def run_test(test_data):
''' Used the test_data to fit the number of events for each process
'''
global config
data_scale = 1.2
fit_data_collection = FitDataCollection()
for fit_variable, fit_input in test_data.iteritems():
# create the histograms
mc_histograms = {}
for sample, h_input in fit_input.iteritems():
mc_histograms[sample] = value_tuplelist_to_hist(
h_input['distribution'], fit_variable_bin_edges[fit_variable])
real_data = sum(mc_histograms[sample]
for sample in mc_histograms.keys())
# scale data so that the fit does not start in the minimum
real_data.Scale(data_scale)
fit_data = FitData(real_data,
mc_histograms,
fit_boundaries=config.fit_boundaries[fit_variable])
fit_data_collection.add(fit_data, fit_variable)
# do fit
fitter = Minuit(fit_data_collection)
fitter.fit()
fit_results = fitter.results
# calculate chi2 for each sample
chi2_results = {}
for sample in fit_results.keys():
true_normalisation = fit_input[sample]['normalisation'] * data_scale
# fit_result, fit_error = fit_results[sample]
# chi2 = pow( true_normalisation - fit_result, 2 ) / pow( fit_error, 2 )
fit_result, _ = fit_results[sample]
chi2 = pow(true_normalisation - fit_result, 2)
chi2_results[sample] = chi2
return chi2_results
def simultaneous_fit(self, histograms):
from dps.utils.Fitting import FitData, FitDataCollection, Minuit
print('not in production yet')
fitter = None
fit_data_collection = FitDataCollection()
for fit_variable in self.fit_variables:
mc_histograms = {
'TTJet': histograms['TTJet'],
'SingleTop': histograms['SingleTop'],
'V+Jets': histograms['V+Jets'],
'QCD': histograms['QCD'],
}
h_data = histograms['data']
fit_data = FitData(h_data, mc_histograms,
fit_boundaries=self.config.fit_boundaries[fit_variable])
fit_data_collection.add(fit_data, name=fit_variable)
fitter = Minuit(fit_data_collection)
fitter.fit()
fit_results = fitter.readResults()
normalisation = fit_data_collection.mc_normalisation(
self.fit_variables[0])
normalisation_errors = fit_data_collection.mc_normalisation_errors(
self.fit_variables[0])
print normalisation, normalisation_errors
def run_test ( test_data ):
''' Used the test_data to fit the number of events for each process
'''
global config
data_scale = 1.2
fit_data_collection = FitDataCollection()
for fit_variable, fit_input in test_data.iteritems():
# create the histograms
mc_histograms = {}
for sample, h_input in fit_input.iteritems():
mc_histograms[sample] = value_tuplelist_to_hist( h_input['distribution'],
fit_variable_bin_edges[fit_variable] )
real_data = sum( mc_histograms[sample] for sample in mc_histograms.keys() )
# scale data so that the fit does not start in the minimum
real_data.Scale( data_scale )
fit_data = FitData( real_data, mc_histograms, fit_boundaries = config.fit_boundaries[fit_variable] )
fit_data_collection.add( fit_data, fit_variable )
# do fit
fitter = Minuit( fit_data_collection )
fitter.fit()
fit_results = fitter.results
# calculate chi2 for each sample
chi2_results = {}
for sample in fit_results.keys():
true_normalisation = fit_input[sample]['normalisation'] * data_scale
# fit_result, fit_error = fit_results[sample]
# chi2 = pow( true_normalisation - fit_result, 2 ) / pow( fit_error, 2 )
fit_result, _ = fit_results[sample]
chi2 = pow( true_normalisation - fit_result, 2 )
chi2_results[sample] = chi2
return chi2_results
def setUp(self):
# create histograms
h_bkg1_1 = Hist(100, 40, 200, title="Background")
h_signal_1 = h_bkg1_1.Clone(title="Signal")
h_data_1 = h_bkg1_1.Clone(title="Data")
