def read_fit_templates_and_results_as_histograms( category, channel ):
global path_to_JSON, variable, met_type, phase_space
templates = read_data_from_JSON( path_to_JSON + '/fit_results/' + category + '/templates_' + channel + '_' + met_type + '.txt' )
data_values = read_data_from_JSON( path_to_JSON + '/fit_results/' + category + '/initial_values_' + channel + '_' + met_type + '.txt' )['data']
fit_results = read_data_from_JSON( path_to_JSON + '/fit_results/' + category + '/fit_results_' + channel + '_' + met_type + '.txt' )
fit_variables = templates.keys()
template_histograms = {fit_variable: {} for fit_variable in fit_variables}
fit_results_histograms = {fit_variable: {} for fit_variable in fit_variables}
variableBins = None
if phase_space == 'VisiblePS':
variableBins = variable_bins_visiblePS_ROOT
elif phase_space == 'FullPS':
variableBins = variable_bins_ROOT
for bin_i, variable_bin in enumerate( variableBins[variable] ):
for fit_variable in fit_variables:
h_template_data = value_tuplelist_to_hist( templates[fit_variable]['data'][bin_i], fit_variable_bin_edges[fit_variable] )
h_template_ttjet = value_tuplelist_to_hist( templates[fit_variable]['TTJet'][bin_i], fit_variable_bin_edges[fit_variable] )
h_template_singletop = value_tuplelist_to_hist( templates[fit_variable]['SingleTop'][bin_i], fit_variable_bin_edges[fit_variable] )
h_template_VJets = value_tuplelist_to_hist( templates[fit_variable]['V+Jets'][bin_i], fit_variable_bin_edges[fit_variable] )
h_template_QCD = value_tuplelist_to_hist( templates[fit_variable]['QCD'][bin_i], fit_variable_bin_edges[fit_variable] )
template_histograms[fit_variable][variable_bin] = {
'TTJet' : h_template_ttjet,
'SingleTop' : h_template_singletop,
'V+Jets':h_template_VJets,
'QCD':h_template_QCD
}
h_data = h_template_data.Clone()
h_ttjet = h_template_ttjet.Clone()
h_singletop = h_template_singletop.Clone()
h_VJets = h_template_VJets.Clone()
h_QCD = h_template_QCD.Clone()
data_normalisation = data_values[bin_i][0]
n_ttjet = fit_results['TTJet'][bin_i][0]
n_singletop = fit_results['SingleTop'][bin_i][0]
VJets_normalisation = fit_results['V+Jets'][bin_i][0]
QCD_normalisation = fit_results['QCD'][bin_i][0]
h_data.Scale( data_normalisation )
h_ttjet.Scale( n_ttjet )
h_singletop.Scale( n_singletop )
h_VJets.Scale( VJets_normalisation )
h_QCD.Scale( QCD_normalisation )
h_background = h_VJets + h_QCD + h_singletop
for bin_i_data in range( len( h_data ) ):
h_data.SetBinError( bin_i_data + 1, sqrt( h_data.GetBinContent( bin_i_data + 1 ) ) )
fit_results_histograms[fit_variable][variable_bin] = {
'data' : h_data,
'signal' : h_ttjet,
'background' : h_background
}
return template_histograms, fit_results_histograms
def read_fit_templates_and_results_as_histograms(category, channel):
global path_to_JSON, variable, met_type
templates = read_data_from_JSON(path_to_JSON + '/' + variable +
'/fit_results/' + category +
'/templates_' + channel + '_' + met_type +
'.txt')
data_values = read_data_from_JSON(path_to_JSON + '/' + variable +
'/fit_results/' + category +
'/initial_values_' + channel + '_' +
met_type + '.txt')['data']
fit_results = read_data_from_JSON(path_to_JSON + '/' + variable +
'/fit_results/' + category +
'/fit_results_' + channel + '_' +
met_type + '.txt')
template_histograms = {}
fit_results_histograms = {}
for bin_i, variable_bin in enumerate(variable_bins_ROOT[variable]):
h_template_data = value_tuplelist_to_hist(templates['data'][bin_i],
eta_bin_edges)
h_template_signal = value_tuplelist_to_hist(templates['signal'][bin_i],
eta_bin_edges)
h_template_VJets = value_tuplelist_to_hist(templates['V+Jets'][bin_i],
eta_bin_edges)
h_template_QCD = value_tuplelist_to_hist(templates['QCD'][bin_i],
eta_bin_edges)
template_histograms[variable_bin] = {
'signal': h_template_signal,
'V+Jets': h_template_VJets,
'QCD': h_template_QCD
}
h_data = h_template_data.Clone()
h_signal = h_template_signal.Clone()
h_VJets = h_template_VJets.Clone()
h_QCD = h_template_QCD.Clone()
data_normalisation = data_values[bin_i]
signal_normalisation = fit_results['signal'][bin_i][0]
VJets_normalisation = fit_results['V+Jets'][bin_i][0]
QCD_normalisation = fit_results['QCD'][bin_i][0]
h_data.Scale(data_normalisation)
h_signal.Scale(signal_normalisation)
h_VJets.Scale(VJets_normalisation)
h_QCD.Scale(QCD_normalisation)
h_background = h_VJets + h_QCD
for bin_i in range(len(h_data)):
h_data.SetBinError(bin_i + 1,
sqrt(h_data.GetBinContent(bin_i + 1)))
fit_results_histograms[variable_bin] = {
'data': h_data,
'signal': h_signal,
'background': h_background
}
return template_histograms, fit_results_histograms
def compare_combine_before_after_unfolding(measurement='normalised_xsection',
add_before_unfolding=False):
file_template = 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += '{measurement}_{channel}_RooUnfold{method}.txt'
variables = [
'MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'
]
for variable in variables:
combineBefore = file_template.format(variable=variable,
method='Svd',
channel='combinedBeforeUnfolding',
measurement=measurement)
combineAfter = file_template.format(variable=variable,
method='Svd',
channel='combined',
measurement=measurement)
data = read_data_from_JSON(combineBefore)
before_unfolding = data['TTJet_measured']
combineBefore_data = data['TTJet_unfolded']
combineAfter_data = read_data_from_JSON(combineAfter)['TTJet_unfolded']
h_combineBefore = value_error_tuplelist_to_hist(
combineBefore_data, bin_edges_vis[variable])
h_combineAfter = value_error_tuplelist_to_hist(combineAfter_data,
bin_edges_vis[variable])
h_before_unfolding = value_error_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = '{0}_compare_combine_before_after_unfolding_{1}'.format(
measurement, variable)
properties.title = 'Comparison of combining before/after unfolding'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = True
properties.x_limits = (bin_edges_vis[variable][0],
bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
if 'xsection' in measurement:
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
else:
properties.y_axis_title = r'$t\bar{t}$ normalisation'
histograms = {
'Combine before unfolding': h_combineBefore,
'Combine after unfolding': h_combineAfter
}
if add_before_unfolding:
histograms['before unfolding'] = h_before_unfolding
properties.name += '_ext'
properties.has_ratio = False
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def compare_unfolding_methods(measurement='normalised_xsection',
add_before_unfolding=False,
channel='combined'):
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += '{measurement}_{channel}_RooUnfold{method}.txt'
variables = [
'MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'
]
for variable in variables:
svd = file_template.format(variable=variable,
method='Svd',
channel=channel,
measurement=measurement)
bayes = file_template.format(variable=variable,
method='Bayes',
channel=channel,
measurement=measurement)
data = read_data_from_JSON(svd)
before_unfolding = data['TTJet_measured_withoutFakes']
svd_data = data['TTJet_unfolded']
bayes_data = read_data_from_JSON(bayes)['TTJet_unfolded']
h_svd = value_error_tuplelist_to_hist(svd_data,
bin_edges_vis[variable])
h_bayes = value_error_tuplelist_to_hist(bayes_data,
bin_edges_vis[variable])
h_before_unfolding = value_error_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = '{0}_compare_unfolding_methods_{1}_{2}'.format(
measurement, variable, channel)
properties.title = 'Comparison of unfolding methods'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = True
properties.x_limits = (bin_edges_vis[variable][0],
bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
if 'xsection' in measurement:
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
else:
properties.y_axis_title = r'$t\bar{t}$ normalisation'
histograms = {'SVD': h_svd, 'Bayes': h_bayes}
if add_before_unfolding:
histograms['before unfolding'] = h_before_unfolding
properties.name += '_ext'
properties.has_ratio = False
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
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 read_fit_templates_and_results_as_histograms(category, channel):
global path_to_JSON, variable, met_type
templates = read_data_from_JSON(
path_to_JSON + "/" + variable + "/fit_results/" + category + "/templates_" + channel + "_" + met_type + ".txt"
)
data_values = read_data_from_JSON(
path_to_JSON
+ "/"
+ variable
+ "/fit_results/"
+ category
+ "/initial_values_"
+ channel
+ "_"
+ met_type
+ ".txt"
)["data"]
fit_results = read_data_from_JSON(
path_to_JSON + "/" + variable + "/fit_results/" + category + "/fit_results_" + channel + "_" + met_type + ".txt"
)
template_histograms = {}
fit_results_histograms = {}
for bin_i, variable_bin in enumerate(variable_bins_ROOT[variable]):
h_template_data = value_tuplelist_to_hist(templates["data"][bin_i], eta_bin_edges)
h_template_signal = value_tuplelist_to_hist(templates["signal"][bin_i], eta_bin_edges)
h_template_VJets = value_tuplelist_to_hist(templates["V+Jets"][bin_i], eta_bin_edges)
h_template_QCD = value_tuplelist_to_hist(templates["QCD"][bin_i], eta_bin_edges)
template_histograms[variable_bin] = {
"signal": h_template_signal,
"V+Jets": h_template_VJets,
"QCD": h_template_QCD,
}
h_data = h_template_data.Clone()
h_signal = h_template_signal.Clone()
h_VJets = h_template_VJets.Clone()
h_QCD = h_template_QCD.Clone()
data_normalisation = data_values[bin_i]
signal_normalisation = fit_results["signal"][bin_i][0]
VJets_normalisation = fit_results["V+Jets"][bin_i][0]
QCD_normalisation = fit_results["QCD"][bin_i][0]
h_data.Scale(data_normalisation)
h_signal.Scale(signal_normalisation)
h_VJets.Scale(VJets_normalisation)
h_QCD.Scale(QCD_normalisation)
h_background = h_VJets + h_QCD
for bin_i in range(len(h_data)):
h_data.SetBinError(bin_i + 1, sqrt(h_data.GetBinContent(bin_i + 1)))
fit_results_histograms[variable_bin] = {"data": h_data, "signal": h_signal, "background": h_background}
return template_histograms, fit_results_histograms
def main():
global config, options
parser = OptionParser()
parser.add_option("-p",
"--path",
dest="path",
default='data/fit_checks/no_merging',
help="set path to JSON files")
parser.add_option('--create_fit_data',
dest="create_fit_data",
action="store_true",
help="create the fit data for testing.")
