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obtain_mass.py
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#!/usr/bin/env python
import subprocess
import ctypes
import array
import math
import ROOT
ROOT.gROOT.SetBatch(True)
do_individual=False
do_individual=True
dir_output = "EOS_output_mass"
rootfile = "plots_20211126/myhist_signal.root"
rootfile = "plots_20211127_v2/myhist_combine_RunII.root"
rootfile = "plots_20211128/myhist_combine_RunII.root"
rootfile = "shortcut_plots/plots_20211128/myhist_combine_RunII.root"
rootfile = "shortcut_plots/plots_20220120_results_ReReco/myhist_combine_RunII.root"
rootfile = "shortcut_plots/plots_20220215_ultraLegacy/myhist_combine_RunII.root"
rootfile = "EOS_backup_path/plots_20220310_updatedLumi/myhist_combine_RunII.root"
fin = ROOT.TFile.Open(rootfile, "R")
ROOT.gStyle.SetOptStat("e")
ROOT.gStyle.SetOptFit(1111)
v_hist_collector, v_herrs_collector = [], []
# setup {{{
c1 = ROOT.TCanvas("c1", "", 800, 600)
c1.SetGrid()
c1.SetLeftMargin(0.12)
c1.SetRightMargin(0.08)
c2 = ROOT.TCanvas("c2", "", 1200, 600)
c2.SetGrid()
c2.SetTopMargin(0.10)
c2.SetBottomMargin(0.12)
c2.SetLeftMargin(0.12)
c2.SetRightMargin(0.08)
c3 = ROOT.TCanvas("c3", "", 800, 800)
c3.SetGrid()
c3.SetTopMargin(0.10)
c3.SetBottomMargin(0.12)
c3.SetLeftMargin(0.15)
c3.SetRightMargin(0.05)
d_fit_const = {"central":[], "error":[]}
d_fit_mean = {"central":[], "error":[]}
d_fit_sigma = {"central":[], "error":[]}
#--------------------------------------------------
lumi={"2016":35.9,"2017":41.5,"2018":59.76,"RunII":138}
masses = [600, 625, 650, 675, 700, 800, 900, 1000, 1100, 1200]
masses_v2 = [600, 625, 650, 675, 700, 750, 800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200]
mass_M600_M700 = [600, 625, 650, 675, 700]
mass_M800_M1000 = [800, 900, 1000]
mass_M1100_M1200 = [1100, 1200]
ytitles = {
"yields": {
"hMass_fine":"Events / 0.5 GeV",
"hMass_pass_BDTG_smh_cut_mixed03_fine":"Events / 0.5 GeV",
"hMass_pass_BDTG_smh_cut_mixed04_fine":"Events / 0.5 GeV",
"hMass_pass_BDTG_smh_cut_mixed05_fine":"Events / 0.5 GeV",
"hmass_tprime_cov_fine":"Events / 5 GeV",
"h_mass_tm_tprime":"Events / 5 GeV",
"hTprime_Mass_pass_BDTG_smh_cut_mixed03_SR_fine":"Events / 5 GeV",
"hTprime_Mass_pass_BDTG_smh_cut_mixed04_SR_fine":"Events / 5 GeV",
"hTprime_Mass_pass_BDTG_smh_cut_mixed05_SR_fine":"Events / 5 GeV",
},
"normalized": {
"hMass_fine":"Arbitrary unit / 0.5 GeV",
"hMass_pass_BDTG_smh_cut_mixed03_fine":"Arbitrary unit / 0.5 GeV",
"hMass_pass_BDTG_smh_cut_mixed04_fine":"Arbitrary unit / 0.5 GeV",
"hMass_pass_BDTG_smh_cut_mixed05_fine":"Arbitrary unit / 0.5 GeV",
"hmass_tprime_cov_fine":"Arbitrary unit / 5 GeV",
"h_mass_tm_tprime":"Arbitrary unit / 5 GeV",
"hTprime_Mass_pass_BDTG_smh_cut_mixed03_SR_fine":"Arbitrary unit / 5 GeV",
"hTprime_Mass_pass_BDTG_smh_cut_mixed04_SR_fine":"Arbitrary unit / 5 GeV",
"hTprime_Mass_pass_BDTG_smh_cut_mixed05_SR_fine":"Arbitrary unit / 5 GeV",
},
}
xtitles = {
"hMass_fine":"M_{#gamma#gamma} (GeV)",
"hMass_pass_BDTG_smh_cut_mixed03_fine":"M_{#gamma#gamma} (GeV)",
"hMass_pass_BDTG_smh_cut_mixed04_fine":"M_{#gamma#gamma} (GeV)",
"hMass_pass_BDTG_smh_cut_mixed05_fine":"M_{#gamma#gamma} (GeV)",
"hmass_tprime_cov_fine":"M_{bjj#gamma#gamma} (GeV)",
"h_mass_tm_tprime":"M_{bjj#gamma#gamma} (GeV)",
"hTprime_Mass_pass_BDTG_smh_cut_mixed03_SR_fine":"M_{bjj#gamma#gamma} (GeV)",
"hTprime_Mass_pass_BDTG_smh_cut_mixed04_SR_fine":"M_{bjj#gamma#gamma} (GeV)",
