<liclass="toctree-l2"><aclass="reference internal"href="examples.html#implementing-your-own-data-importer">Implementing your own data importer</a></li>
<spanid="classes-abstract-importer-module"></span><h2>classes.abstract_importer module<aclass="headerlink"href="#module-classes.abstract_importer"title="Permalink to this headline">¶</a></h2>
<emclass="property">class </em><codeclass="sig-prename descclassname">classes.abstract_importer.</code><codeclass="sig-name descname">AbstractImporter</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">file_path</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#classes.abstract_importer.AbstractImporter"title="Permalink to this definition">¶</a></dt>
<p>Abstract class that exposes all the necessary methods to process the trajectories and the net structure.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>file_path</strong> (<em>str</em>) – the file path</p>
</dd>
<dtclass="field-even">_concatenated_samples</dt>
<ddclass="field-even"><p>Dataframe containing the concatenation of all the processed trajectories</p>
</dd>
<dtclass="field-odd">_df_structure</dt>
<ddclass="field-odd"><p>Dataframe containing the structure of the network (edges)</p>
</dd>
<dtclass="field-even">_df_variables</dt>
<ddclass="field-even"><p>Dataframe containing the nodes cardinalities</p>
</dd>
<dtclass="field-odd">_sorter</dt>
<ddclass="field-odd"><p>A list containing the columns header (excluding the time column) of the <codeclass="docutils literal notranslate"><spanclass="pre">_concatenated_samples</span></code></p>
</dd>
</dl>
<divclass="admonition warning">
<pclass="admonition-title">Warning</p>
<p>The class members <codeclass="docutils literal notranslate"><spanclass="pre">_df_variables</span></code> and <codeclass="docutils literal notranslate"><spanclass="pre">_df_structure</span></code> HAVE to be properly constructed
as Pandas Dataframes with the following structure:
Header of _df_structure = [From_Node | To_Node]
Header of _df_variables = [Variable_Label | Variable_Cardinality]</p>
</div>
<divclass="admonition note">
<pclass="admonition-title">Note</p>
<p>If you don’t have prior network structure just leave <codeclass="docutils literal notranslate"><spanclass="pre">_df_structure</span></code> set to None.</p>
</div>
<divclass="admonition note">
<pclass="admonition-title">Note</p>
<p>See :class:<codeclass="docutils literal notranslate"><spanclass="pre">JsonImporter</span></code> for an example implementation</p>
<codeclass="sig-name descname">build_list_of_samples_array</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">data_frame</span><spanclass="p">:</span><spanclass="n">pandas.core.frame.DataFrame</span></em><spanclass="sig-paren">)</span>→ List<aclass="headerlink"href="#classes.abstract_importer.AbstractImporter.build_list_of_samples_array"title="Permalink to this definition">¶</a></dt>
<dd><p>Builds a List containing the columns of data_frame and converts them to a numpy array.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>data_frame</strong> (<em>pandas.Dataframe</em>) – the dataframe from which the columns have to be extracted and converted</p>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>the resulting list of numpy arrays</p>
<emclass="property">abstract </em><codeclass="sig-name descname">build_sorter</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">sample_frame</span><spanclass="p">:</span><spanclass="n">pandas.core.frame.DataFrame</span></em><spanclass="sig-paren">)</span>→ List<aclass="headerlink"href="#classes.abstract_importer.AbstractImporter.build_sorter"title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes the <codeclass="docutils literal notranslate"><spanclass="pre">_sorter</span></code> class member from a trajectory dataframe, exctracting the header of the frame
and keeping ONLY the variables symbolic labels, cutting out the time label in the header.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>sample_frame</strong> (<em>pandas.DataFrame</em>) – The dataframe from which extract the header</p>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>A list containing the processed header.</p>
<codeclass="sig-name descname">clear_concatenated_frame</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.abstract_importer.AbstractImporter.clear_concatenated_frame"title="Permalink to this definition">¶</a></dt>
<dd><p>Removes all values in the dataframe concatenated_samples.</p>
<codeclass="sig-name descname">compute_row_delta_in_all_samples_frames</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">df_samples_list</span><spanclass="p">:</span><spanclass="n">List</span></em><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.abstract_importer.AbstractImporter.compute_row_delta_in_all_samples_frames"title="Permalink to this definition">¶</a></dt>
<dd><p>Calls the method <codeclass="docutils literal notranslate"><spanclass="pre">compute_row_delta_sigle_samples_frame</span></code> on every dataframe present in the list
Concatenates the result in the dataframe <codeclass="docutils literal notranslate"><spanclass="pre">concatanated_samples</span></code></p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>df_samples_list</strong> (<em>List</em>) – the datframe’s list to be processed and concatenated</p>
</dd>
</dl>
<divclass="admonition warning">
<pclass="admonition-title">Warning</p>
<p>The Dataframe sample_frame has to follow the column structure of this header:
Header of sample_frame = [Time | Variable values]
The class member self._sorter HAS to be properly INITIALIZED (See class members definition doc)</p>
</div>
<divclass="admonition note">
<pclass="admonition-title">Note</p>
<p>After the call of this method the class member <codeclass="docutils literal notranslate"><spanclass="pre">concatanated_samples</span></code> will contain all processed
<codeclass="sig-name descname">compute_row_delta_sigle_samples_frame</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">sample_frame</span><spanclass="p">:</span><spanclass="n">pandas.core.frame.DataFrame</span></em>, <emclass="sig-param"><spanclass="n">columns_header</span><spanclass="p">:</span><spanclass="n">List</span></em>, <emclass="sig-param"><spanclass="n">shifted_cols_header</span><spanclass="p">:</span><spanclass="n">List</span></em><spanclass="sig-paren">)</span>→ pandas.core.frame.DataFrame<aclass="headerlink"href="#classes.abstract_importer.AbstractImporter.compute_row_delta_sigle_samples_frame"title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the difference between each value present in th time column.
