def uncertaintyForSystematicSource( name, centralMC, bin_edges, path, channel ):
variation_up_prefix = '_up'
variation_down_prefix = '_down'
if 'En' in name:
variation_up_prefix = 'Up'
variation_down_prefix = 'Down'
elif 'luminosity' in name or 'V+Jets' in name or 'SingleTop' in name:
variation_up_prefix = '+'
variation_down_prefix = '-'
elif 'QCD' in name:
variation_up_prefix = ''
variation_down_prefix = ''
normalisation_results_up = read_tuple_from_file( '{path}/{variation}{prefix}/normalisation_{channel}.txt'.format( path = path, variation=name, prefix = variation_up_prefix, channel=channel ) )
normalisation_results_down = read_tuple_from_file( '{path}/{variation}{prefix}/normalisation_{channel}.txt'.format( path = path, variation=name, prefix = variation_down_prefix, channel=channel ) )
histograms_up = getHistogramsFromNormalisationResults(normalisation_results_up, bin_edges)
histograms_down = getHistogramsFromNormalisationResults(normalisation_results_down, bin_edges)
total_up = sumMCHistograms( histograms_up )
total_down = sumMCHistograms( histograms_down )
total_up.Add( centralMC, -1 )
total_down.Add( centralMC, -1 )
relative_uncertainties = []
for i in range(1,centralMC.GetNbinsX()+1):
uncertainty = max( abs(total_down.GetBinContent(i)), abs( total_up.GetBinContent(i) ) )
centralValue = centralMC.GetBinContent(i)
if centralValue != 0:
relative_uncertainties.append( uncertainty/centralMC.GetBinContent(i) )
else:
relative_uncertainties.append( 0 )
# print name, relative_uncertainties
return relative_uncertainties
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 = self.phaseSpace
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 )
self.h_refolded = None
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_tuple_from_file(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 __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_tuple_from_file(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 calculateChi2ForModels( modelsForComparing, variable, channel, path_to_input, uncertainty_type ):
# Paths to statistical Covariance/Correlation matrices.
covariance_filename = '{input_path}/covarianceMatrices/{type}/Total_Covariance_{channel}.txt'.format(input_path=path_to_input, type = uncertainty_type, channel=channel)
# Convert to numpy matrix and create total
cov_full = matrix_from_df( file_to_df(covariance_filename) )
covariance_filename_withMCTheoryUncertainties = '{input_path}/covarianceMatrices/mcUncertainty/{type}/Total_Covariance_{channel}.txt'.format(input_path=path_to_input, type = uncertainty_type, channel=channel)
cov_full_withMCTHeoryUncertainties = matrix_from_df( file_to_df(covariance_filename_withMCTheoryUncertainties) )
xsections_filename = '{input_path}/xsection_{type}_{channel}_TUnfold.txt'.format(input_path=path_to_input, type = uncertainty_type, channel=channel)
# Collect the cross section measured/unfolded results from dataframes
xsections = read_tuple_from_file( xsections_filename )
xsection_unfolded = [ i[0] for i in xsections['TTJets_unfolded'] ]
xsectionsOfmodels = {}
chi2OfModels = {}
for model in modelsForComparing:
# print "\nModel is {} for {} {}".format(model, uncertainty_type, channel)
chi2 = None
xsectionsOfmodels[model] = None
if 'withMCTheoryUnc' in model:
# print "With Theory Uncertainties"
xsectionsOfmodels[model] = np.array( [ i[0] for i in xsections[model.replace('_withMCTheoryUnc','')] ] )
chi2 = calculateChi2( xsection_unfolded, xsectionsOfmodels[model], cov_full_withMCTHeoryUncertainties)
