def Plot(jsonfile, opts):
Verbose("Plotting")
with open(os.path.abspath(jsonfile)) as jfile:
j = json.load(jfile)
Verbose("Plotting %s. Will save under \"%s\"" % (j["saveName"], j["saveDir"]), True)
# Setup the style
style = tdrstyle.TDRStyle()
style.setGridX(j["gridX"]=="True")
style.setGridY(j["gridY"]=="True")
# Set ROOT batch mode boolean
ROOT.gROOT.SetBatch(opts.batchMode)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts, j)
if 0:
datasetsMgr.loadLuminosities()
#datasetsMgr.updateNAllEventsToPUWeighted()
# Print information
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)
## Print dataset information (before merge)
#datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information (after merge)
#datasetsMgr.PrintInfo() #marinaT
# Plot the histogram
MCPlot(datasetsMgr, j)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
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()
if 0:
datasetsMgr.loadLuminosities() # from lumi.json
# Custom filtering of datasets
datasetsToRemove = []
# For-loop: All dsets to be removed
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()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information
datasetsMgr.PrintInfo()
if opts.verbose:
datasetsMgr.PrintCrossSections()
# Define signal and background datasets names
dsets_signal = []
dsets_minBias = []
for d in datasetsMgr.getAllDatasetNames():
if "SingleNeutrino" in d:
dsets_minBias.append(d)
else:
dsets_signal.append(d)
# ROC curve ingredients (histograms)
effLists = [[
"Calo_Eff", "Tk_Eff", "VtxIso_Eff", "RelIso_Eff", "Iso_Eff"
],
[
"DiTau_Eff_Calo", "DiTau_Eff_Tk", "DiTau_Eff_VtxIso",
"DiTau_Eff_RelIso", "DiTau_Eff_Iso"
]]
rateLists = [
["Calo_Rate", "Tk_Rate", "VtxIso_Rate", "RelIso_Rate", "Iso_Rate"],
[
"DiTau_Rate_Calo", "DiTau_Rate_Tk", "DiTau_Rate_VtxIso",
"DiTau_Rate_RelIso", "DiTau_Rate_Iso"
]
]
turnOnLists = [[
"Calo_TurnOn25", "Tk_TurnOn25", "RelIso_TurnOn25",
"VtxIso_TurnOn25", "Iso_TurnOn25"
],
[
"Calo_TurnOn50", "Tk_TurnOn50", "RelIso_TurnOn50",
"VtxIso_TurnOn50", "Iso_TurnOn50"
]]
# For-loop: All background histos (min bias)
for i, b in enumerate(dsets_minBias, 1):
bPU = b.split("PU")[1]
# Create rate plots (SingleTau, DiTau)
PlotRate(datasetsMgr, rateLists[0], b, bPU)
PlotRate(datasetsMgr, rateLists[1], b, bPU)
# For-loop: All signal histos
for j, s in enumerate(dsets_signal, 1):
sPU = s.split("PU")[1]
# Create rate plots (SingleTau, DiTau)
PlotEfficiency(datasetsMgr, effLists[0], s, sPU)
PlotEfficiency(datasetsMgr, effLists[1], s, sPU)
if bPU != sPU:
continue
else:
PU = sPU
# For-loop: All triggers (Calo, Calo+Tk, Calo+VtxIso)
for k in range(0, len(effLists)):
eff = effLists[k]
rate = rateLists[k]
Verbose("Bkg = %s, Signal = %s" % (b, s), False)
PlotRateVsEff(datasetsMgr, eff, rate, s, b, PU)
# For-loop: All signal histos
for i, s in enumerate(dsets_signal, 1):
PU = s.split("PU")[1]
# Create rate plots (SingleTau, DiTau)
PlotTurnOns(datasetsMgr, turnOnLists[0], s, PU)
PlotTurnOns(datasetsMgr, turnOnLists[1], s, PU)
print
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
def main(opts):
# Set the ROOTeError verbosity
ROOT.gErrorIgnoreLevel = 3000 # kUnset=-1, kPrint=0, kInfo=1000, kWarning=2000, kError=3000, kBreak=4000
# 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)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintInfo()
# Setup & configure the dataset manager (no collision data => not needed)
if 0:
datasetsMgr.loadLuminosities()
datasetsMgr.updateNAllEventsToPUWeighted()
# Print information
if opts.verbose:
datasetsMgr.PrintCrossSections()
# datasetsMgr.PrintLuminosities()
# Print dataset information (before merge)
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
if 0:
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Apply new dataset order?
