commondqm.py

#!/usr/bin/env python3
 

 
import basf2 as b2
from svd import add_svd_create_recodigits
from svd.dqm_utils import add_svd_dqm_dose
from geometry import check_components, is_detector_present, is_any_detector_present
from analysisDQM import add_analysis_dqm, add_mirabelle_dqm, get_hadB_path
import neurotrigger
 
 
def add_common_dqm(path, components=None, dqm_environment="expressreco", dqm_mode="dont_care", create_hlt_unit_histograms=False):
    """
    This function adds DQMs which are common for Cosmic runs and Collion runs
 
    @param components: A list of the detector components which are available in this
                       run of basf2
    @param dqm_environment: The environment the DQM modules are running in
                            "expressreco" (default) if running on the ExpressReco system
                            "hlt" if running on the HLT online reconstructon nodes
                            If running on the hlt, you may want to output less or other DQM plots
                            due to the limited bandwidth of the HLT nodes.
    @param dqm_mode: How to split up the path for online/HLT.
                     For dqm_mode == "dont_care" all the DQM modules should be added.
                     For dqm_mode == "all_events" only the DQM modules which should run on all events
                            (filtered and dismissed) should be added
                     For dqm_mode == "before_reco" only the DQM modules which should run before
                            all reconstruction
                     For dqm_mode == "filtered"  only the DQM modules which should run on filtered
                            events should be added
    @param create_hlt_unit_histograms: Parameter for SoftwareTiggerHLTDQMModule.
                                         Should be True only when running on the HLT servers
    """
    assert dqm_mode in ["dont_care", "all_events", "filtered", "before_filter"]
    # Check components.
    check_components(components)
 
    if dqm_environment == "expressreco" and (dqm_mode in ["dont_care"]):
        # PXD (not useful on HLT)
        if is_detector_present("PXD", components):
            path.add_module('PXDDAQDQM', histogramDirectoryName='PXDDAQ')
            path.add_module('PXDROIDQM', histogramDirectoryName='PXDROI')
            path.add_module('PXDDQMExpressReco', histogramDirectoryName='PXDER')
            path.add_module('SetupGenfitExtrapolation')
            path.add_module('PXDROIFinder',
                            recoTrackListName='RecoTracks',
                            PXDInterceptListName='PXDIntercepts')
            # moved to cosmics/collision as we need different cuts
            # path.add_module('PXDDQMEfficiency', histogramDirectoryName='PXDEFF')
            path.add_module('PXDTrackClusterDQM', histogramDirectoryName='PXDER')
            path.add_module('PXDInjectionDQM', histogramDirectoryName='PXDINJ', eachModule=True)
            path.add_module("PXDDQMBowing")
 
        # SVD
        if is_detector_present("SVD", components):
            # reconstruct SVDRecoDigits first of all
            add_svd_create_recodigits(path)
 
            # SVD DATA FORMAT
            svdunpackerdqm = b2.register_module('SVDUnpackerDQM')
            path.add_module(svdunpackerdqm)
            # offline ZS emulator
            path.add_module(
                'SVDZeroSuppressionEmulator',
                SNthreshold=5,
                ShaperDigits='SVDShaperDigits',
                ShaperDigitsIN='SVDShaperDigitsZS5',
                FADCmode=True)
            # SVD Occupancy after Injection
            path.add_module('SVDDQMInjection', ShaperDigits='SVDShaperDigitsZS5')
            # SVDDQMExpressReco General
            path.add_module('SVDDQMExpressReco',
                            offlineZSShaperDigits='SVDShaperDigitsZS5')
            # SVD HIT TIME
            path.add_module('SVDDQMHitTime')
            # SVD EFFICIENCY
            path.add_module('SetupGenfitExtrapolation')
            path.add_module('SVDROIFinder',
                            recoTrackListName='RecoTracks',
                            SVDInterceptListName='SVDIntercepts')
            path.add_module('SVDDQMEfficiency')
            # SVD CLUSTERS ON TRACK
            path.add_module('SVDDQMClustersOnTrack')
            # SVD DOSE
            add_svd_dqm_dose(path, 'SVDShaperDigitsZS5')
 
