def PurityHistograms(datasetsMgr, qcdDatasetName):
'''
'''
Verbose("Plotting Purity Histograms")
# Definitions
histoNames = []
saveFormats = [".png"] #[".C", ".png", ".eps"]
dataPath = "ForDataDrivenCtrlPlots"
histoList = datasetsMgr.getDataset(qcdDatasetName).getDirectoryContent(dataPath)
histoList = [h for h in histoList if "_Purity" in h]
# histoList = [h for h in histoList if "_MCEWK" in h]
histoPaths = [dataPath+"/"+h for h in histoList]
histoKwargs = GetHistoKwargs(histoPaths, opts)
# For-loop: All histograms in list
for histoName in histoPaths:
if "_Vs_" in histoName:
continue
if "subldg" in histoName.lower():
continue
kwargs_ = histoKwargs[histoName]
saveName = histoName.replace("/", "_")
# Create the plotting object
p = plots.MCPlot(datasetsMgr, histoName, normalizeToLumi=True, saveFormats=[])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply QCD data-driven style
p.histoMgr.forHisto(qcdDatasetName, styles.getQCDLineStyle())
p.histoMgr.setHistoDrawStyle(qcdDatasetName, "AP")
p.histoMgr.setHistoLegendStyle(qcdDatasetName, "LP")
p.histoMgr.setHistoLegendLabelMany({
#qcdDatasetName: "QCD (Data)",
qcdDatasetName: "QCD",
})
# Draw and save the plot
plots.drawPlot(p, saveName, **kwargs_) #the "**" unpacks the kwargs_ dictionary
SavePlot(p, saveName, os.path.join(opts.saveDir, "", opts.optMode) )
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
#_opts = {"ymin": 1e-3, "ymax": 60}
if "pt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
#_opts["xmax"] = 505 #1005
if "ht" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "H_{T} (%s)" % _units
_opts["xmax"] = 2000
#_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
#_opts["xmax"] = 505 #1005
if "eta" in histo.lower():
_units = ""
_format = "%0.1f " + _units
_xlabel = "#eta %s" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_opts["xmax"] = 2.5
_opts["xmin"] = -2.5
if "trijetmass" in histo.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 505 #1005
if "ldg" in histo.lower():
_xlabel = "m_{jjb}^{ldg} (%s)" % _units
elif "tetrajetmass" in histo.lower():
_rebinX = 10 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 2500 #3500.0
elif "tetrajetpt" in histo.lower():
_rebinX = 2 #5 #10 #4
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 1000 #3500.0
elif "dijetmass" in histo.lower():
_rebinX = 2 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800 #3500.0
_cutBox = {"cutValue": 80.4, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 300
if "ldg" in histo.lower():
_xlabel = "m_{jj}^{ldg} (%s)" % (_units)
elif "tetrajetbjetpt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,bjet} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "ldgtrijetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,jjb}^{ldg} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "ldgtrijetdijetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,jj}^{ldg} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "topbdt_" in histo.lower():
_format = "%0.1f"
_cutBox = {"cutValue": +0.40, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_xlabel = "BDTG discriminant"
if "gentop_pt" in histo.lower():
_rebinX = 1
if "genquark_pt" in histo.lower():
_rebinX = 1
_opts["xmax"] = 500
else:
pass
if "delta" in histo.lower():
_format = "%0.1f "
if "phi" in histo.lower():
_xlabel = "#Delta #phi"
if "deltar" in histo.lower():
_rebinX = 2
if "DiBjetMaxMass_Mass" in histo:
_units = "GeV"
_rebinX = 4
_xlabel = "m_{max}(bb) (%s)" % (_units)
_format = "%0.0f " + _units
if "DiJetDeltaRmin_Mass" in histo:
_units = "GeV"
_format = "%0.0f " + _units
_xlabel = "m_{#DeltaR_{min}(jj)}(jj) (%s)" % (_units)
_opts["xmax"] = 200
if "DiBjetDeltaRmin_Mass" in histo:
_units = "GeV"
_format = "%0.0f " + _units
_rebinX= 2
_xlabel = "m_{#DeltaR_{min}(bb)}(bb) (%s)" % (_units)
_opts["xmax"] = 600
if "LdgBjet_SubldgBjet_Mass" in histo:
_units = "GeV"
_format = "%0.0f " + _units
_rebinX= 4
_xlabel = "m(b_{ldg}b_{sldg}) (%s)" % (_units)
if "DeltaR_LdgTop_DiBjetDeltaRmin" in histo:
_rebinX= 2
_format = "%0.1f "
_xlabel = "#DeltaR (top_{ldg}, bb_{#Delta Rmin})"
if "DeltaR_SubldgTop_DiBjetDeltaRmin" in histo:
_rebinX= 2
_format = "%0.1f "
_xlabel = "#DeltaR (top_{sldg}, bb_{#Delta Rmin})"
if "over" in histo.lower():
_opts["xmax"] = 15
_opts["xmax"] = +0.8
_opts["xmin"] = -0.8
_xlabel = "#Delta p_{T}(j-q)/p_{T,q}"
_cutBox = {"cutValue": +0.32, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
if "within" in histo.lower():
_opts["xmax"] = +0.8
_opts["xmin"] = -0.8
_xlabel = "#Delta p_{T}(j-q)/p_{T,q}"
_cutBox = {"cutValue": +0.32, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
if "axis2" in histo.lower():
_opts["xmax"] = +0.2
_opts["xmin"] = 0.0
_xlabel = "axis2"
_units = ""
_format = "%0.1f "+_units
if "axis2" in histo.lower():
_opts["xmax"] = +0.2
_opts["xmin"] = 0.0
_xlabel = "axis2"
_units = ""
_format = "%0.2f "+_units
if "mass" in histo.lower():
_xlabel = "M (GeV/c^{2})"
_units = "GeV/c^{2}"
_format = "%0.0f "+_units
if "mult" in histo.lower():
_xlabel = "mult"
_units = ""
_format = "%0.0f "+_units
if "BQuarkFromH_Pt" in histo:
_opts["xmax"] = 200
_rebinX= 1
if "order" in histo.lower():
_opts["xmax"] = 15
_rebinX= 1
if "pt" in histo.lower():
_xlabel = "indx_{p_{T}}"
if "csv" in histo.lower():
_xlabel = "indx_{csv}"
if "phi_alpha" in histo.lower() or "phi_beta" in histo.lower() or "r_alpha" in histo.lower() or "r_beta" in histo.lower():
_format = "%0.1f "
_opts["xmin"] = 0.0
if "phi" in histo.lower():
_xlabel = "#phi"
_opts["xmax"] = 5.5
else:
_xlabel = "r"
_opts["xmax"] = 6.5
if "alpha" in histo.lower():
_xlabel = _xlabel+"_{#alpha}"
else:
_xlabel = _xlabel+"_{#beta}"
if opts.normaliseToOne:
logY = False
Ylabel = "Arbitrary Units / %s" % (_format)
else:
logY = True
Ylabel = "Events / %s" % (_format)
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
if "order" in histo.lower():
_opts["ymin"] = 1e-3
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
if "QCD" in datasetsMgr.getAllDatasetNames():
p.histoMgr.forHisto("QCD", styles.getQCDLineStyle()) #getQCDFillStyle() )
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasetNames():
TTStyle = styles.StyleCompound([styles.StyleFill(fillColor=ROOT.kMagenta-2), styles.StyleLine(lineColor=ROOT.kMagenta-2, lineStyle=ROOT.kSolid, lineWidth=3),
styles.StyleMarker(markerSize=1.2, markerColor=ROOT.kMagenta-2, markerSizes=None, markerStyle=ROOT.kFullTriangleUp)])
p.histoMgr.forHisto("TT", TTStyle)
p.histoMgr.setHistoDrawStyle("TT", "HIST") #AP
p.histoMgr.setHistoLegendStyle("TT", "F") #LP
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, styles.getSignalStyleHToTB_M(m))
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = Ylabel,
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
#createLegend = {"x1": 0.59, "y1": 0.65, "x2": 0.92, "y2": 0.92},
createLegend = {"x1": 0.59, "y1": 0.70, "x2": 0.92, "y2": 0.92},
#createLegend = {"x1": 0.73, "y1": 0.85, "x2": 0.97, "y2": 0.77},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, histo.split("/")[0], opts.optMode)
'''
if opts.normaliseToOne:
save_path = savePath + opts.MVAcut
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
else:
save_path = savePath + opts.MVAcut + "/normToLumi/"
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
'''
SavePlot(p, saveName, savePath)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
datasetsMgr40 = GetDatasetsFromDir_second(
opts, opts.mcrab.replace("_BDT85", "_BDT40"))
datasetsMgr40.updateNAllEventsToPUWeighted()
datasetsMgr40.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for datasetsMgr_ in [datasetsMgr, datasetsMgr40]:
for d in datasetsMgr_.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr_.getDataset(d.getName()).setCrossSection(1.0)
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
if 0:
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information before removing anything?