newOrder = ReorderDatasets(datasetsMgr.getAllDatasetNames(),
bReverse=False)
datasetsMgr.selectAndReorder(newOrder)
# Print dataset information (after merge)
if 0:
datasetsMgr.PrintInfo() #Requires python 2.7.6 or 2.6.6
# Plot Histograms
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
histoPaths = [os.path.join(opts.folder, h) for h in histoList]
histoType = type(
datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()
[0]).getDatasetRootHisto(h).getHistogram())
plotCount = 0
inList = ["ETResolution"]
# For-loop: All# histos in opts.folder
for i, h in enumerate(histoPaths, 1):
# Only do Et (don't expect dependence on PU for eta, phi
if "etresolution" not in h.lower():
continue
# Forwards region not yet available for Calos
if "_F" in h:
continue
bSkip = True
for s in inList:
bSkip = False
if bSkip:
continue
histoType = str(
type(
datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDatasetRootHisto(
h).getHistogram()))
if "TH1" not in histoType:
continue
aux.PrintFlushed(h, plotCount == 0)
plotCount += 1
PlotHisto(datasetsMgr, h)
print
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)
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()
if 0:
datasetsMgr.loadLuminosities() # from lumi.json
# Custom filtering of datasets
datasetsToRemove = []
# For-loop: All dsets to be removed
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()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information
datasetsMgr.PrintInfo()
if opts.verbose:
datasetsMgr.PrintCrossSections()
# Define signal and background datasets names
dsets_signal = []
dsets_minBias = []
for d in datasetsMgr.getAllDatasetNames():
if "SingleNeutrino" in d:
dsets_minBias.append(d)
else:
dsets_signal.append(d)
# ROC curve ingredients (histograms)
resList = [
["Calo_ResolutionEt" , "Calo_ResolutionEt_1pr" , "Calo_ResolutionEt_3pr" , "Calo_ResolutionEt_withNeutrals" , "Calo_ResolutionEt_noNeutrals" ],
["Calo_ResolutionEta", "Calo_ResolutionEta_1pr", "Calo_ResolutionEta_3pr", "Calo_ResolutionEta_withNeutrals", "Calo_ResolutionEta_noNeutrals"],
["Calo_ResolutionPhi", "Calo_ResolutionPhi_1pr", "Calo_ResolutionPhi_3pr", "Calo_ResolutionPhi_withNeutrals", "Calo_ResolutionPhi_noNeutrals"],
["Calo_ResolutionEt" , "Calo_ResolutionEt_C" , "Calo_ResolutionEt_I"], # , "Calo_ResolutionEt_F" ],
["Calo_ResolutionEta", "Calo_ResolutionEta_C" , "Calo_ResolutionEta_I"],# , "Calo_ResolutionEta_F"],
["Calo_ResolutionPhi", "Calo_ResolutionPhi_C" , "Calo_ResolutionPhi_I"],# , "CaloIso_ResolutionPhi_F"],
]
# For-loop: All signal histos
for i, s in enumerate(dsets_signal, 1):
PU = s.split("PU")[1]
# By decay mode
PlotHistos(datasetsMgr, resList[0], s, PU, "ResolutionEt_%s_all" % (s) )
PlotHistos(datasetsMgr, resList[1], s, PU, "ResolutionEta_%s_all" % (s) )
PlotHistos(datasetsMgr, resList[2], s, PU, "ResolutionPhi_%s_all" % (s) )
# By eta region (central, intermediate, forward)
PlotHistos(datasetsMgr, resList[3], s, PU, "ResolutionEt_%s_cif" % (s) )
PlotHistos(datasetsMgr, resList[4], s, PU, "ResolutionEta_%s_cif" % (s) )
PlotHistos(datasetsMgr, resList[5], s, PU, "ResolutionPhi_%s_cif" % (s) )
print
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Set the ROOTeError verbosity
ROOT.gErrorIgnoreLevel = 3000 # kUnset=-1, kPrint=0, kInfo=1000, kWarning=2000, kError=3000, kBreak=4000
# 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)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintInfo()
# Setup & configure the dataset manager (no collision data => not needed)
if 0:
datasetsMgr.loadLuminosities()
datasetsMgr.updateNAllEventsToPUWeighted()
# Print information
if opts.verbose:
datasetsMgr.PrintCrossSections()
# datasetsMgr.PrintLuminosities()
# Print dataset information (before merge)
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
if 0:
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Apply new dataset order?
newOrder = ReorderDatasets(datasetsMgr.getAllDatasetNames())
datasetsMgr.selectAndReorder(newOrder)
# Print dataset information (after merge)
if 0:
datasetsMgr.PrintInfo() #Requires python 2.7.6 or 2.6.6
# Plot Histograms
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
histoPaths = [os.path.join(opts.folder, h) for h in histoList]
histoType = type(
datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()
[0]).getDatasetRootHisto(h).getHistogram())
plotCount = 0
skipList = [
"L1TkTau_MatchTk_d0", "L1TkTau_MatchTk_d0Abs", "L1TkTau_SigTks_d0",
"L1TkTau_SigTks_d0Abs", "L1TkTau_SigTks_d0Sig",
"L1TkTau_SigTks_d0SigAbs", "L1TkTau_IsoTks_d0", "L1TkTau_IsoTks_d0Abs",
"L1TkTau_IsoTks_d0Sig", "L1TkTau_IsoTks_d0SigAbs",
"L1TkTau_ResolutionCaloEt_F", "L1TkTau_ResolutionCaloEta_F",
"L1TkTau_ResolutionCaloPhi_F", "L1TkIsoTau_ResolutionCaloEt_F",
"L1TkIsoTau_ResolutionCaloEta_F", "L1TkIsoTau_ResolutionCaloPhi_F",
"DiTau_Rate_Calo_F", "DiTau_Rate_Tk_F", "DiTau_Rate_VtxIso_F",
"DiTau_Rate_RelIso_F", "Calo_Rate_F", "Tk_Rate_F", "VtxIso_Rate_F",
"RelIso_Rate_F"
]
# For-loop: All histos in opts.folder
for i, h in enumerate(histoPaths, 1):
# Obsolete quantity
if h in skipList:
continue
histoType = str(
type(
datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDatasetRootHisto(
h).getHistogram()))
if "TH1" not in histoType:
continue
aux.PrintFlushed(h, plotCount == 0)
plotCount += 1
PlotHisto(datasetsMgr, h)
print
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() #marina
if 0:
datasetsMgr.loadLuminosities() # from lumi.json
# Set/Overwrite cross-sections
datasetsToRemove = []
for d in datasetsMgr.getAllDatasets():
datasetsMgr.getDataset(d.getName()).setCrossSection(1.0)
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()))
#if opts.verbose:
#datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
if 0:
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Re-order datasets (different for inverted than default=baseline)
newOrder = []
# For-loop: All MC datasets
for d in datasetsMgr.getMCDatasets():
newOrder.append(d.getName())
