# antropy.spectral_entropy

antropy.spectral_entropy(x, sf, method='fft', nperseg=None, normalize=False, axis=- 1)[source]

Spectral Entropy.

Parameters
xlist or np.array

1D or N-D data.

sffloat

Sampling frequency, in Hz.

methodstr

Spectral estimation method:

npersegint or None

Length of each FFT segment for Welch method. If None (default), uses scipy default of 256 samples.

normalizebool

If True, divide by log2(psd.size) to normalize the spectral entropy between 0 and 1. Otherwise, return the spectral entropy in bit.

axisint

The axis along which the entropy is calculated. Default is -1 (last).

Returns
sefloat

Spectral Entropy

Notes

Spectral Entropy is defined to be the Shannon entropy of the power spectral density (PSD) of the data:

$H(x, sf) = -\sum_{f=0}^{f_s/2} P(f) \log_2[P(f)]$

Where $$P$$ is the normalised PSD, and $$f_s$$ is the sampling frequency.

References

Examples

Spectral entropy of a pure sine using FFT

>>> import numpy as np
>>> import antropy as ant
>>> sf, f, dur = 100, 1, 4
>>> N = sf * dur # Total number of discrete samples
>>> t = np.arange(N) / sf # Time vector
>>> x = np.sin(2 * np.pi * f * t)
>>> np.round(ant.spectral_entropy(x, sf, method='fft'), 2)
0.0


Spectral entropy of a random signal using Welchâ€™s method

>>> np.random.seed(42)
>>> x = np.random.rand(3000)
>>> ant.spectral_entropy(x, sf=100, method='welch')
6.980045662371389


Normalized spectral entropy

>>> ant.spectral_entropy(x, sf=100, method='welch', normalize=True)
0.9955526198316071


Normalized spectral entropy of 2D data

>>> np.random.seed(42)
>>> x = np.random.normal(size=(4, 3000))
>>> np.round(ant.spectral_entropy(x, sf=100, normalize=True), 4)
array([0.9464, 0.9428, 0.9431, 0.9417])


Fractional Gaussian noise with H = 0.5

>>> import stochastic.processes.noise as sn
>>> rng = np.random.default_rng(seed=42)
>>> x = sn.FractionalGaussianNoise(hurst=0.5, rng=rng).sample(10000)
>>> print(f"{ant.spectral_entropy(x, sf=100, normalize=True):.4f}")
0.9505


Fractional Gaussian noise with H = 0.9

>>> rng = np.random.default_rng(seed=42)
>>> x = sn.FractionalGaussianNoise(hurst=0.9, rng=rng).sample(10000)
>>> print(f"{ant.spectral_entropy(x, sf=100, normalize=True):.4f}")
0.8477


Fractional Gaussian noise with H = 0.1

>>> rng = np.random.default_rng(seed=42)
>>> x = sn.FractionalGaussianNoise(hurst=0.1, rng=rng).sample(10000)
>>> print(f"{ant.spectral_entropy(x, sf=100, normalize=True):.4f}")
0.9248