def getPorbeHisto(PlotBaseObj, sample, xVarBase, selection, probeselection,
tagselection, isMC):
logging.subinfo("Making tag histo for sample: {0}".format(sample.name))
if isMC:
weight = "{0} * {1}".format(sample.getSampleWeight(), sample.weight)
else:
weight = "1"
logging.subdebug("Probe Selection: {0}".format(tagselection))
sel = "{0} && {1} && {2}".format(selection, probeselection, tagselection)
return modules.plotting.getHistoFromTree(sample.tree,
xVarBase,
PlotBaseObj.binning,
sel,
weight=weight)
def efficiencies(loglev, run, doMC, doData, doCSV, doDeepCSV, CSVWPs,
DeepCSVWPs, ptordered, csvordered, doinclusive,
nInclusiveIter, dosplit, nSplitIter, doCross, compWPs,
looseSel):
import ROOT
import modules.classes
import modules.effPlots
setup_logging(loglevel=loglev,
logname="efficiencyoutput",
errname="efficiencyerror")
logger = logging.getLogger(__name__)
logger.info("Starting shape comparison")
t0 = time.time()
if not (doMC or doData):
if __name__ == "__main__":
logging.warning(
"At least on of the flags --mc and --data should to be set")
logging.warning("Falling back the only mc")
else:
logging.warning(
"At least on of the paramters doMC and doData should to be set"
)
logging.warning("Falling back the only mc")
doMC = True
if not (ptordered or csvordered):
if __name__ == "__main__":
logging.warning(
"At least on of the flags --ptorderd and --csvordered should to be set"
)
logging.warning("Falling back to pt ordered")
else:
logging.warning(
"At least on of the paramters ptordered and ptordered should to be set"
)
logging.warning("Falling back to pt ordered")
ptordered = True
if loglev > 0:
ROOT.gErrorIgnoreLevel = ROOT.kBreak # kPrint, kInfo, kWarning, kError, kBreak, kSysError, kFatal;
if run == "CD":
DataInput = "/mnt/t3nfs01/data01/shome/koschwei/scratch/HLTBTagging/DiLepton_v8/MuonEG/MuonEG_RunCD_phase1_0p9970p996_mod_mod_mod.root"
basepaths = "v8nTuples/Efficiencies/RunCD/"
fileprefix = "RunCD_"
if run == "E":
DataInput = "/mnt/t3nfs01/data01/shome/koschwei/scratch/HLTBTagging/DiLepton_v8/MuonEG/RunE/phase1/MuonEG_RunE_phase1_mod_mod_mod.root"
basepaths = "v8nTuples/Efficiencies/RunE/"
fileprefix = "RunE_EmilSel_"
if run == "F":
DataInput = "/mnt/t3nfs01/data01/shome/koschwei/scratch/HLTBTagging/DiLepton_v8/MuonEG/RunF/phase1/MuonEG_RunF_phase1_mod_mod_mod.root"
basepaths = "v8nTuples/Efficiencies/RunF/"
fileprefix = "RunF_"
if run == "Test":
DataInput = "/mnt/t3nfs01/data01/shome/koschwei/trigger/onlineBTV/CMSSW_9_2_12_patch1/src/HLTBTagging/nTuples/tree_phase1.root"
basepaths = "testing/emilComp3K/"
fileprefix = "Tesing_"
#MCInput = "/mnt/t3nfs01/data01/shome/koschwei/scratch/HLTBTagging/DiLepton_v3/ttbar/ttbar_v3.root"
MCInput = "/mnt/t3nfs01/data01/shome/koschwei/scratch/HLTBTagging/DiLepton_v8/ttbar/phase1/ttbar_0p85_mod_mod_mod.root"
MCSelection = "1"
VarSelection = "1"
DeepCSVSelWP = "0.8958"
if looseSel:
TriggerSelection = "1"
LeptonSelection = "1"
offlineSelection = "1"
CSVSelection = "1"
else:
TriggerSelection = "HLT_Mu12_TrkIsoVVL_Ele23_CaloIdL_TrackIdL_IsoVL_v4 > 0 || HLT_Mu23_TrkIsoVVL_Ele12_CaloIdL_TrackIdL_IsoVL_v4 > 0"
LeptonSelection = "Sum$((abs(offTightElectrons_superClusterEta) <= 1.4442 || abs(offTightElectrons_superClusterEta) >= 1.5660) && offTightElectrons_pt > 30 && abs(offTightElectrons_eta) < 2.4) > 0 && Sum$(offTightMuons_iso < 0.25 && offTightMuons_pt > 20 && abs(offTightMuons_eta) < 2.4) > 0"
offlineSelection = "Sum$(cleanJets_pt > 30 && abs(cleanJets_eta) < 2.4) > 2"
CSVSelection = "Sum$(cleanJets_deepcsv > {0}) >= 1".format(
DeepCSVSelWP)
logging.info(
"Using: doMC: {0} | doData: {1} | doCSV: {2} | doDeepCSV: {3}".format(
doMC, doData, doCSV, doDeepCSV))
samples = []
if doMC:
