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File indexing completed on 2025-07-14 08:11:37

 from pathlib import Path
 import os
 import json
 import functools
 import tarfile
 import urllib.request
 import subprocess
 import sys
 import re
 import collections
 import shutil
 
 import pytest
 
 from helpers import (
     geant4Enabled,
     dd4hepEnabled,
     hepmc3Enabled,
     pythia8Enabled,
     exatrkxEnabled,
     onnxEnabled,
     hashingSeedingEnabled,
     AssertCollectionExistsAlg,
     failure_threshold,
 )
 
 import acts
 from acts.examples import (
     Sequencer,
     GenericDetector,
 )
 from acts.examples.odd import getOpenDataDetector, getOpenDataDetectorDirectory
 
 
 u = acts.UnitConstants
 
 
 @pytest.fixture
 def field():
     return acts.ConstantBField(acts.Vector3(0, 0, 2 * u.T))
 
 
 @pytest.fixture
 def seq():
     return Sequencer(events=10, numThreads=1)
 
 
 def assert_csv_output(csv_path, stem):
     __tracebackhide__ = True
     # print(list(csv_path.iterdir()))
     assert len([f for f in csv_path.iterdir() if f.name.endswith(stem + ".csv")]) > 0
     assert all(
         [
             f.stat().st_size > 100
             for f in csv_path.iterdir()
             if f.name.endswith(stem + ".csv")
         ]
     )
 
 
 def assert_entries(root_file, tree_name, exp=None, non_zero=False):
     __tracebackhide__ = True
     import ROOT
 
     ROOT.PyConfig.IgnoreCommandLineOptions = True
     ROOT.gROOT.SetBatch(True)
 
     rf = ROOT.TFile.Open(str(root_file))
     keys = [k.GetName() for k in rf.GetListOfKeys()]
     assert tree_name in keys
     print("Entries:", rf.Get(tree_name).GetEntries())
     if non_zero:
         assert rf.Get(tree_name).GetEntries() > 0, f"{root_file}:{tree_name}"
     if exp is not None:
         assert rf.Get(tree_name).GetEntries() == exp, f"{root_file}:{tree_name}"
 
 
 def assert_has_entries(root_file, tree_name):
     __tracebackhide__ = True
     assert_entries(root_file, tree_name, non_zero=True)
 
 
 @pytest.mark.slow
 @pytest.mark.skipif(not pythia8Enabled, reason="Pythia8 not set up")
 def test_pythia8(tmp_path, seq, assert_root_hash):
     from pythia8 import runPythia8
 
     (tmp_path / "csv").mkdir()
 
     assert not (tmp_path / "particles.root").exists()
     assert len(list((tmp_path / "csv").iterdir())) == 0
 
     events = seq.config.events
 
     vtxGen = acts.examples.GaussianVertexGenerator(
         stddev=acts.Vector4(50 * u.um, 50 * u.um, 150 * u.mm, 0),
         mean=acts.Vector4(0, 0, 0, 0),
     )
 
     runPythia8(
         str(tmp_path), outputRoot=True, outputCsv=True, vtxGen=vtxGen, s=seq
     ).run()
 
     fp = tmp_path / "particles.root"
     assert fp.exists()
     assert fp.stat().st_size > 2**10 * 50
     assert_entries(fp, "particles", events)
     assert_root_hash(fp.name, fp)
 
     assert len(list((tmp_path / "csv").iterdir())) > 0
     assert_csv_output(tmp_path / "csv", "particles")
 
 
 def test_fatras(trk_geo, tmp_path, field, assert_root_hash):
     from fatras import runFatras
 
     csv = tmp_path / "csv"
     csv.mkdir()
 
     nevents = 10
 
     root_files = [
         (
             "particles_simulation.root",
             "particles",
         ),
         (
             "hits.root",
             "hits",
         ),
     ]
 
     assert len(list(csv.iterdir())) == 0
     for rf, _ in root_files:
         assert not (tmp_path / rf).exists()
 
     seq = Sequencer(events=nevents)
     runFatras(trk_geo, field, str(tmp_path), s=seq).run()
 
     assert_csv_output(csv, "particles_simulated")
     assert_csv_output(csv, "hits")
     for f, tn in root_files:
         rfp = tmp_path / f
         assert rfp.exists()
         assert rfp.stat().st_size > 2**10 * 10
 
         assert_has_entries(rfp, tn)
         assert_root_hash(f, rfp)
 
 
 @pytest.mark.slow
 @pytest.mark.odd
 @pytest.mark.skipif(not geant4Enabled, reason="Geant4 not set up")
 @pytest.mark.skipif(not dd4hepEnabled, reason="DD4hep not set up")
 def test_geant4(tmp_path, assert_root_hash):
     # This test literally only ensures that the geant 4 example can run without erroring out
 
