Reference to quickstart in doc

soupeeli · Feb 20, 2022 · 4cb74b0 · 4cb74b0
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diff --git a/README.rst b/README.rst
@@ -27,8 +27,7 @@
 * Spectral analyses: bandpower, phase-amplitude coupling, 1/f slope, and more!
 * Hypnogram analysis: sleep statistics and stage tranisitions.
 
-For more details, check out the `API documentation <https://raphaelvallat.com/yasa/build/html/index.html>`_, try the
-`tutorial (Jupyter notebooks) <https://github.com/raphaelvallat/yasa/tree/master/notebooks>`_ or read the `FAQ <https://raphaelvallat.com/yasa/build/html/faq.html>`_.
+For more details, try the `quickstart <https://raphaelvallat.com/yasa/build/html/quickstart.html>`_ or read the `FAQ <https://raphaelvallat.com/yasa/build/html/faq.html>`_.
 
 ----------------
 
@@ -68,7 +67,7 @@ If you have sleep EEG data in standard formats (e.g. EDF or BrainVision), you ca
 How do I get started with YASA?
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-If you want to dive right in, you can simply go to the main `documentation <https://raphaelvallat.com/yasa/build/html/api.html>`_ and try to apply YASA's functions on your own EEG data.
+If you want to dive right in, you can simply go to the main `documentation <https://raphaelvallat.com/yasa/build/html/quickstart.html>`_ and try to apply YASA's functions on your own EEG data.
 However, for most users, we strongly recommend that you first try running the examples Jupyter notebooks to get a sense of how YASA works and what it can do!
 The notebooks also come with example datasets so they should work right out of the box as long as you've installed YASA first.
 The notebooks and datasets can be found on `GitHub <https://github.com/raphaelvallat/yasa/tree/master/notebooks>`_ (make sure that you download the whole *notebooks/* folder). A short description of all notebooks is provided below:

diff --git a/docs/build/html/_modules/yasa/detection.html b/docs/build/html/_modules/yasa/detection.html
@@ -937,7 +937,7 @@ <h1>Source code for yasa.detection</h1><div class="highlight"><pre>
         <span class="c1"># ####################################################################</span>
         <span class="n">df_chan</span><span class="p">[</span><span class="s1">&#39;Channel&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">ch_names</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
         <span class="n">df_chan</span><span class="p">[</span><span class="s1">&#39;IdxChannel&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
-        <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_chan</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df</span><span class="p">,</span> <span class="n">df_chan</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
     <span class="c1"># If no spindles were detected, return None</span>
     <span class="k">if</span> <span class="n">df</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
@@ -1631,7 +1631,7 @@ <h1>Source code for yasa.detection</h1><div class="highlight"><pre>
 
         <span class="n">df_chan</span><span class="p">[</span><span class="s1">&#39;Channel&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">ch_names</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
         <span class="n">df_chan</span><span class="p">[</span><span class="s1">&#39;IdxChannel&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
-        <span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_chan</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df</span><span class="p">,</span> <span class="n">df_chan</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
     <span class="c1"># If no SW were detected, return None</span>
     <span class="k">if</span> <span class="n">df</span><span class="o">.</span><span class="n">empty</span><span class="p">:</span>
@@ -2295,7 +2295,7 @@ <h1>Source code for yasa.detection</h1><div class="highlight"><pre>
             <span class="n">df_chan</span> <span class="o">=</span> <span class="n">df_chan</span><span class="o">.</span><span class="n">melt</span><span class="p">(</span><span class="n">id_vars</span><span class="o">=</span><span class="s1">&#39;Time&#39;</span><span class="p">,</span> <span class="n">var_name</span><span class="o">=</span><span class="s1">&#39;Event&#39;</span><span class="p">,</span> <span class="n">value_name</span><span class="o">=</span><span class="s1">&#39;Amplitude&#39;</span><span class="p">)</span>
             <span class="n">df_chan</span><span class="p">[</span><span class="s1">&#39;Channel&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">ch</span>
             <span class="n">df_chan</span><span class="p">[</span><span class="s1">&#39;IdxChannel&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">i</span>
-            <span class="n">df_sync</span> <span class="o">=</span> <span class="n">df_sync</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">df_chan</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="n">df_sync</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df_sync</span><span class="p">,</span> <span class="n">df_chan</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">ignore_index</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
         <span class="k">return</span> <span class="n">df_sync</span></div>
 

