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authordschult <dschult@colgate.edu>2022-12-20 16:45:01 +0000
committerdschult <dschult@colgate.edu>2022-12-20 16:45:01 +0000
commit65f76c5aa8328fed6995bb118fc38082003e9435 (patch)
tree0aaa3382556a37e6dd9f275f203de04e968c70c8
parent0f58b11d05d40689b53b018224e0b6a545b8bfec (diff)
downloadnetworkx-65f76c5aa8328fed6995bb118fc38082003e9435.tar.gz
Deploying to gh-pages from @ networkx/networkx@bbc8dd7e74c9417247009210872d30b5fbd59f48 🚀
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+++ b/_images/sphx_glr_plot_words_001.png
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diff --git a/_images/sphx_glr_plot_words_thumb.png b/_images/sphx_glr_plot_words_thumb.png
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diff --git a/_modules/networkx/generators/classic.html b/_modules/networkx/generators/classic.html
index 6a62601a..bd5d97aa 100644
--- a/_modules/networkx/generators/classic.html
+++ b/_modules/networkx/generators/classic.html
@@ -608,7 +608,26 @@
<div class="viewcode-block" id="barbell_graph"><a class="viewcode-back" href="../../../reference/generated/networkx.generators.classic.barbell_graph.html#networkx.generators.classic.barbell_graph">[docs]</a><span class="k">def</span> <span class="nf">barbell_graph</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">m2</span><span class="p">,</span> <span class="n">create_using</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">&quot;&quot;&quot;Returns the Barbell Graph: two complete graphs connected by a path.</span>
-<span class="sd"> For $m1 &gt; 1$ and $m2 &gt;= 0$.</span>
+<span class="sd"> Parameters</span>
+<span class="sd"> ----------</span>
+<span class="sd"> m1 : int</span>
+<span class="sd"> Size of the left and right barbells, must be greater than 2.</span>
+
+<span class="sd"> m2 : int</span>
+<span class="sd"> Length of the path connecting the barbells.</span>
+
+<span class="sd"> create_using : NetworkX graph constructor, optional (default=nx.Graph)</span>
+<span class="sd"> Graph type to create. If graph instance, then cleared before populated.</span>
+<span class="sd"> Only undirected Graphs are supported.</span>
+
+<span class="sd"> Returns</span>
+<span class="sd"> -------</span>
+<span class="sd"> G : NetworkX graph</span>
+<span class="sd"> A barbell graph.</span>
+
+<span class="sd"> Notes</span>
+<span class="sd"> -----</span>
+
<span class="sd"> Two identical complete graphs $K_{m1}$ form the left and right bells,</span>
<span class="sd"> and are connected by a path $P_{m2}$.</span>
@@ -640,14 +659,17 @@
<span class="n">G</span><span class="o">.</span><span class="n">add_nodes_from</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">m1</span> <span class="o">+</span> <span class="n">m2</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
<span class="k">if</span> <span class="n">m2</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(</span><span class="n">pairwise</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span> <span class="n">m1</span> <span class="o">+</span> <span class="n">m2</span><span class="p">)))</span>
+
<span class="c1"># right barbell</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">(</span>
<span class="p">(</span><span class="n">u</span><span class="p">,</span> <span class="n">v</span><span class="p">)</span> <span class="k">for</span> <span class="n">u</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">m1</span> <span class="o">+</span> <span class="n">m2</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">m1</span> <span class="o">+</span> <span class="n">m2</span><span class="p">)</span> <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">u</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">m1</span> <span class="o">+</span> <span class="n">m2</span><span class="p">)</span>
<span class="p">)</span>
+
<span class="c1"># connect it up</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">m1</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">m1</span><span class="p">)</span>
<span class="k">if</span> <span class="n">m2</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">G</span><span class="o">.</span><span class="n">add_edge</span><span class="p">(</span><span class="n">m1</span> <span class="o">+</span> <span class="n">m2</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">m1</span> <span class="o">+</span> <span class="n">m2</span><span class="p">)</span>
+
<span class="k">return</span> <span class="n">G</span></div>
diff --git a/auto_examples/3d_drawing/plot_basic.html b/auto_examples/3d_drawing/plot_basic.html
index d93e0e02..92b69005 100644
--- a/auto_examples/3d_drawing/plot_basic.html
+++ b/auto_examples/3d_drawing/plot_basic.html
@@ -540,7 +540,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.074 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.078 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-3d-drawing-plot-basic-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/79beefddd68fa45123e60db5559f52aa/plot_basic.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_basic.py</span></code></a></p>
diff --git a/auto_examples/3d_drawing/sg_execution_times.html b/auto_examples/3d_drawing/sg_execution_times.html
index 84b6c71f..2a8c64d4 100644
--- a/auto_examples/3d_drawing/sg_execution_times.html
+++ b/auto_examples/3d_drawing/sg_execution_times.html
@@ -463,11 +463,11 @@
<section id="computation-times">
<span id="sphx-glr-auto-examples-3d-drawing-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1>
-<p><strong>00:00.074</strong> total execution time for <strong>auto_examples_3d_drawing</strong> files:</p>
+<p><strong>00:00.078</strong> total execution time for <strong>auto_examples_3d_drawing</strong> files:</p>
<table class="table">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_basic.html#sphx-glr-auto-examples-3d-drawing-plot-basic-py"><span class="std std-ref">Basic matplotlib</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_basic.py</span></code>)</p></td>
-<td><p>00:00.074</p></td>
+<td><p>00:00.078</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="mayavi2_spring.html#sphx-glr-auto-examples-3d-drawing-mayavi2-spring-py"><span class="std std-ref">Mayavi2</span></a> (<code class="docutils literal notranslate"><span class="pre">mayavi2_spring.py</span></code>)</p></td>
diff --git a/auto_examples/algorithms/plot_beam_search.html b/auto_examples/algorithms/plot_beam_search.html
index 70af165b..32d03984 100644
--- a/auto_examples/algorithms/plot_beam_search.html
+++ b/auto_examples/algorithms/plot_beam_search.html
@@ -612,7 +612,7 @@ the progressive widening search in order to find a node of high centrality.</p>
<img src="../../_images/sphx_glr_plot_beam_search_001.png" srcset="../../_images/sphx_glr_plot_beam_search_001.png" alt="plot beam search" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>found node 73 with centrality 0.12598283530728402
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.210 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.211 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-beam-search-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/ccbccb63fd600240faf98d07876c0e92/plot_beam_search.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_beam_search.py</span></code></a></p>
diff --git a/auto_examples/algorithms/plot_betweenness_centrality.html b/auto_examples/algorithms/plot_betweenness_centrality.html
index 9e5c3092..986641ad 100644
--- a/auto_examples/algorithms/plot_betweenness_centrality.html
+++ b/auto_examples/algorithms/plot_betweenness_centrality.html
@@ -582,7 +582,7 @@ using WormNet v.3-GS.</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.830 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.532 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-betweenness-centrality-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/b3018a1aab7bffbd1426574de5a8c65a/plot_betweenness_centrality.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_betweenness_centrality.py</span></code></a></p>
diff --git a/auto_examples/algorithms/plot_blockmodel.html b/auto_examples/algorithms/plot_blockmodel.html
index b66538cb..afb4db6a 100644
--- a/auto_examples/algorithms/plot_blockmodel.html
+++ b/auto_examples/algorithms/plot_blockmodel.html
@@ -579,7 +579,7 @@ used is the Hartford, CT drug users network:</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.358 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.360 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-blockmodel-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/efbe368eaa1e457c6c03d3f5a636063a/plot_blockmodel.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_blockmodel.py</span></code></a></p>
diff --git a/auto_examples/algorithms/plot_circuits.html b/auto_examples/algorithms/plot_circuits.html
index 757286d1..8fa22897 100644
--- a/auto_examples/algorithms/plot_circuits.html
+++ b/auto_examples/algorithms/plot_circuits.html
@@ -603,7 +603,7 @@ fourth layer.</p>
<img src="../../_images/sphx_glr_plot_circuits_001.png" srcset="../../_images/sphx_glr_plot_circuits_001.png" alt="((x ∨ y) ∧ (y ∨ ¬(z)))" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>((x ∨ y) ∧ (y ∨ ¬(z)))
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.101 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.105 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-circuits-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/bd2ce07c5ba253eb7b45764c94237a4c/plot_circuits.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_circuits.py</span></code></a></p>
diff --git a/auto_examples/algorithms/plot_davis_club.html b/auto_examples/algorithms/plot_davis_club.html
index 235cdc64..74f684a5 100644
--- a/auto_examples/algorithms/plot_davis_club.html
+++ b/auto_examples/algorithms/plot_davis_club.html
@@ -639,7 +639,7 @@ The graph is bipartite (clubs, women).</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.070 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.071 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-davis-club-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/6a1e333663010969e61d07b33c7845f0/plot_davis_club.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_davis_club.py</span></code></a></p>
diff --git a/auto_examples/algorithms/plot_dedensification.html b/auto_examples/algorithms/plot_dedensification.html
index 8bb6bde7..798d7d31 100644
--- a/auto_examples/algorithms/plot_dedensification.html
+++ b/auto_examples/algorithms/plot_dedensification.html
@@ -593,7 +593,7 @@ would result in fewer edges in the compressed graph.</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.230 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.244 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-dedensification-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/868e28431bab2565b22bfbab847e1153/plot_dedensification.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_dedensification.py</span></code></a></p>
diff --git a/auto_examples/algorithms/plot_iterated_dynamical_systems.html b/auto_examples/algorithms/plot_iterated_dynamical_systems.html
index 362a94f5..3706a8c1 100644
--- a/auto_examples/algorithms/plot_iterated_dynamical_systems.html
+++ b/auto_examples/algorithms/plot_iterated_dynamical_systems.html
@@ -699,7 +699,7 @@ fixed points are []
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;fixed points are </span><span class="si">{</span><span class="n">fixed_points</span><span class="p">(</span><span class="n">G</span><span class="p">)</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.094 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.096 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-iterated-dynamical-systems-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/d947686c24b50c278c1228ff766cda27/plot_iterated_dynamical_systems.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_iterated_dynamical_systems.py</span></code></a></p>
diff --git a/auto_examples/algorithms/plot_krackhardt_centrality.html b/auto_examples/algorithms/plot_krackhardt_centrality.html
index 452e6dfb..2c82fb26 100644
--- a/auto_examples/algorithms/plot_krackhardt_centrality.html
+++ b/auto_examples/algorithms/plot_krackhardt_centrality.html
@@ -569,7 +569,7 @@ Closeness centrality
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.057 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.060 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-krackhardt-centrality-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/e77acafa90a347f4353549d3bffbb72c/plot_krackhardt_centrality.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_krackhardt_centrality.py</span></code></a></p>
diff --git a/auto_examples/algorithms/plot_parallel_betweenness.html b/auto_examples/algorithms/plot_parallel_betweenness.html
index 3fa49c83..3dbe1799 100644
--- a/auto_examples/algorithms/plot_parallel_betweenness.html
+++ b/auto_examples/algorithms/plot_parallel_betweenness.html
@@ -517,29 +517,29 @@ faster. This is a limitation of our CI/CD pipeline running on a single core.</p>
<img src="../../_images/sphx_glr_plot_parallel_betweenness_001.png" srcset="../../_images/sphx_glr_plot_parallel_betweenness_001.png" alt="plot parallel betweenness" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Computing betweenness centrality for:
Graph with 1000 nodes and 2991 edges
Parallel version
- Time: 1.7270 seconds
- Betweenness centrality for node 0: 0.16254
+ Time: 1.7594 seconds
+ Betweenness centrality for node 0: 0.16147
Non-Parallel version
- Time: 2.7598 seconds
- Betweenness centrality for node 0: 0.16254
+ Time: 2.9510 seconds
+ Betweenness centrality for node 0: 0.16147
Computing betweenness centrality for:
-Graph with 1000 nodes and 5013 edges
+Graph with 1000 nodes and 5026 edges
Parallel version
- Time: 2.1628 seconds
- Betweenness centrality for node 0: 0.00323
+ Time: 2.2668 seconds
+ Betweenness centrality for node 0: 0.00287
Non-Parallel version
- Time: 3.6820 seconds
- Betweenness centrality for node 0: 0.00323
+ Time: 3.9150 seconds
+ Betweenness centrality for node 0: 0.00287
Computing betweenness centrality for:
Graph with 1000 nodes and 2000 edges
Parallel version
- Time: 1.4452 seconds
- Betweenness centrality for node 0: 0.01172
+ Time: 1.5164 seconds
+ Betweenness centrality for node 0: 0.00149
Non-Parallel version
- Time: 2.5251 seconds
- Betweenness centrality for node 0: 0.01172
+ Time: 2.7020 seconds
+ Betweenness centrality for node 0: 0.00149
</pre></div>
</div>
<div class="line-block">
@@ -611,7 +611,7 @@ Graph with 1000 nodes and 2000 edges
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 19.774 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 20.650 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-parallel-betweenness-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/8a9ce246f32a6cf6abd470292c7ffa6a/plot_parallel_betweenness.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_parallel_betweenness.py</span></code></a></p>
diff --git a/auto_examples/algorithms/plot_rcm.html b/auto_examples/algorithms/plot_rcm.html
index 430d73a6..9c75e02c 100644
--- a/auto_examples/algorithms/plot_rcm.html
+++ b/auto_examples/algorithms/plot_rcm.html
@@ -615,7 +615,7 @@ bandwidth: 7
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.988 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.941 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-rcm-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/544d21367fbc1520a180d8891369bb49/plot_rcm.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_rcm.py</span></code></a></p>
diff --git a/auto_examples/algorithms/plot_snap.html b/auto_examples/algorithms/plot_snap.html
index 8ea0f75a..7b373626 100644
--- a/auto_examples/algorithms/plot_snap.html
+++ b/auto_examples/algorithms/plot_snap.html
@@ -610,7 +610,7 @@ graph.</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.164 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.177 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-snap-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/0a756ab7ea4b899fa151e327a4dce8d2/plot_snap.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_snap.py</span></code></a></p>
diff --git a/auto_examples/algorithms/plot_subgraphs.html b/auto_examples/algorithms/plot_subgraphs.html
index 1b665e4d..b56eca4d 100644
--- a/auto_examples/algorithms/plot_subgraphs.html
+++ b/auto_examples/algorithms/plot_subgraphs.html
@@ -678,7 +678,7 @@ of subgraphs that contain only entirely <code class="xref py py-obj docutils lit
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<img src="../../_images/sphx_glr_plot_subgraphs_007.png" srcset="../../_images/sphx_glr_plot_subgraphs_007.png" alt="The reconstructed graph." class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.651 seconds)</p>
+<img src="../../_images/sphx_glr_plot_subgraphs_007.png" srcset="../../_images/sphx_glr_plot_subgraphs_007.png" alt="The reconstructed graph." class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.662 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-subgraphs-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/7c14530887a80b15e4b4f3d68b23d114/plot_subgraphs.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_subgraphs.py</span></code></a></p>
diff --git a/auto_examples/algorithms/sg_execution_times.html b/auto_examples/algorithms/sg_execution_times.html
index 63e73f9e..7ec739a4 100644
--- a/auto_examples/algorithms/sg_execution_times.html
+++ b/auto_examples/algorithms/sg_execution_times.html
@@ -463,55 +463,55 @@
<section id="computation-times">
<span id="sphx-glr-auto-examples-algorithms-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1>
-<p><strong>00:26.527</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p>
+<p><strong>00:27.110</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p>
<table class="table">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_parallel_betweenness.html#sphx-glr-auto-examples-algorithms-plot-parallel-betweenness-py"><span class="std std-ref">Parallel Betweenness</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_parallel_betweenness.py</span></code>)</p></td>
-<td><p>00:19.774</p></td>
+<td><p>00:20.650</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_betweenness_centrality.html#sphx-glr-auto-examples-algorithms-plot-betweenness-centrality-py"><span class="std std-ref">Betweeness Centrality</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_betweenness_centrality.py</span></code>)</p></td>
-<td><p>00:03.830</p></td>
+<td><p>00:03.532</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_rcm.html#sphx-glr-auto-examples-algorithms-plot-rcm-py"><span class="std std-ref">Reverse Cuthill–McKee</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_rcm.py</span></code>)</p></td>
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</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_beam_search.html#sphx-glr-auto-examples-algorithms-plot-beam-search-py"><span class="std std-ref">Beam Search</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_beam_search.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_snap.html#sphx-glr-auto-examples-algorithms-plot-snap-py"><span class="std std-ref">SNAP Graph Summary</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_snap.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_davis_club.html#sphx-glr-auto-examples-algorithms-plot-davis-club-py"><span class="std std-ref">Davis Club</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_davis_club.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
</tbody>
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index 7e60a0f6..c6240224 100644
--- a/auto_examples/basic/plot_properties.html
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</pre></div>
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<p><a class="reference download internal" download="" href="../../_downloads/40632926e1e0842cea9103529e4bea12/plot_properties.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_properties.py</span></code></a></p>
diff --git a/auto_examples/basic/plot_read_write.html b/auto_examples/basic/plot_read_write.html
index f16d4e4c..993cf4e1 100644
--- a/auto_examples/basic/plot_read_write.html
+++ b/auto_examples/basic/plot_read_write.html
@@ -545,7 +545,7 @@ to download the full example code</p>
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<p><a class="reference download internal" download="" href="../../_downloads/63b2264e53e5d28aeb43b6aa768515b9/plot_read_write.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_read_write.py</span></code></a></p>
diff --git a/auto_examples/basic/plot_simple_graph.html b/auto_examples/basic/plot_simple_graph.html
index ce05f6b1..968ddaf2 100644
--- a/auto_examples/basic/plot_simple_graph.html
+++ b/auto_examples/basic/plot_simple_graph.html
@@ -550,7 +550,7 @@ to download the full example code</p>
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</pre></div>
</div>
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<p><a class="reference download internal" download="" href="../../_downloads/0f222beedce48fe624efff9ff2fdc89f/plot_simple_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_simple_graph.py</span></code></a></p>
diff --git a/auto_examples/basic/sg_execution_times.html b/auto_examples/basic/sg_execution_times.html
index 5fe91f2c..7efcad80 100644
--- a/auto_examples/basic/sg_execution_times.html
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@@ -463,19 +463,19 @@
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+<p><strong>00:00.494</strong> total execution time for <strong>auto_examples_basic</strong> files:</p>
<table class="table">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_simple_graph.html#sphx-glr-auto-examples-basic-plot-simple-graph-py"><span class="std std-ref">Simple graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_simple_graph.py</span></code>)</p></td>
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diff --git a/auto_examples/drawing/plot_center_node.html b/auto_examples/drawing/plot_center_node.html
index 70c4ac51..420d7356 100644
--- a/auto_examples/drawing/plot_center_node.html
+++ b/auto_examples/drawing/plot_center_node.html
@@ -530,7 +530,7 @@ to download the full example code</p>
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<p><a class="reference download internal" download="" href="../../_downloads/8561539ed0b99621dbdbe53646ac5075/plot_center_node.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_center_node.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_chess_masters.html b/auto_examples/drawing/plot_chess_masters.html
index dd5dd3eb..c767dcdc 100644
--- a/auto_examples/drawing/plot_chess_masters.html
+++ b/auto_examples/drawing/plot_chess_masters.html
@@ -536,7 +536,7 @@ to black and contains selected game info.</p>
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Note the disconnected component consisting of:
-[&#39;Kasparov, Gary&#39;, &#39;Korchnoi, Viktor L&#39;, &#39;Karpov, Anatoly&#39;]
+[&#39;Karpov, Anatoly&#39;, &#39;Kasparov, Gary&#39;, &#39;Korchnoi, Viktor L&#39;]
From a total of 237 different openings,
the following games used the Sicilian opening
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<p><a class="reference download internal" download="" href="../../_downloads/388158421a67216f605c1bbf9aa310bf/plot_chess_masters.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_chess_masters.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_custom_node_icons.html b/auto_examples/drawing/plot_custom_node_icons.html
index 3f747993..c399eff7 100644
--- a/auto_examples/drawing/plot_custom_node_icons.html
+++ b/auto_examples/drawing/plot_custom_node_icons.html
@@ -585,7 +585,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<p><a class="reference download internal" download="" href="../../_downloads/b580b9776494e714c1fb1880f03524a8/plot_custom_node_icons.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_custom_node_icons.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_degree.html b/auto_examples/drawing/plot_degree.html
index 160b5204..cab48c11 100644
--- a/auto_examples/drawing/plot_degree.html
+++ b/auto_examples/drawing/plot_degree.html
@@ -561,7 +561,7 @@ each node is determined, and a figure is generated showing three things:
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<p><a class="reference download internal" download="" href="../../_downloads/70eaef0d99343cf8d3d6e70c803ad5a8/plot_degree.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_degree.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_directed.html b/auto_examples/drawing/plot_directed.html
index c51f6737..0bd7f700 100644
--- a/auto_examples/drawing/plot_directed.html
+++ b/auto_examples/drawing/plot_directed.html
@@ -556,7 +556,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-directed-py">
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<p><a class="reference download internal" download="" href="../../_downloads/6c2f9c3544cb695b31867eecc0f7fb1e/plot_directed.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_directed.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_edge_colormap.html b/auto_examples/drawing/plot_edge_colormap.html
index d4e651c9..4f0b7e62 100644
--- a/auto_examples/drawing/plot_edge_colormap.html
+++ b/auto_examples/drawing/plot_edge_colormap.html
@@ -534,7 +534,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.060 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.062 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-edge-colormap-py">
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<p><a class="reference download internal" download="" href="../../_downloads/7ea4dc8cf44604668540ed81d6abebda/plot_edge_colormap.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_edge_colormap.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_ego_graph.html b/auto_examples/drawing/plot_ego_graph.html
index c8bac49b..ed4eebdd 100644
--- a/auto_examples/drawing/plot_ego_graph.html
+++ b/auto_examples/drawing/plot_ego_graph.html
@@ -546,7 +546,7 @@ the largest hub in a Barabási-Albert network.</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
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<p><a class="reference download internal" download="" href="../../_downloads/773fa56bdb128b8bd2a4f4a0e4dd38aa/plot_ego_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_ego_graph.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_eigenvalues.html b/auto_examples/drawing/plot_eigenvalues.html
index b3fb0300..31f1ca55 100644
--- a/auto_examples/drawing/plot_eigenvalues.html
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<p><a class="reference download internal" download="" href="../../_downloads/a8660a7bb6b65b5a644025485c973cb9/plot_eigenvalues.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_eigenvalues.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_four_grids.html b/auto_examples/drawing/plot_four_grids.html
index 97afe41c..7aefaf32 100644
--- a/auto_examples/drawing/plot_four_grids.html
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<p><a class="reference download internal" download="" href="../../_downloads/4136c066ab1d073cf527e9dc02bfec77/plot_four_grids.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_four_grids.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_house_with_colors.html b/auto_examples/drawing/plot_house_with_colors.html
index fa33e411..245a380a 100644
--- a/auto_examples/drawing/plot_house_with_colors.html
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@@ -538,7 +538,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<p><a class="reference download internal" download="" href="../../_downloads/98363b3c011ceaffb10684a5ba5de25b/plot_house_with_colors.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_house_with_colors.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_knuth_miles.html b/auto_examples/drawing/plot_knuth_miles.html
index faf231c0..ec71c1be 100644
--- a/auto_examples/drawing/plot_knuth_miles.html
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@@ -660,7 +660,7 @@ Graph with 128 nodes and 8128 edges
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</pre></div>
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<p><a class="reference download internal" download="" href="../../_downloads/e921c603ea1764485dc9acff178a2f05/plot_knuth_miles.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_knuth_miles.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_labels_and_colors.html b/auto_examples/drawing/plot_labels_and_colors.html
index ce3da2b9..d2b999b5 100644
--- a/auto_examples/drawing/plot_labels_and_colors.html
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<p><a class="reference download internal" download="" href="../../_downloads/cff4f78bc18685caa50507ced57e7c6f/plot_labels_and_colors.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_labels_and_colors.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_multipartite_graph.html b/auto_examples/drawing/plot_multipartite_graph.html
index aa2b3c29..f8074c0e 100644
--- a/auto_examples/drawing/plot_multipartite_graph.html
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@@ -553,7 +553,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<p><a class="reference download internal" download="" href="../../_downloads/6cb4bf689cf53c849bce13cbab13eaec/plot_multipartite_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_multipartite_graph.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_node_colormap.html b/auto_examples/drawing/plot_node_colormap.html
index 7111f1ab..c0c2035a 100644
--- a/auto_examples/drawing/plot_node_colormap.html
+++ b/auto_examples/drawing/plot_node_colormap.html
@@ -526,7 +526,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<p><a class="reference download internal" download="" href="../../_downloads/19db6fb1da12c9b9c0afca26691448c8/plot_node_colormap.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_node_colormap.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_rainbow_coloring.html b/auto_examples/drawing/plot_rainbow_coloring.html
index 30212364..d70f19bd 100644
--- a/auto_examples/drawing/plot_rainbow_coloring.html
+++ b/auto_examples/drawing/plot_rainbow_coloring.html
@@ -578,7 +578,7 @@ helpful in determining how to place the tree copies.</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<p><a class="reference download internal" download="" href="../../_downloads/b64fd85d6e5ba509e65b2cb30a8274ed/plot_rainbow_coloring.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_rainbow_coloring.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_random_geometric_graph.html b/auto_examples/drawing/plot_random_geometric_graph.html
index 184f15b2..48b24d8a 100644
--- a/auto_examples/drawing/plot_random_geometric_graph.html
+++ b/auto_examples/drawing/plot_random_geometric_graph.html
@@ -555,7 +555,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<p><a class="reference download internal" download="" href="../../_downloads/f8f8cacecc651443537b92fc341fba08/plot_random_geometric_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_random_geometric_graph.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_sampson.html b/auto_examples/drawing/plot_sampson.html
index 0c22e33c..3fed5ff4 100644
--- a/auto_examples/drawing/plot_sampson.html
+++ b/auto_examples/drawing/plot_sampson.html
@@ -557,7 +557,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<p><a class="reference download internal" download="" href="../../_downloads/838bbb120e1c43a61657821eddf29c25/plot_sampson.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_sampson.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_selfloops.html b/auto_examples/drawing/plot_selfloops.html
index a849449c..e8a0626f 100644
--- a/auto_examples/drawing/plot_selfloops.html
+++ b/auto_examples/drawing/plot_selfloops.html
@@ -540,7 +540,7 @@ This example shows how to draw self-loops with <code class="xref py py-obj docut
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<p><a class="reference download internal" download="" href="../../_downloads/b6f62567cb843f23abdd4b7268921c0b/plot_selfloops.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_selfloops.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_simple_path.html b/auto_examples/drawing/plot_simple_path.html
index 9796d449..f5a07323 100644
--- a/auto_examples/drawing/plot_simple_path.html
+++ b/auto_examples/drawing/plot_simple_path.html
@@ -526,7 +526,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<p><a class="reference download internal" download="" href="../../_downloads/2c281c05b18d8d3cf43a312fc3d67a3b/plot_simple_path.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_simple_path.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_spectral_grid.html b/auto_examples/drawing/plot_spectral_grid.html
index b6fb7e7a..2577c1cd 100644
--- a/auto_examples/drawing/plot_spectral_grid.html
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@@ -568,7 +568,7 @@ As you remove internal nodes, this effect increases.</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<p><a class="reference download internal" download="" href="../../_downloads/5479a9bd23bf1ace2ef03c13b4ac9d7f/plot_spectral_grid.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_spectral_grid.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_tsp.html b/auto_examples/drawing/plot_tsp.html
index 30fb7240..705ad268 100644
--- a/auto_examples/drawing/plot_tsp.html
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<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<p><a class="reference download internal" download="" href="../../_downloads/cc9848c15dd2eeae1872b955a8f34d15/plot_tsp.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_tsp.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_unix_email.html b/auto_examples/drawing/plot_unix_email.html
index afdb9b21..93c60742 100644
--- a/auto_examples/drawing/plot_unix_email.html
+++ b/auto_examples/drawing/plot_unix_email.html
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<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<p><a class="reference download internal" download="" href="../../_downloads/213697eef7dec7ebca6ee2e064eb9c24/plot_unix_email.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_unix_email.py</span></code></a></p>
diff --git a/auto_examples/drawing/plot_weighted_graph.html b/auto_examples/drawing/plot_weighted_graph.html
index d500eaf2..afb1dc65 100644
--- a/auto_examples/drawing/plot_weighted_graph.html
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-geospatial-plot-polygons-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/9be63872be08214edeb4d5a2d5f66987/plot_polygons.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_polygons.py</span></code></a></p>
diff --git a/auto_examples/geospatial/sg_execution_times.html b/auto_examples/geospatial/sg_execution_times.html
index 80924775..778a5ed6 100644
--- a/auto_examples/geospatial/sg_execution_times.html
+++ b/auto_examples/geospatial/sg_execution_times.html
@@ -463,27 +463,27 @@
<section id="computation-times">
<span id="sphx-glr-auto-examples-geospatial-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1>
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+<p><strong>00:17.256</strong> total execution time for <strong>auto_examples_geospatial</strong> files:</p>
<table class="table">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_osmnx.html#sphx-glr-auto-examples-geospatial-plot-osmnx-py"><span class="std std-ref">OpenStreetMap with OSMnx</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_osmnx.py</span></code>)</p></td>
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+<td><p>00:05.843</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_delaunay.html#sphx-glr-auto-examples-geospatial-plot-delaunay-py"><span class="std std-ref">Delaunay graphs from geographic points</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_delaunay.py</span></code>)</p></td>
-<td><p>00:03.717</p></td>
+<td><p>00:04.080</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_lines.html#sphx-glr-auto-examples-geospatial-plot-lines-py"><span class="std std-ref">Graphs from a set of lines</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_lines.py</span></code>)</p></td>
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+<td><p>00:03.612</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_points.html#sphx-glr-auto-examples-geospatial-plot-points-py"><span class="std std-ref">Graphs from geographic points</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_points.py</span></code>)</p></td>
-<td><p>00:02.997</p></td>
+<td><p>00:03.280</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_polygons.html#sphx-glr-auto-examples-geospatial-plot-polygons-py"><span class="std std-ref">Graphs from Polygons</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_polygons.py</span></code>)</p></td>
-<td><p>00:00.423</p></td>
+<td><p>00:00.440</p></td>
<td><p>0.0 MB</p></td>
</tr>
</tbody>
diff --git a/auto_examples/graph/plot_dag_layout.html b/auto_examples/graph/plot_dag_layout.html
index 32a7e65f..9999d6a9 100644
--- a/auto_examples/graph/plot_dag_layout.html
+++ b/auto_examples/graph/plot_dag_layout.html
@@ -541,7 +541,7 @@ order.</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.109 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.116 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-dag-layout-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/317508b452046ab7944bed07a87a11a5/plot_dag_layout.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_dag_layout.py</span></code></a></p>
diff --git a/auto_examples/graph/plot_degree_sequence.html b/auto_examples/graph/plot_degree_sequence.html
index 645361ca..9ac1c068 100644
--- a/auto_examples/graph/plot_degree_sequence.html
+++ b/auto_examples/graph/plot_degree_sequence.html
@@ -548,7 +548,7 @@ degree #nodes
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.055 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.058 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-degree-sequence-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/27102b9986eea2f742603c5d8496d2f8/plot_degree_sequence.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_degree_sequence.py</span></code></a></p>
diff --git a/auto_examples/graph/plot_erdos_renyi.html b/auto_examples/graph/plot_erdos_renyi.html
index c532b522..1215d07c 100644
--- a/auto_examples/graph/plot_erdos_renyi.html
+++ b/auto_examples/graph/plot_erdos_renyi.html
@@ -562,7 +562,7 @@ the adjacency list
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.056 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.058 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-erdos-renyi-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/1dae7040b667b61c3253579b3b21fe83/plot_erdos_renyi.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_erdos_renyi.py</span></code></a></p>
diff --git a/auto_examples/graph/plot_expected_degree_sequence.html b/auto_examples/graph/plot_expected_degree_sequence.html
index c5290b14..69344699 100644
--- a/auto_examples/graph/plot_expected_degree_sequence.html
+++ b/auto_examples/graph/plot_expected_degree_sequence.html
@@ -538,45 +538,47 @@ degree (#nodes) ****
28 ( 0)
29 ( 0)
30 ( 0)
-31 ( 2) **
+31 ( 0)
32 ( 1) *
-33 ( 1) *
+33 ( 0)
34 ( 1) *
-35 ( 0)
-36 ( 2) **
-37 ( 2) **
-38 (10) **********
-39 ( 2) **
-40 ( 4) ****
-41 ( 9) *********
-42 (18) ******************
-43 (11) ***********
-44 (17) *****************
-45 (18) ******************
-46 (29) *****************************
-47 (32) ********************************
-48 (29) *****************************
-49 (25) *************************
-50 (27) ***************************
-51 (31) *******************************
-52 (31) *******************************
-53 (31) *******************************
-54 (32) ********************************
-55 (21) *********************
-56 (23) ***********************
-57 (20) ********************
-58 (17) *****************
-59 (16) ****************
+35 ( 1) *
+36 ( 1) *
+37 ( 5) *****
+38 ( 3) ***
+39 ( 6) ******
+40 (12) ************
+41 (10) **********
+42 ( 4) ****
+43 (17) *****************
+44 (24) ************************
+45 (27) ***************************
+46 (27) ***************************
+47 (24) ************************
+48 (33) *********************************
+49 (21) *********************
+50 (33) *********************************
+51 (30) ******************************
+52 (36) ************************************
+53 (35) ***********************************
+54 (22) **********************
+55 (28) ****************************
+56 (22) **********************
+57 (11) ***********
+58 (22) **********************
+59 (12) ************
60 ( 9) *********
-61 ( 4) ****
-62 ( 3) ***
-63 ( 2) **
-64 ( 5) *****
-65 ( 4) ****
-66 ( 3) ***
-67 ( 4) ****
-68 ( 1) *
-69 ( 3) ***
+61 ( 8) ********
+62 ( 2) **
+63 ( 4) ****
+64 ( 3) ***
+65 ( 0)
+66 ( 1) *
+67 ( 3) ***
+68 ( 0)
+69 ( 1) *
+70 ( 0)
+71 ( 1) *
</pre></div>
</div>
<div class="line-block">
diff --git a/auto_examples/graph/plot_football.html b/auto_examples/graph/plot_football.html
index d9d65dba..ce484553 100644
--- a/auto_examples/graph/plot_football.html
+++ b/auto_examples/graph/plot_football.html
@@ -686,7 +686,7 @@ Hawaii 11
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.555 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.257 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-football-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/ca0a30060f60faf520286faa348f4700/plot_football.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_football.py</span></code></a></p>
diff --git a/auto_examples/graph/plot_karate_club.html b/auto_examples/graph/plot_karate_club.html
index cc319240..b9ae1b93 100644
--- a/auto_examples/graph/plot_karate_club.html
+++ b/auto_examples/graph/plot_karate_club.html
@@ -562,7 +562,7 @@ Journal of Anthropological Research, 33, 452-473.</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.084 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.089 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-karate-club-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/373a1e407e4caee6fc7b7b46704a985c/plot_karate_club.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_karate_club.py</span></code></a></p>
diff --git a/auto_examples/graph/plot_morse_trie.html b/auto_examples/graph/plot_morse_trie.html
index 854a516d..65019d20 100644
--- a/auto_examples/graph/plot_morse_trie.html
+++ b/auto_examples/graph/plot_morse_trie.html
@@ -602,7 +602,7 @@ the path.</p>
<span class="nb">print</span><span class="p">(</span><span class="s2">&quot; &quot;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">morse_encode</span><span class="p">(</span><span class="n">ltr</span><span class="p">)</span> <span class="k">for</span> <span class="n">ltr</span> <span class="ow">in</span> <span class="s2">&quot;ilovenetworkx&quot;</span><span class="p">]))</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.168 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.182 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-morse-trie-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/60379a4283563d425090aaae07ab115a/plot_morse_trie.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_morse_trie.py</span></code></a></p>
diff --git a/auto_examples/graph/plot_napoleon_russian_campaign.html b/auto_examples/graph/plot_napoleon_russian_campaign.html
index 4a354b21..2d412d64 100644
--- a/auto_examples/graph/plot_napoleon_russian_campaign.html
+++ b/auto_examples/graph/plot_napoleon_russian_campaign.html
@@ -632,7 +632,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.120 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.125 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-napoleon-russian-campaign-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/87e75a2d09fb817a4616bb71aa44546f/plot_napoleon_russian_campaign.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_napoleon_russian_campaign.py</span></code></a></p>
diff --git a/auto_examples/graph/plot_roget.html b/auto_examples/graph/plot_roget.html
index d25a8e19..c3d0f7b3 100644
--- a/auto_examples/graph/plot_roget.html
+++ b/auto_examples/graph/plot_roget.html
@@ -588,7 +588,7 @@ DiGraph with 1022 nodes and 5075 edges
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.226 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.227 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-roget-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/118b3a0c87610e4910d74143c904d290/plot_roget.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_roget.py</span></code></a></p>
diff --git a/auto_examples/graph/plot_triad_types.html b/auto_examples/graph/plot_triad_types.html
index ea745d44..7630448d 100644
--- a/auto_examples/graph/plot_triad_types.html
+++ b/auto_examples/graph/plot_triad_types.html
@@ -563,7 +563,7 @@ the Orientation as Up (U), Down (D) , Cyclical (C) or Transitive (T).</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.000 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.069 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-triad-types-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/b7a826e19c8bd8bafecaae1ae69c7d1d/plot_triad_types.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_triad_types.py</span></code></a></p>
diff --git a/auto_examples/graph/plot_words.html b/auto_examples/graph/plot_words.html
index 88df253b..5fb89257 100644
--- a/auto_examples/graph/plot_words.html
+++ b/auto_examples/graph/plot_words.html
@@ -624,7 +624,7 @@ None
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.362 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.369 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-words-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/e6a489a8b2deb49ed237fac38a28f429/plot_words.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_words.py</span></code></a></p>
diff --git a/auto_examples/graph/sg_execution_times.html b/auto_examples/graph/sg_execution_times.html
index 20ce5524..85b54f20 100644
--- a/auto_examples/graph/sg_execution_times.html
+++ b/auto_examples/graph/sg_execution_times.html
@@ -463,47 +463,47 @@
<section id="computation-times">
<span id="sphx-glr-auto-examples-graph-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1>
-<p><strong>00:02.765</strong> total execution time for <strong>auto_examples_graph</strong> files:</p>
+<p><strong>00:02.580</strong> total execution time for <strong>auto_examples_graph</strong> files:</p>
<table class="table">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_triad_types.html#sphx-glr-auto-examples-graph-plot-triad-types-py"><span class="std std-ref">Triads</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_triad_types.py</span></code>)</p></td>
-<td><p>00:00.1000</p></td>
+<td><p>00:01.069</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="plot_football.html#sphx-glr-auto-examples-graph-plot-football-py"><span class="std std-ref">Football</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_football.py</span></code>)</p></td>
-<td><p>00:00.555</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="plot_words.html#sphx-glr-auto-examples-graph-plot-words-py"><span class="std std-ref">Words/Ladder Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_words.py</span></code>)</p></td>
+<td><p>00:00.369</p></td>
<td><p>0.0 MB</p></td>
</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_words.html#sphx-glr-auto-examples-graph-plot-words-py"><span class="std std-ref">Words/Ladder Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_words.py</span></code>)</p></td>
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+<tr class="row-odd"><td><p><a class="reference internal" href="plot_football.html#sphx-glr-auto-examples-graph-plot-football-py"><span class="std std-ref">Football</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_football.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_roget.html#sphx-glr-auto-examples-graph-plot-roget-py"><span class="std std-ref">Roget</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_roget.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_morse_trie.html#sphx-glr-auto-examples-graph-plot-morse-trie-py"><span class="std std-ref">Morse Trie</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_morse_trie.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_napoleon_russian_campaign.html#sphx-glr-auto-examples-graph-plot-napoleon-russian-campaign-py"><span class="std std-ref">Napoleon Russian Campaign</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_napoleon_russian_campaign.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_dag_layout.html#sphx-glr-auto-examples-graph-plot-dag-layout-py"><span class="std std-ref">DAG - Topological Layout</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_dag_layout.py</span></code>)</p></td>
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+<td><p>00:00.116</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_karate_club.html#sphx-glr-auto-examples-graph-plot-karate-club-py"><span class="std std-ref">Karate Club</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_karate_club.py</span></code>)</p></td>
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+<td><p>00:00.089</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_erdos_renyi.html#sphx-glr-auto-examples-graph-plot-erdos-renyi-py"><span class="std std-ref">Erdos Renyi</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_erdos_renyi.py</span></code>)</p></td>
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+<td><p>00:00.058</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_degree_sequence.html#sphx-glr-auto-examples-graph-plot-degree-sequence-py"><span class="std std-ref">Degree Sequence</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_degree_sequence.py</span></code>)</p></td>
-<td><p>00:00.055</p></td>
+<td><p>00:00.058</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_expected_degree_sequence.html#sphx-glr-auto-examples-graph-plot-expected-degree-sequence-py"><span class="std std-ref">Expected Degree Sequence</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_expected_degree_sequence.py</span></code>)</p></td>
diff --git a/auto_examples/graphviz_drawing/plot_attributes.html b/auto_examples/graphviz_drawing/plot_attributes.html
index 0236b13f..b1ab8ba7 100644
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+++ b/auto_examples/graphviz_drawing/plot_attributes.html
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<p><a class="reference download internal" download="" href="../../_downloads/52bb0ebd52824aa460a3ecb45c1cb5e5/plot_attributes.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_attributes.py</span></code></a></p>
diff --git a/auto_examples/graphviz_drawing/plot_grid.html b/auto_examples/graphviz_drawing/plot_grid.html
index f8ade20a..2ec2ce64 100644
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<p><a class="reference download internal" download="" href="../../_downloads/26e3cd745ae317a76a0df34cbf4999d8/plot_grid.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_grid.py</span></code></a></p>
diff --git a/auto_examples/graphviz_drawing/plot_mini_atlas.html b/auto_examples/graphviz_drawing/plot_mini_atlas.html
index 150af660..5d543b0d 100644
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</div>
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<p><a class="reference download internal" download="" href="../../_downloads/cc271806f4fdfe8710206c593b90e506/plot_mini_atlas.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_mini_atlas.py</span></code></a></p>
diff --git a/auto_examples/graphviz_drawing/sg_execution_times.html b/auto_examples/graphviz_drawing/sg_execution_times.html
index c60ed8a8..7bd49ac4 100644
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+++ b/auto_examples/graphviz_drawing/sg_execution_times.html
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+<p><strong>00:00.274</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p>
<table class="table">
<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="plot_mini_atlas.html#sphx-glr-auto-examples-graphviz-drawing-plot-mini-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mini_atlas.py</span></code>)</p></td>
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</tr>
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+<tr class="row-odd"><td><p><a class="reference internal" href="plot_grid.html#sphx-glr-auto-examples-graphviz-drawing-plot-grid-py"><span class="std std-ref">2D Grid</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_grid.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
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index 85bbc3a7..a84553d7 100644
--- a/auto_examples/graphviz_layout/plot_atlas.html
+++ b/auto_examples/graphviz_layout/plot_atlas.html
@@ -549,7 +549,7 @@ We don’t plot the empty graph nor the single node graph.
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-atlas-py">
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<p><a class="reference download internal" download="" href="../../_downloads/37c712582f2a7575f32a59a1389228a7/plot_atlas.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_atlas.py</span></code></a></p>
diff --git a/auto_examples/graphviz_layout/plot_circular_tree.html b/auto_examples/graphviz_layout/plot_circular_tree.html
index a6f75eae..a45bd53d 100644
--- a/auto_examples/graphviz_layout/plot_circular_tree.html
+++ b/auto_examples/graphviz_layout/plot_circular_tree.html
@@ -510,7 +510,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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</div>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-circular-tree-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/e854482dd498b1c5f7f158a5717b999d/plot_circular_tree.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_circular_tree.py</span></code></a></p>
diff --git a/auto_examples/graphviz_layout/plot_decomposition.html b/auto_examples/graphviz_layout/plot_decomposition.html
index 5aa94922..79eeeef8 100644
--- a/auto_examples/graphviz_layout/plot_decomposition.html
+++ b/auto_examples/graphviz_layout/plot_decomposition.html
@@ -535,7 +535,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-decomposition-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/533257c084adfbb38066f806a87784c5/plot_decomposition.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_decomposition.py</span></code></a></p>
diff --git a/auto_examples/graphviz_layout/plot_giant_component.html b/auto_examples/graphviz_layout/plot_giant_component.html
index 5d316683..bd9ac885 100644
--- a/auto_examples/graphviz_layout/plot_giant_component.html
+++ b/auto_examples/graphviz_layout/plot_giant_component.html
@@ -543,7 +543,7 @@ giant connected component in a binomial random graph.</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-giant-component-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/f5d29b33ff492f40e4749050b3f5e7dd/plot_giant_component.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_giant_component.py</span></code></a></p>
diff --git a/auto_examples/graphviz_layout/plot_lanl_routes.html b/auto_examples/graphviz_layout/plot_lanl_routes.html
index d4ed3f89..1e45b2e7 100644
--- a/auto_examples/graphviz_layout/plot_lanl_routes.html
+++ b/auto_examples/graphviz_layout/plot_lanl_routes.html
@@ -561,7 +561,7 @@ to download the full example code</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
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<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-lanl-routes-py">
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<p><a class="reference download internal" download="" href="../../_downloads/30e04b92b8aefc7afe7f634d84ae925a/plot_lanl_routes.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_lanl_routes.py</span></code></a></p>
diff --git a/auto_examples/graphviz_layout/sg_execution_times.html b/auto_examples/graphviz_layout/sg_execution_times.html
index e128d0ee..b99391e9 100644
--- a/auto_examples/graphviz_layout/sg_execution_times.html
+++ b/auto_examples/graphviz_layout/sg_execution_times.html
@@ -463,27 +463,27 @@
<section id="computation-times">
<span id="sphx-glr-auto-examples-graphviz-layout-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1>
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<table class="table">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_atlas.html#sphx-glr-auto-examples-graphviz-layout-plot-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_atlas.py</span></code>)</p></td>
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<tr class="row-even"><td><p><a class="reference internal" href="plot_giant_component.html#sphx-glr-auto-examples-graphviz-layout-plot-giant-component-py"><span class="std std-ref">Giant Component</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_giant_component.py</span></code>)</p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="plot_lanl_routes.html#sphx-glr-auto-examples-graphviz-layout-plot-lanl-routes-py"><span class="std std-ref">Lanl Routes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_lanl_routes.py</span></code>)</p></td>
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<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_decomposition.html#sphx-glr-auto-examples-graphviz-layout-plot-decomposition-py"><span class="std std-ref">Decomposition</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_decomposition.py</span></code>)</p></td>
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+<td><p>00:00.302</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_circular_tree.html#sphx-glr-auto-examples-graphviz-layout-plot-circular-tree-py"><span class="std std-ref">Circular Tree</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_circular_tree.py</span></code>)</p></td>
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</tr>
</tbody>
diff --git a/auto_examples/subclass/plot_antigraph.html b/auto_examples/subclass/plot_antigraph.html
index 7c23ee3f..628c46dc 100644
--- a/auto_examples/subclass/plot_antigraph.html
+++ b/auto_examples/subclass/plot_antigraph.html
@@ -680,7 +680,7 @@ algorithms.</p>
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.088 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.092 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-subclass-plot-antigraph-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/652afbfc3c52c8cdd7689321df2e696a/plot_antigraph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_antigraph.py</span></code></a></p>
diff --git a/auto_examples/subclass/plot_printgraph.html b/auto_examples/subclass/plot_printgraph.html
index 1ca92436..5c05eff8 100644
--- a/auto_examples/subclass/plot_printgraph.html
+++ b/auto_examples/subclass/plot_printgraph.html
@@ -616,7 +616,7 @@ Add edge: 9-12
<a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span>
</pre></div>
</div>
-<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.055 seconds)</p>
+<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.059 seconds)</p>
<div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-subclass-plot-printgraph-py">
<div class="sphx-glr-download sphx-glr-download-python docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/1b5e7bf8d2514d71280314171170de85/plot_printgraph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_printgraph.py</span></code></a></p>
diff --git a/auto_examples/subclass/sg_execution_times.html b/auto_examples/subclass/sg_execution_times.html
index 9661f7dc..a504561f 100644
--- a/auto_examples/subclass/sg_execution_times.html
+++ b/auto_examples/subclass/sg_execution_times.html
@@ -463,15 +463,15 @@
<section id="computation-times">
<span id="sphx-glr-auto-examples-subclass-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1>
-<p><strong>00:00.144</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p>
+<p><strong>00:00.152</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p>
<table class="table">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="plot_antigraph.html#sphx-glr-auto-examples-subclass-plot-antigraph-py"><span class="std std-ref">Antigraph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_antigraph.py</span></code>)</p></td>
-<td><p>00:00.088</p></td>
+<td><p>00:00.092</p></td>
<td><p>0.0 MB</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="plot_printgraph.html#sphx-glr-auto-examples-subclass-plot-printgraph-py"><span class="std std-ref">Print Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_printgraph.py</span></code>)</p></td>
-<td><p>00:00.055</p></td>
+<td><p>00:00.059</p></td>
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diff --git a/reference/generated/networkx.generators.classic.barbell_graph.html b/reference/generated/networkx.generators.classic.barbell_graph.html
index 322ac594..3d3de7ad 100644
--- a/reference/generated/networkx.generators.classic.barbell_graph.html
+++ b/reference/generated/networkx.generators.classic.barbell_graph.html
@@ -630,7 +630,26 @@
<dt class="sig sig-object py" id="networkx.generators.classic.barbell_graph">
<span class="sig-name descname"><span class="pre">barbell_graph</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">m1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">m2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">create_using</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/networkx/generators/classic.html#barbell_graph"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#networkx.generators.classic.barbell_graph" title="Permalink to this definition">#</a></dt>
<dd><p>Returns the Barbell Graph: two complete graphs connected by a path.</p>
-<p>For <span class="math notranslate nohighlight">\(m1 &gt; 1\)</span> and <span class="math notranslate nohighlight">\(m2 &gt;= 0\)</span>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>m1</strong><span class="classifier">int</span></dt><dd><p>Size of the left and right barbells, must be greater than 2.</p>
+</dd>
+<dt><strong>m2</strong><span class="classifier">int</span></dt><dd><p>Length of the path connecting the barbells.</p>
+</dd>
+<dt><strong>create_using</strong><span class="classifier">NetworkX graph constructor, optional (default=nx.Graph)</span></dt><dd><p>Graph type to create. If graph instance, then cleared before populated.
+Only undirected Graphs are supported.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>G</strong><span class="classifier">NetworkX graph</span></dt><dd><p>A barbell graph.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Notes</p>
<p>Two identical complete graphs <span class="math notranslate nohighlight">\(K_{m1}\)</span> form the left and right bells,
and are connected by a path <span class="math notranslate nohighlight">\(P_{m2}\)</span>.</p>
<dl class="simple">
diff --git a/reference/introduction-7.hires.png b/reference/introduction-7.hires.png
index b85eefba..54b1480e 100644
--- a/reference/introduction-7.hires.png
+++ b/reference/introduction-7.hires.png
Binary files differ
diff --git a/reference/introduction-7.pdf b/reference/introduction-7.pdf
index 71ec386f..f2e19a7d 100644
--- a/reference/introduction-7.pdf
+++ b/reference/introduction-7.pdf
Binary files differ
diff --git a/reference/introduction-7.png b/reference/introduction-7.png
index 7d2313f3..7b5205fe 100644
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+++ b/reference/introduction-7.png
Binary files differ
diff --git a/reference/introduction.ipynb b/reference/introduction.ipynb
index 16153f65..dcff8a56 100644
--- a/reference/introduction.ipynb
+++ b/reference/introduction.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "70823638",
+ "id": "457f4466",
"metadata": {},
"source": [
"## Introduction\n",
@@ -34,7 +34,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "b5fa0c35",
+ "id": "a410cd9f",
"metadata": {},
"outputs": [],
"source": [
@@ -43,7 +43,7 @@
},
{
"cell_type": "markdown",
- "id": "ab9af010",
+ "id": "60e223dd",
"metadata": {},
"source": [
"To save repetition, in the documentation we assume that\n",
@@ -82,7 +82,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "cf1980fc",
+ "id": "d311e2e9",
"metadata": {},
"outputs": [],
"source": [
@@ -94,7 +94,7 @@
},
{
"cell_type": "markdown",
- "id": "55f31619",
