def main():
config = XSectionConfig(13)
# method = 'RooUnfoldSvd'
method = 'RooUnfoldBayes'
file_for_unfolding = File(config.unfolding_central, '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,
)
unfolding = Unfolding(
h_truth, h_measured, h_response, h_fakes,
method=method, k_value=-1, tau=tau_value)
unfolded_data = unfolding.closureTest()
plot_closure(h_truth, unfolded_data, variable, channel,
config.centre_of_mass_energy, method)
def main():
config = XSectionConfig(13)
# method = 'RooUnfoldSvd'
method = 'RooUnfoldBayes'
file_for_unfolding = File(config.unfolding_central, '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,
)
unfolding = Unfolding(h_truth,
h_measured,
h_response,
h_fakes,
method=method,
k_value=-1,
tau=tau_value)
unfolded_data = unfolding.closureTest()
plot_closure(h_truth, unfolded_data, variable, channel,
config.centre_of_mass_energy, method)
def setUp(self):
# load histograms
self.input_file = File("data/unfolding_merged_asymmetric.root")
self.k_value = 3
self.unfold_method = "RooUnfoldSvd"
self.met_type = "patType1CorrectedPFMet"
self.variables = ["MET", "WPT", "MT", "ST", "HT"]
self.channels = ["electron", "muon", "combined"]
self.dict = {}
for channel in self.channels:
self.dict[channel] = {}
for variable in self.variables:
self.dict[variable] = {}
h_truth, h_measured, h_response, _ = get_unfold_histogram_tuple(
inputfile=self.input_file, variable=variable, channel=channel, met_type=self.met_type
)
unfolding_object = Unfolding(
h_truth, h_measured, h_response, k_value=self.k_value, method=self.unfold_method
)
self.dict[channel][variable] = {
"h_truth": h_truth,
"h_measured": h_measured,
"h_response": h_response,
"unfolding_object": unfolding_object,
}
def setUp( self ):
# load histograms
self.input_file = File('test/data/unfolding_merged_asymmetric.root')
self.k_value = 3
self.unfold_method = 'RooUnfoldSvd'
self.met_type = 'patType1CorrectedPFMet'
self.variables = ['MET', 'WPT', 'MT' , 'ST', 'HT']
self.channels = ['electron', 'muon', 'combined']
self.dict = {}
for channel in self.channels:
self.dict[channel] = {}
for variable in self.variables:
self.dict[variable] = {}
h_truth, h_measured, h_response, _ = get_unfold_histogram_tuple(
inputfile = self.input_file,
variable = variable,
channel = channel,
met_type = self.met_type)
unfolding_object = Unfolding( h_truth,
h_measured,
h_response,
k_value = self.k_value,
method = self.unfold_method
)
self.dict[channel][variable] = {'h_truth' : h_truth,
'h_measured' : h_measured,
'h_response' : h_response,
'unfolding_object' : unfolding_object
}
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 config.variables:
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 __set_unfolding_histograms__( self ):
# at the moment only one file is supported for the unfolding input
files = set( [self.truth['file'],
self.gen_vs_reco['file'],
self.measured['file']]
)
if len( files ) > 1:
print "Currently not supported to have different files for truth, gen_vs_reco and measured"
sys.exit()
input_file = files.pop()
visiblePS = False
if self.phaseSpace == 'VisiblePS':
visiblePS = True
t, m, r, f = get_unfold_histogram_tuple( File(input_file),
self.variable,
self.channel,
centre_of_mass = self.centre_of_mass_energy,
ttbar_xsection=self.measurement_config.ttbar_xsection,
luminosity=self.measurement_config.luminosity,
load_fakes = True,
visiblePS = visiblePS
)
self.h_truth = asrootpy ( t )
self.h_response = asrootpy ( r )
self.h_measured = asrootpy ( m )
self.h_fakes = asrootpy ( f )
data_file = self.data['file']
if data_file.endswith('.root'):
self.h_data = get_histogram_from_file(self.data['histogram'], self.data['file'])
elif data_file.endswith('.json') or data_file.endswith('.txt'):
data_key = self.data['histogram']
# assume configured bin edges
edges = []
edges = reco_bin_edges_vis[self.variable]
json_input = read_data_from_JSON(data_file)
if data_key == "": # JSON file == histogram
self.h_data = value_error_tuplelist_to_hist(json_input, edges)