diff --git a/docs/build/html/_modules/yasa/spectral.html b/docs/build/html/_modules/yasa/spectral.html
@@ -242,7 +242,7 @@ <h1>Source code for yasa.spectral</h1><div class="highlight"><pre>
             <span class="n">bp_stage</span> <span class="o">=</span> <span class="n">bandpower_from_psd</span><span class="p">(</span><span class="n">psd</span><span class="p">,</span> <span class="n">freqs</span><span class="p">,</span> <span class="n">ch_names</span><span class="p">,</span> <span class="n">bands</span><span class="o">=</span><span class="n">bands</span><span class="p">,</span>
                                           <span class="n">relative</span><span class="o">=</span><span class="n">relative</span><span class="p">)</span>
             <span class="n">bp_stage</span><span class="p">[</span><span class="s1">&#39;Stage&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">stage</span>
-            <span class="n">df_bp</span> <span class="o">=</span> <span class="n">df_bp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">bp_stage</span><span class="p">)</span>
+            <span class="n">df_bp</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">df_bp</span><span class="p">,</span> <span class="n">bp_stage</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
         <span class="k">return</span> <span class="n">df_bp</span><span class="o">.</span><span class="n">set_index</span><span class="p">([</span><span class="s1">&#39;Stage&#39;</span><span class="p">,</span> <span class="s1">&#39;Chan&#39;</span><span class="p">])</span></div>
 
 

diff --git a/docs/build/html/index.html b/docs/build/html/index.html
@@ -109,8 +109,7 @@
 <li><p>Spectral analyses: bandpower, phase-amplitude coupling, 1/f slope, and more!</p></li>
 <li><p>Hypnogram analysis: sleep statistics and stage tranisitions.</p></li>
 </ul>
-<p>For more details, check out the <a class="reference external" href="https://raphaelvallat.com/yasa/build/html/index.html">API documentation</a>, try the
-<a class="reference external" href="https://github.com/raphaelvallat/yasa/tree/master/notebooks">tutorial (Jupyter notebooks)</a> or read the <a class="reference external" href="https://raphaelvallat.com/yasa/build/html/faq.html">FAQ</a>.</p>
+<p>For more details, try the <a class="reference external" href="https://raphaelvallat.com/yasa/build/html/quickstart.html">quickstart</a> or read the <a class="reference external" href="https://raphaelvallat.com/yasa/build/html/faq.html">FAQ</a>.</p>
 <hr class="docutils" />
 <div class="section" id="installation">
 <h1>Installation<a class="headerlink" href="#installation" title="Permalink to this headline">¶</a></h1>
@@ -176,9 +175,9 @@ <h1>How do I get started with YASA?<a class="headerlink" href="#how-do-i-get-sta
 <div class="section" id="gallery">
 <h1>Gallery<a class="headerlink" href="#gallery" title="Permalink to this headline">¶</a></h1>
 <p>Below some plots demonstrating the functionalities of YASA. To reproduce these, check out the <a class="reference external" href="https://github.com/raphaelvallat/yasa/tree/master/notebooks">tutorial (Jupyter notebooks)</a>.</p>
-<div class="figure align-center" id="id2">
+<div class="figure align-center" id="id1">
 <img alt="_images/gallery.png" src="_images/gallery.png" />
-<p class="caption"><span class="caption-text"><em>The top plot show an overlay of the detected spindles on real EEG data. The middle left panel shows a time-frequency representation of the whole-night recording (spectrogram), plotted with the hypnogram (sleep stages) on top. The middle right panel shows the sleep stage probability transition matrix, calculated across the entire night. The bottom row shows, from left to right: a topographic plot, the average template of all detected slow-waves across the entire night stratified by channels, and a phase-amplitude coupling comodulogram.</em></span><a class="headerlink" href="#id2" title="Permalink to this image">¶</a></p>
+<p class="caption"><span class="caption-text"><em>The top plot show an overlay of the detected spindles on real EEG data. The middle left panel shows a time-frequency representation of the whole-night recording (spectrogram), plotted with the hypnogram (sleep stages) on top. The middle right panel shows the sleep stage probability transition matrix, calculated across the entire night. The bottom row shows, from left to right: a topographic plot, the average template of all detected slow-waves across the entire night stratified by channels, and a phase-amplitude coupling comodulogram.</em></span><a class="headerlink" href="#id1" title="Permalink to this image">¶</a></p>
 </div>
 </div>
 <hr class="docutils" />

diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
diff --git a/docs/index.rst b/docs/index.rst
@@ -27,8 +27,7 @@
 * Spectral analyses: bandpower, phase-amplitude coupling, 1/f slope, and more!
 * Hypnogram analysis: sleep statistics and stage tranisitions.
 
-For more details, check out the `API documentation <https://raphaelvallat.com/yasa/build/html/index.html>`_, try the
-`tutorial (Jupyter notebooks) <https://github.com/raphaelvallat/yasa/tree/master/notebooks>`_ or read the `FAQ <https://raphaelvallat.com/yasa/build/html/faq.html>`_.
+For more details, try the `quickstart <https://raphaelvallat.com/yasa/build/html/quickstart.html>`_ or read the `FAQ <https://raphaelvallat.com/yasa/build/html/faq.html>`_.
 
 **********