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| author | dschult <dschult@colgate.edu> | 2023-01-04 14:38:54 +0000 |
|---|---|---|
| committer | dschult <dschult@colgate.edu> | 2023-01-04 14:38:54 +0000 |
| commit | 7373c258b5ac70011f7b681b557ada5ed0ee6886 (patch) | |
| tree | 23dc472430986234a0178fb2c6bdfb5eddf9db97 | |
| parent | 3b81a8d34faa70dfe10f2d6aa951b423a5f3452b (diff) | |
| download | networkx-7373c258b5ac70011f7b681b557ada5ed0ee6886.tar.gz | |
Deploying to gh-pages from @ networkx/networkx@814b295eddcad7374d494fa5f5b7fe08ee765ad7 🚀
116 files changed, 592 insertions, 595 deletions
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Binary files differindex 486863c4..fab6469d 100644 --- a/_images/sphx_glr_plot_subgraphs_thumb.png +++ 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 57d5c4ee..cb372c87 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 65897de3..f3ed17ed 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/auto_examples/3d_drawing/plot_basic.html b/auto_examples/3d_drawing/plot_basic.html index 513ce66c..25a3d173 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 0.110 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-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 f671292c..cc78e7af 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>00:00.110</strong> total execution time for <strong>auto_examples_3d_drawing</strong> files:</p> +<p><strong>00:00.089</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>00:00.110</p></td> +<td><p>00:00.089</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 17caa4fc..654b397b 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 0.264 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.232 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 79b7a921..b43001ad 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 5.121 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.685 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 1b2c8956..1546a67d 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 0.511 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.467 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 c7227fa5..a7c00777 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 0.145 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.119 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 6e0c9372..0ef7985b 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 0.097 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-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 d7d8dad0..eb55893f 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 0.365 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.274 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 a60f2dab..cc86ce9f 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 0.118 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.106 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 ef39dfdb..8eb94f55 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 0.082 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.070 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 237d6760..90f4f694 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: 2.2950 seconds - Betweenness centrality for node 0: 0.13599 + Time: 2.2248 seconds + Betweenness centrality for node 0: 0.06085 Non-Parallel version - Time: 3.7246 seconds - Betweenness centrality for node 0: 0.13599 + Time: 3.3705 seconds + Betweenness centrality for node 0: 0.06085 Computing betweenness centrality for: -Graph with 1000 nodes and 4913 edges +Graph with 1000 nodes and 4973 edges Parallel version - Time: 2.9741 seconds - Betweenness centrality for node 0: 0.00449 + Time: 2.5278 seconds + Betweenness centrality for node 0: 0.00191 Non-Parallel version - Time: 4.8691 seconds - Betweenness centrality for node 0: 0.00449 + Time: 4.3282 seconds + Betweenness centrality for node 0: 0.00191 Computing betweenness centrality for: Graph with 1000 nodes and 2000 edges Parallel version - Time: 1.9223 seconds - Betweenness centrality for node 0: 0.00074 + Time: 1.7547 seconds + Betweenness centrality for node 0: 0.00094 Non-Parallel version - Time: 3.3657 seconds - Betweenness centrality for node 0: 0.00074 + Time: 2.9823 seconds + Betweenness centrality for node 0: 0.00094 </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 27.490 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 23.104 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 78b95f42..b8dc64e7 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 1.351 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.317 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 5a007955..03eb00d7 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 0.251 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.187 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 a311cf52..f4069f8a 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 0.912 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 0.770 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 1401a6e0..ea08b9d5 