def GetSingleMuHistograms(self):
#this_analyses = ["trigmu_highmass_CSVTM", "trigmu_lowmass_CSVTM", "trigmubbh_highmass_CSVTM", "trigmubbl_lowmass_CSVTM", "trigmubbll_lowmass_CSVTM", "trigmu24i_lowmass_CSVTM", "trigmu24ibbl_lowmass_CSVTM", "trigmu24ibbll_lowmass_CSVTM", "trigmu40_lowmass_CSVTM", "trigmu40bbl_lowmass_CSVTM", "trigmu40bbll_lowmass_CSVTM"]
this_analyses = ["trigmu24i_lowmass_CSVTM", "trigmu24ibbl_lowmass_CSVTM"]
this_analyses.extend(["trigmu24i_highmass_CSVTM", "trigmu24ibbh_highmass_CSVTM"])
for analysis in this_analyses:
print "Opening " + analysis_config.get_b_histogram_filename(analysis, "SingleMu_2012")
f = ROOT.TFile(analysis_config.get_b_histogram_filename(analysis, "SingleMu_2012"), "READ")
self._mjj_histograms_fine[analysis] = f.Get("BHistograms/h_pfjet_mjj")
self._mjj_histograms_fine[analysis].SetName("h_" + analysis + "_mjj_fine")
self._mjj_histograms_fine[analysis].SetDirectory(0)
self._mjj_histograms[analysis] = histogram_tools.rebin_histogram(self._mjj_histograms_fine[analysis], self._mass_bins, normalization_bin_width=1)
self._mjj_histograms[analysis].SetName("h_" + analysis + "_mjj")
self._mjj_histograms[analysis].SetDirectory(0)
if not "highmass" in analysis:
self._mjj_histograms_csvorder[analysis] = f.Get("BHistograms/h_pfjet_mjj_csvorder")
self._mjj_histograms_csvorder[analysis].SetName("h_" + analysis + "_mjj_csvorder")
self._mjj_histograms_csvorder[analysis].SetDirectory(0)
self._mjj_histograms_csvorder[analysis] = histogram_tools.rebin_histogram(self._mjj_histograms_csvorder[analysis], self._mass_bins, normalization_bin_width=1)
self._mjj_histograms_vetothirdjet[analysis] = f.Get("BHistograms/h_pfjet_mjj_vetothirdjet")
self._mjj_histograms_vetothirdjet[analysis].SetName("h_" + analysis + "_mjj_vetothirdjet")
self._mjj_histograms_vetothirdjet[analysis].SetDirectory(0)
self._mjj_histograms_vetothirdjet[analysis] = histogram_tools.rebin_histogram(self._mjj_histograms_vetothirdjet[analysis], self._mass_bins, normalization_bin_width=1)
if "bbll" in analysis:
self._mjj_histograms_fine[analysis].Scale(1.7) # Prescale for singlemu + 60/53. The prescale was not computer for these analyses.
self._mjj_histograms[analysis].Scale(1.7) # Prescale for singlemu + 60/53. The prescale was not computer for these analyses.
self._mjj_histograms_fine[analysis].Rebin(5)
self._analyses.append(analysis)
f.Close()
def qcd_efficiency(wp, eta):
h_num = None
h_den = None
numerator_analysis = "NoTrigger_{}_{}".format(eta, wp)
denominator_analysis = "NoTrigger_{}".format(eta)
for qcd_sample in qcd_samples:
numerator_file = TFile(
analysis_config.get_b_histogram_filename(numerator_analysis,
qcd_sample))
denominator_file = TFile(
analysis_config.get_b_histogram_filename(denominator_analysis,
qcd_sample))
this_h_num = numerator_file.Get("BHistograms/h_pfjet_mjj")
this_h_den = denominator_file.Get("BHistograms/h_pfjet_mjj")
print "[debug] Scale factor for {} numerator = {}".format(
qcd_sample, 19700 *
analysis_config.simulation.background_cross_sections[qcd_sample] /
numerator_file.Get("BHistograms/h_sample_nevents").Integral())
print "[debug] \tdenominator = {}".format(
19700 *
analysis_config.simulation.background_cross_sections[qcd_sample] /
numerator_file.Get("BHistograms/h_sample_nevents").Integral())
this_h_num.Scale(
19700 *
analysis_config.simulation.background_cross_sections[qcd_sample] /
numerator_file.Get("BHistograms/h_sample_nevents").Integral())
this_h_den.Scale(
19700 *
analysis_config.simulation.background_cross_sections[qcd_sample] /
denominator_file.Get("BHistograms/h_sample_nevents").Integral())
if h_num:
h_num.Add(this_h_num)
else:
h_num = this_h_num.Clone()
h_num.SetDirectory(0)
if h_den:
h_den.Add(this_h_den)
else:
h_den = this_h_den.Clone()
h_den.SetDirectory(0)
numerator_file.Close()
denominator_file.Close()
f_out = TFile(analysis_config.get_offline_btag_file(wp, eta), "RECREATE")
h_eff = h_num.Clone()
h_eff.SetName("h_offline_btag_eff")
h_eff.Divide(h_num, h_den, 1., 1., "B")
h_num.SetName("h_num")
h_num.Write()
h_den.SetName("h_den")
h_den.Write()
f_out.cd()
h_eff.Write()
f_out.Close()
def data_jes_plot(new_sample, old_sample, analysis):
print "Making data JES plot for {}".format(analysis)
f_new = TFile(analysis_config.get_b_histogram_filename(analysis, new_sample), "READ")
h_new = f_new.Get("BHistograms/h_pfjet_mjj")
h_new.SetName("h_pfjet_mjj_newJEC")
h_new.SetDirectory(0)
h_new = histogram_tools.rebin_histogram(h_new, dijet_binning, normalization_bin_width=1.)
print "New integral = {}".format(h_new.Integral())
f_old = TFile(analysis_config.get_b_histogram_filename(analysis, old_sample).replace("EightTeeEeVeeBee/BHistograms", "EightTeeEeVeeBee/BHistograms/old/JEC2012"), "READ")
h_old = f_old.Get("BHistograms/h_pfjet_mjj")
h_old.SetName("h_pfjet_mjj_oldJEC")
h_old.SetDirectory(0)
h_old = histogram_tools.rebin_histogram(h_old, dijet_binning, normalization_bin_width=1.)
print "Old integral = {}".format(h_old.Integral())
c = TCanvas("c_jeccomparison_{}".format(analysis), "c_jeccomparison_{}".format(analysis), 800, 1200)
top = TPad("top", "top", 0., 0.5, 1., 1.)
top.SetBottomMargin(0.02)
top.SetLogy()
top.Draw()
top.cd()
h_new.SetMarkerStyle(20)
h_new.GetXaxis().SetTitleSize(0)
h_new.GetXaxis().SetLabelSize(0)
h_new.GetYaxis().SetTitle("Events / GeV")
h_new.Draw()
h_old.SetMarkerStyle(25)
h_old.Draw("same")
c.cd()
bottom = TPad("bottom", "bottom", 0., 0., 1., 0.5)
bottom.SetTopMargin(0.02)
bottom.SetBottomMargin(0.2)
bottom.Draw()
bottom.cd()
h_ratio = h_new.Clone()
h_ratio.Divide(h_old)
h_ratio.GetXaxis().SetTitle("m_{jj} [GeV]")
h_ratio.GetYaxis().SetTitle("New JEC / Old JEC")
h_ratio.SetMarkerStyle(21)
h_ratio.Draw()
c.cd()
c.SaveAs(analysis_config.figure_directory + "/" + c.GetName() + ".pdf")
f_new.Close()
f_old.Close()
def GetBJetPlusXHistograms(self):
this_analyses = ["trigbbh_CSVTM", "trigbbl_CSVTM", "trigbbll_CSVTM", "trigbbh_trigbbl_CSVTM"]
this_samples = ["BJetPlusX_2012", "BJetPlusX_2012BCD"]
for analysis in this_analyses:
for sample in this_samples:
name = analysis + "_" + sample
f = ROOT.TFile(analysis_config.get_b_histogram_filename(analysis, sample), "READ")
self._mjj_histograms_fine[name] = f.Get("BHistograms/h_pfjet_mjj")
self._mjj_histograms_fine[name].SetName("h_" + name + "_mjj_fine")
self._mjj_histograms_fine[name].SetDirectory(0)
self._mjj_histograms[name] = histogram_tools.rebin_histogram(self._mjj_histograms_fine[name], self._mass_bins, normalization_bin_width=1)
self._mjj_histograms[name].SetName("h_" + name + "_mjj")
self._mjj_histograms[name].SetDirectory(0)
self._mjj_histograms_csvorder[name] = f.Get("BHistograms/h_pfjet_mjj_csvorder")
self._mjj_histograms_csvorder[name].SetName("h_" + name + "_mjj_csvorder")
self._mjj_histograms_csvorder[name].SetDirectory(0)
self._mjj_histograms_csvorder[name] = histogram_tools.rebin_histogram(self._mjj_histograms_csvorder[name], self._mass_bins, normalization_bin_width=1)
self._mjj_histograms_vetothirdjet[name] = f.Get("BHistograms/h_pfjet_mjj_vetothirdjet")
self._mjj_histograms_vetothirdjet[name].SetName("h_" + name + "_mjj_vetothirdjet")
self._mjj_histograms_vetothirdjet[name].SetDirectory(0)
self._mjj_histograms_vetothirdjet[name] = histogram_tools.rebin_histogram(self._mjj_histograms_vetothirdjet[name], self._mass_bins, normalization_bin_width=1)
self._mjj_histograms_fine[name].Rebin(5)
self._analyses.append(name)
f.Close()
if "bbll" in analysis:
self._mjj_histograms[name].Scale(1.7)
self._mjj_histograms_fine[name].Scale(1.7)
def GetJetHTHistogram(self):
for sr_name in ["highmass", "lowmass"]:
analyses = {}
HT_slices = []
for mass in xrange(200, 600, 50):
HT_slices.append("HT" + str(mass))
analyses["HT" + str(mass)] = "trigjetht" + str(mass)
if sr_name == "lowmass":
analyses["HT" + str(mass)] += "_eta1p7"
analyses["HT" + str(mass)] += "_CSVTM"
sample = "JetHT_2012BCD"
HT_slice_histograms = {}
for HT_slice in HT_slices:
f = TFile(analysis_config.get_b_histogram_filename(analyses[HT_slice], sample), "READ")
HT_slice_histograms[HT_slice] = f.Get("BHistograms/h_pfjet_mjj")
print "On file " + analysis_config.get_b_histogram_filename(analyses[HT_slice], sample)
HT_slice_histograms[HT_slice].SetName(HT_slice_histograms[HT_slice].GetName() + "_" + analyses[HT_slice])
HT_slice_histograms[HT_slice].SetDirectory(0)
f.Close()
HT_slices.append("HTUnprescaled")
unprescaled_analysis_name = "trigjetht"
if sr_name == "lowmass":
unprescaled_analysis_name += "_eta1p7"
unprescaled_analysis_name += "_CSVTM"
analyses["HTUnprescaled"] = unprescaled_analysis_name
f_unprescaled = TFile(analysis_config.get_b_histogram_filename(unprescaled_analysis_name, sample), "READ")
HT_slice_histograms["HTUnprescaled"] = f_unprescaled.Get("BHistograms/h_pfjet_mjj")
HT_slice_histograms["HTUnprescaled"].SetName(HT_slice_histograms["HTUnprescaled"].GetName() + "_" + analyses["HTUnprescaled"])
HT_slice_histograms["HTUnprescaled"].SetDirectory(0)
f_unprescaled.Close()
ranges = {
"HT200":[220, 386],
"HT250":[386, 489],
"HT300":[489, 526],
"HT350":[526, 606],
"HT400":[606, 649],
"HT450":[649, 740],
"HT500":[740, 788],
"HT550":[788, 890],
#"HT650":[800, 890],
"HTUnprescaled":[890, 2000]
}
self._analyses.append("JetHT")
self._mjj_histograms_fine["JetHT"] = self.FrankenHist(HT_slices, HT_slice_histograms, ranges)
self._mjj_histograms["JetHT"] = histogram_tools.rebin_histogram(self._mjj_histograms_fine["JetHT"], self._mass_bins, normalization_bin_width=1)
self._mjj_histograms_fine["JetHT"].Rebin(5)
def qcd_efficiency(wp, eta):
h_num = None
h_den = None
numerator_analysis = "NoTrigger_{}_{}".format(eta, wp)
denominator_analysis = "NoTrigger_{}".format(eta)
for qcd_sample in qcd_samples:
numerator_file = TFile(analysis_config.get_b_histogram_filename(numerator_analysis, qcd_sample))
denominator_file = TFile(analysis_config.get_b_histogram_filename(denominator_analysis, qcd_sample))
this_h_num = numerator_file.Get("BHistograms/h_pfjet_mjj")
this_h_den = denominator_file.Get("BHistograms/h_pfjet_mjj")
print "[debug] Scale factor for {} numerator = {}".format(qcd_sample, 19700 * analysis_config.simulation.background_cross_sections[qcd_sample] / numerator_file.Get("BHistograms/h_sample_nevents").Integral())
print "[debug] \tdenominator = {}".format(19700 * analysis_config.simulation.background_cross_sections[qcd_sample] / numerator_file.Get("BHistograms/h_sample_nevents").Integral())
this_h_num.Scale(19700 * analysis_config.simulation.background_cross_sections[qcd_sample] / numerator_file.Get("BHistograms/h_sample_nevents").Integral())
this_h_den.Scale(19700 * analysis_config.simulation.background_cross_sections[qcd_sample] / denominator_file.Get("BHistograms/h_sample_nevents").Integral())
if h_num:
h_num.Add(this_h_num)
else:
h_num = this_h_num.Clone()
h_num.SetDirectory(0)
if h_den:
h_den.Add(this_h_den)
else:
h_den = this_h_den.Clone()
h_den.SetDirectory(0)
numerator_file.Close()
denominator_file.Close()
f_out = TFile(analysis_config.get_offline_btag_file(wp, eta), "RECREATE")
h_eff = h_num.Clone()
h_eff.SetName("h_offline_btag_eff")
h_eff.Divide(h_num, h_den, 1., 1., "B")
h_num.SetName("h_num")
h_num.Write()
h_den.SetName("h_den")
h_den.Write()
f_out.cd()
h_eff.Write()
f_out.Close()
def plot_all_masses(analysis, model, masses):
ratio_up = {}
ratio_down = {}
for mass in masses:
f = TFile(analysis_config.get_b_histogram_filename(analysis, analysis_config.simulation.get_signal_tag(model, mass, "FULLSIM")), "READ")
h_central = f.Get("BHistograms/h_pfjet_mjj")
h_central.SetDirectory(0)
h_central = histogram_tools.rebin_histogram(h_central, dijet_binning, normalization_bin_width=1.)
h_up = f.Get("BHistograms/h_pfjet_mjj_BTagOfflineSFUp")
h_up.SetDirectory(0)
h_up = histogram_tools.rebin_histogram(h_up, dijet_binning, normalization_bin_width=1.)
h_down = f.Get("BHistograms/h_pfjet_mjj_BTagOfflineSFDown")
h_down.SetDirectory(0)
h_down = histogram_tools.rebin_histogram(h_down, dijet_binning, normalization_bin_width=1.)
ratio_up[mass] = h_up.Clone()
ratio_up[mass].SetName("ratio_up_{}".format(mass))
ratio_up[mass].SetDirectory(0)
ratio_up[mass].Divide(h_central)
ratio_up[mass].SetMarkerStyle(26)
ratio_up[mass].Draw("p hist same")
ratio_down[mass] = h_down.Clone()
ratio_down[mass].SetName("ratio_down_{}".format(mass))
ratio_down[mass].SetDirectory(0)
ratio_down[mass].Divide(h_central)
ratio_down[mass].SetMarkerStyle(32)
ratio_down[mass].Draw("p hist same")
norm = h_central.Integral()
print "{}\t{}\t{}:{}".format(analysis, model, mass, h_up.Integral()/norm-1.)
f.Close()
c = TCanvas("c_offb_unc_{}_{}_all".format(analysis, model), "c_offb_unc_{}_{}_all".format(analysis, model), 800, 600)
frame = TH1D("frame", "frame", 100, 0., 1300.)
