def run(self, plotData=None):
super(EstimateWjets, self).run(plotData)
for wjets_from_mc, wjets_shape_nick, wjets_data_control_nick, wjets_data_substract_nicks, wjets_mc_signal_nick, wjets_mc_control_nick in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
if not wjets_from_mc:
yield_data_control = tools.PoissonYield(
plotData.plotdict["root_objects"]
[wjets_data_control_nick])()
for nick in wjets_data_substract_nicks:
yield_bkg_control = tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
if nick in plotData.metadata:
yield_bkg_control = uncertainties.ufloat(
plotData.metadata[nick].get(
"yield", yield_bkg_control.nominal_value),
plotData.metadata[nick].get(
"yield_unc", yield_bkg_control.std_dev))
yield_data_control -= yield_bkg_control
yield_data_control = max(0.0, yield_data_control)
yield_mc_signal = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_mc_signal_nick])()
yield_mc_control = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_mc_control_nick])()
assert (yield_data_control * yield_mc_signal
== 0.0) or (yield_mc_control != 0.0)
final_yield = yield_data_control * yield_mc_signal
if final_yield != 0.0:
final_yield /= yield_mc_control
log.debug(
"Relative statistical uncertainty of the yield for process W+jets (nick \"{nick}\") is {unc}."
.format(nick=wjets_shape_nick,
unc=final_yield.std_dev / final_yield.nominal_value
if final_yield.nominal_value != 0.0 else 0.0))
plotData.metadata[wjets_shape_nick] = {
"yield":
final_yield.nominal_value,
"yield_unc":
final_yield.std_dev,
"yield_unc_rel":
abs(final_yield.std_dev / final_yield.nominal_value
if final_yield.nominal_value != 0.0 else 0.0),
}
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_shape_nick])()
if integral_shape != 0.0:
scale_factor = final_yield / integral_shape
log.debug(
"Scale factor for process W+jets (nick \"{nick}\") is {scale_factor}."
.format(nick=wjets_shape_nick,
scale_factor=scale_factor))
plotData.plotdict["root_objects"][wjets_shape_nick].Scale(
scale_factor.nominal_value)
def run(self, plotData=None):
super(EstimateTtbar, self).run(plotData)
for ttbar_shape_nick, ttbar_data_control_nick, ttbar_data_subtract_nicks, ttbar_mc_signal_nick, ttbar_mc_control_nick in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
yield_data_control = tools.PoissonYield(
plotData.plotdict["root_objects"][ttbar_data_control_nick])()
#print "data_control_yield from nick " + ttbar_data_control_nick + " : " + str(yield_data_control)
for nick in ttbar_data_subtract_nicks:
yield_bkg_control = tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
#print "\t minus " + nick + " : " + str(yield_bkg_control)
if nick in plotData.metadata:
yield_bkg_control = uncertainties.ufloat(
plotData.metadata[nick].get(
"yield", yield_bkg_control.nominal_value),
plotData.metadata[nick].get("yield_unc",
yield_bkg_control.std_dev))
yield_data_control -= yield_bkg_control
#print "data_control_yield final " + str(yield_data_control)
yield_data_control = max(0.0, yield_data_control)
yield_mc_signal = tools.PoissonYield(
plotData.plotdict["root_objects"][ttbar_mc_signal_nick])()
yield_mc_control = tools.PoissonYield(
plotData.plotdict["root_objects"][ttbar_mc_control_nick])()
#print "data_mc_signal " + str(yield_mc_signal)
#print "data_mc_control " + str(yield_mc_control)
integral_shape = plotData.plotdict["root_objects"][
ttbar_shape_nick].Integral()
final_yield = yield_mc_control / yield_data_control * integral_shape
log.debug(
"Relative statistical uncertainty of the yield for process ttbar+jets (nick \"{nick}\") is {unc}."
.format(nick=ttbar_shape_nick,
unc=final_yield.std_dev / final_yield.nominal_value
if final_yield.nominal_value != 0.0 else 0.0))
plotData.metadata[ttbar_shape_nick] = {
"yield":
final_yield.nominal_value,
"yield_unc":
final_yield.std_dev,
"yield_unc_rel":
abs(final_yield.std_dev / final_yield.nominal_value
if final_yield.nominal_value != 0.0 else 0.0),
}
if integral_shape != 0.0:
scale_factor = integral_shape / final_yield
#print "scale factor: " + str(scale_factor)
log.debug(
"Scale factor for process ttbar+jets (nick \"{nick}\") is {scale_factor}."
.format(nick=ttbar_shape_nick, scale_factor=scale_factor))
plotData.plotdict["root_objects"][ttbar_shape_nick].Scale(
scale_factor.nominal_value)
def run(self, plotData=None):
super(EstimateQcdTauHadTauHad, self).run(plotData)
for qcd_data_shape_nick, qcd_data_signal_control_nick, qcd_data_relaxed_control_nick, qcd_data_subtract_nicks, qcd_control_signal_subtract_nicks, qcd_control_relaxed_subtract_nicks in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
for nick in qcd_data_subtract_nicks:
plotData.plotdict["root_objects"][qcd_data_shape_nick].Add(
plotData.plotdict["root_objects"][nick], -1.0)
yield_control_signal = tools.PoissonYield(
plotData.plotdict["root_objects"]
[qcd_data_signal_control_nick])()
for nick in qcd_control_signal_subtract_nicks:
yield_bgk_control = tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
yield_control_signal -= yield_bgk_control
yield_control_signal = max(0.0, yield_control_signal)
yield_control_relaxed = tools.PoissonYield(
plotData.plotdict["root_objects"]
[qcd_data_relaxed_control_nick])()
for nick in qcd_control_relaxed_subtract_nicks:
yield_bgk_control = tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
yield_control_relaxed -= yield_bgk_control
yield_control_relaxed = max(0.0, yield_control_relaxed)
scale_factor = yield_control_signal
if yield_control_relaxed != 0.0:
scale_factor /= yield_control_relaxed
log.debug(
"Scale factor for process QCD (nick \"{nick}\") is {scale_factor}."
.format(nick=qcd_data_shape_nick, scale_factor=scale_factor))
plotData.plotdict["root_objects"][qcd_data_shape_nick].Scale(
scale_factor.nominal_value)
final_yield = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_data_shape_nick])()
log.debug(
"Relative statistical uncertainty of the yield for process QCD (nick \"{nick}\") is {unc}."
