Merge pull request #901 from neuropsychology/dev

0.2.7

docs/functions/complexity.rst

@@ -80,11 +80,11 @@ Fractal Dimension
 Entropy
 ^^^^^^^^^^^^^^^^^
 *entropy_shannon()*
-"""""""""""""""""""
+"""""""""""""""""""""
 .. autofunction:: neurokit2.complexity.entropy_shannon
 
 *entropy_maximum()*
-"""""""""""""""""""
+"""""""""""""""""""""
 .. autofunction:: neurokit2.complexity.entropy_maximum
 
 *entropy_differential()*
@@ -103,6 +103,18 @@ Entropy
 """""""""""""""""""
 .. autofunction:: neurokit2.complexity.entropy_renyi
 
+*entropy_approximate()*
+""""""""""""""""""""""""
+.. autofunction:: neurokit2.complexity.entropy_approximate
+
+*entropy_sample()*
+""""""""""""""""""""
+.. autofunction:: neurokit2.complexity.entropy_sample
+
+*entropy_quadratic()*
+""""""""""""""""""""""
+.. autofunction:: neurokit2.complexity.entropy_quadratic
+
 *entropy_cumulativeresidual()*
 """""""""""""""""""""""""""""""""
 .. autofunction:: neurokit2.complexity.entropy_cumulativeresidual
@@ -155,14 +167,6 @@ Entropy
 """""""""""""""""""""""
 .. autofunction:: neurokit2.complexity.entropy_ofentropy
 
-*entropy_approximate()*
-""""""""""""""""""""""""
-.. autofunction:: neurokit2.complexity.entropy_approximate
-
-*entropy_sample()*
-""""""""""""""""""""
-.. autofunction:: neurokit2.complexity.entropy_sample
-
 *entropy_permutation()*
 """"""""""""""""""""""""
 .. autofunction:: neurokit2.complexity.entropy_permutation
@@ -274,4 +278,4 @@ Joint/Multivariate
 
 .. .. automodule:: neurokit2.complexity
 ..    :members:
-..    :exclude-members: complexity, complexity_delay, complexity_dimension, complexity_tolerance, complexity_k, fractal_katz, fractal_petrosian, fractal_sevcik, fractal_nld, fractal_psdslope, fractal_higuchi, fractal_correlation, entropy_shannon, entropy_maximum, entropy_differential, entropy_tsallis, entropy_renyi, entropy_cumulativeresidual, entropy_svd, entropy_spectral, entropy_phase, entropy_grid, entropy_attention, entropy_increment, entropy_slope, entropy_symbolicdynamic, entropy_dispersion, entropy_ofentropy, entropy_approximate, entropy_sample, entropy_permutation, entropy_bubble, entropy_range, entropy_fuzzy, entropy_multiscale, entropy_hierarchical, fisher_information, complexity_hjorth, complexity_lempelziv, complexity_relativeroughness, fractal_hurst, complexity_lyapunov, complexity_rqa, fractal_mandelbrot, complexity_simulate, complexity_attractor, complexity_symbolize, complexity_coarsegraining, complexity_ordinalpatterns, recurrence_matrix, entropy_shannon_joint, entropy_rate
+..    :exclude-members: complexity, complexity_delay, complexity_dimension, complexity_tolerance, complexity_k, fractal_katz, fractal_petrosian, fractal_sevcik, fractal_nld, fractal_psdslope, fractal_higuchi, fractal_correlation, entropy_shannon, entropy_maximum, entropy_differential, entropy_tsallis, entropy_renyi, entropy_cumulativeresidual, entropy_svd, entropy_spectral, entropy_phase, entropy_grid, entropy_attention, entropy_increment, entropy_slope, entropy_symbolicdynamic, entropy_dispersion, entropy_ofentropy, entropy_approximate, entropy_sample, entropy_permutation, entropy_bubble, entropy_range, entropy_fuzzy, entropy_multiscale, entropy_hierarchical, fisher_information, complexity_hjorth, complexity_lempelziv, complexity_relativeroughness, fractal_hurst, complexity_lyapunov, complexity_rqa, fractal_mandelbrot, complexity_simulate, complexity_attractor, complexity_symbolize, complexity_coarsegraining, complexity_ordinalpatterns, recurrence_matrix, entropy_shannon_joint, entropy_rate, entropy_quadratic