parser.add_option('--refit',
dest="refit",
action="store_true",
help="Fit again even if the output already exists")
parser.add_option('--test',
dest="test",
action="store_true",
help="Test only: run just one selected sample")
variables = config.histogram_path_templates.keys()
fit_variables = fit_var_inputs
mc_samples = ['TTJet', 'SingleTop', 'QCD', 'V+Jets']
tests = closure_tests
channels = ['electron', 'muon']
COMEnergies = ['7', '8']
(options, _) = parser.parse_args()
print 'Running from path', options.path
if (options.create_fit_data):
create_fit_data(options.path,
variables,
fit_variables,
mc_samples,
COMEnergies=COMEnergies,
channels=channels)
output_file = options.path + '/fit_test_output.txt'
if options.test:
output_file = options.path + '/fit_test_output_test.txt'
if options.refit or not os.path.isfile(output_file) or options.test:
if os.path.isfile(options.path + '/fit_check_data.txt'):
fit_data = read_data_from_JSON(options.path +
'/fit_check_data.txt')
results = run_tests(fit_data, COMEnergies, variables,
fit_variables, mc_samples, channels, tests)
write_data_to_JSON(results, output_file)
else:
print 'Please run bin/prepare_data_for_fit_checks first'
print 'Then run this script with the option --create_fit_data.'
results = read_data_from_JSON(output_file)
plot_results(results)
def compare_combine_before_after_unfolding(measurement='normalised_xsection',
add_before_unfolding=False):
file_template = 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += '{measurement}_{channel}_RooUnfold{method}.txt'
variables = ['MET', 'HT', 'ST', 'NJets',
'lepton_pt', 'abs_lepton_eta', 'WPT']
for variable in variables:
combineBefore = file_template.format(
variable=variable,
method='Svd',
channel='combinedBeforeUnfolding',
measurement=measurement)
combineAfter = file_template.format(
variable=variable,
method='Svd',
channel='combined',
measurement=measurement)
data = read_data_from_JSON(combineBefore)
before_unfolding = data['TTJet_measured']
combineBefore_data = data['TTJet_unfolded']
combineAfter_data = read_data_from_JSON(combineAfter)['TTJet_unfolded']
h_combineBefore = value_error_tuplelist_to_hist(
combineBefore_data, bin_edges_vis[variable])
h_combineAfter = value_error_tuplelist_to_hist(
combineAfter_data, bin_edges_vis[variable])
h_before_unfolding = value_error_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = '{0}_compare_combine_before_after_unfolding_{1}'.format(
measurement, variable)
properties.title = 'Comparison of combining before/after unfolding'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = True
properties.x_limits = (
bin_edges_vis[variable][0], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
if 'xsection' in measurement:
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
else:
properties.y_axis_title = r'$t\bar{t}$ normalisation'
histograms = {'Combine before unfolding': h_combineBefore, 'Combine after unfolding': h_combineAfter}
if add_before_unfolding:
histograms['before unfolding'] = h_before_unfolding
properties.name += '_ext'
properties.has_ratio = False
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def compare_unfolding_methods(measurement='normalised_xsection',
add_before_unfolding=False, channel='combined'):
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += '{measurement}_{channel}_RooUnfold{method}.txt'
variables = ['MET', 'HT', 'ST', 'NJets',
'lepton_pt', 'abs_lepton_eta', 'WPT']
for variable in variables:
svd = file_template.format(
variable=variable,
method='Svd',
channel=channel,
measurement=measurement)
bayes = file_template.format(
variable=variable,
method='Bayes', channel=channel,
measurement=measurement)
data = read_data_from_JSON(svd)
before_unfolding = data['TTJet_measured_withoutFakes']
svd_data = data['TTJet_unfolded']
bayes_data = read_data_from_JSON(bayes)['TTJet_unfolded']
h_svd = value_error_tuplelist_to_hist(
svd_data, bin_edges_vis[variable])
h_bayes = value_error_tuplelist_to_hist(
bayes_data, bin_edges_vis[variable])
h_before_unfolding = value_error_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = '{0}_compare_unfolding_methods_{1}_{2}'.format(
measurement, variable, channel)
properties.title = 'Comparison of unfolding methods'
properties.path = 'plots'
properties.has_ratio = True
properties.xerr = True
properties.x_limits = (
bin_edges_vis[variable][0], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
if 'xsection' in measurement:
properties.y_axis_title = r'$\frac{1}{\sigma} \frac{d\sigma}{d' + \
variables_latex[variable] + '}$'
else:
properties.y_axis_title = r'$t\bar{t}$ normalisation'
histograms = {'SVD': h_svd, 'Bayes': h_bayes}
if add_before_unfolding:
histograms['before unfolding'] = h_before_unfolding
properties.name += '_ext'
properties.has_ratio = False
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def read_xsection_measurement_results_with_errors(channel):
global path_to_JSON, variable, k_values, met_type
category = 'central'
file_template = path_to_JSON + '/' + variable + '/xsection_measurement_results/' + channel + '/kv' + str(k_values[channel]) + '/' + category + '/normalised_xsection_' + met_type + '.txt'
if channel == 'combined':
file_template = file_template.replace('kv' + str(k_values[channel]), '')
file_name = file_template
normalised_xsection_unfolded = read_data_from_JSON( file_name )
normalised_xsection_measured_unfolded = {'measured':normalised_xsection_unfolded['TTJet_measured'],
'unfolded':normalised_xsection_unfolded['TTJet_unfolded']}
file_name = file_template.replace('.txt', '_with_errors.txt')
normalised_xsection_unfolded_with_errors = read_data_from_JSON( file_name )
file_name = file_template.replace('.txt', '_ttbar_generator_errors.txt')
normalised_xsection_ttbar_generator_errors = read_data_from_JSON( file_name )
file_name = file_template.replace('.txt', '_MET_errors.txt')
normalised_xsection_MET_errors = read_data_from_JSON( file_name )
file_name = file_template.replace('.txt', '_topMass_errors.txt')
normalised_xsection_topMass_errors = read_data_from_JSON( file_name )
file_name = file_template.replace('.txt', '_kValue_errors.txt')
normalised_xsection_kValue_errors = read_data_from_JSON( file_name )
file_name = file_template.replace('.txt', '_PDF_errors.txt')
normalised_xsection_PDF_errors = read_data_from_JSON( file_name )
file_name = file_template.replace('.txt', '_other_errors.txt')
normalised_xsection_other_errors = read_data_from_JSON( file_name )
file_name = file_template.replace('.txt', '_new_errors.txt')
normalised_xsection_new_errors = read_data_from_JSON( file_name )
normalised_xsection_measured_unfolded.update({'measured_with_systematics':normalised_xsection_unfolded_with_errors['TTJet_measured'],
'unfolded_with_systematics':normalised_xsection_unfolded_with_errors['TTJet_unfolded']})
normalised_xsection_measured_errors = normalised_xsection_ttbar_generator_errors['TTJet_measured']
normalised_xsection_measured_errors.update(normalised_xsection_PDF_errors['TTJet_measured'])
normalised_xsection_measured_errors.update(normalised_xsection_MET_errors['TTJet_measured'])
normalised_xsection_measured_errors.update(normalised_xsection_topMass_errors['TTJet_measured'])
normalised_xsection_measured_errors.update(normalised_xsection_kValue_errors['TTJet_measured'])
normalised_xsection_measured_errors.update(normalised_xsection_other_errors['TTJet_measured'])
normalised_xsection_measured_errors.update(normalised_xsection_new_errors['TTJet_measured'])
normalised_xsection_unfolded_errors = normalised_xsection_ttbar_generator_errors['TTJet_unfolded']
normalised_xsection_unfolded_errors.update(normalised_xsection_PDF_errors['TTJet_unfolded'])
normalised_xsection_unfolded_errors.update(normalised_xsection_MET_errors['TTJet_unfolded'])
normalised_xsection_unfolded_errors.update(normalised_xsection_topMass_errors['TTJet_unfolded'])
normalised_xsection_unfolded_errors.update(normalised_xsection_kValue_errors['TTJet_unfolded'])
normalised_xsection_unfolded_errors.update(normalised_xsection_other_errors['TTJet_unfolded'])
normalised_xsection_unfolded_errors.update(normalised_xsection_new_errors['TTJet_unfolded'])
return normalised_xsection_measured_unfolded, normalised_xsection_measured_errors, normalised_xsection_unfolded_errors
def compare_combine_before_after_unfolding_uncertainties():
