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neuropsychology · Jul 22, 2024 · d19af31 · d19af31
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diff --git a/.buildinfo b/.buildinfo
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 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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+config: 547af28cce977486a0fb22c0a3782d2b
 tags: 645f666f9bcd5a90fca523b33c5a78b7
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diff --git a/_images/p_signal_psd4.png b/_images/p_signal_psd4.png
diff --git a/_images/p_signal_psd5.png b/_images/p_signal_psd5.png
diff --git a/_images/p_signal_recompose1.png b/_images/p_signal_recompose1.png
diff --git a/_images/p_signal_smooth1.png b/_images/p_signal_smooth1.png
diff --git a/_images/p_signal_smooth2.png b/_images/p_signal_smooth2.png
diff --git a/_images/p_signal_surrogate1.png b/_images/p_signal_surrogate1.png
diff --git a/_images/p_signal_surrogate2.png b/_images/p_signal_surrogate2.png
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diff --git a/_modules/neurokit2/eda/eda_intervalrelated.html b/_modules/neurokit2/eda/eda_intervalrelated.html
@@ -399,6 +399,7 @@ <h1>Source code for neurokit2.eda.eda_intervalrelated</h1><div class="highlight"
 
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
+
 <span class="sd">    data : Union[dict, pd.DataFrame]</span>
 <span class="sd">        A DataFrame containing the different processed signal(s) as different columns, typically</span>
 <span class="sd">        generated by :func:`eda_process` or :func:`bio_process`. Can also take a dict containing</span>
@@ -413,6 +414,7 @@ <h1>Source code for neurokit2.eda.eda_intervalrelated</h1><div class="highlight"
 <span class="sd">    DataFrame</span>
 <span class="sd">        A dataframe containing the analyzed EDA features. The analyzed</span>
 <span class="sd">        features consist of the following:</span>
+
 <span class="sd">        * ``&quot;SCR_Peaks_N&quot;``: the number of occurrences of Skin Conductance Response (SCR).</span>
 <span class="sd">        * ``&quot;SCR_Peaks_Amplitude_Mean&quot;``: the mean amplitude of the SCR peak occurrences.</span>
 <span class="sd">        * ``&quot;EDA_Tonic_SD&quot;``: the mean amplitude of the SCR peak occurrences.</span>
@@ -426,22 +428,22 @@ <h1>Source code for neurokit2.eda.eda_intervalrelated</h1><div class="highlight"
 <span class="sd">    .bio_process, eda_eventrelated</span>
 
 <span class="sd">    Examples</span>
-<span class="sd">    ----------</span>
+<span class="sd">    --------</span>
 <span class="sd">    .. ipython:: python</span>
 
-<span class="sd">      import neurokit2 as nk</span>
+<span class="sd">        import neurokit2 as nk</span>
 
-<span class="sd">      # Download data</span>
-<span class="sd">      data = nk.data(&quot;bio_resting_8min_100hz&quot;)</span>
+<span class="sd">        # Download data</span>
+<span class="sd">        data = nk.data(&quot;bio_resting_8min_100hz&quot;)</span>
 
-<span class="sd">      # Process the data</span>
-<span class="sd">      df, info = nk.eda_process(data[&quot;EDA&quot;], sampling_rate=100)</span>
+<span class="sd">        # Process the data</span>
+<span class="sd">        df, info = nk.eda_process(data[&quot;EDA&quot;], sampling_rate=100)</span>
 
-<span class="sd">      # Single dataframe is passed</span>
-<span class="sd">      nk.eda_intervalrelated(df, sampling_rate=100)</span>
+<span class="sd">        # Single dataframe is passed</span>
+<span class="sd">        nk.eda_intervalrelated(df, sampling_rate=100)</span>
 
-<span class="sd">      epochs = nk.epochs_create(df, events=[0, 25300], sampling_rate=100, epochs_end=20)</span>
-<span class="sd">      nk.eda_intervalrelated(epochs, sampling_rate=100)</span>
+<span class="sd">        epochs = nk.epochs_create(df, events=[0, 25300], sampling_rate=100, epochs_end=20)</span>
+<span class="sd">        nk.eda_intervalrelated(epochs, sampling_rate=100)</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
 

diff --git a/_modules/neurokit2/eeg/mne_to_df.html b/_modules/neurokit2/eeg/mne_to_df.html
@@ -468,15 +468,26 @@ <h1>Source code for neurokit2.eeg.mne_to_df</h1><div class="highlight"><pre>
 