+ "id": "c51d89fb",
"metadata": {},
"source": [
"All graph classes allow any [hashable](https://docs.python.org/3/glossary.html#term-hashable) object as a node.\n",
@@ -193,7 +193,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "87c124c8",
+ "id": "fea302df",
"metadata": {},
"outputs": [],
"source": [
@@ -205,7 +205,7 @@
},
{
"cell_type": "markdown",
- "id": "c085d136",
+ "id": "138280f7",
"metadata": {},
"source": [
"Edge attributes can be anything:"
@@ -214,7 +214,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "c17b567e",
+ "id": "1b094415",
"metadata": {},
"outputs": [],
"source": [
@@ -225,7 +225,7 @@
},
{
"cell_type": "markdown",
- "id": "04edde96",
+ "id": "8c139880",
"metadata": {},
"source": [
"You can add many edges at one time:"
@@ -234,7 +234,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "2d632a67",
+ "id": "23e9c0fd",
"metadata": {},
"outputs": [],
"source": [
@@ -246,7 +246,7 @@
},
{
"cell_type": "markdown",
- "id": "fed905e5",
+ "id": "50ec4cb0",
"metadata": {},
"source": [
"See the Tutorial for more examples.\n",
@@ -311,7 +311,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "be556332",
+ "id": "92ac246f",
"metadata": {},
"outputs": [],
"source": [
@@ -323,7 +323,7 @@
},
{
"cell_type": "markdown",
- "id": "b3b15584",
+ "id": "4a111aa2",
"metadata": {},
"source": [
"# Drawing\n",
@@ -344,7 +344,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "fba91ea0",
+ "id": "ac90228b",
"metadata": {},
"outputs": [],
"source": [
@@ -358,7 +358,7 @@
},
{
"cell_type": "markdown",
- "id": "4dc3c4a1",
+ "id": "33ae2cbe",
"metadata": {},
"source": [
"See the examples for more ideas.\n",
@@ -398,7 +398,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "e319f22c",
+ "id": "2b0baedb",
"metadata": {},
"outputs": [],
"source": [
@@ -410,7 +410,7 @@
},
{
"cell_type": "markdown",
- "id": "ad581721",
+ "id": "a8dfa3c1",
"metadata": {},
"source": [
"The data structure gets morphed slightly for each base graph class.\n",
@@ -428,7 +428,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "389d61af",
+ "id": "afe7074f",
"metadata": {},
"outputs": [],
"source": [
diff --git a/reference/introduction_full.ipynb b/reference/introduction_full.ipynb
index a4a8c9ac..125eeec4 100644
--- a/reference/introduction_full.ipynb
+++ b/reference/introduction_full.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "70823638",
+ "id": "457f4466",
"metadata": {},
"source": [
"## Introduction\n",
@@ -34,13 +34,13 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "b5fa0c35",
+ "id": "a410cd9f",
"metadata": {
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- "iopub.status.busy": "2022-12-20T11:40:15.822808Z",
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- "shell.execute_reply": "2022-12-20T11:40:15.891938Z"
+ "iopub.execute_input": "2022-12-20T16:43:38.483341Z",
+ "iopub.status.busy": "2022-12-20T16:43:38.482955Z",
+ "iopub.status.idle": "2022-12-20T16:43:38.554764Z",
+ "shell.execute_reply": "2022-12-20T16:43:38.554078Z"
}
},
"outputs": [],
@@ -50,7 +50,7 @@
},
{
"cell_type": "markdown",
- "id": "ab9af010",
+ "id": "60e223dd",
"metadata": {},
"source": [
"To save repetition, in the documentation we assume that\n",
@@ -89,13 +89,13 @@
{
"cell_type": "code",
"execution_count": 2,
- "id": "cf1980fc",
+ "id": "d311e2e9",
"metadata": {
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- "iopub.status.busy": "2022-12-20T11:40:15.895541Z",
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- "shell.execute_reply": "2022-12-20T11:40:15.898448Z"
+ "iopub.execute_input": "2022-12-20T16:43:38.558601Z",
+ "iopub.status.busy": "2022-12-20T16:43:38.558373Z",
+ "iopub.status.idle": "2022-12-20T16:43:38.561902Z",
+ "shell.execute_reply": "2022-12-20T16:43:38.561221Z"
}
},
"outputs": [],
@@ -108,7 +108,7 @@
},
{
"cell_type": "markdown",
- "id": "55f31619",
+ "id": "c51d89fb",
"metadata": {},
"source": [
"All graph classes allow any [hashable](https://docs.python.org/3/glossary.html#term-hashable) object as a node.\n",
@@ -207,13 +207,13 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "87c124c8",
+ "id": "fea302df",
"metadata": {
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- "iopub.status.busy": "2022-12-20T11:40:15.901694Z",
- "iopub.status.idle": "2022-12-20T11:40:15.904888Z",
- "shell.execute_reply": "2022-12-20T11:40:15.904301Z"
+ "iopub.execute_input": "2022-12-20T16:43:38.565049Z",
+ "iopub.status.busy": "2022-12-20T16:43:38.564840Z",
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+ "shell.execute_reply": "2022-12-20T16:43:38.567534Z"
}
},
"outputs": [],
@@ -226,7 +226,7 @@
},
{
"cell_type": "markdown",
- "id": "c085d136",
+ "id": "138280f7",
"metadata": {},
"source": [
"Edge attributes can be anything:"
@@ -235,13 +235,13 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "c17b567e",
+ "id": "1b094415",
"metadata": {
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- "iopub.execute_input": "2022-12-20T11:40:15.907610Z",
- "iopub.status.busy": "2022-12-20T11:40:15.907405Z",
- "iopub.status.idle": "2022-12-20T11:40:15.910514Z",
- "shell.execute_reply": "2022-12-20T11:40:15.909910Z"
+ "iopub.execute_input": "2022-12-20T16:43:38.570997Z",
+ "iopub.status.busy": "2022-12-20T16:43:38.570792Z",
+ "iopub.status.idle": "2022-12-20T16:43:38.573879Z",
+ "shell.execute_reply": "2022-12-20T16:43:38.573237Z"
}
},
"outputs": [],
@@ -253,7 +253,7 @@
},
{
"cell_type": "markdown",
- "id": "04edde96",
+ "id": "8c139880",
"metadata": {},
"source": [
"You can add many edges at one time:"
@@ -262,13 +262,13 @@
{
"cell_type": "code",
"execution_count": 5,
- "id": "2d632a67",
+ "id": "23e9c0fd",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:15.913408Z",
- "iopub.status.busy": "2022-12-20T11:40:15.913197Z",
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- "shell.execute_reply": "2022-12-20T11:40:15.916336Z"
+ "iopub.execute_input": "2022-12-20T16:43:38.576815Z",
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}
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@@ -281,7 +281,7 @@
},
{
"cell_type": "markdown",
- "id": "fed905e5",
+ "id": "50ec4cb0",
"metadata": {},
"source": [
"See the Tutorial for more examples.\n",
@@ -346,13 +346,13 @@
{
"cell_type": "code",
"execution_count": 6,
- "id": "be556332",
+ "id": "92ac246f",
"metadata": {
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- "shell.execute_reply": "2022-12-20T11:40:15.923073Z"
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@@ -373,7 +373,7 @@
},
{
"cell_type": "markdown",
- "id": "b3b15584",
+ "id": "4a111aa2",
"metadata": {},
"source": [
"# Drawing\n",
@@ -394,19 +394,19 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "fba91ea0",
+ "id": "ac90228b",
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- "shell.execute_reply": "2022-12-20T11:40:16.464246Z"
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+ "shell.execute_reply": "2022-12-20T16:43:39.152766Z"
}
},
"outputs": [
{
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\n",
+ "image/png": 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JkyeTkJCgyxxCuKNz5+D556FHD3j6afWq4fbtjY7K/eXNq7Yy/t//1JuUq1ZV72XwrTVu7flEMrBsSxLTog5oMta0qAMsv22F4NKlS0yePJnSpUsTHh5O+fLliYmJYePGjTz//PNuWxT4sGw2G7lz56aBjhVMo0aNonz58oSHh5Mh1UHCB3z/PYSGqpcMff+9eptfwYJGR+VZ2rVTE6jnnoPXXlN//eMPg4PyYF6fDJy4mMrY1Xs0HXPM6j1s/P0AQ4YMoWTJkowfP55nnnmGvXv3smrVKpo0aeI1V/VGRUVhtVrx9/fXbY6AgAAWLFjA1q1b+fTTT3WbRwijXbwI3bvDCy+oFwzt3g2dOhkdlecqUEBdFfjhB9i0SV0l+M9/jI7KM3l9MjDyhwQcWdwWeJD0Ww46Tl7B4sWLGTp0KMePH2fevHlUqlRJ03mMlpqaSmxsrG5bBLdr2LAhgwYNYvTo0Rw5ckT3+YRwtV9+Ud+sfvlFrRP47jvwsBIit/Xcc2pi1aIFdO4MXbtCcrLRUXkWr04GDp69RsyhC1muEXggk5mcZWoSvX0vEyZMoFixYtqO7yY2bNjAzZs3XZIMAEyaNImiRYvSq1cvudlQeI2rV9UTAk89pTbT2b1bXR3wksVDt1GkiLoqsHQpREWpidd//2t0VJ7Dq5OBJfFJ+Jn1+T/Oz2xi5a7zuoztLqKioihRooTLVjzy5MnDvHnzWLduHV9++aVL5hTiLtevw86d6vV5O3eqv39E69ZBWBgsW6b2EPj5Z3jiCc0iFXcwmaBbNzXhql0bOnSAN96AK1eyMaiGz4M78+pkIHr/Oe1XBf6U4VSIPnBOl7HdRWYLYlfWP7Rt25ZXX32VYcOGcfr0aZfNK3xcYiIMHgwhIerVeTVrqm3/atZUfx8Son48MfGhhktNVT+9VSv1mt6EBOjZU1YDXKV4cXVVYMECdTsmLAzWrMnCABo/D57Aa5OB6+kOki6m6jpHUnKqbq2LjXbmzBl2796tS3+BB/n4448JCAhg4MCBLp9b+JijR6FtW7W0f84cOHz47jNqiqL+9zlz1M9r21Z93T+Ii1O3AyIi1PsF1q6F0qV1/VOIezCZ1FWBhAQoXx7atIEBAyAl5T4v0uF58BRemwwcT05B711nBTiWfL8ny3PZbDYAWrVq5fK5CxUqxKxZs/jhhx/4/vvvXT6/8BEREVClCkRHq79/0NV4mR+PjlZfFxHxtw+np8OIEdC0qdpOeOdO9YdHs9d+l/UMpUqBzQYzZ6ptjKtXV4903kXj58HTeO1jetPh9Kp5XM1ms1GzZk2KFCliyPwvvPACzz77LAMGDODixYuGxCC82KRJalVfWlrW78d1ONTX9eqljsP/t+2dMQMmT1bfbCpU0CFu8UjMZhg4UO1eWKyYeqxz+HD1rxHQ/HnwRF6bDPhbXPNHc9U8rqQoCjabzZAtgkwmk4nZs2eTlpbG22+/bVgcwgtFRMDo0dqMNXo0q59dQP364O8PW7fCu++Ch/ca81rly8OGDfDhh/DZZ+p9C8dGa/s8sGCBNmO5mPe9k/2pdFBu9K7VMf05j7dJSEjg7NmzLjtS+E+KFy/ORx99xFdfffXXtoUQ2XL0KAwapNlwCtBm9UCm9jvKpk1qoZpwb35+6qrA9u1QznyUYpMGabulPHCgR9YQeG0ykDvAQnAWbxnMquCgQHIHeN8t0DabjZw5c9K4cWOjQ6Fnz55YrVZ69+5Nyn0rf4R4CH36ZH0Z+D5MQE4/B0P39UHHJp1CB6GhsOrxPvibHdr+4OhwqM+Zh/HaZADAWrGorn0GrBW8s32YzWajefPm5MyZ0+hQMJlMzJs3j7NnzzJaq6U84ZsSE9VKMg2TAQBThkMdd+9eTccVOktMxLzGhp9T4xNhDs98Hrw6GeheP1jXPgMvNwjWZWwjpaWlsX79esO3CG4XEhLC+PHj+fTTT9m0aZPR4QhP9cUXYNFpJc9iUY+aCc8hz8PfeHUyUL5YXpqGFNZ8dcDPbKJpSGFCinrfpeOxsbGkpaW5VTIAMGTIEGrXrk14eDjp6elGhyM80S+/aL4q8BeHA379VZ+xhT7kefgbr04GACZ3DMOicTJgMZuY3NE7K4WioqIoVqwYYW5WCWWxWFiwYAEHDhxgypQpRocjPM21a6D3BViHD3ttq1qvI8/DXbw+GShZKJB/dwjVdMzxHUIpqXNxolGMaEH8sKpVq8aIESOYPHkyu3fvNjoc4Unu1UlOa4oChw7pO4fQhjwPd/H6ZACga91g3m6bvQ4gmbfoDW9bkS51va9WAOD8+fPs2LHD7bYIbjd69GjKlStHz549ycjIMDoc4SlctbUkW1ieQZ6Hu/hEMgAw0FqeDzqFEWAxZ7mGwM9swmJSuPjrTEJNJ3WK0Hhr/rzJw52TgYCAACIiIti8eTMzZ840OhzhKQICvGsekT3yPNzFZ5IBUFcI1gxtTqOyQQD4PSAnyEwaGpUNYu1bLagbdIuXXnqJc+e887ZCm81G1apVefzxx40O5b4aN27MwIEDGTVqFEc9sLmHMEBIiP5XBppM6jzC/cnzcBefSgZArSFYHF4f25Bm1M6Xyq2Lp+9qOGECSgUF0qN+KdYMbcbi8PqULpKXJUuWkJGRQY8ePXA6vetOAndoQZwVkydPpnDhwvTu3fuvLRwh/lGePOpdwnoqV06dR7g/eR7u4n3t8x5S+WJ5yXPgV4ps305s/FaOJadw0+HE32KmdFDue3YWfOyxx1iyZAlt27blgw8+YOTIkQZEro99+/Zx8uRJt94iuF2ePHmYO3cu7du3Z+HChbz++utGhyTc3ZNPqme/9ThOZrFA+/bajyv0I8/D3/jcysDt7HY7TZs2JXeAhdDi+akZXJDQ4vnv22K4devWjBw5kvfff5+YmBgXRqsvm82Gv78/zZo1MzqUh/avf/2LHj168NZbb/HHH38YHY5wd3376nuuvF8/fcYW+pDn4W98Nhn4448/OHjwIE2aNMnya8eNG0fjxo3p1q0bFy5c0CE617PZbDRp0oTAQM86Mjljxgxy5MjBwIEDjQ5FuLsqVaBNG827zjmwkNq4DVSurOm4Qmc6PQ9YLOq4HvY8+GwyEBsbC/BIyYDFYuHbb78lPT2dV1991ePrB27evEl0dLTHbBHcLigoiJkzZ/L999+zcuVKo8MR7m7uXE2/+SuAw2Shzra5zJ4NHv6twPdo/DwA6nhz52o7pgv4bDJgt9spU6YMTzzxxCO9/oknnmDx4sX88ssvTJ8+XePoXGvTpk2kpKR4ZDIA0LlzZzp06MCAAQO4dOmS0eEId1amDGh4JNUEMHMWLV4vw4AB0K4dJCVpNrzQm8bPAwCzZqnjehifTQZiYmIeaVXgdv/617949913ee+999i4caNGkbmezWYjKCiImjVrGh3KIzGZTMyePZvU1FTefvtto8MR7q5nT5g4UZuxJk0i54BwZs+GqCjYtw/CwmDhQv0b3AmNaPw8EB6uzViupvigq1evKmazWZk7d262x7p586bSqFEjJTg4WElOTtYgOterV6+e0qVLF6PDyLa5c+cqgLJmzRqjQxGeYP58RcmZU1EsFkVR37sf7h+LRX1dRMRdQ166pCivvaZ+2jPPKMqZM67/Y2lp2zb1z7Jtm9GRuIAOz4Mn8cmVgfj4eJxOJ02bNs32WDly5GDZsmVcv36d119/3ePOvF+8eJGtW7d67BbB7Xr27EmLFi3o1asXKSkpRocj3F3PnpCYCFar+vsH7R1nftxqVV93j58ACxSAr76CVatg82YIDYXly7UNW+hEh+fBk/hkMhATE0NQUBCVKlXSZLySJUuyaNEiVq9ezSeffKLJmK6ybt06nE6nVyQDZrOZ+fPnc+bMGcaMGWN0OMITlCmjru/v2aMeBbtXZ7rMTnL9+qnf9KOiHrgn3KED7N4NrVtD167QpQt4ycEj7/YQz4OCicOmEDL6PPzz4BGMXpowQsuWLZUOHTpoPu6wYcOUHDlyKPHx8ZqPrZfevXsrFStWNDoMTU2dOlUxm80e9fcg3Mi1a0ri0h1KPTYpiUt3KMq1a9kabtkyRSlUSFGKFVOUVau0CdFVfGqb4J/c8Txsib6mgKLExRkdmLZ8bmXg1q1bbNq0SZMtgjtNnjyZmjVr0qVLFy5fvqz5+FpTFIWoqCivWBW43dChQ6lZsybh4eHcvHnT6HCEp8mThxsVa7CZ+tyoWCPbLWW7dFF/0KxbF559Fl5/Ha5c0SZU4QJ3PA81muQhb16IjjY6MG35XDKwY8cOUlNTs32S4F78/f1Zvnw5ly9fJjw83O3rBw4fPsyxY8c85j6Ch2WxWFiwYAH79u3jgw8+MDocIXjsMVi9Wq0nWLkSqlYFm83oqMSjsFigaVP47TejI9GWzyUDdrudXLlyUatWLV3GL126NF999RUrV67k888/12UOrdhsNiwWCy1atDA6FM1Vr16dd955h4kTJ5KYmGh0OEJgMsFrr0FCAlSqBG3bQv/+cP260ZGJrLJaITYWvGnh0SeTgfr16+Pv76/bHM899xyDBw9m2LBhbN++Xbd5sstms9GwYUPy5s1rdCi6eP/99ylbtizh4eFkZGQYHY4QAAQHQ2QkfP45LFoE1auDF11z4hOsVkhNVU+MeAufSgYURcFut+uyRXCnqVOnEhYWRufOnbl69aru82WVw+Fg7dq1XlcvcLucOXOyYMEC4uPjmTVrltHhCPEXs1ldFdi1C4oXh+bN4e234cYNoyMTD6NGDcif37u2CnwqGThw4ADnz593STIQEBDAihUrOH/+PL169XK7+oEtW7Zw9epVr04GABo3bkz//v0ZOXIkx44dMzocIf4mJER9Q/noI7WLba1asGWL0VGJB/Hzg2bNvKuI0KeSAbvdjtlspmHDhi6Zr2zZsixYsIAVK1Yw180urrDZbBQoUIA6deoYHYrupkyZQlBQEL1793a7pEwIPz8YNgy2b1cPLjRsCO+/71370d7IaoW4OEhPNzoSbfhUMhATE0P16tXJly+fy+Z84YUX6N+/P0OGDGHnzp0um/dBoqKiaNmyJRatb+xyQ3nz5mXu3LnYbDa+/vpro8MR4p6qVFHfXMaOhQ8+gHr14PffjY5K/JMWLSAtDeLjjY5EGz6VDLiqXuBO06dPp3LlynTp0oVr1665fP47Xb16lU2bNnn9FsHt2rdvz8svv8zQoUM5e/as0eEIcU85cqirAps3q9ch16kDU6aAw2F0ZOJO1atDwYLes1XgM8nAmTNnOHz4sC7Nhh4kZ86crFixgtOnT9OvXz/Dl6p/++03MjIyvK6/wIPMmDEDPz8/Bg0aZHQoQtxXzZpq7cDbb8Po0dC4sXojonAfZrNa+CnJgIex2+2AWlBmhPLlyzNv3jyWLFnCl19+aUgMmaKioihbtixly5Y1NA5XK1y4MDNnzuQ///kPP/74o9HhCHFfAQEwebJ6nv3yZTVB+OQTdcVAuAerFTZtUrcLPJ1PJQNly5alePHihsXQrVs3evXqxaBBg9i9e7dhcdhsNp/aIrhdly5dePrpp+nfv79HtIwWokED2LED+vSBoUOhZUs4etToqASodQPp6bBxo9GRZJ9PJQNGbBHc6dNPPyUkJITOnTsbcs3u8ePHOXDggM9tEWQymUzMmTOH69evM3z4cKPDEeKhBAaqqwLR0XD8OISFwbx5IIdjjFW1KgQFecdWgU8kA1evXmXnzp2GFA/eKVeuXKxYsYKkpCQGDBjg8vltNhtms5mWLVu6fG53UaJECT766CMiIiJYt26d0eEI8dBatFBPGHTvrq4UtG8PJ08aHZXvMpvVvxNvaD7kE8nApk2bcDqdbpEMAFSqVIk5c+awaNEiFi1a5NK5bTYbdevWpUCBAi6d19306tWLZs2a0atXL1JTU40OR4iHljcvzJ0Lv/6q3nNQtSosXiyrBEZp0UKtG/D0byM+kQzY7XYKFy5MxYoVjQ7lLz169OD111+nf//+LrtIJyMjgzVr1vjsFsHtzGYz8+fP5/Tp04wZM8bocITIsn/9C3bvhmeegVdegU6dQE7Nup7VCrduqT0iPJlPJAMxMTE0adIEk8lkdCh/M3PmTEqXLk2XLl1c8tPpjh07uHjxos8WD96pQoUKjBs3jhkzZrBFesAKD1SwoLoqsHKleuqgalX47jujo/ItVapAkSKeXzfg9cnAzZs3iY+Pd5stgtvlzp2bFStWcPjwYd58803d57PZbOTJk4cGDRroPpenGDZsGDVq1CA8PJyb0v9VeKiOHWHPHrVf/osvwksvwcWLRkflG0wm76gb8PpkYMeOHdy4ccMtThLcS2hoKLNmzSIiIoKlS5fqOldUVBRWq5UcOXLoOo8nsVgsLFiwgMTERKZOnWp0OEI8siJF1FWBJUvUeoKqVeHnn42OyjdYrWrXyOvXjY7k0Xl9MhATE0OuXLmoWbOm0aH8o9dff52XX36ZPn36cODAAV3mSElJITY2VrYI7qFGjRq88847TJgwgb179xodjhCPzGRSVwX27FGv2X36aejZE9zwFnWvYrWqLaNjY42O5NF5fTJgt9tp0KCBW/80nHn2/YknnqBz586k6dDOasOGDdy6dUuKB//BmDFjKF26NOHh4WRkZBgdjhDZUry4uioQEQHLl6t9CeQUrX4qVoTHHvPsrQKvTgYURXGbZkMPkidPHlasWMH+/fsZOnSo5uNHRUVRsmRJKlSooPnY3iBnzpxERESwceNGZs+ebXQ4QmSbyQTh4erxw3LloFUrGDQIDOh15vUy6wY8uYjQq5OBffv2kZyc7JbFg/dSrVo1Pv30U7744gtWrFih6diZLYjd7USFO2natCn9+/fnvffe4/jx40aHI4QmSpeGNWvgs89gwQJ1+8DTj8G5I6sVtm4FN7iY9pF4dTJgt9sxm80eVT3fq1cvunbtSs+ePTl06JAmY54+fZo9e/bIFsFDmDJlCgULFqRPnz6G3y4phFbMZnVVYOdOKFwYmjaFd9/1jgt23EWLFpCRAX/eiedxvD4ZqFmzJnnz5jU6lIdmMpmYO3cuxYoVo0uXLqSnp2d7TJvNhslkolWrVhpE6N3y5cvHF198QWRkJN98843R4QihqQoV1DerKVPUuw7q1IFt24yOyjuUL6/WanjqVoFXJwOZzYY8Tb58+VixYgW7d+/W5DIdm81GzZo1KVy4sAbReb+nnnqKl156iSFDhnBWWroJL+PnB++8oyYB/v7qrYjjxqld9MSjM5nUrQJJBtzMqVOnOHr0qEcmAwA1a9bk448/ZubMmaxcufKRx3E6ndKC+BF88sknmM1mBg8ebHQoQuiialWIj4dRo2DiRDUpMPBmda/QogVs3w5XrhgdSdZ5bTIQ++eBT09NBgD69+/PCy+8wBtvvMHRR7zAPCEhgbNnz0p/gSwqUqQIn376KStWrGDVqlVGhyOELnLkUFcF4uPV+oHatWHqVHXvW2Sd1QpOJ8TEGB1J1nltMhATE0NISAiPPfaY0aE8MpPJREREBIUKFaJLly6P1C7XZrORK1cuGjdurEOE3q1bt2489dRT9O/fnyuemOoL8ZBq11a3DYYMgREj1ALDgweNjsrzlC0LJUt65laB1yYDdrvdo1cFMuXPn58VK1awc+dORowYkeXXR0VF0bx5cwICAnSIzrtlNoO6du0a77zzjtHhCKGrnDnhww/Vn2rPn4fq1WHZMqOj8iyefE+BVyYDV65c4ffff/eIZkMPo06dOnz00UfMmDGD1atXP/Tr0tLSiImJkS2CbChZsiQffvgh8+bN4zdP/D9ciCxq3Fg9ghgeDh99pP63M2cMDcmjWK2wYwdcumR0JFnjlcnAxo0bcTqdXrEykGnw4ME899xzvPbaaw/dEMdut5OWlibFg9nUp08fmjZtSq9evVxy1bQQRsudG2bOhDlz1N937qw2LJLWGw9mtapfpw0bjI4ka7wyGbDb7RQtWpTy5csbHYpmTCYTX375Jfny5aNr167ceohzQFFRUTz++OOEhoa6IELvZTabiYiI4MSJE4wbN87ocIRwmXr11F/btFEvPHr6aTh92tiY3F3p0lCqlOfVDXhtMtCkSROva71bsGBBli1bxtatWxk9evQDP99ms9G6dWuv+zoYoUKFCowbN47p06ezdetWo8MRwqXGjIGfflKPzVWtCkuXyirB/Vitnlc34HXJQHp6OvHx8V61RXC7Bg0aMGXKFKZOncovv/zyj5937tw5du7cKVsEGho2bBjVqlUjPDz8oVZmhPAmTz2l9iH417+ge3d48UW10FDczWqFXbsgOdnoSB6e1yUD27dvJy0tzWuTAYC33nqLp556ildeeYWTJ0/e83PWrFkDQOvWrV0ZmlfLkSMHCxYsYM+ePXyUWVklhA8JClJXBVasUH/yDQ2FH380Oir306KF+uv69YaGkSVelwzY7XZy585NzZo1jQ5FN2azmUWLFpErVy66deuGw+G463NsNhthYWEe3WfBHdWqVYvhw4fz73//m3379hkdjhCGePFF2LMHGjWCjh3hlVc8r3peT8HBas8BT9oq8LpkICYmhgYNGmCxWIwORVdBQUEsW7aMjRs3Mnbs2L99TFEUbDabbBHoZMyYMZQqVYqePXvidDqNDkcIQxQrBj/8AF9/DatXQ1gYREYaHZX78LR7CrwqGXA6ncTGxnr1FsHtGjduzMSJE5kyZQpRUVF//fe9e/dy6tQp6S+gk1y5chEREUFsbCxzMs9e3SEl3cGe01fYkXSJPaevkJJ+9+qNEJ7OZIIePdRagtBQtZ6gTx+4ds3oyIxntapfF0+pq/CqH5/37dvHxYsXvabZ0MN45513WL9+PS+//DI7d+6kePHi2Gw2/P39ferr4GrNmjWjb9++jBgxgmeeeYbg4GAOnr3GkvgkovefI+liKrcXW5uA4EKBWCsWpXv9YMoX85xrtYV4kBIl4H//g3nzYNgwiIqChQuheXOjIzPO7XUDL7xgaCgPxatWBmJiYvDz86N+/fpGh+IyZrOZr7/+mhw5ctC9e3cyMjKw2Ww0bdqUwMBAo8Pzah9++CEFChTgjcHDeXlBPG0+2cDi+OMcvyMRAFCA4xdTWRx/nDafbKDHgnhOXJQGRsJ7mEzqqsDvv6t75i1awNChcOOG0ZEZ44knoHx5z9kq8KpkwG63U7NmTfLkyWN0KC5VpEgRli5dyoYNGxg3bhy//fabbBG4QL58+egxdg4HQroQd0hdC8xw3v/wdebH444k03rGepZtSdI9TiFcqWxZ9Q1wxgz44guoWVO9FdEXeVLdgNclA766NN68eXPGjRvHxIkTSUlJkeJBF5gVfZClh02YLf44yVpjpwynQrrDyYiVCcyKluvhhHcxm9UbEHfsgPz51VMHI0dCerrRkblWixawdy+cPWt0JA/mNcnAyZMnOXbsmM8UD97LyJEjKV26NGazWY4U6mzZliSmRR1Qf5PNDo/Tog6wXFYIhBeqVAliY2HCBJg2DerWVS9B8hWZdQOecMTQa5IBu90OqBX2vsrPz48CBQqQI0cOXnnlFTIyMowOySuduJjK2NV7NB1zzOo9UkMgvJLFoq4KbNmirhjUrQsTJ8I92qN4nccfVxMiT9gq8KpkoEKFChQrVszoUAxz8eJFdu3axaBBg1i7di1TpkwxOiSvNPKHBBwPqA3IKodTYeQPCZqOKYQ7qV4dNm+GESNg3Dh162DvXqOj0l+LFrIy4FIxMTE+vUUAsHbtWhRF4c033+T9999n7NixrPekfpge4ODZa8QcuvDAQsGsynAqxBy6wKFzckBbeC9/f3XLIC5O7UVQsyZMnw7evIhptcL+/e5/26NXJAOXL18mISHB55MBm81GpUqVKFGiBGPGjKFZs2a89NJLnPeUrhceYEl8En5mfW6B9DOb+GaT1A4I71evnnoD4oABMHy4+tPz4cNGR6UPT6kb8IpkYOPGjSiK4rMnCUBtQRwVFfXXKQI/Pz+WLFnCrVu36NGjh7TN1Uj0/nOarwpkynAqRB84p8vYQribXLnUVYH169WfmqtVgzlzvO9q5KJFoUoV968b8IpkICYmhmLFilGuXDmjQzHMoUOHOH78+N/6CxQvXpxvvvmGyMhIuWVPA9fTHSTpXOSXlJwqrYuFT2naVL3u99VXoX9/aNcOTpwwOiptWa2yMuASdrudJk2aYMrmES9PZrPZsFgsNL+j/2fbtm157733GDVqFLGxsQZF5x2OJ6fc1VlQawpwLDlF51mEcC958sDs2Wob4717oWpVtZ2xt6wSWK1w6BD8w43zbsHjk4H09HQ2b97s01sEoCYDjRo1Im/eu3vejx8/noYNG9K1a1eSk5MNiM473HS4ZqvFVfMI4W7atIGEBOjUCV5/HZ59Fv74w+iosi/zZzR33irw+GRg69atpKen+3TxoMPhYN26df/YgthisfDtt99y48YNXnvtNRRvSbddzN/imv9dXDWPEO6oQAH46itYtUo9ihgaCsuXGx1V9hQurF7x7M5bBR7/Xcdut5MnTx6qV69udCiG2bx5M1evXr3vfQQlSpTg66+/5qeffuLjjz92YXTeo3RQ7iw2Hc4605/zCOHrOnRQrwBu3Rq6doUuXeDCBaOjenTufk+BVyQDDRs2xGLxqtuYs8Rms1GgQAHq1Klz38978sknGT58OCNGjGDTpk0uis575A6wEFxI35sgg4MCyR3gu8+yELcrXFhdFVi2DNasUWsJVq82OqpH06IFHD0Kx48bHcm9eXQy4HQ6iY2N9ektAoCoqChatWqFn5/fAz930qRJ1K1bl65du3Lp0iUXROc90tPTKea8AE59OqT4mU1YKxTVZWwhPFmXLrBnj9rK+Nln1XqCK1eMjiprmjdXrzFx160Cj04GEhMTuXTpkk8nA1euXCE+Pv6hbynMkSMHy5Yt4+rVq7z++utSP/AQLly4wMSJEylVqhQ/fjQMzA9Ouh5FhlPh5QbBuowthKd77DF1VeCrr2DlSnWVwGYzOqqHV6iQ2pLZXbcKPDoZsNvtWCwW6tevb3QohomOjiYjI+O+9QJ3Cg4OZuHChaxatYrPPvtMx+g82969e+nTpw8lS5Zk8uTJdOzYkd/tUTQNKax5F0I/s4mmIYUJKXr3aRAhhMpkgtdeU08cVKoEbduqvQmuXzc6sofTooWaDLjjz2AenQzExMRQq1Ytcuf23YIrm81GuXLlKFOmTJZe16FDB4YMGcLw4cPZunWrTtF5HkVRWLNmDU899RRVqlRh9erVjB49mqSkJObMmUOlSpWY3DEMi8bJgMVsYnLHME3HFMJbBQdDZCR8/jksWqT+xB0TY3RUD2a1QlISHDtmdCR38+hkILPZkC+z2WwPvUVwpw8//JAaNWrQpUsXrnjaBpzG0tPTWbhwIdWrV6dNmzacOnWKRYsWcezYMUaNGkXhwoX/+tyShQL5d4dQTecf3yGUkjoXJwrhTcxmdVVg1y4oXlzdk3/7bbhxw+jI/lmzZurqhjtuFXhsMpCUlERSUpJPNxs6duwYBw8ezNIWwe38/f1Zvnw5ycnJ9OzZ0yfrB86fP8+ECRMoVaoUr7/+OqVKlWLt2rXs2LGDV155hYCAgHu+rmvdYN5uW0GTGIa3rUiXulIrIMSjCAlRi/I++ghmzYJatWDLFqOjurcCBdSbGiUZ0JDdbgegcePGBkdiHJvNhtlsxmq1PvIYZcqU4csvv+S7775jzpw5Gkbn3jLrAYKDg5kyZQodO3Zk3759/Pe//6Vly5YP1dp6oLU8H3QKI8BiznINgZ/ZRIDFzIedwhhgDXnUP4YQAvDzg2HD1JsQ8+SBhg3h/ffh5k2jI7tb5j0F7vazl0cnAxUrVqRIkSJGh2IYm81G/fr1KVCgQLbG6dSpEwMHDmTo0KHs2LFDm+DcUGY9wJNPPvlXPcD777/PiRMnmDNnDhUrVszymF3rBrNmaHMalQ0CwPSA2wsyk4ZGZYNYM7S5rAgIoaEqVSAuDsaOhQ8+UK9K/v13o6P6O6tVvaPA3a5s9uhkwJe3CDIyMlizZs0jbxHcadq0aVStWpXOnTtz9epVTcZ0F+np6Xz11Vd/1QOcPn36r3qAkSNHEhQUlK3xSxYKZHF4fWxDmlHk8l4sNy7e1anQBJQKCqRH/VKsGdqMxeH1pUZACB3kyKGuCmzeDE4n1KkDU6aAw00uA23SRK13cLutAsUDXbx4UTGZTMrChQuNDsUwmzdvVgAlJiZGszEPHjyo5M2bV+natavidDo1G9co586dU8aPH68UK1ZMAZSnn35aWbdunW5/NqfTqTz++OPKiBEjlOtpt5Tdpy4r249fVHafuqxcT7uly5xCH9u2KQqov/oyT/86pKUpynvvKYrZrCj16inK3r2PNo7WX4e6dRWlWzdtxtKKR64MxMXFoSiKT58ksNls5M2bV9MeCyEhIURERLBs2TLmz5+v2biulpiYSO/evf+qB+jUqdNf9QBWq1W3q66PHj3KmTNnaNKkCbkDLIQWz0/N4IKEFs8vLYaFMEBAAEyeDLGxcPmyWrz3ySfqioGR3LFuwCOTAbvdzuOPP07ZsmWNDsUwUVFRWK1WcuTIoem4nTt3pm/fvrz55pv87m6bbfehKAo2m40nn3yS0NBQfvrpp7/qAWbPnv1I9QBZlVnU2qhRI93nEkI8vAYNYMcO6NMHhg6Fli3VewKMYrXCmTNw4IBxMdzJI5OBmJgYmjRpottPeO7u+vXrxMXFaVYvcKePP/6YChUq0LlzZ667eWuv2+sB2rZty5kzZ/j66681qwfICrvdTmhoKAULFnTZnEKIhxMYqK4KREerlwWFhcG8ecb8dN64sXoCwp3qBjwuGUhLS2PLli0+vUWwYcMGbt269cjNhh4kV65crFixgpMnT9K/f3+37D9w/vx5xo8fT6lSpXjjjTcoXbo069atY/v27fTo0QN/f3+XxySXZgnh/lq0UE8YdO+urhS0b69W97tS3rzqpUvudGmRxyUDW7du5ebNmz59kiAqKorg4GDKly+v2xwVK1Zk7ty5LF68mIULF/7j56WkO9hz+go7ki6x5/QVUtL1LdnNrAcoWbIkH3zwwV/1AKtXr9a1HuBBkpOTSUxMlGRACA+QNy/MnQu//qrec1C1Kixe7NpVAnerG/C4qqaYmBjy5s1LtWrVjA7FMJktiPV+4+vevTvR0dEMGDCAevXqERqqtuA9ePYaS+KTiN5/jqSLqX87WW8CggsFYq1YlO71gylfLPsX7yh/9gf4+OOP+d///sfjjz/O2LFj6d27t0u3Ae4nLi4OQJIBITzIv/4Fu3fD4MHwyivqbYhffAHFiuk/d4sW6pHHvXvV/ghG87iVAbvdTqNGjfDz0+caWXd38uRJEhMTdasXuNNnn31GuXLl6Ny5M/tPXqDHgnjafLKBxfHHOX5HIgCgAMcvprI4/jhtPtlAjwXxnLiY+khzp6Wl8eWXX1KtWjXatm3LH3/88Vc9wHvvvec2iQCoz+UTTzxBqVKljA5FCJEFBQuqqwIrV6qnDqpWhe++03/exo3VngjuslXgUclARkaGz+/LrlmzBpPJRKtWrVwyX2BgICtWrOCP3GVpPyuOuCPJAGQ477+2lfnxuCPJtJ6xnmVbkh56ztvrAcLDwylTpgzR0dGG1gM8SGxsLI0bN/bZolYhPF3HjrBnj3qZ0IsvwksvwcWL+s2XO7faIdFdigg9KhnYs2cPV65c8elkwGazUbt2bZf+VLz2Dwt5W/UlA/MDk4A7ZTgV0h1ORqxMYFb0wft+bmJiIr169fqrHuCFF15g//79rF69mhYtWrjtG60UtQrhHYoUUVcFlixR6wmqVoWff9ZvvhYt1JUBo/segIclA3a7nRw5clCvXj2jQzGE0+nEZrO5bIsAYNmWJKZFqYdhs/tmPC3qAMvvWCHI7A/Qvn17QkND+eWXXxg7diwnT57k888/p0IFbW4G1FNmUaskA0J4PpNJXRXYswdq1ICnn4bx4/WZy2qFCxcgMVGf8bPCo5KBmJgYateuTWCgb/Z0//333zl//rzLkoETF1MZu3qPpmOOWb2HExdT76oHOHv2LIsXL+bo0aO89957FCpUSNN59WS328mbNy9hYWFGhyKE0Ejx4uqqQEQEREWp/23zZm3naNgQ/P3dY6vAY5IBRVH+ajbkq2w2G4GBgS7rcDfyhwQcWdwWeBBHhpMu036kVKlS9OzZk7JlyxIdHc22bdt4+eWX3bIe4EHsdjsNGjTAYvG4wzlCiPswmSA8HFasUH/frx8MGgQpKdqMHxgI9etLMpAlSUlJnDp1yuf7CzRv3pyAgADd5zp49hoxhy5kuUbgQTIUOK3kp13nV9m3bx+rVq1y63qAB3E6ncTFxfl0kiqEtyteXP11+HBYsEDdPvjzNHG2Wa2wfr3xdQMekwzExMQAvtv3/caNG8TExLhsi2BJfBJ+Zn3eoP1MULbNqx5RD/Age/fu5dKlS5IMCOEDunaFnTuhcGFo2hTefRfS0rI3ptWqnlpISNAkxEfmMcmA3W6ncuXKFC5c2OhQDGG320lPT9etBfGdovef03xVIFOGAtEHzukytqvZ7Xb8/Pw0vT1SCOG+KlQAu11tGPTJJ1CnDmzb9ujjNWig3q5o9FaBRyUDvr5FULx4caq4oFXV9XQHSY/YKOhhJSWn6t662BXsdju1atUid+7cRocihHARPz945x01CfD3V9/Qx42DW7eyPlbOnGohoSQDDyE5OZk9e/b49FKszWajdevWLtlbP56ccldnQa0pwLFkjapwDJTZbEjo7Pp1dX02Pl791c1v0xS+oWpV9ZEcNQomTlSTgt27sz6O1QobNkDGFeOec49IBny97/vZs2fZtWuXy7YIbjpcU8niqnn0curUKY4ePeqzz6XuEhPVpvEhIZAvH9SsqX63rVlT/X1IiPpxdzikLXxWjhzqqkB8vFo/ULs2TJ0KGRkPOUBiIuG7BrPlcgjmgsY95x6RDGT2fS9durTRoRhizZo1ALRu3dol8/lbXPNYuGoevcTGxgLIyoDWjh6Ftm0hNBTmzIHDh+++2k1R1P8+Z476eW3bqq8TwiC1a6vbBkOGwIgRaoHhwfs1Xb3tOS++ag4hHMZk4HPuEd+NM/sLeOrxs+yy2WxUr16dYq64SgsoHZQbvb/Spj/n8WR2u52QkBAee+wxo0PxHhER6hVumRuojgfUlWR+PDpafV1EhL7xCXEfOXPChx9CTAycPw/Vq8PMmfc4NnjHc27KMP45d/tk4MaNG2zdutVnl2IVRSEqKsqlLYhzB1gILqRvl8fgoEByB3h2kx673S6rAlqaNAl69VLXWh+UBNzJ4VBf16uXOo4QBmrcWN3yDw9XV/hbt4bjx//8oJs+526fDGzZsoVbt2757EmCxMREzpw549JkAMBasah+fQbMJqwViuoytqtcu3aNXbt2+WySqrmICBg9WpuxRo9WO8MIYaDcudVVgTVr1JX+sDCIedV9n3O3TwZiYmLIly8fVatWNToUQ9hsNgICAlyeDHWvH6xfnwGnwssNgnUZ21U2bdqE0+mUZEALR4+qPV61NHCg1BAIt9CqldpQqH/7o9T5epC2J7U0fM7dPhmw2+00atQIPz8/o0MxRFRUFE2bNiVXrlwunbd8sbw0DSms+eqAn9lE05DChBTNq+m4rma32wkKCqJixYpGh+L5+vTJ+nLpgzgc6rhCuIF8+eCDS30IMDu0rcfS8Dl362QgIyODuLg4n90iSE9PZ/369S7fIsg0uWMYFo2TAYvZxOSOnn+7X2a9gK8WtWomMRFsNn2SAZsN9u7VdlwhHsWfz7nZ6b7PuVsnAwkJCVy9etVnl2I3btxIamqqy/oL3KlkoUD+3SFU0zHHdwilpM7FiXq7desW8fHxPvtcauqLL0Cv2x4tFvVIlhBG84Dn3K2TAbvdTo4cOahbt67RoRgiKiqKIkWKUK1aNcNi6Fo3mLfbanOh0PC2FelS17NrBQB27dpFSkqKJANa+OUX7VcFMjkc8Ouv+owtRFZ4wHPu9slA3bp1Xb5f7i4yWxCbzcb+NQ20lueDTmEEWMxZriHwM5sIsJj5sFMYA6whOkXoWna7nZw5c1KrVi2jQ/Fs167BkSP6znH4sLQuFsbykOfcbQ96K4pCTEwML7/8stGhGCI5OZlt27YxYMAAo0MB1BWCxuUKM/KHBGIOXQDFCaZ/TlL8zCYynAqNygYxuWOYx28N3M5ut1OvXj0CAgKMDsWz3auzoNYUhb3/PcSNijWy9LLMLVhfLzmQr4MqO1+HXPsPU9kFzzmHDkGNGo88hNsmA8eOHeP06dM+sxSbku7gWHIKNx1O/C1mtq9fi6IohhUP3kvJQoEsDq9PQtIFWvYeQ/E6bbia4f+3ozIm1IZC1gpFeblBsMefGriToijY7XbeeOMNo0PxfOnpLpnmtZfS2fyIr/XRn0XuIl8H1aN8HeqRTrz2odwtm/8/uW0yYLfbAe/u+37w7DWWxCcRvf8cSRdT/37+VAmk9MCFzN92me6WfJQv5j5vqqf3buNC5BzWfzyQUuUq/C2JKR2U2+M7C97PkSNHOHv2rM8kqbpy0crKwqUB3MjiCdC9e9Vv/N98A5Ur6xOXJ5Cvgyo7X4dc+wPgJX3i+pts/v/ktt+1Y2JiCA0NpVChQkaHorkTF1P/Wm7PXE6/i8mEkqcwi+OPs3DjMZqGFHab5fbIyEhKlChB5cqVMZlMhBbPb3RILmO32zGZTDRs2NDoUDxfSAiYTPpuFZhMVH4mBPI82ssrVwYpDZGvQ6ZH+jpUcM1zTkj2arLctoDQbrd75U9fy7Yk0XrGeuKOJAM8sMtf5sfjjiTTesZ6lm1J0j3GB4mMjKRdu3Y+ecbebrdTtWpVChYsaHQoni9PHihbVt85ypVT5xHCKB7ynLtlMnDhwgX27t3rdc2GZkUfZMTKBNIdziy3+s1wKqQ7nIxYmcCs6Pvdi6mvEydOkJiYSLt27QyLwUhyOZHGnnxS3/PX7dvrM7YQWeEBz7lbJgOZ98R708rAsi1JTIs6oMlY06IOsNygFYKoqCjMZjOtW7c2ZH4jXbhwgX379nnVc2m4vn31PX/dr58+YwuRFR7wnLtlMmC32ylRogTBwZ7foAbUGoGxq/doOuaY1Xs4cTFV0zEfRmRkJPXq1fPJZfK4uDjAu5JUw1WpAm3aaP5TU4bJgqNlG9+uehPuQ6fnHItFHVeD59xtk4GmTZt6zZ70yB8ScGh8A6DDqTDyhwRNx3yQjIwM1qxZ49NbBN6UpLqNuXM1/SapADcVC60OziUmRrNhhcgejZ9zQB1v7lxNhnK7ZCA1NZWtW7d6zU9fB89eI+bQBc2vA85wKsQcusChc9c0Hfd+tm7dyqVLlwy7K8FomUWt3pKkuo0yZdSL3zViAq5OnkVGcBmaN4e334YbNzQbXohHo/FzDsCsWeq4GnC7ZGDz5s04HA6vSQaWxCdpfg1wJj+ziW82ua52IDIykvz581OvXj2Xzekubty4wdatW6V4UC89e8LEidqMNWkSxd4LZ/16mDpV/X5ZqxZs2aLN8EI8Mo2fc8LDtRkLN0wG7HY7+fPnp2rVqkaHoono/ec0XxXIlOFUiD5wTpex7yUyMpLWrVtj0asq1o1t3bqVW7dueU2S6pZGjYL58yFnzqwvp1os6usiImDkSAD8/NRVge3bIXduaNgQ3n8fbt7UIXYhHpbGz7lW3C4ZiImJoXHjxoZfzqOF6+kOknQu8ktKTiUlXacq1dtcvnyZ+Ph4n64XyJs3L2FhYUaH4t169lTvfrda1d8/6Jtl5setVvV19/hJqUoV2LgRxo6FDz6AevXg9981jluIrNDhOc8ut3rHdTgcxMXFec1PX8eTU9D5egoU4Fhyis6zwNq1a8nIyPDpZKBRo0b4+fkZHYr3K1MGoqJgzx71yFRmp8LbKJg4bA5B6dtP/eYYFXXfvdMcOdRVgc2bISMD6tSBKVP0O+0lxAM9xHP+V2fBfg/3nGeHW633JiQkcP36da9pNnTT4fSaeSIjI6lUqZJPVtI7nU7i4uJ46623jA7Ft1SpAp99pv779evqrWzp6RAQgP1MCM2ezMPOnlA9C6eqataErVth3DgYPRp+/BEWLYJKlfT4AwjxEO7znBMS4rIOmm61MhATE4O/vz916tQxOhRN+Ftc8+XVex5FUYiKivLZUwSJiYlcvnzZa1asPFKePOr1rPXrQ40a1LXmISAAoqOzPlRAgLoqEBsLly+rCcInn4DTNbm7EP/sjufcla203SoZsNvt1K1bl5w5cxodiiZKB+VG70Nopj/n0dOBAwc4fvy4T28RWCwWnzxF4a5y5oRGjR4tGcjUoAHs2AF9+sDQodCyJRw9ql2MQngSt0kGMu+J95YtAoDcARbdbxkMDgrU/crgyMhI/P39ad68ua7zuCu73U6tWrXInVvfpEtkTYsWsGGDWgPwqAID1VWBdevg2DEIC4N58/S9YE4Id+Q2ycCRI0c4c+aM1yzF3rhxgy+//JKzO9aiOLPx3eo+/MwmrBWK6jL27SIjI2natKnPvhl66w2ans5qVZf5d+3SZqyEBHjpJXWlYNCg7I8phCdxm2Qg8574Ro0aGR1KtiQlJTFixAhKlixJeHg4JdKOYTLrU4Ge4VR4uYG+BX3p6en89ttvPrtFcPLkSY4fPy7NhtxQvXqQK1f2tgpulzevuirwyy9qDRfAzz/LKoHwDW6VDHjqPfGKohAdHU2nTp0oU6YMc+bMoUePHhw8eJC1K7+haUhhzbsQ+plNNA0pTEjRvJqOeye73U5qaqrPJgOZN2hKMuB+AgLUuoHfftN23PbtYcUK9d/HjIFOneDsWW3nEMLduE0yEBMT43FLsSkpKcydO5dq1arRsmVL9u/fz6xZszh16hQzZswgJCQEgMkdw7BonAxYzCYmd9S/AU5UVBSPPfaYzzbbsdvtlC9fnmLFihkdirgHq1WtG9C6X0C+fOqvH32knjqoWhW++07bOYRwJ26RDJw/f579+/d7TDJw5MgRhg0bRokSJejfvz/lypVjzZo17N69m379+pHnjuMgJQsF8u8OoZrGML5DqO7FiaDWC7Rt29ZnL+eRegH3ZrXC1avqqQA9tGwJu3dDs2bw4otqTcHFi/rMJYSR3CIZyFyKdeeTBE6nk6ioKJ555hlCQkL46quv6NWrF4cPH+bHH3+kVatW933D7Fo3mLfbVsheEH9uXg5vW5EudfVv/vPHH3+wa9cun90iuHr1Kr///rtsEbixOnXUEwFabxXcrmhRdVXgm2/g11/VVYKff9ZvPiGM4BbJQExMDMHBwZQsWdLoUO5y9epVZs6cSZUqVWjXrh0nTpxg/vz5nDx5kqlTp1K6dOmHHmugtTwfdAojwGLOcg2B2QSK4yY10vcwwBqSxT/Fo4mKisJkMtGmTRuXzOduNm3ahNPplJUBN+bvD02aaFdE+E9MJujeXV0lqFEDnn5abS9/9aq+8wrhKm6RDLjjUuz+/fsZNGgQJUqUYOjQoVSrVo0NGzawY8cOwsPDCQx8tCX6rnWDWTO0OY3KBgE8MCnI/HjjcoXp9cRZVs14l1WrVj3S3FkVGRlJrVq1KFKkiEvmczd2u53ChQtToUI2V3SErqxWiImBW7f0n+uJJ9RVgfnzYflytS/BunX6zyuE3gxPBlJSUti+fbtbbBE4nU5++ukn2rVrR6VKlVi+fDmDBw/m2LFjrFixgqZNm2qyd16yUCCLw+tjG9KMHvVLUSoo8K5OhSagVFAgPeqXYs3QZiwOr8+oIX157rnneP3110lKSsp2HPeTuS3iq1sE8P9Jqq/WS3gKq1Vt6b59u2vmM5nUVYGEBChbFlq1UvsSpOh/X5gQujH8oqL4+HgcDoehKwOXL1/myy+/5PPPP+fIkSPUqVOHRYsW0blzZ11bI5cvlpdxHUIZRygp6Q6OJadw0+HE32KmdFDuuzoLmkwmFixYQM2aNenWrRu//fYbOXLk0CW2HTt2cOHCBZ9NBm7dusWmTZsYP3680aGIB6hVS23hHh2ttnR3ldKlYe1amDULRoyA//1PvfTIw1ulCB9l+MqA3W6nYMGCVKlSxeVz7969m759+/LEE08wYsQIGjRowMaNG9m8eTOvvPKKS+9IyB1gIbR4fmoGFyS0eP5/bDFcqFAhvv32W+Lj4xk7dqxu8URFRZEnTx4aNmyo2xzubOfOndy4ccPttq/E3XLkgKZN9a8buBezGQYPhp07oXBhNY5334W0NNfHIkR2uEUy0LhxY8xm14TicDj44YcfaNmyJWFhYaxatYp33nmHpKQklixZQoMGDdx+WbhRo0ZMnDiRKVOmEBUVpcsckZGRtGzZUreVB3dnt9vJmTMntWrVMjoU8RCsVrDbXVM3cC8VKqjzT56s3nVQpw5s22ZMLEI8CkOTAYfDwcaNG13y01dycjIffvgh5cqVo1OnTqSlpbF06VKOHz/O2LFjeeyxx3SPQUvvvPMObdu2pUePHvzxxx+ajn3t2jViY2N9dosA1GSgfv36+Pv7Gx2KeAgtWkBqKmzZYlwMfn7qqsDWreophwYNYNw44xIUIbLC5clASrqDPaevsCPpEj+u30pKur71Ajt37lTvCChRgrFjx9KyZUu2bt1KXFwc3bp189hv9mazmcWLF2M2m+nevTsZ2bm67Q7R0dE4HA6fTQYyb9CULQLPUbOm2jXQiK2CO4WFwaZNMHIkTJyoJgW7dxsdlRD355ICwoNnr7EkPono/edIupjK7fd+lHzrP7wXm07L5D10rx9M+WLZ77V/69YtfvjhB2bOnIndbqdEiRKMGTOGnj17etUxuaJFi7JkyRJat27NlClTGD16tCbjRkZGUq5cOcqVK6fJeJ7m0KFDnDt3TpoNeRCLRe0SGB0No0YZHY26MvDvf8Mzz8Crr0Lt2jBhAgwbpq4gCOFudF0ZOHExlR4L4mnzyQYWxx/n+B2JAKgV8kmXbrA4/jhtPtlAjwXxnLiY+kjznT17lgkTJlC6dGm6dOmCn58f3333HUePHuW9997zqkQgU8uWLRk9ejRjx44lJiZGkzEjIyN9dlUA1I6YJpPJZ4snPVWLFhAXB+npRkfy/zJrB958Uz1x0LQpHDxodFRC3E23ZGDZliRaz1hP3JFkQL1u934yPx53JJnWM9azbMvDn6PfvHkzPXr0IDg4mClTpvDUU0+xa9cufvvtN55//nksFsNPUOpqzJgxNG7cmG7dunHhwoVsjXX48GEOHz7s08mA3W4nLCyMAgUKGB2KyAKrFW7cgM2bjY7k73LmhKlT1cZI585B9eowcyY4nUZHJsT/0yUZmBV9kBErE0h3OB+YBNwpw6mQ7nAyYmUCs6L/OYVOT0/nm2++oX79+tSvXx+73c6kSZM4efIk8+bNo1q1atn9Y3gMi8XC0qVLSUtL47XXXkPJxgXsUVFRWCwWWrRooV2AHkbqBTxT9epQoIB71A3cS+PGsGsXvPGGehyxdWs4ftzoqIRQaZ4MLNuSxLSoA5qMNS3qAMvvWCE4ffo0Y8aMoVSpUvTo0YN8+fKxatUqDh06xNtvv02hQoU0mdvTlChRgkWLFvHzzz8zY8aMRx4nMjKSRo0akS/zDlcfk3mDptQLeB4/P7VuQM9Li7Ird261SdGaNXDokFpsuGDBX3eQCWEYTZOBExdTGbt6j5ZDMmb1HpKSU4iNjaVr166UKlWKGTNm8MILL5CYmIjNZqNDhw74SVUOTz31FMOGDWPEiBFseYQzVrdu3WLdunU+vUUQFxcHICsDHspqVesG3L3pT6tWajvjF19UWxs//TScPm10VMKXaZoMjPwhAUcWtwUe5JYjg5YjImjSpAnbt29n+vTpnDx5klmzZlG5cmVN5/IGkydPpmbNmnTp0oUrV65k6bUbN27k2rVrPp0M2O12SpYsSXCw/ldEC+1ZrWoB4aZNRkfyYPnzq6sC//2veq9C1aqwdKmsEghjaJYMHDx7jZhDF7JcI/AgTkw4Cofw5Xe/sG/fPgYPHkz+/Pk1ncOb+Pv7s2zZMi5evEivXr2yVD8QGRlJkSJFqFmzpo4RujepF/BsYWFQqJB7bxXc6emn1T4E7dqp1yS/+CKcP290VMLXaJYMLIlPeuB1vI/Kz2zieI5gl7Us9nRlypQhIiKC//znP8ybN++hXxcZGUmbNm189ut848YNtm3bJsmABzOboXlz9y0i/CdBQfDtt7BihZrIhIbCjz8aHZXwJZp914/ef07zVYFMGU6F6APndBnbW73wwgv07duXN998k99///2Bn3/+/Hm2b9/u01sEW7Zs4datW1I86OGsVnWb4MYNoyPJuhdfhD17oGFD6NgRXnkFLl0yOirhCzRJBq6nO0h6xEZBDyspOZWUdIeuc3ibjz/+mIoVK9KlSxeuX79+389ds2YNiqLQpk0bF0Xnfux2O/ny5aNq1apGhyKyoUULuHkTNm40OpJHU6yYuiqwaBGsXq1ufURGGh2V8HaaJAPHk1Pu6iyoNQU4lpyi8yzeJVeuXCxfvpykpCQGDhx438+NjIykWrVqPP744y6Kzv3Y7XYaNWokJ1M8XGioep2wp20V3M5kUlcFEhKgShX417+gTx+4ds3oyIS30iQZuOlwTSstV83jTSpVqsScOXNYtGgRixcvvufnKIpCVFSUT28RZGRkEBcXJ/UCXsBsVlcHPDkZyFSypLoq8MUXsGQJVKsG69cbHZXwRpokA/4W1xScuWoeb/PKK6/wyiuv0K9fP/bv33/XxxMSEjhz5oxPJwN79uzhypUrUi/gJVq0UNsSp3jBYqLJpK4K/P67mhy0aAFDh3pmTYRwX5q8u5YOyo0+5wj+n+nPecSj+fzzzylRogSdO3cm7Y6OLJGRkQQGBvr0T8WxsbFYLBbq1atndChCA1Yr3LqlNiDyFmXLqicNPv4Y5sxRr22Ojzc6KuEtNEkGcgdYCC4UqMVQ/yg4KJDcAd594ZCe8uTJw4oVK9i/fz/Dhg3728ciIyNp0aIFAQEBBkVnPLvdTu3atQkM1Pc5Fq5RuTIULeodWwW3M5vVVYEdOyBfPmjUCEaOdK+bGoVn0mzd3VqxqK59BqwViuoyti+pVq0aM2bMYPbs2Xz//fcApKamEhMTQ9u2bQ2OzljSbMi7mEzeUzdwL5Urq6seEybAtGlQty7s3Gl0VMKTaZYMdK8frGufgZcbSHtYLfTt25cXXniB8PBwjh49yvr167l586ZP1wucOHGCpKQkSQa8jNUKW7bAA07VeiyLRV0V2LJFTX7q1oWJE8EhJ7DFI9AsGShfLC9NQwprvjrgZzbRNKQwIUXzajqurzKZTMyfP5+CBQvStWtXfvnlF4KDg6lYsaLRoRkmNjYWgEaNGhkcidCS1QoZGWC3Gx2JvqpXVxOCd9+FsWPVrYO9e42OSngaTcvzJ3cMw6JxMmAxm5jcMUzTMX1dgQIFWL58Odu3b2fp0qW0a9cOk0nvElD3ZbfbqVChAkWLylaUN6lQAR5/3Hu3Cm7n76+uCsTFwdWranHh9OlqMiTEw9A0GShZKJB/dwjVckjGdwilpM7Fib6oXr16vPvuu1y8eJEiRYoYHY6hpF7AO2XWDXjSpUXZVb++WlzYvz8MH67++Q8fNjoq4Qk0P7jftW4wb7etoMlYw9tWpEtdqRXQS8mSJTGZTMyZM4dTp04ZHY4hrly5wu+//y7JgJeyWmHbNvWnZV+RK5d6/PC33+DUKbVR0Zw5cjWyuD9duvgMtJbng05hBFjMWa4hUJwZ+PuZ+LBTGAOsIXqEJ/5ks9moU6cOgYGBvPTSSzh8sPJo48aNKIoizYa8VGbdQEyM0ZG4XrNmaqOiV15RVwratYMTJ4yOSrgr3Vr6da0bzJqhzWlUNgjggUlB5sf9LhwiX8ynvFDrCb1CE4DD4WDt2rU89dRTLF26FLvdzoQJE4wOy+ViY2MpUqQI5cuXNzoUoYNy5eCJJ3xrq+B2efKoqwL/+x8kJkLVqrBwoawSiLvp2t+3ZKFAFofXxzakGT3ql6JUUOBdnQpNQKmgQHrUL8Waoc1Y0rMh22NsfPbZZ3qG5vO2bNnC5cuXadeuHc2aNWPcuHFMmDCBaF+otrpNZr2ALxdQejOTSV0d8LHH+i7t2sHu3fDcc/D66/Dss/DHH0ZHJdyK4mLX024pu09dVrYfv6jsPnVZuZ52667PGTRokJIrVy7l8OHDrg7PZ4wdO1YpWLCg4nA4FEVRFIfDobRs2VJ5/PHHlbNnzxocnWukp6cruXLlUqZPn250KEJHCxYoitmsKJcuPfxrtm1TFFB/9TY//qgoRYsqSqFCirJ8+f0/15u/DlnhC18Hl9/8kzvAQmjx/NQMLkho8fz3bDE8efJkihQpQp8+fVBkPUsXkZGRtG7d+q/rev38/Pjmm29wOBy88sorOJ3ef0Pkjh07uHHjhtQLeLkWLcDp9M26gXt59lnYswdatoQuXaBrV0hONjoqYTS3vAYwT548zJs3jzVr1rBw4UKjw/E6ly5dYvPmzXd1HXz88cdZvHgxkZGRTJs2zaDoXCc2NpZcuXJRs2ZNo0MROipTBoKDZavgdoULw4oV8O23EBUFoaHw3/8aHZUwklsmAwDt2rWjR48evPXWW/whm1uaWrNmDU6n854tiNu1a8eIESMYOXIkGzduNCA617Hb7dSvXx9/f3+jQxE6krqBezOZ1FWBPXugTh3o0EGtJ7hyxejIhBHcNhkAmDFjBjly5GDgwIFGh+JVIiMjqVKlCiVKlLjnx8ePH0+9evXo2rUrly5dcnF0rqEoijQb8iEtWsCuXXDxotGRuJ/HH1dXBb78Er7/HsLCYM0ao6MSrubWyUBQUBAzZ87k+++/54cffjA6HK+gKApRUVH3vaUwR44cfPvtt1y9epU33njDK+s2Dh48yPnz5yUZ8BFWq3qcbsMGoyNxTyaTuiqQkADly0ObNjBgAKSmGh2ZcBW3TgYAOnfuzDPPPEP//v299qdUV9q3bx8nTpx44C2FpUqV4quvvuLHH3/k888/d1F0rhMbG4vJZKJBgwZGhyJcoFQptXZAtgrur1QpsNlg1iy1H0G3bkZHJFzF7ZMBk8nE7NmzSU1NZfjw4UaH4/EiIyMJCAigWbNmD/zc5557jkGDBjFs2DB27Njhguhcx263U61aNfLnz290KMJFWrSQZOBhmM3qqsDOnRCk9oxjxgxISzM0LKEzt08GAEqUKMHUqVNZsGAB69atMzocjxYZGUmzZs0IDHy4y58++ugjqlatSufOnbl27ZrO0bmO1Av4HqtVXQa/cMHoSDxD+fIwf77678uXQ61a6lXJwjt5RDIA0KtXL5o1a0avXr1IlY2sR5KWlsb69esfuEVwu4CAAJYtW8Yff/xB3759vaJ+4Ny5cxw4cECSAR/TooX66/r1hobhUf5sQ8KSJRAYCA0bwpgxcPOmsXEJ7XlMMmA2m5k/fz6nT59mzJgxRofjkWJiYrhx40aWkgGA8uXLM2/ePJYuXcpXX32lU3SuExsbCyDNhnxMyZLqXQWyVZB15crBxo1qIjBlinpVckKC0VEJLXlMMgBQoUIFxo0bx4wZM9gi61VZFhUVRfHixQkNDc3ya7t160Z4eDgDBw4kMTFRh+hcJzY2luDgYEqWLGl0KMLFrFbfvbQou3LkUJOB+Hi4dQtq11YTAx+87NQreVQyADBs2DCqV69OeHg4N2WtKksiIyNp27btI1/K89lnn1GmTBm6dOni0Vs1Ui/gu6xWtcnOuXNGR+K5atWCbdtg2DAYPRqaNIH9+42OSmSXxyUDFouFBQsWkJiYyNSpU40Ox2OcPn2ahISELG8R3C4wMJAVK1Zw+PBhhgwZol1wLpSamsq2bdskGfBRmXUDsjqQPQEB6qqA3a42cqpRAz79VL0DQngmj0sGAGrWrMnw4cOZMGECe/fuNTocjxAVFYXJZKJNmzbZGic0NJTPPvuM+fPns2zZMo2ic53NmzfjcDgkGfBRxYtDhQqSDGilYUP1CGLv3jBkiHr50dGjRkclHoVHJgMAY8aMoVSpUvTs2dMnbtjLrsjISOrUqUNQ5sHhbAgPD6dbt2707t2bQ4cOaRCd68TGxpI/f/5HqpsQ3kHuKdBWYKC6KrBuHRw7BtWqwbx5asdH4Tk8NhnIlSsXERERxMXFMXv2bKPDcWsZGRnYbLZsbRHczmQy8cUXX1C0aFG6du1Kenq6JuO6gt1up1GjRpjNHvvoi2yyWmHfPjhzxuhIvIvVCr//rnYt7NMH2reHU6eMjko8LI/+jtisWTP69u3Le++9R1JSktHhuK3t27eTnJysWTIAkC9fPlasWEFCQgLvvvuuZuPqKSMjg7i4ONki8HHNm6u/ylaB9vLlU1cFfvlFTQyqVoVvvpFVAk/g0ckAwIcffkj+/Pm9piGOHqKiosibNy/169fXdNxatWrx0Ucf8emnn7J69WpNx9bD7t27uXr1qiQDPu6xx6ByZUkG9NS+PezeDU89BT16wPPPywkOd+fxyUC+fPmYM2cOv/76K0uWLDE6HLcUGRlJq1atyJEjh+ZjDxo0iGeffZbXXnvN7Vdn7HY7OXLkoG7dukaHIgwmdQP6K1RIXRX47juIiYHQUPWKZOGePD4ZAHjmmWfo2rUrQ4YM4fz580aH41auXr3Kxo0bNd0iuJ3JZOLLL78kT548vPTSSzjcuANJbGwstWvXJleuXEaHIgzWogUcPCh72q7w/PNqb4emTeGFF6B7d5ALaN2PVyQDAJ9++imKovDmm28aHYpbWbduHQ6HQ7dkAKBQoUIsW7aMTZs2MXbsWN3myS5pNiQySb8B1ypaVF0V+OYbtZ6galX49VejoxK385pkoGjRonz66ad8++23/PTTT0aH4zYiIyMpX748ZcqU0XWeRo0aMXHiRKZMmYLNZtN1rkeRlJTEiRMnJBkQABQpor4hyVaB65hM6qrA7t3q8cMnn4ReveDqVaMjE+BFyQBA9+7dad++Pf369eOqPGEoikJkZKSuqwK3e+edd2jTpg0vv/wyf/zxh0vmfFh2ux1QkxYhQF0dkGTA9Z54Ql0dmDcPli1TEwP5ezCeVyUDJpOJOXPmcOnSJUaMGGF0OIY7dOgQR48edVkyYDab+frrrzGbzbz88stkZGS4ZN6HERsbS8WKFSlSpIjRoQg3YbXCkSPg5nWvXslkUlcFEhKgTBm1c+Gbb4IHX3ni8bwqGQAoVaoUH3zwAXPmzGHDhg1Gh2OoqKgocuTIQYvMDVIXKFasGN988w3r1q3jgw8+cNm8DyL1AuJO0m/AeKVLw9q1agfDefPUOw42bjQ6Kt/kdckAQP/+/WnUqBG9evUiLS3N6HAMExkZSePGjcmTJ49L523VqhWjRo1izJgxxMTEuHTue7l8+TIJCQmSDIi/CQqSJWp3YDbD4MHqHQdBQeotiCNGgAc1NvUKXpkMmM1mIiIiOHbsGOPHjzc6HEPcvHmT6Ohol20R3Gns2LE0btyYl156ieTkZENiyLRx40YURZFkQNzFapWVAXdRsaLaj2DSJPj4Y6hTB7ZvNzoq3+GVyQBA5cqVef/995k6dSo7duwwOhyXi4uL4/r164YlAxaLhaVLl3Ljxg1ee+01Q7tD2u12ihYtSrly5QyLQbgnq1W9XOfYMaMjEQAWi7oqsG2b+u/168P48XDrltGReT+vTQZArW6vUqUKPXv2dOtmOHqIjIykaNGiVK9e3bAYSpQowaJFi/jpp5/45JNPDIsjNjaWJk2aYDKZDItBuKdmzdRiNtkqcC9hYRAfD++9pyYDDRuqjYuEfrw6GfD392fBggXs3LmTjz/+2OhwXCoyMpK2bdsafjvfU089xVtvvcW7777L1q1bXT7/zZs3iY+Ply0CcU8FC6pFa7JV4H78/dVEYONG9ZRBrVrw0UfgRoeUvIpXJwMAdevWZejQoYwdO5aDBw8aHY5LnDt3jh07dtC2bVujQwFgypQp1KhRgy5dunDlyhWXzr19+3bS0tIkGRD/KPOeArnnzD3VravWDgweDO++q67mHDpkdFTex+uTAYDx48dTvHhxevXqhdPpNDoc3WV2AHSXZMDf359ly5Zx4cIFevfu7dL6AbvdTmBgIDVq1HDZnMKztGgBJ06oPQeEe8qZU10V2LAB/vgDqleHzz8HH/h27jI+kQwEBgYyb9481q9fT0REhNHh6C4yMpIaNWpQrFgxo0P5S9myZYmIiGDFihXMnz/fZfPGxsZSv359XW5sFN6hWTP1eJvUDbi/Jk1g1y547TUYOBDatpWmUVrxiWQA1LPv4eHhDB8+nFNefFWZ0+kkKirKsFME9/Piiy/St29f3nzzTRISEnSfT1EUaTYkHih/fnU/WuoGPEOePOqqQFQU7N+v3jHx5ZeyzZNdPpMMAHz00UcEBgbSv39/Q4+66en333/n7NmzbpkMAHz88cdUqFCBzp07k5KSoutcBw4c4MKFC5IMiAfKvKfAS78teKU2bdRLj154AcLD4Zln4MwZo6PyXD6VDBQsWJDPP/+c1atX85///MfocHQRGRlJ7ty5ady4sdGh3FOuXLlYvnw5SUlJDBw4UNe57HY7ZrOZBg0a6DqP8HxWK5w+DT5SY+w18udXVwVWr4atWyE0VL38SJK6rPOpZACgU6dOPP/88wwaNMjwznh6iIyMxGq14u/vb3Qo/6hSpUrMnj2bhQsX8s033+g2T2xsLNWqVSNfvny6zSG8Q5Mm4OcnWwWe6pln1D4EbdtCt27QpQtcuGB0VJ7F55IBgFmzZnHz5k3eeusto0PRVEpKCna73W1OEdzPq6++So8ePejbty8HDhzQZQ6pFxAPK18+qF1bigg9WVCQuiqwbJl6+VFoKKxaZXRUnsMnk4HHHnuM6dOn8/XXXxMZGWl0OJr57bffuHXrltvWC9xp9uzZPPHEE3Tu3FnzC6XOnj3LwYMHJRkQD+2vfgPXrpNr/07qEU+u/Tvh+nWjQxNZ0KWLukpQvz489xy8+ipcvpyNAa/7yPOg+Cin06m0atVKKVWqlHLt2jWjw9HEoEGDlNKlSytOp9PoUB7azp07lYCAAGXAgAGajvv9998rgHLixAlNxxVeas8e5ViHQcpByilOk0lR1G1n9R+TSVHKlVOUQYMUZc8eoyN1qW3b1C/Btm1GR5J1TqeiLFyoKPnyKcoTTyhKZGQWXrxnj/r3Xa6c+vfvA8+DzyYDiqIohw8fVgIDA5XBgwcbHYomKlSooPTp08foMLJs9uzZCqB89913mo05dOhQpVSpUpqNJ7zUkSOK0qaNooDi9LP8/Zv+nf9Y/vx4mzbq63yAJycDmZKSFKV1a/XP0bevotz3Z7/bnoe//r595Hnw6WRAURRl+vTpislkUuLi4owOJVuOHj2qAMrKlSuNDiXLnE6n8vzzzyv58+dXjh49qsmY9erVU7p3767JWMJLzZ+vKDlzPvib/r3eBHLmVF/v5bwhGVAUdZVg9mxFCQxUlDJlFGX9+nt8ko8/Dz5ZM3C7N998k7p169KzZ0/S09ONDueRRUZG4ufnR8uWLY0OJctMJhMREREULFiQrl27ciub95WmpKSwfft2qRcQ/2zSJOjVC9LSIKs3mjoc6ut69VLHEW7PZIJ+/eD33+GJJ9S+Em+9BTdu/PkJ8jz4ZgHh7fz8/IiIiODAgQNMnjzZ6HAeWWRkJA0aNCB//vxGh/JIChQowPLly9m2bRujRo3K1libN2/G4XBIMiDuLSICRo/WZqzRo2HBAm3GErorV049PjptGsyeDTVrwtFR8jyAJAMAhIWF8d577zFlyhR2795tdDhZ5nA4WLt2rcecIvgn9erVY8qUKXz00Uf8+uuvjzyO3W6nQIECVKlSRcPohFc4ehQGDdJ2zIED1XGFR/DzU1cFduyAiv5HeWzyIDTtUeShz4MkA38aNWoUISEhhIeHk+FhF2bHx8dz9epVj08GAN566y2efPJJXnnllUe+QyI2NpZGjRphNsvjLe7Qp0/Wl4EfxOFQxxUepXJl+KFYH/zNDkxaDuyhz4N8t/xTQEAACxYsYMuWLXz22WdGh5MlkZGRFCpUiNq1axsdSraZzWYWLVqEv78/3bt3z3JilpGRQVxcnGwRiLslJoLNpk8yYLPB3r3ajiv0lZiIeY0NP6c8DyDJwN80bNiQgQMHMnr0aI560DJPZGQkbdq0wc/Pz+hQNFG4cGGWLl1KTEwMEyZMyNJrExISuHbtmiQD4m5ffAEWiz5jWywwZ44+Ywt9yPPwN5IM3GHy5MkULlyY3r17e8TNhsnJyWzZssUrtghu17x5c8aOHcuECRP4LQsN4+12O/7+/tStW1e/4IRn+uUX7VcFMjkckI06F2EAeR7+RpKBO+TJk4d58+axZs0aFi5caHQ4D7RmzRoURfGI+wiyatSoUTRv3pyXXnqJ8+fP3/NzUtId7Dl9hR1Jl9hz+grrYzdRu3ZtcubM6eJohVu7dg2OHNF3jsOHvbdVrbeR5+EuJsUTfvw1wCuvvMJ///tf9u7dy2OPPWZ0OP8oPDyc+Ph4jzwF8TDOnDlD9erVqV27Nj///DNms5mDZ6+xJD6J6P3nSLqY+rdKYEVRyEMaLzaqTPf6wZQvltew2IUb2blTPUemtx07oEYN/edxke3b1Quctm2DWrWMjkZD8jzcRVYG/sGMGTPIkSMHg7Q+hqQhRVGIjIz0ui2C2z3++ON8/fXX/O9//2Ps1M/osSCeNp9sYHH8cY7fkQiA2sAoxZSLxfHHafPJBnosiOfExVRDYhduxFUNxTy4cZlPkefhLpIM/IOgoCBmzpzJd999xw8//GB0OPeUmJjIqVOnvDoZAPjXv/7FC+9+zNcXgok9rF5SnuG8/4JW5sfjjiTTesZ6lm1J0j1O4cYCArxrHpE98jzcRZKB++jcuTPPPPMMAwYM4HK27sDUR2RkJDlz5qRp06ZGh6KrWdEH2WKqgMnizwNygLtkOBXSHU5GrExgVvRBfQIU7i8kRO1JqyeTSZ1HuD95Hu4iycB9mEwmZs+eTUpKCsOHDzc6nLtERkbSvHlzcuXKZXQoulm2JYlpUQfU32Tzf95pUQdYLisEvilPHihbVt85ypVT5xHuT56Hu0gy8AAlSpRg6tSpREREsG7dOqPD+cuNGzfYsGGDV28RnLiYytjVezQdc8zqPVJD4KuefFLfc+Xt2+szttCHPA9/I8nAQ+jVqxfNmjWjV69epKa6xxvJhg0bSEtL8+pkYOQPCTiyui/wAA6nwsgfEjQdU3iIvn31PVfer58+Ywt9yPPwN5IMPASz2cz8+fM5ffo0Y8eONTocAKKionjiiSeoXLmy0aHo4uDZa8QcuvDAQsGsynAqxBy6wKFz1zQdV3iAKlWgTRvNfxp0YCGlcRu12b3wGM5KVUiq1IZbaLw6YLGoz5mHPQ+SDDykChUqMG7cOD7++GO2bNlidDh/HSk06V0EY5Al8Un4mfX5s/mZTXyzSWoHfNLcuZomAwrgMFmos3UuM2eC06nZ0EJHSUnQti003zcXxc+i7a2FFov6nHkYSQayYNiwYVSvXp2ePXty69Ytw+I4efIke/bs8eotguj95zRfFciU4VSIPnBOl7GFmytTBmbO1Gw4E2CaNYvWvcoweDC0bg3Hjmk2vNCYosBXX0FYGOzfD/OiyuD/xUxtby2cNUt9zjyMJANZYLFYWLBgAXv27GHq1KmGxREVFYXZbKZ169aGxaCn6+kOknQu8ktKTiUlXaf9QuHeevaEiRO1GWvSJAL6hzNzJqxZo3agDQuDiAj1jUe4jzNnoEMHeOMN6NQJEhLU1XytnwfCw7UZy8UkGciimjVrMnz4cMaPH8++ffsMiSEyMpK6detSqFAhQ+bX2/HkFG2X7e5BAY4lp+g8i3Bbo0bB/PmQM2fWtw0sFvV1EREwcuRf/7lVK/UNpksX6NULnnoKTp/WOG6RZYoCy5ZB1aqwZQusXq2uDhQocNsn6fA8eBpJBh7BmDFjKFWqFD179sTp4k3CjIwMbDabV28R3HS45mvqqnmEm+rZExITwWpVf/+gN4HMj1ut6uvu8RNgvnzqe8JPP6nt76tWhaVLZZXAKBcuqMlZt27qKsCePfDMM//wyTo8D55EkoFHkCtXLiIiIoiNjWWOi++s3rp1K5cuXfLKWwoz+Vtc81i6ah7hxsqUgago9V2iX797d6bL7CTXr5/6TT8q6oF7wk89Bbt3q0fNu3eHF1+Ef7h4U+hk1SoIDYW1a9WVgWXLICjoAS/S6XnwBHJrYTb069ePb775hj179hAcHOySOSdMmMD06dO5cOECFr0aZhgsJd1B1XGRum4VmIDd49qRO8A7v4YiG65fh0OH1EtmAgLUb/zZ6CT33Xfq+4bJpBaZd+yoYaw688RbCy9fhjffhK+/VlcB5s2DbF08q/Hz4K7kR6Ns+PDDD8mfPz99+/bFVTlVZGQkrVq18tpEACB3gIWShQJ1nSM4KFASAXFvefKo187Wr6/+ms1v/C+8oK4SNG6sFq716AGXLmkSqbhDVJS6NfPjj7Bwobo6kO0b6DV+HtyVJAPZkC9fPubMmcOvv/7K0qVLdZ/vypUrbNq0yavrBVJTU/niiy84tSUSxZmhyxx+ZhPWCkV1GVuIeylWDFauVH9a/e9/1Tes//3P6Ki8x/Xr6upLu3Zqr5/du+HVV/W/i8ibSDKQTc888wxdu3blzTff5LzOm4Jr164lIyPDK5OBU6dO8d5771GyZEkGDBhAJct5TGY/XebKcCq83MA12zpCZDKZ1FWB3bvV44ft20OfPnBNmmFmy4YNUK2ammjNnq2uDpQsaXRUnkeSAQ18+umnKIrCm2++qes8kZGRVKxYkVKlSuk6jytt2bKFl156idKlSzN79mxee+01Dh8+zM9LF9A0pLDmXQj9zCaahhQmpGheTccV4mGVKAG//qrWDyxZor6R/fab0VF5nhs34K23oEULeOIJ+P33/6/NEFknyYAGihYtyqeffsq3337Lzz//rMsciqL81YLY0zkcDr777jsaN25MvXr1iI+PZ/r06Zw8eZLp06dTunRpACZ3DMOicTJgMZuY3DFM0zGFyCqTCXr3Vt/AgoPV02lDhoCb3IPm9jZvhpo11ZWAadPUZKpcOaOj8mySDGike/futG/fnr59+3L16lXNxz9w4ADHjx/36COFV65cYfr06YSEhPDiiy+SI0cOfvzxRw4cOMDgwYPJm/fvP62XLBTIvzuEahrD+A6huhcnCvGwypaF6GiYMUNdKahZEzZtMjoq93XzJoweDQ0bQt68sGOHujrgp8+Ook+RZEAjJpOJOXPmcOnSJUaMGKH5+FFRUfj7+9OiRQvNx9bb4cOHGTx4MCVKlOC9996jefPmbNu2jd9++41nn30Wv/v8n9y1bjBvt62gSRzD21akS12pFRDuxWxWVwV27oSCBdVTByNHqifZxP/btQvq1oUPP4R//xs2bvS4iwHdmiQDGipVqhQffPABc+bMISYmRtOxIyMjadKkCblz59Z0XL0oisJvv/3Gc889R/ny5fn2228ZOnQox48fZ9GiRdTKwqHlgdbyfNApjACLOcs1BH5mEwEWMx92CmOANSSrfwwhXKZiRbDb1Tb506apb3w7dhgdlfEcDrXlf926aifHLVvU1QEvPl1tCEkGNNa/f38aNWpEz549SUtL02TM9PR0oqOjPaJeID09na+//ppatWphtVo5dOgQ8+bNIykpifHjx/P4448/0rhd6wazZmhzGpVVW4g9KCnI/HijskGsGdpcVgSER7BY4L33YOtWdcWgXj2YMAEMvCTVUPv2QaNGMGYMDB+uJgI1ahgdlXeSZEBjZrOZiIgIjh07xvjx4zUZMzY2ltTUVLdOBs6fP8+ECRMoXbo0r776KsWLFycqKoqEhAR69uxJrly5sj1HyUKBLA6vj21IM3rUL0WpoMC7rh41AaWCAulRvxRrhjZjcXh9qREQHqdaNbVIbsQIdUm8USO1862vcDrVOoqaNeHKFYiLU1cHAgKMjsyLKUIXEyZMUPz8/JQdO3Zke6x33nlHKVasmOJ0OrMfmMYSEhKU8PBwJSAgQMmVK5fSt29fZe/evS6b/3raLWX3qcvK9uMXld2nLivX0265bG4hXGHzZkWpVElRAgIUZdo0RXE4XDf3tm2KAuqvrnL4sKI0a6bOO2SIoqSkuG5uXyZ3E+jk5s2b1KlThxw5chAfH5+t9sE1atSgWrVqfP311xpG+OicTif/+9//mDFjBmvWrOGJJ55g4MCB9O7d22uvVRbCSDduwPvvw8cfq6sECxeqLfL15sq7CRRFPVHx9ttQpIh6zbAH1kt7LNkm0Im/vz8LFixg586dfPzxx488zh9//MGuXbvcYosgJSWFOXPmUKVKFZ566imuXLnC0qVLOXr0KCNGjJBEQAid5MqlFhWuXw9nzkD16uoZexffoK6bEyfgX/9Smwa9/LLaf0ESAdeSZEBHdevWZciQIYwdO5aDBw8+0hg2mw2ANm3aaBlalpw4cYIRI0ZQsmRJBg4cSFhYGLGxscTHx9OtWzdy5MhhWGxC+JKmTdUjdq++CgMGqL34k5KMjurRKYraRjgsTL01+Ndf4Ysv1B4CwrUkGdDZ+PHjKV68OL169cL5CGl8ZGQktWrVomhR11+ss3nzZrp160aZMmWYM2cOb7zxBkeOHOE///kPjRo1wiR9P4VwuTx5/r8H/7596hvpwoXqG6snOXtWvc751VehQwdISFBXB4QxJBnQWe7cuZk3bx7r168nIiIiS691Op1ERUW5dIvA4XD89WZfv359tmzZwowZMzh58iTTpk3zqnsRhPBkbdqob6CdOsHrr8Ozz8Iffxgd1cP5z38gNFQ9JZB5m2PBgkZH5dskGXCBVq1a8cYbbzB8+HBOnTr10K/buXMn58+fd0kycPnyZaZNm0a5cuXo3LkzAQEBrFq1iv379zNo0KC7WgULIYxXoIBaaLdqlXoUMTQUli83Oqp/lpwM3bpB585qTcCePerqgDCeJAMuMm3aNAIDA+nfvz8Pe4AjMjKSPHny0LBhQ93iOnToEIMGDaJEiRKMGjUKq9XKjh07iI6OpkOHDvdtFSyEcA8dOqhXI7duDV27QpcucOGC0VH93U8/QdWqEBkJS5eqqwNFihgdlcgkyYCLFCxYkM8//5zVq1fzn//8556fk5LuYM/pK+xIusSe01f41bYOq9WKv7+/prEoivLXm32FChVYvnw5w4YN4/jx4yxcuJAa0uJLCI9TuLC6KrBsGaxZo64SrF6djQGvXyfX/p3UI55c+3fC9euPNMyVKxAeDs88ox5P3L1bXR2QkiP3In0GXOz555/HbreTmJhIUFAQB89eY0l8EtH7z5F0MZXb/zIURaGgxcFz9crTvX4w5Ytlb6k+PT2db7/9lk8++YRdu3ZRtWpVhg4dyksvvUTOnDmz9wcTQriNP/6AXr3Un8ZffRU++UTdUnigxES1nP+XX+DIkb9XJZpM6jWLTz4JfftClSoPHG7tWrWe4fJltaPgG29IEuCuJBlwsTNnzlClShXaduqGf6NXiTl0AT+ziQznP/81ZH68aUhhJncMy3J73XPnzjFnzhxmz57NuXPneOqppxgyZAitWrWSEwFCeClFgUWL4M03IV8+WLAA/vEG9KNHoU8fsNnUCxIcjn8eOPPjbdqoXYLKlLnrU1JS4N134fPPwWpV6xqk9ti9STJggIEzvmX1qQAsOQLIymFDP7MJi9nEvzuE0vUhLt5JSEjgk08+YcmSJfj5+fHaa68xePBgKlas+OjBCyE8SlKSuky/Zo36A/1HH6nHE/8SEQGDBqlv8PdLAu5ksaj/zJwJPXv+9Z9jY9XViNOnYepU6N9fvXRJuDf5K3KxWdEH+elcPswW/ywlAgAZToV0h5MRKxOYFX3vJkZOp5OffvqJ1q1bU61aNaKiovj3v//NiRMn+PzzzyUREMLHBAerRXuff64e4ateHTZs+PODkyap+wlpaVlLBED9/LQ09fWTJpGWBu+8ozZGKlpUbY40cKAkAp5CVgZcaNmWJEasTNBsvA87hf11NW9KSgqLFi3i008/5cCBA9SrV4+hQ4fy/PPPS4dAIQQAhw6pe/ixsbCsdQSdbb00G/v9xyOYmhzOhAkwbBjIQSTPIsmAi5y4mErrGetJd2jXTDzAYubrrhVY+fU85s2bx7Vr13j++ecZMmSIrscRhRCeKyMDvnz/KC9PqUJO0u66BvxRKEC6KSfHf0mk4r/uriEQ7k+SARfpsSCeuCPJ9y0UzDLFSdqxXaT9bxq9e/dm4MCBBAc/uJZACOHj2rZFWReNKSOLWwP3oVgsmKxWtU+y8DiSDLjAwbPXaPPJhgd/4iNa1acO1UsX0218IYQXSUxUmxDoOX7lyvqNL3QhpR0usCQ+CT+zPkf4/MwmfvjdzVqNCSHc1xdfqKcA9GCxwJw5+owtdCXJgAtE7z+n7fbAbTKcCtEHzukythDCC/3yS9ZPDjwsh0O9h1h4HEkGdHY93UHSxVRd50hKTiUlXaf/uYUQ3uPaNbWzoJ4OH37k1sXCOJIM6Ox4cgp6F2UowLHkFJ1nEUJ4vMOH/95iWA+Kop5hFB5FkgGd3dTwKKE7zCOE8GDp6d41j9CMJAM687e45kvsqnmEEB4sIMC75hGakXcQnZUOyq1JU4/7Mf05jxBC3FdIiP7XBppM6jzCo0gyoLPcARaCs3jLYFYFBwWSO0Cno0JCCO+RJ496DbGeypW74yYk4QkkGXABa8WiuvYZsFYoqsvYQggv9OST+vYZaN9en7GFriQZcIHu9YN17TPwcgNpQSyEeEh9++rbZ6BfP33GFrqSZMAFyhfLS9OQwpqvDviZTTQNKUxI0byajiuE8GJVqkCbNtqvDlgs6rjSitgjSTLgIpM7hmHROBmwmE1M7him6ZhCCB8wd64+ycDcudqOKVxGkgEXKVkokH930PZykPEdQimpc3GiEMILlSkDM2dqO+asWeq4wiNJMuBCXesG83bbCpqMNbxtRbrUlVoBIcQj6tkTJk7UZqxJkyA8XJuxhCHkCmMDLNuSxNjVe3A4lSwVFvqZTVjMJsZ3CJVEQAihjYgIGDRILf7LSmGhxaL+M2uWJAJeQJIBg5y4mMrIHxKIOXQBP7PpvklB5sebhhRmcscw2RoQQmjr6FHo0wdsNvUN/n5JQebH27RRawRka8ArSDJgsINnr7EkPonoA+dISk7926VGJtSGQtYKRXm5QbCcGhBC6CsxEb74Qr2G+M5LjUwmtaFQ+/bq8UE5NeBVJBlwIynpDo4lp3DT4cTfYqZ0UG7pLCiEMMb16+rtg+np6l0DISHSWdCLSTIghBBC+Dg5TSCEEEL4OEkGhBBCCB8nyYAQQgjh4yQZEEIIIXycJANCCCGEj5NkQAghhPBxkgwIIYQQPk6SASGEEMLHSTIghBBC+DhJBoQQQggfJ8mAEEII4eMkGRBCCCF8nCQDQgghhI+TZEAIIYTwcZIMCCGEED5OkgEhhBDCx0kyIIQQQvg4SQaEEEIIHyfJgBBCCOHjJBkQQgghfJwkA0IIIYSPk2RACCGE8HGSDAghhBA+TpIBIYQQwsdJMiCEEEL4OEkGhBBCCB8nyYAQQgjh4/4PghUonj2Qu+cAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
@@ -426,7 +426,7 @@
},
{
"cell_type": "markdown",
- "id": "4dc3c4a1",
+ "id": "33ae2cbe",
"metadata": {},
"source": [
"See the examples for more ideas.\n",
@@ -466,13 +466,13 @@
{
"cell_type": "code",
"execution_count": 8,
- "id": "e319f22c",
+ "id": "2b0baedb",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:16.468911Z",
- "iopub.status.busy": "2022-12-20T11:40:16.468445Z",
- "iopub.status.idle": "2022-12-20T11:40:16.472096Z",
- "shell.execute_reply": "2022-12-20T11:40:16.471629Z"
+ "iopub.execute_input": "2022-12-20T16:43:39.157031Z",
+ "iopub.status.busy": "2022-12-20T16:43:39.156651Z",
+ "iopub.status.idle": "2022-12-20T16:43:39.160553Z",
+ "shell.execute_reply": "2022-12-20T16:43:39.159914Z"
}
},
"outputs": [
@@ -493,7 +493,7 @@
},
{
"cell_type": "markdown",
- "id": "ad581721",
+ "id": "a8dfa3c1",
"metadata": {},
"source": [
"The data structure gets morphed slightly for each base graph class.\n",
@@ -511,13 +511,13 @@
{
"cell_type": "code",
"execution_count": 9,
- "id": "389d61af",
+ "id": "afe7074f",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:16.474767Z",
- "iopub.status.busy": "2022-12-20T11:40:16.474437Z",
- "iopub.status.idle": "2022-12-20T11:40:16.478210Z",
- "shell.execute_reply": "2022-12-20T11:40:16.477741Z"
+ "iopub.execute_input": "2022-12-20T16:43:39.164369Z",
+ "iopub.status.busy": "2022-12-20T16:43:39.164154Z",
+ "iopub.status.idle": "2022-12-20T16:43:39.168232Z",
+ "shell.execute_reply": "2022-12-20T16:43:39.167587Z"
}
},
"outputs": [
diff --git a/searchindex.js b/searchindex.js
index fe1e1be0..dce6047f 100644
--- a/searchindex.js
+++ b/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["auto_examples/3d_drawing/index", "auto_examples/3d_drawing/mayavi2_spring", "auto_examples/3d_drawing/plot_basic", "auto_examples/3d_drawing/sg_execution_times", "auto_examples/algorithms/index", "auto_examples/algorithms/plot_beam_search", "auto_examples/algorithms/plot_betweenness_centrality", "auto_examples/algorithms/plot_blockmodel", "auto_examples/algorithms/plot_circuits", "auto_examples/algorithms/plot_davis_club", "auto_examples/algorithms/plot_dedensification", "auto_examples/algorithms/plot_iterated_dynamical_systems", "auto_examples/algorithms/plot_krackhardt_centrality", "auto_examples/algorithms/plot_parallel_betweenness", "auto_examples/algorithms/plot_rcm", "auto_examples/algorithms/plot_snap", "auto_examples/algorithms/plot_subgraphs", "auto_examples/algorithms/sg_execution_times", "auto_examples/basic/index", "auto_examples/basic/plot_properties", "auto_examples/basic/plot_read_write", "auto_examples/basic/plot_simple_graph", "auto_examples/basic/sg_execution_times", "auto_examples/drawing/index", "auto_examples/drawing/plot_center_node", "auto_examples/drawing/plot_chess_masters", "auto_examples/drawing/plot_custom_node_icons", "auto_examples/drawing/plot_degree", "auto_examples/drawing/plot_directed", "auto_examples/drawing/plot_edge_colormap", "auto_examples/drawing/plot_ego_graph", "auto_examples/drawing/plot_eigenvalues", "auto_examples/drawing/plot_four_grids", "auto_examples/drawing/plot_house_with_colors", "auto_examples/drawing/plot_knuth_miles", "auto_examples/drawing/plot_labels_and_colors", "auto_examples/drawing/plot_multipartite_graph", "auto_examples/drawing/plot_node_colormap", "auto_examples/drawing/plot_rainbow_coloring", "auto_examples/drawing/plot_random_geometric_graph", "auto_examples/drawing/plot_sampson", "auto_examples/drawing/plot_selfloops", "auto_examples/drawing/plot_simple_path", "auto_examples/drawing/plot_spectral_grid", "auto_examples/drawing/plot_tsp", "auto_examples/drawing/plot_unix_email", "auto_examples/drawing/plot_weighted_graph", "auto_examples/drawing/sg_execution_times", "auto_examples/external/index", "auto_examples/external/javascript_force", "auto_examples/external/plot_igraph", "auto_examples/external/sg_execution_times", "auto_examples/geospatial/extended_description", "auto_examples/geospatial/index", "auto_examples/geospatial/plot_delaunay", "auto_examples/geospatial/plot_lines", "auto_examples/geospatial/plot_osmnx", "auto_examples/geospatial/plot_points", "auto_examples/geospatial/plot_polygons", "auto_examples/geospatial/sg_execution_times", "auto_examples/graph/index", "auto_examples/graph/plot_dag_layout", "auto_examples/graph/plot_degree_sequence", "auto_examples/graph/plot_erdos_renyi", "auto_examples/graph/plot_expected_degree_sequence", "auto_examples/graph/plot_football", "auto_examples/graph/plot_karate_club", "auto_examples/graph/plot_morse_trie", "auto_examples/graph/plot_napoleon_russian_campaign", 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100, 103, 110, 151, 152, 158, 161, 171, 204, 206, 207, 214, 215, 216, 219, 230, 231, 232, 252, 253, 257, 258, 259, 260, 261, 262, 264, 267, 268, 269, 271, 273, 276, 281, 285, 297, 298, 306, 307, 315, 316, 317, 318, 319, 324, 325, 327, 329, 330, 342, 361, 362, 363, 378, 382, 385, 391, 410, 411, 412, 413, 425, 429, 440, 471, 472, 473, 474, 475, 545, 546, 547, 548, 549, 550, 551, 553, 555, 556, 557, 558, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 577, 578, 579, 580, 584, 585, 586, 587, 588, 589, 593, 597, 599, 601, 602, 603, 604, 615, 626, 627, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 680, 690, 692, 698, 699, 707, 721, 734, 735, 736, 737, 789, 796, 853, 854, 857, 867, 891, 892, 898, 899, 902, 912, 928, 934, 938, 972, 973, 979, 983, 1010, 1037, 1038, 1039, 1040, 1063, 1071, 1085, 1102, 1133, 1137, 1162, 1165, 1173, 1176, 1186, 1188, 1190, 1193, 1197, 1199, 1209, 1213, 1217, 1219, 1235, 1239, 1240, 1270, 1275, 1276, 1277, 1278, 1279, 1295, 1296, 1298, 1307, 1309, 1310, 1311, 1312, 1315, 1333, 1337, 1338, 1339, 1340, 1359, 1361, 1362, 1363, 1364, 1365, 1366, 1376, 1393, 1394, 1395, 1407, 1426], "NOT": [8, 110, 199, 549, 550, 551, 748, 887, 925, 968, 1007], "util": [8, 14, 36, 44, 45, 93, 97, 102, 103, 229, 230, 231, 316, 374, 423, 425, 426, 429, 460, 496, 678, 679, 758, 1044, 1242, 1299, 1301, 1303, 1310, 1319, 1320, 1321, 1325, 1402, 1406, 1407, 1411, 1413, 1416, 1419], "arbitrary_el": [8, 1392, 1413], "nb": [8, 1331, 1334], "left": [8, 71, 115, 183, 311, 312, 322, 324, 325, 385, 559, 560, 584, 616, 688, 689, 739, 1106, 1134, 1136, 1146, 1179, 1206, 1280, 1355, 1358, 1404], "right": [8, 71, 110, 111, 115, 152, 206, 322, 385, 427, 428, 500, 559, 560, 584, 585, 587, 588, 615, 616, 688, 689, 739, 854, 935, 980, 1134, 1136, 1146, 1155, 1157, 1179, 1206, 1213, 1215, 1270, 1280], "littl": [8, 94, 298, 307], "mislead": 8, "That": [8, 97, 132, 165, 212, 221, 227, 295, 385, 436, 465, 525, 535, 555, 588, 657, 671, 672, 673, 674, 691, 704, 717, 791, 862, 907, 943, 988, 1046, 1162, 1210, 1296, 1388, 1404, 1409], "okai": 8, "becaus": [8, 11, 54, 69, 94, 99, 101, 102, 103, 112, 132, 161, 215, 216, 220, 255, 311, 378, 387, 389, 390, 394, 411, 412, 427, 494, 498, 499, 500, 510, 569, 585, 587, 615, 616, 632, 652, 934, 979, 1038, 1236, 1273, 1296, 1303, 1326, 1345, 1350, 1404, 1407, 1416], "AND": [8, 110, 598, 748, 762], "OR": [8, 110, 157, 175, 188, 856, 869, 877, 901, 937, 947, 950, 959, 982, 992], "symmetr": [8, 145, 148, 237, 545, 586, 593, 761, 1173, 1192, 1235, 1246, 1250, 1251, 1256, 1258, 1269, 1320, 1321, 1387], "It": [8, 52, 56, 58, 92, 93, 94, 97, 99, 101, 102, 104, 107, 110, 112, 115, 132, 172, 184, 207, 214, 215, 216, 229, 230, 231, 249, 260, 261, 262, 264, 278, 310, 316, 324, 325, 326, 343, 346, 347, 351, 353, 412, 414, 415, 416, 417, 418, 419, 429, 438, 440, 452, 457, 464, 480, 496, 500, 508, 530, 540, 545, 559, 560, 565, 566, 567, 582, 588, 594, 595, 598, 600, 601, 615, 619, 628, 629, 630, 652, 658, 659, 663, 671, 674, 692, 717, 718, 719, 760, 761, 762, 791, 796, 868, 873, 892, 913, 916, 928, 949, 955, 973, 994, 998, 1010, 1012, 1013, 1018, 1037, 1038, 1039, 1040, 1054, 1117, 1170, 1174, 1200, 1201, 1206, 1207, 1210, 1217, 1223, 1227, 1234, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1253, 1254, 1258, 1261, 1263, 1264, 1269, 1275, 1276, 1277, 1280, 1296, 1297, 1323, 1324, 1326, 1328, 1343, 1382, 1383, 1393, 1395, 1398, 1402, 1404, 1407, 1408, 1409, 1411, 1412, 1413, 1426], "just": [8, 99, 102, 103, 104, 184, 199, 338, 374, 439, 464, 559, 560, 577, 660, 661, 662, 692, 791, 873, 887, 916, 925, 946, 955, 960, 968, 991, 998, 1007, 1120, 1126, 1229, 1278, 1279, 1296, 1328, 1393, 1404, 1406], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1325, 1402, 1406, 1412], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1202, 1383], "doe": [8, 77, 93, 94, 99, 101, 102, 103, 104, 114, 115, 132, 147, 153, 154, 165, 168, 189, 207, 208, 227, 228, 229, 230, 231, 232, 293, 308, 339, 340, 342, 343, 352, 357, 373, 382, 385, 410, 414, 426, 450, 469, 494, 495, 496, 497, 498, 499, 500, 502, 503, 506, 507, 509, 510, 511, 512, 534, 544, 549, 550, 551, 564, 566, 583, 584, 586, 589, 601, 612, 626, 627, 678, 691, 693, 694, 698, 699, 717, 718, 721, 722, 723, 724, 725, 726, 762, 862, 865, 878, 892, 907, 910, 928, 943, 946, 960, 973, 988, 991, 1010, 1038, 1043, 1066, 1070, 1072, 1081, 1102, 1103, 1105, 1106, 1107, 1109, 1114, 1173, 1175, 1177, 1192, 1207, 1222, 1223, 1227, 1229, 1234, 1241, 1296, 1300, 1303, 1326, 1333, 1334, 1341, 1342, 1344, 1351, 1353, 1354, 1355, 1356, 1357, 1358, 1371, 1379, 1380, 1381, 1383, 1393, 1404, 1405, 1406, 1410, 1417, 1426], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1219], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1229, 1296, 1395, 1404], "everi": [8, 11, 57, 88, 93, 109, 112, 120, 144, 157, 161, 177, 211, 212, 220, 221, 229, 230, 231, 235, 243, 264, 287, 295, 300, 324, 325, 343, 352, 380, 397, 437, 439, 440, 450, 462, 471, 472, 473, 474, 475, 477, 483, 484, 491, 512, 516, 565, 606, 614, 615, 619, 632, 633, 635, 636, 663, 685, 687, 688, 717, 718, 791, 856, 901, 937, 982, 1052, 1053, 1054, 1070, 1071, 1072, 1085, 1086, 1102, 1103, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1148, 1162, 1195, 1216, 1217, 1257, 1264, 1278, 1279, 1296, 1407], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1177, 1207, 1208, 1404, 1406, 1407, 1426], "ha": [8, 11, 16, 44, 67, 88, 91, 93, 94, 95, 97, 99, 100, 101, 102, 103, 105, 107, 110, 112, 116, 120, 127, 152, 161, 165, 166, 173, 174, 175, 184, 188, 198, 207, 212, 214, 215, 219, 220, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 241, 242, 243, 244, 247, 249, 252, 269, 271, 272, 273, 274, 275, 276, 282, 289, 291, 293, 294, 295, 300, 305, 310, 324, 331, 343, 352, 355, 356, 363, 364, 365, 373, 378, 380, 381, 383, 384, 385, 386, 391, 393, 394, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 424, 427, 428, 429, 439, 450, 458, 460, 466, 467, 468, 471, 472, 473, 474, 475, 476, 477, 480, 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 522, 564, 566, 577, 578, 581, 590, 593, 605, 607, 610, 611, 622, 623, 624, 628, 629, 630, 632, 633, 634, 635, 636, 638, 646, 647, 649, 652, 657, 658, 682, 688, 690, 692, 697, 711, 717, 718, 729, 730, 731, 739, 749, 786, 791, 854, 862, 863, 869, 873, 877, 886, 892, 899, 907, 908, 916, 924, 928, 935, 943, 944, 948, 950, 955, 959, 967, 973, 980, 988, 989, 993, 998, 1006, 1010, 1040, 1043, 1045, 1066, 1068, 1070, 1072, 1075, 1080, 1084, 1098, 1099, 1101, 1102, 1103, 1105, 1122, 1130, 1145, 1154, 1160, 1162, 1165, 1176, 1180, 1185, 1193, 1195, 1196, 1197, 1198, 1199, 1207, 1210, 1211, 1215, 1217, 1222, 1234, 1239, 1243, 1244, 1248, 1249, 1254, 1259, 1261, 1264, 1267, 1269, 1270, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1283, 1284, 1285, 1286, 1289, 1291, 1293, 1296, 1300, 1326, 1328, 1330, 1333, 1334, 1353, 1354, 1371, 1372, 1379, 1382, 1393, 1394, 1395, 1398, 1403, 1404, 1405, 1406, 1407, 1409, 1413, 1414, 1416, 1423, 1425], "output": [8, 13, 16, 89, 93, 101, 102, 103, 109, 197, 287, 288, 345, 374, 380, 494, 498, 499, 509, 510, 575, 588, 677, 678, 691, 722, 1045, 1193, 1197, 1199, 1269, 1296, 1326, 1334, 1341, 1344, 1355, 1358, 1399, 1402, 1404, 1406, 1411, 1413, 1414, 1426], "two": [8, 11, 16, 27, 34, 38, 43, 54, 55, 57, 58, 65, 67, 71, 88, 93, 95, 99, 100, 102, 109, 112, 114, 115, 120, 132, 151, 171, 175, 184, 185, 188, 202, 207, 211, 212, 213, 214, 215, 216, 217, 220, 221, 226, 227, 230, 231, 232, 245, 249, 251, 252, 253, 257, 258, 260, 261, 262, 265, 269, 270, 271, 272, 273, 274, 275, 276, 282, 285, 286, 287, 289, 305, 311, 315, 316, 322, 326, 329, 330, 337, 341, 343, 345, 351, 352, 358, 359, 377, 380, 381, 383, 391, 411, 412, 419, 423, 428, 429, 430, 431, 442, 443, 444, 445, 447, 452, 453, 454, 457, 462, 471, 472, 473, 474, 475, 476, 480, 491, 494, 498, 499, 500, 502, 503, 506, 508, 509, 510, 511, 521, 545, 549, 550, 551, 555, 559, 560, 561, 562, 563, 564, 565, 566, 568, 569, 572, 574, 578, 584, 585, 586, 587, 588, 593, 598, 605, 607, 608, 610, 611, 615, 619, 626, 627, 629, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 680, 692, 694, 731, 732, 738, 739, 760, 761, 762, 780, 786, 791, 796, 853, 867, 869, 873, 874, 877, 890, 892, 898, 912, 916, 917, 926, 928, 934, 946, 948, 950, 955, 956, 959, 960, 971, 973, 979, 991, 993, 998, 999, 1008, 1010, 1019, 1020, 1021, 1022, 1036, 1037, 1039, 1040, 1056, 1084, 1088, 1098, 1100, 1101, 1106, 1107, 1108, 1109, 1114, 1116, 1134, 1146, 1147, 1149, 1151, 1152, 1156, 1174, 1185, 1186, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1204, 1207, 1210, 1211, 1215, 1217, 1218, 1243, 1244, 1253, 1271, 1272, 1275, 1276, 1294, 1295, 1296, 1323, 1324, 1326, 1328, 1359, 1360, 1363, 1393, 1394, 1395, 1397, 1402, 1404, 1405, 1406, 1407, 1410, 1411, 1413, 1425], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1420], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1217, 1226, 1262, 1263, 1326, 1407], "appear": [8, 83, 93, 95, 99, 100, 102, 179, 204, 230, 231, 238, 243, 246, 247, 277, 363, 364, 365, 378, 451, 452, 453, 455, 466, 470, 584, 585, 587, 588, 675, 679, 707, 730, 734, 736, 891, 972, 1036, 1088, 1102, 1136, 1150, 1152, 1154, 1157, 1159, 1187, 1188, 1277, 1282, 1323, 1324, 1345, 1348, 1349, 1350, 1382, 1407, 1413, 1414], "both": [8, 52, 55, 92, 93, 94, 100, 101, 102, 103, 115, 161, 164, 204, 214, 215, 216, 217, 240, 257, 258, 259, 264, 282, 286, 287, 289, 337, 358, 379, 383, 415, 417, 418, 419, 423, 427, 440, 470, 502, 506, 545, 575, 581, 598, 600, 601, 602, 603, 604, 605, 606, 607, 610, 611, 615, 621, 635, 636, 653, 654, 655, 676, 711, 720, 760, 761, 762, 782, 891, 972, 1020, 1036, 1066, 1075, 1080, 1084, 1088, 1097, 1120, 1126, 1144, 1165, 1189, 1192, 1199, 1207, 1210, 1211, 1213, 1215, 1282, 1296, 1326, 1328, 1358, 1363, 1364, 1387, 1393, 1395, 1402, 1413, 1416, 1417, 1425, 1426], "negat": 8, "sole": [8, 786, 1278, 1279, 1326], "fourth": [8, 230, 231, 1326, 1404], "digraph": [8, 10, 11, 16, 21, 25, 41, 45, 56, 61, 67, 69, 70, 82, 88, 101, 102, 115, 132, 151, 152, 156, 157, 158, 160, 162, 163, 165, 166, 168, 170, 171, 172, 175, 176, 185, 186, 187, 188, 189, 192, 193, 194, 195, 196, 198, 199, 202, 204, 207, 208, 216, 227, 229, 230, 231, 240, 246, 247, 299, 308, 314, 318, 319, 321, 327, 328, 334, 335, 336, 337, 339, 340, 342, 343, 388, 391, 393, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 430, 431, 437, 450, 452, 453, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 481, 482, 492, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 513, 514, 518, 519, 523, 555, 566, 575, 576, 577, 588, 590, 613, 615, 623, 630, 636, 643, 644, 652, 656, 657, 658, 659, 663, 678, 688, 690, 693, 696, 697, 698, 699, 700, 701, 702, 706, 707, 708, 709, 711, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 740, 741, 744, 745, 746, 747, 748, 749, 750, 752, 760, 789, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 911, 912, 913, 915, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 957, 958, 963, 964, 965, 966, 967, 968, 972, 973, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 1000, 1001, 1003, 1004, 1005, 1006, 1007, 1010, 1035, 1037, 1038, 1039, 1040, 1041, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1135, 1150, 1154, 1168, 1169, 1170, 1173, 1177, 1178, 1180, 1182, 1183, 1184, 1185, 1189, 1217, 1270, 1272, 1273, 1274, 1283, 1284, 1287, 1290, 1292, 1298, 1323, 1326, 1333, 1337, 1342, 1356, 1357, 1362, 1365, 1366, 1371, 1393, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "add_nod": [8, 11, 26, 34, 69, 74, 89, 102, 157, 184, 246, 339, 340, 398, 422, 491, 492, 496, 504, 505, 508, 522, 523, 605, 607, 610, 611, 691, 796, 856, 873, 901, 916, 937, 955, 982, 998, 1037, 1039, 1040, 1086, 1275, 1326, 1345, 1407, 1408, 1417, 1426], "get_node_attribut": [8, 39, 44, 71, 1213, 1404], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1133, 1134, 1136], "22": [8, 35, 64, 66, 383, 384, 1271, 1323, 1403, 1408, 1412, 1422], "multipartite_layout": [8, 36, 61, 67, 1412, 1414, 1420], "subset_kei": [8, 36, 61, 67, 1109], "equal": [8, 36, 81, 144, 214, 215, 216, 230, 231, 238, 269, 271, 273, 276, 288, 297, 298, 300, 303, 306, 307, 310, 311, 312, 315, 316, 320, 323, 324, 325, 329, 330, 331, 373, 410, 411, 412, 413, 418, 419, 428, 471, 474, 476, 491, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 525, 535, 545, 552, 553, 554, 555, 568, 572, 605, 623, 657, 671, 672, 673, 674, 687, 688, 689, 690, 721, 722, 740, 741, 753, 761, 791, 1112, 1116, 1162, 1165, 1198, 1204, 1230, 1239, 1271, 1280, 1291, 1307, 1309, 1312, 1398, 1399], "101": [8, 17, 39, 47, 239, 240, 555, 762], "plot_circuit": [8, 17], "southern": [9, 1265], "women": [9, 1265, 1398, 1406], "unipartit": [9, 115, 258, 259, 358], "properti": [9, 11, 18, 22, 33, 63, 86, 101, 102, 103, 112, 134, 159, 161, 166, 168, 175, 176, 179, 184, 188, 189, 190, 200, 284, 285, 286, 287, 288, 363, 364, 365, 388, 476, 500, 545, 569, 619, 685, 858, 863, 865, 869, 870, 873, 877, 878, 879, 888, 903, 908, 910, 916, 939, 944, 946, 950, 951, 955, 959, 960, 969, 984, 989, 991, 998, 1085, 1086, 1122, 1134, 1136, 1193, 1202, 1217, 1219, 1269, 1283, 1284, 1326, 1328, 1383, 1398, 1405, 1406, 1407, 1408, 1413, 1417, 1426], "These": [9, 52, 58, 73, 79, 86, 93, 94, 105, 112, 336, 385, 494, 512, 559, 671, 673, 732, 748, 779, 786, 1038, 1045, 1047, 1323, 1326, 1385, 1387, 1392, 1394, 1395, 1397, 1399, 1404, 1405, 1411, 1426], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1199, 1393, 1395, 1399, 1402, 1405, 1406, 1407, 1413, 1416], "et": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202], "al": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202, 1407, 1413], "1930": [9, 1396], "thei": [9, 54, 58, 65, 71, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 112, 132, 151, 165, 207, 213, 220, 249, 285, 287, 288, 296, 297, 298, 301, 302, 306, 307, 308, 309, 351, 362, 374, 391, 396, 427, 451, 452, 453, 454, 464, 465, 471, 472, 473, 474, 475, 496, 504, 505, 508, 512, 546, 547, 548, 559, 560, 576, 583, 586, 588, 600, 604, 675, 676, 704, 717, 750, 760, 786, 853, 862, 892, 898, 907, 928, 934, 943, 962, 973, 979, 988, 1002, 1010, 1036, 1038, 1066, 1085, 1088, 1109, 1120, 1126, 1133, 1135, 1137, 1151, 1159, 1165, 1193, 1197, 1198, 1217, 1271, 1272, 1323, 1328, 1353, 1354, 1356, 1357, 1359, 1363, 1394, 1396, 1402, 1404, 1406, 1409, 1414, 1426], "repres": [9, 11, 26, 43, 52, 54, 57, 67, 92, 99, 107, 115, 230, 231, 265, 281, 283, 286, 287, 288, 291, 292, 338, 350, 361, 362, 363, 377, 378, 380, 381, 382, 385, 386, 391, 448, 452, 453, 455, 457, 460, 465, 466, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 521, 565, 577, 578, 579, 580, 586, 588, 609, 615, 618, 619, 656, 660, 664, 667, 676, 679, 691, 692, 695, 697, 698, 699, 700, 702, 728, 730, 731, 734, 736, 739, 752, 786, 791, 796, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1045, 1081, 1102, 1140, 1151, 1185, 1193, 1194, 1196, 1197, 1198, 1199, 1209, 1217, 1240, 