def __init__(self):
self.bins = to_array(0., 0.04, 0.07, 4)
self.nbins = len(self.bins)-1
# 2015 is included in 2016. We scale/sum up 2015, 2016hip, 2016nonhip below, instead of that being done separately in
# SignalEfficiencyCombiner--this simplifies the datacard and plot making downstream.
self.years = '2016', '2017', '2018'
self.nyears = len(self.years)
import JMTucker.MFVNeutralino.AnalysisConstants as ac
self.int_lumis = ac.scaled_int_lumi_2015p6, ac.scaled_int_lumi_2017, ac.scaled_int_lumi_2018
self.fn = 'limitsinput.root'
self.l1eeprefiring_2017 = True # whether to simulate L1 EE prefiring in 2017
self.hem1516_2018 = True # whether to simulate the HEM 15/16 failure in for part of 2018
def fmt(h, name, color, save=[]):
binning = to_array(0., 0.04, 0.07, 0.15)
h = h.Rebin(len(binning)-1, name, binning)
h.Sumw2()
h.SetStats(0)
h.SetLineWidth(3)
h.SetLineColor(color)
h.SetTitle(';d_{VV} (cm);event rate (unit norm.)')
move_overflow_into_last_bin(h)
h.Scale(1./h.Integral(0,h.GetNbinsX()+2))
save.append(h)
return h
def axisize(l):
l = sorted(set(l))
delta = l[-1] - l[-2]
l.append(l[-1] + delta)
return to_array(l)
rec_eff,l,u = clopper_pearson(rec_hist.GetEntries(), nrec)
rec_err = (u-l)/2
if generated[i] != '':
gen_hist = f.Get('mfvGen%s/h_gen%s_dvv' % (generated[i], match))
gen_eff,l,u = clopper_pearson(gen_hist.GetEntries(), ngen)
gen_err = (u-l)/2
gen_rec_div = gen_eff/rec_eff if rec_eff != 0 else 9999
gen_rec_err = (gen_rec_div * ((rec_err/rec_eff)**2 + (gen_err/gen_eff)**2))**0.5 if rec_eff != 0 and gen_eff != 0 else 9999
if generated[i] == gen_num:
print '%20s%6d%20s%6d%10.3f%10.3f%10.3f%10.3f%10.3f%10.3f\n' % (rec, rec_hist.GetEntries(), generated[i], gen_hist.GetEntries(), rec_eff, rec_err, gen_eff, gen_err, gen_rec_div, gen_rec_err)
print r'%s & $%4.3f \pm %4.3f$ & $%4.3f \pm %4.3f$ & $%4.3f \pm %4.3f$ \\' % (sample.latex, rec_eff, rec_err, gen_eff, gen_err, gen_rec_div, gen_rec_err)
x.append(rec_eff)
y.append(gen_eff)
ex.append(rec_err)
ey.append(gen_err)
g = ROOT.TGraphErrors(1, to_array(rec_eff), to_array(gen_eff), to_array(rec_err), to_array(gen_err))
g.SetMarkerStyle(style(sample))
g.SetMarkerColor(color(sample))
gs.append(g)
label = sample.latex.split(',')[0] + sample.latex.split(',')[2]
label = label.replace('\\','#').replace('#GeV',' GeV').replace('$','').replace(' M',', M')
if sample.tau == 1000 or ctau != '':
if style(sample) == 20:
l1.AddEntry(g, label.split(', ')[1], 'P')
if color(sample) == 6:
l2.AddEntry(g, label.split(', ')[0], 'P')
if gen_eff >= (1-0.01*gen_rec_cut)*rec_eff and gen_eff <= (1+0.01*gen_rec_cut)*rec_eff:
matched.append(sample.name)
else:
not_matched.append(sample.name)
break