67 lines
2.7 KiB
Python
67 lines
2.7 KiB
Python
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import os
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import pandas as pd
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import numpy as np
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import pywt
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import matplotlib.pyplot as plt
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# 读取 CSV 文件，提取多列数据
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def read_csv_columns(csv_file_path, header_names):
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df = pd.read_csv(csv_file_path)
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return df[header_names].values
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# 小波变换降噪函数
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def denoise_wavelet(data, wavelet='db4', level=4):
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coeffs = pywt.wavedec(data, wavelet, level=level)
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threshold = np.std(coeffs[-1]) * np.sqrt(2 * np.log(len(data)))
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coeffs_thresh = [pywt.threshold(c, threshold, mode='soft') for c in coeffs]
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return pywt.waverec(coeffs_thresh, wavelet)
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# 保存处理后的数据到新的 CSV 文件
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def save_to_csv(data, header_names, output_file):
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df = pd.DataFrame(data, columns=header_names)
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df.to_csv(output_file, index=False)
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# 绘制对比图表并保存
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def plot_comparison_and_save(original_data, denoised_data, header_names, save_path):
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plt.figure(figsize=(12, 8))
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for i, col_name in enumerate(header_names):
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plt.plot(original_data[:, i], label='Original ' + col_name)
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plt.plot(denoised_data[:, i], label='Denoised ' + col_name, linestyle='--', linewidth=2)
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plt.title('Wavelet Denoising Comparison')
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plt.xlabel('Time')
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plt.ylabel('Value')
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plt.legend(loc='lower left')
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plt.tight_layout()
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plt.savefig(save_path) # 保存图表为文件
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def processALL_Wavalet(csv_file_directory, output_file_directory, output_png_direcotry, columns_to_process):
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for filename in os.listdir(csv_file_directory):
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if filename.endswith('.csv'):
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csv_file_path = os.path.join(csv_file_directory, filename)
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save_path = os.path.join(output_file_directory, filename.split('.')[0] + '_wavelet_denoised.csv')
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output_plot_file = os.path.join(output_png_direcotry, filename.split('.')[0] + '_wavelet_denoised.png')
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data = read_csv_columns(csv_file_path, columns_to_process)
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denoised_data = []
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for col in data.T: # 转置以便对每列进行处理
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denoised_col = denoise_wavelet(col)
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denoised_data.append(denoised_col)
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save_to_csv(np.array(denoised_data).T, columns_to_process, save_path)
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plot_comparison_and_save(data, np.array(denoised_data).T, columns_to_process, output_plot_file)
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if __name__ == "__main__":
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input_csv_directory = r'D:\pycharmProjects\python_server\data'
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output_csv_directory = r'D:\pycharmProjects\python_server\Wavelet_DenoisedData'
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output_plot_directory = r'D:\pycharmProjects\python_server\Wavelet_DenoisedPng'
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# 要处理的列名列表
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columns_to_process = ['accX', 'accY', 'accZ', 'gyroX', 'gyroY', 'gyroZ', 'magX', 'magY', 'magZ']
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processALL_Wavalet(input_csv_directory, output_csv_directory, output_plot_directory, columns_to_process)
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