1243, 1246, 1250, 1258, 1267, 1269, 1272, 1273, 1278, 1279, 1323, 1324, 1326, 1329, 1330, 1346, 1347, 1388, 1393, 1406], "observ": [9, 13, 132, 223, 1414, 1426], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 64, 66, 71, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1150, 1242, 1250, 1262, 1406, 1408, 1426], "event": [9, 25, 99, 100, 110, 1165, 1229, 1300], "18": [9, 44, 64, 66, 93, 324, 325, 345, 383, 384, 618, 1169, 1249, 1255, 1258, 1260, 1263, 1269, 1393, 1406, 1416, 1417, 1421, 1426], "bipartit": [9, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 350, 351, 358, 377, 439, 440, 443, 581, 588, 758, 1043, 1106, 1151, 1203, 1204, 1205, 1265, 1325, 1395, 1398, 1399, 1400, 1401, 1406, 1407, 1411, 1413, 1417, 1421, 1425], "biadjac": [9, 282, 283, 1400, 1406], "7": [9, 12, 14, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 89, 99, 101, 102, 115, 125, 151, 158, 170, 171, 192, 207, 232, 268, 297, 299, 314, 322, 327, 332, 333, 339, 340, 342, 362, 374, 380, 391, 403, 410, 413, 414, 415, 423, 424, 425, 426, 441, 445, 446, 483, 496, 501, 508, 511, 512, 555, 581, 586, 618, 619, 630, 652, 658, 663, 671, 674, 680, 695, 703, 706, 707, 708, 730, 747, 750, 761, 796, 853, 857, 866, 867, 881, 892, 898, 902, 911, 912, 915, 920, 928, 934, 938, 947, 973, 979, 983, 992, 996, 1010, 1037, 1039, 1040, 1052, 1053, 1085, 1100, 1104, 1148, 1212, 1242, 1248, 1250, 1251, 1255, 1258, 1260, 1273, 1323, 1326, 1330, 1339, 1340, 1345, 1348, 1349, 1350, 1382, 1392, 1394, 1402, 1403, 1405, 1408, 1409, 1410, 1411, 1412, 1413, 1426], "12": [9, 11, 19, 25, 44, 50, 55, 58, 64, 65, 66, 89, 91, 93, 229, 230, 231, 265, 345, 380, 381, 392, 399, 405, 406, 407, 449, 486, 501, 516, 568, 572, 574, 606, 616, 1052, 1053, 1054, 1133, 1136, 1150, 1244, 1245, 1249, 1254, 1257, 1263, 1335, 1406, 1408, 1412, 1426], "9": [9, 11, 12, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 82, 89, 101, 102, 111, 115, 125, 232, 293, 295, 339, 340, 342, 346, 347, 356, 374, 380, 405, 406, 424, 438, 449, 494, 496, 501, 504, 505, 508, 545, 566, 581, 586, 676, 706, 707, 708, 761, 1100, 1104, 1148, 1150, 1194, 1199, 1212, 1217, 1235, 1246, 1255, 1267, 1273, 1283, 1284, 1323, 1326, 1328, 1396, 1403, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "11": [9, 25, 33, 44, 64, 65, 66, 68, 89, 102, 110, 115, 157, 210, 239, 240, 297, 298, 303, 306, 307, 323, 392, 399, 405, 406, 407, 413, 415, 417, 422, 501, 514, 517, 606, 618, 680, 721, 738, 856, 901, 937, 982, 1052, 1053, 1054, 1100, 1150, 1287, 1403, 1410, 1413, 1414, 1419, 1424, 1425, 1426], "13": [9, 11, 38, 44, 64, 66, 89, 91, 156, 229, 230, 231, 343, 501, 703, 855, 900, 936, 981, 1150, 1192, 1406, 1420, 1426], "16": [9, 19, 31, 44, 45, 59, 64, 66, 70, 229, 230, 231, 346, 347, 387, 389, 390, 394, 453, 508, 511, 512, 519, 571, 592, 606, 748, 749, 750, 1109, 1205, 1256, 1271, 1286, 1323, 1406, 1411, 1426], "17": [9, 21, 44, 64, 66, 103, 229, 230, 231, 297, 508, 680, 693, 1405, 1406, 1426], "friend": [9, 545, 1407, 1412], "member": [9, 92, 93, 94, 100, 112, 315, 317, 318, 319, 330, 391, 483, 484, 586, 691, 1222, 1267, 1403], "evelyn": 9, "jefferson": 9, "laura": 9, "mandevil": 9, "theresa": 9, "anderson": 9, "brenda": 9, "roger": 9, "charlott": 9, "mcdowd": 9, "franc": 9, "eleanor": 9, "nye": 9, "pearl": [9, 132], "oglethorp": 9, "ruth": 9, "desand": 9, "vern": 9, "sanderson": 9, "myra": 9, "liddel": 9, "katherina": 9, "sylvia": 9, "avondal": 9, "nora": 9, "fayett": 9, "helen": 9, "lloyd": 9, "dorothi": 9, "murchison": 9, "olivia": 9, "carleton": 9, "flora": 9, "price": 9, "meet": [9, 94, 1165, 1196, 1197, 1198], "50": [9, 25, 30, 34, 40, 50, 54, 55, 56, 57, 64, 65, 272, 312, 1117, 1193, 1197, 1198, 1251, 1297, 1302], "45": [9, 58, 64, 110, 226, 300, 409, 1175], "57": [9, 64], "46": [9, 64, 235, 564, 619, 1264], "24": [9, 19, 37, 64, 66, 68, 103, 383, 384, 496, 505, 508, 703, 1212, 1229, 1244, 1262, 1271, 1403], "32": [9, 64, 66, 68, 209, 211, 212, 383, 384, 564, 703, 1403, 1411], "36": [9, 21, 64, 68, 752, 1150, 1262, 1271, 1353, 1354, 1379, 1403], "31": [9, 64, 66, 229, 230, 231, 260, 261, 262, 289, 383, 384, 409, 703, 1226, 1235, 1403], "40": [9, 50, 64, 80, 101, 297, 300, 555, 672, 1173, 1240, 1271], "38": [9, 64, 688, 1271], "33": [9, 58, 64, 66, 68, 93, 383, 384, 500, 514, 703, 1267, 1271, 1403, 1414], "37": [9, 56, 64, 68, 303, 311, 312, 323, 324, 325, 496, 508, 1039, 1040, 1271, 1393, 1403, 1408, 1425], "43": [9, 64, 324, 325, 606, 1244, 1271], "34": [9, 64, 68, 331, 508, 762, 1271, 1403], "algorithm": [9, 14, 15, 44, 52, 54, 88, 93, 94, 95, 96, 102, 103, 107, 109, 110, 111, 112, 114, 115, 117, 120, 121, 122, 125, 127, 128, 132, 133, 136, 141, 151, 210, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 228, 229, 230, 231, 232, 235, 249, 251, 252, 253, 254, 255, 256, 258, 260, 261, 262, 263, 264, 265, 266, 267, 272, 275, 277, 278, 280, 282, 284, 285, 286, 287, 288, 289, 290, 293, 296, 297, 298, 299, 301, 302, 303, 306, 307, 308, 309, 311, 312, 315, 320, 322, 323, 324, 325, 326, 329, 330, 331, 332, 333, 337, 339, 340, 341, 342, 343, 345, 346, 347, 352, 358, 361, 362, 366, 371, 372, 373, 374, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 389, 390, 394, 399, 405, 406, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 421, 422, 424, 425, 426, 427, 428, 429, 430, 432, 433, 435, 437, 440, 449, 451, 452, 453, 454, 455, 460, 464, 466, 468, 481, 482, 483, 488, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 512, 513, 514, 516, 519, 520, 521, 527, 537, 546, 547, 548, 552, 553, 554, 555, 556, 557, 558, 564, 566, 569, 577, 581, 582, 583, 589, 591, 592, 593, 600, 614, 616, 618, 619, 624, 625, 626, 627, 628, 629, 630, 632, 633, 635, 636, 639, 652, 653, 657, 658, 659, 660, 663, 664, 667, 671, 672, 673, 674, 676, 677, 678, 680, 681, 682, 683, 686, 690, 691, 692, 693, 695, 696, 697, 698, 699, 700, 701, 702, 711, 717, 721, 722, 729, 731, 732, 734, 735, 736, 737, 738, 749, 764, 765, 768, 770, 775, 776, 780, 786, 789, 790, 791, 853, 898, 934, 979, 1011, 1038, 1042, 1043, 1105, 1106, 1107, 1109, 1114, 1116, 1117, 1125, 1126, 1155, 1165, 1168, 1169, 1177, 1178, 1179, 1180, 1181, 1185, 1186, 1187, 1188, 1193, 1195, 1200, 1201, 1202, 1205, 1207, 1209, 1210, 1216, 1223, 1224, 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1235, 1239, 1260, 1269, 1275, 1276, 1277, 1298, 1302, 1319, 1320, 1321, 1323, 1325, 1328, 1367, 1368, 1386, 1393, 1394, 1395, 1400, 1401, 1402, 1403, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1422, 1424, 1425, 1426], "davis_southern_women_graph": [9, 88, 263], "top": [9, 34, 52, 67, 106, 111, 112, 115, 125, 260, 272, 284, 350, 381, 670, 675, 770, 1106, 1134, 1136, 1252, 1396, 1399, 1407, 1412, 1413, 1416], "bottom": [9, 91, 115, 260, 272, 274, 284, 285, 286, 287, 288, 350, 381, 1134, 1136, 1155, 1404, 1416], "biadjacency_matrix": [9, 283], "onto": [9, 284, 285, 286, 287, 288, 559, 560], "projected_graph": [9, 115, 284, 285, 286, 288, 351], "keep": [9, 92, 93, 94, 115, 204, 345, 346, 347, 362, 377, 387, 389, 390, 394, 583, 598, 693, 694, 891, 972, 1117, 1207, 1210, 1278, 1279, 1296, 1376, 1394, 1411, 1414], "co": [9, 26, 94, 99, 144, 752, 1326], "occur": [9, 93, 95, 100, 230, 231, 277, 278, 280, 383, 581, 582, 583, 588, 1043, 1117, 1120, 1126, 1282, 1296], "count": [9, 185, 237, 238, 242, 243, 245, 297, 298, 310, 315, 330, 360, 386, 443, 568, 597, 619, 749, 753, 874, 917, 944, 950, 956, 959, 999, 1060, 1179, 1278, 1279, 1406, 1407, 1416], "share": [9, 54, 58, 92, 94, 112, 165, 199, 214, 215, 216, 221, 278, 285, 287, 288, 294, 358, 359, 376, 418, 419, 460, 462, 480, 569, 578, 691, 732, 862, 887, 907, 925, 943, 968, 988, 1007, 1217, 1328], "contact": [9, 92, 688, 1195, 1326], "weighted_projected_graph": [9, 284, 285, 286, 287, 1417], "648": 9, "070": [9, 17], "plot_davis_club": [9, 17], "retain": [10, 102, 110, 230, 284, 285, 286, 287, 288, 1100, 1187, 1295], "pattern": [10, 54, 93, 103, 236, 241, 244, 248, 385, 494, 519, 555, 671, 672, 673, 674, 690, 691, 693, 762, 786, 1036, 1088, 1388, 1413], "add": [10, 11, 26, 34, 41, 45, 49, 52, 61, 71, 88, 89, 91, 93, 94, 101, 102, 105, 106, 115, 151, 152, 153, 154, 156, 157, 158, 164, 207, 222, 223, 229, 282, 285, 341, 374, 411, 412, 423, 428, 430, 431, 450, 460, 581, 582, 583, 589, 614, 615, 618, 619, 654, 690, 701, 717, 718, 796, 850, 853, 854, 855, 856, 857, 892, 895, 898, 899, 900, 901, 902, 928, 931, 934, 935, 936, 937, 938, 973, 976, 979, 980, 981, 982, 983, 984, 1010, 1037, 1038, 1039, 1040, 1042, 1049, 1052, 1053, 1054, 1100, 1154, 1165, 1172, 1185, 1207, 1210, 1217, 1219, 1233, 1234, 1236, 1302, 1326, 1353, 1354, 1356, 1357, 1379, 1380, 1383, 1393, 1394, 1395, 1398, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "compressor": [10, 690, 786], "do": [10, 55, 75, 88, 92, 93, 94, 96, 99, 101, 102, 106, 107, 109, 111, 115, 133, 165, 184, 199, 202, 204, 230, 231, 238, 243, 277, 278, 280, 362, 380, 410, 411, 412, 418, 419, 458, 459, 467, 470, 589, 598, 632, 690, 692, 734, 735, 736, 737, 791, 796, 862, 873, 887, 890, 891, 907, 916, 925, 926, 927, 943, 954, 955, 968, 971, 972, 988, 997, 998, 1007, 1008, 1009, 1037, 1038, 1039, 1040, 1061, 1082, 1102, 1165, 1177, 1189, 1193, 1207, 1210, 1216, 1217, 1227, 1272, 1328, 1393, 1401, 1402, 1407, 1411, 1426], "would": [10, 92, 93, 95, 96, 100, 101, 102, 103, 104, 105, 107, 289, 305, 414, 415, 416, 417, 422, 428, 579, 583, 588, 632, 679, 690, 693, 717, 718, 751, 1217, 1236, 1295, 1296, 1300, 1303, 1326, 1416, 1417], "result": [10, 11, 25, 71, 92, 95, 101, 103, 109, 110, 112, 142, 165, 209, 218, 220, 230, 231, 255, 269, 271, 273, 276, 283, 284, 285, 286, 287, 288, 289, 299, 300, 305, 324, 325, 330, 374, 380, 381, 382, 385, 386, 391, 411, 412, 416, 418, 440, 464, 466, 467, 490, 494, 498, 499, 509, 510, 511, 512, 564, 565, 566, 584, 585, 587, 601, 609, 615, 626, 627, 629, 676, 678, 690, 692, 704, 710, 717, 786, 791, 862, 907, 943, 984, 988, 1038, 1042, 1082, 1094, 1098, 1099, 1102, 1103, 1105, 1112, 1113, 1114, 1116, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1150, 1152, 1154, 1157, 1159, 1160, 1163, 1175, 1177, 1180, 1201, 1222, 1225, 1239, 1278, 1279, 1281, 1296, 1299, 1303, 1308, 1326, 1328, 1331, 1334, 1359, 1402, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1425, 1426], "fewer": [10, 420, 421, 681, 683, 690, 692, 693, 694, 762, 786, 1213, 1215], "compress": [10, 25, 268, 512, 577, 690, 786, 1116, 1242, 1333, 1334, 1339, 1340, 1344, 1350, 1357, 1358, 1371, 1372, 1376], "suptitl": [10, 15], "original_graph": [10, 15, 690], "white_nod": 10, "red_nod": 10, "250": [10, 32, 1165], "white": [10, 21, 25, 82, 83, 127, 214, 215, 216, 220, 427, 1395, 1398, 1406], "add_nodes_from": [10, 15, 16, 36, 70, 71, 82, 89, 115, 156, 165, 199, 207, 236, 237, 248, 265, 267, 268, 423, 425, 426, 469, 555, 690, 796, 855, 862, 887, 892, 900, 907, 925, 928, 936, 943, 968, 973, 981, 988, 1007, 1010, 1037, 1039, 1040, 1065, 1194, 1217, 1291, 1404, 1406, 1413, 1426], "add_edges_from": [10, 15, 16, 36, 41, 70, 82, 89, 115, 132, 151, 158, 165, 199, 204, 207, 236, 248, 287, 327, 376, 422, 423, 425, 426, 460, 469, 501, 511, 512, 572, 574, 588, 688, 690, 705, 706, 707, 709, 730, 742, 743, 796, 853, 857, 862, 887, 891, 892, 898, 902, 907, 925, 927, 928, 934, 938, 943, 956, 962, 963, 968, 972, 973, 979, 983, 988, 999, 1002, 1003, 1007, 1009, 1010, 1037, 1039, 1040, 1070, 1085, 1094, 1135, 1154, 1217, 1287, 1291, 1326, 1404, 1407, 1426], "base_opt": [10, 15], "dict": [10, 15, 19, 25, 39, 54, 57, 58, 67, 70, 88, 101, 102, 107, 109, 144, 145, 148, 157, 159, 160, 165, 168, 169, 176, 179, 184, 189, 190, 195, 197, 200, 202, 204, 207, 220, 237, 239, 240, 252, 290, 309, 310, 329, 334, 336, 353, 408, 411, 412, 416, 422, 427, 470, 473, 481, 482, 496, 502, 512, 545, 561, 563, 565, 566, 575, 577, 578, 579, 580, 588, 614, 628, 631, 636, 637, 638, 640, 642, 644, 645, 646, 647, 648, 649, 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1426], "ax1": [10, 15, 27, 50, 82], "number_of_edg": [10, 15, 25, 28, 198, 690, 886, 924, 967, 1006, 1059, 1154, 1271, 1406, 1407, 1426], "nonexp_graph": 10, "compression_nod": 10, "summar": [10, 15, 100, 101, 690, 691, 758, 791, 1325, 1328, 1413], "dedensifi": [10, 758], "threshold": [10, 57, 83, 112, 220, 229, 231, 380, 381, 690, 692, 695, 696, 758, 786, 1117, 1193, 1194, 1196, 1197, 1198, 1325, 1398, 1406, 1407, 1408, 1412, 1414], "copi": [10, 16, 38, 44, 93, 95, 106, 167, 196, 199, 202, 203, 204, 205, 284, 285, 286, 287, 288, 341, 388, 390, 392, 406, 433, 434, 435, 436, 437, 453, 460, 469, 521, 584, 585, 587, 596, 599, 602, 603, 605, 606, 607, 610, 611, 613, 614, 633, 636, 690, 864, 885, 887, 890, 891, 909, 925, 926, 927, 945, 963, 966, 968, 971, 972, 990, 1003, 1007, 1008, 1009, 1035, 1038, 1057, 1061, 1063, 1066, 1082, 1083, 1122, 1183, 1189, 1217, 1223, 1227, 1251, 1270, 1294, 1295, 1296, 1403, 1404, 1406, 1407, 1408, 1409, 1412, 1413, 1422, 1425], "nonexp_node_color": 10, "nonexp_node_s": 10, "yellow": [10, 15, 598, 760, 1426], "nonexp_po": 10, "75": [10, 34, 239, 260, 299, 314, 355, 356, 386, 682, 1169, 1170, 1171, 1173, 1404, 1408, 1426], "c_node": [10, 690], "spot": 10, "230": [10, 17, 40, 47], "plot_dedensif": [10, 17], "153": [11, 455], "curiou": 11, "let": [11, 55, 58, 93, 97, 101, 103, 217, 257, 280, 282, 299, 300, 313, 322, 371, 372, 383, 586, 619, 762, 1219, 1278, 1279, 1326, 1425], "defin": [11, 24, 52, 58, 69, 97, 112, 127, 213, 222, 223, 239, 240, 260, 261, 262, 263, 285, 289, 311, 316, 329, 334, 335, 345, 346, 347, 356, 385, 386, 390, 424, 425, 426, 429, 432, 433, 434, 435, 436, 437, 449, 464, 465, 466, 469, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 519, 567, 569, 570, 571, 573, 574, 575, 577, 586, 614, 615, 619, 621, 625, 652, 671, 673, 674, 676, 684, 685, 686, 687, 688, 689, 728, 730, 738, 751, 752, 753, 762, 791, 796, 1037, 1038, 1039, 1040, 1045, 1047, 1071, 1081, 1098, 1147, 1154, 1170, 1172, 1195, 1197, 1280, 1286, 1287, 1288, 1296, 1320, 1321, 1326, 1344, 1353, 1354, 1359, 1363, 1379, 1395, 1402, 1407, 1408, 1412, 1426], "an": [11, 15, 24, 25, 31, 34, 38, 41, 44, 46, 49, 52, 54, 55, 58, 63, 66, 67, 71, 75, 76, 77, 88, 91, 92, 93, 94, 95, 96, 99, 100, 101, 102, 103, 104, 107, 110, 112, 114, 115, 116, 120, 121, 127, 128, 132, 141, 151, 152, 157, 158, 160, 165, 166, 167, 168, 170, 175, 179, 180, 181, 184, 188, 189, 191, 192, 193, 194, 195, 198, 199, 201, 204, 206, 207, 208, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 243, 249, 250, 251, 255, 256, 264, 266, 267, 269, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 291, 292, 293, 294, 295, 297, 298, 299, 301, 302, 306, 307, 308, 309, 311, 312, 315, 316, 318, 319, 320, 322, 324, 325, 326, 329, 330, 332, 341, 342, 343, 345, 346, 347, 348, 349, 350, 351, 353, 357, 362, 363, 364, 365, 366, 370, 373, 374, 375, 377, 378, 379, 380, 381, 383, 384, 385, 387, 388, 389, 390, 392, 394, 395, 400, 402, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 427, 428, 429, 431, 432, 433, 437, 438, 439, 440, 449, 450, 451, 455, 456, 457, 460, 462, 466, 467, 468, 469, 471, 472, 473, 474, 475, 477, 480, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 516, 517, 519, 520, 521, 522, 523, 524, 525, 530, 534, 535, 540, 544, 545, 555, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 577, 578, 579, 580, 584, 586, 588, 589, 590, 593, 594, 595, 596, 597, 598, 601, 604, 605, 607, 610, 611, 615, 616, 618, 619, 624, 626, 627, 631, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 678, 679, 680, 681, 682, 683, 684, 686, 690, 691, 692, 694, 695, 696, 697, 701, 703, 704, 705, 706, 707, 708, 716, 717, 719, 721, 722, 723, 724, 725, 726, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 743, 748, 752, 760, 761, 762, 767, 775, 782, 791, 796, 801, 806, 810, 814, 818, 822, 827, 832, 837, 842, 847, 849, 850, 851, 853, 854, 856, 857, 859, 862, 863, 864, 865, 866, 869, 871, 872, 873, 877, 878, 880, 881, 882, 883, 884, 886, 887, 889, 891, 892, 894, 895, 896, 898, 899, 901, 902, 904, 907, 908, 909, 910, 911, 914, 915, 916, 920, 921, 922, 923, 924, 925, 927, 928, 930, 931, 932, 934, 935, 937, 938, 940, 943, 944, 945, 946, 947, 948, 950, 952, 953, 954, 955, 959, 960, 961, 962, 963, 964, 965, 967, 968, 970, 972, 973, 975, 976, 977, 979, 980, 982, 983, 985, 988, 989, 990, 991, 992, 993, 995, 996, 997, 998, 1002, 1003, 1004, 1005, 1006, 1007, 1009, 1010, 1012, 1013, 1018, 1020, 1036, 1037, 1038, 1039, 1040, 1042, 1043, 1045, 1046, 1049, 1050, 1051, 1061, 1062, 1066, 1068, 1074, 1075, 1081, 1082, 1084, 1085, 1086, 1087, 1088, 1090, 1094, 1098, 1099, 1100, 1101, 1102, 1103, 1105, 1115, 1117, 1122, 1133, 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995, 1045, 1100, 1175, 1177, 1180, 1216, 1219, 1222, 1225, 1245, 1280, 1290, 1295, 1296, 1299, 1301, 1383, 1395, 1407, 1408, 1412, 1413, 1414, 1419, 1426], "follow": [11, 25, 44, 49, 52, 53, 65, 67, 83, 86, 91, 92, 93, 94, 95, 97, 99, 100, 101, 102, 103, 108, 110, 111, 128, 132, 151, 161, 171, 183, 207, 213, 227, 229, 230, 231, 243, 280, 305, 338, 343, 351, 362, 373, 378, 380, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 440, 452, 453, 465, 466, 496, 502, 503, 504, 505, 506, 507, 508, 588, 598, 599, 602, 615, 636, 679, 748, 750, 760, 762, 791, 853, 867, 892, 898, 912, 928, 934, 948, 973, 979, 993, 1010, 1102, 1103, 1105, 1144, 1165, 1175, 1179, 1185, 1188, 1200, 1201, 1209, 1219, 1225, 1233, 1234, 1241, 1251, 1260, 1274, 1275, 1276, 1277, 1281, 1296, 1315, 1323, 1326, 1328, 1329, 1388, 1393, 1395, 1399, 1404, 1406, 1407, 1409, 1411, 1412, 1413, 1425, 1426], "given": [11, 38, 44, 62, 64, 67, 91, 99, 101, 103, 112, 116, 141, 142, 144, 152, 158, 193, 197, 208, 211, 212, 227, 229, 235, 236, 248, 249, 260, 264, 266, 269, 271, 273, 274, 276, 279, 281, 283, 284, 285, 286, 287, 288, 320, 329, 331, 338, 344, 351, 353, 357, 362, 363, 364, 365, 373, 378, 380, 381, 385, 439, 454, 455, 460, 462, 470, 477, 478, 480, 497, 511, 512, 513, 559, 560, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 576, 578, 579, 580, 588, 589, 590, 614, 615, 616, 622, 623, 659, 660, 661, 662, 676, 677, 678, 679, 681, 683, 684, 686, 690, 691, 693, 697, 698, 699, 700, 702, 703, 704, 706, 707, 708, 709, 728, 729, 730, 731, 732, 739, 748, 753, 761, 782, 786, 854, 857, 882, 899, 902, 921, 935, 938, 963, 980, 983, 1003, 1046, 1085, 1086, 1094, 1101, 1102, 1135, 1144, 1151, 1162, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1189, 1199, 1200, 1201, 1206, 1207, 1208, 1209, 1210, 1221, 1222, 1240, 1269, 1273, 1274, 1276, 1295, 1300, 1302, 1315, 1323, 1353, 1354, 1379, 1380, 1394, 1395, 1406], "digit": [11, 70, 99], "base": [11, 15, 38, 43, 55, 58, 69, 93, 94, 100, 101, 102, 103, 107, 128, 132, 199, 203, 205, 212, 216, 220, 229, 296, 297, 301, 302, 303, 308, 309, 310, 311, 312, 322, 323, 324, 325, 329, 330, 337, 343, 346, 347, 362, 371, 373, 374, 380, 381, 382, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 425, 426, 427, 428, 430, 431, 449, 464, 466, 494, 498, 499, 500, 509, 510, 545, 555, 564, 566, 569, 574, 581, 614, 616, 660, 667, 680, 688, 691, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 717, 732, 738, 758, 761, 762, 786, 791, 796, 887, 925, 934, 935, 968, 979, 980, 1007, 1036, 1037, 1038, 1041, 1043, 1082, 1088, 1182, 1229, 1235, 1253, 1267, 1296, 1320, 1321, 1323, 1326, 1383, 1387, 1392, 1395, 1402, 1403, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1421, 1425], "obtain": [11, 91, 165, 207, 282, 345, 346, 347, 380, 383, 387, 388, 389, 390, 394, 465, 511, 606, 618, 619, 656, 722, 742, 743, 760, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1164, 1253, 1272, 1278, 1279, 1323, 1326, 1356, 1357, 1402, 1426], "seri": [11, 444, 616, 680, 1215, 1286], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1177, 1179, 1192, 1222], "end": [11, 25, 36, 52, 95, 101, 106, 153, 154, 206, 215, 227, 267, 268, 300, 332, 333, 342, 371, 372, 427, 614, 618, 619, 626, 627, 631, 632, 634, 635, 636, 639, 640, 650, 651, 652, 653, 654, 655, 660, 664, 667, 677, 678, 680, 734, 736, 1038, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1133, 1135, 1152, 1165, 1206, 1229, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1342, 1344, 1350, 1353, 1357, 1358, 1368, 1371, 1372, 1375, 1376, 1379, 1404, 1413], "In": [11, 16, 27, 43, 54, 57, 58, 88, 92, 93, 94, 95, 97, 99, 100, 101, 103, 110, 115, 127, 132, 133, 175, 184, 199, 217, 229, 230, 231, 235, 240, 257, 258, 259, 278, 283, 286, 288, 289, 299, 311, 312, 324, 325, 329, 350, 357, 378, 379, 380, 410, 413, 414, 415, 422, 429, 443, 447, 450, 458, 460, 494, 498, 499, 501, 510, 565, 568, 572, 574, 590, 591, 615, 619, 621, 652, 653, 654, 657, 658, 663, 670, 675, 676, 690, 691, 701, 703, 717, 718, 719, 730, 732, 740, 741, 742, 743, 761, 762, 767, 770, 789, 791, 796, 869, 873, 887, 916, 925, 954, 955, 968, 997, 998, 1007, 1037, 1038, 1039, 1040, 1042, 1043, 1066, 1100, 1101, 1117, 1154, 1168, 1199, 1203, 1206, 1207, 1208, 1210, 1216, 1217, 1222, 1226, 1231, 1233, 1241, 1295, 1296, 1300, 1320, 1321, 1326, 1328, 1350, 1394, 1398, 1399, 1404, 1405, 1406, 1407, 1408, 1409, 1413, 1414, 1426], "languag": [11, 92, 99, 110, 1042, 1324, 1341, 1342, 1344, 1381, 1382, 1383, 1411], "discret": [11, 104, 235, 249, 362, 409, 513, 517, 518, 618, 1164, 1165, 1178, 1180, 1186, 1190, 1204, 1278, 1279, 1282, 1314, 1315, 1323, 1406], "global": [11, 103, 314, 341, 410, 477, 486, 487, 509, 592, 1045, 1269, 1296, 1301, 1304, 1305, 1328, 1407, 1409, 1411], "attractor": [11, 388], "map": [11, 34, 38, 52, 67, 101, 102, 103, 115, 125, 144, 145, 148, 166, 169, 197, 238, 243, 264, 350, 369, 391, 412, 416, 417, 418, 419, 423, 424, 425, 426, 431, 440, 460, 530, 531, 534, 540, 541, 544, 545, 559, 560, 561, 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1426], "108": [11, 1216], "513": [11, 1398, 1406], "reach": [11, 99, 100, 314, 324, 327, 376, 383, 387, 389, 390, 394, 410, 411, 412, 418, 419, 494, 498, 499, 510, 564, 566, 626, 627, 632, 640, 643, 652, 693, 711, 758, 1188, 1207, 1210, 1407], "orbit": 11, "up": [11, 70, 80, 93, 94, 97, 99, 100, 101, 104, 107, 132, 133, 346, 347, 377, 423, 427, 509, 530, 540, 577, 619, 652, 653, 657, 748, 1036, 1038, 1061, 1066, 1082, 1088, 1102, 1144, 1148, 1173, 1213, 1215, 1272, 1326, 1328, 1355, 1358, 1395, 1396, 1402, 1404, 1406, 1410, 1411, 1413, 1414, 1416, 1417, 1420, 1426], "reveal": [11, 711, 786], "maximum": [11, 112, 115, 209, 210, 211, 212, 214, 215, 217, 222, 224, 227, 257, 259, 264, 277, 278, 279, 281, 288, 296, 304, 311, 312, 315, 316, 317, 318, 319, 321, 324, 328, 330, 339, 341, 342, 343, 346, 347, 352, 356, 361, 373, 377, 380, 382, 383, 385, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 428, 440, 472, 473, 494, 498, 499, 500, 501, 502, 503, 506, 507, 509, 510, 520, 521, 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775, 872, 880, 889, 953, 961, 970, 1060, 1151, 1155, 1157, 1169, 1175, 1177, 1179, 1181, 1199, 1201, 1202, 1203, 1204, 1205, 1207, 1208, 1209, 1210, 1211, 1213, 1215, 1216, 1218, 1219, 1220, 1222, 1223, 1226, 1229, 1230, 1231, 1233, 1234, 1235, 1240, 1256, 1265, 1269, 1271, 1278, 1279, 1280, 1287, 1288, 1292, 1323, 1387, 1406, 1409, 1426], "node_color_list": 16, "nc": [16, 56], "spectral_layout": [16, 43, 1141, 1399, 1406], "subgraphs_of_g_ex": 16, "removed_edg": 16, "node_color_list_c": 16, "One": [16, 52, 55, 101, 102, 103, 115, 545, 559, 560, 679, 684, 761, 1177, 1186, 1272, 1315, 1326, 1404, 1426], "g_ex_r": 16, "compos": [16, 269, 270, 271, 272, 273, 274, 275, 276, 600, 604, 758, 1400, 1406, 1407, 1417, 1423, 1425], "previous": [16, 91, 108, 112, 322, 614, 1182, 1183, 1184, 1395, 1407, 1417], "store": [16, 25, 39, 53, 54, 55, 57, 67, 86, 93, 97, 101, 102, 110, 158, 219, 220, 283, 290, 345, 346, 347, 431, 470, 471, 472, 473, 474, 475, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 585, 587, 615, 660, 664, 667, 719, 733, 739, 762, 786, 796, 857, 902, 938, 983, 1037, 1038, 1039, 1040, 1046, 1085, 1086, 1101, 1102, 1104, 1165, 1170, 1193, 1196, 1197, 1198, 1199, 1213, 1215, 1278, 1294, 1296, 1330, 1333, 1334, 1345, 1348, 1349, 1350, 1359, 1360, 1363, 1364, 1365, 1366, 1371, 1382, 1388, 1390, 1394, 1404, 1414], "assert": [16, 67, 88, 102, 1411, 1414, 1424, 1425, 1426], "is_isomorph": [16, 584, 585, 587, 588, 608, 671, 690, 739, 758, 761, 762, 1399, 1406], "651": [16, 17], "plot_subgraph": [16, 17, 1414], "26": [17, 64, 66, 68, 383, 384, 494, 577, 703, 762, 1197, 1295, 1403], "527": [17, 1166, 1167], "auto_examples_algorithm": 17, "03": [17, 21, 25, 47, 59, 85, 112, 217, 274, 300], "read": [18, 22, 25, 40, 52, 54, 55, 57, 58, 65, 75, 86, 93, 94, 100, 115, 159, 165, 167, 190, 200, 267, 583, 618, 796, 858, 862, 864, 879, 888, 903, 907, 909, 939, 943, 945, 947, 969, 984, 988, 990, 992, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1035, 1036, 1037, 1038, 1039, 1040, 1042, 1043, 1061, 1066, 1082, 1083, 1088, 1121, 1143, 1144, 1270, 1296, 1325, 1326, 1329, 1330, 1333, 1337, 1338, 1342, 1343, 1345, 1348, 1349, 1350, 1351, 1352, 1354, 1356, 1357, 1367, 1368, 1371, 1375, 1377, 1378, 1380, 1381, 1382, 1383, 1386, 1387, 1388, 1389, 1390, 1394, 1395, 1397, 1398, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1413, 1414, 1418, 1424, 1425], "write": [18, 22, 49, 52, 75, 76, 77, 86, 89, 93, 99, 105, 110, 115, 267, 268, 470, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1047, 1123, 1129, 1300, 1325, 1326, 1329, 1330, 1334, 1337, 1339, 1340, 1344, 1345, 1348, 1349, 1350, 1352, 1354, 1357, 1358, 1372, 1375, 1376, 1378, 1380, 1381, 1382, 1383, 1387, 1388, 1390, 1395, 1397, 1398, 1399, 1401, 1402, 1405, 1406, 1411, 1412, 1414, 1425, 1426], "simpl": [18, 22, 23, 32, 47, 86, 93, 94, 97, 100, 103, 109, 110, 132, 184, 220, 229, 230, 231, 249, 287, 293, 300, 304, 313, 321, 328, 332, 333, 338, 343, 371, 372, 373, 380, 381, 423, 425, 438, 452, 453, 468, 479, 481, 482, 490, 496, 500, 504, 505, 508, 514, 517, 518, 594, 608, 624, 632, 677, 678, 679, 680, 686, 693, 758, 775, 780, 796, 873, 916, 955, 998, 1037, 1038, 1039, 1040, 1098, 1099, 1100, 1130, 1133, 1175, 1177, 1180, 1181, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1252, 1269, 1296, 1323, 1325, 1326, 1328, 1330, 1351, 1352, 1353, 1354, 1382, 1388, 1395, 1401, 1404, 1406, 1407, 1412, 1413, 1421, 1426], "lollipop": [19, 1157, 1426], "vertex": [19, 115, 211, 235, 249, 281, 289, 315, 322, 330, 338, 359, 360, 373, 387, 394, 397, 427, 428, 432, 438, 477, 491, 580, 606, 615, 616, 619, 622, 623, 624, 688, 689, 758, 1164, 1185, 1190, 1206, 1218, 1219, 1222, 1251, 1323, 1326, 1400, 1406, 1407], "length": [19, 39, 52, 67, 102, 120, 151, 232, 288, 295, 297, 298, 299, 306, 307, 310, 314, 315, 316, 320, 322, 326, 327, 329, 330, 332, 333, 341, 343, 345, 346, 347, 371, 372, 383, 384, 451, 459, 462, 467, 469, 470, 473, 513, 515, 516, 517, 520, 521, 591, 592, 627, 628, 629, 630, 632, 633, 636, 637, 638, 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"single_source_shortest_path_length": [19, 39, 636, 644], "histogram": [19, 27, 31, 62, 64, 511, 1315], "dist": [19, 34, 44, 56, 57, 106, 626, 647, 652, 656, 658, 1108, 1193, 1197, 1199, 1414], "vert": 19, "3068": 19, "085": [19, 22], "plot_properti": [19, 22], "5x5": [20, 76], "adjac": [20, 43, 54, 58, 63, 88, 101, 112, 114, 120, 159, 166, 169, 175, 188, 190, 194, 200, 207, 210, 212, 215, 238, 241, 242, 243, 244, 247, 249, 252, 282, 300, 311, 312, 313, 324, 325, 332, 333, 341, 343, 352, 371, 372, 376, 383, 384, 385, 412, 428, 480, 483, 484, 512, 519, 584, 585, 587, 588, 593, 605, 606, 608, 679, 775, 796, 849, 858, 863, 869, 877, 879, 883, 888, 892, 894, 903, 908, 922, 928, 930, 939, 944, 950, 964, 969, 973, 975, 984, 989, 1004, 1010, 1019, 1020, 1037, 1039, 1040, 1075, 1091, 1092, 1094, 1095, 1098, 1099, 1101, 1102, 1103, 1105, 1167, 1191, 1217, 1220, 1269, 1271, 1278, 1279, 1280, 1281, 1285, 1286, 1287, 1288, 1289, 1323, 1325, 1326, 1327, 1330, 1331, 1332, 1333, 1334, 1359, 1360, 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1104, 1105, 1112, 1213, 1215, 1271, 1278, 1279, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1323, 1395, 1406, 1411, 1412], "left_nod": 21, "middle_nod": 21, "right_nod": 21, "accord": [21, 70, 94, 100, 103, 197, 233, 240, 282, 289, 345, 377, 380, 385, 565, 566, 588, 619, 670, 690, 691, 728, 729, 731, 1102, 1103, 1105, 1165, 1173, 1185, 1186, 1222, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1295, 1344, 1348, 1349, 1390, 1413], "coord": [21, 34], "updat": [21, 93, 94, 95, 99, 101, 102, 106, 111, 151, 152, 156, 157, 158, 199, 204, 233, 322, 337, 362, 366, 370, 373, 378, 460, 500, 506, 511, 598, 600, 604, 626, 627, 692, 796, 853, 854, 855, 856, 857, 887, 891, 898, 899, 900, 901, 902, 925, 934, 935, 936, 937, 938, 968, 979, 980, 981, 982, 983, 1007, 1037, 1039, 1040, 1085, 1086, 1122, 1296, 1302, 1392, 1393, 1394, 1398, 1399, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "378": [21, 22, 387, 389, 390, 394], "plot_simple_graph": [21, 22], "522": 22, "auto_examples_bas": 22, "custom": [23, 32, 33, 35, 47, 86, 102, 115, 204, 285, 464, 546, 547, 548, 552, 553, 554, 556, 557, 558, 704, 706, 707, 708, 796, 891, 934, 935, 979, 980, 1037, 1039, 1040, 1094, 1100, 1193, 1197, 1198, 1202, 1302, 1383, 1407, 1408, 1412, 1413, 1426], "chess": [23, 47, 86, 1406], "master": [23, 47, 86, 476, 1406], "icon": [23, 47, 86, 93, 1413], "ego": [23, 47, 86, 305, 688, 1325, 1406, 1407], "eigenvalu": [23, 47, 86, 311, 312, 313, 324, 325, 332, 371, 566, 593, 1115, 1191, 1275, 1276, 1277, 1289, 1290, 1291, 1292, 1293, 1327, 1406, 1413], "hous": [23, 47, 86, 1252, 1253, 1413], "With": [23, 47, 54, 86, 101, 103, 110, 337, 511, 760, 1118, 1130, 1184, 1229, 1297, 1330, 1338, 1388, 1394, 1402, 1404, 1405, 1407], "knuth": [23, 47, 69, 71, 86, 455, 1226, 1268, 1302, 1413], "mile": [23, 47, 86, 1406, 1413], "multipartit": [23, 47, 86, 1109, 1151, 1162, 1395, 1406, 1407, 1413], "rainbow": [23, 47, 86, 1413], "geometr": [23, 47, 86, 105, 356, 1196, 1197, 1198, 1264, 1325, 1407, 1408, 1413], "sampson": [23, 47, 86, 1406], "self": [23, 45, 47, 52, 69, 86, 88, 89, 101, 152, 158, 168, 176, 180, 189, 224, 246, 247, 304, 321, 328, 331, 335, 345, 346, 347, 355, 356, 360, 432, 433, 434, 435, 436, 437, 438, 453, 467, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 519, 567, 575, 584, 585, 587, 593, 612, 619, 625, 675, 700, 735, 737, 854, 857, 865, 870, 871, 878, 899, 902, 910, 914, 935, 938, 946, 951, 952, 959, 960, 980, 983, 991, 995, 1038, 1060, 1075, 1102, 1103, 1105, 1135, 1173, 1175, 1177, 1179, 1185, 1193, 1196, 1197, 1198, 1199, 1217, 1222, 1239, 1281, 1325, 1326, 1330, 1353, 1354, 1389, 1401, 1403, 1406, 1408, 1411, 1412, 1413, 1414, 1417, 1425], "loop": [23, 45, 47, 52, 69, 86, 224, 230, 231, 246, 247, 304, 321, 328, 331, 345, 346, 347, 355, 356, 360, 432, 433, 434, 435, 436, 437, 438, 449, 450, 451, 453, 467, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 567, 584, 585, 587, 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1151, 1165, 1192, 1207, 1212, 1215, 1227, 1267, 1296, 1297, 1324, 1328, 1387, 1393, 1405, 1425], "draw_spectr": [43, 1426], "similar": [43, 99, 101, 102, 103, 104, 202, 204, 236, 241, 244, 248, 260, 335, 352, 390, 424, 425, 426, 427, 436, 511, 512, 577, 604, 670, 671, 674, 675, 676, 682, 691, 704, 717, 758, 760, 786, 791, 849, 890, 891, 894, 926, 927, 930, 971, 972, 975, 1008, 1009, 1120, 1126, 1269, 1285, 1296, 1300, 1323, 1325, 1328, 1404, 1411, 1413, 1426], "incid": [43, 96, 112, 166, 167, 175, 176, 180, 188, 235, 246, 264, 380, 387, 389, 390, 394, 412, 437, 439, 440, 578, 580, 584, 585, 587, 598, 616, 863, 864, 869, 870, 871, 877, 908, 909, 914, 944, 945, 950, 951, 952, 959, 989, 990, 995, 1061, 1062, 1165, 1187, 1267, 1282, 1327, 1426], "highli": [43, 99, 373, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 791, 1042, 1402, 1407], "closer": [43, 752, 1394, 1414], "particularli": [43, 94, 97, 1269], "strike": 43, "pull": [43, 91, 93, 96, 97, 99, 100, 101, 104, 106, 107, 111, 1408, 1410, 1411, 1412, 1413, 1414, 1416, 1425], "apart": [43, 1117, 1193], "effect": [43, 102, 103, 112, 152, 303, 323, 434, 438, 450, 476, 688, 762, 791, 796, 854, 899, 935, 980, 1037, 1039, 1040, 1177, 1222, 1302, 1404], "c0": 43, "332": 43, "remove_edg": [43, 89, 193, 390, 391, 397, 500, 690, 699, 740, 741, 882, 921, 963, 1003, 1393, 1394, 1426], "334": 43, "335": 43, "336": [43, 441, 445, 446], "337": 43, "338": 43, "339": 43, "213": [43, 47, 1239], "plot_spectral_grid": [43, 47], "christofid": [44, 112, 232, 1413], "calcul": [44, 56, 223, 280, 295, 297, 298, 299, 305, 306, 307, 315, 316, 317, 318, 319, 320, 329, 335, 336, 341, 380, 385, 391, 470, 476, 564, 566, 614, 619, 626, 627, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 658, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 673, 749, 776, 791, 1165, 1199, 1404, 1407, 1412, 1413, 1416], "rout": [44, 49, 55, 79, 85, 86, 112, 1038, 1039, 1040, 1199], "minim": [44, 56, 102, 112, 115, 144, 227, 228, 229, 230, 231, 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535, 555, 671, 672, 673, 674], "messag": [45, 92, 93, 94, 100, 101, 152, 157, 158, 195, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1412, 1413, 1414, 1425], "hold": [45, 88, 100, 151, 159, 166, 175, 188, 190, 196, 198, 200, 208, 227, 239, 240, 241, 242, 243, 244, 247, 252, 266, 297, 298, 303, 306, 307, 311, 315, 316, 323, 324, 325, 326, 329, 330, 352, 355, 356, 380, 381, 383, 384, 385, 491, 593, 647, 687, 688, 689, 738, 796, 853, 858, 863, 869, 877, 879, 885, 886, 888, 898, 903, 908, 924, 939, 944, 950, 959, 966, 967, 969, 984, 989, 1006, 1020, 1037, 1039, 1040, 1102, 1103, 1105, 1108, 1112, 1115, 1117, 1287, 1288, 1393, 1407, 1409, 1426], "call": [45, 55, 58, 63, 93, 94, 97, 101, 102, 112, 114, 132, 141, 164, 168, 184, 189, 206, 212, 230, 231, 244, 249, 338, 341, 346, 347, 394, 410, 412, 414, 416, 417, 418, 419, 426, 450, 452, 453, 464, 470, 491, 492, 494, 498, 499, 502, 503, 506, 507, 509, 510, 517, 525, 530, 535, 540, 545, 555, 584, 586, 588, 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1397, 1398, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1425, 1426], "mail": [45, 92, 93, 94, 99, 100, 103, 104, 106, 1393, 1404, 1405, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "target_nam": 45, "target_addr": 45, "227": 45, "plot_unix_email": [45, 47], "elarg": 46, "esmal": 46, "700": 46, "font_famili": [46, 1133, 1134, 1136], "san": [46, 132, 732, 1133, 1134, 1136, 1239], "serif": [46, 1133, 1134, 1136], "edge_label": [46, 67, 1134], "get_edge_attribut": [46, 1085, 1404], "draw_networkx_edge_label": [46, 67, 1130, 1133, 1135, 1136, 1137, 1413], "080": [46, 47], "plot_weighted_graph": [46, 47], "771": 47, "auto_examples_draw": 47, "javascript": [48, 51, 86, 1359, 1363, 1365, 1399, 1406, 1410, 1413], "igraph": [48, 51, 86, 1413], "json": [49, 58, 1325, 1359, 1361, 1362, 1363, 1364, 1365, 1384, 1399, 1402, 1406, 1407, 1411, 1412], "d3": [49, 1385, 1399, 1406], "need": [49, 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1393, 1399, 1405, 1406, 1413], "primal": [52, 55, 508, 581], "dual": [52, 54, 55, 581, 1227, 1410, 1413], "sens": [52, 97, 99, 104, 199, 310, 460, 586, 791, 887, 925, 968, 1007, 1217, 1234, 1269, 1326, 1403, 1404], "approach": [52, 55, 99, 101, 103, 104, 107, 115, 341, 345, 462, 464, 466, 500, 519, 616, 678, 1094, 1175, 1188, 1202, 1222, 1407, 1413], "segment": [52, 55, 338], "major": [52, 95, 98, 99, 100, 102, 103, 104, 106, 107, 1393, 1394, 1403, 1404, 1407], "studi": [52, 91, 110, 606, 1192, 1196, 1323, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "topologi": [52, 55, 435, 436, 512, 681, 683, 748, 1202, 1217, 1225, 1229, 1233, 1241, 1326], "encod": [52, 55, 58, 67, 99, 141, 249, 267, 268, 619, 758, 775, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1345, 1348, 1349, 1350, 1354, 1355, 1358, 1363, 1368, 1371, 1372, 1375, 1376, 1382, 1406, 1407, 1412], "angular": [52, 55], "inform": [52, 66, 92, 93, 99, 100, 101, 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409, 412, 431, 441, 445, 446, 453, 476, 498, 618, 619, 680, 681, 683, 1203, 1223, 1255], "pydata": [52, 1413, 1423, 1424, 1425], "stack": [52, 111, 346, 693, 1045, 1046], "showcas": [53, 86, 93, 109], "analys": [53, 70, 86, 310], "ecosystem": [53, 86, 99, 100, 104, 107, 110, 1425], "descript": [53, 86, 93, 97, 464, 466, 704, 717, 786, 1130, 1131, 1132, 1133, 1138, 1139, 1140, 1141, 1142, 1207, 1222, 1242, 1407, 1411, 1413, 1421, 1422, 1425], "plu": [54, 386, 583, 1036, 1088, 1148, 1253], "voronoi": [54, 752, 758, 1325, 1407], "cholera": [54, 57], "broad": [54, 57, 1296], "pump": [54, 57], "record": [54, 57, 94, 99, 691, 1426], "john": [54, 57, 91, 278, 568, 572, 685, 1205, 1250, 1408, 1413], "snow": [54, 57], "1853": [54, 57], "method": [54, 57, 58, 75, 88, 92, 93, 95, 101, 102, 103, 107, 112, 143, 161, 164, 165, 185, 186, 187, 190, 200, 202, 204, 206, 207, 226, 231, 232, 250, 260, 261, 262, 299, 301, 302, 303, 308, 309, 311, 312, 323, 324, 336, 374, 376, 379, 380, 381, 385, 423, 440, 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[54, 57], "reli": [54, 57, 99, 103, 362, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 502, 503, 506, 507, 1393, 1407, 1411, 1425], "fiona": [54, 57], "level": [54, 57, 101, 103, 104, 106, 111, 112, 115, 125, 165, 220, 322, 334, 336, 374, 380, 381, 387, 389, 390, 394, 423, 427, 640, 691, 770, 786, 862, 907, 943, 988, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1094, 1108, 1155, 1202, 1207, 1208, 1236, 1296, 1323, 1328, 1396, 1399, 1407, 1412, 1413, 1414], "interfac": [54, 57, 58, 75, 76, 96, 98, 99, 101, 102, 107, 109, 110, 184, 429, 496, 673, 758, 761, 762, 780, 873, 916, 955, 998, 1042, 1044, 1326, 1328, 1393, 1396, 1398, 1402, 1404, 1405, 1406, 1409, 1413, 1414, 1426], "kind": [54, 57, 58, 92, 93, 94, 99, 208, 466, 722, 1202, 1326, 1383], "read_fil": [54, 55, 57, 58], "cholera_cas": [54, 57], "gpkg": [54, 56, 57], "correctli": [54, 164, 324, 325, 1393, 1404, 1406, 1411, 1412, 1419], "construct": [54, 55, 56, 57, 58, 67, 94, 102, 227, 229, 230, 231, 232, 269, 273, 276, 352, 423, 450, 460, 513, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 609, 685, 695, 708, 716, 732, 1046, 1047, 1052, 1053, 1101, 1102, 1103, 1104, 1105, 1153, 1154, 1175, 1177, 1178, 1180, 1186, 1190, 1191, 1192, 1195, 1203, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1229, 1236, 1251, 1259, 1263, 1269, 1272, 1278, 1279, 1296, 1323, 1327, 1395, 1399, 1406, 1409, 1415], "column_stack": [54, 57, 58], "could": [54, 93, 101, 102, 103, 165, 215, 216, 224, 581, 679, 862, 907, 943, 988, 1066, 1094, 1102, 1103, 1120, 1126, 1174, 1296, 1300, 1326, 1393, 1404, 1414, 1426], "present": [54, 58, 93, 107, 110, 132, 184, 220, 226, 315, 316, 330, 357, 359, 429, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 567, 581, 594, 595, 597, 600, 601, 604, 632, 633, 635, 636, 659, 670, 749, 786, 873, 916, 955, 998, 1043, 1045, 1061, 1082, 1150, 1152, 1157, 1159, 1160, 1163, 1165, 1278, 1279, 1353, 1354, 1357, 1381, 1383, 1407, 1411, 1426], "alongsid": [54, 438], "diagram": 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460, 512, 565, 701, 789, 1133, 1135, 1326, 1405, 1413, 1425], "ladder": [60, 72, 86, 1149, 1156], "topological_gener": [61, 67, 758, 1413], "numer": [61, 88, 110, 151, 166, 175, 188, 198, 209, 211, 212, 239, 240, 241, 242, 243, 244, 247, 248, 252, 283, 355, 356, 378, 380, 381, 383, 384, 385, 453, 556, 557, 558, 581, 593, 626, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 658, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 853, 863, 869, 877, 886, 898, 908, 924, 944, 950, 959, 967, 989, 1006, 1100, 1101, 1102, 1103, 1105, 1108, 1115, 1117, 1133, 1135, 1137, 1287, 1288, 1295, 1296, 1326, 1338, 1340, 1358, 1393, 1394, 1399, 1402, 1404, 1406, 1407, 1409, 1413, 1414, 1416, 1419, 1426], "109": [61, 72, 494, 1173], "plot_dag_layout": [61, 72], "668273": 62, "is_graph": [62, 758, 1175, 1181], "configuration_model": [62, 275, 1177, 1178, 1181], "reproducibl": 62, "plot_degree_sequ": [62, 72], "report": [63, 88, 91, 93, 96, 100, 102, 112, 128, 166, 168, 175, 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1398, 1406, 1407, 1410, 1411, 1412, 1413, 1414, 1425, 1426], "statistc": 65, "unpack": [65, 101, 111, 192, 688, 881, 920, 962, 1002, 1393, 1408, 1426], "internet": [65, 84, 92, 93, 210, 320, 435, 436, 1202, 1323, 1411], "person": [65, 92, 93, 94, 97, 238, 565, 566, 688, 1257, 1266, 1407], "umich": 65, "mejn": 65, "netdata": 65, "american": [65, 220, 311, 312, 427, 444, 687, 689], "ia": 65, "colleg": 65, "dure": [65, 74, 93, 97, 99, 152, 157, 158, 195, 329, 345, 346, 347, 494, 525, 535, 555, 614, 640, 671, 672, 673, 674, 703, 704, 717, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1117, 1165, 1412, 1413], "season": 65, "fall": 65, "girvan": [65, 374, 1407], "newman": [65, 110, 214, 215, 216, 220, 236, 241, 244, 248, 284, 301, 302, 308, 309, 311, 312, 324, 325, 326, 374, 383, 385, 625, 1175, 1177, 1222, 1233, 1269, 1287, 1288, 1292, 1382, 1395, 1407, 1409, 1411], "confer": [65, 110, 132, 315, 322, 330, 345, 346, 347, 426, 444, 568, 572, 574, 590, 593, 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1413], "filterwarn": 93, "remind": [93, 94], "misc": [93, 103, 1413, 1416], "generate_unique_nod": [93, 1413], "4281": [93, 1413], "read_yaml": [93, 1405, 1413], "write_yaml": [93, 1405, 1413], "123": [93, 380, 1106], "longer": [93, 94, 99, 102, 103, 107, 215, 216, 511, 512, 579, 1117, 1217, 1275, 1393, 1394, 1396, 1398, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1413, 1416, 1425], "fetch": 93, "unmerg": 93, "modifi": [93, 94, 99, 101, 103, 109, 152, 157, 158, 195, 226, 322, 377, 585, 587, 677, 678, 692, 693, 694, 719, 733, 734, 736, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1045, 1063, 1102, 1103, 1105, 1154, 1177, 1270, 1281, 1295, 1393, 1406, 1413, 1426], "file_with_conflict": 93, "insid": [93, 101, 111, 220, 719, 1253, 1296, 1413], "kept": [93, 106], "delet": [93, 95, 106, 322, 618, 619, 671, 672, 673, 674, 767, 1154, 1300, 1326, 1352, 1354, 1378, 1380, 1393, 1394, 1406, 1407, 1413, 1425], "rest": [93, 107, 184, 214, 410, 414, 873, 916, 955, 998, 1425], "advanc": [93, 103, 574, 592, 618, 673, 758, 796, 1037, 1039, 1040, 1192, 1280, 1290, 1413, 1414], "rebas": [93, 94], "squash": [93, 94], "often": [93, 94, 99, 101, 102, 105, 378, 383, 384, 388, 464, 732, 780, 786, 796, 1037, 1038, 1039, 1040, 1234, 1296, 1326, 1328, 1405, 1425], "typic": [93, 97, 103, 127, 305, 796, 1037, 1039, 1040, 1102, 1103, 1175, 1323, 1413], "propos": [93, 97, 98, 99, 101, 102, 103, 104, 105, 107, 215, 230, 299, 578, 688, 1382, 1412, 1413, 1414, 1422, 1425], "easi": [93, 97, 102, 107, 109, 297, 298, 384, 760, 1326, 1328, 1383, 1412], "demonstr": [93, 100, 310, 1404, 1406], "spread": [93, 301, 302, 308, 309, 329], "sp": [93, 470, 473, 1101, 1387, 1426], "pd": [93, 1099, 1100, 1103, 1412], "stat": [93, 244, 380, 381, 748, 750, 1193, 1197, 1224, 1228, 1232], "optim": [93, 107, 112, 125, 208, 212, 226, 230, 231, 330, 353, 362, 380, 381, 382, 385, 422, 429, 496, 508, 672, 692, 720, 722, 723, 724, 725, 726, 729, 731, 732, 760, 780, 1108, 1117, 1235, 1320, 1321, 1402, 1411, 1412, 1416], "subpackag": [93, 767, 1326, 1413, 1425], "particular": [93, 97, 110, 115, 357, 374, 517, 618, 750, 1175, 1278, 1279, 1328, 1350, 1409], "decor": [93, 102, 103, 1045, 1046, 1047, 1297, 1298, 1299, 1300, 1301, 1325, 1405, 1407, 1411, 1413, 1414, 1417], "not_implemented_for": [93, 1296, 1407, 1417], "doesn": [93, 94, 97, 101, 102, 156, 170, 561, 562, 563, 761, 796, 855, 866, 900, 911, 936, 947, 981, 992, 1037, 1039, 1040, 1117, 1175, 1177, 1179, 1216, 1222, 1296, 1326, 1404, 1406, 1407, 1412, 1414, 1425], "function_not_for_multidigraph": 93, "function_only_for_graph": 93, "framework": [93, 102, 1358], "submodul": [93, 1413], "specif": [93, 96, 99, 101, 107, 110, 111, 157, 184, 232, 346, 347, 370, 458, 502, 503, 506, 507, 517, 681, 683, 703, 856, 873, 901, 916, 937, 947, 955, 982, 992, 998, 1123, 1133, 1135, 1137, 1165, 1193, 1199, 1287, 1288, 1296, 1326, 1343, 1345, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1377, 1378, 1379, 1380, 1381, 1382, 1383, 1398, 1405, 1409, 1412, 1414, 1424, 1425, 1426], "readwrit": [93, 95, 1345, 1347, 1348, 1349, 1350, 1359, 1360, 1365, 1366, 1402, 1406, 1407, 1413], "test_edgelist": 93, "test_parse_edgelist_with_data_list": 93, "doctest": [93, 106, 1407, 1408, 1411, 1412, 1413], "ideal": [93, 1383], "coverag": [93, 97, 109, 386, 1407, 1411, 1412, 1413, 1420, 1424, 1425], "cov": 93, "stmt": 93, "miss": [93, 105, 470, 569, 573, 605, 607, 610, 611, 1155, 1343, 1401, 1406, 1407, 1411, 1412, 1413, 1414, 1416, 1424, 1425], "brpart": 93, "91": [93, 625, 1413], "114": [93, 488, 490, 494, 1406], "cliqu": [93, 209, 210, 211, 224, 234, 339, 340, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 376, 423, 437, 443, 549, 732, 758, 1166, 1167, 1171, 1172, 1174, 1188, 1217, 1276, 1325, 1395, 1399, 1400, 1406, 1408, 1411, 1412, 1413, 1414], "97": [93, 110, 357], "troubl": [93, 224, 1409, 1413], "anywai": [93, 101, 1409], "gather": [93, 99], "assembl": [93, 1046, 1047, 1296], "idea": [93, 94, 97, 99, 102, 105, 132, 217, 373, 423, 428, 687, 689, 1326, 1382, 1404, 1407], "plot_": 93, "plot_new_exampl": 93, "highlight": [93, 106, 1403], "resourc": [93, 96, 476, 477, 478, 572, 573, 618, 1165, 1200], "docstr": [93, 94, 95, 97, 109, 1345, 1348, 1349, 1350, 1399, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1416, 1417, 1420, 1421, 1422, 1423, 1425], "chicago": [93, 1265], "citat": [93, 97, 346, 347, 566, 1239, 1412], "quickest": 93, "scholar": 93, "paywal": 93, "arxiv": [93, 110, 128, 217, 220, 300, 305, 332, 333, 355, 358, 371, 372, 373, 385, 386, 427, 432, 433, 437, 512, 573, 619, 625, 685, 693, 1153, 1169, 1170, 1171, 1185, 1227, 1269, 1280], "access": [93, 101, 112, 125, 151, 168, 189, 429, 471, 472, 473, 474, 475, 496, 606, 626, 627, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 693, 721, 760, 770, 789, 796, 853, 865, 878, 898, 910, 915, 934, 946, 960, 979, 991, 996, 1037, 1038, 1039, 1040, 1135, 1326, 1392, 1393, 1394, 1396, 1398, 1399, 1402, 1406, 1407, 1408, 1410], "cheong": 93, "se": 93, "hang": 93, "yain": 93, "whar": 93, "schemat": 93, "placement": [93, 614], "survei": [93, 110, 564, 566, 581, 786, 1201], "2020": [93, 99, 100, 101, 102, 569, 1406, 1412], "1177": 93, "2f1473871618821740": 93, "upload": [93, 106, 217], "pdf": [93, 105, 110, 112, 128, 214, 215, 216, 217, 220, 235, 305, 311, 312, 315, 322, 324, 325, 330, 342, 355, 356, 373, 410, 411, 412, 413, 414, 415, 417, 426, 427, 430, 442, 447, 448, 476, 483, 490, 494, 511, 512, 519, 564, 566, 567, 570, 571, 573, 618, 619, 690, 693, 748, 749, 750, 760, 762, 1193, 1197, 1198, 1326, 1407, 1412, 1426], "docx": 93, "ppt": 93, "lectur": [93, 110, 412, 431, 498, 616, 1203], "wayback": [93, 1413], "machin": [93, 312, 331, 494, 511, 512, 762, 1396, 1406, 1413], "snapshot": 93, "unreach": 93, "pyarg": [93, 111, 1038], "tell": [93, 99, 102, 760, 1275, 1278, 1279, 1296, 1328, 1412], "compar": [93, 464, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 559, 560, 561, 562, 563, 615, 760, 782, 1165, 1302, 1414], "baselin": [93, 1134, 1136], "ones": [93, 99, 107, 109, 282, 680, 1038, 1395, 1402, 1404], "savefig": [93, 1426], "mpl_image_compar": 93, "test_barbel": 93, "barbel": [93, 293, 294, 391, 424, 1146, 1157, 1276, 1426], "conduct": [93, 96, 100, 109, 447, 448, 758], "contributor": [94, 96, 99, 105, 106, 110, 1271, 1323, 1403], "shepherd": [94, 99], "mission": [94, 96, 97, 100, 107], "approv": [94, 100], "nuclear": 94, "launch": 94, "carefulli": 94, "clean": [94, 106, 530, 540, 1300, 1406, 1407, 1411, 1413, 1420], "nearli": 94, "volunt": [94, 107, 1413], "tremend": 94, "felt": 94, "evalu": [94, 130, 152, 157, 158, 195, 330, 618, 619, 626, 627, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1296, 1417], "novic": 94, "strongli": [94, 217, 232, 388, 391, 397, 398, 399, 403, 405, 406, 423, 480, 491, 492, 519, 588, 633, 697, 699, 751, 753, 1185, 1402, 1406, 1411, 1414, 1417, 1425], "mentorship": 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414, 415, 416, 417, 418, 419, 479, 502, 503, 506, 507, 734, 735, 736, 737, 780, 891, 972, 1038, 1042, 1225, 1241, 1280, 1326, 1426], "wish": [94, 619, 1066, 1393], "bring": [94, 101, 566], "advis": [94, 110, 1414], "aris": [94, 110, 238, 243, 1217, 1245], "experienc": 94, "credit": [94, 105], "send": [94, 99, 496, 497, 501, 504, 505, 508, 1393, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "notif": 94, "maintain": [94, 95, 99, 100, 103, 105, 107, 109, 230, 231, 614, 796, 1037, 1039, 1040, 1406, 1425], "concern": [94, 101, 103, 132, 789, 791, 1382], "mere": [94, 1146, 1157], "understood": 94, "made": [94, 99, 100, 102, 222, 282, 284, 285, 286, 287, 288, 324, 325, 331, 693, 694, 1122, 1210, 1326, 1393, 1403, 1404, 1407, 1412, 1425], "freeli": 94, "consult": [94, 111], "extern": [94, 107, 619, 1326, 1383, 1407], "insight": 94, "opportun": [94, 99], "patch": [94, 99, 102, 1042, 1133, 1135, 1412, 1413], "vouch": 94, "fulli": 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1382, 1417], "past": [94, 106, 1405], "pep8": [94, 1407, 1412, 1416], "pep257": 94, "superset": [94, 582], "stackoverflow": 94, "monitor": [94, 101], "signatur": [95, 97, 103, 109, 545, 1045, 1296, 1399, 1404, 1407, 1413, 1419, 1422, 1425], "buggi": 95, "usual": [95, 101, 168, 176, 189, 291, 292, 329, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 438, 440, 467, 615, 753, 762, 796, 865, 870, 878, 910, 946, 951, 960, 991, 1039, 1040, 1045, 1094, 1174, 1199, 1217, 1272, 1296, 1326, 1403], "minor": [95, 100, 106, 584, 758, 1325, 1394, 1395, 1403, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "strict": [95, 110, 214, 215, 216, 619, 1408, 1413], "rule": [95, 100, 199, 508, 760, 887, 925, 968, 1007, 1061, 1082, 1144, 1298], "procedur": [95, 97, 99, 217, 220, 281, 305, 377, 508, 680, 1188, 1417], "upon": [95, 102, 580, 1296, 1413, 1416], "justif": [95, 104], "literal_string": [95, 1345, 1350, 1384, 1412], "literal_destring": 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1125, 1296, 1405, 1413], "google_matrix": [95, 566, 1414], "futurewarn": [95, 1413, 1414], "attrmatrix": 95, "reflect": [95, 99, 103, 199, 296, 301, 302, 303, 308, 309, 323, 466, 887, 925, 968, 1007, 1061, 1066, 1082, 1085, 1086, 1326, 1406, 1407, 1420], "ndarrai": [95, 107, 565, 629, 1098, 1102, 1278, 1387, 1405, 1414], "distance_measur": [95, 217, 1411], "extrema_bound": [95, 1416], "maxcardin": [95, 581, 583, 1416, 1425], "min_weight_match": [95, 758, 1416, 1425], "scale_free_graph": [95, 1413, 1420], "nx_pydot": [95, 1041, 1042, 1124, 1125, 1126, 1127, 1128, 1396, 1408, 1425, 1426], "5723": [95, 1425], "node_link": [95, 1407, 1422, 1425], "node_link_graph": [95, 1363, 1384], "0rc2": [96, 110, 1325], "dev0": [96, 110, 1325], "dec": [96, 110, 342, 606, 1271, 1323, 1325], "2022": [96, 103, 105, 110, 693, 1325, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424], "about": [96, 99, 100, 101, 103, 111, 115, 230, 231, 249, 413, 423, 488, 494, 498, 499, 509, 510, 619, 761, 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115, 184, 350, 423, 476, 534, 544, 545, 734, 736, 761, 791, 796, 873, 916, 947, 955, 979, 992, 998, 1036, 1037, 1039, 1040, 1088, 1117, 1195, 1272, 1296, 1302, 1326, 1345, 1348, 1349, 1350, 1381, 1382, 1383, 1395, 1403, 1404, 1405, 1406, 1407, 1413, 1414, 1425, 1426], "fit": [97, 110, 1326], "enhanc": [98, 99, 107, 341, 508, 1296, 1412, 1425], "berkelei": [99, 100, 103, 618, 619], "draft": [99, 100, 102, 103, 104, 1411, 1412, 1413, 1416], "stand": [99, 545, 1387], "primari": [99, 103, 1414], "gone": 99, "concis": [99, 110, 791, 1413, 1414], "rational": 99, "consensu": [99, 100], "dissent": 99, "opinion": [99, 100, 104], "revis": [99, 444, 732], "track": [99, 101, 102, 103, 104, 107, 115, 370, 387, 389, 390, 394, 598, 1296, 1302, 1406, 1411, 1412], "codebas": [99, 1296, 1404, 1405, 1412], "meta": [99, 106], "inject": 99, "repo": [99, 106, 1413, 1425], "success": [99, 315, 330, 496, 608, 692, 1180, 1242, 1426], "tend": [99, 593, 1175, 1326], "doubt": [99, 1426], "champion": 99, 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1412], "incorpor": [99, 1399, 1426], "stabil": [99, 334, 335, 681, 683], "provision": 99, "short": [99, 104, 161, 227, 1038, 1066, 1195, 1406], "unlik": [99, 100, 212, 366, 425, 426, 1383], "reject": [99, 100, 104, 1319], "withdrawn": [99, 104], "wherev": [99, 1282], "defer": [99, 101, 104, 280], "challeng": 99, "wider": 99, "done": [99, 101, 102, 230, 231, 249, 373, 440, 466, 517, 564, 566, 614, 690, 762, 1046, 1219, 1296, 1326, 1404], "fact": [99, 352, 460, 619, 1207, 1210, 1404], "actual": [99, 115, 132, 165, 210, 213, 214, 215, 216, 220, 288, 385, 450, 577, 625, 692, 717, 718, 862, 907, 943, 988, 1102, 1103, 1199, 1296, 1324, 1326, 1402, 1416], "compet": [99, 583], "accordingli": [99, 454, 1110, 1407, 1425], "supersed": [99, 104], "render": [99, 216, 410, 413, 1406], "obsolet": [99, 267, 1337, 1406, 1407], "never": [99, 184, 388, 608, 873, 916, 955, 998, 1236], "meant": [99, 291, 292, 631, 1217, 1326, 1413, 1417], "concret": [99, 100], "think": [99, 102, 230, 231, 299, 761, 1426], "bodi": [99, 1243], "briefli": 99, "sentenc": [99, 100], "substant": 99, "pipermail": 99, "2018": [99, 315, 330, 437, 1406, 1408, 1409], "june": [99, 691, 1255, 1398, 1402, 1406, 1419, 1420], "078345": 99, "verg": 99, "chanc": [99, 230, 1234, 1296], "period": [99, 1211, 1212, 1213, 1215, 1297, 1403, 1406, 1412], "beyond": [99, 107, 383, 1210, 1236], "fine": 99, "shouldn": [99, 102], "rigid": 99, "compromis": 99, "followup": [99, 1413], "notifi": [99, 1414], "celebratori": 99, "emoji": 99, "again": [99, 428, 761, 1217, 1403, 1407, 1411, 1416], "unusu": [99, 1393], "disagr": [99, 100], "escal": [99, 100], "controversi": [99, 107], "ultim": 99, "practic": [99, 210, 220, 481, 482, 494, 619, 653, 1328, 1405], "precis": [99, 312, 568, 572, 581, 1269, 1395, 1409], "natur": [99, 102, 109, 376, 443, 466, 585, 587, 618, 753, 1154, 1217, 1225, 1241, 1296, 1326, 1393, 1410], "utf": [99, 267, 268, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1355, 1358, 1368, 1371, 1372, 1375, 1376, 1387, 1406], "restructuredtext": 99, "restructuredtextprim": 99, "dd": [99, 104, 1094], "mmm": 99, "yyyi": [99, 104], "dom": 99, "ain": 99, "separ": [99, 102, 106, 152, 157, 158, 195, 214, 215, 258, 265, 266, 267, 268, 299, 322, 343, 427, 428, 454, 464, 758, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1045, 1112, 1116, 1193, 1195, 1325, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1369, 1370, 1371, 1372, 1395, 1406, 1407, 1412, 1413, 1425, 1426], "older": 99, "brows": 99, "colgat": [100, 110], "deadlock": 100, "websit": [100, 106, 1165, 1382, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "ongo": [100, 1405], "trust": [100, 1381, 1383], "cast": [100, 101, 1412, 1422, 1425], "vote": [100, 337, 1412], "therebi": 100, "adher": 100, "nomin": 100, "lazi": [100, 1283, 1284], "unanim": 100, "agreement": [100, 1202], "initi": [100, 102, 141, 230, 231, 282, 315, 324, 325, 338, 373, 377, 378, 466, 495, 511, 512, 525, 535, 615, 692, 719, 733, 796, 850, 895, 931, 976, 1037, 1039, 1040, 1102, 1105, 1108, 1117, 1185, 1186, 1187, 1188, 1223, 1227, 1234, 1278, 1279, 1296, 1302, 1323, 1394, 1395, 1406, 1411, 1412, 1413, 1414], "voic": 100, "smooth": 100, "strateg": 100, "plan": [100, 1394, 1405, 1407, 1413], "fund": [100, 1414, 1425], "theirs": 100, "pursu": 100, "pictur": 100, "perspect": [100, 104, 1195, 1326], "timefram": 100, "entiti": [100, 1345, 1348, 1349, 1350, 1382, 1426], "occasion": [100, 230], "seek": [100, 762, 1352, 1354, 1378, 1380, 1387], "tri": [100, 112, 343, 380, 931, 976, 1039, 1040, 1175, 1181, 1225, 1237, 1238, 1404], "distinguish": [100, 934, 962, 979, 1002, 1040], "fundament": [100, 107, 110, 338, 449, 618, 619, 1217, 1413], "flaw": 100, "forward": [100, 217, 450, 711, 717, 718], "typo": [100, 1396, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1421], "land": 100, "outlin": [100, 249, 336, 462, 1407], "templat": [100, 1413], "taken": [100, 101, 145, 148, 207, 443, 450, 717, 718, 749, 761, 892, 928, 973, 1010, 1117, 1409], "suffici": [100, 101, 1326], "scikit": [100, 103, 109], "expos": [101, 374, 1405], "nodeview": [101, 184, 391, 598, 599, 601, 602, 603, 604, 695, 873, 916, 955, 998, 1036, 1088, 1349, 1362, 1404, 1407], "nodedataview": [101, 184, 391, 591, 592, 600, 873, 916, 955, 998, 1217, 1426], "edgeview": [101, 590, 591, 592, 598, 599, 600, 601, 602, 603, 604, 612, 624, 770, 910, 1036, 1088, 1098, 1404, 1413], "edgedataview": [101, 168, 189, 865, 878, 910, 946, 960, 991, 1098, 1217, 1362, 1412, 1426], "semant": [101, 531, 541, 762, 1403, 1405], "inher": [101, 220, 427], "impli": [101, 110, 132, 220, 312, 314, 327, 455, 466, 511, 512, 545, 1296], "element": [101, 102, 230, 231, 270, 291, 292, 311, 350, 371, 391, 457, 464, 518, 559, 560, 578, 579, 580, 586, 640, 656, 671, 673, 675, 677, 728, 730, 739, 749, 752, 1036, 1038, 1048, 1049, 1050, 1051, 1087, 1088, 1135, 1137, 1173, 1206, 1211, 1212, 1217, 1237, 1238, 1240, 1249, 1272, 1277, 1278, 1279, 1282, 1287, 1288, 1296, 1302, 1303, 1311, 1318, 1323, 1355, 1358, 1361, 1362, 1405], "intend": [101, 104, 107, 111, 327, 567, 1038, 1269, 1296, 1393], "impos": [101, 103, 545, 791], "due": [101, 102, 109, 231, 264, 440, 581, 583, 626, 627, 1217, 1405, 1412, 1414, 1423, 1425], "bit": [101, 209, 211, 212, 453, 511, 512, 786, 1345, 1348, 1349, 1350, 1382, 1411], "lot": [101, 452, 1326, 1405], "screen": 101, "instinct": 101, "error": [101, 102, 152, 157, 158, 195, 280, 288, 296, 311, 324, 414, 422, 471, 472, 473, 474, 475, 489, 497, 501, 504, 505, 508, 556, 557, 558, 564, 566, 581, 584, 653, 660, 667, 675, 676, 796, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1037, 1043, 1117, 1144, 1396, 1401, 1404, 1406, 1407, 1411, 1412, 1413, 1414, 1417, 1419, 1425], "definit": [101, 132, 235, 238, 243, 289, 291, 292, 303, 323, 342, 356, 398, 435, 437, 464, 467, 549, 550, 551, 608, 618, 619, 620, 625, 676, 685, 687, 700, 735, 737, 791, 1192, 1193, 1197, 1217, 1235, 1287, 1326, 1406, 1413, 1426], "coupl": [101, 102, 132, 1257, 1402, 1404], "realis": 101, "But": [101, 102, 107, 143, 170, 238, 243, 256, 277, 278, 281, 297, 298, 583, 796, 866, 911, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1094, 1328, 1393, 1425], "seem": [101, 102, 298, 307, 791, 1234], "eas": [101, 107, 1409], "idiom": [101, 159, 190, 200, 858, 879, 888, 903, 939, 969, 984, 1296, 1394, 1404, 1411], "subscript": [101, 151, 159, 200, 796, 853, 858, 888, 898, 903, 934, 939, 969, 979, 984, 1037, 1039, 1040, 1394, 1426], "repr": [101, 1347, 1413], "4950": [101, 1414], "traceback": [101, 450, 464, 584, 652, 658, 1302, 1303], "recent": [101, 437, 450, 464, 584, 652, 658, 963, 1003, 1302, 1303, 1411], "typeerror": [101, 382, 464, 1206, 1302, 1404], "opaqu": 101, "ambigu": [101, 103, 115, 252, 253, 464, 762, 1043, 1406], "ambigi": 101, "counter": [101, 153, 357], "nativ": [101, 109], "caveat": 101, "nodes_it": [101, 1404, 1407], "toward": [101, 685, 1407, 1413], "inner": [101, 230, 231, 380, 796, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1086], "synonym": 101, "primarili": [101, 1426], "becam": [101, 1411], "concept": [101, 132, 220, 310, 427, 688, 1043], "intuit": [101, 109], "On": [101, 105, 156, 217, 294, 297, 298, 306, 307, 315, 380, 405, 406, 514, 515, 518, 593, 855, 900, 936, 981, 1180, 1202, 1224, 1228, 1232], "front": [101, 619, 1036, 1088], "constuct": 101, "indx": 101, "desir": [101, 102, 142, 143, 204, 346, 347, 422, 425, 426, 598, 629, 647, 891, 972, 1085, 1094, 1102, 1103, 1105, 1150, 1152, 1157, 1159, 1160, 1163, 1165, 1187, 1218, 1220, 1221, 1234, 1281, 1356, 1357, 1414, 1426], "prelimanari": 101, "impelement": 101, "4086": 101, "rid": [101, 1413], "getitem": 101, "dunder": [101, 107, 1296, 1413], "isinst": [101, 103, 464, 1086, 1411, 1412, 1413], "_node": [101, 1422, 1425], "exclus": [101, 449, 476], "necess": 101, "unhash": [101, 1404], "impel": 101, "insipir": 101, "colon": [101, 1421], "syntax": [101, 102, 171, 796, 867, 912, 948, 993, 1037, 1039, 1040, 1296, 1382, 1383, 1410, 1412], "introspect": 101, "neither": [101, 110, 305, 427, 625, 635, 636, 671, 672, 673, 674, 676, 700, 748], "downsid": 101, "drawback": 101, "discover": 101, "complic": [101, 1296, 1326], "nix": 101, "background": 101, "pertain": 101, "arguabl": [101, 102], "overrid": [101, 671, 672, 673, 674, 1411], "mix": [101, 236, 237, 238, 241, 242, 243, 244, 245, 248, 445, 758, 1100, 1341, 1342, 1344, 1355, 1356, 1357, 1358, 1381, 1383, 1393, 1406, 1407, 1411], "pervas": 101, "unforeseen": 101, "preced": [101, 152, 157, 464, 598, 703, 854, 856, 899, 901, 935, 937, 980, 982, 1045, 1363, 1364], "un": [101, 464, 732, 1407, 1413], "sliceabl": 101, "notabl": [101, 1042], "dict_kei": [101, 1303, 1414], "dict_valu": [101, 379, 1404, 1413], "cpython": [101, 107, 429, 496, 1038, 1402, 1413], "consider": [101, 103, 324, 325, 346, 347, 353, 525, 535, 555, 671, 672, 673, 674, 732, 760, 1168, 1413], "cours": [101, 105, 217, 618, 1326, 1426], "action": [101, 106, 1413, 1417], "allevi": 101, "dig": 101, "enough": [101, 468, 509, 1165], "satisfactorili": 101, "reconsid": [101, 1412], "went": [101, 502], "ahead": 101, "4300": [101, 1413], "4304": [101, 1413], "path_edg": 102, "former": [102, 103, 791], "stylist": 102, "creation": [102, 107, 110, 249, 275, 788, 1154, 1170, 1224, 1228, 1230, 1232, 1325, 1399, 1404, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "cleaner": [102, 1401, 1406], "creativ": [102, 464, 466], "demand": [102, 496, 497, 501, 504, 505, 508], "had": [102, 652, 1217, 1296, 1409, 1416], "node_iter": 102, "isn": [102, 346, 347, 720, 1331, 1334, 1406, 1414, 1425], "leav": [102, 231, 388, 500, 508, 584, 585, 586, 587, 678, 1145, 1155, 1296, 1404, 1409, 1426], "dg": [102, 207, 322, 455, 456, 457, 458, 459, 461, 462, 464, 465, 466, 467, 468, 469, 892, 928, 973, 1010, 1041, 1404, 1426], "mdg": [102, 207, 892, 928, 973, 1010, 1420], "customgraph": 102, "elist": [102, 1326], "isol": [102, 355, 380, 435, 491, 492, 522, 524, 621, 735, 737, 758, 1218, 1325, 1330, 1398, 1401, 1406, 1407, 1417], "ekei": [102, 207, 892, 928, 934, 973, 979, 1010, 1084, 1104], "protocol": [102, 1404], "hashabl": [102, 144, 151, 156, 171, 180, 267, 545, 546, 547, 548, 761, 796, 853, 855, 867, 871, 898, 900, 912, 914, 934, 936, 947, 948, 952, 962, 979, 981, 992, 993, 995, 1002, 1037, 1038, 1039, 1040, 1087, 1207, 1278, 1279, 1295, 1310, 1324, 1326, 1333, 1337, 1338, 1426], "logic": [102, 103, 220, 760, 762, 1298, 1406, 1407, 1419, 1425], "denot": [102, 114, 212, 219, 299, 300, 322, 567, 568, 569, 570, 571, 572, 573, 608, 619, 687, 688, 689, 690, 691, 1174], "multiedg": [102, 553, 934, 979, 1039, 1040, 1085, 1326, 1356, 1357, 1393, 1406, 1412, 1414], "attrdict": [102, 157, 856, 901, 937, 982, 1406], "edge_kei": [102, 489, 1039, 1040, 1100, 1104, 1413], "networkxinvalidedgelist": 102, "flexibl": [102, 110, 467, 1326, 1382, 1383, 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1333, 1334, 1337, 1338, 1339, 1340, 1371, 1372, 1426], "bipart": [115, 290], "routin": [116, 180, 343, 355, 559, 560, 577, 760, 871, 914, 952, 995, 1042, 1091, 1326, 1395, 1396, 1404, 1406, 1411, 1412, 1413], "outsid": [116, 310, 1404, 1406, 1413], "chord": [120, 341, 343, 1190, 1208, 1215], "chordal_graph": [120, 341], "clique_problem": 121, "character": [122, 313, 782], "triangl": [122, 213, 227, 295, 356, 357, 358, 359, 437, 549, 550, 758, 1098, 1101, 1215, 1219, 1222, 1234, 1243, 1247, 1252, 1263, 1323, 1326, 1406, 1412], "greedy_color": [123, 758, 1395, 1406, 1411], "communities_gener": 125, "girvan_newman": 125, "top_level_commun": 125, "next_level_commun": 125, "kernighan": [125, 377, 1413], "lin": [125, 377, 1407, 1413], "luke": [125, 382, 1412], "asynchron": [125, 373, 378, 379, 1407, 1414], "edge_kcompon": [127, 424], "determen": 127, "maxim": [127, 209, 220, 221, 222, 315, 316, 330, 339, 346, 347, 348, 349, 350, 351, 353, 354, 366, 370, 380, 383, 384, 389, 390, 422, 425, 426, 427, 432, 433, 437, 517, 579, 581, 582, 583, 589, 682, 691, 732, 758, 1043, 1201, 1323, 1325, 1398, 1406, 1407, 1413, 1414], "moodi": [127, 220, 427, 1395], "kanevski": [127, 427, 428, 1395], "recurs": [128, 141, 224, 346, 347, 352, 387, 389, 390, 394, 406, 452, 460, 530, 540, 697, 728, 730, 760, 1045, 1046, 1061, 1082, 1147, 1296, 1406, 1412, 1413], "prune": [128, 760, 1236], "vladimir": [128, 275, 432, 433, 494, 588, 749, 1230], "batagelj": [128, 275, 432, 433, 588, 749, 1230], "matjaz": [128, 432, 433], "zaversnik": [128, 432, 433], "0310049": [128, 432, 433], "0202039": 128, "degeneraci": 128, "christo": 128, "giatsidi": 128, "thiliko": 128, "michali": 