     # just to make sure it can build the odd
     with getOpenDataDetector():
         pass
 
     csv = tmp_path / "csv"
     csv.mkdir()
 
     root_files = [
         "particles_simulation.root",
         "hits.root",
     ]
 
     assert len(list(csv.iterdir())) == 0
     for rf in root_files:
         assert not (tmp_path / rf).exists()
 
     script = (
         Path(__file__).parent.parent.parent.parent
         / "Examples"
         / "Scripts"
         / "Python"
         / "geant4.py"
     )
     assert script.exists()
     env = os.environ.copy()
     env["ACTS_LOG_FAILURE_THRESHOLD"] = "WARNING"
     try:
         subprocess.check_call(
             [sys.executable, str(script)],
             cwd=tmp_path,
             env=env,
             stderr=subprocess.STDOUT,
         )
     except subprocess.CalledProcessError as e:
         if e.output is not None:
             print(e.output.decode("utf-8"))
         if e.stderr is not None:
             print(e.stderr.decode("utf-8"))
         raise
 
     assert_csv_output(csv, "particles_simulated")
     assert_csv_output(csv, "hits")
     for f in root_files:
         rfp = tmp_path / f
         assert rfp.exists()
         assert rfp.stat().st_size > 2**10 * 10
 
         assert_root_hash(f, rfp)
 
 
 def test_seeding(tmp_path, trk_geo, field, assert_root_hash):
     from seeding import runSeeding
 
     field = acts.ConstantBField(acts.Vector3(0, 0, 2 * acts.UnitConstants.T))
 
     csv = tmp_path / "csv"
     csv.mkdir()
 
     seq = Sequencer(events=10, numThreads=1)
 
     root_files = [
         (
             "estimatedparams.root",
             "estimatedparams",
         ),
         (
             "performance_seeding.root",
             None,
         ),
         (
             "particles.root",
             "particles",
         ),
         (
             "particles_simulation.root",
             "particles",
         ),
     ]
 
     for fn, _ in root_files:
         fp = tmp_path / fn
         assert not fp.exists()
 
     assert len(list(csv.iterdir())) == 0
 
     runSeeding(trk_geo, field, outputDir=str(tmp_path), s=seq).run()
 
     for fn, tn in root_files:
         fp = tmp_path / fn
         assert fp.exists()
         assert fp.stat().st_size > 100
 
         if tn is not None:
             assert_has_entries(fp, tn)
             assert_root_hash(fn, fp)
 
     assert_csv_output(csv, "particles")
     assert_csv_output(csv, "particles_simulated")
 
 
 @pytest.mark.slow
 @pytest.mark.skipif(not hashingSeedingEnabled, reason="HashingSeeding not set up")
 def test_hashing_seeding(tmp_path, trk_geo, field, assert_root_hash):
     from hashing_seeding import runHashingSeeding, Config
 
     field = acts.ConstantBField(acts.Vector3(0, 0, 2 * acts.UnitConstants.T))
 
     seq = Sequencer(events=10, numThreads=1)
 
     root_files = [
         (
             "estimatedparams.root",
             "estimatedparams",
         ),
         (
             "performance_seeding.root",
             None,
         ),
     ]
 
     for fn, _ in root_files:
         fp = tmp_path / fn
         assert not fp.exists(), f"{fp} exists"
 
     config = Config(
         mu=50,
     )
 
     _, _, digiConfig, geoSelectionConfigFile = config.getDetectorInfo()
 
     runHashingSeeding(
         10,
         trk_geo,
         field,
         outputDir=str(tmp_path),
         saveFiles=True,
         npileup=config.mu,
         seedingAlgorithm=config.seedingAlgorithm,
         maxSeedsPerSpM=config.maxSeedsPerSpM,
         digiConfig=digiConfig,
         geoSelectionConfigFile=geoSelectionConfigFile,
         config=config,
         s=seq,
     ).run()
 
     del seq
 
     for fn, tn in root_files:
         fp = tmp_path / fn
         assert fp.exists(), f"{fp} does not exist"
         assert fp.stat().st_size > 100, f"{fp} is too small: {fp.stat().st_size} bytes"
 
         if tn is not None:
             assert_has_entries(fp, tn)
             assert_root_hash(fn, fp)
 
     assert_csv_output(tmp_path, "particles_simulated")
     assert_csv_output(tmp_path, "buckets")
     assert_csv_output(tmp_path, "seed")
 
 
 def test_seeding_orthogonal(tmp_path, trk_geo, field, assert_root_hash):
     from seeding import runSeeding, SeedingAlgorithm
 
     field = acts.ConstantBField(acts.Vector3(0, 0, 2 * acts.UnitConstants.T))
 
     csv = tmp_path / "csv"
     csv.mkdir()
 
     seq = Sequencer(events=10, numThreads=1)
 
     root_files = [
         (
             "estimatedparams.root",
             "estimatedparams",
         ),
         (
             "performance_seeding.root",
             None,
         ),
         (
             "particles.root",
             "particles",
         ),
         (
             "particles_simulation.root",
             "particles",
         ),
     ]
 
