def get_k_from_d_i(h_truth, h_measured, h_response, h_fakes=None, h_data=None):
global method
k_start = h_measured.nbins()
unfolding = Unfolding(h_truth,
h_measured,
h_response,
h_fakes,
method=method,
k_value=k_start,
error_treatment=0,
verbose=1)
unfolding.unfold(h_data)
hist_d_i = None
if method == 'RooUnfoldSvd':
hist_d_i = asrootpy(unfolding.unfoldObject.Impl().GetD())
elif method == 'TSVDUnfold':
hist_d_i = asrootpy(unfolding.unfoldObject.GetD())
best_k = k_start
for i, d_i in enumerate(hist_d_i.y()):
# i count starts at 0
if d_i >= 1:
continue
else:
# first i when d_i < 0, is k
# because i starts at 0
best_k = i
break
return best_k, hist_d_i.clone()
def get_k_from_d_i( h_truth, h_measured, h_response, h_fakes = None, h_data = None ):
global method
k_start = h_measured.nbins()
unfolding = Unfolding( h_truth,
h_measured,
h_response,
h_fakes,
method = method,
k_value = k_start,
error_treatment = 0,
verbose = 1 )
unfolding.unfold( h_data )
hist_d_i = None
if method == 'RooUnfoldSvd':
hist_d_i = asrootpy( unfolding.unfoldObject.Impl().GetD() )
elif method == 'TSVDUnfold':
hist_d_i = asrootpy( unfolding.unfoldObject.GetD() )
best_k = k_start
for i, d_i in enumerate( hist_d_i.y() ):
# i count starts at 0
if d_i >= 1:
continue
else:
# first i when d_i < 0, is k
# because i starts at 0
best_k = i
break
return best_k, hist_d_i.clone()
def get_tau_from_L_shape( h_truth, h_measured, h_response, h_data = None ):
tau_min = 1e-7
tau_max = 0.2
number_of_scans = 10000
# the best values depend on the variable!!!
# number_of_scans = 60
# tau_min = 1e-6
# tau_max = 1e-7 * 20000 + tau_min
# tau_min = 1e-7
# tau_max = 1e-2
unfolding = Unfolding( h_truth,
h_measured,
h_response,
method = 'RooUnfoldTUnfold',
tau = tau_min )
if h_data:
unfolding.unfold( h_data )
else: # closure test
unfolding.unfold( h_measured )
l_curve = TGraph()
unfolding.unfoldObject.Impl().ScanLcurve( number_of_scans, tau_min, tau_max, l_curve )
best_tau = unfolding.unfoldObject.Impl().GetTau()
x_value = unfolding.unfoldObject.Impl().GetLcurveX()
y_value = unfolding.unfoldObject.Impl().GetLcurveY()
return best_tau, l_curve, x_value, y_value
def get_best_k_from_global_correlation(regularisation_settings):
'''
returns optimal_k, k_values, tau_values, rho_values
- optimal_k: k-value with lowest rho
- minimal_rho: lowest rho value
- k_values: all scanned k-values
- tau_values: tau values for all scanned k-values
- rho_values: rho values for all scanned k-values
'''
h_truth, h_response, h_measured, h_data = regularisation_settings.get_histograms(
)
n_toy = regularisation_settings.n_toy
# initialise variables
optimal_k = 0
minimal_rho = 9999
n_bins = h_data.nbins()
k_values = []
tau_values = []
rho_values = []
# first calculate one set to get the matrices
# tau = 0 is equal to k = nbins
unfolding = Unfolding(
h_truth,
h_measured,
h_response,
method='RooUnfoldSvd',
tau=0., # no regularisation
k_value=-1,
)
unfolding.unfold(h_data)
# get unfolding object
svd_unfold = unfolding.Impl()
# get covariance matrix
cov = svd_unfold.get_data_covariance_matrix(h_data)
# cache functions and save time in the loop
SetTau = svd_unfold.SetTau
GetCovMatrix = svd_unfold.GetUnfoldCovMatrix
GetRho = svd_unfold.get_global_correlation
kToTau = svd_unfold.kToTau
add_k = k_values.append
add_tau = tau_values.append
add_rho = rho_values.append
# now lets loop over all possible k-values
for k in range(2, n_bins + 1):
tau_from_k = kToTau(k)
SetTau(tau_from_k)
cov_matrix = GetCovMatrix(cov, n_toy, 1)
