def create_unfolding_pull_data(input_file_name,
method,
channel,
centre_of_mass,
variable,
n_toy_mc,
n_toy_data,
output_folder,
offset_toy_mc,
offset_toy_data,
k_value,
tau_value=-1,
run_matrix=None):
'''
Sets up all variables for check_multiple_data_multiple_unfolding
'''
timer = Timer()
input_file = File(input_file_name, 'read')
folder_template = '{path}/{centre_of_mass}TeV/{variable}/'
folder_template += '{n_toy_mc}_input_toy_mc/{n_toy_data}_input_toy_data/'
folder_template += '{vtype}_value_{value}/'
msg_template = 'Producing unfolding pull data for {variable},'
msg_template += ' {vtype}-value {value}'
inputs = {
'path': output_folder,
'centre_of_mass': centre_of_mass,
'variable': variable,
'n_toy_mc': n_toy_mc,
'n_toy_data': n_toy_data,
'vtype': 'k',
'value': k_value,
}
if tau_value >= 0:
inputs['vtype'] = 'tau'
inputs['value'] = round(tau_value, 1)
output_folder = folder_template.format(**inputs)
make_folder_if_not_exists(output_folder)
print(msg_template.format(**inputs))
print('Output folder: {0}'.format(output_folder))
check_multiple_data_multiple_unfolding(
input_file,
method,
channel,
variable,
n_toy_mc,
n_toy_data,
output_folder,
offset_toy_mc,
offset_toy_data,
k_value,
tau_value,
run_matrix,
)
print('Runtime', timer.elapsed_time())
def convert_unfolding_histograms(file_name,
histograms_to_load=[
'truth',
'fake',
'measured',
'response',
'response_withoutFakes',
'response_without_fakes',
'EventCounter',
]):
file_start = Timer()
print 'Converting', file_name
histograms = {}
with File(file_name) as f:
for path, _, objects in f.walk():
# keep only unfolding and EventFilter
if path.startswith('unfolding_') or path == 'EventFilter':
histograms[path] = {}
for hist_name in objects:
if hist_name in histograms_to_load:
hist = f.Get(path + '/' + hist_name).Clone()
hist.SetDirectory(0)
histograms[path][hist_name] = hist
new_histograms = {}
# rebin
for path, hists in histograms.iteritems():
new_histograms[path] = {}
variable = ''
if not path == 'EventFilter':
variable = path.split('_')[1]
for name, hist in hists.iteritems():
if name == 'EventCounter':
new_histograms[path][name] = hist.Clone()
else:
new_hist = hist.rebinned(bin_edges[variable])
if 'TH2' in new_hist.class_name():
new_hist = new_hist.rebinned(bin_edges[variable], axis=1)
new_histograms[path][name] = new_hist
# save_to_file
output = File(file_name.replace('.root', '_asymmetric.root'), 'recreate')
for path, hists in new_histograms.iteritems():
directory = output.mkdir(path)
directory.cd()
for name, hist in hists.iteritems():
if name == 'response_withoutFakes': # fix this name
hist.Write('response_without_fakes')
else:
hist.Write(name)
output.close()
secs = file_start.elapsed_time()
print 'File %s converted in %d seconds' % (file_name, secs)
def convert_unfolding_histograms( file_name,
histograms_to_load = ['truth',
'fake', 'measured', 'response',
'response_withoutFakes', 'response_without_fakes',
'EventCounter',
] ):
file_start = Timer()
print 'Converting', file_name
histograms = {}
with File( file_name ) as f:
for path, _, objects in f.walk():
# keep only unfolding and EventFilter
if path.startswith( 'unfolding_' ) or path == 'EventFilter':
histograms[path] = {}
for hist_name in objects:
if hist_name in histograms_to_load:
hist = f.Get( path + '/' + hist_name ).Clone()
hist.SetDirectory( 0 )
histograms[path][hist_name] = hist
new_histograms = {}
# rebin
for path, hists in histograms.iteritems():
new_histograms[path] = {}
variable = ''
if not path == 'EventFilter':
variable = path.split( '_' )[1]
for name, hist in hists.iteritems():
if name == 'EventCounter':
new_histograms[path][name] = hist.Clone()
else:
new_hist = hist.rebinned( bin_edges[variable] )
if 'TH2' in new_hist.class_name():
new_hist = new_hist.rebinned( bin_edges[variable], axis = 1 )
new_histograms[path][name] = new_hist