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>00:36.707</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p> +<p><strong>00:30.410</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>00:27.490</p></td> +<td><p>00:23.104</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>00:05.121</p></td> +<td><p>00:03.685</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>00:01.351</p></td> +<td><p>00:01.317</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>00:00.912</p></td> +<td><p>00:00.770</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>00:00.511</p></td> +<td><p>00:00.467</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>00:00.365</p></td> +<td><p>00:00.274</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>00:00.264</p></td> +<td><p>00:00.232</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>00:00.251</p></td> +<td><p>00:00.187</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>00:00.145</p></td> +<td><p>00:00.119</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>00:00.118</p></td> +<td><p>00:00.106</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>00:00.097</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="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>00:00.082</p></td> +<td><p>00:00.070</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 656e51db..d6e7ee50 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 0.127 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.099 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 aa975138..55e1a791 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 0.089 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.069 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 37f3799f..6cd077c6 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 0.476 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 0.433 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 a0cdf1a1..ca2fdd7c 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>00:00.692</strong> total execution time for <strong>auto_examples_basic</strong> files:</p> +<p><strong>00:00.601</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>00:00.476</p></td> +<td><p>00:00.433</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>00:00.127</p></td> +<td><p>00:00.099</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>00:00.089</p></td> +<td><p>00:00.069</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..9cac3b3b 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 0.077 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 d538e965..7aa7db9c 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: -['Karpov, Anatoly', 'Korchnoi, Viktor L', 'Kasparov, Gary'] +['Kasparov, Gary', 'Karpov, Anatoly', 'Korchnoi, Viktor L'] 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 0.513 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.434 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 87753e63..9a6ec175 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 0.416 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.319 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 3a52248c..59a18962 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 0.383 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.296 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 5f9cad4d..1434a714 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 0.323 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-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..bfadf5f7 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 0.070 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 28509d72..52df8f20 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 0.133 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.108 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 671d9ba8..f1257184 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 0.948 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.715 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 51303a1d..78fa6dc6 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 0.494 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.371 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 24c26842..88f9c14a 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 0.109 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-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 7dc6ed25..b7610fbf 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 0.139 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-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 11fb13a9..8121bbe3 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 0.264 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.210 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 8f111477..0096ed79 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 0.096 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-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..89273df8 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 0.056 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 8688f091..a2a305e0 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 0.159 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.148 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 7e4c4ae5..dda6e51a 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 0.130 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.114 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 15292407..3a120450 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 0.352 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.274 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 51d82084..d5b2aa4d 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 0.115 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-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 b6b33cb1..d2fd5be2 