diff --git a/data_matching/logs/best_seed_effs_testJpsi_NewParams.log b/data_matching/logs/best_seed_effs_testJpsi_NewParams.log new file mode 100644 index 0000000..153a903 --- /dev/null +++ b/data_matching/logs/best_seed_effs_testJpsi_NewParams.log @@ -0,0 +1,309 @@ +# setting LC_ALL to "C" +# --> Including file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_best_seed_data.py' +/***** User ApplicationOptions/ApplicationOptions ************************************************** +|-append_decoding_keys_to_output_manifest = True (default: True) +|-auditors = [] (default: []) +|-buffer_events = 20000 (default: 20000) +|-conddb_tag = 'sim-20210617-vc-md100' (default: '') +|-conditions_version = '' (default: '') +|-control_flow_file = '' (default: '') +|-data_flow_file = '' (default: '') +|-data_type = 'Upgrade' (default: 'Upgrade') +|-dddb_tag = 'dddb-20210617' (default: '') +|-event_store = 'HiveWhiteBoard' (default: 'HiveWhiteBoard') +|-evt_max = -1 (default: -1) +|-first_evt = 0 (default: 0) +|-geometry_version = '' (default: '') +|-histo_file = '' (default: '') +|-input_files = ['/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi'] +| (default: []) +|-input_manifest_file = '' (default: '') +|-input_process = '' (default: '') +|-input_raw_format = 0.5 (default: 0.5) +|-input_type = 'ROOT' (default: '') +|-lines_maker = None +|-memory_pool_size = 10485760 (default: 10485760) +|-monitoring_file = '' (default: '') +|-msg_svc_format = '% F%35W%S %7W%R%T %0W%M' (default: '% F%35W%S %7W%R%T %0W%M') +|-msg_svc_time_format = '%Y-%m-%d %H:%M:%S UTC' (default: '%Y-%m-%d %H:%M:%S UTC') +|-n_event_slots = 1 (default: -1) +|-n_threads = 1 (default: 1) +|-ntuple_file = '/work/cetin/LHCb/reco_tuner/data_matching/NewParams/best_seed_effs_testJpsi_NewParams.root' +| (default: '') +|-output_file = '' (default: '') +|-output_level = 3 (default: 3) +|-output_manifest_file = '' (default: '') +|-output_type = '' (default: '') +|-persistreco_version = 1.0 (default: 1.0) +|-phoenix_filename = '' (default: '') +|-preamble_algs = [] (default: []) +|-print_freq = 10000 (default: 10000) +|-python_logging_level = 20 (default: 20) +|-require_specific_decoding_keys = [] (default: []) +|-scheduler_legacy_mode = True (default: True) +|-simulation = True (default: None) +|-use_iosvc = False (default: False) +|-velo_motion_system_yaml = '' (default: '') +|-write_decoding_keys_to_git = True (default: True) +\----- (End of User ApplicationOptions/ApplicationOptions) ----------------------------------------- +# Overrule specified for keys +# <-- End of file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_best_seed_data.py' +ApplicationMgr SUCCESS +==================================================================================================================================== + Welcome to Moore version 55.2 + running on lhcba2 on Mon Mar 11 12:34:15 2024 +==================================================================================================================================== +ApplicationMgr INFO Application Manager Configured successfully +ToolSvc.GitDDDB INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/DDDB.git' +ToolSvc.GitDDDB INFO using commit 'upgrade/dddb-20210617' corresponding to 1871f1bb5c0d68c81dda62e84cf1eb3a45513521 +ToolSvc.GitSIMCOND INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/SIMCOND.git' +ToolSvc.GitSIMCOND INFO using commit 'upgrade/sim-20210617-vc-md100' corresponding to 9aa116c7143d21760d1be07ce1ef22c0f8f07bdf +DetectorPersistencySvc INFO Added successfully Conversion service:XmlCnvSvc +DetectorDataSvc SUCCESS Detector description database: git:/lhcb.xml +EventClockSvc.FakeEventTime INFO Event times generated from 0 with steps of 0 +EventClockSvc.FakeEventTime INFO Run numbers generated from 0 every 0 events +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c1.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c2.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c3.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c4.down.cdf +MagneticFieldSvc INFO Map scaled by factor 1 with polarity internally used: -1 signed relative current: -1 +NTupleSvc INFO Added stream file:/work/cetin/LHCb/reco_tuner/data_matching/NewParams/best_seed_effs_testJpsi_NewParams.root as FILE1 +HLTControlFlowMgr INFO Start initialization +RootHistSvc INFO Writing ROOT histograms to: /work/cetin/LHCb/reco_tuner/data_matching/NewParams/best_seed_effs_testJpsi_NewParams.root +HistogramPersistencySvc INFO Added successfully Conversion service:RootHistSvc +DeFTDetector INFO Current FT geometry version = 64 +HLTControlFlowMgr INFO Concurrency level information: +HLTControlFlowMgr INFO o Number of events slots: 1 +HLTControlFlowMgr INFO o TBB thread pool size: 'ThreadPoolSize':1 +HLTControlFlowMgr INFO ---> End of Initialization. This took 28548 ms +ApplicationMgr INFO Application Manager Initialized successfully +ApplicationMgr INFO Application Manager Started successfully +EventPersistencySvc INFO Added successfully Conversion service:RootCnvSvc +EventSelector INFO Stream:EventSelector.DataStreamTool_1 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO Will measure time between events 10 and 2147483647 (stop might be some events later) +HLTControlFlowMgr INFO Starting loop on events +EventSelector SUCCESS Reading Event record 1. Record number within stream 1: 1 +FTRawBankDecoder INFO Conditions DB is compatible with FT bank version 4, 5, 6. +FTRawBankDecoder INFO Building the readout map with version 0 +HLTControlFlowMgr INFO Timing started at: 12:35:17 +EventSelector INFO Stream:EventSelector.DataStreamTool_2 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +EventSelector INFO Stream:EventSelector.DataStreamTool_3 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO No more events in event selection +HLTControlFlowMgr INFO ---> Loop over 2955 Events Finished - WSS 1756.6, timed 2945 Events: 183344 ms, Evts/s = 16.0627 +DefaultGECFilter INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb Events Processed" | 2955 | + | "Nb events removed" | 666 | +HLTControlFlowMgr INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Processed events" | 2955 | +MatchTrackChecker_23e28c5b.LoKi:... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +MatchUTHitsChecker_5891c098.LoKi... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 4 | +PrHybridSeeding_4d0337cc INFO Number of counters : 21 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Created T2x1 three-hit combinations in case 0" | 3981395 | 2438467 | 0.61247 | 0.62452 | 0.0000 | 6.0000 | + | "Created T2x1 three-hit combinations in case 1" | 4961664 | 3252259 | 0.65548 | 0.75200 | 0.0000 | 12.000 | + | "Created T2x1 three-hit combinations in case 2" | 7644512 | 6133331 | 0.80232 | 1.0193 | 0.0000 | 23.000 | + | "Created XZ tracks (part 0)" | 6867 | 363280 | 52.902 | 44.400 | 0.0000 | 844.00 | + | "Created XZ tracks (part 1)" | 6867 | 360418 | 52.486 | 47.084 | 0.0000 | 1257.0 | + | "Created XZ tracks in case 0" | 4578 | 269789 | 58.932 | 37.398 | 1.0000 | 363.00 | + | "Created XZ tracks in case 1" | 4578 | 267868 | 58.512 | 44.098 | 1.0000 | 709.00 | + | "Created XZ tracks in case 2" | 4578 | 186041 | 40.638 | 52.165 | 0.0000 | 1257.0 | + | "Created full hit combinations in case 0" | 407934 | 407934 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 1" | 310355 | 310355 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 2" | 280325 | 280325 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created seed tracks" | 4578 | 284763 | 62.202 | 22.650 | 3.0000 | 141.00 | + | "Created seed tracks (part 0)" | 2289 | 159664 | 69.753 | 25.912 | 4.0000 | 161.00 | + | "Created seed tracks (part 1)" | 2289 | 157869 | 68.969 | 25.854 | 3.0000 | 159.00 | + | "Created seed tracks in case 0" | 4578 | 148622 | 32.464 | 12.801 | 1.0000 | 86.000 | + | "Created seed tracks in case 1" | 4578 | 270703 | 59.131 | 21.736 | 2.0000 | 132.00 | + | "Created seed tracks in case 2" | 4578 | 302221 | 66.016 | 24.642 | 3.0000 | 153.00 | + | "Created seed tracks in recovery step" | 2289 | 15312 | 6.6894 | 3.8772 | 0.0000 | 26.000 | + | "Created two-hit combinations in case 0" | 677723 |1.546134e+07 | 22.814 | 15.827 | 0.0000 | 117.00 | + | "Created two-hit combinations in case 1" | 584001 |1.760625e+07 | 30.148 | 18.628 | 0.0000 | 262.00 | + | "Created two-hit combinations in case 2" | 461883 |2.056474e+07 | 44.524 | 28.512 | 0.0000 | 333.00 | +PrLHCbID2MCParticle_a906d17d INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#removed null MCParticles" | 16672433 | 0 | 0.0000 | +PrMatchNN_fd9a8305 INFO Number of counters : 3 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#MatchingChi2" | 2289 | 8439531 | 3687.0 | + | "#MatchingMLP" | 26443 | 22595.62 | 0.85450 | + | "#MatchingTracks" | 2289 | 26443 | 11.552 | +PrMatchNN_fd9a8305.PrAddUTHitsTool INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 24019 | 92930 | 3.8690 | + | "#tracks with hits added" | 24019 | +PrStorePrUTHits_df75b912 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrStoreSciFiHits_fb0eba02 INFO Number of counters : 25 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Average X in T1U" | 690489 |-2.482423e+07 | -35.952 | 1141.3 | -2656.4 | 2656.3 | + | "Average X in T1V" | 696122 |-2.060219e+07 | -29.596 | 1128.0 | -2656.4 | 2656.3 | + | "Average X in T1X1" | 677723 |-3.438883e+07 | -50.742 | 1162.3 | -2646.2 | 2646.2 | + | "Average X in T1X2" | 705312 |-1.014161e+07 | -14.379 | 1120.8 | -2646.2 | 2646.2 | + | "Average X in T2U" | 673541 |-1.658606e+07 | -24.625 | 1135.5 | -2656.4 | 2656.3 | + | "Average X in T2V" | 693923 |-1.479371e+07 | -21.319 | 1129.9 | -2656.4 | 2656.3 | + | "Average X in T2X1" | 645225 |-1.705455e+07 | -26.432 | 1138.8 | -2646.2 | 2646.2 | + | "Average X in T2X2" | 716059 | -9891920 | -13.814 | 1124.6 | -2646.2 | 2646.2 | + | "Average X in T3U" | 731421 |-1.225062e+07 | -16.749 | 1333.5 | -3188.4 | 3188.4 | + | "Average X in T3V" | 753478 |-1.409381e+07 | -18.705 | 1328.7 | -3188.4 | 3188.4 | + | "Average X in T3X1" | 704173 |-1.010873e+07 | -14.355 | 1334.4 | -3176.2 | 3176.2 | + | "Average X in T3X2" | 782214 |-1.938375e+07 | -24.781 | 1321.3 | -3176.2 | 3176.2 | + | "Hits in T1U" | 9156 | 690489 | 75.414 | 27.984 | 5.0000 | 232.00 | + | "Hits in T1V" | 9156 | 696122 | 76.029 | 27.670 | 3.0000 | 245.00 | + | "Hits in T1X1" | 9156 | 677723 | 74.020 | 27.325 | 4.0000 | 205.00 | + | "Hits in T1X2" | 9156 | 705312 | 77.033 | 28.024 | 6.0000 | 266.00 | + | "Hits in T2U" | 9156 | 673541 | 73.563 | 26.210 | 3.0000 | 198.00 | + | "Hits in T2V" | 9156 | 693923 | 75.789 | 27.194 | 6.0000 | 374.00 | + | "Hits in T2X1" | 9156 | 645225 | 70.470 | 25.869 | 3.0000 | 288.00 | + | "Hits in T2X2" | 9156 | 716059 | 78.207 | 27.736 | 6.0000 | 287.00 | + | "Hits in T3U" | 9156 | 731421 | 79.884 | 27.669 | 2.0000 | 239.00 | + | "Hits in T3V" | 9156 | 753478 | 82.293 | 28.471 | 6.0000 | 207.00 | + | "Hits in T3X1" | 9156 | 704173 | 76.908 | 27.098 | 5.0000 | 339.00 | + | "Hits in T3X2" | 9156 | 782214 | 85.432 | 29.532 | 6.0000 | 204.00 | + | "Total number of hits" | 2289 | 8469680 | 3700.2 | 1120.3 | 604.00 | 6365.0 | +PrStoreUTHit_6220b56a INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrTrackAssociator_16ad4612 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 284763 | 279294 |( 98.07946 +- 0.02571932)% | + | "MC particles per track" | 279294 | 279304 | 1.0000 | +PrTrackAssociator_b8868774 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 26443 | 12200 |( 46.13697 +- 0.3065593)% | + | "MC particles per track" | 12200 | 13727 | 1.1252 | +PrTrackAssociator_d68377ee INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 593239 | 578457 |( 97.50826 +- 0.02023753)% | + | "MC particles per track" | 578457 | 581059 | 1.0045 | +SeedTrackChecker_ad9abe4e.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +VeloClusterTrackingSIMD_87c18651 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Clusters" | 2289 | 5397790 | 2358.1 | + | "Nb of Produced Tracks" | 2289 | 593239 | 259.17 | +fromPrMatchTracksV1Tracks_b22bdfde INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 26443 | 11.552 | +fromPrSeedingTracksV1Tracks_84cd... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 284763 | 124.40 | +fromPrVeloTracksV1TracksMerger_f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 593239 | 259.17 | +ApplicationMgr INFO Application Manager Stopped successfully +MatchTrackChecker_23e28c5b INFO Results +MatchTrackChecker_23e28c5b INFO **** Match 26443 tracks including 14243 ghosts [53.86 %], Event average 43.78 % **** +MatchTrackChecker_23e28c5b INFO 01_long : 1 from 152279 [ 0.00 %] 0 clones [ 0.00 %], purity:100.00 %, hitEff:100.00 % +MatchTrackChecker_23e28c5b INFO 02_long_P>5GeV : 1 from 98421 [ 0.00 %] 0 clones [ 0.00 %], purity:100.00 %, hitEff:100.00 % +MatchTrackChecker_23e28c5b INFO 03_long_strange : 0 from 8121 [ 0.00 %] 0 clones [ 0.00 %], purity: 0.00 %, hitEff: 0.00 % +MatchTrackChecker_23e28c5b INFO 04_long_strange_P>5GeV : 0 from 3856 [ 0.00 %] 0 clones [ 0.00 %], purity: 0.00 %, hitEff: 0.00 % +MatchTrackChecker_23e28c5b INFO 05_long_fromB : 0 from 7959 [ 0.00 %] 0 clones [ 0.00 %], purity: 0.00 %, hitEff: 0.00 % +MatchTrackChecker_23e28c5b INFO 05_long_fromD : 0 from 4226 [ 0.00 %] 0 clones [ 0.00 %], purity: 0.00 %, hitEff: 0.00 % +MatchTrackChecker_23e28c5b INFO 06_long_fromB_P>5GeV : 0 from 5983 [ 0.00 %] 0 clones [ 0.00 %], purity: 0.00 %, hitEff: 0.00 % +MatchTrackChecker_23e28c5b INFO 06_long_fromD_P>5GeV : 0 from 2894 [ 0.00 %] 0 clones [ 0.00 %], purity: 0.00 %, hitEff: 0.00 % +MatchTrackChecker_23e28c5b INFO 07_long_electrons : 11648 from 15125 [ 77.01 %] 183 clones [ 1.55 %], purity: 97.73 %, hitEff: 98.15 % +MatchTrackChecker_23e28c5b INFO 07_long_electrons_pairprod : 7822 from 10831 [ 72.22 %] 143 clones [ 1.80 %], purity: 97.10 %, hitEff: 97.86 % +MatchTrackChecker_23e28c5b INFO 08_long_fromB_electrons : 3649 from 4210 [ 86.67 %] 43 clones [ 1.16 %], purity: 99.05 %, hitEff: 98.87 % +MatchTrackChecker_23e28c5b INFO 09_long_fromB_electrons_P>5GeV : 3427 from 3850 [ 89.01 %] 40 clones [ 1.15 %], purity: 99.15 %, hitEff: 98.99 % +MatchTrackChecker_23e28c5b INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 0 from 5182 [ 0.00 %] 0 clones [ 0.00 %], purity: 0.00 %, hitEff: 0.00 % +MatchTrackChecker_23e28c5b INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3306 from 3659 [ 90.35 %] 37 clones [ 1.11 %], purity: 99.22 %, hitEff: 98.99 % +MatchTrackChecker_23e28c5b INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 0 from 2343 [ 0.00 %] 0 clones [ 0.00 %], purity: 0.00 %, hitEff: 0.00 % +MatchTrackChecker_23e28c5b INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 0 from 2010 [ 0.00 %] 0 clones [ 0.00 %], purity: 0.00 %, hitEff: 0.00 % +MatchTrackChecker_23e28c5b INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 0 from 5164 [ 0.00 %] 0 clones [ 0.00 %], purity: 0.00 %, hitEff: 0.00 % +MatchTrackChecker_23e28c5b INFO +MatchUTHitsChecker_5891c098 INFO Results +MatchUTHitsChecker_5891c098 INFO **** UT Efficiency for /Event/fromPrMatchTracksV1Tracks_b22bdfde/OutputTracksLocation **** 14243 ghost, 3.16 UT per track +MatchUTHitsChecker_5891c098 INFO 01_long : 1 tr 4.00 from 4.00 mcUT [100.0 %] 0.00 ghost hits on real tracks [ 0.0 %] +MatchUTHitsChecker_5891c098 INFO 01_long >3UT : 1 tr 4.00 from 4.00 mcUT [100.0 %] 0.00 ghost hits on real tracks [ 0.0 %] +MatchUTHitsChecker_5891c098 INFO 02_long_P>5GeV : 1 tr 4.00 from 4.00 mcUT [100.0 %] 0.00 ghost hits on real tracks [ 0.0 %] +MatchUTHitsChecker_5891c098 INFO 02_long_P>5GeV >3UT : 1 tr 4.00 from 4.00 mcUT [100.0 %] 0.00 ghost hits on real tracks [ 0.0 %] +MatchUTHitsChecker_5891c098 INFO +SeedTrackChecker_ad9abe4e INFO Results +SeedTrackChecker_ad9abe4e INFO **** Seed 284763 tracks including 5469 ghosts [ 1.92 %], Event average 1.56 % **** +SeedTrackChecker_ad9abe4e INFO 01_hasT : 198532 from 234618 [ 84.62 %] 7 clones [ 0.00 %], purity: 99.60 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 02_long : 143630 from 152279 [ 94.32 %] 6 clones [ 0.00 %], purity: 99.71 %, hitEff: 98.42 % +SeedTrackChecker_ad9abe4e INFO 03_long_P>5GeV : 95859 from 98421 [ 97.40 %] 5 clones [ 0.01 %], purity: 99.69 %, hitEff: 99.09 % +SeedTrackChecker_ad9abe4e INFO 04_long_fromB : 7598 from 7959 [ 95.46 %] 1 clones [ 0.01 %], purity: 99.75 %, hitEff: 98.65 % +SeedTrackChecker_ad9abe4e INFO 05_long_fromB_P>5GeV : 5835 from 5983 [ 97.53 %] 1 clones [ 0.02 %], purity: 99.76 %, hitEff: 99.13 % +SeedTrackChecker_ad9abe4e INFO 06_UT+T_strange : 16417 from 17658 [ 92.97 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.00 % +SeedTrackChecker_ad9abe4e INFO 07_UT+T_strange_P>5GeV : 8615 from 8825 [ 97.62 %] 0 clones [ 0.00 %], purity: 99.71 %, hitEff: 99.05 % +SeedTrackChecker_ad9abe4e INFO 08_noVelo+UT+T_strange : 8949 from 9658 [ 92.66 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.03 % +SeedTrackChecker_ad9abe4e INFO 09_noVelo+UT+T_strange_P>5GeV : 4914 from 5043 [ 97.44 %] 0 clones [ 0.00 %], purity: 99.70 %, hitEff: 99.01 % +SeedTrackChecker_ad9abe4e INFO 10_UT+T_SfromDB : 1133 from 1220 [ 92.87 %] 0 clones [ 0.00 %], purity: 99.77 %, hitEff: 97.99 % +SeedTrackChecker_ad9abe4e INFO 11_UT+T_SfromDB_P>5GeV : 612 from 623 [ 98.23 %] 0 clones [ 0.00 %], purity: 99.72 %, hitEff: 99.22 % +SeedTrackChecker_ad9abe4e INFO 12_noVelo+UT+T_SfromDB_P>5GeV : 420 from 428 [ 98.13 %] 0 clones [ 0.00 %], purity: 99.69 %, hitEff: 99.12 % +SeedTrackChecker_ad9abe4e INFO 13_hasT_electrons : 40669 from 74476 [ 54.61 %] 2 clones [ 0.00 %], purity: 99.69 %, hitEff: 97.16 % +SeedTrackChecker_ad9abe4e INFO 14_long_electrons : 13360 from 15125 [ 88.33 %] 1 clones [ 0.01 %], purity: 99.81 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 15_long_fromB_electrons : 3922 from 4210 [ 93.16 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.70 % +SeedTrackChecker_ad9abe4e INFO 16_long_electrons_P>5GeV : 8676 from 9420 [ 92.10 %] 0 clones [ 0.00 %], purity: 99.80 %, hitEff: 98.73 % +SeedTrackChecker_ad9abe4e INFO 17_long_fromB_electrons_P>5GeV : 3632 from 3850 [ 94.34 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.85 % +SeedTrackChecker_ad9abe4e INFO +HLTControlFlowMgr INFO Memory pool: used 3.89287 +/- 0.0385995 MiB (min: 0, max: 5) in 1 +/- 0 blocks (allocated >once in 0 +/- 0% events). Allocated capacity was 10 +/- 0 MiB (min: 10, max: 10) and 272.803 +/- 2.67012 (min: 4, max: 385) requests were served +HLTControlFlowMgr INFO Timing table: +HLTControlFlowMgr INFO + | Name of Algorithm | Execution Count | Total Time / s | Avg. Time / us | + | Sum of all Algorithms | 2955 | 179.266 | 60665.279 | + | "Fetch__Event_DAQ_RawEvent" | 2955 | 111.075 | 37588.778 | + | "SeedTrackChecker_ad9abe4e" | 2289 | 20.177 | 8814.972 | + | "MatchTrackChecker_23e28c5b" | 2289 | 15.929 | 6958.724 | + | "MatchUTHitsChecker_5891c098" | 2289 | 6.544 | 2859.090 | + | "PrMatchNN_fd9a8305" | 2289 | 4.969 | 2170.605 | + | "PrHybridSeeding_4d0337cc" | 2289 | 4.394 | 1919.529 | + | "PrLHCbID2MCParticle_a906d17d" | 2289 | 3.884 | 1696.797 | + | "Unpack__Event_MC_Vertices" | 2289 | 3.363 | 1469.397 | + | "Unpack__Event_MC_Particles" | 2289 | 3.282 | 1433.793 | + | "VeloClusterTrackingSIMD_87c18651" | 2289 | 1.035 | 452.058 | + | "VPClusFull_38754d8c" | 2289 | 0.870 | 380.105 | + | "VPFullCluster2MCParticleLinker_17386552" | 2289 | 0.858 | 374.976 | + | "PrStoreUTHit_6220b56a" | 2289 | 0.615 | 268.789 | + | "PrStorePrUTHits_df75b912" | 2289 | 0.485 | 211.746 | + | "PrTrackAssociator_d68377ee" | 2289 | 0.379 | 165.393 | + | "PrTrackAssociator_16ad4612" | 2289 | 0.337 | 147.050 | + | "fromPrVeloTracksV1TracksMerger_fa66a5de" | 2289 | 0.313 | 136.753 | + | "PrStoreSciFiHits_fb0eba02" | 2289 | 0.200 | 87.553 | + | "fromPrSeedingTracksV1Tracks_84cd46c2" | 2289 | 0.193 | 84.501 | + | "FTRawBankDecoder" | 2289 | 0.085 | 37.293 | + | "PrTrackAssociator_b8868774" | 2289 | 0.084 | 36.894 | + | "fromPrMatchTracksV1Tracks_b22bdfde" | 2289 | 0.055 | 24.218 | + | "UnpackRawEvent_FTCluster" | 2955 | 0.033 | 11.272 | + | "reserveIOV" | 2289 | 0.032 | 13.994 | + | "DefaultGECFilter" | 2955 | 0.008 | 2.754 | + | "Decode_ODIN" | 2289 | 0.008 | 3.496 | + | "Fetch__Event_Link_Raw_VP_Digits" | 2289 | 0.007 | 3.209 | + | "Fetch__Event_pSim_MCVertices" | 2289 | 0.007 | 2.891 | + | "UnpackRawEvent_VP" | 2289 | 0.006 | 2.766 | + | "UnpackRawEvent_UT" | 2955 | 0.006 | 2.121 | + | "Fetch__Event_pSim_MCParticles" | 2289 | 0.006 | 2.675 | + | "Fetch__Event_MC_TrackInfo" | 2289 | 0.006 | 2.556 | + | "Fetch__Event_Link_Raw_UT_Clusters" | 2289 | 0.006 | 2.429 | + | "UnpackRawEvent_ODIN" | 2289 | 0.005 | 2.160 | + | "DummyEventTime" | 2289 | 0.004 | 1.899 | + | "Fetch__Event_Link_Raw_FT_LiteClusters" | 2289 | 0.004 | 1.598 | + +HLTControlFlowMgr INFO StateTree: CFNode #executed #passed +LAZY_AND: hlt2_reco_decision #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + PrGECFilter/DefaultGECFilter #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + NONLAZY_OR: hlt2_reco_data #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/MatchTrackChecker_23e28c5b #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrUTHitChecker/MatchUTHitsChecker_5891c098 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/SeedTrackChecker_ad9abe4e #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + +HLTControlFlowMgr INFO Histograms converted successfully according to request. +ToolSvc INFO Removing all tools created by ToolSvc +SeedTrackChecker_ad9abe4e.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +MatchUTHitsChecker_5891c098.PrCh... SUCCESS Booked 36 Histogram(s) : 1D=32 2D=4 +MatchTrackChecker_23e28c5b.PrChe... SUCCESS Booked 589 Histogram(s) : 1D=420 2D=169 +RootCnvSvc INFO Disconnected data IO:9E3B8940-FB87-11EB-ADCA-FA163E643B60 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi] +RootCnvSvc INFO Disconnected data IO:AFCB9710-FB21-11EB-9E91-FA163ED3A4EB [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi] +RootCnvSvc INFO Disconnected data IO:B9D7CC62-FB38-11EB-8B01-3CECEF5D2C24 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi] +ApplicationMgr INFO Application Manager Finalized successfully +ApplicationMgr INFO Application Manager Terminated successfully diff --git a/data_matching/logs/calo_effs_testJpsi_NewParams.log b/data_matching/logs/calo_effs_testJpsi_NewParams.log new file mode 100644 index 0000000..96e564e --- /dev/null +++ b/data_matching/logs/calo_effs_testJpsi_NewParams.log @@ -0,0 +1,485 @@ +# setting LC_ALL to "C" +# --> Including file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_calo_data.py' +/***** User ApplicationOptions/ApplicationOptions ************************************************** +|-append_decoding_keys_to_output_manifest = True (default: True) +|-auditors = [] (default: []) +|-buffer_events = 20000 (default: 20000) +|-conddb_tag = 'sim-20210617-vc-md100' (default: '') +|-conditions_version = '' (default: '') +|-control_flow_file = '' (default: '') +|-data_flow_file = '' (default: '') +|-data_type = 'Upgrade' (default: 'Upgrade') +|-dddb_tag = 'dddb-20210617' (default: '') +|-event_store = 'HiveWhiteBoard' (default: 'HiveWhiteBoard') +|-evt_max = -1 (default: -1) +|-first_evt = 0 (default: 0) +|-geometry_version = '' (default: '') +|-histo_file = '' (default: '') +|-input_files = ['/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi'] +| (default: []) +|-input_manifest_file = '' (default: '') +|-input_process = '' (default: '') +|-input_raw_format = 0.5 (default: 0.5) +|-input_type = 'ROOT' (default: '') +|-lines_maker = None +|-memory_pool_size = 10485760 (default: 10485760) +|-monitoring_file = '' (default: '') +|-msg_svc_format = '% F%35W%S %7W%R%T %0W%M' (default: '% F%35W%S %7W%R%T %0W%M') +|-msg_svc_time_format = '%Y-%m-%d %H:%M:%S UTC' (default: '%Y-%m-%d %H:%M:%S UTC') +|-n_event_slots = 1 (default: -1) +|-n_threads = 1 (default: 1) +|-ntuple_file = '/work/cetin/LHCb/reco_tuner/data_matching/NewParams/calo_data_testJpsi_NewParams.root' +| (default: '') +|-output_file = '' (default: '') +|-output_level = 3 (default: 3) +|-output_manifest_file = '' (default: '') +|-output_type = '' (default: '') +|-persistreco_version = 1.0 (default: 1.0) +|-phoenix_filename = '' (default: '') +|-preamble_algs = [] (default: []) +|-print_freq = 10000 (default: 10000) +|-python_logging_level = 20 (default: 20) +|-require_specific_decoding_keys = [] (default: []) +|-scheduler_legacy_mode = True (default: True) +|-simulation = True (default: None) +|-use_iosvc = False (default: False) +|-velo_motion_system_yaml = '' (default: '') +|-write_decoding_keys_to_git = True (default: True) +\----- (End of User ApplicationOptions/ApplicationOptions) ----------------------------------------- +# Overrule specified for keys +# <-- End of file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_calo_data.py' +ApplicationMgr SUCCESS +==================================================================================================================================== + Welcome to Moore version 55.2 + running on lhcba2 on Mon Mar 11 12:24:51 2024 +==================================================================================================================================== +ApplicationMgr INFO Application Manager Configured successfully +ToolSvc.GitDDDB INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/DDDB.git' +ToolSvc.GitDDDB INFO using commit 'upgrade/dddb-20210617' corresponding to 1871f1bb5c0d68c81dda62e84cf1eb3a45513521 +ToolSvc.GitSIMCOND INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/SIMCOND.git' +ToolSvc.GitSIMCOND INFO using commit 'upgrade/sim-20210617-vc-md100' corresponding to 9aa116c7143d21760d1be07ce1ef22c0f8f07bdf +DetectorPersistencySvc INFO Added successfully Conversion service:XmlCnvSvc +DetectorDataSvc SUCCESS Detector description database: git:/lhcb.xml +EventClockSvc.FakeEventTime INFO Event times generated from 0 with steps of 0 +EventClockSvc.FakeEventTime INFO Run numbers generated from 0 every 0 events +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c1.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c2.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c3.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c4.down.cdf +MagneticFieldSvc INFO Map scaled by factor 1 with polarity internally used: -1 signed relative current: -1 +NTupleSvc INFO Added stream file:/work/cetin/LHCb/reco_tuner/data_matching/NewParams/calo_data_testJpsi_NewParams.root as FILE1 +HLTControlFlowMgr INFO Start initialization +RootHistSvc INFO Writing ROOT histograms to: /work/cetin/LHCb/reco_tuner/data_matching/NewParams/calo_data_testJpsi_NewParams.root +HistogramPersistencySvc INFO Added successfully Conversion service:RootHistSvc +DeFTDetector INFO Current FT geometry version = 64 +CaloTrackBasedElectronShowerAlg_... INFO getting parametrization histograms from paramfile://data/CaloPID/eshower_trackbased_parametrization.root +HLTControlFlowMgr INFO Concurrency level information: +HLTControlFlowMgr INFO o Number of events slots: 1 +HLTControlFlowMgr INFO o TBB thread pool size: 'ThreadPoolSize':1 +HLTControlFlowMgr INFO ---> End of Initialization. This took 29405 ms +ApplicationMgr INFO Application Manager Initialized successfully +FunctorFactory INFO Reusing functor library: "/tmp/FunctorJitLib_0xc5b1c410f2cf976c_0xc808b52dd7b87121.so" +ApplicationMgr INFO Application Manager Started successfully +EventPersistencySvc INFO Added successfully Conversion service:RootCnvSvc +EventSelector INFO Stream:EventSelector.DataStreamTool_1 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO Will measure time between events 10 and 2147483647 (stop might be some events later) +HLTControlFlowMgr INFO Starting loop on events +EventSelector SUCCESS Reading Event record 1. Record number within stream 1: 1 +FTRawBankDecoder INFO Conditions DB is compatible with FT bank version 4, 5, 6. +FTRawBankDecoder INFO Building the readout map with version 0 +CaloFutureClusterCovarianceAlg_1... INFO == Parameters for covariance estimation == +CaloFutureClusterCovarianceAlg_1... INFO Stochastic : [0.21, 0.14, 0.14] Sqrt(GeV) +CaloFutureClusterCovarianceAlg_1... INFO GainError : [0.045, 0.025, 0.025] +CaloFutureClusterCovarianceAlg_1... INFO IncoherentNoise : [2.2, 2.2, 2.2] ADC +CaloFutureClusterCovarianceAlg_1... INFO CoherentNoise : [1.3, 1.3, 1.3] ADC +CaloFutureClusterCovarianceAlg_1... INFO ConstantE : [0, 0, 0] MeV +CaloFutureClusterCovarianceAlg_1... INFO ConstantX : [9, 2, 0.5] mm +CaloFutureClusterCovarianceAlg_1... INFO ConstantY : [9, 2, 0.5] mm +CaloFutureClusterCovarianceAlg_1... INFO Energy mask : (from DB) +CaloFutureClusterCovarianceAlg_1... INFO Position mask : (from DB) +HLTControlFlowMgr INFO Timing started at: 12:25:50 +EventSelector INFO Stream:EventSelector.DataStreamTool_2 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +EventSelector INFO Stream:EventSelector.DataStreamTool_3 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO No more events in event selection +HLTControlFlowMgr INFO ---> Loop over 2955 Events Finished - WSS 1804.18, timed 2945 Events: 206959 ms, Evts/s = 14.2299 +CaloAcceptanceEcalAlg_Ttrack_1ad... INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#total tracks" | 2289 | 284763 | 124.40 | 43.203 | 7.0000 | 248.00 | + | "#tracks in acceptance" | 2289 | 233690 | 102.09 | 35.860 | 7.0000 | 212.00 | +CaloFutureClusterCovarianceAlg_1... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# clusters" | 460619 | +CaloFutureClusterCovarianceAlg_1... INFO Number of counters : 3 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Corrected Clusters: # cells " | 42592 | 185661 | 4.3591 | 1.3800 | 2.0000 | 14.000 | + | "Corrected Clusters: ET" | 42592 |1.217924e+07 | 285.95 | 492.01 | 0.60000 | 19198. | + | "Corrected Clusters: size ratio" | 42592 | 21653.6 | 0.50840 | 0.45223 | -1.1017e-15 | 7.0882 | +CaloSelectiveElectronMatchAlg_Tt... INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#links in table" | 2289 | 196473 | 85.834 | 32.359 | 4.0000 | 186.00 | + | "average chi2" | 196473 | 28600.87 | 0.14557 | 0.18097 | 2.5694e-07 | 8.8763 | +CaloSelectiveTrackMatchAlg_Ttrac... INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#links in table" | 2289 | 197985 | 86.494 | 32.486 | 4.0000 | 186.00 | + | "average chi2" | 197985 | 5063.975 | 0.025578 | 0.045867 | 7.4238e-08 | 3.6636 | +CaloTrackBasedElectronShowerAlg_... INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "average DLL" | 233690 | -5899.35 | -0.025244 | 0.042736 | -1.6606 | 0.49540 | + | "average E/p" | 233690 | 950.3228 | 0.0040666 | 0.0046573 | 0.0000 | 0.20127 | +ClassifyPhotonElectronAlg_3be601a8 INFO Number of counters : 14 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Electron Delta(E)" | 164102 |-6.43632e+07 | -392.21 | 527.89 | -12989. | 9687.1 | + | "Electron Delta(X)" | 164102 | -52538.47 | -0.32016 | 12.236 | -102.44 | 73.909 | + | "Electron Delta(Y)" | 164102 | -42581.65 | -0.25948 | 12.219 | -90.385 | 90.646 | + | "Electron Delta(Z)" | 164102 |1.085137e+07 | 66.126 | 14.233 | -9.9102 | 134.58 | + | "Electron corrected energy" | 164102 |1.07999e+09 | 6581.2 | 8795.1 | 20.865 | 6.0331e+05 | + | "Electrons pT-rejected after correction" | 1176 | + | "Photon Delta(E)" | 297172 |-6.845382e+07 | -230.35 | 398.21 | -8742.9 | 8635.4 | + | "Photon Delta(X)" | 297172 | -88809.13 | -0.29885 | 12.805 | -92.061 | 86.241 | + | "Photon Delta(Y)" | 297172 | -100248.4 | -0.33734 | 12.794 | -92.484 | 73.654 | + | "Photon Delta(Z)" | 297172 |1.657882e+07 | 55.789 | 13.183 | -10.359 | 128.42 | + | "Photon corrected energy" | 297172 |1.041506e+09 | 3504.7 | 6206.4 | 20.198 | 3.5395e+05 | + | "Photons pT-rejected after correction" | 5064 | + | "electronHypos" | 2289 | 162926 | 71.178 | 23.775 | 4.0000 | 140.00 | + | "photonHypos" | 2289 | 292108 | 127.61 | 35.793 | 11.000 | 214.00 | +ClassifyPhotonElectronAlg_3be601... INFO Number of counters : 7 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | " Inner" | 126684 | 125993.2 | 0.99455 | 0.019853 | 0.96422 | 1.2194 | + | " Middle" | 123144 | 123893 | 1.0061 | 0.020270 | 0.97669 | 1.2090 | + | " Outer" | 210566 | 210420.9 | 0.99931 | 0.016327 | 0.97360 | 1.1546 | + | "Pileup offset" | 460394 |1.64556e+08 | 357.42 | 422.51 | -4249.0 | 4724.6 | + | "Pileup scale" | 461274 | 2574610 | 5.5815 | 1.7679 | 1.0000 | 14.000 | + | "Pileup subtracted ratio" | 460394 | 406791.8 | 0.88357 | 0.12017 | 6.7550e-05 | 1.6696 | + | "Skip negative energy correction" | 880 | +DefaultGECFilter INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb Events Processed" | 2955 | + | "Nb events removed" | 666 | +ForwardTrackChecker_482fda95.LoK... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +ForwardUTHitsChecker_fe9d9ac2.Lo... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 4 | +GraphClustering_72971694 INFO Number of counters : 4 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# clusters" | 2289 | 460619 | 201.23 | 56.959 | 14.000 | 333.00 | + | "Cluster energy" | 460619 |2.244434e+09 | 4872.6 | 7606.7 | 3.6000 | 5.9362e+05 | + | "Cluster size" | 460619 | 4680898 | 10.162 | 2.4013 | 4.0000 | 28.000 | + | "Negative energy clusters" | 25 | 26 | 1.0400 | 0.19596 | 1.0000 | 2.0000 | +HLTControlFlowMgr INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Processed events" | 2955 | +LHCb__Converters__Track__SOA__fr... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Tracks" | 2289 | 284763 | 124.40 | +MatchTrackChecker_ac9fdd0b.LoKi:... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +MatchUTHitsChecker_69ac963b.LoKi... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 4 | +PrFilterTracks2CaloClusters_cae3... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Cut selection efficiency" | 284763 | 186532 |( 65.50430 +- 0.08907906)% | +PrFilterTracks2ElectronMatch_426... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Cut selection efficiency" | 284763 | 144590 |( 50.77556 +- 0.09368628)% | +PrFilterTracks2ElectronShower_72... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Cut selection efficiency" | 284763 | 173169 |( 60.81162 +- 0.09148084)% | +PrForwardTrackingVelo_6024f9ec INFO Number of counters : 10 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Accepted input tracks" | 2289 | 363254 | 158.70 | + | "Created long tracks" | 2289 | 181236 | 79.177 | + | "Input tracks" | 2289 | 380749 | 166.34 | + | "Number of candidate bins per track" | 363254 | 1665217 | 4.5842 | 5.0318 | 0.0000 | 56.000 | + | "Number of complete candidates/track 1st Loop" | 305079 | 195005 | 0.63920 | 0.65005 | 0.0000 | 6.0000 | + | "Number of complete candidates/track 2nd Loop" | 148403 | 13248 | 0.089270 | 0.29669 | 0.0000 | 3.0000 | + | "Number of x candidates per track 1st Loop" | 305079 | 426093 | 1.3967 | 1.3487 | + | "Number of x candidates per track 2nd Loop" | 148403 | 347932 | 2.3445 | 2.6098 | + | "Percentage second loop execution" | 305079 | 148403 | 0.48644 | + | "Removed duplicates" | 2289 | 9647 | 4.2145 | +PrForwardTrackingVelo_6024f9ec.P... INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 166072 | 673152 | 4.0534 | + | "#tracks with hits added" | 166072 | +PrHybridSeeding_4d0337cc INFO Number of counters : 21 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Created T2x1 three-hit combinations in case 0" | 3981395 | 2438467 | 0.61247 | 0.62452 | 0.0000 | 6.0000 | + | "Created T2x1 three-hit combinations in case 1" | 4961664 | 3252259 | 0.65548 | 0.75200 | 0.0000 | 12.000 | + | "Created T2x1 three-hit combinations in case 2" | 7644512 | 6133331 | 0.80232 | 1.0193 | 0.0000 | 23.000 | + | "Created XZ tracks (part 0)" | 6867 | 363280 | 52.902 | 44.400 | 0.0000 | 844.00 | + | "Created XZ tracks (part 1)" | 6867 | 360418 | 52.486 | 47.084 | 0.0000 | 1257.0 | + | "Created XZ tracks in case 0" | 4578 | 269789 | 58.932 | 37.398 | 1.0000 | 363.00 | + | "Created XZ tracks in case 1" | 4578 | 267868 | 58.512 | 44.098 | 1.0000 | 709.00 | + | "Created XZ tracks in case 2" | 4578 | 186041 | 40.638 | 52.165 | 0.0000 | 1257.0 | + | "Created full hit combinations in case 0" | 407934 | 407934 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 1" | 310355 | 310355 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 2" | 280325 | 280325 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created seed tracks" | 4578 | 284763 | 62.202 | 22.650 | 3.0000 | 141.00 | + | "Created seed tracks (part 0)" | 2289 | 159664 | 69.753 | 25.912 | 4.0000 | 161.00 | + | "Created seed tracks (part 1)" | 2289 | 157869 | 68.969 | 25.854 | 3.0000 | 159.00 | + | "Created seed tracks in case 0" | 4578 | 148622 | 32.464 | 12.801 | 1.0000 | 86.000 | + | "Created seed tracks in case 1" | 4578 | 270703 | 59.131 | 21.736 | 2.0000 | 132.00 | + | "Created seed tracks in case 2" | 4578 | 302221 | 66.016 | 24.642 | 3.0000 | 153.00 | + | "Created seed tracks in recovery step" | 2289 | 15312 | 6.6894 | 3.8772 | 0.0000 | 26.000 | + | "Created two-hit combinations in case 0" | 677723 |1.546134e+07 | 22.814 | 15.827 | 0.0000 | 117.00 | + | "Created two-hit combinations in case 1" | 584001 |1.760625e+07 | 30.148 | 18.628 | 0.0000 | 262.00 | + | "Created two-hit combinations in case 2" | 461883 |2.056474e+07 | 44.524 | 28.512 | 0.0000 | 333.00 | +PrLHCbID2MCParticle_a906d17d INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#removed null MCParticles" | 16672433 | 0 | 0.0000 | +PrMatchNNv3_92e0e3ea INFO Number of counters : 3 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#MatchingChi2" | 2289 | 6351575 | 2774.8 | + | "#MatchingMLP" | 152302 | 132224.7 | 0.86817 | + | "#MatchingTracks" | 2289 | 152302 | 66.536 | +PrMatchNNv3_92e0e3ea.PrAddUTHits... INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 128503 | 500109 | 3.8918 | + | "#tracks with hits added" | 128503 | +PrStorePrUTHits_df75b912 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrStoreSciFiHits_fb0eba02 INFO Number of counters : 25 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Average X in T1U" | 690489 |-2.482423e+07 | -35.952 | 1141.3 | -2656.4 | 2656.3 | + | "Average X in T1V" | 696122 |-2.060219e+07 | -29.596 | 1128.0 | -2656.4 | 2656.3 | + | "Average X in T1X1" | 677723 |-3.438883e+07 | -50.742 | 1162.3 | -2646.2 | 2646.2 | + | "Average X in T1X2" | 705312 |-1.014161e+07 | -14.379 | 1120.8 | -2646.2 | 2646.2 | + | "Average X in T2U" | 673541 |-1.658606e+07 | -24.625 | 1135.5 | -2656.4 | 2656.3 | + | "Average X in T2V" | 693923 |-1.479371e+07 | -21.319 | 1129.9 | -2656.4 | 2656.3 | + | "Average X in T2X1" | 645225 |-1.705455e+07 | -26.432 | 1138.8 | -2646.2 | 2646.2 | + | "Average X in T2X2" | 716059 | -9891920 | -13.814 | 1124.6 | -2646.2 | 2646.2 | + | "Average X in T3U" | 731421 |-1.225062e+07 | -16.749 | 1333.5 | -3188.4 | 3188.4 | + | "Average X in T3V" | 753478 |-1.409381e+07 | -18.705 | 1328.7 | -3188.4 | 3188.4 | + | "Average X in T3X1" | 704173 |-1.010873e+07 | -14.355 | 1334.4 | -3176.2 | 3176.2 | + | "Average X in T3X2" | 782214 |-1.938375e+07 | -24.781 | 1321.3 | -3176.2 | 3176.2 | + | "Hits in T1U" | 9156 | 690489 | 75.414 | 27.984 | 5.0000 | 232.00 | + | "Hits in T1V" | 9156 | 696122 | 76.029 | 27.670 | 3.0000 | 245.00 | + | "Hits in T1X1" | 9156 | 677723 | 74.020 | 27.325 | 4.0000 | 205.00 | + | "Hits in T1X2" | 9156 | 705312 | 77.033 | 28.024 | 6.0000 | 266.00 | + | "Hits in T2U" | 9156 | 673541 | 73.563 | 26.210 | 3.0000 | 198.00 | + | "Hits in T2V" | 9156 | 693923 | 75.789 | 27.194 | 6.0000 | 374.00 | + | "Hits in T2X1" | 9156 | 645225 | 70.470 | 25.869 | 3.0000 | 288.00 | + | "Hits in T2X2" | 9156 | 716059 | 78.207 | 27.736 | 6.0000 | 287.00 | + | "Hits in T3U" | 9156 | 731421 | 79.884 | 27.669 | 2.0000 | 239.00 | + | "Hits in T3V" | 9156 | 753478 | 82.293 | 28.471 | 6.0000 | 207.00 | + | "Hits in T3X1" | 9156 | 704173 | 76.908 | 27.098 | 5.0000 | 339.00 | + | "Hits in T3X2" | 9156 | 782214 | 85.432 | 29.532 | 6.0000 | 204.00 | + | "Total number of hits" | 2289 | 8469680 | 3700.2 | 1120.3 | 604.00 | 6365.0 | +PrStoreUTHit_6220b56a INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrTrackAssociator_16ad4612 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 284763 | 279294 |( 98.07946 +- 0.02571932)% | + | "MC particles per track" | 279294 | 279304 | 1.0000 | +PrTrackAssociator_3adf94fb INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 181236 | 155077 |( 85.56633 +- 0.08255009)% | + | "MC particles per track" | 155077 | 181813 | 1.1724 | +PrTrackAssociator_924c9da5 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 152302 | 82999 |( 54.49633 +- 0.1276010)% | + | "MC particles per track" | 82999 | 95416 | 1.1496 | +SeedTrackChecker_ad9abe4e.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +TrackBeamLineVertexFinderSoA_f85... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb PVs" | 2289 | 12075 | 5.2752 | +VeloClusterTrackingSIMD_87c18651 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Clusters" | 2289 | 5397790 | 2358.1 | + | "Nb of Produced Tracks" | 2289 | 593239 | 259.17 | +fromPrForwardTracksV1Tracks_f53f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 181236 | 79.177 | +fromPrMatchTracksV1Tracks_8e5d998e INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 152302 | 66.536 | +fromPrSeedingTracksV1Tracks_84cd... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 284763 | 124.40 | +fromPrVeloTracksV1TracksMerger_f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 593239 | 259.17 | +fromV3TrackV1Track_99589441 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Tracks" | 2289 | 173169 | 75.653 | +ApplicationMgr INFO Application Manager Stopped successfully +ForwardTrackChecker_482fda95 INFO Results +ForwardTrackChecker_482fda95 INFO **** Forward 181236 tracks including 26159 ghosts [14.43 %], Event average 13.11 % **** +ForwardTrackChecker_482fda95 INFO 01_long : 133702 from 152279 [ 87.80 %] 513 clones [ 0.38 %], purity: 99.21 %, hitEff: 98.43 % +ForwardTrackChecker_482fda95 INFO 02_long_P>5GeV : 91867 from 98421 [ 93.34 %] 307 clones [ 0.33 %], purity: 99.32 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 03_long_strange : 6588 from 8121 [ 81.12 %] 20 clones [ 0.30 %], purity: 98.87 %, hitEff: 98.21 % +ForwardTrackChecker_482fda95 INFO 04_long_strange_P>5GeV : 3465 from 3856 [ 89.86 %] 8 clones [ 0.23 %], purity: 99.05 %, hitEff: 98.80 % +ForwardTrackChecker_482fda95 INFO 05_long_fromB : 7199 from 7959 [ 90.45 %] 26 clones [ 0.36 %], purity: 99.34 %, hitEff: 98.69 % +ForwardTrackChecker_482fda95 INFO 05_long_fromD : 3793 from 4226 [ 89.75 %] 10 clones [ 0.26 %], purity: 99.25 %, hitEff: 98.50 % +ForwardTrackChecker_482fda95 INFO 06_long_fromB_P>5GeV : 5664 from 5983 [ 94.67 %] 18 clones [ 0.32 %], purity: 99.45 %, hitEff: 98.93 % +ForwardTrackChecker_482fda95 INFO 06_long_fromD_P>5GeV : 2732 from 2894 [ 94.40 %] 7 clones [ 0.26 %], purity: 99.35 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons : 10559 from 15125 [ 69.81 %] 108 clones [ 1.01 %], purity: 97.96 %, hitEff: 98.31 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons_pairprod : 6890 from 10831 [ 63.61 %] 86 clones [ 1.23 %], purity: 97.36 %, hitEff: 98.08 % +ForwardTrackChecker_482fda95 INFO 08_long_fromB_electrons : 3548 from 4210 [ 84.28 %] 22 clones [ 0.62 %], purity: 99.07 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 09_long_fromB_electrons_P>5GeV : 3333 from 3850 [ 86.57 %] 21 clones [ 0.63 %], purity: 99.15 %, hitEff: 98.96 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 4902 from 5182 [ 94.60 %] 17 clones [ 0.35 %], purity: 99.55 %, hitEff: 98.93 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3220 from 3659 [ 88.00 %] 19 clones [ 0.59 %], purity: 99.22 %, hitEff: 98.94 % +ForwardTrackChecker_482fda95 INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 2218 from 2343 [ 94.66 %] 6 clones [ 0.27 %], purity: 99.49 %, hitEff: 98.85 % +ForwardTrackChecker_482fda95 INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 1801 from 2010 [ 89.60 %] 4 clones [ 0.22 %], purity: 99.36 %, hitEff: 98.68 % +ForwardTrackChecker_482fda95 INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4889 from 5164 [ 94.67 %] 17 clones [ 0.35 %], purity: 99.55 %, hitEff: 98.94 % +ForwardTrackChecker_482fda95 INFO +ForwardUTHitsChecker_fe9d9ac2 INFO Results +ForwardUTHitsChecker_fe9d9ac2 INFO **** UT Efficiency for /Event/fromPrForwardTracksV1Tracks_f53f50a8/OutputTracksLocation **** 26159 ghost, 2.61 UT per track +ForwardUTHitsChecker_fe9d9ac2 INFO 01_long :134215 tr 3.91 from 4.07 mcUT [ 95.9 %] 0.12 ghost hits on real tracks [ 3.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 01_long >3UT :132800 tr 3.94 from 4.10 mcUT [ 96.2 %] 0.12 ghost hits on real tracks [ 2.9 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 02_long_P>5GeV : 92174 tr 3.94 from 4.07 mcUT [ 96.8 %] 0.10 ghost hits on real tracks [ 2.4 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 02_long_P>5GeV >3UT : 90908 tr 3.99 from 4.11 mcUT [ 97.2 %] 0.09 ghost hits on real tracks [ 2.2 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV : 4919 tr 4.00 from 4.07 mcUT [ 98.2 %] 0.05 ghost hits on real tracks [ 1.1 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4897 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.04 ghost hits on real tracks [ 1.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4906 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.05 ghost hits on real tracks [ 1.1 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4897 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.04 ghost hits on real tracks [ 1.