algorithms = [ 'shrinkingCone', 'TaNC', 'hps', 'calo' ]
if __name__ == "__main__":
ROOT.gROOT.SetBatch(True)
ROOT.gROOT.SetStyle("Plain")
ROOT.gStyle.SetOptStat(0)
# Get ZTT samples
ztt = samples.zttPU156bx_mc # particleFlow
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
# Add each sample we want to plot/compare
plotter.add_sample(ztt, "Z #rightarrow #ell #ell, BX156", **style.QCD_MC_STYLE_HIST)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data_wjets.effective_luminosity())
# Get the ntuple we produced
ntuple_manager = ztt.build_ntuple_manager("tauIdEffNtuple")
# Get generator level tau ntuple
genTaus = ntuple_manager.get_ntuple("tauGenJets")
# Get ntuple with vertex information
vertex = ntuple_manager.get_ntuple("offlinePrimaryVertices")
custom_LUMI_LABEL_UPPER_LEFT = copy.deepcopy(style.LUMI_LABEL_UPPER_LEFT)
custom_LUMI_LABEL_UPPER_LEFT.SetTextSize(0.0375)
custom_LUMI_LABEL_UPPER_LEFT.Clear()
custom_LUMI_LABEL_UPPER_LEFT.AddText("Data, L = 8.4nb^{-1}")
custom_LUMI_LABEL_UPPER_LEFT.Draw()
style.SQRTS_LABEL_UPPER_LEFT.Draw()
for extra_label in fakerate_results['calo'][x_var]['extra_labels']:
extra_label.Draw()
# Save the plot
canvas.SaveAs("plots/%s.png" % '_'.join(['fakerate_algo_comparison', 'vs', x_var]))
canvas.SaveAs("plots/%s.pdf" % '_'.join(['fakerate_algo_comparison', 'vs', x_var]))
if __name__ == "__main__":
plotter = PlotManager()
# Add each sample we want to plot/compare
# Uncomment to add QCD
plotter.add_sample(samples.qcddijet_mc, "Simulation", **style.QCD_MC_PYTHIA6_STYLE_HIST)
plotter.add_sample(samples.data_dijet, "Data", **style.DATA_STYLE)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data_dijet.effective_luminosity())
# Build the ntuple manager
ntuple_manager = samples.data_dijet.build_ntuple_manager("tauIdEffNtuple")
nTuples = {
"shrinkingCone": ntuple_manager.get_ntuple("patPFTausDijetTagAndProbeShrinkingCone"),
algorithms = ['shrinkingCone', 'TaNC', 'hps', 'calo']
if __name__ == "__main__":
ROOT.gROOT.SetBatch(True)
ROOT.gROOT.SetStyle("Plain")
ROOT.gStyle.SetOptStat(0)
# Get ZTT samples
ztt = samples.zttPU156bx_mc # particleFlow
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
# Add each sample we want to plot/compare
plotter.add_sample(ztt, "Z #rightarrow #ell #ell, BX156",
**style.QCD_MC_STYLE_HIST)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data_wjets.effective_luminosity())
# Get the ntuple we produced
ntuple_manager = ztt.build_ntuple_manager("tauIdEffNtuple")
# Get generator level tau ntuple
genTaus = ntuple_manager.get_ntuple("tauGenJets")
# Get ntuple with vertex information
canvas_diff.cd()
canvas_diff.Update()
canvas_diff.SaveAs(outputFileName[:outputFileName.find(".")] +
"_%s_diff.png" % algorithm)
canvas_diff.SaveAs(outputFileName[:outputFileName.find(".")] +
"_%s_diff.pdf" % algorithm)
return result_algorithm
if __name__ == "__main__":
# define PlotManagers for QCD Monte Carlo and data samples
plotter_dijet_mc = PlotManager()
plotter_dijet_mc.add_sample(sample_dijet_mc, "QCDj Simulation",
**custom_style_dijet_mc)
plotters_dijet_data = {}
for dijet_dataHLTpath in dijet_dataHLTpaths:
plotter_dijet_data = PlotManager()
plotter_dijet_data.add_sample(
samples_dijet_data[dijet_dataHLTpath],
"QCDj Data: %s" % dijet_dataHLTpath,
**custom_styles_dijet_data[dijet_dataHLTpath])
plotters_dijet_data[dijet_dataHLTpath] = plotter_dijet_data
# Build the ntuple manager
ntuple_manager = sample_dijet_mc.build_ntuple_manager("tauIdEffNtuple")
