def rts_smooth(data, window_size): data = np.array(data) m, n = data.shape smoothed_data = np.copy(data) # 创建一个与原数组相同的副本，用于存储平滑后的数据 # 遍历每一列 for j in range(n): # 使用原数据填充每一列首尾的窗口大小一半的位置 fill_value = np.concatenate((data[0, j] * np.ones(window_size // 2), data[:, j], data[-1, j] * np.ones(window_size // 2))) # 遍历每一行 for i in range(window_size // 2, m + window_size // 2): # 取窗口内的数据进行RTS平滑处理 weights = np.abs(np.arange(-window_size // 2, window_size // 2 + 1) / (window_size - 1)) smoothed_data[i - window_size // 2, j] = np.sum(fill_value[i - window_size // 2:i + window_size // 2 + 1] * weights) / np.sum(weights) return smoothed_data 示例用法 data = pd.read_csv(r'E:\PycharmProjects\pythonProject\stage2\数据预处理\2 快速傅里叶变换\fft\Learning_set\Bearing1_1.csv', header=None, on_bad_lines=None) print(data.shape) window_size = 3 smoothed_data = rts_smooth(data, window_size) print(smoothed_data.shape)以上代码不在平滑前填充,在平滑后用原来的值替换掉首尾的0值

你可以在 `rts_smooth` 函数中添加以下代码来替换平滑后填充的首尾0值：

# 替换首尾的0值
smoothed_data[:window_size//2, :] = data[:window_size//2, :]
smoothed_data[-window_size//2:, :] = data[-window_size//2:, :]

将以上代码添加到 `rts_smooth` 函数的最后一行 `return smoothed_data` 前面即可。这样就会使用原始数据来替换平滑后的首尾0值。

相关问题

mport numpy as np def rts_smooth(data, window_size, smooth_factor): assert window_size % 2 == 1, "Window size must be odd" assert 0 <= smooth_factor <= 1, "Smooth factor must be between 0 and 1" half_window = (window_size - 1) // 2 data_length = len(data) smoothed_data = np.zeros(data_length) for i in range(half_window, data_length - half_window): window = data[i - half_window:i + half_window + 1] median = np.median(window) deviation = np.abs(window - median) threshold = smooth_factor * np.median(deviation) if np.abs(data[i] - median) > threshold: smoothed_data[i] = median else: smoothed_data[i] = data[i] # 处理首尾值 smoothed_data[:half_window] = data[:half_window] smoothed_data[data_length - half_window:] = data[data_length - half_window:] return smoothed_data 使用示例 data = [10, 15, 20, 12, 18, 22, 25, 16, 14, 23] window_size = 3 smooth_factor = 0.6 smoothed_data = rts_smooth(data, window_size, smooth_factor) print(smoothed_data)这段代码如果输入数据有很多维度怎么改

如果输入数据有多个维度，可以使用`np.apply_along_axis`函数来应用平滑函数到每个维度上。下面是修改后的代码示例：

import numpy as np

def rts_smooth(data, window_size, smooth_factor):
    assert window_size % 2 == 1, "Window size must be odd"
    assert 0 <= smooth_factor <= 1, "Smooth factor must be between 0 and 1"
    
    half_window = (window_size - 1) // 2
    data_length = len(data)
    smoothed_data = np.zeros_like(data)

    def smooth_func(x):
        for i in range(half_window, data_length - half_window):
            window = x[i - half_window:i + half_window + 1]
            median = np.median(window)
            deviation = np.abs(window - median)
            threshold = smooth_factor * np.median(deviation)
            if np.abs(x[i] - median) > threshold:
                smoothed_data[i] = median
            else:
                smoothed_data[i] = x[i]

        smoothed_data[:half_window] = x[:half_window]
        smoothed_data[data_length - half_window:] = x[data_length - half_window:]
        return smoothed_data
    
    smoothed_data = np.apply_along_axis(smooth_func, axis=0, arr=data)
    return smoothed_data

# 使用示例
data = np.array([[10, 15, 20, 12, 18, 22, 25, 16, 14, 23],
                 [5, 8, 10, 12, 9, 7, 15, 20, 18, 17]])
window_size = 3
smooth_factor = 0.6

smoothed_data = rts_smooth(data, window_size, smooth_factor)
print(smoothed_data)

这样，`rts_smooth`函数将会对输入数据的每个维度进行平滑处理。输出结果将保持与输入数据相同的维度。

def moving_average_filter(signal, window_size): window = np.ones(window_size) / window_size smoothed_signal = np.convolve(signal, window, mode='same') return smoothed_signal 这段代码具体如何使用呢

这段代码是一个实现移动平均滤波的函数。下面是使用该函数的一个示例：

import numpy as np

def moving_average_filter(signal, window_size):
    window = np.ones(window_size) / window_size
    smoothed_signal = np.convolve(signal, window, mode='same')
    return smoothed_signal

# 示例使用
signal = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])  # 原始信号
window_size = 3  # 滑动窗口大小

smoothed_signal = moving_average_filter(signal, window_size)
print(smoothed_signal)

在上述示例中，首先定义了一个原始信号 `signal`，然后定义了滑动窗口的大小 `window_size`。接着调用 `moving_average_filter` 函数，传入原始信号和窗口大小参数，得到平滑后的信号 `smoothed_signal`。最后打印输出平滑后的信号。

注意，在该示例中，使用的是一维的原始信号和滑动窗口，但该函数也适用于多维信号的平滑处理。另外，滑动窗口大小的选择需要根据具体情况进行调整，较大的窗口可以更平滑地去除噪声，但也会损失一些细节。