def produce_limit_plot(pattern="T1qqqqLL_allbins",
draw_xsecs=True,
smoothing=False):
xsecGlu = getxsecGlu()
hexp = TH2F('hexp', 'hexp', 113, -12.5, 2812.5, 113, -12.5, 2812.5)
hexpdown = TH2F('hexpdown', 'hexpdown', 113, -12.5, 2812.5, 113, -12.5,
2812.5)
hexpup = TH2F('hexpup', 'hexpup', 113, -12.5, 2812.5, 113, -12.5, 2812.5)
hobs = TH2F('hobs', 'hobs', 113, -12.5, 2812.5, 113, -12.5, 2812.5)
vmx = []
vmy = []
vxsec = []
vobs = []
vobsup = []
vobsdown = []
vexp = []
vup = []
vdown = []
vlim = []
for signal_point_file in glob.glob(pattern + '/higgsCombine*root'):
print signal_point_file
mGo = int(signal_point_file.split("Signalg")[-1].split("_chi")[0])
mLSP = int(
signal_point_file.split("_chi")[-1].split(".AsymptoticLimits")[0])
print 'mGo', mGo
print 'mLSP', mLSP
f = TFile.Open(signal_point_file, 'read')
t = f.Get('limit')
try:
entries = t.GetEntries()
except:
continue
if "T1qqqq" in pattern:
xsec = xsecGlu[mGo][0]
theorySys = xsecGlu[mGo][1]
elif "T2bt" in pattern:
xsec = get_sbottom_antisbottom_cross_section(mGo)
theorySys = get_sbottom_antisbottom_cross_section(mGo)
rExp = 0
rObs = 0
rExp1SigmaDown = 0
rExp1SigmaUp = 0
factor = 1.0
if mGo < 1400:
factor = 100.0
for entry in t:
q = entry.quantileExpected
if q == 0.5: rExp = entry.limit / factor
if q == -1: rObs = entry.limit / factor
if q < 0.4 and q > 0.14: rExp1SigmaDown = entry.limit / factor
if q < 0.14: rExp2SigmaDown = entry.limit / factor
if q > 0.6 and q < 0.9: rExp1SigmaUp = entry.limit / factor
if q > 0.9: rExp2SigmaUp = entry.limit / factor
hexp.Fill(mGo, mLSP, rExp)
hexpdown.Fill(mGo, mLSP, rExp1SigmaDown)
hexpup.Fill(mGo, mLSP, rExp1SigmaUp)
hobs.Fill(mGo, mLSP, rObs)
vmx.append(mGo)
vmy.append(mLSP)
vxsec.append(xsec)
vlim.append(xsec * rObs)
vobs.append(rObs)
vobsup.append(rObs * (1 + theorySys / 100.0))
vobsdown.append(rObs * (1 - theorySys / 100.0))
vexp.append(rExp)
vup.append(rExp1SigmaUp)
vdown.append(rExp1SigmaDown)
f.Close()
#hexp.SaveAs(pattern + '/testexp_' + pattern + '.root')
#hobs.SaveAs(pattern + '/testobs_' + pattern + '.root')
aexp = array("d", vexp)
alim = array("d", vlim)
aup = array("d", vup)
adown = array("d", vdown)
aobs = array("d", vobs)
aobsup = array("d", vobsup)
aobsdown = array("d", vobsdown)
amx = array("d", vmx)
amy = array("d", vmy)
glim = TGraph2D("glim", "Cross-section limt", len(vlim), amx, amy, alim)
gexp = TGraph2D("gexp", "Expected Limit", len(vexp), amx, amy, aexp)
gup = TGraph2D("gup", "Expected Limit 1sigma up", len(vup), amx, amy, aup)
gdown = TGraph2D("gdown", "Expected Limit 1sigma down", len(vdown), amx,
amy, adown)
gobs = TGraph2D("gobs", "Observed Limit", len(vobs), amx, amy, aobs)
gobsup = TGraph2D("gobsup", "theory 1sigma up", len(vobsup), amx, amy,
aobsup)
gobsdown = TGraph2D("gobsdown", "theory 1sigma down", len(vobsdown), amx,
amy, aobsdown)
c = TCanvas("c", "c", 900, 800)
c.SetLeftMargin(0.15)
c.SetRightMargin(0.15)
c.SetTopMargin(0.1)
c.SetLogz(True)
xmin = min(vmx)
xmax = max(vmx)
ymin = min(vmy)
ymax = max(vmy)
bin_size = 12.5
nxbins = max(1, min(500, (math.ceil((xmax - xmin) / bin_size))))
nybins = max(1, min(500, (math.ceil((ymax - ymin) / bin_size))))
glim.SetNpx(int(nxbins))
glim.SetNpy(int(nybins))
cexp = TryToGetContours(gexp, 2, 1, smoothing=smoothing)
cup = TryToGetContours(gup, 2, 2, smoothing=smoothing)
cdown = TryToGetContours(gdown, 2, 2, smoothing=smoothing)
cobs = TryToGetContours(gobs, 1, 1, smoothing=smoothing)
cobsup = TryToGetContours(gobsup, 1, 2, smoothing=smoothing)
cobsdown = TryToGetContours(gobsdown, 1, 2, smoothing=smoothing)
hlim = glim.GetHistogram()
if "T1qqqq" in pattern:
hlim.SetTitle(";m_{gluino} [GeV];m_{LSP} [GeV]")
elif "T2bt" in pattern:
hlim.SetTitle(";m_{stop} [GeV];m_{LSP} [GeV]")
hlim.Draw("colz")
#hlim.Draw("text")
if "T2bt" in pattern:
hlim.GetXaxis().SetRangeUser(0, 2000)
hlim.GetYaxis().SetRangeUser(50, 2000)
cexp.Draw("same")
cup.Draw("same")
cdown.Draw("same")
#cobs.Draw("same")
#cobsup.Draw("same")
#cobsdown.Draw("same")
flimit = TFile(pattern + "/limit_scan.root", "recreate")
cobs.SetTitle("Observed Limit")
cobsup.SetTitle("Observed -1#sigma Limit")
cobsdown.SetTitle("Observed +1#sigma Limit")
cexp.SetTitle("Expected Limit")
cup.SetTitle("Expected -1#sigma Limit")
cdown.SetTitle("Expected +1#sigma Limit")
hlim.Write("T1ttttObservedExcludedXsec")
cobs.Write("T1ttttObservedLimit")
cobsup.Write("T1ttttObservedLimitDown")
cobsdown.Write("T1ttttObservedLimitUp")
cexp.Write("T1ttttExpectedLimit")
cup.Write("T1ttttExpectedLimitDown")
cdown.Write("T1ttttExpectedLimitUp")
shared_utils.stamp()
label = pattern.split("/")[-1].replace("_", ", ")
latex_label = TLatex()
latex_label.SetNDC()
latex_label.SetTextColor(kBlack)
latex_label.SetTextAlign(11)
latex_label.SetTextSize(0.035)
latex_label.DrawLatex(0.2, 0.8, label)
c.SaveAs("../" + pattern.split("/")[-1] + '.pdf')
def get_reweighting_factor(histofolder, suffix):
periods = [
"Summer16",
"Fall17",
"Autumn18",
"Run2016B",
"Run2016C",
"Run2016D",
"Run2016E",
"Run2016F",
"Run2016G",
"Run2016H",
"Run2017B",
"Run2017C",
"Run2017D",
"Run2017E",
"Run2017F",
"Run2018A",
"Run2018B",
"Run2018C",
"Run2018D",
]
histolabels = [
#"track_nValidPixelHits",
#"track_nValidPixelHits_short",
#"track_nValidPixelHits_long",
"h_muonPtCand",
#"h_muonPt2Cand",
"h_muonPt",
#"track_pt",
"track_pt_short",
"track_pt_long",
]
hists = {}
for period in periods:
hists[period] = {}
for label in histolabels:
fin = TFile(
"%s/histograms%s_%s.root" % (histofolder, suffix, period),
"open")
print label, histofolder, suffix, period
hists[period][label] = fin.Get("Histograms/" + label)
hists[period][label].SetDirectory(0)
hists[period][label].SetLineWidth(2)
shared_utils.histoStyler(hists[period][label])
fin.Close()
print "all loaded"
# hweight = histTarget_NPixHits.Clone(); hweight.Divide(histSimulation)
hweight = {}
for label in histolabels:
hweight[label] = {}
for year in ["2016", "2017", "2018"]:
canvas = shared_utils.mkcanvas()
legend = shared_utils.mklegend(x1=0.6, y1=0.6, x2=0.85, y2=0.85)
colors = range(209, 250)[::3]
for i, period in enumerate(sorted(periods)):
if year not in period: continue
if "Run2016" in period:
mc = "Summer16"
elif "Run2017" in period:
mc = "Fall17"
elif "Run2018" in period:
mc = "Autumn18"
else:
continue
#num = hists[period]["track_nValidPixelHits"].Clone()
#denom = hists[mc]["track_nValidPixelHits"].Clone()
num = hists[period][label].Clone()
denom = hists[mc][label].Clone()
if num.Integral() > 0:
num.Scale(1.0 / num.Integral())
else:
print label, year, period, num.Integral()
if denom.Integral() > 0:
denom.Scale(1.0 / denom.Integral())
else:
print label, year, period, denom.Integral()
hweight[label][period] = num.Clone()
hweight[label][period].Divide(denom)
hweight[label][period].SetName(period + "_" + label)
shared_utils.histoStyler(hweight[label][period])
hweight[label][period].SetLineColor(colors.pop(0))
hweight[label][period].GetYaxis().SetRangeUser(0, 5)
#hweight[label][period].SetTitle(";number of pixel hits;weight")
hweight[label][period].SetTitle(";track p_{T} (GeV);weight")
hweight[label][period].GetXaxis().SetRangeUser(0, 200)
if i == 0:
hweight[label][period].Draw("hist e")
else:
hweight[label][period].Draw("hist e same")
# Draw overflow:
last_bin = hweight[label][period].GetNbinsX() + 1
overflow = hweight[label][period].GetBinContent(last_bin)
print overflow
hweight[label][period].AddBinContent((last_bin - 1), overflow)
legend.SetTextSize(0.035)
legend.AddEntry(hweight[label][period], period)
legend.Draw()
shared_utils.stamp()
canvas.SaveAs("plots/hweights_%s_%s.pdf" % (label, year))
# save weights:
fout = TFile("hweights.root", "recreate")
for label in histolabels:
for period in hweight[label]:
hweight[label][period].Write()
fout.Close()
ndepth = tmva_folder.split("ndepth")[1].split("-")[0]
legend.AddEntry(histos[tmva_folder],
"depth=%s, %s trees" % (ndepth, ntrees))
area_under_curve = histos[tmva_folder].Integral() / 100.0
rocarea["%s, %s tracks" % (phase, category)].SetBinContent(
iBin, area_under_curve)
binLabels[iBin] = "%s, %s" % (ndepth, ntrees)
