import numpy as np

from os import listdir

from os.path import isfile, join

def read_raw_dataset():

    """

    Returns a dictionary `raw_dataset` which contains following keys pairs.

    A, B, C, D, and F with values as 2D numpy array of shape (m, n).

    m = no. of training examples in each set

    n - no. of data points in time series

    """

    # Directory names are saved as F, O, N, etc.

    # And set names are processed as A, B, C, etc.

    mapping_set_to_dir = {

        'A': (0,'Z'),

        'B': (1,'O'),

        'C': (2,'N'),

        'D': (3,'F'),

        'E': (4,'S')

    }

    file_lists = []

    # get the list of files for each set

    # 1 file corresponds to 1 training example

    for s,d in mapping_set_to_dir.items():

        file_lists.insert(d[0], [f for f in listdir(d[1]) if isfile(join(d[1], f))])

    raw_dataset = { }

    # loop over all sets

    for s,d in mapping_set_to_dir.items():

        # loop over every file (training example) in each set

        for f in file_lists[d[0]]:

            # read the time series data

            curr_example = np.loadtxt(join(d[1], f))

            # create a key in the raw_database dict in case it doesn't exist already

            # otherwise just append the new example in the 2D array

            if (s in raw_dataset):

                raw_dataset[s] = np.append(raw_dataset[s], [curr_example], axis=0)

            else:

                raw_dataset[s] = np.array([curr_example])

    return raw_dataset