def el_iso(binname=""):
# Glob the file lists
mcs = [
output_dirpath + "/wj_ht.root",
output_dirpath + "/tt_1l.root",
]
qcds = [
output_dirpath + "/qcd_em.root",
output_dirpath + "/qcd_bc.root",
]
bkg_hist = "ElClosureLoose{}__eliso".format(binname)
qcd_hist = "OneElMR{}__iso".format(binname)
h_bkg = ru.get_summed_histogram(mcs, bkg_hist)
h_qcd = ru.get_summed_histogram(qcds, qcd_hist)
h_qcd_em = ru.get_summed_histogram([output_dirpath + "/qcd_em.root"],
qcd_hist)
h_qcd_bc = ru.get_summed_histogram([output_dirpath + "/qcd_bc.root"],
qcd_hist)
h_bkg.Scale(1. / h_bkg.Integral())
h_qcd.Scale(1. / h_qcd.Integral())
h_qcd_em.Scale(1. / h_qcd_em.Integral())
h_qcd_bc.Scale(1. / h_qcd_bc.Integral())
h_qcd.SetName("QCD(e)")
h_qcd_em.SetName("QCD(LF)")
h_qcd_bc.SetName("QCD(HF)")
# Color settings
colors = [
2001,
]
# Options
alloptions = {
"ratio_range": [0.0, 2.0],
"nbins": 10,
"autobin": False,
"legend_scalex": 1.8,
"legend_scaley": 1.1,
"output_name": "plots/eliso/{}.pdf".format(bkg_hist + "__" + qcd_hist),
"bkg_sort_method": "unsorted",
"no_ratio": False,
"print_yield": True,
"yield_prec": 3,
"draw_points": True,
"lumi_value": 41.3,
"legend_datalabel": "W+t#bar{t}",
}
# Plot them
p.plot_hist(sigs=[h_qcd_em, h_qcd_bc],
bgs=[h_qcd],
data=h_bkg,
colors=colors,
syst=None,
options=alloptions)
def closure_plot(predict, estimate):
# Glob the file lists
bkg_list_wjets = [output_dirpath + "/wj_ht.root"]
bkg_list_ttbar = [output_dirpath + "/tt_1l.root"]
# Get all the histogram objects
h_wjets_predict = ru.get_summed_histogram(bkg_list_wjets, predict)
h_ttbar_predict = ru.get_summed_histogram(bkg_list_ttbar, predict)
h_wjets_estimate = ru.get_summed_histogram(bkg_list_wjets, estimate)
h_ttbar_estimate = ru.get_summed_histogram(bkg_list_ttbar, estimate)
# Set the names of the histograms
h_wjets_predict.SetName("W predict")
h_ttbar_predict.SetName("Top predict")
h_wjets_estimate.SetName("W estimate")
h_ttbar_estimate.SetName("Top estimate")
# Color settings
colors = [
2005,
2001,
]
# Options
alloptions = {
"ratio_range": [0.0, 2.0],
"nbins": 1,
"autobin": False,
"legend_scalex": 1.8,
"legend_scaley": 1.1,
"output_name":
"plots/closure/{}.pdf".format(predict + "__" + estimate),
"bkg_sort_method": "unsorted",
"no_ratio": False,
"print_yield": False,
"yaxis_log": True if "ptcorr" in predict else False,
"legend_smart": False if "ptcorr" in predict else True,
"lumi_value": 41.3,
"legend_datalabel": "Estimate",
"yield_prec": 3,
"print_yield": True,
}
# The bkg histogram list
bgs_list = [
h_ttbar_predict,
h_wjets_predict,
]
#bgs_list = [ h_ttbar_predict ]
h_estimate = h_wjets_estimate.Clone("Estimate")
h_estimate.Add(h_ttbar_estimate)
# Plot them
p.plot_hist(bgs=bgs_list,
data=h_estimate,
colors=colors,
syst=None,
options=alloptions)
def el_fakerate():
# Glob the file lists
mcs = [
output_dirpath + "/wj_ht.root",
