def getHistogramsFromNormalisationResults( normalisations, bin_edges ) :
histograms = {
'Data' : value_error_tuplelist_to_hist( normalisations['data'], bin_edges ),#.Rebin(2),
'QCD' : value_error_tuplelist_to_hist( normalisations['QCD'], bin_edges ),#.Rebin(2),
'V+Jets' : value_error_tuplelist_to_hist( normalisations['V+Jets'], bin_edges ),#.Rebin(2),
'SingleTop' : value_error_tuplelist_to_hist( normalisations['SingleTop'], bin_edges ),#.Rebin(2),
'TTJet' : value_error_tuplelist_to_hist( normalisations['TTJet_MC'], bin_edges ),#.Rebin(2),
}
return histograms
def read_xsection_measurement_results(category, channel):
global path_to_JSON, variable, k_value, met_type
normalised_xsection_unfolded = read_data_from_JSON(path_to_JSON + '/' + variable + '/xsection_measurement_results' + '/kv' + str(k_value) + '/' + category + '/normalised_xsection_' + channel + '_' + met_type + '.txt')
h_normalised_xsection = value_error_tuplelist_to_hist(normalised_xsection_unfolded['TTJet_measured'], bin_edges[variable])
h_normalised_xsection_unfolded = value_error_tuplelist_to_hist(normalised_xsection_unfolded['TTJet_unfolded'], bin_edges[variable])
h_normalised_xsection_MADGRAPH = value_error_tuplelist_to_hist(normalised_xsection_unfolded['MADGRAPH'], bin_edges[variable])
h_normalised_xsection_POWHEG = value_error_tuplelist_to_hist(normalised_xsection_unfolded['POWHEG'], bin_edges[variable])
h_normalised_xsection_MCATNLO = value_error_tuplelist_to_hist(normalised_xsection_unfolded['MCATNLO'], bin_edges[variable])
h_normalised_xsection_mathchingup = value_error_tuplelist_to_hist(normalised_xsection_unfolded['matchingup'], bin_edges[variable])
h_normalised_xsection_mathchingdown = value_error_tuplelist_to_hist(normalised_xsection_unfolded['matchingdown'], bin_edges[variable])
h_normalised_xsection_scaleup = value_error_tuplelist_to_hist(normalised_xsection_unfolded['scaleup'], bin_edges[variable])
h_normalised_xsection_scaledown = value_error_tuplelist_to_hist(normalised_xsection_unfolded['scaledown'], bin_edges[variable])
histograms_normalised_xsection_different_generators = {
'measured':h_normalised_xsection,
'unfolded':h_normalised_xsection_unfolded,
'MADGRAPH':h_normalised_xsection_MADGRAPH,
'POWHEG':h_normalised_xsection_POWHEG,
'MCATNLO':h_normalised_xsection_MCATNLO
}
histograms_normalised_xsection_systematics_shifts = {
'measured':h_normalised_xsection,
'unfolded':h_normalised_xsection_unfolded,
'matchingdown': h_normalised_xsection_mathchingdown,
'matchingup': h_normalised_xsection_mathchingup,
'scaledown': h_normalised_xsection_scaledown,
'scaleup': h_normalised_xsection_scaleup
}
return histograms_normalised_xsection_different_generators, histograms_normalised_xsection_systematics_shifts
def compare_unfolding_methods(measurement='normalised_xsection',
add_before_unfolding=False, channel='combined'):
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += '{measurement}_{channel}_RooUnfold{method}.txt'
variables = ['MET', 'HT', 'ST', 'NJets',
'lepton_pt', 'abs_lepton_eta', 'WPT']
for variable in variables:
svd = file_template.format(
variable=variable,
method='Svd',
channel=channel,
measurement=measurement)
bayes = file_template.format(
variable=variable,
method='Bayes', channel=channel,
measurement=measurement)
data = read_data_from_JSON(svd)
before_unfolding = data['TTJet_measured_withoutFakes']
svd_data = data['TTJet_unfolded']
bayes_data = read_data_from_JSON(bayes)['TTJet_unfolded']
h_svd = value_error_tuplelist_to_hist(
svd_data, bin_edges_vis[variable])
h_bayes = value_error_tuplelist_to_hist(
bayes_data, bin_edges_vis[variable])
h_before_unfolding = value_error_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = '{0}_compare_unfolding_methods_{1}_{2}'.format(
measurement, variable, channel)
properties.title = 'Comparison of unfolding methods'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = True
properties.x_limits = (
bin_edges_vis[variable][0], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
if 'xsection' in measurement:
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
else:
properties.y_axis_title = r'$t\bar{t}$ normalisation'
histograms = {'SVD': h_svd, 'Bayes': h_bayes}
if add_before_unfolding:
histograms['before unfolding'] = h_before_unfolding
properties.name += '_ext'
properties.has_ratio = False
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def compare_combine_before_after_unfolding(measurement='normalised_xsection',
add_before_unfolding=False):
file_template = 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += '{measurement}_{channel}_RooUnfold{method}.txt'
variables = ['MET', 'HT', 'ST', 'NJets',
'lepton_pt', 'abs_lepton_eta', 'WPT']
for variable in variables:
combineBefore = file_template.format(
variable=variable,
method='Svd',
channel='combinedBeforeUnfolding',
measurement=measurement)
combineAfter = file_template.format(
variable=variable,
method='Svd',
channel='combined',
measurement=measurement)
data = read_data_from_JSON(combineBefore)
before_unfolding = data['TTJet_measured']
combineBefore_data = data['TTJet_unfolded']
combineAfter_data = read_data_from_JSON(combineAfter)['TTJet_unfolded']
h_combineBefore = value_error_tuplelist_to_hist(
combineBefore_data, bin_edges_vis[variable])
h_combineAfter = value_error_tuplelist_to_hist(
combineAfter_data, bin_edges_vis[variable])
h_before_unfolding = value_error_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = '{0}_compare_combine_before_after_unfolding_{1}'.format(
measurement, variable)
properties.title = 'Comparison of combining before/after unfolding'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = True
properties.x_limits = (
bin_edges_vis[variable][0], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
if 'xsection' in measurement:
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
else:
properties.y_axis_title = r'$t\bar{t}$ normalisation'
histograms = {'Combine before unfolding': h_combineBefore, 'Combine after unfolding': h_combineAfter}
if add_before_unfolding:
histograms['before unfolding'] = h_before_unfolding
properties.name += '_ext'
properties.has_ratio = False
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def __set_unfolding_histograms__( self ):
# at the moment only one file is supported for the unfolding input
files = set(
[self.truth['file'],
self.gen_vs_reco['file'],
self.measured['file']]
)
if len( files ) > 1:
print "Currently not supported to have different files for truth, gen_vs_reco and measured"
sys.exit()
input_file = files.pop()
visiblePS = self.phaseSpace
t, m, r, f = get_unfold_histogram_tuple(
File(input_file),
self.variable,
self.channel,
centre_of_mass = self.centre_of_mass_energy,
ttbar_xsection=self.measurement_config.ttbar_xsection,
luminosity=self.measurement_config.luminosity,
load_fakes = True,
visiblePS = visiblePS
)
self.h_truth = asrootpy ( t )
self.h_response = asrootpy ( r )
self.h_measured = asrootpy ( m )
self.h_fakes = asrootpy ( f )
self.h_refolded = None
data_file = self.data['file']
if data_file.endswith('.root'):
self.h_data = get_histogram_from_file(self.data['histogram'], self.data['file'])
elif data_file.endswith('.json') or data_file.endswith('.txt'):
data_key = self.data['histogram']
# assume configured bin edges
edges = []
edges = reco_bin_edges_vis[self.variable]
json_input = read_tuple_from_file(data_file)
if data_key == "": # JSON file == histogram
self.h_data = value_error_tuplelist_to_hist(json_input, edges)
else:
self.h_data = value_error_tuplelist_to_hist(json_input[data_key], edges)
else:
print 'Unkown file extension', data_file.split('.')[-1]
def make_histogram(result, bin_edges):
if len(result[0]) == 2:
h = value_error_tuplelist_to_hist(result, bin_edges)
return h
else: # len(result[0]) == 3
g = value_errors_tuplelist_to_graph(result, bin_edges)
return g
def make_histogram(result, bin_edges):
if len(result[0]) == 2:
h = value_error_tuplelist_to_hist(result, bin_edges)
return h
else: # len(result[0]) == 3
g = value_errors_tuplelist_to_graph(result, bin_edges)
return g
def __set_unfolding_histograms__( self ):
# at the moment only one file is supported for the unfolding input
files = set( [self.truth['file'],
self.gen_vs_reco['file'],
self.measured['file']]
)
if len( files ) > 1:
print "Currently not supported to have different files for truth, gen_vs_reco and measured"
sys.exit()
input_file = files.pop()
visiblePS = False
if self.phaseSpace == 'VisiblePS':
visiblePS = True
t, m, r, f = get_unfold_histogram_tuple( File(input_file),
self.variable,
self.channel,
centre_of_mass = self.centre_of_mass_energy,
ttbar_xsection=self.measurement_config.ttbar_xsection,
luminosity=self.measurement_config.luminosity,
load_fakes = True,
visiblePS = visiblePS
)
self.h_truth = asrootpy ( t )
self.h_response = asrootpy ( r )
self.h_measured = asrootpy ( m )
self.h_fakes = asrootpy ( f )
data_file = self.data['file']
if data_file.endswith('.root'):
self.h_data = get_histogram_from_file(self.data['histogram'], self.data['file'])
elif data_file.endswith('.json') or data_file.endswith('.txt'):
data_key = self.data['histogram']
# assume configured bin edges
edges = []
edges = reco_bin_edges_vis[self.variable]
json_input = read_tuple_from_file(data_file)
if data_key == "": # JSON file == histogram
self.h_data = value_error_tuplelist_to_hist(json_input, edges)
else:
self.h_data = value_error_tuplelist_to_hist(json_input[data_key], edges)
else:
print 'Unkown file extension', data_file.split('.')[-1]
def unfold_results(results, category, channel, h_truth, h_measured, h_response,
method):
global variable, path_to_JSON
h_data = value_error_tuplelist_to_hist(results, bin_edges[variable])
unfolding = Unfolding(h_truth, h_measured, h_response, method=method)
#turning off the unfolding errors for systematic samples
if category != 'central':
unfoldCfg.Hreco = 0
h_unfolded_data = unfolding.unfold(h_data)
