Repository: neuropsychology/NeuroKit (language: python)

Issue:
Replace ba output with sos for ECG filters and determine initial conditions
Closes #609

I replaced `ba` output with `sos` when needed, and I added the initial condition `zi` if `filt` was used instead of `filtfilt`.
Even if you want to replicate as closely as possible a method, an implementation, IMO, using `sos` instead of `ba` should always be preferred as it should not induce significant differences while being more numerically stable. 

I didn't see I had to branch from `dev` and not `master`; let's see if any conflicts arise.
Also, I added `.DS_Store` to the .gitignore for macOS developers.
Error running ecg_process and ecg_analyze examples (TypeError: unsupported operand type(s) for /: 'str' and 'int')
I receive the following error:

TypeError: unsupported operand type(s) for /: 'str' and 'int'

when running the ecg_process and ecg_analyze examples from https://neurokit2.readthedocs.io/en/latest/functions.html#module-neurokit2.ecg

For example, the following returns the unsupported operand error:

>>> import neurokit2 as nk
>>>
>>> ecg = nk.ecg_simulate(duration=15, sampling_rate=1000, heart_rate=80)
>>> signals, info = nk.ecg_process(ecg, sampling_rate=1000)

I know that the example ecg data are float types, and manually converting the dataframe values to float does not change the outcome.

System Specifications: 
- OS: Windows (WindowsPE 64bit)
- Python: 3.10.2
- NeuroKit2: 0.1.5

- NumPy: 1.22.1
- Pandas: 1.4.0
- SciPy: 1.7.3
- sklearn: 1.0.2
- matplotlib: 3.5.1
Error running ecg_process and ecg_analyze examples (TypeError: unsupported operand type(s) for /: 'str' and 'int')
I receive the following error:

TypeError: unsupported operand type(s) for /: 'str' and 'int'

when running the ecg_process and ecg_analyze examples from https://neurokit2.readthedocs.io/en/latest/functions.html#module-neurokit2.ecg

For example, the following returns the unsupported operand error:

>>> import neurokit2 as nk
>>>
>>> ecg = nk.ecg_simulate(duration=15, sampling_rate=1000, heart_rate=80)
>>> signals, info = nk.ecg_process(ecg, sampling_rate=1000)

I know that the example ecg data are float types, and manually converting the dataframe values to float does not change the outcome.

System Specifications: 
- OS: Windows (WindowsPE 64bit)
- Python: 3.10.2
- NeuroKit2: 0.1.5

- NumPy: 1.22.1
- Pandas: 1.4.0
- SciPy: 1.7.3
- sklearn: 1.0.2
- matplotlib: 3.5.1


Discussion / hints:

Fix patch:
diff --git a/.github/workflows/checks.yml b/.github/workflows/checks.yml
index c93e971e74..7b3ba5be66 100644
--- a/.github/workflows/checks.yml
+++ b/.github/workflows/checks.yml
@@ -12,7 +12,7 @@ jobs:
     - name: Set up Python ${{ matrix.python-version }}
       uses: actions/setup-python@v2
       with:
-        python-version: '3.7'
+        python-version: '3.8'
 
     - name: Install dependencies
       run: |
diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml
deleted file mode 100644
index 4a09bc5dc7..0000000000
--- a/.github/workflows/release.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-name: Release Drafter
-
-on:
-  push:
-    branches:
-      - master
-
-jobs:
-  update_release_draft:
-    runs-on: ubuntu-latest
-    steps:
-      # Drafts your next Release notes as Pull Requests are merged into "master"
-      - uses: release-drafter/release-drafter@v5
-        with:
-          # (Optional) specify config name to use, relative to .github/. Default: release-drafter.yml
-          config-name: release-drafter-config.yml
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
\ No newline at end of file
diff --git a/.github/workflows/style.yml b/.github/workflows/style.yml
index ffac47e820..a88f34ce92 100644
--- a/.github/workflows/style.yml
+++ b/.github/workflows/style.yml
@@ -49,9 +49,9 @@ jobs:
 
     - name: Detect remaining style errors with Pylint (fail under)
       run: |
-        pip install pylint-fail-under
+        pip install pylint
         # See http://pylint.pycqa.org/en/latest/index.html
-        pylint-fail-under neurokit2 --max-line-length=127 --load-plugins=pylint.extensions.docparams --load-plugins=pylint.extensions.docstyle --variable-naming-style=any --argument-naming-style=any --reports=n --suggestion-mode=y --disable=E303 --disable=R0913 --disable=R0801 --disable=C0114 --disable=E203 --disable=E0401 --disable=W9006 --disable=C0330 --disable=R0914 --disable=R0912 --disable=R0915 --disable=W0102 --disable=W0511 --disable=C1801 --disable=C0111 --disable=R1705 --disable=R1720 --disable=C0301 --disable=C0415 --disable=C0103 --disable=C0302 --disable=R1716 --disable=W0632 --disable=E1136 --extension-pkg-whitelist=numpy --exit-zero --fail_under 9.80
+        pylint neurokit2 --max-line-length=127 --load-plugins=pylint.extensions.docparams --load-plugins=pylint.extensions.docstyle --variable-naming-style=any --argument-naming-style=any --reports=n --suggestion-mode=y --disable=E303 --disable=R0913 --disable=R0801 --disable=C0114 --disable=E203 --disable=E0401 --disable=W9006 --disable=C0330 --disable=R0914 --disable=R0912 --disable=R0915 --disable=W0102 --disable=W0511 --disable=C1801 --disable=C0111 --disable=R1705 --disable=R1720 --disable=C0301 --disable=C0415 --disable=C0103 --disable=C0302 --disable=R1716 --disable=W0632 --disable=E1136 --extension-pkg-whitelist=numpy --exit-zero --fail-under 9.80
 
     - name: Commit changes
       uses: EndBug/add-and-commit@v4
diff --git a/.gitignore b/.gitignore
index 60b7f9bd92..080ca7e3c7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -196,4 +196,4 @@ data/mit_long-term/mit-bih-long-term-ecg-database-1.0.0/RECORDS
 data/mit_long-term/mit-bih-long-term-ecg-database-1.0.0/SHA256SUMS.txt
 data/mit_long-term/ECGs.csv
 data/mit_long-term/ECGs.csv
-
+.DS_Store
diff --git a/.idea/NeuroKit.iml b/.idea/NeuroKit.iml
index 3d4da0ccbe..d81aebdb5d 100644
--- a/.idea/NeuroKit.iml
+++ b/.idea/NeuroKit.iml
@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
+    <orderEntry type="jdk" jdkName="Python 3.9 (DefaulCondaEnv)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="PyDocumentationSettings">
diff --git a/.idea/misc.xml b/.idea/misc.xml
index 399908725e..7ed51bebe8 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -3,5 +3,5 @@
   <component name="JavaScriptSettings">
     <option name="languageLevel" value="ES6" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (DefaulCondaEnv)" project-jdk-type="Python SDK" />
 </project>
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index 3f28ce087b..9f977b1e6e 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -1,7 +1,11 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
   <component name="ChangeListManager">
-    <list default="true" id="a5fbd7de-1bfb-4b79-bdac-dd068dddfe1f" name="Default Changelist" comment="" />
+    <list default="true" id="a5fbd7de-1bfb-4b79-bdac-dd068dddfe1f" name="Default Changelist" comment="">
+      <change beforePath="$PROJECT_DIR$/.idea/NeuroKit.iml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/NeuroKit.iml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/.idea/misc.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/misc.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
+    </list>
     <option name="SHOW_DIALOG" value="false" />
     <option name="HIGHLIGHT_CONFLICTS" value="true" />
     <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
@@ -29,10 +33,10 @@
   <component name="ProjectLevelVcsManager" settingsEditedManually="true" />
   <component name="ProjectViewState">
     <option name="hideEmptyMiddlePackages" value="true" />
-    <option name="showExcludedFiles" value="true" />
     <option name="showLibraryContents" value="true" />
   </component>
   <component name="PropertiesComponent">
+    <property name="RunOnceActivity.OpenProjectViewOnStart" value="true" />
     <property name="RunOnceActivity.ShowReadmeOnStart" value="true" />
     <property name="WebServerToolWindowFactoryState" value="false" />
     <property name="last_opened_file_path" value="$PROJECT_DIR$" />
@@ -74,6 +78,7 @@
       </list>
     </option>
   </component>
+  <component name="SpellCheckerSettings" RuntimeDictionaries="0" Folders="0" CustomDictionaries="0" DefaultDictionary="application-level" UseSingleDictionary="true" transferred="true" />
   <component name="SvnConfiguration">
     <configuration />
   </component>
@@ -87,11 +92,12 @@
       <workItem from="1576387677929" duration="1147000" />
       <workItem from="1576465425012" duration="255000" />
       <workItem from="1576573439935" duration="81000" />
+      <workItem from="1642197624417" duration="2153000" />
     </task>
     <servers />
   </component>
   <component name="TypeScriptGeneratedFilesManager">
-    <option name="version" value="1" />
+    <option name="version" value="3" />
   </component>
   <component name="WindowStateProjectService">
     <state x="137" y="18" key="#com.intellij.execution.impl.EditConfigurationsDialog" timestamp="1576465541167">
diff --git a/CITATION.cff b/CITATION.cff
index e18ca95cda..b5eb2d949f 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -11,7 +11,7 @@ authors:
 - family-names: "Brammer"
   given-names: "Jan C."
 title: "NeuroKit2: The Python Toolbox for Neurophysiological Signal Processing"
-version: 0.1.5
+version: 0.1.6
 doi: 10.3758/s13428-020-01516-y
 date-released: 2021-02-02
 url: "https://github.com/neuropsychology/NeuroKit"
diff --git a/NEWS.rst b/NEWS.rst
index 28879cef08..59e3324a5a 100644
--- a/NEWS.rst
+++ b/NEWS.rst
@@ -2,6 +2,25 @@ News
 =====
 
 
+0.1.6
+-------------------
+
+Breaking Changes
++++++++++++++++++
+
+* Argument `type` changed to `out` in `expspace()`
+
+
+New Features
++++++++++++++
+
+Fixes
++++++++++++++
+
+
+
+
+
 0.1.5
 -------------------
 
diff --git a/README.rst b/README.rst
index 27fa356f82..2731f7aed0 100644
--- a/README.rst
+++ b/README.rst
@@ -304,7 +304,7 @@ Electromyography (EMG)
     emg = nk.emg_simulate(duration=10, sampling_rate=250, burst_number=3)
 
     # Process it
-    signal, info = nk.emg_process(emg, sampling_rate=250)
+    signals, info = nk.emg_process(emg, sampling_rate=250)
 
     # Visualise the processing
     nk.emg_plot(signals, sampling_rate=250)
diff --git a/neurokit2/__init__.py b/neurokit2/__init__.py
index 389b1dd5bb..92906290c1 100644
--- a/neurokit2/__init__.py
+++ b/neurokit2/__init__.py
@@ -32,7 +32,7 @@
 
 
 # Info
-__version__ = "0.1.5"
+__version__ = "0.1.6"
 
 
 # Maintainer info
diff --git a/neurokit2/bio/bio_analyze.py b/neurokit2/bio/bio_analyze.py
index 7948426021..fe005ba26b 100644
--- a/neurokit2/bio/bio_analyze.py
+++ b/neurokit2/bio/bio_analyze.py
@@ -228,11 +228,11 @@ def _bio_analyze_slicewindow(data, window_lengths, signal='ECG'):
         start = window_lengths[signal][0]
         end = window_lengths[signal][1]
         epochs = {}
-        for i, label in enumerate(data):
+        for _, label in enumerate(data):
             # Slice window
             epoch = data[label].loc[(data[label].index > start) & (data[label].index < end)]
             epochs[label] = epoch
-            epochs
+
     return epochs
 
 
diff --git a/neurokit2/complexity/__init__.py b/neurokit2/complexity/__init__.py
index 423f58cb79..1cf708b5c1 100644
--- a/neurokit2/complexity/__init__.py
+++ b/neurokit2/complexity/__init__.py
@@ -2,11 +2,13 @@
 import functools
 
 from .complexity import complexity
+from .complexity_attractor import complexity_attractor
 from .complexity_embedding import complexity_embedding
 from .complexity_hjorth import complexity_hjorth
 from .complexity_hurst import complexity_hurst
 from .complexity_lempelziv import complexity_lempelziv
 from .complexity_lyapunov import complexity_lyapunov
+from .complexity_recurrence import complexity_recurrence
 from .complexity_rqa import complexity_rqa
 from .complexity_rr import complexity_rr
 from .complexity_simulate import complexity_simulate
@@ -78,7 +80,9 @@
 
 __all__ = [
     "complexity",
+    "complexity_attractor",
     "complexity_embedding",
+    "complexity_recurrence",
     "complexity_delay",
     "complexity_dimension",
     "complexity_optimize",
diff --git a/neurokit2/complexity/complexity.py b/neurokit2/complexity/complexity.py
index 4870767219..a6fc437d13 100644
--- a/neurokit2/complexity/complexity.py
+++ b/neurokit2/complexity/complexity.py
@@ -30,7 +30,7 @@
 
 
 def complexity(
-    signal, which=["fast", "medium", "slow"], delay=1, dimension=2, tolerance="default", **kwargs
+    signal, which=["fast", "medium"], delay=1, dimension=2, tolerance="default", **kwargs
 ):
     """Comprehensive Complexity Analysis
 
@@ -53,8 +53,8 @@ def complexity(
         See for example :func:`entropy_permutation`.
     dimension : int
         See for example :func:`entropy_permutation`.
-    **kwargs : optional
-        Other arguments to be passed to the functions.
+    tolerance : float
+        See for example :func:`entropy_permutation`.
 
     Returns
     --------
diff --git a/neurokit2/complexity/complexity_attractor.py b/neurokit2/complexity/complexity_attractor.py
new file mode 100644
index 0000000000..f673d99aa6
--- /dev/null
+++ b/neurokit2/complexity/complexity_attractor.py
@@ -0,0 +1,283 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy
+
+
+def complexity_attractor(
+    embedded="lorenz", alpha="time", color="last_dim", shadows=True, linewidth=1, **kwargs
+):
+    """Attractor graph
+
+
+    Parameters
+    ----------
+    embedded : Union[str, np.ndarray]
+        Output of ``complexity_embedding()``. Can also be a string, such as ``"lorenz"`` (Lorenz
+        attractor) or ``"rossler"`` (Rössler attractor).
+    alpha : Union[str, float]
+        Transparency of the lines. If ``"time"``, the lines will be transparent as a function of
+        time (slow).
+    color : str
+        Color of the plot. If ``"last_dim"``, the last dimension (max 4th) of the embedded data
+        will be used when the dimensions are higher than 2. Useful to visualize the depth (for
+        3-dimensions embedding), or the fourth dimension, but it is slow.
+    shadows : bool
+        If ``True``, 2D projections will be added to the sides of the 3D attractor.
+    linewidth: float
+        Set the line width in points.
+    **kwargs
+        Additional keyword arguments are passed to the color palette (e.g., ``name="plasma"``), or
+        to the Lorenz system simulator, such as ``duration`` (default = 100), ``sampling_rate``
+        (default = 10), ``sigma`` (default = 10), ``beta`` (default = 8/3), ``rho`` (default = 28).
+
+    Examples
+    ---------
+    >>> import neurokit2 as nk
+    >>>
+    >>> # Lorenz attractors
+    >>> nk.complexity_attractor(color = "last_dim", alpha="time", sampling_rate=500) # doctest: +ELLIPSIS
+    <Figure ...
+    >>>
+    >>> # Fast result (fixed alpha and color)
+    >>> nk.complexity_attractor(color = "red", alpha=1, sampling_rate=5000, linewidth=0.2) # doctest: +ELLIPSIS
+    <Figure ...
+    >>>
+    >>> # Rössler attractors
+    >>> nk.complexity_attractor("rossler", color = "blue", alpha=1, sampling_rate=5000) # doctest: +ELLIPSIS
+    <Figure ...
+    >>>
+    >>> # Simulate Signal
+    >>> signal = nk.signal_simulate(duration=10, sampling_rate=100, frequency = [0.1, 5, 7, 10])
+    >>>
+    >>> # 2D Attractor
+    >>> embedded = nk.complexity_embedding(signal, delay = 3, dimension = 2)
+    >>>
+    >>> # Fast (fixed alpha and color)
+    >>> nk.complexity_attractor(embedded, color = "red", alpha = 1) # doctest: +ELLIPSIS
+    <Figure ...
+    >>>
+    >>> # Slow
+    >>> nk.complexity_attractor(embedded, color = "last_dim", alpha = "time") # doctest: +ELLIPSIS
+    <Figure ...
+    >>>
+    >>> # 3D Attractor
+    >>> embedded = nk.complexity_embedding(signal, delay = 3, dimension = 3)
+    >>>
+    >>> # Fast (fixed alpha and color)
+    >>> nk.complexity_attractor(embedded, color = "red", alpha = 1) # doctest: +ELLIPSIS
+    <Figure ...
+    >>>
+    >>> # Slow
+    >>> nk.complexity_attractor(embedded, color = "last_dim", alpha = "time") # doctest: +ELLIPSIS
+    <Figure ...
+    >>>
+    >>> # Animated rotation
+    >>> import matplotlib.animation as animation
+    >>> fig = nk.complexity_attractor(embedded, color = "black", alpha = 0.5, shadows=False)
+    >>>
+    >>> # ax = fig.get_axes()[0]
+    >>> # def rotate(angle):
+    >>> #     ax.view_init(azim=angle)
+    >>> # anim = animation.FuncAnimation(fig, rotate, frames=np.arange(0, 361, 10), interval=10)
+    >>> # import IPython
+    >>> # IPython.display.HTML(anim.to_jshtml())
+
+
+    """
+    if isinstance(embedded, str):
+        embedded = _attractor_equation(embedded, **kwargs)
+
+    # Parameters -----------------------------
+    # Color
+    if color == "last_dim":
+        # Get data
+        last_dim = min(3, embedded.shape[1] - 1)  # Find last dim with max = 3
+        color = embedded[:, last_dim]
+
+        # Create color palette
+        palette = kwargs["name"] if "name" in kwargs else "plasma"
+        cmap = plt.get_cmap(palette)
+        colors = cmap(plt.Normalize(color.min(), color.max())(color))
+    else:
+        colors = [color] * len(embedded[:, 0])
+
+    # Alpha
+    if alpha == "time":
+        alpha = np.linspace(0.01, 1, len(embedded[:, 0]))
+    else:
+        alpha = [alpha] * len(embedded[:, 0])
+
+    # Plot ------------------------------------
+    fig = plt.figure()
+    # 2D
+    if embedded.shape[1] == 2:
+        ax = plt.axes(projection=None)
+        # Fast
+        if len(np.unique(colors)) == 1 and len(np.unique(alpha)) == 1:
+            ax.plot(
+                embedded[:, 0], embedded[:, 1], color=colors[0], alpha=alpha[0], linewidth=linewidth
+            )
+        # Slow (color and/or alpha)
+        else:
+            ax = _attractor_2D(ax, embedded, colors, alpha, linewidth)
+    # 3D
+    else:
+        ax = plt.axes(projection="3d")
+        # Fast
+        if len(np.unique(colors)) == 1 and len(np.unique(alpha)) == 1:
+            ax = _attractor_3D_fast(ax, embedded, embedded, 0, colors, alpha, shadows, linewidth)
+        else:
+            ax = _attractor_3D(ax, embedded, colors, alpha, shadows, linewidth)
+
+    return fig
+
+
+# =============================================================================
+# 2D Attractors
+# =============================================================================
+def _attractor_2D(ax, embedded, colors, alpha=0.8, linewidth=1.5):
+    # Create a set of line segments
+    points = np.array([embedded[:, 0], embedded[:, 1]]).T.reshape(-1, 1, 2)
+    segments = np.concatenate([points[:-1], points[1:]], axis=1)
+
+    for i in range(len(segments)):
+        ax.plot(
+            segments[i][:, 0],
+            segments[i][:, 1],
+            color=colors[i],
+            alpha=alpha[i],
+            linewidth=linewidth,
+            solid_capstyle="round",
+        )
+    return ax
+
+
+# =============================================================================
+# Slow plots
+# =============================================================================
+
+
+def _attractor_3D_fast(ax, embedded, seg, i, colors, alpha, shadows, linewidth):
+
+    # Plot 2D shadows
+    if shadows is True:
+        ax.plot(
+            seg[:, 0],
+            seg[:, 2],
+            zs=np.max(embedded[:, 1]),
+            zdir="y",
+            color="lightgrey",
+            alpha=alpha[i],
+            linewidth=linewidth,
+            zorder=i + 1,
+            solid_capstyle="round",
+        )
+        ax.plot(
+            seg[:, 1],
+            seg[:, 2],
+            zs=np.min(embedded[:, 0]),
+            zdir="x",
+            color="lightgrey",
+            alpha=alpha[i],
+            linewidth=linewidth,
+            zorder=i + 1 + len(embedded),
+            solid_capstyle="round",
+        )
+        ax.plot(
+            seg[:, 0],
+            seg[:, 1],
+            zs=np.min(embedded[:, 2]),
+            zdir="z",
+            color="lightgrey",
+            alpha=alpha[i],
+            linewidth=linewidth,
+            zorder=i + 1 + len(embedded) * 2,
+            solid_capstyle="round",
+        )
+
+    ax.plot(
+        seg[:, 0],
+        seg[:, 1],
+        seg[:, 2],
+        color=colors[i],
+        alpha=alpha[i],
+        linewidth=linewidth,
+        zorder=i + 1 + len(embedded) * 3,
+    )
+    return ax
+
+
+def _attractor_3D(ax, embedded, colors, alpha=0.8, shadows=True, linewidth=1.5):
+    # Create a set of line segments
+    points = np.array([embedded[:, 0], embedded[:, 1], embedded[:, 2]]).T.reshape(-1, 1, 3)
+    segments = np.concatenate([points[:-1], points[1:]], axis=1)
+
+    for i in range(len(segments)):
+        ax = _attractor_3D_fast(ax, embedded, segments[i], i, colors, alpha, shadows, linewidth)
+
+    return ax
+
+
+# =============================================================================
+# Equations (must be located here to avoid circular imports from complexity_embedding)
+# =============================================================================
+def _attractor_equation(name, **kwargs):
+    if name == "lorenz":
+        return _attractor_lorenz(**kwargs)
+    elif name == "clifford":
+        return _attractor_clifford(**kwargs)
+    else:
+        return _attractor_rossler(**kwargs)
+
+
+def _attractor_lorenz(duration=1, sampling_rate=1000, sigma=10.0, beta=8.0 / 3, rho=28.0):
+    """Simulate Data from Lorenz System"""
+
+    def lorenz_equation(coord, t0, sigma, beta, rho):
+        return [
+            sigma * (coord[1] - coord[0]),
+            coord[0] * (rho - coord[2]) - coord[1],
+            coord[0] * coord[1] - beta * coord[2],
+        ]
+
+    x0 = [1, 1, 1]  # starting vector
+    t = np.linspace(0, duration * 100, int(duration * sampling_rate))
+    return scipy.integrate.odeint(lorenz_equation, x0, t, args=(sigma, beta, rho))
+
+
+def _attractor_rossler(duration=1, sampling_rate=1000, a=0.1, b=0.1, c=14):
+    """Simulate Data from Rössler System"""
+
+    def rossler_equation(coord, t0, a, b, c):
+        return [-coord[1] - coord[2], coord[0] + a * coord[1], b + coord[2] * (coord[0] - c)]
+
+    x0 = [0.1, 0.0, 0.1]  # starting vector
+    t = np.linspace(0, duration * 500, int(duration * sampling_rate))
+    return scipy.integrate.odeint(rossler_equation, x0, t, args=(a, b, c))
+
+
+def _attractor_clifford(duration=1, sampling_rate=1000, a=-1.4, b=1.6, c=1.0, d=0.7, x0=0, y0=0):
+    """Simulate Data from Clifford System
+
+    >>> import neurokit2 as nk
+    >>>
+    >>> emb = nk.complexity_embedding("clifford", sampling_rate=100000)
+    >>> plt.plot(emb[:, 0], emb[:, 1], '.', alpha=0.2, markersize=0.5) #doctest: +ELLIPSIS
+    [...
+    >>> emb = nk.complexity_embedding("clifford", sampling_rate=100000, a=1.9, b=1.0, c=1.9, d=-1.1)
+    >>> plt.plot(emb[:, 0], emb[:, 1], '.', alpha=0.2, markersize=0.5) #doctest: +ELLIPSIS
+    [...
+
+    """
+
+    def clifford_equation(coord, a, b, c, d):
+        return [
+            np.sin(a * coord[1]) + c * np.cos(a * coord[0]),
+            np.sin(b * coord[0]) + d * np.cos(b * coord[1]),
+        ]
+
+    emb = np.tile([x0, y0], (int(duration * sampling_rate), 1)).astype(float)
+    for i in range(len(emb) - 1):
+        emb[i + 1, :] = clifford_equation(emb[i, :], a, b, c, d)
+
+    return emb
diff --git a/neurokit2/complexity/complexity_embedding.py b/neurokit2/complexity/complexity_embedding.py
index 73550e061e..8df2921f21 100644
--- a/neurokit2/complexity/complexity_embedding.py
+++ b/neurokit2/complexity/complexity_embedding.py
@@ -1,14 +1,13 @@
 # -*- coding: utf-8 -*-
 from warnings import warn
 
-import matplotlib.animation
-import matplotlib.pyplot as plt
 import numpy as np
 
 from ..misc import NeuroKitWarning
+from .complexity_attractor import _attractor_equation, complexity_attractor
 
 
-def complexity_embedding(signal, delay=1, dimension=3, show=False):
+def complexity_embedding(signal, delay=1, dimension=3, show=False, **kwargs):
     """Time-delay embedding of a signal
 
     A dynamical system can be described by a vector of numbers, called its 'state', that aims to provide
@@ -34,7 +33,9 @@ def complexity_embedding(signal, delay=1, dimension=3, show=False):
     Parameters
     ----------
     signal : Union[list, np.array, pd.Series]
-        The signal (i.e., a time series) in the form of a vector of values.
+        The signal (i.e., a time series) in the form of a vector of values. Can also be a string, such as
+        ``"lorenz"`` (Lorenz attractor), ``"rossler"`` (Rössler attractor), or ``"clifford"`` (Clifford attractor)
+        to obtain a pre-defined attractor.
     delay : int
         Time delay (often denoted 'Tau', sometimes referred to as 'lag'). In practice, it is common
         to have a fixed time lag (corresponding for instance to the sampling rate; Gautama, 2003), or
@@ -45,6 +46,8 @@ def complexity_embedding(signal, delay=1, dimension=3, show=False):
         an array with two columns corresponding to the original signal and its delayed (by Tau) version.
     show : bool
         Plot the reconstructed attractor.
+    **kwargs
+        Other arguments to be passed to the plotting of the attractor (see ``complexity_attractor()``).
 
     Returns
     -------
@@ -85,6 +88,10 @@ def complexity_embedding(signal, delay=1, dimension=3, show=False):
       on Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03). (Vol. 6, pp. VI-29). IEEE.
 
     """
+    # If string
+    if isinstance(signal, str):
+        return _attractor_equation(signal, **kwargs)
+
     N = len(signal)
 
     # Sanity checks
@@ -108,90 +115,6 @@ def complexity_embedding(signal, delay=1, dimension=3, show=False):
     embedded = Y.T
 
     if show is True:
-        _embedding_plot(embedded)
+        complexity_attractor(embedded, **kwargs)
 
     return embedded
-
-
-# =============================================================================
-# Internals
-# =============================================================================
-
-
-def _embedding_plot(embedded):
-    """Plot reconstructed attractor.
-
-    The input for this function must be obtained via `nk.complexity_embedding()`
-
-    """
-    if embedded.shape[1] == 2:
-        figure = _embedding_plot_2D(embedded)
-    elif embedded.shape[1] == 3:
-        figure = _embedding_plot_3D(embedded)
-    else:
-        figure = _embedding_plot_4D(embedded)
-
-    return figure
-
-
-# =============================================================================
-# Internal plots
-# =============================================================================
-
-
-def _embedding_plot_2D(embedded):
-    return plt.plot(embedded[:, 0], embedded[:, 1], color="#3F51B5")
-
-
-def _embedding_plot_3D(embedded):
-    return _plot_3D_colored(
-        x=embedded[:, 0], y=embedded[:, 1], z=embedded[:, 2], color=embedded[:, 2], rotate=False
-    )
-
-
-def _embedding_plot_4D(embedded):
-    return _plot_3D_colored(
-        x=embedded[:, 0], y=embedded[:, 1], z=embedded[:, 2], color=embedded[:, 3], rotate=False
-    )
-
-
-# =============================================================================
-# Plotting
-# =============================================================================
-def _plot_3D_colored(x, y, z, color=None, rotate=False):
-    if color is None:
-        color = z
-
-    # Create a set of line segments
-    points = np.array([x, y, z]).T.reshape(-1, 1, 3)
-    segments = np.concatenate([points[:-1], points[1:]], axis=1)
-
-    # Color
-    norm = plt.Normalize(color.min(), color.max())
-    cmap = plt.get_cmap("plasma")
-    colors = cmap(norm(color))
-
-    # Plot
-    fig = plt.figure()
-    ax = plt.axes(projection="3d")
-
-    for i in range(len(x) - 1):
-        seg = segments[i]
-        (l,) = ax.plot(seg[:, 0], seg[:, 1], seg[:, 2], color=colors[i])
-        l.set_solid_capstyle("round")
-
-    if rotate is True:
-        fig = _plot_3D_colored_rotate(fig, ax)
-
-    return fig
-
-
-def _plot_3D_colored_rotate(fig, ax):
-    def rotate(angle):
-        ax.view_init(azim=angle)
-
-    fig = matplotlib.animation.FuncAnimation(
-        fig, rotate, frames=np.arange(0, 361, 1), interval=10, cache_frame_data=False
-    )
-
-    return fig
diff --git a/neurokit2/complexity/complexity_hurst.py b/neurokit2/complexity/complexity_hurst.py
index 511a1307dc..a23a5f67b1 100644
--- a/neurokit2/complexity/complexity_hurst.py
+++ b/neurokit2/complexity/complexity_hurst.py
@@ -108,8 +108,7 @@ def complexity_hurst(signal, windows="default", corrected=True, show=False):
 
 
 def expected_rs(n):
-    """
-    Calculates the expected (R/S)_n for white noise for a given n.
+    """Calculates the expected (R/S)_n for white noise for a given n.
     This is used as a correction factor in the function hurst_rs. It uses the
     formula of Anis-Lloyd-Peters.
 
@@ -127,8 +126,7 @@ def expected_rs(n):
 
 
 def _complexity_hurst_rs(signal, window):
-    """
-    Calculates an individual R/S value in the rescaled range approach for
+    """Calculates an individual R/S value in the rescaled range approach for
     a given window size (the size of the subseries in which data should be split).
     """
     n = len(signal)
diff --git a/neurokit2/complexity/complexity_lyapunov.py b/neurokit2/complexity/complexity_lyapunov.py
index 5c181c255c..e8eb71b1ae 100644
--- a/neurokit2/complexity/complexity_lyapunov.py
+++ b/neurokit2/complexity/complexity_lyapunov.py
@@ -188,8 +188,7 @@ def _complexity_lyapunov_rosenstein(
 def _complexity_lyapunov_eckmann(
     signal, delay=1, dimension=2, tolerance=None, matrix_dim=4, min_neighbors="default"
 ):
-    """
-    TODO: check implementation
+    """TODO: check implementation
 
     From https://github.com/CSchoel/nolds
     """
@@ -310,8 +309,7 @@ def _complexity_lyapunov_checklength(
     matrix_dim=4,
     min_neighbors="default",
 ):
-    """
-    Helper function that calculates the minimum number of data points required.
+    """Helper function that calculates the minimum number of data points required.
     """
     if method in ["rosenstein", "rosenstein1993"]:
         # minimum length required to find single orbit vector
diff --git a/neurokit2/complexity/complexity_recurrence.py b/neurokit2/complexity/complexity_recurrence.py
new file mode 100644
index 0000000000..dab4e679c6
--- /dev/null
+++ b/neurokit2/complexity/complexity_recurrence.py
@@ -0,0 +1,107 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import scipy.spatial
+
+from .complexity_embedding import complexity_embedding
+from .optim_complexity_tolerance import complexity_tolerance
+
+
+def complexity_recurrence(signal, delay=1, dimension=3, tolerance="default", show=False):
+    """Recurrence matrix (Python implementation)
+
+    Fast Python implementation of recurrence matrix (tested against pyRQA). Returns a tuple
+    with the recurrence matrix (made of 0s and 1s) and the distance matrix (the non-binarized
+    version of the former).
+
+    Parameters
+    ----------
+    signal : Union[list, np.ndarray, pd.Series]
+        The signal (i.e., a time series) in the form of a vector of values.
+    delay : int
+        Time delay (often denoted 'Tau', sometimes referred to as 'lag'). In practice, it is common
+        to have a fixed time lag (corresponding for instance to the sampling rate; Gautama, 2003),
+        or to find a suitable value using some algorithmic heuristics (see ``delay_optimal()``).
+    dimension : int
+        Embedding dimension (often denoted 'm' or 'd', sometimes referred to as 'order'). Typically
+        2 or 3. It corresponds to the number of compared runs of lagged data. If 2, the embedding
+        returns an array with two columns corresponding to the original signal and its delayed (by
+        Tau) version.
+    tolerance : float
+        Tolerance (similarity threshold, often denoted as 'r'). The radius used for detecting
+        neighbours (states considered as recurrent). A rule of thumb is to set 'r' so that the
+        percentage of points classified as recurrences is about 2-5%.
+    show : bool
+        Visualise recurrence matrix.
+
+    See Also
+    --------
+    complexity_embedding, complexity_tolerance
+
+    Returns
+    -------
+    np.ndarray
+        The recurrence matrix.
+    np.ndarray
+        The distance matrix.
+
+    Examples
+    ----------
+    >>> import neurokit2 as nk
+    >>>
+    >>> signal = nk.signal_simulate(duration=5, sampling_rate=100, frequency=[5, 6], noise=0.01)
+    >>>
+    >>> # Default r
+    >>> rc, _ = nk.complexity_recurrence(signal, show=True)
+    >>>
+    >>> # Larger radius
+    >>> rc, d = nk.complexity_recurrence(signal, tolerance=0.5, show=True)
+    >>>
+    >>> # Optimization of tolerance via recurrence matrix
+    >>> rc, d = nk.complexity_tolerance(signal, delay=1, dimension=3, method="recurrence", show=True)
+
+
+
+    References
+    ----------
+    - Rawald, T., Sips, M., Marwan, N., & Dransch, D. (2014). Fast computation of recurrences
+    in long time series. In Translational Recurrences (pp. 17-29). Springer, Cham.
+    - Dabiré, H., Mestivier, D., Jarnet, J., Safar, M. E., & Chau, N. P. (1998). Quantification of
+    sympathetic and parasympathetic tones by nonlinear indexes in normotensive rats. American
+    Journal of Physiology-Heart and Circulatory Physiology, 275(4), H1290-H1297.
+    """
+    if tolerance == "default":
+        tolerance, _ = complexity_tolerance(
+            signal, method="sd", delay=None, dimension=None, show=False
+        )
+
+    # Time-delay embedding
+    emb = complexity_embedding(signal, delay=delay, dimension=dimension)
+
+    # Compute distance matrix
+    d = scipy.spatial.distance.cdist(emb, emb, metric="euclidean")
+
+    # Flip the matrix to match traditional RQA representation
+    d = np.flip(d, axis=0)
+
+    # Initialize the recurrence matrix filled with 0s
+    recmat = np.zeros((len(d), len(d)))
+    # If lower than tolerance, then 1
+    recmat[d <= tolerance] = 1
+
+    # Plotting
+    if show is True:
+        try:
+            fig, axes = plt.subplots(ncols=2)
+            axes[0].imshow(recmat, cmap="Greys")
+            axes[0].set_title("Recurrence Matrix")
+            im = axes[1].imshow(d)
+            axes[1].set_title("Distance")
+            cbar = fig.colorbar(im, ax=axes[1], fraction=0.046, pad=0.04)
+            cbar.ax.plot([0, 1], [tolerance] * 2, color="r")
+        except MemoryError as e:
+            raise MemoryError(
+                "NeuroKit error: complexity_rqa(): the recurrence plot is too large to display. ",
+                "You can recover the matrix from the parameters and try to display parts of it.",
+            ) from e
+
+    return recmat, d
diff --git a/neurokit2/complexity/complexity_rqa.py b/neurokit2/complexity/complexity_rqa.py
index 09e213b870..3c2b1f0832 100644
--- a/neurokit2/complexity/complexity_rqa.py
+++ b/neurokit2/complexity/complexity_rqa.py
@@ -59,7 +59,7 @@ def complexity_rqa(signal, dimension=3, delay=1, tolerance="default", linelength
     linelength : int
         Minimum length of a diagonal and vertical lines. Default to 2.
     show : bool
-        Visualise plot of RQA.
+        Visualise recurrence matrix.
 
     Returns
     ----------
@@ -104,8 +104,10 @@ def complexity_rqa(signal, dimension=3, delay=1, tolerance="default", linelength
 
     # Get neighbourhood
     if tolerance == "default":
-        r, _ = complexity_tolerance(signal, method="sd", delay=None, dimension=None, show=False)
-    r = pyrqa.neighbourhood.FixedRadius(r)
+        tolerance, _ = complexity_tolerance(
+            signal, method="sd", delay=None, dimension=None, show=False
+        )
+    r = pyrqa.neighbourhood.FixedRadius(tolerance)
 
     # Convert signal to time series
     signal = pyrqa.time_series.TimeSeries(signal, embedding_dimension=dimension, time_delay=delay)
@@ -157,3 +159,12 @@ def complexity_rqa(signal, dimension=3, delay=1, tolerance="default", linelength
             ) from e
 
     return results, {"RQA": rqa, "RP": rp, "Recurrence_Matrix": rp.recurrence_matrix_reverse}
+
+
+
+# def _complexity_rqa_rr(recmat):
+#     """Compute recurrence rate (imported in complexity_rqa)"""
+#     # Indices of the lower triangular (without the diagonal)
+#     idx = np.tril_indices(len(recmat), k=-1)
+#     # Compute percentage
+#     return recmat[idx].sum() / len(recmat[idx])
diff --git a/neurokit2/complexity/complexity_simulate.py b/neurokit2/complexity/complexity_simulate.py
index b26cc4cfc6..e23d7c5b64 100644
--- a/neurokit2/complexity/complexity_simulate.py
+++ b/neurokit2/complexity/complexity_simulate.py
@@ -1,8 +1,12 @@
 # -*- coding: utf-8 -*-
 import numpy as np
 
+from .complexity_attractor import _attractor_lorenz
 
-def complexity_simulate(duration=10, sampling_rate=1000, method="ornstein", hurst_exponent=0.5, **kwargs):
+
+def complexity_simulate(
+    duration=10, sampling_rate=1000, method="ornstein", hurst_exponent=0.5, **kwargs
+):
     """Simulate chaotic time series.
 
     Generates time series using the discrete approximation of the
@@ -18,8 +22,8 @@ def complexity_simulate(duration=10, sampling_rate=1000, method="ornstein", hurs
     duration : int
         The desired length in samples.
     method : str
-        The method. can be 'hurst' for a (fractional) Ornstein–Uhlenbeck process or 'mackeyglass' to
-        use the Mackey-Glass equation.
+        The method. can be 'hurst' for a (fractional) Ornstein–Uhlenbeck process, 'lorenz' for the
+        first dimension of a Lorenz system or 'mackeyglass' to use the Mackey-Glass equation.
     hurst_exponent : float
         Defaults to 0.5.
     **kwargs
@@ -34,9 +38,17 @@ def complexity_simulate(duration=10, sampling_rate=1000, method="ornstein", hurs
     ------------
     >>> import neurokit2 as nk
     >>>
-    >>> signal1 = nk.complexity_simulate(duration=30, sampling_rate=100, method="ornstein")
-    >>> signal2 = nk.complexity_simulate(duration=30, sampling_rate=100, method="mackeyglass")
-    >>> nk.signal_plot([signal1, signal2])
+    >>> signal = nk.complexity_simulate(duration=10, sampling_rate=100, method="lorenz")
+    >>> nk.complexity_attractor(nk.complexity_embedding(signal, delay = 10), alpha=1, color="blue") #doctest: +ELLIPSIS
+    <Figure ...>
+    >>>
+    >>> signal = nk.complexity_simulate(duration=30, sampling_rate=100, method="ornstein")
+    >>> nk.complexity_attractor(nk.complexity_embedding(signal, delay = 10), alpha=1, color="red") #doctest: +ELLIPSIS
+    <Figure ...>
+    >>>
+    >>> signal = nk.complexity_simulate(duration=30, sampling_rate=100, method="mackeyglass")
+    >>> nk.complexity_attractor(nk.complexity_embedding(signal, delay = 10), alpha=1, color="green") #doctest: +ELLIPSIS
+    <Figure ...>
 
     Returns
     -------
@@ -45,12 +57,18 @@ def complexity_simulate(duration=10, sampling_rate=1000, method="ornstein", hurs
 
     """
     method = method.lower()
-    if method in ["fractal", "fractional", "husrt", "ornsteinuhlenbeck", "ornstein"]:
+    if method in ["fractal", "fractional", "hurst", "ornsteinuhlenbeck", "ornstein"]:
         signal = _complexity_simulate_ornstein(
             duration=duration, sampling_rate=sampling_rate, hurst_exponent=hurst_exponent, **kwargs
         )
+    elif method in ["lorenz"]:
+        # x-dimension of Lorenz system
+        signal = _attractor_lorenz(sampling_rate=sampling_rate, duration=duration, **kwargs)[:, 0]
+
     else:
-        signal = _complexity_simulate_mackeyglass(duration=duration, sampling_rate=sampling_rate, **kwargs)
+        signal = _complexity_simulate_mackeyglass(
+            duration=duration, sampling_rate=sampling_rate, **kwargs
+        )
     return signal
 
 
@@ -109,11 +127,15 @@ def _complexity_simulate_mackeyglass(
     B = a * tau / (2 * n + b * tau)
 
     for i in range(n - 1, grids - 1):
-        x[i + 1] = A * x[i] + B * (x[i - n] / (1 + x[i - n] ** c) + x[i - n + 1] / (1 + x[i - n + 1] ** c))
+        x[i + 1] = A * x[i] + B * (
+            x[i - n] / (1 + x[i - n] ** c) + x[i - n + 1] / (1 + x[i - n + 1] ** c)
+        )
     return x[n * discard :: sampling_rate]
 
 
-def _complexity_simulate_ornstein(duration=10, sampling_rate=1000, theta=0.3, sigma=0.1, hurst_exponent=0.7):
+def _complexity_simulate_ornstein(
+    duration=10, sampling_rate=1000, theta=0.3, sigma=0.1, hurst_exponent=0.7
+):
     """This is based on https://github.com/LRydin/MFDFA.
 
     Parameters
@@ -139,7 +161,9 @@ def _complexity_simulate_ornstein(duration=10, sampling_rate=1000, theta=0.3, si
     length = duration * sampling_rate
 
     # The fractional Gaussian noise
-    dB = (duration ** hurst_exponent) * _complexity_simulate_fractionalnoise(size=length, hurst_exponent=hurst_exponent)
+    dB = (duration ** hurst_exponent) * _complexity_simulate_fractionalnoise(
+        size=length, hurst_exponent=hurst_exponent
+    )
 
     # Initialise the array y
     y = np.zeros([length])
diff --git a/neurokit2/complexity/entropy_coalition.py b/neurokit2/complexity/entropy_coalition.py
index 4334eccf2c..bee384793f 100644
--- a/neurokit2/complexity/entropy_coalition.py
+++ b/neurokit2/complexity/entropy_coalition.py
@@ -8,8 +8,7 @@
 
 
 def entropy_coalition(signal, method="amplitude"):
-    """
-    Amplitude Coalition Entropy (ACE) reflects the entropy over time of the constitution of the set
+    """Amplitude Coalition Entropy (ACE) reflects the entropy over time of the constitution of the set
     of most active channels (Shanahan, 2010), and is similar to Lempel-Ziv complexity, in the sense
     that it quantifies variability in space and time of the activity. ACE is normalized by dividing
     the raw by the value obtained for the same binary input but randomly shuffled. The
diff --git a/neurokit2/complexity/entropy_cumulative_residual.py b/neurokit2/complexity/entropy_cumulative_residual.py
index 1a892dbe1b..aab1c222bf 100644
--- a/neurokit2/complexity/entropy_cumulative_residual.py
+++ b/neurokit2/complexity/entropy_cumulative_residual.py
@@ -65,6 +65,6 @@ def entropy_cumulative_residual(signal):
 def _entropy_cumulative_residual_pairwise(events):
     pairs = itertools.tee(events, 2)
     pairs = list(zip(*pairs))
-    for i, p in enumerate(pairs[:-1]):
+    for i, _ in enumerate(pairs[:-1]):
         pairs[i] = (pairs[i][0], pairs[i + 1][0])
     return pairs[:-1]
diff --git a/neurokit2/complexity/fractal_correlation.py b/neurokit2/complexity/fractal_correlation.py
index fff44b33f5..67aafb343d 100644
--- a/neurokit2/complexity/fractal_correlation.py
+++ b/neurokit2/complexity/fractal_correlation.py
@@ -56,17 +56,17 @@ def fractal_correlation(signal, delay=1, dimension=2, radius=64, show=False, **k
     >>>
     >>> signal = nk.signal_simulate(duration=2, frequency=5)
     >>>
-    >>> fractal1, info = nk.fractal_correlation(signal, radius="nolds", show=True)
-    >>> fractal1 #doctest: +SKIP
-    >>> fractal2, info = nk.fractal_correlation(signal, radius=32, show=True)
-    >>> fractal2 #doctest: +SKIP
+    >>> fractal1, info = nk.fractal_correlation(signal, radius=32, show=True)
+    >>> fractal1 # doctest: +ELLIPSIS
+    0.7888...
     >>>
-    >>> signal = nk.rsp_simulate(duration=120, sampling_rate=50)
+    >>> fractal2, info = nk.fractal_correlation(signal, radius="nolds", show=True)
+    >>> fractal2 # doctest: +ELLIPSIS
+    0.8316...
     >>>
-    >>> fractal3, info = nk.fractal_correlation(signal, radius="nolds", show=True)
-    >>> fractal3 #doctest: +SKIP
-    >>> fractal4, info = nk.fractal_correlation(signal, radius=32, show=True)
-    >>> fractal4 #doctest: +SKIP
+    >>> fractal3, info = nk.fractal_correlation(signal, radius='boon2008', show=True)
+    >>> fractal3 # doctest: +ELLIPSIS
+    0.7501...
 
 
     References
@@ -193,7 +193,7 @@ def _fractal_correlation_get_r(radius, signal, dist):
     if isinstance(radius, int):
         dist_range = np.max(dist) - np.min(dist)
         r_min, r_max = (np.min(dist) + 0.025 * dist_range), (np.min(dist) + 0.5 * dist_range)
-        r_vals = expspace(r_min, r_max, radius, base=2, type=float)
+        r_vals = expspace(r_min, r_max, radius, base=2, out="float")
 
     return r_vals
 
diff --git a/neurokit2/complexity/fractal_higuchi.py b/neurokit2/complexity/fractal_higuchi.py
index 536584fee8..fab93e7e02 100644
--- a/neurokit2/complexity/fractal_higuchi.py
+++ b/neurokit2/complexity/fractal_higuchi.py
@@ -7,8 +7,7 @@
 
 
 def fractal_higuchi(signal, k_max="default", show=False, **kwargs):
-    """
-    Computes Higuchi's Fractal Dimension (HFD) by reconstructing k-max number of new
+    """Computes Higuchi's Fractal Dimension (HFD) by reconstructing k-max number of new
     data sets. For each reconstructed data set, curve length is computed and plotted
     against its corresponding k value on a log-log scale. HFD equates to the slope obtained
     from fitting a least-squares method.
diff --git a/neurokit2/complexity/optim_complexity_delay.py b/neurokit2/complexity/optim_complexity_delay.py
index 0864da1b6a..373d44d521 100644
--- a/neurokit2/complexity/optim_complexity_delay.py
+++ b/neurokit2/complexity/optim_complexity_delay.py
@@ -11,7 +11,8 @@
 import scipy.stats
 
 from ..misc import NeuroKitWarning, find_closest
-from ..signal import signal_autocor, signal_findpeaks, signal_zerocrossings
+from ..signal import (signal_autocor, signal_findpeaks, signal_psd,
+                      signal_zerocrossings)
 from .complexity_embedding import complexity_embedding
 from .information_mutual import mutual_information
 
@@ -27,25 +28,28 @@ def complexity_delay(
     Several authors suggested different methods to guide the choice of Tau:
 
     - Fraser and Swinney (1986) suggest using the first local minimum of the mutual information
-      between the delayed and non-delayed time series, effectively identifying a value of tau for which
-      they share the least information.
-
+    between the delayed and non-delayed time series, effectively identifying a value of tau for
+    which they share the least information.
     - Theiler (1990) suggested to select Tau where the autocorrelation between the signal and its
-      lagged version at Tau first crosses the value 1/e.
-
+    lagged version at Tau first crosses the value 1/e.
     - Casdagli (1991) suggests instead taking the first zero-crossing of the autocorrelation.
-
     - Rosenstein (1993) suggests to approximate the point where the autocorrelation function drops
-      to (1 − 1 / e) of its maximum value.
-
+    to (1 − 1 / e) of its maximum value.
     - Rosenstein (1994) suggests to the point close to 40% of the slope of the average displacement
-      from the diagonal (ADFD).
-
+    from the diagonal (ADFD).
     - Kim (1999) suggests estimating Tau using the correlation integral, called the C-C method,
-      which has shown to agree with those obtained using the Mutual Information. This method
-      makes use of a statistic within the reconstructed phase space, rather than analyzing the temporal
-      evolution of the time series. However, computation times are significantly long for this method
-      due to the need to compare every unique pair of pairwise vectors within the embedded signal per delay.
+    which has shown to agree with those obtained using the Mutual Information. This method
+    makes use of a statistic within the reconstructed phase space, rather than analyzing the
+    temporal evolution of the time series. However, computation times are significantly long for
+    this method due to the need to compare every unique pair of pairwise vectors within the
+    embedded signal per delay.
+    - Lyle (2021) describes the 'Symmetric Projection Attractor Reconstruction' (SPAR), where 1/3
+    of the the dominant frequency (i.e., of the length of the average "cycle") can be a suitable
+    value for approximately periodic data, and makes the attractor sensitive to morphological
+    changes. See also `Aston's talk
+    <https://youtu.be/GGrOJtcTcHA?t=730>`_. This
+    method is also the fastest but might not be suitable for aperiodic signals.
+    The 'algorithm' argument (default to 'fft') and will be passed as the 'method' argument of ``signal_psd()``.
 
     Parameters
     ----------
@@ -54,11 +58,12 @@ def complexity_delay(
     delay_max : int
         The maximum time delay (Tau or lag) to test.
     method : str
-        The method that defines what to compute for each tested value of Tau. Can be one of 'fraser1986',
-        'theiler1990', 'casdagli1991', 'rosenstein1993', 'rosenstein1994', or 'kim1999'.
+        The method that defines what to compute for each tested value of Tau. Can be one of
+        'fraser1986', 'theiler1990', 'casdagli1991', 'rosenstein1993', 'rosenstein1994', 'kim1999', or 'dominantfreq'.
     algorithm : str
-        The method used to find the optimal value of Tau given the values computed by the method. If `None` (default),
-        will select the algorithm according to the method. Modify only if you know what you are doing.
+        The method used to find the optimal value of Tau given the values computed by the method.
+        If `None` (default), will select the algorithm according to the method. Modify only if you
+        know what you are doing.
     show : bool
         If true, will plot the metric values for each value of tau.
     **kwargs : optional
@@ -87,33 +92,35 @@ def complexity_delay(
     >>> delay, parameters = nk.complexity_delay(signal, delay_max=1000, show=True, method="fraser1986")
     >>> delay, parameters = nk.complexity_delay(signal, delay_max=1000, show=True, method="theiler1990")
     >>> delay, parameters = nk.complexity_delay(signal, delay_max=1000, show=True, method="casdagli1991")
-    >>> delay, parameters = nk.complexity_delay(signal, delay_max=1000, show=True, method="rosenstein1994")
     >>> delay, parameters = nk.complexity_delay(signal, delay_max=1000, show=True, method="rosenstein1993")
+    >>> delay, parameters = nk.complexity_delay(signal, delay_max=1000, show=True, method="rosenstein1994")
+    >>> delay, parameters = nk.complexity_delay(signal, delay_max=1000, show=True, method="lyle2021")
     >>>
     >>> # Realistic example
-    >>> ecg = nk.ecg_simulate(duration=60*6, sampling_rate=150)
-    >>> signal = nk.ecg_rate(nk.ecg_peaks(ecg, sampling_rate=150), sampling_rate=150, desired_length=len(ecg))
+    >>> ecg = nk.ecg_simulate(duration=60*6, sampling_rate=200)
+    >>> signal = nk.ecg_rate(nk.ecg_peaks(ecg, sampling_rate=200), sampling_rate=200, desired_length=len(ecg))
     >>> nk.signal_plot(signal)
     >>>
     >>> delay, parameters = nk.complexity_delay(signal, delay_max=1000, show=True)
 
     References
     ------------
-    - Gautama, T., Mandic, D. P., & Van Hulle, M. M. (2003, April). A differential entropy based method
-      for determining the optimal embedding parameters of a signal. In 2003 IEEE International Conference
-      on Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03). (Vol. 6, pp. VI-29). IEEE.
-
+    - Lyle, J. V., Nandi, M., & Aston, P. J. (2021). Symmetric Projection Attractor Reconstruction:
+    Sex Differences in the ECG. Frontiers in cardiovascular medicine, 1034.
+    - Gautama, T., Mandic, D. P., & Van Hulle, M. M. (2003, April). A differential entropy based
+    method for determining the optimal embedding parameters of a signal. In 2003 IEEE International
+    Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings.(ICASSP'03). (Vol. 6,
+    pp. VI-29). IEEE.
     - Camplani, M., & Cannas, B. (2009). The role of the embedding dimension and time delay in time
-      series forecasting. IFAC Proceedings Volumes, 42(7), 316-320.
-
+    series forecasting. IFAC Proceedings Volumes, 42(7), 316-320.
     - Rosenstein, M. T., Collins, J. J., & De Luca, C. J. (1993). A practical method for calculating
-      largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 65(1-2), 117-134.
-
+    largest Lyapunov exponents from small data sets. Physica D: Nonlinear Phenomena, 65(1-2),
+    117-134.
     - Rosenstein, M. T., Collins, J. J., & De Luca, C. J. (1994). Reconstruction expansion as a
-      geometry-based framework for choosing proper delay times. Physica-Section D, 73(1), 82-98.
+    geometry-based framework for choosing proper delay times. Physica-Section D, 73(1), 82-98.
+    - Kim, H., Eykholt, R., & Salas, J. D. (1999). Nonlinear dynamics, delay times, and embedding
+    windows. Physica D: Nonlinear Phenomena, 127(1-2), 48-60.
 
-    - Kim, H., Eykholt, R., & Salas, J. D. (1999). Nonlinear dynamics, delay times, and embedding windows.
-      Physica D: Nonlinear Phenomena, 127(1-2), 48-60.
     """
     # Initalize vectors
     if isinstance(delay_max, int):
@@ -147,6 +154,8 @@ def complexity_delay(
         metric = "Correlation Integral"
         if algorithm is None:
             algorithm = "first local minimum"
+    elif method in ["aston2020", "lyle2021", "spar"]:
+        return _embedding_delay_spar(signal, algorithm=algorithm, show=show, **kwargs)
     else:
         raise ValueError("NeuroKit error: complexity_delay(): 'method' not recognized.")
 
