def butter_bandpass_filter(data, lowcut, highcut, fs, order=2):
    nyq = 0.5 * fs
    low = lowcut /nyq
    high = highcut/nyq
    b, a = butter(order, [low, high], btype='band')
    #print(b,a)
    y = lfilter(b, a, data)
    return y

for i in np.arange(n):
    alpha1 = butter_bandpass_filter(fft1[i, :], 8.1, 12.0, 256)
    beta1 = butter_bandpass_filter(fft1[i, :], 16.0, 36.0, 256)
    gamma1 = butter_bandpass_filter(fft1[i, :], 36.1, 80, 256)
    delta1 = butter_bandpass_filter(fft1[i, :], 0.0, 4.0, 256)
    sigma1 = butter_bandpass_filter(fft1[i, :], 12.1, 16.0, 256)
    theta1 = butter_bandpass_filter(fft1[i, :], 4.1, 8.0, 256)
    sumalpha1 = sum(abs(alpha1))
    sumbeta1 = sum(abs(beta1))
    sumgamma1 = sum(abs(gamma1))
    sumdelta1 = sum(abs(delta1))
    sumsigma1 = sum(abs(sigma1))
    sumtheta1 = sum(abs(theta1))
    objects = [sumalpha1, sumbeta1, sumgamma1, sumdelta1, sumsigma1, sumtheta1]
    N = len(objects)
    ra = range(N)
    plt.title(signal_labels[i])
    plt.autoscale
    somestuffneeded = np.arange(6)
    ticks = ['alpha','beta','gamma','delta','sigma','theta']
    plt.xticks(somestuffneeded, ticks)
    plt.bar(ra, objects)
    plt.show()