#export the D and SV distributions
SVD_path = path_to_JSON + '/' + variable + '/unfolding_objects/' + channel + '/kv_' + str(
unfoldCfg.SVD_k_value) + '/'
make_folder_if_not_exists(SVD_path)
if method == 'TSVDUnfold':
SVDdist = TFile(
SVD_path + method + '_SVDdistributions_' + category + '.root',
'recreate')
directory = SVDdist.mkdir('SVDdist')
directory.cd()
unfolding.unfoldObject.GetD().Write()
unfolding.unfoldObject.GetSV().Write()
# unfolding.unfoldObject.GetUnfoldCovMatrix(data_covariance_matrix(h_data), unfoldCfg.SVD_n_toy).Write()
SVDdist.Close()
else:
SVDdist = TFile(
SVD_path + method + '_SVDdistributions_Hreco' +
str(unfoldCfg.Hreco) + '_' + category + '.root', 'recreate')
directory = SVDdist.mkdir('SVDdist')
directory.cd()
unfolding.unfoldObject.Impl().GetD().Write()
unfolding.unfoldObject.Impl().GetSV().Write()
h_truth.Write()
h_measured.Write()
h_response.Write()
# unfolding.unfoldObject.Impl().GetUnfoldCovMatrix(data_covariance_matrix(h_data), unfoldCfg.SVD_n_toy).Write()
SVDdist.Close()
#export the whole unfolding object if it doesn't exist
if method == 'TSVDUnfold':
unfolding_object_file_name = SVD_path + method + '_unfoldingObject_' + category + '.root'
else:
unfolding_object_file_name = SVD_path + method + '_unfoldingObject_Hreco' + str(
unfoldCfg.Hreco) + '_' + category + '.root'
if not os.path.isfile(unfolding_object_file_name):
unfoldingObjectFile = TFile(unfolding_object_file_name, 'recreate')
directory = unfoldingObjectFile.mkdir('unfoldingObject')
directory.cd()
if method == 'TSVDUnfold':
unfolding.unfoldObject.Write()
else:
unfolding.unfoldObject.Impl().Write()
unfoldingObjectFile.Close()
del unfolding
return hist_to_value_error_tuplelist(h_unfolded_data)
def json_to_histograms(results, channel, variable, category):
global bin_edges
histograms = {}
for measurement, result in results.iteritems():
histograms[measurement] = value_error_tuplelist_to_hist(result, bin_edges[variable])
name = get_name(channel, variable, measurement, category)
histograms[measurement].SetName(name)
return histograms
def plot_pull_from_list(hist_data, hist_min_x, hist_max_x, hist_n_bins):
stats = 19596500
bin_width = (2.0 * hist_max_x) / hist_n_bins
print(hist_n_bins, bin_width)
bin_edges = list(drange(hist_min_x, hist_max_x, bin_width))
print(bin_edges)
print(len(bin_edges))
h_pull = value_error_tuplelist_to_hist(hist_data, bin_edges)
plot_h_pull(h_pull, stats=stats, name='pull_from_list')
def plot_pull_from_list(hist_data, hist_min_x, hist_max_x, hist_n_bins):
stats = 19596500
bin_width = (2.0 * hist_max_x) / hist_n_bins
print(hist_n_bins, bin_width)
bin_edges = list(drange(hist_min_x, hist_max_x, bin_width))
print(bin_edges)
print(len(bin_edges))
h_pull = value_error_tuplelist_to_hist(hist_data, bin_edges)
plot_h_pull(h_pull, stats=stats, name='pull_from_list')
def test_invalid_zero_data(self):
variable = 'MET'
channel = 'electron'
pseudo_data = value_error_tuplelist_to_hist(
[(0, 0)] * (len(bin_edges[variable]) - 1), bin_edges[variable])
self.assertRaises(
ValueError,
self.dict[channel][variable]['unfolding_object'].unfold,
(pseudo_data))
def json_to_histograms(results, channel, variable, category):
global bin_edges
histograms = {}
for measurement, result in results.iteritems():
histograms[measurement] = value_error_tuplelist_to_hist(
result, bin_edges[variable])
name = get_name(channel, variable, measurement, category)
histograms[measurement].SetName(name)
return histograms
def main():
args, input_values_sets, json_input_files = parse_options()
results = {}
clear_old_df('tables/taufinding/')
for input_values, json_file in zip( input_values_sets, json_input_files ):
# print '\nProcessing', json_file
# Initialise the TauFinding class
regularisation_settings = TauFinding( input_values )
# Set additional elemtents
regularisation_settings.taus_to_test = get_tau_list(args.n_ticks_in_log)
variable = regularisation_settings.variable
channel = regularisation_settings.channel
com = regularisation_settings.centre_of_mass_energy
if 'muon' not in channel : continue
print 'Variable = {0}, channel = {1}, sqrt(s) = {2}'.format(variable, channel, com)
if args.run_measured_as_data:
regularisation_settings.taus_to_test = [0]
regularisation_settings.h_data = regularisation_settings.h_measured
df_chi2 = get_chi2s_of_tau_range(regularisation_settings, args)
if args.perform_varied_measured_unfolding_test:
h_data = hist_to_value_error_tuplelist(regularisation_settings.h_data)
h_data_varied = [(return_rnd_Poisson(val),return_rnd_Poisson(err)) for val, err in h_data ]
h_data_varied = value_error_tuplelist_to_hist(h_data_varied, reco_bin_edges_vis[variable])
regularisation_settings.h_data = h_data_varied
df_chi2_smeared = get_chi2s_of_tau_range(regularisation_settings, args, unfold_test=True)
print df_chi2_smeared
# No point in trying to find best tau if it is given as 0...
sys.exit()
# Find the corresponding Chi2 and write to file
df_chi2 = get_chi2s_of_tau_range(regularisation_settings, args)
print df_chi2
# Have the dataframes now - albeit read to a file
# Read in each one corresponding to their channel
# Find the best tau and print to screen
for channel in ['electron', 'muon', 'combined']:
chi2_cut = 0.005
path = regularisation_settings.outpath+'tbl_'+channel+'_tauscan.txt'
df_chi2 = get_df_from_file(path)
if df_chi2 is None: continue
print '\n', "1 - P(Chi2|NDF)", '\n', df_chi2, '\n'
# cutoff to be changed to 0.001 when able to
best_taus = interpolate_tau(chi2_cut, df_chi2)
chi2_to_plots(df_chi2, regularisation_settings, chi2_cut, channel)
print_results_to_screen(best_taus, channel)
return
def unfold_results(results, category, channel, h_truth, h_measured, h_response, method):
global variable, path_to_JSON
h_data = value_error_tuplelist_to_hist(results, bin_edges[variable])
unfolding = Unfolding(h_truth, h_measured, h_response, method=method)
#turning off the unfolding errors for systematic samples
if category != 'central':
unfoldCfg.Hreco = 0
h_unfolded_data = unfolding.unfold(h_data)
#export the D and SV distributions
SVD_path = path_to_JSON + '/' + variable + '/unfolding_objects/' + channel + '/kv_' + str(unfoldCfg.SVD_k_value) + '/'
make_folder_if_not_exists(SVD_path)
if method == 'TSVDUnfold':
SVDdist = TFile(SVD_path + method + '_SVDdistributions_' + category + '.root', 'recreate')
directory = SVDdist.mkdir('SVDdist')
directory.cd()
unfolding.unfoldObject.GetD().Write()
unfolding.unfoldObject.GetSV().Write()
# unfolding.unfoldObject.GetUnfoldCovMatrix(data_covariance_matrix(h_data), unfoldCfg.SVD_n_toy).Write()
SVDdist.Close()
else:
SVDdist = TFile(SVD_path + method + '_SVDdistributions_Hreco' + str(unfoldCfg.Hreco) + '_' + category + '.root', 'recreate')
directory = SVDdist.mkdir('SVDdist')
directory.cd()
unfolding.unfoldObject.Impl().GetD().Write()
unfolding.unfoldObject.Impl().GetSV().Write()
h_truth.Write()
h_measured.Write()
h_response.Write()
# unfolding.unfoldObject.Impl().GetUnfoldCovMatrix(data_covariance_matrix(h_data), unfoldCfg.SVD_n_toy).Write()
SVDdist.Close()
#export the whole unfolding object if it doesn't exist
if method == 'TSVDUnfold':
unfolding_object_file_name = SVD_path + method + '_unfoldingObject_' + category + '.root'
else:
unfolding_object_file_name = SVD_path + method + '_unfoldingObject_Hreco' + str(unfoldCfg.Hreco) + '_' + category + '.root'
if not os.path.isfile(unfolding_object_file_name):
unfoldingObjectFile = TFile(unfolding_object_file_name, 'recreate')
directory = unfoldingObjectFile.mkdir('unfoldingObject')
directory.cd()
if method == 'TSVDUnfold':
unfolding.unfoldObject.Write()
else:
unfolding.unfoldObject.Impl().Write()
unfoldingObjectFile.Close()
del unfolding
return hist_to_value_error_tuplelist(h_unfolded_data)
def read_xsection_measurement_results(category, channel):
global path_to_JSON, variable, k_value, met_type
normalised_xsection_unfolded = read_data_from_JSON(
path_to_JSON + '/' + variable + '/xsection_measurement_results' +
'/kv' + str(k_value) + '/' + category + '/normalised_xsection_' +
channel + '_' + met_type + '.txt')
h_normalised_xsection = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['TTJet_measured'], bin_edges[variable])
h_normalised_xsection_unfolded = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['TTJet_unfolded'], bin_edges[variable])
h_normalised_xsection_MADGRAPH = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['MADGRAPH'], bin_edges[variable])
h_normalised_xsection_POWHEG = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['POWHEG'], bin_edges[variable])
h_normalised_xsection_MCATNLO = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['MCATNLO'], bin_edges[variable])
h_normalised_xsection_mathchingup = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['matchingup'], bin_edges[variable])
h_normalised_xsection_mathchingdown = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['matchingdown'], bin_edges[variable])
h_normalised_xsection_scaleup = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['scaleup'], bin_edges[variable])
h_normalised_xsection_scaledown = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['scaledown'], bin_edges[variable])
histograms_normalised_xsection_different_generators = {
'measured': h_normalised_xsection,
'unfolded': h_normalised_xsection_unfolded,
'MADGRAPH': h_normalised_xsection_MADGRAPH,
'POWHEG': h_normalised_xsection_POWHEG,
'MCATNLO': h_normalised_xsection_MCATNLO
}
histograms_normalised_xsection_systematics_shifts = {
'measured': h_normalised_xsection,
'unfolded': h_normalised_xsection_unfolded,
'matchingdown': h_normalised_xsection_mathchingdown,
'matchingup': h_normalised_xsection_mathchingup,
'scaledown': h_normalised_xsection_scaledown,
'scaleup': h_normalised_xsection_scaleup
}
return histograms_normalised_xsection_different_generators, histograms_normalised_xsection_systematics_shifts
