antropy 0.2.2

Entropy

Fractal dimension

Development installation

git clone https://github.com/raphaelvallat/antropy.git
cd antropy
uv pip install --group=test --editable .
pytest --verbose

Entropy measures

import numpy as np
import antropy as ant

np.random.seed(1234567)
x = np.random.normal(size=3000)

print(ant.perm_entropy(x, normalize=True))
print(ant.spectral_entropy(x, sf=100, method='welch', normalize=True))
print(ant.svd_entropy(x, normalize=True))
print(ant.app_entropy(x))
print(ant.sample_entropy(x))
print(ant.hjorth_params(x))             # mobility in samples⁻¹
print(ant.hjorth_params(x, sf=100))     # mobility in Hz
print(ant.num_zerocross(x))
print(ant.lziv_complexity('01111000011001', normalize=True))

0.9995              # perm_entropy        (0 = regular, 1 = random)
0.9941              # spectral_entropy     (0 = pure tone, 1 = white noise)
0.9999              # svd_entropy
2.0152              # app_entropy
2.1986              # sample_entropy
(1.4313, 1.2153)    # hjorth (mobility, complexity)
(143.1339, 1.2153)  # hjorth with sf=100 Hz
1531                # num_zerocross
1.3598              # lziv_complexity (normalized)

Fractal dimension

print(ant.petrosian_fd(x))
print(ant.katz_fd(x))
print(ant.higuchi_fd(x))
print(ant.detrended_fluctuation(x))

1.0311    # petrosian_fd
5.9543    # katz_fd
2.0037    # higuchi_fd   (≈ 2 for white noise)
0.4790    # DFA alpha    (≈ 0.5 for white noise)

N-D arrays

Some functions accept N-D arrays and an axis argument, making it easy to process multi-channel data in a single call:

import numpy as np
import antropy as ant

# 4 channels × 3000 samples
X = np.random.normal(size=(4, 3000))

pe   = ant.perm_entropy(X, normalize=True, axis=-1)          # shape (4,)
mob, com = ant.hjorth_params(X, sf=256, axis=-1)             # shape (4,) each
nzc  = ant.num_zerocross(X, normalize=True, axis=-1)         # shape (4,)
se   = ant.spectral_entropy(X, sf=256, normalize=True)       # shape (4,)