.SetBinContent(1, all)
p = ROOT.TH1F("hpassed_"+dataset.getName(), "passed", 1, 0, 1)
p.SetBinContent(1, passed)
self.effobj = ROOT.TEfficiency(p, a)
self.effobj.SetStatisticOption(ROOT.TEfficiency.kFCP)
if dataset.isMC():
self.effobj.SetWeight(dataset.getCrossSection())
def addConfigInfo(of, dataset):
d = of.mkdir("configInfo")
d.cd()
# configinfo histogram
configinfo = ROOT.TH1F("configinfo", "configinfo", 3, 0, 3)
axis = configinfo.GetXaxis()
def setValue(bin, name, value):
axis.SetBinLabel(bin, name)
configinfo.SetBinContent(bin, value)
setValue(1, "control", 1)
if dataset.isData():
setValue(2, "luminosity", dataset.getLuminosity())
setValue(3, "isData", 1)
elif dataset.isMC():
setValue(2, "crossSection", dataset.getCrossSection())
setValue(3, "isData", 0)
configinfo.Write()
configinfo.Delete()
# dataVersion
ds = dataset
if dataset.isData():
ds = dataset.datasets[0]
dataVersion = ROOT.TNamed("dataVersion", ds.dataVersion)
dataVersion.Write()
dataVersion.Delete()
# codeVersion
codeVersion = ROOT.TNamed("codeVersion", git.getCommitId())
codeVersion.Write()
codeVersion.Delete()
of.cd()
def addConfigInfo(of, dataset):
d = of.mkdir("configInfo")
d.cd()
# configinfo histogram
configinfo = ROOT.TH1F("configinfo", "configinfo", 3, 0, 3)
axis = configinfo.GetXaxis()
def setValue(bin, name, value):
axis.SetBinLabel(bin, name)
configinfo.SetBinContent(bin, value)
setValue(1, "control", 1)
if dataset.isData():
setValue(2, "luminosity", dataset.getLuminosity())
setValue(3, "isData", 1)
elif dataset.isMC():
setValue(2, "crossSection", dataset.getCrossSection())
setValue(3, "isData", 0)
configinfo.Write()
configinfo.Delete()
# dataVersion
ds = dataset
if dataset.isData():
ds = dataset.datasets[0]
dataVersion = ROOT.TNamed("dataVersion", ds.dataVersion)
dataVersion.Write()
dataVersion.Delete()
# codeVersion
codeVersion = ROOT.TNamed("codeVersion", git.getCommitId())
codeVersion.Write()
codeVersion.Delete()
of.cd()
def __init__(self, all, passed, dataset):
self.all = all
self.passed = passed
if all == 0:
self.eff = 0
else:
self.eff = passed / all
self.eff_up = ROOT.TEfficiency.ClopperPearson(int(all), int(passed),
0.95, True)
self.eff_down = ROOT.TEfficiency.ClopperPearson(
int(all), int(passed), 0.95, False)
self.eff_up = self.eff_up - self.eff
self.eff_down = self.eff - self.eff_down
a = ROOT.TH1F("hall_" + dataset.getName(), "all", 1, 0, 1)
a.SetBinContent(1, all)
p = ROOT.TH1F("hpassed_" + dataset.getName(), "passed", 1, 0, 1)
p.SetBinContent(1, passed)
self.effobj = ROOT.TEfficiency(p, a)
self.effobj.SetStatisticOption(ROOT.TEfficiency.kFCP)
if dataset.isMC():
self.effobj.SetWeight(dataset.getCrossSection())
def plotTurnOn(datasets,
pathAll,
pathPassed,
commonText,
dataText=None,
mcText=None,
rebin=1,
ratio=False):
dataLabel = "Data"
mcLabel = "Simulation"
if dataText != None and mcText == None:
raise Exception("mcText must not be None when dataText is not")