rho = GetRho(cov_matrix, h_data)
add_k(k)
add_tau(tau_from_k)
add_rho(rho)
if rho < minimal_rho:
optimal_k = k
minimal_rho = rho
return optimal_k, minimal_rho, k_values, tau_values, rho_values
def get_best_tau_from_global_correlation(regularisation_settings):
'''
returns optimal_tau, tau_values, rho_values
- optimal_tau: k-value with lowest rho
- minimal_rho: lowest rho value
- tau_values: all scanned tau values
- rho_values: rho values for all scanned tau-values
'''
h_truth, h_response, h_measured, h_data = regularisation_settings.get_histograms(
)
n_toy = regularisation_settings.n_toy
number_of_iterations = regularisation_settings.n_tau_scan_points
tau_min = 0.1
tau_max = 1000
optimal_tau = 0
minimal_rho = 9999
tau_values = []
rho_values = []
# first calculate one set to get the matrices
# tau = 0 is equal to k = nbins
unfolding = Unfolding(
h_truth,
h_measured,
h_response,
method='RooUnfoldSvd',
tau=0., # no regularisation
k_value=-1,
)
unfolding.unfold(h_data)
# get unfolding object
svd_unfold = unfolding.Impl()
# get covariance matrix
cov = svd_unfold.get_data_covariance_matrix(h_data)
# cache functions and save time in the loop
SetTau = svd_unfold.SetTau
GetCovMatrix = svd_unfold.GetUnfoldCovMatrix
GetRho = svd_unfold.get_global_correlation
add_tau = tau_values.append
add_rho = rho_values.append
# now lets loop over all tau-values in range
for current_tau in get_tau_range(tau_min, tau_max, number_of_iterations):
SetTau(current_tau)
cov_matrix = GetCovMatrix(cov, n_toy, 1)
current_rho = GetRho(cov_matrix, h_data)
add_tau(current_tau)
add_rho(current_rho)
if current_rho < minimal_rho:
minimal_rho = current_rho
optimal_tau = current_tau
print 'Best tau for', regularisation_settings.channel, ':', optimal_tau
return optimal_tau, minimal_rho, tau_values, rho_values
def get_best_k_from_global_correlation( regularisation_settings ):
'''
returns optimal_k, k_values, tau_values, rho_values
- optimal_k: k-value with lowest rho
- minimal_rho: lowest rho value
- k_values: all scanned k-values
- tau_values: tau values for all scanned k-values
- rho_values: rho values for all scanned k-values
'''
h_truth, h_response, h_measured, h_data = regularisation_settings.get_histograms()
n_toy = regularisation_settings.n_toy
# initialise variables
optimal_k = 0
minimal_rho = 9999
n_bins = h_data.nbins()
k_values = []
tau_values = []
rho_values = []
# first calculate one set to get the matrices
# tau = 0 is equal to k = nbins
unfolding = Unfolding( h_truth,
h_measured,
h_response,
method = 'RooUnfoldSvd',
tau = 0., # no regularisation
k_value = -1, )
unfolding.unfold( h_data )
# get unfolding object
svd_unfold = unfolding.Impl()
# get covariance matrix
cov = svd_unfold.get_data_covariance_matrix( h_data )
# cache functions and save time in the loop
SetTau = svd_unfold.SetTau
GetCovMatrix = svd_unfold.GetUnfoldCovMatrix
GetRho = svd_unfold.get_global_correlation
kToTau = svd_unfold.kToTau
add_k = k_values.append
add_tau = tau_values.append
add_rho = rho_values.append
# now lets loop over all possible k-values
for k in range( 2, n_bins + 1 ):
tau_from_k = kToTau( k )
SetTau( tau_from_k )
cov_matrix = GetCovMatrix( cov, n_toy, 1 )
rho = GetRho( cov_matrix, h_data )
add_k( k )
add_tau( tau_from_k )
add_rho( rho )
if rho < minimal_rho:
optimal_k = k
minimal_rho = rho
return optimal_k, minimal_rho, k_values, tau_values, rho_values
def get_best_tau_from_global_correlation( regularisation_settings ):
'''
