def test_gen_fs_mc_stack_n_data_sum(self):
res = gen.fs_mc_stack_n_data_sum({"name": "histo"})
mc, data = res.next()
# correct instances
self.assertTrue(isinstance(mc, StackWrapper))
self.assertTrue(isinstance(data, HistoWrapper))
# ... of equal lumi (from data)
self.assertEqual(mc.lumi, settings.data_samples()["tt"].lumi)
self.assertEqual(data.lumi, settings.data_samples()["tt"].lumi)
# check stacking order by history
h = str(mc.history)
self.assertTrue(h.index("ttgamma") < h.index("zjets"))
settings.stacking_order.reverse()
mc, data = res.next()
h = str(mc.history)
self.assertTrue(h.index("zjets") < h.index("ttgamma"))
def test_gen_fs_mc_stack_n_data_sum(self):
res = gen.fs_mc_stack_n_data_sum({"name": "histo"})
mc, data = res.next()
# correct instances
self.assertTrue(isinstance(mc, StackWrapper))
self.assertTrue(isinstance(data, HistoWrapper))
# ... of equal lumi (from data)
self.assertEqual(mc.lumi, settings.data_samples()["tt"].lumi)
self.assertEqual(data.lumi, settings.data_samples()["tt"].lumi)
# check stacking order by history
h = str(mc.history)
self.assertTrue(h.index("ttgamma") < h.index("zjets"))
settings.stacking_order.reverse()
mc, data = res.next()
h = str(mc.history)
self.assertTrue(h.index("zjets") < h.index("ttgamma"))
def run(self):
self.configure()
if not self.n_sig_ttgam_wrp:
self.message("WARNING Did not find result in post_proc_dict. Skipping...")
return
# store results in wrapper
r = copy.deepcopy(self.n_sig_ttgam_wrp)
r.name = self.name
self.result = r
# prepare mc counts
class counts(object): pass
c = counts()
c.sig_pre = 0.
c.sig_fid = 0.
c.sig_post = 0.
c.bkg_pre = 0.
c.bkg_post = 0.
c.tt_pre = 0.
c.tt_post = 0
for smp in settings.mc_samples().itervalues():
legend = smp.legend
if legend == "t#bar{t}#gamma (Signal)":
c.sig_pre += smp.log_event_counts[self.get_sig_count_name(self.pre_count_name)] / smp.lumi
c.sig_fid += smp.log_event_counts[self.get_sig_count_name(self.fid_count_name)] / smp.lumi
c.sig_post += smp.log_event_counts[self.get_sig_count_name(self.post_count_name)] / smp.lumi
else:
c.bkg_pre += smp.log_event_counts[self.pre_count_name] / smp.lumi
c.bkg_post += smp.log_event_counts[self.post_count_name] / smp.lumi
if legend == "t#bar{t} inclusive":
c.tt_pre += smp.log_event_counts[self.pre_count_name] / smp.lumi
c.tt_post += smp.log_event_counts[self.post_count_name] / smp.lumi
data_lumi_sum = settings.data_lumi_sum()
for k in c.__dict__.keys():
c.__dict__[k] *= data_lumi_sum
# prepare data counts
c.data_pre = 0.
c.data_post = 0.
for smp in settings.data_samples().itervalues():
c.data_pre += smp.log_event_counts[self.pre_count_name]
c.data_post += smp.log_event_counts[self.post_count_name]
# selection performance
r.eff_gamma = c.sig_post / c.sig_fid
r.eff_gamma_fid = c.sig_fid / c.sig_pre
r.pur_tt = (c.tt_pre + c.sig_pre) / (c.bkg_pre + c.sig_pre)
r.N_presel_data = c.data_pre
r.N_sel_data = c.data_post
r.StoB_gamma = c.sig_post / c.bkg_post
r.StoB_presel = c.tt_pre / (c.bkg_pre - c.tt_pre)
# background-substracted number of ttgamma signal events
# r.n_sig_ttgam = self.n_sig_ttgam_wrp.n_sig_ttgam
# R_fid
R_fid_denom = r.eff_gamma * r.N_presel_data * r.pur_tt
r.R_fid = r.n_sig_ttgam / R_fid_denom
r.R_fid_err_stat= r.n_sig_ttgam_err / R_fid_denom
# R
R_denom = r.eff_gamma_fid * r.eff_gamma * r.N_presel_data * r.pur_tt
r.R = r.n_sig_ttgam / R_denom
r.R_err_stat = r.n_sig_ttgam_err / R_denom
# xsec
r.xsec = r.R * settings.ttbar_xsec_cms
r.xsec_err_stat = r.xsec * r.R_err_stat / r.R
self.message(str(r))
def prepare_for_systematic(self):
mc_samples = settings.mc_samples().keys()
da_samples = settings.data_samples().keys()
pu_samples = list(s + "_TrigMinus" for s in mc_samples)
settings.active_samples = pu_samples + da_samples
super(SysTrigMinus, self).prepare_for_systematic()
def prepare_for_systematic(self):
mc_samples = settings.mc_samples().keys()
da_samples = settings.data_samples().keys()
btag_samples = list(s + "_BTagWeightUDSGPlus" for s in mc_samples)
settings.active_samples = btag_samples + da_samples
super(SysBTagWeightUDSGPlus, self).prepare_for_systematic()
def get_shilp_counts(self, c):
for smp in settings.data_samples().itervalues():
c.tight += smp.log_event_counts[self.tight_cnt]
c.shilp += smp.log_event_counts[self.shilp_cnt]