"hTprime_Mass_pass_BDTG_smh_cut_mixed05_SR_fine":"M_{bjj#gamma#gamma} (GeV)",
}
v_set1 = {
#600 : ROOT.kMagenta,
#625 : ROOT.kMagenta+2,
600 : ROOT.kRed-9,
625 : ROOT.kRed-7,
650 : ROOT.kRed,
675 : ROOT.kRed+2,
700 : ROOT.kRed+4,
800 : ROOT.kGreen-5,
900 : ROOT.kGreen+1,
1000 : ROOT.kGreen+3,
1100 : ROOT.kBlue,
1200 : ROOT.kBlue+2,
}
v_set2 = {
600 : ROOT.kOrange,
625 : ROOT.kOrange+2,
650 : ROOT.kRed,
675 : ROOT.kRed+3,
700 : ROOT.kBlack,
800 : ROOT.kGreen+1,
900 : ROOT.kGreen+3,
1000 : ROOT.kCyan,
1100 : ROOT.kBlue,
1200 : ROOT.kBlue+2,
}
v_blue = {
600 : ROOT.kBlue-10,
625 : ROOT.kBlue-9,
650 : ROOT.kBlue-7,
675 : ROOT.kBlue-4,
700 : ROOT.kBlue,
800 : ROOT.kBlue+1,
900 : ROOT.kBlue+2,
1000 : ROOT.kBlue+3,
1100 : ROOT.kBlue+4,
1200 : ROOT.kBlack
}
v_red = {
600 : ROOT.kRed-10,
625 : ROOT.kRed-9,
650 : ROOT.kRed-7,
675 : ROOT.kRed-4,
700 : ROOT.kRed,
800 : ROOT.kRed+1,
900 : ROOT.kRed+2,
1000 : ROOT.kRed+3,
1100 : ROOT.kRed+4,
1200 : ROOT.kBlack
}
colors = {
"hMass_fine":v_set1,
"hMass_pass_BDTG_smh_cut_mixed03_fine":v_set1,
"hMass_pass_BDTG_smh_cut_mixed04_fine":v_set1,
"hMass_pass_BDTG_smh_cut_mixed05_fine":v_set1,
"hmass_tprime_cov_fine":v_set1,
"h_mass_tm_tprime":v_set1,
"hTprime_Mass_pass_BDTG_smh_cut_mixed03_SR_fine":v_set1,
"hTprime_Mass_pass_BDTG_smh_cut_mixed04_SR_fine":v_set1,
"hTprime_Mass_pass_BDTG_smh_cut_mixed05_SR_fine":v_set1,
"collective":v_set2,
#"hMass_fine":v_blue,
#"hMass_pass_BDTG_smh_cut_mixed03_fine":v_blue,
#"hMass_pass_BDTG_smh_cut_mixed04_fine":v_blue,
#"hMass_pass_BDTG_smh_cut_mixed05_fine":v_blue,
#"hmass_tprime_cov_fine":v_red,
#"hTprime_Mass_pass_BDTG_smh_cut_mixed03_SR_fine":v_red,
#"hTprime_Mass_pass_BDTG_smh_cut_mixed04_SR_fine":v_red,
#"hTprime_Mass_pass_BDTG_smh_cut_mixed05_SR_fine":v_red,
}
ranges = {
"hMass_fine":[100,180],
"hMass_pass_BDTG_smh_cut_mixed03_fine":[100,180],
"hMass_pass_BDTG_smh_cut_mixed04_fine":[100,180],
"hMass_pass_BDTG_smh_cut_mixed05_fine":[100,180],
"hmass_tprime_cov_fine":[200,2500],
"h_mass_tm_tprime":[200,2500],
"hTprime_Mass_pass_BDTG_smh_cut_mixed03_SR_fine":[480,800],
"hTprime_Mass_pass_BDTG_smh_cut_mixed04_SR_fine":[630,1150],
"hTprime_Mass_pass_BDTG_smh_cut_mixed05_SR_fine":[750,1600],
"fullTprime":[350, 1650],
#"fullTprime":[450, 1650],
}
leg_pos_set1 = {
# key = number of mass points to be ploted in a figure
10 : [0.58, 0.25, 0.85, 0.85],
5 : [0.58, 0.55, 0.85, 0.85],
3 : [0.58, 0.67, 0.85, 0.85],
2 : [0.58, 0.73, 0.85, 0.85],
}
leg_pos_set2 = {
# key = number of mass points to be ploted in a figure
10 : [0.58, 0.25, 0.85, 0.85],
3 : [0.58, 0.67, 0.85, 0.85],
2 : [0.58, 0.73, 0.85, 0.85],
5 : [0.12, 0.55, 0.30, 0.85],
}
leg_pos_set3 = [0.15, 0.75, 0.35, 0.87]
leg_pos_set4 = [0.17, 0.45, 0.35, 0.57]
leg_pos_set5 = [0.57, 0.20, 0.87, 0.35]
legend_position = {
"hMass_fine":leg_pos_set1,
"hMass_pass_BDTG_smh_cut_mixed03_fine":leg_pos_set1,
"hMass_pass_BDTG_smh_cut_mixed04_fine":leg_pos_set1,
"hMass_pass_BDTG_smh_cut_mixed05_fine":leg_pos_set1,
"hmass_tprime_cov_fine":leg_pos_set2,
"h_mass_tm_tprime":leg_pos_set2,
"hTprime_Mass_pass_BDTG_smh_cut_mixed03_SR_fine":leg_pos_set2,
"hTprime_Mass_pass_BDTG_smh_cut_mixed04_SR_fine":leg_pos_set2,
"hTprime_Mass_pass_BDTG_smh_cut_mixed05_SR_fine":leg_pos_set2,
# individual plot with fit
"tprime":leg_pos_set5,
600 :leg_pos_set3,
625 :leg_pos_set3,
650 :leg_pos_set3,
675 :leg_pos_set4,
700 :leg_pos_set4,