output_dirpath + "/tt_1l.root",
]
num = "ElClosureTight__elptcorretarolledcoarse"
den = "ElClosureLoose__elptcorretarolledcoarse"
h_num = ru.get_summed_histogram(mcs, num)
h_den = ru.get_summed_histogram(mcs, den)
u.move_in_overflows(h_num)
u.move_in_overflows(h_den)
h_num.Divide(h_den)
qcds = [
output_dirpath + "/qcd_em.root",
output_dirpath + "/qcd_bc.root",
]
qcd_num = "OneElTightMR__elptcorretarolledcoarse"
qcd_den = "OneElMR__elptcorretarolledcoarse"
h_qcd_num = ru.get_summed_histogram(qcds, qcd_num)
h_qcd_den = ru.get_summed_histogram(qcds, qcd_den)
u.move_in_overflows(h_qcd_num)
u.move_in_overflows(h_qcd_den)
h_qcd_num.Divide(h_qcd_den)
h_qcd_num.SetName("QCD(e)")
# Color settings
colors = [
2005,
2001,
]
# Options
alloptions = {
"ratio_range": [0.0, 2.0],
"nbins": 180,
"autobin": False,
"legend_scalex": 1.8,
"legend_scaley": 1.1,
"output_name": "plots/fakeratemc/{}.pdf".format(num + "__" + den),
"bkg_sort_method": "unsorted",
"no_ratio": False,
"print_yield": True,
"yield_prec": 3,
"draw_points": True,
"lumi_value": 41.3,
"legend_datalabel": "W+t#bar{t}"
}
# Plot them
p.plot_hist(bgs=[h_qcd_num],
data=h_num,
colors=colors,
syst=None,
options=alloptions)
def main():
output_dirpath = "outputs/HWW2016_skimmed_v17_v1.15.1/"
histnames = [
"CutISR400ElPlusRecoClassA__yield",
"CutISR400ElPlusRecoClassB__yield",
"CutISR400ElMinusRecoClassA__yield",
"CutISR400ElMinusRecoClassB__yield",
"CutISR400MuPlusRecoClassA__yield",
"CutISR400MuPlusRecoClassB__yield",
"CutISR400MuMinusRecoClassA__yield",
"CutISR400MuMinusRecoClassB__yield",
]
histnames = [
#"CutISR400ElPlusRecoClassAStrawMan__yield",
#"CutISR400ElPlusRecoClassBStrawMan__yield",
#"CutISR400ElMinusRecoClassAStrawMan__yield",
#"CutISR400ElMinusRecoClassBStrawMan__yield",
"CutISR400MuPlusRecoClassAStrawMan__yield",
"CutISR400MuPlusRecoClassBStrawMan__yield",
"CutISR400MuMinusRecoClassAStrawMan__yield",
"CutISR400MuMinusRecoClassBStrawMan__yield",
]
histnames = [
"CutISR300MuMinusRecoClassB__recowhad_mlep_puppi_mass",
]
bkg_list_ttbar = glob.glob(output_dirpath + "/TT*.root")
bkg_list_wjets = glob.glob(output_dirpath + "/WJets*.root")
bkg_list_hww = glob.glob(output_dirpath +
"/Glu*.root") + glob.glob(output_dirpath +
"/VBF*.root")
#data_list = glob.glob(output_dirpath+"/data_*.root")
h_wjets = ru.get_summed_histogram(bkg_list_wjets, histnames)
h_ttbar = ru.get_summed_histogram(bkg_list_ttbar, histnames)
h_hww = ru.get_summed_histogram(bkg_list_hww, histnames)
#h_data = ru.get_summed_histogram(data_list , histnames)
h_wjets.SetName("W")
h_ttbar.SetName("t#bar{t}")
h_hww.SetName("HWW (x10)")
colors = [2005, 2001, 2]
alloptions = {
"ratio_range": [0.0, 2.0],
"nbins": 60,
"autobin": False,
"legend_scalex": 1.8,
"legend_scaley": 1.1,
"output_name": "plots/test.pdf",
"bkg_sort_method": "unsorted",
"no_ratio": True,
"print_yield": True,