nTuples = {
# Figure out how to get the information about the pileup information. This
# has to be determiend from one of the MC samples.
ntuple_mananger_wPUinfo = sample_dijet_mc.build_ntuple_manager(
"tauIdEffNtuple")
# Define the expression which re-weights events for PU.
puinfo_ntuple = ntuple_mananger_wPUinfo.get_ntuple("addPileupInfo")
pu_reweight_expr = puinfo_ntuple.expr('$vtxMultReweight')
# Uncomment following line to DISABLE reweighting
# pu_reweight_expr = "1.0"
# define PlotManagers for QCD multi-jet, QCD muon enriched and W + jets samples
# For the MC samples, reweight according to PU vertices
plotter_dijet = PlotManager()
plotter_dijet.add_sample(sample_dijet_data, "QCDj Data",
**custom_style_dijet_data)
plotter_dijet.add_sample(sample_dijet_mc,
"QCDj Simulation",
weight_expr=pu_reweight_expr,
**custom_style_dijet_mc)
plotter_dijet.set_integrated_lumi(intLumiData)
plotter_ppmux = PlotManager()
plotter_ppmux.add_sample(sample_ppmux_data, "QCD#mu Data",
**custom_style_ppmux_data)
plotter_ppmux.add_sample(sample_ppmux_mc,
"QCD#mu Simulation",
weight_expr=pu_reweight_expr,
**custom_style_ppmux_mc)
#!/usr/bin/env python
import ROOT
from TauAnalysis.TauIdEfficiency.ntauples.PlotManager import PlotManager
import TauAnalysis.TauIdEfficiency.ntauples.styles as style
# Definition of input files.
import samples_cache as samples
plotter = PlotManager()
# Add each sample we want to plot/compare
# Uncomment to add QCD
plotter.add_sample(samples.qcd_mc_pythia8, "Simulation", **style.QCD_MC_PYTHIA8_STYLE_HIST)
#plotter.add_sample(samples.qcd_mc_pythia6, "QCD (Pythia 6)", **style.QCD_MC_PYTHIA6_STYLE_HIST)
#plotter.add_sample(samples.minbias_mc, "Minbias MC", **style.MINBIAS_MC_STYLE)
plotter.add_sample(samples.data, "Data", **style.DATA_STYLE)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data.effective_luminosity())
# Build the ntuple manager
ntuple_manager = samples.data.build_ntuple_manager("tauIdEffNtuple")
shrinking_ntuple = ntuple_manager.get_ntuple(
"patPFTausDijetTagAndProbeShrinkingCone")
hlt = ntuple_manager.get_ntuple("TriggerResults")
import ROOT
from TauAnalysis.TauIdEfficiency.ntauples.PlotManager import PlotManager
import TauAnalysis.TauIdEfficiency.ntauples.styles as style
import os
import samples_cache as samples
if __name__ == "__main__":
ROOT.gROOT.SetBatch(True)
if not os.path.isdir('plots_Control'):
os.mkdir('plots_Control')
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
plotter.add_sample(samples.qcd_mc_pythia6, "PYTHIA 6",
**style.QCD_MC_PYTHIA6_STYLE_DOTS)
plotter.add_sample(samples.qcd_mc_pythia8, "PYTHIA 8",
**style.QCD_MC_PYTHIA8_STYLE_HIST)
plotter.add_sample(samples.data, "Data", **style.DATA_STYLE)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data.effective_luminosity())
# Build the ntuple maanger
ntuple_manager = samples.data.build_ntuple_manager("tauIdEffNtuple")
# Figure out how to get the information about the pileup information. This
# has to be determiend from one of the MC samples.
ntuple_mananger_wPUinfo = sample_dijet_mc.build_ntuple_manager(
"tauIdEffNtuple")
