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<li><a class="reference internal" href="#">deepof.visuals.plot_transitions</a><ul>
<li><a class="reference internal" href="#deepof.visuals.plot_transitions"><code class="docutils literal notranslate"><span class="pre">plot_transitions()</span></code></a></li>
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<section id="deepof-visuals-plot-transitions">
<h1>deepof.visuals.plot_transitions<a class="headerlink" href="#deepof-visuals-plot-transitions" title="Permalink to this heading"></a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="deepof.visuals.plot_transitions">
<span class="sig-prename descclassname"><span class="pre">deepof.visuals.</span></span><span class="sig-name descname"><span class="pre">plot_transitions</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">coordinates</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">embeddings</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">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">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">bin_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">bin_index</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">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exp_condition</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">visualization</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'networks'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">silence_diagonal</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cluster</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">axes</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">list</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">save</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">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#deepof.visuals.plot_transitions" title="Permalink to this definition"></a></dt>
<dd><p>Compute and plots transition matrices for all data or per condition. Plots can be heatmaps or networks.</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>coordinates</strong> (<em>coordinates</em>) – deepOF project where the data is stored.</p></li>
<li><p><strong>embeddings</strong> (<em>table_dict</em>) – table dict with neural embeddings per animal experiment across time.</p></li>
<li><p><strong>soft_counts</strong> (<em>table_dict</em>) – table dict with soft cluster assignments per animal experiment across time.</p></li>
<li><p><strong>breaks</strong> (<em>table_dict</em>) – table dict with changepoint detection breaks per experiment.</p></li>
<li><p><strong>exp_condition</strong> (<em>str</em>) – Name of the experimental condition to use when plotting. If None (default) the first one available is used.</p></li>
<li><p><strong>bin_size</strong> (<em>int</em>) – bin size for time filtering.</p></li>
<li><p><strong>bin_index</strong> (<em>int</em>) – index of the bin of size bin_size to select along the time dimension. new figure will be created.</p></li>
<li><p><strong>visualization</strong> (<em>str</em>) – visualization mode. Can be either ‘networks’, or ‘heatmaps’.</p></li>
<li><p><strong>silence_diagonal</strong> (<em>bool</em>) – If True, diagonals are set to zero.</p></li>
<li><p><strong>cluster</strong> (<em>bool</em>) – If True (default) rows and columns on heatmaps are hierarchically clustered.</p></li>
<li><p><strong>axes</strong> (<em>list</em>) – axes where to plot the current figure. If not provided, a new figure will be created.</p></li>
<li><p><strong>save</strong> (<em>bool</em>) – Saves a time-stamped vectorized version of the figure if True.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
</section>
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