From a499113fa4daedcf58f75ab95a66aea1ff0853f6 Mon Sep 17 00:00:00 2001 From: Matt Date: Mon, 8 Jan 2024 10:45:01 +0100 Subject: [PATCH] Change Python Docstring Directory --- docs/_build/doctrees/environment.pickle | Bin 97972 -> 100456 bytes .../doctrees/python/Functions/Base.doctree | Bin 320162 -> 298030 bytes .../python/Functions/Bidimensional.doctree | Bin 157865 -> 143802 bytes .../doctrees/python/Functions/Cross.doctree | Bin 145851 -> 126016 bytes .../python/Functions/Multiscale.doctree | Bin 137969 -> 131184 bytes .../python/Functions/MultiscaleCross.doctree | Bin 137493 -> 131874 bytes docs/_build/html/python/Functions/Base.html | 114 +++--------------- .../html/python/Functions/Bidimensional.html | 101 +++------------- docs/_build/html/python/Functions/Cross.html | 100 ++------------- .../html/python/Functions/Multiscale.html | 48 +------- .../python/Functions/MultiscaleCross.html | 44 +------ docs/_build/html/searchindex.js | 2 +- docs/conf.py | 2 +- 13 files changed, 55 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znedL!)ydGY&k_D|Yw$zS{wEz?oeVLD-`eqX*(l6!9rvGFMJ7)s@~kh_(M-Ru@MtFA z-^!F7iT2rk>y%YTGrer+!_ca`4Luugo#UyLel(*^5AtT9TaIRK7bm}!M>8Xw8{cr_ z?GyZ`4)1VO9{d(hXl`mPxEs;kIBRfkuu5B8W6+?&7S}ixmrgykxX4kS>Mxyo`e+#a zEKW*|Rf4GJ&3Te~LJbwQ9o03h_6A`@GGbw#J1{!MJRhTPl6lH+ig~6s`_4mkTlSH^ zng0NvwY~dUvf5Ao?V|6ViLK?6Qiy`xcK-I#C4*J)N4ZKR8?0r z4tH6+l+hn?$fYu%-L;O#)zol$1s-6Fb*Br-X{V5z5MPx10`YEYVe#e0%jed{6j7tz zxWR}hqF)_)F!Q2gMm@B6i#z?+k-T4oQKk=%vwEk{p3%MUmZ23>Xu7KeJA%crcE!d> zlHxq7dpdH>IB8o&cb;l-r=JFunctions for estimating the entropy of a single univariate time series.

Returns the cosine similarity entropy (CoSi) and the corresponding -global probabilities (Bm) estimated from the data sequence (Sig) using the +global probabilities estimated from the data sequence (Sig) using the default parameters: embedding dimension = 2, time delay = 1, angular threshold = .1, logarithm = base 2,

CoSi, Bm = CoSiEn(Sig, keyword = value, ...)
@@ -692,7 +692,7 @@ 
Functions for estimating the entropy of a single univariate time series.
 

-FuzzEn(Sig, m=2, tau=1, r=(0.2, 2.0), Fx='default', Logx=numpy.exp)

+FuzzEn(Sig, m=2, tau=1, r=(0.2, 2), Fx='default', Logx=numpy.exp)
 
FuzzEn  estimates the fuzzy entropy of a univariate data sequence.

 
Fuzz, Ps1, Ps2 = FuzzEn(Sig) 
 

@@ -721,26 +721,25 @@ 
Functions for estimating the entropy of a single univariate time series.
 
Fx:

 
    
-
  • Fuzzy function name, one of the following strings:
    • 
+
  • Fuzzy function name, one of the following strings:  {'sigmoid', 'modsampen', 'default', 'gudermannian', 'linear'}
    • 
 

-
{'sigmoid', 'modsampen', 'default', 'gudermannian', 'bell', 'triangular', 'trapezoidal1', 'trapezoidal2', 'z_shaped', 'gaussian', 'constgaussian'}

 

 
r:

 
    
-
  • Fuzzy function parameters, a 1 element scalar or a 2 element vector of positive values.
    • 
+
  • Fuzzy function parameters, a 1 element scalar or a 2 element vector of positive values. The r parameters for each fuzzy
    • 
 

-

-
The r parameters for each fuzzy  function are defined as follows:   [default: (.2 2)]

-

 

 

 
Logx:

@@ -848,11 +794,6 @@ 
Functions for estimating the entropy of a single univariate time series.
 