.format(nick=qcd_data_shape_nick,
unc=final_yield.std_dev / final_yield.nominal_value
if final_yield.nominal_value != 0.0 else 0.0))
plotData.metadata[qcd_data_shape_nick] = {
"yield":
final_yield.nominal_value,
"yield_unc":
final_yield.std_dev,
"yield_unc_rel":
abs(final_yield.std_dev / final_yield.nominal_value
if final_yield.nominal_value != 0.0 else 0.0),
}
def prepare_args(self, parser, plotData):
super(CalculateWJetsOSSSFactor, self).prepare_args(parser, plotData)
self._plotdict_keys = [
"wjets_from_mc_os_nicks", "wjets_from_mc_ss_nicks"
]
self.prepare_list_args(plotData, self._plotdict_keys)
def run(self, plotData=None):
super(CalculateWJetsOSSSFactor, self).run(plotData)
# make sure that all necessary histograms are available
for nicks in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
for nick in nicks:
if isinstance(nick, basestring):
assert isinstance(
plotData.plotdict["root_objects"].get(nick),
ROOT.TH1)
elif not isinstance(nick, bool):
for subnick in nick:
assert isinstance(
plotData.plotdict["root_objects"].get(subnick),
ROOT.TH1)
for wjets_from_mc_os_nicks, wjets_from_mc_ss_nicks in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
yield_wj_mc_os = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_from_mc_os_nicks])()
yield_wj_mc_ss = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_from_mc_ss_nicks])()
# make sure we don't have negative yields
yield_wj_mc_os = uncertainties.ufloat(
max(0.0, yield_wj_mc_os.nominal_value), yield_wj_mc_os.std_dev)
yield_wj_mc_ss = uncertainties.ufloat(
max(0.0, yield_wj_mc_ss.nominal_value), yield_wj_mc_ss.std_dev)
print(yield_wj_mc_os, yield_wj_mc_ss)
assert (yield_wj_mc_os != 0.0) or (yield_wj_mc_ss != 0.0)
# the final yield in the signal region is N_data, WJ^{SR} = N_data, WJ^{CR} * N_MC,WJ^{SR} / N_MC,WJ^{CR}
os_ss_factor = yield_wj_mc_os / yield_wj_mc_ss
plotData.metadata[wjets_from_mc_os_nicks] = {
"os_ss_factor":
os_ss_factor.nominal_value,
"os_ss_factor_unc":
os_ss_factor.std_dev,
"yield_unc_rel":
abs(os_ss_factor.std_dev / os_ss_factor.nominal_value
if os_ss_factor.nominal_value != 0.0 else 0.0),
}
plotData.metadata
print(plotData.metadata)
def run(self, plotData=None):
super(EstimateQcd, self).run(plotData)
for qcd_shape_nick, qcd_yield_nick, qcd_yield_subtract_nicks, qcd_shape_subtract_nicks, qcd_extrapolation_factor_ss_os in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
yield_qcd = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_yield_nick])()
# estimate the QCD yield
# print "yield qcd total: " + str(yield_qcd)
for nick in qcd_yield_subtract_nicks:
yield_bkg = tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
#print "minus " + nick + " " + str(yield_bkg)
yield_qcd -= yield_bkg
yield_qcd = max(0.0, yield_qcd)
if (yield_qcd == 0.0):
log.warning("QCD yield is 0!")
# QCD shape
#print "shape total: " + str(tools.PoissonYield(plotData.plotdict["root_objects"][qcd_shape_nick])())
for nick in qcd_shape_subtract_nicks:
#print "\t minus " + nick + " " + str(tools.PoissonYield(plotData.plotdict["root_objects"][nick])())
plotData.plotdict["root_objects"][qcd_shape_nick].Add(
plotData.plotdict["root_objects"][nick],
-1.0 / plotData.plotdict["qcd_scale_factor"])
shape_yield = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_shape_nick])()
if shape_yield != 0.0:
scale_factor = yield_qcd / shape_yield * qcd_extrapolation_factor_ss_os
final_yield_qcd = yield_qcd * qcd_extrapolation_factor_ss_os
#log.debug("Relative statistical uncertainty of the yield for process QCD (nick \"{nick}\") is {unc}.".format(nick=qcd_data_shape_nick, unc=final_yield.std_dev/final_yield.nominal_value if final_yield.nominal_value != 0.0 else 0.0))
plotData.plotdict["root_objects"][qcd_shape_nick].Scale(
scale_factor.nominal_value)
#print "QCD em estimation summary"
#print "scale factor : " + str(scale_factor)
#print "shape_yield :" + str(shape_yield)
#print "yield_qcd :" + str(yield_qcd)
# save to be picked up
plotData.metadata[qcd_shape_nick] = {
"yield":
final_yield_qcd.nominal_value,
"yield_unc":
final_yield_qcd.std_dev,
"yield_unc_rel":
abs(final_yield_qcd.std_dev / final_yield_qcd.nominal_value
if final_yield_qcd.nominal_value != 0.0 else 0.0),
}
def run(self, plotData=None):
super(EstimateFF, self).run(plotData)
for (ff_data_nick, ff_mc_subtract_nicks, ff_norm_data_nick,
ff_norm_mc_subtract_nicks) in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
for nick in ff_mc_subtract_nicks:
plotData.plotdict["root_objects"][ff_data_nick].Add(
plotData.plotdict["root_objects"][nick], -1.0)
for nick in ff_norm_mc_subtract_nicks:
plotData.plotdict["root_objects"][ff_norm_data_nick].Add(
plotData.plotdict["root_objects"][nick], -1.0)
ff_yield = tools.PoissonYield(
plotData.plotdict["root_objects"][ff_data_nick])()
ff_norm_yield = tools.PoissonYield(
plotData.plotdict["root_objects"][ff_norm_data_nick])()
if ff_yield != 0.0:
scale_factor = ff_norm_yield / ff_yield
plotData.plotdict["root_objects"][ff_data_nick].Scale(