 <span class="sd">      # Raw objects</span>
 <span class="sd">      eeg = nk.mne_data(&quot;filt-0-40_raw&quot;)</span>
-<span class="sd">      nk.mne_to_dict(eeg)</span>
+<span class="sd">      eeg_dict = nk.mne_to_dict(eeg)</span>
+
+<span class="sd">      # Print function result summary</span>
+<span class="sd">      eeg_dict_view = {k: f&quot;Signal with length: {len(v)}&quot; for k, v in eeg_dict.items()}</span>
+<span class="sd">      eeg_dict_view</span>
+
 
 <span class="sd">      # Epochs objects</span>
 <span class="sd">      eeg = nk.mne_data(&quot;epochs&quot;)</span>
-<span class="sd">      nk.mne_to_dict(eeg)</span>
+<span class="sd">      eeg_epoch_dict = nk.mne_to_dict(eeg)</span>
+
+<span class="sd">      # Print function result summary</span>
+<span class="sd">      list(eeg_epoch_dict.items())[:2]</span>
 
 <span class="sd">      # Evoked objects</span>
 <span class="sd">      eeg = nk.mne_data(&quot;evoked&quot;)</span>
-<span class="sd">      nk.mne_to_dict(eeg)</span>
+<span class="sd">      eeg_evoked_dict = nk.mne_to_dict(eeg)</span>
+
+<span class="sd">      # Print function result summary</span>
+<span class="sd">      eeg_evoked_dict</span>
 
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="k">return</span> <span class="n">_mne_convert</span><span class="p">(</span><span class="n">eeg</span><span class="p">,</span> <span class="n">to_what</span><span class="o">=</span><span class="s2">&quot;dict&quot;</span><span class="p">)</span></div>

diff --git a/_modules/neurokit2/events/events_find.html b/_modules/neurokit2/events/events_find.html
@@ -519,6 +519,7 @@ <h1>Source code for neurokit2.events.events_find</h1><div class="highlight"><pre
 <span class="sd">    Convert the event condition results its human readable representation</span>
 
 <span class="sd">    .. ipython:: python</span>
+
 <span class="sd">      value_to_condition = {1: &quot;Stimulus 1&quot;, 2: &quot;Stimulus 2&quot;, 3: &quot;Stimulus 3&quot;}</span>
 <span class="sd">      events[&quot;condition&quot;] = [value_to_condition[id] for id in events[&quot;condition&quot;]]</span>
 <span class="sd">      events</span>

diff --git a/_modules/neurokit2/misc/progress_bar.html b/_modules/neurokit2/misc/progress_bar.html
@@ -401,7 +401,7 @@ <h1>Source code for neurokit2.misc.progress_bar</h1><div class="highlight"><pre>
 
 <span class="sd">    Examples</span>
 <span class="sd">    --------</span>
-<span class="sd">    ..ipython:: python</span>
+<span class="sd">    .. ipython:: python</span>
 
 <span class="sd">      import neurokit2 as nk</span>
 

diff --git a/_modules/neurokit2/rsp/rsp_process.html b/_modules/neurokit2/rsp/rsp_process.html
@@ -450,7 +450,8 @@ <h1>Source code for neurokit2.rsp.rsp_process</h1><div class="highlight"><pre>
 <span class="sd">        * ``&quot;RSP_Phase&quot;``: breathing phase, marked by &quot;1&quot; for inspiration and &quot;0&quot; for expiration.</span>
 <span class="sd">        * ``&quot;RSP_Phase_Completion&quot;``: breathing phase completion, expressed in percentage (from 0 to</span>
 <span class="sd">          1), representing the stage of the current respiratory phase.</span>
-<span class="sd">         * ``&quot;RSP_RVT&quot;``: respiratory volume per time (RVT).</span>
+<span class="sd">        * ``&quot;RSP_RVT&quot;``: respiratory volume per time (RVT).</span>
+
 <span class="sd">    info : dict</span>
 <span class="sd">        A dictionary containing the samples at which inhalation peaks and exhalation troughs occur,</span>
 <span class="sd">        accessible with the keys ``&quot;RSP_Peaks&quot;``, and ``&quot;RSP_Troughs&quot;`` respectively, as well as the</span>