Copies and shift by one position up all the values present in the remaining columns.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>sample_frame</strong> (<em>pandas.Dataframe</em>) – the traj to be processed</p></li>
<li><p><strong>columns_header</strong> (<em>List</em>) – the original header of sample_frame</p></li>
<li><p><strong>shifted_cols_header</strong> (<em>List</em>) – a copy of columns_header with changed names of the contents</p></li>
<p>the Dataframe <codeclass="docutils literal notranslate"><spanclass="pre">sample_frame</span></code> has to follow the column structure of this header:
Header of sample_frame = [Time | Variable values]</p>
<emclass="property">property </em><codeclass="sig-name descname">concatenated_samples</code><aclass="headerlink"href="#classes.abstract_importer.AbstractImporter.concatenated_samples"title="Permalink to this definition">¶</a></dt>
<emclass="property">abstract </em><codeclass="sig-name descname">dataset_id</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ object<aclass="headerlink"href="#classes.abstract_importer.AbstractImporter.dataset_id"title="Permalink to this definition">¶</a></dt>
<dd><p>If the original dataset contains multiple dataset, this method returns a unique id to identify the current
<emclass="property">property </em><codeclass="sig-name descname">file_path</code><aclass="headerlink"href="#classes.abstract_importer.AbstractImporter.file_path"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">sorter</code><aclass="headerlink"href="#classes.abstract_importer.AbstractImporter.sorter"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">structure</code><aclass="headerlink"href="#classes.abstract_importer.AbstractImporter.structure"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">variables</code><aclass="headerlink"href="#classes.abstract_importer.AbstractImporter.variables"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<divclass="section"id="module-classes.cache">
<spanid="classes-cache-module"></span><h2>classes.cache module<aclass="headerlink"href="#module-classes.cache"title="Permalink to this headline">¶</a></h2>
<dlclass="py class">
<dtid="classes.cache.Cache">
<emclass="property">class </em><codeclass="sig-prename descclassname">classes.cache.</code><codeclass="sig-name descname">Cache</code><aclass="headerlink"href="#classes.cache.Cache"title="Permalink to this definition">¶</a></dt>
<ddclass="field-odd"><p>a list of <codeclass="docutils literal notranslate"><spanclass="pre">Sets</span></code> objects of the parents to which the cim in cache at SAME
index is related</p>
</dd>
<dtclass="field-even">_actual_cache</dt>
<ddclass="field-even"><p>a list of setOfCims objects</p>
</dd>
</dl>
<dlclass="py method">
<dtid="classes.cache.Cache.clear">
<codeclass="sig-name descname">clear</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.cache.Cache.clear"title="Permalink to this definition">¶</a></dt>
<dd><p>Clear the contents both of <codeclass="docutils literal notranslate"><spanclass="pre">_actual_cache</span></code> and <codeclass="docutils literal notranslate"><spanclass="pre">_list_of_sets_of_parents</span></code>.</p>
</dd></dl>
<dlclass="py method">
<dtid="classes.cache.Cache.find">
<codeclass="sig-name descname">find</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">parents_comb</span><spanclass="p">:</span><spanclass="n">Set</span></em><spanclass="sig-paren">)</span>→<aclass="reference internal"href="#classes.set_of_cims.SetOfCims"title="classes.set_of_cims.SetOfCims">classes.set_of_cims.SetOfCims</a><aclass="headerlink"href="#classes.cache.Cache.find"title="Permalink to this definition">¶</a></dt>
<dd><p>Tries to find in cache given the symbolic parents combination <codeclass="docutils literal notranslate"><spanclass="pre">parents_comb</span></code> the <codeclass="docutils literal notranslate"><spanclass="pre">SetOfCims</span></code>
related to that <codeclass="docutils literal notranslate"><spanclass="pre">parents_comb</span></code>.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>parents_comb</strong> (<em>Set</em>) – the parents related to that <codeclass="docutils literal notranslate"><spanclass="pre">SetOfCims</span></code></p>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>A <codeclass="docutils literal notranslate"><spanclass="pre">SetOfCims</span></code> object if the <codeclass="docutils literal notranslate"><spanclass="pre">parents_comb</span></code> index is found in <codeclass="docutils literal notranslate"><spanclass="pre">_list_of_sets_of_parents</span></code>.
<codeclass="sig-name descname">put</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">parents_comb</span><spanclass="p">:</span><spanclass="n">Set</span></em>, <emclass="sig-param"><spanclass="n">socim</span><spanclass="p">:</span><spanclass="n"><aclass="reference internal"href="#classes.set_of_cims.SetOfCims"title="classes.set_of_cims.SetOfCims">classes.set_of_cims.SetOfCims</a></span></em><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.cache.Cache.put"title="Permalink to this definition">¶</a></dt>
<dd><p>Place in cache the <codeclass="docutils literal notranslate"><spanclass="pre">SetOfCims</span></code> object, and the related symbolic index <codeclass="docutils literal notranslate"><spanclass="pre">parents_comb</span></code> in
<li><p><strong>parents_comb</strong> (<em>Set</em>) – the symbolic set index</p></li>
<li><p><strong>socim</strong> (<aclass="reference internal"href="#classes.set_of_cims.SetOfCims"title="classes.set_of_cims.SetOfCims"><em>SetOfCims</em></a>) – the related SetOfCims object</p></li>
<spanid="classes-conditional-intensity-matrix-module"></span><h2>classes.conditional_intensity_matrix module<aclass="headerlink"href="#module-classes.conditional_intensity_matrix"title="Permalink to this headline">¶</a></h2>
<emclass="property">class </em><codeclass="sig-prename descclassname">classes.conditional_intensity_matrix.</code><codeclass="sig-name descname">ConditionalIntensityMatrix</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">state_residence_times</span><spanclass="p">:</span><spanclass="n">numpy.array</span></em>, <emclass="sig-param"><spanclass="n">state_transition_matrix</span><spanclass="p">:</span><spanclass="n">numpy.array</span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#classes.conditional_intensity_matrix.ConditionalIntensityMatrix"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">cim</code><aclass="headerlink"href="#classes.conditional_intensity_matrix.ConditionalIntensityMatrix.cim"title="Permalink to this definition">¶</a></dt>
<codeclass="sig-name descname">compute_cim_coefficients</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.conditional_intensity_matrix.ConditionalIntensityMatrix.compute_cim_coefficients"title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the coefficients of the matrix _cim by using the following equality q_xx’ = M[x, x’] / T[x].
The class member <codeclass="docutils literal notranslate"><spanclass="pre">_cim</span></code> will contain the computed cim</p>
<emclass="property">property </em><codeclass="sig-name descname">state_residence_times</code><aclass="headerlink"href="#classes.conditional_intensity_matrix.ConditionalIntensityMatrix.state_residence_times"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">state_transition_matrix</code><aclass="headerlink"href="#classes.conditional_intensity_matrix.ConditionalIntensityMatrix.state_transition_matrix"title="Permalink to this definition">¶</a></dt>
<spanid="classes-json-importer-module"></span><h2>classes.json_importer module<aclass="headerlink"href="#module-classes.json_importer"title="Permalink to this headline">¶</a></h2>
<dlclass="py class">
<dtid="classes.json_importer.JsonImporter">
<emclass="property">class </em><codeclass="sig-prename descclassname">classes.json_importer.</code><codeclass="sig-name descname">JsonImporter</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">file_path</span><spanclass="p">:</span><spanclass="n">str</span></em>, <emclass="sig-param"><spanclass="n">samples_label</span><spanclass="p">:</span><spanclass="n">str</span></em>, <emclass="sig-param"><spanclass="n">structure_label</span><spanclass="p">:</span><spanclass="n">str</span></em>, <emclass="sig-param"><spanclass="n">variables_label</span><spanclass="p">:</span><spanclass="n">str</span></em>, <emclass="sig-param"><spanclass="n">time_key</span><spanclass="p">:</span><spanclass="n">str</span></em>, <emclass="sig-param"><spanclass="n">variables_key</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#classes.json_importer.JsonImporter"title="Permalink to this definition">¶</a></dt>
