我们先看看你的信号(我添加了 endpoint=False 以使除法均匀):

t = np.linspace(0, 10*np.pi, 100, endpoint=False)x = np.sin(t)

让我们划分弧度(主要是采用 t/= 2*np.pi)并通过与频率相关来创建相同的信号:

fs = 20 # 采样率为 100/5 = 20(例如 Hz)f = 1 # 信号频率为 1(例如 Hz)t = np.linspace(0, 5, 5*fs, 端点=假)x = np.sin(2*np.pi*f*t)

这使得 f/fs == 1/20 == 0.05(即信号的周期性正好是 20 个样本)更加突出.正如您已经猜到的那样，数字信号的频率总是与其采样率有关.请注意，无论f和fs的值是多少，只要它们的比例相同，实际信号都是完全一样的:

fs = 1 # 自然单位f = 0.05t = np.linspace(0, 100, 100*fs, 端点=假)x = np.sin(2*np.pi*f*t)

下面我将使用这些自然单位(fs = 1).唯一的区别在于 t 以及生成的频率轴.

自相关

您对自相关函数作用的理解是正确的.它检测信号与其自身滞后版本的相关性.它通过将信号滑过自身来实现这一点，如右列所示(来自

请注意，由于相关函数的两个输入是相同的，生成的信号必然是对称的.这就是为什么 np.correlate 的输出通常从中间:

acf = np.correlate(x, x, 'full')[-len(x):]

现在索引 0 对应于信号的两个副本之间的 0 延迟.

接下来，您需要找到相关性最大的索引或延迟.由于重叠缩小，默认情况下这也是索引 0，因此以下内容将不起作用:

acf.argmax() # 总是返回 0

我建议改为找到最大的峰值，其中峰值被定义为任何值大于其直接邻居的任何索引:

inflection = np.diff(np.sign(np.diff(acf))) # 求二阶差分peaks = (inflection < 0).nonzero()[0] + 1 # 找出它们是负数的地方delay = peaks[acf[peaks].argmax()] # 其中，找到最大值的索引

现在delay == 20，它告诉您信号的频率是其采样率的1/20:

signal_freq = fs/delay # 给出 0.05

傅里叶变换

您使用以下方法计算 FFT:

omega = np.fft.fft(x)频率 = np.fft.fftfreq(x.size, 1)

这些函数针对复值信号重新设计.它们适用于实值信号，但您将获得对称输出，因为负频率分量与正频率分量相同.NumPy 为实值信号提供了单独的函数:

ft = np.fft.rfft(x)freqs = np.fft.rfftfreq(len(x), t[1]-t[0]) # 从时间轴获取频率轴mags = abs(ft) # 我们这里不关心相位信息

让我们看看:

plt.plot(freqs, mags)plt.show()

注意两点:峰值在频率 0.05 处，轴上的最大频率为 0.5(

这条曲线显然与x上的直接FFT略有不同，但主要内容是相同的:频率轴范围从0到0.5*fs，我们在与之前相同的信号频率处找到一个峰值:freqs[abs(pdg).argmax()] == 0.05.

要测量np.sin的实际周期性，我们可以只使用我们传递给np.sin的角度轴"而不是生成时的时间轴频率轴:

freqs = np.fft.rfftfreq(len(x), 2*np.pi*f*(t[1]-t[0]))rad_period = 1/freqs[mags.argmax()] # 6.283185307179586

虽然这看起来毫无意义，对吧?我们传入 2*np.pi 并得到 2*np.pi.但是，我们可以对任何常规时间轴执行相同操作，而无需在任何时候预先假定 pi:

fs = 10t = np.arange(1000)/fsx = np.sin(t)rad_period = 1/np.fft.rfftfreq(len(x), 1/fs)[abs(np.fft.rfft(x)).argmax()] # 6.25

自然，真正的价值现在位于两个箱子之间.这就是插值的用武之地，相关的需要选择合适的窗口函数.

Suppose one wanted to find the period of a given sinusoidal wave signal. From what I have read online, it appears that the two main approaches employ either fourier analysis or autocorrelation. I am trying to automate the process using python and my usage case is to apply this concept to similar signals that come from the time-series of positions (or speeds or accelerations) of simulated bodies orbiting a star.