else:
self.h_data = value_error_tuplelist_to_hist(json_input[data_key], edges)
else:
print 'Unkown file extension', data_file.split('.')[-1]
def create_toy_mc(input_file, sample, output_folder, n_toy, centre_of_mass, ttbar_xsection):
from tools.file_utilities import make_folder_if_not_exists
from tools.toy_mc import generate_toy_MC_from_distribution, generate_toy_MC_from_2Ddistribution
from tools.Unfolding import get_unfold_histogram_tuple
make_folder_if_not_exists(output_folder)
input_file_hists = File(input_file)
output_file_name = get_output_file_name(output_folder, sample, n_toy, centre_of_mass)
variable_bins = bin_edges_vis.copy()
with root_open(output_file_name, 'recreate') as f_out:
for channel in ['combined']:
for variable in variable_bins:
output_dir = f_out.mkdir(channel + '/' + variable, recurse=True)
cd = output_dir.cd
mkdir = output_dir.mkdir
h_truth, h_measured, h_response, _ = get_unfold_histogram_tuple(input_file_hists,
variable,
channel,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
visiblePS = True,
load_fakes=False)
cd()
mkdir('Original')
cd ('Original')
h_truth.Write('truth')
h_measured.Write('measured')
h_response.Write('response')
for i in range(1, n_toy+1):
toy_id = 'toy_{0}'.format(i)
mkdir(toy_id)
cd(toy_id)
# create histograms
# add tuples (truth, measured, response) of histograms
truth = generate_toy_MC_from_distribution(h_truth)
measured = generate_toy_MC_from_distribution(h_measured)
response = generate_toy_MC_from_2Ddistribution(h_response)
truth.SetName('truth')
measured.SetName('measured')
response.SetName('response')
truth.Write()
measured.Write()
response.Write()
def __set_unfolding_histograms__(self):
# at the moment only one file is supported for the unfolding input
files = set([
self.truth['file'], self.gen_vs_reco['file'], self.measured['file']
])
if len(files) > 1:
print "Currently not supported to have different files for truth, gen_vs_reco and measured"
sys.exit()
input_file = files.pop()
t, m, r, _ = get_unfold_histogram_tuple(
File(input_file),
self.variable,
self.channel,
centre_of_mass=self.centre_of_mass_energy,
ttbar_xsection=self.measurement_config.ttbar_xsection,
luminosity=self.measurement_config.luminosity,
)
self.h_truth = t
self.h_response = r
self.h_measured = m
data_file = self.data['file']
if data_file.endswith('.root'):
self.h_data = get_histogram_from_file(self.data['histogram'],
self.data['file'])
elif data_file.endswith('.json') or data_file.endswith('.txt'):
data_key = self.data['histogram']
# assume configured bin edges
edges = []
if self.phaseSpace == 'FullPS':
edges = bin_edges[self.variable]
elif self.phaseSpace == 'VisiblePS':
edges = bin_edges_vis[self.variable]
json_input = read_data_from_JSON(data_file)
if data_key == "": # JSON file == histogram
self.h_data = value_error_tuplelist_to_hist(json_input, edges)
else:
self.h_data = value_error_tuplelist_to_hist(
json_input[data_key], edges)
else:
print 'Unkown file extension', data_file.split('.')[-1]
def setUp( self ):
# load histograms
self.input_file = File('tests/data/unfolding_merged_asymmetric.root')
self.k_value = 3
self.unfold_method = 'RooUnfoldSvd'
self.met_type = 'patType1CorrectedPFMet'
self.variables = ['MET', 'WPT', 'MT' , 'ST', 'HT']
self.channels = ['electron', 'muon', 'combined']
self.dict = {}
for channel in self.channels:
self.dict[channel] = {}
for variable in self.variables:
self.dict[variable] = {}
h_truth, h_measured, h_response, _ = get_unfold_histogram_tuple(
inputfile = self.input_file,
variable = variable,
channel = channel,
met_type = self.met_type)
unfolding_object = Unfolding( h_truth,
h_measured,
h_response,
k_value = self.k_value,
method = self.unfold_method
)
tau_unfolding_object = Unfolding( h_truth,
h_measured,
h_response,
tau=100,
k_value= -1,
method='RooUnfoldSvd')
self.dict[channel][variable] = {'h_truth' : h_truth,
'h_measured' : h_measured,
'h_response' : h_response,
'unfolding_object' : unfolding_object,