file_template = 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += 'unfolded_normalisation_{channel}_RooUnfoldSvd.txt'
variables = [
'MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'
]
# variables = ['ST']
for variable in variables:
beforeUnfolding = file_template.format(
variable=variable, channel='combinedBeforeUnfolding')
afterUnfolding = file_template.format(variable=variable,
channel='combined')
data = read_data_from_JSON(beforeUnfolding)
before_unfolding = data['TTJet_measured']
beforeUnfolding_data = data['TTJet_unfolded']
afterUnfolding_data = read_data_from_JSON(
afterUnfolding)['TTJet_unfolded']
before_unfolding = [e / v * 100 for v, e in before_unfolding]
beforeUnfolding_data = [e / v * 100 for v, e in beforeUnfolding_data]
afterUnfolding_data = [e / v * 100 for v, e in afterUnfolding_data]
h_beforeUnfolding = value_tuplelist_to_hist(beforeUnfolding_data,
bin_edges_vis[variable])
h_afterUnfolding = value_tuplelist_to_hist(afterUnfolding_data,
bin_edges_vis[variable])
h_before_unfolding = value_tuplelist_to_hist(before_unfolding,
bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = 'compare_combine_before_after_unfolding_uncertainties_{0}'.format(
variable)
properties.title = 'Comparison of unfolding uncertainties'
properties.path = 'plots'
properties.has_ratio = False
properties.xerr = True
properties.x_limits = (bin_edges_vis[variable][0],
bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = 'relative uncertainty (\\%)'
properties.legend_location = (0.98, 0.95)
histograms = {
'Combine before unfolding': h_beforeUnfolding,
'Combine after unfolding': h_afterUnfolding,
# 'before unfolding': h_before_unfolding
}
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def read_normalised_xsection_measurement(category, channel):
global path_to_JSON, met_type, met_uncertainties
normalised_xsection = None
if category in met_uncertainties and variable == 'HT':
normalised_xsection = read_data_from_JSON(path_to_JSON + 'central' + '/normalised_xsection_' + channel + '_' + met_type + '.txt')
else:
normalised_xsection = read_data_from_JSON(path_to_JSON + category + '/normalised_xsection_' + channel + '_' + met_type + '.txt')
measurement = normalised_xsection['TTJet_measured']
measurement_unfolded = normalised_xsection['TTJet_unfolded']
return measurement, measurement_unfolded
def compare_combine_before_after_unfolding_uncertainties():
file_template = 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += 'unfolded_normalisation_{channel}_RooUnfoldSvd.txt'
variables = ['MET', 'HT', 'ST', 'NJets',
'lepton_pt', 'abs_lepton_eta', 'WPT']
# variables = ['ST']
for variable in variables:
beforeUnfolding = file_template.format(
variable=variable, channel='combinedBeforeUnfolding')
afterUnfolding = file_template.format(
variable=variable, channel='combined')
data = read_data_from_JSON(beforeUnfolding)
before_unfolding = data['TTJet_measured']
beforeUnfolding_data = data['TTJet_unfolded']
afterUnfolding_data = read_data_from_JSON(afterUnfolding)['TTJet_unfolded']
before_unfolding = [e / v * 100 for v, e in before_unfolding]
beforeUnfolding_data = [e / v * 100 for v, e in beforeUnfolding_data]
afterUnfolding_data = [e / v * 100 for v, e in afterUnfolding_data]
h_beforeUnfolding = value_tuplelist_to_hist(
beforeUnfolding_data, bin_edges_vis[variable])
h_afterUnfolding = value_tuplelist_to_hist(
afterUnfolding_data, bin_edges_vis[variable])
h_before_unfolding = value_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = 'compare_combine_before_after_unfolding_uncertainties_{0}'.format(
variable)
properties.title = 'Comparison of unfolding uncertainties'
properties.path = 'plots'
properties.has_ratio = False
properties.xerr = True
properties.x_limits = (
bin_edges_vis[variable][0], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = 'relative uncertainty (\\%)'
properties.legend_location = (0.98, 0.95)
histograms = {'Combine before unfolding': h_beforeUnfolding, 'Combine after unfolding': h_afterUnfolding,
# 'before unfolding': h_before_unfolding
}
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def compare_unfolding_uncertainties():
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += 'unfolded_normalisation_combined_RooUnfold{method}.txt'
variables = ['MET', 'HT', 'ST', 'NJets',
'lepton_pt', 'abs_lepton_eta', 'WPT']
# variables = ['ST']
for variable in variables:
svd = file_template.format(
variable=variable, method='Svd')
bayes = file_template.format(
variable=variable, method='Bayes')
data = read_data_from_JSON(svd)
before_unfolding = data['TTJet_measured_withoutFakes']
svd_data = data['TTJet_unfolded']
bayes_data = read_data_from_JSON(bayes)['TTJet_unfolded']
before_unfolding = [e / v * 100 for v, e in before_unfolding]
svd_data = [e / v * 100 for v, e in svd_data]
bayes_data = [e / v * 100 for v, e in bayes_data]
h_svd = value_tuplelist_to_hist(
svd_data, bin_edges_vis[variable])
h_bayes = value_tuplelist_to_hist(
bayes_data, bin_edges_vis[variable])
h_before_unfolding = value_tuplelist_to_hist(
before_unfolding, bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = 'compare_unfolding_uncertainties_{0}'.format(
variable)
properties.title = 'Comparison of unfolding uncertainties'
properties.path = 'plots'
properties.has_ratio = False
properties.xerr = True
properties.x_limits = (
bin_edges_vis[variable][0], bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = 'relative uncertainty (\\%)'
properties.legend_location = (0.98, 0.95)
histograms = {'SVD': h_svd, 'Bayes': h_bayes,
'before unfolding': h_before_unfolding}
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def compare_unfolding_uncertainties():
file_template = '/hdfs/TopQuarkGroup/run2/dpsData/'
file_template += 'data/normalisation/background_subtraction/13TeV/'
file_template += '{variable}/VisiblePS/central/'
file_template += 'unfolded_normalisation_combined_RooUnfold{method}.txt'
variables = [
'MET', 'HT', 'ST', 'NJets', 'lepton_pt', 'abs_lepton_eta', 'WPT'
]
# variables = ['ST']
for variable in variables:
svd = file_template.format(variable=variable, method='Svd')
bayes = file_template.format(variable=variable, method='Bayes')
data = read_data_from_JSON(svd)
before_unfolding = data['TTJet_measured_withoutFakes']
svd_data = data['TTJet_unfolded']
bayes_data = read_data_from_JSON(bayes)['TTJet_unfolded']
before_unfolding = [e / v * 100 for v, e in before_unfolding]
svd_data = [e / v * 100 for v, e in svd_data]
bayes_data = [e / v * 100 for v, e in bayes_data]
h_svd = value_tuplelist_to_hist(svd_data, bin_edges_vis[variable])
h_bayes = value_tuplelist_to_hist(bayes_data, bin_edges_vis[variable])
h_before_unfolding = value_tuplelist_to_hist(before_unfolding,
bin_edges_vis[variable])
properties = Histogram_properties()
properties.name = 'compare_unfolding_uncertainties_{0}'.format(
variable)
properties.title = 'Comparison of unfolding uncertainties'
properties.path = 'plots'
properties.has_ratio = False
properties.xerr = True
properties.x_limits = (bin_edges_vis[variable][0],
bin_edges_vis[variable][-1])
properties.x_axis_title = variables_latex[variable]
properties.y_axis_title = 'relative uncertainty (\\%)'
properties.legend_location = (0.98, 0.95)
histograms = {
'SVD': h_svd,
'Bayes': h_bayes,
'before unfolding': h_before_unfolding
}
plot = Plot(histograms, properties)
plot.draw_method = 'errorbar'
compare_histograms(plot)
def read_normalised_xsection_measurement(options, category):
'''
Returns the normalised measurement and normalised unfolded measurement for
the file associated with the variable under study
'''
variable=options['variable']
variables_no_met=options['variables_no_met']
met_specific_systematics=options['met_specific_systematics']
path_to_JSON=options['path_to_JSON']
method=options['method']
channel=options['channel']
filename = '{path}/{category}/normalised_xsection_{channel}_{method}.txt'
# Disregarding Met Uncertainties if variable does not use MET
if (category in met_specific_systematics) and (variable in variables_no_met):
filename = filename.format(
path = path_to_JSON,
channel = channel,
category = 'central',
method = method,
)
else:
filename = filename.format(
path = path_to_JSON,
channel = channel,
category = category,
method = method
)
normalised_xsection = read_data_from_JSON( filename )
measurement = normalised_xsection['TTJet_measured']#should this be measured without fakes???
