Advanced, Common, Features
Contents
Advanced, Common, Features¶
The ATLAS data model’s primary objects, like the Jet or Electron (or any others) have some features in common. A description of accessing them is gathered here.
from config import ds_bphys as ds
import matplotlib.pyplot as plt
import awkward as ak
import numpy as np
from func_adl_servicex_xaodr21 import cpp_float, cpp_string
Object Decorations¶
The XAOD data model is designed to be extensible. Another object member can be added at anytime, even if the object doesn’t have an accessor method. In fact, almost all data in the data model is stored as a named column: when you access jet.pt() the pt method actually does a lookup on a decorator with the name pt associated with the jet object.
This feature is used extensively in reconstruction and calibration processing. Since decorators can be almost any type of access, in func_adl you have to used parameterized access to get at them. Here is an example from a b-physics derived dataset. We’ll demo this by translating a snippet of b-physics code provided by Peter Onyisi.
If you’ve not worked with derived BPHYS datasets, they are unique-ish in the ATLAS world. Since statistics are the name of the game, almost everything is stripped out. Here we want to go after the QUAD_mass decoration on the BPHY4Quads collection in the sample. First job is to figure out what the BPHY4Quads collection is. The ATLAS checkxAOD.py tool is very useful here. If you do that, you’ll find the following:
[bash][atlas AnalysisBase-21.2.62]:data > checkxAOD.py DAOD_BPHY4.999031._000001.pool.root.1
======================================================================================================================== File: DAOD_BPHY4.999031._000001.pool.root.1
------------------------------------------------------------------------------------------------------------------------ Memory size Disk Size Size/Event Compression Entries Name (Type)
------------------------------------------------------------------------------------------------------------------------ 333.24 kB 50.19 kB 0.00 kB/event 6.64 20788 TrigConfKeys (xAOD::TrigConfKeys_v1) [Trig]
2544.13 kB 516.87 kB 0.02 kB/event 4.92 20788 GSFConversionVertices (DataVector<xAOD::Vertex_v1>) [egamma]
4089.23 kB 1132.31 kB 0.05 kB/event 3.61 20788 LVL1MuonRoIs (DataVector<xAOD::MuonRoI_v1>) [Trig]
6630.11 kB 1189.60 kB 0.06 kB/event 5.57 20788 InDetForwardTrackParticles (DataVector<xAOD::TrackParticle_v1>) [InDet]
12605.41 kB 1757.67 kB 0.08 kB/event 7.17 20788 EventInfo (xAOD::EventInfo_v1) [EvtId]
7545.86 kB 2787.27 kB 0.13 kB/event 2.71 20788 HLT_xAOD__MuonContainer_MuonEFInfo_FullScan (DataVector<xAOD::Muon_v1>) [Trig]
23009.07 kB 2875.53 kB 0.14 kB/event 8.00 20788 McEventInfo (PileUpEventInfo_p5) [EvtId]
14812.24 kB 3296.27 kB 0.16 kB/event 4.49 20788 HLT_xAOD__TrigBphysContainer_EFTrackMass (DataVector<xAOD::TrigBphys_v1>) [Trig]
21498.52 kB 4903.93 kB 0.24 kB/event 4.38 20788 HLT_xAOD__MuonContainer_MuonEFInfo (DataVector<xAOD::Muon_v1>) [Trig]
61415.48 kB 4976.95 kB 0.24 kB/event 12.34 20788 HLT_xAOD__TrigBphysContainer_EFBMuMuXFex (DataVector<xAOD::TrigBphys_v1>) [Trig]
217772.43 kB 5590.42 kB 0.27 kB/event 38.95 20788 xTrigDecision (xAOD::TrigDecision_v1) [Trig]
