def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
ROOT.gErrorIgnoreLevel = ROOT.kFatal # [Options: Print, kInfo, kWarning, kError, kBreak, kSysError, kFatal]
ROOT.gROOT.SetBatch()
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetOptTitle(0)
ROOT.gStyle.SetNdivisions(5, "X")
# Find GoF directories (generated by GoF.sh script)
myDirs = []
for d in os.listdir('.'):
if not os.path.isdir(d):
continue
if "GoF_" in d:
myDirs.append(d)
if len(myDirs) < 1:
raise Exception(
"No goodness-of-fit directories found. Did your run the GoF.sh script to create them?"
)
else:
Verbose(
"Found %d GoF directories: %s" % (len(myDirs), ", ".join(myDirs)),
True)
# For-loop: All GoF directories
myAlgos = []
allowedAlgos = ["saturated", "KS",
"AD"] # KS = Kolmogorov-Smirnov, AD = Anderson-Darling
for d in myDirs:
algo = d.split("_")[-1]
if algo not in allowedAlgos:
raise Exception(
"The algorithm \"%s\" is invalid. Expected one of the following: %s"
% (opts.algorithm, ", ".join(allowedAlgos)))
else:
myAlgos.append(algo)
# For-loop: All GoF algorithms ran
Print(
"Found %d GoF algorithm results: %s" %
(len(myDirs), ", ".join(myAlgos)), True)
for i, algo in enumerate(myAlgos, 1):
# Definitions
opts.algorithm = algo
opts.inputDir = "%s/GoF_%s" % (os.getcwd(), algo)
opts.inputfile = "GoF_%s/higgsCombinetoys*.GoodnessOfFit.mH%s.*.root" % (
algo, opts.mass)
opts.outputfile = "GoF_%s/GoF_%s_mH%s.root" % (algo, algo, opts.mass)
doPlots(i, opts)
Verbose(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main():
# Apply TDR style
style = tdrstyle.TDRStyle()
# Enable/Disable grids for axes
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Enable/Disable logscale for axes
style.setLogX(opts.logX)
style.setLogY(opts.logY)
# Definitions
savePath = opts.saveDir
if opts.url:
savePath = opts.saveDir.replace(
"/afs/cern.ch/user/a/attikis/public/html",
"https://cmsdoc.cern.ch/~%s" % getpass.getuser())
Verbose(
"Load module for reading the BR limits from JSON file produced by Combine",
True)
# Definitions
resultsList = []
for i, d in enumerate(opts.dirList, 1):
dirs = glob.glob(d)
dirs.sort()
if len(dirs) == 0:
raise Exception("No directories for pattern '%s'" % path)
directory = dirs[-1]
Verbose("Picked %s" % directory, True)
resultsList.append(_results.Output(directory, excludePoints=[]))
# Do comparison plot
msg = "Creating comparison plots (%d) using the following results directories:%s\n\t%s" % (
len(opts.dirList), sh_t, "\n\t".join(
[os.path.basename(d) for d in opts.dirList]))
Print(msg + sh_n, True)
if "output" in opts.plotType.lower():
doOutput(opts.saveName, resultsList)
elif "efficiency" in opts.plotType.lower():
doEfficiency(opts.saveName, resultsList)
elif "significance" in opts.plotType.lower():
doSignificance(opts.saveName, resultsList)
elif "roc" in opts.plotType.lower():
doROC(opts.saveName, resultsList)
else:
pass
# inform user of output location
Print(
"Plots saved under directory %s" %
(sh_s + aux.convertToURL(opts.saveDir, opts.url) + sh_n), True)
return
def doPlot(legList, graphList, saveName, **kwargs):
# Definitions
hgList = []
lList = {}
# For-loop: All TGraphs
for i, g in enumerate(graphList, 0):
if opts.boldText:
gName = legList[i]
else:
gName = "#font[42]{%s}" % legList[i]
hg = histograms.HistoGraph(graphList[i],
gName,
drawStyle="L",
legendStyle="l")
hgList.append(hg)
# Create a plot-base object
Verbose("Creating the plot-base object", True)
# plot = plots.PlotBase(hgList, saveFormats=[])
hgList.insert(0, hgList.pop(opts.refIndex))
plot = plots.ComparisonManyPlot(hgList[0], hgList[1:], saveFormats=[])
#plot = plots.ComparisonManyPlot(hgList[-1], hgList[:-1], saveFormats=[])
# Apply histo style
Verbose("Applying the histogram styles (generator)", True)
plot.histoMgr.forEachHisto(styles.generator())
# plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetMarkerSize(1.2))
# plot.histoMgr.forEachHisto(lambda h: h.getRootHisto().SetLineWidth(3))
def sty(h):
r = h.getRootHisto()
r.SetLineWidth(3)
r.SetMarkerSize(1.2)
return
# Apply style and set label
Verbose("Applying the histogram styles (forEachHisto)", True)
plot.histoMgr.forEachHisto(sty)
if opts.plotType != "var":
plot.setLegendHeader("Sequential Model (Keras)")
# Draw the plot
Verbose("Drawing the plot", True)
plots.drawPlot(plot, saveName, **kwargs)
if opts.removeLegend:
plot.removeLegend()
# Save plots and return
Verbose("Saving the plot as %s" % (saveName), True)
SavePlot(plot, opts.saveDir, saveName, opts.saveFormats)
Verbose(
"Plots saved under directory %s" %
(sh_s + aux.convertToURL(opts.saveDir, opts.url) + sh_n), True)
return
def SavePlot(plot, plotName, saveDir, saveFormats=[".C", ".png", ".pdf"]):
if not os.path.exists(saveDir):
os.makedirs(saveDir)
# Create the name under which plot will be saved
saveName = os.path.join(saveDir, plotName.replace("/", "_"))
# For-loop: All save formats
for i, ext in enumerate(saveFormats):
saveNameURL = saveName + ext
saveNameURL = aux.convertToURL(saveNameURL, opts.url)
Print(saveNameURL, i == 0)
plot.saveAs(saveName, formats=saveFormats)
return
def SavePlot(plot, plotName, saveDir, saveFormats = [".C", ".png", ".pdf"]):
if not os.path.exists(saveDir):
os.makedirs(saveDir)
# Create the name under which plot will be saved
saveName = os.path.join(saveDir, plotName.replace("/", "_"))
# For-loop: All save formats
for i, ext in enumerate(saveFormats):
saveNameURL = saveName + ext
saveNameURL = aux.convertToURL(saveNameURL, opts.url)
Print(saveNameURL, i==0)
plot.saveAs(saveName, formats=saveFormats)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(False)
style.setGridY(False)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts, 0)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
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 histograms (see NtupleAnalysis/python/tools/plots.py)
if 1:
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print datasets info summary
datasetsMgr.PrintInfo()
# Define the mapping histograms in numerator->denominator pairs
VariableList = ["TetrajetMass"]
counter = 0
opts.nDatasets = len(datasetsMgr.getAllDatasets())
nPlots = len(VariableList)*opts.nDatasets
# For-loop: All datasets
for dataset in datasetsMgr.getAllDatasets():
# For-looop: All variables
for hName in VariableList:
hPath = os.path.join(opts.folder, hName)
counter+=1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (nPlots), "%s" % (dataset.getName()))
aux.Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
PlotHistos(dataset.getName(), hPath) # For each dataset: Plot histos from different multicrabs on same canvas
aux.Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
ROOT.gErrorIgnoreLevel = ROOT.kFatal # [Options: Print, kInfo, kWarning, kError, kBreak, kSysError, kFatal]
ROOT.gROOT.SetBatch()
ROOT.gStyle.SetOptStat(0)
ROOT.gStyle.SetOptTitle(0)
ROOT.gStyle.SetNdivisions(5, "X")
# Find GoF directories (generated by GoF.sh script)
myDirs = []
for d in os.listdir('.'):
if not os.path.isdir(d):
continue
if "GoF_" in d:
myDirs.append(d)
if len(myDirs) < 1:
raise Exception("No goodness-of-fit directories found. Did your run the GoF.sh script to create them?" )
else:
Verbose("Found %d GoF directories: %s" % (len(myDirs), ", ".join(myDirs)), True)
# For-loop: All GoF directories
myAlgos = []
allowedAlgos = ["saturated", "KS", "AD"] # KS = Kolmogorov-Smirnov, AD = Anderson-Darling
for d in myDirs:
algo = d.split("_")[-1]
if algo not in allowedAlgos:
raise Exception("The algorithm \"%s\" is invalid. Expected one of the following: %s" % (opts.algorithm, ", ".join(allowedAlgos)))
else:
myAlgos.append(algo)
# For-loop: All GoF algorithms ran
Print("Found %d GoF algorithm results: %s" % (len(myDirs), ", ".join(myAlgos)), True)
for i, algo in enumerate(myAlgos, 1):
# Definitions
opts.algorithm = algo
opts.inputDir = "%s/GoF_%s" % (os.getcwd(), algo)
opts.inputfile = "GoF_%s/higgsCombinetoys*.GoodnessOfFit.mH%s.*.root" % (algo, opts.mass)
opts.outputfile = "GoF_%s/GoF_%s_mH%s.root" % (algo, algo, opts.mass)
doPlots(i, opts)
Verbose("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def SavePlot(plot, plotName, saveDir, saveFormats = [".pdf", ".png", ".C"]):
Verbose("Saving the plot in %s formats: %s" % (len(saveFormats), ", ".join(saveFormats) ) )
# Check that path exists
if not os.path.exists(saveDir):
os.makedirs(saveDir)
# Create the name under which plot will be saved
saveName = os.path.join(saveDir, plotName.replace("/", "_"))
# For-loop: All save formats
for i, ext in enumerate(saveFormats):
saveNameURL = saveName + ext
saveNameURL = aux.convertToURL(saveNameURL, opts.url)
Verbose(saveNameURL, i==0)
plot.saveAs(saveName, formats=saveFormats)
return
def SavePlot(plot, plotName, saveDir, saveFormats = [".C", ".png", ".pdf"]):
Verbose("Saving the plot in %s formats: %s" % (len(saveFormats), ", ".join(saveFormats) ) )
# Check that path exists
if not os.path.exists(saveDir):
os.makedirs(saveDir)
# Create the name under which plot will be saved
saveName = os.path.join(saveDir, plotName.replace("/", "_"))
# For-loop: All save formats
for i, ext in enumerate(saveFormats):
saveNameURL = saveName + ext
saveNameURL = aux.convertToURL(saveNameURL, opts.url)
Print(saveNameURL, i==0)
plot.saveAs(saveName, formats=saveFormats)
# Something buggy introduced with "except" added in plots.py to fix the error when
# using "divideByBinWidth" option with "addMCUncertainty" option. Must call exit otherwise hangs
if opts.fitUncert:
Print("EXIT!", True)
os._exit(0) #sys.exit()
return
def SavePlot(plot, plotName, saveDir, saveFormats = [".C", ".png", ".pdf"]):
Verbose("Saving the plot in %s formats: %s" % (len(saveFormats), ", ".join(saveFormats) ) )
# Check that path exists
if not os.path.exists(saveDir):
os.makedirs(saveDir)
# Create the name under which plot will be saved
saveName = os.path.join(saveDir, plotName.replace("/", "_"))
# For-loop: All save formats
for i, ext in enumerate(saveFormats):
saveNameURL = saveName + ext
saveNameURL = aux.convertToURL(saveNameURL, opts.url)
Print(saveNameURL, i==0)
plot.saveAs(saveName, formats=saveFormats)
# Something buggy introduced with "except" added in plots.py to fix the error when
# using "divideByBinWidth" option with "addMCUncertainty" option. Must call exit otherwise hangs
if opts.fitUncert:
Print("EXIT!", True)
os._exit(0) #sys.exit()
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities(fname="lumi.json")
# Get Luminosity
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = datasetsMgr.loadLumi()
