def test_plot(self):
tdrstyle()
paths = get_paths()
dataset = 'Jul15'
lepton = 'mu'
systematic = 'nominal'
iso = 'iso'
#Define the different plots to make
items = [
('tchan1', paths[dataset]['mc'][lepton][systematic][iso], 'T_t_ToLeptons.root', Cuts.n_jets(2), '1.0'),
('tbarchan1', '-.-', 'Tbar_t_ToLeptons.root', Cuts.n_jets(2), '1.0'),
('tchan2', '-.-', 'T_t_ToLeptons.root', Cuts.n_jets(2)*Cuts.n_tags(1), '1.0'),
('tbarchan2', '-.-', 'Tbar_t_ToLeptons.root', Cuts.n_jets(2)*Cuts.n_tags(1), '1.0'),
]
items = fill_list(items)
#Dewfine a callbakc for normalization
def normalize(hn, h):
norm(h)
return h
#The final plot definition to draw
plot_def = {
'var': 'mt_mu',
'range': [20, 0, 300],
'items': items,
'merge_cmds': {'signal 2J1T':['tchan2', 'tbarchan2'], 'signal 2J':['tchan1', 'tbarchan1']},
'xlab': 'M_t(W)'
#'hist_callback': normalize
}
#Now do the draw comparison
canv, hists = compare_plot(plot_def)
#Do some post-checking that everything went fine
for hn, h in hists.items():
if hn=='signal 2J':
self.assertAlmostEqual(h.Integral(), 39472.452918)
elif hn=='signal 2J1T':
self.assertAlmostEqual(h.Integral(), 16414.4804941)
print "Saving the plot to test_plot.png"
canv.SaveAs('test_plot.png')
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*"-"
print name
print "t-channel | %d | %d | %d " % (norms["t-channel incl"][0], norms["t-channel excl"][0], refT)
print "Wjets | %d | %d | %d " % (norms["WJets incl"][0], norms["WJets excl"][0], refW)
print "ttbar | %d | %d | %d " % (norms["t#bar{t} incl"][0], norms["t#bar{t} excl"][0], refTT)
#isolated files are by default in $STPOL_DIR/step3_latest/mu/iso/nominal/*.root
datadirs["iso"] = "/".join((os.environ["STPOL_DIR"], "step3_latest", "mu" ,"iso", "nominal"))
#Use the anti-isolated data for QCD $STPOL_DIR/step3_latest/mu/antiiso/nominal/SingleMu.root
datadirs["antiiso"] = "/".join((os.environ["STPOL_DIR"], "step3_latest", "mu" ,"antiiso", "nominal"))
#Load all the samples in the isolated directory
samples = Sample.fromDirectory(datadirs["iso"], out_type="dict")
samples["SingleMu_aiso"] = Sample.fromFile(datadirs["antiiso"] + "/SingleMu.root")
hists_mc = dict()
hist_data = None
#Define the variable, cut, weight and lumi
var = "cos_theta"
cut_name = "2j1t"
cut_str = str(Cuts.n_jets(2)*Cuts.n_tags(1)*Cuts.lepton_veto*Cuts.one_muon*Cuts.mt_mu*Cuts.top_mass_sig*Cuts.eta_lj)
weight_str = "1.0"
lumi = 20000 #FIXME: take from the step2 output filelists/step2/latest/iso/nominal/luminosity.txt
#nbins, min, max
plot_range= [20, -1, 1]
for name, sample in samples.items():
if sample.isMC:
hist = sample.drawHistogram(var, cut_str, weight=weight_str, plot_range=plot_range)
hist.Scale(sample.lumiScaleFactor(lumi)*mc_sf)
hists_mc[sample.name] = hist.hist
Styling.mc_style(hists_mc[sample.name], sample.name)
elif name == "SingleMu":
hist_data = sample.drawHistogram(var, cut_str, weight="1.0", plot_range=plot_range)
Styling.data_style(hist_data)
parser = argparse.ArgumentParser(
description='Produces a hierarchy of histograms corresponding to cuts and weights.'
)
parser.add_argument('outfile', action='store',
help="The output file name."
)
parser.add_argument('infiles', nargs='+',
help="The input file names"
)
args = parser.parse_args()
cuts_jet_tag = [
("%dj%dt"%(n,m), Cuts.n_jets(n)*Cuts.n_tags(m)) for n in [2,3] for m in [0,1,2]
]
# cuts_jet_tag = [
# ("2j1t", Cuts.n_jets(2)*Cuts.n_tags(1))
# ]
cuts = []
for cutname, cbline in cuts_jet_tag:
for lep in ["mu", "ele"]:
cn = "%s_%s" % (lep, cutname)
baseline = Cuts.lepton(lep) * Cuts.hlt(lep) * Cuts.metmt(lep) * Cuts.rms_lj
cuts += [
#Without the MET cut
("%s_nomet" % cn, Cuts.lepton(lep) * Cuts.hlt(lep) * Cuts.rms_lj * cbline),
#Baseline for fit
from plots.common.cuts import Cuts,Cut
import copy
from plots.common.utils import PhysicsProcess, NestedDict
cp = copy.deepcopy
#FIXME: these cutlists need to be factorised away badly!