     for fn, _ in root_files:
         fp = tmp_path / fn
         assert not fp.exists()
 
     assert len(list(csv.iterdir())) == 0
 
     runSeeding(
         trk_geo,
         field,
         outputDir=str(tmp_path),
         s=seq,
         seedingAlgorithm=SeedingAlgorithm.Orthogonal,
     ).run()
 
     for fn, tn in root_files:
         fp = tmp_path / fn
         assert fp.exists()
         assert fp.stat().st_size > 100
 
         if tn is not None:
             assert_has_entries(fp, tn)
             assert_root_hash(fn, fp)
 
     assert_csv_output(csv, "particles")
     assert_csv_output(csv, "particles_simulated")
 
 
 def test_itk_seeding(tmp_path, trk_geo, field, assert_root_hash):
     field = acts.ConstantBField(acts.Vector3(0, 0, 2 * acts.UnitConstants.T))
 
     csv = tmp_path / "csv"
     csv.mkdir()
 
     seq = Sequencer(events=10, numThreads=1)
 
     root_files = [
         (
             "estimatedparams.root",
             "estimatedparams",
         ),
         (
             "performance_seeding.root",
             None,
         ),
         (
             "particles.root",
             "particles",
         ),
         (
             "particles_simulation.root",
             "particles",
         ),
     ]
 
     for fn, _ in root_files:
         fp = tmp_path / fn
         assert not fp.exists()
 
     assert len(list(csv.iterdir())) == 0
 
     rnd = acts.examples.RandomNumbers(seed=42)
 
     from acts.examples.simulation import (
         addParticleGun,
         EtaConfig,
         MomentumConfig,
         ParticleConfig,
         addFatras,
         addDigitization,
         ParticleSelectorConfig,
         addDigiParticleSelection,
     )
 
     addParticleGun(
         seq,
         MomentumConfig(1.0 * u.GeV, 10.0 * u.GeV, True),
         EtaConfig(-4.0, 4.0, True),
         ParticleConfig(1, acts.PdgParticle.eMuon, True),
         outputDirCsv=tmp_path / "csv",
         outputDirRoot=str(tmp_path),
         rnd=rnd,
     )
 
     addFatras(
         seq,
         trk_geo,
         field,
         outputDirCsv=tmp_path / "csv",
         outputDirRoot=str(tmp_path),
         rnd=rnd,
     )
 
     srcdir = Path(__file__).resolve().parent.parent.parent.parent
     addDigitization(
         seq,
         trk_geo,
         field,
         digiConfigFile=srcdir / "Examples/Configs/generic-digi-smearing-config.json",
         rnd=rnd,
     )
 
     addDigiParticleSelection(
         seq,
         ParticleSelectorConfig(
             pt=(0.9 * u.GeV, None),
             eta=(-4, 4),
             measurements=(9, None),
             removeNeutral=True,
         ),
     )
 
     from acts.examples.reconstruction import (
         addSeeding,
     )
     from acts.examples.itk import itkSeedingAlgConfig, InputSpacePointsType
 
     addSeeding(
         seq,
         trk_geo,
         field,
         *itkSeedingAlgConfig(InputSpacePointsType.PixelSpacePoints),
         acts.logging.VERBOSE,
         geoSelectionConfigFile=srcdir / "Examples/Configs/generic-seeding-config.json",
         outputDirRoot=str(tmp_path),
     )
 
     seq.run()
 
     for fn, tn in root_files:
         fp = tmp_path / fn
         assert fp.exists()
         assert fp.stat().st_size > 100
 
         if tn is not None:
             assert_has_entries(fp, tn)
             assert_root_hash(fn, fp)
 
     assert_csv_output(csv, "particles")
     assert_csv_output(csv, "particles_simulated")
 
 
 @pytest.mark.slow
 def test_propagation(tmp_path, trk_geo, field, seq, assert_root_hash):
     from propagation import runPropagation
 
     root_files = [
         (
             "propagation_summary.root",
             "propagation_summary",
             10000,
         )
     ]
 
     for fn, _, _ in root_files:
         fp = tmp_path / fn
         assert not fp.exists()
 
     runPropagation(trk_geo, field, str(tmp_path), s=seq).run()
 
     for fn, tn, ee in root_files:
         fp = tmp_path / fn
         assert fp.exists()
         assert fp.stat().st_size > 2**10 * 50
         assert_entries(fp, tn, ee)
         assert_root_hash(fn, fp)
 