128, "vazirgianni": 128, "icdm": 128, "2011": [128, 331, 377, 383, 385, 441, 445, 446, 511, 512, 519, 619, 682, 1179, 1397, 1398, 1399, 1406, 1407], "graphdegeneraci": 128, "dcores_icdm_2011": 128, "anomali": [128, 438], "onion": [128, 438, 1411], "h\u00e9bert": [128, 438], "dufresn": [128, 438], "grochow": [128, 438], "allard": [128, 438, 1411], "31708": [128, 438], "2016": [128, 337, 352, 385, 438, 476, 690, 1197, 1251, 1396, 1406], "1038": [128, 337, 376, 380, 438, 569], "srep31708": [128, 438], "factor": [132, 226, 293, 294, 299, 300, 324, 325, 370, 462, 497, 501, 504, 505, 508, 513, 565, 592, 624, 676, 697, 1106, 1107, 1108, 1109, 1110, 1114, 1115, 1116, 1117, 1145, 1155, 1178, 1180, 1275, 1276, 1277], "graphic": [132, 454, 517, 518, 693, 758, 1175, 1177, 1180, 1181, 1222, 1325, 1383, 1398, 1401, 1406], "overview": [132, 476, 1038, 1296], "collid": [132, 454], "triplet": [132, 745], "successor": [132, 159, 174, 181, 191, 200, 240, 282, 387, 389, 390, 394, 501, 687, 707, 715, 858, 872, 880, 888, 903, 939, 953, 961, 969, 984, 1055, 1183, 1184, 1189, 1326, 1404, 1407, 1416, 1426], "descend": [132, 454, 456, 465, 709, 758, 1272, 1401, 1404, 1406, 1413, 1414], "unblock": 132, "commonli": [132, 280, 454, 684, 782], "probabilist": [132, 378], "causal": 132, "markov": [132, 462, 565, 692, 1188], "hmm": 132, "s1": [132, 1242, 1313, 1363], "s2": [132, 1242, 1313], "s3": [132, 1313], "s4": 132, "s5": 132, "o1": 132, "o2": 132, "o3": 132, "o4": 132, "o5": 132, "ob": 132, "d_separ": [132, 758, 1412], "darwich": 132, "shachter": 132, "1998": [132, 1143, 1144, 1225, 1241, 1407], "bay": 132, "ball": 132, "ration": 132, "pastim": 132, "irrelev": [132, 1407], "requisit": 132, "influenc": [132, 324, 325, 512, 786], "fourteenth": [132, 1186], "uncertainti": [132, 590, 732], "artifici": [132, 574, 590, 732], "480": [132, 426, 514, 518, 1398, 1406], "487": 132, "francisco": [132, 732], "morgan": [132, 732], "kaufmann": [132, 732], "koller": 132, "friedman": 132, "mit": [132, 342, 519, 618], "causal_markov_condit": 132, "ness": [133, 684, 782], "classmethod": [141, 1047], "auxiliari": [141, 142, 143, 220, 411, 412, 413, 415, 416, 417, 418, 419, 423, 430, 431, 1402], "sink": [141, 302, 309, 416, 418, 494, 495, 498, 499, 501, 502, 503, 506, 507, 509, 510, 565], "pick": [141, 217, 331, 657, 1188, 1207, 1210, 1407], "st": [141, 415, 417], "cut": [141, 222, 223, 293, 377, 382, 387, 389, 390, 394, 411, 412, 414, 415, 416, 417, 419, 427, 428, 429, 442, 443, 444, 445, 447, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 619, 758, 760, 1038, 1066, 1115, 1262, 1325, 1395, 1402, 1406, 1413], "refin": [143, 215, 423, 438], "auxgraph": [143, 423], "node_partit": 144, "permut": [144, 368, 452, 453, 455, 466, 748, 1285, 1320, 1321], "containin": 144, "frozenset": [144, 267, 339, 383, 586, 588, 752, 1165, 1333, 1337, 1338, 1412], "abc": [144, 545, 1154, 1206, 1303, 1412, 1413], "interchang": [144, 362], "bool": [145, 146, 148, 149, 165, 168, 171, 176, 184, 189, 196, 204, 208, 232, 237, 238, 242, 243, 245, 249, 250, 258, 265, 266, 267, 268, 272, 275, 286, 287, 288, 291, 294, 295, 296, 297, 298, 299, 301, 302, 305, 306, 307, 308, 309, 310, 314, 315, 322, 324, 325, 326, 327, 330, 343, 350, 355, 362, 393, 394, 395, 396, 397, 398, 439, 454, 462, 463, 467, 479, 480, 488, 489, 491, 494, 498, 499, 509, 510, 513, 514, 515, 516, 517, 518, 520, 521, 522, 545, 562, 564, 578, 579, 580, 581, 588, 613, 614, 616, 617, 622, 623, 625, 640, 652, 663, 673, 679, 685, 690, 696, 698, 699, 700, 704, 708, 719, 723, 724, 725, 726, 728, 730, 733, 734, 735, 736, 737, 738, 740, 741, 742, 743, 862, 865, 867, 870, 873, 878, 885, 891, 907, 910, 912, 916, 927, 931, 943, 946, 948, 951, 955, 960, 966, 972, 976, 988, 991, 993, 998, 1039, 1040, 1045, 1057, 1068, 1070, 1071, 1072, 1084, 1091, 1097, 1116, 1133, 1134, 1135, 1136, 1169, 1179, 1185, 1189, 1209, 1211, 1212, 1213, 1215, 1224, 1228, 1230, 1231, 1232, 1275, 1276, 1277, 1278, 1279, 1282, 1295, 1296, 1307, 1309, 1312, 1335, 1336, 1337, 1339, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1360, 1364, 1379, 1380], "account": [145, 148, 398, 448, 749, 761, 1270, 1393, 1413], "graph_nod": [145, 148], "subgraph_nod": [145, 148], "find_isomorph": [147, 150], "induc": [148, 167, 199, 211, 226, 342, 388, 392, 406, 427, 436, 437, 470, 487, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 512, 586, 589, 752, 761, 762, 864, 887, 909, 925, 945, 968, 990, 1007, 1038, 1061, 1066, 1087, 1102, 1103, 1105, 1189, 1283, 1284, 1393], "u_of_edg": [151, 853, 898], "v_of_edg": [151, 853, 898], "capac": [151, 265, 296, 301, 302, 303, 308, 309, 323, 411, 412, 415, 416, 417, 418, 419, 430, 431, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 758, 853, 898, 934, 979, 1335, 1402], "342": [151, 853, 898, 934, 979, 1255], "ebunch_to_add": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "add_weighted_edges_from": [152, 229, 230, 231, 508, 581, 630, 657, 659, 721, 854, 899, 935, 980, 1070, 1326, 1404, 1407, 1426], "runtimeerror": [152, 157, 158, 195, 464, 465, 466, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005], "happen": [152, 157, 158, 195, 380, 584, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1403, 1404, 1425], "iterator_of_edg": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "wn2898": [152, 854, 899, 935, 980], "wrong": [152, 157, 158, 722, 854, 856, 857, 899, 901, 902, 935, 937, 938, 980, 982, 983, 1406, 1411, 1416, 1425], "start_nod": [153, 154, 155], "end_nod": [153, 154, 155], "reference_neighbor": [153, 154], "half": [153, 154, 155, 164, 177, 183, 206, 297, 298, 615, 653], "clockwis": [153, 154, 169, 182, 197, 615], "networkxexcept": [153, 154, 161, 331, 588, 593, 724, 726, 1043, 1110, 1138, 1180, 1325], "add_half_edge_cw": [153, 155, 164, 615], "connect_compon": [153, 154, 155, 615], "add_half_edge_first": [153, 154, 164, 615], "add_half_edge_ccw": [154, 155, 164, 615], "node_for_ad": [156, 855, 900, 936, 981], "mutabl": [156, 855, 900, 936, 981, 1061, 1066, 1082, 1085, 1086], "hash": [156, 511, 512, 758, 855, 900, 936, 981, 1324, 1325, 1414, 1426], "hello": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1303], "k3": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1217], "utm": [156, 855, 900, 936, 981], "382871": [156, 855, 900, 936, 981], "3972649": [156, 855, 900, 936, 981], "nodes_for_ad": [157, 856, 901, 937, 982], "iterator_of_nod": [157, 195, 856, 884, 901, 923, 937, 965, 982, 1005], "datadict": [159, 190, 200, 207, 734, 736, 858, 879, 888, 892, 903, 928, 939, 969, 973, 1010, 1084, 1312, 1326], "foovalu": [159, 190, 200, 858, 879, 888, 903, 939, 969], "nbrdict": [160, 859, 904, 940, 985, 1019, 1094], "fulfil": [161, 615], "cw": [161, 615], "ccw": [161, 615], "planar": [161, 614, 616, 617, 758, 1110, 1138, 1243, 1246, 1247, 1249, 1325, 1409, 1410], "first_nbr": [161, 615], "invalid": [161, 615, 1413], "alter": [163, 861, 906, 942, 987], "afterward": 164, "as_view": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1089, 1090], "shallow": [165, 202, 204, 284, 285, 286, 287, 288, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1394], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1409], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1404, 1407, 1409, 1410, 1411], "fresh": [165, 862, 907, 943, 988, 1404], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1226, 1323, 1404], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1265, 1394], "degreeview": [166, 863, 908, 944, 950, 989, 1404, 1426], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1404, 1418], "ddict": [168, 176, 184, 189, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998], "in_edg": [168, 189, 865, 878, 946, 960, 1404, 1406, 1407], "out_edg": [168, 865, 946, 1062, 1404, 1406, 1407, 1426], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1426], "outedgedataview": [168, 189, 865, 878, 1404, 1411], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1404, 1412], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1406, 1407, 1417], "indegreeview": [175, 869, 1404], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1165, 1179, 1222, 1404], "inedgeview": [176, 870, 1404], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1127, 1353, 1354, 1359, 1363, 1406, 1413], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1414], "__iter__": [180, 871, 914, 952, 995, 1303], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1394, 1426], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1216], "nedg": [185, 588, 874, 917, 956, 999], "__len__": [186, 187, 875, 876, 918, 919, 957, 958, 1000, 1001], "outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 882, 921, 963, 1003, 1404, 1414], "get_data": [197, 616], "inplac": [199, 690, 887, 925, 968, 1007, 1066, 1393], "reduct": [199, 469, 618, 786, 887, 925, 968, 1007, 1066, 1320, 1321, 1413, 1414], "sg": [199, 887, 925, 968, 1007], "largest_wcc": [199, 887, 925, 968, 1007], "is_multigraph": [199, 758, 887, 925, 968, 1007, 1154, 1412], "keydict": [199, 207, 887, 892, 925, 928, 968, 973, 1007, 1010, 1039, 1040], "contrast": [202, 204, 301, 302, 308, 309, 890, 891, 926, 927, 971, 972, 1008, 1009, 1066, 1233, 1241, 1426], "reciproc": [204, 299, 320, 322, 356, 411, 430, 447, 476, 620, 758, 891, 972, 1325, 1416, 1425], "mark_half_edg": 206, "li": [206, 619, 670, 675, 685, 775, 1207, 1210, 1425], "straightforward": [207, 892, 928, 973, 1010], "slightli": [207, 326, 437, 520, 521, 581, 892, 928, 973, 1010, 1165, 1326, 1404, 1407, 1412, 1414, 1425], "singleton": [207, 588, 892, 928, 973, 1010, 1218, 1251, 1407], "preserve_attr": [208, 723, 724, 725, 726], "optimum": [208, 231, 583, 720, 722, 791, 1395, 1406], "arboresc": [208, 460, 719, 720, 722, 724, 726, 740, 743, 758, 1272, 1395, 1406], "span": [208, 226, 227, 228, 295, 508, 618, 619, 624, 719, 720, 722, 724, 726, 732, 733, 734, 735, 736, 737, 738, 758, 1394, 1397, 1406, 1407, 1420], "max_ind_cliqu": 209, "networkxnotimpl": [209, 210, 211, 212, 220, 224, 227, 293, 294, 295, 318, 319, 321, 328, 343, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 403, 404, 405, 406, 407, 422, 424, 425, 426, 427, 429, 455, 457, 458, 459, 460, 468, 481, 482, 500, 589, 590, 608, 680, 732, 1043, 1216, 1275, 1276, 1298, 1325, 1353, 1354, 1379, 1407, 1408], "boppana": [209, 211, 212], "halld\u00f3rsson": [209, 211, 212], "1992": [209, 211, 212, 517, 518, 1407], "exclud": [209, 211, 212, 215, 216, 261, 262, 453, 688, 719, 723, 724, 725, 726, 733, 751, 1036, 1038, 1088, 1217, 1412], "196": [209, 211, 212], "heurist": [210, 220, 228, 233, 234, 377, 380, 381, 427, 494, 509, 626, 627, 652, 663, 703, 758, 1173, 1320, 1321, 1325, 1395, 1408, 1412, 1413], "max_cliqu": 210, "rigor": 210, "pattabiraman": 210, "bharath": 210, 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How do I find it in the source code?": [[97, "q-i-want-to-work-on-a-specific-function-how-do-i-find-it-in-the-source-code"]], "Q: What is the policy for deciding whether to include a new algorithm?": [[97, "q-what-is-the-policy-for-deciding-whether-to-include-a-new-algorithm"]], "NXEPs": [[98, "nxeps"], [1413, "nxeps"]], "NXEP 0 \u2014 Purpose and Process": [[99, "nxep-0-purpose-and-process"]], "What is a NXEP?": [[99, "what-is-a-nxep"]], "Types": [[99, "types"]], "NXEP Workflow": [[99, "nxep-workflow"]], "Review and Resolution": [[99, "review-and-resolution"]], "How a NXEP becomes Accepted": [[99, "how-a-nxep-becomes-accepted"]], "Maintenance": [[99, "maintenance"]], "Format and Template": [[99, "format-and-template"]], "Header Preamble": [[99, "header-preamble"]], "References and Footnotes": [[99, "references-and-footnotes"]], "NXEP 1 \u2014 Governance and Decision Making": [[100, "nxep-1-governance-and-decision-making"]], "Abstract": [[100, "abstract"], [101, "abstract"], [102, 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"difference": [[599, "difference"]], "disjoint_union": [[600, "disjoint-union"]], "full_join": [[601, "full-join"]], "intersection": [[602, "intersection"]], "symmetric_difference": [[603, "symmetric-difference"]], "union": [[604, "union"]], "cartesian_product": [[605, "cartesian-product"]], "corona_product": [[606, "corona-product"]], "lexicographic_product": [[607, "lexicographic-product"]], "power": [[608, "power"]], "rooted_product": [[609, "rooted-product"]], "strong_product": [[610, "strong-product"]], "tensor_product": [[611, "tensor-product"]], "complement": [[612, "complement"]], "reverse": [[613, "reverse"]], "combinatorial_embedding_to_pos": [[614, "combinatorial-embedding-to-pos"]], "networkx.algorithms.planarity.PlanarEmbedding": [[615, "networkx-algorithms-planarity-planarembedding"]], "check_planarity": [[616, "check-planarity"]], "is_planar": [[617, "is-planar"]], "chromatic_polynomial": [[618, "chromatic-polynomial"]], "tutte_polynomial": [[619, "tutte-polynomial"]], "overall_reciprocity": [[620, "overall-reciprocity"]], "reciprocity": [[621, "reciprocity"]], "is_k_regular": [[622, "is-k-regular"]], "is_regular": [[623, "is-regular"]], "k_factor": [[624, "k-factor"]], "rich_club_coefficient": [[625, "rich-club-coefficient"]], "astar_path": [[626, "astar-path"]], "astar_path_length": [[627, "astar-path-length"]], "floyd_warshall": [[628, "floyd-warshall"]], "floyd_warshall_numpy": [[629, "floyd-warshall-numpy"]], "floyd_warshall_predecessor_and_distance": [[630, "floyd-warshall-predecessor-and-distance"]], "reconstruct_path": [[631, "reconstruct-path"]], "all_shortest_paths": [[632, "all-shortest-paths"]], "average_shortest_path_length": [[633, "average-shortest-path-length"]], "has_path": [[634, "has-path"]], "shortest_path": [[635, "shortest-path"]], "shortest_path_length": [[636, "shortest-path-length"]], "all_pairs_shortest_path": [[637, "all-pairs-shortest-path"]], "all_pairs_shortest_path_length": [[638, "all-pairs-shortest-path-length"]], "bidirectional_shortest_path": [[639, "bidirectional-shortest-path"]], "predecessor": [[640, "predecessor"]], "single_source_shortest_path": [[641, "single-source-shortest-path"]], "single_source_shortest_path_length": [[642, "single-source-shortest-path-length"]], "single_target_shortest_path": [[643, "single-target-shortest-path"]], "single_target_shortest_path_length": [[644, "single-target-shortest-path-length"]], "all_pairs_bellman_ford_path": [[645, "all-pairs-bellman-ford-path"]], "all_pairs_bellman_ford_path_length": [[646, "all-pairs-bellman-ford-path-length"]], "all_pairs_dijkstra": [[647, "all-pairs-dijkstra"]], "all_pairs_dijkstra_path": [[648, "all-pairs-dijkstra-path"]], "all_pairs_dijkstra_path_length": [[649, "all-pairs-dijkstra-path-length"]], "bellman_ford_path": [[650, "bellman-ford-path"]], "bellman_ford_path_length": [[651, "bellman-ford-path-length"]], "bellman_ford_predecessor_and_distance": [[652, "bellman-ford-predecessor-and-distance"]], "bidirectional_dijkstra": [[653, "bidirectional-dijkstra"]], "dijkstra_path": [[654, "dijkstra-path"]], "dijkstra_path_length": [[655, "dijkstra-path-length"]], "dijkstra_predecessor_and_distance": [[656, "dijkstra-predecessor-and-distance"]], "find_negative_cycle": [[657, "find-negative-cycle"]], "goldberg_radzik": [[658, "goldberg-radzik"]], "johnson": [[659, "johnson"]], "multi_source_dijkstra": [[660, "multi-source-dijkstra"]], "multi_source_dijkstra_path": [[661, "multi-source-dijkstra-path"]], "multi_source_dijkstra_path_length": [[662, "multi-source-dijkstra-path-length"]], "negative_edge_cycle": [[663, "negative-edge-cycle"]], "single_source_bellman_ford": [[664, "single-source-bellman-ford"]], "single_source_bellman_ford_path": [[665, "single-source-bellman-ford-path"]], "single_source_bellman_ford_path_length": [[666, "single-source-bellman-ford-path-length"]], "single_source_dijkstra": [[667, "single-source-dijkstra"]], "single_source_dijkstra_path": [[668, "single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[669, "single-source-dijkstra-path-length"]], "generate_random_paths": [[670, "generate-random-paths"]], "graph_edit_distance": [[671, "graph-edit-distance"]], "optimal_edit_paths": [[672, "optimal-edit-paths"]], "optimize_edit_paths": [[673, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[674, "optimize-graph-edit-distance"]], "panther_similarity": [[675, "panther-similarity"]], "simrank_similarity": [[676, "simrank-similarity"]], "all_simple_edge_paths": [[677, "all-simple-edge-paths"]], "all_simple_paths": [[678, "all-simple-paths"]], "is_simple_path": [[679, "is-simple-path"]], "shortest_simple_paths": [[680, "shortest-simple-paths"]], "lattice_reference": [[681, "lattice-reference"]], "omega": [[682, "omega"]], "random_reference": [[683, "random-reference"]], "sigma": [[684, "sigma"]], "s_metric": [[685, "s-metric"]], "spanner": [[686, "spanner"]], "constraint": [[687, "constraint"]], "effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "Recognition Tests": [[791, "recognition-tests"]], "Branchings and Spanning Arborescences": [[791, "module-networkx.algorithms.tree.branchings"]], "Encoding and decoding": [[791, "module-networkx.algorithms.tree.coding"]], "Operations": [[791, "module-networkx.algorithms.tree.operations"]], "Spanning Trees": [[791, "module-networkx.algorithms.tree.mst"]], "Exceptions": [[791, "exceptions"], [1043, "module-networkx.exception"]], "Vitality": [[793, "module-networkx.algorithms.vitality"]], "Voronoi cells": [[794, "module-networkx.algorithms.voronoi"]], "Wiener index": [[795, "module-networkx.algorithms.wiener"]], "DiGraph\u2014Directed graphs with self loops": [[796, "digraph-directed-graphs-with-self-loops"]], "Overview": [[796, "overview"], [1037, "overview"], [1039, "overview"], [1040, "overview"]], "Methods": [[796, "methods"], [1037, "methods"], [1039, "methods"], [1040, "methods"]], "Adding and removing nodes and edges": [[796, "adding-and-removing-nodes-and-edges"], [1037, "adding-and-removing-nodes-and-edges"], [1040, "adding-and-removing-nodes-and-edges"]], "Reporting nodes edges and neighbors": [[796, "reporting-nodes-edges-and-neighbors"], [1037, "reporting-nodes-edges-and-neighbors"], [1039, "reporting-nodes-edges-and-neighbors"], [1040, "reporting-nodes-edges-and-neighbors"]], "Counting nodes edges and neighbors": [[796, "counting-nodes-edges-and-neighbors"], [1037, "counting-nodes-edges-and-neighbors"], [1039, "counting-nodes-edges-and-neighbors"], [1040, "counting-nodes-edges-and-neighbors"]], "Making copies and subgraphs": [[796, "making-copies-and-subgraphs"], [1037, "making-copies-and-subgraphs"], [1039, "making-copies-and-subgraphs"], [1040, "making-copies-and-subgraphs"]], "AdjacencyView.copy": [[797, "adjacencyview-copy"]], "AdjacencyView.get": [[798, "adjacencyview-get"]], "AdjacencyView.items": [[799, "adjacencyview-items"]], "AdjacencyView.keys": [[800, "adjacencyview-keys"]], "AdjacencyView.values": [[801, "adjacencyview-values"]], "AtlasView.copy": [[802, "atlasview-copy"]], "AtlasView.get": [[803, "atlasview-get"]], "AtlasView.items": [[804, "atlasview-items"]], "AtlasView.keys": [[805, "atlasview-keys"]], "AtlasView.values": [[806, "atlasview-values"]], "FilterAdjacency.get": [[807, "filteradjacency-get"]], "FilterAdjacency.items": [[808, "filteradjacency-items"]], "FilterAdjacency.keys": [[809, "filteradjacency-keys"]], "FilterAdjacency.values": [[810, "filteradjacency-values"]], "FilterAtlas.get": [[811, "filteratlas-get"]], "FilterAtlas.items": [[812, "filteratlas-items"]], "FilterAtlas.keys": [[813, "filteratlas-keys"]], "FilterAtlas.values": [[814, "filteratlas-values"]], "FilterMultiAdjacency.get": [[815, "filtermultiadjacency-get"]], "FilterMultiAdjacency.items": [[816, "filtermultiadjacency-items"]], "FilterMultiAdjacency.keys": [[817, "filtermultiadjacency-keys"]], "FilterMultiAdjacency.values": [[818, "filtermultiadjacency-values"]], "FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": [[836, "unionatlas-keys"]], "UnionAtlas.values": [[837, "unionatlas-values"]], "UnionMultiAdjacency.copy": [[838, "unionmultiadjacency-copy"]], "UnionMultiAdjacency.get": [[839, "unionmultiadjacency-get"]], "UnionMultiAdjacency.items": [[840, "unionmultiadjacency-items"]], "UnionMultiAdjacency.keys": [[841, "unionmultiadjacency-keys"]], "UnionMultiAdjacency.values": [[842, "unionmultiadjacency-values"]], "UnionMultiInner.copy": [[843, "unionmultiinner-copy"]], "UnionMultiInner.get": [[844, "unionmultiinner-get"]], "UnionMultiInner.items": [[845, "unionmultiinner-items"]], "UnionMultiInner.keys": [[846, "unionmultiinner-keys"]], "UnionMultiInner.values": [[847, "unionmultiinner-values"]], "DiGraph.__contains__": [[848, "digraph-contains"]], "DiGraph.__getitem__": [[849, "digraph-getitem"]], "DiGraph.__init__": [[850, "digraph-init"]], "DiGraph.__iter__": [[851, "digraph-iter"]], "DiGraph.__len__": [[852, "digraph-len"]], "DiGraph.add_edge": [[853, "digraph-add-edge"]], "DiGraph.add_edges_from": [[854, "digraph-add-edges-from"]], "DiGraph.add_node": [[855, "digraph-add-node"]], "DiGraph.add_nodes_from": [[856, "digraph-add-nodes-from"]], "DiGraph.add_weighted_edges_from": [[857, "digraph-add-weighted-edges-from"]], "DiGraph.adj": [[858, "digraph-adj"]], "DiGraph.adjacency": [[859, "digraph-adjacency"]], "DiGraph.clear": [[860, "digraph-clear"]], "DiGraph.clear_edges": [[861, "digraph-clear-edges"]], "DiGraph.copy": [[862, "digraph-copy"]], "DiGraph.degree": [[863, "digraph-degree"]], "DiGraph.edge_subgraph": [[864, "digraph-edge-subgraph"]], "DiGraph.edges": [[865, "digraph-edges"]], "DiGraph.get_edge_data": [[866, "digraph-get-edge-data"]], "DiGraph.has_edge": [[867, "digraph-has-edge"]], "DiGraph.has_node": [[868, "digraph-has-node"]], "DiGraph.in_degree": [[869, "digraph-in-degree"]], "DiGraph.in_edges": [[870, "digraph-in-edges"]], "DiGraph.nbunch_iter": [[871, "digraph-nbunch-iter"]], "DiGraph.neighbors": [[872, "digraph-neighbors"]], "DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, "digraph-to-undirected"]], "DiGraph.update": [[892, "digraph-update"]], "Graph.__contains__": [[893, "graph-contains"]], "Graph.__getitem__": [[894, "graph-getitem"]], "Graph.__init__": [[895, "graph-init"]], "Graph.__iter__": [[896, "graph-iter"]], "Graph.__len__": [[897, "graph-len"]], "Graph.add_edge": [[898, "graph-add-edge"]], "Graph.add_edges_from": [[899, "graph-add-edges-from"]], "Graph.add_node": [[900, "graph-add-node"]], "Graph.add_nodes_from": [[901, "graph-add-nodes-from"]], "Graph.add_weighted_edges_from": [[902, "graph-add-weighted-edges-from"]], "Graph.adj": [[903, "graph-adj"]], "Graph.adjacency": [[904, "graph-adjacency"]], "Graph.clear": [[905, "graph-clear"]], "Graph.clear_edges": [[906, "graph-clear-edges"]], "Graph.copy": [[907, "graph-copy"]], "Graph.degree": [[908, "graph-degree"]], "Graph.edge_subgraph": [[909, "graph-edge-subgraph"]], "Graph.edges": [[910, "graph-edges"]], "Graph.get_edge_data": [[911, "graph-get-edge-data"]], "Graph.has_edge": [[912, "graph-has-edge"]], "Graph.has_node": [[913, "graph-has-node"]], "Graph.nbunch_iter": [[914, "graph-nbunch-iter"]], "Graph.neighbors": [[915, "graph-neighbors"]], "Graph.nodes": [[916, "graph-nodes"]], "Graph.number_of_edges": [[917, "graph-number-of-edges"]], "Graph.number_of_nodes": [[918, "graph-number-of-nodes"]], "Graph.order": [[919, "graph-order"]], "Graph.remove_edge": [[920, "graph-remove-edge"]], "Graph.remove_edges_from": [[921, "graph-remove-edges-from"]], "Graph.remove_node": [[922, "graph-remove-node"]], "Graph.remove_nodes_from": [[923, "graph-remove-nodes-from"]], "Graph.size": [[924, "graph-size"]], "Graph.subgraph": [[925, "graph-subgraph"]], "Graph.to_directed": [[926, "graph-to-directed"]], "Graph.to_undirected": [[927, "graph-to-undirected"]], "Graph.update": [[928, "graph-update"]], "MultiDiGraph.__contains__": [[929, "multidigraph-contains"]], "MultiDiGraph.__getitem__": [[930, "multidigraph-getitem"]], "MultiDiGraph.__init__": [[931, "multidigraph-init"]], 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"removed-functionalities"]], "Miscellaneous changes": [[1395, "miscellaneous-changes"], [1396, "miscellaneous-changes"], [1402, "miscellaneous-changes"]], "NetworkX 1.11": [[1396, "networkx-1-11"], [1406, "networkx-1-11"]], "NetworkX 1.4": [[1397, "networkx-1-4"], [1406, "networkx-1-4"]], "Algorithms changed": [[1397, "algorithms-changed"]], "Shortest path": [[1397, "shortest-path"]], "astar_path(), astar_path_length(), shortest_path(), shortest_path_length(),": [[1397, "astar-path-astar-path-length-shortest-path-shortest-path-length"]], "bidirectional_shortest_path(), dijkstra_path(), dijkstra_path_length(),": [[1397, "bidirectional-shortest-path-dijkstra-path-dijkstra-path-length"]], "bidirectional_dijkstra()": [[1397, "bidirectional-dijkstra"]], "NetworkX 1.5": [[1398, "networkx-1-5"], [1406, "networkx-1-5"]], "Weighted graph algorithms": [[1398, "weighted-graph-algorithms"], [1399, "weighted-graph-algorithms"]], "Random geometric graph": [[1398, "random-geometric-graph"]], "NetworkX 1.6": [[1399, "networkx-1-6"], [1406, "networkx-1-6"]], "Graph Classes": [[1399, "graph-classes"]], "Isomorphisms": [[1399, "isomorphisms"]], "Other": [[1399, "other"], [1400, "other"]], "NetworkX 1.7": [[1400, "networkx-1-7"], [1406, "networkx-1-7"]], "NetworkX 1.8": [[1401, "networkx-1-8"], [1406, "networkx-1-8"]], "NetworkX 1.9": [[1402, "networkx-1-9"], [1406, "networkx-1-9"]], "Flow package": [[1402, "flow-package"]], "Main changes": [[1402, "main-changes"]], "Connectivity package": [[1402, "connectivity-package"]], "Other new functionalities": [[1402, "other-new-functionalities"]], "Releases": [[1403, "releases"]], "Migration guide from 1.X to 2.0": [[1404, "migration-guide-from-1-x-to-2-0"]], "Writing code that works for both versions": [[1404, "writing-code-that-works-for-both-versions"]], "Using Pickle with v1 and v2": [[1404, "using-pickle-with-v1-and-v2"]], "Migration guide from 2.X to 3.0": [[1405, "migration-guide-from-2-x-to-3-0"]], "Default dependencies": [[1405, "default-dependencies"]], "Improved integration with scientific Python": [[1405, "improved-integration-with-scientific-python"]], "Replacing NumPy/SciPy matrices with arrays": [[1405, "replacing-numpy-scipy-matrices-with-arrays"]], "Switch to NumPy/SciPy implementations by default for some algorithms": [[1405, "switch-to-numpy-scipy-implementations-by-default-for-some-algorithms"]], "Supporting numpy.random.Generator": [[1405, "supporting-numpy-random-generator"]], "NumPy structured dtypes for multi-attribute adjacency matrices": [[1405, "numpy-structured-dtypes-for-multi-attribute-adjacency-matrices"]], "Deprecated code": [[1405, "deprecated-code"]], "Old Release Log": [[1406, "old-release-log"]], "NetworkX 2.5": [[1406, "networkx-2-5"], [1412, "networkx-2-5"]], "Release notes": [[1406, "release-notes"], [1406, "id1"], [1406, "id2"], [1406, "id3"], [1406, "id4"], [1406, "id5"]], "NetworkX 2.4": [[1406, "networkx-2-4"], [1411, "networkx-2-4"]], "NetworkX 2.3": [[1406, 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"networkx-0-30"]], "NetworkX 0.29": [[1406, "networkx-0-29"]], "NetworkX 0.28": [[1406, "networkx-0-28"]], "NetworkX 0.27": [[1406, "networkx-0-27"]], "NetworkX 0.26": [[1406, "networkx-0-26"]], "NetworkX 0.25": [[1406, "networkx-0-25"]], "NetworkX 0.24": [[1406, "networkx-0-24"]], "NetworkX 0.23": [[1406, "networkx-0-23"]], "Important Change": [[1406, "important-change"]], "NetworkX 0.22": [[1406, "networkx-0-22"]], "API Changes": [[1407, "api-changes"], [1408, "api-changes"], [1409, "api-changes"], [1410, "api-changes"], [1411, "api-changes"], [1412, "api-changes"], [1413, "api-changes"], [1414, "api-changes"], [1416, "api-changes"], [1425, "api-changes"]], "Merged PRs": [[1407, "merged-prs"], [1408, "merged-prs"], [1411, "merged-prs"], [1412, "merged-prs"], [1413, "merged-prs"], [1414, "merged-prs"], [1415, "merged-prs"], [1416, "merged-prs"], [1417, "merged-prs"], [1418, "merged-prs"], [1419, "merged-prs"], [1420, "merged-prs"], [1421, "merged-prs"], [1422, "merged-prs"], [1423, "merged-prs"], [1424, "merged-prs"], [1425, "merged-prs"]], "Improvements": [[1408, "improvements"], [1409, "improvements"], [1410, "improvements"], [1411, "improvements"], [1412, "improvements"], [1413, "improvements"], [1414, "improvements"], [1416, "improvements"], [1417, "improvements"], [1422, "improvements"], [1423, "improvements"], [1425, "improvements"]], "NetworkX 2.6": [[1413, "networkx-2-6"]], "NetworkX 2.7": [[1414, "networkx-2-7"]], "GSoC PRs": [[1414, "gsoc-prs"]], "NetworkX 2.7.1": [[1415, "networkx-2-7-1"]], "NetworkX 2.8": [[1416, "networkx-2-8"]], "NetworkX 2.8.1": [[1417, "networkx-2-8-1"]], "NetworkX 2.8.2": [[1418, "networkx-2-8-2"]], "NetworkX 2.8.3": [[1419, "networkx-2-8-3"]], "NetworkX 2.8.4": [[1420, "networkx-2-8-4"]], "NetworkX 2.8.5": [[1421, "networkx-2-8-5"]], "NetworkX 2.8.6": [[1422, "networkx-2-8-6"]], "NetworkX 2.8.7": [[1423, "networkx-2-8-7"], [1424, "networkx-2-8-7"]], "NetworkX 3.0 (unreleased)": [[1425, "networkx-3-0-unreleased"]], "Tutorial": [[1426, "tutorial"]], "Creating a graph": [[1426, "creating-a-graph"]], "Examining elements of a graph": [[1426, "examining-elements-of-a-graph"]], "Removing elements from a graph": [[1426, "removing-elements-from-a-graph"]], "Using the graph constructors": [[1426, "using-the-graph-constructors"]], "What to use as nodes and edges": [[1426, "what-to-use-as-nodes-and-edges"]], "Accessing edges and neighbors": [[1426, "accessing-edges-and-neighbors"]], "Adding attributes to graphs, nodes, and edges": [[1426, "adding-attributes-to-graphs-nodes-and-edges"]], "Edge Attributes": [[1426, "edge-attributes"]], "Directed graphs": [[1426, "directed-graphs"]], "Multigraphs": [[1426, "multigraphs"]], "Graph generators and graph operations": [[1426, "graph-generators-and-graph-operations"]], "1. Applying classic graph operations, such as:": [[1426, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1426, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1426, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1426, "using-a-stochastic-graph-generator-e-g"]], "5. Reading a graph stored in a file using common graph formats": [[1426, "reading-a-graph-stored-in-a-file-using-common-graph-formats"]], "Analyzing graphs": [[1426, "analyzing-graphs"]], "Drawing graphs": [[1426, "drawing-graphs"]]}, "indexentries": {"module": [[112, "module-networkx.algorithms.approximation"], [112, "module-networkx.algorithms.approximation.clique"], [112, "module-networkx.algorithms.approximation.clustering_coefficient"], [112, "module-networkx.algorithms.approximation.connectivity"], [112, "module-networkx.algorithms.approximation.distance_measures"], [112, "module-networkx.algorithms.approximation.dominating_set"], [112, "module-networkx.algorithms.approximation.kcomponents"], [112, "module-networkx.algorithms.approximation.matching"], [112, "module-networkx.algorithms.approximation.maxcut"], [112, "module-networkx.algorithms.approximation.ramsey"], [112, "module-networkx.algorithms.approximation.steinertree"], [112, "module-networkx.algorithms.approximation.traveling_salesman"], [112, "module-networkx.algorithms.approximation.treewidth"], [112, "module-networkx.algorithms.approximation.vertex_cover"], [113, "module-networkx.algorithms.assortativity"], [114, "module-networkx.algorithms.asteroidal"], [115, "module-networkx.algorithms.bipartite"], [115, "module-networkx.algorithms.bipartite.basic"], [115, "module-networkx.algorithms.bipartite.centrality"], [115, "module-networkx.algorithms.bipartite.cluster"], [115, "module-networkx.algorithms.bipartite.covering"], [115, "module-networkx.algorithms.bipartite.edgelist"], [115, "module-networkx.algorithms.bipartite.generators"], [115, "module-networkx.algorithms.bipartite.matching"], [115, "module-networkx.algorithms.bipartite.matrix"], [115, "module-networkx.algorithms.bipartite.projection"], [115, "module-networkx.algorithms.bipartite.redundancy"], [115, "module-networkx.algorithms.bipartite.spectral"], [116, "module-networkx.algorithms.boundary"], [117, "module-networkx.algorithms.bridges"], [118, "module-networkx.algorithms.centrality"], [119, "module-networkx.algorithms.chains"], [120, "module-networkx.algorithms.chordal"], [121, "module-networkx.algorithms.clique"], [122, "module-networkx.algorithms.cluster"], [123, "module-networkx.algorithms.coloring"], [124, "module-networkx.algorithms.communicability_alg"], [125, "module-networkx.algorithms.community"], [125, "module-networkx.algorithms.community.asyn_fluid"], [125, "module-networkx.algorithms.community.centrality"], [125, "module-networkx.algorithms.community.community_utils"], [125, "module-networkx.algorithms.community.kclique"], [125, "module-networkx.algorithms.community.kernighan_lin"], [125, "module-networkx.algorithms.community.label_propagation"], [125, "module-networkx.algorithms.community.louvain"], [125, "module-networkx.algorithms.community.lukes"], [125, "module-networkx.algorithms.community.modularity_max"], [125, "module-networkx.algorithms.community.quality"], [126, "module-networkx.algorithms.components"], [127, "module-networkx.algorithms.connectivity"], [127, "module-networkx.algorithms.connectivity.connectivity"], [127, "module-networkx.algorithms.connectivity.cuts"], [127, "module-networkx.algorithms.connectivity.disjoint_paths"], [127, "module-networkx.algorithms.connectivity.edge_augmentation"], [127, "module-networkx.algorithms.connectivity.edge_kcomponents"], [127, "module-networkx.algorithms.connectivity.kcomponents"], [127, "module-networkx.algorithms.connectivity.kcutsets"], [127, "module-networkx.algorithms.connectivity.stoerwagner"], [127, "module-networkx.algorithms.connectivity.utils"], [128, "module-networkx.algorithms.core"], [129, "module-networkx.algorithms.covering"], [130, "module-networkx.algorithms.cuts"], [131, "module-networkx.algorithms.cycles"], [132, "module-networkx.algorithms.d_separation"], [133, "module-networkx.algorithms.dag"], [134, 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"module-networkx.algorithms.approximation.clustering_coefficient"]], "networkx.algorithms.approximation.connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"]], "networkx.algorithms.approximation.distance_measures": [[112, "module-networkx.algorithms.approximation.distance_measures"]], "networkx.algorithms.approximation.dominating_set": [[112, "module-networkx.algorithms.approximation.dominating_set"]], "networkx.algorithms.approximation.kcomponents": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "networkx.algorithms.approximation.matching": [[112, "module-networkx.algorithms.approximation.matching"]], "networkx.algorithms.approximation.maxcut": [[112, "module-networkx.algorithms.approximation.maxcut"]], "networkx.algorithms.approximation.ramsey": [[112, "module-networkx.algorithms.approximation.ramsey"]], "networkx.algorithms.approximation.steinertree": [[112, "module-networkx.algorithms.approximation.steinertree"]], "networkx.algorithms.approximation.traveling_salesman": [[112, "module-networkx.algorithms.approximation.traveling_salesman"]], "networkx.algorithms.approximation.treewidth": [[112, "module-networkx.algorithms.approximation.treewidth"]], "networkx.algorithms.approximation.vertex_cover": [[112, "module-networkx.algorithms.approximation.vertex_cover"]], "networkx.algorithms.assortativity": [[113, "module-networkx.algorithms.assortativity"]], "networkx.algorithms.asteroidal": [[114, "module-networkx.algorithms.asteroidal"]], "networkx.algorithms.bipartite": [[115, "module-networkx.algorithms.bipartite"]], "networkx.algorithms.bipartite.basic": [[115, "module-networkx.algorithms.bipartite.basic"]], "networkx.algorithms.bipartite.centrality": [[115, "module-networkx.algorithms.bipartite.centrality"]], "networkx.algorithms.bipartite.cluster": [[115, "module-networkx.algorithms.bipartite.cluster"]], "networkx.algorithms.bipartite.covering": [[115, 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"networkx.algorithms.chains": [[119, "module-networkx.algorithms.chains"]], "networkx.algorithms.chordal": [[120, "module-networkx.algorithms.chordal"]], "networkx.algorithms.clique": [[121, "module-networkx.algorithms.clique"]], "networkx.algorithms.cluster": [[122, "module-networkx.algorithms.cluster"]], "networkx.algorithms.coloring": [[123, "module-networkx.algorithms.coloring"]], "networkx.algorithms.communicability_alg": [[124, "module-networkx.algorithms.communicability_alg"]], "networkx.algorithms.community": [[125, "module-networkx.algorithms.community"]], "networkx.algorithms.community.asyn_fluid": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "networkx.algorithms.community.centrality": [[125, "module-networkx.algorithms.community.centrality"]], "networkx.algorithms.community.community_utils": [[125, "module-networkx.algorithms.community.community_utils"]], "networkx.algorithms.community.kclique": [[125, "module-networkx.algorithms.community.kclique"]], "networkx.algorithms.community.kernighan_lin": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "networkx.algorithms.community.label_propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "networkx.algorithms.community.louvain": [[125, "module-networkx.algorithms.community.louvain"]], "networkx.algorithms.community.lukes": [[125, "module-networkx.algorithms.community.lukes"]], "networkx.algorithms.community.modularity_max": [[125, "module-networkx.algorithms.community.modularity_max"]], "networkx.algorithms.community.quality": [[125, "module-networkx.algorithms.community.quality"]], "networkx.algorithms.components": [[126, "module-networkx.algorithms.components"]], "networkx.algorithms.connectivity": [[127, "module-networkx.algorithms.connectivity"]], "networkx.algorithms.connectivity.connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "networkx.algorithms.connectivity.cuts": [[127, 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"networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, 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"networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": 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"networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[751, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[752, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[753, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[755, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[757, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[759, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[760, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[764, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[766, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[767, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[768, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[769, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[770, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[771, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[772, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[772, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[772, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[772, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[774, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[775, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[777, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[778, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "networkx.algorithms.shortest_paths.weighted": [[779, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[780, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[781, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[782, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[783, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[785, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[786, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[787, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[788, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[789, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "networkx.algorithms.traversal.breadth_first_search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "networkx.algorithms.traversal.depth_first_search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "networkx.algorithms.traversal.edgebfs": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "networkx.algorithms.traversal.edgedfs": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "networkx.algorithms.tree.branchings": [[791, "module-networkx.algorithms.tree.branchings"]], "networkx.algorithms.tree.coding": [[791, "module-networkx.algorithms.tree.coding"]], "networkx.algorithms.tree.decomposition": [[791, "module-networkx.algorithms.tree.decomposition"]], "networkx.algorithms.tree.mst": [[791, "module-networkx.algorithms.tree.mst"]], "networkx.algorithms.tree.operations": [[791, "module-networkx.algorithms.tree.operations"]], "networkx.algorithms.tree.recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "networkx.algorithms.triads": [[792, "module-networkx.algorithms.triads"]], "networkx.algorithms.vitality": [[793, "module-networkx.algorithms.vitality"]], "networkx.algorithms.voronoi": [[794, "module-networkx.algorithms.voronoi"]], "networkx.algorithms.wiener": [[795, "module-networkx.algorithms.wiener"]], "digraph (class in networkx)": [[796, "networkx.DiGraph"]], "copy() (adjacencyview method)": [[797, "networkx.classes.coreviews.AdjacencyView.copy"]], "get() (adjacencyview method)": [[798, "networkx.classes.coreviews.AdjacencyView.get"]], "items() (adjacencyview method)": [[799, "networkx.classes.coreviews.AdjacencyView.items"]], "keys() (adjacencyview method)": [[800, "networkx.classes.coreviews.AdjacencyView.keys"]], "values() (adjacencyview method)": [[801, "networkx.classes.coreviews.AdjacencyView.values"]], "copy() (atlasview method)": [[802, "networkx.classes.coreviews.AtlasView.copy"]], "get() (atlasview method)": [[803, "networkx.classes.coreviews.AtlasView.get"]], "items() (atlasview method)": [[804, "networkx.classes.coreviews.AtlasView.items"]], "keys() (atlasview method)": [[805, "networkx.classes.coreviews.AtlasView.keys"]], "values() (atlasview method)": [[806, "networkx.classes.coreviews.AtlasView.values"]], "get() (filteradjacency method)": [[807, "networkx.classes.coreviews.FilterAdjacency.get"]], "items() (filteradjacency method)": [[808, "networkx.classes.coreviews.FilterAdjacency.items"]], "keys() (filteradjacency method)": [[809, "networkx.classes.coreviews.FilterAdjacency.keys"]], "values() (filteradjacency method)": [[810, "networkx.classes.coreviews.FilterAdjacency.values"]], "get() (filteratlas method)": [[811, "networkx.classes.coreviews.FilterAtlas.get"]], "items() (filteratlas method)": [[812, "networkx.classes.coreviews.FilterAtlas.items"]], "keys() (filteratlas method)": [[813, "networkx.classes.coreviews.FilterAtlas.keys"]], "values() (filteratlas method)": [[814, "networkx.classes.coreviews.FilterAtlas.values"]], "get() (filtermultiadjacency method)": [[815, "networkx.classes.coreviews.FilterMultiAdjacency.get"]], "items() (filtermultiadjacency method)": [[816, "networkx.classes.coreviews.FilterMultiAdjacency.items"]], "keys() (filtermultiadjacency method)": [[817, "networkx.classes.coreviews.FilterMultiAdjacency.keys"]], "values() (filtermultiadjacency method)": [[818, "networkx.classes.coreviews.FilterMultiAdjacency.values"]], "get() (filtermultiinner method)": [[819, "networkx.classes.coreviews.FilterMultiInner.get"]], "items() (filtermultiinner method)": [[820, "networkx.classes.coreviews.FilterMultiInner.items"]], "keys() (filtermultiinner method)": [[821, "networkx.classes.coreviews.FilterMultiInner.keys"]], "values() (filtermultiinner method)": [[822, "networkx.classes.coreviews.FilterMultiInner.values"]], "copy() (multiadjacencyview method)": [[823, "networkx.classes.coreviews.MultiAdjacencyView.copy"]], "get() (multiadjacencyview method)": [[824, "networkx.classes.coreviews.MultiAdjacencyView.get"]], "items() (multiadjacencyview method)": [[825, "networkx.classes.coreviews.MultiAdjacencyView.items"]], "keys() (multiadjacencyview method)": [[826, "networkx.classes.coreviews.MultiAdjacencyView.keys"]], "values() (multiadjacencyview method)": [[827, "networkx.classes.coreviews.MultiAdjacencyView.values"]], "copy() (unionadjacency method)": [[828, "networkx.classes.coreviews.UnionAdjacency.copy"]], "get() (unionadjacency method)": [[829, "networkx.classes.coreviews.UnionAdjacency.get"]], "items() (unionadjacency method)": [[830, "networkx.classes.coreviews.UnionAdjacency.items"]], "keys() (unionadjacency method)": [[831, "networkx.classes.coreviews.UnionAdjacency.keys"]], "values() (unionadjacency method)": [[832, "networkx.classes.coreviews.UnionAdjacency.values"]], "copy() (unionatlas method)": [[833, "networkx.classes.coreviews.UnionAtlas.copy"]], "get() (unionatlas method)": [[834, "networkx.classes.coreviews.UnionAtlas.get"]], "items() (unionatlas method)": [[835, "networkx.classes.coreviews.UnionAtlas.items"]], "keys() (unionatlas method)": [[836, "networkx.classes.coreviews.UnionAtlas.keys"]], "values() (unionatlas method)": [[837, "networkx.classes.coreviews.UnionAtlas.values"]], "copy() (unionmultiadjacency method)": [[838, "networkx.classes.coreviews.UnionMultiAdjacency.copy"]], "get() (unionmultiadjacency method)": [[839, "networkx.classes.coreviews.UnionMultiAdjacency.get"]], "items() (unionmultiadjacency method)": [[840, "networkx.classes.coreviews.UnionMultiAdjacency.items"]], "keys() (unionmultiadjacency method)": [[841, "networkx.classes.coreviews.UnionMultiAdjacency.keys"]], "values() (unionmultiadjacency method)": [[842, "networkx.classes.coreviews.UnionMultiAdjacency.values"]], "copy() (unionmultiinner method)": [[843, "networkx.classes.coreviews.UnionMultiInner.copy"]], "get() (unionmultiinner method)": [[844, "networkx.classes.coreviews.UnionMultiInner.get"]], "items() (unionmultiinner method)": [[845, "networkx.classes.coreviews.UnionMultiInner.items"]], "keys() (unionmultiinner method)": [[846, "networkx.classes.coreviews.UnionMultiInner.keys"]], "values() (unionmultiinner method)": [[847, "networkx.classes.coreviews.UnionMultiInner.values"]], "__contains__() (digraph method)": [[848, "networkx.DiGraph.__contains__"]], "__getitem__() (digraph method)": [[849, "networkx.DiGraph.__getitem__"]], "__init__() (digraph method)": [[850, "networkx.DiGraph.__init__"]], "__iter__() (digraph method)": [[851, "networkx.DiGraph.__iter__"]], "__len__() (digraph method)": [[852, "networkx.DiGraph.__len__"]], "add_edge() (digraph method)": [[853, "networkx.DiGraph.add_edge"]], "add_edges_from() (digraph method)": [[854, "networkx.DiGraph.add_edges_from"]], "add_node() (digraph method)": [[855, "networkx.DiGraph.add_node"]], "add_nodes_from() (digraph method)": [[856, "networkx.DiGraph.add_nodes_from"]], "add_weighted_edges_from() (digraph method)": [[857, "networkx.DiGraph.add_weighted_edges_from"]], "adj (digraph property)": [[858, "networkx.DiGraph.adj"]], "adjacency() (digraph method)": [[859, "networkx.DiGraph.adjacency"]], "clear() (digraph method)": [[860, "networkx.DiGraph.clear"]], "clear_edges() (digraph method)": [[861, "networkx.DiGraph.clear_edges"]], "copy() (digraph method)": [[862, "networkx.DiGraph.copy"]], "degree (digraph property)": [[863, "networkx.DiGraph.degree"]], "edge_subgraph() (digraph method)": [[864, "networkx.DiGraph.edge_subgraph"]], "edges (digraph property)": [[865, "networkx.DiGraph.edges"]], "get_edge_data() (digraph method)": [[866, "networkx.DiGraph.get_edge_data"]], "has_edge() (digraph method)": [[867, "networkx.DiGraph.has_edge"]], "has_node() (digraph method)": [[868, "networkx.DiGraph.has_node"]], "in_degree (digraph property)": [[869, "networkx.DiGraph.in_degree"]], "in_edges (digraph property)": [[870, "networkx.DiGraph.in_edges"]], "nbunch_iter() (digraph method)": [[871, "networkx.DiGraph.nbunch_iter"]], "neighbors() (digraph method)": [[872, "networkx.DiGraph.neighbors"]], "nodes (digraph property)": [[873, "networkx.DiGraph.nodes"]], "number_of_edges() (digraph method)": [[874, "networkx.DiGraph.number_of_edges"]], "number_of_nodes() (digraph method)": [[875, "networkx.DiGraph.number_of_nodes"]], "order() (digraph method)": [[876, "networkx.DiGraph.order"]], "out_degree (digraph property)": [[877, "networkx.DiGraph.out_degree"]], "out_edges (digraph property)": [[878, "networkx.DiGraph.out_edges"]], "pred (digraph property)": [[879, "networkx.DiGraph.pred"]], "predecessors() (digraph method)": [[880, "networkx.DiGraph.predecessors"]], "remove_edge() (digraph method)": [[881, "networkx.DiGraph.remove_edge"]], "remove_edges_from() (digraph method)": [[882, "networkx.DiGraph.remove_edges_from"]], "remove_node() (digraph method)": [[883, "networkx.DiGraph.remove_node"]], "remove_nodes_from() (digraph method)": 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"add_edges_from() (graph method)": [[899, "networkx.Graph.add_edges_from"]], "add_node() (graph method)": [[900, "networkx.Graph.add_node"]], "add_nodes_from() (graph method)": [[901, "networkx.Graph.add_nodes_from"]], "add_weighted_edges_from() (graph method)": [[902, "networkx.Graph.add_weighted_edges_from"]], "adj (graph property)": [[903, "networkx.Graph.adj"]], "adjacency() (graph method)": [[904, "networkx.Graph.adjacency"]], "clear() (graph method)": [[905, "networkx.Graph.clear"]], "clear_edges() (graph method)": [[906, "networkx.Graph.clear_edges"]], "copy() (graph method)": [[907, "networkx.Graph.copy"]], "degree (graph property)": [[908, "networkx.Graph.degree"]], "edge_subgraph() (graph method)": [[909, "networkx.Graph.edge_subgraph"]], "edges (graph property)": [[910, "networkx.Graph.edges"]], "get_edge_data() (graph method)": [[911, "networkx.Graph.get_edge_data"]], "has_edge() (graph method)": [[912, "networkx.Graph.has_edge"]], "has_node() (graph method)": [[913, "networkx.Graph.has_node"]], "nbunch_iter() (graph method)": [[914, "networkx.Graph.nbunch_iter"]], "neighbors() (graph method)": [[915, "networkx.Graph.neighbors"]], "nodes (graph property)": [[916, "networkx.Graph.nodes"]], "number_of_edges() (graph method)": [[917, "networkx.Graph.number_of_edges"]], "number_of_nodes() (graph method)": [[918, "networkx.Graph.number_of_nodes"]], "order() (graph method)": [[919, "networkx.Graph.order"]], "remove_edge() (graph method)": [[920, "networkx.Graph.remove_edge"]], "remove_edges_from() (graph method)": [[921, "networkx.Graph.remove_edges_from"]], "remove_node() (graph method)": [[922, "networkx.Graph.remove_node"]], "remove_nodes_from() (graph method)": [[923, "networkx.Graph.remove_nodes_from"]], "size() (graph method)": [[924, "networkx.Graph.size"]], "subgraph() (graph method)": [[925, "networkx.Graph.subgraph"]], "to_directed() (graph method)": [[926, "networkx.Graph.to_directed"]], "to_undirected() (graph method)": [[927, 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"networkx.MultiDiGraph.remove_edges_from"]], "remove_node() (multidigraph method)": [[964, "networkx.MultiDiGraph.remove_node"]], "remove_nodes_from() (multidigraph method)": [[965, "networkx.MultiDiGraph.remove_nodes_from"]], "reverse() (multidigraph method)": [[966, "networkx.MultiDiGraph.reverse"]], "size() (multidigraph method)": [[967, "networkx.MultiDiGraph.size"]], "subgraph() (multidigraph method)": [[968, "networkx.MultiDiGraph.subgraph"]], "succ (multidigraph property)": [[969, "networkx.MultiDiGraph.succ"]], "successors() (multidigraph method)": [[970, "networkx.MultiDiGraph.successors"]], "to_directed() (multidigraph method)": [[971, "networkx.MultiDiGraph.to_directed"]], "to_undirected() (multidigraph method)": [[972, "networkx.MultiDiGraph.to_undirected"]], "update() (multidigraph method)": [[973, "networkx.MultiDiGraph.update"]], "__contains__() (multigraph method)": [[974, "networkx.MultiGraph.__contains__"]], "__getitem__() (multigraph method)": [[975, "networkx.MultiGraph.__getitem__"]], "__init__() (multigraph method)": [[976, "networkx.MultiGraph.__init__"]], "__iter__() (multigraph method)": [[977, "networkx.MultiGraph.__iter__"]], "__len__() (multigraph method)": [[978, "networkx.MultiGraph.__len__"]], "add_edge() (multigraph method)": [[979, "networkx.MultiGraph.add_edge"]], "add_edges_from() (multigraph method)": [[980, "networkx.MultiGraph.add_edges_from"]], "add_node() (multigraph method)": [[981, "networkx.MultiGraph.add_node"]], "add_nodes_from() (multigraph method)": [[982, "networkx.MultiGraph.add_nodes_from"]], "add_weighted_edges_from() (multigraph method)": [[983, "networkx.MultiGraph.add_weighted_edges_from"]], "adj (multigraph property)": [[984, "networkx.MultiGraph.adj"]], "adjacency() (multigraph method)": [[985, "networkx.MultiGraph.adjacency"]], "clear() (multigraph method)": [[986, "networkx.MultiGraph.clear"]], "clear_edges() (multigraph method)": [[987, "networkx.MultiGraph.clear_edges"]], "copy() (multigraph method)": [[988, "networkx.MultiGraph.copy"]], "degree (multigraph property)": [[989, "networkx.MultiGraph.degree"]], "edge_subgraph() (multigraph method)": [[990, "networkx.MultiGraph.edge_subgraph"]], "edges (multigraph property)": [[991, "networkx.MultiGraph.edges"]], "get_edge_data() (multigraph method)": [[992, "networkx.MultiGraph.get_edge_data"]], "has_edge() (multigraph method)": [[993, "networkx.MultiGraph.has_edge"]], "has_node() (multigraph method)": [[994, "networkx.MultiGraph.has_node"]], "nbunch_iter() (multigraph method)": [[995, "networkx.MultiGraph.nbunch_iter"]], "neighbors() (multigraph method)": [[996, "networkx.MultiGraph.neighbors"]], "new_edge_key() (multigraph method)": [[997, "networkx.MultiGraph.new_edge_key"]], "nodes (multigraph property)": [[998, "networkx.MultiGraph.nodes"]], "number_of_edges() (multigraph method)": [[999, "networkx.MultiGraph.number_of_edges"]], "number_of_nodes() (multigraph method)": [[1000, "networkx.MultiGraph.number_of_nodes"]], "order() (multigraph method)": [[1001, "networkx.MultiGraph.order"]], "remove_edge() (multigraph method)": [[1002, "networkx.MultiGraph.remove_edge"]], "remove_edges_from() (multigraph method)": [[1003, "networkx.MultiGraph.remove_edges_from"]], "remove_node() (multigraph method)": [[1004, "networkx.MultiGraph.remove_node"]], "remove_nodes_from() (multigraph method)": [[1005, "networkx.MultiGraph.remove_nodes_from"]], "size() (multigraph method)": [[1006, "networkx.MultiGraph.size"]], "subgraph() (multigraph method)": [[1007, "networkx.MultiGraph.subgraph"]], "to_directed() (multigraph method)": [[1008, "networkx.MultiGraph.to_directed"]], "to_undirected() (multigraph method)": [[1009, "networkx.MultiGraph.to_undirected"]], "update() (multigraph method)": [[1010, "networkx.MultiGraph.update"]], "_dispatch() (in module networkx.classes.backends)": [[1011, "networkx.classes.backends._dispatch"]], "adjacencyview (class in networkx.classes.coreviews)": [[1012, "networkx.classes.coreviews.AdjacencyView"]], "__init__() (adjacencyview method)": [[1012, "networkx.classes.coreviews.AdjacencyView.__init__"]], "atlasview (class in networkx.classes.coreviews)": [[1013, "networkx.classes.coreviews.AtlasView"]], "__init__() (atlasview method)": [[1013, "networkx.classes.coreviews.AtlasView.__init__"]], "filteradjacency (class in networkx.classes.coreviews)": [[1014, "networkx.classes.coreviews.FilterAdjacency"]], "__init__() (filteradjacency method)": [[1014, "networkx.classes.coreviews.FilterAdjacency.__init__"]], "filteratlas (class in networkx.classes.coreviews)": [[1015, "networkx.classes.coreviews.FilterAtlas"]], "__init__() (filteratlas method)": [[1015, "networkx.classes.coreviews.FilterAtlas.__init__"]], "filtermultiadjacency (class in networkx.classes.coreviews)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency"]], "__init__() (filtermultiadjacency method)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency.__init__"]], "filtermultiinner (class in networkx.classes.coreviews)": [[1017, "networkx.classes.coreviews.FilterMultiInner"]], "__init__() (filtermultiinner method)": [[1017, "networkx.classes.coreviews.FilterMultiInner.__init__"]], "multiadjacencyview (class in networkx.classes.coreviews)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView"]], "__init__() (multiadjacencyview method)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView.__init__"]], "unionadjacency (class in networkx.classes.coreviews)": [[1019, "networkx.classes.coreviews.UnionAdjacency"]], "__init__() (unionadjacency method)": [[1019, "networkx.classes.coreviews.UnionAdjacency.__init__"]], "unionatlas (class in networkx.classes.coreviews)": [[1020, "networkx.classes.coreviews.UnionAtlas"]], "__init__() (unionatlas method)": [[1020, "networkx.classes.coreviews.UnionAtlas.__init__"]], "unionmultiadjacency (class in networkx.classes.coreviews)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency"]], "__init__() (unionmultiadjacency method)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency.__init__"]], "unionmultiinner (class in networkx.classes.coreviews)": [[1022, "networkx.classes.coreviews.UnionMultiInner"]], "__init__() (unionmultiinner method)": [[1022, "networkx.classes.coreviews.UnionMultiInner.__init__"]], "hide_diedges() (in module networkx.classes.filters)": [[1023, "networkx.classes.filters.hide_diedges"]], "hide_edges() (in module networkx.classes.filters)": [[1024, "networkx.classes.filters.hide_edges"]], "hide_multidiedges() (in module networkx.classes.filters)": [[1025, "networkx.classes.filters.hide_multidiedges"]], "hide_multiedges() (in module networkx.classes.filters)": [[1026, "networkx.classes.filters.hide_multiedges"]], "hide_nodes() (in module networkx.classes.filters)": [[1027, "networkx.classes.filters.hide_nodes"]], "no_filter() (in module networkx.classes.filters)": [[1028, "networkx.classes.filters.no_filter"]], "show_diedges() (in module networkx.classes.filters)": [[1029, "networkx.classes.filters.show_diedges"]], "show_edges() (in module networkx.classes.filters)": [[1030, "networkx.classes.filters.show_edges"]], "show_multidiedges() (in module networkx.classes.filters)": [[1031, "networkx.classes.filters.show_multidiedges"]], "show_multiedges() (in module networkx.classes.filters)": [[1032, "networkx.classes.filters.show_multiedges"]], "__init__() (show_nodes method)": [[1033, "networkx.classes.filters.show_nodes.