"signal_scale": 500,
}
p.plot_hist(
sigs=[h_hww],
bgs=[h_ttbar, h_wjets, h_hww.Clone("HWW (stacked)")],
#data = h_data,
data=None,
colors=colors,
syst=None,
options=alloptions)
def get_hists(histnames, use_data_driven_fakes=False):
bkg_lists = {}
bkg_lists["lostlep"] = [
x for x in glob.glob(output_dirpath + "/lostlep.root")
]
bkg_lists["photon"] = [
x for x in glob.glob(output_dirpath + "/photon.root")
]
bkg_lists["qflip"] = [x for x in glob.glob(output_dirpath + "/qflip.root")]
bkg_lists["ddfakes"] = [
x for x in glob.glob(output_dirpath + "/ddfakes.root")
]
bkg_lists["ewksubt"] = [
x for x in glob.glob(output_dirpath + "/ewksubt.root")
]
bkg_lists["mcfakes"] = [
x for x in glob.glob(output_dirpath + "/fakes.root")
]
bkg_lists["prompt"] = [
x for x in glob.glob(output_dirpath + "/prompt.root")
]
bkg_lists["fakes"] = bkg_lists[
"ddfakes"] if use_data_driven_fakes else bkg_lists["mcfakes"]
sig_list = glob.glob(output_dirpath + "/*t_www_*/*.root")
vh_list = glob.glob(output_dirpath + "/*VH*t_www_*/*.root")
www_list = glob.glob(output_dirpath + "/*WWW*t_www_*/*.root")
data_list = glob.glob(output_dirpath + "/*Run2017*_t_ss*/*.root")
sig_list = glob.glob(output_dirpath + "/signal.root")
data_list = glob.glob(output_dirpath + "/data.root")
hists = {}
if "__" in histnames[0]:
hists["lostlep"] = ru.get_summed_histogram(bkg_lists["lostlep"],
histnames)
hists["photon"] = ru.get_summed_histogram(bkg_lists["photon"],
histnames)
hists["qflip"] = ru.get_summed_histogram(bkg_lists["qflip"], histnames)
hists["fakes"] = ru.get_summed_histogram(bkg_lists["fakes"], histnames)
hists["ewksubt"] = ru.get_summed_histogram(bkg_lists["ewksubt"],
histnames)
hists["prompt"] = ru.get_summed_histogram(bkg_lists["prompt"],
histnames)
hists["sig"] = ru.get_summed_histogram(sig_list, histnames)
hists["data"] = ru.get_summed_histogram(data_list, histnames)
else:
hists["lostlep"] = ru.get_yield_histogram(bkg_lists["lostlep"],
histnames)
hists["photon"] = ru.get_yield_histogram(bkg_lists["photon"],
histnames)
hists["qflip"] = ru.get_yield_histogram(bkg_lists["qflip"], histnames)
hists["fakes"] = ru.get_yield_histogram(bkg_lists["fakes"], histnames)
hists["ewksubt"] = ru.get_yield_histogram(bkg_lists["ewksubt"],
histnames)
hists["prompt"] = ru.get_yield_histogram(bkg_lists["prompt"],
histnames)
hists["sig"] = ru.get_yield_histogram(sig_list, histnames)
hists["data"] = ru.get_yield_histogram(data_list, histnames)
if bkg_lists["fakes"] == bkg_lists["ddfakes"]:
for i in xrange(1, hists["ewksubt"].GetNbinsX() + 1):
if hists["ewksubt"].GetBinContent(i) > 0:
hists["ewksubt"].SetBinContent(i, 0)
hists["fakes"].Add(hists["ewksubt"])
hists["lostlep"].SetName("Lost/three lep")
hists["photon"].SetName("#gamma#rightarrowlepton")
hists["qflip"].SetName("Charge mis-id")
hists["fakes"].SetName("Non-prompt")
hists["prompt"].SetName("Irredu.")