 
 @pytest.mark.slow
 @pytest.mark.odd
 @pytest.mark.skipif(not geant4Enabled, reason="Geant4 not set up")
 @pytest.mark.skipif(not dd4hepEnabled, reason="DD4hep not set up")
 def test_material_recording(tmp_path, material_recording, assert_root_hash):
     root_files = [
         (
             "geant4_material_tracks.root",
             "material-tracks",
             200,
         )
     ]
 
     for fn, tn, ee in root_files:
         fp = material_recording / fn
         assert fp.exists()
         assert fp.stat().st_size > 2**10 * 50
         assert_entries(fp, tn, ee)
         assert_root_hash(fn, fp)
 
 
 @pytest.mark.parametrize("revFiltMomThresh", [0 * u.GeV, 1 * u.TeV])
 def test_truth_tracking_kalman(
     tmp_path, assert_root_hash, revFiltMomThresh, detector_config
 ):
     root_files = [
         ("trackstates_kf.root", "trackstates", 19),
         ("tracksummary_kf.root", "tracksummary", 10),
         ("performance_kf.root", None, -1),
     ]
 
     for fn, _, _ in root_files:
         fp = tmp_path / fn
         assert not fp.exists()
 
     with detector_config.detector:
         from truth_tracking_kalman import runTruthTrackingKalman
 
         field = acts.ConstantBField(acts.Vector3(0, 0, 2 * u.T))
 
         seq = Sequencer(events=10, numThreads=1)
 
         runTruthTrackingKalman(
             trackingGeometry=detector_config.trackingGeometry,
             field=field,
             digiConfigFile=detector_config.digiConfigFile,
             outputDir=tmp_path,
             reverseFilteringMomThreshold=revFiltMomThresh,
             s=seq,
         )
 
         seq.run()
 
     for fn, tn, ee in root_files:
         fp = tmp_path / fn
         assert fp.exists()
         assert fp.stat().st_size > 1024
         if tn is not None:
             assert_has_entries(fp, tn)
             assert_root_hash(fn, fp)
 
     import ROOT
 
     ROOT.PyConfig.IgnoreCommandLineOptions = True
     ROOT.gROOT.SetBatch(True)
     rf = ROOT.TFile.Open(str(tmp_path / "tracksummary_kf.root"))
     keys = [k.GetName() for k in rf.GetListOfKeys()]
     assert "tracksummary" in keys
     for entry in rf.Get("tracksummary"):
         assert entry.hasFittedParams
 
 
 def test_truth_tracking_gsf(tmp_path, assert_root_hash, detector_config):
     from truth_tracking_gsf import runTruthTrackingGsf
 
     field = acts.ConstantBField(acts.Vector3(0, 0, 2 * u.T))
 
     seq = Sequencer(
         events=10,
         numThreads=1,
         fpeMasks=[
             (
                 "Core/include/Acts/TrackFitting/detail/GsfUtils.hpp:197",
                 acts.FpeType.FLTUND,
                 1,
             ),
         ],
     )
 
     root_files = [
         ("trackstates_gsf.root", "trackstates"),
         ("tracksummary_gsf.root", "tracksummary"),
     ]
 
     for fn, _ in root_files:
         fp = tmp_path / fn
         assert not fp.exists()
 
     with detector_config.detector:
         runTruthTrackingGsf(
             trackingGeometry=detector_config.trackingGeometry,
             decorators=detector_config.decorators,
             field=field,
             digiConfigFile=detector_config.digiConfigFile,
             outputDir=tmp_path,
             s=seq,
         )
 
         # See https://github.com/acts-project/acts/issues/1300
         with failure_threshold(acts.logging.FATAL):
             seq.run()
 
     for fn, tn in root_files:
         fp = tmp_path / fn
         assert fp.exists()
         assert fp.stat().st_size > 1024
         if tn is not None:
             assert_root_hash(fn, fp)
 
 
 def test_refitting(tmp_path, detector_config, assert_root_hash):
     from truth_tracking_gsf_refitting import runRefittingGsf
 
     field = acts.ConstantBField(acts.Vector3(0, 0, 2 * u.T))
 
     seq = Sequencer(
         events=10,
         numThreads=1,
     )
 
     with detector_config.detector:
         # Only check if it runs without errors right known
         # Changes in fitter behaviour should be caught by other tests
         runRefittingGsf(
             trackingGeometry=detector_config.trackingGeometry,
             field=field,
             digiConfigFile=detector_config.digiConfigFile,
             outputDir=tmp_path,
             s=seq,
         ).run()
 
     root_files = [
         ("trackstates_gsf_refit.root", "trackstates"),
         ("tracksummary_gsf_refit.root", "tracksummary"),
     ]
 
     for fn, tn in root_files:
         fp = tmp_path / fn
         assert fp.exists()
         assert fp.stat().st_size > 1024
         if tn is not None:
             assert_root_hash(fn, fp)
 
 
 def test_particle_gun(tmp_path, assert_root_hash):
     from particle_gun import runParticleGun
 
     s = Sequencer(events=20, numThreads=-1)
 
     csv_dir = tmp_path / "csv"
     root_file = tmp_path / "particles.root"
 
     assert not csv_dir.exists()
     assert not root_file.exists()
 
     runParticleGun(str(tmp_path), s=s).run()
 
     assert csv_dir.exists()
     assert root_file.exists()
 
     assert len([f for f in csv_dir.iterdir() if f.name.endswith("particles.csv")]) > 0
     assert all([f.stat().st_size > 100 for f in csv_dir.iterdir()])
 
     assert root_file.stat().st_size > 200
     assert_entries(root_file, "particles", 20)
     assert_root_hash(root_file.name, root_file)
 