else:
# print "Without Theory Uncertainties"
xsectionsOfmodels[model] = np.array( [ i[0] for i in xsections[model] ] )
chi2 = calculateChi2( xsection_unfolded, xsectionsOfmodels[model], cov_full)
chi2OfModels[model] = chi2
chi2OfModels_df = pd.DataFrame( {
'Variable' : np.array( [variable] * len(modelsForComparing) ),
'Model' : np.array( [model for model in modelsForComparing] ),
'Chi2' : np.array( [chi2OfModels[model].chi2 for model in modelsForComparing] ),
'NDF' : np.array( [chi2OfModels[model].ndf for model in modelsForComparing] ),
'p-Value' : np.array( [chi2OfModels[model].pValue for model in modelsForComparing] ),
} )
output_filename = '{input_path}/chi2OfModels_{channel}_{type}.txt'.format(input_path=path_to_input,channel=channel, type = uncertainty_type)
df_to_file( output_filename, chi2OfModels_df )
return chi2OfModels_df
def main():
config = XSectionConfig(13)
file_for_powhegPythia = File(config.unfolding_central_firstHalf, 'read')
file_for_ptReweight_up = File(config.unfolding_ptreweight_up_firstHalf, 'read')
file_for_ptReweight_down = File(config.unfolding_ptreweight_down_firstHalf, 'read')
file_for_amcatnlo_pythia8 = File(config.unfolding_amcatnlo_pythia8, 'read')
file_for_powhegHerwig = File(config.unfolding_powheg_herwig, 'read')
file_for_etaReweight_up = File(config.unfolding_etareweight_up, 'read')
file_for_etaReweight_down = File(config.unfolding_etareweight_down, 'read')
file_for_data_template = 'data/normalisation/background_subtraction/13TeV/{variable}/VisiblePS/central/normalisation_{channel}.txt'
for channel in config.analysis_types.keys():
if channel is 'combined':continue
for variable in config.variables:
print variable
# for variable in ['HT']:
# Get the central powheg pythia distributions
_, _, response_central, fakes_central = get_unfold_histogram_tuple(
inputfile=file_for_powhegPythia,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=True,
visiblePS=True
)
measured_central = asrootpy(response_central.ProjectionX('px',1))
truth_central = asrootpy(response_central.ProjectionY())
# Get the reweighted powheg pythia distributions
_, _, response_pt_reweighted_up, _ = get_unfold_histogram_tuple(
inputfile=file_for_ptReweight_up,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True
)
measured_pt_reweighted_up = asrootpy(response_pt_reweighted_up.ProjectionX('px',1))
truth_pt_reweighted_up = asrootpy(response_pt_reweighted_up.ProjectionY())
_, _, response_pt_reweighted_down, _ = get_unfold_histogram_tuple(
inputfile=file_for_ptReweight_down,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True
)
measured_pt_reweighted_down = asrootpy(response_pt_reweighted_down.ProjectionX('px',1))
truth_pt_reweighted_down = asrootpy(response_pt_reweighted_down.ProjectionY())
# _, _, response_eta_reweighted_up, _ = get_unfold_histogram_tuple(
# inputfile=file_for_etaReweight_up,
# variable=variable,
# channel=channel,
# centre_of_mass=13,
# load_fakes=False,
# visiblePS=True
# )
# measured_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionX('px',1))
# truth_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionY())
# _, _, response_eta_reweighted_down, _ = get_unfold_histogram_tuple(
# inputfile=file_for_etaReweight_down,
# variable=variable,
# channel=channel,
# centre_of_mass=13,
# load_fakes=False,
# visiblePS=True
# )
# measured_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionX('px',1))
# truth_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionY())
# Get the distributions for other MC models
_, _, response_amcatnlo_pythia8, _ = get_unfold_histogram_tuple(