__init__"]], "show_nodes (class in networkx.classes.filters)": [[1033, "networkx.classes.filters.show_nodes"]], "generic_graph_view() (in module networkx.classes.graphviews)": [[1034, "networkx.classes.graphviews.generic_graph_view"]], "reverse_view() (in module networkx.classes.graphviews)": [[1035, "networkx.classes.graphviews.reverse_view"]], "subgraph_view() (in module networkx.classes.graphviews)": [[1036, "networkx.classes.graphviews.subgraph_view"]], "graph (class in networkx)": [[1037, "networkx.Graph"]], "networkx.classes.backends": [[1038, "module-networkx.classes.backends"]], "networkx.classes.coreviews": [[1038, "module-networkx.classes.coreviews"]], "networkx.classes.filters": [[1038, "module-networkx.classes.filters"]], "networkx.classes.graphviews": [[1038, "module-networkx.classes.graphviews"]], "multidigraph (class in networkx)": [[1039, "networkx.MultiDiGraph"]], "multigraph (class in networkx)": [[1040, "networkx.MultiGraph"]], "networkx.convert": [[1041, "module-networkx.convert"]], "networkx.convert_matrix": [[1041, "module-networkx.convert_matrix"]], "networkx.drawing.layout": [[1042, "module-networkx.drawing.layout"]], "networkx.drawing.nx_agraph": [[1042, "module-networkx.drawing.nx_agraph"]], "networkx.drawing.nx_pydot": [[1042, "module-networkx.drawing.nx_pydot"]], "networkx.drawing.nx_pylab": [[1042, "module-networkx.drawing.nx_pylab"]], "ambiguoussolution (class in networkx)": [[1043, "networkx.AmbiguousSolution"]], "exceededmaxiterations (class in networkx)": [[1043, "networkx.ExceededMaxIterations"]], "hasacycle (class in networkx)": [[1043, "networkx.HasACycle"]], "networkxalgorithmerror (class in networkx)": [[1043, "networkx.NetworkXAlgorithmError"]], "networkxerror (class in networkx)": [[1043, "networkx.NetworkXError"]], "networkxexception (class in networkx)": [[1043, "networkx.NetworkXException"]], "networkxnocycle (class in networkx)": [[1043, "networkx.NetworkXNoCycle"]], "networkxnopath (class in networkx)": [[1043, "networkx.NetworkXNoPath"]], "networkxnotimplemented (class in networkx)": [[1043, "networkx.NetworkXNotImplemented"]], "networkxpointlessconcept (class in networkx)": [[1043, "networkx.NetworkXPointlessConcept"]], "networkxunbounded (class in networkx)": [[1043, "networkx.NetworkXUnbounded"]], "networkxunfeasible (class in networkx)": [[1043, "networkx.NetworkXUnfeasible"]], "nodenotfound (class in networkx)": [[1043, "networkx.NodeNotFound"]], "poweriterationfailedconvergence (class in networkx)": [[1043, "networkx.PowerIterationFailedConvergence"]], "networkx.exception": [[1043, "module-networkx.exception"]], "networkx.classes.function": [[1044, "module-networkx.classes.function"]], "assemble() (argmap method)": [[1045, "networkx.utils.decorators.argmap.assemble"]], "compile() (argmap method)": [[1046, "networkx.utils.decorators.argmap.compile"]], "signature() (argmap class method)": [[1047, "networkx.utils.decorators.argmap.signature"]], "pop() (mappedqueue method)": [[1048, "networkx.utils.mapped_queue.MappedQueue.pop"]], "push() (mappedqueue method)": [[1049, "networkx.utils.mapped_queue.MappedQueue.push"]], "remove() (mappedqueue method)": [[1050, "networkx.utils.mapped_queue.MappedQueue.remove"]], "update() (mappedqueue method)": [[1051, "networkx.utils.mapped_queue.MappedQueue.update"]], "add_cycle() (in module networkx.classes.function)": [[1052, "networkx.classes.function.add_cycle"]], "add_path() (in module networkx.classes.function)": [[1053, "networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, 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"kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], 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module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, 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module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() 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networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, 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networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, 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"networkx.linalg.bethehessianmatrix": [[1327, "module-networkx.linalg.bethehessianmatrix"]], "networkx.linalg.graphmatrix": [[1327, "module-networkx.linalg.graphmatrix"]], "networkx.linalg.laplacianmatrix": [[1327, "module-networkx.linalg.laplacianmatrix"]], "networkx.linalg.modularitymatrix": [[1327, "module-networkx.linalg.modularitymatrix"]], "networkx.linalg.spectrum": [[1327, "module-networkx.linalg.spectrum"]], "networkx.readwrite.adjlist": [[1329, "module-networkx.readwrite.adjlist"]], "networkx.readwrite.edgelist": [[1330, "module-networkx.readwrite.edgelist"]], "generate_adjlist() (in module networkx.readwrite.adjlist)": [[1331, "networkx.readwrite.adjlist.generate_adjlist"]], "parse_adjlist() (in module networkx.readwrite.adjlist)": [[1332, "networkx.readwrite.adjlist.parse_adjlist"]], "read_adjlist() (in module networkx.readwrite.adjlist)": [[1333, "networkx.readwrite.adjlist.read_adjlist"]], "write_adjlist() (in module networkx.readwrite.adjlist)": [[1334, 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1147, 1272], "must": [8, 11, 93, 94, 95, 99, 100, 103, 110, 151, 152, 158, 161, 171, 204, 206, 207, 214, 215, 216, 219, 230, 231, 232, 252, 253, 257, 258, 259, 260, 261, 262, 264, 267, 268, 269, 271, 273, 276, 281, 285, 297, 298, 306, 307, 315, 316, 317, 318, 319, 324, 325, 327, 329, 330, 342, 361, 362, 363, 378, 382, 385, 391, 410, 411, 412, 413, 425, 429, 440, 471, 472, 473, 474, 475, 545, 546, 547, 548, 549, 550, 551, 553, 555, 556, 557, 558, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 577, 578, 579, 580, 584, 585, 586, 587, 588, 589, 593, 597, 599, 601, 602, 603, 604, 615, 626, 627, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 680, 690, 692, 698, 699, 707, 721, 734, 735, 736, 737, 789, 796, 853, 854, 857, 867, 891, 892, 898, 899, 902, 912, 928, 934, 938, 972, 973, 979, 983, 1010, 1037, 1038, 1039, 1040, 1063, 1071, 1085, 1102, 1133, 1137, 1146, 1162, 1165, 1173, 1176, 1186, 1188, 1190, 1193, 1197, 1199, 1209, 1213, 1217, 1219, 1235, 1239, 1240, 1270, 1275, 1276, 1277, 1278, 1279, 1295, 1296, 1298, 1307, 1309, 1310, 1311, 1312, 1315, 1333, 1337, 1338, 1339, 1340, 1359, 1361, 1362, 1363, 1364, 1365, 1366, 1376, 1393, 1394, 1395, 1407, 1426], "NOT": [8, 110, 199, 549, 550, 551, 748, 887, 925, 968, 1007], "util": [8, 14, 36, 44, 45, 93, 97, 102, 103, 229, 230, 231, 316, 374, 423, 425, 426, 429, 460, 496, 678, 679, 758, 1044, 1242, 1299, 1301, 1303, 1310, 1319, 1320, 1321, 1325, 1402, 1406, 1407, 1411, 1413, 1416, 1419], "arbitrary_el": [8, 1392, 1413], "nb": [8, 1331, 1334], "left": [8, 71, 115, 183, 311, 312, 322, 324, 325, 385, 559, 560, 584, 616, 688, 689, 739, 1106, 1134, 1136, 1146, 1179, 1206, 1280, 1355, 1358, 1404], "right": [8, 71, 110, 111, 115, 152, 206, 322, 385, 427, 428, 500, 559, 560, 584, 585, 587, 588, 615, 616, 688, 689, 739, 854, 935, 980, 1134, 1136, 1146, 1155, 1157, 1179, 1206, 1213, 1215, 1270, 1280], "littl": [8, 94, 298, 307], "mislead": 8, "That": [8, 97, 132, 165, 212, 221, 227, 295, 385, 436, 465, 525, 535, 555, 588, 657, 671, 672, 673, 674, 691, 704, 717, 791, 862, 907, 943, 988, 1046, 1162, 1210, 1296, 1388, 1404, 1409], "okai": 8, "becaus": [8, 11, 54, 69, 94, 99, 101, 102, 103, 112, 132, 161, 215, 216, 220, 255, 311, 378, 387, 389, 390, 394, 411, 412, 427, 494, 498, 499, 500, 510, 569, 585, 587, 615, 616, 632, 652, 934, 979, 1038, 1236, 1273, 1296, 1303, 1326, 1345, 1350, 1404, 1407, 1416], "AND": [8, 110, 598, 748, 762], "OR": [8, 110, 157, 175, 188, 856, 869, 877, 901, 937, 947, 950, 959, 982, 992], "symmetr": [8, 145, 148, 237, 545, 586, 593, 761, 1173, 1192, 1235, 1246, 1250, 1251, 1256, 1258, 1269, 1320, 1321, 1387], "It": [8, 52, 56, 58, 92, 93, 94, 97, 99, 101, 102, 104, 107, 110, 112, 115, 132, 172, 184, 207, 214, 215, 216, 229, 230, 231, 249, 260, 261, 262, 264, 278, 310, 316, 324, 325, 326, 343, 346, 347, 351, 353, 412, 414, 415, 416, 417, 418, 419, 429, 438, 440, 452, 457, 464, 480, 496, 500, 508, 530, 540, 545, 559, 560, 565, 566, 567, 582, 588, 594, 595, 598, 600, 601, 615, 619, 628, 629, 630, 652, 658, 659, 663, 671, 674, 692, 717, 718, 719, 760, 761, 762, 791, 796, 868, 873, 892, 913, 916, 928, 949, 955, 973, 994, 998, 1010, 1012, 1013, 1018, 1037, 1038, 1039, 1040, 1054, 1117, 1170, 1174, 1200, 1201, 1206, 1207, 1210, 1217, 1223, 1227, 1234, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1253, 1254, 1258, 1261, 1263, 1264, 1269, 1275, 1276, 1277, 1280, 1296, 1297, 1323, 1324, 1326, 1328, 1343, 1382, 1383, 1393, 1395, 1398, 1402, 1404, 1407, 1408, 1409, 1411, 1412, 1413, 1426], "just": [8, 99, 102, 103, 104, 184, 199, 338, 374, 439, 464, 559, 560, 577, 660, 661, 662, 692, 791, 873, 887, 916, 925, 946, 955, 960, 968, 991, 998, 1007, 1120, 1126, 1229, 1278, 1279, 1296, 1328, 1393, 1404, 1406], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1325, 1402, 1406, 1412], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1202, 1383], "doe": [8, 77, 93, 94, 99, 101, 102, 103, 104, 114, 115, 132, 147, 153, 154, 165, 168, 189, 207, 208, 227, 228, 229, 230, 231, 232, 293, 308, 339, 340, 342, 343, 352, 357, 373, 382, 385, 410, 414, 426, 450, 469, 494, 495, 496, 497, 498, 499, 500, 502, 503, 506, 507, 509, 510, 511, 512, 534, 544, 549, 550, 551, 564, 566, 583, 584, 586, 589, 601, 612, 626, 627, 678, 691, 693, 694, 698, 699, 717, 718, 721, 722, 723, 724, 725, 726, 762, 862, 865, 878, 892, 907, 910, 928, 943, 946, 960, 973, 988, 991, 1010, 1038, 1043, 1066, 1070, 1072, 1081, 1102, 1103, 1105, 1106, 1107, 1109, 1114, 1173, 1175, 1177, 1192, 1207, 1222, 1223, 1227, 1229, 1234, 1241, 1296, 1300, 1303, 1326, 1333, 1334, 1341, 1342, 1344, 1351, 1353, 1354, 1355, 1356, 1357, 1358, 1371, 1379, 1380, 1381, 1383, 1393, 1404, 1405, 1406, 1410, 1417, 1426], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1219], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1229, 1296, 1395, 1404], "everi": [8, 11, 57, 88, 93, 109, 112, 120, 144, 157, 161, 177, 211, 212, 220, 221, 229, 230, 231, 235, 243, 264, 287, 295, 300, 324, 325, 343, 352, 380, 397, 437, 439, 440, 450, 462, 471, 472, 473, 474, 475, 477, 483, 484, 491, 512, 516, 565, 606, 614, 615, 619, 632, 633, 635, 636, 663, 685, 687, 688, 717, 718, 791, 856, 901, 937, 982, 1052, 1053, 1054, 1070, 1071, 1072, 1085, 1086, 1102, 1103, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1148, 1162, 1195, 1216, 1217, 1257, 1264, 1278, 1279, 1296, 1407], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1177, 1207, 1208, 1404, 1406, 1407, 1426], "ha": [8, 11, 16, 44, 67, 88, 91, 93, 94, 95, 97, 99, 100, 101, 102, 103, 105, 107, 110, 112, 116, 120, 127, 152, 161, 165, 166, 173, 174, 175, 184, 188, 198, 207, 212, 214, 215, 219, 220, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 241, 242, 243, 244, 247, 249, 252, 269, 271, 272, 273, 274, 275, 276, 282, 289, 291, 293, 294, 295, 300, 305, 310, 324, 331, 343, 352, 355, 356, 363, 364, 365, 373, 378, 380, 381, 383, 384, 385, 386, 391, 393, 394, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 424, 427, 428, 429, 439, 450, 458, 460, 466, 467, 468, 471, 472, 473, 474, 475, 476, 477, 480, 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 522, 564, 566, 577, 578, 581, 590, 593, 605, 607, 610, 611, 622, 623, 624, 628, 629, 630, 632, 633, 634, 635, 636, 638, 646, 647, 649, 652, 657, 658, 682, 688, 690, 692, 697, 711, 717, 718, 729, 730, 731, 739, 749, 786, 791, 854, 862, 863, 869, 873, 877, 886, 892, 899, 907, 908, 916, 924, 928, 935, 943, 944, 948, 950, 955, 959, 967, 973, 980, 988, 989, 993, 998, 1006, 1010, 1040, 1043, 1045, 1066, 1068, 1070, 1072, 1075, 1080, 1084, 1098, 1099, 1101, 1102, 1103, 1105, 1122, 1130, 1145, 1154, 1160, 1162, 1165, 1176, 1180, 1185, 1193, 1195, 1196, 1197, 1198, 1199, 1207, 1210, 1211, 1215, 1217, 1222, 1234, 1239, 1243, 1244, 1248, 1249, 1254, 1259, 1261, 1264, 1267, 1269, 1270, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1283, 1284, 1285, 1286, 1289, 1291, 1293, 1296, 1300, 1326, 1328, 1330, 1333, 1334, 1353, 1354, 1371, 1372, 1379, 1382, 1393, 1394, 1395, 1398, 1403, 1404, 1405, 1406, 1407, 1409, 1413, 1414, 1416, 1423, 1425], "output": [8, 13, 16, 89, 93, 101, 102, 103, 109, 197, 287, 288, 345, 374, 380, 494, 498, 499, 509, 510, 575, 588, 677, 678, 691, 722, 1045, 1193, 1197, 1199, 1269, 1296, 1326, 1334, 1341, 1344, 1355, 1358, 1399, 1402, 1404, 1406, 1411, 1413, 1414, 1426], "two": [8, 11, 16, 27, 34, 38, 43, 54, 55, 57, 58, 65, 67, 71, 88, 93, 95, 99, 100, 102, 109, 112, 114, 115, 120, 132, 151, 171, 175, 184, 185, 188, 202, 207, 211, 212, 213, 214, 215, 216, 217, 220, 221, 226, 227, 230, 231, 232, 245, 249, 251, 252, 253, 257, 258, 260, 261, 262, 265, 269, 270, 271, 272, 273, 274, 275, 276, 282, 285, 286, 287, 289, 305, 311, 315, 316, 322, 326, 329, 330, 337, 341, 343, 345, 351, 352, 358, 359, 377, 380, 381, 383, 391, 411, 412, 419, 423, 428, 429, 430, 431, 442, 443, 444, 445, 447, 452, 453, 454, 457, 462, 471, 472, 473, 474, 475, 476, 480, 491, 494, 498, 499, 500, 502, 503, 506, 508, 509, 510, 511, 521, 545, 549, 550, 551, 555, 559, 560, 561, 562, 563, 564, 565, 566, 568, 569, 572, 574, 578, 584, 585, 586, 587, 588, 593, 598, 605, 607, 608, 610, 611, 615, 619, 626, 627, 629, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 680, 692, 694, 731, 732, 738, 739, 760, 761, 762, 780, 786, 791, 796, 853, 867, 869, 873, 874, 877, 890, 892, 898, 912, 916, 917, 926, 928, 934, 946, 948, 950, 955, 956, 959, 960, 971, 973, 979, 991, 993, 998, 999, 1008, 1010, 1019, 1020, 1021, 1022, 1036, 1037, 1039, 1040, 1056, 1084, 1088, 1098, 1100, 1101, 1106, 1107, 1108, 1109, 1114, 1116, 1134, 1146, 1147, 1149, 1151, 1152, 1156, 1174, 1185, 1186, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1204, 1207, 1210, 1211, 1215, 1217, 1218, 1243, 1244, 1253, 1271, 1272, 1275, 1276, 1294, 1295, 1296, 1323, 1324, 1326, 1328, 1359, 1360, 1363, 1393, 1394, 1395, 1397, 1402, 1404, 1405, 1406, 1407, 1410, 1411, 1413, 1425], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1420], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1217, 1226, 1262, 1263, 1326, 1407], "appear": [8, 83, 93, 95, 99, 100, 102, 179, 204, 230, 231, 238, 243, 246, 247, 277, 363, 364, 365, 378, 451, 452, 453, 455, 466, 470, 584, 585, 587, 588, 675, 679, 707, 730, 734, 736, 891, 972, 1036, 1088, 1102, 1136, 1150, 1152, 1154, 1157, 1159, 1187, 1188, 1277, 1282, 1323, 1324, 1345, 1348, 1349, 1350, 1382, 1407, 1413, 1414], "both": [8, 52, 55, 92, 93, 94, 100, 101, 102, 103, 115, 161, 164, 204, 214, 215, 216, 217, 240, 257, 258, 259, 264, 282, 286, 287, 289, 337, 358, 379, 383, 415, 417, 418, 419, 423, 427, 440, 470, 502, 506, 545, 575, 581, 598, 600, 601, 602, 603, 604, 605, 606, 607, 610, 611, 615, 621, 635, 636, 653, 654, 655, 676, 711, 720, 760, 761, 762, 782, 891, 972, 1020, 1036, 1066, 1075, 1080, 1084, 1088, 1097, 1120, 1126, 1144, 1165, 1189, 1192, 1199, 1207, 1210, 1211, 1213, 1215, 1282, 1296, 1326, 1328, 1358, 1363, 1364, 1387, 1393, 1395, 1402, 1413, 1416, 1417, 1425, 1426], "negat": 8, "sole": [8, 786, 1278, 1279, 1326], "fourth": [8, 230, 231, 1326, 1404], "digraph": [8, 10, 11, 16, 21, 25, 41, 45, 56, 61, 67, 69, 70, 82, 88, 101, 102, 115, 132, 151, 152, 156, 157, 158, 160, 162, 163, 165, 166, 168, 170, 171, 172, 175, 176, 185, 186, 187, 188, 189, 192, 193, 194, 195, 196, 198, 199, 202, 204, 207, 208, 216, 227, 229, 230, 231, 240, 246, 247, 299, 308, 314, 318, 319, 321, 327, 328, 334, 335, 336, 337, 339, 340, 342, 343, 388, 391, 393, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 430, 431, 437, 450, 452, 453, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 481, 482, 492, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 513, 514, 518, 519, 523, 555, 566, 575, 576, 577, 588, 590, 613, 615, 623, 630, 636, 643, 644, 652, 656, 657, 658, 659, 663, 678, 688, 690, 693, 696, 697, 698, 699, 700, 701, 702, 706, 707, 708, 709, 711, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 740, 741, 744, 745, 746, 747, 748, 749, 750, 752, 760, 789, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 911, 912, 913, 915, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 957, 958, 963, 964, 965, 966, 967, 968, 972, 973, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 1000, 1001, 1003, 1004, 1005, 1006, 1007, 1010, 1035, 1037, 1038, 1039, 1040, 1041, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1135, 1150, 1154, 1168, 1169, 1170, 1173, 1177, 1178, 1180, 1182, 1183, 1184, 1185, 1189, 1217, 1270, 1272, 1273, 1274, 1283, 1284, 1287, 1290, 1292, 1298, 1323, 1326, 1333, 1337, 1342, 1356, 1357, 1362, 1365, 1366, 1371, 1393, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "add_nod": [8, 11, 26, 34, 69, 74, 89, 102, 157, 184, 246, 339, 340, 398, 422, 491, 492, 496, 504, 505, 508, 522, 523, 605, 607, 610, 611, 691, 796, 856, 873, 901, 916, 937, 955, 982, 998, 1037, 1039, 1040, 1086, 1275, 1326, 1345, 1407, 1408, 1417, 1426], "get_node_attribut": [8, 39, 44, 71, 1213, 1404], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1133, 1134, 1136], "22": [8, 35, 64, 66, 383, 384, 1271, 1323, 1403, 1408, 1412, 1422], "multipartite_layout": [8, 36, 61, 67, 1412, 1414, 1420], "subset_kei": [8, 36, 61, 67, 1109], "equal": [8, 36, 81, 144, 214, 215, 216, 230, 231, 238, 269, 271, 273, 276, 288, 297, 298, 300, 303, 306, 307, 310, 311, 312, 315, 316, 320, 323, 324, 325, 329, 330, 331, 373, 410, 411, 412, 413, 418, 419, 428, 471, 474, 476, 491, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 525, 535, 545, 552, 553, 554, 555, 568, 572, 605, 623, 657, 671, 672, 673, 674, 687, 688, 689, 690, 721, 722, 740, 741, 753, 761, 791, 1112, 1116, 1162, 1165, 1198, 1204, 1230, 1239, 1271, 1280, 1291, 1307, 1309, 1312, 1398, 1399], "105": [8, 17, 517, 518, 1166, 1167], "plot_circuit": [8, 17], "southern": [9, 1265], "women": [9, 1265, 1398, 1406], "unipartit": [9, 115, 258, 259, 358], "properti": [9, 11, 18, 22, 33, 63, 86, 101, 102, 103, 112, 134, 159, 161, 166, 168, 175, 176, 179, 184, 188, 189, 190, 200, 284, 285, 286, 287, 288, 363, 364, 365, 388, 476, 500, 545, 569, 619, 685, 858, 863, 865, 869, 870, 873, 877, 878, 879, 888, 903, 908, 910, 916, 939, 944, 946, 950, 951, 955, 959, 960, 969, 984, 989, 991, 998, 1085, 1086, 1122, 1134, 1136, 1193, 1202, 1217, 1219, 1269, 1283, 1284, 1326, 1328, 1383, 1398, 1405, 1406, 1407, 1408, 1413, 1417, 1426], "These": [9, 52, 58, 73, 79, 86, 93, 94, 105, 112, 336, 385, 494, 512, 559, 671, 673, 732, 748, 779, 786, 1038, 1045, 1047, 1323, 1326, 1385, 1387, 1392, 1394, 1395, 1397, 1399, 1404, 1405, 1411, 1426], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1199, 1393, 1395, 1399, 1402, 1405, 1406, 1407, 1413, 1416], "et": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202], "al": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202, 1407, 1413], "1930": [9, 1396], "thei": [9, 54, 58, 65, 71, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 112, 132, 151, 165, 207, 213, 220, 249, 285, 287, 288, 296, 297, 298, 301, 302, 306, 307, 308, 309, 351, 362, 374, 391, 396, 427, 451, 452, 453, 454, 464, 465, 471, 472, 473, 474, 475, 496, 504, 505, 508, 512, 546, 547, 548, 559, 560, 576, 583, 586, 588, 600, 604, 675, 676, 704, 717, 750, 760, 786, 853, 862, 892, 898, 907, 928, 934, 943, 962, 973, 979, 988, 1002, 1010, 1036, 1038, 1066, 1085, 1088, 1109, 1120, 1126, 1133, 1135, 1137, 1151, 1159, 1165, 1193, 1197, 1198, 1217, 1271, 1272, 1323, 1328, 1353, 1354, 1356, 1357, 1359, 1363, 1394, 1396, 1402, 1404, 1406, 1409, 1414, 1426], "repres": [9, 11, 26, 43, 52, 54, 57, 67, 92, 99, 107, 115, 230, 231, 265, 281, 283, 286, 287, 288, 291, 292, 338, 350, 361, 362, 363, 377, 378, 380, 381, 382, 385, 386, 391, 448, 452, 453, 455, 457, 460, 465, 466, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 521, 565, 577, 578, 579, 580, 586, 588, 609, 615, 618, 619, 656, 660, 664, 667, 676, 679, 691, 692, 695, 697, 698, 699, 700, 702, 728, 730, 731, 734, 736, 739, 752, 786, 791, 796, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1045, 1081, 1102, 1140, 1151, 1185, 1193, 1194, 1196, 1197, 1198, 1199, 1209, 1217, 1240, 1243, 1246, 1250, 1258, 1267, 1269, 1272, 1273, 1278, 1279, 1323, 1324, 1326, 1329, 1330, 1346, 1347, 1388, 1393, 1406], "observ": [9, 13, 132, 223, 1414, 1426], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 64, 66, 71, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1150, 1242, 1250, 1262, 1406, 1408, 1426], "event": [9, 25, 99, 100, 110, 1165, 1229, 1300], "18": [9, 44, 64, 66, 93, 324, 325, 345, 383, 384, 618, 1169, 1249, 1255, 1258, 1260, 1263, 1269, 1393, 1406, 1416, 1417, 1421, 1426], "bipartit": [9, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 350, 351, 358, 377, 439, 440, 443, 581, 588, 758, 1043, 1106, 1151, 1203, 1204, 1205, 1265, 1325, 1395, 1398, 1399, 1400, 1401, 1406, 1407, 1411, 1413, 1417, 1421, 1425], "biadjac": [9, 282, 283, 1400, 1406], "7": [9, 12, 14, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 89, 99, 101, 102, 115, 125, 151, 158, 170, 171, 192, 207, 232, 268, 297, 299, 314, 322, 327, 332, 333, 339, 340, 342, 362, 374, 380, 391, 403, 410, 413, 414, 415, 423, 424, 425, 426, 441, 445, 446, 483, 496, 501, 508, 511, 512, 555, 581, 586, 618, 619, 630, 652, 658, 663, 671, 674, 680, 695, 703, 706, 707, 708, 730, 747, 750, 761, 796, 853, 857, 866, 867, 881, 892, 898, 902, 911, 912, 915, 920, 928, 934, 938, 947, 973, 979, 983, 992, 996, 1010, 1037, 1039, 1040, 1052, 1053, 1085, 1100, 1104, 1148, 1212, 1242, 1248, 1250, 1251, 1255, 1258, 1260, 1273, 1323, 1326, 1330, 1339, 1340, 1345, 1348, 1349, 1350, 1382, 1392, 1394, 1402, 1403, 1405, 1408, 1409, 1410, 1411, 1412, 1413, 1426], "12": [9, 11, 19, 25, 44, 50, 55, 58, 64, 65, 66, 89, 91, 93, 229, 230, 231, 265, 345, 380, 381, 392, 399, 405, 406, 407, 449, 486, 501, 516, 568, 572, 574, 606, 616, 1052, 1053, 1054, 1133, 1136, 1150, 1244, 1245, 1249, 1254, 1257, 1263, 1335, 1406, 1408, 1412, 1426], "9": [9, 11, 12, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 82, 89, 101, 102, 111, 115, 125, 232, 293, 295, 339, 340, 342, 346, 347, 356, 374, 380, 405, 406, 424, 438, 449, 494, 496, 501, 504, 505, 508, 545, 566, 581, 586, 676, 706, 707, 708, 761, 1100, 1104, 1148, 1150, 1194, 1199, 1212, 1217, 1235, 1246, 1255, 1267, 1273, 1283, 1284, 1323, 1326, 1328, 1396, 1403, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "11": [9, 25, 33, 44, 64, 65, 66, 68, 89, 102, 110, 115, 157, 210, 239, 240, 297, 298, 303, 306, 307, 323, 392, 399, 405, 406, 407, 413, 415, 417, 422, 501, 514, 517, 606, 618, 680, 721, 738, 856, 901, 937, 982, 1052, 1053, 1054, 1100, 1150, 1287, 1403, 1410, 1413, 1414, 1419, 1424, 1425, 1426], "13": [9, 11, 38, 44, 64, 66, 89, 91, 156, 229, 230, 231, 343, 501, 703, 855, 900, 936, 981, 1150, 1192, 1406, 1420, 1426], "16": [9, 19, 31, 44, 45, 64, 66, 70, 229, 230, 231, 346, 347, 387, 389, 390, 394, 453, 508, 511, 512, 519, 571, 592, 606, 748, 749, 750, 1109, 1205, 1256, 1271, 1286, 1323, 1406, 1411, 1426], "17": [9, 21, 44, 59, 64, 66, 103, 229, 230, 231, 297, 508, 680, 693, 1405, 1406, 1426], "friend": [9, 545, 1407, 1412], "member": [9, 92, 93, 94, 100, 112, 315, 317, 318, 319, 330, 391, 483, 484, 586, 691, 1222, 1267, 1403], "evelyn": 9, "jefferson": 9, "laura": 9, "mandevil": 9, "theresa": 9, "anderson": 9, "brenda": 9, "roger": 9, "charlott": 9, "mcdowd": 9, "franc": 9, "eleanor": 9, "nye": 9, "pearl": [9, 132], "oglethorp": 9, "ruth": 9, "desand": 9, "vern": 9, "sanderson": 9, "myra": 9, "liddel": 9, "katherina": 9, "sylvia": 9, "avondal": 9, "nora": 9, "fayett": 9, "helen": 9, "lloyd": 9, "dorothi": 9, "murchison": 9, "olivia": 9, "carleton": 9, "flora": 9, "price": 9, "meet": [9, 94, 1165, 1196, 1197, 1198], "50": [9, 25, 30, 34, 40, 50, 54, 55, 56, 57, 64, 65, 272, 312, 1117, 1193, 1197, 1198, 1251, 1297, 1302], "45": [9, 58, 64, 110, 226, 300, 409, 1175], "57": [9, 64], "46": [9, 64, 235, 564, 619, 1264], "24": [9, 19, 37, 64, 66, 68, 103, 383, 384, 496, 505, 508, 703, 1212, 1229, 1244, 1262, 1271, 1403], "32": [9, 64, 66, 68, 209, 211, 212, 383, 384, 564, 703, 1403, 1411], "36": [9, 21, 64, 68, 752, 1150, 1262, 1271, 1353, 1354, 1379, 1403], "31": [9, 64, 66, 229, 230, 231, 260, 261, 262, 289, 383, 384, 409, 703, 1226, 1235, 1403], "40": [9, 50, 64, 80, 101, 297, 300, 555, 672, 1173, 1240, 1271], "38": [9, 64, 688, 1271], "33": [9, 58, 64, 66, 68, 93, 383, 384, 500, 514, 703, 1267, 1271, 1403, 1414], "37": [9, 56, 64, 68, 303, 311, 312, 323, 324, 325, 496, 508, 1039, 1040, 1271, 1393, 1403, 1408, 1425], "43": [9, 64, 324, 325, 606, 1244, 1271], "34": [9, 64, 68, 331, 508, 762, 1271, 1403], "algorithm": [9, 14, 15, 44, 52, 54, 88, 93, 94, 95, 96, 102, 103, 107, 109, 110, 111, 112, 114, 115, 117, 120, 121, 122, 125, 127, 128, 132, 133, 136, 141, 151, 210, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 228, 229, 230, 231, 232, 235, 249, 251, 252, 253, 254, 255, 256, 258, 260, 261, 262, 263, 264, 265, 266, 267, 272, 275, 277, 278, 280, 282, 284, 285, 286, 287, 288, 289, 290, 293, 296, 297, 298, 299, 301, 302, 303, 306, 307, 308, 309, 311, 312, 315, 320, 322, 323, 324, 325, 326, 329, 330, 331, 332, 333, 337, 339, 340, 341, 342, 343, 345, 346, 347, 352, 358, 361, 362, 366, 371, 372, 373, 374, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 389, 390, 394, 399, 405, 406, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 421, 422, 424, 425, 426, 427, 428, 429, 430, 432, 433, 435, 437, 440, 449, 451, 452, 453, 454, 455, 460, 464, 466, 468, 481, 482, 483, 488, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 512, 513, 514, 516, 519, 520, 521, 527, 537, 546, 547, 548, 552, 553, 554, 555, 556, 557, 558, 564, 566, 569, 577, 581, 582, 583, 589, 591, 592, 593, 600, 614, 616, 618, 619, 624, 625, 626, 627, 628, 629, 630, 632, 633, 635, 636, 639, 652, 653, 657, 658, 659, 660, 663, 664, 667, 671, 672, 673, 674, 676, 677, 678, 680, 681, 682, 683, 686, 690, 691, 692, 693, 695, 696, 697, 698, 699, 700, 701, 702, 711, 717, 721, 722, 729, 731, 732, 734, 735, 736, 737, 738, 749, 764, 765, 768, 770, 775, 776, 780, 786, 789, 790, 791, 853, 898, 934, 979, 1011, 1038, 1042, 1043, 1105, 1106, 1107, 1109, 1114, 1116, 1117, 1125, 1126, 1155, 1165, 1168, 1169, 1177, 1178, 1179, 1180, 1181, 1185, 1186, 1187, 1188, 1193, 1195, 1200, 1201, 1202, 1205, 1207, 1209, 1210, 1216, 1223, 1224, 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1235, 1239, 1260, 1269, 1275, 1276, 1277, 1298, 1302, 1319, 1320, 1321, 1323, 1325, 1328, 1367, 1368, 1386, 1393, 1394, 1395, 1400, 1401, 1402, 1403, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1422, 1424, 1425, 1426], "davis_southern_women_graph": [9, 88, 263], "top": [9, 34, 52, 67, 106, 111, 112, 115, 125, 260, 272, 284, 350, 381, 670, 675, 770, 1106, 1134, 1136, 1252, 1396, 1399, 1407, 1412, 1413, 1416], "bottom": [9, 91, 115, 260, 272, 274, 284, 285, 286, 287, 288, 350, 381, 1134, 1136, 1155, 1404, 1416], "biadjacency_matrix": [9, 283], "onto": [9, 284, 285, 286, 287, 288, 559, 560], "projected_graph": [9, 115, 284, 285, 286, 288, 351], "keep": [9, 92, 93, 94, 115, 204, 345, 346, 347, 362, 377, 387, 389, 390, 394, 583, 598, 693, 694, 891, 972, 1117, 1207, 1210, 1278, 1279, 1296, 1376, 1394, 1411, 1414], "co": [9, 26, 94, 99, 144, 752, 1326], "occur": [9, 93, 95, 100, 230, 231, 277, 278, 280, 383, 581, 582, 583, 588, 1043, 1117, 1120, 1126, 1282, 1296], "count": [9, 185, 237, 238, 242, 243, 245, 297, 298, 310, 315, 330, 360, 386, 443, 568, 597, 619, 749, 753, 874, 917, 944, 950, 956, 959, 999, 1060, 1179, 1278, 1279, 1406, 1407, 1416], "share": [9, 54, 58, 92, 94, 112, 165, 199, 214, 215, 216, 221, 278, 285, 287, 288, 294, 358, 359, 376, 418, 419, 460, 462, 480, 569, 578, 691, 732, 862, 887, 907, 925, 943, 968, 988, 1007, 1217, 1328], "contact": [9, 92, 688, 1195, 1326], "weighted_projected_graph": [9, 284, 285, 286, 287, 1417], "648": 9, "071": [9, 17], "plot_davis_club": [9, 17], "retain": [10, 102, 110, 230, 284, 285, 286, 287, 288, 1100, 1187, 1295], "pattern": [10, 54, 93, 103, 236, 241, 244, 248, 385, 494, 519, 555, 671, 672, 673, 674, 690, 691, 693, 762, 786, 1036, 1088, 1388, 1413], "add": [10, 11, 26, 34, 41, 45, 49, 52, 61, 71, 88, 89, 91, 93, 94, 101, 102, 105, 106, 115, 151, 152, 153, 154, 156, 157, 158, 164, 207, 222, 223, 229, 282, 285, 341, 374, 411, 412, 423, 428, 430, 431, 450, 460, 581, 582, 583, 589, 614, 615, 618, 619, 654, 690, 701, 717, 718, 796, 850, 853, 854, 855, 856, 857, 892, 895, 898, 899, 900, 901, 902, 928, 931, 934, 935, 936, 937, 938, 973, 976, 979, 980, 981, 982, 983, 984, 1010, 1037, 1038, 1039, 1040, 1042, 1049, 1052, 1053, 1054, 1100, 1154, 1165, 1172, 1185, 1207, 1210, 1217, 1219, 1233, 1234, 1236, 1302, 1326, 1353, 1354, 1356, 1357, 1379, 1380, 1383, 1393, 1394, 1395, 1398, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "compressor": [10, 690, 786], "do": [10, 55, 75, 88, 92, 93, 94, 96, 99, 101, 102, 106, 107, 109, 111, 115, 133, 165, 184, 199, 202, 204, 230, 231, 238, 243, 277, 278, 280, 362, 380, 410, 411, 412, 418, 419, 458, 459, 467, 470, 589, 598, 632, 690, 692, 734, 735, 736, 737, 791, 796, 862, 873, 887, 890, 891, 907, 916, 925, 926, 927, 943, 954, 955, 968, 971, 972, 988, 997, 998, 1007, 1008, 1009, 1037, 1038, 1039, 1040, 1061, 1082, 1102, 1165, 1177, 1189, 1193, 1207, 1210, 1216, 1217, 1227, 1272, 1328, 1393, 1401, 1402, 1407, 1411, 1426], "would": [10, 92, 93, 95, 96, 100, 101, 102, 103, 104, 105, 107, 289, 305, 414, 415, 416, 417, 422, 428, 579, 583, 588, 632, 679, 690, 693, 717, 718, 751, 1217, 1236, 1295, 1296, 1300, 1303, 1326, 1416, 1417], "result": [10, 11, 25, 71, 92, 95, 101, 103, 109, 110, 112, 142, 165, 209, 218, 220, 230, 231, 255, 269, 271, 273, 276, 283, 284, 285, 286, 287, 288, 289, 299, 300, 305, 324, 325, 330, 374, 380, 381, 382, 385, 386, 391, 411, 412, 416, 418, 440, 464, 466, 467, 490, 494, 498, 499, 509, 510, 511, 512, 564, 565, 566, 584, 585, 587, 601, 609, 615, 626, 627, 629, 676, 678, 690, 692, 704, 710, 717, 786, 791, 862, 907, 943, 984, 988, 1038, 1042, 1082, 1094, 1098, 1099, 1102, 1103, 1105, 1112, 1113, 1114, 1116, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1150, 1152, 1154, 1157, 1159, 1160, 1163, 1175, 1177, 1180, 1201, 1222, 1225, 1239, 1278, 1279, 1281, 1296, 1299, 1303, 1308, 1326, 1328, 1331, 1334, 1359, 1402, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1425, 1426], "fewer": [10, 420, 421, 681, 683, 690, 692, 693, 694, 762, 786, 1213, 1215], "compress": [10, 25, 268, 512, 577, 690, 786, 1116, 1242, 1333, 1334, 1339, 1340, 1344, 1350, 1357, 1358, 1371, 1372, 1376], "suptitl": [10, 15], "original_graph": [10, 15, 690], "white_nod": 10, "red_nod": 10, "250": [10, 32, 1165], "white": [10, 21, 25, 82, 83, 127, 214, 215, 216, 220, 427, 1395, 1398, 1406], "add_nodes_from": [10, 15, 16, 36, 70, 71, 82, 89, 115, 156, 165, 199, 207, 236, 237, 248, 265, 267, 268, 423, 425, 426, 469, 555, 690, 796, 855, 862, 887, 892, 900, 907, 925, 928, 936, 943, 968, 973, 981, 988, 1007, 1010, 1037, 1039, 1040, 1065, 1194, 1217, 1291, 1404, 1406, 1413, 1426], "add_edges_from": [10, 15, 16, 36, 41, 70, 82, 89, 115, 132, 151, 158, 165, 199, 204, 207, 236, 248, 287, 327, 376, 422, 423, 425, 426, 460, 469, 501, 511, 512, 572, 574, 588, 688, 690, 705, 706, 707, 709, 730, 742, 743, 796, 853, 857, 862, 887, 891, 892, 898, 902, 907, 925, 927, 928, 934, 938, 943, 956, 962, 963, 968, 972, 973, 979, 983, 988, 999, 1002, 1003, 1007, 1009, 1010, 1037, 1039, 1040, 1070, 1085, 1094, 1135, 1154, 1217, 1287, 1291, 1326, 1404, 1407, 1426], "base_opt": [10, 15], "dict": [10, 15, 19, 25, 39, 54, 57, 58, 67, 70, 88, 101, 102, 107, 109, 144, 145, 148, 157, 159, 160, 165, 168, 169, 176, 179, 184, 189, 190, 195, 197, 200, 202, 204, 207, 220, 237, 239, 240, 252, 290, 309, 310, 329, 334, 336, 353, 408, 411, 412, 416, 422, 427, 470, 473, 481, 482, 496, 502, 512, 545, 561, 563, 565, 566, 575, 577, 578, 579, 580, 588, 614, 628, 631, 636, 637, 638, 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1133, 1134, 1136, 1412, 1413, 1414, 1416, 1426], "ax1": [10, 15, 27, 50, 82], "number_of_edg": [10, 15, 25, 28, 198, 690, 886, 924, 967, 1006, 1059, 1154, 1271, 1406, 1407, 1426], "nonexp_graph": 10, "compression_nod": 10, "summar": [10, 15, 100, 101, 690, 691, 758, 791, 1325, 1328, 1413], "dedensifi": [10, 758], "threshold": [10, 57, 83, 112, 220, 229, 231, 380, 381, 690, 692, 695, 696, 758, 786, 1117, 1193, 1194, 1196, 1197, 1198, 1325, 1398, 1406, 1407, 1408, 1412, 1414], "copi": [10, 16, 38, 44, 93, 95, 106, 167, 196, 199, 202, 203, 204, 205, 284, 285, 286, 287, 288, 341, 388, 390, 392, 406, 433, 434, 435, 436, 437, 453, 460, 469, 521, 584, 585, 587, 596, 599, 602, 603, 605, 606, 607, 610, 611, 613, 614, 633, 636, 690, 864, 885, 887, 890, 891, 909, 925, 926, 927, 945, 963, 966, 968, 971, 972, 990, 1003, 1007, 1008, 1009, 1035, 1038, 1057, 1061, 1063, 1066, 1082, 1083, 1122, 1183, 1189, 1217, 1223, 1227, 1251, 1270, 1294, 1295, 1296, 1403, 1404, 1406, 1407, 1408, 1409, 1412, 1413, 1422, 1425], "nonexp_node_color": 10, "nonexp_node_s": 10, "yellow": [10, 15, 598, 760, 1426], "nonexp_po": 10, "75": [10, 34, 239, 260, 299, 314, 355, 356, 386, 682, 1169, 1170, 1171, 1173, 1404, 1408, 1426], "c_node": [10, 690], "spot": 10, "244": [10, 17, 338], "plot_dedensif": [10, 17], "153": [11, 455], "curiou": 11, "let": [11, 55, 58, 93, 97, 101, 103, 217, 257, 280, 282, 299, 300, 313, 322, 371, 372, 383, 586, 619, 762, 1219, 1278, 1279, 1326, 1425], "defin": [11, 24, 52, 58, 69, 97, 112, 127, 213, 222, 223, 239, 240, 260, 261, 262, 263, 285, 289, 311, 316, 329, 334, 335, 345, 346, 347, 356, 385, 386, 390, 424, 425, 426, 429, 432, 433, 434, 435, 436, 437, 449, 464, 465, 466, 469, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 519, 567, 569, 570, 571, 573, 574, 575, 577, 586, 614, 615, 619, 621, 625, 652, 671, 673, 674, 676, 684, 685, 686, 687, 688, 689, 728, 730, 738, 751, 752, 753, 762, 791, 796, 1037, 1038, 1039, 1040, 1045, 1047, 1071, 1081, 1098, 1147, 1154, 1170, 1172, 1195, 1197, 1280, 1286, 1287, 1288, 1296, 1320, 1321, 1326, 1344, 1353, 1354, 1359, 1363, 1379, 1395, 1402, 1407, 1408, 1412, 1426], "an": [11, 15, 24, 25, 31, 34, 38, 41, 44, 46, 49, 52, 54, 55, 58, 63, 66, 67, 71, 75, 76, 77, 88, 91, 92, 93, 94, 95, 96, 99, 100, 101, 102, 103, 104, 107, 110, 112, 114, 115, 116, 120, 121, 127, 128, 132, 141, 151, 152, 157, 158, 160, 165, 166, 167, 168, 170, 175, 179, 180, 181, 184, 188, 189, 191, 192, 193, 194, 195, 198, 199, 201, 204, 206, 207, 208, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 243, 249, 250, 251, 255, 256, 264, 266, 267, 269, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 291, 292, 293, 294, 295, 297, 298, 299, 301, 302, 306, 307, 308, 309, 311, 312, 315, 316, 318, 319, 320, 322, 324, 325, 326, 329, 330, 332, 341, 342, 343, 345, 346, 347, 348, 349, 350, 351, 353, 357, 362, 363, 364, 365, 366, 370, 373, 374, 375, 377, 378, 379, 380, 381, 383, 384, 385, 387, 388, 389, 390, 392, 394, 395, 400, 402, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 427, 428, 429, 431, 432, 433, 437, 438, 439, 440, 449, 450, 451, 455, 456, 457, 460, 462, 466, 467, 468, 469, 471, 472, 473, 474, 475, 477, 480, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 516, 517, 519, 520, 521, 522, 523, 524, 525, 530, 534, 535, 540, 544, 545, 555, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 577, 578, 579, 580, 584, 586, 588, 589, 590, 593, 594, 595, 596, 597, 598, 601, 604, 605, 607, 610, 611, 615, 616, 618, 619, 624, 626, 627, 631, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 678, 679, 680, 681, 682, 683, 684, 686, 690, 691, 692, 694, 695, 696, 697, 701, 703, 704, 705, 706, 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1103, 1105, 1115, 1117, 1122, 1133, 1135, 1137, 1143, 1144, 1146, 1149, 1150, 1151, 1152, 1154, 1155, 1157, 1159, 1160, 1163, 1166, 1167, 1175, 1177, 1178, 1179, 1181, 1182, 1185, 1186, 1187, 1188, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1202, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1216, 1217, 1218, 1222, 1224, 1225, 1227, 1228, 1229, 1230, 1232, 1234, 1235, 1236, 1239, 1242, 1244, 1250, 1259, 1262, 1263, 1267, 1269, 1270, 1271, 1272, 1273, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1287, 1288, 1291, 1294, 1295, 1296, 1300, 1302, 1303, 1319, 1320, 1321, 1323, 1324, 1326, 1328, 1329, 1331, 1333, 1334, 1336, 1341, 1344, 1352, 1362, 1363, 1365, 1371, 1377, 1378, 1379, 1380, 1381, 1383, 1387, 1393, 1394, 1395, 1397, 1398, 1399, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "process": [11, 13, 52, 76, 92, 93, 94, 96, 97, 98, 102, 104, 180, 222, 226, 232, 274, 331, 338, 373, 383, 405, 406, 440, 455, 464, 465, 466, 592, 624, 691, 760, 786, 871, 914, 952, 995, 1045, 1100, 1175, 1177, 1180, 1216, 1219, 1222, 1225, 1245, 1280, 1290, 1295, 1296, 1299, 1301, 1383, 1395, 1407, 1408, 1412, 1413, 1414, 1419, 1426], "follow": [11, 25, 44, 49, 52, 53, 65, 67, 83, 86, 91, 92, 93, 94, 95, 97, 99, 100, 101, 102, 103, 108, 110, 111, 128, 132, 151, 161, 171, 183, 207, 213, 227, 229, 230, 231, 243, 280, 305, 338, 343, 351, 362, 373, 378, 380, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 440, 452, 453, 465, 466, 496, 502, 503, 504, 505, 506, 507, 508, 588, 598, 599, 602, 615, 636, 679, 748, 750, 760, 762, 791, 853, 867, 892, 898, 912, 928, 934, 948, 973, 979, 993, 1010, 1102, 1103, 1105, 1144, 1165, 1175, 1179, 1185, 1188, 1200, 1201, 1209, 1219, 1225, 1233, 1234, 1241, 1251, 1260, 1274, 1275, 1276, 1277, 1281, 1296, 1315, 1323, 1326, 1328, 1329, 1388, 1393, 1395, 1399, 1404, 1406, 1407, 1409, 1411, 1412, 1413, 1425, 1426], "given": [11, 38, 44, 62, 64, 67, 91, 99, 101, 103, 112, 116, 141, 142, 144, 152, 158, 193, 197, 208, 211, 212, 227, 229, 235, 236, 248, 249, 260, 264, 266, 269, 271, 273, 274, 276, 279, 281, 283, 284, 285, 286, 287, 288, 320, 329, 331, 338, 344, 351, 353, 357, 362, 363, 364, 365, 373, 378, 380, 381, 385, 439, 454, 455, 460, 462, 470, 477, 478, 480, 497, 511, 512, 513, 559, 560, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 576, 578, 579, 580, 588, 589, 590, 614, 615, 616, 622, 623, 659, 660, 661, 662, 676, 677, 678, 679, 681, 683, 684, 686, 690, 691, 693, 697, 698, 699, 700, 702, 703, 704, 706, 707, 708, 709, 728, 729, 730, 731, 732, 739, 748, 753, 761, 782, 786, 854, 857, 882, 899, 902, 921, 935, 938, 963, 980, 983, 1003, 1046, 1085, 1086, 1094, 1101, 1102, 1135, 1144, 1151, 1162, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1189, 1199, 1200, 1201, 1206, 1207, 1208, 1209, 1210, 1221, 1222, 1240, 1269, 1273, 1274, 1276, 1295, 1300, 1302, 1315, 1323, 1353, 1354, 1379, 1380, 1394, 1395, 1406], "digit": [11, 70, 99], "base": [11, 15, 38, 43, 55, 58, 69, 93, 94, 100, 101, 102, 103, 107, 128, 132, 199, 203, 205, 212, 216, 220, 229, 296, 297, 301, 302, 303, 308, 309, 310, 311, 312, 322, 323, 324, 325, 329, 330, 337, 343, 346, 347, 362, 371, 373, 374, 380, 381, 382, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 425, 426, 427, 428, 430, 431, 449, 464, 466, 494, 498, 499, 500, 509, 510, 545, 555, 564, 566, 569, 574, 581, 614, 616, 660, 667, 680, 688, 691, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 717, 732, 738, 758, 761, 762, 786, 791, 796, 887, 925, 934, 935, 968, 979, 980, 1007, 1036, 1037, 1038, 1041, 1043, 1082, 1088, 1182, 1229, 1235, 1253, 1267, 1296, 1320, 1321, 1323, 1326, 1383, 1387, 1392, 1395, 1402, 1403, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1421, 1425], "obtain": [11, 91, 165, 207, 282, 345, 346, 347, 380, 383, 387, 388, 389, 390, 394, 465, 511, 606, 618, 619, 656, 722, 742, 743, 760, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1164, 1253, 1272, 1278, 1279, 1323, 1326, 1356, 1357, 1402, 1426], "seri": [11, 444, 616, 680, 1215, 1286], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1177, 1179, 1192, 1222], "end": [11, 25, 36, 52, 95, 101, 106, 153, 154, 206, 215, 227, 267, 268, 300, 332, 333, 342, 371, 372, 427, 614, 618, 619, 626, 627, 631, 632, 634, 635, 636, 639, 640, 650, 651, 652, 653, 654, 655, 660, 664, 667, 677, 678, 680, 734, 736, 1038, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1133, 1135, 1152, 1165, 1206, 1229, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1342, 1344, 1350, 1353, 1357, 1358, 1368, 1371, 1372, 1375, 1376, 1379, 1404, 1413], "In": [11, 16, 27, 43, 54, 57, 58, 88, 92, 93, 94, 95, 97, 99, 100, 101, 103, 110, 115, 127, 132, 133, 175, 184, 199, 217, 229, 230, 231, 235, 240, 257, 258, 259, 278, 283, 286, 288, 289, 299, 311, 312, 324, 325, 329, 350, 357, 378, 379, 380, 410, 413, 414, 415, 422, 429, 443, 447, 450, 458, 460, 494, 498, 499, 501, 510, 565, 568, 572, 574, 590, 591, 615, 619, 621, 652, 653, 654, 657, 658, 663, 670, 675, 676, 690, 691, 701, 703, 717, 718, 719, 730, 732, 740, 741, 742, 743, 761, 762, 767, 770, 789, 791, 796, 869, 873, 887, 916, 925, 954, 955, 968, 997, 998, 1007, 1037, 1038, 1039, 1040, 1042, 1043, 1066, 1100, 1101, 1117, 1154, 1168, 1199, 1203, 1206, 1207, 1208, 1210, 1216, 1217, 1222, 1226, 1231, 1233, 1241, 1295, 1296, 1300, 1320, 1321, 1326, 1328, 1350, 1394, 1398, 1399, 1404, 1405, 1406, 1407, 1408, 1409, 1413, 1414, 1426], "languag": [11, 92, 99, 110, 1042, 1324, 1341, 1342, 1344, 1381, 1382, 1383, 1411], "discret": [11, 104, 235, 249, 362, 409, 513, 517, 518, 618, 1164, 1165, 1178, 1180, 1186, 1190, 1204, 1278, 1279, 1282, 1314, 1315, 1323, 1406], "global": [11, 103, 314, 341, 410, 477, 486, 487, 509, 592, 1045, 1269, 1296, 1301, 1304, 1305, 1328, 1407, 1409, 1411], "attractor": [11, 388], "map": [11, 34, 38, 52, 67, 101, 102, 103, 115, 125, 144, 145, 148, 166, 169, 197, 238, 243, 264, 350, 369, 391, 412, 416, 417, 418, 419, 423, 424, 425, 426, 431, 440, 460, 530, 531, 534, 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1422, 1423, 1424, 1425, 1426], "108": [11, 1216], "513": [11, 1398, 1406], "reach": [11, 99, 100, 314, 324, 327, 376, 383, 387, 389, 390, 394, 410, 411, 412, 418, 419, 494, 498, 499, 510, 564, 566, 626, 627, 632, 640, 643, 652, 693, 711, 758, 1188, 1207, 1210, 1407], "orbit": 11, "up": [11, 70, 80, 93, 94, 97, 99, 100, 101, 104, 107, 132, 133, 346, 347, 377, 423, 427, 509, 530, 540, 577, 619, 652, 653, 657, 748, 1036, 1038, 1061, 1066, 1082, 1088, 1102, 1144, 1148, 1173, 1213, 1215, 1272, 1326, 1328, 1355, 1358, 1395, 1396, 1402, 1404, 1406, 1410, 1411, 1413, 1414, 1416, 1417, 1420, 1426], "reveal": [11, 711, 786], "maximum": [11, 112, 115, 209, 210, 211, 212, 214, 215, 217, 222, 224, 227, 257, 259, 264, 277, 278, 279, 281, 288, 296, 304, 311, 312, 315, 316, 317, 318, 319, 321, 324, 328, 330, 339, 341, 342, 343, 346, 347, 352, 356, 361, 373, 377, 380, 382, 383, 385, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 428, 440, 472, 473, 494, 498, 499, 500, 501, 502, 503, 506, 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1184, 1404, 1413, 1426], "magenta": 16, "six": 16, "classifi": [16, 512, 684, 750], "four": [16, 23, 47, 86, 99, 102, 165, 263, 585, 587, 692, 862, 907, 943, 988, 1039, 1040, 1164, 1193, 1199, 1211, 1323, 1407, 1408, 1414, 1426], "green": [16, 32, 38, 70, 93, 115, 464, 598, 760, 1302, 1330, 1394, 1412, 1426], "goal": [16, 88, 92, 99, 105, 107, 127, 383, 626, 627, 717, 718, 1042], "g_ex": 16, "m": [16, 25, 28, 30, 31, 63, 65, 67, 91, 93, 96, 102, 106, 110, 112, 128, 181, 191, 201, 209, 211, 212, 219, 227, 231, 235, 236, 238, 239, 240, 241, 243, 244, 248, 257, 258, 259, 263, 272, 274, 275, 278, 280, 282, 284, 293, 294, 296, 300, 301, 302, 308, 309, 315, 316, 317, 330, 338, 341, 343, 345, 352, 355, 356, 361, 362, 370, 380, 383, 385, 412, 429, 431, 432, 433, 451, 462, 479, 494, 498, 499, 509, 510, 511, 512, 519, 545, 555, 569, 582, 584, 585, 587, 588, 606, 614, 619, 625, 652, 658, 659, 684, 686, 691, 692, 706, 748, 749, 761, 762, 775, 872, 880, 889, 953, 961, 970, 1060, 1151, 1155, 1157, 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438, 452, 453, 468, 479, 481, 482, 490, 496, 500, 504, 505, 508, 514, 517, 518, 594, 608, 624, 632, 677, 678, 679, 680, 686, 693, 758, 775, 780, 796, 873, 916, 955, 998, 1037, 1038, 1039, 1040, 1098, 1099, 1100, 1130, 1133, 1175, 1177, 1180, 1181, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1252, 1269, 1296, 1323, 1325, 1326, 1328, 1330, 1351, 1352, 1353, 1354, 1382, 1388, 1395, 1401, 1404, 1406, 1407, 1412, 1413, 1421, 1426], "lollipop": [19, 1157, 1426], "vertex": [19, 115, 211, 235, 249, 281, 289, 315, 322, 330, 338, 359, 360, 373, 387, 394, 397, 427, 428, 432, 438, 477, 491, 580, 606, 615, 616, 619, 622, 623, 624, 688, 689, 758, 1164, 1185, 1190, 1206, 1218, 1219, 1222, 1251, 1323, 1326, 1400, 1406, 1407], "length": [19, 39, 52, 67, 102, 120, 151, 232, 288, 295, 297, 298, 299, 306, 307, 310, 314, 315, 316, 320, 322, 326, 327, 329, 330, 332, 333, 341, 343, 345, 346, 347, 371, 372, 383, 384, 451, 459, 462, 467, 469, 470, 473, 513, 515, 516, 517, 520, 521, 591, 592, 627, 628, 629, 630, 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1425, 1426], "plot_simple_graph": [21, 22], "494": 22, "auto_examples_bas": 22, "custom": [23, 32, 33, 35, 47, 86, 102, 115, 204, 285, 464, 546, 547, 548, 552, 553, 554, 556, 557, 558, 704, 706, 707, 708, 796, 891, 934, 935, 979, 980, 1037, 1039, 1040, 1094, 1100, 1193, 1197, 1198, 1202, 1302, 1383, 1407, 1408, 1412, 1413, 1426], "chess": [23, 47, 86, 1406], "master": [23, 47, 86, 476, 1406], "icon": [23, 47, 86, 93, 1413], "ego": [23, 47, 86, 305, 688, 1325, 1406, 1407], "eigenvalu": [23, 47, 86, 311, 312, 313, 324, 325, 332, 371, 566, 593, 1115, 1191, 1275, 1276, 1277, 1289, 1290, 1291, 1292, 1293, 1327, 1406, 1413], "hous": [23, 47, 86, 1252, 1253, 1413], "With": [23, 47, 54, 86, 101, 103, 110, 337, 511, 760, 1118, 1130, 1184, 1229, 1297, 1330, 1338, 1388, 1394, 1402, 1404, 1405, 1407], "knuth": [23, 47, 69, 71, 86, 455, 1226, 1268, 1302, 1413], "mile": [23, 47, 86, 1406, 1413], "multipartit": [23, 47, 86, 1109, 1151, 1162, 1395, 1406, 1407, 1413], "rainbow": [23, 47, 86, 1413], 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1037, 1039, 1040, 1133, 1135, 1173, 1194, 1393, 1402, 1406, 1408, 1410, 1411, 1412, 1413, 1414, 1425], "bear": 52, "also": [52, 54, 55, 56, 57, 58, 63, 75, 88, 92, 93, 94, 95, 97, 99, 101, 102, 103, 107, 110, 111, 156, 159, 162, 168, 176, 177, 180, 184, 189, 190, 200, 207, 208, 211, 226, 230, 280, 287, 293, 301, 302, 303, 308, 309, 323, 324, 325, 342, 369, 388, 391, 411, 412, 416, 417, 418, 419, 423, 424, 425, 427, 435, 440, 450, 464, 465, 466, 467, 470, 500, 501, 502, 503, 506, 507, 508, 509, 511, 512, 545, 555, 577, 581, 585, 587, 597, 600, 604, 605, 607, 610, 611, 612, 615, 618, 676, 679, 688, 690, 691, 741, 760, 761, 786, 796, 850, 855, 858, 860, 865, 870, 871, 873, 878, 879, 888, 892, 895, 900, 903, 905, 910, 914, 916, 928, 931, 936, 939, 941, 946, 948, 951, 952, 955, 960, 969, 973, 976, 981, 984, 986, 991, 993, 995, 998, 1010, 1037, 1039, 1040, 1082, 1094, 1102, 1103, 1117, 1130, 1133, 1134, 1135, 1136, 1137, 1142, 1145, 1154, 1165, 1190, 1192, 1193, 1195, 1199, 1217, 1222, 1224, 1228, 1230, 1232, 1247, 1253, 1257, 1269, 1270, 1272, 1278, 1279, 1295, 1296, 1297, 1302, 1303, 1324, 1326, 1343, 1352, 1363, 1378, 1380, 1382, 1393, 1395, 1402, 1404, 1407, 1409, 1411, 1412, 1413, 1414, 1417, 1425, 1426], "osm": [52, 56], "footprint": [52, 88], "public": [52, 92, 100, 110, 257, 258, 259, 286, 288, 330, 442, 447, 448, 1328, 1412, 1413, 1414, 1419, 1426], "park": 52, "school": 52, "transit": [52, 70, 103, 213, 467, 468, 469, 545, 565, 566, 586, 748, 750, 758, 761, 1202, 1234, 1235, 1246, 1283, 1284, 1395, 1404, 1406, 1408, 1411, 1413], "etc": [52, 88, 94, 95, 99, 101, 102, 107, 111, 151, 152, 156, 157, 158, 160, 162, 163, 165, 168, 170, 171, 172, 186, 187, 189, 192, 193, 194, 195, 198, 199, 202, 204, 232, 267, 345, 615, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 859, 860, 861, 862, 865, 866, 867, 868, 875, 876, 878, 881, 882, 883, 884, 886, 887, 890, 891, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 910, 911, 912, 913, 915, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 954, 957, 958, 963, 964, 965, 967, 968, 972, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 997, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1038, 1052, 1066, 1075, 1080, 1084, 1130, 1134, 1136, 1154, 1296, 1303, 1324, 1333, 1337, 1338, 1395, 1404, 1405, 1407, 1426], "essenti": [52, 103, 346, 1038, 1217, 1234, 1326], "task": [52, 466, 1042], "relationship": [52, 55, 58, 70, 305, 688, 1326], "featur": [52, 91, 93, 94, 97, 99, 102, 103, 104, 107, 110, 382, 494, 512, 619, 796, 1037, 1038, 1039, 1040, 1042, 1117, 1130, 1133, 1217, 1296, 1328, 1382, 1383, 1396, 1400, 1401, 1403, 1404, 1407, 1410, 1411, 1412, 1425], "queen": [52, 55, 58], "rook": [52, 54, 58], "brief": [52, 93, 132, 619], "explan": [52, 94, 105, 161, 679], "represent": [52, 110, 202, 204, 237, 242, 245, 246, 247, 265, 266, 268, 282, 283, 327, 512, 555, 629, 728, 730, 762, 786, 890, 891, 926, 971, 972, 1008, 1091, 1092, 1094, 1095, 1098, 1099, 1100, 1101, 1117, 1120, 1126, 1130, 1270, 1281, 1326, 1332, 1335, 1336, 1339, 1341, 1347, 1370, 1383, 1393, 1399, 1405, 1406, 1413], "primal": [52, 55, 508, 581], "dual": [52, 54, 55, 581, 1227, 1410, 1413], "sens": [52, 97, 99, 104, 199, 310, 460, 586, 791, 887, 925, 968, 1007, 1217, 1234, 1269, 1326, 1403, 1404], "approach": [52, 55, 99, 101, 103, 104, 107, 115, 341, 345, 462, 464, 466, 500, 519, 616, 678, 1094, 1175, 1188, 1202, 1222, 1407, 1413], "segment": [52, 55, 338], "major": [52, 95, 98, 99, 100, 102, 103, 104, 106, 107, 1393, 1394, 1403, 1404, 1407], "studi": [52, 91, 110, 606, 1192, 1196, 1323, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "topologi": [52, 55, 435, 436, 512, 681, 683, 748, 1202, 1217, 1225, 1229, 1233, 1241, 1326], "encod": [52, 55, 58, 67, 99, 141, 249, 267, 268, 619, 758, 775, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1345, 1348, 1349, 1350, 1354, 1355, 1358, 1363, 1368, 1371, 1372, 1375, 1376, 1382, 1406, 1407, 1412], "angular": [52, 55], "inform": [52, 66, 92, 93, 99, 100, 101, 102, 103, 107, 111, 112, 121, 132, 159, 165, 200, 202, 204, 220, 226, 230, 231, 249, 301, 302, 303, 308, 309, 314, 323, 324, 325, 338, 405, 406, 438, 453, 455, 480, 488, 500, 512, 564, 566, 568, 572, 573, 574, 583, 592, 614, 619, 624, 691, 775, 782, 786, 796, 858, 862, 888, 890, 891, 903, 907, 926, 927, 939, 943, 969, 971, 972, 984, 988, 1008, 1009, 1037, 1039, 1040, 1042, 1112, 1141, 1143, 1185, 1206, 1214, 1216, 1217, 1218, 1219, 1267, 1280, 1290, 1296, 1356, 1373, 1375, 1376, 1381, 1383, 1389, 1390, 1393, 1394, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "angl": [52, 55, 1114, 1116], "instead": [52, 93, 94, 101, 102, 103, 106, 141, 165, 170, 282, 320, 338, 366, 370, 390, 392, 399, 405, 406, 407, 411, 412, 416, 417, 418, 419, 424, 425, 427, 500, 561, 562, 563, 585, 587, 632, 727, 729, 731, 733, 734, 735, 736, 737, 796, 862, 866, 907, 911, 943, 947, 988, 992, 1037, 1038, 1039, 1040, 1097, 1102, 1103, 1124, 1127, 1135, 1172, 1179, 1184, 1186, 1192, 1193, 1199, 1207, 1217, 1300, 1342, 1375, 1383, 1393, 1394, 1395, 1397, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1420, 1421, 1423, 1424, 1425, 1426], "nonplanar": [52, 1250], "form": [52, 55, 110, 151, 170, 220, 238, 377, 381, 391, 422, 427, 440, 449, 450, 451, 488, 500, 517, 521, 567, 568, 569, 570, 571, 572, 573, 574, 578, 579, 580, 588, 589, 677, 679, 697, 711, 717, 718, 719, 729, 730, 731, 748, 752, 767, 786, 791, 853, 866, 898, 911, 934, 947, 979, 992, 1038, 1064, 1085, 1146, 1167, 1199, 1206, 1215, 1217, 1222, 1240, 1243, 1245, 1248, 1252, 1399, 1406, 1407, 1426], "flow": [52, 66, 105, 278, 296, 301, 302, 303, 308, 309, 323, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 427, 428, 430, 431, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 519, 559, 756, 758, 1267, 1325, 1395, 1399, 1400, 1403, 1406, 1407, 1408, 1411, 1414, 1425], "dead": 52, "detail": [52, 53, 86, 92, 93, 97, 99, 100, 128, 252, 253, 256, 257, 258, 259, 260, 277, 278, 281, 282, 284, 285, 286, 287, 288, 297, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 422, 427, 476, 494, 498, 499, 500, 509, 510, 511, 512, 574, 691, 711, 720, 735, 737, 791, 796, 1037, 1039, 1040, 1102, 1105, 1133, 1137, 1140, 1207, 1296, 1319, 1345, 1348, 1349, 1350, 1393, 1399, 1400, 1401, 1402, 1406, 1413, 1414, 1426], "methodologi": 52, "avail": [52, 93, 99, 100, 101, 103, 141, 184, 226, 232, 280, 422, 425, 426, 585, 587, 780, 873, 916, 955, 998, 1039, 1042, 1194, 1196, 1197, 1198, 1328, 1331, 1334, 1393, 1394, 1396, 1402, 1405, 1406, 1409, 1412, 1413, 1426], "1016": [52, 112, 226, 231, 274, 297, 298, 299, 303, 306, 307, 313, 322, 323, 338, 346, 347, 455, 1233], "compenvurbsi": 52, "2017": [52, 227, 512, 1207, 1208, 1406, 1407], "004": [52, 341], "scienc": [52, 91, 101, 105, 107, 109, 110, 112, 219, 228, 249, 296, 301, 302, 303, 308, 309, 323, 346, 347, 409, 412, 431, 441, 445, 446, 453, 476, 498, 618, 619, 680, 681, 683, 1203, 1223, 1255], "pydata": [52, 1413, 1423, 1424, 1425], "stack": [52, 111, 346, 693, 1045, 1046], "showcas": [53, 86, 93, 109], "analys": [53, 70, 86, 310], "ecosystem": [53, 86, 99, 100, 104, 107, 110, 1425], "descript": [53, 86, 93, 97, 464, 466, 704, 717, 786, 1130, 1131, 1132, 1133, 1138, 1139, 1140, 1141, 1142, 1207, 1222, 1242, 1407, 1411, 1413, 1421, 1422, 1425], "plu": [54, 386, 583, 1036, 1088, 1148, 1253], "voronoi": [54, 752, 758, 1325, 1407], "cholera": [54, 57], "broad": [54, 57, 1296], "pump": [54, 57], "record": [54, 57, 94, 99, 691, 1426], "john": [54, 57, 91, 278, 568, 572, 685, 1205, 1250, 1408, 1413], "snow": [54, 57], "1853": [54, 57], "method": [54, 57, 58, 75, 88, 92, 93, 95, 101, 102, 103, 107, 112, 143, 161, 164, 165, 185, 186, 187, 190, 200, 202, 204, 206, 207, 226, 231, 232, 250, 260, 261, 262, 299, 301, 302, 303, 308, 309, 311, 312, 323, 324, 336, 374, 376, 379, 380, 381, 385, 423, 440, 451, 462, 476, 500, 514, 527, 537, 545, 564, 566, 568, 572, 581, 583, 600, 604, 615, 632, 633, 635, 636, 654, 655, 656, 671, 672, 673, 674, 684, 692, 719, 720, 733, 738, 752, 775, 786, 852, 862, 874, 875, 876, 879, 888, 890, 891, 892, 897, 907, 917, 918, 919, 926, 927, 928, 933, 934, 935, 943, 956, 957, 958, 971, 972, 973, 978, 979, 980, 988, 999, 1000, 1001, 1008, 1009, 1010, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1033, 1038, 1043, 1044, 1045, 1046, 1066, 1174, 1182, 1184, 1193, 1197, 1275, 1276, 1277, 1280, 1296, 1301, 1302, 1323, 1326, 1363, 1395, 1399, 1403, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1422, 1425, 1426], "shown": [54, 57, 100, 102, 517, 518, 947, 992, 1275, 1276, 1277, 1300, 1349, 1404], "centroid": [54, 57, 58], "libpys": [54, 55, 57, 58], "cg": [54, 102, 296, 301, 302, 303, 308, 309, 323, 588], "voronoi_fram": 54, "contextili": [54, 55, 57], "add_basemap": [54, 55, 57], "geopackag": [54, 55, 56, 57], "sqlite": [54, 57], "reli": [54, 57, 99, 103, 362, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 502, 503, 506, 507, 1393, 1407, 1411, 1425], "fiona": [54, 57], "level": [54, 57, 101, 103, 104, 106, 111, 112, 115, 125, 165, 220, 322, 334, 336, 374, 380, 381, 387, 389, 390, 394, 423, 427, 640, 691, 770, 786, 862, 907, 943, 988, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1094, 1108, 1155, 1202, 1207, 1208, 1236, 1296, 1323, 1328, 1396, 1399, 1407, 1412, 1413, 1414], "interfac": [54, 57, 58, 75, 76, 96, 98, 99, 101, 102, 107, 109, 110, 184, 429, 496, 673, 758, 761, 762, 780, 873, 916, 955, 998, 1042, 1044, 1326, 1328, 1393, 1396, 1398, 1402, 1404, 1405, 1406, 1409, 1413, 1414, 1426], "kind": [54, 57, 58, 92, 93, 94, 99, 208, 466, 722, 1202, 1326, 1383], "read_fil": [54, 55, 57, 58], "cholera_cas": [54, 57], "gpkg": [54, 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"kazimierz": [91, 1412], "wojciechowski": [91, 1412], "gaetano": [91, 1412], "pietro": 91, "paolo": [91, 320, 1412], "carpinato": [91, 1412], "carghaez": 91, "gaetanocarpinato": 91, "arun": 91, "nampal": 91, "arunwis": [91, 1412], "b57845b7": 91, "duve": [91, 1412], "shashi": [91, 1412], "prakash": 91, "tripathi": [91, 517, 1412], "itsshavar": 91, "itsshashitripathi": 91, "danni": [91, 1412], "niquett": [91, 1412], "trimbl": [91, 1412, 1414], "jamestrimbl": 91, "matthia": [91, 1412, 1413, 1416, 1422], "bruhn": [91, 1412], "mbruhn": 91, "philip": 91, "boalch": 91, "knyazev": [91, 1414], "sultan": [91, 1414, 1416, 1422, 1425], "orazbayev": [91, 1414, 1416, 1422, 1425], "supplementari": 91, "incomplet": [91, 112, 1406, 1408], "commit": [91, 92, 93, 94, 99, 100, 105, 106, 1407, 1409, 1411, 1412, 1413, 1414, 1415, 1417, 1419, 1425], "git": [91, 93, 94, 97, 99, 106, 111, 1416, 1419], "repositori": [91, 93, 99, 106, 1406], "grep": [91, 97], "uniq": 91, "histor": [91, 99, 101, 1217], 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1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424], "about": [96, 99, 100, 101, 103, 111, 115, 230, 231, 249, 413, 423, 488, 494, 498, 499, 509, 510, 619, 761, 762, 1038, 1061, 1066, 1141, 1217, 1296, 1323, 1326, 1406, 1407, 1411, 1412, 1413, 1414, 1416, 1422, 1426], "emeritu": 96, "introduct": [96, 110, 311, 312, 324, 325, 383, 385, 464, 466, 618, 619, 1155, 1269, 1302, 1325, 1411], "guidelin": [96, 99, 1416, 1419], "divers": [96, 107], "enforc": [96, 115, 693, 694, 1419, 1425], "endnot": 96, "diverg": [96, 1187, 1325, 1395], "upstream": [96, 464, 1419], "comparison": [96, 107, 231, 464, 494, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 561, 562, 563, 615, 671, 673, 1413], "mentor": [96, 109, 1413, 1414, 1425], "pedagog": [96, 109, 347, 452, 722, 1405, 1414], "me": [96, 1393], "roadmap": [96, 1412, 1413], "linear": [96, 110, 112, 132, 142, 217, 280, 296, 301, 302, 303, 308, 309, 313, 323, 325, 338, 343, 378, 405, 406, 423, 488, 515, 614, 619, 686, 1108, 1133, 1135, 1180, 1182, 1269, 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97, "offici": [97, 99, 1402], "inclus": [97, 99, 109, 220, 534, 544, 729, 731, 1188, 1214], "criteria": [97, 1425], "addit": [97, 99, 100, 103, 107, 111, 115, 184, 350, 423, 476, 534, 544, 545, 734, 736, 761, 791, 796, 873, 916, 947, 955, 979, 992, 998, 1036, 1037, 1039, 1040, 1088, 1117, 1195, 1272, 1296, 1302, 1326, 1345, 1348, 1349, 1350, 1381, 1382, 1383, 1395, 1403, 1404, 1405, 1406, 1407, 1413, 1414, 1425, 1426], "fit": [97, 110, 1326], "enhanc": [98, 99, 107, 341, 508, 1296, 1412, 1425], "berkelei": [99, 100, 103, 618, 619], "draft": [99, 100, 102, 103, 104, 1411, 1412, 1413, 1416], "stand": [99, 545, 1387], "primari": [99, 103, 1414], "gone": 99, "concis": [99, 110, 791, 1413, 1414], "rational": 99, "consensu": [99, 100], "dissent": 99, "opinion": [99, 100, 104], "revis": [99, 444, 732], "track": [99, 101, 102, 103, 104, 107, 115, 370, 387, 389, 390, 394, 598, 1296, 1302, 1406, 1411, 1412], "codebas": [99, 1296, 1404, 1405, 1412], "meta": [99, 106], "inject": 99, "repo": [99, 106, 1413, 1425], "success": [99, 315, 330, 496, 608, 692, 1180, 1242, 1426], "tend": [99, 593, 1175, 1326], "doubt": [99, 1426], "champion": 99, "attempt": [99, 101, 194, 202, 204, 282, 284, 285, 286, 287, 288, 361, 362, 377, 425, 426, 584, 692, 693, 694, 786, 883, 890, 891, 922, 926, 927, 964, 971, 972, 1004, 1008, 1009, 1041, 1122, 1225, 1237, 1238, 1302, 1333, 1347, 1371, 1393, 1394, 1406, 1411, 1412, 1421, 1425], "ascertain": 99, "suitabl": [99, 110, 659, 693, 694, 1165, 1359, 1363, 1365, 1385, 1390], "0000": 99, "backward": [99, 217, 1199, 1402, 1404, 1406], "compat": [99, 429, 496, 691, 1302, 1404, 1405, 1406, 1412, 1414], "impact": [99, 100, 107, 329, 796, 1037, 1039, 1040], "broader": 99, "scope": [99, 107, 1045, 1413], "earliest": [99, 465], "conveni": [99, 101, 152, 497, 501, 504, 505, 508, 615, 796, 854, 899, 935, 980, 1037, 1038, 1039, 1040, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1270, 1296, 1326, 1394, 1405, 1409, 1426], "expand": [99, 101, 373, 653, 1038, 1190, 1325, 1395, 1406, 1407, 1408, 1413, 1424, 1425], "prototyp": 99, "sound": 99, "principl": [99, 100, 103, 132], "impract": 99, "wip": [99, 1407, 1408, 1412], "incorpor": [99, 1399, 1426], "stabil": [99, 334, 335, 681, 683], "provision": 99, "short": [99, 104, 161, 227, 1038, 1066, 1195, 1406], "unlik": [99, 100, 212, 366, 425, 426, 1383], "reject": [99, 100, 104, 1319], "withdrawn": [99, 104], "wherev": [99, 1282], "defer": [99, 101, 104, 280], "challeng": 99, "wider": 99, "done": [99, 101, 102, 230, 231, 249, 373, 440, 466, 517, 564, 566, 614, 690, 762, 1046, 1219, 1296, 1326, 1404], "fact": [99, 352, 460, 619, 1207, 1210, 1404], "actual": [99, 115, 132, 165, 210, 213, 214, 215, 216, 220, 288, 385, 450, 577, 625, 692, 717, 718, 862, 907, 943, 988, 1102, 1103, 1199, 1296, 1324, 1326, 1402, 1416], "compet": [99, 583], "accordingli": [99, 454, 1110, 1407, 1425], "supersed": [99, 104], "render": [99, 216, 410, 413, 1406], "obsolet": [99, 267, 1337, 1406, 1407], "never": [99, 184, 388, 608, 873, 916, 955, 998, 1236], "meant": [99, 291, 292, 631, 1217, 1326, 1413, 1417], "concret": [99, 100], "think": [99, 102, 230, 231, 299, 761, 1426], "bodi": [99, 1243], "briefli": 99, "sentenc": [99, 100], "substant": 99, "pipermail": 99, "2018": [99, 315, 330, 437, 1406, 1408, 1409], "june": [99, 691, 1255, 1398, 1402, 1406, 1419, 1420], "078345": 99, "verg": 99, "chanc": [99, 230, 1234, 1296], "period": [99, 1211, 1212, 1213, 1215, 1297, 1403, 1406, 1412], "beyond": [99, 107, 383, 1210, 1236], "fine": 99, "shouldn": [99, 102], "rigid": 99, "compromis": 99, "followup": [99, 1413], "notifi": [99, 1414], "celebratori": 99, "emoji": 99, "again": [99, 428, 761, 1217, 1403, 1407, 1411, 1416], "unusu": [99, 1393], "disagr": [99, 100], "escal": [99, 100], "controversi": [99, 107], "ultim": 99, "practic": [99, 210, 220, 481, 482, 494, 619, 653, 1328, 1405], "precis": [99, 312, 568, 572, 581, 1269, 1395, 1409], "natur": [99, 102, 109, 376, 443, 466, 585, 587, 618, 753, 1154, 1217, 1225, 1241, 1296, 1326, 1393, 1410], "utf": [99, 267, 268, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1355, 1358, 1368, 1371, 1372, 1375, 1376, 1387, 1406], "restructuredtext": 99, "restructuredtextprim": 99, "dd": [99, 104, 1094], "mmm": 99, "yyyi": [99, 104], "dom": 99, "ain": 99, "separ": [99, 102, 106, 152, 157, 158, 195, 214, 215, 258, 265, 266, 267, 268, 299, 322, 343, 427, 428, 454, 464, 758, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1045, 1112, 1116, 1193, 1195, 1325, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1369, 1370, 1371, 1372, 1395, 1406, 1407, 1412, 1413, 1425, 1426], "older": 99, "brows": 99, "colgat": [100, 110], "deadlock": 100, "websit": [100, 106, 1165, 1382, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "ongo": [100, 1405], "trust": [100, 1381, 1383], "cast": [100, 101, 1412, 1422, 1425], "vote": [100, 337, 1412], "therebi": 100, "adher": 100, "nomin": 100, "lazi": [100, 1283, 1284], "unanim": 100, "agreement": [100, 1202], "initi": [100, 102, 141, 230, 231, 282, 315, 324, 325, 338, 373, 377, 378, 466, 495, 511, 512, 525, 535, 615, 692, 719, 733, 796, 850, 895, 931, 976, 1037, 1039, 1040, 1102, 1105, 1108, 1117, 1185, 1186, 1187, 1188, 1223, 1227, 1234, 1278, 1279, 1296, 1302, 1323, 1394, 1395, 1406, 1411, 1412, 1413, 1414], "voic": 100, "smooth": 100, "strateg": 100, "plan": [100, 1394, 1405, 1407, 1413], "fund": [100, 1414, 1425], "theirs": 100, "pursu": 100, "pictur": 100, "perspect": [100, 104, 1195, 1326], "timefram": 100, "entiti": [100, 1345, 1348, 1349, 1350, 1382, 1426], "occasion": [100, 230], "seek": [100, 762, 1352, 1354, 1378, 1380, 1387], "tri": [100, 112, 343, 380, 931, 976, 1039, 1040, 1175, 1181, 1225, 1237, 1238, 1404], "distinguish": [100, 934, 962, 979, 1002, 1040], "fundament": [100, 107, 110, 338, 449, 618, 619, 1217, 1413], "flaw": 100, "forward": [100, 217, 450, 711, 717, 718], "typo": [100, 1396, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1421], "land": 100, "outlin": [100, 249, 336, 462, 1407], "templat": [100, 1413], "taken": [100, 101, 145, 148, 207, 443, 450, 717, 718, 749, 761, 892, 928, 973, 1010, 1117, 1409], "suffici": [100, 101, 1326], "scikit": [100, 103, 109], "expos": [101, 374, 1405], "nodeview": [101, 184, 391, 598, 599, 601, 602, 603, 604, 695, 873, 916, 955, 998, 1036, 1088, 1349, 1362, 1404, 1407], "nodedataview": [101, 184, 391, 591, 592, 600, 873, 916, 955, 998, 1217, 1426], "edgeview": [101, 590, 591, 592, 598, 599, 600, 601, 602, 603, 604, 612, 624, 770, 910, 1036, 1088, 1098, 1404, 1413], "edgedataview": [101, 168, 189, 865, 878, 910, 946, 960, 991, 1098, 1217, 1362, 1412, 1426], "semant": [101, 531, 541, 762, 1403, 1405], "inher": [101, 220, 427], "impli": [101, 110, 132, 220, 312, 314, 327, 455, 466, 511, 512, 545, 1296], "element": [101, 102, 230, 231, 270, 291, 292, 311, 350, 371, 391, 457, 464, 518, 559, 560, 578, 579, 580, 586, 640, 656, 671, 673, 675, 677, 728, 730, 739, 749, 752, 1036, 1038, 1048, 1049, 1050, 1051, 1087, 1088, 1135, 1137, 1173, 1206, 1211, 1212, 1217, 1237, 1238, 1240, 1249, 1272, 1277, 1278, 1279, 1282, 1287, 1288, 1296, 1302, 1303, 1311, 1318, 1323, 1355, 1358, 1361, 1362, 1405], "intend": [101, 104, 107, 111, 327, 567, 1038, 1269, 1296, 1393], "impos": [101, 103, 545, 791], "due": [101, 102, 109, 231, 264, 440, 581, 583, 626, 627, 1217, 1405, 1412, 1414, 1423, 1425], "bit": [101, 209, 211, 212, 453, 511, 512, 786, 1345, 1348, 1349, 1350, 1382, 1411], "lot": [101, 452, 1326, 1405], "screen": 101, "instinct": 101, "error": [101, 102, 152, 157, 158, 195, 280, 288, 296, 311, 324, 414, 422, 471, 472, 473, 474, 475, 489, 497, 501, 504, 505, 508, 556, 557, 558, 564, 566, 581, 584, 653, 660, 667, 675, 676, 796, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1037, 1043, 1117, 1144, 1396, 1401, 1404, 1406, 1407, 1411, 1412, 1413, 1414, 1417, 1419, 1425], "definit": [101, 132, 235, 238, 243, 289, 291, 292, 303, 323, 342, 356, 398, 435, 437, 464, 467, 549, 550, 551, 608, 618, 619, 620, 625, 676, 685, 687, 700, 735, 737, 791, 1192, 1193, 1197, 1217, 1235, 1287, 1326, 1406, 1413, 1426], "coupl": [101, 102, 132, 1257, 1402, 1404], "realis": 101, "But": [101, 102, 107, 143, 170, 238, 243, 256, 277, 278, 281, 297, 298, 583, 796, 866, 911, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1094, 1328, 1393, 1425], "seem": [101, 102, 298, 307, 791, 1234], "eas": [101, 107, 1409], "idiom": [101, 159, 190, 200, 858, 879, 888, 903, 939, 969, 984, 1296, 1394, 1404, 1411], "subscript": [101, 151, 159, 200, 796, 853, 858, 888, 898, 903, 934, 939, 969, 979, 984, 1037, 1039, 1040, 1394, 1426], "repr": [101, 1347, 1413], "4950": [101, 1414], "traceback": [101, 450, 464, 584, 652, 658, 1302, 1303], "recent": [101, 437, 450, 464, 584, 652, 658, 963, 1003, 1302, 1303, 1411], "typeerror": [101, 382, 464, 1206, 1302, 1404], "opaqu": 101, "ambigu": [101, 103, 115, 252, 253, 464, 762, 1043, 1406], "ambigi": 101, "counter": [101, 153, 357], "nativ": [101, 109], "caveat": 101, "nodes_it": [101, 1404, 1407], "toward": [101, 685, 1407, 1413], "inner": [101, 230, 231, 380, 796, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1086], "synonym": 101, "primarili": [101, 1426], "becam": [101, 1411], "concept": [101, 132, 220, 310, 427, 688, 1043], "intuit": [101, 109], "On": [101, 105, 156, 217, 294, 297, 298, 306, 307, 315, 380, 405, 406, 514, 515, 518, 593, 855, 900, 936, 981, 1180, 1202, 1224, 1228, 1232], "front": [101, 619, 1036, 1088], "constuct": 101, "indx": 101, "desir": [101, 102, 142, 143, 204, 346, 347, 422, 425, 426, 598, 629, 647, 891, 972, 1085, 1094, 1102, 1103, 1105, 1150, 1152, 1157, 1159, 1160, 1163, 1165, 1187, 1218, 1220, 1221, 1234, 1281, 1356, 1357, 1414, 1426], "prelimanari": 101, "impelement": 101, "4086": 101, "rid": [101, 1413], "getitem": 101, "dunder": [101, 107, 1296, 1413], "isinst": [101, 103, 464, 1086, 1411, 1412, 1413], "_node": [101, 1422, 1425], "exclus": [101, 449, 476], "necess": 101, "unhash": [101, 1404], "impel": 101, "insipir": 101, "colon": [101, 1421], "syntax": [101, 102, 171, 796, 867, 912, 948, 993, 1037, 1039, 1040, 1296, 1382, 1383, 1410, 1412], "introspect": 101, "neither": [101, 110, 305, 427, 625, 635, 636, 671, 672, 673, 674, 676, 700, 748], "downsid": 101, "drawback": 101, "discover": 101, "complic": [101, 1296, 1326], "nix": 101, "background": 101, "pertain": 101, "arguabl": [101, 102], "overrid": [101, 671, 672, 673, 674, 1411], "mix": [101, 236, 237, 238, 241, 242, 243, 244, 245, 248, 445, 758, 1100, 1341, 1342, 1344, 1355, 1356, 1357, 1358, 1381, 1383, 1393, 1406, 1407, 1411], "pervas": 101, "unforeseen": 101, "preced": [101, 152, 157, 464, 598, 703, 854, 856, 899, 901, 935, 937, 980, 982, 1045, 1363, 1364], "un": [101, 464, 732, 1407, 1413], "sliceabl": 101, "notabl": [101, 1042], "dict_kei": [101, 1303, 1414], "dict_valu": [101, 379, 1404, 1413], "cpython": [101, 107, 429, 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467, 704, 706, 707, 708, 710, 734, 736], "2009": [112, 132, 217, 300, 573, 593, 616, 624, 729, 731, 1201, 1222, 1271, 1323, 1394, 1407], "discov": [112, 293, 345, 385, 1038, 1393], "utrecht": 112, "uu": [112, 333, 1179], "018": 112, "nl": [112, 476, 1250, 1259], "wang": [112, 423, 425, 513, 729, 731, 1178, 1180, 1412], "lu": [112, 296, 301, 302, 303, 308, 309, 323, 520, 521, 573, 1179, 1275, 1276, 1277, 1413], "hick": [112, 352], "20210507025929": 112, "eec": 112, "utk": 112, "cphill25": 112, "cs594_spring2015_project": 112, "vertic": [114, 115, 211, 212, 249, 281, 322, 373, 387, 389, 390, 437, 477, 478, 479, 480, 488, 491, 492, 514, 515, 518, 618, 619, 767, 1098, 1101, 1106, 1109, 1134, 1136, 1164, 1169, 1180, 1190, 1192, 1206, 1213, 1215, 1217, 1218, 1219, 1250, 1253, 1263, 1264, 1271, 1323, 1426], "v_j": [114, 282, 332], "v_k": 114, "v_i": 114, "AT": [114, 249, 250, 1411], "polynomi": [114, 264, 440, 618, 619, 758, 762, 1271, 1323, 1325, 1416, 1420], "amongst": 114, "opposit": [115, 177, 259, 615, 762, 962, 1002, 1174, 1253, 1287], "literatur": [115, 468, 616, 732, 762], "analogi": 115, "namespac": [115, 125, 269, 270, 271, 272, 273, 274, 275, 276, 411, 412, 416, 417, 494, 498, 499, 509, 510, 770, 1392, 1395, 1396, 1399, 1402, 1404, 1407, 1412, 1413, 1414], "easiest": [115, 1038, 1326], "is_connect": [115, 394, 396, 397, 398, 1406], "bottom_nod": 115, "top_nod": [115, 256, 277, 278, 279, 280, 281], "refus": [115, 1043], "temptat": [115, 1043], "guess": [115, 1041, 1043], "ambiguoussolut": [115, 256, 277, 278, 281, 1043, 1325], "rb": [115, 267, 1333, 1337, 1338, 1371, 1405], "random_graph": 115, "rb_top": 115, "rb_bottom": 115, "maximum_match": [115, 278, 281], "complete_bipartite_graph": [115, 252, 253, 281, 285, 588, 1151, 1426], "minimum_weight_full_match": 115, "whose": [115, 116, 144, 218, 219, 226, 229, 235, 281, 291, 292, 293, 294, 295, 311, 350, 351, 352, 375, 380, 387, 460, 490, 501, 584, 585, 587, 619, 692, 728, 739, 1055, 1077, 1194, 1206, 