eventSelection = "({0}) && ({1}) && ({2}) && ({3})".format(
VarSelection, TriggerSelection, LeptonSelection, MCSelection)
samples.append(
(modules.classes.Sample("ttbar",
MCInput,
eventSelection,
831.76,
4591.622871124,
ROOT.kRed,
9937324,
legendText="Dataset: t#bar{t}"), "MC",
getLabel("Dataset: t#bar{t}", 0.7)))
if doData:
eventSelection = "({0}) && ({1}) && ({2})".format(
VarSelection, TriggerSelection, LeptonSelection)
samples.append(
(modules.classes.Sample("data",
DataInput,
eventSelection,
legendText="Dataset: MuonEG"), "MuonEG",
getLabel("Dataset: MuonEG", 0.7)))
CSVSelWP = "0.9535"
WPColors = [
ROOT.kRed, ROOT.kBlue, ROOT.kGreen + 2, ROOT.kPink + 7, ROOT.kViolet,
ROOT.kCyan
]
#CSVSelection = "Sum$(cleanJets_csv > {0}) >= 1".format(CSVSelWP)
refbyCSVRank = {
0: "cleanJets_ileadingCSV",
1: "cleanJets_isecondCSV",
2: "cleanJets_ithirdCSV",
3: "cleanJets_ifourthCSV"
}
refbyDeepCSVRank = {
0: "cleanJets_ileadingDeepCSV",
1: "cleanJets_isecondDeepCSV",
2: "cleanJets_ithirdDeepCSV",
3: "cleanJets_ifourthDeepCSV"
}
if doCSV:
logging.info("Processing plots for CSV")
CSVPlotBaseObjs = {}
collections = []
if ptordered:
#collections.append( ("pT", "cleanJets") )
collections.append(("pT", "offJets"))
logging.debug("Adding {1} to collecton as ordered by {0}".format(
"ptOrdered", "offJets"))
if csvordered:
collections.append(("CSV", "cleanCSVJets"))
logging.debug("Adding {1} to collecton as ordered by {0}".format(
"csvOrdered", "cleanCSVJets"))
for WP in CSVWPs:
WPLabel = getLabel("PF WP: {0}".format(WP), 0.7, "under")
CaloWPLabel = getLabel("Calo WP: {0}".format(WP), 0.7, "under")
logging.info("Using WP: {0}".format(WP))
for collectionName, collection in collections:
if dosplit:
for i in range(nSplitIter):
if looseSel:
JetSelection = "abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30".format(
i, collection)
else:
JetSelection = "abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30 && {1}_passesTightLeptVetoID[{0}] > 0".format(
i, collection)
logging.info(
"Making effciency for Jet {0} ordered by {1} w/ CSV as tagger"
.format(i, collectionName))
logging.subinfo(
"Using collection: {0}".format(collection))
CSVPlotBaseObjs[i] = modules.classes.PlotBase(
"{1}_csv[{0}]".format(i, collection),
"{0} && {1} && ({2})".format(
CSVSelection, offlineSelection, JetSelection),
"1", [20, 0, 1],
modules.utils.getAxisTitle("csv", i,
collectionName.lower()),
LegendPosition=[0.1, 0.6, 0.4, 0.76])
CSVPlotBaseObjs[i].color = WPColors[i]
onlysamples = []
for sampleStuff in samples:
sample, name, label = sampleStuff
onlysamples.append(sample)
if not (doMC and doData):
modules.effPlots.makeEffPlot(
CSVPlotBaseObjs[i],
sample,
"pfJets_csv[{2}_matchPF[{1}]] >= {0}".
format(WP, i, collection),
basepaths +
"CSV/{3}/{4}Eff_{2}_Jet{0}_pfWP_{1}_CSV_order{3}"
.format(i, WP, name, collectionName,
fileprefix),
addSel="{1}_matchPF[{0}] >= 0".format(
i, collection),
label=[label, WPLabel])
modules.effPlots.makeEffPlot(
CSVPlotBaseObjs[i],
sample,
"caloJets_csv[{2}_matchCalo[{1}]] >= {0}".
format(WP, i, collection),
basepaths +
"CSV/{3}/{4}Eff_{2}_Jet{0}_caloWP_{1}_CSV_order{3}"
.format(i, WP, name, collectionName,
fileprefix),
addSel="{1}_matchCalo[{0}] >= 0".format(
i, collection),
label=[label, CaloWPLabel])
if doMC and doData:
modules.effPlots.makeEffSCompPlot(
CSVPlotBaseObjs[i],
onlysamples,
"pfJets_csv[{2}_matchPF[{1}]] >= {0}".format(
WP, i, collection),
basepaths +
"CSV/{2}/{3}Eff_DataMC_Jet{0}_pfWP_{1}_CSV_order{2}"
.format(i, WP, collectionName, fileprefix),
addSel="{1}_matchPF[{0}] >= 0".format(
i, collection),
label=WPLabel)
modules.effPlots.makeEffSCompPlot(
CSVPlotBaseObjs[i],
onlysamples,
"caloJets_csv[{2}_matchCalo[{1}]] >= {0}".