if dataText == None and mcText != None:
raise Exception("dataText must not be None when mcText is not")
# if dataText != None:
# dataLabel += ": "+dataText
# mcLabel += ": "+mcText
mc_effs = []
data_eff_gr = None
binWidth = None
luminosity = 0
for dataset in datasets.getAllDatasets():
all = dataset.getDatasetRootHisto(pathAll).getHistogram()
passed = dataset.getDatasetRootHisto(pathPassed).getHistogram()
if rebin > 1:
all.Rebin(rebin)
passed.Rebin(rebin)
binWidth = all.GetBinWidth(1)
if dataset.isMC() and not "TTTo" in dataset.getName():
#if dataset.isMC() and not "TTTo" in dataset.getName() and not "QCD" in dataset.getName():
#if dataset.isMC() and "QCD" in dataset.getName():
mc_effs.append(HistoEff(all, passed, dataset))
elif dataset.isData():
data_eff_gr = ROOT.TGraphAsymmErrors(passed, all,
"cp") # 0.683 cl is default
luminosity += dataset.getLuminosity()
mc_eff_gr = combineHistoEffs(mc_effs)
p = plots.ComparisonPlot(
histograms.HistoGraph(data_eff_gr, "Data", "p", "P"),
histograms.HistoGraph(mc_eff_gr, "Simulation", "p", "P"))
p.histoMgr.forEachHisto(
styles.generator2(styles.StyleMarker(markerSizes=[1.0, 1.5]),
plotStyles))
p.histoMgr.setHistoLegendLabelMany({
"Data": dataLabel,
"Simulation": mcLabel,
})
p.setLuminosity(luminosity)
#p.addGraph(data_eff_gr, "Data")
#p.addGraph(mc_eff_gr, "Simulation")
#p.finalize()
opts = {"ymin": 0.0, "ymax": 1.1}
#opts2 = {"ymin": 0.5, "ymax": 1.5}
opts2 = {"ymin": 0.7, "ymax": 1.3}
name = "calomet_turnon_" + runs
if not mcDataDefinition:
name += "_McSummer11"
if caloMetNoHF:
name += "_caloMetNoHF"
p.createFrame(name, createRatio=ratio, opts=opts, opts2=opts2)
if ratio:
p.getFrame2().GetYaxis().SetTitle("Ratio")
p.cf.canvas.cd(2)
p.line1 = plots._createRatioLine(p.cf.frame.getXmin(),
p.cf.frame.getXmax(), 0.9)
p.line2 = plots._createRatioLine(p.cf.frame.getXmin(),
p.cf.frame.getXmax(), 1.1)
p.line1.SetLineColor(ROOT.kBlue)
p.line2.SetLineColor(ROOT.kBlue)
p.line1.Draw("L")
p.line2.Draw("L")
p.cf.canvas.cd(1)
p.setLegend(
histograms.moveLegend(
histograms.createLegend(y1=0.95, y2=0.85),
#dx=-0.55, dy=-0.05
#dx=-0.44, dy=-0.58
dx=-0.3,
dy=-0.4))
x = 0.25
y = 0.3
dy = 0.035
mcColor = mc_eff_gr.GetMarkerColor()
size = 17
p.appendPlotObject(
histograms.PlotText(x, y, "Data (runs %s):" % runs, size=size))
y -= dy
p.appendPlotObject(histograms.PlotText(x, y, dataText, size=size))
y -= dy
y -= 0.01
p.appendPlotObject(
histograms.PlotText(x, y, "Simulation:", size=size, color=mcColor))
y -= dy
p.appendPlotObject(
histograms.PlotText(x, y, mcText, size=size, color=mcColor))
y -= dy
def text():
l = ROOT.TLatex()
l.SetNDC()
# l.SetTextFont(l.GetTextFont()-20) # bold -> normal
l.SetTextSize(l.GetTextSize() * 0.65)
#l.DrawLatex(0.35, 0.4, commonText)
l.DrawLatex(0.48, 0.32, commonText)
textFunction = text
if dataText != None:
textFunction = None