# Set/Overwrite cross-sections
datasetsToRemove = []
for d in datasetsMgr.getAllDatasets():
mass = "M_%s" % (opts.signalMass)
if mass in d.getName():
if ("%s" % opts.signalMass) != d.getName().split("M_")[-1]:
datasetsMgr.remove(d.getName())
else:
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
else:
#datasetsToRemove.append(d.getName())
datasetsMgr.remove(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# # Custom Filtering of datasets
# for i, d in enumerate(datasetsToRemove, 0):
# msg = "Removing dataset %s" % d
# Verbose(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
# datasetsMgr.remove(filter(lambda name: d == name, datasetsMgr.getAllDatasetNames()))
if opts.verbose:
datasetsMgr.PrintInfo()
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
keepList = ["LdgTetrajetMass_AfterAllSelections"]
#keepList = ["LdgTetrajetMass_AfterStandardSelections"]
myHistos = []
for h in histoPaths:
if h.split("/")[-1] not in keepList:
continue
else:
myHistos.append(h)
for i, h in enumerate(myHistos, 1):
PlotHistograms(datasetsMgr, h)
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts):
# Suppress warnings about weight being re-applied
ROOT.gErrorIgnoreLevel = ROOT.kError
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
opts.optMode = ""
mcrabName = opts.mcrab
RunEra = mcrabName.split("_")[1]
# Setup ROOT and style
ROOT.gROOT.SetBatch(opts.batchMode)
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(True)
style.setGridY(True)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
# Remove some QCD samples (the cross sections of these samples are not calculated)
if 0:
msg = "Removing following samples:"
Print(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(), True)
for d in getDatasetsToExclude():
Print(d, False)
datasetsMgr.remove(d)
# Get run-range
minRunRange, maxRunRange, runRange = GetRunRange(datasetsMgr)
# Get int lumi
intLumi = GetLumi(datasetsMgr)
# Update to PU & load luminosities
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities()
#datasetsMgr.normalizeMCByLuminosity()
# Print luminisoties and cross-sections
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintCrossSections()
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get datasets
datasetsMgr.mergeMC()
dataset_Data = datasetsMgr.getDataDatasets()
dataset_MC = datasetsMgr.getMCDatasets()
# Define lists of Triggers to be plotted and Variables
xVars = ["pt6thJet", "eta6thJet", "phi6thJet", "Ht", "nBTagJets", "pu", "JetMulti", "BJetMulti"]
trgList = ["1BTag", "2BTag", "OR", "OR_PFJet450"]
if opts.fast:
trgList = ["OR_PFJet450"]
xVars = ["pt6thJet", "Ht"]
nPlots = len(trgList)*len(xVars)
counter = 0
# For-loop: All signal triggers
for i, trg in enumerate(trgList, 1):
# For-loop: All x-variables
for j, xVar in enumerate(xVars, 1):
counter+=1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (nPlots), "%s Vs %s" % (trg, xVar))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
# Define names
hNumerator = "hNum_%s_RefTrg_OfflineSel_Signal%s" % (xVar, trg)
hDenominator = "hDen_%s_RefTrg_OfflineSel" % (xVar)
plotName = "Eff_%s_%s" % (xVar, trg)
# Get Efficiency Plots
_kwargs = GetHistoKwargs(xVar, opts)
eff_Data = GetEfficiency(datasetsMgr, dataset_Data, hNumerator, hDenominator , **_kwargs)
eff_MC = GetEfficiency(datasetsMgr, dataset_MC, hNumerator, hDenominator, **_kwargs)
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.mcStyle.apply(eff_MC)
# Create the plot
p = plots.ComparisonPlot(histograms.HistoGraph(eff_Data, "eff_Data", "p", "P"),
histograms.HistoGraph(eff_MC, "eff_MC" , "p", "P"),
saveFormats=[])
# Define the legend entries
p.histoMgr.setHistoLegendLabelMany(
{
"eff_Data": "Data",
"eff_MC" : "Simulation"
}
)
# Draw and save the plot
p.setLuminosity(intLumi)
plots.drawPlot(p, plotName, **_kwargs)
# Draw
histograms.addText(0.65, 0.06, "Runs "+ runRange, 17)
histograms.addText(0.65, 0.10, "2016", 17)
# Save the canvas to a file
SavePlot(p, plotName, os.path.join(opts.saveDir, opts.optMode), saveFormats=[".pdf", ".png", ".C"] )
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(False)
style.setGridY(False)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab1)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Get the datasets from the directory
datasetsMgr1 = GetDatasetsFromDir(opts.mcrab1, opts)
datasetsMgr2 = GetDatasetsFromDir(opts.mcrab2, opts)
datasetsMgr3 = GetDatasetsFromDir(opts.mcrab3, opts)
# Setup the dataset managers
datasetsMgr1.updateNAllEventsToPUWeighted()
datasetsMgr1.loadLuminosities() # from lumi.json
datasetsMgr2.updateNAllEventsToPUWeighted()
datasetsMgr2.loadLuminosities() # from lumi.json
datasetsMgr3.updateNAllEventsToPUWeighted()
datasetsMgr3.loadLuminosities() # from lumi.json
# Print dataset info?
if opts.verbose:
datasetsMgr1.PrintCrossSections()
datasetsMgr1.PrintLuminosities()
datasetsMgr2.PrintCrossSections()
datasetsMgr2.PrintLuminosities()
datasetsMgr2.PrintCrossSections()
datasetsMgr2.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr1)
plots.mergeRenameReorderForDataMC(datasetsMgr2)
plots.mergeRenameReorderForDataMC(datasetsMgr3)
# Get Luminosity
lumi1 = datasetsMgr1.getDataset("Data").getLuminosity()
lumi2 = datasetsMgr2.getDataset("Data").getLuminosity()
lumi3 = datasetsMgr3.getDataset("Data").getLuminosity()
if lumi1 != lumi2 != lumi3:
raise Exception("Lumi1 (=%.2f) != Lumi2 (=%.2f) != Lumi3 (=%.2f" % (lumi1, lumi2, lumi3))
else:
opts.intLumi = datasetsMgr1.getDataset("Data").getLuminosity()
# Merge EWK samples
datasetsMgr1.merge("EWK", aux.GetListOfEwkDatasets())
datasetsMgr2.merge("EWK", aux.GetListOfEwkDatasets())
datasetsMgr3.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr1.PrintInfo()
if 0:
datasetsMgr2.PrintInfo()
datasetsMgr3.PrintInfo()
# Get all the histograms and their paths (e.g. ForFakeBMeasurement/Baseline_DeltaRLdgTrijetBJetTetrajetBJet_AfterCRSelections)
hList = datasetsMgr1.getDataset(datasetsMgr1.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
hPaths = [os.path.join(opts.folder, h) for h in hList]
# Create a smaller list with only histos of interest
hListS = []
for h in hList:
if "StandardSelections" in h:
continue
if "IsGenuineB" in h:
continue
if "_Bjet" in h:
continue
if "_Jet" in h:
continue
if "_SubLdg" in h:
continue
if "_Njets" in h:
continue
if "_NBjets" in h:
continue
if "_Delta" in h:
continue
if "Dijet" in h:
continue
if "Bdisc" in h:
continue
#if "MVA" in h:
# continue
if "MET" in h:
continue
if "HT" in h:
continue
# Otherwise keep the histogram
hListS.append(h)
hPathsS = [os.path.join(opts.folder, h) for h in hListS]
# Create two lists of paths: one for "Baseline" (SR) and one for "Inverted" (CR)
path_SR = [] # baseline, _AfterAllSelections
path_CR1 = [] # baseline, _AfterCRSelections
path_VR = [] # inverted, _AfterAllSelections
path_CR2 = [] # inverted, _AfterCRSelections
# For-loop: All histogram paths
for p in hPathsS: #hPaths:
if "Baseline" in p:
if "AllSelections" in p:
path_SR.append(p)
if "CRSelections" in p:
path_CR1.append(p)
if "Inverted" in p:
if "AllSelections" in p:
path_VR.append(p)
if "CRSelections" in p:
path_CR2.append(p)
counter = 1
# For-loop: All histogram pairs
for hCR1, hCR2 in zip(path_CR1, path_CR2):
if "IsGenuineB" in hCR1:
continue
#hName = hCR1.replace("_AfterCRSelections", "_CR1vCR2").replace("ForFakeBMeasurement/Baseline_", "")
hName = hCR1.replace("_AfterCRSelections", " (CR1 and R2)").replace("ForFakeBMeasurement/Baseline_", "")
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (len(path_CR1)), hName)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hCR1, hCR2, "CR1")
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hCR1, hCR2, "CR2") #iro
counter+=1
# WARNING! This unblinds the Signal Region (SR)
for hSR, hVR in zip(path_SR, path_VR):
if "IsGenuineB" in hSR:
continue
if 1:
continue
#hName = hCR1.replace("_AfterCRSelections", "_SRvVR").replace("ForFakeBMeasurement/Baseline_", "")
hName = hCR1.replace("_AfterCRSelections", " (SR and VR)").replace("ForFakeBMeasurement/Baseline_", "")
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (len(path_CR1)), hName)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hSR, hVR, "SR")
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hSR, hVR, "VR")
counter+=1
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
datasetsMgr_matched = GetDatasetsFromDir(opts)
datasetsMgr_matched.updateNAllEventsToPUWeighted()
datasetsMgr_matched.loadLuminosities() # from lumi.json
plots.mergeRenameReorderForDataMC(datasetsMgr)
datasetsMgr.remove(filter(lambda name: "QCD_b" in name, datasetsMgr.getAllDatasetNames())) #soti
datasetsMgr_matched.remove(filter(lambda name: "QCD" in name, datasetsMgr_matched.getAllDatasetNames())) #soti
# Set/Overwrite cross-sections
datasetsToRemove = ["QCD-b"]#, "QCD_HT50to100", "QCD_HT100to200"]#, "QCD_HT200to300"]#, "QCD_HT300to500"]
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
#if d.getName() != opts.signal:
if "M_650" in d.getName(): #soti fixmi
datasetsToRemove.append(d.getName())
if "M_800" in d.getName():
datasetsToRemove.append(d.getName())
if "M_200" in d.getName():
datasetsToRemove.append(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Custom Filtering of datasets
for i, d in enumerate(datasetsToRemove, 0):
msg = "Removing dataset %s" % d
Print(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
datasetsMgr.remove(filter(lambda name: d in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr_matched.remove(filter(lambda name: d in name, datasetsMgr_matched.getAllDatasetNames())) #soti
if opts.verbose:
datasetsMgr.PrintInfo()
# ZJets and DYJets overlap
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.merge("QCD", GetListOfQCDatasets())
#plots._plotStyles["QCD"] = styles.getQCDLineStyle()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
# Get Luminosity
#intLumi = datasetsMgr.getDataset("Data").getLuminosity() Soti
intLumi = 35920
# Re-order datasets (different for inverted than default=baseline)
newOrder = []
# For-loop: All MC datasets
for d in datasetsMgr.getMCDatasets():
newOrder.append(d.getName())
# Move signal to top
# if opts.signal in newOrder:
# s = newOrder.pop( newOrder.index(opts.signal) )
# newOrder.insert(0, s)
print len(newOrder), "newOrder"
signalMass = ["M_300", "M_500", "M_1000"]
for d in datasetsMgr.getMCDatasets():
for m in signalMass:
if m in d.getName():
s = newOrder.pop( newOrder.index(d.getName()) )
newOrder.insert(0, s)
#datasetsMgr.selectAndReorder(newOrder)
print len(newOrder), "newOrder"
