def METHOD_NAME(data):
	'''
	Multilevel SURE adaptive thresholding of wavelet coefficients from:
	'Adapting to Unknown Smoothness via Wavelet Shrinkage', D. Donoho, I. Johnstone,
	Dec 1995
	Performs adaptive SURE denoising of input signal ('data') by adaptive softmax thresholding
	each level of wavelet coefficients representing the original signal. Works under the assumption
	of Gaussian additive noise in the case when energy signal is concentrated in relatively
	few wavelet transform components allowing softmax thresholding to remove unwanted noise contributions.
	Args:
        data (float list): .
    Returns:
        float list: SUREShrink Denoised signal.
	'''
	mode = "periodic"
	wl = "sym8"
	n = data.shape[0]
	J = np.ceil(np.log2(n))
	dwt = pywt.wavedec(data,wavelet=wl,mode=mode)  
	for i in range(len(dwt)):
		dwt[i] = SUREThresh(dwt[i])
	return pywt.waverec(dwt,wavelet=wl,mode=mode)