'tau_unfolding_object': tau_unfolding_object,
}
for channel in ['electron', 'muon']:
taus_from_global_correlaton[channel] = {}
taus_from_L_shape[channel] = {}
k_values = {}
k_values_crosscheck = {}
for variable in variables:
if variable is 'MT': continue
print 'Doing variable', variable, 'in', channel, 'channel'
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple(
inputfile = input_file,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes)
# print 'h_fakes = ', h_fakes
h_data = None
if test == 'data':
h_data = get_fit_results_histogram( data_path = path_to_JSON,
centre_of_mass = centre_of_mass,
channel = channel,
variable = variable,
met_type = met_type,
bin_edges = bin_edges_full[variable] )
elif test == 'bias':
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_unfolded_normalisation( TTJet_fit_results, category, channel, k_value ):
global variable, met_type, path_to_JSON, file_for_unfolding, file_for_powheg_pythia, file_for_powheg_herwig, file_for_mcatnlo, file_for_ptreweight, files_for_pdfs
global centre_of_mass, luminosity, ttbar_xsection, load_fakes, method
global file_for_matchingdown, file_for_matchingup, file_for_scaledown, file_for_scaleup
global ttbar_generator_systematics, ttbar_theory_systematics, pdf_uncertainties
global use_ptreweight
files_for_systematics = {
ttbar_theory_systematic_prefix + 'matchingdown':file_for_matchingdown,
ttbar_theory_systematic_prefix + 'matchingup':file_for_matchingup,
ttbar_theory_systematic_prefix + 'scaledown':file_for_scaledown,
ttbar_theory_systematic_prefix + 'scaleup':file_for_scaleup,
ttbar_theory_systematic_prefix + 'powheg_pythia':file_for_powheg_pythia,
ttbar_theory_systematic_prefix + 'powheg_herwig':file_for_powheg_herwig,
ttbar_theory_systematic_prefix + 'ptreweight':file_for_ptreweight,
}
h_truth, h_measured, h_response, h_fakes = None, None, None, None
if category in ttbar_generator_systematics or category in ttbar_theory_systematics:
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple( inputfile = files_for_systematics[category],
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes
)
elif category in pdf_uncertainties:
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple( inputfile = files_for_pdfs[category],
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes
)
elif use_ptreweight:
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple( inputfile = file_for_ptreweight,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes
)
else:
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple( inputfile = file_for_unfolding,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes
)
h_truth_POWHEG_PYTHIA, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_powheg_pythia,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes
)
h_truth_POWHEG_HERWIG, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_powheg_herwig,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes
)
h_truth_MCATNLO, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_mcatnlo,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes
)
h_truth_matchingdown, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_matchingdown,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes
)
h_truth_matchingup, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_matchingup,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes
)
h_truth_scaledown, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_scaledown,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes
)
h_truth_scaleup, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_scaleup,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes
)
h_truth_ptreweight, _, _, _ = get_unfold_histogram_tuple(
inputfile = file_for_ptreweight,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes
)
MADGRAPH_results = hist_to_value_error_tuplelist( h_truth )
MADGRAPH_ptreweight_results = hist_to_value_error_tuplelist( h_truth_ptreweight )
POWHEG_PYTHIA_results = hist_to_value_error_tuplelist( h_truth_POWHEG_PYTHIA )