returns optimal_tau, tau_values, rho_values
- optimal_tau: k-value with lowest rho
- minimal_rho: lowest rho value
- tau_values: all scanned tau values
- rho_values: rho values for all scanned tau-values
'''
h_truth, h_response, h_measured, h_data = regularisation_settings.get_histograms()
n_toy = regularisation_settings.n_toy
number_of_iterations = regularisation_settings.n_tau_scan_points
tau_min = 0.1
tau_max = 1000
optimal_tau = 0
minimal_rho = 9999
tau_values = []
rho_values = []
# first calculate one set to get the matrices
# tau = 0 is equal to k = nbins
unfolding = Unfolding( h_truth,
h_measured,
h_response,
method = 'RooUnfoldSvd',
tau = 0., # no regularisation
k_value = -1, )
unfolding.unfold( h_data )
# get unfolding object
svd_unfold = unfolding.Impl()
# get covariance matrix
cov = svd_unfold.get_data_covariance_matrix( h_data )
# cache functions and save time in the loop
SetTau = svd_unfold.SetTau
GetCovMatrix = svd_unfold.GetUnfoldCovMatrix
GetRho = svd_unfold.get_global_correlation
add_tau = tau_values.append
add_rho = rho_values.append
# now lets loop over all tau-values in range
for current_tau in get_tau_range(tau_min, tau_max, number_of_iterations):
SetTau( current_tau )
cov_matrix = GetCovMatrix(cov, n_toy, 1)
current_rho = GetRho(cov_matrix, h_data)
add_tau( current_tau )
add_rho( current_rho )
if current_rho < minimal_rho:
minimal_rho = current_rho
optimal_tau = current_tau
print 'Best tau for',regularisation_settings.channel,':',optimal_tau
return optimal_tau, minimal_rho, tau_values, rho_values
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 = bin_edges[variable]
if visiblePS:
edges = bin_edges_vis[variable]
h_data = value_error_tuplelist_to_hist(results, edges)
# Remove fakes before unfolding
h_measured, h_data = removeFakes(h_measured, h_data, h_response)
unfolding = Unfolding(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(h_data)
del unfolding
return hist_to_value_error_tuplelist(
h_unfolded_data), hist_to_value_error_tuplelist(h_data)
def unfold_results(results, h_truth, h_measured, h_response, method):
global bin_edges
h_data = value_error_tuplelist_to_hist(results, bin_edges)
unfolding = Unfolding(h_truth, h_measured, h_response, method=method)
h_unfolded_data = unfolding.unfold(h_data)
return hist_to_value_error_tuplelist(h_unfolded_data)
def unfold_results(results, category, channel, h_truth, h_measured, h_response,
method):
global variable, path_to_JSON
h_data = value_error_tuplelist_to_hist(results, bin_edges[variable])
unfolding = Unfolding(h_truth, h_measured, h_response, method=method)
#turning off the unfolding errors for systematic samples
if category != 'central':
unfoldCfg.Hreco = 0
h_unfolded_data = unfolding.unfold(h_data)
#export the D and SV distributions
SVD_path = path_to_JSON + '/' + variable + '/unfolding_objects/' + channel + '/kv_' + str(
unfoldCfg.SVD_k_value) + '/'
make_folder_if_not_exists(SVD_path)
if method == 'TSVDUnfold':
SVDdist = TFile(
SVD_path + method + '_SVDdistributions_' + category + '.root',
'recreate')
directory = SVDdist.mkdir('SVDdist')
directory.cd()
unfolding.unfoldObject.GetD().Write()
unfolding.unfoldObject.GetSV().Write()
# unfolding.unfoldObject.GetUnfoldCovMatrix(data_covariance_matrix(h_data), unfoldCfg.SVD_n_toy).Write()
SVDdist.Close()
else:
SVDdist = TFile(
SVD_path + method + '_SVDdistributions_Hreco' +
str(unfoldCfg.Hreco) + '_' + category + '.root', 'recreate')
directory = SVDdist.mkdir('SVDdist')
directory.cd()
