def get_histogram(samples, proc, cut, variable, applysf=False):
# Check the requested process
available_processes = ["data", "top", "w", "dy", "qcd"]
if proc not in available_processes:
print "ERROR: unrecognized process", proc
sys.exit(-1)
# Check the cut to determine whether this is one lepton process or two lepton
# This is because the ntuple used for one lepton and two lepton is different
# Therefore one need to check this to retrieve the right mc ntuple
isonelep = cut.find("One") != -1
# Checking the flavor of the lepton this histogram retreival is interested in
ismu = cut.find("Mu") != -1
# Checking if it is a 2d histogram
# The convention is that the histogram names in loop.py all the 2d histogram must have "_vs_" name
is2d = variable.find("_vs_") != -1
# Checking if it is a three lepton fake rate channel
# The convention is that if it is a three lepton related it will have "3l" in the cut name
is3l = cut.find("3l") != -1
# Setting the proc path
tqprocpath = "/"
if proc == "data": tqprocpath = "/data/mm" if ismu else "/data/ee"
if proc == "top": tqprocpath = "/top"
if proc == "w": tqprocpath = "/Wonelep" if isonelep else "/W/HT"
if proc == "dy": tqprocpath = "/Zonelep" if isonelep else "/Z"
if proc == "qcd": tqprocpath = "/qcd/mu" if ismu else "/qcd/el"
# Setting the nice name
nicename = "/"
if proc == "data": nicename = "Data"
if proc == "top": nicename = "t#bar{t}"
if proc == "w": nicename = "W"
if proc == "dy": nicename = "DY"
if proc == "qcd": nicename = "QCD"
# Retrieve the histograms
hist = samples.getHistogram(tqprocpath, "{}/{}".format(cut, variable)).Clone(nicename)
# Apply ewk sf
if applysf:
sf = 1.0
if samples == samples_cn: sf = (musf_cn if ismu else elsf_cn) if not is3l else (mu3lsf_cn if ismu else el3lsf_cn)
if samples == samples_up: sf = (musf_up if ismu else elsf_up) if not is3l else (mu3lsf_up if ismu else el3lsf_up)
if samples == samples_dn: sf = (musf_dn if ismu else elsf_dn) if not is3l else (mu3lsf_dn if ismu else el3lsf_dn)
if proc == "top": hist.Scale(sf)
if proc == "w" : hist.Scale(sf)
if proc == "dy" : hist.Scale(sf)
# If 2D plot, flatten it
if is2d:
hist = p.flatten_th2(hist)
return hist
def plot_stack(histname,
output_name,
systs=None,
options={},
plotfunc=p.plot_hist):
ismu = str(histname).find("Mu") != -1
is2d = str(histname).find("_vs_") != -1
# Options
alloptions = {
"ratio_range": [0.4, 1.6],
#"nbins": 30,
#"autobin": True,
"legend_scalex": 1.0,
"legend_scaley": 1.0,
"legend_smart": True,
"legend_alignment": "topleft",
"output_name": "{}/{}_stack.pdf".format(output_plot_dir, output_name),
"bkg_sort_method": "unsorted",
"divide_by_bin_width":
True if output_name.find("varbin") != -1 else False,
"yaxis_log": True if output_name.find("varbin") != -1 else False,
"yaxis_range": [1e3, 1e10] if output_name.find("varbin") != -1 else [],
}
alloptions.update(options)
sigs = []
bgs = [
samples.getHistogram("/top", histname).Clone("Top") if not is2d else
p.flatten_th2(samples.getHistogram("/top", histname).Clone("Top")),
samples.getHistogram("/Zonelep", histname).Clone("DY") if not is2d else
p.flatten_th2(samples.getHistogram("/Zonelep", histname).Clone("DY")),
samples.getHistogram("/Wonelep", histname).Clone("W") if not is2d else
p.flatten_th2(samples.getHistogram("/Wonelep", histname).Clone("W")),
