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<li><a class="reference internal" href="#deepof.visuals.plot_embeddings"><code class="docutils literal notranslate"><span class="pre">plot_embeddings()</span></code></a></li>
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<section id="deepof-visuals-plot-embeddings">
<h1>deepof.visuals.plot_embeddings<a class="headerlink" href="#deepof-visuals-plot-embeddings" title="Permalink to this heading"></a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="deepof.visuals.plot_embeddings">
<span class="sig-prename descclassname"><span class="pre">deepof.visuals.</span></span><span class="sig-name descname"><span class="pre">plot_embeddings</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">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">soft_counts</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">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">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">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">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">precomputed_bins</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">ndarray</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">normative_model</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">add_stats</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">'Mann-Whitney'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">verbose</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="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">aggregate_experiments</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">False</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">500</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_aggregated_density</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">colour_by</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">'cluster'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">show_break_size_as_radius</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="n"><span class="pre">ax</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">Any</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><span class="sig-paren">)</span><a class="headerlink" href="#deepof.visuals.plot_embeddings" title="Permalink to this definition"></a></dt>
<dd><p>Return a scatter plot of the passed projection. Allows for temporal and quality filtering, animal aggregation, and changepoint detection size visualization.</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>supervised_annotations</strong> (<em>table_dict</em>) – table dict with supervised annotations 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>normative_model</strong> (<em>str</em>) – Name of the cohort to use as controls. If provided, fits a Gaussian density to the control global animal embeddings, and reports the difference in likelihood across all instances of the provided experimental condition. Statistical parameters can be controlled via <a href="#id1"><span class="problematic" id="id2">**</span></a>kwargs (see full documentation for details).</p></li>
<li><p><strong>add_stats</strong> (<em>str</em>) – test to use. Mann-Whitney (non-parametric) by default. See statsannotations documentation for details.</p></li>
<li><p><strong>verbose</strong> (<em>bool</em>) – if True, prints test results and p-value cutoffs. False by default.</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>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.</p></li>
<li><p><strong>precomputed_bins</strong> (<em>np.ndarray</em>) – precomputed time bins. If provided, bin_size and bin_index are ignored.</p></li>
<li><p><strong>aggregate_experiments</strong> (<em>str</em>) – Whether to aggregate embeddings by experiment (by time on cluster, mean, or median) or not (default).</p></li>
<li><p><strong>samples</strong> (<em>int</em>) – Number of samples to take from the time embeddings. None leads to plotting all time-points, which may hurt performance.</p></li>
<li><p><strong>show_aggregated_density</strong> (<em>bool</em>) – if True, a density plot is added to the aggregated embeddings.</p></li>
<li><p><strong>colour_by</strong> (<em>str</em>) – hue by which to colour the embeddings. Can be one of ‘cluster’ (default), ‘exp_condition’, or ‘exp_id’.</p></li>
<li><p><strong>show_break_size_as_radius</strong> (<em>bool</em>) – Only usable when embeddings come from a model using changepoint detection. If True, the size of each chunk is depicted as the radius of each dot.</p></li>
<li><p><strong>ax</strong> (<em>plt.AxesSubplot</em>) – axes where to plot the current figure. If not provided, 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>
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