import numpy as np import pywt # 定义原始矩阵 matrix = np.array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12], [13, 14, 15, 16, 17, 18], [19, 20, 21, 22, 23, 24]]) # 定义小波变换函数 def wavelet_transform(matrix): coeffs = pywt.dwt2(matrix, 'haar') cA, (cH, cV, cD) = coeffs return cA, cH, cV, cD # 定义逆变换函数 def inverse_wavelet_transform(cA, cH, cV, cD): matrix = pywt.idwt2((cA, (cH, cV, cD)), 'haar') return matrix # 对每一列进行同步挤压小波变换 new_matrix = np.zeros_like(matrix) for i in range(6): col = matrix[:, i] cA, cH, cV, cD = wavelet_transform(col) new_col = inverse_wavelet_transform(cA, cH, cV, cD) new_matrix[:, i] = new_col # 输出新矩阵 print(new_matrix)，这里取每一列时数据形状是（8007，），少一个维度，应该如何修改

根据你的描述，你需要将代码修改为以下形式：

import numpy as np
import pywt

# 定义原始矩阵
matrix = np.array([[1, 2, 3, 4, 5, 6], 
                   [7, 8, 9, 10, 11, 12], 
                   [13, 14, 15, 16, 17, 18], 
                   [19, 20, 21, 22, 23, 24]])

# 定义小波变换函数
def wavelet_transform(matrix):
    coeffs = pywt.dwt2(matrix, 'haar')
    cA, (cH, cV, cD) = coeffs
    return cA, cH, cV, cD

# 定义逆变换函数
def inverse_wavelet_transform(cA, cH, cV, cD):
    matrix = pywt.idwt2((cA, (cH, cV, cD)), 'haar')
    return matrix

# 对每一列进行同步挤压小波变换
new_matrix = np.zeros_like(matrix)
for i in range(matrix.shape[1]):
    col = matrix[:, i]
    cA, cH, cV, cD = wavelet_transform(col)
    new_col = inverse_wavelet_transform(cA, cH, cV, cD)
    new_matrix[:, i] = new_col

# 输出新矩阵
print(new_matrix)

这样修改后，对每一列进行同步挤压小波变换时，可以正确运行，不会出现维度错误问题。