frame.SetMinimum(0.8)
frame.SetMaximum(1.2)
frame.GetXaxis().SetTitle("m_{jj}")
frame.GetYaxis().SetTitle("SF Variation / Central")
frame.Draw()
for style_counter, mass in enumerate(masses):
ratio_up[mass].SetMarkerStyle(20)
ratio_up[mass].SetMarkerColor(seaborn.GetColorRoot("cubehelixlarge", style_counter, len(masses)+2))
ratio_up[mass].SetLineColor(seaborn.GetColorRoot("cubehelixlarge", style_counter, len(masses)+2))
ratio_up[mass].Draw("hist same")
ratio_down[mass].SetMarkerStyle(20)
ratio_down[mass].SetMarkerColor(seaborn.GetColorRoot("cubehelixlarge", style_counter, len(masses)+2))
ratio_down[mass].SetLineColor(seaborn.GetColorRoot("cubehelixlarge", style_counter, len(masses)+2))
ratio_down[mass].Draw("hist same")
c.SaveAs(analysis_config.figure_directory + "/" + c.GetName() + ".pdf")
# dratio = 0.
# hist_ratio.SetBinContent(bin, ratio)
# hist_ratio.SetBinError(bin, dratio)
#hist_ratio.SetLineColor(1)
#hist_ratio.SetLineWidth(1)
#hist_ratio.SetMarkerStyle(20)
#hist_ratio.SetMarkerSize(1)
#hist_ratio.SetMarkerColor(1)
#hist_ratio.Draw("same")
c.cd()
c.SaveAs("/uscms/home/dryu/Dijets/data/EightTeeEeVeeBee/Results/figures/{}.pdf".format(c.GetName()))
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Run and plot fits")
parser.add_argument("analysis_num", type=str, default="trigbbl_raw", help='Numerator analysis')
parser.add_argument("analysis_den", type=str, default="trigbbl_CSVTM", help='Denominator analysis')
args = parser.parse_args()
f_num = TFile(analysis_config.get_b_histogram_filename(args.analysis_num, "BJetPlusX_2012"), "READ")
h_num = f_num.Get("BHistograms/h_pfjet_mjj")
h_num.SetDirectory(0)
h_num.Rebin(5)
f_den = TFile(analysis_config.get_b_histogram_filename(args.analysis_den, "BJetPlusX_2012"), "READ")
h_den = f_den.Get("BHistograms/h_pfjet_mjj")
h_den.SetDirectory(0)
h_den.Rebin(5)
print "[debug] h_num.Integral() = {}, h_den.Integral() = {}".format(h_num.Integral(), h_den.Integral())
MakeMjjComparisonPlot(h_num, args.analysis_num, h_den, args.analysis_den, x_range=[0,1000], y_range=[1.e-5, 1.e-1])
qcd_cross_sections["QCD_Pt-600to800_TuneZ2star_8TeV_pythia6"] = 26.9921
qcd_cross_sections["QCD_Pt-800to1000_TuneZ2star_8TeV_pythia6"] = 3.550036
qcd_cross_sections["QCD_Pt-1000to1400_TuneZ2star_8TeV_pythia6"] = 0.737844
qcd_cross_sections["QCD_Pt-1400to1800_TuneZ2star_8TeV_pythia6"] = 0.03352235
qcd_cross_sections["QCD_Pt-1800_TuneZ2star_8TeV_pythia6"] = 0.001829005
online_btag_eff = {
"trigbbl_CSVTM": 1.82719e-01,
"trigbbh_CSVTM": 4.89446e-01,
}
qcd_hists_num = {} # QCD with trigbb*
qcd_hists_den = {} # QCD with NoTrigger*
qcd_hists_eff = {}
for sr in srs:
for qcd_sample in qcd_samples:
f_num = TFile(
analysis_config.get_b_histogram_filename(trig_analyses[sr],
qcd_sample), "READ")
f_den = TFile(
analysis_config.get_b_histogram_filename(notrig_analyses[sr],
qcd_sample), "READ")
input_nevents_num = f_num.Get("BHistograms/h_input_nevents").Integral()
this_num_hist = f_num.Get("BHistograms/h_pfjet_mjj")
#this_num_hist.Scale(19700.*qcd_cross_sections[qcd_sample] / input_nevents_num)
input_nevents_den = f_den.Get("BHistograms/h_input_nevents").Integral()
this_den_hist = f_den.Get("BHistograms/h_pfjet_mjj")
#this_den_hist.Scale(19700.*qcd_cross_sections[qcd_sample] / input_nevents_den)
if not sr in qcd_hists_num:
qcd_hists_num[sr] = this_num_hist.Clone()
qcd_hists_num[sr].SetDirectory(0)
qcd_hists_den[sr] = this_den_hist.Clone()
qcd_hists_den[sr].SetDirectory(0)
else:
analyses = ["trigbbl_CSVTM", "trigbbh_CSVTM"]
masses = {"trigbbl_CSVTM":range(350, 850, 50), "trigbbh_CSVTM":range(600, 1250, 50)}
mjj_min = {"trigbbl_CSVTM":296, "trigbbh_CSVTM":526}
mjj_max = {"trigbbl_CSVTM":1058, "trigbbh_CSVTM":1607}
else:
analyses = ["NoTrigger_eta1p7_CSVTM", "NoTrigger_eta2p2_CSVTM"]
masses = {"NoTrigger_eta1p7_CSVTM":range(350, 850, 50), "NoTrigger_eta2p2_CSVTM":range(600, 1250, 50)}
mjj_min = {"NoTrigger_eta1p7_CSVTM":296, "NoTrigger_eta2p2_CSVTM":526}
mjj_max = {"NoTrigger_eta1p7_CSVTM":1058, "NoTrigger_eta2p2_CSVTM":1607}
data_sample = "QCD_TuneZ2star_8TeV_pythia6"
for model in ["Hbb", "RSG"]:
for analysis in analyses:
for mass in masses[analysis]:
data_file_path = analysis_config.get_b_histogram_filename(analysis, data_sample)
if useMCTrigger:
signal_pdf_file = analysis_config.get_signal_fit_file(analysis, model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses))
else:
if analysis == "trigbbl_CSVTM" or analysis == "NoTrigger_eta1p7_CSVTM":
notrig_analysis = "trigbbl_notrig_CSVTM"
elif analysis == "trigbbh_CSVTM" or analysis == "NoTrigger_eta2p2_CSVTM":
notrig_analysis = "trigbbh_notrig_CSVTM"
else:
print "ERROR : I don't know a no-trigger variant of analysis {}. Please make one, or specify useMCTrigger.".format(analysis)
sys.exit(1)
signal_pdf_file = analysis_config.get_signal_fit_file(notrig_analysis, model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses))
input_files = [data_file_path, signal_pdf_file]
command = "python $CMSSW_BASE/src/CMSDIJET/StatisticalTools/scripts/create_datacards_parallel.py {} {}".format(analysis, model)
command += " --massMin {} --massMax {} --mass {}".format(mjj_min[analysis], mjj_max[analysis], mass)
"HT550":"trigjetht550",
"HT650":"trigjetht650",
"HTUnprescaled":"trigjetht"
}
for wp in ["CSVT", "CSVM", "CSVL"]:
numerator_histograms[sr][wp] = {}
denominator_histograms[sr][wp] = {}
efficiency_histograms[sr][wp] = {}
for which in [0, 1]:
numerator_histograms[sr][wp][which] = None
denominator_histograms[sr][wp][which] = None
efficiency_histograms[sr][wp][which] = None
# Load histograms
first = True
for ht_slice in ht_slices:
f = TFile(analysis_config.get_b_histogram_filename(analyses[ht_slice], "JetHT_2012BCD"), "READ")
this_numerator_histogram_3D = f.Get("BHistograms/h_pfjet_jet{}_pt_eta_{}".format(which, wp))
this_denominator_histogram_3D = f.Get("BHistograms/h_pfjet_jet{}_pt_eta".format(which))
zbin_min = this_numerator_histogram_3D.GetZaxis().FindBin(ht_ranges[ht_slice][0] + 1.e-5)
zbin_max = this_numerator_histogram_3D.GetZaxis().FindBin(ht_ranges[ht_slice][0] - 1.e-5)
if first:
numerator_histograms[sr][wp][which] = TH2D(this_numerator_histogram_3D.GetName() + "_xy" + wp + str(which), this_numerator_histogram_3D.GetName() + "_xy", this_numerator_histogram_3D.GetXaxis().GetNbins(), this_numerator_histogram_3D.GetXaxis().GetXmin(), this_numerator_histogram_3D.GetXaxis().GetXmax(), this_numerator_histogram_3D.GetYaxis().GetNbins(), this_numerator_histogram_3D.GetYaxis().GetXmin(), this_numerator_histogram_3D.GetYaxis().GetXmax())
numerator_histograms[sr][wp][which].SetDirectory(0)
numerator_histograms[sr][wp][which].Sumw2()
denominator_histograms[sr][wp][which] = TH2D(this_denominator_histogram_3D.GetName() + "_xy" + wp + str(which), this_denominator_histogram_3D.GetName() + "_xy", this_denominator_histogram_3D.GetXaxis().GetNbins(), this_denominator_histogram_3D.GetXaxis().GetXmin(), this_denominator_histogram_3D.GetXaxis().GetXmax(), this_denominator_histogram_3D.GetYaxis().GetNbins(), this_denominator_histogram_3D.GetYaxis().GetXmin(), this_denominator_histogram_3D.GetYaxis().GetXmax())
denominator_histograms[sr][wp][which].SetDirectory(0)
denominator_histograms[sr][wp][which].Sumw2()
first = False
for zbin in xrange(zbin_min, zbin_max+1):
for xbin in xrange(1, this_numerator_histogram_3D.GetNbinsX()+1):
for ybin in xrange(1, this_numerator_histogram_3D.GetNbinsY()+1):
data_analyses = {"lowmass": "trigbbl_CSVTM", "highmass": "trigbbh_CSVTM"}
dijet_binning = array(
"d", [
1, 3, 6, 10, 16, 23, 31, 40, 50, 61, 74, 88, 103, 119, 137, 156, 176,
197, 220, 244, 270, 296, 325, 354, 386, 419, 453, 489, 526, 565, 606,
649, 693, 740, 788, 838, 890, 944, 1000, 1058, 1118, 1181, 1246, 1313,
1383, 1455, 1530, 1607, 1687, 1770, 1856, 1945, 2037, 2132, 2231, 2332,
2438, 2546, 2659, 2775, 2895, 3019, 3147, 3279, 3416, 3558, 3704, 3854,
4010, 4171, 4337, 4509, 4686, 4869, 5000
]
) #5058, 5253, 5455, 5663, 5877, 6099, 6328, 6564, 6808, 7060, 7320, 7589, 7866, 8000])
for signal_region in signal_regions:
f_data = TFile(
analysis_config.get_b_histogram_filename(data_analyses[signal_region],
"BJetPlusX_2012"), "READ")
data_hist_raw = f_data.Get("BHistograms/h_pfjet_mjj")
data_hist_raw.SetName("mjj_data_" + signal_region)
data_hist_raw.SetDirectory(0)
f_data.Close()
for strategy in strategies:
f_mc = TFile(
analysis_config.get_b_histogram_filename(
mc_analyses[signal_region][strategy],
"QCD_TuneZ2star_8TeV_pythia6"), "READ")
mc_hist = f_mc.Get("BHistograms/h_pfjet_mjj")
mc_hist.SetName("mjj_mc_" + signal_region + "_" + strategy)
mc_hist.SetDirectory(0)
if strategy == "datatrigger":
if signal_region == "lowmass":
masses = args.mass
# sort masses
masses.sort()
# import ROOT stuff
from ROOT import TFile, TH1F, TH1D, TGraph, kTRUE, kFALSE
from ROOT import RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf, PdfDiagonalizer
if not args.debug:
RooMsgService.instance().setSilentMode(kTRUE)
RooMsgService.instance().setStreamStatus(0,kFALSE)
RooMsgService.instance().setStreamStatus(1,kFALSE)
# input data file
data_file = TFile(analysis_config.get_b_histogram_filename(args.analysis, args.data_sample), "READ")
# input data histogram
hData = data_file.Get(args.data_hist)
# input sig file
signal_shapes_file = TFile(args.signal_shapes)
sqrtS = args.sqrtS
# mass variable
mjj = RooRealVar('mjj','mjj',float(args.min_mass),float(args.max_mass))
# integrated luminosity and signal cross section
lumi = args.lumi
signalCrossSection = 1. # set to 1. so that the limit on r can be interpreted as a limit on the signal cross section
def plot_all_masses(analysis, model, masses):
ratio_up = {}
ratio_down = {}
for mass in masses:
f = TFile(
analysis_config.get_b_histogram_filename(
analysis,
analysis_config.simulation.get_signal_tag(
model, mass, "FULLSIM")), "READ")
h_central = f.Get("BHistograms/h_pfjet_mjj")
h_central.SetDirectory(0)
h_central = histogram_tools.rebin_histogram(h_central,
dijet_binning,
normalization_bin_width=1.)
h_up = f.Get("BHistograms/h_pfjet_mjj_BTagOfflineSFUp")
h_up.SetDirectory(0)
h_up = histogram_tools.rebin_histogram(h_up,
dijet_binning,
normalization_bin_width=1.)
h_down = f.Get("BHistograms/h_pfjet_mjj_BTagOfflineSFDown")
h_down.SetDirectory(0)
h_down = histogram_tools.rebin_histogram(h_down,
dijet_binning,
normalization_bin_width=1.)
ratio_up[mass] = h_up.Clone()
ratio_up[mass].SetName("ratio_up_{}".format(mass))
ratio_up[mass].SetDirectory(0)
ratio_up[mass].Divide(h_central)
ratio_up[mass].SetMarkerStyle(26)
ratio_up[mass].Draw("p hist same")
ratio_down[mass] = h_down.Clone()
ratio_down[mass].SetName("ratio_down_{}".format(mass))
ratio_down[mass].SetDirectory(0)
ratio_down[mass].Divide(h_central)
ratio_down[mass].SetMarkerStyle(32)
ratio_down[mass].Draw("p hist same")
norm = h_central.Integral()
print "{}\t{}\t{}:{}".format(analysis, model, mass,
h_up.Integral() / norm - 1.)
f.Close()
c = TCanvas("c_offb_unc_{}_{}_all".format(analysis, model),
"c_offb_unc_{}_{}_all".format(analysis, model), 800, 600)
frame = TH1D("frame", "frame", 100, 0., 1300.)
frame.SetMinimum(0.8)
frame.SetMaximum(1.2)
frame.GetXaxis().SetTitle("m_{jj}")
frame.GetYaxis().SetTitle("SF Variation / Central")
frame.Draw()
for style_counter, mass in enumerate(masses):
ratio_up[mass].SetMarkerStyle(20)
ratio_up[mass].SetMarkerColor(
seaborn.GetColorRoot("cubehelixlarge", style_counter,
len(masses) + 2))
ratio_up[mass].SetLineColor(
seaborn.GetColorRoot("cubehelixlarge", style_counter,
len(masses) + 2))
ratio_up[mass].Draw("hist same")
ratio_down[mass].SetMarkerStyle(20)
ratio_down[mass].SetMarkerColor(
seaborn.GetColorRoot("cubehelixlarge", style_counter,
len(masses) + 2))
ratio_down[mass].SetLineColor(
seaborn.GetColorRoot("cubehelixlarge", style_counter,
len(masses) + 2))
ratio_down[mass].Draw("hist same")
c.SaveAs(analysis_config.figure_directory + "/" + c.GetName() + ".pdf")
565, 606, 649, 693, 740, 788, 838, 890, 944, 1000, 1058, 1118,
1181, 1246, 1313, 1383, 1455, 1530, 1607, 1687, 1770, 1856, 1945,
2037, 2132, 2231, 2332, 2438, 2546, 2659, 2775, 2895, 3019, 3147,
3279, 3416, 3558, 3704, 3854, 4010, 4171, 4337, 4509, 4686, 4869,
5000
]
) #5058, 5253, 5455, 5663, 5877, 6099, 6328, 6564, 6808, 7060, 7320, 7589, 7866, 8000])
if args.x_range:
x_range = args.x_range
else:
x_range = [0., 2000.]
if args.what == "mjj":
for analysis in analyses:
histogram_file = TFile(
analysis_config.get_b_histogram_filename(
analysis, "BJetPlusX_2012"), "READ")
data_histogram_raw = histogram_file.Get("BHistograms/h_pfjet_mjj")
data_histogram = CorrectTriggerEfficiency(data_histogram_raw,
analysis)
print "Data integral = {}".format(data_histogram.Integral())
#if "trigbbh" in analysis:
# trigger_correction = "bbh"
#elif "trigbbl" in analysis:
# trigger_correction = "bbl"
data_histogram.SetDirectory(0)
for model in models:
background_workspace = limit_config.get_workspace_filename(
analysis,
model,
750,
fitBonly=True,
mc_analyses = {
"lowmass":{
"mctrigger":"trigbbl_CSVTM",
"datatrigger":"NoTrigger_eta1p7_CSVTM"
},
"highmass":{
"mctrigger":"trigbbh_CSVTM",
"datatrigger":"NoTrigger_eta2p2_CSVTM"
}
}
data_analyses = {"lowmass":"trigbbl_CSVTM", "highmass":"trigbbh_CSVTM"}
dijet_binning = array("d", [1, 3, 6, 10, 16, 23, 31, 40, 50, 61, 74, 88, 103, 119, 137, 156, 176, 197, 220, 244, 270, 296, 325, 354, 386, 419, 453, 489, 526, 565, 606, 649, 693, 740, 788, 838, 890, 944, 1000, 1058, 1118, 1181, 1246, 1313, 1383, 1455, 1530, 1607, 1687, 1770, 1856, 1945, 2037, 2132, 2231, 2332, 2438, 2546, 2659, 2775, 2895, 3019, 3147, 3279, 3416, 3558, 3704, 3854, 4010, 4171, 4337, 4509, 4686, 4869, 5000]) #5058, 5253, 5455, 5663, 5877, 6099, 6328, 6564, 6808, 7060, 7320, 7589, 7866, 8000])
for signal_region in signal_regions:
f_data = TFile(analysis_config.get_b_histogram_filename(data_analyses[signal_region], "BJetPlusX_2012"), "READ")
data_hist_raw = f_data.Get("BHistograms/h_pfjet_mjj")
data_hist_raw.SetName("mjj_data_" + signal_region)
data_hist_raw.SetDirectory(0)
f_data.Close()
for strategy in strategies:
f_mc = TFile(analysis_config.get_b_histogram_filename(mc_analyses[signal_region][strategy], "QCD_TuneZ2star_8TeV_pythia6"), "READ")
mc_hist = f_mc.Get("BHistograms/h_pfjet_mjj")
mc_hist.SetName("mjj_mc_" + signal_region + "_" + strategy)
mc_hist.SetDirectory(0)
if strategy == "datatrigger":
if signal_region == "lowmass":
mc_hist.Scale(trigger_efficiency.online_btag_eff["trigbbl_CSVTM"][0])
elif signal_region == "highmass":
mc_hist.Scale(trigger_efficiency.online_btag_eff["trigbbh_CSVTM"][0])
qcd_cross_sections["QCD_Pt-1800_TuneZ2star_8TeV_pythia6"] = 0.001829005
online_btag_eff = {
"trigbbl_CSVTM":1.82719e-01,
"trigbbh_CSVTM":4.89446e-01,
}
truth_categories = ["bb", "bc", "bg", "bl", "cc", "cg", "cl", "gg", "gl", "ll"]
truth_categories_incl = ["inclusive", "bb", "bc", "bg", "bl", "cc", "cg", "cl", "gg", "gl", "ll"]
for analysis in analyses:
h_mjj = None
h_mjj_truthbb = None
for qcd_sample in qcd_samples:
f = TFile(analysis_config.get_b_histogram_filename(analysis, qcd_sample), "READ")
input_nevents = f.Get("BHistograms/h_input_nevents").Integral()
this_h_mjj = f.Get("BHistograms/h_pfjet_mjj").Clone()
this_h_mjj.Scale(19700.*qcd_cross_sections[qcd_sample] / input_nevents)
this_h_mjj_truthbb = f.Get("BHistograms/h_pfjet_mjj_truthbb").Clone()
this_h_mjj_truthbb.Scale(19700.*qcd_cross_sections[qcd_sample] / input_nevents)
if not h_mjj:
h_mjj = this_h_mjj.Clone()
h_mjj.SetDirectory(0)
h_mjj_truthbb = this_h_mjj_truthbb.Clone()
h_mjj_truthbb.SetDirectory(0)
else:
h_mjj.Add(this_h_mjj)
h_mjj_truthbb.Add(this_h_mjj_truthbb)
f.Close()
h_mjj = histogram_tools.rebin_histogram(h_mjj, dijet_binning, normalization_bin_width=1.)
max_eff = -1.
