def calculate_normalised_xsections(normalisation, category, channel, normalise_to_one = False):
global variable, met_type, path_to_JSON
TTJet_normalised_xsection = calculate_normalised_xsection(normalisation['TTJet_measured'], bin_widths[variable], normalise_to_one)
TTJet_normalised_xsection_unfolded = calculate_normalised_xsection(normalisation['TTJet_unfolded'], bin_widths[variable], normalise_to_one)
MADGRAPH_normalised_xsection = calculate_normalised_xsection(normalisation['MADGRAPH'], bin_widths[variable], normalise_to_one)
POWHEG_normalised_xsection = calculate_normalised_xsection(normalisation['POWHEG'], bin_widths[variable], normalise_to_one)
MCATNLO_normalised_xsection = calculate_normalised_xsection(normalisation['MCATNLO'], bin_widths[variable], normalise_to_one)
matchingdown_normalised_xsection = calculate_normalised_xsection(normalisation['matchingdown'], bin_widths[variable], normalise_to_one)
matchingup_normalised_xsection = calculate_normalised_xsection(normalisation['matchingup'], bin_widths[variable], normalise_to_one)
scaledown_normalised_xsection = calculate_normalised_xsection(normalisation['scaledown'], bin_widths[variable], normalise_to_one)
scaleup_normalised_xsection = calculate_normalised_xsection(normalisation['scaleup'], bin_widths[variable], normalise_to_one)
normalised_xsection = {'TTJet_measured' : TTJet_normalised_xsection,
'TTJet_unfolded' : TTJet_normalised_xsection_unfolded,
'MADGRAPH': MADGRAPH_normalised_xsection,
'POWHEG': POWHEG_normalised_xsection,
'MCATNLO': MCATNLO_normalised_xsection,
#systematics
'matchingdown': matchingdown_normalised_xsection,
'matchingup': matchingup_normalised_xsection,
'scaledown': scaledown_normalised_xsection,
'scaleup': scaleup_normalised_xsection
}
filename = path_to_JSON + '/' + variable + '/xsection_measurement_results' + '/kv' + str(unfoldCfg.SVD_k_value) + '/' + category + '/normalised_xsection_' + channel + '_' + met_type + '.txt'
if normalise_to_one:
filename = filename.replace('normalised_xsection', 'normalised_to_one_xsection')
write_data_to_JSON(normalised_xsection, filename)
def setUp( self ):
self.n_events = [1, 2, 3]
self.n_events_errors = [1, 1, 1]
self.u_n_events = [ufloat( 1, 1 ), ufloat( 2, 1 ), ufloat( 3, 1 )]
self.n_total = sum( self.n_events )
self.inputs = zip( self.n_events, self.n_events_errors )
self.bin_widths = [2, 5, 7]
self.results = calculate_normalised_xsection( self.inputs,
self.bin_widths )
def setUp(self):
self.n_events = [1, 2, 3]
self.n_events_errors = [1, 1, 1]
self.u_n_events = [ufloat(1, 1), ufloat(2, 1), ufloat(3, 1)]
self.n_total = sum(self.n_events)
self.inputs = zip(self.n_events, self.n_events_errors)
self.bin_widths = [2, 5, 7]
self.results = calculate_normalised_xsection(self.inputs,
self.bin_widths)
def calculate_normalised_xsections(normalisation,
category,
channel,
normalise_to_one=False):
global variable, met_type, path_to_JSON
TTJet_normalised_xsection = calculate_normalised_xsection(
normalisation['TTJet_measured'], bin_widths[variable],
normalise_to_one)
TTJet_normalised_xsection_unfolded = calculate_normalised_xsection(
normalisation['TTJet_unfolded'], bin_widths[variable],
normalise_to_one)
MADGRAPH_normalised_xsection = calculate_normalised_xsection(
normalisation['MADGRAPH'], bin_widths[variable], normalise_to_one)
POWHEG_normalised_xsection = calculate_normalised_xsection(
normalisation['POWHEG'], bin_widths[variable], normalise_to_one)
MCATNLO_normalised_xsection = calculate_normalised_xsection(
normalisation['MCATNLO'], bin_widths[variable], normalise_to_one)
matchingdown_normalised_xsection = calculate_normalised_xsection(
normalisation['matchingdown'], bin_widths[variable], normalise_to_one)
matchingup_normalised_xsection = calculate_normalised_xsection(
normalisation['matchingup'], bin_widths[variable], normalise_to_one)
scaledown_normalised_xsection = calculate_normalised_xsection(
