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This file was created from the following Jupyter-notebook: <a href="https://github.com/ihrke/pypillometry/tree/master/docs/summary.ipynb">docs/summary.ipynb</a>
<br>
Interactive version:
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</div><div class="section" id="Summarizing-pupillometric-data">
<h1>Summarizing pupillometric data<a class="headerlink" href="#Summarizing-pupillometric-data" title="Permalink to this headline">¶</a></h1>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">sys</span>
<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="s2">".."</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">pypillometry</span> <span class="k">as</span> <span class="nn">pp</span>
</pre></div>
</div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">pypillometry</span></code> package provides several functions for summarizing datasets. Simply <code class="docutils literal notranslate"><span class="pre">print()</span></code>ing a <code class="docutils literal notranslate"><span class="pre">PupilData</span></code> object gives a readable summary of the main properties of the dataset and also prints the complete history of operations applied to the dataset.</p>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
<span></span><span class="n">d</span><span class="o">=</span><span class="n">pp</span><span class="o">.</span><span class="n">create_fake_pupildata</span><span class="p">(</span><span class="n">ntrials</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">d</span><span class="p">)</span>
</pre></div>
</div>
</div>
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<div class="highlight"><pre>
PupilData(fake_nutuheho, 13.9MiB):
n : 113453
nmiss : 0
perc_miss : 0.0
nevents : 200
nblinks : 0
ninterpolated : 0
blinks_per_min : 0.0
fs : 1000.0
duration_minutes : 1.8908833333333335
start_min : 0.0
end_min : 1.8908780800185903
baseline_estimated: False
response_estimated: False
History:
*
</pre></div></div>
</div>
<p>By calling the function <code class="docutils literal notranslate"><span class="pre">summary()</span></code>, summary data is returned in <code class="docutils literal notranslate"><span class="pre">dict</span></code> form:</p>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
<span></span><span class="n">d</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>
</pre></div>
</div>
</div>
<div class="nboutput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
</pre></div>
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{'name': 'fake_nutuheho',
'n': 113453,
'nmiss': 0,
'perc_miss': 0.0,
'nevents': 200,
'nblinks': 0,
'ninterpolated': 0,
'blinks_per_min': 0.0,
'fs': 1000.0,
'duration_minutes': 1.8908833333333335,
'start_min': 0.0,
'end_min': 1.8908780800185903,
'baseline_estimated': False,
'response_estimated': False}
</pre></div></div>
</div>
<p>Using the package <code class="docutils literal notranslate"><span class="pre">pandas</span></code>, this allows to rapidly summarize properties of many datasets in a table.</p>
<p>To illustrate, we create a list of datasets, each corresponding to one individual subject:</p>
<div class="nbinput nblast docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
<span></span><span class="n">nsubjs</span><span class="o">=</span><span class="mi">10</span>
<span class="n">datasets</span><span class="o">=</span><span class="p">[</span><span class="n">pp</span><span class="o">.</span><span class="n">create_fake_pupildata</span><span class="p">(</span><span class="n">ntrials</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nsubjs</span><span class="p">)]</span>
