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(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 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 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 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 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( 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 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
