peelers.py

 
 
"""
A set of common purose translators from complex framework objects to flat dictionaries
"""
 
import functools
 
import math
import numpy as np
 
import ROOT
 
from ROOT import Belle2
 
import basf2
from tracking.validation.tolerate_missing_key_formatter import TolerateMissingKeyFormatter
 
from tracking.validation.utilities import getObjectList
from tracking.validation.utilities import getHelixFromMCParticle
 
Belle2.RecoTrack.getRightLeftInformation["Belle2::CDCHit"]
ROOT.gSystem.Load("libtracking")
 
formatter = TolerateMissingKeyFormatter()
 
 
 
 
def format_crop_keys(peel_func):
    @functools.wraps(peel_func)
    def peel_func_formatted_keys(*args, key="{part_name}", **kwargs):
        crops = peel_func(*args, **kwargs, key=key)
        if key and hasattr(crops, 'items'):
            crops_with_formatted_keys = dict()
            for part_name, value in list(crops.items()):
                formatted_key = formatter.format(key, part_name=part_name)
                crops_with_formatted_keys[formatted_key] = value
            return crops_with_formatted_keys
 
        else:
            return crops
 
    return peel_func_formatted_keys
 
 
 
 
@format_crop_keys
def peel_mc_particle(mc_particle, key="{part_name}"):
    if mc_particle:
        helix = getHelixFromMCParticle(mc_particle)
        momentum = mc_particle.getMomentum()
        vertex = mc_particle.getVertex()
        charge = mc_particle.getCharge()
        pdg_code = mc_particle.getPDG()
        is_primary = bool(mc_particle.hasStatus(Belle2.MCParticle.c_PrimaryParticle))
 
        decay_vertex = mc_particle.getDecayVertex()
        number_of_daughters = mc_particle.getNDaughters()
        status = mc_particle.getStatus()
 
        return dict(
            # At origin assuming perfect magnetic field
            d0_truth=helix.getD0(),
            phi0_truth=helix.getPhi0() % (2.0 * math.pi),
            omega_truth=helix.getOmega(),
            z0_truth=helix.getZ0(),
            tan_lambda_truth=helix.getTanLambda(),
 
            # At the vertex position
            pt_truth=momentum.Rho(),
            px_truth=momentum.X(),
            py_truth=momentum.Y(),
            pz_truth=momentum.Z(),
            x_truth=vertex.X(),
            y_truth=vertex.Y(),
            z_truth=vertex.Z(),
 
            decay_vertex_radius_truth=decay_vertex.R(),
            decay_vertex_x_truth=decay_vertex.X(),
            decay_vertex_y_truth=decay_vertex.Y(),
            decay_vertex_z_truth=decay_vertex.Z(),
            number_of_daughters_truth=number_of_daughters,
            status_truth=status,
 
            # MC Particle information
            charge_truth=charge,
            pdg_code_truth=pdg_code,
            is_primary=is_primary,
        )
 
    else:
        nan = float('nan')
        return dict(
            # At origin assuming perfect magnetic field
            d0_truth=nan,
            phi0_truth=nan,
            omega_truth=nan,
            z0_truth=nan,
            tan_lambda_truth=nan,
 
            # At the vertex position
            pt_truth=nan,
            px_truth=nan,
            py_truth=nan,
            pz_truth=nan,
            x_truth=nan,
            y_truth=nan,
            z_truth=nan,
 
            decay_vertex_radius_truth=nan,
            decay_vertex_x_truth=nan,
            decay_vertex_y_truth=nan,
            decay_vertex_z_truth=nan,
            number_of_daughters_truth=nan,
            status_truth=nan,
 
 
            # MC Particle information
            charge_truth=nan,
            pdg_code_truth=0,
            is_primary=False,
        )
 
 
 
