def summarise_systematics( list_of_central_measurements, dictionary_of_systematics, pdf_calculation = False, hadronisation_systematic = False ):
global symmetrise_errors
# number of bins
number_of_bins = len( list_of_central_measurements )
down_errors = [0] * number_of_bins
up_errors = [0] * number_of_bins
for bin_i in range( number_of_bins ):
central_value = list_of_central_measurements[bin_i][0] # 0 = value, 1 = error
error_down, error_up = 0, 0
if pdf_calculation:
pdf_uncertainty_values = {systematic:measurement[bin_i][0] for systematic, measurement in dictionary_of_systematics.iteritems()}
error_down, error_up = calculate_lower_and_upper_PDFuncertainty( central_value, pdf_uncertainty_values )
if symmetrise_errors:
error_down = max( error_down, error_up )
error_up = max( error_down, error_up )
elif hadronisation_systematic:
# always symmetric: absolute value of the difference between powheg_herwig and powheg_pythia
difference = dictionary_of_systematics['TTJets_powheg_herwig'][bin_i][0]-dictionary_of_systematics['TTJets_powheg_pythia'][bin_i][0]
error_down = abs(difference)
error_up = error_down
else:
list_of_systematics = [systematic[bin_i][0] for systematic in dictionary_of_systematics.values()]
error_down, error_up = calculate_lower_and_upper_systematics( central_value, list_of_systematics, symmetrise_errors )
down_errors[bin_i] = error_down
up_errors[bin_i] = error_up
return down_errors, up_errors
def summarise_systematics(list_of_central_measurements, dictionary_of_systematics, pdf_calculation=False):
global symmetrise_errors
#number of bins
number_of_bins = len(list_of_central_measurements)
down_errors = [0] * number_of_bins
up_errors = [0] * number_of_bins
for bin_i in range(number_of_bins):
central_value = list_of_central_measurements[bin_i][0] # 0 = value, 1 = error
error_down, error_up = 0, 0
if pdf_calculation:
pdf_uncertainty_values = {systematic:measurement[bin_i][0] for systematic, measurement in dictionary_of_systematics.iteritems()}
error_down, error_up = calculate_lower_and_upper_PDFuncertainty(central_value, pdf_uncertainty_values)
if symmetrise_errors:
error_down = max(error_down, error_up)
error_up = max(error_down, error_up)
else:
list_of_systematics = [systematic[bin_i][0] for systematic in dictionary_of_systematics.values()]
error_down, error_up = calculate_lower_and_upper_systematics(central_value, list_of_systematics, symmetrise_errors)
down_errors[bin_i] = error_down
up_errors[bin_i] = error_up
return down_errors, up_errors
def summarise_systematics( list_of_central_measurements, dictionary_of_systematics, pdf_calculation = False, hadronisation_systematic = False, mass_systematic = False, kValueSystematic = False, experimentalUncertainty = False, actualCentralMeasurements = [] ):
global symmetrise_errors
# number of bins
number_of_bins = len( list_of_central_measurements )
down_errors = [0] * number_of_bins
up_errors = [0] * number_of_bins
for bin_i in range( number_of_bins ):
central_value = list_of_central_measurements[bin_i][0] # 0 = value, 1 = error
error_down, error_up = 0, 0
if pdf_calculation:
pdf_uncertainty_values = {systematic:measurement[bin_i][0] for systematic, measurement in dictionary_of_systematics.iteritems()}
error_down, error_up = calculate_lower_and_upper_PDFuncertainty( central_value, pdf_uncertainty_values )
if symmetrise_errors:
error_down = max( error_down, error_up )
error_up = max( error_down, error_up )
elif hadronisation_systematic:
# always symmetric: absolute value of the difference between powheg_herwig and powheg_pythia
powheg_herwig = dictionary_of_systematics['TTJets_hadronisation'][bin_i][0]
powheg_pythia = dictionary_of_systematics['TTJets_NLOgenerator'][bin_i][0]
difference = powheg_herwig - powheg_pythia
mean = (powheg_herwig + powheg_pythia)/2.0
difference = abs(difference)
# now scale the error to the central value
relative_error = difference/mean
error_down = relative_error * central_value
error_up = error_down
elif mass_systematic:
list_of_systematics = [systematic[bin_i][0] for systematic in dictionary_of_systematics.values()]