800 :leg_pos_set3,
900 :leg_pos_set3,
1000:leg_pos_set4,
1100:leg_pos_set3,
1200:leg_pos_set3,
}
stat_pos_set1 = [0.89, 0.87, 0.20, 0.20]
stat_pos_set2 = [0.89, 0.87, 0.15, 0.15]
stat_pos_set3 = [0.43, 0.87, 0.15, 0.15]
stat_position = {
"hMass_fine":stat_pos_set1,
"hMass_pass_BDTG_smh_cut_mixed03_fine":stat_pos_set1,
"hMass_pass_BDTG_smh_cut_mixed04_fine":stat_pos_set1,
"hMass_pass_BDTG_smh_cut_mixed05_fine":stat_pos_set1,
"hmass_tprime_cov_fine":stat_pos_set1,
"h_mass_tm_tprime":stat_pos_set1,
600 :stat_pos_set2,
625 :stat_pos_set2,
650 :stat_pos_set2,
675 :stat_pos_set3,
700 :stat_pos_set3,
800 :stat_pos_set2,
900 :stat_pos_set2,
1000:stat_pos_set3,
1100:stat_pos_set2,
1200:stat_pos_set2,
#"hTprime_Mass_pass_BDTG_smh_cut_mixed03_SR_fine":stat_pos_set2,
#"hTprime_Mass_pass_BDTG_smh_cut_mixed04_SR_fine":stat_pos_set2,
#"hTprime_Mass_pass_BDTG_smh_cut_mixed05_SR_fine":stat_pos_set2,
}
fit_ranges = {
# for fit summary
"mgg" :{ "range":[122., 128.] , "mean" :[123.5, 125.5], "sigma":[1.0, 1.8] },
"tprime":{ "range":[500., 1300.], "mean" :[500., 1300.] , "sigma":[0., 80.] },
# for individual fit
600 :{ "range":[550. ,650.] },
625 :{ "range":[575. ,675.] },
650 :{ "range":[600. ,700.] },
675 :{ "range":[625. ,725.] },
700 :{ "range":[650. ,750.] },
800 :{ "range":[740. ,860.] },
900 :{ "range":[840. ,960.] },
1000:{ "range":[940. ,1060.] },
1100:{ "range":[1020.,1180.] },
1200:{ "range":[1120.,1280.] },
#600 :{ "range":[540. ,660.] },
#625 :{ "range":[565. ,685.] },
#650 :{ "range":[590. ,710.] },
#675 :{ "range":[615. ,735.] },
#700 :{ "range":[640. ,760.] },
#800 :{ "range":[720. ,880.] },
#900 :{ "range":[820. ,980.] },
#1000:{ "range":[920. ,1080.] },
#1100:{ "range":[1020.,1180.] },
#1200:{ "range":[1120.,1280.] },
}
fit_ytitles = {
"mgg":{
"range":"Mean and sigma of m(#gamma#gamma) from Gaussian fit",
"mean" :"Mean of m(#gamma#gamma) from Gaussian fit",
"sigma":"Sigma of m(#gamma#gamma) from Gaussian fit",
},
"tprime":{
"range":"Mean and sigma of M_{bjj#gamma#gamma} from Gaussian fit",
"mean" :"Mean of M_{bjj#gamma#gamma} from Gaussian fit",
"sigma":"Sigma of M_{bjj#gamma#gamma} from Gaussian fit",
},
}
#}}}
def get_maximum(v): #{{{
result = 0.
for h in v:
hmax = h.GetMaximum()
if hmax > result:
result = hmax
return result
#}}}
def annotate(rshift=0., r2shift=0.): #{{{
latex = ROOT.TLatex()
latex.SetNDC()
latex.SetTextFont(42)
latex.SetTextAlign(11)
#latex.SetTextSize(24)
latex.SetTextSize(0.04)
#latex.DrawLatex( 0.12, 0.912, "#bf{CMS} #it{work in progress}" )
#latex.DrawLatex( 0.12, 0.912, "#bf{CMS} #it{Preliminary}" )
#latex.DrawLatex( 0.18+r2shift, 0.850, "#bf{CMS} #it{Preliminary}" )
#latex.DrawLatex( 0.14, 0.840, "#bf{CMS} #it{Preliminary}" )
latex.DrawLatex( 0.18, 0.840, "#bf{CMS} #it{Preliminary}" )
latex.DrawLatex( 0.70+rshift, 0.912, "%s fb^{-1} (13 TeV)" % str(lumi["RunII"]) )
#latex.DrawLatex( 0.60, 0.800, "Pre-selection" )
#}}}
def set_the_hist(h, varName, rangeTag, plotType, color): #{{{
h.SetStats(0)
#h.SetLineColor(colors[varName][myMasses[i]])
h.SetLineColor(color)
h.SetLineWidth(2)
h.GetXaxis().SetTitleOffset(1.2)
h.GetYaxis().SetTitle(ytitles[plotType][varName])
h.GetXaxis().SetTitle(xtitles[varName])
h.GetXaxis().SetRangeUser(ranges[rangeTag][0], ranges[rangeTag][1])
#}}}
def set_the_error_band(h): #{{{
#h.SetFillStyle(3144)
h.SetFillStyle(3001)
h.SetFillColorAlpha(ROOT.kBlack, 0.5)
h.SetMarkerStyle(20)
h.SetMarkerSize(0.)
