def PlotHistograms(datasetsMgr, histoName):
# Get Histogram name and its kwargs
saveName = histoName.rsplit("/")[-1]
kwargs = GetHistoKwargs(saveName, opts)
# Create the plot
if "Data" in datasetsMgr.getAllDatasetNames():
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
else:
if opts.normalizeToLumi:
p = plots.MCPlot(datasetsMgr,
histoName,
normalizeToLumi=opts.intLumi,
saveFormats=[])
elif opts.normalizeByCrossSection:
p = plots.MCPlot(datasetsMgr,
histoName,
normalizeByCrossSection=True,
saveFormats=[],
**{})
elif opts.normalizeToOne:
p = plots.MCPlot(datasetsMgr,
histoName,
normalizeToOne=True,
saveFormats=[],
**{})
else:
raise Exception(
"One of the options --normalizeToOne, --normalizeByCrossSection, --normalizeToLumi must be enabled (set to \"True\")."
)
# Customise z-axis
if 0:
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().GetZaxis().SetTitle(kwargs["zlabel"]))
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().GetZaxis().SetTitleOffset(1.3))
# Drawing style
if 1:
p.histoMgr.setHistoDrawStyleAll("AP")
p.histoMgr.setHistoLegendStyleAll("LP")
else:
p.histoMgr.setHistoDrawStyleAll("HIST")
p.histoMgr.setHistoLegendStyleAll("F")
# Add dataset name on canvas
# p.appendPlotObject(histograms.PlotText(0.18, 0.88, plots._legendLabels[opts.dataset], bold=True, size=22))
# Draw the plot
plots.drawPlot(p, saveName,
**kwargs) #the "**" unpacks the kwargs_ dictionary
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(opts.saveDir), [".png", ".pdf"])
return
def MCPlot(datasetsMgr, json):
Verbose("Creating MC plot")
# Create the MC Plot with selected normalization ("normalizeToOne", "normalizeByCrossSection", "normalizeToLumi")
if json["normalization"]=="normalizeToLumi":
kwargs = {}
p = plots.MCPlot(datasetsMgr, json["histogram"], normalizeToLumi=float(json["luminosity"]), **kwargs)
else:
kwargs = {json["normalization"]: True}
p = plots.MCPlot(datasetsMgr, json["histogram"], **kwargs)
# Label size (optional. Commonly Used in counters)
xlabelSize = None
if "xlabelsize" in json:
xlabelSize = json["xlabelsize"]
ylabelSize = None
if "ylabelsize" in json:
ylabelSize = json["ylabelsize"]
# Draw a customised plot
saveName = os.path.join(json["saveDir"], json["title"])
plots.drawPlot(p,
saveName,
xlabel = json["xlabel"],
ylabel = json["ylabel"],
rebinX = json["rebinX"],
rebinY = json["rebinY"],
stackMCHistograms = json["stackMCHistograms"]=="True",
addMCUncertainty = json["addMCUncertainty"]=="True" and json["normalization"]!="normalizeToOne",
addLuminosityText = json["normalization"]=="normalizeToLumi",
addCmsText = json["addCmsText"]=="True",
cmsExtraText = json["cmsExtraText"],
opts = json["opts"],
log = json["logY"]=="True",
errorBarsX = json["errorBarsX"]=="True",
moveLegend = json["moveLegend"],
# cutLine = json["cutValue"], #cannot have this and "cutBox" defined
cutBox = {"cutValue": json["cutValue"], "fillColor": json["cutFillColour"], "box": json["cutBox"]=="True", "line": json["cutLine"]=="True", "greaterThan": json["cutGreaterThan"]=="True"},
xlabelsize = xlabelSize,
ylabelsize = ylabelSize,
)
# Remove legend?
if json["removeLegend"] == "True":
p.removeLegend()
# Additional text
histograms.addText(json["extraText"].get("x"), json["extraText"].get("y"), json["extraText"].get("text"), json["extraText"].get("size") )
# Save in all formats chosen by user
saveFormats = json["saveFormats"]
for i, ext in enumerate(saveFormats):
Print("%s" % saveName + ext, i==0)
p.saveAs(saveName, formats=saveFormats)
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}
logY = True
if "_Pt" in histo:
_opts["xmax"] = 1000
_rebinX = 1
logY = False
_xlabel = "p_{T} (GeV)"
elif "Eta" in histo:
_xlabel = "#eta"
logY = False
_opts["xmin"] = -3.0
_opts["xmax"] = +3.0
_rebinX = 2
_format = "%0.2f"
elif "_E" in histo:
_opts["xmax"] = 2000
_rebinX = 1
logY = False
_xlabel = "E (GeV)"
elif "Phi" in histo:
_rebinX = 2
_format = "%0.2f"
_xlabel = "#phi"
logY = False
_opts["xmin"] = -4.0
_opts["xmax"] = 4.0
elif "tau21" in histo:
_rebinX = 2
_format = "%0.2f"
logY = False
_xlabel = "#tau_{2}/#tau_{1}"
def PlotHistograms(datasetsMgr, histoName, signalsList, cutDir):
# Get Histogram name and its kwargs
saveName = histoName.rsplit("/")[-1]
kwargs = GetHistoKwargs(saveName, opts)
# Create the plot
if "Data" in datasetsMgr.getAllDatasetNames():
p1 = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
else:
if opts.normalizeToLumi:
p1 = plots.MCPlot(datasetsMgr, histoName, normalizeToLumi=opts.intLumi, saveFormats=[])
#elif opts.normalizeByCrossSection:
# p1 = plots.MCPlot(datasetsMgr, histoName, normalizeByCrossSection=True, saveFormats=[], **{})
else:
raise Exception("One of the options --normalizeToOne, --normalizeByCrossSection, --normalizeToLumi must be enabled (set to \"True\").")
# Create significance plots
hList = []
for s in signalsList:
hList.append(GetSignificanceHisto(p1, datasetsMgr, cutDir=cutDir, signalDataset=s))
#p2 = plots.ComparisonManyPlot(hList[0], hList[1:])
p2 = plots.PlotBase(hList, saveFormats=[])
p2.setLuminosity(opts.intLumi)
# Drawing style
# p2.histoMgr.setHistoDrawStyleAll("LP")
# p2.histoMgr.setHistoLegendStyleAll("LP")
p2.histoMgr.setHistoDrawStyleAll("HIST")
p2.histoMgr.setHistoLegendStyleAll("L")
p2.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(1.0))
p2.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p2.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerStyle(ROOT.kFullCircle))
# Draw the plot
style.setLogX(opts.logX)
style.setLogY(opts.logY)
plots.drawPlot(p1, saveName, **kwargs)
# SavePlot(p1, saveName, os.path.join(opts.saveDir), [".png", ".pdf"] )
# Draw the significance
xMax, yMax = getXMaxYMax(hList)
kwargs["opts"]["ymax"] = yMax*kwargs["opts"]["ymaxfactor"]
kwargs["cutBox"] = {"cutValue": xMax, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
kwargs["cutBoxY"] = {"cutValue": yMax, "fillColor": 16, "box": False, "line": True, "greaterThan": True, "mainCanvas": True, "ratioCanvas": False}
kwargs["moveLegend"]["dh"] = -0.15
for s in signalsList:
p2.histoMgr.setHistoLegendLabelMany({
s: plots._legendLabels[s]
})
if cutDir == ">=":
name = saveName + "_Signif" + "GE"
else:
name = saveName + "_Signif" + "LE"
plots.drawPlot(p2, name, **kwargs)
SavePlot(p2, name, os.path.join(opts.saveDir), [".png"])#, ".pdf"] )
return
def createPlot(name, **kwargs):
if mcOnly:
plot = plots.MCPlot(datasets,
name,
normalizeToLumi=mcOnlyLumi,
**kwargs)
else:
plot = plots.DataMCPlot(datasets, name, **kwargs)
plot.histoMgr.removeHisto("EWK")
return plot
def Plot2dHistograms(datasetsMgr, histoName):
Verbose("Plotting Data-MC Histograms")
# Get Histogram name and its kwargs
saveName = histoName.rsplit("/")[-1]
kwargs = GetHistoKwargs(saveName, opts)
# Create the 2d plot
if opts.dataset == "Data":
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
else:
if opts.normalizeToLumi:
p = plots.MCPlot(datasetsMgr, histoName, normalizeToLumi=opts.intLumi, saveFormats=[])
elif opts.normalizeByCrossSection:
p = plots.MCPlot(datasetsMgr, histoName, normalizeByCrossSection=True, saveFormats=[], **{})
elif opts.normalizeToOne:
p = plots.MCPlot(datasetsMgr, histoName, normalizeToOne=True, saveFormats=[], **{})
else:
raise Exception("One of the options --normalizeToOne, --normalizeByCrossSection, --normalizeToLumi must be enabled (set to \"True\").")
