sampler.py

#!/usr/bin/env python
 

import argparse
import basf2 as b2
import modularAnalysis as ma
from tflat.flavorTagger import flavorTagger
 
 
def reconstructB2nunubar(path):
    """
    Defines the procedure to create a B0 list for the benchmark channel 'B0 -> nu_tau anti-nu_tau'
    """
    ma.fillParticleListFromMC('nu_tau:MC', '', path=path)
    ma.reconstructMCDecay('B0:sig -> nu_tau:MC anti-nu_tau:MC', '', writeOut=True, path=path)
 
 
def buildROE(path):
    """
    Creates the rest of event for the signal particle list.'
    """
    # perform MC matching (MC truth association).
    ma.matchMCTruth(list_name='B0:sig', path=path)
 
    # build the rest of the event associated to the B0
    ma.buildRestOfEvent(target_list_name='B0:sig', path=path)
 
 
if __name__ == '__main__':
    '''
    Samples a chunk of training data for TFlat
    '''
    parser = argparse.ArgumentParser(description='Sample TFlat')
    parser.add_argument(  # input parser
        '--uniqueIdentifier',
        metavar='uniqueIdentifier',
        dest='uniqueIdentifier',
        type=str,
        default="TFlaT_MC16rd_light_2601_hyperion",
        help='Name of both the config .yaml to be used and the produced weightfile'
    )
    args = parser.parse_args()
    uniqueIdentifier = args.uniqueIdentifier
 
    b2.set_log_level(b2.LogLevel.ERROR)
    b2.conditions.prepend_globaltag(ma.getAnalysisGlobaltag())
    path = b2.Path()
 
    ma.inputMdstList(environmentType="default", filelist='', path=path)
    reconstructB2nunubar(path)
    buildROE(path)
 
    flavorTagger(
        'B0:sig',
        mode='Sampler',
        working_dir='',
        uniqueIdentifier=uniqueIdentifier,
        path=path)
    ma.summaryOfLists(particleLists=['B0:sig'], path=path)
    b2.process(path)
    print(b2.statistics)