POWHEG_HERWIG_results = hist_to_value_error_tuplelist( h_truth_POWHEG_HERWIG )
MCATNLO_results = hist_to_value_error_tuplelist( h_truth_MCATNLO )
matchingdown_results = hist_to_value_error_tuplelist( h_truth_matchingdown )
matchingup_results = hist_to_value_error_tuplelist( h_truth_matchingup )
scaledown_results = hist_to_value_error_tuplelist( h_truth_scaledown )
scaleup_results = hist_to_value_error_tuplelist( h_truth_scaleup )
TTJet_fit_results_unfolded = unfold_results( TTJet_fit_results,
category,
channel,
k_value,
h_truth,
h_measured,
h_response,
h_fakes,
method
)
normalisation_unfolded = {
'TTJet_measured' : TTJet_fit_results,
'TTJet_unfolded' : TTJet_fit_results_unfolded,
'MADGRAPH': MADGRAPH_results,
'MADGRAPH_ptreweight': MADGRAPH_ptreweight_results,
# other generators
'POWHEG_PYTHIA': POWHEG_PYTHIA_results,
'POWHEG_HERWIG': POWHEG_HERWIG_results,
'MCATNLO': MCATNLO_results,
# systematics
'matchingdown': matchingdown_results,
'matchingup': matchingup_results,
'scaledown': scaledown_results,
'scaleup': scaleup_results
}
return normalisation_unfolded
for channel in ['electron', 'muon']:
taus_from_global_correlaton[channel] = {}
taus_from_L_shape[channel] = {}
k_values = {}
k_values_crosscheck = {}
for variable in variables:
if variable is 'MT': continue
print 'Doing variable', variable, 'in', channel, 'channel'
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple(
inputfile=input_file,
variable=variable,
channel=channel,
met_type=met_type,
centre_of_mass=centre_of_mass,
ttbar_xsection=ttbar_xsection,
luminosity=luminosity,
load_fakes=load_fakes)
# print 'h_fakes = ', h_fakes
h_data = None
if test == 'data':
h_data = get_fit_results_histogram(
data_path=path_to_JSON,
centre_of_mass=centre_of_mass,
channel=channel,
variable=variable,
met_type=met_type,
bin_edges=bin_edges[variable])
# taus_from_L_shape = {}
for variable in variables:
# taus_from_global_correlaton[channel] = {}
# taus_from_L_shape[channel] = {}
for channel in ['electron', 'muon']:
if channel == 'electron':
used_k = measurement_config.k_values_electron[variable]
if channel == 'muon':
used_k = measurement_config.k_values_muon[variable]
print 'Doing variable"', variable, '" in', channel, 'channel'
h_truth, h_measured, h_response, _ = get_unfold_histogram_tuple(
inputfile = input_file,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity )
h_data = None
if use_data:
h_data = get_data_histogram( channel, variable, met_type )
else:
h_data = deepcopy( h_measured )
tau, rho, tau_values, rho_values, tau_from_k = get_tau_from_global_correlation( h_truth, h_measured, h_response, h_data )
draw_global_correlation( tau_values, rho_values, tau, rho, channel, variable, tau_from_k )
#tau, l_curve, x, y = get_tau_from_L_shape( h_truth, h_measured, h_response, h_data )
def main():
set_root_defaults()
# prevent directory ownership of ROOT histograms (python does the garbage collection)
TH1F.AddDirectory( False )
parser = OptionParser()
parser.add_option( "-n", "--n_toy_mc",
dest = "n_toy_mc", default = 300,
help = "number of toy MC to create", type = int )
parser.add_option( "-o", "--output",
dest = "output_folder", default = 'data/toy_mc/',
help = "output folder for toy MC" )
parser.add_option( "-v", "--variable", dest = "variable", default = 'MET',
help = "set the variable to analyse (MET, HT, ST, MT, WPT)" )
parser.add_option( "-m", "--metType", dest = "metType", default = 'type1',
help = "set MET type for analysis of MET, ST or MT" )
parser.add_option( "-c", "--centre-of-mass-energy", dest = "CoM", default = 8,
help = "set the centre of mass energy for analysis. Default = 8 [TeV]", type = int )
parser.add_option( '-V', '--verbose', dest = "verbose", action = "store_true",
help = "Print the event number, reco and gen variable value" )
( options, _ ) = parser.parse_args()
measurement_config = XSectionConfig( options.CoM )
centre_of_mass = options.CoM
ttbar_xsection = measurement_config.ttbar_xsection
variable = options.variable
