带有傅立叶变换的信号处理

发布于 2025-02-07 19:55:21 字数 613 浏览 2 评论 0原文

我需要处理周期性信号并获得其频率。这可以通过 scipy.fft 轻松完成。

但是，我的特定信号不是严格周期性的。周期略有变化但随机变化。将 fft 应用于此信号非常困难。获得了许多峰，我不能仅推断我感兴趣的（1/ofere）（1/ofere）的频率范围。

我该怎么做？

这是我正在做的事情的简单代码片段：

df = pd.read_csv('data.txt', header=None)
x = np.asarray(df.iloc[:, 0])
y = np.asarray(df.iloc[:, 1])

yf = fft(y)
xf = fftfreq(len(y))

plt.plot(xf, abs(yf))

您可以在以下github存储库中找到此类信号的示例： https://github.com/crazydecibel/stack-overflow-question

原文

I need to process a periodic signal and obtain its frequency.
This can be easily done with scipy.fft and it works fine.

However, I have a particular signal that is not strictly periodic. The period changes slightly but randomly.
Applying the fft to this signal is quite hard. Many peaks are obtained and I cannot extrapolate only the range of frequencies that are near the (1/period) I am interested in.

How can I do this?

This is a simple code snippet of what I am doing:

df = pd.read_csv('data.txt', header=None)
x = np.asarray(df.iloc[:, 0])
y = np.asarray(df.iloc[:, 1])

yf = fft(y)
xf = fftfreq(len(y))

plt.plot(xf, abs(yf))

You can find an example of such signal at the following GitHub repository, inside the README file: https://github.com/crazydecibel/Stack-Overflow-question

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oО清风挽发oО 2025-02-14 19:55:21

如何使用顶部能源箱的加权平均值？

import numpy as np
import matplotlib.pyplot as plt

your_data = '15.042,...' # your github data
t, y = np.array([list(map(float, row.split(','))) for row in your_data.split()]).T

# here is the solution in terms of t and y.
Y = np.fft.fftshift(np.fft.fft(y))
Ts = (t[-1] - t[0]) / (len(t) + 1) # sampling period
Fs = 1 / Ts # sampling frequency
f = np.fft.fftshift(np.fft.fftfreq(len(y))) * Fs
# get top 5%
top = np.argsort(abs(Y))[-10:]

plt.subplot(211)
plt.stem(f, abs(Y), 'o')
plt.stem(f[top], abs(Y[top]), '+r')
plt.xlim(-0.05 * Fs, 0.05 * Fs)

fc = np.sum(abs(f[top]*Y[top]**2))/np.sum(abs(Y[top])**2)
plt.subplot(212)
plt.plot(t, y)
plt.plot(t, np.sin(t*(2*np.pi*fc)))

What about taking the weighted average of frequency of top energy bins?

import numpy as np
import matplotlib.pyplot as plt

your_data = '15.042,...' # your github data
t, y = np.array([list(map(float, row.split(','))) for row in your_data.split()]).T

# here is the solution in terms of t and y.
Y = np.fft.fftshift(np.fft.fft(y))
Ts = (t[-1] - t[0]) / (len(t) + 1) # sampling period
Fs = 1 / Ts # sampling frequency
f = np.fft.fftshift(np.fft.fftfreq(len(y))) * Fs
# get top 5%
top = np.argsort(abs(Y))[-10:]

plt.subplot(211)
plt.stem(f, abs(Y), 'o')
plt.stem(f[top], abs(Y[top]), '+r')
plt.xlim(-0.05 * Fs, 0.05 * Fs)

fc = np.sum(abs(f[top]*Y[top]**2))/np.sum(abs(Y[top])**2)
plt.subplot(212)
plt.plot(t, y)
plt.plot(t, np.sin(t*(2*np.pi*fc)))