unfolding.unfoldObject.Impl().GetD().Write()
unfolding.unfoldObject.Impl().GetSV().Write()
h_truth.Write()
h_measured.Write()
h_response.Write()
# unfolding.unfoldObject.Impl().GetUnfoldCovMatrix(data_covariance_matrix(h_data), unfoldCfg.SVD_n_toy).Write()
SVDdist.Close()
#export the whole unfolding object if it doesn't exist
if method == 'TSVDUnfold':
unfolding_object_file_name = SVD_path + method + '_unfoldingObject_' + category + '.root'
else:
unfolding_object_file_name = SVD_path + method + '_unfoldingObject_Hreco' + str(
unfoldCfg.Hreco) + '_' + category + '.root'
if not os.path.isfile(unfolding_object_file_name):
unfoldingObjectFile = TFile(unfolding_object_file_name, 'recreate')
directory = unfoldingObjectFile.mkdir('unfoldingObject')
directory.cd()
if method == 'TSVDUnfold':
unfolding.unfoldObject.Write()
else:
unfolding.unfoldObject.Impl().Write()
unfoldingObjectFile.Close()
del unfolding
return hist_to_value_error_tuplelist(h_unfolded_data)
def unfold_results( results, category, channel, k_value, h_truth, h_measured, h_response, h_fakes, method ):
global variable, path_to_JSON, options
h_data = value_error_tuplelist_to_hist( results, bin_edges[variable] )
unfolding = Unfolding( h_truth, h_measured, h_response, h_fakes, method = method, k_value = k_value )
# turning off the unfolding errors for systematic samples
if not category == 'central':
unfoldCfg.Hreco = 0
else:
unfoldCfg.Hreco = options.Hreco
h_unfolded_data = unfolding.unfold( h_data )
if options.write_unfolding_objects:
# export the D and SV distributions
SVD_path = path_to_JSON + '/unfolding_objects/' + channel + '/kv_' + str( k_value ) + '/'
make_folder_if_not_exists( SVD_path )
if method == 'TSVDUnfold':
SVDdist = File( SVD_path + method + '_SVDdistributions_' + category + '.root', 'recreate' )
directory = SVDdist.mkdir( 'SVDdist' )
directory.cd()
unfolding.unfoldObject.GetD().Write()
unfolding.unfoldObject.GetSV().Write()
# unfolding.unfoldObject.GetUnfoldCovMatrix(data_covariance_matrix(h_data), unfoldCfg.SVD_n_toy).Write()
SVDdist.Close()
else:
SVDdist = File( SVD_path + method + '_SVDdistributions_Hreco' + str( unfoldCfg.Hreco ) + '_' + category + '.root', 'recreate' )
directory = SVDdist.mkdir( 'SVDdist' )
directory.cd()
unfolding.unfoldObject.Impl().GetD().Write()
unfolding.unfoldObject.Impl().GetSV().Write()
h_truth.Write()
h_measured.Write()
h_response.Write()
# unfolding.unfoldObject.Impl().GetUnfoldCovMatrix(data_covariance_matrix(h_data), unfoldCfg.SVD_n_toy).Write()
SVDdist.Close()
# export the whole unfolding object if it doesn't exist
if method == 'TSVDUnfold':
unfolding_object_file_name = SVD_path + method + '_unfoldingObject_' + category + '.root'
else:
unfolding_object_file_name = SVD_path + method + '_unfoldingObject_Hreco' + str( unfoldCfg.Hreco ) + '_' + category + '.root'
if not os.path.isfile( unfolding_object_file_name ):
unfoldingObjectFile = File( unfolding_object_file_name, 'recreate' )
directory = unfoldingObjectFile.mkdir( 'unfoldingObject' )
directory.cd()
if method == 'TSVDUnfold':
unfolding.unfoldObject.Write()
else:
unfolding.unfoldObject.Impl().Write()
unfoldingObjectFile.Close()
del unfolding
return hist_to_value_error_tuplelist( h_unfolded_data )
def get_tau_from_global_correlation( h_truth, h_measured, h_response, h_data = None ):
tau_0 = 1e-7
tau_max = 0.2
number_of_iterations = 10000
# tau_step = ( tau_max - tau_0 ) / number_of_iterations
optimal_tau = 0
minimal_rho = 9999
bias_scale = 0.