For simple-examples-sake, consider x = sin(t) for 0 ≤ t ≤ 10 pi.

import numpy as np
from scipy import signal
import matplotlib.pyplot as plt

## sample data
t = np.linspace(0, 10 * np.pi, 100)
x = np.sin(t)
fig, ax = plt.subplots()
ax.plot(t, x, color='b', marker='o')
ax.grid(color='k', alpha=0.3, linestyle=':')
plt.show()
plt.close(fig)

Given a sine-wave of the form x = a sin(b(t+c)) + d, the period of the sine-wave is obtained as 2 * pi / b. Since b=1 (or by visual inspection), the period of our sine wave is 2 * pi. I can check the results obtained from other methods against this baseline.

Attempt 1: Autocorrelation

As I understand it (please correct me if I'm wrong), correlation can be used to see if one signal is a time-lagged copy of another signal (similar to how cosine and sine differ by a phase difference). So autocorrelation is testing a signal against itself to measure the times at which the time-lag repeats said signal. Using the example posted here:

result = np.correlate(x, x, mode='full')

Since x and t each consist of 100 elements and result consists of 199 elements, I am not sure why I should arbitrarily select the last 100 elements.

print("
 autocorrelation (shape={}):
{}
".format(result.shape, result))  

 autocorrelation (shape=(199,)):
[ 0.00000000e+00 -3.82130761e-16 -9.73648712e-02 -3.70014208e-01
 -8.59889695e-01 -1.56185995e+00 -2.41986054e+00 -3.33109112e+00
 -4.15799070e+00 -4.74662427e+00 -4.94918053e+00 -4.64762251e+00
 -3.77524157e+00 -2.33298717e+00 -3.97976240e-01  1.87752669e+00
  4.27722402e+00  6.54129270e+00  8.39434617e+00  9.57785701e+00
  9.88331103e+00  9.18204933e+00  7.44791758e+00  4.76948221e+00
  1.34963425e+00 -2.50822289e+00 -6.42666652e+00 -9.99116299e+00
 -1.27937834e+01 -1.44791297e+01 -1.47873668e+01 -1.35893098e+01
 -1.09091510e+01 -6.93157447e+00 -1.99159756e+00  3.45267493e+00
  8.86228186e+00  1.36707567e+01  1.73433176e+01  1.94357232e+01
  1.96463736e+01  1.78556800e+01  1.41478477e+01  8.81191526e+00
  2.32100171e+00 -4.70897483e+00 -1.15775811e+01 -1.75696560e+01
 -2.20296487e+01 -2.44327920e+01 -2.44454330e+01 -2.19677060e+01
 -1.71533510e+01 -1.04037163e+01 -2.33560966e+00  6.27458308e+00
  1.45655029e+01  2.16769872e+01  2.68391837e+01  2.94553896e+01
  2.91697473e+01  2.59122266e+01  1.99154591e+01  1.17007613e+01
  2.03381596e+00 -8.14633251e+00 -1.78184255e+01 -2.59814393e+01
 -3.17580589e+01 -3.44884934e+01 -3.38046447e+01 -2.96763956e+01
 -2.24244433e+01 -1.26974172e+01 -1.41464998e+00  1.03204331e+01
  2.13281784e+01  3.04712823e+01  3.67721634e+01  3.95170295e+01
  3.83356037e+01  3.32477037e+01  2.46710643e+01  1.33886439e+01
  4.77778141e-01 -1.27924775e+01 -2.50860560e+01 -3.51343866e+01
 -4.18671622e+01 -4.45258983e+01 -4.27482779e+01 -3.66140001e+01
 -2.66465884e+01 -1.37700036e+01  7.76494745e-01  1.55574483e+01
  2.90828312e+01  3.99582426e+01  4.70285203e+01  4.95000000e+01
  4.70285203e+01  3.99582426e+01  2.90828312e+01  1.55574483e+01
  7.76494745e-01 -1.37700036e+01 -2.66465884e+01 -3.66140001e+01
 -4.27482779e+01 -4.45258983e+01 -4.18671622e+01 -3.51343866e+01
 -2.50860560e+01 -1.27924775e+01  4.77778141e-01  1.33886439e+01
  2.46710643e+01  3.32477037e+01  3.83356037e+01  3.95170295e+01
  3.67721634e+01  3.04712823e+01  2.13281784e+01  1.03204331e+01
 -1.41464998e+00 -1.26974172e+01 -2.24244433e+01 -2.96763956e+01
 -3.38046447e+01 -3.44884934e+01 -3.17580589e+01 -2.59814393e+01
 -1.78184255e+01 -8.14633251e+00  2.03381596e+00  1.17007613e+01
  1.99154591e+01  2.59122266e+01  2.91697473e+01  2.94553896e+01
  2.68391837e+01  2.16769872e+01  1.45655029e+01  6.27458308e+00
 -2.33560966e+00 -1.04037163e+01 -1.71533510e+01 -2.19677060e+01
 -2.44454330e+01 -2.44327920e+01 -2.20296487e+01 -1.75696560e+01
 -1.15775811e+01 -4.70897483e+00  2.32100171e+00  8.81191526e+00
  1.41478477e+01  1.78556800e+01  1.96463736e+01  1.94357232e+01
  1.73433176e+01  1.36707567e+01  8.86228186e+00  3.45267493e+00
 -1.99159756e+00 -6.93157447e+00 -1.09091510e+01 -1.35893098e+01
 -1.47873668e+01 -1.44791297e+01 -1.27937834e+01 -9.99116299e+00
 -6.42666652e+00 -2.50822289e+00  1.34963425e+00  4.76948221e+00
  7.44791758e+00  9.18204933e+00  9.88331103e+00  9.57785701e+00
  8.39434617e+00  6.54129270e+00  4.27722402e+00  1.87752669e+00
 -3.97976240e-01 -2.33298717e+00 -3.77524157e+00 -4.64762251e+00
 -4.94918053e+00 -4.74662427e+00 -4.15799070e+00 -3.33109112e+00
 -2.41986054e+00 -1.56185995e+00 -8.59889695e-01 -3.70014208e-01
 -9.73648712e-02 -3.82130761e-16  0.00000000e+00]

