sub.add_link('arXiv', 'https://arxiv.org/abs/%s' % arxiv)
sub.add_link('CDS', 'https://cds.cern.ch/record/%s' % cds)
sub.add_link('DOI', 'https://doi.org/%s' % doi)
sub.add_record_id(inspire, 'inspire')
####
f = fig002
t = f.tables['a']
add_variable(t, hepdata.Variable('$d_{VV}$', is_independent=True, is_binned=True, units='mm'), f.reps['a']['x_edges'])
for signame in signames:
o = f.objs['a'][signame]
v = hepdata.Variable(r'Predicted multijet signal yield, $c\tau = %s~\textrm{mm}$, $m = 800~\textrm{GeV}$, $\sigma = 1~\textrm{fb}$' % nicesigname[signame], is_independent=False, is_binned=False)
u = hepdata.Uncertainty('Statistical (~sqrt(n_generated))')
add_variable(t, v, o['y'], [(u, o['dy'])])
sub.add_table(t)
####
f = fig003
for subfig in 'ab':
o = f.reps[subfig]
t = f.tables[subfig]
t.location += ' (%s plot)' % {'a': 'upper', 'b': 'lower'}[subfig]
particle = {'a': r'\tilde{\chi}^{0} / \tilde{g}', 'b': r'\tilde{t}'}[subfig]
add_variable(t, hepdata.Variable(r'$m_{%s}$' % particle, is_independent=True, is_binned=False, units='GeV'), o['x'])
add_variable(t, hepdata.Variable(r'$c\tau_{%s}$' % particle, is_independent=True, is_binned=False, units='mm'), o['y'])
]
table.add_variable(x)
y = hepdata_lib.Variable('y',
is_independent=False,
is_binned=False,
units='GeV')
y.values = [
0.051528779333327564, 1.6546832058356766, 6.320493448437572,
3.6741550071911946, 2.2145138336614534, 1.520367789083682,
1.2488500519692582, 0.866646224820301, 0.8382429211482135,
0.7893714421512917, 0.7696185170346984
]
table.add_variable(y)
yerr1 = hepdata_lib.Uncertainty('err1', is_symmetric=True)
yerr1.values = [
0.021099959427066828, 0.15962100141035176, 0.3694098739929236,
0.2663855349397847, 0.16382789804566147, 0.1589536500131101,
0.13023012060144698, 0.10656990869338458, 0.11757103350238172,
0.09700799171590394, 0.10694129311692076
]
y.add_uncertainty(yerr1)
yerr2 = hepdata_lib.Uncertainty('err2', is_symmetric=True)
yerr2.values = [
13.496569905975372, 3.5963862583510835, 1.3764269292639162,
2.1596102403742337, 1.134859741048597, 0.9572467734877419,
3.295904153818115, 3.4138799690957224, 1.696395158770446,
3.905857923961752, 3.4993529618066215
]
def add_hepdata_table(self, i, jetR, obs_label, obs_setting, grooming_setting, min_pt, max_pt):
index = self.set_hepdata_table_index(i, jetR, min_pt)
table = hepdata_lib.Table(f'Table {index}')
table.keywords["reactions"] = ['P P --> jet+X']
table.keywords["cmenergies"] = ['5020']
# Set observable-specific info in table
x_label, y_label = self.set_hepdata_table_descriptors(table, jetR, obs_label, obs_setting, grooming_setting, min_pt, max_pt)
# Create readers to read histograms
final_result_root_filename = os.path.join(getattr(self, 'output_dir_final_results'), 'fFinalResults.root')
hepdata_reader = hepdata_lib.RootFileReader(final_result_root_filename)
systematics_root_filename = os.path.join(self.output_dir_systematics, 'fSystematics.root')
hepdata_reader_systematics = hepdata_lib.RootFileReader(systematics_root_filename)
# Define variables
h_name = 'hmain_{}_R{}_{}_{}-{}'.format(self.observable, jetR, obs_label, min_pt, max_pt)
if self.observable == 'ang':
h_name += '_trunc'
h = hepdata_reader.read_hist_1d(h_name)
n_significant_digits = 3 # Note: use 2 significant digits for uncertainties
x = hepdata_lib.Variable(x_label, is_independent=True, is_binned=True, units='')
x.digits = n_significant_digits
x.values = h['x_edges']
y = hepdata_lib.Variable(y_label, is_independent=False, is_binned=False, units='')
y.digits = n_significant_digits
y.values = h['y']
if self.is_pp:
y.add_qualifier('RE', 'P P --> jet+X')
else:
y.add_qualifier('RE', 'Pb Pb --> jet+X')
y.add_qualifier('SQRT(S)', 5.02, 'TeV')
y.add_qualifier('ETARAP', '|0.9-R|')
y.add_qualifier('jet radius', jetR)