samples.getHistogram("/qcd/mu" if ismu else "/qcd/el",
histname).Clone("QCD")
if not is2d else p.flatten_th2(
samples.getHistogram("/qcd/mu" if ismu else "/qcd/el",
histname).Clone("QCD")),
]
dataname = "/data"
if histname.find("El") != -1: dataname = "/data/ee"
if histname.find("Mu") != -1: dataname = "/data/mm"
data = samples.getHistogram(
dataname, histname).Clone("Data") if not is2d else p.flatten_th2(
samples.getHistogram(dataname, histname).Clone("Data"))
if histname.find("HLT") != -1:
totalbkg = p.get_total_hist(bgs)
ratio = samples.getHistogram(
dataname, histname).Clone("Data") if not is2d else p.flatten_th2(
samples.getHistogram(dataname, histname).Clone("Data"))
ratio.Rebin(ratio.GetNbinsX())
totalbkg.Rebin(totalbkg.GetNbinsX())
totalbkg.Divide(ratio)
print histname
totalbkg.Print("all")
if (histname.find("OneMuTightEWKCR3NoNvtxRewgt/nvtx") != -1
or histname.find("OneElTightEWKCR3NoNvtxRewgt/nvtx") != -1
) and options["nbins"] != 5:
totalbkg = p.get_total_hist(bgs[:-1])
nvtxweight = samples.getHistogram(
dataname,
histname).Clone(output_name) if not is2d else p.flatten_th2(
samples.getHistogram(dataname, histname).Clone(output_name))
scale = nvtxweight.Integral() / totalbkg.Integral()
nvtxweight.Divide(totalbkg)
nvtxweight.Scale(1. / scale)
#nvtxweight.Print("all")
#f = ROOT.TFile("{}.root".format(output_name),"recreate")
#nvtxweight.Write()
#f.Close()
if options["nbins"] == 5 and histname.find("TightEWKCR/MT") != -1:
totalbkg = p.get_total_hist(bgs[:-1])
ratio = samples.getHistogram(
dataname, histname).Clone("Data") if not is2d else p.flatten_th2(
samples.getHistogram(dataname, histname).Clone("Data"))
ratio.Rebin(4)
totalbkg.Rebin(4)
ratio.Divide(totalbkg)
print "EWK NF", histname, ratio.GetBinContent(3), ratio.GetBinError(3)
colors = [2005, 2003, 2001, 920]
sf = musf if ismu else elsf
if histname.find("MTOneLep") == -1:
bgs[0].Scale(sf)
bgs[1].Scale(sf)
bgs[2].Scale(sf)
if histname.find("HLT") != -1:
bgs = bgs[:-1]
plotfunc(sigs=sigs,
bgs=bgs,
data=data,
colors=colors,
syst=systs,
options=alloptions)
def fakerate2dcomp(histname,
output_name,
systs=None,
options={},
plotfunc=p.plot_hist):
if str(histname).find("Loose") == -1: return
if str(histname).find("One") == -1: return
if str(histname).find("corr") == -1: return
ismu = str(histname).find("Mu") != -1
qcdsample = "/qcd/mu" if ismu else ((
"/qcd/el/bcToE" if not doW else "/qcd/el/EM")
if not docombinedqcdel else "/qcd/el")
ttbarsample = testsample
qcdsamplename = "QCD" if ismu else "QCD"
ttbarsamplename = testsamplename
qcdhistname = histname
ttbarhistname = histname.replace("One", "Two")
ttbarhistname = ttbarhistname.replace(
"lep_", "mu_") if ismu else ttbarhistname.replace("lep_", "el_")
#print qcdhistname, ttbarhistname
qcdhistnum = p.flatten_th2(
samples.getHistogram(qcdsample,
str(qcdhistname).replace(
"Loose", "Tight")).Clone(qcdsamplename))
qcdhistden = p.flatten_th2(
samples.getHistogram(qcdsample, qcdhistname).Clone(qcdsamplename))
ttbarhistnum = p.flatten_th2(
samples.getHistogram(ttbarsample,
str(ttbarhistname).replace(
"Loose", "Tight")).Clone(ttbarsamplename))
ttbarhistden = p.flatten_th2(
samples.getHistogram(ttbarsample,
ttbarhistname).Clone(ttbarsamplename))
qcdhistnum.Divide(qcdhistden)
ttbarhistnum.Divide(ttbarhistden)
# Options
alloptions = {