Attempt 2: Fourier

Since I am not sure where to go from the last attempt, I sought a new attempt. To my understanding, Fourier analysis basically shifts a signal from/to the time-domain (x(t) vs t) to/from the frequency domain (x(t) vs f=1/t); the signal in frequency-space should appear as a sinusoidal wave that dampens over time. The period is obtained from the most observed frequency since this is the location of the peak of the distribution of frequencies.

Since my values are all real-valued, applying the Fourier transform should mean my output values are all complex-valued. I wouldn't think this is a problem, except for the fact that scipy has methods for real-values. I do not fully understand the differences between all of the different scipy methods. That makes following the algorithm proposed in this posted solution hard for me to follow (ie, how/why is the threshold value picked?).

omega = np.fft.fft(x)
freq = np.fft.fftfreq(x.size, 1)
threshold = 0
idx = np.where(abs(omega)>threshold)[0][-1]
max_f = abs(freq[idx])
print(max_f)

This outputs 0.01, meaning the period is 1/0.01 = 100. This doesn't make sense either.

Attempt 3: Power Spectral Density

According to the scipy docs, I should be able to estimate the power spectral density (psd) of the signal using a periodogram (which, according to wikipedia, is the fourier transform of the autocorrelation function). By selecting the dominant frequency fmax at which the signal peaks, the period of the signal can be obtained as 1 / fmax.

freq, pdensity = signal.periodogram(x)

fig, ax = plt.subplots()
ax.plot(freq, pdensity, color='r')
ax.grid(color='k', alpha=0.3, linestyle=':')
plt.show()
plt.close(fig)

The periodogram shown below peaks at 49.076... at a frequency of fmax = 0.05. So, period = 1/fmax = 20. This doesn't make sense to me. I have a feeling it has something to do with the sampling rate, but don't know enough to confirm or progress further.

I realize I am missing some fundamental gaps in understanding how these things work. There are a lot of resources online, but it's hard to find this needle in the haystack. Can someone help me learn more about this?

Let's first look at your signal (I've added endpoint=False to make the division even):

t = np.linspace(0, 10*np.pi, 100, endpoint=False)
x = np.sin(t)

Let's divide out the radians (essentially by taking t /= 2*np.pi) and create the same signal by relating to frequencies:

fs = 20 # Sampling rate of 100/5 = 20 (e.g. Hz)
f = 1 # Signal frequency of 1 (e.g. Hz)
t = np.linspace(0, 5, 5*fs, endpoint=False)
x = np.sin(2*np.pi*f*t)

This makes it more salient that f/fs == 1/20 == 0.05 (i.e. the periodicity of the signal is exactly 20 samples). Frequencies in a digital signal always relate to its sampling rate, as you have already guessed. Note that the actual signal is exactly the same no matter what the values of f and fs are, as long as their ratio is the same:

fs = 1 # Natural units
f = 0.05
t = np.linspace(0, 100, 100*fs, endpoint=False)
x = np.sin(2*np.pi*f*t)

In the following I'll use these natural units (fs = 1). The only difference will be in t and hence the generated frequency axes.

