def start_process(self):
"""
The function given to the process to start the calculation.
Do not call by yourself.
Resets the logging system, logs onto console and a file and sets the queues
(the result queue and the process queue) correctly.
"""
if not self.path:
return
try:
if self.random_seed is not None:
basf2.set_random_seed(self.random_seed)
basf2.reset_log()
basf2.logging.zero_counters()
basf2.log_to_file(self.log_file_name)
basf2.process(self.path)
self.result_queue.put(
"ipython.statistics",
Basf2CalculationQueueStatistics(basf2.statistics))
except:
raise
finally:
self.progress_queue_remote.send("end")
def simulation(input_file, output_file):
my_path = b2.create_path()
print(f"Number of processes: {b2.get_nprocesses()}")
# load input ROOT file
ma.inputMdst(environmentType="default", filename=input_file, path=my_path)
"""
Loads the specified ROOT (DST/mDST/muDST) files with the RootInput module.
The correct environment (e.g. magnetic field settings) are determined from the specified environment type.
The currently available environments are:
- 'MC5': for analysis of Belle II MC samples produced with releases prior to build-2016-05-01.
This environment sets the constant magnetic field (B = 1.5 T)
- 'MC6': for analysis of Belle II MC samples produced with build-2016-05-01 or newer but prior to release-00-08-00
- 'MC7': for analysis of Belle II MC samples produced with build-2016-05-01 or newer but prior to release-00-08-00
- 'MC8', for analysis of Belle II MC samples produced with release-00-08-00 or newer but prior to release-02-00-00
- 'MC9', for analysis of Belle II MC samples produced with release-00-08-00 or newer but prior to release-02-00-00
- 'MC10', for analysis of Belle II MC samples produced with release-00-08-00 or newer but prior to release-02-00-00
- 'default': for analysis of Belle II MC samples produced with releases with release-02-00-00 or newer.
This environment sets the default magnetic field (see geometry settings)
- 'Belle': for analysis of converted (or during of conversion of) Belle MC/DATA samples
- 'None': for analysis of generator level information or during simulation/my_reconstruction of
previously generated events
Note that there is no difference between MC6 and MC7. Both are given for sake of completion.
The same is true for MC8, MC9 and MC10
Parameters:
environmentType (str): type of the environment to be loaded
filename (str): the name of the file to be loaded
path (basf2.Path): modules are added to this path
skipNEvents (int): N events of the input files are skipped
entrySequences (list(str)): The number sequences (e.g. 23:42,101) defining
the entries which are processed for each inputFileName.
parentLevel (int): Number of generations of parent files (files used as input when creating a file) to be read
"""
# In case of conflict with geometry, you may use this line instead:
# analysis_main.add_module("RootInput", inputFileName='B2A101-Y4SEventGeneration-evtgen.root')
# simulation
si.add_simulation(path=my_path)
# my_reconstruction
re.add_reconstruction(path=my_path)
# dump in MDST format
mdst.add_mdst_output(path=my_path, mc=True, filename=output_file)
# Show progress of processing
progress = b2.register_module("ProgressBar")
my_path.add_module(progress)
# Process the events
b2.process(my_path)
print(b2.statistics)
def main(input_file,output_file, channel,isdata):
path = makeFSPs(input_file, isdata)
if channel == 'kst0mumu':
Kst0mumu(path)
# ft.flavorTagger(particleLists=['B0:kst0mumu'],
# belleOrBelle2="Belle2", weightFiles=weightfiles, path=path)
b_vars = vc.deltae_mbc + vc.mc_truth + vc.kinematics
variablesToNtuple('B0:kst0Jpsi', variables=b_vars, filename='B0:kst0Jpsi_mumu.root', treename='tree', path=path)
gv.varKst0mumu(path, output_file)
elif channel == 'kst0ee':
Kst0ee(path)