@@ -202,9 +211,17 @@ def _embedding_delay_select(metric_values, algorithm="first local minimum"):
             )["Peaks"]
     elif algorithm == "first local minimum":
         # Find reversed peaks
-        optimal = signal_findpeaks(-1 * metric_values, relative_height_min=0.1, relative_max=True)[
-            "Peaks"
-        ]
+        try:
+            optimal = signal_findpeaks(
+                -1 * metric_values, relative_height_min=0.1, relative_max=True
+            )["Peaks"]
+        except ValueError:
+            warn(
+                "First local minimum detection failed. Try setting "
+                + "`algorithm = 'first local minimum (corrected)'` or using another method.",
+                category=NeuroKitWarning,
+            )
+
     elif algorithm == "first 1/e crossing":
         metric_values = metric_values - 1 / np.exp(1)
         optimal = signal_zerocrossings(metric_values)
@@ -233,8 +250,7 @@ def _embedding_delay_metric(
     dimensions=[2, 3, 4, 5],
     r_vals=[0.5, 1.0, 1.5, 2.0],
 ):
-    """
-    Iterating through dimensions and r values is relevant only if metric used is Correlation Integral.
+    """Iterating through dimensions and r values is relevant only if metric used is Correlation Integral.
     For this method, either first zero crossing of the statistic averages or the first local
     minimum of deviations to obtain optimal tau. This implementation takes the latter since in practice,
     they are both in close proximity.
@@ -245,7 +261,7 @@ def _embedding_delay_metric(
         values = values[: len(tau_sequence)]  # upper limit
 
     elif metric == "Autocorrelation (FFT)":
-        values, _ = signal_autocor(signal, demean=False, method='fft')
+        values, _ = signal_autocor(signal, demean=False, method="fft")
         values = values[: len(tau_sequence)]
 
     elif metric == "Correlation Integral":
@@ -293,14 +309,37 @@ def _embedding_delay_metric(
     return values
 
 
+def _embedding_delay_spar(signal, algorithm=None, show=False, **kwargs):
+    if algorithm is None:
+        algorithm = "fft"
+    # Compute power in freqency domain
+    psd = signal_psd(signal, sampling_rate=1000, method=algorithm, show=False, **kwargs)
+    power = psd["Power"].values
+    freqs = 1000 / psd["Frequency"].values  # Convert to samples
+
+    # Get the 1/3 max frequency (in samples) (https://youtu.be/GGrOJtcTcHA?t=730)
+    idx = np.argmax(power)
+    optimal = int(freqs[idx] / 3)
+
+    if show is True:
+        idxs = freqs <= optimal * 6
+        _embedding_delay_plot(
+            signal,
+            metric_values=power[idxs],
+            tau_sequence=freqs[idxs],
+            tau=optimal,
+            metric="Power",
+        )
+    return optimal, {"Algorithm": algorithm, "Method": "SPAR"}
+
+
 # =============================================================================
 # Internals for C-C method, Kim et al. (1999)
 # =============================================================================
 
 
 def _embedding_delay_cc_integral_sum(signal, dimension=3, delay=10, r=0.02):
-    """
-    Correlation integral is a cumulative distribution function, which denotes
+    """Correlation integral is a cumulative distribution function, which denotes
     the probability of distance between any pairs of points in phase space
     not greater than the specified `r`.
     """
diff --git a/neurokit2/complexity/optim_complexity_dimension.py b/neurokit2/complexity/optim_complexity_dimension.py
index d1bee5d87d..24e9fd928e 100644
--- a/neurokit2/complexity/optim_complexity_dimension.py
+++ b/neurokit2/complexity/optim_complexity_dimension.py
@@ -143,7 +143,7 @@ def complexity_dimension(signal, delay=1, dimension_max=20, method="afnn", show=
             )
 
     else:
-        raise ValueError("NeuroKit error: complexity_dimension(): 'method' " "not recognized.")
+        raise ValueError("NeuroKit error: complexity_dimension(): 'method' not recognized.")
 
     return min_dimension, info
 
diff --git a/neurokit2/complexity/optim_complexity_k.py b/neurokit2/complexity/optim_complexity_k.py
index f779745340..75cdaae298 100644
--- a/neurokit2/complexity/optim_complexity_k.py
+++ b/neurokit2/complexity/optim_complexity_k.py
@@ -132,7 +132,7 @@ def _complexity_k_slope(kmax, signal, k_number="max"):
     else:
         k_values = np.unique(np.linspace(1, kmax + 1, k_number).astype(int))
 
-    """Step 3 of Vega & Noel (2015)"""
+    # Step 3 of Vega & Noel (2015)
     vectorized_Lk = np.vectorize(_complexity_k_Lk, excluded=[1])
 
     # Compute length of the curve, Lm(k)
@@ -163,7 +163,7 @@ def _complexity_k_plot(k_range, slope_values, k_optimal, ax=None):
 
     # if single time series
     ax.plot(k_range, slope_values, color="#2196F3", zorder=1)
-    for i, j in enumerate(k_range):
+    for i, _ in enumerate(k_range):
         ax.scatter(k_range[i], slope_values[i], color=colors[i], marker="o", zorder=2)
     ax.axvline(x=k_optimal, color="#E91E63", label="Optimal $k_{max}$: " + str(k_optimal))
     ax.legend(loc="upper right")
diff --git a/neurokit2/complexity/optim_complexity_tolerance.py b/neurokit2/complexity/optim_complexity_tolerance.py
index 2cc2f7fbd8..8046edb342 100644
--- a/neurokit2/complexity/optim_complexity_tolerance.py
+++ b/neurokit2/complexity/optim_complexity_tolerance.py
@@ -1,7 +1,10 @@
 # -*- coding: utf-8 -*-
 import matplotlib.pyplot as plt
+import matplotlib.ticker
 import numpy as np
+import scipy.spatial
 
+from .complexity_embedding import complexity_embedding
 from .entropy_approximate import entropy_approximate
 
 
@@ -20,7 +23,8 @@ def complexity_tolerance(
     method : str
         If 'maxApEn', different values of tolerance will be tested and the one where ApEn is
         maximized will be selected and returned. If 'sd' (as in Standard Deviation),
-        r = 0.2 * standard deviation of the signal will be returned.
+        r = 0.2 * standard deviation of the signal will be returned. If 'recurrence', the tolerance that
+        yields a recurrence rate (see ``RQA``) close to 5% will be returned.
     r_range : Union[list, int]
         Only used if ``method='maxApEn'``. The range of tolerance values to test.
         If an integer, will be set to ``np.linspace(0.02, 0.8, r_range) * np.std(signal, ddof=1)``.
@@ -54,9 +58,13 @@ def complexity_tolerance(
     >>> r
     0.07072836242007384
     >>>
+    >>> r, info = nk.complexity_tolerance(signal, delay=1, dimension=10, method = 'recurrence', show=True)
+    >>> r
+    0.06298107683978625
+    >>>
     >>> # Slow
     >>> r, info = nk.complexity_tolerance(signal, delay=8, dimension=6, method = 'maxApEn', show=True)
-    >>> r #doctest: +SKIP
+    >>> r
     0.014145672484014769
     >>>
     >>> # Narrower range
@@ -75,21 +83,56 @@ def complexity_tolerance(
     method = method.lower()
     if method in ["traditional", "sd", "std"]:
         r = 0.2 * np.std(signal, ddof=1)
-        info = {"Method": method}
+        info = {"Method": "20% SD"}
     elif method in ["maxapen", "optimize"]:
         r, info = _optimize_tolerance_maxapen(
-            signal, r_range=r_range, delay=delay, dimension=dimension, show=show
+            signal, r_range=r_range, delay=delay, dimension=dimension
         )
-        if show is True:
-            _optimize_tolerance_plot(r, info["Values"], info["Scores"])
-        info.update({"Method": method})
+        info.update({"Method": "Max ApEn"})
+    elif method in ["recurrence", "rqa"]:
+        r, info = _optimize_tolerance_recurrence(
+            signal, r_range=r_range, delay=delay, dimension=dimension
+        )
+        info.update({"Method": "5% Recurrence Rate"})
+    else:
+        raise ValueError("NeuroKit error: complexity_tolerance(): 'method' not recognized.")
+
+    if show is True:
+        _optimize_tolerance_plot(r, info["Values"], info["Scores"], method=method)
     return r, info
 
 
 # =============================================================================
 # Internals
 # =============================================================================
-def _optimize_tolerance_maxapen(signal, r_range=None, delay=None, dimension=None, show=False):
+def _optimize_tolerance_recurrence(signal, r_range=None, delay=None, dimension=None):
+
+    # Optimize missing parameters
+    if delay is None or dimension is None:
+        raise ValueError("If method='RQA', both delay and dimension must be specified.")
+
+    # Compute distance matrix
+    emb = complexity_embedding(signal, delay=delay, dimension=dimension)
+    d = scipy.spatial.distance.cdist(emb, emb, metric="euclidean")
+
+    if r_range is None:
+        r_range = 50
+    if isinstance(r_range, int):
+        r_range = np.linspace(0, np.max(d), r_range + 1)[1:]
+
+    recurrence_rate = np.zeros_like(r_range)
+    # Indices of the lower triangular (without the diagonal)
+    idx = np.tril_indices(len(d), k=-1)
+    n = len(d[idx])
+    for i, r in enumerate(r_range):
+        recurrence_rate[i] = (d[idx] <= r).sum() / n
+    # Closest to 0.05 (5%)
+    optimal = r_range[np.abs(r_range - 0.05).argmin()]
+
+    return optimal, {"Values": r_range, "Scores": recurrence_rate}
+
+
+def _optimize_tolerance_maxapen(signal, r_range=None, delay=None, dimension=None):
 
     # Optimize missing parameters
     if delay is None or dimension is None:
@@ -109,16 +152,32 @@ def _optimize_tolerance_maxapen(signal, r_range=None, delay=None, dimension=None
     return r_range[np.argmax(ApEn)], {"Values": r_range, "Scores": ApEn}
 
 
-def _optimize_tolerance_plot(r, r_range, ApEn, ax=None):
+# =============================================================================
+# Plotting
+# =============================================================================
+def _optimize_tolerance_plot(r, r_range, y_values, ax=None, method="maxApEn"):
+    if method in ["traditional", "sd", "std"]:
+        return None
 
     if ax is None:
         fig, ax = plt.subplots()
     else:
         fig = None
-    ax.set_title("Optimization of Tolerence Threshold (r)")
-    ax.set_xlabel("Tolerence threshold $r$")
-    ax.set_ylabel("Approximate Entropy $ApEn$")
-    ax.plot(r_range, ApEn, "o-", label="$ApEn$", color="#80059c")
+
+    # Custom legend depending on method
+    if method in ["maxapen", "optimize"]:
+        ylabel = "Approximate Entropy $ApEn$"
+        legend = "$ApEn$"
+    else:
+        ylabel = "Recurrence Rate $RR$"
+        legend = "$RR$"
+        y_values *= 100  # Convert to percentage
+        ax.yaxis.set_major_formatter(matplotlib.ticker.PercentFormatter())
+
+    ax.set_title("Optimization of Tolerance Threshold (r)")
+    ax.set_xlabel("Tolerance threshold $r$")
+    ax.set_ylabel(ylabel)
+    ax.plot(r_range, y_values, "o-", label=legend, color="#80059c")
     ax.axvline(x=r, color="#E91E63", label="Optimal r: " + str(np.round(r, 3)))
     ax.legend(loc="upper right")
 
diff --git a/neurokit2/complexity/utils.py b/neurokit2/complexity/utils.py
index 07754f4fa6..cc3db785f4 100644
--- a/neurokit2/complexity/utils.py
+++ b/neurokit2/complexity/utils.py
@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 import matplotlib.pyplot as plt
 import numpy as np
+import sklearn.metrics
 import sklearn.neighbors
 
 from .complexity_embedding import complexity_embedding
@@ -127,7 +128,11 @@ def distrange(x, y):
 
 
 def _get_count_fuzzy(embedded, tolerance, distance="chebyshev", n=1):
-    dist = sklearn.neighbors.DistanceMetric.get_metric(distance)
+    # TODO: remove this compatibility patch once sklearn > 1.2 is out
+    if sklearn.__version__ < "1.0":
+        dist = sklearn.neighbors.DistanceMetric.get_metric(distance)
+    else:
+        dist = sklearn.metrics.DistanceMetric.get_metric(distance)
     dist = dist.pairwise(embedded)
 
     if n > 1:
diff --git a/neurokit2/data/data.py b/neurokit2/data/data.py
index d4d5c5a6ea..f5dfe63f55 100644
--- a/neurokit2/data/data.py
+++ b/neurokit2/data/data.py
@@ -3,7 +3,7 @@
 import os
 
 import pandas as pd
-import sklearn.datasets
+from sklearn import datasets as sklearn_datasets
 
 
 def data(dataset="bio_eventrelated_100hz"):
@@ -40,7 +40,7 @@ def data(dataset="bio_eventrelated_100hz"):
 
     # Specific requests
     if dataset == "iris":
-        data = sklearn.datasets.load_iris()
+        data = sklearn_datasets.load_iris()
         return pd.DataFrame(data.data, columns=data["feature_names"])
 
     if dataset in ["eeg", "eeg.txt"]:
diff --git a/neurokit2/data/read_bitalino.py b/neurokit2/data/read_bitalino.py
index 3581c53207..6d9658fa27 100644
--- a/neurokit2/data/read_bitalino.py
+++ b/neurokit2/data/read_bitalino.py
@@ -1,5 +1,6 @@
 # -*- coding: utf-8 -*-
 import json
+import os
 
 import numpy as np
 import pandas as pd
@@ -7,7 +8,13 @@
 from ..signal import signal_resample
 
 
-def read_bitalino(filename, sampling_rate="max", resample_method="interpolation"):
+def read_bitalino(
+    filename,
+    sampling_rate="max",
+    resample_method="interpolation",
+    events_annotation=False,
+    events_annotation_directory=None,
+):
     """Read and format a  OpenSignals file (e.g., from BITalino) into a pandas' dataframe.
 
     The function outputs both the dataframe and the sampling rate (retrieved from the
@@ -24,13 +31,20 @@ def read_bitalino(filename, sampling_rate="max", resample_method="interpolation"
         along with the data.
     resample_method : str
         Method of resampling (see `signal_resample()`).
+    events_annotation : bool
+        Defaults to False. If True, will read signal annotation events.
+    events_annotation_directory : str
+        If None (default), reads signal annotation events from the same location where the acquired file is stored. If not,
+        specify the predefined OpenSignals (r)evolution folder directory of where the 'EventsAnnotation.txt' file is stored.
 
     Returns
     ----------
-    df : DataFrame
-        The BITalino file as a pandas dataframe.
-    sampling rate: int
-        The sampling rate at which the data is sampled.
+    df : DataFrame, dict
+        The BITalino file as a pandas dataframe if one device was read, or a dictionary
+        of pandas dataframes (one dataframe per device) if multiple devices are read.
+    info : dict
+        The metadata information containing the sensors, corresponding channel names, sampling rate, and the
+        events annotation timings if `events_annotation` is True.
 
     See Also
     --------
@@ -40,8 +54,9 @@ def read_bitalino(filename, sampling_rate="max", resample_method="interpolation"
     --------
     >>> import neurokit2 as nk
     >>>
-    >>> #data, sampling_rate = nk.read_bitalino("data.txt")
+    >>> # data, sampling_rate = nk.read_bitalino("data.txt")
     """
+
     # read metadata
     with open(filename, "r") as f:
 
@@ -52,29 +67,223 @@ def read_bitalino(filename, sampling_rate="max", resample_method="interpolation"
 
         metadata = json.loads(f.readline()[1:])  # read second line
 
+    if len(list(metadata.keys())) == 1:
+        return _read_bitalino_onedevice(
+            filename,
+            metadata,
+            sampling_rate,
+            resample_method,
+            events_annotation,
+            events_annotation_directory,
+        )
+
+    else:
+        return _read_bitalino_multipledevice(
+            filename,
+            metadata,
+            sampling_rate,
+            resample_method,
+            events_annotation,
+            events_annotation_directory,
+        )
+
+
+# =============================================================================
+# Convenience functions
+# =============================================================================
+def _read_bitalino_onedevice(
+    filename,
+    metadata,
+    sampling_rate="max",
+    resample_method="interpolation",
+    events_annotation=False,
+    events_annotation_directory=None,
+):
+    info = {}  # Initialize empty dict for storing
+
+    # If only one device
     metadata = metadata[
         list(metadata.keys())[0]
     ]  # convert json header to dict (only select first device / MAC address)
     channels = np.arange(len(metadata["channels"])) + 5  # analog channels start from column 5
 
-    # Get desired frequency and produce output accordingly
     data = pd.read_csv(filename, sep="\t", usecols=channels, header=None, comment="#")
+    data.columns = metadata["sensor"]
+    info["sensors"] = metadata["sensor"]
+    info["channel_names"] = metadata["label"]
 
+    # Adjust sampling rate
     if sampling_rate == "max":
         sampling_rate = metadata["sampling rate"]
     else:
-        signals = pd.DataFrame()
-        for i in data:
-            signal = signal_resample(
-                data[i],
-                sampling_rate=metadata["sampling rate"],
-                desired_sampling_rate=sampling_rate,
-                method=resample_method,
+        # resample
+        colnames = list(data.columns)
+        data, sampling_rate = _read_bitalino_resample(
+            data,
+            original_sampling_rate=metadata["sampling rate"],
+            sampling_rate=sampling_rate,
+            resample_method=resample_method,
+        )
+        data.columns = colnames
+    info["sampling_rate"] = sampling_rate
+
+    # Add manual events annotation
+    if events_annotation:
+        events = _read_bitalino_events_annotation(
+            events_annotation_directory, info["channel_names"]
+        )
+
+        for event in events.keys():
+            for chname in events[event].keys():
+                # Initiate event columns in dataframe
+                start = np.zeros(len(data))
+                stop = np.zeros(len(data))
+
+                # Convert timings to samples
+                start_times = [int(i * sampling_rate) for i in events[event][chname]["start"]]
+                stop_times = [int(i * sampling_rate) for i in events[event][chname]["stop"]]
+                start[start_times] = 1
+                stop[stop_times] = 1
+                data[chname + "_" + event + "_start"] = start.astype(int)
+                data[chname + "_" + event + "_stop"] = stop.astype(int)
+        info["events annotation"] = events
+
+    return data, info
+
+
+def _read_bitalino_multipledevice(
+    filename,
+    metadata,
+    sampling_rate="max",
+    resample_method="interpolation",
+    events_annotation=False,
+    events_annotation_directory=None,
+):
+    info = {}  # Initialize empty dict for storing
+
+    # Read from multiple devices
+    devices = list(metadata.keys())
+    data = {}
+    for index, name in enumerate(devices):
+
+        info[name] = {}
+        channels = np.arange(len(metadata[name]["channels"])) + 5 + (5 * index) + (2 * index)
+        # analog channels start from column 5 for each device
+
+        df = pd.read_csv(filename, sep="\t", usecols=channels, header=None, comment="#")
+        df.columns = metadata[name]["sensor"]
+        info[name]["sensors"] = metadata[name]["sensor"]
+        info[name]["channel_names"] = metadata[name]["label"]
+
+        # Adjust sampling rate
+        if sampling_rate == "max":
+            sampling_rate = metadata[name]["sampling rate"]
+        else:
+            # resample
+            colnames = list(df.columns)
+            df, sampling_rate = _read_bitalino_resample(
+                df,
+                original_sampling_rate=metadata[name]["sampling rate"],
+                resampling_rate=sampling_rate,
+                resample_method=resample_method,
             )
-            signal = pd.Series(signal)
-            signals = pd.concat([signals, signal], axis=1)
-        data = signals.copy()
+            df.columns = colnames
+        info[name]["sampling_rate"] = sampling_rate
 
-    data.columns = metadata["sensor"]
+        # Add manual events annotation
+        if events_annotation:
+            metaevents = _read_bitalino_events_annotation(
+                events_annotation_directory, info[name]["channel_names"]
+            )
+            events = metaevents[name.replace(":", "")]
+
+            for event in events.keys():
+                for chname in events[event].keys():
+                    # Initiate event columns in dataframe
+                    start = np.zeros(len(df))
+                    stop = np.zeros(len(df))
+
+                    # Convert timings to samples
+                    start_times = [int(i * sampling_rate) for i in events[event][chname]["start"]]
+                    stop_times = [int(i * sampling_rate) for i in events[event][chname]["stop"]]
+                    start[start_times] = 1
+                    stop[stop_times] = 1
+                    df[chname + "_" + event + "_start"] = start.astype(int)
+                    df[chname + "_" + event + "_stop"] = stop.astype(int)
+            info[name]["events annotation"] = events
+
+    data[name] = df  # dict of dataframes for each device
+    return data, info
+
+
+# =============================================================================
+# Internals
+# =============================================================================
+def _read_bitalino_resample(
+    data, original_sampling_rate, resampling_rate, resample_method="interpolation"
+):
+
+    signals = pd.DataFrame()
+    for i in data:
+        signal = signal_resample(
+            data[i],
+            sampling_rate=original_sampling_rate,
+            desired_sampling_rate=resampling_rate,
+            method=resample_method,
+        )
+        signal = pd.Series(signal)
+        signals = pd.concat([signals, signal], axis=1)
+    data = signals.copy()
+
+    return data, resampling_rate
+
+
+def _read_bitalino_events_annotation(events_annotation_directory=None, channel_names=None):
+    """Read events that are annotated during BITalino signal acquisition.
+    Returns a dictionary containing the start and stop times (in seconds) in each channel detected per unique event
+    (label) within each device."""
+
+    # Get working directory of data file (assume events stored together in same folder)
+    folder = os.listdir(events_annotation_directory)
+    if len([i for i in folder if "_EventsAnnotation.txt" in i]) == 0:
+        raise ValueError(
+            "NeuroKit error: _read_bitalino_events_annotation(): No events annotation file found in the working directory. "
+            + "Please specify `events_annotation_directory` argument to where the events annotation text file is stored."
+        )
+    else:
+        events_file = [i for i in folder if "_EventsAnnotation.txt" in i][0]
+
+    # read metadata
+    with open(events_file, "r") as f:
+
+        if "OpenSignals" not in f.readline():  # read first line
+            raise ValueError(
+                "NeuroKit error: read_bitalino(): Events text file is not in OpenSignals format."
+            )
+
+        eventdata = json.loads(f.readline()[1:])  # read second line
+
+    df = pd.read_csv(events_file, sep="\t", header=None, comment="#").dropna(axis=1)
+    df.columns = eventdata["columns"]["labels"]
+
+    df = df[df["CHANNEL"].isin(channel_names)]  # read only from recorded channels
+
+    # Initialize data
+    metaevents = {}
+    for device in np.unique(df["MAC"]):
+        metaevents[device] = {}
+        for key in np.unique(df["ID"]):
+            key = "label" + str(key)
+            metaevents[device][key] = {}
+
+            device_data = df[df["MAC"] == device]
+            for channel in np.unique(df["CHANNEL"]):
+                metaevents[device][key][channel] = {}
+
+                # Append data
+                start = list(device_data[device_data["CHANNEL"] == channel]["START"])
+                stop = list(device_data[device_data["CHANNEL"] == channel]["END"])
+                metaevents[device][key][channel]["start"] = start
+                metaevents[device][key][channel]["stop"] = stop
 
-    return data, sampling_rate
+    return metaevents
diff --git a/neurokit2/ecg/ecg_clean.py b/neurokit2/ecg/ecg_clean.py
index 4119f75277..dabb415d47 100644
--- a/neurokit2/ecg/ecg_clean.py
+++ b/neurokit2/ecg/ecg_clean.py
@@ -5,7 +5,7 @@
 import pandas as pd
 import scipy.signal
 
-from ..misc import as_vector, NeuroKitWarning
+from ..misc import NeuroKitWarning, as_vector
 from ..signal import signal_filter
 
 
@@ -69,7 +69,7 @@ def ecg_clean(ecg_signal, sampling_rate=1000, method="neurokit"):
         warn(
             "There are " + str(n_missing) + " missing data points in your signal."
             " Filling missing values by using the forward filling method.",
-            category=NeuroKitWarning
+            category=NeuroKitWarning,
         )
         ecg_signal = _ecg_clean_missing(ecg_signal)
 
@@ -118,15 +118,20 @@ def _ecg_clean_missing(ecg_signal):
 
     return ecg_signal
 
+
 # =============================================================================
 # Neurokit
 # =============================================================================
 def _ecg_clean_nk(ecg_signal, sampling_rate=1000):
 
     # Remove slow drift and dc offset with highpass Butterworth.
-    clean = signal_filter(signal=ecg_signal, sampling_rate=sampling_rate, lowcut=0.5, method="butterworth", order=5)
+    clean = signal_filter(
+        signal=ecg_signal, sampling_rate=sampling_rate, lowcut=0.5, method="butterworth", order=5
+    )
 
-    clean = signal_filter(signal=clean, sampling_rate=sampling_rate, method="powerline", powerline=50)
+    clean = signal_filter(
+        signal=clean, sampling_rate=sampling_rate, method="powerline", powerline=50
+    )
     return clean
 
 
@@ -146,7 +151,9 @@ def _ecg_clean_biosppy(ecg_signal, sampling_rate=1000):
 
     #   -> get_filter()
     #     -> _norm_freq()
-    frequency = 2 * np.array(frequency) / sampling_rate  # Normalize frequency to Nyquist Frequency (Fs/2).
+    frequency = (
+        2 * np.array(frequency) / sampling_rate
+    )  # Normalize frequency to Nyquist Frequency (Fs/2).
 
     #     -> get coeffs
     a = np.array([1])
@@ -165,13 +172,15 @@ def _ecg_clean_pantompkins(ecg_signal, sampling_rate=1000):
     """Adapted from https://github.com/PIA-
     Group/BioSPPy/blob/e65da30f6379852ecb98f8e2e0c9b4b5175416c3/biosppy/signals/ecg.py#L69."""
 
-    f1 = 5 / sampling_rate
-    f2 = 15 / sampling_rate
+    f1 = 5 / (0.5 * sampling_rate)
+    f2 = 15 / (0.5 * sampling_rate)
     order = 1
 
-    b, a = scipy.signal.butter(order, [f1 * 2, f2 * 2], btype="bandpass")
+    sos = scipy.signal.butter(order, [f1, f2], btype="bandpass", output="sos")
+    zi_coeff = scipy.signal.sosfilt_zi(sos)
+    zi = zi_coeff * np.mean(ecg_signal)
 
-    return scipy.signal.lfilter(b, a, ecg_signal)  # Return filtered
+    return scipy.signal.sosfilt(sos, ecg_signal, zi=zi)[0]  # Return filtered
 
 
 # =============================================================================
@@ -186,12 +195,14 @@ def _ecg_clean_elgendi(ecg_signal, sampling_rate=1000):
 
     """
 
-    f1 = 8 / sampling_rate
-    f2 = 20 / sampling_rate
+    f1 = 8 / (0.5 * sampling_rate)
+    f2 = 20 / (0.5 * sampling_rate)
 
-    b, a = scipy.signal.butter(2, [f1 * 2, f2 * 2], btype="bandpass")
+    sos = scipy.signal.butter(2, [f1, f2], btype="bandpass", output="sos")
+    zi_coeff = scipy.signal.sosfilt_zi(sos)
+    zi = zi_coeff * np.mean(ecg_signal)
 
-    return scipy.signal.lfilter(b, a, ecg_signal)  # Return filtered
+    return scipy.signal.sosfilt(sos, ecg_signal, zi=zi)[0]  # Return filtered
 
 
 # =============================================================================
@@ -201,12 +212,14 @@ def _ecg_clean_hamilton(ecg_signal, sampling_rate=1000):
     """Adapted from https://github.com/PIA-
     Group/BioSPPy/blob/e65da30f6379852ecb98f8e2e0c9b4b5175416c3/biosppy/signals/ecg.py#L69."""
 
-    f1 = 8 / sampling_rate
-    f2 = 16 / sampling_rate
+    f1 = 8 / (0.5 * sampling_rate)
+    f2 = 16 / (0.5 * sampling_rate)
 
-    b, a = scipy.signal.butter(1, [f1 * 2, f2 * 2], btype="bandpass")
+    sos = scipy.signal.butter(1, [f1, f2], btype="bandpass", output="sos")
+    zi_coeff = scipy.signal.sosfilt_zi(sos)
+    zi = zi_coeff * np.mean(ecg_signal)
 
-    return scipy.signal.lfilter(b, a, ecg_signal)  # Return filtered
+    return scipy.signal.sosfilt(sos, ecg_signal, zi=zi)[0]  # Return filtered
 
 
 # =============================================================================
@@ -223,7 +236,11 @@ def _ecg_clean_engzee(ecg_signal, sampling_rate=1000):
 
     """
 
-    f1 = 48 / sampling_rate
-    f2 = 52 / sampling_rate
-    b, a = scipy.signal.butter(4, [f1 * 2, f2 * 2], btype="bandstop")
-    return scipy.signal.lfilter(b, a, ecg_signal)  # Return filtered
+    f1 = 48 / (0.5 * sampling_rate)
+    f2 = 52 / (0.5 * sampling_rate)
+
+    sos = scipy.signal.butter(4, [f1, f2], btype="bandstop", output="sos")
+    zi_coeff = scipy.signal.sosfilt_zi(sos)
+    zi = zi_coeff * np.mean(ecg_signal)
+
+    return scipy.signal.sosfilt(sos, ecg_signal, zi=zi)[0]  # Return filtered
diff --git a/neurokit2/ecg/ecg_delineate.py b/neurokit2/ecg/ecg_delineate.py
index 67fd3843b3..84240029b0 100644
--- a/neurokit2/ecg/ecg_delineate.py
+++ b/neurokit2/ecg/ecg_delineate.py
@@ -5,15 +5,21 @@
 import scipy.signal
 
 from ..epochs import epochs_create, epochs_to_df
-from ..signal import (signal_findpeaks, signal_formatpeaks, signal_resample,
-                      signal_smooth, signal_zerocrossings, signal_rate)
+from ..signal import (signal_findpeaks, signal_formatpeaks, signal_rate,
+                      signal_resample, signal_smooth, signal_zerocrossings)
 from ..stats import standardize
 from .ecg_peaks import ecg_peaks
 from .ecg_segment import ecg_segment
 
 
 def ecg_delineate(
-    ecg_cleaned, rpeaks=None, sampling_rate=1000, method="dwt", show=False, show_type="peaks", check=False
+    ecg_cleaned,
+    rpeaks=None,
+    sampling_rate=1000,
+    method="dwt",
+    show=False,
+    show_type="peaks",
+    check=False,
 ):
     """Delineate QRS complex.
 
@@ -96,7 +102,10 @@ def ecg_delineate(
         if cols:
             ecg_cleaned = ecg_cleaned[cols[0]].values
         else:
-            raise ValueError("NeuroKit error: ecg_delineate(): Wrong input, we couldn't extract" "cleaned signal.")
+            raise ValueError(
+                "NeuroKit error: ecg_delineate(): Wrong input, we couldn't extract"
+                "cleaned signal."
+            )
 
     elif isinstance(ecg_cleaned, dict):
         for i in ecg_cleaned:
@@ -106,7 +115,10 @@ def ecg_delineate(
                 ecg_cleaned = signals[cols[0]].values
 
             else:
-                raise ValueError("NeuroKit error: ecg_delineate(): Wrong input, we couldn't extract" "cleaned signal.")
+                raise ValueError(
+                    "NeuroKit error: ecg_delineate(): Wrong input, we couldn't extract"
+                    "cleaned signal."
+                )
 
     # Sanitize input for rpeaks
     if rpeaks is None:
@@ -125,7 +137,9 @@ def ecg_delineate(
         waves = _dwt_ecg_delineator(ecg_cleaned, rpeaks, sampling_rate=sampling_rate)
 
     else:
-        raise ValueError("NeuroKit error: ecg_delineate(): 'method' should be one of 'peak'," "'cwt' or 'dwt'.")
+        raise ValueError(
+            "NeuroKit error: ecg_delineate(): 'method' should be one of 'peak'," "'cwt' or 'dwt'."
+        )
 
     # Remove NaN in Peaks, Onsets, and Offsets
     waves_noNA = waves.copy()
@@ -141,7 +155,11 @@ def ecg_delineate(
 
     if show is True:
         _ecg_delineate_plot(
-            ecg_cleaned, rpeaks=rpeaks, signals=signals, signal_features_type=show_type, sampling_rate=sampling_rate
+            ecg_cleaned,
+            rpeaks=rpeaks,
+            signals=signals,
+            signal_features_type=show_type,
+            sampling_rate=sampling_rate,
         )
 
     if check is True:
@@ -155,10 +173,10 @@ def ecg_delineate(
 # =============================================================================
 def _dwt_resample_points(peaks, sampling_rate, desired_sampling_rate):
     """Resample given points to a different sampling rate."""
-    if isinstance(peaks, np.ndarray):    # peaks are passed in from previous processing steps
+    if isinstance(peaks, np.ndarray):  # peaks are passed in from previous processing steps
         # Prevent overflow by converting to np.int64 (peaks might be passed in containing np.int32).
         peaks = peaks.astype(dtype=np.int64)
-    elif isinstance(peaks, list):    # peaks returned from internal functions
+    elif isinstance(peaks, list):  # peaks returned from internal functions
         # Cannot be converted to int since list might contain np.nan. Automatically cast to np.float64 if list contains np.nan.
         peaks = np.array(peaks)
     peaks_resample = peaks * desired_sampling_rate / sampling_rate
@@ -204,7 +222,9 @@ def _dwt_ecg_delineator(ecg, rpeaks, sampling_rate, analysis_sampling_rate=2000)
         speaks.append(S_index)
 
     # dwt to delineate tp waves, onsets, offsets and qrs ontsets and offsets
-    ecg = signal_resample(ecg, sampling_rate=sampling_rate, desired_sampling_rate=analysis_sampling_rate)
+    ecg = signal_resample(
+        ecg, sampling_rate=sampling_rate, desired_sampling_rate=analysis_sampling_rate
+    )
     dwtmatr = _dwt_compute_multiscales(ecg, 9)
 
     # # only for debugging
@@ -216,28 +236,54 @@ def _dwt_ecg_delineator(ecg, rpeaks, sampling_rate, analysis_sampling_rate=2000)
     # plt.show()
     rpeaks_resampled = _dwt_resample_points(rpeaks, sampling_rate, analysis_sampling_rate)
 
-    tpeaks, ppeaks = _dwt_delineate_tp_peaks(ecg, rpeaks_resampled, dwtmatr, sampling_rate=analysis_sampling_rate)
+    tpeaks, ppeaks = _dwt_delineate_tp_peaks(
+        ecg, rpeaks_resampled, dwtmatr, sampling_rate=analysis_sampling_rate
+    )
     qrs_onsets, qrs_offsets = _dwt_delineate_qrs_bounds(
         rpeaks_resampled, dwtmatr, ppeaks, tpeaks, sampling_rate=analysis_sampling_rate
     )
-    ponsets, poffsets = _dwt_delineate_tp_onsets_offsets(ppeaks, rpeaks_resampled, dwtmatr, sampling_rate=analysis_sampling_rate)
+    ponsets, poffsets = _dwt_delineate_tp_onsets_offsets(
+        ppeaks, rpeaks_resampled, dwtmatr, sampling_rate=analysis_sampling_rate
+    )
     tonsets, toffsets = _dwt_delineate_tp_onsets_offsets(
-        tpeaks, rpeaks_resampled, dwtmatr, sampling_rate=analysis_sampling_rate, onset_weight=0.6, duration_onset=0.6
+        tpeaks,
+        rpeaks_resampled,
+        dwtmatr,
+        sampling_rate=analysis_sampling_rate,
+        onset_weight=0.6,
+        duration_onset=0.6,
     )
 
     return dict(
-        ECG_P_Peaks=_dwt_resample_points(ppeaks, analysis_sampling_rate, desired_sampling_rate=sampling_rate),
-        ECG_P_Onsets=_dwt_resample_points(ponsets, analysis_sampling_rate, desired_sampling_rate=sampling_rate),
-        ECG_P_Offsets=_dwt_resample_points(poffsets, analysis_sampling_rate, desired_sampling_rate=sampling_rate),
+        ECG_P_Peaks=_dwt_resample_points(
+            ppeaks, analysis_sampling_rate, desired_sampling_rate=sampling_rate
+        ),
+        ECG_P_Onsets=_dwt_resample_points(
+            ponsets, analysis_sampling_rate, desired_sampling_rate=sampling_rate
+        ),
+        ECG_P_Offsets=_dwt_resample_points(
+            poffsets, analysis_sampling_rate, desired_sampling_rate=sampling_rate
+        ),
         ECG_Q_Peaks=qpeaks,
-        ECG_R_Onsets=_dwt_resample_points(qrs_onsets, analysis_sampling_rate, desired_sampling_rate=sampling_rate),
-        ECG_R_Offsets=_dwt_resample_points(qrs_offsets, analysis_sampling_rate, desired_sampling_rate=sampling_rate),
+        ECG_R_Onsets=_dwt_resample_points(
+            qrs_onsets, analysis_sampling_rate, desired_sampling_rate=sampling_rate
+        ),
+        ECG_R_Offsets=_dwt_resample_points(
+            qrs_offsets, analysis_sampling_rate, desired_sampling_rate=sampling_rate
+        ),
         ECG_S_Peaks=speaks,
-        ECG_T_Peaks=_dwt_resample_points(tpeaks, analysis_sampling_rate, desired_sampling_rate=sampling_rate),
-        ECG_T_Onsets=_dwt_resample_points(tonsets, analysis_sampling_rate, desired_sampling_rate=sampling_rate),
-        ECG_T_Offsets=_dwt_resample_points(toffsets, analysis_sampling_rate, desired_sampling_rate=sampling_rate),
+        ECG_T_Peaks=_dwt_resample_points(
+            tpeaks, analysis_sampling_rate, desired_sampling_rate=sampling_rate
+        ),
+        ECG_T_Onsets=_dwt_resample_points(
+            tonsets, analysis_sampling_rate, desired_sampling_rate=sampling_rate
+        ),
+        ECG_T_Offsets=_dwt_resample_points(
+            toffsets, analysis_sampling_rate, desired_sampling_rate=sampling_rate
+        ),
     )
 
+
 def _dwt_adjust_parameters(rpeaks, sampling_rate, duration=None, target=None):
     average_rate = np.median(signal_rate(peaks=rpeaks, sampling_rate=sampling_rate))
     if target == "degree":
@@ -248,6 +294,7 @@ def _dwt_adjust_parameters(rpeaks, sampling_rate, duration=None, target=None):
         # adjust duration of search by HR
         return np.round(duration * (60 / average_rate), 3)
 
+
 def _dwt_delineate_tp_peaks(
     ecg,
     rpeaks,
@@ -289,12 +336,14 @@ def _dwt_delineate_tp_peaks(
         Epsilon of RMS value of wavelet transform. Appendix (A.4).
     """
     srch_bndry = int(0.5 * qrs_width * sampling_rate)
-    degree_add = _dwt_adjust_parameters(rpeaks, sampling_rate, target='degree')
+    degree_add = _dwt_adjust_parameters(rpeaks, sampling_rate, target="degree")
     # sanitize search duration by HR
-    p2r_duration = _dwt_adjust_parameters(rpeaks, sampling_rate,
-                                          duration=p2r_duration, target='duration')
-    rt_duration = _dwt_adjust_parameters(rpeaks, sampling_rate,
-                                         duration=rt_duration, target='duration')
+    p2r_duration = _dwt_adjust_parameters(
+        rpeaks, sampling_rate, duration=p2r_duration, target="duration"
+    )
+    rt_duration = _dwt_adjust_parameters(
+        rpeaks, sampling_rate, duration=rt_duration, target="duration"
+    )
 
     tpeaks = []
     for rpeak_ in rpeaks:
@@ -313,7 +362,9 @@ def _dwt_delineate_tp_peaks(
 
         ecg_local = ecg[srch_idx_start:srch_idx_end]
         peaks, __ = scipy.signal.find_peaks(np.abs(dwt_local), height=height)
-        peaks = list(filter(lambda p: np.abs(dwt_local[p]) > 0.025 * max(dwt_local), peaks))  # pylint: disable=W0640
+        peaks = list(
+            filter(lambda p: np.abs(dwt_local[p]) > 0.025 * max(dwt_local), peaks)
+        )  # pylint: disable=W0640
         if dwt_local[0] > 0:  # just append
             peaks = [0] + peaks
 
@@ -321,12 +372,18 @@ def _dwt_delineate_tp_peaks(
         candidate_peaks = []
         candidate_peaks_scores = []
         for idx_peak, idx_peak_nxt in zip(peaks[:-1], peaks[1:]):
-            correct_sign = dwt_local[idx_peak] > 0 and dwt_local[idx_peak_nxt] < 0  # pylint: disable=R1716
+            correct_sign = (
+                dwt_local[idx_peak] > 0 and dwt_local[idx_peak_nxt] < 0
+            )  # pylint: disable=R1716
             if correct_sign:
-                idx_zero = signal_zerocrossings(dwt_local[idx_peak:idx_peak_nxt+1])[0] + idx_peak
+                idx_zero = (
+                    signal_zerocrossings(dwt_local[idx_peak : idx_peak_nxt + 1])[0] + idx_peak
+                )
                 # This is the score assigned to each peak. The peak with the highest score will be
                 # selected.
-                score = ecg_local[idx_zero] - (float(idx_zero) / sampling_rate - (rt_duration - 0.5 * qrs_width))
+                score = ecg_local[idx_zero] - (
+                    float(idx_zero) / sampling_rate - (rt_duration - 0.5 * qrs_width)
+                )
                 candidate_peaks.append(idx_zero)
                 candidate_peaks_scores.append(score)
 
@@ -362,9 +419,13 @@ def _dwt_delineate_tp_peaks(
         candidate_peaks = []
         candidate_peaks_scores = []
         for idx_peak, idx_peak_nxt in zip(peaks[:-1], peaks[1:]):
-            correct_sign = dwt_local[idx_peak] > 0 and dwt_local[idx_peak_nxt] < 0  # pylint: disable=R1716
+            correct_sign = (
+                dwt_local[idx_peak] > 0 and dwt_local[idx_peak_nxt] < 0
+            )  # pylint: disable=R1716
             if correct_sign:
-                idx_zero = signal_zerocrossings(dwt_local[idx_peak:idx_peak_nxt+1])[0] + idx_peak
+                idx_zero = (
+                    signal_zerocrossings(dwt_local[idx_peak : idx_peak_nxt + 1])[0] + idx_peak
+                )
                 # This is the score assigned to each peak. The peak with the highest score will be
                 # selected.
                 score = ecg_local[idx_zero] - abs(
@@ -395,11 +456,13 @@ def _dwt_delineate_tp_onsets_offsets(
     degree_offset=2,
 ):
     # sanitize search duration by HR
-    duration_onset = _dwt_adjust_parameters(rpeaks, sampling_rate,
-                                            duration=duration_onset, target='duration')
-    duration_offset = _dwt_adjust_parameters(rpeaks, sampling_rate,
-                                             duration=duration_offset, target='duration')
-    degree = _dwt_adjust_parameters(rpeaks, sampling_rate, target='degree')
+    duration_onset = _dwt_adjust_parameters(
+        rpeaks, sampling_rate, duration=duration_onset, target="duration"
+    )
+    duration_offset = _dwt_adjust_parameters(
+        rpeaks, sampling_rate, duration=duration_offset, target="duration"
+    )
+    degree = _dwt_adjust_parameters(rpeaks, sampling_rate, target="degree")
     onsets = []
     offsets = []
     for i in range(len(peaks)):  # pylint: disable=C0200
@@ -442,10 +505,12 @@ def _dwt_delineate_tp_onsets_offsets(
         if not (-dwt_local[offset_slope_peaks[0] :] < epsilon_offset).any():
             offsets.append(np.nan)
             continue
-        candidate_offsets = np.where(-dwt_local[offset_slope_peaks[0] :] < epsilon_offset)[0] + offset_slope_peaks[0]
+        candidate_offsets = (
+            np.where(-dwt_local[offset_slope_peaks[0] :] < epsilon_offset)[0]
+            + offset_slope_peaks[0]
+        )
         offsets.append(candidate_offsets[0] + srch_idx_start)
 
-
         # # only for debugging
         # events_plot([candidate_offsets, offset_slope_peaks], dwt_local)
         # plt.plot(ecg[srch_idx_start: srch_idx_end], '--', label='ecg')
@@ -455,7 +520,7 @@ def _dwt_delineate_tp_onsets_offsets(
 
 
 def _dwt_delineate_qrs_bounds(rpeaks, dwtmatr, ppeaks, tpeaks, sampling_rate=250):
-    degree = _dwt_adjust_parameters(rpeaks, sampling_rate, target='degree')
+    degree = _dwt_adjust_parameters(rpeaks, sampling_rate, target="degree")
     onsets = []
     for i in range(len(rpeaks)):  # pylint: disable=C0200
         # look for onsets
@@ -477,11 +542,11 @@ def _dwt_delineate_qrs_bounds(rpeaks, dwtmatr, ppeaks, tpeaks, sampling_rate=250
         onsets.append(candidate_onsets[-1] + srch_idx_start)
 
         # only for debugging
-#        import neurokit as nk
-#        events_plot(candidate_onsets, -dwt_local)
-#        plt.plot(ecg[srch_idx_start: srch_idx_end], '--', label='ecg')
-#        plt.legend()
-#        plt.show()
+    #        import neurokit as nk
+    #        events_plot(candidate_onsets, -dwt_local)
+    #        plt.plot(ecg[srch_idx_start: srch_idx_end], '--', label='ecg')
+    #        plt.legend()
+    #        plt.show()
 
     offsets = []
     for i in range(len(rpeaks)):  # pylint: disable=C0200
@@ -500,7 +565,9 @@ def _dwt_delineate_qrs_bounds(rpeaks, dwtmatr, ppeaks, tpeaks, sampling_rate=250
         if not (dwt_local[onset_slope_peaks[0] :] < epsilon_offset).any():
             offsets.append(np.nan)
             continue
-        candidate_offsets = np.where(dwt_local[onset_slope_peaks[0] :] < epsilon_offset)[0] + onset_slope_peaks[0]
+        candidate_offsets = (
+            np.where(dwt_local[onset_slope_peaks[0] :] < epsilon_offset)[0] + onset_slope_peaks[0]
+        )
         offsets.append(candidate_offsets[0] + srch_idx_start)
 
         # # only for debugging
@@ -519,7 +586,13 @@ def _apply_H_filter(signal_i, power=0):
         zeros = np.zeros(2 ** power - 1)
         timedelay = 2 ** power
         banks = np.r_[
-            1.0 / 8, zeros, 3.0 / 8, zeros, 3.0 / 8, zeros, 1.0 / 8,
+            1.0 / 8,
+            zeros,
+            3.0 / 8,
+            zeros,
+            3.0 / 8,
+            zeros,
+            1.0 / 8,
         ]
         signal_f = scipy.signal.convolve(signal_i, banks, mode="full")
         signal_f[:-timedelay] = signal_f[timedelay:]  # timeshift: 2 steps
@@ -553,13 +626,19 @@ def _ecg_delineator_cwt(ecg, rpeaks=None, sampling_rate=1000):
     tpeaks, ppeaks = _peaks_delineator(ecg, rpeaks, sampling_rate=sampling_rate)
 
     # qrs onsets and offsets
-    qrs_onsets, qrs_offsets = _onset_offset_delineator(ecg, rpeaks, peak_type="rpeaks", sampling_rate=sampling_rate)
+    qrs_onsets, qrs_offsets = _onset_offset_delineator(
+        ecg, rpeaks, peak_type="rpeaks", sampling_rate=sampling_rate
+    )
 
     # ppeaks onsets and offsets
-    p_onsets, p_offsets = _onset_offset_delineator(ecg, ppeaks, peak_type="ppeaks", sampling_rate=sampling_rate)
+    p_onsets, p_offsets = _onset_offset_delineator(
+        ecg, ppeaks, peak_type="ppeaks", sampling_rate=sampling_rate
+    )
 
     # tpeaks onsets and offsets
-    t_onsets, t_offsets = _onset_offset_delineator(ecg, tpeaks, peak_type="tpeaks", sampling_rate=sampling_rate)
+    t_onsets, t_offsets = _onset_offset_delineator(
+        ecg, tpeaks, peak_type="tpeaks", sampling_rate=sampling_rate
+    )
 
     # No dwt defined method for Q and S peak
     # Adopting manual method from "peak" method
@@ -622,14 +701,18 @@ def _onset_offset_delineator(ecg, peaks, peak_type="rpeaks", sampling_rate=1000)
             search_window = cwtmatr[2, index_peak - half_wave_width : index_peak]
             prominence = 0.20 * max(search_window)
             height = 0.0
-            wt_peaks, wt_peaks_data = scipy.signal.find_peaks(search_window, height=height, prominence=prominence)
+            wt_peaks, wt_peaks_data = scipy.signal.find_peaks(
+                search_window, height=height, prominence=prominence
+            )
 
         elif peak_type in ["tpeaks", "ppeaks"]:
             search_window = -cwtmatr[4, index_peak - half_wave_width : index_peak]
 
             prominence = 0.10 * max(search_window)
             height = 0.0
-            wt_peaks, wt_peaks_data = scipy.signal.find_peaks(search_window, height=height, prominence=prominence)
+            wt_peaks, wt_peaks_data = scipy.signal.find_peaks(
+                search_window, height=height, prominence=prominence
+            )
 
         if len(wt_peaks) == 0:
             # print("Fail to find onset at index: %d", index_peak)
@@ -646,9 +729,13 @@ def _onset_offset_delineator(ecg, peaks, peak_type="rpeaks", sampling_rate=1000)
                 epsilon_onset = 0.25 * wt_peaks_data["peak_heights"][-1]
             leftbase = wt_peaks_data["left_bases"][-1] + index_peak - half_wave_width
             if peak_type == "rpeaks":
-                candidate_onsets = np.where(cwtmatr[2, nfirst - 100 : nfirst] < epsilon_onset)[0] + nfirst - 100
+                candidate_onsets = (
+                    np.where(cwtmatr[2, nfirst - 100 : nfirst] < epsilon_onset)[0] + nfirst - 100
+                )
             elif peak_type in ["tpeaks", "ppeaks"]:
-                candidate_onsets = np.where(-cwtmatr[4, nfirst - 100 : nfirst] < epsilon_onset)[0] + nfirst - 100
+                candidate_onsets = (
+                    np.where(-cwtmatr[4, nfirst - 100 : nfirst] < epsilon_onset)[0] + nfirst - 100
+                )
 
             candidate_onsets = candidate_onsets.tolist() + [leftbase]
             if len(candidate_onsets) == 0:
@@ -660,12 +747,16 @@ def _onset_offset_delineator(ecg, peaks, peak_type="rpeaks", sampling_rate=1000)
         if peak_type == "rpeaks":
             search_window = -cwtmatr[2, index_peak : index_peak + half_wave_width]
             prominence = 0.50 * max(search_window)
-            wt_peaks, wt_peaks_data = scipy.signal.find_peaks(search_window, height=height, prominence=prominence)
+            wt_peaks, wt_peaks_data = scipy.signal.find_peaks(
+                search_window, height=height, prominence=prominence
+            )
 
         elif peak_type in ["tpeaks", "ppeaks"]:
             search_window = cwtmatr[4, index_peak : index_peak + half_wave_width]
             prominence = 0.10 * max(search_window)
-            wt_peaks, wt_peaks_data = scipy.signal.find_peaks(search_window, height=height, prominence=prominence)
+            wt_peaks, wt_peaks_data = scipy.signal.find_peaks(
+                search_window, height=height, prominence=prominence
+            )
 
         if len(wt_peaks) == 0:
             # print("Fail to find offsets at index: %d", index_peak)
@@ -681,9 +772,13 @@ def _onset_offset_delineator(ecg, peaks, peak_type="rpeaks", sampling_rate=1000)
                 epsilon_offset = 0.4 * wt_peaks_data["peak_heights"][0]
             rightbase = wt_peaks_data["right_bases"][0] + index_peak
             if peak_type == "rpeaks":
-                candidate_offsets = np.where((-cwtmatr[2, nlast : nlast + 100]) < epsilon_offset)[0] + nlast
+                candidate_offsets = (
+                    np.where((-cwtmatr[2, nlast : nlast + 100]) < epsilon_offset)[0] + nlast
+                )
             elif peak_type in ["tpeaks", "ppeaks"]:
-                candidate_offsets = np.where((cwtmatr[4, nlast : nlast + 100]) < epsilon_offset)[0] + nlast
+                candidate_offsets = (
+                    np.where((cwtmatr[4, nlast : nlast + 100]) < epsilon_offset)[0] + nlast
+                )
 
             candidate_offsets = candidate_offsets.tolist() + [rightbase]
             if len(candidate_offsets) == 0:
@@ -724,9 +819,13 @@ def _peaks_delineator(ecg, rpeaks, sampling_rate=1000):
         # set threshold for heights of peaks to find significant peaks in wavelet
         threshold = 0.125 * max(search_window)
         significant_peaks_tp = []
-        significant_peaks_tp = [peaks_tp[j] for j in range(len(peaks_tp)) if heights_tp["peak_heights"][j] > threshold]
+        significant_peaks_tp = [
+            peaks_tp[j] for j in range(len(peaks_tp)) if heights_tp["peak_heights"][j] > threshold
+        ]
 
-        significant_peaks_groups.append(_find_tppeaks(ecg, significant_peaks_tp, sampling_rate=sampling_rate))
+        significant_peaks_groups.append(
+            _find_tppeaks(ecg, significant_peaks_tp, sampling_rate=sampling_rate)
+        )
 
     tpeaks, ppeaks = zip(*[(g[0], g[-1]) for g in significant_peaks_groups])
 
@@ -751,13 +850,19 @@ def _find_tppeaks(ecg, keep_tp, sampling_rate=1000):
     tppeaks = []
     for index_cur, index_next in zip(keep_tp[:-1], keep_tp[1:]):
         # limit 1
-        correct_sign = cwtmatr[4, :][index_cur] < 0 and cwtmatr[4, :][index_next] > 0  # pylint: disable=R1716
+        correct_sign = (
+            cwtmatr[4, :][index_cur] < 0 and cwtmatr[4, :][index_next] > 0
+        )  # pylint: disable=R1716
         #    near = (index_next - index_cur) < max_wv_peak_dist #limit 2
         #    if near and correct_sign:
         if correct_sign:
-            index_zero_cr = signal_zerocrossings(cwtmatr[4, :][index_cur:index_next+1])[0] + index_cur
+            index_zero_cr = (
+                signal_zerocrossings(cwtmatr[4, :][index_cur : index_next + 1])[0] + index_cur
+            )
             nb_idx = int(max_search_duration * sampling_rate)
-            index_max = np.argmax(ecg[index_zero_cr - nb_idx : index_zero_cr + nb_idx]) + (index_zero_cr - nb_idx)
+            index_max = np.argmax(ecg[index_zero_cr - nb_idx : index_zero_cr + nb_idx]) + (
+                index_zero_cr - nb_idx
+            )
             tppeaks.append(index_max)
     if len(tppeaks) == 0:
         tppeaks = [np.nan]
@@ -828,7 +933,10 @@ def _ecg_delineator_peak(ecg, rpeaks=None, sampling_rate=1000):
 def _ecg_delineator_peak_Q(rpeak, heartbeat, R):
     segment = heartbeat[:0]  # Select left hand side
 
-    Q = signal_findpeaks(-1 * segment["Signal"], height_min=0.05 * (segment["Signal"].max() - segment["Signal"].min()))
+    Q = signal_findpeaks(
+        -1 * segment["Signal"],
+        height_min=0.05 * (segment["Signal"].max() - segment["Signal"].min()),
+    )
     if len(Q["Peaks"]) == 0:
         return np.nan, None
     Q = Q["Peaks"][-1]  # Select most right-hand side
@@ -841,7 +949,9 @@ def _ecg_delineator_peak_P(rpeak, heartbeat, R, Q):
         return np.nan, None
 
     segment = heartbeat.iloc[:Q]  # Select left of Q wave
-    P = signal_findpeaks(segment["Signal"], height_min=0.05 * (segment["Signal"].max() - segment["Signal"].min()))
+    P = signal_findpeaks(
+        segment["Signal"], height_min=0.05 * (segment["Signal"].max() - segment["Signal"].min())
+    )
 
     if len(P["Peaks"]) == 0:
         return np.nan, None
@@ -852,7 +962,9 @@ def _ecg_delineator_peak_P(rpeak, heartbeat, R, Q):
 
 def _ecg_delineator_peak_S(rpeak, heartbeat):
     segment = heartbeat[0:]  # Select right hand side
-    S = signal_findpeaks(-segment["Signal"], height_min=0.05 * (segment["Signal"].max() - segment["Signal"].min()))
+    S = signal_findpeaks(
+        -segment["Signal"], height_min=0.05 * (segment["Signal"].max() - segment["Signal"].min())
+    )
 
     if len(S["Peaks"]) == 0:
         return np.nan, None
@@ -865,7 +977,9 @@ def _ecg_delineator_peak_T(rpeak, heartbeat, R, S):
         return np.nan, None
 
     segment = heartbeat.iloc[R + S :]  # Select right of S wave
-    T = signal_findpeaks(segment["Signal"], height_min=0.05 * (segment["Signal"].max() - segment["Signal"].min()))
+    T = signal_findpeaks(
+        segment["Signal"], height_min=0.05 * (segment["Signal"].max() - segment["Signal"].min())
+    )
 
     if len(T["Peaks"]) == 0:
         return np.nan, None
@@ -917,44 +1031,46 @@ def _ecg_delineator_peak_T_offset(rpeak, heartbeat, R, T):
 # =============================================================================
 
 
-def _ecg_delineate_plot(ecg_signal, rpeaks=None, signals=None, signal_features_type="all", sampling_rate=1000):
+def _ecg_delineate_plot(
+    ecg_signal, rpeaks=None, signals=None, signal_features_type="all", sampling_rate=1000
+):
 
     """#    Examples.
 
-#    --------
-#    >>> import neurokit2 as nk
-#    >>> import numpy as np
-#    >>> import pandas as pd
-#    >>> import matplotlib.pyplot as plt
-#
-#    >>> ecg_signal = np.array(pd.read_csv(
-#    "https://raw.githubusercontent.com/neuropsychology/NeuroKit/dev/data/ecg_1000hz.csv"))[:, 1]
-#
-#    >>> # Extract R-peaks locations
-#    >>> _, rpeaks = nk.ecg_peaks(ecg_signal, sampling_rate=1000)
-#
-#    >>> # Delineate the ECG signal with ecg_delineate()
-#    >>> signals, waves = nk.ecg_delineate(ecg_signal, rpeaks, sampling_rate=1000)
-#
-#    >>> # Plot the ECG signal with markings on ECG peaks
-#    >>> _ecg_delineate_plot(ecg_signal, rpeaks=rpeaks, signals=signals,
-#                            signal_features_type='peaks', sampling_rate=1000)
-#
-#    >>> # Plot the ECG signal with markings on boundaries of R peaks
-#    >>> _ecg_delineate_plot(ecg_signal, rpeaks=rpeaks, signals=signals,
-#                            signal_features_type='bound_R', sampling_rate=1000)
-#
-#    >>> # Plot the ECG signal with markings on boundaries of P peaks
-#    >>> _ecg_delineate_plot(ecg_signal, rpeaks=rpeaks, signals=signals,
-#                            signal_features_type='bound_P', sampling_rate=1000)
-#
-#    >>> # Plot the ECG signal with markings on boundaries of T peaks
-#    >>> _ecg_delineate_plot(ecg_signal, rpeaks=rpeaks, signals=signals,
-#                            signal_features_type='bound_T', sampling_rate=1000)
-#
-#    >>> # Plot the ECG signal with markings on all peaks and boundaries
-#    >>> _ecg_delineate_plot(ecg_signal, rpeaks=rpeaks, signals=signals,
-#                            signal_features_type='all', sampling_rate=1000)
+    #    --------
+    #    >>> import neurokit2 as nk
+    #    >>> import numpy as np
+    #    >>> import pandas as pd
+    #    >>> import matplotlib.pyplot as plt
+    #
+    #    >>> ecg_signal = np.array(pd.read_csv(
+    #    "https://raw.githubusercontent.com/neuropsychology/NeuroKit/dev/data/ecg_1000hz.csv"))[:, 1]
+    #
+    #    >>> # Extract R-peaks locations
+    #    >>> _, rpeaks = nk.ecg_peaks(ecg_signal, sampling_rate=1000)
+    #
+    #    >>> # Delineate the ECG signal with ecg_delineate()
+    #    >>> signals, waves = nk.ecg_delineate(ecg_signal, rpeaks, sampling_rate=1000)
+    #
+    #    >>> # Plot the ECG signal with markings on ECG peaks
+    #    >>> _ecg_delineate_plot(ecg_signal, rpeaks=rpeaks, signals=signals,
+    #                            signal_features_type='peaks', sampling_rate=1000)
+    #
+    #    >>> # Plot the ECG signal with markings on boundaries of R peaks
+    #    >>> _ecg_delineate_plot(ecg_signal, rpeaks=rpeaks, signals=signals,
+    #                            signal_features_type='bound_R', sampling_rate=1000)
+    #
+    #    >>> # Plot the ECG signal with markings on boundaries of P peaks
+    #    >>> _ecg_delineate_plot(ecg_signal, rpeaks=rpeaks, signals=signals,
+    #                            signal_features_type='bound_P', sampling_rate=1000)
+    #
+    #    >>> # Plot the ECG signal with markings on boundaries of T peaks
+    #    >>> _ecg_delineate_plot(ecg_signal, rpeaks=rpeaks, signals=signals,
+    #                            signal_features_type='bound_T', sampling_rate=1000)
+    #
+    #    >>> # Plot the ECG signal with markings on all peaks and boundaries
+    #    >>> _ecg_delineate_plot(ecg_signal, rpeaks=rpeaks, signals=signals,
+    #                            signal_features_type='all', sampling_rate=1000)
 
     """
 
@@ -965,7 +1081,9 @@ def _ecg_delineate_plot(ecg_signal, rpeaks=None, signals=None, signal_features_t
     if isinstance(rpeaks, dict):
         rpeaks = rpeaks["ECG_R_Peaks"]
     # Segment the signal around the R-peaks
-    epochs = epochs_create(data, events=rpeaks, sampling_rate=sampling_rate, epochs_start=-0.35, epochs_end=0.55)
+    epochs = epochs_create(
+        data, events=rpeaks, sampling_rate=sampling_rate, epochs_start=-0.35, epochs_end=0.55
+    )
     data = epochs_to_df(epochs)
     data_cols = data.columns.values
 
@@ -997,7 +1115,7 @@ def _ecg_delineate_plot(ecg_signal, rpeaks=None, signals=None, signal_features_t
     data.Label = data.Label.astype(int)
     for label in data.Label.unique():
         epoch_data = data[data.Label == label]
-        ax.plot(epoch_data.Time, epoch_data.Signal, color="grey", alpha=0.2, label="_nolegend_")
+        ax.plot(epoch_data.Time, epoch_data.Signal, color="grey", alpha=0.2)
     for i, feature_type in enumerate(features.columns.values):  # pylint: disable=W0612
         event_data = data[data[feature_type] == 1.0]
         ax.scatter(event_data.Time, event_data.Signal, label=feature_type, alpha=0.5, s=200)
diff --git a/neurokit2/ecg/ecg_findpeaks.py b/neurokit2/ecg/ecg_findpeaks.py
index 558228d8f4..4e5c112b20 100644
--- a/neurokit2/ecg/ecg_findpeaks.py
+++ b/neurokit2/ecg/ecg_findpeaks.py
@@ -5,10 +5,16 @@
 import scipy.signal
 import scipy.stats
 
-from ..signal import signal_findpeaks, signal_plot, signal_sanitize, signal_smooth, signal_zerocrossings
+from ..signal import (
+    signal_findpeaks,
+    signal_plot,
+    signal_sanitize,
+    signal_smooth,
+    signal_zerocrossings,
+)
 
 
-def ecg_findpeaks(ecg_cleaned, sampling_rate=1000, method="neurokit", show=False):
+def ecg_findpeaks(ecg_cleaned, sampling_rate=1000, method="neurokit", show=False, **kwargs):
     """Find R-peaks in an ECG signal.
 
     Low-level function used by `ecg_peaks()` to identify R-peaks in an ECG signal using a different
@@ -22,12 +28,13 @@ def ecg_findpeaks(ecg_cleaned, sampling_rate=1000, method="neurokit", show=False
         The sampling frequency of `ecg_signal` (in Hz, i.e., samples/second).
         Defaults to 1000.
     method : string
-        The algorithm to be used for R-peak detection. Can be one of 'neurokit' (default),
-        'pantompkins1985', 'hamilton2002', 'christov2004', 'gamboa2008', 'elgendi2010', 'engzeemod2012',
-        'kalidas2017', 'martinez2003', 'rodrigues2021' or 'promac'.
+        The algorithm to be used for R-peak detection. For a list of acceptable methods,
+        please refer to the documentation of `ecg_peaks`.
     show : bool
         If True, will return a plot to visualizing the thresholds used in the algorithm.
         Useful for debugging.
+    **kwargs
+        Additional keyword arguments, usually specific for each `method`.
 
