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| author | rossbar <rossbar@berkeley.edu> | 2023-01-03 20:28:59 +0000 |
|---|---|---|
| committer | rossbar <rossbar@berkeley.edu> | 2023-01-03 20:28:59 +0000 |
| commit | 01d9427f67a41e4fc88ad09f8ae42ce3c82234d7 (patch) | |
| tree | fd5a91eedc37930a82f0dfa02ec42707eda09b46 | |
| parent | 0b9a02d6b3796e8ce4fed6cbce282fced15e486a (diff) | |
| download | networkx-01d9427f67a41e4fc88ad09f8ae42ce3c82234d7.tar.gz | |
Deploying to gh-pages from @ networkx/networkx@78d447f0c83aea5c023849971719d5bda6c09a43 🚀
121 files changed, 611 insertions, 607 deletions
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b/_images/sphx_glr_plot_subgraphs_thumb.png diff --git a/_images/sphx_glr_plot_words_001.png b/_images/sphx_glr_plot_words_001.png Binary files differindex 1ba7bb67..226ea024 100644 --- a/_images/sphx_glr_plot_words_001.png +++ b/_images/sphx_glr_plot_words_001.png diff --git a/_images/sphx_glr_plot_words_thumb.png b/_images/sphx_glr_plot_words_thumb.png Binary files differindex 459ca272..278e8d3c 100644 --- a/_images/sphx_glr_plot_words_thumb.png +++ b/_images/sphx_glr_plot_words_thumb.png diff --git a/_images/tutorial-35.png b/_images/tutorial-35.png Binary files differindex 5ae1b78d..d7651092 100644 --- a/_images/tutorial-35.png +++ b/_images/tutorial-35.png diff --git a/_modules/networkx/algorithms/centrality/katz.html b/_modules/networkx/algorithms/centrality/katz.html index 135b5be8..6535b2aa 100644 --- a/_modules/networkx/algorithms/centrality/katz.html +++ b/_modules/networkx/algorithms/centrality/katz.html @@ -632,7 +632,7 @@ <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_iter</span><span class="p">):</span> <span class="n">xlast</span> <span class="o">=</span> <span class="n">x</span> <span class="n">x</span> <span class="o">=</span> <span class="nb">dict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="n">xlast</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span> - <span class="c1"># do the multiplication y^T = Alpha * x^T A - Beta</span> + <span class="c1"># do the multiplication y^T = Alpha * x^T A + Beta</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">x</span><span class="p">:</span> <span class="k">for</span> <span class="n">nbr</span> <span class="ow">in</span> <span class="n">G</span><span class="p">[</span><span class="n">n</span><span class="p">]:</span> <span class="n">x</span><span class="p">[</span><span class="n">nbr</span><span class="p">]</span> <span class="o">+=</span> <span class="n">xlast</span><span class="p">[</span><span class="n">n</span><span class="p">]</span> <span class="o">*</span> <span class="n">G</span><span class="p">[</span><span class="n">n</span><span class="p">][</span><span class="n">nbr</span><span class="p">]</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">weight</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> diff --git a/auto_examples/3d_drawing/plot_basic.html b/auto_examples/3d_drawing/plot_basic.html index 7bcc911c..a8a85335 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 0.111 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.080 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 cc3b2701..3452e135 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>00:00.111</strong> total execution time for <strong>auto_examples_3d_drawing</strong> files:</p> +<p><strong>00:00.080</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>00:00.111</p></td> +<td><p>00:00.080</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 2b67881a..e27f68f9 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 0.286 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 c623a337..b108be3d 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 5.094 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.702 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 73a3ee27..675da717 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 0.518 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.374 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 3b824024..2a26da29 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 0.151 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.107 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 e2648654..96430796 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 0.109 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.074 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 982b3fba..75c6ef9c 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 0.382 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 ce9657d8..91cb1c1f 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">"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">"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.127 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.094 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 52a49101..5397ef5e 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 0.090 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.061 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 fa707947..af681ab2 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: 2.3222 seconds - Betweenness centrality for node 0: 0.13594 + Time: 1.8126 seconds + Betweenness centrality for node 0: 0.27818 Non-Parallel version - Time: 3.7747 seconds - Betweenness centrality for node 0: 0.13594 + Time: 3.0053 seconds + Betweenness centrality for node 0: 0.27818 Computing betweenness centrality for: -Graph with 1000 nodes and 4875 edges +Graph with 1000 nodes and 5065 edges Parallel version - Time: 2.8966 seconds - Betweenness centrality for node 0: 0.00110 + Time: 2.3279 seconds + Betweenness centrality for node 0: 0.00104 Non-Parallel version - Time: 4.9253 seconds - Betweenness centrality for node 0: 0.00110 + Time: 4.0067 seconds + Betweenness centrality for node 0: 0.00104 Computing betweenness centrality for: Graph with 1000 nodes and 2000 edges Parallel version - Time: 2.0504 seconds - Betweenness centrality for node 0: 0.00281 + Time: 1.5613 seconds + Betweenness centrality for node 0: 0.00307 Non-Parallel version - Time: 3.4283 seconds - Betweenness centrality for node 0: 0.00281 + Time: 2.7442 seconds + Betweenness centrality for node 0: 0.00307 </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 28.196 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 20.926 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 a4ff5a05..92feb87d 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 1.285 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.131 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 b4fd7f66..b4142ac7 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 0.272 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 456efc61..681a3349 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 0.937 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 0.685 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 cd6c6d05..bdca6374 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>00:37.446</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p> +<p><strong>00:27.785</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>00:28.196</p></td> +<td><p>00:20.926</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>00:05.094</p></td> +<td><p>00:03.702</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> -<td><p>00:01.285</p></td> +<td><p>00:01.131</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_subgraphs.html#sphx-glr-auto-examples-algorithms-plot-subgraphs-py"><span class="std std-ref">Subgraphs</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_subgraphs.py</span></code>)</p></td> -<td><p>00:00.937</p></td> +<td><p>00:00.685</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_blockmodel.html#sphx-glr-auto-examples-algorithms-plot-blockmodel-py"><span class="std std-ref">Blockmodel</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_blockmodel.py</span></code>)</p></td> -<td><p>00:00.518</p></td> +<td><p>00:00.374</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_dedensification.html#sphx-glr-auto-examples-algorithms-plot-dedensification-py"><span class="std std-ref">Dedensification</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_dedensification.py</span></code>)</p></td> -<td><p>00:00.382</p></td> +<td><p>00:00.244</p></td> <td><p>0.0 MB</p></td> </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> -<td><p>00:00.286</p></td> +<td><p>00:00.211</p></td> <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> -<td><p>00:00.272</p></td> +<td><p>00:00.177</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_circuits.html#sphx-glr-auto-examples-algorithms-plot-circuits-py"><span class="std std-ref">Circuits</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_circuits.py</span></code>)</p></td> -<td><p>00:00.151</p></td> +<td><p>00:00.107</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_iterated_dynamical_systems.html#sphx-glr-auto-examples-algorithms-plot-iterated-dynamical-systems-py"><span class="std std-ref">Iterated Dynamical Systems</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_iterated_dynamical_systems.py</span></code>)</p></td> -<td><p>00:00.127</p></td> +<td><p>00:00.094</p></td> <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> -<td><p>00:00.109</p></td> +<td><p>00:00.074</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_krackhardt_centrality.html#sphx-glr-auto-examples-algorithms-plot-krackhardt-centrality-py"><span class="std std-ref">Krackhardt Centrality</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_krackhardt_centrality.py</span></code>)</p></td> -<td><p>00:00.090</p></td> +<td><p>00:00.061</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/basic/plot_properties.html b/auto_examples/basic/plot_properties.html index 30c0f1ee..7165a1d8 100644 --- a/auto_examples/basic/plot_properties.html +++ b/auto_examples/basic/plot_properties.html @@ -574,7 +574,7 @@ density: 0.26666666666666666 <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 0.122 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.098 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-basic-plot-properties-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 4958bad7..068ab5a5 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> <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 0.063 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-basic-plot-read-write-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 0cf2a21d..d5379f6a 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> <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_simple_graph_002.png" srcset="../../_images/sphx_glr_plot_simple_graph_002.png" alt="plot simple graph" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.488 seconds)</p> +<img src="../../_images/sphx_glr_plot_simple_graph_002.png" srcset="../../_images/sphx_glr_plot_simple_graph_002.png" alt="plot simple graph" class = "sphx-glr-single-img"/><p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.426 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-basic-plot-simple-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 e5eca5e5..9e8b9dd4 100644 --- a/auto_examples/basic/sg_execution_times.html +++ b/auto_examples/basic/sg_execution_times.html @@ -463,19 +463,19 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-basic-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.698</strong> total execution time for <strong>auto_examples_basic</strong> files:</p> +<p><strong>00:00.587</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> -<td><p>00:00.488</p></td> +<td><p>00:00.426</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_properties.html#sphx-glr-auto-examples-basic-plot-properties-py"><span class="std std-ref">Properties</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_properties.py</span></code>)</p></td> -<td><p>00:00.122</p></td> +<td><p>00:00.098</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_read_write.html#sphx-glr-auto-examples-basic-plot-read-write-py"><span class="std std-ref">Read and write graphs.</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_read_write.py</span></code>)</p></td> -<td><p>00:00.088</p></td> +<td><p>00:00.063</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/drawing/plot_center_node.html b/auto_examples/drawing/plot_center_node.html index 69f836a4..52a5c01d 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> <span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pos</span></a><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.093 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.068 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-center-node-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 bf9409ad..76555a20 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> <img src="../../_images/sphx_glr_plot_chess_masters_001.png" srcset="../../_images/sphx_glr_plot_chess_masters_001.png" alt="World Chess Championship Games: 1886 - 1985" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Loaded 685 chess games between 25 players Note the disconnected component consisting of: -['Kasparov, Gary', 'Korchnoi, Viktor L', 'Karpov, Anatoly'] +['Korchnoi, Viktor L', 'Karpov, Anatoly', 'Kasparov, Gary'] From a total of 237 different openings, the following games used the Sicilian opening @@ -702,7 +702,7 @@ findfont: Font family 'Helvetica' not found. <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 0.526 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.390 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-chess-masters-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 12011819..c0c1abe3 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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.413 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.279 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-custom-node-icons-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 fa6eeb5c..7c7fa28a 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: <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 0.391 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.266 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-degree-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 a38656ca..16436076 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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.363 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.215 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-directed-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 8daf0065..76f698aa 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 0.085 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.063 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-edge-colormap-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 fe63eb00..18cd1c8e 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> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.130 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.098 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-ego-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 453a04e1..0aed5d7a 100644 --- a/auto_examples/drawing/plot_eigenvalues.html +++ b/auto_examples/drawing/plot_eigenvalues.html @@ -517,8 +517,8 @@ to download the full example code</p> <section class="sphx-glr-example-title" id="eigenvalues"> <span id="sphx-glr-auto-examples-drawing-plot-eigenvalues-py"></span><h1>Eigenvalues<a class="headerlink" href="#eigenvalues" title="Permalink to this heading">#</a></h1> <p>Create an G{n,m} random graph and compute the eigenvalues.</p> -<img src="../../_images/sphx_glr_plot_eigenvalues_001.png" srcset="../../_images/sphx_glr_plot_eigenvalues_001.png" alt="plot eigenvalues" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Largest eigenvalue: 1.5924617911775805 -Smallest eigenvalue: 4.0699282104742547e-16 +<img src="../../_images/sphx_glr_plot_eigenvalues_001.png" srcset="../../_images/sphx_glr_plot_eigenvalues_001.png" alt="plot eigenvalues" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Largest eigenvalue: 1.592461791177574 +Smallest eigenvalue: -2.5363890312656235e-16 </pre></div> </div> <div class="line-block"> @@ -541,7 +541,7 @@ Smallest eigenvalue: 4.0699282104742547e-16 <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 0.953 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.645 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-eigenvalues-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 0e45bcba..18db3904 100644 --- a/auto_examples/drawing/plot_four_grids.html +++ b/auto_examples/drawing/plot_four_grids.html @@ -562,7 +562,7 @@ customize the visualization of a simple Graph comprising a 4x4 grid.</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 0.501 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.324 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-four-grids-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 90a3c070..f3fb9fc1 100644 --- a/auto_examples/drawing/plot_house_with_colors.html +++ b/auto_examples/drawing/plot_house_with_colors.html @@ -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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.106 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.076 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-house-with-colors-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 6e78bf9c..ba6d5dfc 100644 --- a/auto_examples/drawing/plot_knuth_miles.html +++ b/auto_examples/drawing/plot_knuth_miles.html @@ -660,7 +660,7 @@ Graph with 128 nodes and 8128 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 0.126 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.101 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-knuth-miles-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 408eb7c3..48f9a37b 100644 --- a/auto_examples/drawing/plot_labels_and_colors.html +++ b/auto_examples/drawing/plot_labels_and_colors.html @@ -566,7 +566,7 @@ components of a 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 0.257 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.185 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-labels-and-colors-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 a71870db..d87b0728 100644 --- a/auto_examples/drawing/plot_multipartite_graph.html +++ b/auto_examples/drawing/plot_multipartite_graph.html @@ -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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.098 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.077 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-multipartite-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 c6507901..1e184179 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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.068 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.052 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-node-colormap-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 10df64d0..308d7915 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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.166 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.127 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-rainbow-coloring-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 3fea9848..1fd7553f 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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.134 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-drawing-plot-random-geometric-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 7e1eaffa..82d517f3 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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.366 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.245 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-sampson-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 51db7cc7..b6343948 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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.117 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.080 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-selfloops-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 b67c8326..032ca9d7 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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.080 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.057 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-simple-path-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 c80cc34f..83fed910 100644 --- a/auto_examples/drawing/plot_spectral_grid.html +++ b/auto_examples/drawing/plot_spectral_grid.html @@ -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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.386 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-drawing-plot-spectral-grid-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 4e5e8cf2..9f2fdef7 100644 --- a/auto_examples/drawing/plot_tsp.html +++ b/auto_examples/drawing/plot_tsp.html @@ -568,7 +568,7 @@ that the traveler has to follow in order to minimize the total cost.</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 0.135 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.087 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-tsp-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 3f9d0e73..2f0d32ca 100644 --- a/auto_examples/drawing/plot_unix_email.html +++ b/auto_examples/drawing/plot_unix_email.html @@ -583,7 +583,7 @@ From: ted@com To: alice@edu Subject: get together for lunch to discuss Networks? <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 0.197 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.162 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-unix-email-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 4556c42c..078092b7 100644 --- a/auto_examples/drawing/plot_weighted_graph.html +++ b/auto_examples/drawing/plot_weighted_graph.