if 0:
datasetsMgr.PrintInfo()
# Determine integrated Lumi before removing data
if "Data" in datasetsMgr.getAllDatasetNames():
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
intLumi = 35920
# Remove datasets
filterKeys = ["Data", "TTZToQQ", "TTWJets", "TTTT"]
#filterKeys = ["Data", "TTZToQQ", "TTWJets", "TTTT"]
for key in filterKeys:
datasetsMgr.remove(
filter(lambda name: key in name,
datasetsMgr.getAllDatasetNames()))
datasetsMgr40.remove(
filter(lambda name: key in name,
datasetsMgr40.getAllDatasetNames()))
# Re-order datasets
datasetOrder = []
for d in datasetsMgr.getAllDatasets():
datasetOrder.append(d.getName())
# Select and re-order
datasetsMgr.selectAndReorder(datasetOrder)
datasetsMgr40.selectAndReorder(datasetOrder)
# Print dataset information
datasetsMgr.PrintInfo()
# QCD multijet
datasetsMgr.merge("QCD", GetListOfQCDatasets())
datasetsMgr40.merge("QCD", GetListOfQCDatasets())
plots._plotStyles["QCD"] = styles.getQCDLineStyle()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# For-loop: All numerator-denominator pairs
PlotEfficiency(datasetsMgr, datasetsMgr40, intLumi)
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
#_opts = {"ymin": 1e-3, "ymax": 60}
if "pt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
#_opts["xmax"] = 505 #1005
if "eta" in histo.lower():
_units = ""
_format = "%0.1f " + _units
_xlabel = "#eta %s" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_opts["xmax"] = 2.5
_opts["xmin"] = -2.5
if "trijetmass" in histo.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 505 #1005
if "ldg" in histo.lower():
_xlabel = "m_{jjb}^{ldg} (%s)" % _units
elif "tetrajetmass" in histo.lower():
_rebinX = 5 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 2500 #3500.0
elif "tetrajetpt" in histo.lower():
_rebinX = 2 #5 #10 #4
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 1000 #3500.0
elif "dijetmass" in histo.lower():
_rebinX = 2 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800 #3500.0
_cutBox = {"cutValue": 80.4, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 300
if "ldg" in histo.lower():
_xlabel = "m_{jj}^{ldg} (%s)" % (_units)
elif "tetrajetbjetpt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,bjet} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "ldgtrijetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,jjb}^{ldg} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "ldgtrijetdijetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T,jj}^{ldg} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "topbdt_" in histo.lower():
_format = "%0.1f"
_cutBox = {"cutValue": +0.40, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_xlabel = "BDTG discriminant"
if "gentop_pt" in histo.lower():
_rebinX = 1
if "genquark_pt" in histo.lower():
_rebinX = 1
_opts["xmax"] = 500
else:
pass
if "delta" in histo.lower():
_format = "%0.1f "
if "phi" in histo.lower():
_xlabel = "#Delta #phi"
if "deltar" in histo.lower():
_rebinX = 2
if "DiBjetMaxMass_Mass" in histo:
_units = "GeV"
_rebinX = 4
_xlabel = "m_{max}(bb) (%s)" % (_units)
_format = "%0.0f " + _units
if "DiJetDeltaRmin_Mass" in histo:
_units = "GeV"
_format = "%0.0f " + _units
_xlabel = "m_{#DeltaR_{min}(jj)}(jj) (%s)" % (_units)
_opts["xmax"] = 200
if "DiBjetDeltaRmin_Mass" in histo:
_units = "GeV"
_format = "%0.0f " + _units
_rebinX= 2
_xlabel = "m_{#DeltaR_{min}(bb)}(bb) (%s)" % (_units)
_opts["xmax"] = 600
if "LdgBjet_SubldgBjet_Mass" in histo:
_units = "GeV"
_format = "%0.0f " + _units
_rebinX= 4
_xlabel = "m(b_{ldg}b_{sldg}) (%s)" % (_units)
if "DeltaR_LdgTop_DiBjetDeltaRmin" in histo:
_rebinX= 2
_format = "%0.1f "
_xlabel = "#DeltaR (top_{ldg}, bb_{#Delta Rmin})"
if "DeltaR_SubldgTop_DiBjetDeltaRmin" in histo:
_rebinX= 2
_format = "%0.1f "
_xlabel = "#DeltaR (top_{sldg}, bb_{#Delta Rmin})"
if "phi_alpha" in histo.lower() or "phi_beta" in histo.lower() or "r_alpha" in histo.lower() or "r_beta" in histo.lower():
_format = "%0.1f "
_opts["xmin"] = 0.0
if "phi" in histo.lower():
_xlabel = "#phi"
_opts["xmax"] = 5.5
else:
_xlabel = "r"
_opts["xmax"] = 6.5
if "alpha" in histo.lower():
_xlabel = _xlabel+"_{#alpha}"
else:
_xlabel = _xlabel+"_{#beta}"
if opts.normaliseToOne:
logY = False
Ylabel = "Arbitrary Units / %s" % (_format)
else:
logY = True
Ylabel = "Events / %s" % (_format)
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
if "QCD" in datasetsMgr.getAllDatasetNames():
p.histoMgr.forHisto("QCD", styles.getQCDLineStyle()) #getQCDFillStyle() )
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasetNames():
TTStyle = styles.StyleCompound([styles.StyleFill(fillColor=ROOT.kMagenta-2), styles.StyleLine(lineColor=ROOT.kMagenta-2, lineStyle=ROOT.kSolid, lineWidth=3),
styles.StyleMarker(markerSize=1.2, markerColor=ROOT.kMagenta-2, markerSizes=None, markerStyle=ROOT.kFullTriangleUp)])
p.histoMgr.forHisto("TT", TTStyle)
p.histoMgr.setHistoDrawStyle("TT", "HIST") #AP
p.histoMgr.setHistoLegendStyle("TT", "F") #LP
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, styles.getSignalStyleHToTB_M(m))
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = Ylabel,
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
#createLegend = {"x1": 0.59, "y1": 0.65, "x2": 0.92, "y2": 0.92},
createLegend = {"x1": 0.59, "y1": 0.70, "x2": 0.92, "y2": 0.92},
#createLegend = {"x1": 0.73, "y1": 0.85, "x2": 0.97, "y2": 0.77},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, histo.split("/")[0], opts.optMode)
'''
if opts.normaliseToOne:
save_path = savePath + opts.MVAcut
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
else:
save_path = savePath + opts.MVAcut + "/normToLumi/"
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
'''
SavePlot(p, saveName, savePath)
return
def PlotHistosAndCalculateTF(datasetsMgr, histoList, binLabels, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CRone", "CRtwo"]
rhDict = GetRootHistos(datasetsMgr, histoList, regions, binLabels)
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
manager = FakeBNormalization.FakeBNormalizationManager(binLabels,
opts.mcrab,
opts.optMode,
verbose=False)
if opts.inclusiveOnly:
#manager.CalculateTransferFactor(binLabels[0], rhDict["CRone-FakeB"], rhDict["CRtwo-FakeB"])
binLabel = "Inclusive"
manager.CalculateTransferFactor("Inclusive",
rhDict["FakeB-CRone-Inclusive"],
rhDict["FakeB-CRtwo-Inclusive"])
else:
for bin in binLabels:
manager.CalculateTransferFactor(bin,
rhDict["FakeB-CRone-%s" % bin],
rhDict["FakeB-CRtwo-%s" % bin])
# Get unique a style for each region
for k in rhDict:
dataset = k.split("-")[0]
region = k.split("-")[1]
styles.getABCDStyle(region).apply(rhDict[k])
if "FakeB" in k:
styles.getFakeBStyle().apply(rhDict[k])
# sr.apply(rhDict[k])
# =========================================================================================
# Create the final plot object
# =========================================================================================
rData_SR = rhDict["Data-SR-Inclusive"]
rEWKGenuineB_SR = rhDict["EWK-SR-Inclusive-EWKGenuineB"]
rBkgSum_SR = rhDict["FakeB-VR-Inclusive"].Clone("BkgSum-SR-Inclusive")
rBkgSum_SR.Reset()
if opts.inclusiveOnly:
bin = "Inclusive"
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print(
"Applying TF = %s%0.6f%s to VR shape" %
(ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()),
True)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
else:
# For-loop: All bins
for i, bin in enumerate(binLabels, 1):
if bin == "Inclusive":
continue
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print(
"Applying TF = %s%0.6f%s to VR shape" %
(ShellStyles.NoteStyle(), VRtoSR_TF,
ShellStyles.NormalStyle()), i == 1)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
#Print("Got Verification Region (VR) shape %s%s%s" % (ShellStyles.NoteStyle(), rFakeB_VR.GetName(), ShellStyles.NormalStyle()), True)
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
#VRtoSR_TF = manager.GetTransferFactor("Inclusive")
#Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
#rBkgSum_SR.Scale(VRtoSR_TF)
# Plot histograms
if opts.altPlot:
# Add the SR EWK Genuine-b to the SR FakeB ( BkgSum = [FakeB] + [GenuineB-MC] = [VR * (CR1/CR2)] + [GenuineB-MC] )
rBkgSum_SR.Add(rEWKGenuineB_SR, +1)
# Change style
styles.getGenuineBStyle().apply(rBkgSum_SR)
# Remove unsupported settings of kwargs
_kwargs["stackMCHistograms"] = False
_kwargs["addLuminosityText"] = False
# Create the plot
p = plots.ComparisonManyPlot(rData_SR, [rBkgSum_SR], saveFormats=[])
# Set draw / legend style
p.histoMgr.setHistoDrawStyle("Data-SR-Inclusive", "P")
p.histoMgr.setHistoLegendStyle("Data-SR-Inclusive", "LP")
p.histoMgr.setHistoDrawStyle("BkgSum-SR-Inclusive", "HIST")
p.histoMgr.setHistoLegendStyle("BkgSum-SR-Inclusive", "F")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Data-SR": "Data",
"BkgSum-SR": "Fake-b + Gen-b",
})
else:
# Create empty histogram stack list
myStackList = []
# Signal
p2 = plots.DataMCPlot(datasetsMgr,
"ForTestQGLR/QGLR_SR/QGLR_SRInclusive",
saveFormats=[])
hSignal_800 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_800').getRootHisto()
hhSignal_800 = histograms.Histo(
hSignal_800, 'ChargedHiggs_HplusTB_HplusToTB_M_800',
"H^{+} m_{H^+}=800 GeV")
hhSignal_800.setIsDataMC(isData=False, isMC=True)
myStackList.append(hhSignal_800)
hSignal_250 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_250').getRootHisto()
hhSignal_250 = histograms.Histo(
hSignal_250, 'ChargedHiggs_HplusTB_HplusToTB_M_250',
"H^{+} m_{H^+}=250 GeV"
) #plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_250'])
hhSignal_250.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_250)
hSignal_500 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_500').getRootHisto()
hhSignal_500 = histograms.Histo(
hSignal_500, 'ChargedHiggs_HplusTB_HplusToTB_M_500',
"H^{+} m_{H^+}=500 GeV"
) #plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_500.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_500)
hSignal_1000 = p2.histoMgr.getHisto(
'ChargedHiggs_HplusTB_HplusToTB_M_1000').getRootHisto()