unfolding = Unfolding( h_truth,
h_measured,
h_response,
method = 'RooUnfoldTUnfold',
tau = tau_0 )
if h_data:
unfolding.unfold( h_data )
else: # closure test
unfolding.unfold( h_measured )
# cache functions and save time in the loop
Unfold = unfolding.unfoldObject.Impl().DoUnfold
GetRho = unfolding.unfoldObject.Impl().GetRhoI
# create lists
tau_values = []
rho_values = []
add_tau = tau_values.append
add_rho = rho_values.append
# for current_tau in drange(tau_0, tau_max, tau_step):
for current_tau in get_tau_range( tau_0, tau_max, number_of_iterations ):
Unfold( current_tau, h_data, bias_scale )
current_rho = GetRho( TH1F() )
add_tau( current_tau )
add_rho( current_rho )
if current_rho < minimal_rho:
minimal_rho = current_rho
optimal_tau = current_tau
return optimal_tau, minimal_rho, tau_values, rho_values
def run_test( h_truth, h_measured, h_response, h_data, h_fakes = None, variable = 'MET' ):
global method, load_fakes
k_values = get_test_k_values( h_truth, h_measured, h_response, h_data )
k_value_results = {}
for k_value in k_values:
unfolding = Unfolding( h_truth,
h_measured,
h_response,
fakes = h_fakes,
method = method,
k_value = k_value )
unfolded_data = unfolding.unfold( h_data )
k_value_results[k_value] = deepcopy( unfolded_data )
return { 'k_value_results' : k_value_results }
def main():
config = XSectionConfig(13)
# method = 'RooUnfoldSvd'
method = 'RooUnfoldBayes'
file_for_data = File(config.unfolding_powheg_herwig, 'read')
file_for_unfolding = File(config.unfolding_madgraphMLM, 'read')
for channel in ['electron', 'muon', 'combined']:
for variable in bin_edges.keys():
tau_value = get_tau_value(config, channel, variable)
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple(
inputfile=file_for_unfolding,
variable=variable,
channel=channel,
met_type=config.met_type,
centre_of_mass=config.centre_of_mass_energy,
ttbar_xsection=config.ttbar_xsection,
luminosity=config.luminosity,
load_fakes=False,
visiblePS=False,
)
h_data_model, h_data, _, _ = get_unfold_histogram_tuple(
inputfile=file_for_data,
variable=variable,
channel=channel,
met_type=config.met_type,
centre_of_mass=config.centre_of_mass_energy,
ttbar_xsection=config.ttbar_xsection,
luminosity=config.luminosity,
load_fakes=False,
visiblePS=False,
)
unfolding = Unfolding(h_truth,
h_measured,
h_response,
h_fakes,
method=method,
k_value=-1,
tau=tau_value)
unfolded_data = unfolding.unfold(h_data)
plot_bias(h_truth, h_data_model, unfolded_data, variable, channel,
config.centre_of_mass_energy, method)
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 = bin_edges[variable]
if visiblePS:
edges = bin_edges_vis[variable]
h_data = value_error_tuplelist_to_hist( results, edges )
# Remove fakes before unfolding
h_measured, h_data = removeFakes( h_measured, h_data, h_response )
unfolding = Unfolding( 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( h_data )
del unfolding
return hist_to_value_error_tuplelist( h_unfolded_data ), hist_to_value_error_tuplelist( h_data )
def main():
config = XSectionConfig(13)
# method = 'RooUnfoldSvd'
method = 'RooUnfoldBayes'
file_for_data = File(config.unfolding_powheg_herwig, 'read')
file_for_unfolding = File(config.unfolding_madgraphMLM, 'read')
for channel in ['electron', 'muon', 'combined']:
for variable in bin_edges.keys():
tau_value = get_tau_value(config, channel, variable)
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple(
inputfile=file_for_unfolding,
variable=variable,
channel=channel,
met_type=config.met_type,
centre_of_mass=config.centre_of_mass_energy,
ttbar_xsection=config.ttbar_xsection,
luminosity=config.luminosity,
load_fakes=False,
visiblePS=False,
)
