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 #!/usr/bin/env python3
 
 import os
 import argparse
 import pathlib
 
 import acts
 import acts.examples
 from acts.examples.simulation import (
     MomentumConfig,
     EtaConfig,
     PhiConfig,
     ParticleConfig,
     ParticleSelectorConfig,
     addParticleGun,
     addPythia8,
     addGenParticleSelection,
     addFatras,
     addGeant4,
     addSimParticleSelection,
     addDigitization,
     addDigiParticleSelection,
 )
 from acts.examples.reconstruction import (
     addSeeding,
     CkfConfig,
     addCKFTracks,
     TrackSelectorConfig,
     addAmbiguityResolution,
     AmbiguityResolutionConfig,
     addAmbiguityResolutionML,
     AmbiguityResolutionMLConfig,
     addScoreBasedAmbiguityResolution,
     ScoreBasedAmbiguityResolutionConfig,
     addVertexFitting,
     VertexFinder,
     addSeedFilterML,
     SeedFilterMLDBScanConfig,
 )
 from acts.examples.odd import getOpenDataDetector, getOpenDataDetectorDirectory
 
 u = acts.UnitConstants
 
 
 parser = argparse.ArgumentParser(description="Full chain with the OpenDataDetector")
 parser.add_argument(
     "--output",
     "-o",
     help="Output directory",
     type=pathlib.Path,
     default=pathlib.Path.cwd() / "odd_output",
 )
 parser.add_argument("--events", "-n", help="Number of events", type=int, default=100)
 parser.add_argument("--skip", "-s", help="Number of events", type=int, default=0)
 parser.add_argument(
     "--jobs",
     "-j",
     help="Number of worker threads (-1 uses all cores)",
     type=int,
     default=None,
 )
 parser.add_argument("--edm4hep", help="Use edm4hep inputs", type=pathlib.Path)
 parser.add_argument(
     "--geant4", help="Use Geant4 instead of fatras", action="store_true"
 )
 parser.add_argument(
     "--ttbar",
     help="Use Pythia8 (ttbar, pile-up 200) instead of particle gun",
     action="store_true",
 )
 parser.add_argument(
     "--ttbar-pu",
     help="Number of pile-up events for ttbar",
     type=int,
     default=200,
 )
 parser.add_argument(
     "--gun-particles",
     help="Multiplicity (no. of particles) of the particle gun",
     type=int,
     default=4,
 )
 parser.add_argument(
     "--gun-multiplicity",
     help="Multiplicity (no. of vertices) of the particle gun",
     type=int,
     default=200,
 )
 parser.add_argument(
     "--gun-eta-range",
     nargs=2,
     help="Eta range of the particle gun",
     type=float,
     default=[-3.0, 3.0],
 )
 parser.add_argument(
     "--gun-pt-range",
     nargs=2,
     help="Pt range of the particle gun (GeV)",
     type=float,
     default=[1.0 * u.GeV, 10.0 * u.GeV],
 )
 parser.add_argument(
     "--digi-config", help="Digitization configuration file", type=pathlib.Path
 )
 parser.add_argument(
     "--material-config", help="Material map configuration file", type=pathlib.Path
 )
 parser.add_argument(
     "--ambi-solver",
     help="Set which ambiguity solver to use, default is the classical one",
     type=str,
     choices=["greedy", "scoring", "ML"],
     default="greedy",
 )
 parser.add_argument(
     "--ambi-config",
     help="Set the configuration file for the Score Based ambiguity resolution",
     type=pathlib.Path,
     default=pathlib.Path.cwd() / "ambi_config.json",
 )
 
 parser.add_argument(
     "--MLSeedFilter",
     help="Use the Ml seed filter to select seed after the seeding step",
     action="store_true",
 )
 parser.add_argument(
     "--reco",
     help="Switch reco on/off",
     default=True,
     action=argparse.BooleanOptionalAction,
 )
 parser.add_argument(
     "--output-root",
     help="Switch root output on/off",
     default=False,
     action=argparse.BooleanOptionalAction,
 )
 parser.add_argument(
     "--output-csv",
     help="Switch csv output on/off",
     default=False,
     action=argparse.BooleanOptionalAction,
 )
 parser.add_argument(
     "--output-obj",
     help="Switch obj output on/off",
     default=False,
     action=argparse.BooleanOptionalAction,
 )
 parser.add_argument(
     "--output-parquet",
     help="Switch parquet output on/off (requires ACTS_BUILD_EXAMPLES_PARQUET=ON)",
     default=False,
     action=argparse.BooleanOptionalAction,
 )
 
