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| Original file line number | Diff line number | Diff line change |
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@@ -2,7 +2,6 @@ | |
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| from __future__ import unicode_literals | ||
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| import itertools | ||
| import numpy as np | ||
| from math import factorial | ||
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@@ -73,8 +72,8 @@ def util_granulate_time_series(time_series, scale): | |
| """ | ||
| n = len(time_series) | ||
| b = int(np.fix(n / scale)) | ||
| temp = np.reshape(time_series[0:b*scale], (b, scale)) | ||
| cts = np.mean(temp, axis = 1) | ||
| temp = np.reshape(time_series[0:b * scale], (b, scale)) | ||
| cts = np.mean(temp, axis=1) | ||
| return cts | ||
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@@ -110,7 +109,7 @@ def shannon_entropy(time_series): | |
| return ent | ||
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| def sample_entropy(time_series, sample_length, tolerance = None): | ||
| def sample_entropy(time_series, sample_length, tolerance=None): | ||
| """Calculates the sample entropy of degree m of a time_series. | ||
| This method uses chebychev norm. | ||
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@@ -135,36 +134,35 @@ def sample_entropy(time_series, sample_length, tolerance = None): | |
| [3] Madalena Costa, Ary Goldberger, CK Peng. Multiscale entropy analysis | ||
| of biological signals | ||
| """ | ||
| #The code below follows the sample length convention of Ref [1] so: | ||
| M = sample_length - 1; | ||
| # The code below follows the sample length convention of Ref [1] so: | ||
| M = sample_length - 1 | ||
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| time_series = np.array(time_series) | ||
| if tolerance is None: | ||
| tolerance = 0.1*np.std(time_series) | ||
| tolerance = 0.1 * np.std(time_series) | ||
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| n = len(time_series) | ||
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| #Ntemp is a vector that holds the number of matches. N[k] holds matches templates of length k | ||
| # Ntemp is a vector that holds the number of matches. N[k] holds matches templates of length k | ||
| Ntemp = np.zeros(M + 2) | ||
| #Templates of length 0 matches by definition: | ||
| Ntemp[0] = n*(n - 1) / 2 | ||
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| # Templates of length 0 matches by definition: | ||
| Ntemp[0] = n * (n - 1) / 2 | ||
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| for i in range(n - M - 1): | ||
| template = time_series[i:(i+M+1)];#We have 'M+1' elements in the template | ||
| rem_time_series = time_series[i+1:] | ||
| template = time_series[i:(i + M + 1)] # We have 'M+1' elements in the template | ||
| rem_time_series = time_series[i + 1:] | ||
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| searchlist = np.arange(len(rem_time_series) - M, dtype=np.int32) | ||
| for length in range(1, len(template)+1): | ||
| hitlist = np.abs(rem_time_series[searchlist] - template[length-1]) < tolerance | ||
| Ntemp[length] += np.sum(hitlist) | ||
| searchlist = searchlist[hitlist] + 1 | ||
| search_list = np.arange(len(rem_time_series) - M, dtype=np.int32) | ||
| for length in range(1, len(template) + 1): | ||
| hit_list = np.abs(rem_time_series[search_list] - template[length - 1]) < tolerance | ||
| Ntemp[length] += np.sum(hit_list) | ||
| search_list = search_list[hit_list] + 1 | ||
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| sampen = - np.log(Ntemp[1:] / Ntemp[:-1]) | ||
| sampen = -np.log(Ntemp[1:] / Ntemp[:-1]) | ||
| return sampen | ||
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| def multiscale_entropy(time_series, sample_length, tolerance = None, maxscale = 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. | ||
|
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@@ -181,15 +179,16 @@ def multiscale_entropy(time_series, sample_length, tolerance = None, maxscale = | |
| """ | ||
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| if tolerance is None: | ||
| #we need to fix the tolerance at this level. If it remains 'None' it will be changed in call to sample_entropy() | ||
| tolerance = 0.1*np.std(time_series) | ||
| # We need to fix the tolerance at this level | ||
| # If it remains 'None' it will be changed in call to sample_entropy() | ||
| tolerance = 0.1 * np.std(time_series) | ||
| if maxscale is None: | ||
| maxscale = len(time_series) | ||
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| mse = np.zeros(maxscale) | ||
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| for i in range(maxscale): | ||
| temp = util_granulate_time_series(time_series, i+1) | ||
| temp = util_granulate_time_series(time_series, i + 1) | ||
| mse[i] = sample_entropy(temp, sample_length, tolerance)[-1] | ||
| return mse | ||
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@@ -289,73 +288,72 @@ def multiscale_permutation_entropy(time_series, m, delay, scale): | |
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| def weighted_permutation_entropy(time_series, order=2, delay=1, normalize=False): | ||
| """Calculate the weighted permuation 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 | ||
| ---------- | ||
| time_series : list or np.array | ||
| Time series | ||
| order : int | ||
| Order of permutation entropy | ||
| delay : int | ||
| Time delay | ||
| normalize : bool | ||
| If True, divide by log2(factorial(m)) to normalize the entropy | ||
| between 0 and 1. Otherwise, return the permutation entropy in bit. | ||