# ft.flavorTagger(particleLists=['B0:kst0Jpsi'],
# belleOrBelle2="Belle2", weightFiles=weightfiles, path=path)
b_vars = vc.deltae_mbc + vc.mc_truth + vc.kinematics
variablesToNtuple('B0:kst0Jpsi', variables=b_vars, filename='B0_kst0Jpsi_ee.root', treename='tree', path=path)
gv.varKst0ee(path, output_file)
# varKst0ee_brem(path, output_file)
elif channel == 'kst_ksee':
Kstee_ks(path)
ft.flavorTagger(particleLists=['B+:kst_ksee'],
belleOrBelle2="Belle2", weightFiles=weightfiles, path=path)
varKstee_ks(path, output_file)
varKstee_ks_brem(path, output_file)
elif channel == 'kst_pi0ee':
Kstee_pi0(path)
ft.flavorTagger(particleLists=['B+:kst_pi0ee'],
belleOrBelle2="Belle2", weightFiles=weightfiles, path=path)
varKstee_pi0(path, output_file)
varKstee_pi0_brem(path, output_file)
elif channel == 'kst_ksmumu':
Kstmumu_ks(path)
ft.flavorTagger(particleLists=['B+:kst_ksmumu'],
belleOrBelle2="Belle2", weightFiles=weightfiles, path=path)
varKstmumu_ks(path, output_file)
elif channel == 'kst_pi0mumu':
Kstmumu_pi0(path)
ft.flavorTagger(particleLists=['B+:kst_pi0mumu'],
belleOrBelle2="Belle2", weightFiles=weightfiles, path=path)
varKstmumu_pi0(path, output_file)
varKst0mumu(path, output_file)
varKstmumu_ks(path, output_file)
varKstmumu_pi0(path, output_file)
varKst0ee(path, output_file)
varKstee_ks(path, output_file)
varKstee_pi0(path, output_file)
progress = register_module('Progress')
path.add_module(progress)
b2.process(path)
# print out the summary
print(b2.statistics)
def submit_generation(n_events, dec_file, out_name, random_seed):
# Command line args passed as strings, so ensure they're corrected to ints
n_events = int(n_events)
random_seed = int(random_seed)
ge.set_random_seed(random_seed)
my_path = b2.create_path()
ma.setupEventInfo(noEvents=n_events, path=my_path)
ge.add_evtgen_generator(path=my_path,
finalstate="signal",
signaldecfile=dec_file)
ma.loadGearbox(path=my_path)
my_path.add_module("RootOutput", outputFileName=out_name)
b2.process(path=my_path)
print(b2.statistics)
def main(input_file, output_file, channel=None, isdata=None):
path = makeFSPs(input_file, isdata)
if channel == 'kst0mumu':
gv.Kst0mumu(path)
ft.flavorTagger(particleLists=['B0:kst0mumu'],
belleOrBelle2="Belle2", weightFiles=weightfiles, path=path)
varKst0mumu(path, output_file)
elif channel == 'kst0ee':
Kst0ee(path)
ft.flavorTagger(particleLists=['B0:kst0ee'],
belleOrBelle2="Belle2", weightFiles=weightfiles, path=path)
varKst0ee(path, output_file)
varKst0ee_brem(path, output_file)
elif channel == 'kst_ksee':
Kstee_ks(path)
ft.flavorTagger(particleLists=['B+:kst_ksee'],
belleOrBelle2="Belle2", weightFiles=weightfiles, path=path)
varKstee_ks(path, output_file)
varKstee_ks_brem(path, output_file)
elif channel == 'kst_pi0ee':
Kstee_pi0(path)
ft.flavorTagger(particleLists=['B+:kst_pi0ee'],
belleOrBelle2="Belle2", weightFiles=weightfiles, path=path)
varKstee_pi0(path, output_file)
varKstee_pi0_brem(path, output_file)
elif channel == 'kst_ksmumu':
Kstmumu_ks(path)
ft.flavorTagger(particleLists=['B+:kst_ksmumu'],
belleOrBelle2="Belle2", weightFiles=weightfiles, path=path)
varKstmumu_ks(path, output_file)
elif channel == 'kst_pi0mumu':
Kstmumu_pi0(path)
ft.flavorTagger(particleLists=['B+:kst_pi0mumu'],
belleOrBelle2="Belle2", weightFiles=weightfiles, path=path)
varKstmumu_pi0(path, output_file)
varKst0mumu(path, output_file)
varKstmumu_ks(path, output_file)
varKstmumu_pi0(path, output_file)
varKst0ee(path, output_file)
varKstee_ks(path, output_file)
varKstee_pi0(path, output_file)
progress = register_module('Progress')
path.add_module(progress)
b2.process(path)
# print out the summary
print(b2.statistics)
def process(self):
assert get_basf2_git_hash() == self.git_hash
try:
import basf2
import ROOT
except ImportError:
raise ImportError("Can not find ROOT or basf2. Can not use the basf2 task.")
