帮我编写基于python的，但不使用PyWavelets库的小波分解代码

好的，以下是基于Python的小波分解代码，但不使用PyWavelets库：

import numpy as np

def wavelet_transform(signal, wavelet='haar', level=1):
    # Define the wavelet filter coefficients
    if wavelet == 'haar':
        dec_lo = np.array([1, 1]) / np.sqrt(2)
        dec_hi = np.array([1, -1]) / np.sqrt(2)
    elif wavelet == 'db4':
        dec_lo = np.array([0.482962913145, 0.836516303738, 0.224143868042, -0.129409522551])
        dec_hi = np.array([-0.129409522551, -0.224143868042, 0.836516303738, -0.482962913145])
    else:
        raise ValueError('Unsupported wavelet')
    
    # Perform the wavelet transform
    coeffs = []
    for i in range(level):
        # Apply the low-pass and high-pass filters
        cA = np.convolve(signal, dec_lo, mode='valid')[::2]
        cD = np.convolve(signal, dec_hi, mode='valid')[::2]
        coeffs.append(cD)
        signal = cA
    
    coeffs.append(signal)
    coeffs.reverse()
    return coeffs

希望这个代码对你有所帮助！