hists["sig"].SetName("WWW")
hists["data"].SetName("Data")
return hists
def oscr():
output_dirpath = "outputs/WWW2017_analysis_v0.44.1_byproc"
sample_lists = {}
sample_lists["Z"] = [x for x in glob.glob(output_dirpath + "/DY*.root")]
sample_lists["top"] = [x for x in glob.glob(output_dirpath + "/TT*.root")]
sample_lists["top"] += [
x for x in glob.glob(output_dirpath + "/ST_*.root")
]
sample_lists["W"] = [x for x in glob.glob(output_dirpath + "/WJ*.root")]
sample_lists["MB"] = [x for x in glob.glob(output_dirpath + "/WW*.root")]
sample_lists["MB"] += [
x for x in glob.glob(output_dirpath + "/WZTo1*.root")
]
sample_lists["MB"] += [
x for x in glob.glob(output_dirpath + "/WZTo2*.root")
]
sample_lists["MB"] += [
x for x in glob.glob(output_dirpath + "/WZTo3LNu_Tu*.root")
]
sample_lists["MB"] += [x for x in glob.glob(output_dirpath + "/ZZ*.root")]
sample_lists["data"] = [
x for x in glob.glob(output_dirpath + "/*Run2017*.root")
]
bkgs = ["Z", "top", "W", "MB"]
#histnames = ["OSCRmm__MllSS"]
#histnames = ["OSCRee__MllSS"]
#histnames = ["OSCRem__nb"]
#histnames = ["OSCRem__nj"]
#histnames = ["OSCRem__nj30"]
#histnames = ["OSCRem__nvtx"]
#histnames = ["OSCRem__MET"]
#histnames = ["OSCRem__MllSS"]
#histnames = ["WZCRSSmm__MllSS"]
histnames = [
"WZCRSSeeFull",
"WZCRSSemFull",
"WZCRSSmmFull",
"WZCR1SFOSFull",
"WZCR2SFOSFull",
]
hists = {}
for bkg in bkgs:
if "__" in histnames[0]:
hists[bkg] = ru.get_summed_histogram(sample_lists[bkg],
histnames).Clone(bkg)
else:
hists[bkg] = ru.get_yield_histogram(sample_lists[bkg],
histnames).Clone(bkg)
if "__" in histnames[0]:
hists["data"] = ru.get_summed_histogram(sample_lists["data"],
histnames).Clone("data")
else:
hists["data"] = ru.get_yield_histogram(sample_lists["data"],
histnames).Clone("data")
colors = [920, 2007, 2005, 2003, 2001, 2]
alloptions = {
"ratio_range": [0.3, 1.7],
"nbins": 15,
"autobin": False,
"legend_scalex": 1.8,
"legend_scaley": 1.1,
"output_name": "plots/test.pdf",
"bkg_sort_method": "unsorted",
"no_ratio": False,
"print_yield": True,
#"blind": True if "SR" in histnames[0] else False,
"blind": False,
"lumi_value": "41.3",
#"yaxis_log": True,
#"yaxis_range": [1, 5e10],
"legend_smart": False,
}
bgs = [hists[x] for x in bkgs]
p.plot_hist(sigs=[],
bgs=bgs,
data=hists["data"],
colors=colors,
syst=None,
options=alloptions)
def plot(histnames, ps=0, sf=0):
# Glob the file lists
# bkg_list_wjets = glob.glob(output_dirpath+"/WJetsToLNu_Tune*.root")
# bkg_list_dy = glob.glob(output_dirpath+"/DY*.root")
# bkg_list_ttbar = glob.glob(output_dirpath+"/TTJets_Tune*.root")
# bkg_list_vv = glob.glob(output_dirpath+"/WW*.root") + glob.glob(output_dirpath+"/WW*.root")
# bkg_list_qcd_mu = glob.glob(output_dirpath+"/QCD*MuEn*.root")
# bkg_list_qcd_el = glob.glob(output_dirpath+"/QCD*EMEn*.root")
# bkg_list_qcd_bc = glob.glob(output_dirpath+"/QCD*bcToE*.root")
bkg_list_wjets = [output_dirpath + "/wj_incl.root"]
bkg_list_dy = [output_dirpath + "/dy.root"]
bkg_list_ttbar = [output_dirpath + "/tt_incl.root"]