# Customise z-axis
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetTitle(kwargs["zlabel"]))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetTitleOffset(1.3))
if kwargs.get("zmin") != None:
zMin = float(kwargs.get("zmin"))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMinimum(zMin))
if kwargs.get("zmax") != None:
zMax = float(kwargs.get("zmax"))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMaximum(zMax))
# Drawing style
p.histoMgr.setHistoDrawStyleAll("COLZ")
# Add dataset name on canvas
p.appendPlotObject(histograms.PlotText(0.18, 0.88, plots._legendLabels[opts.dataset], bold=True, size=22))
# Draw the plot
plots.drawPlot(p, saveName, **kwargs) #the "**" unpacks the kwargs_ dictionary
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode, opts.folder, opts.dataset), [".png", ".pdf"] )
return
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 PlotSignalBackground(datasetsMgr, hG, hF, intLumi):
kwargs = {}
_kwargs = {}
if opts.normaliseToOne:
pG = plots.MCPlot(datasetsMgr,
hG,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
pF = plots.MCPlot(datasetsMgr,
hF,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
else:
pG = plots.MCPlot(datasetsMgr,
hG,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
pF = plots.MCPlot(datasetsMgr,
hF,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.2f"
_xlabel = None
logY = False
_opts = {"ymin": 0, "ymaxfactor": 1.1}
if "mass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "M (%s)" % _units
if "trijetmass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{top} (%s)" % _units
#_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 805 #1005
_opts = {"xmin": 0.0, "xmax": 805}
if "bjetmass" in hG.lower():
_xlabel = "m_{b-tagged jet} (%s)" % _units
_opts["xmax"] = 50
if "bjetldgjet_mass" in hG.lower():
_xlabel = "m_{b, ldg jet} (%s)" % _units
_opts["xmax"] = 705
if "bjetsubldgjet_mass" in hG.lower():
_xlabel = "m_{b-tagged, subldg jet} (%s)" % _units
_opts["xmax"] = 705
if "jet_mass" in hG.lower():
_opts["xmax"] = 750
if "mult" in hG.lower():
_format = "%0.0f"
if "ldg" in hG.lower():
_xlabel = "Leading jet mult"
if "subldg" in hG.lower():
_xlabel = "Subleading jet mult"
if "avg" in hG.lower():
_xlabel = "avg CvsL"
if "cvsl" in hG.lower():
_format = "%0.2f"
if "ldg" in hG.lower():
_xlabel = "Leading jet CvsL"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CvsL"
if "avg" in hG.lower():
_xlabel = "avg CvsL"
if "axis2" in hG.lower():
_format = "%0.3f"
if "ldg" in hG.lower():
_xlabel = "Leading jet axis2"
if "subldg" in hG.lower():
_xlabel = "Subleading jet axis2"
if "avg" in hG.lower():
_xlabel = "avg axis2"
if "dijetmass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % _units
_opts["xmax"] = 600
_opts = {"xmin": 0.0, "xmax": 605, "ymin": 1e-3, "ymaxfactor": 1.0}
if "trijetptdr" in hG.lower():
_opts["xmax"] = 800
_format = "%0.0f"
_xlabel = "p_{T}#Delta R_{t}"
if "dijetptdr" in hG.lower():
_opts["xmax"] = 800
_format = "%0.0f"
_xlabel = "p_{T}#Delta R_{W}"
if "dgjetptd" in hG.lower():
_format = "%0.2f"
_xlabel = "Leading jet p_{T}D"
if "subldg" in hG.lower():
_xlabel = "Subleading jet p_{T}D"
if "bdisc" in hG.lower():
_format = "%0.2f"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.06}
elif "ldg" in hG.lower():
_xlabel = "Leading jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.06}
else:
_xlabel = "b-tagged jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.35}
if "over" in hG.lower():
_format = "%0.2f "
_xlabel = "m_{W}/m_{t}"
_opts["xmax"] = 1
_opts["xmin"] = 0
if "likelihood" in hG.lower():
_format = "%0.2f"
if "ldg" in hG.lower():
_xlabel = "Leading jet QGL"
if "subldg" in hG.lower():
_xlabel = "Subleading jet QGL"
if "avg" in hG.lower():
_xlabel = "avg QGL"
else:
pass
'''
if "bdisc" in hG.lower():
_format = "%0.2f"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CSV"
elif "ldg" in hG.lower():
_xlabel = "Leading jet CSV"
else:
_xlabel = "b-tagged jet CSV"
'''
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
myList = []
# Customise styling
pG.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pF.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
#Dataset: ttbar
dataset = datasetsMgr.getDataset(opts.dataset) #soti
#dataset = datasetsMgr.getDataset("ChargedHiggs_HplusTB_HplusToTB_M_1000")
#Get Genuine-top histogram
h = dataset.getDatasetRootHisto(hG)
h.normalizeToOne()
HG = h.getHistogram()
#Get Fake-top histogram
h = dataset.getDatasetRootHisto(hF)
h.normalizeToOne()
HF = h.getHistogram()
#Define Signal style
altSignalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kAzure + 9,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kAzure + 9,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kAzure + 9)
])
#Define Background style
altBackgroundBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kRed - 4,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kRed - 4,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kRed - 4, fillStyle=3001)
])
signalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kTeal + 2,
markerSizes=None,
markerStyle=ROOT.kFullTriangleUp),
styles.StyleLine(lineColor=ROOT.kTeal + 2,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kTeal + 2)
]) #, fillStyle=3001)])
signalGrayStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kGray + 1,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kGray + 1,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kGray + 1)
])
backgroundGrayStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kGray + 3,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kGray + 3,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kGray + 3, fillStyle=3001)
])
#Comparison Plot
p = plots.ComparisonPlot(
histograms.Histo(HF, "Fake", "p", "P"),
histograms.Histo(HG, "Genuine", "pl", "PL"),
)
#Set labels
p.histoMgr.setHistoLegendLabelMany({
"Fake": "Unmatched",
"Genuine": "Truth-matched"
})
#Set Draw style
p.histoMgr.forHisto("Fake", altBackgroundBDTGStyle)
p.histoMgr.setHistoDrawStyle("Fake", "LP")
p.histoMgr.setHistoLegendStyle("Fake", "LP") #F
p.histoMgr.forHisto("Genuine", altSignalBDTGStyle)
p.histoMgr.setHistoDrawStyle("Genuine", "HIST")
p.histoMgr.setHistoLegendStyle("Genuine", "LP") #LP
if "avg" in hG.lower() or "likelihood" in hG.lower() or "over" in hG.lower(
):
p.histoMgr.forHisto("Genuine", signalBDTGStyle)
p.histoMgr.setHistoDrawStyle("Genuine", "HIST")
p.histoMgr.setHistoLegendStyle("Genuine", "LP") #LP
if "Gray" in opts.mcrab:
p.histoMgr.forHisto("Fake", backgroundGrayStyle)
p.histoMgr.forHisto("Genuine", signalGrayStyle)
histoG = histograms.Histo(HG, "TT", "Signal")
histoG.setIsDataMC(isData=False, isMC=True)
histoF = histograms.Histo(HF, "QCD", "Signal")
histoF.setIsDataMC(isData=False, isMC=True)
styleG = styles.ttStyle
styleF = styles.signalStyleHToTB1000
styleG.apply(HG)
styleF.apply(HF)
myList.append(histoG)
myList.append(histoF)
_kwargs = {
"xlabel": _xlabel,
"ylabel": "Arbitrary Units / %s" % (_format),
"ratioYlabel": "Ratio ",
"ratio": False,
"ratioInvert": False,
"stackMCHistograms": False,
"addMCUncertainty": False,
"addLuminosityText": False,
"addCmsText": True,
"cmsExtraText": "Preliminary",
#"opts" : {"ymin": 0.0, "ymaxfactor": 1.1},
"opts": _opts,
"opts2": {
"ymin": 0.6,
"ymax": 1.5
},
"log": False,
#"createLegend" : {"x1": 0.5, "y1": 0.75, "x2": 0.9, "y2": 0.9},
"createLegend": {
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.82
},
}
# Save plot in all formats
saveName = hG.split("/")[-1]
#plots.drawPlot(p, saveName, **_kwargs)
savePath = os.path.join(opts.saveDir + ANALYSISNAME, "HplusMasses",
hG.split("/")[0], opts.optMode)
plots.drawPlot(p, savePath, **_kwargs)
SavePlot(p,
saveName,
os.path.join(opts.saveDir + opts.mcrab, opts.optMode),
saveFormats=[".png"])
return
def main(hName, opts):
# Setup the style
style = tdrstyle.TDRStyle()
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts.mcrab, opts, **kwargs)
datasetsMgr.updateNAllEventsToPUWeighted()
# datasetsMgr.PrintCrossSections()
# datasetsMgr.PrintLuminosities()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
# Merge datasets: All JetHT to "Data", QCD_Pt to "QCD", QCD_bEnriched to "QCD_b", single-top to "SingleTop", WW, WZ, ZZ to "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Remove datasets
# datasetsMgr.remove("QCD-b")
# Print dataset information
# datasetsMgr.PrintInfo()
# Create plot, with the default
s = {"normalizeToOne": True}
p = plots.MCPlot(datasetsMgr, hName, **s)
p.histoMgr.setHistoLegendStyleAll("LP")
p.histoMgr.setHistoDrawStyleAll("EP")
p.histoMgr.setHistoLegendStyle("ChargedHiggs_HplusTB_HplusToTB_M_500", "F")
p.histoMgr.setHistoDrawStyle ("ChargedHiggs_HplusTB_HplusToTB_M_500", "HIST")
# Create a comparison plot
ratioOpts = {"ymin": 0.0, "ymax": 2.0}
if kwargs.get("logY")==True:
canvOpts = {"xmin": 0.0, "ymin": 1e-5, "ymaxfactor": 10}
else:
canvOpts = {"ymin": 0.0, "ymaxfactor": 1.2}
# Draw a customised plot & Save it
plots.drawPlot(p,
os.path.join(kwargs.get("savePath"), hName.replace("/", "_").replace(" ", "_").replace("(", "_").replace(")", "") ),
xlabel=kwargs.get("xlabel"),
ylabel=kwargs.get("ylabel"),
rebinX=kwargs.get("rebinX"),
rebinY=kwargs.get("rebinY"),
xlabelsize=kwargs.get("xlabelsize"),
ratio=kwargs.get("ratio"),
stackMCHistograms=kwargs.get("stackMCHistograms"),
ratioYlabel=kwargs.get("ratioYlabel"),
ratioInvert=kwargs.get("ratioInvert"),
addMCUncertainty=kwargs.get("addMCUncertainty"),
addLuminosityText=kwargs.get("addLuminosityText"),
addCmsText=kwargs.get("addCmsText"),
opts=canvOpts, opts2=ratioOpts,
log=kwargs.get("logY"),
errorBarsX=kwargs.get("errorBarsX"),
cmsExtraText=kwargs.get("cmsExtraText"),
moveLegend=kwargs.get("moveLegend"),
drawStyle="P",
legendStyle="LP",
#cutLine=kwargs.get("cutValue"),
cutBox={"cutValue": kwargs.get("cutValue"), "fillColor": kwargs.get("cutFillColour"), "box": kwargs.get("cutBox"), "line": kwargs.get("cutLine"), "lessThan": kwargs.get("cutLessthan")},
)
# Remove legend?
if kwargs.get("removeLegend"):
p.removeLegend()
# Additional text
# histograms.addText(0.4, 0.9, "Alexandros Attikis", 17)
# histograms.addText(0.4, 0.11, "Runs " + datasetsMgr.loadRunRange(), 17)
if not opts.batchMode:
raw_input("=== plotEventVars.py:\n\tPress any key to quit ROOT ...")
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
logY = True
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "dijetm" in histo.lower():
_rebinX = 2
_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}
_opts["xmax"] = 400.0
if "trijetm" in histo.lower():
_rebinX = 1
logY = False
_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 #1500.0
if "pt" in histo.lower():
_rebinX = 2
_format = "%0.0f GeV/c"
if "eta" in histo.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 histo.lower():
_format = "%0.2f"
_opts["xmin"] = 0.0
_opts["xmax"] = 6.0
if "bdisc" in histo.lower():
_format = "%0.2f"
if "tetrajetm" in histo.lower():
_rebinX = 10
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjjb} (%s)" % (_units)
_opts["xmax"] = 3500.0
if "chisqr" in histo.lower():
_rebinX = 10
_units = ""
_format = "%0.0f " + _units
_xlabel = "#chi^{2}"
#_opts["xmax"] = 3500.0
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 Dataset styling
p.histoMgr.forHisto("QCD", styles.getQCDFillStyle() )
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
p.histoMgr.setHistoDrawStyle("TT", "AP")
p.histoMgr.setHistoLegendStyle("TT", "LP")
#p.histoMgr.setHistoDrawStyle("TTZToQQ", "AP")
#p.histoMgr.setHistoLegendStyle("TTZToQQ", "LP")
# Customise style
if 0:
p.histoMgr.forHisto("ChargedHiggs_HplusTB_HplusToTB_M_500", styles.getSignalStyleHToTB())
plots.drawPlot(p,
histo,
xlabel = _xlabel,
ylabel = "Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.62, "y1": 0.65, "x2": 0.92, "y2": 0.92},
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)
SavePlot(p, saveName, savePath)
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)
# 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 "ChiSqr" in histo:
_rebinX = 1
# logY = True
_units = ""
_format = "%0.1f " + _units
_xlabel = "#chi^{2}"
_cutBox = {"cutValue": 10.0, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 100
elif "trijetmass" in histo.lower():
_rebinX = 2
# logY = False
_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 "ht" in histo.lower():
_rebinX = 2