for i, label in enumerate(histos):
if i == 0:
histos[label].Draw("hist")
else:
histos[label].Draw("hist same")
legend.Draw()
shared_utils.stamp(showlumi=False)
canvas.SaveAs("optimize_%s_%s.pdf" % (phase, category))
# ROC plot:
canvas = shared_utils.mkcanvas("c1")
colors = [kBlue, kRed, kOrange, kMagenta]
legend = shared_utils.mklegend(x1=.2, y1=.2, x2=.5, y2=.4)
legend.SetTextSize(0.035)
for i, label in enumerate(rocarea):
if i == 0:
rocarea[label].Draw("hist")
else:
rocarea[label].Draw("hist same")
rocarea[label].GetYaxis().SetRangeUser(0.85, 1)
def get_fakerate(path,
variable,
rootfile,
foldername,
base_cuts,
numerator_cuts,
denominator_cuts,
selected_sample,
extra_text,
binning,
threads,
nBinsX=False,
xmin=False,
xmax=False,
nBinsY=False,
ymin=False,
ymax=False,
xlabel=False,
ylabel=False,
do_interpolation=False):
print "Getting fake rate for", variable, selected_sample, extra_text
if ":" in variable:
plot2D = True
else:
plot2D = False
if not plot2D:
if binning[0] == "variable":
nbins = len(binning[1]) - 1
histos[label] = histos[label].Rebin(nbins, label,
array.array('d', binning[1]))
else:
nBinsX = binning[0]
xmin = binning[1]
xmax = binning[2]
else:
if binning[0] == "variable":
nbinsX = len(binning[1]) - 1
nbinsY = len(binning[2]) - 1
histos[label] = histos[label].RebinX(nbinsX, label,
array.array('d', binning[1]))
histos[label] = histos[label].RebinY(nbinsY, label,
array.array('d', binning[2]))
else:
nBinsX = binning[0]
xmin = binning[1]
xmax = binning[2]
nBinsY = binning[3]
ymin = binning[4]
ymax = binning[5]
if plot2D:
fakes_numerator = plotting.get_histogram(
variable,
base_cuts + numerator_cuts,
nBinsX=nBinsX,
xmin=xmin,
xmax=xmax,
nBinsY=nBinsY,
ymin=ymin,
ymax=ymax,
path=path,
selected_sample=selected_sample,
threads=threads)
fakes_denominator = plotting.get_histogram(
variable,
base_cuts + denominator_cuts,
nBinsX=nBinsX,
xmin=xmin,
xmax=xmax,
nBinsY=nBinsY,
ymin=ymin,
ymax=ymax,
path=path,
selected_sample=selected_sample,
threads=threads)
else:
fakes_numerator = plotting.get_histogram(
variable,
base_cuts + numerator_cuts,
nBinsX=nBinsX,
xmin=xmin,
xmax=xmax,
path=path,
selected_sample=selected_sample,
threads=threads)
fakes_denominator = plotting.get_histogram(
variable,
base_cuts + denominator_cuts,
nBinsX=nBinsX,
xmin=xmin,
xmax=xmax,
path=path,
selected_sample=selected_sample,
threads=threads)
try:
print fakes_numerator.GetEntries()
print fakes_denominator.GetEntries()
except:
print "Error while getting histogram!"
return
fout = TFile(rootfile, "update")
fout.cd()
gDirectory.mkdir(foldername)
fout.cd(foldername)
fake_rate = fakes_numerator.Clone()
fake_rate.Divide(fakes_denominator)
fake_rate.SetName("fakerate_%s" % (variable.replace(":", "_")))
labels = {
"HT": "H_{T} (GeV)",
"MHT": "missing H_{T} (GeV)",
"HT_cleaned": "cleaned H_{T} (GeV)",
"MHT_cleaned": "cleaned missing H_{T} (GeV)",
"n_allvertices": "number of vertices"
}
if not xlabel:
xlabel = variable.split(":")[0]
if xlabel in labels:
xlabel = labels[xlabel]
if not ylabel and plot2D:
ylabel = variable.split(":")[1]
if ylabel in labels:
ylabel = labels[ylabel]
if plot2D:
fake_rate.SetTitle(";%s; %s; fake rate" % (ylabel, xlabel))
else:
fake_rate.SetTitle(";%s; fake rate" % xlabel)
shared_utils.histoStyler(fake_rate)
shared_utils.stamp()
fake_rate.SetName("fakerate_%s" %
(variable.replace(":", "_").replace("_cleaned", "")))
fake_rate.SetDirectory(gDirectory)
fake_rate.Write()
# also save interpolated histogram:
if plot2D and do_interpolation:
fake_rate_interpolated = get_interpolated_histogram(fake_rate)
fake_rate_interpolated.SetName("fakerate_%s_interpolated" %
(variable.replace(":", "_")))
fake_rate_interpolated.SetDirectory(gDirectory)
fake_rate_interpolated.Write()
fout.Close()
def combinedplots(channel,
variable,
outputfolder,
folderlabel,
cuts_channel,
skim_folder,
use_or_trigger=True):
if channel == "SEl" and "leadingmuon" in variable:
return
if channel == "SMu" and "leadingelectron" in variable:
return
if channel == "MHT" and "leading" in variable:
return
numevents = -1
pdffile = "%s/triggereff_%s_%s.pdf" % (outputfolder, folderlabel,
variable.replace(":", "_"))
histos = {}
# select/unselect specific run period:
period = ""
for year in [
2016,
2017,
2018,
]:
# switches
if "switchdenom" in folderlabel:
glob_sel_num = "Run%s%s*SingleMuon*.root" % (year, period)
glob_sel_denom = "Run%s%s*SingleMuon*.root" % (year, period)
glob_smu_num = "Run%s%s*SingleElectron*.root" % (year, period)
glob_smu_denom = "Run%s%s*SingleElectron*.root" % (year, period)
if channel == "SEl":
denom = "triggered_singlemuon==1"
denom_label = "mu trigger"
extra_label = "SingleMuon dataset"
elif channel == "SMu":
denom = "triggered_singleelectron==1"
denom_label = "el trigger"
extra_label = "SingleElectron dataset"
elif "jethtother" in folderlabel:
glob_sel_num = "Run%s%s*JetHT*.root" % (year, period)
glob_sel_denom = "Run%s%s*SingleMuon*.root" % (year, period)
glob_smu_num = "Run%s%s*JetHT*.root" % (year, period)
glob_smu_denom = "Run%s%s*SingleElectron*.root" % (year, period)
if channel == "SEl":
denom = "triggered_singlemuon==1"
denom_label = "mu trigger"
extra_label = "num.: JetHT, denom.: SingleMu"
elif channel == "SMu":
denom = "triggered_singleelectron==1"
denom_label = "el trigger"
extra_label = "num.: JetHT, denom.: SingleEl"
elif "jetht" in folderlabel:
glob_sel_num = "Run%s%s*JetHT*.root" % (year, period)
glob_sel_denom = "Run%s%s*JetHT*.root" % (year, period)
glob_smu_num = "Run%s%s*JetHT*.root" % (year, period)
glob_smu_denom = "Run%s%s*JetHT*.root" % (year, period)
denom = "triggered_ht==1"
denom_label = "HT trigger"
extra_label = "JetHT dataset"
else:
glob_sel_num = "Run%s%s*MET*.root" % (year, period)
glob_sel_denom = "Run%s%s*MET*.root" % (year, period)
glob_smu_num = "Run%s%s*MET*.root" % (year, period)
glob_smu_denom = "Run%s%s*MET*.root" % (year, period)
denom = "triggered_met==1"
denom_label = "MHT trigger"
extra_label = "MET dataset"
use_or_trigger = False
if channel == "MHT":
glob_mht_num = "Run%s%s*SingleElectron*.root" % (year, period)
glob_mht_denom = "Run%s%s*SingleElectron*.root" % (year, period)
denom = "triggered_singleelectron==1"
denom_label = "el. trigger"
extra_label = "SingleElectron dataset"
if ":" in variable:
nMinus1 = False
extra_label += ", trigger efficiency"
else:
if variable == "MHT" and "DtTurnon" in folderlabel:
# don't peek into SR
nMinus1 = False
else:
nMinus1 = True
nBinsX = binnings[variable][0]
xmin = binnings[variable][1]
xmax = binnings[variable][2]
if ":" in variable:
nBinsY = binnings[variable][3]
ymin = binnings[variable][4]
ymax = binnings[variable][5]
else:
nBinsY = False
ymin = False
ymax = False
def get_treff_histogram(globstring, variable, cutA, cutB):
if year == 2018 and "SingleElectron" in globstring:
globstring = globstring.replace("SingleElectron", "EGamma")
extra_label = "Egamma dataset"
if ":" in variable:
extra_label = "Egamma dataset, trigger efficiency"
histo = plotting.get_all_histos([skim_folder + "/" + globstring],
"Events",
variable,
numevents=numevents,
nMinus1=nMinus1,
cutstring=cuts_channel + cutA +
cutB,
nBinsX=nBinsX,
xmin=xmin,
xmax=xmax,
nBinsY=nBinsY,
ymin=ymin,
ymax=ymax)
histo.SetDirectory(0)
return histo
# denominator:
if channel == "MHT":
histos["num_%s_%s_%s" %
(variable, channel, year)] = get_treff_histogram(
glob_mht_num, variable, denom, " && triggered_met==1")
histos["denom_%s_%s_%s" %
(variable, channel, year)] = get_treff_histogram(
glob_mht_denom, variable, denom, "")
elif channel == "SEl":
if use_or_trigger:
histos[
"num_%s_%s_%s" %
(variable, channel, year)] = get_treff_histogram(
glob_sel_num, variable, denom,
" && (triggered_met==1 || triggered_singleelectron==1)"
)
else:
histos["num_%s_%s_%s" %
(variable, channel, year)] = get_treff_histogram(
glob_sel_num, variable, denom,
" && triggered_singleelectron==1")
histos["denom_%s_%s_%s" %
(variable, channel, year)] = get_treff_histogram(
glob_sel_denom, variable, denom, "")
elif channel == "SMu":
if use_or_trigger:
histos["num_%s_%s_%s" %
(variable, channel, year)] = get_treff_histogram(