# Define the expression which re-weights events for PU.
puinfo_ntuple = ntuple_mananger_wPUinfo.get_ntuple("addPileupInfo")
pu_reweight_expr = puinfo_ntuple.expr('$vtxMultReweight')
# Uncomment following line to DISABLE reweighting
# pu_reweight_expr = "1.0"
# define PlotManagers for QCD multi-jet, QCD muon enriched and W + jets samples
# For the MC samples, reweight according to PU vertices
plotter_dijet = PlotManager()
plotter_dijet.add_sample(sample_dijet_data, "QCDj Data", **custom_style_dijet_data)
plotter_dijet.add_sample(sample_dijet_mc, "QCDj Simulation",
weight_expr = pu_reweight_expr, **custom_style_dijet_mc)
plotter_dijet.set_integrated_lumi(intLumiData)
plotter_ppmux = PlotManager()
plotter_ppmux.add_sample(sample_ppmux_data, "QCD#mu Data", **custom_style_ppmux_data)
plotter_ppmux.add_sample(sample_ppmux_mc, "QCD#mu Simulation",
weight_expr = pu_reweight_expr, **custom_style_ppmux_mc)
plotter_ppmux.set_integrated_lumi(intLumiData)
plotter_wjets = PlotManager()
plotter_wjets.add_sample(sample_wjets_data, "W #rightarrow #mu #nu Data", **custom_style_wjets_data)
plotter_wjets.add_sample(sample_wjets_mc, "W #rightarrow #mu #nu Simulation",
weight_expr = pu_reweight_expr, **custom_style_wjets_mc)
import TauAnalysis.TauIdEfficiency.ntauples.styles as style
# Defintion of input files.
import samples_cache as samples
import os
if __name__ == "__main__":
ROOT.gROOT.SetBatch(True)
if not os.path.isdir('plots'):
os.mkdir('plots')
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
# Add each sample we want to plot/compare
# Uncomment to add QCD
plotter.add_sample(samples.ztautau_mc, "Z->#tau#tau MC",
**style.QCD_MC_STYLE_HIST)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data.effective_luminosity())
# Build the ntuple maanger
ntuple_manager = samples.ztautau_mc.build_ntuple_manager("tauIdEffNtuple")
# Get the shrinking ntuple
shrinking_ntuple = ntuple_manager.get_ntuple(
"patPFTausDijetTagAndProbeShrinkingCone")
# Defintion of input files.
import samples as samples
import os
import sys
if __name__ == "__main__":
ROOT.gROOT.SetBatch(True)
if not os.path.isdir('plots'):
os.mkdir('plots')
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
# Add each sample we want to plot/compare
# Uncomment to add QCD
plotter.add_sample(samples.ztautau_mc, "Z->#tau#tau MC", **style.QCD_MC_STYLE_HIST)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data.effective_luminosity())
# Build the ntuple maanger
ntuple_manager = samples.ztautau_mc.build_ntuple_manager("tauIdEffNtuple")
# Get the shrinking ntuple
hps_ntuple = ntuple_manager.get_ntuple(
"patPFTausDijetTagAndProbeHPS")
canvas_diff.Update()
canvas_diff.SaveAs("plots/" + filename + "_diff.png")
canvas_diff.SaveAs("plots/" + filename + "_diff.pdf")
canvas_diff.IsA().Destructor(canvas_diff)
if __name__ == "__main__":
ROOT.gROOT.SetBatch(True)
if not os.path.isdir('plots'):
os.mkdir('plots')
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
# Add each sample we want to plot/compare
plotter.add_sample(sample_wjets_data, "W #rightarrow #mu #nu Data", **custom_style_wjets_data)
plotter.add_sample(sample_wjets_mc, "W #rightarrow #mu #nu Simulation", **custom_style_wjets_mc)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(sample_wjets_data.effective_luminosity())
# Build the ntuple manager
ntuple_manager = sample_wjets_data.build_ntuple_manager("tauIdEffNtuple")
# Get HLT trigger decisions
hlt_ntuple = ntuple_manager.get_ntuple("patTriggerEvent")
######################################################
canvas_diff.SaveAs("plots/" + filename + "_diff.png")
canvas_diff.SaveAs("plots/" + filename + "_diff.pdf")
canvas_diff.IsA().Destructor(canvas_diff)
if __name__ == "__main__":
ROOT.gROOT.SetBatch(True)
if not os.path.isdir('plots'):
os.mkdir('plots')
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
# Add each sample we want to plot/compare
plotter.add_sample(sample_wjets_data, "W #rightarrow #mu #nu Data",
**custom_style_wjets_data)
plotter.add_sample(sample_wjets_mc, "W #rightarrow #mu #nu Simulation",
**custom_style_wjets_mc)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(sample_wjets_data.effective_luminosity())
# Build the ntuple manager
ntuple_manager = sample_wjets_data.build_ntuple_manager("tauIdEffNtuple")
# Get HLT trigger decisions
hlt_ntuple = ntuple_manager.get_ntuple("patTriggerEvent")