measurement_unfolded = normalised_xsection['TTJet_unfolded']
return measurement, measurement_unfolded
def parse_options():
parser = OptionParser(__doc__)
parser.add_option("-c",
"--compare",
dest="compare",
action="store_true",
help="Compare to current values (k vs tau)",
default=False)
parser.add_option(
"-t",
"--table-style",
dest="style",
default='simple',
help="Style for table printing: simple|latex|twiki (default = simple)")
(options, args) = parser.parse_args()
input_values_sets = []
json_input_files = []
add_set = input_values_sets.append
add_json_file = json_input_files.append
for arg in args:
input_values = read_data_from_JSON(arg)
add_set(input_values)
add_json_file(arg)
return options, input_values_sets, json_input_files
def read_unfolded_xsections(channel):
global path_to_JSON, variable, k_value, met_type, b_tag_bin
TTJet_xsection_unfolded = {}
for category in categories:
normalised_xsections = read_data_from_JSON(path_to_JSON + '/xsection_measurement_results' + '/kv' + str(k_value) + '/' + category + '/normalised_xsection_' + channel + '_' + met_type + '.txt')
TTJet_xsection_unfolded[category] = normalised_xsections['TTJet_unfolded']
return TTJet_xsection_unfolded
def get_fitted_normalisation(variable, channel):
global path_to_JSON, category, met_type
fit_results = read_data_from_JSON(path_to_JSON + variable +
'/fit_results/' + category +
'/fit_results_' + channel + '_' +
met_type + '.txt')
N_fit_ttjet = [0, 0]
N_fit_singletop = [0, 0]
N_fit_vjets = [0, 0]
N_fit_qcd = [0, 0]
bins = variable_bins_ROOT[variable]
for bin_i, _ in enumerate(bins):
#central values
N_fit_ttjet[0] += fit_results['TTJet'][bin_i][0]
N_fit_singletop[0] += fit_results['SingleTop'][bin_i][0]
N_fit_vjets[0] += fit_results['V+Jets'][bin_i][0]
N_fit_qcd[0] += fit_results['QCD'][bin_i][0]
#errors
N_fit_ttjet[1] += fit_results['TTJet'][bin_i][1]
N_fit_singletop[1] += fit_results['SingleTop'][bin_i][1]
N_fit_vjets[1] += fit_results['V+Jets'][bin_i][1]
N_fit_qcd[1] += fit_results['QCD'][bin_i][1]
fitted_normalisation = {
'TTJet': N_fit_ttjet,
'SingleTop': N_fit_singletop,
'V+Jets': N_fit_vjets,
'QCD': N_fit_qcd
}
return fitted_normalisation
def get_fitted_normalisation(variable, channel):
global path_to_JSON, category, met_type
fit_results = read_data_from_JSON(path_to_JSON + variable + '/fit_results/' + category + '/fit_results_' + channel + '_' + met_type + '.txt')
N_fit_ttjet = [0, 0]
N_fit_singletop = [0, 0]
N_fit_vjets = [0, 0]
N_fit_qcd = [0, 0]
bins = variable_bins_ROOT[variable]
for bin_i, _ in enumerate(bins):
#central values
N_fit_ttjet[0] += fit_results['TTJet'][bin_i][0]
N_fit_singletop[0] += fit_results['SingleTop'][bin_i][0]
N_fit_vjets[0] += fit_results['V+Jets'][bin_i][0]
N_fit_qcd[0] += fit_results['QCD'][bin_i][0]
#errors
N_fit_ttjet[1] += fit_results['TTJet'][bin_i][1]
N_fit_singletop[1] += fit_results['SingleTop'][bin_i][1]
N_fit_vjets[1] += fit_results['V+Jets'][bin_i][1]
N_fit_qcd[1] += fit_results['QCD'][bin_i][1]
fitted_normalisation = {
'TTJet': N_fit_ttjet,
'SingleTop': N_fit_singletop,
'V+Jets': N_fit_vjets,
'QCD': N_fit_qcd
}
return fitted_normalisation
def get_fit_results(variable, channel):
global path_to_JSON, category, met_type
fit_results = read_data_from_JSON(path_to_JSON + variable +
'/fit_results/' + category +
'/fit_results_' + channel + '_' +
met_type + '.txt')
return fit_results
def get_fitted_normalisation( variable, channel, path_to_JSON, category, met_type ):
'''
This function now gets the error on the fit correctly,
so that it can be applied if the --normalise_to_fit option is used
'''
import config.variable_binning
variable_bins_ROOT = config.variable_binning.variable_bins_ROOT
fit_results = read_data_from_JSON( path_to_JSON + variable + '/fit_results/' + category + '/fit_results_' + channel + '_' + met_type + '.txt' )
N_fit_ttjet = [0, 0]
N_fit_singletop = [0, 0]
N_fit_vjets = [0, 0]
N_fit_qcd = [0, 0]
bins = variable_bins_ROOT[variable]
for bin_i, _ in enumerate( bins ):
# central values
N_fit_ttjet[0] += fit_results['TTJet'][bin_i][0]
N_fit_singletop[0] += fit_results['SingleTop'][bin_i][0]
N_fit_vjets[0] += fit_results['V+Jets'][bin_i][0]
N_fit_qcd[0] += fit_results['QCD'][bin_i][0]
# errors
N_fit_ttjet[1] += fit_results['TTJet'][bin_i][1]
N_fit_singletop[1] += fit_results['SingleTop'][bin_i][1]
N_fit_vjets[1] += fit_results['V+Jets'][bin_i][1]
N_fit_qcd[1] += fit_results['QCD'][bin_i][1]
fitted_normalisation = {
'TTJet': N_fit_ttjet,
'SingleTop': N_fit_singletop,
'V+Jets': N_fit_vjets,
'QCD': N_fit_qcd
}
return fitted_normalisation
def main(options, args):
config = XSectionConfig(options.CoM)
variables = ['MET', 'HT', 'ST', 'WPT']
channels = ['electron', 'muon', 'combined']
m_file = 'normalised_xsection_patType1CorrectedPFMet.txt'
m_with_errors_file = 'normalised_xsection_patType1CorrectedPFMet_with_errors.txt'
path_template = args[0]
output_file = 'measurement_{0}TeV.root'.format(options.CoM)
f = File(output_file, 'recreate')
for channel in channels:
d = f.mkdir(channel)
d.cd()
for variable in variables:
dv = d.mkdir(variable)
dv.cd()
if channel == 'combined':
path = path_template.format(variable=variable,
channel=channel,
centre_of_mass_energy=options.CoM)
else:
kv = channel + \
'/kv{0}/'.format(config.k_values[channel][variable])
path = path_template.format(variable=variable,
channel=kv,
centre_of_mass_energy=options.CoM)
m = read_data_from_JSON(path + '/' + m_file)
m_with_errors = read_data_from_JSON(path + '/' +
m_with_errors_file)
for name, result in m.items():
h = make_histogram(result, bin_edges[variable])
h.SetName(name)
h.write()
for name, result in m_with_errors.items():
if not 'TTJet' in name:
continue
h = make_histogram(result, bin_edges[variable])
h.SetName(name + '_with_syst')
h.write()
dv.write()
d.cd()
d.write()
f.write()
f.close()
def get_data_histogram( channel, variable, met_type ):
fit_result_input = 'data/M3_angle_bl/13TeV/%(variable)s/fit_results/central/fit_results_%(channel)s_%(met_type)s.txt'
fit_results = read_data_from_JSON( fit_result_input % {'channel': channel, 'variable': variable, 'met_type':met_type} )
fit_data = fit_results['TTJet']
print fit_data
print bin_edges[variable]
h_data = value_error_tuplelist_to_hist( fit_data, bin_edges[variable] )
return h_data
def main(options, args):
config = XSectionConfig(options.CoM)
variables = ['MET', 'HT', 'ST', 'WPT']
channels = ['electron', 'muon', 'combined']