 
@format_crop_keys
def peel_reco_track_hit_content(reco_track, key="{part_name}"):
    nan = float('nan')
 
    first_pxd_layer = nan
    last_pxd_layer = nan
    first_svd_layer = nan
    last_svd_layer = nan
    first_cdc_layer = nan
    last_cdc_layer = nan
    sum_of_weights = nan
    last_fit_layer = nan
 
    if reco_track:
        sum_of_weights = 0
        last_fit_layer = 0
 
        n_cdc_hits = reco_track.getNumberOfCDCHits()
        n_svd_hits = reco_track.getNumberOfSVDHits()
        n_pxd_hits = reco_track.getNumberOfPXDHits()
        ndf = 2 * n_pxd_hits + n_svd_hits + n_cdc_hits
 
        pxd_hits = [hit.getSensorID().getLayerNumber() for hit in getObjectList(reco_track.getPXDHitList())]
        if pxd_hits:
            first_pxd_layer = min(pxd_hits)
            last_pxd_layer = max(pxd_hits)
        svd_hits = [hit.getSensorID().getLayerNumber() for hit in getObjectList(reco_track.getSVDHitList())]
        if svd_hits:
            first_svd_layer = min(svd_hits)
            last_svd_layer = max(svd_hits)
        cdc_hits = [hit.getICLayer() for hit in getObjectList(reco_track.getCDCHitList())]
        if cdc_hits:
            first_cdc_layer = min(cdc_hits)
            last_cdc_layer = max(cdc_hits)
        for hit_info in getObjectList(reco_track.getRelationsWith("RecoHitInformations")):
            track_point = reco_track.getCreatedTrackPoint(hit_info)
            if track_point:
                fitted_state = track_point.getFitterInfo()
                if fitted_state:
                    W = max(fitted_state.getWeights())
                    sum_of_weights += W
                    if W > 0.5 and hit_info.getTrackingDetector() == Belle2.RecoHitInformation.c_CDC:
                        layer = hit_info.getRelated("CDCHits").getICLayer()
                        if layer > last_fit_layer:
                            last_fit_layer = layer
        return dict(
            n_pxd_hits=n_pxd_hits,
            n_svd_hits=n_svd_hits,
            n_cdc_hits=n_cdc_hits,
            n_hits=n_pxd_hits + n_svd_hits + n_cdc_hits,
            ndf_hits=ndf,
            first_pxd_layer=first_pxd_layer,
            last_pxd_layer=last_pxd_layer,
            first_svd_layer=first_svd_layer,
            last_svd_layer=last_svd_layer,
            first_cdc_layer=first_cdc_layer,
            last_cdc_layer=last_cdc_layer,
            sum_of_weights=sum_of_weights,
            last_fit_layer=last_fit_layer
        )
    else:
        return dict(
            n_pxd_hits=nan,
            n_svd_hits=nan,
            n_cdc_hits=nan,
            n_hits=nan,
            ndf_hits=nan,
            first_pxd_layer=first_pxd_layer,
            last_pxd_layer=last_pxd_layer,
            first_svd_layer=first_svd_layer,
            last_svd_layer=last_svd_layer,
            first_cdc_layer=first_cdc_layer,
            last_cdc_layer=last_cdc_layer,
            sum_of_weights=sum_of_weights,
            last_fit_layer=last_fit_layer
        )
 
 
 
 
@format_crop_keys
def peel_reco_track_seed(reco_track, key="{part_name}"):
    if reco_track:
        seed_fit_result = get_seed_track_fit_result(reco_track)
        return peel_track_fit_result(seed_fit_result, key="seed_{part_name}")
 
    else:
        return peel_track_fit_result(None, key="seed_{part_name}")
 
 
 
 
@format_crop_keys
def peel_event_info(event_meta_data, key="{part_name}"):
    return dict(
        event_number=event_meta_data.getEvent(),
        run_number=event_meta_data.getRun(),
        experiment_number=event_meta_data.getExperiment(),
    )
 
 
 
 
@format_crop_keys
def peel_store_array_info(item, key="{part_name}"):
    if item:
        return dict(
            store_array_number=item.getArrayIndex()
        )
    else:
        return dict(
            store_array_number=float("nan")
        )
 
 
 
 
@format_crop_keys
def peel_store_array_size(array_name, key="{part_name}"):
    array = Belle2.PyStoreArray(array_name)
    return {str(array_name) + "_size": array.getEntries() if array else 0}
 