-
[3] Hamed Azami, et al.
“Fuzzy Entropy Metrics for the Analysis of Biomedical Signals: 
-Assessment and Comparison”
-IEEE Access
-7 (2019): 104833-104847

-

 
 
 
@@ -1120,7 +1061,7 @@ 
Functions for estimating the entropy of a single univariate time series.
Permutation entropy variation, one of the following:

 
 

-
{'uniquant', 'finegrain', 'modified', 'ampaware', 'weighted', 'edge', 'phase'}
See the EntropyHub guide for more info on PermEn variants.

+
{'uniquant', 'finegrain', 'modified', 'ampaware', 'weighted', 'edge'}
See the EntropyHub guide for more info on PermEn variants.

 

 

 
@@ -1184,11 +1125,6 @@ 
Functions for estimating the entropy of a single univariate time series.
 
-
[9] Kang Huan, Xiaofeng Zhang, and Guangbin Zhang,
“Phase permutation entropy: A complexity measure for nonlinear time 
-series incorporating phase information.” 
-Physica A: Statistical Mechanics and its Applications 
-568 (2021): 125686.

-

 
 
 
@@ -1263,7 +1199,7 @@ 
Functions for estimating the entropy of a single univariate time series.
 

-SampEn(Sig, m=2, tau=1, r=None, Logx=numpy.exp, Vcp=False)

+SampEn(Sig, m=2, tau=1, r=None, Logx=numpy.exp)
 
SampEn  estimates the sample entropy of a univariate data sequence.

 
Samp, A, B = SampEn(Sig) 
 

@@ -1271,18 +1207,7 @@ 
Functions for estimating the entropy of a single univariate time series.Returns the sample entropy estimates (Samp) and the number of matched state
 vectors (m: B, m+1: A) for m = [0, 1, 2] estimated from the data sequence (Sig)
 using the default parameters: embedding dimension = 2, time delay = 1, 
-radius threshold = 0.2*SD(Sig), logarithm = natural.

-
Samp, A, B, (Vcp, Ka, Kb) = SampEn(Sig, ..., Vcp = True) 
-

-

-
If Vcp == True, an additional tuple (Vcp, Ka, Kb) is returned with    
-the sample entropy estimates (Samp) and the number of matched state
-vectors (m: B, m+1: A). (Vcp, Ka, Kb)  contains the variance of the conditional
-probabilities (Vcp), i.e. CP = A/B,  and the number of overlapping
-matching vector pairs of lengths m+1 (Ka) and m (Kb),
-respectively.  Note Vcp is undefined for the zeroth embedding dimension (m = 0) 
-and due to the computational demand, will take substantially more time to return function outputs.
-See Appendix B in [2] for more info.

+radius threshold = 0.2*SD(Sig), logarithm = natural

 
Samp, A, B = SampEn(Sig, keyword = value, ...)
 

 

@@ -1310,11 +1235,6 @@ 
Functions for estimating the entropy of a single univariate time series.
Logarithm base, a positive scalar

 
 

-
Vcp:

-
    
-
  • Option to return variance of conditional probabilities and the number of overlapping matching vector pairs, a boolean [default: False]
    • 
-

-
@@ -1328,10 +1248,6 @@

Functions for estimating the entropy of a single univariate time series. -
[2] Douglas E Lake, Joshua S Richman, M.P. Griffin, J. Randall Moorman

“Sample entropy analysis of neonatal heart rate variability.” -American Journal of Physiology-Regulatory, Integrative and Comparative Physiology -283, no. 3 (2002): R789-R797.

