def create_new_trees(input_file, suffix = ''):
tree1_name = 'TTbar_plus_X_analysis/MuPlusJets/QCD non iso mu+jets/FitVariables' + suffix
tree2_name = 'TTbar_plus_X_analysis/MuPlusJets/QCD non iso mu+jets/Muon/Muons' + suffix
s_cr1 = 'relIso_04_deltaBeta <= 0.3 && relIso_04_deltaBeta > 0.12'
s_cr2 = 'relIso_04_deltaBeta > 0.3'
cr1 = 'TTbar_plus_X_analysis/MuPlusJets/QCD 0.12 < iso <= 0.3'
cr2 = 'TTbar_plus_X_analysis/MuPlusJets/QCD iso > 0.3'
with root_open(input_file) as file:
t1 = file.Get(tree1_name)
t2 = file.Get(tree2_name)
t1.AddFriend(t2)
# h1 = t1.Draw('MET', 'relIso_04_deltaBeta > 0.3')
# h2 = t1.Draw(
# 'MET', 'relIso_04_deltaBeta <= 0.3 && relIso_04_deltaBeta > 0.12')
# h3 = t1.Draw('MET', 'relIso_04_deltaBeta > 0.12')
# h4 = t2.Draw('relIso_04_deltaBeta', 'relIso_04_deltaBeta > 0.12')
# print h1.integral()
# print h2.integral()
# print h3.integral(), h1.integral() + h2.integral()
output = File('test.root', 'recreate')
output.mkdir(cr1, recurse=True)
output.mkdir(cr2, recurse=True)
output.cd(cr2)
new_tree1 = t1.CopyTree(s_cr2)
new_tree1.Write()
output.cd(cr1)
new_tree2 = t1.CopyTree(s_cr1)
new_tree2.Write()
output.close()
new_tree1 = None
new_tree2 = None
f_out = File(input_file, 'update')
root_mkdir(f_out, cr1)
root_mkdir(f_out, cr2)
with root_open('test.root') as f_in:
f_out.cd(cr1)
new_tree1 = f_in.Get(cr1 + '/FitVariables' + suffix).CloneTree()
f_out.cd(cr2)
new_tree2 = f_in.Get(cr2 + '/FitVariables' + suffix).CloneTree()
def create_new_trees(input_file, suffix=''):
tree1_name = 'TTbar_plus_X_analysis/MuPlusJets/QCD non iso mu+jets/FitVariables' + suffix
tree2_name = 'TTbar_plus_X_analysis/MuPlusJets/QCD non iso mu+jets/Muon/Muons' + suffix
s_cr1 = 'relIso_04_deltaBeta <= 0.3 && relIso_04_deltaBeta > 0.12'
s_cr2 = 'relIso_04_deltaBeta > 0.3'
cr1 = 'TTbar_plus_X_analysis/MuPlusJets/QCD 0.12 < iso <= 0.3'
cr2 = 'TTbar_plus_X_analysis/MuPlusJets/QCD iso > 0.3'
with root_open(input_file) as file:
t1 = file.Get(tree1_name)
t2 = file.Get(tree2_name)
t1.AddFriend(t2)
# h1 = t1.Draw('MET', 'relIso_04_deltaBeta > 0.3')
# h2 = t1.Draw(
# 'MET', 'relIso_04_deltaBeta <= 0.3 && relIso_04_deltaBeta > 0.12')
# h3 = t1.Draw('MET', 'relIso_04_deltaBeta > 0.12')
# h4 = t2.Draw('relIso_04_deltaBeta', 'relIso_04_deltaBeta > 0.12')
# print h1.integral()
# print h2.integral()
# print h3.integral(), h1.integral() + h2.integral()
output = File('test.root', 'recreate')
output.mkdir(cr1, recurse=True)
output.mkdir(cr2, recurse=True)
output.cd(cr2)
new_tree1 = t1.CopyTree(s_cr2)
new_tree1.Write()
output.cd(cr1)
new_tree2 = t1.CopyTree(s_cr1)
new_tree2.Write()
output.close()
new_tree1 = None
new_tree2 = None
f_out = File(input_file, 'update')
root_mkdir(f_out, cr1)
root_mkdir(f_out, cr2)
with root_open('test.root') as f_in:
f_out.cd(cr1)
new_tree1 = f_in.Get(cr1 + '/FitVariables' + suffix).CloneTree()
f_out.cd(cr2)
new_tree2 = f_in.Get(cr2 + '/FitVariables' + suffix).CloneTree()
def create_toy_mc(input_files, sample, output_folder, n_toy, centre_of_mass, config):
from dps.utils.file_utilities import make_folder_if_not_exists
from dps.utils.toy_mc import generate_toy_MC_from_distribution, generate_toy_MC_from_2Ddistribution
from dps.utils.Unfolding import get_unfold_histogram_tuple
make_folder_if_not_exists(output_folder)