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 0.081 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.064 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 54b490eb..fdf389a9 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 0.351 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.256 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 0956a289..a29a6637 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 0.120 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.096 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 819b681e..d5dcb15d 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 0.199 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.126 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 88853e64..2bde1672 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 0.121 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-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 34044215..db471575 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>00:05.692</strong> total execution time for <strong>auto_examples_drawing</strong> files:</p> +<p><strong>00:04.449</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>00:00.948</p></td> +<td><p>00:00.715</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>00:00.513</p></td> +<td><p>00:00.434</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>00:00.494</p></td> +<td><p>00:00.371</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>00:00.416</p></td> +<td><p>00:00.319</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>00:00.383</p></td> +<td><p>00:00.296</p></td> <td><p>0.0 MB</p></td> </tr> <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>00:00.352</p></td> +<td><p>00:00.274</p></td> <td><p>0.0 MB</p></td> </tr> <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 class="pre">plot_spectral_grid.py</span></code>)</p></td> -<td><p>00:00.351</p></td> +<td><p>00:00.256</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_directed.html#sphx-glr-auto-examples-drawing-plot-directed-py"><span class="std std-ref">Directed Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_directed.py</span></code>)</p></td> -<td><p>00:00.323</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_labels_and_colors.html#sphx-glr-auto-examples-drawing-plot-labels-and-colors-py"><span class="std std-ref">Labels And Colors</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_labels_and_colors.py</span></code>)</p></td> -<td><p>00:00.264</p></td> +<td><p>00:00.210</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_unix_email.html#sphx-glr-auto-examples-drawing-plot-unix-email-py"><span class="std std-ref">Unix Email</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_unix_email.py</span></code>)</p></td> -<td><p>00:00.199</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_rainbow_coloring.html#sphx-glr-auto-examples-drawing-plot-rainbow-coloring-py"><span class="std std-ref">Rainbow Coloring</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_rainbow_coloring.py</span></code>)</p></td> +<td><p>00:00.148</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" 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class="pre">plot_lines.py</span></code>)</p></td> -<td><p>00:04.172</p></td> +<td><p>00:03.175</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>00:00.543</p></td> +<td><p>00:00.465</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 44c04b00..ae3263e4 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 0.180 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.134 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 5a15eb16..a5ca79ba 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 0.079 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.066 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 50c12d93..5d5410b9 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 0.079 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.066 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 8b7796d7..202fd8da 100644 --- a/auto_examples/graph/plot_expected_degree_sequence.html +++ b/auto_examples/graph/plot_expected_degree_sequence.html @@ -539,48 +539,45 @@ degree (#nodes) **** 29 ( 0) 30 ( 1) * 31 ( 0) -32 ( 0) -33 ( 1) * -34 ( 1) * +32 ( 1) * +33 ( 3) *** +34 ( 2) ** 35 ( 3) *** -36 ( 2) ** -37 ( 1) * -38 ( 5) ***** -39 ( 4) **** -40 (10) ********** -41 (14) ************** -42 (19) ******************* -43 (15) *************** -44 (25) ************************* -45 (30) ****************************** -46 (21) ********************* -47 (30) ****************************** -48 (20) ******************** -49 (25) ************************* -50 (33) ********************************* -51 (28) **************************** -52 (35) *********************************** -53 (25) ************************* -54 (26) ************************** -55 (25) ************************* -56 (24) ************************ -57 (11) *********** -58 (15) *************** -59 (10) ********** -60 (11) *********** -61 (10) ********** +36 ( 3) *** +37 ( 5) ***** +38 ( 4) **** +39 ( 9) ********* +40 ( 6) ****** +41 (12) ************ +42 (15) *************** +43 (19) ******************* +44 (23) *********************** +45 (27) *************************** +46 (30) ****************************** +47 (26) ************************** +48 (25) ************************* +49 (24) ************************ +50 (35) *********************************** +51 (25) ************************* +52 (29) ***************************** +53 (28) **************************** +54 (22) ********************** +55 (17) ***************** +56 (22) ********************** +57 (16) **************** +58 (13) ************* +59 (13) ************* +60 (13) ************* +61 (11) *********** 62 ( 7) ******* -63 ( 3) *** -64 ( 3) *** -65 ( 1) * -66 ( 2) ** +63 ( 1) * +64 ( 5) ***** +65 ( 0) +66 ( 3) *** 67 ( 1) * 68 ( 0) -69 ( 2) ** -70 ( 0) -71 ( 0) -72 ( 0) -73 ( 1) * +69 ( 0) +70 ( 1) * </pre></div> </div> <div class="line-block"> @@ -600,7 +597,7 @@ 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 0.045 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.035 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 c575869c..b47788a4 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 0.500 seconds)</p> +<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-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 6c915824..e90d7f2e 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 0.123 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-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 8aa647aa..18296f95 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 0.267 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.199 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 e33e2a8f..75306537 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 0.177 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.144 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 69cf56be..fc0d781f 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 0.281 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.263 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 cceb1f35..f68eb1b8 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 1.616 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.166 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 2009da00..4e0f19e8 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 0.523 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.437 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 ddfde9ee..b45b4b0f 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>00:03.870</strong> total execution time for <strong>auto_examples_graph</strong> files:</p> +<p><strong>00:03.036</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>00:01.616</p></td> +<td><p>00:01.166</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>00:00.523</p></td> +<td><p>00:00.437</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>00:00.500</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_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>00:00.281</p></td> +<td><p>00:00.263</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>00:00.267</p></td> +<td><p>00:00.199</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>00:00.180</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>00:00.144</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>00:00.177</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>00:00.134</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>00:00.123</p></td> +<td><p>00:00.101</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_erdos_renyi.html#sphx-glr-auto-examples-graph-plot-erdos-renyi-py"><span class="std std-ref">Erdos Renyi</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_erdos_renyi.py</span></code>)</p></td> -<td><p>00:00.079</p></td> +<td><p>00:00.066</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_degree_sequence.html#sphx-glr-auto-examples-graph-plot-degree-sequence-py"><span class="std std-ref">Degree Sequence</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_degree_sequence.py</span></code>)</p></td> -<td><p>00:00.079</p></td> +<td><p>00:00.066</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>00:00.045</p></td> +<td><p>00:00.035</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 cf8a02b5..841d7132 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 0.040 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.033 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_grid.html b/auto_examples/graphviz_drawing/plot_grid.html index aecadee2..9de7c58b 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 0.081 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-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 0cfaef51..51247777 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 0.114 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.093 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 8a09c859..089b9da1 100644 --- a/auto_examples/graphviz_drawing/sg_execution_times.html +++ b/auto_examples/graphviz_drawing/sg_execution_times.html @@ -463,19 +463,19 @@ <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>00:00.265</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> +<p><strong>00:00.232</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>00:00.114</p></td> +<td><p>00:00.093</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><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>00:00.081</p></td> +<td><p>00:00.076</p></td> <td><p>0.0 MB</p></td> </tr> <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>00:00.040</p></td> +<td><p>00:00.033</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> diff --git a/auto_examples/graphviz_layout/plot_atlas.html b/auto_examples/graphviz_layout/plot_atlas.html index 602eb298..853b691a 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 4.760 