 args = parser.parse_args()
 
 outputDir = args.output
 ambi_ML = args.ambi_solver == "ML"
 ambi_scoring = args.ambi_solver == "scoring"
 ambi_config = args.ambi_config
 seedFilter_ML = args.MLSeedFilter
 geoDir = getOpenDataDetectorDirectory()
 actsDir = pathlib.Path(__file__).parent.parent.parent.parent
 # acts.examples.dump_args_calls()  # show python binding calls
 
 oddMaterialMap = (
     args.material_config
     if args.material_config
     else geoDir / "data/odd-material-maps.root"
 )
 
 oddDigiConfig = (
     args.digi_config
     if args.digi_config
     else actsDir / "Examples/Configs/odd-digi-smearing-config.json"
 )
 
 oddSeedingSel = actsDir / "Examples/Configs/odd-seeding-config.json"
 oddMaterialDeco = acts.IMaterialDecorator.fromFile(oddMaterialMap)
 
 detector = getOpenDataDetector(odd_dir=geoDir, materialDecorator=oddMaterialDeco)
 trackingGeometry = detector.trackingGeometry()
 decorators = detector.contextDecorators()
 field = acts.ConstantBField(acts.Vector3(0.0, 0.0, 2.0 * u.T))
 rnd = acts.examples.RandomNumbers(seed=42)
 
 s = acts.examples.Sequencer(
     events=args.events,
     skip=args.skip,
     numThreads=args.jobs if args.jobs is not None else (1 if args.geant4 else -1),
     outputDir=str(outputDir),
 )
 
 if args.edm4hep:
     import acts.examples.edm4hep
     from acts.examples.edm4hep import PodioReader
 
     s.addReader(
         PodioReader(
             level=acts.logging.DEBUG,
             inputPath=str(args.edm4hep),
             outputFrame="events",
             category="events",
         )
     )
 
     edm4hepReader = acts.examples.edm4hep.EDM4hepSimInputConverter(
         inputFrame="events",
         inputSimHits=[
             "PixelBarrelReadout",
             "PixelEndcapReadout",
             "ShortStripBarrelReadout",
             "ShortStripEndcapReadout",
             "LongStripBarrelReadout",
             "LongStripEndcapReadout",
         ],
         outputParticlesGenerator="particles_generated",
         outputParticlesSimulation="particles_simulated",
         outputSimHits="simhits",
         outputSimVertices="vertices_truth",
         dd4hepDetector=detector,
         trackingGeometry=trackingGeometry,
         sortSimHitsInTime=False,
         particleRMax=1080 * u.mm,
         particleZ=(-3030 * u.mm, 3030 * u.mm),
         particlePtMin=150 * u.MeV,
         level=acts.logging.DEBUG,
     )
     s.addAlgorithm(edm4hepReader)
 
     s.addWhiteboardAlias("particles", edm4hepReader.config.outputParticlesSimulation)
 
     addSimParticleSelection(
         s,
         ParticleSelectorConfig(
             rho=(0.0, 24 * u.mm),
             absZ=(0.0, 1.0 * u.m),
             eta=(-3.0, 3.0),
             removeNeutral=True,
         ),
     )
 else:
     if not args.ttbar:
         addParticleGun(
             s,
             MomentumConfig(
                 args.gun_pt_range[0] * u.GeV,
                 args.gun_pt_range[1] * u.GeV,
                 transverse=True,
             ),
             EtaConfig(args.gun_eta_range[0], args.gun_eta_range[1]),
             PhiConfig(0.0, 360.0 * u.degree),
             ParticleConfig(
                 args.gun_particles, acts.PdgParticle.eMuon, randomizeCharge=True
             ),
             vtxGen=acts.examples.GaussianVertexGenerator(
                 mean=acts.Vector4(0, 0, 0, 0),
                 stddev=acts.Vector4(
                     0.0125 * u.mm, 0.0125 * u.mm, 55.5 * u.mm, 1.0 * u.ns
                 ),
             ),
             multiplicity=args.gun_multiplicity,
             rnd=rnd,
         )
     else:
         addPythia8(
             s,
             hardProcess=["Top:qqbar2ttbar=on"],
             npileup=args.ttbar_pu,
             vtxGen=acts.examples.GaussianVertexGenerator(
                 mean=acts.Vector4(0, 0, 0, 0),
                 stddev=acts.Vector4(
                     0.0125 * u.mm, 0.0125 * u.mm, 55.5 * u.mm, 5.0 * u.ns
                 ),
             ),
             rnd=rnd,
             outputDirRoot=outputDir if args.output_root else None,
             outputDirCsv=outputDir if args.output_csv else None,
         )
 
         addGenParticleSelection(
             s,
             ParticleSelectorConfig(
                 rho=(0.0, 24 * u.mm),
                 absZ=(0.0, 1.0 * u.m),
                 eta=(-3.0, 3.0),
                 pt=(150 * u.MeV, None),
             ),
         )
 
     if args.geant4:
         if s.config.numThreads != 1:
             raise ValueError("Geant 4 simulation does not support multi-threading")
 