h_data_model, h_data, _, _ = get_unfold_histogram_tuple(
inputfile=file_for_data,
variable=variable,
channel=channel,
met_type=config.met_type,
centre_of_mass=config.centre_of_mass_energy,
ttbar_xsection=config.ttbar_xsection,
luminosity=config.luminosity,
load_fakes=False,
visiblePS=False,
)
unfolding = Unfolding(
h_truth, h_measured, h_response, h_fakes,
method=method, k_value=-1, tau=tau_value)
unfolded_data = unfolding.unfold(h_data)
plot_bias(h_truth, h_data_model, unfolded_data, variable, channel,
config.centre_of_mass_energy, method)
def main():
config = XSectionConfig(13)
method = 'TUnfold'
# A few different files for testing different inputs
file_for_unfolding = File(config.unfolding_central, 'read')
madgraph_file = File(config.unfolding_madgraphMLM, 'read')
for channel in ['combined']:
# for variable in config.variables:
for variable in ['HT']:
print variable
# tau_value = get_tau_value(config, channel, variable)
# tau_value = 0.000228338590921
tau_value = 0.0
# h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple(
# inputfile=file_for_unfolding,
# variable=variable,
# channel=channel,
# met_type=config.met_type,
# centre_of_mass=config.centre_of_mass_energy,
# ttbar_xsection=config.ttbar_xsection,
# luminosity=config.luminosity,
# load_fakes=False,
# visiblePS=True,
# )
# measured = asrootpy(h_response.ProjectionX('px',1))
# print 'Measured from response :',list(measured.y())
# truth = asrootpy(h_response.ProjectionY())
# print 'Truth from response :',list(truth.y())
h_truth_mad, h_measured_mad, h_response_mad, h_fakes_mad = get_unfold_histogram_tuple(
inputfile=madgraph_file,
variable=variable,
channel=channel,
met_type=config.met_type,
centre_of_mass=config.centre_of_mass_energy,
ttbar_xsection=config.ttbar_xsection,
luminosity=config.luminosity,
load_fakes=False,
visiblePS=True,
)
measured = asrootpy(h_response_mad.ProjectionX('px',1))
print 'Measured from response :',list(measured.y())
truth = asrootpy(h_response_mad.ProjectionY())
print 'Truth from response :',list(truth.y())
# Unfold
unfolding = Unfolding( measured,
truth, measured, h_response_mad, None,
method=method, k_value=-1, tau=tau_value)
# unfolded_data = unfolding.closureTest()
# print 'Measured :',list( h_measured.y() )
# h_measured, _ = removeFakes( h_measured, None, h_response)
# for binx in range(0,h_truth.GetNbinsX()+2):
# for biny in range(0,h_truth.GetNbinsX()+2):
# print binx, biny,h_response.GetBinContent(binx,biny)
# print bin,h_truth.GetBinContent(bin)
print 'Tau :',tau_value
unfolded_results = unfolding.unfold()
print 'Unfolded :',list( unfolded_results.y() )
print unfolding.unfoldObject.GetTau()
def main():
config = XSectionConfig(13)
method = 'TUnfold'
file_for_response = File(config.unfolding_central, 'read')
file_for_powhegPythia = File(config.unfolding_central, 'read')
file_for_madgraph = File(config.unfolding_madgraphMLM, 'read')
file_for_amcatnlo = File(config.unfolding_amcatnlo, 'read')
file_for_ptReweight_up = File(config.unfolding_ptreweight_up, 'read')
file_for_ptReweight_down = File(config.unfolding_ptreweight_down, 'read')
samples_and_files_to_compare = {
'Central' : file_for_powhegPythia,
'Reweighted Up' : file_for_ptReweight_up,
'Reweighted Down' : file_for_ptReweight_down,
# 'Madgraph' : file_for_madgraph,
# 'amc@NLO' : file_for_amcatnlo
}
for channel in ['combined']:
for variable in config.variables:
# for variable in ['ST']:
print 'Variable :',variable
# Always unfold with the same response matrix and tau value
tau_value = get_tau_value(config, channel, variable)
_, _, h_response, _ = get_unfold_histogram_tuple(
inputfile=file_for_response,
variable=variable,
channel=channel,
met_type=config.met_type,