# Defintion of input files.
import samples as samples
import os
import sys
if __name__ == "__main__":
ROOT.gROOT.SetBatch(True)
if not os.path.isdir('plots'):
os.mkdir('plots')
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
# Add each sample we want to plot/compare
# Uncomment to add QCD
plotter.add_sample(samples.qcd_mc_pythia8, "QCD MC",
**style.QCD_MC_STYLE_HIST)
#plotter.add_sample(samples.minbias_mc, "Minbias MC", **style.MINBIAS_MC_STYLE)
plotter.add_sample(samples.data, "Data (7 TeV)", **style.DATA_STYLE)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data.effective_luminosity())
# Build the ntuple maanger
ntuple_manager = samples.data.build_ntuple_manager("tauIdEffNtuple")
phi_resol['result'].GetXaxis().SetTitleOffset(1.2)
# Make a pave text w/ mean rms
stat_label = style.make_mean_rms_pave(
phi_resol['samples']['mc_ztt']['plot'])
stat_label.Draw()
canvas.SaveAs("plots/%s%s_phi_resolution.png" %
(algorithm, selection["style_name"]))
canvas.SaveAs("plots/%s%s_phi_resolution.pdf" %
(algorithm, selection["style_name"]))
if __name__ == "__main__":
plotter = PlotManager()
# Add each sample we want to plot/compare
# Uncomment to add QCD
plotter.add_sample(samples.ztautau_mc, "Z->#tau#tau MC",
**style.QCD_MC_STYLE_HIST)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data.effective_luminosity())
# Build the ntuple manager
ntuple_manager = samples.ztautau_mc.build_ntuple_manager("tauIdEffNtuple")
nTuples = {
"shrinkingCone":
ntuple_manager.get_ntuple("patPFTausDijetTagAndProbeShrinkingCone"),
# Defintion of input files.
import samples_cache as samples
import os
import sys
if __name__ == "__main__":
ROOT.gROOT.SetBatch(True)
if not os.path.isdir('plots'):
os.mkdir('plots')
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
# Add each sample we want to plot/compare
plotter.add_sample(samples.qcd_mc, "QCD MC", **style.QCD_MC_STYLE_HIST)
# plotter.add_sample(samples.minbias_mc, "Minbias MC", **style.MINBIAS_MC_STYLE)
plotter.add_sample(samples.data, "Data (7 TeV)", **style.DATA_STYLE)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data.effective_luminosity())
# Build the ntuple maanger
ntuple_manager = samples.data.build_ntuple_manager("tauIdEffNtuple")
import ROOT
from TauAnalysis.TauIdEfficiency.ntauples.PlotManager import PlotManager
import TauAnalysis.TauIdEfficiency.ntauples.styles as style
# Definition of input files.
import samples_cache as samples
import os
import copy
import sys
from optparse import OptionParser
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
# Add each sample we want to plot/compare
# Uncomment to add QCD
plotter.add_sample(samples.qcd_mc_pythia8, "Pythia 8",
**style.QCD_MC_PYTHIA8_STYLE_HIST)
plotter.add_sample(samples.qcd_mc_pythia6, "Pythia 6",
**style.QCD_MC_PYTHIA6_STYLE_HIST)
#plotter.add_sample(samples.minbias_mc, "Minbias MC", **style.MINBIAS_MC_STYLE)
plotter.add_sample(samples.data, "Data", **style.DATA_STYLE)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data.effective_luminosity())
