# need to be flattened before training to 0 or 1
NNApplyModule_m = NNApplyModule(
model_file=args.model,
model_type='combined-wideCNN',
threshold=0.,
# threshold=args.threshold,
extra_info_var='smartBKG')
# dead_path = b2.create_path()
# NNApplyModule_m.if_false(dead_path)
path.add_module(NNApplyModule_m)
# We'll keep just one Btag per event and train on that
ma.rankByHighest(particle_list,
'extraInfo(SignalProbability)',
outputVariable='FEIProbabilityRank',
numBest=1,
path=path)
# Write output
# ma.variablesToNTuple(
ma.variablesToNtuple(
decayString=particle_list,
variables=B_vars,
filename=args.out_file,
path=path,
)
b2.process(path)
print(b2.statistics)
main_path = b2.create_path() # Declaration of main path
bp.add_beamparameters(main_path,'Y4S')
ma.inputMdst('MC10', inputFilename, path=main_path)
sv.goodBelleKshort(path=main_path)
sg.stdPhotons('loose', path=main_path)
sc.stdPi('95eff', path=main_path)
ma.applyCuts('gamma:loose','1.4 < E < 4', path=main_path)
krescuts = " and daughterInvM(0,1,2) < 2 and daughterInvM(0,1) > 0.6 and daughterInvM(0,1) < 0.9"
#reconstructDecay(Kres+":all -> pi+:good pi-:good K_S0:all", krescuts)
ma.reconstructDecay("B0:signal -> pi+:95eff pi-:95eff K_S0:legacyGoodKS gamma:loose",
"Mbc > 5.2 and deltaE < 0.2 and deltaE > -0.2 and -0.65 < daughter(1, cosTheta) < 0.85"+krescuts, path=main_path)
ma.vertexRave('B0:signal',0.0001, 'B0 -> ^pi+ ^pi- ^K_S0 gamma', path=main_path)
#vertexTree('B0:signal',0.0001)
ma.rankByHighest('B0:signal',ratingVar, 1, outputVariable='myRating', path=main_path)
ma.buildRestOfEvent('B0:signal', path=main_path)
# define the "cleaner" mask
eclCut = '[E > 0.062 and abs(clusterTiming) < 18 and clusterReg==1] or \
[E>0.060 and abs(clusterTiming) < 20 and clusterReg==2] or \
[E>0.056 and abs(clusterTiming) < 44 and clusterReg==3]'
cleanMask = ('cleanMask', 'abs(d0) < 10.0 and abs(z0) < 20.0', eclCut)
# append both masks to ROE
ma.appendROEMasks('B0:signal', [cleanMask], path=main_path)
# choose one mask which is applied
ma.buildContinuumSuppression('B0:signal', 'cleanMask', path=main_path)
list_name='D0',
conf_level=
-1, # keep all cadidates, 0:keep only fit survivors, optimise this cut for your need
ipConstraint=True,
# pins the B0 PRODUCTION vertex to the IP (increases SIG and BKG rejection) use for better vertex resolution
updateAllDaughters=True, # update momenta off ALL particles
path=my_path)
# smaller |M_rec - M| is better, add here a different output variable name, due to parentheses
ma.rankByLowest(particleList='D0',
variable='abs(dM)',
outputVariable='abs_dM_rank',
path=my_path)
# maybe not the best idea, but might cut away candidates with failed fits
ma.rankByHighest(particleList='D0', variable='chiProb', path=my_path)
# Now let's do mixed ranking:
# First, we want to rank D candiadtes by the momentum of the pions
# Second, we want to rank those D candidates that were built with the highest-p by the vertex Chi2
# This doesn't have any sense, but shows how to work with consequetive rankings
#
# Let's add alias for the momentum rank of pions in D
va.variables.addAlias('D1_pi_p_rank', 'daughter(1,pi_p_rank)')
# Ranking D candidates by this variable.
# Candidates built with the same pion get the same rank (allowMultiRank=True).
ma.rankByHighest(particleList='D0',
variable='D1_pi_p_rank',
allowMultiRank=True,
outputVariable="first_D_rank",
path=my_path)
def reconstruct(infile='default.root', outfile='output_beta.root', path=None):
"""
Args:
infile: Input file name (use overwrite from basf2)
outfile: output file name (use overwrite from basf2)
path: (optional) basf2 path
Returns:
path
"""
setup()
# EXAMPE RECONSTRUCTION CODE
# DELETE OR MODIFY FROM HERE
just_an_example = True
if just_an_example:
with open(outfile, 'w') as f:
f.write("Proccessed example input " + infile)
else:
path = b2.create_path() if path is None else path
# Input file
ma.inputMdstList("default", infile, path=path)
# Event level cuts examples
ma.applyEventCuts('R2EventLevel<0.6 and nTracks>=3', path=path)
#
# Load Primary particles
#
from stdPhotons import stdPhotons
stdPhotons('cdc', path)
ma.cutAndCopyList('gamma:sig', 'gamma:cdc',
'clusterNHits > 1.5 and E > 1.5', True, path)
from stdPi0s import stdPi0s
stdPi0s('eff20', path)
# Loading charged tracks
good_track = 'thetaInCDCAcceptance and nCDCHits>20 and dr < 0.5 and abs(dz) < 2'
ma.fillParticleList("pi+:sig",
good_track + " and pionID > 0.0",
path=path)
ma.fillParticleList("K+:sig",
good_track + " and kaonID > 0.0",
path=path)
#
# Combine particles
#
ma.reconstructDecay('K*0:sig -> K+:sig pi-:sig',
'0.6 < M < 1.6',
path=path)
ma.reconstructDecay('B0:sig -> K*0:sig gamma:sig',
'5.22 < Mbc < 5.3 and abs(deltaE)< 1',
path=path)
# Final Calculateions
ma.rankByHighest("B0:ch1",
"formula(-1*abs(deltaE))",
outputVariable='Rank_deltaE',
path=path)
ma.buildEventShape(allMoments=True, path=path)
ma.matchMCTruth("B0:sig", path=path)
#
# Write out Ntuples
#
all_vars = get_variables()
ma.variablesToNtuple('B0:ch1',
all_vars,
filename=outfile,
treename="B0",
path=path)
# TO HERE
#ma.printMCParticles(path=path)
return path
# It is also possible to train different combiners consecutively using the same weightFiles name.
# You just need always to specify the desired category combination while using the expert mode as:
#
# flavorTagger(particleLists=['B0:jspipi0'], mode = 'Expert', weightFiles='B2JpsiKs_mu',
# categories=['Electron', 'Muon', 'Kaon', ... etc.])
#
# Another possibility is to train a combiner for a specific category combination using the default weight files
# You can apply cuts using the flavor Tagger: qrOutput(FBDT) > -2 rejects all events which do not
# provide flavor information using the tag side
ma.applyCuts(list_name='B0:jspipi0', cut='qrOutput(FBDT) > -2', path=my_path)
# If you applied the cut on qrOutput(FBDT) > -2 before then you can rank by highest r- factor
ma.rankByHighest(particleList='B0:jspipi0',
variable='abs(qrOutput(FBDT))',
numBest=0,
outputVariable='Dilution_rank',
path=my_path)
# Fit Vertex of the B0 on the tag side
vx.TagV(list_name='B0:jspipi0',
MCassociation='breco',
confidenceLevel=0.001,
useFitAlgorithm='standard_PXD',
path=my_path)
# Select variables that we want to store to ntuple
fs_vars = vc.pid + vc.track + vc.mc_truth
jpsiandk0s_vars = vc.mc_truth
bvars = vc.reco_stats + \
vc.deltae_mbc + \