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',
k_value = -1,
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_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, k_value = -1, 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, 'read')
file_for_ptReweight_up = File(config.unfolding_ptreweight_up, 'read')
file_for_ptReweight_down = File(config.unfolding_ptreweight_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_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_reweighted_up = asrootpy(response_reweighted_up.ProjectionX('px',1))
truth_reweighted_up = asrootpy(response_reweighted_up.ProjectionY())
_, _, response_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_reweighted_down = asrootpy(response_reweighted_down.ProjectionX('px',1))
truth_reweighted_down = asrootpy(response_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_reweighted_up.Rebin(2)
measured_reweighted_down.Rebin(2)
data.Rebin(2)
measured_central.Scale( 1 / measured_central.Integral() )
measured_reweighted_up.Scale( 1 / measured_reweighted_up.Integral() )
measured_reweighted_down.Scale( 1 / measured_reweighted_down.Integral() )
data.Scale( 1 / data.Integral() )
compare_measurements(
models = {'Central' : measured_central, 'Reweighted Up' : measured_reweighted_up, 'Reweighted Down' : measured_reweighted_down},
measurements = {'Data' : data},
show_measurement_errors=True,
histogram_properties=hp,
save_folder='plots/unfolding/reweighting_check',
save_as=['pdf']
)