        # Event time measuring detectors
        if is_any_detector_present(["CDC", "ECL", "TOP"], components):
            eventT0DQMmodule = b2.register_module('EventT0DQM')
            path.add_module(eventT0DQMmodule)
 
    if dqm_environment == "hlt" and (dqm_mode in ["dont_care", "before_filter"]):
        path.add_module(
            "SoftwareTriggerHLTDQM",
            createHLTUnitHistograms=create_hlt_unit_histograms,
            createTotalResultHistograms=False,
            createExpRunEventHistograms=False,
            createErrorFlagHistograms=True,
            cutResultIdentifiers={},
            histogramDirectoryName="softwaretrigger_before_filter",
            pathLocation="before filter",
        ).set_name("SoftwareTriggerHLTDQM_before_filter")
 
        path.add_module(
            "TrackingAbortDQM",
            histogramDirectoryName="TrackingAbort_before_filter",
        ).set_name("TrackingAbortDQM_before_filter")
 
        path.add_module("DetectorOccupanciesDQM", histogramDirectoryName="DetectorOccupancies_before_filter").set_name(
            "DetectorOccupanciesDQM_before_filter")
 
        path.add_module("StatisticsTimingHLTDQM",
                        createHLTUnitHistograms=create_hlt_unit_histograms,
                        )
        # TTD trigger and bunch injection monitoring
        path.add_module('TTDDQM')
 
    if dqm_environment == "hlt" and (dqm_mode in ["dont_care", "filtered"]):
        # HLT
        hlt_trigger_lines_in_plot = []
        hlt_skim_lines_in_plot = []
 
        hlt_trigger_lines_per_unit_in_plot = [
            "ge3_loose_tracks_inc_1_tight_not_ee2leg",
            "Elab_gt_0.5_plus_2_others_with_Elab_gt_0.18_plus_no_clust_with_Ecms_gt_2.0",
            "selectee",
            "Estargt2_GeV_cluster",
        ]
        cutResultIdentifiers = {}
 
        from softwaretrigger import filter_categories, skim_categories
 
        filter_cat = [method for method in dir(filter_categories) if method.startswith('__') is False if method != 'RESULTS']
        skim_cat = [method for method in dir(skim_categories) if method.startswith('__') is False]
 
        def read_lines(category):
            return [i.split(" ", 1)[1].replace(" ", "_") for i in category]
 
        for i in filter_cat:
            cutResultIdentifiers[i] = {"filter": read_lines(getattr(filter_categories, i))}
            hlt_trigger_lines_in_plot += read_lines(getattr(filter_categories, i))
 
        for i in skim_cat:
            cutResultIdentifiers[i] = {"skim": read_lines(getattr(skim_categories, i))}
            hlt_skim_lines_in_plot += read_lines(getattr(skim_categories, i))
 
        cutResultIdentifiers["skim"] = {"skim": hlt_skim_lines_in_plot}
        cutResultIdentifiers["filter"] = {"filter": hlt_trigger_lines_in_plot}
 
        additionalL1Identifiers = [
            'ffy',
            'fyo',
            'c4',
            'hie',
            'mu_b2b',
            'mu_eb2b',
            'beklm',
            'eklm2',
            'cdcklm1',
            'seklm1',
            'ieklm1',
            'ecleklm1',
            'fso',
            'fioiecl1',
            'ff30',
            'stt',
            'ioiecl1',
            'ioiecl2',
            'lml1',
            'lml2',
            'lml3',
            'lml4',
            'lml5',
            'lml6',
            'lml7',
            'lml8',
            'lml9',
            'lml10',
            'lml12',
            'lml13',
            'bhapur']
 
        # Default plot
        path.add_module(
            "SoftwareTriggerHLTDQM",
            cutResultIdentifiers=cutResultIdentifiers,
            l1Identifiers=["fff", "ffo", "lml0", "ffb", "fp", "passive_veto"],
            additionalL1Identifiers=additionalL1Identifiers,
            createHLTUnitHistograms=create_hlt_unit_histograms,
            cutResultIdentifiersPerUnit=hlt_trigger_lines_per_unit_in_plot,
            pathLocation="after filter",
        )
 
        path.add_module("StatisticsTimingHLTDQM",
                        histogramDirectoryName="timing_statistics_after_filter"
                        ).set_name("StatisticsTimingHLTDQM_after_filter")
 
        path.add_module("TrackingAbortDQM")
 
        path.add_module("DetectorOccupanciesDQM")
 
