def get_best_tau(regularisation_settings):
'''
returns TODO
- optimal_tau: TODO
'''
h_truth, h_response, h_measured, h_data, h_fakes = regularisation_settings.get_histograms(
)
variable = regularisation_settings.variable
h_data = removeFakes(h_measured, h_fakes, h_data)
unfolding = Unfolding(h_data,
h_truth,
h_measured,
h_response,
fakes=None,
method='TUnfold',
tau=-1)
# bestTau_LCurve = tau_from_L_curve( unfolding.unfoldObject )
# unfolding.tau = bestTau_LCurve
bestTauScan = tau_from_scan(unfolding.unfoldObject,
regularisation_settings)
unfolding.tau = bestTauScan
return unfolding.tau
def get_best_tau( regularisation_settings ):
'''
returns TODO
- optimal_tau: TODO
'''
h_truth, h_response, h_measured, h_data, h_fakes = regularisation_settings.get_histograms()
variable = regularisation_settings.variable
h_data = removeFakes( h_measured, h_fakes, h_data )
unfolding = Unfolding(
h_data,
h_truth,
h_measured,
h_response,
fakes = None,
method = 'TUnfold',
tau = -1
)
# bestTau_LCurve = tau_from_L_curve( unfolding.unfoldObject )
# unfolding.tau = bestTau_LCurve
bestTauScan = tau_from_scan( unfolding.unfoldObject, regularisation_settings )
unfolding.tau = bestTauScan
return unfolding.tau
def unfold_results( results, category, channel, tau_value, h_truth, h_measured, h_response, h_fakes, method, visiblePS ):
global variable, path_to_DF, args
edges = reco_bin_edges_full[variable]
if visiblePS:
edges = reco_bin_edges_vis[variable]
h_data = value_error_tuplelist_to_hist( results, edges )
# Rebin original TTJet_Measured in terms of final binning (h_data is later replaced with h_data_no_fakes)
h_data_rebinned = h_data.rebinned(2)
# Remove fakes before unfolding
h_data_no_fakes = removeFakes( h_measured, h_fakes, h_data )
# unfold
unfolding = Unfolding( h_data_no_fakes, h_truth, h_measured, h_response, h_fakes, method = method, tau = tau_value )
# turning off the unfolding errors for systematic samples
if not category == 'central':
unfoldCfg.error_treatment = 0
else:
unfoldCfg.error_treatment = args.error_treatment
h_unfolded_data = unfolding.unfold()
h_data_no_fakes = h_data_no_fakes.rebinned(2)
covariance_matrix = None
if category == 'central':
# Return the covariance matrices (They have been normailsed)
covariance_matrix, correlation_matrix = unfolding.get_covariance_matrix()
# Write covariance matrices
covariance_output_tempalte = '{path_to_DF}/central/covarianceMatrices/{cat}_{label}_{channel}.txt'
# Unfolded number of events
table_outfile=covariance_output_tempalte.format( path_to_DF=path_to_DF, channel = channel, label='Covariance', cat='Stat_unfoldedNormalisation' )
create_covariance_matrix( covariance_matrix, table_outfile)
table_outfile=covariance_output_tempalte.format( path_to_DF=path_to_DF, channel = channel, label='Correlation', cat='Stat_unfoldedNormalisation' )
create_covariance_matrix( correlation_matrix, table_outfile )
# # Normalised cross section
# table_outfile=covariance_output_tempalte.format( path_to_DF=path_to_DF, channel = channel, label='Covariance', cat='Stat_normalisedXsection' )
# create_covariance_matrix( norm_covariance_matrix, table_outfile)
# table_outfile=covariance_output_tempalte.format( path_to_DF=path_to_DF, channel = channel, label='Correlation', cat='Stat_normalisedXsection' )
# create_covariance_matrix( norm_correlation_matrix, table_outfile )
del unfolding
return hist_to_value_error_tuplelist( h_data_rebinned ), hist_to_value_error_tuplelist( h_unfolded_data ), hist_to_value_error_tuplelist( h_data_no_fakes ), covariance_matrix
def unfold_results( results, category, channel, tau_value, h_truth, h_measured, h_response, h_fakes, method, visiblePS ):
global variable, path_to_JSON, options
edges = reco_bin_edges_full[variable]
if visiblePS:
edges = reco_bin_edges_vis[variable]
h_data = value_error_tuplelist_to_hist( results, edges )
# Remove fakes before unfolding
h_data = removeFakes( h_measured, h_fakes, h_data )
unfolding = Unfolding( h_data, h_truth, h_measured, h_response, h_fakes, method = method, tau = tau_value )
# turning off the unfolding errors for systematic samples
if not category == 'central':
unfoldCfg.error_treatment = 0
else:
unfoldCfg.error_treatment = options.error_treatment
h_unfolded_data = unfolding.unfold()
print "h_response bin edges : ", h_response
print "h_unfolded_data bin edges : ", h_unfolded_data
del unfolding
return hist_to_value_error_tuplelist( h_unfolded_data ), hist_to_value_error_tuplelist( h_data )
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] )
]),
)
def main():
config = XSectionConfig(13)
file_for_powhegPythia = File(config.unfolding_central, "read")
file_for_ptReweight_up = File(config.unfolding_ptreweight_up, "read")
file_for_ptReweight_down = File(config.unfolding_ptreweight_down, "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_combined_patType1CorrectedPFMet.txt"
for channel in ["combined"]:
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 data input (data after background subtraction, and fake removal)
file_for_data = file_for_data_template.format(variable=variable)
data = read_data_from_JSON(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.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)
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_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())
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,
},
measurements={"Data": data},
show_measurement_errors=True,
histogram_properties=hp,
save_folder="plots/unfolding/reweighting_check",
save_as=["pdf"],
)
def get_chi2s_of_tau_range( regularisation_settings, args, unfold_test=False ):
'''
Takes each tau value, unfolds and refolds, calcs the chi2, the prob of chi2 given ndf (n_bins)
and returns a dictionary of (1-P(Chi2|NDF)) for each tau
For measured test where we only worry about tau=0 outputs tau variables to data frame (+smeared measured values)
'''
h_truth, h_response, h_measured, h_data, h_fakes = regularisation_settings.get_histograms()
if not args.run_measured_as_data :
h_data = removeFakes( h_measured, h_fakes, h_data )
variable = regularisation_settings.variable
taus = regularisation_settings.taus_to_test
chi2_ndf = []
for tau in taus:
unfolding = Unfolding(
h_data,
h_truth,
h_measured,
h_response,
fakes = None,#Fakes or no?