scale_factor.nominal_value)
def run(self, plotData=None): super(EstimateFF, self).run(plotData) # make sure that all necessary histograms are available for nicks in zip(*[plotData.plotdict[key] for key in self._plotdict_keys]): for nick in nicks: if isinstance(nick, basestring): assert isinstance(plotData.plotdict["root_objects"].get(nick), ROOT.TH1) elif (not isinstance(nick, float) and not isinstance(nick, bool)): for subnick in nick: assert isinstance(plotData.plotdict["root_objects"].get(subnick), ROOT.TH1) for (ff_data_nick, ff_mc_subtract_nicks, ff_norm_data_nick, ff_norm_mc_subtract_nicks) in zip(*[plotData.plotdict[key] for key in self._plotdict_keys]): for nick in ff_mc_subtract_nicks: plotData.plotdict["root_objects"][ff_data_nick].Add(plotData.plotdict["root_objects"][nick], -1.0) for nick in ff_norm_mc_subtract_nicks: plotData.plotdict["root_objects"][ff_norm_data_nick].Add(plotData.plotdict["root_objects"][nick], -1.0) ff_yield = tools.PoissonYield(plotData.plotdict["root_objects"][ff_data_nick])() ff_norm_yield = tools.PoissonYield(plotData.plotdict["root_objects"][ff_norm_data_nick])() if ff_yield != 0.0: scale_factor = ff_norm_yield/ff_yield plotData.plotdict["root_objects"][ff_data_nick].Scale(scale_factor.nominal_value)
def run(self, plotData=None):
super(EstimateQcdTauHadTauHad, self).run(plotData)
# make sure that all necessary histograms are available
for nicks in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
for nick in nicks:
if isinstance(nick, basestring):
assert isinstance(
plotData.plotdict["root_objects"].get(nick), ROOT.TH1)
elif (not isinstance(nick, float)
and not isinstance(nick, bool)):
for subnick in nick:
assert isinstance(
plotData.plotdict["root_objects"].get(subnick),
ROOT.TH1)
for qcd_data_shape_nick, qcd_data_signal_control_nick, qcd_data_relaxed_control_nick, qcd_data_subtract_nicks, qcd_control_signal_subtract_nicks, qcd_control_relaxed_subtract_nicks in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
for nick in qcd_data_subtract_nicks:
plotData.plotdict["root_objects"][qcd_data_shape_nick].Add(
plotData.plotdict["root_objects"][nick], -1.0)
yield_control_signal = tools.PoissonYield(
plotData.plotdict["root_objects"]
[qcd_data_signal_control_nick])()
for nick in qcd_control_signal_subtract_nicks:
yield_bgk_control = tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
yield_control_signal -= yield_bgk_control
yield_control_signal = uncertainties.ufloat(
max(0.0, yield_control_signal.nominal_value),
yield_control_signal.std_dev)
yield_control_relaxed = tools.PoissonYield(
plotData.plotdict["root_objects"]
[qcd_data_relaxed_control_nick])()
for nick in qcd_control_relaxed_subtract_nicks:
yield_bgk_control = tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
yield_control_relaxed -= yield_bgk_control
yield_control_relaxed = uncertainties.ufloat(
max(0.0, yield_control_relaxed.nominal_value),
yield_control_relaxed.std_dev)
scale_factor = yield_control_signal
if yield_control_relaxed != 0.0:
scale_factor /= yield_control_relaxed
log.debug(
"Scale factor for process QCD (nick \"{nick}\") is {scale_factor}."
.format(nick=qcd_data_shape_nick, scale_factor=scale_factor))
plotData.plotdict["root_objects"][qcd_data_shape_nick].Scale(
scale_factor.nominal_value)
final_yield = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_data_shape_nick])()
log.debug(
"Relative statistical uncertainty of the yield for process QCD (nick \"{nick}\") is {unc}."
.format(nick=qcd_data_shape_nick,
unc=final_yield.std_dev / final_yield.nominal_value
if final_yield.nominal_value != 0.0 else 0.0))
plotData.metadata[qcd_data_shape_nick] = {
"yield":
final_yield.nominal_value,
"yield_unc":
final_yield.std_dev,
"yield_unc_rel":
abs(final_yield.std_dev / final_yield.nominal_value
if final_yield.nominal_value != 0.0 else 0.0),
}
def run(self, plotData=None):
super(EstimateQcdPrefit, self).run(plotData)
# make sure that all necessary histograms are available
for nicks in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
for nick in nicks:
if isinstance(nick, basestring):
assert isinstance(
plotData.plotdict["root_objects"].get(nick), ROOT.TH1)
elif (not isinstance(nick, float)
and not isinstance(nick, bool)):
for subnick in nick:
assert isinstance(
plotData.plotdict["root_objects"].get(subnick),
ROOT.TH1)
for qcd_shape_nick, qcd_yield_nick, qcd_yield_subtract_nicks, qcd_shape_subtract_nicks, qcd_extrapolation_factor_ss_os in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
######################
# ------- Step 1 -----
# 1. Get Yield in Control region and subtract all backgrounds.
yield_qcd = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_yield_nick])()
# estimate the QCD yield
for nick in qcd_yield_subtract_nicks:
yield_bkg = tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
yield_qcd -= yield_bkg
yield_qcd = uncertainties.ufloat(max(0.0, yield_qcd.nominal_value),
yield_qcd.std_dev)
if (yield_qcd.nominal_value == 0.0):
log.warning("QCD yield is 0!")