diff --git a/_modules/neurokit2/signal/signal_detrend.html b/_modules/neurokit2/signal/signal_detrend.html
@@ -400,10 +400,12 @@ <h1>Source code for neurokit2.signal.signal_detrend</h1><div class="highlight"><
     <span class="n">sampling_rate</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span>
 <span class="p">):</span>
 <span class="w">    </span><span class="sd">&quot;&quot;&quot;**Signal Detrending**</span>
+
 <span class="sd">    Apply a baseline (order = 0), linear (order = 1), or polynomial (order &gt; 1) detrending to the</span>
 <span class="sd">    signal (i.e., removing a general trend). One can also use other methods, such as smoothness</span>
 <span class="sd">    priors approach described by Tarvainen (2002) or LOESS regression, but these scale badly for</span>
 <span class="sd">    long signals.</span>
+
 <span class="sd">    Parameters</span>
 <span class="sd">    ----------</span>
 <span class="sd">    signal : Union[list, np.array, pd.Series]</span>

diff --git a/_modules/neurokit2/signal/signal_interpolate.html b/_modules/neurokit2/signal/signal_interpolate.html
@@ -414,13 +414,13 @@ <h1>Source code for neurokit2.signal.signal_interpolate</h1><div class="highligh
 <span class="sd">    method : str</span>
 <span class="sd">        Method of interpolation. Can be ``&quot;linear&quot;``, ``&quot;nearest&quot;``, ``&quot;zero&quot;``, ``&quot;slinear&quot;``,</span>
 <span class="sd">        ``&quot;quadratic&quot;``, ``&quot;cubic&quot;``, ``&quot;previous&quot;``, ``&quot;next&quot;``, ``&quot;monotone_cubic&quot;``, or ``&quot;akima&quot;``.</span>
-<span class="sd">        The methods ``&quot;zero&quot;``, ``&quot;slinear&quot;``,``&quot;quadratic&quot;`` and ``&quot;cubic&quot;`` refer to a spline</span>
+<span class="sd">        The methods ``&quot;zero&quot;``, ``&quot;slinear&quot;``, ``&quot;quadratic&quot;`` and ``&quot;cubic&quot;`` refer to a spline</span>
 <span class="sd">        interpolation of zeroth, first, second or third order; whereas ``&quot;previous&quot;`` and</span>
 <span class="sd">        ``&quot;next&quot;`` simply return the previous or next value of the point. An integer specifying the</span>
 <span class="sd">        order of the spline interpolator to use.</span>
-<span class="sd">        See `here &lt;https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.</span>
+<span class="sd">        See `monotone cubic method &lt;https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.</span>
 <span class="sd">        PchipInterpolator.html&gt;`_ for details on the ``&quot;monotone_cubic&quot;`` method.</span>
-<span class="sd">        See `here &lt;https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.</span>
+<span class="sd">        See `akima method &lt;https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.</span>
 <span class="sd">        Akima1DInterpolator.html&gt;`_ for details on the ``&quot;akima&quot;`` method.</span>
 <span class="sd">    fill_value : float or tuple or str</span>
 <span class="sd">        If a ndarray (or float), this value will be used to fill in for</span>
@@ -469,6 +469,7 @@ <h1>Source code for neurokit2.signal.signal_interpolate</h1><div class="highligh
 <span class="sd">      plt.scatter(x_values, signal, label=&quot;original datapoints&quot;, zorder=3)</span>
 <span class="sd">      @suppress</span>
 <span class="sd">      plt.close()</span>
+
 <span class="sd">    &quot;&quot;&quot;</span>
     <span class="c1"># Sanity checks</span>
     <span class="k">if</span> <span class="n">x_values</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>