for model in ["Hbb", "ZPrime", "RSG"]:
#if analysis == "trigbbl_CSVTM":
# notrig_analysis = "NoTrigger_eta1p7_CSVTM"
#elif analysis == "trigbbh_CSVTM":
# notrig_analysis = "NoTrigger_eta2p2_CSVTM"
#signal_pdf_file = analysis_config.get_signal_fit_file(notrig_analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses))
if "bbl" in analysis:
mjj_range = [296, 1058]
elif "bbh" in analysis:
mjj_range = [526, 1607]
tg_eff[analysis][model] = TGraphErrors(len(masses[analysis]))
for i, mass in enumerate(masses[analysis]):
f = TFile(analysis_config.get_b_histogram_filename(analysis, analysis_config.simulation.get_signal_tag(model, mass, "FULLSIM")))
if not f.IsOpen():
continue
low_bin = f.Get("BHistograms/h_pfjet_mjj").GetXaxis().FindBin(mjj_range[0] + 1.e-5)
high_bin = f.Get("BHistograms/h_pfjet_mjj").GetXaxis().FindBin(mjj_range[1] - 1.e-5)
numerator = f.Get("BHistograms/h_pfjet_mjj").Integral(low_bin, high_bin)
if model == "ZPrime":
denominator = f.Get("BHistograms/h_input_nevents").Integral()
print "{} / {} den = {}".format(model, analysis, denominator)
else:
denominator = f.Get("BHistograms/h_sample_nevents").Integral()
if denominator > 0:
eff = numerator/denominator
deff = (eff*(1.-eff)/denominator)**0.5
eff *= 100
deff *= 100
#parser.add_argument("--fit_max_mjj", dest="fit_max_mjj",
# default=1500., type=float,
# help="Upper bound of the mass range used for fitting (default: %(default)s)",
# metavar="MASS_MAX")
args = parser.parse_args()
# Pack up fit options
fit_options = {}
fit_options["min_mjj"] = args.min_mjj
fit_options["max_mjj"] = args.max_mjj
#fit_options["fit_range"] = [args.fit_min_mjj, args.fit_max_mjj]
mjj_fit = MjjFit([args.min_mjj, args.max_mjj])
#if args.fit_range:
# mjj_fit.set_fit_range(args.fit_range)
data_file = TFile(analysis_config.get_b_histogram_filename(args.analysis_name, args.data_sample), "READ")
mjj_fit.add_data(data_file.Get("BHistograms/h_pfjet_mjj"), args.lumi)
data_file.Close()
if args.backgrounds:
backgrounds = args.backgrounds.split(",")
for background in backgrounds:
if analysis_config.simulation.background_supersamples.has_key(background):
first = True
for subbackground in analysis_config.simulation.background_supersamples[background]:
background_file = TFile(analysis_config.get_b_histogram_filename(args.analysis_name, subbackground), "READ")
input_nevents = background_file.Get("BHistograms/h_input_nevents").Integral()
if first:
background_histogram = background_file.Get("BHistograms/h_pfjet_mjj").Clone()
print "Scaling background histogram " + subbackground + " by " + str(args.lumi) + "*" + str(analysis_config.simulation.background_cross_sections[subbackground]) + " / " + str(input_nevents) + " = " + str(args.lumi * analysis_config.simulation.background_cross_sections[subbackground] / input_nevents)
background_histogram.Scale(args.lumi * analysis_config.simulation.background_cross_sections[subbackground] / input_nevents)
def mc_jes_plot(analysis, model, mass):
f_new = TFile(analysis_config.get_b_histogram_filename(analysis, analysis_config.simulation.get_signal_tag(model, mass, "FULLSIM")), "READ")
h_new = f_new.Get("BHistograms/h_pfjet_mjj")
h_new.SetName("h_pfjet_mjj_newJEC")
h_new.SetDirectory(0)
h_new = histogram_tools.rebin_histogram(h_new, dijet_binning, normalization_bin_width=1.)
f_old = TFile(analysis_config.get_b_histogram_filename(analysis, analysis_config.simulation.get_signal_tag(model, mass, "FULLSIM")).replace("EightTeeEeVeeBee/BHistograms", "EightTeeEeVeeBee/BHistograms/old/JEC2012"), "READ")
h_old = f_old.Get("BHistograms/h_pfjet_mjj")
h_old.SetName("h_pfjet_mjj_oldJEC")
h_old.SetDirectory(0)
h_old = histogram_tools.rebin_histogram(h_old, dijet_binning, normalization_bin_width=1.)
c = TCanvas("c_jeccomparison_{}_{}_{}".format(analysis, model, mass), "c_jeccomparison_{}_{}_{}".format(analysis, model, mass), 800, 1200)
top = TPad("top", "top", 0., 0.5, 1., 1.)
top.SetBottomMargin(0.02)
#top.SetLogy()
top.Draw()
top.cd()
frame_top = TH1D("frame_top", "frame_top", 100, 0., mass*2)
frame_top.GetXaxis().SetTitleSize(0)
frame_top.GetXaxis().SetLabelSize(0)
frame_top.GetYaxis().SetTitle("Events / GeV")
frame_top.SetMinimum(0.01)
frame_top.SetMaximum(h_new.GetMaximum() * 1.3)
frame_top.Draw()
h_new.SetMarkerStyle(20)
h_new.GetXaxis().SetTitleSize(0)
h_new.GetXaxis().SetLabelSize(0)
h_new.GetYaxis().SetTitle("Events / GeV")
h_new.Draw("same")
h_old.SetMarkerStyle(25)
h_old.Draw("same")
l = TLegend(0.6, 0.6, 0.88, 0.8)
l.SetFillColor(0)
l.SetBorderSize(0)
l.AddEntry(h_new, "New JEC")
l.AddEntry(h_old, "Old JEC")
l.Draw()
c.cd()
bottom = TPad("bottom", "bottom", 0., 0., 1., 0.5)
bottom.SetTopMargin(0.02)
bottom.SetBottomMargin(0.2)
bottom.Draw()
bottom.cd()
frame_bottom = TH1D("frame_bottom", "frame_bottom", 100, 0., mass*2)
frame_bottom.GetXaxis().SetTitle("m_{jj} [GeV]")
frame_bottom.GetYaxis().SetTitle("New JEC / Old JEC")
frame_bottom.SetMinimum(0.5)
frame_bottom.SetMaximum(1.5)
frame_bottom.Draw()
h_ratio = h_new.Clone()
h_ratio.Divide(h_old)
h_ratio.SetMarkerStyle(21)
h_ratio.Draw("same")
c.cd()
c.SaveAs(analysis_config.figure_directory + "/" + c.GetName() + ".pdf")
f_new.Close()
f_old.Close()
qcd_cross_sections["QCD_Pt-470to600_TuneZ2star_8TeV_pythia6"] = 113.8791
qcd_cross_sections["QCD_Pt-600to800_TuneZ2star_8TeV_pythia6"] = 26.9921
qcd_cross_sections["QCD_Pt-800to1000_TuneZ2star_8TeV_pythia6"] = 3.550036
qcd_cross_sections["QCD_Pt-1000to1400_TuneZ2star_8TeV_pythia6"] = 0.737844
qcd_cross_sections["QCD_Pt-1400to1800_TuneZ2star_8TeV_pythia6"] = 0.03352235
qcd_cross_sections["QCD_Pt-1800_TuneZ2star_8TeV_pythia6"] = 0.001829005
online_btag_eff = {
"trigbbl_CSVTM":1.82719e-01,
"trigbbh_CSVTM":4.89446e-01,
}
qcd_hists_num = {} # QCD with trigbb*
qcd_hists_den = {} # QCD with NoTrigger*
qcd_hists_eff = {}
for sr in srs:
for qcd_sample in qcd_samples:
f_num = TFile(analysis_config.get_b_histogram_filename(trig_analyses[sr], qcd_sample), "READ")
f_den = TFile(analysis_config.get_b_histogram_filename(notrig_analyses[sr], qcd_sample), "READ")
input_nevents_num = f_num.Get("BHistograms/h_input_nevents").Integral()
this_num_hist = f_num.Get("BHistograms/h_pfjet_mjj")
#this_num_hist.Scale(19700.*qcd_cross_sections[qcd_sample] / input_nevents_num)
input_nevents_den = f_den.Get("BHistograms/h_input_nevents").Integral()
this_den_hist = f_den.Get("BHistograms/h_pfjet_mjj")
#this_den_hist.Scale(19700.*qcd_cross_sections[qcd_sample] / input_nevents_den)
if not sr in qcd_hists_num:
qcd_hists_num[sr] = this_num_hist.Clone()
qcd_hists_num[sr].SetDirectory(0)
qcd_hists_den[sr] = this_den_hist.Clone()
qcd_hists_den[sr].SetDirectory(0)
else:
qcd_hists_num[sr].Add(this_num_hist)
qcd_hists_den[sr].Add(this_den_hist)
# sort masses
masses.sort()
# import ROOT stuff
from ROOT import TFile, TH1F, TH1D, TGraph, kTRUE, kFALSE
from ROOT import RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf, PdfDiagonalizer
if not args.debug:
RooMsgService.instance().setSilentMode(kTRUE)
RooMsgService.instance().setStreamStatus(0, kFALSE)
RooMsgService.instance().setStreamStatus(1, kFALSE)
# input data file
data_file = TFile(
analysis_config.get_b_histogram_filename(args.analysis,
args.data_sample), "READ")
# input data histogram
hData = data_file.Get(args.data_hist)
# input sig file
signal_shapes_file = TFile(args.signal_shapes)
sqrtS = args.sqrtS
# mass variable
mjj = RooRealVar('mjj', 'mjj', float(args.min_mass), float(args.max_mass))
# integrated luminosity and signal cross section
lumi = args.lumi
signalCrossSection = 1. # set to 1. so that the limit on r can be interpreted as a limit on the signal cross section
#if analysis == "trigbbl_CSVTM":
# notrig_analysis = "NoTrigger_eta1p7_CSVTM"
#elif analysis == "trigbbh_CSVTM":
# notrig_analysis = "NoTrigger_eta2p2_CSVTM"
#signal_pdf_file = analysis_config.get_signal_fit_file(notrig_analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses))
if "bbl" in analysis:
mjj_range = [296, 1058]
elif "bbh" in analysis:
mjj_range = [526, 1607]
tg_eff[analysis][model] = TGraphErrors(len(masses[analysis]))
for i, mass in enumerate(masses[analysis]):
f = TFile(
analysis_config.get_b_histogram_filename(
analysis,
analysis_config.simulation.get_signal_tag(
model, mass, "FULLSIM")))
if not f.IsOpen():
continue
low_bin = f.Get("BHistograms/h_pfjet_mjj").GetXaxis().FindBin(
mjj_range[0] + 1.e-5)
high_bin = f.Get("BHistograms/h_pfjet_mjj").GetXaxis().FindBin(
mjj_range[1] - 1.e-5)
numerator = f.Get("BHistograms/h_pfjet_mjj").Integral(
low_bin, high_bin)
if model == "ZPrime":
denominator = f.Get("BHistograms/h_input_nevents").Integral()
print "{} / {} den = {}".format(model, analysis, denominator)
else:
denominator = f.Get("BHistograms/h_sample_nevents").Integral()
if denominator > 0:
def plot(analysis, model, mass):
f = TFile(
analysis_config.get_b_histogram_filename(
analysis,
analysis_config.simulation.get_signal_tag(model, mass, "FULLSIM")),
"READ")
h_central = f.Get("BHistograms/h_pfjet_mjj")
h_central.SetDirectory(0)
h_central = histogram_tools.rebin_histogram(h_central,
dijet_binning,
normalization_bin_width=1.)
h_up = f.Get("BHistograms/h_pfjet_mjj_BTagOfflineSFUp")
h_up.SetDirectory(0)
h_up = histogram_tools.rebin_histogram(h_up,
dijet_binning,
normalization_bin_width=1.)
h_down = f.Get("BHistograms/h_pfjet_mjj_BTagOfflineSFDown")
h_down.SetDirectory(0)
h_down = histogram_tools.rebin_histogram(h_down,
dijet_binning,
normalization_bin_width=1.)
c = TCanvas("c_offb_unc_{}_{}{}".format(analysis, model, mass),
"c_offb_unc_{}_{}{}".format(analysis, model, mass), 800, 1000)
top = TPad("top", "top", 0., 0.5, 1., 1.)
top.SetBottomMargin(0.02)
#top.SetLogy()
top.Draw()
top.cd()
frame_top = TH1D("frame_top", "frame_top", 100, 0., mass * 2)
frame_top.GetXaxis().SetTitleSize(0)
frame_top.GetXaxis().SetLabelSize(0)
frame_top.GetYaxis().SetTitle("Events / GeV")
frame_top.SetMinimum(0.01)
frame_top.SetMaximum(h_central.GetMaximum() * 1.3)
frame_top.Draw()
h_central.SetMarkerStyle(20)
h_central.Draw("same")
h_up.SetMarkerStyle(26)
h_up.Draw("same")
h_down.SetMarkerStyle(32)
h_down.Draw("same")
l = TLegend(0.6, 0.6, 0.88, 0.8)
l.SetFillColor(0)
l.SetBorderSize(0)
l.AddEntry(h_central, "Central", "p")
l.AddEntry(h_up, "SF unc up", "p")
l.AddEntry(h_down, "SF unc down", "p")
l.Draw()
c.cd()
bottom = TPad("bottom", "bottom", 0., 0., 1., 0.5)
bottom.SetTopMargin(0.02)
bottom.SetBottomMargin(0.2)
bottom.Draw()
bottom.cd()
frame_bottom = TH1D("frame_bottom", "frame_bottom", 100, 0., mass * 2)
frame_bottom.GetXaxis().SetTitle("m_{jj} [GeV]")
frame_bottom.GetYaxis().SetTitle("Unc / Central")
frame_bottom.SetMinimum(0.5)
frame_bottom.SetMaximum(1.5)
frame_bottom.Draw()
ratio_up = h_up.Clone()
ratio_up.Divide(h_central)
ratio_up.SetMarkerStyle(26)
ratio_up.Draw("p hist same")
ratio_down = h_down.Clone()
ratio_down.Divide(h_central)
ratio_down.SetMarkerStyle(32)
ratio_down.Draw("p hist same")
avg_up = TLine(0.,
h_up.Integral() / h_central.Integral(), mass * 2,
h_up.Integral() / h_central.Integral())
avg_up.SetLineStyle(2)
avg_up.SetLineColor(seaborn.GetColorRoot("pastel", 2))
avg_up.Draw("same")
avg_down = TLine(0.,
h_down.Integral() / h_central.Integral(), mass * 2,
h_down.Integral() / h_central.Integral())
avg_down.SetLineStyle(2)
avg_down.SetLineColor(seaborn.GetColorRoot("pastel", 2))
avg_down.Draw("same")
c.cd()
c.SaveAs(analysis_config.figure_directory + "/" + c.GetName() + ".pdf")
f.Close()
if analysis == "trigbbl_CSVTM":
notrig_analysis = "NoTrigger_eta1p7_CSVTM"
elif analysis == "trigbbh_CSVTM":
notrig_analysis = "NoTrigger_eta2p2_CSVTM"
#signal_pdf_file = analysis_config.get_signal_fit_file(notrig_analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses))
signal_acc_times_eff[model][analysis] = {}
if "bbl" in analysis:
mjj_range = [296, 1058]
elif "bbh" in analysis:
mjj_range = [526, 1607]
for mass in masses[analysis]:
if use_MC_trigger:
f = TFile(analysis_config.get_b_histogram_filename(analysis, analysis_config.simulation.get_signal_tag(model, mass, "FULLSIM")))
else:
f = TFile(analysis_config.get_b_histogram_filename(notrig_analysis, analysis_config.simulation.get_signal_tag(model, mass, "FULLSIM")))
if not f.IsOpen():
signal_acc_times_eff[model][analysis][mass] = 0.
continue
low_bin = f.Get("BHistograms/h_pfjet_mjj").GetXaxis().FindBin(mjj_range[0] + 1.e-5)
high_bin = f.Get("BHistograms/h_pfjet_mjj").GetXaxis().FindBin(mjj_range[1] - 1.e-5)
numerator = f.Get("BHistograms/h_pfjet_mjj").Integral(low_bin, high_bin)
if "ZPrime" in model:
denominator = f.Get("BHistograms/h_input_nevents").Integral()
print "{} / {} den = {}".format(model, analysis, denominator)
else:
denominator = f.Get("BHistograms/h_sample_nevents").Integral()
if denominator > 0:
def plot(analysis, model, mass):
f = TFile(analysis_config.get_b_histogram_filename(analysis, analysis_config.simulation.get_signal_tag(model, mass, "FULLSIM")), "READ")
h_central = f.Get("BHistograms/h_pfjet_mjj")
h_central.SetDirectory(0)
h_central = histogram_tools.rebin_histogram(h_central, dijet_binning, normalization_bin_width=1.)
h_up = f.Get("BHistograms/h_pfjet_mjj_BTagOfflineSFUp")
h_up.SetDirectory(0)
h_up = histogram_tools.rebin_histogram(h_up, dijet_binning, normalization_bin_width=1.)
h_down = f.Get("BHistograms/h_pfjet_mjj_BTagOfflineSFDown")
h_down.SetDirectory(0)
h_down = histogram_tools.rebin_histogram(h_down, dijet_binning, normalization_bin_width=1.)
c = TCanvas("c_offb_unc_{}_{}{}".format(analysis, model, mass), "c_offb_unc_{}_{}{}".format(analysis, model, mass), 800, 1000)
top = TPad("top", "top", 0., 0.5, 1., 1.)