-

diff --git a/docs/_build/html/python/Functions/Bidimensional.html b/docs/_build/html/python/Functions/Bidimensional.html index 08e4054..b0e457f 100644 --- a/docs/_build/html/python/Functions/Bidimensional.html +++ b/docs/_build/html/python/Functions/Bidimensional.html @@ -464,107 +464,49 @@

Functions for estimating the entropy of a two-dimensional univariate matrix.
Fx:
    -

  • Fuzzy function name, one of the following strings:

    • +

  • Fuzzy funtion name, one of the following:

-

{'sigmoid', 'modsampen', 'default', 'gudermannian', 'bell', 'triangular', 'trapezoidal1', 'trapezoidal2', 'z_shaped', 'gaussian', 'constgaussian'}

+

{'sigmoid', 'modsampen', 'default', 'gudermannian', 'linear'}

r:

  • Fuzzy function parameters, a 1 element scalar or a 2 element vector of positive values.

-
-
The r parameters for each fuzzy function are defined as follows: [default: (.2 2)]
    -
  • -
    default: [Tuple]
      -

    • r(1) = divisor of the exponential argument

      • -

    • r(2) = argument exponent (pre-division)

      • -
    -
    -
    -
    • -
  • -
    sigmoid: [Tuple]
      -

    • r(1) = divisor of the exponential argument

      • -

    • r(2) = value subtracted from argument (pre-division)

      • -
    -
    -
    -
    • -
  • -
    modsampen: [Tuple]
      -

    • r(1) = divisor of the exponential argument

      • -

    • r(2) = value subtracted from argument (pre-division)

      • -
    -
    -
    -
    • -
  • -
    gudermannian:
      -

    • r = a scalar whose value is the numerator of argument to gudermannian function: GD(x) = atan(tanh(r/x)). GD(x) is normalised to have a maximum value of 1.

      • -
    -
    -
    -
    • -
  • -
    triangular:
      -

    • r = a scalar whose value is the threshold (corner point) of the triangular function.

      • -
    -
    -
    -
    • -
  • -
    trapezoidal1:
      -

    • r = a scalar whose value corresponds to the upper (2r) and lower (r) corner points of the trapezoid.

      • -
    -
    -
    -
    • -
  • -
    trapezoidal2: [Tuple]
      -

    • r(1) = a value corresponding to the upper corner point of the trapezoid.

      • -

    • r(2) = a value corresponding to the lower corner point of the trapezoid.

      • -
    -
    -
    -
    • +

    The r parameters for each fuzzy function are defined as follows:

    +
    +
    • -
      z_shaped:
        -

      • r = a scalar whose value corresponds to the upper (2r) and lower (r) corner points of the z-shape.

        • -
      +
      sigmoid:

      r(1) = divisor of the exponential argument +r(2) = value subtracted from argument (pre-division)

    • -
      bell:
        -

      • r(1) = divisor of the distance value

        • -

      • r(2) = exponent of generalized bell-shaped function

        • -
      +
      modsampen:

      r(1) = divisor of the exponential argument +r(2) = value subtracted from argument (pre-division)

    • -
      gaussian:
        -

      • r = a scalar whose value scales the slope of the Gaussian curve.

        • -
      +
      default:

      r(1) = divisor of the exponential argument +r(2) = argument exponent (pre-division)

    • -
      constgaussian:
        -

      • r = a scalar whose value defines the lower threshod and shape of the Gaussian curve.

        • -
      +
      gudermannian:

      r = a scalar whose value is the numerator of argument to gudermannian function: +GD(x) = atan(tanh(r/x)). GD(x) is normalised to have a maximum value of 1.

    • -
      [DEPRICATED] linear:

      r = an integer value. When r = 0, the argument of the exponential function is +

      linear:

      r = an integer value. When r = 0, the argument of the exponential function is normalised between [0 1]. When r = 1, the minimuum value of the exponential argument is set to 0.

    -
-
+

Logx:
    @@ -598,11 +540,6 @@

    Functions for estimating the entropy of a two-dimensional univariate matrix. 41st Annual International Conference of the IEEE (EMBC) Society 2019.

-
[3] Hamed Azami, et al.