met_type = measurement_config.translate_options[options.metType]
n_toy_mc = options.n_toy_mc
make_folder_if_not_exists( options.output_folder )
# get histograms
input_file_hists = File( measurement_config.unfolding_madgraph )
# define output file
out_file_template = '%s/toy_mc_%s_N_%d_%dTeV.root'
out_file_name = out_file_template % (options.output_folder, variable, n_toy_mc, centre_of_mass)
output = File( out_file_name, 'recreate' )
for channel in ['electron', 'muon']:
# first get the weights
h_truth, h_measured, h_response, _ = get_unfold_histogram_tuple( input_file_hists,
variable,
channel,
met_type,
centre_of_mass,
ttbar_xsection,
load_fakes = False )
# create directories
directory = output.mkdir( channel )
mkdir = directory.mkdir
cd = directory.cd
cd()
# generate toy MC
for i in range( 1, n_toy_mc + 1 ):
mkdir( 'toy_%d' % i )
cd( 'toy_%d' % i )
# create histograms
# add tuples (truth, measured, response) of histograms
truth = generate_toy_MC_from_distribution(h_truth)
measured = generate_toy_MC_from_distribution(h_measured)
response = generate_toy_MC_from_2Ddistribution(h_response)
truth.SetName('truth')
measured.SetName('measured')
response.SetName('response')
truth.Write()
measured.Write()
response.Write()
output.Write()
output.Close()
def get_unfolded_normalisation(TTJet_fit_results, category, channel, tau_value,
visiblePS):
global variable, met_type, path_to_JSON, file_for_unfolding, file_for_powheg_pythia, file_for_amcatnlo_herwig, file_for_ptreweight, files_for_pdfs
global centre_of_mass, luminosity, ttbar_xsection, load_fakes, method
global file_for_powhegPythia8, file_for_madgraphMLM, file_for_amcatnlo
# global file_for_matchingdown, file_for_matchingup
global file_for_scaledown, file_for_scaleup
global file_for_massdown, file_for_massup
global ttbar_generator_systematics, ttbar_theory_systematics, pdf_uncertainties
global use_ptreweight
files_for_systematics = {
# ttbar_theory_systematic_prefix + 'matchingdown' : file_for_matchingdown,
# ttbar_theory_systematic_prefix + 'matchingup' : file_for_matchingup,
ttbar_theory_systematic_prefix + 'scaledown':
file_for_scaledown,
ttbar_theory_systematic_prefix + 'scaleup':
file_for_scaleup,
ttbar_theory_systematic_prefix + 'massdown':
file_for_massdown,
ttbar_theory_systematic_prefix + 'massup':
file_for_massup,
# 'JES_down' : file_for_jesdown,
# 'JES_up' : file_for_jesup,
# 'JER_down' : file_for_jerdown,
# 'JER_up' : file_for_jerup,
# 'BJet_up' : file_for_bjetdown,
# 'BJet_down' : file_for_bjetup,
ttbar_theory_systematic_prefix + 'hadronisation':
file_for_amcatnlo_herwig,
ttbar_theory_systematic_prefix + 'NLOgenerator':
file_for_amcatnlo,
# 'ElectronEnUp' : file_for_ElectronEnUp,
# 'ElectronEnDown' : file_for_ElectronEnDown,
# 'MuonEnUp' : file_for_MuonEnUp,
# 'MuonEnDown' : file_for_MuonEnDown,
# 'TauEnUp' : file_for_TauEnUp,
# 'TauEnDown' : file_for_TauEnDown,
# 'UnclusteredEnUp' : file_for_UnclusteredEnUp,
# 'UnclusteredEnDown' : file_for_UnclusteredEnDown,
# 'Muon_up' : file_for_LeptonUp,
# 'Muon_down' : file_for_LeptonDown,
# 'Electron_up' : file_for_LeptonUp,
# 'Electron_down' : file_for_LeptonDown,
# 'PileUpSystematic' : file_for_PUSystematic,
}
h_truth, h_measured, h_response, h_fakes = None, None, None, None
# Systematics where you change the response matrix
if category in ttbar_generator_systematics or category in files_for_systematics:
print 'Doing category', category, 'by changing response matrix'
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple(
inputfile=files_for_systematics[category],
variable=variable,
channel=channel,
met_type=met_type,
centre_of_mass=centre_of_mass,
ttbar_xsection=ttbar_xsection,
luminosity=luminosity,
load_fakes=load_fakes,
visiblePS=visiblePS,
)
elif category in pdf_uncertainties:
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple(
inputfile=files_for_pdfs[category],
variable=variable,
channel=channel,
met_type=met_type,
centre_of_mass=centre_of_mass,
ttbar_xsection=ttbar_xsection,
luminosity=luminosity,