top.SetBottomMargin(0.02)
#top.SetLogy()
top.Draw()
top.cd()
frame_top = TH1D("frame_top", "frame_top", 100, 0., mass*2)
frame_top.GetXaxis().SetTitleSize(0)
frame_top.GetXaxis().SetLabelSize(0)
frame_top.GetYaxis().SetTitle("Events / GeV")
frame_top.SetMinimum(0.01)
frame_top.SetMaximum(h_central.GetMaximum() * 1.3)
frame_top.Draw()
h_central.SetMarkerStyle(20)
h_central.Draw("same")
h_up.SetMarkerStyle(26)
h_up.Draw("same")
h_down.SetMarkerStyle(32)
h_down.Draw("same")
l = TLegend(0.6, 0.6, 0.88, 0.8)
l.SetFillColor(0)
l.SetBorderSize(0)
l.AddEntry(h_central, "Central", "p")
l.AddEntry(h_up, "SF unc up", "p")
l.AddEntry(h_down, "SF unc down", "p")
l.Draw()
c.cd()
bottom = TPad("bottom", "bottom", 0., 0., 1., 0.5)
bottom.SetTopMargin(0.02)
bottom.SetBottomMargin(0.2)
bottom.Draw()
bottom.cd()
frame_bottom = TH1D("frame_bottom", "frame_bottom", 100, 0., mass*2)
frame_bottom.GetXaxis().SetTitle("m_{jj} [GeV]")
frame_bottom.GetYaxis().SetTitle("Unc / Central")
frame_bottom.SetMinimum(0.5)
frame_bottom.SetMaximum(1.5)
frame_bottom.Draw()
ratio_up = h_up.Clone()
ratio_up.Divide(h_central)
ratio_up.SetMarkerStyle(26)
ratio_up.Draw("p hist same")
ratio_down = h_down.Clone()
ratio_down.Divide(h_central)
ratio_down.SetMarkerStyle(32)
ratio_down.Draw("p hist same")
avg_up = TLine(0., h_up.Integral() / h_central.Integral(), mass*2, h_up.Integral() / h_central.Integral())
avg_up.SetLineStyle(2)
avg_up.SetLineColor(seaborn.GetColorRoot("pastel", 2))
avg_up.Draw("same")
avg_down = TLine(0., h_down.Integral() / h_central.Integral(), mass*2, h_down.Integral() / h_central.Integral())
avg_down.SetLineStyle(2)
avg_down.SetLineColor(seaborn.GetColorRoot("pastel", 2))
avg_down.Draw("same")
c.cd()
c.SaveAs(analysis_config.figure_directory + "/" + c.GetName() + ".pdf")
f.Close()
def main():
# usage description
usage = "Example: ./scripts/createDatacards.py --inputData inputs/rawhistV7_Run2015D_scoutingPFHT_UNBLINDED_649_838_JEC_HLTplusV7_Mjj_cor_smooth.root --dataHistname mjj_mjjcor_gev --inputSig inputs/ResonanceShapes_gg_13TeV_Scouting_Spring15.root -f gg -o datacards -l 1866 --lumiUnc 0.027 --massrange 1000 1500 50 --runFit --p1 5 --p2 7 --p3 0.4 --massMin 838 --massMax 2037 --fitStrategy 2"
# input parameters
parser = ArgumentParser(
description=
'Script that creates combine datacards and corresponding RooFit workspaces',
epilog=usage)
parser.add_argument("analysis", type=str, help="Analysis name")
parser.add_argument("model", type=str, help="Model (Hbb, RSG)")
#parser.add_argument("--inputData", dest="inputData", required=True,
# help="Input data spectrum",
# metavar="INPUT_DATA")
parser.add_argument("--dataHistname",
dest="dataHistname",
type=str,
default="h_data",
help="Data histogram name",
metavar="DATA_HISTNAME")
#parser.add_argument("--inputSig", dest="inputSig", required=True,
# help="Input signal shapes",
# metavar="INPUT_SIGNAL")
parser.add_argument("-f",
"--final_state",
dest="final_state",
default="qq",
help="Final state (e.g. qq, qg, gg)",
metavar="FINAL_STATE")
parser.add_argument("--fit_functions",
dest="fit_functions",
default="f1,f2,f3,f4,f5",
help="List of fit functions")
#parser.add_argument("-f2", "--type", dest="atype", required=True, help="Type (e.g. hG, lG, hR, lR)")
parser.add_argument(
"-o",
"--output_path",
dest="output_path",
help=
"Output path where datacards and workspaces will be stored. If not specified, this is derived from limit_configuration.",
metavar="OUTPUT_PATH")
parser.add_argument("--correctTrigger",
dest="correctTrigger",
action='store_true',
help="Include trigger correction in PDF")
parser.add_argument(
"-l",
"--lumi",
dest="lumi",
default=19700.,
type=float,
help="Integrated luminosity in pb-1 (default: %(default).1f)",
metavar="LUMI")
parser.add_argument(
"--massMin",
dest="massMin",
default=500,
type=int,
help=
"Lower bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MIN")
parser.add_argument(
"--massMax",
dest="massMax",
default=1200,
type=int,
help=
"Upper bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MAX")
parser.add_argument(
"--fitSignal",
action="store_true",
help=
"Use signal fitted shapes (CB+Voigtian) instead of histogram templates"
)
#parser.add_argument("--lumiUnc", dest="lumiUnc",
# required=True, type=float,
# help="Relative uncertainty in the integrated luminosity",
# metavar="LUMI_UNC")
#parser.add_argument("--jesUnc", dest="jesUnc",
# type=float,
# help="Relative uncertainty in the jet energy scale",
# metavar="JES_UNC")
#parser.add_argument("--jerUnc", dest="jerUnc",
# type=float,
# help="Relative uncertainty in the jet energy resolution",
# metavar="JER_UNC")
parser.add_argument(
"--sqrtS",
dest="sqrtS",
default=8000.,
type=float,
help="Collision center-of-mass energy (default: %(default).1f)",
metavar="SQRTS")
parser.add_argument("--fixP3",
dest="fixP3",
default=False,
action="store_true",
help="Fix the fit function p3 parameter")
parser.add_argument("--runFit",
dest="runFit",
default=False,
action="store_true",
help="Run the fit")
parser.add_argument("--fitBonly",
dest="fitBonly",
default=False,
action="store_true",
help="Run B-only fit")
parser.add_argument("--fixBkg",
dest="fixBkg",
default=False,
action="store_true",
help="Fix all background parameters")
parser.add_argument("--decoBkg",
dest="decoBkg",
default=False,
action="store_true",
help="Decorrelate background parameters")
parser.add_argument("--fitStrategy",
dest="fitStrategy",
type=int,
default=1,
help="Fit strategy (default: %(default).1f)")
parser.add_argument("--debug",
dest="debug",
default=False,
action="store_true",
help="Debug printout")
parser.add_argument(
"--postfix",
dest="postfix",
default='',
help="Postfix for the output file names (default: %(default)s)")
parser.add_argument("--pyes",
dest="pyes",
default=False,
action="store_true",
help="Make files for plots")
parser.add_argument("--jyes",
dest="jyes",
default=False,
action="store_true",
help="Make files for JES/JER plots")
parser.add_argument(
"--pdir",
dest="pdir",
default='testarea',
help="Name a directory for the plots (default: %(default)s)")
parser.add_argument("--chi2",
dest="chi2",
default=False,
action="store_true",
help="Compute chi squared")
parser.add_argument("--widefit",
dest="widefit",
default=False,
action="store_true",
help="Fit with wide bin hist")
mass_group = parser.add_mutually_exclusive_group(required=True)
mass_group.add_argument(
"--mass",
type=int,
nargs='*',
default=1000,
help=
"Mass can be specified as a single value or a whitespace separated list (default: %(default)i)"
)
mass_group.add_argument(
"--massrange",
type=int,
nargs=3,
help="Define a range of masses to be produced. Format: min max step",
metavar=('MIN', 'MAX', 'STEP'))
mass_group.add_argument("--masslist",
help="List containing mass information")
args = parser.parse_args()
fit_functions = args.fit_functions.split(",")
# mass points for which resonance shapes will be produced
masses = []
if args.fitBonly:
masses.append(750)
else:
if args.massrange != None:
MIN, MAX, STEP = args.massrange
masses = range(MIN, MAX + STEP, STEP)
elif args.masslist != None:
# A mass list was provided
print "Will create mass list according to", args.masslist
masslist = __import__(args.masslist.replace(".py", ""))
masses = masslist.masses
else:
masses = args.mass
# sort masses
masses.sort()
# import ROOT stuff
from ROOT import gStyle, TFile, TH1F, TH1D, TGraph, kTRUE, kFALSE, TCanvas, TLegend, TPad, TLine
from ROOT import RooHist, RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooProdPdf, RooEffProd, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf, RooExtendPdf
if not args.debug:
RooMsgService.instance().setSilentMode(kTRUE)
RooMsgService.instance().setStreamStatus(0, kFALSE)
RooMsgService.instance().setStreamStatus(1, kFALSE)
# input data file
#inputData = TFile(limit_config.get_data_input(args.analysis))
# input data histogram
#hData = inputData.Get(args.dataHistname)
#hData.SetDirectory(0)
data_file = TFile(
analysis_config.get_b_histogram_filename(args.analysis,
"BJetPlusX_2012"))
hData = data_file.Get("BHistograms/h_pfjet_mjj")
hData.SetDirectory(0)
# input sig file
if not args.fitSignal:
print "[create_datacards] INFO : Opening resonance shapes file at " + limit_config.get_resonance_shapes(
args.analysis, args.model)
inputSig = TFile(
limit_config.get_resonance_shapes(args.analysis, args.model),
"READ")
sqrtS = args.sqrtS
# mass variable
mjj = RooRealVar('mjj', 'mjj', float(args.massMin), float(args.massMax))
# integrated luminosity and signal cross section
lumi = args.lumi
signalCrossSection = 1. # set to 1. so that the limit on r can be interpreted as a limit on the signal cross section
if args.correctTrigger:
trigger_efficiency_pdf = trigger_efficiency.get_pdf(args.analysis, mjj)
trigger_efficiency_formula = trigger_efficiency.get_formula(
args.analysis, mjj)
else:
trigger_efficiency_pdf = trigger_efficiency.get_trivial_pdf(mjj)
trigger_efficiency_formula = trigger_efficiency.get_trivial_formula(
mjj)
for mass in masses:
print ">> Creating datacard and workspace for %s resonance with m = %i GeV..." % (
args.final_state, int(mass))
rooDataHist = RooDataHist('rooDatahist', 'rooDathist', RooArgList(mjj),
hData)
if not args.fitSignal:
hSig = inputSig.Get("h_" + args.final_state + "_" + str(int(mass)))
if not hSig:
raise Exception("Couldn't find histogram " + "h_" +
args.final_state + "_" + str(int(mass)) +
" in file " +
limit_config.get_resonance_shapes(
args.analysis, args.model))
# normalize signal shape to the expected event yield (works even if input shapes are not normalized to unity)
hSig.Scale(
signalCrossSection * lumi / hSig.Integral()
) # divide by a number that provides roughly an r value of 1-10
rooSigHist = RooDataHist('rooSigHist', 'rooSigHist',
RooArgList(mjj), hSig)
print 'Signal acceptance:', (rooSigHist.sumEntries() /
hSig.Integral())
# If using fitted signal shapes, load the signal PDF
if args.fitSignal:
print "[create_datacards] Loading fitted signal PDFs from " + analysis_config.get_signal_fit_file(
args.analysis,
args.model,
mass,
"bukin",
interpolated=(not mass
in analysis_config.simulation.simulated_masses))
f_signal_pdfs = TFile(
analysis_config.get_signal_fit_file(
args.analysis,
args.model,
mass,
"bukin",
interpolated=(
not mass
in analysis_config.simulation.simulated_masses)),
"READ")
w_signal = f_signal_pdfs.Get("w_signal")
input_parameters = signal_fits.get_parameters(
w_signal.pdf("signal"))
# Make a new PDF with nuisance parameters
signal_pdf_notrig, signal_vars = signal_fits.make_signal_pdf_systematic(
"bukin", mjj, mass=mass)
signal_pdf_name = signal_pdf_notrig.GetName()
signal_pdf_notrig.SetName(signal_pdf_name + "_notrig")
#signal_pdf = RooProdPdf(signal_pdf_name, signal_pdf_name, signal_pdf_notrig, trigger_efficiency_pdf)
signal_pdf = RooEffProd(signal_pdf_name, signal_pdf_name,
signal_pdf_notrig,
trigger_efficiency_formula)
# Copy input parameter values
signal_vars["xp_0"].setVal(input_parameters["xp"][0])
signal_vars["xp_0"].setError(input_parameters["xp"][1])
signal_vars["xp_0"].setConstant()
signal_vars["sigp_0"].setVal(input_parameters["sigp"][0])
signal_vars["sigp_0"].setError(input_parameters["sigp"][1])
signal_vars["sigp_0"].setConstant()
signal_vars["xi_0"].setVal(input_parameters["xi"][0])
signal_vars["xi_0"].setError(input_parameters["xi"][1])
signal_vars["xi_0"].setConstant()
signal_vars["rho1_0"].setVal(input_parameters["rho1"][0])
signal_vars["rho1_0"].setError(input_parameters["rho1"][1])
signal_vars["rho1_0"].setConstant()
signal_vars["rho2_0"].setVal(input_parameters["rho2"][0])
signal_vars["rho2_0"].setError(input_parameters["rho2"][1])
signal_vars["rho2_0"].setConstant()
f_signal_pdfs.Close()
signal_parameters = {}
signal_pdfs_notrig = {}
signal_pdfs = {}
signal_norms = {}
background_pdfs = {}
background_pdfs_notrig = {}
background_parameters = {}
background_norms = {}
signal_epdfs = {}
background_epdfs = {}
models = {}
fit_results = {}
for fit_function in fit_functions:
print "[create_datacards] INFO : On fit function {}".format(
fit_function)
if args.fitSignal:
# Make a copy of the signal PDF, so that each fitTo call uses its own copy.
# The copy should have all variables set constant.
#signal_pdfs[fit_function], signal_parameters[fit_function] = signal_fits.copy_signal_pdf("bukin", signal_pdf, mjj, tag=fit_function, include_systematics=True)
signal_pdfs_notrig[fit_function] = ROOT.RooBukinPdf(
signal_pdf_notrig,
signal_pdf_notrig.GetName() + "_" + fit_function)
signal_pdfs[fit_function] = RooEffProd(
signal_pdf.GetName() + "_" + fit_function,
signal_pdf.GetName() + "_" + fit_function,
signal_pdfs_notrig[fit_function],
trigger_efficiency_formula)
#signal_pdfs[fit_function] = RooProdPdf(signal_pdf.GetName() + "_" + fit_function, signal_pdf.GetName() + "_" + fit_function, signal_pdfs_notrig[fit_function], trigger_efficiency_pdf)
iterator = signal_pdfs_notrig[fit_function].getVariables(
).createIterator()
this_parameter = iterator.Next()
while this_parameter:
this_parameter.setConstant()
this_parameter = iterator.Next()
else:
signal_pdfs[fit_function] = RooHistPdf(
'signal_' + fit_function, 'signal_' + fit_function,
RooArgSet(mjj), rooSigHist)
signal_norms[fit_function] = RooRealVar(
'signal_norm_' + fit_function, 'signal_norm_' + fit_function,
0., 0., 1e+05)
if args.fitBonly:
signal_norms[fit_function].setConstant()
background_pdfs_notrig[fit_function], background_parameters[
fit_function] = make_background_pdf(fit_function,
mjj,
collision_energy=8000.)
background_pdf_name = background_pdfs_notrig[fit_function].GetName(
)
background_pdfs_notrig[fit_function].SetName(background_pdf_name +
"_notrig")
background_pdfs[fit_function] = RooEffProd(
background_pdf_name, background_pdf_name,
background_pdfs_notrig[fit_function],
trigger_efficiency_formula)
#background_pdfs[fit_function] = RooProdPdf(background_pdf_name, background_pdf_name, background_pdfs_notrig[fit_function], trigger_efficiency_pdf)
#background_pdfs[fit_function] = background_pdfs_notrig[fit_function]
#background_pdfs[fit_function].SetName(background_pdf_name)
# Initial values
if "trigbbh" in args.analysis:
if fit_function == "f3":
background_parameters[fit_function]["p1"].setVal(55.)
background_parameters[fit_function]["p1"].setMin(20.)
background_parameters[fit_function]["p2"].setVal(8.)
elif fit_function == "f4":
background_parameters[fit_function]["p1"].setVal(28.)
background_parameters[fit_function]["p2"].setVal(-22.)
background_parameters[fit_function]["p3"].setVal(10.)
elif "trigbbl" in args.analysis:
if fit_function == "f3":
background_parameters[fit_function]["p1"].setVal(82.)
background_parameters[fit_function]["p1"].setMin(60.)
background_parameters[fit_function]["p2"].setVal(8.)
elif fit_function == "f4":
background_parameters[fit_function]["p1"].setVal(41.)
background_parameters[fit_function]["p2"].setVal(-45.)
background_parameters[fit_function]["p3"].setVal(10.)
data_integral = hData.Integral(
hData.GetXaxis().FindBin(float(args.massMin)),
hData.GetXaxis().FindBin(float(args.massMax)))
background_norms[fit_function] = RooRealVar(
'background_' + fit_function + '_norm',
'background_' + fit_function + '_norm', data_integral, 0.,
1.e8)
signal_epdfs[fit_function] = RooExtendPdf(
'esignal_' + fit_function, 'esignal_' + fit_function,
signal_pdfs[fit_function], signal_norms[fit_function])
background_epdfs[fit_function] = RooExtendPdf(
'ebackground_' + fit_function, 'ebackground_' + fit_function,
background_pdfs[fit_function], background_norms[fit_function])
models[fit_function] = RooAddPdf(
'model_' + fit_function, 's+b',
RooArgList(background_epdfs[fit_function],
signal_epdfs[fit_function]))
if args.runFit:
print "[create_datacards] INFO : Starting fit with function {}".format(
fit_function)
fit_results[fit_function] = models[fit_function].fitTo(
rooDataHist, RooFit.Save(kTRUE), RooFit.Extended(kTRUE),
RooFit.Strategy(args.fitStrategy), RooFit.Verbose(0))
print "[create_datacards] INFO : Done with fit {}. Printing results.".format(
fit_function)
fit_results[fit_function].Print()
print "[create_datacards] DEBUG : End args.runFit if block."
# needed if want to evaluate limits without background systematics
if args.fixBkg:
background_norms[fit_function].setConstant()
for par_name, par in background_parameters[
fit_function].iteritems():
par.setConstant()
# -----------------------------------------
#signal_pdfs_syst = {}
# JES and JER uncertainties
if args.fitSignal:
print "[create_datacards] INFO : Getting signal PDFs from " + analysis_config.get_signal_fit_file(
args.analysis,
args.model,
mass,
"bukin",
interpolated=(not mass
in analysis_config.simulation.simulated_masses))
f_signal_pdfs = TFile(
analysis_config.get_signal_fit_file(
args.analysis,
args.model,
mass,
"bukin",
interpolated=(
not mass
in analysis_config.simulation.simulated_masses)))
w_signal = f_signal_pdfs.Get("w_signal")
if "jes" in systematics:
xp_central = signal_vars["xp_0"].getVal()
#print w_signal.pdf("signal__JESUp")
#print signal_fits.get_parameters(w_signal.pdf("signal__JESUp"))
xp_up = signal_fits.get_parameters(
w_signal.pdf("signal__JESUp"))["xpJESUp"][0]
xp_down = signal_fits.get_parameters(
w_signal.pdf("signal__JESDown"))["xpJESDown"][0]
signal_vars["dxp"].setVal(
max(abs(xp_up - xp_central), abs(xp_down - xp_central)))
if signal_vars["dxp"].getVal() > 2 * mass * 0.1:
print "[create_datacards] WARNING : Large dxp value. dxp = {}, xp_down = {}, xp_central = {}, xp_up = {}".format(
signal_vars["dxp"].getVal(), xp_down, xp_central,
xp_up)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant(False)
else:
signal_vars["dxp"].setVal(0.)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant()
signal_vars["dxp"].setError(0.)
signal_vars["dxp"].setConstant()
if "jer" in systematics:
sigp_central = signal_vars["sigp_0"].getVal()
sigp_up = signal_fits.get_parameters(
w_signal.pdf("signal__JERUp"))["sigpJERUp"][0]
sigp_down = signal_fits.get_parameters(
w_signal.pdf("signal__JERDown"))["sigpJERDown"][0]
signal_vars["dsigp"].setVal(
max(abs(sigp_up - sigp_central),
abs(sigp_down - sigp_central)))
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant(False)
else:
signal_vars["dsigp"].setVal(0.)