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO +GraphClustering_72971694 INFO Built <201.232> graph calo clustering clusters/event +MatchTrackChecker_ac9fdd0b INFO Results +MatchTrackChecker_ac9fdd0b INFO **** Match 152302 tracks including 69303 ghosts [45.50 %], Event average 42.09 % **** +MatchTrackChecker_ac9fdd0b INFO 01_long : 66226 from 152279 [ 43.49 %] 427 clones [ 0.64 %], purity: 99.27 %, hitEff: 98.40 % +MatchTrackChecker_ac9fdd0b INFO 02_long_P>5GeV : 41782 from 98421 [ 42.45 %] 211 clones [ 0.50 %], purity: 99.43 %, hitEff: 99.27 % +MatchTrackChecker_ac9fdd0b INFO 03_long_strange : 3361 from 8121 [ 41.39 %] 19 clones [ 0.56 %], purity: 98.92 %, hitEff: 97.84 % +MatchTrackChecker_ac9fdd0b INFO 04_long_strange_P>5GeV : 1614 from 3856 [ 41.86 %] 8 clones [ 0.49 %], purity: 99.20 %, hitEff: 99.28 % +MatchTrackChecker_ac9fdd0b INFO 05_long_fromB : 3243 from 7959 [ 40.75 %] 25 clones [ 0.76 %], purity: 99.39 %, hitEff: 98.65 % +MatchTrackChecker_ac9fdd0b INFO 05_long_fromD : 1768 from 4226 [ 41.84 %] 11 clones [ 0.62 %], purity: 99.29 %, hitEff: 98.43 % +MatchTrackChecker_ac9fdd0b INFO 06_long_fromB_P>5GeV : 2368 from 5983 [ 39.58 %] 11 clones [ 0.46 %], purity: 99.55 %, hitEff: 99.28 % +MatchTrackChecker_ac9fdd0b INFO 06_long_fromD_P>5GeV : 1150 from 2894 [ 39.74 %] 5 clones [ 0.43 %], purity: 99.51 %, hitEff: 99.21 % +MatchTrackChecker_ac9fdd0b INFO 07_long_electrons : 11446 from 15125 [ 75.68 %] 175 clones [ 1.51 %], purity: 97.79 %, hitEff: 98.19 % +MatchTrackChecker_ac9fdd0b INFO 07_long_electrons_pairprod : 7675 from 10831 [ 70.86 %] 138 clones [ 1.77 %], purity: 97.16 %, hitEff: 97.89 % +MatchTrackChecker_ac9fdd0b INFO 08_long_fromB_electrons : 3605 from 4210 [ 85.63 %] 41 clones [ 1.12 %], purity: 99.06 %, hitEff: 98.90 % +MatchTrackChecker_ac9fdd0b INFO 09_long_fromB_electrons_P>5GeV : 3386 from 3850 [ 87.95 %] 38 clones [ 1.11 %], purity: 99.16 %, hitEff: 99.02 % +MatchTrackChecker_ac9fdd0b INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 2060 from 5182 [ 39.75 %] 13 clones [ 0.63 %], purity: 99.63 %, hitEff: 99.17 % +MatchTrackChecker_ac9fdd0b INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3268 from 3659 [ 89.31 %] 35 clones [ 1.06 %], purity: 99.23 %, hitEff: 99.02 % +MatchTrackChecker_ac9fdd0b INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 937 from 2343 [ 39.99 %] 6 clones [ 0.64 %], purity: 99.63 %, hitEff: 99.06 % +MatchTrackChecker_ac9fdd0b INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 842 from 2010 [ 41.89 %] 2 clones [ 0.24 %], purity: 99.54 %, hitEff: 99.16 % +MatchTrackChecker_ac9fdd0b INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 2050 from 5164 [ 39.70 %] 13 clones [ 0.63 %], purity: 99.63 %, hitEff: 99.19 % +MatchTrackChecker_ac9fdd0b INFO +MatchUTHitsChecker_69ac963b INFO Results +MatchUTHitsChecker_69ac963b INFO **** UT Efficiency for /Event/fromPrMatchTracksV1Tracks_8e5d998e/OutputTracksLocation **** 69303 ghost, 2.56 UT per track +MatchUTHitsChecker_69ac963b INFO 01_long : 66653 tr 3.87 from 4.08 mcUT [ 94.8 %] 0.14 ghost hits on real tracks [ 3.6 %] +MatchUTHitsChecker_69ac963b INFO 01_long >3UT : 65906 tr 3.91 from 4.11 mcUT [ 95.1 %] 0.14 ghost hits on real tracks [ 3.4 %] +MatchUTHitsChecker_69ac963b INFO 02_long_P>5GeV : 41993 tr 3.94 from 4.09 mcUT [ 96.5 %] 0.10 ghost hits on real tracks [ 2.5 %] +MatchUTHitsChecker_69ac963b INFO 02_long_P>5GeV >3UT : 41375 tr 4.00 from 4.12 mcUT [ 96.9 %] 0.10 ghost hits on real tracks [ 2.3 %] +MatchUTHitsChecker_69ac963b INFO 03_long_fromB_P>3GeV_Pt>0.5GeV : 2073 tr 3.98 from 4.08 mcUT [ 97.7 %] 0.05 ghost hits on real tracks [ 1.3 %] +MatchUTHitsChecker_69ac963b INFO 03_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 2059 tr 4.01 from 4.09 mcUT [ 97.9 %] 0.05 ghost hits on real tracks [ 1.3 %] +MatchUTHitsChecker_69ac963b INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV : 2063 tr 4.00 from 4.09 mcUT [ 97.9 %] 0.05 ghost hits on real tracks [ 1.3 %] +MatchUTHitsChecker_69ac963b INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 2059 tr 4.01 from 4.09 mcUT [ 97.9 %] 0.05 ghost hits on real tracks [ 1.3 %] +MatchUTHitsChecker_69ac963b INFO +SeedTrackChecker_ad9abe4e INFO Results +SeedTrackChecker_ad9abe4e INFO **** Seed 284763 tracks including 5469 ghosts [ 1.92 %], Event average 1.56 % **** +SeedTrackChecker_ad9abe4e INFO 01_hasT : 198532 from 234618 [ 84.62 %] 7 clones [ 0.00 %], purity: 99.60 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 02_long : 143630 from 152279 [ 94.32 %] 6 clones [ 0.00 %], purity: 99.71 %, hitEff: 98.42 % +SeedTrackChecker_ad9abe4e INFO 03_long_P>5GeV : 95859 from 98421 [ 97.40 %] 5 clones [ 0.01 %], purity: 99.69 %, hitEff: 99.09 % +SeedTrackChecker_ad9abe4e INFO 04_long_fromB : 7598 from 7959 [ 95.46 %] 1 clones [ 0.01 %], purity: 99.75 %, hitEff: 98.65 % +SeedTrackChecker_ad9abe4e INFO 05_long_fromB_P>5GeV : 5835 from 5983 [ 97.53 %] 1 clones [ 0.02 %], purity: 99.76 %, hitEff: 99.13 % +SeedTrackChecker_ad9abe4e INFO 06_UT+T_strange : 16417 from 17658 [ 92.97 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.00 % +SeedTrackChecker_ad9abe4e INFO 07_UT+T_strange_P>5GeV : 8615 from 8825 [ 97.62 %] 0 clones [ 0.00 %], purity: 99.71 %, hitEff: 99.05 % +SeedTrackChecker_ad9abe4e INFO 08_noVelo+UT+T_strange : 8949 from 9658 [ 92.66 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.03 % +SeedTrackChecker_ad9abe4e INFO 09_noVelo+UT+T_strange_P>5GeV : 4914 from 5043 [ 97.44 %] 0 clones [ 0.00 %], purity: 99.70 %, hitEff: 99.01 % +SeedTrackChecker_ad9abe4e INFO 10_UT+T_SfromDB : 1133 from 1220 [ 92.87 %] 0 clones [ 0.00 %], purity: 99.77 %, hitEff: 97.99 % +SeedTrackChecker_ad9abe4e INFO 11_UT+T_SfromDB_P>5GeV : 612 from 623 [ 98.23 %] 0 clones [ 0.00 %], purity: 99.72 %, hitEff: 99.22 % +SeedTrackChecker_ad9abe4e INFO 12_noVelo+UT+T_SfromDB_P>5GeV : 420 from 428 [ 98.13 %] 0 clones [ 0.00 %], purity: 99.69 %, hitEff: 99.12 % +SeedTrackChecker_ad9abe4e INFO 13_hasT_electrons : 40669 from 74476 [ 54.61 %] 2 clones [ 0.00 %], purity: 99.69 %, hitEff: 97.16 % +SeedTrackChecker_ad9abe4e INFO 14_long_electrons : 13360 from 15125 [ 88.33 %] 1 clones [ 0.01 %], purity: 99.81 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 15_long_fromB_electrons : 3922 from 4210 [ 93.16 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.70 % +SeedTrackChecker_ad9abe4e INFO 16_long_electrons_P>5GeV : 8676 from 9420 [ 92.10 %] 0 clones [ 0.00 %], purity: 99.80 %, hitEff: 98.73 % +SeedTrackChecker_ad9abe4e INFO 17_long_fromB_electrons_P>5GeV : 3632 from 3850 [ 94.34 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.85 % +SeedTrackChecker_ad9abe4e INFO +HLTControlFlowMgr INFO Memory pool: used 4.78838 +/- 0.0475562 MiB (min: 0, max: 6) in 1 +/- 0 blocks (allocated >once in 0 +/- 0% events). Allocated capacity was 10 +/- 0 MiB (min: 10, max: 10) and 705.46 +/- 6.98485 (min: 4, max: 1064) requests were served +HLTControlFlowMgr INFO Timing table: +HLTControlFlowMgr INFO + | Name of Algorithm | Execution Count | Total Time / s | Avg. Time / us | + | Sum of all Algorithms | 2955 | 203.111 | 68734.534 | + | "Fetch__Event_DAQ_RawEvent" | 2955 | 107.901 | 36514.637 | + | "SeedTrackChecker_ad9abe4e" | 2289 | 18.371 | 8025.917 | + | "ForwardTrackChecker_482fda95" | 2289 | 16.913 | 7388.852 | + | "MatchTrackChecker_ac9fdd0b" | 2289 | 14.268 | 6233.267 | + | "ForwardUTHitsChecker_fe9d9ac2" | 2289 | 6.511 | 2844.470 | + | "MatchUTHitsChecker_69ac963b" | 2289 | 6.289 | 2747.290 | + | "PrForwardTrackingVelo_6024f9ec" | 2289 | 5.682 | 2482.425 | + | "PrHybridSeeding_4d0337cc" | 2289 | 4.314 | 1884.622 | + | "PrLHCbID2MCParticle_a906d17d" | 2289 | 3.526 | 1540.320 | + | "Unpack__Event_MC_Vertices" | 2289 | 2.959 | 1292.608 | + | "Unpack__Event_MC_Particles" | 2289 | 2.814 | 1229.233 | + | "GraphClustering_72971694" | 2289 | 2.210 | 965.374 | + | "CaloTrackBasedElectronShowerAlg_Ttrack_6c238bce" | 2289 | 1.252 | 547.112 | + | "VeloClusterTrackingSIMD_87c18651" | 2289 | 1.031 | 450.248 | + | "ClassifyPhotonElectronAlg_3be601a8" | 2289 | 0.810 | 353.804 | + | "VPFullCluster2MCParticleLinker_17386552" | 2289 | 0.792 | 345.858 | + | "VPClusFull_38754d8c" | 2289 | 0.787 | 344.035 | + | "PrStorePrUTHits_df75b912" | 2289 | 0.771 | 336.698 | + | "PrMatchNNv3_92e0e3ea" | 2289 | 0.722 | 315.285 | + | "FutureEcalZSup" | 2289 | 0.648 | 283.301 | + | "CaloFutureClusterCovarianceAlg_1a2d4ea3" | 2289 | 0.564 | 246.356 | + | "PrStoreUTHit_6220b56a" | 2289 | 0.529 | 231.104 | + | "PrTrackAssociator_3adf94fb" | 2289 | 0.502 | 219.239 | + | "PrTrackAssociator_924c9da5" | 2289 | 0.353 | 154.003 | + | "PrTrackAssociator_16ad4612" | 2289 | 0.351 | 153.480 | + | "fromPrVeloTracksV1TracksMerger_fa66a5de" | 2289 | 0.271 | 118.489 | + | "fromPrSeedingTracksV1Tracks_84cd46c2" | 2289 | 0.193 | 84.506 | + | "fromPrForwardTracksV1Tracks_f53f50a8" | 2289 | 0.189 | 82.409 | + | "PrStoreSciFiHits_fb0eba02" | 2289 | 0.187 | 81.848 | + | "fromPrMatchTracksV1Tracks_8e5d998e" | 2289 | 0.184 | 80.172 | + | "LHCb__Converters__Track__SOA__fromV1Track_854f0d04" | 2289 | 0.171 | 74.858 | + | "fromV3TrackV1Track_99589441" | 2289 | 0.155 | 67.575 | + | "CaloSelectiveTrackMatchAlg_Ttrack_bd1b5be2" | 2289 | 0.132 | 57.534 | + | "CaloAcceptanceEcalAlg_Ttrack_1ad7ead8" | 2289 | 0.107 | 46.940 | + | "TrackBeamLineVertexFinderSoA_f85e7c3b" | 2289 | 0.105 | 45.808 | + | "FTRawBankDecoder" | 2289 | 0.091 | 39.805 | + | "CaloSelectiveElectronMatchAlg_Ttrack_7febcd2c" | 2289 | 0.089 | 39.081 | + | "PrFilterTracks2CaloClusters_cae3b638" | 2289 | 0.062 | 27.252 | + | "PrFilterTracks2ElectronMatch_4265680d" | 2289 | 0.060 | 26.305 | + | "PrFilterTracks2ElectronShower_72362ae8" | 2289 | 0.059 | 25.967 | + | "UnpackRawEvent_UT" | 2955 | 0.039 | 13.103 | + | "reserveIOV" | 2289 | 0.032 | 13.915 | + | "Decode_ODIN" | 2289 | 0.014 | 5.992 | + | "CaloMergeTrackMatchTables_2ce8beb5" | 2289 | 0.013 | 5.499 | + | "UniqueIDGeneratorAlg_26e527e9" | 2289 | 0.012 | 5.144 | + | "DefaultGECFilter" | 2955 | 0.011 | 3.652 | + | "Fetch__Event_pSim_MCParticles" | 2289 | 0.009 | 3.739 | + | "DummyEventTime" | 2289 | 0.007 | 3.122 | + | "UnpackRawEvent_FTCluster" | 2955 | 0.007 | 2.321 | + | "Fetch__Event_Link_Raw_UT_Clusters" | 2289 | 0.006 | 2.715 | + | "Fetch__Event_MC_TrackInfo" | 2289 | 0.006 | 2.633 | + | "UnpackRawEvent_VP" | 2289 | 0.005 | 2.376 | + | "UnpackRawEvent_EcalPackedError" | 2289 | 0.005 | 2.210 | + | "UnpackRawEvent_ODIN" | 2289 | 0.005 | 2.209 | + | "UnpackRawEvent_EcalPacked" | 2289 | 0.005 | 2.062 | + | "Fetch__Event_pSim_MCVertices" | 2289 | 0.004 | 1.651 | + | "Fetch__Event_Link_Raw_FT_LiteClusters" | 2289 | 0.004 | 1.587 | + | "Fetch__Event_Link_Raw_VP_Digits" | 2289 | 0.004 | 1.547 | + +HLTControlFlowMgr INFO StateTree: CFNode #executed #passed +LAZY_AND: hlt2_reco_decision #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + PrGECFilter/DefaultGECFilter #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + NONLAZY_OR: hlt2_reco_data #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrMatchNNv3/PrMatchNNv3_92e0e3ea #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrFilterTracks2CaloClusters/PrFilterTracks2CaloClusters_cae3b638 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrFilterTracks2ElectronMatch/PrFilterTracks2ElectronMatch_4265680d #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrFilterTracks2ElectronShower/PrFilterTracks2ElectronShower_72362ae8 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/ForwardTrackChecker_482fda95 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrUTHitChecker/ForwardUTHitsChecker_fe9d9ac2 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/MatchTrackChecker_ac9fdd0b #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrUTHitChecker/MatchUTHitsChecker_69ac963b #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/SeedTrackChecker_ad9abe4e #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + +HLTControlFlowMgr INFO Histograms converted successfully according to request. +ToolSvc INFO Removing all tools created by ToolSvc +SeedTrackChecker_ad9abe4e.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +MatchUTHitsChecker_69ac963b.PrCh... SUCCESS Booked 44 Histogram(s) : 1D=40 2D=4 +MatchTrackChecker_ac9fdd0b.PrChe... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +ForwardUTHitsChecker_fe9d9ac2.Pr... SUCCESS Booked 44 Histogram(s) : 1D=40 2D=4 +ForwardTrackChecker_482fda95.PrC... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +RootCnvSvc INFO Disconnected data IO:9E3B8940-FB87-11EB-ADCA-FA163E643B60 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi] +RootCnvSvc INFO Disconnected data IO:AFCB9710-FB21-11EB-9E91-FA163ED3A4EB [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi] +RootCnvSvc INFO Disconnected data IO:B9D7CC62-FB38-11EB-8B01-3CECEF5D2C24 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi] +ApplicationMgr INFO Application Manager Finalized successfully +ApplicationMgr INFO Application Manager Terminated successfully diff --git a/data_matching/logs/match_effs_testJpsi_EDef7_yCorrCut_mlp5.log b/data_matching/logs/match_effs_testJpsi_EDef7_yCorrCut_mlp5.log new file mode 100644 index 0000000..a0c18a4 --- /dev/null +++ b/data_matching/logs/match_effs_testJpsi_EDef7_yCorrCut_mlp5.log @@ -0,0 +1,378 @@ +# setting LC_ALL to "C" +# --> Including file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_match_eff_data.py' +/***** User ApplicationOptions/ApplicationOptions ************************************************** +|-append_decoding_keys_to_output_manifest = True (default: True) +|-auditors = [] (default: []) +|-buffer_events = 20000 (default: 20000) +|-conddb_tag = 'sim-20210617-vc-md100' (default: '') +|-conditions_version = '' (default: '') +|-control_flow_file = '' (default: '') +|-data_flow_file = '' (default: '') +|-data_type = 'Upgrade' (default: 'Upgrade') +|-dddb_tag = 'dddb-20210617' (default: '') +|-event_store = 'HiveWhiteBoard' (default: 'HiveWhiteBoard') +|-evt_max = -1 (default: -1) +|-first_evt = 0 (default: 0) +|-geometry_version = '' (default: '') +|-histo_file = '' (default: '') +|-input_files = ['/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi'] +| (default: []) +|-input_manifest_file = '' (default: '') +|-input_process = '' (default: '') +|-input_raw_format = 0.5 (default: 0.5) +|-input_type = 'ROOT' (default: '') +|-lines_maker = None +|-memory_pool_size = 10485760 (default: 10485760) +|-monitoring_file = '' (default: '') +|-msg_svc_format = '% F%35W%S %7W%R%T %0W%M' (default: '% F%35W%S %7W%R%T %0W%M') +|-msg_svc_time_format = '%Y-%m-%d %H:%M:%S UTC' (default: '%Y-%m-%d %H:%M:%S UTC') +|-n_event_slots = 1 (default: -1) +|-n_threads = 1 (default: 1) +|-ntuple_file = '/work/cetin/LHCb/reco_tuner/data_matching/match_effs_testJpsi_EDef7_yCorrCut_mlp5.root' +| (default: '') +|-output_file = '' (default: '') +|-output_level = 3 (default: 3) +|-output_manifest_file = '' (default: '') +|-output_type = '' (default: '') +|-persistreco_version = 1.0 (default: 1.0) +|-phoenix_filename = '' (default: '') +|-preamble_algs = [] (default: []) +|-print_freq = 10000 (default: 10000) +|-python_logging_level = 20 (default: 20) +|-require_specific_decoding_keys = [] (default: []) +|-scheduler_legacy_mode = True (default: True) +|-simulation = True (default: None) +|-use_iosvc = False (default: False) +|-velo_motion_system_yaml = '' (default: '') +|-write_decoding_keys_to_git = True (default: True) +\----- (End of User ApplicationOptions/ApplicationOptions) ----------------------------------------- +# Overrule specified for keys +# <-- End of file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_match_eff_data.py' +ApplicationMgr SUCCESS +==================================================================================================================================== + Welcome to Moore version 55.2 + running on lhcba2 on Mon Mar 11 10:02:04 2024 +==================================================================================================================================== +ApplicationMgr INFO Application Manager Configured successfully +ToolSvc.GitDDDB INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/DDDB.git' +ToolSvc.GitDDDB INFO using commit 'upgrade/dddb-20210617' corresponding to 1871f1bb5c0d68c81dda62e84cf1eb3a45513521 +ToolSvc.GitSIMCOND INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/SIMCOND.git' +ToolSvc.GitSIMCOND INFO using commit 'upgrade/sim-20210617-vc-md100' corresponding to 9aa116c7143d21760d1be07ce1ef22c0f8f07bdf +DetectorPersistencySvc INFO Added successfully Conversion service:XmlCnvSvc +DetectorDataSvc SUCCESS Detector description database: git:/lhcb.xml +EventClockSvc.FakeEventTime INFO Event times generated from 0 with steps of 0 +EventClockSvc.FakeEventTime INFO Run numbers generated from 0 every 0 events +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c1.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c2.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c3.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c4.down.cdf +MagneticFieldSvc INFO Map scaled by factor 1 with polarity internally used: -1 signed relative current: -1 +NTupleSvc INFO Added stream file:/work/cetin/LHCb/reco_tuner/data_matching/match_effs_testJpsi_EDef7_yCorrCut_mlp5.root as FILE1 +HLTControlFlowMgr INFO Start initialization +RootHistSvc INFO Writing ROOT histograms to: /work/cetin/LHCb/reco_tuner/data_matching/match_effs_testJpsi_EDef7_yCorrCut_mlp5.root +HistogramPersistencySvc INFO Added successfully Conversion service:RootHistSvc +DeFTDetector INFO Current FT geometry version = 64 +HLTControlFlowMgr INFO Concurrency level information: +HLTControlFlowMgr INFO o Number of events slots: 1 +HLTControlFlowMgr INFO o TBB thread pool size: 'ThreadPoolSize':1 +HLTControlFlowMgr INFO ---> End of Initialization. This took 63801 ms +ApplicationMgr INFO Application Manager Initialized successfully +ApplicationMgr INFO Application Manager Started successfully +EventPersistencySvc INFO Added successfully Conversion service:RootCnvSvc +EventSelector INFO Stream:EventSelector.DataStreamTool_1 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO Will measure time between events 10 and 2147483647 (stop might be some events later) +HLTControlFlowMgr INFO Starting loop on events +EventSelector SUCCESS Reading Event record 1. Record number within stream 1: 1 +FTRawBankDecoder INFO Conditions DB is compatible with FT bank version 4, 5, 6. +FTRawBankDecoder INFO Building the readout map with version 0 +HLTControlFlowMgr INFO Timing started at: 10:03:31 +EventSelector INFO Stream:EventSelector.DataStreamTool_2 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +EventSelector INFO Stream:EventSelector.DataStreamTool_3 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO No more events in event selection +HLTControlFlowMgr INFO ---> Loop over 2955 Events Finished - WSS 1793.88, timed 2945 Events: 163767 ms, Evts/s = 17.9829 +DefaultGECFilter INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb Events Processed" | 2955 | + | "Nb events removed" | 666 | +ForwardTrackChecker_482fda95.LoK... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +ForwardUTHitsChecker_fe9d9ac2.Lo... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 4 | +HLTControlFlowMgr INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Processed events" | 2955 | +MatchTrackChecker_eb7a9e21.LoKi:... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +MatchUTHitsChecker_eda3b901.LoKi... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 4 | +PrForwardTrackingVelo_6024f9ec INFO Number of counters : 10 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Accepted input tracks" | 2289 | 363254 | 158.70 | + | "Created long tracks" | 2289 | 181236 | 79.177 | + | "Input tracks" | 2289 | 380749 | 166.34 | + | "Number of candidate bins per track" | 363254 | 1665217 | 4.5842 | 5.0318 | 0.0000 | 56.000 | + | "Number of complete candidates/track 1st Loop" | 305079 | 195005 | 0.63920 | 0.65005 | 0.0000 | 6.0000 | + | "Number of complete candidates/track 2nd Loop" | 148403 | 13248 | 0.089270 | 0.29669 | 0.0000 | 3.0000 | + | "Number of x candidates per track 1st Loop" | 305079 | 426093 | 1.3967 | 1.3487 | + | "Number of x candidates per track 2nd Loop" | 148403 | 347932 | 2.3445 | 2.6098 | + | "Percentage second loop execution" | 305079 | 148403 | 0.48644 | + | "Removed duplicates" | 2289 | 9647 | 4.2145 | +PrForwardTrackingVelo_6024f9ec.P... INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 166072 | 673152 | 4.0534 | + | "#tracks with hits added" | 166072 | +PrHybridSeeding_4d0337cc INFO Number of counters : 21 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Created T2x1 three-hit combinations in case 0" | 3981395 | 2438467 | 0.61247 | 0.62452 | 0.0000 | 6.0000 | + | "Created T2x1 three-hit combinations in case 1" | 4961664 | 3252259 | 0.65548 | 0.75200 | 0.0000 | 12.000 | + | "Created T2x1 three-hit combinations in case 2" | 7644512 | 6133331 | 0.80232 | 1.0193 | 0.0000 | 23.000 | + | "Created XZ tracks (part 0)" | 6867 | 363280 | 52.902 | 44.400 | 0.0000 | 844.00 | + | "Created XZ tracks (part 1)" | 6867 | 360418 | 52.486 | 47.084 | 0.0000 | 1257.0 | + | "Created XZ tracks in case 0" | 4578 | 269789 | 58.932 | 37.398 | 1.0000 | 363.00 | + | "Created XZ tracks in case 1" | 4578 | 267868 | 58.512 | 44.098 | 1.0000 | 709.00 | + | "Created XZ tracks in case 2" | 4578 | 186041 | 40.638 | 52.165 | 0.0000 | 1257.0 | + | "Created full hit combinations in case 0" | 407934 | 407934 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 1" | 310355 | 310355 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 2" | 280325 | 280325 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created seed tracks" | 4578 | 284763 | 62.202 | 22.650 | 3.0000 | 141.00 | + | "Created seed tracks (part 0)" | 2289 | 159664 | 69.753 | 25.912 | 4.0000 | 161.00 | + | "Created seed tracks (part 1)" | 2289 | 157869 | 68.969 | 25.854 | 3.0000 | 159.00 | + | "Created seed tracks in case 0" | 4578 | 148622 | 32.464 | 12.801 | 1.0000 | 86.000 | + | "Created seed tracks in case 1" | 4578 | 270703 | 59.131 | 21.736 | 2.0000 | 132.00 | + | "Created seed tracks in case 2" | 4578 | 302221 | 66.016 | 24.642 | 3.0000 | 153.00 | + | "Created seed tracks in recovery step" | 2289 | 15312 | 6.6894 | 3.8772 | 0.0000 | 26.000 | + | "Created two-hit combinations in case 0" | 677723 |1.546134e+07 | 22.814 | 15.827 | 0.0000 | 117.00 | + | "Created two-hit combinations in case 1" | 584001 |1.760625e+07 | 30.148 | 18.628 | 0.0000 | 262.00 | + | "Created two-hit combinations in case 2" | 461883 |2.056474e+07 | 44.524 | 28.512 | 0.0000 | 333.00 | +PrLHCbID2MCParticle_a906d17d INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#removed null MCParticles" | 16672433 | 0 | 0.0000 | +PrMatchNN_e3e0ccb5 INFO Number of counters : 3 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#MatchingChi2" | 2289 | 8439531 | 3687.0 | + | "#MatchingMLP" | 215755 | 198052.3 | 0.91795 | + | "#MatchingTracks" | 2289 | 215755 | 94.257 | +PrMatchNN_e3e0ccb5.PrAddUTHitsTool INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 190959 | 761644 | 3.9885 | + | "#tracks with hits added" | 190959 | +PrStorePrUTHits_df75b912 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrStoreSciFiHits_fb0eba02 INFO Number of counters : 25 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Average X in T1U" | 690489 |-2.482423e+07 | -35.952 | 1141.3 | -2656.4 | 2656.3 | + | "Average X in T1V" | 696122 |-2.060219e+07 | -29.596 | 1128.0 | -2656.4 | 2656.3 | + | "Average X in T1X1" | 677723 |-3.438883e+07 | -50.742 | 1162.3 | -2646.2 | 2646.2 | + | "Average X in T1X2" | 705312 |-1.014161e+07 | -14.379 | 1120.8 | -2646.2 | 2646.2 | + | "Average X in T2U" | 673541 |-1.658606e+07 | -24.625 | 1135.5 | -2656.4 | 2656.3 | + | "Average X in T2V" | 693923 |-1.479371e+07 | -21.319 | 1129.9 | -2656.4 | 2656.3 | + | "Average X in T2X1" | 645225 |-1.705455e+07 | -26.432 | 1138.8 | -2646.2 | 2646.2 | + | "Average X in T2X2" | 716059 | -9891920 | -13.814 | 1124.6 | -2646.2 | 2646.2 | + | "Average X in T3U" | 731421 |-1.225062e+07 | -16.749 | 1333.5 | -3188.4 | 3188.4 | + | "Average X in T3V" | 753478 |-1.409381e+07 | -18.705 | 1328.7 | -3188.4 | 3188.4 | + | "Average X in T3X1" | 704173 |-1.010873e+07 | -14.355 | 1334.4 | -3176.2 | 3176.2 | + | "Average X in T3X2" | 782214 |-1.938375e+07 | -24.781 | 1321.3 | -3176.2 | 3176.2 | + | "Hits in T1U" | 9156 | 690489 | 75.414 | 27.984 | 5.0000 | 232.00 | + | "Hits in T1V" | 9156 | 696122 | 76.029 | 27.670 | 3.0000 | 245.00 | + | "Hits in T1X1" | 9156 | 677723 | 74.020 | 27.325 | 4.0000 | 205.00 | + | "Hits in T1X2" | 9156 | 705312 | 77.033 | 28.024 | 6.0000 | 266.00 | + | "Hits in T2U" | 9156 | 673541 | 73.563 | 26.210 | 3.0000 | 198.00 | + | "Hits in T2V" | 9156 | 693923 | 75.789 | 27.194 | 6.0000 | 374.00 | + | "Hits in T2X1" | 9156 | 645225 | 70.470 | 25.869 | 3.0000 | 288.00 | + | "Hits in T2X2" | 9156 | 716059 | 78.207 | 27.736 | 6.0000 | 287.00 | + | "Hits in T3U" | 9156 | 731421 | 79.884 | 27.669 | 2.0000 | 239.00 | + | "Hits in T3V" | 9156 | 753478 | 82.293 | 28.471 | 6.0000 | 207.00 | + | "Hits in T3X1" | 9156 | 704173 | 76.908 | 27.098 | 5.0000 | 339.00 | + | "Hits in T3X2" | 9156 | 782214 | 85.432 | 29.532 | 6.0000 | 204.00 | + | "Total number of hits" | 2289 | 8469680 | 3700.2 | 1120.3 | 604.00 | 6365.0 | +PrStoreUTHit_6220b56a INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrTrackAssociator_16ad4612 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 284763 | 279294 |( 98.07946 +- 0.02571932)% | + | "MC particles per track" | 279294 | 279304 | 1.0000 | +PrTrackAssociator_3adf94fb INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 181236 | 155077 |( 85.56633 +- 0.08255009)% | + | "MC particles per track" | 155077 | 181813 | 1.1724 | +PrTrackAssociator_43e58d3b INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 215755 | 155547 |( 72.09427 +- 0.09656430)% | + | "MC particles per track" | 155547 | 181754 | 1.1685 | +SeedTrackChecker_ad9abe4e.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +VeloClusterTrackingSIMD_87c18651 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Clusters" | 2289 | 5397790 | 2358.1 | + | "Nb of Produced Tracks" | 2289 | 593239 | 259.17 | +fromPrForwardTracksV1Tracks_f53f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 181236 | 79.177 | +fromPrMatchTracksV1Tracks_13de62af INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 215755 | 94.257 | +fromPrSeedingTracksV1Tracks_84cd... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 284763 | 124.40 | +fromPrVeloTracksV1TracksMerger_f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 593239 | 259.17 | +ApplicationMgr INFO Application Manager Stopped successfully +ForwardTrackChecker_482fda95 INFO Results +ForwardTrackChecker_482fda95 INFO **** Forward 181236 tracks including 26159 ghosts [14.43 %], Event average 13.11 % **** +ForwardTrackChecker_482fda95 INFO 01_long : 133702 from 152279 [ 87.80 %] 513 clones [ 0.38 %], purity: 99.21 %, hitEff: 98.43 % +ForwardTrackChecker_482fda95 INFO 02_long_P>5GeV : 91867 from 98421 [ 93.34 %] 307 clones [ 0.33 %], purity: 99.32 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 03_long_strange : 6588 from 8121 [ 81.12 %] 20 clones [ 0.30 %], purity: 98.87 %, hitEff: 98.21 % +ForwardTrackChecker_482fda95 INFO 04_long_strange_P>5GeV : 3465 from 3856 [ 89.86 %] 8 clones [ 0.23 %], purity: 99.05 %, hitEff: 98.80 % +ForwardTrackChecker_482fda95 INFO 05_long_fromB : 7199 from 7959 [ 90.45 %] 26 clones [ 0.36 %], purity: 99.34 %, hitEff: 98.69 % +ForwardTrackChecker_482fda95 INFO 05_long_fromD : 3793 from 4226 [ 89.75 %] 10 clones [ 0.26 %], purity: 99.25 %, hitEff: 98.50 % +ForwardTrackChecker_482fda95 INFO 06_long_fromB_P>5GeV : 5664 from 5983 [ 94.67 %] 18 clones [ 0.32 %], purity: 99.45 %, hitEff: 98.93 % +ForwardTrackChecker_482fda95 INFO 06_long_fromD_P>5GeV : 2732 from 2894 [ 94.40 %] 7 clones [ 0.26 %], purity: 99.35 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons : 10559 from 15125 [ 69.81 %] 108 clones [ 1.01 %], purity: 97.96 %, hitEff: 98.31 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons_pairprod : 6890 from 10831 [ 63.61 %] 86 clones [ 1.23 %], purity: 97.36 %, hitEff: 98.08 % +ForwardTrackChecker_482fda95 INFO 08_long_fromB_electrons : 3548 from 4210 [ 84.28 %] 22 clones [ 0.62 %], purity: 99.07 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 09_long_fromB_electrons_P>5GeV : 3333 from 3850 [ 86.57 %] 21 clones [ 0.63 %], purity: 99.15 %, hitEff: 98.96 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 4902 from 5182 [ 94.60 %] 17 clones [ 0.35 %], purity: 99.55 %, hitEff: 98.93 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3220 from 3659 [ 88.00 %] 19 clones [ 0.59 %], purity: 99.22 %, hitEff: 98.94 % +ForwardTrackChecker_482fda95 INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 2218 from 2343 [ 94.66 %] 6 clones [ 0.27 %], purity: 99.49 %, hitEff: 98.85 % +ForwardTrackChecker_482fda95 INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 1801 from 2010 [ 89.60 %] 4 clones [ 0.22 %], purity: 99.36 %, hitEff: 98.68 % +ForwardTrackChecker_482fda95 INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4889 from 5164 [ 94.67 %] 17 clones [ 0.35 %], purity: 99.55 %, hitEff: 98.94 % +ForwardTrackChecker_482fda95 INFO +ForwardUTHitsChecker_fe9d9ac2 INFO Results +ForwardUTHitsChecker_fe9d9ac2 INFO **** UT Efficiency for /Event/fromPrForwardTracksV1Tracks_f53f50a8/OutputTracksLocation **** 26159 ghost, 2.61 UT per track +ForwardUTHitsChecker_fe9d9ac2 INFO 01_long :134215 tr 3.91 from 4.07 mcUT [ 95.9 %] 0.12 ghost hits on real tracks [ 3.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 01_long >3UT :132800 tr 3.94 from 4.10 mcUT [ 96.2 %] 0.12 ghost hits on real tracks [ 2.9 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 02_long_P>5GeV : 92174 tr 3.94 from 4.07 mcUT [ 96.8 %] 0.10 ghost hits on real tracks [ 2.4 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 02_long_P>5GeV >3UT : 90908 tr 3.99 from 4.11 mcUT [ 97.2 %] 0.09 ghost hits on real tracks [ 2.2 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV : 4919 tr 4.00 from 4.07 mcUT [ 98.2 %] 0.05 ghost hits on real tracks [ 1.1 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4897 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.04 ghost hits on real tracks [ 1.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4906 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.05 ghost hits on real tracks [ 1.1 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4897 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.04 ghost hits on real tracks [ 1.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO +MatchTrackChecker_eb7a9e21 INFO Results +MatchTrackChecker_eb7a9e21 INFO **** Match 215755 tracks including 60208 ghosts [27.91 %], Event average 25.57 % **** +MatchTrackChecker_eb7a9e21 INFO 01_long : 132596 from 152279 [ 87.07 %] 804 clones [ 0.60 %], purity: 99.33 %, hitEff: 98.63 % +MatchTrackChecker_eb7a9e21 INFO 02_long_P>5GeV : 90070 from 98421 [ 91.52 %] 449 clones [ 0.50 %], purity: 99.45 %, hitEff: 99.25 % +MatchTrackChecker_eb7a9e21 INFO 03_long_strange : 6520 from 8121 [ 80.29 %] 31 clones [ 0.47 %], purity: 98.95 %, hitEff: 98.21 % +MatchTrackChecker_eb7a9e21 INFO 04_long_strange_P>5GeV : 3430 from 3856 [ 88.95 %] 12 clones [ 0.35 %], purity: 99.18 %, hitEff: 99.23 % +MatchTrackChecker_eb7a9e21 INFO 05_long_fromB : 7169 from 7959 [ 90.07 %] 49 clones [ 0.68 %], purity: 99.44 %, hitEff: 98.82 % +MatchTrackChecker_eb7a9e21 INFO 05_long_fromD : 3773 from 4226 [ 89.28 %] 17 clones [ 0.45 %], purity: 99.36 %, hitEff: 98.67 % +MatchTrackChecker_eb7a9e21 INFO 06_long_fromB_P>5GeV : 5590 from 5983 [ 93.43 %] 28 clones [ 0.50 %], purity: 99.57 %, hitEff: 99.24 % +MatchTrackChecker_eb7a9e21 INFO 06_long_fromD_P>5GeV : 2690 from 2894 [ 92.95 %] 9 clones [ 0.33 %], purity: 99.52 %, hitEff: 99.21 % +MatchTrackChecker_eb7a9e21 INFO 07_long_electrons : 11755 from 15125 [ 77.72 %] 178 clones [ 1.49 %], purity: 97.73 %, hitEff: 98.10 % +MatchTrackChecker_eb7a9e21 INFO 07_long_electrons_pairprod : 7967 from 10831 [ 73.56 %] 142 clones [ 1.75 %], purity: 97.09 %, hitEff: 97.77 % +MatchTrackChecker_eb7a9e21 INFO 08_long_fromB_electrons : 3622 from 4210 [ 86.03 %] 41 clones [ 1.12 %], purity: 99.07 %, hitEff: 98.91 % +MatchTrackChecker_eb7a9e21 INFO 09_long_fromB_electrons_P>5GeV : 3385 from 3850 [ 87.92 %] 37 clones [ 1.08 %], purity: 99.18 %, hitEff: 99.05 % +MatchTrackChecker_eb7a9e21 INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 4854 from 5182 [ 93.67 %] 27 clones [ 0.55 %], purity: 99.66 %, hitEff: 99.13 % +MatchTrackChecker_eb7a9e21 INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3264 from 3659 [ 89.20 %] 34 clones [ 1.03 %], purity: 99.26 %, hitEff: 99.04 % +MatchTrackChecker_eb7a9e21 INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 2194 from 2343 [ 93.64 %] 9 clones [ 0.41 %], purity: 99.65 %, hitEff: 99.11 % +MatchTrackChecker_eb7a9e21 INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 1782 from 2010 [ 88.66 %] 6 clones [ 0.34 %], purity: 99.52 %, hitEff: 98.99 % +MatchTrackChecker_eb7a9e21 INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4842 from 5164 [ 93.76 %] 27 clones [ 0.55 %], purity: 99.66 %, hitEff: 99.13 % +MatchTrackChecker_eb7a9e21 INFO +MatchUTHitsChecker_eda3b901 INFO Results +MatchUTHitsChecker_eda3b901 INFO **** UT Efficiency for /Event/fromPrMatchTracksV1Tracks_13de62af/OutputTracksLocation **** 60208 ghost, 2.60 UT per track +MatchUTHitsChecker_eda3b901 INFO 01_long :133400 tr 3.89 from 4.08 mcUT [ 95.4 %] 0.13 ghost hits on real tracks [ 3.2 %] +MatchUTHitsChecker_eda3b901 INFO 01_long >3UT :132035 tr 3.93 from 4.10 mcUT [ 95.7 %] 0.12 ghost hits on real tracks [ 3.1 %] +MatchUTHitsChecker_eda3b901 INFO 02_long_P>5GeV : 90519 tr 3.94 from 4.08 mcUT [ 96.7 %] 0.10 ghost hits on real tracks [ 2.4 %] +MatchUTHitsChecker_eda3b901 INFO 02_long_P>5GeV >3UT : 89346 tr 3.99 from 4.11 mcUT [ 97.1 %] 0.09 ghost hits on real tracks [ 2.3 %] +MatchUTHitsChecker_eda3b901 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV : 4881 tr 3.99 from 4.07 mcUT [ 98.0 %] 0.05 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_eda3b901 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4859 tr 4.01 from 4.08 mcUT [ 98.1 %] 0.04 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_eda3b901 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4869 tr 4.00 from 4.08 mcUT [ 98.1 %] 0.05 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_eda3b901 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4859 tr 4.01 from 4.08 mcUT [ 98.1 %] 0.04 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_eda3b901 INFO +SeedTrackChecker_ad9abe4e INFO Results +SeedTrackChecker_ad9abe4e INFO **** Seed 284763 tracks including 5469 ghosts [ 1.92 %], Event average 1.56 % **** +SeedTrackChecker_ad9abe4e INFO 01_hasT : 198532 from 234618 [ 84.62 %] 7 clones [ 0.00 %], purity: 99.60 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 02_long : 143630 from 152279 [ 94.32 %] 6 clones [ 0.00 %], purity: 99.71 %, hitEff: 98.42 % +SeedTrackChecker_ad9abe4e INFO 03_long_P>5GeV : 95859 from 98421 [ 97.40 %] 5 clones [ 0.01 %], purity: 99.69 %, hitEff: 99.09 % +SeedTrackChecker_ad9abe4e INFO 04_long_fromB : 7598 from 7959 [ 95.46 %] 1 clones [ 0.01 %], purity: 99.75 %, hitEff: 98.65 % +SeedTrackChecker_ad9abe4e INFO 05_long_fromB_P>5GeV : 5835 from 5983 [ 97.53 %] 1 clones [ 0.02 %], purity: 99.76 %, hitEff: 99.13 % +SeedTrackChecker_ad9abe4e INFO 06_UT+T_strange : 16417 from 17658 [ 92.97 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.00 % +SeedTrackChecker_ad9abe4e INFO 07_UT+T_strange_P>5GeV : 8615 from 8825 [ 97.62 %] 0 clones [ 0.00 %], purity: 99.71 %, hitEff: 99.05 % +SeedTrackChecker_ad9abe4e INFO 08_noVelo+UT+T_strange : 8949 from 9658 [ 92.66 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.03 % +SeedTrackChecker_ad9abe4e INFO 09_noVelo+UT+T_strange_P>5GeV : 4914 from 5043 [ 97.44 %] 0 clones [ 0.00 %], purity: 99.70 %, hitEff: 99.01 % +SeedTrackChecker_ad9abe4e INFO 10_UT+T_SfromDB : 1133 from 1220 [ 92.87 %] 0 clones [ 0.00 %], purity: 99.77 %, hitEff: 97.99 % +SeedTrackChecker_ad9abe4e INFO 11_UT+T_SfromDB_P>5GeV : 612 from 623 [ 98.23 %] 0 clones [ 0.00 %], purity: 99.72 %, hitEff: 99.22 % +SeedTrackChecker_ad9abe4e INFO 12_noVelo+UT+T_SfromDB_P>5GeV : 420 from 428 [ 98.13 %] 0 clones [ 0.00 %], purity: 99.69 %, hitEff: 99.12 % +SeedTrackChecker_ad9abe4e INFO 13_hasT_electrons : 40669 from 74476 [ 54.61 %] 2 clones [ 0.00 %], purity: 99.69 %, hitEff: 97.16 % +SeedTrackChecker_ad9abe4e INFO 14_long_electrons : 13360 from 15125 [ 88.33 %] 1 clones [ 0.01 %], purity: 99.81 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 15_long_fromB_electrons : 3922 from 4210 [ 93.16 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.70 % +SeedTrackChecker_ad9abe4e INFO 16_long_electrons_P>5GeV : 8676 from 9420 [ 92.10 %] 0 clones [ 0.00 %], purity: 99.80 %, hitEff: 98.73 % +SeedTrackChecker_ad9abe4e INFO 17_long_fromB_electrons_P>5GeV : 3632 from 3850 [ 94.34 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.85 % +SeedTrackChecker_ad9abe4e INFO +HLTControlFlowMgr INFO Memory pool: used 3.94312 +/- 0.039102 MiB (min: 0, max: 5) in 1 +/- 0 blocks (allocated >once in 0 +/- 0% events). Allocated capacity was 10 +/- 0 MiB (min: 10, max: 10) and 347.612 +/- 3.41441 (min: 4, max: 489) requests were served +HLTControlFlowMgr INFO Timing table: +HLTControlFlowMgr INFO + | Name of Algorithm | Execution Count | Total Time / s | Avg. Time / us | + | Sum of all Algorithms | 2955 | 161.175 | 54543.310 | + | "Fetch__Event_DAQ_RawEvent" | 2955 | 94.901 | 32115.316 | + | "SeedTrackChecker_ad9abe4e" | 2289 | 13.415 | 5860.453 | + | "ForwardTrackChecker_482fda95" | 2289 | 12.396 | 5415.600 | + | "MatchTrackChecker_eb7a9e21" | 2289 | 11.078 | 4839.744 | + | "ForwardUTHitsChecker_fe9d9ac2" | 2289 | 4.908 | 2144.118 | + | "MatchUTHitsChecker_eda3b901" | 2289 | 4.844 | 2116.304 | + | "PrForwardTrackingVelo_6024f9ec" | 2289 | 4.487 | 1960.277 | + | "PrHybridSeeding_4d0337cc" | 2289 | 3.371 | 1472.604 | + | "PrLHCbID2MCParticle_a906d17d" | 2289 | 2.582 | 1127.991 | + | "Unpack__Event_MC_Vertices" | 2289 | 2.056 | 898.377 | + | "Unpack__Event_MC_Particles" | 2289 | 1.963 | 857.610 | + | "VeloClusterTrackingSIMD_87c18651" | 2289 | 0.738 | 322.613 | + | "VPFullCluster2MCParticleLinker_17386552" | 2289 | 0.586 | 255.962 | + | "VPClusFull_38754d8c" | 2289 | 0.555 | 242.478 | + | "PrMatchNN_e3e0ccb5" | 2289 | 0.506 | 221.098 | + | "PrStorePrUTHits_df75b912" | 2289 | 0.471 | 205.950 | + | "PrTrackAssociator_43e58d3b" | 2289 | 0.388 | 169.589 | + | "PrTrackAssociator_3adf94fb" | 2289 | 0.385 | 168.183 | + | "PrStoreUTHit_6220b56a" | 2289 | 0.360 | 157.252 | + | "PrTrackAssociator_16ad4612" | 2289 | 0.261 | 114.091 | + | "fromPrVeloTracksV1TracksMerger_fa66a5de" | 2289 | 0.197 | 86.197 | + | "fromPrMatchTracksV1Tracks_13de62af" | 2289 | 0.188 | 81.950 | + | "fromPrForwardTracksV1Tracks_f53f50a8" | 2289 | 0.137 | 60.012 | + | "fromPrSeedingTracksV1Tracks_84cd46c2" | 2289 | 0.125 | 54.486 | + | "PrStoreSciFiHits_fb0eba02" | 2289 | 0.112 | 48.897 | + | "FTRawBankDecoder" | 2289 | 0.062 | 27.241 | + | "UnpackRawEvent_FTCluster" | 2955 | 0.026 | 8.771 | + | "reserveIOV" | 2289 | 0.023 | 10.130 | + | "Decode_ODIN" | 2289 | 0.007 | 3.078 | + | "Fetch__Event_pSim_MCVertices" | 2289 | 0.006 | 2.727 | + | "DefaultGECFilter" | 2955 | 0.006 | 1.983 | + | "UnpackRawEvent_UT" | 2955 | 0.005 | 1.558 | + | "Fetch__Event_pSim_MCParticles" | 2289 | 0.004 | 1.894 | + | "Fetch__Event_Link_Raw_UT_Clusters" | 2289 | 0.004 | 1.788 | + | "Fetch__Event_Link_Raw_VP_Digits" | 2289 | 0.004 | 1.775 | + | "Fetch__Event_MC_TrackInfo" | 2289 | 0.004 | 1.680 | + | "DummyEventTime" | 2289 | 0.004 | 1.577 | + | "UnpackRawEvent_ODIN" | 2289 | 0.003 | 1.503 | + | "UnpackRawEvent_VP" | 2289 | 0.003 | 1.414 | + | "Fetch__Event_Link_Raw_FT_LiteClusters" | 2289 | 0.002 | 1.023 | + +HLTControlFlowMgr INFO StateTree: CFNode #executed #passed +LAZY_AND: hlt2_matching_reco_decision #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + PrGECFilter/DefaultGECFilter #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + NONLAZY_OR: hlt2_matching_reco_data #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrMatchNN/PrMatchNN_e3e0ccb5 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/ForwardTrackChecker_482fda95 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrUTHitChecker/ForwardUTHitsChecker_fe9d9ac2 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/MatchTrackChecker_eb7a9e21 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrUTHitChecker/MatchUTHitsChecker_eda3b901 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/SeedTrackChecker_ad9abe4e #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + +HLTControlFlowMgr INFO Histograms converted successfully according to request. +ToolSvc INFO Removing all tools created by ToolSvc +SeedTrackChecker_ad9abe4e.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +MatchUTHitsChecker_eda3b901.PrCh... SUCCESS Booked 44 Histogram(s) : 1D=40 2D=4 +MatchTrackChecker_eb7a9e21.PrChe... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +ForwardUTHitsChecker_fe9d9ac2.Pr... SUCCESS Booked 44 Histogram(s) : 1D=40 2D=4 +ForwardTrackChecker_482fda95.PrC... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +RootCnvSvc INFO Disconnected data IO:9E3B8940-FB87-11EB-ADCA-FA163E643B60 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi] +RootCnvSvc INFO Disconnected data IO:AFCB9710-FB21-11EB-9E91-FA163ED3A4EB [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi] +RootCnvSvc INFO Disconnected data IO:B9D7CC62-FB38-11EB-8B01-3CECEF5D2C24 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi] +ApplicationMgr INFO Application Manager Finalized successfully +ApplicationMgr INFO Application Manager Terminated successfully diff --git a/data_matching/logs/match_effs_testJpsi_EDef7_yCorrCut_mlp6.log b/data_matching/logs/match_effs_testJpsi_EDef7_yCorrCut_mlp6.log new file mode 100644 index 0000000..e2780c9 --- /dev/null +++ b/data_matching/logs/match_effs_testJpsi_EDef7_yCorrCut_mlp6.log @@ -0,0 +1,378 @@ +# setting LC_ALL to "C" +# --> Including file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_match_eff_data.py' +/***** User ApplicationOptions/ApplicationOptions ************************************************** +|-append_decoding_keys_to_output_manifest = True (default: True) +|-auditors = [] (default: []) +|-buffer_events = 20000 (default: 20000) +|-conddb_tag = 'sim-20210617-vc-md100' (default: '') +|-conditions_version = '' (default: '') +|-control_flow_file = '' (default: '') +|-data_flow_file = '' (default: '') +|-data_type = 'Upgrade' (default: 'Upgrade') +|-dddb_tag = 'dddb-20210617' (default: '') +|-event_store = 'HiveWhiteBoard' (default: 'HiveWhiteBoard') +|-evt_max = -1 (default: -1) +|-first_evt = 0 (default: 0) +|-geometry_version = '' (default: '') +|-histo_file = '' (default: '') +|-input_files = ['/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi'] +| (default: []) +|-input_manifest_file = '' (default: '') +|-input_process = '' (default: '') +|-input_raw_format = 0.5 (default: 0.5) +|-input_type = 'ROOT' (default: '') +|-lines_maker = None +|-memory_pool_size = 10485760 (default: 10485760) +|-monitoring_file = '' (default: '') +|-msg_svc_format = '% F%35W%S %7W%R%T %0W%M' (default: '% F%35W%S %7W%R%T %0W%M') +|-msg_svc_time_format = '%Y-%m-%d %H:%M:%S UTC' (default: '%Y-%m-%d %H:%M:%S UTC') +|-n_event_slots = 1 (default: -1) +|-n_threads = 1 (default: 1) +|-ntuple_file = '/work/cetin/LHCb/reco_tuner/data_matching/match_effs_testJpsi_EDef7_yCorrCut.root' +| (default: '') +|-output_file = '' (default: '') +|-output_level = 3 (default: 3) +|-output_manifest_file = '' (default: '') +|-output_type = '' (default: '') +|-persistreco_version = 1.0 (default: 1.0) +|-phoenix_filename = '' (default: '') +|-preamble_algs = [] (default: []) +|-print_freq = 10000 (default: 10000) +|-python_logging_level = 20 (default: 20) +|-require_specific_decoding_keys = [] (default: []) +|-scheduler_legacy_mode = True (default: True) +|-simulation = True (default: None) +|-use_iosvc = False (default: False) +|-velo_motion_system_yaml = '' (default: '') +|-write_decoding_keys_to_git = True (default: True) +\----- (End of User ApplicationOptions/ApplicationOptions) ----------------------------------------- +# Overrule specified for keys +# <-- End of file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_match_eff_data.py' +ApplicationMgr SUCCESS +==================================================================================================================================== + Welcome to Moore version 55.2 + running on lhcba2 on Mon Mar 11 09:56:29 2024 +==================================================================================================================================== +ApplicationMgr INFO Application Manager Configured successfully +ToolSvc.GitDDDB INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/DDDB.git' +ToolSvc.GitDDDB INFO using commit 'upgrade/dddb-20210617' corresponding to 1871f1bb5c0d68c81dda62e84cf1eb3a45513521 +ToolSvc.GitSIMCOND INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/SIMCOND.git' +ToolSvc.GitSIMCOND INFO using commit 'upgrade/sim-20210617-vc-md100' corresponding to 9aa116c7143d21760d1be07ce1ef22c0f8f07bdf +DetectorPersistencySvc INFO Added successfully Conversion service:XmlCnvSvc +DetectorDataSvc SUCCESS Detector description database: git:/lhcb.xml +EventClockSvc.FakeEventTime INFO Event times generated from 0 with steps of 0 +EventClockSvc.FakeEventTime INFO Run numbers generated from 0 every 0 events +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c1.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c2.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c3.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c4.down.cdf +MagneticFieldSvc INFO Map scaled by factor 1 with polarity internally used: -1 signed relative current: -1 +NTupleSvc INFO Added stream file:/work/cetin/LHCb/reco_tuner/data_matching/match_effs_testJpsi_EDef7_yCorrCut.root as FILE1 +HLTControlFlowMgr INFO Start initialization +RootHistSvc INFO Writing ROOT histograms to: /work/cetin/LHCb/reco_tuner/data_matching/match_effs_testJpsi_EDef7_yCorrCut.root +HistogramPersistencySvc INFO Added successfully Conversion service:RootHistSvc +DeFTDetector INFO Current FT geometry version = 64 +HLTControlFlowMgr INFO Concurrency level information: +HLTControlFlowMgr INFO o Number of events slots: 1 +HLTControlFlowMgr INFO o TBB thread pool size: 'ThreadPoolSize':1 +HLTControlFlowMgr INFO ---> End of Initialization. This took 21518 ms +ApplicationMgr INFO Application Manager Initialized successfully +ApplicationMgr INFO Application Manager Started successfully +EventPersistencySvc INFO Added successfully Conversion service:RootCnvSvc +EventSelector INFO Stream:EventSelector.DataStreamTool_1 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO Will measure time between events 10 and 2147483647 (stop might be some events later) +HLTControlFlowMgr INFO Starting loop on events +EventSelector SUCCESS Reading Event record 1. Record number within stream 1: 1 +FTRawBankDecoder INFO Conditions DB is compatible with FT bank version 4, 5, 6. +FTRawBankDecoder INFO Building the readout map with version 0 +HLTControlFlowMgr INFO Timing started at: 09:57:09 +EventSelector INFO Stream:EventSelector.DataStreamTool_2 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +EventSelector INFO Stream:EventSelector.DataStreamTool_3 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO No more events in event selection +HLTControlFlowMgr INFO ---> Loop over 2955 Events Finished - WSS 1796.18, timed 2945 Events: 159439 ms, Evts/s = 18.471 +DefaultGECFilter INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb Events Processed" | 2955 | + | "Nb events removed" | 666 | +ForwardTrackChecker_482fda95.LoK... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +ForwardUTHitsChecker_fe9d9ac2.Lo... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 4 | +HLTControlFlowMgr INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Processed events" | 2955 | +MatchTrackChecker_386d067b.LoKi:... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +MatchUTHitsChecker_a4d04726.LoKi... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 4 | +PrForwardTrackingVelo_6024f9ec INFO Number of counters : 10 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Accepted input tracks" | 2289 | 363254 | 158.70 | + | "Created long tracks" | 2289 | 181236 | 79.177 | + | "Input tracks" | 2289 | 380749 | 166.34 | + | "Number of candidate bins per track" | 363254 | 1665217 | 4.5842 | 5.0318 | 0.0000 | 56.000 | + | "Number of complete candidates/track 1st Loop" | 305079 | 195005 | 0.63920 | 0.65005 | 0.0000 | 6.0000 | + | "Number of complete candidates/track 2nd Loop" | 148403 | 13248 | 0.089270 | 0.29669 | 0.0000 | 3.0000 | + | "Number of x candidates per track 1st Loop" | 305079 | 426093 | 1.3967 | 1.3487 | + | "Number of x candidates per track 2nd Loop" | 148403 | 347932 | 2.3445 | 2.6098 | + | "Percentage second loop execution" | 305079 | 148403 | 0.48644 | + | "Removed duplicates" | 2289 | 9647 | 4.2145 | +PrForwardTrackingVelo_6024f9ec.P... INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 166072 | 673152 | 4.0534 | + | "#tracks with hits added" | 166072 | +PrHybridSeeding_4d0337cc INFO Number of counters : 21 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Created T2x1 three-hit combinations in case 0" | 3981395 | 2438467 | 0.61247 | 0.62452 | 0.0000 | 6.0000 | + | "Created T2x1 three-hit combinations in case 1" | 4961664 | 3252259 | 0.65548 | 0.75200 | 0.0000 | 12.000 | + | "Created T2x1 three-hit combinations in case 2" | 7644512 | 6133331 | 0.80232 | 1.0193 | 0.0000 | 23.000 | + | "Created XZ tracks (part 0)" | 6867 | 363280 | 52.902 | 44.400 | 0.0000 | 844.00 | + | "Created XZ tracks (part 1)" | 6867 | 360418 | 52.486 | 47.084 | 0.0000 | 1257.0 | + | "Created XZ tracks in case 0" | 4578 | 269789 | 58.932 | 37.398 | 1.0000 | 363.00 | + | "Created XZ tracks in case 1" | 4578 | 267868 | 58.512 | 44.098 | 1.0000 | 709.00 | + | "Created XZ tracks in case 2" | 4578 | 186041 | 40.638 | 52.165 | 0.0000 | 1257.0 | + | "Created full hit combinations in case 0" | 407934 | 407934 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 1" | 310355 | 310355 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 2" | 280325 | 280325 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created seed tracks" | 4578 | 284763 | 62.202 | 22.650 | 3.0000 | 141.00 | + | "Created seed tracks (part 0)" | 2289 | 159664 | 69.753 | 25.912 | 4.0000 | 161.00 | + | "Created seed tracks (part 1)" | 2289 | 157869 | 68.969 | 25.854 | 3.0000 | 159.00 | + | "Created seed tracks in case 0" | 4578 | 148622 | 32.464 | 12.801 | 1.0000 | 86.000 | + | "Created seed tracks in case 1" | 4578 | 270703 | 59.131 | 21.736 | 2.0000 | 132.00 | + | "Created seed tracks in case 2" | 4578 | 302221 | 66.016 | 24.642 | 3.0000 | 153.00 | + | "Created seed tracks in recovery step" | 2289 | 15312 | 6.6894 | 3.8772 | 0.0000 | 26.000 | + | "Created two-hit combinations in case 0" | 677723 |1.546134e+07 | 22.814 | 15.827 | 0.0000 | 117.00 | + | "Created two-hit combinations in case 1" | 584001 |1.760625e+07 | 30.148 | 18.628 | 0.0000 | 262.00 | + | "Created two-hit combinations in case 2" | 461883 |2.056474e+07 | 44.524 | 28.512 | 0.0000 | 333.00 | +PrLHCbID2MCParticle_a906d17d INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#removed null MCParticles" | 16672433 | 0 | 0.0000 | +PrMatchNN_d80b5038 INFO Number of counters : 3 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#MatchingChi2" | 2289 | 8439531 | 3687.0 | + | "#MatchingMLP" | 208603 | 194112.3 | 0.93053 | + | "#MatchingTracks" | 2289 | 208603 | 91.133 | +PrMatchNN_d80b5038.PrAddUTHitsTool INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 186550 | 745719 | 3.9974 | + | "#tracks with hits added" | 186550 | +PrStorePrUTHits_df75b912 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrStoreSciFiHits_fb0eba02 INFO Number of counters : 25 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Average X in T1U" | 690489 |-2.482423e+07 | -35.952 | 1141.3 | -2656.4 | 2656.3 | + | "Average X in T1V" | 696122 |-2.060219e+07 | -29.596 | 1128.0 | -2656.4 | 2656.3 | + | "Average X in T1X1" | 677723 |-3.438883e+07 | -50.742 | 1162.3 | -2646.2 | 2646.2 | + | "Average X in T1X2" | 705312 |-1.014161e+07 | -14.379 | 1120.8 | -2646.2 | 2646.2 | + | "Average X in T2U" | 673541 |-1.658606e+07 | -24.625 | 1135.5 | -2656.4 | 2656.3 | + | "Average X in T2V" | 693923 |-1.479371e+07 | -21.319 | 1129.9 | -2656.4 | 2656.3 | + | "Average X in T2X1" | 645225 |-1.705455e+07 | -26.432 | 1138.8 | -2646.2 | 2646.2 | + | "Average X in T2X2" | 716059 | -9891920 | -13.814 | 1124.6 | -2646.2 | 2646.2 | + | "Average X in T3U" | 731421 |-1.225062e+07 | -16.749 | 1333.5 | -3188.4 | 3188.4 | + | "Average X in T3V" | 753478 |-1.409381e+07 | -18.705 | 1328.7 | -3188.4 | 3188.4 | + | "Average X in T3X1" | 704173 |-1.010873e+07 | -14.355 | 1334.4 | -3176.2 | 3176.2 | + | "Average X in T3X2" | 782214 |-1.938375e+07 | -24.781 | 1321.3 | -3176.2 | 3176.2 | + | "Hits in T1U" | 9156 | 690489 | 75.414 | 27.984 | 5.0000 | 232.00 | + | "Hits in T1V" | 9156 | 696122 | 76.029 | 27.670 | 3.0000 | 245.00 | + | "Hits in T1X1" | 9156 | 677723 | 74.020 | 27.325 | 4.0000 | 205.00 | + | "Hits in T1X2" | 9156 | 705312 | 77.033 | 28.024 | 6.0000 | 266.00 | + | "Hits in T2U" | 9156 | 673541 | 73.563 | 26.210 | 3.0000 | 198.00 | + | "Hits in T2V" | 9156 | 693923 | 75.789 | 27.194 | 6.0000 | 374.00 | + | "Hits in T2X1" | 9156 | 645225 | 70.470 | 25.869 | 3.0000 | 288.00 | + | "Hits in T2X2" | 9156 | 716059 | 78.207 | 27.736 | 6.0000 | 287.00 | + | "Hits in T3U" | 9156 | 731421 | 79.884 | 27.669 | 2.0000 | 239.00 | + | "Hits in T3V" | 9156 | 753478 | 82.293 | 28.471 | 6.0000 | 207.00 | + | "Hits in T3X1" | 9156 | 704173 | 76.908 | 27.098 | 5.0000 | 339.00 | + | "Hits in T3X2" | 9156 | 782214 | 85.432 | 29.532 | 6.0000 | 204.00 | + | "Total number of hits" | 2289 | 8469680 | 3700.2 | 1120.3 | 604.00 | 6365.0 | +PrStoreUTHit_6220b56a INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrTrackAssociator_16ad4612 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 284763 | 279294 |( 98.07946 +- 0.02571932)% | + | "MC particles per track" | 279294 | 279304 | 1.0000 | +PrTrackAssociator_3adf94fb INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 181236 | 155077 |( 85.56633 +- 0.08255009)% | + | "MC particles per track" | 155077 | 181813 | 1.1724 | +PrTrackAssociator_8c8024ec INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 208603 | 153948 |( 73.79951 +- 0.09627669)% | + | "MC particles per track" | 153948 | 179619 | 1.1668 | +SeedTrackChecker_ad9abe4e.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +VeloClusterTrackingSIMD_87c18651 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Clusters" | 2289 | 5397790 | 2358.1 | + | "Nb of Produced Tracks" | 2289 | 593239 | 259.17 | +fromPrForwardTracksV1Tracks_f53f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 181236 | 79.177 | +fromPrMatchTracksV1Tracks_aaf8b514 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 208603 | 91.133 | +fromPrSeedingTracksV1Tracks_84cd... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 284763 | 124.40 | +fromPrVeloTracksV1TracksMerger_f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 593239 | 259.17 | +ApplicationMgr INFO Application Manager Stopped successfully +ForwardTrackChecker_482fda95 INFO Results +ForwardTrackChecker_482fda95 INFO **** Forward 181236 tracks including 26159 ghosts [14.43 %], Event average 13.11 % **** +ForwardTrackChecker_482fda95 INFO 01_long : 133702 from 152279 [ 87.80 %] 513 clones [ 0.38 %], purity: 99.21 %, hitEff: 98.43 % +ForwardTrackChecker_482fda95 INFO 02_long_P>5GeV : 91867 from 98421 [ 93.34 %] 307 clones [ 0.33 %], purity: 99.32 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 03_long_strange : 6588 from 8121 [ 81.12 %] 20 clones [ 0.30 %], purity: 98.87 %, hitEff: 98.21 % +ForwardTrackChecker_482fda95 INFO 04_long_strange_P>5GeV : 3465 from 3856 [ 89.86 %] 8 clones [ 0.23 %], purity: 99.05 %, hitEff: 98.80 % +ForwardTrackChecker_482fda95 INFO 05_long_fromB : 7199 from 7959 [ 90.45 %] 26 clones [ 0.36 %], purity: 99.34 %, hitEff: 98.69 % +ForwardTrackChecker_482fda95 INFO 05_long_fromD : 3793 from 4226 [ 89.75 %] 10 clones [ 0.26 %], purity: 99.25 %, hitEff: 98.50 % +ForwardTrackChecker_482fda95 INFO 06_long_fromB_P>5GeV : 5664 from 5983 [ 94.67 %] 18 clones [ 0.32 %], purity: 99.45 %, hitEff: 98.93 % +ForwardTrackChecker_482fda95 INFO 06_long_fromD_P>5GeV : 2732 from 2894 [ 94.40 %] 7 clones [ 0.26 %], purity: 99.35 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons : 10559 from 15125 [ 69.81 %] 108 clones [ 1.01 %], purity: 97.96 %, hitEff: 98.31 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons_pairprod : 6890 from 10831 [ 63.61 %] 86 clones [ 1.23 %], purity: 97.36 %, hitEff: 98.08 % +ForwardTrackChecker_482fda95 INFO 08_long_fromB_electrons : 3548 from 4210 [ 84.28 %] 22 clones [ 0.62 %], purity: 99.07 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 09_long_fromB_electrons_P>5GeV : 3333 from 3850 [ 86.57 %] 21 clones [ 0.63 %], purity: 99.15 %, hitEff: 98.96 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 4902 from 5182 [ 94.60 %] 17 clones [ 0.35 %], purity: 99.55 %, hitEff: 98.93 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3220 from 3659 [ 88.00 %] 19 clones [ 0.59 %], purity: 99.22 %, hitEff: 98.94 % +ForwardTrackChecker_482fda95 INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 2218 from 2343 [ 94.66 %] 6 clones [ 0.27 %], purity: 99.49 %, hitEff: 98.85 % +ForwardTrackChecker_482fda95 INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 1801 from 2010 [ 89.60 %] 4 clones [ 0.22 %], purity: 99.36 %, hitEff: 98.68 % +ForwardTrackChecker_482fda95 INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4889 from 5164 [ 94.67 %] 17 clones [ 0.35 %], purity: 99.55 %, hitEff: 98.94 % +ForwardTrackChecker_482fda95 INFO +ForwardUTHitsChecker_fe9d9ac2 INFO Results +ForwardUTHitsChecker_fe9d9ac2 INFO **** UT Efficiency for /Event/fromPrForwardTracksV1Tracks_f53f50a8/OutputTracksLocation **** 26159 ghost, 2.61 UT per track +ForwardUTHitsChecker_fe9d9ac2 INFO 01_long :134215 tr 3.91 from 4.07 mcUT [ 95.9 %] 0.12 ghost hits on real tracks [ 3.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 01_long >3UT :132800 tr 3.94 from 4.10 mcUT [ 96.2 %] 0.12 ghost hits on real tracks [ 2.9 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 02_long_P>5GeV : 92174 tr 3.94 from 4.07 mcUT [ 96.8 %] 0.10 ghost hits on real tracks [ 2.4 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 02_long_P>5GeV >3UT : 90908 tr 3.99 from 4.11 mcUT [ 97.2 %] 0.09 ghost hits on real tracks [ 2.2 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV : 4919 tr 4.00 from 4.07 mcUT [ 98.2 %] 0.05 ghost hits on real tracks [ 1.1 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4897 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.04 ghost hits on real tracks [ 1.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4906 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.05 ghost hits on real tracks [ 1.1 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4897 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.04 ghost hits on real tracks [ 1.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO +MatchTrackChecker_386d067b INFO Results +MatchTrackChecker_386d067b INFO **** Match 208603 tracks including 54655 ghosts [26.20 %], Event average 23.95 % **** +MatchTrackChecker_386d067b INFO 01_long : 131508 from 152279 [ 86.36 %] 783 clones [ 0.59 %], purity: 99.34 %, hitEff: 98.65 % +MatchTrackChecker_386d067b INFO 02_long_P>5GeV : 89497 from 98421 [ 90.93 %] 440 clones [ 0.49 %], purity: 99.46 %, hitEff: 99.27 % +MatchTrackChecker_386d067b INFO 03_long_strange : 6459 from 8121 [ 79.53 %] 30 clones [ 0.46 %], purity: 98.96 %, hitEff: 98.25 % +MatchTrackChecker_386d067b INFO 04_long_strange_P>5GeV : 3407 from 3856 [ 88.36 %] 12 clones [ 0.35 %], purity: 99.19 %, hitEff: 99.25 % +MatchTrackChecker_386d067b INFO 05_long_fromB : 7110 from 7959 [ 89.33 %] 48 clones [ 0.67 %], purity: 99.45 %, hitEff: 98.85 % +MatchTrackChecker_386d067b INFO 05_long_fromD : 3746 from 4226 [ 88.64 %] 16 clones [ 0.43 %], purity: 99.37 %, hitEff: 98.70 % +MatchTrackChecker_386d067b INFO 06_long_fromB_P>5GeV : 5560 from 5983 [ 92.93 %] 27 clones [ 0.48 %], purity: 99.57 %, hitEff: 99.26 % +MatchTrackChecker_386d067b INFO 06_long_fromD_P>5GeV : 2673 from 2894 [ 92.36 %] 8 clones [ 0.30 %], purity: 99.53 %, hitEff: 99.24 % +MatchTrackChecker_386d067b INFO 07_long_electrons : 11645 from 15125 [ 76.99 %] 177 clones [ 1.50 %], purity: 97.74 %, hitEff: 98.13 % +MatchTrackChecker_386d067b INFO 07_long_electrons_pairprod : 7883 from 10831 [ 72.78 %] 141 clones [ 1.76 %], purity: 97.10 %, hitEff: 97.80 % +MatchTrackChecker_386d067b INFO 08_long_fromB_electrons : 3595 from 4210 [ 85.39 %] 41 clones [ 1.13 %], purity: 99.08 %, hitEff: 98.93 % +MatchTrackChecker_386d067b INFO 09_long_fromB_electrons_P>5GeV : 3361 from 3850 [ 87.30 %] 37 clones [ 1.09 %], purity: 99.19 %, hitEff: 99.08 % +MatchTrackChecker_386d067b INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 4832 from 5182 [ 93.25 %] 26 clones [ 0.54 %], purity: 99.66 %, hitEff: 99.14 % +MatchTrackChecker_386d067b INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3243 from 3659 [ 88.63 %] 34 clones [ 1.04 %], purity: 99.27 %, hitEff: 99.07 % +MatchTrackChecker_386d067b INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 2183 from 2343 [ 93.17 %] 8 clones [ 0.37 %], purity: 99.66 %, hitEff: 99.12 % +MatchTrackChecker_386d067b INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 1774 from 2010 [ 88.26 %] 6 clones [ 0.34 %], purity: 99.52 %, hitEff: 99.00 % +MatchTrackChecker_386d067b INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4820 from 5164 [ 93.34 %] 26 clones [ 0.54 %], purity: 99.66 %, hitEff: 99.14 % +MatchTrackChecker_386d067b INFO +MatchUTHitsChecker_a4d04726 INFO Results +MatchUTHitsChecker_a4d04726 INFO **** UT Efficiency for /Event/fromPrMatchTracksV1Tracks_aaf8b514/OutputTracksLocation **** 54655 ghost, 2.67 UT per track +MatchUTHitsChecker_a4d04726 INFO 01_long :132291 tr 3.90 from 4.08 mcUT [ 95.5 %] 0.13 ghost hits on real tracks [ 3.2 %] +MatchUTHitsChecker_a4d04726 INFO 01_long >3UT :130954 tr 3.93 from 4.10 mcUT [ 95.8 %] 0.12 ghost hits on real tracks [ 3.0 %] +MatchUTHitsChecker_a4d04726 INFO 02_long_P>5GeV : 89937 tr 3.95 from 4.08 mcUT [ 96.8 %] 0.10 ghost hits on real tracks [ 2.4 %] +MatchUTHitsChecker_a4d04726 INFO 02_long_P>5GeV >3UT : 88789 tr 3.99 from 4.11 mcUT [ 97.2 %] 0.09 ghost hits on real tracks [ 2.2 %] +MatchUTHitsChecker_a4d04726 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV : 4858 tr 4.00 from 4.07 mcUT [ 98.1 %] 0.05 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_a4d04726 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4836 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.04 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_a4d04726 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4846 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.05 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_a4d04726 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4836 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.04 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_a4d04726 INFO +SeedTrackChecker_ad9abe4e INFO Results +SeedTrackChecker_ad9abe4e INFO **** Seed 284763 tracks including 5469 ghosts [ 1.92 %], Event average 1.56 % **** +SeedTrackChecker_ad9abe4e INFO 01_hasT : 198532 from 234618 [ 84.62 %] 7 clones [ 0.00 %], purity: 99.60 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 02_long : 143630 from 152279 [ 94.32 %] 6 clones [ 0.00 %], purity: 99.71 %, hitEff: 98.42 % +SeedTrackChecker_ad9abe4e INFO 03_long_P>5GeV : 95859 from 98421 [ 97.40 %] 5 clones [ 0.01 %], purity: 99.69 %, hitEff: 99.09 % +SeedTrackChecker_ad9abe4e INFO 04_long_fromB : 7598 from 7959 [ 95.46 %] 1 clones [ 0.01 %], purity: 99.75 %, hitEff: 98.65 % +SeedTrackChecker_ad9abe4e INFO 05_long_fromB_P>5GeV : 5835 from 5983 [ 97.53 %] 1 clones [ 0.02 %], purity: 99.76 %, hitEff: 99.13 % +SeedTrackChecker_ad9abe4e INFO 06_UT+T_strange : 16417 from 17658 [ 92.97 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.00 % +SeedTrackChecker_ad9abe4e INFO 07_UT+T_strange_P>5GeV : 8615 from 8825 [ 97.62 %] 0 clones [ 0.00 %], purity: 99.71 %, hitEff: 99.05 % +SeedTrackChecker_ad9abe4e INFO 08_noVelo+UT+T_strange : 8949 from 9658 [ 92.66 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.03 % +SeedTrackChecker_ad9abe4e INFO 09_noVelo+UT+T_strange_P>5GeV : 4914 from 5043 [ 97.44 %] 0 clones [ 0.00 %], purity: 99.70 %, hitEff: 99.01 % +SeedTrackChecker_ad9abe4e INFO 10_UT+T_SfromDB : 1133 from 1220 [ 92.87 %] 0 clones [ 0.00 %], purity: 99.77 %, hitEff: 97.99 % +SeedTrackChecker_ad9abe4e INFO 11_UT+T_SfromDB_P>5GeV : 612 from 623 [ 98.23 %] 0 clones [ 0.00 %], purity: 99.72 %, hitEff: 99.22 % +SeedTrackChecker_ad9abe4e INFO 12_noVelo+UT+T_SfromDB_P>5GeV : 420 from 428 [ 98.13 %] 0 clones [ 0.00 %], purity: 99.69 %, hitEff: 99.12 % +SeedTrackChecker_ad9abe4e INFO 13_hasT_electrons : 40669 from 74476 [ 54.61 %] 2 clones [ 0.00 %], purity: 99.69 %, hitEff: 97.16 % +SeedTrackChecker_ad9abe4e INFO 14_long_electrons : 13360 from 15125 [ 88.33 %] 1 clones [ 0.01 %], purity: 99.81 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 15_long_fromB_electrons : 3922 from 4210 [ 93.16 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.70 % +SeedTrackChecker_ad9abe4e INFO 16_long_electrons_P>5GeV : 8676 from 9420 [ 92.10 %] 0 clones [ 0.00 %], purity: 99.80 %, hitEff: 98.73 % +SeedTrackChecker_ad9abe4e INFO 17_long_fromB_electrons_P>5GeV : 3632 from 3850 [ 94.34 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.85 % +SeedTrackChecker_ad9abe4e INFO +HLTControlFlowMgr INFO Memory pool: used 3.94312 +/- 0.039102 MiB (min: 0, max: 5) in 1 +/- 0 blocks (allocated >once in 0 +/- 0% events). Allocated capacity was 10 +/- 0 MiB (min: 10, max: 10) and 347.612 +/- 3.41441 (min: 4, max: 489) requests were served +HLTControlFlowMgr INFO Timing table: +HLTControlFlowMgr INFO + | Name of Algorithm | Execution Count | Total Time / s | Avg. Time / us | + | Sum of all Algorithms | 2955 | 156.797 | 53061.598 | + | "Fetch__Event_DAQ_RawEvent" | 2955 | 92.551 | 31320.086 | + | "SeedTrackChecker_ad9abe4e" | 2289 | 12.958 | 5660.968 | + | "ForwardTrackChecker_482fda95" | 2289 | 12.000 | 5242.621 | + | "MatchTrackChecker_386d067b" | 2289 | 10.783 | 4710.787 | + | "ForwardUTHitsChecker_fe9d9ac2" | 2289 | 4.777 | 2086.871 | + | "MatchUTHitsChecker_a4d04726" | 2289 | 4.718 | 2061.173 | + | "PrForwardTrackingVelo_6024f9ec" | 2289 | 4.377 | 1912.147 | + | "PrHybridSeeding_4d0337cc" | 2289 | 3.264 | 1425.786 | + | "PrLHCbID2MCParticle_a906d17d" | 2289 | 2.506 | 1094.590 | + | "Unpack__Event_MC_Vertices" | 2289 | 1.990 | 869.550 | + | "Unpack__Event_MC_Particles" | 2289 | 1.895 | 827.684 | + | "VeloClusterTrackingSIMD_87c18651" | 2289 | 0.720 | 314.395 | + | "VPFullCluster2MCParticleLinker_17386552" | 2289 | 0.565 | 246.955 | + | "VPClusFull_38754d8c" | 2289 | 0.542 | 236.569 | + | "PrMatchNN_d80b5038" | 2289 | 0.469 | 204.856 | + | "PrStorePrUTHits_df75b912" | 2289 | 0.468 | 204.573 | + | "PrTrackAssociator_3adf94fb" | 2289 | 0.371 | 162.237 | + | "PrTrackAssociator_8c8024ec" | 2289 | 0.364 | 159.203 | + | "PrStoreUTHit_6220b56a" | 2289 | 0.338 | 147.867 | + | "PrTrackAssociator_16ad4612" | 2289 | 0.253 | 110.341 | + | "fromPrVeloTracksV1TracksMerger_fa66a5de" | 2289 | 0.191 | 83.556 | + | "fromPrMatchTracksV1Tracks_aaf8b514" | 2289 | 0.176 | 76.676 | + | "fromPrForwardTracksV1Tracks_f53f50a8" | 2289 | 0.134 | 58.409 | + | "fromPrSeedingTracksV1Tracks_84cd46c2" | 2289 | 0.121 | 52.778 | + | "PrStoreSciFiHits_fb0eba02" | 2289 | 0.112 | 48.776 | + | "FTRawBankDecoder" | 2289 | 0.059 | 25.806 | + | "UnpackRawEvent_UT" | 2955 | 0.026 | 8.646 | + | "reserveIOV" | 2289 | 0.022 | 9.692 | + | "Decode_ODIN" | 2289 | 0.007 | 3.021 | + | "DefaultGECFilter" | 2955 | 0.006 | 1.961 | + | "Fetch__Event_pSim_MCVertices" | 2289 | 0.006 | 2.421 | + | "UnpackRawEvent_FTCluster" | 2955 | 0.004 | 1.429 | + | "Fetch__Event_pSim_MCParticles" | 2289 | 0.004 | 1.795 | + | "Fetch__Event_Link_Raw_UT_Clusters" | 2289 | 0.004 | 1.659 | + | "Fetch__Event_Link_Raw_VP_Digits" | 2289 | 0.004 | 1.578 | + | "Fetch__Event_MC_TrackInfo" | 2289 | 0.004 | 1.549 | + | "DummyEventTime" | 2289 | 0.003 | 1.462 | + | "UnpackRawEvent_ODIN" | 2289 | 0.003 | 1.336 | + | "UnpackRawEvent_VP" | 2289 | 0.003 | 1.232 | + | "Fetch__Event_Link_Raw_FT_LiteClusters" | 2289 | 0.002 | 0.879 | + +HLTControlFlowMgr INFO StateTree: CFNode #executed #passed +LAZY_AND: hlt2_matching_reco_decision #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + PrGECFilter/DefaultGECFilter #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + NONLAZY_OR: hlt2_matching_reco_data #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrMatchNN/PrMatchNN_d80b5038 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/ForwardTrackChecker_482fda95 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrUTHitChecker/ForwardUTHitsChecker_fe9d9ac2 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/MatchTrackChecker_386d067b #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrUTHitChecker/MatchUTHitsChecker_a4d04726 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/SeedTrackChecker_ad9abe4e #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + +HLTControlFlowMgr INFO Histograms converted successfully according to request. +ToolSvc INFO Removing all tools created by ToolSvc +SeedTrackChecker_ad9abe4e.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +MatchUTHitsChecker_a4d04726.PrCh... SUCCESS Booked 44 Histogram(s) : 1D=40 2D=4 +MatchTrackChecker_386d067b.PrChe... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +ForwardUTHitsChecker_fe9d9ac2.Pr... SUCCESS Booked 44 Histogram(s) : 1D=40 2D=4 +ForwardTrackChecker_482fda95.PrC... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +RootCnvSvc INFO Disconnected data IO:9E3B8940-FB87-11EB-ADCA-FA163E643B60 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi] +RootCnvSvc INFO Disconnected data IO:AFCB9710-FB21-11EB-9E91-FA163ED3A4EB [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi] +RootCnvSvc INFO Disconnected data IO:B9D7CC62-FB38-11EB-8B01-3CECEF5D2C24 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi] +ApplicationMgr INFO Application Manager Finalized successfully +ApplicationMgr INFO Application Manager Terminated successfully diff --git a/data_matching/match_effs_testJpsi_EDef7_yCorrCut_mlp5.root b/data_matching/match_effs_testJpsi_EDef7_yCorrCut_mlp5.root new file mode 100644 index 0000000..a85ca37 Binary files /dev/null and b/data_matching/match_effs_testJpsi_EDef7_yCorrCut_mlp5.root differ diff --git a/data_matching/match_effs_testJpsi_EDef7_yCorrCut_mlp6.root b/data_matching/match_effs_testJpsi_EDef7_yCorrCut_mlp6.root new file mode 100644 index 0000000..421ff46 Binary files /dev/null and b/data_matching/match_effs_testJpsi_EDef7_yCorrCut_mlp6.root differ diff --git a/data_matching/match_effs_testJpsi_EDef_yCorrCut.root b/data_matching/match_effs_testJpsi_EDef_yCorrCut.root new file mode 100644 index 0000000..59ffd41 Binary files /dev/null and b/data_matching/match_effs_testJpsi_EDef_yCorrCut.root differ diff --git a/data_matching/match_effs_testJpsi_EDef_yCorrNoCut.root b/data_matching/match_effs_testJpsi_EDef_yCorrNoCut.root new file mode 100644 index 0000000..22f4c4b Binary files /dev/null and b/data_matching/match_effs_testJpsi_EDef_yCorrNoCut.root differ diff --git a/efficiencies/logs/effs_BJpsi_EDef.log b/efficiencies/logs/effs_BJpsi_EDef.log new file mode 100644 index 0000000..cf5c999 --- /dev/null +++ b/efficiencies/logs/effs_BJpsi_EDef.log @@ -0,0 +1,550 @@ +# setting LC_ALL to "C" +# --> Including file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_resolution_and_eff_data.py' +/***** User ApplicationOptions/ApplicationOptions ************************************************** +|-append_decoding_keys_to_output_manifest = True (default: True) +|-auditors = [] (default: []) +|-buffer_events = 20000 (default: 20000) +|-conddb_tag = 'sim-20210617-vc-md100' (default: '') +|-conditions_version = '' (default: '') +|-control_flow_file = '' (default: '') +|-data_flow_file = '' (default: '') +|-data_type = 'Upgrade' (default: 'Upgrade') +|-dddb_tag = 'dddb-20210617' (default: '') +|-event_store = 'HiveWhiteBoard' (default: 'HiveWhiteBoard') +|-evt_max = -1 (default: -1) +|-first_evt = 0 (default: 0) +|-geometry_version = '' (default: '') +|-histo_file = '' (default: '') +|-input_files = ['/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000036_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000074_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000084_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000096_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000085_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000039_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000047_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000100_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000029_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000058_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000078_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000070_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000082_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000099_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000043_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000031_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000076_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000094_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000037_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000092_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000064_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000054_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000083_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000056_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000052_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000079_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000066_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000033_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000025_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000053_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000045_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000048_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000040_1.xdigi'] +| (default: []) +|-input_manifest_file = '' (default: '') +|-input_process = '' (default: '') +|-input_raw_format = 0.5 (default: 0.5) +|-input_type = 'ROOT' (default: '') +|-lines_maker = None +|-memory_pool_size = 10485760 (default: 10485760) +|-monitoring_file = '' (default: '') +|-msg_svc_format = '% F%35W%S %7W%R%T %0W%M' (default: '% F%35W%S %7W%R%T %0W%M') +|-msg_svc_time_format = '%Y-%m-%d %H:%M:%S UTC' (default: '%Y-%m-%d %H:%M:%S UTC') +|-n_event_slots = 1 (default: -1) +|-n_threads = 1 (default: 1) +|-ntuple_file = '/work/cetin/LHCb/reco_tuner/efficiencies/effs_BJpsi_EDef.root' +| (default: '') +|-output_file = '' (default: '') +|-output_level = 3 (default: 3) +|-output_manifest_file = '' (default: '') +|-output_type = '' (default: '') +|-persistreco_version = 1.0 (default: 1.0) +|-phoenix_filename = '' (default: '') +|-preamble_algs = [] (default: []) +|-print_freq = 10000 (default: 10000) +|-python_logging_level = 20 (default: 20) +|-require_specific_decoding_keys = [] (default: []) +|-scheduler_legacy_mode = True (default: True) +|-simulation = True (default: None) +|-use_iosvc = False (default: False) +|-velo_motion_system_yaml = '' (default: '') +|-write_decoding_keys_to_git = True (default: True) +\----- (End of User ApplicationOptions/ApplicationOptions) ----------------------------------------- +# Overrule specified for keys +# <-- End of file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_resolution_and_eff_data.py' +ApplicationMgr SUCCESS +==================================================================================================================================== + Welcome to Moore version 55.2 + running on lhcba2 on Sun Mar 10 19:06:10 2024 +==================================================================================================================================== +ApplicationMgr INFO Application Manager Configured successfully +ToolSvc.GitDDDB INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/DDDB.git' +ToolSvc.GitDDDB INFO using commit 'upgrade/dddb-20210617' corresponding to 1871f1bb5c0d68c81dda62e84cf1eb3a45513521 +ToolSvc.GitSIMCOND INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/SIMCOND.git' +ToolSvc.GitSIMCOND INFO using commit 'upgrade/sim-20210617-vc-md100' corresponding to 9aa116c7143d21760d1be07ce1ef22c0f8f07bdf +DetectorPersistencySvc INFO Added successfully Conversion service:XmlCnvSvc +DetectorDataSvc SUCCESS Detector description database: git:/lhcb.xml +EventClockSvc.FakeEventTime INFO Event times generated from 0 with steps of 0 +EventClockSvc.FakeEventTime INFO Run numbers generated from 0 every 0 events +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c1.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c2.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c3.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c4.down.cdf +MagneticFieldSvc INFO Map scaled by factor 1 with polarity internally used: -1 signed relative current: -1 +NTupleSvc INFO Added stream file:/work/cetin/LHCb/reco_tuner/efficiencies/effs_BJpsi_EDef.root as FILE1 +HLTControlFlowMgr INFO Start initialization +RootHistSvc INFO Writing ROOT histograms to: /work/cetin/LHCb/reco_tuner/efficiencies/effs_BJpsi_EDef.root +HistogramPersistencySvc INFO Added successfully Conversion service:RootHistSvc +DeFTDetector INFO Current FT geometry version = 64 +HLTControlFlowMgr INFO Concurrency level information: +HLTControlFlowMgr INFO o Number of events slots: 1 +HLTControlFlowMgr INFO o TBB thread pool size: 'ThreadPoolSize':1 +HLTControlFlowMgr INFO ---> End of Initialization. This took 112590 ms +ApplicationMgr INFO Application Manager Initialized successfully +ApplicationMgr INFO Application Manager Started successfully +EventPersistencySvc INFO Added successfully Conversion service:RootCnvSvc +EventSelector INFO Stream:EventSelector.DataStreamTool_1 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000036_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO Will measure time between events 10 and 2147483647 (stop might be some events later) +HLTControlFlowMgr INFO Starting loop on events +EventSelector SUCCESS Reading Event record 1. Record number within stream 1: 1 +FTRawBankDecoder INFO Conditions DB is compatible with FT bank version 4, 5, 6. +FTRawBankDecoder INFO Building the readout map with version 0 +HLTControlFlowMgr INFO Timing started at: 19:08:23 +EventSelector INFO Stream:EventSelector.DataStreamTool_2 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000074_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +EventSelector INFO Stream:EventSelector.DataStreamTool_3 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000084_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +EventSelector INFO Stream:EventSelector.DataStreamTool_4 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000096_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000036_1.xdigi [0B898020-FB50-11EB-8654-FA163E6857C2] +RootCnvSvc INFO Removed disconnected IO stream:0B898020-FB50-11EB-8654-FA163E6857C2 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000036_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_5 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000085_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000074_1.xdigi [5DCC4124-FC68-11EB-BDA2-FA163E58303C] +RootCnvSvc INFO Removed disconnected IO stream:5DCC4124-FC68-11EB-BDA2-FA163E58303C [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000074_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_6 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000039_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000084_1.xdigi [8EB58942-FC7E-11EB-A61E-FA163EE79BF6] +RootCnvSvc INFO Removed disconnected IO stream:8EB58942-FC7E-11EB-A61E-FA163EE79BF6 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000084_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_7 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000047_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000096_1.xdigi [BECF3234-FE56-11EB-968E-FA163E94D94F] +RootCnvSvc INFO Removed disconnected IO stream:BECF3234-FE56-11EB-968E-FA163E94D94F [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000096_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_8 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000100_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000085_1.xdigi [E516F964-FC84-11EB-B1AC-FA163E0712FF] +RootCnvSvc INFO Removed disconnected IO stream:E516F964-FC84-11EB-B1AC-FA163E0712FF [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000085_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_9 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000029_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000039_1.xdigi [C7B4B038-FB52-11EB-A14B-FA163EF0D557] +RootCnvSvc INFO Removed disconnected IO stream:C7B4B038-FB52-11EB-A14B-FA163EF0D557 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000039_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_10 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000058_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000047_1.xdigi [6D30047A-FB5A-11EB-BF88-FA163E3787B1] +RootCnvSvc INFO Removed disconnected IO stream:6D30047A-FB5A-11EB-BF88-FA163E3787B1 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000047_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_11 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000078_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000100_1.xdigi [123C7EA8-FEE4-11EB-947C-FA163E5E0D5F] +RootCnvSvc INFO Removed disconnected IO stream:123C7EA8-FEE4-11EB-947C-FA163E5E0D5F [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000100_1.xdigi] +EventSelector SUCCESS Reading Event record 10001. Record number within stream 11: 648 +EventSelector INFO Stream:EventSelector.DataStreamTool_12 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000070_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000029_1.xdigi [1559743C-FB48-11EB-ABD6-FA163ECF2D71] +RootCnvSvc INFO Removed disconnected IO stream:1559743C-FB48-11EB-ABD6-FA163ECF2D71 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000029_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_13 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000082_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000058_1.xdigi [3C8722E6-FB7C-11EB-B214-FA163E7AC841] +RootCnvSvc INFO Removed disconnected IO stream:3C8722E6-FB7C-11EB-B214-FA163E7AC841 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000058_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_14 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000078_1.xdigi [971A74C4-FC71-11EB-9B7A-FA163EA1849A] +RootCnvSvc INFO Removed disconnected IO stream:971A74C4-FC71-11EB-9B7A-FA163EA1849A [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000078_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_15 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000099_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000070_1.xdigi [5FE45F74-FC53-11EB-AD8A-FA163E974EB1] +RootCnvSvc INFO Removed disconnected IO stream:5FE45F74-FC53-11EB-AD8A-FA163E974EB1 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000070_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_16 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000043_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000082_1.xdigi [A43AC110-FC79-11EB-BF3F-FA163E72700E] +RootCnvSvc INFO Removed disconnected IO stream:A43AC110-FC79-11EB-BF3F-FA163E72700E [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000082_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_17 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi [B9D7CC62-FB38-11EB-8B01-3CECEF5D2C24] +RootCnvSvc INFO Removed disconnected IO stream:B9D7CC62-FB38-11EB-8B01-3CECEF5D2C24 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_18 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000031_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000099_1.xdigi [91F55774-FE8E-11EB-9355-FA163E426AD6] +RootCnvSvc INFO Removed disconnected IO stream:91F55774-FE8E-11EB-9355-FA163E426AD6 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000099_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_19 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000076_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000043_1.xdigi [6EC8F9B2-FB56-11EB-8DB9-FA163E6BFC32] +RootCnvSvc INFO Removed disconnected IO stream:6EC8F9B2-FB56-11EB-8DB9-FA163E6BFC32 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000043_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_20 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000094_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi [AFCB9710-FB21-11EB-9E91-FA163ED3A4EB] +RootCnvSvc INFO Removed disconnected IO stream:AFCB9710-FB21-11EB-9E91-FA163ED3A4EB [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_21 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000037_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000031_1.xdigi [00D845C2-FB4A-11EB-85C8-3CFDFE9E1FB8] +RootCnvSvc INFO Removed disconnected IO stream:00D845C2-FB4A-11EB-85C8-3CFDFE9E1FB8 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000031_1.xdigi] +EventSelector SUCCESS Reading Event record 20001. Record number within stream 21: 613 +EventSelector INFO Stream:EventSelector.DataStreamTool_22 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000092_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000076_1.xdigi [1BE698B6-FC6F-11EB-A5EC-FA163E212E5B] +RootCnvSvc INFO Removed disconnected IO stream:1BE698B6-FC6F-11EB-A5EC-FA163E212E5B [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000076_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_23 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000064_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000094_1.xdigi [DE6396AC-FD6C-11EB-85E6-FA163EDC144C] +RootCnvSvc INFO Removed disconnected IO stream:DE6396AC-FD6C-11EB-85E6-FA163EDC144C [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000094_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_24 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000054_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000037_1.xdigi [CC17E46C-FB50-11EB-8CCD-3CECEF0DE5A0] +RootCnvSvc INFO Removed disconnected IO stream:CC17E46C-FB50-11EB-8CCD-3CECEF0DE5A0 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000037_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_25 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000083_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000092_1.xdigi [02C64118-FD5C-11EB-8618-FA163E8AF260] +RootCnvSvc INFO Removed disconnected IO stream:02C64118-FD5C-11EB-8618-FA163E8AF260 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000092_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_26 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000056_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000064_1.xdigi [22CD60BE-FBC6-11EB-BEED-FA163E1EE769] +RootCnvSvc INFO Removed disconnected IO stream:22CD60BE-FBC6-11EB-BEED-FA163E1EE769 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000064_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_27 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000054_1.xdigi [8FE2489A-FB67-11EB-9FC8-FA163E35CDB2] +RootCnvSvc INFO Removed disconnected IO stream:8FE2489A-FB67-11EB-9FC8-FA163E35CDB2 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000054_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_28 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000052_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000083_1.xdigi [E09CA29E-FC7A-11EB-9806-FA163E6E9F48] +RootCnvSvc INFO Removed disconnected IO stream:E09CA29E-FC7A-11EB-9806-FA163E6E9F48 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000083_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_29 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000079_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000056_1.xdigi [C0EA9202-FB6D-11EB-9EC2-3CECEF5D2AEE] +RootCnvSvc INFO Removed disconnected IO stream:C0EA9202-FB6D-11EB-9EC2-3CECEF5D2AEE [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000056_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_30 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000066_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi [9E3B8940-FB87-11EB-ADCA-FA163E643B60] +RootCnvSvc INFO Removed disconnected IO stream:9E3B8940-FB87-11EB-ADCA-FA163E643B60 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_31 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000033_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000052_1.xdigi [78850EB8-FB61-11EB-91C7-FA163E8B3E79] +RootCnvSvc INFO Removed disconnected IO stream:78850EB8-FB61-11EB-91C7-FA163E8B3E79 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000052_1.xdigi] +EventSelector SUCCESS Reading Event record 30001. Record number within stream 31: 516 +EventSelector INFO Stream:EventSelector.DataStreamTool_32 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000025_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000079_1.xdigi [D90EB734-FC74-11EB-B12A-FA163EF491BE] +RootCnvSvc INFO Removed disconnected IO stream:D90EB734-FC74-11EB-B12A-FA163EF491BE [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000079_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_33 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000053_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000066_1.xdigi [E20E8376-FC30-11EB-AC14-000017009605] +RootCnvSvc INFO Removed disconnected IO stream:E20E8376-FC30-11EB-AC14-000017009605 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000066_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_34 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000045_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000033_1.xdigi [CF32C3CC-FB4D-11EB-B55F-FA163E3286CE] +RootCnvSvc INFO Removed disconnected IO stream:CF32C3CC-FB4D-11EB-B55F-FA163E3286CE [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000033_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_35 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000048_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000025_1.xdigi [C97B8D2E-FB3E-11EB-9555-FA163E09F528] +RootCnvSvc INFO Removed disconnected IO stream:C97B8D2E-FB3E-11EB-9555-FA163E09F528 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000025_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_36 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000040_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000053_1.xdigi [97FD3520-FB63-11EB-9A46-FA163E714668] +RootCnvSvc INFO Removed disconnected IO stream:97FD3520-FB63-11EB-9A46-FA163E714668 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000053_1.xdigi] +HLTControlFlowMgr INFO No more events in event selection +HLTControlFlowMgr INFO ---> Loop over 35323 Events Finished - WSS 1849.87, timed 35313 Events: 2658468 ms, Evts/s = 13.2832 +BestLongTrackChecker_8a93d154.Lo... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +CloneKillerMatch_c1af047d INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "nTracksInput" | 27023 | 3007547 | 111.30 | + | "nTracksSelected" | 27023 | 1219629 | 45.133 | +DefaultGECFilter INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb Events Processed" | 35323 | + | "Nb events removed" | 8300 | +ForwardTrackChecker_482fda95.LoK... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +HLTControlFlowMgr INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Processed events" | 35323 | +MatchTrackChecker_8a39005f.LoKi:... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +PrForwardTrackingVelo_6024f9ec INFO Number of counters : 10 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Accepted input tracks" | 27023 | 4322823 | 159.97 | + | "Created long tracks" | 27023 | 2155350 | 79.760 | + | "Input tracks" | 27023 | 4529074 | 167.60 | + | "Number of candidate bins per track" | 4322823 |2.042276e+07 | 4.7244 | 5.1650 | 0.0000 | 58.000 | + | "Number of complete candidates/track 1st Loop" | 3639547 | 2320691 | 0.63763 | 0.64847 | 0.0000 | 7.0000 | + | "Number of complete candidates/track 2nd Loop" | 1776672 | 156795 | 0.088252 | 0.29462 | 0.0000 | 5.0000 | + | "Number of x candidates per track 1st Loop" | 3639547 | 5142402 | 1.4129 | 1.3643 | + | "Number of x candidates per track 2nd Loop" | 1776672 | 4267839 | 2.4022 | 2.6608 | + | "Percentage second loop execution" | 3639547 | 1776672 | 0.48816 | + | "Removed duplicates" | 27023 | 116306 | 4.3040 | +PrForwardTrackingVelo_6024f9ec.P... INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 1974692 | 7997020 | 4.0498 | + | "#tracks with hits added" | 1974692 | +PrHybridSeeding_4d0337cc INFO Number of counters : 21 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Created T2x1 three-hit combinations in case 0" | 48341313 |2.955738e+07 | 0.61143 | 0.62121 | 0.0000 | 6.0000 | + | "Created T2x1 three-hit combinations in case 1" | 59736068 |3.890531e+07 | 0.65129 | 0.73914 | 0.0000 | 12.000 | + | "Created T2x1 three-hit combinations in case 2" | 92062305 |7.348832e+07 | 0.79825 | 1.0005 | 0.0000 | 25.000 | + | "Created XZ tracks (part 0)" | 81069 | 4362313 | 53.810 | 45.987 | 0.0000 | 1698.0 | + | "Created XZ tracks (part 1)" | 81069 | 4372824 | 53.940 | 46.383 | 0.0000 | 1257.0 | + | "Created XZ tracks in case 0" | 54046 | 3250382 | 60.141 | 38.259 | 0.0000 | 503.00 | + | "Created XZ tracks in case 1" | 54046 | 3226826 | 59.705 | 45.131 | 0.0000 | 1144.0 | + | "Created XZ tracks in case 2" | 54046 | 2257929 | 41.778 | 51.760 | 0.0000 | 1698.0 | + | "Created full hit combinations in case 0" | 4960359 | 4960359 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 1" | 3736423 | 3736423 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 2" | 3395516 | 3395516 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created seed tracks" | 54046 | 3390744 | 62.738 | 22.781 | 2.0000 | 186.00 | + | "Created seed tracks (part 0)" | 27023 | 1892022 | 70.015 | 25.958 | 3.0000 | 207.00 | + | "Created seed tracks (part 1)" | 27023 | 1889881 | 69.936 | 26.105 | 2.0000 | 215.00 | + | "Created seed tracks in case 0" | 54046 | 1770384 | 32.757 | 12.817 | 0.0000 | 96.000 | + | "Created seed tracks in case 1" | 54046 | 3221597 | 59.608 | 21.826 | 2.0000 | 166.00 | + | "Created seed tracks in case 2" | 54046 | 3598130 | 66.575 | 24.744 | 2.0000 | 205.00 | + | "Created seed tracks in recovery step" | 27023 | 183773 | 6.8006 | 3.9574 | 0.0000 | 30.000 | + | "Created two-hit combinations in case 0" | 8064491 |1.859307e+08 | 23.055 | 16.090 | 0.0000 | 278.00 | + | "Created two-hit combinations in case 1" | 6971955 |2.107604e+08 | 30.230 | 18.520 | 0.0000 | 262.00 | + | "Created two-hit combinations in case 2" | 5497566 |2.463124e+08 | 44.804 | 28.350 | 0.0000 | 333.00 | +PrKalmanFilterForward_a6e62848 INFO Number of counters : 7 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Add states failed" | 5 | 0 | 0.0000 | + | "Pre outlier chi2 cut" | 36393 | + | "chi2 cut" | 204409 | + | "nIterations" | 2155350 | 4886810 | 2.2673 | + | "nOutlierIterations" | 2118957 | 1329607 | 0.62748 | + | "nTracksInput" | 27023 | 2155350 | 79.760 | + | "nTracksOutput" | 27023 | 1914543 | 70.849 | +PrKalmanFilterMatch_e1944f26 INFO Number of counters : 7 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Add states failed" | 2 | 0 | 0.0000 | + | "Pre outlier chi2 cut" | 338811 | + | "chi2 cut" | 606434 | + | "nIterations" | 1219629 | 3274545 | 2.6849 | + | "nOutlierIterations" | 880818 | 1009475 | 1.1461 | + | "nTracksInput" | 27023 | 1219629 | 45.133 | + | "nTracksOutput" | 27023 | 274382 | 10.154 | +PrLHCbID2MCParticle_a906d17d INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#removed null MCParticles" | 198107424 | 0 | 0.0000 | +PrMatchNN_3856ae45 INFO Number of counters : 3 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#MatchingChi2" | 27023 |1.012465e+08 | 3746.7 | + | "#MatchingMLP" | 3007547 | 2506987 | 0.83357 | + | "#MatchingTracks" | 27023 | 3007547 | 111.30 | +PrMatchNN_3856ae45.PrAddUTHitsTool INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 2493400 | 9811039 | 3.9348 | + | "#tracks with hits added" | 2493400 | +PrStorePrUTHits_df75b912 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 27023 | 5836968 | 216.00 | +PrStoreSciFiHits_fb0eba02 INFO Number of counters : 25 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Average X in T1U" | 8196488 |-2.970498e+08 | -36.241 | 1138.7 | -2656.4 | 2656.3 | + | "Average X in T1V" | 8302998 |-2.232378e+08 | -26.886 | 1127.1 | -2656.4 | 2656.3 | + | "Average X in T1X1" | 8064491 |-3.988098e+08 | -49.453 | 1159.2 | -2646.2 | 2646.2 | + | "Average X in T1X2" | 8414851 |-1.355164e+08 | -16.104 | 1119.5 | -2646.2 | 2646.2 | + | "Average X in T2U" | 7999640 |-1.870835e+08 | -23.386 | 1136.2 | -2656.4 | 2656.3 | + | "Average X in T2V" | 8247240 |-1.660776e+08 | -20.137 | 1130.6 | -2656.4 | 2656.3 | + | "Average X in T2X1" | 7652852 |-1.971999e+08 | -25.768 | 1140.3 | -2646.2 | 2646.2 | + | "Average X in T2X2" | 8508327 |-1.284413e+08 | -15.096 | 1126.2 | -2646.2 | 2646.2 | + | "Average X in T3U" | 8684086 |-1.041572e+08 | -11.994 | 1335.9 | -3188.4 | 3188.4 | + | "Average X in T3V" | 8961033 |-1.375357e+08 | -15.348 | 1330.5 | -3188.4 | 3188.4 | + | "Average X in T3X1" | 8348239 |-8.469251e+07 | -10.145 | 1336.3 | -3176.2 | 3176.2 | + | "Average X in T3X2" | 9294885 |-1.774036e+08 | -19.086 | 1321.1 | -3176.2 | 3176.2 | + | "Hits in T1U" | 108092 | 8196488 | 75.829 | 27.842 | 4.0000 | 327.00 | + | "Hits in T1V" | 108092 | 8302998 | 76.814 | 27.983 | 3.0000 | 375.00 | + | "Hits in T1X1" | 108092 | 8064491 | 74.608 | 27.731 | 4.0000 | 375.00 | + | "Hits in T1X2" | 108092 | 8414851 | 77.849 | 28.195 | 4.0000 | 428.00 | + | "Hits in T2U" | 108092 | 7999640 | 74.008 | 26.743 | 3.0000 | 341.00 | + | "Hits in T2V" | 108092 | 8247240 | 76.298 | 27.429 | 4.0000 | 381.00 | + | "Hits in T2X1" | 108092 | 7652852 | 70.799 | 25.759 | 2.0000 | 374.00 | + | "Hits in T2X2" | 108092 | 8508327 | 78.714 | 27.978 | 3.0000 | 356.00 | + | "Hits in T3U" | 108092 | 8684086 | 80.340 | 28.058 | 2.0000 | 331.00 | + | "Hits in T3V" | 108092 | 8961033 | 82.902 | 28.941 | 4.0000 | 399.00 | + | "Hits in T3X1" | 108092 | 8348239 | 77.233 | 27.004 | 3.0000 | 339.00 | + | "Hits in T3X2" | 108092 | 9294885 | 85.990 | 29.878 | 2.0000 | 355.00 | + | "Total number of hits" | 27023 |1.006751e+08 | 3725.5 | 1130.7 | 418.00 | 6405.0 | +PrStoreUTHit_6220b56a INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 27023 | 5836968 | 216.00 | +PrTrackAssociator_16ad4612 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 3390744 | 3322103 |( 97.97564 +- 0.007648140)% | + | "MC particles per track" | 3322103 | 3322179 | 1.0000 | +PrTrackAssociator_24d3bad6 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 3007547 | 1859622 |( 61.83185 +- 0.02801241)% | + | "MC particles per track" | 1859622 | 2182592 | 1.1737 | +PrTrackAssociator_3adf94fb INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 2155350 | 1844072 |( 85.55789 +- 0.02394343)% | + | "MC particles per track" | 1844072 | 2163436 | 1.1732 | +PrTrackAssociator_cbe8f3ce INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 2182277 | 1859055 |( 85.18877 +- 0.02404539)% | + | "MC particles per track" | 1859055 | 2172260 | 1.1685 | +PrTrackAssociator_d68377ee INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 7059265 | 6885105 |( 97.53289 +- 0.005838352)% | + | "MC particles per track" | 6885105 | 6916103 | 1.0045 | +PrVPHitsToVPLightClusters_599554c8 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Clusters" | 27023 |6.416351e+07 | 2374.4 | +SeedTrackChecker_ad9abe4e.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +TBTCMatch_4755c68a INFO Number of counters : 3 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"BadInput" | 273313 | 0 |( 0.000000 +- 0.000000)% | + |*"FitFailed" | 273313 | 0 |( 0.000000 +- 0.000000)% | + | "FittedBefore" | 273313 | +TBTC_Forward_3523b81b INFO Number of counters : 3 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"BadInput" | 1908964 | 0 |( 0.000000 +- 0.000000)% | + |*"FitFailed" | 1908964 | 0 |( 0.000000 +- 0.000000)% | + | "FittedBefore" | 1908964 | +VeloClusterTrackingSIMD_87c18651 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Clusters" | 27023 |6.416351e+07 | 2374.4 | + | "Nb of Produced Tracks" | 27023 | 7059265 | 261.23 | +VeloTrackChecker_e83d0cf5.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +fromPrForwardTracksV1Tracks_f53f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 27023 | 2155350 | 79.760 | +fromPrMatchTracksV1Tracks_67f41548 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 27023 | 3007547 | 111.30 | +fromPrSeedingTracksV1Tracks_84cd... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 27023 | 3390744 | 125.48 | +fromPrVeloTracksV1TracksMerger_f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 27023 | 7059265 | 261.23 | +ApplicationMgr INFO Application Manager Stopped successfully +BestLongTrackChecker_8a93d154 INFO Results +BestLongTrackChecker_8a93d154 INFO **** BestLong 2182277 tracks including 323222 ghosts [14.81 %], Event average 13.80 % **** +BestLongTrackChecker_8a93d154 INFO 01_long : 1608317 from 1811265 [ 88.80 %] 4950 clones [ 0.31 %], purity: 99.36 %, hitEff: 97.48 % +BestLongTrackChecker_8a93d154 INFO 02_long_P>5GeV : 1082174 from 1172326 [ 92.31 %] 2403 clones [ 0.22 %], purity: 99.45 %, hitEff: 98.01 % +BestLongTrackChecker_8a93d154 INFO 03_long_strange : 80436 from 98994 [ 81.25 %] 175 clones [ 0.22 %], purity: 99.13 %, hitEff: 97.12 % +BestLongTrackChecker_8a93d154 INFO 04_long_strange_P>5GeV : 41036 from 46918 [ 87.46 %] 52 clones [ 0.13 %], purity: 99.29 %, hitEff: 98.01 % +BestLongTrackChecker_8a93d154 INFO 05_long_fromB : 86117 from 94402 [ 91.22 %] 255 clones [ 0.30 %], purity: 99.49 %, hitEff: 97.79 % +BestLongTrackChecker_8a93d154 INFO 05_long_fromD : 45844 from 50932 [ 90.01 %] 151 clones [ 0.33 %], purity: 99.40 %, hitEff: 97.59 % +BestLongTrackChecker_8a93d154 INFO 06_long_fromB_P>5GeV : 66778 from 71030 [ 94.01 %] 156 clones [ 0.23 %], purity: 99.56 %, hitEff: 98.15 % +BestLongTrackChecker_8a93d154 INFO 06_long_fromD_P>5GeV : 32729 from 35044 [ 93.39 %] 90 clones [ 0.27 %], purity: 99.50 %, hitEff: 98.07 % +BestLongTrackChecker_8a93d154 INFO 07_long_electrons : 128580 from 181213 [ 70.96 %] 470 clones [ 0.36 %], purity: 98.35 %, hitEff: 96.00 % +BestLongTrackChecker_8a93d154 INFO 07_long_electrons_pairprod : 85645 from 130212 [ 65.77 %] 317 clones [ 0.37 %], purity: 97.94 %, hitEff: 95.24 % +BestLongTrackChecker_8a93d154 INFO 08_long_fromB_electrons : 40559 from 48919 [ 82.91 %] 136 clones [ 0.33 %], purity: 99.17 %, hitEff: 97.60 % +BestLongTrackChecker_8a93d154 INFO 09_long_fromB_electrons_P>5GeV : 37884 from 44696 [ 84.76 %] 130 clones [ 0.34 %], purity: 99.24 %, hitEff: 97.77 % +BestLongTrackChecker_8a93d154 INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 58233 from 61675 [ 94.42 %] 140 clones [ 0.24 %], purity: 99.62 %, hitEff: 98.19 % +BestLongTrackChecker_8a93d154 INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 36851 from 42838 [ 86.02 %] 124 clones [ 0.34 %], purity: 99.29 %, hitEff: 97.80 % +BestLongTrackChecker_8a93d154 INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 26550 from 28214 [ 94.10 %] 72 clones [ 0.27 %], purity: 99.58 %, hitEff: 98.13 % +BestLongTrackChecker_8a93d154 INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 21108 from 24129 [ 87.48 %] 22 clones [ 0.10 %], purity: 99.48 %, hitEff: 98.20 % +BestLongTrackChecker_8a93d154 INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 58102 from 61506 [ 94.47 %] 140 clones [ 0.24 %], purity: 99.62 %, hitEff: 98.21 % +BestLongTrackChecker_8a93d154 INFO +ForwardTrackChecker_482fda95 INFO Results +ForwardTrackChecker_482fda95 INFO **** Forward 2155350 tracks including 311278 ghosts [14.44 %], Event average 13.14 % **** +ForwardTrackChecker_482fda95 INFO 01_long : 1589453 from 1811265 [ 87.75 %] 5716 clones [ 0.36 %], purity: 99.20 %, hitEff: 98.42 % +ForwardTrackChecker_482fda95 INFO 02_long_P>5GeV : 1093695 from 1172326 [ 93.29 %] 3358 clones [ 0.31 %], purity: 99.32 %, hitEff: 98.82 % +ForwardTrackChecker_482fda95 INFO 03_long_strange : 79529 from 98994 [ 80.34 %] 216 clones [ 0.27 %], purity: 98.86 %, hitEff: 98.18 % +ForwardTrackChecker_482fda95 INFO 04_long_strange_P>5GeV : 41749 from 46918 [ 88.98 %] 84 clones [ 0.20 %], purity: 99.10 %, hitEff: 98.81 % +ForwardTrackChecker_482fda95 INFO 05_long_fromB : 85595 from 94402 [ 90.67 %] 303 clones [ 0.35 %], purity: 99.38 %, hitEff: 98.71 % +ForwardTrackChecker_482fda95 INFO 05_long_fromD : 45356 from 50932 [ 89.05 %] 165 clones [ 0.36 %], purity: 99.25 %, hitEff: 98.57 % +ForwardTrackChecker_482fda95 INFO 06_long_fromB_P>5GeV : 67394 from 71030 [ 94.88 %] 217 clones [ 0.32 %], purity: 99.48 %, hitEff: 98.98 % +ForwardTrackChecker_482fda95 INFO 06_long_fromD_P>5GeV : 33032 from 35044 [ 94.26 %] 110 clones [ 0.33 %], purity: 99.39 %, hitEff: 98.93 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons : 125946 from 181213 [ 69.50 %] 1382 clones [ 1.09 %], purity: 97.93 %, hitEff: 98.26 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons_pairprod : 82370 from 130212 [ 63.26 %] 988 clones [ 1.19 %], purity: 97.36 %, hitEff: 98.03 % +ForwardTrackChecker_482fda95 INFO 08_long_fromB_electrons : 41503 from 48919 [ 84.84 %] 400 clones [ 0.95 %], purity: 99.00 %, hitEff: 98.76 % +ForwardTrackChecker_482fda95 INFO 09_long_fromB_electrons_P>5GeV : 39040 from 44696 [ 87.35 %] 383 clones [ 0.97 %], purity: 99.07 %, hitEff: 98.87 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 58515 from 61675 [ 94.88 %] 195 clones [ 0.33 %], purity: 99.57 %, hitEff: 98.96 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 37907 from 42838 [ 88.49 %] 359 clones [ 0.94 %], purity: 99.14 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 26624 from 28214 [ 94.36 %] 90 clones [ 0.34 %], purity: 99.52 %, hitEff: 98.90 % +ForwardTrackChecker_482fda95 INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 21422 from 24129 [ 88.78 %] 43 clones [ 0.20 %], purity: 99.39 %, hitEff: 98.73 % +ForwardTrackChecker_482fda95 INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 58387 from 61506 [ 94.93 %] 195 clones [ 0.33 %], purity: 99.57 %, hitEff: 98.97 % +ForwardTrackChecker_482fda95 INFO +MatchTrackChecker_8a39005f INFO Results +MatchTrackChecker_8a39005f INFO **** Match 3007547 tracks including 1147925 ghosts [38.17 %], Event average 35.40 % **** +MatchTrackChecker_8a39005f INFO 01_long : 1584238 from 1811265 [ 87.47 %] 9510 clones [ 0.60 %], purity: 99.33 %, hitEff: 98.62 % +MatchTrackChecker_8a39005f INFO 02_long_P>5GeV : 1084211 from 1172326 [ 92.48 %] 5207 clones [ 0.48 %], purity: 99.45 %, hitEff: 99.22 % +MatchTrackChecker_8a39005f INFO 03_long_strange : 77791 from 98994 [ 78.58 %] 405 clones [ 0.52 %], purity: 98.99 %, hitEff: 98.24 % +MatchTrackChecker_8a39005f INFO 04_long_strange_P>5GeV : 41578 from 46918 [ 88.62 %] 171 clones [ 0.41 %], purity: 99.24 %, hitEff: 99.21 % +MatchTrackChecker_8a39005f INFO 05_long_fromB : 85553 from 94402 [ 90.63 %] 529 clones [ 0.61 %], purity: 99.47 %, hitEff: 98.87 % +MatchTrackChecker_8a39005f INFO 05_long_fromD : 45317 from 50932 [ 88.98 %] 297 clones [ 0.65 %], purity: 99.36 %, hitEff: 98.72 % +MatchTrackChecker_8a39005f INFO 06_long_fromB_P>5GeV : 67010 from 71030 [ 94.34 %] 346 clones [ 0.51 %], purity: 99.58 %, hitEff: 99.25 % +MatchTrackChecker_8a39005f INFO 06_long_fromD_P>5GeV : 32786 from 35044 [ 93.56 %] 176 clones [ 0.53 %], purity: 99.51 %, hitEff: 99.25 % +MatchTrackChecker_8a39005f INFO 07_long_electrons : 138327 from 181213 [ 76.33 %] 2233 clones [ 1.59 %], purity: 97.75 %, hitEff: 98.09 % +MatchTrackChecker_8a39005f INFO 07_long_electrons_pairprod : 93382 from 130212 [ 71.72 %] 1592 clones [ 1.68 %], purity: 97.15 %, hitEff: 97.77 % +MatchTrackChecker_8a39005f INFO 08_long_fromB_electrons : 42442 from 48919 [ 86.76 %] 628 clones [ 1.46 %], purity: 99.02 %, hitEff: 98.87 % +MatchTrackChecker_8a39005f INFO 09_long_fromB_electrons_P>5GeV : 39740 from 44696 [ 88.91 %] 605 clones [ 1.50 %], purity: 99.11 %, hitEff: 99.02 % +MatchTrackChecker_8a39005f INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 58270 from 61675 [ 94.48 %] 305 clones [ 0.52 %], purity: 99.67 %, hitEff: 99.16 % +MatchTrackChecker_8a39005f INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 38479 from 42838 [ 89.82 %] 572 clones [ 1.46 %], purity: 99.19 %, hitEff: 99.00 % +MatchTrackChecker_8a39005f INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 26503 from 28214 [ 93.94 %] 140 clones [ 0.53 %], purity: 99.64 %, hitEff: 99.12 % +MatchTrackChecker_8a39005f INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 21386 from 24129 [ 88.63 %] 91 clones [ 0.42 %], purity: 99.53 %, hitEff: 98.98 % +MatchTrackChecker_8a39005f INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 58164 from 61506 [ 94.57 %] 305 clones [ 0.52 %], purity: 99.68 %, hitEff: 99.16 % +MatchTrackChecker_8a39005f INFO +SeedTrackChecker_ad9abe4e INFO Results +SeedTrackChecker_ad9abe4e INFO **** Seed 3390744 tracks including 68641 ghosts [ 2.02 %], Event average 1.63 % **** +SeedTrackChecker_ad9abe4e INFO 01_hasT : 2362888 from 2795799 [ 84.52 %] 92 clones [ 0.00 %], purity: 99.60 %, hitEff: 97.84 % +SeedTrackChecker_ad9abe4e INFO 02_long : 1707963 from 1811265 [ 94.30 %] 46 clones [ 0.00 %], purity: 99.71 %, hitEff: 98.41 % +SeedTrackChecker_ad9abe4e INFO 03_long_P>5GeV : 1141970 from 1172326 [ 97.41 %] 33 clones [ 0.00 %], purity: 99.70 %, hitEff: 99.08 % +SeedTrackChecker_ad9abe4e INFO 04_long_fromB : 90231 from 94402 [ 95.58 %] 2 clones [ 0.00 %], purity: 99.76 %, hitEff: 98.72 % +SeedTrackChecker_ad9abe4e INFO 05_long_fromB_P>5GeV : 69302 from 71030 [ 97.57 %] 2 clones [ 0.00 %], purity: 99.75 %, hitEff: 99.17 % +SeedTrackChecker_ad9abe4e INFO 06_UT+T_strange : 195676 from 211050 [ 92.72 %] 3 clones [ 0.00 %], purity: 99.73 %, hitEff: 98.00 % +SeedTrackChecker_ad9abe4e INFO 07_UT+T_strange_P>5GeV : 102766 from 105626 [ 97.29 %] 0 clones [ 0.00 %], purity: 99.71 %, hitEff: 99.07 % +SeedTrackChecker_ad9abe4e INFO 08_noVelo+UT+T_strange : 105019 from 113340 [ 92.66 %] 2 clones [ 0.00 %], purity: 99.72 %, hitEff: 98.02 % +SeedTrackChecker_ad9abe4e INFO 09_noVelo+UT+T_strange_P>5GeV : 57865 from 59507 [ 97.24 %] 0 clones [ 0.00 %], purity: 99.70 %, hitEff: 99.04 % +SeedTrackChecker_ad9abe4e INFO 10_UT+T_SfromDB : 13279 from 14317 [ 92.75 %] 0 clones [ 0.00 %], purity: 99.76 %, hitEff: 98.13 % +SeedTrackChecker_ad9abe4e INFO 11_UT+T_SfromDB_P>5GeV : 7443 from 7643 [ 97.38 %] 0 clones [ 0.00 %], purity: 99.76 %, hitEff: 99.15 % +SeedTrackChecker_ad9abe4e INFO 12_noVelo+UT+T_SfromDB_P>5GeV : 4731 from 4865 [ 97.25 %] 0 clones [ 0.00 %], purity: 99.75 %, hitEff: 99.12 % +SeedTrackChecker_ad9abe4e INFO 13_hasT_electrons : 483995 from 890297 [ 54.36 %] 22 clones [ 0.00 %], purity: 99.67 %, hitEff: 97.17 % +SeedTrackChecker_ad9abe4e INFO 14_long_electrons : 159229 from 181213 [ 87.87 %] 8 clones [ 0.01 %], purity: 99.78 %, hitEff: 97.81 % +SeedTrackChecker_ad9abe4e INFO 15_long_fromB_electrons : 45387 from 48919 [ 92.78 %] 3 clones [ 0.01 %], purity: 99.77 %, hitEff: 98.69 % +SeedTrackChecker_ad9abe4e INFO 16_long_electrons_P>5GeV : 102808 from 112140 [ 91.68 %] 6 clones [ 0.01 %], purity: 99.77 %, hitEff: 98.68 % +SeedTrackChecker_ad9abe4e INFO 17_long_fromB_electrons_P>5GeV : 41974 from 44696 [ 93.91 %] 3 clones [ 0.01 %], purity: 99.77 %, hitEff: 98.88 % +SeedTrackChecker_ad9abe4e INFO +VeloTrackChecker_e83d0cf5 INFO Results +VeloTrackChecker_e83d0cf5 INFO **** Velo 7059265 tracks including 174160 ghosts [ 2.47 %], Event average 2.56 % **** +VeloTrackChecker_e83d0cf5 INFO 01_velo : 3088200 from 3153550 [ 97.93 %] 47327 clones [ 1.51 %], purity: 99.62 %, hitEff: 95.63 %, hitEffFirst3: 95.51 %, hitEffLast: 95.34 % +VeloTrackChecker_e83d0cf5 INFO 02_long : 1796773 from 1811265 [ 99.20 %] 18312 clones [ 1.01 %], purity: 99.71 %, hitEff: 96.60 %, hitEffFirst3: 96.49 %, hitEffLast: 96.44 % +VeloTrackChecker_e83d0cf5 INFO 03_long_P>5GeV : 1166697 from 1172326 [ 99.52 %] 9016 clones [ 0.77 %], purity: 99.71 %, hitEff: 97.01 %, hitEffFirst3: 96.86 %, hitEffLast: 96.95 % +VeloTrackChecker_e83d0cf5 INFO 04_long_strange : 95149 from 98994 [ 96.12 %] 902 clones [ 0.94 %], purity: 99.18 %, hitEff: 96.15 %, hitEffFirst3: 96.25 %, hitEffLast: 95.18 % +VeloTrackChecker_e83d0cf5 INFO 05_long_strange_P>5GeV : 45287 from 46918 [ 96.52 %] 289 clones [ 0.63 %], purity: 99.03 %, hitEff: 96.85 %, hitEffFirst3: 96.96 %, hitEffLast: 96.05 % +VeloTrackChecker_e83d0cf5 INFO 06_long_fromB : 93725 from 94402 [ 99.28 %] 855 clones [ 0.90 %], purity: 99.69 %, hitEff: 96.64 %, hitEffFirst3: 96.53 %, hitEffLast: 96.48 % +VeloTrackChecker_e83d0cf5 INFO 06_long_fromD : 50520 from 50932 [ 99.19 %] 523 clones [ 1.02 %], purity: 99.67 %, hitEff: 96.54 %, hitEffFirst3: 96.41 %, hitEffLast: 96.37 % +VeloTrackChecker_e83d0cf5 INFO 07_long_fromB_P>5GeV : 70725 from 71030 [ 99.57 %] 496 clones [ 0.70 %], purity: 99.70 %, hitEff: 96.97 %, hitEffFirst3: 96.87 %, hitEffLast: 96.84 % +VeloTrackChecker_e83d0cf5 INFO 07_long_fromD_P>5GeV : 34866 from 35044 [ 99.49 %] 267 clones [ 0.76 %], purity: 99.68 %, hitEff: 96.93 %, hitEffFirst3: 96.82 %, hitEffLast: 96.80 % +VeloTrackChecker_e83d0cf5 INFO 08_long_electrons : 174045 from 181213 [ 96.04 %] 3111 clones [ 1.76 %], purity: 98.10 %, hitEff: 94.64 %, hitEffFirst3: 93.13 %, hitEffLast: 94.83 % +VeloTrackChecker_e83d0cf5 INFO 09_long_fromB_electrons : 47652 from 48919 [ 97.41 %] 765 clones [ 1.58 %], purity: 99.20 %, hitEff: 96.20 %, hitEffFirst3: 95.93 %, hitEffLast: 96.12 % +VeloTrackChecker_e83d0cf5 INFO 10_long_fromB_electrons_P>5GeV : 43877 from 44696 [ 98.17 %] 720 clones [ 1.61 %], purity: 99.30 %, hitEff: 96.30 %, hitEffFirst3: 96.15 %, hitEffLast: 96.17 % +VeloTrackChecker_e83d0cf5 INFO 11_long_fromB_P>3GeV_Pt>0.5GeV : 61365 from 61675 [ 99.50 %] 372 clones [ 0.60 %], purity: 99.72 %, hitEff: 96.98 %, hitEffFirst3: 96.88 %, hitEffLast: 96.84 % +VeloTrackChecker_e83d0cf5 INFO 11_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 42320 from 42838 [ 98.79 %] 676 clones [ 1.57 %], purity: 99.38 %, hitEff: 96.39 %, hitEffFirst3: 96.31 %, hitEffLast: 96.22 % +VeloTrackChecker_e83d0cf5 INFO 11_long_fromD_P>3GeV_Pt>0.5GeV : 28057 from 28214 [ 99.44 %] 178 clones [ 0.63 %], purity: 99.68 %, hitEff: 96.94 %, hitEffFirst3: 96.85 %, hitEffLast: 96.77 % +VeloTrackChecker_e83d0cf5 INFO 11_long_strange_P>3GeV_Pt>0.5GeV : 22890 from 24129 [ 94.87 %] 122 clones [ 0.53 %], purity: 98.78 %, hitEff: 96.86 %, hitEffFirst3: 96.60 %, hitEffLast: 96.63 % +VeloTrackChecker_e83d0cf5 INFO 12_UT_long_fromB_P>3GeV_Pt>0.5GeV : 61198 from 61506 [ 99.50 %] 372 clones [ 0.60 %], purity: 99.71 %, hitEff: 96.98 %, hitEffFirst3: 96.88 %, hitEffLast: 96.84 % +VeloTrackChecker_e83d0cf5 INFO +HLTControlFlowMgr INFO Memory pool: used 3.89435 +/- 0.011476 MiB (min: 0, max: 5) in 1 +/- 0 blocks (allocated >once in 0 +/- 0% events). Allocated capacity was 10 +/- 0 MiB (min: 10, max: 10) and 343.336 +/- 1.00196 (min: 4, max: 505) requests were served +HLTControlFlowMgr INFO Timing table: +HLTControlFlowMgr INFO + | Name of Algorithm | Execution Count | Total Time / s | Avg. Time / us | + | Sum of all Algorithms | 35323 | 2608.974 | 73860.490 | + | "Fetch__Event_DAQ_RawEvent" | 35323 | 1440.099 | 40769.436 | + | "SeedTrackChecker_ad9abe4e" | 27023 | 157.356 | 5823.028 | + | "VeloTrackChecker_e83d0cf5" | 27023 | 155.874 | 5768.205 | + | "ForwardTrackChecker_482fda95" | 27023 | 145.953 | 5401.078 | + | "MatchTrackChecker_8a39005f" | 27023 | 133.069 | 4924.269 | + | "BestLongTrackChecker_8a93d154" | 27023 | 130.609 | 4833.249 | + | "PrKalmanFilterForward_a6e62848" | 27023 | 123.471 | 4569.110 | + | "PrKalmanFilterMatch_e1944f26" | 27023 | 66.971 | 2478.286 | + | "PrForwardTrackingVelo_6024f9ec" | 27023 | 53.104 | 1965.157 | + | "PrHybridSeeding_4d0337cc" | 27023 | 39.394 | 1457.777 | + | "PrLHCbID2MCParticle_a906d17d" | 27023 | 30.012 | 1110.627 | + | "Unpack__Event_MC_Vertices" | 27023 | 24.222 | 896.330 | + | "Unpack__Event_MC_Particles" | 27023 | 22.738 | 841.439 | + | "VeloClusterTrackingSIMD_87c18651" | 27023 | 8.650 | 320.096 | + | "CloneKillerMatch_c1af047d" | 27023 | 7.126 | 263.690 | + | "VPFullCluster2MCParticleLinker_17386552" | 27023 | 6.872 | 254.313 | + | "PrStorePrUTHits_df75b912" | 27023 | 6.530 | 241.636 | + | "VPClusFull_38754d8c" | 27023 | 6.440 | 238.302 | + | "PrMatchNN_3856ae45" | 27023 | 6.129 | 226.797 | + | "PrTrackAssociator_24d3bad6" | 27023 | 5.187 | 191.935 | + | "TBTC_Forward_3523b81b" | 27023 | 5.099 | 188.707 | + | "PrTrackAssociator_3adf94fb" | 27023 | 4.475 | 165.609 | + | "PrStoreUTHit_6220b56a" | 27023 | 4.316 | 159.706 | + | "PrTrackAssociator_cbe8f3ce" | 27023 | 4.021 | 148.783 | + | "PrTrackAssociator_d68377ee" | 27023 | 3.313 | 122.602 | + | "PrTrackAssociator_16ad4612" | 27023 | 3.058 | 113.156 | + | "fromPrMatchTracksV1Tracks_67f41548" | 27023 | 2.552 | 94.451 | + | "PrVPHitsToVPLightClusters_599554c8" | 27023 | 2.518 | 93.172 | + | "fromPrVeloTracksV1TracksMerger_fa66a5de" | 27023 | 2.221 | 82.205 | + | "fromPrForwardTracksV1Tracks_f53f50a8" | 27023 | 1.521 | 56.283 | + | "PrStoreSciFiHits_fb0eba02" | 27023 | 1.422 | 52.608 | + | "fromPrSeedingTracksV1Tracks_84cd46c2" | 27023 | 1.373 | 50.812 | + | "FTRawBankDecoder" | 27023 | 0.780 | 28.866 | + | "TrackContainersMerger_511ac736" | 27023 | 0.745 | 27.567 | + | "TBTCMatch_4755c68a" | 27023 | 0.541 | 20.019 | + | "UnpackRawEvent_UT" | 35323 | 0.320 | 9.069 | + | "UniqueIDGeneratorAlg_26e527e9" | 27023 | 0.169 | 6.246 | + | "reserveIOV" | 27023 | 0.098 | 3.622 | + | "Decode_ODIN" | 27023 | 0.078 | 2.868 | + | "DefaultGECFilter" | 35323 | 0.077 | 2.183 | + | "UnpackRawEvent_FTCluster" | 35323 | 0.060 | 1.709 | + | "Fetch__Event_Link_Raw_VP_Digits" | 27023 | 0.057 | 2.114 | + | "UnpackRawEvent_VP" | 27023 | 0.053 | 1.963 | + | "Fetch__Event_pSim_MCVertices" | 27023 | 0.052 | 1.924 | + | "Fetch__Event_Link_Raw_UT_Clusters" | 27023 | 0.052 | 1.918 | + | "Fetch__Event_pSim_MCParticles" | 27023 | 0.048 | 1.761 | + | "Fetch__Event_MC_TrackInfo" | 27023 | 0.044 | 1.624 | + | "DummyEventTime" | 27023 | 0.043 | 1.594 | + | "UnpackRawEvent_ODIN" | 27023 | 0.036 | 1.341 | + | "Fetch__Event_Link_Raw_FT_LiteClusters" | 27023 | 0.028 | 1.024 | + +HLTControlFlowMgr INFO StateTree: CFNode #executed #passed +LAZY_AND: run_tracking_debug_decision #=35323 Sum=27023 Eff=|( 76.50256 +- 0.225590)%| + PrGECFilter/DefaultGECFilter #=35323 Sum=27023 Eff=|( 76.50256 +- 0.225590)%| + NONLAZY_OR: run_tracking_debug_data #=27023 Sum=27023 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/ForwardTrackChecker_482fda95 #=27023 Sum=27023 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/MatchTrackChecker_8a39005f #=27023 Sum=27023 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/BestLongTrackChecker_8a93d154 #=27023 Sum=27023 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/SeedTrackChecker_ad9abe4e #=27023 Sum=27023 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/VeloTrackChecker_e83d0cf5 #=27023 Sum=27023 Eff=|( 100.0000 +- 0.00000 )%| + +HLTControlFlowMgr INFO Histograms converted successfully according to request. +ToolSvc INFO Removing all tools created by ToolSvc +VeloTrackChecker_e83d0cf5.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +SeedTrackChecker_ad9abe4e.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +BestLongTrackChecker_8a93d154.Pr... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +MatchTrackChecker_8a39005f.PrChe... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +ForwardTrackChecker_482fda95.PrC... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +RootCnvSvc INFO Disconnected data IO:148972FE-FB5D-11EB-861A-FA163E8E4EFB [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000048_1.xdigi] +RootCnvSvc INFO Disconnected data IO:1665270C-FB54-11EB-A7EB-FA163E95EADE [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000040_1.xdigi] +RootCnvSvc INFO Disconnected data IO:FACBF624-FB58-11EB-B4CE-FA163E92C5A4 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000045_1.xdigi] +ApplicationMgr INFO Application Manager Finalized successfully +ApplicationMgr INFO Application Manager Terminated successfully diff --git a/efficiencies/logs/effs_testJpsi_EDef_yCorrCut.log b/efficiencies/logs/effs_testJpsi_EDef_yCorrCut.log new file mode 100644 index 0000000..764046f --- /dev/null +++ b/efficiencies/logs/effs_testJpsi_EDef_yCorrCut.log @@ -0,0 +1,448 @@ +# setting LC_ALL to "C" +# --> Including file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_resolution_and_eff_data.py' +/***** User ApplicationOptions/ApplicationOptions ************************************************** +|-append_decoding_keys_to_output_manifest = True (default: True) +|-auditors = [] (default: []) +|-buffer_events = 20000 (default: 20000) +|-conddb_tag = 'sim-20210617-vc-md100' (default: '') +|-conditions_version = '' (default: '') +|-control_flow_file = '' (default: '') +|-data_flow_file = '' (default: '') +|-data_type = 'Upgrade' (default: 'Upgrade') +|-dddb_tag = 'dddb-20210617' (default: '') +|-event_store = 'HiveWhiteBoard' (default: 'HiveWhiteBoard') +|-evt_max = -1 (default: -1) +|-first_evt = 0 (default: 0) +|-geometry_version = '' (default: '') +|-histo_file = '' (default: '') +|-input_files = ['/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi'] +| (default: []) +|-input_manifest_file = '' (default: '') +|-input_process = '' (default: '') +|-input_raw_format = 0.5 (default: 0.5) +|-input_type = 'ROOT' (default: '') +|-lines_maker = None +|-memory_pool_size = 10485760 (default: 10485760) +|-monitoring_file = '' (default: '') +|-msg_svc_format = '% F%35W%S %7W%R%T %0W%M' (default: '% F%35W%S %7W%R%T %0W%M') +|-msg_svc_time_format = '%Y-%m-%d %H:%M:%S UTC' (default: '%Y-%m-%d %H:%M:%S UTC') +|-n_event_slots = 1 (default: -1) +|-n_threads = 1 (default: 1) +|-ntuple_file = '/work/cetin/LHCb/reco_tuner/efficiencies/effs_testJpsi_EDef_yCorrCut.root' +| (default: '') +|-output_file = '' (default: '') +|-output_level = 3 (default: 3) +|-output_manifest_file = '' (default: '') +|-output_type = '' (default: '') +|-persistreco_version = 1.0 (default: 1.0) +|-phoenix_filename = '' (default: '') +|-preamble_algs = [] (default: []) +|-print_freq = 10000 (default: 10000) +|-python_logging_level = 20 (default: 20) +|-require_specific_decoding_keys = [] (default: []) +|-scheduler_legacy_mode = True (default: True) +|-simulation = True (default: None) +|-use_iosvc = False (default: False) +|-velo_motion_system_yaml = '' (default: '') +|-write_decoding_keys_to_git = True (default: True) +\----- (End of User ApplicationOptions/ApplicationOptions) ----------------------------------------- +# Overrule specified for keys +# <-- End of file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_resolution_and_eff_data.py' +ApplicationMgr SUCCESS +==================================================================================================================================== + Welcome to Moore version 55.2 + running on lhcba2 on Mon Mar 11 06:44:19 2024 +==================================================================================================================================== +ApplicationMgr INFO Application Manager Configured successfully +ToolSvc.GitDDDB INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/DDDB.git' +ToolSvc.GitDDDB INFO using commit 'upgrade/dddb-20210617' corresponding to 1871f1bb5c0d68c81dda62e84cf1eb3a45513521 +ToolSvc.GitSIMCOND INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/SIMCOND.git' +ToolSvc.GitSIMCOND INFO using commit 'upgrade/sim-20210617-vc-md100' corresponding to 9aa116c7143d21760d1be07ce1ef22c0f8f07bdf +DetectorPersistencySvc INFO Added successfully Conversion service:XmlCnvSvc +DetectorDataSvc SUCCESS Detector description database: git:/lhcb.xml +EventClockSvc.FakeEventTime INFO Event times generated from 0 with steps of 0 +EventClockSvc.FakeEventTime INFO Run numbers generated from 0 every 0 events +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c1.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c2.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c3.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c4.down.cdf +MagneticFieldSvc INFO Map scaled by factor 1 with polarity internally used: -1 signed relative current: -1 +NTupleSvc INFO Added stream file:/work/cetin/LHCb/reco_tuner/efficiencies/effs_testJpsi_EDef_yCorrCut.root as FILE1 +HLTControlFlowMgr INFO Start initialization +RootHistSvc INFO Writing ROOT histograms to: /work/cetin/LHCb/reco_tuner/efficiencies/effs_testJpsi_EDef_yCorrCut.root +HistogramPersistencySvc INFO Added successfully Conversion service:RootHistSvc +DeFTDetector INFO Current FT geometry version = 64 +HLTControlFlowMgr INFO Concurrency level information: +HLTControlFlowMgr INFO o Number of events slots: 1 +HLTControlFlowMgr INFO o TBB thread pool size: 'ThreadPoolSize':1 +HLTControlFlowMgr INFO ---> End of Initialization. This took 85352 ms +ApplicationMgr INFO Application Manager Initialized successfully +ApplicationMgr INFO Application Manager Started successfully +EventPersistencySvc INFO Added successfully Conversion service:RootCnvSvc +EventSelector INFO Stream:EventSelector.DataStreamTool_1 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO Will measure time between events 10 and 2147483647 (stop might be some events later) +HLTControlFlowMgr INFO Starting loop on events +EventSelector SUCCESS Reading Event record 1. Record number within stream 1: 1 +FTRawBankDecoder INFO Conditions DB is compatible with FT bank version 4, 5, 6. +FTRawBankDecoder INFO Building the readout map with version 0 +HLTControlFlowMgr INFO Timing started at: 06:46:14 +EventSelector INFO Stream:EventSelector.DataStreamTool_2 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +EventSelector INFO Stream:EventSelector.DataStreamTool_3 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO No more events in event selection +HLTControlFlowMgr INFO ---> Loop over 2955 Events Finished - WSS 1872.34, timed 2945 Events: 191967 ms, Evts/s = 15.3412 +BestLongTrackChecker_8a93d154.Lo... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +CloneKillerMatch_c1af047d INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "nTracksInput" | 2289 | 252487 | 110.30 | + | "nTracksSelected" | 2289 | 101849 | 44.495 | +DefaultGECFilter INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb Events Processed" | 2955 | + | "Nb events removed" | 666 | +ForwardTrackChecker_482fda95.LoK... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +HLTControlFlowMgr INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Processed events" | 2955 | +MatchTrackChecker_8a39005f.LoKi:... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +PrForwardTrackingVelo_6024f9ec INFO Number of counters : 10 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Accepted input tracks" | 2289 | 363254 | 158.70 | + | "Created long tracks" | 2289 | 181236 | 79.177 | + | "Input tracks" | 2289 | 380749 | 166.34 | + | "Number of candidate bins per track" | 363254 | 1665217 | 4.5842 | 5.0318 | 0.0000 | 56.000 | + | "Number of complete candidates/track 1st Loop" | 305079 | 195005 | 0.63920 | 0.65005 | 0.0000 | 6.0000 | + | "Number of complete candidates/track 2nd Loop" | 148403 | 13248 | 0.089270 | 0.29669 | 0.0000 | 3.0000 | + | "Number of x candidates per track 1st Loop" | 305079 | 426093 | 1.3967 | 1.3487 | + | "Number of x candidates per track 2nd Loop" | 148403 | 347932 | 2.3445 | 2.6098 | + | "Percentage second loop execution" | 305079 | 148403 | 0.48644 | + | "Removed duplicates" | 2289 | 9647 | 4.2145 | +PrForwardTrackingVelo_6024f9ec.P... INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 166072 | 673152 | 4.0534 | + | "#tracks with hits added" | 166072 | +PrHybridSeeding_4d0337cc INFO Number of counters : 21 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Created T2x1 three-hit combinations in case 0" | 3981395 | 2438467 | 0.61247 | 0.62452 | 0.0000 | 6.0000 | + | "Created T2x1 three-hit combinations in case 1" | 4961664 | 3252259 | 0.65548 | 0.75200 | 0.0000 | 12.000 | + | "Created T2x1 three-hit combinations in case 2" | 7644512 | 6133331 | 0.80232 | 1.0193 | 0.0000 | 23.000 | + | "Created XZ tracks (part 0)" | 6867 | 363280 | 52.902 | 44.400 | 0.0000 | 844.00 | + | "Created XZ tracks (part 1)" | 6867 | 360418 | 52.486 | 47.084 | 0.0000 | 1257.0 | + | "Created XZ tracks in case 0" | 4578 | 269789 | 58.932 | 37.398 | 1.0000 | 363.00 | + | "Created XZ tracks in case 1" | 4578 | 267868 | 58.512 | 44.098 | 1.0000 | 709.00 | + | "Created XZ tracks in case 2" | 4578 | 186041 | 40.638 | 52.165 | 0.0000 | 1257.0 | + | "Created full hit combinations in case 0" | 407934 | 407934 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 1" | 310355 | 310355 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 2" | 280325 | 280325 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created seed tracks" | 4578 | 284763 | 62.202 | 22.650 | 3.0000 | 141.00 | + | "Created seed tracks (part 0)" | 2289 | 159664 | 69.753 | 25.912 | 4.0000 | 161.00 | + | "Created seed tracks (part 1)" | 2289 | 157869 | 68.969 | 25.854 | 3.0000 | 159.00 | + | "Created seed tracks in case 0" | 4578 | 148622 | 32.464 | 12.801 | 1.0000 | 86.000 | + | "Created seed tracks in case 1" | 4578 | 270703 | 59.131 | 21.736 | 2.0000 | 132.00 | + | "Created seed tracks in case 2" | 4578 | 302221 | 66.016 | 24.642 | 3.0000 | 153.00 | + | "Created seed tracks in recovery step" | 2289 | 15312 | 6.6894 | 3.8772 | 0.0000 | 26.000 | + | "Created two-hit combinations in case 0" | 677723 |1.546134e+07 | 22.814 | 15.827 | 0.0000 | 117.00 | + | "Created two-hit combinations in case 1" | 584001 |1.760625e+07 | 30.148 | 18.628 | 0.0000 | 262.00 | + | "Created two-hit combinations in case 2" | 461883 |2.056474e+07 | 44.524 | 28.512 | 0.0000 | 333.00 | +PrKalmanFilterForward_a6e62848 INFO Number of counters : 7 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Add states failed" | 1 | 0 | 0.0000 | + | "Pre outlier chi2 cut" | 3031 | + | "chi2 cut" | 16997 | + | "nIterations" | 181236 | 410655 | 2.2659 | + | "nOutlierIterations" | 178205 | 110979 | 0.62276 | + | "nTracksInput" | 2289 | 181236 | 79.177 | + | "nTracksOutput" | 2289 | 161207 | 70.427 | +PrKalmanFilterMatch_e1944f26 INFO Number of counters : 7 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Add states failed" | 1 | 0 | 0.0000 | + | "Pre outlier chi2 cut" | 28168 | + | "chi2 cut" | 50760 | + | "nIterations" | 101849 | 273302 | 2.6834 | + | "nOutlierIterations" | 73681 | 84103 | 1.1414 | + | "nTracksInput" | 2289 | 101849 | 44.495 | + | "nTracksOutput" | 2289 | 22920 | 10.013 | +PrLHCbID2MCParticle_a906d17d INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#removed null MCParticles" | 16672433 | 0 | 0.0000 | +PrMatchNN_3856ae45 INFO Number of counters : 3 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#MatchingChi2" | 2289 | 8439531 | 3687.0 | + | "#MatchingMLP" | 252487 | 210561.2 | 0.83395 | + | "#MatchingTracks" | 2289 | 252487 | 110.30 | +PrMatchNN_3856ae45.PrAddUTHitsTool INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 209172 | 823901 | 3.9389 | + | "#tracks with hits added" | 209172 | +PrStorePrUTHits_df75b912 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrStoreSciFiHits_fb0eba02 INFO Number of counters : 25 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Average X in T1U" | 690489 |-2.482423e+07 | -35.952 | 1141.3 | -2656.4 | 2656.3 | + | "Average X in T1V" | 696122 |-2.060219e+07 | -29.596 | 1128.0 | -2656.4 | 2656.3 | + | "Average X in T1X1" | 677723 |-3.438883e+07 | -50.742 | 1162.3 | -2646.2 | 2646.2 | + | "Average X in T1X2" | 705312 |-1.014161e+07 | -14.379 | 1120.8 | -2646.2 | 2646.2 | + | "Average X in T2U" | 673541 |-1.658606e+07 | -24.625 | 1135.5 | -2656.4 | 2656.3 | + | "Average X in T2V" | 693923 |-1.479371e+07 | -21.319 | 1129.9 | -2656.4 | 2656.3 | + | "Average X in T2X1" | 645225 |-1.705455e+07 | -26.432 | 1138.8 | -2646.2 | 2646.2 | + | "Average X in T2X2" | 716059 | -9891920 | -13.814 | 1124.6 | -2646.2 | 2646.2 | + | "Average X in T3U" | 731421 |-1.225062e+07 | -16.749 | 1333.5 | -3188.4 | 3188.4 | + | "Average X in T3V" | 753478 |-1.409381e+07 | -18.705 | 1328.7 | -3188.4 | 3188.4 | + | "Average X in T3X1" | 704173 |-1.010873e+07 | -14.355 | 1334.4 | -3176.2 | 3176.2 | + | "Average X in T3X2" | 782214 |-1.938375e+07 | -24.781 | 1321.3 | -3176.2 | 3176.2 | + | "Hits in T1U" | 9156 | 690489 | 75.414 | 27.984 | 5.0000 | 232.00 | + | "Hits in T1V" | 9156 | 696122 | 76.029 | 27.670 | 3.0000 | 245.00 | + | "Hits in T1X1" | 9156 | 677723 | 74.020 | 27.325 | 4.0000 | 205.00 | + | "Hits in T1X2" | 9156 | 705312 | 77.033 | 28.024 | 6.0000 | 266.00 | + | "Hits in T2U" | 9156 | 673541 | 73.563 | 26.210 | 3.0000 | 198.00 | + | "Hits in T2V" | 9156 | 693923 | 75.789 | 27.194 | 6.0000 | 374.00 | + | "Hits in T2X1" | 9156 | 645225 | 70.470 | 25.869 | 3.0000 | 288.00 | + | "Hits in T2X2" | 9156 | 716059 | 78.207 | 27.736 | 6.0000 | 287.00 | + | "Hits in T3U" | 9156 | 731421 | 79.884 | 27.669 | 2.0000 | 239.00 | + | "Hits in T3V" | 9156 | 753478 | 82.293 | 28.471 | 6.0000 | 207.00 | + | "Hits in T3X1" | 9156 | 704173 | 76.908 | 27.098 | 5.0000 | 339.00 | + | "Hits in T3X2" | 9156 | 782214 | 85.432 | 29.532 | 6.0000 | 204.00 | + | "Total number of hits" | 2289 | 8469680 | 3700.2 | 1120.3 | 604.00 | 6365.0 | +PrStoreUTHit_6220b56a INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrTrackAssociator_16ad4612 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 284763 | 279294 |( 98.07946 +- 0.02571932)% | + | "MC particles per track" | 279294 | 279304 | 1.0000 | +PrTrackAssociator_24d3bad6 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 252487 | 156295 |( 61.90220 +- 0.09664591)% | + | "MC particles per track" | 156295 | 183173 | 1.1720 | +PrTrackAssociator_3adf94fb INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 181236 | 155077 |( 85.56633 +- 0.08255009)% | + | "MC particles per track" | 155077 | 181813 | 1.1724 | +PrTrackAssociator_cbe8f3ce INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 183550 | 156297 |( 85.15227 +- 0.08299480)% | + | "MC particles per track" | 156297 | 182435 | 1.1672 | +PrTrackAssociator_d68377ee INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 593239 | 578457 |( 97.50826 +- 0.02023753)% | + | "MC particles per track" | 578457 | 581059 | 1.0045 | +PrVPHitsToVPLightClusters_599554c8 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Clusters" | 2289 | 5397790 | 2358.1 | +SeedTrackChecker_ad9abe4e.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +TBTCMatch_4755c68a INFO Number of counters : 3 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"BadInput" | 22826 | 0 |( 0.000000 +- 0.000000)% | + |*"FitFailed" | 22826 | 0 |( 0.000000 +- 0.000000)% | + | "FittedBefore" | 22826 | +TBTC_Forward_3523b81b INFO Number of counters : 3 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"BadInput" | 160724 | 0 |( 0.000000 +- 0.000000)% | + |*"FitFailed" | 160724 | 0 |( 0.000000 +- 0.000000)% | + | "FittedBefore" | 160724 | +VeloClusterTrackingSIMD_87c18651 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Clusters" | 2289 | 5397790 | 2358.1 | + | "Nb of Produced Tracks" | 2289 | 593239 | 259.17 | +VeloTrackChecker_e83d0cf5.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +fromPrForwardTracksV1Tracks_f53f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 181236 | 79.177 | +fromPrMatchTracksV1Tracks_67f41548 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 252487 | 110.30 | +fromPrSeedingTracksV1Tracks_84cd... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 284763 | 124.40 | +fromPrVeloTracksV1TracksMerger_f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 593239 | 259.17 | +ApplicationMgr INFO Application Manager Stopped successfully +BestLongTrackChecker_8a93d154 INFO Results +BestLongTrackChecker_8a93d154 INFO **** BestLong 183550 tracks including 27253 ghosts [14.85 %], Event average 13.82 % **** +BestLongTrackChecker_8a93d154 INFO 01_long : 135188 from 152279 [ 88.78 %] 421 clones [ 0.31 %], purity: 99.37 %, hitEff: 97.48 % +BestLongTrackChecker_8a93d154 INFO 02_long_P>5GeV : 90829 from 98421 [ 92.29 %] 215 clones [ 0.24 %], purity: 99.45 %, hitEff: 97.99 % +BestLongTrackChecker_8a93d154 INFO 03_long_strange : 6663 from 8121 [ 82.05 %] 14 clones [ 0.21 %], purity: 99.12 %, hitEff: 97.15 % +BestLongTrackChecker_8a93d154 INFO 04_long_strange_P>5GeV : 3417 from 3856 [ 88.62 %] 6 clones [ 0.18 %], purity: 99.26 %, hitEff: 97.97 % +BestLongTrackChecker_8a93d154 INFO 05_long_fromB : 7214 from 7959 [ 90.64 %] 23 clones [ 0.32 %], purity: 99.48 %, hitEff: 97.68 % +BestLongTrackChecker_8a93d154 INFO 05_long_fromD : 3809 from 4226 [ 90.13 %] 12 clones [ 0.31 %], purity: 99.42 %, hitEff: 97.58 % +BestLongTrackChecker_8a93d154 INFO 06_long_fromB_P>5GeV : 5583 from 5983 [ 93.31 %] 9 clones [ 0.16 %], purity: 99.55 %, hitEff: 98.12 % +BestLongTrackChecker_8a93d154 INFO 06_long_fromD_P>5GeV : 2700 from 2894 [ 93.30 %] 6 clones [ 0.22 %], purity: 99.48 %, hitEff: 98.06 % +BestLongTrackChecker_8a93d154 INFO 07_long_electrons : 10843 from 15125 [ 71.69 %] 31 clones [ 0.29 %], purity: 98.33 %, hitEff: 96.09 % +BestLongTrackChecker_8a93d154 INFO 07_long_electrons_pairprod : 7194 from 10831 [ 66.42 %] 24 clones [ 0.33 %], purity: 97.87 %, hitEff: 95.30 % +BestLongTrackChecker_8a93d154 INFO 08_long_fromB_electrons : 3478 from 4210 [ 82.61 %] 7 clones [ 0.20 %], purity: 99.24 %, hitEff: 97.72 % +BestLongTrackChecker_8a93d154 INFO 09_long_fromB_electrons_P>5GeV : 3250 from 3850 [ 84.42 %] 6 clones [ 0.18 %], purity: 99.31 %, hitEff: 97.90 % +BestLongTrackChecker_8a93d154 INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 4873 from 5182 [ 94.04 %] 10 clones [ 0.20 %], purity: 99.61 %, hitEff: 98.15 % +BestLongTrackChecker_8a93d154 INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3142 from 3659 [ 85.87 %] 6 clones [ 0.19 %], purity: 99.36 %, hitEff: 97.93 % +BestLongTrackChecker_8a93d154 INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 2200 from 2343 [ 93.90 %] 6 clones [ 0.27 %], purity: 99.56 %, hitEff: 98.10 % +BestLongTrackChecker_8a93d154 INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 1790 from 2010 [ 89.05 %] 3 clones [ 0.17 %], purity: 99.46 %, hitEff: 98.14 % +BestLongTrackChecker_8a93d154 INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4860 from 5164 [ 94.11 %] 10 clones [ 0.21 %], purity: 99.61 %, hitEff: 98.17 % +BestLongTrackChecker_8a93d154 INFO +ForwardTrackChecker_482fda95 INFO Results +ForwardTrackChecker_482fda95 INFO **** Forward 181236 tracks including 26159 ghosts [14.43 %], Event average 13.11 % **** +ForwardTrackChecker_482fda95 INFO 01_long : 133702 from 152279 [ 87.80 %] 513 clones [ 0.38 %], purity: 99.21 %, hitEff: 98.43 % +ForwardTrackChecker_482fda95 INFO 02_long_P>5GeV : 91867 from 98421 [ 93.34 %] 307 clones [ 0.33 %], purity: 99.32 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 03_long_strange : 6588 from 8121 [ 81.12 %] 20 clones [ 0.30 %], purity: 98.87 %, hitEff: 98.21 % +ForwardTrackChecker_482fda95 INFO 04_long_strange_P>5GeV : 3465 from 3856 [ 89.86 %] 8 clones [ 0.23 %], purity: 99.05 %, hitEff: 98.80 % +ForwardTrackChecker_482fda95 INFO 05_long_fromB : 7199 from 7959 [ 90.45 %] 26 clones [ 0.36 %], purity: 99.34 %, hitEff: 98.69 % +ForwardTrackChecker_482fda95 INFO 05_long_fromD : 3793 from 4226 [ 89.75 %] 10 clones [ 0.26 %], purity: 99.25 %, hitEff: 98.50 % +ForwardTrackChecker_482fda95 INFO 06_long_fromB_P>5GeV : 5664 from 5983 [ 94.67 %] 18 clones [ 0.32 %], purity: 99.45 %, hitEff: 98.93 % +ForwardTrackChecker_482fda95 INFO 06_long_fromD_P>5GeV : 2732 from 2894 [ 94.40 %] 7 clones [ 0.26 %], purity: 99.35 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons : 10559 from 15125 [ 69.81 %] 108 clones [ 1.01 %], purity: 97.96 %, hitEff: 98.31 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons_pairprod : 6890 from 10831 [ 63.61 %] 86 clones [ 1.23 %], purity: 97.36 %, hitEff: 98.08 % +ForwardTrackChecker_482fda95 INFO 08_long_fromB_electrons : 3548 from 4210 [ 84.28 %] 22 clones [ 0.62 %], purity: 99.07 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 09_long_fromB_electrons_P>5GeV : 3333 from 3850 [ 86.57 %] 21 clones [ 0.63 %], purity: 99.15 %, hitEff: 98.96 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 4902 from 5182 [ 94.60 %] 17 clones [ 0.35 %], purity: 99.55 %, hitEff: 98.93 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3220 from 3659 [ 88.00 %] 19 clones [ 0.59 %], purity: 99.22 %, hitEff: 98.94 % +ForwardTrackChecker_482fda95 INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 2218 from 2343 [ 94.66 %] 6 clones [ 0.27 %], purity: 99.49 %, hitEff: 98.85 % +ForwardTrackChecker_482fda95 INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 1801 from 2010 [ 89.60 %] 4 clones [ 0.22 %], purity: 99.36 %, hitEff: 98.68 % +ForwardTrackChecker_482fda95 INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4889 from 5164 [ 94.67 %] 17 clones [ 0.35 %], purity: 99.55 %, hitEff: 98.94 % +ForwardTrackChecker_482fda95 INFO +MatchTrackChecker_8a39005f INFO Results +MatchTrackChecker_8a39005f INFO **** Match 252487 tracks including 96192 ghosts [38.10 %], Event average 35.31 % **** +MatchTrackChecker_8a39005f INFO 01_long : 133127 from 152279 [ 87.42 %] 846 clones [ 0.63 %], purity: 99.33 %, hitEff: 98.62 % +MatchTrackChecker_8a39005f INFO 02_long_P>5GeV : 90983 from 98421 [ 92.44 %] 475 clones [ 0.52 %], purity: 99.45 %, hitEff: 99.22 % +MatchTrackChecker_8a39005f INFO 03_long_strange : 6441 from 8121 [ 79.31 %] 37 clones [ 0.57 %], purity: 98.98 %, hitEff: 98.22 % +MatchTrackChecker_8a39005f INFO 04_long_strange_P>5GeV : 3457 from 3856 [ 89.65 %] 15 clones [ 0.43 %], purity: 99.18 %, hitEff: 99.21 % +MatchTrackChecker_8a39005f INFO 05_long_fromB : 7192 from 7959 [ 90.36 %] 54 clones [ 0.75 %], purity: 99.45 %, hitEff: 98.83 % +MatchTrackChecker_8a39005f INFO 05_long_fromD : 3782 from 4226 [ 89.49 %] 21 clones [ 0.55 %], purity: 99.36 %, hitEff: 98.70 % +MatchTrackChecker_8a39005f INFO 06_long_fromB_P>5GeV : 5632 from 5983 [ 94.13 %] 28 clones [ 0.49 %], purity: 99.57 %, hitEff: 99.24 % +MatchTrackChecker_8a39005f INFO 06_long_fromD_P>5GeV : 2722 from 2894 [ 94.06 %] 9 clones [ 0.33 %], purity: 99.51 %, hitEff: 99.22 % +MatchTrackChecker_8a39005f INFO 07_long_electrons : 11647 from 15125 [ 77.00 %] 175 clones [ 1.48 %], purity: 97.76 %, hitEff: 98.14 % +MatchTrackChecker_8a39005f INFO 07_long_electrons_pairprod : 7809 from 10831 [ 72.10 %] 136 clones [ 1.71 %], purity: 97.13 %, hitEff: 97.84 % +MatchTrackChecker_8a39005f INFO 08_long_fromB_electrons : 3652 from 4210 [ 86.75 %] 42 clones [ 1.14 %], purity: 99.07 %, hitEff: 98.86 % +MatchTrackChecker_8a39005f INFO 09_long_fromB_electrons_P>5GeV : 3429 from 3850 [ 89.06 %] 40 clones [ 1.15 %], purity: 99.14 %, hitEff: 98.99 % +MatchTrackChecker_8a39005f INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 4885 from 5182 [ 94.27 %] 27 clones [ 0.55 %], purity: 99.65 %, hitEff: 99.12 % +MatchTrackChecker_8a39005f INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3310 from 3659 [ 90.46 %] 37 clones [ 1.11 %], purity: 99.22 %, hitEff: 98.98 % +MatchTrackChecker_8a39005f INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 2213 from 2343 [ 94.45 %] 9 clones [ 0.41 %], purity: 99.65 %, hitEff: 99.11 % +MatchTrackChecker_8a39005f INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 1805 from 2010 [ 89.80 %] 6 clones [ 0.33 %], purity: 99.51 %, hitEff: 98.98 % +MatchTrackChecker_8a39005f INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4873 from 5164 [ 94.36 %] 27 clones [ 0.55 %], purity: 99.65 %, hitEff: 99.13 % +MatchTrackChecker_8a39005f INFO +SeedTrackChecker_ad9abe4e INFO Results +SeedTrackChecker_ad9abe4e INFO **** Seed 284763 tracks including 5469 ghosts [ 1.92 %], Event average 1.56 % **** +SeedTrackChecker_ad9abe4e INFO 01_hasT : 198532 from 234618 [ 84.62 %] 7 clones [ 0.00 %], purity: 99.60 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 02_long : 143630 from 152279 [ 94.32 %] 6 clones [ 0.00 %], purity: 99.71 %, hitEff: 98.42 % +SeedTrackChecker_ad9abe4e INFO 03_long_P>5GeV : 95859 from 98421 [ 97.40 %] 5 clones [ 0.01 %], purity: 99.69 %, hitEff: 99.09 % +SeedTrackChecker_ad9abe4e INFO 04_long_fromB : 7598 from 7959 [ 95.46 %] 1 clones [ 0.01 %], purity: 99.75 %, hitEff: 98.65 % +SeedTrackChecker_ad9abe4e INFO 05_long_fromB_P>5GeV : 5835 from 5983 [ 97.53 %] 1 clones [ 0.02 %], purity: 99.76 %, hitEff: 99.13 % +SeedTrackChecker_ad9abe4e INFO 06_UT+T_strange : 16417 from 17658 [ 92.97 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.00 % +SeedTrackChecker_ad9abe4e INFO 07_UT+T_strange_P>5GeV : 8615 from 8825 [ 97.62 %] 0 clones [ 0.00 %], purity: 99.71 %, hitEff: 99.05 % +SeedTrackChecker_ad9abe4e INFO 08_noVelo+UT+T_strange : 8949 from 9658 [ 92.66 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.03 % +SeedTrackChecker_ad9abe4e INFO 09_noVelo+UT+T_strange_P>5GeV : 4914 from 5043 [ 97.44 %] 0 clones [ 0.00 %], purity: 99.70 %, hitEff: 99.01 % +SeedTrackChecker_ad9abe4e INFO 10_UT+T_SfromDB : 1133 from 1220 [ 92.87 %] 0 clones [ 0.00 %], purity: 99.77 %, hitEff: 97.99 % +SeedTrackChecker_ad9abe4e INFO 11_UT+T_SfromDB_P>5GeV : 612 from 623 [ 98.23 %] 0 clones [ 0.00 %], purity: 99.72 %, hitEff: 99.22 % +SeedTrackChecker_ad9abe4e INFO 12_noVelo+UT+T_SfromDB_P>5GeV : 420 from 428 [ 98.13 %] 0 clones [ 0.00 %], purity: 99.69 %, hitEff: 99.12 % +SeedTrackChecker_ad9abe4e INFO 13_hasT_electrons : 40669 from 74476 [ 54.61 %] 2 clones [ 0.00 %], purity: 99.69 %, hitEff: 97.16 % +SeedTrackChecker_ad9abe4e INFO 14_long_electrons : 13360 from 15125 [ 88.33 %] 1 clones [ 0.01 %], purity: 99.81 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 15_long_fromB_electrons : 3922 from 4210 [ 93.16 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.70 % +SeedTrackChecker_ad9abe4e INFO 16_long_electrons_P>5GeV : 8676 from 9420 [ 92.10 %] 0 clones [ 0.00 %], purity: 99.80 %, hitEff: 98.73 % +SeedTrackChecker_ad9abe4e INFO 17_long_fromB_electrons_P>5GeV : 3632 from 3850 [ 94.34 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.85 % +SeedTrackChecker_ad9abe4e INFO +VeloTrackChecker_e83d0cf5 INFO Results +VeloTrackChecker_e83d0cf5 INFO **** Velo 593239 tracks including 14782 ghosts [ 2.49 %], Event average 2.59 % **** +VeloTrackChecker_e83d0cf5 INFO 01_velo : 259695 from 265328 [ 97.88 %] 4074 clones [ 1.54 %], purity: 99.63 %, hitEff: 95.59 %, hitEffFirst3: 95.49 %, hitEffLast: 95.30 % +VeloTrackChecker_e83d0cf5 INFO 02_long : 151005 from 152279 [ 99.16 %] 1638 clones [ 1.07 %], purity: 99.71 %, hitEff: 96.54 %, hitEffFirst3: 96.42 %, hitEffLast: 96.40 % +VeloTrackChecker_e83d0cf5 INFO 03_long_P>5GeV : 97926 from 98421 [ 99.50 %] 841 clones [ 0.85 %], purity: 99.72 %, hitEff: 96.96 %, hitEffFirst3: 96.80 %, hitEffLast: 96.92 % +VeloTrackChecker_e83d0cf5 INFO 04_long_strange : 7805 from 8121 [ 96.11 %] 64 clones [ 0.81 %], purity: 99.18 %, hitEff: 96.27 %, hitEffFirst3: 96.28 %, hitEffLast: 95.54 % +VeloTrackChecker_e83d0cf5 INFO 05_long_strange_P>5GeV : 3719 from 3856 [ 96.45 %] 20 clones [ 0.53 %], purity: 99.06 %, hitEff: 97.00 %, hitEffFirst3: 97.04 %, hitEffLast: 96.45 % +VeloTrackChecker_e83d0cf5 INFO 06_long_fromB : 7894 from 7959 [ 99.18 %] 87 clones [ 1.09 %], purity: 99.65 %, hitEff: 96.46 %, hitEffFirst3: 96.28 %, hitEffLast: 96.34 % +VeloTrackChecker_e83d0cf5 INFO 06_long_fromD : 4188 from 4226 [ 99.10 %] 39 clones [ 0.92 %], purity: 99.64 %, hitEff: 96.54 %, hitEffFirst3: 96.28 %, hitEffLast: 96.50 % +VeloTrackChecker_e83d0cf5 INFO 07_long_fromB_P>5GeV : 5956 from 5983 [ 99.55 %] 48 clones [ 0.80 %], purity: 99.69 %, hitEff: 96.87 %, hitEffFirst3: 96.76 %, hitEffLast: 96.75 % +VeloTrackChecker_e83d0cf5 INFO 07_long_fromD_P>5GeV : 2879 from 2894 [ 99.48 %] 16 clones [ 0.55 %], purity: 99.66 %, hitEff: 97.02 %, hitEffFirst3: 96.80 %, hitEffLast: 97.04 % +VeloTrackChecker_e83d0cf5 INFO 08_long_electrons : 14476 from 15125 [ 95.71 %] 246 clones [ 1.67 %], purity: 98.08 %, hitEff: 94.76 %, hitEffFirst3: 93.30 %, hitEffLast: 94.93 % +VeloTrackChecker_e83d0cf5 INFO 09_long_fromB_electrons : 4080 from 4210 [ 96.91 %] 54 clones [ 1.31 %], purity: 99.31 %, hitEff: 96.44 %, hitEffFirst3: 96.02 %, hitEffLast: 96.34 % +VeloTrackChecker_e83d0cf5 INFO 10_long_fromB_electrons_P>5GeV : 3765 from 3850 [ 97.79 %] 49 clones [ 1.28 %], purity: 99.42 %, hitEff: 96.57 %, hitEffFirst3: 96.29 %, hitEffLast: 96.40 % +VeloTrackChecker_e83d0cf5 INFO 11_long_fromB_P>3GeV_Pt>0.5GeV : 5157 from 5182 [ 99.52 %] 37 clones [ 0.71 %], purity: 99.71 %, hitEff: 96.87 %, hitEffFirst3: 96.86 %, hitEffLast: 96.67 % +VeloTrackChecker_e83d0cf5 INFO 11_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3608 from 3659 [ 98.61 %] 45 clones [ 1.23 %], purity: 99.50 %, hitEff: 96.69 %, hitEffFirst3: 96.40 %, hitEffLast: 96.56 % +VeloTrackChecker_e83d0cf5 INFO 11_long_fromD_P>3GeV_Pt>0.5GeV : 2329 from 2343 [ 99.40 %] 13 clones [ 0.56 %], purity: 99.68 %, hitEff: 96.92 %, hitEffFirst3: 96.74 %, hitEffLast: 96.89 % +VeloTrackChecker_e83d0cf5 INFO 11_long_strange_P>3GeV_Pt>0.5GeV : 1907 from 2010 [ 94.88 %] 11 clones [ 0.57 %], purity: 98.72 %, hitEff: 96.85 %, hitEffFirst3: 96.68 %, hitEffLast: 96.61 % +VeloTrackChecker_e83d0cf5 INFO 12_UT_long_fromB_P>3GeV_Pt>0.5GeV : 5141 from 5164 [ 99.55 %] 37 clones [ 0.71 %], purity: 99.71 %, hitEff: 96.87 %, hitEffFirst3: 96.85 %, hitEffLast: 96.66 % +VeloTrackChecker_e83d0cf5 INFO +HLTControlFlowMgr INFO Memory pool: used 3.94312 +/- 0.039102 MiB (min: 0, max: 5) in 1 +/- 0 blocks (allocated >once in 0 +/- 0% events). Allocated capacity was 10 +/- 0 MiB (min: 10, max: 10) and 347.612 +/- 3.41441 (min: 4, max: 489) requests were served +HLTControlFlowMgr INFO Timing table: +HLTControlFlowMgr INFO + | Name of Algorithm | Execution Count | Total Time / s | Avg. Time / us | + | Sum of all Algorithms | 2955 | 189.128 | 64002.599 | + | "Fetch__Event_DAQ_RawEvent" | 2955 | 91.211 | 30866.610 | + | "SeedTrackChecker_ad9abe4e" | 2289 | 13.192 | 5763.408 | + | "VeloTrackChecker_e83d0cf5" | 2289 | 13.025 | 5690.119 | + | "ForwardTrackChecker_482fda95" | 2289 | 12.217 | 5337.081 | + | "MatchTrackChecker_8a39005f" | 2289 | 11.139 | 4866.422 | + | "BestLongTrackChecker_8a93d154" | 2289 | 10.915 | 4768.442 | + | "PrKalmanFilterForward_a6e62848" | 2289 | 10.351 | 4521.930 | + | "PrKalmanFilterMatch_e1944f26" | 2289 | 5.591 | 2442.540 | + | "PrForwardTrackingVelo_6024f9ec" | 2289 | 4.406 | 1924.922 | + | "PrHybridSeeding_4d0337cc" | 2289 | 3.297 | 1440.275 | + | "PrLHCbID2MCParticle_a906d17d" | 2289 | 2.522 | 1101.835 | + | "Unpack__Event_MC_Vertices" | 2289 | 2.040 | 891.087 | + | "Unpack__Event_MC_Particles" | 2289 | 1.919 | 838.174 | + | "VeloClusterTrackingSIMD_87c18651" | 2289 | 0.766 | 334.809 | + | "CloneKillerMatch_c1af047d" | 2289 | 0.599 | 261.717 | + | "VPFullCluster2MCParticleLinker_17386552" | 2289 | 0.570 | 249.022 | + | "VPClusFull_38754d8c" | 2289 | 0.550 | 240.426 | + | "PrStoreUTHit_6220b56a" | 2289 | 0.550 | 240.079 | + | "PrMatchNN_3856ae45" | 2289 | 0.520 | 227.066 | + | "PrTrackAssociator_24d3bad6" | 2289 | 0.438 | 191.227 | + | "TBTC_Forward_3523b81b" | 2289 | 0.434 | 189.566 | + | "PrTrackAssociator_3adf94fb" | 2289 | 0.384 | 167.643 | + | "PrStorePrUTHits_df75b912" | 2289 | 0.370 | 161.675 | + | "PrTrackAssociator_cbe8f3ce" | 2289 | 0.338 | 147.617 | + | "PrTrackAssociator_d68377ee" | 2289 | 0.274 | 119.608 | + | "PrTrackAssociator_16ad4612" | 2289 | 0.256 | 111.679 | + | "PrVPHitsToVPLightClusters_599554c8" | 2289 | 0.217 | 95.016 | + | "fromPrMatchTracksV1Tracks_67f41548" | 2289 | 0.211 | 92.089 | + | "fromPrVeloTracksV1TracksMerger_fa66a5de" | 2289 | 0.184 | 80.349 | + | "fromPrForwardTracksV1Tracks_f53f50a8" | 2289 | 0.128 | 55.750 | + | "PrStoreSciFiHits_fb0eba02" | 2289 | 0.119 | 52.165 | + | "fromPrSeedingTracksV1Tracks_84cd46c2" | 2289 | 0.114 | 49.901 | + | "TrackContainersMerger_511ac736" | 2289 | 0.064 | 28.049 | + | "FTRawBankDecoder" | 2289 | 0.061 | 26.709 | + | "TBTCMatch_4755c68a" | 2289 | 0.045 | 19.740 | + | "UnpackRawEvent_UT" | 2955 | 0.027 | 9.109 | + | "reserveIOV" | 2289 | 0.022 | 9.575 | + | "UniqueIDGeneratorAlg_26e527e9" | 2289 | 0.010 | 4.577 | + | "Decode_ODIN" | 2289 | 0.009 | 3.812 | + | "DefaultGECFilter" | 2955 | 0.007 | 2.220 | + | "UnpackRawEvent_FTCluster" | 2955 | 0.005 | 1.747 | + | "Fetch__Event_MC_TrackInfo" | 2289 | 0.005 | 2.101 | + | "Fetch__Event_pSim_MCParticles" | 2289 | 0.004 | 1.910 | + | "Fetch__Event_Link_Raw_UT_Clusters" | 2289 | 0.004 | 1.883 | + | "UnpackRawEvent_VP" | 2289 | 0.004 | 1.731 | + | "Fetch__Event_Link_Raw_VP_Digits" | 2289 | 0.004 | 1.663 | + | "DummyEventTime" | 2289 | 0.003 | 1.438 | + | "UnpackRawEvent_ODIN" | 2289 | 0.003 | 1.328 | + | "Fetch__Event_Link_Raw_FT_LiteClusters" | 2289 | 0.002 | 1.040 | + | "Fetch__Event_pSim_MCVertices" | 2289 | 0.002 | 1.037 | + +HLTControlFlowMgr INFO StateTree: CFNode #executed #passed +LAZY_AND: run_tracking_debug_decision #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + PrGECFilter/DefaultGECFilter #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + NONLAZY_OR: run_tracking_debug_data #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/ForwardTrackChecker_482fda95 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/MatchTrackChecker_8a39005f #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/BestLongTrackChecker_8a93d154 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/SeedTrackChecker_ad9abe4e #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/VeloTrackChecker_e83d0cf5 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + +HLTControlFlowMgr INFO Histograms converted successfully according to request. +ToolSvc INFO Removing all tools created by ToolSvc +VeloTrackChecker_e83d0cf5.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +SeedTrackChecker_ad9abe4e.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +BestLongTrackChecker_8a93d154.Pr... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +MatchTrackChecker_8a39005f.PrChe... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +ForwardTrackChecker_482fda95.PrC... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +RootCnvSvc INFO Disconnected data IO:9E3B8940-FB87-11EB-ADCA-FA163E643B60 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi] +RootCnvSvc INFO Disconnected data IO:AFCB9710-FB21-11EB-9E91-FA163ED3A4EB [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi] +RootCnvSvc INFO Disconnected data IO:B9D7CC62-FB38-11EB-8B01-3CECEF5D2C24 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi] +ApplicationMgr INFO Application Manager Finalized successfully +ApplicationMgr INFO Application Manager Terminated successfully diff --git a/efficiencies/logs/match_effs_testJpsi_EDef_yCorrCut.log b/efficiencies/logs/match_effs_testJpsi_EDef_yCorrCut.log new file mode 100644 index 0000000..39e7c2a --- /dev/null +++ b/efficiencies/logs/match_effs_testJpsi_EDef_yCorrCut.log @@ -0,0 +1,378 @@ +# setting LC_ALL to "C" +# --> Including file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_match_eff_data.py' +/***** User ApplicationOptions/ApplicationOptions ************************************************** +|-append_decoding_keys_to_output_manifest = True (default: True) +|-auditors = [] (default: []) +|-buffer_events = 20000 (default: 20000) +|-conddb_tag = 'sim-20210617-vc-md100' (default: '') +|-conditions_version = '' (default: '') +|-control_flow_file = '' (default: '') +|-data_flow_file = '' (default: '') +|-data_type = 'Upgrade' (default: 'Upgrade') +|-dddb_tag = 'dddb-20210617' (default: '') +|-event_store = 'HiveWhiteBoard' (default: 'HiveWhiteBoard') +|-evt_max = -1 (default: -1) +|-first_evt = 0 (default: 0) +|-geometry_version = '' (default: '') +|-histo_file = '' (default: '') +|-input_files = ['/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi'] +| (default: []) +|-input_manifest_file = '' (default: '') +|-input_process = '' (default: '') +|-input_raw_format = 0.5 (default: 0.5) +|-input_type = 'ROOT' (default: '') +|-lines_maker = None +|-memory_pool_size = 10485760 (default: 10485760) +|-monitoring_file = '' (default: '') +|-msg_svc_format = '% F%35W%S %7W%R%T %0W%M' (default: '% F%35W%S %7W%R%T %0W%M') +|-msg_svc_time_format = '%Y-%m-%d %H:%M:%S UTC' (default: '%Y-%m-%d %H:%M:%S UTC') +|-n_event_slots = 1 (default: -1) +|-n_threads = 1 (default: 1) +|-ntuple_file = '/work/cetin/LHCb/reco_tuner/data_matching/match_effs_testJpsi_EDef_yCorrCut.root' +| (default: '') +|-output_file = '' (default: '') +|-output_level = 3 (default: 3) +|-output_manifest_file = '' (default: '') +|-output_type = '' (default: '') +|-persistreco_version = 1.0 (default: 1.0) +|-phoenix_filename = '' (default: '') +|-preamble_algs = [] (default: []) +|-print_freq = 10000 (default: 10000) +|-python_logging_level = 20 (default: 20) +|-require_specific_decoding_keys = [] (default: []) +|-scheduler_legacy_mode = True (default: True) +|-simulation = True (default: None) +|-use_iosvc = False (default: False) +|-velo_motion_system_yaml = '' (default: '') +|-write_decoding_keys_to_git = True (default: True) +\----- (End of User ApplicationOptions/ApplicationOptions) ----------------------------------------- +# Overrule specified for keys +# <-- End of file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_match_eff_data.py' +ApplicationMgr SUCCESS +==================================================================================================================================== + Welcome to Moore version 55.2 + running on lhcba2 on Mon Mar 11 06:55:28 2024 +==================================================================================================================================== +ApplicationMgr INFO Application Manager Configured successfully +ToolSvc.GitDDDB INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/DDDB.git' +ToolSvc.GitDDDB INFO using commit 'upgrade/dddb-20210617' corresponding to 1871f1bb5c0d68c81dda62e84cf1eb3a45513521 +ToolSvc.GitSIMCOND INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/SIMCOND.git' +ToolSvc.GitSIMCOND INFO using commit 'upgrade/sim-20210617-vc-md100' corresponding to 9aa116c7143d21760d1be07ce1ef22c0f8f07bdf +DetectorPersistencySvc INFO Added successfully Conversion service:XmlCnvSvc +DetectorDataSvc SUCCESS Detector description database: git:/lhcb.xml +EventClockSvc.FakeEventTime INFO Event times generated from 0 with steps of 0 +EventClockSvc.FakeEventTime INFO Run numbers generated from 0 every 0 events +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c1.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c2.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c3.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c4.down.cdf +MagneticFieldSvc INFO Map scaled by factor 1 with polarity internally used: -1 signed relative current: -1 +NTupleSvc INFO Added stream file:/work/cetin/LHCb/reco_tuner/data_matching/match_effs_testJpsi_EDef_yCorrCut.root as FILE1 +HLTControlFlowMgr INFO Start initialization +RootHistSvc INFO Writing ROOT histograms to: /work/cetin/LHCb/reco_tuner/data_matching/match_effs_testJpsi_EDef_yCorrCut.root +HistogramPersistencySvc INFO Added successfully Conversion service:RootHistSvc +DeFTDetector INFO Current FT geometry version = 64 +HLTControlFlowMgr INFO Concurrency level information: +HLTControlFlowMgr INFO o Number of events slots: 1 +HLTControlFlowMgr INFO o TBB thread pool size: 'ThreadPoolSize':1 +HLTControlFlowMgr INFO ---> End of Initialization. This took 19554 ms +ApplicationMgr INFO Application Manager Initialized successfully +ApplicationMgr INFO Application Manager Started successfully +EventPersistencySvc INFO Added successfully Conversion service:RootCnvSvc +EventSelector INFO Stream:EventSelector.DataStreamTool_1 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO Will measure time between events 10 and 2147483647 (stop might be some events later) +HLTControlFlowMgr INFO Starting loop on events +EventSelector SUCCESS Reading Event record 1. Record number within stream 1: 1 +FTRawBankDecoder INFO Conditions DB is compatible with FT bank version 4, 5, 6. +FTRawBankDecoder INFO Building the readout map with version 0 +HLTControlFlowMgr INFO Timing started at: 06:56:06 +EventSelector INFO Stream:EventSelector.DataStreamTool_2 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +EventSelector INFO Stream:EventSelector.DataStreamTool_3 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO No more events in event selection +HLTControlFlowMgr INFO ---> Loop over 2955 Events Finished - WSS 1793.25, timed 2945 Events: 156287 ms, Evts/s = 18.8435 +DefaultGECFilter INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb Events Processed" | 2955 | + | "Nb events removed" | 666 | +ForwardTrackChecker_482fda95.LoK... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +ForwardUTHitsChecker_fe9d9ac2.Lo... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 4 | +HLTControlFlowMgr INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Processed events" | 2955 | +MatchTrackChecker_386d067b.LoKi:... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +MatchUTHitsChecker_a4d04726.LoKi... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 4 | +PrForwardTrackingVelo_6024f9ec INFO Number of counters : 10 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Accepted input tracks" | 2289 | 363254 | 158.70 | + | "Created long tracks" | 2289 | 181236 | 79.177 | + | "Input tracks" | 2289 | 380749 | 166.34 | + | "Number of candidate bins per track" | 363254 | 1665217 | 4.5842 | 5.0318 | 0.0000 | 56.000 | + | "Number of complete candidates/track 1st Loop" | 305079 | 195005 | 0.63920 | 0.65005 | 0.0000 | 6.0000 | + | "Number of complete candidates/track 2nd Loop" | 148403 | 13248 | 0.089270 | 0.29669 | 0.0000 | 3.0000 | + | "Number of x candidates per track 1st Loop" | 305079 | 426093 | 1.3967 | 1.3487 | + | "Number of x candidates per track 2nd Loop" | 148403 | 347932 | 2.3445 | 2.6098 | + | "Percentage second loop execution" | 305079 | 148403 | 0.48644 | + | "Removed duplicates" | 2289 | 9647 | 4.2145 | +PrForwardTrackingVelo_6024f9ec.P... INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 166072 | 673152 | 4.0534 | + | "#tracks with hits added" | 166072 | +PrHybridSeeding_4d0337cc INFO Number of counters : 21 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Created T2x1 three-hit combinations in case 0" | 3981395 | 2438467 | 0.61247 | 0.62452 | 0.0000 | 6.0000 | + | "Created T2x1 three-hit combinations in case 1" | 4961664 | 3252259 | 0.65548 | 0.75200 | 0.0000 | 12.000 | + | "Created T2x1 three-hit combinations in case 2" | 7644512 | 6133331 | 0.80232 | 1.0193 | 0.0000 | 23.000 | + | "Created XZ tracks (part 0)" | 6867 | 363280 | 52.902 | 44.400 | 0.0000 | 844.00 | + | "Created XZ tracks (part 1)" | 6867 | 360418 | 52.486 | 47.084 | 0.0000 | 1257.0 | + | "Created XZ tracks in case 0" | 4578 | 269789 | 58.932 | 37.398 | 1.0000 | 363.00 | + | "Created XZ tracks in case 1" | 4578 | 267868 | 58.512 | 44.098 | 1.0000 | 709.00 | + | "Created XZ tracks in case 2" | 4578 | 186041 | 40.638 | 52.165 | 0.0000 | 1257.0 | + | "Created full hit combinations in case 0" | 407934 | 407934 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 1" | 310355 | 310355 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 2" | 280325 | 280325 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created seed tracks" | 4578 | 284763 | 62.202 | 22.650 | 3.0000 | 141.00 | + | "Created seed tracks (part 0)" | 2289 | 159664 | 69.753 | 25.912 | 4.0000 | 161.00 | + | "Created seed tracks (part 1)" | 2289 | 157869 | 68.969 | 25.854 | 3.0000 | 159.00 | + | "Created seed tracks in case 0" | 4578 | 148622 | 32.464 | 12.801 | 1.0000 | 86.000 | + | "Created seed tracks in case 1" | 4578 | 270703 | 59.131 | 21.736 | 2.0000 | 132.00 | + | "Created seed tracks in case 2" | 4578 | 302221 | 66.016 | 24.642 | 3.0000 | 153.00 | + | "Created seed tracks in recovery step" | 2289 | 15312 | 6.6894 | 3.8772 | 0.0000 | 26.000 | + | "Created two-hit combinations in case 0" | 677723 |1.546134e+07 | 22.814 | 15.827 | 0.0000 | 117.00 | + | "Created two-hit combinations in case 1" | 584001 |1.760625e+07 | 30.148 | 18.628 | 0.0000 | 262.00 | + | "Created two-hit combinations in case 2" | 461883 |2.056474e+07 | 44.524 | 28.512 | 0.0000 | 333.00 | +PrLHCbID2MCParticle_a906d17d INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#removed null MCParticles" | 16672433 | 0 | 0.0000 | +PrMatchNN_d80b5038 INFO Number of counters : 3 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#MatchingChi2" | 2289 | 8439531 | 3687.0 | + | "#MatchingMLP" | 212505 | 194680.2 | 0.91612 | + | "#MatchingTracks" | 2289 | 212505 | 92.837 | +PrMatchNN_d80b5038.PrAddUTHitsTool INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 186132 | 741761 | 3.9851 | + | "#tracks with hits added" | 186132 | +PrStorePrUTHits_df75b912 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrStoreSciFiHits_fb0eba02 INFO Number of counters : 25 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Average X in T1U" | 690489 |-2.482423e+07 | -35.952 | 1141.3 | -2656.4 | 2656.3 | + | "Average X in T1V" | 696122 |-2.060219e+07 | -29.596 | 1128.0 | -2656.4 | 2656.3 | + | "Average X in T1X1" | 677723 |-3.438883e+07 | -50.742 | 1162.3 | -2646.2 | 2646.2 | + | "Average X in T1X2" | 705312 |-1.014161e+07 | -14.379 | 1120.8 | -2646.2 | 2646.2 | + | "Average X in T2U" | 673541 |-1.658606e+07 | -24.625 | 1135.5 | -2656.4 | 2656.3 | + | "Average X in T2V" | 693923 |-1.479371e+07 | -21.319 | 1129.9 | -2656.4 | 2656.3 | + | "Average X in T2X1" | 645225 |-1.705455e+07 | -26.432 | 1138.8 | -2646.2 | 2646.2 | + | "Average X in T2X2" | 716059 | -9891920 | -13.814 | 1124.6 | -2646.2 | 2646.2 | + | "Average X in T3U" | 731421 |-1.225062e+07 | -16.749 | 1333.5 | -3188.4 | 3188.4 | + | "Average X in T3V" | 753478 |-1.409381e+07 | -18.705 | 1328.7 | -3188.4 | 3188.4 | + | "Average X in T3X1" | 704173 |-1.010873e+07 | -14.355 | 1334.4 | -3176.2 | 3176.2 | + | "Average X in T3X2" | 782214 |-1.938375e+07 | -24.781 | 1321.3 | -3176.2 | 3176.2 | + | "Hits in T1U" | 9156 | 690489 | 75.414 | 27.984 | 5.0000 | 232.00 | + | "Hits in T1V" | 9156 | 696122 | 76.029 | 27.670 | 3.0000 | 245.00 | + | "Hits in T1X1" | 9156 | 677723 | 74.020 | 27.325 | 4.0000 | 205.00 | + | "Hits in T1X2" | 9156 | 705312 | 77.033 | 28.024 | 6.0000 | 266.00 | + | "Hits in T2U" | 9156 | 673541 | 73.563 | 26.210 | 3.0000 | 198.00 | + | "Hits in T2V" | 9156 | 693923 | 75.789 | 27.194 | 6.0000 | 374.00 | + | "Hits in T2X1" | 9156 | 645225 | 70.470 | 25.869 | 3.0000 | 288.00 | + | "Hits in T2X2" | 9156 | 716059 | 78.207 | 27.736 | 6.0000 | 287.00 | + | "Hits in T3U" | 9156 | 731421 | 79.884 | 27.669 | 2.0000 | 239.00 | + | "Hits in T3V" | 9156 | 753478 | 82.293 | 28.471 | 6.0000 | 207.00 | + | "Hits in T3X1" | 9156 | 704173 | 76.908 | 27.098 | 5.0000 | 339.00 | + | "Hits in T3X2" | 9156 | 782214 | 85.432 | 29.532 | 6.0000 | 204.00 | + | "Total number of hits" | 2289 | 8469680 | 3700.2 | 1120.3 | 604.00 | 6365.0 | +PrStoreUTHit_6220b56a INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrTrackAssociator_16ad4612 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 284763 | 279294 |( 98.07946 +- 0.02571932)% | + | "MC particles per track" | 279294 | 279304 | 1.0000 | +PrTrackAssociator_3adf94fb INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 181236 | 155077 |( 85.56633 +- 0.08255009)% | + | "MC particles per track" | 155077 | 181813 | 1.1724 | +PrTrackAssociator_8c8024ec INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 212505 | 149408 |( 70.30799 +- 0.09911458)% | + | "MC particles per track" | 149408 | 174321 | 1.1667 | +SeedTrackChecker_ad9abe4e.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +VeloClusterTrackingSIMD_87c18651 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Clusters" | 2289 | 5397790 | 2358.1 | + | "Nb of Produced Tracks" | 2289 | 593239 | 259.17 | +fromPrForwardTracksV1Tracks_f53f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 181236 | 79.177 | +fromPrMatchTracksV1Tracks_aaf8b514 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 212505 | 92.837 | +fromPrSeedingTracksV1Tracks_84cd... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 284763 | 124.40 | +fromPrVeloTracksV1TracksMerger_f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 593239 | 259.17 | +ApplicationMgr INFO Application Manager Stopped successfully +ForwardTrackChecker_482fda95 INFO Results +ForwardTrackChecker_482fda95 INFO **** Forward 181236 tracks including 26159 ghosts [14.43 %], Event average 13.11 % **** +ForwardTrackChecker_482fda95 INFO 01_long : 133702 from 152279 [ 87.80 %] 513 clones [ 0.38 %], purity: 99.21 %, hitEff: 98.43 % +ForwardTrackChecker_482fda95 INFO 02_long_P>5GeV : 91867 from 98421 [ 93.34 %] 307 clones [ 0.33 %], purity: 99.32 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 03_long_strange : 6588 from 8121 [ 81.12 %] 20 clones [ 0.30 %], purity: 98.87 %, hitEff: 98.21 % +ForwardTrackChecker_482fda95 INFO 04_long_strange_P>5GeV : 3465 from 3856 [ 89.86 %] 8 clones [ 0.23 %], purity: 99.05 %, hitEff: 98.80 % +ForwardTrackChecker_482fda95 INFO 05_long_fromB : 7199 from 7959 [ 90.45 %] 26 clones [ 0.36 %], purity: 99.34 %, hitEff: 98.69 % +ForwardTrackChecker_482fda95 INFO 05_long_fromD : 3793 from 4226 [ 89.75 %] 10 clones [ 0.26 %], purity: 99.25 %, hitEff: 98.50 % +ForwardTrackChecker_482fda95 INFO 06_long_fromB_P>5GeV : 5664 from 5983 [ 94.67 %] 18 clones [ 0.32 %], purity: 99.45 %, hitEff: 98.93 % +ForwardTrackChecker_482fda95 INFO 06_long_fromD_P>5GeV : 2732 from 2894 [ 94.40 %] 7 clones [ 0.26 %], purity: 99.35 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons : 10559 from 15125 [ 69.81 %] 108 clones [ 1.01 %], purity: 97.96 %, hitEff: 98.31 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons_pairprod : 6890 from 10831 [ 63.61 %] 86 clones [ 1.23 %], purity: 97.36 %, hitEff: 98.08 % +ForwardTrackChecker_482fda95 INFO 08_long_fromB_electrons : 3548 from 4210 [ 84.28 %] 22 clones [ 0.62 %], purity: 99.07 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 09_long_fromB_electrons_P>5GeV : 3333 from 3850 [ 86.57 %] 21 clones [ 0.63 %], purity: 99.15 %, hitEff: 98.96 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 4902 from 5182 [ 94.60 %] 17 clones [ 0.35 %], purity: 99.55 %, hitEff: 98.93 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3220 from 3659 [ 88.00 %] 19 clones [ 0.59 %], purity: 99.22 %, hitEff: 98.94 % +ForwardTrackChecker_482fda95 INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 2218 from 2343 [ 94.66 %] 6 clones [ 0.27 %], purity: 99.49 %, hitEff: 98.85 % +ForwardTrackChecker_482fda95 INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 1801 from 2010 [ 89.60 %] 4 clones [ 0.22 %], purity: 99.36 %, hitEff: 98.68 % +ForwardTrackChecker_482fda95 INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4889 from 5164 [ 94.67 %] 17 clones [ 0.35 %], purity: 99.55 %, hitEff: 98.94 % +ForwardTrackChecker_482fda95 INFO +ForwardUTHitsChecker_fe9d9ac2 INFO Results +ForwardUTHitsChecker_fe9d9ac2 INFO **** UT Efficiency for /Event/fromPrForwardTracksV1Tracks_f53f50a8/OutputTracksLocation **** 26159 ghost, 2.61 UT per track +ForwardUTHitsChecker_fe9d9ac2 INFO 01_long :134215 tr 3.91 from 4.07 mcUT [ 95.9 %] 0.12 ghost hits on real tracks [ 3.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 01_long >3UT :132800 tr 3.94 from 4.10 mcUT [ 96.2 %] 0.12 ghost hits on real tracks [ 2.9 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 02_long_P>5GeV : 92174 tr 3.94 from 4.07 mcUT [ 96.8 %] 0.10 ghost hits on real tracks [ 2.4 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 02_long_P>5GeV >3UT : 90908 tr 3.99 from 4.11 mcUT [ 97.2 %] 0.09 ghost hits on real tracks [ 2.2 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV : 4919 tr 4.00 from 4.07 mcUT [ 98.2 %] 0.05 ghost hits on real tracks [ 1.1 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4897 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.04 ghost hits on real tracks [ 1.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4906 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.05 ghost hits on real tracks [ 1.1 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4897 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.04 ghost hits on real tracks [ 1.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO +MatchTrackChecker_386d067b INFO Results +MatchTrackChecker_386d067b INFO **** Match 212505 tracks including 63097 ghosts [29.69 %], Event average 27.13 % **** +MatchTrackChecker_386d067b INFO 01_long : 128320 from 152279 [ 84.27 %] 760 clones [ 0.59 %], purity: 99.35 %, hitEff: 98.72 % +MatchTrackChecker_386d067b INFO 02_long_P>5GeV : 89484 from 98421 [ 90.92 %] 445 clones [ 0.49 %], purity: 99.46 %, hitEff: 99.26 % +MatchTrackChecker_386d067b INFO 03_long_strange : 6037 from 8121 [ 74.34 %] 28 clones [ 0.46 %], purity: 99.00 %, hitEff: 98.34 % +MatchTrackChecker_386d067b INFO 04_long_strange_P>5GeV : 3399 from 3856 [ 88.15 %] 12 clones [ 0.35 %], purity: 99.18 %, hitEff: 99.23 % +MatchTrackChecker_386d067b INFO 05_long_fromB : 7016 from 7959 [ 88.15 %] 48 clones [ 0.68 %], purity: 99.46 %, hitEff: 98.89 % +MatchTrackChecker_386d067b INFO 05_long_fromD : 3661 from 4226 [ 86.63 %] 17 clones [ 0.46 %], purity: 99.39 %, hitEff: 98.78 % +MatchTrackChecker_386d067b INFO 06_long_fromB_P>5GeV : 5573 from 5983 [ 93.15 %] 28 clones [ 0.50 %], purity: 99.57 %, hitEff: 99.25 % +MatchTrackChecker_386d067b INFO 06_long_fromD_P>5GeV : 2679 from 2894 [ 92.57 %] 9 clones [ 0.33 %], purity: 99.52 %, hitEff: 99.24 % +MatchTrackChecker_386d067b INFO 07_long_electrons : 11295 from 15125 [ 74.68 %] 166 clones [ 1.45 %], purity: 97.81 %, hitEff: 98.20 % +MatchTrackChecker_386d067b INFO 07_long_electrons_pairprod : 7537 from 10831 [ 69.59 %] 131 clones [ 1.71 %], purity: 97.18 %, hitEff: 97.90 % +MatchTrackChecker_386d067b INFO 08_long_fromB_electrons : 3591 from 4210 [ 85.30 %] 38 clones [ 1.05 %], purity: 99.09 %, hitEff: 98.91 % +MatchTrackChecker_386d067b INFO 09_long_fromB_electrons_P>5GeV : 3376 from 3850 [ 87.69 %] 36 clones [ 1.06 %], purity: 99.17 %, hitEff: 99.03 % +MatchTrackChecker_386d067b INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 4829 from 5182 [ 93.19 %] 27 clones [ 0.56 %], purity: 99.65 %, hitEff: 99.14 % +MatchTrackChecker_386d067b INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3258 from 3659 [ 89.04 %] 33 clones [ 1.00 %], purity: 99.25 %, hitEff: 99.03 % +MatchTrackChecker_386d067b INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 2178 from 2343 [ 92.96 %] 9 clones [ 0.41 %], purity: 99.65 %, hitEff: 99.13 % +MatchTrackChecker_386d067b INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 1768 from 2010 [ 87.96 %] 6 clones [ 0.34 %], purity: 99.51 %, hitEff: 99.00 % +MatchTrackChecker_386d067b INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4817 from 5164 [ 93.28 %] 27 clones [ 0.56 %], purity: 99.65 %, hitEff: 99.14 % +MatchTrackChecker_386d067b INFO +MatchUTHitsChecker_a4d04726 INFO Results +MatchUTHitsChecker_a4d04726 INFO **** UT Efficiency for /Event/fromPrMatchTracksV1Tracks_aaf8b514/OutputTracksLocation **** 63097 ghost, 2.51 UT per track +MatchUTHitsChecker_a4d04726 INFO 01_long :129080 tr 3.90 from 4.08 mcUT [ 95.6 %] 0.13 ghost hits on real tracks [ 3.1 %] +MatchUTHitsChecker_a4d04726 INFO 01_long >3UT :127762 tr 3.93 from 4.10 mcUT [ 95.9 %] 0.12 ghost hits on real tracks [ 3.0 %] +MatchUTHitsChecker_a4d04726 INFO 02_long_P>5GeV : 89929 tr 3.95 from 4.08 mcUT [ 96.7 %] 0.10 ghost hits on real tracks [ 2.4 %] +MatchUTHitsChecker_a4d04726 INFO 02_long_P>5GeV >3UT : 88789 tr 3.99 from 4.11 mcUT [ 97.1 %] 0.09 ghost hits on real tracks [ 2.2 %] +MatchUTHitsChecker_a4d04726 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV : 4856 tr 3.99 from 4.07 mcUT [ 98.1 %] 0.05 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_a4d04726 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4834 tr 4.01 from 4.08 mcUT [ 98.2 %] 0.04 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_a4d04726 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4844 tr 4.00 from 4.08 mcUT [ 98.2 %] 0.05 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_a4d04726 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4834 tr 4.01 from 4.08 mcUT [ 98.2 %] 0.04 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_a4d04726 INFO +SeedTrackChecker_ad9abe4e INFO Results +SeedTrackChecker_ad9abe4e INFO **** Seed 284763 tracks including 5469 ghosts [ 1.92 %], Event average 1.56 % **** +SeedTrackChecker_ad9abe4e INFO 01_hasT : 198532 from 234618 [ 84.62 %] 7 clones [ 0.00 %], purity: 99.60 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 02_long : 143630 from 152279 [ 94.32 %] 6 clones [ 0.00 %], purity: 99.71 %, hitEff: 98.42 % +SeedTrackChecker_ad9abe4e INFO 03_long_P>5GeV : 95859 from 98421 [ 97.40 %] 5 clones [ 0.01 %], purity: 99.69 %, hitEff: 99.09 % +SeedTrackChecker_ad9abe4e INFO 04_long_fromB : 7598 from 7959 [ 95.46 %] 1 clones [ 0.01 %], purity: 99.75 %, hitEff: 98.65 % +SeedTrackChecker_ad9abe4e INFO 05_long_fromB_P>5GeV : 5835 from 5983 [ 97.53 %] 1 clones [ 0.02 %], purity: 99.76 %, hitEff: 99.13 % +SeedTrackChecker_ad9abe4e INFO 06_UT+T_strange : 16417 from 17658 [ 92.97 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.00 % +SeedTrackChecker_ad9abe4e INFO 07_UT+T_strange_P>5GeV : 8615 from 8825 [ 97.62 %] 0 clones [ 0.00 %], purity: 99.71 %, hitEff: 99.05 % +SeedTrackChecker_ad9abe4e INFO 08_noVelo+UT+T_strange : 8949 from 9658 [ 92.66 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.03 % +SeedTrackChecker_ad9abe4e INFO 09_noVelo+UT+T_strange_P>5GeV : 4914 from 5043 [ 97.44 %] 0 clones [ 0.00 %], purity: 99.70 %, hitEff: 99.01 % +SeedTrackChecker_ad9abe4e INFO 10_UT+T_SfromDB : 1133 from 1220 [ 92.87 %] 0 clones [ 0.00 %], purity: 99.77 %, hitEff: 97.99 % +SeedTrackChecker_ad9abe4e INFO 11_UT+T_SfromDB_P>5GeV : 612 from 623 [ 98.23 %] 0 clones [ 0.00 %], purity: 99.72 %, hitEff: 99.22 % +SeedTrackChecker_ad9abe4e INFO 12_noVelo+UT+T_SfromDB_P>5GeV : 420 from 428 [ 98.13 %] 0 clones [ 0.00 %], purity: 99.69 %, hitEff: 99.12 % +SeedTrackChecker_ad9abe4e INFO 13_hasT_electrons : 40669 from 74476 [ 54.61 %] 2 clones [ 0.00 %], purity: 99.69 %, hitEff: 97.16 % +SeedTrackChecker_ad9abe4e INFO 14_long_electrons : 13360 from 15125 [ 88.33 %] 1 clones [ 0.01 %], purity: 99.81 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 15_long_fromB_electrons : 3922 from 4210 [ 93.16 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.70 % +SeedTrackChecker_ad9abe4e INFO 16_long_electrons_P>5GeV : 8676 from 9420 [ 92.10 %] 0 clones [ 0.00 %], purity: 99.80 %, hitEff: 98.73 % +SeedTrackChecker_ad9abe4e INFO 17_long_fromB_electrons_P>5GeV : 3632 from 3850 [ 94.34 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.85 % +SeedTrackChecker_ad9abe4e INFO +HLTControlFlowMgr INFO Memory pool: used 3.94312 +/- 0.039102 MiB (min: 0, max: 5) in 1 +/- 0 blocks (allocated >once in 0 +/- 0% events). Allocated capacity was 10 +/- 0 MiB (min: 10, max: 10) and 347.612 +/- 3.41441 (min: 4, max: 489) requests were served +HLTControlFlowMgr INFO Timing table: +HLTControlFlowMgr INFO + | Name of Algorithm | Execution Count | Total Time / s | Avg. Time / us | + | Sum of all Algorithms | 2955 | 153.574 | 51970.798 | + | "Fetch__Event_DAQ_RawEvent" | 2955 | 89.274 | 30211.268 | + | "SeedTrackChecker_ad9abe4e" | 2289 | 12.942 | 5653.928 | + | "ForwardTrackChecker_482fda95" | 2289 | 12.018 | 5250.359 | + | "MatchTrackChecker_386d067b" | 2289 | 10.759 | 4700.275 | + | "ForwardUTHitsChecker_fe9d9ac2" | 2289 | 4.756 | 2077.710 | + | "MatchUTHitsChecker_a4d04726" | 2289 | 4.721 | 2062.669 | + | "PrForwardTrackingVelo_6024f9ec" | 2289 | 4.378 | 1912.819 | + | "PrHybridSeeding_4d0337cc" | 2289 | 3.278 | 1432.022 | + | "PrLHCbID2MCParticle_a906d17d" | 2289 | 2.506 | 1094.586 | + | "Unpack__Event_MC_Vertices" | 2289 | 1.996 | 872.057 | + | "Unpack__Event_MC_Particles" | 2289 | 1.915 | 836.722 | + | "VeloClusterTrackingSIMD_87c18651" | 2289 | 0.728 | 318.014 | + | "VPFullCluster2MCParticleLinker_17386552" | 2289 | 0.572 | 250.069 | + | "VPClusFull_38754d8c" | 2289 | 0.544 | 237.763 | + | "PrMatchNN_d80b5038" | 2289 | 0.471 | 205.633 | + | "PrStorePrUTHits_df75b912" | 2289 | 0.467 | 204.202 | + | "PrTrackAssociator_8c8024ec" | 2289 | 0.374 | 163.572 | + | "PrTrackAssociator_3adf94fb" | 2289 | 0.373 | 162.935 | + | "PrStoreUTHit_6220b56a" | 2289 | 0.344 | 150.391 | + | "PrTrackAssociator_16ad4612" | 2289 | 0.254 | 111.073 | + | "fromPrVeloTracksV1TracksMerger_fa66a5de" | 2289 | 0.194 | 84.790 | + | "fromPrMatchTracksV1Tracks_aaf8b514" | 2289 | 0.182 | 79.321 | + | "fromPrForwardTracksV1Tracks_f53f50a8" | 2289 | 0.136 | 59.378 | + | "fromPrSeedingTracksV1Tracks_84cd46c2" | 2289 | 0.121 | 52.924 | + | "PrStoreSciFiHits_fb0eba02" | 2289 | 0.113 | 49.523 | + | "FTRawBankDecoder" | 2289 | 0.060 | 26.183 | + | "UnpackRawEvent_FTCluster" | 2955 | 0.024 | 8.211 | + | "reserveIOV" | 2289 | 0.021 | 9.147 | + | "Decode_ODIN" | 2289 | 0.006 | 2.807 | + | "Fetch__Event_pSim_MCVertices" | 2289 | 0.006 | 2.481 | + | "DefaultGECFilter" | 2955 | 0.006 | 1.918 | + | "UnpackRawEvent_UT" | 2955 | 0.004 | 1.473 | + | "Fetch__Event_pSim_MCParticles" | 2289 | 0.004 | 1.836 | + | "Fetch__Event_Link_Raw_UT_Clusters" | 2289 | 0.004 | 1.737 | + | "Fetch__Event_MC_TrackInfo" | 2289 | 0.004 | 1.674 | + | "Fetch__Event_Link_Raw_VP_Digits" | 2289 | 0.004 | 1.660 | + | "DummyEventTime" | 2289 | 0.004 | 1.606 | + | "UnpackRawEvent_VP" | 2289 | 0.003 | 1.453 | + | "UnpackRawEvent_ODIN" | 2289 | 0.003 | 1.339 | + | "Fetch__Event_Link_Raw_FT_LiteClusters" | 2289 | 0.002 | 0.966 | + +HLTControlFlowMgr INFO StateTree: CFNode #executed #passed +LAZY_AND: hlt2_matching_reco_decision #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + PrGECFilter/DefaultGECFilter #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + NONLAZY_OR: hlt2_matching_reco_data #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrMatchNN/PrMatchNN_d80b5038 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/ForwardTrackChecker_482fda95 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrUTHitChecker/ForwardUTHitsChecker_fe9d9ac2 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/MatchTrackChecker_386d067b #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrUTHitChecker/MatchUTHitsChecker_a4d04726 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/SeedTrackChecker_ad9abe4e #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + +HLTControlFlowMgr INFO Histograms converted successfully according to request. +ToolSvc INFO Removing all tools created by ToolSvc +SeedTrackChecker_ad9abe4e.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +MatchUTHitsChecker_a4d04726.PrCh... SUCCESS Booked 44 Histogram(s) : 1D=40 2D=4 +MatchTrackChecker_386d067b.PrChe... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +ForwardUTHitsChecker_fe9d9ac2.Pr... SUCCESS Booked 44 Histogram(s) : 1D=40 2D=4 +ForwardTrackChecker_482fda95.PrC... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +RootCnvSvc INFO Disconnected data IO:9E3B8940-FB87-11EB-ADCA-FA163E643B60 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi] +RootCnvSvc INFO Disconnected data IO:AFCB9710-FB21-11EB-9E91-FA163ED3A4EB [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi] +RootCnvSvc INFO Disconnected data IO:B9D7CC62-FB38-11EB-8B01-3CECEF5D2C24 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi] +ApplicationMgr INFO Application Manager Finalized successfully +ApplicationMgr INFO Application Manager Terminated successfully diff --git a/efficiencies/logs/match_effs_testJpsi_EDef_yCorrNoCut.log b/efficiencies/logs/match_effs_testJpsi_EDef_yCorrNoCut.log new file mode 100644 index 0000000..30392f0 --- /dev/null +++ b/efficiencies/logs/match_effs_testJpsi_EDef_yCorrNoCut.log @@ -0,0 +1,378 @@ +# setting LC_ALL to "C" +# --> Including file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_match_eff_data.py' +/***** User ApplicationOptions/ApplicationOptions ************************************************** +|-append_decoding_keys_to_output_manifest = True (default: True) +|-auditors = [] (default: []) +|-buffer_events = 20000 (default: 20000) +|-conddb_tag = 'sim-20210617-vc-md100' (default: '') +|-conditions_version = '' (default: '') +|-control_flow_file = '' (default: '') +|-data_flow_file = '' (default: '') +|-data_type = 'Upgrade' (default: 'Upgrade') +|-dddb_tag = 'dddb-20210617' (default: '') +|-event_store = 'HiveWhiteBoard' (default: 'HiveWhiteBoard') +|-evt_max = -1 (default: -1) +|-first_evt = 0 (default: 0) +|-geometry_version = '' (default: '') +|-histo_file = '' (default: '') +|-input_files = ['/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi'] +| (default: []) +|-input_manifest_file = '' (default: '') +|-input_process = '' (default: '') +|-input_raw_format = 0.5 (default: 0.5) +|-input_type = 'ROOT' (default: '') +|-lines_maker = None +|-memory_pool_size = 10485760 (default: 10485760) +|-monitoring_file = '' (default: '') +|-msg_svc_format = '% F%35W%S %7W%R%T %0W%M' (default: '% F%35W%S %7W%R%T %0W%M') +|-msg_svc_time_format = '%Y-%m-%d %H:%M:%S UTC' (default: '%Y-%m-%d %H:%M:%S UTC') +|-n_event_slots = 1 (default: -1) +|-n_threads = 1 (default: 1) +|-ntuple_file = '/work/cetin/LHCb/reco_tuner/data_matching/match_effs_testJpsi_EDef_yCorrNoCut.root' +| (default: '') +|-output_file = '' (default: '') +|-output_level = 3 (default: 3) +|-output_manifest_file = '' (default: '') +|-output_type = '' (default: '') +|-persistreco_version = 1.0 (default: 1.0) +|-phoenix_filename = '' (default: '') +|-preamble_algs = [] (default: []) +|-print_freq = 10000 (default: 10000) +|-python_logging_level = 20 (default: 20) +|-require_specific_decoding_keys = [] (default: []) +|-scheduler_legacy_mode = True (default: True) +|-simulation = True (default: None) +|-use_iosvc = False (default: False) +|-velo_motion_system_yaml = '' (default: '') +|-write_decoding_keys_to_git = True (default: True) +\----- (End of User ApplicationOptions/ApplicationOptions) ----------------------------------------- +# Overrule specified for keys +# <-- End of file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_match_eff_data.py' +ApplicationMgr SUCCESS +==================================================================================================================================== + Welcome to Moore version 55.2 + running on lhcba2 on Mon Mar 11 07:16:42 2024 +==================================================================================================================================== +ApplicationMgr INFO Application Manager Configured successfully +ToolSvc.GitDDDB INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/DDDB.git' +ToolSvc.GitDDDB INFO using commit 'upgrade/dddb-20210617' corresponding to 1871f1bb5c0d68c81dda62e84cf1eb3a45513521 +ToolSvc.GitSIMCOND INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/SIMCOND.git' +ToolSvc.GitSIMCOND INFO using commit 'upgrade/sim-20210617-vc-md100' corresponding to 9aa116c7143d21760d1be07ce1ef22c0f8f07bdf +DetectorPersistencySvc INFO Added successfully Conversion service:XmlCnvSvc +DetectorDataSvc SUCCESS Detector description database: git:/lhcb.xml +EventClockSvc.FakeEventTime INFO Event times generated from 0 with steps of 0 +EventClockSvc.FakeEventTime INFO Run numbers generated from 0 every 0 events +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c1.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c2.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c3.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c4.down.cdf +MagneticFieldSvc INFO Map scaled by factor 1 with polarity internally used: -1 signed relative current: -1 +NTupleSvc INFO Added stream file:/work/cetin/LHCb/reco_tuner/data_matching/match_effs_testJpsi_EDef_yCorrNoCut.root as FILE1 +HLTControlFlowMgr INFO Start initialization +RootHistSvc INFO Writing ROOT histograms to: /work/cetin/LHCb/reco_tuner/data_matching/match_effs_testJpsi_EDef_yCorrNoCut.root +HistogramPersistencySvc INFO Added successfully Conversion service:RootHistSvc +DeFTDetector INFO Current FT geometry version = 64 +HLTControlFlowMgr INFO Concurrency level information: +HLTControlFlowMgr INFO o Number of events slots: 1 +HLTControlFlowMgr INFO o TBB thread pool size: 'ThreadPoolSize':1 +HLTControlFlowMgr INFO ---> End of Initialization. This took 19653 ms +ApplicationMgr INFO Application Manager Initialized successfully +ApplicationMgr INFO Application Manager Started successfully +EventPersistencySvc INFO Added successfully Conversion service:RootCnvSvc +EventSelector INFO Stream:EventSelector.DataStreamTool_1 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO Will measure time between events 10 and 2147483647 (stop might be some events later) +HLTControlFlowMgr INFO Starting loop on events +EventSelector SUCCESS Reading Event record 1. Record number within stream 1: 1 +FTRawBankDecoder INFO Conditions DB is compatible with FT bank version 4, 5, 6. +FTRawBankDecoder INFO Building the readout map with version 0 +HLTControlFlowMgr INFO Timing started at: 07:17:20 +EventSelector INFO Stream:EventSelector.DataStreamTool_2 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +EventSelector INFO Stream:EventSelector.DataStreamTool_3 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO No more events in event selection +HLTControlFlowMgr INFO ---> Loop over 2955 Events Finished - WSS 1804.29, timed 2945 Events: 156936 ms, Evts/s = 18.7656 +DefaultGECFilter INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb Events Processed" | 2955 | + | "Nb events removed" | 666 | +ForwardTrackChecker_482fda95.LoK... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +ForwardUTHitsChecker_fe9d9ac2.Lo... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 4 | +HLTControlFlowMgr INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Processed events" | 2955 | +MatchTrackChecker_386d067b.LoKi:... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +MatchUTHitsChecker_a4d04726.LoKi... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 4 | +PrForwardTrackingVelo_6024f9ec INFO Number of counters : 10 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Accepted input tracks" | 2289 | 363254 | 158.70 | + | "Created long tracks" | 2289 | 181236 | 79.177 | + | "Input tracks" | 2289 | 380749 | 166.34 | + | "Number of candidate bins per track" | 363254 | 1665217 | 4.5842 | 5.0318 | 0.0000 | 56.000 | + | "Number of complete candidates/track 1st Loop" | 305079 | 195005 | 0.63920 | 0.65005 | 0.0000 | 6.0000 | + | "Number of complete candidates/track 2nd Loop" | 148403 | 13248 | 0.089270 | 0.29669 | 0.0000 | 3.0000 | + | "Number of x candidates per track 1st Loop" | 305079 | 426093 | 1.3967 | 1.3487 | + | "Number of x candidates per track 2nd Loop" | 148403 | 347932 | 2.3445 | 2.6098 | + | "Percentage second loop execution" | 305079 | 148403 | 0.48644 | + | "Removed duplicates" | 2289 | 9647 | 4.2145 | +PrForwardTrackingVelo_6024f9ec.P... INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 166072 | 673152 | 4.0534 | + | "#tracks with hits added" | 166072 | +PrHybridSeeding_4d0337cc INFO Number of counters : 21 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Created T2x1 three-hit combinations in case 0" | 3981395 | 2438467 | 0.61247 | 0.62452 | 0.0000 | 6.0000 | + | "Created T2x1 three-hit combinations in case 1" | 4961664 | 3252259 | 0.65548 | 0.75200 | 0.0000 | 12.000 | + | "Created T2x1 three-hit combinations in case 2" | 7644512 | 6133331 | 0.80232 | 1.0193 | 0.0000 | 23.000 | + | "Created XZ tracks (part 0)" | 6867 | 363280 | 52.902 | 44.400 | 0.0000 | 844.00 | + | "Created XZ tracks (part 1)" | 6867 | 360418 | 52.486 | 47.084 | 0.0000 | 1257.0 | + | "Created XZ tracks in case 0" | 4578 | 269789 | 58.932 | 37.398 | 1.0000 | 363.00 | + | "Created XZ tracks in case 1" | 4578 | 267868 | 58.512 | 44.098 | 1.0000 | 709.00 | + | "Created XZ tracks in case 2" | 4578 | 186041 | 40.638 | 52.165 | 0.0000 | 1257.0 | + | "Created full hit combinations in case 0" | 407934 | 407934 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 1" | 310355 | 310355 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 2" | 280325 | 280325 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created seed tracks" | 4578 | 284763 | 62.202 | 22.650 | 3.0000 | 141.00 | + | "Created seed tracks (part 0)" | 2289 | 159664 | 69.753 | 25.912 | 4.0000 | 161.00 | + | "Created seed tracks (part 1)" | 2289 | 157869 | 68.969 | 25.854 | 3.0000 | 159.00 | + | "Created seed tracks in case 0" | 4578 | 148622 | 32.464 | 12.801 | 1.0000 | 86.000 | + | "Created seed tracks in case 1" | 4578 | 270703 | 59.131 | 21.736 | 2.0000 | 132.00 | + | "Created seed tracks in case 2" | 4578 | 302221 | 66.016 | 24.642 | 3.0000 | 153.00 | + | "Created seed tracks in recovery step" | 2289 | 15312 | 6.6894 | 3.8772 | 0.0000 | 26.000 | + | "Created two-hit combinations in case 0" | 677723 |1.546134e+07 | 22.814 | 15.827 | 0.0000 | 117.00 | + | "Created two-hit combinations in case 1" | 584001 |1.760625e+07 | 30.148 | 18.628 | 0.0000 | 262.00 | + | "Created two-hit combinations in case 2" | 461883 |2.056474e+07 | 44.524 | 28.512 | 0.0000 | 333.00 | +PrLHCbID2MCParticle_a906d17d INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#removed null MCParticles" | 16672433 | 0 | 0.0000 | +PrMatchNN_d80b5038 INFO Number of counters : 3 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#MatchingChi2" | 2289 | 8809473 | 3848.6 | + | "#MatchingMLP" | 214837 | 196735.2 | 0.91574 | + | "#MatchingTracks" | 2289 | 214837 | 93.856 | +PrMatchNN_d80b5038.PrAddUTHitsTool INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#UT hits added" | 187439 | 746377 | 3.9820 | + | "#tracks with hits added" | 187439 | +PrStorePrUTHits_df75b912 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrStoreSciFiHits_fb0eba02 INFO Number of counters : 25 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Average X in T1U" | 690489 |-2.482423e+07 | -35.952 | 1141.3 | -2656.4 | 2656.3 | + | "Average X in T1V" | 696122 |-2.060219e+07 | -29.596 | 1128.0 | -2656.4 | 2656.3 | + | "Average X in T1X1" | 677723 |-3.438883e+07 | -50.742 | 1162.3 | -2646.2 | 2646.2 | + | "Average X in T1X2" | 705312 |-1.014161e+07 | -14.379 | 1120.8 | -2646.2 | 2646.2 | + | "Average X in T2U" | 673541 |-1.658606e+07 | -24.625 | 1135.5 | -2656.4 | 2656.3 | + | "Average X in T2V" | 693923 |-1.479371e+07 | -21.319 | 1129.9 | -2656.4 | 2656.3 | + | "Average X in T2X1" | 645225 |-1.705455e+07 | -26.432 | 1138.8 | -2646.2 | 2646.2 | + | "Average X in T2X2" | 716059 | -9891920 | -13.814 | 1124.6 | -2646.2 | 2646.2 | + | "Average X in T3U" | 731421 |-1.225062e+07 | -16.749 | 1333.5 | -3188.4 | 3188.4 | + | "Average X in T3V" | 753478 |-1.409381e+07 | -18.705 | 1328.7 | -3188.4 | 3188.4 | + | "Average X in T3X1" | 704173 |-1.010873e+07 | -14.355 | 1334.4 | -3176.2 | 3176.2 | + | "Average X in T3X2" | 782214 |-1.938375e+07 | -24.781 | 1321.3 | -3176.2 | 3176.2 | + | "Hits in T1U" | 9156 | 690489 | 75.414 | 27.984 | 5.0000 | 232.00 | + | "Hits in T1V" | 9156 | 696122 | 76.029 | 27.670 | 3.0000 | 245.00 | + | "Hits in T1X1" | 9156 | 677723 | 74.020 | 27.325 | 4.0000 | 205.00 | + | "Hits in T1X2" | 9156 | 705312 | 77.033 | 28.024 | 6.0000 | 266.00 | + | "Hits in T2U" | 9156 | 673541 | 73.563 | 26.210 | 3.0000 | 198.00 | + | "Hits in T2V" | 9156 | 693923 | 75.789 | 27.194 | 6.0000 | 374.00 | + | "Hits in T2X1" | 9156 | 645225 | 70.470 | 25.869 | 3.0000 | 288.00 | + | "Hits in T2X2" | 9156 | 716059 | 78.207 | 27.736 | 6.0000 | 287.00 | + | "Hits in T3U" | 9156 | 731421 | 79.884 | 27.669 | 2.0000 | 239.00 | + | "Hits in T3V" | 9156 | 753478 | 82.293 | 28.471 | 6.0000 | 207.00 | + | "Hits in T3X1" | 9156 | 704173 | 76.908 | 27.098 | 5.0000 | 339.00 | + | "Hits in T3X2" | 9156 | 782214 | 85.432 | 29.532 | 6.0000 | 204.00 | + | "Total number of hits" | 2289 | 8469680 | 3700.2 | 1120.3 | 604.00 | 6365.0 | +PrStoreUTHit_6220b56a INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 2289 | 494424 | 216.00 | +PrTrackAssociator_16ad4612 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 284763 | 279294 |( 98.07946 +- 0.02571932)% | + | "MC particles per track" | 279294 | 279304 | 1.0000 | +PrTrackAssociator_3adf94fb INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 181236 | 155077 |( 85.56633 +- 0.08255009)% | + | "MC particles per track" | 155077 | 181813 | 1.1724 | +PrTrackAssociator_8c8024ec INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 214837 | 149689 |( 69.67561 +- 0.09917036)% | + | "MC particles per track" | 149689 | 174616 | 1.1665 | +SeedTrackChecker_ad9abe4e.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +VeloClusterTrackingSIMD_87c18651 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Clusters" | 2289 | 5397790 | 2358.1 | + | "Nb of Produced Tracks" | 2289 | 593239 | 259.17 | +fromPrForwardTracksV1Tracks_f53f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 181236 | 79.177 | +fromPrMatchTracksV1Tracks_aaf8b514 INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 214837 | 93.856 | +fromPrSeedingTracksV1Tracks_84cd... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 284763 | 124.40 | +fromPrVeloTracksV1TracksMerger_f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 2289 | 593239 | 259.17 | +ApplicationMgr INFO Application Manager Stopped successfully +ForwardTrackChecker_482fda95 INFO Results +ForwardTrackChecker_482fda95 INFO **** Forward 181236 tracks including 26159 ghosts [14.43 %], Event average 13.11 % **** +ForwardTrackChecker_482fda95 INFO 01_long : 133702 from 152279 [ 87.80 %] 513 clones [ 0.38 %], purity: 99.21 %, hitEff: 98.43 % +ForwardTrackChecker_482fda95 INFO 02_long_P>5GeV : 91867 from 98421 [ 93.34 %] 307 clones [ 0.33 %], purity: 99.32 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 03_long_strange : 6588 from 8121 [ 81.12 %] 20 clones [ 0.30 %], purity: 98.87 %, hitEff: 98.21 % +ForwardTrackChecker_482fda95 INFO 04_long_strange_P>5GeV : 3465 from 3856 [ 89.86 %] 8 clones [ 0.23 %], purity: 99.05 %, hitEff: 98.80 % +ForwardTrackChecker_482fda95 INFO 05_long_fromB : 7199 from 7959 [ 90.45 %] 26 clones [ 0.36 %], purity: 99.34 %, hitEff: 98.69 % +ForwardTrackChecker_482fda95 INFO 05_long_fromD : 3793 from 4226 [ 89.75 %] 10 clones [ 0.26 %], purity: 99.25 %, hitEff: 98.50 % +ForwardTrackChecker_482fda95 INFO 06_long_fromB_P>5GeV : 5664 from 5983 [ 94.67 %] 18 clones [ 0.32 %], purity: 99.45 %, hitEff: 98.93 % +ForwardTrackChecker_482fda95 INFO 06_long_fromD_P>5GeV : 2732 from 2894 [ 94.40 %] 7 clones [ 0.26 %], purity: 99.35 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons : 10559 from 15125 [ 69.81 %] 108 clones [ 1.01 %], purity: 97.96 %, hitEff: 98.31 % +ForwardTrackChecker_482fda95 INFO 07_long_electrons_pairprod : 6890 from 10831 [ 63.61 %] 86 clones [ 1.23 %], purity: 97.36 %, hitEff: 98.08 % +ForwardTrackChecker_482fda95 INFO 08_long_fromB_electrons : 3548 from 4210 [ 84.28 %] 22 clones [ 0.62 %], purity: 99.07 %, hitEff: 98.84 % +ForwardTrackChecker_482fda95 INFO 09_long_fromB_electrons_P>5GeV : 3333 from 3850 [ 86.57 %] 21 clones [ 0.63 %], purity: 99.15 %, hitEff: 98.96 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 4902 from 5182 [ 94.60 %] 17 clones [ 0.35 %], purity: 99.55 %, hitEff: 98.93 % +ForwardTrackChecker_482fda95 INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3220 from 3659 [ 88.00 %] 19 clones [ 0.59 %], purity: 99.22 %, hitEff: 98.94 % +ForwardTrackChecker_482fda95 INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 2218 from 2343 [ 94.66 %] 6 clones [ 0.27 %], purity: 99.49 %, hitEff: 98.85 % +ForwardTrackChecker_482fda95 INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 1801 from 2010 [ 89.60 %] 4 clones [ 0.22 %], purity: 99.36 %, hitEff: 98.68 % +ForwardTrackChecker_482fda95 INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4889 from 5164 [ 94.67 %] 17 clones [ 0.35 %], purity: 99.55 %, hitEff: 98.94 % +ForwardTrackChecker_482fda95 INFO +ForwardUTHitsChecker_fe9d9ac2 INFO Results +ForwardUTHitsChecker_fe9d9ac2 INFO **** UT Efficiency for /Event/fromPrForwardTracksV1Tracks_f53f50a8/OutputTracksLocation **** 26159 ghost, 2.61 UT per track +ForwardUTHitsChecker_fe9d9ac2 INFO 01_long :134215 tr 3.91 from 4.07 mcUT [ 95.9 %] 0.12 ghost hits on real tracks [ 3.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 01_long >3UT :132800 tr 3.94 from 4.10 mcUT [ 96.2 %] 0.12 ghost hits on real tracks [ 2.9 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 02_long_P>5GeV : 92174 tr 3.94 from 4.07 mcUT [ 96.8 %] 0.10 ghost hits on real tracks [ 2.4 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 02_long_P>5GeV >3UT : 90908 tr 3.99 from 4.11 mcUT [ 97.2 %] 0.09 ghost hits on real tracks [ 2.2 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV : 4919 tr 4.00 from 4.07 mcUT [ 98.2 %] 0.05 ghost hits on real tracks [ 1.1 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4897 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.04 ghost hits on real tracks [ 1.