Autocorrelation

Your understanding of what the autocorrelation function does is correct. It detects the correlation of a signal with a time-lagged version of itself. It does this by sliding the signal over itself as seen in the right column here (from Wikipedia):

Note that as both inputs to the correlation function are the same, the resulting signal is necessarily symmetric. That is why the output of np.correlate is usually sliced from the middle:

acf = np.correlate(x, x, 'full')[-len(x):]

Now index 0 corresponds to 0 delay between the two copies of the signal.

Next you'll want to find the index or delay that presents the largest correlation. Due to the shrinking overlap this will by default also be index 0, so the following won't work:

acf.argmax() # Always returns 0

Instead I recommend to find the largest peak instead, where a peak is defined to be any index with a larger value than both its direct neighbours:

inflection = np.diff(np.sign(np.diff(acf))) # Find the second-order differences
peaks = (inflection < 0).nonzero()[0] + 1 # Find where they are negative
delay = peaks[acf[peaks].argmax()] # Of those, find the index with the maximum value

Now delay == 20, which tells you that the signal has a frequency of 1/20 of its sampling rate:

signal_freq = fs/delay # Gives 0.05

Fourier transform

You used the following to calculate the FFT:

omega = np.fft.fft(x)
freq = np.fft.fftfreq(x.size, 1)

Thhese functions re designed for complex-valued signals. They will work for real-valued signals, but you'll get a symmetric output as the negative frequency components will be identical to the positive frequency components. NumPy provides separate functions for real-valued signals:

ft = np.fft.rfft(x)
freqs = np.fft.rfftfreq(len(x), t[1]-t[0]) # Get frequency axis from the time axis
mags = abs(ft) # We don't care about the phase information here

Let's have a look:

plt.plot(freqs, mags)
plt.show()

Note two things: the peak is at frequency 0.05, and the maximum frequency on the axis is 0.5 (the Nyquist frequency, which is exactly half the sampling rate). If we had picked fs = 20, this would be 10.

Now let's find the maximum. The thresholding method you have tried can work, but the target frequency bin is selected blindly and so this method would suffer in the presence of other signals. We could just select the maximum value:

signal_freq = freqs[mags.argmax()] # Gives 0.05

However, this would fail if, e.g., we have a large DC offset (and hence a large component in index 0). In that case we could just select the highest peak again, to make it more robust:

inflection = np.diff(np.sign(np.diff(mags)))
peaks = (inflection < 0).nonzero()[0] + 1
peak = peaks[mags[peaks].argmax()]
signal_freq = freqs[peak] # Gives 0.05

If we had picked fs = 20, this would have given signal_freq == 1.0 due to the different time axis from which the frequency axis was generated.

Periodogram

The method here is essentially the same. The autocorrelation function of x has the same time axis and period as x, so we can use the FFT as above to find the signal frequency:

pdg = np.fft.rfft(acf)
freqs = np.fft.rfftfreq(len(x), t[1]-t[0])

plt.plot(freqs, abs(pdg))
plt.show()

This curve obviously has slightly different characteristics from the direct FFT on x, but the main takeaways are the same: the frequency axis ranges from 0 to 0.5*fs, and we find a peak at the same signal frequency as before: freqs[abs(pdg).argmax()] == 0.05.

Edit:

To measure the actual periodicity of np.sin, we can just use the "angle axis" that we passed to np.sin instead of the time axis when generating the frequency axis:

freqs = np.fft.rfftfreq(len(x), 2*np.pi*f*(t[1]-t[0]))
rad_period = 1/freqs[mags.argmax()] # 6.283185307179586

Though that seems pointless, right? We pass in 2*np.pi and we get 2*np.pi. However, we can do the same with any regular time axis, without presupposing pi at any point:

fs = 10
t = np.arange(1000)/fs
x = np.sin(t)
rad_period = 1/np.fft.rfftfreq(len(x), 1/fs)[abs(np.fft.rfft(x)).argmax()] # 6.25

Naturally, the true value now lies in between two bins. That's where interpolation comes in and the associated need to choose a suitable window function.