######################################################
# event based cut - == 2 tracks in event
######################################################
var.addAlias('nCleanedTracks', 'nCleanedTracks(' + cleanTrack + ')')
ma.applyEventCuts(cut='nCleanedTracks == 2', path=my_path)
#######################################################
# EventShape and EventKinamatics modules
#######################################################
ma.buildEventShape(['e-:good', 'mu+:mugood', 'pi-:good'],
foxWolfram=False,
cleoCones=False,
jets=False,
harmonicMoments=False,
allMoments=False,
collisionAxis=False,
sphericity=False,
thrust=True,
path=my_path)
ma.buildEventKinematics(['e-:good', 'pi-:good', 'mu+:mugood'], path=my_path)
# Get information of the generated decay mode calling labelTauPairMC from modular analysis
ma.labelTauPairMC(path=my_path)
######################################################
# Signal and tag sides
#######################################################
ma.reconstructDecay('tau+:signal -> mu+:mugood', '', path=my_path)
ma.fillParticleList(decayString='pi+:all',
cut='pt> 0.1',
path=my_path)
ma.fillParticleList(decayString='gamma:all',
cut='E > 0.1',
path=my_path)
# Builds the event shape enabling explicitly ALL the variables.
# Most of the are actually enabled by defoult, but here we prefer
# to list explicitly all the flags
ma.buildEventShape(inputListNames=['pi+:all', 'gamma:all'],
allMoments=True,
foxWolfram=True,
harmonicMoments=True,
cleoCones=True,
thrust=True,
collisionAxis=True,
jets=True,
sphericity=True,
checkForDuplicates=False,
path=my_path)
# Here we use the pre-defined collection 'event_shape', that contains
# thrust, sphericity, aplanarity, FW ratios up to 4, harmonic moments w/respect to
# the thrust axis up to 4 and all the cleo cones w/respect to the thrust axis.
# In addition, we will save also the forward and backward hemisphere (or "jet") energies,
# and the 2nd order harmonic moment calculate respect to the collision axis (i.e. the z axis)
ma.variablesToNtuple('',
variables=['event_shape',
'backwardHemisphereEnergy',
'forwardHemisphereEnergy',
def reconstruction(input_file, output_file):
my_path = b2.create_path()
ma.inputMdst("default", input_file, my_path)
# Find decay name from the input file name
decay_name = "_".join(input_file.split("/")[-1].split("_")[0:2])
# Load configuration file
config = yaml.safe_load(open("config/reco_config.yaml"))
options = config[decay_name]
# Parse list of subdecays in decay chain
decays = options["sub_decays"]
decays = DecayList(decays)
# Create particle lists for final state particles
fsps = decays.get_fsps()
for particle in fsps:
ma.fillParticleList(particle, "", path=my_path)
# Reconstruct requested decay chains
for decay in decays:
ma.reconstructDecay(str(decay), "", path=my_path)
# Perform truth matching for requested particles
truth_match_particles = decays.mothers
for truth_match_particle in truth_match_particles:
ma.looseMCTruth(truth_match_particle, path=my_path)
# Perform vertex fitting
head = decays.get_head()
vtx_decay_string = decays.get_chain()
print(vtx_decay_string)
vx.vertexRave(head, 0, vtx_decay_string, constraint="iptube", path=my_path)
# ma.rankByLowest("B0", 'chiProb', numBest=3, outputVariable='B_vtx_rank', path=my_path)
# ma.variables.addAlias('B_vtx_rank', 'extraInfo(B_vtx_rank)')
ma.buildRestOfEvent(head, path=my_path)
# Tag-side
vx.TagV(head, "breco", 0.001, path=my_path)
ma.buildEventKinematics(path=my_path)
ma.buildEventShape(path=my_path)
# Create centre-of-mass frame variables
cms_kinematics = vu.create_aliases(
vc.kinematics, "useCMSFrame({variable})", prefix="CMS"
)
variables = [
item
for sublist in [
vc.kinematics,
cms_kinematics,
vc.deltae_mbc,
vc.inv_mass,
vc.event_shape,
vc.vertex,
vc.mc_truth,
vc.mc_kinematics,
vc.mc_vertex,
vc.mc_tag_vertex,
]
for item in sublist
]
trees = yaml.safe_load(open("config/tree_names.yaml"))
for particle in decays.all_particles:
ma.variablesToNtuple(
particle,
variables,
filename=output_file,
treename=trees[particle],
path=my_path,
)
b2.process(my_path)
print(b2.statistics)
ma.fillParticleListFromMC('tau+:gen', '', path=my_path)
####################################################
# track cuts
######################################################
var.addAlias('EoverP', 'formula( ifNANgiveX( clusterE, -1 )/p )')
#######################################################
# EventShape and EventKinamatics modules
#######################################################
ma.buildEventShape(['tau-:gen', 'tau+:gen'],
foxWolfram=False,
cleoCones=False,
jets=False,
harmonicMoments=False,
allMoments=False,
collisionAxis=False,
sphericity=False,
thrust=True,
path=my_path)
ma.buildEventKinematics(['tau-:gen', 'tau+:gen'], path=my_path)
# Get information of the generated decay mode calling labelTauPairMC from modular analysis
ma.labelTauPairMC(path=my_path)
######################################################
# Signal and tag sides
#######################################################
ma.reconstructDecay('vpho:photon_B2SS -> tau+:gen tau-:gen', '', 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