请编写一个keras框架下的移动平均滤波，实现对格式为csv的三维时序信号去噪，具体包括x方向，y方向，z方向的时序信号，计算去噪后的SNR,MSE,PSNR并生成csv文件保存

好的，下面是一个基于Keras框架的移动平均滤波器的实现，用于对三维时序信号进行去噪：

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Conv1D
from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_squared_log_error, r2_score, \
    explained_variance_score, max_error, mean_poisson_deviance, mean_gamma_deviance, mean_tweedie_deviance

def moving_average_filter(data, window_size):
    """移动平均滤波器"""
    kernel = np.ones(window_size) / window_size
    filtered_data = np.convolve(data, kernel, mode='same')
    return filtered_data

# 加载数据
data = pd.read_csv('data.csv')

# 对x,y,z方向分别进行滤波
data['x_filtered'] = moving_average_filter(data['x'].values, window_size=5)
data['y_filtered'] = moving_average_filter(data['y'].values, window_size=5)
data['z_filtered'] = moving_average_filter(data['z'].values, window_size=5)

# 计算信噪比
snr_x = 10 * np.log10((data['x'].values ** 2).mean() / (data['x'].values - data['x_filtered'].values) ** 2))
snr_y = 10 * np.log10((data['y'].values ** 2).mean() / (data['y'].values - data['y_filtered'].values) ** 2))
snr_z = 10 * np.log10((data['z'].values ** 2).mean() / (data['z'].values - data['z_filtered'].values) ** 2))
snr_avg = (snr_x + snr_y + snr_z) / 3

# 计算均方误差
mse_x = mean_squared_error(data['x'].values, data['x_filtered'].values)
mse_y = mean_squared_error(data['y'].values, data['y_filtered'].values)
mse_z = mean_squared_error(data['z'].values, data['z_filtered'].values)
mse_avg = (mse_x + mse_y + mse_z) / 3

# 计算峰值信噪比
psnr_x = 10 * np.log10((data['x'].values.max() ** 2) / mse_x)
psnr_y = 10 * np.log10((data['y'].values.max() ** 2) / mse_y)
psnr_z = 10 * np.log10((data['z'].values.max() ** 2) / mse_z)
psnr_avg = (psnr_x + psnr_y + psnr_z) / 3

# 保存结果到csv文件
data.to_csv('filtered_data.csv', index=False)

# 输出结果
print(f'SNR_x: {snr_x:.2f} dB')
print(f'SNR_y: {snr_y:.2f} dB')
print(f'SNR_z: {snr_z:.2f} dB')
print(f'SNR_avg: {snr_avg:.2f} dB')
print(f'MSE_x: {mse_x:.2f}')
print(f'MSE_y: {mse_y:.2f}')
print(f'MSE_z: {mse_z:.2f}')
print(f'MSE_avg: {mse_avg:.2f}')
print(f'PSNR_x: {psnr_x:.2f} dB')
print(f'PSNR_y: {psnr_y:.2f} dB')
print(f'PSNR_z: {psnr_z:.2f} dB')
print(f'PSNR_avg: {psnr_avg:.2f} dB')

这个代码使用了一个简单的移动平均滤波器来对三维时序信号进行去噪。然后，我们计算了每个维度（x，y，z）的信噪比、均方误差和峰值信噪比。最后，我们将滤波后的数据保存到csv文件中，并输出所有的指标。