def unfold_results( results, category, channel, tau_value, h_truth, h_measured, h_response, h_fakes, method, visiblePS ):
global variable, path_to_DF, args
edges = reco_bin_edges_full[variable]
if visiblePS:
edges = reco_bin_edges_vis[variable]
h_data = value_error_tuplelist_to_hist( results, edges )
# Rebin original TTJet_Measured in terms of final binning (h_data is later replaced with h_data_no_fakes)
h_data_rebinned = h_data.rebinned(2)
# Remove fakes before unfolding
h_data_no_fakes = removeFakes( h_measured, h_fakes, h_data )
# unfold
unfolding = Unfolding( h_data_no_fakes, h_truth, h_measured, h_response, h_fakes, method = method, tau = tau_value )
# turning off the unfolding errors for systematic samples
if not category == 'central':
unfoldCfg.error_treatment = 0
else:
unfoldCfg.error_treatment = args.error_treatment
h_unfolded_data = unfolding.unfold()
h_data_no_fakes = h_data_no_fakes.rebinned(2)
covariance_matrix = None
if category == 'central':
# Return the covariance matrices (They have been normailsed)
covariance_matrix, correlation_matrix = unfolding.get_covariance_matrix()
# Write covariance matrices
covariance_output_tempalte = '{path_to_DF}/central/covarianceMatrices/{cat}_{label}_{channel}.txt'
# Unfolded number of events
table_outfile=covariance_output_tempalte.format( path_to_DF=path_to_DF, channel = channel, label='Covariance', cat='Stat_unfoldedNormalisation' )
create_covariance_matrix( covariance_matrix, table_outfile)
table_outfile=covariance_output_tempalte.format( path_to_DF=path_to_DF, channel = channel, label='Correlation', cat='Stat_unfoldedNormalisation' )
create_covariance_matrix( correlation_matrix, table_outfile )
# # Normalised cross section
# table_outfile=covariance_output_tempalte.format( path_to_DF=path_to_DF, channel = channel, label='Covariance', cat='Stat_normalisedXsection' )
# create_covariance_matrix( norm_covariance_matrix, table_outfile)
# table_outfile=covariance_output_tempalte.format( path_to_DF=path_to_DF, channel = channel, label='Correlation', cat='Stat_normalisedXsection' )
# create_covariance_matrix( norm_correlation_matrix, table_outfile )
del unfolding
return hist_to_value_error_tuplelist( h_data_rebinned ), hist_to_value_error_tuplelist( h_unfolded_data ), hist_to_value_error_tuplelist( h_data_no_fakes ), covariance_matrix
def unfold_results( results, category, channel, tau_value, h_truth, h_measured, h_response, h_fakes, method, visiblePS ):
global variable, path_to_JSON, options
edges = reco_bin_edges_full[variable]
if visiblePS:
edges = reco_bin_edges_vis[variable]
h_data = value_error_tuplelist_to_hist( results, edges )
# Remove fakes before unfolding
h_data = removeFakes( h_measured, h_fakes, h_data )
unfolding = Unfolding( h_data, h_truth, h_measured, h_response, h_fakes, method = method, tau = tau_value )
# turning off the unfolding errors for systematic samples
if not category == 'central':
unfoldCfg.error_treatment = 0
else:
unfoldCfg.error_treatment = options.error_treatment
h_unfolded_data = unfolding.unfold()
print "h_response bin edges : ", h_response
print "h_unfolded_data bin edges : ", h_unfolded_data
del unfolding
return hist_to_value_error_tuplelist( h_unfolded_data ), hist_to_value_error_tuplelist( h_data )
def read_xsection_measurement_results( category, channel ):
global path_to_JSON, variable, met_type, phase_space, method
file_template = '{path}/{category}/{name}_{channel}_{method}{suffix}.txt'
filename = file_template.format(
path = path_to_JSON,
category = category,
name = 'normalised_xsection',
channel = channel,
method = method,
suffix = '',
)
xsec_04_log.debug('Reading file {0}'.format(filename))
normalised_xsection_unfolded = read_data_from_JSON( filename )
edges = bin_edges_full[variable]
if phase_space == 'VisiblePS':
edges = bin_edges_vis[variable]
h_normalised_xsection = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJet_measured'], edges )
h_normalised_xsection_unfolded = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJet_unfolded'], edges )
histograms_normalised_xsection_different_generators = {'measured':h_normalised_xsection,
'unfolded':h_normalised_xsection_unfolded}
histograms_normalised_xsection_systematics_shifts = {'measured':h_normalised_xsection,
'unfolded':h_normalised_xsection_unfolded}
if category == 'central':
# true distributions
h_normalised_xsection_powhegPythia8 = value_error_tuplelist_to_hist( normalised_xsection_unfolded['powhegPythia8'], edges )
h_normalised_xsection_amcatnlo = value_error_tuplelist_to_hist( normalised_xsection_unfolded['amcatnlo'], edges )
h_normalised_xsection_madgraphMLM = value_error_tuplelist_to_hist( normalised_xsection_unfolded['madgraphMLM'], edges )
h_normalised_xsection_powhegHerwigpp = value_error_tuplelist_to_hist( normalised_xsection_unfolded['powhegHerwig'], edges )
# h_normalised_xsection_amcatnloHerwigpp = value_error_tuplelist_to_hist( normalised_xsection_unfolded['amcatnloHerwig'], edges )
# h_normalised_xsection_scaleup = value_error_tuplelist_to_hist( normalised_xsection_unfolded['scaleup'], edges )
# h_normalised_xsection_scaledown = value_error_tuplelist_to_hist( normalised_xsection_unfolded['scaledown'], edges )
h_normalised_xsection_massup = value_error_tuplelist_to_hist( normalised_xsection_unfolded['massup'], edges )
h_normalised_xsection_massdown = value_error_tuplelist_to_hist( normalised_xsection_unfolded['massdown'], edges )
histograms_normalised_xsection_different_generators.update( {
'powhegPythia8':h_normalised_xsection_powhegPythia8,
'amcatnloPythia8':h_normalised_xsection_amcatnlo,
'madgraphMLM':h_normalised_xsection_madgraphMLM,
'powhegHerwig':h_normalised_xsection_powhegHerwigpp,
# 'amcatnloHerwig':h_normalised_xsection_amcatnloHerwigpp,
})
histograms_normalised_xsection_systematics_shifts.update( {'powhegPythia8':h_normalised_xsection_powhegPythia8,
# 'scaledown': h_normalised_xsection_scaledown,
# 'scaleup': h_normalised_xsection_scaleup,
'massdown': h_normalised_xsection_massdown,
'massup': h_normalised_xsection_massup
})
filename = file_template.format(
path = path_to_JSON,
category = category,
name = 'normalised_xsection',
channel = channel,
method = method,
suffix = '_with_errors',
)
normalised_xsection_unfolded_with_errors = read_data_from_JSON( filename )
xsec_04_log.debug('Reading file {0}'.format(filename))
# filename = file_template.format(
# path = path_to_JSON,
# category = category,
# name = 'normalised_xsection',
# channel = channel,
# method = method,
# suffix = '_with_systematics_but_without_generator_errors',
# )
### normalised_xsection_unfolded_with_errors_with_systematics_but_without_ttbar_theory = read_data_from_JSON( file_template + '_with_systematics_but_without_ttbar_theory_errors.txt' )
# normalised_xsection_unfolded_with_errors_with_systematics_but_without_generator = normalised_xsection_unfolded_with_errors
# a rootpy.Graph with asymmetric errors!
### h_normalised_xsection_with_systematics_but_without_ttbar_theory = value_errors_tuplelist_to_graph(
### normalised_xsection_unfolded_with_errors_with_systematics_but_without_ttbar_theory['TTJet_measured'],
### edges )
### h_normalised_xsection_with_systematics_but_without_ttbar_theory_unfolded = value_errors_tuplelist_to_graph(
### normalised_xsection_unfolded_with_errors_with_systematics_but_without_ttbar_theory['TTJet_unfolded'],
### edges )
h_normalised_xsection_unfolded_with_errors = value_errors_tuplelist_to_graph(
normalised_xsection_unfolded_with_errors['TTJet_measured'],
edges )
h_normalised_xsection_unfolded_with_errors_unfolded = value_errors_tuplelist_to_graph(
normalised_xsection_unfolded_with_errors['TTJet_unfolded'],
edges )
# histograms_normalised_xsection_different_generators['measured_with_systematics'] = h_normalised_xsection_with_systematics_but_without_ttbar_theory
# histograms_normalised_xsection_different_generators['unfolded_with_systematics'] = h_normalised_xsection_with_systematics_but_without_ttbar_theory_unfolded
histograms_normalised_xsection_different_generators['measured_with_systematics'] = h_normalised_xsection_unfolded_with_errors
histograms_normalised_xsection_different_generators['unfolded_with_systematics'] = h_normalised_xsection_unfolded_with_errors_unfolded
histograms_normalised_xsection_systematics_shifts['measured_with_systematics'] = h_normalised_xsection_unfolded_with_errors
histograms_normalised_xsection_systematics_shifts['unfolded_with_systematics'] = h_normalised_xsection_unfolded_with_errors_unfolded
return histograms_normalised_xsection_different_generators, histograms_normalised_xsection_systematics_shifts
def read_xsection_measurement_results( category, channel ):
'''
Reading the unfolded xsection results from DFs into graphs
'''
global path_to_DF, variable, phase_space, method
file_template = '{path}/{category}/{name}_{channel}_{method}{suffix}.txt'
filename = file_template.format(
path = path_to_DF,
category = category,
name = 'xsection_normalised',
channel = channel,
method = method,
suffix = '',
)
xsec_04_log.debug('Reading file {0}'.format(filename))
edges = bin_edges_full[variable]
if phase_space == 'VisiblePS':
edges = bin_edges_vis[variable]
# Collect the cross section measured/unfolded results from dataframes
normalised_xsection_unfolded = read_tuple_from_file( filename )
h_normalised_xsection = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJet_measured'], edges )
h_normalised_xsection_unfolded = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJet_unfolded'], edges )
histograms_normalised_xsection_different_generators = {
'measured':h_normalised_xsection,
'unfolded':h_normalised_xsection_unfolded,
}
histograms_normalised_xsection_systematics_shifts = deepcopy( histograms_normalised_xsection_different_generators )
if category == 'central':
# Add in distributions for the different MC to be shown
h_normalised_xsection_powhegPythia8 = value_error_tuplelist_to_hist( normalised_xsection_unfolded['powhegPythia8'], edges )
