caf_boostvector.py

 
 
"""
Airflow script to perform BoostVector calibration.
"""
 
from prompt import CalibrationSettings, INPUT_DATA_FILTERS
from prompt.calibrations.caf_beamspot import settings as beamspot
from basf2 import get_file_metadata, B2WARNING
from reconstruction import prepare_cdst_analysis
import os
 
 
settings = CalibrationSettings(
    name="BoostVector Calibrations",
    expert_username="zlebcr",
    description=__doc__,
    input_data_formats=["cdst"],
    input_data_names=["mumu_tight_or_highm_calib"],
    input_data_filters={
      "mumu_tight_or_highm_calib": [
        INPUT_DATA_FILTERS["Data Tag"]["mumu_tight_or_highm_calib"],
        INPUT_DATA_FILTERS["Run Type"]["physics"],
        INPUT_DATA_FILTERS["Data Quality Tag"]["Good Or Recoverable"],
        INPUT_DATA_FILTERS["Magnet"]["On"]]},
    expert_config={
        "outerLoss": "pow(rawTime - 8.0, 2) + 10 * pow(maxGap, 2)",
        "innerLoss": "pow(rawTime - 8.0, 2) + 10 * pow(maxGap, 2)",
        "minPXDhits": 0},
    depends_on=[beamspot])
 
 
 
 
def is_cDST_file(fName):
    """ Check if the file is cDST based on the metadata """
 
    metaData = get_file_metadata(fName)
    description = metaData.getDataDescription()
 
    # if dataLevel is missing, determine from file name
    if 'dataLevel' not in description:
        B2WARNING('The cdst/mdst info is not stored in file metadata')
        return ('cdst' in os.path.basename(fName))
 
    return (description['dataLevel'] == 'cdst')
 
 
def get_calibrations(input_data, **kwargs):
    """
    Parameters:
      input_data (dict): Should contain every name from the 'input_data_names' variable as a key.
        Each value is a dictionary with {"/path/to/file_e1_r5.root": IoV(1,5,1,5), ...}. Useful for
        assigning to calibration.files_to_iov
 
      **kwargs: Configuration options to be sent in. Since this may change we use kwargs as a way to help prevent
        backwards compatibility problems. But you could use the correct arguments in b2caf-prompt-run for this
        release explicitly if you want to.
 
        Currently only kwargs["output_iov"] is used. This is the output IoV range that your payloads should
        correspond to. Generally your highest ExpRun payload should be open ended e.g. IoV(3,4,-1,-1)
 
    Returns:
      list(caf.framework.Calibration): All of the calibration objects we want to assign to the CAF process
    """
    import basf2
    # Set up config options
 
    # In this script we want to use one sources of input data.
    # Get the input files  from the input_data variable
    file_to_iov_physics = input_data["mumu_tight_or_highm_calib"]
 
    # We might have requested an enormous amount of data across a run range.
    # There's a LOT more files than runs!
    # Lets set some limits because this calibration doesn't need that much to run.
    max_files_per_run = 1000000
 
    # We filter out any more than 100 files per run. The input data files are sorted alphabetically by b2caf-prompt-run
    # already. This procedure respects that ordering
    from prompt.utils import filter_by_max_files_per_run
 
    reduced_file_to_iov_physics = filter_by_max_files_per_run(file_to_iov_physics, max_files_per_run)
    input_files_physics = list(reduced_file_to_iov_physics.keys())
    basf2.B2INFO(f"Total number of files actually used as input = {len(input_files_physics)}")
 
    isCDST = is_cDST_file(input_files_physics[0]) if len(input_files_physics) > 0 else True
 
    # Get the overall IoV we our process should cover. Includes the end values that we may want to ignore since our output
    # IoV should be open ended. We could also use this as part of the input data selection in some way.
    requested_iov = kwargs.get("requested_iov", None)
 
    from caf.utils import IoV
    # The actual value our output IoV payload should have. Notice that we've set it open ended.
    output_iov = IoV(requested_iov.exp_low, requested_iov.run_low, -1, -1)
 
    
 
    from ROOT import Belle2  # noqa: make the Belle2 namespace available
    from ROOT.Belle2 import BoostVectorAlgorithm
    from basf2 import create_path, register_module
    import modularAnalysis as ana
    import vertex
 
    
 
    from caf.framework import Calibration
    from caf.strategies import SingleIOV
 
    # module to be run prior the collector
    rec_path_1 = create_path()
    if isCDST:
        prepare_cdst_analysis(path=rec_path_1, components=['SVD', 'CDC', 'ECL', 'KLM'])
 
    minPXDhits = kwargs['expert_config']['minPXDhits']
    muSelection = '[p>1.0]'
    muSelection += ' and abs(dz)<2.0 and abs(dr)<0.5'
    muSelection += f' and nPXDHits >= {minPXDhits} and nSVDHits >= 8 and nCDCHits >= 20'
    ana.fillParticleList('mu+:BV', muSelection, path=rec_path_1)
    ana.reconstructDecay('Upsilon(4S):BV -> mu+:BV mu-:BV', '9.5<M<11.5', path=rec_path_1)
    vertex.treeFit('Upsilon(4S):BV', updateAllDaughters=True, ipConstraint=True, path=rec_path_1)
 
    collector_bv = register_module('BoostVectorCollector', Y4SPListName='Upsilon(4S):BV')
    algorithm_bv = BoostVectorAlgorithm()
    algorithm_bv.setOuterLoss(kwargs['expert_config']['outerLoss'])
    algorithm_bv.setInnerLoss(kwargs['expert_config']['innerLoss'])
 
    calibration_bv = Calibration('BoostVector',
                                 collector=collector_bv,
                                 algorithms=algorithm_bv,
                                 input_files=input_files_physics,
                                 pre_collector_path=rec_path_1)
 
    calibration_bv.strategies = SingleIOV
 
    # Do this for the default AlgorithmStrategy to force the output payload IoV
    # It may be different if you are using another strategy like SequentialRunByRun
    for algorithm in calibration_bv.algorithms:
        algorithm.params = {"iov_coverage": output_iov}
 
    # Most other options like database chain and backend args will be overwritten by b2caf-prompt-run.
    # So we don't bother setting them.
 
    # You must return all calibrations you want to run in the prompt process, even if it's only one
    return [calibration_bv]