diff --git a/_sources/examples/eeg_microstates/eeg_microstates.ipynb b/_sources/examples/eeg_microstates/eeg_microstates.ipynb
@@ -589,7 +589,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Several different clustering algorithms can be used to segment your EEG recordings into microstates. These algorithms mainly differ in how they define cluster membership and the cost functionals to be optimized ([Xu & Tian, 2015](10.1007/s40745-015-0040-1)). The method to use hence depends on your data and the underlying assumptions of the methods (e.g., some methods ignore polarity). There is no one true method that gives the best results but you can refer to [Poulsen et al., 2018](https://www.researchgate.net/publication/331367421_Microstate_EEGlab_toolbox_An_introductory_guide#pf6) if you would like a more detailed review of the different clustering methods."
+    "Several different clustering algorithms can be used to segment your EEG recordings into microstates. These algorithms mainly differ in how they define cluster membership and the cost functionals to be optimized ([Xu & Tian, 2015](https://doi.org/10.1007/s40745-015-0040-1)). The method to use hence depends on your data and the underlying assumptions of the methods (e.g., some methods ignore polarity). There is no one true method that gives the best results but you can refer to [Poulsen et al., 2018](https://www.researchgate.net/publication/331367421_Microstate_EEGlab_toolbox_An_introductory_guide#pf6) if you would like a more detailed review of the different clustering methods."
    ]
   },
   {

diff --git a/authors.html b/authors.html
@@ -509,6 +509,7 @@ <h2>Contributors<a class="headerlink" href="#contributors" title="Link to this h
 <li><p><a class="reference external" href="https://github.com/rostro36">Jannik Gut</a></p></li>
 <li><p><a class="reference external" href="https://github.com/Nattapong-OnePlanet">Nattapong Thammasan</a> <em>(OnePlanet, Netherlands)</em></p></li>
 <li><p><a class="reference external" href="https://github.com/sokolmarek">Marek Sokol</a> <em>(Faculty of Biomedical Engineering of the CTU in Prague, Czech Republic)</em></p></li>
+<li><p><a class="reference external" href="https://github.com/DerAndereJohannes">Johannes Herforth</a> <em>(University of Luxembourg, Luxembourg)</em></p></li>
 </ul>
 <p>Thanks also to <a class="reference external" href="https://github.com/hcp4715">Chuan-Peng Hu</a>, <a class="reference external" href="https://github.com/ucohen">&#64;ucohen</a>, <a class="reference external" href="https://github.com/gattia">Anthony Gatti</a>, <a class="reference external" href="https://github.com/lamourj">Julien Lamour</a>, <a class="reference external" href="https://github.com/renatosc">&#64;renatosc</a>, <a class="reference external" href="https://github.com/Fegalf">Nicolas Beaudoin-Gagnon</a> and <a class="reference external" href="https://github.com/rubinovitz">&#64;rubinovitz</a> for their contribution in <a class="reference external" href="https://github.com/neuropsychology/NeuroKit.py">NeuroKit 1</a>.</p>
 <div class="admonition seealso">

diff --git a/examples/bio_custom/bio_custom.html b/examples/bio_custom/bio_custom.html
@@ -490,7 +490,7 @@ <h2>The Default NeuroKit processing pipeline<a class="headerlink" href="#the-def
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../_images/0c08c76a7b07f3fe27cd7dd31c1fcaee0f2df13a07f8313b1ef6cbba448bcc01.png" src="../../_images/0c08c76a7b07f3fe27cd7dd31c1fcaee0f2df13a07f8313b1ef6cbba448bcc01.png" />
+<img alt="../../_images/6d389455c535fa4cbb21150e33f84258fd3410396a41c47131bccfcdc207db9c.png" src="../../_images/6d389455c535fa4cbb21150e33f84258fd3410396a41c47131bccfcdc207db9c.png" />
 </div>
 </div>
 </section>
@@ -538,7 +538,7 @@ <h2>Building your own <code class="docutils literal notranslate"><span class="pr
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../_images/614ce7b0ea5b2bca1ca796a1b4e9e5d90bfca9d3a52c22a82b3acbcfe97b1689.png" src="../../_images/614ce7b0ea5b2bca1ca796a1b4e9e5d90bfca9d3a52c22a82b3acbcfe97b1689.png" />
+<img alt="../../_images/a1df997e6c2a5dd77c63ce8a6bccdf6d8eb59b67024f5d79bcc5c2436ad86cb3.png" src="../../_images/a1df997e6c2a5dd77c63ce8a6bccdf6d8eb59b67024f5d79bcc5c2436ad86cb3.png" />
 </div>
 </div>
 </section>
@@ -630,7 +630,7 @@ <h2>Customize even more!<a class="headerlink" href="#customize-even-more" title=
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../_images/e7711d23a2d4cb6f0c704eb903e02b9c8839904f6950e56c89838dd5ac467bab.png" src="../../_images/e7711d23a2d4cb6f0c704eb903e02b9c8839904f6950e56c89838dd5ac467bab.png" />
+<img alt="../../_images/5bc7d67109d0670364743fbb0b85914920221860ef86e2f8302bfb11d026d1c1.png" src="../../_images/5bc7d67109d0670364743fbb0b85914920221860ef86e2f8302bfb11d026d1c1.png" />
 </div>
 </div>
 <p>This doesn’t look bad :) <strong>Can you do better?</strong></p>

