请显示修改后完整的代码

以下是将降噪处理融入到原有代码中的完整版本：

import numpy as np
import os
from scipy.signal import butter, filtfilt
import matplotlib.pyplot as plt

# 定义文件夹路径
folder_path = r'E:\试验数据（1）\速度\处理后数据集'

# 定义低通滤波器参数
cutoff_freq = 10  # 频率截止值
fs = 100  # 采样频率
filter_order = 2  # 滤波器阶数

def butter_lowpass_filter(data, cutoff, fs, order=filter_order):
    nyquist = 0.5 * fs
    normal_cutoff = cutoff / nyquist
    b, a = butter(order, normal_cutoff, btype='low', analog=False)
    filtered_data = filtfilt(b, a, data)
    return filtered_data

# 检查文件夹下所有的.DAT文件
for filename in os.listdir(folder_path):
    if filename.endswith('.DAT'):
        # 构建完整文件路径
        file_path = os.path.join(folder_path, filename)

        # 读取.DAT文件并进行降噪处理
        with open(file_path, 'rb') as f:
            raw_data = f.read()

        try:
            cleaned_data = butter_lowpass_filter(np.frombuffer(raw_data, dtype=np.float64), cutoff_freq, fs)
        except Exception as e:
            print(f"Error processing file {filename}: {e}")
            continue
        
        # 从处理过的数据中提取频域信息
        transformed_data = np.fft.fft(cleaned_data)

        # 计算幅度谱
        amplitude_spectrum = np.abs(transformed_data)

        # 绘制幅度谱图形
        plt.figure()
        plt.plot(amplitude_spectrum)
        plt.title(f'Amplitude Spectrum after noise reduction - {filename}')
        plt.xlabel('Frequency')
        plt.ylabel('Amplitude')
        plt.grid(True)

# 在所有文件处理完毕后，显示所有图形
plt.show()

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