from plots.common.sample import Sample
from plots.common.cuts import Cuts
import sys
if __name__=="__main__":
if len(sys.argv)!=2:
print "Usage: %s /path/to/mu/iso/nominal/T_t_ToLeptons.root" % sys.argv[1]
sys.exit(0)
s = Sample.fromFile(sys.argv[1])
cut = Cuts.final(2,1)
nentries = s.tree.Draw("cos_theta", str(cut))
print "Nentries=",nentries
merge_cmd["t#bar{t} excl"] = ["TTJets_FullLept", "TTJets_SemiLept"]
merge_cmd["QCD (MC)"] = ["QCDMu"]
merge_cmd["t#bar{t} incl"] = ["TTJets_MassiveBinDECAY"]
merge_cmd["WJets incl"] = ["WJets_inclusive"]
merge_cmd["WJets excl"] = ["W1Jets_exclusive", "W2Jets_exclusive", "W3Jets_exclusive", "W4Jets_exclusive"]
merged_hists = merge_hists(histsD, merge_cmd)
normd = dict()
for hn, h in merged_hists.items():
normd[hn] = get_hist_int_err(h)
return merged_hists, normd
if __name__=="__main__":
#cut = Cuts.mu * Cuts.n_jets(2) * Cuts.mt_mu * Cuts.top_mass_sig * Cuts.eta_lj * Cuts.n_tags(1)
cut = Cuts.mu * Cuts.final(2,1)
cutsref = [
("1mu", Cuts.mu, (5879691, 268238, 50236)),
("2J", Cuts.mu*Cuts.n_jets(2), (972069, 75674, 22387)),
("met", Cuts.mu*Cuts.n_jets(2)*Cut("met>45"), (404707, 46185, 11052)),
("0T", Cuts.mu*Cuts.n_jets(2)*Cut("met>45")*Cuts.n_tags(0), (-1, -1, -1)),
("rmslj_0T", Cuts.mu*Cuts.n_jets(2)*Cut("met>45")*Cuts.n_tags(0)*Cuts.rms_lj, (-1, -1, -1)),
("1T", Cuts.mu*Cuts.n_jets(2)*Cut("met>45")*Cuts.n_tags(1), (6197, 17505, 4173)),
("rmslj_1T", Cuts.mu*Cuts.n_jets(2)*Cut("met>45")*Cuts.n_tags(1)*Cuts.rms_lj, (5095, 14197, 3664)),
]
for (name, cut, (refW, refTT, refT)) in cutsref:
#hist, norms = mc_amount(cut, "pu_weight*muon_IDWeight*muon_IsoWeight")
hist, norms = mc_amount(cut, "1.0")
print 80*"-"
from plots.common.sample import Sample
from plots.common.sample_style import ColorStyleGen
from plots.common.hist_plots import plot_hists
from plots.common.cuts import Cuts, Weights
from plots.common.legend import legend
from plots.common.tdrstyle import tdrstyle
if __name__=="__main__":
samp = Sample.fromFile("~/Documents/stpol/data/out_step3_joosep_11_07_19_44/mu/iso/nominal/W4Jets_exclusive.root")
samp.tree.AddFriend("trees/WJets_weights", samp.file_name)
tdrstyle()
cut = str(Cuts.final(2,0)*Cuts.Wflavour("W_heavy"))
mean_weight = samp.drawHistogram(str(Weights.wjets_madgraph_weight("nominal")), cut, weight="1.0", plot_range=[200, 0, 2]).hist.GetMean()
print "mean weight=%.2f" % mean_weight
hi0 = samp.drawHistogram("cos_theta", cut, weight="1.0", plot_range=[20, -1, 1]).hist
hi0.Scale(samp.lumiScaleFactor(20000))
hi1 = samp.drawHistogram("cos_theta", cut, weight=str(Weights.wjets_madgraph_weight("nominal")), plot_range=[20, -1, 1]).hist
hi1.Scale(samp.lumiScaleFactor(20000))
hi2 = samp.drawHistogram("cos_theta", cut, weight=str(Weights.wjets_madgraph_weight("wjets_up")), plot_range=[20, -1, 1]).hist
hi2.Scale(samp.lumiScaleFactor(20000))
hi3 = samp.drawHistogram("cos_theta", cut, weight=str(Weights.wjets_madgraph_weight("wjets_down")), plot_range=[20, -1, 1]).hist
hi3.Scale(samp.lumiScaleFactor(20000))
hists = [hi0, hi1, hi2, hi3]
#for h in hists:
# h.Scale(1.0/h.Integral())
hi0.SetTitle("unweighted")
hi1.SetTitle("weighted")
hi2.SetTitle("weighted wjets_up")
if args.tag:
out_dir += "/" + args.tag
mkdir_p(out_dir)
# plot_ratios("2J0T", Cuts.final(2,0), samples, out_dir, args.recreate)
# plot_ratios("2J1T", Cuts.final(2,1), samples, out_dir, args.recreate)
colls_in, colls_out = plot_sherpa_vs_madgraph(