#}}}
def set_graph(gr, ytitle, color): #{{{
gr.SetTitle("")
gr.SetLineColor(color)
gr.SetLineWidth(2)
gr.SetMarkerStyle(20)
gr.SetMarkerColor(color)
#gr.GetXaxis().SetTitle("Hypothetical M_{T'} (GeV)")
gr.GetXaxis().SetTitle("M_{T'} (GeV)")
gr.GetXaxis().SetTitleSize(0.045)
gr.GetXaxis().SetTitleOffset(1.05)
gr.GetYaxis().SetTitle(ytitle)
gr.GetYaxis().SetTitleSize(0.052)
gr.GetYaxis().SetTitleOffset(1.2)
#}}}
def init_fit_containers(): #{{{
global d_fit_const, d_fit_mean, d_fit_sigma
d_fit_const = {"central":[], "error":[]}
d_fit_mean = {"central":[], "error":[]}
d_fit_sigma = {"central":[], "error":[]}
#}}}
def init_hist_collector(): #{{{
global v_hist_collector, v_herrs_collector
v_hist_collector, v_herrs_collector = [], []
#}}}
def record_fit_result(func): #{{{
global d_fit_const, d_fit_mean, d_fit_sigma
fit_const = func.GetParameter(0)
fit_mean = func.GetParameter(1)
fit_sigma = func.GetParameter(2)
fitError_const = func.GetParError(0)
fitError_mean = func.GetParError(1)
fitError_sigma = func.GetParError(2)
print ">>> result:", fit_const, fit_mean, fit_sigma
print ">>> fit error:", fitError_const, fitError_mean, fitError_sigma
d_fit_const["central"].append( func.GetParameter(0) )
d_fit_mean ["central"].append( func.GetParameter(1) )
d_fit_sigma["central"].append( func.GetParameter(2) )
d_fit_const[ "error" ].append( func.GetParError(0) )
d_fit_mean [ "error" ].append( func.GetParError(1) )
d_fit_sigma[ "error" ].append( func.GetParError(2) )
#}}}
def make_fit_summary(d_raw, v_bound, ytitle, output): #{{{
n = len(d_raw["central"])
x = array.array('d')
y = array.array('d')
ex = array.array('d')
ey = array.array('d')
for i in range(n):
x.append(masses[i])
y.append(d_raw["central"][i])
ex.append(0.)
ey.append(d_raw["error"][i])
gr = ROOT.TGraphErrors(n, x, y, ex, ey)
set_graph(gr, ytitle, ROOT.kBlack)
gr.Draw('ACP')
gr.SetMinimum(v_bound[0])
gr.SetMaximum(v_bound[1])
# local leg
#legend = ROOT.TLegend(0.15, 0.60, 0.40, 0.85)
#legend.SetLineColor(0)
#legend.SetTextSize(0.04)
#legend.AddEntry(gr1, "[600, 700]", "lp")
#legend.AddEntry(gr2, "[800, 1000]", "lp")
#legend.AddEntry(gr3, "[1100, 1200]", "lp")
#legend.Draw("same")
print ">>>>> central:", output, d_raw["central"]
print ">>>>> error: ", output, d_raw["error"]
# wrap up
annotate()
output = dir_output + "/fitSummary_" + output + ".png"
c1.SaveAs(output)
#}}}
def make_plot(varName, myMasses, plotType, pauseFit): #{{{
c1.cd()
nameTag = varName + "_" + plotType
pos = legend_position[varName][len(myMasses)]
legend = ROOT.TLegend(pos[0], pos[1], pos[2], pos[3])
legend.SetLineColor(0)
legend.SetTextSize(0.04)
legend.Clear()
if len(myMasses)==5 and 'prime' in varName: legend.SetTextSize(0.03)
# load hists {{{
v_hists, v_herrs = [], []
for m in myMasses:
histName = varName + "_TprimeBToTH_M-" + str(m)
h = fin.Get(histName)
v_hists.append(h)
h_err = h.Clone()
v_herrs.append(h_err)
#}}}
# loop over hists
for i, h in enumerate(v_hists):
h_err = v_herrs[i]
# normalize area #{{{
if plotType == "normalized":
unc = ctypes.c_double(0.)
nbins = h.GetSize()-1
tot_yields = h.IntegralAndError(0, nbins, unc)
if tot_yields > 0.:
h.Scale(1./tot_yields)
h_err.Scale(1./tot_yields)
#}}}
if do_individual: #{{{
my_fit_range = fit_ranges["mgg"]["range"] if 'hMass' in varName else fit_ranges[myMasses[i]]["range"]
set_the_hist(h, varName, varName, plotType, ROOT.kBlack)
h.SetMaximum( h.GetMaximum()*1.2 )
h.Draw("hist")
h.Fit("gaus", "", "", my_fit_range[0], my_fit_range[1])
# necessary for showing fit curves
h_err = h.Clone()
set_the_error_band(h_err)
h_err.Draw("E2;same")
my_leg_pos = legend_position[myMasses[i]] if 'prime' in varName and 'BDTG' in varName else legend_position["tprime"]
leg = ROOT.TLegend(my_leg_pos[0],my_leg_pos[1],my_leg_pos[2],my_leg_pos[3])
leg.SetLineColor(0)
leg.SetTextSize(0.04)
leg.AddEntry(h, "M_{T'} = " + str(myMasses[i]) + " GeV", "l")
leg.AddEntry(h_err, "Stat. unc.", "f")
leg.Draw("same")
h.SetStats(1)
record_fit_result( h.GetListOfFunctions().FindObject("gaus") )
my_stat_pos = stat_position[myMasses[i]] if 'prime' in varName and 'BDTG' in varName else stat_position[varName]
ROOT.gStyle.SetStatX(my_stat_pos[0])
ROOT.gStyle.SetStatY(my_stat_pos[1])
ROOT.gStyle.SetStatW(my_stat_pos[2])
ROOT.gStyle.SetStatH(my_stat_pos[3])
annotate()
output = dir_output + "/individual_" + nameTag + "_" + str(myMasses[i]) + ".png"
c1.SaveAs(output)
output = dir_output + "/individual_" + nameTag + "_" + str(myMasses[i]) + ".pdf"
c1.SaveAs(output)
#}}}
else: # multi hists {{{
