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refactor: update ruff lint rules and fix tests
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The old ` numpy.math ` module is replaced with `math`. Added numpy and doc-specific lint rules. Tests use `numpy.testing` instead of standard asserts with rounds
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@nikdon
nikdon committed Jul 6, 2024
1 parent f2c6581 commit c5b4293
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65 changes: 33 additions & 32 deletions pyentrp/entropy.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,11 @@
import math
from collections import Counter

import numpy as np


def time_delay_embedding(time_series, embedding_dimension, delay=1):
"""
Time-delayed embedding.
def time_delay_embedding(time_series, embedding_dimension, delay):
"""Calculate time-delayed embedding.
Parameters
----------
Expand All @@ -20,6 +20,7 @@ def time_delay_embedding(time_series, embedding_dimension, delay=1):
-------
embedded : ndarray
The embedded time series with shape (n_times - (order - 1) * delay, order).
"""
series_length = len(time_series)
embedded_series = np.empty((embedding_dimension, series_length - (embedding_dimension - 1) * delay))
Expand All @@ -29,8 +30,7 @@ def time_delay_embedding(time_series, embedding_dimension, delay=1):


def util_pattern_space(time_series, lag, dim):
"""
Create a set of sequences with given lag and dimension
"""Create a set of sequences with a given lag and dimension.
Parameters
----------
Expand All @@ -49,6 +49,7 @@ def util_pattern_space(time_series, lag, dim):
Raises
------
ValueError: If the lag is less than 1 or the result matrix exceeds the size limit.
"""
n = len(time_series)

Expand All @@ -66,8 +67,7 @@ def util_pattern_space(time_series, lag, dim):


def util_granulate_time_series(time_series, scale):
"""
Extract coarse-grained time series
"""Extract coarse-grained time series.
Parameters
----------
Expand All @@ -79,7 +79,8 @@ def util_granulate_time_series(time_series, scale):
Returns
-------
cts : np.ndarray
Array of coarse-grained time series with given scale factor
Array of coarse-grained time series with a given scale factor
"""
if not isinstance(time_series, np.ndarray):
time_series = np.array(time_series)
Expand All @@ -91,8 +92,7 @@ def util_granulate_time_series(time_series, scale):


def shannon_entropy(time_series):
"""
Return the Shannon Entropy of the sample data.
"""Calculate Shannon Entropy of the sample data.
Parameters
----------
Expand All @@ -103,6 +103,7 @@ def shannon_entropy(time_series):
-------
ent: float
The Shannon Entropy as float value
"""
if isinstance(time_series, str):
# Calculate frequency counts
Expand Down Expand Up @@ -130,11 +131,10 @@ def shannon_entropy(time_series):


def sample_entropy(time_series, sample_length, tolerance=None):
"""
Calculates the sample entropy of degree m of a time_series.
"""Calculate the sample entropy of degree m of a time_series.
This method uses Chebyshev norm.
It is quite fast for random data, but can be slower is there is
It is quite fast for random data but can be slower is there is
structure in the input time series.
Parameters
Expand All @@ -150,7 +150,7 @@ def sample_entropy(time_series, sample_length, tolerance=None):
-------
sampen: np.ndarray
Array of Sample Entropies SE.
SE[k] is ratio `#templates of length k+1` / `#templates of length k`
SE[k] is the ratio `#templates of length k+1` / `#templates of length k`
where `#templates of length 0` = n*(n - 1) / 2, by definition
Notes
Expand All @@ -162,6 +162,7 @@ def sample_entropy(time_series, sample_length, tolerance=None):
.. [1] http://en.wikipedia.org/wiki/Sample_Entropy
.. [2] http://physionet.incor.usp.br/physiotools/sampen/
.. [3] Madalena Costa, Ary Goldberger, CK Peng. Multiscale entropy analysis of biological signals
"""
if not isinstance(time_series, np.ndarray):
time_series = np.array(time_series)
Expand All @@ -177,7 +178,7 @@ def sample_entropy(time_series, sample_length, tolerance=None):
# N_temp[k] holds matches templates of length k
N_temp = np.zeros(sample_length + 2)