# Apply new dataset order!
datasetsMgr.selectAndReorder(newOrder)
# Print dataset information
#datasetsMgr.PrintInfo()
# Apply TDR style
style = tdrstyle.TDRStyle()
#style.setOptStat(True)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Plot Histograms
folder = "" #opts.folder
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(folder)
histoPaths = [os.path.join(folder, h) for h in histoList]
histoType = type(datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDatasetRootHisto(h).getHistogram())
for i, h in enumerate(histoPaths, 1):
histoType = str(type(datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDatasetRootHisto(h).getHistogram()))
if "TH2" not in histoType:
continue
PlotHistograms(datasetsMgr, h)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
optModes = [""]
if opts.optMode != None:
optModes = [opts.optMode]
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintInfo()
# Setup & configure the dataset manager (no collision data => not needed)
if 0:
datasetsMgr.loadLuminosities()
datasetsMgr.updateNAllEventsToPUWeighted()
# Print information
if opts.verbose:
datasetsMgr.PrintCrossSections()
# datasetsMgr.PrintLuminosities()
# Print dataset information (before merge)
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
if 0:
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Apply new dataset order?
newOrder = ReorderDatasets(datasetsMgr.getAllDatasetNames())
if 0:
datasetsMgr.selectAndReorder(newOrder)
# Sanity check (exactly 1 dataset)
nDsets = len(datasetsMgr.getAllDatasetNames())
if nDsets > 1:
msg = "Must have exactly 1 dataset (got %d). Please use the -i and -e options to choose exactly 1 dataset!" % (
nDsets)
Print(msg, True)
sys.exit()
# Print dataset information (after merge)
if 0:
datasetsMgr.PrintInfo() #Requires python 2.7.6 or 2.6.6
# Plot Histograms
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
histoPaths = [os.path.join(opts.folder, h) for h in histoList]
skipList = ["eff_", "counter", "match_trk", "match_tp", "tp_pt",
"tp_eta"] #, "resVsEta_ptRel", "resVsPt_ptRel"]
histoList = []
allowedReg = ["C", "I", "F", "L", "M", "H"]
# For-loop: All histograms (in region)
for h in histoPaths:
skip = False
for s in skipList:
if s in h.lower():
skip = True
if skip:
continue
region = h.split("_")[-1]
if region in allowedReg:
hName = h.replace(region, "")
histoList.append(hName)
# Plot the resolution histograms
uniqueList = set(histoList)
# For-loop: All histo (generic) names
for i, h in enumerate(uniqueList, 1):
if h.endswith("_"):
h = h[:-1]
aux.Print("%d/%d: %s" % (i, len(uniqueList), h), i == 1)
PlotHistograms(datasetsMgr, h)
#print
Print(
"All plots saved under directory %s" %
(ts + aux.convertToURL(opts.saveDir, opts.url) + ns), True)
return
def main(opts):
# Apply TDR style
style = tdrstyle.TDRStyle()
style.setOptStat(False)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts, 0)
datasetsMgr.updateNAllEventsToPUWeighted()
if 0:
datasetsMgr.loadLuminosities() # from lumi.json
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintLuminosities()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print datasets info summary
datasetsMgr.PrintInfo()
# Define the mapping histograms in numerator->denominator pairs
VariableList = [
"L1Taus_SingleTau_Eff", "L1Taus_SingleTau_Rate", "L1Taus_TurnOn25",
"L1Taus_TurnOn50"
]
counter = 0
opts.nDatasets = len(datasetsMgr.getAllDatasets())
nPlots = len(VariableList) * opts.nDatasets
# For-loop: All datasets
for dataset in datasetsMgr.getAllDatasets():
opts.saveDir = aux.getSaveDirPath(opts.saveDirBase,
prefix="hltaus/",
postfix="ROC")
PlotRateVsEff(datasetsMgr, dataset, "SingleTau", "PU140")
PlotRateVsEff(datasetsMgr, dataset, "SingleTau", "PU200")
PlotRateVsEff(datasetsMgr, dataset, "DiTau", "PU140")
PlotRateVsEff(datasetsMgr, dataset, "DiTau", "PU200")
# 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)
if "neutrino" in dataset.getName().lower():
if "rate" in hName.lower():
opts.saveDir = aux.getSaveDirPath(opts.saveDirBase,