#common(p, "PF E_{T}^{miss} (GeV)", "Efficiency / %.0f GeV"%binWidth, afterDraw=textFunction)
common(p,
"Uncorrected PF E_{T}^{miss} (GeV)",
"Efficiency",
afterDraw=textFunction)
def printEfficiency(datasets, bin, function):
backup = ROOT.gErrorIgnoreLevel
ROOT.gErrorIgnoreLevel = ROOT.kError
mc_all = 0
mc_passed = 0
data_all = 0
data_passed = 0
# mc_eff = ROOT.TEfficiency("mc", "mc", 1, 0, 1)
# mc_eff.SetStatisticOption(ROOT.TEfficiency.kFCP)
mc_effs = []
for dataset in datasets.getAllDatasets():
# print histo.getRootHisto().GetNbinsX(), histo.getXmin(), histo.getXmax()
# hpass = histograms.dist2pass(histo.getRootHisto(), greaterThan=True)
# hpass = histograms.dist2pass(dataset.getDatasetRootHisto(path).getHistogram(), greaterThan=True)
# all = hpass.GetBinContent(1)
# passed = hpass.GetBinContent(bin)
# cutvalue = hpass.GetBinCenter(bin)
(all, passed, cutvalue) = function(dataset, bin)
eff = Eff(all, passed, dataset)
print "Dataset %-35s, eff %f + %f - %f" % (dataset.getName(), eff.eff,
eff.eff_up, eff.eff_down)
# print all, passed, cutvalue
# e = ROOT.TEfficiency("mc"+dataset.getName(), "mc_"+dataset.getName(), 1, 0, 1)
# e.SetStatisticOption(ROOT.TEfficiency.kFCP)
if dataset.isMC() and not "TTTo" in dataset.getName():
mc_all += all
mc_passed += passed
mc_effs.append(eff)
elif dataset.isData():
data_all += all
data_passed += passed
#cl = 0.95
cl = 0.683
mc_eff = mc_passed / mc_all
mc_eff_up = ROOT.TEfficiency.ClopperPearson(int(mc_all), int(mc_passed),
cl, True)
mc_eff_down = ROOT.TEfficiency.ClopperPearson(int(mc_all), int(mc_passed),
cl, False)
mc_eff_up = mc_eff_up - mc_eff
mc_eff_down = mc_eff - mc_eff_down
(cmc_eff, cmc_eff_up, cmc_eff_down) = combineEffs(mc_effs)
cmc_eff_err = max(cmc_eff_up, cmc_eff_down)
data_eff = data_passed / data_all
data_eff_up = ROOT.TEfficiency.ClopperPearson(int(data_all),
int(data_passed), cl, True)
data_eff_down = ROOT.TEfficiency.ClopperPearson(int(data_all),
int(data_passed), cl,
False)
data_eff_up = data_eff_up - data_eff
data_eff_down = data_eff - data_eff_down
data_eff_err = max(data_eff_up, data_eff_down)
rho = data_eff / cmc_eff
rho_err = math.sqrt((data_eff_err / cmc_eff)**2 + (data_eff * cmc_eff_err /
(cmc_eff**2))**2)
print "Cut value %f, bin %d:" % (cutvalue, bin)
# print " MC %f/%f = %f + %f - %f" % (mc_all, mc_passed, mc_eff, mc_eff_up, mc_eff_down)
print " Comb. MC %f + %f - %f" % (cmc_eff, cmc_eff_up, cmc_eff_down)
print " data %f/%f = %f + %f - %f" % (data_all, data_passed, data_eff,
data_eff_up, data_eff_down)
print " scale factor = %f \\pm %f" % (rho, rho_err)
ROOT.gErrorIgnoreLevel = backup
def __init__(self, all, passed, dataset):
self.effobj = ROOT.TEfficiency(passed, all)
self.effobj.SetStatisticOption(ROOT.TEfficiency.kFCP)
if dataset.isMC():
self.effobj.SetWeight(dataset.getCrossSection())
def plotTurnOn(datasets, pathAll, pathPassed, commonText, dataText=None, mcText=None, rebin=1, ratio=False):
