新增了项目说明
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README.md
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README.md
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shengwutanzhen项目是安卓项目，可以进行手机传感器数据以及屏幕点击数据的采集，python_server为接收并且保存数据的Python服务器
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项目部署：Android Studio（gradle-8.0）
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采集方式：进入姓名拼音及手机号输入界面和随机输入界面开始采集，两个界面均采集了屏幕按压数据和手机运动传感器数据
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采集结果说明：
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运动传感器部分：
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```python
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accX,accY,accZ：加速度传感器的三轴数据
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gyroX,gyroY,gyroZ：陀螺仪传感器的三轴数据
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magX,magY,magZ：磁力计传感器的三轴数据
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以上三个传感器的采集频率为10毫秒一次
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```
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采集结果示例：
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每一行代表间隔10毫秒的一个时间步
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屏幕触摸数据部分：
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```python
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create_date：文件创建时间
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uuid：随机生成的文件名
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data_stamp：数据上传时的时间戳
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label：在采集数据时用户输入的id
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gesture：用户操作手机时的状态（静止or行走）
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gesture_phone：用户操作手机的方式（单手、双手等等）
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input_duration：用户输入数据所花的时间
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usrn_len：用户拼音姓名长度
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pswd_len：用户手机号长度
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hold-time：两次按压之间的间隔时间
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inter-time：每次按压屏幕的时间
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distance：两次按压之间的距离
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distanceMove：每次按压时手指的滑动距离
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pressures：每次按压时手指的压力
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touch_size：每次按压时手指的面积
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```
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数据示例：
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="PublishConfigData" remoteFilesAllowedToDisappearOnAutoupload="false">
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<serverData>
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<paths name="zjt@192.168.202.200:22 password">
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<serverdata>
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<mappings>
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<mapping local="$PROJECT_DIR$" web="/" />
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</mappings>
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</serverdata>
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</paths>
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</serverData>
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</component>
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</project>
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="PySciProjectComponent">
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<option name="PY_INTERACTIVE_PLOTS_SUGGESTED" value="true" />
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</component>
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</project>
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import pandas as pd
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import matplotlib.pyplot as plt
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plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
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plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
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def read_csv_columns(csv_file_path, column_names):
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df = pd.read_csv(csv_file_path)
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return df[column_names].values
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def plot_comparison(csv_file_path1, csv_file_path2, column_names):
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data1 = read_csv_columns(csv_file_path1, column_names)
