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 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
def fakerate(num, den, ps=0, sf=0, sferr=0, tfile=None):
# Obtain histograms
h_num, h_num_qcd_mu, h_num_qcd_esum, h_num_qcd_el, h_num_qcd_bc = get_fakerate_histograms(
num, den, ps, sf)
herr_num, herr_num_qcd_mu, herr_num_qcd_esum, herr_num_qcd_el, herr_num_qcd_bc = get_fakerate_histograms(
num, den, ps, sf + sferr)
# Set data-driven QCD estimate systematics stemming from EWK SF uncertainty
add_systematics(h_num, herr_num)
# Options
alloptions = {
"ratio_range": [0.0, 2.0],
"nbins": 180,
"autobin": False,
"legend_scalex": 1.8,
"legend_scaley": 1.1,
"output_name": "plots/fakerate/{}.pdf".format(num + "__" + den),
"bkg_sort_method": "unsorted",
"no_ratio": False,
"print_yield": True,
"yield_prec": 3,
"draw_points": True,
"lumi_value": 41.3,
}
bgs_list = [h_num_qcd_mu] if "Mu" in num else [h_num_qcd_esum]
#bgs_list = [h_num_qcd_mu] if "Mu" in num else [h_num_qcd_esum, h_num_qcd_el, h_num_qcd_bc]
sigs_list = [] if "Mu" in num else [h_num_qcd_el, h_num_qcd_bc]
p.plot_hist(
sigs=sigs_list,
bgs=bgs_list,
data=h_num,
#data = None,
syst=None,
options=alloptions)
if tfile:
tfile.cd()
channel = "Mu" if "Mu" in num else "El"
histname = num.split("__")[1]
data_fakerate = h_num.Clone(channel + "_" + histname +
"_data_fakerate")
qcd_fakerate = bgs_list[0].Clone(channel + "_" + histname +
"_qcd_fakerate")
if histname == "etacorrvarbin":
create_varbin(data_fakerate, "eta_bounds").Write()
create_varbin(qcd_fakerate, "eta_bounds").Write()
elif histname == "ptcorrvarbin":
create_varbin(data_fakerate, "ptcorr_bounds").Write()
create_varbin(qcd_fakerate, "ptcorr_bounds").Write()
elif histname == "ptcorrvarbincoarse":
create_varbin(data_fakerate, "ptcorrcoarse_bounds").Write()
create_varbin(qcd_fakerate, "ptcorrcoarse_bounds").Write()
elif histname == "ptcorretarolled":
create_varbin(data_fakerate, "ptcorr_bounds", "eta_bounds").Write()
create_varbin(qcd_fakerate, "ptcorr_bounds", "eta_bounds").Write()
elif histname == "ptcorretarolledcoarse":
create_varbin(data_fakerate, "ptcorrcoarse_bounds",
"eta_bounds").Write()
create_varbin(qcd_fakerate, "ptcorrcoarse_bounds",
"eta_bounds").Write()
