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<li><a class="reference internal" href="#">deepof.post_hoc.condition_distance_binning</a><ul>
<li><a class="reference internal" href="#deepof.post_hoc.condition_distance_binning"><code class="docutils literal notranslate"><span class="pre">condition_distance_binning()</span></code></a></li>
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<section id="deepof-post-hoc-condition-distance-binning">
<h1>deepof.post_hoc.condition_distance_binning<a class="headerlink" href="#deepof-post-hoc-condition-distance-binning" title="Permalink to this heading"></a></h1>
<dl class="py function">
<dt class="sig sig-object py" id="deepof.post_hoc.condition_distance_binning">
<span class="sig-prename descclassname"><span class="pre">deepof.post_hoc.</span></span><span class="sig-name descname"><span class="pre">condition_distance_binning</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">embedding</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">deepof_table_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">exp_conditions</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_bin</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">end_bin</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">step_bin</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">scan_mode</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">'growing_window'</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">agg</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>, <em class="sig-param"><span class="n"><span class="pre">metric</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">'auc'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</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">12</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#deepof.post_hoc.condition_distance_binning" title="Permalink to this definition"></a></dt>
<dd><p>Compute the distance between the embeddings of two conditions, using the specified aggregation method.</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>embedding</strong> (<a class="reference internal" href="deepof.data.TableDict.html#deepof.data.TableDict" title="deepof.data.TableDict"><em>TableDict</em></a>) – A dictionary of embeddings, where the keys are the names of the experimental conditions, and the values are the embeddings for each condition.</p></li>
<li><p><strong>soft_counts</strong> (<a class="reference internal" href="deepof.data.TableDict.html#deepof.data.TableDict" title="deepof.data.TableDict"><em>TableDict</em></a>) – A dictionary of soft counts, where the keys are the names of the experimental conditions, and the values are the soft counts for each condition.</p></li>
<li><p><strong>breaks</strong> (<a class="reference internal" href="deepof.data.TableDict.html#deepof.data.TableDict" title="deepof.data.TableDict"><em>TableDict</em></a>) – A dictionary of breaks, where the keys are the names of the experimental conditions, and the values are the breaks for each condition.</p></li>
<li><p><strong>exp_conditions</strong> (<em>dict</em>) – A dictionary of experimental conditions, where the keys are the names of the experiments, and the values are the names of their corresponding experimental conditions.</p></li>
<li><p><strong>start_bin</strong> (<em>int</em>) – The index of the first bin to compute the distance for.</p></li>
<li><p><strong>end_bin</strong> (<em>int</em>) – The index of the last bin to compute the distance for.</p></li>
<li><p><strong>step_bin</strong> (<em>int</em>) – The step size of the bins to compute the distance for.</p></li>
<li><p><strong>scan_mode</strong> (<em>str</em>) – The mode to use for computing the distance. Can be one of “growing-window” (used to select optimal binning), “per-bin” (used to evaluate how discriminability evolves in subsequent bins of a specified size) or “precomputed”, which requires a numpy ndarray with bin IDs to be passed to precomputed_bins.</p></li>
<li><p><strong>precomputed_bins</strong> (<em>np.ndarray</em>) – numpy array with IDs mapping to different bins, not necessarily having the same size. Difference across conditions for each of these bins will be reported.</p></li>
<li><p><strong>agg</strong> (<em>str</em>) – The aggregation method to use. Can be either “mean”, “median”, or “time_on_cluster”.</p></li>
<li><p><strong>metric</strong> (<em>str</em>) – The distance metric to use. Can be either “auc” (where the reported ‘distance’ is based on performance of a classifier when separating aggregated embeddings), or “wasserstein” (which computes distances based on optimal transport).</p></li>
<li><p><strong>n_jobs</strong> (<em>int</em>) – The number of jobs to use for parallel processing.</p></li>
</ul>
</dd>
<dt class="field-even">Returns<span class="colon">:</span></dt>
<dd class="field-even"><p>An array with distances between conditions across the resulting time bins</p>
</dd>
</dl>
</dd></dl>
</section>
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