# Add Data to list of samples!
if not opts.onlyMC:
newOrder.insert(0, "Data")
# Apply new dataset order!
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths1 = [os.path.join(folder, h) for h in histoList]
histoPaths2 = [h for h in histoPaths1]# if "jet" not in h.lower()]
nHistos = len(histoPaths2)
# For-loop: All histograms
for i, h in enumerate(histoPaths2, 1):
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (nHistos), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistograms(datasetsMgr, datasetsMgr_matched, h, intLumi)
ROOT.gStyle.SetNdivisions(10, "X")
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setLogX(opts.logX)
style.setLogY(opts.logY)
style.setLogZ(opts.logZ)
style.setWide(True, 0.15)
# style.setPadRightMargin()#0.13)
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print merged datasets and MC samples
if 0:
datasetsMgr.PrintInfo()
# Get Luminosity
if opts.intLumi < 0:
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Merge EWK samples
if opts.dataset == "EWK":
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Re-order datasets (different for inverted than default=baseline)
newOrder = []
for d in datasetsMgr.getAllDatasets():
if d.getName() == opts.dataset:
newOrder.append(d.getName())
# Sanity check on selected dataset
nDatasets = len(newOrder)
if nDatasets < 1:
msg = "Please select a valid dataset. Dataset \"%s\" does not exist!" % (
opts.dataset)
Print(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(),
True)
datasetsMgr.PrintInfo()
sys.exit()
if nDatasets > 1:
msg = "Please select only 1 valid dataset. Requested %i datasets for plotting!" % (
nDatasets)
Print(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(),
True)
datasetsMgr.PrintInfo()
sys.exit()
# Select only given dataset
datasetsMgr.selectAndReorder(newOrder)
# Print dataset information
msg = "Plotting for single dataset \"%s\". Integrated luminosity is %.2f 1/fb" % (
opts.dataset, opts.intLumi)
Print(ShellStyles.NoteStyle() + msg + ShellStyles.NormalStyle(), True)
datasetsMgr.PrintInfo()
# Get list of histogram paths
folder = opts.folder
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
# For-loop: All histograms
for h in histoPaths:
if "_vs_" not in h.lower():
continue
Plot2dHistograms(datasetsMgr, h)
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Print PSets used for FakeBMeasurement
if 0:
datasetsMgr.printSelections()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ZJetsToQQ_HT600toInf" in d.getName():
datasetsMgr.remove(d.getName())
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if d.getName() not in opts.signal:
if not opts.acceptance:
datasetsMgr.remove(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
# For acceptance plot use all available masses
if opts.acceptance:
opts.signal = []
opts.signalMasses = []
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
#dName = d.getName().replace("_ext1", "").split("M_")
dName = d.getName().split("M_")
m = int(dName[-1])
opts.signal.append(d.getName())
opts.signalMasses.append(m)
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 1.0
# Merge EWK samples
if 1:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print post EWK-merge dataset summary
datasetsMgr.PrintInfo()
# Get the efficiency graphs
hGraphList = []
histoName = os.path.join(opts.folder, "counter")
hGraphList, _kwargs = GetHistoGraphs(datasetsMgr, opts.folder,
histoName)
# Plot the histo graphs
PlotHistoGraphs(hGraphList, _kwargs)
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts):
# Suppress warnings about weight being re-applied
ROOT.gErrorIgnoreLevel = ROOT.kError
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
opts.optMode = ""
mcrabName = opts.mcrab
RunEra = mcrabName.split("_")[1]
# Setup ROOT and style
ROOT.gROOT.SetBatch(opts.batchMode)
style = tdrstyle.TDRStyle()
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
if opts.paper:
style.setGridX(False)
style.setGridY(False)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
# Remove some QCD samples (the cross sections of these samples are not calculated)
if 0:
msg = "Removing following samples:"
Print(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(), True)
for d in getDatasetsToExclude():
Print(d, False)
datasetsMgr.remove(d)
# Get run-range
minRunRange, maxRunRange, runRange = GetRunRange(datasetsMgr)
# Get int lumi
intLumi = GetLumi(datasetsMgr)
# Update to PU & load luminosities
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities()
#datasetsMgr.normalizeMCByLuminosity()
# Print luminisoties and cross-sections
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintCrossSections()
# Default merging & ordering: "Data", "QCD", "SingleTop", "Diboson"
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get datasets
datasetsMgr.mergeMC()
dataset_Data = datasetsMgr.getDataDatasets()
dataset_MC = datasetsMgr.getMCDatasets()
# Define lists of Triggers to be plotted and Variables
xVars = ["pt6thJet", "eta6thJet", "phi6thJet", "Ht", "nBTagJets", "pu", "JetMulti", "BJetMulti"]
trgList = ["1BTag", "2BTag", "OR", "OR_PFJet450"]
if opts.fast:
trgList = ["OR_PFJet450"]
xVars = ["pt6thJet", "Ht"]
nPlots = len(trgList)*len(xVars)
counter = 0
# For-loop: All signal triggers
for i, trg in enumerate(trgList, 1):
# For-loop: All x-variables
for j, xVar in enumerate(xVars, 1):
counter+=1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (nPlots), "%s Vs %s" % (trg, xVar))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
# Define names
hNumerator = "hNum_%s_RefTrg_OfflineSel_Signal%s" % (xVar, trg)
hDenominator = "hDen_%s_RefTrg_OfflineSel" % (xVar)
plotName = "Eff_%s_%s" % (xVar, trg)
# Get Efficiency Plots
_kwargs = GetHistoKwargs(xVar, opts)
eff_Data = GetEfficiency(datasetsMgr, dataset_Data, hNumerator, hDenominator , **_kwargs)
eff_MC = GetEfficiency(datasetsMgr, dataset_MC, hNumerator, hDenominator, **_kwargs)
# Apply Styles
styles.dataStyle.apply(eff_Data)
styles.mcStyle.apply(eff_MC)
# Create the plot
p = plots.ComparisonPlot(histograms.HistoGraph(eff_Data, "eff_Data", "p", "P"),
histograms.HistoGraph(eff_MC, "eff_MC" , "p", "P"),
saveFormats=[])
# Define the legend entries
p.histoMgr.setHistoLegendLabelMany(
{
"eff_Data": "Data",
"eff_MC" : "Simulation"
}
)
# Draw and save the plot
p.setLuminosity(intLumi)
plots.drawPlot(p, plotName, **_kwargs)
# Draw
histograms.addText(0.65, 0.06, "Runs "+ runRange, 17)
histograms.addText(0.65, 0.10, "2016", 17)
# Save the canvas to a file
SavePlot(p, plotName, os.path.join(opts.saveDir, opts.optMode), saveFormats=[".pdf", ".png", ".C"] )
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(True) #opts.gridX)
style.setGridY(True) #opts.gridY)
style.setLogX(False) #opts.logX)
style.setLogY(False) #opts.logY)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Custom Filtering of datasets
if 0:
datasetsMgr.remove(filter(lambda name: "Charged" in name and not "M_500" in name, datasetsMgr.getAllDatasetNames()))
# ZJets and DYJets overlap!
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
datasetsMgr.PrintInfo()
# Get Luminosity
if opts.intLumi < 0.0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 1.0
# Merge EWK samples
if opts.dataset == "EWK":
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Get all histogram names in the given ROOT folder
histoNames = datasetsMgr.getAllDatasets()[0].getDirectoryContent(opts.folder)
histoList_ = [os.path.join(opts.folder, h) for h in histoNames if "_SR" in h]
histoList = [h for h in histoList_ if "Pt" in h] + [h for h in histoList_ if "Eta" in h]
# For-loop: All histos in SR
nHistos = len(histoList)
for i, h in enumerate(histoList, 1):
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i,"/", "%s:" % (nHistos), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
# print "*** h = ", h
PlotHistograms(datasetsMgr, h)
#break
# Inform user where the plots where saved
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
style.setOptStat(False)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
# PrintPSet("BJetSelection", datasetsMgr)
# PrintPSet("TopSelectionBDT", datasetsMgr)
# PrintPSet("FakeBMeasurement", datasetsMgr)
sys.exit()
