# Path: mimic3models/common_utils.py
# def convert_to_dict(data, header, channel_info):
# def extract_features_from_rawdata(chunk, header, period, features):
# def read_chunk(reader, chunk_size):
# def sort_and_shuffle(data, batch_size):
# def add_common_arguments(parser):
#     def __init__(self, dataset_dir, listfile=None, small_part=False):
#     def _read_timeseries(self, ts_filename):
# def create_directory(directory):
# def pad_zeros(arr, min_length=None):
# class DeepSupervisionDataLoader:


from mimic3models import common_utils
import numpy as np
import os
from __future__ import absolute_import
from __future__ import print_function



def load_data(reader, discretizer, normalizer, small_part=False, return_names=False):
    N = reader.get_number_of_examples()
    if small_part:
        N = 1000

# Path: mimic3models/common_utils.py
# def convert_to_dict(data, header, channel_info):
# def extract_features_from_rawdata(chunk, header, period, features):
# def read_chunk(reader, chunk_size):
# def sort_and_shuffle(data, batch_size):
# def add_common_arguments(parser):
#     def __init__(self, dataset_dir, listfile=None, small_part=False):
#     def _read_timeseries(self, ts_filename):
# def create_directory(directory):
# def pad_zeros(arr, min_length=None):
# class DeepSupervisionDataLoader:


# Path: mimic3models/in_hospital_mortality/utils.py
from mimic3models import common_utils
import numpy as np
import os

from __future__ import absolute_import
from __future__ import print_function



def load_data(reader, discretizer, normalizer, small_part=False, return_names=False):
    N = reader.get_number_of_examples()
    if small_part:
        N = 1000

    ret = common_utils.read_chunk(reader, N)