h_bkg1_2 = h_bkg1_1.Clone(title="Background")
h_signal_2 = h_bkg1_1.Clone(title="Signal")
h_data_2 = h_bkg1_1.Clone(title="Data")
# fill the histograms with our distributions
map(h_bkg1_1.Fill, x1)
map(h_signal_1.Fill, x2)
map(h_data_1.Fill, x1_obs)
map(h_data_1.Fill, x2_obs)
map(h_bkg1_2.Fill, x3)
map(h_signal_2.Fill, x4)
map(h_data_2.Fill, x3_obs)
map(h_data_2.Fill, x4_obs)
h_data_1.Scale(data_scale)
h_data_2.Scale(data_scale)
histograms_1 = {"signal": h_signal_1, "bkg1": h_bkg1_1}
histograms_2 = {"signal": h_signal_2, "bkg1": h_bkg1_2}
fit_data_1 = FitData(h_data_1, histograms_1, fit_boundaries=(40, 200))
fit_data_2 = FitData(h_data_2, histograms_2, fit_boundaries=(40, 200))
single_fit_collection = FitDataCollection()
single_fit_collection.add(fit_data_1)
collection_1 = FitDataCollection()
collection_1.add(fit_data_1, "var1")
collection_1.add(fit_data_2, "var2")
collection_2 = FitDataCollection()
collection_2.add(fit_data_1, "var1")
collection_2.add(fit_data_2, "var2")
collection_2.set_normalisation_constraints({"bkg1": 0.5})
collection_3 = FitDataCollection()
collection_3.add(fit_data_1, "var1")
collection_3.add(fit_data_2, "var2")
collection_3.set_normalisation_constraints({"bkg1": 0.001})
self.minuit_fitter = Minuit(single_fit_collection)
self.minuit_fitter.fit()
self.simultaneous_fit = Minuit(collection_1)
self.simultaneous_fit.fit()
self.simultaneous_fit_with_constraints = Minuit(collection_2)
self.simultaneous_fit_with_constraints.fit()
self.simultaneous_fit_with_bad_constraints = Minuit(collection_3)
self.simultaneous_fit_with_bad_constraints.fit()
def setUp(self):
# create histograms
h_bkg1_1 = Hist(100, 40, 200, title='Background')
h_signal_1 = h_bkg1_1.Clone(title='Signal')
h_data_1 = h_bkg1_1.Clone(title='Data')
h_bkg1_2 = h_bkg1_1.Clone(title='Background')
h_signal_2 = h_bkg1_1.Clone(title='Signal')
h_data_2 = h_bkg1_1.Clone(title='Data')
# fill the histograms with our distributions
map(h_bkg1_1.Fill, x1)
map(h_signal_1.Fill, x2)
map(h_data_1.Fill, x1_obs)
map(h_data_1.Fill, x2_obs)
map(h_bkg1_2.Fill, x3)
map(h_signal_2.Fill, x4)
map(h_data_2.Fill, x3_obs)
map(h_data_2.Fill, x4_obs)
h_data_1.Scale(data_scale)
h_data_2.Scale(data_scale)
histograms_1 = {'signal': h_signal_1, 'bkg1': h_bkg1_1}
histograms_2 = {'signal': h_signal_2, 'bkg1': h_bkg1_2}
fit_data_1 = FitData(h_data_1, histograms_1, fit_boundaries=(40, 200))
fit_data_2 = FitData(h_data_2, histograms_2, fit_boundaries=(40, 200))
single_fit_collection = FitDataCollection()
single_fit_collection.add(fit_data_1)
collection_1 = FitDataCollection()
collection_1.add(fit_data_1, 'var1')
collection_1.add(fit_data_2, 'var2')
collection_2 = FitDataCollection()
collection_2.add(fit_data_1, 'var1')
collection_2.add(fit_data_2, 'var2')
collection_2.set_normalisation_constraints({'bkg1': 0.5})
collection_3 = FitDataCollection()
collection_3.add(fit_data_1, 'var1')
collection_3.add(fit_data_2, 'var2')
collection_3.set_normalisation_constraints({'bkg1': 0.001})
self.minuit_fitter = Minuit(single_fit_collection)
self.minuit_fitter.fit()
self.simultaneous_fit = Minuit(collection_1)
self.simultaneous_fit.fit()
self.simultaneous_fit_with_constraints = Minuit(collection_2)
self.simultaneous_fit_with_constraints.fit()
self.simultaneous_fit_with_bad_constraints = Minuit(collection_3)
self.simultaneous_fit_with_bad_constraints.fit()
class Test(unittest.TestCase):
def setUp(self):
# create histograms
h_bkg1_1 = Hist(100, 40, 200, title='Background')