######################
# ------- Step 2 -----
# QCD shape
# 1. Subtract the shapes of all backgrounds from data in the control region.
# 2. Scale the qcd by the qcd_os_ss_extrapolation_factor to get the prefit estimate.
# Sidemark: In this script the factor is supposed to be 1.0 because we want to determine the
# factor with this first estimate.
for nick in qcd_shape_subtract_nicks:
plotData.plotdict["root_objects"][qcd_shape_nick].Add(
plotData.plotdict["root_objects"][nick],
-1.0 / plotData.plotdict["qcd_scale_factor"])
shape_yield = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_shape_nick])()
if shape_yield != 0.0:
qcd_extrapolation_factor_ss_os = 1.0
scale_factor = yield_qcd / shape_yield * qcd_extrapolation_factor_ss_os
plotData.plotdict["root_objects"][qcd_shape_nick].Scale(
scale_factor.nominal_value)
#log.debug("Relative statistical uncertainty of the yield for process QCD (nick \"{nick}\") is {unc}.".format(nick=qcd_data_shape_nick, unc=final_yield.std_dev/final_yield.nominal_value if final_yield.nominal_value != 0.0 else 0.0))
final_yield_qcd = yield_qcd * qcd_extrapolation_factor_ss_os
# save to be picked up
plotData.metadata[qcd_shape_nick] = {
"yield":
final_yield_qcd.nominal_value,
"yield_unc":
final_yield_qcd.std_dev,
"yield_unc_rel":
abs(final_yield_qcd.std_dev / final_yield_qcd.nominal_value
if final_yield_qcd.nominal_value != 0.0 else 0.0),
}
def run(self, plotData=None):
super(EstimateWjetsAndQCD, self).run(plotData)
for qcd_extrapolation_factor_ss_os, qcd_shape_nick, qcd_ss_lowmt_nick, qcd_ss_highmt_shape_nick, qcd_os_highmt_nick, qcd_shape_highmt_substract_nick, qcd_yield_nick, qcd_shape_substract_nick, qcd_yield_substract_nick, wjets_os_highmt_mc_nick, wjets_ss_highmt_mc_nick, wjets_ss_substract_nick, wjets_ss_data_nick, wjets_os_substract_nick, wjets_os_data_nick, wjets_shape_nick, wjets_relaxed_os_highmt_nick, wjets_relaxed_os_lowmt_nick, wjets_scale_factor_shift, wjets_final_selection in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
########################################
# estimate QCD for the highmT region
# get qcd ss high mt shape
for nick in qcd_shape_highmt_substract_nick + [
wjets_ss_highmt_mc_nick
]:
plotData.plotdict["root_objects"][
qcd_ss_highmt_shape_nick].Add(
plotData.plotdict["root_objects"][nick], -1)
# get qcd yield in ss high mt region
yield_qcd_ss_highmt = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_ss_data_nick])()
for nick in wjets_ss_substract_nick + [wjets_ss_highmt_mc_nick]:
yield_qcd_ss_highmt -= tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
yield_qcd_ss_highmt = max(
uncertainties.ufloat(0.0, yield_qcd_ss_highmt.std_dev),
yield_qcd_ss_highmt)
# scale qcd ss high mt shape by qcd yield found in data
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_ss_highmt_shape_nick])()
if integral_shape != 0.0:
scale_factor = yield_qcd_ss_highmt / integral_shape
plotData.plotdict["root_objects"][
qcd_ss_highmt_shape_nick].Scale(scale_factor.nominal_value)
# scale qcd os high mt shape by qcd yield found in ss data and ss->os extrapolation factor
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_os_highmt_nick])()
if integral_shape != 0.0:
scale_factor = yield_qcd_ss_highmt * qcd_extrapolation_factor_ss_os / integral_shape
plotData.plotdict["root_objects"][qcd_os_highmt_nick].Scale(
scale_factor.nominal_value)
########################################
# estimate W+jets
# get w+jets yield in os high mt region
yield_wjets_os_highmt = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_os_data_nick])()
for nick in wjets_os_substract_nick:
yield_wjets_os_highmt -= tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
yield_wjets_os_highmt -= qcd_extrapolation_factor_ss_os * yield_qcd_ss_highmt
yield_wjets_os_highmt = max(
uncertainties.ufloat(0.0, yield_wjets_os_highmt.std_dev),
yield_wjets_os_highmt)
if yield_wjets_os_highmt.nominal_value == 0.0:
log.warning(
"W+jets & QCD estimation: data yield in high mT region after background subtraction is 0!"
)
# get high mt -> low mt extrapolation factor from MC
if tools.PoissonYield(plotData.plotdict["root_objects"]
[wjets_relaxed_os_highmt_nick])() != 0.0:
wjets_extrapolation_factor_mt = tools.PoissonYield(
plotData.plotdict["root_objects"]
[wjets_relaxed_os_lowmt_nick])() / tools.PoissonYield(
plotData.plotdict["root_objects"]
[wjets_relaxed_os_highmt_nick])()
else:
log.warning(
"W+jets & QCD estimation: W+jets high mT region in MC has no entries. High->low mT extrapolation factor is set to 1.0!"
)
wjets_extrapolation_factor_mt = 1.0
# get w+jets yield in low mt region
wjets_yield = yield_wjets_os_highmt * wjets_extrapolation_factor_mt
# extrapolate to final selection
if wjets_final_selection != None:
wjets_yield = wjets_yield * tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_final_selection])(
) / tools.PoissonYield(plotData.plotdict["root_objects"]
[wjets_relaxed_os_lowmt_nick])()
# scale signal region histograms
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_shape_nick])()
if integral_shape != 0.0:
scale_factor = wjets_yield / integral_shape
log.debug(
"Scale factor for process W+jets (nick \"{nick}\") is {scale_factor}."
.format(nick=wjets_shape_nick, scale_factor=scale_factor))
plotData.plotdict["root_objects"][wjets_shape_nick].Scale(
scale_factor.nominal_value)
plotData.metadata[wjets_shape_nick] = {
"yield":
wjets_yield.nominal_value,
"yield_unc":
wjets_yield.std_dev,
"yield_unc_rel":
abs(wjets_yield.std_dev / wjets_yield.nominal_value
if wjets_yield.nominal_value != 0.0 else 0.0),
}
########################################
# estimate QCD for the lowmT
# define w+jets scale factor for qcd estimation
if tools.PoissonYield(plotData.plotdict["root_objects"]
[wjets_os_highmt_mc_nick])() != 0.0:
wjets_scale_factor = yield_wjets_os_highmt / tools.PoissonYield(
plotData.plotdict["root_objects"]
[wjets_os_highmt_mc_nick])()
else:
log.warning(
"W+jets & QCD estimation: W+jets high mT region in MC has no entries. Scale factor for W+jets in QCD estimation is set to 1.0!"
)
wjets_scale_factor = uncertainties.ufloat(1.0, 0.0)
for nick in qcd_shape_substract_nick:
if "wj" in nick:
scale_factor = wjets_scale_factor * wjets_scale_factor_shift
plotData.plotdict["root_objects"][nick].Scale(
scale_factor.nominal_value)
plotData.plotdict["root_objects"][qcd_shape_nick].Add(
plotData.plotdict["root_objects"][nick], -1)
yield_qcd_ss_lowmt = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_yield_nick])()
for nick in qcd_yield_substract_nick:
if "wj" in nick and tools.PoissonYield(
plotData.plotdict["root_objects"][nick])() != 0.0:
scale_factor = wjets_scale_factor * wjets_scale_factor_shift
plotData.plotdict["root_objects"][nick].Scale(
scale_factor.nominal_value)
yield_qcd_ss_lowmt -= tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
yield_qcd_ss_lowmt = max(
uncertainties.ufloat(0.0, yield_qcd_ss_lowmt.std_dev),
yield_qcd_ss_lowmt)
if yield_qcd_ss_lowmt.nominal_value == 0.0:
log.warning(
"W+jets & QCD estimation: data yield in low mT SS region after background subtraction is 0!"