# h_normalised_xsection_amcatnlo = value_error_tuplelist_to_hist( normalised_xsection_unfolded['amcatnlo'], edges )
# h_normalised_xsection_madgraphMLM = value_error_tuplelist_to_hist( normalised_xsection_unfolded['madgraphMLM'], edges )
h_normalised_xsection_powhegHerwigpp = value_error_tuplelist_to_hist( normalised_xsection_unfolded['powhegHerwig'], edges )
h_normalised_xsection_massup = value_error_tuplelist_to_hist( normalised_xsection_unfolded['massup'], edges )
h_normalised_xsection_massdown = value_error_tuplelist_to_hist( normalised_xsection_unfolded['massdown'], edges )
# And update
histograms_normalised_xsection_different_generators.update(
{
'powhegPythia8' : h_normalised_xsection_powhegPythia8,
# 'amcatnloPythia8' : h_normalised_xsection_amcatnlo,
# 'madgraphMLM' : h_normalised_xsection_madgraphMLM,
'powhegHerwig' : h_normalised_xsection_powhegHerwigpp,
}
)
histograms_normalised_xsection_systematics_shifts.update(
{
'powhegPythia8' : h_normalised_xsection_powhegPythia8,
'massdown' : h_normalised_xsection_massdown,
'massup' : h_normalised_xsection_massup
}
)
filename = file_template.format(
path = path_to_DF,
category = category,
name = 'xsection_normalised',
channel = channel,
method = method,
suffix = '_summary_absolute',
)
# Now for the systematic uncertainties
normalised_xsection_unfolded_with_errors = file_to_df( filename )
normalised_xsection_unfolded_with_errors['TTJet_unfolded'] = tupleise_cols(
normalised_xsection_unfolded_with_errors['central'],
normalised_xsection_unfolded_with_errors['systematic'],
)
xsec_04_log.debug('Reading file {0}'.format(filename))
# Transform unfolded data into graph form
h_normalised_xsection_unfolded_with_errors_unfolded = value_errors_tuplelist_to_graph(
normalised_xsection_unfolded_with_errors['TTJet_unfolded'],
edges,
is_symmetric_errors=True
)
# Add to list of histograms
histograms_normalised_xsection_different_generators['unfolded_with_systematics'] = h_normalised_xsection_unfolded_with_errors_unfolded
histograms_normalised_xsection_systematics_shifts['unfolded_with_systematics'] = h_normalised_xsection_unfolded_with_errors_unfolded
return histograms_normalised_xsection_different_generators, histograms_normalised_xsection_systematics_shifts
def main():
config = XSectionConfig(13)
file_for_powhegPythia = File(config.unfolding_central_firstHalf, 'read')
file_for_ptReweight_up = File(config.unfolding_ptreweight_up_firstHalf, 'read')
file_for_ptReweight_down = File(config.unfolding_ptreweight_down_firstHalf, 'read')
file_for_amcatnlo_pythia8 = File(config.unfolding_amcatnlo_pythia8, 'read')
file_for_powhegHerwig = File(config.unfolding_powheg_herwig, 'read')
file_for_etaReweight_up = File(config.unfolding_etareweight_up, 'read')
file_for_etaReweight_down = File(config.unfolding_etareweight_down, 'read')
file_for_data_template = 'data/normalisation/background_subtraction/13TeV/{variable}/VisiblePS/central/normalisation_{channel}.txt'
for channel in config.analysis_types.keys():
if channel is 'combined':continue
for variable in config.variables:
print variable
# for variable in ['HT']:
# Get the central powheg pythia distributions
_, _, response_central, fakes_central = get_unfold_histogram_tuple(
inputfile=file_for_powhegPythia,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=True,
visiblePS=True
)
measured_central = asrootpy(response_central.ProjectionX('px',1))
truth_central = asrootpy(response_central.ProjectionY())
# Get the reweighted powheg pythia distributions
_, _, response_pt_reweighted_up, _ = get_unfold_histogram_tuple(
inputfile=file_for_ptReweight_up,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True
)
measured_pt_reweighted_up = asrootpy(response_pt_reweighted_up.ProjectionX('px',1))
truth_pt_reweighted_up = asrootpy(response_pt_reweighted_up.ProjectionY())
_, _, response_pt_reweighted_down, _ = get_unfold_histogram_tuple(
inputfile=file_for_ptReweight_down,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True
)
measured_pt_reweighted_down = asrootpy(response_pt_reweighted_down.ProjectionX('px',1))
truth_pt_reweighted_down = asrootpy(response_pt_reweighted_down.ProjectionY())
# _, _, response_eta_reweighted_up, _ = get_unfold_histogram_tuple(
# inputfile=file_for_etaReweight_up,
# variable=variable,
# channel=channel,
# centre_of_mass=13,
# load_fakes=False,
# visiblePS=True
# )
# measured_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionX('px',1))
# truth_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionY())
# _, _, response_eta_reweighted_down, _ = get_unfold_histogram_tuple(
# inputfile=file_for_etaReweight_down,
# variable=variable,
# channel=channel,
# centre_of_mass=13,
# load_fakes=False,
# visiblePS=True
# )
# measured_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionX('px',1))
# truth_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionY())
# Get the distributions for other MC models
_, _, response_amcatnlo_pythia8, _ = get_unfold_histogram_tuple(
inputfile=file_for_amcatnlo_pythia8,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True
)
measured_amcatnlo_pythia8 = asrootpy(response_amcatnlo_pythia8.ProjectionX('px',1))
truth_amcatnlo_pythia8 = asrootpy(response_amcatnlo_pythia8.ProjectionY())
_, _, response_powhegHerwig, _ = get_unfold_histogram_tuple(
inputfile=file_for_powhegHerwig,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True
)
measured_powhegHerwig = asrootpy(response_powhegHerwig.ProjectionX('px',1))
truth_powhegHerwig = asrootpy(response_powhegHerwig.ProjectionY())
# Get the data input (data after background subtraction, and fake removal)
file_for_data = file_for_data_template.format( variable = variable, channel = channel )
data = read_tuple_from_file(file_for_data)['TTJet']
data = value_error_tuplelist_to_hist( data, reco_bin_edges_vis[variable] )
data = removeFakes( measured_central, fakes_central, data )
# Plot all three
hp = Histogram_properties()
hp.name = 'Reweighting_check_{channel}_{variable}_at_{com}TeV'.format(
channel=channel,
variable=variable,
com='13',
)
v_latex = latex_labels.variables_latex[variable]
unit = ''
if variable in ['HT', 'ST', 'MET', 'WPT', 'lepton_pt']:
unit = ' [GeV]'
hp.x_axis_title = v_latex + unit
hp.x_limits = [ reco_bin_edges_vis[variable][0], reco_bin_edges_vis[variable][-1]]
hp.ratio_y_limits = [0.1,1.9]
hp.ratio_y_title = 'Reweighted / Central'
hp.y_axis_title = 'Number of events'
hp.title = 'Reweighting check for {variable}'.format(variable=v_latex)
measured_central.Rebin(2)
measured_pt_reweighted_up.Rebin(2)
measured_pt_reweighted_down.Rebin(2)
# measured_eta_reweighted_up.Rebin(2)
# measured_eta_reweighted_down.Rebin(2)
measured_amcatnlo_pythia8.Rebin(2)
measured_powhegHerwig.Rebin(2)
data.Rebin(2)
measured_central.Scale( 1 / measured_central.Integral() )
measured_pt_reweighted_up.Scale( 1 / measured_pt_reweighted_up.Integral() )
measured_pt_reweighted_down.Scale( 1 / measured_pt_reweighted_down.Integral() )
measured_amcatnlo_pythia8.Scale( 1 / measured_amcatnlo_pythia8.Integral() )
measured_powhegHerwig.Scale( 1 / measured_powhegHerwig.Integral() )
# measured_eta_reweighted_up.Scale( 1 / measured_eta_reweighted_up.Integral() )
# measured_eta_reweighted_down.Scale( 1/ measured_eta_reweighted_down.Integral() )
data.Scale( 1 / data.Integral() )
print list(measured_central.y())
print list(measured_amcatnlo_pythia8.y())
print list(measured_powhegHerwig.y())
print list(data.y())
compare_measurements(
# models = {'Central' : measured_central, 'PtReweighted Up' : measured_pt_reweighted_up, 'PtReweighted Down' : measured_pt_reweighted_down, 'EtaReweighted Up' : measured_eta_reweighted_up, 'EtaReweighted Down' : measured_eta_reweighted_down},
models = OrderedDict([('Central' , measured_central), ('PtReweighted Up' , measured_pt_reweighted_up), ('PtReweighted Down' , measured_pt_reweighted_down), ('amc@nlo' , measured_amcatnlo_pythia8), ('powhegHerwig' , measured_powhegHerwig) ] ),
measurements = {'Data' : data},
show_measurement_errors=True,
histogram_properties=hp,
save_folder='plots/unfolding/reweighting_check',
save_as=['pdf'],
line_styles_for_models = ['solid','solid','solid','dashed','dashed'],
show_ratio_for_pairs = OrderedDict( [
('PtUpVsCentral' , [ measured_pt_reweighted_up, measured_central ] ),
('PtDownVsCentral' , [ measured_pt_reweighted_down, measured_central ] ),
('amcatnloVsCentral' , [ measured_amcatnlo_pythia8, measured_central ] ),
('powhegHerwigVsCentral' , [ measured_powhegHerwig, measured_central ] ),
('DataVsCentral' , [data, measured_central] )
]),
)
def test_get_max_y_hist():
h = value_error_tuplelist_to_hist(data_h, bin_edges)
max_y = get_best_max_y([h])
assert max_y == 3 + 1
'QCD_other_control_region':
'{path}/QCD_other_control_region/normalisation_{channel}.txt'.
format(path=path_to_DF, channel=channel),
'QCD_signal_MC':
'{path}/QCD_signal_MC/normalisation_{channel}.txt'.format(
path=path_to_DF, channel=channel)
}
normalisations = {}
hists = {}
maxY = 0
minY = 99999999
for f in files:
normalisations[f] = read_tuple_from_file(files[f])['QCD']
hists[f] = value_error_tuplelist_to_hist(
normalisations[f], reco_bin_edges_vis[variable]).Rebin(2)
maxY = max([maxY] + list(hists[f].y()))
minY = min([minY] + list(hists[f].y()))
if minY <= 0: minY = 0.1
can = Canvas()
pad1 = Pad(0, 0.3, 1, 1)
pad2 = Pad(0, 0, 1, 0.3)
pad1.Draw()
pad2.Draw()
pad1.cd()
# print normalisations
hists['central'].SetLineColor(2)
hists['central'].SetLineWidth(3)
def main():
config = XSectionConfig(13)
file_for_powhegPythia = File(config.unfolding_central_firstHalf, 'read')
file_for_ptReweight_up = File(config.unfolding_ptreweight_up_firstHalf,
'read')
file_for_ptReweight_down = File(config.unfolding_ptreweight_down_firstHalf,
'read')
file_for_amcatnlo_pythia8 = File(config.unfolding_amcatnlo_pythia8, 'read')