| Returns | ||
| ------- | ||
| wpe : float | ||
| Weighted Permutation Entropy | ||
| References | ||
| ---------- | ||
| .. [1] Bilal Fadlallah, Badong Chen, Andreas Keil, and José Príncipe | ||
| Phys. Rev. E 87, 022911 – Published 20 February 2013 | ||
| Notes | ||
| ----- | ||
| Last updated (March 2021) by Samuel Dotson ([email protected]) | ||
| Examples | ||
| -------- | ||
| 1. Weighted permutation entropy with order 2 | ||
| >>> x = [4, 7, 9, 10, 6, 11, 3] | ||
| >>> # Return a value between 0 and log2(factorial(order)) | ||
| >>> print(permutation_entropy(x, order=2)) | ||
| 0.912 | ||
| 2. Normalized weighted permutation entropy with order 3 | ||
| >>> x = [4, 7, 9, 10, 6, 11, 3] | ||
| >>> # Return a value comprised between 0 and 1. | ||
| >>> print(permutation_entropy(x, order=3, normalize=True)) | ||
| 0.547 | ||
| """ | ||
| x = _embed(time_series, order=order, delay=delay) | ||
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| weights = np.var(x, axis=1) | ||
| sorted_idx = x.argsort(kind='quicksort', axis=1) | ||
| motifs, c = np.unique(sorted_idx, return_counts=True, axis=0) | ||
| pw = np.zeros(len(motifs)) | ||
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| # TODO hashmap | ||
| for i, j in zip(weights, sorted_idx): | ||
| idx = int(np.where((j==motifs).sum(1)==order)[0]) | ||
| pw[idx] += i | ||
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| pw /= weights.sum() | ||
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| b = np.log2(pw) | ||
| wpe = -np.dot(pw, b) | ||
| if normalize: | ||
| wpe /= np.log2(factorial(order)) | ||
| return wpe | ||
| """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 | ||
| ---------- | ||
| time_series : list or np.array | ||
| Time series | ||
| order : int | ||
| Order of permutation entropy | ||
| delay : int | ||
| Time delay | ||
| normalize : bool | ||
| If True, divide by log2(factorial(m)) to normalize the entropy | ||
| between 0 and 1. Otherwise, return the permutation entropy in bit. | ||
| Returns | ||
| ------- | ||
| wpe : float | ||
| Weighted Permutation Entropy | ||
| References | ||
| ---------- | ||
| .. [1] Bilal Fadlallah, Badong Chen, Andreas Keil, and José Príncipe | ||
| Phys. Rev. E 87, 022911 – Published 20 February 2013 | ||
| Notes | ||
| ----- | ||
| Last updated (March 2021) by Samuel Dotson ([email protected]) | ||
| Examples | ||
| -------- | ||
| 1. Weighted permutation entropy with order 2 | ||
| >>> x = [4, 7, 9, 10, 6, 11, 3] | ||
| >>> # Return a value between 0 and log2(factorial(order)) | ||
| >>> print(permutation_entropy(x, order=2)) | ||
| 0.912 | ||
| 2. Normalized weighted permutation entropy with order 3 | ||
| >>> x = [4, 7, 9, 10, 6, 11, 3] | ||
| >>> # Return a value comprised between 0 and 1. | ||
| >>> print(permutation_entropy(x, order=3, normalize=True)) | ||
| 0.547 | ||
| """ | ||
| x = _embed(time_series, order=order, delay=delay) | ||
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| weights = np.var(x, axis=1) | ||
| sorted_idx = x.argsort(kind='quicksort', axis=1) | ||
| motifs, c = np.unique(sorted_idx, return_counts=True, axis=0) | ||
| pw = np.zeros(len(motifs)) | ||
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| # TODO hashmap | ||
| for i, j in zip(weights, sorted_idx): | ||
| idx = int(np.where((j == motifs).sum(1) == order)[0]) | ||
| pw[idx] += i | ||
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| pw /= weights.sum() | ||
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| b = np.log2(pw) | ||
| wpe = -np.dot(pw, b) | ||
| if normalize: | ||
| wpe /= np.log2(factorial(order)) | ||
| return wpe | ||
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| # TODO add tests | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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@@ -2,10 +2,10 @@ | |
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| setup( | ||
| name='pyentrp', | ||
| version='0.6.0', | ||
| version='0.7.0', | ||
| description='Functions on top of NumPy for computing different types of entropy', | ||
| url='https://github.com/nikdon/pyEntropy', | ||
| download_url='https://github.com/nikdon/pyEntropy/archive/0.6.0.tar.gz', | ||
| download_url='https://github.com/nikdon/pyEntropy/archive/0.7.0.tar.gz', | ||
| author='Nikolay Donets', | ||
| author_email='[email protected]', | ||
| maintainer='Nikolay Donets', | ||
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@@ -18,8 +18,15 @@ | |
| ], | ||
| test_suite="tests.test_entropy", | ||
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| keywords=['entropy', 'python', 'sample entropy', 'multiscale entropy', 'permutation entropy', | ||
| 'composite multiscale entropy'], | ||
| keywords=[ | ||
| 'python', | ||
| 'entropy', | ||
| 'sample entropy', | ||
| 'multiscale entropy', | ||
| 'permutation entropy', | ||
| 'composite multiscale entropy', | ||
| 'multiscale permutation entropy' | ||
| ], | ||
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| classifiers=[ | ||
| 'Development Status :: 5 - Production/Stable', | ||
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@@ -36,6 +43,9 @@ | |
| 'Programming Language :: Python :: 3.4', | ||
| 'Programming Language :: Python :: 3.5', | ||
| 'Programming Language :: Python :: 3.6', | ||
| 'Programming Language :: Python :: 3.7', | ||
| 'Programming Language :: Python :: 3.8', | ||
| 'Programming Language :: Python :: 3.9', | ||
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| 'Topic :: Scientific/Engineering :: Bio-Informatics', | ||
| 'Topic :: Scientific/Engineering :: Information Analysis', | ||
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