y.add_qualifier('jet method', 'Anti-$k_{T}$')
# Define uncertainties
stat = hepdata_lib.Uncertainty('stat', is_symmetric=True)
stat.values = [float('{:.2g}'.format(dy)) for dy in h['dy']]
# Add tables to submission
table.add_variable(x)
table.add_variable(y)
y.add_uncertainty(stat)
# Add unfolding systematic
name = 'hSystematic_Unfolding_R{}_{}_{}-{}'.format(self.utils.remove_periods(jetR), obs_label,
int(min_pt), int(max_pt))
h_sys_unfolding = hepdata_reader_systematics.read_hist_1d(getattr(self, name).GetName())
sys_unfolding = hepdata_lib.Uncertainty('sys,unfolding', is_symmetric=True)
sys_unfolding.values = ['{:.2g}%'.format(y) for y in h_sys_unfolding['y']]
y.add_uncertainty(sys_unfolding)
# Add systematic uncertainty breakdown
for systematic in self.systematics_list:
if systematic in ['main', 'prior1', 'truncation', 'binning']:
continue
h_sys = self.retrieve_systematic(systematic, jetR, obs_label,
None, min_pt, max_pt)
if not h_sys:
continue
if 'generator' in systematic:
sys_label = 'generator'
else:
sys_label = systematic
h_sys = hepdata_reader_systematics.read_hist_1d(h_sys.GetName())
sys = hepdata_lib.Uncertainty('sys,{}'.format(sys_label), is_symmetric=True)
sys.values = ['{:.2g}%'.format(y) for y in h_sys['y']]
y.add_uncertainty(sys)
# Add table to the submission
self.hepdata_submission.add_table(table)
def add_hepdata_table(self, is_pp=False, is_ratio=False):
result_index = self.set_hepdata_table_index()
if is_ratio:
h = self.h_ratio
h_sys = self.h_ratio_sys
index = 3*result_index
elif is_pp:
h = self.h_main_pp
h_sys = self.h_sys_pp
index = 3*result_index-2
else:
h = self.h_main_AA
h_sys = self.h_sys_AA
index = 3*result_index-1
table = hepdata_lib.Table(f'Table {index}')
if is_ratio:
table.keywords["reactions"] = ['P P --> jet+X, Pb Pb --> jet+X']
elif is_pp:
table.keywords["reactions"] = ['P P --> jet+X']
else:
table.keywords["reactions"] = ['Pb Pb --> jet+X']
table.keywords["cmenergies"] = ['5020']
# Set observable-specific info in table
x_label, y_label = self.set_hepdata_table_descriptors(table, result_index, is_pp, is_ratio)
# Define variables
h = hepdata_lib.root_utils.get_hist_1d_points(h)
n_significant_digits = 3 # Note: use 2 significant digits for uncertainties
x = hepdata_lib.Variable(x_label, is_independent=True, is_binned=True, units='')
x.digits = n_significant_digits
x.values = h['x_edges']
y = hepdata_lib.Variable(y_label, is_independent=False, is_binned=False, units='')
y.digits = n_significant_digits
y.values = h['y']
if is_ratio:
y.add_qualifier('RE', 'P P --> jet+X, Pb Pb --> jet+X')
y.add_qualifier('CENTRALITY', str(self.centrality))
elif is_pp:
y.add_qualifier('RE', 'P P --> jet+X')
else:
y.add_qualifier('RE', 'Pb Pb --> jet+X')
y.add_qualifier('CENTRALITY', str(self.centrality))
y.add_qualifier('SQRT(S)', 5.02, 'TeV')
y.add_qualifier('ETARAP', '|0.9-R|')
y.add_qualifier('jet radius', self.jetR)
y.add_qualifier('jet method', 'Anti-$k_{T}$')
# Define uncertainties
stat = hepdata_lib.Uncertainty('stat', is_symmetric=True)
stat.values = [float('{:.2g}'.format(dy)) for dy in h['dy']]
# Add tables to submission
table.add_variable(x)
table.add_variable(y)
y.add_uncertainty(stat)
# Add unfolding systematic
if self.centrality == [0,10]:
h_sys = hepdata_lib.root_utils.get_hist_1d_points(h_sys)
sys = hepdata_lib.Uncertainty('sys', is_symmetric=True)
sys.values = [float('{:.2g}'.format(dy)) for dy in h_sys['dy']]
elif self.centrality == [30,50]:
h_sys = hepdata_lib.root_utils.get_graph_points(h_sys)
sys = hepdata_lib.Uncertainty('sys', is_symmetric=False)
sys.values = [(float('{:.2g}'.format(dy[0])), float('{:.2g}'.format(dy[1]))) for dy in h_sys['dy']]
y.add_uncertainty(sys)
# Add table to the submission
self.hepdata_submission.add_table(table)