92 lines
2.9 KiB
Python
92 lines
2.9 KiB
Python
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# flake8: noqa
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from Moore import options, run_reconstruction
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from RecoConf.hlt2_tracking import (
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make_hlt2_tracks,
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get_default_out_track_types_for_light_reco,
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convert_tracks_to_v3_from_v1,
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get_global_ut_hits_tool,
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)
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from RecoConf.hlt1_tracking import make_all_pvs
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from RecoConf.event_filters import require_gec
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from RecoConf.mc_checking import get_track_checkers, get_fitted_tracks_checkers
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from RecoConf.calorimeter_reconstruction import (
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make_photons_and_electrons,
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make_clusters,
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make_acceptance,
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make_track_cluster_matching,
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make_digits,
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make_track_electron_and_brem_matching,
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make_trackbased_eshower,
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)
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from Moore.config import Reconstruction
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from PyConf.Algorithms import (
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PrFilterTracks2CaloClusters,
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PrMatchNNv3,
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PrFilterTracks2ElectronMatch,
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PrFilterTracks2ElectronShower,
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fromPrMatchTracksV1Tracks,
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fromV3TrackV1Track,
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)
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import Functors as F
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import glob
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options.evt_max = -1
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options.ntuple_file = f"data/calo_data_test_tinker.root"
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options.input_files = ["/auto/data/guenther/Bd_Kstee/00151673_00000002_1.xdigi"]
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options.input_type = "ROOT"
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options.dddb_tag = "dddb-20210617"
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options.conddb_tag = "sim-20210617-vc-md100"
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options.simulation = True
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def standalone_hlt2_fastest_reco():
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hlt2_tracks = make_hlt2_tracks(light_reco=True, fast_reco=False, use_pr_kf=True)
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digisEcal = make_digits(calo_raw_bank=False)["digitsEcal"]
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tracks_v3, trackrels = convert_tracks_to_v3_from_v1(
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hlt2_tracks["Seed"]["v1"],
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track_types=["Ttrack"],
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)
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# track acceptances
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tracks_incalo = make_acceptance(tracks_v3)
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eshower = make_trackbased_eshower(tracks_incalo, digisEcal)
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# should be best; shape of shower etc; E/p statt chi2; DLL even better? GET(1)
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# Cut=F.FILTER(F.ALL)).Output
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shower_matched_seeds = PrFilterTracks2ElectronShower(
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Relation=eshower["Ttrack"], Cut=F.FILTER((F.GET(0) @ F.WEIGHT) > 0.7)
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).Output
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matched_seeds = {}
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matched_seeds["v3"] = shower_matched_seeds
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matched_seeds["v1"] = fromV3TrackV1Track(
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InputTracks=matched_seeds["v3"]
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).OutputTracks
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calo_long = PrMatchNNv3(
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VeloInput=hlt2_tracks["Velo"]["Pr"],
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SeedInput=matched_seeds["v3"],
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AddUTHitsToolName=get_global_ut_hits_tool(),
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).MatchOutput
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match_tracks = {}
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match_tracks["Pr"] = calo_long
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match_tracks["v1"] = fromPrMatchTracksV1Tracks(
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InputTracksLocation=match_tracks["Pr"],
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VeloTracksLocation=hlt2_tracks["Velo"]["v1"],
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SeedTracksLocation=matched_seeds["v1"],
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).OutputTracksLocation
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data = [calo_long, shower_matched_seeds]
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types_and_locations_for_checkers = {
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"Match": match_tracks,
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}
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data += get_track_checkers(types_and_locations_for_checkers)
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# data += get_fitted_tracks_checkers(best_tracks)
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return Reconstruction("hlt2_reco", data, [require_gec()])
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run_reconstruction(options, standalone_hlt2_fastest_reco)
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