)
# get qcd yield in low mt region
qcd_yield = yield_qcd_ss_lowmt * qcd_extrapolation_factor_ss_os
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_shape_nick])()
if integral_shape != 0.0:
scale_factor = qcd_yield / integral_shape
plotData.plotdict["root_objects"][qcd_shape_nick].Scale(
scale_factor.nominal_value)
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_ss_lowmt_nick])()
if integral_shape != 0.0:
scale_factor = yield_qcd_ss_lowmt / integral_shape
plotData.plotdict["root_objects"][qcd_ss_lowmt_nick].Scale(
scale_factor.nominal_value)
# write relative uncertainties to metadata to pick them up with combine
plotData.metadata[qcd_shape_nick] = {
"yield":
qcd_yield.nominal_value,
"yield_unc":
qcd_yield.std_dev,
"yield_unc_rel":
abs(qcd_yield.std_dev / qcd_yield.nominal_value
if qcd_yield.nominal_value != 0.0 else 0.0),
}
def run(self, plotData=None):
super(EstimateWjetsAndQCD, self).run(plotData)
for qcd_extrapolation_factor_ss_os, qcd_shape_nick, qcd_ss_lowmt_nick, qcd_ss_highmt_shape_nick, qcd_os_highmt_nick, qcd_shape_highmt_substract_nick, qcd_yield_nick, qcd_shape_substract_nick, qcd_yield_substract_nick, wjets_ss_mc_nick, wjets_os_mc_nick, wjets_os_highmt_mc_nick, wjets_os_lowmt_mc_nick, wjets_ss_highmt_mc_nick, wjets_ss_substract_nick, wjets_ss_data_nick, wjets_os_substract_nick, wjets_os_data_nick, wjets_shape_nick, wjets_final_selection in zip(*[plotData.plotdict[key] for key in self._plotdict_keys]):
# estimate QCD for the lowmT
for nick in qcd_shape_substract_nick:
plotData.plotdict["root_objects"][qcd_shape_nick].Add(plotData.plotdict["root_objects"][nick], -1)
yield_qcd_control = tools.PoissonYield(plotData.plotdict["root_objects"][qcd_yield_nick])()
for nick in qcd_yield_substract_nick:
yield_bkg_control = tools.PoissonYield(plotData.plotdict["root_objects"][nick])()
if nick in plotData.metadata:
yield_bkg_control = uncertainties.ufloat(
plotData.metadata[nick].get("yield", yield_bkg_control.nominal_value),
plotData.metadata[nick].get("yield_unc", yield_bkg_control.std_dev)
)
yield_qcd_control -= yield_bkg_control
yield_qcd_control = max(0.0, yield_qcd_control)
integral_shape = tools.PoissonYield(plotData.plotdict["root_objects"][qcd_shape_nick])()
if integral_shape != 0.0:
scale_factor = yield_qcd_control * qcd_extrapolation_factor_ss_os / integral_shape
final_qcd_yield = yield_qcd_control * qcd_extrapolation_factor_ss_os
plotData.plotdict["root_objects"][qcd_shape_nick].Scale(scale_factor.nominal_value)
integral_shape = tools.PoissonYield(plotData.plotdict["root_objects"][qcd_ss_lowmt_nick])()
if integral_shape != 0.0:
scale_factor = yield_qcd_control / integral_shape
plotData.plotdict["root_objects"][qcd_ss_lowmt_nick].Scale(scale_factor.nominal_value)
# write relative uncertainties to metadata to pick them up with combine
plotData.metadata[qcd_shape_nick] = {
"yield" : final_qcd_yield.nominal_value,
"yield_unc" : final_qcd_yield.std_dev,
"yield_unc_rel" : abs(final_qcd_yield.std_dev/final_qcd_yield.nominal_value if final_qcd_yield.nominal_value != 0.0 else 0.0),
}
# estimate W+jets
yield_ss_control = tools.PoissonYield(plotData.plotdict["root_objects"][wjets_ss_data_nick])()
for nick in wjets_ss_substract_nick:
yield_bkg_control = tools.PoissonYield(plotData.plotdict["root_objects"][nick])()
yield_ss_control -= yield_bkg_control
yield_os_control = tools.PoissonYield(plotData.plotdict["root_objects"][wjets_os_data_nick])()
for nick in wjets_os_substract_nick:
yield_bkg_control = tools.PoissonYield(plotData.plotdict["root_objects"][nick])()
yield_os_control -= yield_bkg_control
wjets_extrapolation_factor_ss_os = tools.PoissonYield(plotData.plotdict["root_objects"][wjets_os_mc_nick])()/tools.PoissonYield(plotData.plotdict["root_objects"][wjets_ss_mc_nick])()
wjets_extrapolation_factor_mt = tools.PoissonYield(plotData.plotdict["root_objects"][wjets_os_lowmt_mc_nick])()/tools.PoissonYield(plotData.plotdict["root_objects"][wjets_os_highmt_mc_nick])()
wjets_yield = (yield_os_control-qcd_extrapolation_factor_ss_os*yield_ss_control)*wjets_extrapolation_factor_ss_os/(wjets_extrapolation_factor_ss_os-qcd_extrapolation_factor_ss_os)*wjets_extrapolation_factor_mt
# extrapolate to final selection
if wjets_final_selection != None:
wjets_yield = wjets_yield * tools.PoissonYield(plotData.plotdict["root_objects"][wjets_final_selection])() / tools.PoissonYield(plotData.plotdict["root_objects"][wjets_os_lowmt_mc_nick])()
#print "scale factor inclusive to categorized"
#print str(tools.PoissonYield(plotData.plotdict["root_objects"][wjets_final_selection])() / tools.PoissonYield(plotData.plotdict["root_objects"][wjets_os_lowmt_mc_nick])())
#print "final selection " + str(tools.PoissonYield(plotData.plotdict["root_objects"][wjets_final_selection])())
#print "inclusive " + str(tools.PoissonYield(plotData.plotdict["root_objects"][wjets_os_lowmt_mc_nick])())
# scale signal region histograms
integral_shape = tools.PoissonYield(plotData.plotdict["root_objects"][wjets_shape_nick])()
#print "QCD/WJets estimation summary"
#print "Extrapolation factors"
#print "\t QCD OS/SS: " + str(qcd_extrapolation_factor_ss_os)
#print "\t WJets OS/SS: " + str(wjets_extrapolation_factor_ss_os)
#print "\t WJets high-low mT: " + str(wjets_extrapolation_factor_mt)
#print "WJets yield"
#print "\t before: " + str(integral_shape)
#print "\t after: " + str(wjets_yield)
#print "Scale factor: " + str(wjets_yield / integral_shape)
if integral_shape != 0.0:
scale_factor = wjets_yield / integral_shape