"ratio_range": [0.0, 2.0 if ismu else 3.0],
"yaxis_range": [0.0, 0.35 if ismu else (0.6 if not doW else 1.0)],
#"nbins": 30,
"draw_points": True,
"autobin": True,
"legend_scalex": 0.8,
"legend_scaley": 0.8,
"legend_smart": True,
"legend_alignment": "topleft",
"legend_datalabel": testsamplelegendname,
"output_name": "{}/fr_closure_{}.pdf".format(output_plot_dir,
output_name)
}
#samples.getHistogram(sample, str(histname).replace("Loose", "Tight")).Clone(samplename).Print("all")
#histnum = p.flatten_th2(samples.getHistogram(sample, str(histname).replace("Loose", "Tight")).Clone(samplename))
#histden = p.flatten_th2(samples.getHistogram(sample, histname).Clone(samplename))
alloptions.update(options)
sigs = []
bgs = [qcdhistnum]
data = ttbarhistnum
colors = [2]
plotfunc(sigs=sigs,
bgs=bgs,
data=data,
colors=colors,
syst=systs,
options=alloptions)
def fakerate2d(histname,
output_name,
systs=None,
options={},
plotfunc=p.plot_hist):
if str(histname).find("Loose") == -1: return
isqcd = str(histname).find("One") != -1
ismu = str(histname).find("Mu") != -1
ispredict = str(histname).find("Predict") != -1
sample = testsample
if isqcd and ismu:
sample = "/qcd/mu"
elif isqcd and not ismu:
if doW:
sample = "/qcd/el/EM"
else:
sample = "/qcd/el/bcToE"
#sample = "/qcd/el"
if docombinedqcdel:
sample = "/qcd/el"
samplename = (
("t#bar{t} estimation" if not doW else "W estimation") if
not docombinedqcdel else "W + t#bar{t} estimation") if ispredict else (
"QCD Loose" if isqcd else
("ttbar Loose" if not doW else "W Loose"))
color = 920 if isqcd else (2005 if not doW else 2001)
# Options
alloptions = {
"ratio_range": [0.0, 2.0] if ispredict else [0.0, 0.3],
#"nbins": 30,
"autobin":
True,
"legend_scalex":
0.8,
"legend_scaley":
0.8,
"legend_smart":
True,
"legend_alignment":
"topleft",
"legend_datalabel":
samplename.replace("estimation", "prediction")
if ispredict else samplename.replace("Loose", "Tight"),
"output_name":
"{}/fr_{}.pdf".format(output_plot_dir, output_name),
"hist_disable_xerrors":
True if str(histname).find("varbin") != -1 else False,
}
#samples.getHistogram(sample, str(histname).replace("Loose", "Tight")).Clone(samplename).Print("all")
histnum = p.flatten_th2(
samples.getHistogram(sample,
str(histname).replace("Loose",
"Tight")).Clone(samplename))
histden = p.flatten_th2(
samples.getHistogram(sample, histname).Clone(samplename))
alloptions.update(options)
sigs = []
bgs = [histden]
data = histnum
colors = [color]
if docombinedqcdel:
bgs = [
p.flatten_th2(
samples.getHistogram("/top",
histname).Clone("t#bar{t} estimation")),
p.flatten_th2(
samples.getHistogram("/W/HT", histname).Clone("W estimation"))
]
colors = [2005, 2001]
plotfunc(sigs=sigs,
bgs=bgs,
data=data,
colors=colors,
syst=systs,
options=alloptions)
def plot_datadriven_fakeratecomp(histname,
output_name,
systs=None,
options={},
plotfunc=p.plot_hist):
if str(histname).find("Loose") == -1: return
ismu = str(histname).find("Mu") != -1
is2d = str(histname).find("_vs_") != -1
# Options
alloptions = {
"ratio_range": [0.0, 2.0],
#"nbins": 30,
"autobin":
True,
"legend_scalex":
1.0,
"legend_scaley":
1.0,
"legend_smart":
True,
"legend_alignment":
"topleft",
"legend_datalabel":
"Data FR",
"output_name":
"{}/{}_fakeratecomp.pdf".format(output_plot_dir, output_name),
"bkg_sort_method":
"unsorted",
"draw_points":
True,
}