prefix="hltaus/",
postfix="Rates")
PlotHistos(dataset.getName(), hPath,
hName.split("_")[0] + "_")
else:
pass
else:
if "rate" in hName.lower():
continue
else:
if "eff" in hName.lower():
opts.saveDir = aux.getSaveDirPath(
opts.saveDirBase,
prefix="hltaus/",
postfix="Efficiencies")
PlotHistos(dataset.getName(), hPath,
hName.split("_")[0] + "_")
if "turnon" in hName.lower():
opts.saveDir = aux.getSaveDirPath(opts.saveDirBase,
prefix="hltaus/",
postfix="TurnOns")
PlotHistos(dataset.getName(), hPath,
hName.split("_")[1] + "_")
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()
style.setOptStat(False)
style.setGridX(opts.gridX)
style.setGridY(opts.gridY)
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()
if 0:
datasetsMgr.loadLuminosities() # from lumi.json
# Custom filtering of datasets
datasetsToRemove = []
# For-loop: All dsets to be removed
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()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information
datasetsMgr.PrintInfo()
if opts.verbose:
datasetsMgr.PrintCrossSections()
# Define signal and background datasets names
dsets_signal = []
dsets_minBias = []
for d in datasetsMgr.getAllDatasetNames():
if "SingleNeutrino" in d:
dsets_minBias.append(d)
else:
dsets_signal.append(d)
# ROC curve ingredients (histograms)
effLists = [[
"Tk_Eff", "RelIso_Eff", "VtxIso_Eff", "VtxIsoLoose_Eff",
"VtxIsoTight_Eff", "RelIsoLoose_Eff", "RelIsoTight_Eff"
],
[
"DiTau_Eff_Tk",
"DiTau_Eff_RelIso",
"DiTau_Eff_VtxIso",
"DiTau_Eff_VtxIsoLoose",
"DiTau_Eff_VtxIsoTight",
"DiTau_Eff_RelIsoLoose",
"DiTau_Eff_RelIsoTight",
]]
rateLists = [[
"Tk_Rate", "RelIso_Rate", "VtxIso_Rate", "VtxIsoLoose_Rate",
"VtxIsoTight_Rate", "RelIsoLoose_Rate", "RelIsoTight_Rate"
],
[
"DiTau_Rate_Tk", "DiTau_Rate_RelIso",
"DiTau_Rate_VtxIso", "DiTau_Rate_VtxIsoLoose",
"DiTau_Rate_VtxIsoTight", "DiTau_Rate_RelIsoLoose",
"DiTau_Rate_RelIsoTight"
]]
turnOnLists = [
[
"Tk_TurnOn25", "RelIso_TurnOn25", "VtxIso_TurnOn25"
], # "VtxIsoLoose_TurnOn25", "VtxIsoTight_TurnOn25", "RelIsoLoose_TurnOn25", "RelIsoTight_TurnOn25"],
["Tk_TurnOn50", "RelIso_TurnOn50", "VtxIso_TurnOn50"]
] #, "VtxIsoLoose_TurnOn50", "VtxIsoTight_TurnOn50", "RelIsoLoose_TurnOn50", "RelIsoTight_TurnOn50"]]
turnOnLists_noNeutrals = [[
"Tk_TurnOn25_noNeutrals", "RelIso_TurnOn25_noNeutrals",
"VtxIso_TurnOn25_noNeutrals"
],
[
"Tk_TurnOn50_noNeutrals",
"RelIso_TurnOn50_noNeutrals",
"VtxIso_TurnOn50_noNeutrals"
]]
turnOnLists_withNeutrals = [[
"Tk_TurnOn25_withNeutrals", "RelIso_TurnOn25_withNeutrals",
"VtxIso_TurnOn25_withNeutrals"
],
[
"Tk_TurnOn50_withNeutrals",
"RelIso_TurnOn50_withNeutrals",
"VtxIso_TurnOn50_withNeutrals"
]]
turnOnLists_1pr = [[
"Tk_TurnOn25_1pr", "RelIso_TurnOn25_1pr", "VtxIso_TurnOn25_1pr"
], ["Tk_TurnOn50_1pr", "RelIso_TurnOn50_1pr", "VtxIso_TurnOn50_1pr"]]
turnOnLists_3pr = [[
"Tk_TurnOn25_3pr", "RelIso_TurnOn25_3pr", "VtxIso_TurnOn25_3pr"
], ["Tk_TurnOn50_3pr", "RelIso_TurnOn50_3pr", "VtxIso_TurnOn50_3pr"]]
turnOnLists_all = [[
"VtxIso_TurnOn25", "VtxIso_TurnOn25_1pr", "VtxIso_TurnOn25_3pr",
"VtxIso_TurnOn25_withNeutrals", "VtxIso_TurnOn25_noNeutrals"
],
[
"VtxIso_TurnOn50", "VtxIso_TurnOn50_1pr",
"VtxIso_TurnOn50_3pr",
"VtxIso_TurnOn50_withNeutrals",
"VtxIso_TurnOn50_noNeutrals"
]]
# For-loop: All background histos (min bias)
for i, b in enumerate(dsets_minBias, 1):
bPU = b.split("PU")[1]
# Create rate plots (SingleTau, DiTau)
if 1:
opts.saveDir = aux.getSaveDirPath(opts.mcrab,
prefix="hltaus/",
postfix="Rates")
PlotRate(datasetsMgr, rateLists[0], b, bPU, "SingleTau")
PlotRate(datasetsMgr, rateLists[1], b, bPU, "DiTau")
# For-loop: All signal histos
for j, s in enumerate(dsets_signal, 1):
sPU = s.split("PU")[1]
# Create rate plots (SingleTau, DiTau)
if i == 1: # (since inside minBias loop)
opts.saveDir = aux.getSaveDirPath(opts.mcrab,
prefix="hltaus/",
postfix="Efficiencies")
PlotEfficiency(datasetsMgr, effLists[0], s, sPU,
"SingleTau")
PlotEfficiency(datasetsMgr, effLists[1], s, sPU, "DiTau")