file_for_powhegHerwig = File(config.unfolding_powheg_herwig, 'read')
file_for_etaReweight_up = File(config.unfolding_etareweight_up, 'read')
file_for_etaReweight_down = File(config.unfolding_etareweight_down, 'read')
file_for_data_template = 'data/normalisation/background_subtraction/13TeV/{variable}/VisiblePS/central/normalisation_{channel}.txt'
for channel in config.analysis_types.keys():
if channel is 'combined': continue
for variable in config.variables:
print variable
# for variable in ['HT']:
# Get the central powheg pythia distributions
_, _, response_central, fakes_central = get_unfold_histogram_tuple(
inputfile=file_for_powhegPythia,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=True,
visiblePS=True)
measured_central = asrootpy(response_central.ProjectionX('px', 1))
truth_central = asrootpy(response_central.ProjectionY())
# Get the reweighted powheg pythia distributions
_, _, response_pt_reweighted_up, _ = get_unfold_histogram_tuple(
inputfile=file_for_ptReweight_up,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True)
measured_pt_reweighted_up = asrootpy(
response_pt_reweighted_up.ProjectionX('px', 1))
truth_pt_reweighted_up = asrootpy(
response_pt_reweighted_up.ProjectionY())
_, _, response_pt_reweighted_down, _ = get_unfold_histogram_tuple(
inputfile=file_for_ptReweight_down,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True)
measured_pt_reweighted_down = asrootpy(
response_pt_reweighted_down.ProjectionX('px', 1))
truth_pt_reweighted_down = asrootpy(
response_pt_reweighted_down.ProjectionY())
# _, _, response_eta_reweighted_up, _ = get_unfold_histogram_tuple(
# inputfile=file_for_etaReweight_up,
# variable=variable,
# channel=channel,
# centre_of_mass=13,
# load_fakes=False,
# visiblePS=True
# )
# measured_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionX('px',1))
# truth_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionY())
# _, _, response_eta_reweighted_down, _ = get_unfold_histogram_tuple(
# inputfile=file_for_etaReweight_down,
# variable=variable,
# channel=channel,
# centre_of_mass=13,
# load_fakes=False,
# visiblePS=True
# )
# measured_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionX('px',1))
# truth_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionY())
# Get the distributions for other MC models
_, _, response_amcatnlo_pythia8, _ = get_unfold_histogram_tuple(
inputfile=file_for_amcatnlo_pythia8,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True)
measured_amcatnlo_pythia8 = asrootpy(
response_amcatnlo_pythia8.ProjectionX('px', 1))
truth_amcatnlo_pythia8 = asrootpy(
response_amcatnlo_pythia8.ProjectionY())
_, _, response_powhegHerwig, _ = get_unfold_histogram_tuple(
inputfile=file_for_powhegHerwig,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True)
measured_powhegHerwig = asrootpy(
response_powhegHerwig.ProjectionX('px', 1))
truth_powhegHerwig = asrootpy(response_powhegHerwig.ProjectionY())
# Get the data input (data after background subtraction, and fake removal)
file_for_data = file_for_data_template.format(variable=variable,
channel=channel)
data = read_tuple_from_file(file_for_data)['TTJet']
data = value_error_tuplelist_to_hist(data,
reco_bin_edges_vis[variable])
data = removeFakes(measured_central, fakes_central, data)
# Plot all three
hp = Histogram_properties()
hp.name = 'Reweighting_check_{channel}_{variable}_at_{com}TeV'.format(
channel=channel,
variable=variable,
com='13',
)
v_latex = latex_labels.variables_latex[variable]
unit = ''
if variable in ['HT', 'ST', 'MET', 'WPT', 'lepton_pt']:
unit = ' [GeV]'
hp.x_axis_title = v_latex + unit
hp.x_limits = [
reco_bin_edges_vis[variable][0],
reco_bin_edges_vis[variable][-1]
]
hp.ratio_y_limits = [0.1, 1.9]
hp.ratio_y_title = 'Reweighted / Central'
hp.y_axis_title = 'Number of events'
hp.title = 'Reweighting check for {variable}'.format(
variable=v_latex)
measured_central.Rebin(2)
measured_pt_reweighted_up.Rebin(2)
measured_pt_reweighted_down.Rebin(2)
# measured_eta_reweighted_up.Rebin(2)
# measured_eta_reweighted_down.Rebin(2)
measured_amcatnlo_pythia8.Rebin(2)
measured_powhegHerwig.Rebin(2)
data.Rebin(2)
measured_central.Scale(1 / measured_central.Integral())
measured_pt_reweighted_up.Scale(
1 / measured_pt_reweighted_up.Integral())
measured_pt_reweighted_down.Scale(
1 / measured_pt_reweighted_down.Integral())
measured_amcatnlo_pythia8.Scale(
1 / measured_amcatnlo_pythia8.Integral())
measured_powhegHerwig.Scale(1 / measured_powhegHerwig.Integral())
# measured_eta_reweighted_up.Scale( 1 / measured_eta_reweighted_up.Integral() )
# measured_eta_reweighted_down.Scale( 1/ measured_eta_reweighted_down.Integral() )
data.Scale(1 / data.Integral())
print list(measured_central.y())
print list(measured_amcatnlo_pythia8.y())
print list(measured_powhegHerwig.y())
print list(data.y())
compare_measurements(
# models = {'Central' : measured_central, 'PtReweighted Up' : measured_pt_reweighted_up, 'PtReweighted Down' : measured_pt_reweighted_down, 'EtaReweighted Up' : measured_eta_reweighted_up, 'EtaReweighted Down' : measured_eta_reweighted_down},
models=OrderedDict([
('Central', measured_central),
('PtReweighted Up', measured_pt_reweighted_up),
('PtReweighted Down', measured_pt_reweighted_down),
('amc@nlo', measured_amcatnlo_pythia8),
('powhegHerwig', measured_powhegHerwig)
]),
measurements={'Data': data},
show_measurement_errors=True,
histogram_properties=hp,
save_folder='plots/unfolding/reweighting_check',
save_as=['pdf'],
line_styles_for_models=[
'solid', 'solid', 'solid', 'dashed', 'dashed'
],
show_ratio_for_pairs=OrderedDict([
('PtUpVsCentral',
[measured_pt_reweighted_up, measured_central]),
('PtDownVsCentral',
[measured_pt_reweighted_down, measured_central]),
('amcatnloVsCentral',
[measured_amcatnlo_pythia8, measured_central]),
('powhegHerwigVsCentral',
[measured_powhegHerwig, measured_central]),
('DataVsCentral', [data, measured_central])
]),
)
def get_data_histogram( channel, variable, met_type ):
fit_result_input = 'data/M3_angle_bl/13TeV/%(variable)s/fit_results/central/fit_results_%(channel)s_%(met_type)s.txt'
fit_results = read_data_from_JSON( fit_result_input % {'channel': channel, 'variable': variable, 'met_type':met_type} )
fit_data = fit_results['TTJet']
h_data = value_error_tuplelist_to_hist( fit_data, bin_edges[variable] )
return h_data
def read_xsection_measurement_results(path_to_JSON,
variable,
bin_edges,
category,
channel,
k_values,
met_type='patType1CorrectedPFMet',
met_uncertainties=[]):
filename = ''
if category in met_uncertainties and variable == 'HT' and not 'JES' in category and not 'JER' in category:
filename = path_to_JSON + '/xsection_measurement_results/' + \
channel + '/central/normalised_xsection_' + met_type + '.txt'
else:
filename = path_to_JSON + '/xsection_measurement_results/' + channel + \
'/' + category + '/normalised_xsection_' + met_type + '.txt'
if channel == 'combined':
filename = filename.replace('kv' + str(k_values[channel]), '')
normalised_xsection_unfolded = read_data_from_JSON(filename)
h_normalised_xsection = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['TTJet_measured'], bin_edges[variable])
h_normalised_xsection_unfolded = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['TTJet_unfolded'], bin_edges[variable])
histograms_normalised_xsection_different_generators = {
'measured': h_normalised_xsection,
'unfolded': h_normalised_xsection_unfolded
}
histograms_normalised_xsection_systematics_shifts = {
'measured': h_normalised_xsection,
'unfolded': h_normalised_xsection_unfolded
}
if category == 'central':
# true distributions
h_normalised_xsection_MADGRAPH = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['MADGRAPH'], bin_edges[variable])
h_normalised_xsection_POWHEG_PYTHIA = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['POWHEG_PYTHIA'], bin_edges[variable])
h_normalised_xsection_POWHEG_HERWIG = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['POWHEG_HERWIG'], bin_edges[variable])
h_normalised_xsection_MCATNLO = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['MCATNLO'], bin_edges[variable])
h_normalised_xsection_mathchingup = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['matchingup'], bin_edges[variable])
h_normalised_xsection_mathchingdown = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['matchingdown'], bin_edges[variable])
h_normalised_xsection_scaleup = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['scaleup'], bin_edges[variable])
h_normalised_xsection_scaledown = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['scaledown'], bin_edges[variable])
histograms_normalised_xsection_different_generators.update({
'MADGRAPH':
h_normalised_xsection_MADGRAPH,
'POWHEG_PYTHIA':
h_normalised_xsection_POWHEG_PYTHIA,
'POWHEG_HERWIG':
h_normalised_xsection_POWHEG_HERWIG,
'MCATNLO':
h_normalised_xsection_MCATNLO
})
histograms_normalised_xsection_systematics_shifts.update({
'MADGRAPH':
h_normalised_xsection_MADGRAPH,
'matchingdown':
h_normalised_xsection_mathchingdown,
'matchingup':
h_normalised_xsection_mathchingup,
'scaledown':
h_normalised_xsection_scaledown,
'scaleup':
h_normalised_xsection_scaleup
})
file_template = path_to_JSON + '/xsection_measurement_results/' + channel + \
'/kv' + str(k_values[channel]) + '/' + \
category + '/normalised_xsection_' + met_type
if channel == 'combined':
file_template = file_template.replace(
'kv' + str(k_values[channel]), '')
# normalised_xsection_unfolded_with_errors = read_data_from_JSON( file_template + '_with_errors.txt' )
normalised_xsection_unfolded_with_errors_with_systematics_but_without_ttbar_theory = read_data_from_JSON(
file_template +
'_with_systematics_but_without_ttbar_theory_errors.txt')
normalised_xsection_unfolded_with_errors_with_systematics_but_without_generator = read_data_from_JSON(
file_template +
'_with_systematics_but_without_generator_errors.txt')