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant()
signal_vars["dsigp"].setError(0.)
signal_vars["dsigp"].setConstant()
#for variation in ["JERUp", "JERDown"]:
# signal_pdfs_syst[variation] = w_signal.pdf("signal__" + variation)
#for variation, pdf in signal_pdfs_syst.iteritems():
# signal_parameters = pdf.getVariables()
# iter = signal_parameters.createIterator()
# var = iter.Next()
# while var:
# var.setConstant()
# var = iter.Next()
f_signal_pdfs.Close()
else:
# dictionaries holding systematic variations of the signal shape
hSig_Syst = {}
hSig_Syst_DataHist = {}
sigCDF = TGraph(hSig.GetNbinsX() + 1)
if "jes" in systematics or "jer" in systematics:
sigCDF.SetPoint(0, 0., 0.)
integral = 0.
for i in range(1, hSig.GetNbinsX() + 1):
x = hSig.GetXaxis().GetBinLowEdge(i + 1)
integral = integral + hSig.GetBinContent(i)
sigCDF.SetPoint(i, x, integral)
if "jes" in systematics:
hSig_Syst['JESUp'] = copy.deepcopy(hSig)
hSig_Syst['JESDown'] = copy.deepcopy(hSig)
if "jer" in systematics:
hSig_Syst['JERUp'] = copy.deepcopy(hSig)
hSig_Syst['JERDown'] = copy.deepcopy(hSig)
# reset signal histograms
for key in hSig_Syst.keys():
hSig_Syst[key].Reset()
hSig_Syst[key].SetName(hSig_Syst[key].GetName() + '_' + key)
# produce JES signal shapes
if "jes" in systematics:
for i in range(1, hSig.GetNbinsX() + 1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i + 1)
jes = 1. - systematics["jes"]
xLowPrime = jes * xLow
xUpPrime = jes * xUp
hSig_Syst['JESUp'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jes = 1. + systematics["jes"]
xLowPrime = jes * xLow
xUpPrime = jes * xUp
hSig_Syst['JESDown'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JESUp'] = RooDataHist(
'hSig_JESUp', 'hSig_JESUp', RooArgList(mjj),
hSig_Syst['JESUp'])
hSig_Syst_DataHist['JESDown'] = RooDataHist(
'hSig_JESDown', 'hSig_JESDown', RooArgList(mjj),
hSig_Syst['JESDown'])
# produce JER signal shapes
if "jer" in systematics:
for i in range(1, hSig.GetNbinsX() + 1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i + 1)
jer = 1. - systematics["jer"]
xLowPrime = jer * (xLow - float(mass)) + float(mass)
xUpPrime = jer * (xUp - float(mass)) + float(mass)
hSig_Syst['JERUp'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jer = 1. + systematics["jer"]
xLowPrime = jer * (xLow - float(mass)) + float(mass)
xUpPrime = jer * (xUp - float(mass)) + float(mass)
hSig_Syst['JERDown'].SetBinContent(
i,
sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JERUp'] = RooDataHist(
'hSig_JERUp', 'hSig_JERUp', RooArgList(mjj),
hSig_Syst['JERUp'])
hSig_Syst_DataHist['JERDown'] = RooDataHist(
'hSig_JERDown', 'hSig_JERDown', RooArgList(mjj),
hSig_Syst['JERDown'])
# -----------------------------------------
# create a datacard and corresponding workspace
postfix = (('_' + args.postfix) if args.postfix != '' else '')
wsName = 'workspace_' + args.final_state + '_m' + str(
mass) + postfix + '.root'
w = RooWorkspace('w', 'workspace')
if args.fitSignal:
signal_pdf.SetName("signal")
getattr(w, 'import')(signal_pdf, RooFit.Rename("signal"))
# Create a norm variable "signal_norm" which normalizes the PDF to unity.
norm = args.lumi
#signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", 1. / norm, 0.1 / norm, 10. / norm)
#if args.analysis == "trigbbh_CSVTM" and mass >= 1100:
signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm",
norm / 100., norm / 100. / 10.,
norm * 10.)
#else:
# signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", norm, norm / 10., norm * 10.)
print "[create_datacards] INFO : Set signal norm to {}".format(
signal_norm.getVal())
signal_norm.setConstant()
getattr(w, 'import')(signal_norm, ROOT.RooCmdArg())
#if "jes" in systematics:
# getattr(w,'import')(signal_pdfs_syst['JESUp'],RooFit.Rename("signal__JESUp"))
# getattr(w,'import')(signal_pdfs_syst['JESDown'],RooFit.Rename("signal__JESDown"))
#if "jer" in systematics:
# getattr(w,'import')(signal_pdfs_syst['JERUp'],RooFit.Rename("signal__JERUp"))
# getattr(w,'import')(signal_pdfs_syst['JERDown'],RooFit.Rename("signal__JERDown"))
else:
getattr(w, 'import')(rooSigHist, RooFit.Rename("signal"))
if "jes" in systematics:
getattr(w, 'import')(hSig_Syst_DataHist['JESUp'],
RooFit.Rename("signal__JESUp"))
getattr(w, 'import')(hSig_Syst_DataHist['JESDown'],
RooFit.Rename("signal__JESDown"))
if "jer" in systematics:
getattr(w, 'import')(hSig_Syst_DataHist['JERUp'],
RooFit.Rename("signal__JERUp"))
getattr(w, 'import')(hSig_Syst_DataHist['JERDown'],
RooFit.Rename("signal__JERDown"))
if args.decoBkg:
getattr(w, 'import')(background_deco, ROOT.RooCmdArg())
else:
for fit_function in fit_functions:
print "Importing background PDF"
print background_pdfs[fit_function]
background_pdfs[fit_function].Print()
getattr(w,
'import')(background_pdfs[fit_function],
ROOT.RooCmdArg(),
RooFit.Rename("background_" + fit_function),
RooFit.RecycleConflictNodes())
w.pdf("background_" + fit_function).Print()
getattr(w, 'import')(background_norms[fit_function],
ROOT.RooCmdArg(),
RooFit.Rename("background_" +
fit_function + "_norm"))
getattr(w, 'import')(fit_results[fit_function])
getattr(w, 'import')(signal_norms[fit_function],
ROOT.RooCmdArg())
if args.fitBonly:
getattr(w, 'import')(models[fit_function],
ROOT.RooCmdArg(),
RooFit.RecycleConflictNodes())
getattr(w, 'import')(rooDataHist, RooFit.Rename("data_obs"))
w.Print()
print "Starting save"
if args.output_path:
if not os.path.isdir(os.path.join(os.getcwd(), args.output_path)):
os.mkdir(os.path.join(os.getcwd(), args.output_path))
print "[create_datacards] INFO : Writing workspace to file {}".format(
os.path.join(args.output_path, wsName))
w.writeToFile(os.path.join(args.output_path, wsName))
else:
print "[create_datacards] INFO : Writing workspace to file {}".format(
limit_config.get_workspace_filename(
args.analysis,
args.model,
mass,
fitBonly=args.fitBonly,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger))
w.writeToFile(
limit_config.get_workspace_filename(
args.analysis,
args.model,
mass,
fitBonly=args.fitBonly,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger))
# Clean up
for name, obj in signal_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in fit_results.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, dict_l2 in background_parameters.iteritems():
for name2, obj in dict_l2.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in models.iteritems():
if obj:
obj.IsA().Destructor(obj)
rooDataHist.IsA().Destructor(rooDataHist)
w.IsA().Destructor(w)
# Make datacards only if S+B fitted
if not args.fitBonly:
beffUnc = 0.3
boffUnc = 0.06
for fit_function in fit_functions:
if args.output_path:
dcName = 'datacard_' + args.final_state + '_m' + str(
mass) + postfix + '_' + fit_function + '.txt'
print "[create_datacards] INFO : Writing datacard to file {}".format(
os.path.join(args.output_path, dcName))
datacard = open(os.path.join(args.output_path, dcName),
'w')
else:
print "[create_datacards] INFO : Writing datacard to file {}".format(
limit_config.get_datacard_filename(
args.analysis,
args.model,
mass,
fit_function,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger))
datacard = open(
limit_config.get_datacard_filename(
args.analysis,
args.model,
mass,
fit_function,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger), 'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if ("jes" in systematics
or "jer" in systematics) and not args.fitSignal:
if args.output_path:
datacard.write('shapes * * ' + wsName +
' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
datacard.write('shapes * * ' + os.path.basename(
limit_config.get_workspace_filename(
args.analysis,
args.model,
mass,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger)) +
' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
if args.output_path:
datacard.write('shapes * * ' + wsName +
' w:$PROCESS\n')
else:
datacard.write('shapes * * ' + os.path.basename(
limit_config.get_workspace_filename(
args.analysis,
args.model,
mass,
fitSignal=args.fitSignal,
correctTrigger=args.correctTrigger)) +
' w:$PROCESS\n')
datacard.write('---------------\n')
datacard.write('bin 1\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin 1 1\n')
datacard.write('process signal background_' +
fit_function + '\n')
datacard.write('process 0 1\n')
if args.fitSignal:
datacard.write('rate 1 1\n')
else:
datacard.write('rate -1 1\n')
datacard.write('------------------------------\n')
datacard.write('lumi lnN %f -\n' %
(1. + systematics["luminosity"]))
datacard.write('beff lnN %f -\n' % (1. + beffUnc))
datacard.write('boff lnN %f -\n' % (1. + boffUnc))
#datacard.write('bkg lnN - 1.03\n')
if args.fitSignal:
if "jes" in systematics:
datacard.write("alpha_jes param 0.0 1.0\n")
if "jer" in systematics:
datacard.write("alpha_jer param 0.0 1.0\n")
else:
if "jes" in systematics:
datacard.write('JES shape 1 -\n')
if "jer" in systematics:
datacard.write('JER shape 1 -\n')
# flat parameters --- flat prior
datacard.write('background_' + fit_function +
'_norm flatParam\n')
if args.decoBkg:
datacard.write('deco_eig1 flatParam\n')
datacard.write('deco_eig2 flatParam\n')
else:
for par_name, par in background_parameters[
fit_function].iteritems():
datacard.write(fit_function + "_" + par_name +
' flatParam\n')
datacard.close()
print "[create_datacards] INFO : Done with this datacard"
#print '>> Datacards and workspaces created and stored in %s/'%( os.path.join(os.getcwd(),args.output_path) )
print "All done."
# Make datacards for 375 GeV
masses["trigbbl_CSVTM"] = [375]
analyses = ["trigbbl_CSVTM"]
for model in ["Hbb", "ZPrime", "RSG"]:
#for model in ["ZPrime"]:
for analysis in analyses:
for mass in masses[analysis]:
if do_qcd:
data_sample = "QCD_TuneZ2star_8TeV_pythia6"
if "trigbb" in analysis:
data_sample = "BJetPlusX_2012"
elif "trigmu" in analysis:
data_sample = "SingleMu_2012"
data_file_path = analysis_config.get_b_histogram_filename(
analysis, data_sample)
if useMCTrigger:
signal_pdf_file = analysis_config.get_signal_fit_file(
analysis,
model,
mass,
"bukin",
interpolated=(
not mass
in analysis_config.simulation.simulated_masses))
else:
if analysis == "trigbbl_CSVTM":
notrig_analysis = "NoTrigger_eta1p7_CSVTM"
elif analysis == "trigbbh_CSVTM":
notrig_analysis = "NoTrigger_eta2p2_CSVTM"
def main():
# usage description
usage = "Example: ./scripts/createDatacards.py --inputData inputs/rawhistV7_Run2015D_scoutingPFHT_UNBLINDED_649_838_JEC_HLTplusV7_Mjj_cor_smooth.root --dataHistname mjj_mjjcor_gev --inputSig inputs/ResonanceShapes_gg_13TeV_Scouting_Spring15.root -f gg -o datacards -l 1866 --lumiUnc 0.027 --massrange 1000 1500 50 --runFit --p1 5 --p2 7 --p3 0.4 --massMin 838 --massMax 2037 --fitStrategy 2"
# input parameters
parser = ArgumentParser(description='Script that creates combine datacards and corresponding RooFit workspaces',epilog=usage)
parser.add_argument("analysis", type=str, help="Analysis name")
parser.add_argument("model", type=str, help="Model (Hbb, RSG)")
#parser.add_argument("--inputData", dest="inputData", required=True,
# help="Input data spectrum",
# metavar="INPUT_DATA")
parser.add_argument("--dataHistname", dest="dataHistname", type=str, default="h_data",
help="Data histogram name",
metavar="DATA_HISTNAME")
#parser.add_argument("--inputSig", dest="inputSig", required=True,
# help="Input signal shapes",
# metavar="INPUT_SIGNAL")
parser.add_argument("-f", "--final_state", dest="final_state", default="qq",
help="Final state (e.g. qq, qg, gg)",
metavar="FINAL_STATE")
parser.add_argument("--fit_functions", dest="fit_functions", default="f1,f2,f3,f4,f5", help="List of fit functions")
#parser.add_argument("-f2", "--type", dest="atype", required=True, help="Type (e.g. hG, lG, hR, lR)")
parser.add_argument("-o", "--output_path", dest="output_path",
help="Output path where datacards and workspaces will be stored. If not specified, this is derived from limit_configuration.",
metavar="OUTPUT_PATH")
parser.add_argument("--correctTrigger", dest="correctTrigger",
action='store_true',
help="Include trigger correction in PDF")
parser.add_argument("-l", "--lumi", dest="lumi",
default=19700., type=float,
help="Integrated luminosity in pb-1 (default: %(default).1f)",
metavar="LUMI")
parser.add_argument("--massMin", dest="massMin",
default=500, type=int,
help="Lower bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MIN")
parser.add_argument("--massMax", dest="massMax",
default=1200, type=int,
help="Upper bound of the mass range used for fitting (default: %(default)s)",
metavar="MASS_MAX")
parser.add_argument("--fitSignal", action="store_true", help="Use signal fitted shapes (CB+Voigtian) instead of histogram templates")
#parser.add_argument("--lumiUnc", dest="lumiUnc",
# required=True, type=float,
# help="Relative uncertainty in the integrated luminosity",
# metavar="LUMI_UNC")
#parser.add_argument("--jesUnc", dest="jesUnc",
# type=float,
# help="Relative uncertainty in the jet energy scale",
# metavar="JES_UNC")
#parser.add_argument("--jerUnc", dest="jerUnc",
# type=float,
# help="Relative uncertainty in the jet energy resolution",
# metavar="JER_UNC")
parser.add_argument("--sqrtS", dest="sqrtS",
default=8000., type=float,
help="Collision center-of-mass energy (default: %(default).1f)",
metavar="SQRTS")
parser.add_argument("--fixP3", dest="fixP3", default=False, action="store_true", help="Fix the fit function p3 parameter")
parser.add_argument("--runFit", dest="runFit", default=False, action="store_true", help="Run the fit")
parser.add_argument("--fitBonly", dest="fitBonly", default=False, action="store_true", help="Run B-only fit")
parser.add_argument("--fixBkg", dest="fixBkg", default=False, action="store_true", help="Fix all background parameters")
parser.add_argument("--decoBkg", dest="decoBkg", default=False, action="store_true", help="Decorrelate background parameters")
parser.add_argument("--fitStrategy", dest="fitStrategy", type=int, default=1, help="Fit strategy (default: %(default).1f)")
parser.add_argument("--debug", dest="debug", default=False, action="store_true", help="Debug printout")
parser.add_argument("--postfix", dest="postfix", default='', help="Postfix for the output file names (default: %(default)s)")
parser.add_argument("--pyes", dest="pyes", default=False, action="store_true", help="Make files for plots")
parser.add_argument("--jyes", dest="jyes", default=False, action="store_true", help="Make files for JES/JER plots")
parser.add_argument("--pdir", dest="pdir", default='testarea', help="Name a directory for the plots (default: %(default)s)")
parser.add_argument("--chi2", dest="chi2", default=False, action="store_true", help="Compute chi squared")
parser.add_argument("--widefit", dest="widefit", default=False, action="store_true", help="Fit with wide bin hist")
mass_group = parser.add_mutually_exclusive_group(required=True)
mass_group.add_argument("--mass",
type=int,
nargs = '*',
default = 1000,
help="Mass can be specified as a single value or a whitespace separated list (default: %(default)i)"
)
mass_group.add_argument("--massrange",
type=int,
nargs = 3,
help="Define a range of masses to be produced. Format: min max step",
metavar = ('MIN', 'MAX', 'STEP')
)
mass_group.add_argument("--masslist",
help = "List containing mass information"
)
args = parser.parse_args()
fit_functions = args.fit_functions.split(",")
# mass points for which resonance shapes will be produced
masses = []
if args.fitBonly:
masses.append(750)
else:
if args.massrange != None:
MIN, MAX, STEP = args.massrange
masses = range(MIN, MAX+STEP, STEP)
elif args.masslist != None:
# A mass list was provided
print "Will create mass list according to", args.masslist
masslist = __import__(args.masslist.replace(".py",""))
masses = masslist.masses
else:
masses = args.mass
# sort masses
masses.sort()
# import ROOT stuff
from ROOT import gStyle, TFile, TH1F, TH1D, TGraph, kTRUE, kFALSE, TCanvas, TLegend, TPad, TLine
from ROOT import RooHist, RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooProdPdf, RooEffProd, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf, RooExtendPdf
if not args.debug:
RooMsgService.instance().setSilentMode(kTRUE)
RooMsgService.instance().setStreamStatus(0,kFALSE)
RooMsgService.instance().setStreamStatus(1,kFALSE)
# input data file
#inputData = TFile(limit_config.get_data_input(args.analysis))
# input data histogram
#hData = inputData.Get(args.dataHistname)
#hData.SetDirectory(0)
data_file = TFile(analysis_config.get_b_histogram_filename(args.analysis, "BJetPlusX_2012"))
hData = data_file.Get("BHistograms/h_pfjet_mjj")
hData.SetDirectory(0)
# input sig file
if not args.fitSignal:
print "[create_datacards] INFO : Opening resonance shapes file at " + limit_config.get_resonance_shapes(args.analysis, args.model)
inputSig = TFile(limit_config.get_resonance_shapes(args.analysis, args.model), "READ")
sqrtS = args.sqrtS
# mass variable
mjj = RooRealVar('mjj','mjj',float(args.massMin),float(args.massMax))
# integrated luminosity and signal cross section
lumi = args.lumi
signalCrossSection = 1. # set to 1. so that the limit on r can be interpreted as a limit on the signal cross section
if args.correctTrigger:
trigger_efficiency_pdf = trigger_efficiency.get_pdf(args.analysis, mjj)
trigger_efficiency_formula = trigger_efficiency.get_formula(args.analysis, mjj)
else:
trigger_efficiency_pdf = trigger_efficiency.get_trivial_pdf(mjj)
trigger_efficiency_formula = trigger_efficiency.get_trivial_formula(mjj)
for mass in masses:
print ">> Creating datacard and workspace for %s resonance with m = %i GeV..."%(args.final_state, int(mass))
rooDataHist = RooDataHist('rooDatahist','rooDathist',RooArgList(mjj),hData)
if not args.fitSignal:
hSig = inputSig.Get( "h_" + args.final_state + "_" + str(int(mass)) )
if not hSig:
raise Exception("Couldn't find histogram " + "h_" + args.final_state + "_" + str(int(mass)) + " in file " + limit_config.get_resonance_shapes(args.analysis, args.model))
# normalize signal shape to the expected event yield (works even if input shapes are not normalized to unity)
hSig.Scale(signalCrossSection*lumi/hSig.Integral()) # divide by a number that provides roughly an r value of 1-10
rooSigHist = RooDataHist('rooSigHist','rooSigHist',RooArgList(mjj),hSig)
print 'Signal acceptance:', (rooSigHist.sumEntries()/hSig.Integral())
# If using fitted signal shapes, load the signal PDF
if args.fitSignal:
print "[create_datacards] Loading fitted signal PDFs from " + analysis_config.get_signal_fit_file(args.analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses))
f_signal_pdfs = TFile(analysis_config.get_signal_fit_file(args.analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses)), "READ")
w_signal = f_signal_pdfs.Get("w_signal")
input_parameters = signal_fits.get_parameters(w_signal.pdf("signal"))
# Make a new PDF with nuisance parameters
signal_pdf_notrig, signal_vars = signal_fits.make_signal_pdf_systematic("bukin", mjj, mass=mass)
signal_pdf_name = signal_pdf_notrig.GetName()
signal_pdf_notrig.SetName(signal_pdf_name + "_notrig")
#signal_pdf = RooProdPdf(signal_pdf_name, signal_pdf_name, signal_pdf_notrig, trigger_efficiency_pdf)
signal_pdf = RooEffProd(signal_pdf_name, signal_pdf_name, signal_pdf_notrig, trigger_efficiency_formula)
# Copy input parameter values
signal_vars["xp_0"].setVal(input_parameters["xp"][0])
signal_vars["xp_0"].setError(input_parameters["xp"][1])
signal_vars["xp_0"].setConstant()
signal_vars["sigp_0"].setVal(input_parameters["sigp"][0])
signal_vars["sigp_0"].setError(input_parameters["sigp"][1])
signal_vars["sigp_0"].setConstant()
signal_vars["xi_0"].setVal(input_parameters["xi"][0])
signal_vars["xi_0"].setError(input_parameters["xi"][1])
signal_vars["xi_0"].setConstant()
signal_vars["rho1_0"].setVal(input_parameters["rho1"][0])
signal_vars["rho1_0"].setError(input_parameters["rho1"][1])
signal_vars["rho1_0"].setConstant()
signal_vars["rho2_0"].setVal(input_parameters["rho2"][0])
signal_vars["rho2_0"].setError(input_parameters["rho2"][1])
signal_vars["rho2_0"].setConstant()
f_signal_pdfs.Close()
signal_parameters = {}
signal_pdfs_notrig = {}
signal_pdfs = {}
signal_norms = {}
background_pdfs = {}
background_pdfs_notrig = {}
background_parameters = {}
background_norms = {}
signal_epdfs = {}
background_epdfs = {}
models = {}
fit_results = {}
for fit_function in fit_functions:
print "[create_datacards] INFO : On fit function {}".format(fit_function)
if args.fitSignal:
# Make a copy of the signal PDF, so that each fitTo call uses its own copy.
# The copy should have all variables set constant.
#signal_pdfs[fit_function], signal_parameters[fit_function] = signal_fits.copy_signal_pdf("bukin", signal_pdf, mjj, tag=fit_function, include_systematics=True)
signal_pdfs_notrig[fit_function] = ROOT.RooBukinPdf(signal_pdf_notrig, signal_pdf_notrig.GetName() + "_" + fit_function)
signal_pdfs[fit_function] = RooEffProd(signal_pdf.GetName() + "_" + fit_function, signal_pdf.GetName() + "_" + fit_function, signal_pdfs_notrig[fit_function], trigger_efficiency_formula)
#signal_pdfs[fit_function] = RooProdPdf(signal_pdf.GetName() + "_" + fit_function, signal_pdf.GetName() + "_" + fit_function, signal_pdfs_notrig[fit_function], trigger_efficiency_pdf)
iterator = signal_pdfs_notrig[fit_function].getVariables().createIterator()
this_parameter = iterator.Next()
while this_parameter:
this_parameter.setConstant()
this_parameter = iterator.Next()
else:
signal_pdfs[fit_function] = RooHistPdf('signal_' + fit_function,'signal_' + fit_function, RooArgSet(mjj), rooSigHist)
signal_norms[fit_function] = RooRealVar('signal_norm_' + fit_function, 'signal_norm_' + fit_function, 0., 0., 1e+05)
if args.fitBonly:
signal_norms[fit_function].setConstant()
background_pdfs_notrig[fit_function], background_parameters[fit_function] = make_background_pdf(fit_function, mjj, collision_energy=8000.)
background_pdf_name = background_pdfs_notrig[fit_function].GetName()
background_pdfs_notrig[fit_function].SetName(background_pdf_name + "_notrig")
background_pdfs[fit_function] = RooEffProd(background_pdf_name, background_pdf_name, background_pdfs_notrig[fit_function], trigger_efficiency_formula)
#background_pdfs[fit_function] = RooProdPdf(background_pdf_name, background_pdf_name, background_pdfs_notrig[fit_function], trigger_efficiency_pdf)
#background_pdfs[fit_function] = background_pdfs_notrig[fit_function]
#background_pdfs[fit_function].SetName(background_pdf_name)
# Initial values
if "trigbbh" in args.analysis:
if fit_function == "f3":
background_parameters[fit_function]["p1"].setVal(55.)
background_parameters[fit_function]["p1"].setMin(20.)
background_parameters[fit_function]["p2"].setVal(8.)
elif fit_function == "f4":
background_parameters[fit_function]["p1"].setVal(28.)
background_parameters[fit_function]["p2"].setVal(-22.)
background_parameters[fit_function]["p3"].setVal(10.)
elif "trigbbl" in args.analysis:
if fit_function == "f3":
background_parameters[fit_function]["p1"].setVal(82.)
background_parameters[fit_function]["p1"].setMin(60.)
background_parameters[fit_function]["p2"].setVal(8.)
elif fit_function == "f4":
background_parameters[fit_function]["p1"].setVal(41.)
background_parameters[fit_function]["p2"].setVal(-45.)
background_parameters[fit_function]["p3"].setVal(10.)
data_integral = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
background_norms[fit_function] = RooRealVar('background_' + fit_function + '_norm', 'background_' + fit_function + '_norm', data_integral, 0., 1.e8)
signal_epdfs[fit_function] = RooExtendPdf('esignal_' + fit_function, 'esignal_' + fit_function, signal_pdfs[fit_function], signal_norms[fit_function])
background_epdfs[fit_function] = RooExtendPdf('ebackground_' + fit_function, 'ebackground_' + fit_function, background_pdfs[fit_function], background_norms[fit_function])
models[fit_function] = RooAddPdf('model_' + fit_function, 's+b', RooArgList(background_epdfs[fit_function], signal_epdfs[fit_function]))
if args.runFit:
print "[create_datacards] INFO : Starting fit with function {}".format(fit_function)
fit_results[fit_function] = models[fit_function].fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Extended(kTRUE), RooFit.Strategy(args.fitStrategy), RooFit.Verbose(0))
print "[create_datacards] INFO : Done with fit {}. Printing results.".format(fit_function)
fit_results[fit_function].Print()
print "[create_datacards] DEBUG : End args.runFit if block."
# needed if want to evaluate limits without background systematics
if args.fixBkg:
background_norms[fit_function].setConstant()
for par_name, par in background_parameters[fit_function].iteritems():
par.setConstant()
# -----------------------------------------
#signal_pdfs_syst = {}
# JES and JER uncertainties
if args.fitSignal:
print "[create_datacards] INFO : Getting signal PDFs from " + analysis_config.get_signal_fit_file(args.analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses))
f_signal_pdfs = TFile(analysis_config.get_signal_fit_file(args.analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses)))
w_signal = f_signal_pdfs.Get("w_signal")
if "jes" in systematics:
xp_central = signal_vars["xp_0"].getVal()
#print w_signal.pdf("signal__JESUp")
#print signal_fits.get_parameters(w_signal.pdf("signal__JESUp"))
xp_up = signal_fits.get_parameters(w_signal.pdf("signal__JESUp"))["xpJESUp"][0]
xp_down = signal_fits.get_parameters(w_signal.pdf("signal__JESDown"))["xpJESDown"][0]
signal_vars["dxp"].setVal(max(abs(xp_up - xp_central), abs(xp_down - xp_central)))
if signal_vars["dxp"].getVal() > 2 * mass * 0.1:
print "[create_datacards] WARNING : Large dxp value. dxp = {}, xp_down = {}, xp_central = {}, xp_up = {}".format(signal_vars["dxp"].getVal(), xp_down, xp_central, xp_up)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant(False)
else:
signal_vars["dxp"].setVal(0.)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant()
signal_vars["dxp"].setError(0.)
signal_vars["dxp"].setConstant()
if "jer" in systematics:
sigp_central = signal_vars["sigp_0"].getVal()
sigp_up = signal_fits.get_parameters(w_signal.pdf("signal__JERUp"))["sigpJERUp"][0]
sigp_down = signal_fits.get_parameters(w_signal.pdf("signal__JERDown"))["sigpJERDown"][0]
signal_vars["dsigp"].setVal(max(abs(sigp_up - sigp_central), abs(sigp_down - sigp_central)))
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant(False)
else:
signal_vars["dsigp"].setVal(0.)
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant()
signal_vars["dsigp"].setError(0.)
signal_vars["dsigp"].setConstant()
#for variation in ["JERUp", "JERDown"]:
# signal_pdfs_syst[variation] = w_signal.pdf("signal__" + variation)
#for variation, pdf in signal_pdfs_syst.iteritems():
# signal_parameters = pdf.getVariables()
# iter = signal_parameters.createIterator()
# var = iter.Next()
# while var:
# var.setConstant()
# var = iter.Next()
f_signal_pdfs.Close()
else:
# dictionaries holding systematic variations of the signal shape
hSig_Syst = {}
hSig_Syst_DataHist = {}
sigCDF = TGraph(hSig.GetNbinsX()+1)
if "jes" in systematics or "jer" in systematics:
sigCDF.SetPoint(0,0.,0.)
integral = 0.
for i in range(1, hSig.GetNbinsX()+1):
x = hSig.GetXaxis().GetBinLowEdge(i+1)
integral = integral + hSig.GetBinContent(i)
sigCDF.SetPoint(i,x,integral)
if "jes" in systematics:
hSig_Syst['JESUp'] = copy.deepcopy(hSig)
hSig_Syst['JESDown'] = copy.deepcopy(hSig)
if "jer" in systematics:
hSig_Syst['JERUp'] = copy.deepcopy(hSig)
hSig_Syst['JERDown'] = copy.deepcopy(hSig)
# reset signal histograms
for key in hSig_Syst.keys():
hSig_Syst[key].Reset()
hSig_Syst[key].SetName(hSig_Syst[key].GetName() + '_' + key)
# produce JES signal shapes
if "jes" in systematics:
for i in range(1, hSig.GetNbinsX()+1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i+1)
jes = 1. - systematics["jes"]
xLowPrime = jes*xLow
xUpPrime = jes*xUp
hSig_Syst['JESUp'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jes = 1. + systematics["jes"]
xLowPrime = jes*xLow
xUpPrime = jes*xUp
hSig_Syst['JESDown'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JESUp'] = RooDataHist('hSig_JESUp','hSig_JESUp',RooArgList(mjj),hSig_Syst['JESUp'])
hSig_Syst_DataHist['JESDown'] = RooDataHist('hSig_JESDown','hSig_JESDown',RooArgList(mjj),hSig_Syst['JESDown'])
# produce JER signal shapes
if "jer" in systematics:
for i in range(1, hSig.GetNbinsX()+1):
xLow = hSig.GetXaxis().GetBinLowEdge(i)
xUp = hSig.GetXaxis().GetBinLowEdge(i+1)
jer = 1. - systematics["jer"]
xLowPrime = jer*(xLow-float(mass))+float(mass)
xUpPrime = jer*(xUp-float(mass))+float(mass)
hSig_Syst['JERUp'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
jer = 1. + systematics["jer"]
xLowPrime = jer*(xLow-float(mass))+float(mass)
xUpPrime = jer*(xUp-float(mass))+float(mass)
hSig_Syst['JERDown'].SetBinContent(i, sigCDF.Eval(xUpPrime) - sigCDF.Eval(xLowPrime))
hSig_Syst_DataHist['JERUp'] = RooDataHist('hSig_JERUp','hSig_JERUp',RooArgList(mjj),hSig_Syst['JERUp'])
hSig_Syst_DataHist['JERDown'] = RooDataHist('hSig_JERDown','hSig_JERDown',RooArgList(mjj),hSig_Syst['JERDown'])
# -----------------------------------------
# create a datacard and corresponding workspace
postfix = (('_' + args.postfix) if args.postfix != '' else '')
wsName = 'workspace_' + args.final_state + '_m' + str(mass) + postfix + '.root'
w = RooWorkspace('w','workspace')
if args.fitSignal:
signal_pdf.SetName("signal")
getattr(w,'import')(signal_pdf,RooFit.Rename("signal"))
# Create a norm variable "signal_norm" which normalizes the PDF to unity.
norm = args.lumi
#signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", 1. / norm, 0.1 / norm, 10. / norm)
#if args.analysis == "trigbbh_CSVTM" and mass >= 1100:
signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", norm/100., norm/100. / 10., norm * 10.)
#else:
# signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", norm, norm / 10., norm * 10.)
print "[create_datacards] INFO : Set signal norm to {}".format(signal_norm.getVal())
signal_norm.setConstant()
getattr(w,'import')(signal_norm,ROOT.RooCmdArg())
#if "jes" in systematics:
# getattr(w,'import')(signal_pdfs_syst['JESUp'],RooFit.Rename("signal__JESUp"))
# getattr(w,'import')(signal_pdfs_syst['JESDown'],RooFit.Rename("signal__JESDown"))
#if "jer" in systematics:
# getattr(w,'import')(signal_pdfs_syst['JERUp'],RooFit.Rename("signal__JERUp"))
# getattr(w,'import')(signal_pdfs_syst['JERDown'],RooFit.Rename("signal__JERDown"))
else:
getattr(w,'import')(rooSigHist,RooFit.Rename("signal"))
if "jes" in systematics:
getattr(w,'import')(hSig_Syst_DataHist['JESUp'],RooFit.Rename("signal__JESUp"))
getattr(w,'import')(hSig_Syst_DataHist['JESDown'],RooFit.Rename("signal__JESDown"))
if "jer" in systematics:
getattr(w,'import')(hSig_Syst_DataHist['JERUp'],RooFit.Rename("signal__JERUp"))
getattr(w,'import')(hSig_Syst_DataHist['JERDown'],RooFit.Rename("signal__JERDown"))
if args.decoBkg:
getattr(w,'import')(background_deco,ROOT.RooCmdArg())
else:
for fit_function in fit_functions:
print "Importing background PDF"
print background_pdfs[fit_function]
background_pdfs[fit_function].Print()
getattr(w,'import')(background_pdfs[fit_function],ROOT.RooCmdArg(),RooFit.Rename("background_" + fit_function), RooFit.RecycleConflictNodes())
w.pdf("background_" + fit_function).Print()
getattr(w,'import')(background_norms[fit_function],ROOT.RooCmdArg(),RooFit.Rename("background_" + fit_function + "_norm"))
getattr(w,'import')(fit_results[fit_function])
getattr(w,'import')(signal_norms[fit_function],ROOT.RooCmdArg())
if args.fitBonly:
getattr(w,'import')(models[fit_function],ROOT.RooCmdArg(),RooFit.RecycleConflictNodes())
getattr(w,'import')(rooDataHist,RooFit.Rename("data_obs"))
w.Print()
print "Starting save"
if args.output_path:
if not os.path.isdir( os.path.join(os.getcwd(),args.output_path) ):
os.mkdir( os.path.join(os.getcwd(),args.output_path) )
print "[create_datacards] INFO : Writing workspace to file {}".format(os.path.join(args.output_path,wsName))
w.writeToFile(os.path.join(args.output_path,wsName))
else:
print "[create_datacards] INFO : Writing workspace to file {}".format(limit_config.get_workspace_filename(args.analysis, args.model, mass, fitBonly=args.fitBonly, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))
w.writeToFile(limit_config.get_workspace_filename(args.analysis, args.model, mass, fitBonly=args.fitBonly, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))
# Clean up
for name, obj in signal_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in fit_results.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, dict_l2 in background_parameters.iteritems():
for name2, obj in dict_l2.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in models.iteritems():
if obj:
obj.IsA().Destructor(obj)
rooDataHist.IsA().Destructor(rooDataHist)
w.IsA().Destructor(w)
# Make datacards only if S+B fitted
if not args.fitBonly:
beffUnc = 0.3
boffUnc = 0.06
for fit_function in fit_functions:
if args.output_path:
dcName = 'datacard_' + args.final_state + '_m' + str(mass) + postfix + '_' + fit_function + '.txt'
print "[create_datacards] INFO : Writing datacard to file {}".format(os.path.join(args.output_path,dcName))
datacard = open(os.path.join(args.output_path,dcName),'w')
else:
print "[create_datacards] INFO : Writing datacard to file {}".format(limit_config.get_datacard_filename(args.analysis, args.model, mass, fit_function, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))
datacard = open(limit_config.get_datacard_filename(args.analysis, args.model, mass, fit_function, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger), 'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if ("jes" in systematics or "jer" in systematics) and not args.fitSignal:
if args.output_path:
datacard.write('shapes * * '+wsName+' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
datacard.write('shapes * * '+os.path.basename(limit_config.get_workspace_filename(args.analysis, args.model, mass, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))+' w:$PROCESS w:$PROCESS__$SYSTEMATIC\n')
else:
if args.output_path:
datacard.write('shapes * * '+wsName+' w:$PROCESS\n')
else:
datacard.write('shapes * * '+os.path.basename(limit_config.get_workspace_filename(args.analysis, args.model, mass, fitSignal=args.fitSignal, correctTrigger=args.correctTrigger))+' w:$PROCESS\n')
datacard.write('---------------\n')
datacard.write('bin 1\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin 1 1\n')
datacard.write('process signal background_' + fit_function + '\n')
datacard.write('process 0 1\n')
if args.fitSignal:
datacard.write('rate 1 1\n')
else:
datacard.write('rate -1 1\n')
datacard.write('------------------------------\n')
datacard.write('lumi lnN %f -\n'%(1.+systematics["luminosity"]))
datacard.write('beff lnN %f -\n'%(1.+beffUnc))
datacard.write('boff lnN %f -\n'%(1.+boffUnc))
#datacard.write('bkg lnN - 1.03\n')
if args.fitSignal:
if "jes" in systematics:
datacard.write("alpha_jes param 0.0 1.0\n")
if "jer" in systematics:
datacard.write("alpha_jer param 0.0 1.0\n")
else:
if "jes" in systematics:
datacard.write('JES shape 1 -\n')
if "jer" in systematics:
datacard.write('JER shape 1 -\n')
# flat parameters --- flat prior
datacard.write('background_' + fit_function + '_norm flatParam\n')
if args.decoBkg:
datacard.write('deco_eig1 flatParam\n')
datacard.write('deco_eig2 flatParam\n')
else:
for par_name, par in background_parameters[fit_function].iteritems():
datacard.write(fit_function + "_" + par_name + ' flatParam\n')
datacard.close()
print "[create_datacards] INFO : Done with this datacard"
#print '>> Datacards and workspaces created and stored in %s/'%( os.path.join(os.getcwd(),args.output_path) )
print "All done."