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4906 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.05 ghost hits on real tracks [ 1.1 %] +ForwardUTHitsChecker_fe9d9ac2 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4897 tr 4.01 from 4.08 mcUT [ 98.3 %] 0.04 ghost hits on real tracks [ 1.0 %] +ForwardUTHitsChecker_fe9d9ac2 INFO +MatchTrackChecker_386d067b INFO Results +MatchTrackChecker_386d067b INFO **** Match 214837 tracks including 65148 ghosts [30.32 %], Event average 27.74 % **** +MatchTrackChecker_386d067b INFO 01_long : 128435 from 152279 [ 84.34 %] 764 clones [ 0.59 %], purity: 99.35 %, hitEff: 98.73 % +MatchTrackChecker_386d067b INFO 02_long_P>5GeV : 89386 from 98421 [ 90.82 %] 447 clones [ 0.50 %], purity: 99.46 %, hitEff: 99.27 % +MatchTrackChecker_386d067b INFO 03_long_strange : 6046 from 8121 [ 74.45 %] 29 clones [ 0.48 %], purity: 99.00 %, hitEff: 98.36 % +MatchTrackChecker_386d067b INFO 04_long_strange_P>5GeV : 3397 from 3856 [ 88.10 %] 12 clones [ 0.35 %], purity: 99.18 %, hitEff: 99.24 % +MatchTrackChecker_386d067b INFO 05_long_fromB : 7033 from 7959 [ 88.37 %] 48 clones [ 0.68 %], purity: 99.46 %, hitEff: 98.90 % +MatchTrackChecker_386d067b INFO 05_long_fromD : 3662 from 4226 [ 86.65 %] 17 clones [ 0.46 %], purity: 99.39 %, hitEff: 98.80 % +MatchTrackChecker_386d067b INFO 06_long_fromB_P>5GeV : 5572 from 5983 [ 93.13 %] 28 clones [ 0.50 %], purity: 99.57 %, hitEff: 99.26 % +MatchTrackChecker_386d067b INFO 06_long_fromD_P>5GeV : 2680 from 2894 [ 92.61 %] 9 clones [ 0.33 %], purity: 99.52 %, hitEff: 99.24 % +MatchTrackChecker_386d067b INFO 07_long_electrons : 11296 from 15125 [ 74.68 %] 172 clones [ 1.50 %], purity: 97.80 %, hitEff: 98.22 % +MatchTrackChecker_386d067b INFO 07_long_electrons_pairprod : 7549 from 10831 [ 69.70 %] 134 clones [ 1.74 %], purity: 97.18 %, hitEff: 97.93 % +MatchTrackChecker_386d067b INFO 08_long_fromB_electrons : 3579 from 4210 [ 85.01 %] 40 clones [ 1.11 %], purity: 99.09 %, hitEff: 98.92 % +MatchTrackChecker_386d067b INFO 09_long_fromB_electrons_P>5GeV : 3364 from 3850 [ 87.38 %] 38 clones [ 1.12 %], purity: 99.17 %, hitEff: 99.05 % +MatchTrackChecker_386d067b INFO 10_long_fromB_P>3GeV_Pt>0.5GeV : 4832 from 5182 [ 93.25 %] 27 clones [ 0.56 %], purity: 99.66 %, hitEff: 99.15 % +MatchTrackChecker_386d067b INFO 10_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 3246 from 3659 [ 88.71 %] 35 clones [ 1.07 %], purity: 99.24 %, hitEff: 99.05 % +MatchTrackChecker_386d067b INFO 10_long_fromD_P>3GeV_Pt>0.5GeV : 2183 from 2343 [ 93.17 %] 9 clones [ 0.41 %], purity: 99.65 %, hitEff: 99.13 % +MatchTrackChecker_386d067b INFO 10_long_strange_P>3GeV_Pt>0.5GeV : 1769 from 2010 [ 88.01 %] 6 clones [ 0.34 %], purity: 99.52 %, hitEff: 99.01 % +MatchTrackChecker_386d067b INFO 11_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4820 from 5164 [ 93.34 %] 27 clones [ 0.56 %], purity: 99.66 %, hitEff: 99.15 % +MatchTrackChecker_386d067b INFO +MatchUTHitsChecker_a4d04726 INFO Results +MatchUTHitsChecker_a4d04726 INFO **** UT Efficiency for /Event/fromPrMatchTracksV1Tracks_aaf8b514/OutputTracksLocation **** 65148 ghost, 2.49 UT per track +MatchUTHitsChecker_a4d04726 INFO 01_long :129199 tr 3.90 from 4.08 mcUT [ 95.6 %] 0.13 ghost hits on real tracks [ 3.2 %] +MatchUTHitsChecker_a4d04726 INFO 01_long >3UT :127872 tr 3.93 from 4.10 mcUT [ 95.9 %] 0.12 ghost hits on real tracks [ 3.0 %] +MatchUTHitsChecker_a4d04726 INFO 02_long_P>5GeV : 89833 tr 3.94 from 4.08 mcUT [ 96.7 %] 0.10 ghost hits on real tracks [ 2.4 %] +MatchUTHitsChecker_a4d04726 INFO 02_long_P>5GeV >3UT : 88692 tr 3.99 from 4.11 mcUT [ 97.1 %] 0.09 ghost hits on real tracks [ 2.2 %] +MatchUTHitsChecker_a4d04726 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV : 4859 tr 3.99 from 4.07 mcUT [ 98.0 %] 0.05 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_a4d04726 INFO 03_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4837 tr 4.01 from 4.08 mcUT [ 98.1 %] 0.04 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_a4d04726 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV : 4847 tr 4.00 from 4.08 mcUT [ 98.1 %] 0.05 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_a4d04726 INFO 04_UT_long_fromB_P>3GeV_Pt>0.5GeV >3UT : 4837 tr 4.01 from 4.08 mcUT [ 98.1 %] 0.04 ghost hits on real tracks [ 1.1 %] +MatchUTHitsChecker_a4d04726 INFO +SeedTrackChecker_ad9abe4e INFO Results +SeedTrackChecker_ad9abe4e INFO **** Seed 284763 tracks including 5469 ghosts [ 1.92 %], Event average 1.56 % **** +SeedTrackChecker_ad9abe4e INFO 01_hasT : 198532 from 234618 [ 84.62 %] 7 clones [ 0.00 %], purity: 99.60 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 02_long : 143630 from 152279 [ 94.32 %] 6 clones [ 0.00 %], purity: 99.71 %, hitEff: 98.42 % +SeedTrackChecker_ad9abe4e INFO 03_long_P>5GeV : 95859 from 98421 [ 97.40 %] 5 clones [ 0.01 %], purity: 99.69 %, hitEff: 99.09 % +SeedTrackChecker_ad9abe4e INFO 04_long_fromB : 7598 from 7959 [ 95.46 %] 1 clones [ 0.01 %], purity: 99.75 %, hitEff: 98.65 % +SeedTrackChecker_ad9abe4e INFO 05_long_fromB_P>5GeV : 5835 from 5983 [ 97.53 %] 1 clones [ 0.02 %], purity: 99.76 %, hitEff: 99.13 % +SeedTrackChecker_ad9abe4e INFO 06_UT+T_strange : 16417 from 17658 [ 92.97 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.00 % +SeedTrackChecker_ad9abe4e INFO 07_UT+T_strange_P>5GeV : 8615 from 8825 [ 97.62 %] 0 clones [ 0.00 %], purity: 99.71 %, hitEff: 99.05 % +SeedTrackChecker_ad9abe4e INFO 08_noVelo+UT+T_strange : 8949 from 9658 [ 92.66 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.03 % +SeedTrackChecker_ad9abe4e INFO 09_noVelo+UT+T_strange_P>5GeV : 4914 from 5043 [ 97.44 %] 0 clones [ 0.00 %], purity: 99.70 %, hitEff: 99.01 % +SeedTrackChecker_ad9abe4e INFO 10_UT+T_SfromDB : 1133 from 1220 [ 92.87 %] 0 clones [ 0.00 %], purity: 99.77 %, hitEff: 97.99 % +SeedTrackChecker_ad9abe4e INFO 11_UT+T_SfromDB_P>5GeV : 612 from 623 [ 98.23 %] 0 clones [ 0.00 %], purity: 99.72 %, hitEff: 99.22 % +SeedTrackChecker_ad9abe4e INFO 12_noVelo+UT+T_SfromDB_P>5GeV : 420 from 428 [ 98.13 %] 0 clones [ 0.00 %], purity: 99.69 %, hitEff: 99.12 % +SeedTrackChecker_ad9abe4e INFO 13_hasT_electrons : 40669 from 74476 [ 54.61 %] 2 clones [ 0.00 %], purity: 99.69 %, hitEff: 97.16 % +SeedTrackChecker_ad9abe4e INFO 14_long_electrons : 13360 from 15125 [ 88.33 %] 1 clones [ 0.01 %], purity: 99.81 %, hitEff: 97.85 % +SeedTrackChecker_ad9abe4e INFO 15_long_fromB_electrons : 3922 from 4210 [ 93.16 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.70 % +SeedTrackChecker_ad9abe4e INFO 16_long_electrons_P>5GeV : 8676 from 9420 [ 92.10 %] 0 clones [ 0.00 %], purity: 99.80 %, hitEff: 98.73 % +SeedTrackChecker_ad9abe4e INFO 17_long_fromB_electrons_P>5GeV : 3632 from 3850 [ 94.34 %] 0 clones [ 0.00 %], purity: 99.79 %, hitEff: 98.85 % +SeedTrackChecker_ad9abe4e INFO +HLTControlFlowMgr INFO Memory pool: used 3.94312 +/- 0.039102 MiB (min: 0, max: 5) in 1 +/- 0 blocks (allocated >once in 0 +/- 0% events). Allocated capacity was 10 +/- 0 MiB (min: 10, max: 10) and 347.612 +/- 3.41441 (min: 4, max: 489) requests were served +HLTControlFlowMgr INFO Timing table: +HLTControlFlowMgr INFO + | Name of Algorithm | Execution Count | Total Time / s | Avg. Time / us | + | Sum of all Algorithms | 2955 | 154.247 | 52198.537 | + | "Fetch__Event_DAQ_RawEvent" | 2955 | 89.765 | 30377.465 | + | "SeedTrackChecker_ad9abe4e" | 2289 | 12.997 | 5677.876 | + | "ForwardTrackChecker_482fda95" | 2289 | 11.993 | 5239.299 | + | "MatchTrackChecker_386d067b" | 2289 | 10.823 | 4728.077 | + | "ForwardUTHitsChecker_fe9d9ac2" | 2289 | 4.796 | 2095.109 | + | "MatchUTHitsChecker_a4d04726" | 2289 | 4.748 | 2074.371 | + | "PrForwardTrackingVelo_6024f9ec" | 2289 | 4.350 | 1900.303 | + | "PrHybridSeeding_4d0337cc" | 2289 | 3.270 | 1428.426 | + | "PrLHCbID2MCParticle_a906d17d" | 2289 | 2.564 | 1120.332 | + | "Unpack__Event_MC_Vertices" | 2289 | 1.976 | 863.336 | + | "Unpack__Event_MC_Particles" | 2289 | 1.888 | 824.850 | + | "VeloClusterTrackingSIMD_87c18651" | 2289 | 0.721 | 314.957 | + | "VPFullCluster2MCParticleLinker_17386552" | 2289 | 0.567 | 247.489 | + | "VPClusFull_38754d8c" | 2289 | 0.539 | 235.503 | + | "PrStorePrUTHits_df75b912" | 2289 | 0.516 | 225.497 | + | "PrMatchNN_d80b5038" | 2289 | 0.472 | 206.094 | + | "PrTrackAssociator_8c8024ec" | 2289 | 0.377 | 164.802 | + | "PrTrackAssociator_3adf94fb" | 2289 | 0.371 | 162.280 | + | "PrStoreUTHit_6220b56a" | 2289 | 0.356 | 155.433 | + | "PrTrackAssociator_16ad4612" | 2289 | 0.253 | 110.646 | + | "fromPrVeloTracksV1TracksMerger_fa66a5de" | 2289 | 0.193 | 84.417 | + | "fromPrMatchTracksV1Tracks_aaf8b514" | 2289 | 0.181 | 78.889 | + | "fromPrForwardTracksV1Tracks_f53f50a8" | 2289 | 0.134 | 58.406 | + | "fromPrSeedingTracksV1Tracks_84cd46c2" | 2289 | 0.121 | 53.006 | + | "PrStoreSciFiHits_fb0eba02" | 2289 | 0.111 | 48.299 | + | "FTRawBankDecoder" | 2289 | 0.061 | 26.835 | + | "UnpackRawEvent_FTCluster" | 2955 | 0.027 | 9.014 | + | "reserveIOV" | 2289 | 0.026 | 11.202 | + | "Decode_ODIN" | 2289 | 0.007 | 3.151 | + | "DefaultGECFilter" | 2955 | 0.007 | 2.307 | + | "Fetch__Event_pSim_MCVertices" | 2289 | 0.006 | 2.482 | + | "UnpackRawEvent_UT" | 2955 | 0.005 | 1.614 | + | "Fetch__Event_Link_Raw_UT_Clusters" | 2289 | 0.004 | 1.778 | + | "Fetch__Event_pSim_MCParticles" | 2289 | 0.004 | 1.747 | + | "Fetch__Event_Link_Raw_VP_Digits" | 2289 | 0.004 | 1.588 | + | "Fetch__Event_MC_TrackInfo" | 2289 | 0.004 | 1.573 | + | "UnpackRawEvent_VP" | 2289 | 0.004 | 1.545 | + | "DummyEventTime" | 2289 | 0.003 | 1.410 | + | "UnpackRawEvent_ODIN" | 2289 | 0.003 | 1.399 | + | "Fetch__Event_Link_Raw_FT_LiteClusters" | 2289 | 0.002 | 0.941 | + +HLTControlFlowMgr INFO StateTree: CFNode #executed #passed +LAZY_AND: hlt2_matching_reco_decision #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + PrGECFilter/DefaultGECFilter #=2955 Sum=2289 Eff=|( 77.46193 +- 0.768641)%| + NONLAZY_OR: hlt2_matching_reco_data #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrMatchNN/PrMatchNN_d80b5038 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/ForwardTrackChecker_482fda95 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrUTHitChecker/ForwardUTHitsChecker_fe9d9ac2 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/MatchTrackChecker_386d067b #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrUTHitChecker/MatchUTHitsChecker_a4d04726 #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/SeedTrackChecker_ad9abe4e #=2289 Sum=2289 Eff=|( 100.0000 +- 0.00000 )%| + +HLTControlFlowMgr INFO Histograms converted successfully according to request. +ToolSvc INFO Removing all tools created by ToolSvc +SeedTrackChecker_ad9abe4e.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +MatchUTHitsChecker_a4d04726.PrCh... SUCCESS Booked 44 Histogram(s) : 1D=40 2D=4 +MatchTrackChecker_386d067b.PrChe... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +ForwardUTHitsChecker_fe9d9ac2.Pr... SUCCESS Booked 44 Histogram(s) : 1D=40 2D=4 +ForwardTrackChecker_482fda95.PrC... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +RootCnvSvc INFO Disconnected data IO:9E3B8940-FB87-11EB-ADCA-FA163E643B60 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi] +RootCnvSvc INFO Disconnected data IO:AFCB9710-FB21-11EB-9E91-FA163ED3A4EB [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi] +RootCnvSvc INFO Disconnected data IO:B9D7CC62-FB38-11EB-8B01-3CECEF5D2C24 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi] +ApplicationMgr INFO Application Manager Finalized successfully +ApplicationMgr INFO Application Manager Terminated successfully diff --git a/efficiencies/logs/velo_and_seed_effs_B.log b/efficiencies/logs/velo_and_seed_effs_B.log new file mode 100644 index 0000000..3f4dcb7 --- /dev/null +++ b/efficiencies/logs/velo_and_seed_effs_B.log @@ -0,0 +1,353 @@ +# setting LC_ALL to "C" +# --> Including file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_resolution_and_eff_data.py' +/***** User ApplicationOptions/ApplicationOptions ************************************************** +|-append_decoding_keys_to_output_manifest = True (default: True) +|-auditors = [] (default: []) +|-buffer_events = 20000 (default: 20000) +|-conddb_tag = 'sim-20210617-vc-md100' (default: '') +|-conditions_version = '' (default: '') +|-control_flow_file = '' (default: '') +|-data_flow_file = '' (default: '') +|-data_type = 'Upgrade' (default: 'Upgrade') +|-dddb_tag = 'dddb-20210617' (default: '') +|-event_store = 'HiveWhiteBoard' (default: 'HiveWhiteBoard') +|-evt_max = -1 (default: -1) +|-first_evt = 0 (default: 0) +|-geometry_version = '' (default: '') +|-histo_file = '' (default: '') +|-input_files = ['/auto/data/guenther/Bd_Kstee/00151673_00000098_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000078_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000114_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000115_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000090_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000096_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000127_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000137_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000049_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000019_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000111_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000077_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000027_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000058_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000159_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000072_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000129_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000126_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000017_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000093_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000124_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000089_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000018_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000148_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000176_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000068_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000135_1.xdigi', '/auto/data/guenther/Bd_Kstee/00151673_00000071_1.xdigi'] +| (default: []) +|-input_manifest_file = '' (default: '') +|-input_process = '' (default: '') +|-input_raw_format = 0.5 (default: 0.5) +|-input_type = 'ROOT' (default: '') +|-lines_maker = None +|-memory_pool_size = 10485760 (default: 10485760) +|-monitoring_file = '' (default: '') +|-msg_svc_format = '% F%35W%S %7W%R%T %0W%M' (default: '% F%35W%S %7W%R%T %0W%M') +|-msg_svc_time_format = '%Y-%m-%d %H:%M:%S UTC' (default: '%Y-%m-%d %H:%M:%S UTC') +|-n_event_slots = 1 (default: -1) +|-n_threads = 1 (default: 1) +|-ntuple_file = '/work/cetin/LHCb/reco_tuner/efficiencies/Velo_and_Seed_effs_B_baseline.root' +| (default: '') +|-output_file = '' (default: '') +|-output_level = 3 (default: 3) +|-output_manifest_file = '' (default: '') +|-output_type = '' (default: '') +|-persistreco_version = 1.0 (default: 1.0) +|-phoenix_filename = '' (default: '') +|-preamble_algs = [] (default: []) +|-print_freq = 10000 (default: 10000) +|-python_logging_level = 20 (default: 20) +|-require_specific_decoding_keys = [] (default: []) +|-scheduler_legacy_mode = True (default: True) +|-simulation = True (default: None) +|-use_iosvc = False (default: False) +|-velo_motion_system_yaml = '' (default: '') +|-write_decoding_keys_to_git = True (default: True) +\----- (End of User ApplicationOptions/ApplicationOptions) ----------------------------------------- +# Overrule specified for keys +# <-- End of file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_resolution_and_eff_data.py' +ApplicationMgr SUCCESS +==================================================================================================================================== + Welcome to Moore version 55.2 + running on lhcba2 on Sat Mar 9 09:49:42 2024 +==================================================================================================================================== +ApplicationMgr INFO Application Manager Configured successfully +ToolSvc.GitDDDB INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/DDDB.git' +ToolSvc.GitDDDB INFO using commit 'upgrade/dddb-20210617' corresponding to 1871f1bb5c0d68c81dda62e84cf1eb3a45513521 +ToolSvc.GitSIMCOND INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/SIMCOND.git' +ToolSvc.GitSIMCOND INFO using commit 'upgrade/sim-20210617-vc-md100' corresponding to 9aa116c7143d21760d1be07ce1ef22c0f8f07bdf +DetectorPersistencySvc INFO Added successfully Conversion service:XmlCnvSvc +DetectorDataSvc SUCCESS Detector description database: git:/lhcb.xml +EventClockSvc.FakeEventTime INFO Event times generated from 0 with steps of 0 +EventClockSvc.FakeEventTime INFO Run numbers generated from 0 every 0 events +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c1.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c2.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c3.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c4.down.cdf +MagneticFieldSvc INFO Map scaled by factor 1 with polarity internally used: -1 signed relative current: -1 +NTupleSvc INFO Added stream file:/work/cetin/LHCb/reco_tuner/efficiencies/Velo_and_Seed_effs_B_baseline.root as FILE1 +HLTControlFlowMgr INFO Start initialization +RootHistSvc INFO Writing ROOT histograms to: /work/cetin/LHCb/reco_tuner/efficiencies/Velo_and_Seed_effs_B_baseline.root +HistogramPersistencySvc INFO Added successfully Conversion service:RootHistSvc +DeFTDetector INFO Current FT geometry version = 64 +HLTControlFlowMgr INFO Concurrency level information: +HLTControlFlowMgr INFO o Number of events slots: 1 +HLTControlFlowMgr INFO o TBB thread pool size: 'ThreadPoolSize':1 +HLTControlFlowMgr INFO ---> End of Initialization. This took 110868 ms +ApplicationMgr INFO Application Manager Initialized successfully +ApplicationMgr INFO Application Manager Started successfully +EventPersistencySvc INFO Added successfully Conversion service:RootCnvSvc +EventSelector INFO Stream:EventSelector.DataStreamTool_1 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000098_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO Will measure time between events 10 and 2147483647 (stop might be some events later) +HLTControlFlowMgr INFO Starting loop on events +EventSelector SUCCESS Reading Event record 1. Record number within stream 1: 1 +FTRawBankDecoder INFO Conditions DB is compatible with FT bank version 4, 5, 6. +FTRawBankDecoder INFO Building the readout map with version 0 +HLTControlFlowMgr INFO Timing started at: 09:52:06 +EventSelector INFO Stream:EventSelector.DataStreamTool_2 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000078_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +EventSelector INFO Stream:EventSelector.DataStreamTool_3 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000114_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +EventSelector INFO Stream:EventSelector.DataStreamTool_4 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000115_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000098_1.xdigi [8D5E10AA-5DE5-11EC-900E-48FD8EE739FD] +RootCnvSvc INFO Removed disconnected IO stream:8D5E10AA-5DE5-11EC-900E-48FD8EE739FD [/auto/data/guenther/Bd_Kstee/00151673_00000098_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_5 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000090_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000078_1.xdigi [016CE698-5DE0-11EC-AA65-F02FA78BD289] +RootCnvSvc INFO Removed disconnected IO stream:016CE698-5DE0-11EC-AA65-F02FA78BD289 [/auto/data/guenther/Bd_Kstee/00151673_00000078_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_6 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000096_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000114_1.xdigi [96F78B2E-5DEE-11EC-A774-3CECEF0DB336] +RootCnvSvc INFO Removed disconnected IO stream:96F78B2E-5DEE-11EC-A774-3CECEF0DB336 [/auto/data/guenther/Bd_Kstee/00151673_00000114_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_7 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000127_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000115_1.xdigi [E90D9CE0-5DEF-11EC-9A8E-D85ED3091D9F] +RootCnvSvc INFO Removed disconnected IO stream:E90D9CE0-5DEF-11EC-9A8E-D85ED3091D9F [/auto/data/guenther/Bd_Kstee/00151673_00000115_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_8 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000137_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000090_1.xdigi [2F87F326-5DDE-11EC-BE06-6CC21739CEE0] +RootCnvSvc INFO Removed disconnected IO stream:2F87F326-5DDE-11EC-BE06-6CC21739CEE0 [/auto/data/guenther/Bd_Kstee/00151673_00000090_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_9 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000049_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000096_1.xdigi [FB24F114-5DE3-11EC-A481-C4346BC8E730] +RootCnvSvc INFO Removed disconnected IO stream:FB24F114-5DE3-11EC-A481-C4346BC8E730 [/auto/data/guenther/Bd_Kstee/00151673_00000096_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_10 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000019_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000127_1.xdigi [B810C602-5DFA-11EC-9B02-3CECEF0DB326] +RootCnvSvc INFO Removed disconnected IO stream:B810C602-5DFA-11EC-9B02-3CECEF0DB326 [/auto/data/guenther/Bd_Kstee/00151673_00000127_1.xdigi] +EventSelector SUCCESS Reading Event record 10001. Record number within stream 10: 829 +EventSelector INFO Stream:EventSelector.DataStreamTool_11 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000111_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000137_1.xdigi [2895F650-5E04-11EC-9D63-BC97E1CA35E0] +RootCnvSvc INFO Removed disconnected IO stream:2895F650-5E04-11EC-9D63-BC97E1CA35E0 [/auto/data/guenther/Bd_Kstee/00151673_00000137_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_12 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000077_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000049_1.xdigi [E3AB1D28-5DB2-11EC-986D-F02FA768CDD0] +RootCnvSvc INFO Removed disconnected IO stream:E3AB1D28-5DB2-11EC-986D-F02FA768CDD0 [/auto/data/guenther/Bd_Kstee/00151673_00000049_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_13 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000027_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000019_1.xdigi [03088410-5DA1-11EC-9AFE-A4BF010F110E] +RootCnvSvc INFO Removed disconnected IO stream:03088410-5DA1-11EC-9AFE-A4BF010F110E [/auto/data/guenther/Bd_Kstee/00151673_00000019_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_14 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000058_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000111_1.xdigi [FC11F736-5DEB-11EC-97D4-B42E99AB00C4] +RootCnvSvc INFO Removed disconnected IO stream:FC11F736-5DEB-11EC-97D4-B42E99AB00C4 [/auto/data/guenther/Bd_Kstee/00151673_00000111_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_15 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000159_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000077_1.xdigi [2B0AD454-5DD8-11EC-ADC9-0242AC1C0534] +RootCnvSvc INFO Removed disconnected IO stream:2B0AD454-5DD8-11EC-ADC9-0242AC1C0534 [/auto/data/guenther/Bd_Kstee/00151673_00000077_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_16 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000072_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000027_1.xdigi [76253C8E-5DA8-11EC-B7C8-0242AC1C0539] +RootCnvSvc INFO Removed disconnected IO stream:76253C8E-5DA8-11EC-B7C8-0242AC1C0539 [/auto/data/guenther/Bd_Kstee/00151673_00000027_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_17 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000129_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000058_1.xdigi [3231FD90-5DD6-11EC-B2FD-A4BF010F1246] +RootCnvSvc INFO Removed disconnected IO stream:3231FD90-5DD6-11EC-B2FD-A4BF010F1246 [/auto/data/guenther/Bd_Kstee/00151673_00000058_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_18 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000126_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000159_1.xdigi [5BC2B228-5E1A-11EC-BA37-0242AC1C052C] +RootCnvSvc INFO Removed disconnected IO stream:5BC2B228-5E1A-11EC-BA37-0242AC1C052C [/auto/data/guenther/Bd_Kstee/00151673_00000159_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_19 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000017_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000072_1.xdigi [69B0AE80-5DDE-11EC-BDE6-A4C64F4163F6] +RootCnvSvc INFO Removed disconnected IO stream:69B0AE80-5DDE-11EC-BDE6-A4C64F4163F6 [/auto/data/guenther/Bd_Kstee/00151673_00000072_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_20 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000093_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000129_1.xdigi [9B23AA88-5DFE-11EC-A56D-F02FA78BD09F] +RootCnvSvc INFO Removed disconnected IO stream:9B23AA88-5DFE-11EC-A56D-F02FA78BD09F [/auto/data/guenther/Bd_Kstee/00151673_00000129_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_21 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000124_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000126_1.xdigi [1D28E18E-5DF9-11EC-9195-0242AC1C050D] +RootCnvSvc INFO Removed disconnected IO stream:1D28E18E-5DF9-11EC-9195-0242AC1C050D [/auto/data/guenther/Bd_Kstee/00151673_00000126_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_22 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000089_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000017_1.xdigi [A9F30DC8-5DA0-11EC-A530-80D4A5B16D11] +RootCnvSvc INFO Removed disconnected IO stream:A9F30DC8-5DA0-11EC-A530-80D4A5B16D11 [/auto/data/guenther/Bd_Kstee/00151673_00000017_1.xdigi] +EventSelector SUCCESS Reading Event record 20001. Record number within stream 22: 144 +EventSelector INFO Stream:EventSelector.DataStreamTool_23 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000018_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000093_1.xdigi [A9ADB61C-5DDF-11EC-B270-18C04D0AD672] +RootCnvSvc INFO Removed disconnected IO stream:A9ADB61C-5DDF-11EC-B270-18C04D0AD672 [/auto/data/guenther/Bd_Kstee/00151673_00000093_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_24 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000124_1.xdigi [609AEFF4-5DF7-11EC-A950-A4BF0112D64C] +RootCnvSvc INFO Removed disconnected IO stream:609AEFF4-5DF7-11EC-A950-A4BF0112D64C [/auto/data/guenther/Bd_Kstee/00151673_00000124_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_25 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000148_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000089_1.xdigi [97630F54-5DDD-11EC-B8C0-A4BF0112BC72] +RootCnvSvc INFO Removed disconnected IO stream:97630F54-5DDD-11EC-B8C0-A4BF0112BC72 [/auto/data/guenther/Bd_Kstee/00151673_00000089_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_26 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000176_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000018_1.xdigi [01D7F180-5DA0-11EC-9E4C-B42E99AB00C0] +RootCnvSvc INFO Removed disconnected IO stream:01D7F180-5DA0-11EC-9E4C-B42E99AB00C0 [/auto/data/guenther/Bd_Kstee/00151673_00000018_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_27 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000068_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi [38264BD6-5B59-11EC-B4D7-0800383D3666] +RootCnvSvc INFO Removed disconnected IO stream:38264BD6-5B59-11EC-B4D7-0800383D3666 [/auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_28 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000135_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000148_1.xdigi [2DD034A4-5E0F-11EC-AE5B-0242AC1C0558] +RootCnvSvc INFO Removed disconnected IO stream:2DD034A4-5E0F-11EC-AE5B-0242AC1C0558 [/auto/data/guenther/Bd_Kstee/00151673_00000148_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_29 Def:DATAFILE='/auto/data/guenther/Bd_Kstee/00151673_00000071_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_Kstee/00151673_00000176_1.xdigi [CD8DF0F6-5E2A-11EC-A006-EC0D9A8DE50E] +RootCnvSvc INFO Removed disconnected IO stream:CD8DF0F6-5E2A-11EC-A006-EC0D9A8DE50E [/auto/data/guenther/Bd_Kstee/00151673_00000176_1.xdigi] +HLTControlFlowMgr INFO No more events in event selection +HLTControlFlowMgr INFO ---> Loop over 27892 Events Finished - WSS 1747.91, timed 27882 Events: 1520892 ms, Evts/s = 18.3327 +DefaultGECFilter INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb Events Processed" | 27892 | + | "Nb events removed" | 6553 | +HLTControlFlowMgr INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Processed events" | 27892 | +PrHybridSeeding_4d0337cc INFO Number of counters : 21 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Created T2x1 three-hit combinations in case 0" | 38013635 |2.322837e+07 | 0.61105 | 0.62118 | 0.0000 | 7.0000 | + | "Created T2x1 three-hit combinations in case 1" | 46882559 |3.047896e+07 | 0.65011 | 0.73801 | 0.0000 | 12.000 | + | "Created T2x1 three-hit combinations in case 2" | 72355118 |5.76419e+07 | 0.79665 | 0.99896 | 0.0000 | 25.000 | + | "Created XZ tracks (part 0)" | 64017 | 3419765 | 53.420 | 44.024 | 0.0000 | 868.00 | + | "Created XZ tracks (part 1)" | 64017 | 3430423 | 53.586 | 45.021 | 0.0000 | 968.00 | + | "Created XZ tracks in case 0" | 42678 | 2556814 | 59.909 | 38.071 | 1.0000 | 447.00 | + | "Created XZ tracks in case 1" | 42678 | 2527878 | 59.231 | 43.541 | 0.0000 | 671.00 | + | "Created XZ tracks in case 2" | 42678 | 1765496 | 41.368 | 48.797 | 0.0000 | 968.00 | + | "Created full hit combinations in case 0" | 3902332 | 3902332 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 1" | 2925960 | 2925960 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 2" | 2652087 | 2652087 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created seed tracks" | 42678 | 2669003 | 62.538 | 22.804 | 2.0000 | 150.00 | + | "Created seed tracks (part 0)" | 21339 | 1489106 | 69.783 | 26.004 | 2.0000 | 160.00 | + | "Created seed tracks (part 1)" | 21339 | 1488397 | 69.750 | 26.073 | 2.0000 | 171.00 | + | "Created seed tracks in case 0" | 42678 | 1394075 | 32.665 | 12.822 | 1.0000 | 96.000 | + | "Created seed tracks in case 1" | 42678 | 2536578 | 59.435 | 21.840 | 2.0000 | 139.00 | + | "Created seed tracks in case 2" | 42678 | 2832760 | 66.375 | 24.739 | 2.0000 | 163.00 | + | "Created seed tracks in recovery step" | 21339 | 144743 | 6.7830 | 3.9453 | 0.0000 | 28.000 | + | "Created two-hit combinations in case 0" | 6349985 |1.462553e+08 | 23.032 | 16.066 | 0.0000 | 245.00 | + | "Created two-hit combinations in case 1" | 5486883 |1.655901e+08 | 30.179 | 18.484 | 0.0000 | 228.00 | + | "Created two-hit combinations in case 2" | 4329193 |1.936761e+08 | 44.737 | 28.290 | 0.0000 | 286.00 | +PrLHCbID2MCParticle_a906d17d INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#removed null MCParticles" | 153632681 | 0 | 0.0000 | +PrStoreSciFiHits_fb0eba02 INFO Number of counters : 25 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Average X in T1U" | 6446810 |-2.269135e+08 | -35.198 | 1139.3 | -2656.4 | 2656.3 | + | "Average X in T1V" | 6536201 |-1.736483e+08 | -26.567 | 1127.6 | -2656.4 | 2656.3 | + | "Average X in T1X1" | 6349985 |-3.099046e+08 | -48.804 | 1159.6 | -2646.2 | 2646.2 | + | "Average X in T1X2" | 6622589 |-1.059345e+08 | -15.996 | 1119.8 | -2646.2 | 2646.2 | + | "Average X in T2U" | 6293416 |-1.479677e+08 | -23.512 | 1137.2 | -2656.4 | 2656.3 | + | "Average X in T2V" | 6499947 |-1.344745e+08 | -20.689 | 1130.8 | -2656.4 | 2656.3 | + | "Average X in T2X1" | 6027778 |-1.469288e+08 | -24.375 | 1141.9 | -2646.2 | 2646.2 | + | "Average X in T2X2" | 6696840 |-1.035574e+08 | -15.464 | 1125.7 | -2646.2 | 2646.2 | + | "Average X in T3U" | 6838149 |-8.463564e+07 | -12.377 | 1335.1 | -3188.4 | 3188.4 | + | "Average X in T3V" | 7052301 |-1.103353e+08 | -15.645 | 1330.6 | -3188.4 | 3188.4 | + | "Average X in T3X1" | 6566761 |-6.593846e+07 | -10.041 | 1335.6 | -3176.2 | 3176.2 | + | "Average X in T3X2" | 7321855 |-1.45122e+08 | -19.820 | 1322.4 | -3176.2 | 3176.2 | + | "Hits in T1U" | 85356 | 6446810 | 75.528 | 27.778 | 0.0000 | 294.00 | + | "Hits in T1V" | 85356 | 6536201 | 76.576 | 28.094 | 2.0000 | 464.00 | + | "Hits in T1X1" | 85356 | 6349985 | 74.394 | 27.726 | 1.0000 | 330.00 | + | "Hits in T1X2" | 85356 | 6622589 | 77.588 | 28.179 | 1.0000 | 318.00 | + | "Hits in T2U" | 85356 | 6293416 | 73.731 | 26.747 | 0.0000 | 282.00 | + | "Hits in T2V" | 85356 | 6499947 | 76.151 | 27.620 | 2.0000 | 497.00 | + | "Hits in T2X1" | 85356 | 6027778 | 70.619 | 25.796 | 2.0000 | 314.00 | + | "Hits in T2X2" | 85356 | 6696840 | 78.458 | 28.094 | 2.0000 | 386.00 | + | "Hits in T3U" | 85356 | 6838149 | 80.113 | 28.240 | 2.0000 | 346.00 | + | "Hits in T3V" | 85356 | 7052301 | 82.622 | 28.989 | 1.0000 | 291.00 | + | "Hits in T3X1" | 85356 | 6566761 | 76.934 | 27.129 | 1.0000 | 309.00 | + | "Hits in T3X2" | 85356 | 7321855 | 85.780 | 30.007 | 2.0000 | 316.00 | + | "Total number of hits" | 21339 |7.925263e+07 | 3714.0 | 1137.2 | 250.00 | 6455.0 | +PrStoreUTHit_6220b56a INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 21339 | 4609224 | 216.00 | +PrTrackAssociator_16ad4612 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 2669003 | 2615753 |( 98.00487 +- 0.008559228)% | + | "MC particles per track" | 2615753 | 2615818 | 1.0000 | +PrTrackAssociator_d68377ee INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 5459078 | 5338075 |( 97.78345 +- 0.006301029)% | + | "MC particles per track" | 5338075 | 5363403 | 1.0047 | +SeedTrackChecker_ad9abe4e.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +VeloClusterTrackingSIMD_87c18651 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Clusters" | 21339 |4.811547e+07 | 2254.8 | + | "Nb of Produced Tracks" | 21339 | 5459078 | 255.83 | +VeloTrackChecker_e83d0cf5.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +fromPrSeedingTracksV1Tracks_84cd... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 21339 | 2669003 | 125.08 | +fromPrVeloTracksV1TracksMerger_f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 21339 | 5459078 | 255.83 | +ApplicationMgr INFO Application Manager Stopped successfully +SeedTrackChecker_ad9abe4e INFO Results +SeedTrackChecker_ad9abe4e INFO **** Seed 2669003 tracks including 53250 ghosts [ 2.00 %], Event average 1.61 % **** +SeedTrackChecker_ad9abe4e INFO 01_hasT : 1863005 from 2205354 [ 84.48 %] 80 clones [ 0.00 %], purity: 99.61 %, hitEff: 97.84 % +SeedTrackChecker_ad9abe4e INFO 02_long : 1346563 from 1428344 [ 94.27 %] 38 clones [ 0.00 %], purity: 99.71 %, hitEff: 98.41 % +SeedTrackChecker_ad9abe4e INFO 03_long_P>5GeV : 901403 from 925632 [ 97.38 %] 24 clones [ 0.00 %], purity: 99.70 %, hitEff: 99.08 % +SeedTrackChecker_ad9abe4e INFO 04_long_fromB : 71500 from 74786 [ 95.61 %] 1 clones [ 0.00 %], purity: 99.75 %, hitEff: 98.74 % +SeedTrackChecker_ad9abe4e INFO 05_long_fromB_P>5GeV : 55118 from 56518 [ 97.52 %] 1 clones [ 0.00 %], purity: 99.74 %, hitEff: 99.17 % +SeedTrackChecker_ad9abe4e INFO 06_UT+T_strange : 154523 from 166675 [ 92.71 %] 8 clones [ 0.01 %], purity: 99.74 %, hitEff: 98.01 % +SeedTrackChecker_ad9abe4e INFO 07_UT+T_strange_P>5GeV : 81453 from 83700 [ 97.32 %] 1 clones [ 0.00 %], purity: 99.72 %, hitEff: 99.07 % +SeedTrackChecker_ad9abe4e INFO 08_noVelo+UT+T_strange : 83454 from 90044 [ 92.68 %] 4 clones [ 0.00 %], purity: 99.73 %, hitEff: 98.04 % +SeedTrackChecker_ad9abe4e INFO 09_noVelo+UT+T_strange_P>5GeV : 45983 from 47337 [ 97.14 %] 0 clones [ 0.00 %], purity: 99.72 %, hitEff: 99.03 % +SeedTrackChecker_ad9abe4e INFO 10_UT+T_SfromDB : 10673 from 11399 [ 93.63 %] 0 clones [ 0.00 %], purity: 99.74 %, hitEff: 98.21 % +SeedTrackChecker_ad9abe4e INFO 11_UT+T_SfromDB_P>5GeV : 6030 from 6194 [ 97.35 %] 0 clones [ 0.00 %], purity: 99.73 %, hitEff: 99.14 % +SeedTrackChecker_ad9abe4e INFO 12_noVelo+UT+T_SfromDB_P>5GeV : 3854 from 3961 [ 97.30 %] 0 clones [ 0.00 %], purity: 99.72 %, hitEff: 99.08 % +SeedTrackChecker_ad9abe4e INFO 13_hasT_electrons : 377908 from 697843 [ 54.15 %] 23 clones [ 0.01 %], purity: 99.68 %, hitEff: 97.18 % +SeedTrackChecker_ad9abe4e INFO 14_long_electrons : 122765 from 139900 [ 87.75 %] 5 clones [ 0.00 %], purity: 99.77 %, hitEff: 97.77 % +SeedTrackChecker_ad9abe4e INFO 15_long_fromB_electrons : 33970 from 36843 [ 92.20 %] 0 clones [ 0.00 %], purity: 99.77 %, hitEff: 98.63 % +SeedTrackChecker_ad9abe4e INFO 16_long_electrons_P>5GeV : 78364 from 85616 [ 91.53 %] 3 clones [ 0.00 %], purity: 99.76 %, hitEff: 98.67 % +SeedTrackChecker_ad9abe4e INFO 17_long_fromB_electrons_P>5GeV : 30822 from 32947 [ 93.55 %] 0 clones [ 0.00 %], purity: 99.78 %, hitEff: 98.88 % +SeedTrackChecker_ad9abe4e INFO +VeloTrackChecker_e83d0cf5 INFO Results +VeloTrackChecker_e83d0cf5 INFO **** Velo 5459078 tracks including 121003 ghosts [ 2.22 %], Event average 2.31 % **** +VeloTrackChecker_e83d0cf5 INFO 01_velo : 2430356 from 2480071 [ 98.00 %] 34596 clones [ 1.40 %], purity: 99.69 %, hitEff: 95.80 %, hitEffFirst3: 95.85 %, hitEffLast: 95.41 % +VeloTrackChecker_e83d0cf5 INFO 02_long : 1417133 from 1428344 [ 99.22 %] 13565 clones [ 0.95 %], purity: 99.77 %, hitEff: 96.72 %, hitEffFirst3: 96.84 %, hitEffLast: 96.42 % +VeloTrackChecker_e83d0cf5 INFO 03_long_P>5GeV : 921303 from 925632 [ 99.53 %] 6649 clones [ 0.72 %], purity: 99.78 %, hitEff: 97.14 %, hitEffFirst3: 97.31 %, hitEffLast: 96.88 % +VeloTrackChecker_e83d0cf5 INFO 04_long_strange : 74819 from 77647 [ 96.36 %] 703 clones [ 0.93 %], purity: 99.30 %, hitEff: 96.26 %, hitEffFirst3: 96.51 %, hitEffLast: 95.17 % +VeloTrackChecker_e83d0cf5 INFO 05_long_strange_P>5GeV : 35809 from 36993 [ 96.80 %] 222 clones [ 0.62 %], purity: 99.16 %, hitEff: 96.90 %, hitEffFirst3: 97.10 %, hitEffLast: 96.02 % +VeloTrackChecker_e83d0cf5 INFO 06_long_fromB : 74183 from 74786 [ 99.19 %] 596 clones [ 0.80 %], purity: 99.77 %, hitEff: 96.79 %, hitEffFirst3: 96.84 %, hitEffLast: 96.53 % +VeloTrackChecker_e83d0cf5 INFO 06_long_fromD : 39449 from 39770 [ 99.19 %] 389 clones [ 0.98 %], purity: 99.76 %, hitEff: 96.66 %, hitEffFirst3: 96.76 %, hitEffLast: 96.36 % +VeloTrackChecker_e83d0cf5 INFO 07_long_fromB_P>5GeV : 56233 from 56518 [ 99.50 %] 346 clones [ 0.61 %], purity: 99.78 %, hitEff: 97.08 %, hitEffFirst3: 97.19 %, hitEffLast: 96.83 % +VeloTrackChecker_e83d0cf5 INFO 07_long_fromD_P>5GeV : 27088 from 27248 [ 99.41 %] 199 clones [ 0.73 %], purity: 99.76 %, hitEff: 97.04 %, hitEffFirst3: 97.23 %, hitEffLast: 96.72 % +VeloTrackChecker_e83d0cf5 INFO 08_long_electrons : 134486 from 139900 [ 96.13 %] 2681 clones [ 1.95 %], purity: 98.05 %, hitEff: 94.54 %, hitEffFirst3: 93.12 %, hitEffLast: 94.74 % +VeloTrackChecker_e83d0cf5 INFO 09_long_fromB_electrons : 35830 from 36843 [ 97.25 %] 820 clones [ 2.24 %], purity: 98.74 %, hitEff: 95.49 %, hitEffFirst3: 95.13 %, hitEffLast: 95.42 % +VeloTrackChecker_e83d0cf5 INFO 10_long_fromB_electrons_P>5GeV : 32310 from 32947 [ 98.07 %] 765 clones [ 2.31 %], purity: 98.79 %, hitEff: 95.57 %, hitEffFirst3: 95.35 %, hitEffLast: 95.45 % +VeloTrackChecker_e83d0cf5 INFO 11_long_fromB_P>3GeV_Pt>0.5GeV : 49295 from 49577 [ 99.43 %] 285 clones [ 0.57 %], purity: 99.80 %, hitEff: 97.04 %, hitEffFirst3: 97.13 %, hitEffLast: 96.76 % +VeloTrackChecker_e83d0cf5 INFO 11_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 30514 from 30915 [ 98.70 %] 727 clones [ 2.33 %], purity: 98.88 %, hitEff: 95.64 %, hitEffFirst3: 95.49 %, hitEffLast: 95.48 % +VeloTrackChecker_e83d0cf5 INFO 11_long_fromD_P>3GeV_Pt>0.5GeV : 21975 from 22125 [ 99.32 %] 148 clones [ 0.67 %], purity: 99.76 %, hitEff: 96.95 %, hitEffFirst3: 97.13 %, hitEffLast: 96.61 % +VeloTrackChecker_e83d0cf5 INFO 11_long_strange_P>3GeV_Pt>0.5GeV : 18143 from 19077 [ 95.10 %] 107 clones [ 0.59 %], purity: 98.93 %, hitEff: 96.74 %, hitEffFirst3: 96.61 %, hitEffLast: 96.29 % +VeloTrackChecker_e83d0cf5 INFO 12_UT_long_fromB_P>3GeV_Pt>0.5GeV : 49146 from 49428 [ 99.43 %] 285 clones [ 0.58 %], purity: 99.80 %, hitEff: 97.04 %, hitEffFirst3: 97.13 %, hitEffLast: 96.76 % +VeloTrackChecker_e83d0cf5 INFO +HLTControlFlowMgr INFO Memory pool: used 3.50909 +/- 0.011634 MiB (min: 0, max: 4) in 1 +/- 0 blocks (allocated >once in 0 +/- 0% events). Allocated capacity was 10 +/- 0 MiB (min: 10, max: 10) and 258.328 +/- 0.844755 (min: 4, max: 381) requests were served +HLTControlFlowMgr INFO Timing table: +HLTControlFlowMgr INFO + | Name of Algorithm | Execution Count | Total Time / s | Avg. Time / us | + | Sum of all Algorithms | 27892 | 1483.620 | 53191.587 | + | "Fetch__Event_DAQ_RawEvent" | 27892 | 1109.351 | 39773.088 | + | "SeedTrackChecker_ad9abe4e" | 21339 | 133.190 | 6241.619 | + | "VeloTrackChecker_e83d0cf5" | 21339 | 119.917 | 5619.617 | + | "PrHybridSeeding_4d0337cc" | 21339 | 30.735 | 1440.341 | + | "PrLHCbID2MCParticle_a906d17d" | 21339 | 23.132 | 1084.019 | + | "Unpack__Event_MC_Vertices" | 21339 | 19.275 | 903.297 | + | "Unpack__Event_MC_Particles" | 21339 | 17.922 | 839.893 | + | "VeloClusterTrackingSIMD_87c18651" | 21339 | 6.441 | 301.859 | + | "VPFullCluster2MCParticleLinker_17386552" | 21339 | 5.090 | 238.527 | + | "VPClusFull_38754d8c" | 21339 | 4.793 | 224.632 | + | "PrStoreUTHit_6220b56a" | 21339 | 4.236 | 198.492 | + | "PrTrackAssociator_d68377ee" | 21339 | 2.364 | 110.775 | + | "PrTrackAssociator_16ad4612" | 21339 | 2.261 | 105.974 | + | "fromPrVeloTracksV1TracksMerger_fa66a5de" | 21339 | 1.582 | 74.143 | + | "fromPrSeedingTracksV1Tracks_84cd46c2" | 21339 | 1.045 | 48.973 | + | "PrStoreSciFiHits_fb0eba02" | 21339 | 0.985 | 46.163 | + | "FTRawBankDecoder" | 21339 | 0.550 | 25.773 | + | "UnpackRawEvent_FTCluster" | 27892 | 0.262 | 9.394 | + | "Decode_ODIN" | 21339 | 0.053 | 2.480 | + | "DefaultGECFilter" | 27892 | 0.049 | 1.772 | + | "reserveIOV" | 21339 | 0.046 | 2.177 | + | "Fetch__Event_Link_Raw_VP_Digits" | 21339 | 0.043 | 1.997 | + | "Fetch__Event_pSim_MCVertices" | 21339 | 0.040 | 1.853 | + | "UnpackRawEvent_VP" | 21339 | 0.039 | 1.830 | + | "Fetch__Event_pSim_MCParticles" | 21339 | 0.036 | 1.702 | + | "UnpackRawEvent_UT" | 27892 | 0.036 | 1.299 | + | "Fetch__Event_MC_TrackInfo" | 21339 | 0.035 | 1.662 | + | "UnpackRawEvent_ODIN" | 21339 | 0.034 | 1.585 | + | "Fetch__Event_Link_Raw_UT_Clusters" | 21339 | 0.032 | 1.503 | + | "DummyEventTime" | 21339 | 0.023 | 1.075 | + | "Fetch__Event_Link_Raw_FT_LiteClusters" | 21339 | 0.020 | 0.923 | + +HLTControlFlowMgr INFO StateTree: CFNode #executed #passed +LAZY_AND: run_tracking_debug_decision #=27892 Sum=21339 Eff=|( 76.50581 +- 0.253856)%| + PrGECFilter/DefaultGECFilter #=27892 Sum=21339 Eff=|( 76.50581 +- 0.253856)%| + NONLAZY_OR: run_tracking_debug_data #=21339 Sum=21339 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/SeedTrackChecker_ad9abe4e #=21339 Sum=21339 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/VeloTrackChecker_e83d0cf5 #=21339 Sum=21339 Eff=|( 100.0000 +- 0.00000 )%| + +HLTControlFlowMgr INFO Histograms converted successfully according to request. +ToolSvc INFO Removing all tools created by ToolSvc +VeloTrackChecker_e83d0cf5.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +SeedTrackChecker_ad9abe4e.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +RootCnvSvc INFO Disconnected data IO:0ACB343E-5E02-11EC-9FD8-0C42A132CBA6 [/auto/data/guenther/Bd_Kstee/00151673_00000135_1.xdigi] +RootCnvSvc INFO Disconnected data IO:50B932A6-5DDB-11EC-A1F2-A4BF010F119E [/auto/data/guenther/Bd_Kstee/00151673_00000068_1.xdigi] +RootCnvSvc INFO Disconnected data IO:5CF12ABE-5DD7-11EC-B196-0CC47A50D2CA [/auto/data/guenther/Bd_Kstee/00151673_00000071_1.xdigi] +ApplicationMgr INFO Application Manager Finalized successfully +ApplicationMgr INFO Application Manager Terminated successfully diff --git a/efficiencies/logs/velo_and_seed_effs_BJpsi.log b/efficiencies/logs/velo_and_seed_effs_BJpsi.log new file mode 100644 index 0000000..d99687c --- /dev/null +++ b/efficiencies/logs/velo_and_seed_effs_BJpsi.log @@ -0,0 +1,375 @@ +# setting LC_ALL to "C" +# --> Including file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_resolution_and_eff_data.py' +/***** User ApplicationOptions/ApplicationOptions ************************************************** +|-append_decoding_keys_to_output_manifest = True (default: True) +|-auditors = [] (default: []) +|-buffer_events = 20000 (default: 20000) +|-conddb_tag = 'sim-20210617-vc-md100' (default: '') +|-conditions_version = '' (default: '') +|-control_flow_file = '' (default: '') +|-data_flow_file = '' (default: '') +|-data_type = 'Upgrade' (default: 'Upgrade') +|-dddb_tag = 'dddb-20210617' (default: '') +|-event_store = 'HiveWhiteBoard' (default: 'HiveWhiteBoard') +|-evt_max = -1 (default: -1) +|-first_evt = 0 (default: 0) +|-geometry_version = '' (default: '') +|-histo_file = '' (default: '') +|-input_files = ['/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000036_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000074_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000084_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000096_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000085_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000039_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000047_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000100_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000029_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000058_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000078_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000070_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000082_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000099_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000043_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000031_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000076_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000094_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000037_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000092_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000064_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000054_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000083_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000056_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000052_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000079_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000066_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000033_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000025_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000053_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000045_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000048_1.xdigi', '/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000040_1.xdigi'] +| (default: []) +|-input_manifest_file = '' (default: '') +|-input_process = '' (default: '') +|-input_raw_format = 0.5 (default: 0.5) +|-input_type = 'ROOT' (default: '') +|-lines_maker = None +|-memory_pool_size = 10485760 (default: 10485760) +|-monitoring_file = '' (default: '') +|-msg_svc_format = '% F%35W%S %7W%R%T %0W%M' (default: '% F%35W%S %7W%R%T %0W%M') +|-msg_svc_time_format = '%Y-%m-%d %H:%M:%S UTC' (default: '%Y-%m-%d %H:%M:%S UTC') +|-n_event_slots = 1 (default: -1) +|-n_threads = 1 (default: 1) +|-ntuple_file = '/work/cetin/LHCb/reco_tuner/efficiencies/Velo_and_Seed_effs_BJpsi_baseline.root' +| (default: '') +|-output_file = '' (default: '') +|-output_level = 3 (default: 3) +|-output_manifest_file = '' (default: '') +|-output_type = '' (default: '') +|-persistreco_version = 1.0 (default: 1.0) +|-phoenix_filename = '' (default: '') +|-preamble_algs = [] (default: []) +|-print_freq = 10000 (default: 10000) +|-python_logging_level = 20 (default: 20) +|-require_specific_decoding_keys = [] (default: []) +|-scheduler_legacy_mode = True (default: True) +|-simulation = True (default: None) +|-use_iosvc = False (default: False) +|-velo_motion_system_yaml = '' (default: '') +|-write_decoding_keys_to_git = True (default: True) +\----- (End of User ApplicationOptions/ApplicationOptions) ----------------------------------------- +# Overrule specified for keys +# <-- End of file '/auto/work/cetin/LHCb/reco_tuner/moore_options/get_resolution_and_eff_data.py' +ApplicationMgr SUCCESS +==================================================================================================================================== + Welcome to Moore version 55.2 + running on lhcba2 on Sat Mar 9 10:44:25 2024 +==================================================================================================================================== +ApplicationMgr INFO Application Manager Configured successfully +ToolSvc.GitDDDB INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/DDDB.git' +ToolSvc.GitDDDB INFO using commit 'upgrade/dddb-20210617' corresponding to 1871f1bb5c0d68c81dda62e84cf1eb3a45513521 +ToolSvc.GitSIMCOND INFO opening Git repository '/cvmfs/lhcb.cern.ch/lib/lhcb/git-conddb/SIMCOND.git' +ToolSvc.GitSIMCOND INFO using commit 'upgrade/sim-20210617-vc-md100' corresponding to 9aa116c7143d21760d1be07ce1ef22c0f8f07bdf +DetectorPersistencySvc INFO Added successfully Conversion service:XmlCnvSvc +DetectorDataSvc SUCCESS Detector description database: git:/lhcb.xml +EventClockSvc.FakeEventTime INFO Event times generated from 0 with steps of 0 +EventClockSvc.FakeEventTime INFO Run numbers generated from 0 every 0 events +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c1.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c2.