     Returns
     -------
@@ -37,7 +44,7 @@ def ecg_findpeaks(ecg_cleaned, sampling_rate=1000, method="neurokit", show=False
 
     See Also
     --------
-    ecg_clean, signal_fixpeaks, ecg_peaks, ecg_rate, ecg_process, ecg_plot
+    ecg_peaks, ecg_clean, signal_fixpeaks, ecg_rate, ecg_process, ecg_plot
 
     Examples
     --------
@@ -91,29 +98,7 @@ def ecg_findpeaks(ecg_cleaned, sampling_rate=1000, method="neurokit", show=False
 
     References
     --------------
-    - Rodrigues, Tiago & Samoutphonh, Sirisack & Plácido da Silva, Hugo & Fred, Ana. (2021).
-      A Low-Complexity R-peak Detection Algorithm with Adaptive Thresholding for Wearable Devices.
-
-    - Gamboa, H. (2008). Multi-modal behavioral biometrics based on hci and electrophysiology.
-      PhD ThesisUniversidade.
-
-    - Zong, W., Heldt, T., Moody, G. B., & Mark, R. G. (2003). An open-source algorithm to
-      detect onset of arterial blood pressure pulses. In Computers in Cardiology, 2003 (pp. 259-262). IEEE.
-
-    - Hamilton, P. (2002, September). Open source ECG analysis. In Computers in cardiology (pp. 101-104). IEEE.
-
-    - Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE transactions on
-      biomedical engineering, (3), 230-236.
-
-    - Engelse, W. A. H., & Zeelenberg, C. (1979). A single scan algorithm for QRS detection and feature
-      extraction IEEE Comput Cardiol. Long Beach: IEEE Computer Society.
-
-    - Lourenço, A., Silva, H., Leite, P., Lourenço, R., & Fred, A. L. (2012). Real Time
-      Electrocardiogram Segmentation for Finger based ECG Biometrics. In Biosignals (pp. 49-54).
-
-    - Nabian, M., Yin, Y., Wormwood, J., Quigley, K. S., Barrett, L. F., Ostadabbas, S. (2018).
-      An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data.
-      IEEE Journal of Translational Engineering in Health and Medicine, 6, 1-11. doi:10.1109/jtehm.2018.2878000
+    Please, see the list of references in the documentation of `ecg_peaks`.
 
     """
     # Try retrieving right column
@@ -128,59 +113,126 @@ def ecg_findpeaks(ecg_cleaned, sampling_rate=1000, method="neurokit", show=False
 
     # Sanitize input
     ecg_cleaned = signal_sanitize(ecg_cleaned)
-
     method = method.lower()  # remove capitalised letters
+
     # Run peak detection algorithm
+    try:
+        func = _ecg_findpeaks_findmethod(method)
+        rpeaks = func(ecg_cleaned, sampling_rate=sampling_rate, show=show, **kwargs)
+    except ValueError as error:
+        raise error
+
+    # Prepare output.
+    info = {"ECG_R_Peaks": rpeaks}
+
+    return info
+
+
+# Returns the peak detector function by name
+def _ecg_findpeaks_findmethod(method):
     if method in ["nk", "nk2", "neurokit", "neurokit2"]:
-        rpeaks = _ecg_findpeaks_neurokit(ecg_cleaned, sampling_rate, show=show)
+        return _ecg_findpeaks_neurokit
     elif method in ["pantompkins", "pantompkins1985"]:
-        rpeaks = _ecg_findpeaks_pantompkins(ecg_cleaned, sampling_rate)
+        return _ecg_findpeaks_pantompkins
     elif method in ["nabian", "nabian2018"]:
-        rpeaks = _ecg_findpeaks_nabian2018(ecg_cleaned, sampling_rate)
+        return _ecg_findpeaks_nabian2018
     elif method in ["gamboa2008", "gamboa"]:
-        rpeaks = _ecg_findpeaks_gamboa(ecg_cleaned, sampling_rate)
+        return _ecg_findpeaks_gamboa
     elif method in ["ssf", "slopesumfunction", "zong", "zong2003"]:
-        rpeaks = _ecg_findpeaks_ssf(ecg_cleaned, sampling_rate)
+        return _ecg_findpeaks_ssf
     elif method in ["hamilton", "hamilton2002"]:
-        rpeaks = _ecg_findpeaks_hamilton(ecg_cleaned, sampling_rate)
+        return _ecg_findpeaks_hamilton
     elif method in ["christov", "christov2004"]:
-        rpeaks = _ecg_findpeaks_christov(ecg_cleaned, sampling_rate)
+        return _ecg_findpeaks_christov
     elif method in ["engzee", "engzee2012", "engzeemod", "engzeemod2012"]:
-        rpeaks = _ecg_findpeaks_engzee(ecg_cleaned, sampling_rate)
+        return _ecg_findpeaks_engzee
     elif method in ["elgendi", "elgendi2010"]:
-        rpeaks = _ecg_findpeaks_elgendi(ecg_cleaned, sampling_rate)
+        return _ecg_findpeaks_elgendi
     elif method in ["kalidas2017", "swt", "kalidas", "kalidastamil", "kalidastamil2017"]:
-        rpeaks = _ecg_findpeaks_kalidas(ecg_cleaned, sampling_rate)
+        return _ecg_findpeaks_kalidas
     elif method in ["martinez2003", "martinez"]:
-        rpeaks = _ecg_findpeaks_WT(ecg_cleaned, sampling_rate)
+        return _ecg_findpeaks_WT
     elif method in ["rodrigues2020", "rodrigues2021", "rodrigues", "asi"]:
-        rpeaks = _ecg_findpeaks_rodrigues(ecg_cleaned, sampling_rate)
+        return _ecg_findpeaks_rodrigues
     elif method in ["promac", "all"]:
-        rpeaks = _ecg_findpeaks_promac(ecg_cleaned, sampling_rate=sampling_rate, threshold=0.33, show=show)
+        return _ecg_findpeaks_promac
     else:
-        raise ValueError("NeuroKit error: ecg_findpeaks(): 'method' should be one of 'neurokit'" "or 'pantompkins'.")
-
-    # Prepare output.
-    info = {"ECG_R_Peaks": rpeaks}
-
-    return info
+        raise ValueError(f"NeuroKit error: ecg_findpeaks(): '{method}' not implemented.")
 
 
 # =============================================================================
 # Probabilistic Methods-Agreement via Convolution (ProMAC)
 # =============================================================================
-def _ecg_findpeaks_promac(signal, sampling_rate=1000, threshold=0.33, show=False, **kwargs):
+def _ecg_findpeaks_promac(
+    signal,
+    sampling_rate=1000,
+    show=False,
+    promac_methods=[
+        "neurokit",
+        "gamboa",
+        "ssf",
+        "engzee",
+        "elgendi",
+        "kalidas",
+        "martinez",
+        "rodrigues",
+    ],
+    threshold=0.33,
+    gaussian_sd=100,
+    **kwargs,
+):
+    """Probabilistic Methods-Agreement via Convolution (ProMAC).
+
+    ProMAC combines the result of several R-peak detectors in a probabilistic way. For a given peak
+    detector, the binary signal representing the peak locations is convolved with a Gaussian
+    distribution, resulting in a propabilistic representation of each peak location. This procedure
+    is repeated for all selected 'promac_methods' and the resulting signals are accumulated. Finally,
+    a threshold is used to accept or reject the peak locations.
 
+    See this discussion for more information on the origins of the method:
+    https://github.com/neuropsychology/NeuroKit/issues/222
+
+    Parameters
+    ----------
+    signal : Union[list, np.array, pd.Series]
+        The (cleaned) ECG channel, e.g. as returned by `ecg_clean()`.
+    sampling_rate : int
+        The sampling frequency of `ecg_signal` (in Hz, i.e., samples/second).
+        Defaults to 1000.
+    show : bool
+        If True, will return a plot to visualizing the thresholds used in the algorithm.
+        Useful for debugging.
+    promac_methods : list of string
+        The algorithms to be used for R-peak detection. See the list of acceptable algorithms for
+        the 'ecg_peaks' function.
+    threshold : float
+        The tolerance for peak acceptance. This value is a percentage of the signal's maximum
+        value. Only peaks found above this tolerance will be finally considered as actual peaks.
+    gaussian_sd : int
+        The standard deviation of the Gaussian distribution used to represent the peak location
+        probability. This value should be in millisencods and is usually taken as the size of
+        QRS complexes.
+
+    Returns
+    -------
+    rpeaks : list of int
+        A list of array positions at which R-peaks occur.
+
+    """
     x = np.zeros(len(signal))
+    promac_methods = [method.lower() for method in promac_methods]  # remove capitalised letters
+    error_list = []  # Stores the failed methods
 
-    x = _ecg_findpeaks_promac_addmethod(signal, sampling_rate, x, _ecg_findpeaks_neurokit, **kwargs)
-    x = _ecg_findpeaks_promac_addmethod(signal, sampling_rate, x, _ecg_findpeaks_gamboa, **kwargs)
-    x = _ecg_findpeaks_promac_addmethod(signal, sampling_rate, x, _ecg_findpeaks_ssf, **kwargs)
-    x = _ecg_findpeaks_promac_addmethod(signal, sampling_rate, x, _ecg_findpeaks_engzee, **kwargs)
-    x = _ecg_findpeaks_promac_addmethod(signal, sampling_rate, x, _ecg_findpeaks_elgendi, **kwargs)
-    x = _ecg_findpeaks_promac_addmethod(signal, sampling_rate, x, _ecg_findpeaks_kalidas, **kwargs)
-    x = _ecg_findpeaks_promac_addmethod(signal, sampling_rate, x, _ecg_findpeaks_WT, **kwargs)
-    x = _ecg_findpeaks_promac_addmethod(signal, sampling_rate, x, _ecg_findpeaks_rodrigues, **kwargs)
+    for method in promac_methods:
+        try:
+            func = _ecg_findpeaks_findmethod(method)
+            x = _ecg_findpeaks_promac_addconvolve(
+                signal, sampling_rate, x, func, gaussian_sd=gaussian_sd, **kwargs
+            )
+        except ValueError:
+            error_list.append(f"Method '{method}' is not valid.")
+        except Exception as error:
+            error_list.append(f"{method} error: {error}")
 
     # Rescale
     x = x / np.max(x)
@@ -193,26 +245,32 @@ def _ecg_findpeaks_promac(signal, sampling_rate=1000, threshold=0.33, show=False
 
     if show is True:
         signal_plot([signal, convoluted], standardize=True)
-        [plt.axvline(x=peak, color="red", linestyle="--") for peak in peaks]  # pylint: disable=W0106
+        [
+            plt.axvline(x=peak, color="red", linestyle="--") for peak in peaks
+        ]  # pylint: disable=W0106
+
+    # I am not sure if mandatory print the best option
+    if error_list:  # empty?
+        print(error_list)
 
     return peaks
 
 
-def _ecg_findpeaks_promac_addmethod(signal, sampling_rate, x, fun, **kwargs):
+# _ecg_findpeaks_promac_addmethod + _ecg_findpeaks_promac_convolve
+# Joining them makes parameters exposition more consistent
+def _ecg_findpeaks_promac_addconvolve(signal, sampling_rate, x, fun, gaussian_sd=100, **kwargs):
     peaks = fun(signal, sampling_rate=sampling_rate, **kwargs)
-    x += _ecg_findpeaks_promac_convolve(signal, peaks, sampling_rate=sampling_rate)
-    return x
-
 
-def _ecg_findpeaks_promac_convolve(signal, peaks, sampling_rate=1000):
-    x = np.zeros(len(signal))
-    x[peaks] = 1
+    mask = np.zeros(len(signal))
+    mask[peaks] = 1
 
-    # Because a typical QRS is roughly defined within about 100ms
-    sd = sampling_rate / 10
+    # SD is defined as a typical QRS size, which for adults if 100ms
+    sd = sampling_rate * gaussian_sd / 1000
     shape = scipy.stats.norm.pdf(np.linspace(-sd * 4, sd * 4, num=int(sd * 8)), loc=0, scale=sd)
 
-    return np.convolve(x, shape, "same")  # Return convolved
+    x += np.convolve(mask, shape, "same")
+
+    return x
 
 
 # =============================================================================
@@ -299,7 +357,7 @@ def _ecg_findpeaks_neurokit(
 # =============================================================================
 # Pan & Tompkins (1985)
 # =============================================================================
-def _ecg_findpeaks_pantompkins(signal, sampling_rate=1000):
+def _ecg_findpeaks_pantompkins(signal, sampling_rate=1000, **kwargs):
     """From https://github.com/berndporr/py-ecg-detectors/
 
     - Jiapu Pan and Willis J. Tompkins. A Real-Time QRS Detection Algorithm.
@@ -323,7 +381,7 @@ def _ecg_findpeaks_pantompkins(signal, sampling_rate=1000):
 # ===========================================================================
 # Nabian et al. (2018)
 # ===========================================================================
-def _ecg_findpeaks_nabian2018(signal, sampling_rate=1000):
+def _ecg_findpeaks_nabian2018(signal, sampling_rate=1000, **kwargs):
     """R peak detection method by Nabian et al. (2018) inspired by the Pan-Tompkins algorithm.
 
     - Nabian, M., Yin, Y., Wormwood, J., Quigley, K. S., Barrett, L. F., &amp; Ostadabbas, S. (2018).
@@ -353,7 +411,7 @@ def _ecg_findpeaks_nabian2018(signal, sampling_rate=1000):
 # =============================================================================
 # Hamilton (2002)
 # =============================================================================
-def _ecg_findpeaks_hamilton(signal, sampling_rate=1000):
+def _ecg_findpeaks_hamilton(signal, sampling_rate=1000, **kwargs):
     """From https://github.com/berndporr/py-ecg-detectors/
 
     - Hamilton, Open Source ECG Analysis Software Documentation, E.P.Limited, 2002.
@@ -385,7 +443,9 @@ def _ecg_findpeaks_hamilton(signal, sampling_rate=1000):
 
     for i in range(len(ma)):  # pylint: disable=C0200,R1702
 
-        if i > 0 and i < len(ma) - 1 and ma[i - 1] < ma[i] and ma[i + 1] < ma[i]:  # pylint: disable=R1716
+        if (
+            i > 0 and i < len(ma) - 1 and ma[i - 1] < ma[i] and ma[i + 1] < ma[i]
+        ):  # pylint: disable=R1716
             peak = i
             peaks.append(peak)
             if ma[peak] > th and (peak - QRS[-1]) > 0.3 * sampling_rate:
@@ -399,7 +459,10 @@ def _ecg_findpeaks_hamilton(signal, sampling_rate=1000):
                 if RR_ave != 0.0 and QRS[-1] - QRS[-2] > 1.5 * RR_ave:
                     missed_peaks = peaks[idx[-2] + 1 : idx[-1]]
                     for missed_peak in missed_peaks:
-                        if missed_peak - peaks[idx[-2]] > int(0.360 * sampling_rate) and ma[missed_peak] > 0.5 * th:
+                        if (
+                            missed_peak - peaks[idx[-2]] > int(0.36 * sampling_rate)
+                            and ma[missed_peak] > 0.5 * th
+                        ):
                             QRS.append(missed_peak)
                             QRS.sort()
                             break
@@ -429,7 +492,7 @@ def _ecg_findpeaks_hamilton(signal, sampling_rate=1000):
 # =============================================================================
 # Slope Sum Function (SSF) - Zong et al. (2003)
 # =============================================================================
-def _ecg_findpeaks_ssf(signal, sampling_rate=1000, threshold=20, before=0.03, after=0.01):
+def _ecg_findpeaks_ssf(signal, sampling_rate=1000, threshold=20, before=0.03, after=0.01, **kwargs):
     """From https://github.com/PIA-
     Group/BioSPPy/blob/e65da30f6379852ecb98f8e2e0c9b4b5175416c3/biosppy/signals/ecg.py#L448.
 
@@ -479,7 +542,7 @@ def _ecg_findpeaks_ssf(signal, sampling_rate=1000, threshold=20, before=0.03, af
 # =============================================================================
 # Christov (2004)
 # =============================================================================
-def _ecg_findpeaks_christov(signal, sampling_rate=1000):
+def _ecg_findpeaks_christov(signal, sampling_rate=1000, **kwargs):
     """From https://github.com/berndporr/py-ecg-detectors/
 
     - Ivaylo I. Christov, Real time electrocardiogram QRS detection using combined adaptive threshold,
@@ -611,7 +674,7 @@ def _ecg_findpeaks_christov(signal, sampling_rate=1000):
 # =============================================================================
 # Gamboa (2008)
 # =============================================================================
-def _ecg_findpeaks_gamboa(signal, sampling_rate=1000, tol=0.002):
+def _ecg_findpeaks_gamboa(signal, sampling_rate=1000, tol=0.002, **kwargs):
     """From https://github.com/PIA-
     Group/BioSPPy/blob/e65da30f6379852ecb98f8e2e0c9b4b5175416c3/biosppy/signals/ecg.py#L834.
 
@@ -656,7 +719,7 @@ def _ecg_findpeaks_gamboa(signal, sampling_rate=1000, tol=0.002):
 # =============================================================================
 # Engzee Modified (2012)
 # =============================================================================
-def _ecg_findpeaks_engzee(signal, sampling_rate=1000):
+def _ecg_findpeaks_engzee(signal, sampling_rate=1000, **kwargs):
     """From https://github.com/berndporr/py-ecg-detectors/
 
     - C. Zeelenberg, A single scan algorithm for QRS detection and feature extraction, IEEE Comp.
@@ -761,12 +824,19 @@ def _ecg_findpeaks_engzee(signal, sampling_rate=1000):
 
         if counter > neg_threshold:
             unfiltered_section = signal[thi_list[-1] - int(0.01 * sampling_rate) : i]
-            r_peaks.append(engzee_fake_delay + np.argmax(unfiltered_section) + thi_list[-1] - int(0.01 * sampling_rate))
+            r_peaks.append(
+                engzee_fake_delay
+                + np.argmax(unfiltered_section)
+                + thi_list[-1]
+                - int(0.01 * sampling_rate)
+            )
             counter = 0
             thi = False
             thf = False
 
-    r_peaks.pop(0)  # removing the 1st detection as it 1st needs the QRS complex amplitude for the threshold
+    r_peaks.pop(
+        0
+    )  # removing the 1st detection as it 1st needs the QRS complex amplitude for the threshold
     r_peaks = np.array(r_peaks, dtype="int")
     return r_peaks
 
@@ -774,7 +844,7 @@ def _ecg_findpeaks_engzee(signal, sampling_rate=1000):
 # =============================================================================
 # Stationary Wavelet Transform  (SWT) - Kalidas and Tamil (2017)
 # =============================================================================
-def _ecg_findpeaks_kalidas(signal, sampling_rate=1000):
+def _ecg_findpeaks_kalidas(signal, sampling_rate=1000, **kwargs):
     """From https://github.com/berndporr/py-ecg-detectors/
 
     - Vignesh Kalidas and Lakshman Tamil (2017). Real-time QRS detector using Stationary Wavelet Transform
@@ -811,11 +881,11 @@ def _ecg_findpeaks_kalidas(signal, sampling_rate=1000):
 
     squared = swt_ecg * swt_ecg
 
-    f1 = 0.01 / sampling_rate
-    f2 = 10 / sampling_rate
+    f1 = 0.01 / (0.5 * sampling_rate)
+    f2 = 10 / (0.5 * sampling_rate)
 
-    b, a = scipy.signal.butter(3, [f1 * 2, f2 * 2], btype="bandpass")
-    filtered_squared = scipy.signal.lfilter(b, a, squared)
+    sos = scipy.signal.butter(3, [f1, f2], btype="bandpass", output="sos")
+    filtered_squared = scipy.signal.sosfilt(sos, squared)
 
     # Drop padding to avoid detecting peaks inside it (#456)
     filtered_squared = filtered_squared[:signal_length]
@@ -829,7 +899,7 @@ def _ecg_findpeaks_kalidas(signal, sampling_rate=1000):
 # =============================================================================
 # Elgendi et al. (2010)
 # =============================================================================
-def _ecg_findpeaks_elgendi(signal, sampling_rate=1000):
+def _ecg_findpeaks_elgendi(signal, sampling_rate=1000, **kwargs):
     """From https://github.com/berndporr/py-ecg-detectors/
 
     - Elgendi, Mohamed & Jonkman, Mirjam & De Boer, Friso. (2010). Frequency Bands Effects on QRS Detection.
@@ -874,7 +944,7 @@ def _ecg_findpeaks_elgendi(signal, sampling_rate=1000):
 # Continuous Wavelet Transform (CWT) - Martinez et al. (2003)
 # =============================================================================
 #
-def _ecg_findpeaks_WT(signal, sampling_rate=1000):
+def _ecg_findpeaks_WT(signal, sampling_rate=1000, **kwargs):
     # Try loading pywt
     try:
         import pywt
@@ -940,7 +1010,7 @@ def _ecg_findpeaks_WT(signal, sampling_rate=1000):
 # =============================================================================
 
 
-def _ecg_findpeaks_rodrigues(signal, sampling_rate=1000):
+def _ecg_findpeaks_rodrigues(signal, sampling_rate=1000, **kwargs):
     """Segmenter by Tiago Rodrigues, inspired by on Gutierrez-Rivas (2015) and Sadhukhan (2012).
 
     References
@@ -1011,7 +1081,7 @@ def _ecg_findpeaks_rodrigues(signal, sampling_rate=1000):
 # =============================================================================
 
 
-def _ecg_findpeaks_MWA(signal, window_size):
+def _ecg_findpeaks_MWA(signal, window_size, **kwargs):
     """Based on https://github.com/berndporr/py-ecg-detectors/
 
     Optimized for vectorized computation.
@@ -1040,7 +1110,7 @@ def _ecg_findpeaks_MWA(signal, window_size):
     return mwa
 
 
-def _ecg_findpeaks_peakdetect(detection, sampling_rate=1000):
+def _ecg_findpeaks_peakdetect(detection, sampling_rate=1000, **kwargs):
     """Based on https://github.com/berndporr/py-ecg-detectors/
 
     Optimized for vectorized computation.
@@ -1079,7 +1149,8 @@ def _ecg_findpeaks_peakdetect(detection, sampling_rate=1000):
                 if peak - last_peak > RR_missed:
                     missed_peaks = peaks[last_index + 1 : index]
                     missed_peaks = missed_peaks[
-                        (missed_peaks > last_peak + min_missed_distance) & (missed_peaks < peak - min_missed_distance)
+                        (missed_peaks > last_peak + min_missed_distance)
+                        & (missed_peaks < peak - min_missed_distance)
                     ]
                     threshold_I2 = 0.5 * threshold_I1
                     missed_peaks = missed_peaks[detection[missed_peaks] > threshold_I2]
diff --git a/neurokit2/ecg/ecg_peaks.py b/neurokit2/ecg/ecg_peaks.py
index 5d9068f3a3..58ddf6ff8b 100644
--- a/neurokit2/ecg/ecg_peaks.py
+++ b/neurokit2/ecg/ecg_peaks.py
@@ -4,10 +4,11 @@
 from .ecg_findpeaks import ecg_findpeaks
 
 
-def ecg_peaks(ecg_cleaned, sampling_rate=1000, method="neurokit", correct_artifacts=False):
+def ecg_peaks(ecg_cleaned, sampling_rate=1000, method="neurokit", correct_artifacts=False, **kwargs):
     """Find R-peaks in an ECG signal.
 
-    Find R-peaks in an ECG signal using the specified method.
+    Find R-peaks in an ECG signal using the specified method. The method accepts unfiltered ECG signals
+    as input, althought it is expected that a filtered (cleaned) ECG will result in better results.
 
     Parameters
     ----------
@@ -17,12 +18,15 @@ def ecg_peaks(ecg_cleaned, sampling_rate=1000, method="neurokit", correct_artifa
         The sampling frequency of `ecg_signal` (in Hz, i.e., samples/second).
         Defaults to 1000.
     method : string
-        The algorithm to be used for R-peak detection. Can be one of 'neurokit' (default), 'pantompkins1985',
-        'hamilton2002', 'christov2004', 'gamboa2008', 'elgendi2010', 'engzeemod2012' or 'kalidas2017'.
+        The algorithm to be used for R-peak detection. Can be one of 'neurokit' (default), 'pantompkins1985'
+        'nabian2018', 'gamboa2008', 'zong2003', 'hamilton2002', 'christov2004', 'engzeemod2012', 'elgendi2010',
+        'kalidas2017', 'martinez2003', 'rodrigues2021' or 'promac'.
     correct_artifacts : bool
         Whether or not to identify artifacts as defined by Jukka A. Lipponen & Mika P. Tarvainen (2019):
         A robust algorithm for heart rate variability time series artefact correction using novel beat
         classification, Journal of Medical Engineering & Technology, DOI: 10.1080/03091902.2019.1640306.
+    **kwargs
+        Additional keyword arguments, usually specific for each method.
 
     Returns
     -------
@@ -31,7 +35,8 @@ def ecg_peaks(ecg_cleaned, sampling_rate=1000, method="neurokit", correct_artifa
         in a list of zeros with the same length as `ecg_cleaned`. Accessible with the keys "ECG_R_Peaks".
     info : dict
         A dictionary containing additional information, in this case the samples at which R-peaks occur,
-        accessible with the key "ECG_R_Peaks", as well as the signals' sampling rate.
+        accessible with the key "ECG_R_Peaks", as well as the signals' sampling rate, accessible with
+        the key "sampling_rate".
 
     See Also
     --------
@@ -50,25 +55,64 @@ def ecg_peaks(ecg_cleaned, sampling_rate=1000, method="neurokit", correct_artifa
 
     References
     ----------
-    - Gamboa, H. (2008). Multi-modal behavioral biometrics based on hci and electrophysiology.
-      PhD ThesisUniversidade.
-
-    - W. Zong, T. Heldt, G.B. Moody, and R.G. Mark. An open-source algorithm to detect onset of arterial
-      blood pressure pulses. In Computers in Cardiology, 2003, pages 259–262, 2003.
-
-    - Hamilton, Open Source ECG Analysis Software Documentation, E.P.Limited, 2002.
-
-    - Jiapu Pan and Willis J. Tompkins. A Real-Time QRS Detection Algorithm. In: IEEE Transactions on
-      Biomedical Engineering BME-32.3 (1985), pp. 230–236.
-
-    - C. Zeelenberg, A single scan algorithm for QRS detection and feature extraction, IEEE Comp. in
-      Cardiology, vol. 6, pp. 37-42, 1979
-
-    - A. Lourenco, H. Silva, P. Leite, R. Lourenco and A. Fred, "Real Time Electrocardiogram Segmentation
-      for Finger Based ECG Biometrics", BIOSIGNALS 2012, pp. 49-54, 2012.
-
+    'neurokit'
+        Unpublished. See this discussion for more information on the method:
+        https://github.com/neuropsychology/NeuroKit/issues/476
+    'pantompkins1985'
+        - Pan, J., & Tompkins, W. J. (1985). A real-time QRS detection algorithm. IEEE transactions on
+          biomedical engineering, (3), 230-236.
+        From https://github.com/berndporr/py-ecg-detectors/
+    'nabian2018'
+        - Nabian, M., Yin, Y., Wormwood, J., Quigley, K. S., Barrett, L. F., Ostadabbas, S. (2018).
+          An Open-Source Feature Extraction Tool for the Analysis of Peripheral Physiological Data.
+          IEEE Journal of Translational Engineering in Health and Medicine, 6, 1-11.
+          doi:10.1109/jtehm.2018.2878000
+    'gamboa2008'
+        - Gamboa, H. (2008). Multi-modal behavioral biometrics based on hci and electrophysiology.
+          PhD ThesisUniversidade.
+          From https://github.com/PIA-Group/BioSPPy/blob/e65da30f6379852ecb98f8e2e0c9b4b5175416c3/biosppy/signals/ecg.py
+    'zong2003'
+        - Zong, W., Heldt, T., Moody, G. B., & Mark, R. G. (2003). An open-source algorithm to
+          detect onset of arterial blood pressure pulses. In Computers in Cardiology, 2003 (pp. 259-262). IEEE.
+          From BioSPPy.
+    'hamilton2002'
+        - Hamilton, P. (2002, September). Open source ECG analysis. In Computers in cardiology (pp. 101-104). IEEE.
+        From https://github.com/berndporr/py-ecg-detectors/
+    'christov2004'
+        - Ivaylo I. Christov, Real time electrocardiogram QRS detection using combined adaptive threshold,
+          BioMedical Engineering OnLine 2004, vol. 3:28, 2004.
+        From https://github.com/berndporr/py-ecg-detectors/
+    'engzeemod2012'
+        - C. Zeelenberg, A single scan algorithm for QRS detection and feature extraction, IEEE Comp.
+          in Cardiology, vol. 6, pp. 37-42, 1979
+        - A. Lourenco, H. Silva, P. Leite, R. Lourenco and A. Fred, "Real Time Electrocardiogram Segmentation
+          for Finger Based ECG Biometrics", BIOSIGNALS 2012, pp. 49-54, 2012.
+        From https://github.com/berndporr/py-ecg-detectors/
+    'elgendi2010'
+        - Elgendi, Mohamed & Jonkman, Mirjam & De Boer, Friso. (2010). Frequency Bands Effects on QRS Detection.
+          The 3rd International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS2010).
+          428-431.
+        From https://github.com/berndporr/py-ecg-detectors/
+    'kalidas2017'
+        - Vignesh Kalidas and Lakshman Tamil (2017). Real-time QRS detector using Stationary Wavelet Transform
+          for Automated ECG Analysis. In: 2017 IEEE 17th International Conference on Bioinformatics and
+          Bioengineering (BIBE). Uses the Pan and Tompkins thresolding.
+        From https://github.com/berndporr/py-ecg-detectors/
+    'martinez2003'
+        TO BE DEFINED
+    'rodrigues2021'
+        - Gutiérrez-Rivas, R., García, J. J., Marnane, W. P., & Hernández, A. (2015). Novel real-time
+          low-complexity QRS complex detector based on adaptive thresholding. IEEE Sensors Journal,
+          15(10), 6036-6043.
+        - Sadhukhan, D., & Mitra, M. (2012). R-peak detection algorithm for ECG using double difference
+          and RR interval processing. Procedia Technology, 4, 873-877.
+        - Rodrigues, Tiago & Samoutphonh, Sirisack & Plácido da Silva, Hugo & Fred, Ana. (2021).
+          A Low-Complexity R-peak Detection Algorithm with Adaptive Thresholding for Wearable Devices.
+    'promac'
+        Unpublished. See this discussion for more information on the method:
+        https://github.com/neuropsychology/NeuroKit/issues/222
     """
-    rpeaks = ecg_findpeaks(ecg_cleaned, sampling_rate=sampling_rate, method=method)
+    rpeaks = ecg_findpeaks(ecg_cleaned, sampling_rate=sampling_rate, method=method, **kwargs)
 
     if correct_artifacts:
         _, rpeaks = signal_fixpeaks(rpeaks, sampling_rate=sampling_rate, iterative=True, method="Kubios")
diff --git a/neurokit2/eeg/eeg_1f.py b/neurokit2/eeg/eeg_1f.py
new file mode 100644
index 0000000000..a7f0db17e1
--- /dev/null
+++ b/neurokit2/eeg/eeg_1f.py
@@ -0,0 +1,41 @@
+# -*- coding: utf-8 -*-
+# import numpy as np
+# import pandas as pd
+
+
+# def mne_channel_extract(raw):
+#     """1/f Neural Noise
+
+#     Extract parameters related to the 1/f structure of the EEG power spectrum.
+
+#     Parameters
+#     ----------
+#     raw : mne.io.Raw
+#         Raw EEG data.
+
+#     Returns
+#     ----------
+#     DataFrame
+#         A DataFrame or Series containing the channel(s).
+
+#     Example
+#     ----------
+#     >>> import neurokit2 as nk
+#     >>> import mne
+#     >>>
+#     >>> raw = nk.mne_data("raw")
+#     >>>
+#     """
+#     import mne
+
+#     import neurokit2 as nk
+
+#     raw = nk.mne_data("raw")
+#     raw.plot_psd(fmin=0, fmax=40.0, picks=["EEG 050"])
+
+#     channel = nk.mne_channel_extract(raw, what=["EEG 050"]).values
+#     psd = nk.signal_psd(
+#         channel, sampling_rate=raw.info["sfreq"], show=True, max_frequency=40, method="multitapers"
+#     )
+#     plt.loglog(psd["Frequency"], psd["Power"])
+#     plt.plot(psd["Frequency"], np.log(psd["Power"]))
diff --git a/neurokit2/eeg/eeg_rereference.py b/neurokit2/eeg/eeg_rereference.py
index 03522c4f59..fade7c38a5 100644
--- a/neurokit2/eeg/eeg_rereference.py
+++ b/neurokit2/eeg/eeg_rereference.py
@@ -6,6 +6,18 @@
 def eeg_rereference(eeg, reference="average", robust=False, **kwargs):
     """EEG Rereferencing
 
+    This function can be used for arrays as well as MNE objects.
+
+    EEG recordings measure differences in electrical potentials between two points, which means the
+    signal displayed at any channel is in fact the difference in electrical potential to some other
+    recording site. Primarily, this other recording site is the ground electrode, which picks up
+    electrical noise that does not reach the other scalp electrodes. Consequently, the voltage
+    difference between ground and EEG scalp electrodes is also affected by this noise.
+
+    The idea behind re-referencing is to express the voltage at the EEG scalp channels with respect
+    to another, new reference. It can be composed of any recorded channel or an average of several
+    channels.
+
     Parameters
     -----------
     eeg : np.ndarray
diff --git a/neurokit2/eeg/eeg_source.py b/neurokit2/eeg/eeg_source.py
index 2d795bb1be..aa0ef8cf2e 100644
--- a/neurokit2/eeg/eeg_source.py
+++ b/neurokit2/eeg/eeg_source.py
@@ -7,7 +7,7 @@ def eeg_source(raw, src, bem, method="sLORETA", show=False, verbose="WARNING", *
     ----------
     raw : mne.io.Raw
         Raw EEG data.
-    scr : mne.SourceSpace
+    src : mne.SourceSpace
         Source space. See ``mne_templateMRI()`` to obtain it from an MRI template.
     bem : mne.Bem
         BEM model. See ``mne_templateMRI()`` to obtain it from an MRI template.
diff --git a/neurokit2/epochs/epochs_create.py b/neurokit2/epochs/epochs_create.py
index 8c79e6ac95..3f2d878523 100644
--- a/neurokit2/epochs/epochs_create.py
+++ b/neurokit2/epochs/epochs_create.py
@@ -104,7 +104,9 @@ def epochs_create(
     if isinstance(events, int):
         events = np.linspace(0, len(data), events + 2)[1:-1]
     if isinstance(events, dict) is False:
-        events = _events_find_label({"onset": events}, event_labels=event_labels, event_conditions=event_conditions)
+        events = _events_find_label(
+            {"onset": events}, event_labels=event_labels, event_conditions=event_conditions
+        )
 
     event_onsets = list(events["onset"])
     event_labels = list(events["label"])
@@ -115,7 +117,11 @@ def epochs_create(
     if epochs_end == "from_events":
         epochs_end = [i / sampling_rate for i in events["duration"]]
     parameters = listify(
-        onset=event_onsets, label=event_labels, condition=event_conditions, start=epochs_start, end=epochs_end
+        onset=event_onsets,
+        label=event_labels,
+        condition=event_conditions,
+        start=epochs_start,
+        end=epochs_end,
     )
 
     # Find the maximum numbers of samples in an epoch
@@ -124,9 +130,15 @@ def epochs_create(
 
     # Extend data by the max samples in epochs * NaN (to prevent non-complete data)
     length_buffer = epoch_max_duration
-    buffer = pd.DataFrame(index=range(length_buffer), columns=data.columns)
-    data = data.append(buffer, ignore_index=True, sort=False)
-    data = buffer.append(data, ignore_index=True, sort=False)
+
+    # First createa buffer of the same dtype as data and fill with it 0s
+    buffer = pd.DataFrame(0, index=range(length_buffer), columns=data.columns).astype(
+        dtype=data.dtypes
+    )
+    # Only then, we convert the non-integers to nans (because regular numpy's ints cannot be nan)
+    buffer.select_dtypes(exclude="int64").replace({0.0: np.nan}, inplace=True)
+    # Now we can combine the buffer with the data
+    data = pd.concat([buffer, data, buffer], ignore_index=True, sort=False)
 
     # Adjust the Onset of the events for the buffer
     parameters["onset"] = [i + length_buffer for i in parameters["onset"]]
@@ -162,16 +174,16 @@ def epochs_create(
     # Sanitize dtype of individual columns
     for i in epochs:
 
-        for colname, column in epochs[i].select_dtypes(include=['object']).iteritems():
+        for colname, column in epochs[i].select_dtypes(include=["object"]).iteritems():
 
             # Check whether columns are indices or label/condition
             values = column.unique().tolist()
-            zero_or_one = False if False in [x in [0, 1] for x in values] else True
+            zero_or_one = not (False in [x in [0, 1] for x in values])
 
             if zero_or_one:
                 # Force to int64
-                epochs[i][colname] = epochs[i][colname].astype('int64')
+                epochs[i][colname] = epochs[i][colname].astype("int64")
             else:
-                epochs[i][colname] = epochs[i][colname].astype('string')
+                epochs[i][colname] = epochs[i][colname].astype("string")
 
     return epochs
diff --git a/neurokit2/events/events_find.py b/neurokit2/events/events_find.py
index 58d4318965..1e25613783 100644
--- a/neurokit2/events/events_find.py
+++ b/neurokit2/events/events_find.py
@@ -91,7 +91,7 @@ def events_find(
     if len(events["onset"]) == 0:
         warn(
             "No events found. Check your event_channel or adjust 'threshold' or 'keep' arguments.",
-            category=NeuroKitWarning
+            category=NeuroKitWarning,
         )
         return events
 
@@ -114,8 +114,12 @@ def events_find(
     # Remove based on interval min
     if inter_min > 0:
         inter = np.diff(events["onset"])
-        events["onset"] = np.concatenate([events["onset"][0:1], events["onset"][1::][inter >= inter_min]])
-        events["duration"] = np.concatenate([events["duration"][0:1], events["duration"][1::][inter >= inter_min]])
+        events["onset"] = np.concatenate(
+            [events["onset"][0:1], events["onset"][1::][inter >= inter_min]]
+        )
+        events["duration"] = np.concatenate(
+            [events["duration"][0:1], events["duration"][1::][inter >= inter_min]]
+        )
 
     # Remove first and last n
     if discard_first > 0:
@@ -125,7 +129,9 @@ def events_find(
         events["onset"] = events["onset"][0 : -1 * discard_last]
         events["duration"] = events["duration"][0 : -1 * discard_last]
 
-    events = _events_find_label(events, event_labels=event_labels, event_conditions=event_conditions)
+    events = _events_find_label(
+        events, event_labels=event_labels, event_conditions=event_conditions
+    )
 
     return events
 
@@ -135,13 +141,15 @@ def events_find(
 # =============================================================================
 
 
-def _events_find_label(events, event_labels=None, event_conditions=None, function_name="events_find"):
+def _events_find_label(
+    events, event_labels=None, event_conditions=None, function_name="events_find"
+):
     # Get n events
     n = len(events["onset"])
 
     # Labels
     if event_labels is None:
-        event_labels = (np.arange(n) + 1).astype(np.str)
+        event_labels = (np.arange(n) + 1).astype(str)
 
     if len(list(set(event_labels))) != n:
         raise ValueError(
diff --git a/neurokit2/misc/expspace.py b/neurokit2/misc/expspace.py
index 3cb4e89bc9..79f603657f 100644
--- a/neurokit2/misc/expspace.py
+++ b/neurokit2/misc/expspace.py
@@ -1,7 +1,7 @@
 import numpy as np
 
 
-def expspace(start, stop, num=50, type=int, base=1):
+def expspace(start, stop, num=50, out=int, base=1):
     """Exponential range.
 
     Creates a list of integer values (by default) of a given length from start to stop, spread by
@@ -15,6 +15,8 @@ def expspace(start, stop, num=50, type=int, base=1):
         Maximum range values.
     num : int
         Number of samples to generate. Default is 50. Must be non-negative.
+    out : type
+        Type of the returned values. Default is int.
     base : float
         If 1, will use ``np.exp()``, if 2 will use ``np.exp2()``.
 
@@ -38,7 +40,7 @@ def expspace(start, stop, num=50, type=int, base=1):
         )  # pylint: disable=E1111
 
     # Round and convert to int
-    if type == int:
+    if out == int:
         seq = np.round(seq).astype(int)
 
     return seq
diff --git a/neurokit2/misc/parallel_run.py b/neurokit2/misc/parallel_run.py
index e1083613f7..e887684752 100644
--- a/neurokit2/misc/parallel_run.py
+++ b/neurokit2/misc/parallel_run.py
@@ -36,11 +36,11 @@ def parallel_run(function, arguments_list, n_jobs=-2, **kwargs):
     # Try loading mne
     try:
         import joblib
-    except ImportError:
+    except ImportError as e:
         raise ImportError(
             "NeuroKit error: parallel_run(): the 'joblib' module is required for this function to run. ",
             "Please install it first (`pip install joblib`).",
-        )
+        ) from e
 
     parallel = joblib.Parallel(n_jobs=n_jobs, **kwargs)
     funs = (joblib.delayed(function)(**arguments) for arguments in arguments_list)
diff --git a/neurokit2/ppg/ppg_findpeaks.py b/neurokit2/ppg/ppg_findpeaks.py
index 0e482400e5..30314e39ba 100644
--- a/neurokit2/ppg/ppg_findpeaks.py
+++ b/neurokit2/ppg/ppg_findpeaks.py
@@ -83,8 +83,9 @@ def _ppg_findpeaks_elgendi(
 
     # Ignore the samples with negative amplitudes and square the samples with
     # values larger than zero.
-    signal[signal < 0] = 0
-    sqrd = signal ** 2
+    signal_abs = signal.copy()
+    signal_abs[signal_abs < 0] = 0
+    sqrd = signal_abs ** 2
 
     # Compute the thresholds for peak detection. Call with show=True in order
     # to visualize thresholds.
@@ -141,7 +142,7 @@ def _ppg_findpeaks_elgendi(
     peaks.pop(0)
 
     if show:
-        ax0.scatter(peaks, signal[peaks], c="r")
+        ax0.scatter(peaks, signal_abs[peaks], c="r")
 
     peaks = np.asarray(peaks).astype(int)
     return peaks
diff --git a/neurokit2/signal/signal_decompose.py b/neurokit2/signal/signal_decompose.py
index 5e9fb1d254..c1a8b85e0e 100644
--- a/neurokit2/signal/signal_decompose.py
+++ b/neurokit2/signal/signal_decompose.py
@@ -114,7 +114,9 @@ def _signal_decompose_ssa(signal, n_components=None):
     for i in range(d):
         X_elem = sigma[i] * np.outer(u[:, i], vt[i, :])
         X_rev = X_elem[::-1]
-        components[:, i] = [X_rev.diagonal(j).mean() for j in range(-X_rev.shape[0] + 1, X_rev.shape[1])]
+        components[:, i] = [
+            X_rev.diagonal(j).mean() for j in range(-X_rev.shape[0] + 1, X_rev.shape[1])
+        ]
 
     # Return the components
     return components.T
@@ -160,11 +162,11 @@ def _signal_decompose_emd(signal, ensemble=False):
     """
     try:
         import PyEMD
-    except ImportError:
+    except ImportError as e:
         raise ImportError(
             "NeuroKit error: _signal_decompose_emd(): the 'PyEMD' module is required for this function to run. ",
             "Please install it first (`pip install EMD-signal`).",
-        )
+        ) from e
 
     if ensemble is False:
         emd = PyEMD.EMD(extrema_detection="parabol")
diff --git a/neurokit2/signal/signal_filter.py b/neurokit2/signal/signal_filter.py
index 3a3600d4ae..a2f9ca48a7 100644
--- a/neurokit2/signal/signal_filter.py
+++ b/neurokit2/signal/signal_filter.py
@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 from warnings import warn
 
+import matplotlib.pyplot as plt
 import numpy as np
 import scipy.signal
 
@@ -16,6 +17,7 @@ def signal_filter(
     order=2,
     window_size="default",
     powerline=50,
+    show=False,
 ):
     """Filter a signal using 'butterworth', 'fir' or 'savgol' filters.
 
@@ -46,6 +48,8 @@ def signal_filter(
         (101 if the sampling rate is 1000 Hz).
     powerline : int
         Only used if method is 'powerline'. The powerline frequency (normally 50 Hz or 60 Hz).
+    show : bool
+        If ``True``, plot the filtered signal as an overlay of the original.
 
     See Also
     --------
@@ -114,11 +118,10 @@ def signal_filter(
     """
     method = method.lower()
 
-
     if method in ["sg", "savgol", "savitzky-golay"]:
         filtered = _signal_filter_savgol(signal, sampling_rate, order, window_size=window_size)
     elif method in ["powerline"]:
-            filtered = _signal_filter_powerline(signal, sampling_rate, powerline)
+        filtered = _signal_filter_powerline(signal, sampling_rate, powerline)
     else:
 
         # Sanity checks
@@ -134,13 +137,20 @@ def signal_filter(
         elif method in ["bessel"]:
             filtered = _signal_filter_bessel(signal, sampling_rate, lowcut, highcut, order)
         elif method in ["fir"]:
-            filtered = _signal_filter_fir(signal, sampling_rate, lowcut, highcut, window_size=window_size)
+            filtered = _signal_filter_fir(
+                signal, sampling_rate, lowcut, highcut, window_size=window_size
+            )
         else:
             raise ValueError(
                 "NeuroKit error: signal_filter(): 'method' should be",
                 " one of 'butterworth', 'butterworth_ba', 'bessel',",
-                " 'savgol' or 'fir'."
+                " 'savgol' or 'fir'.",
             )
+
+    if show is True:
+        plt.plot(signal, color="lightgrey")
+        plt.plot(filtered, color="red", alpha=0.9)
+
     return filtered
 
 
@@ -168,7 +178,9 @@ def _signal_filter_savgol(signal, sampling_rate=1000, order=2, window_size="defa
 # =============================================================================
 # FIR
 # =============================================================================
-def _signal_filter_fir(signal, sampling_rate=1000, lowcut=None, highcut=None, window_size="default"):
+def _signal_filter_fir(
+    signal, sampling_rate=1000, lowcut=None, highcut=None, window_size="default"
+):
     """Filter a signal using a FIR filter."""
     try:
         import mne
@@ -206,7 +218,9 @@ def _signal_filter_fir(signal, sampling_rate=1000, lowcut=None, highcut=None, wi
 
 def _signal_filter_butterworth(signal, sampling_rate=1000, lowcut=None, highcut=None, order=5):
     """Filter a signal using IIR Butterworth SOS method."""
-    freqs, filter_type = _signal_filter_sanitize(lowcut=lowcut, highcut=highcut, sampling_rate=sampling_rate)
+    freqs, filter_type = _signal_filter_sanitize(
+        lowcut=lowcut, highcut=highcut, sampling_rate=sampling_rate
+    )
 
     sos = scipy.signal.butter(order, freqs, btype=filter_type, output="sos", fs=sampling_rate)
     filtered = scipy.signal.sosfiltfilt(sos, signal)
@@ -216,7 +230,9 @@ def _signal_filter_butterworth(signal, sampling_rate=1000, lowcut=None, highcut=
 def _signal_filter_butterworth_ba(signal, sampling_rate=1000, lowcut=None, highcut=None, order=5):
     """Filter a signal using IIR Butterworth B/A method."""
     # Get coefficients
-    freqs, filter_type = _signal_filter_sanitize(lowcut=lowcut, highcut=highcut, sampling_rate=sampling_rate)
+    freqs, filter_type = _signal_filter_sanitize(
+        lowcut=lowcut, highcut=highcut, sampling_rate=sampling_rate
+    )
 
     b, a = scipy.signal.butter(order, freqs, btype=filter_type, output="ba", fs=sampling_rate)
     try:
@@ -233,7 +249,9 @@ def _signal_filter_butterworth_ba(signal, sampling_rate=1000, lowcut=None, highc
 
 
 def _signal_filter_bessel(signal, sampling_rate=1000, lowcut=None, highcut=None, order=5):
-    freqs, filter_type = _signal_filter_sanitize(lowcut=lowcut, highcut=highcut, sampling_rate=sampling_rate)
+    freqs, filter_type = _signal_filter_sanitize(
+        lowcut=lowcut, highcut=highcut, sampling_rate=sampling_rate
+    )
 
     sos = scipy.signal.bessel(order, freqs, btype=filter_type, output="sos", fs=sampling_rate)
     filtered = scipy.signal.sosfiltfilt(sos, signal)
@@ -270,7 +288,7 @@ def _signal_filter_sanitize(lowcut=None, highcut=None, sampling_rate=1000, norma
                 "The sampling rate is too low. Sampling rate"
                 " must exceed the Nyquist rate to avoid aliasing problem."
                 f" In this analysis, the sampling rate has to be higher than {2 * highcut} Hz",
-                category=NeuroKitWarning
+                category=NeuroKitWarning,
             )
 
     # Replace 0 by none
diff --git a/neurokit2/signal/signal_fixpeaks.py b/neurokit2/signal/signal_fixpeaks.py
index d8d419858f..0ad42a481e 100644
--- a/neurokit2/signal/signal_fixpeaks.py
+++ b/neurokit2/signal/signal_fixpeaks.py
@@ -112,19 +112,18 @@ def signal_fixpeaks(
 
     # If method Kubios
     if method.lower() == "kubios":
-
         return _signal_fixpeaks_kubios(peaks, sampling_rate=sampling_rate, iterative=iterative, show=show)
-
-    # Else method is NeuroKit
-    return _signal_fixpeaks_neurokit(
-        peaks,
-        sampling_rate=sampling_rate,
-        interval_min=interval_min,
-        interval_max=interval_max,
-        relative_interval_min=relative_interval_min,
-        relative_interval_max=relative_interval_max,
-        robust=robust,
-    )
+    else:
+        # Else method is NeuroKit
+        return _signal_fixpeaks_neurokit(
+            peaks,
+            sampling_rate=sampling_rate,
+            interval_min=interval_min,
+            interval_max=interval_max,
+            relative_interval_min=relative_interval_min,
+            relative_interval_max=relative_interval_max,
+            robust=robust,
+        )
 
 
 # =============================================================================
@@ -417,7 +416,7 @@ def _update_indices(source_idcs, update_idcs, update):
     for s in source_idcs:
         update_idcs = [u + update if u > s else u for u in update_idcs]
 
-    return update_idcs
+    return list(np.unique(update_idcs))
 
 
 def _plot_artifacts_lipponen2019(artifacts, info):
diff --git a/neurokit2/signal/signal_formatpeaks.py b/neurokit2/signal/signal_formatpeaks.py
index 73391a6423..3b6c51d1ed 100644
--- a/neurokit2/signal/signal_formatpeaks.py
+++ b/neurokit2/signal/signal_formatpeaks.py
@@ -11,12 +11,14 @@ def signal_formatpeaks(info, desired_length, peak_indices=None, other_indices=No
     signals = {}
     for feature, values in info.items():
         # Get indices of features
-        if feature != "SCR_RecoveryTime" and any(x in str(feature) for x in ["Peak", "Onset", "Offset", "Trough", "Recovery"]):
+        if feature != "SCR_RecoveryTime" and any(
+            x in str(feature) for x in ["Peak", "Onset", "Offset", "Trough", "Recovery"]
+        ):
             signals[feature] = _signal_from_indices(values, desired_length, 1)
-            signals[feature] = signals[feature].astype('int64')  # indexing of feature using 1 and 0
+            signals[feature] = signals[feature].astype("int64")  # indexing of feature using 1 and 0
 
         # Get values of features
-        elif 'RecoveryTime' in feature:
+        elif "RecoveryTime" in feature:
             # Sanitize indices and values
             other_indices, values = _signal_sanitize_indices(other_indices, values)
             # Append recovery time values to signal
@@ -30,10 +32,12 @@ def signal_formatpeaks(info, desired_length, peak_indices=None, other_indices=No
     signals = pd.DataFrame(signals)
     return signals
 
+
 # =============================================================================
 # Internals
 # =============================================================================
 
+
 def _signal_sanitize_indices(indices, values):
     # Check if nan in indices
     if np.sum(np.isnan(indices)) > 0:
@@ -44,6 +48,7 @@ def _signal_sanitize_indices(indices, values):
 
     return indices, values
 
+
 def _signal_from_indices(indices, desired_length=None, value=1):
     """Generates array of 0 and given values at given indices.
 
@@ -53,9 +58,9 @@ def _signal_from_indices(indices, desired_length=None, value=1):
     # signal = np.zeros(desired_length, dtype=float)
     signal = pd.Series(np.zeros(desired_length, dtype=float))
 
-    if isinstance(indices, list) and (not indices):    # skip empty lists
+    if isinstance(indices, list) and (not indices):  # skip empty lists
         return signal
-    if isinstance(indices, np.ndarray) and (indices.size == 0):    # skip empty arrays
+    if isinstance(indices, np.ndarray) and (indices.size == 0):  # skip empty arrays
         return signal
 
     # Force indices as int
@@ -81,7 +86,9 @@ def _signal_from_indices(indices, desired_length=None, value=1):
     return signal
 
 
-def _signal_formatpeaks_sanitize(peaks, key="Peaks"):    # FIXME: private function not used in this module
+def _signal_formatpeaks_sanitize(
+    peaks, key="Peaks"
+):  # FIXME: private function not used in this module
     # Attempt to retrieve column.
     if isinstance(peaks, tuple):
         if isinstance(peaks[0], (dict, pd.DataFrame)):
diff --git a/neurokit2/signal/signal_noise.py b/neurokit2/signal/signal_noise.py
index 4eea2f998a..8e00272cdc 100644
--- a/neurokit2/signal/signal_noise.py
+++ b/neurokit2/signal/signal_noise.py
@@ -90,7 +90,7 @@ def signal_noise(duration=10, sampling_rate=1000, beta=1):
 
     # If the signal length is even, frequencies +/- 0.5 are equal
     # so the coefficient must be real.
-    if not (n % 2):
+    if not n % 2:
         si[..., -1] = 0
 
     # Regardless of signal length, the DC component must be real
diff --git a/neurokit2/signal/signal_plot.py b/neurokit2/signal/signal_plot.py
index 0504cd5002..fe99a748e2 100644
--- a/neurokit2/signal/signal_plot.py
+++ b/neurokit2/signal/signal_plot.py
@@ -134,15 +134,11 @@ def signal_plot(
             signal[continuous_columns] = nk_standardize(signal[continuous_columns])
 
         if subplots is True:
-            fig, axes = plt.subplots(
-                nrows=len(continuous_columns), ncols=1, sharex=True, **kwargs
-            )
+            _, axes = plt.subplots(nrows=len(continuous_columns), ncols=1, sharex=True, **kwargs)
             for ax, col, color in zip(axes, continuous_columns, colors):
                 ax.plot(signal[col], c=color, **kwargs)
         else:
-            plot = signal[continuous_columns].plot(
-                subplots=False, sharex=True, **kwargs
-            )
+            plot = signal[continuous_columns].plot(subplots=False, sharex=True, **kwargs)
 
         if sampling_rate is None and signal.index.is_integer():
             plt.xlabel("Samples")
diff --git a/neurokit2/signal/signal_psd.py b/neurokit2/signal/signal_psd.py
index b28b376e0d..536d444847 100644
--- a/neurokit2/signal/signal_psd.py
+++ b/neurokit2/signal/signal_psd.py
@@ -22,7 +22,7 @@ def signal_psd(
     order_criteria="KIC",
     order_corrected=True,
     silent=True,
-    **kwargs
+    **kwargs,
 ):
     """Compute the Power Spectral Density (PSD).
 
@@ -109,15 +109,12 @@ def signal_psd(
             signal,
             sampling_rate=sampling_rate,
             min_frequency=min_frequency,
-            max_frequency=max_frequency
+            max_frequency=max_frequency,
         )
 
     # FFT (Numpy)
     elif method in ["fft"]:
-        frequency, power = _signal_psd_fft(
-            signal,
-            sampling_rate=sampling_rate
-        )
+        frequency, power = _signal_psd_fft(signal, sampling_rate=sampling_rate)
 
     # Lombscargle (AtroPy)
     elif method.lower() in ["lombscargle", "lomb"]:
@@ -125,7 +122,7 @@ def signal_psd(
             signal,
             sampling_rate=sampling_rate,
             min_frequency=min_frequency,
-            max_frequency=max_frequency
+            max_frequency=max_frequency,
         )
 
     # Method that are using a window
@@ -158,7 +155,7 @@ def signal_psd(
                 sampling_rate=sampling_rate,
                 nperseg=nperseg,
                 window_type=window_type,
-                **kwargs
+                **kwargs,
             )
 
         # BURG
@@ -212,9 +209,7 @@ def _signal_psd_fft(signal, sampling_rate=1000):
 # =============================================================================
 
 
-def _signal_psd_multitaper(
-    signal, sampling_rate=1000, min_frequency=0, max_frequency=np.inf
-):
+def _signal_psd_multitaper(signal, sampling_rate=1000, min_frequency=0, max_frequency=np.inf):
     try:
         import mne
     except ImportError as e:
@@ -242,9 +237,7 @@ def _signal_psd_multitaper(
 # =============================================================================
 
 
-def _signal_psd_welch(
-    signal, sampling_rate=1000, nperseg=None, window_type="hann", **kwargs
-):
+def _signal_psd_welch(signal, sampling_rate=1000, nperseg=None, window_type="hann", **kwargs):
     if nperseg is not None:
         nfft = int(nperseg * 2)
     else:
@@ -259,7 +252,7 @@ def _signal_psd_welch(
         average="mean",
         nperseg=nperseg,
         window=window_type,
-        **kwargs
+        **kwargs,
     )
 
     return frequency, power
@@ -270,9 +263,7 @@ def _signal_psd_welch(
 # =============================================================================
 
 
-def _signal_psd_lomb(
-    signal, sampling_rate=1000, min_frequency=0, max_frequency=np.inf
-):
+def _signal_psd_lomb(signal, sampling_rate=1000, min_frequency=0, max_frequency=np.inf):
 
     try:
         import astropy.timeseries
@@ -284,12 +275,12 @@ def _signal_psd_lomb(
             minimum_frequency=min_frequency, maximum_frequency=max_frequency
         )
 
-    except ImportError:
+    except ImportError as e:
         raise ImportError(
             "NeuroKit error: signal_psd(): the 'astropy'",
             " module is required for the 'lomb' method to run.",
             " Please install it first (`pip install astropy`).",
-        )
+        ) from e
 
     return frequency, power
 
@@ -391,7 +382,7 @@ def _signal_arma_burg(signal, order=16, criteria="KIC", corrected=True):
         rho = new_rho
         if rho <= 0:
             raise ValueError(
-                "Found a negative value (expected positive strictly) %s." "Decrease the order" % rho
+                f"Found a negative value (expected positive strictly) {rho}. Decrease the order."
             )
 
         ar = np.resize(ar, ar.size + 1)
diff --git a/neurokit2/signal/signal_rate.py b/neurokit2/signal/signal_rate.py
index d21079e9e5..8ad776367f 100644
--- a/neurokit2/signal/signal_rate.py
+++ b/neurokit2/signal/signal_rate.py
@@ -3,7 +3,7 @@
 
 
 def signal_rate(peaks, sampling_rate=1000, desired_length=None, interpolation_method="monotone_cubic"):
-    """Calculate signal rate from a series of peaks.
+    """Calculate signal rate (per minute) from a series of peaks.
 
     This function can also be called either via ``ecg_rate()``, ```ppg_rate()`` or ``rsp_rate()``
     (aliases provided for consistency).
@@ -33,7 +33,7 @@ def signal_rate(peaks, sampling_rate=1000, desired_length=None, interpolation_me
     Returns
     -------
     array
-        A vector containing the rate.
+        A vector containing the rate (peaks per minute).
 