107825.61 kB 7299.25 kB 0.35 kB/event 14.77 20788 HLT_xAOD__TrigBphysContainer_EFMultiMuFex (DataVector<xAOD::TrigBphys_v1>) [Trig]
26836.23 kB 8364.40 kB 0.40 kB/event 3.21 20788 Electrons (DataVector<xAOD::Electron_v1>) [egamma]
104940.43 kB 8684.58 kB 0.42 kB/event 12.08 20788 HLT_xAOD__TrigBphysContainer_L2BMuMuFex (DataVector<xAOD::TrigBphys_v1>) [Trig]
14616.80 kB 8693.32 kB 0.42 kB/event 1.68 20788 MuonSpectrometerTrackParticles (DataVector<xAOD::TrackParticle_v1>) [Muon]
20589.57 kB 11065.87 kB 0.53 kB/event 1.86 20788 GSFTrackParticles (DataVector<xAOD::TrackParticle_v1>) [egamma]
38546.04 kB 11397.73 kB 0.55 kB/event 3.38 20788 Photons (DataVector<xAOD::Photon_v1>) [egamma]
23701.12 kB 13832.01 kB 0.67 kB/event 1.71 20788 HLT_xAOD__TrackParticleContainer_InDetTrigTrackingxAODCnv_Muon_IDTrig (DataVector<xAOD::TrackParticle_v1>) [Trig]
453682.19 kB 22308.57 kB 1.07 kB/event 20.34 20788 HLT_xAOD__TrigBphysContainer_EFBMuMuFex (DataVector<xAOD::TrigBphys_v1>) [Trig]
521885.12 kB 24422.69 kB 1.17 kB/event 21.37 20788 BPHY4RefittedPrimaryVertices (DataVector<xAOD::Vertex_v1>) [*Unknown*]
71110.69 kB 28750.34 kB 1.38 kB/event 2.47 20788 ExtrapolatedMuonTrackParticles (DataVector<xAOD::TrackParticle_v1>) [Muon]
72210.54 kB 31226.66 kB 1.50 kB/event 2.31 20788 CombinedMuonTrackParticles (DataVector<xAOD::TrackParticle_v1>) [Muon]
66508.79 kB 35456.61 kB 1.71 kB/event 1.88 20788 egammaClusters (DataVector<xAOD::CaloCluster_v1>) [egamma]
142288.23 kB 45957.38 kB 2.21 kB/event 3.10 20788 Muons (DataVector<xAOD::Muon_v1>) [Muon]
322969.71 kB 56110.05 kB 2.70 kB/event 5.76 20788 PrimaryVertices (DataVector<xAOD::Vertex_v1>) [InDet]
99516.93 kB 59326.76 kB 2.85 kB/event 1.68 20788 BPHY4Quads (DataVector<xAOD::Vertex_v1>) [*Unknown*]
110682.30 kB 62286.51 kB 3.00 kB/event 1.78 20788 BPHY4Pairs (DataVector<xAOD::Vertex_v1>) [*Unknown*]
421894.21 kB 117419.71 kB 5.65 kB/event 3.59 20788 InDetTrackParticles (DataVector<xAOD::TrackParticle_v1>) [InDet]
1064386.64 kB 292559.83 kB 14.07 kB/event 3.64 20788 TrigNavigation (xAOD::TrigNavigation_v1) [Trig]
------------------------------------------------------------------------------------------------------------------------ 4056446.90 kB 874239.27 kB 42.05 kB/event Total
So we know we can access it as a vertex!
quad_mass = (
ds.Select(lambda e: e.Vertices('BPHY4Quads'))
.Select(lambda quads: {
'mass': quads.Select(lambda q: q.auxdataConst[cpp_float]('QUAD_mass')),
'chi2': quads.Select(lambda q: q.chiSquared()),
'charge_code': quads.Select(lambda q: q.auxdataConst[cpp_string]('ChargeCode')),
})
.AsAwkwardArray()
.value()
)
fig, [ax1, ax2] = plt.subplots(1, 2, sharey=True, figsize=(10, 5))
two_plus_charges = ak.sum(ak.without_parameters(quad_mass.charge_code) == ord('+'), axis=1) == 2
ax1.hist(ak.flatten(quad_mass['mass']), bins=100, range=(0, 2e6), label='All')
ax1.hist(ak.flatten(quad_mass[two_plus_charges]['mass']), bins=100, range=(0, 2e6), label='++')
ax1.set_yscale('log')
ax1.set_xlabel('Mass [eV]')
ax1.legend()
ax1.set_title('QUAD Mass')
ax2.hist(ak.flatten(quad_mass['chi2']), bins=100, range=(0,100), label='All')
ax2.hist(ak.flatten(quad_mass[two_plus_charges]['chi2']), bins=100, range=(0, 100), label='++')
ax2.set_xlabel('$\chi^2$')
ax2.legend()
_ = ax2.set_title('Fit $\chi^2$')
