def METHOD_NAME(self, random_initial_value, first_seed_of_chunk):
    """
    Initialize numpy number generator according to user's parameter
    and indexes from the seeding map.
    Parameters
    ----------
    random_initial_value : int
        The "seed" for the random generator.
    first_seed_of_chunk : int
        Number of seeds to skip (skip parameter + multi-processor skip).
    Return
    ------
    random_generator : numpy random generator
        Initialized numpy number generator.
    indices : ndarray
        Indices of current seeding map.
    """
    random_generator = np.random.RandomState(random_initial_value)
    # 1. Initializing seeding maps indices (shuffling in-place)
    indices = np.arange(len(self.seeds_vox))
    random_generator.shuffle(indices)
    # 2. Initializing the random generator
    # For reproducibility through multi-processing, skipping random numbers
    # (by producing rand numbers without using them) until reaching this
    # process (i.e this chunk)'s set of random numbers. Producing only
    # 100000 at the time to prevent RAM overuse.
    # (Multiplying by 3 for x,y,z)
    random_numbers_to_skip = first_seed_of_chunk * 3
    # toDo: see if 100000 is ok, and if we can create something not
    #  hard-coded
    while random_numbers_to_skip > 100000:
        random_generator.random_sample(100000)
        random_numbers_to_skip -= 100000
    random_generator.random_sample(random_numbers_to_skip)
    return random_generator, indices