set_the_hist(h, varName, varName, plotType, colors[varName][myMasses[i]])
set_the_error_band(h_err)
legend.AddEntry(h, "M_{T'} = " + str(myMasses[i]) + " GeV", "l")
if i == 0: h.Draw("hist")
else: h.Draw("hist;same")
h_err.Draw("E2;same")
#}}}
# wrap up #{{{
if do_individual:
if not pauseFit:
my_fit_range = fit_ranges["mgg"] if 'hMass' in varName else fit_ranges["tprime"]
my_fit_ytitle = fit_ytitles["mgg"] if 'hMass' in varName else fit_ytitles["tprime"]
d_fit = {"central":d_fit_mean["central"], "error":d_fit_sigma["central"]}
make_fit_summary(d_fit , my_fit_range['range'] , my_fit_ytitle['range'], nameTag + "_Gaussian")
make_fit_summary(d_fit_mean , my_fit_range['mean'] , my_fit_ytitle['mean' ], nameTag + "_Mean")
make_fit_summary(d_fit_sigma , my_fit_range['sigma'] , my_fit_ytitle['sigma'], nameTag + "_Sigma")
init_fit_containers()
else:
if not pauseFit:
maximum = get_maximum(v_hists)
v_hists[0].SetMaximum(maximum*1.2)
annotate()
legend.Draw("same")
output = dir_output + "/" + nameTag + ".png"
c1.SaveAs(output)
init_hist_collector()
#}}}
#}}}
def make_collective_plot(v_varName, v_myMasses, plotType): #{{{
nameTag = "collective_" + v_varName[-1] + "_" + plotType
isHiggs = 'hMass' in v_varName[0]
isTprime = not isHiggs
c2.cd()
legend = ROOT.TLegend(0, 0, 0, 0)
if isTprime and plotType == "normalized":
legend = ROOT.TLegend(0.70, 0.25, 0.90, 0.85)
else:
legend = ROOT.TLegend(0.70, 0.25, 0.90, 0.87)
legend.SetLineColor(0)
legend.SetTextSize(0.04)
# load hists {{{
v_hists, v_herrs, myMasses, myVarName= [], [], [], []
for i, varName in enumerate(v_varName):
for m in v_myMasses[i]:
histName = varName + "_TprimeBToTH_M-" + str(m)
h = fin.Get(histName)
v_hists.append(h)
h_err = h.Clone()
v_herrs.append(h_err)
myMasses.append(m)
myVarName.append(varName)
entries = h.GetEntries()
print ">>> load:", histName, entries
#}}}
# loop over hists
for i, h in enumerate(v_hists):
h_err = v_herrs[i]
# normalize area #{{{
if plotType == "normalized":
unc = ctypes.c_double(0.)
nbins = h.GetSize()-1
tot_yields = h.IntegralAndError(0, nbins, unc)
if tot_yields > 0.:
h.Scale(1./tot_yields)
h_err.Scale(1./tot_yields)
#}}}
rangeTag = "fullTprime" if isTprime else myVarName[i]
set_the_hist(h, myVarName[i], rangeTag, plotType, colors["collective"][myMasses[i]])
set_the_error_band(h_err)
legend.AddEntry(h, "M_{T'} = " + str(myMasses[i]) + " GeV", "l")
if i == 0: h.Draw("hist")
else: h.Draw("hist;same")
h_err.Draw("E2;same")
maximum = get_maximum(v_hists)
v_hists[0].SetMaximum(maximum*1.2)
v_hists[0].GetXaxis().SetTitleSize(0.045)
v_hists[0].GetXaxis().SetTitleOffset(1.02)
v_hists[0].GetYaxis().SetTitleSize(0.052)
v_hists[0].GetYaxis().SetTitleOffset(0.80)
legend.Draw("same")
output = dir_output + "/" + nameTag
if isTprime and plotType == "normalized":
annotate(0.0, -0.04)
c2.SetLogy(0)
c2.SaveAs(output + ".png")
c2.SaveAs(output + ".pdf")
else:
#annotate(0.08)
myLatex = ROOT.TLatex()
myLatex.SetNDC()
myLatex.SetTextFont(42)
myLatex.SetTextAlign(11)
myLatex.SetTextSize(0.05)
#myLatex.DrawLatex( 0.32, 0.840, "Hadronic channel, T' #rightarrow t(bjj) H(#gamma#gamma)" )
#myLatex.DrawLatex( 0.40, 0.840, "T' #rightarrow t(bjj) H(#gamma#gamma)" )
#myLatex.DrawLatex( 0.14, 0.840, "#bf{CMS} #it{Preliminary}" )
myLatex.DrawLatex( 0.14, 0.840, "#bf{CMS} #it{Simulation Preliminary}, T' #rightarrow t(bjj) H(#gamma#gamma)" )
myLatex.DrawLatex( 0.75, 0.912, "%s fb^{-1} (13 TeV)" % str(lumi["RunII"]) )
v_hists[0].SetMaximum(0.6)
v_hists[0].SetMinimum(0.0)
c2.SetLogy(0)
c2.SaveAs(output + "_linear.png")
c2.SaveAs(output + "_linear.pdf")
v_hists[0].SetMaximum(2)
v_hists[0].SetMinimum(3e-3)
c2.SetLogy(1)
c2.SaveAs(output + ".png")
c2.SaveAs(output + ".pdf")
#c1.SaveAs(output + ".png")
#c1.SaveAs(output + ".pdf")
#}}}
def truth_matched_make_plot(varName, myMasses, plotType, pauseFit): #{{{
c1.cd()
nameTag = varName + "_" + plotType
pos = legend_position[varName][len(myMasses)]
legend = ROOT.TLegend(pos[0], pos[1], pos[2], pos[3])
legend.SetLineColor(0)
legend.SetTextSize(0.04)
legend.Clear()
# load hists {{{
v_fins, v_hists, v_herrs = [], [], []
for m in myMasses:
#_truthMatching_v1
rootfile = "shortcut_plots/plots_20220219_ultraLegacy_truthMatchStudy/covMatrix_TprimeBToTH_M-%d_merged.root" % m
fin = ROOT.TFile.Open(rootfile, "R")
histName = varName
h = fin.Get(histName)
v_hists.append(h)
h_err = h.Clone()
v_herrs.append(h_err)
v_fins.append(fin)
#}}}
# loop over hists
for i, h in enumerate(v_hists):
h_err = v_herrs[i]
# normalize area #{{{
if plotType == "normalized":
unc = ctypes.c_double(0.)