# Print dataset info?
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set signal cross-section
if 0:
datasetsMgr.getDataset(opts.signal).setCrossSection(1.0)
# Remove unwanted datasets
if 0:
datasetsMgr.remove(
filter(lambda name: "QCD-b" in name,
datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Merge EWK samples
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Get all the histograms and their paths (e.g. ForFakeBMeasurement/Baseline_DeltaRLdgTrijetBJetTetrajetBJet_AfterCRSelections)
hList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(
opts.folder)
hPaths = [os.path.join(opts.folder, h) for h in hList]
# Create two lists of paths: one for "Baseline" (SR) and one for "Inverted" (CR)
path_SR = [] # baseline, _AfterAllSelections
path_CR1 = [] # baseline, _AfterCRSelections
path_VR = [] # inverted, _AfterAllSelections
path_CR2 = [] # inverted, _AfterCRSelections
# For-loop: All histogram paths
for p in hPaths:
if "AfterStandardSelections" in p:
#print p
continue
if "Baseline" in p:
if "AllSelections" in p:
path_SR.append(p)
if "CRSelections" in p:
path_CR1.append(p)
if "Inverted" in p:
if "AllSelections" in p:
path_VR.append(p)
if "CRSelections" in p:
path_CR2.append(p)
# For-loop: All histogram pairs
for hVR, hCR2, hCR1 in zip(path_VR, path_CR2, path_CR1):
break # not needed now
if "IsGenuineB" in hVR:
continue
PlotComparison(datasetsMgr, hVR, hCR2, "VRvCR2")
# For-loop: All histogram pairs
counter = 1
for hCR1, hCR2 in zip(path_CR1, path_CR2):
if "IsGenuineB" in hCR1:
continue
hName = hCR1.replace("_AfterCRSelections", "_CR1vCR2").replace(
"ForFakeBMeasurement/Baseline_", "")
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format(
"Histogram", "%i" % counter, "/", "%s:" % (len(path_CR1)),
hName)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(),
counter == 1)
PlotComparison(datasetsMgr, hCR1, hCR2, "CR1vCR2")
counter += 1
# For-loop: All histogram pairs
for hSR, hVR in zip(path_SR, path_VR):
break
Print("UNBLINDING SR! Are you nuts ? BREAK!", False)
if "IsGenuineB" in hSR:
continue
PlotComparison(datasetsMgr, hSR, hVR, "SRvVR")
# # For-loop: All histogram pairs
# for hSR, hCR1 in zip(path_SR, path_CR1):
# #break
# Print("UNBLINDING SR! Are you nuts ? BREAK!", False)
# raw_input("Press any key to continue")
# if "IsGenuineB" in hSR:
# continue
# PlotComparison(datasetsMgr, hSR, hCR1, "SRvCR1")
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
sys.exit()
# Define datasets to remove by default
QCD_list = ["QCD_HT700to1000", "QCD_HT50to100", "QCD_HT500to700", "QCD_HT300to500",
"QCD_HT200to300", "QCD_HT2000toInf", "QCD_HT1500to2000", "QCD_HT100to200", "QCD_HT1000to1500"]
QCDExt_list = [x+"_ext1" for x in QCD_list]
datasetsToRemove = ["QCD-b"]
# datasetsToRemove.extend(QCD_list)
# datasetsToRemove.extend(QCDExt_list)
# ZJets and DYJets overlap
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "DYJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
if d.getName() != opts.signal:
datasetsToRemove.append(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Custom Filtering of datasets
for i, d in enumerate(datasetsToRemove, 0):
msg = "Removing dataset %s" % d
Verbose(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
datasetsMgr.remove(filter(lambda name: d == name, datasetsMgr.getAllDatasetNames()))
if opts.verbose:
datasetsMgr.PrintInfo()
# Re-order datasets (different for inverted than default=baseline)
newOrder = ["Data"]
for i, d in enumerate(datasetsMgr.getAllDatasets(), 0):
if d.isData():
continue
else:
newOrder.append(d.getName())
# Re-arrange dataset order?
if 0:
s = newOrder.pop( newOrder.index("noTop") )
newOrder.insert(len(newOrder), s) #after "Data"
# Move signal to top
if opts.signal in newOrder:
s = newOrder.pop( newOrder.index(opts.signal) )
newOrder.insert(1, s)
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
ignoreList = ["Aplanarity", "Planarity", "Sphericity", "FoxWolframMoment", "Circularity", "ThirdJetResolution", "Centrality", "_Vs_"]
myHistos = []
for h in histoPaths:
skip = False
# Skip unwanted histos
for i in ignoreList:
if i in h:
skip = True
if skip:
continue
else:
myHistos.append(h)
for i, h in enumerate(myHistos, 1):
# Plot the histograms!
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(myHistos)), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
DataMCHistograms(datasetsMgr, h)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
style.setOptStat(False)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
# Print PSets used for FakeBMeasurement
if 0:
datasetsMgr.printSelections()
PrintPSet("BJetSelection", datasetsMgr)
PrintPSet("TopSelectionBDT", datasetsMgr)
PrintPSet("FakeBMeasurement", datasetsMgr)
sys.exit()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Filter the datasets
datasetsMgr.remove(filter(lambda name: "Charged" in name, datasetsMgr.getAllDatasetNames()))
# datasetsMgr.remove(filter(lambda name: "Charged" in name and not "M_500" in name, datasetsMgr.getAllDatasetNames()))
# ZJets and DYJets overlap!
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 1.0
# Re-order datasets (different for inverted than default=baseline)
if 0:
newOrder = ["Data"]
newOrder.extend(aux.GetListOfEwkDatasets())
datasetsMgr.selectAndReorder(newOrder)
# Print post-merged data dataset summary
if 0:
datasetsMgr.PrintInfo()
# Merge EWK samples
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print post EWK-merge dataset summary
if 1:
datasetsMgr.PrintInfo()
# Get all histograms from the in the selected folder inside the ROOT files
allHistos = datasetsMgr.getAllDatasets()[0].getDirectoryContent(opts.folder)
hList = [h for h in allHistos if "CRSelections" in h and "_Vs" not in h]
hList.extend([h for h in allHistos if "AllSelections" in h and "_Vs" not in h])
# hList.extend([h for h in allHistos if "StandardSelections" in h and "_Vs" not in h])
# Create a list with strings included in the histogram names you want to plot
myHistos = ["LdgTrijetPt", "LdgTrijetMass", "TetrajetBJetPt", "TetrajetBJetEta", "LdgTetrajetPt", "LdgTetrajetMass", "MVAmax2", "MVAmax1", "HT", "MET"]
# For-loop: All histos
for i, h in enumerate(myHistos, 1):
hGraphList = []
for b in ["Baseline_", "Inverted_"]:
for r in [ "_AfterCRSelections", "_AfterAllSelections"]:
histoName = b + h + r
hgQCD, kwargs = GetPurityHistoGraph(datasetsMgr, opts.folder, histoName)
# Do not draw SR in multigraph plot!
if GetControlRegionLabel(histoName) != "SR":
hGraphList.append(hgQCD)
# Plot individual purity graphs?
if 0:
PlotHistoGraph(hgQCD, kwargs)
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(myHistos)), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistoGraphs(hGraphList, kwargs)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(False)
style.setGridY(False)
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager for pseudo-multicrab with default matching definition
datasetsMgr_default = GetDatasetsFromDir(opts, opts.mcrab)
datasetsMgr_default.updateNAllEventsToPUWeighted()
datasetsMgr_default.loadLuminosities()
datasetsMgr_second = GetDatasetsFromDir(opts, opts.mcrab2)
datasetsMgr_second.updateNAllEventsToPUWeighted()
datasetsMgr_second.loadLuminosities()
if opts.verbose:
datasetsMgr_default.PrintCrossSections()
datasetsMgr_default.PrintLuminosities()
# Print datasets info summary
datasetsMgr_default.PrintInfo()
datasetsMgr_second.PrintInfo()
Numerator = ["AllTopQuarkPt_MatchedBDT",
"TrijetFakePt_BDT",
"AllTopQuarkPt_MatchedBDT",
"AllTopQuarkPt_Matched",
]
Denominator = ["AllTopQuarkPt_Matched",
"TrijetFakePt",
"TopQuarkPt",
"TopQuarkPt",
]
eff_def = ["genuineTop",
"fakeTop",
"inclusiveTop",
"matching",
]
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr_default)
plots.mergeRenameReorderForDataMC(datasetsMgr_default)
counter = 0
opts.nDatasets = len(datasetsMgr_default.getAllDatasets())
nPlots = len(Numerator)
# For-loop: All numerator-denominator pairs
for i in range(len(Numerator)):
numerator = os.path.join(opts.folder, Numerator[i])
denominator = os.path.join(opts.folder, Denominator[i])
counter+=1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50} {:<2} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (nPlots), "%s" % (numerator), "/", "%s" % (denominator))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
PlotEfficiency(datasetsMgr_default, datasetsMgr_second, numerator, denominator, eff_def[i])
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setOptStat(False)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
# PrintPSet("BJetSelection", datasetsMgr)
# PrintPSet("TopSelectionBDT", datasetsMgr)
# PrintPSet("FakeBMeasurement", datasetsMgr)
sys.exit()