if self.num_processes:
basf2.set_nprocesses(self.num_processes)
if self.max_event:
ROOT.Belle2.Environment.Instance().setNumberEventsOverride(self.max_event)
path = self.create_path()
path.add_module("Progress")
basf2.print_path(path)
basf2.process(path)
print(basf2.statistics)
def start_process(self):
"""
The function given to the process to start the calculation.
Do not call by yourself.
Resets the logging system, logs onto console and a file and sets the queues
(the result queue and the process queue) correctly.
"""
if not self.path:
return
try:
if self.random_seed is not None:
basf2.set_random_seed(self.random_seed)
basf2.reset_log()
basf2.logging.zero_counters()
basf2.log_to_file(self.log_file_name)
basf2.process(self.path)
self.result_queue.put("ipython.statistics", Basf2CalculationQueueStatistics(basf2.statistics))
except:
raise
finally:
self.progress_queue_remote.send("end")
# mp.add_module('VariablesToNtuple', particleList = 'Sigma+:loose',
# variables=ntuple_vars, treeName='sigma_loose', fileName=sys.argv[2])
# Eff of this cut is about 96% and rejects about 50% of the background for Sigma+
pi0_mass_cut = 'daughter(1, M) >= 0.12 and daughter(1, M) <= 0.15'
ma.cutAndCopyList('Sigma+:good', 'Sigma+:loose', pi0_mass_cut, path=mp)
ma.vertexTree('Sigma+:good',
0,
ipConstraint=True,
massConstraint=[],
updateAllDaughters=False,
path=mp)
ma.applyCuts('Sigma+:good', 'M >= 1.16 and M <= 1.22', path=mp)
ma.matchMCTruth('Sigma+:good', path=mp)
mp.add_module('VariablesToNtuple',
particleList='Sigma+:good',
variables=ntuple_vars,
treeName='sigma_good',
fileName=sys.argv[2])
# # Mass constrain pi0 and update the daughters
# ma.vertexTree('Sigma+:good', 0, ipConstraint = True, massConstraint = [111],
# updateAllDaughters = True, path = mp)
# ma.matchMCTruth('Sigma+:good', path = mp)
# mp.add_module('VariablesToNtuple', particleList = 'Sigma+:good',
# variables=ntuple_vars, treeName='sigma_updated', fileName=sys.argv[2])
b2.process(path=mp)
print(b2.statistics)
main_path.add_module('SeqRootInput', inputFileNames=inputFileName)
main_path.add_module('Progress')
main_path.add_module('ProgressBar')
# set the geometry
main_path.add_module('Gearbox')
main_path.add_module('Geometry', useDB=True)
# unpack bklm raw dawa
main_path.add_module('BKLMUnpacker')
# analyze bklm digits
main_path.add_module('BKLMDigitAnalyzer',
outputRootName=outputPath + 'bklmHitmap')
# reconstruct bklm 2d hits
reco = ma.register_module('BKLMReconstructor')
reco.param('Prompt window (ns)', 2000)
main_path.add_module(reco)
# add bklm stand-alone tracking
main_path.add_module('BKLMTracking',
StudyEffiMode=True,
outputName=outputPath + 'bklmEfficiency_' + date +
'.root')
# process the path
b2.print_path(main_path)
b2.process(main_path, 100000)
print(b2.statistics)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import basf2 as b2 # noqa
import modularAnalysis as ma # noqa
main = b2.create_path()
main.add_module('RootInput', recovery=True)
main.add_module('RootOutput')
b2.process(main)
)
stdK("higheff", path=path)
stdPi("higheff", path=path)
mA.reconstructDecay('D0:Kpi -> K-:higheff pi+:higheff', '1.7 < M < 1.9', path=path)
# use try except to have this code work for both the old and new function names for the tree fit
mA.matchMCTruth('D0:Kpi', path=path)
