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updated docs for 4.2.1.0 #92

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updated docs for 4.2.1.0 #92

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10 changes: 5 additions & 5 deletions docs/_build/html/_modules/tigramite/data_processing.html
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Expand Up @@ -206,10 +206,10 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>

<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> array, xyz [,XYZ] : Tuple of data array of shape (dim, T) and xyz</span>
<span class="sd"> array, xyz [,XYZ] : Tuple of data array of shape (dim, time) and xyz</span>
<span class="sd"> identifier array of shape (dim,) identifying which row in array</span>
<span class="sd"> corresponds to X, Y, and Z. For example:: X = [(0, -1)], Y = [(1,</span>
<span class="sd"> 0)], Z = [(1, -1), (0, -2)] yields an array of shape (5, T) and</span>
<span class="sd"> 0)], Z = [(1, -1), (0, -2)] yields an array of shape (4, T) and</span>
<span class="sd"> xyz is xyz = numpy.array([0,1,2,2]) If return_cleaned_xyz is</span>
<span class="sd"> True, also outputs the cleaned XYZ lists.</span>

Expand Down Expand Up @@ -1017,13 +1017,13 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># Default maximum lag and node ID</span>
<span class="n">max_time_lag</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">max_node_id</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">max_node_id</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">-</span> <span class="mi">1</span>
<span class="c1"># Iterate through the keys in parents_neighbors_coeffs</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">_iter_coeffs</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
<span class="c1"># Find max lag time</span>
<span class="n">max_time_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_time_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span>
<span class="c1"># Find the max node ID</span>
<span class="n">max_node_id</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_node_id</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span>
<span class="c1"># max_node_id = max(max_node_id, j)</span>
<span class="c1"># Return these values</span>
<span class="k">return</span> <span class="n">max_time_lag</span><span class="p">,</span> <span class="n">max_node_id</span>

Expand Down Expand Up @@ -1483,7 +1483,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
<span class="n">noises</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">]</span>
<span class="n">data</span><span class="p">,</span> <span class="n">nonstat</span> <span class="o">=</span> <span class="n">structural_causal_process</span><span class="p">(</span><span class="n">links</span><span class="p">,</span>
<span class="n">T</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">noises</span><span class="o">=</span><span class="n">noises</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
</pre></div>

</div>
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10 changes: 5 additions & 5 deletions docs/_modules/tigramite/data_processing.html
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Original file line number Diff line number Diff line change
Expand Up @@ -206,10 +206,10 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>

<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> array, xyz [,XYZ] : Tuple of data array of shape (dim, T) and xyz</span>
<span class="sd"> array, xyz [,XYZ] : Tuple of data array of shape (dim, time) and xyz</span>
<span class="sd"> identifier array of shape (dim,) identifying which row in array</span>
<span class="sd"> corresponds to X, Y, and Z. For example:: X = [(0, -1)], Y = [(1,</span>
<span class="sd"> 0)], Z = [(1, -1), (0, -2)] yields an array of shape (5, T) and</span>
<span class="sd"> 0)], Z = [(1, -1), (0, -2)] yields an array of shape (4, T) and</span>
<span class="sd"> xyz is xyz = numpy.array([0,1,2,2]) If return_cleaned_xyz is</span>
<span class="sd"> True, also outputs the cleaned XYZ lists.</span>

Expand Down Expand Up @@ -1017,13 +1017,13 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
<span class="sd"> &quot;&quot;&quot;</span>
<span class="c1"># Default maximum lag and node ID</span>
<span class="n">max_time_lag</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">max_node_id</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">max_node_id</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span> <span class="o">-</span> <span class="mi">1</span>
<span class="c1"># Iterate through the keys in parents_neighbors_coeffs</span>
<span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">tau</span><span class="p">,</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">_iter_coeffs</span><span class="p">(</span><span class="n">parents_neighbors_coeffs</span><span class="p">):</span>
<span class="c1"># Find max lag time</span>
<span class="n">max_time_lag</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_time_lag</span><span class="p">,</span> <span class="nb">abs</span><span class="p">(</span><span class="n">tau</span><span class="p">))</span>
<span class="c1"># Find the max node ID</span>
<span class="n">max_node_id</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">max_node_id</span><span class="p">,</span> <span class="n">j</span><span class="p">)</span>
<span class="c1"># max_node_id = max(max_node_id, j)</span>
<span class="c1"># Return these values</span>
<span class="k">return</span> <span class="n">max_time_lag</span><span class="p">,</span> <span class="n">max_node_id</span>

Expand Down Expand Up @@ -1483,7 +1483,7 @@ <h1>Source code for tigramite.data_processing</h1><div class="highlight"><pre>
<span class="n">noises</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randn</span><span class="p">]</span>
<span class="n">data</span><span class="p">,</span> <span class="n">nonstat</span> <span class="o">=</span> <span class="n">structural_causal_process</span><span class="p">(</span><span class="n">links</span><span class="p">,</span>
<span class="n">T</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">noises</span><span class="o">=</span><span class="n">noises</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
</pre></div>

</div>
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