# Print dataset info?
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Set signal cross-section
if 0:
datasetsMgr.getDataset(opts.signal).setCrossSection(1.0)
# Remove unwanted datasets
if 0:
datasetsMgr.remove(filter(lambda name: "QCD-b" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Merge EWK samples
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Get all the histograms and their paths (e.g. ForFakeBMeasurement/Baseline_DeltaRLdgTrijetBJetTetrajetBJet_AfterCRSelections)
hList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
hPaths = [os.path.join(opts.folder, h) for h in hList]
# Create two lists of paths: one for "Baseline" (SR) and one for "Inverted" (CR)
path_SR = [] # baseline, _AfterAllSelections
path_CR1 = [] # baseline, _AfterCRSelections
path_VR = [] # inverted, _AfterAllSelections
path_CR2 = [] # inverted, _AfterCRSelections
# For-loop: All histogram paths
for p in hPaths:
if "AfterStandardSelections" in p:
continue
if "Baseline" in p:
if "AllSelections" in p:
path_SR.append(p)
if "CRSelections" in p:
path_CR1.append(p)
if "Inverted" in p:
if "AllSelections" in p:
path_VR.append(p)
if "CRSelections" in p:
path_CR2.append(p)
# For-loop: All histogram pairs
for hVR, hCR2, hCR1 in zip(path_VR, path_CR2, path_CR1):
break
if "IsGenuineB" in hVR:
continue
PlotComparison(datasetsMgr, hVR, hCR2, "VRvCR2")
# For-loop: All histogram pairs
counter = 1
for hCR1, hCR2 in zip(path_CR1, path_CR2):
if "IsGenuineB" in hCR1:
continue
hName = hCR1.replace("_AfterCRSelections", "_CR1vCR2").replace("ForFakeBMeasurement/Baseline_", "")
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (len(path_CR1)), hName)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
PlotComparison(datasetsMgr, hCR1, hCR2, "CR1vCR2")
counter+=1
# For-loop: All histogram pairs
for hSR, hVR in zip(path_SR, path_VR):
break
# Print("UNBLINDING SR! Are you nuts ? BREAK!", False)
if "IsGenuineB" in hSR:
continue
PlotComparison(datasetsMgr, hSR, hVR, "SRvVR")
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setLogX(opts.logX)
style.setLogY(opts.logY)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
plots.mergeRenameReorderForDataMC(datasetsMgr)
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Do the plot
moduleDict = {}
moduleNames = list(
filter(
lambda x: "FakeBTransferFactors" in x and x.endswith(".py") and
not x.startswith("#"), os.listdir(opts.mcrab)))
sKeys = []
Verbose(
"Found %d import files:%s%s%s" %
(len(moduleNames), hs, "\n\t" + "\n\t".join(moduleNames), ns), True)
# For-loop: All module files to be imported
for i, m in enumerate(moduleNames, 1):
# This is the default file (inclusive). But another inclusive one (.._Run2016.py) is also inclded (=>duplicate)
if "80to1000" in str(m):
continue
if not opts.verbose:
Print(
"Importing module %s" %
(hs + os.path.join(opts.mcrab, m) + ns), i == 1)
else:
Print(
"Importing module %s" %
(hs + os.path.join(opts.mcrab, m) + ns), True)
# Define the path to the .py file (module Path) and the name to be imported as (module name)
mPath = os.path.join(os.getcwd(), opts.mcrab, m)
mName = m.split("_")[-1].replace(".py", "")
sKeys.append(mName)
mObj = imp.load_source(mName, mPath)
moduleDict[mName] = mObj
# Debugging
Verbose(moduleDict[mName].FakeBNormalisation_Value.keys(), False)
Verbose(moduleDict[mName].FakeBNormalisation_Value.values(), False)
gList = []
gListR = []
# For-loop: All modules imported
for i, k in enumerate(sorted(sKeys, key=natural_keys), 0):
g = PlotTFs(i, k, moduleDict[k])
gList.extend(g)
gListR.extend(g)
# Make sure you use correct graph as reference
refPos = 0
for i, g in enumerate(gList, 0):
if g.getName() == opts.refHisto:
refPos = i
break
# Reference histograph
gList.insert(0, gList.pop(refPos))
gListR.insert(0, gListR.pop(refPos))
# Create comparison plot
PlotTFsCompare("transferFactors", sKeys, gList)
# Create comparison ratio plot
gListR = [
divideGraph(g, gListR[0], errorY=True, invRatio=False) for g in gListR
]
PlotTFsCompare("transferFactorsR", sKeys, gListR, isRatio=True)
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(False)
style.setGridY(False)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab1)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Get the datasets from the directory
datasetsMgr1 = GetDatasetsFromDir(opts.mcrab1, opts)
datasetsMgr2 = GetDatasetsFromDir(opts.mcrab2, opts)
datasetsMgr3 = GetDatasetsFromDir(opts.mcrab3, opts)
# Setup the dataset managers
datasetsMgr1.updateNAllEventsToPUWeighted()
datasetsMgr1.loadLuminosities() # from lumi.json
datasetsMgr2.updateNAllEventsToPUWeighted()
datasetsMgr2.loadLuminosities() # from lumi.json
datasetsMgr3.updateNAllEventsToPUWeighted()
datasetsMgr3.loadLuminosities() # from lumi.json
# Print dataset info?
if opts.verbose:
datasetsMgr1.PrintCrossSections()
datasetsMgr1.PrintLuminosities()
datasetsMgr2.PrintCrossSections()
datasetsMgr2.PrintLuminosities()
datasetsMgr2.PrintCrossSections()
datasetsMgr2.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr1)
plots.mergeRenameReorderForDataMC(datasetsMgr2)
plots.mergeRenameReorderForDataMC(datasetsMgr3)
# Get Luminosity
lumi1 = datasetsMgr1.getDataset("Data").getLuminosity()
lumi2 = datasetsMgr2.getDataset("Data").getLuminosity()
lumi3 = datasetsMgr3.getDataset("Data").getLuminosity()
if lumi1 != lumi2 != lumi3:
raise Exception("Lumi1 (=%.2f) != Lumi2 (=%.2f) != Lumi3 (=%.2f" %
(lumi1, lumi2, lumi3))
else:
opts.intLumi = datasetsMgr1.getDataset("Data").getLuminosity()
# Merge EWK samples
datasetsMgr1.merge("EWK", aux.GetListOfEwkDatasets())
datasetsMgr2.merge("EWK", aux.GetListOfEwkDatasets())
datasetsMgr3.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr1.PrintInfo()
if 0:
datasetsMgr2.PrintInfo()
datasetsMgr3.PrintInfo()
# Get all the histograms and their paths (e.g. ForFakeBMeasurement/Baseline_DeltaRLdgTrijetBJetTetrajetBJet_AfterCRSelections)
hList = datasetsMgr1.getDataset(
datasetsMgr1.getAllDatasetNames()[0]).getDirectoryContent(
opts.folder)
hPaths = [os.path.join(opts.folder, h) for h in hList]
# Create a smaller list with only histos of interest
hListS = []
for h in hList:
if "StandardSelections" in h:
continue
if "IsGenuineB" in h:
continue
if "_Bjet" in h:
continue
if "_Jet" in h:
continue
if "_SubLdg" in h:
continue
if "_Njets" in h:
continue
if "_NBjets" in h:
continue
if "_Delta" in h:
continue
if "Dijet" in h:
continue
if "Bdisc" in h:
continue
#if "MVA" in h:
# continue
if "MET" in h:
continue
if "HT" in h:
continue
# Otherwise keep the histogram
hListS.append(h)
hPathsS = [os.path.join(opts.folder, h) for h in hListS]
# Create two lists of paths: one for "Baseline" (SR) and one for "Inverted" (CR)
path_SR = [] # baseline, _AfterAllSelections
path_CR1 = [] # baseline, _AfterCRSelections
path_VR = [] # inverted, _AfterAllSelections
path_CR2 = [] # inverted, _AfterCRSelections
# For-loop: All histogram paths
for p in hPathsS: #hPaths:
if "Baseline" in p:
if "AllSelections" in p:
path_SR.append(p)
if "CRSelections" in p:
path_CR1.append(p)
if "Inverted" in p:
if "AllSelections" in p:
path_VR.append(p)
if "CRSelections" in p:
path_CR2.append(p)
counter = 1
# For-loop: All histogram pairs
for hCR1, hCR2 in zip(path_CR1, path_CR2):
if "IsGenuineB" in hCR1:
continue
#hName = hCR1.replace("_AfterCRSelections", "_CR1vCR2").replace("ForFakeBMeasurement/Baseline_", "")
hName = hCR1.replace("_AfterCRSelections",
" (CR1 and R2)").replace(
"ForFakeBMeasurement/Baseline_", "")
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format(
"Histogram", "%i" % counter, "/", "%s:" % (len(path_CR1)),
hName)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(),
counter == 1)
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hCR1,
hCR2, "CR1")
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hCR1,
hCR2, "CR2") #iro
counter += 1
# WARNING! This unblinds the Signal Region (SR)
for hSR, hVR in zip(path_SR, path_VR):
if "IsGenuineB" in hSR:
continue
if 1:
continue
#hName = hCR1.replace("_AfterCRSelections", "_SRvVR").replace("ForFakeBMeasurement/Baseline_", "")
hName = hCR1.replace("_AfterCRSelections", " (SR and VR)").replace(
"ForFakeBMeasurement/Baseline_", "")
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format(
"Histogram", "%i" % counter, "/", "%s:" % (len(path_CR1)),
hName)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(),
counter == 1)
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hSR, hVR,
"SR")
PlotComparison(datasetsMgr1, datasetsMgr2, datasetsMgr3, hSR, hVR,
"VR")
counter += 1
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setLogX(opts.logX)
style.setLogY(opts.logY)
style.setLogZ(opts.logZ)
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print merged datasets and MC samples
if 0:
datasetsMgr.PrintInfo()
# Get Luminosity
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 1.0
# Set/Overwrite cross-sections. Remove all but 1 signal mass
removeList = []
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if d.getName() != opts.signal:
removeList.append(d.getName())
# Custom filtering of datasets
for i, d in enumerate(removeList, 1):
msg = "Removing datasets %s from dataset manager" % (ShellStyles.NoteStyle() + d + ShellStyles.NormalStyle())
Verbose(msg, i==1)
datasetsMgr.remove(filter(lambda name: d == name, datasetsMgr.getAllDatasetNames()))
# Merge EWK samples
if opts.mergeEWK == "EWK":
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
if 1:
datasetsMgr.PrintInfo()
# Get list of histogram paths
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
# For-loop: All histograms
for i, h in enumerate(histoPaths, 1):
if "_Vs_" in h:
continue
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(histoPaths)), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistograms(datasetsMgr, h)
break
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setOptStat(False)
# If user does not define optimisation mode do all of them
if opts.optMode == None:
optModes = optList
else:
optModes = [opts.optMode]
# Define genuineB and fakeB datasets
mergeMap = {"TT": "GenuineB",
"QCD_HT50to100" : "FakeB", #v. poor stats!