glob_smu_num, variable, denom,
" && (triggered_met==1 || triggered_singlemuon==1)")
else:
histos["num_%s_%s_%s" %
(variable, channel, year)] = get_treff_histogram(
glob_smu_num, variable, denom,
" && triggered_singlemuon==1")
histos["denom_%s_%s_%s" %
(variable, channel, year)] = get_treff_histogram(
glob_smu_denom, variable, denom, "")
c1 = shared_utils.mkcanvas("c1")
#legend = TLegend(0.5, 0.2, 0.88, 0.4)
legend = TLegend(0.55, 0.2, 0.88, 0.5)
legend.SetTextSize(0.03)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
if ":" not in variable:
if isinstance(binnings[variable][0], list):
histo = TH1F("histo", "",
len(binnings[variable][0]) - 1,
array('d', binnings[variable][0]))
else:
histo = TH1F("histo", "", binnings[variable][0],
binnings[variable][1], binnings[variable][2])
else:
if isinstance(binnings[variable][0], list):
histo = TH2F("histo", "",
len(binnings[variable][0]) - 1,
array('d', binnings[variable][0]),
len(binnings[variable][3]) - 1,
array('d', binnings[variable][3]))
else:
histo = TH2F("histo", "", binnings[variable][0],
binnings[variable][1], binnings[variable][2],
binnings[variable][3], binnings[variable][4],
binnings[variable][5])
if ":" not in variable:
htitle = ";%s; trigger efficiency #epsilon" % binnings[variable][3]
else:
htitle = ";%s; trigger efficiency #epsilon" % binnings[variable][6]
histo.SetTitle(htitle)
shared_utils.histoStyler(histo)
#FIXME
gStyle.SetPaintTextFormat("4.2f")
if ":" not in variable:
histo.Draw("hist")
histo.GetYaxis().SetRangeUser(0, 1.1)
c1.SetLogy(False)
c1.SetLogz(False)
else:
histo.Draw("colz text e")
histo.GetZaxis().SetRangeUser(0, 1)
histo.GetZaxis().SetTitle("trigger efficiency #epsilon")
c1.SetRightMargin(.13)
size = 0.15
font = 132
histo.GetZaxis().SetLabelFont(font)
histo.GetZaxis().SetTitleFont(font)
histo.GetZaxis().SetTitleSize(size)
histo.GetZaxis().SetLabelSize(size)
histo.GetZaxis().SetTitleOffset(1)
c1.SetLogy(False)
c1.SetLogz(False)
h_effs = {}
for i_year, year in enumerate([2016, 2017, 2018]):
denom = histos["denom_%s_%s_%s" % (variable, channel, year)].Clone()
num = histos["num_%s_%s_%s" % (variable, channel, year)].Clone()
print "@@", num.GetEntries()
print "@@", denom.GetEntries()
if "normalizeDenom" in folderlabel:
# calc. eff with normalizing to denominator
numNormEff = num.Clone()
denomNormEff = denom.Clone()
if denom.Integral() > 0:
numNormEff.Scale(1.0 / denom.Integral())
if denom.Integral() > 0:
denomNormEff.Scale(1.0 / denom.Integral())
h_effs["eff_%s" % year] = numNormEff.Clone()
h_effs["eff_%s" % year].Divide(denomNormEff)
if "altern." not in extra_label:
extra_label += "; altern."
else:
# default TEfficiency
h_effs["eff_%s_TEff" % year] = TEfficiency(num.Clone(),
denom.Clone())
# convert TEff to normal hist:
h2dPass = h_effs["eff_%s_TEff" % year].GetPassedHistogram()
h2dTotal = h_effs["eff_%s_TEff" % year].GetTotalHistogram()
h_effs["eff_%s" % year] = h2dPass.Clone()
h_effs["eff_%s" % year].Divide(h2dTotal)
h_effs["eff_%s" % year].SetDirectory(0)
shared_utils.histoStyler(h_effs["eff_%s" % year])
h_effs["eff_%s" % year].SetTitle(htitle)
if ":" not in variable:
# 1D histograms:
h_effs["eff_%s" % year].Draw("same")
h_effs["eff_%s" % year].SetLineWidth(2)
if year == 2016:
print "extra"
numNorm = num.Clone()
denomNorm = denom.Clone()
if num.Integral() > 0:
numNorm.Scale(1.0 / num.Integral())
if denom.Integral():
denomNorm.Scale(1.0 / denom.Integral())
denomNorm.SetLineColor(kBlue)
denomNorm.SetFillColor(kBlue)
denomNorm.SetFillStyle(3354)
denomNorm.Draw("hist f same")
numNorm.SetLineColor(kTeal)
numNorm.SetFillColor(kTeal)
numNorm.SetFillStyle(3345)
numNorm.Draw("hist f same")
legend.AddEntry(numNorm, "2016 norm. numerator", "f")
legend.AddEntry(denomNorm, "2016 norm. denominator", "f")
#legend.AddEntry(numNorm, "2016 numerator", "f")
#legend.AddEntry(denomNorm, "2016 denominator", "f")
else:
# 2D histograms:
h_effs["eff_%s" % year].Draw("colz text e same")
savetoroot(
h_effs["eff_%s" % year], "h_triggereff_%s_%s_%s" %
(channel, variable.replace(":", "_"), year), outputfolder,
folderlabel)
if "eff_%s_TEff" % year in h_effs:
savetoroot(
h_effs["eff_%s_TEff" % year], "teff_triggereff_%s_%s_%s" %
(channel, variable.replace(":", "_"), year), outputfolder,
folderlabel)
stamp_cuts(cuts_channel, channel, variable, use_or_trigger,
denom_label, extra_label)
shared_utils.stamp()
c1.SaveAs(pdffile.replace(".pdf", "_%s.pdf" % year))
savetoroot(
c1, "c_triggereff_%s_%s_%s" %
(channel, variable.replace(":", "_"), year), outputfolder,
folderlabel)
continue
if ":" in variable:
return 0
h_effs["eff_2016"].SetFillColorAlpha(0, 0)
h_effs["eff_2017"].SetFillColorAlpha(0, 0)
h_effs["eff_2018"].SetFillColorAlpha(0, 0)
h_effs["eff_2016"].SetLineColor(kBlue)
if channel == "MHT":
legend.AddEntry(h_effs["eff_2016"], "2016")
else:
legend.AddEntry(h_effs["eff_2016"], "2016, %s" % channel)
h_effs["eff_2017"].SetLineColor(kRed)
if channel == "MHT":
legend.AddEntry(h_effs["eff_2017"], "2017")
else:
legend.AddEntry(h_effs["eff_2017"], "2017, %s" % channel)
h_effs["eff_2018"].SetLineColor(kGreen + 2)
if channel == "MHT":
legend.AddEntry(h_effs["eff_2018"], "2018")
else:
legend.AddEntry(h_effs["eff_2018"], "2018, %s" % channel)
stamp_cuts(cuts_channel, channel, variable, use_or_trigger, denom_label,
extra_label)
shared_utils.stamp()
legend.Draw()
c1.SetGrid(True)
c1.SaveAs(pdffile)
savetoroot(
h_effs["eff_2016"],
"h_triggereff_%s_%s_2016" % (channel, variable.replace(":", "_")),
outputfolder, folderlabel)
savetoroot(
h_effs["eff_2017"],
"h_triggereff_%s_%s_2017" % (channel, variable.replace(":", "_")),
outputfolder, folderlabel)
savetoroot(
h_effs["eff_2018"],
"h_triggereff_%s_%s_2018" % (channel, variable.replace(":", "_")),
outputfolder, folderlabel)
if "eff_2016_TEff" in h_effs:
print "@@@@@"
savetoroot(
h_effs["eff_2016_TEff"], "teff_triggereff_%s_%s_2016" %
(channel, variable.replace(":", "_")), outputfolder, folderlabel)
savetoroot(
h_effs["eff_2017_TEff"], "teff_triggereff_%s_%s_2017" %
(channel, variable.replace(":", "_")), outputfolder, folderlabel)
savetoroot(
h_effs["eff_2018_TEff"], "teff_triggereff_%s_%s_2018" %
(channel, variable.replace(":", "_")), outputfolder, folderlabel)
savetoroot(
c1, "c_triggereff_%s_%s" % (folderlabel, variable.replace(":", "_")),
outputfolder, folderlabel)
def plot_run_periods(variable, prediction_folder, header):
histos = collections.OrderedDict()
for period in ["Summer16", "Run2016B_MET", "Run2016C_MET", "Run2016D_MET", "Run2016E_MET", "Run2016F_MET", "Run2016G_MET", "Run2016H_MET"]:
iFile = prediction_folder + "/prediction_%s.root" % period
print iFile
fin = TFile(iFile, "read")
histos[period] = fin.Get(variable)
print histos[period].GetEntries()
histos[period].SetDirectory(0)
fin.Close()
canvas = shared_utils.mkcanvas()
canvas.SetLogy(True)
legend = TLegend(0.55, 0.6, 0.88, 0.88)
legend.SetHeader(header)
legend.SetTextSize(0.025)
legend.SetBorderSize(0)
colors = [kBlack, kRed, kBlue, kGreen, kOrange, kAzure, kMagenta, kYellow, kTeal]
for i_label, label in enumerate(histos):
if i_label == 0:
histos[label].Draw("h")
else:
histos[label].Draw("h same")
shared_utils.histoStyler(histos[label])
xlabel = variable.split("_")[0]
if "DeDx" in xlabel:
xlabel += " (Mev/cm)"
color = colors.pop(0)
histos[label].SetLineColor(color)
histos[label].SetFillColor(0)
histos[label].SetMarkerSize(0)
if histos[label].Integral()>0:
histos[label].Scale(1.0/histos[label].Integral())
histos[label].SetTitle(";%s;Events" % xlabel)
histos[label].GetXaxis().SetRangeUser(1.5,7.5)
histos[label].GetYaxis().SetRangeUser(5e-4,1e-1)
# fit histograms:
if "DeDx_" in variable:
if "Summer16" in label:
fit_x = TF1("fit_x", "gaus", 2.5, 3.6)
else:
fit_x = TF1("fit_x", "gaus", 1.6, 2.8)
fit_x.SetLineColor(kRed)
fit_x.SetLineWidth(2)
histos[label].Fit("fit_x", "r")
legend.AddEntry(histos[label], label + ", (#mu=%1.2f)" % fit_x.GetParameter(1))
else:
legend.AddEntry(histos[label], label)
legend.Draw()
shared_utils.stamp()
canvas.SaveAs("fakebg_%s.pdf" % variable)
def plot2D(variable,
cutstring,
histos,
lumi,
pdffile,