log.debug("Scale factor for process W+jets (nick \"{nick}\") is {scale_factor}.".format(nick=wjets_shape_nick, scale_factor=scale_factor))
plotData.plotdict["root_objects"][wjets_shape_nick].Scale(scale_factor.nominal_value)
integral_shape = tools.PoissonYield(plotData.plotdict["root_objects"][wjets_os_highmt_mc_nick])()
if integral_shape != 0.0:
scale_factor = wjets_yield / (integral_shape * wjets_extrapolation_factor_mt)
log.debug("Scale factor for process W+jets (nick \"{nick}\") is {scale_factor}.".format(nick=wjets_os_highmt_mc_nick, scale_factor=scale_factor))
plotData.plotdict["root_objects"][wjets_os_highmt_mc_nick].Scale(scale_factor.nominal_value)
integral_shape = tools.PoissonYield(plotData.plotdict["root_objects"][wjets_ss_highmt_mc_nick])()
if integral_shape != 0.0:
scale_factor = wjets_yield / (integral_shape * wjets_extrapolation_factor_mt * wjets_extrapolation_factor_ss_os)
log.debug("Scale factor for process W+jets (nick \"{nick}\") is {scale_factor}.".format(nick=wjets_ss_highmt_mc_nick, scale_factor=scale_factor))
plotData.plotdict["root_objects"][wjets_ss_highmt_mc_nick].Scale(scale_factor.nominal_value)
# estimate QCD for the highmT region
for nick in qcd_shape_highmt_substract_nick+[wjets_ss_highmt_mc_nick]:
plotData.plotdict["root_objects"][qcd_ss_highmt_shape_nick].Add(plotData.plotdict["root_objects"][nick], -1)
yield_qcd_ss_highmt = yield_ss_control - tools.PoissonYield(plotData.plotdict["root_objects"][wjets_ss_highmt_mc_nick])()
yield_qcd_ss_highmt = max(0.0, yield_qcd_ss_highmt)
integral_shape = tools.PoissonYield(plotData.plotdict["root_objects"][qcd_ss_highmt_shape_nick])()
if integral_shape != 0.0:
scale_factor = yield_qcd_ss_highmt / integral_shape
plotData.plotdict["root_objects"][qcd_ss_highmt_shape_nick].Scale(scale_factor.nominal_value)
integral_shape = tools.PoissonYield(plotData.plotdict["root_objects"][qcd_os_highmt_nick])()
if integral_shape != 0.0:
scale_factor = yield_qcd_ss_highmt * qcd_extrapolation_factor_ss_os / integral_shape
plotData.plotdict["root_objects"][qcd_os_highmt_nick].Scale(scale_factor.nominal_value)
plotData.metadata[wjets_shape_nick] = {
"yield" : wjets_yield.nominal_value,
"yield_unc" : wjets_yield.std_dev,
"yield_unc_rel" : abs(wjets_yield.std_dev/wjets_yield.nominal_value if wjets_yield.nominal_value != 0.0 else 0.0),
}
def run(self, plotData=None):
super(NormalizeForPolarisation, self).run(plotData)
for ztt_pos_pol_gen_nick, ztt_neg_pol_gen_nick, ztt_pos_pol_reco_nick, ztt_neg_pol_reco_nick, ztt_forced_gen_polarisation, ztt_pos_pol_reco_result_nick, ztt_neg_pol_reco_result_nick in zip(
*[
plotData.plotdict[key] for key in [
"ztt_pos_pol_gen_nicks", "ztt_neg_pol_gen_nicks",
"ztt_pos_pol_reco_nicks", "ztt_neg_pol_reco_nicks",
"ztt_forced_gen_polarisations",
"ztt_pos_pol_reco_result_nicks",
"ztt_neg_pol_reco_result_nicks"
]
]):
if ztt_pos_pol_reco_result_nick != ztt_pos_pol_reco_nick:
new_name = "zttpospol_" + hashlib.md5(
ztt_pos_pol_gen_nick + ztt_pos_pol_reco_nick +
ztt_pos_pol_reco_result_nick).hexdigest()
new_histogram = plotData.plotdict["root_objects"][
ztt_pos_pol_reco_nick].Clone(new_name)
plotData.plotdict["root_objects"][
ztt_pos_pol_reco_result_nick] = new_histogram
if ztt_neg_pol_reco_result_nick != ztt_neg_pol_reco_nick:
new_name = "zttnegpol_" + hashlib.md5(
ztt_neg_pol_gen_nick + ztt_neg_pol_reco_nick +
ztt_neg_pol_reco_result_nick).hexdigest()
new_histogram = plotData.plotdict["root_objects"][
ztt_neg_pol_reco_nick].Clone(new_name)
plotData.plotdict["root_objects"][
ztt_neg_pol_reco_result_nick] = new_histogram
pos_reco_norm = tools.PoissonYield(
plotData.plotdict["root_objects"][ztt_pos_pol_reco_nick])()
neg_reco_norm = tools.PoissonYield(
plotData.plotdict["root_objects"][ztt_neg_pol_reco_nick])()
pos_gen_norm = tools.PoissonYield(
plotData.plotdict["root_objects"][ztt_pos_pol_gen_nick])()
neg_gen_norm = tools.PoissonYield(
plotData.plotdict["root_objects"][ztt_neg_pol_gen_nick])()
scale_factors = polarisationsignalscaling.PolarisationScaleFactors(
pos_reco_norm,
neg_reco_norm,
pos_gen_norm,
neg_gen_norm,
forced_gen_polarisation=ztt_forced_gen_polarisation)
log.debug("Gen. level polarisation = {polarisation}".format(
polarisation=scale_factors.get_gen_polarisation()))
log.debug("Reco. level polarisation = {polarisation}".format(
polarisation=scale_factors.get_reco_polarisation()))
pos_reco_scale_factor = scale_factors.get_bias_removal_factor_pospol(
) if plotData.plotdict[
"ztt_remove_bias_instead_unpolarisation"] else scale_factors.get_scale_factor_pospol(
)
plotData.plotdict["root_objects"][
ztt_pos_pol_reco_result_nick].Scale(
pos_reco_scale_factor.nominal_value)
log.debug(
"Scaled histogram \"{nick}\" by a factor of {factor}".format(
nick=ztt_pos_pol_reco_result_nick,
factor=pos_reco_scale_factor))
neg_reco_scale_factor = scale_factors.get_bias_removal_factor_neg_pol(
) if plotData.plotdict[
"ztt_remove_bias_instead_unpolarisation"] else scale_factors.get_scale_factor_negpol(
)
plotData.plotdict["root_objects"][
ztt_neg_pol_reco_result_nick].Scale(
neg_reco_scale_factor.nominal_value)
log.debug(
"Scaled histogram \"{nick}\" by a factor of {factor}".format(
nick=ztt_neg_pol_reco_result_nick,
factor=neg_reco_scale_factor))
if log.isEnabledFor(logging.DEBUG):
reco_polarisation_before_scaling = (
pos_reco_norm - neg_reco_norm) / (pos_reco_norm +
neg_reco_norm)
reco_polarisation_after_scaling = (
pos_reco_norm * pos_reco_scale_factor -
neg_reco_norm * neg_reco_scale_factor) / (
pos_reco_norm * pos_reco_scale_factor +
neg_reco_norm * neg_reco_scale_factor)
log.debug("Tau polarisation changed from {before} to {after}.".
