def read_samplepoints(file_name):

    sampFreq, snd1 = wavfile.read(file_name)

    samp_points = len(snd1)

    data_type = snd1.dtype

    return samp_points, data_type

def maximum_amplitude(file_name):

    sampFreq, snd = wavfile.read(file_name)

    snd = snd / (2.**15)

    s1 = snd[:,0]

    s2 = snd[:,1]

    max_s1 = np.max(s1)

    max_s2 = np.max(s2)

    return max_s1, max_s2


def plot_signal(file_name):

    sampFreq, snd = wavfile.read(file_name)

    snd = snd / (2.**15)

    s1 = snd[:,0]

    s2 = snd[:,1]

    timeArray = arange(0, len(snd), 1)
    timeArray = timeArray / sampFreq
    timeArray = timeArray * 1000  #scale to milliseconds

    plt.plot(timeArray, s1, color='k')
    plt.ylabel('LeftChannel_Amplitude')
    plt.xlabel('Time (ms)')
    plt.show()

    timeArray2 = arange(0, len(snd), 1)
    timeArray2 = timeArray2 / sampFreq
    timeArray2 = timeArray2 * 1000  #scale to milliseconds

    plt.plot(timeArray2, s2, color='k')
    plt.ylabel('RightChannel_Amplitude')
    plt.xlabel('Time (ms)')
    plt.show()

    n = len(s1)
    p = fft(s1) # take the fourier transform

    m = len(s2)
    p2 = fft(s2)

    nUniquePts = ceil((n+1)/2.0)
    p = p[0:nUniquePts]
    p = abs(p)

    mUniquePts = ceil((m+1)/2.0)
    p2 = p2[0:mUniquePts]
    p2 = abs(p2)

'''
Left Channel
'''
    p = p / float(n) # scale by the number of points so that
             # the magnitude does not depend on the length
             # of the signal or on its sampling frequency
    p = p**2  # square it to get the power




    # multiply by two (see technical document for details)
    # odd nfft excludes Nyquist point
    if n % 2 > 0: # we've got odd number of points fft
        p[1:len(p)] = p[1:len(p)] * 2
    else:
        p[1:len(p) -1] = p[1:len(p) - 1] * 2 # we've got even number of     points fft

    freqArray = arange(0, nUniquePts, 1.0) * (sampFreq / n);
    plt.plot(freqArray/1000, 10*log10(p), color='k')
    plt.xlabel('LeftChannel_Frequency (kHz)')
    plt.ylabel('LeftChannel_Power (dB)')
    plt.show()

'''
Right Channel
'''
    p2 = p2 / float(m) # scale by the number of points so that
             # the magnitude does not depend on the length
             # of the signal or on its sampling frequency
    p2 = p2**2  # square it to get the power




# multiply by two (see technical document for details)
# odd nfft excludes Nyquist point
    if m % 2 > 0: # we've got odd number of points fft
        p2[1:len(p2)] = p2[1:len(p2)] * 2
    else:
        p2[1:len(p2) -1] = p2[1:len(p2) - 1] * 2 # we've got even number of points fft

    freqArray2 = arange(0, mUniquePts, 1.0) * (sampFreq / m);
    plt.plot(freqArray2/1000, 10*log10(p2), color='k')
    plt.xlabel('RightChannel_Frequency (kHz)')
    plt.ylabel('RightChannel_Power (dB)')
    plt.show()

    max_p = np.max(10*log10(p))

    max_p2 = np.max(10*log10(p2))

    return max_p, max_p2