inputfile=file_for_amcatnlo_pythia8,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True
)
measured_amcatnlo_pythia8 = asrootpy(response_amcatnlo_pythia8.ProjectionX('px',1))
truth_amcatnlo_pythia8 = asrootpy(response_amcatnlo_pythia8.ProjectionY())
_, _, response_powhegHerwig, _ = get_unfold_histogram_tuple(
inputfile=file_for_powhegHerwig,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True
)
measured_powhegHerwig = asrootpy(response_powhegHerwig.ProjectionX('px',1))
truth_powhegHerwig = asrootpy(response_powhegHerwig.ProjectionY())
# Get the data input (data after background subtraction, and fake removal)
file_for_data = file_for_data_template.format( variable = variable, channel = channel )
data = read_tuple_from_file(file_for_data)['TTJet']
data = value_error_tuplelist_to_hist( data, reco_bin_edges_vis[variable] )
data = removeFakes( measured_central, fakes_central, data )
# Plot all three
hp = Histogram_properties()
hp.name = 'Reweighting_check_{channel}_{variable}_at_{com}TeV'.format(
channel=channel,
variable=variable,
com='13',
)
v_latex = latex_labels.variables_latex[variable]
unit = ''
if variable in ['HT', 'ST', 'MET', 'WPT', 'lepton_pt']:
unit = ' [GeV]'
hp.x_axis_title = v_latex + unit
hp.x_limits = [ reco_bin_edges_vis[variable][0], reco_bin_edges_vis[variable][-1]]
hp.ratio_y_limits = [0.1,1.9]
hp.ratio_y_title = 'Reweighted / Central'
hp.y_axis_title = 'Number of events'
hp.title = 'Reweighting check for {variable}'.format(variable=v_latex)
measured_central.Rebin(2)
measured_pt_reweighted_up.Rebin(2)
measured_pt_reweighted_down.Rebin(2)
# measured_eta_reweighted_up.Rebin(2)
# measured_eta_reweighted_down.Rebin(2)
measured_amcatnlo_pythia8.Rebin(2)
measured_powhegHerwig.Rebin(2)
data.Rebin(2)
measured_central.Scale( 1 / measured_central.Integral() )
measured_pt_reweighted_up.Scale( 1 / measured_pt_reweighted_up.Integral() )
measured_pt_reweighted_down.Scale( 1 / measured_pt_reweighted_down.Integral() )
measured_amcatnlo_pythia8.Scale( 1 / measured_amcatnlo_pythia8.Integral() )
measured_powhegHerwig.Scale( 1 / measured_powhegHerwig.Integral() )
# measured_eta_reweighted_up.Scale( 1 / measured_eta_reweighted_up.Integral() )
# measured_eta_reweighted_down.Scale( 1/ measured_eta_reweighted_down.Integral() )
data.Scale( 1 / data.Integral() )
print list(measured_central.y())
print list(measured_amcatnlo_pythia8.y())
print list(measured_powhegHerwig.y())
print list(data.y())
compare_measurements(
# models = {'Central' : measured_central, 'PtReweighted Up' : measured_pt_reweighted_up, 'PtReweighted Down' : measured_pt_reweighted_down, 'EtaReweighted Up' : measured_eta_reweighted_up, 'EtaReweighted Down' : measured_eta_reweighted_down},
models = OrderedDict([('Central' , measured_central), ('PtReweighted Up' , measured_pt_reweighted_up), ('PtReweighted Down' , measured_pt_reweighted_down), ('amc@nlo' , measured_amcatnlo_pythia8), ('powhegHerwig' , measured_powhegHerwig) ] ),
measurements = {'Data' : data},
show_measurement_errors=True,
histogram_properties=hp,
save_folder='plots/unfolding/reweighting_check',
save_as=['pdf'],
line_styles_for_models = ['solid','solid','solid','dashed','dashed'],
show_ratio_for_pairs = OrderedDict( [
('PtUpVsCentral' , [ measured_pt_reweighted_up, measured_central ] ),
('PtDownVsCentral' , [ measured_pt_reweighted_down, measured_central ] ),
('amcatnloVsCentral' , [ measured_amcatnlo_pythia8, measured_central ] ),