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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.138 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.085 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-weighted-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/32d3b6ab4dec83957a1981fa91e52e14/plot_weighted_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_weighted_graph.py</span></code></a></p> diff --git a/auto_examples/drawing/sg_execution_times.html b/auto_examples/drawing/sg_execution_times.html index 14f7e4c7..dcfc8b64 100644 --- a/auto_examples/drawing/sg_execution_times.html +++ b/auto_examples/drawing/sg_execution_times.html @@ -463,99 +463,99 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-drawing-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:05.829</strong> total execution time for <strong>auto_examples_drawing</strong> files:</p> +<p><strong>00:04.015</strong> total execution time for <strong>auto_examples_drawing</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_eigenvalues.html#sphx-glr-auto-examples-drawing-plot-eigenvalues-py"><span class="std std-ref">Eigenvalues</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_eigenvalues.py</span></code>)</p></td> -<td><p>00:00.953</p></td> +<td><p>00:00.645</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_chess_masters.html#sphx-glr-auto-examples-drawing-plot-chess-masters-py"><span class="std std-ref">Chess Masters</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_chess_masters.py</span></code>)</p></td> -<td><p>00:00.526</p></td> +<td><p>00:00.390</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_four_grids.html#sphx-glr-auto-examples-drawing-plot-four-grids-py"><span class="std std-ref">Four Grids</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_four_grids.py</span></code>)</p></td> -<td><p>00:00.501</p></td> +<td><p>00:00.324</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_custom_node_icons.html#sphx-glr-auto-examples-drawing-plot-custom-node-icons-py"><span class="std std-ref">Custom node icons</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_custom_node_icons.py</span></code>)</p></td> -<td><p>00:00.413</p></td> +<td><p>00:00.279</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_degree.html#sphx-glr-auto-examples-drawing-plot-degree-py"><span class="std std-ref">Degree Analysis</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_degree.py</span></code>)</p></td> -<td><p>00:00.391</p></td> +<td><p>00:00.266</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_spectral_grid.html#sphx-glr-auto-examples-drawing-plot-spectral-grid-py"><span class="std std-ref">Spectral Embedding</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_spectral_grid.py</span></code>)</p></td> -<td><p>00:00.386</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_sampson.html#sphx-glr-auto-examples-drawing-plot-sampson-py"><span class="std std-ref">Sampson</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_sampson.py</span></code>)</p></td> +<td><p>00:00.245</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_sampson.html#sphx-glr-auto-examples-drawing-plot-sampson-py"><span class="std std-ref">Sampson</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_sampson.py</span></code>)</p></td> -<td><p>00:00.366</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_spectral_grid.html#sphx-glr-auto-examples-drawing-plot-spectral-grid-py"><span class="std std-ref">Spectral Embedding</span></a> (<code class="docutils literal notranslate"><span 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the script:</strong> ( 0 minutes 0.552 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.425 seconds)</p> <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 08559969..6acb5513 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> -<p><strong>00:21.525</strong> total execution time for <strong>auto_examples_geospatial</strong> files:</p> +<p><strong>00:15.561</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> -<td><p>00:06.540</p></td> +<td><p>00:05.609</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>00:05.293</p></td> +<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>00:03.281</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><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>00:04.809</p></td> +<tr class="row-odd"><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>00:03.236</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><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> -<td><p>00:04.332</p></td> +<td><p>00:03.009</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>00:00.552</p></td> +<td><p>00:00.425</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 a0e99fa3..cb638069 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 0.181 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 cb55f342..9205188a 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 0.084 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 b0308aab..338423c4 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 0.081 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-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 12273e61..1bd249b2 100644 --- a/auto_examples/graph/plot_expected_degree_sequence.html +++ b/auto_examples/graph/plot_expected_degree_sequence.html @@ -535,50 +535,54 @@ degree (#nodes) **** 25 ( 0) 26 ( 0) 27 ( 0) -28 ( 0) +28 ( 1) * 29 ( 0) 30 ( 0) 31 ( 0) -32 ( 1) * -33 ( 1) * -34 ( 0) -35 ( 2) ** -36 ( 3) *** +32 ( 0) +33 ( 0) +34 ( 2) ** +35 ( 0) +36 ( 4) **** 37 ( 5) ***** 38 ( 4) **** -39 ( 8) ******** -40 ( 9) ********* -41 ( 7) ******* -42 ( 9) ********* -43 (22) ********************** -44 (32) ******************************** -45 (25) ************************* -46 (25) ************************* -47 (26) ************************** -48 (22) ********************** -49 (25) ************************* +39 ( 6) ****** +40 (13) ************* +41 (13) ************* +42 (13) ************* +43 (23) *********************** +44 (27) *************************** +45 (35) *********************************** +46 (28) **************************** +47 (32) ******************************** +48 (27) *************************** +49 (30) ****************************** 50 (31) ******************************* 51 (27) *************************** -52 (29) ***************************** -53 (27) *************************** -54 (25) ************************* -55 (25) ************************* -56 (16) **************** -57 (25) ************************* -58 (14) ************** -59 (15) *************** -60 ( 8) ******** -61 (10) ********** +52 (28) **************************** +53 (24) ************************ +54 (23) *********************** +55 (16) **************** +56 (19) ******************* +57 (12) ************ +58 ( 6) ****** +59 (10) ********** +60 (12) ************ +61 ( 4) **** 62 ( 4) **** -63 ( 6) ****** -64 ( 4) **** -65 ( 3) *** -66 ( 1) * -67 ( 1) * +63 ( 7) ******* +64 ( 3) *** +65 ( 2) ** +66 ( 3) *** +67 ( 2) ** 68 ( 1) * -69 ( 1) * -70 ( 0) -71 ( 1) * +69 ( 0) +70 ( 1) * +71 ( 0) +72 ( 0) +73 ( 1) * +74 ( 0) +75 ( 1) * </pre></div> </div> <div class="line-block"> @@ -598,7 +602,7 @@ degree (#nodes) **** <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="si">:</span><span class="s2">2</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span></a><span class="si">:</span><span class="s2">2</span><span class="si">}</span><span class="s2">) </span><span class="si">{</span><span class="s1">'*'</span><span class="o">*</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span></a><span class="si">}</span><span class="s2">"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.039 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.031 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-expected-degree-sequence-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/7378087382f40e96e66bce4a35ba0e52/plot_expected_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_expected_degree_sequence.py</span></code></a></p> diff --git a/auto_examples/graph/plot_football.html b/auto_examples/graph/plot_football.html index 78efd4be..403704f2 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 0.468 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.303 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 7654416c..6dd520a8 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 0.134 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 6a46f709..a7054f60 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">" "</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">"ilovenetworkx"</span><span class="p">]))</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.280 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.181 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 f4a9b331..1e13ba38 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 0.174 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.127 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 cfb3f5e4..493b2f1d 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 0.301 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.229 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 e045f309..5479b87b 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 1.676 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.064 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 425da26f..1b1c5b09 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 0.528 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.393 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 22ad4700..45cfd4c5 100644 --- a/auto_examples/graph/sg_execution_times.html +++ b/auto_examples/graph/sg_execution_times.html @@ -463,51 +463,51 @@ <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>00:03.946</strong> total execution time for <strong>auto_examples_graph</strong> files:</p> +<p><strong>00:02.651</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>00:01.676</p></td> +<td><p>00:01.064</p></td> <td><p>0.0 MB</p></td> </tr> <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>00:00.528</p></td> +<td><p>00:00.393</p></td> <td><p>0.0 MB</p></td> </tr> <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> -<td><p>00:00.468</p></td> +<td><p>00:00.303</p></td> <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> -<td><p>00:00.301</p></td> +<td><p>00:00.229</p></td> <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> -<td><p>00:00.280</p></td> +<td><p>00:00.181</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><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> -<td><p>00:00.181</p></td> +<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> +<td><p>00:00.127</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><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> -<td><p>00:00.174</p></td> +<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> +<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> -<td><p>00:00.134</p></td> +<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_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>00:00.084</p></td> +<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> +<td><p>00:00.059</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><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> -<td><p>00:00.081</p></td> +<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>00:00.058</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> -<td><p>00:00.039</p></td> +<td><p>00:00.031</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/graphviz_drawing/plot_attributes.html b/auto_examples/graphviz_drawing/plot_attributes.html index 5f62be49..968d63f6 100644 --- a/auto_examples/graphviz_drawing/plot_attributes.html +++ b/auto_examples/graphviz_drawing/plot_attributes.html @@ -532,7 +532,7 @@ node node attributes <span class="nb">print</span><span class="p">(</span><span class="n">X</span><span class="o">.</span><span class="n">nodes</span><span class="o">.</span><span class="n">data</span><span class="p">(</span><span class="kc">True</span><span class="p">))</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.089 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.083 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-attributes-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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_conversion.html b/auto_examples/graphviz_drawing/plot_conversion.html index 52aa0c0d..4ed8440f 100644 --- a/auto_examples/graphviz_drawing/plot_conversion.html +++ b/auto_examples/graphviz_drawing/plot_conversion.html @@ -514,7 +514,7 @@ to download the full example code</p> <a href="https://pygraphviz.github.io/documentation/stable/reference/agraph.html#pygraphviz.AGraph.draw" title="pygraphviz.AGraph.draw" class="sphx-glr-backref-module-pygraphviz sphx-glr-backref-type-py-method"><span class="n">A</span><span class="o">.</span><span class="n">draw</span></a><span class="p">(</span><span class="s2">"k5.png"</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"neato"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.032 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.027 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-conversion-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/27aa0c08bacf20ba3f5ce4f8d02ac226/plot_conversion.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_conversion.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/plot_grid.html b/auto_examples/graphviz_drawing/plot_grid.html index 2b55aa63..adbfdcf8 100644 --- a/auto_examples/graphviz_drawing/plot_grid.html +++ b/auto_examples/graphviz_drawing/plot_grid.html @@ -519,7 +519,7 @@ Graphviz command line interface to create visualizations.</p> <img src="../../_images/sphx_glr_plot_grid_001.png" srcset="../../_images/sphx_glr_plot_grid_001.png" alt="plot grid" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Now run: neato -Tps grid.dot >grid.ps </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.083 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.068 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-grid-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 d851360e..1798acd3 100644 --- a/auto_examples/graphviz_drawing/plot_mini_atlas.html +++ b/auto_examples/graphviz_drawing/plot_mini_atlas.html @@ -543,7 +543,7 @@ Graph named 'G19' with 5 nodes and 0 edges <a href="https://pygraphviz.github.io/documentation/stable/reference/agraph.html#pygraphviz.AGraph.draw" title="pygraphviz.AGraph.draw" class="sphx-glr-backref-module-pygraphviz sphx-glr-backref-type-py-method"><span class="n">A</span><span class="o">.</span><span class="n">draw</span></a><span class="p">(</span><span class="s2">"A20.png"</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"neato"</span><span class="p">)</span> </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 0.080 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-mini-atlas-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 bebaea4a..93125a86 100644 --- a/auto_examples/graphviz_drawing/sg_execution_times.html +++ b/auto_examples/graphviz_drawing/sg_execution_times.html @@ -463,23 +463,23 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-graphviz-drawing-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.305</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> +<p><strong>00:00.258</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> -<td><p>00:00.101</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_attributes.html#sphx-glr-auto-examples-graphviz-drawing-plot-attributes-py"><span class="std std-ref">Attributes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_attributes.py</span></code>)</p></td> +<td><p>00:00.083</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_attributes.html#sphx-glr-auto-examples-graphviz-drawing-plot-attributes-py"><span class="std std-ref">Attributes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_attributes.py</span></code>)</p></td> -<td><p>00:00.089</p></td> +<tr class="row-even"><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> +<td><p>00:00.080</p></td> <td><p>0.0 MB</p></td> </tr> <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> -<td><p>00:00.083</p></td> +<td><p>00:00.068</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_conversion.html#sphx-glr-auto-examples-graphviz-drawing-plot-conversion-py"><span class="std std-ref">Conversion</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_conversion.py</span></code>)</p></td> -<td><p>00:00.032</p></td> +<td><p>00:00.027</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/graphviz_layout/plot_atlas.html b/auto_examples/graphviz_layout/plot_atlas.html index 1a7ba2e3..3a7bde2d 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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 5.043 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.826 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-atlas-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 daf2dead..cfa0c5c0 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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.192 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.153 seconds)</p> <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 0f5749bb..54b05499 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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.439 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.301 seconds)</p> <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 a73b815b..9d6dd1e5 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> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.106 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.806 seconds)</p> <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 ffa76a67..6d8b4360 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> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.459 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.336 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-lanl-routes-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <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 8f2320e0..bc043ffd 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> -<p><strong>00:07.239</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> +<p><strong>00:05.422</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> <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> -<td><p>00:05.043</p></td> +<td><p>00:03.826</p></td> <td><p>0.0 MB</p></td> </tr> <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> -<td><p>00:01.106</p></td> +<td><p>00:00.806</p></td> <td><p>0.0 MB</p></td> </tr> <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> -<td><p>00:00.459</p></td> +<td><p>00:00.336</p></td> <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> -<td><p>00:00.439</p></td> +<td><p>00:00.301</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> -<td><p>00:00.192</p></td> +<td><p>00:00.153</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/subclass/plot_antigraph.html b/auto_examples/subclass/plot_antigraph.html index 7a6e4ad5..f3cd57d8 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 0.122 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.098 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 38c85ba4..dc92f3ce 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 0.087 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-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 b9259933..3b0fea01 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>00:00.210</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p> +<p><strong>00:00.157</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>00:00.122</p></td> +<td><p>00:00.098</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>00:00.087</p></td> +<td><p>00:00.058</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/developer/index.html b/developer/index.html index 179fa412..cfb855df 100644 --- a/developer/index.html +++ b/developer/index.html @@ -493,7 +493,7 @@ <dd class="field-odd"><p>3.0rc2.dev0</p> </dd> <dt class="field-even">Date<span class="colon">:</span></dt> -<dd class="field-even"><p>Jan 02, 2023</p> +<dd class="field-even"><p>Jan 03, 2023</p> </dd> </dl> <div class="toctree-wrapper compound"> diff --git a/developer/new_contributor_faq.html b/developer/new_contributor_faq.html index 676f6bb0..7471f66f 100644 --- a/developer/new_contributor_faq.html +++ b/developer/new_contributor_faq.html @@ -586,8 +586,8 @@ class is defined.</p> <a class="reference internal" href="../reference/generated/networkx.drawing.layout.kamada_kawai_layout.html#networkx.drawing.layout.kamada_kawai_layout" title="networkx.drawing.layout.kamada_kawai_layout"><code class="xref py py-obj docutils literal notranslate"><span class="pre">kamada_kawai_layout</span></code></a> function, so you need to know where it is defined. In an IPython terminal, you can use <code class="docutils literal notranslate"><span class="pre">?</span></code> — the source file is listed in the <code class="docutils literal notranslate"><span class="pre">File:</span></code> field:</p> -<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span>In [1]: import networkx as nx -In [2]: nx.kamada_kawai_layout? +<div class="highlight-ipython notranslate"><div class="highlight"><pre><span></span><span class="n">In</span> <span class="p">[</span><span class="mi">1</span><span class="p">]:</span> <span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span> +<span class="n">In</span> <span class="p">[</span><span class="mi">2</span><span class="p">]:</span> nx.kamada_kawai_layout<span class="o">?</span> </pre></div> </div> <div class="highlight-text notranslate"><div class="highlight"><pre><span></span>Signature: <clipped for brevity> @@ -464,7 +464,7 @@ <dd class="field-odd"><p>3.0rc2.dev0</p> </dd> <dt class="field-even">Date<span class="colon">:</span></dt> -<dd class="field-even"><p>Jan 02, 2023</p> +<dd class="field-even"><p>Jan 03, 2023</p> </dd> </dl> <p>NetworkX is a Python package for the creation, manipulation, and study diff --git a/reference/index.html b/reference/index.html index aaa1b53c..001c4091 100644 --- a/reference/index.html +++ b/reference/index.html @@ -496,7 +496,7 @@ <dd class="field-odd"><p>3.0rc2.dev0</p> </dd> <dt class="field-even">Date<span class="colon">:</span></dt> -<dd class="field-even"><p>Jan 02, 2023</p> +<dd class="field-even"><p>Jan 03, 2023</p> </dd> </dl> </div></blockquote> diff --git a/reference/introduction-7.hires.png b/reference/introduction-7.hires.png Binary files differindex a338d753..cb016e3b 100644 --- a/reference/introduction-7.hires.png +++ b/reference/introduction-7.hires.png diff --git a/reference/introduction-7.pdf b/reference/introduction-7.pdf Binary files differindex 24078ffd..f8df25ed 100644 --- a/reference/introduction-7.pdf +++ b/reference/introduction-7.pdf diff --git a/reference/introduction-7.png b/reference/introduction-7.png Binary files differindex 1a5a2c98..341bc319 100644 --- a/reference/introduction-7.png +++ b/reference/introduction-7.png diff --git a/reference/introduction.ipynb b/reference/introduction.ipynb index 78824535..a44e5edf 100644 --- a/reference/introduction.ipynb +++ b/reference/introduction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "460bb211", + "id": "e2f57e72", "metadata": {}, "source": [ "## Introduction\n", @@ -34,7 +34,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1ea1fa59", + "id": "bc29f559", "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "e548cbe6", + "id": "ec570782", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": null, - "id": "85de9aeb", + "id": "b95145e0", "metadata": {}, "outputs": [], "source": [ @@ -94,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "aeefd720", + "id": "cc9e3ee7", "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": "37e6adfb", + "id": "0376d71c", "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "1ee398d6", + "id": "55e825f0", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -214,7 +214,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a13afd73", + "id": "f1cb48d5", "metadata": {}, "outputs": [], "source": [ @@ -225,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "9b4f6830", + "id": "10559968", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -234,7 +234,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8577401d", + "id": "2c55ecf6", "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "5d054728", + "id": "aac0c4d6", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -311,7 +311,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2d598f2f", + "id": "ca3d9b8b", "metadata": {}, "outputs": [], "source": [ @@ -323,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "e20d6c24", + "id": "3d3aa733", "metadata": {}, "source": [ "# Drawing\n", @@ -344,7 +344,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0777150c", + "id": "adfd3c21", "metadata": {}, "outputs": [], "source": [ @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "b7fc5217", + "id": "b8a18d63", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -398,7 +398,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d8ddc593", + "id": "a7bbcdc2", "metadata": {}, "outputs": [], "source": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "26cc6ce1", + "id": "12b7f9bb", "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": "097de0f5", + "id": "030fd58c", "metadata": {}, "outputs": [], "source": [ diff --git a/reference/introduction_full.ipynb b/reference/introduction_full.ipynb index cd8b5130..795dff66 100644 --- a/reference/introduction_full.ipynb +++ b/reference/introduction_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "460bb211", + "id": "e2f57e72", "metadata": {}, "source": [ "## Introduction\n", @@ -34,13 +34,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "1ea1fa59", + "id": "bc29f559", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:41.690885Z", - "iopub.status.busy": "2023-01-02T13:06:41.690301Z", - "iopub.status.idle": "2023-01-02T13:06:41.790926Z", - "shell.execute_reply": "2023-01-02T13:06:41.789837Z" + "iopub.execute_input": "2023-01-03T20:27:35.044503Z", + "iopub.status.busy": "2023-01-03T20:27:35.044263Z", + "iopub.status.idle": "2023-01-03T20:27:35.117993Z", + "shell.execute_reply": "2023-01-03T20:27:35.117269Z" } }, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "markdown", - "id": "e548cbe6", + "id": "ec570782", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -89,13 +89,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "85de9aeb", + "id": "b95145e0", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:41.795235Z", - "iopub.status.busy": "2023-01-02T13:06:41.794963Z", - "iopub.status.idle": "2023-01-02T13:06:41.798977Z", - "shell.execute_reply": "2023-01-02T13:06:41.798159Z" + "iopub.execute_input": "2023-01-03T20:27:35.122086Z", + "iopub.status.busy": "2023-01-03T20:27:35.121843Z", + "iopub.status.idle": "2023-01-03T20:27:35.125364Z", + "shell.execute_reply": "2023-01-03T20:27:35.124717Z" } }, "outputs": [], @@ -108,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "aeefd720", + "id": "cc9e3ee7", "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": "37e6adfb", + "id": "0376d71c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:41.803233Z", - "iopub.status.busy": "2023-01-02T13:06:41.802967Z", - "iopub.status.idle": "2023-01-02T13:06:41.807197Z", - "shell.execute_reply": "2023-01-02T13:06:41.806363Z" + "iopub.execute_input": "2023-01-03T20:27:35.128497Z", + "iopub.status.busy": "2023-01-03T20:27:35.128273Z", + "iopub.status.idle": "2023-01-03T20:27:35.131710Z", + "shell.execute_reply": "2023-01-03T20:27:35.131075Z" } }, "outputs": [], @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "1ee398d6", + "id": "55e825f0", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -235,13 +235,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "a13afd73", + "id": "f1cb48d5", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:41.811069Z", - "iopub.status.busy": "2023-01-02T13:06:41.810810Z", - "iopub.status.idle": "2023-01-02T13:06:41.814696Z", - "shell.execute_reply": "2023-01-02T13:06:41.813827Z" + "iopub.execute_input": "2023-01-03T20:27:35.134730Z", + "iopub.status.busy": "2023-01-03T20:27:35.134521Z", + "iopub.status.idle": "2023-01-03T20:27:35.137665Z", + "shell.execute_reply": "2023-01-03T20:27:35.137040Z" } }, "outputs": [], @@ -253,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "9b4f6830", + "id": "10559968", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -262,13 +262,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "8577401d", + "id": "2c55ecf6", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:41.818445Z", - "iopub.status.busy": "2023-01-02T13:06:41.818145Z", - "iopub.status.idle": "2023-01-02T13:06:41.823261Z", - "shell.execute_reply": "2023-01-02T13:06:41.822449Z" + "iopub.execute_input": "2023-01-03T20:27:35.140639Z", + "iopub.status.busy": "2023-01-03T20:27:35.140430Z", + "iopub.status.idle": "2023-01-03T20:27:35.144275Z", + "shell.execute_reply": "2023-01-03T20:27:35.143626Z" } }, "outputs": [], @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "5d054728", + "id": "aac0c4d6", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -346,13 +346,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "2d598f2f", + "id": "ca3d9b8b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:41.826834Z", - "iopub.status.busy": "2023-01-02T13:06:41.826598Z", - "iopub.status.idle": "2023-01-02T13:06:41.835198Z", - "shell.execute_reply": "2023-01-02T13:06:41.834223Z" + "iopub.execute_input": "2023-01-03T20:27:35.147402Z", + "iopub.status.busy": "2023-01-03T20:27:35.147194Z", + "iopub.status.idle": "2023-01-03T20:27:35.151505Z", + "shell.execute_reply": "2023-01-03T20:27:35.150853Z" } }, "outputs": [ @@ -373,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "e20d6c24", + "id": "3d3aa733", "metadata": {}, "source": [ "# Drawing\n", @@ -394,19 +394,19 @@ { "cell_type": "code", "execution_count": 7, - "id": "0777150c", + "id": "adfd3c21", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:41.839092Z", - "iopub.status.busy": "2023-01-02T13:06:41.838825Z", - "iopub.status.idle": "2023-01-02T13:06:42.599406Z", - "shell.execute_reply": "2023-01-02T13:06:42.598612Z" + "iopub.execute_input": "2023-01-03T20:27:35.156101Z", + "iopub.status.busy": "2023-01-03T20:27:35.155889Z", + "iopub.status.idle": "2023-01-03T20:27:35.741006Z", + "shell.execute_reply": "2023-01-03T20:27:35.740353Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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I/KPD6hUuzEcF1q4Fdu3iiddff8kdlXpQMpBJ8fHxGD9+PDp27IjatWuL3n6OHDng5uam2GQgTevWrREZGYkOHTqgX79+MBgMuHz5stxhEZJ9z58DYWH8+LywMP7nLNq3D9DpgHXrgGXLgL//BkqUEC1S8hZB4FszIyL4WQ6tWgE9egBPn2ajURHvByWjZCCTpk+fjidPnmDatGmS9eHp6am4aYL3yZcvH5YuXYp9+/bhzp070Ov1mDZtGl6+fCl3aIRkTlQU8P33gIsLPzrPy4uX/fPy4n92ceFfj4rKUHOJifzljRvzY3rDw4FevWg0wFyKF+ejAsuX8+kYnQ7YsycTDYh8P6gCIxl248YNZmdnx8aPHy9pP3PmzGE5c+ZkRqNR0n7ElJCQwEaOHMk0Gg2rXLkyO3PmjNwhEZK+a9cY8/FhDGBMq+W/f+hX2td9fPj7PuDIEcbKl2csZ07G5s1jLDXVjN+PyM6c4d+ymv8537jBWKNG/Pvo35+x588/8mIJ7ge1oGQgEzp06MCKFi3K4uPjJe1n7969DACLjo6WtB8pnD59mnl6ejIbGxs2evRolpiYKHdIhLzfsmWM2dun/0P/fR8C9vb8/a9JSmJs9GjGNBrGatVi7NIlmb4vEVlCMsAYT8jmz+cJWrlyjB069J4XiXw/qA0lAxl05MgRBoD99NNPkvf177//MgBs3bp1kvclhZSUFBYUFMTs7OxY+fLl2f79++UOiZA3BQZm7gf+h34FBjLG+IeluztjtraMTZvGmIoG9T7KUpKBNJcvM1anDmOCwNiIEYy9ePHfF0S+H9SIkoEMSE1NZTVq1GBeXl4s1UxjfsWLF2djx441S19SiY6OZvXq1WMAWJ8+fdiTJ0/kDokQ/gQnxg/+/35tbhXCtFrGvLwYu3BB7m9OXJaWDDDGE7UZM3jiVqkSY9fHi3s/sJAQub/FLKEFhBnw66+/4uTJkwgODoZGY55LpqRKhFlVsWJFHDhwAAsXLsSaNWvg7u6OLVu2yB0WsWbXrwODBonWHAPgs2UgZvS7juPH+UI1omw2NsDIkcDZs0A5zXUUCRoEUcunDRzI7zOVoWQgHYmJiRgzZgzatm2Lhg0bmq1fS0gGAECj0aB///6IjIyEp6cnWrdujQ4dOuDBgwdyh0asUZ8+gNEoWnMCAHsbI4Ze7ANbW9GaJWbg7g5sLtYHthojRN3kYTTy+0xlKBlIx6xZs/DgwQPMmDHDrP16enrizp07iLOQMlrOzs7YunUr1qxZg71796JSpUpYvXo1lTQm5hMVBezeLWoyAABCqpG3Gx0tartEYlFR0OzZDRuTuPcDjOq8HygZ+Ig7d+5g+vTp+P7771GuXDmz9p1WltgSRgfSCIKATp06ISoqCs2aNUPXrl3RvHlz3Lx5U+7QiDVYvBjQaqVpW6sFFi2Spm0iDbof3kDJwEeMHz8ejo6O8PX1NXvf5cuXh729vSqKD2VW4cKFsWbNGmzduhVRUVFwd3fH/PnzkZqaKndoxJJt2yb6qMArRiOwfbs0bRNp0P3wBkoGPuD06dNYtWoVJk2ahLx585q9f61WCw8PD4saGXhbixYtEBkZiW+//Rbff/896tevjyhLquhFlCM+Hrh2Tdo+rl612FK1Fofuh3dQMvAejDEMHToU7u7u6NWrl2xxWMoiwo/JnTs3FixYgEOHDuHRo0fw8vLCpEmTkJKSIndoxJJcvco3fkmJMSAmRto+iDjofngHJQPv8ccff+Dw4cMIDg6GVqo5pQzw9PREZGSkVdT6r1evHsLCwjBixAhMnjwZVatWxcmTJ+UOi1iK5GTL6odkD90P76Bk4C1JSUkYOXIkWrRoAR8fH1lj0ev1SElJwaVLl2SNw1zs7e0RFBSE06dPw87ODrVr18awYcOQkJAgd2hE7ezsLKsfkj10P7yDkoG3zJs3D7dv38asWbPkDgV6vR6AZe0oyAhPT08cP34c06ZNw6JFi6DT6bB37165wyJq5uIi/ZGBgsD7IcpH98M7KBl4zf379xEUFIT+/fujYsWKcoeD/Pnzw9nZ2eqSAYAvoBw5ciTCw8NRqlQpNGnSBD179sTjx4/lDo2oUa5c/CxhKZUrx/shykf3wzsoGXiNn58ftFot/P395Q7lFWtYRPgxLi4u2LdvH5YuXYoNGzbAzc0Nf/zxh9xhETX67DNp95U3by5N20QadD+8gZKB/1y4cAHLly+Hv78/ChQoIHc4r1h7MgDwYkW9e/dGdHQ0atWqhS+//BLt2rXDP//8k+22E5KNiLz7FOdiHyPy7lMkJEu075jIr29fafeV9+snTdtEGnQ/vEFgVA8WjDH4+Pjg9u3bCA8PR44cOeQO6ZUNGzbgq6++wr1791CkSBG5w5EdYwx//PEHBgwYgJSUFMyaNQs9evSAkIn5vyv347HmRCxCLz1A7KPENw4pEQA4F3CAwdUJnWs6o3yR3KJ/D0RGTZsCoaGifggYoUVKXQMcDu8SrU2lOHsWqFoVOHMGqFJF7mgkIMH9AK0WMBiAXeq6H2hkAMBff/2FvXv3YtasWYpKBID/lyW2xEqEWSEIAr788ktER0ejTZs26NWrF5o0aYKrV6+m+95bjxLRZfkJ+Mw9iNUnbuLmW4kAwE+hu/koEatP3ITP3IPosvwEbj1KlOR7ITJYskTUoWEGwChoUe3MEvz4I2AyidY0MQeR7wcAvL0lS8Rt0wysPhlISUnBiBEj0KRJE7Ro0ULucN5Rrlw5ODo6Wv1UwdsKFCiAFStWYOfOnbh27Rp0Oh1mz579wZLG607FoknwARy9xg9+SjV9fEAs7etHr8WhSfABrDsVK+43QORRpgwwf75ozQkAMH8BvLuXwYABwKefArF0q6iHyPcDAGDBAt6uylh9MvDjjz/i6tWrmDNnTqaGms1Fo9FAp9NRMvABTZs2RXh4OPr06YORI0eidu3a74yiLAi9gjEbw5FsNKWbBLwt1cSQbDRhzMZwLAi9ImboRC69egGBgeK0FRQE+wE98eOPfFT44kVApwNWrpS+wB0Ricj3A3r2FKctM7PqZCAuLg4BAQHo3bs3dDqd3OF8kF6vp2TgI3LlyoXg4GAcPXoUiYmJqFq1Kvz8/JCcnIx1p2Ixa9dlUfqZtesy1tMIgWUYPx5Ytgywt8/8MLFWy98XEgKMG/fqf/v4AOHhQNu2QPfuQOvWwL17IsdNpCHB/aA2Vp0MTJw4ESaTCZMmTZI7lI/y9PREdHQ0klVU2lIOtWrVwtmzZ+Hr64vp06fDs24j+P0ZLmofE7ZE0hoCS9GrFxAVxRd7Ael/CKR93WDg73vPE2C+fMCKFcDmzcDJk4C7O7B+vbhhE4lIcD+oidUmA9HR0Vi0aBF8fX3h5OQkdzgf5enpCaPRiOjoaLlDUTxbW1v4+/vj3LlzMFVtj5SX4m4dMpoYxm0SN8EgMipTho/vR0byrWDvq0yXVkmuXz/+Q3/XrnTnhFu1AiIigCZNgA4dgPbtgYcPJfw+iDgycD8wCLgquCC1T8bvBzWw2q2Fn332GS5duoSoqCjYKbx+dHx8PPLkyYOVK1eiW7ducoejClfux8Nn7kHJ2t8ztAFcnGjboUV6/hzRf8Xg207JWLnWDpU+d8lWJbn164H+/YEcOYClS3mioBYWv7UwI966HxKKuaC6IReOHgVq15Y7OPFY5cjAzp07sX37dsyYMUPxiQDAj/ktW7YsrRvIhDUnYmGjkWZBqI1GwC/Hae2AxcqVCy9cK+MkauKFa+Vsl5Rt354/aFavztcRdO8OPH0qTqjEDN66HyrXy4XcuXl5AktidcmA0WjEsGHD0KBBA7Rt21bucDLM09OTag1kQuilB5neOZBRqSaG0MsPJGmbWKaiRYEtW/h6go0bAQ8PYPduuaMiWaHVAvXrA/v3yx2JuKwuGVi6dCmio6MRHBysyK2EH5JWlthKZ3Uy5XmyEbESL/KLjUuk0sUkUwQB+PZbvuOgYkVe/K5/f+D5c7kjI5llMABHjgApKXJHIh6rSgaePHmCCRMmoFu3bqiisgkwT09PPHz4UJR6/JbuZlzCO5UFxcYA3IhLkLgXYomcnYGdO4GFC4FVqwBPT+DQIbmjIplhMACJiXzHiKWwqmQgMDAQSUlJCAoKkjuUTEsrS0zrBtKXYjRPTVhz9UMsj0bDRwXOnweKFwcaNgRGjABevJA7MpIRlSsDefNa1lSB1SQDV65cwQ8//IAxY8agePHicoeTaaVKlULu3LkpGcgAW615bmtz9UMsl4sL/0CZOZNXsa1SBTh1Su6oSHpsbIAGDSxrEaHV/DQbNWoUihYtiuHDh8sdSpZoNBqqRJhBpQs6QurVIMJ//RCSXTY2wPDhfBtfrlx8u5qfn2XNR1sigwE4ehSwlFpwVpEMhIaG4s8//8T06dORM2dOucPJsrRFhOTjHO20cC7gIGkfzgUd4Ggn8mlnxKq5ufEPF39/YNo0oEYNgDYQKZe3N5CUBJw4IXck4rD4ZCA1NRXDhg1DrVq10KFDB7nDyRZPT09cunQJL2hiMV0GVydJ6wwYKii7aiVRpxw5+KjAyZP8OORq1YCpUwEjbVxRHE9PIH9+y5kqsPhkYOXKlQgLC1PdVsL38fT0hMlkQmRkpNyhKF7nms6S1hn4ppazJG0TAgBeXnztwIgRgK8vULcuPxGRKIdGwxd+UjKgAvHx8Rg/fjw6deqEWrVqyR1Otnl4eEAQBCo+lAHli+RGfZdCoo8O2GgE1HcpRKWIieTs7IApU/h+9idPeIIwdy4fMSDKYDAAx4/z6QK1s+hkYNq0aXj69CmmTp0qdyiicHR0RPny5WndQAZN+UIHrcjJgFYjYMoXyj3umlieWrWAc+eAPn2AoUOBRo2A69fljooAfN1AcjJw7JjckWSfxSYDN27cwOzZszFixAg4O1vOkC4tIsy4kgUcENDKXdQ2J7VyR0mJFycS8jYHBz4qEBoK3LwJ6HT80CMqSCovDw+gYEHLmCqw2GRgzJgxKFCgAEaPHi13KKKissSZ06G6M0Y0rSBKWyObuqJ9dctJLIn6eHvzHQadO/ORgubNgdu35Y7Kemk0/O/EEooPWWQycPToUaxfvx5TpkxBrmyeOKY0er0eT548wa1bt+QORTUGGspjWlsdtDCBpWZuWbaNRoCdVoPpbXUYYHCRKEJCMi53bmDJEmD7dn7OgYcHsHo1jRLIxdubrxtIlPY4FMlZXDJgMpkwZMgQVKlSBV27dpU7HNFRWeKsaeVeCAkbxiH/y4cAkO7CwrSv1ylbEHuGNqQRAaI4zZoBERHA558DXbsCbdsC9+/LHZX1MRiAly95jQg1s7hkYO3atTh16hSCg4Oh0Vjct4eSJUsiX758lAxk0qJFi/Dv9Wj8MdAbu4c0QJeapVCqoMN7KhUylCrogC41S2HP0AZY3bMmrREgipU/Px8V2LiR7zrw8AA2bJA7Kuvi5gYULqz+dQMWVUItISEBY8aMQbt27dCgQQO5w5GEIAi0iDCTnj9/jmnTpqFHjx4oV64cAGBiK3dMhDsSko24EZeAFKMJX37RBi0a1sAPI2bKHDEhmfPFF0C9ekDfvsBXXwEdO/KzDgoUkDsyyycIlrFuwKIenWfNmoV///0XM2bMkDsUSVEykDk//PADnj17Bl9f33e+5minhXvxvPByzg+9cwFEh4eZP0BCRFC4MB8VWLOGryfw8AD+/lvuqKyDwcCrRj5/LnckWWcxycCdO3cwY8YMDB48GGXLlpU7HEl5enoiJiYGCQkJcoeieE+ePMHMmTPx3XffpbvFVK/XIzw83EyRESI+QQA6dQIiI/kxuy1bAr16Ac+eyR2ZZTMYeMnoI0fkjiTrLCYZGDduHBwdHTF+/Hi5Q5Gcp6cnGGOIiIiQOxTFCw4ORlJSEsaNG5fua3U6He7fv48HDx6YITJCpFO8OB8VCAkB1q/ndQn27ZM7Ksvl6goULaruqQKLSAZOnTqFn3/+GZMnT0bevHnlDkdy7u7usLGxoamCdDx8+BDBwcEYMGAAihUrlu7rdTpeWZBGB4glEASgZ0++/bBcOaBxY2DQIIAGFMWXtm5AzYsIVZ8MMMYwdOhQeHh4oGfPnnKHYxb29vZwdXWlZCAdM2fOhMlkynDhKRcXF9jb21MyQCxK6dLAnj3ADz8Ay5fz6QO1b4NTIoMBOH0aiI+XO5KsUX0ysGHDBhw5cgRz5syBVmtRmyM+Sq/XUzLwEffu3cP8+fMxZMgQFC5cOEPvsbGxgbu7Ox0ERSyORsNHBcLCgEKFgPr1gdGjLeOAHaXw9gZSU4HDh+WOJGtUnQwkJSVh1KhRaNmyJXx8fOQOx6w8PT1x4cIFmOgIs/eaNm0abG1tMXz48Ey9jxYREktWoQL/sJo6lZ91UK0acOaM3FFZhvLl+VoNtU4VqDoZmDt3Lm7fvo1Zs2bJHYrZeXp6Ij4+Hjdu3JA7FMW5ffs2Fi1ahOHDhyN//vyZeq9Op0NERARSU1Mlio4QednYAKNG8STA1pafijhxIq+iR7JOEPhUASUDZnbv3j1MmTIFAwYMgKurq9zhmB2VJf6woKAg5M6dG4MHD870e3U6HZKSknD16lUJIiNEOTw8gBMngPHjgcBAnhTQBqXs8fYGzp4Fnj6VO5LMU20y4OfnB61WiwkTJsgdiiyKFSuGQoUKUTLwluvXryMkJASjR49Gnjx5Mv1+vV4PgHYUEOuQIwcfFThxgq8fqFoVmDGDz32TzDMYAJMJOHRI7kgyT5XJwPnz57F8+XJMnDgRBay03mZaWWJa7PamSZMmoWDBghgwYECW3u/k5AQnJye6rsSqVK3Kpw2GDAHGjOELDK9ckTsq9SlbFihZUp1TBapLBtK2ElaoUAH9+vWTOxxZUVniN12+fBk///wzxo0bBweHrB8upNPpaGSAWB17e2D6dP5U+++/gKcnsG6d3FGpi5rPKVBdMrBlyxaEhoZi9uzZyJEjh9zhyMrT0xPXrl3DM6o1CgCYOHEiihcvju+++y5b7dCOAmLN6tblWxB79gRm/ndm1z//yBqSqhgMwLlzwOPHckeSOapKBlJSUjBixAj4+Pjgs88+kzsc2aUtIqQPLn4N1q1bB19fX9jb22erLZ1Oh6tXr9LZD8RqOToC8+cDixbxP3/9NS9YxJi8camBwcCv08GDckeSOapKBhYuXIhr165hzpw5EIR3T6K3NhUrVoRWq6WpAgD+/v4oXbo0unfvnu229Ho9GGOIjIwUITJC1KtGDf67jw8/8KhlS+DuXXljUrrSpYFSpdS3bkA1ycDDhw8REBCA7777Dh4eHnKHowh2dnaoVKmS1ScDZ86cwaZNm+Dv7w9bW9tst+fm5gaNRkOLCAn5z4QJwNatfNuchwewdi2NEnyMwaC+dQOqSQYmTpwIxhgmTZokdyiKQosIgQkTJqBChQro3LmzKO3lzJkTLi4uNP1CyGtatOB1CJo1Azp3Br76ii80JO8yGIDz54G4OLkjyThVJANRUVFYvHgx/Pz8Mlxn3lp4enoiPDzcaivmHTt2DNu2bUNAQICoZ1PQIkJC3lWwIB8V+O03/uTr7g78+afcUSmPtzf//cABWcPIFFUkA8OHD0fp0qUxaNAguUNRHE9PTyQmJlptxTw/Pz94eHjg66+/FrVdnU6HCxcugNFYKCHv+OorIDISqFMH+OILoGtX9a2el5KzM685oKapAsUnAzt27MCOHTswY8YM2NnZyR2O4qTtKLDG+e3Q0FDs3bsXkyZNgkYj7q2s0+kQFxeHe/fuidouIZaiSBFg0ybg55+BLVsAnQ7YuVPuqJRDbecUKDoZMBqNGDZsGBo2bIgvvvhC7nAUycnJCUWLFrW6dQOMMfj5+aFKlSpo06aN6O1TWWJC0icIQJcufC2BuztfT9CnDxAfL3dk8jMY+HVRy7oKRScDS5YswcWLFxEcHExbCT/CGhcR7tq1C0eOHEFgYKAk90aZMmXg6OholSMuhGTWJ58AO3YAixcDa9YAer265suloLZ1A4pNBh4/fgx/f398++238PLykjscRbO2ZIAxBl9fX9SuXRvNmjWTpA+NRgMPDw8aGSAkgwSBjwpcuMDnzL29gaFDgRcv5I5MHiVKAOXLq2eqQLHJQGBgIJKSkhAUFCR3KIqn1+sRGxuLx1aygmfLli04ffq0ZKMCadIWERJCMq5sWf4BGBzMRwq8vPipiNZITesGFJkMXLlyBfPnz8fYsWNRrFgxucNRPGtaRGgymTBhwgQYDAY0atRI0r50Oh2io6NhNBol7YcQS6PR8BMQz50D8ubluw7GjQOSk+WOzLy8vYHoaOD+fbkjSZ8ik4GRI0eiWLFiGDZsmNyhqIKrqytsbW2tYqpgw4YNuHDhAiZPnix5X3q9HsnJybhCZ7kSkiUVKwJHjgCTJwOzZgHVq/NDkKxF2roBNWwxVFwyEBoais2bN2P69OnImTOn3OGoQo4cOeDu7o4zFyIQefcpzsU+RuTdp0hItqwn2tTUVPj7+6NZs2aoW7eu5P3pdDoA1jHiQohUtFo+KnDqFB8xqF4dCAwErGHArVgxnhCpYapAvJJtIkhNTcXQoUNRu3ZttG/fXu5wVOHK/XisORGLxMajccDGEQfmH371NQGAcwEHGFyd0LmmM8oXyS1foCJYu3YtLl68iNWrV5ulv4IFC6JYsWIIDw+n+5GQbPL0BE6e5KMEEyfy2gSrVgGVKskdmbS8vdWRDJh9ZCAh2fjBp9cVK1bg/PnztJUwA249SkSX5SfgM/cgVp+4iSRtLr6c9zUMwM1HiVh94iZ85h5El+UncOtRojwBZ9PLly8xceJEtGnTBtWqVTNbv1SWmBDx2NryZODoUV6LwMsLmD0bsORq6gYDcOmS8k97NMvIQNrTa+ilB4h9lIjXC7ymPb3WLZsPK2YuROfOnVGzZk1zhKVa607Fwn9LJIwmfiVTTR8vmZv29aPX4tAk+AACWrmjQ3VnyeMU08qVK3Ht2jVs2rTJrP3qdDps2LDBrH0SYulq1OAnIPr6AiNH8vMNVq4EypWTOzLxvb5uoFMnOSP5OElHBt5+er35ViIA/P/p9dfTd2DXNhDJtXur9unVHBaEXsGYjeFINprSTQLelmpiSDaaMGZjOBaEqmdRXHJyMiZPnoz27du/qgxoLnq9Hjdu3MCzZ8/M2i8hli5nTj4qcOAAf2rW64FFiyzvaGQnJ8DNTflTBZIlA+tOxaJJ8AEcvcbPcEzvg4uBD3GfvZuAJsEHsO5UrFShqda6U7GYteuyKG3N2nUZ61VyjZctW4Y7d+5g4sSJZu87bRFhRESE2fsmxBrUr8+P++3WDejfH/j0U+DWLbmjEpfBoPwdBZIkA9b49Cq1W48S4b8lUtQ2J2yJVPwoTGJiIoKCgvDNN9+gYsWKZu+/UqVKsLGxoXUDhEgoVy7gxx+BXbv4vnwPDz5tYCmjBAYDEBMD3L4tdyQfJnoyYK1Pr1Ibtyn81RoBsRhNDOM2KftDbtGiRXj48CEmTJggS/92dnZwdXWlZIAQM/DxAcLDgbZtge7dgdatAUs4OLRhQ/67kqcKRE0GrPXpVWpX7sfjUMzDTI+ypCfVxHAo5iFiHijziLH4+HhMmzYNPXr0QDkZVxZRWWJCzCdfPmDFCmDzZr4V0d0dWL9e7qiyp1AhfsSzkqcKRE0GrPXpVWprTsTCRiPNVksbjYBfjitz9OWHH37As2fP4OvrK2scOp0O4eHhYJYyZkmICrRqxY8AbtIE6NABaN8eePhQ7qiyTunnFIiWDFjr06s5hF56IPp1TZNqYgi9/ECStrPjyZMnmDVrFvr06YOSJUvKGoter8eTJ09w584dWeMgxNoUKsRHBdatA/bs4WsJtmyRO6qs8fYGrl8Hbt6UO5L3Ey0ZsNanV6k9TzYiVuJpkti4RMWVLp4zZw6Sk5MxduxYuUOhssSEyKx9eyAykpcybt2aryd4+lTuqDKnYUNeF06pUwWiJQPW+PRqDjfjEt6pzSA2BuBGXILEvWTcw4cPERwcjAEDBiji1MpSpUohd+7ctIiQEBkVLcpHBVasADZu5KMEu3fLHVXGFSjASzIrdapAlGTAWp9ezSHFaLKofjJi5syZAIBRo0bJHAknCMKrdQOEEPkIAvDtt3zHQcWKQNOmvDbB8+dyR5YxaecUKHH5kSjJgDU+vZqLrdY8x0eYq5/03Lt3D/Pnz8eQIUNQuHBhucN5hXYUEKIczs7Azp3AwoX8sCNPT+DQIbmjSp/BAMTGAjduyB3Ju0T5BLDGp1dzKV3QEVIf2ST8148STJ06Fba2thg2bJjcobxBr9fj4sWLSElJkTsUQgj4ccj9+/PqhcWL8zn5ESOAFy/kjuzDGjTgoxtKnCoQJRmwtqdXc3K008K5gIOkfTgXdICjnfynWd+6dQuLFy/GiBEjkD9/frnDeYNOp8PLly9x6dIluUMhhLzGxYUvyps5E1iwAKhSBTh1Su6o3i9fPn5So8UmA9b29GpuBlcnSXdqGCo4SdJ2ZgUFBSF37twYPHiw3KG8w8PDAwBo3QAhCmRjAwwfzk9CzJULqF0b8PMDlDiQl3ZOgdLWDYiSDFjT06scOtd0lnSnxje15D/O+Nq1a1i+fDlGjx6N3Llzyx3OO/Lnz4+SJUtSMkCIgrm5AUePAv7+wLRp/KhkpS31MRj4GQVXr8odyZtEG3e3lqdXOZQvkhv1XQqJfn1tNEB9l0JwcZL/w3fSpEkoWLAgBgwYIHcoH0SLCAlRvhw5+KjAyZOAyQRUqwZMnQoYFbIZrV49vt5BaVMFoiUD1vD0KqcpX+igFTEZYIwh9eVL9NDlFK3NrLp06RJWr16N8ePHw8FB2hGm7KDthYSoh5cXXzswYgTg6wvUrQtcvCh3VEDevEDVqhacDEj19KoRlPP0KqeSBRwQ0MpdtPYEQYDthU1oaaiNVatWidZuVkycOBHFixdH7969ZY0jPXq9Hrdu3cKTJ0/kDoUQkgF2dsCUKcCRI8CTJzxBmDuXjxjISYnrBkRdni/20ysYgzElGcVu70dqaqp47apUh+rOGNG0gihtjWzqinN//Ij27dvj22+/Rbdu3fBchsod4eHhWLduHfz8/GBvb2/2/jMjrSwxjQ4Qoi61agHnzgF9+gBDhwKNGvFzAuRiMAD//ANcvixfDG8TNRkQ++kVgoB69ncwK2AsmjZtin/++Ue8tlVqoKE8prXVwU6rAUyZS5BsNALstBpMb6vDAIMLHB0d8dNPP+Hnn3/GH3/8gWrVqpl9Ttzf3x9ly5ZF9+7dzdpvVri6uiJHjhyUDBCiQg4OfFQgNJQfFqTTAUuXyvN0Xrcu3wGhpKkC0Tfui/30ujZwEPbu3Yvo6Gh4enpi586dorStZh2qO2O0Rwpe3DwPAOlOzaR9vU7ZgtgztCHaV39z/UWXLl1w5swZ2NnZoUaNGliyZIlZjus9c+YMNm3aBH9/f+TIkUPy/rLL1tYWFStWpEWEhKiYtzffYdC5Mx8paN6cr+43p9y5+aFLSjq0SJIqPq8/vWZ2DcHbT68AYDAYEBYWhqpVq6JZs2YYPXo0Xr58KUXoqpCamorgQF/oHh7A7iEN0KVmKZQq6PBOrQcBQKmCDuhSsxT2DG2A1T1rouQHtoC6urri+PHj6NGjB/r27YsOHTrgqcTHgvn5+cHV1RWdO3eWtB8x0SJCQtQvd25gyRJg+3Z+zoGHB7B6tXlHCZS2bkCyjfsdqjujbrlCGLcpHIdiHsJGI3x0t0Ha1+uULYgpX+je+dBycnLC33//jdmzZ2PcuHE4ePAgfv31V5QuXVqqb0Gx1q1bh4iICISEhKB8kdyY2ModE+GOhGQjbsQlIMVogq1Wg9IFHTNVmyFnzpz48ccfYTAY0KtXL1SpUgXr169HtWrVRP8ejh49iu3bt2PdunWwsbERvX2p6PV6/PXXX2CMQRCkLrVFCJFSs2ZARATw/fdA1678NMTFi4EiRaTv29ubb3mMjub1EeQmaX3fkgUcsLpnzY8+vYKxDD+9ajQajBw5EocOHcK9e/fg5eWFjRs3SvktKM7Lly8xYcIEtG7dGjVr1nzja452WrgXzwsv5/xwL543y0WavvrqK5w7dw4FChRAnTp1MHfuXNGnDfz8/KDT6fDVV1+J2q7UdDod4uPjcfPmTblDIYSIIH9+PiqwcSPfdeDhAWzYIH2/devymgiKmSpgZvY86SWLuPOEnb35iH3W6TvWoJFPltp5/Pgxa9euHQPABgwYwF68eCFypMq0ePFiJggCu3DhguR9JScns6FDhzIArFWrViwuLk6Udvft28cAsE2bNonSnjnFxsYyAGzLli1yh0IkdOYMYwD/3ZpZ23V48ICxtm3599yxI2NpP/Kkug516zL25ZfitplVZj/55/Wn11oVP0FE2JksPXXmy5cPv//+O3788UeEhISgVq1aFn+IzIsXLzBp0iR07Njx1TY3Kdna2mLOnDnYvHkzDh06hMqVK+PIkSPZapMxBj8/P1StWhWtW7cWKVLz+eSTT5AvXz5aN0CIBSpcmI8KrFnD1xN4eAB//y1df97efGRA7roHgMTTBOnR6XR49OhRlrcMCoKAfv364cSJE0hKSkLVqlWxevVqkaNUjh9//BH3799HQECAWftt1aoVwsLC4OzsjIYNG2LatGkwZfHu3blzJ44cOYLAwEBVzrkLgkBliQmxYIIAdOoEREYClSsDLVsCkyZJ05fBADx8CERFSdN+ZsiaDOj1egDI9g9WT09PnD59Gl9++SW6du2Kb7/9VpYCOlJ69uwZpk6dip49e8LFxcXs/Ts7O2P//v0YPXo0xo0bh+bNm+PBgweZaiNtVKBOnTr49NNPJYpUenq9nkYGCLFwxYvzUYGQEGDXLv7/Tp4Ut4/atQFbW2XUG5A1GShdujQcHR1F+cGaK1curFy5Ej///DM2bNiAatWq4fz58yJEqQzBwcF4/vw5/Pz8ZItBq9UiKCgIO3bsQFhYGDw9PRGaibt4y5YtOH36tGpHBdLodDpcunQJycnJcodCCJGQIAA9ewK//cb/3K8fMGgQkJAgTvsODkDNmpQMQKPRiD7kmlZAx97eHjVr1sSiRYvMUkBHSnFxcZg9ezYGDBiATz75RO5w0LRpU4SFhcHNzQ2NGzfGxIkT0y0XbTKZ4Ofnh0aNGsFgMJgpUmnodDqkpqYiOjpa7lAIIWZQvDj/feRIYPlyPn1w9Kg4bRsMwIED8q8bkDUZAKQZck0roNOrVy/0798fX331laoPl5k+fToYYxgzZozcobxSrFgx7Nq1CwEBAZg8eTKaNGmCu3fvfvD1v//+O8LDwzF58mQzRikNDw8PAHRGASHWpkMHICwMKFQIqF8fGD0aSErKXpsGA/DoES9+JCfZkwGdToeoqCjRKwra29tjwYIF+OOPP7Bnzx54eXnhxIkTovZhDnfv3sX8+fMxbNgwFC5cWO5w3mBjYwM/Pz/s27cPly9fhqenJ3bs2PHO64xGI/z9/dG8eXPUqVNHhkjFlSdPHpQuXZoWERJihSpUAA4f5gWD5s4FqlUDzpzJenu1avHTFeWeKpA9GdDr9Xj58iUuS3R8U9u2bREWFoaiRYuiXr16mDlzZpZXwsshMDAQDg4OGDZsmNyhfFDDhg0RFhaG6tWro3nz5hgzZswbyd3atWtx6dIlixgVSENliQmxXjY2wKhRPAmwteUf6BMnAll5prW35wsJrT4ZSNsvL+VTVunSpXHw4EEMHz4co0aNQsuWLfHvv/9K1p9Yrl27hmXLlmHMmDHImzev3OF8VOHChbF161bMmDEDs2fPRsOGDXHz5k28fPkSAQEBaNOmDapWrSp3mKKhHQVm9vw5H589cYL/bmG7hYg6eXjwW3L8eCAwkCcFERGZb8dgAA4eBFKfynify1ry6D+ffPIJGzt2rFn62r59OytcuDArVqwYCw0NNUufWdWlSxdWrFgxlpCQIHcomXL06FHm7OzM8ufPz/r372+2ionmtG7dOgaAPXz4UO5QLFdkJGODBjFWrhxjgsBLwKX9EgT+/wcN4q8TmbVV3vsQug5cRq7D6dOMubkxZmvL2PTpjBmNGWw8MpLdbjuIXUE5ZjLzff46RSQDzZs3Zy1atDBbf3fu3GEGg4EJgsAmTJjAjBn+WzOfiIgIJggC+/HHH+UOJUsePXrEPv/8cwaAVahQgSUlJckdkqiioqIYAMUnlKp07RpjPj78h6FW++YPx7d/pX3dx4e/TyT0IcjRdeAyeh1evGBs1Cj+GV67NmOXL3/kxa/d5yYbee7z18k+TQCYf8i1ePHi2L17NwICAhAYGIhGjRrhzp07Zus/I/z8/FC6dGn07NlT7lCyJH/+/GjSpAkEQcD169dRp04dxMTEyB2WaMqXLw87OztaRCi2kBB+hFvaBKrR+PHXp309NJS/LyRE2vgI+Qh7e2D6dODQIeDffwFPT2D+/PdsG3zrPhdS5b/PFZEM6HQ6xMbGmnX7X9pK+NDQUFy9ehWenp74W8oi1Jlw6tQpbNq0CQEBAbC1tZU7nCxJTEzE1KlT0bVrVxw/fhzPnj1DlSpVsG7dOrlDE4VWq0WlSpVo3YCYgoKA3r35Xq30koC3GY38fb1783YIkVHdunzKv2dPfjxykybAq4NOlXqfSzLekEkXLlxgANjBgwdl6f/ff/9lLVu2ZADY8OHDWXJysixxpPHx8WFubm6KnL7IqJkzZzKtVsuuXr3KGGPs6dOnrEOHDgwA++6771hiYqLMEWZf165dWc2aNeUOwzIsW/bxYdLM/goJyVY4NDzO0XXgsnMd9uxhzNmZsdy5GTvYVVn3+esUkQwkJyczrVbLFi5cKFsMJpOJzZkzh+XIkYNVr1791YeYuaUd7/vHH3/I0r8Ynj17xgoWLMi+++67N/6/yWRiy5YtY/b29szDw4NFRUXJFKE4Zs6cyRwdHVlqaqrcoajbtWuM2duL+0PS3j5bc6v0IcjRdeCyex2ePmVs9NfXWCLsmUlB9/nrFDFNYGtri0qVKsk6/yoIAoYOHYojR44gLi4OXl5e+C2tILWZMMYwfvx4VK1aFV988YVZ+xbTDz/8gPj4ePj6+r7x/wVBQK9evXDq1CmkpqaiWrVqWLVqlUxRZp9Op0NCQgKuX78udyjq1qdP5odL02M08nYJUYA8eYBpj/vATmOEqKeyiHifKyIZAJRTxKV69eo4e/Ysmjdvjvbt26Nv37548eKFWfr++++/cezYMUyZMkW1B/k8fvwYM2fORN++fVGyZMn3vsbDwwOnTp1C+/bt8e2336Jr166qPGUy7dRNJdy3qhUVBezeLU0ysHs3QOdHECX47z7XmJR7nysmGUjbUcAUcKhQ3rx58euvv2Lp0qVYtWoVatasKfmhNCaTCePHj0fDhg3h4+MjaV9SmjNnDlJSUjB27NiPvs7R0RE//fQTVq9ejY0bN6rylMmiRYuiYMGCtKMgOxYvBrRaadrWaoFFi6Rpm5DMUMF9rphkQKfTIT4+HjdfLbmUlyAI6N27N06ePAmj0Yhq1aphxYoVkiUrv/32Gy5cuICgoCDVjgo8fPgQc+fOxcCBA1G0aNEMveebb75545TJxYsXKyIhzAhBEBQzoqVa27aJPyqQxmgEtm+Xpm1CMkMF97likoG0IVelPWXpdDqcOnUKHTt2RI8ePdClSxfEx8eL2ofRaMSECRPQokUL1K1bV9S2zWnGjBkAgFGjRmXqfWmnTPbs2RP9+vVD+/bt8fTpUylCFB2VJc6G+Hjg2jVp+7h6lUoXE3mp5D6XaNwi80qUKIH8+fMjPDwcrVq1kjucNzg6OiIkJASNGjVCnz59cPLkSaxfvx5eXl6itL9q1SpcuXLF7AsWxfTPP/9gwYIFGDFiBAoVKpTp99vb22PhwoUwGAzo2bPnq5oE1atXlyBa8eh0OixYsAAvXrxAzpw55Q5HXa5e5WuipcQYov+KwQvXypl6W9qsoLUvOaDrwGXnOuS8dBWVzHCfIyYGqFw5O20oR4MGDdjXX38tdxgfdfnyZValShVma2vLfvjhB2YymbLV3osXL1jJkiVZ+/btRYpQHoMGDWL58uVjjx8/znZbV69eZdWrV2c5cuRgwcHB2b7GUjpx4gQDwE6fPi13KOpz/Li42wk/8KsGjpujG/pFv977qwbMc5+z48ez9c9RMSMDAB9y3bt3r9xhfFT58uVx9OhRjBo1Ct9//z327duH5cuXo0CBAllqb8mSJbh79y4mTZokcqTmc+vWLSxZsgQTJkxAvnz5st1e2bJlcfjwYYwdOxZDhw5FaGgoVqxYkeVrLCV3d3cIgoALFy5Y1KmMZmFnZ5ZuVq61wwvXzL0nOhr45hvgl1+ASpWkiUsN6Dpw2bkOOS/ZAZ2kiesN2f33JFKOL4olS5YwGxsb9uLFC7lDyZA///yT5c+fnzk7O7MjR45k+v3x8fGscOHCrGfPnhJEZz7fffcdK1SoEHv27Jnobf/111+sQIECrGTJkuzw4cOity+GcuXKsaFDh8odhvrEx797GqHYvwSB95NJVGyHo+vAZes6KPg+f51iFhACfGQgNTVV8m18YmndujXCwsJQsmRJNGjQAFOnToXpnRMpPmzevHl4+vQpJkyYIGGU0rp27Rp++uknjB49Grlz5xa9/ZYtWyIsLAylSpVCw4YNM32NzYEWEWZRrlxA2bLS9lGuHO+HELmo5D5XVDLg7u4OQHk7Cj7G2dkZ+/fvx+jRozF+/Hg0a9YM9+/fT/d9jx49wsyZM9GvXz84OzubIVJpTJo0CYUKFUL//v0l66NkyZIIDQ3FmDFjMH78eDRv3jxD19hcdDqdqu5ZRfnsM2n3XzdvLk3bhGSGCu5zRSUDuXPnRtmyZVX3g1Wr1SIoKAi7du3ChQsX4OnpiT179nz0PTNnzoTRaEy3OI+SXbx4EatXr8b48ePh4OAgaV9arRaBgYHYuXMnwsLCULlyZezbt0/SPjNKp9PhwYMHePDggdyhqE/fvtLuv+7XT5q2CckMFdznikoGAOWUJc6KJk2aICwsDHq9Hk2bNoWvry+M77kB7t27h3nz5mHIkCEoUqSIDJGKY+LEiShRogR69+5ttj59fHxw/vx5uLm5oUmTJvD390dqaqrZ+n8fKkucDW5ugI+P6E9NqYIWxkY+1r3qjSiHRPc5tFrergj3ueKSAb1er7qRgdcVLVoUO3bsQFBQEKZNmwZvb2/cunXrjdcEBQXBzs4OI0aMkCnK7Ltw4QLWr18PPz8/2JlpVXiaokWLYteuXZg0aRICAwPRuHFj3Llzx6wxvK5cuXLImTOnqu9bWS1ZIuoPSQYghWnR+MoSHDokWrOEZI/I9zkA3t6SJaI0pbhkQKfT4f79+6oectVoNBg7diwOHDiA2NhYeHp6YsuWLQCAGzduYMmSJRg1apQo2/Dk4u/vj7Jly+Lbb7+VpX8bGxv4+voiNDQUV65cQeXKlbFdptKzNjY2cHd3p5GBrCpTBpg/X7TmBADPpixAqnMZNGwIjBgBmOmsMUI+TOT7HACwYAFvVwzZ2osggYsXLzIAbM+ePXKHIoq4uDjWunVrBoANHjyYdenShTk5ObHnz5/LHVqWnTp1igFgq1atkjsUxhhjDx48YM2bN2cA2KhRo1hKSorZY+jevTurWrWq2fu1KIGB4myzCgpijDFmNDI2cyZjdnaMVazI2MmTGQ+FttRxdB04Ua+DyPe5WBSXDBiNRmZvb8+Cg4PlDkU0JpOJ/fDDDyxHjhwMAPPz85M7pGxp3rw5q1ixIjMajXKH8kpqaiqbOXMm02q1rFatWuzGjRtm7X/OnDnM3t5eUddElZYtY8zenjGtNnM/GLVa/r6QkHeajIxkrGpVxmxsGPP1ZSw5Of0w6EOQo+vAiX4dJLjPs0tx0wRpQ66WNP8qCAIGDRqEBg0aQKvVYu7cufj111/lDitLjhw5gu3btyMgIAA2NjZyh/OKRqPBiBEjcOjQIfzzzz+oXLky/vzzT7P1r9frkZSUhKtXr5qtT4vUqxc/+91g4H9Ob4417esGA39fz57vvMTNDTh2DPD3B6ZNA2rUACzoxwtRIwnu8+xSXDIAqHtHwYecOXMGe/fuxbx58/D555+jU6dO6NWrFxITE+UOLVP8/Pyg1+vx5Zdfyh3Ke9WqVQvnzp1Do0aN8MUXX2Dw4MFITk6WvF+dTgdAXTUyFKtMGWDXLiAykm+ZcnEB3jrWm0HAVY0LWN9+/Ifjrl0fnTvNkQPw8wNOngRSU4Fq1YCpU6Xb7UVIujJwn0MQ+P/vl7H7PFtEH2sQgSUOuTZr1oy5urqyly9fMpPJxH766Sfm4ODA3NzcWHh4uNzhZcjevXsZAPbnn3/KHUq6TCYTW7BgAbO1tWVVqlRhV65ckbxPJycnNmHCBMn7sUrx8YydO8cPYzl3jh3cFs8AxsLCMt9UUhJjY8YwptEwVqMGY9HR776Ghsc5ug6c2a7DW/d5dksMZ4YiRwYsbcj10KFD2LFjByZPngytVgtBENC9e3ecOnUKGo0G1atXx7Jly8AYkzvUD2KMwc/PD9WqVVPcEdPvIwgCBgwYgOPHj+PZs2evjkSWEpUlllCuXPx41po1gcqVUd2QC3Z2QGho5puys+OjAkeOAE+eAF5ewNy5gMKqXBNr9NZ9bs5S2opMBixpyJUxhnHjxsHLywvt2rV742tubm44efIkunbtiu+++w4dO3bEs2fPZIr043bs2IGjR49i8uTJEN4eylIwLy8vnD17Fi1btkTHjh3Rp08fvJBonxmVJTYfe3ugTp2sJQNpatUCzp0D+vQBhg4FGjUCrl8XL0ZC1ESRyYCTkxOKFCliET9Yd+7cicOHDyMoKAgazbuXO2fOnFiyZAnWr1+P7du3w8vLC6dPn5Yh0g9LGxWoW7cuPv30U7nDybTcuXNjzZo1CAkJwerVq1GjRg1ERUWJ3o9er8e1a9fw/Plz0dsm7/L2Bg4e5GsAssrBgY8K7NsH3LgB6HTA0qV86TYh1kSRyQBgGYsITSYTxo0bh3r16qFZs2Yffe3XX3+Nc+fOoUCBAqhTpw6Cg4MVM22wefNmnDlzBoGBgaoaFXidIAjo2bMnTp48CZPJhOrVq2PlypWiXmOdTgfGGCIjI0Vrk3yYwcCH+c+fF6et8HCgUyc+UjBoUPbbJERNFJsMqL0sMQBs3LgR586dw5QpUzL0IVq2bFkcOXIEgwYNwrBhw9CqVSvExcWZIdIPM5lM8PPzQ6NGjeDt7S1rLGLw8PDAyZMn0aFDB3Tv3h3dunUT7Unezc0NGo1G9UmsWtSoAeTMmb2pgtflzs1HBbZtA2Ji+P/7+28aJSDWQbHJgE6nU/WQq9FohJ+fH5o1a4b69etn+H22traYPXs2tm7dimPHjsHT0xOHZCyw/vvvvyMiIgKTJ0+WLQaxOTo6Yvny5Vi9ejU2btyIqlWr4rwIj5c5c+ZE+fLlKRkwEzs7vm5g/35x223eHPjtN/7fEyYAbdsCCjoxmxBJKDYZSDsJLiIiQuZIsuaXX37BxYsXERgYmKX3t2jRAmFhYShXrhy8vb0RGBho9tP5jEYj/P398dlnn6FOnTpm7dscvvnmG5w9exY5c+ZEzZo1sXjx4mxPG9AiQvMyGPi6AbHrBeTJw3+fOZPvOvDwADZsELcPQpREsclApUqVVDvkmpycjIkTJ+LLL79E1apVs9zOJ598gr1798LX1xcTJkxA06ZN8c8//4gY6cetWbMGly5dwqRJk8zWp7lVqFABx48fR8+ePdGvXz+0b98eT58+zXJ7aWtdlLLew9IZDMCzZ3xXgBQaNQIiIoAGDYCvvuJrCh49kqYvQuSk2GQgZ86cqFChgiqfspYtW4Zbt26J8iGq1WoREBCAvXv3Ijo6Gp6enti5c6cIUX7cy5cvERAQgC+++CJbCY0a2NvbY+HChdiwYQN27doFLy8vnDp1Kktt6fV6xMXF4d69eyJHSd6nWjW+I0DsqYLXOTnxUYFffgG2b+ejBH//LV1/hMhBsckAoM4dBQkJCQgMDESXLl1QqVIl0do1GAwICwtDlSpV0KxZM4wZMwYvX74Urf23rVixAjdu3EBAQIBkfShNu3btcO7cORQuXBh169bN0o4OS6qRoQa2tkC9euItIvwQQQA6d+ajBJUrAy1b8vLyCi0LQkimKToZSNtRoKYh1/nz5+PRo0fw9/cXvW0nJyds27YNM2bMwOzZs9GwYUPcvHkzS20lJBsRefcpzsU+RuTdp0hI/v+ka1JSEiZPnowOHTq8+nCzFmXKlMGhQ4fw/fffY9iwYWjdunWmdnSUKVMGjo6OOHMh4oPXl4jLYAAOHQIkzI1fKVGCjwosWwasX8/rEuzbJ32/hEhNYAr+pN28eTPatGmD27dvo0SJEnKHk64nT56gTJky6Ny5MxYsWCBpX8ePH0eHDh3w9OlTLF++HG3btk33PVfux2PNiViEXnqA2EeJeP0vXgDgXMABBlcnIOYQJo8cgKioKLi6ukr2PSjd1q1b0a1bNzg4OGDdunWoW7fuR1+fdn1X7z2L1Jz53zh05PXr27mmM8oXyS1x9NbjxAleTfD4cV7FVQxnzwJVqwJnzgBVqrz/NTduAN278ymKgQP5iYiOjuL0rxQZuQ7WwBqug+JHBgD1DLnOmjULycnJGD9+vOR9vX46X7t27TBw4EAkJSW997W3HiWiy/IT8Jl7EKtP3MTNtxIBAGAAbj5KxOrjN7Dy35LQD14Kh8IlJf8+lKxly5Y4f/48SpcujYYNG2Lq1KkwvaeA/dvXN9WhwHtO2fvv+p64CZ+5B9Fl+QnceqSuEyuVqkoVXsJd6qmCt5UuDezdC8ybByxfzqcPjh41bwyEiEXRyUCpUqWQK1cuVawbuH//PubOnYvvv/8exYoVM0uf+fPnx4YNG/Djjz8iJCQEtWvXxuXLl994zbpTsWgSfABHr/Gh7lTTxweCUv/78jP7omgSfADrTsVKErtafPLJJwgNDcWYMWMwfvx4NG/eHPdf23Se6ev739ePXouj6yuSHDmA+vXNnwwAgEYDfP89EBYGFCrE4xg9GvhAXk6IYik6GdBoNKrZtz116lTY2Nhg1KhRZu1XEAT069cPJ06cQGJiIqpUqYLVq1cDABaEXsGYjeFINprS/ZB6WyoDko0mjNkYjgWhV6QIXTW0Wi0CAwOxc+dOhIWFoXLlyti3b1/2rq+J0fUVkcEAHD5snnUD71OhAu9/yhR+1kG1anxImRC1UHQyAKijLHFsbCwWLVqEkSNHokCBArLE4OnpiTNnzqBdu3bo2rUrmvYLwKxdl9N/YwbM2nUZ6+kJFj4+Pjh//jzc3d3RetgMur4K4u0NJCYCWdwRKgobGz4qcPo03+VQqxYwcaJ8CQohmaH4ZECn0+HixYtISUmRO5QPmjx5MvLmzYvBgwfLGkeuXLmwatUqzF22Gpdy6UUtqj5hSyTNcQMoWrQoQn7dBKfmA0Td5ULXN3u8vHjVQDmmCt6m0/HFjOPGAYGBPClQaSFVYkUUnwzo9Xq8fPkSly5dkjuU97p8+TJWrFiBcePGIXduZawQPy2UhzaH7TuL2LLDaGIYt0n5azfMwW9LFKCxEfUER7q+2aPV8iqBSkgGAD4yEBDAk4KkJL4SfcaM7B23TIiUFJ8MeHh4AIBiFxH6+/ujWLFi6Nu3r9yhAODb2w7FPHy1EFAsqSaGQzEPEfMgXtyGVebV9c3kGoH00PXNPm9vvpo/OVnuSP4vbe3A4MHAmDF8geEVWiJCFEjxyUD+/PlRsmRJRa4bOH/+PNatWwd/f3/Y29vLHQ4AYM2JWNhoxHtifZ2NRsAvx617bpuur3IZDMCLF8DJk3JH8iZ7ez4qcOgQ8OAB4OkJzJ8PvGeXKiGyUXwyACi3LLGvry/Kly+Pbt26yR3KK6GXHoj+1Jom1cQQevmBJG2rBV1f5fL0BPLlU85Uwdvq1gXOnwd69ODbEZs0AbJYQJQQ0akiGVDijoKjR49i69atmDRpEnLkyCF3OACA58lGxEq8CC02LtFqS+vS9VU2Gxu+bkDKQ4uyy9ERWLAA2LMHiInhiw2XLxd1rS8hWaKKZECn0+H27dt4/Pix3KEAABhjGDduHPR6Pb7++mu5w3nlZlzCO5UFxcYA3IhLkLgXZaLrq3wGA183oPSiP40bA+Hh/FjkXr34wUd378odFbFmqkgG0soSK2WqYM+ePThw4ACCgoKg0SjnEqYYzTMJaa5+lIaur/IZDHwB4fHjckeSvrx5+ajAX3/x2vceHsDatTRKQOShnE+yj3B1dUWOHDkUkQykjQrUqlULLVq0kDucN9hqzfPXaa5+lIaur/LpdECBAsqeKnhby5a8DsGnn/Jjkr/6Cvj3X7mjItZGFT91cuTIgUqVKili3cCff/6J06dPY8qUKaLuMxdD6YKOkDoi4b9+rBFdX+XTaICGDZW7iPBDChYEfv0V+O03nsi4uwN//il3VMSaqCIZAKCIMwpSU1Ph6+uLJk2awGAwyBrL+zjaaeFcwEHSPpwLOsDRTitpH0pF11cdDAY+TfDihdyRZN5XXwGRkUDt2sAXXwBduwIKWSpFLJxqkgG9Xo+IiIj3HiFrLmvXrkVUVBSCgoJkiyE9BlcnSffBGyo4SdK2WtD1VT5vbyAlBTh2TO5IsqZIET4qsGoVsGULn/rYuVPuqIilU00yoNPp8Pz5c9y4cUOW/lNSUuDv7482bdqgRo0assSQEZ1rOku6D/6bWs6StK0WdH2Vz92dHyestqmC1wkCHxUIDwfc3IBmzYA+fYB4KlBJJKKaZEDuHQXLly/HjRs3MHnyZFn6z6jyRXKjvksh0Z9ebTQC6rsUgouTMs5fkAtdX+XTaPjogJqTgTQlS/JRgcWLgTVrAL0eOHBA7qiIJVJNMlC8eHEUKFBAlnUDL168wOTJk9G5c+dXZyUo2ZQvdNCK/GGl1QiY8oVO1DbViq6v8nl787LECRZQskEQ+KjAhQs8OfD2BoYOVeeaCKJcqkkGBEGQrSzxwoUL8e+//2LixIlm7zsrShZwQEArd1HbnNTKHSUlXjynFnR9lc9gAF6+5AWILEXZsnynwZw5wKJF/NjmEyfkjopYCtUkA4A8ZYmfPXuGqVOnolevXihXrpxZ+86ODtWdMaJpBVHaGtnUFe2r01z26964vtmsEkPXV3yVKgFOTpYxVfA6jYaPCpw7B+TJA9SpA4wbp6yTGok6qSoZ0Ol0uHLlCl6YcXxszpw5SExMhK+vr9n6FMtAQ3lMa6uDnVaT6TluG40AO60G09vqMMDgIlGE6jbQUB59vXLDZEyBkMlCxXR9pSUIlrNu4H0qVeKjHpMnA7NmAdWrA2FhckdF1ExVyYBer4fJZEJUVJRZ+nv48CFmz56NgQMHokSJEmbpU2wdqjtjz9CGqFO2IACkmxSkfb1O2YLYM7QhPbF+BGMMW+aOQd7DP6BuuUIA6PoqicEAnDoFPH8udyTS0Gr5qMCpUzz5qV4dCAwEjHTOFckCVVU3cXfn87Th4eGoWrWq5P1NmzYNgiBg9OjRkvclpZIFHLC6Z01cuR+PNSdiEXr5AWLjEt94lmWMoWQ+ezRxK4ZvajnTqvYM+PXXX3Hs2DHs27cPBkOtj15fAbygkKGCE11fMzEYgNRU4PBhvjXPUnl68oRg0iTA35/XJli1io8eEJJRqkoGcuXKhXLlypll3cDt27exYMECjB07FoUKFZK8P3MoXyQ3JrZyx0S4IyHZiBtxCUgxmnDv7m20aFADy7f9hSZNxF0YZ6kSEhIwevRotG3b9lU1yg9dX1utBqULOlJlQTOrUAEoVoxPFVhyMgAAtrZ8VODzz4Fu3fjiwqAgYMgQfrQzIelR1TQBALPtKAgMDESuXLkwdOhQyfuSg6OdFu7F88LLOT+aVndDzhwa2cs9q8nMmTPx4MEDzJw5871ff/36uhfPS4mADNLWDajp0KLsqlmTLy7s3x8YOZJ//1evyh0VUQPVJQPm2FEQExOD5cuXY+zYsciTJ4+kfSmBjY0NPDw8KBnIoNjYWEyfPh3Dhg1D2bJl5Q6HfITBAJw5Azx7Jnck5pMzJ99+uH8/cOcOL1S0aBEdjUw+TnXJgE6nw4MHD3D//n3J+pg4cSKcnJzQv39/yfpQGiUcBKUWo0ePRr58+TBu3Di5QyHpSFs3cOiQ3JGYX4MGvFBR1658pODTT4Fbt+SOiiiV6pIBqcsSh4eHY+3atfDz80POnDkl6UOJ9Ho9IiMjYaSlyB91+PBhrFu3DlOnTkXu3LQIUOnKlQNKlLCuqYLX5crFRwV27ACiogAPD2DlSholIO9SXTJQrlw55MyZU7KnWD8/P5QpUwY9evSQpH2l0uv1SElJweXLl+UORbFMJhMGDx6MatWqoWvXrnKHQzJAEPjogKXWG8ioTz8FIiKANm2A7t2B1q2Be/fkjoooieqSARsbG7i7u0syMnDixAls3rwZAQEBsLW1Fb19JdPpeF18uQ6CUoOVK1fi7NmzmDdvHjQa1f3TsVoGA19U9+SJ3JHIK18+vuXwzz95GWN3d+C33+SOiiiFKn+iSTW/PX78eLi7u6Njx46it610BQsWRIkSJWjdwAc8e/YM48aNQ8eOHVGnTh25wyGZ4O0NmEzWuW7gfVq3BiIjgUaNgPbtgQ4dgLg4uaMiclNlMqDX6xEVFSXq/Pa+ffuwd+9eBAYGwsZKN+bKcfaDWkyZMgXPnj3D9OnT5Q6FZFKZMoCzM00VvK5QIT4q8OuvwK5dfJTgr7/kjorISbXJQFJSEmJiYkRpjzGG8ePHo3r16mjdurUobaoR7Sh4v6tXryI4OBijR49GyZIl5Q6HZBKtG3g/QeCjApGRQLVqQKtWfD3B06dyR0bkoMpkQOz57a1bt+L48eOYMmUKBEHcc+rVRK/XIzY2Fk+sfXL1LSNGjECRIkUwcuRIuUMhWeTtDZw/Dzx6JHckylOsGB8V+Okn4I8/AJ0O2LNH7qiIuakyGShcuDCKFi0qylOsyWTC+PHjYTAY0LhxYxGiUy+pt22q0d69e/Hnn39ixowZcHBwkDsckkUGA99Od/Cg3JEokyDwUYHwcKB8ecDHBxgwAEhMlDsyYi6qTAYA8coSr1+/HuHh4QgKCrLqUQEAcHV1RY4cOSgZ+I/RaMSQIUNQt25dtG/fXu5wSDaUKsXXDtBUwceVKgXs3g0sWMDrEVjhWmqrpdpkQIzFbi9fvsSECRPw+eefo3bt2iJFpl62traoVKkSrRv4z7JlyxAZGYl58+ZZfaJoCby9KRnICI2GjwqEhQEF+cnnCA4GkpJkDYtITLXJgE6nw/Xr1xEfH5/lNlauXImYmBgEBgaKGJm60Y4C7vHjx/Dz88O3335rluOyifQMBj4M/vCh3JGoQ/nywLJl/L/XrweqVOFHJRPLpNpkIG1+OyIiIkvvT0pKQkBAADp27PiqLfL/6ReTySR3KLIKCAhAcnIypkyZIncoRCTe3vz3AwdkDUNV0nZZr1kDODgAtWsDEyYAKSnyxkXEp9pkoFKlSrCxscnyU+yiRYtw7949BAQEiByZuun1ejx//hw3btyQOxTZREdHY8GCBfD19UXRokXlDoeIpGRJflYBTRVkXrlywLFjPBGYOpUflUxLiyyLapMBe3t7VKhQIUuL3eLj4zFlyhT06NED5cuXlyA69aIdBcCwYcNQunRpDBkyRO5QiMgMBus9tCi7cuTgycCJE8DLl0DVqjwxoLPNLINqkwEg60Vy5s6di2fPnsHPz0+CqNStWLFiKFiwoNWuG9i2bRt27NiBWbNmwc7OTu5wiMgMBl5k58EDuSNRrypVgDNngOHDAV9foF494NIluaMi2aXqZECv1yM8PBwsE+dxxsXFYdasWejfvz9Vk3sPQRCsdhFhSkoKhg4dikaNGll1JUpLlrZugEYHssfOjo8KHD7MCzlVrgzMm8fPgCDqpOpkQKfT4cmTJ7h9+3aG3zNjxgykpqZi7NixEkambtZalnjhwoWIiYnB3LlzaSuhhSpeHKhQgZIBsdSuzbcgfvcdMGQIP/zo+nW5oyJZoepkILPz23fv3sX8+fMxdOhQODk5SRmaqun1ely5cgWJVlR+7N9//0VAQAD69Onzqtw1sUx0ToG4HBz4qMC+fcCNG4BeDyxdyis+EvVQdTJQqlQp5M6dO8NPsUFBQbC3t8fw4cMljkzd9Ho9GGOIjIyUOxSz8fPzgyAImDRpktyhEIkZDMDFi8A//8gdiWUxGIALF3jVwj59gObNgTt35I6KZJSqkwFBEDJclvj69etYunQpRo8ejXz58kkfnIq5u7tDEASr2VFw/vx5LFu2DBMnTkShQoXkDodIrGFD/jtNFYgvTx4+KrBtG08MPDyAX36hUQI1UHUyAGS8Yl5AQAAKFSqEgQMHmiEqdXNwcED58uWtYt0AYwxDhgxBhQoV0L9/f7nDIWZQtChQqRIlA1Jq3hyIiABatAC6dAHataMdHEqn+mRAp9Ph4sWLSPlISayoqCisXr0avr6+cHR0NGN06mUtOwo2bdqE/fv3Izg4GDly5JA7HGImtG5AegUK8FGBDRuAQ4cAd3d+RDJRJtUnA3q9HkajEefCoxB59ynOxT5G5N2nSEj+fyWMCRMmwNnZGb1795YxUnVJ21GQmW2bapOUlIThw4fjs88+Q7NmzeQOh5iRtzdw5QrNaZtDu3a8tkP9+sCXXwKdOwOPH8sdFXmbVu4AsuPK/Xj8fc8BxfssRfsNtwH8/1+2AMC5gAPc8jNs2X8Sy2ZPhq2trWyxqo1er0dcXBz++ecfFC9eXO5wJBEcHIzbt29jx44dcodCzOz1egOdO8sZiXVwcuKjAmvXAgMH8rUEISF8OoEogypHBm49SkSX5SfgM/cgfjt3DznyFwf/+P8/BuDmo0Rsj0lA8d6LsPulK249sp6tctmVtm3TUqcK7t69i6CgIAwaNAiurq5yh0PMrHBh/oFEUwXmIwg88YqI4NsPP/sM6N0bePZM7sgIoMJkYN2pWDQJPoCj1+IAAKmmdIaxBf4tHrv+CE2CD2DdqVipQ7QIpUuXRq5cuSx2R8G4ceOQM2dOTJgwQe5QiEy8vSkZkEOJEny3wdKlwLp1PDGgvwf5qSoZWBB6BWM2hiPZaEo/CXhLqokh2WjCmI3hWBB6RaIILYdGo7HYSoSnTp3CqlWrEBgYSNtMrZjBAFy7BsTS84HZCQIfFQgPB8qU4ZULBw8GrKjOmeKoJhlYdyoWs3ZdFqWtWbsuYz2NEKTLEncUMMYwePBg6PV69OrVS+5wiIyo3oD8SpcG9u7lFQyXLuVnHBw7JndU1kkVycCtR4nw3yJuNbwJWyJpDUE6dDodoqOjP7ptU21+/fVXHDt2DHPnzoWNjY3c4RAZFSxIQ9RKoNEA33/PzzgoWJCfgjhmDJCcLHdk1kUVycC4TeEwZnJaID1GE8O4TZY5Hy4WvV6Ply9f4pKFnE+akJCAUaNGoW3btjAYDHKHQxTAYKCRAaVwdeX1CIKCgDlzgGrVgLNn5Y7Keig+GbhyPx6HYh5meo1AelJNDIdiHiLmQbyo7VqStAN7LGWqYMaMGfj3338xc+ZMuUMhCmEw8MN1btyQOxICAFotHxU4c4b/d82awKRJwMuXckdm+RSfDKw5EQsbjTTHydpoBPxynNYOfEi+fPng7OxsETsKYmNjMWPGDAwfPhxly5aVOxyiEA0a8MVsNFWgLDodcOIEMHYsTwZq1+aFi4h0FJ8MhF56IPqoQJpUE0PoZSqY/TGWsohw1KhRyJcvH8aOHSt3KERB8ufni9ZoqkB5bG15InDsGN9lUKUKMHMmkJoqd2SWSdHJwPNkI2IlXuQXG5f4Ruli8iZLSAYOHTqE9evXY9q0acidO7fc4RCFSTunwIIrb6ta9ep87cD33wOjR/PRnJgYuaOyPIpOBm7GJUDqf58MwI24BIl7US+dToc7d+4gLi5O7lCyxGQyYciQIahevTq6dOkidzhEgby9gVu3eM0Bokz29nxU4OBB4N49wNMTWLgQMJnkjsxyKDoZSDGa52/aXP2oUVpZYrWuG1i5ciXOnj2LuXPnQqNR9O1OZNKgAd/eRusGlK9ePeD8eeDbb/kZB02bUtEosSj6p6Ot1jzhmasfNapQoQJsbW1VmQw8e/YM48aNQ6dOnVCnTh25wyEKlTcvn4+mdQPqkCsXHxXYtQu4dImfMfHTTzTNk12K/hQsXdAR0uwj+D/hv37I+2m1Wri7u6ty3UBQUBCePXuGadOmyR0KUbi0cwroA0U9fHz4oUdffgn07Al8/jnwzz9yR6Veik4GHO20cC7gIGkfzgUd4Gin6pOcJafGRYQxMTGYO3cuxowZg5IlS8odDlE4gwG4exe4QseWqErevHxUYMsW4PRpwN2dH35ESV3mKToZAACDq5OkdQYMFZwkaduS6HQ6REREIFVFe3pGjBiBIkWKYMSIEXKHQlSgXj3AxoamCtTq8895HYKmTYGOHYH27YGHD+WOSl0Unwx0ruksaZ2Bb2o5S9K2JdHr9UhMTMQ1lSy33rt3LzZv3oyZM2fCwUHakSViGfLkAapWpUWEalawIB8VWLeOH37k7g5s3ix3VOqh+GSgfJHcqO9SSPzRAVMqdIW1cHGifefpSdtRoIapAqPRiCFDhqBevXr4+uuv5Q6HqMiregPxz5HzUhhq4ARyXgoDnj+XOzSSCe3b81GCmjWBNm2Abt2AJ0+y0eBz67gfFJ8MAMCUL3TQip0MMBN2TP4Ww4cPRyIdov1RRYoUgZOTkyp2FCxduhSRkZGYO3cuBEHq5afEYkRFoV/09zh83wXImweVOnnhBGqhUicvPmzg4sKr3kRFyR0pyYCiRfmowMqVwJ9/8h0Hu3ZlooGoKP737eIC5LGO+0EVyUDJAg4IaOUuaptT2lXGlHHDsHDhQnh6euLQoUOitm9p1LCI8NGjR5gwYQK6d++OqlWryh0OUYPr1/lEs7s7nP9eBBdchfD26jPGgKtXgUWL+Nhz06b8fUTRBIGPCkREAJUqAZ9+CvTrl86D/Wv3AxYt4n/vVnI/qCIZAIAO1Z0xomkFUdoa2dQVnWqWxsiRI3H+/Hk4OTmhYcOG+P777/HcQoeAsksNyUBAQABSUlIQFBQkdyhEDUJCADe3VwsFhNR0ypIb//t6aCh/X0iIxAESMZQsyUcFfvwR+PlnQK/nlQzf8db98Orv+0Ms7H5QTTIAAAMN5TGtrQ52Wk2m1xDYaATYaTWY3laHAQaXV//f1dUVBw8eRHBwMEJCQqDX67Fv3z6xQ1c9nU6Hq1evKjZZio6OxsKFC+Hr64uiRYvKHQ5RuqAgoHdvICkp/R/6bzMa+ft69+btEMUTBD4qcOECUKIErysxbBjw4sV/L6D7QV3JAMBHCPYMbYg6ZQsCQLpJQdrX65QtiD1DG6J99Xd3D9jY2GDw4MEIDw+Hs7MzGjdujL59++LZs2fifwMqlbaIMCIiQuZI3sUYw9ChQ1G6dGkMHjxY7nCI0oWEAL6+4rTl6wssXy5OW0Ry5crx7aOzZvGRAi8v4Pp4uh8AQGBMveUZrtyPx5oTsQi9/ACxcYlvHGokgBcUMlRwwje1nDO8a8BkMmHx4sUYNWoUChQogGXLluHTTz+VJH41SUpKgqOjIxYtWoTvvvtO7nDe8Pfff6Nly5bYtGkT2rRpI3c4RMmuX+dDuklJ4rVpb88XkpUpI16bCnH2LN9yeeYML9lsSaKjgTHtr2NduBvskSRetVuV3g+qTgZel5BsxI24BKQYTbDValC6oGO2KgveuHEDvXv3xp49e9C9e3fMmTMH+fLlEy9gFXJzc0Pjxo0xf/58uUN5JSUlBTqdDiVLlsTu3btpBwH5uKZN+RxvZoeCP0ar5fsSM7VcXR0sORkAAJNPU7B9obAx0f2gummCD3G008K9eF54OeeHe/G82S4xXLp0aezatQvLli3DH3/8AXd3d2zdulWkaNVJiYsIFyxYgJiYGAQHB1MiQD4uKgrYvVvcRADg7e3ezR81iXpERUGzZ7e4iQCg2vvBYpIBKQiCgF69eiEyMhKVK1fG559/ji5duiAuLk7u0GSRlgwoZTDpwYMHmDRpEvr27QudTid3OETpFi/mT21S0Gr5VjOiHnQ/vIGSgQz45JNPsHXrVqxatQpbt26Fu7s7Nm7cKHdYZqfT6fDkyRPcvn1b7lAAAH5+fhAEAQEBAXKHQtRg2zbxRwXSGI3A9u3StE2kQffDGygZyCBBENC1a1dERUWhVq1aaNeuHdq3b49///1X7tDMRkllic+fP4+QkBBMnDgRhQoVkjsconTx8YDUZ2tcvWqxpWotDt0P76BkIJOKFSuGTZs2Ye3atdi7dy/c3Nywfv16xQydS8nZ2Rl58uSRvSwxYwxDhgxBhQoV0L9/f1ljISrxvkpyYmMMiImRtg8iDrof3kHJQBYIgoCOHTsiKioKBoMBHTp0QLt27XDv3j25Q5OUIAiKWES4ceNG7N+/H8HBwciRI4essRCVSE62rH5I9tD98A5KBrLByckJv/32GzZs2IAjR47Azc0Nq1evtuhRArmTgaSkJIwYMQItWrRAs2bNZIuDqIydnWX1Q7KH7od3UDIggnbt2iEyMhLNmzdH165d8fnnn+POnTtyhyUJvV6PixcvIlmmjHfOnDm4ffs2Zs+eLUv/RKVcXHhNWikJAu+HKB/dD++gZEAkhQoVwpo1a7B582acPXsWbm5uWL58ucWNEuh0OqSmpiJahj20d+/exZQpUzBo0CC4urqavX+iYrlyAWXLSttHuXK8H6J8dD+8g5IBkbVq1QqRkZFo27YtevXqhWbNmuHmzZtyhyUaDw8PAPLsKBg3bhxy5syJCRMmmL1vYgE++0zafeXNm0vTNpEG3Q9voGRAAvnz58eKFSuwbds2REVFwcPDA4sWLYLJZJI7tGzLkycPypQpY/YdBSdPnsSqVasQGBho9WWhSRb17SvtvvJ+/aRpm0iD7oc3UDIgoebNmyMiIgKdOnVC//790aRJE1yTem+rGZh7ESFjDIMHD4Zer0evXr3M1i+xMG5ugI+P6E+DRmiRUNcHqFRJ1HaJtEwV3RBb0QcvIfLogFbL7zOV3Q+UDEgsb968WLJkCfbs2YPr169Dp9Phhx9+UPUogbmTgbVr1+L48eOYN28ebGxszNYvsUBLloiaDDAARkGLaqeXYP58QMX/rK1KbCw/s6rhxSVgNlqIurJLq+X3mcpQMmAmjRs3Rnh4OLp3747BgwejQYMGuHz5stxhZYlOp8O9e/fw4MEDyftKSEjA6NGj0a5dO3h7e0veH7FwZcoAIp66KQAQFixAk95l8P33QJMmwI0bojVPRMYYsGIFoNMBly4BS3eVge3i+eIdXwwACxao7vhigJIBs8qVKxcWLFiA/fv34969e/D09MSsWbOQmpoqd2iZklaW2BzrBmbMmIGHDx9i5syZkvdFrESvXkBgoDhtBQXBrn9PzJ8P7NnDC9vpdEBIiPQF7kjm/PMP0KoV0KMH0LYtEB7OR/PFvh/Qs6c4bZkbI7JISEhgQ4cOZYIgsJo1a7LIyEi5Q8owo9HI7O3t2Zw5cyTt58aNG8ze3p6NHTtW0n6IlVq2jDF7e8a0Wsb4Z3fGfmm1/H0hIe80+fQpYz178pc1b87YnTsyfF8iOnOGfy9nzsgdSdaZTIz9+itjBQowVqQIY1u2fOCFEtwPakIjAzJxcHDAnDlzcPjwYTx58gReXl6YMmUKjFKtbhWRjY0NPDw8JB8ZGD16NPLnz4+xY8dK2g+xUr16AVFRgMHA/5zeWoK0rxsM/H3veQLMk4ePCmzdCoSFAR4ewNq1NEogl4cPgfbtgY4d+ShAZCTw+ecfeLEE94OqyJ2NEMYSExPZqFGjmEajYVWqVGHnz5+XO6R09ejRg1WtWlWy9g8ePMgAsJUrV0rWByGvREYyNmgQYy4ujAnCm09+gsD//6BBjEVFZbjJuDjGOnXiTbRrx9iDBxLGLxE1jwz8+SdjTk58RGDduky+WYL7QekExihnVYpTp06he/fuuHz5MsaPH4+xY8fC1tZW7rDea968eRgzZgzi4+OhFXmrlslkQvXq1WFjY4Pjx49Do6EBLGJGz5/z0+aSk3lteReXbFWS27CBbzkXBL7I/IsvRIxVYmfPAlWrAmfOAFWqyB1Nxjx5AgweDPz8Mx8FWLoUKFo0Gw2KfD8oltzZCHlTUlIS8/X1ZTY2Nkyv17MzCk3J9+7dywCw6Oho0dtevnw5A8COHj0qetuEyOHePcbatOEPlt98w9ijR3JHlDFqGxnYuZOxEiUYy5OHsZUr+XoBkjH0yKUwdnZ2mDx5Mk6dOgVBEFCjRg2MHz9etoOBPkSn0wEQvyzxs2fPMHbsWHTq1Am1a9cWtW1C5FKkCLBxI39a/esvvpZgxw65o7Icz5/z0ZdPP+W1fiIigG7dpD+LyJJQMqBQXl5eOHXqFPz9/TFz5kxUqVIFJ0+elDusVwoXLoxixYqJngwEBQXh+fPnmD59uqjtEiI3QQC6dOEfVDodL13fpw8QHy93ZOp28CCg1/NE68cfgV27gJIl5Y5KfSgZULAcOXLAz88PZ86cgYODA2rXro1Ro0bhxYsXcocGgNcbEHNHQUxMDIKDgzF69Gh88sknorVLiJJ88gmwfTtfP7BmDf8g279f7qjU58ULYNgwwNsbKFECuHDh/2szSOZRMqACOp0Ox44dw5QpU/DDDz+gcuXKOHLkiNxhiV6WeMSIEShWrBhGjBghWpuEKJEgAN99xz/AnJ357rQhQ4DERLkjU4eTJwEvLz4SMGsWT6bKlZM7KnWjZEAltFotRo8ejXPnzqFAgQKoX78+hgwZgoSEBNli0uv1uHHjBp4+fZrttvbs2YPNmzdjxowZcHBwECE6QpSvbFkgNBQIDuYjBV5ewPHjckelXCkpgK8vULs2kDs3cO4cHx2gI0uyj5IBlalUqRIOHz6MWbNmYcmSJdDr9dgv0xhj2iLCiIiIbLVjNBoxZMgQ1KtXD19//bUYoRGiGhoNHxUICwPy5wfq1gXGjeM72cj/nT8PVK8OTJ8OBAQAx46p7mBARaNkQIVsbGwwbNgwXLhwASVKlIDBYED//v0Rb+aVSBUrVoRWq832VMHSpUsRFRWFuXPnQqAJP2KlXF2Bw4d5mfxZs/gH37lzckclP6ORl/yvXp1X/Tl1io8OiFzexOpRMqBi5cuXx/79+zF//nz8/PPP0Ol02L17t9n6t7OzQ8WKFbO1iPDRo0fw8/ND9+7dUbVqVRGjI0R9tFpg7Fjg9Gk+YlCjBjB5MvDypdyRyePiRaBOHWDCBGDkSJ4IVK4sd1SWiZIBldNoNBg4cCDCw8NRrlw5NG3aFL179xZlHj8jsruIMCAgAC9fvkRQUJCIURGibno9XyQ3ZgwfEq9Th5e/txYmE19H4eUFPH0KHD3KRwfs7OSOzHJRMmAhypQpgz179mDJkiVYv3493N3dsW3bNsn7TUsGWBaqWkdFRWHhwoXw9fVF0WzVCyXE8tja8lGBY8d4UZ0qVYDZswGVnXieadeu8d0Vw4YBffvyqZKaNeWOyvJRMmBBBEHAd999h4iICHh4eKBFixbo1q0bHj16JFmfOp0O8fHxuHnzZqbexxjDsGHDULp0aQwePFii6AhRv+rV+RkBAwfyofKGDXmpfEvDGLB4MR8ViY39/y4L2lxkHpQMWCBnZ2ds374dP/30EzZv3gx3d3ds3rxZkr70ej2AzJcl3rZtG3bu3InZs2fDjsb+CPmonDn5osIDB4B//gE8Pfkee5NJ7sjEcesW0KwZLxr0zTe8/oK3t9xRWRdKBiyUIAjo3r07IiMjUa1aNbRp0wYdO3bEw4cPRe2nRIkSyJ8/f6aSgZSUFAwdOhSNGzdGq1atRI2HEEtWvz7fYtetGzBgAK/FHxsrd1RZxxgvI6zTAZGRvDLj4sW8hgAxL0oGLFyJEiWwZcsW/PLLL9i1axfc3Nzw+++/i9a+IAiZLku8YMECXL16lbYSEpIFuXL9vwb/xYv8g3TlSv7Bqib37/PjnLt1A1q1AsLD+egAkQclA1ZAEAR07twZkZGRqF+/Pr7++mt8+eWXuH//vijtZ2ZHwYMHDxAQEIC+ffvCw8NDlP4JsUY+PvwDtG1boHt3oHVr4N49uaPKmN9/B9zd+S6BtNMc8+eXOyrrRsmAFSlatCg2bNiA9evX48CBA3Bzc8OaNWuytBPgdXq9HpcvX87QAUp+fn7QaDQICAjIVp+EECBfPmDFCmDzZr4V0d0dWL9e7qg+LC4O6NgR+PprviYgMpKPDhD5UTJgZQRBwNdff42oqCg0bdoU33zzDVq3bo27d+9muU2dTgeTyYSodDZCh4WFYdmyZQgICEChQoWy3B8h5E2tWvGjkZs0ATp0ANq3B0ReHpRtW7cCHh7Azp3A2rV8dKBwYbmjImkoGbBShQsXxq+//opNmzbh5MmTcHNzw4oVK7I0SuDu7g5BEHA6LByRd5/iXOxjRN59ioRk46vXMMYwZMgQVKxYEf369RPzWyGEAChUiI8KrFsH7NnDRwm2bMlGg8+fI+elMNTACeS8FMaLHWTB06dAz57A55/zWgkREXx0gJYLKYvAsjtGTFTv0aNHGDJkCFavXo1mzZphyZIlcHZ2ztB7r9yPx5oTsVi5+xTgWOiNf+ECAOcCDjC4OqHws0sY2KUdduzYgU8//VSi74QQAvC1A71786fxbt2AuXP5lEK6oqL4cv5t23j1n9c/HgSBH7P42We8GpCbW7rN7d3L1zM8ecJrBvToQUmAUlEyQF75+++/0adPHzx79gwzZ87Ed99998HV/rceJWLcpnAcinkIG42AVNOHbyMbQUAqY3CMv4Udk7qiZAGqIkKI1BgDVq0CBg8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"text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -426,7 +426,7 @@ }, { "cell_type": "markdown", - "id": "b7fc5217", + "id": "b8a18d63", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -466,13 +466,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "d8ddc593", + "id": "a7bbcdc2", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:42.604374Z", - "iopub.status.busy": "2023-01-02T13:06:42.603650Z", - "iopub.status.idle": "2023-01-02T13:06:42.609093Z", - "shell.execute_reply": "2023-01-02T13:06:42.608292Z" + "iopub.execute_input": "2023-01-03T20:27:35.744553Z", + "iopub.status.busy": "2023-01-03T20:27:35.743997Z", + "iopub.status.idle": "2023-01-03T20:27:35.748034Z", + "shell.execute_reply": "2023-01-03T20:27:35.747494Z" } }, "outputs": [ @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "26cc6ce1", + "id": "12b7f9bb", "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": "097de0f5", + "id": "030fd58c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:42.613625Z", - "iopub.status.busy": "2023-01-02T13:06:42.613122Z", - "iopub.status.idle": "2023-01-02T13:06:42.618868Z", - "shell.execute_reply": "2023-01-02T13:06:42.618069Z" + "iopub.execute_input": "2023-01-03T20:27:35.750994Z", + "iopub.status.busy": "2023-01-03T20:27:35.750631Z", + "iopub.status.idle": "2023-01-03T20:27:35.754891Z", + "shell.execute_reply": "2023-01-03T20:27:35.754237Z" } }, "outputs": [ diff --git a/searchindex.js b/searchindex.js index 94760984..e7a6ec0d 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", 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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], "151": [8, 17], "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, 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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, 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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, 17, 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, 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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, 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93, 94, 95, 96, 98, 99, 100, 103, 105, 106, 107, 109, 1326, 1328, 1393, 1394, 1403, 1404, 1419], "speed": [52, 56, 107, 215, 291, 292, 346, 347, 423, 427, 509, 796, 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, 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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, 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": [54, 132, 381, 752], "intrins": 54, "put": [54, 92, 95, 102, 226, 1326, 1404, 1406], "underli": [54, 101, 102, 132, 152, 157, 158, 161, 195, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 427, 428, 490, 491, 500, 615, 742, 743, 791, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1038, 1225, 1233, 1241, 1326, 1393, 1394, 1402], "quickli": [54, 1239], "Be": [54, 92, 1038, 1135, 1404], "care": [54, 92, 100, 102, 106, 107, 109, 115, 156, 855, 900, 936, 981, 1038, 1326, 1404, 1406], "bound": [54, 112, 214, 215, 216, 217, 220, 224, 227, 264, 300, 342, 352, 437, 440, 675, 1043, 1165, 1235, 1319, 1413, 1414, 1416], "box": [54, 107, 1134, 1136, 1271, 1323], "control": [54, 168, 179, 189, 204, 230, 231, 324, 325, 450, 467, 865, 878, 891, 910, 946, 960, 991, 1328, 1402, 1408, 1409, 1413], "cell": [54, 58, 752, 758, 1271, 1323, 1325, 1407], "convex": 54, "hull": 54, "contigu": [54, 58, 438, 1102, 1277, 1278], "being": [54, 92, 94, 95, 99, 101, 102, 109, 217, 227, 464, 465, 466, 559, 560, 711, 1038, 1045, 1144, 1175, 1236, 1296, 1393, 1394, 1407, 1412, 1413, 1416, 1425], "face": [54, 101, 102, 115, 183, 206, 615, 1043, 1262, 1263], "analogu": [54, 58, 230], "von": 54, "neuman": 54, "neighborhood": [54, 58, 114, 213, 240, 249, 285, 286, 324, 325, 512, 690, 786, 1189], "cardin": [54, 115, 218, 221, 264, 277, 278, 279, 280, 339, 341, 343, 345, 414, 415, 416, 417, 428, 440, 441, 444, 446, 581, 583, 611, 691, 1395], "regular": [54, 58, 65, 88, 99, 477, 478, 479, 480, 622, 623, 624, 758, 1038, 1185, 1190, 1191, 1192, 1239, 1245, 1250, 1251, 1254, 1258, 1261, 1262, 1263, 1264, 1280, 1290, 1323, 1325, 1394, 1395, 1398, 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674, 693, 1325, 1394, 1406], "aagaard": 91, "meier": 91, "henrik": 91, "haugb\u00f8ll": 91, "piotr": 91, "brodka": 91, "gutfraind": 91, "alessandro": [91, 1407], "luongo": [91, 1407], "huston": [91, 1408], "heding": [91, 1408], "olegu": 91, "sagarra": 91, "kazimierz": [91, 1412], "wojciechowski": [91, 1412], "256": [91, 110, 1175, 1266, 1344, 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, 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1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "depart": [91, 494], "physic": [91, 110, 230, 236, 241, 244, 248, 326, 332, 333, 355, 356, 358, 378, 383, 386, 438, 485, 486, 487, 625, 1169, 1170, 1171, 1193, 1222, 1229, 1233], "crutchfield": 91, "institut": [91, 112, 214, 215, 216, 220], "discoveri": [91, 670, 675, 676, 690], "madison": 91, "jessica": 91, "flack": 91, "david": [91, 277, 362, 437, 442, 447, 448, 624, 685, 710, 711, 712, 713, 714, 715, 734, 736, 1146, 1157, 1255, 1408, 1409, 1412], "krakauer": 91, "financi": 91, "summer": [91, 105, 1405, 1413, 1414], "foundat": [91, 110, 412, 431, 441, 445, 446, 619, 751], "grant": [91, 100, 105, 1202], "w911nf": 91, "0288": 91, "darpa": 91, "intellig": [91, 132, 494, 574, 590, 732, 762, 1207, 1210], "subcontract": 91, "No": [91, 92, 228, 282, 284, 285, 286, 287, 288, 444, 450, 460, 680, 1038, 1393, 1394, 1396, 1411], "9060": 91, "000709": 91, "nsf": 91, "phy": [91, 275, 284, 313, 371, 372, 383, 385, 434, 573, 1165, 1177, 1182, 1183, 1184, 1187, 1230, 1234, 1287], "0748828": 91, "templeton": 91, "santa": [91, 214, 215, 216, 220], "fe": [91, 214, 215, 216, 220], "under": [91, 324, 325, 525, 535, 555, 566, 577, 586, 588, 606, 671, 672, 673, 674, 739, 1326, 1412, 1413, 1417], "contract": [91, 110, 391, 500, 584, 585, 587, 618, 619, 767, 1174, 1395, 1413], "0340": 91, "space": [92, 101, 109, 231, 296, 301, 302, 308, 309, 355, 423, 628, 629, 630, 760, 786, 1112, 1144, 1193, 1196, 1197, 1198, 1199, 1239, 1296, 1326, 1331, 1334, 1390, 1398, 1406, 1412, 1417], "manag": [92, 93, 100, 111, 228, 680, 691, 1402, 1411, 1412], "privat": [92, 100, 1412, 1413, 1421, 1425], "tracker": [92, 97, 100, 107], "wiki": [92, 112, 120, 121, 132, 211, 226, 230, 282, 283, 293, 340, 341, 425, 454, 469, 476, 483, 484, 488, 490, 590, 676, 695, 696, 704, 710, 732, 761, 767, 782, 1206, 1219, 1243, 1244, 1245, 1246, 1248, 1249, 1250, 1251, 1256, 1257, 1258, 1259, 1261, 1262, 1263, 1264], "channel": 92, 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100, 109, 127, 207, 270, 422, 488, 892, 928, 973, 1010, 1117, 1330, 1394, 1426], "someth": [92, 94, 101, 103, 107, 527, 537, 796, 1037, 1039, 1040, 1046, 1120, 1126, 1300, 1356, 1357, 1404], "sensit": [92, 100, 1269], "too": [92, 94, 691, 780, 1043, 1165, 1234, 1295, 1326, 1328, 1404, 1425, 1426], "answer": [92, 97, 761, 1407], "question": [92, 97, 693, 1326, 1393, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "inadvert": 92, "mistak": [92, 94, 1424, 1425], "easili": [92, 100, 115, 380, 494, 688, 691, 1328, 1399, 1404, 1426], "detect": [92, 95, 105, 128, 210, 322, 373, 374, 378, 379, 380, 381, 383, 385, 386, 438, 519, 593, 652, 658, 663, 758, 786, 1165, 1169, 1170, 1171, 1326, 1407, 1408, 1409, 1412, 1414], "empathet": 92, "welcom": [92, 94, 109], "patient": 92, "resolv": [92, 93, 94, 97, 99, 100, 101, 464, 1411, 1412, 1425], "assum": [92, 93, 94, 97, 101, 106, 111, 132, 184, 219, 235, 265, 291, 292, 314, 316, 327, 378, 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380, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 862, 907, 943, 988, 1393, 1394, 1398, 1399, 1404, 1407, 1417], "colleagu": 92, "consequ": [92, 101], "serious": [92, 94], "inquisit": 92, "nobodi": [92, 1407], "everyth": 92, "ask": [92, 93, 94, 97, 99, 1278, 1279, 1406], "earli": [92, 93, 383, 652, 663, 760], "avoid": [92, 94, 99, 101, 102, 114, 152, 157, 158, 195, 249, 252, 253, 345, 346, 347, 348, 349, 469, 471, 472, 473, 474, 475, 600, 604, 678, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1038, 1061, 1082, 1295, 1302, 1331, 1334, 1406, 1407, 1408, 1409, 1412, 1417, 1425], "later": [92, 93, 99, 102, 739, 1406, 1426], "encourag": [92, 94, 99, 105, 230, 780, 1399], "although": [92, 698, 699, 762, 1144, 1402], "appropri": [92, 99, 100, 102, 111, 625, 628, 629, 630, 695, 729, 731, 1042, 1098, 1099, 1118, 1296, 1407], "forum": [92, 99], "hard": [92, 101, 106, 112, 212, 422, 780, 1042, 1117, 1218, 1234, 1404, 1412], "respons": [92, 93, 94, 99, 103, 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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, 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1142, 1179, 1203, 1230, 1325, 1386, 1390, 1398, 1399, 1406, 1407, 1408, 1411, 1413], "explor": [94, 105, 107, 110, 704, 711, 717], "corner": [94, 1407, 1414], "tempt": 94, "nitpicki": 94, "spell": [94, 1406, 1412, 1413], "suggest": [94, 102, 105, 632, 635, 636, 1165, 1326, 1402, 1406, 1412, 1414, 1425], "latter": [94, 100, 102, 440, 729, 731, 791, 1299], "choic": [94, 102, 204, 385, 408, 409, 410, 411, 412, 413, 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": 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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": [95, 1347, 1349, 1384, 1412], "coreview": [95, 1413], "filter": [95, 322, 453, 1036, 1061, 1082, 1088, 1269, 1324, 1325, 1413], "link_analysi": [95, 1405], "pagerank_alg": [95, 1405], "replac": [95, 99, 102, 103, 202, 232, 270, 385, 411, 412, 430, 431, 512, 583, 796, 890, 926, 934, 971, 979, 1008, 1037, 1039, 1040, 1051, 1094, 1225, 1241, 1295, 1296, 1297, 1311, 1317, 1326, 1347, 1363, 1364, 1393, 1394, 1396, 1399, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1417, 1422, 1424, 1425], "pagerank": [95, 311, 312, 324, 325, 565, 758, 1283, 1284, 1394, 1398, 1405, 1406, 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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, 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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, 1395, 1401, 1406, 1407, 1411, 1426], "wheel": [102, 106, 1163, 1261, 1411, 1421, 1425], "spoke": 102, "wheel_graph": [102, 341, 671, 672, 674], "star": [102, 260, 300, 615, 626, 627, 779, 1054, 1151, 1160, 1223, 1227, 1394, 1404, 1406, 1407, 1411], "mycustomgraph": 102, "configuration_model_graph": 102, "deg_sequ": [102, 515, 517, 518, 1175, 1176, 1177, 1178, 1180, 1222], "graph_build": 102, "py_random_st": [102, 103, 1296, 1299, 1405], "extended_barabasi_albert_graph": 102, "node_and_edge_build": 102, "ladder_graph": 102, "incompat": [102, 1199, 1402, 1403, 1406], "thrust": 102, "incept": 102, "attach": [102, 214, 274, 357, 569, 571, 621, 1036, 1088, 1122, 1182, 1185, 1223, 1227, 1229, 1326, 1426], 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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, 549, 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], "180": [209, 211, 212, 238], "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, "massiv": [210, 217], "421": 210, "448": 210, "1080": [210, 297, 298, 306, 307, 329], "15427951": 210, "986778": 210, "apx": [211, 212], "subseteq": [211, 280, 289, 618, 675], "omega": [211, 758, 782, 1414], "maximum_cliqu": 211, "1007": [211, 226, 296, 301, 302, 303, 308, 309, 323, 324, 325, 341, 431, 451, 498, 574, 1144, 1181], "bf01994876": 211, "iset": 212, "trial": [213, 230, 231, 1195, 1237, 1238], "estim": [213, 224, 297, 306, 313, 564, 625, 626, 627, 782, 1280, 1407], "coeffici": [213, 248, 260, 261, 262, 263, 289, 355, 356, 358, 570, 618, 619, 625, 682, 684, 778, 782, 1397, 1398, 1399, 1406, 1413], 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How do I get started?": [[97, "q-i-m-new-to-open-source-and-would-like-to-contribute-to-networkx-how-do-i-get-started"]], "Q: I\u2019ve found an issue I\u2019m interested in, can I have it assigned to me?": [[97, "q-i-ve-found-an-issue-i-m-interested-in-can-i-have-it-assigned-to-me"]], "Q: How do I contribute an example to the Gallery?": [[97, "q-how-do-i-contribute-an-example-to-the-gallery"]], "Q: I want to work on a specific function. 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"]], "NetworkX 2.8.8": [[1424, "networkx-2-8-8"]], "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, 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[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, 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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.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.