# logY = False
_units = "GeV"
_format = "%0.0f " + _units
_xlabel = "H_{T} (%s)" % _units
_cutBox = {"cutValue": 500, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 2000
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"] = 1500 #3500.0
elif "dijetmass" in histo.lower():
_rebinX = 1 #5 #10 #4
# logY = False
_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
# logY = False
_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 "trijetbdt_mass" 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
# elif "topcandmass" 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
elif "matched_mass" 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
elif "bdtvalue" in histo.lower():
_format = "%0.1f"
elif "foxwolframmoment" in histo.lower():
_format = "%0.1f"
_cutBox = {"cutValue": 0.5, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
elif "absdeltamvamax_mctruth_sameobjfakes" in histo.lower():
_format = "%0.1f"
_xlabel = "|#Delta BDTG| response"
elif "deltamvamin_mctruth_sameobjfakespassbdt" in histo.lower():
_opts["xmax"] = 1
_opts["xmin"] = -1
elif "bdtmultiplicity" in histo.lower():
_opts["xmax"] = 15
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"] = 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.getAllDatasets():
p.histoMgr.forHisto("QCD", styles.getQCDFillStyle() )
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasets():
p.histoMgr.setHistoDrawStyle("TT", "AP")
p.histoMgr.setHistoLegendStyle("TT", "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,#"Arbitrary Units / %s" % (_format), #"Events / %s" % (_format), #"Arbitrary Units / %s" % (_format),
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, "HplusMasses", 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)
SavePlot(p, saveName, save_path)
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)
# Get histogram<->kwargs dictionary
kwargs = GetHistoKwargs(histo, opts)
# Customise style
for i, m in enumerate(signalMass):
massNum = m.rsplit("M_")[-1]
if i == len(signalMass) - 1:
p.histoMgr.setHistoDrawStyle(m, "HIST")
p.histoMgr.setHistoLegendStyle(m, "F")
p.histoMgr.forHisto(m, styles.getSignalStyleHToTB())
# p.histoMgr.forHisto(m, styles. getSignalfillStyleHToTB())
# p.histoMgr.forHisto(m, datasetsMgr.getDataset(m).getRootHisto().SetMarkerStyle(6))
# h = GetRootHisto(datasetsMgr, m, histo)
# h.SetFillStyle(1001)
# p.histoMgr.setHistoLegendStyle(dName, "LP")
h = datasetsMgr.getDataset(m).getDatasetRootHisto(
histo).getHistogram()
styles.qcdFillStyle.apply(h)
else:
p.histoMgr.forHisto(m, styles.getSignalStyleHToTB_M(massNum))
p.histoMgr.setHistoLegendStyle(m, "LP")
# Plot customised histogram
plots.drawPlot(
p,
histo,
xlabel=kwargs.get("xlabel"),
ylabel=kwargs.get("ylabel"),
log=kwargs.get("log"),
rebinX=kwargs.get("rebinX"),
cmsExtraText="Preliminary",
#createLegend = {"x1": 0.62, "y1": 0.75, "x2": 0.92, "y2": 0.92},
moveLegend=kwargs.get("moveLegend"),
opts=kwargs.get("opts"),
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox=kwargs.get("cutBox"),
)
# Customise styling
if 0:
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(0))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerStyle(6))
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
# Save plot in all formats
saveName = histo.split("/")[-1]
if opts.folder == "":
savePath = os.path.join(opts.saveDir, opts.optMode)
else:
savePath = os.path.join(opts.saveDir,
histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath, [".png", ".pdf"])
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"] = 805 #1005
if "bdisc" in histo.lower():
_rebinX = 2
_units = ""
_format = "%0.2f " + _units
_xlabel = "b-discriminator" #(%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_opts["xmax"] = 1.2
if "ht" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "H_{T} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_opts["xmax"] = 1505 #1005
if "met" in histo.lower():
_rebinX = 1
_units = "GeV"
_format = "%0.0f " + _units
_xlabel = "E_{T,missing} (%s)" % _units
_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": False, "greaterThan": True}
_opts["xmax"] = 205 #1005
if "mult" in histo.lower():
_units = ""
_format = "%0.0f " + _units
_xlabel = "jet multiplicity"
_opts["xmax"] = 10
if "mass" 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"] = 350 #1005
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"] = 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 "tau" in histo.lower():
_format = "%0.2f "
elif "deltar" in histo.lower():
_format = "%0.2f "
elif "bdtvalue" in histo.lower():
_format = "%0.1f"
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"] = 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.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": 0.68, "y1": 0.82, "x2": 1.0, "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 PlotHistograms(datasetsMgr):
# Definitions
kwargs = {}
histos = []
# For-loop: All histos in list
dName = "ChargedHiggs_HplusTB_HplusToTB_M_%s" % (opts.signalMass)
# Matched Leading Trijet
p0 = plots.MCPlot(datasetsMgr,
"AnalysisTriplets/TetrajetMass_LdgTopIsHTop",
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
hTopTrue = p0.histoMgr.getHisto(dName).getRootHisto().Clone(
"Matched-LdgTrijet")
histos.append(hTopTrue)
# Matched Bjet
p1 = plots.MCPlot(datasetsMgr,
"AnalysisTripletsTrue/TetrajetMass",
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
hBjetTrue = p1.histoMgr.getHisto(dName).getRootHisto().Clone(
"Matched-Bjet")
histos.append(hBjetTrue)
# Matched Leading Trijet + Bjet
p2 = plots.MCPlot(datasetsMgr,
"AnalysisTripletsTrue/TetrajetMass_LdgTopIsHTop",
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
hAllTrue = p2.histoMgr.getHisto(dName).getRootHisto().Clone(
"Matched-LdgTrijet-Bjet")
histos.append(hAllTrue)
# Inclusive
p3 = plots.MCPlot(datasetsMgr,
"AnalysisTriplets/TetrajetMass",
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
hInclusive = p3.histoMgr.getHisto(dName).getRootHisto().Clone("Inclusive")
histos.append(hInclusive)
# Unmatched
hUnmatched = p3.histoMgr.getHisto(dName).getRootHisto().Clone("Unmatched")
hUnmatched.Add(hAllTrue, +1)
hUnmatched.Add(hBjetTrue, -1)
hUnmatched.Add(hTopTrue, -1)
histos.append(Unmatched)
# Make comparison plot
nTotal = hInclusive.Integral()
align = "{:>25} {:>25}"
title = align.format("Histogram", "Percentage (%)")
hLine = 50 * "="
table = []
table.append(hLine)
table.append(title)
table.append(hLine)
for h in histos:
n = h.Integral()
perc = (n / nTotal) * 100
table.append(align.format(h.GetName(), " %.1f" % (perc)))
if opts.normaliseToOne:
h = h.Scale(1 / h.Integral())
# Print table info
table.append(hLine)
for row in table:
print row
# Make comparison plot
p = plots.ComparisonManyPlot(hInclusive,
[hBjetTrue, hTopTrue, hAllTrue, hUnmatched],
saveFormats=[])
p.setLuminosity(opts.intLumi)
# Overwite signal style?
style = [200, 500, 800, 1000, 2000, 5000]
lstyle = [
ROOT.kSolid, ROOT.kDashed, ROOT.kDashDotted, ROOT.kDotted,
ROOT.kDotted, ROOT.kSolid
]
p.histoMgr.forHisto("Matched-LdgTrijet-Bjet",
styles.getFakeBLineStyle()) #styles.getFakeBStyle())
p.histoMgr.forHisto("Matched-Bjet",
styles.mcStyle) #getInvertedLineStyle())
p.histoMgr.forHisto("Matched-LdgTrijet", styles.stylesCompound[-1])
p.histoMgr.forHisto("Inclusive", styles.getGenuineBLineStyle())
p.histoMgr.forHisto("Unmatched", styles.errorRatioStatStyle)
# Set draw style
p.histoMgr.setHistoDrawStyle("Inclusive", "AP")
p.histoMgr.setHistoDrawStyle("Matched-Bjet", "AP")
p.histoMgr.setHistoDrawStyle("Matched-LdgTrijet", "HIST")
p.histoMgr.setHistoDrawStyle("Matched-LdgTrijet-Bjet", "HIST")
p.histoMgr.setHistoDrawStyle("Unmatched", "HIST")
# Set legend style
p.histoMgr.setHistoLegendStyle("Inclusive", "LP")
p.histoMgr.setHistoLegendStyle("Matched-Bjet", "LP")
p.histoMgr.setHistoLegendStyle("Matched-LdgTrijet", "F")
p.histoMgr.setHistoLegendStyle("Matched-LdgTrijet-Bjet", "L")
p.histoMgr.setHistoLegendStyle("Unmatched", "F")
p.histoMgr.setHistoLegendLabelMany({
"Inclusive": "Inclusive",
"Matched-Bjet": "b-jet match",
"Matched-LdgTrijet": "top match",
"Matched-LdgTrijet-Bjet": "full match", #"top + b-jet match",
"Unmatched": "Combinatoric",
})
# Draw customised plot
saveName = "TetrajetMass"
_units = "GeV/c^{2}"
if opts.signalMass > 500:
_leg = {"x1": 0.20, "y1": 0.65, "x2": 0.45, "y2": 0.87}
else:
_leg = {"x1": 0.60, "y1": 0.65, "x2": 0.85, "y2": 0.87}
if opts.normaliseToOne:
yLabel = "Arbitrary Units / %0.0f " + _units
else:
yLabel = "Events / %0.0f " + _units
if opts.logY:
ymin = 1e-3
ymaxf = 5
else:
ymin = 0.0
ymaxf = 1.2
ROOT.gStyle.SetNdivisions(6 + 100 * 5 + 10000 * 2, "X")
plots.drawPlot(
p,
saveName,
xlabel="m_{jjbb} (%s)" % (_units),
ylabel=yLabel,
log=opts.logY,
rebinX=2, #2, 5
cmsExtraText="Preliminary",
createLegend=_leg,
opts={
"xmin": 0.0,
"xmax": 1400.0,
"ymin": ymin,
"ymaxfactor": ymaxf
},
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox={
"cutValue": opts.signalMass,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
})
# Save plot in all formats
savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p, "%s_M%s" % (saveName, opts.signalMass), savePath)
return
def PlotHistograms(datasetsMgr, histoName):
# Get Histogram name and its kwargs
rootHistos = []
regionsList = ["SR", "VR", "CRone", "CRtwo"]
hRegion = histoName.split("_")[-1]
saveName = histoName.rsplit("/")[-1]
saveName = saveName.replace("_" + hRegion, "")
kwargs = GetHistoKwargs(saveName, opts)
myRegions = []
# For-loop: All histograms in all regions
for region in regionsList:
hName = histoName.replace(hRegion, region)
if "Data" in datasetsMgr.getAllDatasetNames():
p = plots.DataMCPlot(datasetsMgr, hName, saveFormats=[])
else:
p = plots.MCPlot(datasetsMgr,
hName,
normalizeToLumi=opts.intLumi,
saveFormats=[])
# Append (non-empty) dataset ROOT histos to a list for plotting
h = p.histoMgr.getHisto(opts.dataset).getRootHisto()
if h.GetEntries() > 0:
h.SetName(region)
rootHistos.append(h)
myRegions.append(region)
# Create a comparison plot for a given dataset in all CRs
if len(rootHistos) == 0:
return
else:
p = plots.ComparisonManyPlot(rootHistos[0],
rootHistos[1:],
saveFormats=[])
p.setLuminosity(opts.intLumi)
if opts.normalizeToOne:
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Scale(
1.0 / h.getRootHisto().Integral()))
# Drawing style
p.histoMgr.setHistoDrawStyleAll("AP")
p.histoMgr.setHistoLegendStyleAll("LP")
# Apply styles
for h in rootHistos:
region = h.GetName()
if region == "SR":
styles.fakeBLineStyle1.apply(
p.histoMgr.getHisto(region).getRootHisto())
p.histoMgr.setHistoDrawStyle(region, "HIST")
p.histoMgr.setHistoLegendStyle(region, "L")
else:
styles.getABCDStyle(region).apply(
p.histoMgr.getHisto(region).getRootHisto())
# Add dataset name on canvas
p.appendPlotObject(
histograms.PlotText(0.18,
0.88,
plots._legendLabels[opts.dataset],
bold=True,
size=22))
# Set legend labels
if "CRone" in myRegions:
p.histoMgr.setHistoLegendLabelMany({
"CRone": "CR1",
})
if "CRtwo" in myRegions:
p.histoMgr.setHistoLegendLabelMany({
"CRtwo": "CR2",
})
# Draw the plot
plots.drawPlot(p, saveName,
**kwargs) #the "**" unpacks the kwargs_ dictionary
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(opts.saveDir), [".png", ".pdf"])
return
def PlotHistograms(datasetsMgr, histoName):
# Get Histogram name and its kwargs
rootHistos = []
bdiscList = ["Loose", "Medium"]
saveName = histoName.rsplit("/")[-1]
kwargs = GetHistoKwargs(saveName, opts)
myBdiscs = []
histoName = histoName.replace(opts.folder, "")
stylesList = [styles.FakeBStyle1, styles.FakeBStyle3, styles.FakeBStyle2, styles.FakeBStyle4]
#stylesList = [styles.fakeBLineStyle1, styles.FakeBStyle2, styles.FakeBStyle3, styles.FakeBStyle4]
# For-loop: All histograms in all regions
for bdisc in bdiscList:
hName_ = histoName.replace(opts.refBdisc, bdisc)
hName = opts.folder + "/" + hName_
if "Data" in datasetsMgr.getAllDatasetNames():
p = plots.DataMCPlot(datasetsMgr, hName, saveFormats=[])
else:
p = plots.MCPlot(datasetsMgr, hName, normalizeToLumi=opts.intLumi, saveFormats=[])
# Append (non-empty) dataset ROOT histos to a list for plotting
h = p.histoMgr.getHisto(opts.dataset).getRootHisto()
if h.GetEntries() > 0:
h.SetName(bdisc)
rootHistos.append(h)
myBdiscs.append(bdisc)
# Create a comparison plot for a given dataset in all CRs
if len(rootHistos) < 2:
Print("Cannot plot comparison due to too few non-empty histograms present (=%i)" % (len(rootHistos)), False)
return
# Move ref histo to top of rootHisto list
for rh in rootHistos:
if opts.refBdisc in rh.GetName():
s = rootHistos.pop( rootHistos.index(rh) )
#rootHistos.insert(0, s)
rootHistos.insert(len(rootHistos), s)
# Draw the comparison plot (first argument the reference histo for ratio purposes)
p = plots.ComparisonManyPlot(rootHistos[-1], rootHistos[:-1], saveFormats=[])
# p = plots.ComparisonManyPlot(rootHistos[0], rootHistos[1:], saveFormats=[])
p.setLuminosity(opts.intLumi)
if opts.normalizeToOne:
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().Scale(1.0/h.getRootHisto().Integral()))
# Apply drawing/legend styles
for i, bdisc in enumerate(myBdiscs, 0):
stylesList[i].apply(p.histoMgr.getHisto(bdisc).getRootHisto())
if bdisc == opts.refBdisc:
p.histoMgr.setHistoDrawStyle(bdisc, "AP")
p.histoMgr.setHistoLegendStyle(bdisc,"LP")
else:
p.histoMgr.setHistoDrawStyle(bdisc, "AP")
p.histoMgr.setHistoLegendStyle(bdisc, "LP")
#p.histoMgr.setHistoDrawStyle(bdisc, "HIST")
#p.histoMgr.setHistoLegendStyle(bdisc, "F")
# Add dataset name on canvas
p.appendPlotObject(histograms.PlotText(0.18, 0.88, plots._legendLabels[opts.dataset], bold=True, size=22))
#p.appendPlotObject(histograms.PlotText(0.18, 0.88, plots._legendLabels[opts.dataset + " (%s)" % ], bold=True, size=22))
# Set legend labels
p.histoMgr.setHistoLegendLabelMany({
"Loose" : "Loose (%s)" % (GetCRLabel(histoName)),
"Medium": "Medium (%s)" % (GetCRLabel(histoName)),
})
# Draw the plot
plots.drawPlot(p, saveName, **kwargs) #the "**" unpacks the kwargs_ dictionary
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(opts.saveDir), [".png", ".pdf"] )
return
def PlotHistogram(dsetMgr, histoName, opts):
# Get kistogram argumetns
kwargs = GetHistoKwargs(histoName, opts)
saveName = histoName.replace(opts.folder + "/", "")
# Create the plotting object (Data, "FakeB")
p1 = plots.DataMCPlot(dsetMgr, histoName, saveFormats=[])
# Copy dataset manager before changing datasets. Keep only EWK (GenuineB) datasets
datasetMgr = dsetMgr.deepCopy()
datasetMgr.selectAndReorder(aux.GetListOfEwkDatasets())
# Create the MCPlot for the EWKGenuineB histograms
if opts.useMC:
p2 = plots.MCPlot(datasetMgr, histoName, normalizeToLumi=opts.intLumi, saveFormats=[])
else:
histoNameGenuineB = histoName.replace(opts.folder, opts.folder + "EWKGenuineB")
p2 = plots.MCPlot(datasetMgr, histoNameGenuineB, normalizeToLumi=opts.intLumi, saveFormats=[])
# Add the datasets to be included in the plot
myStackList = []
# Data-driven FakeB background
if not opts.useMC:
hFakeB = p1.histoMgr.getHisto("FakeB").getRootHisto()
hhFakeB = histograms.Histo(hFakeB, "FakeB", legendLabel="Fake-b")
hhFakeB.setIsDataMC(isData=False, isMC=True)
myStackList.append(hhFakeB)
else:
hQCD = p1.histoMgr.getHisto("QCD").getRootHisto()
hhQCD = histograms.Histo(hQCD, "QCD", legendLabel="QCD")
hhQCD.setIsDataMC(isData=False, isMC=True)
myStackList.append(hhQCD)
# EWK GenuineB background (Replace all EWK histos with GenuineB histos)
ewkHistoList = []
# For-loop: All EWK datasets
for dataset in aux.GetListOfEwkDatasets():
h = p2.histoMgr.getHisto(dataset).getRootHisto()
hh = histograms.Histo(h, dataset, plots._legendLabels[dataset])
hh.setIsDataMC(isData=False, isMC=True)
myStackList.append(hh)
# Collision data
hData = p1.histoMgr.getHisto("Data").getRootHisto()
hhData = histograms.Histo(hData, "Data")
hhData.setIsDataMC(isData=True, isMC=False)
myStackList.insert(0, hhData)
# Signal
hSignal = p1.histoMgr.getHisto(opts.signal).getRootHisto()
hhSignal = histograms.Histo(hSignal, opts.signal, plots._legendLabels[opts.signal])
hhSignal.setIsDataMC(isData=False, isMC=True)
myStackList.insert(1, hhSignal)
# Create the final plot by passing the histogram list
p3 = plots.DataMCPlot2(myStackList, saveFormats=[])
p3.setLuminosity(opts.intLumi)
p3.setDefaultStyles()
# Apply blinding of data in Signal Region (After creating the plot)
if "blindingRangeString" in kwargs:
startBlind = float(kwargs["blindingRangeString"].split("-")[1])
endBlind = float(kwargs["blindingRangeString"].split("-")[0])
plots.partiallyBlind(p3, maxShownValue=startBlind, minShownValue=endBlind, invert=True, moveBlindedText=kwargs["moveBlindedText"])
# Draw and save the plot
plots.drawPlot(p3, saveName, **kwargs)
SavePlot(p3, saveName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf"])
return
def PurityTripletPlots(datasetsMgr, analysisType=""):
'''
Create data-MC comparison plot, with the default:
- legend labels (defined in plots._legendLabels)
- plot styles (defined in plots._plotStyles, and in styles)
- drawing styles ('HIST' for MC, 'EP' for data)
- legend styles ('L' for MC, 'P' for data)
'''
Verbose("Plotting the Purity-Triplets for %s" % analysisType)
analysisTypes = ["", "EWKFakeB", "EWKGenuineB"]
if analysisType not in analysisTypes:
raise Exception(
"Invalid analysis type \"%s\". Please select one of the following: %s"
% (analysisType, "\"" + "\", \"".join(analysisTypes) + "\""))
else:
folder = "FakeBPurity" + analysisType
# Definitions
histoNames = []
histoKwargs = {}
# General Settings
if opts.mergeEWK:
_moveLegend = {"dx": -0.05, "dy": 0.0, "dh": -0.15}
else:
_moveLegend = {"dx": -0.05, "dy": 0.0, "dh": 0.1}
_kwargs = {
"xlabel": "",
"ylabel": "",
"rebinX": 1,
"rebinY": None,
"ratioYlabel": "Data/MC",
"ratio": False,
"stackMCHistograms": True,
"ratioInvert": False,
"addMCUncertainty": False,
"addLuminosityText": True,
"addCmsText": True,
"cmsExtraText": "Preliminary",
"opts": {
"ymin": 2e-1,
"ymaxfactor": 10
},
"opts2": {
"ymin": 0.0,
"ymax": 2.0
},
"log": True,
"errorBarsX": True,
"moveLegend": _moveLegend,
"cutBox": {
"cutValue": 0.0,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
}
}
# Create/Draw the plots
histoNames.append("%s/Inverted_FailedBJetPt_AfterAllSelections" % folder)
kwargs = copy.deepcopy(_kwargs)
kwargs["xlabel"] = "jet p_{T} (GeV/c)"
kwargs["ylabel"] = "Events / %.0f GeV/c"
kwargs["cutBox"] = {
"cutValue": 40.0,
"fillColor": 16,
"box": True,
"line": True,
"greaterThan": True
}
histoKwargs["%s/Inverted_FailedBJetPt_AfterAllSelections" %
folder] = kwargs
histoNames.append("%s/Inverted_FailedBJetEta_AfterAllSelections" % folder)
kwargs = copy.deepcopy(_kwargs)
kwargs["xlabel"] = "jet #eta"
kwargs["ylabel"] = "Events / %.2f"
histoKwargs["%s/Inverted_FailedBJetEta_AfterAllSelections" %
folder] = kwargs
histoNames.append("%s/Inverted_FailedBJetBDisc_AfterAllSelections" %
folder)
kwargs = copy.deepcopy(_kwargs)
kwargs["xlabel"] = "jet b-tag discriminator"
kwargs["ylabel"] = "Events / %.2f"
kwargs["cutBox"] = {
"cutValue": 0.8484,
"fillColor": 16,
"box": True,
"line": True,
"greaterThan": False
}
histoKwargs["%s/Inverted_FailedBJetBDisc_AfterAllSelections" %
folder] = kwargs
histoNames.append("%s/Inverted_FailedBJetPdgId_AfterAllSelections" %
folder)
kwargs = copy.deepcopy(_kwargs)
kwargs["xlabel"] = "jet pdgId"
kwargs["ylabel"] = "Events / %.0f"
histoKwargs["%s/Inverted_FailedBJetPdgId_AfterAllSelections" %
folder] = kwargs
histoNames.append(
"%s/Inverted_FailedBJetPartonFlavour_AfterAllSelections" % folder)
kwargs = copy.deepcopy(_kwargs)
kwargs["xlabel"] = "jet parton flavour"
kwargs["ylabel"] = "Events / %.0f"
histoKwargs["%s/Inverted_FailedBJetPartonFlavour_AfterAllSelections" %
folder] = kwargs
histoNames.append(
"%s/Inverted_FailedBJetHadronFlavour_AfterAllSelections" % folder)
kwargs = copy.deepcopy(_kwargs)
kwargs["xlabel"] = "jet hadron flavour"
kwargs["ylabel"] = "Events / %.0f"
histoKwargs["%s/Inverted_FailedBJetHadronFlavour_AfterAllSelections" %
folder] = kwargs
histoNames.append("%s/Inverted_FailedBJetAncestry_AfterAllSelections" %
folder)
kwargs = copy.deepcopy(_kwargs)
kwargs["xlabel"] = "ancestor bit"
kwargs["ylabel"] = "Events / %.0f"
histoKwargs["%s/Inverted_FailedBJetAncestry_AfterAllSelections" %
folder] = kwargs
# For-loop: All histograms in list
for histoName in histoNames:
kwargs_ = histoKwargs[histoName]
saveName = histoName.replace(folder + "/", "")
if opts.mcOnly:
p = plots.MCPlot(datasetsMgr,
histoName,
normalizeToLumi=opts.intLumi,
saveFormats=[])
kwargs_.pop("ratio", None)
kwargs_.pop("ratioYlabel", None)
kwargs_.pop("ratioInvert", None)
kwargs_.pop("opts2", None)
plots.drawPlot(p, histoName,
**kwargs_) #the "**" unpacks the kwargs_ dictionary
else:
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
plots.drawPlot(p, histoName,
**kwargs_) #the "**" unpacks the kwargs_ dictionary
# Save plot in all formats
SavePlot(p,
saveName,
os.path.join(opts.saveDir, folder),
saveFormats=[".png"])
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)
# Get histogram<->kwargs dictionary
kwargs = GetHistoKwargs(histo, opts)
# Customise styling
if 0:
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(0))
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerStyle(6))
# Customise signal
for d in datasetsMgr.getAllDatasetNames():
if "Charged" not in d:
continue
else:
#p.histoMgr.setHistoDrawStyle(d, "HIST")
p.histoMgr.setHistoLegendStyle(d, "L")
p.histoMgr.setHistoLegendStyle(d, "L")
# Customise QCD style
p.histoMgr.setHistoDrawStyle("QCD", "P")
p.histoMgr.setHistoLegendStyle("QCD", "P")
p.histoMgr.setHistoLegendLabelMany({
"QCD": "QCD (MC)",
})
# Customise style
signalM = []
for m in signalMass:
signalM.append(m.rsplit("M_")[-1])
for m in signalM:
dName = "ChargedHiggs_HplusTB_HplusToTB_M_%s" % m
if dName in datasetsMgr.getAllDatasetNames():
p.histoMgr.forHisto(dName, styles.getSignalStyleHToTB_M(m))
p.histoMgr.setHistoLegendStyle(dName, "LP")
# Plot customised histogram
plots.drawPlot(
p,
histo,
xlabel=kwargs.get("xlabel"),
ylabel=kwargs.get("ylabel"),
log=kwargs.get("log"),
rebinX=kwargs.get("rebinX"),
cmsExtraText="Preliminary",
#createLegend = {"x1": 0.62, "y1": 0.75, "x2": 0.92, "y2": 0.92},
moveLegend=kwargs.get("moveLegend"),
opts=kwargs.get("opts"),
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox=kwargs.get("cutBox"),
)
# Save plot in all formats
saveName = histo.split("/")[-1]
if opts.folder == "":
savePath = os.path.join(opts.saveDir, opts.optMode)
else:
savePath = os.path.join(opts.saveDir,
histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath, [".png", ".pdf"])
return
def PlotHistograms(datasetsMgr_Mcrab644_TopPtRew,
datasetsMgr_Mcrab644_noTopPtRew,
datasetsMgr_Mcrab905_TopPtRew,
datasetsMgr_Mcrab905_noTopPtRew):
# Definitions
kwargs = {}
histos = []
# For-loop: All histos in list
dName = "TT"
# Multicrab *644, top pt reweighting:
p0 = plots.MCPlot(datasetsMgr_Mcrab644_TopPtRew,
"Analysis_/TopQuarkPt",
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
h_Mcrab644_TopPtRew = p0.histoMgr.getHisto(dName).getRootHisto().Clone(
"Mcrab644-ptRew")
histos.append(h_Mcrab644_TopPtRew)
# Multicrab *644, no top pt reweighting:
p1 = plots.MCPlot(datasetsMgr_Mcrab644_noTopPtRew,
"Analysis_/TopQuarkPt",
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
h_Mcrab644_noTopPtRew = p1.histoMgr.getHisto(dName).getRootHisto().Clone(
"Mcrab644-noptRew")
histos.append(h_Mcrab644_noTopPtRew)
# Multicrab *905, top pt reweighting:
p2 = plots.MCPlot(datasetsMgr_Mcrab905_TopPtRew,
"Analysis_/TopQuarkPt",
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
h_Mcrab905_TopPtRew = p2.histoMgr.getHisto(dName).getRootHisto().Clone(
"Mcrab905-ptRew")
histos.append(h_Mcrab905_TopPtRew)
# Multicrab *905, no top pt reweighting:
p3 = plots.MCPlot(datasetsMgr_Mcrab905_noTopPtRew,
"Analysis_/TopQuarkPt",
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
h_Mcrab905_noTopPtRew = p3.histoMgr.getHisto(dName).getRootHisto().Clone(
"Mcrab905-noptRew")
histos.append(h_Mcrab905_noTopPtRew)
# Normalise histos
for h in histos:
if opts.normaliseToOne:
h = h.Scale(1 / h.Integral())
# Make comparison plot
if opts.eight:
# p = plots.ComparisonManyPlot(h_Mcrab905_TopPtRew, [ h_Mcrab905_noTopPtRew], saveFormats=[])
p = plots.ComparisonManyPlot(h_Mcrab905_noTopPtRew,
[h_Mcrab905_TopPtRew],
saveFormats=[])
else:
# p = plots.ComparisonManyPlot(h_Mcrab644_TopPtRew, [h_Mcrab644_noTopPtRew], saveFormats=[])
# p = plots.ComparisonManyPlot(h_Mcrab644_noTopPtRew, [h_Mcrab644_TopPtRew], saveFormats=[])
p = plots.ComparisonManyPlot(
h_Mcrab644_noTopPtRew, [h_Mcrab644_TopPtRew, h_Mcrab905_TopPtRew],
saveFormats=[])
p.setLuminosity(opts.intLumi)
