def draw_channel(channel,
fit=None,
no_data=False,
ypadding=None,
log_ypadding=None,
**kwargs):
"""
Draw a HistFactory::Channel only include OverallSys systematics
in resulting band as an illustration of the level of uncertainty
since correlations of the NPs are not known and it is not
possible to draw the statistically correct error band.
"""
if fit is not None:
log.warning("applying snapshot on channel {0}".format(channel.name))
channel = channel.apply_snapshot(fit)
if channel.data and channel.data.hist and not no_data:
data_hist = channel.data.hist
else:
data_hist = None
model_hists = []
signal_hists = []
systematics_terms = {}
for sample in channel.samples:
nominal_hist = sample.hist.Clone(shallow=True)
_systematics = {}
for sys_name, osys, hsys in sample.iter_sys():
systematics_terms[sys_name] = (sys_name + '_UP',
sys_name + '_DOWN')
if hsys is not None:
# include only overallsys component
norm, shape = split_norm_shape(hsys, nominal_hist)
if osys is not None:
osys.low *= norm.low
osys.high *= norm.high
else:
osys = norm
_systematics[sys_name + '_DOWN'] = nominal_hist * osys.low
_systematics[sys_name + '_UP'] = nominal_hist * osys.high
log.debug("sample: {0} overallsys: {1} high: {2} low: {3}".format(
sample.name, sys_name, osys.high, osys.low))
nominal_hist.systematics = _systematics
if sample.GetNormFactor('SigXsecOverSM') is not None:
signal_hists.append(nominal_hist)
else:
model_hists.append(nominal_hist)
if 'systematics' in kwargs:
del kwargs['systematics']
figs = []
for logy in (False, True):
figs.append(
draw(data=data_hist,
model=model_hists or None,
signal=signal_hists or None,
systematics=systematics_terms,
logy=logy,
ypadding=(log_ypadding or ypadding) if logy else ypadding,
**kwargs))
return figs
def draw_channel(channel, fit=None, no_data=False,
ypadding=None, log_ypadding=None, **kwargs):
"""
Draw a HistFactory::Channel only include OverallSys systematics
in resulting band as an illustration of the level of uncertainty
since correlations of the NPs are not known and it is not
possible to draw the statistically correct error band.
"""
if fit is not None:
log.warning("applying snapshot on channel {0}".format(channel.name))
channel = channel.apply_snapshot(fit)
if channel.data and channel.data.hist and not no_data:
data_hist = channel.data.hist
else:
data_hist = None
model_hists = []
signal_hists = []
systematics_terms = {}
for sample in channel.samples:
nominal_hist = sample.hist.Clone(shallow=True)
_systematics = {}
for sys_name, osys, hsys in sample.iter_sys():
systematics_terms[sys_name] = (
sys_name + '_UP',
sys_name + '_DOWN')
if hsys is not None:
# include only overallsys component
norm, shape = split_norm_shape(hsys, nominal_hist)
if osys is not None:
osys.low *= norm.low
osys.high *= norm.high
else:
osys = norm
_systematics[sys_name + '_DOWN'] = nominal_hist * osys.low
_systematics[sys_name + '_UP'] = nominal_hist * osys.high
log.debug("sample: {0} overallsys: {1} high: {2} low: {3}".format(
sample.name, sys_name, osys.high, osys.low))
nominal_hist.systematics = _systematics
if sample.GetNormFactor('SigXsecOverSM') is not None:
signal_hists.append(nominal_hist)
else:
model_hists.append(nominal_hist)
if 'systematics' in kwargs:
del kwargs['systematics']
figs = []
for logy in (False, True):
figs.append(draw(
data=data_hist,
model=model_hists or None,
signal=signal_hists or None,
systematics=systematics_terms,
logy=logy,