ymin=1e-1,
ymax=1e5,
showdata=False,
drawoption="colz"):
# BDT sideband region plot
for label in histos:
histos[label].SetLineWidth(2)
shared_utils.histoStyler(histos[label])
size = 0.059
font = 132
histos[label].GetZaxis().SetLabelFont(font)
histos[label].GetZaxis().SetTitleFont(font)
histos[label].GetZaxis().SetTitleSize(size)
histos[label].GetZaxis().SetLabelSize(size)
histos[label].GetZaxis().SetTitleOffset(1.2)
histos[label].GetXaxis().SetNdivisions(5)
histos[label].SetMarkerStyle(20)
histos[label].SetMarkerSize(0.2)
histos[label].SetMarkerColorAlpha(histos[label].GetFillColor(), 0.5)
variable = variable.replace("tracks_mva_loose", "MVA score")
variable = variable.replace("tracks_dxyVtx", "d_{xy} (cm)")
histos[label].SetTitle(
";%s;%s;Events" % (variable.split(":")[1], variable.split(":")[0]))
legend = shared_utils.mklegend(x1=0.6, y1=0.4, x2=0.9, y2=0.8)
canvas = shared_utils.mkcanvas()
canvas.SetLogz(True)
canvas.SetRightMargin(0.2)
# combine backgrounds:
histos["CombinedBg"] = 0
for label in histos:
if "Signal" not in label and "CombinedBg" not in label:
if histos["CombinedBg"] == 0:
histos["CombinedBg"] = histos[label].Clone()
else:
histos["CombinedBg"].Add(histos[label])
if histos["CombinedBg"] == 0:
del histos["CombinedBg"]
#if "/" in pdffile:
# os.system("mkdir -p %s" % "/".join(pdffile.split("/")[:-1]))
# os.chdir("/".join(pdffile.split("/")[:-1]))
for label in histos:
if "CombinedBg" in label or "Signal" in label:
histos[label].Draw(drawoption)
shared_utils.stamp()
canvas.SaveAs(pdffile + "_" +
label.replace(":", "_").replace(" ", "_") + ".pdf")
# draw some funky lcines
text = TLatex()
text.SetTextFont(132)
text.SetTextSize(0.059)
if "short" in pdffile:
#upper_line = TLine(0, -0.5, (1 + 0.5)/(0.65/0.01), 1)
upper_line = TLine(0, -0.5, (0.8 + 0.5) / (0.65 / 0.01), 0.8)
upper_line.SetLineWidth(2)
upper_line.Draw("same")
lower_line = TLine(0.02, -1, 0.02, 1)
lower_line.SetLineWidth(2)
lower_line.Draw("same")
text.DrawLatex(0.005, 0.75, "SR")
text.DrawLatex(0.03, 0.75, "CR")
if "long" in pdffile:
upper_line = TLine(0, -0.05, (1 + 0.05) / (0.7 / 0.01), 1)
upper_line.SetLineWidth(2)
upper_line.Draw("same")
lower_line = TLine(0.02, -1, 0.02, 1)
lower_line.SetLineWidth(2)
lower_line.Draw("same")
text.DrawLatex(0.003, 0.75, "SR")
text.DrawLatex(0.03, 0.75, "CR")
canvas.SaveAs(pdffile + "_" +
label.replace(":", "_").replace(" ", "_") +
"_lines.pdf")
def significances(sg_filelist, bg_filelist, phase, batchname, signalcut=""):
bdt_short = " && ".join(cutflow.cuts["BDT_short"][:-2])
bdt_long = " && ".join(cutflow.cuts["BDT_long"][:-2])
#FIXME phase 1 dE/dx
if phase == 1:
bdt_short = bdt_short.replace("tracks_deDxHarmonic2pixel>2.0",
"MHT>=0")
bdt_long = bdt_long.replace("tracks_deDxHarmonic2pixel>2.0", "MHT>=0")
lumi = 137000
else:
lumi = 35000
histos = collections.OrderedDict()
drawoptions = collections.OrderedDict()
# get histograms:
def fill_histos(label, variable, shortcuts, longcuts):
histos["bg_short_%s" % label] = plotting.get_all_histos(
bg_filelist,
"Events",
variable,
"tracks_is_pixel_track==1 && " + shortcuts,
nBinsX=200,
xmin=-1,
xmax=1)
histos["bg_long_%s" % label] = plotting.get_all_histos(
bg_filelist,
"Events",
variable,
"tracks_is_pixel_track==0 && tracks_nMissingOuterHits>=2 && " +
longcuts,
nBinsX=200,
xmin=-1,
xmax=1)
histos["sg_short_%s" % label] = plotting.get_all_histos(
sg_filelist,
"Events",
variable,
"tracks_is_pixel_track==1 && tracks_chiCandGenMatchingDR<0.01 && "
+ shortcuts + signalcut,
nBinsX=200,
xmin=-1,
xmax=1)
histos["sg_long_%s" % label] = plotting.get_all_histos(
sg_filelist,
"Events",
variable,
"tracks_is_pixel_track==0 && tracks_nMissingOuterHits>=2 && tracks_chiCandGenMatchingDR<0.01 && "
+ longcuts + signalcut,
nBinsX=200,
xmin=-1,
xmax=1)
# get common denominator:
fill_histos(
"study", "tracks_mva_tight_may20_chi2_pt15",
"tracks_pt>15 && tracks_matchedCaloEnergy/tracks_p<0.20 && " +
bdt_short,
"tracks_pt>40 && tracks_matchedCaloEnergy/tracks_p<0.20 && " +
bdt_long)
fill_histos("denom", "tracks_mva_tight_may20_chi2_pt15", "tracks_pt>15",
"tracks_pt>40")
# scale with lumi
for label in histos:
shared_utils.histoStyler(histos[label])
histos[label].Scale(lumi)
# get efficiencies:
efficiencies = {}
for label in histos:
if "denom" in label: continue
efficiencies[label] = []
#denominator = histos[label.replace("study", "denom")].Integral(histos[label].GetXaxis().FindBin(-1), histos[label].GetXaxis().FindBin(1))
denominator = histos[label.replace("study", "denom")].Integral()
#denominator = histos[label].Integral(histos[label].GetXaxis().FindBin(-1), histos[label].GetXaxis().FindBin(1))
for i_score in numpy.arange(-1.0, 1.0, 0.005):
numerator = histos[label].Integral(
histos[label].GetXaxis().FindBin(i_score),
histos[label].GetXaxis().FindBin(1))
if denominator > 0:
efficiencies[label].append(
[i_score, numerator / denominator, numerator])
else:
efficiencies[label].append([i_score, 0, numerator])
# build TGraphs
graphs_roc = {}
graphs_sgeff = {}
graphs_bgeff = {}
graphs_significance = {}
scalingfactor = 1.0
signlabel = ""
for label in efficiencies:
if "bg" in label: continue
graphs_roc[label] = TGraph()
graphs_sgeff[label] = TGraph()
graphs_bgeff[label] = TGraph()
graphs_significance[label] = TGraph()
for i in range(len(efficiencies[label])):
score = efficiencies[label][i][0]
eff_sg = efficiencies[label][i][1]
eff_bg = efficiencies[label.replace("sg", "bg")][i][1]
N_sg = efficiencies[label][i][2]
N_bg = efficiencies[label.replace("sg", "bg")][i][2]
graphs_roc[label].SetPoint(graphs_roc[label].GetN(), eff_sg,
1 - eff_bg)
graphs_sgeff[label].SetPoint(graphs_sgeff[label].GetN(), score,
eff_sg)
graphs_bgeff[label].SetPoint(graphs_bgeff[label].GetN(), score,
eff_bg)
try:
#N = 1000
#significance = N * eff_sg / math.sqrt(N*eff_sg+N*eff_bg)
#signlabel = "#epsilon_{sg} / #sqrt{#epsilon_{sg} + #epsilon_{bg}}"
#significance = 5e4 * N_sg / math.sqrt(5e4*N_sg + N_bg)
significance = N_sg / math.sqrt(N_sg + N_bg)
signlabel = "significance = N_{sg} / #sqrt{N_{sg} + N_{bg}}"
#significance = 1e3 * N_sg / math.sqrt( N_bg + (0.2*N_bg)**2 )
#signlabel = "significance = 1e3 * N_{sg} / #sqrt{N_{bg} + (0.2*N_{bg})^{2}}"
except:
significance = 0
#scalingfactor = 1.0/1000
#scalingfactor = 1.0/100
graphs_significance[label].SetPoint(
graphs_significance[label].GetN(), score, significance)
for category in ["short", "long"]:
# plot significances:
#####################
canvas = shared_utils.mkcanvas()
if category == "short":
histo = TH2F("empty", "empty", 1, -1, 1, 1, 0, 1)
else:
histo = TH2F("empty", "empty", 1, -1, 1, 1, 0, 1)
shared_utils.histoStyler(histo)
histo.Draw()
histo.SetTitle(";BDT response;efficiency, significance")
legend = shared_utils.mklegend(x1=0.17, y1=0.2, x2=0.65, y2=0.45)
first = True
for label in graphs_significance:
if category not in label: continue
graphs_significance[label].Draw("same")
graphStyler(graphs_significance[label])
graphs_significance[label].SetLineColor(210)
graphs_sgeff[label].Draw("same")
graphStyler(graphs_sgeff[label])
graphs_sgeff[label].SetLineColor(kBlue)
graphs_sgeff[label].SetFillColor(0)
graphs_bgeff[label].Draw("same")
graphStyler(graphs_bgeff[label])
graphs_bgeff[label].SetLineColor(kRed)
graphs_bgeff[label].SetFillColor(0)
legendlabel = label.replace("sg_", "").replace(
"short_",
"short tracks ").replace("long_", "long tracks ").replace(
"p0", " (phase 0)").replace("p1", " (phase 1)")
legend.SetHeader("Phase %s, %s tracks" % (phase, category))
legend.AddEntry(graphs_sgeff["sg_short_study"],
"signal efficiency #epsilon_{sg}")
legend.AddEntry(graphs_bgeff["sg_short_study"],
"background efficiency #epsilon_{bg}")
legend.AddEntry(graphs_significance["sg_short_study"], signlabel)
legend.Draw()
shared_utils.stamp()
canvas.Print("plots/significance_%s_%s_phase%s_completestats.pdf" %
(batchname, category, phase))
canvas.Print("plots/significance_%s_%s_phase%s_completestats.root" %
(batchname, category, phase))
histos[label].GetZaxis().SetLabelFont(font)
histos[label].GetZaxis().SetTitleFont(font)
