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Namespaces
	generate_datataset

Functions
def	generate_datataset.normalize (value)

def	generate_datataset.normalize2 (value)

Variables
string	generate_datataset.__version__ = '1.0'

string	generate_datataset.__author__ = 'Saul Alonso-Monsalve'

string	generate_datataset.__email__ = "saul.alonso.monsalve@cern.ch"

	generate_datataset.stream

	generate_datataset.stdout

	generate_datataset.level

	generate_datataset.config = configparser.ConfigParser()

	generate_datataset.SEED = int(config['random']['seed'])

	generate_datataset.IMAGES_PATH = config['images']['path']

	generate_datataset.VIEWS = int(config['images']['views'])

	generate_datataset.PLANES = int(config['images']['planes'])

	generate_datataset.CELLS = int(config['images']['cells'])

	generate_datataset.DATASET_PATH = config['dataset']['path']

	generate_datataset.PARTITION_PREFIX = config['dataset']['partition_prefix']

	generate_datataset.LABELS_PREFIX = config['dataset']['labels_prefix']

	generate_datataset.UNIFORM = ast.literal_eval(config['dataset']['uniform'])

	generate_datataset.OUTPUTS = int(config['model']['outputs'])

	generate_datataset.TRAIN_FRACTION = float(config['train']['fraction'])

	generate_datataset.WEIGHTED_LOSS_FUNCTION = ast.literal_eval(config['train']['weighted_loss_function'])

	generate_datataset.CLASS_WEIGHTS_PREFIX = config['train']['class_weights_prefix']

	generate_datataset.VALIDATION_FRACTION = float(config['validation']['fraction'])

	generate_datataset.TEST_FRACTION = float(config['test']['fraction'])

int	generate_datataset.count_flavour = [0]*4

int	generate_datataset.count_category = [0]*14

dictionary	generate_datataset.partition = {'train' : [], 'validation' : [], 'test' : []}

dictionary	generate_datataset.labels = {}

list	generate_datataset.y_train = []

list	generate_datataset.y1_class_weights = []

list	generate_datataset.y2_class_weights = []

list	generate_datataset.only_train = ['nutau2', 'nutau3']

int	generate_datataset.count_neutrinos = 0

int	generate_datataset.count_antineutrinos = 0

int	generate_datataset.count_empty_views = 0

int	generate_datataset.count_empty_events = 0

int	generate_datataset.count_less_10nonzero_views = 0

int	generate_datataset.count_less_10nonzero_events = 0

	generate_datataset.count_train

	generate_datataset.count_val

	generate_datataset.count_test

	generate_datataset.files = list(glob.iglob(images_path + "/images/*"))

	generate_datataset.ID = imagefile.split("/")[-1][:-3]

string	generate_datataset.infofile = images_path+'/info/'

	generate_datataset.info = open(infofile, 'r').readlines()

	generate_datataset.fInt = int(info[0].strip())

int	generate_datataset.flavour = fInt//4

int	generate_datataset.interaction = fInt%4

	generate_datataset.fNuEnergy = float(info[1].strip())

	generate_datataset.fLepEnergy = float(info[2].strip())

	generate_datataset.fRecoNueEnergy = float(info[3].strip())

	generate_datataset.fRecoNumuEnergy = float(info[4].strip())

	generate_datataset.fEventWeight = float(info[5].strip())

	generate_datataset.fNuPDG = normalize2(int(info[6].strip()))

	generate_datataset.fNProton = normalize(int(info[7].strip()))

	generate_datataset.fNPion = normalize(int(info[8].strip()))

	generate_datataset.fNPizero = normalize(int(info[9].strip()))

	generate_datataset.fNNeutron = normalize(int(info[10].strip()))

	generate_datataset.random_value = np.random.uniform(0,1)

	generate_datataset.pixels = np.fromstring(zlib.decompress(image_file.read()), dtype=np.uint8, sep='').reshape(VIEWS, PLANES, CELLS)

list	generate_datataset.views = [None]*VIEWS

list	generate_datataset.empty_view = [0,0,0]

list	generate_datataset.non_empty_view = [0,0,0]

int	generate_datataset.count_empty = 0

int	generate_datataset.count_less_10nonzero = 0

	generate_datataset.maxi = np.max(views[i])

	generate_datataset.mini = np.min(views[i])

	generate_datataset.nonzero = np.count_nonzero(views[i])

	generate_datataset.total = np.sum(views[i])

	generate_datataset.avg = np.mean(views[i])

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