 
 @pytest.mark.slow
 @pytest.mark.odd
 @pytest.mark.skipif(not dd4hepEnabled, reason="DD4hep not set up")
 def test_material_mapping(material_recording, tmp_path, assert_root_hash):
     from material_mapping import runMaterialMapping
     from material_validation import runMaterialValidation
 
     map_file = tmp_path / "material-map_tracks.root"
     assert not map_file.exists()
 
     odd_dir = getOpenDataDetectorDirectory()
     config = acts.MaterialMapJsonConverter.Config()
     materialDecorator = acts.JsonMaterialDecorator(
         level=acts.logging.INFO,
         rConfig=config,
         jFileName=str(odd_dir / "config/odd-material-mapping-config.json"),
     )
 
     s = Sequencer(numThreads=1)
 
     with getOpenDataDetector(materialDecorator) as detector:
         trackingGeometry = detector.trackingGeometry()
         decorators = detector.contextDecorators()
 
         runMaterialMapping(
             trackingGeometry,
             decorators,
             outputDir=str(tmp_path),
             inputDir=material_recording,
             mappingStep=1,
             s=s,
         )
 
         s.run()
 
     mat_file = tmp_path / "material-map.json"
 
     assert mat_file.exists()
     assert mat_file.stat().st_size > 10
 
     with mat_file.open() as fh:
         assert json.load(fh)
 
     assert map_file.exists()
     assert_entries(map_file, "material-tracks", 200)
     assert_root_hash(map_file.name, map_file)
 
     val_file = tmp_path / "propagation-material.root"
     assert not val_file.exists()
 
     # test the validation as well
 
     field = acts.NullBField()
 
     s = Sequencer(events=10, numThreads=1)
 
     with getOpenDataDetector(
         materialDecorator=acts.IMaterialDecorator.fromFile(mat_file)
     ) as detector:
         trackingGeometry = detector.trackingGeometry()
         decorators = detector.contextDecorators()
 
         runMaterialValidation(
             10, 1000, trackingGeometry, decorators, field, outputDir=str(tmp_path), s=s
         )
 
         s.run()
 
     assert val_file.exists()
     assert_entries(val_file, "material-tracks", 10000)
     assert_root_hash(val_file.name, val_file)
 
 
 @pytest.mark.slow
 @pytest.mark.odd
 @pytest.mark.skipif(not dd4hepEnabled, reason="DD4hep not set up")
 def test_volume_material_mapping(material_recording, tmp_path, assert_root_hash):
     from material_mapping import runMaterialMapping
     from material_validation import runMaterialValidation
 
     map_file = tmp_path / "material-map-volume_tracks.root"
     assert not map_file.exists()
 
     geo_map = Path(__file__).parent / "geometry-volume-map.json"
     assert geo_map.exists()
     assert geo_map.stat().st_size > 10
     with geo_map.open() as fh:
         assert json.load(fh)
 
     s = Sequencer(numThreads=1)
 
     with getOpenDataDetector(
         materialDecorator=acts.IMaterialDecorator.fromFile(geo_map)
     ) as detector:
         trackingGeometry = detector.trackingGeometry()
         decorators = detector.contextDecorators()
 
         runMaterialMapping(
             trackingGeometry,
             decorators,
             mapName="material-map-volume",
             outputDir=str(tmp_path),
             inputDir=material_recording,
             mappingStep=1,
             s=s,
         )
 
         s.run()
 
     mat_file = tmp_path / "material-map-volume.json"
 
     assert mat_file.exists()
     assert mat_file.stat().st_size > 10
 
     with mat_file.open() as fh:
         assert json.load(fh)
 
     assert map_file.exists()
     assert_entries(map_file, "material-tracks", 200)
     assert_root_hash(map_file.name, map_file)
 
     val_file = tmp_path / "propagation-volume-material.root"
     assert not val_file.exists()
 
     # test the validation as well
 
     field = acts.NullBField()
 
     s = Sequencer(events=10, numThreads=1)
 
     with getOpenDataDetector(
         materialDecorator=acts.IMaterialDecorator.fromFile(mat_file)
     ) as detector:
         trackingGeometry = detector.trackingGeometry()
         decorators = detector.contextDecorators()
 
         runMaterialValidation(
             10,
             1000,
             trackingGeometry,
             decorators,
             field,
             outputDir=str(tmp_path),
             outputName="propagation-volume-material",
             s=s,
         )
 
         s.run()
 
     assert val_file.exists()
 
     assert_root_hash(val_file.name, val_file)
 
