def build_fit_function(self, fitted, mc_tmplts, x_min, x_max):
self.x_min, self.x_max = x_min, x_max
self.fitted = fitted
self.mc_tmplts = mc_tmplts
theta_root_wrp = varial.wrappers.Wrapper(
name="ThetaHistos",
histo__DATA=fitted.histo,
)
self.template_names = []
for i, tmplt in enumerate(mc_tmplts):
name = 'template%02d' % (i + 1)
self.template_names.append(name)
setattr(theta_root_wrp, 'histo__' + name, tmplt.histo)
self._store_histos_for_theta(theta_root_wrp)
theta_auto.config.workdir = varial.analysis.cwd
self.model = theta_auto.build_model_from_rootfile(
os.path.join(varial.analysis.cwd, "ThetaHistos.root"),
include_mc_uncertainties=True
)
self.model.set_signal_processes([self.template_names[-1]])
for tmplt_name in self.template_names[:-1]:
self.model.get_coeff(
"histo", tmplt_name).add_factor(
'id', parameter='bg_' + tmplt_name)
self.model.distribution.set_distribution(
'bg_' + tmplt_name,
'gauss', 1.0, theta_auto.inf, [0.0, theta_auto.inf]
)
self.ndf = fitted.histo.GetNbinsX() - len(self.template_names)
def get_bkg_only_model_flat(input_file=input0H, signal='TpTp_M-*', histogram_filter=lambda w: True):
model = theta_auto.build_model_from_rootfile(input_file, histogram_filter=histogram_filter, include_mc_uncertainties=True)#,histogram_filter = (lambda s: s.count('jec')==0 and s.count('jer')==0)
model.fill_histogram_zerobins()
if signal:
model.set_signal_processes(signal)
else:
model.set_signal_process_groups({'':[]})
procs = model.processes
obsvs = model.observables.keys()
for obs in obsvs:
if 'isE' in obs:
try: model.add_lognormal_uncertainty('sfel_trg', math.log(1.05), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('sfel_id', math.log(1.01), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('sfel_iso', math.log(1.01), '*', obs)
except RuntimeError: pass
elif 'isM' in obs:
try: model.add_lognormal_uncertainty('sfmu_trg', math.log(1.05), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('sfmu_id', math.log(1.01), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('sfmu_iso', math.log(1.01), '*', obs)
except RuntimeError: pass
elif 'El45' in obs:
try: model.add_lognormal_uncertainty('sfel_trg', math.log(1.02), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('luminosity', math.log(1.027), '*', '*')
except RuntimeError: pass
for obs in obsvs:
# if 'SignalRegion' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.80), 'TTbar', obs) # from ttbar CR
except RuntimeError: pass
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.80), 'WJets', obs) # from ttbar CR
except RuntimeError: pass
try: model.add_lognormal_uncertainty('QCD_rate', math.log(2.00), 'QCD', obs) # from ttbar CR
except RuntimeError: pass
try: model.add_lognormal_uncertainty('SingleTop_rate', math.log(1.16), 'SingleTop', obs) # from ttbar CR
except RuntimeError: pass
try: model.add_lognormal_uncertainty('Diboson_rate', math.log(1.15), 'Diboson', obs) # from ttbar CR
except RuntimeError: pass
dist_dict = {'mean': 0.0,
'range': [float('-inf'), float('inf')],
'typ': 'gauss',
'width': float('inf')}
# model.distribution.distributions.update({
# 'TTbar_rate' : dist_dict,
# 'WJets_rate' : dist_dict})
return model
def build_fit_function(self, fitted, tmplts, x_min, x_max):
self.x_min, self.x_max = x_min, x_max
self.fitted = fitted
self.tmplts = tmplts
theta_root_wrp = varial.wrappers.Wrapper(name="ThetaHistos")
if fitted:
setattr(theta_root_wrp, 'histo__DATA', fitted.histo)
self.template_names = []
signal_procs = []
for i, tmplt in enumerate(tmplts):
name = 'template%02d' % (i + 1)
self.template_names.append(name)
setattr(theta_root_wrp, 'histo__' + name, tmplt.histo)
if tmplt.is_signal:
signal_procs.append(name)
self._store_histos_for_theta(theta_root_wrp)
theta_auto.config.workdir = varial.analysis.cwd
theta_auto.config.report = theta_auto.html_report(os.path.join(
varial.analysis.cwd, 'theta.html'
))
plt_dir = os.path.join(varial.analysis.cwd, 'plots')
if not os.path.exists(plt_dir):
os.mkdir(plt_dir)
self.model = theta_auto.build_model_from_rootfile(
os.path.join(varial.analysis.cwd, "ThetaHistos.root"),
include_mc_uncertainties=True
)
self.model.fill_histogram_zerobins()
self.model.set_signal_processes(signal_procs)
if self.model_callback:
self.model_callback(self.model)
#for tmplt_name in self.template_names[:-1]:
# self.model.get_coeff(
# "histo", tmplt_name).add_factor(
# 'id', parameter='bg_' + tmplt_name)
# self.model.distribution.set_distribution(
# 'bg_' + tmplt_name,
# 'gauss', 1.0, theta_auto.inf, [0.0, theta_auto.inf]
# )
# TODO: get ndf from theta directly
self.ndf = tmplts[0].histo.GetNbinsX() - len(self.template_names)
def build_fit_function(self, fitted, mc_tmplts, x_min, x_max):
self.x_min, self.x_max = x_min, x_max
self.fitted = fitted
self.mc_tmplts = mc_tmplts
theta_root_wrp = wrp.Wrapper(
name="ThetaHistos",
chhadiso__DATA=fitted.histo,
chhadiso__real=mc_tmplts[1].histo,
chhadiso__fake=mc_tmplts[0].histo,
)
self._store_histos_for_theta(theta_root_wrp)
theta_auto.config.workdir = settings.dir_result
self.model = theta_auto.build_model_from_rootfile(
os.path.join(settings.dir_result, "ThetaHistos.root"),
include_mc_uncertainties=True
)
self.model.set_signal_processes(["real"])
self.model.add_lognormal_uncertainty("fake_rate", 1., "fake")