<codeclass="sig-name descname">build_sorter</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">sample_frame</span><spanclass="p">:</span><spanclass="n">pandas.core.frame.DataFrame</span></em><spanclass="sig-paren">)</span>→ List<aclass="headerlink"href="#classes.json_importer.JsonImporter.build_sorter"title="Permalink to this definition">¶</a></dt>
<dd><p>Implements the abstract method build_sorter of the <codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">AbstractImporter</span></code> for this dataset.</p>
<codeclass="sig-name descname">clear_data_frame_list</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.json_importer.JsonImporter.clear_data_frame_list"title="Permalink to this definition">¶</a></dt>
<dd><p>Removes all values present in the dataframes in the list <codeclass="docutils literal notranslate"><spanclass="pre">_df_samples_list</span></code>.</p>
<codeclass="sig-name descname">dataset_id</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ object<aclass="headerlink"href="#classes.json_importer.JsonImporter.dataset_id"title="Permalink to this definition">¶</a></dt>
<dd><p>If the original dataset contains multiple dataset, this method returns a unique id to identify the current
<codeclass="sig-name descname">import_data</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">indx</span><spanclass="p">:</span><spanclass="n">int</span></em><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.json_importer.JsonImporter.import_data"title="Permalink to this definition">¶</a></dt>
<dd><p>Implements the abstract method of <codeclass="xref py py-class docutils literal notranslate"><spanclass="pre">AbstractImporter</span></code>.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>indx</strong> (<em>int</em>) – the index of the outer JsonArray to extract the data from</p>
<codeclass="sig-name descname">import_sampled_cims</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">raw_data</span><spanclass="p">:</span><spanclass="n">List</span></em>, <emclass="sig-param"><spanclass="n">indx</span><spanclass="p">:</span><spanclass="n">int</span></em>, <emclass="sig-param"><spanclass="n">cims_key</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→ Dict<aclass="headerlink"href="#classes.json_importer.JsonImporter.import_sampled_cims"title="Permalink to this definition">¶</a></dt>
<dd><p>Imports the synthetic CIMS in the dataset in a dictionary, using variables labels
as keys for the set of CIMS of a particular node.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p></li>
<li><p><strong>indx</strong> (<em>int</em>) – The index of the array from which the data have to be extracted</p></li>
<li><p><strong>cims_key</strong> (<em>string</em>) – the key where the json object cims are placed</p></li>
</ul>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>a dictionary containing the sampled CIMS for all the variables in the net</p>
<codeclass="sig-name descname">import_structure</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">raw_data</span><spanclass="p">:</span><spanclass="n">List</span></em><spanclass="sig-paren">)</span>→ pandas.core.frame.DataFrame<aclass="headerlink"href="#classes.json_importer.JsonImporter.import_structure"title="Permalink to this definition">¶</a></dt>
<dd><p>Imports in a dataframe the data in the list raw_data at the key <codeclass="docutils literal notranslate"><spanclass="pre">_structure_label</span></code></p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>Dataframe containg the starting node a ending node of every arc of the network</p>
<codeclass="sig-name descname">import_trajectories</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">raw_data</span><spanclass="p">:</span><spanclass="n">List</span></em><spanclass="sig-paren">)</span>→ List<aclass="headerlink"href="#classes.json_importer.JsonImporter.import_trajectories"title="Permalink to this definition">¶</a></dt>
<dd><p>Imports the trajectories from the list of dicts <codeclass="docutils literal notranslate"><spanclass="pre">raw_data</span></code>.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>List of dataframes containing all the trajectories</p>
<codeclass="sig-name descname">import_variables</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">raw_data</span><spanclass="p">:</span><spanclass="n">List</span></em><spanclass="sig-paren">)</span>→ pandas.core.frame.DataFrame<aclass="headerlink"href="#classes.json_importer.JsonImporter.import_variables"title="Permalink to this definition">¶</a></dt>
<dd><p>Imports the data in <codeclass="docutils literal notranslate"><spanclass="pre">raw_data</span></code> at the key <codeclass="docutils literal notranslate"><spanclass="pre">_variables_label</span></code>.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>Datframe containg the variables simbolic labels and their cardinalities</p>
<codeclass="sig-name descname">normalize_trajectories</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">raw_data</span><spanclass="p">:</span><spanclass="n">List</span></em>, <emclass="sig-param"><spanclass="n">indx</span><spanclass="p">:</span><spanclass="n">int</span></em>, <emclass="sig-param"><spanclass="n">trajectories_key</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→ List<aclass="headerlink"href="#classes.json_importer.JsonImporter.normalize_trajectories"title="Permalink to this definition">¶</a></dt>
<dd><p>Extracts the trajectories in <codeclass="docutils literal notranslate"><spanclass="pre">raw_data</span></code> at the index <codeclass="docutils literal notranslate"><spanclass="pre">index</span></code> at the key <codeclass="docutils literal notranslate"><spanclass="pre">trajectories</span><spanclass="pre">key</span></code>.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p></li>
<li><p><strong>indx</strong> (<em>int</em>) – The index of the array from which the data have to be extracted</p></li>
<li><p><strong>trajectories_key</strong> (<em>string</em>) – the key of the trajectories objects</p></li>
</ul>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>A list of daframes containg the trajectories</p>
<codeclass="sig-name descname">one_level_normalizing</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">raw_data</span><spanclass="p">:</span><spanclass="n">List</span></em>, <emclass="sig-param"><spanclass="n">indx</span><spanclass="p">:</span><spanclass="n">int</span></em>, <emclass="sig-param"><spanclass="n">key</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→ pandas.core.frame.DataFrame<aclass="headerlink"href="#classes.json_importer.JsonImporter.one_level_normalizing"title="Permalink to this definition">¶</a></dt>
<dd><p>Extracts the one-level nested data in the list <codeclass="docutils literal notranslate"><spanclass="pre">raw_data</span></code> at the index <codeclass="docutils literal notranslate"><spanclass="pre">indx</span></code> at the key <codeclass="docutils literal notranslate"><spanclass="pre">key</span></code>.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>raw_data</strong> (<em>List</em>) – List of Dicts</p></li>
<li><p><strong>indx</strong> (<em>int</em>) – The index of the array from which the data have to be extracted</p></li>
<li><p><strong>key</strong> (<em>string</em>) – the key for the Dicts from which exctract data</p></li>
<codeclass="sig-name descname">read_json_file</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ List<aclass="headerlink"href="#classes.json_importer.JsonImporter.read_json_file"title="Permalink to this definition">¶</a></dt>
<dd><p>Reads the JSON file in the path self.filePath.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Returns</dt>
<ddclass="field-odd"><p>The contents of the json file</p>
<spanid="classes-network-graph-module"></span><h2>classes.network_graph module<aclass="headerlink"href="#module-classes.network_graph"title="Permalink to this headline">¶</a></h2>
<dlclass="py class">
<dtid="classes.network_graph.NetworkGraph">
<emclass="property">class </em><codeclass="sig-prename descclassname">classes.network_graph.</code><codeclass="sig-name descname">NetworkGraph</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">graph_struct</span><spanclass="p">:</span><spanclass="n"><aclass="reference internal"href="#classes.structure.Structure"title="classes.structure.Structure">classes.structure.Structure</a></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#classes.network_graph.NetworkGraph"title="Permalink to this definition">¶</a></dt>
<p>Abstracts the infos contained in the Structure class in the form of a directed graph.
