caf_ecl_E.py

 
"""ECL single crystal energy calibration using three control samples."""
 
from prompt import CalibrationSettings, INPUT_DATA_FILTERS
 
# --------------------------------------------------------------
# ..Tell the automated script some required details
settings = CalibrationSettings(
    name="ecl_energy",
    expert_username="hearty",
    subsystem="ecl",
    description=__doc__,
    input_data_formats=["cdst"],
    input_data_names=[
        "bhabha_all_calib",
        "gamma_gamma_calib",
        "mumu_tight_or_highm_calib"],
    input_data_filters={
        "bhabha_all_calib": [
            INPUT_DATA_FILTERS["Data Tag"]["bhabha_all_calib"],
            INPUT_DATA_FILTERS["Data Quality Tag"]["Good Or Recoverable"],
            INPUT_DATA_FILTERS["Beam Energy"]["4S"],
            INPUT_DATA_FILTERS["Run Type"]["physics"],
            INPUT_DATA_FILTERS["Magnet"]["On"]],
        "gamma_gamma_calib": [
            INPUT_DATA_FILTERS["Data Tag"]["gamma_gamma_calib"],
            INPUT_DATA_FILTERS["Data Quality Tag"]["Good Or Recoverable"],
            INPUT_DATA_FILTERS["Beam Energy"]["4S"],
            INPUT_DATA_FILTERS["Run Type"]["physics"],
            INPUT_DATA_FILTERS["Magnet"]["On"]],
        "mumu_tight_or_highm_calib": [
            INPUT_DATA_FILTERS["Data Tag"]["mumu_tight_or_highm_calib"],
            INPUT_DATA_FILTERS["Data Quality Tag"]["Good Or Recoverable"],
            INPUT_DATA_FILTERS["Beam Energy"]["4S"],
            INPUT_DATA_FILTERS["Run Type"]["physics"],
            INPUT_DATA_FILTERS["Magnet"]["On"]]},
    depends_on=[],
    expert_config={"eCmsScale": 1.0,  # Ecms/10.58, typically 0.9943 for offpeak
                   "ee5x5_min_entries": 100},
    produced_payloads=["ECLCrystalEnergy", "ECLCrystalEnergyMuMu", "ECLCrystalEnergyGammaGamma", "ECLCrystalEnergyee5x5"])
 
# --------------------------------------------------------------
# ..Raise clustering seed threshold in ECLCRFinder
 
 
def touch_CRFinder(path, new_seed):
    import basf2
    """
    Speed up ECL clustering by increasing seed threshold
    """
    new_path = basf2.create_path()
    found = False
    for m in path.modules():
        if str(m) != "ECLCRFinder":  # search module by name
            new_path.add_module(m)
        else:
            crfinder = basf2.register_module('ECLCRFinder')
            crfinder.param('energyCut0', new_seed)
            basf2.print_params(crfinder)
            new_path.add_module(crfinder)
            found = True
    if not found:
        raise KeyError("Could not find ECLCRFinder in path")
    return new_path
 
# --------------------------------------------------------------
# ..Raise threshold in ECLWaveformFit
 
 
def touch_WaveformFit(path, energy_threshold):
    import basf2
    """
    Speed up ECL reconstruction by increasing energy threshold
    """
    new_path = basf2.create_path()
    found = False
    for m in path.modules():
        if str(m) != "ECLWaveformFit":  # search module by name
            new_path.add_module(m)
        else:
            waveformfit = basf2.register_module('ECLWaveformFit')
            waveformfit.param('EnergyThreshold', energy_threshold)
            basf2.print_params(waveformfit)
            new_path.add_module(waveformfit)
            found = True
    if not found:
        raise KeyError("Could not find ECLWaveformFit in path")
    return new_path
 
# --------------------------------------------------------------
# ..The calibration functions
 
 
def get_calibrations(input_data, **kwargs):
    import basf2
    from ROOT import Belle2
    from caf.utils import IoV
    from caf.framework import Calibration
    from reconstruction import prepare_cdst_analysis
 
    # --------------------------------------------------------------
    # ..Bhabha
 
    # ..Input data
    file_to_iov_bhabha = input_data["bhabha_all_calib"]
    input_files_bhabha = list(file_to_iov_bhabha.keys())
 
    # ..Algorithm
    algo_ee5x5 = Belle2.ECL.eclee5x5Algorithm()
    expert_config = kwargs.get("expert_config")
    ee5x5minEntries = expert_config["ee5x5_min_entries"]
    algo_ee5x5.setMinEntries(ee5x5minEntries)
    algo_ee5x5.setPayloadName("ECLCrystalEnergy5x5")
    algo_ee5x5.setStoreConst(True)
 