# Templates of length 0 matches by definition:
# Templates of length 0 match by definition:
N_temp[0] = n * (n - 1) / 2

for i in range(n - sample_length - 1):
Expand All @@ -195,9 +196,7 @@ def sample_entropy(time_series, sample_length, tolerance=None):


def multiscale_entropy(time_series, sample_length, tolerance=None, maxscale=None):
"""
Calculate the Multiscale Entropy of the given time series considering
different time-scales of the time series.
"""Calculate Multiscale Entropy considering different time-scales of the time series.
Parameters
----------
Expand All @@ -219,6 +218,7 @@ def multiscale_entropy(time_series, sample_length, tolerance=None, maxscale=None
----------
.. [1] http://en.pudn.com/downloads149/sourcecode/math/detail646216_en.html
Can be viewed at https://web.archive.org/web/20170207221539/http://en.pudn.com/downloads149/sourcecode/math/detail646216_en.html
"""
if tolerance is None:
tolerance = 0.1 * np.std(time_series)
Expand All @@ -234,12 +234,11 @@ def multiscale_entropy(time_series, sample_length, tolerance=None, maxscale=None


def permutation_entropy(time_series, order=3, delay=1, normalize=False):
"""
Permutation Entropy.
"""Calculate Permutation Entropy.
Parameters
----------
time_series : list | np.array
time_series : list | np.ndarray
Time series
order : int
Order of permutation entropy
Expand Down Expand Up @@ -286,6 +285,7 @@ def permutation_entropy(time_series, order=3, delay=1, normalize=False):
>>> # Return a value comprised between 0 and 1.
>>> print(permutation_entropy(x, order=3, normalize=True))
0.589
"""
x = np.array(time_series)
hashmult = np.power(order, np.arange(order))
Expand All @@ -299,13 +299,13 @@ def permutation_entropy(time_series, order=3, delay=1, normalize=False):
p = np.true_divide(c, c.sum())
pe = -np.multiply(p, np.log2(p)).sum()
if normalize:
pe /= np.log2(np.math.factorial(order))
factorial = math.factorial(order)
pe /= np.log2(factorial)
return pe


def multiscale_permutation_entropy(time_series, m, delay, scale):
"""
Calculate the Multiscale Permutation Entropy
"""Calculate the Multiscale Permutation Entropy.
Parameters
----------
Expand All @@ -328,6 +328,7 @@ def multiscale_permutation_entropy(time_series, m, delay, scale):
.. [1] Francesco Carlo Morabito et al. Multivariate Multi-Scale Permutation Entropy for
Complexity Analysis of Alzheimer`s Disease EEG. www.mdpi.com/1099-4300/14/7/1186
.. [2] http://www.mathworks.com/matlabcentral/fileexchange/37288-multiscale-permutation-entropy-mpe/content/MPerm.m
"""
mspe = np.empty(scale)
for i in range(scale):
Expand All @@ -337,11 +338,10 @@ def multiscale_permutation_entropy(time_series, m, delay, scale):


def weighted_permutation_entropy(time_series, order=2, delay=1, normalize=False):
"""
Calculate the weighted permutation entropy. Weighted permutation
entropy captures the information in the amplitude of a signal where
standard permutation entropy only measures the information in the
ordinal pattern, "motif".
"""Calculate the weighted permutation entropy.
Weighted permutation entropy captures the information in the amplitude of a signal where
standard permutation entropy only measures the information in the ordinal pattern, "motif".
Parameters
----------
Expand Down Expand Up @@ -385,6 +385,7 @@ def weighted_permutation_entropy(time_series, order=2, delay=1, normalize=False)
>>> # Return a value comprised between 0 and 1.
>>> print(permutation_entropy(x, order=3, normalize=True))
0.547
"""
x = time_delay_embedding(time_series, embedding_dimension=order, delay=delay)

Expand All @@ -406,14 +407,13 @@ def weighted_permutation_entropy(time_series, order=2, delay=1, normalize=False)
wpe = -np.dot(pw, b)

if normalize:
wpe /= np.log2(np.math.factorial(order))
wpe /= np.log2(math.factorial(order))

return wpe


def composite_multiscale_entropy(time_series, sample_length, scale, tolerance=None):
"""
Composite Multiscale Entropy of the given time series
"""Calculate Composite Multiscale Entropy.
Parameters
----------
Expand All @@ -435,6 +435,7 @@ def composite_multiscale_entropy(time_series, sample_length, scale, tolerance=No
----------
.. [1] Wu, Shuen-De, et al. "Time series analysis using
composite multiscale entropy." Entropy 15.3 (2013): 1069-1084.
"""
if tolerance is None:
tolerance = 0.1 * np.std(time_series)
Expand Down
13 changes: 12 additions & 1 deletion pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -15,11 +15,11 @@ numpy = ">=1.26,<3.0"


[tool.poetry.group.dev.dependencies]
ruff = ">=0.5.0,<0.5.1"
black = ">=23.3,<25.0"
codecov = "^2.1.13"
commitizen = "^3.2.2"
pre-commit = "^3.3.2"
ruff = ">=0.5.0,<0.5.1"