Has the task of creating all the necessary filtering and indexing structures for parameters estimation</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>graph_struct</strong> (<aclass="reference internal"href="#classes.structure.Structure"title="classes.structure.Structure"><em>Structure</em></a>) – the <codeclass="docutils literal notranslate"><spanclass="pre">Structure</span></code> object from which infos about the net will be extracted</p>
<codeclass="sig-name descname">add_edges</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">list_of_edges</span><spanclass="p">:</span><spanclass="n">List</span></em><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.network_graph.NetworkGraph.add_edges"title="Permalink to this definition">¶</a></dt>
<dd><p>Add the edges to the <codeclass="docutils literal notranslate"><spanclass="pre">_graph</span></code> contained in the list <codeclass="docutils literal notranslate"><spanclass="pre">list_of_edges</span></code>.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>list_of_edges</strong> (<em>List</em>) – the list containing of tuples containing the edges</p>
<codeclass="sig-name descname">add_nodes</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">list_of_nodes</span><spanclass="p">:</span><spanclass="n">List</span></em><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.network_graph.NetworkGraph.add_nodes"title="Permalink to this definition">¶</a></dt>
<dd><p>Adds the nodes to the <codeclass="docutils literal notranslate"><spanclass="pre">_graph</span></code> contained in the list of nodes <codeclass="docutils literal notranslate"><spanclass="pre">list_of_nodes</span></code>.
Sets all the properties that identify a nodes (index, positional index, cardinality)</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>list_of_nodes</strong> (<em>List</em>) – the nodes to add to <codeclass="docutils literal notranslate"><spanclass="pre">_graph</span></code></p>
<emclass="property">static </em><codeclass="sig-name descname">build_p_comb_structure_for_a_node</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">parents_values</span><spanclass="p">:</span><spanclass="n">List</span></em><spanclass="sig-paren">)</span>→ numpy.ndarray<aclass="headerlink"href="#classes.network_graph.NetworkGraph.build_p_comb_structure_for_a_node"title="Permalink to this definition">¶</a></dt>
<dd><p>Builds the combinatorial structure that contains the combinations of all the values contained in
<emclass="property">static </em><codeclass="sig-name descname">build_time_columns_filtering_for_a_node</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_indx</span><spanclass="p">:</span><spanclass="n">int</span></em>, <emclass="sig-param"><spanclass="n">p_indxs</span><spanclass="p">:</span><spanclass="n">List</span></em><spanclass="sig-paren">)</span>→ numpy.ndarray<aclass="headerlink"href="#classes.network_graph.NetworkGraph.build_time_columns_filtering_for_a_node"title="Permalink to this definition">¶</a></dt>
<dd><p>Builds the necessary structure to filter the desired columns indicated by <codeclass="docutils literal notranslate"><spanclass="pre">node_indx</span></code> and <codeclass="docutils literal notranslate"><spanclass="pre">p_indxs</span></code>
in the dataset.
This structute will be used in the computation of the state res times.
:param node_indx: the index of the node
:type node_indx: int
:param p_indxs: the indexes of the node’s parents
:type p_indxs: List
:return: The filtering structure for times estimation
<emclass="property">static </em><codeclass="sig-name descname">build_time_scalar_indexing_structure_for_a_node</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_states</span><spanclass="p">:</span><spanclass="n">int</span></em>, <emclass="sig-param"><spanclass="n">parents_vals</span><spanclass="p">:</span><spanclass="n">List</span></em><spanclass="sig-paren">)</span>→ numpy.ndarray<aclass="headerlink"href="#classes.network_graph.NetworkGraph.build_time_scalar_indexing_structure_for_a_node"title="Permalink to this definition">¶</a></dt>
<dd><p>Builds an indexing structure for the computation of state residence times values.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>node_states</strong> (<em>int</em>) – the node cardinality</p></li>
<li><p><strong>parents_vals</strong> (<em>List</em>) – the caridinalites of the node’s parents</p></li>
</ul>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>The time indexing structure</p>
<emclass="property">static </em><codeclass="sig-name descname">build_transition_filtering_for_a_node</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_indx</span><spanclass="p">:</span><spanclass="n">int</span></em>, <emclass="sig-param"><spanclass="n">p_indxs</span><spanclass="p">:</span><spanclass="n">List</span></em>, <emclass="sig-param"><spanclass="n">nodes_number</span><spanclass="p">:</span><spanclass="n">int</span></em><spanclass="sig-paren">)</span>→ numpy.ndarray<aclass="headerlink"href="#classes.network_graph.NetworkGraph.build_transition_filtering_for_a_node"title="Permalink to this definition">¶</a></dt>
<dd><p>Builds the necessary structure to filter the desired columns indicated by <codeclass="docutils literal notranslate"><spanclass="pre">node_indx</span></code> and <codeclass="docutils literal notranslate"><spanclass="pre">p_indxs</span></code>
in the dataset.
This structure will be used in the computation of the state transitions values.
:param node_indx: the index of the node
:type node_indx: int
:param p_indxs: the indexes of the node’s parents
:type p_indxs: List
:param nodes_number: the total number of nodes in the dataset
:type nodes_number: int
:return: The filtering structure for transitions estimation
<emclass="property">static </em><codeclass="sig-name descname">build_transition_scalar_indexing_structure_for_a_node</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_states_number</span><spanclass="p">:</span><spanclass="n">int</span></em>, <emclass="sig-param"><spanclass="n">parents_vals</span><spanclass="p">:</span><spanclass="n">List</span></em><spanclass="sig-paren">)</span>→ numpy.ndarray<aclass="headerlink"href="#classes.network_graph.NetworkGraph.build_transition_scalar_indexing_structure_for_a_node"title="Permalink to this definition">¶</a></dt>
<dd><p>Builds an indexing structure for the computation of state transitions values.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>node_states_number</strong> (<em>int</em>) – the node cardinality</p></li>
<li><p><strong>parents_vals</strong> (<em>List</em>) – the caridinalites of the node’s parents</p></li>
<codeclass="sig-name descname">clear_indexing_filtering_structures</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.network_graph.NetworkGraph.clear_indexing_filtering_structures"title="Permalink to this definition">¶</a></dt>
<dd><p>Initialize all the filtering/indexing structures.</p>
</dd></dl>
<dlclass="py method">
<dtid="classes.network_graph.NetworkGraph.edges">
<emclass="property">property </em><codeclass="sig-name descname">edges</code><aclass="headerlink"href="#classes.network_graph.NetworkGraph.edges"title="Permalink to this definition">¶</a></dt>
<codeclass="sig-name descname">fast_init</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_id</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.network_graph.NetworkGraph.fast_init"title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes all the necessary structures for parameters estimation of the node identified by the label
node_id</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the label of the node</p>
<codeclass="sig-name descname">get_node_indx</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_id</span></em><spanclass="sig-paren">)</span>→ int<aclass="headerlink"href="#classes.network_graph.NetworkGraph.get_node_indx"title="Permalink to this definition">¶</a></dt>
<codeclass="sig-name descname">get_ordered_by_indx_set_of_parents</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→ Tuple<aclass="headerlink"href="#classes.network_graph.NetworkGraph.get_ordered_by_indx_set_of_parents"title="Permalink to this definition">¶</a></dt>
<dd><p>Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely
<codeclass="sig-name descname">get_parents_by_id</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_id</span></em><spanclass="sig-paren">)</span>→ List<aclass="headerlink"href="#classes.network_graph.NetworkGraph.get_parents_by_id"title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a list of labels of the parents of the node <codeclass="docutils literal notranslate"><spanclass="pre">node_id</span></code></p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>a List of labels of the parents</p>
<codeclass="sig-name descname">get_positional_node_indx</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_id</span></em><spanclass="sig-paren">)</span>→ int<aclass="headerlink"href="#classes.network_graph.NetworkGraph.get_positional_node_indx"title="Permalink to this definition">¶</a></dt>
<codeclass="sig-name descname">get_states_number</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_id</span></em><spanclass="sig-paren">)</span>→ int<aclass="headerlink"href="#classes.network_graph.NetworkGraph.get_states_number"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dlclass="py method">
<dtid="classes.network_graph.NetworkGraph.nodes">
<emclass="property">property </em><codeclass="sig-name descname">nodes</code><aclass="headerlink"href="#classes.network_graph.NetworkGraph.nodes"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">nodes_indexes</code><aclass="headerlink"href="#classes.network_graph.NetworkGraph.nodes_indexes"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">nodes_values</code><aclass="headerlink"href="#classes.network_graph.NetworkGraph.nodes_values"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">p_combs</code><aclass="headerlink"href="#classes.network_graph.NetworkGraph.p_combs"title="Permalink to this definition">¶</a></dt>
<codeclass="sig-name descname">remove_node</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_id</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.network_graph.NetworkGraph.remove_node"title="Permalink to this definition">¶</a></dt>
<dd><p>Remove the node <codeclass="docutils literal notranslate"><spanclass="pre">node_id</span></code> from all the class members.
