wavelet denoising

def maddest(d, axis=None):
    return np.mean(np.absolute(d - np.mean(d, axis)), axis)

def denoise_signal(x, wavelet='db4', level=1):
    coeff = pywt.wavedec(x, wavelet, mode="per")
    sigma = (1/0.6745) * maddest(coeff[-level])

    uthresh = sigma * np.sqrt(2*np.log(len(x)))
    coeff[1:] = (pywt.threshold(i, value=uthresh, mode='hard') for i in coeff[1:])

    return pywt.waverec(coeff, wavelet, mode='per')