h_signal_1 = h_bkg1_1.Clone(title='Signal')
h_data_1 = h_bkg1_1.Clone(title='Data')
h_bkg1_2 = h_bkg1_1.Clone(title='Background')
h_signal_2 = h_bkg1_1.Clone(title='Signal')
h_data_2 = h_bkg1_1.Clone(title='Data')
# fill the histograms with our distributions
map(h_bkg1_1.Fill, x1)
map(h_signal_1.Fill, x2)
map(h_data_1.Fill, x1_obs)
map(h_data_1.Fill, x2_obs)
map(h_bkg1_2.Fill, x3)
map(h_signal_2.Fill, x4)
map(h_data_2.Fill, x3_obs)
map(h_data_2.Fill, x4_obs)
h_data_1.Scale(data_scale)
h_data_2.Scale(data_scale)
histograms_1 = {'signal': h_signal_1, 'bkg1': h_bkg1_1}
histograms_2 = {'signal': h_signal_2, 'bkg1': h_bkg1_2}
fit_data_1 = FitData(h_data_1, histograms_1, fit_boundaries=(40, 200))
fit_data_2 = FitData(h_data_2, histograms_2, fit_boundaries=(40, 200))
single_fit_collection = FitDataCollection()
single_fit_collection.add(fit_data_1)
collection_1 = FitDataCollection()
collection_1.add(fit_data_1, 'var1')
collection_1.add(fit_data_2, 'var2')
collection_2 = FitDataCollection()
collection_2.add(fit_data_1, 'var1')
collection_2.add(fit_data_2, 'var2')
collection_2.set_normalisation_constraints({'bkg1': 0.5})
collection_3 = FitDataCollection()
collection_3.add(fit_data_1, 'var1')
collection_3.add(fit_data_2, 'var2')
collection_3.set_normalisation_constraints({'bkg1': 0.001})
self.minuit_fitter = Minuit(single_fit_collection)
self.minuit_fitter.fit()
self.simultaneous_fit = Minuit(collection_1)
self.simultaneous_fit.fit()
self.simultaneous_fit_with_constraints = Minuit(collection_2)
self.simultaneous_fit_with_constraints.fit()
self.simultaneous_fit_with_bad_constraints = Minuit(collection_3)
self.simultaneous_fit_with_bad_constraints.fit()
def tearDown(self):
pass
def test_normalisation(self):
normalisation = self.minuit_fitter.normalisation
self.assertAlmostEqual(normalisation["data"],
N_data,
delta=sqrt(N_data))
self.assertAlmostEqual(normalisation["bkg1"],
N_bkg1,
delta=sqrt(N_bkg1))
self.assertAlmostEqual(normalisation["signal"],
N_signal,
delta=sqrt(N_signal))
def test_result(self):
results = self.minuit_fitter.readResults()
self.assertAlmostEqual(N_signal_obs * data_scale,
results['signal'][0],
delta=2 * results['signal'][1])
self.assertAlmostEqual(N_bkg1_obs * data_scale,
results['bkg1'][0],
delta=2 * results['bkg1'][1])
def test_result_simultaneous(self):
results = self.simultaneous_fit.readResults()
self.assertAlmostEqual(N_signal_obs * data_scale,
results['signal'][0],
delta=2 * results['signal'][1])
self.assertAlmostEqual(N_bkg1_obs * data_scale,
results['bkg1'][0],
delta=2 * results['bkg1'][1])
def test_result_simultaneous_with_constraints(self):
results = self.simultaneous_fit_with_constraints.readResults()
self.assertAlmostEqual(N_signal_obs * data_scale,
results['signal'][0],
delta=2 * results['signal'][1])
self.assertAlmostEqual(N_bkg1_obs * data_scale,
results['bkg1'][0],
delta=2 * results['bkg1'][1])
def test_result_simultaneous_with_bad_constraints(self):
results = self.simultaneous_fit_with_bad_constraints.readResults()
self.assertNotAlmostEqual(N_signal_obs * data_scale,
results['signal'][0],
delta=results['signal'][1])
self.assertNotAlmostEqual(N_bkg1_obs * data_scale,
results['bkg1'][0],
delta=results['bkg1'][1])
def test_relative_error(self):
results = self.minuit_fitter.readResults()
self.assertLess(results['signal'][1] / results['signal'][0], 0.1)
self.assertLess(results['bkg1'][1] / results['bkg1'][0], 0.1)
h_t3.Draw('SAME HIST')
h_t4.Draw('SAME HIST')
templates = {
}
if useT1: templates['t1'] = h_t1