hhSignal_1000 = histograms.Histo(
hSignal_1000, 'ChargedHiggs_HplusTB_HplusToTB_M_1000',
"H^{+} m_{H^+}=1000 GeV"
) #plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_1000.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_1000)
# Add the FakeB data-driven background to the histogram list
hFakeB = histograms.Histo(rBkgSum_SR, "FakeB", "Fake-b")
hFakeB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hFakeB)
# Add the EWKGenuineB MC background to the histogram list
hGenuineB = histograms.Histo(rEWKGenuineB_SR, "GenuineB",
"EWK Genuine-b")
hGenuineB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hGenuineB)
# Add the collision datato the histogram list
hData = histograms.Histo(rData_SR, "Data", "Data")
hData.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, hData)
p = plots.DataMCPlot2(myStackList, saveFormats=[])
p.setLuminosity(opts.intLumi)
p.setDefaultStyles()
# Draw the plot and save it
hName = "test"
plots.drawPlot(p, hName, **_kwargs)
SavePlot(p,
hName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf"])
#==========================================================================================
# Calculate Cut-Flow Efficiency
#==========================================================================================
kwargs = {
"rebinX": 1,
"xlabel": "QGLR",
"ylabel": "Significance / %.02f ",
"opts": {
"ymin": 0.0,
"ymaxfactor": 1.3
},
"createLegend": {
"x1": 0.55,
"y1": 0.70,
"x2": 0.92,
"y2": 0.92
},
# "cutBox" : {"cutValue": 0.0, "fillColor" : 16, "box": False, "line": False, "greaterThan": True},
}
efficiencyList = []
xValues = []
yValues_250 = []
yValues_500 = []
yValues_800 = []
yValues_1000 = []
yValues_Bkg = []
nBins = hSignal_250.GetNbinsX() + 1
hBkg = p.histoMgr.getHisto(
"ChargedHiggs_HplusTB_HplusToTB_M_800").getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
for i in range(0, nBins):
# Cut value
cut = hSignal_250.GetBinCenter(i)
passed_250 = hSignal_250.Integral(i, hSignal_250.GetXaxis().GetNbins())
passed_500 = hSignal_500.Integral(i, hSignal_500.GetXaxis().GetNbins())
passed_800 = hSignal_800.Integral(i, hSignal_800.GetXaxis().GetNbins())
passed_1000 = hSignal_1000.Integral(i,
hSignal_1000.GetXaxis().GetNbins())
passed_Bkg = hBkg.Integral(i, hBkg.GetXaxis().GetNbins())
total_250 = hSignal_250.Integral()
total_500 = hSignal_500.Integral()
total_800 = hSignal_800.Integral()
total_1000 = hSignal_1000.Integral()
total_Bkg = hBkg.Integral()
eff_250 = float(passed_250) / total_250
eff_500 = float(passed_500) / total_500
eff_800 = float(passed_800) / total_800
eff_1000 = float(passed_1000) / total_1000
eff_Bkg = float(passed_Bkg) / total_Bkg
xValues.append(cut)
yValues_250.append(eff_250)
yValues_500.append(eff_500)
yValues_800.append(eff_800)
yValues_1000.append(eff_1000)
yValues_Bkg.append(eff_Bkg)
# Create the Efficiency Plot
tGraph_250 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_250))
tGraph_500 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_500))
tGraph_800 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_800))
tGraph_1000 = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_1000))
tGraph_Bkg = ROOT.TGraph(len(xValues), array.array("d", xValues),
array.array("d", yValues_Bkg))
styles.getSignalStyleHToTB_M("200").apply(tGraph_250)
styles.getSignalStyleHToTB_M("500").apply(tGraph_500)
styles.getSignalStyleHToTB_M("800").apply(tGraph_800)
styles.getSignalStyleHToTB_M("1000").apply(tGraph_1000)
styles.getQCDLineStyle().apply(tGraph_Bkg)
drawStyle = "CPE"
effGraph_250 = histograms.HistoGraph(tGraph_250,
"H^{+} m_{H^{+}} = 250 GeV", "lp",
drawStyle)
effGraph_500 = histograms.HistoGraph(tGraph_500,
"H^{+} m_{H^{+}} = 500 GeV", "lp",
drawStyle)
effGraph_800 = histograms.HistoGraph(tGraph_800,
"H^{+} m_{H^{+}} = 800 GeV", "lp",
drawStyle)
effGraph_1000 = histograms.HistoGraph(tGraph_1000,
"H^{+} m_{H^{+}} = 1000 GeV", "lp",
drawStyle)
effGraph_Bkg = histograms.HistoGraph(tGraph_Bkg, "Bkg", "lp", drawStyle)
efficiencyList.append(effGraph_250)
efficiencyList.append(effGraph_500)
efficiencyList.append(effGraph_800)
efficiencyList.append(effGraph_1000)
efficiencyList.append(effGraph_Bkg)
# Efficiency plot
pE = plots.PlotBase(efficiencyList, saveFormats=["pdf"])
pE.createFrame("QGLR_Efficiency")
pE.setEnergy("13")
pE.getFrame().GetYaxis().SetLabelSize(18)
pE.getFrame().GetXaxis().SetLabelSize(20)
pE.getFrame().GetYaxis().SetTitle("Efficiency / 0.01")
pE.getFrame().GetXaxis().SetTitle("QGLR Cut")
# Add Standard Texts to plot
histograms.addStandardTexts()
# Customise Legend
moveLegend = {"dx": -0.50, "dy": -0.5, "dh": -0.1}
pE.setLegend(histograms.moveLegend(histograms.createLegend(),
**moveLegend))
pE.draw()
# plots.drawPlot(pE, "QGLR_Efficiency", **kwargs)
SavePlot(pE,
"QGLR_Efficiency",
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf"])
#==========================================================================================
# Calculate Significance
#==========================================================================================
SignalName = "ChargedHiggs_HplusTB_HplusToTB_M_800"
hSignif_250 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_500 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_800 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_1000 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(
SignalName)
hSignif_250.Reset()
hSignif_500.Reset()
hSignif_800.Reset()
hSignif_1000.Reset()
hBkg = p.histoMgr.getHisto(SignalName).getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
nBins = hSignif_250.GetNbinsX() + 1
# For-loop: All histo bins
for i in range(1, nBins + 1):
sigmaB = ROOT.Double(0)
b = hBkg.IntegralAndError(i, nBins, sigmaB)
s_250 = hSignal_250.Integral(i, nBins)
s_500 = hSignal_500.Integral(i, nBins)
s_800 = hSignal_800.Integral(i, nBins)
s_1000 = hSignal_1000.Integral(i, nBins)
# Calculate significance
signif_250 = stat.significance(s_250, b, sigmaB,
option="Simple") #Asimov")
signif_500 = stat.significance(s_500, b, sigmaB,
option="Simple") #Asimov")
signif_800 = stat.significance(s_800, b, sigmaB,
option="Simple") #Asimov")
signif_1000 = stat.significance(s_1000, b, sigmaB,
option="Simple") #"Asimov")
# Set signif for this bin
hSignif_250.SetBinContent(i, signif_250)
hSignif_500.SetBinContent(i, signif_500)
hSignif_800.SetBinContent(i, signif_800)
hSignif_1000.SetBinContent(i, signif_1000)
# Apply style
s_250 = styles.getSignalStyleHToTB_M("200")
s_250.apply(hSignif_250)
s_500 = styles.getSignalStyleHToTB_M("500")
s_500.apply(hSignif_500)
s_800 = styles.getSignalStyleHToTB_M("800")
s_800.apply(hSignif_800)
s_1000 = styles.getSignalStyleHToTB_M("1000")
s_1000.apply(hSignif_1000)
hList = []
hList.append(hSignif_250)
hList.append(hSignif_500)
hList.append(hSignif_800)
hList.append(hSignif_1000)
hSignif_250.SetName("H^{+} m_{H^{+}} = 250 GeV")
hSignif_500.SetName("H^{+} m_{H^{+}} = 500 GeV")
hSignif_800.SetName("H^{+} m_{H^{+}} = 800 GeV")
hSignif_1000.SetName("H^{+} m_{H^{+}} = 1000 GeV")
pS = plots.PlotBase(hList, saveFormats=["png", "pdf"])
pS.setLuminosity(opts.intLumi)
# Drawing style
pS.histoMgr.setHistoDrawStyleAll("HIST")
pS.histoMgr.setHistoLegendStyleAll("L")
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(1.0))
pS.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pS.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetMarkerStyle(ROOT.kFullCircle))
# Draw the plot
name = "QGLR_Signif" + "GE"
plots.drawPlot(pS, name, **kwargs)
SavePlot(pS,
name,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf"])
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
Verbose("Write the normalisation factors to a python file", True)
fileName = os.path.join(
opts.mcrab, "FakeBTransferFactors%s.py" % (getModuleInfoString(opts)))
manager.writeNormFactorFile(fileName, opts)
return
def main(opts, signalMass):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(False)
style.setGridY(False)
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
dir = opts.mcrab.replace("_BDT85", "_BDT")
print dir
datasetsMgr30 = GetDatasetsFromDir_second(opts, dir+"30")
datasetsMgr30.updateNAllEventsToPUWeighted()
datasetsMgr30.loadLuminosities() # from lumi.json
datasetsMgr40 = GetDatasetsFromDir_second(opts, dir+"40")
datasetsMgr40.updateNAllEventsToPUWeighted()
datasetsMgr40.loadLuminosities() # from lumi.json
datasetsMgr50 = GetDatasetsFromDir_second(opts, dir+"50")
datasetsMgr50.updateNAllEventsToPUWeighted()
datasetsMgr50.loadLuminosities() # from lumi.json
datasetsMgr60 = GetDatasetsFromDir_second(opts, dir+"60")
datasetsMgr60.updateNAllEventsToPUWeighted()
datasetsMgr60.loadLuminosities() # from lumi.json
datasetsMgr70 = GetDatasetsFromDir_second(opts, dir+"70")
datasetsMgr70.updateNAllEventsToPUWeighted()
datasetsMgr70.loadLuminosities() # from lumi.json
datasetsMgr80 = GetDatasetsFromDir_second(opts, dir+"80")
datasetsMgr80.updateNAllEventsToPUWeighted()
datasetsMgr80.loadLuminosities() # from lumi.json
datasetsMgr90 = GetDatasetsFromDir_second(opts, dir+"90")
datasetsMgr90.updateNAllEventsToPUWeighted()
datasetsMgr90.loadLuminosities() # from lumi.json
datasetsMgr95 = GetDatasetsFromDir_second(opts, dir+"95")
datasetsMgr95.updateNAllEventsToPUWeighted()
datasetsMgr95.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for datasetsMgr_ in [datasetsMgr, datasetsMgr30, datasetsMgr40, datasetsMgr50, datasetsMgr60, datasetsMgr70, datasetsMgr80, datasetsMgr90, datasetsMgr95]:
for d in datasetsMgr_.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr_.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr50.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr60.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr70.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr80.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr90.getDataset(d.getName()).setCrossSection(1.0)
#datasetsMgr95.getDataset(d.getName()).setCrossSection(1.0)
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
#plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information before removing anything?