histos[label].GetZaxis().SetTitleSize(size)
histos[label].GetZaxis().SetLabelSize(size)
histos[label].GetZaxis().SetLabelSizeser(3e-3, 2e-1)
histos[label].GetZaxis().SetTitleOffset(1.2)
histos[label].GetXaxis().SetNdivisions(5)
histos[label].SetTitle(";%s;%s;fake rate" %
(myvariable.split(":")[0],
myvariable.split(":")[1]))
canvas = shared_utils.mkcanvas()
canvas.SetRightMargin(0.2)
histos[label].Draw("colz")
canvas.SetLogz(True)
shared_utils.stamp()
text = TLatex()
text.SetTextFont(132)
text.SetTextSize(0.05)
text.SetNDC()
text.DrawLatex(
0.175, 0.825, "%s, %s tracks" %
(dataset, category.replace("_", "")))
canvas.SaveAs(folder + "_plots/" + label + ".pdf")
if ":" not in variable:
for category in ["long", "short"]:
if category == "combined" and variable != "tracks_is_pixel_track":
def stack_histograms(histos, samples, variable, xlabel, ylabel, folder, normalize = False, ratioplot = False, signal_scaling_factor=1.0, suffix="", logx=False, logy=True, miniylabel="Data/MC", lumi=False, ymin=False, ymax=False, xmin=False, xmax=False, yaxis_label="Events", output_folder = ".", include_data = True, width=900):
if ratioplot:
canvas = TCanvas("canvas", "canvas", width, 800)
else:
canvas = shared_utils.mkcanvas()
if ratioplot:
pad1 = TPad("pad1", "pad1", 0, 0.16, 1, 1.0)
pad1.SetRightMargin(0.05)
pad1.SetLogy(logy)
pad1.SetTopMargin(0.07)
pad1.Draw()
pad2 = TPad("pad2", "pad2", 0.0, 0.025, 1.0, 0.235)
pad2.SetBottomMargin(0.25)
pad2.SetRightMargin(0.05)
pad2.Draw()
pad1.cd()
l = canvas.GetLeftMargin()
t = canvas.GetTopMargin()
r = canvas.GetRightMargin()
b = canvas.GetBottomMargin()
#canvas.SetTopMargin(0.05)
#canvas.SetBottomMargin(1.2*b)
#canvas.SetLeftMargin(1.2*l)
#canvas.SetRightMargin(0.7*r)
canvas.SetLogx(logx)
canvas.SetLogy(logy)
legend = TLegend(0.6, 0.65, 0.9, 0.9)
legend.SetTextSize(0.03)
minimum_y_value = 1e6
global_minimum = 1e10
global_maximum = 1e-10
mcstack = THStack("mcstack-%s" % str(uuid.uuid1()), "")
samples_for_sorting = []
# get total lumi value:
plot_has_data = False
total_lumi = 0
for label in sorted(histos):
if samples[label]["type"] == "data" and include_data:
plot_has_data = True
total_lumi += samples[label]["lumi"]
if not lumi and plot_has_data:
lumi = total_lumi
elif lumi:
pass
else:
lumi = 1.0
totals_background = 0
totals_signal = 0
# plot backgrounds:
for label in sorted(histos):
if samples[label]["type"] == "bg" or samples[label]["type"] == "sg":
histos[label].Scale(lumi * 1000.0)
totals_background += histos[label].Integral()
if histos[label].GetMinimum(0) < global_minimum:
global_minimum = histos[label].GetMinimum(0)
if histos[label].GetMaximum() > global_maximum:
global_maximum = histos[label].GetMaximum()
if samples[label]["type"] == "bg":
histos[label].SetFillColor(samples[label]["color"])
histos[label].SetLineColor(samples[label]["color"])
histos[label].SetMarkerColor(samples[label]["color"])
histos[label].SetLineWidth(0)
samples_for_sorting.append([label, histos[label].Integral()])
if samples[label]["type"] == "sg":
totals_signal += histos[label].Integral()
if normalize:
histos[label].Scale(1.0/histos[label].Integral())
#if not ratioplot:
# shared_utils.histoStyler(histos[label])
# stack histograms with the largest integral to appear on the top of the stack:
def Sort(sub_li, i_index):
return(sorted(sub_li, key = lambda x: x[i_index]))
print "Stacking"
for label in Sort(samples_for_sorting, 1):
mcstack.Add(histos[label[0]])
legend.AddEntry(histos[label[0]], label[0])
mcstack.Draw("hist")
if ratioplot:
mcstack.GetXaxis().SetLabelSize(0)
mcstack.SetTitle(";;%s" % yaxis_label)
mcstack.GetYaxis().SetTitleOffset(1.3)
mcstack.GetXaxis().SetTitleOffset(1.3)
else:
mcstack.SetTitle(";%s;%s" % (xlabel, yaxis_label))
#canvas.SetGridx(True)
#canvas.SetGridy(True)
#mcstack.GetXaxis().SetTitleSize(0.13)
#mcstack.GetYaxis().SetTitleSize(0.13)
mcstack.GetYaxis().SetTitleOffset(0.38)
#mcstack.GetYaxis().SetRangeUser(0,2)
#mcstack.GetYaxis().SetNdivisions(4)
#mcstack.GetXaxis().SetLabelSize(0.15)
#mcstack.GetYaxis().SetLabelSize(0.15)
shared_utils.histoStyler(mcstack)
# plot signal:
for label in sorted(histos):
if samples[label]["type"] == "sg":
histos[label].SetLineColor(samples[label]["color"])
histos[label].SetMarkerColor(samples[label]["color"])
histos[label].SetLineWidth(3)
histos[label].Scale(signal_scaling_factor)
histos[label].Draw("same hist")
legend.AddEntry(histos[label], label)
# plot data:
if plot_has_data:
combined_data = 0
for label in sorted(histos):
if samples[label]["type"] == "data":
if combined_data == 0:
combined_data = histos[label].Clone()
else:
combined_data.Add(histos[label])
combined_data.SetLineColor(kBlack)
combined_data.SetMarkerColor(kBlack)
combined_data.SetMarkerColor(kBlack)
combined_data.SetMarkerStyle(20)
combined_data.SetMarkerSize(1)
combined_data.SetLineWidth(3)
combined_data.Draw("same E & X0")
legend.AddEntry(combined_data, "Data")
# set minimum/maximum ranges
if global_minimum != 0:
mcstack.SetMinimum(1e-2 * global_minimum)
else:
mcstack.SetMinimum(1e-2)
if ymin:
mcstack.SetMinimum(ymin)
if logy:
global_maximum_scale = 100
else:
global_maximum_scale = 1
if not ymax:
mcstack.SetMaximum(global_maximum_scale * global_maximum)
else:
mcstack.SetMaximum(ymax)
legend.SetBorderSize(0)
legend.SetFillStyle(0)
legend.Draw()
latex=TLatex()
latex.SetNDC()
latex.SetTextAngle(0)
latex.SetTextColor(kBlack)
latex.SetTextFont(62)
latex.SetTextAlign(31)
latex.SetTextSize(0.35 * t)
latex.DrawLatex(0.915, 0.915, "%.1f fb^{-1} (13 TeV)" % lumi)
#latex.SetTextSize(0.35*t)
#latex.SetTextFont(52)
#latex.DrawLatex(0.4, 1-0.5*t+0.15*0.5*t, "CMS Work in Progress")
if ratioplot:
# plot ratio
pad2.cd()
combined_mc_background = 0
for label in sorted(histos):
if samples[label]["type"] == "bg":
if combined_mc_background == 0:
combined_mc_background = histos[label].Clone()
else:
combined_mc_background.Add(histos[label])
if plot_has_data:
ratio = combined_data.Clone()
else:
ratio = combined_mc_background.Clone()
ratio.Divide(combined_mc_background)
if xmax:
ratio.GetXaxis().SetRangeUser(xmin, xmax)
ratio.Draw("same e0")
ratio.SetTitle(";%s;%s" % (xlabel, miniylabel))
pad2.SetGridx(True)
pad2.SetGridy(True)
ratio.GetXaxis().SetTitleSize(0.13)
ratio.GetYaxis().SetTitleSize(0.13)
ratio.GetYaxis().SetTitleOffset(0.38)
ratio.GetYaxis().SetRangeUser(0,2)
ratio.GetYaxis().SetNdivisions(4)
ratio.GetXaxis().SetLabelSize(0.15)
ratio.GetYaxis().SetLabelSize(0.15)
shared_utils.stamp()
if len(output_folder)>0:
os.system("mkdir -p %s" % output_folder)
canvas.SaveAs("%s/%s%s.pdf" % (output_folder, variable, suffix))
#with open("%s/%s%s.txt" % (output_folder, variable, suffix), "w+") as fo:
# fo.write("bg = %s\n" % totals_background)
# fo.write("sg = %s\n" % totals_signal)
#canvas.SaveAs("%s/%s%s.root" % (output_folder, variable, suffix))
print "\n****************\n"
def main(options):
histofolder = options.histofolder
suffix = options.suffix
plotfolder = "plots_%s" % options.suffix
os.system("mkdir -p %s" % plotfolder)
if options.mc_reweighted:
periods = [
"Run2016B",
"Run2016C",
"Run2016D",
"Run2016E",
"Run2016F",
"Run2016G",
"Run2016H",
"Run2017B",
"Run2017C",
"Run2017D",
"Run2017E",
"Run2017F",
"Run2018A",
"Run2018B",
"Run2018C",
"Run2018D",
"Summer16rwRun2016B",
"Summer16rwRun2016C",
"Summer16rwRun2016D",
"Summer16rwRun2016E",
"Summer16rwRun2016F",
"Summer16rwRun2016G",
"Summer16rwRun2016H",
"Fall17rwRun2017B",
"Fall17rwRun2017C",
"Fall17rwRun2017D",
"Fall17rwRun2017E",
"Fall17rwRun2017F",
"Autumn18rwRun2018A",
"Autumn18rwRun2018B",
"Autumn18rwRun2018C",
"Autumn18rwRun2018D",
]
else:
periods = [
"Run2016B",
"Run2016C",
"Run2016D",
"Run2016E",
"Run2016F",
"Run2016G",
"Run2016H",
"Run2017B",
"Run2017C",
"Run2017D",
"Run2017E",
"Run2017F",
"Run2018A",
"Run2018B",
"Run2018C",
"Run2018D",
"Summer16",
"Fall17",
"Autumn18",
]
exact = "layers_remaining==track_trackerLayersWithMeasurement && "
cuts = {
"baseline": {
"base_cuts": "layers_remaining>=3 && ",
"taggedextra": "",
"legendheader": "baseline",
},
#"lowdxydz": {
# "base_cuts": "layers_remaining>=3 && ",
# "taggedextra": "track_dxyVtx<0.01 && track_dzVtx<0.01 && ",
# "legendheader": "lowdxydz",
# },
}