if useT2: templates['t2'] = h_t2
if useT3: templates['t3'] = h_t3
if useT4: templates['t4'] = h_t4
fit_data = FitData( h_data, templates, fit_boundaries = ( 0, h_data.nbins() ) )
fit_collection = FitDataCollection()
fit_collection.add( fit_data )
minuit_fitter = Minuit( fit_collection, method = 'logLikelihood', verbose = True )
minuit_fitter.fit()
results = minuit_fitter.readResults()
c.cd(2)
ymax = h_data.GetBinContent( h_data.GetMaximumBin() ) * 1.1
h_data.GetYaxis().SetRangeUser(0,ymax)
h_data.Draw('PE')
leg = Legend(nTemplates+2)
leg.AddEntry( h_data, style='LEP')
h_tSumAfter=0
print '----> Target \t Fit Result'
if useT1:
h_t1After = h_t1.Clone()
class Test(unittest.TestCase):
def setUp(self):
# create histograms
h_bkg1_1 = Hist(100, 40, 200, title="Background")
h_signal_1 = h_bkg1_1.Clone(title="Signal")
h_data_1 = h_bkg1_1.Clone(title="Data")
h_bkg1_2 = h_bkg1_1.Clone(title="Background")
h_signal_2 = h_bkg1_1.Clone(title="Signal")
h_data_2 = h_bkg1_1.Clone(title="Data")
# fill the histograms with our distributions
map(h_bkg1_1.Fill, x1)
map(h_signal_1.Fill, x2)
map(h_data_1.Fill, x1_obs)
map(h_data_1.Fill, x2_obs)
map(h_bkg1_2.Fill, x3)
map(h_signal_2.Fill, x4)
map(h_data_2.Fill, x3_obs)
map(h_data_2.Fill, x4_obs)
h_data_1.Scale(data_scale)
h_data_2.Scale(data_scale)
histograms_1 = {"signal": h_signal_1, "bkg1": h_bkg1_1}
histograms_2 = {"signal": h_signal_2, "bkg1": h_bkg1_2}
fit_data_1 = FitData(h_data_1, histograms_1, fit_boundaries=(40, 200))
fit_data_2 = FitData(h_data_2, histograms_2, fit_boundaries=(40, 200))
single_fit_collection = FitDataCollection()
single_fit_collection.add(fit_data_1)
collection_1 = FitDataCollection()
collection_1.add(fit_data_1, "var1")
collection_1.add(fit_data_2, "var2")
collection_2 = FitDataCollection()
collection_2.add(fit_data_1, "var1")
collection_2.add(fit_data_2, "var2")
collection_2.set_normalisation_constraints({"bkg1": 0.5})
collection_3 = FitDataCollection()
collection_3.add(fit_data_1, "var1")
collection_3.add(fit_data_2, "var2")
collection_3.set_normalisation_constraints({"bkg1": 0.001})
self.minuit_fitter = Minuit(single_fit_collection)
self.minuit_fitter.fit()
self.simultaneous_fit = Minuit(collection_1)
self.simultaneous_fit.fit()
self.simultaneous_fit_with_constraints = Minuit(collection_2)
self.simultaneous_fit_with_constraints.fit()
self.simultaneous_fit_with_bad_constraints = Minuit(collection_3)
self.simultaneous_fit_with_bad_constraints.fit()
def tearDown(self):
pass
def test_normalisation(self):
normalisation = self.minuit_fitter.normalisation
self.assertAlmostEqual(normalisation["data"], N_data, delta=sqrt(N_data))
self.assertAlmostEqual(normalisation["bkg1"], N_bkg1, delta=sqrt(N_bkg1))
self.assertAlmostEqual(normalisation["signal"], N_signal, delta=sqrt(N_signal))
def test_result(self):
results = self.minuit_fitter.readResults()
self.assertAlmostEqual(N_signal_obs * data_scale, results["signal"][0], delta=2 * results["signal"][1])
self.assertAlmostEqual(N_bkg1_obs * data_scale, results["bkg1"][0], delta=2 * results["bkg1"][1])
def test_result_simultaneous(self):
results = self.simultaneous_fit.readResults()
self.assertAlmostEqual(N_signal_obs * data_scale, results["signal"][0], delta=2 * results["signal"][1])
self.assertAlmostEqual(N_bkg1_obs * data_scale, results["bkg1"][0], delta=2 * results["bkg1"][1])
def test_result_simultaneous_with_constraints(self):
results = self.simultaneous_fit_with_constraints.readResults()