    # ..The calibration
    eclee5x5_collector = basf2.register_module("eclee5x5Collector")
    eclee5x5_collector.param("thetaLabMinDeg", 17.)
    eclee5x5_collector.param("thetaLabMaxDeg", 150.)
    eclee5x5_collector.param("minE0", 0.45)
    eclee5x5_collector.param("minE1", 0.40)
    eclee5x5_collector.param("maxdThetaSum", 2.)
    eclee5x5_collector.param("dPhiScale", 1.)
    eclee5x5_collector.param("maxTime", 10.)
    eclee5x5_collector.param("useCalDigits", False)
    eclee5x5_collector.param("requireL1", False)
    eclee5x5_collector.param("granularity", "all")
 
    # ..Adjust the expected energies for offpeak calibration
    eCmsScale = expert_config["eCmsScale"]
    eclee5x5_collector.param("expectedEnergyScale", eCmsScale)
 
    cal_ecl_ee5x5 = Calibration("ecl_ee5x5",
                                collector=eclee5x5_collector,
                                algorithms=[algo_ee5x5],
                                input_files=input_files_bhabha
                                )
    cal_ecl_ee5x5.backend_args = {"request_memory": "4 GB"}
 
    # ..Add prepare_cdst_analysis to pre_collector_path
    ee5x5_pre_path = basf2.create_path()
    prepare_cdst_analysis(ee5x5_pre_path, components=['ECL'])
    new_threshold = 1.0  # GeV
    ee5x5_pre_path = touch_CRFinder(ee5x5_pre_path, new_threshold)
    waveform_threshold = 99.  # GeV
    ee5x5_pre_path = touch_WaveformFit(ee5x5_pre_path, waveform_threshold)
    cal_ecl_ee5x5.pre_collector_path = ee5x5_pre_path
 
    # --------------------------------------------------------------
    # ..gamma gamma
 
    # ..Input data
    file_to_iov_gamma_gamma = input_data["gamma_gamma_calib"]
    input_files_gamma_gamma = list(file_to_iov_gamma_gamma.keys())
 
    # ..Algorithm
    algo_gamma_gamma = Belle2.ECL.eclGammaGammaEAlgorithm()
    algo_gamma_gamma.setOutputName("eclGammaGammaE_algorithm.root")
    algo_gamma_gamma.setCellIDLo(1)
    algo_gamma_gamma.setCellIDHi(8736)
    algo_gamma_gamma.setMinEntries(150)
    algo_gamma_gamma.setMaxIterations(10)
    algo_gamma_gamma.setTRatioMin(0.45)
    algo_gamma_gamma.setTRatioMax(0.70)
    algo_gamma_gamma.setTRatioMinHiStat(0.70)
    algo_gamma_gamma.setTRatioMaxHiStat(0.95)
    algo_gamma_gamma.setUpperEdgeThresh(0.02)
    algo_gamma_gamma.setPerformFits(True)
    algo_gamma_gamma.setFindExpValues(False)
    algo_gamma_gamma.setStoreConst(0)
 
    # ..The calibration
    eclGammaGamma_collector = basf2.register_module("eclGammaGammaECollector")
    eclGammaGamma_collector.param("granularity", "all")
    eclGammaGamma_collector.param("thetaLabMinDeg", 0.)
    eclGammaGamma_collector.param("thetaLabMaxDeg", 180.)
    eclGammaGamma_collector.param("minPairMass", 9.)
    eclGammaGamma_collector.param("mindPhi", 179.)
    eclGammaGamma_collector.param("maxTime", 999.)
    eclGammaGamma_collector.param("measureTrueEnergy", False)
    eclGammaGamma_collector.param("requireL1", False)
 
    # ..Adjust the expected energies for offpeak calibration
    eclGammaGamma_collector.param("expectedEnergyScale", eCmsScale)
 
    cal_ecl_gamma_gamma = Calibration("ecl_gamma_gamma",
                                      collector=eclGammaGamma_collector,
                                      algorithms=[algo_gamma_gamma],
                                      input_files=input_files_gamma_gamma
                                      )
 
    # ..Add prepare_cdst_analysis to pre_collector_path
    gamma_gamma_pre_path = basf2.create_path()
    prepare_cdst_analysis(gamma_gamma_pre_path, components=['ECL'])
    new_threshold = 1.0  # GeV
    gamma_gamma_pre_path = touch_CRFinder(gamma_gamma_pre_path, new_threshold)
    waveform_threshold = 99.  # GeV
    gamma_gamma_pre_path = touch_WaveformFit(gamma_gamma_pre_path, waveform_threshold)
    cal_ecl_gamma_gamma.pre_collector_path = gamma_gamma_pre_path
 