load_fakes=load_fakes,
visiblePS=visiblePS,
)
# Systematics where you change input MC
else:
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple(
inputfile=file_for_unfolding,
variable=variable,
channel=channel,
met_type=met_type,
centre_of_mass=centre_of_mass,
ttbar_xsection=ttbar_xsection,
luminosity=luminosity,
load_fakes=load_fakes,
visiblePS=visiblePS,
)
# central_results = hist_to_value_error_tuplelist( h_truth )
TTJet_fit_results_unfolded, TTJet_fit_results_withoutFakes = unfold_results(
TTJet_fit_results,
category,
channel,
tau_value,
h_truth,
h_measured,
h_response,
h_fakes,
method,
visiblePS,
)
normalisation_unfolded = {
'TTJet_measured': TTJet_fit_results,
'TTJet_measured_withoutFakes': TTJet_fit_results_withoutFakes,
'TTJet_unfolded': TTJet_fit_results_unfolded
}
#
# THESE ARE FOR GETTING THE HISTOGRAMS FOR COMPARING WITH UNFOLDED DATA
#
if category == 'central':
# h_truth_matchingdown, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_matchingdown,
# variable = variable,
# channel = channel,
# met_type = met_type,
# centre_of_mass = centre_of_mass,
# ttbar_xsection = ttbar_xsection,
# luminosity = luminosity,
# load_fakes = load_fakes
# )
# h_truth_matchingup, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_matchingup,
# variable = variable,
# channel = channel,
# met_type = met_type,
# centre_of_mass = centre_of_mass,
# ttbar_xsection = ttbar_xsection,
# luminosity = luminosity,
# load_fakes = load_fakes
# )
h_truth_scaledown, _, _, _ = get_unfold_histogram_tuple(
inputfile=file_for_scaledown,
variable=variable,
channel=channel,
met_type=met_type,
centre_of_mass=centre_of_mass,
ttbar_xsection=ttbar_xsection,
luminosity=luminosity,
load_fakes=load_fakes,
visiblePS=visiblePS,
)
h_truth_scaleup, _, _, _ = get_unfold_histogram_tuple(
inputfile=file_for_scaleup,
variable=variable,
channel=channel,
met_type=met_type,
centre_of_mass=centre_of_mass,
ttbar_xsection=ttbar_xsection,
luminosity=luminosity,
load_fakes=load_fakes,
visiblePS=visiblePS,
)
h_truth_massdown, _, _, _ = get_unfold_histogram_tuple(
inputfile=file_for_massdown,
variable=variable,
channel=channel,
met_type=met_type,
centre_of_mass=centre_of_mass,
ttbar_xsection=ttbar_xsection,
luminosity=luminosity,
load_fakes=load_fakes,
visiblePS=visiblePS,
)
h_truth_massup, _, _, _ = get_unfold_histogram_tuple(
inputfile=file_for_massup,
variable=variable,
channel=channel,
met_type=met_type,
centre_of_mass=centre_of_mass,
ttbar_xsection=ttbar_xsection,
luminosity=luminosity,
load_fakes=load_fakes,
visiblePS=visiblePS,
)
h_truth_powhegPythia8, _, _, _ = get_unfold_histogram_tuple(
inputfile=file_for_powhegPythia8,
variable=variable,
channel=channel,
met_type=met_type,
centre_of_mass=centre_of_mass,
ttbar_xsection=ttbar_xsection,
luminosity=luminosity,
load_fakes=load_fakes,
visiblePS=visiblePS,
)
h_truth_amcatnlo, _, _, _ = get_unfold_histogram_tuple(
inputfile=file_for_amcatnlo,
variable=variable,
channel=channel,
met_type=met_type,
centre_of_mass=centre_of_mass,
ttbar_xsection=ttbar_xsection,
luminosity=luminosity,
load_fakes=load_fakes,
visiblePS=visiblePS,
)
h_truth_madgraphMLM, _, _, _ = get_unfold_histogram_tuple(
inputfile=file_for_madgraphMLM,
variable=variable,
channel=channel,
met_type=met_type,
centre_of_mass=centre_of_mass,
ttbar_xsection=ttbar_xsection,
luminosity=luminosity,
load_fakes=load_fakes,
visiblePS=visiblePS,
)
h_truth_amcatnlo_HERWIG, _, _, _ = get_unfold_histogram_tuple(
inputfile=file_for_amcatnlo_herwig,
variable=variable,
channel=channel,
met_type=met_type,
centre_of_mass=centre_of_mass,
ttbar_xsection=ttbar_xsection,
luminosity=luminosity,
load_fakes=load_fakes,
visiblePS=visiblePS,
)
# MADGRAPH_ptreweight_results = hist_to_value_error_tuplelist( h_truth_ptreweight )
# POWHEG_PYTHIA_results = hist_to_value_error_tuplelist( h_truth_POWHEG_PYTHIA )
# MCATNLO_results = None
powhegPythia8_results = hist_to_value_error_tuplelist(
h_truth_powhegPythia8)
madgraphMLM_results = hist_to_value_error_tuplelist(
