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(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] return smoothed_data 使用示例 data = [10, 15, 20, 12, 18, 22, 25, 16, 14, 23] window_size = 5 smooth_factor = 0.6 smoothed_data = rts_smooth(data, window_size, smooth_factor) print(smoothed_data)怎么修改不影响首尾值
时间: 2024-04-11 16:27:34 浏览: 11
如果你想修改代码,使得平滑后的结果不影响首尾值,你可以在循环中对边界值进行特殊处理。以下是修改后的代码示例:
```python
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(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 = 5
smooth_factor = 0.6
smoothed_data = rts_smooth(data, window_size, smooth_factor)
print(smoothed_data)
```
在这个示例中,我们在循环结束后,对首尾的值进行了处理,将原始的数据的首尾值赋给平滑后的结果。这样就能保持首尾值不受平滑处理的影响。