ypadding=(log_ypadding or ypadding) if logy else ypadding,
**kwargs))
return figs
def apply_split_norm_shape(s):
from rootpy.stats.histfactory import split_norm_shape
for histosys in s.histo_sys:
# skip histosys for which overallsys already exist
if s.GetOverallSys(histosys.name) is not None:
continue
log.info("splitting HistoSys `{0}` in sample `{1}`".format(
histosys.name, s.name))
norm, shape = split_norm_shape(histosys, s.hist)
histosys.high = shape.high
histosys.low = shape.low
s.AddOverallSys(norm)
def apply_split_norm_shape(s):
from rootpy.stats.histfactory import split_norm_shape
for histosys in s.histo_sys:
# skip histosys for which overallsys already exist
if s.GetOverallSys(histosys.name) is not None:
continue
log.info("splitting HistoSys `{0}` in sample `{1}`".format(
histosys.name, s.name))
norm, shape = split_norm_shape(histosys, s.hist)
histosys.high = shape.high
histosys.low = shape.low
s.AddOverallSys(norm)
def histfactory(self, sample, category, systematics=False,
rec=None, weights=None, mva=False,
uniform=False, nominal=None):
if not systematics:
return
if len(self.modes) != 1:
raise TypeError(
'histfactory sample only valid for single production mode')
if len(self.masses) != 1:
raise TypeError(
'histfactory sample only valid for single mass point')
# isolation systematic
sample.AddOverallSys(
'ATLAS_ANA_HH_{0:d}_Isolation'.format(self.year),
1. - 0.06,
1. + 0.06)
mode = self.modes[0]
if mode in ('Z', 'W'):
_uncert_mode = 'VH'
else:
_uncert_mode = self.MODES_WORKSPACE[mode]
if self.year == 2011:
energy = 7
elif self.year == 2012:
energy = 8
else:
raise ValueError(
"collision energy is unknown for year {0:d}".format(self.year))
# QCD_SCALE
for qcd_scale_term, qcd_scale_mode, qcd_scale_category, values in self.QCD_SCALE:
if qcd_scale_mode == _uncert_mode and qcd_scale_category == category.name:
high, low = map(float, values.split('/'))
sample.AddOverallSys(qcd_scale_term, low, high)
# UE UNCERTAINTY
for ue_term, ue_mode, ue_category, values in self.UE_UNCERT:
if ue_mode == _uncert_mode and ue_category == category.name:
high, low = map(float, values.split('/'))
sample.AddOverallSys(ue_term, low, high)
# PDF ACCEPTANCE UNCERTAINTY (OverallSys)
for pdf_term, pdf_mode, pdf_category, values in self.PDF_ACCEPT_NORM_UNCERT:
if pdf_mode == _uncert_mode and pdf_category == category.name:
high, low = map(float, values.split('/'))
sample.AddOverallSys(pdf_term, low, high)
sample_nom = sample.hist
# PDF ACCEPTANCE UNCERTAINTY (HistoSys) ONLY FOR MVA
if mva:
for pdf_term, pdf_mode, pdf_category, hist_names in self.PDF_ACCEPT_SHAPE_UNCERT:
if pdf_mode == _uncert_mode and pdf_category == category.name:
high_name, low_name = hist_names.format(energy).split('/')
high_shape, low_shape = self.PDF_ACCEPT_file[high_name], self.PDF_ACCEPT_file[low_name]
if len(high_shape) != len(sample.hist):
log.warning("skipping pdf acceptance shape systematic "
"since histograms are not compatible")
continue
high = sample_nom.Clone(shallow=True, name=sample_nom.name + '_{0}_UP'.format(pdf_term))
low = sample_nom.Clone(shallow=True, name=sample_nom.name + '_{0}_DOWN'.format(pdf_term))
high *= high_shape
low *= low_shape
histsys = histfactory.HistoSys(
pdf_term, low=low, high=high)
sample.AddHistoSys(histsys)
#mixing Norms
if self.SM:
log.info('adding norm factor')
sample.AddNormFactor('ATLAS_epsilon', 1., -200., 200., False)
elif self.BSM:
log.info('adding norm factor')