 
 ACTS_DIR = Path(__file__).parent.parent.parent.parent
 CONFIG_DIR = ACTS_DIR / "Examples/Configs"
 DIGI_SHARE_DIR = (
     Path(__file__).parent.parent.parent.parent
     / "Examples/Algorithms/Digitization/share"
 )
 
 
 @pytest.mark.parametrize(
     "digi_config_file",
     [
         CONFIG_DIR / "generic-digi-smearing-config.json",
         CONFIG_DIR / "generic-digi-geometric-config.json",
     ],
     ids=["smeared", "geometric"],
 )
 def test_digitization_example(trk_geo, tmp_path, assert_root_hash, digi_config_file):
     from digitization import runDigitization
 
     s = Sequencer(events=10, numThreads=-1)
 
     csv_dir = tmp_path / "csv"
     root_file = tmp_path / "measurements.root"
 
     assert not root_file.exists()
     assert not csv_dir.exists()
 
     field = acts.ConstantBField(acts.Vector3(0, 0, 2 * u.T))
     runDigitization(
         trk_geo, field, outputDir=tmp_path, digiConfigFile=digi_config_file, s=s
     )
 
     s.run()
 
     assert root_file.exists()
     assert csv_dir.exists()
 
     assert len(list(csv_dir.iterdir())) == 3 * s.config.events
     assert all(f.stat().st_size > 50 for f in csv_dir.iterdir())
 
     assert_root_hash(root_file.name, root_file)
 
 
 @pytest.mark.parametrize(
     "digi_config_file",
     [
         CONFIG_DIR / "generic-digi-smearing-config.json",
         CONFIG_DIR / "generic-digi-geometric-config.json",
         pytest.param(
             (ACTS_DIR / "Examples/Configs" / "odd-digi-smearing-config.json"),
             marks=[
                 pytest.mark.odd,
             ],
         ),
         pytest.param(
             (ACTS_DIR / "Examples/Configs" / "odd-digi-geometric-config.json"),
             marks=[
                 pytest.mark.odd,
             ],
         ),
     ],
     ids=["smeared", "geometric", "odd-smeared", "odd-geometric"],
 )
 def test_digitization_example_input_parsing(digi_config_file):
     from acts.examples import readDigiConfigFromJson
 
     readDigiConfigFromJson(str(digi_config_file))
 
 
 @pytest.mark.parametrize(
     "digi_config_file",
     [
         CONFIG_DIR / "generic-digi-smearing-config.json",
         CONFIG_DIR / "generic-digi-geometric-config.json",
     ],
     ids=["smeared", "geometric"],
 )
 def test_digitization_example_input(
     trk_geo, tmp_path, assert_root_hash, digi_config_file
 ):
     from particle_gun import runParticleGun
     from digitization import runDigitization
 
     ptcl_dir = tmp_path / "ptcl"
     ptcl_dir.mkdir()
     pgs = Sequencer(events=20, numThreads=-1)
     runParticleGun(str(ptcl_dir), s=pgs)
 
     pgs.run()
 
     s = Sequencer(numThreads=-1)
 
     csv_dir = tmp_path / "csv"
     root_file = tmp_path / "measurements.root"
 
     assert not root_file.exists()
     assert not csv_dir.exists()
 
     assert_root_hash(
         "particles.root",
         ptcl_dir / "particles.root",
     )
 
     field = acts.ConstantBField(acts.Vector3(0, 0, 2 * u.T))
     runDigitization(
         trk_geo,
         field,
         outputDir=tmp_path,
         digiConfigFile=digi_config_file,
         particlesInput=ptcl_dir / "particles.root",
         s=s,
         doMerge=True,
     )
 
     s.run()
 
     assert root_file.exists()
     assert csv_dir.exists()
 
     assert len(list(csv_dir.iterdir())) == 3 * pgs.config.events
     assert all(f.stat().st_size > 50 for f in csv_dir.iterdir())
 
     assert_root_hash(root_file.name, root_file)
 
 
 def test_digitization_config_example(trk_geo, tmp_path):
     from digitization_config import runDigitizationConfig
 
     out_file = tmp_path / "output.json"
     assert not out_file.exists()
 
     input = (
         Path(__file__).parent
         / "../../../Examples/Configs/generic-digi-smearing-config.json"
     )
     assert input.exists(), input.resolve()
 
     runDigitizationConfig(trk_geo, input=input, output=out_file)
 
     assert out_file.exists()
 
     with out_file.open() as fh:
         data = json.load(fh)
     assert len(data.keys()) == 2
     assert data["acts-geometry-hierarchy-map"]["format-version"] == 0
     assert (
         data["acts-geometry-hierarchy-map"]["value-identifier"]
         == "digitization-configuration"
     )
     assert len(data["entries"]) == 27
 