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data2 = read_csv_columns(csv_file_path2, column_names)
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plt.figure(figsize=(12, 8))
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plt.plot(data1[:, 0], label=f'Gesture 1 - {column_names[0]}', color='#FF4081')
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plt.plot(data2[:, 0], label=f'Gesture 2 - {column_names[0]}', linestyle='--', color='#FF4081')
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plt.plot(data1[:, 1], label=f'Gesture 1 - {column_names[1]}', color='#1da1e6')
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plt.plot(data2[:, 1], label=f'Gesture 2 - {column_names[1]}', linestyle='--', color='#1da1e6')
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plt.plot(data1[:, 2], label=f'Gesture 1 - {column_names[2]}', color='#000210')
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plt.plot(data2[:, 2], label=f'Gesture 2 - {column_names[2]}', linestyle='--', color='#000210')
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plt.title('同一个人的不同姿势对比')
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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('ComparsionACC_SameP_Diff.png')
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plt.figure(figsize=(12, 8))
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plt.plot(data1[:, 3], label=f'Gesture 1 - {column_names[3]}', color='#FF4081')
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plt.plot(data2[:, 3], label=f'Gesture 2 - {column_names[3]}', linestyle='--', color='#FF4081')
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plt.plot(data1[:, 4], label=f'Gesture 1 - {column_names[4]}', color='#1da1e6')
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plt.plot(data2[:, 4], label=f'Gesture 2 - {column_names[4]}', linestyle='--', color='#1da1e6')
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plt.plot(data1[:, 5], label=f'Gesture 1 - {column_names[5]}', color='#000210')
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plt.plot(data2[:, 5], label=f'Gesture 2 - {column_names[5]}', linestyle='--', color='#000210')
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plt.title('同一个人的不同姿势对比')
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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('ComparsionGyro_SameP_Diff.png')
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plt.figure(figsize=(12, 8))
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plt.plot(data1[:, 6], label=f'Gesture 1 - {column_names[6]}', color='#FF4081')
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plt.plot(data2[:, 6], label=f'Gesture 2 - {column_names[6]}', linestyle='--', color='#FF4081')
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plt.plot(data1[:, 7], label=f'Gesture 1 - {column_names[7]}', color='#1da1e6')
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plt.plot(data2[:, 7], label=f'Gesture 2 - {column_names[7]}', linestyle='--', color='#1da1e6')
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plt.plot(data1[:, 8], label=f'Gesture 1 - {column_names[8]}', color='#000210')
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plt.plot(data2[:, 8], label=f'Gesture 2 - {column_names[8]}', linestyle='--', color='#000210')
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plt.title('同一个人的不同姿势对比')
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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('ComparsionMag_SameP_Diff.png')
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plt.show()
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if __name__ == "__main__":
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csv_file1 = r'D:\pycharmProjects\python_server\NormalizeData\4d9fddfe684048ccbbe100bf40a51180_wavelet_denoised_kalman_denoised_normalized.csv'
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csv_file2 = r'D:\pycharmProjects\python_server\NormalizeData\c744b2c8330e41c4980f57fbd1ffa4f8_wavelet_denoised_kalman_denoised_normalized.csv'
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columns_to_compare = ['accX', 'accY', 'accZ', 'gyroX', 'gyroY', 'gyroZ', 'magX', 'magY', 'magZ']
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plot_comparison(csv_file1, csv_file2, columns_to_compare)
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import pandas as pd
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import matplotlib.pyplot as plt
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plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
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plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