“Fuzzy Entropy Metrics for the Analysis of Biomedical Signals: -Assessment and Comparison” -IEEE Access -7 (2019): 104833-104847

-
@@ -662,16 +599,16 @@

Functions for estimating the entropy of a two-dimensional univariate matrix.
-
NOTE:

The original bidimensional permutation entropy algorithms [1][2] -do not account for equal-valued elements of the embedding matrices. +

NOTE - ``The original bidimensional permutation entropy algorithms

[1][2] do not account for equal-valued elements of the embedding +matrices. `` To overcome this, PermEn2D uses the lowest common rank for such instances. For example, given an embedding matrix A where, A = [3.4 5.5 7.3]

-

[2.1 6 9.9] +

|2.1 6 9.9| [7.3 1.1 2.1]

-

would normally be mapped to an ordinal pattern like so, +

would normally be mapped to an ordinal pattern like so, [3.4 5.5 7.3 2.1 6 9.9 7.3 1.1 2.1] => [ 8 4 9 1 2 5 3 7 6 ] However, indices 4 & 9, and 3 & 7 have the same values, 2.1 diff --git a/docs/_build/html/python/Functions/Cross.html b/docs/_build/html/python/Functions/Cross.html index 32584cf..9b580ac 100644 --- a/docs/_build/html/python/Functions/Cross.html +++ b/docs/_build/html/python/Functions/Cross.html @@ -398,7 +398,7 @@

Functions for estimating the entropy between two univariate time series.Returns the cross-fuzzy entropy estimates (XFuzz) for dimensions = [1, …, m] estimated for the data sequences in ‘Sig’ using the specified ‘keyword’ arguments:

-
+
m:
Logx:
@@ -534,11 +477,6 @@

Functions for estimating the entropy between two univariate time series.

-
[2] Hamed Azami, et al.

“Fuzzy Entropy Metrics for the Analysis of Biomedical Signals: -Assessment and Comparison” -IEEE Access -7 (2019): 104833-104847

-
@@ -652,7 +590,7 @@

Functions for estimating the entropy between two univariate time series.
-XSampEn(Sig, m=2, tau=1, r=None, Logx=numpy.exp, Vcp=False)
+XSampEn(Sig, m=2, tau=1, r=None, Logx=numpy.exp)

XSampEn Estimates the cross-sample entropy between two univariate data sequences.

XSamp, A, B = XSampEn(Sig)
@@ -660,19 +598,7 @@

Functions for estimating the entropy between two univariate time series.Returns the cross-sample entropy estimates (XSamp) and the number of matched vectors (m: B, m+1: A) for m = [0,1,2] estimated for the two univariate data sequences contained in Sig using the default parameters: -embedding dimension = 2, time delay = 1, radius = 0.2*SD(Sig), -logarithm = natural

-
XSamp, A, B, (Vcp, Ka, Kb) = XSampEn(Sig, ..., Vcp = True) 
-
-
-

If Vcp == True, an additional tuple (Vcp, Ka, Kb) is returned with -the cross-sample entropy estimates (XSamp) and the number of matched state -vectors (m: B, m+1: A). (Vcp, Ka, Kb) contains the variance of the conditional -probabilities (Vcp), i.e. CP = A/B, and the number of overlapping -matching vector pairs of lengths m+1 (Ka) and m (Kb), -respectively. Note Vcp is undefined for the zeroth embedding dimension (m = 0) -and due to the computational demand, will take substantially more time to return function outputs. -See Appendix B in [2] for more info.

+embedding dimension = 2, time delay = 1, radius = 0.2*SD(Sig), logarithm = natural

XSamp, A, B = XSampEn(Sig, keyword = value, ...)
@@ -713,10 +639,6 @@

Functions for estimating the entropy between two univariate time series.

-
[2] Douglas E Lake, Joshua S Richman, M.P. Griffin, J. Randall Moorman

“Sample entropy analysis of neonatal heart rate variability.” -American Journal of Physiology-Regulatory, Integrative and Comparative Physiology -283, no. 3 (2002): R789-R797.

-
diff --git a/docs/_build/html/python/Functions/Multiscale.html b/docs/_build/html/python/Functions/Multiscale.html index 0a4fd0f..a1efac4 100644 --- a/docs/_build/html/python/Functions/Multiscale.html +++ b/docs/_build/html/python/Functions/Multiscale.html @@ -201,7 +201,7 @@

Functions for estimating the multiscale entropy of a univariate time series. To see the default parameters for a particular entropy method, type: help(EnType) (e.g. help(SampEn))

EnType can be any of the following (case sensitive) string names:

-
+
Base Entropies:
'ApEn':
@@ -295,8 +295,6 @@

Functions for estimating the multiscale entropy of a univariate time series.