costheta, "2J",
Cuts.mu*Cuts.final_jet(2),
samples.values(), out_dir, recreate=args.recreate, legend_pos="top-left", nudge_x=-0.03, nudge_y=0, systematic="nominal"
)
coll_in, coll_out = plot_sherpa_vs_madgraph(
costheta, "2J0T",
Cuts.mu*Cuts.final(2,0),
samples.values(), out_dir, recreate=args.recreate, legend_pos="top-left", nudge_x=-0.03, nudge_y=0
)
coll_in, coll_out = plot_sherpa_vs_madgraph(
costheta, "2J1T",
Cuts.mu*Cuts.final(2,1),
samples.values(), out_dir, recreate=args.recreate, legend_pos="top-left", nudge_x=-0.03, nudge_y=0
)
hi_mg = colls_out["madgraph/weighted"].hists["WJets_hf"] + colls_out["madgraph/weighted"].hists["WJets_lf"]
hi_sh = colls_out["sherpa/unweighted"].hists["WJets_hf"] + colls_out["sherpa/unweighted"].hists["WJets_lf"]
hi_sh_w = colls_out["sherpa/weighted"].hists["WJets_hf"] + colls_out["sherpa/weighted"].hists["WJets_lf"]
hf_mg = colls_out["madgraph/weighted"].hists["WJets_hf"].Integral() / hi_mg.Integral()
lf_mg = colls_out["madgraph/weighted"].hists["WJets_lf"].Integral() / hi_mg.Integral()
weights = [
("weight__nominal", Weights.total_weight("mu")),
]
c1 = (
"2j0t",
Cuts.mt_mu()*Cuts.n_jets(2)*Cuts.n_tags(0)*Cuts.lepton("mu")*Cuts.hlt("mu"),
)
c2 = (
"2j1t",
Cuts.mt_mu()*Cuts.n_jets(2)*Cuts.n_tags(1)*Cuts.lepton("mu")*Cuts.hlt("mu"),
)
c3 = (
"final_cb_2j1t",
Cuts.final(2,1),
)
cuts = [c1, c2, c3]
varnodes = {}
for k, v in variables.items():
for c in cuts:
vn = tree.hist_node(graph, v, c, weights)
vn.addParents(top.children())
tree.HistNode.logger.setLevel(logging.INFO)
print "Recursing down"
for snode in snodes:
snode.recurseDown()
merge_cmd["WJets incl"] = ["WJets_inclusive"]
merge_cmd["WJets excl"] = [
"W1Jets_exclusive", "W2Jets_exclusive", "W3Jets_exclusive",
"W4Jets_exclusive"
]
merged_hists = merge_hists(histsD, merge_cmd)
normd = dict()
for hn, h in merged_hists.items():
normd[hn] = get_hist_int_err(h)
return merged_hists, normd
if __name__ == "__main__":
#cut = Cuts.mu * Cuts.n_jets(2) * Cuts.mt_mu * Cuts.top_mass_sig * Cuts.eta_lj * Cuts.n_tags(1)
cut = Cuts.mu * Cuts.final(2, 1)
cutsref = [
("1mu", Cuts.mu, (5879691, 268238, 50236)),
("2J", Cuts.mu * Cuts.n_jets(2), (972069, 75674, 22387)),
("met", Cuts.mu * Cuts.n_jets(2) * Cut("met>45"), (404707, 46185,
11052)),
("0T", Cuts.mu * Cuts.n_jets(2) * Cut("met>45") * Cuts.n_tags(0),
(-1, -1, -1)),
("rmslj_0T", Cuts.mu * Cuts.n_jets(2) * Cut("met>45") *
Cuts.n_tags(0) * Cuts.rms_lj, (-1, -1, -1)),
("1T", Cuts.mu * Cuts.n_jets(2) * Cut("met>45") * Cuts.n_tags(1),
(6197, 17505, 4173)),
("rmslj_1T", Cuts.mu * Cuts.n_jets(2) * Cut("met>45") *
Cuts.n_tags(1) * Cuts.rms_lj, (5095, 14197, 3664)),
]
#ROOT functions can be used
("abs_eta_lj", "abs(eta_lj)", [20, 0, 5]),
("bdt", Cuts.mva_vars['mu'], [60, -1, 1]),
]
#Define all the weight strategies that we want to apply
weights = [
("weight__nominal", Weights.total_weight("mu")),
#Demonstrate reweighting
("weight__puw", Weights.pu("nominal")),
("weight__puw_up", Weights.pu("up")),
]
#All the cut regions that we are interested in
cuts = [
("2j0t", Cuts.mt_mu()*Cuts.n_jets(2)*Cuts.n_tags(0)*Cuts.lepton("mu")*Cuts.hlt("mu")),
("2j1t", Cuts.mt_mu()*Cuts.n_jets(2)*Cuts.n_tags(1)*Cuts.lepton("mu")*Cuts.hlt("mu")),
("final_cb_2j1t", Cuts.final(2,1))
]
#Construct the analysis chain
snodes, out = analysis_tree(cuts, weights, variables, args.infiles, args.outfile)
print "Recursing down"
for sn in snodes:
sn.recurseDown()
out.close()