self.assertAlmostEqual(N_signal_obs * data_scale, results["signal"][0], delta=2 * results["signal"][1])
self.assertAlmostEqual(N_bkg1_obs * data_scale, results["bkg1"][0], delta=2 * results["bkg1"][1])
def test_result_simultaneous_with_bad_constraints(self):
results = self.simultaneous_fit_with_bad_constraints.readResults()
self.assertNotAlmostEqual(N_signal_obs * data_scale, results["signal"][0], delta=results["signal"][1])
self.assertNotAlmostEqual(N_bkg1_obs * data_scale, results["bkg1"][0], delta=results["bkg1"][1])
def test_relative_error(self):
results = self.minuit_fitter.readResults()
self.assertLess(results["signal"][1] / results["signal"][0], 0.1)
self.assertLess(results["bkg1"][1] / results["bkg1"][0], 0.1)
h_t2.Draw('SAME HIST')
h_t3.Draw('SAME HIST')
h_t4.Draw('SAME HIST')
templates = {}
if useT1: templates['t1'] = h_t1
if useT2: templates['t2'] = h_t2
if useT3: templates['t3'] = h_t3
if useT4: templates['t4'] = h_t4
fit_data = FitData(h_data, templates, fit_boundaries=(0, h_data.nbins()))
fit_collection = FitDataCollection()
fit_collection.add(fit_data)
minuit_fitter = Minuit(fit_collection, method='logLikelihood', verbose=True)
minuit_fitter.fit()
results = minuit_fitter.readResults()
c.cd(2)
ymax = h_data.GetBinContent(h_data.GetMaximumBin()) * 1.1
h_data.GetYaxis().SetRangeUser(0, ymax)
h_data.Draw('PE')
leg = Legend(nTemplates + 2)
leg.AddEntry(h_data, style='LEP')
h_tSumAfter = 0
print '----> Target \t Fit Result'
if useT1:
h_t1After = h_t1.Clone()
def get_fitted_normalisation_from_ROOT( channel, input_files, variable, met_systematic, met_type, b_tag_bin, treePrefix, weightBranch, scale_factors = None ):
'''
Retrieves the number of ttbar events from fits to one or more distribution
(fit_variables) for each bin in the variable.
'''
global use_fitter, measurement_config, verbose, fit_variables, options
# results and initial values are the same across different fit variables
# templates are not
results = {}
initial_values = {}
templates = {fit_variable: {} for fit_variable in fit_variables}
for variable_bin in variable_bins_ROOT[variable]:
fitter = None
fit_data_collection = FitDataCollection()
for fit_variable in fit_variables:
histograms = get_histograms( channel,
input_files,
variable = variable,
met_systematic = met_systematic,
met_type = met_type,
variable_bin = variable_bin,
b_tag_bin = b_tag_bin,
rebin = measurement_config.rebin[fit_variable],
fit_variable = fit_variable,
scale_factors = scale_factors,
treePrefix = treePrefix,
weightBranch = weightBranch,
)
# create data sets
h_fit_variable_signal = None
mc_histograms = None
# if options.make_combined_signal:
# h_fit_variable_signal = histograms['TTJet'] + histograms['SingleTop']
# mc_histograms = {
# 'signal' : h_fit_variable_signal,
# 'V+Jets': histograms['V+Jets'],
# 'QCD': histograms['QCD'],
# }
# else:
mc_histograms = {
'TTJet': histograms['TTJet'],
'SingleTop': histograms['SingleTop'],
'V+Jets': histograms['V+Jets'],
'QCD': histograms['QCD'],
}
h_data = histograms['data']
# if options.closure_test:
# ct_type = options.closure_test_type
# ct_norm = closure_tests[ct_type]
# h_data = histograms['TTJet'] * ct_norm['TTJet'] + histograms['SingleTop'] * ct_norm['SingleTop'] + histograms['V+Jets'] * ct_norm['V+Jets'] + histograms['QCD'] * ct_norm['QCD']
fit_data = FitData( h_data,
mc_histograms,
fit_boundaries = measurement_config.fit_boundaries[fit_variable] )
fit_data_collection.add( fit_data, name = fit_variable )
# if options.enable_constraints:
# fit_data_collection.set_normalisation_constraints( {'QCD': 2.0, 'V+Jets': 0.5} )
if use_fitter == 'RooFit':