format(WP, i, collection),
basepaths +
"CSV/{2}/{3}Eff_DataMC_Jet{0}_caloWP_{1}_CSV_order{2}"
.format(i, WP, collectionName, fileprefix),
addSel="{1}_matchCalo[{0}] >= 0".format(
i, collection),
label=CaloWPLabel)
if doinclusive:
JetSelectionIter = "abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30 && {1}_passesTightLeptVetoID[{0}] > 0".format(
"?", collection)
logging.info(
"Making inclusive effciency ordered by {0} w/ CSV as tagger"
.format(collectionName))
logging.subinfo("Using collection: {0}".format(collection))
CSVPlotBaseObjIter = modules.classes.PlotBase(
"{1}_csv[{0}]".format("?", collection),
"{0} && {1} && ({2})".format(CSVSelection,
offlineSelection,
JetSelectionIter),
"1", [20, 0, 1],
modules.utils.getAxisTitle("csv",
0,
collectionName.lower(),
inclusive=True),
LegendPosition=[0.1, 0.6, 0.4, 0.76])
#CSVPlotBaseObjIter.color = WPColors[0]
onlysamples = []
for sampleStuff in samples:
sample, name, label = sampleStuff
onlysamples.append(sample)
if not (doMC and doData):
modules.effPlots.makeEffSumPlot(
CSVPlotBaseObjIter,
sample,
"pfJets_csv[{2}_matchPF[{1}]] >= {0}".format(
WP, "?", collection),
nInclusiveIter,
basepaths +
"CSV/{3}/{4}Eff_{2}_Jet{0}_pfWP_{1}_CSV_order{3}"
.format("incl", WP, name, collectionName,
fileprefix),
addSel="{1}_matchPF[{0}] >= 0".format(
"?", collection),
label=[label, WPLabel])
modules.effPlots.makeEffSumPlot(
CSVPlotBaseObjIter,
sample,
"caloJets_csv[{2}_matchCalo[{1}]] >= {0}".
format(WP, "?", collection),
nInclusiveIter,
basepaths +
"CSV/{3}/{4}Eff_{2}_Jet{0}_caloWP_{1}_CSV_order{3}"
.format("incl", WP, name, collectionName,
fileprefix),
addSel="{1}_matchCalo[{0}] >= 0".format(
"?", collection),
label=[label, CaloWPLabel])
if doMC and doData:
modules.effPlots.makeEffSummSCompPlot(
CSVPlotBaseObjIter,
onlysamples,
"pfJets_csv[{2}_matchPF[{1}]] >= {0}".format(
WP, "?", collection),
nInclusiveIter,
basepaths +
"CSV/{2}/{3}Eff_DataMC_Jet{0}_pfWP_{1}_CSV_order{2}"
.format("incl", WP, collectionName, fileprefix),
addSel="{1}_matchPF[{0}] >= 0".format(
"?", collection),
label=WPLabel)
modules.effPlots.makeEffSummSCompPlot(
CSVPlotBaseObjIter,
onlysamples,
"caloJets_csv[{2}_matchCalo[{1}]] >= {0}".format(
WP, "?", collection),
nInclusiveIter,
basepaths +
"CSV/{2}/{3}Eff_DataMC_Jet{0}_caloWP_{1}_CSV_order{2}"
.format("incl", WP, collectionName, fileprefix),
addSel="{1}_matchCalo[{0}] >= 0".format(
"?", collection),
label=CaloWPLabel)
if doCross:
if dosplit:
pass
if doinclusive:
if looseSel:
JetSelectionIter = "{1}_deepcsv[{0}] >= 0 && abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30".format(
"?", collection)
else:
JetSelectionIter = "{1}_deepcsv[{0}] >= 0 && abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30 && {1}_passesTightLeptVetoID[{0}] > 0".format(
"?", collection)
logging.info(
"Making inclusive effciency ordered by {0} w/ DeepCSV as tagger"
.format(collectionName))
logging.subinfo(
"Using collection: {0}".format(collection))
DeepCSVPlotBaseIter = modules.classes.PlotBase(
"{1}_deepcsv[{0}]".format("?", collection),
"{0} && {1} && ({2})".format(
CSVSelection, offlineSelection,
JetSelectionIter),
"1", [20, 0, 1],
modules.utils.getAxisTitle("deepcsv",
0,
collectionName.lower(),
inclusive=True),
LegendPosition=[0.1, 0.6, 0.4, 0.76])
onlysamples = []
for sampleStuff in samples:
sample, name, label = sampleStuff
onlysamples.append(sample)
if doMC and doData:
modules.effPlots.makeEffSummSCompPlot(
DeepCSVPlotBaseIter,
onlysamples,
"pfJets_csv[{2}_matchPF[{1}]] >= {0}".format(
WP, "?", collection),
nInclusiveIter,
basepaths +
"CSV/{2}/{3}Eff_DataMC_Jet{0}_pfWP_{1}_CSV_order{2}"
.format("cross", WP, collectionName,
fileprefix),
addSel="{1}_matchPF[{0}] >= 0".format(
"?", collection),
label=WPLabel)
modules.effPlots.makeEffSummSCompPlot(
DeepCSVPlotBaseIter,
onlysamples,
"caloJets_csv[{2}_matchCalo[{1}]] >= {0}".