# Overwite signal style?
if opts.eight:
p.histoMgr.forHisto("Mcrab905-ptRew", styles.getABCDStyle("VR"))
p.histoMgr.forHisto("Mcrab905-noptRew", styles.getFakeBLineStyle())
else:
p.histoMgr.forHisto("Mcrab644-ptRew", styles.getABCDStyle("VR"))
p.histoMgr.forHisto("Mcrab644-noptRew", styles.getFakeBLineStyle())
# Set draw style
if opts.eight:
p.histoMgr.setHistoDrawStyle("Mcrab905-noptRew", "HIST")
p.histoMgr.setHistoDrawStyle("Mcrab905-ptRew", "AP")
else:
p.histoMgr.setHistoDrawStyle("Mcrab644-noptRew", "HIST")
p.histoMgr.setHistoDrawStyle("Mcrab644-ptRew", "AP")
# Set legend style
if opts.eight:
p.histoMgr.setHistoLegendStyle("Mcrab905-noptRew", "L") #"F"
p.histoMgr.setHistoLegendStyle("Mcrab905-ptRew", "LP")
else:
p.histoMgr.setHistoLegendStyle("Mcrab644-noptRew", "L") #"F"
p.histoMgr.setHistoLegendStyle("Mcrab644-ptRew", "LP")
if opts.eight:
p.histoMgr.setHistoLegendLabelMany({
"Mcrab905-ptRew": "p_{T} reweight (8 TeV)",
"Mcrab905-noptRew": "Default",
})
else:
p.histoMgr.setHistoLegendLabelMany({
"Mcrab644-ptRew": "p_{T} reweight (13 TeV)",
"Mcrab644-noptRew": "Default",
})
# Draw customised plot
if opts.eight:
saveName = "TopQuarkPt_PtReweight_8TeV"
else:
saveName = "TopQuarkPt_PtReweight_13TeV"
_units = "GeV/c"
_leg = {"x1": 0.55, "y1": 0.75, "x2": 0.85, "y2": 0.87}
if opts.normaliseToOne:
yLabel = "Arbitrary Units / %0.0f " + _units
else:
yLabel = "Events / %0.0f " + _units
plots.drawPlot(
p,
saveName,
xlabel="generated top p_{T} (%s)" % (_units),
ylabel=yLabel,
log=False,
rebinX=2,
cmsExtraText="Simulation", #"Preliminary",
ratio=True,
ratioType="errorPropagation", #"errorScale", "binomial"
divideByBinWidth=False,
ratioErrorOptions={
"numeratorStatSyst": False,
"denominatorStatSyst": True
},
ratioMoveLegend={
"dx": +0.21,
"dy": 0.03,
"dh": -0.08
},
ratioYlabel="Ratio ",
createLegend=_leg,
opts={
"xmin": 0.0,
"xmax": 800.0,
"ymin": 0.0,
"ymaxfactor": 1.2
},
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox={
"cutValue": 0.0,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
})
# Save plot in all formats
savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p, saveName, savePath)
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 Fit_Gaus(datasetsMgr, signalMass):
# Definitions
kwargs = {}
histos = []
dName = "ChargedHiggs_HplusTB_HplusToTB_M_%s" % (signalMass)
hName = "AnalysisTripletsTrue/TetrajetMass_LdgTopIsHTop"
dataset = datasetsMgr.getDataset(dName)
dx = signalMass / 2 + 50
xmin = signalMass - dx
xmax = signalMass + dx
_rebin = 2
# Get histograms
pp = plots.MCPlot(datasetsMgr,
hName,
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
myhist = pp.histoMgr.getHisto(dName).getRootHisto().Clone(
"TetrajetMass_LdgTopIsHTop0")
if (opts.normaliseToOne):
myhist.Scale(1. / myhist.Integral())
myhist.Rebin(_rebin)
#Initialize parameters
#===Gaus
g_const = myhist.Integral() #myhist.GetMaximum()
g_mean = signalMass
g_sigma = signalMass * 0.1
xminR = signalMass - g_sigma
xmaxR = signalMass + g_sigma
par = array.array('f', [g_const, g_mean, g_sigma])
xminR, xmaxR = GetFitRange(signalMass)
G1 = ROOT.TF1("G1", "gaus", xminR, xmaxR)
#Set parameters
G1.SetParameters(par[0], par[1], par[2])
# Fit my function to the histo (https://root.cern.ch/root/htmldoc/guides/users-guide/FittingHistograms.html)
r = myhist.Fit("G1", "WL Q R S")
print "=" * 50
print "***Fitting Results:***"
print "Signal Mass = %.0f GeV" % (signalMass)
if 0:
print "chi2 = %.1f " % (r.Chi2())
print "ndf = %.0f " % (r.Ndf())
print "chi2-N = %.1f " % (r.Chi2() / r.Ndf())
print "const = %.2f " % (r.Parameter(0))
print "constE = %.2f " % (r.ParError(0))
print "mean = %.1f +/- %.1f GeV" % (r.Parameter(1), r.ParError(1))
print "sigma = %.1f +/- %.1f GeV" % (r.Parameter(2), r.ParError(2))
FWHM = 2 * math.sqrt(2 * math.log(2)) * r.Parameter(2)
FWHM_E = 2 * math.sqrt(2 * math.log(2)) * r.ParError(2)
print "FWHM = %.1f +/- %.1f GeV" % (FWHM, FWHM_E)
print "=" * 50
par = G1.GetParameters()
myhist.GetFunction("G1").SetLineWidth(3)
myhist.Draw()
#Update parameters
width = myhist.GetXaxis().GetBinWidth(0) #bin width
ibinmn = myhist.GetXaxis().FindBin(xmin) #first bin
ibinmx = myhist.GetXaxis().FindBin(xmax) #last bin
g1_integral = G1.Integral(xmin, xmax) / width
g1_mean = G1.GetParameter(1)
g1_sigma = G1.GetParameter(2)
myhist.SetMarkerStyle(20)
myhist.SetMarkerSize(0.4)
myhist.GetXaxis().SetRangeUser(xmin - 50, xmax + 50)
G1.SetLineColor(kRed)
G1.SetLineWidth(3)
G1.Draw("same")
ROOT.gStyle.SetOptFit(1)
sb1 = ROOT.TPaveStats()
ROOT.gPad.Update()
sb1 = myhist.FindObject("stats")
sb1.SetX1NDC(0.65)
sb1.SetX2NDC(0.92)
sb1.SetY1NDC(0.65)
sb1.SetY2NDC(0.8)
sb1.SetLineColor(2)
p = plots.MCPlot(datasetsMgr,
"AnalysisTripletsTrue/TetrajetMass_LdgTopIsHTop",
normalizeToLumi=opts.intLumi,
saveFormats=[],
**kwargs)
hTopTrue = p.histoMgr.getHisto(dName).getRootHisto().Clone("Matched")
histos.append(hTopTrue)
p0 = plots.PlotBase(datasetRootHistos=[hTopTrue], saveFormats=[])
p0.histoMgr.forHisto("Matched", styles.getGenuineBLineStyle())
p0.histoMgr.setHistoDrawStyle("Matched", "AP")
p0.histoMgr.setHistoLegendLabelMany(
{"Matched": "m_{H^{+}} = %s GeV" % (signalMass)})
p0.appendPlotObject(G1)
saveName = "TetrajetMass_G1"
_units = "GeV/c^{2}"
if opts.normaliseToOne:
yLabel = "Arbitrary Units / %0.0f " + _units
else:
yLabel = "Events / %0.0f " + _units
#if signalMass > 500:
if 0:
_leg = {"x1": 0.20, "y1": 0.65, "x2": 0.45, "y2": 0.87}
else:
_leg = {"x1": 0.60, "y1": 0.65, "x2": 0.85, "y2": 0.87}
if opts.logY:
ymin = 1e-3
ymaxf = 5
else:
ymin = 0.0
ymaxf = 1.3
ROOT.gStyle.SetNdivisions(6 + 100 * 5 + 10000 * 2, "X")
plots.drawPlot(
p0,
saveName,
xlabel="m_{jjbb} (%s)" % (_units),
ylabel=yLabel,
log=opts.logY,
rebinX=_rebin, #2, 5
cmsExtraText="Preliminary",
createLegend=_leg,
opts={
"xmin": signalMass - 5 * g_sigma,
"xmax": signalMass + 5 * g_sigma,
"ymin": ymin,
"ymaxfactor": ymaxf
}, #xmin - 50, xmax + 50
opts2={
"ymin": 0.6,
"ymax": 1.4
},
cutBox={
"cutValue": signalMass,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
})
# Save plot in all formats
savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p0, "%s_M%s" % (saveName, signalMass), savePath)
return r
def PlotSignalBackground(datasetsMgr, hG, hF, intLumi):
kwargs = {}
_kwargs = {}
#kwargs = GetHistoKwargs(hG, opts)
if opts.normaliseToOne:
pG = plots.MCPlot(datasetsMgr,
hG,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
pF = plots.MCPlot(datasetsMgr,
hF,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
else:
pG = plots.MCPlot(datasetsMgr,
hG,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
pF = plots.MCPlot(datasetsMgr,
hF,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.2f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "mass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "M (%s)" % _units
if "trijetmass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{top} (%s)" % _units
#_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_opts["xmax"] = 805 #1005
if "bjetmass" in hG.lower():
_xlabel = "m_{b-tagged jet} (%s)" % _units
_opts["xmax"] = 50
if "bjetldgjet_mass" in hG.lower():
_xlabel = "m_{b, ldg jet} (%s)" % _units
if "bjetsubldgjet_mass" in hG.lower():
_xlabel = "m_{b-tagged, subldg jet} (%s)" % _units
if "jet_mass" in hG.lower():
_opts["xmax"] = 750
if "mult" in hG.lower():
_format = "%0.0f"
_xlabel = "Leading jet mult"
if "subldg" in hG.lower():
_xlabel = "Subleading jet mult"
if "cvsl" in hG.lower():
_format = "%0.2f"
_xlabel = "Leading jet CvsL"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CvsL"
if "axis2" in hG.lower():
_format = "%0.3f"
_xlabel = "Leading jet axis2"
if "subldg" in hG.lower():
_xlabel = "Subleading jet axis2"
if "dijetmass" in hG.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % _units
_opts["xmax"] = 600
if "trijetptdr" in hG.lower():
_opts["xmax"] = 800
_format = "%0.0f"
_xlabel = "p_{T}#Delta R_{t}"
if "dijetptdr" in hG.lower():
_opts["xmax"] = 800
_format = "%0.0f"
_xlabel = "p_{T}#Delta R_{W}"
if "dgjetptd" in hG.lower():
_format = "%0.2f"
_xlabel = "Leading jet p_{T}D"
if "subldg" in hG.lower():
_xlabel = "Subleading jet p_{T}D"
else:
pass
'''
if "bdisc" in hG.lower():
_format = "%0.2f"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CSV"
elif "ldg" in hG.lower():
_xlabel = "Leading jet CSV"
else:
_xlabel = "b-tagged jet CSV"
'''
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.2
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
if "bdisc" in hG.lower():
_format = "%0.2f"
if "subldg" in hG.lower():
_xlabel = "Subleading jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.06}
elif "ldg" in hG.lower():
_xlabel = "Leading jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.06}
else:
_xlabel = "b-tagged jet CSV"
_opts = {"ymin": 1e-3, "ymax": 0.35}
myList = []
# Customise styling
pG.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pF.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
'''
plots.drawPlot(pG,
hG,
xlabel = _xlabel,
ylabel = "Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.92},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
plots.drawPlot(pF,
hF,
xlabel = _xlabel,
ylabel = "Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.92},
opts = _opts,
opts2 = {"ymin": 0.6, "ymax": 1.4},
cutBox = _cutBox,
)
'''
dataset = datasetsMgr.getDataset("TT")