error_down, error_up = calculate_lower_and_upper_systematics( central_value, list_of_systematics, False )
# Scale errors calculated using very different top masses
error_down, error_up = scaleTopMassSystematicErrors( [error_down], [error_up] )
error_down = error_down[0]
error_up = error_up[0]
elif kValueSystematic:
list_of_systematics = [systematic[bin_i][0] for systematic in dictionary_of_systematics.values()]
error_down, error_up = calculate_lower_and_upper_systematics( central_value, list_of_systematics, True )
elif experimentalUncertainty:
list_of_systematics = [systematic[bin_i][0] for systematic in dictionary_of_systematics.values()]
error_down, error_up = calculate_lower_and_upper_systematics( central_value, list_of_systematics, symmetrise_errors )
actualCentralValue = actualCentralMeasurements[bin_i][0]
error_down = error_down / central_value * actualCentralValue
error_up = error_up / central_value * actualCentralValue
else:
list_of_systematics = [systematic[bin_i][0] for systematic in dictionary_of_systematics.values()]
error_down, error_up = calculate_lower_and_upper_systematics( central_value, list_of_systematics, symmetrise_errors )
down_errors[bin_i] = error_down
up_errors[bin_i] = error_up
return down_errors, up_errors
def summarise_systematics(list_of_central_measurements,
dictionary_of_systematics,
pdf_calculation=False,
hadronisation_systematic=False,
mass_systematic=False,
kValueSystematic=False,
experimentalUncertainty=False,
actualCentralMeasurements=[]):
global symmetrise_errors
# number of bins
number_of_bins = len(list_of_central_measurements)
down_errors = [0] * number_of_bins
up_errors = [0] * number_of_bins
for bin_i in range(number_of_bins):
central_value = list_of_central_measurements[bin_i][
0] # 0 = value, 1 = error
error_down, error_up = 0, 0
if pdf_calculation:
pdf_uncertainty_values = {
systematic: measurement[bin_i][0]
for systematic, measurement in
dictionary_of_systematics.iteritems()
}
error_down, error_up = calculate_lower_and_upper_PDFuncertainty(
central_value, pdf_uncertainty_values)
if symmetrise_errors:
error_down = max(error_down, error_up)
error_up = max(error_down, error_up)
elif hadronisation_systematic:
# always symmetric: absolute value of the difference between powheg_herwig and powheg_pythia
powheg_herwig = dictionary_of_systematics['TTJets_hadronisation'][
bin_i][0]
powheg_pythia = dictionary_of_systematics['TTJets_NLOgenerator'][
bin_i][0]
difference = powheg_herwig - powheg_pythia
mean = (powheg_herwig + powheg_pythia) / 2.0
difference = abs(difference)
# now scale the error to the central value
relative_error = difference / mean
error_down = relative_error * central_value
error_up = error_down
elif mass_systematic:
list_of_systematics = [
systematic[bin_i][0]
for systematic in dictionary_of_systematics.values()
]
error_down, error_up = calculate_lower_and_upper_systematics(
central_value, list_of_systematics, False)
# Scale errors calculated using very different top masses
error_down, error_up = scaleTopMassSystematicErrors([error_down],
[error_up])
error_down = error_down[0]
error_up = error_up[0]
elif kValueSystematic:
list_of_systematics = [
systematic[bin_i][0]
for systematic in dictionary_of_systematics.values()
]
error_down, error_up = calculate_lower_and_upper_systematics(
central_value, list_of_systematics, True)
elif experimentalUncertainty:
list_of_systematics = [
systematic[bin_i][0]
for systematic in dictionary_of_systematics.values()
]
error_down, error_up = calculate_lower_and_upper_systematics(
central_value, list_of_systematics, symmetrise_errors)
actualCentralValue = actualCentralMeasurements[bin_i][0]
error_down = error_down / central_value * actualCentralValue
error_up = error_up / central_value * actualCentralValue
else:
list_of_systematics = [
systematic[bin_i][0]
for systematic in dictionary_of_systematics.values()
]
error_down, error_up = calculate_lower_and_upper_systematics(
central_value, list_of_systematics, symmetrise_errors)
down_errors[bin_i] = error_down
up_errors[bin_i] = error_up
return down_errors, up_errors