Initialize all the filtering/indexing structures.</p>
<emclass="property">property </em><codeclass="sig-name descname">time_filtering</code><aclass="headerlink"href="#classes.network_graph.NetworkGraph.time_filtering"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">time_scalar_indexing_strucure</code><aclass="headerlink"href="#classes.network_graph.NetworkGraph.time_scalar_indexing_strucure"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">transition_filtering</code><aclass="headerlink"href="#classes.network_graph.NetworkGraph.transition_filtering"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">transition_scalar_indexing_structure</code><aclass="headerlink"href="#classes.network_graph.NetworkGraph.transition_scalar_indexing_structure"title="Permalink to this definition">¶</a></dt>
<spanid="classes-parameters-estimator-module"></span><h2>classes.parameters_estimator module<aclass="headerlink"href="#module-classes.parameters_estimator"title="Permalink to this headline">¶</a></h2>
<emclass="property">class </em><codeclass="sig-prename descclassname">classes.parameters_estimator.</code><codeclass="sig-name descname">ParametersEstimator</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">trajectories</span><spanclass="p">:</span><spanclass="n"><aclass="reference internal"href="#classes.trajectory.Trajectory"title="classes.trajectory.Trajectory">classes.trajectory.Trajectory</a></span></em>, <emclass="sig-param"><spanclass="n">net_graph</span><spanclass="p">:</span><spanclass="n"><aclass="reference internal"href="#classes.network_graph.NetworkGraph"title="classes.network_graph.NetworkGraph">classes.network_graph.NetworkGraph</a></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#classes.parameters_estimator.ParametersEstimator"title="Permalink to this definition">¶</a></dt>
<p>Has the task of computing the cims of particular node given the trajectories and the net structure
in the graph <codeclass="docutils literal notranslate"><spanclass="pre">_net_graph</span></code>.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>trajectories</strong> (<aclass="reference internal"href="#classes.trajectory.Trajectory"title="classes.trajectory.Trajectory"><em>Trajectory</em></a>) – the trajectories</p></li>
<li><p><strong>net_graph</strong> (<aclass="reference internal"href="#classes.network_graph.NetworkGraph"title="classes.network_graph.NetworkGraph"><em>NetworkGraph</em></a>) – the net structure</p></li>
</ul>
</dd>
<dtclass="field-even">_single_set_of_cims</dt>
<ddclass="field-even"><p>the set of cims object that will hold the cims of the node</p>
<codeclass="sig-name descname">compute_parameters_for_node</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_id</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→<aclass="reference internal"href="#classes.set_of_cims.SetOfCims"title="classes.set_of_cims.SetOfCims">classes.set_of_cims.SetOfCims</a><aclass="headerlink"href="#classes.parameters_estimator.ParametersEstimator.compute_parameters_for_node"title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the CIMS of the node identified by the label <codeclass="docutils literal notranslate"><spanclass="pre">node_id</span></code>.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>A SetOfCims object filled with the computed CIMS</p>
<emclass="property">static </em><codeclass="sig-name descname">compute_state_res_time_for_node</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">times</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em>, <emclass="sig-param"><spanclass="n">trajectory</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em>, <emclass="sig-param"><spanclass="n">cols_filter</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em>, <emclass="sig-param"><spanclass="n">scalar_indexes_struct</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em>, <emclass="sig-param"><spanclass="n">T</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.parameters_estimator.ParametersEstimator.compute_state_res_time_for_node"title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the state residence times for a node and fill the matrix <codeclass="docutils literal notranslate"><spanclass="pre">T</span></code> with the results</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>node_indx</strong> (<em>int</em>) – the index of the node</p></li>
<li><p><strong>times</strong> (<em>numpy.array</em>) – the times deltas vector</p></li>
<li><p><strong>trajectory</strong> (<em>numpy.ndArray</em>) – the trajectory</p></li>
<li><p><strong>cols_filter</strong> (<em>numpy.array</em>) – the columns filtering structure</p></li>
<li><p><strong>scalar_indexes_struct</strong> (<em>numpy.array</em>) – the indexing structure</p></li>
<li><p><strong>T</strong> (<em>numpy.ndArray</em>) – the state residence times vectors</p></li>
<emclass="property">static </em><codeclass="sig-name descname">compute_state_transitions_for_a_node</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_indx</span><spanclass="p">:</span><spanclass="n">int</span></em>, <emclass="sig-param"><spanclass="n">trajectory</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em>, <emclass="sig-param"><spanclass="n">cols_filter</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em>, <emclass="sig-param"><spanclass="n">scalar_indexing</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em>, <emclass="sig-param"><spanclass="n">M</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.parameters_estimator.ParametersEstimator.compute_state_transitions_for_a_node"title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the state residence times for a node and fill the matrices <codeclass="docutils literal notranslate"><spanclass="pre">M</span></code> with the results.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>node_indx</strong> (<em>int</em>) – the index of the node</p></li>
<li><p><strong>trajectory</strong> (<em>numpy.ndArray</em>) – the trajectory</p></li>
<li><p><strong>cols_filter</strong> (<em>numpy.array</em>) – the columns filtering structure</p></li>
<li><p><strong>scalar_indexing</strong> (<em>numpy.array</em>) – the indexing structure</p></li>
<li><p><strong>M</strong> (<em>numpy.ndArray</em>) – the state transitions matrices</p></li>
<codeclass="sig-name descname">fast_init</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_id</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.parameters_estimator.ParametersEstimator.fast_init"title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes all the necessary structures for the parameters estimation for the node <codeclass="docutils literal notranslate"><spanclass="pre">node_id</span></code>.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p>
<spanid="classes-sample-path-module"></span><h2>classes.sample_path module<aclass="headerlink"href="#module-classes.sample_path"title="Permalink to this headline">¶</a></h2>
<dlclass="py class">
<dtid="classes.sample_path.SamplePath">
<emclass="property">class </em><codeclass="sig-prename descclassname">classes.sample_path.</code><codeclass="sig-name descname">SamplePath</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">importer</span><spanclass="p">:</span><spanclass="n"><aclass="reference internal"href="#classes.abstract_importer.AbstractImporter"title="classes.abstract_importer.AbstractImporter">classes.abstract_importer.AbstractImporter</a></span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#classes.sample_path.SamplePath"title="Permalink to this definition">¶</a></dt>
<p>Aggregates all the informations about the trajectories, the real structure of the sampled net and variables
cardinalites. Has the task of creating the objects <codeclass="docutils literal notranslate"><spanclass="pre">Trajectory</span></code> and <codeclass="docutils literal notranslate"><spanclass="pre">Structure</span></code> that will
contain the mentioned data.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>importer</strong> (<aclass="reference internal"href="#classes.abstract_importer.AbstractImporter"title="classes.abstract_importer.AbstractImporter"><em>AbstractImporter</em></a>) – the Importer object which contains the imported and processed data</p>
</dd>
<dtclass="field-even">_trajectories</dt>
<ddclass="field-even"><p>the <codeclass="docutils literal notranslate"><spanclass="pre">Trajectory</span></code> object that will contain all the concatenated trajectories</p>
</dd>
<dtclass="field-odd">_structure</dt>
<ddclass="field-odd"><p>the <codeclass="docutils literal notranslate"><spanclass="pre">Structure</span></code> Object that will contain all the structural infos about the net</p>
</dd>
<dtclass="field-even">_total_variables_count</dt>
<ddclass="field-even"><p>the number of variables in the net</p>
<codeclass="sig-name descname">build_structure</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.sample_path.SamplePath.build_structure"title="Permalink to this definition">¶</a></dt>
<dd><p>Builds the <codeclass="docutils literal notranslate"><spanclass="pre">Structure</span></code> object that aggregates all the infos about the net.</p>
<codeclass="sig-name descname">build_trajectories</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.sample_path.SamplePath.build_trajectories"title="Permalink to this definition">¶</a></dt>
<dd><p>Builds the Trajectory object that will contain all the trajectories.
