if args.ana_output == None:
args.ana_output = "default_ana_output.root"
print "No ana output file selected. If necessary, output will go to:"
print "\t" + args.ana_output
mgr = fmwk.storage_manager()
mgr.set_io_mode(mgr.READ)
for source in args.source:
mgr.add_in_filename(source)
if len(sys.argv) > 2:
mgr.set_in_rootdir("scanner")
mgr.open()
algo = cluster.ClusterParamsExecutor()
algo.SetUseHitBlurring(false)
algo.DisableFANN()
if args.argoneut != None:
algo.SetArgoneutGeometry()
fann = cluster.TrainingModule()
fann.setFeatureVectorLength(13)
fann.setOutputVectorLength(2)
fann.setOutputFileName("track_shower.net")
fann.init()
# Here is the neural network that has been more rigorously trained:
cascadeFANN = cluster.FANNModule()
cascadeFANN.LoadFromFile("cascade_net.net")
# ana_proc.add_input_file(args.source)
mgr = fmwk.storage_manager()
mgr.set_io_mode(mgr.READ)
mgr.add_in_filename(args.source)
if len(sys.argv) > 2:
mgr.set_in_rootdir("scanner")
mgr.open()
chit = TCanvas("chit", "chit", 800, 700)
chit.SetGridx(1)
chit.SetGridy(1)
algo = cluster.ClusterParamsExecutor()
algo.SetUseHitBlurring(false)
merger = cluster.ClusterMergeAlgNew()
processed_events = 0
fGSer = larutil.GeometryUtilities.GetME()
fGeo = larutil.Geometry.GetME()
while mgr.next_event():
# Get event_cluster ... std::vector<larlight::cluster>
cluster_v = mgr.get_data(fmwk.DATA.FuzzyCluster)