# Defintion of input files.
import samples as samples
import os
import sys
if __name__ == "__main__":
ROOT.gROOT.SetBatch(True)
if not os.path.isdir('plots'):
os.mkdir('plots')
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
# Add each sample we want to plot/compare
# Uncomment to add QCD
plotter.add_sample(samples.qcd_mc_pythia8, "QCD MC", **style.QCD_MC_STYLE_HIST)
#plotter.add_sample(samples.minbias_mc, "Minbias MC", **style.MINBIAS_MC_STYLE)
plotter.add_sample(samples.data, "Data (7 TeV)", **style.DATA_STYLE)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data.effective_luminosity())
# Build the ntuple maanger
ntuple_manager = samples.data.build_ntuple_manager("tauIdEffNtuple")
h_mcDen.Delete()
h_mc2Den.Delete()
h_mc0Den.Delete()
canvas_diff.IsA().Destructor(canvas_diff)
if __name__ == "__main__":
ROOT.gROOT.SetBatch(True)
if not os.path.isdir('plots'):
os.mkdir('plots')
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
# Add each sample we want to plot/compare
plotter.add_sample(mySamples.data_ppmux_runs132440to145761, "Muon Data ", **style.DATA_STYLE)
plotter.add_sample(mySamples.ppmux_mc, "PPMuX MC", **style.QCD_MC_PYTHIA6_STYLE_HIST)
plotter.add_sample(mySamples.data_dijet_runs132440to135802, "JetMETTau Data ", **style.MINBIAS_MC_STYLE )
plotter.add_sample(mySamples.qcddijet_mc, "QCD MC", **style.QCD_MC_PYTHIA8_STYLE_HIST)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(mySamples.data_ppmux_runs132440to145761.effective_luminosity())
# Build the ntuple manager
ntuple_manager = mySamples.data_ppmux_runs132440to145761.build_ntuple_manager("tauIdEffNtuple")
# Get HLT trigger decisions
# hlt = ntuple_manager.get_ntuple("TriggerResults")
logy = False
)
phi_resol['result'].GetXaxis().SetTitleOffset(1.2)
# Make a pave text w/ mean rms
stat_label = style.make_mean_rms_pave(phi_resol['samples']['mc_ztt']['plot'])
stat_label.Draw()
canvas.SaveAs("plots/%s%s_phi_resolution.png"%(algorithm,selection["style_name"]))
canvas.SaveAs("plots/%s%s_phi_resolution.pdf"%(algorithm,selection["style_name"]))
if __name__ == "__main__":
plotter = PlotManager()
# Add each sample we want to plot/compare
# Uncomment to add QCD
plotter.add_sample(samples.ztautau_mc, "Z->#tau#tau MC", **style.QCD_MC_STYLE_HIST)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data.effective_luminosity())
# Build the ntuple manager
ntuple_manager = samples.ztautau_mc.build_ntuple_manager("tauIdEffNtuple")
nTuples = {
"shrinkingCone": ntuple_manager.get_ntuple("patPFTausDijetTagAndProbeShrinkingCone"),
"fixedCone": ntuple_manager.get_ntuple("patPFTausDijetTagAndProbeFixedCone"),
"TaNC": ntuple_manager.get_ntuple("patPFTausDijetTagAndProbeShrinkingCone"),
canvas_diff.Update()
canvas_diff.SaveAs("plots/" + filename + "_diff.png")
canvas_diff.SaveAs("plots/" + filename + "_diff.pdf")
canvas_diff.IsA().Destructor(canvas_diff)
if __name__ == "__main__":
ROOT.gROOT.SetBatch(True)
if not os.path.isdir('plots'):
os.mkdir('plots')
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
# Add each sample we want to plot/compare
plotter.add_sample(sample_qcddijet_mc, "Simulation", **style.QCD_MC_STYLE_HIST)
plotter.add_sample(sample_qcddijet_data, "Data", **style.DATA_STYLE)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(sample_qcddijet_data.effective_luminosity())
# Build the ntuple manager
ntuple_manager = sample_qcddijet_data.build_ntuple_manager("tauIdEffNtuple")
# Get HLT trigger decisions
hlt_ntuple = ntuple_manager.get_ntuple("patTriggerEvent")
######################################################