# Skip non-matching signal and bkg PU pairs?
if sPU != bPU and sPU != "":
continue
else:
if sPU == "":
sPU = "0" #rename before saving
# For-loop: All triggers
for k in range(0, len(effLists)):
eff = effLists[k]
rate = rateLists[k]
Verbose("Bkg = %s, Signal = %s" % (b, s), False)
if 1:
opts.saveDir = aux.getSaveDirPath(opts.mcrab,
prefix="hltaus/",
postfix="ROC")
PlotRateVsEff(datasetsMgr, eff, rate, s, b, sPU, bPU)
# For-loop: All signal histos
for i, s in enumerate(dsets_signal, 1):
PU = s.split("PU")[1]
# Create rate plots (SingleTau, DiTau)
if 1:
opts.saveDir = aux.getSaveDirPath(opts.mcrab,
prefix="hltaus/",
postfix="TurnOns")
PlotTurnOns(datasetsMgr, turnOnLists[0], s, PU,
"TurnOns_25GeV_%s_Inclusive" % (s))
PlotTurnOns(datasetsMgr, turnOnLists[1], s, PU,
"TurnOns_50GeV_%s_Inclusive" % (s))
PlotTurnOns(datasetsMgr, turnOnLists_noNeutrals[0], s, PU,
"TurnOns_25GeV_%s_noNeutrals" % (s))
PlotTurnOns(datasetsMgr, turnOnLists_noNeutrals[1], s, PU,
"TurnOns_50GeV_%s_noNeutrals" % (s))
PlotTurnOns(datasetsMgr, turnOnLists_withNeutrals[0], s, PU,
"TurnOns_25GeV_%s_withNeutrals" % (s))
PlotTurnOns(datasetsMgr, turnOnLists_withNeutrals[1], s, PU,
"TurnOns_50GeV_%s_withNeutrals" % (s))
PlotTurnOns(datasetsMgr, turnOnLists_1pr[0], s, PU,
"TurnOns_25GeV_%s_1pr" % (s))
PlotTurnOns(datasetsMgr, turnOnLists_1pr[1], s, PU,
"TurnOns_50GeV_%s_1pr" % (s))
PlotTurnOns(datasetsMgr, turnOnLists_3pr[0], s, PU,
"TurnOns_25GeV_%s_3pr" % (s))
PlotTurnOns(datasetsMgr, turnOnLists_3pr[1], s, PU,
"TurnOns_50GeV_%s_3pr" % (s))
PlotTurnOns(datasetsMgr, turnOnLists_all[0], s, PU,
"TurnOns_25GeV_%s_VtxIso_allModes" % (s))
PlotTurnOns(datasetsMgr, turnOnLists_all[1], s, PU,
"TurnOns_50GeV_%s_VtxIso_allModes" % (s))
print
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)
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()
if 0:
datasetsMgr.loadLuminosities() # from lumi.json
# Custom filtering of datasets
datasetsToRemove = []
# For-loop: All dsets to be removed
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()))
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Print dataset information
datasetsMgr.PrintInfo()
if opts.verbose:
datasetsMgr.PrintCrossSections()
# Define signal and background datasets names
dsets_signal = []
dsets_minBias = []
for d in datasetsMgr.getAllDatasetNames():
if "SingleNeutrino" in d:
dsets_minBias.append(d)
else:
dsets_signal.append(d)
# ROC curve ingredients (histograms)
resList = [
[
"TkEG_EtResolution", "TkEG_EtResolution_1pr",
"TkEG_EtResolution_3pr", "TkEG_EtResolution_withNeutrals",
"TkEG_EtResolution_noNeutrals"
],
[
"TkEG_EtaResolution", "TkEG_EtaResolution_1pr",
"TkEG_EtaResolution_3pr", "TkEG_EtaResolution_withNeutrals",
"TkEG_EtaResolution_noNeutrals"
],
[
"TkEG_PhiResolution", "TkEG_PhiResolution_1pr",
"TkEG_PhiResolution_3pr", "TkEG_PhiResolution_withNeutrals",
"TkEG_PhiResolution_noNeutrals"
],
[
"TkEG_EtResolution", "TkEG_EtResolution_C",
"TkEG_EtResolution_I", "TkEG_EtResolution_F"
],
[
"TkEG_EtaResolution", "TkEG_EtaResolution_C",
"TkEG_EtaResolution_I", "TkEG_EtaResolution_F"
],
[
"TkEG_PhiResolution", "TkEG_PhiResolution_C",
"TkEG_PhiResolution_I", "TkEG_PhiResolution_F"
],
[
"TkEG_EtResolution_F", "TkEG_EtResolution_1pr_F",
"TkEG_EtResolution_3pr_F", "TkEG_EtResolution_withNeutrals_F",
"TkEG_EtResolution_noNeutrals_F"
],
[
"TkEG_EtaResolution_F", "TkEG_EtaResolution_1pr_F",
"TkEG_EtaResolution_3pr_F",
"TkEG_EtaResolution_withNeutrals_F",
"TkEG_EtaResolution_noNeutrals_F"
],
[
"TkEG_PhiResolution_F", "TkEG_PhiResolution_1pr_F",
"TkEG_PhiResolution_3pr_F",
"TkEG_PhiResolution_withNeutrals_F",
"TkEG_PhiResolution_noNeutrals_F"
],
[
"TkEG_EtResolution_F_withEGs",
"TkEG_EtResolution_1pr_F_withEGs",
"TkEG_EtResolution_3pr_F_withEGs",
"TkEG_EtResolution_withNeutrals_F_withEGs",
"TkEG_EtResolution_noNeutrals_F_withEGs"
],
[
"TkEG_EtaResolution_F_withEGs",
