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<li><a class="reference internal" href="#deepof.post_hoc.annotate_time_chunks"><code class="docutils literal notranslate"><span class="pre">annotate_time_chunks()</span></code></a></li>
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<section id="deepof-post-hoc-annotate-time-chunks">
<h1>deepof.post_hoc.annotate_time_chunks<a class="headerlink" href="#deepof-post-hoc-annotate-time-chunks" title="Permalink to this heading"></a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="deepof.post_hoc.annotate_time_chunks">
<span class="sig-prename descclassname"><span class="pre">deepof.post_hoc.</span></span><span class="sig-name descname"><span class="pre">annotate_time_chunks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">deepof_project</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">deepof_coordinates</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">soft_counts</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">deepof_table_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">breaks</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">deepof_table_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">supervised_annotations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">deepof_table_dict</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_size</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">int</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_step</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">animal_id</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Optional</span><span class="p"><span class="pre">[</span></span><span class="pre">str</span><span class="p"><span class="pre">]</span></span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">samples</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">10000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">min_confidence</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">float</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kin_derivative</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">int</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_distances</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_angles</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">include_areas</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">bool</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">aggregate</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span><span class="w"> </span><span class="o"><span class="pre">=</span></span><span class="w"> </span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#deepof.post_hoc.annotate_time_chunks" title="Permalink to this definition"></a></dt>
<dd><p>Annotate time chunks produced after change-point detection using the unsupervised pipeline.</p>
<p>Uses a set of summary statistics coming from kinematics, distances, angles, and supervised labels when provided.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters<span class="colon">:</span></dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>deepof_project</strong> (<em>coordinates</em>) – Project object.</p></li>
<li><p><strong>soft_counts</strong> (<em>table_dict</em>) – matrix with soft cluster assignments produced by the unsupervised pipeline.</p></li>
<li><p><strong>breaks</strong> (<em>table_dict</em>) – the breaks for each condition.</p></li>
<li><p><strong>supervised_annotations</strong> (<em>table_dict</em>) – set of supervised annotations produced by the supervised pipeline withing deepof.</p></li>
<li><p><strong>window_size</strong> (<em>int</em>) – Minimum size of the applied ruptures. If automatic_changepoints is False, specifies the size of the sliding window to pass through the data to generate training instances. None defaults to video frame-rate.</p></li>
<li><p><strong>window_step</strong> (<em>int</em>) – Specifies the minimum jump for the rupture algorithms. If automatic_changepoints is False, specifies the step to take when sliding the aforementioned window. In this case, a value of 1 indicates a true sliding window, and a value equal to window_size splits the data into non-overlapping chunks.</p></li>
<li><p><strong>animal_id</strong> (<em>str</em>) – The animal ID to use, in case of multi-animal projects.</p></li>
<li><p><strong>samples</strong> (<em>int</em>) – Time chunks samples to take to reduce computational time. Defaults to the minimum between 10000 and the number of available chunks.</p></li>
<li><p><strong>min_confidence</strong> (<em>float</em>) – minimum confidence in cluster assignments used for quality control filtering.</p></li>
<li><p><strong>kin_derivative</strong> (<em>int</em>) – The order of the derivative to use for the kinematics. 1 = speed, 2 = acceleration, etc.</p></li>
<li><p><strong>include_distances</strong> (<em>bool</em>) – Whether to include distances in the alignment. kin_derivative is taken into account.</p></li>
<li><p><strong>include_angles</strong> (<em>bool</em>) – Whether to include angles in the alignment. kin_derivative is taken into account.</p></li>
<li><p><strong>include_areas</strong> (<em>bool</em>) – Whether to include areas in the alignment. kin_derivative is taken into account.</p></li>
<li><p><strong>aggregate</strong> (<em>str</em>) – aggregation mode. Can be either “mean” (computationally cheapest), just use the average per feature, or “seglearn” which runs a thorough feature extraction and selection pipeline on each time series.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>A dataframe of kinematic features, of shape chunks by features.</p>
</dd>
</dl>
</dd></dl>
</section>
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