parser.add_argument('outputFilePrefix')
args = parser.parse_args()
print('reading data collection')
dc = DataCollection()
dc.readFromFile(args.inputDataCollection)
print('producing feature array')
feat = dc.getAllFeatures()
print('producing truth array')
truth = dc.getAllLabels()
print('producing weight array')
weight = dc.getAllWeights()
print('producing means and norms array')
means = dc.means
from numpy import save
print('saving output')
for i in range(len(feat)):
save(args.outputFilePrefix + '_features_' + str(i) + '.npy', feat[i])
for i in range(len(truth)):
save(args.outputFilePrefix + '_truth_' + str(i) + '.npy', truth[i])
for i in range(len(weight)):
save(args.outputFilePrefix + '_weights_' + str(i) + '.npy', weight[i])
from DeepJetCore.DataCollection import DataCollection
print('reading data collection')
dc=DataCollection()
dc.readFromFile(args.inputDataCollection)
nfiles = args.nfiles
print('producing feature array')
feat=dc.getAllFeatures(nfiles=nfiles)
print('producing truth array')
truth=dc.getAllLabels(nfiles=nfiles)
print('producing weight array')
weight=dc.getAllWeights(nfiles=nfiles)
from numpy import save
print('saving output')
for i in range(len(feat)):
save(args.outputFilePrefix+'_features_'+str(i) +'.npy', feat[i])
for i in range(len(truth)):
save(args.outputFilePrefix+'_truth_'+str(i) +'.npy', truth[i])
for i in range(len(weight)):
save(args.outputFilePrefix+'_weights_'+str(i) +'.npy', weight[i])