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
时间: 2024-04-11 07:28:19 浏览: 152
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:
```python
# 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:
```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_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 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) 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以上代码运行出现smoothed_data[i - window_size // 2, j] = np.sum(fill_value[i - window_size // 2:i + window_size // 2 + 1] * weights) / np.sum(weights) ValueError: operands could not be broadcast together with shapes (3,) (4,)
这个错误是由于窗口大小 `window_size` 的值不正确导致的。
根据代码,窗口大小 `window_size` 应该是一个奇数,因为代码中使用了 `window_size // 2` 来确定填充值和平滑数据的位置。但是,根据错误信息,窗口大小 `window_size` 的值为偶数,导致无法正确广播数组。
请确保将窗口大小 `window_size` 设置为一个奇数,以解决这个问题。如果你想使用偶数窗口大小,可以根据具体情况修改代码适应偶数窗口的情况。例如,在计算平滑值时,可以将权重数组的长度修改为 `window_size + 1`,以匹配填充值的长度。
另外,还需要确保输入的 `data` 是一个二维数组,其中每列代表一个信号序列。如果输入的 `data` 不是二维数组,请先进行相应的处理以使其满足要求。
如果你仍然遇到问题,请提供更多关于输入数据和窗口大小的详细信息,以便更好地帮助你解决问题。
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