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def read_csv_columns(csv_file_path, column_names):
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df = pd.read_csv(csv_file_path)
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return df[column_names].values
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def plot_comparison(csv_file_path1, csv_file_path2, column_names):
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data1 = read_csv_columns(csv_file_path1, column_names)
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data2 = read_csv_columns(csv_file_path2, column_names)
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plt.figure(figsize=(12, 8))
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plt.plot(data1[:, 0], label=f'Person 1 - {column_names[0]}', color='#FF4081')
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plt.plot(data2[:, 0], label=f'Person 2 - {column_names[0]}', linestyle='--', color='#FF4081')
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plt.plot(data1[:, 1], label=f'Person 1 - {column_names[1]}', color='#1da1e6')
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plt.plot(data2[:, 1], label=f'Person 2 - {column_names[1]}', linestyle='--', color='#1da1e6')
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plt.plot(data1[:, 2], label=f'Person 1 - {column_names[2]}', color='#000210')
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plt.plot(data2[:, 2], label=f'Person 2 - {column_names[2]}', linestyle='--', color='#000210')
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plt.title('不同用户对比')
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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('ComparsionACC_Diff.png')
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plt.figure(figsize=(12, 8))
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plt.plot(data1[:, 3], label=f'Person 1 - {column_names[3]}', color='#FF4081')
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plt.plot(data2[:, 3], label=f'Person 2 - {column_names[3]}', linestyle='--', color='#FF4081')
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plt.plot(data1[:, 4], label=f'Person 1 - {column_names[4]}', color='#1da1e6')
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plt.plot(data2[:, 4], label=f'Person 2 - {column_names[4]}', linestyle='--', color='#1da1e6')
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plt.plot(data1[:, 5], label=f'Person 1 - {column_names[5]}', color='#000210')
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plt.plot(data2[:, 5], label=f'Person 2 - {column_names[5]}', linestyle='--', color='#000210')
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plt.title('不同用户对比')
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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('ComparsionGyro_Diff.png')
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plt.figure(figsize=(12, 8))
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plt.plot(data1[:, 6], label=f'Person 1 - {column_names[6]}', color='#FF4081')
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plt.plot(data2[:, 6], label=f'Person 2 - {column_names[6]}', linestyle='--', color='#FF4081')
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plt.plot(data1[:, 7], label=f'Person 1 - {column_names[7]}', color='#1da1e6')
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plt.plot(data2[:, 7], label=f'Person 2 - {column_names[7]}', linestyle='--', color='#1da1e6')
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plt.plot(data1[:, 8], label=f'Person 1 - {column_names[8]}', color='#000210')
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plt.plot(data2[:, 8], label=f'Person 2 - {column_names[8]}', linestyle='--', color='#000210')
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plt.title('不同用户对比')
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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('ComparsionMag_Diff.png')
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plt.show()
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if __name__ == "__main__":
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csv_file1 = r'D:\pycharmProjects\python_server\NormalizeData\44c3071c5d754df686c47ce236428bf6_wavelet_denoised_kalman_denoised_normalized.csv'
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csv_file2 = r'D:\pycharmProjects\python_server\NormalizeData\57edc01fed864a18b095fe7d5cf41ea2_wavelet_denoised_kalman_denoised_normalized.csv'
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columns_to_compare = ['accX', 'accY', 'accZ', 'gyroX', 'gyroY', 'gyroZ', 'magX', 'magY', 'magZ']
