Python实现信号的时域与频域之间的转换

Python028

Python实现信号的时域与频域之间的转换,第1张

用FFT（快速傅里叶变换）可以将时域的数字信号转换为频域信号，转换为频域信号之后就可以分析出信号的频率成分，最后还可以将处理完毕的频域信号通过IFFT（逆变换）转换为时域信号。

这里使用Scipy模块中的fft实现时域信号的FFT变换，如下：

时域信号：该信号为带有噪声的正弦信号经过小波去噪后的图像

转换结果：

利用Python scipy.signal.filtfilt() 实现信号滤波

https://blog.csdn.net/weixin_37996604/article/details/82864680

https://www.cnblogs.com/xiaosongshine/p/10831931.html

https://stackoverflow.com/questions/35565540/designing-an-fir-notch-filter-with-python

Required input defintions are as follows

time: Time between samples

band: The bandwidth around the centerline freqency that you wish to filter

freq: The centerline frequency to be filtered

ripple: The maximum passband ripple that is allowed in db

order: The filter order. For FIR notch filters this is best set to 2 or 3, IIR filters are best suited for high values of order. This algorithm is hard coded to FIR filters

filter_type: 'butter', 'bessel', 'cheby1', 'cheby2', 'ellip'

data: the data to be filtered

用python设计FIR陷波滤波器

https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.iirnotch.html

https://www.cnpython.com/qa/85882

https://vimsky.com/examples/usage/python-scipy.signal.iirnotch.html

https://github.com/scipy/scipy/blob/master/scipy/signal/filter_design.py

https://blog.csdn.net/qq_41978536/article/details/90736793

你可以使用Python的scipy.signal模块中的find_peaks()函数来把一堆周期信号里面提取出一个周期的信号。该函数将在输入信号中寻找极大值，这些极大值将构成一个完整的周期。