 
 
 
@format_crop_keys
def peel_event_level_tracking_info(event_level_tracking_info, key="{part_name}"):
    if not event_level_tracking_info:
        return dict(
            has_vxdtf2_failure_flag=False,
            has_svdckf_failure_flag=False,
            has_pxdckf_failure_flag=False,
            has_unspecified_trackfinding_failure=False,
        )
    return dict(has_vxdtf2_failure_flag=event_level_tracking_info.hasVXDTF2AbortionFlag(),
                has_svdckf_failure_flag=event_level_tracking_info.hasSVDCKFAbortionFlag(),
                has_pxdckf_failure_flag=event_level_tracking_info.hasPXDCKFAbortionFlag(),
                has_unspecified_trackfinding_failure=event_level_tracking_info.hasUnspecifiedTrackFindingFailure(),
                )
 
 
 
 
@format_crop_keys
def peel_quality_indicators(reco_track, key="{part_name}"):
    nan = float("nan")
 
    svd_qi = nan
    cdc_qi = nan
    qi = nan
 
    if reco_track:
        qi = reco_track.getQualityIndicator()
        space_point_track_cand = reco_track.getRelated('SPTrackCands')
 
        # adjust relations if SVD->CDC CKF enabled
        if not space_point_track_cand:
            svd_track_cand = reco_track.getRelated('SVDRecoTracks')
            if not svd_track_cand:
                svd_cdc_track_cand = reco_track.getRelated('SVDCDCRecoTracks')
                if svd_cdc_track_cand:
                    svd_track_cand = svd_cdc_track_cand.getRelated('SVDRecoTracks')
                    if not svd_track_cand:
                        temp_svd_track_cand = svd_cdc_track_cand.getRelated('SVDPlusCDCStandaloneRecoTracks')
                        if temp_svd_track_cand:
                            svd_track_cand = temp_svd_track_cand.getRelated('SVDRecoTracks')
            if svd_track_cand:
                space_point_track_cand = svd_track_cand.getRelated('SPTrackCands')
 
        if space_point_track_cand:
            svd_qi = space_point_track_cand.getQualityIndicator()
 
        cdc_track_cand = reco_track.getRelated('CDCRecoTracks')
        if not cdc_track_cand:
            svd_cdc_track_cand = reco_track.getRelated('SVDCDCRecoTracks')
            if svd_cdc_track_cand:
                cdc_track_cand = svd_cdc_track_cand.getRelated('CDCRecoTracks')
                if not cdc_track_cand:
                    cdc_track_cand = svd_cdc_track_cand.getRelated('CKFCDCRecoTracks')
                if not cdc_track_cand:
                    temp_cdc_track_cand = svd_cdc_track_cand.getRelated('SVDPlusCDCStandaloneRecoTracks')
                    if temp_cdc_track_cand:
                        cdc_track_cand = temp_cdc_track_cand.getRelated('CDCRecoTracks')
 
        if cdc_track_cand:
            cdc_qi = cdc_track_cand.getQualityIndicator()
 
    crops = dict(
        quality_indicator=qi,
        svd_quality_indicator=svd_qi,
        cdc_qualityindicator=cdc_qi,
    )
 
    return crops
 
 
 
 
@format_crop_keys
def peel_trackfinder(reco_track, key="{part_name}"):
    used_CDCTrackFinder = False
    used_VXDTrackFinder = False
    used_SVDHough = False
    used_SVDtoCDCCKF = False
    used_ECLtoCDCCKF = False
    used_CDCtoSVDCKF = False
 
    if reco_track:
        if reco_track.getNumberOfSVDHits() > 0:
            info = get_reco_hit_information(reco_track, reco_track.getSVDHitList()[0])
            svd_tf = info.getFoundByTrackFinder()
            used_VXDTrackFinder = svd_tf == Belle2.RecoHitInformation.c_VXDTrackFinder
            used_CDCtoSVDCKF = svd_tf == Belle2.RecoHitInformation.c_CDCtoSVDCKF
            used_SVDHough = svd_tf == Belle2.RecoHitInformation.c_SVDHough
 