self.model.distribution.set_distribution_parameters(
'fake_rate',
width=theta_auto.inf
)
self.ndf = fitted.histo.GetNbinsX() - 2
def get_bkg_only_model(input_file=input0H, signal='TpTp_M-*', histogram_filter=lambda w: True):
model = theta_auto.build_model_from_rootfile(input_file, histogram_filter=histogram_filter, include_mc_uncertainties=True)#,histogram_filter = (lambda s: s.count('jec')==0 and s.count('jer')==0)
model.fill_histogram_zerobins()
if signal:
model.set_signal_processes(signal)
else:
model.set_signal_process_groups({'':[]})
procs = model.processes
obsvs = model.observables.keys()
for obs in obsvs:
if 'SignalRegion' in obs or 'SidebandRegion' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.087), 'TTbar', obs) # from ttbar CR
except RuntimeError: pass
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.06), 'WJets', obs) # from ttbar CR
except RuntimeError: pass
# try: model.add_lognormal_uncertainty('DYJets_rate', math.log(1.2), 'DYJets', obs) # from ttbar CR
# except RuntimeError: pass
try: model.add_lognormal_uncertainty('QCD_rate', math.log(2.00), 'QCD', obs) # from ttbar CR
except RuntimeError: pass
# try: model.add_lognormal_uncertainty('SingleTop_rate', math.log(1.16), 'SingleTop', obs) # from ttbar CR
# except RuntimeError: pass
# try: model.add_lognormal_uncertainty('Diboson_rate', math.log(1.15), 'Diboson', obs) # from ttbar CR
# except RuntimeError: pass
for obs in obsvs:
if 'isE' in obs:
try: model.add_lognormal_uncertainty('sfel_trg', math.log(1.05), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('sfel_id', math.log(1.01), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('sfel_iso', math.log(1.01), '*', obs)
except RuntimeError: pass
elif 'isM' in obs:
try: model.add_lognormal_uncertainty('sfmu_trg', math.log(1.05), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('sfmu_id', math.log(1.01), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('sfmu_iso', math.log(1.01), '*', obs)
except RuntimeError: pass
elif 'El45' in obs:
try: model.add_lognormal_uncertainty('sfel_trg', math.log(1.02), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('luminosity', math.log(1.027), '*', '*')
except RuntimeError: pass
try: model.add_lognormal_uncertainty('SingleTop_rate', math.log(1.16), 'SingleTop', '*') # from ttbar CR
except RuntimeError: pass
try: model.add_lognormal_uncertainty('Diboson_rate', math.log(1.15), 'Diboson', '*') # from ttbar CR
except RuntimeError: pass
for proc in procs:
if proc != 'TTbar': continue # and proc != 'SingleTop': continue
for obs in obsvs:
if 'nW0_nB0' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.111), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW0_nB1' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.051), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW0_nB2' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.055), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW0_nB3p' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.055), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW1p_nB0' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.111), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW1p_nB1' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.051), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW1p_nB2' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.055), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW1p_nB3p' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate',math.log(1.055), proc, obs) # from ttbar CR
except RuntimeError: pass
for proc in procs:
if proc != 'WJets': continue #proc != 'DYJets' and
for obs in obsvs:
if 'nW0_nB0' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.182), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW0_nB1' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.182), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW0_nB2' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.182), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW0_nB3p' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.182), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW1p_nB0' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.046), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW1p_nB1' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.046), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW1p_nB2' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.046), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW1p_nB3p' in obs:
try: model.add_lognormal_uncertainty('WJets_rate',math.log(1.046), proc, obs) # from Wjets CR
except RuntimeError: pass
return model
def tmp(input_file=input0H):
print input_file
model = theta_auto.build_model_from_rootfile(input_file, histogram_filter=histogram_filter, include_mc_uncertainties=True)#,histogram_filter = (lambda s: s.count('jec')==0 and s.count('jer')==0)
model.fill_histogram_zerobins()
if signal:
model.set_signal_processes(signal)
else:
model.set_signal_process_groups({'':[]})
procs = model.processes
obsvs = model.observables.keys()
for obs in obsvs:
if 'SignalRegion' in obs or 'SidebandRegion' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.087), 'TTbar', obs) # from ttbar CR
except RuntimeError: pass