    # --------------------------------------------------------------
    # ..muon pair
 
    # ..Input data
    file_to_iov_mu_mu = input_data["mumu_tight_or_highm_calib"]
    input_files_mu_mu = list(file_to_iov_mu_mu.keys())
 
    # ..Algorithm
    algo_mu_mu = Belle2.ECL.eclMuMuEAlgorithm()
    algo_mu_mu.cellIDLo = 1
    algo_mu_mu.cellIDHi = 8736
    algo_mu_mu.minEntries = 150
    algo_mu_mu.nToRebin = 1000
    algo_mu_mu.tRatioMin = 0.05
    algo_mu_mu.tRatioMax = 0.40
    algo_mu_mu.lowerEdgeThresh = 0.10
    algo_mu_mu.performFits = True
    algo_mu_mu.findExpValues = False
    algo_mu_mu.storeConst = 0
 
    # ..The calibration
    eclmumu_collector = basf2.register_module("eclMuMuECollector")
    eclmumu_collector.param("granularity", "all")
    eclmumu_collector.param("minPairMass", 9.0)
    eclmumu_collector.param("minTrackLength", 30.)
    eclmumu_collector.param("MaxNeighbourE", 0.010)
    eclmumu_collector.param("thetaLabMinDeg", 17.)
    eclmumu_collector.param("thetaLabMaxDeg", 150.)
    eclmumu_collector.param("measureTrueEnergy", False)
    eclmumu_collector.param("requireL1", False)
    cal_ecl_mu_mu = Calibration(name="ecl_mu_mu", collector=eclmumu_collector, algorithms=algo_mu_mu, input_files=input_files_mu_mu)
 
    # ..Need to include track extrapolation in the path before collector
    ext_path = basf2.create_path()
    prepare_cdst_analysis(ext_path, components=['ECL'])
    ext_path.add_module("Ext", pdgCodes=[13])
    new_threshold = 0.1  # GeV
    ext_path = touch_CRFinder(ext_path, new_threshold)
    waveform_threshold = 99.  # GeV
    ext_path = touch_WaveformFit(ext_path, waveform_threshold)
    cal_ecl_mu_mu.pre_collector_path = ext_path
 
    # --------------------------------------------------------------
    # Include a merging Calibration that doesn't require input data but instead creates the final
    # payload from the previous calibration payloads.
 
    # We use a dummy collector that barely outputs any data and we set the input files to a single file so
    # we spawn only one very fast job.
    # It doesn't matter which input file we choose as the output is never used.
 
    merging_alg = Belle2.ECL.eclMergingCrystalEAlgorithm()
    cal_ecl_merge = Calibration(name="ecl_merge", collector="DummyCollector", algorithms=[merging_alg],
                                input_files=input_files_mu_mu[:1])
 
    # The important part is that we depend on all 3 previous calibrations
    cal_ecl_merge.depends_on(cal_ecl_ee5x5)
    cal_ecl_merge.depends_on(cal_ecl_gamma_gamma)
    cal_ecl_merge.depends_on(cal_ecl_mu_mu)
 
    # ..Uses cdst data so it requires prepare_cdst_analysis
    ecl_merge_pre_path = basf2.create_path()
    prepare_cdst_analysis(ecl_merge_pre_path, components=['ECL'])
    new_threshold = 0.15  # GeV
    ecl_merge_pre_path = touch_CRFinder(ecl_merge_pre_path, new_threshold)
    waveform_threshold = 99.  # GeV
    ecl_merge_pre_path = touch_WaveformFit(ecl_merge_pre_path, waveform_threshold)
    ecl_merge_pre_path.pre_collector_path = ecl_merge_pre_path
 
    # --------------------------------------------------------------
    # ..Force the output iovs to be open
    requested_iov = kwargs.get("requested_iov", None)
    output_iov = IoV(requested_iov.exp_low, requested_iov.run_low, -1, -1)
    for algorithm in cal_ecl_ee5x5.algorithms:
        algorithm.params = {"apply_iov": output_iov}
    for algorithm in cal_ecl_gamma_gamma.algorithms:
        algorithm.params = {"apply_iov": output_iov}
    for algorithm in cal_ecl_mu_mu.algorithms:
        algorithm.params = {"apply_iov": output_iov}
    for algorithm in cal_ecl_merge.algorithms:
        algorithm.params = {"apply_iov": output_iov}
 
    # --------------------------------------------------------------
    # ..Return the calibrations
    return [cal_ecl_ee5x5, cal_ecl_gamma_gamma, cal_ecl_mu_mu, cal_ecl_merge]