dataLabel = "Data"
mcLabel = "Simulation"
if dataText != None and mcText == None:
raise Exception("mcText must not be None when dataText is not")
if dataText == None and mcText != None:
raise Exception("dataText must not be None when mcText is not")
# if dataText != None:
# dataLabel += ": "+dataText
# mcLabel += ": "+mcText
mc_effs = []
data_eff_gr = None
binWidth = None
luminosity = 0
for dataset in datasets.getAllDatasets():
all = dataset.getDatasetRootHisto(pathAll).getHistogram()
passed = dataset.getDatasetRootHisto(pathPassed).getHistogram()
if rebin > 1:
all.Rebin(rebin)
passed.Rebin(rebin)
binWidth = all.GetBinWidth(1)
if dataset.isMC() and not "TTTo" in dataset.getName():
#if dataset.isMC() and not "TTTo" in dataset.getName() and not "QCD" in dataset.getName():
#if dataset.isMC() and "QCD" in dataset.getName():
mc_effs.append(HistoEff(all, passed, dataset))
elif dataset.isData():
data_eff_gr = ROOT.TGraphAsymmErrors(passed, all, "cp") # 0.683 cl is default
luminosity += dataset.getLuminosity()
mc_eff_gr = combineHistoEffs(mc_effs)
p = plots.ComparisonPlot(
histograms.HistoGraph(data_eff_gr, "Data", "p", "P"),
histograms.HistoGraph(mc_eff_gr, "Simulation", "p", "P")
)
p.histoMgr.forEachHisto(styles.generator2(styles.StyleMarker(markerSizes=[1.0, 1.5]), plotStyles))
p.histoMgr.setHistoLegendLabelMany({
"Data": dataLabel,
"Simulation": mcLabel,
})
p.setLuminosity(luminosity)
#p.addGraph(data_eff_gr, "Data")
#p.addGraph(mc_eff_gr, "Simulation")
#p.finalize()
opts = {"ymin": 0.0, "ymax": 1.1}
#opts2 = {"ymin": 0.5, "ymax": 1.5}
opts2 = {"ymin": 0.7, "ymax": 1.3}
name = "calomet_turnon_"+runs
if not mcDataDefinition:
name += "_McSummer11"
if caloMetNoHF:
name += "_caloMetNoHF"
p.createFrame(name, createRatio=ratio, opts=opts, opts2=opts2)
if ratio:
p.getFrame2().GetYaxis().SetTitle("Data / MC")
p.cf.canvas.cd(2)
p.line1 = plots._createRatioLine(p.cf.frame.getXmin(), p.cf.frame.getXmax(), 0.9)
p.line2 = plots._createRatioLine(p.cf.frame.getXmin(), p.cf.frame.getXmax(), 1.1)
p.line1.SetLineColor(ROOT.kBlue)
p.line2.SetLineColor(ROOT.kBlue)
p.line1.Draw("L")
p.line2.Draw("L")
p.cf.canvas.cd(1)
p.setLegend(histograms.moveLegend(histograms.createLegend(y1=0.95, y2=0.85),
#dx=-0.55, dy=-0.05
#dx=-0.44, dy=-0.58
dx=-0.3, dy=-0.4
))
x = 0.25
y = 0.3
dy = 0.035
mcColor = mc_eff_gr.GetMarkerColor()
size = 17
p.appendPlotObject(histograms.PlotText(x, y, "Data (runs %s):"%runs, size=size)); y -= dy
p.appendPlotObject(histograms.PlotText(x, y, dataText, size=size)); y -= dy
y -= 0.01
p.appendPlotObject(histograms.PlotText(x, y, "Simulation:", size=size, color=mcColor)); y -= dy
p.appendPlotObject(histograms.PlotText(x, y, mcText, size=size, color=mcColor)); y -= dy
def text():
l = ROOT.TLatex()
l.SetNDC()
# l.SetTextFont(l.GetTextFont()-20) # bold -> normal
l.SetTextSize(l.GetTextSize()*0.65)
#l.DrawLatex(0.35, 0.4, commonText)