format(WP, "?", collection),
nInclusiveIter,
basepaths +
"CSV/{2}/{3}Eff_DataMC_Jet{0}_caloWP_{1}_CSV_order{2}"
.format("cross", WP, collectionName,
fileprefix),
addSel="{1}_matchCalo[{0}] >= 0".format(
"?", collection),
label=CaloWPLabel)
if compWPs:
logging.info("Makeing CSV WP comparison")
for collectionName, collection in collections:
#JetSelectionIter = "abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30".format("?", collection)
if looseSel:
JetSelectionIter = "abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30".format(
"?", collection)
else:
JetSelectionIter = "abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30 && {1}_passesTightLeptVetoID[{0}] > 0".format(
"?", collection)
logging.info(
"Making inclusive WP comparison ordered by {0} w/ CSV as tagger"
.format(collectionName))
logging.subinfo("Using collection: {0}".format(collection))
CSVPlotBaseObjIter = modules.classes.PlotBase(
"{1}_csv[{0}]".format("?", collection),
"{0} && {1} && ({2})".format(CSVSelection,
offlineSelection,
JetSelectionIter),
"1", [20, 0, 1],
modules.utils.getAxisTitle("csv",
0,
collectionName.lower(),
inclusive=True),
LegendPosition=[0.1, 0.6, 0.4, 0.76])
for sampleStuff in samples:
sample, name, label = sampleStuff
logging.info("Plotting sample {0}".format(name))
modules.effPlots.makeSumWPComp(
CSVPlotBaseObjIter,
sample,
"pfJets_csv[{1}_matchPF[{0}]]".format("?", collection),
nInclusiveIter,
CSVWPs,
basepaths +
"CSV/{3}/{4}Eff_{2}_Jet{0}_pfWPComp_{1}_CSV_order{3}".
format("incl", WP, name, collectionName, fileprefix),
addSel="{1}_matchPF[{0}] >= 0".format("?", collection))
modules.effPlots.makeSumWPComp(
CSVPlotBaseObjIter,
sample,
"caloJets_csv[{1}_matchCalo[{0}]]".format(
"?", collection),
nInclusiveIter,
CSVWPs,
basepaths +
"CSV/{3}/{4}Eff_{2}_Jet{0}_caloWPComp_{1}_CSV_order{3}"
.format("incl", WP, name, collectionName, fileprefix),
addSel="{1}_matchCalo[{0}] >= 0".format(
"?", collection))
if doDeepCSV:
logging.info("Processing plots for DeepCSV")
DeepCSVPlotBaseObjs = {}
collections = []
if ptordered:
collections.append(("pT", "offJets"))
logging.debug("Adding {1} to collecton as ordered by {0}".format(
"ptOrdered", "cleanJets"))
if csvordered:
collections.append(("DeepCSV", "cleanDeepCSVJets"))
logging.debug("Adding {1} to collecton as ordered by {0}".format(
"csvOrdered", "cleanDeepCSVJets"))
for WP in DeepCSVWPs:
WPLabel = getLabel("PF WP: {0}".format(WP), 0.7, "under")
CaloWPLabel = getLabel("Calo WP: {0}".format(WP), 0.7, "under")
logging.info("Using WP: {0}".format(WP))
for collectionName, collection in collections:
if dosplit:
for i in range(nSplitIter):
if looseSel:
JetSelection = "{1}_deepcsv[{0}] >= 0 && abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30".format(
i, collection)
else:
JetSelection = "{1}_deepcsv[{0}] >= 0 && abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30 && {1}_passesTightLeptVetoID[{0}] > 0".format(
i, collection)
logging.info(
"Making effciency for Jet {0} ordered by {1} w/ DeepCSV as tagger"
.format(i, collectionName))
logging.subinfo(
"Using collection: {0}".format(collection))
DeepCSVPlotBaseObjs[i] = modules.classes.PlotBase(
"{1}_deepcsv[{0}]".format(i, collection),
"{0} && {1} && ({2})".format(
CSVSelection, offlineSelection, JetSelection),
"1", [20, 0, 1],
modules.utils.getAxisTitle("deepcsv", i,
collectionName.lower()),
LegendPosition=[0.1, 0.6, 0.4, 0.76])
DeepCSVPlotBaseObjs[i].color = WPColors[i]
onlysamples = []
for sampleStuff in samples:
sample, name, label = sampleStuff
onlysamples.append(sample)
if not (doMC and doData):
modules.effPlots.makeEffPlot(
DeepCSVPlotBaseObjs[i],
sample,
"(pfJets_deepcsv[{2}_matchPF[{1}]]) >= {0}"
.format(WP, i, collection),
basepaths +
"DeepCSV/{3}/{4}Eff_{2}_Jet{0}_pfWP_{1}_DeepCSV_order{3}"
.format(i, WP, name, collectionName,
fileprefix),
addSel="{1}_matchPF[{0}] >= 0".format(
i, collection),
label=[label, WPLabel])
modules.effPlots.makeEffPlot(
DeepCSVPlotBaseObjs[i],
sample,
"(caloJets_deepcsv[{2}_matchCalo[{1}]]) >= {0}"
.format(WP, i, collection),
basepaths +
"DeepCSV/{3}/{4}Eff_{2}_Jet{0}_caloWP_{1}_DeepCSV_order{3}"
.format(i, WP, name, collectionName,
fileprefix),
addSel="{1}_matchCalo[{0}] >= 0".format(
i, collection),
label=[label, CaloWPLabel])
if doMC and doData:
modules.effPlots.makeEffSCompPlot(
DeepCSVPlotBaseObjs[i],
onlysamples,
"(pfJets_deepcsv[{2}_matchPF[{1}]]) >= {0}".