mA.reconstructDecay('B- -> D0:Kpi pi-:higheff', f"{mbc_range[0]} < Mbc < {mbc_range[1]}", path=path)
try:
vx.treeFit('B+', 0.1, path=path)
except AttributeError:
vx.vertexTree('B+', 0.1, path=path)
mA.setAnalysisConfigParams({"mcMatchingVersion": "BelleII"}, path)
mA.matchMCTruth('B-', path=path)
vm.addAlias("p_cms", "useCMSFrame(p)") # include aliases to test if they work
vm.addAlias("E_cms", "useCMSFrame(E)")
mA.variablesToNtuple('D0:Kpi', ['M', 'p', 'E', 'E_cms', 'p_cms', 'daughter(0, kaonID)',
'daughter(1, pionID)', 'isSignal', 'mcErrors'],
filename=d_ntuple_filename, treename="D", path=path)
mA.variablesToNtuple('B-', ['Mbc', 'deltaE', 'isSignal', 'mcErrors', 'M'],
filename=b_ntuple_filename, treename="B", path=path)
return path
if __name__ == '__main__':
# Use this to execute the analysis path if this file is called as a main
# file. It can be used to test the analysis path independently of the gbasf2
# luigi task. But if this module is only imported, the following is not executed.
path = create_analysis_path()
basf2.print_path(path)
basf2.process(path)
# variables to ntuple
vars = ['combinationID',
'deltaPhi',
'deltaPhiCMS',
'deltaTheta',
'charge_0',
'charge_1',
'clusterE_0',
'clusterE_1',
'clusterTheta_0',
'clusterTheta_1',
'clusterPhi_0',
'clusterPhi_1',
'clusterECMS_0',
'clusterECMS_1',
'clusterPhiCMS_0',
'clusterPhiCMS_1',
'clusterThetaCMS_0',
'clusterThetaCMS_1'
]
# store variables
variablesToNtuple('vpho:bhabha', vars, filename='bhabha.root', path=mypath)
# Process the events
b2.process(mypath)
# print out the summary
print(b2.statistics)
ntuple = []
# May have more feature than needed.
features = [
'dr', 'dz', 'cosaXY', 'min_dr', 'min_dz', 'pt', 'pz', 'chiProb', 'p'
]
features += create_aliases_for_selected(['PIDppi', 'PIDpk', 'PIDkpi', 'p'],
'Lambda0 -> ^p+ ^pi-',
prefix=['proton', 'pi'])
spectator = ['isSignal', 'isPrimarySignal', 'mcPDG', 'genMotherPDG']
spectator += ['IPX', 'IPY', 'IPZ', 'M', 'p', 'goodBelleLambda', 'distance']
ntuple = features + spectator
return ntuple
if __name__ == '__main__':
infile, outfile = sys.argv[1], sys.argv[2]
mypath = b2.create_path()
b2c.convertBelleMdstToBelleIIMdst(infile, path=mypath)
reconstructLambda('Lambda0:belle',
match=True,
only='background',
path=mypath)
ntuple = make_ntuple()
ma.variablesToNtuple('Lambda0:belle',
ntuple,
'Lambda',
outfile,
path=mypath)
b2.process(path=mypath)
ft_variables = makePretty(['qrOutput(FBDT)', 'qrOutput(FANN)', 'isRelatedRestOfEventB0Flavor', 'qOutput(FANN)', 'rBinBelle(FANN)', 'qOutput(FBDT)', 'rBinBelle(FBDT)', 'EW90', 'pMissCMS', 'cosThetaMissCMS'])
deltat_variables = ['DeltaT', 'DeltaTErr', 'MCDeltaT', 'TagVz', 'TagVzErr','mcTagVz', 'DeltaZ', 'MCTagBFlavor']
cs_variables = makePretty(['R2', 'cosTBTO', 'cosTBz','CleoConeCS(2)','KSFWVariables(hso02)', 'CleoConeCS(4)', 'CleoConeCS(9)','KSFWVariables(hso10)', 'KSFWVariables(hso12)', 'KSFWVariables(hso14)', 'thrustBm', 'thrustOm'])
roe_vars_clean = makePretty(['ROE_E(cleanMask)','ROE_P(cleanMask)', 'ROE_M(cleanMask)', 'ROE_deltae(cleanMask)', 'ROE_mbc(cleanMask)', 'nROE_Tracks(cleanMask)', 'nROE_NeutralECLClusters(cleanMask)', 'ROE_PTheta(cleanMask)', 'ROE_MC_MissFlags(cleanMask)'])
vtx_variables = ['z', 'z_uncertainty', 'pValue']
dalitz_vars = ['m12', 'm13', 'm23']
mix_variables = []#['mixFBDT', 'mixFANN', 'mixTrue']
