def get_ff_stat(ntp, tree):
ham_ok, wff = read_branches(ntp, tree, ['ham_ok', 'wff'])
print(f' Reweight success rate: {ham_ok.sum()/ham_ok.size:.2f}')
wff_ok = wff[ham_ok]
details = f' FF avg wt: {wff_ok.mean():.3f}, max wt: {wff_ok.max():.3f}'
details += f', wt > 1 ratio: {wff_ok[wff_ok > 1].size / wff_ok.size:.2f}'
print(details)
def cutflow_uniq_events_outer(ntp,
tree,
run_branch='runNumber',
event_branch='eventNumber'):
run, event = read_branches(ntp, tree, (run_branch, event_branch))
def inner(ntp_i, tree_i, arr):
return extract_uid(ntp_i,
tree_i,
conditional=arr,
run_array=run,
event_array=event)[3]
return inner
# License: BSD 2-clause
# Last Change: Thu Feb 24, 2022 at 11:01 PM -0500
import numpy as np
from argparse import ArgumentParser
from pyTuplingUtils.io import read_branches
def parse_input():
parser = ArgumentParser(
description='Print RDX skim (e.g. ISO) sizes in step-2 ntuples.')
parser.add_argument('ntp', help='specify input ntuple.')
return parser.parse_args()
if __name__ == '__main__':
args = parse_input()
print('From ntuple: {}'.format(args.ntp))
skims = ['ISO', '1OS', '2OS', 'DD']
skim_branches = read_branches(args.ntp, 'tree',
['is_' + i.lower() for i in skims])
# skim_branches = read_branches(args.ntp, 'tree', ['is_normal & d_mass_window_ok & ' + 'is_'+i.lower() for i in skims])
for name, arr in zip(skims, skim_branches):
cut = np.logical_and.reduce([arr])
print('{:>6}: {:>12,}'.format(name, arr[cut].size))
parser.add_argument('tree', help='specify ntuple tree.')
parser.add_argument('--l0', default='L0DUTCK',
help='specify L0 TCK branch.')
parser.add_argument('--hlt1', default='HLT1TCK',
help='specify HLT1 TCK branch.')
parser.add_argument('--hlt2', default='HLT2TCK',
help='specify HLT2 TCK branch.')
return parser.parse_args()
########
# Main #
########
if __name__ == '__main__':
args = parse_input()
tck_raw = read_branches(args.ntp, args.tree, (args.l0, args.hlt1, args.hlt2),
transpose=True)
tck = list(map(lambda x: '-'.join([hex(i) for i in x]), tck_raw))
tck_uniq = np.unique(tck)
print('Unique TCKs (L0-HLT1-HLT2):')
for t in tck_uniq:
print(t)
# 220,
230,
# 240,
]
debugMode = len(sys.argv) == 1
print(f'Running in debug mode or not: {debugMode}')
for ntp in ntpsIn:
hep.style.use('LHCb2')
plotCommonName = plotBaseName(ntp)
# particles = findDssInNtp(ntp)
weight, tm, q2True, mass = read_branches(
ntp, 'tree', ['wff', 'truthmatch', 'q2_true', 'ff_d_mass'])
# massB = read_branch(ntp, 'tree', 'ff_b_mass')
# rFac = mass / massB
for p in particles:
subplotCommonName = plotCommonName + f'_{p}'
pNum = weight[tm == p].size
pWeight = weight[tm == p].sum()
pMass = mass[tm == p]
xMin, xMax = pMass.min(), pMass.max()
labels = [f'ISGW2 ({pNum})', f'BLR ({pWeight:.1f})']
label = fr'\$B \\rightarrow {findDss(p)} {findLep(p)}$'
plotNoComp(
ntp,
},
'mu': {
'title': r'$\mu$'
},
},
}
plotTitleAddOn = {
'Dst': r'$D^{*}$ tree',
'D0': r'$D^{0}$ tree',
}
for ntpName in ntpsIn:
hep.style.use('LHCb2')
ntp = uproot.open(ntpName)
for treeId, scheme in plotScheme.items():
if treeId in ntpName:
for part in scheme:
brP, brEta, brWt = read_branches(
ntp, 'tree', [f'{part}_p', f'{part}_eta', f'wtrk_{part}'])
effRatio = f', {plotTitleAddOn[treeId]}, tracking eff: {brWt.sum() / brWt.size:.2f}'
plotPEta(brP,
brEta,
f'{treeId}_{part}_p_eta.png',
binning=plotRange,
title=scheme[part]['title'] + effRatio)
break