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plot_comparison(csv_file1, csv_file2, columns_to_compare)
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accX,accY,accZ,gyroX,gyroY,gyroZ,magX,magY,magZ
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-0.03671236584162741,5.770850569100772,7.66632293083667,0.03873503732986191,-0.015339542657760698,-0.008234461070924399,-9.472549762885553,-26.258119192233433,-51.74610373614889
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-0.038251103855316465,5.776534685604118,7.651810087672519,0.037069198011055945,-0.01604700415300229,-0.008049512119431479,-9.485152556217132,-26.263364745370954,-51.794408592284334
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-0.04142560151399896,5.782406556196552,7.617683343408211,0.0325526645448036,-0.017830890972611986,-0.0077589570419007246,-9.496482956636068,-26.24006975111409,-51.84865751697536
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-0.042959493275976965,5.7842150937678305,7.600497535838384,0.0299084689061325,-0.018709108564426726,-0.007609258687204094,-9.498253709139236,-26.2185320106631,-51.872673457942504
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-0.04656009414749672,5.788833087333803,7.555833128443868,0.021658671676553727,-0.02008305016996847,-0.006929480215156198,-9.493089728847306,-26.129928940776242,-51.98763711326244
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-0.04725420689456309,5.790230106941286,7.54467990458744,0.01872860457732412,-0.020155118193633255,-0.006642618382177191,-9.487241331200973,-26.095891518763512,-52.034842593853234
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-0.04408536540033367,5.799131437819345,7.475849875685212,-0.00785649024639735,-0.017378976507522216,-0.003516917344311663,-9.399598862867036,-25.926852657817456,-52.2510168593234
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-0.04373513801268771,5.799946033089342,7.472197351765472,-0.010796717392090524,-0.016738775796551135,-0.003054500484088856,-9.38972069748888,-25.920600948436167,-52.26366970575998
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-0.04343189978613439,5.800940844392229,7.470125952949516,-0.013553225703759116,-0.016031113814958902,-0.0025774606939470285,-9.381327306417079,-25.915841134929337,-52.27301572601111
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-0.04318659790620461,5.802198222662641,7.469803335233162,-0.016093477730954302,-0.015254301347250785,-0.002086154883286152,-9.374825286693214,-25.91190468949712,-52.27906383194442
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-0.042994357200596134,5.803533646135864,7.4710676430354415,-0.018449119416527736,-0.0144447395293103,-0.0015968845048332785,-9.369453689596375,-25.909149356051035,-52.28316101531734
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-0.04286426954427383,5.804762362380529,7.473774465279964,-0.020643515190293532,-0.013639366629955128,-0.001125728872177616,-9.364528099071336,-25.90797104294594,-52.286670600908636
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-0.04278632505164291,5.805884881006645,7.477982536405718,-0.022671758195870757,-0.012848612533417217,-0.0006789514656536785,-9.360009404453123,-25.907889647020255,-52.29007583224153
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-0.04275198783046184,5.806839492863294,7.4836814812462435,-0.024540657202861187,-0.012092144007336507,-0.00026654840068889165,-9.355614963930428,-25.908669345991,-52.294187894179075
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-0.04278708561484977,5.807689737698561,7.491037168933773,-0.026210351851063046,-0.01137270811551255,0.00010989467316790285,-9.351266463470733,-25.909860276869015,-52.29902567082557
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-0.0429138427051428,5.8085105308719935,7.500228289205912,-0.027640217971135868,-0.010691730962033085,0.00044909842346405076,-9.346918232622953,-25.910926395138386,-52.304578244346445
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-0.0431373381357799,5.809265920708721,7.511293140309568,-0.02882238664864203,-0.010066550060338911,0.0007424494855918901,-9.342252592214978,-25.911599075167246,-52.31151079807846
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||||
|
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,823 @@
|
|||
accX,accY,accZ,gyroX,gyroY,gyroZ,magX,magY,magZ
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||||
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
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Load Diff
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@ -0,0 +1,959 @@
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|||
