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') # 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) histograms_1 = {'signal': h_signal_1, 'bkg1': h_bkg1_1, # 'data': h_data_1 } fit_data_1 = FitData(h_data_1, histograms_1, fit_boundaries=(40, 200)) self.single_fit_collection = FitDataCollection() self.single_fit_collection.add( fit_data_1 ) # self.roofitFitter = RooFitFit(histograms_1, dataLabel='data', fit_boundries=(40, 200)) self.roofitFitter = RooFitFit(self.single_fit_collection) def tearDown(self): pass def test_normalisation(self): normalisation = self.roofitFitter.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_signal_result(self): self.roofitFitter.fit() results = self.roofitFitter.readResults() self.assertAlmostEqual(N_signal_obs, results['signal'][0], delta=2 * results['signal'][1]) self.assertAlmostEqual(N_bkg1_obs, results['bkg1'][0], delta=2 * results['bkg1'][1]) def test_constraints(self): self.single_fit_collection.set_normalisation_constraints({'signal': 0.8, 'bkg1': 0.5}) self.roofitFitter = RooFitFit(self.single_fit_collection) # self.roofitFitter.set_fit_constraints({'signal': 0.8, 'bkg1': 0.5}) self.roofitFitter.fit() results = self.roofitFitter.readResults() self.assertAlmostEqual(N_signal_obs, results['signal'][0], delta=2 * results['signal'][1]) self.assertAlmostEqual(N_bkg1_obs, results['bkg1'][0], delta=2 * results['bkg1'][1])
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') # 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) histograms_1 = { 'signal': h_signal_1, 'bkg1': h_bkg1_1, # 'data': h_data_1 } fit_data_1 = FitData(h_data_1, histograms_1, fit_boundaries=(40, 200)) self.single_fit_collection = FitDataCollection() self.single_fit_collection.add(fit_data_1) # self.roofitFitter = RooFitFit(histograms_1, dataLabel='data', fit_boundries=(40, 200)) self.roofitFitter = RooFitFit(self.single_fit_collection) def tearDown(self): pass def test_normalisation(self): normalisation = self.roofitFitter.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_signal_result(self): self.roofitFitter.fit() results = self.roofitFitter.readResults() self.assertAlmostEqual(N_signal_obs, results['signal'][0], delta=2 * results['signal'][1]) self.assertAlmostEqual(N_bkg1_obs, results['bkg1'][0], delta=2 * results['bkg1'][1]) def test_constraints(self): self.single_fit_collection.set_normalisation_constraints({ 'signal': 0.8, 'bkg1': 0.5 }) self.roofitFitter = RooFitFit(self.single_fit_collection) # self.roofitFitter.set_fit_constraints({'signal': 0.8, 'bkg1': 0.5}) self.roofitFitter.fit() results = self.roofitFitter.readResults() self.assertAlmostEqual(N_signal_obs, results['signal'][0], delta=2 * results['signal'][1]) self.assertAlmostEqual(N_bkg1_obs, results['bkg1'][0], delta=2 * results['bkg1'][1])
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) self.histograms_1 = {'signal': h_signal_1, 'bkg1': h_bkg1_1} self.histograms_2 = {'signal': h_signal_2, 'bkg1': h_bkg1_2} self.histograms_3 = {'var1': h_signal_1, 'bkg1': h_bkg1_1} self.fit_data_1 = FitData( h_data_1, self.histograms_1, fit_boundaries = ( x_min, x_max )) self.fit_data_2 = FitData( h_data_2, self.histograms_2, fit_boundaries = ( x_min, x_max )) self.fit_data_3 = FitData( h_data_1, self.histograms_3, fit_boundaries = ( x_min, x_max )) self.collection_1 = FitDataCollection() self.collection_1.add( self.fit_data_1, 'signal region' ) self.collection_1.add( self.fit_data_2, 'control region' ) self.collection_1.set_normalisation_constraints({'bkg1': 0.5}) self.collection_2 = FitDataCollection() self.collection_2.add( self.fit_data_1 ) self.collection_2.add( self.fit_data_2 ) self.collection_2.set_normalisation_constraints({'bkg1': 0.5}) self.single_collection = FitDataCollection() self.single_collection.add( self.fit_data_1 ) self.single_collection.set_normalisation_constraints({'bkg1': 0.5}) self.non_simultaneous_fit_collection = FitDataCollection() self.non_simultaneous_fit_collection.add( self.fit_data_1 ) self.non_simultaneous_fit_collection.add( self.fit_data_3 ) self.h_data = h_data_1 self.h_bkg1 = h_bkg1_1 self.h_signal = h_signal_1 def tearDown( self ): pass def test_is_valid_for_simultaneous_fit( self ): self.assertTrue( self.collection_1.is_valid_for_simultaneous_fit(), msg = 'has_same_n_samples: ' + str(self.collection_1.has_same_n_samples) + ', has_same_n_data: ' + str(self.collection_1.has_same_n_data) ) self.assertTrue( self.collection_2.is_valid_for_simultaneous_fit(), msg = 'has_same_n_samples: ' + str(self.collection_1.has_same_n_samples) + ', has_same_n_data: ' + str(self.collection_1.has_same_n_data) ) self.assertFalse( self.non_simultaneous_fit_collection.is_valid_for_simultaneous_fit() ) def test_samples( self ): samples = sorted( self.histograms_1.keys() ) samples_from_fit_data = sorted( self.fit_data_1.samples ) samples_from_fit_data_collection = self.collection_1.mc_samples() self.assertEqual( samples, samples_from_fit_data ) self.assertEqual( samples, samples_from_fit_data_collection ) def test_normalisation( self ): normalisation = {name:adjust_overflow_to_limit(histogram, x_min, x_max).Integral() for name, histogram in self.histograms_1.iteritems()} normalisation_from_fit_data = self.fit_data_1.normalisation normalisation_from_single_collection = self.single_collection.mc_normalisation() normalisation_from_collection = self.collection_1.mc_normalisation( 'signal region' ) normalisation_from_collection_1 = self.collection_1.mc_normalisation()['signal region'] for sample in normalisation.keys(): self.assertEqual( normalisation[sample], normalisation_from_fit_data[sample] ) self.assertEqual( normalisation[sample], normalisation_from_single_collection[sample] ) self.assertEqual( normalisation[sample], normalisation_from_collection[sample] ) self.assertEqual( normalisation[sample], normalisation_from_collection_1[sample] ) # data normalisation normalisation = self.h_data.integral( overflow = True ) normalisation_from_fit_data = self.fit_data_1.n_data() normalisation_from_single_collection = self.single_collection.n_data() normalisation_from_collection = self.collection_1.n_data( 'signal region' ) normalisation_from_collection_1 = self.collection_1.n_data()['signal region'] self.assertEqual( normalisation, normalisation_from_fit_data ) self.assertEqual( normalisation, normalisation_from_single_collection ) self.assertEqual( normalisation, normalisation_from_collection ) self.assertEqual( normalisation, normalisation_from_collection_1 ) self.assertAlmostEqual(normalisation, self.collection_1.max_n_data(), delta = 1 ) def test_real_data( self ): real_data = self.fit_data_1.real_data_histogram() self.assertEqual( self.h_data.integral( overflow = True ), real_data.Integral() ) def test_overwrite_warning( self ): c = FitDataCollection() c.add( self.fit_data_1, 'var1' ) self.assertRaises( UserWarning, c.add, ( self.fit_data_1, 'var1' ) ) def test_vectors( self ): h_signal = adjust_overflow_to_limit( self.h_signal, x_min, x_max ) h_signal.Scale(1/h_signal.Integral()) h_bkg1 = adjust_overflow_to_limit( self.h_bkg1, x_min, x_max ) h_bkg1.Scale(1/h_bkg1.Integral()) signal = list( h_signal.y() ) bkg1 = list( h_bkg1.y() ) v_from_fit_data = self.fit_data_1.vectors v_from_single_collection = self.single_collection.vectors() # v_from_collection = self.collection_1.vectors( 'signal region' ) # v_from_collection_1 = self.collection_1.vectors()['signal region'] self.assertEqual(signal, v_from_fit_data['signal']) self.assertEqual(bkg1, v_from_fit_data['bkg1']) self.assertEqual(signal, v_from_single_collection['signal']) self.assertEqual(bkg1, v_from_single_collection['bkg1']) def test_constraints(self): constraint_from_single_collection = self.single_collection.constraints()['bkg1'] self.assertEqual(0.5, constraint_from_single_collection)
def get_fitted_normalisation_from_ROOT( channel, input_files, variable, met_type, b_tag_bin, 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_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, ) # create data sets h_fit_variable_signal = None mc_histograms = None if options.make_combined_signal: if measurement_config.include_higgs: h_fit_variable_signal = histograms['TTJet'] + histograms['SingleTop'] + histograms['Higgs'] else: 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 = ", results return results, initial_values, templates
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()