[tool.commitizen]
Expand Down Expand Up @@ -102,4 +102,15 @@ select = [
"PGH",
# Ruff-specific rules
"RUF",
# NumPy-specific rules
"NPY",
# pydocstyle
"D",
]
ignore = [
"D100", "D104",
"D203", "D213"
]

[tool.ruff.lint.per-file-ignores]
"**/{tests}/*" = ["D101", "D102", "D103"]
58 changes: 28 additions & 30 deletions tests/test_entropy.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,7 @@

from pyentrp import entropy as ent

np.random.seed(1234567)

rng = np.random.default_rng(1234567)

TIME_SERIES = [1, 1, 1, 2, 3, 4, 5]
TIME_SERIES_STRING = "1112345"
Expand All @@ -22,15 +21,15 @@

PERM_ENTROPY_BANDT = [4, 7, 9, 10, 6, 11, 3]

RANDOM_TIME_SERIES = np.random.rand(1000)
RANDOM_TIME_SERIES = rng.random(1000)


class TestEntropy(unittest.TestCase):
def test_shannon_entropy_string(self):
self.assertEqual(round(ent.shannon_entropy(TIME_SERIES_STRING), 5), SHANNON_ENTROPY)
np.testing.assert_allclose(ent.shannon_entropy(TIME_SERIES_STRING), SHANNON_ENTROPY, rtol=1e-5)

def test_shannon_entropy_numerical(self):
self.assertEqual(round(ent.shannon_entropy(TIME_SERIES), 5), SHANNON_ENTROPY)
np.testing.assert_allclose(ent.shannon_entropy(TIME_SERIES), SHANNON_ENTROPY, rtol=1e-5)

def test_sample_entropy(self):
ts = TS_SAMPLE_ENTROPY
Expand All @@ -42,49 +41,48 @@ def test_multiscale_entropy(self):
multi_scale_entropy = ent.multiscale_entropy(RANDOM_TIME_SERIES, 4, maxscale=4)
np.testing.assert_allclose(
multi_scale_entropy,
np.array([2.52572864, 2.31911439, 1.65292302, 1.86075234]),
np.array([3.178054, 3.178054, 2.890372, 3.401197]),
rtol=1e-6,
)

def test_permutation_entropy(self):
self.assertEqual(
np.round(ent.permutation_entropy(PERM_ENTROPY_BANDT, order=2, delay=1), 3),
np.testing.assert_allclose(
ent.permutation_entropy(PERM_ENTROPY_BANDT, order=2, delay=1),
0.918,
rtol=1e-3,
)
self.assertEqual(
np.round(ent.permutation_entropy(PERM_ENTROPY_BANDT, order=3, delay=1), 3),

np.testing.assert_allclose(
ent.permutation_entropy(PERM_ENTROPY_BANDT, order=3, delay=1),
1.522,
rtol=1e-3,
)

# Assert that a fully random vector has an entropy of 0.99999...
self.assertEqual(
np.round(
ent.permutation_entropy(RANDOM_TIME_SERIES, order=3, delay=1, normalize=True),
3,
),
np.testing.assert_allclose(
ent.permutation_entropy(RANDOM_TIME_SERIES, order=3, delay=1, normalize=True),
0.999,
rtol=1e-3,
)

def test_weighted_permutation_entropy(self):
self.assertEqual(
np.round(
ent.weighted_permutation_entropy(PERM_ENTROPY_BANDT, order=2, delay=1),
3,
),
np.testing.assert_allclose(
ent.weighted_permutation_entropy(PERM_ENTROPY_BANDT, order=2, delay=1),
0.913,
rtol=1e-3,
)
self.assertEqual(
np.round(
ent.weighted_permutation_entropy(PERM_ENTROPY_BANDT, order=3, delay=1),
3,
),

np.testing.assert_allclose(
ent.weighted_permutation_entropy(PERM_ENTROPY_BANDT, order=3, delay=1),
1.414,
rtol=1e-3,
)

# Assert that a fully random vector has an entropy of 0.99999...
self.assertEqual(
np.round(
ent.weighted_permutation_entropy(RANDOM_TIME_SERIES, order=3, delay=1, normalize=True),
3,
),
np.testing.assert_allclose(
ent.weighted_permutation_entropy(RANDOM_TIME_SERIES, order=3, delay=1, normalize=True),
0.999,
rtol=1e-3,
)

def test_multiscale_permutation_entropy(self):
Expand Down

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