-

['DivEn']: - Diversity Entropy -['RangEn']: - Range Entropy

Cross Entropies:
@@ -342,42 +340,8 @@

Functions for estimating the multiscale entropy of a univariate time series.

-
Bidimensional Entropies:
-
-
'SampEn2D':
-
    -

  • Bidimensional Sample Entropy

    • -
-
-
'FuzzEn2D':
-
    -

  • Bidimensional Fuzzy Entropy

    • -
-
-
'DispEn2D':
-
    -

  • Bidimensional Dispersion Entropy

    • -
-
-
'DistEn2D':
-
    -

  • Bidimensional Distribution Entropy

    • -
-
-
'PermEn2D':
-
    -

  • Bidimensional Permutation Entropy

    • -
-
-
'EspEn2D':
-
    -

  • Bidimensional Espinosa Entropy

    • -
-
-
-
-
See also:
-

MSEn, cMSEn, rMSEn, hMSEn, XMSEn, rXMSEn, cXMSEn, hXMSEn

+
See also:
+

MSEn, cMSEn, rMSEn, hMSEn, XMSEn, rXMSEn, cXMSEn, hXMSEn

@@ -411,7 +375,7 @@

Functions for estimating the multiscale entropy of a univariate time series.
Methodx:
    -

  • Graining method, one of the following: [default: 'coarse'] {'coarse', 'modified', 'imf' , 'timeshift' , 'generalized'}

    • +

  • Graining method, one of the following: [default: 'coarse'] {'coarse', 'modified', 'imf' , 'timeshift'}

RadNew:
@@ -494,10 +458,6 @@

Functions for estimating the multiscale entropy of a univariate time series. and dispersion entropy.” Entropy 20.2 (2018): 138.

-
[12] Madalena Costa and Ary L. Goldberger,

“Generalized multiscale entropy analysis: Application to quantifying -the complex volatility of human heartbeat time series.” -Entropy 17.3 (2015): 1197-1203.

-
diff --git a/docs/_build/html/python/Functions/MultiscaleCross.html b/docs/_build/html/python/Functions/MultiscaleCross.html index 735ac13..21aa791 100644 --- a/docs/_build/html/python/Functions/MultiscaleCross.html +++ b/docs/_build/html/python/Functions/MultiscaleCross.html @@ -205,7 +205,7 @@

Functions for estimating the multiscale cross-entropy between two univariate To see the default parameters for a particular entropy method, type: help(EnType) (e.g. help(SampEn))

EnType can be any of the following (case sensitive) string names:

-
+
Base Entropies:
'ApEn':
@@ -299,8 +299,6 @@

Functions for estimating the multiscale cross-entropy between two univariate

-

['DivEn']: - Diversity Entropy -['RangEn']: - Range Entropy

Cross Entropies:
@@ -346,42 +344,8 @@

Functions for estimating the multiscale cross-entropy between two univariate

-
Bidimensional Entropies:
-
-
'SampEn2D':
-
    -

  • Bidimensional Sample Entropy

    • -
-
-
'FuzzEn2D':
-
    -

  • Bidimensional Fuzzy Entropy

    • -
-
-
'DispEn2D':
-
    -

  • Bidimensional Dispersion Entropy

    • -
-
-
'DistEn2D':
-
    -

  • Bidimensional Distribution Entropy

    • -
-
-
'PermEn2D':
-
    -

  • Bidimensional Permutation Entropy

    • -
-
-
'EspEn2D':
-
    -

  • Bidimensional Espinosa Entropy

    • -
-
-
-
-
See also:
-

MSEn, cMSEn, rMSEn, hMSEn, XMSEn, rXMSEn, cXMSEn, hXMSEn

+
See also:
+

MSEn, cMSEn, rMSEn, hMSEn, XMSEn, rXMSEn, cXMSEn, hXMSEn

@@ -416,7 +380,7 @@

Functions for estimating the multiscale cross-entropy between two univariate
Methodx:
    -

  • Graining method, one of the following: [default: 'coarse'] {'coarse', 'modified', 'imf' , 'timeshift', 'generalized'}

    • +

  • Graining method, one of the following: [default: 'coarse'] {'coarse', 'modified', 'imf' , 'timeshift'}

RadNew:
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