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<li><a class="reference internal" href="#">deepof.data.TableDict</a><ul>
<li><a class="reference internal" href="#deepof.data.TableDict"><code class="docutils literal notranslate"><span class="pre">TableDict</span></code></a><ul>
<li><a class="reference internal" href="#deepof.data.TableDict.__init__"><code class="docutils literal notranslate"><span class="pre">TableDict.__init__()</span></code></a></li>
<li><a class="reference internal" href="#id0"><code class="docutils literal notranslate"><span class="pre">TableDict.__init__()</span></code></a></li>
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<section id="deepof-data-tabledict">
<h1>deepof.data.TableDict<a class="headerlink" href="#deepof-data-tabledict" title="Permalink to this heading"></a></h1>
<dl class="py class">
<dt class="sig sig-object py" id="deepof.data.TableDict">
<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">deepof.data.</span></span><span class="sig-name descname"><span class="pre">TableDict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tabs</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">typ</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arena</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">arena_dims</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">array</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">animal_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</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">('',)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</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">connectivity</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">Graph</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">polar</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">bool</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">exp_conditions</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">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">propagate_labels</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">propagate_annotations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Dict</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">bool</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">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#deepof.data.TableDict" title="Permalink to this definition"></a></dt>
<dd><p>Main class for storing a single dataset as a dictionary with individuals as keys and pandas.DataFrames as values.</p>
<p>Includes methods for generating training and testing datasets for the supervised and unsupervised models.</p>
<dl class="py method">
<dt class="sig sig-object py" id="deepof.data.TableDict.__init__">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tabs</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">typ</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arena</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">arena_dims</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">array</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">animal_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</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">('',)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</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">connectivity</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">Graph</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">polar</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">bool</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">exp_conditions</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">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">propagate_labels</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">propagate_annotations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Dict</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">bool</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">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#deepof.data.TableDict.__init__" title="Permalink to this definition"></a></dt>
<dd><p>Store single datasets as dictionaries with individuals as keys and pandas.DataFrames as values.</p>
<p>Includes methods for generating training and testing datasets for the autoencoders.</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>tabs</strong> (<em>Dict</em>) – Dictionary of pandas.DataFrames with individual experiments as keys.</p></li>
<li><p><strong>typ</strong> (<em>str</em>) – Type of the dataset. Examples are “coords”, “dists”, and “angles”. For logging purposes only.</p></li>
<li><p><strong>arena</strong> (<em>str</em>) – Type of the arena. Must be one of “circular-autodetect”, “circular-manual”, or “polygon-manual”. Handled internally.</p></li>
<li><p><strong>arena_dims</strong> (<em>np.array</em>) – Dimensions of the arena in mm.</p></li>
<li><p><strong>animal_ids</strong> (<em>list</em>) – list of animal ids.</p></li>
<li><p><strong>center</strong> (<em>str</em>) – Type of the center. Handled internally.</p></li>
<li><p><strong>polar</strong> (<em>bool</em>) – Whether the dataset is in polar coordinates. Handled internally.</p></li>
<li><p><strong>exp_conditions</strong> (<em>dict</em>) – dictionary with experiment IDs as keys and experimental conditions as values.</p></li>
<li><p><strong>propagate_labels</strong> (<em>bool</em>) – Whether to propagate phenotypic labels from the original experiments to the transformed dataset.</p></li>
<li><p><strong>propagate_annotations</strong> (<em>Dict</em>) – Dictionary of annotations to propagate. If provided, the supervised annotations of the individual experiments are propagated to the dataset.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