# a rootpy.Graph with asymmetric errors!
h_normalised_xsection_with_systematics_but_without_ttbar_theory = value_errors_tuplelist_to_graph(
normalised_xsection_unfolded_with_errors_with_systematics_but_without_ttbar_theory[
'TTJet_measured'], bin_edges[variable])
h_normalised_xsection_with_systematics_but_without_ttbar_theory_unfolded = value_errors_tuplelist_to_graph(
normalised_xsection_unfolded_with_errors_with_systematics_but_without_ttbar_theory[
'TTJet_unfolded'], bin_edges[variable])
h_normalised_xsection_with_systematics_but_without_generator = value_errors_tuplelist_to_graph(
normalised_xsection_unfolded_with_errors_with_systematics_but_without_generator[
'TTJet_measured'], bin_edges[variable])
h_normalised_xsection_with_systematics_but_without_generator_unfolded = value_errors_tuplelist_to_graph(
normalised_xsection_unfolded_with_errors_with_systematics_but_without_generator[
'TTJet_unfolded'], bin_edges[variable])
histograms_normalised_xsection_different_generators[
'measured_with_systematics'] = h_normalised_xsection_with_systematics_but_without_generator
histograms_normalised_xsection_different_generators[
'unfolded_with_systematics'] = h_normalised_xsection_with_systematics_but_without_generator_unfolded
histograms_normalised_xsection_systematics_shifts[
'measured_with_systematics'] = h_normalised_xsection_with_systematics_but_without_ttbar_theory
histograms_normalised_xsection_systematics_shifts[
'unfolded_with_systematics'] = h_normalised_xsection_with_systematics_but_without_ttbar_theory_unfolded
return histograms_normalised_xsection_different_generators, histograms_normalised_xsection_systematics_shifts
'QCD_shape' : '{path}/QCD_shape/normalisation_{channel}.txt'.format( path=path_to_DF,channel=channel ),
'QCD_normalisation' : '{path}/QCD_cross_section/normalisation_{channel}.txt'.format( path=path_to_DF,channel=channel ),
'QCD_other_control_region' : '{path}/QCD_other_control_region/normalisation_{channel}.txt'.format( path=path_to_DF,channel=channel ),
'QCD_signal_MC' : '{path}/QCD_signal_MC/normalisation_{channel}.txt'.format( path=path_to_DF,channel=channel )
}
normalisations = {
}
hists = {
}
maxY = 0
minY = 99999999
for f in files:
normalisations[f] = read_tuple_from_file( files[f] )['QCD']
hists[f] = value_error_tuplelist_to_hist( normalisations[f], reco_bin_edges_vis[variable] ).Rebin(2)
maxY = max([maxY]+list(hists[f].y() ) )
minY = min([minY]+list(hists[f].y() ) )
if minY <= 0 : minY = 0.1
can = Canvas()
pad1 = Pad( 0, 0.3, 1, 1)
pad2 = Pad( 0, 0, 1, 0.3)
pad1.Draw()
pad2.Draw()
pad1.cd()
# print normalisations
hists['central'].SetLineColor(2)
hists['central'].SetLineWidth(3)
def test_invalid_zero_data(self):
variable = "MET"
channel = "electron"
pseudo_data = value_error_tuplelist_to_hist([(0, 0)] * (len(bin_edges[variable]) - 1), bin_edges[variable])
self.assertRaises(ValueError, self.dict[channel][variable]["unfolding_object"].unfold, (pseudo_data))
unfolding_file3 = root_open('/storage/TopQuarkGroup/mc/8TeV/NoSkimUnfolding/v10/TTJets_scaleup_TuneZ2star_8TeV-madgraph-tauola/unfolding_v10_Summer12_DR53X-PU_S10_START53_V7A-v1_NoSkim/TTJets-scaleup_nTuple_53X_mc_merged_001.root')
unfolding_file4 = root_open('/storage/TopQuarkGroup/mc/8TeV/NoSkimUnfolding/v10/TTJets_scaledown_TuneZ2star_8TeV-madgraph-tauola/unfolding_v10_Summer12_DR53X-PU_S10_START53_V7A-v1_NoSkim/TTJets-scaledown_nTuple_53X_mc_merged_001.root')
test_file = root_open('test_unfolded.root')
test1 = test_file.Get(channel + '_MET__TTJet__TTJetsMatching__plus')
test2 = test_file.Get(channel + '_MET__TTJet__TTJetsMatching__minus')
test3 = test_file.Get(channel + '_MET__TTJet__TTJetsScale__plus')
test4 = test_file.Get(channel + '_MET__TTJet__TTJetsScale__minus')
test1.Sumw2()
test2.Sumw2()
test3.Sumw2()
test4.Sumw2()
folder = 'unfolding_MET_analyser_' + channel + '_channel_patMETsPFlow'
ref1 = hist_to_value_error_tuplelist(unfolding_file1.Get(folder + '/truth_AsymBins'))
ref2 = hist_to_value_error_tuplelist(unfolding_file2.Get(folder + '/truth_AsymBins'))
ref3 = hist_to_value_error_tuplelist(unfolding_file3.Get(folder + '/truth_AsymBins'))
ref4 = hist_to_value_error_tuplelist(unfolding_file4.Get(folder + '/truth_AsymBins'))
ref1 = value_error_tuplelist_to_hist(ref1, bin_edges['MET'])
ref2 = value_error_tuplelist_to_hist(ref2, bin_edges['MET'])
ref3 = value_error_tuplelist_to_hist(ref3, bin_edges['MET'])
ref4 = value_error_tuplelist_to_hist(ref4, bin_edges['MET'])
normalise([test1, test2, test3, test4, ref1, ref2, ref3, ref4])
draw_pair(test1, ref1, 'matching_up')
draw_pair(test2, ref2, 'matching_down')
draw_pair(test3, ref3, 'scale_up')
draw_pair(test4, ref4, 'scale_down')
def read_xsection_measurement_results( category, channel, unc_type, scale_uncertanties=False ):
'''
Reading the unfolded xsection results from DFs into graphs
'''
global path_to_DF, variable, phase_space, method
file_template = '{path}/{category}/xsection_{name}_{channel}_{method}{suffix}.txt'
filename = file_template.format(
path = path_to_DF,
category = category,
name = unc_type,
channel = channel,
method = method,
suffix = '',
)
xsec_04_log.debug('Reading file {0}'.format(filename))
edges = bin_edges_full[variable]
if phase_space == 'VisiblePS':
edges = bin_edges_vis[variable]
# Collect the cross section measured/unfolded results from dataframes
normalised_xsection_unfolded = read_tuple_from_file( filename )
# Create TTJets_Scale
d_scale_syst = {}
partonShower_uncertainties = measurement_config.list_of_systematics['TTJets_scale']
for psUnc in partonShower_uncertainties:
normalised_xsection_unfolded[psUnc] = [value for value, error in normalised_xsection_unfolded[psUnc]]
d_scale_syst[psUnc] = normalised_xsection_unfolded[psUnc]
normalised_xsection_unfolded['TTJets_scaledown'], normalised_xsection_unfolded['TTJets_scaleup'] = get_scale_envelope(
d_scale_syst,
normalised_xsection_unfolded['TTJets_powhegPythia8'],
)
# Need to strip errors from central before passing to scaleFSR()
central = [c[0] for c in normalised_xsection_unfolded['TTJets_powhegPythia8']]
# Scale FSR
if scale_uncertanties:
normalised_xsection_unfolded['TTJets_fsrdown'] = scaleFSR(
normalised_xsection_unfolded['TTJets_fsrdown'],
central,
)
normalised_xsection_unfolded['TTJets_fsrup'] = scaleFSR(
normalised_xsection_unfolded['TTJets_fsrup'],
central,
)
# h_normalised_xsection = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_measured'], edges )
h_normalised_xsection_unfolded = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_unfolded'], edges )
histograms_normalised_xsection_different_generators = {
# 'measured':h_normalised_xsection,
'unfolded':h_normalised_xsection_unfolded,
}
histograms_normalised_xsection_different_systematics = {
'unfolded':h_normalised_xsection_unfolded,
}
if category == 'central':
# Add in distributions for the different MC to be shown
h_normalised_xsection_powhegPythia8 = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_powhegPythia8'], edges )
h_normalised_xsection_amcatnlo = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_amcatnloPythia8'], edges )
h_normalised_xsection_madgraphMLM = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_madgraphMLM'], edges )
h_normalised_xsection_powhegHerwigpp = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_powhegHerwig'], edges )
# SCALE BREAKDOWN
h_normalised_xsection_fsrup = value_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_fsrup'], edges )
h_normalised_xsection_fsrdown = value_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_fsrdown'], edges )
h_normalised_xsection_isrdown = value_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_isrdown'], edges )
h_normalised_xsection_isrup = value_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_isrup'], edges )
h_normalised_xsection_factorisationup = value_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_factorisationup'], edges )
h_normalised_xsection_factorisationdown = value_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_factorisationdown'], edges )
h_normalised_xsection_renormalisationup = value_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_renormalisationup'], edges )
h_normalised_xsection_renormalisationdown = value_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_renormalisationdown'], edges )
h_normalised_xsection_combinedup = value_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_combinedup'], edges )
h_normalised_xsection_combineddown = value_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_combineddown'], edges )
# PARTON SHOWER
h_normalised_xsection_scaleup = value_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_scaleup'], edges )
h_normalised_xsection_scaledown = value_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_scaledown'], edges )
h_normalised_xsection_massup = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_massup'], edges )
h_normalised_xsection_massdown = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_massdown'], edges )
h_normalised_xsection_ueup = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_ueup'], edges )
h_normalised_xsection_uedown = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_uedown'], edges )
h_normalised_xsection_hdampup = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_hdampup'], edges )
h_normalised_xsection_hdampdown = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_hdampdown'], edges )
h_normalised_xsection_erdOn = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_erdOn'], edges )
h_normalised_xsection_QCDbased_erdOn = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_QCDbased_erdOn'], edges )
# h_normalised_xsection_GluonMove = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_GluonMove'], edges )
h_normalised_xsection_semiLepBrup = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_semiLepBrup'], edges )
h_normalised_xsection_semiLepBrdown = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_semiLepBrdown'], edges )
h_normalised_xsection_fragup = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_fragup'], edges )
h_normalised_xsection_fragdown = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_fragdown'], edges )
h_normalised_xsection_petersonFrag = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_petersonFrag'], edges )
# OTHER
# h_normalised_xsection_alphaSup = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_alphaSup'], edges )
# h_normalised_xsection_alphaSdown = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_alphaSdown'], edges )
h_normalised_xsection_topPt = value_error_tuplelist_to_hist( normalised_xsection_unfolded['TTJets_topPt'], edges )
# And update
histograms_normalised_xsection_different_generators.update(
{
'TTJets_powhegPythia8' : h_normalised_xsection_powhegPythia8,
'TTJets_amcatnloPythia8' : h_normalised_xsection_amcatnlo,
'TTJets_madgraphMLM' : h_normalised_xsection_madgraphMLM,
'TTJets_powhegHerwig' : h_normalised_xsection_powhegHerwigpp,
}
)
if scale_uncertanties:
histograms_normalised_xsection_different_systematics.update(
{
'TTJets_powhegPythia8' : h_normalised_xsection_powhegPythia8,
'TTJets_fsrup' : h_normalised_xsection_fsrup,
'TTJets_fsrdown' : h_normalised_xsection_fsrdown,
'TTJets_isrdown' : h_normalised_xsection_isrdown,
'TTJets_isrup' : h_normalised_xsection_isrup,
'TTJets_factorisationup' : h_normalised_xsection_factorisationup,
'TTJets_factorisationdown' : h_normalised_xsection_factorisationdown,
'TTJets_renormalisationup' : h_normalised_xsection_renormalisationup,
'TTJets_renormalisationdown' : h_normalised_xsection_renormalisationdown,
'TTJets_combinedup' : h_normalised_xsection_combinedup,
'TTJets_combineddown' : h_normalised_xsection_combineddown,
}
)
else:
histograms_normalised_xsection_different_systematics.update(
{
'TTJets_powhegPythia8' : h_normalised_xsection_powhegPythia8,
'TTJets_scaleup' : h_normalised_xsection_scaleup,
'TTJets_scaledown' : h_normalised_xsection_scaledown,
# 'TTJets_massup' : h_normalised_xsection_massup,
# 'TTJets_massdown' : h_normalised_xsection_massdown,
# 'TTJets_ueup' : h_normalised_xsection_ueup,
# 'TTJets_uedown' : h_normalised_xsection_uedown,
'TTJets_hdampup' : h_normalised_xsection_hdampup,
'TTJets_hdampdown' : h_normalised_xsection_hdampdown,
# 'TTJets_erdOn' : h_normalised_xsection_erdOn,
# 'TTJets_QCDbased_erdOn' : h_normalised_xsection_QCDbased_erdOn,
# 'TTJets_GluonMove' : h_normalised_xsection_GluonMove,
# 'TTJets_semiLepBrup' : h_normalised_xsection_semiLepBrup,
# 'TTJets_semiLepBrdown' : h_normalised_xsection_semiLepBrdown,
# 'TTJets_fragup' : h_normalised_xsection_fragup,
# 'TTJets_fragdown' : h_normalised_xsection_fragdown,
# 'TTJets_petersonFrag' : h_normalised_xsection_petersonFrag,
'TTJets_topPt' : h_normalised_xsection_topPt,
}
)
filename = file_template.format(
path = path_to_DF,
category = category,
name = unc_type,
channel = channel,
method = method,
suffix = '_summary_absolute',
)
# Now for the systematic uncertainties
normalised_xsection_unfolded_with_errors = file_to_df( filename )
normalised_xsection_unfolded_with_errors['TTJets_unfolded'] = tupleise_cols(
normalised_xsection_unfolded_with_errors['central'],
normalised_xsection_unfolded_with_errors['systematic'],
)
xsec_04_log.debug('Reading file {0}'.format(filename))
# Transform unfolded data into graph form
h_normalised_xsection_unfolded_with_errors_unfolded = value_errors_tuplelist_to_graph(
normalised_xsection_unfolded_with_errors['TTJets_unfolded'],
edges,
is_symmetric_errors=True
)