m_file = 'normalised_xsection_patType1CorrectedPFMet.txt'
m_with_errors_file = 'normalised_xsection_patType1CorrectedPFMet_with_errors.txt'
path_template = args[0]
output_file = 'measurement_{0}TeV.root'.format(options.CoM)
f = File(output_file, 'recreate')
for channel in channels:
d = f.mkdir(channel)
d.cd()
for variable in variables:
dv = d.mkdir(variable)
dv.cd()
if channel == 'combined':
path = path_template.format(variable=variable,
channel=channel,
centre_of_mass_energy=options.CoM)
else:
kv = channel + \
'/kv{0}/'.format(config.k_values[channel][variable])
path = path_template.format(variable=variable,
channel=kv,
centre_of_mass_energy=options.CoM)
m = read_data_from_JSON(path + '/' + m_file)
m_with_errors = read_data_from_JSON(
path + '/' + m_with_errors_file)
for name, result in m.items():
h = make_histogram(result, bin_edges[variable])
h.SetName(name)
h.write()
for name, result in m_with_errors.items():
if not 'TTJet' in name:
continue
h = make_histogram(result, bin_edges[variable])
h.SetName(name + '_with_syst')
h.write()
dv.write()
d.cd()
d.write()
f.write()
f.close()
def main():
global config, options
parser = OptionParser()
parser.add_option( "-p", "--path", dest = "path", default = 'data/fit_checks/no_merging',
help = "set path to JSON files" )
parser.add_option( '--create_fit_data', dest = "create_fit_data", action = "store_true",
help = "create the fit data for testing." )
parser.add_option( '--refit', dest = "refit", action = "store_true",
help = "Fit again even if the output already exists" )
parser.add_option( '--test', dest = "test", action = "store_true",
help = "Test only: run just one selected sample" )
variables = config.histogram_path_templates.keys()
fit_variables = fit_var_inputs
mc_samples = ['TTJet', 'SingleTop', 'QCD', 'V+Jets']
tests = closure_tests
channels = ['electron', 'muon']
COMEnergies = ['7', '8']
( options, _ ) = parser.parse_args()
print 'Running from path', options.path
if ( options.create_fit_data ):
create_fit_data( options.path, variables, fit_variables, mc_samples,
COMEnergies = COMEnergies, channels = channels )
output_file = options.path + '/fit_test_output.txt'
if options.test:
output_file = options.path + '/fit_test_output_test.txt'
if options.refit or not os.path.isfile( output_file ) or options.test:
if os.path.isfile( options.path + '/fit_check_data.txt' ):
fit_data = read_data_from_JSON( options.path + '/fit_check_data.txt' )
results = run_tests( fit_data,
COMEnergies,
variables,
fit_variables,
mc_samples,
channels,
tests )
write_data_to_JSON(results, output_file )
else:
print 'Please run bin/prepare_data_for_fit_checks first'
print 'Then run this script with the option --create_fit_data.'
results = read_data_from_JSON(output_file)
plot_results( results )
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_fit_templates_and_results_as_histograms(category, channel):
global path_to_JSON, variable, met_type
templates = read_data_from_JSON(path_to_JSON + '/fit_results/' + category + '/templates_' + channel + '_' + met_type + '.txt')
data_values = read_data_from_JSON(path_to_JSON + '/fit_results/' + category + '/initial_values_' + channel + '_' + met_type + '.txt')['data']
fit_results = read_data_from_JSON(path_to_JSON + '/fit_results/' + category + '/fit_results_' + channel + '_' + met_type + '.txt')
template_histograms = {}
fit_results_histograms = {}
for bin_i, variable_bin in enumerate(variable_bins_ROOT[variable]):
h_template_data = value_tuplelist_to_hist(templates['data'][bin_i], eta_bin_edges)
h_template_signal = value_tuplelist_to_hist(templates['signal'][bin_i], eta_bin_edges)
h_template_VJets = value_tuplelist_to_hist(templates['V+Jets'][bin_i], eta_bin_edges)
h_template_QCD = value_tuplelist_to_hist(templates['QCD'][bin_i], eta_bin_edges)
template_histograms[variable_bin] = {
'signal':h_template_signal,
'V+Jets':h_template_VJets,
'QCD':h_template_QCD
}
h_data = h_template_data.Clone()
h_signal = h_template_signal.Clone()
h_VJets = h_template_VJets.Clone()
h_QCD = h_template_QCD.Clone()
data_normalisation = data_values[bin_i]
signal_normalisation = fit_results['signal'][bin_i][0]
VJets_normalisation = fit_results['V+Jets'][bin_i][0]
QCD_normalisation = fit_results['QCD'][bin_i][0]
h_data.Scale(data_normalisation)
h_signal.Scale(signal_normalisation)
h_VJets.Scale(VJets_normalisation)
h_QCD.Scale(QCD_normalisation)
h_background = h_VJets + h_QCD
for bin_i in range(len(h_data)):
h_data.SetBinError(bin_i+1, sqrt(h_data.GetBinContent(bin_i+1)))
fit_results_histograms[variable_bin] = {
'data':h_data,
'signal':h_signal,
'background':h_background
}
return template_histograms, fit_results_histograms
def get_fit_results_histogram( data_path = 'data/M3_angle_bl',
centre_of_mass = 8,
channel = 'electron',
variable = 'MET',
met_type = 'patType1CorrectedPFMet',
bin_edges = [] ):
fit_result_input = data_path + '/%(CoM)dTeV/%(variable)s/fit_results/central/fit_results_%(channel)s_%(met_type)s.txt'
fit_results = read_data_from_JSON( fit_result_input % {'CoM': centre_of_mass, 'channel': channel, 'variable': variable, 'met_type':met_type} )
fit_data = fit_results['TTJet']
h_data = value_error_tuplelist_to_hist( fit_data, bin_edges )
return h_data
def read_from_fit_results_folder(path_to_JSON = 'data',
variable = 'MET',
category = 'central',
channel = 'combined',
met_type = 'patType1CorrectedPFMet',
data_type = 'fit_results'):
filename = path_to_JSON + '/' + variable + '/fit_results/' + category + '/'
filename += data_type + '_' + channel + '_' + met_type + '.txt'
results = read_data_from_JSON( filename )
return results
def read_from_fit_results_folder(path_to_JSON='data',
variable='MET',
category='central',
channel='combined',
met_type='patType1CorrectedPFMet',
data_type='fit_results'):
filename = path_to_JSON + '/' + category + '/'
filename += data_type + '_' + channel + '_' + met_type + '.txt'
results = read_data_from_JSON(filename)
return results
def read_unfolded_xsections(channel):
global path_to_JSON, variable, k_value, met_type, b_tag_bin
TTJet_xsection_unfolded = {}
for category in categories:
normalised_xsections = read_data_from_JSON(
path_to_JSON + '/' + variable + '/xsection_measurement_results' +
'/kv' + str(k_value) + '/' + category + '/normalised_xsection_' +
channel + '_' + met_type + '.txt')
TTJet_xsection_unfolded[category] = normalised_xsections[
'TTJet_unfolded']
return TTJet_xsection_unfolded
def get_data(file_name, subset=""):