import ROOT from TauAnalysis.TauIdEfficiency.ntauples.PlotManager import PlotManager import TauAnalysis.TauIdEfficiency.ntauples.styles as style # Definition of input files. import samples_cache as samples import os import copy import sys from optparse import OptionParser # Build the plot manager. The plot manager keeps track of all the samples # and ensures they are correctly normalized w.r.t. luminosity. See # samples.py for available samples. plotter = PlotManager() # Add each sample we want to plot/compare # Uncomment to add QCD plotter.add_sample(samples.qcd_mc_pythia8, "Pythia 8", **style.QCD_MC_PYTHIA8_STYLE_HIST) plotter.add_sample(samples.qcd_mc_pythia6, "Pythia 6", **style.QCD_MC_PYTHIA6_STYLE_HIST) #plotter.add_sample(samples.minbias_mc, "Minbias MC", **style.MINBIAS_MC_STYLE) plotter.add_sample(samples.data, "Data", **style.DATA_STYLE) # Normalize everything to the data luminosity plotter.set_integrated_lumi(samples.data.effective_luminosity()) # Build the ntuple manager
custom_LUMI_LABEL_UPPER_LEFT.AddText("Data, L = 8.4nb^{-1}")
custom_LUMI_LABEL_UPPER_LEFT.Draw()
style.SQRTS_LABEL_UPPER_LEFT.Draw()
for extra_label in fakerate_results['calo'][x_var]['extra_labels']:
extra_label.Draw()
# Save the plot
canvas.SaveAs("plots/%s.png" %
'_'.join(['fakerate_algo_comparison', 'vs', x_var]))
canvas.SaveAs("plots/%s.pdf" %
'_'.join(['fakerate_algo_comparison', 'vs', x_var]))
if __name__ == "__main__":
plotter = PlotManager()
# Add each sample we want to plot/compare
# Uncomment to add QCD
plotter.add_sample(samples.qcddijet_mc, "Simulation",
**style.QCD_MC_PYTHIA6_STYLE_HIST)
plotter.add_sample(samples.data_dijet, "Data", **style.DATA_STYLE)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data_dijet.effective_luminosity())
# Build the ntuple manager
ntuple_manager = samples.data_dijet.build_ntuple_manager("tauIdEffNtuple")
nTuples = {
import ROOT
from TauAnalysis.TauIdEfficiency.ntauples.PlotManager import PlotManager
import TauAnalysis.TauIdEfficiency.ntauples.styles as style
import os
import samples_cache as samples
if __name__ == "__main__":
ROOT.gROOT.SetBatch(True)
if not os.path.isdir('plots_Control'):
os.mkdir('plots_Control')
# Build the plot manager. The plot manager keeps track of all the samples
# and ensures they are correctly normalized w.r.t. luminosity. See
# samples.py for available samples.
plotter = PlotManager()
plotter.add_sample(samples.qcd_mc_pythia6, "PYTHIA 6", ** style.QCD_MC_PYTHIA6_STYLE_DOTS)
plotter.add_sample(samples.qcd_mc_pythia8, "PYTHIA 8", ** style.QCD_MC_PYTHIA8_STYLE_HIST)
plotter.add_sample(samples.data, "Data", ** style.DATA_STYLE)
# Normalize everything to the data luminosity
plotter.set_integrated_lumi(samples.data.effective_luminosity())
# Build the ntuple maanger
ntuple_manager = samples.data.build_ntuple_manager("tauIdEffNtuple")
# Get the shrinking ntuple
for dijet_dataHLTpath in dijet_dataHLTpaths:
result_algorithm['%s_diff' % dijet_dataHLTpath]['samples'][samples_dijet_data[dijet_dataHLTpath].name].Draw("epsame")
canvas_diff.cd()
canvas_diff.Update()
canvas_diff.SaveAs(outputFileName[:outputFileName.find(".")] + "_%s_diff.png" % algorithm)
canvas_diff.SaveAs(outputFileName[:outputFileName.find(".")] + "_%s_diff.pdf" % algorithm)
return result_algorithm
if __name__ == "__main__":
# define PlotManagers for QCD Monte Carlo and data samples
plotter_dijet_mc = PlotManager()
plotter_dijet_mc.add_sample(sample_dijet_mc, "QCDj Simulation",
**custom_style_dijet_mc)
plotters_dijet_data = {}
for dijet_dataHLTpath in dijet_dataHLTpaths:
plotter_dijet_data = PlotManager()
plotter_dijet_data.add_sample(samples_dijet_data[dijet_dataHLTpath], "QCDj Data: %s" % dijet_dataHLTpath,
**custom_styles_dijet_data[dijet_dataHLTpath])
plotters_dijet_data[dijet_dataHLTpath] = plotter_dijet_data
# Build the ntuple manager
ntuple_manager = sample_dijet_mc.build_ntuple_manager("tauIdEffNtuple")
nTuples = {
"shrinkingCone" : ntuple_manager.get_ntuple("shrinking"),
"fixedCone" : ntuple_manager.get_ntuple("fixed"),