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:
input_file_index = 0
for input_file in input_files:
input_file_hists = File(input_file)
for channel in config.analysis_types.keys():
if channel is 'combined':continue
for variable in variable_bins:
output_dir = f_out.mkdir(str(input_file_index) + '/' + 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,
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()
input_file_index += 1
def create_toy_mc(input_file, output_folder, n_toy, centre_of_mass, ttbar_xsection, met_type, start_at=1):
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, start_at, n_toy, centre_of_mass)
variable_bins = bin_edges.copy()
with root_open(output_file_name, 'recreate') as f_out:
for channel in ['electron', 'muon']:
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,
met_type,
centre_of_mass,
ttbar_xsection,
load_fakes=False)
cd()
for i in range(start_at, start_at + n_toy):
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 generate_toy(n_toy,
n_input_mc,
config,
output_folder,
start_at=0,
split=1):
from progressbar import Percentage, Bar, ProgressBar, ETA
set_root_defaults()
genWeight = '( EventWeight * {0})'.format(config.luminosity_scale)
file_name = config.ttbar_category_templates_trees['central']
make_folder_if_not_exists(output_folder)
outfile = get_output_file_name(output_folder, n_toy, start_at, n_input_mc,
config.centre_of_mass_energy)
variable_bins = bin_edges.copy()
widgets = ['Progress: ', Percentage(), ' ', Bar(), ' ', ETA()]
with root_open(file_name, 'read') as f_in, root_open(outfile,
'recreate') as f_out:
tree = f_in.Get("TTbar_plus_X_analysis/Unfolding/Unfolding")
n_events = tree.GetEntries()
print("Number of entries in tree : ", n_events)
for channel in ['electron', 'muon']:
print('Channel :', channel)
gen_selection, gen_selection_vis = '', ''
if channel is 'muon':
gen_selection = '( isSemiLeptonicMuon == 1 )'
gen_selection_vis = '( isSemiLeptonicMuon == 1 && passesGenEventSelection )'
else:
gen_selection = '( isSemiLeptonicElectron == 1 )'
gen_selection_vis = '( isSemiLeptonicElectron == 1 && passesGenEventSelection )'
selection = '( {0} ) * ( {1} )'.format(genWeight, gen_selection)
selection_vis = '( {0} ) * ( {1} )'.format(genWeight,
gen_selection_vis)
weighted_entries = get_weighted_entries(tree, selection)
weighted_entries_vis = get_weighted_entries(tree, selection_vis)
pbar = ProgressBar(widgets=widgets, maxval=n_input_mc).start()
toy_mc_sets = []
for variable in ['MET', 'HT', 'ST', 'WPT']: # variable_bins:
toy_mc = ToySet(f_out, variable, channel, n_toy)
toy_mc_sets.append(toy_mc)
count = 0
for event in tree:
# generate 300 weights for each event
mc_weights = get_mc_weight(weighted_entries, n_toy)
mc_weights_vis = get_mc_weight(weighted_entries_vis, n_toy)
if count >= n_input_mc:
break
count += 1
if count < start_at:
continue
# weight = event.EventWeight * config.luminosity_scale
# # rescale to N input events
# weight *= n_events / n_input_mc / split
weight = 1
for toy_mc in toy_mc_sets:
toy_mc.fill(event, weight, mc_weights, mc_weights_vis)
if count % 1000 == 1:
pbar.update(count)
print('Processed {0} events'.format(count))
pbar.finish()
for toy_mc in toy_mc_sets:
toy_mc.write()
print('Toy MC was saved to file:', outfile)
