使用scipy.signal.spectral.lombscargle进行周期发现

Question

新的Scipy v0.11提供了一个用于频谱分析的包。不幸的是，文档很稀疏，并且没有太多可用的示例。

作为一个小例子，我正在尝试做一个正弦波的周期发现。不幸的是，它预测的是1的周期，而不是预期的2pi。有什么想法吗？

# imports the numerical array and scientific computing packages
import numpy as np
import scipy as sp
from scipy.signal import spectral

# generates 100 evenly spaced points between 1 and 1000
time = np.linspace(1, 1000, 100)

# computes the sine value of each of those points
mags = np.sin(time)

# scales the sine values so that the mean is 0 and the variance is 1 (the documentation specifies that this must be done)
scaled_mags = (mags-mags.mean())/mags.std()

# generates 1000 frequencies between 0.01 and 1
freqs = np.linspace(0.01, 1, 1000)

# computes the Lomb Scargle Periodogram of the time and scaled magnitudes using each frequency as a guess
periodogram = spectral.lombscargle(time, scaled_mags, freqs)

# returns the inverse of the frequence (i.e. the period) of the largest periodogram value
1/freqs[np.argmax(periodogram)]

这将返回1，而不是预期的2pi ~= 1/0.6366周期。有什么想法吗？

Answer 1

请注意，根据docstring，spectral.lombscargle的最后一个参数是角频率

Parameters
----------
x : array_like
Sample times.
y : array_like
Measurement values.
freqs : array_like
Angular frequencies for output periodogram.