format(before=reco_polarisation_before_scaling,
after=reco_polarisation_after_scaling))
def run(self, plotData=None):
super(EstimateWjets, self).run(plotData)
# make sure that all necessary histograms are available
for nicks in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
for nick in nicks:
if isinstance(nick, basestring):
assert isinstance(
plotData.plotdict["root_objects"].get(nick), ROOT.TH1)
elif not isinstance(nick, bool):
for subnick in nick:
assert isinstance(
plotData.plotdict["root_objects"].get(subnick),
ROOT.TH1)
for wjets_from_mc, wjets_shape_nick, wjets_data_control_nick, wjets_data_substract_nicks, wjets_mc_signal_nick, wjets_mc_control_nick in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
if not wjets_from_mc: # skips the full estimation of this module and uses MC estimate of WJ instead.
# Get yield and uncertainity in data in the control region
yield_data_control = tools.PoissonYield(
plotData.plotdict["root_objects"]
[wjets_data_control_nick])()
for nick in wjets_data_substract_nicks:
# subtract yields of all backgrounds
yield_bkg_control = tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
if nick in plotData.metadata:
yield_bkg_control = uncertainties.ufloat(
plotData.metadata[nick].get(
"yield", yield_bkg_control.nominal_value),
plotData.metadata[nick].get(
"yield_unc", yield_bkg_control.std_dev))
yield_data_control -= yield_bkg_control
# make sure we don't have negative yields
yield_data_control = uncertainties.ufloat(
max(0.0, yield_data_control.nominal_value),
yield_data_control.std_dev)
yield_mc_signal = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_mc_signal_nick])()
yield_mc_control = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_mc_control_nick])()
assert (yield_data_control * yield_mc_signal
== 0.0) or (yield_mc_control != 0.0)
# the final yield in the signal region is N_data, WJ^{SR} = N_data, WJ^{CR} * N_MC,WJ^{SR} / N_MC,WJ^{CR}
final_yield = yield_data_control * yield_mc_signal
if final_yield != 0.0:
final_yield /= yield_mc_control
log.debug(
"Relative statistical uncertainty of the yield for process W+jets (nick \"{nick}\") is {unc}."
.format(nick=wjets_shape_nick,
unc=final_yield.std_dev / final_yield.nominal_value
if final_yield.nominal_value != 0.0 else 0.0))
plotData.metadata[wjets_shape_nick] = {
"yield":
final_yield.nominal_value,
"yield_unc":
final_yield.std_dev,
"yield_unc_rel":
abs(final_yield.std_dev / final_yield.nominal_value
if final_yield.nominal_value != 0.0 else 0.0),
}
plotData.metadata
# scale the wj file by the ratio of the estimated yield and the yield given by MC.
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_shape_nick])()
if integral_shape != 0.0:
scale_factor = final_yield / integral_shape
log.debug(
"Scale factor for process W+jets (nick \"{nick}\") is {scale_factor}."
.format(nick=wjets_shape_nick,
scale_factor=scale_factor))
plotData.plotdict["root_objects"][wjets_shape_nick].Scale(
scale_factor.nominal_value)
def run(self, plotData=None):
super(EstimateQcd, self).run(plotData)
for qcd_data_shape_nick, qcd_data_yield_nick, qcd_data_control_nick, qcd_data_substract_nicks, qcd_extrapolation_factor_ss_os, qcd_subtract_shape in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
yield_data_control = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_data_control_nick])()
yield_qcd_control = yield_data_control
for nick in qcd_data_substract_nicks:
yield_bkg_control = tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
if nick in plotData.metadata:
yield_bkg_control = uncertainties.ufloat(
plotData.metadata[nick].get(
"yield", yield_bkg_control.nominal_value),
plotData.metadata[nick].get("yield_unc",
yield_bkg_control.std_dev))
yield_qcd_control -= yield_bkg_control
if qcd_subtract_shape:
plotData.plotdict["root_objects"][
qcd_data_control_nick].Add(
plotData.plotdict["root_objects"][nick],
-1.0 / plotData.plotdict["qcd_scale_factor"])
if qcd_subtract_shape:
plotData.plotdict["root_objects"][
qcd_data_shape_nick] = plotData.plotdict["root_objects"][
qcd_data_control_nick]
yield_qcd_control = max(0.0, yield_qcd_control)
scale_factor = yield_qcd_control * qcd_extrapolation_factor_ss_os
if yield_data_control != 0.0:
scale_factor /= yield_data_control
final_yield = tools.PoissonYield(
plotData.plotdict["root_objects"]
[qcd_data_yield_nick])() * scale_factor
log.debug(
"Relative statistical uncertainty of the yield for process QCD (nick \"{nick}\") is {unc}."