        # Skim plots where bhabha contamination is removed
        path.add_module(
            "SoftwareTriggerHLTDQM",
            cutResultIdentifiers={
                "skim": {"skim": hlt_skim_lines_in_plot},
            },
            cutResultIdentifiersIgnored={
                "skim": [
                    "accept_bhabha_trk_ecl",
                ]
            },
            createTotalResultHistograms=False,
            createExpRunEventHistograms=False,
            histogramDirectoryName="softwaretrigger_skim_nobhabha",
        ).set_name("SoftwareTriggerHLTDQM_skim_nobhabha")
 
    if dqm_environment == "hlt" and (dqm_mode in ["dont_care", "filtered"]):
        # SVD DATA FORMAT
        if is_detector_present("SVD", components):
            svdunpackerdqm = b2.register_module('SVDUnpackerDQM')
            path.add_module(svdunpackerdqm)
 
    # CDC
    if is_detector_present("CDC", components) and (dqm_mode in ["dont_care", "filtered"]):
        cdcdqm = b2.register_module('cdcDQM7')
        path.add_module(cdcdqm)
 
        module_names = [m.name() for m in path.modules()]
        if ('SoftwareTrigger' in module_names):
            cdcdedxdqm = b2.register_module('CDCDedxDQM')
            path.add_module(cdcdedxdqm)
 
        if dqm_environment == "expressreco":
            path.add_module('CDCDQM')
 
    # ECL
    if is_detector_present("ECL", components) and (dqm_mode in ["dont_care", "filtered"]):
        ecldqm = b2.register_module('ECLDQM')
        path.add_module(ecldqm)
        ecldqmext = b2.register_module('ECLDQMEXTENDED')
        path.add_module(ecldqmext)
        path.add_module('ECLDQMOutOfTimeDigits')
        path.add_module('ECLDQMConnectedRegions')
        # we dont want to create large histograms on HLT, thus ERECO only
        if dqm_environment == "expressreco":
            path.add_module('ECLDQMInjection', histogramDirectoryName='ECLINJ')
 
    # TOP
    if is_detector_present("TOP", components) and (dqm_mode in ["dont_care", "filtered"]):
        topdqm = b2.register_module('TOPDQM')
        path.add_module(topdqm)
 
    # KLM
    if is_detector_present("KLM", components) and (dqm_mode in ["dont_care", "filtered"]):
        klmdqm = b2.register_module("KLMDQM")
        path.add_module(klmdqm)
 
    # TRG before all reconstruction runs (so on all events with all unpacked information)
    if is_detector_present("TRG", components) and (dqm_mode in ["dont_care", "before_filter"]):
        # TRGECL
        trgecldqm = b2.register_module('TRGECLDQM')
        path.add_module(trgecldqm)
        trgecltimingdqm = b2.register_module('TRGECLEventTimingDQM')
        path.add_module(trgecltimingdqm)
        # TRGGDL
        trggdldqm = b2.register_module('TRGGDLDQM')
        trggdldqm.param('skim', 0)
        path.add_module(trggdldqm)
        # TRGGRL
        trggrldqm = b2.register_module('TRGGRLDQM')
        path.add_module(trggrldqm)
        # TRGCDCTSF
        nmod_tsf = [0, 1, 2, 3, 4, 5, 6]
        for mod_tsf in nmod_tsf:
            path.add_module('TRGCDCTSFDQM', TSFMOD=mod_tsf)
        # TRGCDC2D
        trgcdct2ddqm = b2.register_module('TRGCDCT2DDQM')
        path.add_module(trgcdct2ddqm)
        # TRGCDC3D
        nmod_t3d = [0, 1, 2, 3]
        for mod_t3d in nmod_t3d:
            path.add_module('TRGCDCT3DConverter',
                            hitCollectionName='FirmCDCTriggerSegmentHits' + str(mod_t3d),
                            addTSToDatastore=True,
                            EventTimeName='FirmBinnedEventT0' + str(mod_t3d),
                            addEventTimeToDatastore=True,
                            inputCollectionName='FirmTRGCDC2DFinderTracks' + str(mod_t3d),
                            add2DFinderToDatastore=True,
                            outputCollectionName='FirmTRGCDC3DFitterTracks' + str(mod_t3d),
                            add3DToDatastore=True,
                            fit3DWithTSIM=0,
                            firmwareResultCollectionName='TRGCDCT3DUnpackerStore' + str(mod_t3d),
                            isVerbose=0)
            path.add_module('TRGCDCT3DDQM', T3DMOD=mod_t3d)
        # CDCTriggerNeuro
        if dqm_environment != "expressreco":
            neurotrigger.add_neurotrigger_hw(path)
            path.add_module('CDCTriggerNeuroDQMOnline', histogramDirectoryName="TRGCDCTNN2", useRecoTracks=False)
        path.add_module('CDCTriggerNeuroDQM')
 