 
 @pytest.mark.parametrize(
     "truthSmeared,truthEstimated",
     [
         [False, False],
         [False, True],
         [True, False],
     ],
     ids=["full_seeding", "truth_estimated", "truth_smeared"],
 )
 @pytest.mark.slow
 def test_ckf_tracks_example(
     tmp_path, assert_root_hash, truthSmeared, truthEstimated, detector_config
 ):
     csv = tmp_path / "csv"
 
     assert not csv.exists()
 
     field = acts.ConstantBField(acts.Vector3(0, 0, 2 * u.T))
     events = 100
     s = Sequencer(events=events, numThreads=-1)
 
     root_files = [
         (
             "performance_finding_ckf.root",
             None,
         ),
         (
             "trackstates_ckf.root",
             "trackstates",
         ),
         (
             "tracksummary_ckf.root",
             "tracksummary",
         ),
     ]
 
     if not truthSmeared:
         root_files += [
             (
                 "performance_seeding.root",
                 None,
             ),
         ]
 
     for rf, _ in root_files:
         assert not (tmp_path / rf).exists()
 
     from ckf_tracks import runCKFTracks
 
     with detector_config.detector:
         runCKFTracks(
             detector_config.trackingGeometry,
             detector_config.decorators,
             field=field,
             outputCsv=True,
             outputDir=tmp_path,
             geometrySelection=detector_config.geometrySelection,
             digiConfigFile=detector_config.digiConfigFile,
             truthSmearedSeeded=truthSmeared,
             truthEstimatedSeeded=truthEstimated,
             s=s,
         )
 
         s.run()
 
     assert csv.exists()
     for rf, tn in root_files:
         rp = tmp_path / rf
         assert rp.exists()
         if tn is not None:
             assert_root_hash(rf, rp)
 
     assert (
         len([f for f in csv.iterdir() if f.name.endswith("tracks_ckf.csv")]) == events
     )
     assert all([f.stat().st_size > 300 for f in csv.iterdir()])
 
 
 @pytest.mark.skipif(not dd4hepEnabled, reason="DD4hep not set up")
 @pytest.mark.odd
 @pytest.mark.slow
 def test_full_chain_odd_example(tmp_path):
     # This test literally only ensures that the full chain example can run without erroring out
 
     # just to make sure it can build the odd
     with getOpenDataDetector():
         pass
 
     script = (
         Path(__file__).parent.parent.parent.parent
         / "Examples"
         / "Scripts"
         / "Python"
         / "full_chain_odd.py"
     )
     assert script.exists()
     env = os.environ.copy()
     env["ACTS_LOG_FAILURE_THRESHOLD"] = "ERROR"
     try:
         subprocess.check_call(
             [sys.executable, str(script), "-n1"],
             cwd=tmp_path,
             env=env,
             stderr=subprocess.STDOUT,
         )
     except subprocess.CalledProcessError as e:
         print(e.output.decode("utf-8"))
         raise
 
 
 @pytest.mark.skipif(
     not dd4hepEnabled or not geant4Enabled, reason="DD4hep and/or Geant4 not set up"
 )
 @pytest.mark.slow
 def test_full_chain_odd_example_pythia_geant4(tmp_path):
     # This test literally only ensures that the full chain example can run without erroring out
 
     # just to make sure it can build the odd
     with getOpenDataDetector():
         pass
 
     script = (
         Path(__file__).parent.parent.parent.parent
         / "Examples"
         / "Scripts"
         / "Python"
         / "full_chain_odd.py"
     )
     assert script.exists()
     env = os.environ.copy()
     env["ACTS_LOG_FAILURE_THRESHOLD"] = "ERROR"
     try:
         stdout = subprocess.check_output(
             [
                 sys.executable,
                 str(script),
                 "-n1",
                 "--geant4",
                 "--ttbar",
                 "--ttbar-pu",
                 "50",
             ],
             cwd=tmp_path,
             env=env,
             stderr=subprocess.STDOUT,
         )
         stdout = stdout.decode("utf-8")
     except subprocess.CalledProcessError as e:
         print(e.output.decode("utf-8"))
         raise
 
     # collect and compare known errors
     errors = []
     error_regex = re.compile(r"^\d\d:\d\d:\d\d\s+(\w+)\s+ERROR\s+", re.MULTILINE)
     for match in error_regex.finditer(stdout):
         (algo,) = match.groups()
         errors.append(algo)
     errors = collections.Counter(errors)
     assert dict(errors) == {}, stdout
 
 
 @pytest.mark.skipif(not dd4hepEnabled, reason="DD4hep not set up")
 @pytest.mark.skipif(not onnxEnabled, reason="ONNX plugin not enabled")
 @pytest.mark.slow
 def test_ML_Ambiguity_Solver(tmp_path, assert_root_hash):
     # This test literally only ensures that the full chain example can run without erroring out
 
     root_file = "performance_finding_ambiML.root"
     output_dir = "odd_output"
     assert not (tmp_path / root_file).exists()
 