format(WP, i, collection),
basepaths +
"DeepCSV/{2}/{3}Eff_DataMC_Jet{0}_pfWP_{1}_DeepCSV_order{2}"
.format(i, WP, collectionName, fileprefix),
addSel="{1}_matchPF[{0}] >= 0".format(
i, collection),
label=WPLabel)
modules.effPlots.makeEffSCompPlot(
DeepCSVPlotBaseObjs[i],
onlysamples,
"(caloJets_deepcsv[{2}_matchCalo[{1}]]) >= {0}"
.format(WP, i, collection),
basepaths +
"DeepCSV/{2}/{3}Eff_DataMC_Jet{0}_caloWP_{1}_DeepCSV_order{2}"
.format(i, WP, collectionName, fileprefix),
addSel="{1}_matchCalo[{0}] >= 0".format(
i, collection),
label=CaloWPLabel)
if doinclusive:
if looseSel:
JetSelectionIter = "{1}_deepcsv[{0}] >= 0 && abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30".format(
"?", collection)
else:
JetSelectionIter = "{1}_deepcsv[{0}] >= 0 && abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30 && {1}_passesTightLeptVetoID[{0}] > 0".format(
"?", collection)
logging.info(
"Making inclusive effciency ordered by {0} w/ DeepCSV as tagger"
.format(collectionName))
logging.subinfo("Using collection: {0}".format(collection))
DeepCSVPlotBaseIter = modules.classes.PlotBase(
"{1}_deepcsv[{0}]".format("?", collection),
"{0} && {1} && ({2})".format(CSVSelection,
offlineSelection,
JetSelectionIter),
"1", [20, 0, 1],
modules.utils.getAxisTitle("deepcsv",
0,
collectionName.lower(),
inclusive=True),
LegendPosition=[0.1, 0.6, 0.4, 0.76])
onlysamples = []
for sampleStuff in samples:
sample, name, label = sampleStuff
onlysamples.append(sample)
if not (doMC and doData):
modules.effPlots.makeEffSumPlot(
DeepCSVPlotBaseIter,
sample,
"(pfJets_deepcsv[{2}_matchPF[{1}]]) >= {0}".
format(WP, "?", collection),
nInclusiveIter,
basepaths +
"DeepCSV/{3}/{4}Eff_{2}_Jet{0}_pfWP_{1}_DeepCSV_order{3}"
.format("incl", WP, name, collectionName,
fileprefix),
addSel="{1}_matchPF[{0}] >= 0".format(
"?", collection),
label=[label, WPLabel])
modules.effPlots.makeEffSumPlot(
DeepCSVPlotBaseIter,
sample,
"(caloJets_deepcsv[{2}_matchCalo[{1}]]) >= {0}"
.format(WP, "?", collection),
nInclusiveIter,
basepaths +
"DeepCSV/{3}/{4}Eff_{2}_Jet{0}_caloWP_{1}_DeepCSV_order{3}"
.format("incl", WP, name, collectionName,
fileprefix),
addSel="{1}_matchCalo[{0}] >= 0".format(
"?", collection),
label=[label, CaloWPLabel])
if doMC and doData:
modules.effPlots.makeEffSummSCompPlot(
DeepCSVPlotBaseIter,
onlysamples,
"(pfJets_deepcsv[{2}_matchPF[{1}]]) >= {0} && {2}_matchPF[{1}] >= 0"
.format(WP, "?", collection),
nInclusiveIter,
basepaths +
"DeepCSV/{2}/{3}Eff_DataMC_Jet{0}_pfWP_{1}_DeepCSV_order{2}"
.format("incl", WP, collectionName, fileprefix),
addSel="{1}_matchPF[{0}] >= 0".format(
"?", collection),
label=WPLabel)
modules.effPlots.makeEffSummSCompPlot(
DeepCSVPlotBaseIter,
onlysamples,
"(caloJets_deepcsv[{2}_matchCalo[{1}]]) >= {0}".