fitter = RooFitFit( fit_data_collection )
elif use_fitter == 'Minuit':
fitter = Minuit( fit_data_collection, verbose = verbose )
else: # not recognised
sys.stderr.write( 'Do not recognise fitter "%s". Using default (Minuit).\n' % fitter )
fitter = Minuit ( fit_data_collection )
if verbose:
print "FITTING: " + channel + '_' + variable + '_' + variable_bin + '_' + met_type + '_' + b_tag_bin
fitter.fit()
fit_results = fitter.readResults()
normalisation = fit_data_collection.mc_normalisation( fit_variables[0] )
normalisation_errors = fit_data_collection.mc_normalisation_errors( fit_variables[0] )
# if options.make_combined_signal:
# N_ttbar_before_fit = histograms['TTJet'].Integral()
# N_SingleTop_before_fit = histograms['SingleTop'].Integral()
# N_ttbar_error_before_fit = sum(histograms['TTJet'].yerravg())
# N_SingleTop_error_before_fit = sum(histograms['SingleTop'].yerravg())
# N_Higgs_before_fit = 0
# N_Higgs_error_before_fit = 0
# if measurement_config.include_higgs:
# N_Higgs_before_fit = histograms['Higgs'].Integral()
# N_Higgs_error_before_fit = sum(histograms['Higgs'].yerravg())
# if (N_SingleTop_before_fit != 0):
# TTJet_SingleTop_ratio = (N_ttbar_before_fit + N_Higgs_before_fit) / N_SingleTop_before_fit
# else:
# print 'Bin ', variable_bin, ': ttbar/singleTop ratio undefined for %s channel! Setting to 0.' % channel
# TTJet_SingleTop_ratio = 0
# N_ttbar_all, N_SingleTop = decombine_result(fit_results['signal'], TTJet_SingleTop_ratio)
# if (N_Higgs_before_fit != 0):
# TTJet_Higgs_ratio = N_ttbar_before_fit/ N_Higgs_before_fit
# else:
# TTJet_Higgs_ratio = 0
# N_ttbar, N_Higgs = decombine_result(N_ttbar_all, TTJet_Higgs_ratio)
# fit_results['TTJet'] = N_ttbar
# fit_results['SingleTop'] = N_SingleTop
# fit_results['Higgs'] = N_Higgs
# normalisation['TTJet'] = N_ttbar_before_fit
# normalisation['SingleTop'] = N_SingleTop_before_fit
# normalisation['Higgs'] = N_Higgs_before_fit
# normalisation_errors['TTJet'] = N_ttbar_error_before_fit
# normalisation_errors['SingleTop'] = N_SingleTop_error_before_fit
# normalisation_errors['Higgs'] = N_Higgs_error_before_fit
if results == {}: # empty
initial_values['data'] = [( normalisation['data'], normalisation_errors['data'] )]
for fit_variable in fit_variables:
templates[fit_variable]['data'] = [fit_data_collection.vectors( fit_variable )['data']]
for sample in fit_results.keys():
results[sample] = [fit_results[sample]]
initial_values[sample] = [( normalisation[sample], normalisation_errors[sample] )]
if sample in ['TTJet', 'SingleTop', 'Higgs'] and options.make_combined_signal:
continue
for fit_variable in fit_variables:
templates[fit_variable][sample] = [fit_data_collection.vectors( fit_variable )[sample]]
else:
initial_values['data'].append( [normalisation['data'], normalisation_errors['data']] )
for fit_variable in fit_variables:
templates[fit_variable]['data'].append( fit_data_collection.vectors( fit_variable )['data'] )
for sample in fit_results.keys():
results[sample].append( fit_results[sample] )
initial_values[sample].append( [normalisation[sample], normalisation_errors[sample]] )
if sample in ['TTJet', 'SingleTop', 'Higgs'] and options.make_combined_signal:
continue
for fit_variable in fit_variables:
templates[fit_variable][sample].append( fit_data_collection.vectors( fit_variable )[sample] )
# print results
# print "results = ", results
# print 'templates = ',templates
return results, initial_values, templates