Clears all the unused dataframes in <codeclass="docutils literal notranslate"><spanclass="pre">_importer</span></code> Object</p>
</dd></dl>
<dlclass="py method">
<dtid="classes.sample_path.SamplePath.structure">
<emclass="property">property </em><codeclass="sig-name descname">structure</code><aclass="headerlink"href="#classes.sample_path.SamplePath.structure"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">total_variables_count</code><aclass="headerlink"href="#classes.sample_path.SamplePath.total_variables_count"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">trajectories</code><aclass="headerlink"href="#classes.sample_path.SamplePath.trajectories"title="Permalink to this definition">¶</a></dt>
<spanid="classes-set-of-cims-module"></span><h2>classes.set_of_cims module<aclass="headerlink"href="#module-classes.set_of_cims"title="Permalink to this headline">¶</a></h2>
<dlclass="py class">
<dtid="classes.set_of_cims.SetOfCims">
<emclass="property">class </em><codeclass="sig-prename descclassname">classes.set_of_cims.</code><codeclass="sig-name descname">SetOfCims</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_id</span><spanclass="p">:</span><spanclass="n">str</span></em>, <emclass="sig-param"><spanclass="n">parents_states_number</span><spanclass="p">:</span><spanclass="n">List</span></em>, <emclass="sig-param"><spanclass="n">node_states_number</span><spanclass="p">:</span><spanclass="n">int</span></em>, <emclass="sig-param"><spanclass="n">p_combs</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#classes.set_of_cims.SetOfCims"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">actual_cims</code><aclass="headerlink"href="#classes.set_of_cims.SetOfCims.actual_cims"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dlclass="py method">
<dtid="classes.set_of_cims.SetOfCims.build_cims">
<codeclass="sig-name descname">build_cims</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">state_res_times</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em>, <emclass="sig-param"><spanclass="n">transition_matrices</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.set_of_cims.SetOfCims.build_cims"title="Permalink to this definition">¶</a></dt>
<dd><p>Build the <codeclass="docutils literal notranslate"><spanclass="pre">ConditionalIntensityMatrix</span></code> objects given the state residence times and transitions matrices.
Compute the cim coefficients.The class member <codeclass="docutils literal notranslate"><spanclass="pre">_actual_cims</span></code> will contain the computed cims.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>state_res_times</strong> (<em>numpy.ndArray</em>) – the state residence times matrix</p></li>
<li><p><strong>transition_matrices</strong> (<em>numpy.ndArray</em>) – the transition matrices</p></li>
<codeclass="sig-name descname">build_times_and_transitions_structures</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.set_of_cims.SetOfCims.build_times_and_transitions_structures"title="Permalink to this definition">¶</a></dt>
<dd><p>Initializes at the correct dimensions the state residence times matrix and the state transition matrices.</p>
<codeclass="sig-name descname">filter_cims_with_mask</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">mask_arr</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em>, <emclass="sig-param"><spanclass="n">comb</span><spanclass="p">:</span><spanclass="n">List</span></em><spanclass="sig-paren">)</span>→ numpy.ndarray<aclass="headerlink"href="#classes.set_of_cims.SetOfCims.filter_cims_with_mask"title="Permalink to this definition">¶</a></dt>
<dd><p>Filter the cims contained in the array <codeclass="docutils literal notranslate"><spanclass="pre">_actual_cims</span></code> given the boolean mask <codeclass="docutils literal notranslate"><spanclass="pre">mask_arr</span></code> and the index
<codeclass="sig-name descname">get_cims_number</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="headerlink"href="#classes.set_of_cims.SetOfCims.get_cims_number"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dlclass="py method">
<dtid="classes.set_of_cims.SetOfCims.p_combs">
<emclass="property">property </em><codeclass="sig-name descname">p_combs</code><aclass="headerlink"href="#classes.set_of_cims.SetOfCims.p_combs"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<divclass="section"id="module-classes.structure">
<spanid="classes-structure-module"></span><h2>classes.structure module<aclass="headerlink"href="#module-classes.structure"title="Permalink to this headline">¶</a></h2>
<dlclass="py class">
<dtid="classes.structure.Structure">
<emclass="property">class </em><codeclass="sig-prename descclassname">classes.structure.</code><codeclass="sig-name descname">Structure</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">nodes_labels_list</span><spanclass="p">:</span><spanclass="n">List</span></em>, <emclass="sig-param"><spanclass="n">nodes_indexes_arr</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em>, <emclass="sig-param"><spanclass="n">nodes_vals_arr</span><spanclass="p">:</span><spanclass="n">numpy.ndarray</span></em>, <emclass="sig-param"><spanclass="n">edges_list</span><spanclass="p">:</span><spanclass="n">List</span></em>, <emclass="sig-param"><spanclass="n">total_variables_number</span><spanclass="p">:</span><spanclass="n">int</span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#classes.structure.Structure"title="Permalink to this definition">¶</a></dt>
<p>Contains all the infos about the network structure(nodes labels, nodes caridinalites, edges, indexes)</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>nodes_labels_list</strong> (<em>List</em>) – the symbolic names of the variables</p></li>
<li><p><strong>nodes_indexes_arr</strong> (<em>numpy.ndArray</em>) – the indexes of the nodes</p></li>
<li><p><strong>nodes_vals_arr</strong> (<em>numpy.ndArray</em>) – the cardinalites of the nodes</p></li>
<li><p><strong>edges_list</strong> (<em>List</em>) – the edges of the network</p></li>
<li><p><strong>total_variables_number</strong> (<em>int</em>) – the total number of variables in the dataset</p></li>
</ul>
</dd>
</dl>
<dlclass="py method">
<dtid="classes.structure.Structure.edges">
<emclass="property">property </em><codeclass="sig-name descname">edges</code><aclass="headerlink"href="#classes.structure.Structure.edges"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dlclass="py method">
<dtid="classes.structure.Structure.get_node_id">
<codeclass="sig-name descname">get_node_id</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_indx</span><spanclass="p">:</span><spanclass="n">int</span></em><spanclass="sig-paren">)</span>→ str<aclass="headerlink"href="#classes.structure.Structure.get_node_id"title="Permalink to this definition">¶</a></dt>
<dd><p>Given the <codeclass="docutils literal notranslate"><spanclass="pre">node_index</span></code> returns the node label.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>node_indx</strong> (<em>int</em>) – the node index</p>