if not options.get_from_tree:
cuts = {
"baseline": {
"base_cuts": "",
"taggedextra": "",
"legendheader": "baseline",
},
}
for cut_label in cuts:
if options.weightkinematic:
cuts[cut_label]["base_cuts"] += " weight_kinematicMLP2>0 && "
elif options.weighttrackprop:
cuts[cut_label]["base_cuts"] += " weight_trackpropMLP2>0 && "
histolabels = {
"h_tracks_reco": [
"track_reco", cuts[cut_label]["base_cuts"] + "track_reco==1",
1, 1, 2
],
"h_tracks_rereco_short": [
"track_reco",
cuts[cut_label]["base_cuts"] + cuts[cut_label]["taggedextra"] +
exact + " track_rereco==1 && track_is_pixel_track==1", 1, 1, 2
],
"h_tracks_rereco_long": [
"track_reco",
cuts[cut_label]["base_cuts"] + cuts[cut_label]["taggedextra"] +
exact + " track_rereco==1 && track_is_pixel_track==0", 1, 1, 2
],
"h_tracks_tagged_short": [
"track_reco", cuts[cut_label]["base_cuts"] +
cuts[cut_label]["taggedextra"] + exact +
" track_preselected==1 && track_mva>0.1 && track_pt>25 && track_is_pixel_track==1 && (track_matchedCaloEnergy<15 || track_matchedCaloEnergy/track_p<0.15)",
1, 1, 2
],
"h_tracks_tagged_long": [
"track_reco", cuts[cut_label]["base_cuts"] +
cuts[cut_label]["taggedextra"] + exact +
" track_preselected==1 && track_mva>0.1 && track_pt>40 && track_is_pixel_track==0 && (track_matchedCaloEnergy<15 || track_matchedCaloEnergy/track_p<0.15)",
1, 1, 2
],
#"h_tracks_tagged_short": ["track_reco", cuts[cut_label]["base_cuts"] + cuts[cut_label]["taggedextra"] + exact + " track_preselected==1 && track_tagged==1 && track_is_pixel_track==1", 1, 1, 2],
#"h_tracks_tagged_long": ["track_reco", cuts[cut_label]["base_cuts"] + cuts[cut_label]["taggedextra"] + exact + " track_preselected==1 && track_tagged==1 && track_is_pixel_track==0", 1, 1, 2],
#h_tracks_tagged_short": ["track_reco", base_cutstagged + "track_tagged==1 && track_is_pixel_track==1", 1, 1, 2],
#"h_tracks_tagged_short": ["track_reco", base_cutstagged + "track_is_pixel_track==1", 1, 1, 2],
#"h_tracks_tagged_short": ["track_reco", base_cuts + exact + " abs(track_dxyVtx)<0.005 && abs(track_dzVtx)<0.005 && track_tagged==1 && track_pt>25 && track_is_pixel_track==1", 1, 1, 2],
#"h_tracks_tagged_short": ["track_reco", base_cuts + exact + base_cutstagged + " track_is_pixel_track==1", 1, 1, 2],
#"h_tracks_tagged_short": ["track_reco", exo + " && track_is_pixel_track==1", 1, 1, 2],
#"h_tracks_tagged_short": ["track_reco", base_cuts + "track_pt>25 && track_dxyVtx<0.001 && track_dzVtx<0.001 && track_tagged==1 && track_is_pixel_track==1", 1, 1, 2],
#"h_tracks_tagged_short": ["track_reco", "track_rereco==1 && track_is_pixel_track==1 && " + exo, 1, 1, 2],
}
categories = ["_short", "_long"]
# get all histos:
hists = {}
for period in periods:
if "Run201" in period and "rw" not in period:
is_data = False
else:
is_data = True
hists[period] = {}
for label in histolabels:
print period, label
filename = "%s/histograms%s_%s.root" % (histofolder, suffix,
period)
if options.get_from_tree:
tree = TChain("Events")
tree.Add(filename)
# apply weights before getting the histograms from the tree:
treecuts = "(%s)" % histolabels[label][1]
if not is_data and options.weightkinematic:
treecuts += "*weight_kinematicMLP2/(1.0-weight_kinematicMLP2)"
if not is_data and options.weighttrackprop:
treecuts += "*weight_trackpropMLP2/(1.0-weight_trackpropMLP2)"
if options.mc_reweighted:
treecuts += "*weight_ptreweighting"
hists[period][label] = plotting.get_histogram_from_tree(
tree,
histolabels[label][0],
cutstring=treecuts,
nBinsX=histolabels[label][2],
xmin=histolabels[label][3],
xmax=histolabels[label][4])
hists[period][label].SetDirectory(0)
hists[period][label].SetLineWidth(2)
else:
fin = TFile(filename, "open")
print "%s/histograms%s_%s.root" % (histofolder, suffix,
period)
hists[period][label] = fin.Get("Histograms/" + label)
hists[period][label].SetDirectory(0)
hists[period][label].SetLineWidth(2)
fin.Close()
shared_utils.histoStyler(hists[period][label])
# denom. fix:
for period in periods:
hists[period]["h_tracks_reco_short"] = hists[period][
"h_tracks_reco"].Clone()
hists[period]["h_tracks_reco_long"] = hists[period][
"h_tracks_reco"].Clone()
# global SF:
fitresults = {}
fitresults["fit_sf"] = {}
fitresults["fit_uncert"] = {}
fitresults["fit_sfreco"] = {}
fitresults["fit_uncertreco"] = {}
fitresults["fit_sftag"] = {}
fitresults["fit_uncerttag"] = {}
# calculated histograms:
finaleff_global = {}
finaleff_reco = {}
finaleff_tag = {}
finaleff_global_num = {}
finaleff_reco_num = {}
finaleff_tag_num = {}
finaleff_global_denom = {}
finaleff_reco_denom = {}
finaleff_tag_denom = {}
h_sf_global = {}
h_sf_reco = {}
h_sf_tag = {}
g1_global = {}
g1_reco = {}
g1_tag = {}
for category in categories:
# first, get efficiencies:
finaleff_global[category] = {}
finaleff_reco[category] = {}
finaleff_tag[category] = {}
finaleff_global_num[category] = {}
finaleff_reco_num[category] = {}
finaleff_tag_num[category] = {}
finaleff_global_denom[category] = {}
finaleff_reco_denom[category] = {}
finaleff_tag_denom[category] = {}
for period in periods:
finaleff_global_num[category][period] = hists[period][
"h_tracks_tagged" + category].Clone()
finaleff_global_denom[category][period] = hists[period][
"h_tracks_reco" + category].Clone()
finaleff_global[category][period] = finaleff_global_num[
category][period].Clone()
finaleff_global[category][period].Divide(
finaleff_global_denom[category][period])
finaleff_reco_num[category][period] = hists[period][
"h_tracks_rereco" + category].Clone()
finaleff_reco_denom[category][period] = hists[period][
"h_tracks_reco" + category].Clone()
finaleff_reco[category][period] = finaleff_reco_num[category][
period].Clone()
finaleff_reco[category][period].Divide(
finaleff_reco_denom[category][period])
finaleff_tag_num[category][period] = hists[period][
"h_tracks_tagged" + category].Clone()
finaleff_tag_denom[category][period] = hists[period][
"h_tracks_rereco" + category].Clone()
finaleff_tag[category][period] = finaleff_tag_num[category][
period].Clone()
finaleff_tag[category][period].Divide(
finaleff_tag_denom[category][period])
# get SF:
h_sf_global[category] = {}
h_sf_reco[category] = {}
h_sf_tag[category] = {}
g1_global[category] = {}
g1_reco[category] = {}
g1_tag[category] = {}
for period in periods:
print category, period
if "rw" in period: continue
if "Run201" not in period: continue
if not options.mc_reweighted:
if "Run2016" in period:
mcperiod = "Summer16"
elif "Run2017" in period:
mcperiod = "Fall17"
elif "Run2018" in period:
mcperiod = "Autumn18"
else:
if "Run2016" in period:
mcperiod = "Summer16rw" + period
elif "Run2017" in period:
mcperiod = "Fall17rw" + period
elif "Run2018" in period:
mcperiod = "Autumn18rw" + period
if not options.get_from_tree:
print "fitting global SF..."
g1_global[category][period] = TF1('g1_global', '[0]', 3,
20)
h_sf_global[category][period] = finaleff_global[category][
period].Clone()
h_sf_global[category][period].Divide(
finaleff_global[category][mcperiod])
fit = h_sf_global[category][period].Fit(
g1_global[category][period], "", "same", 3, 20)
fitresults["fit_sf"][
period +
category] = g1_global[category][period].GetParameter(0)
if "short" in category:
fitresults["fit_uncert"][
period + category] = h_sf_global[category][
period].GetBinError(4)
else:
fitresults["fit_uncert"][
period + category] = h_sf_global[category][
period].GetBinError(6)
print "fitting reco SF..."
g1_reco[category][period] = TF1('g1_reco', '[0]', 3, 20)
h_sf_reco[category][period] = finaleff_reco[category][
period].Clone()
h_sf_reco[category][period].Divide(
finaleff_reco[category][mcperiod])
fit = h_sf_reco[category][period].Fit(
g1_reco[category][period], "", "same", 3, 20)
fitresults["fit_sfreco"][
period +
category] = g1_reco[category][period].GetParameter(0)
if "short" in category:
fitresults["fit_uncertreco"][
period + category] = h_sf_reco[category][
period].GetBinError(4)
else:
fitresults["fit_uncertreco"][
period + category] = h_sf_reco[category][
period].GetBinError(6)
print "fitting tagging SF..."