('powhegHerwigVsCentral' , [ measured_powhegHerwig, measured_central ] ),
('DataVsCentral' , [data, measured_central] )
]),
)
# For systematics not run in 01 [PDF and TTJet_] then use the central normalisations
if category not in measurement_config.normalisation_systematics:
electron_file = path_to_DF + '/central/normalisation_electron.txt'
muon_file = path_to_DF + '/central/normalisation_muon.txt'
combined_file = path_to_DF + '/central/normalisation_combined.txt'
# Read the normalisations
normalisation_results_electron = None
normalisation_results_muon = None
normalisation_results_combined = None
# Read the normalisation files
# For LeptonUp/Down return other lepton type to central normailsation
# THINK HOW TO READ MUON:ELECTRON/UP:DOWN WITH COMBINEDBEFOREUNFOLDING
if category == 'Muon_up' or category == 'Muon_down':
normalisation_results_electron = read_tuple_from_file( path_to_DF + '/central/normalisation_electron.txt' )
normalisation_results_muon = read_tuple_from_file( muon_file )
elif category == 'Electron_up' or category == 'Electron_down':
normalisation_results_electron = read_tuple_from_file( electron_file )
normalisation_results_muon = read_tuple_from_file( path_to_DF + '/central/normalisation_muon.txt' )
else:
normalisation_results_electron = read_tuple_from_file( electron_file )
normalisation_results_muon = read_tuple_from_file( muon_file )
# Combine the normalisations (beforeUnfolding)
normalisation_results_combined = combine_complex_df(normalisation_results_electron, normalisation_results_muon)
TTJet_normalisation_results_electron = normalisation_results_electron['TTJet']
TTJet_normalisation_results_muon = normalisation_results_muon['TTJet']
TTJet_normalisation_results_combined = normalisation_results_combined['TTJet']
# # get unfolded normalisations and xsections
path=path_to_DF, channel=channel),
'QCD_other_control_region':
'{path}/QCD_other_control_region/normalisation_{channel}.txt'.
format(path=path_to_DF, channel=channel),
'QCD_signal_MC':
'{path}/QCD_signal_MC/normalisation_{channel}.txt'.format(
path=path_to_DF, channel=channel)
}
normalisations = {}
hists = {}
maxY = 0
minY = 99999999
for f in files:
normalisations[f] = read_tuple_from_file(files[f])['QCD']
hists[f] = value_error_tuplelist_to_hist(
normalisations[f], reco_bin_edges_vis[variable]).Rebin(2)
maxY = max([maxY] + list(hists[f].y()))
minY = min([minY] + list(hists[f].y()))
if minY <= 0: minY = 0.1
can = Canvas()
pad1 = Pad(0, 0.3, 1, 1)
pad2 = Pad(0, 0, 1, 0.3)
pad1.Draw()
pad2.Draw()
pad1.cd()
# print normalisations
'central' : '{path}/central/normalisation_{channel}.txt'.format( path=path_to_DF,channel=channel ),
'QCD_shape' : '{path}/QCD_shape/normalisation_{channel}.txt'.format( path=path_to_DF,channel=channel ),
'QCD_normalisation' : '{path}/QCD_cross_section/normalisation_{channel}.txt'.format( path=path_to_DF,channel=channel ),
'QCD_other_control_region' : '{path}/QCD_other_control_region/normalisation_{channel}.txt'.format( path=path_to_DF,channel=channel ),
'QCD_signal_MC' : '{path}/QCD_signal_MC/normalisation_{channel}.txt'.format( path=path_to_DF,channel=channel )
}
normalisations = {
}
hists = {
}
maxY = 0
minY = 99999999
for f in files:
normalisations[f] = read_tuple_from_file( files[f] )['QCD']
hists[f] = value_error_tuplelist_to_hist( normalisations[f], reco_bin_edges_vis[variable] ).Rebin(2)
maxY = max([maxY]+list(hists[f].y() ) )
minY = min([minY]+list(hists[f].y() ) )
if minY <= 0 : minY = 0.1
can = Canvas()
pad1 = Pad( 0, 0.3, 1, 1)