.format(nick=qcd_data_shape_nick,
unc=final_yield.std_dev / final_yield.nominal_value
if final_yield.nominal_value != 0.0 else 0.0))
plotData.metadata[qcd_data_shape_nick] = {
"yield":
final_yield.nominal_value,
"yield_unc":
final_yield.std_dev,
"yield_unc_rel":
abs(final_yield.std_dev / final_yield.nominal_value
if final_yield.nominal_value != 0.0 else 0.0),
}
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_data_shape_nick])()
if integral_shape != 0.0:
scale_factor = final_yield / integral_shape
log.debug(
"Scale factor for process QCD (nick \"{nick}\") is {scale_factor}."
.format(nick=qcd_data_shape_nick,
scale_factor=scale_factor))
plotData.plotdict["root_objects"][qcd_data_shape_nick].Scale(
scale_factor.nominal_value)
def run(self, plotData=None):
super(EstimateWjetsAndQCD, self).run(plotData)
for qcd_extrapolation_factor_ss_os, qcd_shape_nick, qcd_ss_lowmt_nick, qcd_ss_highmt_shape_nick, qcd_os_highmt_nick, qcd_shape_highmt_substract_nick, qcd_yield_nick, qcd_shape_substract_nick, qcd_yield_substract_nick, wjets_ss_mc_nick, wjets_os_mc_nick, wjets_os_highmt_mc_nick, wjets_os_lowmt_mc_nick, wjets_ss_highmt_mc_nick, wjets_ss_substract_nick, wjets_ss_data_nick, wjets_os_substract_nick, wjets_os_data_nick, wjets_shape_nick in zip(
*[plotData.plotdict[key] for key in self._plotdict_keys]):
# estimate QCD for the lowmT
for nick in qcd_shape_substract_nick:
plotData.plotdict["root_objects"][qcd_shape_nick].Add(
plotData.plotdict["root_objects"][nick], -1)
yield_qcd_control = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_yield_nick])()
for nick in qcd_yield_substract_nick:
yield_bkg_control = tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
if nick in plotData.metadata:
yield_bkg_control = uncertainties.ufloat(
plotData.metadata[nick].get(
"yield", yield_bkg_control.nominal_value),
plotData.metadata[nick].get("yield_unc",
yield_bkg_control.std_dev))
yield_qcd_control -= yield_bkg_control
yield_qcd_control = max(0.0, yield_qcd_control)
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_shape_nick])()
if integral_shape != 0.0:
scale_factor = yield_qcd_control * qcd_extrapolation_factor_ss_os / integral_shape
plotData.plotdict["root_objects"][qcd_shape_nick].Scale(
scale_factor.nominal_value)
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_ss_lowmt_nick])()
if integral_shape != 0.0:
scale_factor = yield_qcd_control / integral_shape
plotData.plotdict["root_objects"][qcd_ss_lowmt_nick].Scale(
scale_factor.nominal_value)
# estimate W+jets
yield_ss_control = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_ss_data_nick])()
for nick in wjets_ss_substract_nick:
yield_bkg_control = tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
yield_ss_control -= yield_bkg_control
yield_os_control = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_os_data_nick])()
for nick in wjets_os_substract_nick:
yield_bkg_control = tools.PoissonYield(
plotData.plotdict["root_objects"][nick])()
yield_os_control -= yield_bkg_control
wjets_extrapolation_factor_ss_os = tools.PoissonYield(
plotData.plotdict["root_objects"]
[wjets_os_mc_nick])() / tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_ss_mc_nick])()
wjets_extrapolation_factor_mt = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_os_lowmt_mc_nick]
)() / tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_os_highmt_mc_nick])()
wjets_yield = (
yield_os_control -
qcd_extrapolation_factor_ss_os * yield_ss_control
) * wjets_extrapolation_factor_ss_os / (
wjets_extrapolation_factor_ss_os -
qcd_extrapolation_factor_ss_os) * wjets_extrapolation_factor_mt
# scale signal region histograms
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_shape_nick])()
if integral_shape != 0.0:
scale_factor = wjets_yield / integral_shape
log.debug(
"Scale factor for process W+jets (nick \"{nick}\") is {scale_factor}."
.format(nick=wjets_shape_nick, scale_factor=scale_factor))
plotData.plotdict["root_objects"][wjets_shape_nick].Scale(
scale_factor.nominal_value)
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_os_highmt_mc_nick])()
if integral_shape != 0.0:
scale_factor = wjets_yield / (integral_shape *
wjets_extrapolation_factor_mt)
log.debug(
"Scale factor for process W+jets (nick \"{nick}\") is {scale_factor}."
.format(nick=wjets_os_highmt_mc_nick,
scale_factor=scale_factor))
plotData.plotdict["root_objects"][
wjets_os_highmt_mc_nick].Scale(scale_factor.nominal_value)
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_ss_highmt_mc_nick])()
if integral_shape != 0.0:
scale_factor = wjets_yield / (integral_shape *
wjets_extrapolation_factor_mt *
wjets_extrapolation_factor_ss_os)
log.debug(
"Scale factor for process W+jets (nick \"{nick}\") is {scale_factor}."
.format(nick=wjets_ss_highmt_mc_nick,
scale_factor=scale_factor))
plotData.plotdict["root_objects"][
wjets_ss_highmt_mc_nick].Scale(scale_factor.nominal_value)
# estimate QCD for the highmT region
for nick in qcd_shape_highmt_substract_nick + [
wjets_ss_highmt_mc_nick
]:
plotData.plotdict["root_objects"][
qcd_ss_highmt_shape_nick].Add(
plotData.plotdict["root_objects"][nick], -1)
yield_qcd_ss_highmt = yield_ss_control - tools.PoissonYield(
plotData.plotdict["root_objects"][wjets_ss_highmt_mc_nick])()
yield_qcd_ss_highmt = max(0.0, yield_qcd_ss_highmt)
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_ss_highmt_shape_nick])()
if integral_shape != 0.0:
scale_factor = yield_qcd_ss_highmt / integral_shape
plotData.plotdict["root_objects"][
qcd_ss_highmt_shape_nick].Scale(scale_factor.nominal_value)
integral_shape = tools.PoissonYield(
plotData.plotdict["root_objects"][qcd_os_highmt_nick])()
if integral_shape != 0.0:
scale_factor = yield_qcd_ss_highmt * qcd_extrapolation_factor_ss_os / integral_shape
plotData.plotdict["root_objects"][qcd_os_highmt_nick].Scale(
scale_factor.nominal_value)