h_truth_madgraphMLM)
AMCATNLO_results = hist_to_value_error_tuplelist(h_truth_amcatnlo)
amcatnlo_HERWIG_results = hist_to_value_error_tuplelist(
h_truth_amcatnlo_HERWIG)
# matchingdown_results = hist_to_value_error_tuplelist( h_truth_matchingdown )
# matchingup_results = hist_to_value_error_tuplelist( h_truth_matchingup )
scaledown_results = hist_to_value_error_tuplelist(h_truth_scaledown)
scaleup_results = hist_to_value_error_tuplelist(h_truth_scaleup)
massdown_results = hist_to_value_error_tuplelist(h_truth_massdown)
massup_results = hist_to_value_error_tuplelist(h_truth_massup)
normalisation_unfolded['powhegPythia8'] = powhegPythia8_results
normalisation_unfolded['amcatnlo'] = AMCATNLO_results
normalisation_unfolded['madgraphMLM'] = madgraphMLM_results
normalisation_unfolded['amcatnlo_HERWIG'] = amcatnlo_HERWIG_results
normalisation_unfolded['scaledown'] = scaledown_results
normalisation_unfolded['scaleup'] = scaleup_results
normalisation_unfolded['massdown'] = massdown_results
normalisation_unfolded['massup'] = massup_results
return normalisation_unfolded
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 get_unfolded_normalisation( TTJet_fit_results, category, channel, tau_value, visiblePS ):
global variable, met_type, path_to_JSON, file_for_unfolding, file_for_powheg_pythia, file_for_amcatnlo_herwig, file_for_ptreweight, files_for_pdfs
global centre_of_mass, luminosity, ttbar_xsection, load_fakes, method
global file_for_powhegPythia8, file_for_madgraphMLM, file_for_amcatnlo
# global file_for_matchingdown, file_for_matchingup
global file_for_scaledown, file_for_scaleup
global file_for_massdown, file_for_massup
global ttbar_generator_systematics, ttbar_theory_systematics, pdf_uncertainties
global use_ptreweight
files_for_systematics = {
# ttbar_theory_systematic_prefix + 'matchingdown' : file_for_matchingdown,
# ttbar_theory_systematic_prefix + 'matchingup' : file_for_matchingup,
ttbar_theory_systematic_prefix + 'scaledown' : file_for_scaledown,
ttbar_theory_systematic_prefix + 'scaleup' : file_for_scaleup,
ttbar_theory_systematic_prefix + 'massdown' : file_for_massdown,
ttbar_theory_systematic_prefix + 'massup' : file_for_massup,
# 'JES_down' : file_for_jesdown,
# 'JES_up' : file_for_jesup,
# 'JER_down' : file_for_jerdown,
# 'JER_up' : file_for_jerup,
# 'BJet_up' : file_for_bjetdown,
# 'BJet_down' : file_for_bjetup,
ttbar_theory_systematic_prefix + 'hadronisation' : file_for_amcatnlo_herwig,
ttbar_theory_systematic_prefix + 'NLOgenerator' : file_for_amcatnlo,
# 'ElectronEnUp' : file_for_ElectronEnUp,
# 'ElectronEnDown' : file_for_ElectronEnDown,
# 'MuonEnUp' : file_for_MuonEnUp,
# 'MuonEnDown' : file_for_MuonEnDown,
# 'TauEnUp' : file_for_TauEnUp,
# 'TauEnDown' : file_for_TauEnDown,
# 'UnclusteredEnUp' : file_for_UnclusteredEnUp,
# 'UnclusteredEnDown' : file_for_UnclusteredEnDown,
# 'Muon_up' : file_for_LeptonUp,
# 'Muon_down' : file_for_LeptonDown,
# 'Electron_up' : file_for_LeptonUp,
# 'Electron_down' : file_for_LeptonDown,
# 'PileUpSystematic' : file_for_PUSystematic,
}
h_truth, h_measured, h_response, h_fakes = None, None, None, None
# Systematics where you change the response matrix
if category in ttbar_generator_systematics or category in files_for_systematics :
print 'Doing category',category,'by changing response matrix'
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple( inputfile = files_for_systematics[category],
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes,
visiblePS = visiblePS,
)
elif category in pdf_uncertainties:
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple( inputfile = files_for_pdfs[category],
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes,
visiblePS = visiblePS,
)
# Systematics where you change input MC
else:
h_truth, h_measured, h_response, h_fakes = get_unfold_histogram_tuple( inputfile = file_for_unfolding,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes,
visiblePS = visiblePS,
)
