import numpy as np

def create_augmented_dataset(raw_dataset, window_size = 512, stride = 64, verbose=False):
    augmented_dataset = { }

    for s,Xset_raw in raw_dataset.items():
        
        if (verbose):
            print('Processing set ' + s)

        total_points = Xset_raw.shape[1]

        # no. of examples generated from single training example using sliding window
        # = floor((total_points - window_size) / stride) + 1
        iterations = ((total_points - window_size) // stride) + 1

        for x_raw in Xset_raw:

            for i in range(iterations):
                window_slice_from = i*stride
                window_slice_to = i*stride + window_size

                if (s in augmented_dataset):
                    augmented_dataset[s] = np.append(augmented_dataset[s], [x_raw[window_slice_from:window_slice_to]], axis=0)
                else:
                    augmented_dataset[s] = np.array([x_raw[window_slice_from:window_slice_to]])

    
    if (verbose):
        print('Done.')
    return augmented_dataset