pad2 = Pad( 0, 0, 1, 0.3)
pad1.Draw()
pad2.Draw()
pad1.cd()
# print normalisations
hists['central'].SetLineColor(2)
def read_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_DF = options['path_to_DF']
method = options['method']
channel = options['channel']
norm = options['normalisation_type']
filename = '{path}/{category}/xsection_{norm}_{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_DF,
channel = channel,
category = 'central',
method = method,
norm = norm,
)
else:
filename = filename.format(
path = path_to_DF,
channel = channel,
category = category,
method = method,
norm = norm,
)
measurement = read_tuple_from_file( filename )
xsection_unfolded = measurement['TTJets_unfolded']
if category is 'central':
theoryUncertaintySources = options['mcTheoryUncertainties']
xsection_mc = { 'central' : measurement['TTJets_powhegPythia8' ]}
for source in theoryUncertaintySources:
variations = theoryUncertaintySources[source]
if source is 'TTJets_scale':
# if source is 'TTJets_scale' or source is 'TTJets_CR':
scale_xsections = {}
for variation in variations:
xsectionWithUncertainty = measurement[variation]
for i in range(0,len(xsectionWithUncertainty)):
xsectionWithUncertainty[i] = xsectionWithUncertainty[i][0]
scale_xsections[variation] = xsectionWithUncertainty
scale_envelope_lower, scale_envelope_upper = get_scale_envelope(scale_xsections, measurement['TTJets_powhegPythia8' ])
xsection_mc[source] = [
scale_envelope_lower,
scale_envelope_upper,
]
pass
else:
xsectionWithUncertainty_lower = deepcopy( measurement[variations[0]] )
xsectionWithUncertainty_upper = deepcopy( measurement[variations[1]] )
for i in range(0,len(xsectionWithUncertainty_lower)):
xsectionWithUncertainty_lower[i] = xsectionWithUncertainty_lower[i][0]
xsectionWithUncertainty_upper[i] = xsectionWithUncertainty_upper[i][0]
xsection_mc[source] = [
xsectionWithUncertainty_lower,
xsectionWithUncertainty_upper,
]
return xsection_unfolded, xsection_mc
return xsection_unfolded
uncertaintiesForEachChannel = {}
binEdges = control_plots_bins_for01[variable]
bin_low = binEdges[0]
bin_high = binEdges[-1]
nBins = control_plot_nbins[variable]
binWidth = ( bin_high - bin_low ) / nBins
reco_bin_edges = [ bin_low + binWidth * i for i in range(0, nBins + 1) ]
for channel in config.analysis_types.keys():
if channel == 'combined': continue
path_to_DF = 'data/normalisation/background_subtraction/13TeV/{variable}/VisiblePS/'.format( variable = variable )
normalisation_fileName = 'normalisation_{channel}.txt'.format(channel=channel)
normalisation_results_electron = read_tuple_from_file( '{path}/central/{filename}'.format(path=path_to_DF,filename=normalisation_fileName) )
dict_histograms = getHistogramsFromNormalisationResults(normalisation_results_electron, reco_bin_edges )
totalMC = sumMCHistograms( dict_histograms )
statisticalUncertainties = [ totalMC.GetBinError(i) / totalMC.GetBinContent(i) if totalMC.GetBinContent(i) else 0 for i in range(1, totalMC.GetNbinsX()+1)]
systematicUncertainties = {}
systematicUncertainties['stat'] = statisticalUncertainties
for source in systmematicSourceToPlot:
if channel == 'electron' and source == 'Muon': continue
elif channel == 'muon' and source == 'Electron': continue
if variable in config.variables_no_met and 'En' in source : continue
systematicUncertainties[source] = uncertaintyForSystematicSource( source, totalMC, reco_bin_edges, path_to_DF, channel )
def calculateChi2ForModels(modelsForComparing, variable, channel,
path_to_input, uncertainty_type):