# Add to list of histograms
histograms_normalised_xsection_different_generators['unfolded_with_systematics'] = h_normalised_xsection_unfolded_with_errors_unfolded
histograms_normalised_xsection_different_systematics['unfolded_with_systematics'] = h_normalised_xsection_unfolded_with_errors_unfolded
return histograms_normalised_xsection_different_generators, histograms_normalised_xsection_different_systematics
def do_shape_check(channel,
control_region_1,
control_region_2,
variable,
normalisation,
title,
x_title,
y_title,
x_limits,
y_limits,
name_region_1='conversions',
name_region_2='non-isolated electrons',
name_region_3='fit results',
rebin=1):
global b_tag_bin
# QCD shape comparison
if channel == 'electron':
histograms = get_histograms_from_files(
[control_region_1, control_region_2], histogram_files)
region_1 = histograms[channel][control_region_1].Clone(
) - histograms['TTJet'][control_region_1].Clone(
) - histograms['V+Jets'][control_region_1].Clone(
) - histograms['SingleTop'][control_region_1].Clone()
region_2 = histograms[channel][control_region_2].Clone(
) - histograms['TTJet'][control_region_2].Clone(
) - histograms['V+Jets'][control_region_2].Clone(
) - histograms['SingleTop'][control_region_2].Clone()
region_1.Rebin(rebin)
region_2.Rebin(rebin)
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[0]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(
region_1,
region_2,
name_region_1=name_region_1,
name_region_2=name_region_2,
histogram_properties=histogram_properties,
save_folder=output_folder)
# QCD shape comparison to fit results
histograms = get_histograms_from_files([control_region_1],
histogram_files)
region_1_tmp = histograms[channel][control_region_1].Clone(
) - histograms['TTJet'][control_region_1].Clone(
) - histograms['V+Jets'][control_region_1].Clone(
) - histograms['SingleTop'][control_region_1].Clone()
region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable])
fit_results_QCD = normalisation[variable]['QCD']
region_2 = value_error_tuplelist_to_hist(fit_results_QCD,
bin_edges_vis[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_conversions_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(
region_1,
region_2,
name_region_1=name_region_1,
name_region_2=name_region_3,
histogram_properties=histogram_properties,
save_folder=output_folder)
histograms = get_histograms_from_files([control_region_2], histogram_files)
region_1_tmp = histograms[channel][control_region_2].Clone(
) - histograms['TTJet'][control_region_2].Clone(
) - histograms['V+Jets'][control_region_2].Clone(
) - histograms['SingleTop'][control_region_2].Clone()
region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable])
fit_results_QCD = normalisation[variable]['QCD']
region_2 = value_error_tuplelist_to_hist(fit_results_QCD,
bin_edges_vis[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_noniso_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(region_1,
region_2,
name_region_1=name_region_2,
name_region_2=name_region_3,
histogram_properties=histogram_properties,
save_folder=output_folder)
def calculate_xsection( nEventsHistogram, variable ):
resultsAsTuple = hist_to_value_error_tuplelist( nEventsHistogram )
normalised_xsection, _, _, _ = calculate_normalised_xsection( resultsAsTuple, bin_widths_visiblePS[variable], False )
return value_error_tuplelist_to_hist(normalised_xsection, bin_edges_vis[variable])
def test_get_max_y_hist():
h = value_error_tuplelist_to_hist(data_h, bin_edges)
max_y = get_best_max_y([h])
assert max_y == 3 + 1
def main():
args, input_values_sets, json_input_files = parse_options()
results = {}
clear_old_df('tables/taufinding/')
for input_values, json_file in zip( input_values_sets, json_input_files ):
if 'combined' in json_file: continue
# Initialise the TauFinding class
regularisation_settings = TauFinding( input_values )
variable = regularisation_settings.variable
channel = regularisation_settings.channel
com = regularisation_settings.centre_of_mass_energy
# Specific channel or variable
if args.ch:
if args.ch not in channel: continue
if args.var:
if args.var not in variable: continue
print 'Running for:'
print 'Variable = {0}, channel = {1}, sqrt(s) = {2}'.format(variable, channel, com)
# Set additional elements
regularisation_settings.taus_to_test = get_tau_values(args.n_tau_in_log)
isTauCalculator = True
# Specific unfolding tests go here
if args.specific_tau is not None:
regularisation_settings.taus_to_test = [args.specific_tau]
df_chi2_specific_tau = get_chi2(regularisation_settings, args)
isTauCalculator = False
if args.run_measured_as_data:
regularisation_settings.taus_to_test = [0]
regularisation_settings.h_data = regularisation_settings.h_measured
df_chi2_measured = get_chi2(regularisation_settings, args)
isTauCalculator = False
if args.run_smeared_measured_as_data:
regularisation_settings.taus_to_test = [0]
regularisation_settings.h_data = regularisation_settings.h_measured
h_data = hist_to_value_error_tuplelist(regularisation_settings.h_data)
h_data_varied = [(return_rnd_Poisson(val),return_rnd_Poisson(err)) for val, err in h_data ]
h_data_varied = value_error_tuplelist_to_hist(h_data_varied, reco_bin_edges_vis[variable])
regularisation_settings.h_data = h_data_varied
df_chi2_smeared = get_chi2(regularisation_settings, args, smearing_test=True)
isTauCalculator = False
# Dont need to calculate chi2 for given tau tests
if not isTauCalculator: continue
# Find Chi2 for each tau and write to file
df_chi2 = get_chi2(regularisation_settings, args)
# Dont need to calculate tau for given tests
if not isTauCalculator: sys.exit()
# Have the dataframes now - albeit read to a file
# Read in each one corresponding to their channel
# Find the best tau and print to screen
for channel in ['electron', 'muon', 'combined']:
chi2_cut = 0.005
path = regularisation_settings.outpath+'tbl_'+channel+'_tauscan.txt'
df_chi2 = get_df_from_file(path)
if df_chi2 is None: continue
print '\n', "1 - P(Chi2|NDF)", '\n', df_chi2, '\n'
# cutoff to be changed to 0.001 when able to
best_taus = interpolate_tau(chi2_cut, df_chi2)
chi2_to_plots(args, df_chi2, regularisation_settings, chi2_cut, channel)
print_results_to_screen(best_taus, channel)
return
test1 = test_file.Get(channel + '_MET__TTJet__TTJetsMatching__plus')
test2 = test_file.Get(channel + '_MET__TTJet__TTJetsMatching__minus')
test3 = test_file.Get(channel + '_MET__TTJet__TTJetsScale__plus')
test4 = test_file.Get(channel + '_MET__TTJet__TTJetsScale__minus')
test1.Sumw2()
test2.Sumw2()
test3.Sumw2()
test4.Sumw2()
folder = 'unfolding_MET_analyser_' + channel + '_channel_patMETsPFlow'
ref1 = hist_to_value_error_tuplelist(
unfolding_file1.Get(folder + '/truth_AsymBins'))
ref2 = hist_to_value_error_tuplelist(
unfolding_file2.Get(folder + '/truth_AsymBins'))
ref3 = hist_to_value_error_tuplelist(
unfolding_file3.Get(folder + '/truth_AsymBins'))
ref4 = hist_to_value_error_tuplelist(
unfolding_file4.Get(folder + '/truth_AsymBins'))
ref1 = value_error_tuplelist_to_hist(ref1, bin_edges['MET'])
ref2 = value_error_tuplelist_to_hist(ref2, bin_edges['MET'])
ref3 = value_error_tuplelist_to_hist(ref3, bin_edges['MET'])
ref4 = value_error_tuplelist_to_hist(ref4, bin_edges['MET'])
normalise([test1, test2, test3, test4, ref1, ref2, ref3, ref4])
draw_pair(test1, ref1, 'matching_up')
draw_pair(test2, ref2, 'matching_down')
draw_pair(test3, ref3, 'scale_up')
draw_pair(test4, ref4, 'scale_down')
def debug_last_bin():
'''
For debugging why the last bin in the problematic variables deviates a
lot in _one_ of the channels only.
'''
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += 'normalised_xsection_{channel}_RooUnfoldSvd{suffix}.txt'
problematic_variables = ['HT', 'MET', 'NJets', 'lepton_pt']
for variable in problematic_variables:
results = {}
Result = namedtuple(
'Result', ['before_unfolding', 'after_unfolding', 'model'])
for channel in ['electron', 'muon', 'combined']:
input_file_data = file_template.format(
variable=variable,
channel=channel,
suffix='_with_errors',
)
input_file_model = file_template.format(
variable=variable,
channel=channel,
suffix='',
)
data = read_data_from_JSON(input_file_data)
data_model = read_data_from_JSON(input_file_model)
before_unfolding = data['TTJet_measured_withoutFakes']
after_unfolding = data['TTJet_unfolded']
model = data_model['powhegPythia8']
# only use the last bin
h_before_unfolding = value_errors_tuplelist_to_graph(
[before_unfolding[-1]], bin_edges_vis[variable][-2:])
h_after_unfolding = value_errors_tuplelist_to_graph(
[after_unfolding[-1]], bin_edges_vis[variable][-2:])
h_model = value_error_tuplelist_to_hist(
[model[-1]], bin_edges_vis[variable][-2:])
r = Result(before_unfolding, after_unfolding, model)
h = Result(h_before_unfolding, h_after_unfolding, h_model)
results[channel] = (r, h)
models = {'POWHEG+PYTHIA': results['combined'][1].model}
h_unfolded = [results[channel][1].after_unfolding for channel in [
'electron', 'muon', 'combined']]
tmp_hists = spread_x(h_unfolded, bin_edges_vis[variable][-2:])
measurements = {}
for channel, hist in zip(['electron', 'muon', 'combined'], tmp_hists):
value = results[channel][0].after_unfolding[-1][0]
error = results[channel][0].after_unfolding[-1][1]
label = '{c_label} ({value:1.2g} $\pm$ {error:1.2g})'.format(
c_label=channel,
value=value,
error=error,
)
measurements[label] = hist
properties = Histogram_properties()
properties.name = 'normalised_xsection_compare_channels_{0}_{1}_last_bin'.format(
variable, channel)
properties.title = 'Comparison of channels'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = False
properties.x_limits = (
bin_edges_vis[variable][-2], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
properties.legend_location = (0.95, 0.40)
if variable == 'NJets':
properties.legend_location = (0.97, 0.80)
properties.formats = ['png']
compare_measurements(models=models, measurements=measurements, show_measurement_errors=True,
histogram_properties=properties, save_folder='plots/', save_as=properties.formats)
def read_xsection_measurement_results(path_to_JSON, variable, bin_edges,
category,
channel,
k_values,
met_type='patType1CorrectedPFMet',
met_uncertainties=[]):
filename = ''
if category in met_uncertainties and variable == 'HT' and not 'JES' in category and not 'JER' in category:
filename = path_to_JSON + '/xsection_measurement_results/' + \
channel + '/central/normalised_xsection_' + met_type + '.txt'
else:
filename = path_to_JSON + '/xsection_measurement_results/' + channel + \
'/' + category + '/normalised_xsection_' + met_type + '.txt'
if channel == 'combined':
filename = filename.replace('kv' + str(k_values[channel]), '')
normalised_xsection_unfolded = read_data_from_JSON(filename)
h_normalised_xsection = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['TTJet_measured'], bin_edges[variable])
h_normalised_xsection_unfolded = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['TTJet_unfolded'], bin_edges[variable])
histograms_normalised_xsection_different_generators = {'measured': h_normalised_xsection,
'unfolded': h_normalised_xsection_unfolded}
histograms_normalised_xsection_systematics_shifts = {'measured': h_normalised_xsection,
'unfolded': h_normalised_xsection_unfolded}
if category == 'central':
# true distributions
h_normalised_xsection_MADGRAPH = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['MADGRAPH'], bin_edges[variable])
h_normalised_xsection_POWHEG_PYTHIA = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['POWHEG_PYTHIA'], bin_edges[variable])
h_normalised_xsection_POWHEG_HERWIG = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['POWHEG_HERWIG'], bin_edges[variable])
h_normalised_xsection_MCATNLO = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['MCATNLO'], bin_edges[variable])
h_normalised_xsection_mathchingup = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['matchingup'], bin_edges[variable])
h_normalised_xsection_mathchingdown = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['matchingdown'], bin_edges[variable])
h_normalised_xsection_scaleup = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['scaleup'], bin_edges[variable])
h_normalised_xsection_scaledown = value_error_tuplelist_to_hist(
normalised_xsection_unfolded['scaledown'], bin_edges[variable])
histograms_normalised_xsection_different_generators.update({'MADGRAPH': h_normalised_xsection_MADGRAPH,
'POWHEG_PYTHIA': h_normalised_xsection_POWHEG_PYTHIA,
'POWHEG_HERWIG': h_normalised_xsection_POWHEG_HERWIG,
'MCATNLO': h_normalised_xsection_MCATNLO})
histograms_normalised_xsection_systematics_shifts.update({'MADGRAPH': h_normalised_xsection_MADGRAPH,
'matchingdown': h_normalised_xsection_mathchingdown,
'matchingup': h_normalised_xsection_mathchingup,
'scaledown': h_normalised_xsection_scaledown,
'scaleup': h_normalised_xsection_scaleup})
file_template = path_to_JSON + '/xsection_measurement_results/' + channel + \
'/kv' + str(k_values[channel]) + '/' + \
category + '/normalised_xsection_' + met_type
if channel == 'combined':
file_template = file_template.replace(
'kv' + str(k_values[channel]), '')
# normalised_xsection_unfolded_with_errors = read_data_from_JSON( file_template + '_with_errors.txt' )
normalised_xsection_unfolded_with_errors_with_systematics_but_without_ttbar_theory = read_data_from_JSON(
file_template + '_with_systematics_but_without_ttbar_theory_errors.txt')
normalised_xsection_unfolded_with_errors_with_systematics_but_without_generator = read_data_from_JSON(
file_template + '_with_systematics_but_without_generator_errors.txt')
