python 写一个类似matlab的五点平滑smooth函数
时间: 2023-06-03 07:02:32 浏览: 193
好的,我可以回答这个问题。以下是一个示例 Python 代码,可用于实现五点平滑函数:
```
def smooth(x, window_len=5, window='hanning'):
if window_len < 3:
return x
if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
raise ValueError("Window not one of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'")
s = np.r_[x[window_len-1:0:-1], x, x[-2:-window_len-1:-1]]
if window == 'flat': #moving average
w = np.ones(window_len,'d')
else:
w = eval('np.'+window+'(window_len)')
y = np.convolve(w/w.sum(), s, mode='valid')
return y[(window_len/2-1):-(window_len/2)]
```
注意,此代码依赖于 NumPy 库,因此请确保您已经将其安装在您的 Python 环境中。
相关问题
python 写一个类似matlab的smooth平滑函数
好的,关于python写一个类似matlab的smooth平滑函数,您可以参考下面的代码实现:
```
import numpy as np
def smooth(x, window_len=3, window='hanning'):
if window_len < 3:
return x
if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
raise ValueError('Window is not one of the allowed types.')
s = np.r_[x[window_len-1:0:-1], x, x[-2:-window_len-1:-1]]
if window == 'flat': # moving average
w = np.ones(window_len,'d')
else:
w = eval('np.' + window + '(window_len)')
y = np.convolve(w/w.sum(), s, mode='valid')
return y[(window_len//2-1):-(window_len//2)]
```
这个函数可以用于对输入的数据进行平滑处理。其中,x是输入的一维数组,window_len是平滑的窗口大小,window为平滑窗口的类型。如果window_len小于3,函数将直接返回输入的数组;如果window不在['flat', 'hanning', 'hamming', 'bartlett', 'blackman']中,则函数会抛出异常。
希望对您有所帮助!
python写一个类似matlab中的smooth平滑函数
可以使用numpy库中的convolve函数来实现平滑滤波,代码如下:
```python
import numpy as np
def smooth(x, window_len=11, window='hanning'):
if window_len < 3:
return x
if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
return x
s = np.r_[x[window_len-1:0:-1], x, x[-2:-window_len-1:-1]]
if window == 'flat':
w = np.ones(window_len, 'd')
else:
w = eval('np.'+window+'(window_len)')
y = np.convolve(w/w.sum(), s, mode='valid')
return y[(window_len//2):-(window_len//2)]
```
其中,参数x是输入的序列,window_len是窗口长度,默认为11,window是选择平滑窗口的类型,默认为'hanning'窗口。可以按照以下方式进行调用:
```python
import matplotlib.pyplot as plt
x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x) + np.random.random(100) * 0.5
ysmooth = smooth(y)
plt.plot(x, y)
plt.plot(x, ysmooth)
plt.show()
```
该代码可以实现对sin函数加噪声后的平滑滤波,并绘制出原始信号和平滑后的信号。
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