nbins = h.GetSize()-1
tot_yields = h.IntegralAndError(0, nbins, unc)
if tot_yields > 0.:
h.Scale(1./tot_yields)
h_err.Scale(1./tot_yields)
#}}}
if do_individual: #{{{
my_fit_range = fit_ranges["mgg"]["range"] if 'hMass' in varName else fit_ranges[myMasses[i]]["range"]
set_the_hist(h, varName, varName, plotType, ROOT.kBlack)
h.SetMaximum( h.GetMaximum()*1.2 )
h.Draw("hist")
h.Fit("gaus", "", "", my_fit_range[0], my_fit_range[1])
# necessary for showing fit curves
h_err = h.Clone()
set_the_error_band(h_err)
h_err.Draw("E2;same")
my_leg_pos = legend_position[myMasses[i]] if 'prime' in varName and 'BDTG' in varName else legend_position["tprime"]
leg = ROOT.TLegend(my_leg_pos[0],my_leg_pos[1],my_leg_pos[2],my_leg_pos[3])
leg.SetLineColor(0)
leg.SetTextSize(0.04)
leg.AddEntry(h, "M_{T'} = " + str(myMasses[i]) + " GeV", "l")
leg.AddEntry(h_err, "Stat. unc.", "f")
leg.Draw("same")
h.SetStats(1)
record_fit_result( h.GetListOfFunctions().FindObject("gaus") )
my_stat_pos = stat_position[myMasses[i]] if 'prime' in varName and 'BDTG' in varName else stat_position[varName]
ROOT.gStyle.SetStatX(my_stat_pos[0])
ROOT.gStyle.SetStatY(my_stat_pos[1])
ROOT.gStyle.SetStatW(my_stat_pos[2])
ROOT.gStyle.SetStatH(my_stat_pos[3])
annotate()
output = dir_output + "/individual_" + nameTag + "_" + str(myMasses[i]) + ".png"
c1.SaveAs(output)
output = dir_output + "/individual_" + nameTag + "_" + str(myMasses[i]) + ".pdf"
c1.SaveAs(output)
#}}}
else: # multi hists {{{
set_the_hist(h, varName, varName, plotType, colors[varName][myMasses[i]])
set_the_error_band(h_err)
legend.AddEntry(h, "M_{T'} = " + str(myMasses[i]) + " GeV", "l")
if i == 0: h.Draw("hist")
else: h.Draw("hist;same")
h_err.Draw("E2;same")
#}}}
# wrap up #{{{
if do_individual:
if not pauseFit:
my_fit_range = fit_ranges["mgg"] if 'hMass' in varName else fit_ranges["tprime"]
my_fit_ytitle = fit_ytitles["mgg"] if 'hMass' in varName else fit_ytitles["tprime"]
d_fit = {"central":d_fit_mean["central"], "error":d_fit_sigma["central"]}
make_fit_summary(d_fit , my_fit_range['range'] , my_fit_ytitle['range'], nameTag + "_Gaussian")
make_fit_summary(d_fit_mean , my_fit_range['mean'] , my_fit_ytitle['mean' ], nameTag + "_Mean")
make_fit_summary(d_fit_sigma , my_fit_range['sigma'] , my_fit_ytitle['sigma'], nameTag + "_Sigma")
init_fit_containers()
else:
if not pauseFit:
maximum = get_maximum(v_hists)
v_hists[0].SetMaximum(maximum*1.2)
annotate()
legend.Draw("same")
output = dir_output + "/" + nameTag + ".png"
c1.SaveAs(output)
init_hist_collector()
#}}}
#}}}
def truth_matched_make_collective_plot(v_varName, v_myMasses, plotType): #{{{
nameTag = "collective_" + v_varName[-1] + "_" + plotType
isHiggs = 'hMass' in v_varName[0]
isTprime = not isHiggs
c2.cd()
legend = ROOT.TLegend(0, 0, 0, 0)
if isTprime and plotType == "normalized":
legend = ROOT.TLegend(0.70, 0.25, 0.90, 0.85)
legend.SetTextSize(0.04)
else:
#legend = ROOT.TLegend(0.62, 0.40, 0.87, 0.87)
legend = ROOT.TLegend(0.70, 0.25, 0.90, 0.87)
legend.SetTextSize(0.03)
legend.SetLineColor(0)
legend.SetTextSize(0.04)
# load hists {{{
v_fins, v_hists, v_herrs, myMasses, myVarName = [], [], [], [], []
for i, varName in enumerate(v_varName):
for m in v_myMasses[i]:
rootfile = "shortcut_plots/plots_20220219_ultraLegacy_truthMatchStudy/covMatrix_TprimeBToTH_M-%d_merged.root" % m
fin = ROOT.TFile.Open(rootfile, "R")
histName = varName
h = fin.Get(histName)
v_hists.append(h)
h_err = h.Clone()
v_herrs.append(h_err)
myMasses.append(m)
myVarName.append(varName)
v_fins.append(fin)
entries = h.GetEntries()
print ">>> load:", histName, entries
#}}}
# loop over hists
for i, h in enumerate(v_hists):
h_err = v_herrs[i]
# normalize area #{{{
if plotType == "normalized":
unc = ctypes.c_double(0.)
