# 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
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,
    PrMatchNNv3,
    PrFilterTracks2ElectronMatch,
    PrFilterTracks2ElectronShower,
    fromPrMatchTracksV1Tracks,
    fromV3TrackV1Track,
)
import Functors as F
import glob

options.evt_max = -1
options.ntuple_file = f"data/calo_data_test_tinker.root"
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():
    hlt2_tracks = make_hlt2_tracks(light_reco=True, fast_reco=False, use_pr_kf=True)

    digisEcal = make_digits(calo_raw_bank=False)["digitsEcal"]
    tracks_v3, trackrels = convert_tracks_to_v3_from_v1(
        hlt2_tracks["Seed"]["v1"],
        track_types=["Ttrack"],
    )
    # track acceptances
    tracks_incalo = make_acceptance(tracks_v3)

    eshower = make_trackbased_eshower(tracks_incalo, digisEcal)

    # should be best; shape of shower etc; E/p statt chi2; DLL even better? GET(1)
    # Cut=F.FILTER(F.ALL)).Output
    shower_matched_seeds = PrFilterTracks2ElectronShower(
        Relation=eshower["Ttrack"], Cut=F.FILTER((F.GET(0) @ F.WEIGHT) > 0.7)
    ).Output

    matched_seeds = {}
    matched_seeds["v3"] = shower_matched_seeds
    matched_seeds["v1"] = fromV3TrackV1Track(
        InputTracks=matched_seeds["v3"]
    ).OutputTracks

    calo_long = PrMatchNNv3(
        VeloInput=hlt2_tracks["Velo"]["Pr"],
        SeedInput=matched_seeds["v3"],
        AddUTHitsToolName=get_global_ut_hits_tool(),
    ).MatchOutput

    match_tracks = {}
    match_tracks["Pr"] = calo_long
    match_tracks["v1"] = fromPrMatchTracksV1Tracks(
        InputTracksLocation=match_tracks["Pr"],
        VeloTracksLocation=hlt2_tracks["Velo"]["v1"],
        SeedTracksLocation=matched_seeds["v1"],
    ).OutputTracksLocation

    data = [calo_long, shower_matched_seeds]
    types_and_locations_for_checkers = {
        "Match": match_tracks,
    }
    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)