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
import HLTausAnalysis.NtupleAnalysis.tools.dataset as dataset
import HLTausAnalysis.NtupleAnalysis.tools.histograms as histograms
import HLTausAnalysis.NtupleAnalysis.tools.counter as counter
import HLTausAnalysis.NtupleAnalysis.tools.tdrstyle as tdrstyle
import HLTausAnalysis.NtupleAnalysis.tools.styles as styles
import HLTausAnalysis.NtupleAnalysis.tools.plots as plots
import HLTausAnalysis.NtupleAnalysis.tools.crosssection as xsect
import HLTausAnalysis.NtupleAnalysis.tools.aux as aux
import HLTausAnalysis.NtupleAnalysis.tools.multicrabConsistencyCheck as consistencyCheck
import HLTausAnalysis.NtupleAnalysis.tools.ShellStyles as ShellStyles
#================================================================================================
# Variable definition
#================================================================================================
ss = ShellStyles.SuccessStyle()
ns = ShellStyles.NormalStyle()
ts = ShellStyles.NoteStyle()
hs = ShellStyles.HighlightAltStyle()
ls = ShellStyles.HighlightStyle()
es = ShellStyles.ErrorStyle()
cs = ShellStyles.CaptionStyle()
#================================================================================================
# Function Definition
#================================================================================================
def Print(msg, printHeader=False):
fName = __file__.split("/")[-1]
if printHeader == True:
print "=== ", fName
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):
# 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):
#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
import HLTausAnalysis.NtupleAnalysis.tools.dataset as dataset import HLTausAnalysis.NtupleAnalysis.tools.tdrstyle as tdrstyle import HLTausAnalysis.NtupleAnalysis.tools.styles as styles import HLTausAnalysis.NtupleAnalysis.tools.plots as plots import HLTausAnalysis.NtupleAnalysis.tools.histograms as histograms import HLTausAnalysis.NtupleAnalysis.tools.aux as aux import HLTausAnalysis.NtupleAnalysis.tools.ShellStyles as ShellStyles import ROOT ROOT.gROOT.SetBatch(True) #================================================================================================ # Shell Styles #================================================================================================ ws = ShellStyles.WarningStyle() es = ShellStyles.ErrorStyle() ls = ShellStyles.HighlightStyle() hs = ShellStyles.HighlightAltStyle() cs = ShellStyles.CaptionStyle() ns = ShellStyles.NormalStyle() rs = ShellStyles.ResultStyle() ss = ShellStyles.SuccessStyle() # s = ShellStyles.NoteStyle() # s = ShellStyles.AltStyle() # s = ShellStyles.NoteLabel() # s = ShellStyles.WarningLabel() # s = ShellStyles.ErrorLabel() # s = ShellStyles.SuccessLabel()
import HLTausAnalysis.NtupleAnalysis.tools.histograms as histograms
import HLTausAnalysis.NtupleAnalysis.tools.counter as counter
import HLTausAnalysis.NtupleAnalysis.tools.tdrstyle as tdrstyle
import HLTausAnalysis.NtupleAnalysis.tools.styles as styles
import HLTausAnalysis.NtupleAnalysis.tools.plots as plots
import HLTausAnalysis.NtupleAnalysis.tools.crosssection as xsect
import HLTausAnalysis.NtupleAnalysis.tools.aux as aux
import HLTausAnalysis.NtupleAnalysis.tools.multicrabConsistencyCheck as consistencyCheck
import HLTausAnalysis.NtupleAnalysis.tools.ShellStyles as ShellStyles
ROOT.gErrorIgnoreLevel = ROOT.kError
#================================================================================================
# Variable definition
#================================================================================================
ss = ShellStyles.SuccessStyle()
ns = ShellStyles.NormalStyle()
ts = ShellStyles.NoteStyle()
hs = ShellStyles.HighlightAltStyle()
ls = ShellStyles.HighlightStyle()
es = ShellStyles.ErrorStyle()
cs = ShellStyles.CaptionStyle()
#================================================================================================
# Function Definition
#================================================================================================
def Print(msg, printHeader=False):
fName = __file__.split("/")[-1]
if printHeader == True:
print "=== ", fName
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 Plot2dHistograms(datasetsMgr, dsetName, histoName, index):
msg = "%s%s (%s)%s" % (ShellStyles.SuccessStyle(), histoName, dsetName, ShellStyles.NormalStyle() )
aux.PrintFlushed(msg, index==1)
# Custom Filtering of datasets
dsetsMgr = datasetsMgr.deepCopy()
if opts.verbose:
dsetsMgr.PrintInfo()
# Get Histogram name and its kwargs
saveName = histoName.rsplit("/")[-1] + "_" + dsetName.split("_")[0] + dsetName.split("_")[-1]
kwargs_ = GetHistoKwargs(saveName, opts)
for i, d in enumerate(dsetsMgr.getAllDatasetNames(), 0):
if d == dsetName:
continue
else:
# Remove dataset from manager but do NOT close the file!
dsetsMgr.remove(d, close=False)
# Sanity check
nDatasets = len(dsetsMgr.getAllDatasets())
if nDatasets > 1:
raise Exception("More than 1 datasets detected in the dataset manager! Can only support 1 dataset. Please use the -i option to choose exactly 1 dataset'")
# Get the reference histo and the list of histos to compare
datasets0 = dsetsMgr.getAllDatasets()[0].getName()
histoList = [getHisto(dsetsMgr, datasets0, histoName)]
# Create the 2d plot
Verbose("Creating the 2d plot", True)
if opts.normalizeToLumi:
p = plots.MCPlot(dsetsMgr, histoName, normalizeToLumi=opts.intLumi, saveFormats=[])
elif opts.normalizeByCrossSection:
p = plots.MCPlot(dsetsMgr, histoName, normalizeByCrossSection=True, saveFormats=[], **{})
elif opts.normalizeToOne:
p = plots.MCPlot(dsetsMgr, histoName, normalizeToOne=True, saveFormats=[], **{})
else:
raise Exception("One of the options --normalizeToOne, --normalizeByCrossSection, --normalizeToLumi must be enabled (set to \"True\").")
Verbose("Setting universal histo styles", True)
p.histoMgr.setHistoDrawStyleAll("COLZ")
#p.histoMgr.setHistoLegendStyleAll("L")
Verbose("Customising histograms", True)
p.histoMgr.forEachHisto(lambda h: h.getRootHisto().GetZaxis().SetTitleOffset(1.3)) #fixme
Verbose("Setting plot styles to histograms", True)
for index, h in enumerate(p.histoMgr.getHistos()):
plots._plotStyles[p.histoMgr.getHistos()[index].getDataset().getName()].apply(p.histoMgr.getHistos()[index].getRootHisto())
Verbose("Drawing the plot", True)
plots.drawPlot(p, saveName, **kwargs_) #the "**" unpacks the kwargs_ dictionary
# Add fit line for shrinking cone?
const= 0.0
coeff= 0.0
step = 1
xmin = 0
xmax = 0
if "GenP_VisEt_Vs" in histoName:
const= 3.5
coeff= 1.0
step = 100
xmin = 0
xmax = 30
if "GenP_PtLdg_Vs" in histoName:
const= 2.5 #2.0
coeff= 1.0
step = 100
xmin = 0
xmax = 30
if "VtxIso_Vs_RelIso" in histoName:
const= -0.5
coeff= 0.3 #0.4
step = 100
xmin = 0
xmax = 0#3
if "GenP_VisEt_Vs" in histoName or "GenP_PtLdg_Vs" in histoName:
gr = getCustomTGraph(histoName, const, coeff, xmin, xmax, step)
gr.SetLineWidth(3)
gr.Draw("L same")
Verbose("Removing the legend", True)
p.removeLegend()
Verbose("Adding text on canvas", True)
histograms.addText(0.22, 0.89, plots._legendLabels[datasets0], 18)
#histograms.addText(0.5, 0.89, plots._legendLabels[datasets0], 18)
Verbose("Saving the canvas", True)
aux.SavePlot(p, opts.saveDir, saveName, opts.saveFormats, opts.url)
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