if 0:
datasetsMgr.PrintInfo()
# Determine integrated Lumi before removing data
if "Data" in datasetsMgr.getAllDatasetNames():
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
intLumi = 35920
# Remove datasets
filterKeys = ["Data", "TTZToQQ", "TTWJets", "TTTT"]
#filterKeys = ["Data", "TTZToQQ", "TTWJets", "TTTT"]
for key in filterKeys:
datasetsMgr.remove(filter(lambda name: key in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr30.remove(filter(lambda name: key in name, datasetsMgr30.getAllDatasetNames()))
datasetsMgr40.remove(filter(lambda name: key in name, datasetsMgr40.getAllDatasetNames()))
datasetsMgr50.remove(filter(lambda name: key in name, datasetsMgr50.getAllDatasetNames()))
datasetsMgr60.remove(filter(lambda name: key in name, datasetsMgr60.getAllDatasetNames()))
datasetsMgr70.remove(filter(lambda name: key in name, datasetsMgr70.getAllDatasetNames()))
datasetsMgr80.remove(filter(lambda name: key in name, datasetsMgr80.getAllDatasetNames()))
datasetsMgr90.remove(filter(lambda name: key in name, datasetsMgr90.getAllDatasetNames()))
datasetsMgr95.remove(filter(lambda name: key in name, datasetsMgr95.getAllDatasetNames()))
# Re-order datasets
datasetOrder = []
for d in datasetsMgr.getAllDatasets():
if "M_" in d.getName():
if d not in signalMass:
continue
datasetOrder.append(d.getName())
# Append signal datasets
for m in signalMass:
datasetOrder.insert(0, m)
datasetsMgr.selectAndReorder(datasetOrder)
datasetsMgr30.selectAndReorder(datasetOrder)
datasetsMgr40.selectAndReorder(datasetOrder)
datasetsMgr50.selectAndReorder(datasetOrder)
datasetsMgr60.selectAndReorder(datasetOrder)
datasetsMgr70.selectAndReorder(datasetOrder)
datasetsMgr80.selectAndReorder(datasetOrder)
datasetsMgr90.selectAndReorder(datasetOrder)
datasetsMgr95.selectAndReorder(datasetOrder)
# Print dataset information
datasetsMgr.PrintInfo()
# Define the mapping histograms in numerator->denominator pairs
Numerators = ["AllTopQuarkPt_MatchedBDT",
"AllTopQuarkPt_Matched",
"EventTrijetPt2T_MatchedBDT",
"EventTrijetPt2T_MatchedBDT",
"EventTrijetPt2T_MatchedBDT",
"AllTopQuarkPt_MatchedBDT",
"SelectedTrijetsPt_BjetPassCSVdisc_afterCuts",
"TrijetFakePt_BDT",
]
Denominators = ["AllTopQuarkPt_Matched",
"TopQuarkPt",
"EventTrijetPt2T_BDT",
"EventTrijetPt2T_Matched",
"EventTrijetPt2T",
"TopQuarkPt",
"SelectedTrijetsPt_afterCuts",
"TrijetFakePt",
]
if 1:
datasetsMgr.merge("QCD", GetListOfQCDatasets())
datasetsMgr30.merge("QCD", GetListOfQCDatasets())
datasetsMgr40.merge("QCD", GetListOfQCDatasets())
datasetsMgr50.merge("QCD", GetListOfQCDatasets())
datasetsMgr60.merge("QCD", GetListOfQCDatasets())
datasetsMgr70.merge("QCD", GetListOfQCDatasets())
datasetsMgr80.merge("QCD", GetListOfQCDatasets())
datasetsMgr90.merge("QCD", GetListOfQCDatasets())
datasetsMgr95.merge("QCD", GetListOfQCDatasets())
plots._plotStyles["QCD"] = styles.getQCDLineStyle()
#Background1_Dataset = datasetsMgr.getDataset("QCD")
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# For-loop: All numerator-denominator pairs
for i in range(len(Numerators)):
numerator = os.path.join(opts.folder, Numerators[i])
denominator = os.path.join(opts.folder, Denominators[i])
PlotEfficiency_comparison(datasetsMgr, datasetsMgr30, datasetsMgr40, datasetsMgr50, datasetsMgr60, datasetsMgr70, datasetsMgr80, datasetsMgr90, datasetsMgr95, numerator, denominator, intLumi)
return
def DataEwkQcd(datasetsMgr, histoName, analysisType):
'''
Create plots with "Data", "QCD=Data-EWK", and "EWK" on the same canvas
Mostly for sanity checks and visualisation purposes
'''
Verbose(
"Plotting histogram %s for Data, EWK, QCD for %s" %
(histoName, analysisType), True)
# Sanity check
IsBaselineOrInverted(analysisType)
# Define histogram names (full path)
h1 = "topSelection_Baseline/%s" % (histoName)
h2 = "topSelection_Inverted/%s" % (histoName)
# Create plot object for Data
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, "Data", h1, h2))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Create plot object for EWK
p2 = plots.ComparisonPlot(*getHistos(datasetsMgr, "EWK", h1, h2))
p2.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Get Data and EWK histos
Data = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
"-Data")
EWK = p2.histoMgr.getHisto(analysisType +
"-EWK").getRootHisto().Clone(analysisType +
"-EWK")
# Create QCD histo: QCD = Data-EWK
QCD = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
"-QCD")
QCD.Add(EWK, -1)
# Create the final plot object. The Data is treated as the reference histo.
# All other histograms are compared with respect to that.
p = plots.ComparisonManyPlot(Data, [QCD, EWK], saveFormats=[])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply histo styles
p.histoMgr.forHisto(analysisType + "-Data", styles.getDataStyle())
p.histoMgr.forHisto(analysisType + "-QCD", styles.getQCDLineStyle())
p.histoMgr.forHisto(analysisType + "-EWK", styles.getAltEWKStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle(analysisType + "-Data", "P")
p.histoMgr.setHistoLegendStyle(analysisType + "-Data", "P")
p.histoMgr.setHistoDrawStyle(analysisType + "-QCD", "LP")
p.histoMgr.setHistoLegendStyle(analysisType + "-QCD", "LP")
p.histoMgr.setHistoDrawStyle(analysisType + "-EWK", "HIST")
p.histoMgr.setHistoLegendStyle(analysisType + "-EWK", "LFP")
# p.histoMgr.setHistoLegendStyleAll("LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
analysisType + "-Data": "Data",
analysisType + "-QCD": "QCD", #=Data-EWK",
analysisType + "-EWK": "EWK",
# analysisType + "-Data": "%s (Data)" % (analysisType),
# analysisType + "-QCD" : "%s (QCD)" % (analysisType),
# analysisType + "-EWK" : "%s (EWK)" % (analysisType),
})
# Draw the histograms
_cutBox = None
_rebinX = 1
_opts = {"ymin": 1e0, "ymaxfactor": 10}
_format = "%0.0f"
if "mass" in histoName.lower():
_rebinX = 2
_format = "%0.0f GeV/c^{2}"
if "TrijetMass" in histoName:
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "DijetMass" in histoName:
_cutBox = {
"cutValue": 80.399,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "pt" in histoName.lower():
_rebinX = 2
_format = "%0.0f GeV/c"
if "eta" in histoName.lower():
_rebinX = 2
_format = "%0.2f"
_cutBox = {
"cutValue": 0.,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "bdisc" in histoName.lower():
_format = "%0.2f"
_rebinX = 1
if "chisqr" in histoName.lower():
_format = "%0.0f"
_rebinX = 10
if "after" in histoName.lower():
_rebinX = 1
_opts["xmax"] = 20.0
if "tetrajet" in histoName.lower():
if "mass" in histoName.lower():
_rebinX = 10
_opts["xmax"] = 3500.0
else:
pass
histoName += "_" + analysisType
plots.drawPlot(
p,
histoName,
ylabel="Events / %s" % (_format),
log=True,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.75,
"y1": 0.76,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 1e-5,
"ymax": 1e0
},
ratio=True,
ratioInvert=False,
ratioYlabel="Ratio",
cutBox=_cutBox,
)
# Save plot in all formats
SavePlot(p,
histoName,
os.path.join(opts.saveDir, "DataEwkQcd"),
saveFormats=[".png"])
return
def PlotMC(datasetsMgr, datasetsMgr1, histo, intLumi):
kwargs = {}
print "here1"
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[".pdf", "png"], **kwargs)
p = plots.MCPlot(datasetsMgr1, histo, normalizeToOne=True, saveFormats=[".pdf", ".png"], **kwargs)
else:
p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[".pdf", ".png"], **kwargs)
p = plots.MCPlot(datasetsMgr1, histo, normalizeToOne=True, saveFormats=[".pdf", "png"], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
print "here2"
if "Pt" in histo:
_rebinX = 2
xMin = 0.0
# rebinX = 2
xMax = 805.0
# xMax = 555.0 # For topPt < 500GeV
xTitle = "p_{T} (GeV/c)"
units = "GeV/c"
_format = "%0.0f" + units
# yTitle = "Efficiency / " + str(binwidth) + " "+units
# yMin = 0.0
# yMax = 1.1
else:
pass
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
print "here3"
histoDef = datasetsMgr.getDataset("TT").getDatasetRootHisto(histo)
histoDR = datasetsMgr1.getDataset("TT").getDatasetRootHisto(histo)
histoDef.normalizeToOne()
histoDR.normalizeToOne()
hDef = histoDef.getHistogram()
hDR = histoDR.getHistogram()
p = plots.ComparisonPlot(histograms.Histo(hDR,"DeltaR08", "l", "L"),
histograms.Histo(hDef,"Default", "l", "L"))
p.histoMgr.setHistoLegendLabelMany({"DeltaR08": "#DeltaR(q,q')>0.8",
"Default": "Default"})
p.histoMgr.forHisto("DeltaR08", styles.getQCDLineStyle() )
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.setHistoDrawStyle("DeltaR08", "HIST") #HIST
p.histoMgr.setHistoLegendStyle("DeltaR08", "L") #F
p.histoMgr.setHistoDrawStyle("Default", "HIST") #HIST
p.histoMgr.setHistoLegendStyle("Default", "L") #F
p.histoMgr.forHisto("Default", styles.ttStyle)
print "here4"
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, styles.getSignalStyleHToTB_M(m))
print "here5"
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = "Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.58, "y1": 0.75, "x2": 0.92, "y2": 0.92},
# createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.92},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
print "here6"
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, "HplusMasses", histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath)
print "here7"
return
def EWKvQCD(datasetsMgr, histoName, analysisType=""):
Verbose("Plotting EWK Vs QCD unity-normalised histograms for %s" %
analysisType)
# Sanity check
IsBaselineOrInverted(analysisType)
p1 = plots.ComparisonPlot(
*getHistos(datasetsMgr, "Data", "topSelection_Baseline/%s" %
histoName, "topSelection_Inverted/%s" % histoName))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
p2 = plots.ComparisonPlot(
*getHistos(datasetsMgr, "EWK", "topSelection_Baseline/%s" %
histoName, "topSelection_Inverted/%s" % histoName))
p2.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Get histos
data = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
" -Data")
EWK = p2.histoMgr.getHisto(analysisType +
"-EWK").getRootHisto().Clone(analysisType +
"-EWK")
# Create QCD histos: QCD = Data-EWK
QCD = p1.histoMgr.getHisto(analysisType +
"-Data").getRootHisto().Clone(analysisType +
"-QCD")
QCD.Add(EWK, -1)
# Normalize histograms to unit area
QCD.Scale(1.0 / QCD.Integral())
EWK.Scale(1.0 / EWK.Integral())
# Create the final plot object
p = plots.ComparisonManyPlot(QCD, [EWK],
saveFormats=[]) #[".C", ".png", ".pdf"])
p.setLuminosity(GetLumi(datasetsMgr))
# Apply styles
p.histoMgr.forHisto(analysisType + "-QCD", styles.getQCDLineStyle())
p.histoMgr.forHisto(analysisType + "-EWK", styles.getAltEWKStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle(analysisType + "-QCD", "LP")
p.histoMgr.setHistoLegendStyle(analysisType + "-QCD", "LP")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
analysisType + "-QCD":
analysisType + " (QCD)",
analysisType + "-EWK":
analysisType + " (EWK)",
})
# Append analysisType to histogram name
saveName = histoName + "_" + analysisType
# Draw the histograms #alex
plots.drawPlot(p, saveName,
**GetHistoKwargs(histoName)) #the "**" unpacks the kwargs_
# _kwargs = {"lessThan": True}
# p.addCutBoxAndLine(cutValue=200, fillColor=ROOT.kRed, box=False, line=True, ***_kwargs)
# Save plot in all formats
saveDir = os.path.join(opts.saveDir, "EWKvQCD", opts.optMode)
SavePlot(p, saveName, saveDir)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
datasetsMgr40 = GetDatasetsFromDir_second(opts, opts.mcrab.replace("_BDT85", "_BDT40"))
datasetsMgr40.updateNAllEventsToPUWeighted()
datasetsMgr40.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for datasetsMgr_ in [datasetsMgr, datasetsMgr40]:
for d in datasetsMgr_.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr_.getDataset(d.getName()).setCrossSection(1.0)
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
if 0:
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information before removing anything?
if 0:
datasetsMgr.PrintInfo()
# Determine integrated Lumi before removing data
if "Data" in datasetsMgr.getAllDatasetNames():
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
intLumi = 35920
# Remove datasets
filterKeys = ["Data", "TTZToQQ", "TTWJets", "TTTT"]
#filterKeys = ["Data", "TTZToQQ", "TTWJets", "TTTT"]
for key in filterKeys:
datasetsMgr.remove(filter(lambda name: key in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr40.remove(filter(lambda name: key in name, datasetsMgr40.getAllDatasetNames()))
# Re-order datasets
datasetOrder = []
for d in datasetsMgr.getAllDatasets():
datasetOrder.append(d.getName())
# Select and re-order
datasetsMgr.selectAndReorder(datasetOrder)
datasetsMgr40.selectAndReorder(datasetOrder)
# Print dataset information
datasetsMgr.PrintInfo()
# QCD multijet
datasetsMgr.merge("QCD", GetListOfQCDatasets())
datasetsMgr40.merge("QCD", GetListOfQCDatasets())
plots._plotStyles["QCD"] = styles.getQCDLineStyle()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# For-loop: All numerator-denominator pairs
PlotEfficiency(datasetsMgr, datasetsMgr40, intLumi)
return
def PlotHistosAndCalculateTF(datasetsMgr, histoList, binLabels, opts):
# Get the histogram customisations (keyword arguments)
_kwargs = GetHistoKwargs(histoList[0])
# Get the root histos for all datasets and Control Regions (CRs)
regions = ["SR", "VR", "CRone", "CRtwo"]
rhDict = GetRootHistos(datasetsMgr, histoList, regions, binLabels)
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
manager = FakeBNormalization.FakeBNormalizationManager(binLabels, opts.mcrab, opts.optMode, verbose=False)
if opts.inclusiveOnly:
#manager.CalculateTransferFactor(binLabels[0], rhDict["CRone-FakeB"], rhDict["CRtwo-FakeB"])
binLabel = "Inclusive"
manager.CalculateTransferFactor("Inclusive", rhDict["FakeB-CRone-Inclusive"], rhDict["FakeB-CRtwo-Inclusive"])
else:
for bin in binLabels:
manager.CalculateTransferFactor(bin, rhDict["FakeB-CRone-%s" % bin], rhDict["FakeB-CRtwo-%s" % bin])
# Get unique a style for each region
for k in rhDict:
dataset = k.split("-")[0]
region = k.split("-")[1]
styles.getABCDStyle(region).apply(rhDict[k])
if "FakeB" in k:
styles.getFakeBStyle().apply(rhDict[k])
# sr.apply(rhDict[k])
# =========================================================================================
# Create the final plot object
# =========================================================================================
rData_SR = rhDict["Data-SR-Inclusive"]
rEWKGenuineB_SR = rhDict["EWK-SR-Inclusive-EWKGenuineB"]
rBkgSum_SR = rhDict["FakeB-VR-Inclusive"].Clone("BkgSum-SR-Inclusive")
rBkgSum_SR.Reset()
if opts.inclusiveOnly:
bin = "Inclusive"
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
else:
# For-loop: All bins
for i, bin in enumerate(binLabels, 1):
if bin == "Inclusive":
continue
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
binHisto_VR = rhDict["FakeB-VR-%s" % (bin)]
VRtoSR_TF = manager.GetTransferFactor(bin)
Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), i==1)
binHisto_VR.Scale(VRtoSR_TF)
# Add the normalised histogram to the final Inclusive SR (predicted) histo
rBkgSum_SR.Add(binHisto_VR, +1)
#Print("Got Verification Region (VR) shape %s%s%s" % (ShellStyles.NoteStyle(), rFakeB_VR.GetName(), ShellStyles.NormalStyle()), True)
# Normalise the VR histogram with the Transfer Factor ( BkgSum = VR * (CR1/CR2) )
#VRtoSR_TF = manager.GetTransferFactor("Inclusive")
#Print("Applying TF = %s%0.6f%s to VR shape" % (ShellStyles.NoteStyle(), VRtoSR_TF, ShellStyles.NormalStyle()), True)
#rBkgSum_SR.Scale(VRtoSR_TF)
# Plot histograms
if opts.altPlot:
# Add the SR EWK Genuine-b to the SR FakeB ( BkgSum = [FakeB] + [GenuineB-MC] = [VR * (CR1/CR2)] + [GenuineB-MC] )
rBkgSum_SR.Add(rEWKGenuineB_SR, +1)
# Change style
styles.getGenuineBStyle().apply(rBkgSum_SR)
# Remove unsupported settings of kwargs
_kwargs["stackMCHistograms"] = False
_kwargs["addLuminosityText"] = False
# Create the plot
p = plots.ComparisonManyPlot(rData_SR, [rBkgSum_SR], saveFormats=[])
# Set draw / legend style
p.histoMgr.setHistoDrawStyle("Data-SR-Inclusive", "P")
p.histoMgr.setHistoLegendStyle("Data-SR-Inclusive" , "LP")
p.histoMgr.setHistoDrawStyle("BkgSum-SR-Inclusive", "HIST")
p.histoMgr.setHistoLegendStyle("BkgSum-SR-Inclusive" , "F")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Data-SR" : "Data",
"BkgSum-SR" : "Fake-b + Gen-b",
})
else:
# Create empty histogram stack list
myStackList = []
# Signal
p2 = plots.DataMCPlot(datasetsMgr, "ForTestQGLR/QGLR_SR/QGLR_SRInclusive", saveFormats=[])
hSignal_800 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_800').getRootHisto()
hhSignal_800= histograms.Histo(hSignal_800, 'ChargedHiggs_HplusTB_HplusToTB_M_800', "H^{+} m_{H^+}=800 GeV")
hhSignal_800.setIsDataMC(isData=False, isMC=True)
myStackList.append(hhSignal_800)
hSignal_250 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_250').getRootHisto()
hhSignal_250= histograms.Histo(hSignal_250, 'ChargedHiggs_HplusTB_HplusToTB_M_250', "H^{+} m_{H^+}=250 GeV")#plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_250'])
hhSignal_250.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_250)
hSignal_500 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_500').getRootHisto()
hhSignal_500= histograms.Histo(hSignal_500, 'ChargedHiggs_HplusTB_HplusToTB_M_500', "H^{+} m_{H^+}=500 GeV")#plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_500.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_500)
hSignal_1000 = p2.histoMgr.getHisto('ChargedHiggs_HplusTB_HplusToTB_M_1000').getRootHisto()
hhSignal_1000= histograms.Histo(hSignal_1000, 'ChargedHiggs_HplusTB_HplusToTB_M_1000', "H^{+} m_{H^+}=1000 GeV")#plots._legendLabels['ChargedHiggs_HplusTB_HplusToTB_M_500'])
hhSignal_1000.setIsDataMC(isData=False, isMC=True)
#myStackList.append(hhSignal_1000)
# Add the FakeB data-driven background to the histogram list
hFakeB = histograms.Histo(rBkgSum_SR, "FakeB", "Fake-b")
hFakeB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hFakeB)
# Add the EWKGenuineB MC background to the histogram list
hGenuineB = histograms.Histo(rEWKGenuineB_SR, "GenuineB", "EWK Genuine-b")
hGenuineB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hGenuineB)
# Add the collision datato the histogram list
hData = histograms.Histo(rData_SR, "Data", "Data")
hData.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, hData)
p = plots.DataMCPlot2( myStackList, saveFormats=[])
p.setLuminosity(opts.intLumi)
p.setDefaultStyles()
# Draw the plot and save it
hName = "test"
plots.drawPlot(p, hName, **_kwargs)
SavePlot(p, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf"])
#==========================================================================================
# Calculate Cut-Flow Efficiency
#==========================================================================================
kwargs = {
"rebinX" : 1,
"xlabel" : "QGLR",
"ylabel" : "Significance / %.02f ",
"opts" : {"ymin": 0.0, "ymaxfactor": 1.3},
"createLegend": {"x1": 0.55, "y1": 0.70, "x2": 0.92, "y2": 0.92},
# "cutBox" : {"cutValue": 0.0, "fillColor" : 16, "box": False, "line": False, "greaterThan": True},
}
efficiencyList = []
xValues = []
yValues_250 = []
yValues_500 = []
yValues_800 = []
yValues_1000 = []
yValues_Bkg = []
nBins = hSignal_250.GetNbinsX()+1
hBkg = p.histoMgr.getHisto("ChargedHiggs_HplusTB_HplusToTB_M_800").getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
for i in range (0, nBins):
# Cut value
cut = hSignal_250.GetBinCenter(i)
passed_250 = hSignal_250.Integral(i, hSignal_250.GetXaxis().GetNbins())
passed_500 = hSignal_500.Integral(i, hSignal_500.GetXaxis().GetNbins())
passed_800 = hSignal_800.Integral(i, hSignal_800.GetXaxis().GetNbins())