1213, 1249, 1254, 1269, 1272, 1273, 1278, 1279, 1299, 1301, 1310, 1350, 1411], "mode": [115, 260, 261, 262, 267, 268, 289, 1300, 1333, 1334, 1337, 1338, 1339, 1340, 1371, 1372, 1426], "bipart": [115, 290], "routin": [116, 180, 343, 355, 559, 560, 577, 760, 871, 914, 952, 995, 1042, 1091, 1326, 1395, 1396, 1404, 1406, 1411, 1412, 1413], "outsid": [116, 310, 1404, 1406, 1413], "chord": [120, 341, 343, 1190, 1208, 1215], "chordal_graph": [120, 341], "clique_problem": 121, "character": [122, 313, 782], "triangl": [122, 213, 227, 295, 356, 357, 358, 359, 437, 549, 550, 758, 1098, 1101, 1215, 1219, 1222, 1234, 1243, 1247, 1252, 1263, 1323, 1326, 1406, 1412], "greedy_color": [123, 758, 1395, 1406, 1411], "communities_gener": 125, "girvan_newman": 125, "top_level_commun": 125, "next_level_commun": 125, "kernighan": [125, 377, 1413], "lin": [125, 377, 1407, 1413], "luke": [125, 382, 1412], "asynchron": [125, 373, 378, 379, 1407, 1414], "edge_kcompon": [127, 424], "determen": 127, "maxim": [127, 209, 220, 221, 222, 315, 316, 330, 339, 346, 347, 348, 349, 350, 351, 353, 354, 366, 370, 380, 383, 384, 389, 390, 422, 425, 426, 427, 432, 433, 437, 517, 579, 581, 582, 583, 589, 682, 691, 732, 758, 1043, 1201, 1323, 1325, 1398, 1406, 1407, 1413, 1414], "moodi": [127, 220, 427, 1395], "kanevski": [127, 427, 428, 1395], "recurs": [128, 141, 224, 346, 347, 352, 387, 389, 390, 394, 406, 452, 460, 530, 540, 697, 728, 730, 760, 1045, 1046, 1061, 1082, 1147, 1296, 1406, 1412, 1413], "prune": [128, 760, 1236], "vladimir": [128, 275, 432, 433, 494, 588, 749, 1230], "batagelj": [128, 275, 432, 433, 588, 749, 1230], "matjaz": [128, 432, 433], "zaversnik": [128, 432, 433], "0310049": [128, 432, 433], "0202039": 128, "degeneraci": 128, "christo": 128, "giatsidi": 128, "thiliko": 128, "michali": 128, "vazirgianni": 128, "icdm": 128, "2011": [128, 331, 377, 383, 385, 441, 445, 446, 511, 512, 519, 619, 682, 1179, 1397, 1398, 1399, 1406, 1407], "graphdegeneraci": 128, "dcores_icdm_2011": 128, "anomali": [128, 438], "onion": [128, 438, 1411], "h\u00e9bert": [128, 438], "dufresn": [128, 438], "grochow": [128, 438], "allard": [128, 438, 1411], "31708": [128, 438], "2016": [128, 337, 352, 385, 438, 476, 690, 1197, 1251, 1396, 1406], "1038": [128, 337, 376, 380, 438, 569], "srep31708": [128, 438], "factor": [132, 226, 293, 294, 299, 300, 324, 325, 370, 462, 497, 501, 504, 505, 508, 513, 565, 592, 624, 676, 697, 1106, 1107, 1108, 1109, 1110, 1114, 1115, 1116, 1117, 1145, 1155, 1178, 1180, 1275, 1276, 1277], "graphic": [132, 454, 517, 518, 693, 758, 1175, 1177, 1180, 1181, 1222, 1325, 1383, 1398, 1401, 1406], "overview": [132, 476, 1038, 1296], "collid": [132, 454], "triplet": [132, 745], "successor": [132, 159, 174, 181, 191, 200, 240, 282, 387, 389, 390, 394, 501, 687, 707, 715, 858, 872, 880, 888, 903, 939, 953, 961, 969, 984, 1055, 1183, 1184, 1189, 1326, 1404, 1407, 1416, 1426], "descend": [132, 454, 456, 465, 709, 758, 1272, 1401, 1404, 1406, 1413, 1414], "unblock": 132, "commonli": [132, 280, 454, 684, 782], "probabilist": [132, 378], "causal": 132, "markov": [132, 462, 565, 692, 1188], "hmm": 132, "s1": [132, 1242, 1313, 1363], "s2": [132, 1242, 1313], "s3": [132, 1313], "s4": 132, "s5": 132, "o1": 132, "o2": 132, "o3": 132, "o4": 132, "o5": 132, "ob": 132, "d_separ": [132, 758, 1412], "darwich": 132, "shachter": 132, "1998": [132, 1143, 1144, 1225, 1241, 1407], "bay": 132, "ball": 132, "ration": 132, "pastim": 132, "irrelev": [132, 1407], "requisit": 132, "influenc": [132, 324, 325, 512, 786], "fourteenth": [132, 1186], "uncertainti": [132, 590, 732], "artifici": [132, 574, 590, 732], "480": [132, 426, 514, 518, 1398, 1406], "487": 132, "francisco": [132, 732], "morgan": [132, 732], "kaufmann": [132, 732], "koller": 132, "friedman": 132, "mit": [132, 342, 519, 618], "causal_markov_condit": 132, "ness": [133, 684, 782], "classmethod": [141, 1047], "auxiliari": [141, 142, 143, 220, 411, 412, 413, 415, 416, 417, 418, 419, 423, 430, 431, 1402], "sink": [141, 302, 309, 416, 418, 494, 495, 498, 499, 501, 502, 503, 506, 507, 509, 510, 565], "pick": [141, 217, 331, 657, 1188, 1207, 1210, 1407], "st": [141, 415, 417], "cut": [141, 222, 223, 293, 377, 382, 387, 389, 390, 394, 411, 412, 414, 415, 416, 417, 419, 427, 428, 429, 442, 443, 444, 445, 447, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 619, 758, 760, 1038, 1066, 1115, 1262, 1325, 1395, 1402, 1406, 1413], "refin": [143, 215, 423, 438], "auxgraph": [143, 423], "node_partit": 144, "permut": [144, 368, 452, 453, 455, 466, 748, 1285, 1320, 1321], "containin": 144, "frozenset": [144, 267, 339, 383, 586, 588, 752, 1165, 1333, 1337, 1338, 1412], "abc": [144, 545, 1154, 1206, 1303, 1412, 1413], "interchang": [144, 362], "bool": [145, 146, 148, 149, 165, 168, 171, 176, 184, 189, 196, 204, 208, 232, 237, 238, 242, 243, 245, 249, 250, 258, 265, 266, 267, 268, 272, 275, 286, 287, 288, 291, 294, 295, 296, 297, 298, 299, 301, 302, 305, 306, 307, 308, 309, 310, 314, 315, 322, 324, 325, 326, 327, 330, 343, 350, 355, 362, 393, 394, 395, 396, 397, 398, 439, 454, 462, 463, 467, 479, 480, 488, 489, 491, 494, 498, 499, 509, 510, 513, 514, 515, 516, 517, 518, 520, 521, 522, 545, 562, 564, 578, 579, 580, 581, 588, 613, 614, 616, 617, 622, 623, 625, 640, 652, 663, 673, 679, 685, 690, 696, 698, 699, 700, 704, 708, 719, 723, 724, 725, 726, 728, 730, 733, 734, 735, 736, 737, 738, 740, 741, 742, 743, 862, 865, 867, 870, 873, 878, 885, 891, 907, 910, 912, 916, 927, 931, 943, 946, 948, 951, 955, 960, 966, 972, 976, 988, 991, 993, 998, 1039, 1040, 1045, 1057, 1068, 1070, 1071, 1072, 1084, 1091, 1097, 1116, 1133, 1134, 1135, 1136, 1169, 1179, 1185, 1189, 1209, 1211, 1212, 1213, 1215, 1224, 1228, 1230, 1231, 1232, 1275, 1276, 1277, 1278, 1279, 1282, 1295, 1296, 1307, 1309, 1312, 1335, 1336, 1337, 1339, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1360, 1364, 1379, 1380], "account": [145, 148, 398, 448, 749, 761, 1270, 1393, 1413], "graph_nod": [145, 148], "subgraph_nod": [145, 148], "find_isomorph": [147, 150], "induc": [148, 167, 199, 211, 226, 342, 388, 392, 406, 427, 436, 437, 470, 487, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 512, 586, 589, 752, 761, 762, 864, 887, 909, 925, 945, 968, 990, 1007, 1038, 1061, 1066, 1087, 1102, 1103, 1105, 1189, 1283, 1284, 1393], "u_of_edg": [151, 853, 898], "v_of_edg": [151, 853, 898], "capac": [151, 265, 296, 301, 302, 303, 308, 309, 323, 411, 412, 415, 416, 417, 418, 419, 430, 431, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 758, 853, 898, 934, 979, 1335, 1402], "342": [151, 853, 898, 934, 979, 1255], "ebunch_to_add": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "add_weighted_edges_from": [152, 229, 230, 231, 508, 581, 630, 657, 659, 721, 854, 899, 935, 980, 1070, 1326, 1404, 1407, 1426], "runtimeerror": [152, 157, 158, 195, 464, 465, 466, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005], "happen": [152, 157, 158, 195, 380, 584, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1403, 1404, 1425], "iterator_of_edg": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "wn2898": [152, 854, 899, 935, 980], "wrong": [152, 157, 158, 722, 854, 856, 857, 899, 901, 902, 935, 937, 938, 980, 982, 983, 1406, 1411, 1416, 1425], "start_nod": [153, 154, 155], "end_nod": [153, 154, 155], "reference_neighbor": [153, 154], "half": [153, 154, 155, 164, 177, 183, 206, 297, 298, 615, 653], "clockwis": [153, 154, 169, 182, 197, 615], "networkxexcept": [153, 154, 161, 331, 588, 593, 724, 726, 1043, 1110, 1138, 1180, 1325], "add_half_edge_cw": [153, 155, 164, 615], "connect_compon": [153, 154, 155, 615], "add_half_edge_first": [153, 154, 164, 615], "add_half_edge_ccw": [154, 155, 164, 615], "node_for_ad": [156, 855, 900, 936, 981], "mutabl": [156, 855, 900, 936, 981, 1061, 1066, 1082, 1085, 1086], "hash": [156, 511, 512, 758, 855, 900, 936, 981, 1324, 1325, 1414, 1426], "hello": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1303], "k3": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1217], "utm": [156, 855, 900, 936, 981], "382871": [156, 855, 900, 936, 981], "3972649": [156, 855, 900, 936, 981], "nodes_for_ad": [157, 856, 901, 937, 982], "iterator_of_nod": [157, 195, 856, 884, 901, 923, 937, 965, 982, 1005], "datadict": [159, 190, 200, 207, 734, 736, 858, 879, 888, 892, 903, 928, 939, 969, 973, 1010, 1084, 1312, 1326], "foovalu": [159, 190, 200, 858, 879, 888, 903, 939, 969], "nbrdict": [160, 859, 904, 940, 985, 1019, 1094], "fulfil": [161, 615], "cw": [161, 615], "ccw": [161, 615], "planar": [161, 614, 616, 617, 758, 1110, 1138, 1243, 1246, 1247, 1249, 1325, 1409, 1410], "first_nbr": [161, 615], "invalid": [161, 615, 1413], "alter": [163, 861, 906, 942, 987], "afterward": 164, "as_view": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1089, 1090], "shallow": [165, 202, 204, 284, 285, 286, 287, 288, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1394], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1409], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1404, 1407, 1409, 1410, 1411], "fresh": [165, 862, 907, 943, 988, 1404], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1226, 1323, 1404], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1265, 1394], "degreeview": [166, 863, 908, 944, 950, 989, 1404, 1426], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1404, 1418], "ddict": [168, 176, 184, 189, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998], "in_edg": [168, 189, 865, 878, 946, 960, 1404, 1406, 1407], "out_edg": [168, 865, 946, 1062, 1404, 1406, 1407, 1426], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1426], "outedgedataview": [168, 189, 865, 878, 1404, 1411], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1404, 1412], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1406, 1407, 1417], "indegreeview": [175, 869, 1404], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1165, 1179, 1222, 1404], "inedgeview": [176, 870, 1404], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1127, 1353, 1354, 1359, 1363, 1406, 1413], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1414], "__iter__": [180, 871, 914, 952, 995, 1303], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1394, 1426], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1216], "nedg": [185, 588, 874, 917, 956, 999], "__len__": [186, 187, 875, 876, 918, 919, 957, 958, 1000, 1001], "outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 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"vf2pp_is_isomorphic": [[562, "vf2pp-is-isomorphic"]], "vf2pp_isomorphism": [[563, "vf2pp-isomorphism"]], "hits": [[564, "hits"]], "google_matrix": [[565, "google-matrix"]], "pagerank": [[566, "pagerank"]], "adamic_adar_index": [[567, "adamic-adar-index"]], "cn_soundarajan_hopcroft": [[568, "cn-soundarajan-hopcroft"]], "common_neighbor_centrality": [[569, "common-neighbor-centrality"]], "jaccard_coefficient": [[570, "jaccard-coefficient"]], "preferential_attachment": [[571, "preferential-attachment"]], "ra_index_soundarajan_hopcroft": [[572, "ra-index-soundarajan-hopcroft"]], "resource_allocation_index": [[573, "resource-allocation-index"]], "within_inter_cluster": [[574, "within-inter-cluster"]], "all_pairs_lowest_common_ancestor": [[575, "all-pairs-lowest-common-ancestor"]], "lowest_common_ancestor": [[576, "lowest-common-ancestor"]], "tree_all_pairs_lowest_common_ancestor": [[577, "tree-all-pairs-lowest-common-ancestor"]], "is_matching": [[578, "is-matching"]], "is_maximal_matching": [[579, "is-maximal-matching"]], "is_perfect_matching": [[580, "is-perfect-matching"]], "max_weight_matching": [[581, "max-weight-matching"]], "maximal_matching": [[582, "maximal-matching"]], "min_weight_matching": [[583, "min-weight-matching"]], "contracted_edge": [[584, "contracted-edge"]], "contracted_nodes": [[585, "contracted-nodes"]], "equivalence_classes": [[586, "equivalence-classes"]], "identified_nodes": [[587, "identified-nodes"]], "quotient_graph": [[588, "quotient-graph"]], "maximal_independent_set": [[589, "maximal-independent-set"]], "moral_graph": [[590, "moral-graph"]], "harmonic_function": [[591, "harmonic-function"]], "local_and_global_consistency": [[592, "local-and-global-consistency"]], "non_randomness": [[593, "non-randomness"]], "compose_all": [[594, "compose-all"]], "disjoint_union_all": [[595, "disjoint-union-all"]], "intersection_all": [[596, "intersection-all"]], "union_all": [[597, "union-all"]], "compose": [[598, "compose"]], "difference": [[599, "difference"]], "disjoint_union": [[600, "disjoint-union"]], "full_join": [[601, "full-join"]], "intersection": [[602, "intersection"]], "symmetric_difference": [[603, "symmetric-difference"]], "union": [[604, "union"]], "cartesian_product": [[605, "cartesian-product"]], "corona_product": [[606, "corona-product"]], "lexicographic_product": [[607, "lexicographic-product"]], "power": [[608, "power"]], "rooted_product": [[609, "rooted-product"]], "strong_product": [[610, "strong-product"]], "tensor_product": [[611, "tensor-product"]], "complement": [[612, "complement"]], "reverse": [[613, "reverse"]], "combinatorial_embedding_to_pos": [[614, "combinatorial-embedding-to-pos"]], "networkx.algorithms.planarity.PlanarEmbedding": [[615, "networkx-algorithms-planarity-planarembedding"]], "check_planarity": [[616, "check-planarity"]], "is_planar": [[617, "is-planar"]], "chromatic_polynomial": [[618, "chromatic-polynomial"]], "tutte_polynomial": [[619, "tutte-polynomial"]], "overall_reciprocity": [[620, "overall-reciprocity"]], "reciprocity": [[621, "reciprocity"]], "is_k_regular": [[622, "is-k-regular"]], "is_regular": [[623, "is-regular"]], "k_factor": [[624, "k-factor"]], "rich_club_coefficient": [[625, "rich-club-coefficient"]], "astar_path": [[626, "astar-path"]], "astar_path_length": [[627, "astar-path-length"]], "floyd_warshall": [[628, "floyd-warshall"]], "floyd_warshall_numpy": [[629, "floyd-warshall-numpy"]], "floyd_warshall_predecessor_and_distance": [[630, "floyd-warshall-predecessor-and-distance"]], "reconstruct_path": [[631, "reconstruct-path"]], "all_shortest_paths": [[632, "all-shortest-paths"]], "average_shortest_path_length": [[633, "average-shortest-path-length"]], "has_path": [[634, "has-path"]], "shortest_path": [[635, "shortest-path"]], "shortest_path_length": [[636, "shortest-path-length"]], "all_pairs_shortest_path": [[637, "all-pairs-shortest-path"]], "all_pairs_shortest_path_length": [[638, "all-pairs-shortest-path-length"]], "bidirectional_shortest_path": [[639, "bidirectional-shortest-path"]], "predecessor": [[640, "predecessor"]], "single_source_shortest_path": [[641, "single-source-shortest-path"]], "single_source_shortest_path_length": [[642, "single-source-shortest-path-length"]], "single_target_shortest_path": [[643, "single-target-shortest-path"]], "single_target_shortest_path_length": [[644, "single-target-shortest-path-length"]], "all_pairs_bellman_ford_path": [[645, "all-pairs-bellman-ford-path"]], "all_pairs_bellman_ford_path_length": [[646, "all-pairs-bellman-ford-path-length"]], "all_pairs_dijkstra": [[647, "all-pairs-dijkstra"]], "all_pairs_dijkstra_path": [[648, "all-pairs-dijkstra-path"]], "all_pairs_dijkstra_path_length": [[649, "all-pairs-dijkstra-path-length"]], "bellman_ford_path": [[650, "bellman-ford-path"]], "bellman_ford_path_length": [[651, "bellman-ford-path-length"]], "bellman_ford_predecessor_and_distance": [[652, "bellman-ford-predecessor-and-distance"]], "bidirectional_dijkstra": [[653, "bidirectional-dijkstra"]], "dijkstra_path": [[654, "dijkstra-path"]], "dijkstra_path_length": [[655, "dijkstra-path-length"]], "dijkstra_predecessor_and_distance": [[656, "dijkstra-predecessor-and-distance"]], "find_negative_cycle": [[657, "find-negative-cycle"]], "goldberg_radzik": [[658, "goldberg-radzik"]], "johnson": [[659, "johnson"]], "multi_source_dijkstra": [[660, "multi-source-dijkstra"]], "multi_source_dijkstra_path": [[661, "multi-source-dijkstra-path"]], "multi_source_dijkstra_path_length": [[662, "multi-source-dijkstra-path-length"]], "negative_edge_cycle": [[663, "negative-edge-cycle"]], "single_source_bellman_ford": [[664, "single-source-bellman-ford"]], "single_source_bellman_ford_path": [[665, "single-source-bellman-ford-path"]], "single_source_bellman_ford_path_length": [[666, "single-source-bellman-ford-path-length"]], "single_source_dijkstra": [[667, "single-source-dijkstra"]], "single_source_dijkstra_path": [[668, "single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[669, "single-source-dijkstra-path-length"]], "generate_random_paths": [[670, "generate-random-paths"]], "graph_edit_distance": [[671, "graph-edit-distance"]], "optimal_edit_paths": [[672, "optimal-edit-paths"]], "optimize_edit_paths": [[673, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[674, "optimize-graph-edit-distance"]], "panther_similarity": [[675, "panther-similarity"]], "simrank_similarity": [[676, "simrank-similarity"]], "all_simple_edge_paths": [[677, "all-simple-edge-paths"]], "all_simple_paths": [[678, "all-simple-paths"]], "is_simple_path": [[679, "is-simple-path"]], "shortest_simple_paths": [[680, "shortest-simple-paths"]], "lattice_reference": [[681, "lattice-reference"]], "omega": [[682, "omega"]], "random_reference": [[683, "random-reference"]], "sigma": [[684, "sigma"]], "s_metric": [[685, "s-metric"]], "spanner": [[686, "spanner"]], "constraint": [[687, "constraint"]], "effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "Recognition Tests": [[791, "recognition-tests"]], "Branchings and Spanning Arborescences": [[791, "module-networkx.algorithms.tree.branchings"]], "Encoding and decoding": [[791, "module-networkx.algorithms.tree.coding"]], "Operations": [[791, "module-networkx.algorithms.tree.operations"]], "Spanning Trees": [[791, "module-networkx.algorithms.tree.mst"]], "Exceptions": [[791, "exceptions"], [1043, "module-networkx.exception"]], "Vitality": [[793, "module-networkx.algorithms.vitality"]], "Voronoi cells": [[794, "module-networkx.algorithms.voronoi"]], "Wiener index": [[795, "module-networkx.algorithms.wiener"]], "DiGraph\u2014Directed graphs with self loops": [[796, "digraph-directed-graphs-with-self-loops"]], "Overview": [[796, "overview"], [1037, "overview"], [1039, "overview"], [1040, "overview"]], "Methods": [[796, "methods"], [1037, "methods"], [1039, "methods"], [1040, "methods"]], "Adding and removing nodes and edges": [[796, "adding-and-removing-nodes-and-edges"], [1037, "adding-and-removing-nodes-and-edges"], [1040, "adding-and-removing-nodes-and-edges"]], "Reporting nodes edges and neighbors": [[796, "reporting-nodes-edges-and-neighbors"], [1037, "reporting-nodes-edges-and-neighbors"], [1039, "reporting-nodes-edges-and-neighbors"], [1040, "reporting-nodes-edges-and-neighbors"]], "Counting nodes edges and neighbors": [[796, "counting-nodes-edges-and-neighbors"], [1037, "counting-nodes-edges-and-neighbors"], [1039, "counting-nodes-edges-and-neighbors"], [1040, "counting-nodes-edges-and-neighbors"]], "Making copies and subgraphs": [[796, "making-copies-and-subgraphs"], [1037, "making-copies-and-subgraphs"], [1039, "making-copies-and-subgraphs"], [1040, "making-copies-and-subgraphs"]], "AdjacencyView.copy": [[797, "adjacencyview-copy"]], "AdjacencyView.get": [[798, "adjacencyview-get"]], "AdjacencyView.items": [[799, "adjacencyview-items"]], "AdjacencyView.keys": [[800, "adjacencyview-keys"]], "AdjacencyView.values": [[801, "adjacencyview-values"]], "AtlasView.copy": [[802, "atlasview-copy"]], "AtlasView.get": [[803, "atlasview-get"]], "AtlasView.items": [[804, "atlasview-items"]], "AtlasView.keys": [[805, "atlasview-keys"]], "AtlasView.values": [[806, "atlasview-values"]], "FilterAdjacency.get": [[807, "filteradjacency-get"]], "FilterAdjacency.items": [[808, "filteradjacency-items"]], "FilterAdjacency.keys": [[809, "filteradjacency-keys"]], "FilterAdjacency.values": [[810, "filteradjacency-values"]], "FilterAtlas.get": [[811, "filteratlas-get"]], "FilterAtlas.items": [[812, "filteratlas-items"]], "FilterAtlas.keys": [[813, "filteratlas-keys"]], "FilterAtlas.values": [[814, "filteratlas-values"]], "FilterMultiAdjacency.get": [[815, "filtermultiadjacency-get"]], "FilterMultiAdjacency.items": [[816, "filtermultiadjacency-items"]], "FilterMultiAdjacency.keys": [[817, "filtermultiadjacency-keys"]], "FilterMultiAdjacency.values": [[818, "filtermultiadjacency-values"]], "FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": [[836, "unionatlas-keys"]], "UnionAtlas.values": [[837, "unionatlas-values"]], "UnionMultiAdjacency.copy": [[838, "unionmultiadjacency-copy"]], "UnionMultiAdjacency.get": [[839, "unionmultiadjacency-get"]], "UnionMultiAdjacency.items": [[840, "unionmultiadjacency-items"]], "UnionMultiAdjacency.keys": [[841, "unionmultiadjacency-keys"]], "UnionMultiAdjacency.values": [[842, "unionmultiadjacency-values"]], "UnionMultiInner.copy": [[843, "unionmultiinner-copy"]], "UnionMultiInner.get": [[844, "unionmultiinner-get"]], "UnionMultiInner.items": [[845, "unionmultiinner-items"]], "UnionMultiInner.keys": [[846, "unionmultiinner-keys"]], "UnionMultiInner.values": [[847, "unionmultiinner-values"]], "DiGraph.__contains__": [[848, "digraph-contains"]], "DiGraph.__getitem__": [[849, "digraph-getitem"]], "DiGraph.__init__": [[850, "digraph-init"]], "DiGraph.__iter__": [[851, "digraph-iter"]], "DiGraph.__len__": [[852, "digraph-len"]], "DiGraph.add_edge": [[853, "digraph-add-edge"]], "DiGraph.add_edges_from": [[854, "digraph-add-edges-from"]], "DiGraph.add_node": [[855, "digraph-add-node"]], "DiGraph.add_nodes_from": [[856, "digraph-add-nodes-from"]], "DiGraph.add_weighted_edges_from": [[857, "digraph-add-weighted-edges-from"]], "DiGraph.adj": [[858, "digraph-adj"]], "DiGraph.adjacency": [[859, "digraph-adjacency"]], "DiGraph.clear": [[860, "digraph-clear"]], "DiGraph.clear_edges": [[861, "digraph-clear-edges"]], "DiGraph.copy": [[862, "digraph-copy"]], "DiGraph.degree": [[863, "digraph-degree"]], "DiGraph.edge_subgraph": [[864, "digraph-edge-subgraph"]], "DiGraph.edges": [[865, "digraph-edges"]], "DiGraph.get_edge_data": [[866, "digraph-get-edge-data"]], "DiGraph.has_edge": [[867, "digraph-has-edge"]], "DiGraph.has_node": [[868, "digraph-has-node"]], "DiGraph.in_degree": [[869, "digraph-in-degree"]], "DiGraph.in_edges": [[870, "digraph-in-edges"]], "DiGraph.nbunch_iter": [[871, "digraph-nbunch-iter"]], "DiGraph.neighbors": [[872, "digraph-neighbors"]], "DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, 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[[1426, "tutorial"]], "Creating a graph": [[1426, "creating-a-graph"]], "Examining elements of a graph": [[1426, "examining-elements-of-a-graph"]], "Removing elements from a graph": [[1426, "removing-elements-from-a-graph"]], "Using the graph constructors": [[1426, "using-the-graph-constructors"]], "What to use as nodes and edges": [[1426, "what-to-use-as-nodes-and-edges"]], "Accessing edges and neighbors": [[1426, "accessing-edges-and-neighbors"]], "Adding attributes to graphs, nodes, and edges": [[1426, "adding-attributes-to-graphs-nodes-and-edges"]], "Edge Attributes": [[1426, "edge-attributes"]], "Directed graphs": [[1426, "directed-graphs"]], "Multigraphs": [[1426, "multigraphs"]], "Graph generators and graph operations": [[1426, "graph-generators-and-graph-operations"]], "1. Applying classic graph operations, such as:": [[1426, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1426, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1426, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1426, "using-a-stochastic-graph-generator-e-g"]], "5. Reading a graph stored in a file using common graph formats": [[1426, "reading-a-graph-stored-in-a-file-using-common-graph-formats"]], "Analyzing graphs": [[1426, "analyzing-graphs"]], "Drawing graphs": [[1426, "drawing-graphs"]]}, "indexentries": {"module": [[112, "module-networkx.algorithms.approximation"], [112, "module-networkx.algorithms.approximation.clique"], [112, "module-networkx.algorithms.approximation.clustering_coefficient"], [112, "module-networkx.algorithms.approximation.connectivity"], [112, "module-networkx.algorithms.approximation.distance_measures"], [112, "module-networkx.algorithms.approximation.dominating_set"], [112, "module-networkx.algorithms.approximation.kcomponents"], [112, "module-networkx.algorithms.approximation.matching"], [112, "module-networkx.algorithms.approximation.maxcut"], [112, "module-networkx.algorithms.approximation.ramsey"], [112, "module-networkx.algorithms.approximation.steinertree"], [112, 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"module-networkx.algorithms.approximation.clustering_coefficient"]], "networkx.algorithms.approximation.connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"]], "networkx.algorithms.approximation.distance_measures": [[112, "module-networkx.algorithms.approximation.distance_measures"]], "networkx.algorithms.approximation.dominating_set": [[112, "module-networkx.algorithms.approximation.dominating_set"]], "networkx.algorithms.approximation.kcomponents": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "networkx.algorithms.approximation.matching": [[112, "module-networkx.algorithms.approximation.matching"]], "networkx.algorithms.approximation.maxcut": [[112, "module-networkx.algorithms.approximation.maxcut"]], "networkx.algorithms.approximation.ramsey": [[112, "module-networkx.algorithms.approximation.ramsey"]], "networkx.algorithms.approximation.steinertree": [[112, "module-networkx.algorithms.approximation.steinertree"]], "networkx.algorithms.approximation.traveling_salesman": [[112, "module-networkx.algorithms.approximation.traveling_salesman"]], "networkx.algorithms.approximation.treewidth": [[112, "module-networkx.algorithms.approximation.treewidth"]], "networkx.algorithms.approximation.vertex_cover": [[112, "module-networkx.algorithms.approximation.vertex_cover"]], "networkx.algorithms.assortativity": [[113, "module-networkx.algorithms.assortativity"]], "networkx.algorithms.asteroidal": [[114, "module-networkx.algorithms.asteroidal"]], "networkx.algorithms.bipartite": [[115, "module-networkx.algorithms.bipartite"]], "networkx.algorithms.bipartite.basic": [[115, "module-networkx.algorithms.bipartite.basic"]], "networkx.algorithms.bipartite.centrality": [[115, "module-networkx.algorithms.bipartite.centrality"]], "networkx.algorithms.bipartite.cluster": [[115, "module-networkx.algorithms.bipartite.cluster"]], "networkx.algorithms.bipartite.covering": [[115, 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"networkx.algorithms.chains": [[119, "module-networkx.algorithms.chains"]], "networkx.algorithms.chordal": [[120, "module-networkx.algorithms.chordal"]], "networkx.algorithms.clique": [[121, "module-networkx.algorithms.clique"]], "networkx.algorithms.cluster": [[122, "module-networkx.algorithms.cluster"]], "networkx.algorithms.coloring": [[123, "module-networkx.algorithms.coloring"]], "networkx.algorithms.communicability_alg": [[124, "module-networkx.algorithms.communicability_alg"]], "networkx.algorithms.community": [[125, "module-networkx.algorithms.community"]], "networkx.algorithms.community.asyn_fluid": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "networkx.algorithms.community.centrality": [[125, "module-networkx.algorithms.community.centrality"]], "networkx.algorithms.community.community_utils": [[125, "module-networkx.algorithms.community.community_utils"]], "networkx.algorithms.community.kclique": [[125, "module-networkx.algorithms.community.kclique"]], "networkx.algorithms.community.kernighan_lin": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "networkx.algorithms.community.label_propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "networkx.algorithms.community.louvain": [[125, "module-networkx.algorithms.community.louvain"]], "networkx.algorithms.community.lukes": [[125, "module-networkx.algorithms.community.lukes"]], "networkx.algorithms.community.modularity_max": [[125, "module-networkx.algorithms.community.modularity_max"]], "networkx.algorithms.community.quality": [[125, "module-networkx.algorithms.community.quality"]], "networkx.algorithms.components": [[126, "module-networkx.algorithms.components"]], "networkx.algorithms.connectivity": [[127, "module-networkx.algorithms.connectivity"]], "networkx.algorithms.connectivity.connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "networkx.algorithms.connectivity.cuts": [[127, "module-networkx.algorithms.connectivity.cuts"]], "networkx.algorithms.connectivity.disjoint_paths": [[127, "module-networkx.algorithms.connectivity.disjoint_paths"]], "networkx.algorithms.connectivity.edge_augmentation": [[127, "module-networkx.algorithms.connectivity.edge_augmentation"]], "networkx.algorithms.connectivity.edge_kcomponents": [[127, "module-networkx.algorithms.connectivity.edge_kcomponents"]], "networkx.algorithms.connectivity.kcomponents": [[127, "module-networkx.algorithms.connectivity.kcomponents"]], "networkx.algorithms.connectivity.kcutsets": [[127, "module-networkx.algorithms.connectivity.kcutsets"]], "networkx.algorithms.connectivity.stoerwagner": [[127, "module-networkx.algorithms.connectivity.stoerwagner"]], "networkx.algorithms.connectivity.utils": [[127, "module-networkx.algorithms.connectivity.utils"]], "networkx.algorithms.core": [[128, "module-networkx.algorithms.core"]], "networkx.algorithms.covering": [[129, "module-networkx.algorithms.covering"]], "networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[144, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[151, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": [[166, "networkx.algorithms.planarity.PlanarEmbedding.degree"]], "edge_subgraph() (planarembedding method)": [[167, "networkx.algorithms.planarity.PlanarEmbedding.edge_subgraph"]], "edges (planarembedding property)": [[168, "networkx.algorithms.planarity.PlanarEmbedding.edges"]], "get_data() (planarembedding method)": [[169, "networkx.algorithms.planarity.PlanarEmbedding.get_data"]], "get_edge_data() (planarembedding method)": [[170, "networkx.algorithms.planarity.PlanarEmbedding.get_edge_data"]], "has_edge() (planarembedding method)": [[171, "networkx.algorithms.planarity.PlanarEmbedding.has_edge"]], "has_node() (planarembedding method)": [[172, "networkx.algorithms.planarity.PlanarEmbedding.has_node"]], "has_predecessor() (planarembedding method)": [[173, "networkx.algorithms.planarity.PlanarEmbedding.has_predecessor"]], "has_successor() (planarembedding method)": [[174, "networkx.algorithms.planarity.PlanarEmbedding.has_successor"]], "in_degree (planarembedding property)": [[175, "networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[751, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[752, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[753, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[755, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[757, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[759, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[760, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[764, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[766, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[767, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[768, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[769, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[770, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[771, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[772, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[772, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[772, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[772, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[774, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[775, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[777, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[778, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "networkx.algorithms.shortest_paths.weighted": [[779, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[780, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[781, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[782, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[783, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[785, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[786, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[787, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[788, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[789, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "networkx.algorithms.traversal.breadth_first_search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "networkx.algorithms.traversal.depth_first_search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "networkx.algorithms.traversal.edgebfs": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "networkx.algorithms.traversal.edgedfs": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "networkx.algorithms.tree.branchings": [[791, "module-networkx.algorithms.tree.branchings"]], "networkx.algorithms.tree.coding": [[791, "module-networkx.algorithms.tree.coding"]], "networkx.algorithms.tree.decomposition": [[791, "module-networkx.algorithms.tree.decomposition"]], "networkx.algorithms.tree.mst": [[791, "module-networkx.algorithms.tree.mst"]], "networkx.algorithms.tree.operations": [[791, "module-networkx.algorithms.tree.operations"]], "networkx.algorithms.tree.recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "networkx.algorithms.triads": [[792, "module-networkx.algorithms.triads"]], "networkx.algorithms.vitality": [[793, "module-networkx.algorithms.vitality"]], "networkx.algorithms.voronoi": [[794, "module-networkx.algorithms.voronoi"]], "networkx.algorithms.wiener": [[795, "module-networkx.algorithms.wiener"]], "digraph (class in networkx)": [[796, "networkx.DiGraph"]], "copy() (adjacencyview method)": [[797, "networkx.classes.coreviews.AdjacencyView.copy"]], "get() (adjacencyview method)": [[798, "networkx.classes.coreviews.AdjacencyView.get"]], "items() (adjacencyview method)": [[799, "networkx.classes.coreviews.AdjacencyView.items"]], "keys() (adjacencyview method)": [[800, "networkx.classes.coreviews.AdjacencyView.keys"]], "values() (adjacencyview method)": [[801, "networkx.classes.coreviews.AdjacencyView.values"]], "copy() (atlasview method)": [[802, "networkx.classes.coreviews.AtlasView.copy"]], "get() (atlasview method)": [[803, "networkx.classes.coreviews.AtlasView.get"]], "items() (atlasview method)": [[804, "networkx.classes.coreviews.AtlasView.items"]], "keys() (atlasview method)": [[805, "networkx.classes.coreviews.AtlasView.keys"]], "values() (atlasview method)": [[806, "networkx.classes.coreviews.AtlasView.values"]], "get() (filteradjacency method)": [[807, "networkx.classes.coreviews.FilterAdjacency.get"]], "items() (filteradjacency method)": [[808, "networkx.classes.coreviews.FilterAdjacency.items"]], "keys() (filteradjacency method)": [[809, "networkx.classes.coreviews.FilterAdjacency.keys"]], "values() (filteradjacency method)": [[810, "networkx.classes.coreviews.FilterAdjacency.values"]], "get() (filteratlas method)": [[811, "networkx.classes.coreviews.FilterAtlas.get"]], "items() (filteratlas method)": [[812, "networkx.classes.coreviews.FilterAtlas.items"]], "keys() (filteratlas method)": [[813, "networkx.classes.coreviews.FilterAtlas.keys"]], "values() (filteratlas method)": [[814, 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"networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, 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"kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], "spring_layout() (in module networkx.drawing.layout)": [[1117, "networkx.drawing.layout.spring_layout"]], "from_agraph() (in module networkx.drawing.nx_agraph)": [[1118, "networkx.drawing.nx_agraph.from_agraph"]], "graphviz_layout() (in module networkx.drawing.nx_agraph)": [[1119, "networkx.drawing.nx_agraph.graphviz_layout"]], "pygraphviz_layout() (in module networkx.drawing.nx_agraph)": [[1120, "networkx.drawing.nx_agraph.pygraphviz_layout"]], "read_dot() (in module networkx.drawing.nx_agraph)": [[1121, "networkx.drawing.nx_agraph.read_dot"]], "to_agraph() (in module networkx.drawing.nx_agraph)": [[1122, "networkx.drawing.nx_agraph.to_agraph"]], "write_dot() (in module networkx.drawing.nx_agraph)": [[1123, "networkx.drawing.nx_agraph.write_dot"]], "from_pydot() (in module networkx.drawing.nx_pydot)": [[1124, "networkx.drawing.nx_pydot.from_pydot"]], "graphviz_layout() (in module networkx.drawing.nx_pydot)": [[1125, "networkx.drawing.nx_pydot.graphviz_layout"]], "pydot_layout() (in module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, 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module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() 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networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, "networkx.generators.random_graphs.dense_gnm_random_graph"]], "dual_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1227, "networkx.generators.random_graphs.dual_barabasi_albert_graph"]], "erdos_renyi_graph() (in module networkx.generators.random_graphs)": [[1228, "networkx.generators.random_graphs.erdos_renyi_graph"]], "extended_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1229, "networkx.generators.random_graphs.extended_barabasi_albert_graph"]], "fast_gnp_random_graph() (in module networkx.generators.random_graphs)": [[1230, "networkx.generators.random_graphs.fast_gnp_random_graph"]], "gnm_random_graph() (in module networkx.generators.random_graphs)": [[1231, "networkx.generators.random_graphs.gnm_random_graph"]], "gnp_random_graph() (in module networkx.generators.random_graphs)": [[1232, "networkx.generators.random_graphs.gnp_random_graph"]], "newman_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1233, "networkx.generators.random_graphs.newman_watts_strogatz_graph"]], "powerlaw_cluster_graph() (in module networkx.generators.random_graphs)": [[1234, "networkx.generators.random_graphs.powerlaw_cluster_graph"]], "random_kernel_graph() (in module networkx.generators.random_graphs)": [[1235, "networkx.generators.random_graphs.random_kernel_graph"]], "random_lobster() (in module networkx.generators.random_graphs)": [[1236, "networkx.generators.random_graphs.random_lobster"]], "random_powerlaw_tree() (in module networkx.generators.random_graphs)": [[1237, "networkx.generators.random_graphs.random_powerlaw_tree"]], "random_powerlaw_tree_sequence() (in module networkx.generators.random_graphs)": [[1238, "networkx.generators.random_graphs.random_powerlaw_tree_sequence"]], "random_regular_graph() (in module networkx.generators.random_graphs)": [[1239, "networkx.generators.random_graphs.random_regular_graph"]], "random_shell_graph() (in module networkx.generators.random_graphs)": [[1240, "networkx.generators.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1242, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1243, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1244, "networkx.generators.small.chvatal_graph"]], "cubical_graph() (in module networkx.generators.small)": [[1245, "networkx.generators.small.cubical_graph"]], "desargues_graph() (in module networkx.generators.small)": [[1246, "networkx.generators.small.desargues_graph"]], "diamond_graph() (in module networkx.generators.small)": [[1247, "networkx.generators.small.diamond_graph"]], "dodecahedral_graph() (in module networkx.generators.small)": [[1248, "networkx.generators.small.dodecahedral_graph"]], "frucht_graph() (in module networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, "networkx.generators.small.octahedral_graph"]], "pappus_graph() (in module networkx.generators.small)": [[1258, "networkx.generators.small.pappus_graph"]], "petersen_graph() (in module networkx.generators.small)": [[1259, "networkx.generators.small.petersen_graph"]], "sedgewick_maze_graph() (in module networkx.generators.small)": [[1260, "networkx.generators.small.sedgewick_maze_graph"]], "tetrahedral_graph() (in module networkx.generators.small)": [[1261, "networkx.generators.small.tetrahedral_graph"]], "truncated_cube_graph() (in module networkx.generators.small)": [[1262, "networkx.generators.small.truncated_cube_graph"]], "truncated_tetrahedron_graph() (in module networkx.generators.small)": [[1263, "networkx.generators.small.truncated_tetrahedron_graph"]], "tutte_graph() (in module networkx.generators.small)": [[1264, "networkx.generators.small.tutte_graph"]], "davis_southern_women_graph() (in module networkx.generators.social)": [[1265, "networkx.generators.social.davis_southern_women_graph"]], "florentine_families_graph() (in module networkx.generators.social)": [[1266, "networkx.generators.social.florentine_families_graph"]], "karate_club_graph() (in module networkx.generators.social)": [[1267, "networkx.generators.social.karate_club_graph"]], "les_miserables_graph() (in module networkx.generators.social)": [[1268, "networkx.generators.social.les_miserables_graph"]], "spectral_graph_forge() (in module networkx.generators.spectral_graph_forge)": [[1269, "networkx.generators.spectral_graph_forge.spectral_graph_forge"]], "stochastic_graph() (in module networkx.generators.stochastic)": [[1270, "networkx.generators.stochastic.stochastic_graph"]], "sudoku_graph() (in module networkx.generators.sudoku)": [[1271, "networkx.generators.sudoku.sudoku_graph"]], "prefix_tree() (in module networkx.generators.trees)": [[1272, "networkx.generators.trees.prefix_tree"]], "random_tree() (in module networkx.generators.trees)": [[1273, "networkx.generators.trees.random_tree"]], "triad_graph() (in module networkx.generators.triads)": [[1274, "networkx.generators.triads.triad_graph"]], "algebraic_connectivity() (in module networkx.linalg.algebraicconnectivity)": [[1275, "networkx.linalg.algebraicconnectivity.algebraic_connectivity"]], "fiedler_vector() (in module networkx.linalg.algebraicconnectivity)": [[1276, "networkx.linalg.algebraicconnectivity.fiedler_vector"]], "spectral_ordering() (in module networkx.linalg.algebraicconnectivity)": [[1277, "networkx.linalg.algebraicconnectivity.spectral_ordering"]], "attr_matrix() (in module networkx.linalg.attrmatrix)": [[1278, "networkx.linalg.attrmatrix.attr_matrix"]], "attr_sparse_matrix() (in module networkx.linalg.attrmatrix)": [[1279, "networkx.linalg.attrmatrix.attr_sparse_matrix"]], "bethe_hessian_matrix() (in module networkx.linalg.bethehessianmatrix)": [[1280, "networkx.linalg.bethehessianmatrix.bethe_hessian_matrix"]], "adjacency_matrix() (in module networkx.linalg.graphmatrix)": [[1281, "networkx.linalg.graphmatrix.adjacency_matrix"]], "incidence_matrix() (in module networkx.linalg.graphmatrix)": [[1282, "networkx.linalg.graphmatrix.incidence_matrix"]], "directed_combinatorial_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1283, "networkx.linalg.laplacianmatrix.directed_combinatorial_laplacian_matrix"]], "directed_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1284, "networkx.linalg.laplacianmatrix.directed_laplacian_matrix"]], "laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1285, "networkx.linalg.laplacianmatrix.laplacian_matrix"]], "normalized_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1286, "networkx.linalg.laplacianmatrix.normalized_laplacian_matrix"]], "directed_modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1287, "networkx.linalg.modularitymatrix.directed_modularity_matrix"]], "modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1288, "networkx.linalg.modularitymatrix.modularity_matrix"]], "adjacency_spectrum() (in module networkx.linalg.spectrum)": [[1289, "networkx.linalg.spectrum.adjacency_spectrum"]], "bethe_hessian_spectrum() (in module networkx.linalg.spectrum)": [[1290, "networkx.linalg.spectrum.bethe_hessian_spectrum"]], "laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1291, "networkx.linalg.spectrum.laplacian_spectrum"]], "modularity_spectrum() (in module networkx.linalg.spectrum)": [[1292, "networkx.linalg.spectrum.modularity_spectrum"]], "normalized_laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1293, "networkx.linalg.spectrum.normalized_laplacian_spectrum"]], "convert_node_labels_to_integers() (in module networkx.relabel)": [[1294, "networkx.relabel.convert_node_labels_to_integers"]], "relabel_nodes() (in module networkx.relabel)": [[1295, "networkx.relabel.relabel_nodes"]], "__init__() (argmap method)": [[1296, "networkx.utils.decorators.argmap.__init__"]], "argmap (class in networkx.utils.decorators)": [[1296, "networkx.utils.decorators.argmap"]], "nodes_or_number() (in module networkx.utils.decorators)": [[1297, "networkx.utils.decorators.nodes_or_number"]], "not_implemented_for() (in module networkx.utils.decorators)": [[1298, "networkx.utils.decorators.not_implemented_for"]], "np_random_state() (in module networkx.utils.decorators)": [[1299, "networkx.utils.decorators.np_random_state"]], "open_file() (in module networkx.utils.decorators)": [[1300, "networkx.utils.decorators.open_file"]], "py_random_state() (in module networkx.utils.decorators)": [[1301, "networkx.utils.decorators.py_random_state"]], "mappedqueue (class in networkx.utils.mapped_queue)": [[1302, "networkx.utils.mapped_queue.MappedQueue"]], "__init__() (mappedqueue method)": [[1302, "networkx.utils.mapped_queue.MappedQueue.__init__"]], "arbitrary_element() (in module networkx.utils.misc)": [[1303, "networkx.utils.misc.arbitrary_element"]], "create_py_random_state() (in module networkx.utils.misc)": [[1304, "networkx.utils.misc.create_py_random_state"]], "create_random_state() (in module networkx.utils.misc)": [[1305, "networkx.utils.misc.create_random_state"]], "dict_to_numpy_array() (in module networkx.utils.misc)": [[1306, "networkx.utils.misc.dict_to_numpy_array"]], "edges_equal() (in module networkx.utils.misc)": [[1307, "networkx.utils.misc.edges_equal"]], "flatten() (in module networkx.utils.misc)": [[1308, "networkx.utils.misc.flatten"]], "graphs_equal() (in module networkx.utils.misc)": [[1309, "networkx.utils.misc.graphs_equal"]], "groups() (in module networkx.utils.misc)": [[1310, "networkx.utils.misc.groups"]], "make_list_of_ints() (in module networkx.utils.misc)": [[1311, "networkx.utils.misc.make_list_of_ints"]], "nodes_equal() (in module networkx.utils.misc)": [[1312, "networkx.utils.misc.nodes_equal"]], "pairwise() (in module networkx.utils.misc)": [[1313, "networkx.utils.misc.pairwise"]], "cumulative_distribution() (in module networkx.utils.random_sequence)": [[1314, "networkx.utils.random_sequence.cumulative_distribution"]], "discrete_sequence() (in module networkx.utils.random_sequence)": [[1315, "networkx.utils.random_sequence.discrete_sequence"]], "powerlaw_sequence() (in module networkx.utils.random_sequence)": [[1316, "networkx.utils.random_sequence.powerlaw_sequence"]], "random_weighted_sample() (in module networkx.utils.random_sequence)": [[1317, "networkx.utils.random_sequence.random_weighted_sample"]], "weighted_choice() (in module networkx.utils.random_sequence)": [[1318, "networkx.utils.random_sequence.weighted_choice"]], "zipf_rv() (in module networkx.utils.random_sequence)": [[1319, "networkx.utils.random_sequence.zipf_rv"]], "cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1320, "networkx.utils.rcm.cuthill_mckee_ordering"]], "reverse_cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1321, "networkx.utils.rcm.reverse_cuthill_mckee_ordering"]], "union() (unionfind method)": [[1322, "networkx.utils.union_find.UnionFind.union"]], "networkx.generators.atlas": [[1323, "module-networkx.generators.atlas"]], "networkx.generators.classic": [[1323, "module-networkx.generators.classic"]], "networkx.generators.cographs": [[1323, "module-networkx.generators.cographs"]], "networkx.generators.community": [[1323, "module-networkx.generators.community"]], "networkx.generators.degree_seq": [[1323, "module-networkx.generators.degree_seq"]], "networkx.generators.directed": [[1323, "module-networkx.generators.directed"]], "networkx.generators.duplication": [[1323, "module-networkx.generators.duplication"]], "networkx.generators.ego": [[1323, "module-networkx.generators.ego"]], "networkx.generators.expanders": [[1323, "module-networkx.generators.expanders"]], "networkx.generators.geometric": [[1323, "module-networkx.generators.geometric"]], "networkx.generators.harary_graph": [[1323, "module-networkx.generators.harary_graph"]], "networkx.generators.internet_as_graphs": [[1323, "module-networkx.generators.internet_as_graphs"]], "networkx.generators.intersection": [[1323, "module-networkx.generators.intersection"]], "networkx.generators.interval_graph": [[1323, "module-networkx.generators.interval_graph"]], "networkx.generators.joint_degree_seq": [[1323, "module-networkx.generators.joint_degree_seq"]], "networkx.generators.lattice": [[1323, "module-networkx.generators.lattice"]], "networkx.generators.line": [[1323, "module-networkx.generators.line"]], "networkx.generators.mycielski": [[1323, "module-networkx.generators.mycielski"]], "networkx.generators.nonisomorphic_trees": [[1323, "module-networkx.generators.nonisomorphic_trees"]], "networkx.generators.random_clustered": [[1323, "module-networkx.generators.random_clustered"]], "networkx.generators.random_graphs": [[1323, "module-networkx.generators.random_graphs"]], "networkx.generators.small": [[1323, "module-networkx.generators.small"]], "networkx.generators.social": [[1323, "module-networkx.generators.social"]], "networkx.generators.spectral_graph_forge": [[1323, "module-networkx.generators.spectral_graph_forge"]], "networkx.generators.stochastic": [[1323, "module-networkx.generators.stochastic"]], "networkx.generators.sudoku": [[1323, "module-networkx.generators.sudoku"]], "networkx.generators.trees": [[1323, "module-networkx.generators.trees"]], "networkx.generators.triads": [[1323, "module-networkx.generators.triads"]], "dictionary": [[1324, "term-dictionary"]], "ebunch": [[1324, "term-ebunch"]], "edge": [[1324, "term-edge"]], "edge attribute": [[1324, "term-edge-attribute"]], "nbunch": [[1324, "term-nbunch"]], "node": [[1324, "term-node"]], "node attribute": [[1324, "term-node-attribute"]], "networkx.linalg.algebraicconnectivity": [[1327, "module-networkx.linalg.algebraicconnectivity"]], "networkx.linalg.attrmatrix": [[1327, "module-networkx.linalg.attrmatrix"]], "networkx.linalg.bethehessianmatrix": [[1327, "module-networkx.linalg.bethehessianmatrix"]], "networkx.linalg.graphmatrix": [[1327, "module-networkx.linalg.graphmatrix"]], "networkx.linalg.laplacianmatrix": [[1327, "module-networkx.linalg.laplacianmatrix"]], "networkx.linalg.modularitymatrix": [[1327, "module-networkx.linalg.modularitymatrix"]], "networkx.linalg.spectrum": [[1327, "module-networkx.linalg.spectrum"]], "networkx.readwrite.adjlist": [[1329, "module-networkx.readwrite.adjlist"]], "networkx.readwrite.edgelist": [[1330, "module-networkx.readwrite.edgelist"]], "generate_adjlist() (in module networkx.readwrite.adjlist)": [[1331, "networkx.readwrite.adjlist.generate_adjlist"]], "parse_adjlist() (in module networkx.readwrite.adjlist)": [[1332, "networkx.readwrite.adjlist.parse_adjlist"]], "read_adjlist() (in module networkx.readwrite.adjlist)": [[1333, "networkx.readwrite.adjlist.read_adjlist"]], "write_adjlist() (in module networkx.readwrite.adjlist)": [[1334, "networkx.readwrite.adjlist.write_adjlist"]], "generate_edgelist() (in module networkx.readwrite.edgelist)": [[1335, "networkx.readwrite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.readwrite.edgelist)": [[1336, "networkx.readwrite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.readwrite.edgelist)": [[1337, "networkx.readwrite.edgelist.read_edgelist"]], "read_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1338, "networkx.readwrite.edgelist.read_weighted_edgelist"]], "write_edgelist() (in module networkx.readwrite.edgelist)": [[1339, "networkx.readwrite.edgelist.write_edgelist"]], "write_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1340, "networkx.readwrite.edgelist.write_weighted_edgelist"]], "generate_gexf() (in module networkx.readwrite.gexf)": [[1341, "networkx.readwrite.gexf.generate_gexf"]], "read_gexf() (in module networkx.readwrite.gexf)": [[1342, "networkx.readwrite.gexf.read_gexf"]], "relabel_gexf_graph() (in module networkx.readwrite.gexf)": [[1343, "networkx.readwrite.gexf.relabel_gexf_graph"]], "write_gexf() (in module networkx.readwrite.gexf)": [[1344, "networkx.readwrite.gexf.write_gexf"]], "generate_gml() (in module networkx.readwrite.gml)": [[1345, "networkx.readwrite.gml.generate_gml"]], "literal_destringizer() (in module networkx.readwrite.gml)": [[1346, "networkx.readwrite.gml.literal_destringizer"]], "literal_stringizer() (in module networkx.readwrite.gml)": [[1347, "networkx.readwrite.gml.literal_stringizer"]], "parse_gml() (in module networkx.readwrite.gml)": [[1348, "networkx.readwrite.gml.parse_gml"]], "read_gml() (in module networkx.readwrite.gml)": [[1349, "networkx.readwrite.gml.read_gml"]], "write_gml() (in module networkx.readwrite.gml)": [[1350, "networkx.readwrite.gml.write_gml"]], "from_graph6_bytes() (in module networkx.readwrite.graph6)": [[1351, "networkx.readwrite.graph6.from_graph6_bytes"]], "read_graph6() (in module networkx.readwrite.graph6)": [[1352, "networkx.readwrite.graph6.read_graph6"]], "to_graph6_bytes() (in module networkx.readwrite.graph6)": [[1353, "networkx.readwrite.graph6.to_graph6_bytes"]], "write_graph6() (in module networkx.readwrite.graph6)": [[1354, "networkx.readwrite.graph6.write_graph6"]], "generate_graphml() (in module networkx.readwrite.graphml)": [[1355, "networkx.readwrite.graphml.generate_graphml"]], "parse_graphml() (in module networkx.readwrite.graphml)": [[1356, "networkx.readwrite.graphml.parse_graphml"]], "read_graphml() (in module networkx.readwrite.graphml)": [[1357, "networkx.readwrite.graphml.read_graphml"]], "write_graphml() (in module networkx.readwrite.graphml)": [[1358, "networkx.readwrite.graphml.write_graphml"]], "adjacency_data() (in module networkx.readwrite.json_graph)": [[1359, "networkx.readwrite.json_graph.adjacency_data"]], "adjacency_graph() (in module networkx.readwrite.json_graph)": [[1360, "networkx.readwrite.json_graph.adjacency_graph"]], "cytoscape_data() (in module networkx.readwrite.json_graph)": [[1361, "networkx.readwrite.json_graph.cytoscape_data"]], "cytoscape_graph() (in module networkx.readwrite.json_graph)": [[1362, "networkx.readwrite.json_graph.cytoscape_graph"]], "node_link_data() (in module networkx.readwrite.json_graph)": [[1363, "networkx.readwrite.json_graph.node_link_data"]], "node_link_graph() (in module networkx.readwrite.json_graph)": [[1364, "networkx.readwrite.json_graph.node_link_graph"]], "tree_data() (in module networkx.readwrite.json_graph)": [[1365, "networkx.readwrite.json_graph.tree_data"]], "tree_graph() (in module networkx.readwrite.json_graph)": [[1366, "networkx.readwrite.json_graph.tree_graph"]], "parse_leda() (in module networkx.readwrite.leda)": [[1367, "networkx.readwrite.leda.parse_leda"]], "read_leda() (in module networkx.readwrite.leda)": [[1368, "networkx.readwrite.leda.read_leda"]], "generate_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1369, "networkx.readwrite.multiline_adjlist.generate_multiline_adjlist"]], "parse_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1370, "networkx.readwrite.multiline_adjlist.parse_multiline_adjlist"]], "read_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1371, "networkx.readwrite.multiline_adjlist.read_multiline_adjlist"]], "write_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1372, "networkx.readwrite.multiline_adjlist.write_multiline_adjlist"]], "generate_pajek() (in module networkx.readwrite.pajek)": [[1373, "networkx.readwrite.pajek.generate_pajek"]], "parse_pajek() (in module networkx.readwrite.pajek)": [[1374, "networkx.readwrite.pajek.parse_pajek"]], "read_pajek() (in module networkx.readwrite.pajek)": [[1375, "networkx.readwrite.pajek.read_pajek"]], "write_pajek() (in module networkx.readwrite.pajek)": [[1376, "networkx.readwrite.pajek.write_pajek"]], "from_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1377, "networkx.readwrite.sparse6.from_sparse6_bytes"]], "read_sparse6() (in module networkx.readwrite.sparse6)": [[1378, "networkx.readwrite.sparse6.read_sparse6"]], "to_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1379, "networkx.readwrite.sparse6.to_sparse6_bytes"]], "write_sparse6() (in module networkx.readwrite.sparse6)": [[1380, "networkx.readwrite.sparse6.write_sparse6"]], "networkx.readwrite.gexf": [[1381, "module-networkx.readwrite.gexf"]], "networkx.readwrite.gml": [[1382, "module-networkx.readwrite.gml"]], "networkx.readwrite.graphml": [[1383, "module-networkx.readwrite.graphml"]], "networkx.readwrite.json_graph": [[1385, "module-networkx.readwrite.json_graph"]], "networkx.readwrite.leda": [[1386, "module-networkx.readwrite.leda"]], "networkx.readwrite.multiline_adjlist": [[1388, "module-networkx.readwrite.multiline_adjlist"]], "networkx.readwrite.pajek": [[1389, "module-networkx.readwrite.pajek"]], "networkx.readwrite.graph6": [[1390, "module-networkx.readwrite.graph6"]], "networkx.readwrite.sparse6": [[1390, "module-networkx.readwrite.sparse6"]], "networkx.relabel": [[1391, "module-networkx.relabel"]], "networkx.utils": [[1392, "module-networkx.utils"]], "networkx.utils.decorators": [[1392, "module-networkx.utils.decorators"]], "networkx.utils.mapped_queue": [[1392, "module-networkx.utils.mapped_queue"]], "networkx.utils.misc": [[1392, "module-networkx.utils.misc"]], "networkx.utils.random_sequence": [[1392, "module-networkx.utils.random_sequence"]], "networkx.utils.rcm": [[1392, "module-networkx.utils.rcm"]], "networkx.utils.union_find": [[1392, "module-networkx.utils.union_find"]]}}) \ No newline at end of file
diff --git a/tutorial-34.pdf b/tutorial-34.pdf
index 8f8300ee..86cc8f63 100644
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+++ b/tutorial-34.pdf
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index 61ce168e..400560fb 100644
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index b188ab3a..119440d9 100644
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diff --git a/tutorial.ipynb b/tutorial.ipynb
index 6b3063e6..bd27f376 100644
--- a/tutorial.ipynb
+++ b/tutorial.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "c1df7792",
+ "id": "e3b4c6cb",
"metadata": {},
"source": [
"## Tutorial\n",
@@ -17,7 +17,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "c8da7c6c",
+ "id": "5576467a",
"metadata": {},
"outputs": [],
"source": [
@@ -27,7 +27,7 @@
},
{
"cell_type": "markdown",
- "id": "fb8cff6d",
+ "id": "2f9d4265",
"metadata": {},
"source": [
"By definition, a `Graph` is a collection of nodes (vertices) along with\n",
@@ -47,7 +47,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "160a4cb7",
+ "id": "0a791bd4",
"metadata": {},
"outputs": [],
"source": [
@@ -56,7 +56,7 @@
},
{
"cell_type": "markdown",
- "id": "bd486052",
+ "id": "c9fbb085",
"metadata": {},
"source": [
"or add nodes from any [iterable](https://docs.python.org/3/glossary.html#term-iterable) container, such as a list"
@@ -65,7 +65,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "89778ad6",
+ "id": "a55d12ce",
"metadata": {},
"outputs": [],
"source": [
@@ -74,7 +74,7 @@
},
{
"cell_type": "markdown",
- "id": "df75a4d8",
+ "id": "722f5149",
"metadata": {},
"source": [
"You can also add nodes along with node\n",
@@ -96,7 +96,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "8965f552",
+ "id": "c886466a",
"metadata": {},
"outputs": [],
"source": [
@@ -106,7 +106,7 @@
},
{
"cell_type": "markdown",
- "id": "e8d79910",
+ "id": "01b57622",
"metadata": {},
"source": [
"`G` now contains the nodes of `H` as nodes of `G`.\n",
@@ -116,7 +116,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "8130eaa1",
+ "id": "5dfd7b46",
"metadata": {},
"outputs": [],
"source": [
@@ -125,7 +125,7 @@
},
{
"cell_type": "markdown",
- "id": "fd4cb72d",
+ "id": "15fb720b",
"metadata": {},
"source": [
"The graph `G` now contains `H` as a node. This flexibility is very powerful as\n",
@@ -143,7 +143,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "25ac86f8",
+ "id": "54dc3160",
"metadata": {},
"outputs": [],
"source": [
@@ -154,7 +154,7 @@
},
{
"cell_type": "markdown",
- "id": "a7a507ca",
+ "id": "172498d6",
"metadata": {},
"source": [
"by adding a list of edges,"
@@ -163,7 +163,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "3551b660",
+ "id": "21364aac",
"metadata": {},
"outputs": [],
"source": [
@@ -172,7 +172,7 @@
},
{
"cell_type": "markdown",
- "id": "dbeb878d",
+ "id": "622e5aac",
"metadata": {},
"source": [
"or by adding any ebunch of edges. An *ebunch* is any iterable\n",
@@ -185,7 +185,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "bb0bed7b",
+ "id": "7037d73c",
"metadata": {},
"outputs": [],
"source": [
@@ -194,7 +194,7 @@
},
{
"cell_type": "markdown",
- "id": "c978377d",
+ "id": "1d6a97bc",
"metadata": {},
"source": [
"There are no complaints when adding existing nodes or edges. For example,\n",
@@ -204,7 +204,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "118590f5",
+ "id": "4b23410e",
"metadata": {},
"outputs": [],
"source": [
@@ -213,7 +213,7 @@
},
{
"cell_type": "markdown",
- "id": "8128daf6",
+ "id": "1f39f96d",
"metadata": {},
"source": [
"we add new nodes/edges and NetworkX quietly ignores any that are\n",
@@ -223,7 +223,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "29d285b0",
+ "id": "83130d08",
"metadata": {},
"outputs": [],
"source": [
@@ -237,7 +237,7 @@
},
{
"cell_type": "markdown",
- "id": "73405a2b",
+ "id": "a9eba233",
"metadata": {},
"source": [
"At this stage the graph `G` consists of 8 nodes and 3 edges, as can be seen by:"
@@ -246,7 +246,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "7930b9ae",
+ "id": "465cf2c9",
"metadata": {},
"outputs": [],
"source": [
@@ -257,7 +257,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "0bf4652e",
+ "id": "7823b21f",
"metadata": {},
"outputs": [],
"source": [
@@ -272,7 +272,7 @@
},
{
"cell_type": "markdown",
- "id": "e46e1f97",
+ "id": "64aa0773",
"metadata": {},
"source": [
"# Examining elements of a graph\n",
@@ -292,7 +292,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "e8fc834c",
+ "id": "3ff762e2",
"metadata": {},
"outputs": [],
"source": [
@@ -304,7 +304,7 @@
},
{
"cell_type": "markdown",
- "id": "2fae0e4a",
+ "id": "a4b5de2d",
"metadata": {},
"source": [
"One can specify to report the edges and degree from a subset of all nodes\n",
@@ -316,7 +316,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "e1508072",
+ "id": "12bcbc1a",
"metadata": {},
"outputs": [],
"source": [
@@ -326,7 +326,7 @@
},
{
"cell_type": "markdown",
- "id": "aa2f21b1",
+ "id": "32d8fd07",
"metadata": {},
"source": [
"# Removing elements from a graph\n",
@@ -343,7 +343,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "29a3c316",
+ "id": "c2b6b314",
"metadata": {},
"outputs": [],
"source": [
@@ -355,7 +355,7 @@
},
{
"cell_type": "markdown",
- "id": "bffa3a12",
+ "id": "d96f2e94",
"metadata": {},
"source": [
"# Using the graph constructors\n",
@@ -370,7 +370,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "a6204886",
+ "id": "9afdca65",
"metadata": {},
"outputs": [],
"source": [
@@ -387,7 +387,7 @@
},
{
"cell_type": "markdown",
- "id": "51d764d6",
+ "id": "2b87b39b",
"metadata": {},
"source": [
"# What to use as nodes and edges\n",
@@ -416,7 +416,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "45c6451a",
+ "id": "5031e469",
"metadata": {},
"outputs": [],
"source": [
@@ -428,7 +428,7 @@
},
{
"cell_type": "markdown",
- "id": "7d632e8f",
+ "id": "0de80526",
"metadata": {},
"source": [
"You can get/set the attributes of an edge using subscript notation\n",
@@ -438,7 +438,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "dc2f2e19",
+ "id": "29d1d4a9",
"metadata": {},
"outputs": [],
"source": [
@@ -450,7 +450,7 @@
},
{
"cell_type": "markdown",
- "id": "963c5720",
+ "id": "57eed5af",
"metadata": {},
"source": [
"Fast examination of all (node, adjacency) pairs is achieved using\n",
@@ -461,7 +461,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "d902e4cd",
+ "id": "7ac3f870",
"metadata": {},
"outputs": [],
"source": [
@@ -475,7 +475,7 @@
},
{
"cell_type": "markdown",
- "id": "c301ade8",
+ "id": "5e6fd308",
"metadata": {},
"source": [
"Convenient access to all edges is achieved with the edges property."