    # TRG for expressreco:
    if dqm_environment == "expressreco" and is_detector_present("TRG", components) and (dqm_mode in ["dont_care"]):
        # TRGCDCTNN
        neurotrigger.add_neurotrigger_hw(path)
        path.add_module('CDCTriggerRecoMatcher', TrgTrackCollectionName=neurotrigger.hwneurotracks,
                        hitCollectionName=neurotrigger.hwneuroinputsegmenthits, axialOnly=True)
        path.add_module('CDCTriggerRecoMatcher', TrgTrackCollectionName=neurotrigger.hwsimneurotracks,
                        hitCollectionName=neurotrigger.hwneuroinputsegmenthits, axialOnly=True)
        path.add_module('CDCTriggerRecoMatcher', TrgTrackCollectionName=neurotrigger.hwneuroinput2dfindertracks,
                        hitCollectionName=neurotrigger.hwneuroinputsegmenthits, axialOnly=True)
        path.add_module('CDCTriggerNeuroDQMOnline', histogramDirectoryName="TRGCDCTNN2", useRecoTracks=True)
 
    # TRG after skim
    if is_detector_present("TRG", components) and (dqm_mode in ["dont_care", "filtered"]):
        # TRGGDL
        path.add_module('TRGGDLDQM', skim=1)
        path.add_module('TRGEfficiencyDQM')
 
    # TrackDQM, needs at least one VXD components to be present or will crash otherwise
    if is_any_detector_present(["PXD", "SVD"], components) and (dqm_mode in ["dont_care", "filtered"]):
        if (dqm_environment == "hlt"):
            path.add_module('TrackingHLTDQM')
        else:
            path.add_module('ParallelTrackFilter', min_d0=-0.5, max_d0=0.5, min_z0=-1, max_z0=1,
                            inputArrayName="", outputINArrayName="TracksFromIP", outputOUTArrayName="TracksNotFromIP")
            path.add_module('TrackingExpressRecoDQM', histogramDirectoryName="TrackingERDQM_FromIP",
                            tracksStoreArrayName="TracksFromIP", histogramTitleSuffix=" - Tracks from IP") \
                .set_name("TrackingExpressRecoDQM_FromIP")
            path.add_module('TrackingExpressRecoDQM', histogramDirectoryName="TrackingERDQM_NotFromIP",
                            tracksStoreArrayName="TracksNotFromIP", histogramTitleSuffix=" - Tracks not from IP",
                            produce1Dresiduals=False, produce2Dresiduals=False, produceTRClusterPlots=False) \
                .set_name("TrackingExpressRecoDQM_NotFromIP")
 
    # ARICH
    if is_detector_present("ARICH", components) and (dqm_mode in ["dont_care", "filtered"]):
        path.add_module('ARICHDQM')
 
    if dqm_mode in ["dont_care", "filtered"]:
        # PhysicsObjectsDQM
        add_analysis_dqm(path)
    if dqm_environment == "expressreco" and (dqm_mode in ["dont_care"]):
        add_mirabelle_dqm(path)
        get_hadB_path(path)
 
    # We want to see the datasize of all events after removing the raw data
    if dqm_mode in ["dont_care", "all_events"]:
        # DAQ Monitor
        path.add_module('DAQMonitor')