"TkEG_EtaResolution_1pr_F_withEGs",
"TkEG_EtaResolution_3pr_F_withEGs",
"TkEG_EtaResolution_withNeutrals_F_withEGs",
"TkEG_EtaResolution_noNeutrals_F_withEGs"
],
[
"TkEG_PhiResolution_F_withEGs",
"TkEG_PhiResolution_1pr_F_withEGs",
"TkEG_PhiResolution_3pr_F_withEGs",
"TkEG_PhiResolution_withNeutrals_F_withEGs",
"TkEG_PhiResolution_noNeutrals_F_withEGs"
],
[
"TkEG_EtResolution_F_noEGs", "TkEG_EtResolution_1pr_F_noEGs",
"TkEG_EtResolution_3pr_F_noEGs",
"TkEG_EtResolution_withNeutrals_F_noEGs",
"TkEG_EtResolution_noNeutrals_F_noEGs"
],
[
"TkEG_EtaResolution_F_noEGs", "TkEG_EtaResolution_1pr_F_noEGs",
"TkEG_EtaResolution_3pr_F_noEGs",
"TkEG_EtaResolution_withNeutrals_F_noEGs",
"TkEG_EtaResolution_noNeutrals_F_noEGs"
],
[
"TkEG_PhiResolution_F_noEGs", "TkEG_PhiResolution_1pr_F_noEGs",
"TkEG_PhiResolution_3pr_F_noEGs",
"TkEG_PhiResolution_withNeutrals_F_noEGs",
"TkEG_PhiResolution_noNeutrals_F_noEGs"
],
[
"TkEG_EtResolution_F_withEGs_posEta",
"TkEG_EtResolution_1pr_F_withEGs_posEta",
"TkEG_EtResolution_3pr_F_withEGs_posEta",
"TkEG_EtResolution_withNeutrals_F_withEGs_posEta",
"TkEG_EtResolution_noNeutrals_F_withEGs_posEta"
],
[
"TkEG_EtaResolution_F_withEGs_posEta",
"TkEG_EtaResolution_1pr_F_withEGs_posEta",
"TkEG_EtaResolution_3pr_F_withEGs_posEta",
"TkEG_EtaResolution_withNeutrals_F_withEGs_posEta",
"TkEG_EtaResolution_noNeutrals_F_withEGs_posEta"
],
[
"TkEG_PhiResolution_F_withEGs_posEta",
"TkEG_PhiResolution_1pr_F_withEGs_posEta",
"TkEG_PhiResolution_3pr_F_withEGs_posEta",
"TkEG_PhiResolution_withNeutrals_F_withEGs_posEta",
"TkEG_PhiResolution_noNeutrals_F_withEGs_posEta"
],
[
"TkEG_EtResolution_F_noEGs_posEta",
"TkEG_EtResolution_1pr_F_noEGs_posEta",
"TkEG_EtResolution_3pr_F_noEGs_posEta",
"TkEG_EtResolution_withNeutrals_F_noEGs_posEta",
"TkEG_EtResolution_noNeutrals_F_noEGs_posEta"
],
[
"TkEG_EtaResolution_F_noEGs_posEta",
"TkEG_EtaResolution_1pr_F_noEGs_posEta",
"TkEG_EtaResolution_3pr_F_noEGs_posEta",
"TkEG_EtaResolution_withNeutrals_F_noEGs_posEta",
"TkEG_EtaResolution_noNeutrals_F_noEGs_posEta"
],
[
"TkEG_PhiResolution_F_noEGs_posEta",
"TkEG_PhiResolution_1pr_F_noEGs_posEta",
"TkEG_PhiResolution_3pr_F_noEGs_posEta",
"TkEG_PhiResolution_withNeutrals_F_noEGs_posEta",
"TkEG_PhiResolution_noNeutrals_F_noEGs_posEta"
],
[
"TkEG_EtResolution_F_withEGs_negEta",
"TkEG_EtResolution_1pr_F_withEGs_negEta",
"TkEG_EtResolution_3pr_F_withEGs_negEta",
"TkEG_EtResolution_withNeutrals_F_withEGs_negEta",
"TkEG_EtResolution_noNeutrals_F_withEGs_negEta"
],
[
"TkEG_EtaResolution_F_withEGs_negEta",
"TkEG_EtaResolution_1pr_F_withEGs_negEta",
"TkEG_EtaResolution_3pr_F_withEGs_negEta",
"TkEG_EtaResolution_withNeutrals_F_withEGs_negEta",
"TkEG_EtaResolution_noNeutrals_F_withEGs_negEta"
],
[
"TkEG_PhiResolution_F_withEGs_negEta",
"TkEG_PhiResolution_1pr_F_withEGs_negEta",
"TkEG_PhiResolution_3pr_F_withEGs_negEta",
"TkEG_PhiResolution_withNeutrals_F_withEGs_negEta",
"TkEG_PhiResolution_noNeutrals_F_withEGs_negEta"
],
[
"TkEG_EtResolution_F_noEGs_negEta",
"TkEG_EtResolution_1pr_F_noEGs_negEta",
"TkEG_EtResolution_3pr_F_noEGs_negEta",
"TkEG_EtResolution_withNeutrals_F_noEGs_negEta",
"TkEG_EtResolution_noNeutrals_F_noEGs_negEta"
],
[
"TkEG_EtaResolution_F_noEGs_negEta",
"TkEG_EtaResolution_1pr_F_noEGs_negEta",
"TkEG_EtaResolution_3pr_F_noEGs_negEta",
"TkEG_EtaResolution_withNeutrals_F_noEGs_negEta",
"TkEG_EtaResolution_noNeutrals_F_noEGs_negEta"
],
[
"TkEG_PhiResolution_F_noEGs_negEta",
"TkEG_PhiResolution_1pr_F_noEGs_negEta",
"TkEG_PhiResolution_3pr_F_noEGs_negEta",
"TkEG_PhiResolution_withNeutrals_F_noEGs_negEta",
"TkEG_PhiResolution_noNeutrals_F_noEGs_negEta"
],
[
"TkEG_ChargedResolution", "TkEG_ChargedResolution_1pr",
"TkEG_ChargedResolution_3pr",
"TkEG_ChargedResolution_withNeutrals",
"TkEG_ChargedResolution_noNeutrals"
],
[
"TkEG_NeutralsResolution", "TkEG_NeutralsResolution_1pr",
"TkEG_NeutralsResolution_3pr",