@@ -484,7 +484,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "77b61e77",
+ "id": "653c6da2",
"metadata": {},
"outputs": [],
"source": [
@@ -495,7 +495,7 @@
},
{
"cell_type": "markdown",
- "id": "90b14046",
+ "id": "93b10672",
"metadata": {},
"source": [
"# Adding attributes to graphs, nodes, and edges\n",
@@ -517,7 +517,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "088afb6c",
+ "id": "6f783c7d",
"metadata": {},
"outputs": [],
"source": [
@@ -527,7 +527,7 @@
},
{
"cell_type": "markdown",
- "id": "16b697e9",
+ "id": "54b3c521",
"metadata": {},
"source": [
"Or you can modify attributes later"
@@ -536,7 +536,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "67ab7cdb",
+ "id": "edcb6c37",
"metadata": {},
"outputs": [],
"source": [
@@ -546,7 +546,7 @@
},
{
"cell_type": "markdown",
- "id": "7bdc4675",
+ "id": "840049be",
"metadata": {},
"source": [
"# Node attributes\n",
@@ -557,7 +557,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "7adb48a4",
+ "id": "9b3928cb",
"metadata": {},
"outputs": [],
"source": [
@@ -570,7 +570,7 @@
},
{
"cell_type": "markdown",
- "id": "72624cdc",
+ "id": "c4184226",
"metadata": {},
"source": [
"Note that adding a node to `G.nodes` does not add it to the graph, use\n",
@@ -585,7 +585,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "5e6a24ae",
+ "id": "37effb50",
"metadata": {},
"outputs": [],
"source": [
@@ -598,7 +598,7 @@
},
{
"cell_type": "markdown",
- "id": "279603ae",
+ "id": "a26fb909",
"metadata": {},
"source": [
"The special attribute `weight` should be numeric as it is used by\n",
@@ -619,7 +619,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "fb770685",
+ "id": "a83a5aff",
"metadata": {},
"outputs": [],
"source": [
@@ -633,7 +633,7 @@
},
{
"cell_type": "markdown",
- "id": "31452e73",
+ "id": "a8710f62",
"metadata": {},
"source": [
"Some algorithms work only for directed graphs and others are not well\n",
@@ -646,7 +646,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "6ad4b4f4",
+ "id": "04f47562",
"metadata": {},
"outputs": [],
"source": [
@@ -655,7 +655,7 @@
},
{
"cell_type": "markdown",
- "id": "0745992c",
+ "id": "4b4957fd",
"metadata": {},
"source": [
"# Multigraphs\n",
@@ -675,7 +675,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "cda91c67",
+ "id": "34c5a178",
"metadata": {},
"outputs": [],
"source": [
@@ -693,7 +693,7 @@
},
{
"cell_type": "markdown",
- "id": "298eb127",
+ "id": "ffaf9b1d",
"metadata": {},
"source": [
"# Graph generators and graph operations\n",
@@ -713,7 +713,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "74d76d3e",
+ "id": "e5b54464",
"metadata": {},
"outputs": [],
"source": [
@@ -725,7 +725,7 @@
},
{
"cell_type": "markdown",
- "id": "945ac4c7",
+ "id": "43db4093",
"metadata": {},
"source": [
"# 4. Using a stochastic graph generator, e.g,\n",
@@ -736,7 +736,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "93f74903",
+ "id": "15250e99",
"metadata": {},
"outputs": [],
"source": [
@@ -748,7 +748,7 @@
},
{
"cell_type": "markdown",
- "id": "f12a0272",
+ "id": "afa3e227",
"metadata": {},
"source": [
"# 5. Reading a graph stored in a file using common graph formats\n",
@@ -760,7 +760,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "54196e80",
+ "id": "3fd7fa9a",
"metadata": {},
"outputs": [],
"source": [
@@ -770,7 +770,7 @@
},
{
"cell_type": "markdown",
- "id": "22b76e1e",
+ "id": "464f1849",
"metadata": {},
"source": [
"For details on graph formats see Reading and writing graphs\n",
@@ -785,7 +785,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "ff743238",
+ "id": "48f96c72",
"metadata": {},
"outputs": [],
"source": [
@@ -799,7 +799,7 @@
},
{
"cell_type": "markdown",
- "id": "a1540523",
+ "id": "9e5c6eb4",
"metadata": {},
"source": [
"Some functions with large output iterate over (node, value) 2-tuples.\n",
@@ -809,7 +809,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "bf51122c",
+ "id": "97eb312f",
"metadata": {},
"outputs": [],
"source": [
@@ -819,7 +819,7 @@
},
{
"cell_type": "markdown",
- "id": "7ad44534",
+ "id": "933b6776",
"metadata": {},
"source": [
"See Algorithms for details on graph algorithms\n",
@@ -838,7 +838,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "63d9498e",
+ "id": "ce4fd906",
"metadata": {},
"outputs": [],
"source": [
@@ -847,7 +847,7 @@
},
{
"cell_type": "markdown",
- "id": "39c9d7a5",
+ "id": "340778b2",
"metadata": {},
"source": [
"To test if the import of `nx_pylab` was successful draw `G`\n",
@@ -857,7 +857,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "c1a1256b",
+ "id": "5729a65c",
"metadata": {},
"outputs": [],
"source": [
@@ -870,7 +870,7 @@
},
{
"cell_type": "markdown",
- "id": "159f63cd",
+ "id": "3839a528",
"metadata": {},
"source": [
"when drawing to an interactive display. Note that you may need to issue a\n",
@@ -880,7 +880,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "9e94fc92",
+ "id": "6478a32f",
"metadata": {},
"outputs": [],
"source": [
@@ -889,7 +889,7 @@
},
{
"cell_type": "markdown",
- "id": "9905d091",
+ "id": "8cd647f8",
"metadata": {},
"source": [
"command if you are not using matplotlib in interactive mode."
@@ -898,7 +898,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "9d4bb899",
+ "id": "e89f272c",
"metadata": {},
"outputs": [],
"source": [
@@ -919,7 +919,7 @@
},
{
"cell_type": "markdown",
- "id": "805e8019",
+ "id": "04d93eaf",
"metadata": {},
"source": [
"You can find additional options via `draw_networkx()` and\n",
@@ -930,7 +930,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "572d2502",
+ "id": "e494ea7f",
"metadata": {},
"outputs": [],
"source": [
@@ -941,7 +941,7 @@
},
{
"cell_type": "markdown",
- "id": "ddedda46",
+ "id": "55bcbc79",
"metadata": {},
"source": [
"To save drawings to a file, use, for example"
@@ -950,7 +950,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "11cfb1cb",
+ "id": "41b4d36e",
"metadata": {},
"outputs": [],
"source": [
@@ -960,7 +960,7 @@
},
{
"cell_type": "markdown",
- "id": "364d1dfc",
+ "id": "1c61855f",
"metadata": {},
"source": [
"This function writes to the file `path.png` in the local directory. If Graphviz and\n",
@@ -973,7 +973,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "50b2da8a",
+ "id": "e72d52c5",
"metadata": {},
"outputs": [],
"source": [
@@ -985,7 +985,7 @@
},
{
"cell_type": "markdown",
- "id": "3e79a049",
+ "id": "7209e1c5",
"metadata": {},
"source": [
"See Drawing for additional details."
diff --git a/tutorial_full.ipynb b/tutorial_full.ipynb
index 2b52c557..38c0019d 100644
--- a/tutorial_full.ipynb
+++ b/tutorial_full.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "markdown",
- "id": "c1df7792",
+ "id": "e3b4c6cb",
"metadata": {},
"source": [
"## Tutorial\n",
@@ -17,13 +17,13 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "c8da7c6c",
+ "id": "5576467a",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.686365Z",
- "iopub.status.busy": "2022-12-20T11:40:17.686152Z",
- "iopub.status.idle": "2022-12-20T11:40:17.756154Z",
- "shell.execute_reply": "2022-12-20T11:40:17.755523Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.391495Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.391037Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.463272Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.462597Z"
}
},
"outputs": [],
@@ -34,7 +34,7 @@
},
{
"cell_type": "markdown",
- "id": "fb8cff6d",
+ "id": "2f9d4265",
"metadata": {},
"source": [
"By definition, a `Graph` is a collection of nodes (vertices) along with\n",
@@ -54,13 +54,13 @@
{
"cell_type": "code",
"execution_count": 2,
- "id": "160a4cb7",
+ "id": "0a791bd4",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.759727Z",
- "iopub.status.busy": "2022-12-20T11:40:17.759186Z",
- "iopub.status.idle": "2022-12-20T11:40:17.762362Z",
- "shell.execute_reply": "2022-12-20T11:40:17.761771Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.466917Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.466502Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.469670Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.469045Z"
}
},
"outputs": [],
@@ -70,7 +70,7 @@
},
{
"cell_type": "markdown",
- "id": "bd486052",
+ "id": "c9fbb085",
"metadata": {},
"source": [
"or add nodes from any [iterable](https://docs.python.org/3/glossary.html#term-iterable) container, such as a list"
@@ -79,13 +79,13 @@
{
"cell_type": "code",
"execution_count": 3,
- "id": "89778ad6",
+ "id": "a55d12ce",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.765005Z",
- "iopub.status.busy": "2022-12-20T11:40:17.764803Z",
- "iopub.status.idle": "2022-12-20T11:40:17.767828Z",
- "shell.execute_reply": "2022-12-20T11:40:17.767239Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.472349Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.472137Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.475179Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.474579Z"
}
},
"outputs": [],
@@ -95,7 +95,7 @@
},
{
"cell_type": "markdown",
- "id": "df75a4d8",
+ "id": "722f5149",
"metadata": {},
"source": [
"You can also add nodes along with node\n",
@@ -117,13 +117,13 @@
{
"cell_type": "code",
"execution_count": 4,
- "id": "8965f552",
+ "id": "c886466a",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.770562Z",
- "iopub.status.busy": "2022-12-20T11:40:17.770360Z",
- "iopub.status.idle": "2022-12-20T11:40:17.773835Z",
- "shell.execute_reply": "2022-12-20T11:40:17.773236Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.477958Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.477747Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.481105Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.480493Z"
}
},
"outputs": [],
@@ -134,7 +134,7 @@
},
{
"cell_type": "markdown",
- "id": "e8d79910",
+ "id": "01b57622",
"metadata": {},
"source": [
"`G` now contains the nodes of `H` as nodes of `G`.\n",
@@ -144,13 +144,13 @@
{
"cell_type": "code",
"execution_count": 5,
- "id": "8130eaa1",
+ "id": "5dfd7b46",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.776574Z",
- "iopub.status.busy": "2022-12-20T11:40:17.776377Z",
- "iopub.status.idle": "2022-12-20T11:40:17.779142Z",
- "shell.execute_reply": "2022-12-20T11:40:17.778563Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.483890Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.483679Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.486572Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.485965Z"
}
},
"outputs": [],
@@ -160,7 +160,7 @@
},
{
"cell_type": "markdown",
- "id": "fd4cb72d",
+ "id": "15fb720b",
"metadata": {},
"source": [
"The graph `G` now contains `H` as a node. This flexibility is very powerful as\n",
@@ -178,13 +178,13 @@
{
"cell_type": "code",
"execution_count": 6,
- "id": "25ac86f8",
+ "id": "54dc3160",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.781851Z",
- "iopub.status.busy": "2022-12-20T11:40:17.781654Z",
- "iopub.status.idle": "2022-12-20T11:40:17.784675Z",
- "shell.execute_reply": "2022-12-20T11:40:17.784071Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.489460Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.489228Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.492504Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.491872Z"
}
},
"outputs": [],
@@ -196,7 +196,7 @@
},
{
"cell_type": "markdown",
- "id": "a7a507ca",
+ "id": "172498d6",
"metadata": {},
"source": [
"by adding a list of edges,"
@@ -205,13 +205,13 @@
{
"cell_type": "code",
"execution_count": 7,
- "id": "3551b660",
+ "id": "21364aac",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.787358Z",
- "iopub.status.busy": "2022-12-20T11:40:17.787156Z",
- "iopub.status.idle": "2022-12-20T11:40:17.790061Z",
- "shell.execute_reply": "2022-12-20T11:40:17.789451Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.495359Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.495155Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.498078Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.497441Z"
}
},
"outputs": [],
@@ -221,7 +221,7 @@
},
{
"cell_type": "markdown",
- "id": "dbeb878d",
+ "id": "622e5aac",
"metadata": {},
"source": [
"or by adding any ebunch of edges. An *ebunch* is any iterable\n",
@@ -234,13 +234,13 @@
{
"cell_type": "code",
"execution_count": 8,
- "id": "bb0bed7b",
+ "id": "7037d73c",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.792886Z",
- "iopub.status.busy": "2022-12-20T11:40:17.792679Z",
- "iopub.status.idle": "2022-12-20T11:40:17.795507Z",
- "shell.execute_reply": "2022-12-20T11:40:17.794935Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.500931Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.500726Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.503528Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.502912Z"
}
},
"outputs": [],
@@ -250,7 +250,7 @@
},
{
"cell_type": "markdown",
- "id": "c978377d",
+ "id": "1d6a97bc",
"metadata": {},
"source": [
"There are no complaints when adding existing nodes or edges. For example,\n",
@@ -260,13 +260,13 @@
{
"cell_type": "code",
"execution_count": 9,
- "id": "118590f5",
+ "id": "4b23410e",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.798307Z",
- "iopub.status.busy": "2022-12-20T11:40:17.798102Z",
- "iopub.status.idle": "2022-12-20T11:40:17.800850Z",
- "shell.execute_reply": "2022-12-20T11:40:17.800254Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.506419Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.506214Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.508928Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.508320Z"
}
},
"outputs": [],
@@ -276,7 +276,7 @@
},
{
"cell_type": "markdown",
- "id": "8128daf6",
+ "id": "1f39f96d",
"metadata": {},
"source": [
"we add new nodes/edges and NetworkX quietly ignores any that are\n",
@@ -286,13 +286,13 @@
{
"cell_type": "code",
"execution_count": 10,
- "id": "29d285b0",
+ "id": "83130d08",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.803651Z",
- "iopub.status.busy": "2022-12-20T11:40:17.803443Z",
- "iopub.status.idle": "2022-12-20T11:40:17.807088Z",
- "shell.execute_reply": "2022-12-20T11:40:17.806503Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.511865Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.511658Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.515424Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.514810Z"
}
},
"outputs": [],
@@ -307,7 +307,7 @@
},
{
"cell_type": "markdown",
- "id": "73405a2b",
+ "id": "a9eba233",
"metadata": {},
"source": [
"At this stage the graph `G` consists of 8 nodes and 3 edges, as can be seen by:"
@@ -316,13 +316,13 @@
{
"cell_type": "code",
"execution_count": 11,
- "id": "7930b9ae",
+ "id": "465cf2c9",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.809715Z",
- "iopub.status.busy": "2022-12-20T11:40:17.809512Z",
- "iopub.status.idle": "2022-12-20T11:40:17.815660Z",
- "shell.execute_reply": "2022-12-20T11:40:17.815071Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.518372Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.518167Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.524396Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.523787Z"
}
},
"outputs": [
@@ -345,13 +345,13 @@
{
"cell_type": "code",
"execution_count": 12,
- "id": "0bf4652e",
+ "id": "7823b21f",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.819620Z",
- "iopub.status.busy": "2022-12-20T11:40:17.819416Z",
- "iopub.status.idle": "2022-12-20T11:40:17.823451Z",
- "shell.execute_reply": "2022-12-20T11:40:17.822858Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.528568Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.528358Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.532543Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.531925Z"
}
},
"outputs": [],
@@ -367,7 +367,7 @@
},
{
"cell_type": "markdown",
- "id": "e46e1f97",
+ "id": "64aa0773",
"metadata": {},
"source": [
"# Examining elements of a graph\n",
@@ -387,13 +387,13 @@
{
"cell_type": "code",
"execution_count": 13,
- "id": "e8fc834c",
+ "id": "3ff762e2",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.826229Z",
- "iopub.status.busy": "2022-12-20T11:40:17.826031Z",
- "iopub.status.idle": "2022-12-20T11:40:17.830373Z",
- "shell.execute_reply": "2022-12-20T11:40:17.829790Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.535575Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.535363Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.539884Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.539280Z"
}
},
"outputs": [
@@ -417,7 +417,7 @@
},
{
"cell_type": "markdown",
- "id": "2fae0e4a",
+ "id": "a4b5de2d",
"metadata": {},
"source": [
"One can specify to report the edges and degree from a subset of all nodes\n",
@@ -429,13 +429,13 @@
{
"cell_type": "code",
"execution_count": 14,
- "id": "e1508072",
+ "id": "12bcbc1a",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.834022Z",
- "iopub.status.busy": "2022-12-20T11:40:17.833816Z",
- "iopub.status.idle": "2022-12-20T11:40:17.837812Z",
- "shell.execute_reply": "2022-12-20T11:40:17.837222Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.543696Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.543483Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.547666Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.547061Z"
}
},
"outputs": [
@@ -457,7 +457,7 @@
},
{
"cell_type": "markdown",
- "id": "aa2f21b1",
+ "id": "32d8fd07",
"metadata": {},
"source": [
"# Removing elements from a graph\n",
@@ -474,13 +474,13 @@
{
"cell_type": "code",
"execution_count": 15,
- "id": "29a3c316",
+ "id": "c2b6b314",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.841056Z",
- "iopub.status.busy": "2022-12-20T11:40:17.840862Z",
- "iopub.status.idle": "2022-12-20T11:40:17.843931Z",
- "shell.execute_reply": "2022-12-20T11:40:17.843344Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.551377Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.551000Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.554242Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.553627Z"
}
},
"outputs": [],
@@ -493,7 +493,7 @@
},
{
"cell_type": "markdown",
- "id": "bffa3a12",
+ "id": "d96f2e94",
"metadata": {},
"source": [
"# Using the graph constructors\n",
@@ -508,13 +508,13 @@
{
"cell_type": "code",
"execution_count": 16,
- "id": "a6204886",
+ "id": "9afdca65",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:17.846646Z",
- "iopub.status.busy": "2022-12-20T11:40:17.846439Z",
- "iopub.status.idle": "2022-12-20T11:40:18.094746Z",
- "shell.execute_reply": "2022-12-20T11:40:18.094090Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.557097Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.556886Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.810881Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.810216Z"
}
},
"outputs": [
@@ -543,7 +543,7 @@
},
{
"cell_type": "markdown",
- "id": "51d764d6",
+ "id": "2b87b39b",
"metadata": {},
"source": [
"# What to use as nodes and edges\n",
@@ -572,13 +572,13 @@
{
"cell_type": "code",
"execution_count": 17,
- "id": "45c6451a",
+ "id": "5031e469",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.097862Z",
- "iopub.status.busy": "2022-12-20T11:40:18.097544Z",
- "iopub.status.idle": "2022-12-20T11:40:18.102894Z",
- "shell.execute_reply": "2022-12-20T11:40:18.102295Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.814211Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.813880Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.819907Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.819300Z"
}
},
"outputs": [
@@ -602,7 +602,7 @@
},
{
"cell_type": "markdown",
- "id": "7d632e8f",
+ "id": "0de80526",
"metadata": {},
"source": [
"You can get/set the attributes of an edge using subscript notation\n",
@@ -612,13 +612,13 @@
{
"cell_type": "code",
"execution_count": 18,
- "id": "dc2f2e19",
+ "id": "29d1d4a9",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.105640Z",
- "iopub.status.busy": "2022-12-20T11:40:18.105435Z",
- "iopub.status.idle": "2022-12-20T11:40:18.109850Z",
- "shell.execute_reply": "2022-12-20T11:40:18.109251Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.822685Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.822474Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.827019Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.826406Z"
}
},
"outputs": [
@@ -642,7 +642,7 @@
},
{
"cell_type": "markdown",
- "id": "963c5720",
+ "id": "57eed5af",
"metadata": {},
"source": [
"Fast examination of all (node, adjacency) pairs is achieved using\n",
@@ -653,13 +653,13 @@
{
"cell_type": "code",
"execution_count": 19,
- "id": "d902e4cd",
+ "id": "7ac3f870",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.113355Z",
- "iopub.status.busy": "2022-12-20T11:40:18.113139Z",
- "iopub.status.idle": "2022-12-20T11:40:18.117783Z",
- "shell.execute_reply": "2022-12-20T11:40:18.117152Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.830466Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.830253Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.834876Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.834242Z"
}
},
"outputs": [
@@ -685,7 +685,7 @@
},
{
"cell_type": "markdown",
- "id": "c301ade8",
+ "id": "5e6fd308",
"metadata": {},
"source": [
"Convenient access to all edges is achieved with the edges property."
@@ -694,13 +694,13 @@
{
"cell_type": "code",
"execution_count": 20,
- "id": "77b61e77",
+ "id": "653c6da2",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.121210Z",
- "iopub.status.busy": "2022-12-20T11:40:18.121003Z",
- "iopub.status.idle": "2022-12-20T11:40:18.124547Z",
- "shell.execute_reply": "2022-12-20T11:40:18.123943Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.838572Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.838361Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.841935Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.841289Z"
}
},
"outputs": [
@@ -721,7 +721,7 @@
},
{
"cell_type": "markdown",
- "id": "90b14046",
+ "id": "93b10672",
"metadata": {},
"source": [
"# Adding attributes to graphs, nodes, and edges\n",
@@ -743,13 +743,13 @@
{
"cell_type": "code",
"execution_count": 21,
- "id": "088afb6c",
+ "id": "6f783c7d",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.128075Z",
- "iopub.status.busy": "2022-12-20T11:40:18.127875Z",
- "iopub.status.idle": "2022-12-20T11:40:18.131728Z",
- "shell.execute_reply": "2022-12-20T11:40:18.131159Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.845539Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.845314Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.849350Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.848737Z"
}
},
"outputs": [
@@ -771,7 +771,7 @@
},
{
"cell_type": "markdown",
- "id": "16b697e9",
+ "id": "54b3c521",
"metadata": {},
"source": [
"Or you can modify attributes later"
@@ -780,13 +780,13 @@
{
"cell_type": "code",
"execution_count": 22,
- "id": "67ab7cdb",
+ "id": "edcb6c37",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.135036Z",
- "iopub.status.busy": "2022-12-20T11:40:18.134827Z",
- "iopub.status.idle": "2022-12-20T11:40:18.138669Z",
- "shell.execute_reply": "2022-12-20T11:40:18.138091Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.852737Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.852529Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.856594Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.855978Z"
}
},
"outputs": [
@@ -808,7 +808,7 @@
},
{
"cell_type": "markdown",
- "id": "7bdc4675",
+ "id": "840049be",
"metadata": {},
"source": [
"# Node attributes\n",
@@ -819,13 +819,13 @@
{
"cell_type": "code",
"execution_count": 23,
- "id": "7adb48a4",
+ "id": "9b3928cb",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.141880Z",
- "iopub.status.busy": "2022-12-20T11:40:18.141681Z",
- "iopub.status.idle": "2022-12-20T11:40:18.146279Z",
- "shell.execute_reply": "2022-12-20T11:40:18.145680Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.860173Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.859963Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.864748Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.864125Z"
}
},
"outputs": [
@@ -850,7 +850,7 @@
},
{
"cell_type": "markdown",
- "id": "72624cdc",
+ "id": "c4184226",
"metadata": {},
"source": [
"Note that adding a node to `G.nodes` does not add it to the graph, use\n",
@@ -865,13 +865,13 @@
{
"cell_type": "code",
"execution_count": 24,
- "id": "5e6a24ae",
+ "id": "37effb50",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.149692Z",
- "iopub.status.busy": "2022-12-20T11:40:18.149491Z",
- "iopub.status.idle": "2022-12-20T11:40:18.153420Z",
- "shell.execute_reply": "2022-12-20T11:40:18.152800Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.868156Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.867943Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.871969Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.871344Z"
}
},
"outputs": [],
@@ -885,7 +885,7 @@
},
{
"cell_type": "markdown",
- "id": "279603ae",
+ "id": "a26fb909",
"metadata": {},
"source": [
"The special attribute `weight` should be numeric as it is used by\n",
@@ -906,13 +906,13 @@
{
"cell_type": "code",
"execution_count": 25,
- "id": "fb770685",
+ "id": "a83a5aff",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.156229Z",
- "iopub.status.busy": "2022-12-20T11:40:18.156010Z",
- "iopub.status.idle": "2022-12-20T11:40:18.161034Z",
- "shell.execute_reply": "2022-12-20T11:40:18.160436Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.874806Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.874594Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.879737Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.879128Z"
}
},
"outputs": [
@@ -938,7 +938,7 @@
},
{
"cell_type": "markdown",
- "id": "31452e73",
+ "id": "a8710f62",
"metadata": {},
"source": [
"Some algorithms work only for directed graphs and others are not well\n",
@@ -951,13 +951,13 @@
{
"cell_type": "code",
"execution_count": 26,
- "id": "6ad4b4f4",
+ "id": "04f47562",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.164497Z",
- "iopub.status.busy": "2022-12-20T11:40:18.164295Z",
- "iopub.status.idle": "2022-12-20T11:40:18.167195Z",
- "shell.execute_reply": "2022-12-20T11:40:18.166598Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.883296Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.883086Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.885990Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.885345Z"
}
},
"outputs": [],
@@ -967,7 +967,7 @@
},
{
"cell_type": "markdown",
- "id": "0745992c",
+ "id": "4b4957fd",
"metadata": {},
"source": [
"# Multigraphs\n",
@@ -987,13 +987,13 @@
{
"cell_type": "code",
"execution_count": 27,
- "id": "cda91c67",
+ "id": "34c5a178",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.169999Z",
- "iopub.status.busy": "2022-12-20T11:40:18.169794Z",
- "iopub.status.idle": "2022-12-20T11:40:18.175983Z",
- "shell.execute_reply": "2022-12-20T11:40:18.175395Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.888808Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.888598Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.894957Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.894319Z"
}
},
"outputs": [
@@ -1023,7 +1023,7 @@
},
{
"cell_type": "markdown",
- "id": "298eb127",
+ "id": "ffaf9b1d",
"metadata": {},
"source": [
"# Graph generators and graph operations\n",
@@ -1043,13 +1043,13 @@
{
"cell_type": "code",
"execution_count": 28,
- "id": "74d76d3e",
+ "id": "e5b54464",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.179345Z",
- "iopub.status.busy": "2022-12-20T11:40:18.179135Z",
- "iopub.status.idle": "2022-12-20T11:40:18.183262Z",
- "shell.execute_reply": "2022-12-20T11:40:18.182658Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.898452Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.898238Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.902384Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.901756Z"
}
},
"outputs": [],
@@ -1062,7 +1062,7 @@
},
{
"cell_type": "markdown",
- "id": "945ac4c7",
+ "id": "43db4093",
"metadata": {},
"source": [
"# 4. Using a stochastic graph generator, e.g,\n",
@@ -1073,13 +1073,13 @@
{
"cell_type": "code",
"execution_count": 29,
- "id": "93f74903",
+ "id": "15250e99",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.185934Z",
- "iopub.status.busy": "2022-12-20T11:40:18.185721Z",
- "iopub.status.idle": "2022-12-20T11:40:18.242120Z",
- "shell.execute_reply": "2022-12-20T11:40:18.241464Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.905260Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.905047Z",
+ "iopub.status.idle": "2022-12-20T16:43:40.981320Z",
+ "shell.execute_reply": "2022-12-20T16:43:40.980668Z"
}
},
"outputs": [],
@@ -1092,7 +1092,7 @@
},
{
"cell_type": "markdown",
- "id": "f12a0272",
+ "id": "afa3e227",
"metadata": {},
"source": [
"# 5. Reading a graph stored in a file using common graph formats\n",
@@ -1104,13 +1104,13 @@
{
"cell_type": "code",
"execution_count": 30,
- "id": "54196e80",
+ "id": "3fd7fa9a",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:18.245207Z",
- "iopub.status.busy": "2022-12-20T11:40:18.244981Z",
- "iopub.status.idle": "2022-12-20T11:40:19.036083Z",
- "shell.execute_reply": "2022-12-20T11:40:19.035455Z"
+ "iopub.execute_input": "2022-12-20T16:43:40.984733Z",
+ "iopub.status.busy": "2022-12-20T16:43:40.984501Z",
+ "iopub.status.idle": "2022-12-20T16:43:42.315819Z",
+ "shell.execute_reply": "2022-12-20T16:43:42.315124Z"
}
},
"outputs": [],
@@ -1121,7 +1121,7 @@
},
{
"cell_type": "markdown",
- "id": "22b76e1e",
+ "id": "464f1849",
"metadata": {},
"source": [
"For details on graph formats see Reading and writing graphs\n",
@@ -1136,13 +1136,13 @@
{
"cell_type": "code",
"execution_count": 31,
- "id": "ff743238",
+ "id": "48f96c72",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:19.039526Z",
- "iopub.status.busy": "2022-12-20T11:40:19.039282Z",
- "iopub.status.idle": "2022-12-20T11:40:19.045070Z",
- "shell.execute_reply": "2022-12-20T11:40:19.044486Z"
+ "iopub.execute_input": "2022-12-20T16:43:42.319596Z",
+ "iopub.status.busy": "2022-12-20T16:43:42.319357Z",
+ "iopub.status.idle": "2022-12-20T16:43:42.325390Z",
+ "shell.execute_reply": "2022-12-20T16:43:42.324777Z"
}
},
"outputs": [
@@ -1168,7 +1168,7 @@
},
{
"cell_type": "markdown",
- "id": "a1540523",
+ "id": "9e5c6eb4",
"metadata": {},
"source": [
"Some functions with large output iterate over (node, value) 2-tuples.\n",
@@ -1178,13 +1178,13 @@
{
"cell_type": "code",
"execution_count": 32,
- "id": "bf51122c",
+ "id": "97eb312f",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:19.049208Z",
- "iopub.status.busy": "2022-12-20T11:40:19.049003Z",
- "iopub.status.idle": "2022-12-20T11:40:19.053156Z",
- "shell.execute_reply": "2022-12-20T11:40:19.052554Z"
+ "iopub.execute_input": "2022-12-20T16:43:42.329465Z",
+ "iopub.status.busy": "2022-12-20T16:43:42.329236Z",
+ "iopub.status.idle": "2022-12-20T16:43:42.333565Z",
+ "shell.execute_reply": "2022-12-20T16:43:42.332936Z"
}
},
"outputs": [
@@ -1206,7 +1206,7 @@
},
{
"cell_type": "markdown",
- "id": "7ad44534",
+ "id": "933b6776",
"metadata": {},
"source": [
"See Algorithms for details on graph algorithms\n",
@@ -1225,13 +1225,13 @@
{
"cell_type": "code",
"execution_count": 33,
- "id": "63d9498e",
+ "id": "ce4fd906",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:19.056612Z",
- "iopub.status.busy": "2022-12-20T11:40:19.056397Z",
- "iopub.status.idle": "2022-12-20T11:40:19.404203Z",
- "shell.execute_reply": "2022-12-20T11:40:19.403564Z"
+ "iopub.execute_input": "2022-12-20T16:43:42.337154Z",
+ "iopub.status.busy": "2022-12-20T16:43:42.336943Z",
+ "iopub.status.idle": "2022-12-20T16:43:42.698838Z",
+ "shell.execute_reply": "2022-12-20T16:43:42.698144Z"
}
},
"outputs": [],
@@ -1241,7 +1241,7 @@
},
{
"cell_type": "markdown",
- "id": "39c9d7a5",
+ "id": "340778b2",
"metadata": {},
"source": [
"To test if the import of `nx_pylab` was successful draw `G`\n",
@@ -1251,19 +1251,19 @@
{
"cell_type": "code",
"execution_count": 34,
- "id": "c1a1256b",
+ "id": "5729a65c",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:19.407789Z",
- "iopub.status.busy": "2022-12-20T11:40:19.407450Z",
- "iopub.status.idle": "2022-12-20T11:40:19.601936Z",
- "shell.execute_reply": "2022-12-20T11:40:19.601394Z"
+ "iopub.execute_input": "2022-12-20T16:43:42.702749Z",
+ "iopub.status.busy": "2022-12-20T16:43:42.702392Z",
+ "iopub.status.idle": "2022-12-20T16:43:42.901743Z",
+ "shell.execute_reply": "2022-12-20T16:43:42.899554Z"
}
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
@@ -1282,7 +1282,7 @@
},
{
"cell_type": "markdown",
- "id": "159f63cd",
+ "id": "3839a528",
"metadata": {},
"source": [
"when drawing to an interactive display. Note that you may need to issue a\n",
@@ -1292,13 +1292,13 @@
{
"cell_type": "code",
"execution_count": 35,
- "id": "9e94fc92",
+ "id": "6478a32f",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:19.605138Z",
- "iopub.status.busy": "2022-12-20T11:40:19.604927Z",
- "iopub.status.idle": "2022-12-20T11:40:19.607919Z",
- "shell.execute_reply": "2022-12-20T11:40:19.607453Z"
+ "iopub.execute_input": "2022-12-20T16:43:42.905102Z",
+ "iopub.status.busy": "2022-12-20T16:43:42.904863Z",
+ "iopub.status.idle": "2022-12-20T16:43:42.907940Z",
+ "shell.execute_reply": "2022-12-20T16:43:42.907316Z"
}
},
"outputs": [],
@@ -1308,7 +1308,7 @@
},
{
"cell_type": "markdown",
- "id": "9905d091",
+ "id": "8cd647f8",
"metadata": {},
"source": [
"command if you are not using matplotlib in interactive mode."
@@ -1317,19 +1317,19 @@
{
"cell_type": "code",
"execution_count": 36,
- "id": "9d4bb899",
+ "id": "e89f272c",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:19.610312Z",
- "iopub.status.busy": "2022-12-20T11:40:19.610095Z",
- "iopub.status.idle": "2022-12-20T11:40:19.878445Z",
- "shell.execute_reply": "2022-12-20T11:40:19.877790Z"
+ "iopub.execute_input": "2022-12-20T16:43:42.911029Z",
+ "iopub.status.busy": "2022-12-20T16:43:42.910815Z",
+ "iopub.status.idle": "2022-12-20T16:43:43.190898Z",
+ "shell.execute_reply": "2022-12-20T16:43:43.190191Z"
}
},
"outputs": [
{
"data": {
- "image/png": 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mDE3uqVOnal2OpKQkhg3R8uXLtS4Hl1y9ehV/+eWXYmkzQJQBHePGjRtYsmRJRkcqW7YsBgcHCy2e0pw9e5ZRB132mU4ofvj7+3OuDBROZcqUwb59++LWrVvxzZs3OvOinzNnDsMoUtuGxps2baLJYGxsjF+/ftWqDFwRFhaGXbt21aiviEQinf4gIsqADnHkyBHW/axVq1bFqKgoocVTCYqiWL14zZo1S2jRCAYCF34GVE3lypVDb29v3LlzJ4aHhwumHERGRjIUoZ07d2qtfJlMhh4eHrTyBwwYoLXyuUKZuAXKJH3wM0CUAR2Aoihcu3Ytaydq1qyZ3m5zun37NqM+xsbGGBsbK7RoBAOBKw+EbFtmlUmOjo44dOhQ3Lt3L0ZHR2u17oW/ZOvWras15eTatWuMtrh165ZWyuaCvIiGbJ5e85Krq6tKHgivXr0qdLUUQpQBgZFKpTh58mTWTtS7d2/MyMgQWkSN+PXXXxn18vb2FlosggGhrP/4Q4cOobe3t8KXeps2bXDgwIGscUGUSa6urjhq1Cg8dOgQ70rxpUuXGOVry115z549aeXWqFFDZ5ZQiuLy5ctYrVo1ufewbNmyuHv3bpRKpQr7Vl4yMzPTeUUAkSgDgpKRkcGINpaXfH19USqVCi2ixrx48YK1fkLGfycYHhKJBAMCAlgjywUEBNAi0d2/fx+bNGkidzAoWbIkrlu3Dt++fYt79uzBIUOGsBr8KpOqVKmC48aNw+PHj+O3b984rbNUKkVXV1daeUOGDOG0DDZiYmIYX8vajKKqLqGhoXLDwecN6nPnzmXYPcnrW3mpdu3aeqEIEWVAIBISEtDT05O186xfv14vOo+y9O7dm1HHX3/9VWixCAYIRVGYkJCAUVFRmJCQIPc5k8lkePjwYYWDvJubG545cwYpikKKovD9+/f4559/4sCBA7Fs2bJqKQe//PILTpo0CU+fPs3J8uC6deto+ZuamuL37981zlcRCxYsoJVpbW2NqampvJapCfHx8Thp0iQ0MjKSe1/69euHkZGRCvPJ61t79uxhXP/w4UMt1UZ9iDIgAJGRkYwIY3kP6rFjx4QWj3MiIiJYH7Rr164JLRqBoJD09HRcsmQJWlpayh0oWrVqhc+ePaNdR1EUhoaGYmBgIPbp00dh9Dt5SSQSYZ06ddDPzw/Pnz+vlvFZfHw8mpmZ0fJds2YNR63DJDs7m6EITZw4kbfyNCE7Oxs3btyINjY2cu9B/fr18fbt2yrlK5VKGctIw4YN46kW3EGUAS3z9OlTdHBwYHS6UqVK4c2bN4UWjzfGjRvHqHPVqlWLxVIIofjz6dMnHDp0qMKBe/To0XK3zslkMnzx4gVu2rQJf/vtNyxVqpTKyoFYLMaGDRvirFmz8PLly0p/bQ8bNoyWj7OzM2/P3bFjxxhyv3r1ipey1IWiKLxw4QJjt0PBVL58edy/f7/afiTWrFlDy8/MzAzj4+M5rgm3EGVAi1y+fBmtrKwYHc/R0VHnHhiu+fbtG+MLBQBw//79QotGICjNo0ePsFmzZnIHEWtra1y1ahVmZmYqzEcqleLTp09x/fr12KVLF7S2tlZZOTAyMsKmTZvivHnz8Pr163JDAj98+JBx7cWLF/loHmzZsiWtHF1bDnz58iW2a9dObpuam5vjggULMC0tTaNyvn//jqamprS8165dy1Et+IEoA1pi9+7drFPltWrVwk+fPgktnlaYP38+o/729vZ6v2OCYFhQFIXHjx9HJycnuYOKi4sL/vXXX0rb/uTk5OCDBw9w1apV2L59e7W2MpqYmGDLli1x0aJFePPmzXyFhKIorF+/Pu3czp07c94uL1++ZMh0/PhxzstRh7i4OBw3bpzCbYDe3t748eNHzsocMmQILX9XV1edngklygDPUBSFixcvZu18Xl5eNCvm4k5KSgqrd8WVK1cKLRqBoDIZGRm4YsUK1tm+vNSiRQt88uSJynlnZ2fjnTt3cOnSpdi6dWvWWbWikrm5OXp5eeHy5ctx7ty5tN9EIhF++PCB0/aYMGECrYyyZctidnY2p2WoSlZWFq5fv571vZOXGjdujPfu3eO87Pv37zPKunTpEuflcAVRBngkJycHR40axdoBBw0aJPiDIgR//PEHoy0sLCx4t3AmEPjiy5cvOHLkSIWuj4cPH46fP39Wu4zMzEwMCgrChQsXYvPmzTX2iAcAOGPGDM7aICUlhbHUsWDBAs7yVxWKovDMmTNyt/sBAFasWBEPHz7MW3wJiqKwbt26tDK7du3KS1lcQJQBnkhLS5O7Z3Xu3Lk6GeBEG2RmZrJuu5o0aZLQohEIGhEcHMxYMy+YLC0tcdmyZXLX9lUhPT0dr169inPnzsUmTZoo3BYnL4lEIuzUqRNu2LABg4ODNZrC3rp1Ky1vIyMjwZY/Q0JCsHXr1nLrbWFhgUuWLNFKrIZdu3Yx2ryoLYpCQZQBHvj27Rs2aNCA9eELDAwUWjzB2bdvH6NtxGIxvnv3TmjRCASNoCgKT506xXD2UzBVqlQJjx49yqkvkZSUFLx06RLOmDED69evr1aAptKlS2PPnj0xICBApYiMFEUxIvn16tWLs7opy9evX3H06NEK6z506FCtKinp6emMnSOzZ8/WWvmqQJQBjgkLC2N9EZibm+PZs2eFFk8nkEqlrNt6evbsKbRoBAInZGZm4po1a7BEiRJyByZPT0/enNEkJSXhuXPncOrUqVirVi21lhHyIjJu27YN3759K1d5+e+//xjXXr9+nZd6sZGZmYmrV69WuCPD09MTHz16pDWZCjJ16lSaLHZ2dkXuNhECogxwyL1791gDW9jZ2WnNJ7i+cO7cOdaH9u7du0KLRiBwxrdv33DMmDEKv1YHDx6MMTExvMrx/ft3PHnypEJ3u0Wl8uXLs0Zk7N+/P+28KlWqaGUZlKIo/OuvvxTGiXBycsLjx48L6tE1LCyMIdfBgwcFk0ceRBngiDNnzqC5uTnjpru6upLpbxYoisJGjRox2qtx48bFyhUzgYD4cx27TZs2cgctCwsLXLRoEe/r2Lm5uejo6EgrW9HshaJUqVIl7Nu3L8NeYdOmTbzWAfGn87YWLVrIlc3KygpXrFihM9uW27ZtS5OvadOmQovEgCgDHBAYGMiq+Tdo0IDz4CPFiVu3brE+yCdPnhRaNET8qbDEx8djVFQUxsfHEyWFoBEUReG5c+cUWrhXqFABDx48yOuX9fLly2llmpub46tXr/DgwYM4cuRIdHZ2Vnv2wMjICP/880+Ndk4o4vPnzzh8+HC55YtEIhw5ciRv5avL6dOnGbIGBwcLLRYNogwogbxBQSaT4ezZs1k7ZZcuXTT2YmUIdOzYkdF2Li4ugm67lEgk6O/vzxrhzt/fXy0f8QRCHllZWbhhwwaFLokbNWrEy953xJ+GdiYmJrTyNm7cSDsnMjKSs4iMJ06cwLi4OI1kzsjIwOXLlyv06dCyZUt8+vSpRuXwRW5uLqMdfXx8hBaLBlEGFKBoUNiwYQP27duXtVOOHj0ac3NzhRZfLwgJCWFtw4CAAEHkURSfPO+YlZUVXrlyRRD5CMWH79+/44QJExR6xRswYABGR0dzXvbAgQNp5VSuXFnubETBiIwDBgxgja2iTKpRowZOnjxZpYiMFEXh0aNHsVKlSnLzdXFxwVOnTun8zN2yZctocltYWGBSUpLQYuVDlAE5FDUoyOuYS5cu1flOqWsMGjSI0Y42NjZa/wK/cuUKGhkZKXw5A/zcBmlkZEQUAgInvHr1Ctu3by+3v5mZmeH8+fM5nWm8ffs2o5yrV68qdW1eRMaqVauqpRTkvUPr1q2L06ZNwwsXLrB6Yn348KHcMO8AP20d1qxZo5OW+Wx8+fKF4SxKG/YVykKUARaUHRQKJiMjI9y7d6/QouslHz58YHWaMmfOHK3JIJFI0MrKSul7LhaL0crKiiwZEDiBoij8+++/WUOb56Vy5crh3r17ObEnoCgKa9asScu/R48eSl//9u1bhnyDBg3iJCLjwYMHGTMXhRWJMWPG6KU9VuGdFx4eHjrjgI4oA4VQdVDIS7pi9KavTJo0idGmJiYmrIFDKIpCqVSK2dnZmJGRgenp6ZiSkoISiQQTEhLw+/fv+PXrV/z8+TPGxMRgdHQ0RkRE4Pv37zEsLAxDQ0Px1atX+Pz5c3z27Bk+efIEp02bprKjFpFIJNhyBqF4kpOTg/7+/mhjYyO339WvXx9v3bqlcVnbtm1jDMjKBupRtHe+YETGzp07qxWRUV5q3bo1hoSEaFx3oRDaJ4MiRIiIQMgnICAApk2bBqo0i0gkgqVLl8KoUaNAJpMBRVEgk8lo/5f3rzLnGEJ+OTk5EBcXx2hbY2NjMDY2pp2rK11WJBKBm5sbhIeHg0gkElocQjEiMTERli5dCtu2bQOZTMZ6Tp8+fWDdunXg5uamVhlpaWlQsWJFSEtLyz82f/58WLFihcLrfvz4ARUrVoSUlJT8Y7Nnz4a1a9eynp+bmwvBwcFw8+ZNuHnzJty9excyMzNVlveXX36Bvn37gpeXFzRt2hTMzMxUzkNoEBFq1aoFoaGh+cd69eoFZ86cEVCq/0dQVUTHoCgK3d3d1XLlSZLhJmWNoQgEVXnz5g127txZbt8zNTXFOXPmYEpKilr5F56Rc3BwKHInj6b+9rOysvD27du4dOlSrFevnlrv24IRGe/du4c5OTlq1V8ICsdxEIvFOhHGnigDBYiPjxd8YCFJ/1JUVJTQXZdQzPnnn3+wevXqcvugg4MD7ty5U+VgQ69fv2bkdfToUbnncxWJLzo6WqFdgKrJysoKO3bsiGvWrMFHjx7p9G4uXYvwmAdRBgoQFRUl+MBCkv4lMjNA0AY5OTkYGBjI6vI8L9WuXRtv3LihUr6tWrWi5dGiRQu5596/f59R5qVLl5QuKy0tDefPn8/qrTUvtWnTBnfs2KFRRMaSJUtit27d8I8//sBnz57pjJFeHhMmTKDJW7ZsWcFD2hObgQIkJCSAvb09L3mLRCIQi8VgZGQk919Fv6lzrq7ml5ubC0lJSZCYmAjx8fGQkJAA379/h/j4ePj27RskJibycg/YKF26NDg5OUF4eDhkZGSodC2xGSAIgUQigeXLl8OWLVtAKpWyntOjRw9Yv349eHh4FJnfX3/9BQMGDKAde/HiBdSuXZtx7tChQ+Hw4cP5f7u6ukJ4eDgYGRkpLIOiKDh48CDMmzcPvn37xnpOlSpV4I8//oCuXbvSnqfU1FS4c+dOvs1BSEiIynZDpUuXhlatWoGXlxe0adMGatSoIegz++rVK0b7Hj9+nHEftIqgqoiOoa7NgEgkQhcXF0xKSsKUlBRMT0/HjIwMzM7OxtzcXIPyO0BRFCYmJuKzZ8/w7Nmz6O/vj9OmTcPevXtjgwYNsEyZMlr7YheJRFihQgX09PTEgQMH4pw5c3Dbtm34999/46tXrzA1NTVfbn9/f7KbgKBXvHv3Drt37y63f5qYmOCMGTOK3P6anZ2N5cqVo107fvx4xnnfv39HU1NT2nlr164tUs5bt25h/fr15cppY2OD/v7+Sn8ZcxGR0d7eHvv161dkREY+admyJU2mX3/9VesyFIQoA4Ugg4JiZDIZfv78Ge/fv4/Hjh3DNWvW4IQJE7BLly5Yo0YNTrcRFZVMTEzQzc0N27RpgyNGjMDFixfj3r178caNG/jhwwfMyspSul7EzwBBX7l+/TrDZ0DBVKZMGdy2bZvCdfSFCxfSrrGysmIYJa5Zs4Z2jpmZGcbHx8vNMzIyUq6XVoCfvlkmT56s8TJbXkTGiRMnKrSrUJTKly+PgwYNwl27duGHDx+0ohwcO3aMIcerV694L1ceRBkoRN6goGwnKm6DQnZ2NkZERGBQUBDu27cPlyxZgiNHjkQvLy90d3dnfBloO/Xt2xfv3buHsbGxKhtLFYWqHgiV9dhGIPBNbm4u7tixQ+HMW40aNfDatWus13/69ImxNh8YGJj/u1QqZYQKHjZsGGteKSkpOGfOHIXvis6dO2NoaCgvbfHlyxc8evQojhkzBitXrqzWe6ZSpUo4fPhw3L9/v9K+F1QlOzsby5YtSyt34sSJvJSlDEQZYGHVqlVKKwJ5g4K+RLhLS0vD169f46VLl3Dbtm04Z84cHDhwIHp6emKFChW0uq3Szs4O69evj7169UI/Pz/ctGkTnjlzBoODgzE+Ph6bNGnCuMbS0hK/fv3KW/sockOdl6ysrIgiQNBJkpOTcdasWYxARAVTt27dMCwsjHFt7969aef98ssv+e+xv//+m5HPw4cPaddLpVLctWuXwtgF1atXx8uXL2ulLfKIiYnROCKjm5sbjh49Gg8fPsxpRMQFCxbQyrG2tlZ7m6imEGWgEDKZDOvVq6ewYxQMWHP69GmdiXCXp5A8ffoUT58+jRs3bsSpU6diz549sV69egqtkLlOIpEIHR0dsXnz5jho0CCcO3cubt++HS9fvoyhoaFKxW2/efMma97jxo3jtR0lEgkGBASwhpoViUS8fSkQ5KMvyrauEB4ejr169ZL7fBobG+PUqVMxMTEx/5p///2Xcd7NmzcRERm+DurXr0+7B0FBQVinTh255dna2mJgYKBO+APgIiJj1apVcfz48RpHZIyJiWHMRK5du1aQfk6UgUIcOXKEcePt7e0ZA31AQACePn1aqxHupFIpfvr0Ce/evYtHjhzBVatW4bhx47BTp05YvXp1tLS01Npgb2pqipUrV8a2bdviqFGjcOnSpbh//368efMmRkZGcvbQd+rUiVG2WCzGN2/ecJK/IiiKwk+fPjG+si5evMh72YSfkHDSmqHMIL1lyxbMyclBiqIYwYf69euHHz58YLzfdu/ejYg/lY6ePXsqVDr8/Px0KjpfQSiKwnfv3uGOHTs0jsg4ZcoUPHPmDE3BUgZF7afNfk6UgQJkZWUx1sWqV6+OOTk5mJCQgFFRUZiQkIAURfES4S4rKwvDw8Px33//xT179uCiRYtw+PDh2Lp1a3R1dWVEvOIzlShRAmvWrIldu3bFiRMn4tq1a/H48eP44MED/PLli9b27T579oxVvu7du2ulfETmPuzp06drrWxDhoST5gapVIq7d+9mrE8XTNWqVcPLly+jv78/YzAvvCe+VKlS+OXLF5w5c6bC5Yju3buzLkfoMnkRGQMDA7FPnz5qzaYqE5GxIIqWpbXZz4kyUIBNmzYxbsb58+cZ56lref7x40d8+fIlXrx4EQMDA3HWrFnYv39/bNKkCZYvX15rAz3Az9mOhg0bYp8+fXD69OkYEBCA586dw5CQEExKStKpaVhvb2/WOvz3339aKX/JkiW0cuvWrauVcg0ZEk6ae1JSUnDu3LkKDfvatm3LcAhkYWFB+9vLy0uhoWLNmjXlGirqGzKZDJ8/f46bNm3SKCJjo0aNcPbs2fjPP//QQlHn9XNl8uC7nxOnQ/9PSkoKuLu70xzetGjRAm7fvs1wTqFOMCNtIhaLwdHREZycnMDZ2ZmRnJycwNLSUmgxlebDhw9QvXp1hoOVhg0bwqNHj0AsFvNa/p07d+DXX3/N/1skEkF8fDzY2dnxWq6hkpycDI6OjpCZmQkURRV5vlgsBgsLC4iNjQUbGxv+BdRzoqKiYM6cOXDy5EnW30UikVrvtjJlysDy5cvBx8cHjI2NNRVTJ5HJZBASEpLvAOnOnTuQnp6uUh7GxsbQuHFj8PT0hK1bt0JOTo5O9HOiDPw/v//+O6xevZp27P79++Dp6Uk7hojg4eEBkZGRgikD5ubmcgd6Z2dnqFixYrF7GCdOnAjbt29nHD969Ch4e3vzWnZ2djaULl2aFmnt9OnT0Lt3b17LNVTUjRy6YsUKGDNmDI+SFS8ePnwICxYsgJcvX2qUj4mJCYwZMwamTZsGpUqV4kg6/SA3NxdevHgBd+/ehXv37sGjR48gKyuLt/JEIhH4+/uDr68v93kTZQAgNjYWPDw8aDexd+/ecPr0aca5fLoszqNUqVJyB3pnZ2dwcHAwOPe3X79+hcqVKzNcBru4uEBYWBjv4Uw7dOgA169fz/978uTJsGXLFl7LNER0QdkmEHQVPl2gF6/PRzVZvHgxTREwMjKCVatWsZ6r6pQQG8bGxmBvbw/VqlWDunXrgpubG22wNzTtWhnKly8Pfn5+jPsSHR0NgYGBMGPGDF7Lb9OmDU0ZCAoK4rU8QyUxMREiIiKEFoNA0EkQESIiIiApKYnzZUqDnxkIDQ2F2rVr09Zsxo8fzzolDcD9zICJiQk0atQIunTpAq1bt4ZGjRqBqakpZ/kXJ1JSUsDNzQ2SkpJox21sbCAiIgJsbW15K/vRo0fQtGlT2rFv375B2bJleSvTEImOjgZXV1ehxSAQdJqoqChwcXHhNlPeTBP1hG7dutGsNq2srBR6uFM3mJGyyczMDNu3b4+rVq3CBw8e6ISTDl1i/fr1rO3G93a/3NxcLFGiBK3MY8eO8VqmIRIfH6/VXTUkkaSPiY+w6QY9M3D79m1o1aoV7diiRYtg6dKlCq/T5m4CKysraNmyJbRp0wZat24N9evXL3bGgaqQmZkJVapUgdjYWNpxExMTePfuHa9fld26dYNLly7l/z127Fj4888/eSvPEEENbAZMTU3h9OnT0KRJE56kK34kJSXB+vXrYf/+/SCTyVS+3sXFBRYvXgxdunQxODsmTUBEaNKkCXz8+FFlQ1newqZzrl7oCRRFMXzfOzg40MLaykMdPwOWlpa4cOFCrFOnjtLXsaUSJUpgly5dcP369fj06VPOg/XoA7t372Ztm4EDB/Ja7oYNG2jlVa5cmdfyDBV1IocW7gfR0dFCV0Onyc7Oxk2bNqGNjQ0nX6qtW7fGkJAQoaulV+hahFyDVQZOnjzJaOiCUbqKQpMIdykpKXjy5EkcMGAAlixZUqOHsFSpUti9e3fcuHEjPnv2TGueAYUkNzcXq1Wrxtoejx8/5q1cNm+IMTExvJVnqKiqbLMlc3NznD9/Ps3BC+HnR9DFixexSpUqctvOzMxMrTYXiUTo4+PDayCx4oSuhU03SGUgJycHPTw8GF95qq7PK+suVVGEO6lUivfv38d58+apHYu7YCpdujT26NED/f398cWLF8VWOTh9+jRr/X/99VfevCfKZDIsXbo0rbwDBw7wUpaho6yyXVQqX7487tu3r9g+B6rw8uVLbNeunUIFasqUKYw2t7Ozo/3dsWNH9PT0lJuPtbU1rl69GjMzM4Wuss6jS2HTDVIZ2Lp1K6Ox//rrL7XykhfhLi+YUVF+qQsTFRWFgYGB2KlTJ4VuQ5VNdnZ22Lt3b9yyZQu+evVKp9wMawJFUdi4cWPWOrO5kOaKwpHgRowYwVtZhk5RyrYqz0H9+vXx1q1bQldJEL5//47jx49XOODkLa3Mnz+fMbAvX76cdszU1BS/ffuGx44dQycnJ7l5uri44F9//VVs3jl8wcVHJRcYnDKQmprKiEzVuHFjjTssRVGMYEaakpaWhmfPnsVRo0YpDDKiSrK3t8e+ffvi1q1b8c2bN3r9oAYFBbHWsVq1apibm8tLmZs3b6aV5eTkpNdtqOsoUrZnzJiBVlZWjPuvSFHo27cvRkZGCl0trZCVlYXr169XuBTZuHFjvHfvHiL+tCMo/G6cOHEixsfHM5YOVq9ejYiIGRkZuGLFCtb7kJdatGiBT58+FbIpdB6uPyrVweCUgcWLFzM6qz58MchkMnz06BEuXLgQ69Wrx4liAABYtmxZHDBgAG7fvh3DwsL0bmDr0KEDa722b9/OS3mvX79mlPXhwwdeyiL8D3nKdlBQEOsad+FQvIW/bOfMmYMpKSkC14ofKIrCs2fPMgaWgqlixYp46NAh2vLJsWPHGOe9fv0aERGHDRtGO+7s7EwzXv78+TOOHDlSriImEolwxIgR+PnzZ623hz7Bx0elshiUMvD161eGBtutWzehxVKLT58+4Y4dO7Br166MKGOapPLly6O3tzfu3LkTw8PDdV45CA4OZq2HsjtDVIWiKMbX065duzgvh6A8Z8+eZZ0C9/X1VWiH4+DggLt27SpWO3JCQkKwdevWcutsYWGBS5YswfT0dMa1LVu2pJ3bqlWr/N8ePXrEyOvixYuMPJ4+fcrIp2CysrLC5cuXY0ZGBp/NQFADg1IGCsflFovF+ZqvPvPjxw+8cOECjh07FitUqMCZYpD3BTFkyBDcvXs3RkRE6KRyMGDAAFbZFy5cqJXyvL29eSmHoDz79u1j3H+xWIzHjh3DLVu2KIxLX6dOHQwKChK6Chrx7ds39PHxUbhEMmTIEPz06RPr9S9fvmScf+LEifzfKYrCBg0a0H7v1KkTa14UReHJkyfRxcVFriyVKlXCY8eO6eT7xFAxGGXg3bt3jLjRo0aNEloszqEoCoODg3Hp0qXYsGFDThUDgJ9r5MOGDcN9+/ZhVFSU0NVFRMTw8HA0NjZmyGppacnLtOSOHTto5ZQrV4681HSAjRs3MvqAiYkJXrt2DZOSktDPz4+1n+Slnj17Ynh4uNDVUInMzExcvXo1wztmweTp6YmPHj1SmM/48eMZfTo7O5t2zp49exh5K1oiy8zMxDVr1hQp28OHDzlpC4JmGIwy0KdPH1onNDc3l6slFye+fPmCu3fvxh49eqClpSXnyoGLiwuOHDkSDxw4IOie+8Ivs7w0evRozst6//49o5w3b95wXg5BdQpbwwP8nJp+8OABIiKGhYUxXJAXVh5mzpypFYMtTeDy6zslJYWxfMo2q/bjxw+Gk6KZM2cWKevXr181mrUgaAeDUAYePHjA6Hxz584VWiytk5mZiZcvX8aJEydipUqVOFcMAH5av44ePRoPHz6MsbGxrHJQFIXx8fEYFRWF8fHxnHxVf/nyBS0sLBjyiMVifPXqlcb5F4SiKKxYsSKtnK1bt3JaBkE9KIpiLAcC/PS/UbAfXLt2DWvWrCm3H5cpUwa3b9/O264UTeB6XT4wMJB2vZGRkdyBedq0abRzbW1tlS5HGXuGxYsXs9ozEPin2CsDFEUxHhxbW1vevDjpCxRF4YsXL3DlypXYtGlT3gIveXh44NixY/Ho0aP49u1b9Pf3Z90+4+/vr/E9mTdvHqsMnTt35qbRCjB06FBaGX369OG8DIJ6yGQyHDhwIKMflC9fnratMDc3F7dv345lypSR239r1qyJ165dE7A2/+Pz5884YsQIhc+qqhb7FEXhL7/8QsujV69ecs9nmxXbv3+/SuWps9OBwD/FXhm4cOECo7Nt3LhRaLF0jri4ONy/fz/26dMHra2teVEM5KWCjjWuXLmidh0kEgnDQ2Be+vfffzlsLcS9e/fS8rezsyMvLx0iOzsbO3fuzOgH7u7uDHe5EokEZ86ciSYmJnL7aLdu3TAsLEyQumRkZODy5ct52cv/33//qfysFN7O27hxY5XLzcrKwg0bNij0gdCoUaN8HwgE/inWykBubi5D63VxccGsrCyhRdNpsrKy8Nq1a+jr64uurq5aUwryXG5qohCsW7eONe969epxOlhHR0czynj+/Dln+RM058ePH9i8eXPGfapduzbrLFR4eDj27NlTbv80NjZGPz8/TEpK0or8FEUp5eXv5MmTai+19evXj5Zf1apVi8zr3LlzDDmePHmiVvlxcXFKe0ck8EuxVgZ27drF6FiHDx8WWiy9gqIoDA0NxbVr12KLFi009hWvTDIxMcFDhw6pFbM7IyODsZ6flw4ePMhp2xRWlDZt2sRp/gTNkUgkWLt2bUZfaNasmdy16aCgIKxTp47c/mlra4tbtmxROZaJKjx69Ih3//+fP39m7K7w9/cv8rrc3FyGzdHIkSPVlgNRubgJJPAUvxRbZeDHjx+MPfdcfx0aIgkJCXjo0CEcMGAAlipVilelQCQSYZ06ddDPzw/PnTun9BcZmxIIAOjo6IgxMTGcGS6OHj2aln/37t01yo/AD1+/fmVdo+7UqRNj+1weUqkUd+3axXAwVTBVr14d//nnH05l/fTpEw4ZMkThM8FVZMClS5fS8rawsFDabmfFihWMwToxMVEjeZSJqEgCT/FHsVUGVq5cyehIumIIpI+w7QDIycnBoKAgnD59OiMKJF/KQb169XD69Ol48eJFudu/cnNzFbqjzUuaGi4ePnyYll/JkiV10vqcgBgZGcnqkGvgwIEKPRCmpKTg3LlzFQYN69SpE4aGhmokX3p6Oi5evJh1R0xeat26NYaEhGhUTh45OTmM9vDx8VH6+q9fvzJsLP744w9OZMvOzkZ/f3/GNsaCyZADT/FFsVQG4uPjGYYp7du3F1osvUQikSi9A+Ddu3e4YcMGbN26NcPBEx9JLBZjw4YNcebMmXjp0iWa++FTp04ppVxoYrj4+fNnRp6PHz/motkJPPDq1StWA9MJEyYUOUsUERGBffv2lduXjIyMcPLkySovbclkMjx06JDcpa28Z+3s2bOcOrZiCwH+7NkzlfIovGOjcuXKnH6xJyQk4OTJkxW+Swwp8BTfFEtlYOrUqRp3dILyoTXZBlKJRIILFy7kXSEo/EJu3LgxzpkzBy9fvoyVK1dWWqlQ13Cx8AzE2rVruWh6Ak88ePCA1Sp//vz5Sl1/69YtrF+/vty+ZGNjg5s2bZK7/FCQ+/fvyw3DDfBzpmn9+vW8GDy3bduWVpanp6fKedy5c4chMx9hdkNDQ1l3huSl4h54SlsUO2UgIiKCMX01ePBgocXSO65cuYJGRkZFGgwWHkjT0tJw37592KpVK40H97///huXLVuGXl5enAZjklcPKysrlZcMCju46dixIw93g8Al165dY91GqOw0t0wmw3379mH58uXl9qcqVargxYsXWb/mo6OjWf0gFOyL48ePx7i4OK6rjoiIb9++ZZR56NAhlfOhKApr1apFy+e3337jQeKfXL582eACT2mTYqcMeHt7M7RGXfGhry9IJBK0srJSeueAWCxGc3Nz9Pb2VrgXWpXk7u5Oe5FmZmbirVu3cMmSJdiqVSuFa7jqJpFIhAEBASq11V9//UXLw8rKSqmvQoKwnDx5krV/7927V+k80tLScMGCBQoV1Xbt2uHLly/VOp8vfH19aWWWKVNG7V0J27dvZ7wL+NwGmJOTYxCBp4SgWCkDT58+ZXSMadOmCS2W3uHv78+bR0Jlk6mpKVarVg2rVq2Kzs7OWK5cObSxsUELCwveZBOJRAwlpCi+f//OyOfu3bs83h0CV7DtOhGLxXj27FmV8lHmS79NmzYKdyYomkngkvT0dIY91Zw5c9TOLzU1lRGI6Pfff+dQYnaKa+ApISk2ygBFUYx1sFKlSqm1V92QoSgK3d3dBVcGhEyq9pnCU6XLly/n6e4QuGbt2rWsiuiNGzdUzuvevXsKbQDYkio2Blywc+dOhgKsqQHepEmTaHk6ODhozbFbcQk8pQsUG2XgypUrjI6wZs0aocXSO+Lj4wUfjIVOqi4rFTZYbdOmDT83h8ALc+bMYfQBa2trtXaG5O0OKFeunMI+JhaLceLEiRgfH89DjdihKArr1q1Lk6Nr164a5/v69WtG/Y4ePcqBxMqjz4GndIVioQzIZDKGxzBHR0elo2kR/seNGzcEH4w1SaamphrbLag6M1DYPauZmZlGnuEI2oWiKBwzZgyjH9jZ2akcmjrPL4GiOAd5iUu/Acpw//59hgyXL1/mJO/C0QhbtGjBSb6qoG+Bp3SNYqEMHDx4kHHTVTEEMnTS09Nx//79CsOiappEIhGWKFECbW1t0czMjPabjY0NdurUCRctWoSrVq3Cpk2bys1jyJAh+OTJE3z37h1GR0fjt2/fUCKRYGZmZv4eZ3WXOtSxGUD8aXBZ2BiNGDDpF1KplOGnH+DnR4UyBnF5HgvLli2rcp/z8fHBb9++8V7HwYMH08p2dXXlzC9AYUNaAMAXL15wkreq6HrgKV1F75WBzMxMRiCPmjVrku0lRUBRFD58+BDHjBmj9SiFecna2hp//fVXnDdvHp4/f572Qjx58qTcCIR169Yt0uObOkaQ6uwmyKNBgwa0vBYuXKhWPgThyMrKwvbt2zP6hYeHh8JtfsrEMpgxY4ZcJRcAsESJEhrHGlBEXFwcYwfOunXrOMs/JyeHsTQybtw4zvJXB10LPKXr6L0ysGHDBsZN/vvvv4UWS2f5/v07bty4EWvUqMH54O7h4cGwLFY1ubi44IABA3Djxo147tw5bNOmDet55ubmuGXLFrlf8epsj1THz0Aes2bNouXXvHlzDe4SQSjS0tJYB+169eoxjNA+fPiAvXr1UjjYTJ06Nd9nf14UwsJBfgr3f02iEMpj9erVtHLMzMw4t1dYtGgRrQwrKyudMNzThcBT+oBeKwNJSUmMr8dWrVrxvj1H35BKpXj58mXs06ePwq046qaCA6lUKsXXr1/jnj17cOzYsVinTh2NIh0aGRmho6Oj3Dw6deokN2iLso6TRCIRGhkZaeQ97fLly4yBQF5UPIJuk5iYyGqM1rJlS8zIyMDk5GSNpqEzMjJw+fLlCm1bWrZsiU+fPuWkPlKpFJ2dnWn5Dxs2jJO8C/Lp0yeG6+AtW7ZwXo46CBV4Sp/Qa2Vg9uzZjBv66NEjocXSGSIiInDBggUK/Z7LG4AbN26MYrFYaQ+EigbS9PR0vHXrFq5btw779OmDjo6OnCojdnZ2eP78edayFblULlgHdVwRFyQ1NZWhaGmaJ0E4Pn/+zAhRDQBYq1YthQZqNWrUUNpA7fPnzzhixAiFSuqIESPw8+fPGtXl4sWLjLwfPnyoUZ7y6N27N2OA1aWPM20FntJH9FYZiImJYRii9evXT2ixBCcjIwMPHz4sd3pdUapRowZu2LAh/0tb2dgE6nxRf/78Gc+cOYNz5szB1q1bc+K50N3dHdeuXYsPHjygrb1KJBIMCAhgDWObl06dOqVx2xeOP6+JMxeC8Hz48KHILYJ5SZOta0+fPsUWLVrIzdvKygqXL1+u9u6own79GzRowNsAzbYb6ebNm7yUpQl8BZ7SZ/RWGSisURsbGxu0t6ng4GCcOHEilipVSqUB1NbWFidPnoxPnz5lfUHIG0jd3d0xICCAszVBqVSKL1++xF27dqGPjw/WqlVLo+UFExMTbNSoEU6ePBkPHTqE79+/R5lMhgkJCRgeHs6oT9WqVTXegzx//nxano0aNeKkbQjCcf78eYXLASYmJjhjxgy1bU3yoCgKT548iS4uLnLLcnJywmPHjqk0kH/48IGhyO/Zs0cjWYuqR+HgXbr8kcZl4Cl9Ry+VgRcvXjA6+KRJk4QWS+skJibi5s2bFRrHyNN6u3XrhqdOnVLaUxhFUZiQkIBRUVGYkJCglam/1NRUvHnzJq5ZswZ79erFGo9eVcWnU6dOuHjxYvz9998Zv+/atUsjef/9919afiKRCF+8eIHx8fE6NVVqyFAUhfHx8RgVFaXwvijj7rZatWqcf4BkZmbi6tWrFe7w8fT0VHo5dObMmYzB7cePH5zKXJiAgADGh5qmSx18khd4StEskDLuopXtW7qKXioDhae9rK2teYvwpWvIZDK8fv06Dhw4UOVgPYWXAfSRT58+4alTp3DWrFnYsmVLTgMW2dvbazTTkZGRIfcr0t3dHf39/TX+giSoh0QiQX9/f9YZroL3RZlAOAWTultRi+Lr16/o4+OjcHvskCFD8NOnT3LzyMjIYNRDG7FaJBIJWlpa0spdsmQJ7+VqSlpaGs6fP1/lQFLK9i1dR++UgaCgIMYNWrp0qdBi8c7Hjx9xyZIlCrclyfsaVrQMoO/k5ubihQsXWI291ElGRkbYpEkT9PX1xSNHjuCHDx+UbrcrV67IXdooaGNBDAu1i7K2LytWrFAYIrdkyZKsg7M64X+VJSQkhOHdr2CysLDAxYsXs+5c2b9/P+P8d+/e8SZrQQp7dKxQoYLebN1TNsT09+/fle5b+vDM65UyIJPJsGHDhrQGL1u2LKalpQktGi9kZWXhiRMnWB2hFDWgqboMoO/k5OTgokWLNLIzkJfKlCmDXbp0waVLl+KVK1dYnZTkbWMsytFR3u4LfXg5FAeU3V6qKJmamuKcOXMwJSWFEbI373m7cOECb3WgKArPnj2r0AC2YsWKeOjQIZpHwcJBk9q3b8+bjIUJCQlhyMiFka42KSrwlKWlJYpEIqV3XOn6M69XysDx48cZDb19+3ahxeKcFy9eoK+vr8rGgMVhGUBT7t+/j25ubqztY2dnh3379sXmzZsrnApUJlWpUgWHDh2KgYGBePPmTa06OCIoh6qOp9hS3759MSIigpbvypUrGeeZm5vjrVu3eK1PVlYWrl+/nhGCuGBq3Lgx3r9/H588ecL47dy5c7zKV5hmzZrRyvfy8tJq+VyQF3hK1e3Z+vjMixARQQ/IycmB6tWrQ2RkZP6xKlWqwOvXr8HExERAybghOTkZjh8/Djt37oSQkBClrytdujQMHjwYRowYAfXr1weRSMSjlPpBWloaTJ06Ffbt28f4TSQSwcyZM2HRokXw/v17ePToEaxduxY+fvyoVRlFIhH4+/uDr6+vVss1JAICAmDatGmgziuuZs2asHLlSvD09GT8hoiwePFi2LFjB+24tbU1nDt3DmrXrq22zMoQHx8P69atg4MHD8qtm5OTE8TExOT/XaFCBXj69CkYGxvzKltBTp06BRMnTqQdu3v3LlSpUkVrMnDFjx8/YOvWrRAYGAhZWVlq5aHzz7ygqogKbN68maFtnT59WmixNIKiKLx58yYOHjxYqShneckQlwHUQdn4Bu/evWN4TnN2dkZ7e3uNvgYUJXWDIhGUQ91gVSSRxFfS9WdeL5SBlJQUhtcvT09PnW3UooiNjcWVK1cyAiwVlX755ReDXwZQldjYWGzbti1rexaMbzB27Fjab0ZGRvju3TuMjIzEY8eOoZ+fH3p6ejIcXWmaDMmpiTaJj48X/OVPEklsSVefeb1QBhYsWMBo0Dt37ggtlkpkZ2fj6dOnsUOHDip9rdjY2BTr3QDaQCaT4caNG+VuQ+zUqRM+e/aMYUcwYMAARl7Z2dn45MkTDAwMxKFDh2q8iyEqKkr7DWIAREVFCf7SJ4kktqSrz7zOKwNfvnxh7Fnt0aOH0GIpzZs3b3D69OkqGQOKxWLs2rUrWQbgmBcvXrAGoAH4uWOgsF91AMCnT58qdCTy5csXjV4MuvqVoO+QmQGSdDXp6jOv8waE48aNg507d+b/LRaL4fXr11C9enUBpVJMWloanDhxArZu3QrPnz9X+rpq1aqBj48PDB48GMqVK8efgAZMVlYWzJ07FwICAlh/NzU1hZycnPy/LSwsIDMzM/9vd3d3mDJlCgwfPhwePXoEfn5+EBYWprIcIpEI3NzcIDw8nBh98gAigoeHB0RGRqplQKiIihUrQosWLaB58+bQokULcHR0hMTERPjtt98gPDycdm7z5s3h2LFjYG5uzqkM8hg2bBhcuXKlyPNMTExg/Pjx4OfnByVKlOBVphs3boC3tzft2N9//w2NGzfmtVxlycnJgZCQELh37x7cu3cPnjx5oraRoCJ0/pkXVhdRzNu3bxmGXWPGjBFaLFYoisI7d+7gkCFDVPKKV7JkSZw8eTIGBweTZQAtcvXqVSxfvrzKWn2eI5HC/VKdfPjyXkf4ib+/v1YMCN3d3dHHxwc3b97MugWtZ8+eGse9UIaPHz8ytlFOmjRJoSMlBwcH3LVrF0qlUt7kkslkjO2+gwcP5q28osjNzcWHDx/i6tWrsUOHDoyZZ2WSOttVdf2Z12lloGfPnrTGtLCw0Dkf11+/fsXVq1er5BlQLBZjp06dyDKAwMTHx2OvXr14HyzY7r+u7zkuDqjqZ0AsFqOlpSUGBATgoEGD1FIWAYBVURwxYgTNIRAfFI63UaJECUxNTVXKxXKdOnUwKCiIN9nWr19PK8/U1FRrLuSlUikGBwfjhg0bsGvXrliiRAm17mmTJk1w7ty5ePXqVYyNjS12vkV0Vhm4e/cuo0Hnz58vtFiI+D8XuO3atVPpy8PDw4PsBtAxKIrCPXv2aBxCuXLlyigWi5X2RqZO2GeC6ijrgZDtvlAUhWFhYbht2zbs16+fxltNu3fvztt6cVZWFjo4ONDKKxy8LTExEadOnaow+FLPnj15if6akJDA2ImzatUqzstB/DkT8fLlSwwICMCePXvK3V6sKIlEIqxfvz7OmDEDL126hCkpKYxyNOlbuohOKgMURTG8V5UpU4b1hmiTd+/eoZ+fn0IPYIVTiRIlcNKkSWQZQMcJDw9HZ2dntV7y3t7eKJPJlPZTrusvheIGV/eFoih89eoVbt68GXv16qX2IFOvXj2cPn06Xrx4kbN32tGjRxllvX79mvXcsLAw7Natm1wZTUxMcObMmZyFJ89j+PDhtHKcnJw4WZ6gKArfvn2LW7duxb59+zK2oSubateujVOnTsVz586xuhxnozg98zqpDJw9e5Zxo4Raa0lPT8c9e/ZgrVq1lO5UYrEY27dvj6dPnybLAHoCRVFy3RgX9XIv6EhEIpFgQEAAawSzgIAAzl+wBOXg477IZDJ89uwZ/vHHH9itWzeVPhIKvisaN26Mc+bMwStXrrAGHFKGFi1a0PJt1apVkddcu3ZN7u4agJ9RPLdv386ZvcOjR48YZagT04GiKAwPD8edO3eit7e3wtDDilL16tVx4sSJePLkSfz+/bva9Souz7zOKQO5ublYrVo1WqO6ublhdna21mSgKAofPnyIAwYMUMkY0NXVFTds2IDfvn3TmqwEbtB0K1rh6V+KojAhIQGjoqIwISGBzArpCHzel9zcXHz8+DGuWbMGO3bsqNbSk7GxMTZr1gznz5+PN27cwIyMjCLLffHiBSOfEydOKC3z9u3bFX5N16xZE69du6Zp8yBFUdigQQNa3p06dVLq2ujoaNy3bx8OGzYMHR0d1XpGK1eujGPGjMFjx47xslSr78+8zikDf/75J+MmHjt2TCtlf//+HVetWoUVKlRQuoNZWVnhuHHjyDKAnqOpkxpddSRCEI6cnBy8d++ewin5opKpqSm2atUKlyxZgrdu3WKdaRw/fjztmnLlyqkcLlgikeCMGTMUukXv1q0bhoWFadQme/fuZeT74cMHxnmxsbF46NAhHDVqlNqOvVxcXHDkyJF48OBB/PTpk0ZyGwI6pQykp6czpnwaNGjAqxWuVCrFv//+G1u2bKm0MaBIJMLWrVuTZYBiBNczAwRCQZYsWaJR/8pLFhYW2K5dO1y5ciXev38f4+PjGTMQixYtUlvO8PBwxi6ugsnY2Bj9/PyUXlMvzI8fPxi2FjNmzMBv377h8ePHcdy4cVilShW12qZixYo4ZMgQ3LNnD0ZGRqrdBoaKTikDy5YtY9zgGzdu8FJWREQETpw4UaVtJk5OTrh27VqyDFAMUTewja4HHyHoBhRFoa+vL6P/WFlZ4ejRo9HT01Ohlb+8VNhCXywWc/IVHBQUhHXq1JFbrq2tLW7ZskXlGQhExOnTpzNkVmfwd3BwwAEDBuCff/6J79+/J8+ghuiMMhAXF4fW1ta0m63sepKyZGRk4K5duxg2CUVp4iNHjiTLAAaAOk5qdN2RCEF3kMlkOGTIEEYfKlu2LIaHh2NaWhr+888/OHv2bGzUqJFag6SJiQl2794dN27ciCEhIRrNqkqlUty1axdjy2LBVL16dfznn3+KzEsikeD58+fRz89PpfdvYQWkT58+GBgYiKGhoeR9zDE6owxMnjyZ8ZJ98eKFxvlSFIWPHz/GHj16KB0mWCQSoaenJ548eZIsAxgQ6jip0XVHIgTdIicnB7t3787oSy4uLhgbG0s7Nzk5GS9cuIDTpk3DunXrquVN0dbWFnv37o1btmzB169fqzWApqSk4Jw5cxQaU3fu3BnfvHmTf01qaipevnwZZ82ahQ0bNlRLsSlVqhT+9ttvuGnTJnz+/DnvTpsMHZ1QBsLDwxlTZMOGDdMoz8TERFy4cCGWLVtW6c5XoUIFXLFiBVkGMGCKmyMRgu6RkZGBv/76K6NP1ahRAxMTE+Vel5iYiGfOnMHJkydjjRo11Pq6dnBwwP79++OOHTvw3bt3KikHERER2LdvX4XPRIMGDbBhw4Zqueu2tLTEzp074/r16/Hp06e8ukgmMNEJZaB///60TmFmZoYfP35UOR+ZTIbnz5/Hxo0bK61Fm5ubo7e3N1kGIORTnByJEHST5ORkrFevHuN91KRJE0xLSyvy+s+fP2scHyPvA2jw4MG4e/dupY3ubt26hXXr1tW47MLP1vDhwzVsVYImCK4MPH78mNFJZs6cqVIeUVFROHz4cKX39YpEImzQoAEeO3aMLAMQWCkujkQIuktcXByr5Xy7du2KfC8V3p1gaWmJb968wcOHD+Po0aPVcqAFAOjs7IwjRozAAwcOYExMTH552dnZePfuXVy+fDl6eXmhubm5ynmbmZlh69atcenSpXj79m1cunQp48OM7MoRDkGVAYqisHXr1rQOYWNjo3CqLI+srCz09/dXqdM7ODjgggULyDIAQWn03ZEIQbf5+PEjqxOdvn37yp0mz8nJYfhCYYvm+vHjR9y/fz8OGzZMpUBqBZOtrS1WrFiRsWtBleTq6op79uxhOFD69u0bw45rw4YNvLQzoWgEVQYuXbrE6Djr169XeM39+/exbdu2Sm/DMTU1xR49euDjx4/Ji5xAIOgcb968QTs7O8a7y8fHh/WdderUKca5ISEhCsugKAo/fPiAu3bt0igio6KkaGlWLBbj+PHjGW5/vb29aee5u7sTQ0GB0LoyQFEUxsfH44cPHxhbTJycnDAzM5NxTV60LbYHRl6qWbMm7t+/nywDEAgEnefJkyeMrdUAgLNnz0bE/703o6KiGHEIPD09VS5PKpXi+fPnsV+/flihQgW1dioA/Jzar1evHs6fPx+jo6Px7t272KhRI7nnlyxZEtevX5//Xr5z5w7jnBMnTmBUVBTGx8eTDzgtojVlQCKRoL+/P2MNtmA6cOBA/vkUReHhw4dVChBka2uLfn5+JEQwgUDQO4KCglin47t166bwvXno0KEi886L7Ldt2zaNIvsVNTOQF5FxxowZCmcf3N3d8ezZsyiTyRS+493d3dHf359s39UCWlEGFFlnF5xGunTpEoaFhWGvXr2UNlAxNjbG9u3b4927d4kWSSAQ9Jpz586pvEvg4sWLjHwKR/ZTd1mgbNmy6OzsjBYWFipfa2RkhBUrVlS4pNumTRvGUkFhBSNv986VK1cEuCOGgwgREXjk6tWr0LVrV0BEoCiKs3w9PDzAz88PRo8eDWZmZpzlSyAQCEJy4MABGDFihNLnGxkZwaVLl6BatWpw8+bN/PTp0yeVy65cuTK0adMG2rRpA61bt4by5csDAIBUKoVnz57l53337l348eOHyvmri1gsBpFIBJcuXYKOHTtqrVxDgldlIDk5GRwdHSEzM5MTRaBkyZLQv39/WLp0KVSoUIEDCQkEAkG3SE5OhrJly0JOTo7S14hEIlDnVe7s7Axt2rQBLy8vaN26NVSqVEmp63Jzc+HJkydw8+ZNCAoKgvv370NWVpbK5auCWCwGCwsLiI2NBRsbG17LMkSM+cz8wIEDkJGRoVYnzcPIyAg8PT1hyZIl4OXlBSKRiEMJCQQCQbc4cOAA5ObmqnSNsu/YihUr5n/5t2nTBlxdXdUREUxMTKBZs2bQrFkzmD9/PmRlZcGjR4/yZw4ePnyokjKjDBRFQUZGBhw8eBB8fX05zZvA48wAIoKHhwdERkaqpQxUqlQJJk6cCH5+fmBubs6DhAQCgaBbaPreLIyDgwNt8Pfw8NDKB1VGRgbcv38/Xzl48uQJSKVSjfMViUTg5uYG4eHh5MOQY3hTBhISEsDe3l6j6+3s7DiUiEAgEHQbTd+bNjY24OXlBV5eXtCmTRuoXr26Tgya6enpcPfu3fxlheDgYI2UHTI+cA9vywTp6ekaXZ+WlkZuNoFAMCg0fW8GBweDm5sbR9Jwh7W1NXTq1Ak6deoEAAAvX76EOnXqqJ0fGR+4R8xXxtbW1hpdX6JECY4kIRAIBP1A0/dmqVKlOJKEXzQ1ACfjA/fwpgzY2dmBu7u7ylNUIpEI3N3dwdbWlifJCAQCQTcxlPemodRTn+BNGRCJRDBlyhS1rvX19dWJdS4CgUDQJoby3jSUeuoTOuVngOwjJRAIho6hvDcNpZ76Am8zAwA/LVtPnz4NIpEIxGLFReV5mDpz5gy50QQCwWAxlPemodRTX+BVGQAA6NixI1y6dAksLCxAJBIxpnfyjllYWMDly5ehQ4cOfItEIBAIOo2hvDcNpZ76AO/KAMDPGx4bGwv+/v6MbS9ubm7g7+8Pnz9/JjeaQCAQ/h9DeW8aSj11Hd4DFRUGESEpKQnS0tKgRIkSYGtrS4xBCAQCQQGG8t40lHrqIlpXBggEAoFAIOgWWlkmIBAIBAKBoLsQZYBAIBAIBAOHKAMEAoFAIBg4RBkgEAgEAsHAIcoAgUAgEAgGDlEGCAQCgUAwcIgyQCAQCASCgUOUAQKBQCAQDByiDBAIBAKBYOAQZYBAIBAIBAOHKAMEAoFAIBg4RBkgEAgEAsHAIcoAgUAgEAgGDlEGCAQCgUAwcIgyQCAQCASCgUOUAQKBQCAQDByiDBAIBAKBYOAQZYBAIBAIBAOHKAMEAoFAIBg4RBkgEAgEAsHAIcoAgUAgEAgGDlEGCAQCgUAwcIgyQCAQCASCgUOUAQKBQCAQDByiDBAIBAKBYOAQZYBAIBAIBAOHKAMEAoFAIBg4RBkgEAgEAsHAIcoAgUAgEAgGDlEGCAQCgUAwcIgyQCAQCASCgUOUAQKBQCAQDByiDBAIBAKBYOAQZYBAIBAIBAOHKAMEAoFAIBg4RBkgEAgEAsHAIcoAgUAgEAgGDlEGCAQCgUAwcIgyQCAQCASCgUOUAQKBQCAQDByiDBAIBAKBYOAQZYBAIBAIBAOHKAMEAoFAIBg4RBkgEAgEAsHAIcoAgUAgEAgGjrHQAhB0F0SExMRESE9PB2tra7CzswORSCS0WAQCoQDkOSVwAZkZIDBITk6GgIAA8PDwAHt7e3B1dQV7e3vw8PCAgIAASE5OFlpEAsHgIc8pgUtEiIhCC0HQHa5evQp9+vSBjIwMAPj51ZFH3teGpaUlnD59Gjp27CiIjASCoUOeUwLXEGWAkM/Vq1eha9eugIhAUZTc88RiMYhEIrh06RJ50RAIWoY8pwQ+IMoAAQB+Tjk6OjpCZmamwhdMHmKxGCwsLCA2NhZsbGz4F5BAIJDnlMAbxGaAAAAABw4cgIyMDKVeMAAAFEVBRkYGHDx4kGfJCARCHuQ5JfAFmRkwMPIsj6OioiAyMjL/38OHD0NmZqZKeYlEInBzc4Pw8HBivUwg8AwigoeHB0RGRoIqr23ynBKUgSgDxZD09HSIioqSm9LT0zktLyEhAezs7DjNk0Ag0ElISAB7e3u1r+/WrRt4eXlB8+bNoV69emBiYsKhdAR9hygDekhOTg7ExMTIHezj4+O1Kk9UVBS4uLhotUwCwdCIjo4GV1dXTvKysLCAxo0bQ/PmzaF58+bg6ekJpUuX5iRvgn5ClAEdhKIo+Pr1q9zBPjY2Vuk1Q21AZgYIBP7RdGagKGrUqJGvHDRv3hzc3NzIsoIBQZQBAUBEkEgkcgf76OhoyM7O5lUGIyMjcHJyAldXV3BxcYFz586BRCJRaS0SAMDR0RFiYmLIS4NA4BlEhMqVK0NkZKRWyitbtixNOahXrx6YmppqpWyC9iHKAE9kZGRAdHQ062AfGRkJqampvMtQrlw5cHV1zU9ubm75/3d0dARj4/95ow4ICIBp06aprAwYGRnB1q1bYezYsUQhIBB4JCsrCxo0aABv3rxR63oTExMoU6YMfP36Va3rzc3N85cWmjVrBs2aNQNbW1u18iLoHkQZUBOpVAqfPn2SO9jHxcXxLkOpUqXkDvYuLi5gYWGhdF6q7l8ujLe3N/z5559QokQJla8lEAiKyczMhJ49e8K1a9c0zqtfv37Qp08fCAkJgXv37sGTJ0/UnomsXr06bfagcuXK5KNAT9G6MqAvQTUQEeLi4uQO9p8+fQKZTMarDGZmZuDi4sI62Lu6unJu8KOsZzN5VKlSBU6ePAm1a9fmVC4CwZD58eMH/PbbbxAUFKTSdSKRSO5Mn5ubGxw9ehSaNGkC2dnZ8OzZM7h3715+UtcI2cHBAZo1a5avHNSvXx/MzMzUyougXbSmDCQnJ8OBAwdgy5YtEBERkX/c3d0dpkyZAsOHD9e6h6yUlBS5g310dLTK++5VRSwWg6Ojo9zBvly5ciAWa9cvlCKf53mIRCLo0aMHnDt3jvGbubk5bNmyBUaPHq2TSh6BoE+kpaVB165d4c6dO7TjlpaWgIiQlZUFAPJjExw+fBjOnz8P+/fvZ+RtZGQEy5Ytgzlz5oCRkVH+cUSEDx8+wL179+D+/ftw7949tZcmzMzMoFGjRvnKQbNmzYixsY6iFWVAqKAaWVlZ8PHjR8ZAn/d/iUTCWVnyyIsmVnigd3V1hUqVKumkQU5ycjIcPHgQNm/eTFPcCnLnzh1ISEiAESNGQEpKCuP3IUOGwPbt28Ha2ppvcQmEYklKSgp07twZHjx4QDtuY2MDV69ehSpVqrA+p+7u7uDr6wvDhw+HUqVKAQDA8ePHYdy4cay2Sq1atYJDhw5BpUqV5MqSlJQEDx48yJ85ePz4cb4ioirVqlWjLS14eHiQDwcdgHdlgM+gGjKZDD5//ix3sP/y5QtX1ZCLtbW13MHexcVFrwdDRISkpCRISUmBtm3bQnR0dP5vAwcOhGPHjkFUVBT0798fnj59yri+atWqcPLkSahVq5YWpSYQ9B+JRAIdO3aEJ0+e0I7b2trC9evXoX79+vnH8p7TtLQ0KFGiBNja2rIOrtHR0TB48GC4f/8+47fSpUvD7t27oXfv3krJl5OTk29zkJfUtZMqU6YMbWmhYcOGZGlBAHhVBjQNqoGIkJCQIHewj4mJgdzcXL7EB4CfFrjOzs6sg72rq6vO2jxwzR9//AEzZ87M/9vExARiYmKgXLlykJ2dDXPmzIGAgADGdRYWFhAYGAgjR440iHYiEDQlISEB2rdvD8+fP6cdt7e3h3///VcjmxypVAorVqyA5cuXs76Tx44dCxs3bgQrKyuV8kVEiIyMpCkHoaGhasloamoKDRs2pC0t8OlfgfATXpUBdber1ahRA0QiEURFRcGPHz94ku4nIpEIKlasyDrQu7q6QoUKFWjraYZKUlISVKxYkTY1uHz5cliwYEH+32fOnIFRo0axLhsMHToUtm/frvJLhkAwJL5//w7t2rWDV69e0Y6XK1cObty4Ab/88gsn5dy9excGDx4MMTExjN+qVasGx44dg7p162pUhkQiYSwtqGuHVaVKFdrSQtWqVcnHBcfwpgyoG1SDD2xtbeV+2Ts7O5MpKSUZNWoU7Nu3L/9vR0dHiIqKovkriIyMhP79+0NwcDDj+urVq8Nff/0FNWvW1Iq8BII+8fXrV2jbti28ffuWdrxixYoQFBQEVapU4bS85ORkGDduHPz111+M30xNTWHNmjUwdepUzoyYc3Nz4fnz57TZA3V9HtjZ2TGWFszNzTmR01DhTRng23VmQSwtLeV+2bu6ukLJkiW1IkdxJzg4GBo2bEg7dubMGejVqxftWHZ2NsycORMCAwMZeVhYWMC2bdtgxIgRfIpKIOgVsbGx4OXlBeHh4bTjTk5OEBQUBO7u7ryUi4iwf/9+mDJlCussbMeOHWH//v1Qrlw5XsqOjo6mKQevX79W6+PR1NQUGjRoQFtacHBw4Fzm4gxvygCXQTWMjY3zXeeyJQcHBzJlpCWaNGkCjx8/zv+7Xbt2cP36ddZzT506BaNHj2a1YB4+fDhs3bqVLBsQDJ6PHz+Cl5cXw82wq6srBAUFaSUI2Pv372HQoEGsM3oODg6wf/9+6Ny5M+9yJCcnw8OHD/OVg0ePHuXvQlOVypUr05YWqlWrpvWt2noF8kR8fDwCgNopICAAg4KCMCoqCnNzc/kSk6AiBw4cYNyrsLAwueeHh4djvXr1WO/xL7/8gqGhoVqUnkDQLSIiItDZ2ZnxbFSuXBljYmK0Kkt2djbOnj1b7jt56tSpmJmZqVWZcnJy8MmTJ+jv74/9+vXDChUqqD2m2NraYteuXXHVqlV469YtzMjI0GpddB3elAGKotDd3R1FIpHKN61ChQpIURRfohE0IDMzE+3s7BgviaKumThxIuu9trS0xAMHDmhHeAJBh3j//j06Ojoynolq1arh58+fBZPr+vXrWL58edbntU6dOoIq8BRFYVRUFB45cgQnTpyIderUUWuMAQA0MTHBJk2a4PTp0/H06dP47ds3weqlC/CmDCAi+vv7q32jBg8ejF++fOFTPIKaFP56KFWqFKanpxd53fHjx7FEiRKs93vUqFH448cPLUhPIAjPmzdvWAfcGjVq6MSg9P37d+zevTvrs2phYYE7duzQmQ+2lJQUvHr1Ki5atAjbtm2LVlZWas8euLu747Bhw/DPP//E169fo0wmE7p6WoNXZUAikaCVlRWKxWK1bkyJEiXwjz/+wJycHD7FJKhIZGQkQ8nbuXOnUte+f/8e69aty3q/a9SogW/evOFZegJBWF69eoUODg6sX93fv38XWrx8KIrCrVu3orm5Oevz2rNnT0xISBBaTAa5ubkYHByMmzdvxgEDBrDOviibSpcujV26dMGVK1fif//9V6w/WHhVBhARr1y5gkZGRmorBHmDxM2bN/kWlaACXbt2ZbzIlP1SyMzMxPHjx7PeaysrKzx06BDP0hMIwhASEsJYZgMAbNCgASYmJgotHiuvXr3CmjVrsj6vFSpUwKCgIKFFLJKPHz/i0aNHcdKkSVi3bl21xyNjY2Ns3Lgx+vn54cmTJzmfvaYoCuPj4zEqKgrj4+O1OvvCuzKA+FMhsLKyYl0yEIlEKBKJ0MrKCidOnIilSpWSeyO8vb0FXUsj/I9Lly4x7s+9e/dUyuPYsWNobW3Neq99fHyIgQ+hWPHkyRMsXbo0o683bdoUJRKJ0OIpJCMjA6dMmcL6rIpEIpw3b55ezeCmpqbitWvXcPHixdi+fXu57yFlkqurKw4ZMgS3b9+OL1++VGtpQSKRoL+/P7q7u9Pydnd3R39/f630D60oA4g/K7ty5UpGQzo7O2NAQAAmJycjImJcXByOGDFCbsNbW1vjhg0b9KrjFUdkMhm6urrS7s2gQYNUzufdu3dYu3Zt1ntdq1YthTsVCAR94f79+1iyZElGH2/RogWmpKQILZ7SXLx4EcuUKcP6vDZq1AjDw8OFFlEtpFIphoSEYGBgIHp7e6OTk5PaykGpUqWwU6dOuHz5cgwKCirSnqrgx3LhD+aCH8tXrlzhtQ20pgwg/twmUrjh5Fmm3rt3T+7aMsDPbWn6MD1VnFm3bh3tnpiYmGBcXJzK+WRkZODYsWNZ77OVlRUeOXKEB+kJBO1w584d1i/PNm3aYFpamtDiqcyXL1+wffv2cj/WDhw4oDPGhZrw6dMnPH78OE6ZMgXr16+PRkZGaikHRkZG2LBhQ5w6dSr+9ddftNltZZfRxWIxGhkZ8aoQaFUZQESGpef9+/flniuVSnHr1q1oY2Mjt5EGDhyIsbGxWqwBIY+EhAQ0MzOj3Y9Vq1apnd/hw4flWgKPGTOGLBsQ9I6goCC0tLRk9Of27dvrtTGaTCbDDRs2oImJCevz6u3tnT/bW1xIS0vDf//9F5cuXYodOnSQuzNKmeTi4oL9+vVDU1NTpXfcicVitLKy4m3JQOvKQMWKFWkVvHz5cpHXxMXF4ahRo+Q2krW1Na5fv54sHQjA8OHDaffCyckJpVKp2vmFhYVhrVq1WO9z7dq18d27dxxKTyDwx9WrV1kt8bt06aJ15z18ERwcjFWqVJE74Cn62NN3pFIpPn/+HLdu3YqDBg1idR7FdRKJRBgQEMBLfbSuDNSoUYNWuaNHjyp97f379+V6swMArF69Ot64cYNH6QmFefz4MeM+nD9/XqM8f/z4gaNHj5ar+B07dowj6QkEfrh06RJj1gwAsEePHpiVlSW0eJySlpYm93k1MjLCZcuWafSBoE/ExsbiX3/9hVOnTsWGDRuqvbSgSBlwd3fnZRlG68pA8+bNaZXbtm2bStdLpVLctm2bwqWDAQMGkKUDLdKwYUNa+3fo0IGTfA8ePMg6xQoAOH78+GLzdUUoXpw7d451+rxv377FcvYybztcYGAgq5EkAGDLli3x48ePQouqddLT0zEoKAiXL1+OnTp1UrhbTpXEh38HrSsDhfenq7vG/P37d7naKMBPw7O1a9didnY2xzUgFGbfvn2M9n///j0neb9584Yxm5SX6taty1k5BAIX/PXXX2hsbMzoq97e3sUuxoq87XDynBTZ2NjgX3/9JbTYgiKTyfDly5e4YsUKjZSBqKgozmXTujIwePBgWqVmz56tUX4PHjzA+vXry220atWq4b///suR9AQ2MjIyGPunp02bxln+P378kGszUqJECTxx4gRnZREI6nLkyBFWq/Bhw4YVu2nyorbDKRrIRo0apZT78uKMpoH8isXMwKRJk2iVGjNmjMZ5SqVS3L59O6tDj7zUv39//PTpEwc1ILAxY8YMxlcA19bSBw4ckLtsMHHiRLJsQBCM/fv3sw6CPj4+xc6/vbLb4RQpBVWqVMHg4GChqyIY6gbyK1Y2A/Pnz6dVrl+/fpzlHR8fjz4+PnIb2MrKCtesWUOWDnjgw4cPjPbevXs35+WEhobiL7/8wnp/69Wrhx8+fOC8TAJBETt37mR950ycOLHYKQKqxpsRi8WsyyYAP/2SrF+/vti1kbKoE8ivWO0m2LBhA61y7du357yMR48eMYzaCqaqVavi9evXOS/X0OnUqRNjcOZDg01PT2dsacxLJUuWxJMnT3JeJoHAxtatW1n7oZ+fX7FwvFMYdQewwYMHy3X52759e4OMUCuRSFh3nChSrIqVn4Hdu3fTKtioUSNeypFKpbhjxw60tbWV27j9+vXDmJgYXso3RC5evMho4wcPHvBW3r59+9DCwoL13k6ePLnYbeEi6BabNm1i7XuzZ88uloqAplPb79+/x0aNGrGeU6ZMGbx48aLQVdQqb9++VVoZyPNAePXqVd7k0boycOrUKVolPTw8eC0vPj4ex4wZI7cDW1pa4urVq8nSAQdIpVKG442hQ4fyWuarV6+wWrVqrPe2QYMGGBERwWv5BMNk7dq1rH1u4cKFxVIRQPwZulwVJaBwSkhIwJycHJw3b57c9/HkyZMNwtNoZmam3JgshRWpvNgEfCoCiAIoA//++y+tsvb29lop9/HjxwqXDqpUqcJ7YxsCa9asobWrqakp7zHa09LScOjQoaz3tWTJknjq1CleyycYFsuWLWPta8uWLRNaNM7Jzs7G8+fPY//+/dHU1FQjZaDgdrigoCCGN9q8VLNmTXz16pVwldYCkydPZtSbbdbExcWFFsiPT7SuDDx9+pRWWRMTE61p0lKpFP/880+FSwd9+vQxSOcYXPH9+3fGS2PNmjW8l0tRFO7evVvuHmdfX1+ybEDQCIqicMGCBaz9Sxt9XFvIZDK8desWjh07VuEOLXVmBgqSkJCAPXv2ZD3X3Nwct27dWixnWc6ePcuob40aNfDjx4+M4+oEflMXrSsDbFbn2g7YkZCQgOPGjVO4dLBq1SoyeKhJ4a90Z2dnre2zfvnyJVatWpX1vjZq1AgjIyO1IgeheEFRFM6ePZu1X23cuFFo8TjhxYsXOHv2bKxUqRJnCgCA4u1wFEXhjh075Nr+dO/eHePj4wVoDX6IiYlhKFgWFhb4+vVr/P79O6P+fM+qFkTrykBCQgKjwkK5Dn7y5IlcgxaAn/YMfMeQLo48ePCA0ZbaNA5KS0tjOLfKS6VKlcIzZ85oTRaC/kNRFPr5+bH2p8DAQKHF04jo6GhctWoV1qxZU6mBXdkthYWVgaK2w4WGhspdQy9fvnyx2P2Vm5uLLVq0YNTvzz//REQ0PGUgNzeXUeHXr19rW4x8ZDIZ7tq1C+3s7OR25t69e5OlAxWgKIrhFbJTp05al2HXrl1ylw38/PyI0SihSGQyGU6cOJF1gNu5c6fQ4qlFfHw8btu2jXVgkpdatGiB27dvx4iIiHzPg8oqD8puh8vMzJSrdIlEIpw9e7ZeP7OLFi1i1Ktfv375MyYGpwwgIiMO9N27d4UQg0ZCQgKOHz9ebie3sLDAlStXkqUDJSm8hRQABHEI9OLFC7khVhs3bsyLj29C8UAmk6GPjw/rwLR//36hxVOJHz9+4LFjx7Bbt25ynQAVTjVr1sTVq1cznpELFy4orUSIxWKVDbMvXbqE9vb2rPk1aNBAL8OY37x5kzG2ODs705Qkg1QGCq9J/f3330KIwcqTJ0+wcePGcju3h4cH/vPPP0KLqfP8+PGDEVly5syZgsiSmpqKAwcOZL2fNjY2eO7cOUHkIuguUqmU1bGVkZERHjlyRGjxlCI3Nxf/+ecfHDJkCFpZWSk1eFeqVAnnzJmDL168kJvvzp07lVYGOnbsqJbsX79+xY4dO7LmaWVlhXv37tUb48L4+HisUKECox8V9sFikMpArVq1aBU+fPiwEGLIRSaT4e7duxUuHfTq1Qujo6OFFlWnmTZtGq3NbG1tBdtDTFEU/vnnn3KdfEyfPl2vpyAJ3JGbm4uDBg1i9BFjY2Odj7pHURQ+ePAAJ0+eLPfrunAqXbo0jhs3Dm/dulWka2CZTMaYaZNn/Afwc2uxut4FZTIZbtq0Se6Wxv79+/PmjY8rKIrCbt26MWRfvXo141yDVAZatmxJq7CuGuEkJibihAkTFC4dLF++nATIkcP79+8ZbbZv3z5BZQoJCcHKlSuz3s8mTZoQBc/AycnJwb59+zL6homJiU7PIL19+xYXLFiAbm5uSikA5ubm2L9/fzx//rxKSjDbtrh//vkHExISMCoqCkNDQ9HExIT2+9y5czWq27Nnz+Q6FnNycsI7d+5olD+f+Pv7M2Ru164dq9JlkMpA9+7daRVesWKFEGIozdOnT7FJkyZyH6zKlSvj5cuXhRZTJ+nQoQOtrRo2bCi0SJiSkoIDBgyQ+5VkaG5RCT/JysrCHj16MPqEmZkZXrp0SWjxGMTGxuKGDRsUhnAvmMRiMXbo0AEPHDiAqampapXZrFkzWp61a9dmTNePGTOGdk6pUqXULi+P9PR0HDt2rNx6LV68GHNzczUqg2uCg4MZipGDgwN+/fqV9XyDVAYK70MXai1ZFWQyGe7ZswfLlCkj92Hr2bMnMUgrxPnz5xnt9PjxY6HFQoqicNu2bXKnIGfOnIk5OTlCi0nQEpmZmdilSxfWL2hd8kwqkUhw9+7d6OXlpbRFf+PGjTEgIEDuIKQsd+/eZeR96NAhxnlhYWEM2f744w+Nys7j9OnTch0hNWvWTGfev6mpqejh4cGQUVFfMkhlYMqUKbQKjx49Wggx1CIxMREnTpwod7+tubk5Llu2jCwd/D9SqRSdnJxobTR8+HChxcrn2bNn6O7uznovPT09SSArA+DHjx/Yvn17xv23tLTEoKAgocXDzMxMPH36NPbu3VvpwDZVqlTBpUuX4vv37zmTo/CsSaVKleQqzKqcqyoxMTHYqlUr1nqXLFkSjx07xkk5msDmHn327NkKrzFIZWDhwoW0Cvfp00cIMTTi2bNn6OnpKfdhdHd318mpRSFYtWoVrW3MzMwYrkmFJDk5Gfv168d6H21tbXVqtwuBW9LS0rB169aM+25tbY23b98WTC6pVIo3btzAUaNGYalSpZRSAMqVK4d+fn745MkTzi3t3759y/jaV+R58d69e0rNIqiLVCrFlStXopGREWtbjBgxQuOlCXU5cOAA6+xMUcqQQSoDGzdupFW4bdu2QoihMTKZDPfu3avQardHjx46M3UlFHFxcYzp+HXr1gktFg2KojAwMFDussHs2bPJskExIzU1ldXxTsmSJfH+/ftal4eiKAwODsbp06cztqLJSyVKlMARI0bg9evXeXX5XdjfgjJ2AIXtC2rVqsW5kvLw4UO5RpOVK1fW+pLku3fvGNs4S5YsqVT0VINUBvbu3UurcIMGDYQQgzOSkpJw0qRJCpcOli5datBLB4XdA7u6uha5jUkInj59Kvfl0rx5c7JsUEyQSCTYtGlTxj22sbHBJ0+eaFWWDx8+4LJly+RazBdOpqam2LNnTzx58qRWtup+/fqVoSTPmzevyOvYdh7w4d49JSVFbtRSY2NjXLt2rVbeNVlZWVivXj2GDMePH1fqeoNUBs6cOUOrsLu7uxBicE5ISAhDGy6Y3NzcDHbKmW3aUFeXUZKTk7FPnz6s99DOzo7sHNFzEhMTWcOZ29nZYUhIiFZkiIuLw82bN7MqJGxJJBJh69atcdeuXZiUlKQVGfP4/fffGcqIMr4D2HwS8DkLfPjwYYZ327zk5eXFewycqVOnMsr18fFR+nqDVAaCgoIYD2FxQSaT4b59+xQuHXTv3t3goudRFIV16tShtUPXrl2FFksuFEXh5s2bGVuD8tLcuXN1bisToWji4+Oxbt26jPvp4OCAr1694rXs1NRUPHjwIHbq1EnuWnfhVLduXVy3bh1++vSJV9kUyVzYk6gqBt9s3gqDg4N5kzciIkLuNnA7OzvefEWwuWiuXr26ShF5DVIZePbsGa3CRkZGeuNaUlkkEglOmTJF4dLBkiVLBPPIJwSFXwwikUjnlaLHjx+jq6sr6z1s0aKFYBE3Carz7ds31uh85cuXxzdv3vBSZnZ2Nl68eBEHDhyo0FNfweTi4oK///47hoaG8iKTKmzatIkh39u3b5W+PjMzE8uWLUu7fuDAgTxK/NNx1IIFC+RuvZwwYQLjvUtRFMbHx2NUVBTGx8erNB7FxsYyvNWamZnhy5cvVZLbIJWByMhIRqWFsvzkm5CQEGzevLncB9/V1dVgnNykp6czLKOL2m6jC0gkEuzVqxfr/StTpgzrOqgmLxcC93z+/Jl1Td7R0ZHT7XeIP2cH79y5g+PHj1fo0pwtVaxYETdt2qQTbnZzcnIY24J/++03lfNZuXIl4+NPGx8B//33Hzo6OrK28y+//IIvXrxAiUSC/v7+jO3F7u7u6O/vX+R9kEqlrNsct23bprK8BqkMJCUlMSpdnA2zKIrCAwcOoIODg9yXQLdu3ZSyONV3Cq+r2dnZ6YVhJUVR6O/vL3fZ4Pfff8fc3FyNXy4E7omJiWF1Qe3s7MzpM/fq1SucO3cuOjs7q6QAFEwikQhFIhFaWVnxYmynCocPH2bIp06E2aSkJIaF/ZQpU3iQmEliYiL27t2bta1NTEzQ1NQ0v83VuQ9Lly5l5Nu7d2+1lH+DVAakUimj0qpOqegjEokEfX195S4dmJmZ4eLFi4v10kFYWBij3gcOHBBaLKV59OiR3Jd9zZo10dLSUqOXC4FboqKiWJd53NzcOIlD8fHjR1yzZg3Wrl1bqcE+79kvynugWCxGIyMjwfoKm42Pp6en2vn5+fnR8rK0tNSarxGKonDXrl1oaWmpsnKm6D7cvn2b8S53cnJS28DTIJUBRGRMFwvp4EPbPH/+nHV/c15ydXXFCxcuCC0mb7Rr145W38aNGwstkkokJSWx+rDX9OVC4JYPHz4wprkBfnrn08QgLzExEXfs2IG//vqr0ve9WbNmuG7dOrS0tJT7McDWV6ysrASZTbp69SpDnjNnzqidX3R0NMNoctmyZRxKXDRv375lNR5V5z4kJCQwliCMjIzUmjnJQ2hlQISICALg4uICHz9+zP/7woUL0L17dyFEEQREhMOHD8OsWbMgLi6O9ZyuXbtCQEAAuLu7a1k6fjl79iz07t2bduzJkyfQsGFDgSRSHUQEf39/mD17NkilUpWuFYvFYGFhAbGxsWBjY8OPgAbO+/fvwcvLCz5//kw7Xr16dbhx4waUL19epfwyMjLg4sWLcPToUfjnn38gNze3yGuqV68OgwcPhkGDBoGrqysEBATAtGnTQJVXrkgkghUrVsCYMWNUkldT+vbtC7dv387/283NDe7duwdGRkZq5zl+/Hg4c+ZM/t92dnbw7NkzsLCw0EhWVcjOzoaBAwfCvXv3VLpOJBKBv78/+Pr6AiJCr1694Pz587RzVqxYAfPnz1dbtvj4eHBwcKAd+/79O9jb26udp0poTe0oROEpqIMHDwoliqAkJyfj1KlT5W41MjMzw4ULF6q0RUXXyc3NZWjVo0aNElostXjw4AFj65UySSQSYUBAgNDiF0tCQ0OxXLlyjDavVasWxsXFKZ1Pbm4uXr16FYcNG4bW1tZK3deKFSvizJkzMSQkhLZuTFEUuru7Kx1ciCTdSiKRCN3d3ZGiKNyyZQvjdy8vL409QAo9MyCYMlDYAnPz5s1CiaITvHjxQuHSgYuLC547d67YWKUvX76cVj9zc3NMTEwUWiyVoSgKXVxcNHq5ELjj5cuXrD4+6tati/Hx8UVeT1EUPnr0CH19fRlb4uQlGxsb9PHxwZs3b8odEOLj4wUf0EjSPN28eZPhjdHe3h4/f/6scd81WGWg8JqrttePdBGKovDQoUMKX0JdunTB8PBwoUXVmK9fvzIs87kKc6pNPn36pNHLRZcCNuk7z549Y93K16hRoyKNut69e4eLFi1i3XXAlszMzLBv37549uxZzMrKKlK2qKgowQcykjRPbK7KufJIarDKwPDhw2mVnjZtmlCi6BzJycno5+cnd+nA1NS0WCwdDBw4kFYvd3d3nYxXUBCKojA0NBQ3bdqEnTp1UjqkrLxk6EGsuOLx48esyzWenp6YnJzMes2XL19w48aNrK6J2ZJYLMZ27drh3r175eYpDzIzUDzTjBkzuOi+iCi8MiCYAaGfnx8EBATk/z1y5EjYu3evEKLoLK9evYLJkyfTDHkK4uzsDP7+/tCjRw8QiURalk5z7t69Cy1btqQdu3LlCnTs2FEgidhJTEyEf//9F65duwbXrl2D2NhYzvJOSEgAOzs7zvIzRO7fvw+dO3eG1NRU2vGWLVvCpUuXoESJEvnHUlJS4MyZM3D06FEICgoCiqKKzL9hw4YwaNAgGDhwoMqGh3kgInh4eEBkZKRKBoQFqVOnDsydOxe8vLx4ed4lEgnUrVsXMjMz84+NGTMGVq5cyWk5p0+fhgkTJtCOXbhwAZo2bcppOXmEhobC2rVr4cqVK2rnIRKJGPetYcOGcO/ePTA1NdVURAAwYAPCxYsX0zSgXr16CSWKTkNRFB4+fJjVICovde7cWS+XDiiKwlq1atHq0r17d6HFwpycHLx9+zYuWLAAGzVqxIvRF7EZUA15Hh1v3brFatzn5eWF6enpiPgzmtzZs2exb9++Ss/kuLu746JFizAsLIyzOvj7+3PSl5o3b443b97kTK48VqxYQSvHyMiIE18MhcnJyWH46uDjuX/79i0OGDCAlxmBEiVKcP7OFXpmQDBlwN/fn1bpNm3aCCWKXpCSkoLTp09XuHSwYMECvVs62LFjB2OQFGLq/MOHD7h161bs0aOH3MhnXCsDZDdB0Sjy6Dhx4kRWf/8dOnTA9PR0vHnzJvr4+Ci928PBwQF9fX3x0aNHvChpEokErayslPYzUFTy8vLC+/fvcyJbZmYmw0Oqt7c3J3mzUfj9DwCcxYeIiIjAYcOGKWxnNsdgqqQjR45wImtBDFYZ2L9/P63S9erVE0oUveLVq1esvrDzkrOzM545c0ZvvjjT0tKwZMmStDrMnTuX93JTUlLw7NmzOGHCBFajoKKSo6Mjjh49Gk+cOIEREREqveSFdCajT1y5cgWtrKzkenRka9sWLVqgn58fVqxYUal7YW1tjcOGDcOrV69qJQrllStX0MjIqMi+kuecat68eYwwwIVTly5dNI4E+OeffzLyffbsGUe1ZpKWloalS5emlafp9uKYmBgcO3YsGhsby20rOzs7XL9+PZ47d06p+8CWRo4cyVEr0DFYZeDcuXO0Sru6ugolit5BURQeOXIEy5cvL7fDduzYkfMALHwxZcoUxgMbGxvLaZAfqVSKjx49wmXLlmGLFi2UDiGblywsLLBz587o7++Pb968YciU95JX1s3s1atXNa5TcUbZQbNgUnZGx9jYGLt3747Hjx8XZCZNkZKTl6ysrPL7SG5uLu7bt6/ILay9e/dWKwyzTCZjKBzt2rXjutoM5s+fTyvT1NQUv3z5onI+X79+RV9fX8aWv4KpVKlSuHz5clpAvKKUTbZ7U7Vq1fzlJ64xWGXgv//+o1W6dOnSQomit6SkpOCMGTMULh38/vvvvHVernjz5o3Cl5y6QX5iYmJw9+7d2L9/f7S1tVX5C6BOnTo4e/Zs/Pfff5UKpnTlyhW5gYwKxiYgioBiuJ5Oz0stW7bEHTt26MR2TolEggEBAYzlD4CfsxVsfT07Oxu3b9+ucNZDJBKht7c3vnv3TmlZzp49y8jn2rVrHNaWnW/fvjFsOFSZFYyPj8dZs2YpDA1tbW2NCxYskLu1VN59cHZ2ZgRXMjMzw5CQEI5qz8RglYHnz5/TKi0Wi3V+W5mu8vr1a2zdurXcB8LJyQlPnz6t00sHhT1SyhtIFfn0T09Px8uXL+PUqVOxevXqKg8WDg4OOGTIEDx48CB+/fpVrXp07NhRrkITEBCg8pY0Q4QrQzuAn14H16xZgx8/fhS6WqxQFIWPHz9myK3IbiYzMxP9/f0VRkEVi8U4cuRIpexvPD09adfWrVtXa++KMWPG0MouVapUkeHsJRIJLly4UKFXSHNzc5w5c6bSgylFUZiQkIBRUVEYFxeHbdq0YeS5ZcsWLqosF4NVBqKjoxkVJy9K9aEoCo8dO4YVKlSQ+4B06NBBpS8GbXHlyhWlvgILB/mhKAqfP3+Oa9euxbZt2yqcJmRLpqam6OXlhWvXrsWQkBBOlNHCSzf+/v6YkJCg04qYLsGF214nJyecO3eu3kRCpSiKMXN14sSJIq9LT0/HNWvWKJz1MjExwQkTJmBsbCxrHnfv3mVcc/jwYa6rKJewsDDGvZbnfCw1NRVXrFih0CDU1NQUJ0+erNZyQx6Fd1UAAPbo0YP3Z9hglYHk5GRGxfnYxmJopKam4syZM+Ua0ZiYmOC8efMULh3I28alKsrko+qUsFgsRjMzMxwwYIDS7mILpurVq+PUqVPx8uXLnC+ffPnyhVEel1vTDAFNnfNcvHhRL2cYO3XqRKvH9OnTlb42JSUFly5dyjDELZjMzMzQz88Pv337Rrv2t99+YyhSOTk5XFdPIT179qTJ4OjoSJMhIyMDN2zYgGXKlJFbPyMjIxwzZozGM0B3795lLLs6OjpqxVW6wSoDMpmMoRE+f/5cKHGKHaGhoaxTXXmpUqVKeOrUKdoArWgblypr9qrkw+WUMFsqXbo09uvXD3fv3s37VPHFixdpZVtbW+vlwCQkmrrt1VePjoX9rrRo0ULlPBITE3HevHloaWkpt30sLS1x7ty5mJiYiG/fvmX8vmnTJu4rVwT3799nyHHw4EHMysrCwMBAhYbSIpEIhw4dysme/6SkJEbIa7FYjLdv3+aglkVjsMoAIjK2lvz3339CilPsoCgKjx8/rnDpoH379hgWFqaUZW1Ra/aIylno5uXDRyQ3IyMjbNGiBS5btgwfPXqkcSQxVViyZAlNlpYtW2qt7OKCpjMDumAcqA6XLl2i1cPCwkLtrY5xcXE4bdo0hQ6WSpYsifXq1aMds7GxwbS0NI5rphzNmzenyVKxYkXGwFw49evXD0NDQzkpn6Io7N27N6OMpUuXcpK/Mhi0MuDq6kqr+Llz54QUp9iSmpqKs2bNkrt0kLclTtm9z/IUAlX3UJ84cYITBcDV1RXHjx+PZ8+eFdTupPCUq5+fn2Cy6CvqKoj67tGRbSDQ1HI9NjYWJ06cKHeHS+H0+++/c1MZNSi81VxR+u233zi36t+2bRujnFatWmn1Y0JoZUAMAmJjY0P7Ozk5WRA5ijslSpSAdevWwcuXL8HLy4vxu0wmA0Qs0k87RVGAiNCnTx/GvUpOToY+ffoonQ9FUTB48GCV61KQZcuWwYcPHyAiIgKWL18OdevWhdzcXLV9v2tKcHAw7e8GDRoIIoc+IxKJYMqUKSpfh4gwadIkvYzRAQBgb28Pbm5utGOPHz/WKM+KFSvC1q1b4f379zBq1CgwMjJSeL6ZmRlkZWVpVKY6UBQFWVlZRfr479ChAzx69AjOnz8PdevW5az8ly9fwrRp02jH7Ozs4MiRI0W2WbFCa2oHC4XXtP39/YUUxyCgKApPnDihtIc2tsTmSpfvtX+2FBERwYmNAxd8+/aNIR9X7lUNDXX9DHTp0gWzs7OFFl9tCkfx1NQjX2Hev3+PgwcPVvicVqxYEbdt26aVdqQoCi9cuKBwWzEA4K+//srbun16ejrrNuSLFy/yUp4ihJ4ZEFQZ6NWrF63iS5YsEVIcgyItLQ1nz56ttjLg7OyMMTEx+ObNG3zw4IFCuwQ+kq2tLVpaWmps48AVhdd8raystDrFWNxQxwMhAGDXrl2VchCli2zatIlWl5o1a/JSjjLPvYuLC+7du5cXF80UReG1a9ewcePGRcrRqlUrXpd+fHx8GGUKtbxn0MrAyJEjaRUfO3as3q756QNZWVn4/ft3/PDhAz579gzPnz+v1QFc26koGwcuWbZsGa1sdazBCXTUcRcL8NOfRkZGhtDiq8y9e/cYdSzKAY+q5OTkYKVKlZR+hjw8PPDIkSOcKba3bt3CX3/9VaVnODIykpOyC3P8+HFGefXq1cOsrCxeyisKg1UGJBIJa8AdIaZ4dRmpVIrJyckYExODr1+/xgcPHuDVq1fx5MmTuGfPHty0aRMuW7YMZ86ciWPHjsWBAwdi165dsWXLllinTh10dXVFOzs7lR3yFJekraBAPXr0oJU7depUXsszFOS5i83z6Hj58mVWT3Rt2rTReTfchcnIyGAY+XIdqvjw4cOMtvrzzz+xXbt2Cp+jGjVqaOTF9OHDh9i+fXuFZfzyyy+4b98+xtbIKVOmcNoGiD8jGxb2y2BtbS1oPBeDVAbyNH62DiHEFC/XUBSFGRkZ+O3bN3z//j0GBwfjzZs38fz583jo0CHctm0brlmzBn///XecPHkyDhs2DHv16oVt27bFRo0aYdWqVbF8+fIK3W2SpHzSRrhgR0dHWpkHDx7ktTxDo6C72MIeHe/fv8/qcKdly5acf1nzTf369Wl1WLt2LWd5UxSFtWvXpuXfrFmz/N9v3ryJLVq0UPgs1a9fHy9duqS0UhASEoLdu3dXmKe7uzsePnw4f/bBz8+P9rulpSWnW0ZzcnJYlyiEfmYNThlQdfuZNhWC3NxcTEpKwujoaHz16hXeu3cP//nnHzxx4gTu3r0bN27ciEuWLMHp06ejj48PDhgwADt37ozNmzfHWrVqobOzM9ra2ioMoUmS9hPf287i4uIYZb5+/ZqXsgjsPH78mNVNraenp165OR8/fjxN/t69e3OW99WrVxntU3g7N0VReOXKFWzYsKHCZ8rT0xNv3Lght6zQ0FDs27evwjycnJxw9+7dDI+HHz9+ZHgBXLZsGWftwGYzMWzYMM7yVxehlQERovb2YSUnJ4OjoyNkZmYWuf0MAEAsFoOFhQXExsYytiHmgYiQkZEBqampkJaWBqmpqfmp8N9FHcvIyOC4xrqLkZERlCxZEgAAJBKJytcPGDAAxo4dC5mZmSCRSOD06dNw7tw5jqXkloSEBLCzs+M833/++Qe6dOmS/7elpSWkpqYa1rYkHSAkJATat28PiYmJtOMNGzaEa9euQenSpQWSTHn2798PI0eOzP/b0dERPn36xEne7dq1gxs3buT/XbVqVXjz5g2Ixcwd5ogIFy9ehIULF8LLly/l5tm6dWtYsWIFNG/eHAAAPnz4AEuXLoUjR47I3eJbvnx5mD9/Pvj4+ICZmRnrOUOGDIEjR47k/21vbw8fP34ECwsLpeoqj6tXr0KnTp1oxzw8PODZs2dgbW2tUd6aEh8fDw4ODrRj379/B3t7e62Ur1VlICAgAKZNm6byPvCmTZtCpUqV5A7qyigWxQUrKysoUaIElCxZkpZUPWZhYQEikUhlBS0PY2Nj2Lx5M4wfP16tfEQiERgZGYFUKqUdt7a2BoqieFHMoqKiwMXFhfN8V65cCQsWLMj/u1mzZnDv3j3OyyEUzatXr6Bt27YQHx9PO163bl24fv06lClTRiDJlOPNmzdQo0YN2rHPnz9DhQoVNMr32bNnDL8XO3fuhDFjxii8jqIoOHXqFCxatAjevXsn97xWrVqBjY0N/P333yCTyVjPKVOmDMybNw8mTJhQ5KD+4sULhi+B7du3w/jx4xVep4hv375BnTp14Pv37/nHTE1N4cGDB1C/fn218+UKg1EGEBE8PDwgMjJSMKcwQmFsbMwYlNkG66IGdGtrazA2NuZcvqtXr0LXrl2VchhUmCFDhsCOHTvAyspK6XzEYjGIRCJwcHCAr1+/5h+3t7eHd+/eQenSpSEnJwcSExMhISEhP7179w4WLlyodj35mhno3bs3nD17Nv/vKVOmwObNmzkvh6Acb968gbZt28K3b99ox2vWrAk3btxgvHB1CZlMBqVLl4a0tLT8Y2fPnoWePXtqlO+gQYPg2LFj+X+XLVsWoqOjwdzcXKnrpVIpHD16FJYuXQqRkZEqlW1jYwOzZs0CX19flb6+O3bsCNeuXcv/u3LlyhAWFqbWjBtFUdCxY0f4999/acf9/f1h6tSpKufHB0IrA1qzGdDU57gQydraGitUqIDVqlXDxo0bY9u2bbFXr144fPhwnDJlCs6fPx/XrFmD27Ztw8OHD+OFCxfwv//+w+DgYAwPD8e4uDjMzMzUi+2SymzjkmfnUbNmzfzQyMrGJhg6dCgjn3379imUUVdd1Rb2ob5//35eyiEoz7t371gda1WvXl2j8LbawMvLiybzvHnzNMovKiqKsQa/cuVKtfLKycnBnTt3KgwelJcsLS1x0aJFau/kuX79OiPPU6dOqZXXmjVrGHl169ZNp97NQtsMaE0Z0DQambLJxMQEy5Qpg25ublinTh1s2bIldu3aFb29vXHcuHE4c+ZMXLZsGfr7++PevXvx1KlTePXqVXzw4AGGhobip0+fMCUlxSAdxhS1jSshIUGuw5ISJUrg6dOnlcrn+fPnaG5uTvutWbNmSkX4U8fTIZ+7CdiU3FevXvFSFkE1Pnz4wBrsxsPDAz99+iS0eHKZO3cuTV4vLy+N8vP19aXlZ2VlhUlJSWrllZSUhPPnz5e7G6zwczd8+HCMiIhQqyyKohjBlJo0aaLyAP7gwQOGMlShQgWMj49XSy6+MBhlQNOZgenTp+O6detwx44deOTIEbx48SLeunULQ0JCMCIiAr9//y6Ys4jihqJtXIiIZ86ckRs7febMmfley+TlU3irkVgsVjrwiKquavn2M3DlyhVaeZpEmyNwT3R0NCMgGgCgm5sbRkdHCy0eK2fPnmUo2uqGwk5ISGDs21fHw15KSgouW7YMS5UqpfK729jYGMeOHYsxMTEql3v06FFGfqq4JpZIJOji4sJ4J+hihFyDUQY0CVcrEonwjz/+YGxBIQjH+/fvsVatWqz369dff5U7FXvhwgXG+ao6FVF1e+rVq1e5qDIrK1eupJXp6enJW1kE9fj06RN6eHgw+oeTk5PaX6188vnzZ4as6sa5WL58OS0fIyMjlZSgHz9+4Lp169DOzk7hYD9q1CicOXOmQmXB1NQUfX198evXr0qXn5ubi87OzrR8unXrptS1FEVhv379GHIsWrRI6fK1icEoA4iaB7P55ZdfMCgoSJsiExTw48cP1rV/AMBy5crhrVu3aOdnZGQwvtLKli2r1le7srYJfCoCiMiIgT558mReyyOox5cvX7BatWqMflqxYkVBvc7Jo7ATq6LsadjIzMxEBwcHWj6DBw9W+tqAgAAsW7asQmW78DJA3jKCIodpFhYWOGvWLKWn6QMCAhh5hIaGFnndzp07Gde1bNlSZ2fuDEoZUDcaWeHUv39/nV7zMyQoisIdO3awujs2MjLCDRs25C8PLF68mHGOJl6/irJN0IazmcJfLXv37uW9TIJ6fPv2DWvWrMmquOpahMnCSuaECRNUzuPPP/9k1PX58+cKr8nJycE///yToYwUTgMHDsS3b9/Kzef79+84c+ZMhm1QwWRtbY0LFy4s8mMgLS0NS5cuTbu2qIiOr1+/ZpRta2ur1lKFtjAoZQBRtSleRbMIlpaWuHr1amInoCM8fvyY1VgL4KcXtZCQEDQzM6Mdb9GiBSfWvEXZOPBFQkICo64vXrzQStkE9YiPj8e6desy7pu9vT2+fPlSaPHyKWz9Xr9+fZWul0qljKWR9u3bKzz/wIED6ObmpvC93LNnT5X6+JcvX3Dy5MloYmIiN08bGxtcuXIlpqWlyc1nwYIFtGtMTEzw8+fPrOdmZGRgjRo1GOUU9raoaxicMoCo2hTvgwcPGP66C6YqVarobQyD4kZCQgJ27NhRrvJWeNZA3wfOa9eu0epkbm6us1OQhP+RmJjI6m7Xzs4Onz17JrR4iPgzTkBB2YyNjVWKxHjmzBlG/a5fv844TyaT4fHjx1mXUAqmTp064ePHj9WuT3R0NPr4+DCs+gumMmXK4B9//MFaz2/fvjE+JubMmcNa1rhx4xh58xHsiGsMUhlAVG2KVyqV4o4dO9DW1lahxhoVFSVUdQj/j1QqxSVLlhRpGyJUzHAuWb16Na1OTZo0EVokgpIkJydj06ZNWb9SNRn0uCI1NZXxDN2/f1+paymKYtStbt26tBkziqLw3LlzjMBFhVPr1q3x7t27nNUrPDwchw4dqvD9UL58eQwMDGTM+o4dO5Z2XsmSJTElJYV2zsmTJxn51alTBzMzMzmrA18YrDKQhypTvAkJCThu3Di5Hcnc3ByXLl2ql7HMixv//POPXOXN0tIS4+LihBZRYwoHYpk4caLQIhFUIDU1lTVKX8mSJZUeePmksH3Dpk2bFJ5PURTGx8fjX3/9xajT0aNH88/5559/NA5EpCmhoaGslv4FU+FARu/evWO8+5cuXYpRUVEYHx+PkZGRjN0MVlZWGBYWxls9uMTglQF1ePr0KTZp0kRuJ3J1dcXz58/rlHcpQyQ6Olqup7JGjRrhx48fhRZRIwrvjNizZ4/QIhFUJD09Hdu0acPon9bW1irtZ+eDUaNG0WTy9vZmPU8ikaC/vz9jljUvVapUCXNzc/HmzZvYvHlzhQNwvXr1VApRrCkhISH422+/KZTJ3d0dDx06hFKpFHv16iX3vMLLCADq7cIQCqIMqIlMJsO9e/eivb293M7RpUsXDA8PF1pUg+X9+/esuwzykp2dnd7aeyQmJjLqU5SlNkE3+fHjB7Zv3551BkvIrcw7duygyePm5sY4R5H9VV4yMTFhNZosmGrUqIFnzpwR7APq0aNH2KFDB4UyVq9eHYcPH67wnIJp8ODBevVBSJQBDZFIJDhlyhS5uxNMTU1x/vz5mJ6eLrSoBgVFUXKNCQsmkUiES5cuVdvDmlAU9ptuZmZGnGLpMZmZmdilSxdG/zQ3N+fdV4U8QkJCGPIU3Juv7M4sRcnDwwOPHj2qM+7Xb926hS1btlS7PgXTmTNnhK6OShBlgCNevHihsBNVqlQJT548qVeaoj5z+vRpxj0YNmwYVq5cmfX+dO7cGRMSEoQWW2kKb/1q1KiR0CIRNCQrKwt79OjB+kHx999/a12e3NxctLCwoMly+fJlRNTcZ4uzszPu3btXJ3e/UBSF165dw8aNG6utCOTtSOPLDTkfEGWAQyiKwiNHjiiMqNWuXTudczBS3EhPT8dKlSrR2r1ChQqYmpqKycnJ2LNnT7kvqCdPnggtvlIUNn4aP3680CIROCAnJ4fVsM3ExATPnj2rdXkKGzguXrwYEdX35lqyZEnctm0bZmdna70uqkJRFF68eLHIJQ5FCgFfAcr4gCgDPJCSkoIzZ85EY2Nj1k5ibGyMM2fOxNTUVKFFLZbMmzeP0ebHjh3L/52iKFy3bh3rnmNTU1P8888/dX4Gp7Cx1q5du4QWicARubm5OGjQINb3xl9//aVVWaZPn86YQdMkzouVlRX6+fnhpk2b8MyZM/j06VP8/v27Tj9vUqlUoVtkRcoAn6HLuYYoAzzy5s0bbNu2rdzOUr58eTxy5IjedBZ9ICwsjOFtzMvLi7WN//vvP7kP+fDhw/HHjx8C1KBoJBIJQ15dcVZD4AapVMpqrCYWi/HIkSNak+P48eO08u3s7FgHDU2Tubk5VqlSBdu1a4ejR4/GpUuX4r59+/DGjRsYHh4u6D59TSPe6svyI1EGeIaiKDx58iRj2rpg+vXXX/XeG54uQFEUwyrb2NhY4bLMly9fWPd6AwDWrl1bJ3eD3LhxgzGboQ/TrgTVkMlk6OPjw/rFuX//fq3IEBUVxSj/v//+41wZUCaVLVsWGzdujH379sXp06ejv7+/VmYX2NpAlaQvzuiIMqAl0tPT8ffff5e71c3IyAh9fX31yuBE12Dz/jV79uwir8vJycEZM2aw3peSJUvqnE/xdevW0WRs2LCh0CIReEImk+HEiRNZFYKdO3fyXj5FUYzt04W3HOpKsrCwwKpVq2L79u1pswtBQUH44cMHtePIkJkB7WAwykAe79+/x86dO8vtOPb29rh371692+omNGlpaYxIZxUrVlQYfKQwJ0+exBIlSrDelzlz5uiM5fOAAQNoso0bN05okQg8QlEU+vn5sfbLwMBA3svv1q0brUxfX1+1bQZsbGzQ29sbW7RogU5OTgpjBfCRypUrR5tdCAgIwLNnz2JwcDDGx8ezzi6oayNBbAZUw+CUAcSfnev8+fMMD3IFU9OmTfHp06dCi6o3zJ49m9GG6hhbhYWFsUYcA/jpJ/3bt288SK8ahbdHauMLkSAsFEXhnDlzWPvlxo0beS172bJltPI8PT3V2k3AZl2fm5uLHz9+xDt37uCRI0dw9erVOGHCBOzatSvWqlULS5YsqVVloeDsgo+PDy5btgz379+PkyZN4qS+ugxRBgQkIyMDlyxZIjfmtkgkwvHjx+vNNJNQvHnzhrFzo3379mpr5Onp6azW3AA/jT65DJyiKsnJyQyZgoODBZOHoD0oisKFCxey9ss1a9bwVu7Vq1dpZZmZmWFcXJxKfgbEYrHa++6Tk5Px5cuX+Pfff+O2bdtw7ty5OGjQIGzevDlWqlRJI6dHfCVN6isUQisDIkREMHCioqJg+vTpcO7cOdbfbW1tYdWqVeDj4wNGRkbaFU7HQURo164dBAUF5R8zMTGBV69eQdWqVTXKd/v27eDn5we5ubm034yNjWHdunXg5+cHIpFI7TLU4ebNm+Dl5ZX/t4mJCaSlpYGZmZlW5SAIx4oVK2DhwoWM48uWLWM9rilJSUlgZ2dHO/b06VNISEiArl27AiICRVFyrxeLxSASieDy5cvQoUMHzuWTSqXw5csXiImJgZiYGPj48WP+//P+TktL47zcoqhfvz7Ur18fnJycwMnJCZydncHJyQkcHR3B1NRU6/IURXx8PDg4ONCOxcXFMY7xhtbUDj3gn3/+QQ8PD7naZoMGDfDBgwdCi6lTFN76BAA4b948zvJ/+PAhwxYhL/Xt21frviLWr19Pk6F+/fpaLZ+gG6xdu5a1Ty5YsICXNerC76Vt27YhouLYBHnHrKysBHOpnEfB2YWtW7finDlz0NvbW5DZBZFIhOXLl8cmTZpgv379cMaMGbh582Y8d+4cPnv2rMjouXwgkUhwxYoVDFldXFzQ399fKzMcRBkoRFZWFq5evRotLS3ldqaRI0cWixC8mpKamooVKlSgtU2lSpU4jwPx/ft31kAyAIBVq1bF169fc1qeIry9vWnljxkzRmtlE3SLTZs2sfbJ2bNncz6YDBkyhFbG8OHD83+TSCQYEBDAcITl7u6OAQEBmJyczKksfJBnu3D79m08fPgwrlq1CsePH49dunTBmjVryjUs5itZWlpitWrVsGPHjjhmzBhcvnw5HjhwAG/evIkRERGcbiUuqNCxKS55Ch3fQd2IMiCHmJgY7N+/v9zOUqpUKdy8ebPOWLgLwcyZMxntcvr0aV7KkkqlctdrLS0t8+O1802VKlVoZe/YsUMr5RJ0k61bt7L2yalTp3KqEGzevJmWf/Xq1RnnUBSFCQkJGBUVJcjXLd9IJBJ88eIFXrx4Ebdu3YqzZ8/G3r17Y4MGDbB8+fJq7a7QZHahQoUK2LRpU+zfvz/OnDkzf3YhJCQEExMTlWp/ZYNNicViNDIy4lUhIDYDRRAUFARTpkyBN2/esP5eu3ZtCAwMhJYtW2pZMmEJDQ2FunXrglQqzT/WsWNH+Oeff3hdx7906RIMHToUJBIJ47fJkyfDH3/8wdt6YGpqKpQqVYp27MmTJ9CwYUNeyiPoB7t374axY8dC4VfphAkTIDAwEMRiscZlPH78GJo0aZL/t0gkAolEwuiPhkxubi7NdqGwDcPHjx8hPT1da/JYWVnl2ysUtltwcnICKysrcHNzg8zMTIU2H3mIxWKwsLCA2NhYsLGx4V5g3tSMYkROTg7+8ccfCqeqBg8ejJ8/fxZaVK1AURS2atWKVn9TU1N8//69VsqPjIzE+vXrs96Hpk2bYkxMDC/lFvb8ZmJiorYjFULxYv/+/axfdz4+Ppz4LMnKymK4+f733385kNxwoCiKdXZh4MCB2KxZM3R0dNTJnREFE5/bJYkyoAJfvnzBoUOHyr1R1tbWuH79+mIf1/7IkSOMui9YsECrMmRmZuKYMWNY70OZMmXw+vXrnJf5xx9/0MqpV68e52UQ9JcjR46wOvEZNmwYSqVSjfNv1KgRLd9Vq1ZxIDWhIDk5ORgdHY23b9/GQ4cO4cqVK3HcuHHYuXNnrFGjBlpbWwuuDPDlSIkoA2pw584drF27ttwbVr169WKrtaekpGC5cuVo9XV2dhYsqNDevXtZ/USIRCJcsWIFp54kC/s+8PHx4SxvQvHg5MmTrNFSvb29NbYvmjx5Mi3PHj16cCM0QWnyZheeP3+OFy5cwMDAQEFmF/jwfUOUATXJzc3FwMBAtLGxkXvD+vXrx9uUtVBMmzaNUU+hYweEhIQwLKnzUteuXTEpKYmTcqpWrUrLO297F4FQkHPnzjGm9AEA+/Tpo9Gs4cGDB2n5lStXrtgZCRYHcnJyMCoqCm/duoW7d+/GUaNGYcOGDbFMmTKcKQp8BF8iyoCGxMXF4ejRo+XeNEtLS1y5cmWxWFt++fIlYxq0S5cuOvFCkkgk+Ntvv7HeA1dXV429BKampjKslR89esSR9ITixqVLl9DMzIzRF3/77Te13wXv3r1j5FfcPjb0ndjYWDxx4gT6+vpigwYNeIv9QGYGdJiHDx9igwYN5N68ypUr4+XLl4UWU20oisKWLVvS6mRmZoYfPnwQWrR8ZDIZrlmzhlX7NjMzw927d6ud9+3bt2n5GRsbCxrjnaD7XLt2jXUJq3Pnzmr1HZlMxpiJPHXqFA+SE5RBKpViSEgIBgYGore3Nzo5OfEy8BdMxGZAT5BKpbhz5060s7OTezN/++03jIiIEFpUlSk8RQkAuHjxYqHFYiUoKAgdHBxY23/UqFGYkZGhcp6FHczUqVOHe8EJxY6goCBWB2bt27dXy86msPMtZUKEE7ghNTUVr1+/jkuWLMH27dtr5AjJ1dUVGzZsqJYyQHYT6BGJiYk4YcIEuU4wzMzMcPHixWoNSkIgkUgYg6urq6tOyx8bG4vNmjVjbf+6deuqrJAV9gA3atQoniQnFDfu3LnDaoXeunVrlUJ8IyIuWLCAlkerVq34EZqAMTExeOzYMZw8eTLWq1dP7fV+Y2NjbNSoEfr5+eHJkyfxy5cviPjzvaqtYFPKQJQBHgkODkZPT0+5N9fFxQXPnTunE2vuivD19WXIfvHiRaHFKpKcnBy5cehtbGzwwoULSudVvXp12vVbt27lUXJCcePBgwes4YCbN2+OKSkpSudz4cIF2vVWVlacbFs0dHJzc/HZs2e4ZcsWHDhwIFaqVEntr34bGxvs3LkzrlixAm/evKlwBkhVD4R8xpggygDPyGQy3L9/v9xpawDATp064bt374QWlZXnz58zOmr37t2FFkslTpw4gVZWVqxt//vvvxf5Mk1LS2PM8jx8+FBL0hOKC0+ePMHSpUsz+mCTJk2U/tr79u0b4/qXL1/yK3gxJCUlBa9evYqLFi3Cdu3aaeQ/wM3NDYcOHYo7duzAV69eqbydWVeCTRFlQEskJyfj1KlT5VqXmpiY4Ny5czkP8qMJMpkMmzdvTpPT3NwcIyMjhRZNZd68ecP4us9Lbdu2VRh46s6dO7TzjYyMdHqJhKC7hISEsNoUNWjQABMTE5XKw9nZmXatJoaxhgBFURgdHY1HjhzBiRMnYp06dTSa8m/SpAlOmzYNT506hV+/fuVERl0INkWUAS3z8uVLhivfgsnR0RFPnDihE0sH+/fvZ8i3bNkyocVSm7S0NBwwYABru1esWBHv3bvHep2/vz/t3Nq1a2tZckJx4tWrV6wzhXXq1MHv378XeX3hAGpjx47VgtT6Q25uLj59+hQDAgKwf//+WLFiRbW/+kuXLo1du3bFVatW4a1bt3j/CBAy2BRRBgSAoig8duwYI/xvweTl5YWhoaGCyZiUlIT29vYMLVXft9NRFIWbN29m9RJnbGyMAQEBtAeQoijs168f7bwRI0YIWANCceDt27dYvnx5Rh+sUaMGfvv2TeG1GzZsYCgRhkxycjJeuXIFFy5ciF5eXnKXBJVJlStXxuHDh+POnTsxNDSUUw+mug5RBgQkNTUVZ8+ezTow5Q1O06dPV8nAiCsmTZrEkEef/SQU5t69e3K/GAYMGICfPn1Cf39/Vs+Gffr04c2il2A4vH//Hh0dHRn9q1q1agqDnhX2eWFkZKRTy4t8QlEURkZG4uHDh3HChAlYu3ZttUMXm5iYYNOmTXHGjBl45syZIpWw4g5RBnSAt2/fYrt27eR22nLlyuGhQ4e0NmUUHBzMWFPr2bOnVsrWJnFxcejl5cXa5nmGO2wvmjyDHj5jixMMg8jISIYNQN4Xqjzvgunp6Qzbo9u3b2tZcu2Qk5ODjx8/xk2bNmHfvn0VzqYWlWxtbbFbt264evVqvH37NrH7KQRRBnQEiqLw9OnTCr1YtWjRAp8/f86rHDKZDJs2bUor18LCAqOjo3ktVyikUin+/vvvKr9Y8rb6EIWAoCnR0dHo5ubG6GOurq5yfdDXqVOHdu6GDRu0KzRPSCQSvHz5Ms6fPx9bt27N6rBJ2eTh4YEjRozAXbt24Zs3bwxqyl8diDKgY/z48QMXLFiApqamcgehyZMncxZ8pzB79uxhlLly5UpeytIlLly4wLoPvCiFgE8nIATD4dOnT1ilShVGH6tUqRKry++xY8fSzuvfv78AUmsGRVEYERGBBw8exHHjxmHNmjXVnvI3NTXFZs2a4axZs/DcuXNKGWIS6BBlQEcJDw/Hrl27yu389vb2uGfPHk613cTERCxTpgxDuy4OQZaUobB3N2USn+5BCYbFly9fWLe/VqhQAcPCwmjn7t69m3aOs7OzMEKrQE5ODj569Ag3btyIffr0YYRCVyXZ2dnhb7/9hmvXrsW7d+/qvWGzLiBCRASCzvL333/D1KlTITIykvX3xo0bw9atW6Fhw4YalzVhwgTYsWMH7diVK1egY8eOGueta2RkZEBUVBRERkZCZGQkREREwJ49eyAjI0OlfEQiEbi5uUF4eDiIRCKepCUYCt+/f4d27drBq1evaMfLli0LQUFB8MsvvwAAwKtXr6B27dq0c759+wZly5bVmqxFIZFI4P79+3Dv3j24d+8ePHnyBDIzM9XKq2rVqtC8efP8VKVKFfK8cQxRBvSArKwsWL9+PaxatQqysrIYv4tEIvDx8YFVq1ZBmTJl1Crj6dOn0LhxYyjYHfr06QOnTp1SW24hkclk8OXLF4iMjKQN+nkpLi6O0/ISEhLAzs6O0zwJhkliYiK0b98eQkJCaMft7e3h33//hdq1a4NMJoNSpUrBjx8/8n+/cOECdO/eXdviAgAAIkJERET+wH/v3j148+aNWnmZmZlBw4YN8wf+Zs2aqf1eIygPUQb0iOjoaJgxYwacOXOG9ffSpUvDypUrYezYsWBkZKR0vhRFQdOmTeHJkyf5xywtLeHt27fg5OSksdx8kZqayhjk8wb+6OhoyMnJ0ZosUVFR4OLiorXyCMUbiUQCHTt2pD2TAAC2trZw/fp1qF+/PrRu3Rpu3bqV/9uCBQtg+fLlWpEvJycHnj17lj/w379/X20F297eHpo1a5Y/+Ddo0ADMzMw4lphQFEQZ0EOuXbsGvr6+8O7dO9bf69WrB1u3bgVPT0/W3xEREhMTIT09HaytreHMmTMwbtw42jmrV6+GuXPnci67KkilUvj06RNjwM8b9BMTEwWVryBkZoDANSkpKdC5c2d48OAB7biNjQ1cvXoVTp8+DevWrcs/3rJlSzhz5gzY2dlxPoWelJTEmPJnm6VUhurVq9Om/CtXrkym/HUAogzoKTk5OeDv7w/Lli2jTRUWZPjw4bB27dr8dcTk5GQ4cOAAbNmyBSIiIvLPE4vFQFFU/t9Vq1aFly9fgqmpKa91QERISkpifNXnpZiYGJDJZLzKUL58eXB1dQVXV1e4fPkyJCcngyqPBLEZIPBJWloadOvWDW7fvk07XqJECfD19YWVK1cyrnF3d4cpU6bA8OHDwcbGRuUyERHCw8Pzv/jv3bsHb9++VUt+c3NzaNSoUf7A7+npSZRmHYUoA3pObGwszJo1C44fP876e8mSJWHZsmXg4eEB/fv3zzeQU3Tbr1+/Du3ateNEvuzsbPj48SPrVH5kZCSkpqZyUo48LC0twc3NDdzc3MDV1TX//25ubuDi4gKWlpb55wYEBMC0adNUVgb8/f3B19eXD/EJBPjx4wf89ttvEBQUpNT5eUqppaUlnD59ukgD4OzsbAgODqZN+cf/Xzt375JeFMdx/HtuZVyiB5Qswh680lIEEVE0B0o0FPQH9A+UU0NbU0sQSNAfUHtuFjY11BY0tepiYOQgFFg03N9kdH9ppV3N7nm/xsrrgTj3fM73PDw81NXWYDDomPXPzMw0fFIBdxAGPOLi4kI2Njbk9va26t8opb4c6JRScnZ29u0TBLZty/39fcVNetlsVnK5XE2Da62UUhIKhaoO+MFg8Nsz9mKxKKFQSEqlkqNSUo1hGGKapuRyubpmYMB3lUolWV1dlfPz829/xjAMUUpJKpVy9OdCoeAo+V9fX8vLy0td7ZqYmHAM/pFIhArZH0UY8JDX11c5PDyUnZ2dumfclQa498fw/h/0s9lszcfxatXT0+MY4N8P+qOjo65uNkqn07K8vCy2bX8aCMov2tPTU4lGo659P1DN8/OzrKys1BwIOjs7ZW9vT25ubuTq6qrqXqOvmKYpc3Nzbzv8FxYWxO/31/UstB7CgAfl83nZ3t6Wo6Ojup8xOzsrPp9PMpmM5PN5F1v3UVtbm4yMjHwY8MuDvt/vb+psI51Oy9raWsUllfcl2GQySRBAU+3v78vW1lZTvmtwcNAx65+enqbk72GEAQ+7vLyUxcXFph6xqyYQCFQs41uWJcPDw9Le3v7bTXQoFotyfHwsBwcHjs2WkUhE4vG4rK+vS29v7y+2ELqxbVvGx8clk8m4vvSmlJLJyUnH4B8Ohyn5a4Qw4GGFQkH6+/ub8l0+n0/GxsYqrt2Hw+E/O3CWTzw8Pj5Kd3d306sUQJmb/dk0TZmfn3fs8mffi95aazoGVz09Pbn6vIGBgapr90NDQzVddPRXKKUkEAhwHAq/7qf9eWlpSaLR6FvJv6Ojw6WWwQuoDHjYT2cSu7u7MjU19XYMr6ury8XWAajFT/szF2PhM4QBD6t3jZGLdIDWQ39GIxm/3QA0jlJKNjc36/psPB7nxQG0EPozGonKgMdxkQ7gHfRnNAqVAY/r6+uTk5MTUUqJYXz+7y5fpJNMJnlxAC2I/oxGIQxoIBaLSSqVEtM0RSn1oVxY/plpmtyoB7Q4+jMagTCgiVgsJrlcThKJhFiW5fidZVmSSCTk7u6OFwfwB9Cf4Tb2DGiIi3QA76A/ww2EAQAANMcyAQAAmiMMAACgOcIAAACaIwwAAKA5wgAAAJojDAAAoDnCAAAAmiMMAACgOcIAAACaIwwAAKA5wgAAAJojDAAAoDnCAAAAmiMMAACgOcIAAACa+wd65T+eKmqB0gAAAABJRU5ErkJggg==\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 4 Axes>"
]
@@ -1356,7 +1356,7 @@
},
{
"cell_type": "markdown",
- "id": "805e8019",
+ "id": "04d93eaf",
"metadata": {},
"source": [
"You can find additional options via `draw_networkx()` and\n",
@@ -1367,13 +1367,13 @@
{
"cell_type": "code",
"execution_count": 37,
- "id": "572d2502",
+ "id": "e494ea7f",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:19.881517Z",
- "iopub.status.busy": "2022-12-20T11:40:19.881269Z",
- "iopub.status.idle": "2022-12-20T11:40:19.984218Z",
- "shell.execute_reply": "2022-12-20T11:40:19.983630Z"
+ "iopub.execute_input": "2022-12-20T16:43:43.194221Z",
+ "iopub.status.busy": "2022-12-20T16:43:43.193988Z",
+ "iopub.status.idle": "2022-12-20T16:43:43.299064Z",
+ "shell.execute_reply": "2022-12-20T16:43:43.298460Z"
}
},
"outputs": [
@@ -1396,7 +1396,7 @@
},
{
"cell_type": "markdown",
- "id": "ddedda46",
+ "id": "55bcbc79",
"metadata": {},
"source": [
"To save drawings to a file, use, for example"
@@ -1405,19 +1405,19 @@
{
"cell_type": "code",
"execution_count": 38,
- "id": "11cfb1cb",
+ "id": "41b4d36e",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:19.987088Z",
- "iopub.status.busy": "2022-12-20T11:40:19.986863Z",
- "iopub.status.idle": "2022-12-20T11:40:20.117930Z",
- "shell.execute_reply": "2022-12-20T11:40:20.117298Z"
+ "iopub.execute_input": "2022-12-20T16:43:43.302663Z",
+ "iopub.status.busy": "2022-12-20T16:43:43.301976Z",
+ "iopub.status.idle": "2022-12-20T16:43:43.439127Z",
+ "shell.execute_reply": "2022-12-20T16:43:43.438550Z"
}
},
"outputs": [
{
"data": {
- "image/png": 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\n",
+ "image/png": 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\n",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -1433,7 +1433,7 @@
},
{
"cell_type": "markdown",
- "id": "364d1dfc",
+ "id": "1c61855f",
"metadata": {},
"source": [
"This function writes to the file `path.png` in the local directory. If Graphviz and\n",
@@ -1446,13 +1446,13 @@
{
"cell_type": "code",
"execution_count": 39,
- "id": "50b2da8a",
+ "id": "e72d52c5",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-12-20T11:40:20.120937Z",
- "iopub.status.busy": "2022-12-20T11:40:20.120709Z",
- "iopub.status.idle": "2022-12-20T11:40:20.255796Z",
- "shell.execute_reply": "2022-12-20T11:40:20.255203Z"
+ "iopub.execute_input": "2022-12-20T16:43:43.442335Z",
+ "iopub.status.busy": "2022-12-20T16:43:43.441956Z",
+ "iopub.status.idle": "2022-12-20T16:43:43.644331Z",
+ "shell.execute_reply": "2022-12-20T16:43:43.643798Z"
}
},
"outputs": [
@@ -1476,7 +1476,7 @@
},
{
"cell_type": "markdown",
- "id": "3e79a049",
+ "id": "7209e1c5",
"metadata": {},
"source": [
"See Drawing for additional details."