# this takes a LOT of memory, please use subset!!
all_data = []
extend = all_data.extend
data = read_data_from_JSON(file_name)
if subset:
for entry in data: # loop over all data entries
extend(entry[subset])
else:
extend(data)
return all_data
def get_data(files, subset=''):
# this takes a LOT of memory, please use subset!!
all_data = []
extend = all_data.extend
for f in files:
data = read_data_from_JSON(f)
if subset:
for entry in data: # loop over all data entries
extend(entry[subset])
else:
extend(data)
return all_data
def read_unfolded_normalisation(path_to_JSON='data',
variable='MET',
category='central',
channel='combined',
met_type='patType1CorrectedPFMet'):
new_path = '{path}/xsection_measurement_results/{channel}/{category}/{file}'
result_file = 'normalisation_{0}.txt'.format(met_type)
new_path = new_path.format(
path=path_to_JSON,
channel=channel,
category=category,
file=result_file,
)
return read_data_from_JSON(new_path)
def read_unfolded_normalisation(
path_to_JSON='data',
variable='MET',
category='central',
channel='combined',
met_type='patType1CorrectedPFMet'):
new_path = '{path}/xsection_measurement_results/{channel}/{category}/{file}'
result_file = 'normalisation_{0}.txt'.format(met_type)
new_path = new_path.format(
path=path_to_JSON,
channel=channel,
category=category,
file=result_file,
)
return read_data_from_JSON(new_path)
def parse_options():
parser = OptionParser( __doc__ )
( options, args ) = parser.parse_args()
input_values_sets = []
json_input_files = []
add_set = input_values_sets.append
add_json_file = json_input_files.append
for arg in args:
input_values = read_data_from_JSON( arg )
add_set( input_values )
add_json_file( arg )
return options, input_values_sets, json_input_files
def __set_unfolding_histograms__( self ):
# at the moment only one file is supported for the unfolding input
files = set( [self.truth['file'],
self.gen_vs_reco['file'],
self.measured['file']]
)
if len( files ) > 1:
print "Currently not supported to have different files for truth, gen_vs_reco and measured"
sys.exit()
input_file = files.pop()
visiblePS = False
if self.phaseSpace == 'VisiblePS':
visiblePS = True
t, m, r, f = get_unfold_histogram_tuple( File(input_file),
self.variable,
self.channel,
centre_of_mass = self.centre_of_mass_energy,
ttbar_xsection=self.measurement_config.ttbar_xsection,
luminosity=self.measurement_config.luminosity,
load_fakes = True,
visiblePS = visiblePS
)
self.h_truth = asrootpy ( t )
self.h_response = asrootpy ( r )
self.h_measured = asrootpy ( m )
self.h_fakes = asrootpy ( f )
data_file = self.data['file']
if data_file.endswith('.root'):
self.h_data = get_histogram_from_file(self.data['histogram'], self.data['file'])
elif data_file.endswith('.json') or data_file.endswith('.txt'):
data_key = self.data['histogram']
# assume configured bin edges
edges = []
edges = reco_bin_edges_vis[self.variable]
json_input = read_data_from_JSON(data_file)
if data_key == "": # JSON file == histogram
self.h_data = value_error_tuplelist_to_hist(json_input, edges)
else:
self.h_data = value_error_tuplelist_to_hist(json_input[data_key], edges)
else:
print 'Unkown file extension', data_file.split('.')[-1]
def get_fit_results_histogram(data_path='data/M3_angle_bl',
centre_of_mass=8,
channel='electron',
variable='MET',
met_type='patType1CorrectedPFMet',
bin_edges=[]):
fit_result_input = data_path + '/%(CoM)dTeV/%(variable)s/fit_results/central/fit_results_%(channel)s_%(met_type)s.txt'
fit_results = read_data_from_JSON(
fit_result_input % {
'CoM': centre_of_mass,
'channel': channel,
'variable': variable,
'met_type': met_type
})
fit_data = fit_results['TTJet']
h_data = value_error_tuplelist_to_hist(fit_data, bin_edges)
return h_data
def read_normalised_xsection_measurement( category, channel ):
global path_to_JSON, met_type, met_uncertainties_list, k_values
filename = ''
if category in met_uncertainties_list and variable == 'HT':
filename = path_to_JSON + '/' + channel + '/kv' + str( k_values[channel] ) + '/central/normalised_xsection_' + met_type + '.txt'
else:
filename = path_to_JSON + '/' + 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 = read_data_from_JSON( filename )
measurement = normalised_xsection['TTJet_measured']
measurement_unfolded = normalised_xsection['TTJet_unfolded']
return measurement, measurement_unfolded
def parse_options():
parser = OptionParser( __doc__ )
parser.add_option( "-c", "--compare", dest = "compare", action = "store_true",
help = "Compare to current values (k vs tau)", default = False )
parser.add_option( "-t", "--table-style", dest = "style", default = 'simple',
help = "Style for table printing: simple|latex|twiki (default = simple)" )
( options, args ) = parser.parse_args()
input_values_sets = []
json_input_files = []
add_set = input_values_sets.append
add_json_file = json_input_files.append
for arg in args:
input_values = read_data_from_JSON( arg )
add_set( input_values )
add_json_file( arg )
return options, input_values_sets, json_input_files
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 + '/' + variable + '/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])
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_with_errors(channel):
global path_to_JSON, variable, k_value, met_type
category = 'central'
normalised_xsection_unfolded = read_data_from_JSON(path_to_JSON + '/xsection_measurement_results' + '/kv' + str(k_value) + '/'
+ category + '/normalised_xsection_' + channel + '_' + met_type + '.txt')
normalised_xsection_measured_unfolded = {'measured':normalised_xsection_unfolded['TTJet_measured'],
'unfolded':normalised_xsection_unfolded['TTJet_unfolded']}
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')
normalised_xsection_ttbar_theory_errors = read_data_from_JSON(path_to_JSON + '/xsection_measurement_results' + '/kv' +
str(k_value) + '/' + category + '/normalised_xsection_' +
channel + '_' + met_type + '_ttbar_theory_errors.txt')
normalised_xsection_MET_errors = read_data_from_JSON(path_to_JSON + '/xsection_measurement_results' + '/kv' +
str(k_value) + '/' + category + '/normalised_xsection_' +
channel + '_' + met_type + '_MET_errors.txt')
normalised_xsection_PDF_errors = read_data_from_JSON(path_to_JSON + '/xsection_measurement_results' + '/kv' +
str(k_value) + '/' + category + '/normalised_xsection_' +
channel + '_' + met_type + '_PDF_errors.txt')
normalised_xsection_other_errors = read_data_from_JSON(path_to_JSON + '/xsection_measurement_results' + '/kv' +
str(k_value) + '/' + category + '/normalised_xsection_' +
channel + '_' + met_type + '_other_errors.txt')
normalised_xsection_new_errors = read_data_from_JSON(path_to_JSON + '/xsection_measurement_results' + '/kv' +
str(k_value) + '/' + category + '/normalised_xsection_' +
channel + '_' + met_type + '_new_errors.txt')
normalised_xsection_measured_unfolded.update({'measured_with_systematics':normalised_xsection_unfolded_with_errors['TTJet_measured'],
'unfolded_with_systematics':normalised_xsection_unfolded_with_errors['TTJet_unfolded']})
normalised_xsection_measured_errors = normalised_xsection_ttbar_theory_errors['TTJet_measured']
normalised_xsection_measured_errors.update(normalised_xsection_PDF_errors['TTJet_measured'])
normalised_xsection_measured_errors.update(normalised_xsection_MET_errors['TTJet_measured'])
normalised_xsection_measured_errors.update(normalised_xsection_other_errors['TTJet_measured'])
normalised_xsection_measured_errors.update(normalised_xsection_new_errors['TTJet_measured'])
normalised_xsection_unfolded_errors = normalised_xsection_ttbar_theory_errors['TTJet_unfolded']
normalised_xsection_unfolded_errors.update(normalised_xsection_PDF_errors['TTJet_unfolded'])
normalised_xsection_unfolded_errors.update(normalised_xsection_MET_errors['TTJet_unfolded'])
normalised_xsection_unfolded_errors.update(normalised_xsection_other_errors['TTJet_unfolded'])
normalised_xsection_unfolded_errors.update(normalised_xsection_new_errors['TTJet_unfolded'])
return normalised_xsection_measured_unfolded, normalised_xsection_measured_errors, normalised_xsection_unfolded_errors