alloptions.update(options)
sigs = []
# Loose
bgs = [
samples.getHistogram("/top", histname).Clone("Top") if not is2d else
p.flatten_th2(samples.getHistogram("/top", histname).Clone("Top")),
samples.getHistogram("/Zonelep", histname).Clone("DY") if not is2d else
p.flatten_th2(samples.getHistogram("/Zonelep", histname).Clone("DY")),
samples.getHistogram("/Wonelep", histname).Clone("W") if not is2d else
p.flatten_th2(samples.getHistogram("/Wonelep", histname).Clone("W")),
#samples.getHistogram("/qcd/mu" if ismu else "/qcd/el", histname).Clone("QCD") if not is2d else p.flatten_th2(samples.getHistogram("/qcd/mu" if ismu else "/qcd/el", histname).Clone("QCD")),
]
dataname = "/data"
if histname.find("El") != -1: dataname = "/data/ee"
if histname.find("Mu") != -1: dataname = "/data/mm"
ddqcd = samples.getHistogram(
dataname, histname).Clone("Data") if not is2d else p.flatten_th2(
samples.getHistogram(dataname, histname).Clone("Data"))
qcd = samples.getHistogram(
"/qcd/mu" if ismu else "/qcd/el",
histname).Clone("QCD") if not is2d else p.flatten_th2(
samples.getHistogram("/qcd/mu" if ismu else "/qcd/el",
histname).Clone("QCD"))
totalbkg = p.get_total_hist(bgs)
# tight
bgstight = [
samples.getHistogram(
"/top",
str(histname).replace(
"Loose", "Tight")).Clone("Top") if not is2d else p.flatten_th2(
samples.getHistogram(
"/top",
str(histname).replace("Loose", "Tight")).Clone("Top")),
samples.getHistogram(
"/Zonelep",
str(histname).replace(
"Loose", "Tight")).Clone("DY") if not is2d else p.flatten_th2(
samples.getHistogram(
"/Zonelep",
str(histname).replace("Loose", "Tight")).Clone("DY")),
samples.getHistogram(
"/Wonelep",
str(histname).replace(
"Loose", "Tight")).Clone("W") if not is2d else p.flatten_th2(
samples.getHistogram(
"/Wonelep",
str(histname).replace("Loose", "Tight")).Clone("W")),
#samples.getHistogram("/qcd/mu" if ismu else "/qcd/el", str(histname).replace("Loose","Tight")).Clone("QCD") if not is2d else p.flatten_th2(samples.getHistogram("/qcd/mu" if ismu else "/qcd/el", str(histname).replace("Loose","Tight")).Clone("QCD")),
]
dataname = "/data"
if str(histname).replace("Loose", "Tight").find("El") != -1:
dataname = "/data/ee"
if str(histname).replace("Loose", "Tight").find("Mu") != -1:
dataname = "/data/mm"
ddqcdtight = samples.getHistogram(
dataname,
str(histname).replace(
"Loose", "Tight")).Clone("Data") if not is2d else p.flatten_th2(
samples.getHistogram(dataname,
str(histname).replace(
"Loose", "Tight")).Clone("Data"))
qcdtight = samples.getHistogram(
"/qcd/mu" if ismu else "/qcd/el",
str(histname).replace(
"Loose", "Tight")).Clone("QCD") if not is2d else p.flatten_th2(
samples.getHistogram("/qcd/mu" if ismu else "/qcd/el",
str(histname).replace(
"Loose", "Tight")).Clone("QCD"))
totalbkgtight = p.get_total_hist(bgstight)
# Get data fakerate
p.remove_overflow(totalbkg)
p.remove_overflow(totalbkgtight)
p.remove_overflow(ddqcd)
p.remove_overflow(ddqcdtight)
sf = -musf if ismu else -elsf
ddqcd.Add(totalbkg, sf)
ddqcdtight.Add(totalbkgtight, sf)
ddqcdtight.Divide(ddqcd)
# Get QCD fakerate
p.remove_overflow(qcd)
p.remove_overflow(qcdtight)
qcdtight.Divide(qcd)
bgs = [qcdtight.Clone("QCD FR")]
data = ddqcdtight
# Compute data driven fake rate
colors = [2]
plotfunc(sigs=sigs,
bgs=bgs,
data=data,
colors=colors,
syst=systs,
options=alloptions)