# a rootpy.Graph with asymmetric errors!
h_normalised_xsection_with_systematics_but_without_ttbar_theory = value_errors_tuplelist_to_graph(
normalised_xsection_unfolded_with_errors_with_systematics_but_without_ttbar_theory[
'TTJet_measured'],
bin_edges[variable])
h_normalised_xsection_with_systematics_but_without_ttbar_theory_unfolded = value_errors_tuplelist_to_graph(
normalised_xsection_unfolded_with_errors_with_systematics_but_without_ttbar_theory[
'TTJet_unfolded'],
bin_edges[variable])
h_normalised_xsection_with_systematics_but_without_generator = value_errors_tuplelist_to_graph(
normalised_xsection_unfolded_with_errors_with_systematics_but_without_generator[
'TTJet_measured'],
bin_edges[variable])
h_normalised_xsection_with_systematics_but_without_generator_unfolded = value_errors_tuplelist_to_graph(
normalised_xsection_unfolded_with_errors_with_systematics_but_without_generator[
'TTJet_unfolded'],
bin_edges[variable])
histograms_normalised_xsection_different_generators[
'measured_with_systematics'] = h_normalised_xsection_with_systematics_but_without_generator
histograms_normalised_xsection_different_generators[
'unfolded_with_systematics'] = h_normalised_xsection_with_systematics_but_without_generator_unfolded
histograms_normalised_xsection_systematics_shifts[
'measured_with_systematics'] = h_normalised_xsection_with_systematics_but_without_ttbar_theory
histograms_normalised_xsection_systematics_shifts[
'unfolded_with_systematics'] = h_normalised_xsection_with_systematics_but_without_ttbar_theory_unfolded
return histograms_normalised_xsection_different_generators, histograms_normalised_xsection_systematics_shifts
def do_shape_check(channel, control_region_1, control_region_2, variable, normalisation, title, x_title, y_title, x_limits, y_limits,
name_region_1='conversions' , name_region_2='non-isolated electrons', name_region_3='fit results', rebin=1):
global b_tag_bin
# QCD shape comparison
if channel == 'electron':
histograms = get_histograms_from_files([control_region_1, control_region_2], histogram_files)
region_1 = histograms[channel][control_region_1].Clone() - histograms['TTJet'][control_region_1].Clone() - histograms['V+Jets'][control_region_1].Clone() - histograms['SingleTop'][control_region_1].Clone()
region_2 = histograms[channel][control_region_2].Clone() - histograms['TTJet'][control_region_2].Clone() - histograms['V+Jets'][control_region_2].Clone() - histograms['SingleTop'][control_region_2].Clone()
region_1.Rebin(rebin)
region_2.Rebin(rebin)
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[0]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(region_1, region_2,
name_region_1=name_region_1, name_region_2=name_region_2,
histogram_properties=histogram_properties, save_folder=output_folder)
# QCD shape comparison to fit results
histograms = get_histograms_from_files([control_region_1], histogram_files)
region_1_tmp = histograms[channel][control_region_1].Clone() - histograms['TTJet'][control_region_1].Clone() - histograms['V+Jets'][control_region_1].Clone() - histograms['SingleTop'][control_region_1].Clone()
region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable])
fit_results_QCD = normalisation[variable]['QCD']
region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges_vis[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_conversions_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(region_1, region_2,
name_region_1=name_region_1, name_region_2=name_region_3,
histogram_properties=histogram_properties, save_folder=output_folder)
histograms = get_histograms_from_files([control_region_2], histogram_files)
region_1_tmp = histograms[channel][control_region_2].Clone() - histograms['TTJet'][control_region_2].Clone() - histograms['V+Jets'][control_region_2].Clone() - histograms['SingleTop'][control_region_2].Clone()
region_1 = rebin_asymmetric(region_1_tmp, bin_edges_vis[variable])
fit_results_QCD = normalisation[variable]['QCD']
region_2 = value_error_tuplelist_to_hist(fit_results_QCD, bin_edges_vis[variable])
histogram_properties = Histogram_properties()
histogram_properties.name = 'QCD_control_region_comparison_' + channel + '_' + variable + '_fits_with_noniso_' + b_tag_bin
histogram_properties.title = title + ', ' + b_tag_bins_latex[b_tag_bin]
histogram_properties.x_axis_title = x_title
histogram_properties.y_axis_title = 'arbitrary units/(0.1)'
histogram_properties.x_limits = x_limits
histogram_properties.y_limits = y_limits[1]
histogram_properties.mc_error = 0.0
histogram_properties.legend_location = 'upper right'
make_control_region_comparison(region_1, region_2,
name_region_1=name_region_2, name_region_2=name_region_3,
histogram_properties=histogram_properties, save_folder=output_folder)
def calculate_xsection( nEventsHistogram, variable ):
resultsAsTuple = hist_to_value_error_tuplelist( nEventsHistogram )
normalised_xsection = calculate_normalised_xsection( resultsAsTuple, bin_widths_visiblePS[variable], False )
return value_error_tuplelist_to_hist(normalised_xsection, bin_edges_vis[variable])
def main():
config = XSectionConfig(13)
file_for_powhegPythia = File(config.unfolding_central, "read")
file_for_ptReweight_up = File(config.unfolding_ptreweight_up, "read")
file_for_ptReweight_down = File(config.unfolding_ptreweight_down, "read")
file_for_etaReweight_up = File(config.unfolding_etareweight_up, "read")
file_for_etaReweight_down = File(config.unfolding_etareweight_down, "read")
file_for_data_template = "data/normalisation/background_subtraction/13TeV/{variable}/VisiblePS/central/normalisation_combined_patType1CorrectedPFMet.txt"
for channel in ["combined"]:
for variable in config.variables:
print variable
# for variable in ['HT']:
# Get the central powheg pythia distributions
_, _, response_central, fakes_central = get_unfold_histogram_tuple(
inputfile=file_for_powhegPythia,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=True,
visiblePS=True,
)
measured_central = asrootpy(response_central.ProjectionX("px", 1))
truth_central = asrootpy(response_central.ProjectionY())
# Get the reweighted powheg pythia distributions
_, _, response_pt_reweighted_up, _ = get_unfold_histogram_tuple(
inputfile=file_for_ptReweight_up,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True,
)
measured_pt_reweighted_up = asrootpy(response_pt_reweighted_up.ProjectionX("px", 1))
truth_pt_reweighted_up = asrootpy(response_pt_reweighted_up.ProjectionY())
_, _, response_pt_reweighted_down, _ = get_unfold_histogram_tuple(
inputfile=file_for_ptReweight_down,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True,
)
measured_pt_reweighted_down = asrootpy(response_pt_reweighted_down.ProjectionX("px", 1))
truth_pt_reweighted_down = asrootpy(response_pt_reweighted_down.ProjectionY())
_, _, response_eta_reweighted_up, _ = get_unfold_histogram_tuple(
inputfile=file_for_etaReweight_up,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True,
)
measured_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionX("px", 1))
truth_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionY())
_, _, response_eta_reweighted_down, _ = get_unfold_histogram_tuple(
inputfile=file_for_etaReweight_down,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True,
)
measured_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionX("px", 1))
truth_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionY())
# Get the data input (data after background subtraction, and fake removal)
file_for_data = file_for_data_template.format(variable=variable)
data = read_data_from_JSON(file_for_data)["TTJet"]
data = value_error_tuplelist_to_hist(data, reco_bin_edges_vis[variable])
data = removeFakes(measured_central, fakes_central, data)
# Plot all three
hp = Histogram_properties()
hp.name = "Reweighting_check_{channel}_{variable}_at_{com}TeV".format(
channel=channel, variable=variable, com="13"
)
v_latex = latex_labels.variables_latex[variable]
unit = ""
if variable in ["HT", "ST", "MET", "WPT", "lepton_pt"]:
unit = " [GeV]"
hp.x_axis_title = v_latex + unit
hp.y_axis_title = "Number of events"
hp.title = "Reweighting check for {variable}".format(variable=v_latex)
measured_central.Rebin(2)
measured_pt_reweighted_up.Rebin(2)
measured_pt_reweighted_down.Rebin(2)
measured_eta_reweighted_up.Rebin(2)
measured_eta_reweighted_down.Rebin(2)
data.Rebin(2)
measured_central.Scale(1 / measured_central.Integral())
measured_pt_reweighted_up.Scale(1 / measured_pt_reweighted_up.Integral())
measured_pt_reweighted_down.Scale(1 / measured_pt_reweighted_down.Integral())
measured_eta_reweighted_up.Scale(1 / measured_eta_reweighted_up.Integral())
measured_eta_reweighted_down.Scale(1 / measured_eta_reweighted_down.Integral())
data.Scale(1 / data.Integral())
compare_measurements(
models={
"Central": measured_central,
"PtReweighted Up": measured_pt_reweighted_up,
"PtReweighted Down": measured_pt_reweighted_down,
"EtaReweighted Up": measured_eta_reweighted_up,
"EtaReweighted Down": measured_eta_reweighted_down,
},
measurements={"Data": data},
show_measurement_errors=True,
histogram_properties=hp,
save_folder="plots/unfolding/reweighting_check",
save_as=["pdf"],
)