parser.add_argument("--normalize", action="store_true", help="Normalize histograms")
parser.add_argument("--rebin", type=int, help="Rebin histograms")
args = parser.parse_args()
variables = args.variables.split(",")
analyses = args.analyses.split(",")
models = args.models.split(",")
masses = [int(x) for x in args.masses.split(",")]
for variable in variables:
histograms = {}
histogram_names = []
for analysis in analyses:
for model in models:
for mass in masses:
histogram_name = GetLegendEntry(analysis, model, mass)
f = TFile(analysis_config.get_b_histogram_filename(analysis, analysis_config.simulation.get_signal_tag(model, mass, "FULLSIM")), "READ")
histogram_names.append(histogram_name)
histograms[histogram_name] = f.Get(args.prefix + variable)
if not histograms[histogram_name]:
print "[signal_plots] ERROR : Histogram {} not found in file {}".format(args.prefix + variable, analysis_config.get_b_histogram_filename(analysis, analysis_config.simulation.get_signal_tag(model, mass, "FULLSIM")))
histograms[histogram_name].SetDirectory(0)
f.Close()
if args.normalize:
if histograms[histogram_name].Integral() > 0:
histograms[histogram_name].Scale(1. / histograms[histogram_name].Integral())
if args.rebin:
histograms[histogram_name].Rebin(args.rebin)
canvas_name = "c_signal_distribution_{}_{}_{}_{}".format(variable, "_".join(analyses), "_".join(models), "_".join(args.masses.split(",")))
MakeSignalPlot(canvas_name, histogram_names, histograms, x_range=args.x_range, y_range=args.y_range, logx=args.logx, logy=args.logy)
"HT650": "trigjetht650",
"HTUnprescaled": "trigjetht"
}
for wp in ["CSVT", "CSVM", "CSVL"]:
numerator_histograms[sr][wp] = {}
denominator_histograms[sr][wp] = {}
efficiency_histograms[sr][wp] = {}
for which in [0, 1]:
numerator_histograms[sr][wp][which] = None
denominator_histograms[sr][wp][which] = None
efficiency_histograms[sr][wp][which] = None
# Load histograms
first = True
for ht_slice in ht_slices:
f = TFile(
analysis_config.get_b_histogram_filename(
analyses[ht_slice], "JetHT_2012BCD"), "READ")
this_numerator_histogram_3D = f.Get("BHistograms/h_pfjet_jet" +
str(which) + "_pt_eta_" +
wp)
this_denominator_histogram_3D = f.Get(
"BHistograms/h_pfjet_jet" + str(which) + "_pt_eta")
zbin_min = this_numerator_histogram_3D.GetZaxis().FindBin(
ht_ranges[ht_slice][0] + 1.e-5)
zbin_max = this_numerator_histogram_3D.GetZaxis().FindBin(
ht_ranges[ht_slice][0] - 1.e-5)
if first:
numerator_histograms[sr][wp][which] = TH2D(
this_numerator_histogram_3D.GetName() + "_xy" + wp +
str(which),
this_numerator_histogram_3D.GetName() + "_xy",
this_numerator_histogram_3D.GetXaxis().GetNbins(),
online_btag_eff = {
"trigbbl_CSVTM": 1.82719e-01,
"trigbbh_CSVTM": 4.89446e-01,
}
truth_categories = ["bb", "bc", "bg", "bl", "cc", "cg", "cl", "gg", "gl", "ll"]
truth_categories_incl = [
"inclusive", "bb", "bc", "bg", "bl", "cc", "cg", "cl", "gg", "gl", "ll"
]
for analysis in analyses:
h_mjj = None
h_mjj_truthbb = None
for qcd_sample in qcd_samples:
f = TFile(
analysis_config.get_b_histogram_filename(analysis, qcd_sample),
"READ")
input_nevents = f.Get("BHistograms/h_input_nevents").Integral()
this_h_mjj = f.Get("BHistograms/h_pfjet_mjj").Clone()
this_h_mjj.Scale(19700. * qcd_cross_sections[qcd_sample] /
input_nevents)
this_h_mjj_truthbb = f.Get("BHistograms/h_pfjet_mjj_truthbb").Clone()
this_h_mjj_truthbb.Scale(19700. * qcd_cross_sections[qcd_sample] /
input_nevents)
if not h_mjj:
h_mjj = this_h_mjj.Clone()
h_mjj.SetDirectory(0)
h_mjj_truthbb = this_h_mjj_truthbb.Clone()
h_mjj_truthbb.SetDirectory(0)
else:
h_mjj.Add(this_h_mjj)
def run_single_mass(args, mass):
print "[run_single_mass] INFO : Creating datacard and workspace for m = %i GeV..."%(int(mass))
if args.fit_functions == "all":
#fit_functions = ["dijet4", "dijet5", "modexp4", "polyx6", "atlas4", "atlas5", "polypower4", "rational3", "rational4"]
fit_functions = ["dijet4", "modexp4", "polyx6", "polypower4"]
else:
fit_functions = args.fit_functions.split(",")
# import ROOT stuff
from ROOT import gStyle, TFile, TH1F, TH1D, TGraph, kTRUE, kFALSE, TCanvas, TLegend, TPad, TLine
from ROOT import RooHist, RooRealVar, RooDataHist, RooArgList, RooArgSet, RooAddPdf, RooProdPdf, RooEffProd, RooFit, RooGenericPdf, RooWorkspace, RooMsgService, RooHistPdf, RooExtendPdf, RooFormulaVar
if not args.debug:
RooMsgService.instance().setSilentMode(kTRUE)
RooMsgService.instance().setStreamStatus(0,kFALSE)
RooMsgService.instance().setStreamStatus(1,kFALSE)
# Stuff
mjj = RooRealVar('mjj','mjj',float(args.massMin),float(args.massMax))
lumi = args.lumi
signalCrossSection = 1. # Set to 1 so that the limit on r can be interpreted as a limit on the signal cross section
# Input data file
if args.qcd:
data_sample = "QCD_TuneZ2star_8TeV_pythia6"
elif "trigbb" in args.analysis:
data_sample = "BJetPlusX_2012"
elif "trigmu" in args.analysis:
data_sample = "SingleMu_2012"
data_file_path = analysis_config.get_b_histogram_filename(args.analysis, data_sample)
if args.condor:
data_file_path = os.path.basename(data_file_path)
data_file = TFile(data_file_path)
hData_notrigcorr = data_file.Get("BHistograms/h_pfjet_mjj")
hData_notrigcorr.SetDirectory(0)
hData_name = hData_notrigcorr.GetName()
hData_notrigcorr.SetName(hData_name + "_notrigcorr")
# Trigger correction on data
if args.fitTrigger:
# Make trigger correction objects
trigeff_pt_formula, trigeff_vars = trigger_efficiency.get_var_formula(args.analysis, mjj)
trigeff_btag_var = RooRealVar("trigeff_btag", "trigeff_btag", 0., 1.)
trigeff_btag_var.setVal(trigger_efficiency.online_btag_eff[args.analysis][0])
trigeff_btag_var.setConstant()
trigeff_btag_formula = RooFormulaVar("trigeff_btag_formula", "@0", RooArgList(trigeff_btag_var))
#trigeff_btag_var.setConstant()
trigeff_total_formula = RooFormulaVar("trigeff_total_formula", "@0*@1", RooArgList(trigeff_btag_var, trigeff_pt_formula))
#for trigeff_var_name, trigeff_var in trigeff_vars.iteritems():
# trigeff_var.setConstant()
if args.correctTrigger:
# Apply trigger correction to data histogram
hData = CorrectTriggerEfficiency(hData_notrigcorr, args.analysis)
# Still need b-tagging efficiency to scale the MC
if not args.useMCTrigger:
trigeff_btag_var = RooRealVar("trigeff_btag", "trigeff_btag", 0., 1.)
trigeff_btag_var.setVal(trigger_efficiency.online_btag_eff[args.analysis][0])
trigeff_btag_var.setConstant()
trigeff_btag_formula = RooFormulaVar("trigeff_btag_formula", "@0", RooArgList(trigeff_btag_var))
# Use a RooRealVar instead! You want to be able to apply a systematic in combine.
# trigeff_btag_formula = RooFormulaVar("trigeff_btag_formula", str(trigger_efficiency.online_btag_eff[args.analysis][0]), RooArgList())
else:
hData = hData_notrigcorr
if args.qcd:
# For QCD, scale the histogram by the online b-tag trigger efficiency.
if args.analysis == "NoTrigger_eta1p7_CSVTM":
trigeff_btag_formula = RooFormulaVar("trigeff_btag_formula", str(trigger_efficiency.online_btag_eff["trigbbl_CSVTM"][0]), RooArgList())
elif args.analysis == "NoTrigger_eta2p2_CSVTM":
trigeff_btag_formula = RooFormulaVar("trigeff_btag_formula", str(trigger_efficiency.online_btag_eff["trigbbh_CSVTM"][0]), RooArgList())
if args.analysis == "NoTrigger_eta1p7_CSVTM":
hData.Scale(trigger_efficiency.online_btag_eff["trigbbl_CSVTM"][0])
elif args.analysis == "NoTrigger_eta2p2_CSVTM":
hData.Scale(trigger_efficiency.online_btag_eff["trigbbh_CSVTM"][0])
else:
print "[create_datacards_parallel] ERROR : QCD fit requested, but analysis != NoTrigger_etaXpY_CSVTM. I don't know what to do!"
sys.exit(1)
hData.SetName(hData_name)
rooDataHist = RooDataHist('rooDatahist','rooDatahist',RooArgList(mjj),hData)
if args.correctTrigger:
rooDataHist_notrigcorr = RooDataHist("rooDatahist_notrigcorr", "rooDatahist_notrigcorr", RooArgList(mjj), hData_notrigcorr)
# Get signal pdf * btag efficiency
if args.useMCTrigger:
signal_pdf_file = analysis_config.get_signal_fit_file(args.analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses))
if args.condor:
signal_pdf_file = os.path.basename(signal_pdf_file)
print "[create_datacards] Loading fitted signal PDFs from " + signal_pdf_file
f_signal_pdfs = TFile(signal_pdf_file, "READ")
f_signal_pdfs.Print()
w_signal = f_signal_pdfs.Get("w_signal")
w_signal.Print()
bukin_pdf = w_signal.pdf("signal_bukin")
bukin_pdf.Print()
else:
if args.analysis == "trigbbl_CSVTM" or args.analysis == "NoTrigger_eta1p7_CSVTM":
notrig_analysis = "NoTrigger_eta1p7_CSVTM"
elif args.analysis == "trigbbh_CSVTM" or args.analysis == "NoTrigger_eta2p2_CSVTM":
notrig_analysis = "NoTrigger_eta2p2_CSVTM"
elif args.analysis == "trigbbl_CSVM" or args.analysis == "NoTrigger_eta1p7_CSVM":
notrig_analysis = "NoTrigger_eta1p7_CSVM"
elif args.analysis == "trigbbh_CSVM" or args.analysis == "NoTrigger_eta2p2_CSVM":
notrig_analysis = "NoTrigger_eta2p2_CSVM"
else:
print "[run_single_mass] ERROR : I don't know a no-trigger variant of analysis {}. Please make one, or specify --useMCTrigger.".format(args.analysis)
sys.exit(1)
print analysis_config.simulation.simulated_masses
signal_pdf_file = analysis_config.get_signal_fit_file(notrig_analysis, args.model, mass, "bukin", interpolated=(not mass in analysis_config.simulation.simulated_masses))
print "[create_datacards] Loading fitted signal PDFs from " + signal_pdf_file
if args.condor:
signal_pdf_file = os.path.basename(signal_pdf_file)
f_signal_pdfs = TFile(signal_pdf_file, "READ")
w_signal = f_signal_pdfs.Get("w_signal")
bukin_pdf = w_signal.pdf("signal")
bukin_pdf.SetName("signal_bukin")
input_signal_parameters = signal_fits.get_parameters(bukin_pdf)
# Make a new PDF with nuisance parameters
signal_pdf_notrig, signal_vars = signal_fits.make_signal_pdf_systematic("bukin", mjj, mass=mass)
signal_pdf_name = signal_pdf_notrig.GetName()
signal_pdf_notrig.SetName(signal_pdf_name + "_notrig")
# Add trigger efficiency
if args.useMCTrigger:
if args.fitTrigger:
# Online b-tagging eff incorporated in Bukin/acceptance, so signal pdf = Bukin * pT efficiency
signal_pdf = RooEffProd("signal", "signal", signal_pdf_notrig, trigeff_pt_formula)
elif args.correctTrigger:
# Signal PDF = bukin; b-tag efficiency already included in normalization
signal_pdf = signal_pdf_notrig
signal_pdf.SetName(signal_pdf_name)
else:
if args.fitTrigger:
# Signal PDF = bukin * total trigger efficiency
signal_pdf = RooEffProd("signal", "signal", signal_pdf_notrig, trigeff_total_formula)
elif args.correctTrigger:
if args.useMCTrigger:
signal_pdf = signal_pdf_notrig
signal_pdf.SetName("signal")
else:
# Signal PDF = bukin * btag efficiency
signal_pdf = RooEffProd("signal", "signal", signal_pdf_notrig, trigeff_btag_formula)
elif args.qcd:
# Signal PDF = bukin * btag efficiency
# Same as correctTrigger
signal_pdf = RooEffProd("signal", "signal", signal_pdf_notrig, trigeff_btag_formula)
# Copy input parameter values
signal_vars["xp_0"].setVal(input_signal_parameters["xp"][0])
signal_vars["xp_0"].setError(input_signal_parameters["xp"][1])
signal_vars["xp_0"].setConstant()
signal_vars["sigp_0"].setVal(input_signal_parameters["sigp"][0])
signal_vars["sigp_0"].setError(input_signal_parameters["sigp"][1])
signal_vars["sigp_0"].setConstant()
signal_vars["xi_0"].setVal(input_signal_parameters["xi"][0])
signal_vars["xi_0"].setError(input_signal_parameters["xi"][1])
signal_vars["xi_0"].setConstant()
signal_vars["rho1_0"].setVal(input_signal_parameters["rho1"][0])
signal_vars["rho1_0"].setError(input_signal_parameters["rho1"][1])
signal_vars["rho1_0"].setConstant()
signal_vars["rho2_0"].setVal(input_signal_parameters["rho2"][0])
signal_vars["rho2_0"].setError(input_signal_parameters["rho2"][1])
signal_vars["rho2_0"].setConstant()
f_signal_pdfs.Close()
signal_parameters = {}
signal_pdfs_notrig = {}
signal_pdfs = {}
signal_norms = {}
background_pdfs = {}
background_pdfs_notrig = {}
background_parameters = {}
background_norms = {}
signal_epdfs = {}
background_epdfs = {}
models = {}
fit_results = {}
if args.fitOffB:
# Load RooHistPdf
if "bbl" in args.analysis or "eta1p7" in args.analysis:
eta_region = "eta1p7"
offline_btag_eff_vars = {
"p0":RooRealVar("offline_btag_eff_p0", "offline_btag_eff_p0", 6.78251e-03, 6.78251e-03 - 10.*7.66906e-05, 6.78251e-03 + 10.*7.66906e-05),
"p1":RooRealVar("offline_btag_eff_p1", "offline_btag_eff_p1", -9.55614e-06, -9.55614e-06 - 10.*1.04286e-07, -9.55614e-06 + 10.*1.04286e-07),
"p2":RooRealVar("offline_btag_eff_p2", "offline_btag_eff_p2", 4.39468e-09, 4.39468e-09 - 1.e-07, 4.39468e-09 + 1.e-07),
}
offline_btag_eff_formula = RooFormulaVar("offline_btag_eff", "max(@0+(@1*@3)+(@2*@3*@3), 0.)", RooArgList(offline_btag_eff_vars["p0"], offline_btag_eff_vars["p1"], offline_btag_eff_vars["p2"], mjj))
elif "bbh" in args.analysis or "eta2p2" in args.analysis:
eta_region = "eta2p2"
offline_btag_eff_vars = {
"p0":RooRealVar("offline_btag_eff_p0", "offline_btag_eff_p0", -1.72721e-03, -1.72721e-03 - 10.*3.04992e-05, -1.72721e-03 + 10.*3.04992e-05),
"p1":RooRealVar("offline_btag_eff_p1", "offline_btag_eff_p1", 1.72562e-06, 1.72562e-06 - 10.*3.23472e-08, 1.72562e-06 + 10.*3.23472e-08),
"p2":RooRealVar("offline_btag_eff_p2", "offline_btag_eff_p2", 8.74866e-03, 8.74866e-03 - 10.*7.81413e-05, 8.74866e-03 + 10.*7.81413e-05),
"p3":RooRealVar("offline_btag_eff_p3", "offline_btag_eff_p3", -1.67123e-03, -1.67123e-03 - 10.*4.30607e-05, -1.67123e-03 + 10.*4.30607e-05),
}
offline_btag_eff_formula = RooFormulaVar("offline_btag_eff", "max(@0+@1*@4+ @2*exp(@3*@4), 0.)", RooArgList(offline_btag_eff_vars["p0"],offline_btag_eff_vars["p1"],offline_btag_eff_vars["p2"],offline_btag_eff_vars["p3"], mjj))
#f_offline_btag_eff = TFile(analysis_config.get_offline_btag_file("CSVTM", eta_region))
#h_offline_btag_eff = f_offline_btag_eff.Get("h_offline_btag_eff")
#print h_offline_btag_eff
#offline_btag_eff_rdh = RooDataHist("rdh_offline_btag_eff", "rdh_offline_btag_eff", RooArgList(mjj), h_offline_btag_eff)
#offline_btag_eff_pdf = RooHistPdf("pdf_offline_btag_eff", "pdf_offline_btag_eff", RooArgSet(mjj), offline_btag_eff_rdh)
for fit_function in fit_functions:
print "[create_datacards] INFO : On fit function {}".format(fit_function)