method = 'TUnfold',
tau = tau
)
# Cannot refold without first unfolding
h_unfolded_data = unfolding.unfold()
h_refolded_data = unfolding.refold()
# print("Data")
# print (hist_to_value_error_tuplelist(h_data))
# print("Unfolded Data")
# print (hist_to_value_error_tuplelist(h_unfolded_data))
# print("Refolded Data")
# print (hist_to_value_error_tuplelist(h_refolded_data))
regularisation_settings.h_refolded = h_refolded_data
ndf = regularisation_settings.ndf
if args.run_refold_plots:
plot_data_vs_refold(args, regularisation_settings, tau)
if args.unfolded_binning:
unfolding.refolded_data = h_refolded_data.rebinned(2)
unfolding.data = h_data.rebinned(2)
ndf = int(regularisation_settings.ndf / 2)
# print("Data")
# print (hist_to_value_error_tuplelist(regularisation_settings.h_data))
# print("Refolded Data")
# print (hist_to_value_error_tuplelist(regularisation_settings.h_refolded))
chi2 = unfolding.getUnfoldRefoldChi2()
prob = TMath.Prob( chi2, ndf )
chi2_ndf.append(1-prob)
# print( tau, chi2, prob, 1-prob )
# Create pandas dictionary
d_chi2 = {variable : pd.Series( chi2_ndf )}
d_taus = {'tau' : pd.Series( taus )}
if unfold_test:
d_tau_vars = {
variable : {
'Tau' : tau,
'Chi2' : chi2,
'Prob' : prob,
'1-Prob' : 1-prob,
}
}
df_unfold_tests = tau_vars_to_df(d_tau_vars, regularisation_settings)
return df_unfold_tests
df_chi2 = chi2_to_df(d_chi2, d_taus, regularisation_settings )
return df_chi2
def get_chi2( regularisation_settings, args, smearing_test=False ):
'''
Takes each tau value, unfolds and refolds, calcs the chi2, the prob of chi2 given ndf (n_bins)
and returns a dictionary of (1-P(Chi2|NDF)) for each tau
For measured test where we only worry about tau=0 outputs tau variables to data frame (+smeared measured values)
'''
h_truth, h_response, h_measured, h_data, h_fakes = regularisation_settings.get_histograms()
# Dont remove any fakes if we are using the true mc distribution
if not args.run_measured_as_data or not args.run_smeared_measured_as_data:
h_data = removeFakes( h_measured, h_fakes, h_data )
variable = regularisation_settings.variable
taus = regularisation_settings.taus_to_test
chi2_ndf = []
for tau in taus:
unfolding = Unfolding(
h_data,
h_truth,
h_measured,
h_response,
fakes = None,#Fakes or no?
method = 'TUnfold',
tau = tau
)
# Cannot refold without first unfolding
h_unfolded_data = unfolding.unfold()
h_refolded_data = unfolding.refold()
# print("Data")
# print (hist_to_value_error_tuplelist(h_data))
# print("Unfolded Data")
# print (hist_to_value_error_tuplelist(h_unfolded_data))
# print("Refolded Data")
# print (hist_to_value_error_tuplelist(h_refolded_data))
regularisation_settings.h_refolded = h_refolded_data
ndf = regularisation_settings.ndf
if args.unfolded_binning:
unfolding.refolded_data = h_refolded_data.rebinned(2)
unfolding.data = h_data.rebinned(2)
ndf = int(regularisation_settings.ndf / 2)
regularisation_settings.h_refolded = unfolding.refolded_data
regularisation_settings.h_data = unfolding.data
if args.create_refold_plots:
plot_data_vs_refold(args, regularisation_settings, tau)
# Calculate the chi2 between refold and unfold
chi2 = unfolding.getUnfoldRefoldChi2()
# Calculate the Prob chi2 given NDF
prob = TMath.Prob( chi2, ndf )
# 1-P(Chi2|NDF)
chi2_ndf.append(1-prob)
# print( tau, chi2, prob, 1-prob )
# Create tau and Chi2 dictionary
d_chi2 = {variable : pd.Series( chi2_ndf )}
d_taus = {'tau' : pd.Series( taus )}
if smearing_test:
d_tau_vars = {
variable : {
'Tau' : tau,
'Chi2' : chi2,
'Prob' : prob,
'1-Prob' : 1-prob,
}
}
df_unfold_tests = tau_vars_to_df(d_tau_vars, regularisation_settings)
return df_unfold_tests
df_chi2 = chi2_to_df(d_chi2, d_taus, regularisation_settings )
return df_chi2
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])
]),
)