def read_xsection_measurement_results(category, channel):
global path_to_JSON, variable, k_value, met_type
normalised_xsection_unfolded = None
if category in met_uncertainties and variable == 'HT':
normalised_xsection_unfolded = read_data_from_JSON(path_to_JSON + '/xsection_measurement_results' + '/kv' + str(k_value) + '/'
+ 'central' + '/normalised_xsection_' + channel + '_' + met_type + '.txt')
else:
normalised_xsection_unfolded = read_data_from_JSON(path_to_JSON + '/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])
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 = 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.update({'MADGRAPH':h_normalised_xsection_MADGRAPH,
'POWHEG':h_normalised_xsection_POWHEG,
'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})
normalised_xsection_unfolded_with_errors = read_data_from_JSON(path_to_JSON + '/xsection_measurement_results' + '/kv' +
str(k_value) + '/' + category + '/normalised_xsection_' +
channel + '_' + met_type + '_with_errors.txt')
# a rootpy.Graph with asymmetric errors!
h_normalised_xsection_with_systematics = value_errors_tuplelist_to_graph(normalised_xsection_unfolded_with_errors['TTJet_measured'], bin_edges[variable])
h_normalised_xsection_with_systematics_unfolded = value_errors_tuplelist_to_graph(normalised_xsection_unfolded_with_errors['TTJet_unfolded'], bin_edges[variable])
histograms_normalised_xsection_different_generators['measured_with_systematics'] = h_normalised_xsection_with_systematics
histograms_normalised_xsection_different_generators['unfolded_with_systematics'] = h_normalised_xsection_with_systematics_unfolded
histograms_normalised_xsection_systematics_shifts['measured_with_systematics'] = h_normalised_xsection_with_systematics
histograms_normalised_xsection_systematics_shifts['unfolded_with_systematics'] = h_normalised_xsection_with_systematics_unfolded
return histograms_normalised_xsection_different_generators, histograms_normalised_xsection_systematics_shifts
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 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 + '/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])
if category == 'central':
normalised_xsection_unfolded_with_errors = read_data_from_JSON(path_to_JSON + '/xsection_measurement_results' + '/kv' +
str(k_value) + '/' + category + '/normalised_xsection_' +
channel + '_' + met_type + '_with_errors.txt')
h_normalised_xsection = value_errors_tuplelist_to_graph(normalised_xsection_unfolded_with_errors['TTJet_measured'], bin_edges[variable])
h_normalised_xsection_unfolded = value_errors_tuplelist_to_graph(normalised_xsection_unfolded_with_errors['TTJet_unfolded'], bin_edges[variable])
#true distributions
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 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':
filename = path_to_JSON + '/xsection_measurement_results/' + channel + '/kv' + str( k_values[channel] ) + '/central/normalised_xsection_' + met_type + '.txt'
else:
filename = path_to_JSON + '/xsection_measurement_results/' + channel + '/kv' + str( k_values[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 test_get_max_y_graph():
g = value_errors_tuplelist_to_graph(data_g, bin_edges)
max_y = get_best_max_y([g])
assert max_y == 3 + 1
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 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[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_amcatnloHerwigpp = value_error_tuplelist_to_hist( normalised_xsection_unfolded['amcatnlo_HERWIG'], 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,
'amcatnlo':h_normalised_xsection_amcatnlo,
'madgraphMLM':h_normalised_xsection_madgraphMLM,
'amcatnlo_HERWIG':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 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 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 test_get_max_y_graph():
g = value_errors_tuplelist_to_graph(data_g, bin_edges)
max_y = get_best_max_y([g])
assert max_y == 3 + 1