        if reco_track.getNumberOfCDCHits() > 0:
            info = get_reco_hit_information(reco_track, reco_track.getCDCHitList()[0])
            cdc_tf = info.getFoundByTrackFinder()
            used_CDCTrackFinder = cdc_tf == Belle2.RecoHitInformation.c_CDCTrackFinder
            used_SVDtoCDCCKF = cdc_tf == Belle2.RecoHitInformation.c_SVDtoCDCCKF
            used_ECLtoCDCCKF = cdc_tf == Belle2.RecoHitInformation.c_ECLtoCDCCKF
 
    crops = dict(
        foundby_CDCTrackFinder=used_CDCTrackFinder,
        foundby_VXDTrackFinder=used_VXDTrackFinder,
        foundby_SVDHough=used_SVDHough,
        foundby_SVDtoCDCCKF=used_SVDtoCDCCKF,
        foundby_CDCtoSVDCKF=used_CDCtoSVDCKF,
        foundby_ECLtoCDCCKF=used_ECLtoCDCCKF,
    )
 
    return crops
 
 
 
 
@format_crop_keys
def peel_fit_status(reco_track, key="{part_name}"):
    nan = float("nan")
 
    crops = dict(
        is_fitted=nan,
        fit_pion_ok=nan,
        ndf_pion=nan,
        fit_muon_ok=nan,
        ndf_muon=nan,
        fit_electron_ok=nan,
        ndf_electron=nan,
        fit_proton_ok=nan,
        ndf_proton=nan,
        fit_kaon_ok=nan,
        ndf_kaon=nan,
    )
 
    if reco_track:
        crops["is_fitted"] = reco_track.wasFitSuccessful()
 
        for rep in getObjectList(reco_track.getRepresentations()):
            was_successful = reco_track.wasFitSuccessful(rep)
            pdg_code = rep.getPDG()
 
            for crop in crops.keys():
                if crop.startswith("fit_"):
                    particle_name = crop.split("_")[1]
                    if abs(getattr(Belle2.Const, particle_name).getPDGCode()) == abs(pdg_code):
                        crops[crop] = was_successful
 
                        if was_successful:
                            crops[f"ndf_{particle_name}"] = reco_track.getTrackFitStatus(rep).getNdf()
 
    return crops
 
 
 
 
@format_crop_keys
def peel_track_fit_result(track_fit_result, key="{part_name}"):
    nan = float("nan")
    if track_fit_result:
        cov6 = track_fit_result.getCovariance6()
        mom = track_fit_result.getMomentum()
        pos = track_fit_result.getPosition()
 
        pt_estimate = mom.Rho()
 
        pt_variance = np.divide(
            mom.X() ** 2 * cov6(3, 3) + mom.Y() ** 2 * cov6(4, 4) - 2 * mom.X() * mom.Y() * cov6(3, 4),
            mom.Perp2()
        )
 
        pt_resolution = np.divide(pt_variance, pt_estimate)
 
        fit_crops = dict(
            has_trackFitResult=True,
            d0_estimate=track_fit_result.getD0(),
            d0_variance=track_fit_result.getCov()[0],
            phi0_estimate=track_fit_result.getPhi() % (2.0 * math.pi),
            phi0_variance=track_fit_result.getCov()[5],
            omega_estimate=track_fit_result.getOmega(),
            omega_variance=track_fit_result.getCov()[9],
            z0_estimate=track_fit_result.getZ0(),
            z0_variance=track_fit_result.getCov()[12],
            tan_lambda_estimate=track_fit_result.getCotTheta(),
            tan_lambda_variance=track_fit_result.getCov()[14],
 
            x_estimate=pos.X(),
            x_variance=cov6(0, 0),
            y_estimate=pos.Y(),
            y_variance=cov6(1, 1),
            z_estimate=pos.Z(),
            z_variance=cov6(2, 2),
 
            pt_estimate=pt_estimate,
            pt_variance=pt_variance,
            pt_resolution=pt_resolution,
 
