如何扩展pyWavelets以处理N维数据？

import pywt
import sys
import numpy as np

def waveFn(wavelet):
    if not isinstance(wavelet, pywt.Wavelet):
        return pywt.Wavelet(wavelet)
    else:
        return wavelet

# given a single dimensional array ... returns the coefficients.
def wavedec(data, wavelet, mode=\'sym\'):
    wavelet = waveFn(wavelet)

    dLen = len(data)
    coeffs = np.zeros_like(data)
    level = pywt.dwt_max_level(dLen, wavelet.dec_len)

    a = data    
    end_idx = dLen
    for idx in xrange(level):
        a, d = pywt.dwt(a, wavelet, mode)
        begin_idx = end_idx/2
        coeffs[begin_idx:end_idx] = d
        end_idx = begin_idx

    coeffs[:end_idx] = a
    return coeffs

def waverec(data, wavelet, mode=\'sym\'):
    wavelet = waveFn(wavelet)

    dLen = len(data)
    level = pywt.dwt_max_level(dLen, wavelet.dec_len)

    end_idx = 1
    a = data[:end_idx] # approximation ... also the original data 
    d = data[end_idx:end_idx*2]    
    for idx in xrange(level):
        a = pywt.idwt(a, d, wavelet, mode)
        end_idx *= 2
        d = data[end_idx:end_idx*2]
    return a

def fn_dec(arr):
    return np.array(map(lambda row: reduce(lambda x,y : np.hstack((x,y)), pywt.wavedec(row, \'haar\', \'zpd\')), arr))
    # return np.array(map(lambda row: row*2, arr))

if __name__ == \'__main__\':
    test  = 1
    np.random.seed(10)
    wavelet = waveFn(\'haar\')
    if test==0:
        # SIngle dimensional test.
        a = np.random.randn(1,8)
        print \"original values A\"
        print a
        print \"decomposition of A by method in pywt\"
        print fn_dec(a)
        print \" decomposition of A by my method\"
        coeffs =  wavedec(a[0], \'haar\', \'zpd\')
        print coeffs
        print \"recomposition of A by my method\"
        print waverec(coeffs, \'haar\', \'zpd\')
        sys.exit()
    if test==1:
        a = np.random.randn(4,4,4)
        # 2 D test
        print \"original value of A\"
        print a

        # decompose the signal into wavelet coefficients.
        dimensions = a.shape
        for dim in dimensions:
            a = np.rollaxis(a, 0, a.ndim)
            ndim = a.shape
            #a = fn_dec(a.reshape(-1, dim))
            a = np.array(map(lambda row: wavedec(row, wavelet), a.reshape(-1, dim)))
            a = a.reshape(ndim)
        print \" decomposition of signal into coefficients\"
        print a

        # re-composition of the coefficients into original signal
        for dim in dimensions:
            a = np.rollaxis(a, 0, a.ndim)
            ndim = a.shape
            a = np.array(map(lambda row: waverec(row, wavelet), a.reshape(-1, dim)))
            a = a.reshape(ndim)
        print \"recomposition of coefficients to signal\"
        print a