def __init__(self, csv=None, wp_key=None, eff_file=None, pattern=None):
'''SF computation according to method 1a of
https://twiki.cern.ch/twiki/bin/view/CMS/BTagSFMethods
Inputs: csv, wp_key, eff_file, pattern
csv: path to a b-tagging CSV file
wp_key: a tuple of three elements containing (Algo name, SF method, WP name)
eff_file: root file containing the efficiency histograms
pattern: formattable string that accepts one parameter for flavour definition'''
parsed_csv = reshuffle_sf_dict(convert_btag_csv_file(csv))
self.sf_ = recursive_compile(
parsed_csv[wp_key[0]][wp_key[1]][wp_key[2]])
# FIXME: move to correlated/uncorrelated
# Define, by hand, the proper correlation among taggers,
# somewhere unfortunately needs to be hardcoded by hand
# tuple of names for UDSG, B, C
self.schema_ = {
'central': ('UDSG_central', 'C_central', 'B_central'),
'bc_up': ('UDSG_central', 'C_up', 'B_up'),
'bc_down': ('UDSG_central', 'C_down', 'B_down'),
'udgs_up': ('UDSG_up', 'C_central', 'B_central'),
'udsg_down': ('UDSG_down', 'C_central', 'B_central'),
}
effs = convert_histo_root_file(eff_file)
self.eff_ = {
'B':
dense_lookup(*effs[(pattern.format('bottom'), 'dense_lookup')]),
'C':
dense_lookup(*effs[(pattern.format('charm'), 'dense_lookup')]),
'UDSG':
dense_lookup(*effs[(pattern.format('light'), 'dense_lookup')]),
}
def __init__(self):
self.lepSFs_ = {}
for lepton in lep_info[year].keys():
SFfile = convert_histo_root_file(
'%s/inputs/data/%s' %
(proj_dir, lep_info[year][lepton]['filename']))
for sf_type in lep_info[year][lepton]['SFs'].keys():
if lep_info[year][lepton]['SFs'][sf_type]['available']:
self.lepSFs_['%s_%s' % (lepton, sf_type)] = dense_lookup(
*SFfile[(sf_type, 'dense_lookup')])
print('Lepton SF constructed')
def test_554():
import uproot
from coffea.lookup_tools.root_converters import convert_histo_root_file
f_in = "tests/samples/PR554_SkipReadOnlyDirectory.root"
rf = uproot.open(f_in)
# check that input file contains uproot.ReadOnlyDirectory
assert any(isinstance(v, uproot.ReadOnlyDirectory) for v in rf.values())
# check that we can do the conversion now and get histograms out of uproot.ReadOnlyDirectories
out = convert_histo_root_file(f_in)
assert out
# check that output does not contain any Directory-like keys
rfkeys = set(k.rsplit(";")[0] for k in rf.keys())
assert all(not isinstance(rf[k], uproot.ReadOnlyDirectory)
for k, _ in out.keys() if k in rfkeys)
25] # available widths (%)
scales = [
"nominal", "uF_up", "uF_down", "uR_up", "uR_down", "uF_up_uR_up",
"uF_down_uR_down"
] # available scale
channels = {"ll": "DiLep", "lj": "SL"}
procs = {"res": "Res", "int": "Int"}
types = [
"xsec", "xabs"
] # <type> can be either xsec for actual cross section or xabs for the absolute cross section i.e. sum of the magnitude of positive and negative parts of the cross section
pos_evt_fraction_name = "int_mg5_pdf_325500_scale_dyn_0p5mtt_positive_event_fraction" # <A/H>_int_mg5_pdf_325500_scale_dyn_0p5mtt_positive_event_fraction contain the fraction of positive events for each signal points
kfactors_name = "sushi_nnlo_mg5_lo_kfactor_pdf_325500" # <A/H>_<res/int>_sushi_nnlo_mg5_lo_kfactor_pdf_325500_<scale> contain the NNLO to LO k-factors
mg5_LO_xsecs_name = "mg5_pdf_325500_scale_dyn_0p5mtt" # <A/H>_<res/int>_mg5_pdf_325500_scale_dyn_0p5mtt_<scale>_<type>_<channel> contain the MG5 LO cross section for the relevant process and channel