     # just to make sure it can build the odd
     with getOpenDataDetector():
         pass
 
     script = (
         Path(__file__).parent.parent.parent.parent
         / "Examples"
         / "Scripts"
         / "Python"
         / "full_chain_odd.py"
     )
     assert script.exists()
     env = os.environ.copy()
     env["ACTS_LOG_FAILURE_THRESHOLD"] = "ERROR"
     try:
         subprocess.check_call(
             [sys.executable, str(script), "-n1", "--ambi-solver", "ML"],
             cwd=tmp_path,
             env=env,
             stderr=subprocess.STDOUT,
         )
     except subprocess.CalledProcessError as e:
         print(e.output.decode("utf-8"))
         raise
 
     rfp = tmp_path / output_dir / root_file
     assert rfp.exists()
 
     assert_root_hash(root_file, rfp)
 
 
 def test_bfield_writing(tmp_path, seq, assert_root_hash):
     from bfield_writing import runBFieldWriting
 
     root_files = [
         ("solenoid.root", "solenoid", 100),
         ("solenoid2.root", "solenoid", 100),
     ]
 
     for fn, _, _ in root_files:
         fp = tmp_path / fn
         assert not fp.exists()
 
     runBFieldWriting(outputDir=tmp_path, rewrites=1)
 
     for fn, tn, ee in root_files:
         fp = tmp_path / fn
         assert fp.exists()
         assert fp.stat().st_size > 2**10 * 2
         assert_entries(fp, tn, ee)
         assert_root_hash(fn, fp)
 
 
 @pytest.mark.parametrize("backend", ["onnx", "torch"])
 @pytest.mark.parametrize("hardware", ["cpu", "gpu"])
 @pytest.mark.skipif(not exatrkxEnabled, reason="ExaTrkX environment not set up")
 def test_exatrkx(tmp_path, trk_geo, field, assert_root_hash, backend, hardware):
     if backend == "onnx" and hardware == "cpu":
         pytest.skip("Combination of ONNX and CPU not yet supported")
 
     if backend == "torch":
         pytest.skip(
             "Disabled torch support until replacement for torch-scatter is found"
         )
 
     root_file = "performance_track_finding.root"
     assert not (tmp_path / root_file).exists()
 
     # Extract both models, since we currently don't have a working implementation
     # of metric learning with ONNX and we need to use torch here
     onnx_url = "https://acts.web.cern.ch/ci/exatrkx/onnx_models_v01.tar"
     torch_url = "https://acts.web.cern.ch/ci/exatrkx/torchscript_models_v01.tar"
 
     for url in [onnx_url, torch_url]:
         tarfile_name = tmp_path / "models.tar"
         urllib.request.urlretrieve(url, tarfile_name)
         tarfile.open(tarfile_name).extractall(tmp_path)
 
     shutil.copyfile(
         tmp_path / "torchscript_models/embed.pt", tmp_path / "onnx_models/embed.pt"
     )
 
     script = (
         Path(__file__).parent.parent.parent.parent
         / "Examples"
         / "Scripts"
         / "Python"
         / "exatrkx.py"
     )
     assert script.exists()
     env = os.environ.copy()
     env["ACTS_LOG_FAILURE_THRESHOLD"] = "WARNING"
 
     if hardware == "cpu":
         env["CUDA_VISIBLE_DEVICES"] = ""
 
     try:
         subprocess.check_call(
             [sys.executable, str(script), backend],
             cwd=tmp_path,
             env=env,
             stderr=subprocess.STDOUT,
         )
     except subprocess.CalledProcessError as e:
         print(e.output.decode("utf-8"))
         raise
 
     rfp = tmp_path / root_file
     assert rfp.exists()
 
     assert_root_hash(root_file, rfp)
 
 
 @pytest.mark.odd
 def test_strip_spacepoints(detector_config, field, tmp_path, assert_root_hash):
     if detector_config.name == "generic":
         pytest.skip("No strip spacepoint formation for the generic detector currently")
 
     from strip_spacepoints import createStripSpacepoints
 
     s = Sequencer(events=20, numThreads=-1)
 
     config_path = Path(__file__).parent.parent.parent.parent / "Examples" / "Configs"
 
     geo_selection = config_path / "odd-strip-spacepoint-selection.json"
     digi_config_file = config_path / "odd-digi-smearing-config.json"
 
     with detector_config.detector:
         createStripSpacepoints(
             trackingGeometry=detector_config.trackingGeometry,
             field=field,
             digiConfigFile=digi_config_file,
             geoSelection=geo_selection,
             outputDir=tmp_path,
             s=s,
         ).run()
 
     root_file = "strip_spacepoints.root"
     rfp = tmp_path / root_file
 
     assert_root_hash(root_file, rfp)

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