     See Also
     --------
diff --git a/neurokit2/signal/signal_sanitize.py b/neurokit2/signal/signal_sanitize.py
index cb2113a566..cb97ee7323 100644
--- a/neurokit2/signal/signal_sanitize.py
+++ b/neurokit2/signal/signal_sanitize.py
@@ -30,7 +30,7 @@ def signal_sanitize(signal):
     """
 
     # Series check for non-default index
-    if type(signal) is pd.Series and type(signal.index) != pd.RangeIndex:
+    if isinstance(signal, pd.Series) and not isinstance(signal.index, pd.RangeIndex):
         return signal.reset_index(drop=True)
 
     return signal
diff --git a/neurokit2/stats/distance.py b/neurokit2/stats/distance.py
index 62a50ae069..563634f2a3 100644
--- a/neurokit2/stats/distance.py
+++ b/neurokit2/stats/distance.py
@@ -33,7 +33,7 @@ def distance(X=None, method="mahalanobis"):
     >>> vector #doctest: +SKIP
 
     """
-    if isinstance(X, pd.DataFrame) is False:
+    if not isinstance(X, pd.DataFrame):
         X = pd.DataFrame(X)
 
     method = method.lower()  # remove capitalised letters
diff --git a/setup.py b/setup.py
index accc75691d..d7cb7db613 100644
--- a/setup.py
+++ b/setup.py
@@ -77,5 +77,6 @@ def find_version():
         "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
     ],
 )
diff --git a/studies/complexity_benchmark/1_ComputationTime.py b/studies/complexity_benchmark/1_ComputationTime.py
index bc88bd69d7..9a7744e4bd 100644
--- a/studies/complexity_benchmark/1_ComputationTime.py
+++ b/studies/complexity_benchmark/1_ComputationTime.py
@@ -188,9 +188,9 @@ def run_methods(i, x, delay=1, dimension=2):
     data.append(
         time_function(
             x,
-            nk.complexity_mpe,
-            index="MPEn",
-            name="nk_complexity_mpe",
+            nk.complexity_mspe,
+            index="MSPEn",
+            name="nk_complexity_mspe",
             delay=delay,
             dimension=dimension,
         )
diff --git a/studies/complexity_benchmark/README.Rmd b/studies/complexity_benchmark/README.Rmd
index ab56bd8325..47de5bb487 100644
--- a/studies/complexity_benchmark/README.Rmd
+++ b/studies/complexity_benchmark/README.Rmd
@@ -64,19 +64,41 @@ The goal for NeuroKit is to provide the most comprehensive, accurate and fastest
 library(tidyverse)
 library(easystats)
 
-df <- read.csv("data_ComputationTime.csv") 
+df <- read.csv("data_ComputationTime.csv") |> 
+  mutate(Method = factor(Method))
+
+
 
 order <- df |> 
   group_by(Method) |> 
   summarize(Duration = median(Duration)) |> 
   arrange(Duration) |> 
   mutate(Method = factor(Method, levels = Method))
+# order2 <- mgcv::gam(Duration ~ s(Length, by = Method), data = df) |> 
+#   modelbased::estimate_means(at = c("Method", "Length = 10000")) |> 
+#   arrange(Mean) |> 
+#   mutate(Method = factor(Method, levels = Method),
+#          Duration = Mean) 
+
+order |> 
+  ggplot(aes(x = Method, y = Duration)) +
+  geom_point() +
+  theme_modern() +
+  scale_y_log10() +
+  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
 
 df <- mutate(df, Method = fct_relevel(Method, as.character(order$Method)))
+```
 
+
+#### Visualization
+```{r message=FALSE, warning=FALSE}
 dfsummary <- df |>
   group_by(Method, Length) |>
-  summarize(Duration = median(Duration))
+  summarize(CI_low = median(Duration) - sd(Duration),
+            CI_high = median(Duration) + sd(Duration),
+            Duration = median(Duration))
+dfsummary$CI_low[dfsummary$CI_low < 0] <- 0
 