passed_1000 = hSignal_1000.Integral(i, hSignal_1000.GetXaxis().GetNbins())
passed_Bkg = hBkg.Integral(i, hBkg.GetXaxis().GetNbins())
total_250 = hSignal_250.Integral()
total_500 = hSignal_500.Integral()
total_800 = hSignal_800.Integral()
total_1000 = hSignal_1000.Integral()
total_Bkg = hBkg.Integral()
eff_250 = float(passed_250)/total_250
eff_500 = float(passed_500)/total_500
eff_800 = float(passed_800)/total_800
eff_1000 = float(passed_1000)/total_1000
eff_Bkg = float(passed_Bkg)/total_Bkg
xValues.append(cut)
yValues_250.append(eff_250)
yValues_500.append(eff_500)
yValues_800.append(eff_800)
yValues_1000.append(eff_1000)
yValues_Bkg.append(eff_Bkg)
# Create the Efficiency Plot
tGraph_250 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_250))
tGraph_500 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_500))
tGraph_800 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_800))
tGraph_1000 = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_1000))
tGraph_Bkg = ROOT.TGraph(len(xValues), array.array("d", xValues), array.array("d", yValues_Bkg))
styles.getSignalStyleHToTB_M("200").apply(tGraph_250)
styles.getSignalStyleHToTB_M("500").apply(tGraph_500)
styles.getSignalStyleHToTB_M("800").apply(tGraph_800)
styles.getSignalStyleHToTB_M("1000").apply(tGraph_1000)
styles.getQCDLineStyle().apply(tGraph_Bkg)
drawStyle = "CPE"
effGraph_250 = histograms.HistoGraph(tGraph_250, "H^{+} m_{H^{+}} = 250 GeV", "lp", drawStyle)
effGraph_500 = histograms.HistoGraph(tGraph_500, "H^{+} m_{H^{+}} = 500 GeV", "lp", drawStyle)
effGraph_800 = histograms.HistoGraph(tGraph_800, "H^{+} m_{H^{+}} = 800 GeV", "lp", drawStyle)
effGraph_1000 = histograms.HistoGraph(tGraph_1000, "H^{+} m_{H^{+}} = 1000 GeV", "lp", drawStyle)
effGraph_Bkg = histograms.HistoGraph(tGraph_Bkg, "Bkg", "lp", drawStyle)
efficiencyList.append(effGraph_250)
efficiencyList.append(effGraph_500)
efficiencyList.append(effGraph_800)
efficiencyList.append(effGraph_1000)
efficiencyList.append(effGraph_Bkg)
# Efficiency plot
pE = plots.PlotBase(efficiencyList, saveFormats=["pdf"])
pE.createFrame("QGLR_Efficiency")
pE.setEnergy("13")
pE.getFrame().GetYaxis().SetLabelSize(18)
pE.getFrame().GetXaxis().SetLabelSize(20)
pE.getFrame().GetYaxis().SetTitle("Efficiency / 0.01")
pE.getFrame().GetXaxis().SetTitle("QGLR Cut")
# Add Standard Texts to plot
histograms.addStandardTexts()
# Customise Legend
moveLegend = {"dx": -0.50, "dy": -0.5, "dh": -0.1}
pE.setLegend(histograms.moveLegend(histograms.createLegend(), **moveLegend))
pE.draw()
# plots.drawPlot(pE, "QGLR_Efficiency", **kwargs)
SavePlot(pE, "QGLR_Efficiency", os.path.join(opts.saveDir, opts.optMode), saveFormats=[".png", ".pdf"] )
#==========================================================================================
# Calculate Significance
#==========================================================================================
SignalName = "ChargedHiggs_HplusTB_HplusToTB_M_800"
hSignif_250 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_500 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_800 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_1000 = p.histoMgr.getHisto(SignalName).getRootHisto().Clone(SignalName)
hSignif_250.Reset()
hSignif_500.Reset()
hSignif_800.Reset()
hSignif_1000.Reset()
hBkg = p.histoMgr.getHisto(SignalName).getRootHisto().Clone("Bkg")
hBkg.Reset()
# Bkg: FakeB + Genuine B
hBkg.Add(hFakeB.getRootHisto(), +1)
hBkg.Add(hGenuineB.getRootHisto(), +1)
nBins = hSignif_250.GetNbinsX()+1
# For-loop: All histo bins
for i in range (1, nBins+1):
sigmaB = ROOT.Double(0)
b = hBkg.IntegralAndError(i, nBins, sigmaB)
s_250 = hSignal_250.Integral(i, nBins)
s_500 = hSignal_500.Integral(i, nBins)
s_800 = hSignal_800.Integral(i, nBins)
s_1000 = hSignal_1000.Integral(i, nBins)
# Calculate significance
signif_250 = stat.significance(s_250, b, sigmaB, option="Simple")#Asimov")
signif_500 = stat.significance(s_500, b, sigmaB, option="Simple")#Asimov")
signif_800 = stat.significance(s_800, b, sigmaB, option="Simple")#Asimov")
signif_1000 = stat.significance(s_1000, b, sigmaB, option="Simple")#"Asimov")
# Set signif for this bin
hSignif_250.SetBinContent(i, signif_250)
hSignif_500.SetBinContent(i, signif_500)
hSignif_800.SetBinContent(i, signif_800)
hSignif_1000.SetBinContent(i, signif_1000)
# Apply style
s_250 = styles.getSignalStyleHToTB_M("200")
s_250.apply(hSignif_250)
s_500 = styles.getSignalStyleHToTB_M("500")
s_500.apply(hSignif_500)
s_800 = styles.getSignalStyleHToTB_M("800")
s_800.apply(hSignif_800)
s_1000 = styles.getSignalStyleHToTB_M("1000")
s_1000.apply(hSignif_1000)
hList = []
hList.append(hSignif_250)
hList.append(hSignif_500)
hList.append(hSignif_800)
hList.append(hSignif_1000)
hSignif_250.SetName("H^{+} m_{H^{+}} = 250 GeV")
hSignif_500.SetName("H^{+} m_{H^{+}} = 500 GeV")
hSignif_800.SetName("H^{+} m_{H^{+}} = 800 GeV")
hSignif_1000.SetName("H^{+} m_{H^{+}} = 1000 GeV")
pS = plots.PlotBase(hList, saveFormats=["png", "pdf"])
pS.setLuminosity(opts.intLumi)
# Drawing style
pS.histoMgr.setHistoDrawStyleAll("HIST")
pS.histoMgr.setHistoLegendStyleAll("L")
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(1.0))
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pS.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerStyle(ROOT.kFullCircle))
# Draw the plot
name = "QGLR_Signif" + "GE"
plots.drawPlot(pS, name, **kwargs)
SavePlot(pS, name, os.path.join(opts.saveDir, opts.optMode), saveFormats=[".png", ".pdf"] )
#=========================================================================================
# Calculate the Transfer Factor (TF) and save to file
#=========================================================================================
Verbose("Write the normalisation factors to a python file", True)
fileName = os.path.join(opts.mcrab, "FakeBTransferFactors%s.py"% ( getModuleInfoString(opts) ) )
manager.writeNormFactorFile(fileName, opts)
return
def PlotMC(datasetsMgr, datasetsMgr1, histo, intLumi):
kwargs = {}
print "here1"
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToOne=True,
saveFormats=[".pdf", "png"],
**kwargs)
p = plots.MCPlot(datasetsMgr1,
histo,
normalizeToOne=True,
saveFormats=[".pdf", ".png"],
**kwargs)
else:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToLumi=intLumi,
saveFormats=[".pdf", ".png"],
**kwargs)
p = plots.MCPlot(datasetsMgr1,
histo,
normalizeToOne=True,
saveFormats=[".pdf", "png"],
**kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
print "here2"
if "Pt" in histo:
_rebinX = 2
xMin = 0.0
# rebinX = 2
xMax = 805.0
# xMax = 555.0 # For topPt < 500GeV
xTitle = "p_{T} (GeV/c)"
units = "GeV/c"
_format = "%0.0f" + units
# yTitle = "Efficiency / " + str(binwidth) + " "+units
# yMin = 0.0
# yMax = 1.1
else:
pass
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e0
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
print "here3"
histoDef = datasetsMgr.getDataset("TT").getDatasetRootHisto(histo)
histoDR = datasetsMgr1.getDataset("TT").getDatasetRootHisto(histo)
histoDef.normalizeToOne()
histoDR.normalizeToOne()
hDef = histoDef.getHistogram()
hDR = histoDR.getHistogram()
p = plots.ComparisonPlot(histograms.Histo(hDR, "DeltaR08", "l", "L"),
histograms.Histo(hDef, "Default", "l", "L"))
p.histoMgr.setHistoLegendLabelMany({
"DeltaR08": "#DeltaR(q,q')>0.8",
"Default": "Default"
})
p.histoMgr.forHisto("DeltaR08", styles.getQCDLineStyle())
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.setHistoDrawStyle("DeltaR08", "HIST") #HIST
p.histoMgr.setHistoLegendStyle("DeltaR08", "L") #F
p.histoMgr.setHistoDrawStyle("Default", "HIST") #HIST
p.histoMgr.setHistoLegendStyle("Default", "L") #F
p.histoMgr.forHisto("Default", styles.ttStyle)
print "here4"
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" % m,
styles.getSignalStyleHToTB_M(m))
print "here5"
plots.drawPlot(
p,
histo,
xlabel=_xlabel,
ylabel="Arbitrary Units / %s" % (_format),
log=logY,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.58,
"y1": 0.75,
"x2": 0.92,
"y2": 0.92
},
# createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.92},
opts=_opts,
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox=_cutBox,
)
print "here6"
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, "HplusMasses",
histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath)
print "here7"
return
def PlotHisto(datasetsMgr, histoName):
Verbose("Plotting histogram %s for Data, EWK, QCD " % (histoName), True)
# Customize the histograms (BEFORE calculating purity obviously otherwise numbers are nonsense)
_cutBox = None
_rebinX = 1
logY = True
yMaxF = 1.2
if logY:
yMaxF = 10
_opts = {"ymin": 1e-4, "ymaxfactor": yMaxF}
_format = "%0.0f"
_xlabel = None
_ylabel = "Events / "
_format = "/ %.0f "
h = histoName.split("/")[-1]
if "pt" in h.lower():
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % (_units)
_cutBox = {
"cutValue": 40.,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 500.0
if "eta" in h.lower():
_rebinX = 1
_format = "%0.2f"
_cutBox = {
"cutValue": 0.0,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmin"] = -3.0
_opts["xmax"] = +3.0
if "bjetbdisc" in h.lower():
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = 1.2
_cutBox = {
"cutValue": 0.8484,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "pt_" in h.lower():
_rebinX = 2
_ylabel += _format
# Get histos (Data, EWK) for Inclusive
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Clone histograms
Data = p1.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
EWK = p1.histoMgr.getHisto("EWK").getRootHisto().Clone("EWK")
QCD = p1.histoMgr.getHisto("Data").getRootHisto().Clone("QCD")
# Rebin histograms (Before calculating Purity)
Data.Rebin(_rebinX)
EWK.Rebin(_rebinX)
QCD.Rebin(_rebinX)
# Get QCD = Data-EWK
QCD.Add(EWK, -1)