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||||
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Load Diff
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Load Diff
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Load Diff
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|
@ -0,0 +1,859 @@
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|||
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File diff suppressed because it is too large
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Load Diff
|
|
@ -0,0 +1,885 @@
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|||
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File diff suppressed because it is too large
Load Diff
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File diff suppressed because it is too large
Load Diff
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Load Diff
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|
@ -0,0 +1,907 @@
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|||
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||||
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||||
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||||
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|
@ -0,0 +1,25 @@
|
|||
accX,accY,accZ,gyroX,gyroY,gyroZ,magX,magY,magZ
|
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||||
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||||
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|
||||
|
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,79 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
import glob
|
||||
|
||||
|
||||
class KalmanFilter:
|
||||
def __init__(self, process_variance, measurement_variance, initial_estimate, initial_error_covariance):
|
||||
self.process_variance = process_variance
|
||||
self.measurement_variance = measurement_variance
|
||||
self.estimate = initial_estimate
|
||||
self.error_covariance = initial_error_covariance
|
||||
|
||||
def update(self, measurement):
|
||||
predicted_estimate = self.estimate
|
||||
predicted_error_covariance = self.error_covariance + self.process_variance
|
||||
kalman_gain = predicted_error_covariance / (predicted_error_covariance + self.measurement_variance)
|
||||
self.estimate = predicted_estimate + kalman_gain * (measurement - predicted_estimate)
|
||||
self.error_covariance = (1 - kalman_gain) * predicted_error_covariance
|
||||
return self.estimate
|
||||
|
||||
|
||||
def read_csv_columns(csv_file_path, header_names):
|
||||
df = pd.read_csv(csv_file_path)
|
||||
return df[header_names].values
|
||||
|
||||
|
||||
def save_to_csv(data, header_names, output_file):
|
||||
df = pd.DataFrame(data, columns=header_names)
|
||||
df.to_csv(output_file, index=False)
|
||||
|
||||
|
||||
def plot_comparison_and_save(original_data, denoised_data, header_names, save_path):
|
||||
plt.figure(figsize=(14, 10))
|
||||
for i, col_name in enumerate(header_names):
|
||||
plt.plot(original_data[:, i], label='Original ' + col_name)
|
||||
plt.plot(denoised_data[:, i], label='Denoised ' + col_name, linestyle='--', linewidth=2)
|
||||
plt.title('Kalman Filter Denoising Comparison')
|
||||
plt.xlabel('Time')
|
||||
plt.ylabel('Value')
|
||||
plt.legend(loc='lower left')
|
||||
plt.tight_layout()
|
||||
plt.savefig(save_path)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
input_directory = r'D:\pycharmProjects\python_server\Wavelet_DenoisedData' # 输入目录路径
|
||||
output_csv_directory = r'D:\pycharmProjects\python_server\KalManData' # 输出目录路径
|
||||
output_png_directory = r'D:\pycharmProjects\python_server\KalManPng' # 输出png目录路径
|
||||
columns_to_process = ['accX', 'accY', 'accZ', 'gyroX', 'gyroY', 'gyroZ', 'magX', 'magY', 'magZ']
|
||||
|
||||
# 创建输出目录（如果不存在）
|
||||
if not os.path.exists(output_csv_directory):
|
||||
os.makedirs(output_csv_directory)
|
||||
|
||||
if not os.path.exists(output_png_directory):
|
||||
os.makedirs(output_png_directory)
|
||||
|
||||
csv_files = glob.glob(os.path.join(input_directory, '*.csv'))
|
||||
|
||||
for input_csv_file in csv_files:
|
||||
file_name = os.path.basename(input_csv_file)
|
||||
output_csv_file = os.path.join(output_csv_directory,
|
||||
os.path.splitext(file_name)[0] + '_kalman_denoised.csv')
|
||||
output_plot_file = os.path.join(output_png_directory,
|
||||
os.path.splitext(file_name)[0] + '_kalman_denoising_comparison.png')
|
||||
|
||||
data = read_csv_columns(input_csv_file, columns_to_process)
|
||||
|
||||
denoised_data = np.zeros_like(data)
|
||||
for i, col in enumerate(columns_to_process):
|
||||
kf = KalmanFilter(process_variance=1e-5, measurement_variance=0.1 ** 2, initial_estimate=0.0,