 
 ggplot(dfsummary, aes(x = Method, y = Duration)) + 
@@ -95,11 +117,11 @@ ggplot(dfsummary, aes(x = Method, y = Duration)) +
 ```{r message=FALSE, warning=FALSE}
 df |> 
   ggplot(aes(x = as.factor(Length), y = Duration)) +
-  geom_hline(yintercept = c(0.01), linetype = "dotted") +
+  geom_hline(yintercept = c(0.001, 0.01, 0.1, 1), linetype = "dotted") +
   geom_line(data=dfsummary, aes(group = 1)) +
   geom_violin(aes(fill = Length)) +
   facet_wrap(~Method) +
-  scale_y_log10() +
+  scale_y_log10(breaks = c(0.001, 0.01, 0.1, 1)) +
   scale_fill_viridis_c(guide = "none") +
   theme_modern() +
   theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
diff --git a/studies/complexity_benchmark/data_ComputationTime.csv b/studies/complexity_benchmark/data_ComputationTime.csv
index 0e6ac512c6..c87f3ead62 100644
--- a/studies/complexity_benchmark/data_ComputationTime.csv
+++ b/studies/complexity_benchmark/data_ComputationTime.csv
@@ -1,2001 +1,3701 @@
 Duration,Result,Length,Index,Method,Iteration
-0.0001601999999998327,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,0
-0.007362000000000091,0.7284506128345722,300,Hurst,nk_complexity_hurst,0
-0.003058800000000028,1.1246052210344373,300,LZC,nk_complexity_lzc,0
-0.002737999999999907,0.7575577096795095,300,PLZC,nk_complexity_plzc,0
-0.0072168999999999706,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,0
-0.006335299999999933,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,0
-0.00018059999999997522,0.9987340130978268,300,PE,nk_complexity_pe,0
-0.0025602000000000125,0.9956200476605646,300,WPE,nk_complexity_wpe,0
-0.0020407000000000064,0.8899744973388644,300,MPE,nk_complexity_mpe,0
-0.04360530000000007,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,0
-0.046544700000000105,inf,300,RangeEn,nk_entropy_range_mApEn,0
-0.004104299999999839,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,0
-0.0017132999999998066,8.228818690495881,300,ShanEn,nk_entropy_shannon,0
-0.0027124999999998956,0.8726394003530048,300,SpEn,nk_entropy_spectral,0
-9.620000000021278e-05,0.9972506088650914,300,SVDen,nk_entropy_svd,0
-0.01104320000000003,2.115364222027546,300,CD,nk_fractal_correlation,0
-0.013294000000000139,0.577351342862693,300,DFA,nk_fractal_dfa,0
-0.016924700000000126,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,0
-7.109999999999062e-05,4.3572539387418985,300,Katz,nk_fractal_katz,0
-0.0018673000000002382,1.7989767252374387,300,NLD,nk_fractal_nld,0
-4.440000000016653e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,0
-0.02371970000000001,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,0
-0.0011056999999996542,,300,SDA,nk_fractal_sda,0
-0.0004205999999999932,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,0
-9.850000000000136e-05,0.007175249122036997,300,Fisher,nk_fisher_information,0
-0.00018479999999998498,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,1
-0.007604200000000061,0.7284506128345722,300,Hurst,nk_complexity_hurst,1
-0.003242499999999815,1.1246052210344373,300,LZC,nk_complexity_lzc,1
-0.002271799999999935,0.7575577096795095,300,PLZC,nk_complexity_plzc,1
-0.006887700000000052,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,1
-0.007257199999999964,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,1
-0.00015490000000006887,0.9987340130978268,300,PE,nk_complexity_pe,1
-0.0031218999999997887,0.9956200476605646,300,WPE,nk_complexity_wpe,1
-0.0018881000000001702,0.8899744973388644,300,MPE,nk_complexity_mpe,1
-0.048983200000000116,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,1
-0.046608800000000006,inf,300,RangeEn,nk_entropy_range_mApEn,1
-0.0026661000000001156,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,1
-0.0010100999999997917,8.228818690495881,300,ShanEn,nk_entropy_shannon,1
-0.002064100000000124,0.8726394003530048,300,SpEn,nk_entropy_spectral,1
-0.00010439999999967142,0.9972506088650914,300,SVDen,nk_entropy_svd,1
-0.00975439999999983,2.115364222027546,300,CD,nk_fractal_correlation,1
-0.014113299999999995,0.577351342862693,300,DFA,nk_fractal_dfa,1
-0.01549479999999992,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,1
-4.93000000001409e-05,4.3572539387418985,300,Katz,nk_fractal_katz,1
-0.001086700000000107,1.7989767252374387,300,NLD,nk_fractal_nld,1
-3.779999999986572e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,1
-0.02324370000000009,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,1
-0.0008932999999999858,,300,SDA,nk_fractal_sda,1
-0.00038189999999982405,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,1
-9.77999999998147e-05,0.007175249122036997,300,Fisher,nk_fisher_information,1
-0.00014710000000017764,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,2
-0.0015321000000001472,0.7284506128345722,300,Hurst,nk_complexity_hurst,2
-0.002142499999999936,1.1246052210344373,300,LZC,nk_complexity_lzc,2
-0.002500100000000005,0.7575577096795095,300,PLZC,nk_complexity_plzc,2
-0.00799710000000009,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,2
-0.007238899999999937,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,2
-0.00016279999999979644,0.9987340130978268,300,PE,nk_complexity_pe,2
-0.0031537999999999844,0.9956200476605646,300,WPE,nk_complexity_wpe,2
-0.001927500000000082,0.8899744973388644,300,MPE,nk_complexity_mpe,2
-0.050075300000000045,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,2
-0.050297399999999826,inf,300,RangeEn,nk_entropy_range_mApEn,2
-0.0027468999999999966,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,2
-0.000814400000000326,8.228818690495881,300,ShanEn,nk_entropy_shannon,2
-0.002159000000000244,0.8726394003530048,300,SpEn,nk_entropy_spectral,2
-0.00010440000000011551,0.9972506088650914,300,SVDen,nk_entropy_svd,2
-0.010383500000000101,2.115364222027546,300,CD,nk_fractal_correlation,2
-0.014826600000000134,0.577351342862693,300,DFA,nk_fractal_dfa,2
-0.01477750000000011,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,2
-5.089999999974282e-05,4.3572539387418985,300,Katz,nk_fractal_katz,2
-0.0016955999999996862,1.7989767252374387,300,NLD,nk_fractal_nld,2
-5.099999999957916e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,2
-0.02530460000000012,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,2
-0.0009272000000000169,,300,SDA,nk_fractal_sda,2
-0.00013570000000040494,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,2
-0.00011949999999982808,0.007175249122036997,300,Fisher,nk_fisher_information,2
-0.00018319999999993897,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,3
-0.007361100000000009,0.7284506128345722,300,Hurst,nk_complexity_hurst,3
-0.002617099999999928,1.1246052210344373,300,LZC,nk_complexity_lzc,3
-0.0030015000000001013,0.7575577096795095,300,PLZC,nk_complexity_plzc,3
-0.008096100000000162,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,3
-0.007836999999999872,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,3
-0.0001564000000000565,0.9987340130978268,300,PE,nk_complexity_pe,3
-0.0030064000000000757,0.9956200476605646,300,WPE,nk_complexity_wpe,3
-0.0018796999999999287,0.8899744973388644,300,MPE,nk_complexity_mpe,3
-0.05117080000000018,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,3
-0.0503359000000001,inf,300,RangeEn,nk_entropy_range_mApEn,3
-0.0024676000000001252,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,3
-0.0005939999999999834,8.228818690495881,300,ShanEn,nk_entropy_shannon,3
-0.0019466000000001316,0.8726394003530048,300,SpEn,nk_entropy_spectral,3
-9.660000000000224e-05,0.9972506088650914,300,SVDen,nk_entropy_svd,3
-0.010841699999999843,2.115364222027546,300,CD,nk_fractal_correlation,3
-0.014669299999999996,0.577351342862693,300,DFA,nk_fractal_dfa,3
-0.016939999999999955,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,3
-7.409999999996586e-05,4.3572539387418985,300,Katz,nk_fractal_katz,3
-0.0018247000000002345,1.7989767252374387,300,NLD,nk_fractal_nld,3
-4.4700000000119644e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,3
-0.025657800000000286,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,3
-0.0008415999999997759,,300,SDA,nk_fractal_sda,3
-0.00012330000000027042,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,3
-0.0001012000000000235,0.007175249122036997,300,Fisher,nk_fisher_information,3
-0.00022009999999994534,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,4
-0.007365800000000089,0.7284506128345722,300,Hurst,nk_complexity_hurst,4
-0.0033370999999999817,1.1246052210344373,300,LZC,nk_complexity_lzc,4
-0.002904400000000029,0.7575577096795095,300,PLZC,nk_complexity_plzc,4
-0.008372999999999964,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,4
-0.006995900000000166,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,4
-0.00014639999999999098,0.9987340130978268,300,PE,nk_complexity_pe,4
-0.003014700000000037,0.9956200476605646,300,WPE,nk_complexity_wpe,4
-0.0019411999999998653,0.8899744973388644,300,MPE,nk_complexity_mpe,4
-0.04836189999999996,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,4
-0.04882110000000006,inf,300,RangeEn,nk_entropy_range_mApEn,4
-0.002436099999999719,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,4
-0.0006732000000000404,8.228818690495881,300,ShanEn,nk_entropy_shannon,4
-0.002070699999999981,0.8726394003530048,300,SpEn,nk_entropy_spectral,4
-0.00013069999999970605,0.9972506088650914,300,SVDen,nk_entropy_svd,4
-0.010333999999999843,2.115364222027546,300,CD,nk_fractal_correlation,4
-0.015133899999999922,0.577351342862693,300,DFA,nk_fractal_dfa,4
-0.013674899999999823,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,4
-4.93000000001409e-05,4.3572539387418985,300,Katz,nk_fractal_katz,4
-0.0010118000000001182,1.7989767252374387,300,NLD,nk_fractal_nld,4
-3.3199999999844465e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,4
-0.022304400000000335,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,4
-0.0007791999999997579,,300,SDA,nk_fractal_sda,4
-0.00011779999999994573,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,4
-8.889999999972531e-05,0.007175249122036997,300,Fisher,nk_fisher_information,4
-0.00012849999999997586,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,5
-0.007360599999999939,0.7284506128345722,300,Hurst,nk_complexity_hurst,5
-0.002438699999999905,1.1246052210344373,300,LZC,nk_complexity_lzc,5
-0.003016600000000036,0.7575577096795095,300,PLZC,nk_complexity_plzc,5
-0.007947200000000043,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,5
-0.006728200000000184,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,5
-0.00017080000000002649,0.9987340130978268,300,PE,nk_complexity_pe,5
-0.0030157000000001766,0.9956200476605646,300,WPE,nk_complexity_wpe,5
-0.0018987999999999783,0.8899744973388644,300,MPE,nk_complexity_mpe,5
-0.05094430000000005,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,5
-0.050674999999999804,inf,300,RangeEn,nk_entropy_range_mApEn,5
-0.0024125000000001506,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,5
-0.0006639999999999979,8.228818690495881,300,ShanEn,nk_entropy_shannon,5
-0.0020252000000002823,0.8726394003530048,300,SpEn,nk_entropy_spectral,5
-0.0001012000000000235,0.9972506088650914,300,SVDen,nk_entropy_svd,5
-0.010897900000000238,2.115364222027546,300,CD,nk_fractal_correlation,5
-0.014651199999999864,0.577351342862693,300,DFA,nk_fractal_dfa,5
-0.01697240000000022,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,5
-7.249999999991985e-05,4.3572539387418985,300,Katz,nk_fractal_katz,5
-0.0019070999999999394,1.7989767252374387,300,NLD,nk_fractal_nld,5
-4.510000000035319e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,5
-0.023455100000000062,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,5
-0.0009950000000000792,,300,SDA,nk_fractal_sda,5
-0.00016759999999971242,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,5
-0.00012450000000008288,0.007175249122036997,300,Fisher,nk_fisher_information,5
-0.00012459999999991922,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,6
-0.0017365000000000297,0.7284506128345722,300,Hurst,nk_complexity_hurst,6
-0.0025960000000000427,1.1246052210344373,300,LZC,nk_complexity_lzc,6
-0.0029871999999999677,0.7575577096795095,300,PLZC,nk_complexity_plzc,6
-0.008022900000000055,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,6
-0.007569900000000018,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,6
-0.00015940000000003174,0.9987340130978268,300,PE,nk_complexity_pe,6
-0.002076600000000095,0.9956200476605646,300,WPE,nk_complexity_wpe,6
-0.0020677000000000056,0.8899744973388644,300,MPE,nk_complexity_mpe,6
-0.05084500000000003,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,6
-0.05075030000000025,inf,300,RangeEn,nk_entropy_range_mApEn,6
-0.002549899999999994,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,6
-0.0007060000000000954,8.228818690495881,300,ShanEn,nk_entropy_shannon,6
-0.0019948000000002963,0.8726394003530048,300,SpEn,nk_entropy_spectral,6
-9.499999999995623e-05,0.9972506088650914,300,SVDen,nk_entropy_svd,6
-0.009869999999999823,2.115364222027546,300,CD,nk_fractal_correlation,6
-0.014713399999999766,0.577351342862693,300,DFA,nk_fractal_dfa,6
-0.016996799999999812,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,6
-7.830000000019766e-05,4.3572539387418985,300,Katz,nk_fractal_katz,6
-0.0018484000000000833,1.7989767252374387,300,NLD,nk_fractal_nld,6
-4.8600000000398325e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,6
-0.022826299999999744,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,6
-0.0008028000000002145,,300,SDA,nk_fractal_sda,6
-0.00013879999999977244,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,6
-0.00012069999999964054,0.007175249122036997,300,Fisher,nk_fisher_information,6
-0.00016649999999995835,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,7
-0.005266900000000074,0.7284506128345722,300,Hurst,nk_complexity_hurst,7
-0.002592600000000056,1.1246052210344373,300,LZC,nk_complexity_lzc,7
-0.0030064000000000757,0.7575577096795095,300,PLZC,nk_complexity_plzc,7
-0.008428600000000008,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,7
-0.006419599999999859,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,7
-0.00016710000000008662,0.9987340130978268,300,PE,nk_complexity_pe,7
-0.0030898999999999788,0.9956200476605646,300,WPE,nk_complexity_wpe,7
-0.0019328000000000678,0.8899744973388644,300,MPE,nk_complexity_mpe,7
-0.04347089999999998,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,7
-0.045215000000000005,inf,300,RangeEn,nk_entropy_range_mApEn,7
-0.00420660000000006,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,7
-0.0017199000000001075,8.228818690495881,300,ShanEn,nk_entropy_shannon,7
-0.002035299999999962,0.8726394003530048,300,SpEn,nk_entropy_spectral,7
-0.001004299999999958,0.9972506088650914,300,SVDen,nk_entropy_svd,7
-0.009958300000000087,2.115364222027546,300,CD,nk_fractal_correlation,7
-0.012148499999999895,0.577351342862693,300,DFA,nk_fractal_dfa,7
-0.010616100000000017,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,7
-5.01000000001639e-05,4.3572539387418985,300,Katz,nk_fractal_katz,7
-0.0014167000000000485,1.7989767252374387,300,NLD,nk_fractal_nld,7
-5.329999999981183e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,7
-0.024943099999999774,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,7
-0.0005975000000000286,,300,SDA,nk_fractal_sda,7
-7.679999999998799e-05,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,7
-6.880000000020203e-05,0.007175249122036997,300,Fisher,nk_fisher_information,7
-0.0001614999999999256,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,8
-0.002517700000000067,0.7284506128345722,300,Hurst,nk_complexity_hurst,8
-0.002373199999999853,1.1246052210344373,300,LZC,nk_complexity_lzc,8
-0.002927300000000077,0.7575577096795095,300,PLZC,nk_complexity_plzc,8
-0.00862099999999999,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,8
-0.007467899999999972,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,8
-0.00013759999999995998,0.9987340130978268,300,PE,nk_complexity_pe,8
-0.003015299999999943,0.9956200476605646,300,WPE,nk_complexity_wpe,8
-0.0019255000000000244,0.8899744973388644,300,MPE,nk_complexity_mpe,8
-0.05112760000000005,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,8
-0.04984019999999978,inf,300,RangeEn,nk_entropy_range_mApEn,8
-0.0023721999999999355,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,8
-0.0006713000000000413,8.228818690495881,300,ShanEn,nk_entropy_shannon,8
-0.0025740999999999126,0.8726394003530048,300,SpEn,nk_entropy_spectral,8
-9.970000000025792e-05,0.9972506088650914,300,SVDen,nk_entropy_svd,8
-0.0101445,2.115364222027546,300,CD,nk_fractal_correlation,8
-0.015339299999999945,0.577351342862693,300,DFA,nk_fractal_dfa,8
-0.017420800000000014,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,8
-8.570000000007738e-05,4.3572539387418985,300,Katz,nk_fractal_katz,8
-0.0019100000000000783,1.7989767252374387,300,NLD,nk_fractal_nld,8
-4.830000000000112e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,8
-0.025255000000000027,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,8
-0.001230699999999807,,300,SDA,nk_fractal_sda,8
-0.00016209999999983182,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,8
-0.0001335000000000086,0.007175249122036997,300,Fisher,nk_fisher_information,8
-0.00018660000000014776,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,9
-0.0021096000000000448,0.7284506128345722,300,Hurst,nk_complexity_hurst,9
-0.002370500000000053,1.1246052210344373,300,LZC,nk_complexity_lzc,9
-0.002875000000000183,0.7575577096795095,300,PLZC,nk_complexity_plzc,9
-0.007880700000000074,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,9
-0.006972100000000037,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,9
-0.00015249999999999986,0.9987340130978268,300,PE,nk_complexity_pe,9
-0.0025900000000000922,0.9956200476605646,300,WPE,nk_complexity_wpe,9
-0.0013821000000000527,0.8899744973388644,300,MPE,nk_complexity_mpe,9
-0.05009419999999998,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,9
-0.0497649,inf,300,RangeEn,nk_entropy_range_mApEn,9
-0.0028614999999998503,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,9
-0.0008320999999997802,8.228818690495881,300,ShanEn,nk_entropy_shannon,9
-0.003408600000000206,0.8726394003530048,300,SpEn,nk_entropy_spectral,9
-9.559999999986246e-05,0.9972506088650914,300,SVDen,nk_entropy_svd,9
-0.010201899999999764,2.115364222027546,300,CD,nk_fractal_correlation,9
-0.014975900000000042,0.577351342862693,300,DFA,nk_fractal_dfa,9
-0.01715610000000023,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,9
-7.560000000017553e-05,4.3572539387418985,300,Katz,nk_fractal_katz,9
-0.0019996999999998266,1.7989767252374387,300,NLD,nk_fractal_nld,9
-4.779999999993123e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,9
-0.025850099999999987,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,9
-0.00108110000000039,,300,SDA,nk_fractal_sda,9
-0.000128000000000128,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,9
-0.00010530000000041895,0.007175249122036997,300,Fisher,nk_fisher_information,9
-0.0001357999999997972,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,10
-0.0020956999999999226,0.7284506128345722,300,Hurst,nk_complexity_hurst,10
-0.0024162000000000905,1.1246052210344373,300,LZC,nk_complexity_lzc,10
-0.0020948000000000633,0.7575577096795095,300,PLZC,nk_complexity_plzc,10
-0.005657999999999941,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,10
-0.008395199999999825,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,10
-0.00013180000000012626,0.9987340130978268,300,PE,nk_complexity_pe,10
-0.002422499999999994,0.9956200476605646,300,WPE,nk_complexity_wpe,10
-0.0016784999999999162,0.8899744973388644,300,MPE,nk_complexity_mpe,10
-0.04845900000000003,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,10
-0.04845260000000007,inf,300,RangeEn,nk_entropy_range_mApEn,10
-0.0023694999999999133,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,10
-0.0006308000000001535,8.228818690495881,300,ShanEn,nk_entropy_shannon,10
-0.0021825999999998125,0.8726394003530048,300,SpEn,nk_entropy_spectral,10
-0.0001082999999999501,0.9972506088650914,300,SVDen,nk_entropy_svd,10
-0.010123500000000174,2.115364222027546,300,CD,nk_fractal_correlation,10
-0.01221950000000005,0.577351342862693,300,DFA,nk_fractal_dfa,10
-0.013976400000000222,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,10
-6.739999999982871e-05,4.3572539387418985,300,Katz,nk_fractal_katz,10
-0.0014039999999999608,1.7989767252374387,300,NLD,nk_fractal_nld,10
-4.7399999999697684e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,10
-0.023294200000000043,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,10
-0.0008023999999999809,,300,SDA,nk_fractal_sda,10
-0.00010989999999999611,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,10
-8.93999999997952e-05,0.007175249122036997,300,Fisher,nk_fisher_information,10
-0.00014620000000009625,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,11
-0.0021742999999998514,0.7284506128345722,300,Hurst,nk_complexity_hurst,11
-0.002446200000000065,1.1246052210344373,300,LZC,nk_complexity_lzc,11
-0.002995300000000034,0.7575577096795095,300,PLZC,nk_complexity_plzc,11
-0.00866330000000004,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,11
-0.0074800999999999895,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,11
-0.00014730000000007237,0.9987340130978268,300,PE,nk_complexity_pe,11
-0.003058200000000122,0.9956200476605646,300,WPE,nk_complexity_wpe,11
-0.0019295999999999758,0.8899744973388644,300,MPE,nk_complexity_mpe,11
-0.05038659999999995,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,11
-0.04232139999999984,inf,300,RangeEn,nk_entropy_range_mApEn,11
-0.0024796000000000262,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,11
-0.0006859999999999644,8.228818690495881,300,ShanEn,nk_entropy_shannon,11
-0.002063499999999774,0.8726394003530048,300,SpEn,nk_entropy_spectral,11
-0.00010460000000023228,0.9972506088650914,300,SVDen,nk_entropy_svd,11
-0.0100943,2.115364222027546,300,CD,nk_fractal_correlation,11
-0.011771599999999882,0.577351342862693,300,DFA,nk_fractal_dfa,11
-0.017809100000000022,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,11
-8.779999999974919e-05,4.3572539387418985,300,Katz,nk_fractal_katz,11
-0.001908300000000196,1.7989767252374387,300,NLD,nk_fractal_nld,11
-4.2500000000167404e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,11
-0.024312699999999854,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,11
-0.0007413999999998921,,300,SDA,nk_fractal_sda,11
-0.00011969999999994485,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,11
-9.040000000037907e-05,0.007175249122036997,300,Fisher,nk_fisher_information,11
-0.00017339999999999023,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,12
-0.0025808000000000497,0.7284506128345722,300,Hurst,nk_complexity_hurst,12
-0.00219630000000004,1.1246052210344373,300,LZC,nk_complexity_lzc,12
-0.0020871000000000084,0.7575577096795095,300,PLZC,nk_complexity_plzc,12
-0.007688499999999987,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,12
-0.00735219999999992,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,12
-0.00010859999999990322,0.9987340130978268,300,PE,nk_complexity_pe,12
-0.00195309999999993,0.9956200476605646,300,WPE,nk_complexity_wpe,12
-0.0013369000000000852,0.8899744973388644,300,MPE,nk_complexity_mpe,12
-0.04947100000000004,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,12
-0.04613889999999987,inf,300,RangeEn,nk_entropy_range_mApEn,12
-0.0024314000000000835,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,12
-0.0006330000000001057,8.228818690495881,300,ShanEn,nk_entropy_shannon,12
-0.0027433999999999514,0.8726394003530048,300,SpEn,nk_entropy_spectral,12
-9.510000000001462e-05,0.9972506088650914,300,SVDen,nk_entropy_svd,12
-0.013328600000000135,2.115364222027546,300,CD,nk_fractal_correlation,12
-0.014345699999999795,0.577351342862693,300,DFA,nk_fractal_dfa,12
-0.017137899999999817,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,12
-8.620000000014727e-05,4.3572539387418985,300,Katz,nk_fractal_katz,12
-0.001829900000000162,1.7989767252374387,300,NLD,nk_fractal_nld,12
-4.830000000000112e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,12
-0.025708600000000192,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,12
-0.001252599999999937,,300,SDA,nk_fractal_sda,12
-0.00017219999999973368,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,12
-0.00013020000000008025,0.007175249122036997,300,Fisher,nk_fisher_information,12
-0.00011769999999988734,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,13
-0.0018133000000000177,0.7284506128345722,300,Hurst,nk_complexity_hurst,13
-0.0025573000000000956,1.1246052210344373,300,LZC,nk_complexity_lzc,13
-0.002897099999999986,0.7575577096795095,300,PLZC,nk_complexity_plzc,13
-0.008228700000000089,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,13
-0.006465100000000001,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,13
-0.00016740000000003974,0.9987340130978268,300,PE,nk_complexity_pe,13
-0.0031375000000000153,0.9956200476605646,300,WPE,nk_complexity_wpe,13
-0.0019231000000001774,0.8899744973388644,300,MPE,nk_complexity_mpe,13
-0.05052760000000012,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,13
-0.050625599999999826,inf,300,RangeEn,nk_entropy_range_mApEn,13
-0.0024212999999999596,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,13
-0.0007120999999998823,8.228818690495881,300,ShanEn,nk_entropy_shannon,13
-0.0019857999999999265,0.8726394003530048,300,SpEn,nk_entropy_spectral,13
-9.670000000006063e-05,0.9972506088650914,300,SVDen,nk_entropy_svd,13
-0.010425399999999918,2.115364222027546,300,CD,nk_fractal_correlation,13
-0.01460410000000012,0.577351342862693,300,DFA,nk_fractal_dfa,13
-0.017826700000000084,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,13
-7.469999999987209e-05,4.3572539387418985,300,Katz,nk_fractal_katz,13
-0.0021179999999998422,1.7989767252374387,300,NLD,nk_fractal_nld,13
-4.850000000011789e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,13
-0.0264694999999997,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,13
-0.0009516999999998887,,300,SDA,nk_fractal_sda,13
-0.00014020000000014576,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,13
-9.339999999991022e-05,0.007175249122036997,300,Fisher,nk_fisher_information,13
-0.0002985999999998157,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,14
-0.002750699999999995,0.7284506128345722,300,Hurst,nk_complexity_hurst,14
-0.003765200000000135,1.1246052210344373,300,LZC,nk_complexity_lzc,14
-0.0025878999999999763,0.7575577096795095,300,PLZC,nk_complexity_plzc,14
-0.006455700000000064,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,14
-0.0029718999999999163,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,14
-7.7600000000011e-05,0.9987340130978268,300,PE,nk_complexity_pe,14
-0.0014183999999999308,0.9956200476605646,300,WPE,nk_complexity_wpe,14
-0.0009271000000001806,0.8899744973388644,300,MPE,nk_complexity_mpe,14
-0.025390100000000082,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,14
-0.025475600000000043,inf,300,RangeEn,nk_entropy_range_mApEn,14
-0.0016750999999999294,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,14
-0.00038509999999991607,8.228818690495881,300,ShanEn,nk_entropy_shannon,14
-0.0013999000000000095,0.8726394003530048,300,SpEn,nk_entropy_spectral,14
-7.199999999984996e-05,0.9972506088650914,300,SVDen,nk_entropy_svd,14
-0.006137199999999954,2.115364222027546,300,CD,nk_fractal_correlation,14
-0.007510099999999742,0.577351342862693,300,DFA,nk_fractal_dfa,14
-0.01020500000000002,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,14
-7.009999999985084e-05,4.3572539387418985,300,Katz,nk_fractal_katz,14
-0.0017548999999998927,1.7989767252374387,300,NLD,nk_fractal_nld,14
-3.630000000010014e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,14
-0.013800599999999719,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,14
-0.00034409999999995833,,300,SDA,nk_fractal_sda,14
-6.389999999978357e-05,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,14
-5.1899999999882596e-05,0.007175249122036997,300,Fisher,nk_fisher_information,14
-0.00021040000000027703,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,15
-0.0027038999999997593,0.7284506128345722,300,Hurst,nk_complexity_hurst,15
-0.0016113999999998185,1.1246052210344373,300,LZC,nk_complexity_lzc,15
-0.0025273999999999575,0.7575577096795095,300,PLZC,nk_complexity_plzc,15
-0.006790899999999933,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,15
-0.004863799999999863,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,15
-8.009999999991635e-05,0.9987340130978268,300,PE,nk_complexity_pe,15
-0.0014636999999999567,0.9956200476605646,300,WPE,nk_complexity_wpe,15
-0.0009615999999996738,0.8899744973388644,300,MPE,nk_complexity_mpe,15
-0.02950509999999973,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,15
-0.026406099999999988,inf,300,RangeEn,nk_entropy_range_mApEn,15
-0.0016205000000000247,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,15
-0.000642400000000265,8.228818690495881,300,ShanEn,nk_entropy_shannon,15
-0.0018134999999999124,0.8726394003530048,300,SpEn,nk_entropy_spectral,15
-5.919999999992598e-05,0.9972506088650914,300,SVDen,nk_entropy_svd,15
-0.006095599999999646,2.115364222027546,300,CD,nk_fractal_correlation,15
-0.007314299999999996,0.577351342862693,300,DFA,nk_fractal_dfa,15
-0.009381500000000376,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,15
-5.319999999997549e-05,4.3572539387418985,300,Katz,nk_fractal_katz,15
-0.0010262000000000882,1.7989767252374387,300,NLD,nk_fractal_nld,15
-2.7600000000127523e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,15
-0.012336000000000347,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,15
-0.0003293000000001989,,300,SDA,nk_fractal_sda,15
-9.719999999990847e-05,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,15
-5.319999999997549e-05,0.007175249122036997,300,Fisher,nk_fisher_information,15
-0.00017529999999998935,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,16
-0.001839799999999947,0.7284506128345722,300,Hurst,nk_complexity_hurst,16
-0.0036377999999999133,1.1246052210344373,300,LZC,nk_complexity_lzc,16
-0.0029466000000000214,0.7575577096795095,300,PLZC,nk_complexity_plzc,16
-0.003947600000000051,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,16
-0.003918000000000088,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,16
-0.00012370000000005987,0.9987340130978268,300,PE,nk_complexity_pe,16
-0.0014967999999999648,0.9956200476605646,300,WPE,nk_complexity_wpe,16
-0.001044000000000267,0.8899744973388644,300,MPE,nk_complexity_mpe,16
-0.028007500000000185,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,16
-0.025436299999999967,inf,300,RangeEn,nk_entropy_range_mApEn,16
-0.0015477000000001517,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,16
-0.00038110000000024513,8.228818690495881,300,ShanEn,nk_entropy_shannon,16
-0.0012571999999999584,0.8726394003530048,300,SpEn,nk_entropy_spectral,16
-6.210000000006488e-05,0.9972506088650914,300,SVDen,nk_entropy_svd,16
-0.006317099999999964,2.115364222027546,300,CD,nk_fractal_correlation,16
-0.00900330000000027,0.577351342862693,300,DFA,nk_fractal_dfa,16
-0.010972199999999876,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,16
-5.200000000016303e-05,4.3572539387418985,300,Katz,nk_fractal_katz,16
-0.000975500000000018,1.7989767252374387,300,NLD,nk_fractal_nld,16
-2.5400000000175282e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,16
-0.012287500000000229,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,16
-0.00031399999999992545,,300,SDA,nk_fractal_sda,16
-6.370000000011089e-05,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,16
-5.040000000011702e-05,0.007175249122036997,300,Fisher,nk_fisher_information,16
-0.00018049999999991684,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,17
-0.002453099999999875,0.7284506128345722,300,Hurst,nk_complexity_hurst,17
-0.003544400000000003,1.1246052210344373,300,LZC,nk_complexity_lzc,17
-0.0018374000000003221,0.7575577096795095,300,PLZC,nk_complexity_plzc,17
-0.00391470000000016,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,17
-0.0032823999999997966,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,17
-7.860000000015077e-05,0.9987340130978268,300,PE,nk_complexity_pe,17
-0.0018335999999998798,0.9956200476605646,300,WPE,nk_complexity_wpe,17
-0.0011765000000001358,0.8899744973388644,300,MPE,nk_complexity_mpe,17
-0.028597800000000007,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,17
-0.029005100000000006,inf,300,RangeEn,nk_entropy_range_mApEn,17
-0.0016508000000001744,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,17
-0.0003823000000000576,8.228818690495881,300,ShanEn,nk_entropy_shannon,17
-0.001238199999999967,0.8726394003530048,300,SpEn,nk_entropy_spectral,17
-7.040000000024804e-05,0.9972506088650914,300,SVDen,nk_entropy_svd,17
-0.007220100000000063,2.115364222027546,300,CD,nk_fractal_correlation,17
-0.0074566000000002575,0.577351342862693,300,DFA,nk_fractal_dfa,17
-0.008746299999999874,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,17
-4.960000000009401e-05,4.3572539387418985,300,Katz,nk_fractal_katz,17
-0.0010775000000000645,1.7989767252374387,300,NLD,nk_fractal_nld,17
-2.6599999999987745e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,17
-0.012794700000000159,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,17
-0.0003687000000001106,,300,SDA,nk_fractal_sda,17
-6.299999999992423e-05,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,17
-5.049999999995336e-05,0.007175249122036997,300,Fisher,nk_fisher_information,17
-0.0001339999999996344,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,18
-0.00195529999999966,0.7284506128345722,300,Hurst,nk_complexity_hurst,18
-0.0021629000000000786,1.1246052210344373,300,LZC,nk_complexity_lzc,18
-0.0025809999999997224,0.7575577096795095,300,PLZC,nk_complexity_plzc,18
-0.006487200000000026,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,18
-0.0029553999999998304,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,18
-8.13000000001729e-05,0.9987340130978268,300,PE,nk_complexity_pe,18
-0.001455500000000054,0.9956200476605646,300,WPE,nk_complexity_wpe,18
-0.0010431999999997998,0.8899744973388644,300,MPE,nk_complexity_mpe,18
-0.028960500000000167,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,18
-0.025513099999999955,inf,300,RangeEn,nk_entropy_range_mApEn,18
-0.0015785999999997635,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,18
-0.00039739999999977016,8.228818690495881,300,ShanEn,nk_entropy_shannon,18
-0.0012992999999998922,0.8726394003530048,300,SpEn,nk_entropy_spectral,18
-5.670000000002062e-05,0.9972506088650914,300,SVDen,nk_entropy_svd,18
-0.00617560000000017,2.115364222027546,300,CD,nk_fractal_correlation,18
-0.007417999999999925,0.577351342862693,300,DFA,nk_fractal_dfa,18
-0.008384700000000134,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,18
-5.330000000025592e-05,4.3572539387418985,300,Katz,nk_fractal_katz,18
-0.0009875999999997553,1.7989767252374387,300,NLD,nk_fractal_nld,18
-3.510000000028768e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,18
-0.012272199999999955,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,18
-0.0003196999999999228,,300,SDA,nk_fractal_sda,18
-6.510000000004013e-05,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,18
-5.330000000025592e-05,0.007175249122036997,300,Fisher,nk_fisher_information,18
-0.00017839999999980094,1.3129748767882208,300,Hjorth,nk_complexity_hjorth,19
-0.0020801999999999765,0.7284506128345722,300,Hurst,nk_complexity_hurst,19
-0.0020815999999999057,1.1246052210344373,300,LZC,nk_complexity_lzc,19
-0.001330700000000018,0.7575577096795095,300,PLZC,nk_complexity_plzc,19
-0.0038529999999998843,0.8179231026159123,300,MPLZC,nk_complexity_mplzc,19
-0.002983299999999911,0.03827469853362973,300,Lyapunov,nk_complexity_lyapunov,19
-7.519999999994198e-05,0.9987340130978268,300,PE,nk_complexity_pe,19
-0.0015249999999999986,0.9956200476605646,300,WPE,nk_complexity_wpe,19
-0.0013603999999998173,0.8899744973388644,300,MPE,nk_complexity_mpe,19
-0.027096199999999904,1.4166697106258541,300,RangeEn,nk_entropy_range_mSampEn,19
-0.025766100000000236,inf,300,RangeEn,nk_entropy_range_mApEn,19
-0.002317099999999961,2.2216160304603787,300,RangeEn_mApEn,nk_entropy_sample,19
-0.0006239999999997359,8.228818690495881,300,ShanEn,nk_entropy_shannon,19
-0.0021835999999999522,0.8726394003530048,300,SpEn,nk_entropy_spectral,19
-8.919999999967843e-05,0.9972506088650914,300,SVDen,nk_entropy_svd,19
-0.007034900000000288,2.115364222027546,300,CD,nk_fractal_correlation,19
-0.007890300000000128,0.577351342862693,300,DFA,nk_fractal_dfa,19
-0.009733900000000073,[0.80485615 0.73288977 0.66146274 0.60209396 0.55506314 0.51679279],300,MFDFA,nk_fractal_mfdfa,19
-4.790000000021166e-05,4.3572539387418985,300,Katz,nk_fractal_katz,19
-0.001269399999999976,1.7989767252374387,300,NLD,nk_fractal_nld,19
-3.6600000000053257e-05,1.0307248736374652,300,Petrosian,nk_fractal_petrosian,19
-0.013998899999999814,2.4844333714008617,300,PSDslope,nk_fractal_psdslope,19
-0.00031239999999987944,,300,SDA,nk_fractal_sda,19
-6.339999999971369e-05,1.6199005210319442,300,Sevcik,nk_fractal_sevcik,19
-4.770000000009489e-05,0.007175249122036997,300,Fisher,nk_fisher_information,19
-0.00012389999999973256,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,0
-0.0019823000000003255,0.7576767879978331,501,Hurst,nk_complexity_hurst,0
-0.0028265999999996794,1.0024857094190252,501,LZC,nk_complexity_lzc,0
-0.005035299999999854,0.6948540041314585,501,PLZC,nk_complexity_plzc,0
-0.016692599999999835,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,0
-0.008288600000000201,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,0
-0.000172000000000061,0.9983224658769967,501,PE,nk_complexity_pe,0
-0.004463400000000117,0.997017876982581,501,WPE,nk_complexity_wpe,0
-0.0029710999999998933,0.9072420882196771,501,MPE,nk_complexity_mpe,0
-0.09828310000000018,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,0
-0.09465309999999993,inf,501,RangeEn,nk_entropy_range_mApEn,0
-0.003888600000000242,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,0
-0.0007117999999999292,8.968666793195206,501,ShanEn,nk_entropy_shannon,0
-0.0020915999999999713,0.8581824871882455,501,SpEn,nk_entropy_spectral,0
-7.530000000022241e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,0
-0.03197659999999969,2.212029644286709,501,CD,nk_fractal_correlation,0
-0.0313504,0.6780863158404323,501,DFA,nk_fractal_dfa,0
-0.035229900000000036,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,0
-7.640000000019853e-05,4.553693914348758,501,Katz,nk_fractal_katz,0
-0.002755500000000133,1.750356764421105,501,NLD,nk_fractal_nld,0
-4.819999999972069e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,0
-0.022840200000000088,2.548465840430601,501,PSDslope,nk_fractal_psdslope,0
-0.00044410000000016936,,501,SDA,nk_fractal_sda,0
-9.460000000016677e-05,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,0
-7.130000000010739e-05,0.01664832195245951,501,Fisher,nk_fisher_information,0
-0.00011599999999978294,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,1
-0.001953700000000058,0.7576767879978331,501,Hurst,nk_complexity_hurst,1
-0.0028095999999999677,1.0024857094190252,501,LZC,nk_complexity_lzc,1
-0.0032608999999999,0.6948540041314585,501,PLZC,nk_complexity_plzc,1
-0.012680800000000048,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,1
-0.0075950000000002404,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,1
-0.00016009999999999636,0.9983224658769967,501,PE,nk_complexity_pe,1
-0.0049986999999998005,0.997017876982581,501,WPE,nk_complexity_wpe,1
-0.003166799999999803,0.9072420882196771,501,MPE,nk_complexity_mpe,1
-0.09364660000000002,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,1
-0.07615380000000016,inf,501,RangeEn,nk_entropy_range_mApEn,1
-0.004280900000000365,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,1
-0.0007447000000002646,8.968666793195206,501,ShanEn,nk_entropy_shannon,1
-0.00206620000000024,0.8581824871882455,501,SpEn,nk_entropy_spectral,1
-8.430000000014815e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,1
-0.02516150000000028,2.212029644286709,501,CD,nk_fractal_correlation,1
-0.031664899999999996,0.6780863158404323,501,DFA,nk_fractal_dfa,1
-0.025939099999999993,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,1
-7.139999999994373e-05,4.553693914348758,501,Katz,nk_fractal_katz,1
-0.0014533000000001017,1.750356764421105,501,NLD,nk_fractal_nld,1
-2.7300000000174407e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,1
-0.0217432999999998,2.548465840430601,501,PSDslope,nk_fractal_psdslope,1
-0.0005231000000001096,,501,SDA,nk_fractal_sda,1
-0.00011759999999982895,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,1
-8.220000000003225e-05,0.01664832195245951,501,Fisher,nk_fisher_information,1
-0.00010989999999999611,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,2
-0.0019967999999996877,0.7576767879978331,501,Hurst,nk_complexity_hurst,2
-0.003007599999999666,1.0024857094190252,501,LZC,nk_complexity_lzc,2
-0.00319529999999979,0.6948540041314585,501,PLZC,nk_complexity_plzc,2
-0.008951399999999943,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,2
-0.006871099999999686,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,2
-0.000157600000000091,0.9983224658769967,501,PE,nk_complexity_pe,2
-0.004303800000000191,0.997017876982581,501,WPE,nk_complexity_wpe,2
-0.0030186999999997077,0.9072420882196771,501,MPE,nk_complexity_mpe,2
-0.0957545999999998,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,2
-0.09552510000000014,inf,501,RangeEn,nk_entropy_range_mApEn,2
-0.004201300000000074,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,2
-0.0006679999999996689,8.968666793195206,501,ShanEn,nk_entropy_shannon,2
-0.0019450999999999219,0.8581824871882455,501,SpEn,nk_entropy_spectral,2
-8.549999999996061e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,2
-0.030442199999999975,2.212029644286709,501,CD,nk_fractal_correlation,2
-0.029356100000000218,0.6780863158404323,501,DFA,nk_fractal_dfa,2
-0.03472149999999985,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,2
-7.219999999996674e-05,4.553693914348758,501,Katz,nk_fractal_katz,2
-0.002736300000000025,1.750356764421105,501,NLD,nk_fractal_nld,2
-4.3600000000143524e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,2
-0.02297539999999998,2.548465840430601,501,PSDslope,nk_fractal_psdslope,2
-0.0005128999999999273,,501,SDA,nk_fractal_sda,2
-0.0001340000000000785,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,2
-9.430000000021366e-05,0.01664832195245951,501,Fisher,nk_fisher_information,2
-0.00010769999999959978,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,3
-0.0019946000000001796,0.7576767879978331,501,Hurst,nk_complexity_hurst,3
-0.0029823000000002153,1.0024857094190252,501,LZC,nk_complexity_lzc,3
-0.003288100000000238,0.6948540041314585,501,PLZC,nk_complexity_plzc,3
-0.01638300000000026,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,3
-0.00822630000000002,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,3
-0.00014439999999993347,0.9983224658769967,501,PE,nk_complexity_pe,3
-0.005034300000000158,0.997017876982581,501,WPE,nk_complexity_wpe,3
-0.003141400000000072,0.9072420882196771,501,MPE,nk_complexity_mpe,3
-0.09256660000000005,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,3
-0.09294309999999983,inf,501,RangeEn,nk_entropy_range_mApEn,3
-0.004062000000000232,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,3
-0.0006830999999998255,8.968666793195206,501,ShanEn,nk_entropy_shannon,3
-0.0019369000000000192,0.8581824871882455,501,SpEn,nk_entropy_spectral,3
-7.910000000022066e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,3
-0.03149120000000005,2.212029644286709,501,CD,nk_fractal_correlation,3
-0.02411910000000006,0.6780863158404323,501,DFA,nk_fractal_dfa,3
-0.03063640000000012,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,3
-7.62999999999181e-05,4.553693914348758,501,Katz,nk_fractal_katz,3
-0.0028445000000001386,1.750356764421105,501,NLD,nk_fractal_nld,3
-4.230000000005063e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,3
-0.02252989999999988,2.548465840430601,501,PSDslope,nk_fractal_psdslope,3
-0.0005211000000002741,,501,SDA,nk_fractal_sda,3
-0.00011910000000003862,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,3
-8.30999999998916e-05,0.01664832195245951,501,Fisher,nk_fisher_information,3
-0.00013340000000017227,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,4
-0.0023711000000004034,0.7576767879978331,501,Hurst,nk_complexity_hurst,4
-0.0028690000000000104,1.0024857094190252,501,LZC,nk_complexity_lzc,4
-0.004164499999999904,0.6948540041314585,501,PLZC,nk_complexity_plzc,4
-0.010194199999999931,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,4
-0.007244400000000262,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,4
-0.00015330000000002286,0.9983224658769967,501,PE,nk_complexity_pe,4
-0.0045357000000003644,0.997017876982581,501,WPE,nk_complexity_wpe,4
-0.002721399999999985,0.9072420882196771,501,MPE,nk_complexity_mpe,4
-0.08645290000000028,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,4
-0.09069360000000026,inf,501,RangeEn,nk_entropy_range_mApEn,4
-0.004182099999999966,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,4
-0.0006962000000001467,8.968666793195206,501,ShanEn,nk_entropy_shannon,4
-0.0021223999999997467,0.8581824871882455,501,SpEn,nk_entropy_spectral,4
-8.529999999984383e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,4
-0.026778800000000214,2.212029644286709,501,CD,nk_fractal_correlation,4
-0.02921560000000012,0.6780863158404323,501,DFA,nk_fractal_dfa,4
-0.03514829999999991,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,4
-9.630000000004912e-05,4.553693914348758,501,Katz,nk_fractal_katz,4
-0.0028714000000000794,1.750356764421105,501,NLD,nk_fractal_nld,4
-4.049999999988785e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,4
-0.023901099999999786,2.548465840430601,501,PSDslope,nk_fractal_psdslope,4
-0.0004754000000000147,,501,SDA,nk_fractal_sda,4
-0.00011369999999999436,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,4
-7.970000000012689e-05,0.01664832195245951,501,Fisher,nk_fisher_information,4
-0.0002106000000003938,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,5
-0.003097700000000092,0.7576767879978331,501,Hurst,nk_complexity_hurst,5
-0.006015999999999799,1.0024857094190252,501,LZC,nk_complexity_lzc,5
-0.007250099999999815,0.6948540041314585,501,PLZC,nk_complexity_plzc,5
-0.015643100000000132,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,5
-0.008097400000000032,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,5
-0.00016190000000015914,0.9983224658769967,501,PE,nk_complexity_pe,5
-0.005002199999999846,0.997017876982581,501,WPE,nk_complexity_wpe,5
-0.0032868999999999815,0.9072420882196771,501,MPE,nk_complexity_mpe,5
-0.09497020000000012,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,5
-0.09552579999999988,inf,501,RangeEn,nk_entropy_range_mApEn,5
-0.004057100000000258,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,5
-0.0006715999999999944,8.968666793195206,501,ShanEn,nk_entropy_shannon,5
-0.001990999999999854,0.8581824871882455,501,SpEn,nk_entropy_spectral,5
-8.710000000000662e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,5
-0.03329509999999969,2.212029644286709,501,CD,nk_fractal_correlation,5
-0.03179860000000012,0.6780863158404323,501,DFA,nk_fractal_dfa,5
-0.03229020000000027,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,5
-5.389999999971806e-05,4.553693914348758,501,Katz,nk_fractal_katz,5
-0.0022478999999999694,1.750356764421105,501,NLD,nk_fractal_nld,5
-4.399999999993298e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,5
-0.01940469999999994,2.548465840430601,501,PSDslope,nk_fractal_psdslope,5
-0.0005299999999999194,,501,SDA,nk_fractal_sda,5
-0.00011630000000018015,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,5
-8.980000000002875e-05,0.01664832195245951,501,Fisher,nk_fisher_information,5
-0.00016419999999994772,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,6
-0.003713999999999995,0.7576767879978331,501,Hurst,nk_complexity_hurst,6
-0.006231200000000214,1.0024857094190252,501,LZC,nk_complexity_lzc,6
-0.006936699999999796,0.6948540041314585,501,PLZC,nk_complexity_plzc,6
-0.01737780000000022,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,6
-0.00862380000000007,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,6
-0.00018049999999991684,0.9983224658769967,501,PE,nk_complexity_pe,6
-0.004518699999999765,0.997017876982581,501,WPE,nk_complexity_wpe,6
-0.0032654999999999212,0.9072420882196771,501,MPE,nk_complexity_mpe,6
-0.09297380000000022,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,6
-0.0932183000000002,inf,501,RangeEn,nk_entropy_range_mApEn,6
-0.004397199999999657,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,6
-0.00047739999999985017,8.968666793195206,501,ShanEn,nk_entropy_shannon,6
-0.0013070000000001691,0.8581824871882455,501,SpEn,nk_entropy_spectral,6
-6.089999999980833e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,6
-0.034475600000000384,2.212029644286709,501,CD,nk_fractal_correlation,6
-0.031797899999999935,0.6780863158404323,501,DFA,nk_fractal_dfa,6
-0.03507609999999994,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,6
-8.30999999998916e-05,4.553693914348758,501,Katz,nk_fractal_katz,6
-0.002751299999999901,1.750356764421105,501,NLD,nk_fractal_nld,6
-5.4599999999904725e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,6
-0.023158400000000245,2.548465840430601,501,PSDslope,nk_fractal_psdslope,6
-0.002558200000000177,,501,SDA,nk_fractal_sda,6
-0.00013130000000005637,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,6
-8.710000000000662e-05,0.01664832195245951,501,Fisher,nk_fisher_information,6
-0.0001176999999996653,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,7
-0.0022516999999999676,0.7576767879978331,501,Hurst,nk_complexity_hurst,7
-0.005394899999999758,1.0024857094190252,501,LZC,nk_complexity_lzc,7
-0.0058555000000000135,0.6948540041314585,501,PLZC,nk_complexity_plzc,7
-0.015404200000000312,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,7
-0.007915299999999625,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,7
-0.00021169999999992584,0.9983224658769967,501,PE,nk_complexity_pe,7
-0.0033060999999996454,0.997017876982581,501,WPE,nk_complexity_wpe,7
-0.0018482999999998029,0.9072420882196771,501,MPE,nk_complexity_mpe,7
-0.0951124000000001,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,7
-0.09150809999999998,inf,501,RangeEn,nk_entropy_range_mApEn,7
-0.003953699999999838,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,7
-0.0007043000000002131,8.968666793195206,501,ShanEn,nk_entropy_shannon,7
-0.0020923999999999943,0.8581824871882455,501,SpEn,nk_entropy_spectral,7
-8.079999999965892e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,7
-0.03316240000000015,2.212029644286709,501,CD,nk_fractal_correlation,7
-0.02664869999999997,0.6780863158404323,501,DFA,nk_fractal_dfa,7
-0.035294399999999726,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,7
-7.899999999994023e-05,4.553693914348758,501,Katz,nk_fractal_katz,7
-0.0028286999999997953,1.750356764421105,501,NLD,nk_fractal_nld,7
-4.2100000000377946e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,7
-0.023204699999999967,2.548465840430601,501,PSDslope,nk_fractal_psdslope,7
-0.0005104999999998583,,501,SDA,nk_fractal_sda,7
-0.0004675000000000651,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,7
-0.00010099999999990672,0.01664832195245951,501,Fisher,nk_fisher_information,7
-0.00014260000000021478,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,8
-0.002365900000000032,0.7576767879978331,501,Hurst,nk_complexity_hurst,8
-0.004476499999999994,1.0024857094190252,501,LZC,nk_complexity_lzc,8
-0.007210099999999997,0.6948540041314585,501,PLZC,nk_complexity_plzc,8
-0.01749210000000012,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,8
-0.008899700000000177,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,8
-0.00015800000000032455,0.9983224658769967,501,PE,nk_complexity_pe,8
-0.005217000000000027,0.997017876982581,501,WPE,nk_complexity_wpe,8
-0.003356399999999926,0.9072420882196771,501,MPE,nk_complexity_mpe,8
-0.0945081000000001,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,8
-0.09234260000000027,inf,501,RangeEn,nk_entropy_range_mApEn,8
-0.004033399999999965,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,8
-0.0006970000000001697,8.968666793195206,501,ShanEn,nk_entropy_shannon,8
-0.002433400000000141,0.8581824871882455,501,SpEn,nk_entropy_spectral,8
-0.00010710000000013764,0.9934119495808924,501,SVDen,nk_entropy_svd,8
-0.032314500000000024,2.212029644286709,501,CD,nk_fractal_correlation,8
-0.033296400000000226,0.6780863158404323,501,DFA,nk_fractal_dfa,8
-0.03677049999999982,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,8
-8.089999999993935e-05,4.553693914348758,501,Katz,nk_fractal_katz,8
-0.0027789000000000286,1.750356764421105,501,NLD,nk_fractal_nld,8
-5.639999999962342e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,8
-0.02538969999999985,2.548465840430601,501,PSDslope,nk_fractal_psdslope,8
-0.0004989000000001909,,501,SDA,nk_fractal_sda,8
-0.00010440000000011551,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,8
-9.099999999984121e-05,0.01664832195245951,501,Fisher,nk_fisher_information,8
-0.0001385000000002634,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,9
-0.0034279000000001503,0.7576767879978331,501,Hurst,nk_complexity_hurst,9
-0.005348099999999967,1.0024857094190252,501,LZC,nk_complexity_lzc,9
-0.004374900000000181,0.6948540041314585,501,PLZC,nk_complexity_plzc,9
-0.019113099999999772,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,9
-0.00812489999999988,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,9
-0.00015410000000004587,0.9983224658769967,501,PE,nk_complexity_pe,9
-0.004850999999999939,0.997017876982581,501,WPE,nk_complexity_wpe,9
-0.0032645000000002256,0.9072420882196771,501,MPE,nk_complexity_mpe,9
-0.09577869999999988,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,9
-0.09502900000000025,inf,501,RangeEn,nk_entropy_range_mApEn,9
-0.0071517999999999304,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,9
-0.0007052999999999088,8.968666793195206,501,ShanEn,nk_entropy_shannon,9
-0.002035000000000231,0.8581824871882455,501,SpEn,nk_entropy_spectral,9
-7.789999999996411e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,9
-0.026879600000000003,2.212029644286709,501,CD,nk_fractal_correlation,9
-0.03279319999999997,0.6780863158404323,501,DFA,nk_fractal_dfa,9
-0.03333229999999965,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,9
-0.00010250000000011639,4.553693914348758,501,Katz,nk_fractal_katz,9
-0.002724099999999563,1.750356764421105,501,NLD,nk_fractal_nld,9
-4.500000000007276e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,9
-0.0228815,2.548465840430601,501,PSDslope,nk_fractal_psdslope,9
-0.0006081999999998366,,501,SDA,nk_fractal_sda,9
-0.00011999999999989797,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,9
-9.490000000011989e-05,0.01664832195245951,501,Fisher,nk_fisher_information,9
-0.00014480000000016702,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,10
-0.003162499999999735,0.7576767879978331,501,Hurst,nk_complexity_hurst,10
-0.00642200000000015,1.0024857094190252,501,LZC,nk_complexity_lzc,10
-0.007435699999999823,0.6948540041314585,501,PLZC,nk_complexity_plzc,10
-0.01842730000000037,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,10
-0.007267099999999971,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,10
-0.0001529000000002334,0.9983224658769967,501,PE,nk_complexity_pe,10
-0.005040399999999945,0.997017876982581,501,WPE,nk_complexity_wpe,10
-0.0032012999999997405,0.9072420882196771,501,MPE,nk_complexity_mpe,10
-0.08741300000000018,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,10
-0.08850809999999987,inf,501,RangeEn,nk_entropy_range_mApEn,10
-0.00509610000000027,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,10
-0.0007138000000002087,8.968666793195206,501,ShanEn,nk_entropy_shannon,10
-0.001996800000000132,0.8581824871882455,501,SpEn,nk_entropy_spectral,10
-8.609999999986684e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,10
-0.03449649999999993,2.212029644286709,501,CD,nk_fractal_correlation,10
-0.0318786000000002,0.6780863158404323,501,DFA,nk_fractal_dfa,10
-0.027176199999999984,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,10
-5.039999999967293e-05,4.553693914348758,501,Katz,nk_fractal_katz,10
-0.0014897999999998746,1.750356764421105,501,NLD,nk_fractal_nld,10
-2.740000000001075e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,10
-0.021215100000000042,2.548465840430601,501,PSDslope,nk_fractal_psdslope,10
-0.0005350000000001742,,501,SDA,nk_fractal_sda,10
-0.00011329999999976081,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,10
-0.00010139999999969618,0.01664832195245951,501,Fisher,nk_fisher_information,10
-0.00017579999999961515,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,11
-0.002647000000000066,0.7576767879978331,501,Hurst,nk_complexity_hurst,11
-0.00577659999999991,1.0024857094190252,501,LZC,nk_complexity_lzc,11
-0.007048499999999791,0.6948540041314585,501,PLZC,nk_complexity_plzc,11
-0.015851099999999896,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,11
-0.007313399999999692,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,11
-0.00016049999999978581,0.9983224658769967,501,PE,nk_complexity_pe,11
-0.004744599999999988,0.997017876982581,501,WPE,nk_complexity_wpe,11
-0.003159000000000134,0.9072420882196771,501,MPE,nk_complexity_mpe,11
-0.08454539999999966,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,11
-0.0961097999999998,inf,501,RangeEn,nk_entropy_range_mApEn,11
-0.003777499999999989,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,11
-0.0004608999999997643,8.968666793195206,501,ShanEn,nk_entropy_shannon,11
-0.0013700000000000934,0.8581824871882455,501,SpEn,nk_entropy_spectral,11
-8.350000000012514e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,11
-0.03362430000000005,2.212029644286709,501,CD,nk_fractal_correlation,11
-0.030083700000000047,0.6780863158404323,501,DFA,nk_fractal_dfa,11
-0.033163000000000054,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,11
-7.329999999994286e-05,4.553693914348758,501,Katz,nk_fractal_katz,11
-0.0025290000000000035,1.750356764421105,501,NLD,nk_fractal_nld,11
-4.990000000004713e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,11
-0.025716199999999745,2.548465840430601,501,PSDslope,nk_fractal_psdslope,11
-0.0005101000000000688,,501,SDA,nk_fractal_sda,11
-0.00011999999999989797,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,11
-8.159999999968193e-05,0.01664832195245951,501,Fisher,nk_fisher_information,11
-0.00016559999999987696,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,12
-0.003874000000000155,0.7576767879978331,501,Hurst,nk_complexity_hurst,12
-0.00647859999999989,1.0024857094190252,501,LZC,nk_complexity_lzc,12
-0.0042591999999999075,0.6948540041314585,501,PLZC,nk_complexity_plzc,12
-0.014125500000000013,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,12
-0.008141600000000082,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,12
-0.000158400000000114,0.9983224658769967,501,PE,nk_complexity_pe,12
-0.004842500000000083,0.997017876982581,501,WPE,nk_complexity_wpe,12
-0.0032125000000000625,0.9072420882196771,501,MPE,nk_complexity_mpe,12
-0.09025019999999984,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,12
-0.0978226000000002,inf,501,RangeEn,nk_entropy_range_mApEn,12
-0.004757199999999795,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,12
-0.0007423000000001956,8.968666793195206,501,ShanEn,nk_entropy_shannon,12
-0.0021260999999999086,0.8581824871882455,501,SpEn,nk_entropy_spectral,12
-7.880000000026754e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,12
-0.030299999999999994,2.212029644286709,501,CD,nk_fractal_correlation,12
-0.030631000000000075,0.6780863158404323,501,DFA,nk_fractal_dfa,12
-0.027654099999999904,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,12
-7.540000000005875e-05,4.553693914348758,501,Katz,nk_fractal_katz,12
-0.0027641999999996614,1.750356764421105,501,NLD,nk_fractal_nld,12
-4.019999999993473e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,12
-0.02181539999999993,2.548465840430601,501,PSDslope,nk_fractal_psdslope,12
-0.0006002000000000507,,501,SDA,nk_fractal_sda,12
-0.00013620000000003074,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,12
-9.710000000007213e-05,0.01664832195245951,501,Fisher,nk_fisher_information,12
-0.00015439999999999898,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,13
-0.0035213000000000605,0.7576767879978331,501,Hurst,nk_complexity_hurst,13
-0.006857499999999739,1.0024857094190252,501,LZC,nk_complexity_lzc,13
-0.00753880000000029,0.6948540041314585,501,PLZC,nk_complexity_plzc,13
-0.01561760000000012,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,13
-0.007992899999999636,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,13
-0.00016740000000003974,0.9983224658769967,501,PE,nk_complexity_pe,13
-0.004879100000000136,0.997017876982581,501,WPE,nk_complexity_wpe,13
-0.0031680000000000597,0.9072420882196771,501,MPE,nk_complexity_mpe,13
-0.09479969999999982,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,13
-0.08958330000000014,inf,501,RangeEn,nk_entropy_range_mApEn,13
-0.003951700000000002,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,13
-0.0007227999999996904,8.968666793195206,501,ShanEn,nk_entropy_shannon,13
-0.002047300000000085,0.8581824871882455,501,SpEn,nk_entropy_spectral,13
-9.049999999977132e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,13
-0.030918699999999966,2.212029644286709,501,CD,nk_fractal_correlation,13
-0.032450800000000335,0.6780863158404323,501,DFA,nk_fractal_dfa,13
-0.030342700000000278,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,13
-7.849999999987034e-05,4.553693914348758,501,Katz,nk_fractal_katz,13
-0.0028861000000000026,1.750356764421105,501,NLD,nk_fractal_nld,13
-5.720000000009051e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,13
-0.02516100000000021,2.548465840430601,501,PSDslope,nk_fractal_psdslope,13
-0.0005323000000001521,,501,SDA,nk_fractal_sda,13
-0.00013250000000031292,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,13
-9.759999999969793e-05,0.01664832195245951,501,Fisher,nk_fisher_information,13
-0.00014469999999988659,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,14
-0.004316699999999951,0.7576767879978331,501,Hurst,nk_complexity_hurst,14
-0.0067989999999999995,1.0024857094190252,501,LZC,nk_complexity_lzc,14
-0.007416400000000323,0.6948540041314585,501,PLZC,nk_complexity_plzc,14
-0.018168000000000184,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,14
-0.0096387,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,14
-0.00015950000000009013,0.9983224658769967,501,PE,nk_complexity_pe,14
-0.005000400000000127,0.997017876982581,501,WPE,nk_complexity_wpe,14
-0.004254299999999933,0.9072420882196771,501,MPE,nk_complexity_mpe,14
-0.06687399999999988,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,14
-0.06854229999999983,inf,501,RangeEn,nk_entropy_range_mApEn,14
-0.002781099999999981,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,14
-0.000444800000000356,8.968666793195206,501,ShanEn,nk_entropy_shannon,14
-0.0012726999999999045,0.8581824871882455,501,SpEn,nk_entropy_spectral,14
-6.230000000018165e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,14
-0.019038099999999947,2.212029644286709,501,CD,nk_fractal_correlation,14
-0.025268599999999974,0.6780863158404323,501,DFA,nk_fractal_dfa,14
-0.017939699999999892,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,14
-5.119999999969593e-05,4.553693914348758,501,Katz,nk_fractal_katz,14
-0.0014017000000001723,1.750356764421105,501,NLD,nk_fractal_nld,14
-2.529999999989485e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,14
-0.012941200000000208,2.548465840430601,501,PSDslope,nk_fractal_psdslope,14
-0.00034069999999974954,,501,SDA,nk_fractal_sda,14
-6.810000000001537e-05,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,14
-5.160000000037357e-05,0.01664832195245951,501,Fisher,nk_fisher_information,14
-0.00017639999999996547,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,15
-0.004565800000000397,0.7576767879978331,501,Hurst,nk_complexity_hurst,15
-0.0064173999999996845,1.0024857094190252,501,LZC,nk_complexity_lzc,15
-0.007591099999999962,0.6948540041314585,501,PLZC,nk_complexity_plzc,15
-0.017395200000000166,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,15
-0.00807240000000009,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,15
-0.000155200000000022,0.9983224658769967,501,PE,nk_complexity_pe,15
-0.0050343999999999944,0.997017876982581,501,WPE,nk_complexity_wpe,15
-0.0031962000000000934,0.9072420882196771,501,MPE,nk_complexity_mpe,15
-0.05694869999999996,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,15
-0.06430829999999998,inf,501,RangeEn,nk_entropy_range_mApEn,15
-0.0028223999999998917,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,15
-0.00044170000000010035,8.968666793195206,501,ShanEn,nk_entropy_shannon,15
-0.0013337999999998296,0.8581824871882455,501,SpEn,nk_entropy_spectral,15
-6.590000000006313e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,15
-0.01824860000000017,2.212029644286709,501,CD,nk_fractal_correlation,15
-0.015622399999999814,0.6780863158404323,501,DFA,nk_fractal_dfa,15
-0.018460800000000166,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,15
-7.899999999994023e-05,4.553693914348758,501,Katz,nk_fractal_katz,15
-0.0020261000000001417,1.750356764421105,501,NLD,nk_fractal_nld,15
-3.659999999960917e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,15
-0.013141899999999929,2.548465840430601,501,PSDslope,nk_fractal_psdslope,15
-0.00034880000000026,,501,SDA,nk_fractal_sda,15
-9.660000000000224e-05,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,15
-8.229999999986859e-05,0.01664832195245951,501,Fisher,nk_fisher_information,15
-0.0001597000000002069,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,16
-0.003846499999999864,0.7576767879978331,501,Hurst,nk_complexity_hurst,16
-0.006428099999999937,1.0024857094190252,501,LZC,nk_complexity_lzc,16
-0.008831799999999834,0.6948540041314585,501,PLZC,nk_complexity_plzc,16
-0.016205999999999943,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,16
-0.005505199999999988,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,16
-0.00013639999999970343,0.9983224658769967,501,PE,nk_complexity_pe,16
-0.004975700000000138,0.997017876982581,501,WPE,nk_complexity_wpe,16
-0.0031517000000000905,0.9072420882196771,501,MPE,nk_complexity_mpe,16
-0.06640489999999977,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,16
-0.055393000000000026,inf,501,RangeEn,nk_entropy_range_mApEn,16
-0.004139799999999916,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,16
-0.000791500000000056,8.968666793195206,501,ShanEn,nk_entropy_shannon,16
-0.0018791999999998588,0.8581824871882455,501,SpEn,nk_entropy_spectral,16
-9.850000000000136e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,16
-0.025553200000000054,2.212029644286709,501,CD,nk_fractal_correlation,16
-0.018232500000000318,0.6780863158404323,501,DFA,nk_fractal_dfa,16
-0.017589700000000263,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,16
-5.530000000009139e-05,4.553693914348758,501,Katz,nk_fractal_katz,16
-0.0013988999999998697,1.750356764421105,501,NLD,nk_fractal_nld,16
-2.529999999989485e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,16
-0.019584099999999882,2.548465840430601,501,PSDslope,nk_fractal_psdslope,16
-0.00045029999999979253,,501,SDA,nk_fractal_sda,16
-9.760000000014202e-05,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,16
-7.719999999977745e-05,0.01664832195245951,501,Fisher,nk_fisher_information,16
-0.00018770000000012388,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,17
-0.0041135999999997175,0.7576767879978331,501,Hurst,nk_complexity_hurst,17
-0.006432699999999958,1.0024857094190252,501,LZC,nk_complexity_lzc,17
-0.007736400000000199,0.6948540041314585,501,PLZC,nk_complexity_plzc,17
-0.018081299999999967,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,17
-0.007889099999999871,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,17
-0.00011189999999983158,0.9983224658769967,501,PE,nk_complexity_pe,17
-0.0026063999999998977,0.997017876982581,501,WPE,nk_complexity_wpe,17
-0.0017936999999998982,0.9072420882196771,501,MPE,nk_complexity_mpe,17
-0.05621710000000002,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,17
-0.05893630000000005,inf,501,RangeEn,nk_entropy_range_mApEn,17
-0.004077099999999945,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,17
-0.0006615000000000926,8.968666793195206,501,ShanEn,nk_entropy_shannon,17
-0.00197069999999977,0.8581824871882455,501,SpEn,nk_entropy_spectral,17
-7.70999999999411e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,17
-0.027083499999999816,2.212029644286709,501,CD,nk_fractal_correlation,17
-0.017283599999999844,0.6780863158404323,501,DFA,nk_fractal_dfa,17
-0.0176539,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,17
-4.939999999997724e-05,4.553693914348758,501,Katz,nk_fractal_katz,17
-0.001461899999999794,1.750356764421105,501,NLD,nk_fractal_nld,17
-2.8200000000033754e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,17
-0.01745349999999979,2.548465840430601,501,PSDslope,nk_fractal_psdslope,17
-0.0004927000000001236,,501,SDA,nk_fractal_sda,17
-0.00010580000000004475,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,17
-7.430000000008263e-05,0.01664832195245951,501,Fisher,nk_fisher_information,17
-0.00014470000000033068,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,18
-0.004702299999999937,0.7576767879978331,501,Hurst,nk_complexity_hurst,18
-0.008782399999999857,1.0024857094190252,501,LZC,nk_complexity_lzc,18
-0.006229700000000005,0.6948540041314585,501,PLZC,nk_complexity_plzc,18
-0.012601800000000107,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,18
-0.006067200000000383,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,18
-0.0001002000000003278,0.9983224658769967,501,PE,nk_complexity_pe,18
-0.00260859999999985,0.997017876982581,501,WPE,nk_complexity_wpe,18
-0.0018762999999997199,0.9072420882196771,501,MPE,nk_complexity_mpe,18
-0.06366970000000016,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,18
-0.06293819999999961,inf,501,RangeEn,nk_entropy_range_mApEn,18
-0.0027417999999999054,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,18
-0.0004205999999999932,8.968666793195206,501,ShanEn,nk_entropy_shannon,18
-0.001471800000000023,0.8581824871882455,501,SpEn,nk_entropy_spectral,18
-7.040000000024804e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,18
-0.018146799999999796,2.212029644286709,501,CD,nk_fractal_correlation,18
-0.016517799999999916,0.6780863158404323,501,DFA,nk_fractal_dfa,18
-0.01799020000000029,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,18
-5.2900000000022374e-05,4.553693914348758,501,Katz,nk_fractal_katz,18
-0.0013638999999998624,1.750356764421105,501,NLD,nk_fractal_nld,18
-2.6800000000104518e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,18
-0.012889599999999835,2.548465840430601,501,PSDslope,nk_fractal_psdslope,18
-0.0003243999999997804,,501,SDA,nk_fractal_sda,18
-6.82000000002958e-05,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,18
-5.2399999999952485e-05,0.01664832195245951,501,Fisher,nk_fisher_information,18
-0.00015169999999997685,1.3492044661503897,501,Hjorth,nk_complexity_hjorth,19
-0.0041917000000002425,0.7576767879978331,501,Hurst,nk_complexity_hurst,19
-0.006468399999999708,1.0024857094190252,501,LZC,nk_complexity_lzc,19
-0.0075720999999999705,0.6948540041314585,501,PLZC,nk_complexity_plzc,19
-0.014497800000000005,0.7851455508631707,501,MPLZC,nk_complexity_mplzc,19
-0.005280899999999811,0.044917424172302635,501,Lyapunov,nk_complexity_lyapunov,19
-0.0001250999999999891,0.9983224658769967,501,PE,nk_complexity_pe,19
-0.0025842000000002585,0.997017876982581,501,WPE,nk_complexity_wpe,19
-0.0023215999999997017,0.9072420882196771,501,MPE,nk_complexity_mpe,19
-0.06012770000000023,1.3122839404246405,501,RangeEn,nk_entropy_range_mSampEn,19
-0.05738880000000002,inf,501,RangeEn,nk_entropy_range_mApEn,19
-0.0028040000000002507,1.90522657241887,501,RangeEn_mApEn,nk_entropy_sample,19
-0.0004368999999999623,8.968666793195206,501,ShanEn,nk_entropy_shannon,19
-0.001450699999999916,0.8581824871882455,501,SpEn,nk_entropy_spectral,19
-9.01999999998182e-05,0.9934119495808924,501,SVDen,nk_entropy_svd,19
-0.02441570000000004,2.212029644286709,501,CD,nk_fractal_correlation,19
-0.017017199999999733,0.6780863158404323,501,DFA,nk_fractal_dfa,19
-0.01834900000000017,[0.86376489 0.80579681 0.74847755 0.69861215 0.66128122 0.63772889],501,MFDFA,nk_fractal_mfdfa,19
-7.460000000003575e-05,4.553693914348758,501,Katz,nk_fractal_katz,19
-0.0020267999999998842,1.750356764421105,501,NLD,nk_fractal_nld,19
-2.6199999999754198e-05,1.023013708270944,501,Petrosian,nk_fractal_petrosian,19
-0.012223399999999884,2.548465840430601,501,PSDslope,nk_fractal_psdslope,19
-0.00039970000000000283,,501,SDA,nk_fractal_sda,19
-6.750000000010914e-05,1.6274110793534535,501,Sevcik,nk_fractal_sevcik,19
-4.999999999988347e-05,0.01664832195245951,501,Fisher,nk_fisher_information,19
-0.00011010000000011289,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,0
-0.003174999999999706,0.8074103284891481,834,Hurst,nk_complexity_hurst,0
-0.011958900000000217,1.0239130868862474,834,LZC,nk_complexity_lzc,0
-0.020134700000000283,0.7728580092419188,834,PLZC,nk_complexity_plzc,0
-0.042346400000000006,0.88334764654843,834,MPLZC,nk_complexity_mplzc,0
-0.018052999999999653,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,0
-0.0001980000000001425,0.9991435713744474,834,PE,nk_complexity_pe,0
-0.0058339000000002805,0.9991661823125552,834,WPE,nk_complexity_wpe,0
-0.005517400000000006,0.9170944343848886,834,MPE,nk_complexity_mpe,0
-0.20949240000000025,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,0
-0.2027142000000004,inf,834,RangeEn,nk_entropy_range_mApEn,0
-0.006905500000000231,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,0
-0.0008240999999999943,9.703903573444661,834,ShanEn,nk_entropy_shannon,0
-0.0021347999999998812,0.8516134728259882,834,SpEn,nk_entropy_spectral,0
-9.350000000019065e-05,0.9941747987936185,834,SVDen,nk_entropy_svd,0
-0.14054219999999962,2.3105117887772852,834,CD,nk_fractal_correlation,0
-0.04874620000000007,0.769795743965192,834,DFA,nk_fractal_dfa,0
-0.06665300000000007,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,0
-5.1800000000046253e-05,5.12900094216942,834,Katz,nk_fractal_katz,0
-0.004386499999999849,1.755650195582907,834,NLD,nk_fractal_nld,0
-4.500000000007276e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,0
-0.017903500000000072,2.522923280026853,834,PSDslope,nk_fractal_psdslope,0
-0.0003729000000003424,,834,SDA,nk_fractal_sda,0
-8.749999999979607e-05,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,0
-6.040000000018253e-05,0.014800049786141103,834,Fisher,nk_fisher_information,0
-0.00013689999999977331,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,1
-0.0032224000000002917,0.8074103284891481,834,Hurst,nk_complexity_hurst,1
-0.008566800000000097,1.0239130868862474,834,LZC,nk_complexity_lzc,1
-0.020013099999999895,0.7728580092419188,834,PLZC,nk_complexity_plzc,1
-0.04344329999999985,0.88334764654843,834,MPLZC,nk_complexity_mplzc,1
-0.019079899999999927,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,1
-0.00021270000000006561,0.9991435713744474,834,PE,nk_complexity_pe,1
-0.009181100000000164,0.9991661823125552,834,WPE,nk_complexity_wpe,1
-0.0057836,0.9170944343848886,834,MPE,nk_complexity_mpe,1
-0.19787449999999973,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,1
-0.20427730000000022,inf,834,RangeEn,nk_entropy_range_mApEn,1
-0.006968800000000108,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,1
-0.000805900000000026,9.703903573444661,834,ShanEn,nk_entropy_shannon,1
-0.0020278999999998604,0.8516134728259882,834,SpEn,nk_entropy_spectral,1
-9.889999999979082e-05,0.9941747987936185,834,SVDen,nk_entropy_svd,1
-0.13217370000000006,2.3105117887772852,834,CD,nk_fractal_correlation,1
-0.05131439999999987,0.769795743965192,834,DFA,nk_fractal_dfa,1
-0.05525089999999988,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,1
-8.030000000003312e-05,5.12900094216942,834,Katz,nk_fractal_katz,1
-0.003911399999999787,1.755650195582907,834,NLD,nk_fractal_nld,1
-4.2000000000097515e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,1
-0.014587999999999823,2.522923280026853,834,PSDslope,nk_fractal_psdslope,1
-0.0005581999999999532,,834,SDA,nk_fractal_sda,1
-0.00011530000000004037,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,1
-8.520000000000749e-05,0.014800049786141103,834,Fisher,nk_fisher_information,1
-0.00010859999999990322,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,2
-0.0032583000000001583,0.8074103284891481,834,Hurst,nk_complexity_hurst,2
-0.015055499999999888,1.0239130868862474,834,LZC,nk_complexity_lzc,2
-0.019850499999999993,0.7728580092419188,834,PLZC,nk_complexity_plzc,2
-0.03930169999999977,0.88334764654843,834,MPLZC,nk_complexity_mplzc,2
-0.01942490000000019,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,2
-0.00023030000000012762,0.9991435713744474,834,PE,nk_complexity_pe,2
-0.010607100000000091,0.9991661823125552,834,WPE,nk_complexity_wpe,2
-0.0052906999999997595,0.9170944343848886,834,MPE,nk_complexity_mpe,2
-0.20979709999999985,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,2
-0.19972730000000016,inf,834,RangeEn,nk_entropy_range_mApEn,2