'''
Created on 21 Aug 2015
@author: phxlk
'''
from rootpy.plotting.hist import Hist
from rootpy.io.file import root_open
from rootpy.interactive.rootwait import wait
if __name__ == '__main__':
input_file = '/hdfs/TopQuarkGroup/run2/atOutput/13TeV/50ns/data_electron_tree.root'
tree_path = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/FitVariables'
h = Hist(100, 0, 2)
with root_open(input_file) as f:
tree = f.Get(tree_path)
tree.Draw('PUWeight', 'PUWeight > 0', hist=h)
h.Draw()
wait()
def generate_toy(n_toy, n_input_mc, config, output_folder, start_at=0, split=1):
from progressbar import Percentage, Bar, ProgressBar, ETA
set_root_defaults()
genWeight = '( EventWeight * {0})'.format(config.luminosity_scale)
file_name = config.ttbar_category_templates_trees['central']
make_folder_if_not_exists(output_folder)
outfile = get_output_file_name(
output_folder, n_toy, start_at, n_input_mc, config.centre_of_mass_energy)
variable_bins = bin_edges.copy()
widgets = ['Progress: ', Percentage(), ' ', Bar(),
' ', ETA()]
with root_open(file_name, 'read') as f_in, root_open(outfile, 'recreate') as f_out:
tree = f_in.Get("TTbar_plus_X_analysis/Unfolding/Unfolding")
n_events = tree.GetEntries()
print("Number of entries in tree : ", n_events)
for channel in ['electron', 'muon']:
print('Channel :', channel)
gen_selection, gen_selection_vis = '', ''
if channel is 'muon':
gen_selection = '( isSemiLeptonicMuon == 1 )'
gen_selection_vis = '( isSemiLeptonicMuon == 1 && passesGenEventSelection )'
else:
gen_selection = '( isSemiLeptonicElectron == 1 )'
gen_selection_vis = '( isSemiLeptonicElectron == 1 && passesGenEventSelection )'
selection = '( {0} ) * ( {1} )'.format(genWeight, gen_selection)
selection_vis = '( {0} ) * ( {1} )'.format(genWeight,
gen_selection_vis)
weighted_entries = get_weighted_entries(tree, selection)
weighted_entries_vis = get_weighted_entries(tree, selection_vis)
pbar = ProgressBar(widgets=widgets, maxval=n_input_mc).start()
toy_mc_sets = []
for variable in ['MET', 'HT', 'ST', 'WPT']: # variable_bins:
toy_mc = ToySet(f_out, variable, channel, n_toy)
toy_mc_sets.append(toy_mc)
count = 0
for event in tree:
# generate 300 weights for each event
mc_weights = get_mc_weight(weighted_entries, n_toy)
mc_weights_vis = get_mc_weight(weighted_entries_vis, n_toy)
if count >= n_input_mc:
break
count += 1
if count < start_at:
continue
# weight = event.EventWeight * config.luminosity_scale
# # rescale to N input events
# weight *= n_events / n_input_mc / split
weight = 1
for toy_mc in toy_mc_sets:
toy_mc.fill(event, weight, mc_weights, mc_weights_vis)
if count % 1000 == 1:
pbar.update(count)
print('Processed {0} events'.format(count))
pbar.finish()
for toy_mc in toy_mc_sets:
toy_mc.write()
print('Toy MC was saved to file:', outfile)
'''
Created on 21 Aug 2015
@author: phxlk
'''
from rootpy.plotting.hist import Hist
from rootpy.io.file import root_open
from rootpy.interactive.rootwait import wait
if __name__ == '__main__':
input_file = '/hdfs/TopQuarkGroup/run2/atOutput/13TeV/50ns/data_electron_tree.root'
tree_path = 'TTbar_plus_X_analysis/EPlusJets/Ref selection/FitVariables'
h = Hist(100, 0, 2)
with root_open(input_file) as f:
tree = f.Get(tree_path)
tree.Draw('PUWeight', 'PUWeight > 0', hist = h)
h.Draw()
wait()