         # Pythia can sometime simulate particles outside the world volume, a cut on the Z of the track help mitigate this effect
         # Older version of G4 might not work, this as has been tested on version `geant4-11-00-patch-03`
         # For more detail see issue #1578
         addGeant4(
             s,
             detector,
             trackingGeometry,
             field,
             outputDirRoot=outputDir if args.output_root else None,
             outputDirCsv=outputDir if args.output_csv else None,
             outputDirObj=outputDir if args.output_obj else None,
             rnd=rnd,
             killVolume=trackingGeometry.highestTrackingVolume,
             killAfterTime=25 * u.ns,
         )
     else:
         addFatras(
             s,
             trackingGeometry,
             field,
             enableInteractions=True,
             outputDirRoot=outputDir if args.output_root else None,
             outputDirCsv=outputDir if args.output_csv else None,
             outputDirObj=outputDir if args.output_obj else None,
             rnd=rnd,
         )
 
 addDigitization(
     s,
     trackingGeometry,
     field,
     digiConfigFile=oddDigiConfig,
     outputDirRoot=outputDir if args.output_root else None,
     outputDirCsv=outputDir if args.output_csv else None,
     rnd=rnd,
 )
 
 addDigiParticleSelection(
     s,
     ParticleSelectorConfig(
         pt=(1.0 * u.GeV, None),
         eta=(-3.0, 3.0),
         measurements=(9, None),
         removeNeutral=True,
     ),
 )
 
 if args.reco:
     addSeeding(
         s,
         trackingGeometry,
         field,
         initialSigmas=[
             1 * u.mm,
             1 * u.mm,
             1 * u.degree,
             1 * u.degree,
             0 * u.e / u.GeV,
             1 * u.ns,
         ],
         initialSigmaQoverPt=0.1 * u.e / u.GeV,
         initialSigmaPtRel=0.1,
         initialVarInflation=[1.0] * 6,
         particleHypothesis=acts.ParticleHypothesis.muon,
         geoSelectionConfigFile=oddSeedingSel,
         outputDirRoot=outputDir if args.output_root else None,
         outputDirCsv=outputDir if args.output_csv else None,
     )
 
     if seedFilter_ML:
         addSeedFilterML(
             s,
             SeedFilterMLDBScanConfig(
                 epsilonDBScan=0.03, minPointsDBScan=2, minSeedScore=0.1
             ),
             onnxModelFile=os.path.dirname(__file__)
             + "/MLAmbiguityResolution/seedDuplicateClassifier.onnx",
             outputDirRoot=outputDir if args.output_root else None,
             outputDirCsv=outputDir if args.output_csv else None,
         )
 
     addCKFTracks(
         s,
         trackingGeometry,
         field,
         TrackSelectorConfig(
             pt=(1.0 * u.GeV if args.ttbar else 0.0, None),
             absEta=(None, 3.0),
             loc0=(-4.0 * u.mm, 4.0 * u.mm),
             nMeasurementsMin=7,
             maxHoles=2,
             maxOutliers=2,
         ),
         CkfConfig(
             chi2CutOffMeasurement=15.0,
             chi2CutOffOutlier=25.0,
             numMeasurementsCutOff=2,
             seedDeduplication=True,
             stayOnSeed=True,
             pixelVolumes=[16, 17, 18],
             stripVolumes=[23, 24, 25],
             maxPixelHoles=1,
             maxStripHoles=2,
             constrainToVolumes=[
                 2,  # beam pipe
                 32,
                 4,  # beam pip gap
                 16,
                 17,
                 18,  # pixel
                 20,  # PST
                 23,
                 24,
                 25,  # short strip
                 26,
                 8,  # long strip gap
                 28,
                 29,
                 30,  # long strip
             ],
         ),
         outputDirRoot=outputDir if args.output_root else None,
         outputDirCsv=outputDir if args.output_csv else None,
         writeCovMat=True,
     )
 