-0.007087600000000194,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,2
-0.0009871999999999659,9.703903573444661,834,ShanEn,nk_entropy_shannon,2
-0.002185900000000185,0.8516134728259882,834,SpEn,nk_entropy_spectral,2
-9.570000000014289e-05,0.9941747987936185,834,SVDen,nk_entropy_svd,2
-0.13982070000000002,2.3105117887772852,834,CD,nk_fractal_correlation,2
-0.06218350000000017,0.769795743965192,834,DFA,nk_fractal_dfa,2
-0.0660797999999998,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,2
-8.359999999996148e-05,5.12900094216942,834,Katz,nk_fractal_katz,2
-0.004485900000000154,1.755650195582907,834,NLD,nk_fractal_nld,2
-4.8099999999884346e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,2
-0.024700100000000003,2.522923280026853,834,PSDslope,nk_fractal_psdslope,2
-0.0005345000000001043,,834,SDA,nk_fractal_sda,2
-0.00013090000000026691,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,2
-9.719999999990847e-05,0.014800049786141103,834,Fisher,nk_fisher_information,2
-0.00014989999999981407,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,3
-0.00433389999999978,0.8074103284891481,834,Hurst,nk_complexity_hurst,3
-0.01548090000000002,1.0239130868862474,834,LZC,nk_complexity_lzc,3
-0.020739299999999794,0.7728580092419188,834,PLZC,nk_complexity_plzc,3
-0.044630100000000006,0.88334764654843,834,MPLZC,nk_complexity_mplzc,3
-0.019880499999999746,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,3
-0.00037069999999994607,0.9991435713744474,834,PE,nk_complexity_pe,3
-0.010711899999999996,0.9991661823125552,834,WPE,nk_complexity_wpe,3
-0.005755100000000013,0.9170944343848886,834,MPE,nk_complexity_mpe,3
-0.2114495999999999,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,3
-0.2043816999999999,inf,834,RangeEn,nk_entropy_range_mApEn,3
-0.0070822000000001495,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,3
-0.0016025000000001732,9.703903573444661,834,ShanEn,nk_entropy_shannon,3
-0.0027599999999998737,0.8516134728259882,834,SpEn,nk_entropy_spectral,3
-0.0001731000000000371,0.9941747987936185,834,SVDen,nk_entropy_svd,3
-0.1443626,2.3105117887772852,834,CD,nk_fractal_correlation,3
-0.05959899999999996,0.769795743965192,834,DFA,nk_fractal_dfa,3
-0.06973479999999999,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,3
-8.990000000030918e-05,5.12900094216942,834,Katz,nk_fractal_katz,3
-0.004329800000000272,1.755650195582907,834,NLD,nk_fractal_nld,3
-5.309999999969506e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,3
-0.024067699999999803,2.522923280026853,834,PSDslope,nk_fractal_psdslope,3
-0.0007310000000000372,,834,SDA,nk_fractal_sda,3
-0.00013500000000021828,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,3
-0.00011770000000010938,0.014800049786141103,834,Fisher,nk_fisher_information,3
-0.0001166999999999696,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,4
-0.004796400000000034,0.8074103284891481,834,Hurst,nk_complexity_hurst,4
-0.015854800000000058,1.0239130868862474,834,LZC,nk_complexity_lzc,4
-0.020807599999999926,0.7728580092419188,834,PLZC,nk_complexity_plzc,4
-0.050053799999999704,0.88334764654843,834,MPLZC,nk_complexity_mplzc,4
-0.029895600000000133,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,4
-0.0002187000000000161,0.9991435713744474,834,PE,nk_complexity_pe,4
-0.006684299999999865,0.9991661823125552,834,WPE,nk_complexity_wpe,4
-0.005029900000000254,0.9170944343848886,834,MPE,nk_complexity_mpe,4
-0.20889210000000036,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,4
-0.1998230000000003,inf,834,RangeEn,nk_entropy_range_mApEn,4
-0.008355300000000287,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,4
-0.0011626000000002357,9.703903573444661,834,ShanEn,nk_entropy_shannon,4
-0.0029981000000001146,0.8516134728259882,834,SpEn,nk_entropy_spectral,4
-0.00016479999999985395,0.9941747987936185,834,SVDen,nk_entropy_svd,4
-0.1382376999999999,2.3105117887772852,834,CD,nk_fractal_correlation,4
-0.05329169999999994,0.769795743965192,834,DFA,nk_fractal_dfa,4
-0.06927069999999969,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,4
-9.139999999963067e-05,5.12900094216942,834,Katz,nk_fractal_katz,4
-0.004454700000000145,1.755650195582907,834,NLD,nk_fractal_nld,4
-5.620000000039482e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,4
-0.02441419999999983,2.522923280026853,834,PSDslope,nk_fractal_psdslope,4
-0.0006414999999999615,,834,SDA,nk_fractal_sda,4
-0.00013379999999996173,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,4
-0.00011479999999997048,0.014800049786141103,834,Fisher,nk_fisher_information,4
-0.00014849999999988484,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,5
-0.004971100000000117,0.8074103284891481,834,Hurst,nk_complexity_hurst,5
-0.015633699999999973,1.0239130868862474,834,LZC,nk_complexity_lzc,5
-0.020877100000000315,0.7728580092419188,834,PLZC,nk_complexity_plzc,5
-0.047345900000000274,0.88334764654843,834,MPLZC,nk_complexity_mplzc,5
-0.027906399999999998,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,5
-0.0002570000000003958,0.9991435713744474,834,PE,nk_complexity_pe,5
-0.007429800000000153,0.9991661823125552,834,WPE,nk_complexity_wpe,5
-0.005323699999999931,0.9170944343848886,834,MPE,nk_complexity_mpe,5
-0.2116920000000002,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,5
-0.1999013999999999,inf,834,RangeEn,nk_entropy_range_mApEn,5
-0.007057800000000114,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,5
-0.0010457999999999856,9.703903573444661,834,ShanEn,nk_entropy_shannon,5
-0.003027000000000335,0.8516134728259882,834,SpEn,nk_entropy_spectral,5
-0.00016669999999985308,0.9941747987936185,834,SVDen,nk_entropy_svd,5
-0.13629840000000026,2.3105117887772852,834,CD,nk_fractal_correlation,5
-0.06014120000000034,0.769795743965192,834,DFA,nk_fractal_dfa,5
-0.06432770000000021,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,5
-0.0001041000000001624,5.12900094216942,834,Katz,nk_fractal_katz,5
-0.00463310000000039,1.755650195582907,834,NLD,nk_fractal_nld,5
-4.379999999981621e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,5
-0.024420799999999687,2.522923280026853,834,PSDslope,nk_fractal_psdslope,5
-0.0006420999999998678,,834,SDA,nk_fractal_sda,5
-0.0001698999999999451,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,5
-0.0001562999999999981,0.014800049786141103,834,Fisher,nk_fisher_information,5
-0.00016950000000015564,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,6
-0.005412900000000054,0.8074103284891481,834,Hurst,nk_complexity_hurst,6
-0.014646899999999796,1.0239130868862474,834,LZC,nk_complexity_lzc,6
-0.014199800000000096,0.7728580092419188,834,PLZC,nk_complexity_plzc,6
-0.032918800000000026,0.88334764654843,834,MPLZC,nk_complexity_mplzc,6
-0.0160060999999998,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,6
-0.00022199999999994446,0.9991435713744474,834,PE,nk_complexity_pe,6
-0.009909800000000413,0.9991661823125552,834,WPE,nk_complexity_wpe,6
-0.005910000000000082,0.9170944343848886,834,MPE,nk_complexity_mpe,6
-0.21592020000000023,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,6
-0.2128532999999999,inf,834,RangeEn,nk_entropy_range_mApEn,6
-0.00688650000000024,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,6
-0.000830699999999851,9.703903573444661,834,ShanEn,nk_entropy_shannon,6
-0.0022463000000003674,0.8516134728259882,834,SpEn,nk_entropy_spectral,6
-0.00010219999999971918,0.9941747987936185,834,SVDen,nk_entropy_svd,6
-0.14409260000000002,2.3105117887772852,834,CD,nk_fractal_correlation,6
-0.06474439999999992,0.769795743965192,834,DFA,nk_fractal_dfa,6
-0.06767820000000002,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,6
-7.789999999996411e-05,5.12900094216942,834,Katz,nk_fractal_katz,6
-0.004483099999999851,1.755650195582907,834,NLD,nk_fractal_nld,6
-7.489999999998886e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,6
-0.02425310000000014,2.522923280026853,834,PSDslope,nk_fractal_psdslope,6
-0.0007158000000000442,,834,SDA,nk_fractal_sda,6
-0.00013780000000007675,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,6
-0.00010509999999985808,0.014800049786141103,834,Fisher,nk_fisher_information,6
-0.00011300000000025179,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,7
-0.0032416000000003997,0.8074103284891481,834,Hurst,nk_complexity_hurst,7
-0.01561849999999998,1.0239130868862474,834,LZC,nk_complexity_lzc,7
-0.02084620000000026,0.7728580092419188,834,PLZC,nk_complexity_plzc,7
-0.04097840000000019,0.88334764654843,834,MPLZC,nk_complexity_mplzc,7
-0.0211108999999996,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,7
-0.0003353999999999857,0.9991435713744474,834,PE,nk_complexity_pe,7
-0.007998100000000008,0.9991661823125552,834,WPE,nk_complexity_wpe,7
-0.005306499999999659,0.9170944343848886,834,MPE,nk_complexity_mpe,7
-0.2093468999999999,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,7
-0.19498190000000015,inf,834,RangeEn,nk_entropy_range_mApEn,7
-0.008402100000000079,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,7
-0.0009352000000002469,9.703903573444661,834,ShanEn,nk_entropy_shannon,7
-0.0021149000000000306,0.8516134728259882,834,SpEn,nk_entropy_spectral,7
-0.00011289999999997136,0.9941747987936185,834,SVDen,nk_entropy_svd,7
-0.13981759999999976,2.3105117887772852,834,CD,nk_fractal_correlation,7
-0.0608799999999996,0.769795743965192,834,DFA,nk_fractal_dfa,7
-0.06892429999999994,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,7
-8.009999999991635e-05,5.12900094216942,834,Katz,nk_fractal_katz,7
-0.004447300000000265,1.755650195582907,834,NLD,nk_fractal_nld,7
-5.100000000002325e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,7
-0.023679899999999865,2.522923280026853,834,PSDslope,nk_fractal_psdslope,7
-0.0005461000000002159,,834,SDA,nk_fractal_sda,7
-0.00011679999999980595,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,7
-9.579999999997924e-05,0.014800049786141103,834,Fisher,nk_fisher_information,7
-0.0001647000000000176,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,8
-0.0052034999999999165,0.8074103284891481,834,Hurst,nk_complexity_hurst,8
-0.016547300000000043,1.0239130868862474,834,LZC,nk_complexity_lzc,8
-0.021270200000000017,0.7728580092419188,834,PLZC,nk_complexity_plzc,8
-0.045607100000000234,0.88334764654843,834,MPLZC,nk_complexity_mplzc,8
-0.028997299999999893,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,8
-0.00017110000000020165,0.9991435713744474,834,PE,nk_complexity_pe,8
-0.007637600000000244,0.9991661823125552,834,WPE,nk_complexity_wpe,8
-0.005185299999999948,0.9170944343848886,834,MPE,nk_complexity_mpe,8
-0.1974073999999999,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,8
-0.2030088000000001,inf,834,RangeEn,nk_entropy_range_mApEn,8
-0.0070606000000004165,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,8
-0.001479799999999809,9.703903573444661,834,ShanEn,nk_entropy_shannon,8
-0.0026535000000000863,0.8516134728259882,834,SpEn,nk_entropy_spectral,8
-0.00014539999999962916,0.9941747987936185,834,SVDen,nk_entropy_svd,8
-0.13880610000000004,2.3105117887772852,834,CD,nk_fractal_correlation,8
-0.05821220000000027,0.769795743965192,834,DFA,nk_fractal_dfa,8
-0.06766899999999998,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,8
-5.6299999999787076e-05,5.12900094216942,834,Katz,nk_fractal_katz,8
-0.0024036999999998976,1.755650195582907,834,NLD,nk_fractal_nld,8
-3.3699999999914354e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,8
-0.019750900000000016,2.522923280026853,834,PSDslope,nk_fractal_psdslope,8
-0.0005221999999998062,,834,SDA,nk_fractal_sda,8
-0.00013089999999982282,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,8
-9.90999999999076e-05,0.014800049786141103,834,Fisher,nk_fisher_information,8
-0.00019440000000026103,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,9
-0.003352699999999764,0.8074103284891481,834,Hurst,nk_complexity_hurst,9
-0.016361499999999918,1.0239130868862474,834,LZC,nk_complexity_lzc,9
-0.02069259999999984,0.7728580092419188,834,PLZC,nk_complexity_plzc,9
-0.04381970000000024,0.88334764654843,834,MPLZC,nk_complexity_mplzc,9
-0.027371000000000034,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,9
-0.0001921999999998647,0.9991435713744474,834,PE,nk_complexity_pe,9
-0.006860099999999925,0.9991661823125552,834,WPE,nk_complexity_wpe,9
-0.005575899999999745,0.9170944343848886,834,MPE,nk_complexity_mpe,9
-0.1959841,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,9
-0.19935290000000006,inf,834,RangeEn,nk_entropy_range_mApEn,9
-0.007042900000000074,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,9
-0.0008661999999999281,9.703903573444661,834,ShanEn,nk_entropy_shannon,9
-0.002197699999999969,0.8516134728259882,834,SpEn,nk_entropy_spectral,9
-9.470000000000312e-05,0.9941747987936185,834,SVDen,nk_entropy_svd,9
-0.14271430000000018,2.3105117887772852,834,CD,nk_fractal_correlation,9
-0.06153110000000028,0.769795743965192,834,DFA,nk_fractal_dfa,9
-0.06422239999999979,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,9
-8.490000000005438e-05,5.12900094216942,834,Katz,nk_fractal_katz,9
-0.004690199999999756,1.755650195582907,834,NLD,nk_fractal_nld,9
-4.830000000000112e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,9
-0.022585999999999995,2.522923280026853,834,PSDslope,nk_fractal_psdslope,9
-0.0003674000000000177,,834,SDA,nk_fractal_sda,9
-0.00017339999999999023,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,9
-8.879999999988897e-05,0.014800049786141103,834,Fisher,nk_fisher_information,9
-0.00014309999999984058,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,10
-0.005338800000000088,0.8074103284891481,834,Hurst,nk_complexity_hurst,10
-0.016343500000000066,1.0239130868862474,834,LZC,nk_complexity_lzc,10
-0.021170500000000203,0.7728580092419188,834,PLZC,nk_complexity_plzc,10
-0.04557600000000006,0.88334764654843,834,MPLZC,nk_complexity_mplzc,10
-0.0290341999999999,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,10
-0.00017350000000027066,0.9991435713744474,834,PE,nk_complexity_pe,10
-0.007503900000000119,0.9991661823125552,834,WPE,nk_complexity_wpe,10
-0.005152999999999963,0.9170944343848886,834,MPE,nk_complexity_mpe,10
-0.21179459999999972,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,10
-0.1954589000000002,inf,834,RangeEn,nk_entropy_range_mApEn,10
-0.005373700000000259,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,10
-0.0013651999999999553,9.703903573444661,834,ShanEn,nk_entropy_shannon,10
-0.0036616000000000426,0.8516134728259882,834,SpEn,nk_entropy_spectral,10
-0.00019130000000000535,0.9941747987936185,834,SVDen,nk_entropy_svd,10
-0.13865789999999967,2.3105117887772852,834,CD,nk_fractal_correlation,10
-0.06374980000000008,0.769795743965192,834,DFA,nk_fractal_dfa,10
-0.06868929999999995,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,10
-5.840000000034706e-05,5.12900094216942,834,Katz,nk_fractal_katz,10
-0.002538200000000046,1.755650195582907,834,NLD,nk_fractal_nld,10
-5.3600000000209036e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,10
-0.02728980000000014,2.522923280026853,834,PSDslope,nk_fractal_psdslope,10
-0.0007014000000000742,,834,SDA,nk_fractal_sda,10
-0.00016160000000020602,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,10
-0.00011809999999989884,0.014800049786141103,834,Fisher,nk_fisher_information,10
-0.00017259999999996722,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,11
-0.006148400000000276,0.8074103284891481,834,Hurst,nk_complexity_hurst,11
-0.01677549999999961,1.0239130868862474,834,LZC,nk_complexity_lzc,11
-0.02118690000000001,0.7728580092419188,834,PLZC,nk_complexity_plzc,11
-0.04414449999999981,0.88334764654843,834,MPLZC,nk_complexity_mplzc,11
-0.0267849,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,11
-0.00017179999999994422,0.9991435713744474,834,PE,nk_complexity_pe,11
-0.0077816999999997805,0.9991661823125552,834,WPE,nk_complexity_wpe,11
-0.004360699999999884,0.9170944343848886,834,MPE,nk_complexity_mpe,11
-0.20592919999999992,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,11
-0.20104830000000007,inf,834,RangeEn,nk_entropy_range_mApEn,11
-0.00800080000000003,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,11
-0.0015193000000000012,9.703903573444661,834,ShanEn,nk_entropy_shannon,11
-0.004544999999999799,0.8516134728259882,834,SpEn,nk_entropy_spectral,11
-0.00023150000000038418,0.9941747987936185,834,SVDen,nk_entropy_svd,11
-0.13235229999999998,2.3105117887772852,834,CD,nk_fractal_correlation,11
-0.060593200000000014,0.769795743965192,834,DFA,nk_fractal_dfa,11
-0.06859769999999976,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,11
-7.529999999977832e-05,5.12900094216942,834,Katz,nk_fractal_katz,11
-0.00420329999999991,1.755650195582907,834,NLD,nk_fractal_nld,11
-5.9100000000089636e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,11
-0.029749299999999756,2.522923280026853,834,PSDslope,nk_fractal_psdslope,11
-0.0007253000000000398,,834,SDA,nk_fractal_sda,11
-0.00014059999999993522,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,11
-0.00011450000000001737,0.014800049786141103,834,Fisher,nk_fisher_information,11
-0.0001429000000001679,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,12
-0.005557200000000151,0.8074103284891481,834,Hurst,nk_complexity_hurst,12
-0.016382600000000025,1.0239130868862474,834,LZC,nk_complexity_lzc,12
-0.020899900000000304,0.7728580092419188,834,PLZC,nk_complexity_plzc,12
-0.0428506999999998,0.88334764654843,834,MPLZC,nk_complexity_mplzc,12
-0.02566600000000019,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,12
-0.00013210000000007938,0.9991435713744474,834,PE,nk_complexity_pe,12
-0.006530900000000006,0.9991661823125552,834,WPE,nk_complexity_wpe,12
-0.005113300000000098,0.9170944343848886,834,MPE,nk_complexity_mpe,12
-0.2019012,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,12
-0.19765940000000004,inf,834,RangeEn,nk_entropy_range_mApEn,12
-0.00645030000000002,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,12
-0.0011538000000004267,9.703903573444661,834,ShanEn,nk_entropy_shannon,12
-0.003656100000000162,0.8516134728259882,834,SpEn,nk_entropy_spectral,12
-0.00012150000000010763,0.9941747987936185,834,SVDen,nk_entropy_svd,12
-0.14047220000000005,2.3105117887772852,834,CD,nk_fractal_correlation,12
-0.06339650000000008,0.769795743965192,834,DFA,nk_fractal_dfa,12
-0.06724910000000017,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,12
-8.480000000021803e-05,5.12900094216942,834,Katz,nk_fractal_katz,12
-0.004386100000000059,1.755650195582907,834,NLD,nk_fractal_nld,12
-4.6299999999721564e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,12
-0.029977800000000165,2.522923280026853,834,PSDslope,nk_fractal_psdslope,12
-0.0006545000000000023,,834,SDA,nk_fractal_sda,12
-0.00016079999999973893,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,12
-0.0001345000000001484,0.014800049786141103,834,Fisher,nk_fisher_information,12
-0.00015739999999997423,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,13
-0.005433999999999717,0.8074103284891481,834,Hurst,nk_complexity_hurst,13
-0.013617199999999663,1.0239130868862474,834,LZC,nk_complexity_lzc,13
-0.021377100000000038,0.7728580092419188,834,PLZC,nk_complexity_plzc,13
-0.04570810000000014,0.88334764654843,834,MPLZC,nk_complexity_mplzc,13
-0.02704270000000042,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,13
-0.00017690000000003536,0.9991435713744474,834,PE,nk_complexity_pe,13
-0.007928799999999736,0.9991661823125552,834,WPE,nk_complexity_wpe,13
-0.005226500000000023,0.9170944343848886,834,MPE,nk_complexity_mpe,13
-0.19962869999999988,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,13
-0.20230080000000017,inf,834,RangeEn,nk_entropy_range_mApEn,13
-0.008719900000000003,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,13
-0.0013453000000001047,9.703903573444661,834,ShanEn,nk_entropy_shannon,13
-0.003277999999999892,0.8516134728259882,834,SpEn,nk_entropy_spectral,13
-0.00019679999999988596,0.9941747987936185,834,SVDen,nk_entropy_svd,13
-0.13723529999999995,2.3105117887772852,834,CD,nk_fractal_correlation,13
-0.057494399999999946,0.769795743965192,834,DFA,nk_fractal_dfa,13
-0.05182460000000022,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,13
-5.6799999999856965e-05,5.12900094216942,834,Katz,nk_fractal_katz,13
-0.0032260999999995654,1.755650195582907,834,NLD,nk_fractal_nld,13
-4.6999999999908226e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,13
-0.023273800000000122,2.522923280026853,834,PSDslope,nk_fractal_psdslope,13
-0.0005461999999996081,,834,SDA,nk_fractal_sda,13
-0.00011629999999973606,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,13
-8.169999999996236e-05,0.014800049786141103,834,Fisher,nk_fisher_information,13
-0.0001635999999995974,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,14
-0.006060199999999849,0.8074103284891481,834,Hurst,nk_complexity_hurst,14
-0.018235699999999966,1.0239130868862474,834,LZC,nk_complexity_lzc,14
-0.019818799999999914,0.7728580092419188,834,PLZC,nk_complexity_plzc,14
-0.04186220000000018,0.88334764654843,834,MPLZC,nk_complexity_mplzc,14
-0.02535609999999977,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,14
-0.0003234999999999211,0.9991435713744474,834,PE,nk_complexity_pe,14
-0.009450300000000134,0.9991661823125552,834,WPE,nk_complexity_wpe,14
-0.007517100000000276,0.9170944343848886,834,MPE,nk_complexity_mpe,14
-0.13060820000000017,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,14
-0.1377935999999993,inf,834,RangeEn,nk_entropy_range_mApEn,14
-0.00452980000000025,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,14
-0.0005456999999999823,9.703903573444661,834,ShanEn,nk_entropy_shannon,14
-0.0014545999999997505,0.8516134728259882,834,SpEn,nk_entropy_spectral,14
-7.040000000024804e-05,0.9941747987936185,834,SVDen,nk_entropy_svd,14
-0.06018959999999929,2.3105117887772852,834,CD,nk_fractal_correlation,14
-0.03257829999999995,0.769795743965192,834,DFA,nk_fractal_dfa,14
-0.03260040000000064,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,14
-5.089999999974282e-05,5.12900094216942,834,Katz,nk_fractal_katz,14
-0.0020319000000004195,1.755650195582907,834,NLD,nk_fractal_nld,14
-2.5599999999847967e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,14
-0.012220600000000026,2.522923280026853,834,PSDslope,nk_fractal_psdslope,14
-0.0003174999999995265,,834,SDA,nk_fractal_sda,14
-6.679999999992248e-05,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,14
-5.279999999974194e-05,0.014800049786141103,834,Fisher,nk_fisher_information,14
-0.0003280999999999423,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,15
-0.006372799999999845,0.8074103284891481,834,Hurst,nk_complexity_hurst,15
-0.010448199999999908,1.0239130868862474,834,LZC,nk_complexity_lzc,15
-0.018103499999999606,0.7728580092419188,834,PLZC,nk_complexity_plzc,15
-0.03854299999999977,0.88334764654843,834,MPLZC,nk_complexity_mplzc,15
-0.01820239999999984,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,15
-0.00012279999999975644,0.9991435713744474,834,PE,nk_complexity_pe,15
-0.00407959999999985,0.9991661823125552,834,WPE,nk_complexity_wpe,15
-0.002844399999999858,0.9170944343848886,834,MPE,nk_complexity_mpe,15
-0.1306891000000001,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,15
-0.12826119999999985,inf,834,RangeEn,nk_entropy_range_mApEn,15
-0.004619799999999508,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,15
-0.0007068000000005625,9.703903573444661,834,ShanEn,nk_entropy_shannon,15
-0.001355499999999843,0.8516134728259882,834,SpEn,nk_entropy_spectral,15
-6.35000000004382e-05,0.9941747987936185,834,SVDen,nk_entropy_svd,15
-0.05479280000000042,2.3105117887772852,834,CD,nk_fractal_correlation,15
-0.0399162000000004,0.769795743965192,834,DFA,nk_fractal_dfa,15
-0.03976610000000047,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,15
-4.9200000000304556e-05,5.12900094216942,834,Katz,nk_fractal_katz,15
-0.0025440000000003238,1.755650195582907,834,NLD,nk_fractal_nld,15
-3.0999999999892225e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,15
-0.012199700000000036,2.522923280026853,834,PSDslope,nk_fractal_psdslope,15
-0.0003165000000002749,,834,SDA,nk_fractal_sda,15
-7.119999999982696e-05,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,15
-5.05999999997897e-05,0.014800049786141103,834,Fisher,nk_fisher_information,15
-0.00022929999999998785,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,16
-0.007070199999999804,0.8074103284891481,834,Hurst,nk_complexity_hurst,16
-0.013562999999999992,1.0239130868862474,834,LZC,nk_complexity_lzc,16
-0.01282670000000019,0.7728580092419188,834,PLZC,nk_complexity_plzc,16
-0.027275500000000008,0.88334764654843,834,MPLZC,nk_complexity_mplzc,16
-0.01028739999999928,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,16
-0.00015490000000006887,0.9991435713744474,834,PE,nk_complexity_pe,16
-0.004119699999999504,0.9991661823125552,834,WPE,nk_complexity_wpe,16
-0.002963500000000785,0.9170944343848886,834,MPE,nk_complexity_mpe,16
-0.1553431000000005,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,16
-0.1370559,inf,834,RangeEn,nk_entropy_range_mApEn,16
-0.00507809999999953,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,16
-0.0005142999999998565,9.703903573444661,834,ShanEn,nk_entropy_shannon,16
-0.0012856999999995011,0.8516134728259882,834,SpEn,nk_entropy_spectral,16
-5.9600000000159525e-05,0.9941747987936185,834,SVDen,nk_entropy_svd,16
-0.05922250000000062,2.3105117887772852,834,CD,nk_fractal_correlation,16
-0.030745999999999718,0.769795743965192,834,DFA,nk_fractal_dfa,16
-0.04121639999999971,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,16
-4.850000000011789e-05,5.12900094216942,834,Katz,nk_fractal_katz,16
-0.0029351999999995826,1.755650195582907,834,NLD,nk_fractal_nld,16
-3.789999999970206e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,16
-0.01646049999999999,2.522923280026853,834,PSDslope,nk_fractal_psdslope,16
-0.0002836999999997758,,834,SDA,nk_fractal_sda,16
-6.569999999950227e-05,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,16
-4.779999999993123e-05,0.014800049786141103,834,Fisher,nk_fisher_information,16
-0.00018520000000021852,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,17
-0.007313799999999926,0.8074103284891481,834,Hurst,nk_complexity_hurst,17
-0.009675800000000123,1.0239130868862474,834,LZC,nk_complexity_lzc,17
-0.01195180000000029,0.7728580092419188,834,PLZC,nk_complexity_plzc,17
-0.03139580000000031,0.88334764654843,834,MPLZC,nk_complexity_mplzc,17
-0.010529400000000244,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,17
-0.0001445000000002139,0.9991435713744474,834,PE,nk_complexity_pe,17
-0.0056781999999993005,0.9991661823125552,834,WPE,nk_complexity_wpe,17
-0.002768899999999519,0.9170944343848886,834,MPE,nk_complexity_mpe,17
-0.13556969999999957,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,17
-0.13067779999999996,inf,834,RangeEn,nk_entropy_range_mApEn,17
-0.004572000000000465,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,17
-0.0005799999999993588,9.703903573444661,834,ShanEn,nk_entropy_shannon,17
-0.001285900000000062,0.8516134728259882,834,SpEn,nk_entropy_spectral,17
-9.820000000004825e-05,0.9941747987936185,834,SVDen,nk_entropy_svd,17
-0.05874399999999991,2.3105117887772852,834,CD,nk_fractal_correlation,17
-0.03576759999999979,0.769795743965192,834,DFA,nk_fractal_dfa,17
-0.04177320000000062,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,17
-5.180000000049034e-05,5.12900094216942,834,Katz,nk_fractal_katz,17
-0.002617900000000617,1.755650195582907,834,NLD,nk_fractal_nld,17
-4.069999999956053e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,17
-0.012537399999999366,2.522923280026853,834,PSDslope,nk_fractal_psdslope,17
-0.0003064999999997653,,834,SDA,nk_fractal_sda,17
-7.950000000001012e-05,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,17
-5.1400000000256796e-05,0.014800049786141103,834,Fisher,nk_fisher_information,17
-0.0001221999999998502,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,18
-0.004917400000000072,0.8074103284891481,834,Hurst,nk_complexity_hurst,18
-0.015186200000000039,1.0239130868862474,834,LZC,nk_complexity_lzc,18
-0.02012880000000017,0.7728580092419188,834,PLZC,nk_complexity_plzc,18
-0.022314100000000003,0.88334764654843,834,MPLZC,nk_complexity_mplzc,18
-0.011032399999999498,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,18
-0.00012400000000045708,0.9991435713744474,834,PE,nk_complexity_pe,18
-0.003968000000000416,0.9991661823125552,834,WPE,nk_complexity_wpe,18
-0.0026805000000003076,0.9170944343848886,834,MPE,nk_complexity_mpe,18
-0.14184600000000014,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,18
-0.16217840000000017,inf,834,RangeEn,nk_entropy_range_mApEn,18
-0.006340900000000538,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,18
-0.000744300000000031,9.703903573444661,834,ShanEn,nk_entropy_shannon,18
-0.0018120000000001468,0.8516134728259882,834,SpEn,nk_entropy_spectral,18
-6.479999999964292e-05,0.9941747987936185,834,SVDen,nk_entropy_svd,18
-0.058744599999999814,2.3105117887772852,834,CD,nk_fractal_correlation,18
-0.04546040000000051,0.769795743965192,834,DFA,nk_fractal_dfa,18
-0.03796569999999999,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,18
-5.05999999997897e-05,5.12900094216942,834,Katz,nk_fractal_katz,18
-0.0018748000000003984,1.755650195582907,834,NLD,nk_fractal_nld,18
-2.3899999999521526e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,18
-0.010414200000000484,2.522923280026853,834,PSDslope,nk_fractal_psdslope,18
-0.00034549999999988756,,834,SDA,nk_fractal_sda,18
-9.890000000023491e-05,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,18
-4.999999999988347e-05,0.014800049786141103,834,Fisher,nk_fisher_information,18
-0.00024870000000021264,1.3560209154146534,834,Hjorth,nk_complexity_hjorth,19
-0.005716800000000077,0.8074103284891481,834,Hurst,nk_complexity_hurst,19
-0.01565329999999987,1.0239130868862474,834,LZC,nk_complexity_lzc,19
-0.018508600000000097,0.7728580092419188,834,PLZC,nk_complexity_plzc,19
-0.02233580000000046,0.88334764654843,834,MPLZC,nk_complexity_mplzc,19
-0.010922900000000624,0.04340720079394987,834,Lyapunov,nk_complexity_lyapunov,19
-0.0001230000000003173,0.9991435713744474,834,PE,nk_complexity_pe,19
-0.003824100000000108,0.9991661823125552,834,WPE,nk_complexity_wpe,19
-0.002683500000000727,0.9170944343848886,834,MPE,nk_complexity_mpe,19
-0.13722790000000007,1.385636586608372,834,RangeEn,nk_entropy_range_mSampEn,19
-0.14494159999999923,inf,834,RangeEn,nk_entropy_range_mApEn,19
-0.004936700000000016,2.001214821658139,834,RangeEn_mApEn,nk_entropy_sample,19
-0.0005554000000005388,9.703903573444661,834,ShanEn,nk_entropy_shannon,19
-0.0012461000000003608,0.8516134728259882,834,SpEn,nk_entropy_spectral,19
-6.0699999999691556e-05,0.9941747987936185,834,SVDen,nk_entropy_svd,19
-0.06457099999999993,2.3105117887772852,834,CD,nk_fractal_correlation,19
-0.030523899999999493,0.769795743965192,834,DFA,nk_fractal_dfa,19
-0.03483599999999942,[1.01797143 0.93720798 0.85531816 0.79509792 0.74752131 0.71149921],834,MFDFA,nk_fractal_mfdfa,19
-4.899999999974369e-05,5.12900094216942,834,Katz,nk_fractal_katz,19
-0.002101099999999967,1.755650195582907,834,NLD,nk_fractal_nld,19
-2.5899999999801082e-05,1.0238072085971834,834,Petrosian,nk_fractal_petrosian,19
-0.0114958000000005,2.522923280026853,834,PSDslope,nk_fractal_psdslope,19
-0.0003796999999998718,,834,SDA,nk_fractal_sda,19
-7.829999999930948e-05,1.6612204156480344,834,Sevcik,nk_fractal_sevcik,19
-5.01000000001639e-05,0.014800049786141103,834,Fisher,nk_fisher_information,19
-0.00012050000000041194,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,0
-0.005371700000000423,0.856400660990199,1392,Hurst,nk_complexity_hurst,0
-0.02472619999999992,0.9752748954456427,1392,LZC,nk_complexity_lzc,0
-0.06580890000000039,0.9245830025088785,1392,PLZC,nk_complexity_plzc,0
-0.10247259999999958,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,0
-0.03479320000000019,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,0
-0.0003204000000005536,0.9980020873958776,1392,PE,nk_complexity_pe,0
-0.009428900000000517,0.9977955807463277,1392,WPE,nk_complexity_wpe,0
-0.01048030000000022,0.9178262759495832,1392,MPE,nk_complexity_mpe,0
-0.4726756999999999,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,0
-0.4401232999999998,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,0
-0.012617400000000778,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,0
-0.0010408999999995672,10.442943495848729,1392,ShanEn,nk_entropy_shannon,0
-0.001990400000000392,0.8521546269350491,1392,SpEn,nk_entropy_spectral,0
-0.00011109999999980857,0.9895887808353406,1392,SVDen,nk_entropy_svd,0
-0.5158670000000001,2.345641449702419,1392,CD,nk_fractal_correlation,0
-0.11881960000000014,0.8464585379002225,1392,DFA,nk_fractal_dfa,0
-0.13232520000000036,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,0
-7.860000000015077e-05,4.830376816622304,1392,Katz,nk_fractal_katz,0
-0.007102600000000514,1.7690846589657636,1392,NLD,nk_fractal_nld,0
-4.709999999974457e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,0
-0.02273900000000051,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,0
-0.03109309999999965,0.5403462326682851,1392,SDA,nk_fractal_sda,0
-0.00013310000000021915,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,0
-9.579999999953515e-05,0.0257113487615282,1392,Fisher,nk_fisher_information,0
-0.0001492000000000715,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,1
-0.005388100000000229,0.856400660990199,1392,Hurst,nk_complexity_hurst,1
-0.032217400000000396,0.9752748954456427,1392,LZC,nk_complexity_lzc,1
-0.06093409999999988,0.9245830025088785,1392,PLZC,nk_complexity_plzc,1
-0.12225470000000005,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,1
-0.08635039999999972,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,1
-0.000503499999999768,0.9980020873958776,1392,PE,nk_complexity_pe,1
-0.01667919999999956,0.9977955807463277,1392,WPE,nk_complexity_wpe,1
-0.009089499999999973,0.9178262759495832,1392,MPE,nk_complexity_mpe,1
-0.4507186000000001,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,1
-0.4304749999999995,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,1
-0.012702700000000178,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,1
-0.001048800000000405,10.442943495848729,1392,ShanEn,nk_entropy_shannon,1
-0.002270700000000403,0.8521546269350491,1392,SpEn,nk_entropy_spectral,1
-0.00012520000000026954,0.9895887808353406,1392,SVDen,nk_entropy_svd,1
-0.5625434,2.345641449702419,1392,CD,nk_fractal_correlation,1
-0.1251313999999999,0.8464585379002225,1392,DFA,nk_fractal_dfa,1
-0.1319083000000001,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,1
-9.140000000051884e-05,4.830376816622304,1392,Katz,nk_fractal_katz,1
-0.00724830000000054,1.7690846589657636,1392,NLD,nk_fractal_nld,1
-5.560000000048859e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,1
-0.02275310000000008,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,1
-0.029947599999999852,0.5403462326682851,1392,SDA,nk_fractal_sda,1
-0.00014529999999979282,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,1
-0.0001188000000000855,0.0257113487615282,1392,Fisher,nk_fisher_information,1
-0.00019550000000023715,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,2
-0.005472499999999769,0.856400660990199,1392,Hurst,nk_complexity_hurst,2
-0.03195009999999954,0.9752748954456427,1392,LZC,nk_complexity_lzc,2
-0.05067939999999993,0.9245830025088785,1392,PLZC,nk_complexity_plzc,2
-0.11423389999999944,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,2
-0.048180000000000334,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,2
-0.000511200000000045,0.9980020873958776,1392,PE,nk_complexity_pe,2
-0.012569400000000286,0.9977955807463277,1392,WPE,nk_complexity_wpe,2
-0.010144699999999673,0.9178262759495832,1392,MPE,nk_complexity_mpe,2
-0.47158059999999935,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,2
-0.45482979999999973,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,2
-0.012790599999999763,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,2
-0.0012185000000002333,10.442943495848729,1392,ShanEn,nk_entropy_shannon,2
-0.0021899000000003,0.8521546269350491,1392,SpEn,nk_entropy_spectral,2
-0.00011619999999989972,0.9895887808353406,1392,SVDen,nk_entropy_svd,2
-0.5665079999999998,2.345641449702419,1392,CD,nk_fractal_correlation,2
-0.11473469999999963,0.8464585379002225,1392,DFA,nk_fractal_dfa,2
-0.13466030000000018,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,2
-7.599999999996498e-05,4.830376816622304,1392,Katz,nk_fractal_katz,2
-0.0048538999999996335,1.7690846589657636,1392,NLD,nk_fractal_nld,2
-5.439999999978795e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,2
-0.023506600000000155,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,2
-0.030071799999999982,0.5403462326682851,1392,SDA,nk_fractal_sda,2
-0.00014570000000002636,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,2
-9.920000000018803e-05,0.0257113487615282,1392,Fisher,nk_fisher_information,2
-0.00016529999999992384,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,3
-0.008154499999999842,0.856400660990199,1392,Hurst,nk_complexity_hurst,3
-0.038143999999999956,0.9752748954456427,1392,LZC,nk_complexity_lzc,3
-0.06287100000000034,0.9245830025088785,1392,PLZC,nk_complexity_plzc,3
-0.11272560000000009,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,3
-0.08177069999999986,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,3
-0.00150830000000024,0.9980020873958776,1392,PE,nk_complexity_pe,3
-0.015233599999999292,0.9977955807463277,1392,WPE,nk_complexity_wpe,3
-0.009987800000000213,0.9178262759495832,1392,MPE,nk_complexity_mpe,3
-0.4583417000000001,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,3
-0.4285851000000003,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,3
-0.012659600000000104,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,3
-0.00126190000000026,10.442943495848729,1392,ShanEn,nk_entropy_shannon,3
-0.0025732999999998896,0.8521546269350491,1392,SpEn,nk_entropy_spectral,3
-0.00013570000000040494,0.9895887808353406,1392,SVDen,nk_entropy_svd,3
-0.5694295999999994,2.345641449702419,1392,CD,nk_fractal_correlation,3
-0.1029024999999999,0.8464585379002225,1392,DFA,nk_fractal_dfa,3
-0.13026679999999935,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,3
-8.25999999998217e-05,4.830376816622304,1392,Katz,nk_fractal_katz,3
-0.007553800000000166,1.7690846589657636,1392,NLD,nk_fractal_nld,3
-4.649999999983834e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,3
-0.0219750000000003,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,3
-0.020255699999999877,0.5403462326682851,1392,SDA,nk_fractal_sda,3
-0.0001356000000001245,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,3
-9.939999999986071e-05,0.0257113487615282,1392,Fisher,nk_fisher_information,3
-0.00016170000000048645,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,4
-0.005860000000000198,0.856400660990199,1392,Hurst,nk_complexity_hurst,4
-0.039858699999999914,0.9752748954456427,1392,LZC,nk_complexity_lzc,4
-0.06339170000000038,0.9245830025088785,1392,PLZC,nk_complexity_plzc,4
-0.1262762000000004,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,4
-0.09578360000000075,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,4
-0.00038120000000052556,0.9980020873958776,1392,PE,nk_complexity_pe,4
-0.013081200000000237,0.9977955807463277,1392,WPE,nk_complexity_wpe,4
-0.009638200000000374,0.9178262759495832,1392,MPE,nk_complexity_mpe,4
-0.45580960000000026,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,4
-0.43832760000000004,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,4
-0.01248429999999967,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,4
-0.001805399999999402,10.442943495848729,1392,ShanEn,nk_entropy_shannon,4
-0.0029493000000000436,0.8521546269350491,1392,SpEn,nk_entropy_spectral,4
-0.0001613000000002529,0.9895887808353406,1392,SVDen,nk_entropy_svd,4
-0.5744860999999997,2.345641449702419,1392,CD,nk_fractal_correlation,4
-0.09013399999999994,0.8464585379002225,1392,DFA,nk_fractal_dfa,4
-0.13101589999999952,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,4
-6.160000000043908e-05,4.830376816622304,1392,Katz,nk_fractal_katz,4
-0.006764399999999782,1.7690846589657636,1392,NLD,nk_fractal_nld,4
-5.1299999999976365e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,4
-0.02314539999999976,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,4
-0.023749699999999763,0.5403462326682851,1392,SDA,nk_fractal_sda,4
-0.00015770000000081552,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,4
-0.000111200000000089,0.0257113487615282,1392,Fisher,nk_fisher_information,4
-0.00014120000000072963,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,5
-0.007741499999999846,0.856400660990199,1392,Hurst,nk_complexity_hurst,5
-0.04020679999999999,0.9752748954456427,1392,LZC,nk_complexity_lzc,5
-0.047953600000000485,0.9245830025088785,1392,PLZC,nk_complexity_plzc,5
-0.10951470000000008,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,5
-0.05462399999999956,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,5
-0.0005412000000006856,0.9980020873958776,1392,PE,nk_complexity_pe,5
-0.013362299999999827,0.9977955807463277,1392,WPE,nk_complexity_wpe,5
-0.010104899999999972,0.9178262759495832,1392,MPE,nk_complexity_mpe,5
-0.45161230000000074,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,5
-0.4461018000000001,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,5
-0.01267409999999991,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,5
-0.0010212000000002774,10.442943495848729,1392,ShanEn,nk_entropy_shannon,5
-0.002415300000000009,0.8521546269350491,1392,SpEn,nk_entropy_spectral,5
-9.220000000009776e-05,0.9895887808353406,1392,SVDen,nk_entropy_svd,5
-0.5391775000000001,2.345641449702419,1392,CD,nk_fractal_correlation,5
-0.11337210000000031,0.8464585379002225,1392,DFA,nk_fractal_dfa,5
-0.1251648999999997,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,5
-0.00010019999999943963,4.830376816622304,1392,Katz,nk_fractal_katz,5
-0.007288199999999634,1.7690846589657636,1392,NLD,nk_fractal_nld,5
-4.7600000000258547e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,5
-0.022364000000000495,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,5
-0.03238600000000069,0.5403462326682851,1392,SDA,nk_fractal_sda,5
-0.00014230000000026166,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,5
-0.00010129999999985984,0.0257113487615282,1392,Fisher,nk_fisher_information,5
-0.00014890000000011838,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,6
-0.006936800000000076,0.856400660990199,1392,Hurst,nk_complexity_hurst,6
-0.03959129999999966,0.9752748954456427,1392,LZC,nk_complexity_lzc,6
-0.0661833000000005,0.9245830025088785,1392,PLZC,nk_complexity_plzc,6
-0.11394790000000032,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,6
-0.09532190000000007,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,6
-0.0003139000000000891,0.9980020873958776,1392,PE,nk_complexity_pe,6
-0.015451000000000548,0.9977955807463277,1392,WPE,nk_complexity_wpe,6
-0.008573799999999743,0.9178262759495832,1392,MPE,nk_complexity_mpe,6
-0.4577131000000003,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,6
-0.45450690000000016,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,6
-0.013967899999999922,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,6
-0.0018890000000002516,10.442943495848729,1392,ShanEn,nk_entropy_shannon,6
-0.004143100000000288,0.8521546269350491,1392,SpEn,nk_entropy_spectral,6
-0.00027650000000001285,0.9895887808353406,1392,SVDen,nk_entropy_svd,6
-0.5658841999999993,2.345641449702419,1392,CD,nk_fractal_correlation,6
-0.1266778999999998,0.8464585379002225,1392,DFA,nk_fractal_dfa,6
-0.12654929999999975,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,6
-9.090000000000487e-05,4.830376816622304,1392,Katz,nk_fractal_katz,6
-0.00711120000000065,1.7690846589657636,1392,NLD,nk_fractal_nld,6
-4.840000000072564e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,6
-0.026947500000000346,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,6
-0.03165460000000042,0.5403462326682851,1392,SDA,nk_fractal_sda,6
-0.0001756999999997788,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,6
-0.00012080000000036506,0.0257113487615282,1392,Fisher,nk_fisher_information,6
-0.00015720000000030154,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,7
-0.008540500000000506,0.856400660990199,1392,Hurst,nk_complexity_hurst,7
-0.04443100000000033,0.9752748954456427,1392,LZC,nk_complexity_lzc,7
-0.06720430000000022,0.9245830025088785,1392,PLZC,nk_complexity_plzc,7
-0.1267857000000001,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,7
-0.0923765000000003,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,7
-0.0002675000000005312,0.9980020873958776,1392,PE,nk_complexity_pe,7
-0.012860499999999497,0.9977955807463277,1392,WPE,nk_complexity_wpe,7
-0.008595100000000855,0.9178262759495832,1392,MPE,nk_complexity_mpe,7
-0.45837839999999996,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,7
-0.44789869999999965,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,7
-0.011580299999999433,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,7
-0.0021019999999998262,10.442943495848729,1392,ShanEn,nk_entropy_shannon,7
-0.003925799999999313,0.8521546269350491,1392,SpEn,nk_entropy_spectral,7
-0.0001971999999996754,0.9895887808353406,1392,SVDen,nk_entropy_svd,7
-0.5608858999999997,2.345641449702419,1392,CD,nk_fractal_correlation,7
-0.12802839999999982,0.8464585379002225,1392,DFA,nk_fractal_dfa,7
-0.1313550000000001,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,7
-9.30999999999571e-05,4.830376816622304,1392,Katz,nk_fractal_katz,7
-0.006250200000000206,1.7690846589657636,1392,NLD,nk_fractal_nld,7
-5.489999999941375e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,7
-0.02572249999999965,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,7
-0.032319299999999274,0.5403462326682851,1392,SDA,nk_fractal_sda,7
-0.00010359999999920433,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,7
-7.669999999926347e-05,0.0257113487615282,1392,Fisher,nk_fisher_information,7
-0.00010040000000000049,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,8
-0.009265599999999985,0.856400660990199,1392,Hurst,nk_complexity_hurst,8
-0.04276069999999965,0.9752748954456427,1392,LZC,nk_complexity_lzc,8
-0.06035199999999996,0.9245830025088785,1392,PLZC,nk_complexity_plzc,8
-0.12805239999999962,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,8
-0.09434519999999935,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,8
-0.00022940000000026828,0.9980020873958776,1392,PE,nk_complexity_pe,8
-0.010881199999999147,0.9977955807463277,1392,WPE,nk_complexity_wpe,8
-0.008572299999999977,0.9178262759495832,1392,MPE,nk_complexity_mpe,8
-0.4472581999999994,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,8
-0.4421058999999996,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,8
-0.012889200000000045,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,8
-0.0012638000000002592,10.442943495848729,1392,ShanEn,nk_entropy_shannon,8
-0.002387200000000256,0.8521546269350491,1392,SpEn,nk_entropy_spectral,8
-0.00023399999999984544,0.9895887808353406,1392,SVDen,nk_entropy_svd,8
-0.5653698999999994,2.345641449702419,1392,CD,nk_fractal_correlation,8
-0.10986269999999987,0.8464585379002225,1392,DFA,nk_fractal_dfa,8
-0.11920079999999977,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,8
-8.400000000019503e-05,4.830376816622304,1392,Katz,nk_fractal_katz,8
-0.007176499999999919,1.7690846589657636,1392,NLD,nk_fractal_nld,8
-5.270000000034969e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,8
-0.023111899999999963,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,8
-0.027631200000000078,0.5403462326682851,1392,SDA,nk_fractal_sda,8
-0.00015600000000048908,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,8
-0.00010479999999990497,0.0257113487615282,1392,Fisher,nk_fisher_information,8
-0.0001492000000000715,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,9
-0.009943799999999392,0.856400660990199,1392,Hurst,nk_complexity_hurst,9
-0.04436850000000003,0.9752748954456427,1392,LZC,nk_complexity_lzc,9
-0.05763370000000023,0.9245830025088785,1392,PLZC,nk_complexity_plzc,9
-0.1151207000000003,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,9
-0.09456999999999915,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,9
-0.00036020000000025476,0.9980020873958776,1392,PE,nk_complexity_pe,9
-0.012219599999999886,0.9977955807463277,1392,WPE,nk_complexity_wpe,9
-0.008589999999999876,0.9178262759495832,1392,MPE,nk_complexity_mpe,9
-0.45865940000000016,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,9
-0.43055480000000035,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,9
-0.012971600000000194,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,9
-0.0014121999999998636,10.442943495848729,1392,ShanEn,nk_entropy_shannon,9
-0.002711299999999639,0.8521546269350491,1392,SpEn,nk_entropy_spectral,9
-0.000140000000000029,0.9895887808353406,1392,SVDen,nk_entropy_svd,9
-0.5783570000000005,2.345641449702419,1392,CD,nk_fractal_correlation,9
-0.12151569999999978,0.8464585379002225,1392,DFA,nk_fractal_dfa,9
-0.11468770000000017,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,9
-8.829999999981908e-05,4.830376816622304,1392,Katz,nk_fractal_katz,9
-0.00719549999999991,1.7690846589657636,1392,NLD,nk_fractal_nld,9
-5.0299999999836587e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,9
-0.023069999999999702,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,9
-0.03156780000000037,0.5403462326682851,1392,SDA,nk_fractal_sda,9
-0.00019050000000042644,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,9
-0.00015349999999969555,0.0257113487615282,1392,Fisher,nk_fisher_information,9
-0.00012729999999994135,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,10
-0.005754399999999826,0.856400660990199,1392,Hurst,nk_complexity_hurst,10
-0.043350199999999894,0.9752748954456427,1392,LZC,nk_complexity_lzc,10
-0.06808879999999995,0.9245830025088785,1392,PLZC,nk_complexity_plzc,10
-0.12588240000000006,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,10
-0.09000680000000028,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,10
-0.00026420000000015875,0.9980020873958776,1392,PE,nk_complexity_pe,10
-0.012469300000000239,0.9977955807463277,1392,WPE,nk_complexity_wpe,10
-0.00873270000000037,0.9178262759495832,1392,MPE,nk_complexity_mpe,10
-0.4349004000000001,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,10
-0.4575091999999996,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,10
-0.012172900000000375,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,10
-0.0018679999999999808,10.442943495848729,1392,ShanEn,nk_entropy_shannon,10
-0.00333129999999926,0.8521546269350491,1392,SpEn,nk_entropy_spectral,10
-0.00023769999999956326,0.9895887808353406,1392,SVDen,nk_entropy_svd,10
-0.5637289000000001,2.345641449702419,1392,CD,nk_fractal_correlation,10
-0.1153126999999996,0.8464585379002225,1392,DFA,nk_fractal_dfa,10
-0.11382760000000047,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,10
-5.880000000058061e-05,4.830376816622304,1392,Katz,nk_fractal_katz,10
-0.0038669000000002285,1.7690846589657636,1392,NLD,nk_fractal_nld,10
-3.469999999961004e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,10
-0.018905199999999844,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,10
-0.0342911999999993,0.5403462326682851,1392,SDA,nk_fractal_sda,10
-0.00015900000000002024,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,10
-0.000139499999999515,0.0257113487615282,1392,Fisher,nk_fisher_information,10
-0.00014559999999974593,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,11
-0.008926100000000048,0.856400660990199,1392,Hurst,nk_complexity_hurst,11
-0.03930690000000059,0.9752748954456427,1392,LZC,nk_complexity_lzc,11
-0.04813409999999951,0.9245830025088785,1392,PLZC,nk_complexity_plzc,11
-0.11264629999999975,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,11
-0.09707310000000025,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,11
-0.0007146000000002317,0.9980020873958776,1392,PE,nk_complexity_pe,11
-0.01220610000000022,0.9977955807463277,1392,WPE,nk_complexity_wpe,11
-0.008260199999999607,0.9178262759495832,1392,MPE,nk_complexity_mpe,11
-0.45609900000000003,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,11
-0.4287667000000006,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,11
-0.012825300000000261,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,11
-0.0011450000000001737,10.442943495848729,1392,ShanEn,nk_entropy_shannon,11
-0.0023751000000000744,0.8521546269350491,1392,SpEn,nk_entropy_spectral,11
-0.00014620000000054034,0.9895887808353406,1392,SVDen,nk_entropy_svd,11
-0.5798794000000003,2.345641449702419,1392,CD,nk_fractal_correlation,11
-0.11414860000000004,0.8464585379002225,1392,DFA,nk_fractal_dfa,11
-0.11848979999999987,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,11
-8.590000000019415e-05,4.830376816622304,1392,Katz,nk_fractal_katz,11
-0.007386600000000243,1.7690846589657636,1392,NLD,nk_fractal_nld,11
-5.219999999983571e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,11
-0.023409899999999872,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,11
-0.03161970000000025,0.5403462326682851,1392,SDA,nk_fractal_sda,11
-0.0001648999999996903,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,11
-0.00011990000000050571,0.0257113487615282,1392,Fisher,nk_fisher_information,11
-0.0001836000000006166,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,12
-0.009066300000000638,0.856400660990199,1392,Hurst,nk_complexity_hurst,12
-0.04613699999999987,0.9752748954456427,1392,LZC,nk_complexity_lzc,12
-0.06476280000000045,0.9245830025088785,1392,PLZC,nk_complexity_plzc,12
-0.11624970000000001,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,12
-0.09064210000000017,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,12
-0.0002575000000000216,0.9980020873958776,1392,PE,nk_complexity_pe,12
-0.013336699999999979,0.9977955807463277,1392,WPE,nk_complexity_wpe,12
-0.008697999999999873,0.9178262759495832,1392,MPE,nk_complexity_mpe,12
-0.43114649999999966,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,12
-0.44731140000000025,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,12
-0.011360200000000376,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,12
-0.0008091999999999544,10.442943495848729,1392,ShanEn,nk_entropy_shannon,12
-0.0023245000000002847,0.8521546269350491,1392,SpEn,nk_entropy_spectral,12
-8.750000000024016e-05,0.9895887808353406,1392,SVDen,nk_entropy_svd,12
-0.5698683000000004,2.345641449702419,1392,CD,nk_fractal_correlation,12
-0.11569139999999933,0.8464585379002225,1392,DFA,nk_fractal_dfa,12
-0.12100039999999979,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,12
-9.649999999972181e-05,4.830376816622304,1392,Katz,nk_fractal_katz,12
-0.007272699999999688,1.7690846589657636,1392,NLD,nk_fractal_nld,12
-5.0199999999556155e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,12
-0.023293999999999926,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,12
-0.032949399999999684,0.5403462326682851,1392,SDA,nk_fractal_sda,12
-0.00017189999999978056,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,12
-0.00014380000000002724,0.0257113487615282,1392,Fisher,nk_fisher_information,12
-0.0001541999999998822,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,13
-0.009132499999999766,0.856400660990199,1392,Hurst,nk_complexity_hurst,13
-0.04550290000000068,0.9752748954456427,1392,LZC,nk_complexity_lzc,13
-0.056402900000000145,0.9245830025088785,1392,PLZC,nk_complexity_plzc,13
-0.11746670000000048,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,13
-0.09734990000000021,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,13
-0.00023559999999989145,0.9980020873958776,1392,PE,nk_complexity_pe,13
-0.013220200000000126,0.9977955807463277,1392,WPE,nk_complexity_wpe,13
-0.00885770000000008,0.9178262759495832,1392,MPE,nk_complexity_mpe,13
-0.44301559999999984,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,13
-0.45003410000000077,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,13
-0.01336239999999922,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,13
-0.0017985999999998725,10.442943495848729,1392,ShanEn,nk_entropy_shannon,13
-0.005011099999999935,0.8521546269350491,1392,SpEn,nk_entropy_spectral,13
-0.00022440000000045757,0.9895887808353406,1392,SVDen,nk_entropy_svd,13
-0.5669523999999999,2.345641449702419,1392,CD,nk_fractal_correlation,13
-0.1270484999999999,0.8464585379002225,1392,DFA,nk_fractal_dfa,13
-0.13408139999999946,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,13
-0.00011429999999990059,4.830376816622304,1392,Katz,nk_fractal_katz,13
-0.006660800000000577,1.7690846589657636,1392,NLD,nk_fractal_nld,13
-4.280000000012052e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,13
-0.020613299999999946,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,13
-0.03691639999999996,0.5403462326682851,1392,SDA,nk_fractal_sda,13
-0.00017809999999940374,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,13
-0.0001257999999992876,0.0257113487615282,1392,Fisher,nk_fisher_information,13
-0.0002013999999999072,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,14
-0.01139340000000022,0.856400660990199,1392,Hurst,nk_complexity_hurst,14
-0.04909560000000024,0.9752748954456427,1392,LZC,nk_complexity_lzc,14
-0.06831790000000026,0.9245830025088785,1392,PLZC,nk_complexity_plzc,14
-0.09957090000000068,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,14
-0.03813590000000033,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,14
-0.0002855999999997749,0.9980020873958776,1392,PE,nk_complexity_pe,14
-0.01090739999999979,0.9977955807463277,1392,WPE,nk_complexity_wpe,14
-0.006319800000000875,0.9178262759495832,1392,MPE,nk_complexity_mpe,14
-0.3268259000000002,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,14
-0.3179709000000006,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,14
-0.00847209999999965,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,14
-0.0006311999999999429,10.442943495848729,1392,ShanEn,nk_entropy_shannon,14
-0.001305099999999726,0.8521546269350491,1392,SpEn,nk_entropy_spectral,14
-6.750000000010914e-05,0.9895887808353406,1392,SVDen,nk_entropy_svd,14
-0.24929480000000037,2.345641449702419,1392,CD,nk_fractal_correlation,14
-0.06524060000000009,0.8464585379002225,1392,DFA,nk_fractal_dfa,14
-0.06967999999999996,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,14
-8.150000000028967e-05,4.830376816622304,1392,Katz,nk_fractal_katz,14
-0.0046672000000000935,1.7690846589657636,1392,NLD,nk_fractal_nld,14
-4.710000000063275e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,14
-0.01773429999999987,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,14
-0.022250399999999892,0.5403462326682851,1392,SDA,nk_fractal_sda,14
-0.00014359999999946638,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,14
-9.969999999981383e-05,0.0257113487615282,1392,Fisher,nk_fisher_information,14
-0.00017479999999991946,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,15
-0.009450800000000648,0.856400660990199,1392,Hurst,nk_complexity_hurst,15
-0.04874509999999965,0.9752748954456427,1392,LZC,nk_complexity_lzc,15
-0.052282499999999565,0.9245830025088785,1392,PLZC,nk_complexity_plzc,15
-0.10137959999999957,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,15
-0.04068210000000061,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,15
-0.0003790000000005733,0.9980020873958776,1392,PE,nk_complexity_pe,15
-0.014104500000000186,0.9977955807463277,1392,WPE,nk_complexity_wpe,15
-0.00802510000000023,0.9178262759495832,1392,MPE,nk_complexity_mpe,15
-0.3841492999999998,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,15
-0.32704479999999947,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,15
-0.00753830000000022,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,15
-0.0006455000000000766,10.442943495848729,1392,ShanEn,nk_entropy_shannon,15
-0.0018377000000002752,0.8521546269350491,1392,SpEn,nk_entropy_spectral,15
-0.00010729999999981032,0.9895887808353406,1392,SVDen,nk_entropy_svd,15
-0.2493843999999994,2.345641449702419,1392,CD,nk_fractal_correlation,15
-0.07223690000000005,0.8464585379002225,1392,DFA,nk_fractal_dfa,15
-0.08888549999999995,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,15
-5.670000000002062e-05,4.830376816622304,1392,Katz,nk_fractal_katz,15
-0.004170600000000135,1.7690846589657636,1392,NLD,nk_fractal_nld,15
-3.289999999900317e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,15
-0.011524499999998383,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,15
-0.014802800000000005,0.5403462326682851,1392,SDA,nk_fractal_sda,15
-8.379999999874599e-05,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,15
-6.500000000109196e-05,0.0257113487615282,1392,Fisher,nk_fisher_information,15
-0.0001509999999997902,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,16
-0.009065800000000124,0.856400660990199,1392,Hurst,nk_complexity_hurst,16
-0.034953599999999696,0.9752748954456427,1392,LZC,nk_complexity_lzc,16
-0.0350969000000001,0.9245830025088785,1392,PLZC,nk_complexity_plzc,16
-0.08578740000000007,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,16
-0.04194859999999956,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,16
-0.00018739999999972667,0.9980020873958776,1392,PE,nk_complexity_pe,16
-0.007163300000000206,0.9977955807463277,1392,WPE,nk_complexity_wpe,16
-0.00532649999999979,0.9178262759495832,1392,MPE,nk_complexity_mpe,16
-0.3463789000000004,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,16
-0.3350446000000007,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,16
-0.007947099999999985,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,16
-0.0006468000000001695,10.442943495848729,1392,ShanEn,nk_entropy_shannon,16
-0.0014335000000000875,0.8521546269350491,1392,SpEn,nk_entropy_spectral,16
-9.509999999934848e-05,0.9895887808353406,1392,SVDen,nk_entropy_svd,16
-0.25511259999999947,2.345641449702419,1392,CD,nk_fractal_correlation,16
-0.06703169999999936,0.8464585379002225,1392,DFA,nk_fractal_dfa,16
-0.074345000000001,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,16
-5.6199999999506645e-05,4.830376816622304,1392,Katz,nk_fractal_katz,16
-0.003495299999999091,1.7690846589657636,1392,NLD,nk_fractal_nld,16
-3.069999999993911e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,16
-0.017527499999999918,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,16
-0.016924699999998793,0.5403462326682851,1392,SDA,nk_fractal_sda,16
-8.829999999981908e-05,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,16
-7.540000000005875e-05,0.0257113487615282,1392,Fisher,nk_fisher_information,16
-0.00020409999999948525,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,17
-0.009751699999999808,0.856400660990199,1392,Hurst,nk_complexity_hurst,17
-0.043632399999999905,0.9752748954456427,1392,LZC,nk_complexity_lzc,17
-0.04065730000000034,0.9245830025088785,1392,PLZC,nk_complexity_plzc,17
-0.06809719999999952,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,17
-0.03910500000000017,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,17
-0.00021960000000031954,0.9980020873958776,1392,PE,nk_complexity_pe,17
-0.00694410000000012,0.9977955807463277,1392,WPE,nk_complexity_wpe,17
-0.005016100000000634,0.9178262759495832,1392,MPE,nk_complexity_mpe,17
-0.34112900000000046,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,17
-0.3084069000000005,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,17
-0.008532800000000229,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,17
-0.0006604000000001164,10.442943495848729,1392,ShanEn,nk_entropy_shannon,17
-0.001422999999999952,0.8521546269350491,1392,SpEn,nk_entropy_spectral,17
-7.350000000005963e-05,0.9895887808353406,1392,SVDen,nk_entropy_svd,17
-0.2398821,2.345641449702419,1392,CD,nk_fractal_correlation,17
-0.07763149999999985,0.8464585379002225,1392,DFA,nk_fractal_dfa,17
-0.07153679999999962,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,17
-7.9200000000057e-05,4.830376816622304,1392,Katz,nk_fractal_katz,17
-0.003685400000000172,1.7690846589657636,1392,NLD,nk_fractal_nld,17
-3.0100000000032878e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,17
-0.013527999999999984,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,17
-0.020649799999999274,0.5403462326682851,1392,SDA,nk_fractal_sda,17
-8.769999999991285e-05,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,17
-9.010000000131413e-05,0.0257113487615282,1392,Fisher,nk_fisher_information,17
-0.00023060000000008074,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,18
-0.009443300000000043,0.856400660990199,1392,Hurst,nk_complexity_hurst,18
-0.030597600000000114,0.9752748954456427,1392,LZC,nk_complexity_lzc,18
-0.03180920000000054,0.9245830025088785,1392,PLZC,nk_complexity_plzc,18
-0.07940609999999992,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,18
-0.034420100000000176,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,18
-0.00019610000000014338,0.9980020873958776,1392,PE,nk_complexity_pe,18
-0.007426299999999664,0.9977955807463277,1392,WPE,nk_complexity_wpe,18
-0.005367099999999958,0.9178262759495832,1392,MPE,nk_complexity_mpe,18
-0.31828270000000014,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,18
-0.30469360000000023,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,18
-0.008164599999999744,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,18
-0.0006631999999999749,10.442943495848729,1392,ShanEn,nk_entropy_shannon,18
-0.0013746999999995069,0.8521546269350491,1392,SpEn,nk_entropy_spectral,18
-6.829999999968805e-05,0.9895887808353406,1392,SVDen,nk_entropy_svd,18
-0.23497250000000047,2.345641449702419,1392,CD,nk_fractal_correlation,18
-0.08300670000000032,0.8464585379002225,1392,DFA,nk_fractal_dfa,18
-0.08601619999999954,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,18
-5.249999999978883e-05,4.830376816622304,1392,Katz,nk_fractal_katz,18
-0.0036157000000001105,1.7690846589657636,1392,NLD,nk_fractal_nld,18
-2.8900000000220416e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,18
-0.013497599999999998,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,18
-0.023472699999999236,0.5403462326682851,1392,SDA,nk_fractal_sda,18
-9.000000000014552e-05,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,18
-6.909999999926697e-05,0.0257113487615282,1392,Fisher,nk_fisher_information,18
-0.00021139999999952863,1.3987023804419654,1392,Hjorth,nk_complexity_hjorth,19
-0.009911200000000342,0.856400660990199,1392,Hurst,nk_complexity_hurst,19
-0.022149299999999705,0.9752748954456427,1392,LZC,nk_complexity_lzc,19
-0.031064000000000647,0.9245830025088785,1392,PLZC,nk_complexity_plzc,19
-0.0696608000000003,0.8725559358400956,1392,MPLZC,nk_complexity_mplzc,19
-0.041166399999999825,0.042834662538271416,1392,Lyapunov,nk_complexity_lyapunov,19
-0.00017560000000038656,0.9980020873958776,1392,PE,nk_complexity_pe,19
-0.00876739999999998,0.9977955807463277,1392,WPE,nk_complexity_wpe,19
-0.005266999999999911,0.9178262759495832,1392,MPE,nk_complexity_mpe,19
-0.32426880000000047,1.3319902112753281,1392,RangeEn,nk_entropy_range_mSampEn,19
-0.32685220000000026,1.3369403648637719,1392,RangeEn,nk_entropy_range_mApEn,19
-0.010671399999999664,2.0929259053292144,1392,RangeEn_mApEn,nk_entropy_sample,19
-0.0006703999999997379,10.442943495848729,1392,ShanEn,nk_entropy_shannon,19
-0.0013623999999996528,0.8521546269350491,1392,SpEn,nk_entropy_spectral,19
-7.409999999996586e-05,0.9895887808353406,1392,SVDen,nk_entropy_svd,19
-0.24375489999999989,2.345641449702419,1392,CD,nk_fractal_correlation,19
-0.0681086999999998,0.8464585379002225,1392,DFA,nk_fractal_dfa,19
-0.09767450000000011,[0.99843419 0.95461385 0.90930874 0.86624189 0.82815245 0.79620832],1392,MFDFA,nk_fractal_mfdfa,19
-7.800000000024454e-05,4.830376816622304,1392,Katz,nk_fractal_katz,19
-0.00663220000000031,1.7690846589657636,1392,NLD,nk_fractal_nld,19
-4.580000000053985e-05,1.0211545186338082,1392,Petrosian,nk_fractal_petrosian,19
-0.02255089999999882,2.566960605645096,1392,PSDslope,nk_fractal_psdslope,19
-0.016310400000000058,0.5403462326682851,1392,SDA,nk_fractal_sda,19
-8.569999999963329e-05,1.65589534040618,1392,Sevcik,nk_fractal_sevcik,19
-6.229999999973757e-05,0.0257113487615282,1392,Fisher,nk_fisher_information,19
+0.0002562999999966564,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,0
+0.3216113000000007,0.8815000687731794,2000,Hurst,nk_complexity_hurst,0
+0.0754464999999982,0.99788636990425,2000,LZC,nk_complexity_lzc,0
+0.11416690000000074,0.9692679097822192,2000,PLZC,nk_complexity_plzc,0
+0.16407869999999747,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,0
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+0.003270899999996857,1.6061378306189726,2000,RR,nk_complexity_rr,0
+0.02739719999999579,1.0796704855858055,2000,ApEn,nk_entropy_approximate,0
+0.010251099999997848,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,0
+0.001168599999999742,1.5299377592545462,2000,DiffEn,nk_entropy_differential,0
+0.9435339999999997,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,0
+0.04193420000000003,0.875455134560752,2000,MSE,nk_complexity_mse,0
+8.7402527,0.5421120627391649,2000,CMSE,nk_complexity_cmse,0
+8.9826853,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,0
+0.4674315999999976,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,0
+5.1663323000000005,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,0
+2.9260719000000037,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,0
+0.0003418000000010579,0.9995642134676757,2000,PEn,nk_complexity_pe,0
+0.013851799999997638,0.9974282038230982,2000,WPEn,nk_complexity_wpe,0
+0.01190210000000036,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,0
+0.9660077999999999,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,0
+0.9407502999999977,inf,2000,RangeEn,nk_entropy_range_mApEn,0
+0.024564999999995507,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,0
+0.0012544999999946072,10.965784284662085,2000,ShanEn,nk_entropy_shannon,0
+0.0026811000000037666,0.8465376735420068,2000,SpEn,nk_entropy_spectral,0
+0.00020810000000182072,0.9927605014360844,2000,SVDen,nk_entropy_svd,0
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+0.0016481999999982122,1.9990408568403804,2000,HFD,nk_fractal_higuchi,0
+0.00010050000000205728,4.331735672660552,2000,Katz,nk_fractal_katz,0
+0.008943099999996207,1.8040203537176212,2000,NLD,nk_fractal_nld,0
+5.579999999838492e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,0
+0.004279699999997888,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,0
+0.009226500000004023,0.691035660416629,2000,SDA,nk_fractal_sda,0
+0.00015009999999904267,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,0
+0.0001238999999984003,0.018215273069519526,2000,Fisher,nk_fisher_information,0
+0.0002459999999970819,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,1
+0.2923700999999994,0.8815000687731794,2000,Hurst,nk_complexity_hurst,1
+0.07677440000000502,0.99788636990425,2000,LZC,nk_complexity_lzc,1
+0.11403100000000421,0.9692679097822192,2000,PLZC,nk_complexity_plzc,1
+0.2563808000000023,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,1
+0.6981421999999995,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,1
+0.0027822999999997933,1.6061378306189726,2000,RR,nk_complexity_rr,1
+0.027144399999997404,1.0796704855858055,2000,ApEn,nk_entropy_approximate,1
+0.0093293999999986,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,1
+0.0008224000000041087,1.5299377592545462,2000,DiffEn,nk_entropy_differential,1
+1.0978041999999988,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,1
+0.054430099999997594,0.875455134560752,2000,MSE,nk_complexity_mse,1
+5.426725699999999,0.5421120627391649,2000,CMSE,nk_complexity_cmse,1
+5.129987100000001,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,1
+0.45320099999999996,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,1
+3.2402355999999983,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,1
+3.3083291000000017,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,1
+0.00030079999999799156,0.9995642134676757,2000,PEn,nk_complexity_pe,1
+0.013910700000003828,0.9974282038230982,2000,WPEn,nk_complexity_wpe,1
+0.011992300000002842,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,1
+0.9783675999999986,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,1
+0.9725602000000038,inf,2000,RangeEn,nk_entropy_range_mApEn,1
+0.0258176999999975,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,1
+0.002343299999999715,10.965784284662085,2000,ShanEn,nk_entropy_shannon,1
+0.004963300000000004,0.8465376735420068,2000,SpEn,nk_entropy_spectral,1
+0.0007901999999972986,0.9927605014360844,2000,SVDen,nk_entropy_svd,1
+2.9291333000000037,2.132371401023081,2000,CD,nk_fractal_correlation,1
+0.309043299999999,0.9905916277925066,2000,DFA,nk_fractal_dfa,1
+0.22555159999999574,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,1
+0.0027514000000010697,1.9990408568403804,2000,HFD,nk_fractal_higuchi,1
+0.00012170000000111258,4.331735672660552,2000,Katz,nk_fractal_katz,1
+0.01980209999999971,1.8040203537176212,2000,NLD,nk_fractal_nld,1
+9.069999999411493e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,1
+0.006991100000000472,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,1
+0.019803599999995924,0.691035660416629,2000,SDA,nk_fractal_sda,1
+0.00019809999999864658,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,1
+0.0001466000000007739,0.018215273069519526,2000,Fisher,nk_fisher_information,1
+0.00028379999999827987,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,2
+0.3172011999999995,0.8815000687731794,2000,Hurst,nk_complexity_hurst,2
+0.153045800000001,0.99788636990425,2000,LZC,nk_complexity_lzc,2
+0.22565310000000238,0.9692679097822192,2000,PLZC,nk_complexity_plzc,2
+0.16395150000000314,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,2
+0.6763324999999938,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,2
+0.0017923999999993612,1.6061378306189726,2000,RR,nk_complexity_rr,2
+0.025503100000001666,1.0796704855858055,2000,ApEn,nk_entropy_approximate,2
+0.007673799999999176,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,2
+0.000664000000000442,1.5299377592545462,2000,DiffEn,nk_entropy_differential,2
+0.9533035999999981,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,2
+0.04134439999999984,0.875455134560752,2000,MSE,nk_complexity_mse,2
+8.6833983,0.5421120627391649,2000,CMSE,nk_complexity_cmse,2
+8.956139399999998,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,2
+0.7111073000000019,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,2
+5.171562700000003,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,2
+3.000034399999997,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,2
+0.00041850000000209775,0.9995642134676757,2000,PEn,nk_complexity_pe,2
+0.013992200000004118,0.9974282038230982,2000,WPEn,nk_complexity_wpe,2
+0.012106799999997975,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,2
+0.9596059999999937,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,2
+0.9423596000000032,inf,2000,RangeEn,nk_entropy_range_mApEn,2
+0.024466899999993075,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,2
+0.0010817000000002963,10.965784284662085,2000,ShanEn,nk_entropy_shannon,2
+0.0025291999999979,0.8465376735420068,2000,SpEn,nk_entropy_spectral,2
+0.0001965000000012651,0.9927605014360844,2000,SVDen,nk_entropy_svd,2
+0.6100070999999971,2.132371401023081,2000,CD,nk_fractal_correlation,2
+0.17847090000000065,0.9905916277925066,2000,DFA,nk_fractal_dfa,2
+0.19871020000000073,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,2
+0.0015677000000025032,1.9990408568403804,2000,HFD,nk_fractal_higuchi,2
+9.590000000514465e-05,4.331735672660552,2000,Katz,nk_fractal_katz,2
+0.008865600000000029,1.8040203537176212,2000,NLD,nk_fractal_nld,2
+6.399999999473494e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,2
+0.004022599999998988,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,2
+0.009297000000003663,0.691035660416629,2000,SDA,nk_fractal_sda,2
+0.00013839999999731845,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,2
+0.00011409999999756337,0.018215273069519526,2000,Fisher,nk_fisher_information,2
+0.00025229999999965,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,3
+0.27065100000000086,0.8815000687731794,2000,Hurst,nk_complexity_hurst,3
+0.1518931999999964,0.99788636990425,2000,LZC,nk_complexity_lzc,3
+0.14611440000000187,0.9692679097822192,2000,PLZC,nk_complexity_plzc,3
+0.15986170000000044,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,3
+0.7393358000000063,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,3
+0.0028683999999969956,1.6061378306189726,2000,RR,nk_complexity_rr,3
+0.037993000000000166,1.0796704855858055,2000,ApEn,nk_entropy_approximate,3
+0.012576600000002713,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,3
+0.0009001000000026238,1.5299377592545462,2000,DiffEn,nk_entropy_differential,3
+1.1354601999999971,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,3
+0.06201129999999466,0.875455134560752,2000,MSE,nk_complexity_mse,3
+9.1023766,0.5421120627391649,2000,CMSE,nk_complexity_cmse,3
+8.883425700000004,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,3
+1.329406800000001,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,3
+5.476246500000002,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,3
+3.836667200000001,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,3
+0.0002899000000056162,0.9995642134676757,2000,PEn,nk_complexity_pe,3
+0.014408500000001823,0.9974282038230982,2000,WPEn,nk_complexity_wpe,3
+0.012122599999997874,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,3
+0.9856070999999957,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,3