# central_results = hist_to_value_error_tuplelist( h_truth )
TTJet_fit_results_unfolded, TTJet_fit_results_withoutFakes = unfold_results( TTJet_fit_results,
category,
channel,
tau_value,
h_truth,
h_measured,
h_response,
h_fakes,
method,
visiblePS,
)
normalisation_unfolded = {
'TTJet_measured' : TTJet_fit_results,
'TTJet_measured_withoutFakes' : TTJet_fit_results_withoutFakes,
'TTJet_unfolded' : TTJet_fit_results_unfolded
}
#
# THESE ARE FOR GETTING THE HISTOGRAMS FOR COMPARING WITH UNFOLDED DATA
#
if category == 'central':
# h_truth_matchingdown, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_matchingdown,
# variable = variable,
# channel = channel,
# met_type = met_type,
# centre_of_mass = centre_of_mass,
# ttbar_xsection = ttbar_xsection,
# luminosity = luminosity,
# load_fakes = load_fakes
# )
# h_truth_matchingup, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_matchingup,
# variable = variable,
# channel = channel,
# met_type = met_type,
# centre_of_mass = centre_of_mass,
# ttbar_xsection = ttbar_xsection,
# luminosity = luminosity,
# load_fakes = load_fakes
# )
h_truth_scaledown, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_scaledown,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes,
visiblePS = visiblePS,
)
h_truth_scaleup, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_scaleup,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes,
visiblePS = visiblePS,
)
h_truth_massdown, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_massdown,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes,
visiblePS = visiblePS,
)
h_truth_massup, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_massup,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes,
visiblePS = visiblePS,
)
h_truth_powhegPythia8, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_powhegPythia8,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes,
visiblePS = visiblePS,
)
h_truth_amcatnlo, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_amcatnlo,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes,
visiblePS = visiblePS,
)
h_truth_madgraphMLM, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_madgraphMLM,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes,
visiblePS = visiblePS,
)
h_truth_amcatnlo_HERWIG, _, _, _ = get_unfold_histogram_tuple( inputfile = file_for_amcatnlo_herwig,
variable = variable,
channel = channel,
met_type = met_type,
centre_of_mass = centre_of_mass,
ttbar_xsection = ttbar_xsection,
luminosity = luminosity,
load_fakes = load_fakes,
visiblePS = visiblePS,
)
# MADGRAPH_ptreweight_results = hist_to_value_error_tuplelist( h_truth_ptreweight )
# POWHEG_PYTHIA_results = hist_to_value_error_tuplelist( h_truth_POWHEG_PYTHIA )
# MCATNLO_results = None
powhegPythia8_results = hist_to_value_error_tuplelist( h_truth_powhegPythia8 )
madgraphMLM_results = hist_to_value_error_tuplelist( h_truth_madgraphMLM )
AMCATNLO_results = hist_to_value_error_tuplelist( h_truth_amcatnlo )
amcatnlo_HERWIG_results = hist_to_value_error_tuplelist( h_truth_amcatnlo_HERWIG )
# matchingdown_results = hist_to_value_error_tuplelist( h_truth_matchingdown )
# matchingup_results = hist_to_value_error_tuplelist( h_truth_matchingup )
scaledown_results = hist_to_value_error_tuplelist( h_truth_scaledown )
scaleup_results = hist_to_value_error_tuplelist( h_truth_scaleup )
massdown_results = hist_to_value_error_tuplelist( h_truth_massdown )
massup_results = hist_to_value_error_tuplelist( h_truth_massup )
normalisation_unfolded['powhegPythia8'] = powhegPythia8_results
normalisation_unfolded['amcatnlo'] = AMCATNLO_results
normalisation_unfolded['madgraphMLM'] = madgraphMLM_results
normalisation_unfolded['amcatnlo_HERWIG'] = amcatnlo_HERWIG_results
normalisation_unfolded['scaledown'] = scaledown_results
normalisation_unfolded['scaleup'] = scaleup_results
normalisation_unfolded['massdown'] = massdown_results
normalisation_unfolded['massup'] = massup_results
return normalisation_unfolded
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']
)