format(WP, "?", collection),
nInclusiveIter,
basepaths +
"DeepCSV/{2}/{3}Eff_DataMC_Jet{0}_caloWP_{1}_DeepCSV_order{2}"
.format("incl", WP, collectionName, fileprefix),
addSel="{1}_matchCalo[{0}] >= 0".format(
"?", collection),
label=CaloWPLabel)
if compWPs:
logging.info("Makeing DeepCSV WP comparison")
for collectionName, collection in collections:
if looseSel:
#JetSelectionIter = "abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30".format("?", collection)
JetSelectionIter = "{1}_pt[{0}] > 0".format(
"?", collection)
else:
JetSelectionIter = "{1}_deepcsv[{0}] >= 0 && abs({1}_eta[{0}]) < 2.4 && {1}_pt[{0}] > 30 && {1}_passesTightLeptVetoID[{0}] > 0".format(
"?", collection)
logging.info(
"Making inclusive WP comparison ordered by {0} w/ DeepCSV as tagger"
.format(collectionName))
logging.subinfo("Using collection: {0}".format(collection))
DeepCSVPlotBaseIter = modules.classes.PlotBase(
"{1}_deepcsv[{0}]".format("?", collection),
"{0} && {1} && ({2})".format(CSVSelection,
offlineSelection,
JetSelectionIter),
"1", [25, 0, 1],
modules.utils.getAxisTitle("deepcsv",
0,
collectionName.lower(),
inclusive=True),
LegendPosition=[0.1, 0.6, 0.4, 0.76])
for sampleStuff in samples:
sample, name, label = sampleStuff
logging.info("Plotting sample {0}".format(name))
modules.effPlots.makeSumWPComp(
DeepCSVPlotBaseIter,
sample,
"pfJets_deepcsv[{1}_matchPF[{0}]]".format(
"?", collection),
nInclusiveIter,
DeepCSVWPs,
basepaths +
"DeepCSV/{3}/{4}Eff_{2}_Jet{0}_pfWPComp_{1}_DeepCSV_order{3}"
.format("incl", WP, name, collectionName, fileprefix),
#addSel = "pfJets_deepcsv[{1}_matchPF[{0}]] >= 0 && {1}_matchPF[{0}] >= 0 && pfJets_pt[{1}_matchPF[{0}]] > 30 && abs(pfJets_eta[{1}_matchPF[{0}]]) < 2.4".format("?", collection)
addSel="{1}_matchPF[{0}] >= 0 && emilSourceValid == 1".
format("?", collection),
saveGraph=True)
"""
modules.effPlots.makeSumWPComp(DeepCSVPlotBaseIter, sample,
"caloJets_deepcsv[{1}_matchCalo[{0}]]".format("?", collection), nInclusiveIter, DeepCSVWPs,
basepaths+"DeepCSV/{3}/{4}Eff_{2}_Jet{0}_caloWPComp_{1}_DeepCSV_order{3}".format("incl", WP, name, collectionName, fileprefix),
addSel = "caloJets_deepcsv[{1}_matchCalo[{0}]] >= 0 && {1}_matchCalo[{0}] >= 0 && pfJets_pt[{1}_matchCalo[{0}]] > 30 && abs(pfJets_eta[{1}_matchCalo[{0}]]) < 2.4".format("?", collection),
#addSel = "caloJets_deepcsv[{1}_matchCalo[{0}]] >= 0".format("?", collection),
)
"""
logger.info("Runtime: {0:8f}s".format(time.time() - t0))
logger.info("Closing efficiency turnon calc")
def makeSumWPComp(PlotBaseObj,
Sample,
numSelectionVarIter,
nIter,
WPs,
outname=None,
outputformat="pdf",
label=None,
drawHistos=False,
addSel="1",
saveGraph=False):
styleconfig = SafeConfigParser()
styleconfig.read("config/plotting.cfg")
binning = PlotBaseObj.binning
if label is not None and isinstance(label, list):
for l in label:
if not isinstance(l, ROOT.TLatex):
logging.error(
"All labels are required to be of type ROOT.TLatex")
label.remove(l)
elif label is not None and not isinstance(label, ROOT.TLatex):
logging.warning("The label param should be of type ROOT.TLatex")
logging.warning("Ignoring additional label")
label = None
elif (label is not None
and isinstance(label, ROOT.TLatex)) or label is None:
pass
else:
logging.error("Something is happening here... :(")
graphs = []
forlegend = []
colors = [
ROOT.kRed, ROOT.kBlue, ROOT.kGreen - 2, ROOT.kOrange, ROOT.kPink,
ROOT.kTeal
]
if len(colors) < len(WPs):
logging.warning(
"less colors defined than WP. Will repeat colors for additional WPs"
)
while not len(colors) >= len(WPs):
colors += colors
for iWP, WP in enumerate(WPs):
logging.subinfo("Making plot for WP: {0}".format(WP))
numSelection = "{0} >= {1}".format(numSelectionVarIter, WP)
logging.debug("Passing numSelection {0} to makeEffSumPlot()".format(
numSelection))
graphs.append(
makeEffSumPlot(PlotBaseObj,
Sample,
numSelection,
nIter,
addSel=addSel,
forceColor=colors[iWP],
drawHistos=True,
drawEff=False,
outname=outname + "_" + str(WP)))
forlegend.append((graphs[iWP], "WP: {0}".format(WP), "p"))
canvas = modules.plotting.getCanvas()
canvas.cd()
logging.debug("Drawing graph 0")
graphs[0].Draw("AP")