# Paths to statistical Covariance/Correlation matrices.
covariance_filename = '{input_path}/covarianceMatrices/{type}/Total_Covariance_{channel}.txt'.format(
input_path=path_to_input, type=uncertainty_type, channel=channel)
# Convert to numpy matrix and create total
cov_full = matrix_from_df(file_to_df(covariance_filename))
covariance_filename_withMCTheoryUncertainties = '{input_path}/covarianceMatrices/mcUncertainty/{type}/Total_Covariance_{channel}.txt'.format(
input_path=path_to_input, type=uncertainty_type, channel=channel)
cov_full_withMCTHeoryUncertainties = matrix_from_df(
file_to_df(covariance_filename_withMCTheoryUncertainties))
xsections_filename = '{input_path}/xsection_{type}_{channel}_TUnfold.txt'.format(
input_path=path_to_input, type=uncertainty_type, channel=channel)
# Collect the cross section measured/unfolded results from dataframes
xsections = read_tuple_from_file(xsections_filename)
xsection_unfolded = [i[0] for i in xsections['TTJets_unfolded']]
xsectionsOfmodels = {}
chi2OfModels = {}
for model in modelsForComparing:
# print "\nModel is {} for {} {}".format(model, uncertainty_type, channel)
chi2 = None
xsectionsOfmodels[model] = None
if 'withMCTheoryUnc' in model:
# print "With Theory Uncertainties"
xsectionsOfmodels[model] = np.array([
i[0] for i in xsections[model.replace('_withMCTheoryUnc', '')]
])
chi2 = calculateChi2(xsection_unfolded, xsectionsOfmodels[model],
cov_full_withMCTHeoryUncertainties)
else:
# print "Without Theory Uncertainties"
xsectionsOfmodels[model] = np.array(
[i[0] for i in xsections[model]])
chi2 = calculateChi2(xsection_unfolded, xsectionsOfmodels[model],
cov_full)
chi2OfModels[model] = chi2
chi2OfModels_df = pd.DataFrame({
'Variable':
np.array([variable] * len(modelsForComparing)),
'Model':
np.array([model for model in modelsForComparing]),
'Chi2':
np.array([chi2OfModels[model].chi2 for model in modelsForComparing]),
'NDF':
np.array([chi2OfModels[model].ndf for model in modelsForComparing]),
'p-Value':
np.array([chi2OfModels[model].pValue for model in modelsForComparing]),
})
output_filename = '{input_path}/chi2OfModels_{channel}_{type}.txt'.format(
input_path=path_to_input, channel=channel, type=uncertainty_type)
df_to_file(output_filename, chi2OfModels_df)
return chi2OfModels_df
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 read_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_DF = options['path_to_DF']
method = options['method']
channel = options['channel']
norm = options['normalisation_type']
filename = '{path}/{category}/xsection_{norm}_{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_DF,
channel=channel,
category='central',
method=method,
norm=norm,
)
else:
filename = filename.format(
path=path_to_DF,
channel=channel,
category=category,
method=method,
norm=norm,
)
measurement = read_tuple_from_file(filename)
xsection_unfolded = measurement['TTJets_unfolded']
if category is 'central':
theoryUncertaintySources = options['mcTheoryUncertainties']
xsection_mc = {'central': measurement['TTJets_powhegPythia8']}
for source in theoryUncertaintySources:
variations = theoryUncertaintySources[source]
if source is 'TTJets_scale':
# if source is 'TTJets_scale' or source is 'TTJets_CR':
scale_xsections = {}
for variation in variations:
xsectionWithUncertainty = measurement[variation]
for i in range(0, len(xsectionWithUncertainty)):
xsectionWithUncertainty[i] = xsectionWithUncertainty[
i][0]
scale_xsections[variation] = xsectionWithUncertainty
scale_envelope_lower, scale_envelope_upper = get_scale_envelope(