"QCD_HT200to300" : "FakeB", #v. poor stats!
"QCD_HT100to200" : "FakeB", #v. poor stats!
"QCD_HT200to300" : "FakeB", #v. poor stats!
"QCD_HT300to500" : "FakeB",
"QCD_HT500to700" : "FakeB",
"QCD_HT700to1000" : "FakeB",
"QCD_HT1000to1500": "FakeB",
"QCD_HT1500to2000": "FakeB",
"QCD_HT2000toInf" : "FakeB",
"WJetsToQQ_HT_600ToInf": "FakeB"
}
# Definitions
results = []
# For-loop: All optimisation modes
for index, opt in enumerate(optModes, 1):
opts.optMode = opt
# Definitions
genuineB = None
fakeB = None
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Print datasets info ?
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Remove unwanted datasets
removeList = ["QCD_HT50to100", "QCD_HT100to200", "QCD_HT200to300", "QCD_HT200to300_ext1", "WJetsToQQ_HT_600ToInf"]
for k, d in enumerate(datasetsMgr.getAllDatasets(), 1):
if d.getName() in removeList:
datasetsMgr.remove(d.getName())
Verbose(ShellStyles.ErrorStyle() + "Removing dataset %s" % d.getName() + ShellStyles.NormalStyle(), k==1)
if index == 1:
datasetsMgr.PrintInfo()
# Merge datasets into two groups: Genuine-B and Fake-B
datasetsMgr.mergeMany(mergeMap, addition=False)
for d in datasetsMgr.getAllDatasets():
if d.getName() == "GenuineB":
genuineB = d
if d.getName() == "FakeB":
fakeB = d
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get integrated luminosity
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
datasetsMgr.remove(filter(lambda name: "Data" in name, datasetsMgr.getAllDatasetNames()))
# Print dataset information
if index == 1:
datasetsMgr.PrintInfo()
# Do the plot
name = GetPlotName(opts)
myResults = doPlot(name, genuineB, fakeB, opts.errorlevel, opts.optMode, intLumi)
# Save results and msgs
results.extend(myResults)
# For-loop: All points
if opts.verbose:
for item in results:
print item
# Print path of all saved plots
msg = "All plots saved under directory %s" % (aux.convertToURL(opts.saveDir, opts.url))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), True)
# Write results to a json file
with open(opts.json, 'w') as outfile:
json.dump(results, outfile)
msg = "Wrote results to \"%s\"" % (opts.json)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(True) #opts.gridX)
style.setGridY(True) #opts.gridY)
style.setLogX(False) #opts.logX)
style.setLogY(False) #opts.logY)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Custom Filtering of datasets
if 0:
datasetsMgr.remove(filter(lambda name: "Charged" in name and not "M_500" in name, datasetsMgr.getAllDatasetNames()))
# ZJets and DYJets overlap!
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
datasetsMgr.PrintInfo()
# Get Luminosity
if opts.intLumi < 0.0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 1.0
# Merge EWK samples
if opts.dataset == "EWK":
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Get all histogram names in the given ROOT folder
histoNames = datasetsMgr.getAllDatasets()[0].getDirectoryContent(opts.folder)
# histoList = [os.path.join(opts.folder, h) for h in histoNames if "_" + opts.region in h and opts.refBdisc in h]
histoList = [os.path.join(opts.folder, h) for h in histoNames if opts.refBdisc in h]
# For-loop: All histos in CR of interest
nHistos = len(histoList)
for i, h in enumerate(histoList, 1):
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i,"/", "%s:" % (nHistos), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistograms(datasetsMgr, h)
# Inform user where the plots where saved
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setLogX(opts.logX)
# If you want BOTH pads (main and ratio) in log scale
if 0:
style.setLogY(opts.logY)
# Overwrite default legends
plots._legendLabels["MCStatError"] = "Bkg. stat."
plots._legendLabels["MCStatSystError"] = "Bkg. stat.#oplussyst."
plots._legendLabels["BackgroundStatError"] = "Bkg. stat. unc"
plots._legendLabels["BackgroundStatSystError"] = "Bkg. stat.#oplussyst. unc."
# Define optimisatio modes to run on
optModes = [""] #["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
if opts.optMode != None:
optModes = [opts.optMode]
# Inform user of EWK datasets used
Print("The EWK datasets used are the following:", True)
for i,d in enumerate(aux.GetListOfEwkDatasets(), 1):
Print(ShellStyles.NoteStyle() + d + ShellStyles.NormalStyle(), i==0)
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
dsetMgr1 = GetDatasetsFromDir(opts, False)
dsetMgr2 = GetDatasetsFromDir(opts, True)
# Setup the dataset managers
dsetMgr1.updateNAllEventsToPUWeighted()
dsetMgr2.updateNAllEventsToPUWeighted()
# Load luminosities
dsetMgr1.loadLuminosities() # from lumi.json
# dsetMgr2.loadLuminosities()
# Print PSets. Perhaps i can use this to ensure parameters are matching!
if 0:
dsetMgr1.printSelections()
dsetMgr2.printSelections()
PrintPSet("FakeBMeasurement", dsetMgr1)
PrintPSet("TopSelectionBDT" , dsetMgr2)
# Remove datasets with overlap?
removeList = ["QCD-b"]
dsetDY = "DYJetsToQQ_HT180"
dsetZJ = "ZJetsToQQ_HT600toInf"
dsetRM = dsetZJ # datasets with overlap
removeList.append(dsetRM)
# Set/Overwrite cross-sections. Remove all but 1 signal mass
for d in dsetMgr1.getAllDatasets():
if "ChargedHiggs" in d.getName():
dsetMgr1.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
if d.getName() != opts.signal:
removeList.append(d.getName())
# Print useful information?
if opts.verbose:
dsetMgr1.PrintCrossSections()
dsetMgr1.PrintLuminosities()
dsetMgr2.PrintCrossSections()
dsetMgr2.PrintLuminosities()
# Merge histograms
plots.mergeRenameReorderForDataMC(dsetMgr1)
# Get the luminosity
if opts.intLumi < 0:
opts.intLumi = dsetMgr1.getDataset("Data").getLuminosity()
# Custom Filtering of datasets
for i, d in enumerate(removeList, 1):
msg = "Removing datasets %s from dataset manager" % (ShellStyles.NoteStyle() + d + ShellStyles.NormalStyle())
Verbose(msg, i==1)
dsetMgr1.remove(filter(lambda name: d == name, dsetMgr1.getAllDatasetNames()))
# Print dataset information
dsetMgr1.PrintInfo()
dsetMgr2.PrintInfo()
# Replace MC datasets with data-driven
if not opts.useMC:
replaceQCD(dsetMgr1, dsetMgr2, "FakeBMeasurementTrijetMass", "FakeB") #dsetMgr1 now contains "FakeB" pseudo-dataset
# Definitions
allHistos = dsetMgr2.getAllDatasets()[0].getDirectoryContent(opts.folder)
histoPaths = []
ignoreKeys = ["MCEWK", "Purity", "BJetPt", "BJetEta", "BtagDiscriminator", "METPhi", "MHT", "NBjets", "Njets", "_Vs_", "JetEta"]
# For-loop: All histograms in directory
for h in allHistos:
bKeep = True
for k in ignoreKeys:
if k in h:
bKeep = False
continue
if bKeep:
histoPaths.append(os.path.join(opts.folder, h))
# For-loop: All histograms in list
for i, hName in enumerate(histoPaths, 1):
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(histoPaths)), hName)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistogram(dsetMgr1, hName, opts)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
Verbose("Reference histo is \"%s\"" % (rhName), True)
p.histoMgr.setHistoDrawStyle(rhName, "CE4")
p.histoMgr.setHistoLegendStyle(rhName, "LF")
else:
setStyle(i, h.getRootHisto(), False)
p.histoMgr.setHistoDrawStyle(rhName, "P")
p.histoMgr.setHistoLegendStyle(rhName, "LP")
#p.histoMgr.setHistoDrawStyle(styles.styles[i]) #styles.generator(False), "P2")
kwargs = opts._kwargs
kwargs["rebinX"] = systematics.getBinningForPlot(
"LdgTetrajetMass_AfterAllSelections")
kwargs["divideByBinWidth"] = True
kwargs["stackMCHistograms"] = False
kwargs["addLuminosityText"] = False
kwargs["createLegend"] = {"x1": 0.50, "y1": 0.60, "x2": 0.92, "y2": 0.92}
kwargs["opts"] = {"ymin": 1e-6, "ymaxfactor": 5, "xmax": 3000.0}
hName = opts.histoKey + "_AllEras"
dName = aux.getSaveDirPath(opts.mcrab, prefix="", postfix="")
plots.drawPlot(p, hName, **kwargs)
SavePlot(p, hName, dName, saveFormats=[".png", ".pdf", ".C"])
Print(
"Saving \"%s\" under \"%s\"" %
(hName, aux.convertToURL(dName, opts.url)), True)
if not opts.batchMode:
raw_input(
"=== plot_ClosureInPartialDataset.py: Press any key to quit ROOT ..."