"TkEG_NeutralsResolution_withNeutrals",
"TkEG_NeutralsResolution_noNeutrals"
],
[
"TkEG_EtResolution_withEGs", "TkEG_EtResolution_1pr_withEGs",
"TkEG_EtResolution_3pr_withEGs",
"TkEG_EtResolution_withNeutrals_withEGs",
"TkEG_EtResolution_noNeutrals_withEGs"
],
[
"TkEG_EtResolution_noEGs", "TkEG_EtResolution_1pr_noEGs",
"TkEG_EtResolution_3pr_noEGs",
"TkEG_EtResolution_withNeutrals_noEGs",
"TkEG_EtResolution_noNeutrals_noEGs"
],
[
"TkEG_EtResolution_withNeutrals",
"TkEG_EtResolution_withNeutrals_1pr",
"TkEG_EtResolution_withNeutrals_3pr"
],
[
"TkEG_EtResolution_withNeutrals",
"TkEG_EtResolution_withNeutrals_1pion0",
"TkEG_EtResolution_withNeutrals_2pion0",
"TkEG_EtResolution_withNeutrals_3pion0"
], #,"TkEG_EtResolution_withNeutrals_4pion0"],
[
"TkEG_EtResolution_withNeutrals_withEGs",
"TkEG_EtResolution_withNeutrals_withEGs_0to5GeV",
"TkEG_EtResolution_withNeutrals_withEGs_5to10GeV",
"TkEG_EtResolution_withNeutrals_withEGs_10to15GeV",
"TkEG_EtResolution_withNeutrals_withEGs_15to20GeV",
"TkEG_EtResolution_withNeutrals_withEGs_20to30GeV",
"TkEG_EtResolution_withNeutrals_withEGs_30to40GeV",
"TkEG_EtResolution_withNeutrals_withEGs_40to50GeV"
]
]
# For-loop: All signal histos
for i, s in enumerate(dsets_signal, 1):
PU = s.split("PU")[1]
# By decay mode
PlotHistos(datasetsMgr, resList[0], s, PU,
"ResolutionEt_%s_all" % (s))
PlotHistos(datasetsMgr, resList[1], s, PU,
"ResolutionEta_%s_all" % (s))
PlotHistos(datasetsMgr, resList[2], s, PU,
"ResolutionPhi_%s_all" % (s))
# By eta region (central, intermediate, forward)
PlotHistos(datasetsMgr, resList[3], s, PU,
"ResolutionEt_%s_cif" % (s))
PlotHistos(datasetsMgr, resList[4], s, PU,
"ResolutionEta_%s_cif" % (s))
PlotHistos(datasetsMgr, resList[5], s, PU,
"ResolutionPhi_%s_cif" % (s))
PlotHistos(datasetsMgr, resList[6], s, PU,
"ResolutionEt_%s_all_F" % (s))
PlotHistos(datasetsMgr, resList[7], s, PU,
"ResolutionEta_%s_all_F" % (s))
PlotHistos(datasetsMgr, resList[8], s, PU,
"ResolutionPhi_%s_all_F" % (s))
PlotHistos(datasetsMgr, resList[9], s, PU,
"ResolutionEt_%s_all_F_withEGs" % (s))
PlotHistos(datasetsMgr, resList[10], s, PU,
"ResolutionEta_%s_all_F_withEGs" % (s))
PlotHistos(datasetsMgr, resList[11], s, PU,
"ResolutionPhi_%s_all_F_withEGs" % (s))
PlotHistos(datasetsMgr, resList[12], s, PU,
"ResolutionEt_%s_all_F_noEGs" % (s))
PlotHistos(datasetsMgr, resList[13], s, PU,
"ResolutionEta_%s_all_F_noEGs" % (s))
PlotHistos(datasetsMgr, resList[14], s, PU,
"ResolutionPhi_%s_all_F_noEGs" % (s))
PlotHistos(datasetsMgr, resList[15], s, PU,
"ResolutionEt_%s_all_F_withEGs_posEta" % (s))
PlotHistos(datasetsMgr, resList[16], s, PU,
"ResolutionEta_%s_all_F_withEGs_posEta" % (s))
PlotHistos(datasetsMgr, resList[17], s, PU,
"ResolutionPhi_%s_all_F_withEGs_posEta" % (s))
PlotHistos(datasetsMgr, resList[18], s, PU,
"ResolutionEt_%s_all_F_noEGs_posEta" % (s))
PlotHistos(datasetsMgr, resList[19], s, PU,
"ResolutionEta_%s_all_F_noEGs_posEta" % (s))
PlotHistos(datasetsMgr, resList[20], s, PU,
"ResolutionPhi_%s_all_F_noEGs_posEta" % (s))
PlotHistos(datasetsMgr, resList[21], s, PU,
"ResolutionEt_%s_all_F_withEGs_negEta" % (s))
PlotHistos(datasetsMgr, resList[22], s, PU,
"ResolutionEta_%s_all_F_withEGs_negEta" % (s))
PlotHistos(datasetsMgr, resList[23], s, PU,
"ResolutionPhi_%s_all_F_withEGs_negEta" % (s))
PlotHistos(datasetsMgr, resList[24], s, PU,
"ResolutionEt_%s_all_F_noEGs_negEta" % (s))
PlotHistos(datasetsMgr, resList[25], s, PU,
"ResolutionEta_%s_all_F_noEGs_negEta" % (s))
PlotHistos(datasetsMgr, resList[26], s, PU,
"ResolutionPhi_%s_all_F_noEGs_negEta" % (s))
PlotHistos(datasetsMgr, resList[27], s, PU,
"ResolutionCharged_%s_all" % (s))
PlotHistos(datasetsMgr, resList[28], s, PU,
"ResolutionNeutrals_%s_all" % (s))
PlotHistos(datasetsMgr, resList[29], s, PU,
"ResolutionEt_%s_all_withEGs" % (s))
PlotHistos(datasetsMgr, resList[30], s, PU,
"ResolutionEt_%s_all_noEGs" % (s))