     if ambi_ML:
         addAmbiguityResolutionML(
             s,
             AmbiguityResolutionMLConfig(
                 maximumSharedHits=3, maximumIterations=1000000, nMeasurementsMin=7
             ),
             outputDirRoot=outputDir if args.output_root else None,
             outputDirCsv=outputDir if args.output_csv else None,
             onnxModelFile=os.path.dirname(__file__)
             + "/MLAmbiguityResolution/duplicateClassifier.onnx",
         )
 
     elif ambi_scoring:
         addScoreBasedAmbiguityResolution(
             s,
             ScoreBasedAmbiguityResolutionConfig(
                 minScore=0,
                 minScoreSharedTracks=1,
                 maxShared=2,
                 minUnshared=3,
                 maxSharedTracksPerMeasurement=2,
                 useAmbiguityScoring=False,
             ),
             outputDirRoot=outputDir if args.output_root else None,
             outputDirCsv=outputDir if args.output_csv else None,
             ambiVolumeFile=ambi_config,
             writeCovMat=True,
         )
     else:
         addAmbiguityResolution(
             s,
             AmbiguityResolutionConfig(
                 maximumSharedHits=3, maximumIterations=1000000, nMeasurementsMin=7
             ),
             outputDirRoot=outputDir if args.output_root else None,
             outputDirCsv=outputDir if args.output_csv else None,
             writeCovMat=True,
         )
 
     addVertexFitting(
         s,
         field,
         vertexFinder=VertexFinder.AMVF,
         outputDirRoot=outputDir if args.output_root else None,
         outputDirCsv=outputDir if args.output_csv else None,
     )
 
 if args.output_parquet:
     try:
         from acts.arrow import (
             particleSchema,
             simHitSchema,
             trackSchema,
         )
         from acts.examples.arrow import (
             ArrowParticleOutputConverter,
             ArrowSimHitOutputConverter,
             ArrowTrackOutputConverter,
             makeVolumeIdDetectorResolver,
             ParquetWriter,
         )
     except ImportError as e:
         raise RuntimeError(
             "Parquet output requested but acts.examples.arrow is not available; "
             "rebuild with ACTS_BUILD_EXAMPLES_PARQUET=ON."
         ) from e
 
     # ODD volume -> detector enum mapping used in parquet simhit export.
     # 0..8 are subdetector-specific enums; 255 marks unknown/unmatched.
     _odd_detector_resolver = makeVolumeIdDetectorResolver(
         {
             7: 0,  # pixel_neg_endcap
             8: 1,  # pixel_barrel
             9: 2,  # pixel_pos_endcap
             12: 3,  # short_neg_endcap
             13: 4,  # short_barrel
             14: 5,  # short_pos_endcap
             16: 6,  # long_neg_endcap
             17: 7,  # long_barrel
             18: 8,  # long_pos_endcap
         },
         255,
     )
 
     # Each converter parks an arrow::Table on the whiteboard under a fresh
     # key, and one ParquetWriter picks them all up.
     arrParticleConv = ArrowParticleOutputConverter(
         level=acts.logging.INFO,
         inputParticles="particles_simulated",
         outputTable="particles_arrow",
     )
     s.addAlgorithm(arrParticleConv)
 
     arrSimHitConv = ArrowSimHitOutputConverter(
         level=acts.logging.INFO,
         inputSimHits="simhits",
         inputParticles="particles_simulated",
         inputClusters="clusters",
         inputSimHitMeasurementsMap="simhit_measurements_map",
         outputTable="simhits_arrow",
         detectorResolver=_odd_detector_resolver,
     )
     s.addAlgorithm(arrSimHitConv)
 
     if args.reco:
         arrTrackConv = ArrowTrackOutputConverter(
             level=acts.logging.INFO,
             inputTracks="tracks",
             inputTrackParticleMatching="track_particle_matching",
             inputParticles="particles_simulated",
             inputMeasurementSimHitsMap="measurement_simhits_map",
             outputTable="tracks_arrow",
         )
         s.addAlgorithm(arrTrackConv)
 
     s.addWriter(
         ParquetWriter(
             level=acts.logging.INFO,
             outputDir=str(outputDir),
             collections={
                 arrSimHitConv.config.outputTable: "simhits",
                 arrTrackConv.config.outputTable: "tracks",
                 arrParticleConv.config.outputTable: "particles",
             },
             expectedSchemas={
                 arrSimHitConv.config.outputTable: simHitSchema(),
                 arrTrackConv.config.outputTable: trackSchema(),
                 arrParticleConv.config.outputTable: particleSchema(),
             },
         )
     )
 
 s.run()

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