h = dataset.getDatasetRootHisto(hG)
h.normalizeToOne()
HG = h.getHistogram()
h = dataset.getDatasetRootHisto(hF)
h.normalizeToOne()
HF = h.getHistogram()
altSignalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kAzure + 9,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kAzure + 9,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kAzure + 9)
])
altBackgroundBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kRed - 4,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kRed - 4,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kRed - 4, fillStyle=3001)
])
p = plots.ComparisonPlot(
histograms.Histo(HF, "Fake", "p", "P"),
histograms.Histo(HG, "Genuine", "pl", "PL"),
)
p.histoMgr.setHistoLegendLabelMany({
"Fake": "Unmatched",
"Genuine": "Truth-matched"
})
p.histoMgr.forHisto("Fake", altBackgroundBDTGStyle)
p.histoMgr.setHistoDrawStyle("Fake", "LP")
p.histoMgr.setHistoLegendStyle("Fake", "LP") #F
p.histoMgr.forHisto("Genuine", altSignalBDTGStyle)
p.histoMgr.setHistoDrawStyle("Genuine", "HIST")
p.histoMgr.setHistoLegendStyle("Genuine", "LP") #LP
#HF = dataset.getDatasetRootHisto(hF).getHistogram()
#HG = dataset.getDatasetRootHisto(hG).getHistogram()
histoG = histograms.Histo(HG, "TT", "Signal")
histoG.setIsDataMC(isData=False, isMC=True)
histoF = histograms.Histo(HF, "QCD", "Signal")
histoF.setIsDataMC(isData=False, isMC=True)
styleG = styles.ttStyle
styleF = styles.signalStyleHToTB1000
#hG = histograms.Histo(histoBkg3, legNameBkg3, "F", "HIST9" )
#background3_histo = histograms.Histo(histoBkg3, legNameBkg3, "F", "HIST9" )
styleG.apply(HG)
styleF.apply(HF)
myList.append(histoG)
myList.append(histoF)
#myList.append(HF)
#myList.append(HG)
# p = plots.PlotBase(datasetRootHistos=myList, saveFormats=[])
_kwargs = {
"xlabel": _xlabel,
"ylabel": "Arbitrary Units / %s" % (_format),
"ratioYlabel": "Ratio ",
"ratio": False,
"ratioInvert": False,
"stackMCHistograms": False,
"addMCUncertainty": False,
"addLuminosityText": False,
"addCmsText": True,
"cmsExtraText": "Preliminary",
"opts": {
"ymin": 0.0,
"ymaxfactor": 1.1
},
"opts2": {
"ymin": 0.6,
"ymax": 1.5
},
"log": False,
#"createLegend" : {"x1": 0.5, "y1": 0.75, "x2": 0.9, "y2": 0.9},
"createLegend": {
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.82
},
}
'''
plots.drawPlot(p,
hG,
xlabel = _xlabel,
ylabel = "Arbitrary Units / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.58, "y1": 0.65, "x2": 0.92, "y2": 0.82},
opts = _opts,
opts2 = {"ymin": 0.6, "ymaxfactor": 1.1},
cutBox = _cutBox,
)
'''
# Save plot in all formats
saveName = hG.split("/")[-1]
#plots.drawPlot(p, saveName, **_kwargs)
savePath = os.path.join(opts.saveDir, "HplusMasses",
hG.split("/")[0], opts.optMode)
plots.drawPlot(p, savePath, **_kwargs)
SavePlot(p,
saveName,
os.path.join(opts.saveDir, opts.optMode),
saveFormats=[".png", ".pdf", ".C"])
#SavePlot(p, saveName, savePath)
#p = plots.DataMCPlot2( myList, saveFormats=[])
#p.setLuminosity(opts.intLumi)
#p.setDefaultStyles()
# Draw the plot and save it
#hName = hG
#plots.drawPlot(p, hName, **_kwargs)
#SavePlot(p, hName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png"])
return
def TopSelectionHistograms(opts, datasetsMgr, analysisType):
'''
Create data-MC comparison plot, with the default:
- legend labels (defined in plots._legendLabels)
- plot styles (defined in plots._plotStyles, and in styles)
- drawing styles ('HIST' for MC, 'EP' for data)
- legend styles ('L' for MC, 'P' for data)
'''
Verbose("Plotting all topSelection histograms for %s" % analysisType)
# Sanity check
IsBaselineOrInverted(analysisType)
# Definitions
histoNames = getTopSelectionHistos(opts.histoLevel, analysisType)
histoKwargs = {}
saveFormats = [".png", ".pdf"] #[".C", ".png", ".pdf"]
# General Settings
if opts.mergeEWK:
_moveLegend = {"dx": -0.1, "dy": 0.0, "dh": -0.15}
else:
_moveLegend = {"dx": -0.1, "dy": 0.0, "dh": 0.1}
logY = True
if logY:
_opts1 = {"ymin": 1.0, "ymaxfactor": 10}
else:
_opts1 = {"ymin": 0.0, "ymaxfactor": 1.2}
_opts2 = {"ymin": 0.0, "ymax": 2.0},
_kwargs = {
"rebinX": 10,
"rebinY": None,
"ratioYlabel": "Data/MC",
"ratio": False,
"stackMCHistograms": True,
"ratioInvert": False,
"addMCUncertainty": False,
"addLuminosityText": True,
"addCmsText": True,
"cmsExtraText": "Preliminary",
"opts": _opts1,
"opts2": _opts2,
"log": logY,
"errorBarsX": True,
"moveLegend": _moveLegend,
"cutBox": {
"cutValue": 0.0,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
},
"ylabel": "Events / %.0f",
}
# Create/Draw the plots
for histoName in histoNames:
histoKwargs[histoName] = _kwargs
# For-loop: All histograms in list
folder = "topSelection_"
for histoName in histoNames:
kwargs_ = histoKwargs[histoName]
saveName = histoName.replace(folder + analysisType + "/", "")
if opts.mcOnly:
p = plots.MCPlot(datasetsMgr,
histoName,
normalizeToLumi=opts.intLumi,
saveFormats=[])
kwargs_.pop("ratio", None)
kwargs_.pop("ratioYlabel", None)
kwargs_.pop("ratioInvert", None)
kwargs_.pop("opts2", None)
plots.drawPlot(p, saveName,
**kwargs_) #the "**" unpacks the kwargs_ dictionary
else:
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
plots.drawPlot(p, saveName,
**kwargs_) #the "**" unpacks the kwargs_ dictionary
# Save plot in all formats
SavePlot(p, histoName, os.path.join(opts.saveDir, "DataEwkSignal"))
#SavePlot(p, histoName, os.path.join(opts.saveDir, analysisType) )
return
def Plot_Comparisons(datasetsMgr, datasetsMgrRef, hT, hR, intLumi):
#kwargs = {}
_kwargs = {}
#kwargs = GetHistoKwargs(hG, opts)
print
if opts.normaliseToOne:
pT = plots.MCPlot(datasetsMgr,
hT,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
pR = plots.MCPlot(datasetsMgrRef,
hR,
normalizeToOne=True,
saveFormats=[],
**_kwargs)
else:
pT = plots.MCPlot(datasetsMgr,
hT,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
pR = plots.MCPlot(datasetsMgrRef,
hR,
normalizeToLumi=intLumi,
saveFormats=[],
**_kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.2f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
print "HT", hT
xMax = 200
_kwargs = {
"xlabel": _xlabel,
"ylabel": "Events / %s" % (_format),
"ratioYlabel": "Ratio ",
"ratio": False,
"ratioInvert": False,
"stackMCHistograms": False,
"addMCUncertainty": False,
"addLuminosityText": False,
"addCmsText": True,
"cmsExtraText": "Preliminary",
#"opts" : {"ymin": 0.1, "ymaxfactor": 1.2},
"opts": {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": xMax
},
"opts2": {
"ymin": 0.6,
"ymax": 1.5
},
"log": True,
#"createLegend" : {"x1": 0.5, "y1": 0.75, "x2": 0.9, "y2": 0.9},
"createLegend": {
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.82
},
"rebinX": 1,
}
if "counters" in hT.lower():
print "HERE"
ROOT.gStyle.SetLabelSize(16.0, "X")
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 6
}
_kwargs["createLegend"] = {
"x1": 0.65,
"y1": 0.6,
"x2": 0.90,
"y2": 0.77
},
if "pt" in hT.lower():
_units = "GeV/c"
_format = "%0.0f " + _units
_xlabel = "p_{T} (%s)" % _units
_kwargs["xmax"] = 800
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 800
}
if "mass" in hT.lower():
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "M (%s)" % _units
_kwargs["xmax"] = 800
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 800
}
if "tetrajet" in hT.lower():
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 2000
}
_kwargs["rebinX"] = 8
if "trijetmass" in hT.lower():
print "mass"
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{top} (%s)" % _units
#_cutBox = {"cutValue": 173.21, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
_kwargs["xmax"] = 400 #1005
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 400
}
if "eta" in hT.lower():
_units = ""
_format = "%0.1f " + _units
_xlabel = "#eta (%s)" % _units
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": -5.0,
"xmax": +5.0
}
if "mult" in hT.lower():
_format = "%0.0f"
if "cleaned" in hT.lower():
_kwargs["xmax"] = 10
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 10
}
else:
_kwargs["xmax"] = 60
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 60
}
if "bdisc" in hT.lower():
_format = "%0.2f"
_kwargs["xmax"] = 1
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 1
}
if "topbdt" in hT.lower():
_format = "%0.2f"
_kwargs["xmax"] = 1
_kwargs["xmin"] = 0.3
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0.3,
"xmax": 1.0
}
if "dijetmass" in hT.lower():
print "dijet-mass"
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{W} (%s)" % _units
_opts["xmax"] = 600
_kwargs["opts"] = {
"ymin": 0.1,
"ymaxfactor": 1.2,
"xmin": 0,
"xmax": 300
}
else:
pass
if logY:
yMaxFactor = 2.0
else:
yMaxFactor = 1.5
_opts["ymaxfactor"] = yMaxFactor
if opts.normaliseToOne:
_opts["ymin"] = 1e-3
else:
_opts["ymin"] = 1e0
myList = []
# Customise styling
pT.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
pR.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
datasetTarg = datasetsMgr.getDataset(
"QCD") #ChargedHiggs_HplusTB_HplusToTB_M_500
datasetRef = datasetsMgrRef.getDataset(
"QCD") #ChargedHiggs_HplusTB_HplusToTB_M_500
h = datasetTarg.getDatasetRootHisto(hT)
#h.normalizeToOne()
h.normalizeToLuminosity(intLumi)
HT = h.getHistogram()
h = datasetRef.getDatasetRootHisto(hR)
#h.normalizeToOne()
h.normalizeToLuminosity(intLumi)
HR = h.getHistogram()
altSignalBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kAzure + 9,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kAzure + 9,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kAzure + 9, fillStyle=3001)
])
altBackgroundBDTGStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kRed - 4,
markerSizes=None,
markerStyle=ROOT.kFullDiamond),
styles.StyleLine(lineColor=ROOT.kRed - 4,
lineStyle=ROOT.kSolid,
lineWidth=3),
#styles.StyleFill(fillColor=ROOT.kRed-4)
])
signalStyle = styles.StyleCompound([
styles.StyleMarker(markerSize=1.2,
markerColor=ROOT.kTeal + 2,
markerSizes=None,
markerStyle=ROOT.kFullTriangleUp),
styles.StyleLine(lineColor=ROOT.kTeal + 2,
lineStyle=ROOT.kSolid,
lineWidth=3),
styles.StyleFill(fillColor=ROOT.kTeal + 2, fillStyle=3001)
])
p = plots.ComparisonPlot(
histograms.Histo(HT, "Target", "pl", "PL"),
histograms.Histo(HR, "Reference", "pl", "PL"),
)
p.histoMgr.setHistoLegendLabelMany({
"Target": "bug fixed",
"Reference": "bug"
})
if ("TT" in datasetTarg.getName()):
p.histoMgr.forHisto("Target", altBackgroundBDTGStyle)
p.histoMgr.forHisto("Reference", altSignalBDTGStyle)
elif ("QCD" in datasetTarg.getName()):
p.histoMgr.forHisto(
"Target", altBackgroundBDTGStyle) #styles.getABCDStyle("VR"))
p.histoMgr.forHisto("Reference", styles.qcdFillStyle)
elif ("Charged" in datasetTarg.getName()):
p.histoMgr.forHisto("Target", altBackgroundBDTGStyle)
p.histoMgr.forHisto("Reference", signalStyle)
p.histoMgr.setHistoDrawStyle("Target", "HIST")
p.histoMgr.setHistoLegendStyle("Target", "LP") #F
p.histoMgr.setHistoDrawStyle("Reference", "HIST")
p.histoMgr.setHistoLegendStyle("Reference", "F") #LP
'''
histoG = histograms.Histo(HT, "TT", "Signal")
histoG.setIsDataMC(isData=False, isMC=True)
histoF = histograms.Histo(HR, "QCD", "Signal")
histoF.setIsDataMC(isData=False, isMC=True)
'''
#styleT = styles.ttStyle
#styleR = styles.signalStyleHToTB1000
#styleT.apply(HT)
#styleR.apply(HR)
'''
myList.append(histoT)
myList.append(histoR)
'''
# Save plot in all formats
#saveName = hT.split("/")[-1]
#plots.drawPlot(p, saveName, **_kwargs)
#savePath = os.path.join(opts.saveDir, "HplusMasses", hT.split("/")[0], opts.optMode)
#plots.drawPlot(p, savePath, **_kwargs)
#SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode), saveFormats = [".png", ".pdf", ".C"])
dName = datasetTarg.getName()
dName = dName.replace("ChargedHiggs_HplusTB_HplusToTB_", "")
saveName = hT.split("/")[-1] + "_" + dName
#print saveName, hT, opts.saveDir
savePath = os.path.join(opts.saveDir, hT.split("/")[0], opts.optMode)
print "TYPE", type(hT), type(savePath), type(p)
plots.drawPlot(p, savePath, **_kwargs)
#leg = ROOT.TLegend(0.2, 0.8, 0.81, 0.87)
leg = ROOT.TLegend(0.2, 0.8, 0.51, 0.87)
leg.SetFillStyle(0)
leg.SetFillColor(0)
leg.SetBorderSize(0)
#{"dx": -0.55, "dy": -0.55, "dh": -0.08}
datasetName = datasetTarg.getName()
datasetName = datasetName.replace("TT", "t#bar{t}")
if "ChargedHiggs" in datasetName:
datasetName = datasetName.replace("ChargedHiggs_HplusTB_HplusToTB_M_",
"m_{H+} = ")
datasetName = datasetName + "GeV"
#leg.SetHeader("t#bar{t}")
leg.SetHeader(datasetName)
leg.Draw()
#print savePath
#savePath = os.path.join(opts.saveDir, opts.optMode)
SavePlot(p, saveName, savePath)
return
def DataMCHistograms(datasetsMgr, analysisType=""):
'''
Create data-MC comparison plot, with the default:
- legend labels (defined in plots._legendLabels)
- plot styles (defined in plots._plotStyles, and in styles)
- drawing styles ('HIST' for MC, 'EP' for data)
- legend styles ('L' for MC, 'P' for data)
'''
Verbose("Plotting all histograms for %s" % analysisType)
# Sanity check
IsBaselineOrInverted(analysisType)
# Definitions
histoNames = []
histoKwargs = {}
saveFormats = [".png", ".pdf"] #[".C", ".png", ".pdf"]
# General Settings
if opts.mergeEWK:
_moveLegend = {"dx": -0.05, "dy": 0.0, "dh": -0.15}
else:
_moveLegend = {"dx": -0.05, "dy": 0.0, "dh": 0.1}
_kwargs = {
"rebinX": 1,
"rebinY": None,
"ratioYlabel": "Data/MC",
"ratio": False,
"stackMCHistograms": True,
"ratioInvert": False,
"addMCUncertainty": False,
"addLuminosityText": True,
"addCmsText": True,
"cmsExtraText": "Preliminary",
"opts": {
"ymin": 2e-1,
"ymaxfactor": 10
}, #1.2
"opts2": {
"ymin": 0.0,
"ymax": 2.0
},
"log": True,
"errorBarsX": True,
"moveLegend": _moveLegend,
"cutBox": {
"cutValue": 0.0,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
},
}
# Create/Draw the plots
histoName = "%s_TopMassReco_LdgTrijetPt_AfterAllSelections" % analysisType
kwargs = copy.deepcopy(_kwargs)
kwargs["ylabel"] = "Events / %.0f"
histoNames.append(histoName)
histoKwargs[histoName] = kwargs
histoName = "%s_TopMassReco_LdgTrijetM_AfterAllSelections" % analysisType
kwargs = copy.deepcopy(_kwargs)
kwargs["ylabel"] = "Events / %.0f GeV/c^{2}"
kwargs["log"] = False
kwargs["opts"] = {"xmax": 700, "ymin": 2e-1, "ymaxfactor": 1.2}
kwargs["cutBox"] = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
histoNames.append(histoName)
histoKwargs[histoName] = kwargs
histoName = "%s_TopMassReco_SubLdgTrijetPt_AfterAllSelections" % analysisType
kwargs = copy.deepcopy(_kwargs)
kwargs["ylabel"] = "Events / %.0f GeV/c"
histoNames.append(histoName)
histoKwargs[histoName] = kwargs
histoName = "%s_TopMassReco_SubLdgTrijetM_AfterAllSelections" % analysisType
kwargs = copy.deepcopy(_kwargs)
kwargs["ylabel"] = "Events / %.0f GeV/c^{2}"
kwargs["log"] = False
kwargs["opts"] = {"xmax": 700, "ymin": 2e-1, "ymaxfactor": 1.2}
kwargs["cutBox"] = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
histoNames.append(histoName)
histoKwargs[histoName] = kwargs
histoName = "%s_TopMassReco_LdgDijetPt_AfterAllSelections" % analysisType
kwargs = copy.deepcopy(_kwargs)
kwargs["ylabel"] = "Events / %.0f GeV/c"
histoNames.append(histoName)
histoKwargs[histoName] = kwargs
histoName = "%s_TopMassReco_LdgDijetM_AfterAllSelections" % analysisType
kwargs = copy.deepcopy(_kwargs)
kwargs["ylabel"] = "Events / %.0f GeV/c^{2}"
kwargs["cutBox"] = {
"cutValue": 80.399,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
histoNames.append(histoName)
histoKwargs[histoName] = kwargs
histoName = "%s_TopMassReco_SubLdgDijetPt_AfterAllSelections" % analysisType
kwargs = copy.deepcopy(_kwargs)
kwargs["ylabel"] = "Events / %.0f GeV/c"
histoNames.append(histoName)
histoKwargs[histoName] = kwargs
histoName = "%s_TopMassReco_SubLdgDijetM_AfterAllSelections" % analysisType
kwargs = copy.deepcopy(_kwargs)
kwargs["ylabel"] = "Events / %.0f GeV/c^{2}"
kwargs["cutBox"] = {
"cutValue": 80.399,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
histoNames.append(histoName)
histoKwargs[histoName] = kwargs
# For-loop: All histograms in list
for histoName in histoNames:
kwargs_ = histoKwargs[histoName]
#saveName = os.path.join(opts.saveDir, histoName.replace("/", "_"))
saveName = histoName.replace("/", "_")
if opts.mcOnly:
p = plots.MCPlot(datasetsMgr,
histoName,
normalizeToLumi=opts.intLumi,
saveFormats=[])
kwargs_.pop("ratio", None)
kwargs_.pop("ratioYlabel", None)
kwargs_.pop("ratioInvert", None)
kwargs_.pop("opts2", None)
plots.drawPlot(p, saveName,
**kwargs_) #the "**" unpacks the kwargs_ dictionary
else:
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
plots.drawPlot(p, saveName,
**kwargs_) #the "**" unpacks the kwargs_ dictionary
# Save plot in all formats
SavePlot(p, saveName, os.path.join(opts.saveDir, "Signal",
opts.optMode))
return
def PlotMC(datasetsMgr, histo, intLumi):
kwargs = {}
p = plots.MCPlot(datasetsMgr, histo, normalizeToOne=True, saveFormats=[], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.01f"
_xlabel = None
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.2}
if "Pt" in histo:
if "Gluon" in histo:
_xlabel = "Gluon Jets p_{T} (GeV)"
elif "Light" in histo:
_xlabel = "Light Jets p_{T} (GeV)"
if "JetsN" in histo:
if "Gluon" in histo:
_xlabel = "Gluon Jets Multiplicity"
elif "Light" in histo:
_xlabel = "Light Jets Multiplicity"
if "QGL" in histo:
_opts["xmin"] = 0.0
_opts["xmax"] = 1.0
_units = ""
_format = "%0.01f " + _units
if "Gluon" in histo:
_xlabel = "Gluon Jets QGL"
elif "Light" in histo:
_xlabel = "Light Jets QGL"
# Customise styling
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
# 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 = "Arbitrary Units",# / %s" % (_format),
log = logY,
rebinX = _rebinX, cmsExtraText = "Preliminary",
createLegend = {"x1": 0.70, "y1": 0.65, "x2": 0.98, "y2": 0.92},
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, "", "", opts.optMode)
SavePlot(p, saveName, savePath)
return
def PlotHistograms(datasetsMgr, histoName):
# Get Histogram name and its kwargs
saveName = histoName.rsplit("/")[-1] # histoName.replace("/", "_")
kwargs_ = GetHistoKwargs(saveName, opts)
# Create the plotting object
if "Data" in datasetsMgr.getAllDatasetNames():
p = plots.DataMCPlot(datasetsMgr, histoName, saveFormats=[])
else:
if opts.normalizeToLumi:
p = plots.MCPlot(datasetsMgr,
histoName,
normalizeToLumi=opts.intLumi,
saveFormats=[])
elif opts.normalizeByCrossSection:
p = plots.MCPlot(datasetsMgr,
histoName,
normalizeByCrossSection=True,
saveFormats=[],
**{})
elif opts.normalizeToOne:
p = plots.MCPlot(datasetsMgr,
histoName,
normalizeToOne=True,
saveFormats=[],
**{})
else:
raise Exception(
"One of the options --normalizeToOne, --normalizeByCrossSection, --normalizeToLumi must be enabled (set to \"True\")."