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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\ No newline at end of file +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", 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1309, 1312, 1342, 1356, 1357, 1376, 1409, 1413, 1426], "child": [8, 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], "107": [8, 17, 241, 244, 1201], "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, 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, "074": [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, 662, 666, 669, 687, 688, 691, 705, 706, 707, 713, 715, 749, 750, 760, 796, 849, 856, 858, 859, 862, 865, 870, 873, 878, 879, 884, 888, 890, 891, 892, 894, 901, 903, 904, 907, 910, 916, 923, 926, 927, 928, 930, 931, 935, 937, 939, 940, 943, 946, 947, 951, 955, 960, 965, 969, 971, 972, 973, 975, 976, 980, 982, 984, 985, 988, 991, 992, 998, 1005, 1008, 1009, 1010, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1041, 1045, 1047, 1085, 1086, 1091, 1094, 1097, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1120, 1122, 1126, 1134, 1136, 1193, 1196, 1197, 1198, 1207, 1208, 1213, 1295, 1296, 1302, 1303, 1307, 1324, 1326, 1345, 1348, 1349, 1350, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1382, 1393, 1394, 1395, 1402, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1415, 1416, 1425, 1426], "edgecolor": [10, 15, 21, 32, 34, 35, 38, 54, 58, 82, 83, 1137], "black": [10, 15, 21, 25, 65, 69, 93, 598, 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, 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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, 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546, 547, 548, 611], "node_typ": [16, 1342, 1356, 1357], "supported_nod": 16, "unsupported_nod": 16, "remove_edges_from": [16, 89, 192, 453, 602, 881, 920, 962, 1002, 1175, 1177, 1222, 1393, 1394, 1412, 1420, 1426], "nbr": [16, 88, 159, 190, 199, 200, 207, 229, 230, 231, 285, 500, 506, 796, 858, 879, 887, 888, 892, 903, 925, 928, 939, 968, 969, 973, 984, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1426], "adj": [16, 88, 199, 200, 207, 324, 325, 796, 849, 887, 888, 892, 894, 915, 925, 928, 930, 968, 969, 973, 975, 996, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1411, 1417, 1425, 1426], "g_minus_h": 16, "strip": [16, 25, 69, 1215], "_node_color": 16, "_po": 16, "draw_networkx_edg": [16, 25, 26, 27, 28, 33, 35, 38, 39, 40, 41, 44, 46, 68, 83, 1130, 1133, 1134, 1136, 1137, 1411, 1413], "draw_networkx_label": [16, 25, 35, 38, 46, 71, 1130, 1133, 1134, 1135, 1137], "ncl": 16, "undirect": [16, 25, 34, 71, 93, 112, 177, 185, 204, 205, 209, 211, 212, 214, 215, 216, 217, 218, 219, 220, 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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, 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1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "426": [21, 22], "plot_simple_graph": [21, 22], "587": 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, 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1404, 1419], "speed": [52, 56, 107, 215, 291, 292, 346, 347, 423, 427, 509, 796, 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, 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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, 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, 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467, 865, 878, 891, 910, 946, 960, 991, 1328, 1402, 1408, 1409, 1413], "cell": [54, 58, 752, 758, 1271, 1323, 1325, 1407], "convex": 54, "hull": 54, "contigu": [54, 58, 438, 1102, 1277, 1278], "being": [54, 92, 94, 95, 99, 101, 102, 109, 217, 227, 464, 465, 466, 559, 560, 711, 1038, 1045, 1144, 1175, 1236, 1296, 1393, 1394, 1407, 1412, 1413, 1416, 1425], "face": [54, 101, 102, 115, 183, 206, 615, 1043, 1262, 1263], "analogu": [54, 58, 230], "von": 54, "neuman": 54, "neighborhood": [54, 58, 114, 213, 240, 249, 285, 286, 324, 325, 512, 690, 786, 1189], "cardin": [54, 115, 218, 221, 264, 277, 278, 279, 280, 339, 341, 343, 345, 414, 415, 416, 417, 428, 440, 441, 444, 446, 581, 583, 611, 691, 1395], "regular": [54, 58, 65, 88, 99, 477, 478, 479, 480, 622, 623, 624, 758, 1038, 1185, 1190, 1191, 1192, 1239, 1245, 1250, 1251, 1254, 1258, 1261, 1262, 1263, 1264, 1280, 1290, 1323, 1325, 1394, 1395, 1398, 1406, 1412, 1413], "come": [54, 93, 100, 101, 102, 517, 577, 588, 598, 608, 677, 698, 699, 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128, 313, 329, 396, 438, 455, 457, 458, 464, 465, 466, 468, 1398, 1401, 1404, 1406, 1414], "sequenc": [60, 72, 80, 86, 101, 102, 107, 180, 269, 271, 273, 274, 276, 363, 364, 365, 374, 386, 488, 512, 513, 514, 515, 516, 517, 518, 549, 550, 551, 625, 671, 672, 673, 674, 678, 679, 693, 702, 728, 729, 731, 758, 791, 871, 914, 952, 995, 1102, 1133, 1134, 1135, 1136, 1137, 1144, 1165, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1199, 1206, 1207, 1208, 1218, 1222, 1237, 1238, 1272, 1273, 1297, 1311, 1315, 1316, 1325, 1398, 1406, 1407, 1413], "renyi": [60, 72, 86, 593, 1398, 1406], "expect": [60, 61, 72, 83, 86, 100, 103, 105, 109, 275, 280, 429, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 671, 672, 673, 674, 686, 727, 1038, 1043, 1085, 1175, 1177, 1179, 1230, 1235, 1236, 1287, 1296, 1319, 1323, 1328, 1398, 1404, 1405, 1406, 1413, 1414], "footbal": [60, 72, 86, 1406], "karat": [60, 72, 86, 1267, 1398, 1406, 1414], "mors": [60, 72, 86, 1421], "trie": [60, 72, 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1424, 1425], "release_dev": [93, 106], "rst": [93, 99, 106, 1407, 1408, 1411, 1412, 1413, 1414, 1422, 1425], "deprec": [93, 96, 103, 106, 1186, 1363, 1364, 1394, 1395, 1403, 1404, 1406, 1420, 1422], "curly_hair": 93, "deprecationwarn": 93, "conftest": [93, 95, 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": 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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, 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"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": [94, 1413], "handhold": 94, "liber": 94, "workflow": [94, 96, 97, 100, 106, 1413, 1420], "realiz": [94, 513, 514, 515, 516, 517, 518, 693, 1175, 1177, 1180, 1207, 1208, 1209, 1210, 1222, 1264], "gentl": 94, "abandon": 94, "difficult": [94, 1405], "carri": [94, 100, 508], "polici": [94, 96, 99, 1412, 1414], "readabl": [94, 107, 109, 169, 172, 460, 868, 913, 949, 994, 1393, 1414], "effici": [94, 102, 112, 212, 275, 290, 377, 387, 389, 390, 392, 394, 399, 405, 406, 407, 422, 425, 426, 486, 487, 508, 512, 581, 614, 680, 688, 691, 698, 699, 758, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1179, 1203, 1230, 1325, 1386, 1390, 1398, 1399, 1406, 1407, 1408, 1411, 1413], "explor": [94, 105, 107, 110, 704, 711, 717], "corner": [94, 1407, 1414], "tempt": 94, "nitpicki": 94, "spell": [94, 1406, 1412, 1413], "suggest": [94, 102, 105, 632, 635, 636, 1165, 1326, 1402, 1406, 1412, 1414, 1425], "latter": [94, 100, 102, 440, 729, 731, 791, 1299], "choic": [94, 102, 204, 385, 408, 409, 410, 411, 412, 413, 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": [94, 761, 1042, 1188], "behind": [94, 105], "clarif": [94, 299, 322], "deem": 94, "nich": 94, "devot": 94, "sustain": [94, 96], "effort": [94, 107, 1326], "priorit": 94, "similarli": [94, 103, 115, 207, 356, 598, 621, 796, 892, 928, 973, 1010, 1037, 1039, 1040, 1148, 1175, 1177, 1193, 1198, 1207, 1296, 1394, 1404, 1426], "worth": [94, 761, 1426], "mainten": 94, "burden": 94, "necessari": [94, 95, 100, 104, 527, 537, 954, 997, 1135, 1137, 1296, 1406, 1412], "valid": [94, 101, 161, 177, 256, 277, 278, 281, 282, 377, 386, 439, 458, 464, 466, 497, 513, 514, 515, 516, 517, 518, 559, 560, 578, 579, 580, 588, 614, 615, 734, 735, 736, 737, 746, 758, 1038, 1043, 1071, 1087, 1100, 1104, 1105, 1165, 1187, 1193, 1237, 1238, 1274, 1278, 1279, 1296, 1331, 1334, 1407, 1412, 1413, 1414, 1417, 1419, 1422, 1425], "wari": 94, "alien": 94, "visibl": [94, 97], "thread": [94, 97, 99, 103, 104, 1413], "appeal": [94, 100], "empow": 94, "regardless": [94, 99, 1135, 1191, 1404], "outcom": [94, 105, 1036, 1088, 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": [95, 1347, 1349, 1384, 1412], "coreview": [95, 1413], "filter": [95, 322, 453, 1036, 1061, 1082, 1088, 1269, 1324, 1325, 1413], "link_analysi": [95, 1405], "pagerank_alg": [95, 1405], "replac": [95, 99, 102, 103, 202, 232, 270, 385, 411, 412, 430, 431, 512, 583, 796, 890, 926, 934, 971, 979, 1008, 1037, 1039, 1040, 1051, 1094, 1225, 1241, 1295, 1296, 1297, 1311, 1317, 1326, 1347, 1363, 1364, 1393, 1394, 1396, 1399, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1417, 1422, 1424, 1425], "pagerank": [95, 311, 312, 324, 325, 565, 758, 1283, 1284, 1394, 1398, 1405, 1406, 1407, 1413], "pagerank_scipi": [95, 1405, 1411, 1413], "renam": [95, 102, 106, 597, 601, 604, 609, 1295, 1348, 1349, 1357, 1394, 1407, 1412, 1421, 1424, 1425], "pagerank_numpi": 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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, 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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, 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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, 549, 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], "180": [209, 211, 212, 238], "196": [209, 211, 212], "heurist": [210, 220, 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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"]], "NetworkX 2.8.8": [[1424, "networkx-2-8-8"]], "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, 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[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, 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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.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.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, "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 Binary files differindex 3592bbe7..c86e8d29 100644 --- a/tutorial-34.pdf +++ b/tutorial-34.pdf diff --git a/tutorial-35.hires.png b/tutorial-35.hires.png Binary files differindex 51b54532..95fa708b 100644 --- a/tutorial-35.hires.png +++ b/tutorial-35.hires.png diff --git a/tutorial-35.pdf b/tutorial-35.pdf Binary files differindex e3fec9e7..e4fd054c 100644 --- a/tutorial-35.pdf +++ b/tutorial-35.pdf diff --git a/tutorial-35.png b/tutorial-35.png Binary files differindex 5ae1b78d..d7651092 100644 --- a/tutorial-35.png +++ b/tutorial-35.png diff --git a/tutorial-36.pdf b/tutorial-36.pdf Binary files differindex 4bb35b7f..75aaec5d 100644 --- a/tutorial-36.pdf +++ b/tutorial-36.pdf diff --git a/tutorial.ipynb b/tutorial.ipynb index a1eef26c..ebf2f0dc 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "09acc732", + "id": "e59716aa", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,7 +17,7 @@ { "cell_type": "code", "execution_count": null, - "id": "36cfb0a2", + "id": "4c02a2be", "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "c63b6961", + "id": "a68e7605", "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": "e5028e1f", + "id": "04184847", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "aa375677", + "id": "b9be2699", "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": "7e1e53f0", + "id": "1f327dd5", "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "markdown", - "id": "943c9b7e", + "id": "4b467036", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": null, - "id": "268f654d", + "id": "90bd0361", "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "d7b8f39b", + "id": "ac638797", "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": "6f7a04cb", + "id": "308e8588", "metadata": {}, "outputs": [], "source": [ @@ -125,7 +125,7 @@ }, { "cell_type": "markdown", - "id": "6f7d863a", + "id": "490aef47", "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": "e5deadb8", + "id": "7b4d5d97", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +154,7 @@ }, { "cell_type": "markdown", - "id": "873b8364", + "id": "1e95cd0a", "metadata": {}, "source": [ "by adding a list of edges," @@ -163,7 +163,7 @@ { "cell_type": "code", "execution_count": null, - "id": "81f1e49b", + "id": "2b2cddc0", "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "e451cd9a", + "id": "7735d204", "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": "466844ea", + "id": "1a1f832d", "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "9fa30d52", + "id": "bd6b6dbe", "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": "fe5d7a63", + "id": "63b6eff3", "metadata": {}, "outputs": [], "source": [ @@ -213,7 +213,7 @@ }, { "cell_type": "markdown", - "id": "64214131", + "id": "463d8334", "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": "b14b30c4", + "id": "ddf23c52", "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "2e2f7f9d", + "id": "da3789cc", "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": "d0d78b40", + "id": "b0e854a0", "metadata": {}, "outputs": [], "source": [ @@ -257,7 +257,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0a1b3f69", + "id": "de2b7208", "metadata": {}, "outputs": [], "source": [ @@ -272,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "26fb3c24", + "id": "1f9cb39c", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -292,7 +292,7 @@ { "cell_type": "code", "execution_count": null, - "id": "60073a2a", + "id": "3ad2f7b8", "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "markdown", - "id": "568d047d", + "id": "2dd63975", "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": "e139feda", + "id": "3b3a853a", "metadata": {}, "outputs": [], "source": [ @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "8c580f0e", + "id": "c1d5867d", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -343,7 +343,7 @@ { "cell_type": "code", "execution_count": null, - "id": "60a057e7", + "id": "336c3714", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "5d2f0a3e", + "id": "81796970", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -370,7 +370,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0dd2e93d", + "id": "d5a68853", "metadata": {}, "outputs": [], "source": [ @@ -387,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "efd1be5f", + "id": "67496885", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -416,7 +416,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2cca9ec0", + "id": "5dd56a05", "metadata": {}, "outputs": [], "source": [ @@ -428,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "af094850", + "id": "5963217f", "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": "576d0b8e", + "id": "2a14156d", "metadata": {}, "outputs": [], "source": [ @@ -450,7 +450,7 @@ }, { "cell_type": "markdown", - "id": "a47c1fc9", + "id": "b50cdc42", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -461,7 +461,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6071604a", + "id": "297c4229", "metadata": {}, "outputs": [], "source": [ @@ -475,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "f9806f8c", + "id": "79600d0d", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -484,7 +484,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5545ab2e", + "id": "d613ab0a", "metadata": {}, "outputs": [], "source": [ @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "e36d0fa6", + "id": "3d16bd90", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -517,7 +517,7 @@ { "cell_type": "code", "execution_count": null, - "id": "02147c02", + "id": "ed61843c", "metadata": {}, "outputs": [], "source": [ @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "370316f1", + "id": "c987375f", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -536,7 +536,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a166455b", + "id": "9c28c878", "metadata": {}, "outputs": [], "source": [ @@ -546,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "d1aec45b", + "id": "536f6b52", "metadata": {}, "source": [ "# Node attributes\n", @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6559df75", + "id": "8c483d57", "metadata": {}, "outputs": [], "source": [ @@ -570,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "8414f740", + "id": "676ea883", "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": "66ad40c7", + "id": "f5bd44ff", "metadata": {}, "outputs": [], "source": [ @@ -598,7 +598,7 @@ }, { "cell_type": "markdown", - "id": "86b86661", + "id": "07ff2c77", "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": "510da4e2", + "id": "9c32a36f", "metadata": {}, "outputs": [], "source": [ @@ -633,7 +633,7 @@ }, { "cell_type": "markdown", - "id": "eb3106f6", + "id": "8df9a6bc", "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": "4ce14fcf", + "id": "aabbcd4b", "metadata": {}, "outputs": [], "source": [ @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "1c4cac99", + "id": "0852b1fb", "metadata": {}, "source": [ "# Multigraphs\n", @@ -675,7 +675,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ecc058cb", + "id": "cdc9ff8c", "metadata": {}, "outputs": [], "source": [ @@ -693,7 +693,7 @@ }, { "cell_type": "markdown", - "id": "0d509efb", + "id": "8636eabf", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -713,7 +713,7 @@ { "cell_type": "code", "execution_count": null, - "id": "36872fe5", + "id": "bf582f5d", "metadata": {}, "outputs": [], "source": [ @@ -725,7 +725,7 @@ }, { "cell_type": "markdown", - "id": "f8aa2bc4", + "id": "b7049764", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -736,7 +736,7 @@ { "cell_type": "code", "execution_count": null, - "id": "018c814b", + "id": "0d1da7f1", "metadata": {}, "outputs": [], "source": [ @@ -748,7 +748,7 @@ }, { "cell_type": "markdown", - "id": "1f2ee001", + "id": "fe33bd32", "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": "0958110d", + "id": "e4118e78", "metadata": {}, "outputs": [], "source": [ @@ -770,7 +770,7 @@ }, { "cell_type": "markdown", - "id": "a3bfafb7", + "id": "88168e19", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -785,7 +785,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ff811ef3", + "id": "6b8bddf8", "metadata": {}, "outputs": [], "source": [ @@ -799,7 +799,7 @@ }, { "cell_type": "markdown", - "id": "51824ba9", + "id": "7f9bca65", "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": "1df52a7a", + "id": "21c663f1", "metadata": {}, "outputs": [], "source": [ @@ -819,7 +819,7 @@ }, { "cell_type": "markdown", - "id": "1b996922", + "id": "5c8acf2c", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -838,7 +838,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c22301f3", + "id": "f7d43a30", "metadata": {}, "outputs": [], "source": [ @@ -847,7 +847,7 @@ }, { "cell_type": "markdown", - "id": "28bb8512", + "id": "1e3ac651", "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": "c9a9ea9b", + "id": "c32216be", "metadata": {}, "outputs": [], "source": [ @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "410e7b3d", + "id": "fd7250c1", "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": "62b634c9", + "id": "79362d66", "metadata": {}, "outputs": [], "source": [ @@ -889,7 +889,7 @@ }, { "cell_type": "markdown", - "id": "d67a317b", + "id": "d7711d4b", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -898,7 +898,7 @@ { "cell_type": "code", "execution_count": null, - "id": "29d6266e", + "id": "8be5197e", "metadata": {}, "outputs": [], "source": [ @@ -919,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "0d2b3db8", + "id": "751d3c6c", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -930,7 +930,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0dc65281", + "id": "b9a82a38", "metadata": {}, "outputs": [], "source": [ @@ -941,7 +941,7 @@ }, { "cell_type": "markdown", - "id": "7bfd3c65", + "id": "4a4e4c97", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -950,7 +950,7 @@ { "cell_type": "code", "execution_count": null, - "id": "251b1df9", + "id": "331810ca", "metadata": {}, "outputs": [], "source": [ @@ -960,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "22fa85d4", + "id": "0eb4eea0", "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": "c92c0580", + "id": "1802df64", "metadata": {}, "outputs": [], "source": [ @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "98f275f7", + "id": "66284130", "metadata": {}, "source": [ "See Drawing for additional details." diff --git a/tutorial_full.ipynb b/tutorial_full.ipynb index c5a4cece..38bb21a2 100644 --- a/tutorial_full.ipynb +++ b/tutorial_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "09acc732", + "id": "e59716aa", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,13 +17,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "36cfb0a2", + "id": "4c02a2be", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.172035Z", - "iopub.status.busy": "2023-01-02T13:06:44.171100Z", - "iopub.status.idle": "2023-01-02T13:06:44.266446Z", - "shell.execute_reply": "2023-01-02T13:06:44.265507Z" + "iopub.execute_input": "2023-01-03T20:27:36.989696Z", + "iopub.status.busy": "2023-01-03T20:27:36.989182Z", + "iopub.status.idle": "2023-01-03T20:27:37.063917Z", + "shell.execute_reply": "2023-01-03T20:27:37.063168Z" } }, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "markdown", - "id": "c63b6961", + "id": "a68e7605", "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": "e5028e1f", + "id": "04184847", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.270855Z", - "iopub.status.busy": "2023-01-02T13:06:44.270597Z", - "iopub.status.idle": "2023-01-02T13:06:44.274699Z", - "shell.execute_reply": "2023-01-02T13:06:44.273713Z" + "iopub.execute_input": "2023-01-03T20:27:37.067875Z", + "iopub.status.busy": "2023-01-03T20:27:37.067600Z", + "iopub.status.idle": "2023-01-03T20:27:37.070782Z", + "shell.execute_reply": "2023-01-03T20:27:37.070123Z" } }, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "aa375677", + "id": "b9be2699", "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": "7e1e53f0", + "id": "1f327dd5", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.278248Z", - "iopub.status.busy": "2023-01-02T13:06:44.277982Z", - "iopub.status.idle": "2023-01-02T13:06:44.281926Z", - "shell.execute_reply": "2023-01-02T13:06:44.280997Z" + "iopub.execute_input": "2023-01-03T20:27:37.074272Z", + "iopub.status.busy": "2023-01-03T20:27:37.074059Z", + "iopub.status.idle": "2023-01-03T20:27:37.076984Z", + "shell.execute_reply": "2023-01-03T20:27:37.076369Z" } }, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "943c9b7e", + "id": "4b467036", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -117,13 +117,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "268f654d", + "id": "90bd0361", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.286108Z", - "iopub.status.busy": "2023-01-02T13:06:44.285796Z", - "iopub.status.idle": "2023-01-02T13:06:44.290267Z", - "shell.execute_reply": "2023-01-02T13:06:44.289319Z" + "iopub.execute_input": "2023-01-03T20:27:37.079996Z", + "iopub.status.busy": "2023-01-03T20:27:37.079786Z", + "iopub.status.idle": "2023-01-03T20:27:37.083144Z", + "shell.execute_reply": "2023-01-03T20:27:37.082519Z" } }, "outputs": [], @@ -134,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "d7b8f39b", + "id": "ac638797", "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": "6f7a04cb", + "id": "308e8588", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.294167Z", - "iopub.status.busy": "2023-01-02T13:06:44.293896Z", - "iopub.status.idle": "2023-01-02T13:06:44.297829Z", - "shell.execute_reply": "2023-01-02T13:06:44.297000Z" + "iopub.execute_input": "2023-01-03T20:27:37.085998Z", + "iopub.status.busy": "2023-01-03T20:27:37.085791Z", + "iopub.status.idle": "2023-01-03T20:27:37.088562Z", + "shell.execute_reply": "2023-01-03T20:27:37.087944Z" } }, "outputs": [], @@ -160,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "6f7d863a", + "id": "490aef47", "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": "e5deadb8", + "id": "7b4d5d97", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.302405Z", - "iopub.status.busy": "2023-01-02T13:06:44.302169Z", - "iopub.status.idle": "2023-01-02T13:06:44.306059Z", - "shell.execute_reply": "2023-01-02T13:06:44.305218Z" + "iopub.execute_input": "2023-01-03T20:27:37.091380Z", + "iopub.status.busy": "2023-01-03T20:27:37.091175Z", + "iopub.status.idle": "2023-01-03T20:27:37.094215Z", + "shell.execute_reply": "2023-01-03T20:27:37.093601Z" } }, "outputs": [], @@ -196,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "873b8364", + "id": "1e95cd0a", "metadata": {}, "source": [ "by adding a list of edges," @@ -205,13 +205,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "81f1e49b", + "id": "2b2cddc0", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.310030Z", - "iopub.status.busy": "2023-01-02T13:06:44.309791Z", - "iopub.status.idle": "2023-01-02T13:06:44.313681Z", - "shell.execute_reply": "2023-01-02T13:06:44.312781Z" + "iopub.execute_input": "2023-01-03T20:27:37.097130Z", + "iopub.status.busy": "2023-01-03T20:27:37.096918Z", + "iopub.status.idle": "2023-01-03T20:27:37.099845Z", + "shell.execute_reply": "2023-01-03T20:27:37.099220Z" } }, "outputs": [], @@ -221,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "e451cd9a", + "id": "7735d204", "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": "466844ea", + "id": "1a1f832d", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.318227Z", - "iopub.status.busy": "2023-01-02T13:06:44.317805Z", - "iopub.status.idle": "2023-01-02T13:06:44.322291Z", - "shell.execute_reply": "2023-01-02T13:06:44.321329Z" + "iopub.execute_input": "2023-01-03T20:27:37.102842Z", + "iopub.status.busy": "2023-01-03T20:27:37.102633Z", + "iopub.status.idle": "2023-01-03T20:27:37.105499Z", + "shell.execute_reply": "2023-01-03T20:27:37.104880Z" } }, "outputs": [], @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "9fa30d52", + "id": "bd6b6dbe", "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": "fe5d7a63", + "id": "63b6eff3", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.326255Z", - "iopub.status.busy": "2023-01-02T13:06:44.326005Z", - "iopub.status.idle": "2023-01-02T13:06:44.329502Z", - "shell.execute_reply": "2023-01-02T13:06:44.328674Z" + "iopub.execute_input": "2023-01-03T20:27:37.108401Z", + "iopub.status.busy": "2023-01-03T20:27:37.108195Z", + "iopub.status.idle": "2023-01-03T20:27:37.110937Z", + "shell.execute_reply": "2023-01-03T20:27:37.110320Z" } }, "outputs": [], @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "64214131", + "id": "463d8334", "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": "b14b30c4", + "id": "ddf23c52", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.333425Z", - "iopub.status.busy": "2023-01-02T13:06:44.333182Z", - "iopub.status.idle": "2023-01-02T13:06:44.337843Z", - "shell.execute_reply": "2023-01-02T13:06:44.336974Z" + "iopub.execute_input": "2023-01-03T20:27:37.113805Z", + "iopub.status.busy": "2023-01-03T20:27:37.113597Z", + "iopub.status.idle": "2023-01-03T20:27:37.117439Z", + "shell.execute_reply": "2023-01-03T20:27:37.116735Z" } }, "outputs": [], @@ -307,7 +307,7 @@ }, { "cell_type": "markdown", - "id": "2e2f7f9d", + "id": "da3789cc", "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": "d0d78b40", + "id": "b0e854a0", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.341911Z", - "iopub.status.busy": "2023-01-02T13:06:44.341576Z", - "iopub.status.idle": "2023-01-02T13:06:44.350017Z", - "shell.execute_reply": "2023-01-02T13:06:44.349155Z" + "iopub.execute_input": "2023-01-03T20:27:37.120616Z", + "iopub.status.busy": "2023-01-03T20:27:37.120406Z", + "iopub.status.idle": "2023-01-03T20:27:37.126709Z", + "shell.execute_reply": "2023-01-03T20:27:37.126087Z" } }, "outputs": [ @@ -345,13 +345,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "0a1b3f69", + "id": "de2b7208", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.358042Z", - "iopub.status.busy": "2023-01-02T13:06:44.357706Z", - "iopub.status.idle": "2023-01-02T13:06:44.363630Z", - "shell.execute_reply": "2023-01-02T13:06:44.362379Z" + "iopub.execute_input": "2023-01-03T20:27:37.131018Z", + "iopub.status.busy": "2023-01-03T20:27:37.130805Z", + "iopub.status.idle": "2023-01-03T20:27:37.134888Z", + "shell.execute_reply": "2023-01-03T20:27:37.134393Z" } }, "outputs": [], @@ -367,7 +367,7 @@ }, { "cell_type": "markdown", - "id": "26fb3c24", + "id": "1f9cb39c", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -387,13 +387,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "60073a2a", + "id": "3ad2f7b8", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.368341Z", - "iopub.status.busy": "2023-01-02T13:06:44.368082Z", - "iopub.status.idle": "2023-01-02T13:06:44.374612Z", - "shell.execute_reply": "2023-01-02T13:06:44.373752Z" + "iopub.execute_input": "2023-01-03T20:27:37.137616Z", + "iopub.status.busy": "2023-01-03T20:27:37.137273Z", + "iopub.status.idle": "2023-01-03T20:27:37.141541Z", + "shell.execute_reply": "2023-01-03T20:27:37.141046Z" } }, "outputs": [ @@ -417,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "568d047d", + "id": "2dd63975", "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": "e139feda", + "id": "3b3a853a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.379911Z", - "iopub.status.busy": "2023-01-02T13:06:44.379639Z", - "iopub.status.idle": "2023-01-02T13:06:44.385996Z", - "shell.execute_reply": "2023-01-02T13:06:44.385123Z" + "iopub.execute_input": "2023-01-03T20:27:37.144343Z", + "iopub.status.busy": "2023-01-03T20:27:37.143933Z", + "iopub.status.idle": "2023-01-03T20:27:37.147983Z", + "shell.execute_reply": "2023-01-03T20:27:37.147494Z" } }, "outputs": [ @@ -457,7 +457,7 @@ }, { "cell_type": "markdown", - "id": "8c580f0e", + "id": "c1d5867d", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -474,13 +474,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "60a057e7", + "id": "336c3714", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.391322Z", - "iopub.status.busy": "2023-01-02T13:06:44.390961Z", - "iopub.status.idle": "2023-01-02T13:06:44.395330Z", - "shell.execute_reply": "2023-01-02T13:06:44.394450Z" + "iopub.execute_input": "2023-01-03T20:27:37.150735Z", + "iopub.status.busy": "2023-01-03T20:27:37.150331Z", + "iopub.status.idle": "2023-01-03T20:27:37.153442Z", + "shell.execute_reply": "2023-01-03T20:27:37.152965Z" } }, "outputs": [], @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "5d2f0a3e", + "id": "81796970", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -508,13 +508,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "0dd2e93d", + "id": "d5a68853", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.399196Z", - "iopub.status.busy": "2023-01-02T13:06:44.398921Z", - "iopub.status.idle": "2023-01-02T13:06:44.713313Z", - "shell.execute_reply": "2023-01-02T13:06:44.712497Z" + "iopub.execute_input": "2023-01-03T20:27:37.156203Z", + "iopub.status.busy": "2023-01-03T20:27:37.155789Z", + "iopub.status.idle": "2023-01-03T20:27:37.423994Z", + "shell.execute_reply": "2023-01-03T20:27:37.423327Z" } }, "outputs": [ @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "efd1be5f", + "id": "67496885", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -572,13 +572,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "2cca9ec0", + "id": "5dd56a05", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.717607Z", - "iopub.status.busy": "2023-01-02T13:06:44.717192Z", - "iopub.status.idle": "2023-01-02T13:06:44.723755Z", - "shell.execute_reply": "2023-01-02T13:06:44.722977Z" + "iopub.execute_input": "2023-01-03T20:27:37.427516Z", + "iopub.status.busy": "2023-01-03T20:27:37.427168Z", + "iopub.status.idle": "2023-01-03T20:27:37.433317Z", + "shell.execute_reply": "2023-01-03T20:27:37.432709Z" } }, "outputs": [ @@ -602,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "af094850", + "id": "5963217f", "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": "576d0b8e", + "id": "2a14156d", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.728432Z", - "iopub.status.busy": "2023-01-02T13:06:44.728097Z", - "iopub.status.idle": "2023-01-02T13:06:44.733749Z", - "shell.execute_reply": "2023-01-02T13:06:44.732957Z" + "iopub.execute_input": "2023-01-03T20:27:37.436078Z", + "iopub.status.busy": "2023-01-03T20:27:37.435866Z", + "iopub.status.idle": "2023-01-03T20:27:37.440431Z", + "shell.execute_reply": "2023-01-03T20:27:37.439815Z" } }, "outputs": [ @@ -642,7 +642,7 @@ }, { "cell_type": "markdown", - "id": "a47c1fc9", + "id": "b50cdc42", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -653,13 +653,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "6071604a", + "id": "297c4229", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.738677Z", - "iopub.status.busy": "2023-01-02T13:06:44.738397Z", - "iopub.status.idle": "2023-01-02T13:06:44.744231Z", - "shell.execute_reply": "2023-01-02T13:06:44.743467Z" + "iopub.execute_input": "2023-01-03T20:27:37.444156Z", + "iopub.status.busy": "2023-01-03T20:27:37.443946Z", + "iopub.status.idle": "2023-01-03T20:27:37.448614Z", + "shell.execute_reply": "2023-01-03T20:27:37.447967Z" } }, "outputs": [ @@ -685,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "f9806f8c", + "id": "79600d0d", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -694,13 +694,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "5545ab2e", + "id": "d613ab0a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.748148Z", - "iopub.status.busy": "2023-01-02T13:06:44.747467Z", - "iopub.status.idle": "2023-01-02T13:06:44.752426Z", - "shell.execute_reply": "2023-01-02T13:06:44.751519Z" + "iopub.execute_input": "2023-01-03T20:27:37.452219Z", + "iopub.status.busy": "2023-01-03T20:27:37.452008Z", + "iopub.status.idle": "2023-01-03T20:27:37.455593Z", + "shell.execute_reply": "2023-01-03T20:27:37.454948Z" } }, "outputs": [ @@ -721,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "e36d0fa6", + "id": "3d16bd90", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -743,13 +743,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "02147c02", + "id": "ed61843c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.758595Z", - "iopub.status.busy": "2023-01-02T13:06:44.758286Z", - "iopub.status.idle": "2023-01-02T13:06:44.763664Z", - "shell.execute_reply": "2023-01-02T13:06:44.762599Z" + "iopub.execute_input": "2023-01-03T20:27:37.459277Z", + "iopub.status.busy": "2023-01-03T20:27:37.459065Z", + "iopub.status.idle": "2023-01-03T20:27:37.463179Z", + "shell.execute_reply": "2023-01-03T20:27:37.462539Z" } }, "outputs": [ @@ -771,7 +771,7 @@ }, { "cell_type": "markdown", - "id": "370316f1", + "id": "c987375f", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -780,13 +780,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "a166455b", + "id": "9c28c878", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.768644Z", - "iopub.status.busy": "2023-01-02T13:06:44.767953Z", - "iopub.status.idle": "2023-01-02T13:06:44.773379Z", - "shell.execute_reply": "2023-01-02T13:06:44.772547Z" + "iopub.execute_input": "2023-01-03T20:27:37.466906Z", + "iopub.status.busy": "2023-01-03T20:27:37.466690Z", + "iopub.status.idle": "2023-01-03T20:27:37.470836Z", + "shell.execute_reply": "2023-01-03T20:27:37.470189Z" } }, "outputs": [ @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "d1aec45b", + "id": "536f6b52", "metadata": {}, "source": [ "# Node attributes\n", @@ -819,13 +819,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "6559df75", + "id": "8c483d57", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.778154Z", - "iopub.status.busy": "2023-01-02T13:06:44.777910Z", - "iopub.status.idle": "2023-01-02T13:06:44.784020Z", - "shell.execute_reply": "2023-01-02T13:06:44.782894Z" + "iopub.execute_input": "2023-01-03T20:27:37.474738Z", + "iopub.status.busy": "2023-01-03T20:27:37.474521Z", + "iopub.status.idle": "2023-01-03T20:27:37.479492Z", + "shell.execute_reply": "2023-01-03T20:27:37.478858Z" } }, "outputs": [ @@ -850,7 +850,7 @@ }, { "cell_type": "markdown", - "id": "8414f740", + "id": "676ea883", "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": "66ad40c7", + "id": "f5bd44ff", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.789613Z", - "iopub.status.busy": "2023-01-02T13:06:44.789354Z", - "iopub.status.idle": "2023-01-02T13:06:44.794799Z", - "shell.execute_reply": "2023-01-02T13:06:44.793653Z" + "iopub.execute_input": "2023-01-03T20:27:37.483264Z", + "iopub.status.busy": "2023-01-03T20:27:37.483046Z", + "iopub.status.idle": "2023-01-03T20:27:37.487127Z", + "shell.execute_reply": "2023-01-03T20:27:37.486481Z" } }, "outputs": [], @@ -885,7 +885,7 @@ }, { "cell_type": "markdown", - "id": "86b86661", + "id": "07ff2c77", "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": "510da4e2", + "id": "9c32a36f", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.798865Z", - "iopub.status.busy": "2023-01-02T13:06:44.798561Z", - "iopub.status.idle": "2023-01-02T13:06:44.805046Z", - "shell.execute_reply": "2023-01-02T13:06:44.804215Z" + "iopub.execute_input": "2023-01-03T20:27:37.490364Z", + "iopub.status.busy": "2023-01-03T20:27:37.490147Z", + "iopub.status.idle": "2023-01-03T20:27:37.496909Z", + "shell.execute_reply": "2023-01-03T20:27:37.495166Z" } }, "outputs": [ @@ -938,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "eb3106f6", + "id": "8df9a6bc", "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": "4ce14fcf", + "id": "aabbcd4b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.810077Z", - "iopub.status.busy": "2023-01-02T13:06:44.809788Z", - "iopub.status.idle": "2023-01-02T13:06:44.813430Z", - "shell.execute_reply": "2023-01-02T13:06:44.812634Z" + "iopub.execute_input": "2023-01-03T20:27:37.499837Z", + "iopub.status.busy": "2023-01-03T20:27:37.499596Z", + "iopub.status.idle": "2023-01-03T20:27:37.502609Z", + "shell.execute_reply": "2023-01-03T20:27:37.501972Z" } }, "outputs": [], @@ -967,7 +967,7 @@ }, { "cell_type": "markdown", - "id": "1c4cac99", + "id": "0852b1fb", "metadata": {}, "source": [ "# Multigraphs\n", @@ -987,13 +987,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "ecc058cb", + "id": "cdc9ff8c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.817671Z", - "iopub.status.busy": "2023-01-02T13:06:44.817425Z", - "iopub.status.idle": "2023-01-02T13:06:44.825347Z", - "shell.execute_reply": "2023-01-02T13:06:44.824506Z" + "iopub.execute_input": "2023-01-03T20:27:37.505489Z", + "iopub.status.busy": "2023-01-03T20:27:37.505275Z", + "iopub.status.idle": "2023-01-03T20:27:37.511722Z", + "shell.execute_reply": "2023-01-03T20:27:37.511078Z" } }, "outputs": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "markdown", - "id": "0d509efb", + "id": "8636eabf", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -1043,13 +1043,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "36872fe5", + "id": "bf582f5d", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.830432Z", - "iopub.status.busy": "2023-01-02T13:06:44.830187Z", - "iopub.status.idle": "2023-01-02T13:06:44.836070Z", - "shell.execute_reply": "2023-01-02T13:06:44.835000Z" + "iopub.execute_input": "2023-01-03T20:27:37.515192Z", + "iopub.status.busy": "2023-01-03T20:27:37.514977Z", + "iopub.status.idle": "2023-01-03T20:27:37.519299Z", + "shell.execute_reply": "2023-01-03T20:27:37.518654Z" } }, "outputs": [], @@ -1062,7 +1062,7 @@ }, { "cell_type": "markdown", - "id": "f8aa2bc4", + "id": "b7049764", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -1073,13 +1073,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "018c814b", + "id": "0d1da7f1", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.840131Z", - "iopub.status.busy": "2023-01-02T13:06:44.839862Z", - "iopub.status.idle": "2023-01-02T13:06:44.926006Z", - "shell.execute_reply": "2023-01-02T13:06:44.924883Z" + "iopub.execute_input": "2023-01-03T20:27:37.522243Z", + "iopub.status.busy": "2023-01-03T20:27:37.522023Z", + "iopub.status.idle": "2023-01-03T20:27:37.603202Z", + "shell.execute_reply": "2023-01-03T20:27:37.602379Z" } }, "outputs": [], @@ -1092,7 +1092,7 @@ }, { "cell_type": "markdown", - "id": "1f2ee001", + "id": "fe33bd32", "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": "0958110d", + "id": "e4118e78", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:44.930470Z", - "iopub.status.busy": "2023-01-02T13:06:44.930203Z", - "iopub.status.idle": "2023-01-02T13:06:47.012922Z", - "shell.execute_reply": "2023-01-02T13:06:47.011621Z" + "iopub.execute_input": "2023-01-03T20:27:37.606633Z", + "iopub.status.busy": "2023-01-03T20:27:37.606380Z", + "iopub.status.idle": "2023-01-03T20:27:38.447980Z", + "shell.execute_reply": "2023-01-03T20:27:38.447176Z" } }, "outputs": [], @@ -1121,7 +1121,7 @@ }, { "cell_type": "markdown", - "id": "a3bfafb7", + "id": "88168e19", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -1136,13 +1136,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "ff811ef3", + "id": "6b8bddf8", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:47.017450Z", - "iopub.status.busy": "2023-01-02T13:06:47.017205Z", - "iopub.status.idle": "2023-01-02T13:06:47.024635Z", - "shell.execute_reply": "2023-01-02T13:06:47.023673Z" + "iopub.execute_input": "2023-01-03T20:27:38.451965Z", + "iopub.status.busy": "2023-01-03T20:27:38.451649Z", + "iopub.status.idle": "2023-01-03T20:27:38.460902Z", + "shell.execute_reply": "2023-01-03T20:27:38.460277Z" } }, "outputs": [ @@ -1168,7 +1168,7 @@ }, { "cell_type": "markdown", - "id": "51824ba9", + "id": "7f9bca65", "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": "1df52a7a", + "id": "21c663f1", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:47.030119Z", - "iopub.status.busy": "2023-01-02T13:06:47.029863Z", - "iopub.status.idle": "2023-01-02T13:06:47.036049Z", - "shell.execute_reply": "2023-01-02T13:06:47.035153Z" + "iopub.execute_input": "2023-01-03T20:27:38.464246Z", + "iopub.status.busy": "2023-01-03T20:27:38.463778Z", + "iopub.status.idle": "2023-01-03T20:27:38.468581Z", + "shell.execute_reply": "2023-01-03T20:27:38.467927Z" } }, "outputs": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "markdown", - "id": "1b996922", + "id": "5c8acf2c", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -1225,13 +1225,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "c22301f3", + "id": "f7d43a30", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:47.040596Z", - "iopub.status.busy": "2023-01-02T13:06:47.040313Z", - "iopub.status.idle": "2023-01-02T13:06:47.454119Z", - "shell.execute_reply": "2023-01-02T13:06:47.453099Z" + "iopub.execute_input": "2023-01-03T20:27:38.472362Z", + "iopub.status.busy": "2023-01-03T20:27:38.472007Z", + "iopub.status.idle": "2023-01-03T20:27:38.867315Z", + "shell.execute_reply": "2023-01-03T20:27:38.866577Z" } }, "outputs": [], @@ -1241,7 +1241,7 @@ }, { "cell_type": "markdown", - "id": "28bb8512", + "id": "1e3ac651", "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": "c9a9ea9b", + "id": "c32216be", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:47.458882Z", - "iopub.status.busy": "2023-01-02T13:06:47.458405Z", - "iopub.status.idle": "2023-01-02T13:06:47.762965Z", - "shell.execute_reply": "2023-01-02T13:06:47.762082Z" + "iopub.execute_input": "2023-01-03T20:27:38.871475Z", + "iopub.status.busy": "2023-01-03T20:27:38.871088Z", + "iopub.status.idle": "2023-01-03T20:27:39.078550Z", + "shell.execute_reply": "2023-01-03T20:27:39.077938Z" } }, "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": "410e7b3d", + "id": "fd7250c1", "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": "62b634c9", + "id": "79362d66", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:47.766910Z", - "iopub.status.busy": "2023-01-02T13:06:47.766577Z", - "iopub.status.idle": "2023-01-02T13:06:47.770331Z", - "shell.execute_reply": "2023-01-02T13:06:47.769390Z" + "iopub.execute_input": "2023-01-03T20:27:39.082686Z", + "iopub.status.busy": "2023-01-03T20:27:39.082278Z", + "iopub.status.idle": "2023-01-03T20:27:39.085334Z", + "shell.execute_reply": "2023-01-03T20:27:39.084817Z" } }, "outputs": [], @@ -1308,7 +1308,7 @@ }, { "cell_type": "markdown", - "id": "d67a317b", + "id": "d7711d4b", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -1317,19 +1317,19 @@ { "cell_type": "code", "execution_count": 36, - "id": "29d6266e", + "id": "8be5197e", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:47.775156Z", - "iopub.status.busy": "2023-01-02T13:06:47.774865Z", - "iopub.status.idle": "2023-01-02T13:06:48.203569Z", - "shell.execute_reply": "2023-01-02T13:06:48.202634Z" + "iopub.execute_input": "2023-01-03T20:27:39.088269Z", + "iopub.status.busy": "2023-01-03T20:27:39.087917Z", + "iopub.status.idle": "2023-01-03T20:27:39.369281Z", + "shell.execute_reply": "2023-01-03T20:27:39.368719Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 640x480 with 4 Axes>" ] @@ -1356,7 +1356,7 @@ }, { "cell_type": "markdown", - "id": "0d2b3db8", + "id": "751d3c6c", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -1367,13 +1367,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "0dc65281", + "id": "b9a82a38", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:48.207606Z", - "iopub.status.busy": "2023-01-02T13:06:48.207248Z", - "iopub.status.idle": "2023-01-02T13:06:48.449081Z", - "shell.execute_reply": "2023-01-02T13:06:48.447977Z" + "iopub.execute_input": "2023-01-03T20:27:39.373376Z", + "iopub.status.busy": "2023-01-03T20:27:39.372932Z", + "iopub.status.idle": "2023-01-03T20:27:39.479368Z", + "shell.execute_reply": "2023-01-03T20:27:39.478782Z" } }, "outputs": [ @@ -1396,7 +1396,7 @@ }, { "cell_type": "markdown", - "id": "7bfd3c65", + "id": "4a4e4c97", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -1405,19 +1405,19 @@ { "cell_type": "code", "execution_count": 38, - "id": "251b1df9", + "id": "331810ca", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:48.453460Z", - "iopub.status.busy": "2023-01-02T13:06:48.453175Z", - "iopub.status.idle": "2023-01-02T13:06:48.638752Z", - "shell.execute_reply": "2023-01-02T13:06:48.637841Z" + "iopub.execute_input": "2023-01-03T20:27:39.482770Z", + "iopub.status.busy": "2023-01-03T20:27:39.482444Z", + "iopub.status.idle": "2023-01-03T20:27:39.616195Z", + "shell.execute_reply": "2023-01-03T20:27:39.615592Z" } }, "outputs": [ { "data": { - "image/png": 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lS9euXXH//n3cvHkTderU4TsSIf+Rn5+PsLCwktHL6OhoCIVCtGvXDlW7jEOsrCY4HJwsoSUUYKSdBbxdaT0xUR5CvgMQQrgREhICZ2dndOrUCa9fv8bRo0dx48YNuLq6qlQxCQBz587F5cuXsW/fPiomidLS1dVF586d8fvvv+POnTtITk7G5s2bYWZmhpgMyKWYBIAiGUNw3Av5XJyQcqKCkhAV934h+erVKxw5ckRlC0kAOHz4MH7//XesWLECHTt25DsOIaVWr149eHp6wn/Hboiq1JTrvZLSc5GTL5XrPQgpCyooCVFRISEh6Ny58weF5M2bN9GvXz+VLCQBIDY2FmPGjMHAgQPx448/8h2HkHJJTM+BvNeSMQAJ6TlyvgshpUcFJSEqJjQ0tKSQzMzMVItCEgCys7MxYMAA1K1bF/7+/ir9tRDNVsBhmyBluA8hpUEFJSEqoriQ7Nixo1oVksC73n9jx45FUlISDh8+XKoef4QoKx2RYl5aFXUfQkqDfhoJUXIfF5KBgYFqU0gWW7t2Lfbt2wd/f380bdqU7ziEVEh9E33I+zdT8O99CFEWVFASoqRCQ0PRpUsXdOzYERkZGSWFZP/+/dWmkASAK1euYObMmfjxxx8xaNAgvuMQUmH6uiKYy/kkG3MTPejriuR6D0LKggpKQpTM5cuXSwrJ9PR0lSwkc/KluPvsNW4lZeLus9ef3Y2akpKCwYMHQywWY/ny5QpOSYj8ODepAS2hfH5ftYQCOFvVkMu1CSkventDiJK4fPkyvL29cfHiRXz77bcIDAyEq6srhELVeN8nSc3CrsgkBMe+QFJG7ge7XAUAzI314NykBkbYmaNxTUMUFhZiyJAhAID9+/dDW1ubl9yEyMMIO3MEXE2Qy7WLZAzu9uZyuTYh5UUFJSE8+7iQPHz4MPr166cyhWRyRi7mBEbjcnwatISCT55bzAAkZuRiR2QiAq4mwMnSFPr3jiE8PBzBwcGoVauW4oMTIkeNaxrCydJUbmd507GLRNnQ0YuE8OTy5ctYuHAhLly4AFtbW3h7e6tUIQkAe6OSsODYXUhlrEwvmkIwSAsL0M3kNbb84iHHhITwJzkjFy6rQ5DPYXsfXZEQQTM6wkzOazQJKSvVeeUiRE1cuXIFLi4u6NChA16+fInDhw/j1q1bcHNzU6li0idYgtmHo5EvlZV5BEYGAYQiHQS9qQGfYImcEhLCLzNjPSzk+Lztn10aUDFJlJLqvHoRouKKC0knJyeVLiSBdyOTq87FVewi/24wWnUuDvuikjhIRYjyGdrWHDO7WXFyrfxrB/DnlIF48uQJJ9cjhEuq9SpGiAr6uJA8dOiQyhaSwLtpvAXH7nJ6zV+P3UVyRi6n1yREWUx1bozlA2ygKxKWeee3llAAXZEQKwbYIHj9bGRlZUEsFuPevXtySktI+ajeqxkhKuLKlSvo2rXrfwrJAQMGqGQhWWxOYDSkHG4yAACpjGFOYDSn1yREmQxta46gGR3h0NAEAL5aWBY/7tDQBEEzOmJIW3N88803uHr1KqpVq4b27dsjLCxM7rkJKS3alEM0Sk6+FAnpOSiQyqAjEqK+iT7nzYHDwsLg7e2NoKAg2NraYsGCBejfv79KF5HFJKlZ6LomVG7XD5rRgXavErVX3GJrV/AtFOpWLVn+AfzbYstED85WNeBub/7J34dXr16hf//+iIyMxJ49e9C/f3+FZSfkc6igJGqvrP0Ry+v9QtLGxgbe3t5qU0gW8z52FzsiEzltg1JMSyjASDsLeHO8iYEQZdWgQQO4DR4Krx9ml/lNbl5eHkaOHInDhw9j/fr1mDhxogISE/J51IeSqK3y9kdc6mZTpl2UHxeSBw8eVNn1kV8THPtCLsUk8K5Zc3DcC3iDCkqi/vLz85GUlIRmjRvBuk6VMn9+pUqVsHfvXsyYMQOTJk3C8+fP4e3trTKnaRH1o36veITg3S5kl9UhCH+UDgBfLYKKHw9/lA6X1SHYW4pdx2FhYejWrRvat2+P1NRUHDx4EP/88w++++47tSwms/OlSJLzxpmk9NzPHtNIiDp59OgRZDIZGjduXO5raGlp4a+//sLy5cuxaNEijB8/HlIp/f4Qfqjfqx7ReBXpj1gkY8iXyjD7cPRn+yOGh4eXFJIpKSlqX0gWS0zPgbzXxzAACek5cr4LIfyTSN49v1SkoAQAgUCAn3/+GQEBAdi6dSsGDBiA3FzqmEAUT31f/YhG4qQ/4r8+7o9YXEg6Ojri+fPnGlNIFivg8LQPZbgPIXySSCTQ19dH7dq1Obne6NGjcfz4cVy4cAFdunRBeno6J9clpLTU/1WQaAx59Uc8EnT5g0LywIEDuH37tsYUksV0RIr5WhV1H0L4FBcXB0tLS07XPPbs2ROXLl3Cw4cP4ejoiMTERM6uTcjX0DM3URvy6I+YXyjFhC2XPigkBw4cqFGFZLH6JvqQ93J/wb/3IUTdSSQSWFlxc4LO+9q2bYuwsDAUFBRALBbjzp07nN+DkE/RvFdFopYkqVm4HJ/G/Q5kgRCVG7TCofNhGltIFtPXFcFczmcIm5vocd4XlBBlJJFIKrx+8nMaN26M8PBw1KpVC05OTrh06ZJc7kPI+zT31ZGolV2RSWU+0qy0tIQC7L6WLJdrqxrnJjXk9n1msiK8+CcYu3btQkFBgVzuQYgyyM3NxZMnT+RWUAJArVq1EBISgnbt2qF79+7Yv3+/3O5FCEAFJVETiuiPSIA+31SR2/dZINRCjTexcHd3h4WFBRYtWoTU1FS53IsQPj18+BBAxXd4f42hoSFOnjyJgQMHYujQoVi3bp1c70c0GxWUROVRf0T5KyoqwqZNm9DDoQUKk+5AwHEDIS2hAE6Wpgg5tg93795F//79sWLFCpibm2P06NG4ceMGp/cjhE9ctQwqDR0dHezYsQP/+9//8P333+OXX34BHZBH5IEKSqLyqD+ifF27dg329vaYMGEC+vTpgxMLRkBHpMXpPURCAZa62QAAmjVrhg0bNuDJkydYunQpQkND0aZNG7Rv3x779+9HYWEhp/cmRNHi4uJgZGSE6tWrK+R+QqEQK1euxB9//IHly5djzJgx9HtEOEcFJVF51B9RPtLS0jBu3DjY29tDKpUiLCwMAQEBaGlljoUcn7e9yNX6P8ddVqtWDf/73/8QHx+PwMBAaGtrY8iQIWjYsCGWLVuGtLQ0TjMQoijFO7wVfUzijz/+iN27d2PPnj3o27cvsrOzFXp/ot6ooCQqj/ojcquoqAgbNmyAlZUVDh48iHXr1uH69etwcHAo+Zihbc0xsxs3LU9mdWuCIW3NP/u4lpYW+vfvj+DgYNy+fRvdu3fHokWLUK9ePXh5eVFbFKJy5LnD+2uGDRuG06dPIzw8HM7OznjxgtaHE25oxiskUWvUH5E7V69eRdu2bTF58mQMGDAAcXFxmDJlCrS0/jvFPdW5MZYPsIGuSFjmnd9aQgF0RUKsGGCDKc6Wpf48W1tbbNmyBcnJyfD29sa5c+fw7bffolOnTggMDERRUVGZchDCBz4LSgDo0qULQkJCkJycDEdHRzx69Ii3LER9UEFJVB71R6y4Fy9ewMPDAw4ODhAKhYiIiMCWLVu+usZraFtzBM3oCIeGJgDw1cKy+HGHhiYImtHxiyOTX2JqaorZs2fj0aNH2L9/P4qKijBgwAA0atQIK1euRGZmZrmuS4i8ZWVlISUlhdeCEgBatmyJq1evQiAQQCwW08Y3UmFUUBK1IM/+iFpCAZytasjl2nyTSqVYt24drKyscOzYMfj6+iIyMhJ2dnalvoaZsR52eNnh/PQOGGlnAQsTvf+MGAsAWJjoYaSdBYJmdMAOL7v/rJksD21tbQwaNAiXL1/G9evX0alTJ8ybNw/16tXDxIkTce/evQrfgxAuxcfHA1DMDu+vadCgAcLCwmBhYYFOnTrh/PnzfEciKkzAqH8AUQOS1Cx0XRMqt+sHzegAyxqGcrs+H65cuYIpU6YgOjoa48aNw9KlS2FiYsLJtXPypUhIz0GBVAYdkRD1TfQVNsKbmpqKTZs24e+//