|
||||
initial_error_covariance=1.0)
|
||||
denoised_data[:, i] = [kf.update(measurement) for measurement in data[:, i]]
|
||||
|
||||
save_to_csv(denoised_data, columns_to_process, output_csv_file)
|
||||
plot_comparison_and_save(data, denoised_data, columns_to_process, output_plot_file)
|
||||
|
||||
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|
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|
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|
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|
@ -0,0 +1,61 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import os
|
||||
import glob
|
||||
|
||||
|
||||
def read_csv_columns(csv_file_path, header_names):
|
||||
df = pd.read_csv(csv_file_path)
|
||||
return df[header_names].values
|
||||
|
||||
|
||||
def normalize(data):
|
||||
mean = np.mean(data, axis=0)
|
||||
std = np.std(data, axis=0)
|
||||
return (data - mean) / std
|
||||
|
||||
|
||||
def save_to_csv(data, header_names, output_file):
|
||||
df = pd.DataFrame(data, columns=header_names)
|
||||
df.to_csv(output_file, index=False)
|
||||
|
||||
|
||||
def plot_comparison_and_save(original_data, normalized_data, header_names, save_path):
|
||||
plt.figure(figsize=(14, 10))
|
||||
for i, col_name in enumerate(header_names):
|
||||
plt.plot(normalized_data[:, i], label='Normalized ' + col_name)
|
||||
plt.title('Normalization')
|
||||
plt.xlabel('Time')
|
||||
plt.ylabel('Value')
|
||||
plt.legend(loc='lower left') # 固定图例在左下角
|
||||
plt.tight_layout()
|
||||
plt.savefig(save_path)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
input_directory = r'D:\pycharmProjects\python_server\KalManData' # 输入目录路径
|
||||
output_csv_directory = r'D:\pycharmProjects\python_server\NormalizeData' # 输出目录路径
|
||||
output_png_directory = r'D:\pycharmProjects\python_server\NormalizePng' # 输出png目录路径
|
||||
columns_to_process = ['accX', 'accY', 'accZ', 'gyroX', 'gyroY', 'gyroZ', 'magX', 'magY', 'magZ']
|
||||
|
||||
# 创建输出目录（如果不存在）
|
||||
if not os.path.exists(output_csv_directory):
|
||||
os.makedirs(output_csv_directory)
|
||||
|
||||
if not os.path.exists(output_png_directory):
|
||||
os.makedirs(output_png_directory)
|
||||
|
||||
csv_files = glob.glob(os.path.join(input_directory, '*.csv'))
|
||||
|
||||
for input_csv_file in csv_files:
|
||||
file_name = os.path.basename(input_csv_file)
|
||||
output_csv_file = os.path.join(output_csv_directory, os.path.splitext(file_name)[0] + '_normalized.csv')
|
||||
output_plot_file = os.path.join(output_png_directory,
|
||||
os.path.splitext(file_name)[0] + '_normalization_comparison.png')
|
||||
|
||||
data = read_csv_columns(input_csv_file, columns_to_process)
|
||||
normalized_data = normalize(data)
|
||||
|
||||
save_to_csv(normalized_data, columns_to_process, output_csv_file)
|
||||
plot_comparison_and_save(data, normalized_data, columns_to_process, output_plot_file)
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,985 @@
|
|||
accX,accY,accZ,gyroX,gyroY,gyroZ,magX,magY,magZ
|
||||
0.5908718126265949,-0.09518236391420909,0.07683396051346593,0.2913204263256204,0.0053136657088657694,-0.20559206808071684,0.3753117947525117,-0.8504064894187278,0.8608927454757348
|
||||
0.5878127156591066,-0.06241747526207073,0.10134965611743948,0.28903523750711363,0.003095617959715404,-0.20167185736538587,0.3567574392615384,-0.8783189609118313,0.741319117400152
|
||||
0.5853171298260007,-0.05047831494823869,0.09680745848999162,0.2864273629727313,0.0011018784284720772,-0.1986053624405371,0.34889310128364287,-0.8852717430525593,0.7005246388282641
|
||||
0.5834503238356926,-0.04439654784901091,0.08227228678812834,0.28383259955726103,-0.0005993174308562502,-0.1969433229449796,0.3460384625956987,-0.8865039379596564,0.6765961010831145
|
||||
0.5815646306638601,-0.040612560551157265,0.06498493675317532,0.2806592527578624,-0.002567084473349487,-0.195541630605465,0.34441209469773343,-0.8847889328053783,0.6609043042557863
|
||||
0.5795990046424792,-0.03811389172136755,0.04809303728499854,0.2767975057936089,-0.004888937277932198,-0.19433225642855587,0.34347203160418605,-0.8810318795262267,0.6497230794874573
|
||||
0.5777380781531933,-0.036178832105286454,0.030880788724381804,0.27267882398011467,-0.00700074144089151,-0.19298699560973234,0.34307094126894655,-0.8759726048609369,0.6378264215550513
|
||||
0.5760483930144823,-0.03440306864028692,0.013716155172751345,0.26848994565421,-0.008616488988133844,-0.19123267429824264,0.3429767680320439,-0.8699526881662172,0.6225861061707758
|
||||
0.5745719671585987,-0.032775345914448806,-0.0015945201464301233,0.2641910280645449,-0.009751090028617748,-0.18919580639207403,0.3433390925012407,-0.8629970695291846,0.6038457320366788