graphs[0].GetHistogram().SetMaximum(
styleconfig.getfloat("Efficiency", "yMax"))
graphs[0].GetHistogram().SetMinimum(0.0)
graphs[0].GetXaxis().SetRangeUser(binning[1], binning[2])
graphs[0].Draw("AP")
for i in range(1, len(graphs)):
logging.debug("Drawing graph {0}".format(i))
graphs[i].Draw("PSame")
if label is not None:
if isinstance(label, list):
for l in label:
l.Draw("same")
else:
label.Draw("same")
CMSL1, CMSL2 = modules.utils.getCMStext()
CMSL1.Draw("same")
CMSL2.Draw("same")
logging.debug("Making Legend")
xstart = PlotBaseObj.LegendPosition[0]
ystart = PlotBaseObj.LegendPosition[1]
xend = PlotBaseObj.LegendPosition[2]
yend = PlotBaseObj.LegendPosition[3]
leg = modules.utils.getLegend(forlegend,
xstart,
ystart,
xend,
yend,
usingPlotBase=False)
leg.Draw("")
if outname is not None:
modules.utils.savePlot(canvas, outname, outputformat)
if saveGraph:
rout = ROOT.TFile(outname + ".root", "RECREATE")
rout.cd()
graphs[0].Write()
rout.Close()
def LeadingProbe(PlotBaseObj,
Samples2Stack,
probeSelection,
tagSelection,
plottag=False,
convertIterSelection=True,
probeIndex=0,
tagIndex=1,
data=None,
normalized=False,
drawRatio=True,
outname=None,
outputformat="pdf",
label=None,
skipPlots=False):
"""
Function for plotting a distribution of a porbe jet depending on a probe jet selection. Calls
modules.DataMC.StackDMCPlotBase for plotting.
Parameters
----------
PlotBaseObj :
Samples2Stack : list modules.classes.sample objects
probeSelection : string
Selection of the probe jets. I index of jet not fixed, use
convertIterSelection = True and the probeIndex to replace ?
with the index
tagSelection : string
Selection of the tag jets. I index of jet not fixed, use
convertIterSelection = True and the tagIndex to replace ?
with the index
convertIterSelection : bool
If true, selections are expected to have [?] instead of [index]
which will be replaced with probeIndex or tagIndex.
probeIndex : int
Index of the probe Jet in the jet array
tagIndex
Index of the tag Jet in the jet array
data : modules.classes.sample object
If None is provide the sum of all stacked histograms (passed in
Samples2Stack will be used as "data"
"""
StackHistos = []
StackSum = None
if convertIterSelection:
probeSelection = probeSelection.replace("?", str(probeIndex))
tagSelection = tagSelection.replace("?", str(tagIndex))
if plottag:
xVarBase = PlotBaseObj.variable.replace("?", str(tagIndex))
else:
xVarBase = PlotBaseObj.variable.replace("?", str(probeIndex))
else:
xVarBase = PlotBaseObj.variable
for isample, sample in enumerate(Samples2Stack):
selection = "{0} && {1}".format(PlotBaseObj.selection,
sample.selection).replace(
"?", str(probeIndex))
StackHistos.append(
getPorbeHisto(PlotBaseObj,
sample,
xVarBase,
selection,
probeSelection,
tagSelection,
isMC=True))
if isample == 0:
logging.debug("Creating stacksum" + " with name " +
str(sample.name))
StackSum = StackHistos[0].Clone()
StackSum.SetName("Stacksum_" + StackSum.GetName())
StackSum.SetTitle("Stacksum_" + StackSum.GetTitle())
else:
logging.debug("Adding histo to stacksum " + str(isample) +
" with name " + str(sample.name))
StackSum.Add(StackHistos[isample])
logging.subinfo("Stack histos finished")
if data is None:
logging.warning("No data set -> Setting data to Stacksum")
hData = StackSum.Clone()
hData.SetName("Data_" + StackSum.GetName())
else:
selection = "{0} && {1}".format(PlotBaseObj.selection,
data.selection).replace(
"?", str(probeIndex))
hData = getPorbeHisto(PlotBaseObj,
data,
xVarBase,
selection,
probeSelection,
tagSelection,
isMC=False)
if not skipPlots:
modules.DataMC.StackDMCPlotBase(StackSum, StackHistos, hData,
PlotBaseObj, Samples2Stack, data,
normalized, drawRatio, outname,
outputformat, label)
return StackSum, StackHistos, hData
def LeadingProbe(PlotBaseObj,
Samples2Stack,
probeSelection,
tagSelection,
plottag=False,
convertIterSelection=True,
probeIndex=0,
tagIndex=1,
probeWeight="1",
tagWeight="1",
data=None,
normalized=False,
drawRatio=True,
outname=None,
outputformat="pdf",
label=None,
skipPlots=False):
"""
Function for plotting a distribution of a porbe jet depending on a probe jet selection. Calls
modules.DataMC.StackDMCPlotBase for plotting.