</pre></div>
</div>
</div>
<p>We can now call <code class="docutils literal notranslate"><span class="pre">summary()</span></code> for each of the objects and merge the resulting <code class="docutils literal notranslate"><span class="pre">dict</span></code> into a table form:</p>
<div class="nbinput docutils container">
<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
</pre></div>
</div>
<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre>
<span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">([</span><span class="n">d</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">datasets</span><span class="p">])</span>
</pre></div>
</div>
</div>
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
</pre></div>
</div>
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<style scoped>
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<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>name</th>
<th>n</th>
<th>nmiss</th>
<th>perc_miss</th>
<th>nevents</th>
<th>nblinks</th>
<th>ninterpolated</th>
<th>blinks_per_min</th>
<th>fs</th>
<th>duration_minutes</th>
<th>start_min</th>
<th>end_min</th>
<th>baseline_estimated</th>
<th>response_estimated</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>fake_tamofase</td>
<td>113832</td>
<td>0</td>
<td>0.0</td>
<td>200</td>
<td>0</td>
<td>0</td>
<td>0.0</td>
<td>1000.0</td>
<td>1.897200</td>
<td>0.0</td>
<td>1.897196</td>
<td>False</td>
<td>False</td>
</tr>
<tr>
<th>1</th>
<td>fake_lobavifu</td>
<td>114425</td>
<td>0</td>
<td>0.0</td>
<td>200</td>
<td>0</td>
<td>0</td>
<td>0.0</td>
<td>1000.0</td>
<td>1.907083</td>
<td>0.0</td>
<td>1.907083</td>
<td>False</td>
<td>False</td>
</tr>
<tr>
<th>2</th>
<td>fake_paketomu</td>
<td>112977</td>
<td>0</td>
<td>0.0</td>
<td>200</td>
<td>0</td>
<td>0</td>
<td>0.0</td>
<td>1000.0</td>
<td>1.882950</td>
<td>0.0</td>
<td>1.882941</td>
<td>False</td>
<td>False</td>
</tr>
<tr>
<th>3</th>
<td>fake_hiligovi</td>
<td>114345</td>
<td>0</td>
<td>0.0</td>
<td>200</td>
<td>0</td>
<td>0</td>
<td>0.0</td>
<td>1000.0</td>
<td>1.905750</td>
<td>0.0</td>
<td>1.905735</td>
<td>False</td>
<td>False</td>
</tr>
<tr>
<th>4</th>
<td>fake_kolavebo</td>
<td>113732</td>
<td>0</td>
<td>0.0</td>
<td>200</td>
<td>0</td>
<td>0</td>
<td>0.0</td>
<td>1000.0</td>
<td>1.895533</td>
<td>0.0</td>
<td>1.895517</td>
<td>False</td>
<td>False</td>
</tr>
<tr>
<th>5</th>
<td>fake_ropopodi</td>
<td>113661</td>
<td>0</td>
<td>0.0</td>
<td>200</td>
<td>0</td>
<td>0</td>
<td>0.0</td>
<td>1000.0</td>
<td>1.894350</td>
<td>0.0</td>
<td>1.894346</td>
<td>False</td>
<td>False</td>
</tr>
<tr>
<th>6</th>
<td>fake_rilimolo</td>
<td>114402</td>
<td>0</td>
<td>0.0</td>
<td>200</td>
<td>0</td>
<td>0</td>
<td>0.0</td>
<td>1000.0</td>
<td>1.906700</td>
<td>0.0</td>
<td>1.906698</td>
<td>False</td>
<td>False</td>
</tr>
<tr>
<th>7</th>
<td>fake_gapudome</td>
<td>113891</td>
<td>0</td>
<td>0.0</td>
<td>200</td>
<td>0</td>
<td>0</td>
<td>0.0</td>
<td>1000.0</td>
<td>1.898183</td>
<td>0.0</td>
<td>1.898178</td>
<td>False</td>
<td>False</td>
</tr>
<tr>
<th>8</th>
<td>fake_belobeva</td>
<td>113932</td>
<td>0</td>
<td>0.0</td>
<td>200</td>
<td>0</td>
<td>0</td>
<td>0.0</td>
<td>1000.0</td>
<td>1.898867</td>
<td>0.0</td>
<td>1.898865</td>
<td>False</td>
<td>False</td>
</tr>
<tr>
<th>9</th>
<td>fake_rihatofi</td>
<td>113304</td>
<td>0</td>
<td>0.0</td>
<td>200</td>
<td>0</td>
<td>0</td>
<td>0.0</td>
<td>1000.0</td>
<td>1.888400</td>
<td>0.0</td>
<td>1.888393</td>
<td>False</td>
<td>False</td>
</tr>
</tbody>
</table>
</div></div>
</div>
</div>
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