l.DrawLatex(0.48, 0.32, commonText)
textFunction = text
if dataText != None:
textFunction = None
#common(p, "PF E_{T}^{miss} (GeV)", "Efficiency / %.0f GeV"%binWidth, afterDraw=textFunction)
common(p, "Uncorrected PF E_{T}^{miss} (GeV)", "Efficiency", afterDraw=textFunction)
def printEfficiency(datasets, bin, function):
backup = ROOT.gErrorIgnoreLevel
ROOT.gErrorIgnoreLevel = ROOT.kError
mc_all = 0
mc_passed = 0
data_all = 0
data_passed = 0
# mc_eff = ROOT.TEfficiency("mc", "mc", 1, 0, 1)
# mc_eff.SetStatisticOption(ROOT.TEfficiency.kFCP)
mc_effs = []
for dataset in datasets.getAllDatasets():
# print histo.getRootHisto().GetNbinsX(), histo.getXmin(), histo.getXmax()
# hpass = histograms.dist2pass(histo.getRootHisto(), greaterThan=True)
# hpass = histograms.dist2pass(dataset.getDatasetRootHisto(path).getHistogram(), greaterThan=True)
# all = hpass.GetBinContent(1)
# passed = hpass.GetBinContent(bin)
# cutvalue = hpass.GetBinCenter(bin)
(all, passed, cutvalue) = function(dataset, bin)
eff = Eff(all, passed, dataset)
print "Dataset %-35s, eff %f + %f - %f" % (dataset.getName(), eff.eff, eff.eff_up, eff.eff_down)
# print all, passed, cutvalue
# e = ROOT.TEfficiency("mc"+dataset.getName(), "mc_"+dataset.getName(), 1, 0, 1)
# e.SetStatisticOption(ROOT.TEfficiency.kFCP)
if dataset.isMC() and not "TTTo" in dataset.getName():
mc_all += all
mc_passed += passed
mc_effs.append(eff)
elif dataset.isData():
data_all += all
data_passed += passed
#cl = 0.95
cl = 0.683
mc_eff = mc_passed/mc_all
mc_eff_up = ROOT.TEfficiency.ClopperPearson(int(mc_all), int(mc_passed), cl, True)
mc_eff_down = ROOT.TEfficiency.ClopperPearson(int(mc_all), int(mc_passed), cl, False)
mc_eff_up = mc_eff_up-mc_eff
mc_eff_down = mc_eff-mc_eff_down
(cmc_eff, cmc_eff_up, cmc_eff_down) = combineEffs(mc_effs)
cmc_eff_err = max(cmc_eff_up, cmc_eff_down)
data_eff = data_passed/data_all
data_eff_up = ROOT.TEfficiency.ClopperPearson(int(data_all), int(data_passed), cl, True)
data_eff_down = ROOT.TEfficiency.ClopperPearson(int(data_all), int(data_passed), cl, False)
data_eff_up = data_eff_up-data_eff
data_eff_down = data_eff-data_eff_down
data_eff_err = max(data_eff_up, data_eff_down)
rho = data_eff / cmc_eff
rho_err = math.sqrt( (data_eff_err/cmc_eff)**2 + (data_eff*cmc_eff_err/(cmc_eff**2))**2)
print "Cut value %f, bin %d:" % (cutvalue, bin)
# print " MC %f/%f = %f + %f - %f" % (mc_all, mc_passed, mc_eff, mc_eff_up, mc_eff_down)
print " Comb. MC %f + %f - %f" % (cmc_eff, cmc_eff_up, cmc_eff_down)
print " data %f/%f = %f + %f - %f" % (data_all, data_passed, data_eff, data_eff_up, data_eff_down)
print " scale factor = %f \\pm %f" % (rho, rho_err)
ROOT.gErrorIgnoreLevel = backup
def __init__(self, all, passed, dataset):
self.effobj = ROOT.TEfficiency(passed, all)
self.effobj.SetStatisticOption(ROOT.TEfficiency.kFCP)
if dataset.isMC():
self.effobj.SetWeight(dataset.getCrossSection())