# save_to_file
output = File( file_name.replace( '.root', '_asymmetric.root' ), 'recreate' )
for path, hists in new_histograms.iteritems():
directory = output.mkdir( path )
directory.cd()
for name, hist in hists.iteritems():
if name == 'response_withoutFakes': # fix this name
hist.Write( 'response_without_fakes' )
else:
hist.Write( name )
output.close()
secs = file_start.elapsed_time()
print 'File %s converted in %d seconds' % (file_name, secs)
def create_unfolding_pull_data(input_file_name, method, channel,
centre_of_mass, variable,
sample,
responseFile,
n_toy_data,
output_folder,
tau_value,
run_matrix=None):
'''
Sets up all variables for check_multiple_data_multiple_unfolding
'''
set_root_defaults(msg_ignore_level=3001)
timer = Timer()
input_file = File(input_file_name, 'read')
folder_template = '{path}/{centre_of_mass}TeV/{variable}/{sample}/'
msg_template = 'Producing unfolding pull data for {variable},'
msg_template += ' tau-value {value}'
inputs = {
'path': output_folder,
'centre_of_mass': centre_of_mass,
'variable': variable,
'sample': sample,
'value': round(tau_value,4),
}
h_response = get_response_histogram(responseFile, variable, channel)
output_folder = folder_template.format(**inputs)
make_folder_if_not_exists(output_folder)
print(msg_template.format(**inputs))
print('Output folder: {0}'.format(output_folder))
print ('Response here :',h_response)
output_file_name = check_multiple_data_multiple_unfolding(
input_file, method, channel, variable,
h_response,
n_toy_data,
output_folder,
tau_value,
)
print('Runtime', timer.elapsed_time())
return output_file_name
def create_unfolding_pull_data(input_file_name, method, channel,
centre_of_mass, variable, n_toy_mc, n_toy_data,
output_folder, offset_toy_mc, offset_toy_data,
k_value, tau_value=-1, run_matrix=None):
'''
Sets up all variables for check_multiple_data_multiple_unfolding
'''
timer = Timer()
input_file = File(input_file_name, 'read')
folder_template = '{path}/{centre_of_mass}TeV/{variable}/'
folder_template += '{n_toy_mc}_input_toy_mc/{n_toy_data}_input_toy_data/'
folder_template += '{vtype}_value_{value}/'
msg_template = 'Producing unfolding pull data for {variable},'
msg_template += ' {vtype}-value {value}'
inputs = {
'path': output_folder,
'centre_of_mass': centre_of_mass,
'variable': variable,
'n_toy_mc': n_toy_mc,
'n_toy_data': n_toy_data,
'vtype': 'k',
'value': k_value,
}
if tau_value >= 0:
inputs['vtype'] = 'tau'
inputs['value'] = round(tau_value, 1)
output_folder = folder_template.format(**inputs)
make_folder_if_not_exists(output_folder)
print(msg_template.format(**inputs))
print('Output folder: {0}'.format(output_folder))
check_multiple_data_multiple_unfolding(
input_file, method, channel, variable, n_toy_mc, n_toy_data,
output_folder, offset_toy_mc, offset_toy_data, k_value, tau_value,
run_matrix,
)
print('Runtime', timer.elapsed_time())
# set the number of toy MC for error calculation
k_value = options.k_value
use_N_toy = options.n_input_mc
offset_toy_mc = options.offset_toy_mc
offset_toy_data = options.offset_toy_data
method = options.method
variable = options.variable
# define bins
bins = array( 'd', bin_edges[variable] )
nbins = len( bins ) - 1
output_folder = options.output_folder + '/' + str(centre_of_mass) + 'TeV/' + variable + '/%d_input_toy_mc/k_value_%d/' % ( use_N_toy, k_value )
make_folder_if_not_exists( output_folder )
print 'Producing unfolding pull data for %s variable, k-value %s. \nOutput folder: %s' % ( variable, k_value, output_folder )
input_file = File( options.file, 'read' )
timer = Timer()
if options.channel == 'electron':
check_multiple_data_multiple_unfolding( input_file, method, 'electron' )
elif options.channel == 'muon':
check_multiple_data_multiple_unfolding( input_file, method, 'muon' )
else:
check_multiple_data_multiple_unfolding( input_file, method, 'combined' )
end1, end2 = clock(), time()
print 'Runtime', timer.elapsed_time()