sample.AddNormFactor('ATLAS_epsilon_rejected', 1., -200., 200., False)
else:
log.info('no norms for {0}'.format(self.name))
# BR_tautau
_, (br_up, br_down) = yellowhiggs.br(
self.mass, 'tautau', error_type='factor')
sample.AddOverallSys('ATLAS_BR_tautau', br_down, br_up)
# <NormFactor Name="mu_BR_tautau" Val="1" Low="0" High="200" />
sample.AddNormFactor('mu_BR_tautau', 1., 0., 200., True)
#mu_XS[energy]_[mode]
#_, (xs_up, xs_down) = yellowhiggs.xs(
# energy, self.mass, self.MODES_DICT[self.mode][0],
# error_type='factor')
#sample.AddOverallSys(
# 'mu_XS{0:d}_{1}'.format(energy, self.MODES_WORKSPACE[self.mode]),
# xs_down, xs_up)
sample.AddNormFactor(
'mu_XS{0:d}_{1}'.format(energy, self.MODES_WORKSPACE[self.mode]),
1., 0., 200., True)
# https://twiki.cern.ch/twiki/bin/viewauth/AtlasProtected/HSG4Uncertainties
# pdf uncertainty
if mode == 'gg':
if energy == 8:
sample.AddOverallSys('pdf_Higgs_gg', 0.93, 1.08)
else: # 7 TeV
sample.AddOverallSys('pdf_Higgs_gg', 0.92, 1.08)
else:
if energy == 8:
sample.AddOverallSys('pdf_Higgs_qq', 0.97, 1.03)
else: # 7 TeV
sample.AddOverallSys('pdf_Higgs_qq', 0.98, 1.03)
# EWK NLO CORRECTION FOR VBF ONLY
if mode == 'VBF':
sample.AddOverallSys('NLO_EW_Higgs', 0.98, 1.02)
# QCDscale_ggH3in HistoSys ONLY FOR MVA
# also see ggH3in script
if mva and mode == 'gg' and category.name == 'vbf':
Rel_Error_2j = 0.215
Error_exc = 0.08613046469238815 # Abs error on the exclusive xsec
xsec_exc = 0.114866523583739 # Exclusive Xsec
Error_3j = sqrt(Error_exc**2 - (Rel_Error_2j*xsec_exc)**2)
rel_error = Error_3j / xsec_exc
dphi = rec['true_dphi_jj_higgs_no_overlap']
scores = rec['classifier']
idx_2j = ((pi - dphi) < 0.2) & (dphi >= 0)
idx_3j = ((pi - dphi) >= 0.2) & (dphi >= 0)
# get normalization factor
dphi_2j = weights[idx_2j].sum()
dphi_3j = weights[idx_3j].sum()
weight_up = np.ones(len(weights))
weight_dn = np.ones(len(weights))
weight_up[idx_2j] -= (dphi_3j / dphi_2j) * rel_error
weight_dn[idx_2j] += (dphi_3j / dphi_2j) * rel_error
weight_up[idx_3j] += rel_error
weight_dn[idx_3j] -= rel_error
weight_up *= weights
weight_dn *= weights
up_hist = nominal.clone(shallow=True, name=sample_nom.name + '_QCDscale_ggH3in_UP')
up_hist.Reset()
dn_hist = nominal.clone(shallow=True, name=sample_nom.name + '_QCDscale_ggH3in_DOWN')
dn_hist.Reset()
fill_hist(up_hist, scores, weight_up)
fill_hist(dn_hist, scores, weight_dn)
if uniform:
up_hist = uniform_hist(up_hist)
dn_hist = uniform_hist(dn_hist)
shape = histfactory.HistoSys('QCDscale_ggH3in',
low=dn_hist,
high=up_hist)
norm, shape = histfactory.split_norm_shape(shape, sample_nom)
sample.AddHistoSys(shape)
def histfactory(self, sample, category, systematics=True):
if not systematics:
return
if len(self.modes) != 1:
raise TypeError(
'histfactory sample only valid for single production mode')
if len(self.masses) != 1:
raise TypeError(
'histfactory sample only valid for single mass point')
mode = self.modes[0]
if mode in ('Z', 'W'):
_qcd_scale_mode = 'VH'
else:
_qcd_scale_mode = self.MODES_WORKSPACE[mode]
# QCD_SCALE
for qcd_scale_term, qcd_scale_mode, qcd_scale_category, values in self.QCD_SCALE:
if qcd_scale_mode == _qcd_scale_mode and qcd_scale_category.lower() in category.name.lower():
high, low = map(float, values.split('/'))
sample.AddOverallSys(qcd_scale_term, low, high)
# GEN_QMASS
for qmass_term, qmass_mode, qmass_category, values in self.GEN_QMASS:
if qmass_mode == _qcd_scale_mode and qmass_category.lower() in category.name.lower():
high, low = map(float, values.split('/'))
sample.AddOverallSys(qmass_term, low, high)
# BR_tautau
_, (br_up, br_down) = yellowhiggs.br(
self.mass, 'tautau', error_type='factor')
sample.AddOverallSys('ATLAS_BR_tautau', br_down, br_up)
# <NormFactor Name="mu_BR_tautau" Val="1" Low="0" High="200" />
sample.AddNormFactor('mu_BR_tautau', 1., 0., 200., True)
if self.year == 2011:
energy = 7
elif self.year == 2012:
energy = 8
else:
raise ValueError(
"collision energy is unknown for year {0:d}".format(self.year))
#mu_XS[energy]_[mode]
#_, (xs_up, xs_down) = yellowhiggs.xs(
# energy, self.mass, self.MODES_DICT[self.mode][0],
# error_type='factor')
#sample.AddOverallSys(
# 'mu_XS{0:d}_{1}'.format(energy, self.MODES_WORKSPACE[self.mode]),
# xs_down, xs_up)
sample.AddNormFactor(
'mu_XS{0:d}_{1}'.format(energy, self.MODES_WORKSPACE[self.mode]),
1., 0., 200., True)
# https://twiki.cern.ch/twiki/bin/viewauth/AtlasProtected/HSG4Uncertainties
# underlying event uncertainty in the VBF category
if 'vbf' in category.name.lower():
if mode == 'gg':
sample.AddOverallSys('ATLAS_UE_gg', 0.7, 1.3)
elif mode == 'VBF':
sample.AddOverallSys('ATLAS_UE_qq', 0.94, 1.06)
# pdf uncertainty
if mode == 'gg':
if energy == 8:
sample.AddOverallSys('pdf_Higgs_gg', 0.93, 1.08)
else: # 7 TeV
sample.AddOverallSys('pdf_Higgs_gg', 0.92, 1.08)
else:
if energy == 8:
sample.AddOverallSys('pdf_Higgs_qq', 0.97, 1.03)
else: # 7 TeV
sample.AddOverallSys('pdf_Higgs_qq', 0.98, 1.03)
# QCDscale_ggH3in MVA only
if mode == 'gg' and category.name == 'vbf':
up = self.QCDscale_ggH3in_file.up_fit
dn = self.QCDscale_ggH3in_file.dn_fit
nom = sample.hist
up_hist = nom.clone(shallow=True, name=nom.name + '_QCDscale_ggH3in_UP')
dn_hist = nom.clone(shallow=True, name=nom.name + '_QCDscale_ggH3in_DOWN')
up_hist *= up
dn_hist *= dn
shape = histfactory.HistoSys('QCDscale_ggH3in',
low=dn_hist,
high=up_hist)
norm, shape = histfactory.split_norm_shape(shape, nom)
sample.AddHistoSys(shape)
def histfactory(self,
sample,
category,
systematics=False,
rec=None,
weights=None,
mva=False,
uniform=False,
nominal=None):
if not systematics:
return
if len(self.modes) != 1:
raise TypeError(
'histfactory sample only valid for single production mode')
if len(self.masses) != 1:
raise TypeError(
'histfactory sample only valid for single mass point')
# isolation systematic
sample.AddOverallSys('ATLAS_ANA_HH_{0:d}_Isolation'.format(self.year),
1. - 0.06, 1. + 0.06)
mode = self.modes[0]
if mode in ('Z', 'W'):
_uncert_mode = 'VH'
else:
_uncert_mode = self.MODES_WORKSPACE[mode]
if self.year == 2011:
energy = 7
elif self.year == 2012:
energy = 8
else:
raise ValueError(
"collision energy is unknown for year {0:d}".format(self.year))
# QCD_SCALE
for qcd_scale_term, qcd_scale_mode, qcd_scale_category, values in self.QCD_SCALE:
if qcd_scale_mode == _uncert_mode and qcd_scale_category == category.name:
high, low = map(float, values.split('/'))
sample.AddOverallSys(qcd_scale_term, low, high)
# UE UNCERTAINTY
for ue_term, ue_mode, ue_category, values in self.UE_UNCERT:
if ue_mode == _uncert_mode and ue_category == category.name:
high, low = map(float, values.split('/'))
sample.AddOverallSys(ue_term, low, high)
# PDF ACCEPTANCE UNCERTAINTY (OverallSys)
for pdf_term, pdf_mode, pdf_category, values in self.PDF_ACCEPT_NORM_UNCERT:
if pdf_mode == _uncert_mode and pdf_category == category.name:
high, low = map(float, values.split('/'))
sample.AddOverallSys(pdf_term, low, high)
sample_nom = sample.hist
# PDF ACCEPTANCE UNCERTAINTY (HistoSys) ONLY FOR MVA
if mva:
for pdf_term, pdf_mode, pdf_category, hist_names in self.PDF_ACCEPT_SHAPE_UNCERT:
if pdf_mode == _uncert_mode and pdf_category == category.name:
high_name, low_name = hist_names.format(energy).split('/')
high_shape, low_shape = self.PDF_ACCEPT_file[
high_name], self.PDF_ACCEPT_file[low_name]
if len(high_shape) != len(sample.hist):
log.warning("skipping pdf acceptance shape systematic "
"since histograms are not compatible")
continue
high = sample_nom.Clone(shallow=True,
name=sample_nom.name +
'_{0}_UP'.format(pdf_term))
low = sample_nom.Clone(shallow=True,
name=sample_nom.name +
'_{0}_DOWN'.format(pdf_term))
high *= high_shape
low *= low_shape
histsys = histfactory.HistoSys(pdf_term,
low=low,
high=high)
sample.AddHistoSys(histsys)
# BR_tautau
_, (br_up, br_down) = yellowhiggs.br(self.mass,
'tautau',
error_type='factor')
sample.AddOverallSys('ATLAS_BR_tautau', br_down, br_up)
# <NormFactor Name="mu_BR_tautau" Val="1" Low="0" High="200" />
sample.AddNormFactor('mu_BR_tautau', 1., 0., 200., True)
#mu_XS[energy]_[mode]
#_, (xs_up, xs_down) = yellowhiggs.xs(
# energy, self.mass, self.MODES_DICT[self.mode][0],
# error_type='factor')
#sample.AddOverallSys(
# 'mu_XS{0:d}_{1}'.format(energy, self.MODES_WORKSPACE[self.mode]),
# xs_down, xs_up)
sample.AddNormFactor(
'mu_XS{0:d}_{1}'.format(energy, self.MODES_WORKSPACE[self.mode]),
1., 0., 200., True)
# https://twiki.cern.ch/twiki/bin/viewauth/AtlasProtected/HSG4Uncertainties
# pdf uncertainty
if mode == 'gg':
if energy == 8:
sample.AddOverallSys('pdf_Higgs_gg', 0.93, 1.08)
else: # 7 TeV
sample.AddOverallSys('pdf_Higgs_gg', 0.92, 1.08)
else:
if energy == 8:
sample.AddOverallSys('pdf_Higgs_qq', 0.97, 1.03)
else: # 7 TeV
sample.AddOverallSys('pdf_Higgs_qq', 0.98, 1.03)
# EWK NLO CORRECTION FOR VBF ONLY
if mode == 'VBF':
sample.AddOverallSys('NLO_EW_Higgs', 0.98, 1.02)
# QCDscale_ggH3in HistoSys ONLY FOR MVA
# also see ggH3in script
if mva and mode == 'gg' and category.name == 'vbf':
Rel_Error_2j = 0.215
Error_exc = 0.08613046469238815 # Abs error on the exclusive xsec
xsec_exc = 0.114866523583739 # Exclusive Xsec
Error_3j = sqrt(Error_exc**2 - (Rel_Error_2j * xsec_exc)**2)
rel_error = Error_3j / xsec_exc
dphi = rec['true_dphi_jj_higgs_no_overlap']
scores = rec['classifier']
idx_2j = ((pi - dphi) < 0.2) & (dphi >= 0)
idx_3j = ((pi - dphi) >= 0.2) & (dphi >= 0)
# get normalization factor
dphi_2j = weights[idx_2j].sum()
dphi_3j = weights[idx_3j].sum()
weight_up = np.ones(len(weights))
weight_dn = np.ones(len(weights))
weight_up[idx_2j] -= (dphi_3j / dphi_2j) * rel_error
weight_dn[idx_2j] += (dphi_3j / dphi_2j) * rel_error
weight_up[idx_3j] += rel_error
weight_dn[idx_3j] -= rel_error
weight_up *= weights
weight_dn *= weights
up_hist = nominal.clone(shallow=True,
name=sample_nom.name +
'_QCDscale_ggH3in_UP')
up_hist.Reset()
dn_hist = nominal.clone(shallow=True,
name=sample_nom.name +
'_QCDscale_ggH3in_DOWN')
dn_hist.Reset()
fill_hist(up_hist, scores, weight_up)
fill_hist(dn_hist, scores, weight_dn)
if uniform:
up_hist = uniform_hist(up_hist)
dn_hist = uniform_hist(dn_hist)
shape = histfactory.HistoSys('QCDscale_ggH3in',
low=dn_hist,
high=up_hist)
norm, shape = histfactory.split_norm_shape(shape, sample_nom)
sample.AddHistoSys(shape)