def __set_unfolding_histograms__(self):
# at the moment only one file is supported for the unfolding input
files = set([
self.truth['file'], self.gen_vs_reco['file'], self.measured['file']
])
if len(files) > 1:
print "Currently not supported to have different files for truth, gen_vs_reco and measured"
sys.exit()
input_file = files.pop()
t, m, r, _ = get_unfold_histogram_tuple(
File(input_file),
self.variable,
self.channel,
centre_of_mass=self.centre_of_mass_energy,
ttbar_xsection=self.measurement_config.ttbar_xsection,
luminosity=self.measurement_config.luminosity,
)
self.h_truth = t
self.h_response = r
self.h_measured = m
data_file = self.data['file']
if data_file.endswith('.root'):
self.h_data = get_histogram_from_file(self.data['histogram'],
self.data['file'])
elif data_file.endswith('.json') or data_file.endswith('.txt'):
data_key = self.data['histogram']
# assume configured bin edges
edges = []
if self.phaseSpace == 'FullPS':
edges = bin_edges[self.variable]
elif self.phaseSpace == 'VisiblePS':
edges = bin_edges_vis[self.variable]
json_input = read_data_from_JSON(data_file)
if data_key == "": # JSON file == histogram
self.h_data = value_error_tuplelist_to_hist(json_input, edges)
else:
self.h_data = value_error_tuplelist_to_hist(
json_input[data_key], edges)
else:
print 'Unkown file extension', data_file.split('.')[-1]
def read_unfolded_xsections(channel):
global path_to_JSON, variable, k_value, met_type, b_tag_bin
TTJet_xsection_unfolded = {}
for category in categories:
normalised_xsections = read_data_from_JSON(
path_to_JSON
+ "/"
+ variable
+ "/xsection_measurement_results"
+ "/kv"
+ str(k_value)
+ "/"
+ category
+ "/normalised_xsection_"
+ channel
+ "_"
+ met_type
+ ".txt"
)
TTJet_xsection_unfolded[category] = normalised_xsections["TTJet_unfolded"]
return TTJet_xsection_unfolded
def get_fitted_normalisation(variable, channel, path_to_JSON, category,
met_type):
'''
This function now gets the error on the fit correctly,
so that it can be applied if the --normalise_to_fit option is used
'''
import config.variable_binning
variable_bins_ROOT = config.variable_binning.variable_bins_ROOT
fit_results = read_data_from_JSON(path_to_JSON + variable +
'/fit_results/' + category +
'/fit_results_' + channel + '_' +
met_type + '.txt')
N_fit_ttjet = [0, 0]
N_fit_singletop = [0, 0]
N_fit_vjets = [0, 0]
N_fit_qcd = [0, 0]
bins = variable_bins_ROOT[variable]
for bin_i, _ in enumerate(bins):
# central values
N_fit_ttjet[0] += fit_results['TTJet'][bin_i][0]
N_fit_singletop[0] += fit_results['SingleTop'][bin_i][0]
N_fit_vjets[0] += fit_results['V+Jets'][bin_i][0]
N_fit_qcd[0] += fit_results['QCD'][bin_i][0]
# errors
N_fit_ttjet[1] += fit_results['TTJet'][bin_i][1]
N_fit_singletop[1] += fit_results['SingleTop'][bin_i][1]
N_fit_vjets[1] += fit_results['V+Jets'][bin_i][1]
N_fit_qcd[1] += fit_results['QCD'][bin_i][1]
fitted_normalisation = {
'TTJet': N_fit_ttjet,
'SingleTop': N_fit_singletop,
'V+Jets': N_fit_vjets,
'QCD': N_fit_qcd
}
return fitted_normalisation
def read_normalised_xsection_measurement( category, channel ):
global path_to_JSON, met_type, met_uncertainties_list, method
filename = '{path}/{category}/normalised_xsection_{channel}_{method}.txt'
if category in met_systematics_suffixes and ( variable in measurement_config.variables_no_met ) and not ('JES' in category or 'JER' in category):
filename = filename.format(
path = path_to_JSON,
channel = channel,
category = 'central',
method = method,
)
else:
filename = filename.format(
path = path_to_JSON,
channel = channel,
category = category,
method = method)
normalised_xsection = read_data_from_JSON( filename )
measurement = normalised_xsection['TTJet_measured']
measurement_unfolded = normalised_xsection['TTJet_unfolded']
return measurement, measurement_unfolded
def read_normalised_xsection_measurement(category, channel):
global path_to_JSON, met_type, met_uncertainties_list, method
filename = '{path}/{category}/normalised_xsection_{channel}_{method}.txt'
if category in met_systematics_suffixes and (
variable in measurement_config.variables_no_met
) and not ('JES' in category or 'JER' in category):
filename = filename.format(
path=path_to_JSON,
channel=channel,
category='central',
method=method,
)
else:
filename = filename.format(path=path_to_JSON,
channel=channel,
category=category,
method=method)
normalised_xsection = read_data_from_JSON(filename)
measurement = normalised_xsection['TTJet_measured']
measurement_unfolded = normalised_xsection['TTJet_unfolded']
return measurement, measurement_unfolded
def fromJSON(JSON_file):
src = fu.read_data_from_JSON(JSON_file)
m = Measurement.fromDict(src)
return m
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 get_variable_from(variable='MET',
path_to_JSON='data/8TeV',
category='central',
signal='Higgs',
measurement_type='unfolded'):
global met_type
channel = 'electron'
electron_results = read_data_from_JSON(
path_to_JSON + '/' + variable + '/xsection_measurement_results/kv4/' +
category + '/normalisation_' + channel + '_' + met_type + '.txt')
electron_results_signal = read_data_from_JSON(
path_to_JSON + '/' + variable + '/xsection_measurement_results/kv4/' +
category + '/normalisation_' + channel + '_' + met_type + '_' +
signal + '.txt')
channel = 'muon'
muon_results = read_data_from_JSON(path_to_JSON + '/' + variable +
'/xsection_measurement_results/kv4/' +
category + '/normalisation_' + channel +
'_' + met_type + '.txt')
muon_results_signal = read_data_from_JSON(
path_to_JSON + '/' + variable + '/xsection_measurement_results/kv4/' +
category + '/normalisation_' + channel + '_' + met_type + '_' +
signal + '.txt')
channel = 'combined'
combined_results = read_data_from_JSON(
path_to_JSON + '/' + variable + '/xsection_measurement_results/kv4/' +
category + '/normalisation_' + channel + '_' + met_type + '.txt')
combined_results_signal = read_data_from_JSON(
path_to_JSON + '/' + variable + '/xsection_measurement_results/kv4/' +
category + '/normalisation_' + channel + '_' + met_type + '_' +
signal + '.txt')
# we are only interested in the measured ttbar, unfolded TTbar for all categories
# for_all_categories = ['TTJet_unfolded', 'TTJet_measured']
# and the scale_up/down, matching up/down and the generators for central (all)
electron_results_selected = {
'Higgs_125': electron_results_signal['TTJet_' + measurement_type],
}
muon_results_selected = {
'Higgs_125': muon_results_signal['TTJet_' + measurement_type],
}
combined_results_selected = {
'Higgs_125': combined_results_signal['TTJet_' + measurement_type],
}
# note on systematics:
# theta does not understand DATA systematics so they have to be migrated to MADGRAPH
# there are two ways of doing so:
# 1. calculate a scale factor based on data central measurement and MADPGRAPH
# 2. calculate a scale factor based on data central and systematic measurement
# then apply the scale factor (1- [a-b]/a) to MADGRAPH
# The second approach seems more right as it takes into account the effect of the systematic instead of the difference between data and Madgraph
# A way to test this is to calculate the scale factor for ttjet_matching up/down and compare it to the existing distributions
if measurement_type == 'unfolded':
electron_results_selected['TTJet'] = electron_results['MADGRAPH']
muon_results_selected['TTJet'] = muon_results['MADGRAPH']
combined_results_selected['TTJet'] = combined_results['MADGRAPH']
# theta does not understand DATA with systematics
if category == 'central':
electron_results_selected['DATA'] = electron_results[
'TTJet_unfolded']
muon_results_selected['DATA'] = muon_results['TTJet_unfolded']
combined_results_selected['DATA'] = combined_results[
'TTJet_unfolded']
else: # now let's do the systematics
# read central results
channel = 'electron'
tmp_category = 'central'
tmp_electron_results = read_data_from_JSON(
path_to_JSON + '/' + variable +
'/xsection_measurement_results/kv4/' + tmp_category +
'/normalisation_' + channel + '_' + met_type + '.txt')
channel = 'muon'
