如何将过滤器应用于python中的信号 [英] How To apply a filter to a signal in python

问题描述

python 中是否有任何准备好的函数可以将滤波器(例如巴特沃斯滤波器)应用于给定信号?我在 'scipy.signal' 中寻找这样的函数，但我没有找到比过滤器设计更有用的函数.实际上我希望这个函数用信号对滤波器进行卷积.

is there any prepared function in python to apply a filter (for example Butterworth filter) to a given signal? I looking for such a function in 'scipy.signal' but I haven't find any useful functions more than filter design ones. actually I want this function to convolve a filter with the signal.

推荐答案

是的！有两个:

scipy.signal.filtfilt
scipy.signal.lfilter

也有卷积方法(convolve 和 fftconvolve)，但这些可能不适合您的应用，因为它涉及 IIR 滤波器.

There are also methods for convolution (convolve and fftconvolve), but these are probably not appropriate for your application because it involves IIR filters.

完整代码示例:

b, a = scipy.signal.butter(N, Wn, 'low')
output_signal = scipy.signal.filtfilt(b, a, input_signal)

您可以在文档中阅读有关参数和用法的更多信息.一个问题是 Wn 是奈奎斯特频率的一小部分(采样频率的一半).因此，如果采样率为 1000Hz 并且您希望截止频率为 250Hz，则应使用 Wn=0.5.

You can read more about the arguments and usage in the documentation. One gotcha is that Wn is a fraction of the Nyquist frequency (half the sampling frequency). So if the sampling rate is 1000Hz and you want a cutoff of 250Hz, you should use Wn=0.5.

顺便说一句，对于大多数应用程序，我强烈建议使用 filtfilt 而不是 lfilter(在 Matlab 中仅称为 filter).正如文档所述:

By the way, I highly recommend the use of filtfilt over lfilter (which is called just filter in Matlab) for most applications. As the documentation states:

此函数应用线性过滤器两次，一次向前，一次向后.组合滤波器具有线性相位.

This function applies a linear filter twice, once forward and once backwards. The combined filter has linear phase.

这意味着输出的每个值都是输入中过去"和未来"点的函数.因此它不会滞后于输入.

What this means is that each value of the output is a function of both "past" and "future" points in the input equally. Therefore it will not lag the input.

相反，lfilter 仅使用输入的过去"值.这不可避免地引入了时间延迟，这将取决于频率.当然有一些应用程序需要这样做(特别是实时过滤)，但大多数用户使用 filtfilt 效果更好.

In contrast, lfilter uses only "past" values of the input. This inevitably introduces a time lag, which will be frequency-dependent. There are of course a few applications for which this is desirable (notably real-time filtering), but most users are far better off with filtfilt.

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