+0.9906359000000009,inf,2000,RangeEn,nk_entropy_range_mApEn,3
+0.03433460000000821,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,3
+0.00219920000000684,10.965784284662085,2000,ShanEn,nk_entropy_shannon,3
+0.004383099999998308,0.8465376735420068,2000,SpEn,nk_entropy_spectral,3
+0.0005069000000048618,0.9927605014360844,2000,SVDen,nk_entropy_svd,3
+1.2110169999999982,2.132371401023081,2000,CD,nk_fractal_correlation,3
+0.1872259000000014,0.9905916277925066,2000,DFA,nk_fractal_dfa,3
+0.19234030000001212,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,3
+0.0016178999999993948,1.9990408568403804,2000,HFD,nk_fractal_higuchi,3
+0.00011369999999999436,4.331735672660552,2000,Katz,nk_fractal_katz,3
+0.009075799999990863,1.8040203537176212,2000,NLD,nk_fractal_nld,3
+6.419999999707215e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,3
+0.0041370999999941205,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,3
+0.00919209999999282,0.691035660416629,2000,SDA,nk_fractal_sda,3
+0.00014010000001007938,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,3
+0.00011489999999980682,0.018215273069519526,2000,Fisher,nk_fisher_information,3
+0.00024179999999773827,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,4
+0.2669372999999986,0.8815000687731794,2000,Hurst,nk_complexity_hurst,4
+0.14423489999999362,0.99788636990425,2000,LZC,nk_complexity_lzc,4
+0.11333890000000224,0.9692679097822192,2000,PLZC,nk_complexity_plzc,4
+0.1659307000000041,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,4
+0.7079306999999986,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,4
+0.0032284999999987463,1.6061378306189726,2000,RR,nk_complexity_rr,4
+0.02725019999999745,1.0796704855858055,2000,ApEn,nk_entropy_approximate,4
+0.009129600000001403,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,4
+0.000770400000000393,1.5299377592545462,2000,DiffEn,nk_entropy_differential,4
+0.9506725999999972,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,4
+0.04246609999999862,0.875455134560752,2000,MSE,nk_complexity_mse,4
+8.669510299999999,0.5421120627391649,2000,CMSE,nk_complexity_cmse,4
+8.9931475,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,4
+0.6295438000000004,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,4
+6.231326599999996,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,4
+3.8000994000000006,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,4
+0.0002691000000041299,0.9995642134676757,2000,PEn,nk_complexity_pe,4
+0.013845000000003438,0.9974282038230982,2000,WPEn,nk_complexity_wpe,4
+0.011920400000001052,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,4
+0.9624775999999997,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,4
+0.9475649999999973,inf,2000,RangeEn,nk_entropy_range_mApEn,4
+0.034335100000006946,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,4
+0.0011788000000052534,10.965784284662085,2000,ShanEn,nk_entropy_shannon,4
+0.0027648000000084494,0.8465376735420068,2000,SpEn,nk_entropy_spectral,4
+0.0002455000000054497,0.9927605014360844,2000,SVDen,nk_entropy_svd,4
+1.2480633999999924,2.132371401023081,2000,CD,nk_fractal_correlation,4
+0.1948048,0.9905916277925066,2000,DFA,nk_fractal_dfa,4
+0.1992467999999974,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,4
+0.0016652000000050293,1.9990408568403804,2000,HFD,nk_fractal_higuchi,4
+0.00012060000000246873,4.331735672660552,2000,Katz,nk_fractal_katz,4
+0.00937179999999671,1.8040203537176212,2000,NLD,nk_fractal_nld,4
+5.789999998739859e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,4
+0.005192499999992606,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,4
+0.009361100000006672,0.691035660416629,2000,SDA,nk_fractal_sda,4
+0.00015349999999614283,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,4
+0.00013880000000199288,0.018215273069519526,2000,Fisher,nk_fisher_information,4
+0.00024219999999886,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,5
+0.2812677000000008,0.8815000687731794,2000,Hurst,nk_complexity_hurst,5
+0.07657090000000011,0.99788636990425,2000,LZC,nk_complexity_lzc,5
+0.11311700000000258,0.9692679097822192,2000,PLZC,nk_complexity_plzc,5
+0.16318890000000152,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,5
+0.7484948999999972,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,5
+0.0029554999999987785,1.6061378306189726,2000,RR,nk_complexity_rr,5
+0.02782600000000457,1.0796704855858055,2000,ApEn,nk_entropy_approximate,5
+0.011436100000004501,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,5
+0.0010222000000013054,1.5299377592545462,2000,DiffEn,nk_entropy_differential,5
+0.945105599999998,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,5
+0.04279999999999973,0.875455134560752,2000,MSE,nk_complexity_mse,5
+6.516392699999997,0.5421120627391649,2000,CMSE,nk_complexity_cmse,5
+6.130891200000001,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,5
+0.33374569999999437,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,5
+4.6828179999999975,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,5
+3.7538423999999964,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,5
+0.0008288000000007401,0.9995642134676757,2000,PEn,nk_complexity_pe,5
+0.014675900000000297,0.9974282038230982,2000,WPEn,nk_complexity_wpe,5
+0.014090099999997108,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,5
+0.9762236999999985,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,5
+0.9629055999999991,inf,2000,RangeEn,nk_entropy_range_mApEn,5
+0.04747790000000407,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,5
+0.002263599999999144,10.965784284662085,2000,ShanEn,nk_entropy_shannon,5
+0.004449199999996267,0.8465376735420068,2000,SpEn,nk_entropy_spectral,5
+0.000256000000000256,0.9927605014360844,2000,SVDen,nk_entropy_svd,5
+0.7012282999999968,2.132371401023081,2000,CD,nk_fractal_correlation,5
+0.1786557000000002,0.9905916277925066,2000,DFA,nk_fractal_dfa,5
+0.1946808999999945,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,5
+0.0016133999999965454,1.9990408568403804,2000,HFD,nk_fractal_higuchi,5
+9.450000000299497e-05,4.331735672660552,2000,Katz,nk_fractal_katz,5
+0.009069100000004937,1.8040203537176212,2000,NLD,nk_fractal_nld,5
+5.4800000000909677e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,5
+0.004372800000005839,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,5
+0.010727200000005155,0.691035660416629,2000,SDA,nk_fractal_sda,5
+0.00018930000000239033,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,5
+0.0001259000000004562,0.018215273069519526,2000,Fisher,nk_fisher_information,5
+0.0003258000000023742,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,6
+0.3802083999999972,0.8815000687731794,2000,Hurst,nk_complexity_hurst,6
+0.15322660000000354,0.99788636990425,2000,LZC,nk_complexity_lzc,6
+0.22578829999999783,0.9692679097822192,2000,PLZC,nk_complexity_plzc,6
+0.16369770000000017,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,6
+0.6741953000000009,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,6
+0.0017524000000008755,1.6061378306189726,2000,RR,nk_complexity_rr,6
+0.025614599999997267,1.0796704855858055,2000,ApEn,nk_entropy_approximate,6
+0.007820200000004718,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,6
+0.0006540999999984365,1.5299377592545462,2000,DiffEn,nk_entropy_differential,6
+0.9569085000000044,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,6
+0.041788499999995565,0.875455134560752,2000,MSE,nk_complexity_mse,6
+5.3342758,0.5421120627391649,2000,CMSE,nk_complexity_cmse,6
+5.180472199999997,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,6
+0.3896398999999988,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,6
+3.2695849999999993,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,6
+3.3149270999999985,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,6
+0.00027349999999870533,0.9995642134676757,2000,PEn,nk_complexity_pe,6
+0.013816900000001908,0.9974282038230982,2000,WPEn,nk_complexity_wpe,6
+0.011953200000000663,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,6
+0.9651624000000041,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,6
+0.9748237000000017,inf,2000,RangeEn,nk_entropy_range_mApEn,6
+0.025708799999996756,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,6
+0.0018811999999996942,10.965784284662085,2000,ShanEn,nk_entropy_shannon,6
+0.003955699999998785,0.8465376735420068,2000,SpEn,nk_entropy_spectral,6
+0.0006994000000020151,0.9927605014360844,2000,SVDen,nk_entropy_svd,6
+2.6346533000000036,2.132371401023081,2000,CD,nk_fractal_correlation,6
+0.18949280000000357,0.9905916277925066,2000,DFA,nk_fractal_dfa,6
+0.19610070000000235,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,6
+0.0016524999999987244,1.9990408568403804,2000,HFD,nk_fractal_higuchi,6
+0.00010680000000462542,4.331735672660552,2000,Katz,nk_fractal_katz,6
+0.009170400000002132,1.8040203537176212,2000,NLD,nk_fractal_nld,6
+6.059999999763477e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,6
+0.004447499999997717,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,6
+0.009353599999997186,0.691035660416629,2000,SDA,nk_fractal_sda,6
+0.00018030000000379687,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,6
+0.00011889999999681322,0.018215273069519526,2000,Fisher,nk_fisher_information,6
+0.0002479999999991378,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,7
+0.28786509999999765,0.8815000687731794,2000,Hurst,nk_complexity_hurst,7
+0.15264460000000213,0.99788636990425,2000,LZC,nk_complexity_lzc,7
+0.22744050000000016,0.9692679097822192,2000,PLZC,nk_complexity_plzc,7
+0.17706619999999873,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,7
+0.7743421999999995,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,7
+0.004669899999996119,1.6061378306189726,2000,RR,nk_complexity_rr,7
+0.03795079999999729,1.0796704855858055,2000,ApEn,nk_entropy_approximate,7
+0.01925860000000057,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,7
+0.0013678999999982011,1.5299377592545462,2000,DiffEn,nk_entropy_differential,7
+0.9478579000000025,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,7
+0.055151099999996234,0.875455134560752,2000,MSE,nk_complexity_mse,7
+7.9122746000000035,0.5421120627391649,2000,CMSE,nk_complexity_cmse,7
+5.1473682000000025,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,7
+0.3324433999999954,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,7
+2.9654257000000044,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,7
+2.7774266999999995,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,7
+0.0004689000000013266,0.9995642134676757,2000,PEn,nk_complexity_pe,7
+0.014326799999999196,0.9974282038230982,2000,WPEn,nk_complexity_wpe,7
+0.01367270000000076,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,7
+0.9892532000000003,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,7
+0.9690322000000009,inf,2000,RangeEn,nk_entropy_range_mApEn,7
+0.02706320000000062,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,7
+0.0033864000000036754,10.965784284662085,2000,ShanEn,nk_entropy_shannon,7
+0.005937899999999274,0.8465376735420068,2000,SpEn,nk_entropy_spectral,7
+0.000595400000001689,0.9927605014360844,2000,SVDen,nk_entropy_svd,7
+1.6327533999999986,2.132371401023081,2000,CD,nk_fractal_correlation,7
+0.18024920000000577,0.9905916277925066,2000,DFA,nk_fractal_dfa,7
+0.19181460000000072,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,7
+0.0016036000000028139,1.9990408568403804,2000,HFD,nk_fractal_higuchi,7
+0.0001104000000040628,4.331735672660552,2000,Katz,nk_fractal_katz,7
+0.009538700000000233,1.8040203537176212,2000,NLD,nk_fractal_nld,7
+7.159999999828415e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,7
+0.004927799999997262,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,7
+0.009218599999996968,0.691035660416629,2000,SDA,nk_fractal_sda,7
+0.00016109999999969205,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,7
+0.00013690000000110558,0.018215273069519526,2000,Fisher,nk_fisher_information,7
+0.00023990000000040368,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,8
+0.26411560000000023,0.8815000687731794,2000,Hurst,nk_complexity_hurst,8
+0.1518872999999985,0.99788636990425,2000,LZC,nk_complexity_lzc,8
+0.22646749999999827,0.9692679097822192,2000,PLZC,nk_complexity_plzc,8
+0.19276200000000188,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,8
+0.5496133999999984,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,8
+0.0029925000000048385,1.6061378306189726,2000,RR,nk_complexity_rr,8
+0.028088799999999026,1.0796704855858055,2000,ApEn,nk_entropy_approximate,8
+0.012458899999998607,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,8
+0.0012077000000019211,1.5299377592545462,2000,DiffEn,nk_entropy_differential,8
+0.923995699999999,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,8
+0.04068130000000281,0.875455134560752,2000,MSE,nk_complexity_mse,8
+5.591842700000001,0.5421120627391649,2000,CMSE,nk_complexity_cmse,8
+8.805054300000002,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,8
+0.6378975999999952,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,8
+5.086913900000006,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,8
+4.8494401000000025,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,8
+0.00047059999999987667,0.9995642134676757,2000,PEn,nk_complexity_pe,8
+0.014212800000002801,0.9974282038230982,2000,WPEn,nk_complexity_wpe,8
+0.012661899999997672,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,8
+0.974226299999998,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,8
+0.9515808000000021,inf,2000,RangeEn,nk_entropy_range_mApEn,8
+0.03261630000000082,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,8
+0.0010852999999997337,10.965784284662085,2000,ShanEn,nk_entropy_shannon,8
+0.002518699999995988,0.8465376735420068,2000,SpEn,nk_entropy_spectral,8
+0.00019209999999958427,0.9927605014360844,2000,SVDen,nk_entropy_svd,8
+0.5809145000000058,2.132371401023081,2000,CD,nk_fractal_correlation,8
+0.17588139999999441,0.9905916277925066,2000,DFA,nk_fractal_dfa,8
+0.18985769999999746,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,8
+0.0015642999999982976,1.9990408568403804,2000,HFD,nk_fractal_higuchi,8
+8.700000000061436e-05,4.331735672660552,2000,Katz,nk_fractal_katz,8
+0.008883999999994785,1.8040203537176212,2000,NLD,nk_fractal_nld,8
+6.430000000534619e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,8
+0.004075099999994336,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,8
+0.009079199999995069,0.691035660416629,2000,SDA,nk_fractal_sda,8
+0.0001501000000061481,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,8
+0.00012050000000130012,0.018215273069519526,2000,Fisher,nk_fisher_information,8
+0.0002513999999997907,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,9
+0.262641900000002,0.8815000687731794,2000,Hurst,nk_complexity_hurst,9
+0.15339610000000192,0.99788636990425,2000,LZC,nk_complexity_lzc,9
+0.22683019999999487,0.9692679097822192,2000,PLZC,nk_complexity_plzc,9
+0.26502580000000364,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,9
+0.6490846000000019,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,9
+0.0021675999999999362,1.6061378306189726,2000,RR,nk_complexity_rr,9
+0.02609229999999485,1.0796704855858055,2000,ApEn,nk_entropy_approximate,9
+0.007823899999998218,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,9
+0.0007014999999981342,1.5299377592545462,2000,DiffEn,nk_entropy_differential,9
+0.9401758999999998,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,9
+0.04161720000000457,0.875455134560752,2000,MSE,nk_complexity_mse,9
+6.878700899999998,0.5421120627391649,2000,CMSE,nk_complexity_cmse,9
+6.5545683,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,9
+0.4535482000000002,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,9
+3.718595999999998,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,9
+4.465680599999999,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,9
+0.0006108999999980824,0.9995642134676757,2000,PEn,nk_complexity_pe,9
+0.01442339999999831,0.9974282038230982,2000,WPEn,nk_complexity_wpe,9
+0.013596499999998457,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,9
+1.1133611000000059,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,9
+0.9509694999999994,inf,2000,RangeEn,nk_entropy_range_mApEn,9
+0.024642000000000053,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,9
+0.0011264000000039687,10.965784284662085,2000,ShanEn,nk_entropy_shannon,9
+0.002644100000004812,0.8465376735420068,2000,SpEn,nk_entropy_spectral,9
+0.00021100000000018326,0.9927605014360844,2000,SVDen,nk_entropy_svd,9
+1.4074077000000003,2.132371401023081,2000,CD,nk_fractal_correlation,9
+0.19270260000000405,0.9905916277925066,2000,DFA,nk_fractal_dfa,9
+0.20844819999999942,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,9
+0.002210200000000384,1.9990408568403804,2000,HFD,nk_fractal_higuchi,9
+0.00019409999999453476,4.331735672660552,2000,Katz,nk_fractal_katz,9
+0.010559200000002988,1.8040203537176212,2000,NLD,nk_fractal_nld,9
+0.00010789999999616384,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,9
+0.008441500000003543,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,9
+0.012026200000001097,0.691035660416629,2000,SDA,nk_fractal_sda,9
+0.000323400000006302,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,9
+0.000294199999999023,0.018215273069519526,2000,Fisher,nk_fisher_information,9
+0.000244500000000869,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,10
+0.26814230000000094,0.8815000687731794,2000,Hurst,nk_complexity_hurst,10
+0.07532819999999418,0.99788636990425,2000,LZC,nk_complexity_lzc,10
+0.11437519999999779,0.9692679097822192,2000,PLZC,nk_complexity_plzc,10
+0.1641479999999973,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,10
+0.6416585999999995,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,10
+0.0026870000000016603,1.6061378306189726,2000,RR,nk_complexity_rr,10
+0.028765100000001098,1.0796704855858055,2000,ApEn,nk_entropy_approximate,10
+0.011401399999996897,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,10
+0.0010475000000056411,1.5299377592545462,2000,DiffEn,nk_entropy_differential,10
+0.9558864000000042,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,10
+0.04189389999999804,0.875455134560752,2000,MSE,nk_complexity_mse,10
+5.323083000000004,0.5421120627391649,2000,CMSE,nk_complexity_cmse,10
+5.169094300000005,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,10
+0.3455277999999993,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,10
+3.1885231000000047,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,10
+2.9282276999999937,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,10
+0.00029879999999593565,0.9995642134676757,2000,PEn,nk_complexity_pe,10
+0.013936799999996197,0.9974282038230982,2000,WPEn,nk_complexity_wpe,10
+0.012003599999999892,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,10
+0.9670181999999983,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,10
+0.9632880999999998,inf,2000,RangeEn,nk_entropy_range_mApEn,10
+0.02594659999999749,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,10
+0.0013934999999989373,10.965784284662085,2000,ShanEn,nk_entropy_shannon,10
+0.003131899999999632,0.8465376735420068,2000,SpEn,nk_entropy_spectral,10
+0.00034629999999680194,0.9927605014360844,2000,SVDen,nk_entropy_svd,10
+2.1358484000000004,2.132371401023081,2000,CD,nk_fractal_correlation,10
+0.20901839999999794,0.9905916277925066,2000,DFA,nk_fractal_dfa,10
+0.22083390000000236,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,10
+0.002699099999993848,1.9990408568403804,2000,HFD,nk_fractal_higuchi,10
+0.00027150000000375485,4.331735672660552,2000,Katz,nk_fractal_katz,10
+0.011946399999999358,1.8040203537176212,2000,NLD,nk_fractal_nld,10
+0.00010619999999761376,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,10
+0.010339500000000612,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,10
+0.0110968999999983,0.691035660416629,2000,SDA,nk_fractal_sda,10
+0.0004288000000016723,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,10
+0.00037060000000366244,0.018215273069519526,2000,Fisher,nk_fisher_information,10
+0.00030380000000462815,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,11
+0.2892015999999984,0.8815000687731794,2000,Hurst,nk_complexity_hurst,11
+0.07580910000000074,0.99788636990425,2000,LZC,nk_complexity_lzc,11
+0.11501849999999791,0.9692679097822192,2000,PLZC,nk_complexity_plzc,11
+0.16829800000000006,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,11
+0.7289212999999961,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,11
+0.0030548000000010234,1.6061378306189726,2000,RR,nk_complexity_rr,11
+0.04151719999999415,1.0796704855858055,2000,ApEn,nk_entropy_approximate,11
+0.02014190000000582,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,11
+0.0018424999999950842,1.5299377592545462,2000,DiffEn,nk_entropy_differential,11
+1.1092581000000052,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,11
+0.06330650000000304,0.875455134560752,2000,MSE,nk_complexity_mse,11
+9.111430299999995,0.5421120627391649,2000,CMSE,nk_complexity_cmse,11
+8.769295300000003,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,11
+0.7083046999999993,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,11
+5.5294000000000025,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,11
+3.044166699999998,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,11
+0.0002992000000006101,0.9995642134676757,2000,PEn,nk_complexity_pe,11
+0.013877100000001974,0.9974282038230982,2000,WPEn,nk_complexity_wpe,11
+0.012108000000004893,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,11
+0.9578989000000036,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,11
+0.9458704999999981,inf,2000,RangeEn,nk_entropy_range_mApEn,11
+0.033190799999999854,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,11
+0.0011121000000002823,10.965784284662085,2000,ShanEn,nk_entropy_shannon,11
+0.0026527999999998997,0.8465376735420068,2000,SpEn,nk_entropy_spectral,11
+0.00018309999999388538,0.9927605014360844,2000,SVDen,nk_entropy_svd,11
+0.7029951999999966,2.132371401023081,2000,CD,nk_fractal_correlation,11
+0.1870282000000003,0.9905916277925066,2000,DFA,nk_fractal_dfa,11
+0.2012689000000023,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,11
+0.0020260999999948126,1.9990408568403804,2000,HFD,nk_fractal_higuchi,11
+0.000166900000010628,4.331735672660552,2000,Katz,nk_fractal_katz,11
+0.009582600000001662,1.8040203537176212,2000,NLD,nk_fractal_nld,11
+7.91999999876225e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,11
+0.005896299999989196,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,11
+0.00951419999999814,0.691035660416629,2000,SDA,nk_fractal_sda,11
+0.0002430999999916139,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,11
+0.0001414999999980182,0.018215273069519526,2000,Fisher,nk_fisher_information,11
+0.00024259999999998172,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,12
+0.2640434000000056,0.8815000687731794,2000,Hurst,nk_complexity_hurst,12
+0.15316870000000193,0.99788636990425,2000,LZC,nk_complexity_lzc,12
+0.13347210000000587,0.9692679097822192,2000,PLZC,nk_complexity_plzc,12
+0.15957909999999487,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,12
+0.7604839000000041,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,12
+0.002773500000003537,1.6061378306189726,2000,RR,nk_complexity_rr,12
+0.03943450000000581,1.0796704855858055,2000,ApEn,nk_entropy_approximate,12
+0.014211499999994714,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,12
+0.001045699999998817,1.5299377592545462,2000,DiffEn,nk_entropy_differential,12
+1.1548177999999965,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,12
+0.06207099999999599,0.875455134560752,2000,MSE,nk_complexity_mse,12
+5.4192158000000035,0.5421120627391649,2000,CMSE,nk_complexity_cmse,12
+5.163133199999997,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,12
+0.5129729000000012,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,12
+3.1610926999999975,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,12
+3.248938499999994,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,12
+0.0004409000000009655,0.9995642134676757,2000,PEn,nk_complexity_pe,12
+0.014029499999999473,0.9974282038230982,2000,WPEn,nk_complexity_wpe,12
+0.012270599999993692,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,12
+0.9719602999999992,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,12
+0.968790399999996,inf,2000,RangeEn,nk_entropy_range_mApEn,12
+0.0476460000000003,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,12
+0.0034982999999968456,10.965784284662085,2000,ShanEn,nk_entropy_shannon,12
+0.0053879000000023325,0.8465376735420068,2000,SpEn,nk_entropy_spectral,12
+0.0007818000000057168,0.9927605014360844,2000,SVDen,nk_entropy_svd,12
+2.5426849999999988,2.132371401023081,2000,CD,nk_fractal_correlation,12
+0.1840560999999994,0.9905916277925066,2000,DFA,nk_fractal_dfa,12
+0.1942954999999955,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,12
+0.0015514000000038664,1.9990408568403804,2000,HFD,nk_fractal_higuchi,12
+9.839999999883275e-05,4.331735672660552,2000,Katz,nk_fractal_katz,12
+0.008935499999999763,1.8040203537176212,2000,NLD,nk_fractal_nld,12
+6.129999999870961e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,12
+0.004024600000001044,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,12
+0.009144400000003827,0.691035660416629,2000,SDA,nk_fractal_sda,12
+0.00014330000000484233,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,12
+0.0001164999999971883,0.018215273069519526,2000,Fisher,nk_fisher_information,12
+0.00025259999999960314,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,13
+0.29025430000000085,0.8815000687731794,2000,Hurst,nk_complexity_hurst,13
+0.0855509000000012,0.99788636990425,2000,LZC,nk_complexity_lzc,13
+0.22618310000000008,0.9692679097822192,2000,PLZC,nk_complexity_plzc,13
+0.1954457000000005,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,13
+0.7524013999999966,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,13
+0.002307199999997067,1.6061378306189726,2000,RR,nk_complexity_rr,13
+0.035185399999996037,1.0796704855858055,2000,ApEn,nk_entropy_approximate,13
+0.013527799999998535,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,13
+0.0009778000000011389,1.5299377592545462,2000,DiffEn,nk_entropy_differential,13
+1.1210221000000047,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,13
+0.06156469999999814,0.875455134560752,2000,MSE,nk_complexity_mse,13
+8.741865599999997,0.5421120627391649,2000,CMSE,nk_complexity_cmse,13
+8.9087477,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,13
+1.0364760000000004,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,13
+5.607093899999995,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,13
+3.8441338999999957,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,13
+0.00030579999999957863,0.9995642134676757,2000,PEn,nk_complexity_pe,13
+0.013998799999995981,0.9974282038230982,2000,WPEn,nk_complexity_wpe,13
+0.011927200000002358,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,13
+0.9600700000000018,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,13
+0.9459052999999926,inf,2000,RangeEn,nk_entropy_range_mApEn,13
+0.02476910000000032,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,13
+0.0012363000000021884,10.965784284662085,2000,ShanEn,nk_entropy_shannon,13
+0.002664799999990919,0.8465376735420068,2000,SpEn,nk_entropy_spectral,13
+0.0002096999999992022,0.9927605014360844,2000,SVDen,nk_entropy_svd,13
+1.288495399999988,2.132371401023081,2000,CD,nk_fractal_correlation,13
+0.18475030000000459,0.9905916277925066,2000,DFA,nk_fractal_dfa,13
+0.19627699999999493,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,13
+0.001567799999989461,1.9990408568403804,2000,HFD,nk_fractal_higuchi,13
+0.0001012999999971953,4.331735672660552,2000,Katz,nk_fractal_katz,13
+0.009152299999996671,1.8040203537176212,2000,NLD,nk_fractal_nld,13
+5.52999999996473e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,13
+0.00420770000000914,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,13
+0.009169700000001058,0.691035660416629,2000,SDA,nk_fractal_sda,13
+0.00015250000001287844,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,13
+0.00011849999999924421,0.018215273069519526,2000,Fisher,nk_fisher_information,13
+0.0003589999999995541,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,14
+0.36530890000000227,0.8815000687731794,2000,Hurst,nk_complexity_hurst,14
+0.07668499999999767,0.99788636990425,2000,LZC,nk_complexity_lzc,14
+0.11337480000000255,0.9692679097822192,2000,PLZC,nk_complexity_plzc,14
+0.16394959999999514,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,14
+0.8548126999999965,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,14
+0.0028038000000023544,1.6061378306189726,2000,RR,nk_complexity_rr,14
+0.03902959999999922,1.0796704855858055,2000,ApEn,nk_entropy_approximate,14
+0.013177500000004727,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,14
+0.0010752000000024964,1.5299377592545462,2000,DiffEn,nk_entropy_differential,14
+1.1550979000000012,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,14
+0.061789400000002104,0.875455134560752,2000,MSE,nk_complexity_mse,14
+6.7649497000000025,0.5421120627391649,2000,CMSE,nk_complexity_cmse,14
+6.937065499999996,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,14
+0.523499600000001,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,14
+3.803114800000003,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,14
+4.724304500000002,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,14
+0.00027589999999833026,0.9995642134676757,2000,PEn,nk_complexity_pe,14
+0.015521199999994906,0.9974282038230982,2000,WPEn,nk_complexity_wpe,14
+0.012115900000004842,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,14
+1.0170457999999982,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,14
+0.9623721999999972,inf,2000,RangeEn,nk_entropy_range_mApEn,14
+0.02565150000000216,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,14
+0.002296000000001186,10.965784284662085,2000,ShanEn,nk_entropy_shannon,14
+0.004274299999998732,0.8465376735420068,2000,SpEn,nk_entropy_spectral,14
+0.00038079999999496295,0.9927605014360844,2000,SVDen,nk_entropy_svd,14
+1.5876266999999942,2.132371401023081,2000,CD,nk_fractal_correlation,14
+0.17978740000000215,0.9905916277925066,2000,DFA,nk_fractal_dfa,14
+0.19096340000000112,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,14
+0.0015540999999998917,1.9990408568403804,2000,HFD,nk_fractal_higuchi,14
+8.729999999701477e-05,4.331735672660552,2000,Katz,nk_fractal_katz,14
+0.008887600000001328,1.8040203537176212,2000,NLD,nk_fractal_nld,14
+6.110000000347782e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,14
+0.003999999999997783,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,14
+0.009150599999998121,0.691035660416629,2000,SDA,nk_fractal_sda,14
+0.00013960000000423634,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,14
+0.00011260000000135051,0.018215273069519526,2000,Fisher,nk_fisher_information,14
+0.0002710999999990804,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,15
+0.2907353999999991,0.8815000687731794,2000,Hurst,nk_complexity_hurst,15
+0.07669200000000131,0.99788636990425,2000,LZC,nk_complexity_lzc,15
+0.11367100000000363,0.9692679097822192,2000,PLZC,nk_complexity_plzc,15
+0.16331310000000343,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,15
+0.8384545999999986,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,15
+0.003323300000005247,1.6061378306189726,2000,RR,nk_complexity_rr,15
+0.032349199999998746,1.0796704855858055,2000,ApEn,nk_entropy_approximate,15
+0.012722100000004843,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,15
+0.0009003999999990242,1.5299377592545462,2000,DiffEn,nk_entropy_differential,15
+1.1530237000000056,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,15
+0.054801499999996395,0.875455134560752,2000,MSE,nk_complexity_mse,15
+9.031293599999998,0.5421120627391649,2000,CMSE,nk_complexity_cmse,15
+8.861362100000001,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,15
+1.2040166000000028,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,15
+4.957309700000003,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,15
+3.565389699999997,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,15
+0.00027569999999599304,0.9995642134676757,2000,PEn,nk_complexity_pe,15
+0.013934400000003677,0.9974282038230982,2000,WPEn,nk_complexity_wpe,15
+0.01179500000000644,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,15
+0.9633843000000013,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,15
+0.9465004999999991,inf,2000,RangeEn,nk_entropy_range_mApEn,15
+0.024455299999999625,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,15
+0.0010778000000044585,10.965784284662085,2000,ShanEn,nk_entropy_shannon,15
+0.0025629000000009228,0.8465376735420068,2000,SpEn,nk_entropy_spectral,15
+0.00019550000000378986,0.9927605014360844,2000,SVDen,nk_entropy_svd,15
+0.6652600000000035,2.132371401023081,2000,CD,nk_fractal_correlation,15
+0.182944599999999,0.9905916277925066,2000,DFA,nk_fractal_dfa,15
+0.20149920000000066,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,15
+0.001914800000008654,1.9990408568403804,2000,HFD,nk_fractal_higuchi,15
+0.00018649999999809097,4.331735672660552,2000,Katz,nk_fractal_katz,15
+0.010486700000001292,1.8040203537176212,2000,NLD,nk_fractal_nld,15
+0.0001042000000097687,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,15
+0.007739400000005503,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,15
+0.009643900000000372,0.691035660416629,2000,SDA,nk_fractal_sda,15
+0.00020570000000930122,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,15
+0.00019290000000182772,0.018215273069519526,2000,Fisher,nk_fisher_information,15
+0.00023999999999801958,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,16
+0.3520686000000026,0.8815000687731794,2000,Hurst,nk_complexity_hurst,16
+0.15335860000000068,0.99788636990425,2000,LZC,nk_complexity_lzc,16
+0.1366336999999973,0.9692679097822192,2000,PLZC,nk_complexity_plzc,16
+0.16014609999999863,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,16
+0.7070770999999993,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,16
+0.003620300000001464,1.6061378306189726,2000,RR,nk_complexity_rr,16
+0.03942969999999946,1.0796704855858055,2000,ApEn,nk_entropy_approximate,16
+0.014854599999999607,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,16
+0.0010582000000027847,1.5299377592545462,2000,DiffEn,nk_entropy_differential,16
+1.1547192000000024,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,16
+0.06180719999999695,0.875455134560752,2000,MSE,nk_complexity_mse,16
+6.9354853999999975,0.5421120627391649,2000,CMSE,nk_complexity_cmse,16
+6.762663199999999,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,16
+0.41438230000000686,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,16
+4.3943841000000035,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,16
+5.1926451,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,16
+0.0002885999999975297,0.9995642134676757,2000,PEn,nk_complexity_pe,16
+0.01420699999999897,0.9974282038230982,2000,WPEn,nk_complexity_wpe,16
+0.013675399999996785,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,16
+0.969551200000005,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,16
+0.9688992000000027,inf,2000,RangeEn,nk_entropy_range_mApEn,16
+0.025601899999998068,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,16
+0.002346000000002846,10.965784284662085,2000,ShanEn,nk_entropy_shannon,16
+0.0040690000000012105,0.8465376735420068,2000,SpEn,nk_entropy_spectral,16
+0.0004123999999947614,0.9927605014360844,2000,SVDen,nk_entropy_svd,16
+1.0037828000000033,2.132371401023081,2000,CD,nk_fractal_correlation,16
+0.17636579999999924,0.9905916277925066,2000,DFA,nk_fractal_dfa,16
+0.18998640000000222,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,16
+0.001559199999995542,1.9990408568403804,2000,HFD,nk_fractal_higuchi,16
+0.00010310000000401942,4.331735672660552,2000,Katz,nk_fractal_katz,16
+0.008889500000002215,1.8040203537176212,2000,NLD,nk_fractal_nld,16
+6.259999999969068e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,16
+0.0039626000000012596,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,16
+0.00915830000000284,0.691035660416629,2000,SDA,nk_fractal_sda,16
+0.0001487000000039984,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,16
+0.00012109999999410093,0.018215273069519526,2000,Fisher,nk_fisher_information,16
+0.00025259999999960314,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,17
+0.2881834000000012,0.8815000687731794,2000,Hurst,nk_complexity_hurst,17
+0.07538450000000196,0.99788636990425,2000,LZC,nk_complexity_lzc,17
+0.114363400000002,0.9692679097822192,2000,PLZC,nk_complexity_plzc,17
+0.1642719000000028,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,17
+0.6400134000000008,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,17
+0.002713100000001134,1.6061378306189726,2000,RR,nk_complexity_rr,17
+0.027507700000001023,1.0796704855858055,2000,ApEn,nk_entropy_approximate,17
+0.011287699999996903,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,17
+0.0011429000000049427,1.5299377592545462,2000,DiffEn,nk_entropy_differential,17
+0.9477626999999984,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,17
+0.04233819999999611,0.875455134560752,2000,MSE,nk_complexity_mse,17
+5.3268375999999975,0.5421120627391649,2000,CMSE,nk_complexity_cmse,17
+5.175114299999997,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,17
+0.47086859999999575,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,17
+4.706258600000005,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,17
+3.912604899999998,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,17
+0.0006910000000033278,0.9995642134676757,2000,PEn,nk_complexity_pe,17
+0.014352000000002363,0.9974282038230982,2000,WPEn,nk_complexity_wpe,17
+0.0143719999999945,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,17
+0.9815807000000021,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,17
+0.9668495000000021,inf,2000,RangeEn,nk_entropy_range_mApEn,17
+0.027952100000000257,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,17
+0.003605100000001471,10.965784284662085,2000,ShanEn,nk_entropy_shannon,17
+0.006873400000003471,0.8465376735420068,2000,SpEn,nk_entropy_spectral,17
+0.0008533000000028323,0.9927605014360844,2000,SVDen,nk_entropy_svd,17
+1.5706322999999998,2.132371401023081,2000,CD,nk_fractal_correlation,17
+0.17981179999999597,0.9905916277925066,2000,DFA,nk_fractal_dfa,17
+0.1930717000000044,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,17
+0.002065200000004097,1.9990408568403804,2000,HFD,nk_fractal_higuchi,17
+0.00010050000000205728,4.331735672660552,2000,Katz,nk_fractal_katz,17
+0.008944999999997094,1.8040203537176212,2000,NLD,nk_fractal_nld,17
+6.480000000408381e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,17
+0.0044206999999971686,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,17
+0.009218600000004074,0.691035660416629,2000,SDA,nk_fractal_sda,17
+0.00015199999999992997,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,17
+0.0001200000000025625,0.018215273069519526,2000,Fisher,nk_fisher_information,17
+0.00024189999999890688,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,18
+0.27388549999999867,0.8815000687731794,2000,Hurst,nk_complexity_hurst,18
+0.07692940000000448,0.99788636990425,2000,LZC,nk_complexity_lzc,18
+0.1131984000000017,0.9692679097822192,2000,PLZC,nk_complexity_plzc,18
+0.16368539999999854,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,18
+0.7948407000000017,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,18
+0.0034767000000002213,1.6061378306189726,2000,RR,nk_complexity_rr,18
+0.04032360000000068,1.0796704855858055,2000,ApEn,nk_entropy_approximate,18
+0.01463329999999985,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,18
+0.0011561000000028798,1.5299377592545462,2000,DiffEn,nk_entropy_differential,18
+1.028143699999994,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,18
+0.04149149999999935,0.875455134560752,2000,MSE,nk_complexity_mse,18
+8.832296800000002,0.5421120627391649,2000,CMSE,nk_complexity_cmse,18
+8.8881662,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,18
+0.803537200000001,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,18
+5.9020115,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,18
+3.7439999999999998,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,18
+0.0002747999999996864,0.9995642134676757,2000,PEn,nk_complexity_pe,18
+0.014018599999999992,0.9974282038230982,2000,WPEn,nk_complexity_wpe,18
+0.012009499999997786,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,18
+0.9610056,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,18
+0.9461566000000019,inf,2000,RangeEn,nk_entropy_range_mApEn,18
+0.02467049999999915,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,18
+0.0011668999999869811,10.965784284662085,2000,ShanEn,nk_entropy_shannon,18
+0.0025607999999976983,0.8465376735420068,2000,SpEn,nk_entropy_spectral,18
+0.0002324000000015758,0.9927605014360844,2000,SVDen,nk_entropy_svd,18
+1.155692700000003,2.132371401023081,2000,CD,nk_fractal_correlation,18
+0.19776010000001065,0.9905916277925066,2000,DFA,nk_fractal_dfa,18
+0.2159931000000057,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,18
+0.0027501999999941518,1.9990408568403804,2000,HFD,nk_fractal_higuchi,18
+0.00013940000000900454,4.331735672660552,2000,Katz,nk_fractal_katz,18
+0.00945510000001093,1.8040203537176212,2000,NLD,nk_fractal_nld,18
+7.870000000309574e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,18
+0.00601249999999709,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,18
+0.009503100000003428,0.691035660416629,2000,SDA,nk_fractal_sda,18
+0.00021550000001013814,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,18
+0.00016840000000684086,0.018215273069519526,2000,Fisher,nk_fisher_information,18
+0.0002494999999989034,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,19
+0.30791710000000094,0.8815000687731794,2000,Hurst,nk_complexity_hurst,19
+0.07594060000000269,0.99788636990425,2000,LZC,nk_complexity_lzc,19
+0.11531580000000474,0.9692679097822192,2000,PLZC,nk_complexity_plzc,19
+0.16601499999999447,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,19
+0.6309150000000017,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,19
+0.002985400000000027,1.6061378306189726,2000,RR,nk_complexity_rr,19
+0.027601499999995838,1.0796704855858055,2000,ApEn,nk_entropy_approximate,19
+0.011269100000006915,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,19
+0.00117800000000301,1.5299377592545462,2000,DiffEn,nk_entropy_differential,19
+0.9537912000000048,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,19
+0.04228210000000132,0.875455134560752,2000,MSE,nk_complexity_mse,19
+5.3355642,0.5421120627391649,2000,CMSE,nk_complexity_cmse,19
+5.182248900000005,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,19
+0.34594080000000105,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,19
+3.212679399999999,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,19
+2.9374394000000024,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,19
+0.0003049999999973352,0.9995642134676757,2000,PEn,nk_complexity_pe,19
+0.01397529999999847,0.9974282038230982,2000,WPEn,nk_complexity_wpe,19
+0.012027500000002078,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,19
+0.9578306999999953,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,19
+0.9844950999999966,inf,2000,RangeEn,nk_entropy_range_mApEn,19
+0.025178399999994383,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,19
+0.001729399999994996,10.965784284662085,2000,ShanEn,nk_entropy_shannon,19
+0.00382770000000221,0.8465376735420068,2000,SpEn,nk_entropy_spectral,19
+0.0004574000000019396,0.9927605014360844,2000,SVDen,nk_entropy_svd,19
+2.1808625000000035,2.132371401023081,2000,CD,nk_fractal_correlation,19
+0.2015999999999991,0.9905916277925066,2000,DFA,nk_fractal_dfa,19
+0.22458859999999703,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,19
+0.0026453999999986877,1.9990408568403804,2000,HFD,nk_fractal_higuchi,19
+0.0002783000000050606,4.331735672660552,2000,Katz,nk_fractal_katz,19
+0.012118800000003205,1.8040203537176212,2000,NLD,nk_fractal_nld,19
+0.0001485000000016612,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,19
+0.011164100000002009,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,19
+0.010935900000006882,0.691035660416629,2000,SDA,nk_fractal_sda,19
+0.0005426000000028353,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,19
+0.0003949999999974807,0.018215273069519526,2000,Fisher,nk_fisher_information,19
+0.00026240000000044006,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,20
+0.26824729999999875,0.8815000687731794,2000,Hurst,nk_complexity_hurst,20
+0.15241939999999943,0.99788636990425,2000,LZC,nk_complexity_lzc,20
+0.22773350000000292,0.9692679097822192,2000,PLZC,nk_complexity_plzc,20
+0.1639577999999986,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,20
+0.5849272000000028,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,20
+0.0027153999999995904,1.6061378306189726,2000,RR,nk_complexity_rr,20
+0.027642899999996473,1.0796704855858055,2000,ApEn,nk_entropy_approximate,20
+0.011867199999997524,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,20
+0.0010981000000001018,1.5299377592545462,2000,DiffEn,nk_entropy_differential,20
+0.9195735000000056,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,20
+0.04052819999999713,0.875455134560752,2000,MSE,nk_complexity_mse,20
+7.865563900000005,0.5421120627391649,2000,CMSE,nk_complexity_cmse,20
+5.196034900000001,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,20
+0.3125390999999951,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,20
+2.9726532999999975,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,20
+2.816395700000001,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,20
+0.0004563999999973589,0.9995642134676757,2000,PEn,nk_complexity_pe,20
+0.015386199999994687,0.9974282038230982,2000,WPEn,nk_complexity_wpe,20
+0.013550599999994972,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,20
+0.9952143000000007,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,20
+0.9820192000000034,inf,2000,RangeEn,nk_entropy_range_mApEn,20
+0.02712590000000148,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,20
+0.0030476000000021486,10.965784284662085,2000,ShanEn,nk_entropy_shannon,20
+0.00572559999999811,0.8465376735420068,2000,SpEn,nk_entropy_spectral,20
+0.0006994999999960783,0.9927605014360844,2000,SVDen,nk_entropy_svd,20
+1.7879689999999968,2.132371401023081,2000,CD,nk_fractal_correlation,20
+0.18051469999999625,0.9905916277925066,2000,DFA,nk_fractal_dfa,20
+0.19192230000000166,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,20
+0.001666899999996474,1.9990408568403804,2000,HFD,nk_fractal_higuchi,20
+9.399999999715192e-05,4.331735672660552,2000,Katz,nk_fractal_katz,20
+0.009199799999997538,1.8040203537176212,2000,NLD,nk_fractal_nld,20
+5.560000000315313e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,20
+0.004469299999996679,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,20
+0.009306799999997395,0.691035660416629,2000,SDA,nk_fractal_sda,20
+0.00015989999999987958,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,20
+0.0001257999999992876,0.018215273069519526,2000,Fisher,nk_fisher_information,20
+0.00027609999999711476,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,21
+0.3673654000000006,0.8815000687731794,2000,Hurst,nk_complexity_hurst,21
+0.15281360000000177,0.99788636990425,2000,LZC,nk_complexity_lzc,21
+0.22734720000000408,0.9692679097822192,2000,PLZC,nk_complexity_plzc,21
+0.21774800000000027,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,21
+0.7820204999999945,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,21
+0.00439699999999732,1.6061378306189726,2000,RR,nk_complexity_rr,21
+0.03474899999999792,1.0796704855858055,2000,ApEn,nk_entropy_approximate,21
+0.016186900000001003,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,21
+0.001532499999996162,1.5299377592545462,2000,DiffEn,nk_entropy_differential,21
+0.9190745000000007,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,21
+0.05480539999999934,0.875455134560752,2000,MSE,nk_complexity_mse,21
+9.1290348,0.5421120627391649,2000,CMSE,nk_complexity_cmse,21
+8.9740496,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,21
+0.9227886999999981,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,21
+5.434899800000004,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,21
+3.229243699999998,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,21
+0.00028689999999897964,0.9995642134676757,2000,PEn,nk_complexity_pe,21
+0.013888799999996593,0.9974282038230982,2000,WPEn,nk_complexity_wpe,21
+0.012049699999998609,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,21
+0.9599734000000026,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,21
+0.9434519000000066,inf,2000,RangeEn,nk_entropy_range_mApEn,21
+0.02470830000000035,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,21
+0.0013391000000098074,10.965784284662085,2000,ShanEn,nk_entropy_shannon,21
+0.002902500000004693,0.8465376735420068,2000,SpEn,nk_entropy_spectral,21
+0.00025610000000142463,0.9927605014360844,2000,SVDen,nk_entropy_svd,21
+1.1826203999999905,2.132371401023081,2000,CD,nk_fractal_correlation,21
+0.17991290000000504,0.9905916277925066,2000,DFA,nk_fractal_dfa,21
+0.19436190000000408,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,21
+0.0016092999999983704,1.9990408568403804,2000,HFD,nk_fractal_higuchi,21
+0.00010080000001266853,4.331735672660552,2000,Katz,nk_fractal_katz,21
+0.008965699999990306,1.8040203537176212,2000,NLD,nk_fractal_nld,21
+5.190000000254713e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,21
+0.004187800000011066,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,21
+0.009203300000010017,0.691035660416629,2000,SDA,nk_fractal_sda,21
+0.0001661999999953423,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,21
+0.00013020000000096843,0.018215273069519526,2000,Fisher,nk_fisher_information,21
+0.00030049999999803845,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,22
+0.293951400000001,0.8815000687731794,2000,Hurst,nk_complexity_hurst,22
+0.15306340000000063,0.99788636990425,2000,LZC,nk_complexity_lzc,22
+0.22446740000000176,0.9692679097822192,2000,PLZC,nk_complexity_plzc,22
+0.16338520000000045,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,22
+0.6750268999999989,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,22
+0.0018176999999965915,1.6061378306189726,2000,RR,nk_complexity_rr,22
+0.02554209999999557,1.0796704855858055,2000,ApEn,nk_entropy_approximate,22
+0.007730399999999804,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,22
+0.0006713999999945486,1.5299377592545462,2000,DiffEn,nk_entropy_differential,22
+0.9588263999999995,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,22
+0.041203400000000556,0.875455134560752,2000,MSE,nk_complexity_mse,22
+5.438600399999999,0.5421120627391649,2000,CMSE,nk_complexity_cmse,22
+5.1639762000000005,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,22
+0.4305272000000002,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,22
+3.246277500000005,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,22
+3.335442999999998,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,22
+0.00029659999999864795,0.9995642134676757,2000,PEn,nk_complexity_pe,22
+0.013827300000002651,0.9974282038230982,2000,WPEn,nk_complexity_wpe,22
+0.011960399999999538,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,22
+0.9716390000000033,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,22
+1.1483551999999975,inf,2000,RangeEn,nk_entropy_range_mApEn,22
+0.04506719999999831,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,22
+0.00702749999999952,10.965784284662085,2000,ShanEn,nk_entropy_shannon,22
+0.01648670000000152,0.8465376735420068,2000,SpEn,nk_entropy_spectral,22
+0.0014193000000020106,0.9927605014360844,2000,SVDen,nk_entropy_svd,22
+2.791691099999994,2.132371401023081,2000,CD,nk_fractal_correlation,22
+0.29014189999999473,0.9905916277925066,2000,DFA,nk_fractal_dfa,22
+0.24894179999999722,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,22
+0.0025140999999990754,1.9990408568403804,2000,HFD,nk_fractal_higuchi,22
+0.00012699999999910005,4.331735672660552,2000,Katz,nk_fractal_katz,22
+0.017529400000000805,1.8040203537176212,2000,NLD,nk_fractal_nld,22
+6.770000000244636e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,22
+0.005899599999999339,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,22
+0.014045699999996941,0.691035660416629,2000,SDA,nk_fractal_sda,22
+0.0001750000000058094,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,22
+0.00014029999999820575,0.018215273069519526,2000,Fisher,nk_fisher_information,22
+0.00023699999999848842,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,23
+0.26304919999999754,0.8815000687731794,2000,Hurst,nk_complexity_hurst,23
+0.07677770000000095,0.99788636990425,2000,LZC,nk_complexity_lzc,23
+0.11360959999999665,0.9692679097822192,2000,PLZC,nk_complexity_plzc,23
+0.16332080000000104,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,23
+0.8227953000000028,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,23
+0.0025799000000006345,1.6061378306189726,2000,RR,nk_complexity_rr,23
+0.03619579999999445,1.0796704855858055,2000,ApEn,nk_entropy_approximate,23
+0.01346179999999464,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,23
+0.0009142999999980361,1.5299377592545462,2000,DiffEn,nk_entropy_differential,23
+1.1344457999999946,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,23
+0.05926910000000163,0.875455134560752,2000,MSE,nk_complexity_mse,23
+8.952864399999996,0.5421120627391649,2000,CMSE,nk_complexity_cmse,23
+8.897503899999997,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,23
+1.1070206999999996,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,23
+5.499464799999998,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,23
+3.8724281000000005,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,23
+0.00027570000000309847,0.9995642134676757,2000,PEn,nk_complexity_pe,23
+0.013809299999998359,0.9974282038230982,2000,WPEn,nk_complexity_wpe,23
+0.012039000000001465,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,23
+0.9616995000000017,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,23
+0.947143600000004,inf,2000,RangeEn,nk_entropy_range_mApEn,23
+0.026067900000001032,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,23
+0.001218700000009676,10.965784284662085,2000,ShanEn,nk_entropy_shannon,23
+0.002651799999995319,0.8465376735420068,2000,SpEn,nk_entropy_spectral,23
+0.0002628000000015618,0.9927605014360844,2000,SVDen,nk_entropy_svd,23
+1.3318423000000053,2.132371401023081,2000,CD,nk_fractal_correlation,23
+0.18375230000000897,0.9905916277925066,2000,DFA,nk_fractal_dfa,23
+0.19209110000001317,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,23
+0.0015364000000062106,1.9990408568403804,2000,HFD,nk_fractal_higuchi,23
+8.610000000430773e-05,4.331735672660552,2000,Katz,nk_fractal_katz,23
+0.008920199999991496,1.8040203537176212,2000,NLD,nk_fractal_nld,23
+5.990000001077078e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,23
+0.003997800000007601,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,23
+0.009149500000006583,0.691035660416629,2000,SDA,nk_fractal_sda,23
+0.00013649999999643114,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,23
+0.000110899999995695,0.018215273069519526,2000,Fisher,nk_fisher_information,23
+0.0004354999999982567,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,24
+0.3070522000000011,0.8815000687731794,2000,Hurst,nk_complexity_hurst,24
+0.15271290000000093,0.99788636990425,2000,LZC,nk_complexity_lzc,24
+0.22530009999999834,0.9692679097822192,2000,PLZC,nk_complexity_plzc,24
+0.22832059999999643,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,24
+0.7577025000000006,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,24
+0.003286699999996756,1.6061378306189726,2000,RR,nk_complexity_rr,24
+0.03990490000000335,1.0796704855858055,2000,ApEn,nk_entropy_approximate,24
+0.01813510000000207,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,24
+0.0014317000000048097,1.5299377592545462,2000,DiffEn,nk_entropy_differential,24
+1.0555467000000007,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,24
+0.06136860000000155,0.875455134560752,2000,MSE,nk_complexity_mse,24
+6.641950899999998,0.5421120627391649,2000,CMSE,nk_complexity_cmse,24
+6.3826964,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,24
+0.34597740000000243,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,24
+3.989761200000004,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,24
+4.102354699999999,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,24
+0.0006205999999977507,0.9995642134676757,2000,PEn,nk_complexity_pe,24
+0.01475380000000115,0.9974282038230982,2000,WPEn,nk_complexity_wpe,24
+0.016385200000001987,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,24
+1.1553400000000025,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,24
+0.9514759999999995,inf,2000,RangeEn,nk_entropy_range_mApEn,24
+0.024798699999998064,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,24
+0.001115200000000982,10.965784284662085,2000,ShanEn,nk_entropy_shannon,24
+0.002614899999997533,0.8465376735420068,2000,SpEn,nk_entropy_spectral,24
+0.0002253999999979328,0.9927605014360844,2000,SVDen,nk_entropy_svd,24
+0.8219637999999989,2.132371401023081,2000,CD,nk_fractal_correlation,24
+0.18925349999999952,0.9905916277925066,2000,DFA,nk_fractal_dfa,24
+0.2107247000000001,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,24
+0.00223720000000327,1.9990408568403804,2000,HFD,nk_fractal_higuchi,24
+0.00020160000000402079,4.331735672660552,2000,Katz,nk_fractal_katz,24
+0.011334300000001463,1.8040203537176212,2000,NLD,nk_fractal_nld,24
+0.00011229999999784468,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,24
+0.008360900000006666,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,24
+0.010158500000002846,0.691035660416629,2000,SDA,nk_fractal_sda,24
+0.0003066000000018221,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,24
+0.00027330000000347354,0.018215273069519526,2000,Fisher,nk_fisher_information,24
+0.00024099999999904753,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,25
+0.2609037999999977,0.8815000687731794,2000,Hurst,nk_complexity_hurst,25
+0.07657079999999894,0.99788636990425,2000,LZC,nk_complexity_lzc,25
+0.11352360000000061,0.9692679097822192,2000,PLZC,nk_complexity_plzc,25
+0.1633887999999999,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,25
+0.855566599999996,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,25
+0.0027230999999972028,1.6061378306189726,2000,RR,nk_complexity_rr,25
+0.03873060000000095,1.0796704855858055,2000,ApEn,nk_entropy_approximate,25
+0.013420500000002278,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,25
+0.0011587999999989052,1.5299377592545462,2000,DiffEn,nk_entropy_differential,25
+1.152918299999996,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,25
+0.06175490000000394,0.875455134560752,2000,MSE,nk_complexity_mse,25
+9.094341500000006,0.5421120627391649,2000,CMSE,nk_complexity_cmse,25
+9.019561799999998,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,25
+1.3611226000000016,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,25
+5.294848000000002,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,25
+3.794178500000001,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,25
+0.000304500000005703,0.9995642134676757,2000,PEn,nk_complexity_pe,25
+0.013937200000000871,0.9974282038230982,2000,WPEn,nk_complexity_wpe,25
+0.012054499999997859,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,25
+0.96630609999999,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,25
+0.9546858000000071,inf,2000,RangeEn,nk_entropy_range_mApEn,25
+0.03350480000000289,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,25
+0.0018477999999930717,10.965784284662085,2000,ShanEn,nk_entropy_shannon,25
+0.003937000000007629,0.8465376735420068,2000,SpEn,nk_entropy_spectral,25
+0.00038360000000636774,0.9927605014360844,2000,SVDen,nk_entropy_svd,25
+1.2561206999999968,2.132371401023081,2000,CD,nk_fractal_correlation,25
+0.17676879999999073,0.9905916277925066,2000,DFA,nk_fractal_dfa,25
+0.1905100000000033,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,25
+0.0015758999999917478,1.9990408568403804,2000,HFD,nk_fractal_higuchi,25
+0.0001004000000079941,4.331735672660552,2000,Katz,nk_fractal_katz,25
+0.009052900000000363,1.8040203537176212,2000,NLD,nk_fractal_nld,25
+6.220000000212167e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,25
+0.004107700000005821,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,25
+0.009337200000004486,0.691035660416629,2000,SDA,nk_fractal_sda,25
+0.00015559999999936736,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,25
+0.00011930000000859309,0.018215273069519526,2000,Fisher,nk_fisher_information,25
+0.00024900000000016576,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,26
+0.2632286000000015,0.8815000687731794,2000,Hurst,nk_complexity_hurst,26
+0.07702529999999541,0.99788636990425,2000,LZC,nk_complexity_lzc,26
+0.11341750000000417,0.9692679097822192,2000,PLZC,nk_complexity_plzc,26
+0.18467729999999705,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,26
+0.831025099999998,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,26
+0.002835400000002153,1.6061378306189726,2000,RR,nk_complexity_rr,26
+0.03928529999999597,1.0796704855858055,2000,ApEn,nk_entropy_approximate,26
+0.013418099999995547,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,26
+0.001081499999997959,1.5299377592545462,2000,DiffEn,nk_entropy_differential,26
+1.1524969000000027,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,26
+0.06211950000000144,0.875455134560752,2000,MSE,nk_complexity_mse,26
+8.8251688,0.5421120627391649,2000,CMSE,nk_complexity_cmse,26
+8.915812899999999,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,26
+1.060985500000001,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,26
+5.858381600000001,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,26
+3.8011186999999964,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,26
+0.0002854000000027668,0.9995642134676757,2000,PEn,nk_complexity_pe,26
+0.013875999999996225,0.9974282038230982,2000,WPEn,nk_complexity_wpe,26
+0.01212600000000208,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,26
+0.9579086999999973,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,26
+0.9466134000000039,inf,2000,RangeEn,nk_entropy_range_mApEn,26
+0.025204900000005637,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,26
+0.00140190000000473,10.965784284662085,2000,ShanEn,nk_entropy_shannon,26
+0.0032767000000006874,0.8465376735420068,2000,SpEn,nk_entropy_spectral,26
+0.00033249999999895863,0.9927605014360844,2000,SVDen,nk_entropy_svd,26
+1.3155919000000011,2.132371401023081,2000,CD,nk_fractal_correlation,26
+0.17777490000000284,0.9905916277925066,2000,DFA,nk_fractal_dfa,26
+0.19191490000000044,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,26
+0.001586900000006608,1.9990408568403804,2000,HFD,nk_fractal_higuchi,26
+9.660000000621949e-05,4.331735672660552,2000,Katz,nk_fractal_katz,26
+0.00887879999999086,1.8040203537176212,2000,NLD,nk_fractal_nld,26
+6.369999999833453e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,26
+0.00405570000000921,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,26
+0.009116199999994024,0.691035660416629,2000,SDA,nk_fractal_sda,26
+0.00013810000000091804,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,26
+0.00011080000000163182,0.018215273069519526,2000,Fisher,nk_fisher_information,26
+0.00024360000000100968,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,27
+0.2890361000000006,0.8815000687731794,2000,Hurst,nk_complexity_hurst,27
+0.15322789999999742,0.99788636990425,2000,LZC,nk_complexity_lzc,27
+0.22817870000000084,0.9692679097822192,2000,PLZC,nk_complexity_plzc,27
+0.17544730000000186,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,27
+0.6607073999999997,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,27
+0.005482700000001728,1.6061378306189726,2000,RR,nk_complexity_rr,27
+0.04353160000000145,1.0796704855858055,2000,ApEn,nk_entropy_approximate,27
+0.02925340000000176,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,27
+0.0023799000000011006,1.5299377592545462,2000,DiffEn,nk_entropy_differential,27
+0.9907975999999934,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,27
+0.05586990000000469,0.875455134560752,2000,MSE,nk_complexity_mse,27
+5.451325399999995,0.5421120627391649,2000,CMSE,nk_complexity_cmse,27
+5.145505299999996,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,27
+0.4658958999999996,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,27
+3.1034204000000045,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,27
+2.959979599999997,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,27
+0.00028689999999897964,0.9995642134676757,2000,PEn,nk_complexity_pe,27
+0.013839099999998439,0.9974282038230982,2000,WPEn,nk_complexity_wpe,27
+0.012062199999995471,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,27
+0.9613447999999991,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,27
+0.9611209000000045,inf,2000,RangeEn,nk_entropy_range_mApEn,27
+0.025396300000004146,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,27
+0.001784000000000674,10.965784284662085,2000,ShanEn,nk_entropy_shannon,27
+0.0036861999999970863,0.8465376735420068,2000,SpEn,nk_entropy_spectral,27
+0.00044850000000451473,0.9927605014360844,2000,SVDen,nk_entropy_svd,27
+2.4064729999999983,2.132371401023081,2000,CD,nk_fractal_correlation,27
+0.20039420000000518,0.9905916277925066,2000,DFA,nk_fractal_dfa,27
+0.20920110000000136,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,27
+0.0018750000000054001,1.9990408568403804,2000,HFD,nk_fractal_higuchi,27
+0.00014629999999726806,4.331735672660552,2000,Katz,nk_fractal_katz,27
+0.010345499999999674,1.8040203537176212,2000,NLD,nk_fractal_nld,27
+8.309999999767115e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,27
+0.006585099999995236,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,27
+0.00998289999999713,0.691035660416629,2000,SDA,nk_fractal_sda,27
+0.0003074999999981287,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,27
+0.00020750000000191449,0.018215273069519526,2000,Fisher,nk_fisher_information,27
+0.0002763000000030047,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,28
+0.2600839999999991,0.8815000687731794,2000,Hurst,nk_complexity_hurst,28
+0.15339529999999968,0.99788636990425,2000,LZC,nk_complexity_lzc,28
+0.226551299999997,0.9692679097822192,2000,PLZC,nk_complexity_plzc,28
+0.23219080000000503,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,28
+0.7480560999999994,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,28
+0.002704399999998941,1.6061378306189726,2000,RR,nk_complexity_rr,28
+0.04132649999999671,1.0796704855858055,2000,ApEn,nk_entropy_approximate,28
+0.017805500000001473,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,28
+0.0013746999999995069,1.5299377592545462,2000,DiffEn,nk_entropy_differential,28
+1.0564826999999966,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,28
+0.060922900000001334,0.875455134560752,2000,MSE,nk_complexity_mse,28
+5.408242000000001,0.5421120627391649,2000,CMSE,nk_complexity_cmse,28
+5.202005900000003,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,28
+0.5231214000000008,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,28
+3.1194992,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,28
+3.229697899999998,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,28
+0.0002992000000006101,0.9995642134676757,2000,PEn,nk_complexity_pe,28
+0.01388899999999893,0.9974282038230982,2000,WPEn,nk_complexity_wpe,28
+0.012256299999997111,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,28
+0.9702783000000039,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,28
+0.987714700000005,inf,2000,RangeEn,nk_entropy_range_mApEn,28
+0.040329399999997406,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,28
+0.004381699999996158,10.965784284662085,2000,ShanEn,nk_entropy_shannon,28
+0.008761300000003303,0.8465376735420068,2000,SpEn,nk_entropy_spectral,28
+0.00109960000000342,0.9927605014360844,2000,SVDen,nk_entropy_svd,28
+2.777344300000003,2.132371401023081,2000,CD,nk_fractal_correlation,28
+0.3218095999999946,0.9905916277925066,2000,DFA,nk_fractal_dfa,28
+0.2873050000000035,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,28
+0.0015597999999954482,1.9990408568403804,2000,HFD,nk_fractal_higuchi,28
+9.800000000126374e-05,4.331735672660552,2000,Katz,nk_fractal_katz,28
+0.008979199999998855,1.8040203537176212,2000,NLD,nk_fractal_nld,28
+7.029999999730308e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,28
+0.004091000000002509,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,28
+0.00922909999999888,0.691035660416629,2000,SDA,nk_fractal_sda,28
+0.00016180000000076689,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,28
+0.00010999999999938836,0.018215273069519526,2000,Fisher,nk_fisher_information,28
+0.0002552999999991812,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,29
+0.3243645999999991,0.8815000687731794,2000,Hurst,nk_complexity_hurst,29
+0.15207250000000272,0.99788636990425,2000,LZC,nk_complexity_lzc,29
+0.22671309999999778,0.9692679097822192,2000,PLZC,nk_complexity_plzc,29
+0.3991320000000016,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,29
+0.6841995999999995,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,29
+0.005084799999998779,1.6061378306189726,2000,RR,nk_complexity_rr,29
+0.04640269999999447,1.0796704855858055,2000,ApEn,nk_entropy_approximate,29
+0.014516700000001492,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,29
+0.0012261999999978457,1.5299377592545462,2000,DiffEn,nk_entropy_differential,29
+0.8984583000000015,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,29
+0.06284360000000078,0.875455134560752,2000,MSE,nk_complexity_mse,29
+7.2161715000000015,0.5421120627391649,2000,CMSE,nk_complexity_cmse,29
+5.226239800000002,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,29
+0.3025566000000026,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,29
+2.9618938000000057,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,29
+2.8205489999999998,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,29
+0.0005027999999995814,0.9995642134676757,2000,PEn,nk_complexity_pe,29
+0.015534500000001117,0.9974282038230982,2000,WPEn,nk_complexity_wpe,29
+0.013516700000003823,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,29
+0.9953682000000015,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,29
+1.0056837000000058,inf,2000,RangeEn,nk_entropy_range_mApEn,29
+0.027127400000004798,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,29
+0.002511800000000619,10.965784284662085,2000,ShanEn,nk_entropy_shannon,29
+0.004929600000004086,0.8465376735420068,2000,SpEn,nk_entropy_spectral,29
+0.0006271000000026561,0.9927605014360844,2000,SVDen,nk_entropy_svd,29
+1.9416919999999962,2.132371401023081,2000,CD,nk_fractal_correlation,29
+0.1830155999999974,0.9905916277925066,2000,DFA,nk_fractal_dfa,29
+0.19381379999999382,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,29
+0.0016381000000009749,1.9990408568403804,2000,HFD,nk_fractal_higuchi,29
+0.00010900000000191312,4.331735672660552,2000,Katz,nk_fractal_katz,29
+0.0090849999999989,1.8040203537176212,2000,NLD,nk_fractal_nld,29
+5.550000000198452e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,29
+0.0041954999999944675,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,29
+0.009396299999998803,0.691035660416629,2000,SDA,nk_fractal_sda,29
+0.0001449000000022238,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,29
+0.00011909999999915044,0.018215273069519526,2000,Fisher,nk_fisher_information,29
+0.0002571000000024526,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,30
+0.3007399000000035,0.8815000687731794,2000,Hurst,nk_complexity_hurst,30
+0.07535320000000212,0.99788636990425,2000,LZC,nk_complexity_lzc,30
+0.11434970000000533,0.9692679097822192,2000,PLZC,nk_complexity_plzc,30
+0.1649388000000016,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,30
+0.6050560999999988,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,30
+0.003297700000004511,1.6061378306189726,2000,RR,nk_complexity_rr,30
+0.027389800000001685,1.0796704855858055,2000,ApEn,nk_entropy_approximate,30
+0.010369300000000692,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,30
+0.0010373000000001298,1.5299377592545462,2000,DiffEn,nk_entropy_differential,30
+0.9424818999999971,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,30
+0.04214969999999596,0.875455134560752,2000,MSE,nk_complexity_mse,30
+8.738286800000004,0.5421120627391649,2000,CMSE,nk_complexity_cmse,30
+8.983778100000002,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,30
+0.4674145999999979,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,30
+5.130789099999994,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,30
+2.903594400000003,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,30
+0.000301200000002666,0.9995642134676757,2000,PEn,nk_complexity_pe,30
+0.013952500000002033,0.9974282038230982,2000,WPEn,nk_complexity_wpe,30
+0.011846300000001975,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,30
+0.9682268999999977,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,30
+0.9426561000000007,inf,2000,RangeEn,nk_entropy_range_mApEn,30
+0.024557799999996632,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,30
+0.0010891000000015083,10.965784284662085,2000,ShanEn,nk_entropy_shannon,30
+0.0025381999999964933,0.8465376735420068,2000,SpEn,nk_entropy_spectral,30
+0.00019149999999967804,0.9927605014360844,2000,SVDen,nk_entropy_svd,30
+0.6376016000000035,2.132371401023081,2000,CD,nk_fractal_correlation,30
+0.17987190000000197,0.9905916277925066,2000,DFA,nk_fractal_dfa,30
+0.191407599999998,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,30
+0.001582199999994316,1.9990408568403804,2000,HFD,nk_fractal_higuchi,30
+9.16999999986956e-05,4.331735672660552,2000,Katz,nk_fractal_katz,30
+0.008993400000001373,1.8040203537176212,2000,NLD,nk_fractal_nld,30
+5.240000000128475e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,30
+0.004347199999997997,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,30
+0.009308300000000713,0.691035660416629,2000,SDA,nk_fractal_sda,30
+0.00015660000000394803,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,30
+0.00013669999999876836,0.018215273069519526,2000,Fisher,nk_fisher_information,30
+0.00027539999999959264,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,31
+0.3038870999999972,0.8815000687731794,2000,Hurst,nk_complexity_hurst,31
+0.15340719999999664,0.99788636990425,2000,LZC,nk_complexity_lzc,31
+0.22867759999999748,0.9692679097822192,2000,PLZC,nk_complexity_plzc,31
+0.20200759999999462,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,31
+0.7335718,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,31
+0.004407900000003906,1.6061378306189726,2000,RR,nk_complexity_rr,31
+0.040877600000001735,1.0796704855858055,2000,ApEn,nk_entropy_approximate,31
+0.019777100000005987,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,31
+0.0013025000000013165,1.5299377592545462,2000,DiffEn,nk_entropy_differential,31
+0.9578077999999977,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,31
+0.05586600000000175,0.875455134560752,2000,MSE,nk_complexity_mse,31
+9.187262099999998,0.5421120627391649,2000,CMSE,nk_complexity_cmse,31
+9.000230299999998,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,31
+0.9606242999999992,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,31
+5.464725399999999,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,31
+3.222906899999998,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,31
+0.0003297000000017647,0.9995642134676757,2000,PEn,nk_complexity_pe,31
+0.013917599999999197,0.9974282038230982,2000,WPEn,nk_complexity_wpe,31
+0.012114199999999187,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,31
+0.959933399999997,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,31
+0.9433924999999945,inf,2000,RangeEn,nk_entropy_range_mApEn,31
+0.03456070000000011,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,31
+0.001880200000002219,10.965784284662085,2000,ShanEn,nk_entropy_shannon,31
+0.0033628999999990583,0.8465376735420068,2000,SpEn,nk_entropy_spectral,31
+0.0002805999999964115,0.9927605014360844,2000,SVDen,nk_entropy_svd,31
+1.1658750999999938,2.132371401023081,2000,CD,nk_fractal_correlation,31
+0.18078789999999856,0.9905916277925066,2000,DFA,nk_fractal_dfa,31
+0.19218649999999116,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,31
+0.0016224000000022443,1.9990408568403804,2000,HFD,nk_fractal_higuchi,31
+0.00010460000000023228,4.331735672660552,2000,Katz,nk_fractal_katz,31
+0.009111599999997111,1.8040203537176212,2000,NLD,nk_fractal_nld,31
+5.5000000003246896e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,31
+0.004268699999997239,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,31
+0.009321400000004587,0.691035660416629,2000,SDA,nk_fractal_sda,31
+0.00015720000000385426,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,31
+0.00013500000000021828,0.018215273069519526,2000,Fisher,nk_fisher_information,31
+0.00032009999999615957,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,32
+0.27469370000000026,0.8815000687731794,2000,Hurst,nk_complexity_hurst,32