PlotHistos(datasetsMgr, resList[31], s, PU,
"ResolutionEt_%s_withNeutrals" % (s))
PlotHistos(datasetsMgr, resList[32], s, PU,
"ResolutionEt_%s_withNeutrals_Npion0" % (s))
PlotHistos(datasetsMgr, resList[33], s, PU,
"ResolutionEt_%s_withNeutrals_withEGs_etSteps" % (s))
print
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(False, 0.15)
# 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)
# Get Luminosity
if opts.intLumi < 0:
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Custom Filtering of datasets
datasetsToRemove = []
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()))
if opts.verbose:
datasetsMgr.PrintInfo()
# Re-order datasets (different for inverted than default=baseline)
newOrder = []
for d in datasetsMgr.getMCDatasets():
newOrder.append(d.getName())
# Apply new dataset order!
datasetsMgr.selectAndReorder(newOrder)
# Print dataset information
datasetsMgr.PrintInfo()
# Plot Histograms
histoList = datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
histoPaths = [os.path.join(opts.folder, h) for h in histoList]
# For-loop: All histograms in chosen folder
counter = 0
for i, h in enumerate(histoPaths, 1):
histoType = str(type(datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()[0]).getDatasetRootHisto(h).getHistogram()))
if "TH2" not in histoType:
continue
if "DiTau" in h:
continue
if "L1TkIsoTau" in h:
continue
# For-loop: All datasets
for d in datasetsMgr.getAllDatasetNames():
counter += 1
#if d != "SingleNeutrino_L1TPU140":
# continue
#if "reliso" not in h.lower():
# continue
Plot2dHistograms(datasetsMgr, d, h, counter)
print
Print("All plots saved under directory %s" % (ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) + ShellStyles.NormalStyle()), True)
return
def main(opts):
# Set the ROOTeError verbosity
ROOT.gErrorIgnoreLevel = 3000 # kUnset=-1, kPrint=0, kInfo=1000, kWarning=2000, kError=3000, kBreak=4000
# 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)
# Setup & configure the dataset manager
datasetsMgr = GetDatasetsFromDir(opts)
datasetsMgr.updateNAllEventsToPUWeighted()
if opts.verbose:
datasetsMgr.PrintCrossSections()
datasetsMgr.PrintInfo()
# Setup & configure the dataset manager (no collision data => not needed)
if 0:
datasetsMgr.loadLuminosities()
datasetsMgr.updateNAllEventsToPUWeighted()
# Print information
if opts.verbose:
datasetsMgr.PrintCrossSections()
# datasetsMgr.PrintLuminosities()
# Print dataset information (before merge)
datasetsMgr.PrintInfo()
# Merge histograms (see NtupleAnalysis/python/tools/plots.py)
plots.mergeRenameReorderForDataMC(datasetsMgr)
# Get Luminosity
if 0:
intLumi = datasetsMgr.getDataset("Data").getLuminosity()
# Print dataset information (after merge)
if 0:
datasetsMgr.PrintInfo() #Requires python 2.7.6 or 2.6.6
# Sanity check
nDatasets = len(datasetsMgr.getAllDatasetNames())
if (nDatasets) > 1:
raise Exception(
"More than 1 datasets found! Please choose only 1 dataset (-i \"regex\") and retry"
)
# Plot Histograms
histoList = datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDirectoryContent(opts.folder)
histoPaths = [os.path.join(opts.folder, h) for h in histoList]
histoType = type(
datasetsMgr.getDataset(datasetsMgr.getAllDatasetNames()
[0]).getDatasetRootHisto(h).getHistogram())
plotCount = 0
# For-loop: All# histos in opts.folder
for i, h in enumerate(histoPaths, 1):
histoType = str(
type(
datasetsMgr.getDataset(
datasetsMgr.getAllDatasetNames()[0]).getDatasetRootHisto(
h).getHistogram()))
if "TH1" in histoType or "TDirectoryFile" in histoType:
continue
else:
# print "histoType = ", histoType
pass
if "GenP" in h:
continue
aux.PrintFlushed(h, plotCount == 0)
plotCount += 1
PlotHisto(datasetsMgr, h, histoType)
print
Print(
"All plots saved under directory %s" %
(ShellStyles.NoteStyle() + aux.convertToURL(opts.saveDir, opts.url) +
ShellStyles.NormalStyle()), True)
return