# Normalize histograms to unit area?
if opts.normaliseToOne:
if Data.Integral() > 0:
Data.Scale(1.0 / Data.Integral())
if QCD.Integral() > 0:
QCD.Scale(1.0 / QCD.Integral())
if EWK.Integral() > 0:
EWK.Scale(1.0 / EWK.Integral())
# Make the plots
if opts.plotEWK:
p = plots.ComparisonManyPlot(QCD, [EWK], saveFormats=[])
else:
p = plots.PlotBase([QCD], saveFormats=[])
# Apply histo styles
p.histoMgr.forHisto("QCD", styles.getQCDLineStyle())
if opts.plotEWK:
p.histoMgr.forHisto("EWK", styles.getEWKStyle())
#p.histoMgr.forHisto("EWK", styles.getAltEWKLineStyle() )
# Set draw style
p.histoMgr.setHistoDrawStyle("QCD", "P")
if opts.plotEWK:
p.histoMgr.setHistoDrawStyle("EWK", "HIST")
# Set legend style
p.histoMgr.setHistoLegendStyle("QCD", "P")
if opts.plotEWK:
p.histoMgr.setHistoLegendStyle("EWK", "F")
# Set legend labels
if opts.plotEWK:
p.histoMgr.setHistoLegendLabelMany({
"QCD": "QCD",
"EWK": "EWK",
})
else:
p.histoMgr.setHistoLegendLabelMany({
"QCD": "QCD",
})
# Do the plot
plots.drawPlot(
p,
histoName,
xlabel=_xlabel,
ylabel=_ylabel,
log=logY,
rebinX=1,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.76,
"y1": 0.80,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 1e-5,
"ymax": 1e0
},
ratio=False,
ratioInvert=False,
ratioYlabel="Ratio",
cutBox=_cutBox,
)
# Save plot in all formats
SavePlot(p, histoName,
os.path.join(opts.saveDir, "FailedBJet",
opts.optMode)) #, saveFormats = [".png"] )
return
def PlotPurity(datasetsMgr, histoName):
'''
Create plots with "FakeB=Data-EWKGenuineB"
'''
Verbose("Plotting histogram %s for Data, EWK, QCD " % (histoName), True)
# Which folder to use (redundant)
defaultFolder = "FakeBPurity"
genuineBFolder = defaultFolder + "EWKGenuineB"
fakeBFolder = defaultFolder + "EWKFakeB"
# Customize the histograms (BEFORE calculating purity obviously otherwise numbers are nonsense)
_cutBox = None
_rebinX = 1
_opts = {"ymin": 1e-3, "ymax": 1.0} #"ymaxfactor": 1.2}
_format = "%0.0f"
#_opts["xmax"] = xMax
_xlabel = None
_ylabel = "Purity / "
_format = "/ %.0f "
h = histoName.split("/")[-1]
if "dijetm" in h.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % (_units)
_cutBox = {
"cutValue": 80.399,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "trijetm" in h.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 1000.0
if "pt" in h.lower():
_format = "%0.0f GeV/c"
if "chisqr" in h.lower():
_opts["xmax"] = 100.0
if "allselections" in h.lower():
_opts["xmax"] = 10.0
#if histo.lower().endswith("met_et"):
if h.lower().startswith("ht_"):
_rebinX = 5
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "H_{T} (%s)" % _units
_cutBox = {
"cutValue": 500.0,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
}
_opts["xmin"] = 500.0
_opts["xmax"] = 3500.0
if "eta" in h.lower():
_rebinX = 1
_format = "%0.2f"
_cutBox = {
"cutValue": 0.,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmin"] = -3.0
_opts["xmax"] = +3.0
if "deltaeta" in h.lower():
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = 6.0
if "bdisc" in h.lower():
_format = "%0.2f"
if "tetrajetm" in h.lower():
_rebinX = 4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_opts["xmax"] = 2500.0
if "pt_" in h.lower():
_rebinX = 2
_ylabel += _format
# Get histos (Data, EWK) for Inclusive
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName))
p1.histoMgr.normalizeMCToLuminosity(
datasetsMgr.getDataset("Data").getLuminosity())
# Clone histograms
Data = p1.histoMgr.getHisto("Data").getRootHisto().Clone("Data")
EWK = p1.histoMgr.getHisto("EWK").getRootHisto().Clone("EWK")
QCD = p1.histoMgr.getHisto("Data").getRootHisto().Clone("QCD")
# Rebin histograms (Before calculating Purity)
Data.Rebin(_rebinX)
EWK.Rebin(_rebinX)
QCD.Rebin(_rebinX)
# Get QCD = Data-EWK
QCD.Add(EWK, -1)
# Comparison plot. The first argument is the reference histo. All other histograms are compared with respect to that.
QCD_Purity, xMin, xMax, binList, valueDict, upDict, downDict = getPurityHisto(
QCD, Data, inclusiveBins=False, printValues=False)
EWK_Purity, xMin, xMax, binList, valueDict, upDict, downDict = getPurityHisto(
EWK, Data, inclusiveBins=False, printValues=False)
# Create TGraphs
if 0:
gQCD_Purity = MakeGraph(ROOT.kFullTriangleUp, ROOT.kOrange, binList,
valueDict, upDict, downDict)
gEWK_Purity = MakeGraph(ROOT.kFullTriangleDown, ROOT.kPurple, binList,
valueDict, upDict, downDict)
# Make the plots
if opts.plotEWK:
p = plots.ComparisonManyPlot(QCD_Purity, [EWK_Purity], saveFormats=[])
else:
p = plots.PlotBase([QCD_Purity], saveFormats=[])
# Apply histo styles
p.histoMgr.forHisto("QCD", styles.getQCDLineStyle())
if opts.plotEWK:
p.histoMgr.forHisto("EWK", styles.getAltEWKLineStyle())
# Set draw style
p.histoMgr.setHistoDrawStyle("QCD", "P")
if opts.plotEWK:
p.histoMgr.setHistoDrawStyle("EWK", "HIST")
# Set legend style
p.histoMgr.setHistoLegendStyle("QCD", "P")
if opts.plotEWK:
p.histoMgr.setHistoLegendStyle("EWK", "F")
# Set legend labels
if opts.plotEWK:
p.histoMgr.setHistoLegendLabelMany({
"QCD": "QCD",
"EWK": "EWK",
})
else:
p.histoMgr.setHistoLegendLabelMany({
"QCD": "QCD",
})
# Do the plot
plots.drawPlot(
p,
histoName,
xlabel=_xlabel,
ylabel=_ylabel,
log=False,
rebinX=1, # must be done BEFORE calculating purity
cmsExtraText="Preliminary",
createLegend={
"x1": 0.76,
"y1": 0.80,
"x2": 0.92,
"y2": 0.92
},
opts=_opts,
opts2={
"ymin": 1e-5,
"ymax": 1e0
},
ratio=False,
ratioInvert=False,
ratioYlabel="Ratio",
cutBox=_cutBox,
)
# Save plot in all formats
SavePlot(p, histoName,
os.path.join(opts.saveDir, "Purity",
opts.optMode)) #, saveFormats = [".png"] )
return
def PurityPlots(datasetsMgr, histoName, analysisType="Inverted"):
'''
Create plots with "FakeB=Data-EWKGenuineB"
'''
Verbose("Plotting histogram %s for Data, EWK, QCD for %s" % (histoName, analysisType), True)
# Sanity check
IsBaselineOrInverted(analysisType)
# Which folder to use (redundant)
defaultFolder = "FakeBPurity"
genuineBFolder = defaultFolder + "EWKGenuineB"
fakeBFolder = defaultFolder + "EWKFakeB"
# Get histos (Data, EWK) for Inclusive
p1 = plots.ComparisonPlot(*getHistos(datasetsMgr, histoName, analysisType) )
p1.histoMgr.normalizeMCToLuminosity(datasetsMgr.getDataset("Data").getLuminosity())
# Clone histograms
Data = p1.histoMgr.getHisto("Data").getRootHisto().Clone(analysisType + "-Data")
EWK = p1.histoMgr.getHisto("EWK").getRootHisto().Clone(analysisType + "-EWK")
QCD= p1.histoMgr.getHisto("Data").getRootHisto().Clone(analysisType + "-QCD")
# Get QCD = Data-EWK
QCD.Add(EWK, -1)