)
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setLogX(opts.logX)
style.setLogY(opts.logY)
style.setLogZ(opts.logZ)
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print merged datasets and MC samples
if 0:
datasetsMgr.PrintInfo()
# Get Luminosity
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 1.0
# Set/Overwrite cross-sections. Remove all but 1 signal mass
removeList = []
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if d.getName() != opts.signal:
removeList.append(d.getName())
# Custom filtering of datasets
for i, d in enumerate(removeList, 1):
msg = "Removing datasets %s from dataset manager" % (
ShellStyles.NoteStyle() + d + ShellStyles.NormalStyle())
Verbose(msg, i == 1)
datasetsMgr.remove(
filter(lambda name: d == name,
datasetsMgr.getAllDatasetNames()))
# Merge EWK samples
if opts.mergeEWK == "EWK":
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
if 1:
datasetsMgr.PrintInfo()
# Get list of histogram paths
folder = opts.folder
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
# For-loop: All histograms
for i, h in enumerate(histoPaths, 1):
if "_Vs_" in h:
continue
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format(
"Histogram", "%i" % i, "/", "%s:" % (len(histoPaths)), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(),
i == 1)
PlotHistograms(datasetsMgr, h)
break
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
style.setOptStat(False)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Get the PSets:
if 0:
datasetsMgr.printSelections()
#PrintPSet("BJetSelection", datasetsMgr, depth=150)
#PrintPSet("fakeBMeasurement", datasetsMgr, depth=150)
sys.exit()
# ZJets and DYJets overlap!
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames(
) and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print(
"Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..",
True)
datasetsMgr.remove(
filter(lambda name: "ZJetsToQQ" in name,
datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get luminosity if a value is not specified
if opts.intLumi < 0:
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Remove datasets
removeList = ["QCD-b", "Charged"]
if not opts.useMC:
removeList.append("QCD")
for i, d in enumerate(removeList, 0):
msg = "Removing dataset %s" % d
Verbose(
ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(),
i == 0)
datasetsMgr.remove(
filter(lambda name: d in name,
datasetsMgr.getAllDatasetNames()))
# Print summary of datasets to be used
if 0:
datasetsMgr.PrintInfo()
# Merge EWK samples
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
# Print dataset information
datasetsMgr.PrintInfo()
# List of TDirectoryFile (_CRone, _CRtwo, _VR, _SR)
tdirs = [
"LdgTrijetPt_", "LdgTrijetMass_", "TetrajetBJetPt_",
"TetrajetBJetEta_", "LdgTetrajetPt_", "LdgTetrajetMass_"
]
region = ["CRone", "CRtwo"]
hList = []
for d in tdirs:
for r in region:
hList.append(d + r)
# Get the folders with the binned histograms
folderList_ = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(
opts.folder)
folderList = [h for h in folderList_ if h in hList]
# For-loop: All folders
histoPaths = []
for f in folderList:
folderPath = os.path.join(opts.folder, f)
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(
folderPath)
pathList = [os.path.join(folderPath, h) for h in histoList]
histoPaths.extend(pathList)
# Get all the bin labels
binLabels = GetBinLabels("CRone", histoPaths)
for i, t in enumerate(tdirs, 1):
myList = []
for p in histoPaths:
if t in p:
myList.append(p)
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format(
"Histogram", "%i" % i, "/", "%s:" % (len(tdirs)),
t.replace("_", ""))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(),
i == 1)
PlotHistograms(datasetsMgr, myList, binLabels, opts)
# Save the plots
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(False)
style.setGridY(False)
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
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 histograms (see NtupleAnalysis/python/tools/plots.py)
if 0:
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information before removing anything?
if 0:
datasetsMgr.PrintInfo()
# Print datasets info summary
datasetsMgr.PrintInfo()
# Re-order datasets
datasetOrder = []
haveQCD = False
for d in datasetsMgr.getAllDatasets():
if "QCD" in d.getName():
haveQCD = True
datasetOrder.append(d.getName())
# Append signal datasets
datasetsMgr.selectAndReorder(datasetOrder)
# Define the mapping histograms in numerator->denominator pairs
VariableList = ["LeadingTrijet_Pt"]
# VariableList = ["LeadingTrijet_Pt", "LeadingTrijet_Eta", "LeadingTrijet_Phi"]
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
counter = 0
opts.nDatasets = len(datasetsMgr.getAllDatasets())
nPlots = len(VariableList)
# For-loop: All numerator-denominator pairs
for var in VariableList:
hNumerator = "AfterAllSelections_" + var + "_SR"
hDenominator = "AfterStandardSelections_" + var + "_SR"
numerator = os.path.join(opts.folder, hNumerator)
denFolder = opts.folder
#denFolder = denFolder.replace("Genuine", "")
#print "denFolder", denFolder
denominator = os.path.join(denFolder, hDenominator)
counter += 1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format(
"Histogram", "%i" % counter, "/", "%s:" % (nPlots),
"%s" % (var))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(),
counter == 1)
PlotEfficiency(datasetsMgr, numerator, denominator)
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(False)
style.setGridY(False)
style.setOptStat(False)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Get the PSets:
if 0:
datasetsMgr.printSelections()
#PrintPSet("BJetSelection", datasetsMgr, depth=150)
#PrintPSet("fakeBMeasurement", datasetsMgr, depth=150)
sys.exit()
# ZJets and DYJets overlap!
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get luminosity if a value is not specified
if opts.intLumi < 0:
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Remove datasets
removeList = ["QCD-b", "Charged"]
if not opts.useMC:
removeList.append("QCD")
for i, d in enumerate(removeList, 0):
msg = "Removing dataset %s" % d
Verbose(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
datasetsMgr.remove(filter(lambda name: d in name, datasetsMgr.getAllDatasetNames()))
# Print summary of datasets to be used
if 0:
datasetsMgr.PrintInfo()
# Merge EWK samples
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
# Print dataset information
datasetsMgr.PrintInfo()
# List of TDirectoryFile (_CRone, _CRtwo, _VR, _SR)
tdirs = ["LdgTetrajetMass_", "LdgTetrajetPt_", "LdgTrijetPt_", "LdgTrijetMass_", "TetrajetBJetPt_", "TetrajetBJetEta_"]
region = ["CRone", "CRtwo", "SR", "VR"]
hList = []
for d in tdirs:
for r in region:
hList.append(d + r)
# Get the folders with the binned histograms
folderList_ = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
folderList = [h for h in folderList_ if h in hList]
# For-loop: All folders
histoPaths = []
for f in folderList:
folderPath = os.path.join(opts.folder, f)
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folderPath)
pathList = [os.path.join(folderPath, h) for h in histoList]
histoPaths.extend( pathList )
# Get all the bin labels
binLabels = GetBinLabels("CRone", histoPaths)
for i, t in enumerate(tdirs, 1):
myList = []
for p in histoPaths:
if t in p:
myList.append(p)
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(tdirs)), t.replace("_", ""))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistograms(datasetsMgr, myList, binLabels, opts)
# Save the plots
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# Apply TDR style
global style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setLogX(opts.logX)
style.setLogY(opts.logY)
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print merged datasets and MC samples
if 0:
datasetsMgr.PrintInfo()
# Get Luminosity
if opts.intLumi == 0.0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = GetLumi(datasetsMgr)
# Set/Overwrite cross-sections. Remove all but 1 signal mass
signalsList = []
for s in opts.signalList:
signalsList.append("ChargedHiggs_HplusTB_HplusToTB_M_%s" % s)
# Remove datasets with overlap?
removeList = ["QCD-b"]
dsetDY = "DYJetsToQQ_HT180"
dsetZJ = "ZJetsToQQ_HT600toInf"
dsetRM = dsetZJ # datasets with overlap
removeList.append(dsetRM)
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if d.getName() not in signalsList:
removeList.append(d.getName())
# Custom filtering of datasets
for i, d in enumerate(removeList, 1):
msg = "Removing datasets %s from dataset manager" % (ShellStyles.NoteStyle() + d + ShellStyles.NormalStyle())
Verbose(msg, i==1)
datasetsMgr.remove(filter(lambda name: d == name, datasetsMgr.getAllDatasetNames()))
# Merge EWK samples
if opts.mergeEWK == "EWK":
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
if 1:
datasetsMgr.PrintInfo()
# Get list of histogram paths
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList if "AllSelections" in h]
myHistos = []
skipStrings = ["StandardSelections", "JetPt", "JetEta", "JetEtaPhi", "BJetPt", "BJetEta",
"BtagDiscriminator", "Eta", "LdgTrijetTopMassWMassRatio", "Subldg", "Dijet",
"MET", "MHT", "MinDeltaPhiJetMHT", "MaxDeltaPhiJetMHT", "MinDeltaRJetMHT", "MinDeltaRJetMHTReversed"]
for h in histoPaths:
bSkip = False
for s in skipStrings:
if s in h:
bSkip = True
break
if bSkip:
continue
else:
myHistos.append(h)
# For-loop: All histograms
for i, h in enumerate(myHistos, 1):
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(myHistos)), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistograms(datasetsMgr, h, signalsList, opts.cutDir)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
style.setLogX(opts.logX)
style.setLogY(opts.logY)
style.setLogZ(opts.logZ)
style.setWide(True, 0.15)
# style.setPadRightMargin()#0.13)
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print merged datasets and MC samples
if 0:
datasetsMgr.PrintInfo()
# Get Luminosity
if opts.intLumi < 0:
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Merge EWK samples
if opts.dataset == "EWK":
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Re-order datasets (different for inverted than default=baseline)
newOrder = []
for d in datasetsMgr.getAllDatasets():
if d.getName() == opts.dataset:
newOrder.append(d.getName())
# Sanity check on selected dataset
nDatasets = len(newOrder)
if nDatasets < 1:
msg = "Please select a valid dataset. Dataset \"%s\" does not exist!" % (opts.dataset)
Print(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(), True)
datasetsMgr.PrintInfo()
sys.exit()
if nDatasets > 1:
msg = "Please select only 1 valid dataset. Requested %i datasets for plotting!" % (nDatasets)
Print(ShellStyles.ErrorStyle() + msg + ShellStyles.NormalStyle(), True)
datasetsMgr.PrintInfo()
sys.exit()
# Select only given dataset
datasetsMgr.selectAndReorder(newOrder)
# Print dataset information
msg = "Plotting for single dataset \"%s\". Integrated luminosity is %.2f 1/fb" % (opts.dataset, opts.intLumi)
Print(ShellStyles.NoteStyle() + msg + ShellStyles.NormalStyle(), True)
datasetsMgr.PrintInfo()
# Get list of histogram paths
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
# For-loop: All histograms
for h in histoPaths:
if "_vs_" not in h.lower():
continue
Plot2dHistograms(datasetsMgr, h)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if 0:
datasetsMgr.printSelections()
sys.exit()
# Define datasets to remove by default
QCD_list = ["QCD_HT700to1000", "QCD_HT50to100", "QCD_HT500to700", "QCD_HT300to500",
"QCD_HT200to300", "QCD_HT2000toInf", "QCD_HT1500to2000", "QCD_HT100to200", "QCD_HT1000to1500"]
QCDExt_list = [x+"_ext1" for x in QCD_list]
datasetsToRemove = ["QCD-b"]
# datasetsToRemove.extend(QCD_list)
# datasetsToRemove.extend(QCDExt_list)
# ZJets and DYJets overlap
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.remove(filter(lambda name: "DYJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
if d.getName() != opts.signal:
datasetsToRemove.append(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Custom Filtering of datasets
for i, d in enumerate(datasetsToRemove, 0):
msg = "Removing dataset %s" % d
Verbose(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
datasetsMgr.remove(filter(lambda name: d == name, datasetsMgr.getAllDatasetNames()))
if opts.verbose:
datasetsMgr.PrintInfo()
# Re-order datasets (different for inverted than default=baseline)
newOrder = ["Data"]
for i, d in enumerate(datasetsMgr.getAllDatasets(), 0):
if d.isData():
continue
else:
newOrder.append(d.getName())
# Re-arrange dataset order?
if 0:
s = newOrder.pop( newOrder.index("noTop") )
newOrder.insert(len(newOrder), s) #after "Data"
# Move signal to top
if opts.signal in newOrder:
s = newOrder.pop( newOrder.index(opts.signal) )
newOrder.insert(1, s)
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
ignoreList = ["Aplanarity", "Planarity", "Sphericity", "FoxWolframMoment", "Circularity", "ThirdJetResolution", "Centrality", "_Vs_"]
myHistos = []
for h in histoPaths:
skip = False
# Skip unwanted histos
for i in ignoreList:
if i in h:
skip = True
if skip:
continue
else:
myHistos.append(h)
for i, h in enumerate(myHistos, 1):