g1_tag[category][period] = TF1('g1_tag', '[0]', 3, 20)
h_sf_tag[category][period] = finaleff_tag[category][
period].Clone()
h_sf_tag[category][period].Divide(
finaleff_tag[category][mcperiod])
fit = h_sf_tag[category][period].Fit(
g1_tag[category][period], "", "same", 3, 20)
fitresults["fit_sftag"][
period +
category] = g1_tag[category][period].GetParameter(0)
if "short" in category:
fitresults["fit_uncerttag"][
period +
category] = h_sf_tag[category][period].GetBinError(
4)
else:
fitresults["fit_uncerttag"][
period +
category] = h_sf_tag[category][period].GetBinError(
6)
else:
# using the tree:
h_sf_global[category][period] = finaleff_global[category][
period].Clone()
h_sf_global[category][period].Divide(
finaleff_global[category][mcperiod])
fitresults["fit_sf"][period + category] = h_sf_global[
category][period].GetBinContent(1)
fitresults["fit_uncert"][period + category] = h_sf_global[
category][period].GetBinError(1)
h_sf_reco[category][period] = finaleff_reco[category][
period].Clone()
h_sf_reco[category][period].Divide(
finaleff_reco[category][mcperiod])
fitresults["fit_sfreco"][period + category] = h_sf_reco[
category][period].GetBinContent(1)
fitresults["fit_uncertreco"][
period +
category] = h_sf_reco[category][period].GetBinError(1)
h_sf_tag[category][period] = finaleff_tag[category][
period].Clone()
h_sf_tag[category][period].Divide(
finaleff_tag[category][mcperiod])
fitresults["fit_sftag"][
period +
category] = h_sf_tag[category][period].GetBinContent(1)
fitresults["fit_uncerttag"][
period +
category] = h_sf_tag[category][period].GetBinError(1)
# Lumi-weighting:
if options.lumiweighted:
official_lumis = {
"Run2016B": 5.8,
"Run2016C": 2.6,
"Run2016D": 4.2,
"Run2016E": 4.0,
"Run2016F": 3.1,
"Run2016G": 7.5,
"Run2016H": 8.6,
"Run2017B": 4.8,
"Run2017C": 9.7,
"Run2017D": 4.3,
"Run2017E": 9.3,
"Run2017F": 13.5,
"Run2018A": 14,
"Run2018B": 7.1,
"Run2018C": 6.94,
"Run2018D": 31.93,
}
for runyear in ["Run2016", "Run2017", "Run2018"]:
print "runyear", runyear
fitresults["fit_sf"][runyear + category] = 0
fitresults["fit_uncert"][runyear + category] = 0
fitresults["fit_sfreco"][runyear + category] = 0
fitresults["fit_uncertreco"][runyear + category] = 0
fitresults["fit_sftag"][runyear + category] = 0
fitresults["fit_uncerttag"][runyear + category] = 0
sum_lumi = 0.0
for lumiyear in official_lumis:
if runyear in lumiyear:
sum_lumi += official_lumis[lumiyear]
fitresults["fit_sf"][
runyear + category] += fitresults["fit_sf"][
lumiyear +
category] * official_lumis[lumiyear]
fitresults["fit_uncert"][
runyear +
category] += fitresults["fit_uncert"][
lumiyear +
category] * official_lumis[lumiyear]
fitresults["fit_sfreco"][
runyear +
category] += fitresults["fit_sfreco"][
lumiyear +
category] * official_lumis[lumiyear]
fitresults["fit_uncertreco"][
runyear +
category] += fitresults["fit_uncertreco"][
lumiyear +
category] * official_lumis[lumiyear]
fitresults["fit_sftag"][
runyear + category] += fitresults["fit_sftag"][
lumiyear +
category] * official_lumis[lumiyear]
fitresults["fit_uncerttag"][
runyear +
category] += fitresults["fit_uncerttag"][
lumiyear +
category] * official_lumis[lumiyear]
print runyear, sum_lumi
fitresults["fit_sf"][runyear + category] /= sum_lumi
fitresults["fit_uncert"][runyear + category] /= sum_lumi
fitresults["fit_sfreco"][runyear + category] /= sum_lumi
fitresults["fit_uncertreco"][runyear +
category] /= sum_lumi
fitresults["fit_sftag"][runyear + category] /= sum_lumi
fitresults["fit_uncerttag"][runyear + category] /= sum_lumi
# efficiencies:
print "plot SF..."
output_rootfile = TFile(
"%s/allperiods_sf_combined.root" % (plotfolder), "recreate")
for category in ["short", "long"]:
canvas = shared_utils.mkcanvas()
legend = shared_utils.mklegend(x1=0.47, y1=0.65, x2=0.85, y2=0.85)
legend.SetHeader("%s tracks %s" %
(category, cuts[cut_label]["legendheader"]))
legend.SetTextSize(0.035)
h_sf_short = {}
h_sf_long = {}
for label in ["fit_sf", "fit_sfreco", "fit_sftag"]:
if options.lumiweighted:
h_sf_short[label] = TH1F("h_sf_short[label]", "", 3, 0, 3)
h_sf_long[label] = TH1F("h_sf_long[label]", "", 3, 0, 3)
else:
h_sf_short[label] = TH1F("h_sf_short[label]", "", 16, 0,
16)
h_sf_long[label] = TH1F("h_sf_long[label]", "", 16, 0, 16)
shared_utils.histoStyler(h_sf_short[label])
shared_utils.histoStyler(h_sf_long[label])
i_short = 0
i_long = 0
binlabels_short = []
binlabels_long = []
for i, period in enumerate(sorted(fitresults[label])):
sf = fitresults[label][period]
uncert = fitresults[label.replace("_sf",
"_uncert")][period]
if options.lumiweighted:
if period.split("_")[0] == "Run2016" or period.split(
"_")[0] == "Run2017" or period.split(
"_")[0] == "Run2018":
pass
else:
continue
print category, period
if "short" in period:
h_sf_short[label].SetBinContent(i_short + 1, sf)
h_sf_short[label].SetBinError(i_short + 1, uncert)
i_short += 1
binlabels_short.append(
period.replace("Run", "").replace("_short",
"").replace(
"_long", ""))
elif "long" in period:
h_sf_long[label].SetBinContent(i_long + 1, sf)
h_sf_long[label].SetBinError(i_long + 1, uncert)
i_long += 1
binlabels_long.append(
period.replace("Run", "").replace("_short",
"").replace(
"_long", ""))
if category == "short":
if label == "fit_sf":
h_sf_short[label].Draw("hist e")
else:
h_sf_short[label].Draw("hist e same")
outhist = h_sf_short[label].Clone()
outhist.SetDirectory(0)
outhist.SetName(label + "_" + category)
outhist.Write()
if "reco" in label:
h_sf_short[label].SetTitle(
";;fitted track reconstruction scale factor")
elif "tag" in label:
h_sf_short[label].SetTitle(
";;fitted track tagging scale factor")
else:
h_sf_short[label].SetTitle(";;fitted scale factor")
h_sf_short[label].GetYaxis().SetRangeUser(0.4, 1.6)
else:
if label == "fit_sf":
h_sf_long[label].Draw("hist e")
else:
h_sf_long[label].Draw("hist e same")
outhist = h_sf_long[label].Clone()
outhist.SetDirectory(0)
outhist.SetName(label + "_" + category)
outhist.Write()
if "reco" in label:
h_sf_long[label].SetTitle(
";;fitted track reconstruction scale factor")
elif "tag" in label:
h_sf_long[label].SetTitle(
";;fitted track tagging scale factor")
else:
h_sf_long[label].SetTitle(";;fitted scale factor")
h_sf_long[label].GetYaxis().SetRangeUser(0.4, 1.6)
if label == "fit_sf":
#h_sf_short[label].SetLineStyle(1)
#h_sf_long[label].SetLineStyle(1)
h_sf_short[label].SetLineWidth(3)
h_sf_long[label].SetLineWidth(3)
h_sf_short[label].SetLineColor(kRed)
h_sf_long[label].SetLineColor(kBlue)
if category == "short":
legend.AddEntry(h_sf_short[label], "combined SF")
else:
legend.AddEntry(h_sf_long[label], "combined SF")
if label == "fit_sfreco":
#h_sf_short[label].SetLineStyle(2)
#h_sf_long[label].SetLineStyle(2)
h_sf_short[label].SetLineColor(97)
h_sf_long[label].SetLineColor(62)
if category == "short":
legend.AddEntry(h_sf_short[label], "reconstruction SF")
else:
legend.AddEntry(h_sf_long[label], "reconstruction SF")
elif label == "fit_sftag":
h_sf_short[label].SetLineStyle(2)
h_sf_long[label].SetLineStyle(2)
h_sf_short[label].SetLineColor(97)
h_sf_long[label].SetLineColor(62)
if category == "short":
legend.AddEntry(h_sf_short[label], "tagging SF")
else:
legend.AddEntry(h_sf_long[label], "tagging SF")
for i, i_binlabel in enumerate(binlabels_short):
h_sf_short[label].GetXaxis().SetBinLabel(i + 1, i_binlabel)
for i, i_binlabel in enumerate(binlabels_long):
h_sf_long[label].GetXaxis().SetBinLabel(i + 1, i_binlabel)
if options.lumiweighted:
h_sf_short[label].GetXaxis().SetLabelSize(0.09)
h_sf_short[label].GetXaxis().SetTitleSize(0.09)
h_sf_long[label].GetXaxis().SetLabelSize(0.09)
h_sf_long[label].GetXaxis().SetTitleSize(0.09)
else:
h_sf_short[label].GetXaxis().SetTitleSize(0.045)
h_sf_short[label].GetXaxis().SetLabelSize(0.045)
h_sf_long[label].GetXaxis().SetTitleSize(0.045)
h_sf_long[label].GetXaxis().SetLabelSize(0.045)
legend.Draw()
shared_utils.stamp()
pdfname = "%s/allperiods_sf_combined_%s_%s.pdf" % (
plotfolder, category, cut_label)
if options.get_from_tree:
pdfname = pdfname.replace(".pdf", "_tree.pdf")
if options.mc_reweighted:
pdfname = pdfname.replace(".pdf", "_mcreweighted.pdf")
if options.lumiweighted:
pdfname = pdfname.replace(".pdf", "_lumiweighted.pdf")
if options.weightkinematic:
pdfname = pdfname.replace(".pdf", "_weightkinematic.pdf")
if options.weighttrackprop:
pdfname = pdfname.replace(".pdf", "_weighttrackprop.pdf")
canvas.SaveAs(pdfname)
#canvas.SaveAs(pdfname.replace(".pdf", ".root"))
#fout = TFile(pdfname.replace(".pdf", ".root"), "recreate")
#canvas.Write()
#h_sf_short.SetName("h_scalefactor_short")