<codeclass="sig-name descname">get_node_indx</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_id</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→ int<aclass="headerlink"href="#classes.structure.Structure.get_node_indx"title="Permalink to this definition">¶</a></dt>
<dd><p>Given the <codeclass="docutils literal notranslate"><spanclass="pre">node_index</span></code> returns the node label.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>node_id</strong> (<em>string</em>) – the node label</p>
<codeclass="sig-name descname">get_positional_node_indx</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_id</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→ int<aclass="headerlink"href="#classes.structure.Structure.get_positional_node_indx"title="Permalink to this definition">¶</a></dt>
<codeclass="sig-name descname">get_states_number</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→ int<aclass="headerlink"href="#classes.structure.Structure.get_states_number"title="Permalink to this definition">¶</a></dt>
<dd><p>Given the node label <codeclass="docutils literal notranslate"><spanclass="pre">node</span></code> returns the cardinality of the node.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>node</strong> (<em>string</em>) – the node label</p>
<emclass="property">property </em><codeclass="sig-name descname">nodes_indexes</code><aclass="headerlink"href="#classes.structure.Structure.nodes_indexes"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dlclass="py method">
<dtid="classes.structure.Structure.nodes_labels">
<emclass="property">property </em><codeclass="sig-name descname">nodes_labels</code><aclass="headerlink"href="#classes.structure.Structure.nodes_labels"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dlclass="py method">
<dtid="classes.structure.Structure.nodes_values">
<emclass="property">property </em><codeclass="sig-name descname">nodes_values</code><aclass="headerlink"href="#classes.structure.Structure.nodes_values"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dlclass="py method">
<dtid="classes.structure.Structure.remove_node">
<codeclass="sig-name descname">remove_node</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_id</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.structure.Structure.remove_node"title="Permalink to this definition">¶</a></dt>
<dd><p>Remove the node <codeclass="docutils literal notranslate"><spanclass="pre">node_id</span></code> from all the class members.
The class member <codeclass="docutils literal notranslate"><spanclass="pre">_total_variables_number</span></code> since it refers to the total number of variables in the dataset.</p>
<emclass="property">property </em><codeclass="sig-name descname">total_variables_number</code><aclass="headerlink"href="#classes.structure.Structure.total_variables_number"title="Permalink to this definition">¶</a></dt>
<spanid="classes-structure-estimator-module"></span><h2>classes.structure_estimator module<aclass="headerlink"href="#module-classes.structure_estimator"title="Permalink to this headline">¶</a></h2>
<emclass="property">class </em><codeclass="sig-prename descclassname">classes.structure_estimator.</code><codeclass="sig-name descname">StructureEstimator</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">sample_path</span><spanclass="p">:</span><spanclass="n"><aclass="reference internal"href="#classes.sample_path.SamplePath"title="classes.sample_path.SamplePath">classes.sample_path.SamplePath</a></span></em>, <emclass="sig-param"><spanclass="n">exp_test_alfa</span><spanclass="p">:</span><spanclass="n">float</span></em>, <emclass="sig-param"><spanclass="n">chi_test_alfa</span><spanclass="p">:</span><spanclass="n">float</span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#classes.structure_estimator.StructureEstimator"title="Permalink to this definition">¶</a></dt>
<p>Has the task of estimating the network structure given the trajectories in <codeclass="docutils literal notranslate"><spanclass="pre">samplepath</span></code>.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>sample_path</strong> (<aclass="reference internal"href="#classes.sample_path.SamplePath"title="classes.sample_path.SamplePath"><em>SamplePath</em></a>) – the _sample_path object containing the trajectories and the real structure</p></li>
<li><p><strong>exp_test_alfa</strong> (<em>float</em>) – the significance level for the exponential Hp test</p></li>
<li><p><strong>chi_test_alfa</strong> (<em>float</em>) – the significance level for the chi Hp test</p></li>
<ddclass="field-odd"><p>the complete directed graph built using the nodes labels in <codeclass="docutils literal notranslate"><spanclass="pre">_nodes</span></code></p>
<codeclass="sig-name descname">adjacency_matrix</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ numpy.ndarray<aclass="headerlink"href="#classes.structure_estimator.StructureEstimator.adjacency_matrix"title="Permalink to this definition">¶</a></dt>
<dd><p>Converts the estimated structrure <codeclass="docutils literal notranslate"><spanclass="pre">_complete_graph</span></code> to a boolean adjacency matrix representation.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Returns</dt>
<ddclass="field-odd"><p>The adjacency matrix of the graph <codeclass="docutils literal notranslate"><spanclass="pre">_complete_graph</span></code></p>
<emclass="property">static </em><codeclass="sig-name descname">build_complete_graph</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">node_ids</span><spanclass="p">:</span><spanclass="n">List</span></em><spanclass="sig-paren">)</span>→ networkx.classes.digraph.DiGraph<aclass="headerlink"href="#classes.structure_estimator.StructureEstimator.build_complete_graph"title="Permalink to this definition">¶</a></dt>
<dd><p>Builds a complete directed graph (no self loops) given the nodes labels in the list <codeclass="docutils literal notranslate"><spanclass="pre">node_ids</span></code>:</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><p><strong>node_ids</strong> (<em>List</em>) – the list of nodes labels</p>
<codeclass="sig-name descname">complete_test</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">test_parent</span><spanclass="p">:</span><spanclass="n">str</span></em>, <emclass="sig-param"><spanclass="n">test_child</span><spanclass="p">:</span><spanclass="n">str</span></em>, <emclass="sig-param"><spanclass="n">parent_set</span><spanclass="p">:</span><spanclass="n">List</span></em>, <emclass="sig-param"><spanclass="n">child_states_numb</span><spanclass="p">:</span><spanclass="n">int</span></em>, <emclass="sig-param"><spanclass="n">tot_vars_count</span><spanclass="p">:</span><spanclass="n">int</span></em><spanclass="sig-paren">)</span>→ bool<aclass="headerlink"href="#classes.structure_estimator.StructureEstimator.complete_test"title="Permalink to this definition">¶</a></dt>
<dd><p>Performs a complete independence test on the directed graphs G1 = {test_child U parent_set}
G2 = {G1 U test_parent} (added as an additional parent of the test_child).