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c3.down.cdf +MagneticFieldGridReader INFO Opened magnetic field file: /cvmfs/lhcb.cern.ch/lib/lhcb/DBASE/FieldMap/v5r7/cdf//field.v5r0.c4.down.cdf +MagneticFieldSvc INFO Map scaled by factor 1 with polarity internally used: -1 signed relative current: -1 +NTupleSvc INFO Added stream file:/work/cetin/LHCb/reco_tuner/efficiencies/Velo_and_Seed_effs_BJpsi_baseline.root as FILE1 +HLTControlFlowMgr INFO Start initialization +RootHistSvc INFO Writing ROOT histograms to: /work/cetin/LHCb/reco_tuner/efficiencies/Velo_and_Seed_effs_BJpsi_baseline.root +HistogramPersistencySvc INFO Added successfully Conversion service:RootHistSvc +DeFTDetector INFO Current FT geometry version = 64 +HLTControlFlowMgr INFO Concurrency level information: +HLTControlFlowMgr INFO o Number of events slots: 1 +HLTControlFlowMgr INFO o TBB thread pool size: 'ThreadPoolSize':1 +HLTControlFlowMgr INFO ---> End of Initialization. This took 20547 ms +ApplicationMgr INFO Application Manager Initialized successfully +ApplicationMgr INFO Application Manager Started successfully +EventPersistencySvc INFO Added successfully Conversion service:RootCnvSvc +EventSelector INFO Stream:EventSelector.DataStreamTool_1 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000036_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +HLTControlFlowMgr INFO Will measure time between events 10 and 2147483647 (stop might be some events later) +HLTControlFlowMgr INFO Starting loop on events +EventSelector SUCCESS Reading Event record 1. Record number within stream 1: 1 +FTRawBankDecoder INFO Conditions DB is compatible with FT bank version 4, 5, 6. +FTRawBankDecoder INFO Building the readout map with version 0 +HLTControlFlowMgr INFO Timing started at: 10:45:05 +EventSelector INFO Stream:EventSelector.DataStreamTool_2 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000074_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +EventSelector INFO Stream:EventSelector.DataStreamTool_3 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000084_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +EventSelector INFO Stream:EventSelector.DataStreamTool_4 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000096_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000036_1.xdigi [0B898020-FB50-11EB-8654-FA163E6857C2] +RootCnvSvc INFO Removed disconnected IO stream:0B898020-FB50-11EB-8654-FA163E6857C2 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000036_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_5 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000085_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000074_1.xdigi [5DCC4124-FC68-11EB-BDA2-FA163E58303C] +RootCnvSvc INFO Removed disconnected IO stream:5DCC4124-FC68-11EB-BDA2-FA163E58303C [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000074_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_6 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000039_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000084_1.xdigi [8EB58942-FC7E-11EB-A61E-FA163EE79BF6] +RootCnvSvc INFO Removed disconnected IO stream:8EB58942-FC7E-11EB-A61E-FA163EE79BF6 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000084_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_7 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000047_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000096_1.xdigi [BECF3234-FE56-11EB-968E-FA163E94D94F] +RootCnvSvc INFO Removed disconnected IO stream:BECF3234-FE56-11EB-968E-FA163E94D94F [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000096_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_8 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000100_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000085_1.xdigi [E516F964-FC84-11EB-B1AC-FA163E0712FF] +RootCnvSvc INFO Removed disconnected IO stream:E516F964-FC84-11EB-B1AC-FA163E0712FF [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000085_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_9 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000029_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000039_1.xdigi [C7B4B038-FB52-11EB-A14B-FA163EF0D557] +RootCnvSvc INFO Removed disconnected IO stream:C7B4B038-FB52-11EB-A14B-FA163EF0D557 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000039_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_10 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000058_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000047_1.xdigi [6D30047A-FB5A-11EB-BF88-FA163E3787B1] +RootCnvSvc INFO Removed disconnected IO stream:6D30047A-FB5A-11EB-BF88-FA163E3787B1 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000047_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_11 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000078_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000100_1.xdigi [123C7EA8-FEE4-11EB-947C-FA163E5E0D5F] +RootCnvSvc INFO Removed disconnected IO stream:123C7EA8-FEE4-11EB-947C-FA163E5E0D5F [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000100_1.xdigi] +EventSelector SUCCESS Reading Event record 10001. Record number within stream 11: 648 +EventSelector INFO Stream:EventSelector.DataStreamTool_12 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000070_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000029_1.xdigi [1559743C-FB48-11EB-ABD6-FA163ECF2D71] +RootCnvSvc INFO Removed disconnected IO stream:1559743C-FB48-11EB-ABD6-FA163ECF2D71 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000029_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_13 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000082_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000058_1.xdigi [3C8722E6-FB7C-11EB-B214-FA163E7AC841] +RootCnvSvc INFO Removed disconnected IO stream:3C8722E6-FB7C-11EB-B214-FA163E7AC841 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000058_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_14 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000078_1.xdigi [971A74C4-FC71-11EB-9B7A-FA163EA1849A] +RootCnvSvc INFO Removed disconnected IO stream:971A74C4-FC71-11EB-9B7A-FA163EA1849A [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000078_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_15 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000099_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000070_1.xdigi [5FE45F74-FC53-11EB-AD8A-FA163E974EB1] +RootCnvSvc INFO Removed disconnected IO stream:5FE45F74-FC53-11EB-AD8A-FA163E974EB1 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000070_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_16 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000043_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000082_1.xdigi [A43AC110-FC79-11EB-BF3F-FA163E72700E] +RootCnvSvc INFO Removed disconnected IO stream:A43AC110-FC79-11EB-BF3F-FA163E72700E [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000082_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_17 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi [B9D7CC62-FB38-11EB-8B01-3CECEF5D2C24] +RootCnvSvc INFO Removed disconnected IO stream:B9D7CC62-FB38-11EB-8B01-3CECEF5D2C24 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_18 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000031_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000099_1.xdigi [91F55774-FE8E-11EB-9355-FA163E426AD6] +RootCnvSvc INFO Removed disconnected IO stream:91F55774-FE8E-11EB-9355-FA163E426AD6 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000099_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_19 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000076_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000043_1.xdigi [6EC8F9B2-FB56-11EB-8DB9-FA163E6BFC32] +RootCnvSvc INFO Removed disconnected IO stream:6EC8F9B2-FB56-11EB-8DB9-FA163E6BFC32 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000043_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_20 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000094_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi [AFCB9710-FB21-11EB-9E91-FA163ED3A4EB] +RootCnvSvc INFO Removed disconnected IO stream:AFCB9710-FB21-11EB-9E91-FA163ED3A4EB [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_21 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000037_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000031_1.xdigi [00D845C2-FB4A-11EB-85C8-3CFDFE9E1FB8] +RootCnvSvc INFO Removed disconnected IO stream:00D845C2-FB4A-11EB-85C8-3CFDFE9E1FB8 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000031_1.xdigi] +EventSelector SUCCESS Reading Event record 20001. Record number within stream 21: 613 +EventSelector INFO Stream:EventSelector.DataStreamTool_22 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000092_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000076_1.xdigi [1BE698B6-FC6F-11EB-A5EC-FA163E212E5B] +RootCnvSvc INFO Removed disconnected IO stream:1BE698B6-FC6F-11EB-A5EC-FA163E212E5B [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000076_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_23 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000064_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000094_1.xdigi [DE6396AC-FD6C-11EB-85E6-FA163EDC144C] +RootCnvSvc INFO Removed disconnected IO stream:DE6396AC-FD6C-11EB-85E6-FA163EDC144C [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000094_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_24 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000054_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000037_1.xdigi [CC17E46C-FB50-11EB-8CCD-3CECEF0DE5A0] +RootCnvSvc INFO Removed disconnected IO stream:CC17E46C-FB50-11EB-8CCD-3CECEF0DE5A0 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000037_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_25 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000083_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000092_1.xdigi [02C64118-FD5C-11EB-8618-FA163E8AF260] +RootCnvSvc INFO Removed disconnected IO stream:02C64118-FD5C-11EB-8618-FA163E8AF260 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000092_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_26 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000056_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000064_1.xdigi [22CD60BE-FBC6-11EB-BEED-FA163E1EE769] +RootCnvSvc INFO Removed disconnected IO stream:22CD60BE-FBC6-11EB-BEED-FA163E1EE769 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000064_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_27 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000054_1.xdigi [8FE2489A-FB67-11EB-9FC8-FA163E35CDB2] +RootCnvSvc INFO Removed disconnected IO stream:8FE2489A-FB67-11EB-9FC8-FA163E35CDB2 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000054_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_28 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000052_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000083_1.xdigi [E09CA29E-FC7A-11EB-9806-FA163E6E9F48] +RootCnvSvc INFO Removed disconnected IO stream:E09CA29E-FC7A-11EB-9806-FA163E6E9F48 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000083_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_29 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000079_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000056_1.xdigi [C0EA9202-FB6D-11EB-9EC2-3CECEF5D2AEE] +RootCnvSvc INFO Removed disconnected IO stream:C0EA9202-FB6D-11EB-9EC2-3CECEF5D2AEE [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000056_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_30 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000066_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi [9E3B8940-FB87-11EB-ADCA-FA163E643B60] +RootCnvSvc INFO Removed disconnected IO stream:9E3B8940-FB87-11EB-ADCA-FA163E643B60 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_31 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000033_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000052_1.xdigi [78850EB8-FB61-11EB-91C7-FA163E8B3E79] +RootCnvSvc INFO Removed disconnected IO stream:78850EB8-FB61-11EB-91C7-FA163E8B3E79 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000052_1.xdigi] +EventSelector SUCCESS Reading Event record 30001. Record number within stream 31: 516 +EventSelector INFO Stream:EventSelector.DataStreamTool_32 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000025_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000079_1.xdigi [D90EB734-FC74-11EB-B12A-FA163EF491BE] +RootCnvSvc INFO Removed disconnected IO stream:D90EB734-FC74-11EB-B12A-FA163EF491BE [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000079_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_33 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000053_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000066_1.xdigi [E20E8376-FC30-11EB-AC14-000017009605] +RootCnvSvc INFO Removed disconnected IO stream:E20E8376-FC30-11EB-AC14-000017009605 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000066_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_34 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000045_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000033_1.xdigi [CF32C3CC-FB4D-11EB-B55F-FA163E3286CE] +RootCnvSvc INFO Removed disconnected IO stream:CF32C3CC-FB4D-11EB-B55F-FA163E3286CE [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000033_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_35 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000048_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000025_1.xdigi [C97B8D2E-FB3E-11EB-9555-FA163E09F528] +RootCnvSvc INFO Removed disconnected IO stream:C97B8D2E-FB3E-11EB-9555-FA163E09F528 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000025_1.xdigi] +EventSelector INFO Stream:EventSelector.DataStreamTool_36 Def:DATAFILE='/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000040_1.xdigi' SVC='Gaudi::RootEvtSelector' OPT='READ' IgnoreChecksum='YES' +IODataManager INFO Disconnect from dataset /auto/data/guenther/Bd_JpsiKst_ee/00143565_00000053_1.xdigi [97FD3520-FB63-11EB-9A46-FA163E714668] +RootCnvSvc INFO Removed disconnected IO stream:97FD3520-FB63-11EB-9A46-FA163E714668 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000053_1.xdigi] +HLTControlFlowMgr INFO No more events in event selection +HLTControlFlowMgr INFO ---> Loop over 35323 Events Finished - WSS 1778.02, timed 35313 Events: 2011655 ms, Evts/s = 17.5542 +DefaultGECFilter INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb Events Processed" | 35323 | + | "Nb events removed" | 8300 | +HLTControlFlowMgr INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Processed events" | 35323 | +PrHybridSeeding_4d0337cc INFO Number of counters : 21 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Created T2x1 three-hit combinations in case 0" | 48341313 |2.955738e+07 | 0.61143 | 0.62121 | 0.0000 | 6.0000 | + | "Created T2x1 three-hit combinations in case 1" | 59736068 |3.890531e+07 | 0.65129 | 0.73914 | 0.0000 | 12.000 | + | "Created T2x1 three-hit combinations in case 2" | 92062305 |7.348832e+07 | 0.79825 | 1.0005 | 0.0000 | 25.000 | + | "Created XZ tracks (part 0)" | 81069 | 4362313 | 53.810 | 45.987 | 0.0000 | 1698.0 | + | "Created XZ tracks (part 1)" | 81069 | 4372824 | 53.940 | 46.383 | 0.0000 | 1257.0 | + | "Created XZ tracks in case 0" | 54046 | 3250382 | 60.141 | 38.259 | 0.0000 | 503.00 | + | "Created XZ tracks in case 1" | 54046 | 3226826 | 59.705 | 45.131 | 0.0000 | 1144.0 | + | "Created XZ tracks in case 2" | 54046 | 2257929 | 41.778 | 51.760 | 0.0000 | 1698.0 | + | "Created full hit combinations in case 0" | 4960359 | 4960359 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 1" | 3736423 | 3736423 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created full hit combinations in case 2" | 3395516 | 3395516 | 1.0000 | 0.0000 | 1.0000 | 1.0000 | + | "Created seed tracks" | 54046 | 3390744 | 62.738 | 22.781 | 2.0000 | 186.00 | + | "Created seed tracks (part 0)" | 27023 | 1892022 | 70.015 | 25.958 | 3.0000 | 207.00 | + | "Created seed tracks (part 1)" | 27023 | 1889881 | 69.936 | 26.105 | 2.0000 | 215.00 | + | "Created seed tracks in case 0" | 54046 | 1770384 | 32.757 | 12.817 | 0.0000 | 96.000 | + | "Created seed tracks in case 1" | 54046 | 3221597 | 59.608 | 21.826 | 2.0000 | 166.00 | + | "Created seed tracks in case 2" | 54046 | 3598130 | 66.575 | 24.744 | 2.0000 | 205.00 | + | "Created seed tracks in recovery step" | 27023 | 183773 | 6.8006 | 3.9574 | 0.0000 | 30.000 | + | "Created two-hit combinations in case 0" | 8064491 |1.859307e+08 | 23.055 | 16.090 | 0.0000 | 278.00 | + | "Created two-hit combinations in case 1" | 6971955 |2.107604e+08 | 30.230 | 18.520 | 0.0000 | 262.00 | + | "Created two-hit combinations in case 2" | 5497566 |2.463124e+08 | 44.804 | 28.350 | 0.0000 | 333.00 | +PrLHCbID2MCParticle_a906d17d INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#removed null MCParticles" | 198107424 | 0 | 0.0000 | +PrStoreSciFiHits_fb0eba02 INFO Number of counters : 25 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Average X in T1U" | 8196488 |-2.970498e+08 | -36.241 | 1138.7 | -2656.4 | 2656.3 | + | "Average X in T1V" | 8302998 |-2.232378e+08 | -26.886 | 1127.1 | -2656.4 | 2656.3 | + | "Average X in T1X1" | 8064491 |-3.988098e+08 | -49.453 | 1159.2 | -2646.2 | 2646.2 | + | "Average X in T1X2" | 8414851 |-1.355164e+08 | -16.104 | 1119.5 | -2646.2 | 2646.2 | + | "Average X in T2U" | 7999640 |-1.870835e+08 | -23.386 | 1136.2 | -2656.4 | 2656.3 | + | "Average X in T2V" | 8247240 |-1.660776e+08 | -20.137 | 1130.6 | -2656.4 | 2656.3 | + | "Average X in T2X1" | 7652852 |-1.971999e+08 | -25.768 | 1140.3 | -2646.2 | 2646.2 | + | "Average X in T2X2" | 8508327 |-1.284413e+08 | -15.096 | 1126.2 | -2646.2 | 2646.2 | + | "Average X in T3U" | 8684086 |-1.041572e+08 | -11.994 | 1335.9 | -3188.4 | 3188.4 | + | "Average X in T3V" | 8961033 |-1.375357e+08 | -15.348 | 1330.5 | -3188.4 | 3188.4 | + | "Average X in T3X1" | 8348239 |-8.469251e+07 | -10.145 | 1336.3 | -3176.2 | 3176.2 | + | "Average X in T3X2" | 9294885 |-1.774036e+08 | -19.086 | 1321.1 | -3176.2 | 3176.2 | + | "Hits in T1U" | 108092 | 8196488 | 75.829 | 27.842 | 4.0000 | 327.00 | + | "Hits in T1V" | 108092 | 8302998 | 76.814 | 27.983 | 3.0000 | 375.00 | + | "Hits in T1X1" | 108092 | 8064491 | 74.608 | 27.731 | 4.0000 | 375.00 | + | "Hits in T1X2" | 108092 | 8414851 | 77.849 | 28.195 | 4.0000 | 428.00 | + | "Hits in T2U" | 108092 | 7999640 | 74.008 | 26.743 | 3.0000 | 341.00 | + | "Hits in T2V" | 108092 | 8247240 | 76.298 | 27.429 | 4.0000 | 381.00 | + | "Hits in T2X1" | 108092 | 7652852 | 70.799 | 25.759 | 2.0000 | 374.00 | + | "Hits in T2X2" | 108092 | 8508327 | 78.714 | 27.978 | 3.0000 | 356.00 | + | "Hits in T3U" | 108092 | 8684086 | 80.340 | 28.058 | 2.0000 | 331.00 | + | "Hits in T3V" | 108092 | 8961033 | 82.902 | 28.941 | 4.0000 | 399.00 | + | "Hits in T3X1" | 108092 | 8348239 | 77.233 | 27.004 | 3.0000 | 339.00 | + | "Hits in T3X2" | 108092 | 9294885 | 85.990 | 29.878 | 2.0000 | 355.00 | + | "Total number of hits" | 27023 |1.006751e+08 | 3725.5 | 1130.7 | 418.00 | 6405.0 | +PrStoreUTHit_6220b56a INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "#banks" | 27023 | 5836968 | 216.00 | +PrTrackAssociator_16ad4612 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 3390744 | 3322103 |( 97.97564 +- 0.007648140)% | + | "MC particles per track" | 3322103 | 3322179 | 1.0000 | +PrTrackAssociator_d68377ee INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + |*"Efficiency" | 7059265 | 6885105 |( 97.53289 +- 0.005838352)% | + | "MC particles per track" | 6885105 | 6916103 | 1.0045 | +SeedTrackChecker_ad9abe4e.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +VeloClusterTrackingSIMD_87c18651 INFO Number of counters : 2 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of Produced Clusters" | 27023 |6.416351e+07 | 2374.4 | + | "Nb of Produced Tracks" | 27023 | 7059265 | 261.23 | +VeloTrackChecker_e83d0cf5.LoKi::... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "# loaded from PYTHON" | 17 | +fromPrSeedingTracksV1Tracks_84cd... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 27023 | 3390744 | 125.48 | +fromPrVeloTracksV1TracksMerger_f... INFO Number of counters : 1 + | Counter | # | sum | mean/eff^* | rms/err^* | min | max | + | "Nb of converted Tracks" | 27023 | 7059265 | 261.23 | +ApplicationMgr INFO Application Manager Stopped successfully +SeedTrackChecker_ad9abe4e INFO Results +SeedTrackChecker_ad9abe4e INFO **** Seed 3390744 tracks including 68641 ghosts [ 2.02 %], Event average 1.63 % **** +SeedTrackChecker_ad9abe4e INFO 01_hasT : 2362888 from 2795799 [ 84.52 %] 92 clones [ 0.00 %], purity: 99.60 %, hitEff: 97.84 % +SeedTrackChecker_ad9abe4e INFO 02_long : 1707963 from 1811265 [ 94.30 %] 46 clones [ 0.00 %], purity: 99.71 %, hitEff: 98.41 % +SeedTrackChecker_ad9abe4e INFO 03_long_P>5GeV : 1141970 from 1172326 [ 97.41 %] 33 clones [ 0.00 %], purity: 99.70 %, hitEff: 99.08 % +SeedTrackChecker_ad9abe4e INFO 04_long_fromB : 90231 from 94402 [ 95.58 %] 2 clones [ 0.00 %], purity: 99.76 %, hitEff: 98.72 % +SeedTrackChecker_ad9abe4e INFO 05_long_fromB_P>5GeV : 69302 from 71030 [ 97.57 %] 2 clones [ 0.00 %], purity: 99.75 %, hitEff: 99.17 % +SeedTrackChecker_ad9abe4e INFO 06_UT+T_strange : 195676 from 211050 [ 92.72 %] 3 clones [ 0.00 %], purity: 99.73 %, hitEff: 98.00 % +SeedTrackChecker_ad9abe4e INFO 07_UT+T_strange_P>5GeV : 102766 from 105626 [ 97.29 %] 0 clones [ 0.00 %], purity: 99.71 %, hitEff: 99.07 % +SeedTrackChecker_ad9abe4e INFO 08_noVelo+UT+T_strange : 105019 from 113340 [ 92.66 %] 2 clones [ 0.00 %], purity: 99.72 %, hitEff: 98.02 % +SeedTrackChecker_ad9abe4e INFO 09_noVelo+UT+T_strange_P>5GeV : 57865 from 59507 [ 97.24 %] 0 clones [ 0.00 %], purity: 99.70 %, hitEff: 99.04 % +SeedTrackChecker_ad9abe4e INFO 10_UT+T_SfromDB : 13279 from 14317 [ 92.75 %] 0 clones [ 0.00 %], purity: 99.76 %, hitEff: 98.13 % +SeedTrackChecker_ad9abe4e INFO 11_UT+T_SfromDB_P>5GeV : 7443 from 7643 [ 97.38 %] 0 clones [ 0.00 %], purity: 99.76 %, hitEff: 99.15 % +SeedTrackChecker_ad9abe4e INFO 12_noVelo+UT+T_SfromDB_P>5GeV : 4731 from 4865 [ 97.25 %] 0 clones [ 0.00 %], purity: 99.75 %, hitEff: 99.12 % +SeedTrackChecker_ad9abe4e INFO 13_hasT_electrons : 483995 from 890297 [ 54.36 %] 22 clones [ 0.00 %], purity: 99.67 %, hitEff: 97.17 % +SeedTrackChecker_ad9abe4e INFO 14_long_electrons : 159229 from 181213 [ 87.87 %] 8 clones [ 0.01 %], purity: 99.78 %, hitEff: 97.81 % +SeedTrackChecker_ad9abe4e INFO 15_long_fromB_electrons : 45387 from 48919 [ 92.78 %] 3 clones [ 0.01 %], purity: 99.77 %, hitEff: 98.69 % +SeedTrackChecker_ad9abe4e INFO 16_long_electrons_P>5GeV : 102808 from 112140 [ 91.68 %] 6 clones [ 0.01 %], purity: 99.77 %, hitEff: 98.68 % +SeedTrackChecker_ad9abe4e INFO 17_long_fromB_electrons_P>5GeV : 41974 from 44696 [ 93.91 %] 3 clones [ 0.01 %], purity: 99.77 %, hitEff: 98.88 % +SeedTrackChecker_ad9abe4e INFO +VeloTrackChecker_e83d0cf5 INFO Results +VeloTrackChecker_e83d0cf5 INFO **** Velo 7059265 tracks including 174160 ghosts [ 2.47 %], Event average 2.56 % **** +VeloTrackChecker_e83d0cf5 INFO 01_velo : 3088200 from 3153550 [ 97.93 %] 47327 clones [ 1.51 %], purity: 99.62 %, hitEff: 95.63 %, hitEffFirst3: 95.51 %, hitEffLast: 95.34 % +VeloTrackChecker_e83d0cf5 INFO 02_long : 1796773 from 1811265 [ 99.20 %] 18312 clones [ 1.01 %], purity: 99.71 %, hitEff: 96.60 %, hitEffFirst3: 96.49 %, hitEffLast: 96.44 % +VeloTrackChecker_e83d0cf5 INFO 03_long_P>5GeV : 1166697 from 1172326 [ 99.52 %] 9016 clones [ 0.77 %], purity: 99.71 %, hitEff: 97.01 %, hitEffFirst3: 96.86 %, hitEffLast: 96.95 % +VeloTrackChecker_e83d0cf5 INFO 04_long_strange : 95149 from 98994 [ 96.12 %] 902 clones [ 0.94 %], purity: 99.18 %, hitEff: 96.15 %, hitEffFirst3: 96.25 %, hitEffLast: 95.18 % +VeloTrackChecker_e83d0cf5 INFO 05_long_strange_P>5GeV : 45287 from 46918 [ 96.52 %] 289 clones [ 0.63 %], purity: 99.03 %, hitEff: 96.85 %, hitEffFirst3: 96.96 %, hitEffLast: 96.05 % +VeloTrackChecker_e83d0cf5 INFO 06_long_fromB : 93725 from 94402 [ 99.28 %] 855 clones [ 0.90 %], purity: 99.69 %, hitEff: 96.64 %, hitEffFirst3: 96.53 %, hitEffLast: 96.48 % +VeloTrackChecker_e83d0cf5 INFO 06_long_fromD : 50520 from 50932 [ 99.19 %] 523 clones [ 1.02 %], purity: 99.67 %, hitEff: 96.54 %, hitEffFirst3: 96.41 %, hitEffLast: 96.37 % +VeloTrackChecker_e83d0cf5 INFO 07_long_fromB_P>5GeV : 70725 from 71030 [ 99.57 %] 496 clones [ 0.70 %], purity: 99.70 %, hitEff: 96.97 %, hitEffFirst3: 96.87 %, hitEffLast: 96.84 % +VeloTrackChecker_e83d0cf5 INFO 07_long_fromD_P>5GeV : 34866 from 35044 [ 99.49 %] 267 clones [ 0.76 %], purity: 99.68 %, hitEff: 96.93 %, hitEffFirst3: 96.82 %, hitEffLast: 96.80 % +VeloTrackChecker_e83d0cf5 INFO 08_long_electrons : 174045 from 181213 [ 96.04 %] 3111 clones [ 1.76 %], purity: 98.10 %, hitEff: 94.64 %, hitEffFirst3: 93.13 %, hitEffLast: 94.83 % +VeloTrackChecker_e83d0cf5 INFO 09_long_fromB_electrons : 47652 from 48919 [ 97.41 %] 765 clones [ 1.58 %], purity: 99.20 %, hitEff: 96.20 %, hitEffFirst3: 95.93 %, hitEffLast: 96.12 % +VeloTrackChecker_e83d0cf5 INFO 10_long_fromB_electrons_P>5GeV : 43877 from 44696 [ 98.17 %] 720 clones [ 1.61 %], purity: 99.30 %, hitEff: 96.30 %, hitEffFirst3: 96.15 %, hitEffLast: 96.17 % +VeloTrackChecker_e83d0cf5 INFO 11_long_fromB_P>3GeV_Pt>0.5GeV : 61365 from 61675 [ 99.50 %] 372 clones [ 0.60 %], purity: 99.72 %, hitEff: 96.98 %, hitEffFirst3: 96.88 %, hitEffLast: 96.84 % +VeloTrackChecker_e83d0cf5 INFO 11_long_fromB_electrons_P>3GeV_Pt>0.5GeV : 42320 from 42838 [ 98.79 %] 676 clones [ 1.57 %], purity: 99.38 %, hitEff: 96.39 %, hitEffFirst3: 96.31 %, hitEffLast: 96.22 % +VeloTrackChecker_e83d0cf5 INFO 11_long_fromD_P>3GeV_Pt>0.5GeV : 28057 from 28214 [ 99.44 %] 178 clones [ 0.63 %], purity: 99.68 %, hitEff: 96.94 %, hitEffFirst3: 96.85 %, hitEffLast: 96.77 % +VeloTrackChecker_e83d0cf5 INFO 11_long_strange_P>3GeV_Pt>0.5GeV : 22890 from 24129 [ 94.87 %] 122 clones [ 0.53 %], purity: 98.78 %, hitEff: 96.86 %, hitEffFirst3: 96.60 %, hitEffLast: 96.63 % +VeloTrackChecker_e83d0cf5 INFO 12_UT_long_fromB_P>3GeV_Pt>0.5GeV : 61198 from 61506 [ 99.50 %] 372 clones [ 0.60 %], purity: 99.71 %, hitEff: 96.98 %, hitEffFirst3: 96.88 %, hitEffLast: 96.84 % +VeloTrackChecker_e83d0cf5 INFO +HLTControlFlowMgr INFO Memory pool: used 3.52274 +/- 0.0103795 MiB (min: 0, max: 4) in 1 +/- 0 blocks (allocated >once in 0 +/- 0% events). Allocated capacity was 10 +/- 0 MiB (min: 10, max: 10) and 260.277 +/- 0.756528 (min: 4, max: 384) requests were served +HLTControlFlowMgr INFO Timing table: +HLTControlFlowMgr INFO + | Name of Algorithm | Execution Count | Total Time / s | Avg. Time / us | + | Sum of all Algorithms | 35323 | 1967.505 | 55700.380 | + | "Fetch__Event_DAQ_RawEvent" | 35323 | 1486.820 | 42092.133 | + | "SeedTrackChecker_ad9abe4e" | 27023 | 169.730 | 6280.937 | + | "VeloTrackChecker_e83d0cf5" | 27023 | 153.933 | 5696.352 | + | "PrHybridSeeding_4d0337cc" | 27023 | 39.199 | 1450.566 | + | "PrLHCbID2MCParticle_a906d17d" | 27023 | 29.653 | 1097.338 | + | "Unpack__Event_MC_Vertices" | 27023 | 24.420 | 903.692 | + | "Unpack__Event_MC_Particles" | 27023 | 22.720 | 840.750 | + | "VeloClusterTrackingSIMD_87c18651" | 27023 | 8.841 | 327.174 | + | "VPFullCluster2MCParticleLinker_17386552" | 27023 | 6.857 | 253.764 | + | "PrStoreUTHit_6220b56a" | 27023 | 6.493 | 240.289 | + | "VPClusFull_38754d8c" | 27023 | 6.403 | 236.933 | + | "PrTrackAssociator_d68377ee" | 27023 | 3.180 | 117.693 | + | "PrTrackAssociator_16ad4612" | 27023 | 2.893 | 107.044 | + | "fromPrVeloTracksV1TracksMerger_fa66a5de" | 27023 | 2.074 | 76.763 | + | "fromPrSeedingTracksV1Tracks_84cd46c2" | 27023 | 1.340 | 49.570 | + | "PrStoreSciFiHits_fb0eba02" | 27023 | 1.284 | 47.523 | + | "FTRawBankDecoder" | 27023 | 0.721 | 26.664 | + | "UnpackRawEvent_FTCluster" | 35323 | 0.307 | 8.689 | + | "Decode_ODIN" | 27023 | 0.082 | 3.035 | + | "DefaultGECFilter" | 35323 | 0.067 | 1.908 | + | "reserveIOV" | 27023 | 0.062 | 2.301 | + | "Fetch__Event_Link_Raw_VP_Digits" | 27023 | 0.056 | 2.083 | + | "Fetch__Event_pSim_MCParticles" | 27023 | 0.052 | 1.920 | + | "UnpackRawEvent_UT" | 35323 | 0.049 | 1.383 | + | "UnpackRawEvent_VP" | 27023 | 0.048 | 1.786 | + | "UnpackRawEvent_ODIN" | 27023 | 0.046 | 1.717 | + | "Fetch__Event_Link_Raw_UT_Clusters" | 27023 | 0.046 | 1.714 | + | "Fetch__Event_MC_TrackInfo" | 27023 | 0.040 | 1.481 | + | "DummyEventTime" | 27023 | 0.033 | 1.213 | + | "Fetch__Event_Link_Raw_FT_LiteClusters" | 27023 | 0.027 | 1.016 | + | "Fetch__Event_pSim_MCVertices" | 27023 | 0.026 | 0.970 | + +HLTControlFlowMgr INFO StateTree: CFNode #executed #passed +LAZY_AND: run_tracking_debug_decision #=35323 Sum=27023 Eff=|( 76.50256 +- 0.225590)%| + PrGECFilter/DefaultGECFilter #=35323 Sum=27023 Eff=|( 76.50256 +- 0.225590)%| + NONLAZY_OR: run_tracking_debug_data #=27023 Sum=27023 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/SeedTrackChecker_ad9abe4e #=27023 Sum=27023 Eff=|( 100.0000 +- 0.00000 )%| + PrTrackChecker/VeloTrackChecker_e83d0cf5 #=27023 Sum=27023 Eff=|( 100.0000 +- 0.00000 )%| + +HLTControlFlowMgr INFO Histograms converted successfully according to request. +ToolSvc INFO Removing all tools created by ToolSvc +VeloTrackChecker_e83d0cf5.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +SeedTrackChecker_ad9abe4e.PrChec... SUCCESS Booked 857 Histogram(s) : 1D=614 2D=243 +RootCnvSvc INFO Disconnected data IO:148972FE-FB5D-11EB-861A-FA163E8E4EFB [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000048_1.xdigi] +RootCnvSvc INFO Disconnected data IO:1665270C-FB54-11EB-A7EB-FA163E95EADE [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000040_1.xdigi] +RootCnvSvc INFO Disconnected data IO:FACBF624-FB58-11EB-B4CE-FA163E92C5A4 [/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000045_1.xdigi] +ApplicationMgr INFO Application Manager Finalized successfully +ApplicationMgr INFO Application Manager Terminated successfully diff --git a/electron_main.py b/electron_main.py index 2952fa4..723657b 100644 --- a/electron_main.py +++ b/electron_main.py @@ -1,4 +1,5 @@ # flake8: noqaq +# ruff: noqa import os import subprocess import argparse @@ -50,8 +51,8 @@ if args.prepare_weights_data: "hadd", "-fk", ghost_data, - "data/ghost_data_B_NewParams7.root", - "data/ghost_data_BJpsi_NewParams7.root", + "data/ghost_data_B_NewParamsM.root", + "data/ghost_data_BJpsi_NewParamsM.root", ] print("Concatenate decays for neural network training ...") subprocess.run(merge_cmd, check=True) @@ -66,16 +67,16 @@ tree_names["newpars"] = "PrMatchNN_b826666c.PrMCDebugMatchToolNN/MVAInputAndOutp if args.matching_weights: os.chdir(os.path.dirname(os.path.realpath(__file__))) train_matching_ghost_mlp( - input_file="data/ghost_data_B_NewParams7.root", + input_file="data/ghost_data_B_NewParamsM.root", tree_name=tree_names[file_name], exclude_electrons=False, only_electrons=True, filter_velos=False, - filter_seeds=True, - n_train_signal=115e3, - n_train_bkg=115e3, - n_test_signal=5e3, - n_test_bkg=5e3, + filter_seeds=False, + n_train_signal=115e3, # 115e3, + n_train_bkg=115e3, # 115e3, + n_test_signal=8e3, + n_test_bkg=8e3, prepare_data=True, outdir="nn_electron_training", ) diff --git a/moore_options/get_best_seed_data.py b/moore_options/get_best_seed_data.py new file mode 100644 index 0000000..c91a3f7 --- /dev/null +++ b/moore_options/get_best_seed_data.py @@ -0,0 +1,136 @@ +# flake8: noqa +from Moore import options, run_reconstruction +from RecoConf.hlt2_tracking import ( + make_hlt2_tracks, + get_default_out_track_types_for_light_reco, + convert_tracks_to_v3_from_v1, + get_global_ut_hits_tool, +) +from RecoConf.hlt1_tracking import make_all_pvs +from RecoConf.event_filters import require_gec +from RecoConf.mc_checking import ( + get_track_checkers, + get_fitted_tracks_checkers, + check_tracking_efficiency, + make_links_lhcbids_mcparticles_tracking_system, + make_links_tracks_mcparticles, + get_mc_categories, + get_hit_type_mask, +) +from RecoConf.calorimeter_reconstruction import ( + make_photons_and_electrons, + make_clusters, + make_acceptance, + make_track_cluster_matching, + make_digits, + make_track_electron_and_brem_matching, + make_trackbased_eshower, +) +from Moore.config import Reconstruction +from PyConf.Algorithms import ( + PrFilterTracks2CaloClusters, + PrMatchNN, + PrFilterTracks2ElectronMatch, + PrFilterTracks2ElectronShower, + fromPrMatchTracksV1Tracks, + fromV3TrackV1Track, + PrTrackAssociator, +) +from PyConf.Tools import PrMCDebugForwardTool, PrMCDebugMatchToolNN +from PyConf.application import make_data_with_FetchDataFromFile +from RecoConf.data_from_file import mc_unpackers + +import Functors as F +import glob + + +decay = "testJpsi" + +options.evt_max = -1 + +options.ntuple_file = f"/work/cetin/LHCb/reco_tuner/data_matching/NewParams/best_seed_effs_{decay}_NewParams.root" # _dll_NegThree_mlp_NullSix.root" + + +if decay == "B": + options.input_files = glob.glob("/auto/data/guenther/Bd_Kstee/*.xdigi") +elif decay == "BJpsi": + options.input_files = glob.glob("/auto/data/guenther/Bd_JpsiKst_ee/*.xdigi") +elif decay == "D": + options.input_files = glob.glob("/auto/data/guenther/Dst_D0ee/*.xdigi") +elif decay == "testJpsi": + options.input_files = [ + "/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi", + "/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi", + "/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi", + ] +elif decay == "test": + options.input_files = ["/auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi"] + +options.input_type = "ROOT" + +options.dddb_tag = "dddb-20210617" +options.conddb_tag = "sim-20210617-vc-md100" +options.simulation = True + + +def standalone_hlt2_fastest_reco(): + links_to_hits = make_links_lhcbids_mcparticles_tracking_system() + hlt2_tracks = make_hlt2_tracks(light_reco=True, fast_reco=False, use_pr_kf=True) + + links_to_velo_tracks = PrTrackAssociator( + SingleContainer=hlt2_tracks["Velo"]["v1"], + LinkerLocationID=links_to_hits, + MCParticleLocation=mc_unpackers()["MCParticles"], + MCVerticesInput=mc_unpackers()["MCVertices"], + ).OutputLocation + + links_to_seed_tracks = PrTrackAssociator( + SingleContainer=hlt2_tracks["Seed"]["v1"], + LinkerLocationID=links_to_hits, + MCParticleLocation=mc_unpackers()["MCParticles"], + MCVerticesInput=mc_unpackers()["MCVertices"], + ).OutputLocation + + matching_params = dict( + MinMatchNN=0.5, # NN response cut value + PerfectTSelection=1.0, + ) + + match_tracks = {} + match_tracks["Pr"] = PrMatchNN( + VeloInput=hlt2_tracks["Velo"]["Pr"], + SeedInput=hlt2_tracks["Seed"]["Pr"], + MatchDebugToolName=PrMCDebugMatchToolNN( + VeloTracks=hlt2_tracks["Velo"]["v1"], + SeedTracks=hlt2_tracks["Seed"]["v1"], + VeloTrackLinks=links_to_velo_tracks, + SeedTrackLinks=links_to_seed_tracks, + TrackInfo=make_data_with_FetchDataFromFile( + "/Event/MC/TrackInfo", "LHCb::MCProperty" + ), + MCParticles=mc_unpackers()["MCParticles"], + ), + AddUTHitsToolName=get_global_ut_hits_tool(), + **matching_params, + ).MatchOutput + + match_tracks["v1"] = fromPrMatchTracksV1Tracks( + InputTracksLocation=match_tracks["Pr"], + VeloTracksLocation=hlt2_tracks["Velo"]["v1"], + SeedTracksLocation=hlt2_tracks["Seed"]["v1"], + ).OutputTracksLocation + + data = [match_tracks["Pr"]] + # data = [] + types_and_locations_for_checkers = { + "Velo": hlt2_tracks["Velo"], + "Seed": hlt2_tracks["Seed"], + "Match": match_tracks, # hlt2_tracks["Match"], + } + data += get_track_checkers(types_and_locations_for_checkers) + # data += get_fitted_tracks_checkers(best_tracks) + + return Reconstruction("hlt2_reco", data, [require_gec()]) + + +run_reconstruction(options, standalone_hlt2_fastest_reco) diff --git a/moore_options/get_calo_data.py b/moore_options/get_calo_data.py index 0fff42d..6bdb1d3 100644 --- a/moore_options/get_calo_data.py +++ b/moore_options/get_calo_data.py @@ -43,7 +43,7 @@ decay = "testJpsi" options.evt_max = -1 -options.ntuple_file = f"/work/cetin/LHCb/reco_tuner/data_matching/NewParams/calo_data_{decay}_NewParams_dll_NegFive_mlp_NullSix.root" +options.ntuple_file = f"/work/cetin/LHCb/reco_tuner/data_matching/NewParams/calo_data_{decay}_NewParams.root" # _dll_NegThree_mlp_NullSix.root" if decay == "B": @@ -110,7 +110,7 @@ def standalone_hlt2_fastest_reco(): shower_matched_seeds = PrFilterTracks2ElectronShower( # Relation=eshower["Ttrack"], Cut=F.FILTER((F.GET(0) @ F.WEIGHT) > 0.7) Relation=eshower["Ttrack"], - Cut=F.FILTER(((F.GET(1) @ F.WEIGHT) > -5)), # & ((F.GET(0) @ F.WEIGHT) > 0.5)), + Cut=F.FILTER(((F.GET(1) @ F.WEIGHT) > -3) | ((F.GET(0) @ F.WEIGHT) > 0.7)), ).Output matched_seeds = {} @@ -120,7 +120,7 @@ def standalone_hlt2_fastest_reco(): ).OutputTracks matching_params = dict( - MinMatchNN=0.6, # NN response cut value + MinMatchNN=0.5, # NN response cut value ) calo_long = PrMatchNNv3( diff --git a/moore_options/get_ghost_data.py b/moore_options/get_ghost_data.py index 999572a..268e8c6 100644 --- a/moore_options/get_ghost_data.py +++ b/moore_options/get_ghost_data.py @@ -1,4 +1,5 @@ # flake8: noqa +# ruff: noqa """ Moore/run gaudirun.py /work/cetin/LHCb/reco_tuner/moore_options/get_ghost_data.py @@ -35,9 +36,9 @@ import glob options.evt_max = -1 -decay = "BJpsi" # D, B +decay = "B" # D, B options.ntuple_file = ( - f"/work/cetin/LHCb/reco_tuner/data/ghost_data_{decay}_NewParams7.root" + f"/work/cetin/LHCb/reco_tuner/data/ghost_data_{decay}_NewParamsM.root" ) options.input_type = "ROOT" diff --git a/moore_options/get_match_eff_data.py b/moore_options/get_match_eff_data.py index ac60472..41c1f4a 100644 --- a/moore_options/get_match_eff_data.py +++ b/moore_options/get_match_eff_data.py @@ -48,7 +48,7 @@ decay = "testJpsi" options.evt_max = -1 # options.ntuple_file = f"/work/cetin/LHCb/reco_tuner/data_matching/sample4_data/match_effs_{decay}_EFilter.root" -options.ntuple_file = f"/work/cetin/LHCb/reco_tuner/data_matching/match_effs_{decay}_NewParams_EFilter.root" +options.ntuple_file = f"/work/cetin/LHCb/reco_tuner/data_matching/match_effs_{decay}_EDef7_yCorrCut_mlp5.root" options.input_type = "ROOT" diff --git a/moore_options/get_resolution_and_eff_data.py b/moore_options/get_resolution_and_eff_data.py index 640501b..2a1431d 100644 --- a/moore_options/get_resolution_and_eff_data.py +++ b/moore_options/get_resolution_and_eff_data.py @@ -37,11 +37,13 @@ from RecoConf.hlt1_tracking import ( get_global_materiallocator, ) -decay = "B" +decay = "testJpsi" options.evt_max = -1 -options.ntuple_file = f"/work/cetin/LHCb/reco_tuner/efficiencies/resolutions_and_effs_{decay}_baseline.root" +options.ntuple_file = ( + f"/work/cetin/LHCb/reco_tuner/efficiencies/effs_{decay}_EDef_yCorrNoCut.root" +) options.input_type = "ROOT" @@ -51,9 +53,11 @@ elif decay == "BJpsi": options.input_files = glob.glob("/auto/data/guenther/Bd_JpsiKst_ee/*.xdigi") elif decay == "D": options.input_files = glob.glob("/auto/data/guenther/Dst_D0ee/*.xdigi") -elif decay == "test2": +elif decay == "testJpsi": options.input_files = [ - "/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi" + "/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000009_1.xdigi", + "/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000059_1.xdigi", + "/auto/data/guenther/Bd_JpsiKst_ee/00143565_00000020_1.xdigi", ] elif decay == "test": options.input_files = ["/auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi"] @@ -96,7 +100,10 @@ def run_tracking_resolution(): InputTracks=tracks["Seed"], LinksToLHCbIDs=links_to_lhcbids, ) - + links_to_velo = make_links_tracks_mcparticles( + InputTracks=tracks["Velo"], + LinksToLHCbIDs=links_to_lhcbids, + ) # res_checker_forward = check_track_resolution(tracks["Forward"], suffix="Forward") # res_checker_best_long = check_track_resolution( # tracks["BestLong"], @@ -143,7 +150,14 @@ def run_tracking_resolution(): get_mc_categories("Seed"), get_hit_type_mask("Seed"), ) - + eff_checker_velo = check_tracking_efficiency( + "Velo", + tracks["Velo"], + links_to_velo, + links_to_lhcbids, + get_mc_categories("Velo"), + get_hit_type_mask("Velo"), + ) # types_and_locations_for_checkers = { # "Forward": tracks["Forward"], # "Seed": tracks["Seed"], @@ -162,6 +176,7 @@ def run_tracking_resolution(): eff_checker_match, eff_checker_best_long, eff_checker_seed, + eff_checker_velo, ] return Reconstruction("run_tracking_debug", data, [require_gec()]) diff --git a/moore_options/get_tracking_losses.py b/moore_options/get_tracking_losses.py index b7e9110..23a86f4 100644 --- a/moore_options/get_tracking_losses.py +++ b/moore_options/get_tracking_losses.py @@ -40,7 +40,7 @@ tested by mc_matching_example.py """ -decay = "B" +decay = "BJpsi" options.evt_max = -1 @@ -63,7 +63,7 @@ options.simulation = True options.input_type = "ROOT" options.ntuple_file = ( - f"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_{decay}_upstream.root" + f"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_{decay}_NewParamsM.root" ) diff --git a/parameterisations/notebooks/bend_y_params.ipynb b/parameterisations/notebooks/bend_y_params.ipynb index 28f5065..18c15ce 100644 --- a/parameterisations/notebooks/bend_y_params.ipynb +++ b/parameterisations/notebooks/bend_y_params.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 13, + "execution_count": 106, "metadata": {}, "outputs": [], "source": [ @@ -20,27 +20,30 @@ "from sklearn.metrics import mean_squared_error\n", "\n", "mplhep.style.use([\"LHCbTex2\"])\n", - "input_tree = uproot.open({\n", - " \"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_B_BJpsi_def_selected.root\":\n", - " \"Selected\"\n", - "})\n", + "input_tree = uproot.open(\n", + " {\n", + " \"/work/cetin/LHCb/reco_tuner/data/tracking_losses_ntuple_B_BJpsi_def_selected.root\": \"Selected\"\n", + " }\n", + ")\n", "array = input_tree.arrays()\n", "\n", - "array[\"yStraightOut\"] = array[\n", - " \"ideal_state_770_y\"] + array[\"ideal_state_770_ty\"] * (\n", - " array[\"ideal_state_10000_z\"] - array[\"ideal_state_770_z\"])\n", + "array[\"yStraightOut\"] = array[\"ideal_state_770_y\"] + array[\"ideal_state_770_ty\"] * (\n", + " array[\"ideal_state_10000_z\"] - array[\"ideal_state_770_z\"]\n", + ")\n", "array[\"yDiffOut\"] = array[\"ideal_state_10000_y\"] - array[\"yStraightOut\"]\n", - "array[\"yStraightEndT\"] = array[\"ideal_state_770_y\"] + array[\n", - " \"ideal_state_770_ty\"] * (9410.0 - array[\"ideal_state_770_z\"])\n", + "array[\"yStraightEndT\"] = array[\"ideal_state_770_y\"] + array[\"ideal_state_770_ty\"] * (\n", + " 9410.0 - array[\"ideal_state_770_z\"]\n", + ")\n", "array[\"yDiffEndT\"] = array[\"ideal_state_9410_y\"] - array[\"yStraightEndT\"]\n", "\n", - "array[\"dSlope_xEndT\"] = array[\"ideal_state_9410_tx\"] - array[\n", - " \"ideal_state_770_tx\"]\n", - "array[\"dSlope_yEndT\"] = array[\"ideal_state_9410_ty\"] - array[\n", - " \"ideal_state_770_ty\"]\n", + "array[\"dSlope_xEndT\"] = array[\"ideal_state_9410_tx\"] - array[\"ideal_state_770_tx\"]\n", + "array[\"dSlope_yEndT\"] = array[\"ideal_state_9410_ty\"] - array[\"ideal_state_770_ty\"]\n", "array[\"dSlope_xEndT_abs\"] = abs(array[\"dSlope_xEndT\"])\n", "array[\"dSlope_yEndT_abs\"] = abs(array[\"dSlope_yEndT\"])\n", "\n", + "sel_array = array[(array[\"yDiffOut\"] < 100) & (array[\"yDiffOut\"] > -100)]\n", + "# sel_array = array\n", + "\n", "\n", "def format_array(name, coef):\n", " coef = [str(c) + \"f\" for c in coef if c != 0.0]\n", @@ -51,19 +54,47 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 107, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "plt.hist(\n", + " sel_array[\"yDiffOut\"],\n", + " bins=100,\n", + " # range=[5100, 5700],\n", + " color=\"#2A9D8F\",\n", + " density=True,\n", + ")\n", + "plt.xlabel(r\"$y_{corr}$ [mm]\")\n", + "plt.ylabel(\"Number of Tracks (normalised)\")\n", + "mplhep.lhcb.text(\"Simulation\")\n", + "plt.show()\n", + "# plt.savefig(\n", + "# \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/magnet_kink_dist.pdf\",\n", + "# format=\"PDF\",\n", + "# )" + ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 108, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -75,8 +106,8 @@ "source": [ "bins = np.linspace(-200, 200, 50)\n", "sns.regplot(\n", - " x=ak.to_numpy(array[\"ideal_state_770_y\"]),\n", - " y=ak.to_numpy(array[\"yDiffOut\"]),\n", + " x=ak.to_numpy(sel_array[\"ideal_state_770_y\"]),\n", + " y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n", " x_bins=bins,\n", " fit_reg=None,\n", " x_estimator=np.mean,\n", @@ -89,12 +120,12 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 109, "metadata": {}, "outputs": [ { "data": { - "image/png": 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PoZydnY3d3d0olUrnus5R1Wq1r/3M7t+/f7jck3TU6/WeHXwAAMDoynwQdvRkuE6nk3I15EmhEPGCoOtczgqqp6enh1zJ6Q661U7r8qpUKrG4uBizs7PPfR/z8/Oxs7PT93VOhmYrKytx9+7dnocFHJwomdVTMK+6ZrOZ6T3JAACAy8v0ZvnvvvturK6uHgZgOrog284KwrJ0ul63pY4Hm9efVKvVTl3yeZZisXgsyGo2mzEzM9NXR2un0xGCpeBg3zhLUwEA4GrLdBB2sNl0oVCITqcTN2/ejLm5uVheXo7l5eVYXV3t+Tg4rc0LS/JqZWUlCoXChR4zMzPnutZZ+2Jl5f4rFos99/sqlUrHTnE8cNrHujk5vl6vx/T0dCInUtZqtVhcXOzZaddsNmNjYyNmZmaOXfdg+d/169ejUCjE9PT0qctC6/V6rKysHJ6qOT09HYuLi6f+nA+uc/J36KTZ2dnD+Y4+ktzDcWNj49h1rl+/HjMzM6d+jxHPns+bN28+d3jC0frOeq77/VkcaDab8fDhw5idnT18/g/qW1lZ6SuIq9fr8fDhw5ienj5W89GfwfT09OEyYAAA4IhOhhUKhc7Y2FhnbGysU61WLzXX7u5uZ2xsLKHKuKjJyclORHQmJyfTLmVgIuK5x/7+/lCuvbCwcOr1k3yUSqVzX397e3so3/9Jy8vLx+qoVCp9f22xWHzu+zivSqVy6vNRLpc7Ozs755prZ2ens7CwcKyuYrH43Lj9/f3O6upqp1wuH7vm+vp6p9N5/jk5+lhYWDicp9u4Uql05u/0yd+Bs6yurvZ1j+zs7PT9M9jc3Dx8XjY3N4/NUSqVzqx9f3+/s7Oz81xNOzs7xx5H5+vnZ3HS+vr64fjV1dXOzs5OZ3d3t7O5uXns5zU3N/fc1+7v73eWl5cPv4+jNe7v7z/38z5a1+7ubs/aAABgmNLMBjLdEXbQRbK8vBx37ty51FylUilu3ryZQFUwWhYWFmJnZ+dCj/OemndW51dWulLO05l2WufYyY6hXra3tw/3/To5z8zMTN8dO/V6PR49etR311G5XH5uw/ednZ2YmZmJWq0Wm5ubsbu7G/v7+8dO3dzY2IharRazs7NRrVZje3s79vf3Y39/P7a3tw+fv3q9ftixe1K/m/2f9rxcRrVaPbz2/fv3j81fLpdjc3MzIp7Vfu/evWNfWywWo1wuP3eow8HHjn7uvD+LAysrK7G4uBjFYjF2d3djeXk5yuVylEqlmJubi52dncOat7a2Ynp6+tjvxtOnT2N2dva538vHjx/HzZs3D5f17u7uxvr6+uHPqtls2vwfAACOGnr0dg4zMzOdsbGxzrvvvpvIfJftKuPydIQN1mkdWaurqxee72Q3TvToCDvZUXPwOOhGGraTXU2nddqc5aC7KInv46zn5SLzHp2rVxfSyS6hszrzTnauHe0MO+roc3LWtbe3t/vq4Nrf30+0I2xubq7n73yvefqt/UC/P4ujnWC9ftZHO77O6mA8WmOxWDz153ry93dY/wYBAEA/dISd4aAL7OnTp4nOBwxG1jvCzuPkyY8RF/8+lpeXY39//8wuqIPTKvuZ/7S6znL0kIKFhYUz90c7eVLiWZ2AR+tvNpuZ/bme9Xt49ONJbIrf789iZWXl8PoLCwt9jY141uV22iENR7+P733ve6f+XE/+rj1+/LivWgEA4KrLdBD2jW98IzqdzrmXI53l+9//fiLzAKc7KxjY3d0dciWXd1qY8vOf//xS821ubsbOzs5zS/AinoUed+7cSTRc6ncp6NGf23mWj2bphMXvfe97sbq6Gpubmz3DpojhhbMbGxuH1+onODt5uMt5D2k46uj1svSzAgCANGU6CLt27Vp85zvfiUePHsXf//3fX3q+8+53BJzPWR1Hp3W1ZN1pocXLL7986XnL5fKZ+6/VarXn9q8ahoue6plUt24SisViLC8vn7knW1L/h8p5bW9vH77dbwfZ0ftoFO8dAADIskwHYRFxuKFwvxswn+WDDz5I7YUQ5Mlp3U71ej2zy+jO4zzLEntZWFiI3d3d50Kora2tTIcfFw3Nhq1er8fGxkZUq9VTN8IfZh3ndfL37CrcOwAAkBUvpl1AP7a3t+P27dvxyiuvxMrKyrF9b7p5+vRpNJvN2N3djXfeeWfAVQIREW+88capoXO1Wk38pMBBKxaLx0KIJIOwg/kOTnM8ep319fUzu+vorlarxcrKSpTL5UstK0zK0Z9rv6HYyRMpnz59OjIBJAAAZN1IBGE/+clPIuLZPkMXPQa+0+lEoVBIsiwYulqtFg8ePIjNzc20SznTwsLCsQ2/D6yvr49cEHayE6efrqJqtRorKyuxs7PT1zVKpVJ873vfO9b1qnv1YhYXF2NjYyMWFhYyEYJFXGyD/pP/Z0/SASwAAORZ5pdG/of/8B9idnY23n///SgUCtHpdM79gKvi3r17mV8mVSwWTw28qtXqSAU8J5/nfju0SqVS1Gq1cy1vPPl82dj8/A5CsIjLbTCftKMhVr8nbR7de00IBgAAycp0EPa9730v1tfXD8Osi4ZawjCugoMgaXZ29swxSW9eftH5zgoiTusUu6xBBYOPHz8+9n6/3agHwcVFu1cj+us8G3VJ/q7WarXDEKxUKmVqGeEbb7xx7P2DOrs5esrqqHVRAgBA1mV6aeTBqWoHnWCVSiVmZ2ejWCyee5+w7373u/Hhhx8OsFrOo9FoxNTUVNcxS0tLsbS0NKSKsq3ZbB4unTvvC+Of//znidbST4BRKpVidXX1ueCrWq3Gw4cPY3l5OZFa6vV6zM7ORqVSSfxU2JOn/Z3neS+VSlGv12NlZaWv7qSTHWAnw5NRcvLf5mazeWowlWTX29Huu27zptFNOTc3d2yvufX19Z6//0e/n/v37w+yPAAASNza2lqsra11HdNoNIZUzfMyHYTVarUoFApRLBbj8ePHcfPmzQvPde/evXj55ZcTrI7LaLfbsbe313VMq9UaUjXJGcQL7WazGXfu3DkMFM67VCrpmvqdb3l5Oba3t59bIniwkfllN4OvVqsxOzt7GLolbWtr6/Dt84ZsB0HYw4cPY3p6OhYWFrqOPzp/qVRKLCjs92d1dFyvr+n1+ZPdbBsbG899Pwch4VFJbgi/tbX1XHB52vVOOnlv1ev155Y2XqTGo3vA1ev1qFarZ/7+12q1wyXEq6urmepuAwCAfrRarZ6v99OU6aWRBy8A7t+/f6kQ7GCuW7duJVAVSRgbG4vJycmuj/Hx8bTLPLezulEuugysWq3GzMzM4Qvju3fvnvv6l1mCdtmune3t7VM7qWZnZ+Phw4cXmrPZbMbi4uJhCLazs9N3WNBvMLSxsXH4vS8sLJw7tDsaniwuLsbi4uKZ197Y2Dj2XBztRDvN0Z9n2vvFnXX9o8/XyspKLC4uRrVaja2trVhcXIzp6ennQp5+753TrnkyfJufn4+HDx8ediBOT08/F2qtr68fHmxwMOfJIOzg51av14/tQXZabd1+FnNzc8fC2vn5+VPHN5vNuHfvXkQ8+707KxDt9+d+9DlN+3cFAID8GB8f7/l6f2wsxTiqk2GVSqUzNjbWeffddxOZr1arJTIPFzc5OdmJiM7k5GTapQzEwsJCJyKee1Qqlc7u7m7Pr9/f3+9sb293lpeXO6VS6bl5Njc3z/za3d3dU68dEZ39/f0LfT/lcvnU+dbX1881z/Ly8qnzlMvlzvb2dl9z7O/vd1ZXVzvFYvHwOe31fZ28brFY7Hmdo89jpVLpq7aT1tfXT/1+5+bmOsvLy5319fXOwsLCsZ9xv78jJ38mZ/1O7O/vP/dcn2Vubq6vn+/29vaxcQsLC2de++DndPJRLBYPf+ZHx5TL5c7u7u5zP9N+azs57uhjdXW10+mcfn/Ozc0dm+es39XTvtd+fxYHNjc3D7/nYrHYWV9f7+zu7nZ2d3c76+vrh78PB/Wept+fwclxF/1dBgCAQUgzG8h0ELa5udkpFAqd//gf/2PapZCQqxqE7ezsdH0hntTjtOBnf3+/s76+fmbwEBGdUqnUWV9f7zsQ29nZ6VQqla61LC8v9xXcHNje3j4zWCsWi525ubnO+vp6Z3t7u7Ozs9PZ3t7urK+vd1ZXV499XbFY7Bk4HDgtHCyVSmeGb0eDipMByXlsb28f1rm5udlZWFjoVCqVTrlcPvZzOvi+e4WBB79fZ/2My+XyYXjSbWypVOrMzc0d/h6cDONOBicHc25vb5/5+1AqlU4NY/b394/NXy6XO8vLy8fGHHzs5O/R/v5+z+/3tGuurq4eXq9UKnWWl5eP/c7v7+8ffh9Hn7OTjgbR5XL52O/beX4WZ1lfX+9UKpVjodjB1511j3a7J4/+DHZ3d7uO61UbAAAMQ5rZQKHTyfaRijMzMzE2Nhbvvffepef6y7/8y/i3//bfJlAVFzU1NRV7e3sxOTkZT548SbucS9vY2LjU6YDnUS6XY2dn59jHCoXCuecpFouxv79/6ufm5+eP7Y3Vr+3t7b6XD25tbcWDBw8Ol3v2q1QqxeLiYiwsLJxr36SD/ahOfl/FYjFu3759uJH548ePD/eA2tzcvPQeZgAAAJwuzWwg80FYvV6P3/7t34533303/vAP//BSc7366quJBGpc3FULwri4ZrMZ1Wo1Hj16FPV6/XAvpohnodfBwQCvvvpqzM3NnfuQgF7XO7jmwXVu374d8/PzAjAAAIABE4T1sLGxEffv34+f//znF57jo48+ihs3bsQnn3ySYGWclyAMAAAA8i3NbODFoV7tnP7yL/8yIiL+xb/4F3H9+vV45ZVXTj2BrpdmsxnvvPNO0uUBAAAAMEIyHYR9+9vfjvfff//w/U6nEw8fPrzQXJ1O50L7KQEAAABwNYylXUA3d+/ejc6zky0j4mIbgwMAAABARMaDsIPT+AqFwmEgdtEHAAAAAPmW6aWR165di3K5HO+//36srq5GuVyOGzdunHueer0e3/72t+OnP/1p8kUCAAAAMBIyHYRFRLzxxhsxPT0dX/va1y48x61bt+L111+/UIgGAAAAwNWQ6aWRERGVSiWxpY23b99OZB4AAAAARk/mg7Bbt27F6upqInMlNU9a6vV6zM7OxtbW1qXmefjwYczOzsb169ejUCjE9PR0zM/PR7VaTahSAAAAgOzJfBAWEXHz5s1E5rl161Yi8wxbs9mM+fn5mJ6ejmq1Gk+fPr3QPNVqNa5fvx4rKysREbG5uRm7u7uxuroatVotZmdnY3Z2NprNZoLVAwAAAGRDqkFYljavz1ItB5rNZqysrMT169cv3QVWrVYPQ66FhYXY3t6OSqUSpVIp5ubmYnd3N8rlclSr1ZiZmRGGAQAAAFdOqkHYzMxMtFqtNEuIiIiPPvooZmZm0i7jmIcPH8bMzEzUarVLz3XQURYRUSqVYn19/dRxm5ubEfFsCebBeAAAAICrItUgLKlN8JOQpVpqtVpUKpXY3d2N7e3tM4Orfs3Pzx92eB0sizzNQXdYxLMOso2NjUtdFwAAACBLUg3CCoVCmpc/Jku1lMvlKJfLh+9f5rTLer1+bBP8u3fvdh3/xhtvHL7dLTQDAAAAGDU6wkZAsVi88NcePSmzUqn0nOugIyzi2ZLKy+5NBgAAAJAVqZ8a+cEHH6RdQjx+/DjtEgbm6PLGo11m3ZRKpcO3Hz16lHhNAAAAAGl4Me0C7t27F1//+tejWCzGjRs3hnrtp0+fRr1ej+Xl5aFed1hObrT/6quv9vV15XI56vV6RISOMAAAAODKSD0I29nZSf2Ewk6nk6k9wpJydG+wiOOdXt2cHFer1fruJgMAAADIqtSXRh7odDqpPK6y995779j7/e419vLLLx97/yovHQUAAADyI/WOsLTDqLSvP0gHyxsPXLQjbHd3N7GaAAAAANKSahC2v7+f5uWvvJNB2EU1m81E5gEAAABIU6pB2LVr19K8/JV30QDr5BLKp0+fXr6YExqNRkxNTV16nqWlpVhaWkqgIgAAAKCbtbW1WFtbu/Q8jUYjgWouJvWlkWTfIDrC2u127O3tXXqeVquVQDUAAABAL61WK5HX8mkShF1hxWIxkRCr3032z2NsbCwmJiYuPc/4+HgC1QAAAAC9jI+Px+Tk5KXnaTQa0W63E6jo/ARhV9iNGzcSCcJu3Lhx+WJOmJiYiCdPniQ+LwAAADAYSW1PNDU1lVpn2VgqV2UoLtrJdTI8G0RHGAAAAMCwCcKusNu3bx97v9/usJOb409PTydVEgAAAEBqBGFX2MzMzLH36/V6X1+3u7t77P1KpZJYTQAAAABpEYRdYSc7wvoNwo52jhWLxSiVSkmWBQAAAJAKQdgVVi6Xj+3v9d577/X1dY8fPz58+2SYBgAAADCqBGFX3N27dw/frtVqfX3N0XErKyuJ1wQAAACQBkHYFbe4uHj4drVa7Tn+6JhSqWR/MAAAAODKEISNgH5PezxNuVw+FmZtbW11Hb+5uXn4tm4wAAAA4CoRhI2Ak5vcnzcYW19fP3z7wYMHZ45rNpuxsbEREc9OilxYWDjXdQAAAACyTBCWcc1m87nOrEePHp1rjlKpdNjpVavV4uHDh6eOu3PnTkQ8OynyaGcYAAAAwFUgCMugZrMZ8/PzMTs7G9evX3+uI6xWq0WhUIjZ2dmYn5/vaxP8ubm52N7ejmKxGCsrK4df12w2o1qtxszMTNRqtSiXy/HBBx8cO20SAAAA4Cp4Me0CkvaTn/wk1tfX43d/93fj3//7f592ORcyqI6sSqUS+/v78fDhw3j06FHcuXMnms1mFIvFuH37dmxubsbc3Fzi1wUAAADIgkKn0+mkXUSSbty4ER999FFEROzv78f4+HjKFXHU1NRU7O3txeTkZDx58iTtcgAAAIAhSzMbuHJLI2/evBkREbdu3RKCAQAAAHDoygVhm5ub0el04vvf/37apQAAAACQIVcuCCuVSvH48eO4d+9e/N3f/V3a5QAAAACQEVcuCIuIKJfLUa1W4969e3H//v20ywEAAAAgA65cEHbjxo149dVXY3V1Nb7yla9EqVSKV199Nf70T/807dIAAAAASNGVOzXyd3/3d6NarUahUDj8WKlUiqdPn0ZExOLiYiwsLMRv/dZvpVRhvjk1EgAAAPLNqZEJKpfLUSwW4/XXX4+bN29Gp9OJ3d3d2N/fj/39/VhdXY3p6el45ZVX4k//9E/jww8/TLtkAAAAAIbgynWEvfvuu1EsFuPOnTsREfHRRx9FtVqN7e3tqFarUa/XD8cedI1dv349/vt//++p1Js3OsIAAAAg39LMBl4c6tWGoFwuR7PZPHz/2rVr8frrr8frr78eEREffPDBsWCs2WzG/v5+StUCAAAAMCxXLgi7efNmz8/fu3cv7t27FxER77//flSr1WGUBgAAAECKrtweYT/5yU/ijTfeiD//8z/va/ytW7fia1/72oCrAgAAACBtV26PsBs3bsRHH30UERH7+/sxPj6eckUcdbAOeGxsLCYmJrqOXVpaiqWlpSFVBgAAAFzW2tparK2tdR3TaDSi3W7bIywJN2/ejJ/+9Kdx69YtIViGtdvt2Nvb6zqm1WoNqRoAAAAgCa1Wq+fr/TRduSBsc3Mzfvu3fzu+//3vp10KXfTTESbIBAAAgNEyPj4ek5OTXcccdISl4cotjYyIqNVqsbi4GFtbW/E//A//Q9rlcESaR6QCAAAA6UszG7hym+VHRJTL5ahWq3Hv3r24f/9+2uUAAAAAkAFXLgi7ceNGvPrqq7G6uhpf+cpXolQqxauvvhp/+qd/mnZpAAAAAKToyi2N/N3f/d2oVqtRKBQOP1YqleLp06cREbG4uBgLCwvxW7/1WylVmG+WRgIAAEC+WRqZoHK5HMViMV5//fW4efNmdDqd2N3djf39/djf34/V1dWYnp6OV155Jf70T/80Pvzww7RLBgAAAGAIrlxH2LvvvhvFYjHu3LkTEREfffRRVKvV2N7ejmq1GvV6/XDsQdfY9evX47//9/+eSr15oyMMAAAA8i3NbODFoV5tCMrlcnz00UeH71+7di1ef/31eP311yMi4oMPPjgWjDWbzdjf30+rXAAAAACG5MoFYTdv3uz5+Xv37sW9e/ciIuL999+ParU6jNIAAAAASNGVC8LO69atW3Hr1q20ywAAAABgwK5sENZqtQ73BCuVSlEul50UCQAAAJBjVzII+/73vx+Li4vPfXxmZiZWV1fjf/qf/qcUqgIAAAAgTWNpF5C0H//4x7GwsBCdTue5x+PHj6NSqcS/+3f/Lu0yAQAAABiyKxeEra6uRqVSie3t7djd3Y2dnZ3Y3NyMhYWFKJVK0el04p133on/2//t/xZ///d/n3a5AAAAAAzJlVsa+fTp03j8+PGxj926dStef/31iIioVquxuroaP/7xj2NmZiYeP34c4+PjaZQKAAAAwBBduY6w6enprp8/6Bb70Y9+FO12OxYWFoZUGQAAAABpunJB2P7+fl/jKpVKPH78OB4/fhz/5//5fw64KgAAAADSduWCsNnZ2fjGN77R19hisRg/+tGP4rvf/e6AqwIAAAAgbVduj7CFhYW4fv16RER8+9vf7jn+YAN9AAAAAK62K9cRdu3atfjOd74T3/nOd+J//p//5/i7v/u7nl/T73JKAAAAAEbXlQvCIiKWl5fj9ddfjx/96EdRKpXi3/27fxc//elPTx37wQcfRK1WG26BAAAAAAzdlVsaeWBzczPm5+fj3Xffjc3Nzdjc3IxisRiVSiVKpVK8/PLLsbu7GxsbGzE3N5d2uQAAAAAMWKFzxTfIevjwYXz9618/fL9QKBy+ffCt1+v1+K3f+q1hl5ZLU1NTsbe3F5OTk/HkyZO0ywEAAACGLM1s4EoujTxqeXk59vf342tf+9rhxvgHj3K5HDs7O0IwAAAAgBy48h1hp/nggw/i5s2baZeRSzrCAAAAIN90hA1Aq9U683NCMAAAAID8uXJB2EcffRSvvPJKXL9+Pb7xjW+kXQ4AAAAAGXHllkbevXs3tra2IiJieno6/vZv//bUca1WK8bHx4dZGvHr9sexsbGYmJjoOnZpaSmWlpaGVBkAAABwWWtra7G2ttZ1TKPRiHa7ncrSyBeHerUhqNfrsbOzE48fP47bt2+fOe69996Lra2t+M//+T8PsToOtNvt2Nvb6zqm2/JWAAAAIHtarVbP1/tpunJB2PT0dNy6dStu3brVddydO3ei2WzG/fv348GDB0OqjgP9dITp2AMAAIDRMj4+HpOTk13HHHSEpeHKLY189dVX47333ut7/N27d+Mb3/hG/M7v/M7giuKQUyMBAAAg35wamaA7d+7En//5n/c9fnFxUUcYAAAAQA5cuSBsYWEhlpeX4+/+7u/6Gl8qlaJarQ64KgAAAADSduWCsFKpFHNzc1EqlfrqDKvX69FsNgdfGAAAAACpunJBWETE+vp6/M7v/E4sLCzEv/pX/yr+y3/5L6eOa7Vasbi4GKVSacgVAgAAADBsV+7UyAM/+clP4l//638djx8/jrm5uYiIKJfLUSqV4saNG1Gv1w+XRC4sLKRZKgAAAABDcOVOjTxpcXExvve970VERKFQOPa5TqcT169fjw8++CDGx8fTKC93nBoJAAAA+ebUyAFaX1+P3d3d+NrXvhY3b96MTqcTnU4nrl27FnNzc1Gv14VgAAAAADlw5TvCyBYdYQAAAJBvOsIAAAAAYMAyv1l+q9WKlZWVePz4cdy4cSNmZmbijTfeiP/xf/wf0y4NAAAAgBGS+SDsj//4j+Pdd989fH97ezu+853vxPT0dHzlK1+J//V//V9TrA4AAACAUZH5pZHVavXw7Vu3bsX+/n602+34z//5P8d//a//NV5++eW4f/9+tFqtFKsEAAAAIOsyH4TduXMnOp1OFIvF+MlPfhLXrl2LiIhKpRLvvPNO7O7uRrvdjps3b8Y3vvGNlKsFAAAAIKsyH4Rtbm7Gzs5O1Ov1GB8ff+7zxWIxVldXo16vxyeffBIvv/xy/Pmf/3kKlQIAAACQZZkPwiKeLYk86AQ7y7Vr12J1dTXee++9+N//9/89Xnnllfg//o//Y0gVAgAAAJB1md8s/7xKpVK88847UavV4u7du/Hbv/3bsbm5Gb/5m7+ZdmkAAAAApGgkOsLO68MPP4wPP/ww5ubm4kc/+lEUi0XLJQEAAABybuQ7wj788MOoVquxs7MT1Wo16vX6sc8XCoXodDqxsLAQ6+vr8eMf/1h3GAAAAEAOjWQQ9pOf/CQ2NzfjnXfeiWazeexznU7n1K/pdDrx+PHj+K3f+q34/ve/H3/4h384hEoBAAAAyIqRCcJ+8pOfxPr6emxtbR1+7CD0Ouj6OqpcLsft27djZmYmbt++Hbdu3YqIiK2trfj3//7fx3vvvRff/va3h/cNAAAAAJCqzAdhf/mXfxkrKyuHSx5P6/jqdDpRqVRidnY2KpXKYeh1mrm5uahUKnH37t34N//m38TW1palkgAAAAA5kPkgbG5u7rDjq1AoHH68VCrF3NxczM7Oxp07d841Z7FYjB/96Efx8OHDKJfLUavVhGEAAAAAV1zmg7ByuRzvv/9+RDzr/FpeXo7FxcW4efPmpedeXl6OcrksDAMAAADIgbG0C+ilVCpFp9OJ2dnZ2N/fj+985zuJhGAHKpVKPHjwIP71v/7Xic0JAAAAQPYUOmcds5gR//E//sf4zne+Ez//+c8Hep0bN27E7/7u78b/9r/9bwO9Tt5NTU3F3t5ejI2NxcTERNexS0tLsbS0NKTKAAAAgMtaW1uLtbW1rmMajUa02+2YnJyMJ0+eDKmyZ0ZiaeTt27cHeo0PPvggms1mbG5uxte//vX4nd/5nYFej4h2ux17e3tdx7RarSFVAwAAACSh1Wr1fL2fpswHYbdv3z71pMgkzc/PH7796NEjQdgQ9NMRNj4+PqRqAAAAgCSMj4/H5ORk1zEHHWFpyHwQdu3atVhZWRnoNer1+uGJlC+//PJAr8UzExMTQ29/BAAAAAarn22ODrZNSkPmN8uPiLhz585A5//Od74TnU4nyuVyLCwsDPRaAAAAAKRjJIKwQVtYWIh2ux3vvfee5XgAAAAAV5QgDAAAAIBcEIQBAAAAkAuCMAAAAAByQRAGAAAAQC4IwgAAAADIBUEYAAAAALkgCAMAAAAgFwRhAAAAAOSCIAwAAACAXBCEAQAAAJALgjAAAAAAckEQBgAAAEAuCMIAAAAAyAVBGAAAAAC5IAgDAAAAIBcEYQAAAADkgiAMAAAAgFwQhAEAAACQC4IwAAAAAHJBEAYAAABALgjCAAAAAMgFQRgAAAAAufBi2gWQT41GI6amprqOWVpaiqWlpSFVBAAAAFzW2tparK2tdR3TaDSGVM3zBGGkot1ux97eXtcxrVZrSNUAAAAASWi1Wj1f76dJEEYqxsbGYmJiouuY8fHxIVUDAAAAJGF8fDwmJye7jmk0GtFut4dU0XGFTqfTSeXK5NLU1FTs7e3F5ORkPHnyJO1yAAAAgCFLMxuwWT4AAAAAuSAIAwAAACAXBGEAAAAA5IIgDAAAAIBcEIQBAAAAkAuCMAAAAAByQRAGAAAAQC4IwgAAAADIBUEYAAAAALkgCAMAAAAgFwRhAAAAAOSCIIye6vV6zM7Opl0GAAAAwKUIwnKs2WxGoVDo+Zieno5SqZR2uQAAAACXIgjLsY2Njb7HrqysDLASAAAAgMEThOXYgwcP+hpXqVR0hAEAAAAj78W0CyAdGxsb0Ww2Y3l5uef+X7dv3x5SVQAAAACDIwjLqdXV1SiVSrG6upp2KQAAAABDIQjLoa2trajX67G+vp52KQAAAABDY4+wHHrw4EEUi8W4e/du2qUAAAAADI0gLGdqtVrUarVoNptx/fr1mJ6ejsXFxdja2kq7NAAAAICBEoTlzMrKyrH36/V6bGxsxPz8fBQKhZifn49arZZSdQAAAACDIwjLkXq9HtVqteuYra2tmJmZicXFxSFVBQAAADAcNsvPkVKpFOvr69FsNmN3dzeq1WrU6/VTx25sbMTjx49jZ2dnyFUCAAAADIYgLGcWFhaOvd9sNmNjYyMePHgQzWbz2OdqtVrMzs7G9vb2ECsEAAAAGIxCp9PppF0E2bC1tRX37t17LhBbXV2N5eXlRK4xNTUVe3t7MTY2FhMTE5eeb2lpKZaWlhKoDAAAAOhmbW0t1tbWLj1Po9GIdrsdk5OT8eTJkwQq65+OMA7Nzc1FpVKJO3fuHNsw/8GDB4kFYQfa7Xbs7e1dep5Wq5VANQAAAEAvrVYrkdfyaRKEcUyxWIydnZ2YmZk5DMOazWZUq9WoVCqJXSepjrDx8fEEqgEAAAB6GR8fj8nJyUvPc9ARlgZLIzlVrVaLmZmZw/eXl5djdXX10vMeLI1Mo/0RAAAASF+a2cDYUK/GyCiXy8c6wM46XRIAAABgVAjCONPs7GzaJQAAAAAkRhDGmUql0uHbN27cSLESAAAAgMsThHGmo0FYsVhMrxAAAACABAjCONPjx48P37ZMEgAAABh1gjDOtLu7e/j20Y3zAQAAAEaRIIwzbW1tRUTE8vJyypUAAAAAXJ4gjFNtbW1FvV6PYrEY9+/fT7scAAAAgEsThOVEtVqN69evR6FQiNnZ2ajVameOrdfrce/evYiI+PGPf2yjfAAAAOBKEITlxObmZjSbzYh4ForNzMzE4uLic+MOPnfjxo3Y3d2Ncrk85EoBAAAABkMQlhPz8/PPfWxjYyOuX78e8/Pzsbi4GDMzMzE7OxsLCwuxs7MTpVIphUoBAAAABkMQlhOVSiV2d3djYWEhSqXSseWOtVotnj59Gvfv34/9/f1YXV21HBIAAAC4cl5MuwCGp1Qqxfr6etplAAAAAKRCRxgAAAAAuSAIAwAAACAXBGEAAAAA5II9wmDEdDoR7U73MWOFiEJhOPUAAADAqBCEwYhpdyJe/Gb3Mb/6VsQLgjAAAAA4xtJIAAAAAHJBEAYAAABALgjCAAAAAMgFQRgAAAAAuWCzfMgxJ1ACAACQJ4IwyDEnUAIAAJAngjAYAp1XAAAAkD5BGKloNBoxNTXVdczS0lIsLS0NqaLB0nkFAABAHqytrcXa2lrXMY1GY0jVPE8QRira7Xbs7e11HdNqtYZUDQAAAJCEVqvV8/V+mgRhpGJsbCwmJia6jhkfHx9SNQAAAEASxsfHY3JysuuYRqMR7XZ7SBUdJwgjFRMTE/HkyZO0ywAAAAAS1M82R1NTU6l1jY2lclVgoN5+P+0KAAAAIHsEYTBi+gm5vrwlDAMAAICTBGEwQj76OOJP/rq/sV/5q4jWxwMtBwAAAEaKIAxGyF/UIn75T/2N/cU/RrxVG2w9AAAAMEoEYTBCfvCz843/m3OOBwAAgKtMEAYjZP8fBjv+NPYaAwAA4KoQhMEIuf7pZMfbeB8AAIA8EYTBCHntc+cb/3tdxie98X6nE/FJu/uj0+m7dAAAAEjci2kXAPTvS+WIr/+wvw3zP/upiDfLZ3/+Ihvvf/ULZ49pdyJe/Gb3eX71rYgXCv1dEwAAAJKmIwxGyLWXIv7s9/sb+90/iBh/6ezP23j/YnS+AQAAjC4dYZARb78f8eZM73FfvPVs365u3pp7Nq6bNDbevwp0vgEAAIwuHWEwBMPelP6PeoRgEclvvA8AAABZJwiDAUt6U/qkJLnxfpZZyggAAMABSyNhwJLelD4pSW68n7RO59kSxG7GChGFPpYfWsoIAADAAUEYDNhFNqUfRhB2sPF+r/3GInpvvJ804RUAAACDYGkkDFiWN6XvtaF+RH8b78NZLE0FAACyREcYDNiob0rfz8b7cBbdfQAAQJboCIMBy8um9AAAAJB1gjAYsC+VIz7zG/2NHfam9AAAAJAngjAYsINN6fsx7E3ph+3t99OuAAAAgDwThMEQ5GFT+n5Cri9vCcMAAABIjyAMMmKUN6X/6OOIP/nr/sZ+5a8iWh8PtJzUpRH2OZ0RAACgN6dGwogZKzw7Za/XmGH6i1rEL/+pv7G/+MeIt2oRX/3CYGsalH47314Y693h1+k8O1Wxm7FCRKGPn6fTGQEAAHoThMGIKRSyF2b84GfnG/83P8tWEPb2+xFvzvQed97Ot9c+333PN+EVAADAcAnCSEWj0YipqamuY5aWlmJpaWlIFXEZ+/8w2PGXkWQHV14635LsVAMAAPJlbW0t1tbWuo5pNBpDquZ5gjBS0W63Y29vr+uYVqs1pGq4rOufHuz40/TTxZV0B9eod771S6caAABwUa1Wq+fr/TQJwkjF2NhYTExMdB0zPj4+pGq4rNc+F/Gjv+1//O99rvvnk+riSrqDK8udbwAAAFkwPj4ek5OTXcc0Go1ot9tDqug4QRipmJiYiCdPnqRdRu4ltfH+l8oRX/9hf6HTZz8V8Wb57M8n2cWVdAdXGp1vedDvHm0AAED29bPN0dTUVGpdY2OpXBXIhELhWVdVt0c/+0Bdeyniz36/v2t+9w+6Lz+8SBfXWZLu4HqtRyfbSb063/Kg3+6+fsYlqdOJ+KTd/dHpsU8aAAAwenSEAYn44q1ngUY3b8313pA+yS6upDu4kux8y4Ok92hLkn3QAAAgn3SEAUPzRz1CsIhku7iS7uBKsvMtD5Ls7gMAAEiCIAzIlCS7uL5UjvjMb/Q3T78dXL062iL663zLg4t09wEAAAySIAzIlCS7uNLq4Oqn861fw947K0lO2QQAALLGHmEwBEmdzpgHSe/DldTeZYPQ70byL4wlU9+wT2d0yiYAAJA1OsJgCJI6nTEP0ujiSrKDq1/n3Ui+9XH3MVk8ndEpmwAAQNYIwoDMycM+XEluJJ90qJaUQezRBgAAcBmCMGAkpdHFlaQkN5LP6umMTtkEAACyRhAGkIIkN5LP8umMSXb3dToRn7S7Pzqdy9cMAABcXTbLB0hBkhvJp3E6Y5Ib7/fb3dfuRLz4ze5jfvWtiBfstwcAAJxBEAYkwsmY5/Pa5yJ+9Lf9j++2kXzSpzMO+zTLrBr2KZsAAMDgWRoJJMLJmOeT5EbySZ7OmNWN95OWxVM2AQCAwdMRBnAOSXW+HWwk/+Wt3mN7bST/pXLE13/Y34b5vUK1i2y8/9Uv9Dc+K84b9r32eRv5AwDAVaEjDOAckux8S2oj+SRPZ8zyxvtJyeopmwAAwODpCAOutFHfu6zfjeS/eKt3d1k/oVoaG+8P20XCvlHregMAAE4nCAOutELBKYIH+gnVkt54P4uyHvZ1Os9OyOxmrDD8PfeyWhcAAJyHIAzInFHv4hplSZ5mmVVZD/vanYgXv9l9zK++NfyAN6t1AQDAedgjDMgcJ1CmJ8nTLLMqyVM2AQCA0SIIA+BQkhvvZ1Uewj4AAOB0lkYCcExSG+9HZHOZ60HY1+t7jBjdsA8AADidjjAAzq3f0yyzusy1nxCv37APAAAYHYIwAK6Mt99Pbq5+wz4AAGB0CMIAGAn9hFxf3ko2DAMAAK4We4SRikajEVNTU13HLC0txdLS0pAqArLso48j/uSv+xv7lb+KeO3zw93bq9OJaHe6jxkrOO30gOcLAODqWltbi7W1ta5jGo3GkKp5niCMVLTb7djb2+s6ptVqDakaSEeSG8lncVP6JP1FLeKX/9Tf2F/8Y8RbtYivfmGwNR3V7kS8+M3uY371rYgXRvhnkKQkny+hGgBAtrRarZ6v99MkCCMVY2NjMTEx0XXM+Pj4kKqBdBQKyQUjSc6VRT/42fnG/83PhhuEkR4hZLoEkQDASePj4zE5Odl1TKPRiHa7PaSKjhOEkYqJiYl48uRJ2mUAI2L/HwY7ftS8/X7EmzNpVwGCSADgef1sczQ1NZVa15jN8gHIvOufHuz4LBnlQwGyWBMAABwlCAMg81773PnG/945x2fFeQ8FaH080HKOGeWADgAADgjCAMi8L5UjPvMb/Y397Kci3iwPtp5BucihAMOQ5YAOAADOQxAGQOZdeyniz36/v7Hf/YOI8ZcGWs7AXORQgGHIakAHAADnZbN8AI4ZKzzb3LrXmGH74q1nS++6eWvu2bhRldVDAZzaCQDAVSEIA+CYQmF0T3j7oz5DsKyGfVk9FCCrAR0AAJyXpZEA5E6hEPHCWPdHIYUgLKuHAmQ1oAMAgPMShAFARmT1UICsBnQAAHBegjAAyIikDwXodCI+aXd/dDq9r5XVgA4AAM7LHmEAMARvvx/x5kzvcUkeCtDuRLz4ze5jfvWt3nvCHQR0veqKSO7Uzn6fLwAAOA8dYQBwSW+/33vMl7f6G9ePfg8FSFI/wVu/Ad2wny8AADggCAOAS/jo44g/+ev+xn7lryJaHw+0nFT1E9Cl8XwJ1AAAOCAIA4BL+ItaxC//qb+xv/jHiLdqg60n65J+vnSXAQBwHoIwALiEH/zsfOP/5pzjr5okny/deAAAnJcgDAAuYf8fBjv+qkny+dKNBwDAeTk1EoCRMFZ4dsJhrzHDdv3Tgx1/1ST5fF2ku+yrXzj7853Os5M2uxkrRBRS+D0DACAZgjAARkKhEPFCBgOI1z4X8aO/7X/8731ucLWMgiSfr6S78dqdiBe/2X3Mr76Vzd/DLHv7/Yg3Z9KuAgDgGUsjAeASvlSO+Mxv9Df2s5+KeLM82HqyLsnnSzde+hxWAACMGkEYAFzCtZci/uz3+xv73T+IGH9poOVkXpLP12vn7K7Lezde0hxWAACMIkEYAFzSF2/1HvPWXH/jhi2NTp2knq88deN1OhGftLs/Oj32N0uawwoAgFFkjzAAGII/6jMES/JQgH6Xrb0wlr2Qrp/n66C77MtbvceOejdeFvcvS/qwAgCAYRCEAUCGJHUowHmXrb32+e5BUVZP7fzird5BWFa78UZd0ocVAAAMg6WRAHAFJb1srVB41jnW7VHI6GmK/XbjcT4OKwAARpEgDACuoIssW4PzcFgBADCKBGEAcAVZtsag5emwAgDg6rBHGKloNBoxNTXVdczS0lIsLS0NqSKAq8WyNQYtT4cVAAD9W1tbi7W1ta5jGo3GkKp5niCMVLTb7djb2+s6ptVqDakagKvntc9F/Ohv+x9v2RoX4bACAOCkVqvV8/V+mgRhpGJsbCwmJia6jhkfHx9SNQBXz5fKEV//YX8b5lu2xiA5rAAA8mV8fDwmJye7jmk0GtFut4dU0XGCMFIxMTERT548SbsMgCvLsrXBePv9iDdn0q7icjqdiHan+5ixQnZPAQUAsq2fbY6mpqZS6xoThAHAFWXZ2vm8/X7vMV/einhhbLSfs3Yn4sVvdh/zq29FvCAIAwCuIEEYAFzSWOFZcNBrTBZZtvbMRx9H/Mlf9zf2K38V8drnddEBAIwiQRgAXFKhoHvmPLIYHP5Frb/91CIifvGPEW/VIr76hcHWBABA8sbSLgAAyJdC4dnywm6PYe9P9YOfnW/835xzPAAA2aAjDAAYWUl1l+3/w/mue97xw3AVNvIHABg0QRgAMLKSWpZ6/dODHX9ZednIHwBg0CyNBABy77XPnW/87/UY3+lEfNLu/uh0+rvWeTfyb33c31gAgDzSEQYA5N6XyhFf/2F/G+Z/9lMRb5a7j2l3Il78Zvcxv/pWf91sNvIHAEiOjjAAIPeuvRTxZ7/f39jv/kHE+EsDLecYG/kDACRHEAYAEP3trfXW3PD34LoKG/kDAGSFIIx4+PBhzM7OxvXr16NQKMT09HTMz89HtVpNuzQAyJQ/SmEj+qxv5A8AMErsEZZj1Wo15ufno9lsRqVSic3NzSiVSlGr1WJlZSVmZ2cPP14sFtMuF4BzGis824eq1xiy7bXPRfzob/sf32sjfwCAPNMRllPVajVmZ2ej2WzGwsJCbG9vR6VSiVKpFHNzc7G7uxvlcjmq1WrMzMxEs9lMu2QAzqlQiHhhrPujIAjLvC+VIz7zG/2N7WcjfwCAPNMRlkPNZjPm5+cjIqJUKsX6+vqp4zY3N2N6ejrq9XrMz8/H9vb2MMsEAOLXG/l/eav32KQ28n/7/Yg3Z3qP03UIAIwaHWE5dLAcMiJiZWXlzHEH3WERzzrINjY2hlEeAHBCkhv5v/1+7zFf3upvnK5DAGDUCMJypl6vH9sE/+7du13Hv/HGG4dvdwvNAIB09bOR/0cfR/zJX/c331f+KqL18aVKAgDIHEFYzqyurh6+XalUem6Cf9ARFvFsSeXWVh/rMgCATPqLWsQv/6m/sb/4x4i3aoOtBwBg2ARhOXN0eWO53N9uuqVS6fDtR48eJV4TADAcP/jZ+cb/zTnHAwBknSAsR2q14/+37quvvtrX1x0NzHSEAcDo2v+HwY4HAMg6p0bmyNG9wSKOd3p1c3JcrVbru5sMAEbFsE9A7PdkxiRd//RgxwMAZJ2OsBx57733jr3fa3+wAy+//PKx9x8/fpxUSQCQGUmegJjkyYxJeu1z5xv/e+ccz/B0OhGftLs/Op20qwSA7NERliP1ev3Y+xftCNvd3U2sJgC4as57MuNrn48Yf2mgJR36Ujni6z/sb8P8z34q4k0N4JnV7kS8+M3uY371rYgXEuxiBICrQEdYjpwMwi6q2WwmMg8AXEVZPpnx2ksRf/b7/Y397h8ML6ADABgWQViOXDTAOrmE8unTp5cvBgCuqKyfzPjFW73HvDXX3zgAgFFjaSTnlkRHWKPRiKmpqUvPs7S0FEtLS5eeBwCSchVOZvwjIdihTufZMsRuxgr97x8HAKNsbW0t1tbWLj1Po9FIoJqLEYTlSLFYTCTE6neT/W7a7Xbs7e1dep5Wq3XpOQAgSU5mTF+S4ZW9uADg11qtViKv5dMkCMuRGzduJBKE3bhx49JzjI2NxcTExKXnGR8fv/QcAJCk1z4X8aO/7X+8kxmTJ7wCgMEYHx+PycnJS8/TaDSi3W4nUNH5CcJy5KKdXCfDsyQ6wiYmJuLJkyeXngcAsmYQJzOOFZ4FN73GAAAMUlLbE01NTaXWWWaz/By5ffv2sff77Q47uTn+9PR0UiUBwJUziJMZC4WIF8a6P+xRBQDQmyAsR2ZmZo69X6/X+/q63d3dY+9XKpXEagKAq8jJjAAA2SQIy5GTHWH9BmFHO8eKxWKUSqUkywKAXHIyIwDA8AnCcqRcLh/b3+u9997r6+seP358+PbJMA0AAABgVAjCcubu3buHb9dqtb6+5ui4lZWVxGsCAAAAGAZBWM4sLi4evl2tVnuOPzqmVCrZHwwAYES8/X7aFQBA9gjCcqZcLh8Ls7a2trqO39zcPHxbNxgAQDb0E3J9eUsYBgAnCcJyaH19/fDtBw8enDmu2WzGxsZGRDw7KXJhYWHgtQEA0N1HH0f8yV/3N/YrfxXR+nig5QDASHkx7QIYvlKpFJubmzE/Px+1Wi0ePnwYy8vLz427c+dORDw7KfJoZxgAMLrGChG/+lbvMWTXX9QifvlP/Y39xT9GvFWL+OoXBlsTAIwKHWE5NTc3F9vb21EsFmNlZeUwFGs2m1GtVmNmZiZqtVqUy+X44IMPjp02CQCMrkIh4oWx7o+CIGwgOp2IT9rdH51O73l+8LPzXfdvzjkeAK4yHWE5VqlUYn9/Px4+fBiPHj2KO3fuRLPZjGKxGLdv347Nzc2Ym5tLu0wAgCuh3Yl48Zvdx/zqWxEv9Agi9//hfNc973gAuMoEYcTy8vKpSyMBAOjt7fcj3pwZ3vWuf3qw4wHgKrM0EgCA3On3NMUsns742ufON/73zjkeAK4yHWEAAAmzIf35dTrPlg52M1bob/+yfsOrF8Yivnjr7DHnPZ3xtc9HjL/U3/jL+FI54us/7G/D/M9+KuLN8uBrGpQkfy8AIEIQBgCQuEKh9z5PHJfU/llJhldZPZ3x2ksRf/b7z8K8Xr77B8MJ5wYlqd8LADhgaSQAAFfGRcKrs2T5dMZunWwH3prrbxznl9QJoAAMn44wAACujIuEV2d1cY366Yx/JAQbmCQ71Sz/BBguQRgAAFdGkuGV0xkZBss/AYbL0kgAAK6MJMMrpzMCwNUjCAMA4MpIMrz6UjniM7/R3zyjfjojAOSFIAwAgCsjyfDq4HTGfoz66Yw2fwcgL+wRBgDAlXEQXn15q/fYfsKrL97qPddVOJ3RPlUA5IWOMAAArpR+Qqkkw6skT2d8+/3k5gIAnqcjDACA3EkyvOpXPyHXl7ciXhgb/Q6zLOp0nnW+dTNWiCjoegO40gRhpKLRaMTU1FTXMUtLS7G0tDSkigAABuejjyP+5K/7G/uVv4p47fOjvefYML39fsSbM73HWf4JMBxra2uxtrbWdUyj0RhSNc8ThJGKdrsde3t7Xce0Wq0hVQMAMFh/UYv45T/1N/YX/xjxVi3iq18YbE2jQBcdwOhptVo9X++nSRBGKsbGxmJiYqLrmPHx8SFVAwAwWD/42fnG/83PBGGj3kXXb6cawFUzPj4ek5OTXcc0Go1ot9tDqug4QRipmJiYiCdPnqRdBgDAUOz/w2DHZ0lSe3FluYtOpxrA2frZ5mhqaiq1rjFBGAAADNj1Tyc7fqzwbD+rXmPSkNReXFntohv1TjWAvBtLuwAAAOhHP104WfXa5843/vd6jC8UnnUbdXuM+umHWe2iu0inGgDZIQgDACB1/S41G9Uw7EvliM/8Rn9jP/upiDfLg61nFCTdRZeUi3SqXdao/t4DZJEgDACAVJ13qVnr44GWMxDXXor4s9/vb+x3/8BSuojku+iSknSn2lUPgQGyRhAGAECq8rLUrJ9N09+as7n6gax20SXZqZaHEBggawRhAACkKo2lZln1RxkOwYbdkZTVLrokO9XyEgIDZIkgDACAVGV1U/Q8yeryvCx20SXZqSYEBhi+F9MuAACAfEt6U/SxQsSvvtV7DM+cd3nea5/P1h5mSXbRvf1+xJsz3cccdKp9eav3fL061YTAAMMnCAMAIFWvfS7iR3/b//hem6IXChEvJBR05SFUu8jyvK9+YbA1DUK/XW8vjPXuMPvird5BWD+dalk9GRPgKrM0EgCAVGV1U/SIfw7Vxro/CiMehOVheV4am9L306mW1ZMxAa4yQRgAAKnK6qboeZGH5XlZ3ZQ+yyEwwFUlCAMAIHVZ3BQ9L/KwPC+rXW9CYIDhE4QBADASktwUnV/Lw/K8LHe9CYEBhksQBgAAOZbG8rx+Nq5P0qh3vQmBAZIjCAMAgBxLenlev6czDjMMy0PXGwD9EYQBAEDOJbU8L43TGfthU3oADgjCAACAnvpZnpfV0xltSs9ZOp2IT9rdH51O2lUCSXox7QIAACAPxgoRv/pW7zGj7CKnM371C2d/Psnn7Iu3ni3J7Mam9KOh04lo9winxgoRhT5+N9qdiBe/2X3Mr74V8cKI35vArwnCAABgCAqFq/9iOunTGYf9nPW7KX0eQs2IZAOnJAmvgMsQhAEAAIkY9dMZ+5WHUDNC4ARcTfYIAwAAEuF0RgCyThAGAAAkwumMAGSdIAwAAEiE0xkByDp7hAEAAIlxOuP55GXjfYCsEISRikajEVNTU13HLC0txdLS0pAqAgBgWPo9nTEPktx4P6unPEZkuzYgWWtra7G2ttZ1TKPRGFI1zxOEkYp2ux17e3tdx7RarSFVAwAAoy/LpzxmuTYgWa1Wq+fr/TQJwkjF2NhYTExMdB0zPj4+pGoAgLRZHpYuzz8ASRkfH4/JycmuYxqNRrTb7SFVdJwgjFRMTEzEkydP0i4DAMiIJJeHcX6efzjb2+9HvDmTdhUwOvrZ5mhqaiq1rjGnRgIAAJBLb7/fe8yXt/obB4wGHWEAAMCVZ/lnvvTTxfXRxxF/8tf9zfeVv4p47fMR4y9durSR5tADrgJBGAAAcOVZ/jkYaSwb7LeL64WxiC92OaH0L2oRv/yn/q75i3+MeKsW8dUv9Df+qnLoAVeBpZEAAAA8J4vLBs/bxdX6+OzP/+Bn57v235xzPJBNgjAAAACOSTJwStJFurjOsv8P57v2eccD2SQIAwAA4JgkA6ckJdnFdf3T55vrvOOBbBKEAQAAcExWlw0m2cX12ufON9fvnXM8kE2CMAAAAI7J6rLBJLu4vlSO+Mxv9DfPZz8V8Wa5+5hOJ+KTdvdHp8eJi3njOSMNTo0EAAASM1Z4dmpcrzFkWxrLBvs5gfK1z0X86G/7n7NbF9e1lyL+7Pefbfjfy3f/IGL8pe5jsnqiYqfzrLZuxgrPTlYdtqw+Z1xtgjAAACAxhYIXrVdBkoFTRP8nUL4wFvHFW2eP+VI54us/7G//sn66uL54q3cQ9tZc95qyTtgEx1kaCQAAwDFJLhtM8gTKgy6ufvTTxdWPPxrhEAx4niAMAAAgJ/rpzIpINnBK+gTKfrqzRr2LCxgcQRgAAMAV0O/yw37DsKQCpzROoBz1Lq5R3kS+398vSIs9wgAAAEbceZcfvvb54S0bzOoJlFmW1X29ktrrDdKkIwwAAGDEJb38MElpnEBJ8pLc620QRrmLjuHSEQYAADDiLrL88KtfGEwtJyV9AiXpuEjYOqzfsYjsdtGRPTrCAAAARlyWlx8meQIl6UljrzcYBEEYAADAiMvy8sMkT6AkPVkOW+E8BGEAAAAj7rVzLicc9vLDpE6gJD1pha1OoSRpgjAAAIARdxWWH/ZzAiXpGUTY2u8plMIwkmSzfAAAgBF3sPzwy1u9x1p++GtjhWcbqPcak5S33494cya5+ZLST11fKkd8/Yf9bZjfT9h63lMoX/u831uSIQgDAAC4Ar54q3cQdhWWHyYZXhUKyZ0i2G930wtjw/0ZJFVX0mFr1k+h5OqyNBIAACAnrsLyw0LhWWjT7VFIsIurH+ftbmp9PNByDiVdV5J7vTmFkrToCAMAAOA5w142OMqy2t2URl39hq1OoSQtOsIAAAB4ThY7r7Iqq91NWa0rIr1TKEFHGKloNBoxNTXVdczS0lIsLS0NqSIAAICLSaO7qZ8N7rPcdfXa5yJ+9Lf9j+/nFEqyYW1tLdbW1rqOaTQaQ6rmeYIwUtFut2Nvb6/rmFarNaRqAAAALi7p7qakNrjPctdV0qdQ9iOrp3ZeNa1Wq+fr/TQJwkjF2NhYTExMdB0zPj4+pGoAAAAuLsnupvNucP/a588+oTHLXVdJn0KZ1VM782h8fDwmJye7jmk0GtFut4dU0XGFTqfTSeXK5NLU1FTs7e3F5ORkPHnyJO1yAADgyvikHfHiN7uP+dW3ngUBw9bpRLR7vPIcK4zunmMffRwx+e3+u5v27p8d7Py//78R/88f9H/t//Ta2RvcJ1lXRPK/Y/3M99ZcxJd6dHEl/X0yeGlmAzbLBwAAYKCu+sb7B91N/ejV3ZTkBvdJ1pWWfk6hvMjpmOSXIAwAAAAuqZ/ldm/N9R6X9Ab3SdWVZVk+HZPssUcYAADAFTBWeLYsrdcY0tNPd1MaG9z3U1eWZfl0zKu+LHgUCcIAAACugEIh4gUvpkdelje4z2rYmnR4mGR41e70ua+ae3doBGEAAACQEV8qR3z9h/1v/P5mefA1Hchq2Jp0eCi8utrsEQYAAAAZcRU2uB+2L5UjPvMb/Y0ddnhI9gjCAAAAIEPysMF9koSHnIcgDAAAAEbMqG9wnzThIf0ShAEAAABXnvCQCEEYAAAAADnh1EgAAABg6MYKz05f7DUGkiQIAwAAAIauUIh4QdDFkAnCAAAA4JKy2t2U1bogLYIwAAAAuKSsdjdltS5Ii83yAQAAAMgFQRgAAADAObz9ftoVcFGCMAAAAIB/1k/I9eWt5MIwodpw2SMMAAAAMsQG9+n56OOIP/nr/sZ+5a8iXvt8xPhLZ4/pN1R7YSzii7f6uy6XIwgDAACADLHB/fklFR7+RS3il//U3zV/8Y8Rb9UivvqF0z+fdKhGMiyNBAAAAEZaofCsq6rbo9BHEPaDn53vun/TZfxFQjUGTxAGAAAAEBH7/5Dc+CRDNZIjCAMAAACIiOufTm58kqEayRGEAQAAAETEa5873/jf6zI+yVCN5AjCAAAAACLiS+WIz/xGf2M/+6mIN8tnfz7JUI3kODWSVDQajZiamuo6ZmlpKZaWloZUEQAAAHl37aWIP/v9iC9v9R773T/ofsrjl8oRX/9hfxvm9wrVRsna2lqsra11HdNoNIZUzfMEYaSi3W7H3t5e1zGtVmtI1QAAAMAzX7zVOwh7a+7ZuG6SDNVGSavV6vl6P02CMFIxNjYWExMTXceMj48PqRoAAADo3x/1CMEOJBWqjZLx8fGYnJzsOqbRaES73R5SRccJwkjFxMREPHnyJO0yAAAAIFX9hmqjop9tjqamplLrGrNZPgAAAAC5IAgDAAAAIBcEYQAAAADkgiAMAAAAgFwQhAEAAACQC4IwAAAAAHJBEAYAAABALgjCAAAAAMgFQRgAAAAAufBi2gUAAAAAZMVYIeJX3+o9htEkCAMAAAD4Z4VCxAsJBV1CteyxNJKu6vV6zM7Opl0GAAAAjJxCIeKFse6PgiBsqARhOdVsNqNQKPR8TE9PR6lUSrtcAAAAgEsThOXUxsZG32NXVlYGWAkAAADAcAjCcurBgwd9jatUKjrCAAAAgCvBZvk5tLGxEc1mM5aXl3vu/3X79u0hVQUAAAAwWIKwHFpdXY1SqRSrq6tplwIAAAAwNIKwnNna2op6vR7r6+tplwIAAAAwVPYIy5kHDx5EsViMu3fvpl0KAAAAwFAJwnKkVqtFrVaLZrMZ169fj+np6VhcXIytra20SwMAAAAYOEFYjqysrBx7v16vx8bGRszPz0ehUIj5+fmo1WopVQcAAAAwWIKwnKjX61GtVruO2draipmZmVhcXBxSVQAAAADDY7P8nCiVSrG+vh7NZjN2d3ejWq1GvV4/dezGxkY8fvw4dnZ2hlwlAAAAwOAIwnJkYWHh2PvNZjM2NjbiwYMH0Ww2j32uVqvF7OxsbG9vD7FCAAAAgMEpdDqdTtpFkL6tra24d+/ec4HY6upqLC8vJ3adqamp2Nvbi7GxsZiYmLj0fEtLS7G0tJRAZQAAAEA3a2trsba2dul5Go1GtNvtmJycjCdPniRQWf90hBEREXNzc1GpVOLOnTvHNsx/8OBBokHYgXa7HXt7e5eep9VqJVANAAAA0Eur1UrktXyaBGEcKhaLsbOzEzMzM4dhWLPZjGq1GpVKJdFrJdURNj4+nkA1AAAAQC/j4+MxOTl56XkOOsLSYGlkyjY2NhI/pbFcLl9qo/tarRYzMzOH7y8vL8fq6moSpR0ujUyj/REAAABIX5rZwNhQr8ZIKJfLxzrAzjpdEgAAAGCUWBqZskqlEpubm4nOWSwWLz3H7OxsVKvVyxcDAAAAkBGCsJSVSqUolUppl/GcozXduHEjxUoAAAAAkmFpJKc6GoQl0WEGAAAAkDZBGKd6/Pjx4duzs7MpVgIAAACQDEEYp9rd3T18++jG+QAAAACjShDGqba2tiIiYnl5OeVKAAAAAJIhCOM5W1tbUa/Xo1gsxv3799MuBwAAACARgrAcqFarcf369SgUCjE7Oxu1Wu3MsfV6Pe7duxcRET/+8Y9tlA8AAABcGYKwHNjc3IxmsxkRz0KxmZmZWFxcfG7cwedu3LgRu7u7US6Xh1wpAAAAwOAIwnJgfn7+uY9tbGzE9evXY35+PhYXF2NmZiZmZ2djYWEhdnZ2olQqpVApAAAAwOAIwnKgUqnE7u5uLCwsRKlUOrbcsVarxdOnT+P+/fuxv78fq6urlkMCAAAAV9KLaRfAcJRKpVhfX0+7DAAAAIDU6AgDAAAAIBcEYQAAAADkgiAMAAAAgFwQhAEAAACQC4IwAAAAAHJBEAYAAABALgjCAAAAAMgFQRgAAAAAuSAIAwAAACAXBGEAAAAA5IIgDAAAAIBceDHtAsinRqMRU1NTXccsLS3F0tLSkCoCAAAALmttbS3W1ta6jmk0GkOq5nmCMFLRbrdjb2+v65hWqzWkagAAAIAktFqtnq/30yQIIxVjY2MxMTHRdcz4+PiQqgEAAACSMD4+HpOTk13HNBqNaLfbQ6rouEKn0+mkcmVyaWpqKvb29mJycjKePHmSdjkAAADAkKWZDdgsHwAAAIBcEIQBAAAAkAuCMAAAAAByQRAGAAAAQC4IwgAAAADIBUEYAAAAALkgCAMAAAAgFwRhAAAAAOSCIAwAAACAXBCEAQAAAJALgjAAAAAAckEQBgAAAEAuCMIAAAAAyAVBGAAAAAC5IAgDAAAAIBcEYQAAAADkgiAMAAAAgFwQhAEAAACQC4IwAAAAAHJBEAYAAABALgjCAAAAAMgFQRgAAAAAuSAIAwAAACAXBGEAAAAA5IIgDAAAAIBceDHtAsinRqMRU1NTXccsLS3F0tLSkCoCAAAALmttbS3W1ta6jmk0GkOq5nmCMFLRbrdjb2+v65hWqzWkagAAAIAktFqtnq/30yQIIxVjY2MxMTHRdcz4+PiQqgEAAACSMD4+HpOTk13HNBqNaLfbQ6rouEKn0+mkcmVyaWpqKvb29mJycjKePHmSdjkAAADAkKWZDdgsHwAAAIBcEIQBAAAAkAuCMAAAAAByQRAGAAAAQC4IwgAAAADIBUEYAAAAALkgCAMAAAAgFwRhAAAAAOSCIAwAAACAXBCEAQAAAJALgjAAAAAAckEQBgAAAEAuCMIAAAAAyAVBGAAAAAC5IAgDAAAAIBcEYQAAAADkgiAMAAAAgFwQhAEAAACQC4IwAAAAAHJBEAYAAABALgjCAAAAAMgFQRgAAAAAuSAIAwAAACAXBGEAAAAA5MKLaRdAPjUajZiamuo6ZmlpKZaWloZUEQAAAHBZa2trsba21nVMo9EYUjXPE4SRina7HXt7e13HtFqtIVUDAAAAJKHVavV8vZ8mQRipGBsbi4mJia5jxsfHh1QNAAAAkITx8fGYnJzsOqbRaES73R5SRccVOp1OJ5Urk0tTU1Oxt7cXk5OT8eTJk7TLAQAAAIYszWzAZvkAAAAA5IIgDAAAAIBcEIQBAAAAkAuCMAAAAAByQRAGAAAAQC4IwgAAAADIBUEYAAAAALkgCAMAAAAgFwRhAAAAAOSCIAwAAACAXBCEAQAAAJALgjAAAAAAckEQBgAAAEAuCMIAAAAAyAVBGAAAAAC5IAgDAAAAIBcEYQAAAADkgiAMAAAAgFwQhAEAAACQC4IwAAAAAHJBEAYAAABALgjCAAAAAMgFQRgAAAAAuSAIAwAAACAXXky7APKp0WjE1NRU1zFLS0uxtLQ0pIoAAACAy1pbW4u1tbWuYxqNxpCqeZ4gjFS02+3Y29vrOqbVag2pGgAAACAJrVar5+v9NAnCSMXY2FhMTEx0HTM+Pj6kagAAAIAkjI+Px+TkZNcxjUYj2u32kCo6rtDpdDqpXJlcmpqair29vZicnIwnT56kXQ4AAAAwZGlmAzbLBwAAACAXBGEAAAAA5IIgDAAAAIBcEIQBAAAAkAuCMAAAAAByQRAGAAAAQC4IwgAAAADIBUEYAAAAALkgCAMAAAAgFwRhAAAAAOSCIAwAAACAXBCEjaB6vR6zs7OxtbV1qXkePnwYs7Ozcf369SgUCjE9PR3z8/NRrVYTqhQAAAAgOwRhI6TZbMb8/HxMT09HtVqNp0+fXmiearUa169fj5WVlYiI2NzcjN3d3VhdXY1arRazs7MxOzsbzWYzweoBAAAA0vVi2gXQW7PZjAcPHsTDhw8vPVe1Wo3Z2dmIiFhYWIj19fXDz5VKpZibm4uZmZmoVqsxMzMTOzs7USwWL31dAAAAgLTpCMu4hw8fxszMTNRqtUvPddBRFvEs9Doagh21ubkZEc+WYB6MBwAAABh1grAMq9VqUalUYnd3N7a3t88Mrvo1Pz9/uNzxYFnkaQ46wyKedZBtbGxc6roAAAAAWSAIy7ByuRzlcvnw/du3b194rnq9fmwT/Lt373Yd/8Ybbxy+3S00AwAAABgVgrARcpm9ulZXVw/frlQqPec66AiLeLak8rInVAIAAACkTRCWE0eXNx7tMuumVCodvv3o0aPEawIAAAAYJkFYDpzcaP/VV1/t6+uOBmY6wgAAAIBRJwjLgaN7g0Uc7/Tq5uS4JE6uBAAAAEiLICwH3nvvvWPv97vX2Msvv3zs/cePHydVEgAAAMDQCcJyoF6vH3v/oh1hu7u7idUEAAAAMGyCsBw4GYRdVLPZTGQeAAAAgDS8mHYBDN5FA6yTSyifPn16+WL+WaPRiKmpqUvPs7S0FEtLSwlUBAAAAHSztrYWa2trl56n0WgkUM3FCMLoW5IdYe12O/b29i49T6vVSqAaAAAAoJdWq5XIa/k0CcJyoFgsJhJi9bvJfj/GxsZiYmLi0vOMj48nUA0AAADQy/j4eExOTl56nkajEe12O4GKzk8Qdg4bGxuxuLiY6Jzlcjl2dnYSnfOkGzduJBKE3bhx4/LF/LOJiYl48uRJYvMBAAAAg5XU9kRTU1OpdZbZLD8HLtrJdTI8S7IjDAAAAGDYdISdQ6VSic3NzUTnHEa4dPv27ajVaofvN5vNvq57cnP86enppEsDAAAAGBpB2DmUSqUolUppl3FuMzMzx96v1+tRLpd7ft3u7u6x9yuVSqJ1AQAAAAyTpZE5cPv27WPv1+v1vr7u6NLIYrE4kiEgAAAAwAFBWA6Uy+VjSyHfe++9vr7u8ePHh2+fDNMAAAAARo0gLCfu3r17+PbR/cK6OTpuZWUl8ZoAAAAAhkkQlhOLi4uHb1er1Z7jj44plUr2BwMAAABGniBshBzds+u8yuXysTBra2ur6/ijp2PqBgMAAACuAkHYCDm5yf15g7H19fXDtx88eHDmuGazGRsbGxHx7KTIhYWFc10HAAAAIIsEYSOi2Ww+15n16NGjc81RKpUOO71qtVo8fPjw1HF37tyJiGcnRR7tDAMAAAAYZYKwDGs2mzE/Px+zs7Nx/fr15zrCarVaFAqFmJ2djfn5+b42wZ+bm4vt7e0oFouxsrJy+HXNZjOq1WrMzMxErVaLcrkcH3zwwbHTJgEAAABG2YtpF8DZBtWRValUYn9/Px4+fBiPHj2KO3fuRLPZjGKxGLdv347Nzc2Ym5tL/LoAAAAAaSp0Op1O2kWQH1NTU7G3txeTk5Px5MmTtMsBAAAAhizNbMDSSAAAAAByQRAGAAAAQC4IwgAAAADIBUEYAAAAALkgCAMAAAAgFwRhAAAAAOSCIAwAAACAXBCEAQAAAJALgjAAAAAAckEQBgAAAEAuCMIAAAAAyAVBGAAAAAC58GLaBZBPjUYjpqamuo5ZWlqKpaWlIVUEAAAAXNba2lqsra11HdNoNIZUzfMEYaSi3W7H3t5e1zGtVmtI1QAAAABJaLVaPV/vp0kQRirGxsZiYmKi65jx8fEhVQMAAAAkYXx8PCYnJ7uOaTQa0W63h1TRcYVOp9NJ5crk0tTUVOzt7cXk5GQ8efIk7XIAAACAIUszG7BZPgAAAAC5IAgDAAAAIBcEYQAAAADkgiAMAAAAgFwQhAEAAACQC4IwAAAAAHJBEAYAAABALgjCAAAAAMgFQRgAAAAAuSAIAwAAACAXBGEAAAAA5IIgDAAAAIBcEIQBAAAAkAuCMAAAAAByQRAGAAAAQC4IwgAAAADIBUEYAAAAALkgCAMAAAAgFwRhAAAAAOTCi2kXAAAAAEB3nU5Eu9N9zFgholAYTj2jShAGAAAAkHHtTsSL3+w+5lffinhBENaVpZEAAAAA5IIgDAAAAIBcEIQBAAAAkAv2CCMVjUYjpqamuo5ZWlqKpaWlIVUEAAAAXNba2lqsra11HdNoNIZUzfMEYaSi3W7H3t5e1zGtVmtI1QAAAMDoe/v9iDdn0q2h1Wr1fL2fJkEYqRgbG4uJiYmuY8bHx4dUDQAAAGTb2+/3HvPlrYgXxiK+eGvw9ZxlfHw8Jicnu45pNBrRbreHVNFxhU6n00nlyuTS1NRU7O3txeTkZDx58iTtcgAAACDzPvo4YvLbEb/8p95jP/upiL37EeMvDb6ui0ozG7BZPgAAAECG/UWtvxAsIuIX/xjxVm2w9YwyQRgAAABAhv3gZ+cb/zfnHJ8ngjAAAACADNv/h8GOzxNBGAAAAECGXf/0YMfniSAMAAAAIMNe+9z5xv/eOcfniSAMAAAAIMO+VI74zG/0N/azn4p4szzYekaZIAwAAAAgw669FPFnv9/f2O/+QcT4SwMtZ6QJwgAAAAAy7ou3eo95a66/cXkmCAMAAAC4Av5ICNaTIAwAAACAXBCEAQAAAJALL6ZdAAAAAADdjRUifvWt3mPoThAGAAAAkHGFQsQLgq5LszQSAAAAgFwQhAEAAACQC4IwAAAAAHJBEAYAAABALgjCAAAAAMgFQRgAAAAAuSAIAwAAACAXBGEAAAAA5IIgDAAAAIBcEIQBAAAAkAsvpl0AjIq1tbVotVoxPj4eS0tLaZcDnMJ9CtnnPoXR4F6F7HOfXkyh0+l00i6C/Jiamoq9vb0YGxuLiYmJrmOXlpYydTMf1D45ORlPnjxJuxzgFO5TyD73KYwG9ypkX1bv07W1tVhbW+s6ptFoRLvdTqV2HWGkot1ux97eXtcxrVZrSNUAAAAASWi1Wj1f76dJEEYq+ukIGx8fH1I1AAAAQBLGx8djcnKy65iDjrA0CMJIxcTERKZaNwEAAIDL62ebo4NlnWlwaiQAAAAAuSAIAwAAACAXBGEAAAAA5IIgDAAAAIBcsFk+Q/WLX/zi2P8yHGtra9FqtWJ8fLznpoVZMqp1R6id8xvl513t6Rjl2kfVKD/nak/HKNc+ykb5eR/V2ke17ojRrn2UpZkNFDqdTmfoVyW3XnjhhWi32zE2NhaffPJJ2uWcy8GpFpOTkyN34uWo1j6qdUeoPS1qT4fa0zGqtY9q3RFqT4va06H2dIxq7aNad4Ta05JmNmBpJAAAAAC5IAgDAAAAIBcEYQAAAADkgiAMAAAAgFwQhAEAAACQC4IwAAAAAHJBEAYAAABALhQ6nU4n7SLIj0KhcPj25ORkipWcX6PRiHa7HWNjYzExMZF2OecyqrWPat0Rak+L2tOh9nSMau2jWneE2tOi9nSoPR2jWvuo1h2h9rTs7e0dvj3sWEoQxlCNjY0N/ZccAAAAyJ5CoRDtdnuo13xxqFcj9z796U/Hxx9/HC+88EL8y3/5L9MuBwAAABiy//bf/lt88skn8dJLLw392jrCAAAAAMgFm+UDAAAAkAuCMAAAAAByQRAGAAAAQC4IwgAAAADIBUEYAAAAALkgCAMAAAAgFwRhAAAAAOSCIAwAAACAXBCEAQAAAJALgjAAAAAAckEQRu7UarVYXFyM6enpKBQKUSgUYnp6OlZWVqLZbF543ocPH8bs7Gxcv379cM75+fmoVquZmhNGVb1ej9nZ2dja2rrUPO5VSIf7BJLl7yJkm9edGdaBnNjf3+/Mzc11IqLrY319/Vzzbm9vd4rFYiciOpVKpbO9vd3Z3d3tbG5udkql0uHH9/f3U50TRtXJe/e89+gB9yqkw30CyfJ3EbLN687sE4SRC/v7+4c3cj+PhYWFvubd3t7u+TXlcrkTEZ1SqdTXPyCDmBNG0f7+fmd5efnS/9HQ6bhXIS3uE0iOv4uQfV53jgZBGLlQqVQ6EdEpl8udzc3Nzu7u7mHSfdp/UEREZ3Nzs+uc+/v7h+l5qVQ6c9zu7u7hnJVKZehzwihaXV3tlEqlw3v3Mv/B716FdLhPIDn+LsJo8LpzNAjCuPLW19c7EdFZXl4+c8zu7u5h2n3wKBaLXec9+h8ivf4DpN/29UHMCaNmZ2ens7Ozc/j+wT180d939yqkw30CyfB3EUaD152jQxDGlXfw/571cjTtPnhsb2/3NbZXm+jm5mbPf+gGMSdcBTs7Oxf+D373KqTDfQKD4+8iZJPXnaPDqZFcabVaLer1emxubvYcWyqVYnV19bmvP83RcZVKJYrFYte55+bmDt9uNpunnu4ziDnhKuh1L3TjXoV0uE9gcPxdhOzxunO0CMK40h49ehQLCwt9/wdDpVI59v7Pf/7zU8dtbGwcvl0ul/uau1QqHatrGHNC3rlXIR3uE8gmfxdhMLzuHC2CMK60N95447m0vZuT/xBMT08/N+ZkWv/qq6+ee+6TKfog5oS8c69COtwnkE3+LsLgeN05WgRhXGnlcvlc7ePNZvPY+0eT7wPVarXnmNOcHHf0H6FBzAl5516FdLhPIJv8XYTB8bpztAjC4Ih6vX7s/ZMtqxER77333rH3+/0H7+WXXz72/uPHjwc6J+SdexXS4T6BbPJ3EbLD6850CcLgiKM39MLCwqljTv6jddEUfXd3d6BzQt65VyEd7hPIJn8XITu87kyXIAyOWF9fP3x7ZWXl1DEn//G4qKPtsIOYE/LOvQrpcJ9ANvm7CNnhdWe6BGHwz+r1+uFa59XV1TPT8Yve9CfbTp8+fTrQOSHv3KuQDvcJZJO/i5ANXnemTxAG/+zglI9SqRTLy8sDv94gUvRRT+Yhi9yrkA73CWSTv4twOV53pk8QBvHs1IuNjY0oFouxvb3ddex5TgPpd55BzAl5516FdLhPIJv8XYT0ed2ZDYIwUrOxsRGFQiHRx8zMzIVquXfvXkRE/PjHP+65YeCNGzcudI1u8wxiTkhKlu7V83CvQjrcJ5BN/i5C+rzuzAZBGLm3uLgYtVotNjc3o1wu9xx/0fT7ZPtoEsl8tzkh79yrkA73CWSTv4uQLq87s+PFtAsgvyqVSmxubiY653lvyI2NjdjY2Ij19fWYm5vr62tu3759uLlhxLN/FPq57skNBaenpwc6JyQlC/fqRbhXIR3uE8gmfxchPV53ZosgjNSUSqWe7aCDVK1WY3FxMdbX12NhYaHvrzu5pKter/eV6O/u7h57v1KpDHROSEra9+pFuVchHe4TyCZ/FyEdXndmj6WR5FKtVovZ2dlYXV091z9GEc9S9KPq9XpfX3e0nbRYLB4LFgYxJ+SdexXS4T6BbPJ3EYbP685sEoSRO/V6Pe7cuRPLy8sXOq62XC4fax997733+vq6x48fH7598h+gQcwJeedehXS4TyCb/F2E4fK6M7sEYeRKvV6PmZmZWFhYiNXV1b6/5uHDh8c+dvfu3cO3j66x7ubouJWVlec+P4g5Ie/cq5AO9wlkk7+LMBxed2ZcB3Jif3+/UyqVOgsLC+f6unK53NnZ2Tn2sZ2dnU5EHD562d7ePhxbKpVOHTOIOeEq2N3dPXZvrK+v9/217lVIh/sEBsffRcg2rzuzT0cYudBsNmNmZiZKpVKsrKxEvV7v+ahWq4ebCZ7cQLBcLh/bIHBra6vr9Y+euHdWgj6IOeEqOHlc83m4VyEd7hMYHH8XIbu87hwRaSdxMAzlcvlY6n2ex1n/L9vR/zeuXC6fee39/f3DcZVKpWudg5gTRt3m5uaxe3J1dfVcX+9ehXS4T2Aw/F2E7PK6czQIwrjyLvOPUa+s+Oh/iJz1HyEH1y8Wi539/f2e9Q5iThhVB63lR+/Jbn+sz+JehXS4TyBZ/i5CdnndOToEYVxpc3Nzl/rHqJ913dvb251isdiJiM7c3FxnZ2ens7+/39ne3j78h6NcLp/rH45BzAmjYn9/vzM3N9epVCpd789KpXJ4f/TDvQrpcJ/A5fi7CNnndedoKXQ6nU4Al/bw4cN49OhR1Ov1aDabUSwW4/bt27G4uBhzc3OZmRPyzr0K6XCfQDb5uwijxT17eYIwAAAAAHLBqZEAAAAA5IIgDAAAAIBcEIQBAAAAkAuCMAAAAAByQRAGAAAAQC4IwgAAAADIBUEYAAAAALkgCAMAAAAgFwRhAAAAAOSCIAwAAACAXBCEAQAAAJALgjAAAAAAckEQBgAAAEAuCMIAAAAARlSz2Yz5+fmo1WqpXP/hw4exsrKSyrUvQhAGAAAAMIKq1WrcvHkzSqVSlMvlw48XCoWBPw4sLCxErVaL6enpqNfraTwN51LodDqdtIsAAAAAoH8HnVjr6+uxsLBw7HNHg6qjisVi3Lhx4/D904KrUql0+PbTp0+j2WyeOtfJOGlxcTHeeeed+PGPf3wslMsaHWEAAAAAI6RbCHYyuFpYWIidnZ3odDqxv78fu7u7h4+TgVWlUjn2+f39/eh0OrG7uxurq6tda1pfX49KpRIzMzOpLdPshyAMAAAAYERsbGzEyspKLC8vPxeCnbS5uRnr6+uX7tAqlUqxvLwc29vbhx87rVNsc3MzyuVy3Llz58xOsrQJwgAAAABGwNbWViwuLka5XD6zQ+vp06cR8ay7a25uLtHrVyqVqFQqXcdsbm5Gs9mMO3fuJHrtpAjCAAAAADKu2WzGvXv3IiJ6LlOMeLZn1yAczHsQuJ1UKpUON9DP4mmSgjAAAACAjJufn49msxnlcrlrV9bBksRenVsX1c+8BwHYw4cPo1qtDqSOixKEAQAAAGTY1tbWYaB0//79rmNLpVKsr69HsVgcSC3FYjHW19ePnT55Wg0HgdmgOtMuShAGAAAAkGEPHjw4fLvXvl/FYrHnJvqXtbCw0DNom52djYiIer0eGxsbA63nPARhAAAAAP+sWq3G9evXo1AonPqYnp4+c++r2dnZ5752enr60vXUarWIiEuf/jhMR5dQ9rOn2bAIwgAAAAD+WaVSif39/VO7qjY3N2N3d/fMYGd7ezv29/cPu6WWl5djd3f3UvWsr68fq21UHA3t6vV6bG1tpVjNrwnCAAAAAE64zD5bzWYzisViIp1QRwOkV1999dLzDdPR4G57ezvFSn5NEAYAAABwipMb0/cT5hwEV0ns03WwJPJAqVS69JzDdLQr7J133kmxkl8ThAEAAACcYnl5+dj7Gxsb0Ww2u37NwVLGJE5LPDgp8sCoBWEvv/zy4dvNZjPq9XqK1TwjCAMAAAA4w2lh2FmazWZUq9UolUqJhFbvvffesfcvulQzLSefg5MdbmkQhAEAAACc4eTyyAcPHpw59mD531mnSp5Xr+6zrDsZ3D19+jSdQo4QhAEAAACcoVgsHtvvq9lsnnkC4ubmZkRE3L17N5FrZyE4SlIWgj1BGAAAAEAXJzu8Tuv4OlgWOTc3l9gSxqPB0agti4yIuHHjxrH3f/7zn6dUya8JwgAAAAC6KJVKUalUDt+v1+vPbWR/sHdYEpvknyYL3VRXgSAMAAAAoIfV1dWu7z969CiKxeKxwOyyRrEL7KiTSzuPniKZFkEYAAAAQA/lcjnK5fLh+9VqNer1ekQ86xCr1WrH9hJLQhInT2ZJFoI9QRgAAABAH06eIHmwV9jB5vlJL4s8ucfWqC2PPFlvFoI9QRgAAABAH+bm5o6FOVtbW9FsNmN9fT1KpVLiQc/s7Oyx90ftFMmDjrkDt2/fTqmSXxOEAQAAAPTp5ImR8/PzUa/XTz1J8rJO7jd2MljKuqOnRJZKJUsjAQAAAEbJwsLCsc6vg9Mj7969m/i1isXisWttb28nfo2jku44OxrcJXmIwGUIwgAAAADO4eSJkXNzcwPrdpqbm/v/t3e3N6klcQCH/262AKQDoQOwA6UDjnYAHWBogQ6gBLADsYRDB44dwOmA/bCBda/XROT1Zp4nITm85MyEj7/MzNleb6Lbofx6htehzyD7ON+iKA5675+6Wq/X63NPAgAAAOBP0mw2tyueXl5ejrbiqaqquL6+3r4/VMZJKUWz2fz0+Wq1OkjU+zjvRqMRb29ve9/zEKwIAwAAANjRZqVWrVY76ra/Wq0Wg8Fg+/4Qq8IWi8WXK7SKoojFYrH3GNPpdHt9jPPTfkoIAwAAANjRZjVVr9c7+ljD4XB7vc85Ye12O66urqLdbn8Zu+bz+fZ3v1sx9l3j8Tgi/l0Ndor/6LuEMAAAAIAdbYLU4+Pj0ceq1WrbsDSZTH58n7IsY71ef/v10+2MKaVtaJvNZj+e7zEIYQAAAAA7en5+jlarFa1W6yTj9Xq96Ha7UVXVXjHsFPr9fkREDAaDk/0/3+WwfAAAAIAdTCaT6Pf7MR6PT77tr9lsxnK5jPf396M9qXIf8/k8Op1OtFqtKMvy3NP5RAgDAAAA2MEmRq1Wq5OPvXnaY7fbvbhth1VVxc3NTdTr9SjL8iJDna2RAAAAAN80Go0ipXS2A+AbjUaUZRnPz8/bLYiXoKqqaLfbFx3BIqwIAwAAAPik3+9HSilarVZ0Op24vb2N6XQa/X4/arXa2bcmLhaLuLu7i4eHh+1B+ueyiWCNRiNms9nFRrAIIQwAAADgf56enmI0Gn35/TnOBvudlFIURRH1ev1sAWoT5IbDYQwGg5OPv6u/zz0BAAAAgEtSVdWX33W73YuIYBH/bZMcjUaxXC7PEsJSSvH6+npxT4f8ihVhAAAAAB8sFotot9ufPr/EA+rZjRAGAAAA8IuUUozH40gpRb1ej6Io4v7+/tzTYk9CGAAAAABZ+OvcEwAAAACAUxDCAAAAAMiCEAYAAABAFoQwAAAAALIghAEAAACQBSEMAAAAgCwIYQAAAABkQQgDAAAAIAtCGAAAAABZEMIAAAAAyIIQBgAAAEAWhDAAAAAAsiCEAQAAAJAFIQwAAACALAhhAAAAAGRBCAMAAAAgC0IYAAAAAFkQwgAAAADIghAGAAAAQBaEMAAAAACyIIQBAAAAkAUhDAAAAIAsCGEAAAAAZEEIAwAAACALQhgAAAAAWRDCAAAAAMiCEAYAAABAFoQwAAAAALIghAEAAACQhX8Ap7nu+DudAWoAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] @@ -106,8 +137,8 @@ "source": [ "bins = np.linspace(-200, 200, 50)\n", "sns.regplot(\n", - " x=ak.to_numpy(array[\"ideal_state_9410_y\"]),\n", - " y=ak.to_numpy(array[\"yDiffOut\"]),\n", + " x=ak.to_numpy(sel_array[\"ideal_state_9410_y\"]),\n", + " y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n", " x_bins=bins,\n", " fit_reg=None,\n", " x_estimator=np.mean,\n", @@ -120,12 +151,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 110, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] @@ -137,8 +168,8 @@ "source": [ "bins = np.linspace(-1, 1, 50)\n", "sns.regplot(\n", - " x=ak.to_numpy(array[\"ideal_state_9410_ty\"]),\n", - " y=ak.to_numpy(array[\"yDiffOut\"]),\n", + " x=ak.to_numpy(sel_array[\"ideal_state_9410_ty\"]),\n", + " y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n", " x_bins=bins,\n", " fit_reg=None,\n", " x_estimator=np.mean,\n", @@ -151,12 +182,12 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 111, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -168,8 +199,8 @@ "source": [ "bins = np.linspace(-0.5, 0.5, 50)\n", "sns.regplot(\n", - " x=ak.to_numpy(array[\"ideal_state_770_ty\"]),\n", - " y=ak.to_numpy(array[\"yDiffOut\"]),\n", + " x=ak.to_numpy(sel_array[\"ideal_state_770_ty\"]),\n", + " y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n", " x_bins=bins,\n", " fit_reg=None,\n", " x_estimator=np.mean,\n", @@ -186,12 +217,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 112, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -203,8 +234,8 @@ "source": [ "bins = np.linspace(-0.5, 0.5, 100)\n", "sns.regplot(\n", - " x=ak.to_numpy(array[\"dSlope_yEndT\"]),\n", - " y=ak.to_numpy(array[\"yDiffOut\"]),\n", + " x=ak.to_numpy(sel_array[\"dSlope_yEndT\"]),\n", + " y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n", " x_bins=bins,\n", " fit_reg=None,\n", " x_estimator=np.mean,\n", @@ -221,12 +252,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 113, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -238,8 +269,8 @@ "source": [ "bins = np.linspace(-1.0, 1.0, 50)\n", "sns.regplot(\n", - " x=ak.to_numpy(array[\"dSlope_xEndT\"]),\n", - " y=ak.to_numpy(array[\"yDiffOut\"]),\n", + " x=ak.to_numpy(sel_array[\"dSlope_xEndT\"]),\n", + " y=ak.to_numpy(sel_array[\"yDiffOut\"]),\n", " x_bins=bins,\n", " fit_reg=None,\n", " x_estimator=np.mean,\n", @@ -256,17 +287,17 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 119, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "intercept= 0.16257747434812642\n", - "coef= {'dSlope_yEndT': 4379.8079670973475, 'dSlope_xEndT dSlope_yEndT': 37.76975769672208, 'ideal_state_770_ty dSlope_xEndT^2': 1224.0422108322969, 'ideal_state_770_ty dSlope_yEndT^2': -18042.13200642719}\n", - "r2 score= 0.8441034202616307\n", - "RMSE = 10.185808113150346\n" + "intercept= 0.0\n", + "coef= {'dSlope_yEndT': 4089.1594362560113, 'dSlope_xEndT dSlope_yEndT': 25.0456971896117, 'ideal_state_770_ty dSlope_xEndT^2': 1049.7443418962382, 'ideal_state_770_ty dSlope_yEndT^2': 77388.96417801932}\n", + "r2 score= 0.7591964011096296\n", + "RMSE = 9.026349818744066\n" ] } ], @@ -282,8 +313,8 @@ "target_feat = \"yDiffOut\"\n", "order = 3\n", "\n", - "data = np.column_stack([ak.to_numpy(array[feat]) for feat in features])\n", - "target = ak.to_numpy(array[target_feat])\n", + "data = np.column_stack([ak.to_numpy(sel_array[feat]) for feat in features])\n", + "target = ak.to_numpy(sel_array[target_feat])\n", "X_train, X_test, y_train, y_test = train_test_split(data,\n", " target,\n", " test_size=0.2,\n", @@ -296,61 +327,61 @@ "poly_features = poly.get_feature_names_out(input_features=features)\n", "\n", "# keep = [\n", - "# # \"ideal_state_770_ty\", #\n", - "# # \"dSlope_xEndT\", #\n", + "# \"ideal_state_770_ty\", #\n", + "# \"dSlope_xEndT\", #\n", "# \"dSlope_yEndT\", # keep\n", - "# # \"ideal_state_770_ty^2\", #\n", - "# # \"ideal_state_770_ty dSlope_xEndT\", #\n", - "# # \"ideal_state_770_ty dSlope_yEndT\", #\n", - "# # \"dSlope_xEndT^2\", # do not keep\n", + "# \"ideal_state_770_ty^2\", #\n", + "# \"ideal_state_770_ty dSlope_xEndT\", #\n", + "# \"ideal_state_770_ty dSlope_yEndT\", #\n", + "# \"dSlope_xEndT^2\", # do not keep\n", "# \"dSlope_xEndT dSlope_yEndT\", # keep\n", - "# # \"dSlope_yEndT^2\", #\n", - "# # \"ideal_state_770_ty^3\",\n", - "# # \"ideal_state_770_ty^2 dSlope_xEndT\", #\n", - "# # \"ideal_state_770_ty^2 dSlope_yEndT\",\n", + "# \"dSlope_yEndT^2\", #\n", + "# \"ideal_state_770_ty^3\",\n", + "# \"ideal_state_770_ty^2 dSlope_xEndT\", #\n", + "# \"ideal_state_770_ty^2 dSlope_yEndT\",\n", "# \"ideal_state_770_ty dSlope_xEndT^2\", # keep\n", - "# # \"ideal_state_770_ty dSlope_xEndT dSlope_yEndT\", #\n", + "# \"ideal_state_770_ty dSlope_xEndT dSlope_yEndT\", #\n", "# \"ideal_state_770_ty dSlope_yEndT^2\", # keep\n", - "# # \"dSlope_xEndT^3\", # do not keep\n", - "# # \"dSlope_xEndT^2 dSlope_yEndT\", #\n", - "# # \"dSlope_xEndT dSlope_yEndT^2\", #\n", - "# # \"dSlope_yEndT^3\",\n", - "# # \"ideal_state_770_ty^4\",\n", - "# # \"ideal_state_770_ty^3 dSlope_xEndT\",\n", - "# # \"ideal_state_770_ty^3 dSlope_yEndT\",\n", - "# # \"ideal_state_770_ty^2 dSlope_xEndT^2\", #\n", - "# # \"ideal_state_770_ty^2 dSlope_xEndT dSlope_yEndT\",\n", - "# # \"ideal_state_770_ty^2 dSlope_yEndT^2\",\n", - "# # \"ideal_state_770_ty dSlope_xEndT^3\", # do not keep\n", - "# # \"ideal_state_770_ty dSlope_xEndT^2 dSlope_yEndT\", #\n", - "# # \"ideal_state_770_ty dSlope_xEndT dSlope_yEndT^2\",\n", - "# # \"ideal_state_770_ty dSlope_yEndT^3\",\n", - "# # \"dSlope_xEndT^4\", #\n", + "# \"dSlope_xEndT^3\", # do not keep\n", + "# \"dSlope_xEndT^2 dSlope_yEndT\", #\n", + "# \"dSlope_xEndT dSlope_yEndT^2\", #\n", + "# \"dSlope_yEndT^3\",\n", + "# \"ideal_state_770_ty^4\",\n", + "# \"ideal_state_770_ty^3 dSlope_xEndT\",\n", + "# \"ideal_state_770_ty^3 dSlope_yEndT\",\n", + "# \"ideal_state_770_ty^2 dSlope_xEndT^2\", #\n", + "# \"ideal_state_770_ty^2 dSlope_xEndT dSlope_yEndT\",\n", + "# \"ideal_state_770_ty^2 dSlope_yEndT^2\",\n", + "# \"ideal_state_770_ty dSlope_xEndT^3\", # do not keep\n", + "# \"ideal_state_770_ty dSlope_xEndT^2 dSlope_yEndT\", #\n", + "# \"ideal_state_770_ty dSlope_xEndT dSlope_yEndT^2\",\n", + "# \"ideal_state_770_ty dSlope_yEndT^3\",\n", + "# \"dSlope_xEndT^4\", #\n", "# \"dSlope_xEndT^3 dSlope_yEndT\", # keep\n", - "# # \"dSlope_xEndT^2 dSlope_yEndT^2\", #\n", - "# # \"dSlope_xEndT dSlope_yEndT^3\",\n", - "# # \"dSlope_yEndT^4\",\n", - "# # \"ideal_state_770_ty^5\",\n", - "# # \"ideal_state_770_ty^4 dSlope_xEndT\",\n", - "# # \"ideal_state_770_ty^4 dSlope_yEndT\",\n", - "# # \"ideal_state_770_ty^3 dSlope_xEndT^2\",\n", - "# # \"ideal_state_770_ty^3 dSlope_xEndT dSlope_yEndT\",\n", - "# # \"ideal_state_770_ty^3 dSlope_yEndT^2\",\n", - "# # \"ideal_state_770_ty^2 dSlope_xEndT^3\", #\n", - "# # \"ideal_state_770_ty^2 dSlope_xEndT^2 dSlope_yEndT\",\n", - "# # \"ideal_state_770_ty^2 dSlope_xEndT dSlope_yEndT^2\",\n", - "# # \"ideal_state_770_ty^2 dSlope_yEndT^3\",\n", + "# \"dSlope_xEndT^2 dSlope_yEndT^2\", #\n", + "# \"dSlope_xEndT dSlope_yEndT^3\",\n", + "# \"dSlope_yEndT^4\",\n", + "# \"ideal_state_770_ty^5\",\n", + "# \"ideal_state_770_ty^4 dSlope_xEndT\",\n", + "# \"ideal_state_770_ty^4 dSlope_yEndT\",\n", + "# \"ideal_state_770_ty^3 dSlope_xEndT^2\",\n", + "# \"ideal_state_770_ty^3 dSlope_xEndT dSlope_yEndT\",\n", + "# \"ideal_state_770_ty^3 dSlope_yEndT^2\",\n", + "# \"ideal_state_770_ty^2 dSlope_xEndT^3\", #\n", + "# \"ideal_state_770_ty^2 dSlope_xEndT^2 dSlope_yEndT\",\n", + "# \"ideal_state_770_ty^2 dSlope_xEndT dSlope_yEndT^2\",\n", + "# \"ideal_state_770_ty^2 dSlope_yEndT^3\",\n", "# \"ideal_state_770_ty dSlope_xEndT^4\", # keep\n", - "# # \"ideal_state_770_ty dSlope_xEndT^3 dSlope_yEndT\", #\n", - "# # \"ideal_state_770_ty dSlope_xEndT^2 dSlope_yEndT^2\",\n", - "# # \"ideal_state_770_ty dSlope_xEndT dSlope_yEndT^3\",\n", - "# # \"ideal_state_770_ty dSlope_yEndT^4\",\n", - "# # \"dSlope_xEndT^5\", #\n", + "# \"ideal_state_770_ty dSlope_xEndT^3 dSlope_yEndT\", #\n", + "# \"ideal_state_770_ty dSlope_xEndT^2 dSlope_yEndT^2\",\n", + "# \"ideal_state_770_ty dSlope_xEndT dSlope_yEndT^3\",\n", + "# \"ideal_state_770_ty dSlope_yEndT^4\",\n", + "# \"dSlope_xEndT^5\", #\n", "# \"dSlope_xEndT^4 dSlope_yEndT\", # keep\n", - "# # \"dSlope_xEndT^3 dSlope_yEndT^2\", #\n", - "# # \"dSlope_xEndT^2 dSlope_yEndT^3\",\n", - "# # \"dSlope_xEndT dSlope_yEndT^4\",\n", - "# # \"dSlope_yEndT^5\",\n", + "# \"dSlope_xEndT^3 dSlope_yEndT^2\", #\n", + "# \"dSlope_xEndT^2 dSlope_yEndT^3\",\n", + "# \"dSlope_xEndT dSlope_yEndT^4\",\n", + "# \"dSlope_yEndT^5\",\n", "# ]\n", "\n", "keep = [\n", @@ -361,6 +392,10 @@ " # \"dSlope_xEndT^3 dSlope_yEndT\", # do not keep\n", " # \"ideal_state_770_ty dSlope_xEndT^4\", # keep\n", " # \"dSlope_xEndT^4 dSlope_yEndT\", # keep\n", + " ###\n", + " # \"ideal_state_770_ty^5\",\n", + " # \"dSlope_xEndT^5\",\n", + " # \"dSlope_yEndT^5\",\n", "]\n", "\n", "# keep = [\n", @@ -373,12 +408,12 @@ "X_test_model = np.delete(X_test_model, remove, axis=1)\n", "poly_features = np.delete(poly_features, remove)\n", "# print(poly_features)\n", - "lin_reg = LinearRegression()\n", + "lin_reg = LinearRegression(fit_intercept=False)\n", "# lin_reg = Lasso(fit_intercept=False, alpha=0.000001)\n", "# lin_reg = Lasso(alpha=0.1)\n", "# lin_reg = LassoCV(max_iter=2000)\n", "# lin_reg = ElasticNet(alpha=0.1)\n", - "# lin_reg = Ridge(alpha=0)\n", + "# lin_reg = Ridge(alpha=0.1)\n", "lin_reg.fit(X_train_model, y_train)\n", "y_pred_test = lin_reg.predict(X_test_model)\n", "print(\"intercept=\", lin_reg.intercept_)\n", @@ -389,16 +424,16 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 115, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['dSlope_xEndT^2', 'dSlope_xEndT^3']" + "[]" ] }, - "execution_count": 45, + "execution_count": 115, "metadata": {}, "output_type": "execute_result" } @@ -415,42 +450,47 @@ }, { "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "ls_koeffs = []\n", - "for itr in koeffs.items():\n", - " ls_koeffs.append(itr[0])\n", - "# ls_koeffs" - ] - }, - { - "cell_type": "code", - "execution_count": 65, + "execution_count": 116, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "['1', 'dSlope_xEndT^2', 'dSlope_xEndT^3', 'dSlope_xEndT^6']" + "['dSlope_yEndT',\n", + " 'dSlope_xEndT dSlope_yEndT',\n", + " 'ideal_state_770_ty dSlope_xEndT^2',\n", + " 'ideal_state_770_ty dSlope_yEndT^2',\n", + " 'ideal_state_770_ty dSlope_xEndT^4',\n", + " 'dSlope_xEndT^4 dSlope_yEndT']" ] }, - "execution_count": 65, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], + "source": [ + "ls_koeffs = []\n", + "for itr in koeffs.items():\n", + " ls_koeffs.append(itr[0])\n", + "ls_koeffs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 120, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -485,11 +525,11 @@ ")\n", "ax2.set_ylabel(\"Number of Tracks (normalised)\")\n", "mplhep.lhcb.text(\"Simulation\", loc=0)\n", - "# plt.show()\n", - "plt.savefig(\n", - " \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/bend_y_regression_plot.pdf\",\n", - " format=\"PDF\",\n", - ")" + "plt.show()\n", + "# plt.savefig(\n", + "# \"/work/cetin/LHCb/reco_tuner/parameterisations/plots/bend_y_regression_plot.pdf\",\n", + "# format=\"PDF\",\n", + "# )" ] }, { diff --git a/parameterisations/parameterise_track_model_electron.py b/parameterisations/parameterise_track_model_electron.py index 32d6b42..2f29b33 100644 --- a/parameterisations/parameterise_track_model_electron.py +++ b/parameterisations/parameterise_track_model_electron.py @@ -31,6 +31,9 @@ def parameterise_track_model( array["ideal_state_10000_z"] - array["ideal_state_770_z"] ) array["yDiffOut"] = array["ideal_state_10000_y"] - array["yStraightOut"] + + array = array[(array["yDiffOut"] < 100) & (array["yDiffOut"] > -100)] + array["yStraightEndT"] = array["ideal_state_770_y"] + array[ "ideal_state_770_ty" ] * (9410.0 - array["ideal_state_770_z"]) diff --git a/parameterisations/result/track_model_params_electron.hpp b/parameterisations/result/track_model_params_electron.hpp index c790af3..71c78ef 100644 --- a/parameterisations/result/track_model_params_electron.hpp +++ b/parameterisations/result/track_model_params_electron.hpp @@ -2,8 +2,8 @@ // param[2]*ideal_state_770_ty dSlope_xEndT^2 + param[3]*ideal_state_770_ty // dSlope_yEndT^2 static constexpr std::array bendYParamsMatchElectron{ - 4349.958490794569f, 23.412267157820907f, 1217.6046177241865f, - -17594.965665893247f}; + 4089.1594362560113f, 25.0456971896117f, 1049.7443418962382f, + 77388.96417801932f}; // param[0]*dSlope_yEndT + param[1]*ideal_state_770_ty dSlope_xEndT_abs + // param[2]*ideal_state_770_ty dSlope_yEndT_abs + param[3]*ideal_state_770_ty // ideal_state_770_tx dSlope_xEndT + param[4]*ideal_state_770_ty dSlope_xEndT^2 @@ -12,6 +12,6 @@ static constexpr std::array bendYParamsMatchElectron{ // dSlope_xEndT_abs + param[8]*ideal_state_770_ty^3 ideal_state_770_tx // dSlope_xEndT static constexpr std::array bendYParamsElectron{ - 2727.4382768214373f, 171.62746271508692f, -1639.9176239198928f, - 2841.0170296705633f, 490.03818091751873f, 34340.21609055052f, - 2885.8437352087253f, -697.8060002066588f, 17736.16501881911f}; + 2725.327235395602f, 251.23007060378893f, -6061.341551441552f, + 2624.269841207972f, 404.65243171033745f, 193290.96063652748f, + 1854.349846613761f, 120.35295786470361f, 18947.494690363914f}; diff --git a/parameterisations/train_matching_ghost_mlps_electron.py b/parameterisations/train_matching_ghost_mlps_electron.py index 1319dc7..5211ebd 100644 --- a/parameterisations/train_matching_ghost_mlps_electron.py +++ b/parameterisations/train_matching_ghost_mlps_electron.py @@ -1,5 +1,5 @@ # flake8: noqaq - +# ruff: noqa import os import argparse import ROOT @@ -63,7 +63,7 @@ def train_matching_ghost_mlp( "abs(quality) > 0", "Signal is defined as non-zero label", ) - bkg_selection = "quality == 0" + bkg_selection = "quality >= 0" if filter_velos: bkg_selection += " && velo_isElectron == 1" if filter_seeds: @@ -114,7 +114,7 @@ def train_matching_ghost_mlp( dataloader.AddVariable("distY", "F") dataloader.AddVariable("dSlope", "F") dataloader.AddVariable("dSlopeY", "F") - dataloader.AddVariable("zMag", "F") + # dataloader.AddVariable("zMag_electron", "F") # dataloader.AddVariable("eta", "F") # dataloader.AddVariable("dEta", "F") @@ -123,7 +123,7 @@ def train_matching_ghost_mlp( # these cuts are also applied in the algorithm preselectionCuts = ROOT.TCut( - "chi2<15 && distX<250 && distY<250 && dSlope<1.5 && dSlopeY<0.15 && zMag<6000 && zMag>4500", + "chi2<15 && distX<250 && distY<250 && dSlope<1.5 && dSlopeY<0.15", # && zMag_electron<6000 && zMag_electron>4500", ) dataloader.PrepareTrainingAndTestTree( preselectionCuts, @@ -135,7 +135,7 @@ def train_matching_ghost_mlp( dataloader, TMVA.Types.kMLP, "matching_mlp", - "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+4,N+2:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator", + "!H:V:TrainingMethod=BP:NeuronType=ReLU:EstimatorType=CE:VarTransform=Norm:NCycles=700:HiddenLayers=N+2,N:TestRate=50:Sampling=1.0:SamplingImportance=1.0:LearningRate=0.02:DecayRate=0.01:!UseRegulator", ) factory.TrainAllMethods() factory.TestAllMethods() diff --git a/scripts/PrCheckerEfficiency.py b/scripts/PrCheckerEfficiency.py index 8381090..d2dc973 100644 --- a/scripts/PrCheckerEfficiency.py +++ b/scripts/PrCheckerEfficiency.py @@ -60,8 +60,8 @@ def getOriginFolders(): "DefaultMatch": {}, } # evtl anpassen wenn die folders anders heissen - basedict["Velo"]["folder"] = "VeloTrackChecker/" - basedict["Upstream"]["folder"] = "UpstreamTrackChecker/" + basedict["Velo"]["folder"] = "VeloTrackChecker" + unique_name_ext_re() + "/" + basedict["Upstream"]["folder"] = "UpstreamTrackChecker" + unique_name_ext_re() + "/" basedict["Forward"]["folder"] = "ForwardTrackChecker" + unique_name_ext_re() + "/" basedict["Match"]["folder"] = "MatchTrackChecker" + unique_name_ext_re() + "/" basedict["BestLong"]["folder"] = "BestLongTrackChecker" + unique_name_ext_re() + "/" @@ -72,10 +72,6 @@ def getOriginFolders(): basedict["DefaultMatch"]["folder"] = ( "DefaultMatchTrackChecker" + unique_name_ext_re() + "/" ) - # basedict["Forward"]["folder"] = "ForwardTrackChecker_7a0dbfa7/" - # basedict["Match"]["folder"] = "MatchTrackChecker_29e3152a/" - # basedict["BestLong"]["folder"] = "BestLongTrackChecker_4ddacce1/" - # basedict["Seed"]["folder"] = "SeedTrackChecker_1b1d5575/" return basedict @@ -139,7 +135,7 @@ def getGhostHistoNames(): "Seed": {}, } - basedict["Velo"] = ["eta", "nPV"] + basedict["Velo"] = ["eta", "p", "pt", "nPV"] basedict["Upstream"] = ["eta", "p", "pt", "nPV"] basedict["Forward"] = ["eta", "p", "pt", "nPV"] basedict["Match"] = ["eta", "p", "pt", "nPV"] @@ -170,7 +166,7 @@ def argument_parser(): "--trackers", type=str, nargs="+", - default=["Forward", "Match", "BestLong", "Seed"], # DefaultMatch + default=["Forward", "Match", "BestLong", "Seed"], # Velo help="Trackers to plot.", ) parser.add_argument( @@ -256,31 +252,36 @@ def get_eff(eff, hist, tf, histoName, label, var): eff[lab].SetTitle(lab + " Forward, e^{-}") else: eff[lab].SetTitle(lab + " Forward, not e^{-}") - if histoName.find("Merged") != -1: + elif histoName.find("Merged") != -1: if histoName.find("electron") != -1: eff[lab].SetTitle(lab + " MergedMatch, e^{-}") else: eff[lab].SetTitle(lab + " MergedMatch, not e^{-}") - if histoName.find("DefaultMatch") != -1: + elif histoName.find("DefaultMatch") != -1: if histoName.find("electron") != -1: eff[lab].SetTitle(lab + " DefaultMatch, e^{-}") else: eff[lab].SetTitle(lab + " DefaultMatch, not e^{-}") - if histoName.find("Match") != -1: + elif histoName.find("Match") != -1: if histoName.find("electron") != -1: eff[lab].SetTitle(lab + " Match, e^{-}") else: eff[lab].SetTitle(lab + " Match, not e^{-}") - if histoName.find("Seed") != -1: + elif histoName.find("Seed") != -1: if histoName.find("electron") != -1: eff[lab].SetTitle(lab + " Seed, e^{-}") else: eff[lab].SetTitle(lab + " Seed, not e^{-}") - if histoName.find("BestLong") != -1: + elif histoName.find("BestLong") != -1: if histoName.find("electron") != -1: eff[lab].SetTitle(lab + " BestLong, e^{-}") else: eff[lab].SetTitle(lab + " BestLong, not e^{-}") + elif histoName.find("Velo") != -1: + if histoName.find("electron") != -1: + eff[lab].SetTitle(lab + " Velo, e^{-}") + else: + eff[lab].SetTitle(lab + " Velo, not e^{-}") # if histoName.find("EndVelo") != -1: # eff[lab].SetTitle(eff[lab].GetTitle() + " EndVelo") # if histoName.find("EndUT") != -1: diff --git a/scripts/True2VeloPrCheckerEfficiency.py b/scripts/True2VeloPrCheckerEfficiency.py index 690395b..a94462b 100644 --- a/scripts/True2VeloPrCheckerEfficiency.py +++ b/scripts/True2VeloPrCheckerEfficiency.py @@ -435,13 +435,6 @@ def PrCheckerEfficiency( canvas.SetRightMargin(0.1) mg = TMultiGraph() for i, lab in enumerate(label): - if ( - categories[tracker][cut]["plotEndVelo"] - and plot_velo - and (histo == "p" or histo == "pt") - ): # and not plot_velo_only: - mg.Add(eff_velo[lab]) - set_style(eff_velo[lab], colors[i], markers[i], styles[i]) if categories[tracker][cut]["plotElectrons"] and plot_electrons: if (not plot_velo_only) or ( histo == "phi" or histo == "eta" or histo == "nPV" @@ -453,6 +446,13 @@ def PrCheckerEfficiency( elec_markers[i], styles[i], ) + if ( + categories[tracker][cut]["plotEndVelo"] + and plot_velo + and (histo == "p" or histo == "pt") + ): # and not plot_velo_only: + mg.Add(eff_velo[lab]) + set_style(eff_velo[lab], colors[i], markers[i], styles[i]) mg.Draw("AP") mg.GetYaxis().SetRangeUser(0, 1.05) @@ -493,14 +493,6 @@ def PrCheckerEfficiency( hist_velo[lab].Scale(scale) if i == 0: - if ( - categories[tracker][cut]["plotEndVelo"] - and plot_velo - and (histo == "p" or histo == "pt") - ): - set_style(hist_velo[lab], mygray, markers[i], styles[i]) - gStyle.SetPalette(2, array("i", [mygray - 1, myblue + 1])) - hist_velo[lab].Draw("HIST PLC SAME") if categories[tracker][cut]["plotElectrons"] and plot_electrons: if not plot_velo_only or ( histo == "phi" or histo == "eta" or histo == "nPV" @@ -508,8 +500,17 @@ def PrCheckerEfficiency( set_style( hist_elec[lab], myblue, elec_markers[i], styles[i] ) - hist_elec[lab].SetFillColorAlpha(myblue, 0.5) + hist_elec[lab].SetFillColorAlpha(myblue, 0.75) hist_elec[lab].Draw("HIST PLC SAME") + if ( + categories[tracker][cut]["plotEndVelo"] + and plot_velo + and (histo == "p" or histo == "pt") + ): + set_style(hist_velo[lab], mygray, markers[i], styles[i]) + gStyle.SetPalette(2, array("i", [myblue + 1, mygray - 2])) + hist_velo[lab].SetFillColorAlpha(mygray, 0.6) + hist_velo[lab].Draw("HIST PLC SAME") # else: # print( @@ -521,7 +522,7 @@ def PrCheckerEfficiency( # hist_den[lab].Draw("HIST PLC SAME") if histo == "p": - pos = [0.5, 0.35, 1.0, 0.7] # [0.53, 0.4, 1.01, 0.71] + pos = [0.45, 0.4, 0.95, 0.75] # [0.53, 0.4, 1.01, 0.71] elif histo == "pt": pos = [0.5, 0.3, 0.99, 0.5] # [0.5, 0.4, 0.98, 0.71] elif histo == "phi": diff --git a/scripts/utils/ConfigHistos.py b/scripts/utils/ConfigHistos.py index f555293..87e7581 100644 --- a/scripts/utils/ConfigHistos.py +++ b/scripts/utils/ConfigHistos.py @@ -1,3 +1,4 @@ +# flake8: noqa from collections import defaultdict @@ -222,7 +223,7 @@ def categoriesDict(): basedict["Velo"]["05_long_strange_P>5GeV"]["plotElectrons"] = False basedict["Velo"]["06_long_fromB"]["plotElectrons"] = True basedict["Velo"]["07_long_fromB_P>5GeV"]["plotElectrons"] = True - basedict["Velo"]["11_long_fromB_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False + basedict["Velo"]["11_long_fromB_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = True basedict["Velo"]["11_long_strange_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False basedict["Velo"]["12_UT_long_fromB_P>3GeV_Pt>0.5GeV"]["plotElectrons"] = False @@ -235,6 +236,22 @@ def categoriesDict(): "Electrons" ] = "11_long_fromB_electrons_P>3GeV_Pt>0.5GeV" + basedict["Velo"]["01_velo"]["plotEndVelo"] = False + basedict["Velo"]["02_long"]["plotEndVelo"] = True + basedict["Velo"]["03_long_P>5GeV"]["plotEndVelo"] = False + basedict["Velo"]["06_long_fromB"]["plotEndVelo"] = True + basedict["Velo"]["07_long_fromB_P>5GeV"]["plotEndVelo"] = True + basedict["Velo"]["11_long_fromB_P>3GeV_Pt>0.5GeV"]["plotEndVelo"] = True + + basedict["Velo"]["02_long"]["EndVelo"] = "08_long_electrons_EndVelo" + basedict["Velo"]["06_long_fromB"]["EndVelo"] = "09_long_fromB_electrons_EndVelo" + basedict["Velo"]["07_long_fromB_P>5GeV"][ + "EndVelo" + ] = "10_long_fromB_electrons_P>5GeV_EndVelo" + basedict["Velo"]["11_long_fromB_P>3GeV_Pt>0.5GeV"][ + "EndVelo" + ] = "11_long_fromB_electrons_P>3GeV_Pt>0.5GeV_EndVelo" + # UPSTREAM basedict["Upstream"]["01_velo"]["title"] = "Velo, 2 <#eta < 5" basedict["Upstream"]["02_velo+UT"]["title"] = "VeloUT, 2 <#eta < 5" diff --git a/thesis/TMVA_stuff.ipynb b/thesis/TMVA_stuff.ipynb index 15fd225..662ae4e 100644 --- a/thesis/TMVA_stuff.ipynb +++ b/thesis/TMVA_stuff.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -22,16 +22,14 @@ "metadata": {}, "outputs": [], "source": [ - "train_tree = uproot.open(\n", - " {\n", - " \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_NewParams_EFilter/matching_ghost_mlp_training.root\": \"MatchNNDataSet/TrainTree\"\n", - " }\n", - ")\n", - "test_tree = uproot.open(\n", - " {\n", - " \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_NewParams_EFilter/matching_ghost_mlp_training.root\": \"MatchNNDataSet/TestTree\"\n", - " }\n", - ")\n", + "train_tree = uproot.open({\n", + " \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_NewParams_EFilter/matching_ghost_mlp_training.root\":\n", + " \"MatchNNDataSet/TrainTree\"\n", + "})\n", + "test_tree = uproot.open({\n", + " \"/work/cetin/LHCb/reco_tuner/nn_electron_training/result_NewParams_EFilter/matching_ghost_mlp_training.root\":\n", + " \"MatchNNDataSet/TestTree\"\n", + "})\n", "train_array = train_tree.arrays()\n", "test_array = test_tree.arrays()" ] @@ -141,9 +139,7 @@ " label=\"training sample, wrong pairs\",\n", ")\n", "axes[1, 0].set_xlabel(r\"$D_{x}$ [mm]\")\n", - "axes[1, 0].set_ylabel(\"Number of tracks (normalised)\",\n", - " va=\"bottom\",\n", - " ha=\"center\")\n", + "axes[1, 0].set_ylabel(\"Number of tracks (normalised)\", va=\"bottom\", ha=\"center\")\n", "# 0,1\n", "axes[0, 1].hist(\n", " train_sig.teta2,\n", @@ -231,8 +227,8 @@ ")\n", "axes[2, 1].set_xlabel(r\"$|\\Delta t_{y}^{\\mathrm{match}}|$\")\n", "plt.savefig(\n", - " \"/work/cetin/LHCb/reco_tuner/thesis/filtered_NN_elec_variables.pdf\",\n", - " format=\"PDF\")\n", + " \"/work/cetin/LHCb/reco_tuner/thesis/filtered_NN_elec_variables.pdf\", format=\"PDF\"\n", + ")\n", "# plt.show()" ] }, @@ -309,7 +305,9 @@ " label=\"training sample, wrong pairs\",\n", ")\n", "axes[0, 2].set_xlabel(r\"$D_{x}$ [mm]\")\n", - "axes[0, 0].set_ylabel(\"Number of tracks (normalised)\", va=\"bottom\", ha=\"center\")\n", + "axes[0, 0].set_ylabel(\"Number of tracks (normalised)\",\n", + " va=\"bottom\",\n", + " ha=\"center\")\n", "# 1,0\n", "axes[1, 0].hist(\n", " train_sig.distY,\n",