<p class="rubric">Methods</p>
<table class="autosummary longtable docutils align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="#id0" title="deepof.data.TableDict.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(tabs, typ[, arena, arena_dims, ...])</p></td>
<td><p>Store single datasets as dictionaries with individuals as keys and pandas.DataFrames as values.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">clear</span></code>()</p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">copy</span></code>()</p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">filter_condition</span></code>(exp_filters)</p></td>
<td><p>Return a subset of the original table_dict object, containing only videos belonging to the specified experimental condition.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">filter_id</span></code>([selected_id])</p></td>
<td><p>Filter a TableDict object to keep only those columns related to the selected id.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">filter_videos</span></code>(keys)</p></td>
<td><p>Return a subset of the original table_dict object, containing only the specified keys.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">fromkeys</span></code>([value])</p></td>
<td><p>Create a new dictionary with keys from iterable and values set to value.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get</span></code>(key[, default])</p></td>
<td><p>Return the value for key if key is in the dictionary, else default.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_training_set</span></code>(current_table_dict[, ...])</p></td>
<td><p>Generate training and test sets as numpy.array objects for model training.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">items</span></code>()</p></td>
<td><p></p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">keys</span></code>()</p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">merge</span></code>(*args[, ignore_index])</p></td>
<td><p>Take a number of table_dict objects and merges them to the current one.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pca</span></code>([n_components, kernel])</p></td>
<td><p>Return a training set generated from the 2D original data (time x features) and a PCA projection to a n_components space.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">pop</span></code>(k[,d])</p></td>
<td><p>If key is not found, default is returned if given, otherwise KeyError is raised</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">popitem</span></code>()</p></td>
<td><p>Remove and return a (key, value) pair as a 2-tuple.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">preprocess</span></code>([automatic_changepoints, ...])</p></td>
<td><p>Preprocess the loaded dataset before feeding to unsupervised embedding models.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">random_projection</span></code>([n_components, kernel])</p></td>
<td><p>Return a training set generated from the 2D original data (time x features) and a random projection to a n_components space.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">setdefault</span></code>(key[, default])</p></td>
<td><p>Insert key with a value of default if key is not in the dictionary.</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">umap</span></code>([n_components])</p></td>
<td><p>Return a training set generated from the 2D original data (time x features) and a PCA projection to a n_components space.</p></td>
</tr>
<tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code>([E, ]**F)</p></td>
<td><p>If E is present and has a .keys() method, then does:  for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does:  for k, v in E: D[k] = v In either case, this is followed by: for k in F:  D[k] = F[k]</p></td>
</tr>
<tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">values</span></code>()</p></td>
<td><p></p></td>
</tr>
</tbody>
</table>
<dl class="py method">
<dt class="sig sig-object py" id="id0">
<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tabs</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">typ</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">str</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">arena</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">arena_dims</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">array</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">animal_ids</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">List</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">('',)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">center</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">connectivity</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">Graph</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">polar</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">bool</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">exp_conditions</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">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">propagate_labels</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">propagate_annotations</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Union</span><span class="p"><span class="pre">[</span></span><span class="pre">Dict</span><span class="p"><span class="pre">,</span></span><span class="w"> </span><span class="pre">bool</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">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#id0" title="Permalink to this definition"></a></dt>
<dd><p>Store single datasets as dictionaries with individuals as keys and pandas.DataFrames as values.</p>
<p>Includes methods for generating training and testing datasets for the autoencoders.</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>tabs</strong> (<em>Dict</em>) – Dictionary of pandas.DataFrames with individual experiments as keys.</p></li>
<li><p><strong>typ</strong> (<em>str</em>) – Type of the dataset. Examples are “coords”, “dists”, and “angles”. For logging purposes only.</p></li>
<li><p><strong>arena</strong> (<em>str</em>) – Type of the arena. Must be one of “circular-autodetect”, “circular-manual”, or “polygon-manual”. Handled internally.</p></li>
<li><p><strong>arena_dims</strong> (<em>np.array</em>) – Dimensions of the arena in mm.</p></li>
<li><p><strong>animal_ids</strong> (<em>list</em>) – list of animal ids.</p></li>
<li><p><strong>center</strong> (<em>str</em>) – Type of the center. Handled internally.</p></li>
<li><p><strong>polar</strong> (<em>bool</em>) – Whether the dataset is in polar coordinates. Handled internally.</p></li>
<li><p><strong>exp_conditions</strong> (<em>dict</em>) – dictionary with experiment IDs as keys and experimental conditions as values.</p></li>
<li><p><strong>propagate_labels</strong> (<em>bool</em>) – Whether to propagate phenotypic labels from the original experiments to the transformed dataset.</p></li>
<li><p><strong>propagate_annotations</strong> (<em>Dict</em>) – Dictionary of annotations to propagate. If provided, the supervised annotations of the individual experiments are propagated to the dataset.</p></li>
</ul>
</dd>
</dl>
</dd></dl>

</dd></dl>

</section>


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