def to_subs(ts, sub_len, increment):
    """
    Convert a time-series to a 2D array (num_of_subsequences, sub_len)
    ts: raw time-series
    sub_len: length of subsequences
    increment: overlap of subsequence (increment in loop)
    """
    subs=[]
    for i in range(0,len(ts)-sub_len,increment):
        subs.append(ts[i:i+sub_len])
    return subs

def unwrap_predictions(preds, ts, sub_len, increment):
    """
    Unwrap predictions for every point.
    Returns unnormalized predictions and mask to normalize (unnorm_preds / mask)
    preds: predictions x sequence, len: num_of_subsequences
    ts: raw time-series
    sub_len: length of subsequences
    increment: overlap of subsequence (increment in loop)
    """
    unwrapped_preds = np.zeros(len(ts))
    mask = np.ones(len(ts))
    for k in range(len(preds)):
        r = np.min([k+1,int(sub_len/increment)])
        for j in range(r):
            unwrapped_preds[increment*(k-j) + j*increment: increment*(k-j) + ((j+1)*increment)] += preds[k]
            mask[increment*(k-j) + j*increment: increment*(k-j) + ((j+1)*increment)] += 1

    return unwrapped_preds, mask