neurokit2/__init__.py

@@ -32,7 +32,7 @@
 from .video import *
 
 # Info
-__version__ = "0.2.6"
+__version__ = "0.2.7"
 
 
 # Maintainer info

neurokit2/complexity/__init__.py

@@ -30,6 +30,7 @@
 from .entropy_permutation import entropy_permutation
 from .entropy_phase import entropy_phase
 from .entropy_power import entropy_power
+from .entropy_quadratic import entropy_quadratic
 from .entropy_range import entropy_range
 from .entropy_rate import entropy_rate
 from .entropy_renyi import entropy_renyi
@@ -96,7 +97,9 @@
 complexity_rcmse = functools.partial(entropy_multiscale, method="RCMSEn")
 complexity_fuzzymse = functools.partial(entropy_multiscale, fuzzy=True)
 complexity_fuzzycmse = functools.partial(entropy_multiscale, method="CMSEn", fuzzy=True)
-complexity_fuzzyrcmse = functools.partial(entropy_multiscale, method="RCMSEn", fuzzy=True)
+complexity_fuzzyrcmse = functools.partial(
+    entropy_multiscale, method="RCMSEn", fuzzy=True
+)
 
 
 complexity_dfa = fractal_dfa
@@ -175,6 +178,7 @@
     "entropy_symbolicdynamic",
     "entropy_cumulativeresidual",
     "entropy_approximate",
+    "entropy_quadratic",
     "entropy_bubble",
     "entropy_coalition",
     "entropy_sample",

neurokit2/complexity/complexity_decorrelation.py

@@ -1,10 +1,11 @@
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 
 from ..signal import signal_autocor
 
 
-def complexity_decorrelation(signal):
+def complexity_decorrelation(signal, show=False):
     """**Decorrelation Time (DT)**
 
     The decorrelation time (DT) is defined as the time (in samples) of the first zero crossing of
@@ -17,6 +18,8 @@ def complexity_decorrelation(signal):
     ----------
     signal : Union[list, np.array, pd.Series]
         The signal (i.e., a time series) in the form of a vector of values.
+    show : bool
+        If True, will return a plot of the autocorrelation.
 
     Returns
     -------
@@ -36,11 +39,15 @@ def complexity_decorrelation(signal):
 
       import neurokit2 as nk
 
-      # Simulate a signal with duration os 2s
-      signal = nk.signal_simulate(duration=2, frequency=[5, 9, 12])
+      # Simulate a signal
+      signal = nk.signal_simulate(duration=5, sampling_rate=100, frequency=[5, 6], noise=0.5)
 
       # Compute DT
-      dt, _ = nk.complexity_decorrelation(signal)
+      @savefig p_complexity_decorrelation1.png scale=100%
+      dt, _ = nk.complexity_decorrelation(signal, show=True)
+      @suppress
+      plt.close()
+
       dt
 
     References
@@ -60,12 +67,28 @@ def complexity_decorrelation(signal):
         )
 
     # Unbiased autocor (see https://github.com/mne-tools/mne-features/)
-    autocor, _ = signal_autocor(signal, method="unbiased")
+    autocor, _ = signal_autocor(signal, unbiased=True)
 
     # Get zero-crossings
     zc = np.diff(np.sign(autocor)) != 0
     if np.any(zc):
         dt = np.argmax(zc) + 1
     else:
         dt = -1
+
+    if show is True:
+        # Max length of autocorrelation to plot
+        max_len = int(dt * 4)
+        if max_len > len(autocor):
+            max_len = len(autocor)
+
+        plt.plot(autocor[0:max_len])
+        plt.xlabel("Lag")
+        plt.ylabel("Autocorrelation")
+        plt.xticks(np.arange(0, max_len, step=dt).astype(int))
+        plt.axvline(dt, color="red", linestyle="--", label=f"DT = {dt}")
+        plt.axhline(0, color="black", linestyle="--")
+        plt.title("Decorrelation Time (DT)")
+        plt.legend()
+
     return dt, {}