)
# Overwite signal style?
style = [200, 500, 800, 1000, 2000, 3000, 5000]
lstyle = [
ROOT.kSolid, ROOT.kDashed, ROOT.kDashDotted, ROOT.kDotted,
ROOT.kDotted, ROOT.kSolid
]
for i, d in enumerate(datasetsMgr.getAllDatasets(), 0):
p.histoMgr.forHisto(d.getName(),
styles.getSignalStyleHToTB_M(style[i]))
if 1:
p.histoMgr.forEachHisto(
lambda h: h.getRootHisto().SetLineStyle(ROOT.kSolid))
p.histoMgr.setHistoLegendLabelMany({
#"ChargedHiggs_HplusTB_HplusToTB_M_500_MVA0p30": "H^{+} m_{H^{+}} = 500 GeV (BDT #geq 0.3)",
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p30" % (opts.signalMass):
"m_{H^{+}}=%s GeV (BDT #geq 0.3)" % (opts.signalMass),
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p40" % (opts.signalMass):
"m_{H^{+}}=%s GeV (BDT #geq 0.4)" % (opts.signalMass),
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p50" % (opts.signalMass):
"m_{H^{+}}=%s GeV (BDT #geq 0.5)" % (opts.signalMass),
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p60" % (opts.signalMass):
"m_{H^{+}}=%s GeV (BDT #geq 0.6)" % (opts.signalMass),
"ChargedHiggs_HplusTB_HplusToTB_M_%s_MVA0p70" % (opts.signalMass):
"m_{H^{+}}=%s GeV (BDT #geq 0.7)" % (opts.signalMass),
})
# Apply blinding of signal region
if "blindingRangeString" in kwargs_:
startBlind = float(kwargs_["blindingRangeString"].split("-")[1])
endBlind = float(kwargs_["blindingRangeString"].split("-")[0])
plots.partiallyBlind(p,
maxShownValue=startBlind,
minShownValue=endBlind,
invert=True,
moveBlindedText=kwargs_["moveBlindedText"])
# Draw and save the plot
saveName += "_M%s" % (opts.signalMass)
plots.drawPlot(p, saveName,
**kwargs_) #the "**" unpacks the kwargs_ dictionary
# Save the plots in custom list of saveFormats
SavePlot(p, saveName, os.path.join(opts.saveDir, opts.optMode,
opts.folder), [".png", ".pdf"])
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
logY = False
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "ChiSqr" in histo:
_rebinX = 1
logY = True
_units = ""
_format = "%0.1f " + _units
_xlabel = "#chi^{2}"
_cutBox = {
"cutValue": 10.0,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 100
elif "trijetmass" in histo.lower():
_rebinX = 4
logY = False
_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"] = 805 #1005
elif "ht" in histo.lower():
_rebinX = 2
logY = False
_units = "GeV"
_format = "%0.0f " + _units
_xlabel = "H_{T} (%s)" % _units
_cutBox = {
"cutValue": 500,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
#_opts["xmin"] = 500
_opts["xmax"] = 2000
elif "tetrajetmass" in histo.lower():
_rebinX = 5 #5 #10 #4
logY = False
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_cutBox = {
"cutValue": 500.0,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 1500 #3500.0
#_rebinX = 10
#_opts["xmax"] = 3500
elif "tetrajetbjetpt" 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"] = 600
elif "foxwolframmoment" in histo.lower():
_format = "%0.1f"
_cutBox = {
"cutValue": 0.5,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
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))
if "QCD" in datasetsMgr.getAllDatasets():
p.histoMgr.forHisto("QCD", styles.getQCDFillStyle())
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasets():
p.histoMgr.setHistoDrawStyle("TT", "AP")
p.histoMgr.setHistoLegendStyle("TT", "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="Arbitrary Units / %s" % (_format),
log=logY,
rebinX=_rebinX,
cmsExtraText="Preliminary",
createLegend={
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.92
},
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, "HplusMasses",
histo.split("/")[0], opts.optMode)
SavePlot(p, saveName, savePath)
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)
# p = plots.MCPlot(datasetsMgr, histo, normalizeToLumi=intLumi, saveFormats=[], **kwargs)
# Draw the histograms
_cutBox = None
_rebinX = 1
_format = "%0.1f"
_xlabel = None
_opts = {"ymin": 1e-3, "ymaxfactor": 1.0}
if "ChiSqr" in histo:
_rebinX = 1
#logY = True
_units = ""
_format = "%0.1f " + _units
_xlabel = "#chi^{2}"
_cutBox = {
"cutValue": 10.0,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 100
elif "trijetjets_deltarmin" in histo.lower():
_xlabel = "#Delta R_{min}"
_rebinX = 2
_opts["xmax"] = 3
_units = ""
_format = "%0.1f "
_cutBox = {
"cutValue": 0.8,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
elif "trijetjets_deltarmax" in histo.lower():
_xlabel = "#Delta R_{max}"
_rebinX = 2
_opts["xmax"] = 3.5
_units = ""
_format = "%0.1f "
_cutBox = {
"cutValue": 0.8,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
elif "trijetdijetdr" in histo.lower():
_rebinX = 1
units = ""
_format = "%0.1f "
_opts["xmax"] = 3.5
_cutBox = {
"cutValue": 0.8,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
elif "trijetdijetpt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "trijetdijetmass" in histo.lower():
_rebinX = 1
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_opts["xmax"] = 200
_xlabel = "m_{w} (%s)" % _units
_cutBox = {
"cutValue": 80.39,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
if "cevts_ldgtrijetmatchedtofatjet_fatjetpt" in histo.lower():
_rebin = 1
_opts["xmax"] = 3
elif "pt" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_opts["xmax"] = 800
elif "trijetmass" in histo.lower():
_rebinX = 1
# logY = False
_units = "GeV/c^{2}"
# _format = "%0.0f " + _units
_format = "%0.0f" + (_units)
_xlabel = "m_{jjb} (%s)" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 305 #1005
_opts["xmin"] = 55 #1005
elif "topcandmass" in histo.lower():
_rebinX = 1
# logY = False
_units = "GeV/c^{2}"
# _format = "%0.0f " + _units
_format = "%0.0f" + (_units)
_xlabel = "m_{jjb}^{BDTG} (%s)" % _units
_cutBox = {
"cutValue": 173.21,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
_opts["xmax"] = 505 #1005
_opts["xmin"] = 0 #1005
# elif "ht" in histo.lower():
# _rebinX = 2
## logY = False
# _units = "GeV"
# _format = "%0.0f " + _units
# _xlabel = "H_{T} (%s)" % _units
# _cutBox = {"cutValue": 500, "fillColor": 16, "box": False, "line": True, "greaterThan": True}
# _opts["xmax"] = 2000
elif "tetrajetptd" in histo.lower():
_rebinX = 2 #5 #10 #4
_units = "GeV/c^{2}"
# _xlabel = "m_{jjbb} (%s)" % (_units)
_format = "%0.0f " + _units
_opts["xmax"] = 2000 #3500.0
_cutBox = {
"cutValue": 400,
"fillColor": 16,
"box": False,
"line": True,
"greaterThan": True
}
elif "tetrajetmass" in histo.lower() or "tetrajetmass_" in histo.lower():
#ROOT.gStyle.SetNdivisions(10, "X")
# h = dataset.getDatasetRootHisto(histo).getHistogram()
# h.SetTickLength(100, "X")
_rebinX = 5 #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
_cutBox = {
"cutValue": 500,
"fillColor": 16,
"box": False,
"line": False,
"greaterThan": True
}
elif "dijetmass" in histo.lower():
_rebinX = 1 #5 #10 #4
# logY = False
_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"] = 205
elif "trijetmultiplicitypassbdt" in histo.lower():
_rebinX = 1
_opts["xmax"] = 5
_format = "%0.0f "
elif "bdtg" or "bdtvalue" in histo.lower():
_rebinX = 1
_format = "%0.2f "
elif "trijetbdt_mass" in histo.lower():
_rebinX = 2
_units = "GeV/c^{2}"
_format = "%0.0f " + _units
_xlabel = "m_{jjb} (%s)" % _units
_opts["xmax"] = 800
# if "eta" or "phi" or "delta" in histo.lower():
#_rebinX = 1 #5 #10 #4
# _units = ""
# _format = "%0.1f "
# if "eta" in histo.lower():
# _xlabel = "#eta"
# if "phi" in histo.lower():
# _xlabel = "#phi"
# if "pt" in histo.lower():
# _opts["xmax"] = 800
elif "boosted" in histo.lower():
_xlabel = "Trijet Counter"
elif "matched_dijetpt" in histo.lower():
_units = "(GeV/c)"
_xlabel = "p_{T}" + _units
elif "deltar_bdttrijets_tetrajetbjet" in histo.lower():
_xlabel = "#Delta R(Trijet,b_{free})"
elif "tetrajetbjetbdisc" in histo.lower():
_rebinX = 2
_opts["xmax"] = 1.05
# if "matched_dijetmass" in histo.lower():
# _rebinX = 2
# if "higgstop_dijetmass" in histo.lower():
# _rebinX = 2
elif "trijet_deltaeta_trijet_tetrajetbjet" in histo.lower():
_rebinX = 2
_xlabel = "#Delta #eta (Trijet, b_{free})"
elif "trijet_deltaphi_trijet_tetrajetbjet" in histo.lower():
_xlabel = "#Delta #phi (Trijet, b_{free})"
elif "cevts_closejettotetrajetbjet_isbtagged" in histo.lower():
_units = ""
_format = "%0.0f " + _units
# if "higgstop_" in histo.lower():
# _rebinX = 2
if "eventtrijetpt2t" in histo.lower():
_rebinX = 2
if "ldgfatjetpt" in histo.lower():
_opts["xmax"] = 1000
if "deltar_w" in histo.lower():
_rebinX = 2
_xlabel = "#Delta R"
_format = "%0.1f "
_opts["xmax"] = 5
logY = True
if "ldgtrijet_deltar" in histo.lower():
_rebinX = 2
_xlabel = "#Delta R"
_format = "%0.1f "
_opts["xmax"] = 5
logY = True
if "higgstop_deltar" in histo.lower():
_rebinX = 2
_xlabel = "#Delta R"
_format = "%0.1f "
_opts["xmax"] = 5
logY = True
if "allfatjet" in histo.lower():
_rebinX = 2
_units = "GeV/c"
_format = "%0.0f " + _units
_opts["xmax"] = 800
else:
pass
if opts.normaliseToOne:
logY = True
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.getAllDatasets():
p.histoMgr.forHisto("QCD", styles.getQCDFillStyle())
p.histoMgr.setHistoDrawStyle("QCD", "HIST")
p.histoMgr.setHistoLegendStyle("QCD", "F")
if "TT" in datasetsMgr.getAllDatasets():
p.histoMgr.setHistoDrawStyle("TT", "HIST")
p.histoMgr.setHistoLegendStyle("TT", "LP")
# if "M_200" in datasetsMgr.getAllDatasets() or "M_300" in datasetsMgr.getAllDatasets():
# p.histoMgr.forHisto("QCD", styles.getQCDFillStyle() )
# p.histoMgr.setHistoDrawStyle("QCD", "P")
# p.histoMgr.setHistoLegendStyle("QCD", "F")
# elif d.getName() == "TT" or d.getName() == "QCD" or d.getName() == "Data":
# otherHisto = histograms.Histo(histo, legName, "LP", "P")
# otherHistos.append(otherHisto)
# 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))
#soti
# p.histoMgr.setHistoDrawStyle("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, "LP")
# p.histoMgr.setHistoLegendStyle("ChargedHiggs_HplusTB_HplusToTB_M_%s" %m, "P")
plots.drawPlot(
p,
histo,
xlabel=_xlabel,
ylabel=
Ylabel, #"Arbitrary Units / %s" % (_format), #"Events / %s" % (_format), #"Arbitrary Units / %s" % (_format),
# ylabel = "Arbitrary Units / %s" % (_format), #"Events / %s" % (_format), #"Arbitrary Units / %s" % (_format),
log=logY,
rebinX=_rebinX,
cmsExtraText="Preliminary", #_rebinX
#createLegend = {"x1": 0.48, "y1": 0.45, "x2": 0.92, "y2": 0.92}, #All datasets
# createLegend = {"x1": 0.58, "y1": 0.7, "x2": 0.92, "y2": 0.92},
createLegend={
"x1": 0.58,
"y1": 0.65,
"x2": 0.92,
"y2": 0.87
},
#createLegend = {"x1": 0.73, "y1": 0.85, "x2": 0.97, "y2": 0.77}, #One dataset
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, "HplusMasses",
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/TT/"
if opts.noQCD:
save_path = savePath + opts.MVAcut + "/noQCD/"
# SavePlot(p, saveName, savePath)
SavePlot(p, saveName, save_path)
return