+0.15321009999999546,0.99788636990425,2000,LZC,nk_complexity_lzc,32
+0.2294376999999983,0.9692679097822192,2000,PLZC,nk_complexity_plzc,32
+0.36092519999999695,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,32
+0.6774300999999952,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,32
+0.0031364000000024816,1.6061378306189726,2000,RR,nk_complexity_rr,32
+0.032624800000000675,1.0796704855858055,2000,ApEn,nk_entropy_approximate,32
+0.016945499999998503,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,32
+0.0009678999999991333,1.5299377592545462,2000,DiffEn,nk_entropy_differential,32
+0.9343714000000034,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,32
+0.052757999999997196,0.875455134560752,2000,MSE,nk_complexity_mse,32
+9.080618000000001,0.5421120627391649,2000,CMSE,nk_complexity_cmse,32
+8.916108800000003,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,32
+0.8751710000000017,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,32
+5.296330900000001,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,32
+3.1700164999999956,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,32
+0.0002860999999967362,0.9995642134676757,2000,PEn,nk_complexity_pe,32
+0.013946299999993528,0.9974282038230982,2000,WPEn,nk_complexity_wpe,32
+0.011990299999993681,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,32
+0.9558008999999998,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,32
+0.9444506999999973,inf,2000,RangeEn,nk_entropy_range_mApEn,32
+0.02471990000000801,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,32
+0.0012157000000030393,10.965784284662085,2000,ShanEn,nk_entropy_shannon,32
+0.0027477000000004637,0.8465376735420068,2000,SpEn,nk_entropy_spectral,32
+0.000194699999994441,0.9927605014360844,2000,SVDen,nk_entropy_svd,32
+1.0087925999999925,2.132371401023081,2000,CD,nk_fractal_correlation,32
+0.18516979999999705,0.9905916277925066,2000,DFA,nk_fractal_dfa,32
+0.19808629999999994,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,32
+0.001858799999993721,1.9990408568403804,2000,HFD,nk_fractal_higuchi,32
+0.00014299999999423108,4.331735672660552,2000,Katz,nk_fractal_katz,32
+0.009758700000006115,1.8040203537176212,2000,NLD,nk_fractal_nld,32
+8.579999999369647e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,32
+0.005872499999995284,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,32
+0.009513099999992392,0.691035660416629,2000,SDA,nk_fractal_sda,32
+0.00020309999999312822,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,32
+0.0001718999999980042,0.018215273069519526,2000,Fisher,nk_fisher_information,32
+0.0002484000000002595,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,33
+0.2697094,0.8815000687731794,2000,Hurst,nk_complexity_hurst,33
+0.15319860000000318,0.99788636990425,2000,LZC,nk_complexity_lzc,33
+0.22801690000000008,0.9692679097822192,2000,PLZC,nk_complexity_plzc,33
+0.3792694999999995,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,33
+0.6915584000000052,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,33
+0.002366800000004332,1.6061378306189726,2000,RR,nk_complexity_rr,33
+0.03443970000000007,1.0796704855858055,2000,ApEn,nk_entropy_approximate,33
+0.01224709999999618,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,33
+0.0009867000000056692,1.5299377592545462,2000,DiffEn,nk_entropy_differential,33
+0.9152222000000023,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,33
+0.060478099999997426,0.875455134560752,2000,MSE,nk_complexity_mse,33
+9.177694600000002,0.5421120627391649,2000,CMSE,nk_complexity_cmse,33
+8.982625600000006,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,33
+1.0004320999999976,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,33
+5.412091599999997,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,33
+3.217712900000002,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,33
+0.00034849999999408965,0.9995642134676757,2000,PEn,nk_complexity_pe,33
+0.013907599999996023,0.9974282038230982,2000,WPEn,nk_complexity_wpe,33
+0.012459499999998513,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,33
+0.9610204000000024,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,33
+0.9448308999999924,inf,2000,RangeEn,nk_entropy_range_mApEn,33
+0.02535759999999243,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,33
+0.001963200000005827,10.965784284662085,2000,ShanEn,nk_entropy_shannon,33
+0.003781000000003587,0.8465376735420068,2000,SpEn,nk_entropy_spectral,33
+0.00030129999998962376,0.9927605014360844,2000,SVDen,nk_entropy_svd,33
+1.142776799999993,2.132371401023081,2000,CD,nk_fractal_correlation,33
+0.18181290000001127,0.9905916277925066,2000,DFA,nk_fractal_dfa,33
+0.19209979999999405,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,33
+0.0015973000000002457,1.9990408568403804,2000,HFD,nk_fractal_higuchi,33
+0.00010009999999738284,4.331735672660552,2000,Katz,nk_fractal_katz,33
+0.009005500000000666,1.8040203537176212,2000,NLD,nk_fractal_nld,33
+6.280000000913333e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,33
+0.0041709000000054175,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,33
+0.009161100000000033,0.691035660416629,2000,SDA,nk_fractal_sda,33
+0.0001530999999914684,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,33
+0.00011689999999475731,0.018215273069519526,2000,Fisher,nk_fisher_information,33
+0.000366100000000813,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,34
+0.2958406999999994,0.8815000687731794,2000,Hurst,nk_complexity_hurst,34
+0.1537569999999988,0.99788636990425,2000,LZC,nk_complexity_lzc,34
+0.22424929999999677,0.9692679097822192,2000,PLZC,nk_complexity_plzc,34
+0.16429860000000218,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,34
+0.6757569000000032,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,34
+0.0017991000000066038,1.6061378306189726,2000,RR,nk_complexity_rr,34
+0.02561579999999708,1.0796704855858055,2000,ApEn,nk_entropy_approximate,34
+0.008079999999999643,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,34
+0.000663600000002873,1.5299377592545462,2000,DiffEn,nk_entropy_differential,34
+0.9563380999999964,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,34
+0.04145210000000077,0.875455134560752,2000,MSE,nk_complexity_mse,34
+5.344818400000001,0.5421120627391649,2000,CMSE,nk_complexity_cmse,34
+5.198009399999997,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,34
+0.40928669999999556,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,34
+3.303128800000003,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,34
+3.3216146000000037,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,34
+0.0002861000000038416,0.9995642134676757,2000,PEn,nk_complexity_pe,34
+0.013935400000001152,0.9974282038230982,2000,WPEn,nk_complexity_wpe,34
+0.0120804000000021,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,34
+0.967386900000001,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,34
+0.9735510000000005,inf,2000,RangeEn,nk_entropy_range_mApEn,34
+0.02552120000000002,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,34
+0.002146400000000881,10.965784284662085,2000,ShanEn,nk_entropy_shannon,34
+0.00400979999999862,0.8465376735420068,2000,SpEn,nk_entropy_spectral,34
+0.0005335999999971364,0.9927605014360844,2000,SVDen,nk_entropy_svd,34
+2.621120099999999,2.132371401023081,2000,CD,nk_fractal_correlation,34
+0.18820519999999874,0.9905916277925066,2000,DFA,nk_fractal_dfa,34
+0.19760529999999932,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,34
+0.0016176000000029944,1.9990408568403804,2000,HFD,nk_fractal_higuchi,34
+8.749999999935199e-05,4.331735672660552,2000,Katz,nk_fractal_katz,34
+0.009101399999998705,1.8040203537176212,2000,NLD,nk_fractal_nld,34
+6.450000000057798e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,34
+0.0043322999999944045,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,34
+0.009314299999999776,0.691035660416629,2000,SDA,nk_fractal_sda,34
+0.0001658000000048787,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,34
+0.00011519999999620723,0.018215273069519526,2000,Fisher,nk_fisher_information,34
+0.00024850000000142813,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,35
+0.2888188000000014,0.8815000687731794,2000,Hurst,nk_complexity_hurst,35
+0.15380109999999902,0.99788636990425,2000,LZC,nk_complexity_lzc,35
+0.2224812000000007,0.9692679097822192,2000,PLZC,nk_complexity_plzc,35
+0.1600748999999979,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,35
+0.6793706999999998,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,35
+0.0018454000000005522,1.6061378306189726,2000,RR,nk_complexity_rr,35
+0.025639699999999266,1.0796704855858055,2000,ApEn,nk_entropy_approximate,35
+0.00786899999999946,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,35
+0.0006711000000052536,1.5299377592545462,2000,DiffEn,nk_entropy_differential,35
+0.957153500000004,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,35
+0.04168210000000272,0.875455134560752,2000,MSE,nk_complexity_mse,35
+6.558568799999996,0.5421120627391649,2000,CMSE,nk_complexity_cmse,35
+6.529343600000004,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,35
+0.3312939000000057,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,35
+4.472097099999999,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,35
+4.6963072,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,35
+0.0003452999999993267,0.9995642134676757,2000,PEn,nk_complexity_pe,35
+0.013963900000000251,0.9974282038230982,2000,WPEn,nk_complexity_wpe,35
+0.012092100000003825,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,35
+1.0505925999999945,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,35
+0.9531025,inf,2000,RangeEn,nk_entropy_range_mApEn,35
+0.025202999999997644,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,35
+0.0019521999999980721,10.965784284662085,2000,ShanEn,nk_entropy_shannon,35
+0.004320700000000954,0.8465376735420068,2000,SpEn,nk_entropy_spectral,35
+0.00042450000000116006,0.9927605014360844,2000,SVDen,nk_entropy_svd,35
+1.720608900000002,2.132371401023081,2000,CD,nk_fractal_correlation,35
+0.1828175999999999,0.9905916277925066,2000,DFA,nk_fractal_dfa,35
+0.19392639999999517,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,35
+0.0015612999999987665,1.9990408568403804,2000,HFD,nk_fractal_higuchi,35
+9.839999999883275e-05,4.331735672660552,2000,Katz,nk_fractal_katz,35
+0.008928400000002057,1.8040203537176212,2000,NLD,nk_fractal_nld,35
+6.0200000000065756e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,35
+0.004192599999996105,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,35
+0.009177600000001007,0.691035660416629,2000,SDA,nk_fractal_sda,35
+0.00015330000000091104,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,35
+0.00011729999999943175,0.018215273069519526,2000,Fisher,nk_fisher_information,35
+0.00025049999999993133,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,36
+0.26228270000000364,0.8815000687731794,2000,Hurst,nk_complexity_hurst,36
+0.07745940000000218,0.99788636990425,2000,LZC,nk_complexity_lzc,36
+0.1714022999999969,0.9692679097822192,2000,PLZC,nk_complexity_plzc,36
+0.31319829999999627,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,36
+0.7421183000000013,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,36
+0.0022834999999972183,1.6061378306189726,2000,RR,nk_complexity_rr,36
+0.03204399999999907,1.0796704855858055,2000,ApEn,nk_entropy_approximate,36
+0.013763199999999642,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,36
+0.0009624999999999773,1.5299377592545462,2000,DiffEn,nk_entropy_differential,36
+1.1016565999999983,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,36
+0.057038900000002,0.875455134560752,2000,MSE,nk_complexity_mse,36
+9.160787300000003,0.5421120627391649,2000,CMSE,nk_complexity_cmse,36
+9.0024388,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,36
+1.3231463999999988,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,36
+5.240139300000003,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,36
+3.7714143999999976,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,36
+0.0002956000000011727,0.9995642134676757,2000,PEn,nk_complexity_pe,36
+0.014015299999996955,0.9974282038230982,2000,WPEn,nk_complexity_wpe,36
+0.012083600000003969,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,36
+0.9568604999999977,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,36
+0.9466309999999964,inf,2000,RangeEn,nk_entropy_range_mApEn,36
+0.025953499999999963,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,36
+0.0015912000000071203,10.965784284662085,2000,ShanEn,nk_entropy_shannon,36
+0.003076499999991711,0.8465376735420068,2000,SpEn,nk_entropy_spectral,36
+0.0003296000000005961,0.9927605014360844,2000,SVDen,nk_entropy_svd,36
+1.2864019999999954,2.132371401023081,2000,CD,nk_fractal_correlation,36
+0.17789700000000153,0.9905916277925066,2000,DFA,nk_fractal_dfa,36
+0.19210819999999273,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,36
+0.0015681999999941354,1.9990408568403804,2000,HFD,nk_fractal_higuchi,36
+9.930000000224481e-05,4.331735672660552,2000,Katz,nk_fractal_katz,36
+0.008965899999992644,1.8040203537176212,2000,NLD,nk_fractal_nld,36
+6.469999999580978e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,36
+0.0041101000000054455,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,36
+0.009174899999990771,0.691035660416629,2000,SDA,nk_fractal_sda,36
+0.00015989999999987958,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,36
+0.00011580000000321888,0.018215273069519526,2000,Fisher,nk_fisher_information,36
+0.00024879999999782854,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,37
+0.2605625000000025,0.8815000687731794,2000,Hurst,nk_complexity_hurst,37
+0.15352910000000008,0.99788636990425,2000,LZC,nk_complexity_lzc,37
+0.22454570000000018,0.9692679097822192,2000,PLZC,nk_complexity_plzc,37
+0.16061440000000005,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,37
+0.6747924999999952,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,37
+0.0017624999999981128,1.6061378306189726,2000,RR,nk_complexity_rr,37
+0.025749699999998654,1.0796704855858055,2000,ApEn,nk_entropy_approximate,37
+0.007220799999998917,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,37
+0.0006508000000025049,1.5299377592545462,2000,DiffEn,nk_entropy_differential,37
+0.9603911000000025,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,37
+0.04159839999999804,0.875455134560752,2000,MSE,nk_complexity_mse,37
+8.814367499999996,0.5421120627391649,2000,CMSE,nk_complexity_cmse,37
+8.922455499999998,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,37
+0.8290768999999969,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,37
+6.092283999999999,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,37
+3.876650099999999,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,37
+0.0002783999999991238,0.9995642134676757,2000,PEn,nk_complexity_pe,37
+0.01509709999999842,0.9974282038230982,2000,WPEn,nk_complexity_wpe,37
+0.012229099999998994,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,37
+0.9731190999999981,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,37
+0.9613386000000048,inf,2000,RangeEn,nk_entropy_range_mApEn,37
+0.0249761000000035,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,37
+0.0013629000000037195,10.965784284662085,2000,ShanEn,nk_entropy_shannon,37
+0.0028939999999977317,0.8465376735420068,2000,SpEn,nk_entropy_spectral,37
+0.00027029999999683696,0.9927605014360844,2000,SVDen,nk_entropy_svd,37
+1.3212824000000012,2.132371401023081,2000,CD,nk_fractal_correlation,37
+0.17946279999999604,0.9905916277925066,2000,DFA,nk_fractal_dfa,37
+0.19092369999999903,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,37
+0.0015640999999959604,1.9990408568403804,2000,HFD,nk_fractal_higuchi,37
+9.780000000603195e-05,4.331735672660552,2000,Katz,nk_fractal_katz,37
+0.008954000000002793,1.8040203537176212,2000,NLD,nk_fractal_nld,37
+6.159999999511001e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,37
+0.0041133000000002085,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,37
+0.00919489999999712,0.691035660416629,2000,SDA,nk_fractal_sda,37
+0.00017369999999061747,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,37
+0.00011680000000069413,0.018215273069519526,2000,Fisher,nk_fisher_information,37
+0.000259700000000862,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,38
+0.26935370000000347,0.8815000687731794,2000,Hurst,nk_complexity_hurst,38
+0.15298179999999917,0.99788636990425,2000,LZC,nk_complexity_lzc,38
+0.22706879999999785,0.9692679097822192,2000,PLZC,nk_complexity_plzc,38
+0.3896712000000022,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,38
+0.7126328999999956,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,38
+0.0021775000000019418,1.6061378306189726,2000,RR,nk_complexity_rr,38
+0.03487570000000062,1.0796704855858055,2000,ApEn,nk_entropy_approximate,38
+0.015512600000000987,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,38
+0.001218600000001402,1.5299377592545462,2000,DiffEn,nk_entropy_differential,38
+0.8942368999999957,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,38
+0.060690700000002096,0.875455134560752,2000,MSE,nk_complexity_mse,38
+5.414714500000002,0.5421120627391649,2000,CMSE,nk_complexity_cmse,38
+5.180078899999998,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,38
+0.5106730000000042,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,38
+3.0506130000000056,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,38
+2.9668512000000007,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,38
+0.00027599999999949887,0.9995642134676757,2000,PEn,nk_complexity_pe,38
+0.0138383000000033,0.9974282038230982,2000,WPEn,nk_complexity_wpe,38
+0.012081999999999482,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,38
+0.9608386000000024,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,38
+0.9649626999999938,inf,2000,RangeEn,nk_entropy_range_mApEn,38
+0.026933500000005495,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,38
+0.0022975999999985675,10.965784284662085,2000,ShanEn,nk_entropy_shannon,38
+0.005009199999996383,0.8465376735420068,2000,SpEn,nk_entropy_spectral,38
+0.0006860000000017408,0.9927605014360844,2000,SVDen,nk_entropy_svd,38
+2.4425773000000035,2.132371401023081,2000,CD,nk_fractal_correlation,38
+0.1966165999999987,0.9905916277925066,2000,DFA,nk_fractal_dfa,38
+0.20811410000000308,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,38
+0.002212299999996503,1.9990408568403804,2000,HFD,nk_fractal_higuchi,38
+0.00019429999999687197,4.331735672660552,2000,Katz,nk_fractal_katz,38
+0.010065900000000738,1.8040203537176212,2000,NLD,nk_fractal_nld,38
+0.0001178000000052748,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,38
+0.007980099999997492,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,38
+0.010273800000000222,0.691035660416629,2000,SDA,nk_fractal_sda,38
+0.0003685000000004379,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,38
+0.00030979999999658503,0.018215273069519526,2000,Fisher,nk_fisher_information,38
+0.00024540000000072837,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,39
+0.2629425000000012,0.8815000687731794,2000,Hurst,nk_complexity_hurst,39
+0.07744160000000022,0.99788636990425,2000,LZC,nk_complexity_lzc,39
+0.11750930000000182,0.9692679097822192,2000,PLZC,nk_complexity_plzc,39
+0.25131790000000365,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,39
+0.6282816000000011,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,39
+0.003135999999997807,1.6061378306189726,2000,RR,nk_complexity_rr,39
+0.02814189999999428,1.0796704855858055,2000,ApEn,nk_entropy_approximate,39
+0.010370700000002842,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,39
+0.0010614999999987162,1.5299377592545462,2000,DiffEn,nk_entropy_differential,39
+0.9469238000000004,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,39
+0.04347570000000189,0.875455134560752,2000,MSE,nk_complexity_mse,39
+5.307935700000002,0.5421120627391649,2000,CMSE,nk_complexity_cmse,39
+5.162050899999997,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,39
+0.3406536999999972,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,39
+3.2374440999999976,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,39
+3.1919068999999993,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,39
+0.0002743000000009488,0.9995642134676757,2000,PEn,nk_complexity_pe,39
+0.013778899999998373,0.9974282038230982,2000,WPEn,nk_complexity_wpe,39
+0.011950800000001038,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,39
+0.9598677000000038,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,39
+0.9621416999999965,inf,2000,RangeEn,nk_entropy_range_mApEn,39
+0.026552700000003426,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,39
+0.0023802999999986696,10.965784284662085,2000,ShanEn,nk_entropy_shannon,39
+0.0043915000000041005,0.8465376735420068,2000,SpEn,nk_entropy_spectral,39
+0.0005354999999980237,0.9927605014360844,2000,SVDen,nk_entropy_svd,39
+2.595380199999994,2.132371401023081,2000,CD,nk_fractal_correlation,39
+0.1961984000000001,0.9905916277925066,2000,DFA,nk_fractal_dfa,39
+0.21233089999999777,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,39
+0.0023718999999999824,1.9990408568403804,2000,HFD,nk_fractal_higuchi,39
+0.00019369999999696574,4.331735672660552,2000,Katz,nk_fractal_katz,39
+0.010163000000005695,1.8040203537176212,2000,NLD,nk_fractal_nld,39
+9.350000000551972e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,39
+0.008364999999997735,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,39
+0.010156899999998359,0.691035660416629,2000,SDA,nk_fractal_sda,39
+0.00034780000000012024,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,39
+0.0002703000000039424,0.018215273069519526,2000,Fisher,nk_fisher_information,39
+0.000249600000000072,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,40
+0.2621810000000018,0.8815000687731794,2000,Hurst,nk_complexity_hurst,40
+0.15335389999999904,0.99788636990425,2000,LZC,nk_complexity_lzc,40
+0.22647800000000018,0.9692679097822192,2000,PLZC,nk_complexity_plzc,40
+0.2693425000000005,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,40
+0.7135587000000001,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,40
+0.0029807000000019457,1.6061378306189726,2000,RR,nk_complexity_rr,40
+0.035900399999995614,1.0796704855858055,2000,ApEn,nk_entropy_approximate,40
+0.01789839999999998,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,40
+0.0012260000000026139,1.5299377592545462,2000,DiffEn,nk_entropy_differential,40
+1.053026299999999,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,40
+0.06116049999999973,0.875455134560752,2000,MSE,nk_complexity_mse,40
+5.5667872,0.5421120627391649,2000,CMSE,nk_complexity_cmse,40
+5.1581951,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,40
+0.536446699999999,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,40
+3.047097199999996,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,40
+3.1888231000000005,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,40
+0.0003035000000011223,0.9995642134676757,2000,PEn,nk_complexity_pe,40
+0.013881200000000149,0.9974282038230982,2000,WPEn,nk_complexity_wpe,40
+0.01213549999999941,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,40
+0.9693917000000027,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,40
+0.975778499999997,inf,2000,RangeEn,nk_entropy_range_mApEn,40
+0.04033590000000231,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,40
+0.004422099999999318,10.965784284662085,2000,ShanEn,nk_entropy_shannon,40
+0.008748299999993492,0.8465376735420068,2000,SpEn,nk_entropy_spectral,40
+0.0010946999999958962,0.9927605014360844,2000,SVDen,nk_entropy_svd,40
+2.5353291999999996,2.132371401023081,2000,CD,nk_fractal_correlation,40
+0.18075670000000343,0.9905916277925066,2000,DFA,nk_fractal_dfa,40
+0.19232869999999735,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,40
+0.0015714000000031092,1.9990408568403804,2000,HFD,nk_fractal_higuchi,40
+0.00013979999999946813,4.331735672660552,2000,Katz,nk_fractal_katz,40
+0.009970600000002605,1.8040203537176212,2000,NLD,nk_fractal_nld,40
+5.449999999740385e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,40
+0.004127099999998052,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,40
+0.009151500000001533,0.691035660416629,2000,SDA,nk_fractal_sda,40
+0.00015819999999422407,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,40
+0.00012189999999634438,0.018215273069519526,2000,Fisher,nk_fisher_information,40
+0.00027749999999926445,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,41
+0.32654619999999923,0.8815000687731794,2000,Hurst,nk_complexity_hurst,41
+0.07504550000000165,0.99788636990425,2000,LZC,nk_complexity_lzc,41
+0.11456190000000177,0.9692679097822192,2000,PLZC,nk_complexity_plzc,41
+0.16427889999999934,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,41
+0.6381529000000015,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,41
+0.005284500000001913,1.6061378306189726,2000,RR,nk_complexity_rr,41
+0.06024269999999632,1.0796704855858055,2000,ApEn,nk_entropy_approximate,41
+0.015582199999997215,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,41
+0.0012062999999997714,1.5299377592545462,2000,DiffEn,nk_entropy_differential,41
+1.1335240000000013,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,41
+0.07797959999999904,0.875455134560752,2000,MSE,nk_complexity_mse,41
+8.997538499999997,0.5421120627391649,2000,CMSE,nk_complexity_cmse,41
+8.861401900000004,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,41
+0.5945739000000003,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,41
+6.129066700000003,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,41
+3.2509885000000054,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,41
+0.00032339999999919655,0.9995642134676757,2000,PEn,nk_complexity_pe,41
+0.013910400000000323,0.9974282038230982,2000,WPEn,nk_complexity_wpe,41
+0.011808600000001945,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,41
+0.9573582999999957,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,41
+0.9432388999999972,inf,2000,RangeEn,nk_entropy_range_mApEn,41
+0.024871900000000835,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,41
+0.0013316000000003214,10.965784284662085,2000,ShanEn,nk_entropy_shannon,41
+0.003195400000002735,0.8465376735420068,2000,SpEn,nk_entropy_spectral,41
+0.00029059999999958563,0.9927605014360844,2000,SVDen,nk_entropy_svd,41
+1.1796849999999992,2.132371401023081,2000,CD,nk_fractal_correlation,41
+0.18133210000000588,0.9905916277925066,2000,DFA,nk_fractal_dfa,41
+0.19405560000001287,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,41
+0.0016138000000012198,1.9990408568403804,2000,HFD,nk_fractal_higuchi,41
+8.919999999079664e-05,4.331735672660552,2000,Katz,nk_fractal_katz,41
+0.008942399999995132,1.8040203537176212,2000,NLD,nk_fractal_nld,41
+5.4300000002172055e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,41
+0.004326000000006047,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,41
+0.00926979999999844,0.691035660416629,2000,SDA,nk_fractal_sda,41
+0.00017859999999814136,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,41
+0.00013789999999858082,0.018215273069519526,2000,Fisher,nk_fisher_information,41
+0.0002537999999994156,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,42
+0.3331117999999975,0.8815000687731794,2000,Hurst,nk_complexity_hurst,42
+0.07535949999999758,0.99788636990425,2000,LZC,nk_complexity_lzc,42
+0.11439120000000003,0.9692679097822192,2000,PLZC,nk_complexity_plzc,42
+0.16436220000000645,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,42
+0.6330620999999965,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,42
+0.00296400000000574,1.6061378306189726,2000,RR,nk_complexity_rr,42
+0.02745869999999684,1.0796704855858055,2000,ApEn,nk_entropy_approximate,42
+0.011514299999994648,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,42
+0.0010857000000044081,1.5299377592545462,2000,DiffEn,nk_entropy_differential,42
+0.9383910000000029,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,42
+0.04236070000000325,0.875455134560752,2000,MSE,nk_complexity_mse,42
+5.331373400000004,0.5421120627391649,2000,CMSE,nk_complexity_cmse,42
+5.165848099999998,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,42
+0.3385782000000006,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,42
+3.156899200000005,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,42
+2.878350999999995,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,42
+0.0003258999999999901,0.9995642134676757,2000,PEn,nk_complexity_pe,42
+0.014647799999998767,0.9974282038230982,2000,WPEn,nk_complexity_wpe,42
+0.0118396999999959,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,42
+0.967837099999997,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,42
+1.0221628999999979,inf,2000,RangeEn,nk_entropy_range_mApEn,42
+0.031194200000001615,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,42
+0.002246900000002938,10.965784284662085,2000,ShanEn,nk_entropy_shannon,42
+0.004429299999998193,0.8465376735420068,2000,SpEn,nk_entropy_spectral,42
+0.0005121000000016807,0.9927605014360844,2000,SVDen,nk_entropy_svd,42
+2.6084749000000045,2.132371401023081,2000,CD,nk_fractal_correlation,42
+0.3796610000000058,0.9905916277925066,2000,DFA,nk_fractal_dfa,42
+0.36875820000000203,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,42
+0.0029561999999998534,1.9990408568403804,2000,HFD,nk_fractal_higuchi,42
+0.0002307999999970889,4.331735672660552,2000,Katz,nk_fractal_katz,42
+0.015128600000004155,1.8040203537176212,2000,NLD,nk_fractal_nld,42
+0.0001294999999998936,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,42
+0.008648499999999615,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,42
+0.018238900000000058,0.691035660416629,2000,SDA,nk_fractal_sda,42
+0.00037100000000123146,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,42
+0.00029089999999598604,0.018215273069519526,2000,Fisher,nk_fisher_information,42
+0.0002520999999973128,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,43
+0.2710739999999987,0.8815000687731794,2000,Hurst,nk_complexity_hurst,43
+0.07529029999999892,0.99788636990425,2000,LZC,nk_complexity_lzc,43
+0.1140681999999984,0.9692679097822192,2000,PLZC,nk_complexity_plzc,43
+0.16346800000000172,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,43
+0.6422661000000005,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,43
+0.0048245999999991795,1.6061378306189726,2000,RR,nk_complexity_rr,43
+0.06522340000000071,1.0796704855858055,2000,ApEn,nk_entropy_approximate,43
+0.01710740000000044,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,43
+0.0012270999999941523,1.5299377592545462,2000,DiffEn,nk_entropy_differential,43
+1.1324418000000023,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,43
+0.05801999999999907,0.875455134560752,2000,MSE,nk_complexity_mse,43
+9.940655,0.5421120627391649,2000,CMSE,nk_complexity_cmse,43
+9.853645099999994,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,43
+1.6115437999999997,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,43
+3.898878400000001,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,43
+3.0375815000000017,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,43
+0.00031919999999985293,0.9995642134676757,2000,PEn,nk_complexity_pe,43
+0.013927699999996435,0.9974282038230982,2000,WPEn,nk_complexity_wpe,43
+0.012062999999997714,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,43
+0.9575691000000006,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,43
+0.9447329000000053,inf,2000,RangeEn,nk_entropy_range_mApEn,43
+0.025390700000002653,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,43
+0.0015034999999983256,10.965784284662085,2000,ShanEn,nk_entropy_shannon,43
+0.0038009000000016613,0.8465376735420068,2000,SpEn,nk_entropy_spectral,43
+0.00031169999999747233,0.9927605014360844,2000,SVDen,nk_entropy_svd,43
+0.9841889999999864,2.132371401023081,2000,CD,nk_fractal_correlation,43
+0.17918619999998953,0.9905916277925066,2000,DFA,nk_fractal_dfa,43
+0.19569549999999936,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,43
+0.0015920000000022583,1.9990408568403804,2000,HFD,nk_fractal_higuchi,43
+9.870000000944401e-05,4.331735672660552,2000,Katz,nk_fractal_katz,43
+0.008929699999995933,1.8040203537176212,2000,NLD,nk_fractal_nld,43
+6.569999999328502e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,43
+0.004083300000004897,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,43
+0.009192999999996232,0.691035660416629,2000,SDA,nk_fractal_sda,43
+0.00015580000000170457,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,43
+0.00012270000000569325,0.018215273069519526,2000,Fisher,nk_fisher_information,43
+0.0002554000000003498,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,44
+0.27663689999999974,0.8815000687731794,2000,Hurst,nk_complexity_hurst,44
+0.15298870000000164,0.99788636990425,2000,LZC,nk_complexity_lzc,44
+0.22776169999999496,0.9692679097822192,2000,PLZC,nk_complexity_plzc,44
+0.35475909999999544,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,44
+0.7219531000000003,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,44
+0.0025589999999979796,1.6061378306189726,2000,RR,nk_complexity_rr,44
+0.035612700000001496,1.0796704855858055,2000,ApEn,nk_entropy_approximate,44
+0.01270590000000027,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,44
+0.0008407000000048015,1.5299377592545462,2000,DiffEn,nk_entropy_differential,44
+0.9129754000000005,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,44
+0.0606308999999996,0.875455134560752,2000,MSE,nk_complexity_mse,44
+9.118762599999997,0.5421120627391649,2000,CMSE,nk_complexity_cmse,44
+8.897685799999998,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,44
+0.9058701000000013,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,44
+5.113263799999999,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,44
+3.1184121999999945,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,44
+0.000293800000001454,0.9995642134676757,2000,PEn,nk_complexity_pe,44
+0.015997300000002213,0.9974282038230982,2000,WPEn,nk_complexity_wpe,44
+0.012113399999996943,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,44
+0.9601504000000034,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,44
+0.9448311999999959,inf,2000,RangeEn,nk_entropy_range_mApEn,44
+0.02455590000000285,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,44
+0.0011178999999970074,10.965784284662085,2000,ShanEn,nk_entropy_shannon,44
+0.0025852999999997905,0.8465376735420068,2000,SpEn,nk_entropy_spectral,44
+0.00019580000000019027,0.9927605014360844,2000,SVDen,nk_entropy_svd,44
+0.8722792999999953,2.132371401023081,2000,CD,nk_fractal_correlation,44
+0.18303889999999967,0.9905916277925066,2000,DFA,nk_fractal_dfa,44
+0.2013337000000064,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,44
+0.0017878999999965117,1.9990408568403804,2000,HFD,nk_fractal_higuchi,44
+0.00011479999999153279,4.331735672660552,2000,Katz,nk_fractal_katz,44
+0.009623700000005897,1.8040203537176212,2000,NLD,nk_fractal_nld,44
+6.940000000099644e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,44
+0.005135400000000345,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,44
+0.009501700000001279,0.691035660416629,2000,SDA,nk_fractal_sda,44
+0.000201400000008789,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,44
+0.00014620000000320488,0.018215273069519526,2000,Fisher,nk_fisher_information,44
+0.00024099999999904753,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,45
+0.2730567000000015,0.8815000687731794,2000,Hurst,nk_complexity_hurst,45
+0.1532227000000006,0.99788636990425,2000,LZC,nk_complexity_lzc,45
+0.22815059999999932,0.9692679097822192,2000,PLZC,nk_complexity_plzc,45
+0.20187860000000057,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,45
+0.5560144000000022,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,45
+0.0024265999999997234,1.6061378306189726,2000,RR,nk_complexity_rr,45
+0.028407000000001403,1.0796704855858055,2000,ApEn,nk_entropy_approximate,45
+0.012319200000000308,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,45
+0.0012547000000040498,1.5299377592545462,2000,DiffEn,nk_entropy_differential,45
+0.9127287000000024,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,45
+0.04134260000000012,0.875455134560752,2000,MSE,nk_complexity_mse,45
+9.652844800000004,0.5421120627391649,2000,CMSE,nk_complexity_cmse,45
+9.849532799999999,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,45
+1.6495096999999959,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,45
+4.533602599999995,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,45
+3.291302700000003,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,45
+0.00030980000000369046,0.9995642134676757,2000,PEn,nk_complexity_pe,45
+0.017014400000000762,0.9974282038230982,2000,WPEn,nk_complexity_wpe,45
+0.013781100000002766,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,45
+1.0258554999999987,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,45
+1.0208844,inf,2000,RangeEn,nk_entropy_range_mApEn,45
+0.0337312000000054,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,45
+0.0016686999999961927,10.965784284662085,2000,ShanEn,nk_entropy_shannon,45
+0.0034969999999958645,0.8465376735420068,2000,SpEn,nk_entropy_spectral,45
+0.00033339999998815983,0.9927605014360844,2000,SVDen,nk_entropy_svd,45
+0.6830101999999982,2.132371401023081,2000,CD,nk_fractal_correlation,45
+0.18228530000000376,0.9905916277925066,2000,DFA,nk_fractal_dfa,45
+0.19196110000000033,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,45
+0.001598199999989447,1.9990408568403804,2000,HFD,nk_fractal_higuchi,45
+0.00010079999999845768,4.331735672660552,2000,Katz,nk_fractal_katz,45
+0.00891550000000052,1.8040203537176212,2000,NLD,nk_fractal_nld,45
+6.059999999763477e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,45
+0.0040862000000032594,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,45
+0.009197399999990807,0.691035660416629,2000,SDA,nk_fractal_sda,45
+0.00013450000000148066,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,45
+0.00011689999999475731,0.018215273069519526,2000,Fisher,nk_fisher_information,45
+0.00025869999999983406,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,46
+0.2721382000000041,0.8815000687731794,2000,Hurst,nk_complexity_hurst,46
+0.15318820000000244,0.99788636990425,2000,LZC,nk_complexity_lzc,46
+0.2280415999999974,0.9692679097822192,2000,PLZC,nk_complexity_plzc,46
+0.35926059999999893,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,46
+0.6933807999999999,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,46
+0.002995200000000864,1.6061378306189726,2000,RR,nk_complexity_rr,46
+0.03478179999999753,1.0796704855858055,2000,ApEn,nk_entropy_approximate,46
+0.012973799999997482,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,46
+0.0007912999999959425,1.5299377592545462,2000,DiffEn,nk_entropy_differential,46
+0.9171497000000031,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,46
+0.06087859999999523,0.875455134560752,2000,MSE,nk_complexity_mse,46
+5.398836700000004,0.5421120627391649,2000,CMSE,nk_complexity_cmse,46
+5.1461538999999945,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,46
+0.4886562000000012,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,46
+3.0881965000000022,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,46
+2.970656599999998,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,46
+0.00027399999999744296,0.9995642134676757,2000,PEn,nk_complexity_pe,46
+0.013825199999999427,0.9974282038230982,2000,WPEn,nk_complexity_wpe,46
+0.01190210000000036,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,46
+0.9597792000000069,1.3373695971539579,2000,RangeEn,nk_entropy_range_mSampEn,46
+0.9662827000000007,inf,2000,RangeEn,nk_entropy_range_mApEn,46
+0.025749300000001085,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,46
+0.00236590000000092,10.965784284662085,2000,ShanEn,nk_entropy_shannon,46
+0.004918899999999837,0.8465376735420068,2000,SpEn,nk_entropy_spectral,46
+0.000555900000001941,0.9927605014360844,2000,SVDen,nk_entropy_svd,46
+2.443909600000005,2.132371401023081,2000,CD,nk_fractal_correlation,46
+0.1957736999999966,0.9905916277925066,2000,DFA,nk_fractal_dfa,46
+0.20858060000000478,[1.0048312  1.01243887 1.01538856 1.00258515 0.97610769 0.94421012],2000,MFDFA,nk_fractal_mfdfa,46
+0.0022024999999956663,1.9990408568403804,2000,HFD,nk_fractal_higuchi,46
+0.0001716000000016038,4.331735672660552,2000,Katz,nk_fractal_katz,46
+0.010285400000000777,1.8040203537176212,2000,NLD,nk_fractal_nld,46
+9.690000000261989e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,46
+0.007193700000001968,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,46
+0.010218100000003005,0.691035660416629,2000,SDA,nk_fractal_sda,46
+0.00029479999999892925,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,46
+0.00024959999999651927,0.018215273069519526,2000,Fisher,nk_fisher_information,46
+0.0002535000000030152,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,47
+0.3090863000000006,0.8815000687731794,2000,Hurst,nk_complexity_hurst,47
+0.15320859999999925,0.99788636990425,2000,LZC,nk_complexity_lzc,47
+0.22906520000000086,0.9692679097822192,2000,PLZC,nk_complexity_plzc,47
+0.36147040000000175,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,47
+0.677279200000001,0.03482840312535622,2000,Lyapunov,nk_complexity_lyapunov,47
+0.004107600000004652,1.6061378306189726,2000,RR,nk_complexity_rr,47
+0.0525957000000048,1.0796704855858055,2000,ApEn,nk_entropy_approximate,47
+0.016113400000001832,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,47
+0.0009168000000059351,1.5299377592545462,2000,DiffEn,nk_entropy_differential,47
+0.9066706999999994,0.9931171126315198,2000,FuzzyEn,nk_entropy_fuzzy,47
+0.061939999999999884,0.875455134560752,2000,MSE,nk_complexity_mse,47
+5.466874799999999,0.5421120627391649,2000,CMSE,nk_complexity_cmse,47
+5.201330800000001,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,47
+0.501099399999994,0.4103695051357232,2000,FuzzyMSE,nk_complexity_fuzzymse,47
+3.035005499999997,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,47
+2.9525918000000004,0.32380003462994705,2000,FuzzyRCMSE,nk_complexity_fuzzyrcmse,47
+0.00027200000000249247,0.9995642134676757,2000,PEn,nk_complexity_pe,47
+0.01382939999999877,0.9974282038230982,2000,WPEn,nk_complexity_wpe,47
+0.0119883999999999,0.8858210047821046,2000,MSPEn,nk_complexity_mspe,47
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+0.0022908999999984303,10.965784284662085,2000,ShanEn,nk_entropy_shannon,47
+0.0046330999999995015,0.8465376735420068,2000,SpEn,nk_entropy_spectral,47
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+0.009161900000002277,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,47
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+0.0003089000000002784,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,47
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+0.00025059999999754723,1.3615826333453833,2000,Hjorth,nk_complexity_hjorth,48
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+0.16447089999999776,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,48
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+0.01101709999999656,0.030279938087174496,2000,CREn,nk_entropy_cumulative_residual,48
+0.0010756000000000654,1.5299377592545462,2000,DiffEn,nk_entropy_differential,48
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+8.683983500000004,0.5421120627391649,2000,CMSE,nk_complexity_cmse,48
+8.945774100000001,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,48
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+5.690428599999997,0.32428580855237027,2000,FuzzyCMSE,nk_complexity_fuzzycmse,48
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+0.01384859999999577,0.9974282038230982,2000,WPEn,nk_complexity_wpe,48
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+0.9424953999999985,inf,2000,RangeEn,nk_entropy_range_mApEn,48
+0.034947199999997736,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,48
+0.0011762000000032913,10.965784284662085,2000,ShanEn,nk_entropy_shannon,48
+0.0026233000000033257,0.8465376735420068,2000,SpEn,nk_entropy_spectral,48
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+0.00011190000000027567,4.331735672660552,2000,Katz,nk_fractal_katz,48
+0.009036799999996958,1.8040203537176212,2000,NLD,nk_fractal_nld,48
+6.619999999912807e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,48
+0.004812699999995118,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,48
+0.009381799999999885,0.691035660416629,2000,SDA,nk_fractal_sda,48
+0.00014060000000171158,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,48
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+0.11424470000000042,0.9692679097822192,2000,PLZC,nk_complexity_plzc,49
+0.16918730000000082,0.7322589602225493,2000,MPLZC,nk_complexity_mplzc,49
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+0.0018652000000045632,1.5299377592545462,2000,DiffEn,nk_entropy_differential,49
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+9.157165400000004,0.5421120627391649,2000,CMSE,nk_complexity_cmse,49
+8.9901463,0.5283400799952006,2000,RCMSE,nk_complexity_rcmse,49
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+0.939686199999997,inf,2000,RangeEn,nk_entropy_range_mApEn,49
+0.02469289999999802,1.96072244013389,2000,RangeEn_mApEn,nk_entropy_sample,49
+0.001282799999998474,10.965784284662085,2000,ShanEn,nk_entropy_shannon,49
+0.002777799999996944,0.8465376735420068,2000,SpEn,nk_entropy_spectral,49
+0.00020500000000112095,0.9927605014360844,2000,SVDen,nk_entropy_svd,49
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+0.1807881000000009,0.9905916277925066,2000,DFA,nk_fractal_dfa,49
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+0.00010449999999195825,4.331735672660552,2000,Katz,nk_fractal_katz,49
+0.00895280000000298,1.8040203537176212,2000,NLD,nk_fractal_nld,49
+5.640000000539658e-05,1.0211150486568212,2000,Petrosian,nk_fractal_petrosian,49
+0.0044043000000044685,2.548723843847367,2000,PSDslope,nk_fractal_psdslope,49
+0.009336399999995137,0.691035660416629,2000,SDA,nk_fractal_sda,49
+0.00016109999999969205,1.6357431816746955,2000,Sevcik,nk_fractal_sevcik,49
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+29.673998299999766,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,0
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+4.825459300000006,0.9427602513579317,12421,LZC,nk_complexity_lzc,1
+7.503572399999996,0.9569580138119792,12421,PLZC,nk_complexity_plzc,1
+6.196569400000001,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,1
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+0.2726447999998527,1.529570509042314,12421,DiffEn,nk_entropy_differential,1
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+298.10341400000016,0.25586340033253263,12421,RCMSE,nk_complexity_rcmse,1
+39.25697160000027,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,1
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+0.08142000000043481,0.8612920360386087,12421,MSPEn,nk_complexity_mspe,1
+29.893663899999865,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,1
+29.87887159999991,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,1
+0.28015700000014476,1.967993753861321,12421,RangeEn_mApEn,nk_entropy_sample,1
+0.06406890000016574,13.600493707452811,12421,ShanEn,nk_entropy_shannon,1
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+0.0007292999999890526,1.3674116863977033,12421,Hjorth,nk_complexity_hjorth,2
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+4.979428999999996,0.9427602513579317,12421,LZC,nk_complexity_lzc,2
+7.679897499999996,0.9569580138119792,12421,PLZC,nk_complexity_plzc,2
+9.142399499999996,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,2
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+0.7813912000001437,1.529570509042314,12421,DiffEn,nk_entropy_differential,2
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+221.3511669999998,0.2580890780837671,12421,CMSE,nk_complexity_cmse,2
+227.38707290000002,0.25586340033253263,12421,RCMSE,nk_complexity_rcmse,2
+21.231070099999897,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,2
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+40.8516267,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,2
+30.960549400000218,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,2
+0.613956700000017,1.967993753861321,12421,RangeEn_mApEn,nk_entropy_sample,2
+0.062187099999846396,13.600493707452811,12421,ShanEn,nk_entropy_shannon,2
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+0.13293489999978192,1.8217236761199127,12421,NLD,nk_fractal_nld,2
+0.0002678000000742031,1.0163709528562037,12421,Petrosian,nk_fractal_petrosian,2
+0.06950750000032713,2.507678005229455,12421,PSDslope,nk_fractal_psdslope,2
+0.16105659999993804,0.7512984048750675,12421,SDA,nk_fractal_sda,2
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+0.2107258999999999,1.0318115896477018,12421,Hurst,nk_complexity_hurst,3
+2.3772724999999895,0.9427602513579317,12421,LZC,nk_complexity_lzc,3
+3.7766914000000043,0.9569580138119792,12421,PLZC,nk_complexity_plzc,3
+4.697942800000007,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,3
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+38.283812500000295,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,5
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+32.592905900000005,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,6
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+40.80522380000002,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,6
+41.10310410000011,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,6
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+32.145336900000075,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,7
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+5.815885100000003,0.9569580138119792,12421,PLZC,nk_complexity_plzc,8
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+9.1356969,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,12
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+9.251456900000008,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,13
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+1.0513539000000947,1.529570509042314,12421,DiffEn,nk_entropy_differential,14
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+39.896669499999916,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,16
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+0.05370509999966089,13.600493707452811,12421,ShanEn,nk_entropy_shannon,19
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+15.932674799999859,0.9969456853242545,12421,FuzzyEn,nk_entropy_fuzzy,20
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+40.07798899999989,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,20
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+29.804724999999962,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,20
+29.678769799999827,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,20
+0.2793781000000308,1.967993753861321,12421,RangeEn_mApEn,nk_entropy_sample,20
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+4.906938199999999,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,21
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+0.0021681000000626227,1.529570509042314,12421,DiffEn,nk_entropy_differential,21
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+35.83548070000006,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,21
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+41.16178690000015,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,21
+46.938735599999745,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,21
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+6.208756600000015,0.8323446903619242,12421,SpEn,nk_entropy_spectral,21
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+91.90469949999988,2.204715912197064,12421,CD,nk_fractal_correlation,21
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+0.05206219999990935,1.8217236761199127,12421,NLD,nk_fractal_nld,21
+0.00014039999996384722,1.0163709528562037,12421,Petrosian,nk_fractal_petrosian,21
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+4.9451138000000014,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,22
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+288.58346759999995,0.25586340033253263,12421,RCMSE,nk_complexity_rcmse,22
+35.20459230000006,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,22
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+30.980211099999906,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,22
+31.079332400000112,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,22
+0.28446930000018256,1.967993753861321,12421,RangeEn_mApEn,nk_entropy_sample,22
+0.005281799999920622,13.600493707452811,12421,ShanEn,nk_entropy_shannon,22
+0.1252082999999402,0.8323446903619242,12421,SpEn,nk_entropy_spectral,22
+0.001071800000318035,0.9927803730087326,12421,SVDen,nk_entropy_svd,22
+39.19265989999985,2.204715912197064,12421,CD,nk_fractal_correlation,22
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+0.00010430000020278385,1.0163709528562037,12421,Petrosian,nk_fractal_petrosian,22
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+0.0003876999999192776,1.702149180726762,12421,Sevcik,nk_fractal_sevcik,22
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+2.4643106999999986,0.9427602513579317,12421,LZC,nk_complexity_lzc,23
+3.905264900000006,0.9569580138119792,12421,PLZC,nk_complexity_plzc,23
+4.846543699999998,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,23
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+2.8184115999999904,1.694180881801623,12421,ApEn,nk_entropy_approximate,23
+2.3309635000000526,-0.0008177172346955364,12421,CREn,nk_entropy_cumulative_residual,23
+0.2997104000000945,1.529570509042314,12421,DiffEn,nk_entropy_differential,23
+12.524800299999924,0.9969456853242545,12421,FuzzyEn,nk_entropy_fuzzy,23
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+276.83809210000004,0.2580890780837671,12421,CMSE,nk_complexity_cmse,23
+273.5649338999999,0.25586340033253263,12421,RCMSE,nk_complexity_rcmse,23
+46.417638200000056,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,23
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+30.410779000000275,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,23
+30.30028379999976,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,23
+0.2931852000001527,1.967993753861321,12421,RangeEn_mApEn,nk_entropy_sample,23
+0.010854699999981676,13.600493707452811,12421,ShanEn,nk_entropy_shannon,23
+0.22402800000008938,0.8323446903619242,12421,SpEn,nk_entropy_spectral,23
+0.005632300000343093,0.9927803730087326,12421,SVDen,nk_entropy_svd,23
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+0.014551400000073045,2.0000960096260543,12421,HFD,nk_fractal_higuchi,23
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+0.07138839999970514,1.8217236761199127,12421,NLD,nk_fractal_nld,23
+0.00012110000034226687,1.0163709528562037,12421,Petrosian,nk_fractal_petrosian,23
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+0.0007178999999979396,1.3674116863977033,12421,Hjorth,nk_complexity_hjorth,24
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+2.4639292000000097,0.9427602513579317,12421,LZC,nk_complexity_lzc,24
+3.905367499999997,0.9569580138119792,12421,PLZC,nk_complexity_plzc,24
+4.845295000000007,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,24
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+0.11234740000008969,1.529570509042314,12421,DiffEn,nk_entropy_differential,24
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+275.450053,0.25586340033253263,12421,RCMSE,nk_complexity_rcmse,24
+41.5936056999999,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,24
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+0.09680310000021564,0.998682364217253,12421,WPEn,nk_complexity_wpe,24
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+29.894467799999802,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,24
+29.730886000000282,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,24
+0.2785014000000956,1.967993753861321,12421,RangeEn_mApEn,nk_entropy_sample,24
+0.008318899999721907,13.600493707452811,12421,ShanEn,nk_entropy_shannon,24
+0.24738079999997353,0.8323446903619242,12421,SpEn,nk_entropy_spectral,24
+0.004130099999656522,0.9927803730087326,12421,SVDen,nk_entropy_svd,24
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+2.2552695999997923,1.1637199849558086,12421,DFA,nk_fractal_dfa,24
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+0.014186500000050728,2.0000960096260543,12421,HFD,nk_fractal_higuchi,24
+0.0003224000001864624,5.5308729996176815,12421,Katz,nk_fractal_katz,24
+0.0721915999997691,1.8217236761199127,12421,NLD,nk_fractal_nld,24
+8.090000028460054e-05,1.0163709528562037,12421,Petrosian,nk_fractal_petrosian,24
+0.055536400000164576,2.507678005229455,12421,PSDslope,nk_fractal_psdslope,24
+0.07077690000005532,0.7512984048750675,12421,SDA,nk_fractal_sda,24
+0.009386600000198086,1.702149180726762,12421,Sevcik,nk_fractal_sevcik,24
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+0.0007389999999958263,1.3674116863977033,12421,Hjorth,nk_complexity_hjorth,25
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+4.7630841,0.9427602513579317,12421,LZC,nk_complexity_lzc,25
+7.401798499999998,0.9569580138119792,12421,PLZC,nk_complexity_plzc,25
+8.9136192,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,25
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+8.070558099999971,1.694180881801623,12421,ApEn,nk_entropy_approximate,25
+14.225740200000018,-0.0008177172346955364,12421,CREn,nk_entropy_cumulative_residual,25
+1.9239024000000882,1.529570509042314,12421,DiffEn,nk_entropy_differential,25
+10.001059599999962,0.9969456853242545,12421,FuzzyEn,nk_entropy_fuzzy,25
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+225.73863670000014,0.2580890780837671,12421,CMSE,nk_complexity_cmse,25
+282.7248710000001,0.25586340033253263,12421,RCMSE,nk_complexity_rcmse,25
+31.51946679999992,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,25
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+36.80778689999988,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,25
+31.986033800000314,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,25
+0.29009080000014364,1.967993753861321,12421,RangeEn_mApEn,nk_entropy_sample,25
+0.16203760000007605,13.600493707452811,12421,ShanEn,nk_entropy_shannon,25
+0.40947890000006737,0.8323446903619242,12421,SpEn,nk_entropy_spectral,25
+0.01944210000010571,0.9927803730087326,12421,SVDen,nk_entropy_svd,25
+67.13983330000019,2.204715912197064,12421,CD,nk_fractal_correlation,25
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+0.06337579999990339,2.0000960096260543,12421,HFD,nk_fractal_higuchi,25
+0.0003670000000965956,5.5308729996176815,12421,Katz,nk_fractal_katz,25
+0.07959960000016508,1.8217236761199127,12421,NLD,nk_fractal_nld,25
+0.00012619999961316353,1.0163709528562037,12421,Petrosian,nk_fractal_petrosian,25
+0.041840899999897374,2.507678005229455,12421,PSDslope,nk_fractal_psdslope,25
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+4.701207100000005,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,26
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+29.62648999999965,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,26
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+4.701406299999988,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,27
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+34.0381425999999,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,27
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+29.65759190000017,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,27
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+42.98606010000003,2.204715912197064,12421,CD,nk_fractal_correlation,27
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+3.783668000000006,0.9569580138119792,12421,PLZC,nk_complexity_plzc,28
+4.704725600000003,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,28
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+0.351935899999944,1.529570509042314,12421,DiffEn,nk_entropy_differential,28
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+30.579285600000276,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,28
+30.092670199999702,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,28
+0.2801574999998593,1.967993753861321,12421,RangeEn_mApEn,nk_entropy_sample,28
+0.0036625000002459274,13.600493707452811,12421,ShanEn,nk_entropy_shannon,28
+0.23642119999976785,0.8323446903619242,12421,SpEn,nk_entropy_spectral,28
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+0.0535994000001665,1.8217236761199127,12421,NLD,nk_fractal_nld,28
+8.32000000627886e-05,1.0163709528562037,12421,Petrosian,nk_fractal_petrosian,28
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+0.0003050000000257569,1.702149180726762,12421,Sevcik,nk_fractal_sevcik,28
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+0.29965820000006715,1.529570509042314,12421,DiffEn,nk_entropy_differential,29
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+228.9059335999998,0.25586340033253263,12421,RCMSE,nk_complexity_rcmse,29
+31.234474999999975,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,29
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+34.60668580000038,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,29
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+0.2901372999999694,1.967993753861321,12421,RangeEn_mApEn,nk_entropy_sample,29
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+0.08815800000002127,1.8217236761199127,12421,NLD,nk_fractal_nld,29
+0.00028449999990698416,1.0163709528562037,12421,Petrosian,nk_fractal_petrosian,29
+0.03006649999997535,2.507678005229455,12421,PSDslope,nk_fractal_psdslope,29
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+0.0007817000000613916,1.702149180726762,12421,Sevcik,nk_fractal_sevcik,29
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+0.25486089999999706,1.0318115896477018,12421,Hurst,nk_complexity_hurst,30
+4.83800140000001,0.9427602513579317,12421,LZC,nk_complexity_lzc,30
+6.022145600000002,0.9569580138119792,12421,PLZC,nk_complexity_plzc,30
+8.708715900000001,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,30
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+0.002194299999928262,1.529570509042314,12421,DiffEn,nk_entropy_differential,30
+9.730288299999984,0.9969456853242545,12421,FuzzyEn,nk_entropy_fuzzy,30
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+231.61771580000004,0.2580890780837671,12421,CMSE,nk_complexity_cmse,30
+281.09100979999994,0.25586340033253263,12421,RCMSE,nk_complexity_rcmse,30
+26.757373400000233,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,30
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+0.09546030000001338,0.998682364217253,12421,WPEn,nk_complexity_wpe,30
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+30.209511900000052,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,30
+30.39152120000017,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,30
+0.2803957999999511,1.967993753861321,12421,RangeEn_mApEn,nk_entropy_sample,30
+0.00361830000019836,13.600493707452811,12421,ShanEn,nk_entropy_shannon,30
+0.13551189999998314,0.8323446903619242,12421,SpEn,nk_entropy_spectral,30
+0.009365299999899435,0.9927803730087326,12421,SVDen,nk_entropy_svd,30
+33.75919930000009,2.204715912197064,12421,CD,nk_fractal_correlation,30
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+0.00034370000003036694,5.5308729996176815,12421,Katz,nk_fractal_katz,30
+0.052966400000059366,1.8217236761199127,12421,NLD,nk_fractal_nld,30
+9.949999957825639e-05,1.0163709528562037,12421,Petrosian,nk_fractal_petrosian,30
+0.008777999999892927,2.507678005229455,12421,PSDslope,nk_fractal_psdslope,30
+0.06288150000000314,0.7512984048750675,12421,SDA,nk_fractal_sda,30
+0.00038080000012996607,1.702149180726762,12421,Sevcik,nk_fractal_sevcik,30
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+0.0006755999999938922,1.3674116863977033,12421,Hjorth,nk_complexity_hjorth,31
+0.22546429999999873,1.0318115896477018,12421,Hurst,nk_complexity_hurst,31
+4.832222099999996,0.9427602513579317,12421,LZC,nk_complexity_lzc,31
+6.81164969999999,0.9569580138119792,12421,PLZC,nk_complexity_plzc,31
+4.63035450000001,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,31
+1548.5065679,0.043709895215556606,12421,Lyapunov,nk_complexity_lyapunov,31
+1.0152184000000943,1.6041911526957384,12421,RR,nk_complexity_rr,31
+1.1240619000000152,1.694180881801623,12421,ApEn,nk_entropy_approximate,31
+1.2233432000000448,-0.0008177172346955364,12421,CREn,nk_entropy_cumulative_residual,31
+0.07524409999996351,1.529570509042314,12421,DiffEn,nk_entropy_differential,31
+9.406016399999999,0.9969456853242545,12421,FuzzyEn,nk_entropy_fuzzy,31
+2.3469497999999476,1.1036096559584527,12421,MSE,nk_complexity_mse,31
+241.26271429999997,0.2580890780837671,12421,CMSE,nk_complexity_cmse,31
+285.71014149999996,0.25586340033253263,12421,RCMSE,nk_complexity_rcmse,31
+40.02034189999995,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,31
+212.2004962000001,0.23915884493316017,12421,FuzzyCMSE,nk_complexity_fuzzycmse,31
+205.2978449999996,0.2393988785463466,12421,FuzzyRCMSE,nk_complexity_fuzzyrcmse,31
+0.05054829999971844,0.9996245769755556,12421,PEn,nk_complexity_pe,31
+0.10824920000004568,0.998682364217253,12421,WPEn,nk_complexity_wpe,31
+0.08291509999980917,0.8612920360386087,12421,MSPEn,nk_complexity_mspe,31
+30.30626919999986,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,31
+29.816617599999972,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,31
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+35.20120910000014,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,33
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+29.835214799999903,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,34
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+3.795822700000002,0.9569580138119792,12421,PLZC,nk_complexity_plzc,36
+4.716877600000004,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,36
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+0.12938039999971807,1.8217236761199127,12421,NLD,nk_fractal_nld,36
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+0.05661079999981666,2.507678005229455,12421,PSDslope,nk_fractal_psdslope,36
+0.06599840000035329,0.7512984048750675,12421,SDA,nk_fractal_sda,36
+0.002165299999887793,1.702149180726762,12421,Sevcik,nk_fractal_sevcik,36
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+0.25247549999998853,1.0318115896477018,12421,Hurst,nk_complexity_hurst,37
+4.8396604,0.9427602513579317,12421,LZC,nk_complexity_lzc,37
+5.997664700000001,0.9569580138119792,12421,PLZC,nk_complexity_plzc,37
+4.616918600000005,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,37
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+32.814776200000324,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,41
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+4.624300100000255,0.8323446903619242,12421,SpEn,nk_entropy_spectral,41
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+0.10706060000029538,1.8217236761199127,12421,NLD,nk_fractal_nld,41
+0.00016860000005181064,1.0163709528562037,12421,Petrosian,nk_fractal_petrosian,41
+0.05975560000024416,2.507678005229455,12421,PSDslope,nk_fractal_psdslope,41
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+8.554702699999993,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,42
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+61.088372400000026,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,42
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+29.77399289999994,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,42
+29.854395999999724,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,42
+0.2812438999999358,1.967993753861321,12421,RangeEn_mApEn,nk_entropy_sample,42
+0.05760289999989254,13.600493707452811,12421,ShanEn,nk_entropy_shannon,42
+0.2015931000000819,0.8323446903619242,12421,SpEn,nk_entropy_spectral,42
+0.007177400000273337,0.9927803730087326,12421,SVDen,nk_entropy_svd,42
+33.29459309999993,2.204715912197064,12421,CD,nk_fractal_correlation,42
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+39.010008899999775,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,47
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+0.6811407000000145,1.529570509042314,12421,DiffEn,nk_entropy_differential,48
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+47.06863859999976,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,48
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+0.09512230000018462,0.998682364217253,12421,WPEn,nk_complexity_wpe,48
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+29.393327300000237,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,48
+29.362536600000112,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,48
+0.27911020000010467,1.967993753861321,12421,RangeEn_mApEn,nk_entropy_sample,48
+0.011980499999936,13.600493707452811,12421,ShanEn,nk_entropy_shannon,48
+0.2509471999997004,0.8323446903619242,12421,SpEn,nk_entropy_spectral,48
+0.010797299999921961,0.9927803730087326,12421,SVDen,nk_entropy_svd,48
+29.76277970000001,2.204715912197064,12421,CD,nk_fractal_correlation,48
+2.4349732999999105,1.1637199849558086,12421,DFA,nk_fractal_dfa,48
+1.9312411999999313,[1.12839659 1.13814752 1.15702378 1.1652607  1.15977908 1.14721293],12421,MFDFA,nk_fractal_mfdfa,48
+0.019984300000032817,2.0000960096260543,12421,HFD,nk_fractal_higuchi,48
+0.0003754999997909181,5.5308729996176815,12421,Katz,nk_fractal_katz,48
+0.06818010000006325,1.8217236761199127,12421,NLD,nk_fractal_nld,48
+0.00014850000025035115,1.0163709528562037,12421,Petrosian,nk_fractal_petrosian,48
+0.033284699999967415,2.507678005229455,12421,PSDslope,nk_fractal_psdslope,48
+0.0658235999999306,0.7512984048750675,12421,SDA,nk_fractal_sda,48
+0.006418000000394386,1.702149180726762,12421,Sevcik,nk_fractal_sevcik,48
+0.000320099999953527,0.018167624410263664,12421,Fisher,nk_fisher_information,48
+0.00035619999999880747,1.3674116863977033,12421,Hjorth,nk_complexity_hjorth,49
+0.1467236000000014,1.0318115896477018,12421,Hurst,nk_complexity_hurst,49
+4.894372799999999,0.9427602513579317,12421,LZC,nk_complexity_lzc,49
+7.687721299999993,0.9569580138119792,12421,PLZC,nk_complexity_plzc,49
+8.340358899999998,0.6552603660905415,12421,MPLZC,nk_complexity_mplzc,49
+1574.8720735,0.043709895215556606,12421,Lyapunov,nk_complexity_lyapunov,49
+2.659063999999944,1.6041911526957384,12421,RR,nk_complexity_rr,49
+1.8003383000000213,1.694180881801623,12421,ApEn,nk_entropy_approximate,49
+3.2515805999998975,-0.0008177172346955364,12421,CREn,nk_entropy_cumulative_residual,49
+0.7245609999999942,1.529570509042314,12421,DiffEn,nk_entropy_differential,49
+15.091057699999965,0.9969456853242545,12421,FuzzyEn,nk_entropy_fuzzy,49
+2.51674509999998,1.1036096559584527,12421,MSE,nk_complexity_mse,49
+277.88377130000003,0.2580890780837671,12421,CMSE,nk_complexity_cmse,49
+298.1119066000001,0.25586340033253263,12421,RCMSE,nk_complexity_rcmse,49
+59.12068429999999,0.45428590663791013,12421,FuzzyMSE,nk_complexity_fuzzymse,49
+215.12441489999992,0.23915884493316017,12421,FuzzyCMSE,nk_complexity_fuzzycmse,49
+217.82748539999966,0.2393988785463466,12421,FuzzyRCMSE,nk_complexity_fuzzyrcmse,49
+0.06924089999984062,0.9996245769755556,12421,PEn,nk_complexity_pe,49
+0.09673900000007052,0.998682364217253,12421,WPEn,nk_complexity_wpe,49
+0.08265319999964049,0.8612920360386087,12421,MSPEn,nk_complexity_mspe,49
+30.29381280000007,1.304809539197346,12421,RangeEn,nk_entropy_range_mSampEn,49
+30.032645299999785,1.3354518602738343,12421,RangeEn,nk_entropy_range_mApEn,49
+0.2804612999998426,1.967993753861321,12421,RangeEn_mApEn,nk_entropy_sample,49
+0.023671000000376807,13.600493707452811,12421,ShanEn,nk_entropy_shannon,49
+0.19721869999966657,0.8323446903619242,12421,SpEn,nk_entropy_spectral,49
+0.001037699999869801,0.9927803730087326,12421,SVDen,nk_entropy_svd,49
+34.796487499999785,2.204715912197064,12421,CD,nk_fractal_correlation,49
+2.4769952000001467,1.1637199849558086,12421,DFA,nk_fractal_dfa,49
+1.9307381999997233,[1.12839659 1.13814752 1.15702378 1.1652607  1.15977908 1.14721293],12421,MFDFA,nk_fractal_mfdfa,49
+0.007948199999646022,2.0000960096260543,12421,HFD,nk_fractal_higuchi,49
+0.00020670000003519817,5.5308729996176815,12421,Katz,nk_fractal_katz,49
+0.07545129999971323,1.8217236761199127,12421,NLD,nk_fractal_nld,49
+0.000136099999963335,1.0163709528562037,12421,Petrosian,nk_fractal_petrosian,49
+0.016838799999732146,2.507678005229455,12421,PSDslope,nk_fractal_psdslope,49
+0.06662890000006882,0.7512984048750675,12421,SDA,nk_fractal_sda,49
+0.008090100000117673,1.702149180726762,12421,Sevcik,nk_fractal_sevcik,49
+0.0003123999999843363,0.018167624410263664,12421,Fisher,nk_fisher_information,49


Test patch:
diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 65cc1d0151..c6bd12b142 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -19,10 +19,7 @@ jobs:
           - ubuntu-latest  # ubuntu-18.04
         #  - macos-latest  # macOS-10.14
         #  - windows-latest  # windows-2019
-        python-version: [3.7, 3.8, 3.9]
-        #exclude:
-        #  - platform: windows-latest
-        #    python-version: 3.8
+        python-version: ["3.7", "3.8", "3.9", "3.10"]
 
     steps:
     - uses: actions/checkout@master
diff --git a/tests/tests_eeg.py b/tests/tests_eeg.py
index 8f1fe2e644..ab8c67bfea 100644
--- a/tests/tests_eeg.py
+++ b/tests/tests_eeg.py
@@ -1,5 +1,6 @@
 import mne
 import numpy as np
+import pooch
 
 import neurokit2 as nk
 
@@ -10,13 +11,17 @@
 
 def test_eeg_add_channel():
 
-    raw = mne.io.read_raw_fif(mne.datasets.sample.data_path() + "/MEG/sample/sample_audvis_raw.fif", preload=True)
+    raw = mne.io.read_raw_fif(
+        mne.datasets.sample.data_path() + "/MEG/sample/sample_audvis_raw.fif", preload=True
+    )
 
     # len(channel) > len(raw)
     ecg1 = nk.ecg_simulate(length=170000)
 
     # sync_index_raw > sync_index_channel
-    raw1 = nk.mne_channel_add(raw.copy(), ecg1, channel_type="ecg", sync_index_raw=100, sync_index_channel=0)
+    raw1 = nk.mne_channel_add(
+        raw.copy(), ecg1, channel_type="ecg", sync_index_raw=100, sync_index_channel=0
+    )
     df1 = raw1.to_data_frame()
 
     # test if the column of channel is added
@@ -33,7 +38,9 @@ def test_eeg_add_channel():
     ecg2 = nk.ecg_simulate(length=166790)
 
     # sync_index_raw < sync_index_channel
-    raw2 = nk.mne_channel_add(raw.copy(), ecg2, channel_type="ecg", sync_index_raw=0, sync_index_channel=100)
+    raw2 = nk.mne_channel_add(
+        raw.copy(), ecg2, channel_type="ecg", sync_index_raw=0, sync_index_channel=100
+    )
     df2 = raw2.to_data_frame()
 
     # test if the column of channel is added
@@ -42,16 +49,22 @@ def test_eeg_add_channel():
     # test if the NaN is appended properly to the added channel to account for difference in distance between two signals + difference in length
     sync_index_raw = 0
     sync_index_channel = 100
-    for i in df2["Added_Channel"].tail(abs(sync_index_channel - sync_index_raw) + (len(raw) - len(ecg2))):
+    for i in df2["Added_Channel"].tail(
+        abs(sync_index_channel - sync_index_raw) + (len(raw) - len(ecg2))
+    ):
         assert np.isnan(i)
     assert np.isfinite(
-        df2["Added_Channel"].iloc[-abs(sync_index_channel - sync_index_raw) - (len(raw) - len(ecg2)) - 1]
+        df2["Added_Channel"].iloc[
+            -abs(sync_index_channel - sync_index_raw) - (len(raw) - len(ecg2)) - 1
+        ]
     )
 
 
 def test_mne_channel_extract():
 
-    raw = mne.io.read_raw_fif(mne.datasets.sample.data_path() + "/MEG/sample/sample_audvis_raw.fif", preload=True)
+    raw = mne.io.read_raw_fif(
+        mne.datasets.sample.data_path() + "/MEG/sample/sample_audvis_raw.fif", preload=True
+    )
 
     # Extract 1 channel
     what = "EEG 053"
@@ -78,25 +91,29 @@ def test_mne_channel_extract():
 
 def test_mne_to_df():
 
-    raw = mne.io.read_raw_fif(mne.datasets.sample.data_path() + '/MEG/sample/sample_audvis_filt-0-40_raw.fif')
+    raw = mne.io.read_raw_fif(
+        mne.datasets.sample.data_path() + "/MEG/sample/sample_audvis_filt-0-40_raw.fif"
+    )
     assert len(nk.mne_to_df(raw)) == 41700
 
-
-    events = mne.read_events(mne.datasets.sample.data_path() + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif')
-    event_id = {'audio/left': 1, 'audio/right': 2,
-                'visual/left': 3, 'visual/right': 4}
+    events = mne.read_events(
+        mne.datasets.sample.data_path() + "/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif"
+    )
+    event_id = {"audio/left": 1, "audio/right": 2, "visual/left": 3, "visual/right": 4}
 
     # Create epochs (100 ms baseline + 500 ms)
-    epochs = mne.Epochs(raw,
-                        events,
-                        event_id,
-                        tmin=-0.1,
-                        tmax=0.5,
-                        picks='eeg',
-                        preload=True,
-                        detrend=0,
-                        baseline=(None, 0))
+    epochs = mne.Epochs(
+        raw,
+        events,
+        event_id,
+        tmin=-0.1,
+        tmax=0.5,
+        picks="eeg",
+        preload=True,
+        detrend=0,
+        baseline=(None, 0),
+    )
     assert len(nk.mne_to_df(epochs)) == 26208
 
-    evoked = [epochs[name].average() for name in ('audio', 'visual')]
+    evoked = [epochs[name].average() for name in ("audio", "visual")]
     assert len(nk.mne_to_df(evoked)) == 182