nbins = h.GetSize()-1
tot_yields = h.IntegralAndError(0, nbins, unc)
if tot_yields > 0.:
h.Scale(1./tot_yields)
h_err.Scale(1./tot_yields)
#}}}
rangeTag = "fullTprime" if isTprime else myVarName[i]
set_the_hist(h, myVarName[i], rangeTag, plotType, colors["collective"][myMasses[i]])
set_the_error_band(h_err)
legend.AddEntry(h, "M_{T'} = " + str(myMasses[i]) + " GeV", "l")
if i == 0: h.Draw("hist")
else: h.Draw("hist;same")
h_err.Draw("E2;same")
maximum = get_maximum(v_hists)
v_hists[0].SetMaximum(maximum*1.2)
legend.Draw("same")
output = dir_output + "/" + nameTag
if isTprime and plotType == "normalized":
annotate(0.08)
c2.SaveAs(output + ".png")
c2.SaveAs(output + ".pdf")
else:
#v_hists[0].SetMaximum(maximum*1e+3)
#annotate()
#c1.SetLogy(1)
#c1.SaveAs(output + ".png")
#c1.SaveAs(output + ".pdf")
annotate(0.08)
v_hists[0].SetMaximum(2e-2)
v_hists[0].SetMinimum(5e-5)
c2.SetLogy(1)
c2.SaveAs(output + ".png")
c2.SaveAs(output + ".pdf")
#}}}
def run():
plotTypes = ["yields", "normalized"]
for plotType in plotTypes:
#truth_matched_make_collective_plot(["h_mass_tm_tprime"], [masses], plotType)
#truth_matched_make_plot("h_mass_tm_tprime", masses, plotType, pauseFit=False)
make_collective_plot(["hmass_tprime_cov_fine"], [masses], plotType)
#make_plot("hmass_tprime_cov_fine", masses, plotType, pauseFit=False)
#make_plot("hTprime_Mass_pass_BDTG_smh_cut_mixed03_SR_fine", mass_M600_M700, plotType, pauseFit=True);
#make_plot("hTprime_Mass_pass_BDTG_smh_cut_mixed04_SR_fine", mass_M800_M1000, plotType, pauseFit=True);
#make_plot("hTprime_Mass_pass_BDTG_smh_cut_mixed05_SR_fine", mass_M1100_M1200, plotType, pauseFit=False);
break
# truth-matched tprime mass
truth_matched_make_plot("h_mass_tm_tprime", masses, plotType, pauseFit=False)
truth_matched_make_collective_plot(["h_mass_tm_tprime"], [masses], plotType)
# collective mass at pre-selection
make_collective_plot(["hMass_fine"], [masses], plotType)
make_collective_plot(["hmass_tprime_cov_fine"], [masses], plotType)
# collective mass in SRs
v_myMasses = [mass_M600_M700, mass_M800_M1000, mass_M1100_M1200]
v_varName = ["hMass_pass_BDTG_smh_cut_mixed03_fine", "hMass_pass_BDTG_smh_cut_mixed04_fine", "hMass_pass_BDTG_smh_cut_mixed05_fine"]
make_collective_plot(v_varName, v_myMasses, plotType)
v_varName = ["hTprime_Mass_pass_BDTG_smh_cut_mixed03_SR_fine", "hTprime_Mass_pass_BDTG_smh_cut_mixed04_SR_fine", "hTprime_Mass_pass_BDTG_smh_cut_mixed05_SR_fine"]
make_collective_plot(v_varName, v_myMasses, plotType)
# individual tprime mass
make_plot("hmass_tprime_cov_fine", masses, plotType, pauseFit=False)
make_plot("hTprime_Mass_pass_BDTG_smh_cut_mixed03_SR_fine", mass_M600_M700, plotType, pauseFit=True);
make_plot("hTprime_Mass_pass_BDTG_smh_cut_mixed04_SR_fine", mass_M800_M1000, plotType, pauseFit=True);
make_plot("hTprime_Mass_pass_BDTG_smh_cut_mixed05_SR_fine", mass_M1100_M1200, plotType, pauseFit=False);
# individual diphoton mass
make_plot("hMass_fine", masses, plotType, pauseFit=False)
make_plot("hMass_pass_BDTG_smh_cut_mixed03_fine", mass_M600_M700, plotType, pauseFit=True);
make_plot("hMass_pass_BDTG_smh_cut_mixed04_fine", mass_M800_M1000, plotType, pauseFit=True);
make_plot("hMass_pass_BDTG_smh_cut_mixed05_fine", mass_M1100_M1200, plotType, pauseFit=False);
# myParameterSets {{{
myParameterSets = {
"eff":{
"ybound" : [0.0, 0.5],
#"legend" : [0.17, 0.60, 0.42, 0.85],
#"legend" : [0.17, 0.55, 0.45, 0.80],
"legend" : [0.18, 0.55, 0.45, 0.80],
},
"eff-leptonic":{
"ybound" : [0.0, 0.2],
"legend" : [0.18, 0.55, 0.45, 0.80],
},
"ratio":{
"ybound" : [0.8, 2.0],
"legend" : [0.60, 0.60, 0.85, 0.85],
},
"significance":{
"ybound" : [0., 0.1],
"legend" : [0.17, 0.60, 0.42, 0.85],
},
}
#}}}
# collection_efficiency {{{
collection_efficiency = {
"set0":{
# original
"raw_M600_M700" : [ 0.07882, 0.09317, 0.10533, 0.11234, 0.10923, 0.06883, 0.02264, 0.01262, 0.00778, 0.00539 ],
"raw_M800_M1000" : [ 0.00810, 0.01266, 0.02134, 0.03390, 0.04737, 0.12473, 0.17935, 0.19774, 0.16925, 0.09225 ],
"raw_M1100_1200" : [ 0.00242, 0.00311, 0.00408, 0.00535, 0.00699, 0.03057, 0.10157, 0.18667, 0.23183, 0.26597 ],
},
"set1":{