scale_xsections, measurement['TTJets_powhegPythia8'])
xsection_mc[source] = [
scale_envelope_lower,
scale_envelope_upper,
]
pass
else:
xsectionWithUncertainty_lower = deepcopy(
measurement[variations[0]])
xsectionWithUncertainty_upper = deepcopy(
measurement[variations[1]])
for i in range(0, len(xsectionWithUncertainty_lower)):
xsectionWithUncertainty_lower[
i] = xsectionWithUncertainty_lower[i][0]
xsectionWithUncertainty_upper[
i] = xsectionWithUncertainty_upper[i][0]
xsection_mc[source] = [
xsectionWithUncertainty_lower,
xsectionWithUncertainty_upper,
]
return xsection_unfolded, xsection_mc
return xsection_unfolded
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 main():
config = XSectionConfig(13)
file_for_powhegPythia = File(config.unfolding_central_firstHalf, 'read')
file_for_ptReweight_up = File(config.unfolding_ptreweight_up_firstHalf,
'read')
file_for_ptReweight_down = File(config.unfolding_ptreweight_down_firstHalf,
'read')
file_for_amcatnlo_pythia8 = File(config.unfolding_amcatnlo_pythia8, 'read')
file_for_powhegHerwig = File(config.unfolding_powheg_herwig, 'read')
file_for_etaReweight_up = File(config.unfolding_etareweight_up, 'read')
file_for_etaReweight_down = File(config.unfolding_etareweight_down, 'read')
file_for_data_template = 'data/normalisation/background_subtraction/13TeV/{variable}/VisiblePS/central/normalisation_{channel}.txt'
for channel in config.analysis_types.keys():
if channel is 'combined': continue
for variable in config.variables:
print variable
# for variable in ['HT']:
# Get the central powheg pythia distributions
_, _, response_central, fakes_central = get_unfold_histogram_tuple(
inputfile=file_for_powhegPythia,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=True,
visiblePS=True)
measured_central = asrootpy(response_central.ProjectionX('px', 1))
truth_central = asrootpy(response_central.ProjectionY())
# Get the reweighted powheg pythia distributions
_, _, response_pt_reweighted_up, _ = get_unfold_histogram_tuple(
inputfile=file_for_ptReweight_up,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True)
measured_pt_reweighted_up = asrootpy(
response_pt_reweighted_up.ProjectionX('px', 1))
truth_pt_reweighted_up = asrootpy(
response_pt_reweighted_up.ProjectionY())
_, _, response_pt_reweighted_down, _ = get_unfold_histogram_tuple(
inputfile=file_for_ptReweight_down,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True)
measured_pt_reweighted_down = asrootpy(
response_pt_reweighted_down.ProjectionX('px', 1))
truth_pt_reweighted_down = asrootpy(
response_pt_reweighted_down.ProjectionY())
# _, _, response_eta_reweighted_up, _ = get_unfold_histogram_tuple(
# inputfile=file_for_etaReweight_up,
# variable=variable,
# channel=channel,
# centre_of_mass=13,
# load_fakes=False,
# visiblePS=True
# )
# measured_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionX('px',1))
# truth_eta_reweighted_up = asrootpy(response_eta_reweighted_up.ProjectionY())
# _, _, response_eta_reweighted_down, _ = get_unfold_histogram_tuple(
# inputfile=file_for_etaReweight_down,
# variable=variable,
# channel=channel,
# centre_of_mass=13,
# load_fakes=False,
# visiblePS=True
# )
# measured_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionX('px',1))
# truth_eta_reweighted_down = asrootpy(response_eta_reweighted_down.ProjectionY())
# Get the distributions for other MC models
_, _, response_amcatnlo_pythia8, _ = get_unfold_histogram_tuple(