normalisation['scaledown'], bin_widths[variable], normalise_to_one)
scaleup_normalised_xsection = calculate_normalised_xsection(
normalisation['scaleup'], bin_widths[variable], normalise_to_one)
normalised_xsection = {
'TTJet_measured': TTJet_normalised_xsection,
'TTJet_unfolded': TTJet_normalised_xsection_unfolded,
'MADGRAPH': MADGRAPH_normalised_xsection,
'POWHEG': POWHEG_normalised_xsection,
'MCATNLO': MCATNLO_normalised_xsection,
#systematics
'matchingdown': matchingdown_normalised_xsection,
'matchingup': matchingup_normalised_xsection,
'scaledown': scaledown_normalised_xsection,
'scaleup': scaleup_normalised_xsection
}
filename = path_to_JSON + '/' + variable + '/xsection_measurement_results' + '/kv' + str(
unfoldCfg.SVD_k_value
) + '/' + category + '/normalised_xsection_' + channel + '_' + met_type + '.txt'
if normalise_to_one:
filename = filename.replace('normalised_xsection',
'normalised_to_one_xsection')
write_data_to_JSON(normalised_xsection, filename)
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 calculate_normalised_xsections( normalisation, category, channel, normalise_to_one = False, covariance_matrix=None ):
'''
Calculate the normalised cross sections
'''
global variable, path_to_DF, phase_space
binWidths = None
if phase_space == 'VisiblePS':
binWidths = bin_widths_visiblePS
elif phase_space == 'FullPS':
binWidths = bin_widths
normalised_xsection = {}
normalised_xsection['TTJet_measured'], _, _ = calculate_normalised_xsection(
normalisation['TTJet_measured'],
binWidths[variable],
normalise_to_one
)
normalised_xsection['TTJet_measured_withoutFakes'], _, _ = calculate_normalised_xsection(
normalisation['TTJet_measured_withoutFakes'],
binWidths[variable],
normalise_to_one
)
normalised_xsection['TTJet_unfolded'], normalised_covariance_matrix, normalised_correlation_matrix = calculate_normalised_xsection(
normalisation['TTJet_unfolded'],
binWidths[variable],
normalise_to_one,
covariance_matrix
)
if not covariance_matrix is None:
covariance_output_template = '{path_to_DF}/central/covarianceMatrices/{cat}_{label}_{channel}.txt'
# Unfolded number of events
table_outfile=covariance_output_template.format( path_to_DF=path_to_DF, channel = channel, label='Covariance', cat='Stat_normalisedXsection' )
create_covariance_matrix( normalised_covariance_matrix, table_outfile)
table_outfile=covariance_output_template.format( path_to_DF=path_to_DF, channel = channel, label='Correlation', cat='Stat_normalisedXsection' )
create_covariance_matrix( normalised_correlation_matrix, table_outfile )
if category == 'central':
normalised_xsection['powhegPythia8'], _, _ = calculate_normalised_xsection(
normalisation['powhegPythia8'],
binWidths[variable],
normalise_to_one,
)
# normalised_xsection['amcatnlo'], _, _ = calculate_normalised_xsection(
# normalisation['amcatnlo'],
# binWidths[variable],
# normalise_to_one,
# )
normalised_xsection['powhegHerwig'], _, _ = calculate_normalised_xsection(
normalisation['powhegHerwig'],
binWidths[variable],
normalise_to_one,
)
# normalised_xsection['madgraphMLM'], _, _ = calculate_normalised_xsection(
# normalisation['madgraphMLM'],
# binWidths[variable],
# normalise_to_one,
# )
normalised_xsection['massdown'], _, _ = calculate_normalised_xsection(
normalisation['massdown'],
binWidths[variable],
normalise_to_one,
)
normalised_xsection['massup'], _, _ = calculate_normalised_xsection(
normalisation['massup'],
binWidths[variable],
normalise_to_one,
)
normalised_xsection['isrdown'], _, _ = calculate_normalised_xsection(
normalisation['isrdown'],
binWidths[variable],
normalise_to_one,
)
normalised_xsection['isrup'], _, _ = calculate_normalised_xsection(
normalisation['isrup'],
binWidths[variable],
normalise_to_one,
)
# normalised_xsection['fsrdown'], _, _ = calculate_normalised_xsection(
# normalisation['fsrdown'],
# binWidths[variable],
# normalise_to_one,
# )
normalised_xsection['fsrup'], _, _ = calculate_normalised_xsection(