fdict = convert_histo_root_file(rname)
widthTOname = lambda width: str(float(width)).replace('.', 'p')
# create dict of the fraction of positive events for each signal points
#outdict = {}
#for boson in bosons:
# sig_dname = "_".join([boson, pos_evt_fraction_name]) # name of signal dist
# vals = dense_lookup(*fdict[(sig_dname, "dense_lookup")])
# errs = dense_lookup(*fdict[(f"{sig_dname}_error", "dense_lookup")])
# create dict of LO xsection values and errors
#set_trace()
LO_outdict = {}
for boson in bosons:
for proc, proc_name in procs.items():
args = parser.parse_args()
proj_dir = os.environ['PROJECT_DIR']
base_jobid = os.environ['base_jobid']
analyzer = 'NNLOqcd_dists'
f_ext = 'TOT.coffea'
outdir = os.path.join(proj_dir, 'plots', base_jobid, analyzer)
if not os.path.isdir(outdir):
os.makedirs(outdir)
## get values from NNLO root file
nnlo_fname = 'MATRIX_ttmVStheta.root' # 'xsec_central' dist has only statistical uncs
#nnlo_fname = 'matrixhists_NNPDF.root' # 'cen' dist has only statistical uncs
#nnlo_fname = 'MATRIX_17_abs.root' # has scale and statistical uncs
nnlo_file = convert_histo_root_file(
os.path.join(proj_dir, 'NNLO_files', nnlo_fname))
nnlo_var = 'xsec_central'
nnlo_dict = Plotter.root_converters_dict_to_hist(
nnlo_file,
vars=[nnlo_var],
sparse_axes_list=[{
'name': 'dataset',
'label': "Event Process",
'fill': 'nnlo'
}],
#dense_axes_list=[{'name': 'mtt', 'idx' : 1}, {'name' : 'ctstar', 'idx' : 0}],
#transpose_da=True,
dense_axes_list=[{
'name': 'ctstar',
'idx': 0
}, {
'Central': {},
'Error': {},
},
'Trig': {
'Central': {},
'Error': {},
},
},
}
for year in years_to_run
}
for lep in leptons.keys():
for year, fname in leptons[lep]['fnames'].items():
sf_file = convert_histo_root_file(
os.path.join(proj_dir, 'inputs', 'data', base_jobid, 'lepSFs',
fname))
eta_binning = sf_file[(leptons[lep]['eta'], 'dense_lookup')][1][0]
#if lep == 'Muons': set_trace()
sf_output[year][lep]['eta_ranges'] = [
(eta_binning[idx], eta_binning[idx + 1])
for idx in range(len(eta_binning) - 1)
]
for idx in range(len(eta_binning) - 1):
# reco/ID SFs
sf_output[year][lep]['Reco_ID']['Central'][
'eta_bin%i' % idx] = dense_lookup(
*(sf_file[(leptons[lep]['pt']['reco_id'][idx],
'dense_lookup')][0],
sf_file[(leptons[lep]['pt']['reco_id'][idx],
mc_input_dir = os.path.join(proj_dir, 'results',
'%s_%s' % (args.year, base_jobid), analyzer)
f_ext = 'TOT.coffea'
mc_fnames = sorted([
os.path.join(mc_input_dir, fname) for fname in os.listdir(mc_input_dir)
if fname.endswith(f_ext)
])
mc_hdict = plt_tools.add_coffea_files(
mc_fnames) if len(mc_fnames) > 1 else load(mc_fnames[0])
# get hists
mc_nTrueInt_histo = mc_hdict['PU_nTrueInt']
data_input_dir = os.path.join(proj_dir, 'inputs', 'data', base_jobid, 'Pileup')
# central
data_pu_central = convert_histo_root_file(
os.path.join(data_input_dir, '%s_data.meta.pu.root' % args.year))
data_pu_dict = Plotter.root_converters_dict_to_hist(data_pu_central,
vars=['pileup'],
sparse_axes_list=[{
'name':
'dataset',
'label':
"Event Process",
'fill':
'data'
}])
# up
data_pu_up = convert_histo_root_file(
os.path.join(data_input_dir, '%s_data.meta.pu_up.root' % args.year))
data_pu_up_dict = Plotter.root_converters_dict_to_hist(data_pu_up,
vars=['pileup'],