|
||||
0.5733698080410206,-0.03123777199199762,-0.01385233648742468,0.25982868183573177,-0.010304587354806927,-0.18687818201736817,0.34424062623776924,-0.8551594992445143,0.5808774440458319
|
||||
0.5725277063819694,-0.029743019398385633,-0.02502271998491504,0.2552647176957789,-0.010477885582729093,-0.18430030475809958,0.34556533749379476,-0.8471640148875251,0.557493498528479
|
||||
0.5721263997198327,-0.0282783002000226,-0.03628276414483925,0.2503910768496325,-0.010417421216824242,-0.1814983809891927,0.34727013447356075,-0.8395511180274489,0.5366735119437566
|
||||
0.5722348628062895,-0.026809222678373715,-0.04702758707449823,0.24524356716098974,-0.010081058146855561,-0.1784379304013016,0.3493951218726907,-0.832444100383754,0.5183855551575034
|
||||
0.572928419472866,-0.025313707402691992,-0.0572196621338351,0.23981580211199938,-0.009485754518038276,-0.1751008364451139,0.3519556175697992,-0.8260931424222809,0.5035591096502908
|
||||
0.5738490245751857,-0.02390669277730022,-0.06667093648374153,0.23431070254189762,-0.008547007815611291,-0.1715673605213861,0.3547260236082429,-0.8205108891826477,0.49074977493406624
|
||||
0.574697539456716,-0.022685680201158475,-0.07508321218846599,0.22891398002041952,-0.007541553331252065,-0.16789858575294217,0.35752044712238645,-0.8157147722424328,0.47864164376983126
|
||||
0.5754409859423056,-0.021675159354877446,-0.08249509619597757,0.22366432417742493,-0.00639195764934551,-0.16410635522752587,0.3602910729545889,-0.8119275788832035,0.4678395124582586
|
||||
0.575959564542993,-0.020926847777690227,-0.0888591377604262,0.21864768494320644,-0.005163095651855478,-0.16021956421762315,0.36294393715273315,-0.8093166510364318,0.458394475743009
|
||||
0.5763721634832677,-0.0202189673769939,-0.09395864639314626,0.21385494265526425,-0.0038022420698507636,-0.15621126432665428,0.36541709370613146,-0.807456319988889,0.45040832149516646
|
||||
0.5767970614057282,-0.019347384482917472,-0.09761678985394764,0.20927515290922463,-0.0022627847199194434,-0.15205576585586958,0.367654581246591,-0.8059877762036445,0.44414054351664695
|
||||
0.5771649518021337,-0.01828297796551517,-0.09969137559241492,0.20498152936413747,-0.0005611067614920296,-0.14776885876194878,0.3695557549186661,-0.8048696828761941,0.4395108516753336
|
||||
0.5774625534827399,-0.016937635766463656,-0.1000144896678743,0.20102475337030112,0.0013068537740682934,-0.14335375040299897,0.3710285170726733,-0.8039450009815529,0.4365148313434217
|
||||
0.5776957810352007,-0.015508788462190103,-0.09874823668199946,0.19735553221752888,0.003253564968689839,-0.13895693340873613,0.3722452289940106,-0.80329776555173,0.4344852298741032
|
||||
0.5778536041434408,-0.014194113432799901,-0.09603724989288161,0.1939374727393127,0.005190203288674197,-0.1347229041606274,0.3733609166756073,-0.8030209765517909,0.4327467038125444
|
||||
0.57794816685942,-0.012993065416883408,-0.09182270449645775,0.19077821800859393,0.007091688533913994,-0.1307079492805283,0.37438443901202717,-0.8030018564242344,0.43105987120400885
|
||||
0.5779898249777099,-0.01197167062687812,-0.0861149916217604,0.18786716247292362,0.00891072905122329,-0.12700189862964495,0.37537981675074816,-0.8031850098640412,0.42902289941369287
|
||||
0.5779472441396732,-0.011061944187884788,-0.07874798694085643,0.1852663938758262,0.010640719119170671,-0.1236190016111511,0.3763647886788115,-0.8034647628136573,0.4266264339162378
|
||||
0.5777934616936895,-0.010183729796164371,-0.06954272579547248,0.18303918969382585,0.012278229459434336,-0.12057075491668426,0.3773496995375084,-0.8037151969370838,0.42387588302536283
|
||||
0.5775223157253647,-0.009375494241281921,-0.05846085101545209,0.18119780679411943,0.013781569455245623,-0.11793456234903481,0.3784065063312267,-0.8038732113321239,0.42044173816047314
|
||||
0.5771197530085312,-0.008641257229707629,-0.045418786259729255,0.1797716217725182,0.015118846954397724,-0.11577079268622999,0.3795954914055798,-0.8038557025321164,0.41608430090471593
|
||||
0.57660812100033,-0.007973631081155648,-0.030975147085004047,0.1787198827884348,0.01636656957019869,-0.11391142312619246,0.3807743385269241,-0.8036946170177798,0.41130678963595063
|
||||
0.5760112580710727,-0.007376376370270857,-0.01570409048864625,0.17799767968923805,0.017597456260031542,-0.11219470543772213,0.3818085336161155,-0.8034263905793432,0.4065795080594567
|
||||
0.5753283126864538,-0.0068359154321918705,0.00019917768251674075,0.1776099691835758,0.018831488094695998,-0.11057251744350613,0.3826671345557658,-0.8030077569272847,0.40202459795517476
|
||||