cluster_variables = ['clusterReg','clusterTiming',
'clusterErrorTiming','clusterE1E9',
'clusterE9E21','clusterAbsZernikeMoment40',
'clusterAbsZernikeMoment51','minC2TDist',
'clusterTheta','clusterZernikeMVA',
'clusterSecondMoment','clusterNHits']
track_variables = vc.track + vc.track_hits + vc.pid
ma.variablesToNtuple('B0:signal',
vc.deltae_mbc+kin_variables+ft_variables+deltat_variables+cs_variables+vtx_variables+mix_variables+roe_vars_clean+dalitz_vars+
['CSMVA','isContinuumEvent','isNotContinuumEvent','pi0Likeness','etaLikeness']+
vu.create_aliases_for_selected(list_of_variables=kin_variables, decay_string= 'B0 -> ^pi+ ^pi- [ ^K_S0 -> ^pi+ ^pi- ] ^gamma')+
vu.create_aliases_for_selected(list_of_variables=track_variables, decay_string= 'B0 -> ^pi+ ^pi- [ K_S0 -> ^pi+ ^pi- ] gamma')+
vu.create_aliases_for_selected(list_of_variables=cluster_variables, decay_string= 'B0 -> pi+ pi- [ K_S0 -> pi+ pi- ] ^gamma'),
filename = outputFilename, path=main_path)
# Process the events
b2.process(main_path)
# print out the summary
print(b2.statistics)
# Adding generator
# This specific event generator will generate either charged final states (charged B mesons)
# or mixed final states (neutal B mesons)
ge.add_evtgen_generator(path=my_path,
finalstate='signal',
signaldecfile='event_gen.dec')
ma.loadGearbox(path=my_path)
# Set the first parameter to zero if you want to see all the events.
# This will show how many particles are generated in each event.
#ma.printDataStore(0, path=my_path)
# Show the decay chain in each event
#ma.printMCParticles(path=my_path)
# dump generated events in DST format to the output ROOT file
my_path.add_module('RootOutput', outputFileName='event_gen.root')
# Show progress bar
my_path.add_module("ProgressBar")
# Add log file
b2.log_to_file("event_gen.log")
# process all modules added to the analysis_main path
# (note: analysis_main is the default path created in the modularAnalysis.py)
b2.process(path=my_path)
# print out the summary
b2.B2INFO(b2.statistics)
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)
dest='out_dir')
args = parser.parse_args()
# Set up output file (not integrated into basf2 command line yet)
if len(args.in_file) > 0:
out_filename = '{}.h5'.format(
os.path.splitext(os.path.basename(args.in_file))[0])
else:
out_filename = 'train_data.h5'
os.makedirs(args.out_dir, exist_ok=True)
outfile = os.path.join(args.out_dir, out_filename)
print("Output file set to:\t" + outfile)
# Main processing
################################################################################
path = ma.create_path()
# Should change to RootInput with keppParents=True
# Don't need the other junk added by inputMdst since we're only dealing with MCParticles
ma.inputMdstList('default', filelist=[args.in_file], path=path)
TrainDataSaver_module = TrainDataSaver(
evt_num_file=args.evtnum_file,
output_file=outfile,
keep_only=args.keep_only,
)
path.add_module(TrainDataSaver_module)
b2.process(path)
print(b2.statistics)
'KSFWVariables(hso22)',
'KSFWVariables(hso24)',
'KSFWVariables(hoo0)',
'KSFWVariables(hoo1)',
'KSFWVariables(hoo2)',
'KSFWVariables(hoo3)',
'KSFWVariables(hoo4)',
'cleoConeThrust0',
'cleoConeThrust1',
'cleoConeThrust2',
'cleoConeThrust3',
'cleoConeThrust4',
'cleoConeThrust5',
'cleoConeThrust6',
'cleoConeThrust7',
'cleoConeThrust8',
]