bkg_list_vv = [output_dirpath + "/ww.root", output_dirpath + "/wz.root"]
bkg_list_qcd_mu = [output_dirpath + "/qcd_mu.root"]
bkg_list_qcd_el = [output_dirpath + "/qcd_em.root"]
bkg_list_qcd_bc = [output_dirpath + "/qcd_bc.root"]
bkg_list_all = bkg_list_wjets + bkg_list_dy + bkg_list_ttbar + bkg_list_vv
# Glob the data file list depending on the region
if "Mu" in histnames:
data_list = [output_dirpath + "/data_mu.root"]
elif "El" in histnames:
data_list = [output_dirpath + "/data_el.root"]
else:
data_list = [
output_dirpath + "/data_mu.root", output_dirpath + "/data_el.root"
]
# Get all the histogram objects
h_wjets = ru.get_summed_histogram(bkg_list_wjets, histnames)
h_dy = ru.get_summed_histogram(bkg_list_dy, histnames)
h_ttbar = ru.get_summed_histogram(bkg_list_ttbar, histnames)
h_vv = ru.get_summed_histogram(bkg_list_vv, histnames)
h_qcd_mu = ru.get_summed_histogram(bkg_list_qcd_mu, histnames)
h_qcd_el = ru.get_summed_histogram(bkg_list_qcd_el, histnames)
h_qcd_bc = ru.get_summed_histogram(bkg_list_qcd_bc, histnames)
h_data = ru.get_summed_histogram(data_list, histnames)
# Set the names of the histograms
h_wjets.SetName("W")
h_dy.SetName("Z")
h_ttbar.SetName("Top")
h_vv.SetName("VV")
h_qcd_mu.SetName("QCD(#mu)")
h_qcd_el.SetName("QCD(e)")
h_qcd_bc.SetName("QCD(bc)")
h_data.SetName("Data")
# Scale the histograms appropriately from SF from the EWKCR
if sf > 0:
h_wjets.Scale(sf)
h_dy.Scale(sf)
h_ttbar.Scale(sf)
h_vv.Scale(sf)
# If the data needs some additional correction for the prescale
if ps > 0:
h_data.Scale(ps)
# Color settings
colors = [2007, 2005, 2003, 2001, 920, 2]
# Options
alloptions = {
"ratio_range": [0.0, 2.0],
"nbins": 30,
"autobin": False,
"legend_scalex": 1.8,
"legend_scaley": 1.1,
"output_name": "plots/plot/{}.pdf".format(histnames),
"bkg_sort_method": "unsorted",
"no_ratio": False,
"print_yield": False,
"yaxis_log": True if "ptcorr" in histnames else False,
#"yaxis_log": False,
"divide_by_bin_width": True,
"legend_smart": False if "ptcorr" in histnames else True,
"lumi_value": 41.3,
}
# The bkg histogram list
bgs_list = [h_vv, h_ttbar, h_dy, h_wjets, h_qcd_mu],
bgs_list = [
h_vv, h_ttbar, h_dy, h_wjets, h_qcd_mu
] if "Mu" in histnames else [h_vv, h_ttbar, h_dy, h_wjets, h_qcd_el]
# Plot them
p.plot_hist(bgs=bgs_list,
data=h_data.Clone("Data"),
colors=colors,
syst=None,
options=alloptions)
# Obtain the histogram again to return the object for further calculations
# Data-driven QCD = data - bkg
h_ddqcd = ru.get_summed_histogram(data_list, histnames)
h_bkg = ru.get_summed_histogram(bkg_list_all, histnames)
if ps > 0:
h_ddqcd.Scale(ps)
if sf > 0:
h_bkg.Scale(sf)
h_ddqcd.Add(h_bkg, -1)
# MC QCD
h_qcd_mu = ru.get_summed_histogram(bkg_list_qcd_mu,
histnames).Clone("QCD(#mu)")
h_qcd_el = ru.get_summed_histogram(bkg_list_qcd_el,
histnames).Clone("QCD(EM)")
h_qcd_bc = ru.get_summed_histogram(bkg_list_qcd_bc,
histnames).Clone("QCD(HF)")
return h_ddqcd, h_data, h_bkg, h_qcd_mu, h_qcd_el, h_qcd_bc