0.574567304888698,-0.006339625536580759,0.016381890299378952,0.17754410578710184,0.020112786055441572,-0.10894513559196323,0.3832747957485075,-0.8024230761809702,0.3979271566931605
|
||||
0.5737205008872927,-0.005899998572028999,0.032849482179538536,0.1778200309534439,0.021414168415654862,-0.10736158114190336,0.38368739807140556,-0.8015890448618089,0.3940649382483404
|
||||
0.5727782773439174,-0.0055266837983640234,0.049626942953672454,0.17846162718024491,0.022705998406386093,-0.10587610610003075,0.38396703744805627,-0.8004149443319254,0.39019999183711124
|
||||
0.5717459313306726,-0.005216899952870007,0.06642616187939823,0.17946235299847985,0.024014627240863375,-0.1044249002298067,0.38407362432781655,-0.7988654286573799,0.3864854266030688
|
||||
0.5706247214719332,-0.004972362086082886,0.08304064218145803,0.18082407851472293,0.02535014463769654,-0.10297795531956559,0.3839979453052135,-0.7968870044277426,0.38295868043980286
|
||||
0.569468108109693,-0.004723713776283415,0.0994737174348551,0.18247440970839976,0.026659676341162102,-0.10161955924690763,0.3838189939253282,-0.7946806498009905,0.3796978114233776
|
||||
0.5683329553874708,-0.004398238678321339,0.11569770836962409,0.18433518059607074,0.027894038474770996,-0.10042527283253118,0.38360889569302015,-0.7924599790206709,0.3768215391737145
|
||||
0.5672459969880567,-0.003963213037030482,0.1316247500144808,0.18637296758530406,0.029026345014267502,-0.09943272190216638,0.3834146931793746,-0.7902910584072291,0.3743501520452983
|
||||
0.5662477838632015,-0.0033676986839531148,0.1472047618681195,0.18853437866660464,0.03001567462961588,-0.098704164706621,0.38330286832458427,-0.7883049238506143,0.3723265963436253
|
||||
0.5653504726899654,-0.0026502800908925392,0.16219401593891902,0.19080488971623238,0.03087038876927172,-0.09820858184868932,0.3832673296179243,-0.7864889144336417,0.37052225488433915
|
||||
0.5645647708724294,-0.0018537919341150056,0.1763428766280444,0.19317223136086356,0.03159940003819074,-0.09791384128882887,0.3833025203580816,-0.7848211456098733,0.36868551192371296
|
||||
0.5639277800318502,-0.0009594795036293853,0.18952811318702165,0.19558706850666868,0.032174566647414284,-0.09785600418010777,0.3834568853661959,-0.7834074023381828,0.36672538238398833
|
||||
0.5634704839571347,2.9820032856391204e-05,0.20158030913975492,0.1980084593016824,0.032578342175867754,-0.09805078696261238,0.3837639423165312,-0.7823206852691792,0.3644769980883123
|
||||
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|
||||
0.5628144518085105,0.00208148668886663,0.22263972302091556,0.20292497745312452,0.03311936145426614,-0.09910996474315291,0.3844886664237744,-0.7806619386094037,0.36000434923796
|
||||
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|
||||
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|
||||
0.5618639321273938,0.004729727971091083,0.24664807037264241,0.2104253199773884,0.0338700378836155,-0.10217309551637568,0.3851678507393048,-0.7785303640693314,0.35458952632037705
|
||||
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|
||||
0.5611948762077049,0.006376401027015275,0.25665798702269627,0.2151931526027765,0.03446345334992097,-0.10525251783649295,0.38608715612808847,-0.7774899206099769,0.3518233194108078
|
||||
0.5608112383400015,0.007213435507338497,0.2596797204364764,0.21743154911997853,0.03484212371597271,-0.10711139172277313,0.3868937455607827,-0.7770855502776353,0.3507783658226379
|
||||
0.560426962323426,0.008044626964412414,0.26152888362058274,0.21954922971738688,0.03524361692217191,-0.1091233152384909,0.3878374163655407,-0.7767670808691631,0.34984943584361633
|
||||
0.5600766745581311,0.0088434307600299,0.2623988175623821,0.2215335639827722,0.03563075267092489,-0.11122194647902996,0.3887834691370834,-0.7765349194827201,0.3489207131102232
|
||||
0.5597767647962039,0.009620696714060937,0.2623488812219412,0.22336019219360226,0.03598954996625018,-0.11337765044788503,0.389730315184382,-0.7764024300572322,0.3479408731748547
|
||||
0.5595560678527586,0.010377823368117663,0.26150122281017146,0.22500737863954842,0.03629176627142404,-0.11554378492507639,0.39062797263294485,-0.776384249370922,0.3468215520584478
|
||||
0.5593976144705732,0.011122675293226013,0.25995317916576477,0.22645460466773532,0.03653302428527571,-0.11770306630233521,0.3914762177426703,-0.7764590863337865,0.34559388081760006
|
||||
0.5592833254109735,0.011868113453783136,0.25779993753847713,0.22767981186561032,0.03670900868812554,-0.11984030971395032,0.3922865351225308,-0.7766071149301826,0.3442938389036539
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File diff suppressed because it is too large
Load Diff
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