inputfile=file_for_amcatnlo_pythia8,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True)
measured_amcatnlo_pythia8 = asrootpy(
response_amcatnlo_pythia8.ProjectionX('px', 1))
truth_amcatnlo_pythia8 = asrootpy(
response_amcatnlo_pythia8.ProjectionY())
_, _, response_powhegHerwig, _ = get_unfold_histogram_tuple(
inputfile=file_for_powhegHerwig,
variable=variable,
channel=channel,
centre_of_mass=13,
load_fakes=False,
visiblePS=True)
measured_powhegHerwig = asrootpy(
response_powhegHerwig.ProjectionX('px', 1))
truth_powhegHerwig = asrootpy(response_powhegHerwig.ProjectionY())
# Get the data input (data after background subtraction, and fake removal)
file_for_data = file_for_data_template.format(variable=variable,
channel=channel)
data = read_tuple_from_file(file_for_data)['TTJet']
data = value_error_tuplelist_to_hist(data,
reco_bin_edges_vis[variable])
data = removeFakes(measured_central, fakes_central, data)
# Plot all three
hp = Histogram_properties()
hp.name = 'Reweighting_check_{channel}_{variable}_at_{com}TeV'.format(
channel=channel,
variable=variable,
com='13',
)
v_latex = latex_labels.variables_latex[variable]
unit = ''
if variable in ['HT', 'ST', 'MET', 'WPT', 'lepton_pt']:
unit = ' [GeV]'
hp.x_axis_title = v_latex + unit
hp.x_limits = [
reco_bin_edges_vis[variable][0],
reco_bin_edges_vis[variable][-1]
]
hp.ratio_y_limits = [0.1, 1.9]
hp.ratio_y_title = 'Reweighted / Central'
hp.y_axis_title = 'Number of events'
hp.title = 'Reweighting check for {variable}'.format(
variable=v_latex)
measured_central.Rebin(2)
measured_pt_reweighted_up.Rebin(2)
measured_pt_reweighted_down.Rebin(2)
# measured_eta_reweighted_up.Rebin(2)
# measured_eta_reweighted_down.Rebin(2)
measured_amcatnlo_pythia8.Rebin(2)
measured_powhegHerwig.Rebin(2)
data.Rebin(2)
measured_central.Scale(1 / measured_central.Integral())
measured_pt_reweighted_up.Scale(
1 / measured_pt_reweighted_up.Integral())
measured_pt_reweighted_down.Scale(
1 / measured_pt_reweighted_down.Integral())
measured_amcatnlo_pythia8.Scale(
1 / measured_amcatnlo_pythia8.Integral())
measured_powhegHerwig.Scale(1 / measured_powhegHerwig.Integral())
# measured_eta_reweighted_up.Scale( 1 / measured_eta_reweighted_up.Integral() )
# measured_eta_reweighted_down.Scale( 1/ measured_eta_reweighted_down.Integral() )
data.Scale(1 / data.Integral())
print list(measured_central.y())
print list(measured_amcatnlo_pythia8.y())
print list(measured_powhegHerwig.y())
print list(data.y())
compare_measurements(
# models = {'Central' : measured_central, 'PtReweighted Up' : measured_pt_reweighted_up, 'PtReweighted Down' : measured_pt_reweighted_down, 'EtaReweighted Up' : measured_eta_reweighted_up, 'EtaReweighted Down' : measured_eta_reweighted_down},
models=OrderedDict([
('Central', measured_central),
('PtReweighted Up', measured_pt_reweighted_up),
('PtReweighted Down', measured_pt_reweighted_down),
('amc@nlo', measured_amcatnlo_pythia8),
('powhegHerwig', measured_powhegHerwig)
]),
measurements={'Data': data},
show_measurement_errors=True,
histogram_properties=hp,
save_folder='plots/unfolding/reweighting_check',
save_as=['pdf'],
line_styles_for_models=[
'solid', 'solid', 'solid', 'dashed', 'dashed'
],
show_ratio_for_pairs=OrderedDict([
('PtUpVsCentral',
[measured_pt_reweighted_up, measured_central]),
('PtDownVsCentral',
[measured_pt_reweighted_down, measured_central]),
('amcatnloVsCentral',
[measured_amcatnlo_pythia8, measured_central]),
('powhegHerwigVsCentral',
[measured_powhegHerwig, measured_central]),
('DataVsCentral', [data, measured_central])
]),
)