对一个m行n列的列表沿着行进行RTS平滑处理

要对一个m行n列的列表沿着行进行RTS平滑处理，可以使用循环或者`np.apply_along_axis`函数来逐行处理。下面是使用循环的示例代码：

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"

    m, n = data.shape
    half_window = (window_size - 1) // 2
    smoothed_data = np.zeros_like(data)

    for i in range(m):
        row = data[i]
        for j in range(half_window, n - half_window):
            window = row[j - half_window:j + half_window + 1]
            median = np.median(window)
            deviation = np.abs(window - median)
            threshold = smooth_factor * np.median(deviation)
            if np.abs(row[j] - median) > threshold:
                smoothed_data[i, j] = median
            else:
                smoothed_data[i, j] = row[j]

        smoothed_data[i, :half_window] = row[:half_window]
        smoothed_data[i, n - half_window:] = row[n - half_window:]

    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],
                 [2, 4, 6, 8, 10, 12, 14, 16, 18, 20]])
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一行进行平滑处理，输出结果将保持与输入数据相同的形状。每行的数据都会被独立处理。