Generates all the necessary structures and datas to perform the tests.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>test_parent</strong> (<em>string</em>) – the node label of the test parent</p></li>
<li><p><strong>test_child</strong> (<em>string</em>) – the node label of the child</p></li>
<li><p><strong>parent_set</strong> (<em>List</em>) – the common parent set</p></li>
<li><p><strong>child_states_numb</strong> (<em>int</em>) – the cardinality of the <codeclass="docutils literal notranslate"><spanclass="pre">test_child</span></code></p></li>
<li><p><strong>tot_vars_count</strong> (<em>int</em>) – the total number of variables in the net</p></li>
</ul>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>True iff test_child and test_parent are independent given the sep_set parent_set. False otherwise</p>
<codeclass="sig-name descname">ctpc_algorithm</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.structure_estimator.StructureEstimator.ctpc_algorithm"title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the CTPC algorithm over the entire net.</p>
<emclass="property">static </em><codeclass="sig-name descname">generate_possible_sub_sets_of_size</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">u</span><spanclass="p">:</span><spanclass="n">List</span></em>, <emclass="sig-param"><spanclass="n">size</span><spanclass="p">:</span><spanclass="n">int</span></em>, <emclass="sig-param"><spanclass="n">parent_label</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→ Iterator<aclass="headerlink"href="#classes.structure_estimator.StructureEstimator.generate_possible_sub_sets_of_size"title="Permalink to this definition">¶</a></dt>
<dd><p>Creates a list containing all possible subsets of the list <codeclass="docutils literal notranslate"><spanclass="pre">u</span></code> of size <codeclass="docutils literal notranslate"><spanclass="pre">size</span></code>,
that do not contains a the node identified by <codeclass="docutils literal notranslate"><spanclass="pre">parent_label</span></code>.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>u</strong> (<em>List</em>) – the list of nodes</p></li>
<li><p><strong>size</strong> (<em>int</em>) – the size of the subsets</p></li>
<li><p><strong>parent_label</strong> (<em>string</em>) – the node to exclude in the subsets generation</p></li>
</ul>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>an Iterator Object containing a list of lists</p>
<codeclass="sig-name descname">independence_test</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">child_states_numb</span><spanclass="p">:</span><spanclass="n">int</span></em>, <emclass="sig-param"><spanclass="n">cim1</span><spanclass="p">:</span><spanclass="n"><aclass="reference internal"href="#classes.conditional_intensity_matrix.ConditionalIntensityMatrix"title="classes.conditional_intensity_matrix.ConditionalIntensityMatrix">classes.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em>, <emclass="sig-param"><spanclass="n">cim2</span><spanclass="p">:</span><spanclass="n"><aclass="reference internal"href="#classes.conditional_intensity_matrix.ConditionalIntensityMatrix"title="classes.conditional_intensity_matrix.ConditionalIntensityMatrix">classes.conditional_intensity_matrix.ConditionalIntensityMatrix</a></span></em><spanclass="sig-paren">)</span>→ bool<aclass="headerlink"href="#classes.structure_estimator.StructureEstimator.independence_test"title="Permalink to this definition">¶</a></dt>
<dd><p>Compute the actual independence test using two cims.
It is performed first the exponential test and if the null hypothesis is not rejected,
it is performed also the chi_test.</p>
<dlclass="field-list simple">
<dtclass="field-odd">Parameters</dt>
<ddclass="field-odd"><ulclass="simple">
<li><p><strong>child_states_numb</strong> (<em>int</em>) – the cardinality of the test child</p></li>
<li><p><strong>cim1</strong> (<aclass="reference internal"href="#classes.conditional_intensity_matrix.ConditionalIntensityMatrix"title="classes.conditional_intensity_matrix.ConditionalIntensityMatrix"><em>ConditionalIntensityMatrix</em></a>) – a cim belonging to the graph without test parent</p></li>
<li><p><strong>cim2</strong> (<aclass="reference internal"href="#classes.conditional_intensity_matrix.ConditionalIntensityMatrix"title="classes.conditional_intensity_matrix.ConditionalIntensityMatrix"><em>ConditionalIntensityMatrix</em></a>) – a cim belonging to the graph with test parent</p></li>
</ul>
</dd>
<dtclass="field-even">Returns</dt>
<ddclass="field-even"><p>True iff both tests do NOT reject the null hypothesis of independence. False otherwise.</p>
<codeclass="sig-name descname">one_iteration_of_CTPC_algorithm</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">var_id</span><spanclass="p">:</span><spanclass="n">str</span></em><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.structure_estimator.StructureEstimator.one_iteration_of_CTPC_algorithm"title="Permalink to this definition">¶</a></dt>
<dd><p>Performs an iteration of the CTPC algorithm using the node <codeclass="docutils literal notranslate"><spanclass="pre">var_id</span></code> as <codeclass="docutils literal notranslate"><spanclass="pre">test_child</span></code>.</p>
<codeclass="sig-name descname">save_results</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span>→ None<aclass="headerlink"href="#classes.structure_estimator.StructureEstimator.save_results"title="Permalink to this definition">¶</a></dt>
<dd><p>Save the estimated Structure to a .json file in the path where the data are loaded from.
The file is named as the input dataset but the <cite>results_</cite> word is appended to the results file.</p>
<spanid="classes-trajectory-module"></span><h2>classes.trajectory module<aclass="headerlink"href="#module-classes.trajectory"title="Permalink to this headline">¶</a></h2>
<dlclass="py class">
<dtid="classes.trajectory.Trajectory">
<emclass="property">class </em><codeclass="sig-prename descclassname">classes.trajectory.</code><codeclass="sig-name descname">Trajectory</code><spanclass="sig-paren">(</span><emclass="sig-param"><spanclass="n">list_of_columns</span><spanclass="p">:</span><spanclass="n">List</span></em>, <emclass="sig-param"><spanclass="n">original_cols_number</span><spanclass="p">:</span><spanclass="n">int</span></em><spanclass="sig-paren">)</span><aclass="headerlink"href="#classes.trajectory.Trajectory"title="Permalink to this definition">¶</a></dt>
<emclass="property">property </em><codeclass="sig-name descname">complete_trajectory</code><aclass="headerlink"href="#classes.trajectory.Trajectory.complete_trajectory"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dlclass="py method">
<dtid="classes.trajectory.Trajectory.size">
<codeclass="sig-name descname">size</code><spanclass="sig-paren">(</span><spanclass="sig-paren">)</span><aclass="headerlink"href="#classes.trajectory.Trajectory.size"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dlclass="py method">
<dtid="classes.trajectory.Trajectory.times">
<emclass="property">property </em><codeclass="sig-name descname">times</code><aclass="headerlink"href="#classes.trajectory.Trajectory.times"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<dlclass="py method">
<dtid="classes.trajectory.Trajectory.trajectory">
<emclass="property">property </em><codeclass="sig-name descname">trajectory</code><aclass="headerlink"href="#classes.trajectory.Trajectory.trajectory"title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
</dd></dl>
</div>
<divclass="section"id="module-classes">
<spanid="module-contents"></span><h2>Module contents<aclass="headerlink"href="#module-classes"title="Permalink to this headline">¶</a></h2>
Built with <ahref="http://sphinx-doc.org/">Sphinx</a> using a <ahref="https://github.com/jucacrispim/sphinx_pdj_theme">theme</a> provided by <ahref="http://poraodojuca.net">Porão do Juca</a>.