            track_charge=track_fit_result.getChargeSign(),
            b_field=Belle2.BFieldManager.getField(pos).Z(),
 
            px_estimate=mom.X(),
            px_variance=cov6(3, 3),
            py_estimate=mom.Y(),
            py_variance=cov6(4, 4),
            pz_estimate=mom.Z(),
            pz_variance=cov6(5, 5),
 
            p_value=track_fit_result.getPValue(),
        )
 
    else:
        fit_crops = dict(
            has_trackFitResult=False,
            d0_estimate=nan,
            d0_variance=nan,
            phi0_estimate=nan,
            phi0_variance=nan,
            omega_estimate=nan,
            omega_variance=nan,
            z0_estimate=nan,
            z0_variance=nan,
            tan_lambda_estimate=nan,
            tan_lambda_variance=nan,
 
            x_estimate=nan,
            x_variance=nan,
            y_estimate=nan,
            y_variance=nan,
            z_estimate=nan,
            z_variance=nan,
 
            pt_estimate=nan,
            pt_variance=nan,
            pt_resolution=nan,
 
            track_charge=nan,
            b_field=nan,
 
            px_estimate=nan,
            px_variance=nan,
            py_estimate=nan,
            py_variance=nan,
            pz_estimate=nan,
            pz_variance=nan,
 
            p_value=nan,
        )
 
    return fit_crops
 
 
 
 
def get_reco_hit_information(reco_track, hit):
    """Helper function for getting the correct reco hit info"""
    for info in getObjectList(hit.getRelationsFrom("RecoHitInformations")):
        if info.getRelatedFrom(reco_track.getArrayName()) == reco_track:
            return info
 
 
 
@format_crop_keys
def peel_subdetector_hit_efficiency(mc_reco_track, reco_track, key="{part_name}"):
    def get_efficiency(detector_string):
        if not reco_track or not mc_reco_track:
            hit_efficiency = float("nan")
        else:
            mc_reco_hits = getObjectList(getattr(mc_reco_track, "get{}HitList".format(detector_string.upper()))())
            if len(mc_reco_hits) == 0:
                hit_efficiency = float('nan')
            else:
                mc_reco_hit_size = 0
                hit_efficiency = 0.
                for mc_reco_hit in mc_reco_hits:
                    info = get_reco_hit_information(mc_reco_track, mc_reco_hit)
 
                    if info.getFoundByTrackFinder() == Belle2.RecoHitInformation.c_MCTrackFinderAuxiliaryHit:
                        continue
 
                    mc_reco_hit_size += 1
 
                    for reco_hit in getObjectList(getattr(reco_track, "get{}HitList".format(detector_string.upper()))()):
                        if mc_reco_hit.getArrayIndex() == reco_hit.getArrayIndex():
                            hit_efficiency += 1
                            break
 
                if not mc_reco_hit_size:
                    hit_efficiency = float('nan')
                else:
                    hit_efficiency /= mc_reco_hit_size
 
        return {"{}_hit_efficiency".format(detector_string.lower()): hit_efficiency}
 
    return dict(**get_efficiency("CDC"), **get_efficiency("SVD"), **get_efficiency("PXD"))
 
 
 
@format_crop_keys
def peel_subdetector_hit_purity(reco_track, mc_reco_track, key="{part_name}"):
    def get_efficiency(detector_string):
        if not reco_track or not mc_reco_track:
            hit_purity = float("nan")
        else:
            reco_hits = getObjectList(getattr(reco_track, "get{}HitList".format(detector_string.upper()))())
            reco_hit_size = len(reco_hits)
 
            if reco_hit_size == 0:
                hit_purity = float('nan')
            else:
                hit_purity = 0.
                for reco_hit in reco_hits:
                    for mc_reco_hit in getObjectList(getattr(mc_reco_track, "get{}HitList".format(detector_string.upper()))()):
                        if mc_reco_hit.getArrayIndex() == reco_hit.getArrayIndex():
                            hit_purity += 1
                            break
 
                hit_purity /= reco_hit_size
 
        return {"{}_hit_purity".format(detector_string.lower()): hit_purity}
 
    return dict(**get_efficiency("CDC"), **get_efficiency("SVD"), **get_efficiency("PXD"))
 