# Comparison plot. The first argument is the reference histo. All other histograms are compared with respect to that.
QCD_Purity, xMin, xMax, binList, valueDict, upDict, downDict = getPurityHisto(QCD, Data, inclusiveBins=False, printValues=False)
EWK_Purity , xMin, xMax, binList, valueDict, upDict, downDict = getPurityHisto(EWK , Data, inclusiveBins=False, printValues=False)
# Create TGraphs
if 0:
gQCD_Purity = MakeGraph(ROOT.kFullTriangleUp, ROOT.kOrange, binList, valueDict, upDict, downDict)
gEWK_Purity = MakeGraph(ROOT.kFullTriangleDown, ROOT.kPurple, binList, valueDict, upDict, downDict)
# Make the plots
p = plots.ComparisonManyPlot(QCD_Purity, [EWK_Purity], saveFormats=[])
if 0:
p = plots.PlotBase([gQCD_Purity, gEWK_Purity], saveFormats=[]) #legend/styles all wrog
# Apply histo styles
p.histoMgr.forHisto(analysisType + "-QCD", styles.getQCDLineStyle() )
p.histoMgr.forHisto(analysisType + "-EWK" , styles.getAltEWKLineStyle() )
# Set draw style
p.histoMgr.setHistoDrawStyle(analysisType + "-QCD", "P")
p.histoMgr.setHistoDrawStyle(analysisType + "-EWK", "P")
# Set legend style
p.histoMgr.setHistoLegendStyle(analysisType + "-QCD", "P")
p.histoMgr.setHistoLegendStyle(analysisType + "-EWK", "P")
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
analysisType + "-QCD" : "QCD",
analysisType + "-EWK" : "EWK",
})
# Draw the histograms
_cutBox = None
_rebinX = 1
_opts = {"ymin": 1e-3, "ymax": 1.0} #"ymaxfactor": 1.2}
_format = "%0.0f"
_opts["xmax"] = xMax
_xlabel = None
if "dijetm" in histoName.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jj} (%s)" % (_units)
_cutBox = {"cutValue": 80.399, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
if "trijetm" in histoName.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 1000.0
if "pt" in histoName.lower():
_format = "%0.0f GeV/c"
if "eta" in histoName.lower():
_format = "%0.2f"
_cutBox = {"cutValue": 0., "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmin"] = -3.0
_opts["xmax"] = +3.0
if "deltaeta" in histoName.lower():
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = 6.0
if "bdisc" in histoName.lower():
_format = "%0.2f"
if "tetrajetm" in histoName.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjjb} (%s)" % (_units)
_opts["xmax"] = 2500.0
# Do the plot
plots.drawPlot(p,
histoName,
xlabel = _xlabel,
ylabel = "Purity",
log = False,
cmsExtraText = "Preliminary",
createLegend = {"x1": 0.80, "y1": 0.80, "x2": 0.92, "y2": 0.92},
opts = _opts,
opts2 = {"ymin": 1e-5, "ymax": 1e0},
ratio = False,
ratioInvert = False,
ratioYlabel = "Ratio",
cutBox = _cutBox,
)
# Save plot in all formats
SavePlot(p, histoName, os.path.join(opts.saveDir, "Purity", opts.optMode))#, saveFormats = [".png"] )
return
def main(opts, signalMass):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(False)
style.setGridY(False)
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
dir = opts.mcrab.replace("_DptOverPt0p32", "")
print dir
datasetsMgr_DR = GetDatasetsFromDir_second(opts, dir)
datasetsMgr_DR.updateNAllEventsToPUWeighted()
datasetsMgr_DR.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for datasetsMgr_ in [datasetsMgr, datasetsMgr_DR]:
for d in datasetsMgr_.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr_.getDataset(d.getName()).setCrossSection(1.0)
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
#plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information before removing anything?
if 0:
datasetsMgr.PrintInfo()
# Determine integrated Lumi before removing data
if "Data" in datasetsMgr.getAllDatasetNames():
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
intLumi = 35920
# Remove datasets
filterKeys = ["Data", "TTZToQQ", "TTWJets", "TTTT"]
#filterKeys = ["Data", "TTZToQQ", "TTWJets", "TTTT"]
for key in filterKeys:
datasetsMgr.remove(
filter(lambda name: key in name,
datasetsMgr.getAllDatasetNames()))
datasetsMgr_DR.remove(
filter(lambda name: key in name,
datasetsMgr_DR.getAllDatasetNames()))
# Re-order datasets
datasetOrder = []
for d in datasetsMgr.getAllDatasets():
if "M_" in d.getName():
if d not in signalMass:
continue
datasetOrder.append(d.getName())
# Append signal datasets
for m in signalMass:
datasetOrder.insert(0, m)
datasetsMgr.selectAndReorder(datasetOrder)
datasetsMgr_DR.selectAndReorder(datasetOrder)
# Print dataset information
datasetsMgr.PrintInfo()
# Define the mapping histograms in numerator->denominator pairs
Numerators = [
"AllTopQuarkPt_MatchedBDT",
"AllTopQuarkPt_Matched",
"EventTrijetPt2T_MatchedBDT",
"EventTrijetPt2T_MatchedBDT",
"EventTrijetPt2T_MatchedBDT",
"AllTopQuarkPt_MatchedBDT",
"SelectedTrijetsPt_BjetPassCSVdisc_afterCuts",
"TrijetFakePt_BDT",
]
Denominators = [
"AllTopQuarkPt_Matched",
"TopQuarkPt",
"EventTrijetPt2T_BDT",
"EventTrijetPt2T_Matched",
"EventTrijetPt2T",
"TopQuarkPt",
"SelectedTrijetsPt_afterCuts",
"TrijetFakePt",
]
if 0:
datasetsMgr.merge("QCD", GetListOfQCDatasets())
datasetsMgr_DR.merge("QCD", GetListOfQCDatasets())
plots._plotStyles["QCD"] = styles.getQCDLineStyle()
#Background1_Dataset = datasetsMgr.getDataset("QCD")
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# For-loop: All numerator-denominator pairs
#for i in range(len(Numerators)):
if 1:
numerator = os.path.join(opts.folder,
"AfterAllSelections_LeadingTrijet_Pt_SR")
denominator = os.path.join(
opts.folder, "AfterStandardSelections_LeadingTrijet_Pt_SR")
#PlotEfficiency_comparison(datasetsMgr, datasetsMgr_DR, intLumi)
PlotEfficiency_comparison(datasetsMgr, numerator, denominator,
datasetsMgr_DR, intLumi)
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
if opts.normaliseToOne:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToOne=True,
saveFormats=[],
**kwargs)
else:
p = plots.MCPlot(datasetsMgr,
histo,
normalizeToLumi=intLumi,
saveFormats=[],
**kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.0f"
_xlabel = None
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "pt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
}
#_opts["xmax"] = 505 #1005
if "eta" in histo.lower():
_units = ""
_format = "%0.1f " + _units
_xlabel = "#eta %s" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
}
_opts["xmax"] = 2.5
_opts["xmin"] = -2.5
if "trijetmass" in histo.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 505 #1005
elif "tetrajetmass" in histo.lower():
_rebinX = 8 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 1500 #3500.0
elif "dijetmass" in histo.lower():
_rebinX = 1 #5 #10 #4
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800 #3500.0
_cutBox = {
"cutValue": 80.4,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 300
elif "tetrajetbjetpt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "ldgtrijetpt" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "bdt" in histo.lower():
_format = "%0.1f"
if "topTagSF" in histo:
_rebinX = 5
#kwargs["ylabel"] = "Events / %.3f "
_format = "%0.2f "
_opts["xmax"] = +10
_opts["xmin"] = 0.0
_xlabel = "topTag SF"
else:
pass
if opts.normaliseToOne:
logY = False
Ylabel = "Arbitrary Units / %s" % (_format)
else:
logY = True
Ylabel = "Events / %s" % (_format)
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
#_opts = {"ymin": 1e-3, "ymaxfactor": yMaxFactor, "xmax": None}
else:
_opts["ymin"] = 1e-3
#_opts["ymaxfactor"] = yMaxFactor
#_opts = {"ymin": 1e0, "ymaxfactor": yMaxFactor, "xmax": None}
# Customise styling
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
if "QCD" in datasetsMgr.getAllDatasetNames():
p.histoMgr.forHisto("QCD",
styles.getQCDLineStyle()) #getQCDFillStyle() )
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasetNames():
TTStyle = styles.StyleCompound([
styles.StyleFill(fillColor=ROOT.kMagenta - 2),
styles.StyleLine(lineColor=ROOT.kMagenta - 2,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kMagenta - 2,
markerSizes=None,
markerStyle=ROOT.kFullTriangleUp)
])
p.histoMgr.forHisto("TT", TTStyle)
p.histoMgr.setHistoDrawStyle("TT", "HIST") #AP
p.histoMgr.setHistoLegendStyle("TT", "F") #LP
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_%s" % m,
styles.getSignalStyleHToTB_M(m))
plots.drawPlot(
p,
histo,
xlabel=_xlabel,
ylabel=Ylabel,
log=logY,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.92
},
#createLegend = {"x1": 0.73, "y1": 0.85, "x2": 0.97, "y2": 0.77},
opts=_opts,
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox=_cutBox,
)
# Save plot in all formats
saveName = histo.split("/")[-1]
savePath = os.path.join(opts.saveDir, histo.split("/")[0], opts.optMode)
'''
if opts.normaliseToOne:
save_path = savePath + opts.MVAcut
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
else:
save_path = savePath + opts.MVAcut + "/normToLumi/"
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
'''
SavePlot(p, saveName, savePath)
return