# signal efficiency of opt1; from log messages && double check from validation code
"raw_M600_M700" : [ 0.07882, 0.09316, 0.10531, 0.11233, 0.10923, 0.06886, 0.02265, 0.01258, 0.00790, 0.00568 ],
"raw_M800_M1000" : [ 0.00810, 0.01267, 0.02135, 0.03389, 0.04737, 0.12472, 0.17937, 0.19774, 0.16941, 0.09232 ],
"raw_M1100_1200" : [ 0.00214, 0.00280, 0.00364, 0.00491, 0.00635, 0.02916, 0.09825, 0.17936, 0.21772, 0.23706 ],
},
"set2":{
# signal efficiency of opt2
"raw_M600_M700" : [ 0.07882, 0.09316, 0.10531, 0.11233, 0.10923, 0.06886, 0.02265, 0.01258, 0.00790, 0.00568 ],
"raw_M800_M1000" : [ 0.01154, 0.01693, 0.02616, 0.03833, 0.05153, 0.13028, 0.18590, 0.20395, 0.17460, 0.09648 ],
"raw_M1100_1200" : [ 0.00315, 0.00411, 0.00581, 0.00791, 0.01048, 0.03536, 0.10861, 0.19710, 0.24494, 0.28062 ],
},
"set3":{
# UL signal efficiency of opt2
"raw_M600_M700" : [ 0.07114, 0.08517, 0.09706, 0.10479, 0.10776, 0.06358, 0.01886, 0.01027, 0.00701, 0.00462 ],
"raw_M800_M1000" : [ 0.01006, 0.01513, 0.02305, 0.03451, 0.04970, 0.12768, 0.18943, 0.21193, 0.18650, 0.09700 ],
"raw_M1100_1200" : [ 0.00320, 0.00438, 0.00594, 0.00822, 0.01101, 0.03480, 0.10923, 0.20500, 0.26490, 0.29458 ],
},
"leptonic":{
"raw_M600_M700" : [ 0.0377784, 0.0407727, 0.0430395, 0.0446526, 0.0452286, 0.030533, 0.0104256, 0.00559142, 0.00352654, 0.00229087 ],
"raw_M800_M1000" : [ 0.0404852, 0.0468421, 0.0522516, 0.0563517, 0.0596403, 0.0687015, 0.0725982, 0.0726485, 0.0652187, 0.0332645 ],
"raw_M1100_1200" : [ 0.00888833, 0.0138209, 0.0219249, 0.0315641, 0.0398544, 0.0603796, 0.0709918, 0.076363, 0.0789114, 0.0786163 ],
},
}
#}}}
# collection_background {{{
collection_background = {
"set1":{"SR1":13., "SR2":13., "SR3":15.},
"set2":{"SR1":13., "SR2":13., "SR3":19.},
}
#}}}
def get_ratio_list(key): #{{{
result = []
opt1 = collection_efficiency["set1"][key]
opt2 = collection_efficiency["set2"][key]
for i in range(len(opt1)):
ratio = opt2[i] / opt1[i]
result.append( ratio )
return result
#}}}
def get_significance(tag, key): #{{{
result = []
efficiency = collection_efficiency[tag][key]
for i in range(len(efficiency)):
if i<5: nbkg = collection_background[tag]["SR1"]
elif i<8: nbkg = collection_background[tag]["SR2"]
else: nbkg = collection_background[tag]["SR3"]
significance = efficiency[i] / math.sqrt(nbkg)
result.append( significance )
return result
#}}}
def make_efficiency(ytitle, myParameters, tag, output_stem): #{{{
c3.cd()
ymin = myParameters["ybound"][0]
ymax = myParameters["ybound"][1]
leg = myParameters["legend"]
if tag in collection_efficiency.keys():
raw_M600_M700 = collection_efficiency[tag]["raw_M600_M700"]
raw_M800_M1000 = collection_efficiency[tag]["raw_M800_M1000"]
raw_M1100_1200 = collection_efficiency[tag]["raw_M1100_1200"]
elif tag == "ratio":
raw_M600_M700 = get_ratio_list("raw_M600_M700")
raw_M800_M1000 = get_ratio_list("raw_M800_M1000")
raw_M1100_1200 = get_ratio_list("raw_M1100_1200")
elif "significance" in tag:
subtag = tag.split(',')[0]
raw_M600_M700 = get_significance(subtag, "raw_M600_M700")
raw_M800_M1000 = get_significance(subtag, "raw_M800_M1000")
raw_M1100_1200 = get_significance(subtag, "raw_M1100_1200")
else:
print ">>> [ERROR] The tag is not defined. Please check the code."
return
n = len(raw_M600_M700)
x = array.array('d')
efficiency_M600_M700 = array.array('d')
efficiency_M800_M1000 = array.array('d')
efficiency_M1100_1200 = array.array('d')
for i in range(n):
#x.append(masses_v2[i])
x.append(masses[i])
efficiency_M600_M700.append(raw_M600_M700[i])
efficiency_M800_M1000.append(raw_M800_M1000[i])
efficiency_M1100_1200.append(raw_M1100_1200[i])
gr1 = ROOT.TGraph(n, x, efficiency_M600_M700)
gr2 = ROOT.TGraph(n, x, efficiency_M800_M1000)
gr3 = ROOT.TGraph(n, x, efficiency_M1100_1200)
set_graph(gr1, ytitle, ROOT.kRed)
set_graph(gr2, ytitle, ROOT.kGreen+3)
set_graph(gr3, ytitle, ROOT.kBlue)
gr1.SetMaximum(ymax)
gr1.SetMinimum(ymin)
gr1.Draw('ACP')
gr2.Draw('CP')
gr3.Draw('CP')
# local leg
legend = ROOT.TLegend(leg[0], leg[1], leg[2], leg[3])
legend.SetLineColor(0)
legend.SetTextSize(0.04)
legend.Clear()
legend.AddEntry(gr1, "[600, 700]", "lp")
legend.AddEntry(gr2, "[800, 1000]", "lp")
legend.AddEntry(gr3, "[1100, 1200]", "lp")