'p_' + str(p) for p in percentiles_list]
    if iqr and 25 in percentiles_list and 75 in percentiles_list:
        names.append('iqr')
    if amplitude and 1 in percentiles_list and 99 in percentiles_list:
        names.append('perc_amp')
    if len(x) == 0:
        values = [0 for i in range(len(names))]
        return values, names
    if len(percentiles_list) > 0 and all([0 < q < 100 for q in percentiles_list]):
        values = list(np.percentile(x, percentiles_list))
    else:
        values = []
    if iqr:
        q1 = percentiles_list.index(25)
        q3 = percentiles_list.index(75)
        values.append(values[q3] - values[q1])
    if amplitude and 1 in percentiles_list and 99 in percentiles_list:
        q1 = percentiles_list.index(1)
        q3 = percentiles_list.index(99)
        values.append(values[q3] - values[q1])
    return values, names

def stats_calculate_all(x):
    stats_all_names = ['len', 'min', 'max', 'range', 'mean', 'hmean', 'gmean', 'var', 'std', 'skew', 'kurtosis',
                       'median', 'mode', 'energy', 'energy_sample', 'snr']
    xnp = np.array(x)
    n = len(x)
    if n == 0:
        values = [0 for i in range(len(stats_all_names))]
        return values, stats_all_names
    values = []
    # vmin = float(min(xnp))
    # if vmin < 1:
    #     offset = 1 + abs(vmin)
    # else:
    #     offset = 0
    # vmax = float(max(xnp))
    # vrange = vmax - vmin
    vmean = float(np.mean(xnp))
    vstd = float(np.std(xnp))
    # venergy = float(sum(np.array(xnp) ** 2))
    # venergy_sample = venergy / n
    # snr = 0.0
    if vstd != 0:
       snr = vmean / vstd
    # values.append(vmin)
    # values.append(vmax)
    # values.append(vrange)
    values.append(vmean)
    # values.append(float(sp.hmean(xnp + offset)))
    # values.append(float(sp.gmean(xnp + offset)))
    # values.append(vstd ** 2)
    values.append(vstd)
    # values.append(sp.skew(xnp))
    #values.append(sp.kurtosis(xnp))
    values.append(np.median(xnp))
    # vmode = sp.mode(xnp)
    # vmode = float(vmode[0][0])
    # values.append(vmode)
    # values.append(venergy)
    # values.append(venergy_sample)
    # values.append(snr)
    return values, stats_all_names


if __name__ == "__main__":
    x = np.array(X_data)
    w_long = input()
    w_short = input()
    shift = 20



    for i in range(max(w_long, w_short), len(X_data), 20):
        results = []
        for j in range(0, len(x[0, :])):
            s, stat_names = stats_calculate_all(x[i-w_short:i,j])
            s1, stat_names2 = stats_calculate_all(x[i - w_long:i, j])
            results.append([s1[0], s1[2], s1[1], s[0], s[2], s[1], s1[0]-s[0]])
            if s1[0] > s[0]:
                results.append(1)
            else:
                results.append(-1)

        print (i, ['%.2f'% elem for elem in results])