trackidx_holder = []
showeridx_holder = []
if len(dataset) >= 15000 or len(dataset) == 0:
labels = pc.walker(dataset, 6,
20) # Runs clustering and returns labels list
else:
labels = pc.crawlernn(dataset, 6,
20) # Runs clustering and returns labels list
datasetidx_holder = lh.label_to_idxholder(
labels, 20
) # Converts the labels list into a list of indexvalues for datasets [ [ list of index], [list of indexes].. [] ]
Ea.Ana_Object_photons(dataset,
datasetidx_holder,
jcount,
mcinfo,
mcqdep,
filename='photon_ana_obj')
print ' AT THE END'
'''
########################
# mc_datalabel info
# Call this once and get the mc info for the jsons for later
########################
#mc_dl = mh.mc_Obj_points(mh.mc_neutron_induced_OBJ(f))
########################
# if the file is bad then continue and fill
########################
if not file_info[0]:
#print 'size of dataset : ' , str(len(dataset))
if len(dataset) == 0:
continue
########################
# Build the reco testing here
########################
labels = []
#trackidx_holder = []
#showeridx_holder = []
labels = pc.walker(
dataset, nn_dist,
mincluster) # Runs clustering and returns labels list
datasetidx_holder = lh.label_to_idxholder(
labels, mincluster
) # Converts the labels list into a list of indexvalues for datasets [ [ list of index], [list of indexes].. [] ]
Ea.Ana_Object_photons(
dataset,
datasetidx_holder,
jcount,
mcinfo,
mcqdep,
filename='{}/photon_ana_obj_nn_{}_mspt_{}'.format(
method_name, str(nn_dist), str(mincluster)))
print 'AT THE END for nndist={} and mincluster={}'.format(
str(nn_dist), str(mincluster))