else:
sys.exit("ERROR: Output Directory does not exist!")
#add nJets and nTags to output directory
outputdir += "_" + options.category
# the input variables are loaded from the variable_set file
if options.category in variable_set.variables:
variables = variable_set.variables[options.category]
else:
variables = variable_st.all_variables
print("category {} not specified in variable set {} - using all variables".
format(options.category, options.variableSelection))
# load samples
input_samples = df.InputSamples(inPath)
naming = options.naming
# during preprocessing half of the ttH sample is discarded (Even/Odd splitting),
# thus, the event yield has to be multiplied by two. This is done with normalization_weight = 2.
#input_samples.addSample("ttHbb"+naming, label = "ttHbb", normalization_weight = 2.)
#input_samples.addSample("ttbb"+naming, label = "ttbb")
#input_samples.addSample("tt2b"+naming, label = "tt2b")
#input_samples.addSample("ttb"+naming, label = "ttb")
#input_samples.addSample("ttcc"+naming, label = "ttcc")
#input_samples.addSample("ttlf"+naming, label = "ttlf")
sampleDict = pputils.readSampleFile(inPath)
print "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
print sampleDict
print "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!"
pputils.addToInputSamples(input_samples, sampleDict)
options = optionHandler.optionHandler(sys.argv)
# local imports
filedir = os.path.dirname(os.path.realpath(__file__))
basedir = os.path.dirname(filedir)
sys.path.append(basedir)
# import class for DNN training
import DRACO_Frameworks.DNN.DNN as DNN
import DRACO_Frameworks.DNN.data_frame as df
options.initArguments()
# load samples
input_samples = df.InputSamples(options.getInputDirectory(),
options.getActivatedSamples(),
options.getTestPercentage())
# define all samples
# only ttH sample needs even/odd splitting for 2017 MC
input_samples.addSample(options.getDefaultName("ttHbb"),
label="ttH",
normalization_weight=options.getNomWeight())
input_samples.addSample(options.getDefaultName("tthf"),
label="tthf",
normalization_weight=1.)
input_samples.addSample(options.getDefaultName("ttcc"),
label="ttcc",
normalization_weight=1.)
input_samples.addSample(options.getDefaultName("ttlf"),
label="ttlf",