diff --git a/examples/bio_eventrelated/bio_eventrelated.html b/examples/bio_eventrelated/bio_eventrelated.html
@@ -641,11 +641,11 @@ <h2>Manually Extract Event Related Features<a class="headerlink" href="#manually
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-<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>C:\Users\runneradmin\AppData\Local\Temp\ipykernel_7944\4246861920.py:12: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>C:\Users\runneradmin\AppData\Local\Temp\ipykernel_7080\4246861920.py:12: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
   ecg_mean = epoch[&quot;ECG_Rate&quot;][0:4].mean()  # Mean heart rate in the 0-4 seconds
-C:\Users\runneradmin\AppData\Local\Temp\ipykernel_7944\4246861920.py:18: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
+C:\Users\runneradmin\AppData\Local\Temp\ipykernel_7080\4246861920.py:18: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
   rsp_rate = epoch[&quot;RSP_Rate&quot;][0:6].mean()  # Longer window for RSP that has a slower dynamic
-C:\Users\runneradmin\AppData\Local\Temp\ipykernel_7944\4246861920.py:24: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
+C:\Users\runneradmin\AppData\Local\Temp\ipykernel_7080\4246861920.py:24: FutureWarning: The behavior of obj[i:j] with a float-dtype index is deprecated. In a future version, this will be treated as positional instead of label-based. For label-based slicing, use obj.loc[i:j] instead
   scr_max = epoch[&quot;SCR_Amplitude&quot;][0:6].max()  # Maximum SCR peak
 </pre></div>
 </div>

diff --git a/examples/ecg_generate_12leads/ecg_generate_12leads.html b/examples/ecg_generate_12leads/ecg_generate_12leads.html
@@ -481,7 +481,7 @@ <h2>Normal Multi-lead ECG<a class="headerlink" href="#normal-multi-lead-ecg" tit
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../_images/ed0d22b4e3369020330f49441b703d1e70b7863c0f1c44f75ae954eec745a347.png" src="../../_images/ed0d22b4e3369020330f49441b703d1e70b7863c0f1c44f75ae954eec745a347.png" />
+<img alt="../../_images/77fba950a70805d05786377d40adc0b87a29b3efdd0549c9b9183e744c8ad66e.png" src="../../_images/77fba950a70805d05786377d40adc0b87a29b3efdd0549c9b9183e744c8ad66e.png" />
 </div>
 </div>
 </section>
@@ -513,7 +513,7 @@ <h2>Abnormal Multi-lead ECG<a class="headerlink" href="#abnormal-multi-lead-ecg"
 </div>
 </div>
 <div class="cell_output docutils container">
-<img alt="../../_images/f78a85026e7880f28c432717e9b2840e22877cad680226f0da8a7a99f2c7face.png" src="../../_images/f78a85026e7880f28c432717e9b2840e22877cad680226f0da8a7a99f2c7face.png" />
+<img alt="../../_images/98087ffb8dde657e0361b107ec69b5e805257488a10ccf6cca2031598198c098.png" src="../../_images/98087ffb8dde657e0361b107ec69b5e805257488a10ccf6cca2031598198c098.png" />
 </div>
 </div>
 </section>