Vectorized striding window extraction

vectorized_stride.py

def vectorized_stride_v1(array, clearing_time_index, max_time, sub_window_size,
                         stride_size):
    start = clearing_time_index + 1 - sub_window_size + 1
    
    sub_windows = (
        start + 
        np.expand_dims(np.arange(sub_window_size), 0) +
        np.expand_dims(np.arange(max_time + 1), 0).T
    )
    
    # Fancy indexing to select every V rows.
    return array[sub_windows[::stride_size]]


def vectorized_stride_v2(array, clearing_time_index, max_time, sub_window_size,
                         stride_size):
    start = clearing_time_index + 1 - sub_window_size + 1
    
    sub_windows = (
        start + 
        np.expand_dims(np.arange(sub_window_size), 0) +
        # Create a rightmost vector as [0, V, 2V, ...].
        np.expand_dims(np.arange(max_time + 1, step=stride_size), 0).T
    )
    
    return array[sub_windows]