#h_sf_short.Write()
#h_sf_long.SetName("h_scalefactor_long")
#h_sf_long.Write()
#fout.Close()
output_rootfile.Close()
if options.lumiweighted:
#this_periods = finaleff_global_num[category].keys()
this_periods = [
"Run2016",
"Run2017",
"Run2018",
]
else:
this_periods = periods
# plot underlying efficiencies and SFs:
if not options.lumiweighted and not options.get_from_tree:
for i_finaleff, finaleff in enumerate(
[finaleff_global, finaleff_reco, finaleff_tag]):
for year in ["2016", "2017", "2018"]:
for category in categories:
canvas = shared_utils.mkcanvas()
legend = shared_utils.mklegend(x1=0.6,
y1=0.6,
x2=0.85,
y2=0.85)
legend.SetHeader("%s tracks (%s)" %
(category.replace("_", ""), year))
legend.SetTextSize(0.035)
colors = [kBlack, 97, 94, 91, 86, 81, 70, 65, 61, 51]
for i_period, period in enumerate(this_periods):
if "rw" in period: continue
if year == "2016":
mcperiod = "Summer16"
elif year == "2017":
mcperiod = "Fall17"
elif year == "2018":
mcperiod = "Autumn18"
if period == mcperiod or year in period:
color = colors.pop(0)
if i_period == 0:
drawoption = "p e"
else:
drawoption = "hist e same"
print finaleff[category].keys()
if period != mcperiod:
finaleff[category][period].SetMarkerStyle(
20)
finaleff[category][period].SetMarkerColor(
color)
finaleff[category][period].Draw(drawoption)
finaleff[category][period].SetLineColor(color)
finaleff[category][period].SetLineStyle(1)
finaleff[category][period].SetTitle(
";number of remaining tracker layers;efficiency, scale factor"
)
finaleff[category][period].GetXaxis(
).SetRangeUser(0, 20)
finaleff[category][period].GetYaxis(
).SetRangeUser(0.4, 1.6)
legend.AddEntry(finaleff[category][period],
period)
# include scale factor with fit result...:
if "Run" in period and "rw" not in period:
if i_finaleff == 0:
h_sf_global[category][period].Draw(
"e same")
h_sf_global[category][
period].SetLineColor(color)
h_sf_global[category][
period].SetLineWidth(2)
g1_global[category][period].Draw(
"same")
g1_global[category][
period].SetLineColor(color)
g1_global[category][
period].SetLineWidth(2)
#legend.AddEntry(g1_global[category][period], "scale factor")
elif i_finaleff == 1:
h_sf_reco[category][period].Draw(
"e same")
h_sf_reco[category][
period].SetLineColor(color)
h_sf_reco[category][
period].SetLineWidth(2)
g1_reco[category][period].Draw("same")
g1_reco[category][period].SetLineColor(
color)
g1_reco[category][period].SetLineWidth(
2)
#legend.AddEntry(g1_reco[category][period], "scale factor")
elif i_finaleff == 2:
h_sf_tag[category][period].Draw(
"e same")
h_sf_tag[category][
period].SetLineColor(color)
h_sf_tag[category][
period].SetLineWidth(2)
g1_tag[category][period].Draw("same")
g1_tag[category][period].SetLineColor(
color)
g1_tag[category][period].SetLineWidth(
2)
#legend.AddEntry(g1_tag[category][period], "scale factor")
#finaleff_global_num[category][period].Draw("e same")
#finaleff_global_num[category][period].SetLineColor(color)
#finaleff_global_num[category][period].SetLineWidth(2)
#finaleff_global_denom[category][period].Draw("e same")
#finaleff_global_denom[category][period].SetLineColor(color)
#finaleff_global_denom[category][period].SetLineWidth(2)
#finaleff_global_denom[category][period].SetLineStyle(2)
legend.Draw()
shared_utils.stamp()
if i_finaleff == 0:
pdfname = "%s/underlying%s%s.pdf" % (
plotfolder, category, year)
elif i_finaleff == 1:
pdfname = "%s/underlying_reco%s%s.pdf" % (
plotfolder, category, year)
elif i_finaleff == 2:
pdfname = "%s/underlying_tag%s%s.pdf" % (
plotfolder, category, year)
if options.get_from_tree:
pdfname = pdfname.replace(".pdf", "_tree.pdf")
if options.mc_reweighted:
pdfname = pdfname.replace(".pdf",
"_mcreweighted.pdf")
if options.lumiweighted:
pdfname = pdfname.replace(".pdf",
"_lumiweighted.pdf")
#if not plot_sf:
# pdfname = pdfname.replace(".pdf", "_numdenom.pdf")
canvas.SaveAs(pdfname)
def plot_cutflow(files, header, is_signal, prefix):
if is_signal:
signal_cutstring = " && tracks_chiCandGenMatchingDR<0.01"
else:
signal_cutstring = ""
histos = {}
for label in cuts:
histos[label] = TH1D(label, label, 20, 0, 20)
# get consecutive cuts:
cuts_consecutive = {}
for label in cuts:
cuts_consecutive[label] = []
for i, cut in enumerate(cuts[label]):
cuts_consecutive[label].append(" && ".join(cuts[label][:i + 1]))
# get nev:
counts = {}
for label in cuts_consecutive:
counts[label] = []
for i, cut in enumerate(cuts_consecutive[label]):
if "p1" in prefix:
#FIXME phase 1 dE/dx
cut = cut.replace("tracks_deDxHarmonic2pixel>2.0", "MHT>=0")
h_tmp = plotting.get_all_histos(files,
"Events",
"tracks_is_pixel_track",
cut + signal_cutstring,
nBinsX=2,
xmin=0,
xmax=2)
if h_tmp:
count = h_tmp.Integral()
else:
count = 0
counts[label].append(count)
histos[label].Fill(i, count)
# normalize histos:
for label in histos:
normalization = histos[label].GetBinContent(1)
if normalization > 0:
histos[label].Scale(1.0 / normalization)
# set alphanumeric x-axis labels:
for label in histos:
for i in range(histos[label].GetNbinsX()):
if i <= len(cuts[label]):
binlabel = cuts[label][i - 1]
if "tracks_is_pixel_track" in binlabel: binlabel = "category"
elif "tracks_ptErrOverPt2" in binlabel:
binlabel = "#Delta p_{T}"
elif "tracks_neutralPtSum/tracks_pt" in binlabel:
binlabel = "nt. #Sigma p_{T}/pT"
elif "tracks_neutralPtSum" in binlabel:
binlabel = "nt. pTSum"
elif "tracks_chargedPtSum/tracks_pt" in binlabel:
binlabel = "ch. #Sigma p_{T}/pT"
elif "tracks_chargedPtSum" in binlabel:
binlabel = "ch. #Sigma p_{T}"
elif "tracks_pt" in binlabel:
binlabel = "p_{T}"
elif "tracks_passmask" in binlabel:
binlabel = "mask"
elif "tracks_trackQualityHighPurity" in binlabel:
binlabel = "purity"
elif "tracks_eta" in binlabel:
binlabel = "#eta"
elif "tracks_dzVtx" in binlabel:
binlabel = "d_{z}"
elif "tracks_dxyVtx" in binlabel:
binlabel = "d_{xy}"
elif "tracks_trkRelIso" in binlabel:
binlabel = "relIso"
elif "tracks_trackerLayersWithMeasurement" in binlabel:
binlabel = "layers"
elif "tracks_nValidTrackerHits" in binlabel:
binlabel = "tracker hits"
elif "tracks_nMissingInnerHits" in binlabel:
binlabel = "miss. inner hits"
elif "tracks_nValidPixelHits" in binlabel:
binlabel = "pixel hits"
elif "tracks_passPFCandVeto" in binlabel:
binlabel = "PFCand"
elif "tracks_passleptonveto" in binlabel:
binlabel = "lepton veto"
elif "tracks_passpionveto" in binlabel:
binlabel = "pion veto"
elif "tracks_passjetveto" in binlabel:
binlabel = "jet veto"
elif "tracks_deDxHarmonic2pixel" in binlabel:
binlabel = "dE/dx"
elif "tracks_mva_tight_may20_chi2" in binlabel:
binlabel = "BDT"
elif "tracks_matchedCaloEnergy/tracks_p" in binlabel:
binlabel = "EDep/track p"
elif "tracks_nMissingOuterHits" in binlabel:
binlabel = "miss. outer hits"
elif "tracks_nMissingMiddleHits" in binlabel:
binlabel = "miss. middle hits"
elif "tracks_exo_leptoniso" in binlabel:
binlabel = "lepton iso"
elif "tracks_exo_trackiso" in binlabel:
binlabel = "track iso"
elif "tracks_exo_jetiso" in binlabel:
binlabel = "jet iso"
elif "tracks_matchedCaloEnergy" in binlabel:
binlabel = "EDep"
elif "tracks_mt2_leptoniso" in binlabel:
binlabel = "lepton iso"
elif "tracks_mt2_trackiso" in binlabel:
binlabel = "track iso"
elif "tracks_pixelLayersWithMeasurement" in binlabel:
binlabel = "pixel layers"
histos[label].GetXaxis().SetBinLabel(i, binlabel)
for label in histos:
if "long" in label: continue
canvas = TCanvas("c1", "c1", 1000, 630)
canvas.SetBottomMargin(.16)
canvas.SetLeftMargin(.14)
canvas.SetGrid()
if "bg" in prefix:
canvas.SetLogy()
if is_signal:
legend = shared_utils.mklegend(x1=0.17, y1=0.17, x2=0.4, y2=0.4)
else:
legend = shared_utils.mklegend(x1=0.6, y1=0.7, x2=0.9, y2=0.9)
legend.SetTextSize(0.03)
shared_utils.histoStyler(histos[label])
histos[label].Draw("hist")
histos[label].SetLineColor(kRed)
histos[label].SetTitle(";;percentage of tracks remaining")
legend.AddEntry(histos[label], "short tracks")
if "bg" in prefix:
histos[label].GetYaxis().SetRangeUser(1e-4, 2e0)
else:
histos[label].GetYaxis().SetRangeUser(0, 1.1)
label_long = label.replace("short", "long")
shared_utils.histoStyler(histos[label_long])
histos[label_long].Draw("hist same")
histos[label_long].SetLineColor(kBlue)
histos[label_long].SetTitle(";cut stage;tracks")
legend.AddEntry(histos[label_long], "long tracks")
legend.SetTextSize(0.045)
legend.SetHeader(header + ", " +
label.replace("_short", "").replace("_", " ") +
" tag")
legend.Draw()
shared_utils.stamp()
batchname = files[0].split("/")[2]
canvas.Print("plots/cutflow_" + batchname + "_" + prefix + "_" +
label.replace("_short", "") + ".pdf")