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])