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 + '/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_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 )
amcatnlo_HERWIG_normalised_xsection = calculate_normalised_xsection( normalisation['amcatnlo_HERWIG'], 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 )
madgraphMLM_normalised_xsection = calculate_normalised_xsection( normalisation['madgraphMLM'], 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['amcatnlo_HERWIG'] = amcatnlo_HERWIG_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])
def calculate_normalised_xsections( normalisation, category, channel, k_value = None, 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 )
MADGRAPH_ptreweight_normalised_xsection = calculate_normalised_xsection( normalisation['MADGRAPH_ptreweight'], bin_widths[variable], normalise_to_one )
POWHEG_PYTHIA_normalised_xsection = calculate_normalised_xsection( normalisation['POWHEG_PYTHIA'], bin_widths[variable], normalise_to_one )
POWHEG_HERWIG_normalised_xsection = calculate_normalised_xsection( normalisation['POWHEG_HERWIG'], 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,
'MADGRAPH_ptreweight': MADGRAPH_ptreweight_normalised_xsection,
'POWHEG_PYTHIA': POWHEG_PYTHIA_normalised_xsection,
'POWHEG_HERWIG': POWHEG_HERWIG_normalised_xsection,
'MCATNLO': MCATNLO_normalised_xsection,
# systematics
'matchingdown': matchingdown_normalised_xsection,
'matchingup': matchingup_normalised_xsection,
'scaledown': scaledown_normalised_xsection,
'scaleup': scaleup_normalised_xsection
}
if not channel == 'combined':
filename = path_to_JSON + '/xsection_measurement_results/%s/kv%d/%s/normalised_xsection_%s.txt' % ( channel, k_value, category, met_type )
else:
filename = path_to_JSON + '/xsection_measurement_results/%s/%s/normalised_xsection_%s.txt' % ( channel, category, met_type )
if normalise_to_one:
filename = filename.replace( 'normalised_xsection', 'normalised_to_one_xsection' )
write_data_to_JSON( normalised_xsection, filename )
'scaleup': scaleup_results_electron
}
write_data_to_JSON(normalisation_electron_unfolded, 'data/normalisation_electron_unfolded.txt')
# calculate the x-sections and
bin_widths = [25, 20, 25, 30, 150]
TTJet_xsection = calculate_xsection(TTJet_fit_results_electron, 5050, 0.15) # L in pb1
TTJet_xsection_unfolded = calculate_xsection(TTJet_fit_results_electron_unfolded, 5050, 0.15) # L in pb1
MADGRAPH_xsection = calculate_xsection(MADGRAPH_results_electron, 5050, 0.15) # L in pb1
xsection_electron_unfolded = {'TTJet' : TTJet_xsection_unfolded,
'MADGRAPH': MADGRAPH_xsection
}
write_data_to_JSON(xsection_electron_unfolded, 'data/xsection_electron_unfolded.txt')
TTJet_normalised_to_one_xsection = calculate_normalised_xsection(TTJet_fit_results_electron, bin_widths, normalise_to_one=True)
TTJet_normalised_to_one_xsection_unfolded = calculate_normalised_xsection(TTJet_fit_results_electron_unfolded, bin_widths, normalise_to_one=True)
MADGRAPH_normalised_to_one_xsection = calculate_normalised_xsection(MADGRAPH_results_electron, bin_widths, normalise_to_one=True)
normalised_to_one_xsection_electron_unfolded = {'TTJet' : TTJet_normalised_to_one_xsection_unfolded,
'MADGRAPH': MADGRAPH_normalised_to_one_xsection
}
write_data_to_JSON(normalised_to_one_xsection_electron_unfolded, 'data/normalised_to_one_xsection_electron_unfolded.txt')
TTJet_normalised_xsection = calculate_normalised_xsection(TTJet_fit_results_electron, bin_widths, normalise_to_one=False)
TTJet_normalised_xsection_unfolded = calculate_normalised_xsection(TTJet_fit_results_electron_unfolded, bin_widths, normalise_to_one=False)
MADGRAPH__normalised_xsection = calculate_normalised_xsection(MADGRAPH_results_electron, bin_widths, normalise_to_one=False)
normalised_xsection_electron_unfolded = {'TTJet' : TTJet_normalised_xsection_unfolded,
'MADGRAPH': MADGRAPH__normalised_xsection
}
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)
amcatnlo_HERWIG_normalised_xsection = calculate_normalised_xsection(
normalisation['amcatnlo_HERWIG'], 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)
madgraphMLM_normalised_xsection = calculate_normalised_xsection(
normalisation['madgraphMLM'], 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[
'amcatnlo_HERWIG'] = amcatnlo_HERWIG_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 get_normalised_xsection(normalisation_unfolded, normalise_to_one):
bin_widths = [25, 20, 25, 30, 150]
result = {}
for sample in normalisation_unfolded.keys():
result[sample] = calculate_normalised_xsection(normalisation_unfolded[sample], bin_widths, normalise_to_one)
return result
