traind = None
if class_name in class_options:
traind = class_options[class_name]
elif not recover and not testdatafor:
print('available classes:')
for key, val in class_options.iteritems():
print(key)
raise Exception('wrong class selection')
if recover:
dc.recoverCreateDataFromRootFromSnapshot(recover)
elif args.means:
dc.convertListOfRootFiles(infile,
traind,
outPath,
means_only=True,
output_name='batch_template.djcdc',
relpath=relpath)
else:
logging.info('Start conversion')
dc.convertListOfRootFiles(
infile,
traind,
outPath,
takeweightersfrom=usemeansfrom,
output_name=(args.batch if args.batch else 'dataCollection.djcdc'),
relpath=relpath)
if args.inRange is not None:
os.unlink(infile)
traind = class_options[class_name]
elif not recover and not testdatafor:
print('available classes:')
for key, val in class_options.iteritems():
print(key)
raise Exception('wrong class selection')
if testdatafor:
logging.info('converting test data, no weights applied')
dc.createTestDataForDataCollection(
testdatafor,
infile,
outPath,
outname=args.batch if args.batch else 'dataCollection.dc',
batch_mode=bool(args.batch))
elif recover:
dc.recoverCreateDataFromRootFromSnapshot(recover)
elif args.means:
dc.convertListOfRootFiles(infile,
traind(),
outPath,
means_only=True,
output_name='batch_template.dc')
else:
dc.convertListOfRootFiles(
infile,
traind(),
outPath,
usemeansfrom,
output_name=args.batch if args.batch else 'dataCollection.dc',
batch_mode=bool(args.batch))
try:
cls = class_options[args.c]
except KeyError:
raise Exception('wrong class selection')
if not args.classArgs:
args.classArgs = tuple()
dc = DataCollection(nprocs=-1)
dc.meansnormslimit = int(args.nforweighter)
try:
dc.convertListOfRootFiles(
args.infile,
cls(*args.classArgs),
args.out,
means_only=True,
output_name='batch_template.dc',
relpath=('' if args.noRelativePaths else os.path.dirname(
os.path.realpath(args.infile))))
except:
print 'The first round of root conversion failed'
raise
print('means/norms/weighter produced successfully')
elif args.meansfrom:
if not os.path.exists(args.meansfrom):
raise Exception("The file " + args.meansfrom + " does not exist")
print('using means/weighter from ' + args.meansfrom)
os.mkdir(args.out)
if testdatafor:
logging.info('converting test data, no weights applied')
dc.createTestDataForDataCollection(
testdatafor,
infile,
outPath,
outname=(args.batch if args.batch else 'dataCollection.dc'),
traind=(traind(class_args) if traind else None),
relpath=relpath)
elif recover:
dc.recoverCreateDataFromRootFromSnapshot(recover)
elif args.means:
dc.convertListOfRootFiles(infile,
traind(class_args) if class_args else traind(),
outPath,
means_only=True,
output_name='batch_template.dc',
relpath=relpath)
else:
logging.info('Start conversion')
dc.convertListOfRootFiles(
infile,
traind(class_args) if class_args else traind(),
outPath,
takemeansfrom=usemeansfrom,
output_name=(args.batch if args.batch else 'dataCollection.dc'),
relpath=relpath)
if args.inRange is not None:
os.unlink(infile)