def nevents_plot():
in_f = ROOT.TFile(gp.fn)
out_f = ROOT.TFile('nevents.root', 'recreate')
kinds, masses, taus, nmasses, ntaus = axes(in_f)
for kind in kinds:
h = ROOT.TH2D('nevents_%s' % kind, ';mass (GeV);#tau (mm)', nmasses, masses, ntaus, taus)
for ibin in xrange(1, nmasses+1):
mass = h.GetXaxis().GetBinLowEdge(ibin)
for jbin in xrange(1, ntaus+1):
tau = h.GetYaxis().GetBinLowEdge(jbin)
try:
isample = name2isample(in_f, details2name(kind, tau, mass))
except ValueError:
continue
nev = nevents(in_f, isample)
h.SetBinContent(ibin, jbin, nev)
h.SetBinError (ibin, jbin, nev**0.5)
out_f.cd()
h.Write()
out_f.Write()
out_f.Close()
def event_scale_factors():
from signal_efficiency import SignalEfficiencyCombiner
for which, years in ('2017p8', [2017,2018]), :
combiner = SignalEfficiencyCombiner(years)
in_f = combiner.inputs[0].f
out_f = ROOT.TFile('event_TrackMover_scale_factors_%s.root' % which, 'recreate')
kinds, masses, taus, nmasses, ntaus = axes(in_f)
taus.remove(30.)
ntaus -= 1
for kind in kinds:
h = ROOT.TH2D('event_TrackMover_scale_factors_%s' % kind, ';mass (GeV);#tau (mm)', nmasses, masses, ntaus, taus)
for ibin in xrange(1, nmasses+1):
mass = h.GetXaxis().GetBinLowEdge(ibin)
for jbin in xrange(1, ntaus+1):
tau = h.GetYaxis().GetBinLowEdge(jbin)
pt = kind, tau, mass
try:
name = details2name(*pt)
isample = name2isample(in_f, name)
except ValueError as exc:
if tau < 100:
print colors.warning('problem getting %r : isample %r exc %s' % (pt, isample, exc))
continue # leave holes in the plot to know which samples are missing
event_SF = None
if which == '2017p8' :
vtx_SF_2017 = sig_datamcSF_2017p8(name+"_2017")
vtx_SF_2018 = sig_datamcSF_2017p8(name+"_2018")
event_SF_2017 = vtx_SF_2017**2
event_SF_2018 = vtx_SF_2018**2
lumi2017 = gp.int_lumis[gp.years.index("2017")]
lumi2018 = gp.int_lumis[gp.years.index("2018")]
event_SF = (event_SF_2017*lumi2017 + event_SF_2018*lumi2018) / (lumi2017+lumi2018)
# for table of SFs, with bins 0.1--0.3\mm & 0.3--1\mm & 1--10\mm & 10--100\mm,
# take the ~midpoint of each bin and put its SF in the table (averaged where necessary)
if mass == 1600 :
if tau == 0.2 or tau == 0.6 or tau == 0.7 or tau == 4 or tau == 7 or tau == 55 :
print("for SF table:",name, round(event_SF, 4))
else :
sys.exit('event_scale_factors not set up to handle %s! Exiting.' % which)
h.SetBinContent(ibin, jbin, event_SF)
h.SetBinError (ibin, jbin, 0) # JMTBAD
out_f.cd()
h.Write()
out_f.Write()
out_f.Close()
def signal_efficiency():
from signal_efficiency import SignalEfficiencyCombiner
for which, years in ('run2', [2016, 2017, 2018]), ('2017p8', [2017, 2018]):
combiner = SignalEfficiencyCombiner(years)
in_f = combiner.inputs[0].f
out_f = ROOT.TFile('signal_efficiency_%s.root' % which, 'recreate')
kinds, masses, taus, nmasses, ntaus = axes(in_f)
taus.remove(30.)
ntaus -= 1
for kind in kinds:
h = ROOT.TH2D('signal_efficiency_%s' % kind,
';mass (GeV);#tau (mm)', nmasses, masses, ntaus,
taus)
for ibin in xrange(1, nmasses + 1):
mass = h.GetXaxis().GetBinLowEdge(ibin)
for jbin in xrange(1, ntaus + 1):
tau = h.GetYaxis().GetBinLowEdge(jbin)
pt = kind, tau, mass
isample, r = None, None
try:
isample = name2isample(in_f, details2name(*pt))
r = combiner.combine(isample)
except ValueError as exc:
if tau < 100:
print colors.warning(
'problem getting %r : isample %r exc %s' %
(pt, isample, exc))
continue # leave holes in the plot to know which samples are missing
h.SetBinContent(ibin, jbin, r.total_sig_eff)
h.SetBinError(ibin, jbin, 0) # JMTBAD
out_f.cd()
h.Write()
out_f.Write()
out_f.Close()