tmp_muon_results = read_data_from_JSON(
path_to_JSON + '/' + variable +
'/xsection_measurement_results/kv4/' + tmp_category +
'/normalisation_' + channel + '_' + met_type + '.txt')
channel = 'combined'
tmp_combined_results = read_data_from_JSON(
path_to_JSON + '/' + variable +
'/xsection_measurement_results/kv4/' + tmp_category +
'/normalisation_' + channel + '_' + met_type + '.txt')
electron_central = tmp_electron_results['TTJet_unfolded']
muon_central = tmp_muon_results['TTJet_unfolded']
combined_central = tmp_combined_results['TTJet_unfolded']
# set systematics
electron_systematic = electron_results['TTJet_unfolded']
muon_systematic = muon_results['TTJet_unfolded']
combined_systematic = combined_results['TTJet_unfolded']
# calculate scale factors
electron_results_selected['TTJet'] = morph_systematic(
electron_central, electron_systematic,
electron_results_selected['TTJet'])
muon_results_selected['TTJet'] = morph_systematic(
muon_central, muon_systematic, muon_results_selected['TTJet'])
combined_results_selected['TTJet'] = morph_systematic(
combined_central, combined_systematic,
combined_results_selected['TTJet'])
else:
if category == 'JES_up':
met_type += 'JetEnUp'
elif category == 'JES_down':
met_type += 'JetEnDown'
# read initial values
channel = 'electron'
electron_initial = read_data_from_JSON(path_to_JSON + '/' + variable +
'/fit_results/' + category +
'/initial_values_' + channel +
'_' + met_type + '.txt')
electron_fit = read_data_from_JSON(path_to_JSON + '/' + variable +
'/fit_results/' + category +
'/fit_results_' + channel + '_' +
met_type + '.txt')
channel = 'muon'
muon_initial = read_data_from_JSON(path_to_JSON + '/' + variable +
'/fit_results/' + category +
'/initial_values_' + channel + '_' +
met_type + '.txt')
muon_fit = read_data_from_JSON(path_to_JSON + '/' + variable +
'/fit_results/' + category +
'/fit_results_' + channel + '_' +
met_type + '.txt')
electron_results_selected['TTJet'] = electron_results['TTJet_measured']
muon_results_selected['TTJet'] = muon_results['TTJet_measured']
electron_results_selected['SingleTop'] = electron_fit['SingleTop']
muon_results_selected['SingleTop'] = muon_fit['SingleTop']
electron_results_selected['QCD'] = electron_fit['QCD']
muon_results_selected['QCD'] = muon_fit['QCD']
# theta does not understand DATA with systematics
if category == 'central':
electron_results_selected['DATA'] = electron_initial['data']
muon_results_selected['DATA'] = muon_initial['data']
electron_results_selected['VJets'] = electron_fit['V+Jets']
muon_results_selected['VJets'] = muon_fit['V+Jets']
# reset met for JES
if category == 'JES_up':
met_type = met_type.replace('JetEnUp', '')
elif category == 'JES_down':
met_type = met_type.replace('JetEnDown', '')
return electron_results_selected, muon_results_selected, combined_results_selected
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 get_data_histogram( channel, variable, met_type ):
fit_result_input = 'data/M3_angle_bl/13TeV/%(variable)s/fit_results/central/fit_results_%(channel)s_%(met_type)s.txt'
fit_results = read_data_from_JSON( fit_result_input % {'channel': channel, 'variable': variable, 'met_type':met_type} )
fit_data = fit_results['TTJet']
h_data = value_error_tuplelist_to_hist( fit_data, bin_edges[variable] )
return h_data
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)
# muon_file = path_to_JSON + '/'+category+'/initial_normalisation_muon_' + met_type + '.txt'
elif category != 'central':
# electron_file = path_to_JSON + '/'+category+'/initial_normalisation_electron_' + met_type + '.txt'
# muon_file = path_to_JSON + '/'+category+'/initial_normalisation_muon_' + met_type + '.txt'
# electron_file = path_to_JSON + '/central/normalisation_electron_' + translate_options[options.metType] + '.txt'
# muon_file = path_to_JSON + '/central/normalisation_muon_' + translate_options[options.metType] + '.txt'
electron_file = path_to_JSON + '/' + category + '/normalisation_electron_' + met_type + '.txt'
muon_file = path_to_JSON + '/' + category + '/normalisation_muon_' + met_type + '.txt'
fit_results_electron = None
fit_results_muon = None
if category == 'Muon_up' or category == 'Muon_down':
# fit_results_electron = read_data_from_JSON( path_to_JSON + '/central/initial_normalisation_electron_' + met_type + '.txt' )
fit_results_electron = read_data_from_JSON(
path_to_JSON + '/central/normalisation_electron_' + met_type +
'.txt')
fit_results_muon = read_data_from_JSON(muon_file)
elif category == 'Electron_up' or category == 'Electron_down':
fit_results_electron = read_data_from_JSON(electron_file)
# fit_results_muon = read_data_from_JSON( path_to_JSON + '/central/initial_normalisation_muon_' + met_type + '.txt' )
fit_results_muon = read_data_from_JSON(
path_to_JSON + '/central/normalisation_muon_' + met_type +
'.txt')
else:
fit_results_electron = read_data_from_JSON(electron_file)
fit_results_muon = read_data_from_JSON(muon_file)
fit_results_combined = combine_complex_results(fit_results_electron,
fit_results_muon)
TTJet_fit_results_electron = fit_results_electron['TTJet']
TTJet_fit_results_muon = fit_results_muon['TTJet']
# electron_file = path_to_JSON + '/'+category+'/initial_normalisation_electron_' + met_type + '.txt'
# muon_file = path_to_JSON + '/'+category+'/initial_normalisation_muon_' + met_type + '.txt'
elif category != 'central':
# electron_file = path_to_JSON + '/'+category+'/initial_normalisation_electron_' + met_type + '.txt'
# muon_file = path_to_JSON + '/'+category+'/initial_normalisation_muon_' + met_type + '.txt'
# electron_file = path_to_JSON + '/central/normalisation_electron_' + translate_options[options.metType] + '.txt'
# muon_file = path_to_JSON + '/central/normalisation_muon_' + translate_options[options.metType] + '.txt'
electron_file = path_to_JSON + '/' + category + '/normalisation_electron_' + met_type + '.txt'
muon_file = path_to_JSON + '/' + category + '/normalisation_muon_' + met_type + '.txt'
fit_results_electron = None
fit_results_muon = None
if category == 'Muon_up' or category == 'Muon_down':
# fit_results_electron = read_data_from_JSON( path_to_JSON + '/central/initial_normalisation_electron_' + met_type + '.txt' )
fit_results_electron = read_data_from_JSON( path_to_JSON + '/central/normalisation_electron_' + met_type + '.txt' )
fit_results_muon = read_data_from_JSON( muon_file )
elif category == 'Electron_up' or category == 'Electron_down':
fit_results_electron = read_data_from_JSON( electron_file )
# fit_results_muon = read_data_from_JSON( path_to_JSON + '/central/initial_normalisation_muon_' + met_type + '.txt' )
fit_results_muon = read_data_from_JSON( path_to_JSON + '/central/normalisation_muon_' + met_type + '.txt' )
else:
fit_results_electron = read_data_from_JSON( electron_file )
fit_results_muon = read_data_from_JSON( muon_file )
fit_results_combined = combine_complex_results(fit_results_electron, fit_results_muon)
TTJet_fit_results_electron = fit_results_electron['TTJet']
TTJet_fit_results_muon = fit_results_muon['TTJet']
TTJet_fit_results_combined = fit_results_combined['TTJet']
# # change back to original MET type for the unfolding
met_type = translate_options[options.metType]
for category in categories:
#Setting up systematic MET for JES up/down samples
met_type = translateOptions[options.metType]
if category == 'JES_up':
met_type += 'JetEnUp'
if met_type == 'PFMETJetEnUp':
met_type = 'patPFMetJetEnUp'
elif category == 'JES_down':
met_type += 'JetEnDown'
if met_type == 'PFMETJetEnDown':
met_type = 'patPFMetJetEnDown'
#read fit results from JSON
TTJet_fit_results_electron = read_data_from_JSON(
path_to_JSON + '/' + variable + '/fit_results/' + category +
'/fit_results_electron_' + met_type + '.txt')['TTJet']
TTJet_fit_results_muon = read_data_from_JSON(path_to_JSON + '/' +
variable +
'/fit_results/' +
category +
'/fit_results_muon_' +
met_type +
'.txt')['TTJet']
#change back to original MET type for the unfolding
met_type = translateOptions[options.metType]
#ad-hoc switch for PFMET -> patMETsPFlow
if met_type == 'PFMET':
met_type = 'patMETsPFlow'