Python:在傅立叶分析后设计时间序列滤波器 [英] Python: Designing a time-series filter after Fourier analysis

问题描述

我有一个时间序列的 3 小时温度数据，我分析并找到了使用傅立叶分析的功率谱.

I have a time series of 3-hourly temperature data that I have analyzed and found the power spectrum for using Fourier analysis.

data = np.genfromtxt('H:/RData/3hr_obs.txt',
                      skip_header=3)

step = data[:,0]
t    = data[:,1]
y    = data[:,2]
freq = 0.125

yps = np.abs(np.fft.fft(y))**2
yfreqs = np.fft.fftfreq(y.size, freq)
y_idx = np.argsort(yfreqs)

fig = plt.figure(figsize=(14,10))
ax = fig.add_subplot(111)
ax.semilogy(yfreqs[y_idx],yps[y_idx])
ax.set_ylim(1e-3,1e8)

原始数据:

频谱:

功率谱:

既然我知道信号在频率 1 和 2 处最强，我想创建一个滤波器(非 Boxcar)来平滑数据以保留这些主要频率.

Now that I know that the signal is strongest at frequencies of 1 and 2, I want to create a filter (non-boxcar) that can smooth out the data to keep those dominant frequencies.

是否有特定的 numpy 或 scipy 函数可以做到这一点?这是否必须在主包之外创建?

Is there a specific numpy or scipy function that can do this? Will this be something that will have to be created outside the main packages?

推荐答案

一些合成数据的例子:

# fourier filter example (1D)
%matplotlib inline
import matplotlib.pyplot as p
import numpy as np

# make up a noisy signal
dt=0.01
t= np.arange(0,5,dt)
f1,f2= 5, 20  #Hz
n=t.size
s0=  0.2*np.sin(2*np.pi*f1*t)+ 0.15 * np.sin(2*np.pi*f2*t)
sr= np.random.rand(np.size(t)) 
s=s0+sr

#fft
s-= s.mean()  # remove DC (spectrum easier to look at)
fr=np.fft.fftfreq(n,dt)  # a nice helper function to get the frequencies  
fou=np.fft.fft(s) 

#make up a narrow bandpass with a Gaussian
df=0.1
gpl= np.exp(- ((fr-f1)/(2*df))**2)+ np.exp(- ((fr-f2)/(2*df))**2)  # pos. frequencies
gmn= np.exp(- ((fr+f1)/(2*df))**2)+ np.exp(- ((fr+f2)/(2*df))**2)  # neg. frequencies
g=gpl+gmn    
filt=fou*g  #filtered spectrum = spectrum * bandpass 

#ifft
s2=np.fft.ifft(filt)

p.figure(figsize=(12,8))

p.subplot(511)
p.plot(t,s0)
p.title('data w/o noise')

p.subplot(512)
p.plot(t,s)
p.title('data w/ noise')

p.subplot(513)
p.plot(np.fft.fftshift(fr) ,np.fft.fftshift(np.abs(fou) )  )
p.title('spectrum of noisy data')

p.subplot(514)
p.plot(fr,g*50, 'r')  
p.plot(fr,np.abs(filt))
p.title('filter (red)  + filtered spectrum')

p.subplot(515)
p.plot(t,np.real(s2))
p.title('filtered time data')

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