centre_of_mass=config.centre_of_mass_energy,
ttbar_xsection=config.ttbar_xsection,
luminosity=config.luminosity,
load_fakes=False,
visiblePS=True,
)
integralOfResponse = asrootpy(h_response.ProjectionY()).integral(0,-1)
# Dictionary to hold results
unfolded_and_truth_for_sample = {}
for sample, input_file_for_unfolding in samples_and_files_to_compare.iteritems():
_, _, h_response_to_unfold, _ = get_unfold_histogram_tuple(
inputfile=input_file_for_unfolding,
variable=variable,
channel=channel,
met_type=config.met_type,
centre_of_mass=config.centre_of_mass_energy,
ttbar_xsection=config.ttbar_xsection,
luminosity=config.luminosity,
load_fakes=False,
visiblePS=True,
)
measured = asrootpy(h_response_to_unfold.ProjectionX('px',1))
truth = asrootpy(h_response_to_unfold.ProjectionY())
scale = integralOfResponse / truth.integral(0,-1)
measured.Scale( scale )
truth.Scale( scale )
# Unfold, and set 'data' to 'measured'
unfolding = Unfolding( measured,
truth, measured, h_response, None,
method=method, k_value=-1, tau=tau_value)
unfolded_data = unfolding.unfold()
unfolded_xsection = calculate_xsection( unfolded_data, variable )
truth_xsection = calculate_xsection( truth, variable )
bias = calculate_bias( truth, unfolded_data )
unfolded_and_truth_for_sample[sample] = {
'truth' : truth_xsection,
'unfolded' : unfolded_xsection,
'bias' : bias
}
plot_closure(unfolded_and_truth_for_sample, variable, channel,
config.centre_of_mass_energy, method)
plot_bias(unfolded_and_truth_for_sample, variable, channel,
config.centre_of_mass_energy, method)
def get_tau_from_global_correlation( h_truth, h_measured, h_response, h_data = None ):
global used_k
# this gives 9.97e-05 with TUnfold
tau_0 = 1
tau_max = 1000
number_of_iterations = int(100)
n_toy = int(1000)
# tau_step = ( tau_max - tau_0 ) / number_of_iterations
optimal_tau = 0
minimal_rho = 9999
# bias_scale = 0.
unfolding = Unfolding( h_truth,
h_measured,
h_response,
method = 'RooUnfoldSvd',
tau = tau_0,
k_value = -1, )
data = None
if h_data:
data = h_data
else: # closure test
data = h_measured
unfolding.unfold( data )
# get unfolding object
tau_svd_unfold = unfolding.Impl()
# get covariance matrix
cov = tau_svd_unfold.get_data_covariance_matrix(data)
# cache functions and save time in the loop
SetTau = tau_svd_unfold.SetTau
GetCovMatrix = tau_svd_unfold.GetUnfoldCovMatrix
GetRho = tau_svd_unfold.get_global_correlation
n_bins = h_data.nbins()
print 'k to tau'
to_return = None
for k in range(2, n_bins + 1):
tau_from_k = tau_svd_unfold.kToTau(k)
SetTau( tau_from_k )
cov_matrix = GetCovMatrix(cov, n_toy, 1)
rho = GetRho(cov_matrix, data)
if k == used_k:
to_return = (tau_from_k, rho)
print 'k =', k, ', tau = ', tau_from_k, ', rho = ', rho, '<-- currently used'
else:
print 'k =', k, ', tau = ', tau_from_k, ', rho = ', rho
#print 'used k (=%d) to tau' % used_k
tau_from_k = tau_svd_unfold.kToTau(used_k)
#SetTau( tau_from_k )
#cov_matrix = GetCovMatrix(cov, 10, 1)
#rho_from_tau_from_k = GetRho(cov_matrix, data)
#print "tau from k", tau_from_k
#print 'rho for tau from used k', rho_from_tau_from_k
# create lists
tau_values = []
rho_values = []
add_tau = tau_values.append
add_rho = rho_values.append
# for current_tau in drange(tau_0, tau_max, tau_step):
for current_tau in get_tau_range( tau_0, tau_max, number_of_iterations ):
SetTau( current_tau )
cov_matrix = GetCovMatrix(cov, n_toy, 1)
current_rho = GetRho(cov_matrix, data)
add_tau( current_tau )
add_rho( current_rho )
if current_rho < minimal_rho:
minimal_rho = current_rho
optimal_tau = current_tau
del unfolding
print 'optimal tau = %.2f' % optimal_tau
return optimal_tau, minimal_rho, tau_values, rho_values, to_return