cutlist={}
cutlist['2j']=Cuts.n_jets(2)
cutlist['3j']=Cuts.n_jets(3)
cutlist['2j1t']=Cuts.n_jets(2)*Cuts.n_tags(1)
cutlist['2j0t']=Cuts.n_jets(2)*Cuts.n_tags(0)
cutlist['3j0t']=Cuts.n_jets(3)*Cuts.n_tags(0)
cutlist['3j1t']=Cuts.n_jets(3)*Cuts.n_tags(1)
cutlist['3j2t']=Cuts.n_jets(3)*Cuts.n_tags(2)
#Needed for RMS cut validation
cutlist['presel_ele_no_rms']=Cuts.hlt_isoele*Cuts.lepton_veto*Cuts.pt_jet*Cuts.one_electron
cutlist['presel_mu_no_rms']=Cuts.hlt_isomu*Cuts.lepton_veto*Cuts.pt_jet*Cuts.one_muon
cutlist['presel_ele'] = cutlist['presel_ele_no_rms']*Cuts.rms_lj
cutlist['presel_mu'] = cutlist['presel_mu_no_rms']*Cuts.rms_lj
cutlist['nomet_ele']=cutlist['presel_ele']*Cuts.top_mass_sig*Cuts.eta_lj
cutlist['nomt_mu']=cutlist['presel_mu']*Cuts.top_mass_sig*Cuts.eta_lj
cutlist['noeta_ele']=cutlist['presel_ele']*Cuts.top_mass_sig*Cuts.met()
cutlist['noeta_mu']=cutlist['presel_mu']*Cuts.top_mass_sig*Cuts.mt_mu()
cutlist['final_ele']=cutlist['nomet_ele']*Cuts.met()
cutlist['final_mu']=cutlist['nomt_mu']*Cuts.mt_mu()
"binning": [20, -1, 1]
}
variables["bdt"] = {
"name": "bdt",
"var": Cuts.mva_vars['mu'],
"binning": [60, -1, 1]
}
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)
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 * "-"
print name
print "t-channel | %d | %d | %d " % (norms["t-channel incl"][0],
if __name__=="__main__":
for fi in sys.argv[1:]:
print fi
samp = Sample.fromFile(fi)
print "Lumi scale factor: ", samp.lumiScaleFactor(20000)
for lep in ["mu", "ele"]:
if "/%s/"%lep not in fi:
continue
print lep
cut = None
for cutname, _cut in [
("hlt", Cuts.hlt(lep)),
("lep", Cuts.single_lepton(lep)),
("2J", Cuts.n_jets(2)),
("1T", Cuts.n_tags(1)),
("MET/MtW", Cuts.metmt(lep)),
("rms", Cuts.rms_lj),
("Mtop", Cuts.top_mass_sig),
("etalj", Cuts.eta_lj)
]:
if not cut:
cut = _cut
else:
cut *= _cut
try:
hi = samp.drawHistogram("eta_lj", str(cut), binning=[50, -5, 5])
hi.Scale(samp.lumiScaleFactor(20000))
print cutname
print hi.GetEntries(), hi.Integral()
except:
#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()
filter_funcs=[
lambda x: is_samp(x, "antiiso") and is_samp(x, "data")
]
)
isol.append(isos[lep][cn])
# [iso, antiiso] --> jet --> [jets2-3]
jet = Node(graph, "jet", isol, [])
jets = dict()
for i in [2,3]:
jets[i] = CutNode(Cuts.n_jets(i), graph, "%dj"%i, [jet], [])
tag = Node(graph, "tag", jet.children(), [])
tags = dict()
for i in [0,1,2]:
tags[i] = CutNode(Cuts.n_tags(i), graph, "%dt"%i, [tag], [])
#The primary MET/MTW cut node
met = Node(graph, "met", tag.children(), [])
mets = dict()
#No MET cut requirement
mets['off'] = CutNode(Cuts.no_cut, graph, "met__off", [met], [],
)
for met_syst in ["nominal", "up", "down"]:
mets['met_' + met_syst] = CutNode(
Cuts.met(met_syst),
graph, "met__met_" + met_syst,
mvaFileList['sig']['eval']= [
'T_t_ToLeptons',
'Tbar_t_ToLeptons'
]
mvaFileList['bg']['train']= [
'TTJets_MassiveBinDECAY',
'WJets_inclusive'
]
mvaFileList['bg']['eval']= [
'TTJets_FullLept',
'TTJets_SemiLept',
'W1Jets_exclusive',
'W2Jets_exclusive',
'W3Jets_exclusive',
'W4Jets_exclusive'
]
varList={}
varList['ele'] = [ 'top_mass','eta_lj','C','met','mt_el','mass_bj','mass_lj','el_pt','pt_bj' ]
varList['mu'] = [ 'top_mass','eta_lj','C','met','mt_mu','mass_bj','mass_lj','mu_pt','pt_bj' ]
varRank={}
varRank['ele'] = ['top_mass', 'C', 'eta_lj', 'el_pt', 'mt_el', 'pt_bj', 'mass_bj', 'met', 'mass_lj']
varRank['mu'] = ['top_mass', 'eta_lj', 'C', 'mu_pt', 'mt_mu', 'met', 'mass_bj', 'pt_bj', 'mass_lj']
from plots.common.cuts import Cut,Cuts
cuts = {}
cuts['ele'] = Cuts.n_jets(2)*Cuts.n_tags(1)*Cuts.hlt_isoele*Cuts.lepton_veto*Cuts.pt_jet*Cuts.one_electron*Cuts.rms_lj*Cuts.met()
cuts['mu'] = Cuts.n_jets(2)*Cuts.n_tags(1)*Cuts.hlt_isomu*Cuts.lepton_veto*Cuts.pt_jet*Cuts.one_muon*Cuts.rms_lj*Cuts.mt_mu()