Parameters
----------
PlotBaseObj :
Samples2Stack : list modules.classes.sample objects
probeSelection : string
Selection of the probe jets. I index of jet not fixed, use
convertIterSelection = True and the probeIndex to replace ?
with the index
tagSelection : string
Selection of the tag jets. I index of jet not fixed, use
convertIterSelection = True and the tagIndex to replace ?
with the index
convertIterSelection : bool
If true, selections are expected to have [?] instead of [index]
which will be replaced with probeIndex or tagIndex.
probeIndex : int
Index of the probe Jet in the jet array
tagIndex
Index of the tag Jet in the jet array
probeWeight : str
Special weight applyied for the probe Jet. If not [?] in string,
"1" will be uesd. Genera lweights are expected to be set in the
sample definition
tagWeight : str
Special weight applyied for the tag Jet. If not [?] in string,
"1" will be uesd. Genera lweights are expected to be set in the
sample definition
data : modules.classes.sample object
If None is provide the sum of all stacked histograms (passed in
Samples2Stack will be used as "data"
"""
StackHistos = []
StackSum = None
if convertIterSelection:
probeSelection = probeSelection.replace("?", str(probeIndex))
tagSelection = tagSelection.replace("?", str(tagIndex))
if plottag:
xVarBase = PlotBaseObj.variable.replace("?", str(tagIndex))
else:
xVarBase = PlotBaseObj.variable.replace("?", str(probeIndex))
else:
xVarBase = PlotBaseObj.variable
if "[?]" in probeWeight:
probeWeight = probeWeight.replace("?", str(probeIndex))
else:
probeWeight = "1"
if "[?]" in tagWeight:
tagWeight = tagWeight.replace("?", str(tagIndex))
else:
tagWeight = "1"
logging.info("Will use {0} as tag and {1} as probe weight".format(
tagWeight, probeWeight))
for isample, sample in enumerate(Samples2Stack):
selection = "{0} && {1}".format(PlotBaseObj.selection,
sample.selection).replace(
"?", str(probeIndex))
StackHistos.append(
getPorbeHisto(PlotBaseObj,
sample,
xVarBase,
selection,
probeSelection,
tagSelection,
isMC=True,
addWeight="({0} * {1})".format(
probeWeight, tagWeight)))
#for isample, sample in enumerate(Samples2Stack):
# print sample.name, StackHistos[isample], StackHistos[isample].Integral()#
#raw_input(".........................")
""" Merge all sample that end with the same string after _ """
#print Samples2Stack
mergedStackHistos = []
samples2Merge = OrderedDict(
) # Dict with sample name postfix as key and list of indices as
Samples2StackResorted = []
for isample, sample in enumerate(Samples2Stack):
print sample, sample.name, sample.color
postfix = sample.name.split("_")[-1]
if not postfix in samples2Merge:
samples2Merge[postfix] = []
samples2Merge[postfix].append(isample)
Samples2StackResorted.append(sample)
logging.debug("Merging sampes with same postfix")
for merge in samples2Merge: #Loop over gourps
#print "-----------------"
for iIndex, index in enumerate(samples2Merge[merge]):
#iIndex: Number of histos in the current merge
#index: index in the total list of samples
#print merge, StackHistos[index].Integral()
if iIndex == 0:
logging.info("Creating merged histos" + " with postfix " +
str(merge))
mergedHisto = StackHistos[index].Clone()
mergedHisto.SetName("Merge_" + str(merge))
mergedHisto.SetTitle("Merge_" + str(merge))
else:
logging.debug("Adding sample to merge")
mergedHisto.Add(StackHistos[index])
#print "--------",mergedHisto.Integral()
mergedStackHistos.append(mergedHisto)
# mergedStackHistos : Has len() of different sample name postfixes
#print mergedStackHistos
StackSum = None
for imergedHisto, mergedHisto in enumerate(mergedStackHistos):
if imergedHisto == 0:
logging.debug("Creating stacksum" + " with name " +
str(sample.name))
StackSum = mergedHisto.Clone()
StackSum.SetName("Stacksum_" + StackSum.GetName())
StackSum.SetTitle("Stacksum_" + StackSum.GetTitle())
else:
logging.debug("Adding histo to stacksum " + str(isample) +
" with name " + str(sample.name))
StackSum.Add(mergedHisto)
#print "++++++++++++++++****",StackSum.Integral()
logging.subinfo("Stack histos finished")
#print "StackSum:",StackSum.Integral()
if data is None:
logging.warning("No data set -> Setting data to Stacksum")
hData = StackSum.Clone()
hData.SetName("Data_" + StackSum.GetName())
else:
selection = "{0} && {1}".format(PlotBaseObj.selection,
data.selection).replace(
"?", str(probeIndex))
hData = getPorbeHisto(PlotBaseObj,
data,
xVarBase,
selection,
probeSelection,
tagSelection,
isMC=False)
print "StackSum:", StackSum.Integral()
print "hData:", hData.Integral()
if not skipPlots:
logging.info("Will Start printing here")
modules.DataMC.StackDMCPlotBase(StackSum, mergedStackHistos, hData,
PlotBaseObj, Samples2StackResorted,
data, normalized, drawRatio, outname,
outputformat, label)
print "StackSum:", StackSum.Integral()
#raw_input(".........................")
return StackSum, mergedStackHistos, hData