# Make a copy of the signal PDF, so that each fitTo call uses its own copy.
# The copy should have all variables set constant.
#signal_pdfs[fit_function], signal_parameters[fit_function] = signal_fits.copy_signal_pdf("bukin", signal_pdf, mjj, tag=fit_function, include_systematics=True)
signal_pdfs_notrig[fit_function] = ROOT.RooBukinPdf(signal_pdf_notrig, signal_pdf_notrig.GetName() + "_" + fit_function)
iterator = signal_pdfs_notrig[fit_function].getVariables().createIterator()
this_parameter = iterator.Next()
while this_parameter:
this_parameter.setConstant()
this_parameter = iterator.Next()
# Add trigger efficiency
if args.useMCTrigger:
if args.fitTrigger:
signal_pdfs[fit_function] = RooEffProd(signal_pdf.GetName() + "_" + fit_function, signal_pdf.GetName() + "_" + fit_function, signal_pdfs_notrig[fit_function], trigeff_pt_formula)
else:
signal_pdfs[fit_function] = signal_pdfs_notrig[fit_function]
signal_pdfs[fit_functions].SetName(signal_pdf.GetName() + "_" + fit_function)
elif args.fitTrigger:
# Signal PDF = bukin * total trigger efficiency
signal_pdfs[fit_function] = RooEffProd(signal_pdf.GetName() + "_" + fit_function, signal_pdf.GetName() + "_" + fit_function, signal_pdfs_notrig[fit_function], trigeff_total_formula)
elif args.correctTrigger:
# Signal PDF = bukin * btag efficiency
signal_pdfs[fit_function] = RooEffProd(signal_pdf.GetName() + "_" + fit_function, signal_pdf.GetName() + "_" + fit_function, signal_pdfs_notrig[fit_function], trigeff_btag_formula)
elif args.qcd:
# Signal PDF = bukin * btag efficiency
signal_pdfs[fit_function] = RooEffProd(signal_pdf.GetName() + "_" + fit_function, signal_pdf.GetName() + "_" + fit_function, signal_pdfs_notrig[fit_function], trigeff_btag_formula)
signal_norms[fit_function] = RooRealVar('signal_norm_' + fit_function, 'signal_norm_' + fit_function, 0., 0., 1e+05)
if args.fitBonly:
signal_norms[fit_function].setConstant()
# Make background PDF
background_pdfs_notrig[fit_function], background_parameters[fit_function] = make_background_pdf(fit_function, mjj, collision_energy=8000.)
background_pdf_name = background_pdfs_notrig[fit_function].GetName()
background_pdfs_notrig[fit_function].SetName(background_pdf_name + "_notrig")
if args.fitTrigger and args.fitOffB:
background_pdf_intermediate = RooEffProd(background_pdf_name + "_intermediate", background_pdf_name + "_intermediate", background_pdfs_notrig[fit_function], offline_btag_eff_formula)
background_pdfs[fit_function] = RooEffProd(background_pdf_name, background_pdf_name, background_pdf_intermediate, trigeff_pt_formula)
elif args.fitTrigger and not args.fitOffB:
background_pdfs[fit_function] = RooEffProd(background_pdf_name, background_pdf_name, background_pdfs_notrig[fit_function], trigeff_pt_formula)
elif args.fitOffB and not args.fitTrigger:
background_pdfs[fit_function] = RooEffProd(background_pdf_name, background_pdf_name, background_pdfs_notrig[fit_function], offline_btag_eff_formula)
else:
background_pdfs[fit_function] = background_pdfs_notrig[fit_function]
background_pdfs[fit_function].SetName(background_pdf_name)
# Initial values
if "trigbbh" in args.analysis:
if fit_function == "dijet4":
if mass == 650:
background_parameters[fit_function]["p1"].setVal(-2.2473e+01)
background_parameters[fit_function]["p2"].setVal(1.4923e+01)
background_parameters[fit_function]["p3"].setVal(1.3077e+00)
else:
background_parameters[fit_function]["p1"].setVal(-13.5877235358)
background_parameters[fit_function]["p2"].setVal(14.0659901462)
background_parameters[fit_function]["p3"].setVal(1.24550474025)
background_parameters[fit_function]["p1"].setMin(-50.)
background_parameters[fit_function]["p1"].setMax(50.)
elif fit_function == "f2":
background_parameters[fit_function]["p1"].setVal(6.06731321562)
background_parameters[fit_function]["p2"].setVal(6.06264502704)
elif fit_function == "polypower3":
background_parameters[fit_function]["p1"].setVal(50.0270215343)
background_parameters[fit_function]["p2"].setVal(8.17180937688)
background_parameters[fit_function]["p1"].setMin(20.)
elif fit_function == "polypower4":
background_parameters[fit_function]["p1"].setVal(31.3765210572)
background_parameters[fit_function]["p2"].setVal(-22.5800092219)
background_parameters[fit_function]["p3"].setVal(9.94548656557)
elif fit_function == "f5":
background_parameters[fit_function]["p1"].setVal(5.51929170927)
background_parameters[fit_function]["p2"].setVal(4.25521547671)
elif fit_function == "f6":
background_parameters[fit_function]["p1"].setVal(35.)
background_parameters[fit_function]["p2"].setVal(-28.)
background_parameters[fit_function]["p3"].setVal(0.)
background_parameters[fit_function]["p4"].setVal(10.)
elif "trigbbl" in args.analysis:
if fit_function == "dijet4":
background_parameters[fit_function]["p1"].setVal(-32.4727133488)
background_parameters[fit_function]["p2"].setVal(18.7641649883)
background_parameters[fit_function]["p3"].setVal(1.84028034937)
elif fit_function == "f2":
background_parameters[fit_function]["p1"].setVal(4.96261586452)
background_parameters[fit_function]["p2"].setVal(19.0848105961)
if fit_function == "polypower3":
background_parameters[fit_function]["p1"].setVal(60.0000032579)
background_parameters[fit_function]["p2"].setVal(8.00317534363)
background_parameters[fit_function]["p1"].setMin(60.)
elif fit_function == "polypower4":
background_parameters[fit_function]["p1"].setVal(25.4109169544)
background_parameters[fit_function]["p2"].setVal(-42.56719661)
background_parameters[fit_function]["p3"].setVal(12.3295648189)
elif fit_function == "f5":
background_parameters[fit_function]["p1"].setVal(3.74859358646)
background_parameters[fit_function]["p2"].setVal(11.4366903839)
elif fit_function == "f6":
background_parameters[fit_function]["p1"].setVal(35.)
background_parameters[fit_function]["p2"].setVal(-43.)
background_parameters[fit_function]["p3"].setVal(0.)
background_parameters[fit_function]["p4"].setVal(10.)
data_integral = hData.Integral(hData.GetXaxis().FindBin(float(args.massMin)),hData.GetXaxis().FindBin(float(args.massMax)))
background_norms[fit_function] = RooRealVar('background_' + fit_function + '_norm', 'background_' + fit_function + '_norm', data_integral, 0., 1.e8)
signal_epdfs[fit_function] = RooExtendPdf('esignal_' + fit_function, 'esignal_' + fit_function, signal_pdfs[fit_function], signal_norms[fit_function])
background_epdfs[fit_function] = RooExtendPdf('ebackground_' + fit_function, 'ebackground_' + fit_function, background_pdfs[fit_function], background_norms[fit_function])
models[fit_function] = RooAddPdf('model_' + fit_function, 's+b', RooArgList(background_epdfs[fit_function], signal_epdfs[fit_function]))
if args.runFit and not args.dconly:
print "[create_datacards] INFO : Starting fit with function {}".format(fit_function)
models[fit_function].Print()
# Fix the trigger efficiency for this fit
if args.fitTrigger:
trigeff_vars["alpha_trigeff_p0"].setConstant(True)
trigeff_vars["alpha_trigeff_p1"].setConstant(True)
trigeff_btag_var.setConstant(True)
if args.fitOffB:
for var in offline_btag_eff_vars.values():
var.setConstant(True)
fit_results[fit_function] = models[fit_function].fitTo(rooDataHist, RooFit.Save(kTRUE), RooFit.Extended(kTRUE), RooFit.Strategy(args.fitStrategy), RooFit.Verbose(0))
print "[create_datacards] INFO : Done with fit {}. Printing results.".format(fit_function)
fit_results[fit_function].Print()
if args.fitTrigger:
# Current strategy: freeze the trigger nuisance parameters.
trigeff_vars["alpha_trigeff_p0"].setConstant(True)
trigeff_vars["alpha_trigeff_p1"].setConstant(True)
trigeff_btag_var.setConstant(True)
if args.fitOffB:
for var in offline_btag_eff_vars.values():
var.setConstant(False)
print "[create_datacards] DEBUG : End args.runFit if block."
# needed if want to evaluate limits without background systematics
if args.fixBkg:
background_norms[fit_function].setConstant()
for par_name, par in background_parameters[fit_function].iteritems():
par.setConstant()
# -----------------------------------------
# Set values of signal systematic variables
# JES and JER uncertainties
if "jes" in systematics:
xp_central = signal_vars["xp_0"].getVal()
xp_up = signal_fits.get_parameters(w_signal.pdf("signal__JESUp"))["xpJESUp"][0]
xp_down = signal_fits.get_parameters(w_signal.pdf("signal__JESDown"))["xpJESDown"][0]
signal_vars["dxp"].setVal(max(abs(xp_up - xp_central), abs(xp_down - xp_central)))
if signal_vars["dxp"].getVal() > 2 * mass * 0.1:
print "[create_datacards] WARNING : Large dxp value. dxp = {}, xp_down = {}, xp_central = {}, xp_up = {}".format(signal_vars["dxp"].getVal(), xp_down, xp_central, xp_up)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant(False)
else:
signal_vars["dxp"].setVal(0.)
signal_vars["alpha_jes"].setVal(0.)
signal_vars["alpha_jes"].setConstant()
signal_vars["dxp"].setError(0.)
signal_vars["dxp"].setConstant()
if "jer" in systematics:
sigp_central = signal_vars["sigp_0"].getVal()
sigp_up = signal_fits.get_parameters(w_signal.pdf("signal__JERUp"))["sigpJERUp"][0]
sigp_down = signal_fits.get_parameters(w_signal.pdf("signal__JERDown"))["sigpJERDown"][0]
signal_vars["dsigp"].setVal(max(abs(sigp_up - sigp_central), abs(sigp_down - sigp_central)))
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant(False)
else:
signal_vars["dsigp"].setVal(0.)
signal_vars["alpha_jer"].setVal(0.)
signal_vars["alpha_jer"].setConstant()
signal_vars["dsigp"].setError(0.)
signal_vars["dsigp"].setConstant()
# -----------------------------------------
# create a datacard and corresponding workspace
postfix = (('_' + args.postfix) if args.postfix != '' else '')
wsName = 'workspace_' + args.final_state + '_m' + str(mass) + postfix + '.root'
if not args.dconly:
w = RooWorkspace('w','workspace')
signal_pdf.SetName("signal")
getattr(w,'import')(signal_pdf,RooFit.Rename("signal"))
norm = args.lumi
signal_norm = ROOT.RooRealVar("signal_norm", "signal_norm", norm/100., norm/100. / 10., norm * 10.)
print "[create_datacards] INFO : Set signal norm to {}".format(signal_norm.getVal())
signal_norm.setConstant()
getattr(w,'import')(signal_norm,ROOT.RooCmdArg())
for fit_function in fit_functions:
print "Importing background PDF"
print background_pdfs[fit_function]
background_pdfs[fit_function].Print()
getattr(w,'import')(background_pdfs[fit_function],ROOT.RooCmdArg(),RooFit.Rename("background_" + fit_function), RooFit.RecycleConflictNodes())
w.pdf("background_" + fit_function).Print()
getattr(w,'import')(background_norms[fit_function],ROOT.RooCmdArg(),RooFit.Rename("background_" + fit_function + "_norm"))
getattr(w,'import')(fit_results[fit_function])
getattr(w,'import')(signal_norms[fit_function],ROOT.RooCmdArg())
if args.fitBonly:
getattr(w,'import')(models[fit_function],ROOT.RooCmdArg(),RooFit.RecycleConflictNodes())
getattr(w,'import')(rooDataHist,RooFit.Rename("data_obs"))
if args.correctTrigger:
getattr(w,'import')(rooDataHist_notrigcorr, RooFit.Rename("data_obs_notrigcorr"))
w.Print()
print "Starting save"
if args.output_path:
if not os.path.isdir( os.path.join(os.getcwd(),args.output_path) ):
os.mkdir( os.path.join(os.getcwd(),args.output_path) )
workspace_output_path = os.path.join(args.output_path,wsName)
else:
workspace_output_path = limit_config.get_workspace_filename(args.analysis, args.model, mass, fitBonly=args.fitBonly, fitTrigger=args.fitTrigger, correctTrigger=args.correctTrigger, useMCTrigger=args.useMCTrigger, qcd=args.qcd, fitOffB=args.fitOffB)
if args.condor:
workspace_output_path = os.path.basename(workspace_output_path)
print "[create_datacards] INFO : Writing workspace to file {}".format(workspace_output_path)
w.writeToFile(workspace_output_path)
if args.correctTrigger:
f_workspace = TFile(workspace_output_path, "UPDATE")
hData.Write()
hData_notrigcorr.Write()
# Clean up
for name, obj in signal_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_norms.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_pdfs_notrig.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in signal_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in background_epdfs.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in fit_results.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, dict_l2 in background_parameters.iteritems():
for name2, obj in dict_l2.iteritems():
if obj:
obj.IsA().Destructor(obj)
for name, obj in models.iteritems():
if obj:
obj.IsA().Destructor(obj)
rooDataHist.IsA().Destructor(rooDataHist)
if not args.dconly:
w.IsA().Destructor(w)
# Make datacards only if S+B fitted
#beffUnc = 0.3
boffUnc = systematics["boff"][args.analysis][args.model].Eval(mass)
pdfUnc = systematics["pdfunc"][args.analysis][args.model].Eval(mass)
for fit_function in fit_functions:
if args.output_path:
dcName = 'datacard_' + args.final_state + '_m' + str(mass) + postfix + '_' + fit_function + '.txt'
datacard_output_path = os.path.join(args.output_path,dcName)
else:
datacard_output_path = limit_config.get_datacard_filename(args.analysis, args.model, mass, fit_function, fitTrigger=args.fitTrigger, correctTrigger=args.correctTrigger, useMCTrigger=args.useMCTrigger, qcd=args.qcd, fitOffB=args.fitOffB, fitBonly=args.fitBonly)
if args.condor:
datacard_output_path = os.path.basename(datacard_output_path)
print "[create_datacards] INFO : Writing datacard to file {}".format(datacard_output_path)
datacard = open(datacard_output_path, 'w')
datacard.write('imax 1\n')
datacard.write('jmax 1\n')
datacard.write('kmax *\n')
datacard.write('---------------\n')
if args.output_path:
datacard.write('shapes * * '+wsName+' w:$PROCESS\n')
else:
datacard.write('shapes * * '+os.path.basename(limit_config.get_workspace_filename(args.analysis, args.model, mass, fitTrigger=args.fitTrigger, correctTrigger=args.correctTrigger, useMCTrigger=args.useMCTrigger, qcd=args.qcd, fitOffB=args.fitOffB, fitBonly=args.fitBonly))+' w:$PROCESS\n')
datacard.write('---------------\n')
datacard.write('bin 1\n')
datacard.write('observation -1\n')
datacard.write('------------------------------\n')
datacard.write('bin 1 1\n')
datacard.write('process signal background_' + fit_function + '\n')
datacard.write('process 0 1\n')
datacard.write('rate 1 1\n')
datacard.write('------------------------------\n')
datacard.write('lumi lnN %f -\n'%(1.+systematics["luminosity"]))
if not args.useMCTrigger:
datacard.write('bon lnN %f -\n'%(1.+ (trigger_efficiency.online_btag_eff[args.analysis][2] / trigger_efficiency.online_btag_eff[args.analysis][0])))
else:
datacard.write('bon lnN %f -\n'%(1.+ systematics["bon"]))
#datacard.write('beff lnN %f -\n'%(1.+beffUnc))
datacard.write('boff lnN %f -\n'%(1.+boffUnc))
datacard.write('pdf lnN %f -\n'%(1.+pdfUnc))
#datacard.write('bkg lnN - 1.03\n')
if "jes" in systematics:
datacard.write("alpha_jes param 0.0 1.0\n")
if "jer" in systematics:
datacard.write("alpha_jer param 0.0 1.0\n")
if args.fitOffB:
if eta_region == "eta1p7":
datacard.write("offline_btag_eff_p0 param 6.78251e-03 3.82505e-04\n")
datacard.write("offline_btag_eff_p1 param -9.55614e-06 1.13679e-06\n")
datacard.write("offline_btag_eff_p2 param 4.39468e-09 7.90724e-10\n")
elif eta_region == "eta2p2":
datacard.write("offline_btag_eff_p0 param -1.72721e-03 3.04992e-05\n")
datacard.write("offline_btag_eff_p1 param 1.72562e-06 3.23472e-08\n")
datacard.write("offline_btag_eff_p2 param 8.74866e-03 7.81413e-05\n")
datacard.write("offline_btag_eff_p3 param -1.67123e-03 4.30607e-05\n")
# Current decision: don't put in nuisance parameters for trigger efficiency sigmoid. Impact is likely small.
#if args.fitTrigger:
# if "bbl" in args.analysis or "eta1p7" in args.analysis:
# datacard.write("alpha_trigeff_p0 param {} {}\n".format(0.0, 1.0))
# datacard.write("alpha_trigeff_p1 param {} {}\n".format(0.0, 1.0))
# Background fit parameters --- flat prior
datacard.write('background_' + fit_function + '_norm flatParam\n')
for par_name, par in background_parameters[fit_function].iteritems():
datacard.write(fit_function + "_" + par_name + ' flatParam\n')
datacard.close()
print "[create_datacards] INFO : Done with this datacard"
#print '>> Datacards and workspaces created and stored in %s/'%( os.path.join(os.getcwd(),args.output_path) )
print "Done with mass {}.".format(mass)