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.06), 'WJets', obs) # from ttbar CR
except RuntimeError: pass
try: model.add_lognormal_uncertainty('QCD_rate', math.log(2.00), 'QCD', obs) # from ttbar CR
except RuntimeError: pass
# try: model.add_lognormal_uncertainty('SingleTop_rate', math.log(1.16), 'SingleTop', obs) # from ttbar CR
# except RuntimeError: pass
# try: model.add_lognormal_uncertainty('Diboson_rate', math.log(1.15), 'Diboson', obs) # from ttbar CR
# except RuntimeError: pass
# try: model.add_lognormal_uncertainty("btag_bc", math.log(1.03), '*', obs)
except RuntimeError: pass
for proc in procs:
if 'TpTp' in proc or 'BpBp' in proc:
try: model.add_lognormal_uncertainty("higgs_py2hw", math.log(1.05), proc, '*')
except RuntimeError:
print obs, signal, 'COULD NOT ADD higgs_py2hw UNCERTAINTY'
pass
# for obs in obsvs:
# if 'minMlb' in obs:
# try: model.add_lognormal_uncertainty("btag_bc", math.log(0.97), '*', obs)
# except RuntimeError: pass
for obs in obsvs:
if 'isE' in obs:
try: model.add_lognormal_uncertainty('sfel_trg', math.log(1.05), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('sfel_id', math.log(1.01), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('sfel_iso', math.log(1.01), '*', obs)
except RuntimeError: pass
elif 'isM' in obs:
try: model.add_lognormal_uncertainty('sfmu_trg', math.log(1.05), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('sfmu_id', math.log(1.01), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('sfmu_iso', math.log(1.01), '*', obs)
except RuntimeError: pass
elif 'El45' in obs:
try: model.add_lognormal_uncertainty('sfel_trg', math.log(1.02), '*', obs)
except RuntimeError: pass
try: model.add_lognormal_uncertainty('luminosity', math.log(1.027), '*', '*')
except RuntimeError: pass
try: model.add_lognormal_uncertainty('DYJets_rate', math.log(1.03), 'DYJets', obs) # from ttbar CR
except RuntimeError: pass
try: model.add_lognormal_uncertainty('SingleTop_rate', math.log(1.16), 'SingleTop', '*') # from ttbar CR
except RuntimeError: pass
try: model.add_lognormal_uncertainty('Diboson_rate', math.log(1.15), 'Diboson', '*') # from ttbar CR
except RuntimeError: pass
for proc in procs:
if proc != 'TTbar': continue # and proc != 'SingleTop': continue
for obs in obsvs:
if 'nW0_nB0' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.125), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW0_nB1' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.080), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW0_nB2' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.055), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW0_nB3p' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.055), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW1p_nB0' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.125), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW1p_nB1' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.080), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW1p_nB2' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate', math.log(1.055), proc, obs) # from ttbar CR
except RuntimeError: pass
if 'nW1p_nB3p' in obs:
try: model.add_lognormal_uncertainty('TTbar_rate',math.log(1.055), proc, obs) # from ttbar CR
except RuntimeError: pass
for proc in procs:
if proc != 'WJets': continue #proc != 'DYJets' and
for obs in obsvs:
if 'nW0_nB0' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.208), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW0_nB1' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.208), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW0_nB2' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.208), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW0_nB3p' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.208), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW1p_nB0' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.038), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW1p_nB1' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.038), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW1p_nB2' in obs:
try: model.add_lognormal_uncertainty('WJets_rate', math.log(1.038), proc, obs) # from Wjets CR
except RuntimeError: pass
if 'nW1p_nB3p' in obs:
try: model.add_lognormal_uncertainty('WJets_rate',math.log(1.038), proc, obs) # from Wjets CR
except RuntimeError: pass
return model
import os
import theta_auto
theta_auto.config.workdir = os.getcwd()
theta_auto.config.report = theta_auto.html_report(os.path.join(theta_auto.config.workdir, 'index.html'))
theta_auto.config.theta_dir = "/afs/cern.ch/work/h/htholen/private/cmsWorkingDir/CMSSW_5_3_11_patch6/theta"
model = theta_auto.build_model_from_rootfile("ThetaHistos.root", include_mc_uncertainties=True)
model.set_signal_processes(["real"])
model.add_lognormal_uncertainty("fake_rate", 1., "fake")
res = theta_auto.mle(model, "data", 1, chi2=True)
print res
par_values = {"beta_signal":res["real"]["beta_signal"][0][0], "fake_rate":res["real"]["fake_rate"][0][0]}
bkg_val = model.get_coeff("chhadiso", "fake").get_value(par_values)
bkg_err = bkg_val * res["real"]["fake_rate"][0][1] / res["real"]["fake_rate"][0][0]
sig_val = res["real"]["beta_signal"][0][0]
sig_err = res["real"]["beta_signal"][0][1]