normalisation['fsrup'],
binWidths[variable],
normalise_to_one,
)
normalised_xsection['uedown'], _, _ = calculate_normalised_xsection(
normalisation['uedown'],
binWidths[variable],
normalise_to_one,
)
normalised_xsection['ueup'], _, _ = calculate_normalised_xsection(
normalisation['ueup'],
binWidths[variable],
normalise_to_one,
)
file_template = '{path_to_DF}/{category}/xsection_normalised_{channel}_{method}.txt'
if normalise_to_one:
file_template = file_template.replace( 'xsection_normalised', 'xsection_normalised_to_one' )
write_02(normalised_xsection, file_template, path_to_DF, category, channel, method)
def calculate_normalised_xsections( normalisation, category, channel, normalise_to_one = False ):
global variable, met_type, path_to_JSON, phase_space
binWidths = None
if phase_space == 'VisiblePS':
binWidths = bin_widths_visiblePS
elif phase_space == 'FullPS':
binWidths = bin_widths
TTJet_normalised_xsection = calculate_normalised_xsection( normalisation['TTJet_measured'], binWidths[variable], normalise_to_one )
TTJet_withoutFakes_normalised_xsection = calculate_normalised_xsection( normalisation['TTJet_measured_withoutFakes'], binWidths[variable], normalise_to_one )
TTJet_normalised_xsection_unfolded = calculate_normalised_xsection( normalisation['TTJet_unfolded'], binWidths[variable], normalise_to_one )
normalised_xsection = {'TTJet_measured' : TTJet_normalised_xsection,
'TTJet_measured_withoutFakes' : TTJet_withoutFakes_normalised_xsection,
'TTJet_unfolded' : TTJet_normalised_xsection_unfolded
}
if category == 'central':
powhegPythia8_normalised_xsection = calculate_normalised_xsection( normalisation['powhegPythia8'], binWidths[variable], normalise_to_one )
amcatnlo_normalised_xsection = calculate_normalised_xsection( normalisation['amcatnlo'], binWidths[variable], normalise_to_one )
powhegHerwig_normalised_xsection = calculate_normalised_xsection( normalisation['powhegHerwig'], binWidths[variable], normalise_to_one )
# amcatnloHerwig_normalised_xsection = calculate_normalised_xsection( normalisation['amcatnloHerwig'], binWidths[variable], normalise_to_one )
madgraphMLM_normalised_xsection = calculate_normalised_xsection( normalisation['madgraphMLM'], binWidths[variable], normalise_to_one )
scaledown_normalised_xsection = calculate_normalised_xsection( normalisation['scaledown'], binWidths[variable], normalise_to_one )
scaleup_normalised_xsection = calculate_normalised_xsection( normalisation['scaleup'], binWidths[variable], normalise_to_one )
massdown_normalised_xsection = calculate_normalised_xsection( normalisation['massdown'], binWidths[variable], normalise_to_one )
massup_normalised_xsection = calculate_normalised_xsection( normalisation['massup'], binWidths[variable], normalise_to_one )
normalised_xsection['powhegPythia8'] = powhegPythia8_normalised_xsection
normalised_xsection['amcatnlo'] = amcatnlo_normalised_xsection
normalised_xsection['madgraphMLM' ] = madgraphMLM_normalised_xsection
normalised_xsection['powhegHerwig'] = powhegHerwig_normalised_xsection
# normalised_xsection['amcatnloHerwig'] = amcatnloHerwig_normalised_xsection
normalised_xsection['scaledown'] = scaledown_normalised_xsection
normalised_xsection['scaleup'] = scaleup_normalised_xsection
normalised_xsection['massdown'] = massdown_normalised_xsection
normalised_xsection['massup'] = massup_normalised_xsection
file_template = '{path_to_JSON}/{category}/normalised_xsection_{channel}_{method}.txt'
filename = file_template.format(
path_to_JSON = path_to_JSON,
category = category,
channel = channel,
method = method,
)
if normalise_to_one:
filename = filename.replace( 'normalised_xsection', 'normalised_to_one_xsection' )
write_data_to_JSON( normalised_xsection, filename )
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])