0ZKSgpcXFzw/fffo3fv3hp90hFRDvv27cPQoUORnp4OY2NjvuMAAHJycjB48GCcO3cOAQEBGDFiBN+RiAqiZ1eiFuobV0Id4WswGbdr6Ir7I6pTMZmSkoJRo0bByckJlSpVwrVr17Bx40bOikng3TIE6zpV0NK8GqzrVFHocoGaNWti/vz5SExMxK5du5CVlQVXV1dYWVlhzZo1eP36tcKyEPIxiUQCY2NjpSkmAUBfXx9HjhyBu7s73N3d8ccff/AdiaggKiiJyrtz5w7s7Oxww3cmuN2IzT7oj6jqCgsLsWbNGjRp0gSnT5/Gli1bcPXqVbRp04bvaHKho6OD4cOHIyIiAhEREbCzs8OsWbNQr149TJs2DXFxcXxHJBqouGWQstHW1oa/vz/mzp2LmTNn4scff4RMplmt0kjFUEFJVFZhYSF+++03tGnTBoWFhQg7ewxLBrTg8A4CmL24ipoGqr8ZJyQkBK1atcL//vc/uLu7IzY2Fl5eXhozBWxnZ4ddu3YhMTERM2bMwL59+9CkSRP06tULZ86coRdOojB87/D+EoFAgMWLF8PHxwdr1qzBiBEjkJ+fz3csoiI049WEqJ3o6GjY29tj4cKF+Omnn3D9+nW0adOG0/6I3Wvn43LACvTo0QOvXr3i5JqK9uzZMwwfPhydOnWCoaEhrl+/jvXr1yvVdJsi1alTB4sWLUJSUhICAgKQkpKCnj17olmzZli/fj01eiZyp8wFZbEpU6Zg//79CAwMRK9evfDmzRu+IxEVQAUlUSmFhYVYvHgxWrdujfz8fERERGDx4sXQ1dUt+Riu+iNu/H4AgoKC8M8//6B9+/ZISkri+suRm8LCQqxatQpNmjTBhQsXEBAQgCtXrqBly5Z8R1MKlSpVKjkj/PLly7C1tcUPP/yAunXr4scff6S+fEQuMjMzkZaWpvQFJQAMHDgQ586dw40bN9CxY0c8f/6c70hEyVFBSVRG8aikt7c3Zs2ahRs3bnx2/R9X/RGdnJwQHh6OnJwc2Nvb49atWxx+RfJx4cIFfPvtt/j555/h6emJ2NhYjB49WmOmt8tCIBCUnBH++PFjTJ48Gdu2bYOlpSX69euHCxcugBphEK5IJBIAytEyqDQ6dOiAy5cv4+XLl3BwcEBsbCzfkYgSo1cYovSkUimWLFmC1q1bIy8vDxEREViyZMkHo5KfwlV/xG+++QYRERGoW7cuOnTogNOnT3P7BXIkOTkZgwcPhouLC0xMTHDr1i389ddfqFq1Kt/RVIKZmRmWLVuGJ0+eYPPmzXj8+DFcXFxgY2ODTZs2ITc3l++IRMWpWkEJADY2NggPD0flypXh6OiIyMhIviMRZcUIUWLR0dGsdevWTCgUsl9++YXl5eVV6HrZeYUs5ukrdjMxg8U8fcWy8wpL/7nZ2axPnz5MS0uLbd68uUI5uJSfn8+WLVvG9PT0WK1atdiOHTuYTCbjO5bKk8lkLDg4mPXv358JhUJWrVo19tNPP7GEhAS+oxEVtWDBAlazZk2+Y5RLeno6c3BwYHp6euzEiRN8xyFKiApKopQKCwvZ4sWLmba2NmvWrBm7du0a35EYY+9yTZo0iQFgc+fO5b1wO3v2LLOysmJaWlpsxowZ7PXr17zmUVePHj1i//vf/1iVKlWYUChkAwYMYCEhIbz/+xPVMnz4cNa+fXu+Y5Rbbm4u69evH9PS0mL+/v58xyFKhgpKonTeH5WcPXs2e/v2Ld+RPiCTydjvv//OADB3d3eWn5+v8AwJCQlswIABDADr2LEji46OVngGTZSVlcU2bNjAmjZtygCwFi1aMH9/f6X7GSXKqW3btszDw4PvGBVSWFjIxo8fzwCwxYsX05sqUoIKSqI0CgsL2ZIlS5iOjg5r2rQpi4yM5DvSF+3du5fp6OgwZ2dnlpmZqZB7vn37lv3222+scuXKrE6dOmzPnj30hM4DmUzGzp07x/r06cMEAgEzNTVlc+bMYU+ePOE7GlFSMpmMVa1alS1dupTvKBUmk8nYwoULGQA2efJkJpVK+Y5ElAAVlEQpxMTEsDZt2jChUMh+/vlnlRnxCQ0NZdWqVWPW1tYsMTFRrvc6efIka9SoEROJRGzWrFnszZs3cr0fKR2JRMJ++OEHZmhoyEQiERsyZAgLDw+nQp984OXLlwwAO3DgAN9ROLNp06aSJSCq8pxN5Id2eRNeSaVSLFu2DK1atUJ2djbCw8OxfPlyVKpUie9opeLk5ISwsLCStkL//PMP5/d4/Pgx+vXrh969e6N+/fq4c+cOfv/9dxgaqs/54qrM0tISa9aswZMnT/Dnn3/i5s2bcHBwQLt27bBz5046aYQAQMlRn6q0w/trxo0bh8DAQJw6dQrdu3dHZmYm35EIj6igJLy5d+8eHBwcMG/ePEyfPh23bt2CnZ0d37HKrGnTprh69Srq1KkDJycnnDlzhpPrvn37FgsXLkSzZs1w8+ZN7N+/H+fPn0fTpk05uT7hlpGREaZNm4YHDx7g5MmTMDExwciRI2FhYQFvb2+kpKTwHZHwqLhlkKWlJc9JuOXq6ooLFy4gJiYGTk5OePLkCd+RCE+ooCQKJ5VKsXz5crRs2RJZWVkIDw/HihUrVGZU8lNq1aqFkJAQdOrUCX369IGfn1+Frnf8+HFYW1tjyZIlmDFjBh48eIBBgwZBICjbyT9E8YRCYckZ4ffu3cN3332HVatWwdzcHCNHjkRUVBTfEQkPJBIJ6tatC319fb6jcM7BwQFXrlxBVlYWxGIx7t27x3ckwgMqKIlC3bt3D46Ojpg7d65Kj0p+ir6+PgIDAzF+/HiMHTsW8+fPL/MpK/Hx8ejTpw9cXV1hZWWFmJgYLF26VC1fhDRB06ZNsX79ejx58gTLly9HWFgY2rVrBwcHB+zduxeFhYV8RyQKogpneFdE06ZNER4ejmrVqqF9+/YICwvjOxJRMCooiUJIpVKsWLECLVu2xJs3bxAWFqbyo5KfIhKJsH79eqxYsQKLFy/G6NGjUVBQ8NXPy83Nxfz582FtbY3o6GgcPnwYp0+fhpWVlQJSE3mrWrUqfvzxR0gkEhw5cgSVK1fGsGHDUL9+fSxZsgQvX77kOyKRM3UvKAGgbt26CA0Nha2tLVxcXHDkyBG+IxFF4ntXEFF/9+7dY+3atWNCoZDNmjWL5ebm8h1JIfbs2cN0dHRY586d2atXrz75MTKZjB0+fJhZWFgwHR0dNm/ePJaTk6PgpIQPd+7cYePGjWOVKlViurq6zMPDg926dYvvWEQOZDIZMzAwYL///jvfURTi7du3bODAgUwoFLINGzbwHYcoCBWURG6kUilbsWIF09XVZU2aNGHh4eF8R1K4kJAQVrVqVda8eXOWlJT0wWOxsbGse/fuDADr1asXk0gkPKUkfEpLS2PLly9nZmZmDADr0KEDO3jwICssLP2xoES5PXv2jAFggYGBfEdRGKlUyqZOncoAsF9//ZXaaGkAKiiJXNy/f5/Z2dkxgUDAZs6cqTGjkp9y7949Vr9+fVanTh1269Ytlp2dzWbPns20tbVZgwYN2LFjx+jJlrDCwkJ24MAB5uTkxAAwc3NztmLFCpaens53NFJBISEhDACLiYnhO4pCyWQytmzZMgaAjR07lt4kqTkBY2XcNUDIFxQVFeHPP//E/PnzYWFhgYCAAIjFYr5j8S4lJQW9e/fGvXv3YGBggOzsbMyePRs//fQTKleuzHc8omRu3bqFtWvXYvfu3dDS0sLIkSMxbdo0NG/enO9opBz8/Pwwbtw45Obmqt268dLYtm0bvLy80KtXL+zduxd6enp8RyJyQJtyCGcePHgAR0dH/Pzzz5g6dSr++ecfKib/lZmZCSMjI+Tl5SEtLQ0LFizAggULqJgkn9SyZUts3boVycnJmDt3Lk6cOAEbGxt06dIFx44dQ1FREd8RSRlIJBKYm5trZDEJAKNHj8bx48dx4cIFdOnSBenp6XxHInJABSWpsKKiIqxcuRItWrRAZmYmrly5glWrVlGxBCArKws//fQTbG1tkZycjGPHjmHixIn45Zdf8Ouvv5a5rRDRLDVq1MDcuXORkJCAPXv2IDc3F/369YOVlRVWr16NV69e8R2RlIIm7PD+mp49eyI4OBjx8fFwdHREYmIi35EI13iecicq7v79+8ze3p4JBAL2448/avRayffJZDK2e/duVqdOHVa5cmW2ePHikrNuZTIZW758OQPARo4cyfLz83lOS1RJZGQkc3d3Z9ra2kxfX59NmTKF3b9/n+9Y5AtsbGzYpEmT+I6hFOLi4liDBg1Y7dq12e3bt/mOQzhEI5SkXIqKirBq1Sq0aNEC6enpuHz5Mv744w8alQRw9+5ddO7cGcOHD4e9vT3u37+PuXPnlkx3CQQC/Pzzz9i9ezf27duHXr164fXr1zynJqqiXbt22LFjBxITEzFz5kwcOHAATZs2RY8ePXD69GnIZDK+I5L3yGQyGqF8T+PGjREeHo5atWrByckJly5d4jsS4QgVlKTMYmNj4eTkhJ9++gmTJ0/GP//8A0dHR75j8e7Nmzf48ccf8e233+LZs2c4e/YsDh06BAsLi09+/LBhw3Du3DncuHED7du3R3JysoITE1VWu3ZteHt7IykpCdu3b0daWhp69eqFpk2bwsfHB1lZWXxHJACePn2KvLw8KijfU3xUbbt27dC9e3fs37+f70iEA1RQklIrKirCH3/8gRYtWuDly5cIDQ3Fn3/+qfE79hhj2LlzJ5o0aYKNGzdi8eLFuHPnDrp16/bVz+3YsSPCw8ORlZUFe3t73L59WwGJiTrR1dUtOSM8LCwMLVu2xPTp01G3bl1Mnz4d8fHxfEfUaBKJBADo1KuPGBoa4uTJkxg4cCCGDh2KdevW8R2JVBTfc+5ENcTGxjIHBwcmEAjY9OnT6TSXf/3zzz+sffv2DAAbPHjwf5qXl9bz589Zq1atmKGhITt79izHKYmmSU5OZnPmzGEmJiZMIBCwPn36sHPnzlG/Ux74+voyLS0tVlBQwHcUpVRUVMT+97//MQBs9uzZ9DOqwmiEknxRcV/Jb7/9Fi9evEBoaChWr16t8aOSr169wvfff49WrVohPT0dQUFB2LdvH8zMzMp1veIpoA4dOqBXr17w9/fnODHRJPXq1cOSJUuQnJyMLVu2ICkpCd26dUPz5s3h6+uLnJwcviNqDIlEgvr160NbW5vvKEpJKBRi1apV+OOPP7B8+XKMGTMGhYWFfMci5cF3RUuUV2xsLHN0dKRRyfcUFRWxrVu3sho1ajADAwO2cuVKTndpFxYWsgkTJtBxZYRTMpmMXbp0iQ0YMIAJhUJWtWpVNnPmTPb48WO+o6k9V1dX1qNHD75jqITdu3czbW1t1r17d5aVlcV3HFJGVFCS/5BKpezPP/9klSpVYpaWliw0NJTvSErh5s2bTCwWMwBs2LBh7MmTJ3K5z/vHlY0aNYraChFOJSQksFmzZrGqVasyoVDI3NzcWHBwsELevGTnFbKYp6/YzcQMFvP0FcvOU/+j+Jo2bcqmTZvGdwyVcf78eWZgYMDatGnDUlNT+Y5DyoCOXiQfkEgk8PDwQHh4OL7//nssXbpU46e3MzMzMW/ePPj6+pbsoO3UqZPc77t79254eHigQ4cOOHjwIKpUqSL3exLNkZOTg507d2Lt2rW4d+8ebG1t8f3332P48OGctv+SpGZhV2QSgmNfICkjF++/4AgAmBvrwblJDYywM0fjmoac3VcZFBUVQU9PD6tWrcK0adP4jqMybt26hZ49e8LQ0BBnz55Fw4YN+Y5ESoEKSgLg3RPf2rVrMWfOHNStWxf+/v7o0KED37F4JZPJsHXrVsyePRv5+flYtGgRpkyZotC1UCEhIejfvz/MzMxw6tQp1KtXT2H3JpqBMYYLFy5g7dq1OHHiBIyNjTF+/HhMnjy5Qj9vyRm5mBMYjcvxadASClAk+/xLTfHjTpamWOpmAzNj9XgT+/jxYzRs2BBnzpxB9+7d+Y6jUh49eoQePXrg9evXOHXqFFq3bl3qz83JlyIhPQcFUhl0RELUN9GHvq5IjmkJQAUlARAfHw8PDw9cuXKlZFRSX1+f71i8un79OqZMmYJr165h5MiR+P3331GrVi1esty7dw+9evWCVCrFyZMn8e233/KSg6i/hw8fwsfHB/7+/sjJycF3332H77//Hg4ODhAIBKW+zt6oJCw4dhdSGftiIfkxLaEAIqEAC12tMbSteXm+BKVy7tw5dO/eHQ8fPqRRtnJ4+fIlevfujfv37+Pw4cPo2rXrZz9Wk0fClQXt8tZgMpkMf/31F2xtbfHs2TOEhITgr7/+0uhiMj09HRMmTEC7du2Ql5eHy5cvY/v27bwVkwDQrFkzXL16FTVr1oSTkxPOnTvHWxai3ho1aoTVq1fjyZMn+Ouvv/DPP/+gffv2aNu2LbZv3478/PyvXsMnWILZh6ORL5WVqZgEgCIZQ75UhtmHo+ETLCnvl6E0JBIJtLW1YW6u+sUxH6pXr47g4GA4OTmhV69e2LVr138+JjkjFyP9ItF1TSh2RCYi8aNiEgAYgMSMXOyITETXNaEY6ReJ5IxchXwNmoQKSg0VHx+Pjh07Yvr06Rg7dizu3Lmj0VPcRUVF2LhxI6ysrLBv3z789ddfJSfYKIPatWsjJCQETk5O6N27N7Zu3cp3JKLGDA0NMWXKFNy/fx+nT59G9erVMXr0aJibm2PBggV4/vz5Jz9vb1QSVp2L4yTDqnNx2BeVxMm1+CKRSNCwYUOIRDTdWl76+vo4evQo3N3d4e7ujj/++KPksb1RSXBZHYLwR+kA8NU3MMWPhz9Kh8vqEOxV8Z8vZUMFpYb5eFTy0qVLWLt2rUaPSkZGRsLOzg4TJ06Eq6srYmNjMW3aNKV7ETAwMMDRo0fh6ekJT09PeHt7g1asEHkSCoUlZ4Q/ePAAgwcPxh9//AELCwu4u7vj2rVrJR+bnJGLBcfucnr/X4/dVemRJDrDmxva2trw9/fHnDlzMHPmTPz4449YdyGORsKVDBWUGiQ+Ph6dOnXC9OnT4eXlhTt37qBjx458x+LNy5cv4eXlBXt7e8hkMoSHh2Pr1q2oWbMm39E+SyQSwdfXF0uXLsXChQvh4eGBgoICvmMRDdCkSROsW7cOT58+xe+//46rV6/Czs4O9vb22LNnD2Yfug1pGV/Yv0YqY5gTGM3pNRUpLi6OCkqOCAQCLFmyBD4+Pth88R7+COKmEFSHkXBlQQWlBpDJZFi7di1sbW3x5MkTBAcHY926dRo7KllUVIT169fDysoKgYGB+PvvvxEVFQWxWMx3tFIRCAT45ZdfsGvXLuzevRu9e/fGmzdv+I5FNESVKlUwffp0xMXF4dixYzAwMMDoaT8j7FFGmUeKvqZIxnA5Pg3xL7I4va4iFBYW4vHjx3SGN8dch3mgZq+pnM7OqPpIuLKgglLNPXz4EM7Ozvjhhx/g6emJO3fuKKSHorIKDw9HmzZtMG3aNAwcOBBxcXGYNGkStLS0+I5WZsOHD8e5c+cQFRUFJycnPHnyhO9IRINoaWmhb9++CAoKwoRVOyFgMvncRyjAzgjVG0FKSEhAUVERjVBybE5gNJhAWKauA1+j6iPhyoIKSjUlk8mwbt062NraIjk5GRcvXoSPjw8MDAz4jsaL1NRUjBkzBo6OjhCJRIiIiMDmzZthamrKd7QK6dSpE8LCwvDq1SvY29vjzp07fEciGujOyyIwgXxeTopkDMFxL+RybXmSSN5NyVJByR1JahYux6fRSLiSooJSDT169AidO3fG999/Dw8PD9y5cwfOzs58x+KFVCrF2rVrYWVlhRMnTmDTpk2IiIhAu3bt+I7GGWtra0RERKBGjRpo3749zp8/z3ckokGy86VIkvN0YVJ6LnLypXK9B9ckEgkqVapEhxFwaFdkErSE3I1Mvk9VR8KVCRWUakQmk8HHxwc2NjZITEzU+FHJ0NBQtGrVCtOnT8fw4cMRGxuLcePGqeT09tfUrl0boaGhaN++PXr16oWAgAC+IxENkZie85++f1xjAGISU5GdnY23b9+isLAQMpl8pti5IpFI0KhRIwiF9DLLleDYF5yPThZT1ZFwZaJcfVFIuT169Aienp4ICQnB5MmTsWLFCo0tJJ8/f45Zs2Zh165dsLOzQ1RUVJmO7VJVBgYGOHbsGCZPngwPDw8kJibi119/5XStESEfK5AqprDr0KkzCp5/2ONSIBBAS0sLWlpaEIlEX/z71x7n+vPOnDkDXV1drFy5UimyiUQilS5uFTkSTsc0lg9911ScTCbDhg0b8PPPP6N69eq4cOECOnfuzHcsXhQWFmLdunXw9vaGrq4u/Pz8MGbMGJV+Ei0rkUiEjRs3okGDBpgzZw4SEhKwadMmhZ4/TjSLjkgxv18rli1BTZ1CFBUVoaioCFKp9D9//9R/K+vf3/9vBQUF5b5GamoqdHV1sWTJkv88zufoqryLVnkVxmnSSmAwluv3hgFISM+BdZ0qcr2PuqKCUoU9fvwYnp6euHTpEiZNmoQVK1bA0FAzzyi9dOkSpk6divv372PSpEn47bffUK1aNb5j8aK4rZC5uTk8PDzw9OlTHDx4EEZGRnxHI2qovok+BIBcp70FAMYN7a8yI0f5+fnQ09PD2rVrMXbs2P88zhgrKTIVVRwr4hr5+fmc5nj/v+nUtkLt0X/K/d9OUSPu6kg1fjvJB94flTQ1NUVQUBC6dOnCdyxePH36FDNnzsTevXvh4OCAGzduoEWLFnzHUgojRoxAnTp14ObmBicnJ5w8eZI2CBDO6euKYG6sh0Q5Tkeam+ipTDEJvFuCJJPJPrvDWyAQQCQSKd1pXMqKMYaYp6/Qd3243O+lqBF3dUTfORXz+PFjuLi4YOrUqRg5ciSio6M1spgsKCjA77//jiZNmiA4OBjbtm3DlStXqJj8iLOzM7UVInLn3KSGXHffOlvVkMu15YVaBnFLIBCgYXVDyHs1uADvRtxJ+VBBqSKKRyVtbGzw8OFDBAUFYcOGDRo5xR0UFIRvv/0Wc+bMwdixYxEbG4tRo0bR5pPPsLa2xtWrV1GjRg04OTkhKCiI70hEzYywM5fr7lt3e3O5XFteJBIJ9PX1Ubt2bb6jqI3ikXB5UrWRcGVDBaUKSEhIgIuLCyZPngx3d3fExMQo/ahkTr4Ud5+9xq2kTNx99pqTHnLJyckYNGgQunbtiurVq+PWrVtYs2YNqlShBdRfU6dOHYSEhMDBwQE9e/aktkKEU41rGsLJ0pTzUUotoQBOlqawrKFab5zj4uJgaWlJb3I5RiPhyo1KcSUmk8mwceNGzJo1CyYmJjh//jxcXFz4jvVZktQs7IpMQnDsCyRl5H6wSF8AwNxYD85NamCEnTka1yz9C0R+fj7+/PNPLF68GEZGRti5cyeGDx9OT9ZlZGhoiOPHj2PSpEnw8PBAUlIS5s+fT99HwomlbjZwWR3C6UilSCjAUjcbzq6nKBKJhKa75WCEnTkCribI5dqqOBKubKigVFIJCQnw8vLCxYsXMX78eKxcuVJpd+kmZ+RiTmA0LsenQUso+OQLCgOQmJGLHZGJCLiaACdLUyx1s4HZV6Ywzpw5g++//x6PHz/GDz/8gF9//VVpvw+qQCQSYdOmTWjQoAHmzp2LhIQEbNy4kdoKkQozM9bDQldrzD7M3ZnIi1ytv/ocoYwkEgnEYjHfMdRO8Uh4+KN0Tt+4aAkFcGhoonIj4cqGpryVDGMMvr6+sLGxgUQiwblz57Bx40alLaL2RiXBZXUIwh+lA8BXf8mLHw9/lA6X1SHYG/Xpo64SEhLg5uaGnj17ol69erh9+zZWrVqltN8HVSIQCDBnzhzs2LEDO3fuRO/evfHmzRu+YxE1MLStOWZ2s+LkWrO6NcGQtqo3YpSbm4snT57QCKWcLHWzgYjjaW9VHQlXNlRQKpHExER07doVkyZNwrBhwxATE4OuXbvyHeuzfIIlmH04GvlSWZnfLRbJGPKlMsw+HA2fYEnJf8/Ly8Nvv/2Gpk2bIioqCnv37sWFCxfQrFkzruNrPHd3d5w9exbXrl2Dk5MTnj59ynckogamOjfG8gE20BUJy7zeTUsogK5IiBUDbDDF2VJOCeXr4cOHAGiHt7wUj4RzSVVHwpUNFZRKgDGGjRs3onnz5oiLi8PZs2exadMmpR6N2xuVhFXn4r7+gaWw6lwc9kUl4eTJk2jevDl+++03fP/993jw4AGGDBlCa/zkqLitUGZmJuzt7REdzd10JdFcQ9uaI2hGR7SzqAoAEHyl7Xlx4enQ0ARBMzqq5MhkMWoZJH80Eq6cqKDkWWJiIrp164aJEyeWjEp269aN71hflJyRiwXH7nJ6zdkHb6H/CC80aNAAd+7c0eizyBXN2toaERERMDU1Rfv27XHhwgW+IxE1YGashy7Ce3i2ZTLcmpvAwkTvP30EBQAsTPQw0s4CQTM6YIeXncqPFEkkEhgZGaF69ep8R1Frmj4SrowEjDF5nphFPoMxhs2bN2PmzJmoUqUKtmzZgu7du/Mdq1RG+kVyviiayYrQpApw9pe+NCLJk6ysLAwePBhBQUHYsmULRo8ezXckouLatGmDmjVr4uTJkwDetRNLSM9BgVQGHZEQ9U301a7vn5eXF27fvo3r16/zHUUjlGZTaLHix0u7KZSUjXr9JquIpKQkjB07FufPn8fYsWOxatUqlemlKEnNwuX4NM6vKxBqIS4LePgym3ba8cTQ0BDHjh3D5MmTMWbMGCQmJlJbIVJuN27cwI0bN3D06NGS/6avK4J1HdV4risviUQCKytupmPJ15kZ62GHl93/t62Le4Gk9A/b1jHGUN9UH85WNeBub06vMXJCBaUCMcawZcsW/O9//0OVKlVw5swZlRmVLLYrMumr7wLLS0sowM6IJHhzvOCalJ62tjY2bdqE+vXrY968eUhMTISvry+1FSJltnHjRtSrVw+9evXiO4pCSSQSODs78x1D4zSuaQhvV2t4w/qDkfBrEeGY5D4IF+/HoFGjRnzHVGu0hlJBkpKS0KNHD4wfPx6DBw9GTEyMyhWTABAc+0KuR6wFx72Qy7VJ6QkEAsydOxc7duzAjh070KdPH2orRMrkzZs32L17N8aOHQuRSHPGLbKyspCSkkIbcnhWPBLe0rwaBrmIwQrzcPXqVb5jqT0qKOWseK1k8+bNce/ePZw+fRpbtmxRmSnu92XnS5GUkSvXeySl53JyTCOpOHd3d5w5cwaRkZHo0KEDtRUipbZr1y7k5eVh7NixfEdRqPj4eAC0w1uZGBsbo0mTJlRQKgAVlHKUnJyMnj17Yvz48Rg0aBBiYmLQo0cPvmOVW2J6zleaf1QcA5CQniPnu5DS6ty5M65cuYKMjAxqK0RKpfhwhj59+qBu3bp8x1EoahmknMRiMRWUCkAFpRwwxuDn54fmzZsjJiYGp06dgp+fn0qOSr6vQCpTq/uQ0mnevDm1FSKlFhkZiTt37mDixIl8R1G4uLg4GBsbw9jYmO8o5D1isRi3b99GdnY231HUGhWUHCselRw7diy+++47xMTEoGfPnnzHqhCZTIZHjx4h/EqoQu6nI6IfS2VTp04dhIaGQiwWo0ePHti+fTvfkYiS8vX1Rf369ZW+n6480A5v5eTg4ACZTIaoqCi+o6g1zVkt/Rlc9UVjjMHf3x8//vgjDAwMcPLkSZXc3Zieno7o6GhER0fjzp07iI6ORkxMDHJyciDQrgSzHw/ItY2MAEB9E325XZ+Un6GhIY4fP45JkyZh9OjRSExMxLx586itECmRmZmJffv24ddff4VQqHlvDCUSCU13K6FmzZrByMgIV69epR34cqSRBWVJv6rYF0jK+LBflQCAubEenJvUwAg7czSu+fV+VU+ePMG4ceNw5swZjBkzBqtXr0bVqlXlFZ8TeXl5uH///geFY3R0NJ4/fw4A0NHRQbNmzWBjY4PvvvsONjY2sLW1xdBdsXLdmGNuoqd2jY7Viba2NjZv3oz69etj/vz5SExMxIYNG6itEAEAbN++HVKpFB4eHnxH4YVEIlH5GSl1JBQKYWdnR+so5UyjXrlL01GfAUjMyMWOyEQEXE34Ykd9xhi2bt2KGTNmKO2opEwmQ0JCwn8KR4lEgqKiIgBAgwYNYGNjA09Pz5LCsXHjxp9s99G5SSZ2RCbKrQ+ls1UNzq9LuCUQCDBv3jxYWFjA09MTT548wYEDB2BoSM2CNRljDBs3boSbmxtq1arFdxyFe/XqFdLS0miEUkmJxWL8/fffYIzRrIqcaMzRi3ujkrDg2F1IZaxMxZCWUACRUICFrtYY+t4B8k+ePMH48eNx+vRpjB49GqtXr0a1atXkEb3Uiqer3y8ci6ergXftE2xsbEr+2NrawtraukyFgCQ1C13XyG8tZdCMDnSKgQq5cOECBgwYgIYNG+LkyZOoU6cO35EIT0JDQ9GxY0cEBQWhS5cufMdRmOJlU7ej72LMKHdcPLYP7e3a8h2LfOTMmTPo2bMn4uLiqOiXE40oKH2CJVh1Lq7C15nZzQpTOlkiICAAM2bMgL6+PjZt2oTevXtzkLL0iqer3y8cPzddXVw42tjYoHbt2py8M5PHWd5aQgEcGppgh5cdZ9ckilG88UwgEODUqVNo3rw535EID0aMGIGoqCg8ePBA7ddPcr1sishfZmYmjI2NsW3bNowaNYrvOGpJ7QvKvVFJmH2Yu955dZKDcXXXHwoZlSyerv64cPzUdPX7hePnpqu5kpyRC5fVIcjnsL2PrkiIoBkdP7m0gCi/p0+fonfv3nj8+DECAwPRuXNnviMRBUpLS0PdunWxZMkSzJw5k+84clOaZVPFih//0rIpoljNmjVDx44dsWHDBr6jqCW1Lii5LnwYY0BRIRbaaWPMwD6cXLNYWaariwvHsk5Xc4nrQn3FABsMeW9JAVE9b968weDBg3Hx4kX4+flh5MiRfEciCrJq1SrMnTsXT58+hampKd9x5ILrZVNE8by8vHDjxg38888/fEdRS2q9KWdOYDSkHE7LCgQCaGnr4MJrU4wp5zXKMl393XffcT5dzZWhbc2Rlp3PyVKCWd2aUDGpBoyMjHD8+HFMnDgRo0aNQlJSEubMmaNUP7eEezKZDJs2bcLAgQPVtpisyLKpon8L0NmHo5GWnY+pzrR+jy9isRgBAQHIysqiTYRyoLYFpSQ1C5fj0zi/bhEDLsenIf5F1hc3j5RlutrT01Nh09VcmurcGKYGuhV6177I1ZqKSTWira2NLVu2oH79+pg3bx4SEhLw999/U1shNRYcHAyJRIItW7bwHUUu9kYlcfLGGQBWnYtDdQNdes7jiVgsLmlwTstyuKe2U97ex+7Ktb3NSDsLeLtaA3i3fuj9olHZp6u5VpZ1RUxWBIFQi9YVaYDt27fDy8sLLi4u2L9/f6l/3rk6bIAoxuDBgxETE4O7d++q3Wg0rRdXLzKZDMbGxpg5cybmzZvHdxy1o7YFZceVwUiUYwNufZaLere2fHa6urhwVMbpankp2fkY9wJJ6Z/Y+WiiB930eNzYuwZP7l1H5cqV+YpKFKS0bYVo16xqSklJgZmZGVatWoUffviB7zico44W6qdHjx7Q0tLCyZMn+Y6idtSyoMzOl8LG+yzk+oUxhuYPtqKlzf+351Gl6Wp5+9woU/FZt7t27cLw4cP5jkkUIDo6Gr169YJAIMDp06dhbW1d8hjtmlVty5Ytw6JFi/Ds2TPe+/ByjXruqqeFCxdi7dq1SEtL04iBHkVSy4Ly7rPX6L3uitzvc3Jae1jXqSL3+6ibjh07QiQS4cKFC3xHIQpS3FYoISEBhw8fRufOnWnXrIqTyWRo1KgROnbsiICAAL7jcE6Ry6aI4pw7dw7du3fHgwcP0KRJE77jqBW17D5bwOF6F2W4j7rx9PTExYsX8fjxY76jEAWpW7cuQkNDYWdnhx49emDs6oOYfTga+VJZmV+wi2QM+VIZZh+Ohk+wRE6JydecO3cOCQkJmDhxIt9R5CI49oVcikng3c9wcNwLuVybfJmdnR0EAgGd6y0HallQ6ogU82Up6j7qZuDAgTA0NMTWrVv5jkIUyMjICCdOnEDncfMQ9IKb9bOrzsVhX1QSJ9ciZePr6wtbW1vY2anfWsDsfCmS5LgGHwCS0nORky+V6z3If1WpUgXNmjWjglIO1LIiqm+iD3mvjBD8ex9Sdvr6+hg6dCgCAgJKWigRzZCSVYjHxm0BDlc4/3rsLpLl/OJPPvTkyROcOHECEydOVJt1aFKpFG/evEFqaiquRkvkuwYf734DEtJz5HwX8ilisZgKSjlQyx0k+roimBvryXWXt7mJHrUyqQAvLy9s3rwZQUFB6N69O99xiIL8/2ED3BUhUhnDnMBo2jWrQH5+fqhUqRJGjBght3swxpCfn4+3b9/i7du3yM3NLfXfy/KxxX+XSv9/tFCnthVqj/5Tbl9bMVo2xQ+xWAw/Pz+8efMGRkZGfMdRG2pbETk3qSHXBdXOVjU4v64madeuHaytreHv708FpYaQ22EDMlaqwwZIxclkMmRlZWHTpk1wdXXF8+fP8ejRI86Kuo//W1n2jFauXBl6enqoXLnyB38v/l8jIyPUqlXrP49/6u8vpbpYdK1Qjt/Jd2jZFD/EYjEYY7h27RpcXFz4jqM21LagHGFnjoCrCXK5dpGMwd2edpdWhEAggKenJ3755Rekp6fDxMSE70hEznZFJn21NVB5aQkF2BmRpJG7ZgsLCzkdqfvS4/n5+SX33bNnD/bs2fPZXFpaWv8p6j7+e40aNUpV4H3tcV1dXU6n3nPypfjtmnxbz9GyKf40adIEVatWxdWrV6mg5JDaFpSNaxrCydJUbk1paSSk4kaOHImff/4ZO3fuVMumyORDitg16w3+C8riqVp5FXUf/70s65B1dXW/WpyZmJh8sahbt24dsrOzsX79+i8WgKp83CYtm1JvQqEQ9vb2tI6SY2r907zUzQYuq0M4fRETCQVY6mbD2fU0WfXq1eHq6go/Pz98//33arO4n/yXInfNfupFuqioqFxFXXkKvLy8vFJP1QoEgq8WeFWrVkXt2rVLNVL3pb9XqlQJWlpaFfoeJyYmYuzYsdi0aRO6detWoWspO1o2pd4cHBzw559/QiaTQSikpQdcUOuC0sxYDwtdrTH7cDRn11zkak0ndHDIy8sLvXv3xo0bN9CmTRu+4xA5SUzPUciu2fY93VD44vF/ir2CgoJSX0ckEn21OKtZs2aFCzw9PT3o6Oio1BupzZs3w9DQEEOHDuU7itzRsin1JhaL8erVK8TGxqJp06Z8x1ELal1QAsDQtuZIy87HqnNxFb7WrG5NMIRO5uBU9+7dUbduXfj7+1NBqcYUtZu18TfNUMe2UYXW5dHxqZ9WWFgIPz8/uLu7w8DAgO84ckfLptRbu3btShqcU0HJDbU8evFTKnrM2yJXayom5WTu3LlYv349nj9/jsqVuWl4TZQLHYeq+g4dOoSBAwfi9u3bsLW15TuOQiRn5MJldQjyOXxDpCsSImhGR5rpUgLFjfk3b97MdxS1oDELB4a2NUfQjI5waPhuN7GW8MvTTMWPOzQ0QdCMjlRMypGHhwdev36NQ4cO8R2FyAkdNqD6Nm7cCLFYrDHFJPD/y6a4RMumlAc1OOeWxhSUwLsnhx1edjg/vQNG2lnAwkTvPy9yjDGYV6uEkXYWCJrRATu87OiXX84sLS3RsWNH+Pv78x2FyEnxrll5ol2z8hMfH4/z589jwoQJfEdRuKFtzTGzmxUn16JlU8pFLBbj3r17eP36Nd9R1IJGPvs2rmkIb1dreMMaOflSJKTnoEAqQ1LCI7i5tMfWMyfRuTP/7Uc0iZeXF0aNGoWHDx+iUaNGfMchckC7ZlXX5s2bUbVqVQwePJjvKLyY6twYpga6tGxKzRQ3OI+MjFT7rgWKoFEjlJ+iryuCdZ0qaGleDX3bt4SRni7CwsL4jqVxvvvuOxgZGSEgIIDvKERORtiZy7UPJe2alY/8/Hz4+/tj9OjRGr3GuazLppjsXX9QWjalvKysrGBsbEzT3hzR+ILyfUKhEGKxmApKHujp6WHYsGEICAgoU6NmojqKd81+7YW4rLSEAjhZmtKuWTkJDAxEWlqaRk53f6w0y6YEACxM9KCdcBXWCYdp2ZQSEwgE1OCcQxqzy7u0Fi9ejJUrVyIjI6PCTYBJ2URFRaFdu3Y4ffo0evTowXccIge0a1b1dOrUCYwxhISE8B1FKb2/bEpHJER9E33o64rw999/4/vvv8fjx49hZmbGd0zyGYsXL8aqVauQkZFBDc4riL57H3F0dMSbN29w7949vqNonDZt2qB58+bw8/PjOwqRE9o1q1oePHiAkJAQTJw4ke8oSuv9ZVPWdaqUbAwbNWoU9PX14evry3NC8iVisRivX7/G/fv3+Y6i8qig/Ei7du2gpaVF0948EAgE8PLywtGjR/Hy5Uu+4xA5oV2zqmPjxo0wNTXFgAED+I6icgwMDDBmzBhs2rQJeXl5fMchn9GuXTsIhUKa9uYAFZQf0dfXR8uWLamg5Im7uzsAYNeuXTwnIfI01bkxlg+wga5IWOY1lVpCAXRFQqwYYIMpzpZySkjevn2Lbdu2wcPDA7q6unzHUUlTpkxBWloa9u3bx3cU8hmGhoawsbGhgpIDVFB+gqOjIxWUPDE1NUW/fv3g5+cHWt6r3j7eNVu8K/Zz6LABxTp48CAyMzMxfvx4vqOoLCsrK/To0QPr1q2j5zMlRg3OuUEF5Sc4Ojri8ePHeP78Od9RNJKnpydiYmJw/fp1vqMQOTMz1oPfyJbIOfALGuP5F3fN0mEDiuXr6wsXFxdYWtIocEVMnToVN27cQGRkJN9RyGeIxWLcv38fmZmZfEdRaRrZ2PxrHB0dAQBhYWEYOHAgz2k0T7du3VCvXj34+fmhbdu2fMchcnb8+HGkPYzG2tFOaNas2Wd3zRLFiY6ORnh4OA4cOMB3FJXXs2dPNGrUCOvWrYO9vT3fccgniMViAEBkZCR1GKkAGqH8hDp16qB+/fo07c0TLS0tjBkzBnv27EFubi7fcYic+fv7w87ODs2aNQPw+V2zRHE2btyIWrVqoV+/fnxHUXlCoRBTpkzBgQMHkJKSwncc8gmWlpYwNTWlae8KooLyM2gdJb88PDzw5s0bHDp0iO8oRI6ePn2KM2fOwNPTk+8o5F85OTnYsWMHPD09oa2tzXccteDh4QFtbW1s2rSJ7yjkE6jBOTeooPwMR0dH3Lp1i0bIeNKwYUM4OztTT0o1t2PHDujq6mLIkCF8RyH/2rt3L7KysjBu3Di+o6iNqlWrYuTIkfD19UVBQQHfccgniMViREZGQibj7tAFTUMF5Wc4OjpCKpXi2rVrfEfRWJ6enggJCUF8fDzfUYgcMMbg7++P7777DlWqVOE7DvnXxo0b0aNHD9SvX5/vKGpl6tSpeP78OQIDA/mOQj5BLBbToSYVRAXlZ1hbW8PIyIimvXlUXGhs3bqV7yhEDsLCwiCRSGi6W4ncvHkTUVFRdDKOHDRv3hydOnXCunXr+I5CPqFt27bU4LyCqKD8DC0tLYjFYiooeVS5cmUMGzYMAQEBKCr6co9Conr8/f3RoEEDdOzYke8o5F8bN25E3bp10atXL76jqKVp06YhLCwMt27d4jsK+YiBgQFsbW0RHh7OdxSVRQXlFzg6OuLq1au0poJHXl5eePbsGc6ePct3FMKh7Oxs7N+/Hx4eHhAK6WlIGbx58wa7du3C2LFjIRLRznp5cHV1hZmZGXx8fPiOQj7BwcGBRigrgJ7Jv8DR0RGvXr2iNRU8at26NWxtbeHv7893FMKhAwcOIDc3F6NHj+Y7CvnX7t278fbtW4wdO5bvKGpLJBJh0qRJ2L17N9LT0/mOQz4iFosRGxuLjIwMvqOoJCoov8DOzg5aWlo07c0jgUAAT09PHDt2DC9fvuQ7DuGIv78/XFxcYG5OxycqA8YYfH190adPH9SrV4/vOGpt3LhxYIxhy5YtfEchHylucB4REcFzEtVEBeUX6Ovro0WLFlRQ8szd3R0CgQA7duzgOwrhQFxcHK5cuUKbcZTItWvXcPv2bdqMowCmpqYYNmwY/v77b1obrmQaNmyI6tWr07R3OVFB+RXU4Jx/JiYm6NevH/z8/MAY4zsOqaCAgABUrVoV/fv35zsK+dfGjRthYWGBbt268R1FI0ydOhVJSUk4fvw431HIewQCAcRiMRWU5UQF5Vc4Ojri0aNHdGQWz7y8vHDv3j3qC6ripFIptm3bhuHDh6NSpUp8xyEAXr16hb1792L8+PHQ0tLiO45GaN26NcRiMbUQUkLFDc5p9LjsqKD8CkdHRwCgUUqeubi4wMzMjDbnqLhz587h2bNnNN2tRHbs2IHCwkL6N1GwadOm4eLFi7TpU8mIxWJkZ2fj7t27fEdROVRQfkXdunVhYWFBBSXPtLS0MGbMGOzZswc5OTl8xyHl5O/vD1tbW7Rq1YrvKAT/vxmnf//+qFWrFt9xNMp3332HWrVqUQshJdOmTRtoaWnRtHc5UEFZCrSOUjl4eHggKysLBw8e5DsKKYe0tDQcO3YMnp6eEAgEfMcheDfzcu/ePUyYMIHvKBpHR0cHEyZMwPbt2/H69Wu+45B/6evr49tvv6WCshyooCwFR0dH3Lx5E7m5uXxH0WgNGjRA586dadpbRe3atQsAMGLECJ6TkGK+vr6wtLRE586d+Y6ikSZMmID8/HwEBATwHYW8hzbmlA8VlKXg6OgIqVSKqKgovqNoPC8vL4SGhkIikfAdhZQBYwx+fn5wdXWFqakp33EI3o0YHzx4EOPHj6fTinhSu3ZtDBw4ED4+PnQimxIRi8WIi4tDWloa31FUCj2LlELz5s1hZGRE095KwM3NDVWqVMHWrVv5jkLK4ObNm4iOjqaNH0pk27ZtYIxhzJgxfEfRaNOmTUN8fDwdL6tEHBwcAFCD87KigrIUtLS0YG9vTwWlEqhcuTJGjBiBgIAASKVSvuOQUvL390edOnWoz6GSYIxh48aN+O6771C9enW+42g0sViMVq1a0eYcJVK/fn3UrFmTpr3LiArKUnJ0dMTVq1dpWkIJeHl54fnz5/SOXkXk5eVh9+7dGD16NEQiEd9xCIDg4GBIJBI6GUcJCAQCTJ06FadPn0Z8fDzfcQiowXl5UUFZSg4ODsjMzMSDBw/4jqLxWrZsiW+//RZ+fn58RyGlcOTIEbx69YqmVpXIxo0b0bRpUzg5OfEdhQAYOnQojI2NsX79er6jkH+JxWJcu3aNZsLKgArKUrKzs4NQKKRpbyUgEAjg5eWF48ePIzU1le845Cv8/f3Rvn17WFlZ8R2FAEhNTcXhw4cxYcIEat+kJCpXroxx48Zh69atyM7O5jsOwbuCMicnBzExMXxHURlUUJaSoaEhvv32WyoolcTw4cMhFAqxc+dOvqOQL0hKSkJQUBBtxlEiW7duhUgkwqhRo/iOQt4zceJEZGVl0XOakmjTpg1EIhFNe5cBFZRlQA3OlYeJiQnc3Nzg5+cHxhjfcchnbNu2DXp6ehg0aBDfUQgAmUyGTZs2YfDgwahWrRrfcch7LCws0K9fP/j4+NBzmhKoXLkyWrRoQQVlGVBBWQaOjo6Ij4+naVYl4enpifv37yMyMpLvKOQTZDIZtm7disGDB8PAwIDvOATA+fPn8fjxY9qMo6SmTZuGu3fv4tKlS3xHIaAG52VFBWUZODo6AgDCw8N5TkIAwMXFBebm5rQ5R0mFhITg8ePHNN2tRDZu3AgbGxvY29vzHYV8QqdOnWBtbY1169bxHYXgXUEZHx+Ply9f8h1FJVBBWQZmZmYwMzOjaW8lIRQK4eHhgb179yInJ4fvOOQj/v7+aNy4cckbMcKvZ8+e4dixY5g4cSJtxlFSxS2Ejh49isTERL7jaDyxWAyAGpyXFhWUZUTrKJXLmDFjkJOTgwMHDvAdhbzn9evXOHToEDw9Pal4URJ+fn7Q1dWls9SVnLu7OwwNDeHr68t3FI1nYWGBWrVq0axkKVFBWUaOjo64ceMG3r59y3cUgncnGnTp0oWmvZXMvn37kJ+fTzuJlURRURE2b96M4cOHo0qVKnzHIV9gYGAADw8PbN68mV5neCYQCODg4EDrKEuJCsoycnR0RGFhIa5fv853FPIvT09PXLlyBXFxcXxHIf/y9/dHjx49UKdOHb6jEACnT59GcnIyJkyYwHcUUgpTpkxBeno69u3bx3cUjScWixEVFUUNzkuBCsoysrGxgYGBAU17KxE3NzdUrVoV/v7+fEchAO7du4fIyEjajKNEfH190bp1a7Rp04bvKKQULC0t0bNnT6xbt45aCPFMLBYjNzcXd+7c4TuK0qOCsoxEIhHs7e2poFQilSpVwogRI7Bt2zZ6F6kEtm7dClNTU/Tt25fvKARAYmIiTp06RaOTKmbatGm4efMmTbfyrHXr1tDW1qZ/h1KggrIcHB0dER4eDplMxncU8i8vLy+kpKTg9OnTfEfRaIWFhdi+fTtGjBgBHR0dvuMQAFu2bIGBgQGGDRvGdxRSBt27d4elpSV8fHz4jqLRKlWqhJYtW1JBWQpUUJaDo6MjMjIyEBsby3cU8q+WLVuiZcuWNO3Ns1OnTuHFixfw8PDgOwrBuwLfz88P7u7u1FxexQiFQkyZMgUHDhzA8+fP+Y6j0ajBeelQQVkO9vb2EAqFNO2tZDw9PXHixAk6yYhH/v7+aN26Nb799lu+oxAAx48fx/Pnz2m6W0V5eHhAV1cXGzdu5DuKRhOLxXj06BFevHjBdxSlRgVlORgaGsLW1pYKSiUzfPhwaGlpYfv27XxH0UgpKSk4efIkbcZRIhs3boS9vT0V+CqqSpUqGDVqFDZu3IiCggK+42is4gbnNEr5ZVRQlhM1OFc+xsbGcHNzg7+/P+2M5MHOnTshEolorZ6SePjwIc6dO0fndqu4KVOmICUlBYcOHeI7isYyMzNDnTp1qKD8Ciooy8nR0RESiYSGwJWMl5cXHjx4QL/4CsYYg7+/P9zc3FCtWjW+4xAAmzdvRtWqVTF48GC+o5AKsLa2RufOnel8bx4JBAJaR1kKVFCWU/H5xHQkk3Lp3LkzLCwsaHOOgkVGRuL+/fs03a0kCgoK4O/vj9GjR6Ny5cp8xyEVNG3aNFy9ehU3btzgO4rGKm5wXlhYyHcUpUUFZTmZm5ujXr16NO2tZIRCITw8PLBv3z5kZ2fzHUdj+Pv7w9zcHJ07d+Y7CgEQGBiIly9f0mYcNdGnTx+Ym5tTCyEeOTg44O3bt7h9+zbfUZQWFZQVQOsoldOYMWOQk5ODAwcO8B1FI+Tm5mLv3r0YM2YMtLS0+I5D8G4zTocOHdC0aVO+oxAOiEQiTJ48GXv27MHLly/5jqORWrVqBR0dHZr2/gIqKCvA0dERN27cQF5eHt9RyHssLCzg4uICPz8/vqNohEOHDiErKwtjxozhOwoBEBsbi+DgYBqdVDNjx46FQCCg5zWe6OrqolWrVlRQfgEVlBXg6OiIgoICXL9+ne8o5CNeXl4ICwvDgwcP+I6i9vz9/eHs7IwGDRrwHYUA2LRpE0xMTPDdd9/xHYVwyMTEBMOGDcPff/9NR8zyhDbmfBkVlBVga2sLfX192pijhPr164dq1aph69atfEdRa48ePcKlS5doM46SyMvLQ0BAQElDbKJepk2bhuTkZBw7dozvKBpJLBYjISEBKSkpfEdRSlRQVoBIJIK9vT2to1RClSpVgru7O7Zt20a78uQoICAARkZGGDBgAN9RCICDBw8iIyMD48eP5zsKkYOWLVvC0dGRNufwhBqcfxkVlBXk6OiI8PBwaqSthDw9PZGamorTp0/zHUUtFRUVISAgAEOHDoWenh7fcQgAX19fdOnSBY0bN+Y7CpGTqVOnIjg4GDExMXxH0Tj16tVDvXr1qKD8DCooK8jR0RFpaWmIi4vjOwr5SIsWLdCqVStaxC4nFy5cQHJyMk13K4mYmBiEhYXRZhw1991336F27do0SskTWkf5eVRQVpC9vT2EQiFNeyspLy8vnDx5Es+fP+c7itrx9/dHs2bN0K5dO76jELxrFVSzZk3069eP7yhEjrS1tTFx4kTs2LEDr1694juOxhGLxbh+/Tqdrf4JVFBWkJGREWxsbKigVFLDhg2DSCTCjh07+I6iVjIyMnDkyBF4enpCIBDwHUfj5eTkYMeOHfD09ISOjg7fcYicjR8/HoWFhbTpkAdisRh5eXnU4PwTqKDkADU4V17VqlXDd999Bz8/P1rnyqE9e/ZAKpXC3d2d7ygEwL59+/DmzRuMGzeO7yhEAWrVqoVBgwZh/fr1kMlkfMfRKC1btoSOjg51d/kEKig54OjoiNjYWKSlpfEdhXyCp6cn4uLi6AmAQ/7+/ujTpw9q1qzJdxSNkpMvxd1nr3ErKRN3n71GTv67foQbN25E9+7dqReoBpk2bRoePnyIM2fO8B1Fo+jq6qJNmza0jvITRHwHUAeOjo4AgPDwcLi6uvKchnysuOm2n59fyb8VKb/bt2/j5s2bWLBgAd9RNIIkNQu7IpMQHPsCSRm5eH+cXQCgpr4WJEYtsGxUL74iEh7Y2dmhdevWWLduHXr1on97RRKLxXS07yfQCCUHzM3NUadOHZr2VlJCoRAeHh7Yv38/srKy+I6j8rZu3YqaNWuiZ8+efEdRa8kZuRjpF4mua0KxIzIRiR8VkwDAAKTkFMGwVW8suyPCSL9IJGfk8hGXKJhAIMC0adNw5swZ6jKiYGKxGElJSXj27BnfUZQKFZQcEAgEtI5SyY0ePRq5ubnYv38/31FUWn5+Pnbu3ImRI0dCW1ub7zhqa29UElxWhyD8UToAoEj25fW/AqEWACD8UTpcVodgb1SS3DMS/g0ZMgSmpqb4+++/+Y6iUYobnF+6cvWTS1A0lYDRTgVO/PXXX/j555/x+vVrOvJMSfXo0QNv3ryhtZQVcPDgQQwaNAh3795Fs2bN+I6jlnyCJVh1ruIjTjO7WWGqMzU4V3dz5szB+vXr8fTpUxgYGPAdR+0VL0HZei4KAgNT4L0uFwIA5sZ6cG5SAyPszNG4piF/QXlABSVHrl+/jrZt2yIsLAwODg58xyGfsH//fgwZMgT37t1D06ZN+Y6jknr16oXMzExakC4ne6OSMPtwNGfXWzHABkPamnN2PaJ8kpOT0aBBA6xduxaTJ0/mO47aSs7IxZzAaFyOT4OWUPDFWYPix50sTbHUzQZmxppxkhhNeXPk22+/hZ6eHk17K7F+/frB2NgY/v7+fEdRSU+fPsXZs2fpZBw5Sc7IxYJjdzm95q/H7tKaSjVnZmaG/v37w8fHh1qjyUlZl6AUP65pS1CooOSItrY27OzsqKBUYrq6unB3d8f27dtRWFjIdxyVs337dujq6mLIkCF8R1FLcwKjIf3KC1VZSWUMcwK5G/Ekymnq1Km4f/8+Ll68yHcUteMTLMHsw9HIl8q+Wkh+rEjGkC+VYfbhaPgES+SUUHlQQckhR0dHhIeH07tEJebl5YUXL17g5MmTfEdRKYwx+Pv7Y+DAgTAyMuI7jtqRpGbhcnxamV+wvqZIxnA5Pg3xL6i7gTrr2LEjmjdvjnXr1vEdRa3sjUriZD0zAKw6F4d9aj5SSQUlhxwdHfHy5UtIJOr/TkRV2draok2bNh9Me3+uWTT5f1euXEF8fDxNd8vJrsgkaAnlc4SlllCAnRHq/UKm6YpbCB0/fhwJCQl8x1ELtASl7GhTDodev36NatWqwc/PDx4eHnzHIZ+xYcMGzPD+HdP+2odryTmfbBatyTv1PsXDwwOhoaGQSCQQCul9KNc6rgxGohxfaCxM9BAy01lu1yf8y8nJQb169TB+/HisWLGC7zgqb6RfJMIfpXM6a6AlFMChoQl2eNlxdk1lQq8MHKpSpQqaN29O6yiVWHJGLkKENqjl6YODt198tll0YkYudkQmouuaUI1vFp2VlYUDBw7Aw8ODikk5yM6XIknOP19J6bk08q7m9PX14enpiS1btuDt27d8x1FptASlfOjVgWPU4Fx5Fe/Ui0p6AwBg+PIUo6bu1PvYgQMHkJubi9GjR/MdRS0lpuf8500N1xiAhPQcOd+F8G3y5MnIzMzEnj17+I6i0mgJSvnQlDfHik8RSUtLg4mJCd9xyL+oWXT5tW/fHvr6+jh79izfUZRSYWEhsrKy8ObNm//8+dx//+BjdEwg7PGz3HMGTnJAS/Nqcr8P4VefPn3w9OlT3Lx5EwKBfIoidUdLUMpHxHcAdePo6AgACA8PR9++fXlOQwDud+pVN9DVmGbRsbGxCAsLw969e/mOwinGGN6+ffv5Iq80heC/H/Ol6UWBQABDQ0MYGRn950+9evVgaGgIqWEtnFDAbLSOiCakNMG0adPQo0cPhIWFoX379nzHUTmKXIKir6teJZh6fTVKoH79+qhduzbCwsKooFQC8tqp59DIVCNOPwgICEC1atXQr18/vqMAAIqKir5a7JX28aKios/eR1tbG1WqVPlPEVinTh188803ny0S3/9jaGgIfX39r647zcmX4qT3WblOewsA1DfRl+MdiLLo2rUrrKys4OPjQwVlOShyCYp1nSpyvpNiUUHJMYFAQOsolYg8m0Wr6069YlKpFNu2bcPw4cNRqVKlcl+HMYb8/PwKFYHFf3JzvzxyYGBg8MnirlatWiVFXmkKQV1d3XJ/vWWlryuCubGeXKfYzE301G40hHyaUCjElClT8L///Q/Pnj1DnTp1+I6kUgqkMrW6jyLRM4wcODo6Yvbs2SgoKICOjg7fcTRW8U49rr2/U8+yhvq2FDp9+jSeP3+O3r174969exWaGv7SyUQikeiThV2NGjVgaWlZ6kLQwMBAZXehOzepgR2RiZzvKgXebQJwtqrB+XWJ8hozZgzmzp0LX19fLFq0iO84KkVRS0PUcQkKFZRy4OjoiPz8fNy8eRP29vZ8x9FYxTv15PUivTMiCd6u1pxfu6Ly8/PLtAbwc3+ys7MBAL169frkffT09D5Z2DVs2PCD0b6vFYK6uroav3lghJ05Aq4myOXaRTIGd3vNWPNL3jEyMsLo0aOxceNGzJ07V6Ej7qquvok+BAAtQSkHKijloEWLFtDT00NYWBgVlDwKjn0hl2ISePciHRz3At7gpqCUyWTIycnhpBAsKCj47H2EQuEnizpjY2PUr1+/5P8LBAIsWrQII0eOxJAhQz45GigS0dMHVxrXNISTpancGimr80g6+bQpU6Zg/fr1OHjwIEaMGMF3HJWQlpaGEydOQLtABwU68lvfqK5LUKhtkJw4OzujWrVqOHz4MN9RNFJ2vhQ2CtjocGtuZxTl51a4EMzKyvriGfCVK1cu9fq/Lz1euXLlUo0GrlmzBj/99BOePXsGU1NTDr9r5HOSM3LhsjoE+VytrWIMutpaCJrRUSM2kJH/6tq1K7KyshAREcF3FKWVmJiII0eO4MiRIwgNDQVjDM3cFyCnbuuv9iouDy2hACPtLJRydqui1K9EVhKOjo7YvHkzGGMaP53HB0Xt1KvRyBqFLx5/8nGBQPDZQq9evXqlLgYNDQ2hra0t56/mva+LMfj7+6Nfv35UTCqQmbEeFrpaY/bhaG4uKBDA+NF5VBE5cnM9onKmTZuGfv36ISoqCm3btuU7jlJgjCEmJgZHjhxBYGAgbt26BR0dHXTp0gW+vr7o27cvsgT66LomVC73V+clKDRCKSenT59Gr169IJFIYGlpyXccjXMrKRNuG8Llfp9xFq/QtEblTxaCenp6KrlJ5Pr162jbti1OnTqFnj178h1H43DVhN+tkRa2zhwKMzMznDhxAhYWFhykI6qkqKgIlpaW6NChA7Zt28Z3HN4UFRUhIiICgYGBOHLkCB4+fAhDQ0P07t0b/fv3R8+ePWFkZPTB5wxcfwnXk7MAAXfP4ep+ljeNUMqJWCyGQCBAWFgYFZQ8UNQOuv6ufdSul9jWrVtRt25ddOvWje8oGmmqc2OYGuhiwbG7kMpYmdZUagkFEAkFWORqjSFtzTHe8Sp69+6Ndu3a4dixY7CzU88XMvJpWlpamDx5MubNm4eVK1eiRg3N2e2fn5+PCxcu4MiRIzh69ChevHiBmjVrol+/fvDx8YGzs/MnNysxxhAQEICgpYtRZfgqCETcdWoRCQVY6mbD2fWUjeoNn6iIqlWrwtramvpR8qR4p548qeNOvbdv32L37t0YNWoUtLS0+I6jsYa2NUfQjI5waPju+NavnStc/LhDQxMEzehYcpJT06ZNERkZicaNG6NTp07Yv3+/fIMTpePl5QWhUIgtW7bwHUXuXr9+jb1792LIkCEwNTVF7969ERwcjNGjRyMsLAzPnj3Dxo0b0aNHj08WkykpKejXrx88PT3h1q0DvPs24zTfIldrtV7PTCOUcuTo6IjLly/zHUMjUbPo8jly5AhevXoFDw8PvqNoPDNjPezwsoMkNQu7IpMQHPcCSem5H6wNFuDdz6GzVQ2425t/cjd39erVERQUBC8vLwwZMgQSiQRz5syhtd0awtjYGCNGjMCGDRvw008/qV13hpSUFBw9ehRHjhzBhQsXUFhYiNatW+Pnn3+Gm5sbmjVrVqqf9YMHD2LixInQ0tLC0aNH4erqCgDIKRJysgRlVrcman9kL62hlKMdO3Zg1KhRSE9Ph7GxMd9xNI73sbtybRatjjv1unbtivz8fISGymdBOqmYnHwpEtJzUCCVQUckRH0T/VK/qWGM4bfffsOCBQswatQobNq0ifoTaojbt2+jRYsWOHDgAAYOHMh3nAqTSCQlm2oiIiIgFArRoUMH9O/fH/3794e5eekLt8zMTEybNg27du3CgAED4Ovri+rVq3/wMXujkjhZgqLuqKCUo0ePHqFRo0Y4ceIEevfuzXccjSNJzZLbTj0ACJrRQa36+yUmJqJBgwbw9/fHmDFj+I5D5GTPnj3w8PBAu3btcPjwYdrJryE6dOgAoVCIS5cu8R2lzBhjuHnzZsmmmrt376JSpUro3r073Nzc0KdPH5iYmJT5umfPnoWnpydycnLg4+ODESNGfHY0MzkjF3MCo3E5Pu2rB2YUP+5kaYqlbjZqPc39Pioo5Ygxhjp16sDDwwNLly7lO45GGukXKbdm0eq2U2/RokVYuXIlnj9/DgMDA77jEDkKDw9H//79YWRkhJMnT6JJkyZ8RyJytn//fgwZMgS3b9+Gra0t33G+SiqVIjQ0tKRHZHJyMqpVq4a+ffvCzc0NXbt2hb5++dawZ2dnY9asWfD19UXXrl3h7++PevXqlepzK7oERZ1RQSlnAwcOxMuXLxESEsJ3FI3EebNoALoiodo1i5bJZGjUqBE6d+4MPz8/vuMQBXj8+DH69OmDZ8+e4fDhw3B2duY7EpGjwsJCNGjQAL169cKmTZsAVGwJhTzk5ubi3LlzCAwMxIkTJ5CRkQEzM7OSqWwnJ6cK9+S9cuUKRo8ejZSUFKxatQoTJ04s93piZfv+8U1zv3IFcXR0xJw5c1BQUAAdHe7aD5DS4bxZNNRzp96lS5eQkJAAT09PvqMQBWnQoAHCw8MxaNAgdOvWDb6+vvDy8uI7FpETbW1tTJw4ESt8t8P4wA1cTXiDpIxPjLAZ68G5SQ2MsDNH45ryH2FLT0/HiRMncOTIEZw9exZv376FtbU1Jk2aBDc3N7Rq1YqTDWR5eXn49ddfsWrVKojFYpw9e7bCLf30dUVq1zauImiEUs6uXbsGOzs7REREUA84HnHVLHpWtyaY4qx+fUXd3d0RFRWFBw8e0O5fDVNYWIjvv/8evr6++Omnn7Bs2TKVbMhPviw5Ixf/23cd15KyIAD74rGC8l4DmJSUhKNHjyIwMBChoaEoKiqCWCyGm5sb+vfvj8aNG3N6v1u3bmHkyJGQSCRYtGgRZs6cSW3R5IBGKOWsZcuWqFy5MsLCwqig5BGXzaLVzevXr3Ho0CF4e3tTMamBtLW18ffff6NJkyb48ccfIZFIsGPHjnKvTyPK5/1dygC+ekZ18fNj+KN0uKwOwUJXawytwHMfYwz37t0r2VRz48YNaGtro0uXLli/fj1cXV1Ru3btcl//c6RSKZYvX46FCxfC2toa169fh42N+jYW5xuNUCpAp06dYGJigkOHDvEdReO9v1NPKAC+VFdqyk69jRs3YvLkyUhOTkadOnX4jkN4dPz4cQwbNgzffPMNjh07Rj8PaoCr2ZmZ3aww1bn0I4cymQwREREl7X3i4+NhYGCAXr16wc3NDT179kSVKvKbLo6NjcWoUaNw/fp1/PLLL/j1119p2ZmcUUGpAHPnzoWfnx+eP39OI0BKQpKahXnbzuLKw0zoGNfR6J16dnZ2qF69Ok6cOMF3FKIE/vnnH/Tp0wcCgQDHjx9HixYt+I5EymlvVBKn68dXDLD54ixNfn4+goODERgYiKNHjyI1NRU1atRAv3790L9/f3Tp0kXuvU9lMhl8fHzw888/w9zcHNu3b6fZQQWhglIBTp48iT59+iD6fhwERjVoR5iSmDp1KoKCgnDjdozG7tS7e/cumjdvjkOHDmHAgAF8xyFK4tmzZ3B1dcWDBw+wZ88e9O3bl+9IpIwU1eHizZs3OH36NI4cOYJTp07hzZs3aNiwYcl6SLFYrLD1iomJifDw8EBwcDCmTZuG5cuXQ09PPWeWlBEVlHImSc2CX2gcdly4Ce1qtYH31q7wsaOO/L+2bduiWbNm2LZtG99ReDNz5kxs27YNT58+pekg8oGcnByMHDkSR44cwR9//IHp06fTDIsKkWcP3lV96uPYsWMIDAzEhQsXUFBQgJYtW6J///5wc3ND8+bNFfqzwhhDQEAAfvjhB1StWhVbt25Fly5dFHZ/8g4VlHJCXfWVW15eHoyMjLB69WpMmTKF7zi8KCwsRN26dTFixAisXr2a7zhECclkMvzyyy/4/fffMXHiRKxdu7bCfQCJ/Mn7lLBnWyahKOPpB8cdWlhYyO1+X5KSkoLx48fj+PHjGDNmDNasWSPXtZnk8zRjXk/BPt5R97V3iFzvqCNf988//6CwsFCj19acPHkSL1++pN6T5LOEQiFWrFgBKysrTJw4EY8ePcL+/fvpBVvJ7YpM+upARrkxGfrP/AN/eznzfmznwYMHMXHiRGhpaeHIkSPo168fr3k0HTUb45hPsASzD0cjXyor8y9zkYwhXyrD7MPR8AmWyCkhAYDIyEjo6uqqxBFk8uLv7482bdpQGw3yVV5eXjh79iyuXbsGBwcHPH78mO9I5AuCY1/Ip5gEAIEQqVqmvBaTmZmZcHd3x6BBg9CxY0fExMRQMakEqKDk0N6oJE7aMwDAqnNx2BeVxMm1yH9du3YNLVu21Nh1gykpKTh16hSNTpJS69y5MyIiIpCfnw87OzuEh4fzHYl8Qna+FEkZuXK9R1J6LnLypXK9x+ecPXsWNjY2OHHiBHbs2IGDBw+ievXqvGQhH6KCkiPJGblYcOwup9f89dhdJMv5iUFTRUZGavR0944dOyASiTB06FC+oxAV0qRJE0REROCbb75B586dsWfPHr4jkY8kpudA3hsjGICE9Bw53+VD2dnZmDRpEnr06IFmzZohJiYG7u7utFFMiVBByZE5gdElaya5IpUxzAnkrocYeSctLQ0PHz5Eu3bt+I7CC8YY/P39MWDAAFSrVo3vOETFmJqa4vz58xgyZAiGDx+OhQsXgvZ2Ko8CDtsEKcN9ACAsLAwtWrTA9u3b8ffff+Ps2bOoV6+ewu5PSocKSg5IUrNwOT6N8zUrRTKGy/FpiH+Rxel1NV1UVBQAaOwIZUREBB48eEDT3aTcdHV1ERAQgMWLF8Pb2xsjR45EXl4e37EIAB2RYl7WFXGfvLw8/Pzzz3ByckLNmjVx+/ZtTJo0iUYllRQVlBwo3lEnD1pCAXZG0FpKLkVGRsLExAQNGzbkO4rc5eRLcffZa9xKysTdZ6+Rky/F1q1bYW5ujs6dO/Mdj6gwgUCAuXPnYt++fTh06BBcXFzw8uVLvmNpvPom+l85qbviBP/eR55u3bqFNm3aYM2aNVi2bBlCQ0NhaWkp13uSiqG2QRyQ5466IhlDcNwLeMNaLtfXRNeuXUO7du3U9l2uJDULuyKTEBz7AkkZuf85VlKqZQdrdzs8fJlDzfRJhQ0ePBgWFhZwdXWFnZ0dTp48iaZNm/IdS2Pp64pgbqyHRDmuvzc30ZPbaWJSqRTLly/HwoULYW1tjevXr1MnChVBI5QVpO476tQNYwzXrl1Ty+nu5IxcjPSLRNc1odgRmYjEj4pJ4N1ieq2qtSBhtdB1TShG+kXSxi9SYXZ2doiMjIS+vj7EYjGCgoL4jqTRnJvUgJac3i9rCQVwtqohl2vHxsbC0dERCxYswE8//YRr165RMalCqKCsIHXdUaeuHj58iPT0dLXbkLM3Kgkuq0MQ/igdQCma6f/7cHEz/b3UoopUUP369REWFgaxWIwePXpg06ZNfEfSSEVFRRAlXC35Hef8+jIGd3tuD96QyWRYu3YtWrRogczMTISFhWHJkiUa29ZNVdGUdwUpaqfbwMFDUU32GkZGRv/5Y2ho+Mn//v5jIhH9UwPvprsBqFVB6RMsKXf/0yIZQ5GMYfbhaKRl52Oqc2OO0xFNYmRkhOPHj2P69OmYMGEC4uLisGLFCmhpafEdTSNcvXoVU6ZMwa1bt9Byhh9eV6rJaWFZfJa3ZQ3ulsokJibCw8MDwcHBmDZtGpYvXw49PTp+WBVRlVFBitpRZ9emNYRvnuHNmzd49eoVkpKS8ObNm5I/2dnZX2zdUbly5TIVop97vHLlyiq99jAyMhKNGjWCiYkJ31E4wXUz/eoGuhhCx36SChCJRPDx8UGTJk0wffp0SCQS7Nq1CwYGBnxHU1upqan4+eefsW3bNrRu3RoRERGo09gGLqtDUMThoIdIKMBSN26moBljCAgIwA8//ICqVasiKCgIXbp04eTahB8CRg3EKiQnX4rm3mflOu0tABDj3f2Li6BlMhlycnI+KDLf/5OVlVWqx16/fo3CwsLP3kdLS6vUo6JfeszQ0JCXUVOxWIyGDRti165dCr8315IzcuGyOgT5HL5g6IqECJrREWbGNEJAKu7UqVMYMmQILC0tcfz4ceodyLHCwkKsX78eCxYsgLa2NpYtWwZPT8+SEeG9UUmYfZi7XsYrBthw8oYzNTUV48ePx7FjxzB69Gj89ddfdD68GqARygpSlh11QqEQhoaGMDQ0RN26dSt0v/z8/DIXohkZGUhISPjPqOmX6OnplasY/fhPpUqVSjVqWlBQgFu3bmHYsGEV+v4oC3k209/hpX6bloji9erVC2FhYejTpw/s7Oxw/PhxtGrViu9YaqF4ivj+/fuYOHEifvvtNxgbG3/wMUPbmiMtO5+TWYxZ3ZpwUkweOnQIEydOhFAoxJEjR+gMbjVCBSUHnJvUwI7IRLm0DpLnjrrP0dXVRfXq1St8PqpMJkN2dnaZR0w/Lkxfv34NqfTzu9xFIlGppu9fvXqF/Px8ZGVl4ezZs58cNVWVtV7FzfS59n4zfS7XSRHNZWtri2vXrsHV1RVOTk7YtWsX+vfvz3cslfXkyRPMnDkT+/btg4ODA65fv46WLVt+9uOnOjeGqYEuFhy7C+m/a6ZLS0sogEgowCJX6woXk5mZmZg2bRp27dqFAQMGwNfXl87gVjM05c0BSWoWuq4Jldv1g2Z00OgXd8ZYuUZNP/6TkZGB/Pz8L95LX1+/wutMjYyMoKurK9e1pt7H7sr1TcxIOwt4u1LvU8Kd3NxcjB49GocOHcLKlSvx448/qvR6bEXLz8/H6tWr8dtvv8HQ0BArV64s01nWyRm5mBMYjcvxadASCr743FH8uJOlKZa62VR4CczZs2fh5eWF7Oxs+Pj4YMSIEfRvr4aooOTISL9IhD9K5/QFvnhHHU0/cmPUqFG4f/8+goKCyr3G9P0/Xxo11dbWLvc60/cfNzAw+OSoaceVwXJdZmFhooeQmc5yuz7RTDKZDPPmzcOyZcswbtw4rF+/Htra2nzHUnpnzpzB999/j0ePHuGHH37AggULYGRkVK5rlRx8EPcCSen/PfjA3EQPzlY14G5vXuGBjOzsbMyaNQu+vr7o2rUr/Pz8YGZmVqFrEuVFBSVHaIOE8mvSpAm6du0KHx+fCl+LMYa8vLwKjZgWP5aT8+UeowYGBh8Um/pVjPGo1RRAju/wS7MRjJDyCggIwPjx49GhQwccOHAA1apV4zuSUnr8+DFmzJiBo0ePonPnzli7di2srbmbOcjJlyIhPQcFUhl0RELUN9Hn7Hc+LCwMo0ePxvPnz7Fy5Uo6g1sD0KsFR8yM9bDQ1ZrTHXWLXK2pmORIZmYm4uLiMG/ePE6uJxAIULlyZVSuXBk1a9as0LWkUukn15p+rhh9nqcl12IS+P9m+tZ1aOcl4d6YMWPQoEEDDBgwAA4ODjhx4gQaNWrEdyyl8fbtW6xYsQIrVqyAqakp9u/fj4EDB3JekOnrijj/Hc/Pz8evv/6KlStXwt7eHqdPn0bjxtTfVhNQQckhLnfUZYZsw+sGLkDb8RwkI1FRUQCUs6G5SCRC1apVUbVq1VJ9/K2kTLhtCJdvKCiuaT/RTB07dkRERAR69+4NOzs7HDlyBO3bt+c7Fq8YYzh69ChmzJiBZ8+eYebMmZgzZw709fX5jlYq//zzD0aOHInY2FgsXboUs2bNUpmNjqTi6OhFjk11bozlA2ygKxJCS1i2d5NaQgF0RUIsH2ADj3Z1MGHCBGzdulVOSTXLtWvXULVqVbV4p6yoZvqKug/RXI0bN0ZERASaN2+OLl26YOfOnXxH4k1sbCx69uwJNzc3NG3aFDExMViyZIlKFJNSqRRLlixB27ZtIRQKcf36dcyePZuKSQ1DI5RyMLStORwbmZZ5R51DQ5OSHXVD2qxGfn4+vLy8oKOjgxEjRijwK1A/kZGRaNeuHYRC1S+S6pvoQwDIvZl+fRPlfyEjqs/Y2Bjnzp3DhAkTMHLkSMTFxWHhwoUas94uOzsbixcvxp9//ol69erh2LFj6NOnj8p8/bGxsRg9ejSioqIwe/ZsLFiwgM7g1lBUUMqJmbEednjZlXtHnUAgwPr161FQUIBRo0ZBR0cHgwYNUvjXoQ4YY4iMjMSECRP4jsIJZWmmTwhXdHR04O/vjyZNmuCXX36BRCLB1q1bUalSJb6jyQ1jDPv27cPMmTORnp6O+fPnY9asWSrzNctkMvj4+GD27NmoV68ewsLCYG9vz3cswiN6xZCzxjUN4e1qDW9Yl3lHnVAoxKZNm1BQUIDhw4dDR0eHThUoh8TERLx8+RJ2durTfkmezfTBZLDSLwRjTGVGSYjqEwgEmD17Nho3boyRI0fC2dkZR44cqfCmN2UUExODqVOnIiQkBAMGDMAff/yB+vXr8x2r1BITE+Hh4VFyWs/y5cuhp0cbSDWd6s//qZDiHXUtzavBuk6VUo0AaWlpYevWrRgwYAAGDRqEU6dOKSCpeomMjASgnBtyymuEnbl8ikkAEAgRMN8LdnZ22L9//xf7bRLCte+++w4hISFISEiAnZ0d7t69y3ckzrx69QrTp09HixYtkJKSgrNnz+LQoUMqU0wyxrB161bY2NggPj4eQUFBWLt2LRWTBAAVlCpBJBJh586d6NOnDwYMGIDz58/zHUmlXLt2DfXr10eNGoo9wlKeGtc0hJOlaZk3fn2NllCA9pamOL5zM4yMjDBkyBBYWVlh3bp1Xz2bnRCutG3bFpGRkTAyMoKDgwPOnj3Ld6QKkclkCAgIQJMmTbBlyxYsW7YMd+7cQbdu3fiOVmqpqano378/PD09MWDAAERHR6NLly58xyJKhApKFaGtrY29e/fCxcUF/fr1Q0hICN+RVEZkZKRaTXcXW+pmAxHHBaVIKMAyNxv06NEDQUFBuHHjBsRiMWbMmAFzc3PMnTsXKSkpnN6TkE8xNzdHWFgYnJyc0Lt3b2zYsIHvSOVy48YNODo6wsPDAy4uLoiNjcWsWbNUauPKoUOH0Lx5c1y9ehWBgYEICAhAlSrUo5Z8iApKFaKjo4ODBw+WPMGGhYXxHUnpFRYW4saNG2o13V2suJk+lz5upt+qVSvs2rULDx8+xJgxY7B27VpYWFhg7NixuH//Pqf3JuRjhoaGOHr0KKZOnYrJkydj+vTpKCoq4jtWqaSnp2PixIlo27YtcnJyEBISgl27dqFu3bp8Ryu1zMxMuLu7Y+DAgXByckJMTAz69+/PdyyipKigVDGVKlVCYGAg2rVrh549e+LatWt8R1JqMTExyMvLU8sRSuBdi6qZ3aw4udasbk0wpK35Jx+zsLDAn3/+ieTkZCxatAinTp1Cs2bN0LdvX4SEhIBOcCXyoqWlhTVr1mD9+vXw8fFB//79kZWVxXeszyoqKoKvry+srKywd+9e/PXXX7h58yY6dOjAd7QyOXfuHGxsbHDixAls374dhw4dUqtlQ4R7VFCqID09PRw7dgy2trbo1q0bbt68yXckpRUZGQktLS20bNmS7yhyw0Uz/RUDbDDF2fKrH1+1alX8/PPPSEhIwNatW/H48WN06tSJNvAQuZs8eTJOnDiBkJAQtG/fHsnJyXxH+o+rV6+ibdu2mDRpEvr374+4uDhMmzYNIpHqNFTJycnB5MmT0b17dzRt2hTR0dEYOXIkdXwgX0UFpYoyMDDAqVOn8M0336Br1664c+cO35GU0rVr12Bra6v2uxCHtjVH0IyOcGhoAgBfLSyLH3doaIKgGR0/OzL5OTo6OhgzZgyio6Nx+vRpGBoafrCBJycnp3xfCCFf0KNHD4SHh+P169do164drl+/znckAO82rIwZMwYODg4QCoWIiIiAn5+fyo3ohYWF4dtvv0VAQAB8fHxw9uxZmJmZ8R2LqAgBo7kqlfbq1St06dIFycnJuHTpEpo1a8Z3JKVibW2NDh06qOyC/vIobzP9irp58yZWrVqF/fv3w8jICJMnT8bUqVNRq1Ytzu5BCPCugOvXrx/u3LmDnTt3YsCAAbzkKCwsxPr167FgwQJoa2tj2bJl8PT0VLkjB/Pz8/Hrr79i5cqVsLe3x7Zt29TimFqiYIyovLS0NGZra8tq1arFYmNj+Y6jNF6/fs0EAgHz9/fnOwpvsvMKWczTV+xmYgaLefqKZecVyv2eCQkJbMaMGczAwIDp6OiwsWPHsnv37sn9vkSz5ObmsiFDhjAAbPny5Uwmkyn0/hcvXmTW1tZMKBSyyZMns/T0dIXenyu3bt1izZs3Z9ra2mzZsmVMKpXyHYmoKCoo1URqaipr1qwZq1u3Lnv48CHfcZTChQsXGAB29+5dvqNopIyMDLZ8+XJWu3ZtBoD17duXhYSEKPyFn6ivoqIiNm/ePAaAeXp6svz8fLnfMzk5uaSQdXBwYDdv3pT7PeWhsLCQLV68mIlEImZra8tu377NdySi4qigVCPPnz9nVlZWzMLCgiUkJPAdh3dLly5lhoaGrKioiO8oGi0vL49t3bqVWVtbMwCsbdu2bP/+/aywUP6jpUQzbN++neno6LBOnTrJbaQwLy+PLVu2jOnp6bGaNWuy7du3q+ybowcPHjA7OzsmFArZnDlzFFKIE/VHm3LUSK1atXDx4kVoaWmhc+fOePr0Kd+ReBUZGYm2bdtCKKQfcz7p6uqWbOA5deoUDA0NMXjwYFhZWcHHx4c28JAKGzlyJIKCghAdHQ2xWAyJRMLp9c+cOQMbGxvMmzcPEydORFxcnErufJbJZFi7di1atmyJjIwMXLlyBUuWLFGpJutEedErrZqpW7cuLl68CKlUis6dO2vsqSaMMbU9IUdVCQQC9OzZExcuXMD169dhb2+P6dOnw8zMDPPmzUNqairfEYkKc3JyQkREBAQCAezt7REaGlrhaz5+/Bj9+/dHz549YWZmhtu3b+OPP/6AkZERB4kVKykpCV27dsUPP/wALy8v3Lp1C2KxmO9YRI1QQamGLCwsEBwcjJycHHTp0gUvX77kO5LCPXnyBCkpKWp5Qo46aN26NXbv3o34+HiMHj0aa9asgYWFBcaNG4cHDx7wHY+oKEtLS1y9ehUtWrSAi4sLtm3bVq7rvH37Ft7e3mjWrBlu3LiB/fv3IygoCNbW3J5MpQiMMQQEBMDGxgZxcXE4f/481q1bB319fb6jEXXD95w7kZ/Y2FhWq1YtZmtry9LS0viOo1AHDx5kANizZ8/4jkJKISMjgy1btow28BBOFBQUsLFjxzIAbM6cOaVeRy2TyVhgYCCrX78+09HRYXPmzGHZ2dlyTis/KSkpzNXVlQFgo0aNYpmZmXxHImqMCko1d/fuXVa9enXWqlUrjXoymTVrFjMzM+M7BimjvLw85u/vz5o1a8YAsHbt2rH9+/dTKxNSZjKZjK1cuZIJBAI2cOBAlpub+8WPf/DgAevevTsDwHr27Mni4uIUlFQ+Dh48yExNTVn16tVZYGAg33GIBqApbzXXrFkzBAUFISEhAT169MCbN2/4jqQQkZGRNN2tgnR1deHh4YGYmBicOnUK+vr6GDx4MBo3bkwbeEiZCAQCzJw5E4cOHcLJkyfRqVOnT64pz87OxuzZs0umhI8dO4aTJ0+qbGPvzMxMuLu7Y+DAgXByckJMTAz69+/PdyyiAaig1AC2trY4f/48Hjx4gN69eyM7O5vvSHIllUpx/fp12pCjwoo38Fy8eLHk33L69OkwNzfH/PnzaQMPKTU3NzdcvnwZycnJsLOzQ3R0NIB3awv37t2Lb775Bn/99Rfmz5+Pe/fuoW/fviq3e7vYuXPnYGNjgxMnTmD79u04dOiQyh3/SFQXFZQaolWrVjh79ixu374NV1dX5Obm8h1Jbu7du4fc3FwaoVQTrVu3xp49exAfH49Ro0Zh9erVsLCwwPjx42kDDymV1q1b49q1azA2NoajoyM2bNgAZ2dnDBs2DHZ2drh//z7mz5+PSpUq8R21XHJycjB58mR0794dTZs2RXR0tEq2NSKqjc7y1jBhYWHo3r07HB0dcfToUZV9Av2SLVu2YMKECXj9+jUMDAz4jkM4lpmZiY0bN+Kvv/5CSkoK+vbti1mzZqF9+/b0Akq+6MmTJ+jQoQMeP36MGjVqYMeOHejWrRvfsSokLCwMo0ePxrNnz7By5UpMmjSJeu8SXtBPnYZxdHTEiRMncPnyZQwcOBAFBQV8R+JcZGQkmjdvTsWkmqpWrRpmz56NhIQE+Pv74+HDh+jQoQPs7e1x8OBBFBUV8R2RKBmZTIaAgAC0bt0aqampcHJywosXL3D8+HFIpVK+45VLfn4+Zs+ejQ4dOqBGjRq4ffs2pkyZQsUk4Q395GmgTp064ejRowgKCsKQIUNQWFjIdyRO0YYczVC8gSc6OhonT56Evr4+Bg0aRCfwkA/cuHEDjo6O8PDwgIuLC+Li4hAaGgpfX19s2LABrq6uKrdZ8Z9//kHbtm3x559/YsmSJbh8+bLKbiIi6oMKSg3VtWtXHD58GCdPnoS7u7vKvkv/WHZ2Nu7evUsbcjSIUChEr169cPHiRURFRaFdu3a0gYcgPT0dEydORNu2bZGTk4NLly5h165dqFu3LgBgwoQJOH36NMLDw+Ho6IjExESeE3+dVCrFkiVL0K5dOwgEAkRFRWH27NnQ0tLiOxoh1Nhc0wUGBjKRSMTc3d3VotffpUuXGAB2+/ZtvqMQHj1+/Jj98MMPTF9fn+nq6rJx48ax+/fv8x2LKIBUKmUbNmxgxsbGrEqVKmzt2rWssLDwsx9/9+5d1qBBA1azZk0WERGhwKRl8+DBA2ZnZ8eEQiH75ZdfWF5eHt+RCPkAFZSE7d+/nwmFQubp6VnqEyWU1e+//8709fXVojgmFZeRkcGWLl3KatWqxQAwV1dXFhoaSifwqKnw8HDWsmVLBoB5enqy1NTUUn3eixcvmIODA6tUqRLbv3+/nFOWTVFREfvrr79Y5cqVmaWlJQsPD+c7EiGfRFPeBIMGDcL27duxdetWTJ06FUyFN/5HRkaiTZs2NAVEALzbwPPLL7+UbOCJj49Hhw4dIBaLaQOPGklNTcWYMWPg4OAAoVCIiIgI+Pn5lboHY/Xq1XHhwgUMGDAAgwcPxtKlS5XieTApKQldu3bFDz/8AE9PT/zzzz8Qi8V8xyLk0/iuaIny8PPzYwDYDz/8oLIjOPXq1WOzZs3iOwZRUkVFRezEiROsU6dODABr2LAh8/HxUenzmjVZQUEBW716NTMyMmImJiZs06ZNFZqdkMlkzNvbmwFgo0eP5m1aWSaTsa1btzIjIyNWr149dv78eV5yEFIWVFCSD/j6+jIA7KefflK5ovLp06cMADt48CDfUYgKiIqKYkOGDGFCoZAZGxuz+fPns5SUFL5jkVK6ePEis7a2ZkKhkE2ePJmlp6dzdu1du3YxHR0d1qFDB5aWllbqz8vOK2QxT1+xm4kZLObpK5ad9/m1m5+TkpLCXF1dGQA2atQolpmZWeZrEMIHamxO/mPdunX4/vvvMX/+fCxatIjvOKV25MgRuLm5ISkpCWZmZnzHISri8ePHWLNmDfz8/CCVSjF69Gj8+OOPaNKkCd/RyCc8efIEM2fOxL59++Dg4AAfHx+0bNmS8/uEhYWhf//+qFq1Kk6cOPHZnwdJahZ2RSYhOPYFkjJy8f4LqgCAubEenJvUwAg7czSuafjFex46dAgTJ06EQCDApk2b6AxuolKooCSftGrVKsyaNQu//fYb5s2bx3ecUpkzZw4CAgLw9OlTOjGFlFlGRgZ8fX2xdu1apKamwtXVFbNmzYKjoyP9PCmB/Px8rF69Gr/99hsMDQ2xcuVKuLu7y/Xf5tGjR+jTpw9SUlJw6NAhODs7lzyWnJGLOYHRuByfBi2hAEWyz7+UFj/uZGmKpW42MDPW++DxV69eYdq0adi5cyfc3Nzg6+tLZ3ATlUObcsgnzZw5E4sXL8b8+fOxcuVKvuOUSmRkJOzs7OjFn5SLsbEx5syZg8TERPj5+UEikcDJyQlisRiHDh2iDTw8OnPmDGxsbDBv3jxMnDgRsbGxCjmrumHDhggPD0ebNm3QrVs3+Pv7AwD2RiXBZXUIwh+lA8AXi8n3Hw9/lA6X1SHYG5VU8ti5c+fQvHlzHDt2DNu2bcOhQ4eomCQqiQpK8llz587F/Pnz8dNPP+Gvv/7iO84XFRUVlTS1JqQidHV14enpiZiYGJw4cQKVKlXCwIEDYWVlhfXr1yM3N5fviBrj8ePH6N+/P3r27AkzMzPcvn0bf/zxB6pUqaKwDFWrVsXJkyfh5eUFLy8v9J29HrMPRyNfKvtqIfmxItn/tXfncVHV+//AXzPDohBucMmbP8EMQSS6lxLlukOI1XVNy5V785ql4oIsSoizgLLKopJki49S6qI+0sLsBi5ImjZy1VIpYcwE/PlNDPIbgiAw5/dH6a/MDeecOQPzev7pOO/PBx44vDzv8z4fAU0tRsTsOIWMgm+wYMECjBkzBt7e3jh9+jT+8Y9/8D/E1G6x5U13JQgCYmJikJqaipycHMybN0/uLd3WN998Ax8fH+zbtw9BQUFyb4c6mJKSEqSnp2P79u3o3r07FixYgIULF/JKkkSuXbuGlJQUpKSkwMXFBRkZGZgyZYqsYUsQBMxJ3IT9V3uKVvPnvTlIeOlZzJ8/n2dwU7vHn2C6K4VCgeTkZCxZsgTz58+/2fKxNHq9HgqFAgMHDpR7K9QB+fv7Iy8vD2fPnsXMmTORnp4ONzc3vPrqqygrK5N7ex2GIAj46KOPMGDAACQlJSEiIgJnzpzBCy+8IPuVuws/XcMXjY8AEOkajCDAZcwCjJ8+m2GSOgT+FNM9KRQKZGZmYv78+Xj55ZeRm5sr95b+QK/Xw9vbG126dJF7K9SBPfroo1i7di2qqqqgVqvx8ccfw9vbGxMnTsShQ4cs4mHY7VVZWRmeffZZTJo06WYLePXq1XB0dJR7awCA2J2n0GIU8MvstggUCrQKv9Ql6ggYKOm+KBQKZGdn41//+hf++c9/Ytu2bXJv6XeOHj2KwYMHy70NshI3BnjOnz+Pt956C2VlZRg+fDiGDBnCAZ42unr1KmJiYuDr64vy8nLk5+dj9+7d6Nevn9xbu8lwqQ4Hz/7Y5nsm76XVKODg2R9xtrpO1LpEcmCgpPumVCqxceNGzJw5EzNmzMDOnTvl3hIAoKGhASdPnuRADpldp06dMGfOHJSWlmLXrl2wt7fHlClT4OXlhQ0bNnCA5y4EQUBeXh769++PtWvXYuXKlSgtLcW4ceNkb2/f6n19JVRKafakUiqQ+2Xlvf8ikYVjoKQ2UalU2LRpE6ZMmYKpU6fik08+kXtLOHHiBFpbW3mFkmSjVCoxduxYHDhwAEePHsVTTz2FRYsWwc3NDRqNBtXV1XJv0aKcPn0agYGBmD59OgYNGoRvv/0WK1euROfOneXe2m0VlVWLfnXyhlajgKJy/nxQ+8dASW1mY2ODLVu2YOzYsZg8eTIKCwtl3Y9er0fnzp3x+OOPy7oPIuCXAZ6tW7fi7NmzmDFjBtasWXNzgKe8vFzu7cnqypUrCA8Px1//+lf88MMPKCgowI4dO9CnTx+5t3ZHV5taUFkr7ZXmypoG1De1SLoGkdQYKOmB2NraIi8vDyEhIZgwYQKKiopk24ter8eTTz4JW1tb2fZAdKtHH30U69at+90AT//+/TFx4kR88cUXVjXAYzQa8e6778LLywtvv/02kpKScPLkSYSEhMi9tXuqqKkXa677jgQA52vqJV6FSFoMlPTA7OzssH37dowYMQJjx47FoUOHZNkHB3LIkt1ugGfYsGFWM8Bz7NgxDB06FLNnz0ZwcDDKysoQHR0NOzs7ubd2X663GDvUOkRSYaAkk3Tq1AkfffQRBg8ejOeeew56vd6s61dXV+P8+fMcyCGLZ20DPDU1NZg3bx78/f1RX1+PAwcO4P3330evXr3k3lqb2NmY59ekudYhkgp/gslknTt3xq5du/CXv/wFY8aMwbFjx8y29tGjRwGAVyip3fjtAI9er+9wAzytra1444034Onpiby8PKxduxbHjx/HyJEj5d7aA+nj7CjWkyfvSPHrOkTtGQMlicLR0RG7d+9G//79ERISgq+//tos6+r1eri6usLd3d0s6xGJadCgQdi6dSsMBsPNAR53d3fMmzdP8gGe+qYWlF78X5yo/AmlF/9XlKGQI0eOwN/fH/Pnz8eECRNQXl6ORYsWwcbGRoQdy8PR3gZuPRwkXcPN2QGO9u33e0QE8CxvEtmVK1cQHByMiooKHDhwAD4+PpKuFxISAnt7e+zatUvSdYjMoaamBm+88QbWr1+P6upqTJgwAVFRURg6dKgo9Q2X6vC+vhJFZdWorG343bCJAoBbDwcEerli5mA39HvY6b7r/vDDD4iJicF7772Hp556CtnZ2QgICBBlz3Krq6vD9DU7cKqxOxRKlej1VUoFQge7Qzte2s9KIqnxCiWJqlu3bigsLESvXr3w9NNPS3rOsdFoRElJCdvd1GE4OztjxYoVOH/+PN58802cOXPm5gDPjh07HniAp6q2AaHv6DE663Ns0Veg4pYwCfwyaVxR24At+gqMzvocoe/oUXWPx+U0NzcjKysLXl5e+OSTT7Bx40bo9foOESbr6+uRlpaGvn37oujNeEnCJPDLcyhnBbhJUpvInBgoSXQ9evTAnj174OLigqCgIHz33XeSrGMwGHDlyhUO5FCH06lTJ7z88ssoLS1Ffn4+bG1tMXnyZPTv3x85OTltGuDJK6lEcGYxDp+rAYB7PqD7xuuHz9UgOLMYeSW3P8WlqKgIfn5+iIyMxKxZs1BeXo5XXnkFKpU0wctcrl27hszMTPTt2xcrVqzAlClTcOboAQz3cBH9tByVUoHhHi7wcL3/q8FEloqBkiTxpz/9CXv37sVDDz2EoKAgVFRUiL7GjYEcBkrqqJRKJcaNG4fi4mLo9Xr4+flh4cKFcHd3h1arxeXLl+/6/uwiA2J2nEJTi7HNJ720GgU0tRgRs+MUsosMN//8woULmDZtGoKCgtC1a1f897//xeuvv44ePXo80NdoKRobG7F+/Xo89thjiI6Oxrhx41BeXo6cnBz07t0biZN8YSNyoLRRKpA4yVfUmkRyYaAkyfTs2RP79++Hra0tAgMDceHCBVHr6/V6eHl5oVu3bqLWJbJEgwYNwrZt22AwGDBt2jSkpaXBzc3tjgM8eSWVWFMozmDPmsJy5B45h+TkZHh5eeHAgQPYvHkzDh06BD8/P1HWkEtTUxNycnLg4eGB8PBwhISEoKysDG+//fbvTvDp3cMBOpHvc4wf74PeEg/8EJkLh3JIcpWVlRgxYgTs7OxQXFyMP//5z6LU9ff3h7e3NzZv3ixKPaL25MYAz7p163D58mVMmDAB0dHRGDJkCKpqGxCcWYwmMR+W3Xod//NOGBa+NB1qtRpdu3YVr7YMmpub8e6772LVqlWoqqrCjBkzoFar4enpedf3ZRcZRAnq0SFeCAv0MLkOkaVgoCSzOHfuHEaOHAknJyccOHAArq6uJtVrbGxEly5dkJmZibCwMJF2SdT+NDY2Ijc3F2vWrEFZWRn+9re/4aFxr+G7q6o2t7nvSjDC7xEH7Fz8tHg1ZdDS0oLc3FzEx8fj+++/x9SpU6FWqzFgwID7rpFXUglNfilajEKbvscqpQI2SgXix/tgqj8HcahjYcubzKJv377Yv38/fvrpJwQHB6Ompsakel999RWam5s54U1W78YAzzfffIP8/HwIXXqi/GeluGESABRKnPifRpytrhO3rpm0trYiNzcX3t7emD17Nvz8/HDy5Enk5eW1KUwCwDR/N+xdOhJD+joDwD2HdW68PqSvM/YuHckwSR0SAyWZTb9+/bBv3z788MMPCAkJwZUrVx64ll6vh729PZ544gnxNkjUjt0Y4BmzIAEiz47cpFIqkPvl7ae+LZXRaEReXh4ef/xxhIaGYsCAATh+/Dg+/PBD+Po++EBM7x4O2DJnMPaEj0DoYHe4Ozv84UQdBQB3ZweEDnbH3qUjsGXOYN4zSR0WW95kdidPnkRgYCA8PDywZ88edOnSpc01Zs6ciXPnzuHIkSMS7JCo/RqZVoSKezw/0hTuzg4ojgqUrL5YjEYjduzYAa1Wi9LSUjz33HPQ6XQYOHCgZGvWN7XgfE09rrcYYWejRB9nR56AQ1aDVyjJ7J544gns2bMH5eXlePbZZ3H16tV7vufWY+K+/O8JPi6I6BZXm1pQKWGYBIDKmgZRjmmUiiAI+Oijj+Dn54cXXngBvXr1wpEjR7B7925JwyTwyzGNPo90hZ9bd/g80pVhkqwKf9pJFk8++SQKCgoQHByMcePGYffu3XBw+H0r6G7HxAmTUnHQ3ghtfmmbj4kj6qgqaur/cAKO2AQA52vq4fOIZU15C4KATz/9FGq1GsePH0dQUBAOHjyIYcOGyb01IqvAK5Qkm0GDBuGzzz5DSUkJJk6ciMbGRgD3d0ycQqFA7XVVm46JI+rorov5mCALWOd+CIKAgoICBAQEYOzYsXB0dERRURH27dvHMElkRgyUJKshQ4Zg9+7dOHToECZPnozcw+ckOSaOyBrY2ZjnI91c69yNIAg3Q+MzzzwDlUqFPXv2oLi4GKNGjZJ7e0RWR/5PBbJ6I0eORH5+Pr6s64a4Xd+KekwckTXp4+z4h0ljsSl+XUdON0JjcHAwmpub8Z///AdffPEFgoODoVBI/R0gotthoCSL8GNXTzgNnSFKrTWF5djKK5VkhRztbeAm8WNp3JwdZBs2uREaR40ahbq6OuzatQt6vR7PPPMMgySRzBgoSXZVtQ3Q5JeKWlOdX8p7KskqBXq53vNB2w9KpVQg0NO0U64exI3QOGzYMFy+fBk7d+7EsWPHMHbsWAZJIgvBQEmyi915Ci0in+rRYhQQu/OUqDWJ2oOZg93EPyXnV61GAbMCzHfKy43QGBAQgKqqKmzfvh0nTpzAxIkTGSSJLAwDJcnKcKkOB8/+KPovwFajgINnf2y3x8QRPah+DzthuIeL6FcpVUoFhnu4wMNV+kd0ffXVV5g4cSIGDhwIg8GADz74ACdPnsSUKVOgVPLXFpEl4r9MktX7+kpJ23Pt7Zg4IjEkTvKFjVIBiPhUShulAomTHvyowvtx+vRpTJkyBX5+fjh9+jQ2b96M0tJSTJ8+HSqVStK1icg0DJQkq6Kyaknbc0Xl1ZLUJrJkvXs4ILh7LSDizHf8eB/JzqH+9ttvMW3aNDzxxBM4duwYNm3ahDNnziA0NBQ2Njx/g6g9YKAk2fCYOCJpvPXWW9gQOQuejWWi1IsO8cJUf/HvnTQYDJg1axZ8fHxw+PBhbNy4EWVlZZg9ezaDJFE7w0BJsjHnMXFE1uLNN9/EK6+8grCwMBRkLEXy876wt1G2+dYSlVIBexslUp73RVigh6h7PHfuHGbPng1vb28UFRUhOzsbBoMBc+fOhZ2dnahrEZF5MFCSbKzxmDgiKW3cuBGvvvoqFi5ciPXr10OhUGCavxv2Lh2JIX2dAeCewfLG60P6OmPv0pGiXpmsqKjA3Llz4eXlhc8++wwZGRn47rvvsGDBAtjb24u2DhGZH3sKJBtrOiaOSGo5OTlYsGABFi9ejKysrN89Vqd3DwdsmTMYhkt1eF9fiaLyalTWNPyuQ6DALw8tD/R0xawAN1GnuS9cuIDVq1fjnXfeQbdu3ZCSkoJ58+bBwUHah7ATkfkoBEGQuutIdFv1TS14XFsgedv7lHo0nDqzjUYd14YNGxAWFoYlS5YgMzPzvp7RWN/UgvM19bjeYoSdjRJ9nB1FPwHn4sWLSE5OxsaNG+Hk5IRly5YhLCwMjo7yHt1IROLjFUqSzY1j4iokHMxprr2IR3s/guDgYISEhGD06NHo3bu3ZOsRmdvrr7+OhQsXIjw8HBkZGff9wG9Hexv4PNJVkj1dunQJKSkpyMnJQefOnaHRaLBo0SI4OUn/DEsikgevUJKstPml2KKvkOTRQSqlAkG9bdHz/36OwsJClJSUQBAEeHt7Y/To0QgJCcHIkSPx0EMPib42kTlkZ2dj0aJFWLp0KdLT02U/Peby5ctIS0tDdnY2bG1tERERgfDwcHTtKk1wJSLLwUBJsjJcqsPorM8lq7936Yib94LV1tZi37592LNnDwoKClBZWQlbW1sMHTr05tXLJ598kidxULuwfv16LF68GJGRkUhLS5M1TNbU1CA9PR3r1q2DQqFAeHg4IiIi0L17d9n2RETmxUBJsgt9R4/D52pEvUqpUiowpK8ztswZfNvXBUGAwWBAYWEhCgsLUVRUhKtXr8LZ2ZntcbJ469atw5IlSxAVFYXU1FTZwuSVK1eQkZGBrKwstLa2YvHixYiKioKzs7Ms+yEi+TBQkuyqahsQnFmMJhEf72Nvo8TepSPv+2SP5uZmfPnllzcDJtvjZKmysrKwdOlSREdHIyUlRZYw+fPPP2Pt2rVIT0/H9evXERYWhujoaLi6upp9L0RkGRgoySLklVQiZscp0eqlPO9r0vPz2B4nS5SZmYmIiAgsX74cSUlJZg+TdXV1yM7ORlpaGhoaGjB//nwsX74cPXv2NOs+iMjyMFCSxcguMmBNYbnJdaJDvEQ92YPtcbIEGRkZiIyMRExMDBITE80aJuvr67Fhwwakpqbi559/xty5c/Haa6+hV69eZtsDEVk2BkqyKHklldDkl6LFKLTpnkqVUgEbpQLx430kOXP4t9geJ3NLT09HVFQUXnvtNaxevdpsYfLatWt44403kJycjNraWsyZMwexsbFwc5P23xgRtT8MlGRxqmobELvzFA6e/REqpeKuwfLG68M9XJA4yfe+75kUE9vjJKW0tDQsW7YMsbGxWLVqlVnCZGNjI9566y0kJSWhuroaL730EuLi4tCnTx/J1yai9omBkiyWXMfEmYLtcRJTamoqli9fjri4OMTHx0seJq9fv45NmzZh9erVuHjxIkJDQ7Fy5Uo89thjkq5LRO0fAyW1C+Y4Jk4KbI/Tg0pJSUFMTAxWrlwJnU4naZhsbm7Ge++9h4SEBFRVVWH69OnQaDTw9PSUbE0i6lgYKInMiO1xuh9JSUmIjY2FWq2GVquVLEy2tLQgNzcX8fHx+P777/Hiiy9Co9FgwIABkqxHRB0XAyWRTNgep9tJTEzEihUroNFooNVqJVmjtbUV//73v6HT6XD27Fk8//zz0Gq18PX1lWQ9Iur4GCiJLATb47R69WrExcVBq9VCo9GIXt9oNGLbtm3Q6XQ4c+YMxo8fD61WCz8/P9HXIiLrwkBJZKHYHrcuCQkJUKvV0Ol0UKvVotY2Go3YuXMnNBoNSktL8dxzz0Gn02HgwIGirkNE1ouBkqgdYHu8Y4uPj4dGo0FCQgLi4uJEqysIAvLz86HRaPD1118jJCQEOp0OAQEBoq1BRAQwUBK1S2yPdxw6nQ5arRarVq3CihUrRKkpCAI+/fRTqNVqHD9+HEFBQdDpdBg2bJgo9YmIbsVASdQBsD3ePmm1Wuh0OqxevRqxsbEm1xMEAYWFhVCr1Th69CiGDRuGhIQEjBo1yvTNEhHdBQMlUQfD9rjlEwQBWq0W8fHxSEpKQkxMjMn19u/fD7VajcOHDyMgIAAJCQl4+umnzXrmNxFZLwZKog7uTu3x/v37IyQkhO1xMxME4eb9ksnJyVi+fLlJ9YqLi6FWq/H555/D398f8fHxGDNmDIMkEZkVAyWRlamtrcX+/ftRWFjI9riZCYIAtVqNVatWISUlBcuWLXvgWocPH4Zarca+ffvg5+eH+Ph4/P3vf2eQJCJZMFASWTG2x81HEATExcUhMTERqampiI6OfqA6er0eGo0GBQUF8PX1hU6nw8SJExkkiUhWDJREdBPb49IQBAErVqxAUlIS1qxZg8jIyDbXOHbsGDQaDXbv3o0BAwZAq9Vi8uTJvJJMRBaBgZKI7ojtcdMJgoDY2FgkJycjPT0dERERbXr/V199Ba1Wi48//hienp7QarV48cUXoVKpJNoxEVHbMVAS0X1he7ztBEFATEwMUlNTkZGRgaVLl973e0+fPg2tVosPP/wQjz32GDQaDaZPnw4bGxsJd0xE9GAYKInogbA9fneCIGD58uVIS0tDZmYmwsPD7+t9Z86cgU6nw9atW+Hu7g61Wo3Q0FAGSSKyaAyURCQKtsf/P0EQsGzZMqxZswZZWVlYsmTJPd9jMBgQHx+PDz74AL169UJcXBxeeukl2NnZmWHHRESmYaAkItF1pPZ4fVMLztfU43qLEXY2SvRxdoSj/Z2vFgqCgKioKGRkZGDdunVYtGjRXeufO3cOCQkJ2LJlCx5++GGsWLECc+bMgb29vdhfChGRZBgoiUhy7a09brhUh/f1lSgqq0ZlbQN++yGpAODWwwGBXq6YOdgN/R52uvmaIAiIjIxEZmYm1q9fj4ULF95xjYqKCqxatQrvvvsunJ2dERsbi1deeQWdOnWS7gsjIpIIAyURmZ2ltserahsQu/MUDp79ESqlAq3GO3883nh9uIcLEif54v9074yIiAhkZWUhOzsbYWFht33fhQsXkJiYiLfffhvdunVDTEwM5s2bBwcHB6m+LCIiyTFQEpGsLKU9nldSCU1+KVqMwl2D5K1USgVslAp4N5zGxxnL8Prrr2PBggV/+HsXL15EcnIyNm7cCCcnJyxbtgxhYWFwdHQU88sgIpIFAyURWRQ52uPZRQasKSx/8AKCACgUGNHtCjYvn/m7ly5duoSUlBTk5OSgc+fOiIqKwqJFi+Dk5HSHYkRE7Q8DJRFZNKnb43kllYjZcUq0/aY874up/m64fPky0tLSkJ2dDVtbW0RERCA8PBxdu3YVbS0iIkvBQElE7YbY7fGq2gYEZxajqcUo2h7tbRQY01KCTevToFAoEB4ejoiICHTv3l20NYiILA0DJRG1W6a2x0Pf0ePwuZo23TN5L4KxFc1Vp/FP9zpERkbCxcVFtNpERJaKgZKIOoy2tMcNl+owOutzyfayd+kIeLjyPkkisg4MlETUId2rPW7862Qc+9kBrRJ8AqqUCoQOdod2vI/4xYmILBADJRFZhVvb4xf85sK2+58lW8/d2QHFUYGS1ScisiQMlERkda42tcBXWwApP/wUAE5rx9z1mEYioo7CvMdQEBFZgIqaeknDJAAIAM7X1Eu8ChGRZWCgJCKrc13ExwRZwjpERHJjoCQiq2NnY56PPnOtQ0QkN37aEZHV6ePsCIXEayh+XYeIyBowUBKR1XG0t4FbDwdJ13BzduBADhFZDQZKIrJKgV6uUCmluU6pUioQ6OkqSW0iIkvEQElEVmnmYDdRj1z8rVajgFkBbpLUJiKyRAyURGSV+j3shOEeLqJfpVQpFRju4cJjF4nIqjBQEpHVSpzkCxuRA6WNUoHESb6i1iQisnQMlERktXr3cIBO5PO248f7oLfEAz9ERJaGgZKIrNo0fzdEhXiKUis6xAtT/XnvJBFZH57lTUQEIK+kEpr8UrQYhTYN66iUCtgoFYgf78MwSURWi4GSiOhXVbUNiN15CgfP/giVUnHXYHnj9eEeLkic5Ms2NxFZNQZKIqJbGC7V4X19JYrKq1FZ04Dffkgq8MtDywM9XTErwI3T3EREYKAkIrqr+qYWnK+px/UWI+xslOjj7MgTcIiIbsFASUREREQm4ZQ3EREREZmEgZKIiIiITMJASUREREQmYaAkIiIiIpMwUBIRERGRSRgoiYiIiMgkDJREREREZBIGSiIiIiIyCQMlEREREZmEgZKIiIiITMJASUREREQmYaAkIiIiIpMwUBIRERGRSRgoiYiIiMgkDJREREREZBIGSiIiIiIyCQMlEREREZmEgZKIiIiITMJASUREREQmYaAkIiIiIpMwUBIRERGRSRgoiYiIiMgkDJREREREZBIGSiIiIiIyCQMlEREREZmEgZKIiIiITMJASUREREQmYaAkIiIiIpMwUBIRERGRSRgoiYiIiMgkDJREREREZBIGSiIiIiIyCQMlEREREZmEgZKIiIiITMJASUREREQmYaAkIiIiIpMwUBIRERGRSf4f0oPda/0wJ0UAAAAASUVORK5CYII=\n", + "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1433,7 +1433,7 @@ }, { "cell_type": "markdown", - "id": "22fa85d4", + "id": "0eb4eea0", "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": "c92c0580", + "id": "1802df64", "metadata": { "execution": { - "iopub.execute_input": "2023-01-02T13:06:48.643208Z", - "iopub.status.busy": "2023-01-02T13:06:48.642893Z", - "iopub.status.idle": "2023-01-02T13:06:48.830067Z", - "shell.execute_reply": "2023-01-02T13:06:48.829260Z" + "iopub.execute_input": "2023-01-03T20:27:39.619306Z", + "iopub.status.busy": "2023-01-03T20:27:39.618836Z", + "iopub.status.idle": "2023-01-03T20:27:39.849633Z", + "shell.execute_reply": "2023-01-03T20:27:39.849038Z" } }, "outputs": [ @@ -1476,7 +1476,7 @@ }, { "cell_type": "markdown", - "id": "98f275f7", + "id": "66284130", "metadata": {}, "source": [ "See Drawing for additional details." |
