请编写一个keras框架下的均值漂移滤波，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

以下是基于Keras框架的均值漂移滤波代码，可以用于对三维时序信号进行去噪：

import numpy as np
import pandas as pd
from keras.layers import Input, Conv1D, Lambda
from keras.models import Model
import keras.backend as K

def mean_shift_filter(signal, window_size):
    # 定义均值漂移滤波器
    input_tensor = Input(shape=(signal.shape[1], 1))
    shifted_tensor = Lambda(lambda x: K.mean(x[:, :window_size, :], axis=1, keepdims=True))(input_tensor)
    conv_tensor = Conv1D(filters=1, kernel_size=1, padding='same')(shifted_tensor)
    model = Model(inputs=input_tensor, outputs=conv_tensor)

    # 对信号进行均值漂移滤波
    signal_filtered = []
    for i in range(signal.shape[0]):
        signal_i = signal[i, :, np.newaxis]
        signal_filtered_i = model.predict(signal_i)
        signal_filtered.append(signal_filtered_i.squeeze())
    signal_filtered = np.array(signal_filtered)

    return signal_filtered

# 读取csv文件数据
data = pd.read_csv('data.csv')
x_signal = data['x'].values
y_signal = data['y'].values
z_signal = data['z'].values

# 对三个信号分别进行均值漂移滤波
window_size = 30
x_signal_filtered = mean_shift_filter(x_signal, window_size)
y_signal_filtered = mean_shift_filter(y_signal, window_size)
z_signal_filtered = mean_shift_filter(z_signal, window_size)

# 计算SNR、MSE、PSNR
def calculate_metrics(signal, signal_filtered):
    mse = np.mean(np.square(signal - signal_filtered))
    snr = 10 * np.log10(np.mean(np.square(signal)) / mse)
    psnr = 10 * np.log10(np.max(signal) ** 2 / mse)
    return snr, mse, psnr

x_snr, x_mse, x_psnr = calculate_metrics(x_signal, x_signal_filtered)
y_snr, y_mse, y_psnr = calculate_metrics(y_signal, y_signal_filtered)
z_snr, z_mse, z_psnr = calculate_metrics(z_signal, z_signal_filtered)

# 将滤波后的数据保存为csv文件
data_filtered = pd.DataFrame({'x': x_signal_filtered, 'y': y_signal_filtered, 'z': z_signal_filtered})
data_filtered.to_csv('data_filtered.csv', index=False)

# 打印计算出的SNR、MSE、PSNR
print('x: SNR={:.2f}dB, MSE={:.2f}, PSNR={:.2f}dB'.format(x_snr, x_mse, x_psnr))
print('y: SNR={:.2f}dB, MSE={:.2f}, PSNR={:.2f}dB'.format(y_snr, y_mse, y_psnr))
print('z: SNR={:.2f}dB, MSE={:.2f}, PSNR={:.2f}dB'.format(z_snr, z_mse, z_psnr))

需要注意的是，这里假设输入的三维时序信号已经被分别保存在名为 `data.csv` 的CSV文件的 `x`、`y`、`z` 列中。同时，我们定义的均值漂移滤波器采用了Keras框架中的函数式API，其中输入张量的形状为 `(信号长度, 1)`，输出张量的形状与输入相同，且每个样本被替换为其前 `window_size` 个样本的平均值。最后，我们计算并打印了每个方向上的信噪比（SNR）、均方误差（MSE）和峰值信噪比（PSNR），并将滤波后的数据保存为名为 `data_filtered.csv` 的CSV文件。