# Plot the histograms!
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (len(myHistos)), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
DataMCHistograms(datasetsMgr, h)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
#optModes = ["", "OptChiSqrCutValue50", "OptChiSqrCutValue100"]
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
datasetsMgr_matched = GetDatasetsFromDir(opts)
datasetsMgr_matched.updateNAllEventsToPUWeighted()
datasetsMgr_matched.loadLuminosities() # from lumi.json
plots.mergeRenameReorderForDataMC(datasetsMgr)
datasetsMgr.remove(filter(lambda name: "QCD_b" in name, datasetsMgr.getAllDatasetNames())) #soti
datasetsMgr_matched.remove(filter(lambda name: "QCD" in name, datasetsMgr_matched.getAllDatasetNames())) #soti
# Set/Overwrite cross-sections
datasetsToRemove = ["QCD-b"]#, "QCD_HT50to100", "QCD_HT100to200"]#, "QCD_HT200to300"]#, "QCD_HT300to500"]
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0) # ATLAS 13 TeV H->tb exclusion limits
#if d.getName() != opts.signal:
#if "M_650" in d.getName(): #soti fixmi
# datasetsToRemove.append(d.getName())
if "M_800" in d.getName():
datasetsToRemove.append(d.getName())
#if "M_200" in d.getName():
# datasetsToRemove.append(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Custom Filtering of datasets
for i, d in enumerate(datasetsToRemove, 0):
msg = "Removing dataset %s" % d
Print(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
datasetsMgr.remove(filter(lambda name: d in name, datasetsMgr.getAllDatasetNames()))
datasetsMgr_matched.remove(filter(lambda name: d in name, datasetsMgr_matched.getAllDatasetNames())) #soti
if opts.verbose:
datasetsMgr.PrintInfo()
# ZJets and DYJets overlap
if "ZJetsToQQ_HT600toInf" in datasetsMgr.getAllDatasetNames() and "DYJetsToQQ_HT180" in datasetsMgr.getAllDatasetNames():
Print("Cannot use both ZJetsToQQ and DYJetsToQQ due to duplicate events? Investigate. Removing ZJetsToQQ datasets for now ..", True)
datasetsMgr.remove(filter(lambda name: "ZJetsToQQ" in name, datasetsMgr.getAllDatasetNames()))
#datasetsMgr.merge("QCD", GetListOfQCDatasets())
#plots._plotStyles["QCD"] = styles.getQCDLineStyle()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
# Get Luminosity
#intLumi = datasetsMgr.getDataset("Data").getLuminosity() Soti
intLumi = 35920
# Re-order datasets (different for inverted than default=baseline)
newOrder = []
# For-loop: All MC datasets
for d in datasetsMgr.getMCDatasets():
newOrder.append(d.getName())
# Move signal to top
# if opts.signal in newOrder:
# s = newOrder.pop( newOrder.index(opts.signal) )
# newOrder.insert(0, s)
print len(newOrder), "newOrder"
signalMass = ["M_200", "M_500", "M_1000", "M_650"]
for d in datasetsMgr.getMCDatasets():
for m in signalMass:
if m in d.getName():
s = newOrder.pop( newOrder.index(d.getName()) )
newOrder.insert(0, s)
#datasetsMgr.selectAndReorder(newOrder)
print len(newOrder), "newOrder"
# Add Data to list of samples!
if not opts.onlyMC:
newOrder.insert(0, "Data")
# Apply new dataset order!
datasetsMgr.selectAndReorder(newOrder)
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths1 = [os.path.join(folder, h) for h in histoList]
histoPaths2 = [h for h in histoPaths1]# if "jet" not in h.lower()]
nHistos = len(histoPaths2)
# For-loop: All histograms
for i, h in enumerate(histoPaths2, 1):
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % i, "/", "%s:" % (nHistos), h)
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), i==1)
PlotHistograms(datasetsMgr, datasetsMgr_matched, h, intLumi)
ROOT.gStyle.SetNdivisions(10, "X")
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# For-loop: All opt Mode
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities(fname="lumi.json")
# Get Luminosity
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = datasetsMgr.loadLumi()
# Set/Overwrite cross-sections
datasetsToRemove = []
for d in datasetsMgr.getAllDatasets():
if "M_%s" % (opts.signalMass) in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
else:
datasetsToRemove.append(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Custom Filtering of datasets
for i, d in enumerate(datasetsToRemove, 0):
msg = "Removing dataset %s" % d
Verbose(ShellStyles.WarningLabel() + msg + ShellStyles.NormalStyle(), i==0)
datasetsMgr.remove(filter(lambda name: d == name, datasetsMgr.getAllDatasetNames()))
if opts.verbose:
datasetsMgr.PrintInfo()
# Merge EWK samples
if opts.mergeEWK:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print dataset information
datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Do Data-MC histograms with DataDriven QCD
folder = opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
keepList = ["LdgTetrajetMass_AfterAllSelections"]
#keepList = ["LdgTetrajetMass_AfterStandardSelections"]
myHistos = []
for h in histoPaths:
if h.split("/")[-1] not in keepList:
continue
else:
myHistos.append(h)
for i, h in enumerate(myHistos, 1):
PlotHistograms(datasetsMgr, h)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(False)
style.setGridY(False)
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
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 histograms (see NtupleAnalysis/python/tools/plots.py)
if 0:
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information before removing anything?
if 0:
datasetsMgr.PrintInfo()
# Print datasets info summary
datasetsMgr.PrintInfo()
# Re-order datasets
datasetOrder = []
haveQCD = False
for d in datasetsMgr.getAllDatasets():
if "QCD" in d.getName():
haveQCD = True
datasetOrder.append(d.getName())
# Append signal datasets
datasetsMgr.selectAndReorder(datasetOrder)
# Define the mapping histograms in numerator->denominator pairs
VariableList = ["LeadingTrijet_Pt"]
# VariableList = ["LeadingTrijet_Pt", "LeadingTrijet_Eta", "LeadingTrijet_Phi"]
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
counter = 0
opts.nDatasets = len(datasetsMgr.getAllDatasets())
nPlots = len(VariableList)
# For-loop: All numerator-denominator pairs
for var in VariableList:
hNumerator = "AfterAllSelections_" + var + "_SR"
hDenominator = "AfterStandardSelections_" +var + "_SR"
numerator = os.path.join(opts.folder, hNumerator)
denFolder = opts.folder
#denFolder = denFolder.replace("Genuine", "")
#print "denFolder", denFolder
denominator = os.path.join(denFolder, hDenominator)
counter+=1
msg = "{:<9} {:>3} {:<1} {:<3} {:<50}".format("Histogram", "%i" % counter, "/", "%s:" % (nPlots), "%s" % (var))
Print(ShellStyles.SuccessStyle() + msg + ShellStyles.NormalStyle(), counter==1)
PlotEfficiency(datasetsMgr, numerator, denominator)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Obtain dsetMgrCreator and register it to module selector
dsetMgrCreator = dataset.readFromMulticrabCfg(directory=opts.mcrab)
# Get list of eras, modes, and optimisation modes
erasList = dsetMgrCreator.getDataEras()
modesList = dsetMgrCreator.getSearchModes()
optList = dsetMgrCreator.getOptimizationModes()
sysVarList = dsetMgrCreator.getSystematicVariations()
sysVarSrcList = dsetMgrCreator.getSystematicVariationSources()
# If user does not define optimisation mode do all of them
if opts.optMode == None:
if len(optList) < 1:
optList.append("")
else:
pass
optModes = optList
else:
optModes = [opts.optMode]
# For-loop: All optimisation modes
for opt in optModes:
opts.optMode = opt
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
datasetsMgr.loadLuminosities() # from lumi.json
# Print PSets used for FakeBMeasurement
if 0:
datasetsMgr.printSelections()
# Set/Overwrite cross-sections
for d in datasetsMgr.getAllDatasets():
if "ZJetsToQQ_HT600toInf" in d.getName():
datasetsMgr.remove(d.getName())
if "ChargedHiggs" in d.getName():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
if d.getName() not in opts.signal:
if not opts.acceptance:
datasetsMgr.remove(d.getName())
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
# For acceptance plot use all available masses
if opts.acceptance:
opts.signal = []
opts.signalMasses = []
for d in datasetsMgr.getAllDatasets():
if "ChargedHiggs" in d.getName():
#dName = d.getName().replace("_ext1", "").split("M_")
dName = d.getName().split("M_")
m = int(dName[-1])
opts.signal.append(d.getName())
opts.signalMasses.append(m)
if opts.intLumi < 0:
if "Data" in datasetsMgr.getAllDatasetNames():
opts.intLumi = datasetsMgr.getDataset("Data").getLuminosity()
else:
opts.intLumi = 1.0
# Merge EWK samples
if 1:
datasetsMgr.merge("EWK", aux.GetListOfEwkDatasets())
plots._plotStyles["EWK"] = styles.getAltEWKStyle()
# Print post EWK-merge dataset summary
datasetsMgr.PrintInfo()
# Get the efficiency graphs
hGraphList = []
histoName = os.path.join(opts.folder, "counter")
hGraphList, _kwargs = GetHistoGraphs(datasetsMgr, opts.folder, histoName)
# Plot the histo graphs
PlotHistoGraphs(hGraphList, _kwargs)
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