# Save target variable necessary for training.
targetVar = ['isNotContinuumEvent']
# Create output file.
ma.variablesToNtuple(decayString='B0',
variables=trainVars + targetVar,
treename='tree',
filename=outfile,
path=my_path)
b2.process(my_path)
print(b2.statistics)
# model_type=model_type,
# threshold=args.threshold,
# )
# dead_path = b2.create_path()
# NNApplyModule_m.if_false(dead_path)
# main.add_module(NNApplyModule_m)
# # Do I need this here?
# # c1.set_property_flags(b2.ModulePropFlags.PARALLELPROCESSINGCERTIFIED)
# ma.loadGearbox(path=main)
# main.add_module('Geometry', ignoreIfPresent=True, components=['MagneticField'])
simulation.add_simulation(path=main,
bkgfiles=glob.glob(args.bkg_dir + '/*.root'))
reconstruction.add_reconstruction(path=main)
# dump generated events in MDST format to the output ROOT file
reconstruction.add_mdst_output(
path=main,
mc=True,
filename='RootOutput.root',
additionalBranches=['EventExtraInfo'],
)
# Begin processing
################################################################################
main.add_module('ProgressBar')
b2.process(path=main)
print(b2.statistics)
tauVariables += ['mcErrors', 'genMotherPDG', 'mcPDG', 'isSignal']
# -- track level variables
trackVariables = vc.kinematics + vc.pid + vc.track + vc.track_hits + vc.vertex
trackVariables += [
'theta', 'cosTheta', 'phi', 'charge', 'clusterE', 'EoverP', 'mcPDG'
]
variableList = vu.create_aliases_for_selected(list_of_variables=eventVariables,
decay_string='^vpho') + \
vu.create_aliases_for_selected(list_of_variables=tauVariables + ['charge'],
decay_string='vpho -> ^tau+ ^tau-') + \
vu.create_aliases_for_selected(list_of_variables=trackVariables,
decay_string= decay_chain)
if tauMinus == '1':
Outputfile = "tautau_mctruthsel_21_test.root"
if tauMinus == '3':
Outputfile = "tautau_mctruthsel_23_test.root"
ma.variablesToNtuple('vpho:photon_B2SS',
variables=variableList,
treename='tree',
filename=Outputfile,
path=my_path)
b2.process(my_path, max_event=50000)
vm.addAlias('dxDstar', 'dx')
vm.addAlias('dyDstar', 'dy')
vm.addAlias('dzDstar', 'dz')
vm.addAlias('pDstar', 'p')
vm.addAlias('eDstar', 'E')
vm.addAlias('eCmsDstar', 'useCMSFrame(E)')
vm.addAlias('mDstar', 'M')
vm.addAlias('mD', 'daughter(0,M)')
vm.addAlias('slowPiID', 'daughter(1,pionID)')
Vars = [ 'xDstar', 'yDstar', 'zDstar', 'xGenDstar', 'yGenDstar', 'zGenDstar', 'dxDstar', 'dyDstar', 'dzDstar', 'pDstar', 'eDstar', 'eCmsDstar', 'mDstar', 'mD', 'slowPiID']
Vars.append('isSignal')
Vars.append('IPX')
Vars.append('IPCov(0,0)')
# Match to the MC truth information
ma.matchMCTruth('D*+:my', path=path)
ma.variablesToNtuple('D*+:my', Vars,
filename='nTupleNo.root', treename="Dstar", path=path)
b2.process(path, max_event=100000)
print(b2.statistics)