 
 
@format_crop_keys
def peel_hit_information(hit_info, reco_track, key="{part_name}"):
    nan = np.float("nan")
 
    crops = dict(residual=nan,
                 residual_x=nan,
                 residual_y=nan,
                 residuals=nan,
                 weight=nan,
                 tracking_detector=hit_info.getTrackingDetector(),
                 use_in_fit=hit_info.useInFit(),
                 hit_time=nan,
                 layer_number=nan,
                 )
 
    if hit_info.useInFit() and reco_track.hasTrackFitStatus():
        track_point = reco_track.getCreatedTrackPoint(hit_info)
        fitted_state = track_point.getFitterInfo()
        if fitted_state:
            try:
                res_state = fitted_state.getResidual().getState()
                crops["residual"] = np.sqrt(res_state.Norm2Sqr())
                if res_state.GetNoElements() == 2:
                    crops["residual_x"] = res_state[0]
                    crops["residual_y"] = res_state[1]
                weights = fitted_state.getWeights()
                crops['weight'] = max(weights)
            except BaseException:
                pass
 
    if hit_info.getTrackingDetector() == Belle2.RecoHitInformation.c_SVD:
        hit = hit_info.getRelated("SVDClusters")
        crops["hit_time"] = hit.getClsTime()
        crops["layer_number"] = hit.getSensorID().getLayerNumber()
    if hit_info.getTrackingDetector() == Belle2.RecoHitInformation.c_PXD:
        hit = hit_info.getRelated("PXDClusters")
        crops["layer_number"] = hit.getSensorID().getLayerNumber()
    if hit_info.getTrackingDetector() == Belle2.RecoHitInformation.c_CDC:
        hit = hit_info.getRelated("CDCHits")
        crops["layer_number"] = hit.getICLayer()
 
    return crops
 
 
 
@format_crop_keys
def peel_module_statistics(modules=None, key="{part_name}"):
    if modules is None:
        modules = []
    module_stats = dict()
 
    for module in basf2.statistics.modules:
        if module.name in modules:
            module_stats[str(module.name) + "_mem"] = module.memory_sum(basf2.statistics.EVENT)
            module_stats[str(module.name) + "_time"] = module.time_sum(basf2.statistics.EVENT)
            module_stats[str(module.name) + "_calls"] = module.calls(basf2.statistics.EVENT)
 
    return module_stats
 
 
 
 
 
def get_seed_track_fit_result(reco_track):
    position = reco_track.getPositionSeed()
    momentum = reco_track.getMomentumSeed()
    cartesian_covariance = reco_track.getSeedCovariance()
    charge_sign = (-1 if reco_track.getChargeSeed() < 0 else 1)
    # It does not matter, which particle we put in here, so we just use a pion
    particle_type = Belle2.Const.pion
    p_value = float('nan')
    b_field = Belle2.BFieldManager.getField(ROOT.Math.XYZVector(position)).Z() / Belle2.Unit.T
    cdc_hit_pattern = 0
    svd_hit_pattern = 0
    # the value 0xFFFF will cause the TrackFitResult::getNDF() to return -1
    ndf = 0xFFFF
 
    track_fit_result = Belle2.TrackFitResult(
        position,
        momentum,
        cartesian_covariance,
        charge_sign,
        particle_type,
        p_value,
        b_field,
        cdc_hit_pattern,
        svd_hit_pattern,
        ndf,
    )
 
    return track_fit_result
 
 
 
 
def is_correct_rl_information(cdc_hit, reco_track, hit_lookup):
    rl_info = reco_track.getRightLeftInformation["const Belle2::CDCHit"](cdc_hit)
    truth_rl_info = hit_lookup.getRLInfo(cdc_hit)
 
    if rl_info == Belle2.RecoHitInformation.c_right and truth_rl_info == 1:
        return True
    if rl_info == Belle2.RecoHitInformation.c_left and truth_rl_info == 65535:  # -1 as short
        return True
 
    return False