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 4.109 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 8b38d1b9..61e431f9 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 0.188 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.169 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 ff02fa3e..004200a4 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 0.425 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.341 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 f208c0f9..4a094f61 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 1.042 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.960 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 8020fc08..258b1091 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 0.417 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.377 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 1eb6c491..7f655dc9 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>00:06.831</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> +<p><strong>00:05.956</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>00:04.760</p></td> +<td><p>00:04.109</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>00:01.042</p></td> +<td><p>00:00.960</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><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>00:00.425</p></td> +<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>00:00.377</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_lanl_routes.html#sphx-glr-auto-examples-graphviz-layout-plot-lanl-routes-py"><span class="std 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b/auto_examples/subclass/plot_antigraph.html index cfb9c30b..b8a8ba6a 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 0.121 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.102 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 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+++ 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>00:00.201</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p> +<p><strong>00:00.167</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>00:00.121</p></td> +<td><p>00:00.102</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>00:00.080</p></td> +<td><p>00:00.066</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/reference/introduction-7.hires.png b/reference/introduction-7.hires.png Binary files differindex 363f7e65..ebc417ad 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 a4142c62..1a075032 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 4927348a..f59bb09a 100644 --- a/reference/introduction-7.png +++ b/reference/introduction-7.png diff --git a/reference/introduction.ipynb b/reference/introduction.ipynb index 1c156eae..3cdab788 100644 --- a/reference/introduction.ipynb +++ b/reference/introduction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "76061848", + "id": "4b02aa4e", "metadata": {}, "source": [ "## Introduction\n", @@ -34,7 +34,7 @@ { "cell_type": "code", "execution_count": null, - "id": "aacd9a5e", + "id": "33a0bce8", "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "e50a31d0", + "id": "484e02b8", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": null, - "id": "241c63cb", + "id": "5e546053", "metadata": {}, "outputs": [], "source": [ @@ -94,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "6f167e58", + "id": "74c45c46", "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": "1dd38732", + "id": "3377f258", "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "ad6e5c50", + "id": "956ea5e2", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -214,7 +214,7 @@ { "cell_type": "code", "execution_count": null, - "id": "dbde2ed2", + "id": "76e5fdf4", "metadata": {}, "outputs": [], "source": [ @@ -225,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "7eddd2b2", + "id": "09743c25", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -234,7 +234,7 @@ { "cell_type": "code", "execution_count": null, - "id": "674fd085", + "id": "64ee3a79", "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "9e35fe28", + "id": "2b898aa9", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -311,7 +311,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e351abab", + "id": "f0a9eb6a", "metadata": {}, "outputs": [], "source": [ @@ -323,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "f7ef096b", + "id": "c31e2ef8", "metadata": {}, "source": [ "# Drawing\n", @@ -344,7 +344,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3f293fc6", + "id": "242bcc6c", "metadata": {}, "outputs": [], "source": [ @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "80debed5", + "id": "8e6a5b97", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -398,7 +398,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ca69d90b", + "id": "96fa3f07", "metadata": {}, "outputs": [], "source": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "2900c0fc", + "id": "c21afb7d", "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": "0211b3ab", + "id": "49f71f35", "metadata": {}, "outputs": [], "source": [ diff --git a/reference/introduction_full.ipynb b/reference/introduction_full.ipynb index 02011b07..cfe83970 100644 --- a/reference/introduction_full.ipynb +++ b/reference/introduction_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "76061848", + "id": "4b02aa4e", "metadata": {}, "source": [ "## Introduction\n", @@ -34,13 +34,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "aacd9a5e", + "id": "33a0bce8", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:13:58.604200Z", - "iopub.status.busy": "2023-01-04T11:13:58.603277Z", - "iopub.status.idle": "2023-01-04T11:13:58.697635Z", - "shell.execute_reply": "2023-01-04T11:13:58.696478Z" + "iopub.execute_input": "2023-01-04T14:37:22.982341Z", + "iopub.status.busy": "2023-01-04T14:37:22.981914Z", + "iopub.status.idle": "2023-01-04T14:37:23.059258Z", + "shell.execute_reply": "2023-01-04T14:37:23.058052Z" } }, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "markdown", - "id": "e50a31d0", + "id": "484e02b8", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -89,13 +89,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "241c63cb", + "id": "5e546053", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:13:58.702188Z", - "iopub.status.busy": "2023-01-04T11:13:58.701901Z", - "iopub.status.idle": "2023-01-04T11:13:58.706208Z", - "shell.execute_reply": "2023-01-04T11:13:58.705447Z" + "iopub.execute_input": "2023-01-04T14:37:23.064662Z", + "iopub.status.busy": "2023-01-04T14:37:23.064410Z", + "iopub.status.idle": "2023-01-04T14:37:23.068092Z", + "shell.execute_reply": "2023-01-04T14:37:23.067377Z" } }, "outputs": [], @@ -108,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "6f167e58", + "id": "74c45c46", "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": "1dd38732", + "id": "3377f258", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:13:58.710434Z", - "iopub.status.busy": "2023-01-04T11:13:58.710192Z", - "iopub.status.idle": "2023-01-04T11:13:58.714623Z", - "shell.execute_reply": "2023-01-04T11:13:58.713613Z" + "iopub.execute_input": "2023-01-04T14:37:23.071684Z", + "iopub.status.busy": "2023-01-04T14:37:23.071458Z", + "iopub.status.idle": "2023-01-04T14:37:23.074660Z", + "shell.execute_reply": "2023-01-04T14:37:23.074071Z" } }, "outputs": [], @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "ad6e5c50", + "id": "956ea5e2", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -235,13 +235,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "dbde2ed2", + "id": "76e5fdf4", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:13:58.719374Z", - "iopub.status.busy": "2023-01-04T11:13:58.719085Z", - "iopub.status.idle": "2023-01-04T11:13:58.722949Z", - "shell.execute_reply": "2023-01-04T11:13:58.722338Z" + "iopub.execute_input": "2023-01-04T14:37:23.077623Z", + "iopub.status.busy": "2023-01-04T14:37:23.077425Z", + "iopub.status.idle": "2023-01-04T14:37:23.080531Z", + "shell.execute_reply": "2023-01-04T14:37:23.079810Z" } }, "outputs": [], @@ -253,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "7eddd2b2", + "id": "09743c25", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -262,13 +262,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "674fd085", + "id": "64ee3a79", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:13:58.726691Z", - "iopub.status.busy": "2023-01-04T11:13:58.726449Z", - "iopub.status.idle": "2023-01-04T11:13:58.731962Z", - "shell.execute_reply": "2023-01-04T11:13:58.731042Z" + "iopub.execute_input": "2023-01-04T14:37:23.083661Z", + "iopub.status.busy": "2023-01-04T14:37:23.083439Z", + "iopub.status.idle": "2023-01-04T14:37:23.087568Z", + "shell.execute_reply": "2023-01-04T14:37:23.086914Z" } }, "outputs": [], @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "9e35fe28", + "id": "2b898aa9", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -346,13 +346,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "e351abab", + "id": "f0a9eb6a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:13:58.735989Z", - "iopub.status.busy": "2023-01-04T11:13:58.735730Z", - "iopub.status.idle": "2023-01-04T11:13:58.740948Z", - "shell.execute_reply": "2023-01-04T11:13:58.740099Z" + "iopub.execute_input": "2023-01-04T14:37:23.091173Z", + "iopub.status.busy": "2023-01-04T14:37:23.090924Z", + "iopub.status.idle": "2023-01-04T14:37:23.095366Z", + "shell.execute_reply": "2023-01-04T14:37:23.094698Z" } }, "outputs": [ @@ -373,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "f7ef096b", + "id": "c31e2ef8", "metadata": {}, "source": [ "# Drawing\n", @@ -394,19 +394,19 @@ { "cell_type": "code", "execution_count": 7, - "id": "3f293fc6", + "id": "242bcc6c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:13:58.746681Z", - "iopub.status.busy": "2023-01-04T11:13:58.746383Z", - "iopub.status.idle": "2023-01-04T11:13:59.481747Z", - "shell.execute_reply": "2023-01-04T11:13:59.480649Z" + "iopub.execute_input": "2023-01-04T14:37:23.099934Z", + "iopub.status.busy": "2023-01-04T14:37:23.099683Z", + "iopub.status.idle": "2023-01-04T14:37:23.718241Z", + "shell.execute_reply": "2023-01-04T14:37:23.717617Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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"text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -426,7 +426,7 @@ }, { "cell_type": "markdown", - "id": "80debed5", + "id": "8e6a5b97", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -466,13 +466,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "ca69d90b", + "id": "96fa3f07", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:13:59.486522Z", - "iopub.status.busy": "2023-01-04T11:13:59.485902Z", - "iopub.status.idle": "2023-01-04T11:13:59.491045Z", - "shell.execute_reply": "2023-01-04T11:13:59.490154Z" + "iopub.execute_input": "2023-01-04T14:37:23.722108Z", + "iopub.status.busy": "2023-01-04T14:37:23.721537Z", + "iopub.status.idle": "2023-01-04T14:37:23.725821Z", + "shell.execute_reply": "2023-01-04T14:37:23.725244Z" } }, "outputs": [ @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "2900c0fc", + "id": "c21afb7d", "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": "0211b3ab", + "id": "49f71f35", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:13:59.496006Z", - "iopub.status.busy": "2023-01-04T11:13:59.495718Z", - "iopub.status.idle": "2023-01-04T11:13:59.502145Z", - "shell.execute_reply": "2023-01-04T11:13:59.501347Z" + "iopub.execute_input": "2023-01-04T14:37:23.728990Z", + "iopub.status.busy": "2023-01-04T14:37:23.728568Z", + "iopub.status.idle": "2023-01-04T14:37:23.732880Z", + "shell.execute_reply": "2023-01-04T14:37:23.732330Z" } }, "outputs": [ diff --git a/searchindex.js b/searchindex.js index 1ba1b364..665efcee 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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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], "145": [8, 17, 297, 298, 306, 307, 315, 681, 1179], "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, 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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, 17, 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, 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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, "097": [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, 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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, 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"process": [11, 13, 52, 76, 92, 93, 94, 96, 97, 98, 102, 104, 180, 222, 226, 232, 274, 331, 338, 373, 383, 405, 406, 440, 455, 464, 465, 466, 592, 624, 691, 760, 786, 871, 914, 952, 995, 1045, 1100, 1175, 1177, 1180, 1216, 1219, 1222, 1225, 1245, 1280, 1290, 1295, 1296, 1299, 1301, 1383, 1395, 1407, 1408, 1412, 1413, 1414, 1419, 1426], "follow": [11, 25, 44, 49, 52, 53, 65, 67, 83, 86, 91, 92, 93, 94, 95, 97, 99, 100, 101, 102, 103, 108, 110, 111, 128, 132, 151, 161, 171, 183, 207, 213, 227, 229, 230, 231, 243, 280, 305, 338, 343, 351, 362, 373, 378, 380, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 440, 452, 453, 465, 466, 496, 502, 503, 504, 505, 506, 507, 508, 588, 598, 599, 602, 615, 636, 679, 748, 750, 760, 762, 791, 853, 867, 892, 898, 912, 928, 934, 948, 973, 979, 993, 1010, 1102, 1103, 1105, 1144, 1165, 1175, 1179, 1185, 1188, 1200, 1201, 1209, 1219, 1225, 1233, 1234, 1241, 1251, 1260, 1274, 1275, 1276, 1277, 1281, 1296, 1315, 1323, 1326, 1328, 1329, 1388, 1393, 1395, 1399, 1404, 1406, 1407, 1409, 1411, 1412, 1413, 1425, 1426], "given": [11, 38, 44, 62, 64, 67, 91, 99, 101, 103, 112, 116, 141, 142, 144, 152, 158, 193, 197, 208, 211, 212, 227, 229, 235, 236, 248, 249, 260, 264, 266, 269, 271, 273, 274, 276, 279, 281, 283, 284, 285, 286, 287, 288, 320, 329, 331, 338, 344, 351, 353, 357, 362, 363, 364, 365, 373, 378, 380, 381, 385, 439, 454, 455, 460, 462, 470, 477, 478, 480, 497, 511, 512, 513, 559, 560, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 576, 578, 579, 580, 588, 589, 590, 614, 615, 616, 622, 623, 659, 660, 661, 662, 676, 677, 678, 679, 681, 683, 684, 686, 690, 691, 693, 697, 698, 699, 700, 702, 703, 704, 706, 707, 708, 709, 728, 729, 730, 731, 732, 739, 748, 753, 761, 782, 786, 854, 857, 882, 899, 902, 921, 935, 938, 963, 980, 983, 1003, 1046, 1085, 1086, 1094, 1101, 1102, 1135, 1144, 1151, 1162, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1189, 1199, 1200, 1201, 1206, 1207, 1208, 1209, 1210, 1221, 1222, 1240, 1269, 1273, 1274, 1276, 1295, 1300, 1302, 1315, 1323, 1353, 1354, 1379, 1380, 1394, 1395, 1406], "digit": [11, 70, 99], "base": [11, 15, 38, 43, 55, 58, 69, 93, 94, 100, 101, 102, 103, 107, 128, 132, 199, 203, 205, 212, 216, 220, 229, 296, 297, 301, 302, 303, 308, 309, 310, 311, 312, 322, 323, 324, 325, 329, 330, 337, 343, 346, 347, 362, 371, 373, 374, 380, 381, 382, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 425, 426, 427, 428, 430, 431, 449, 464, 466, 494, 498, 499, 500, 509, 510, 545, 555, 564, 566, 569, 574, 581, 614, 616, 660, 667, 680, 688, 691, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 717, 732, 738, 758, 761, 762, 786, 791, 796, 887, 925, 934, 935, 968, 979, 980, 1007, 1036, 1037, 1038, 1041, 1043, 1082, 1088, 1182, 1229, 1235, 1253, 1267, 1296, 1320, 1321, 1323, 1326, 1383, 1387, 1392, 1395, 1402, 1403, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1421, 1425], "obtain": [11, 91, 165, 207, 282, 345, 346, 347, 380, 383, 387, 388, 389, 390, 394, 465, 511, 606, 618, 619, 656, 722, 742, 743, 760, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1164, 1253, 1272, 1278, 1279, 1323, 1326, 1356, 1357, 1402, 1426], "seri": [11, 444, 616, 680, 1215, 1286], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1177, 1179, 1192, 1222], "end": [11, 25, 36, 52, 95, 101, 106, 153, 154, 206, 215, 227, 267, 268, 300, 332, 333, 342, 371, 372, 427, 614, 618, 619, 626, 627, 631, 632, 634, 635, 636, 639, 640, 650, 651, 652, 653, 654, 655, 660, 664, 667, 677, 678, 680, 734, 736, 1038, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1133, 1135, 1152, 1165, 1206, 1229, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1342, 1344, 1350, 1353, 1357, 1358, 1368, 1371, 1372, 1375, 1376, 1379, 1404, 1413], "In": [11, 16, 27, 43, 54, 57, 58, 88, 92, 93, 94, 95, 97, 99, 100, 101, 103, 110, 115, 127, 132, 133, 175, 184, 199, 217, 229, 230, 231, 235, 240, 257, 258, 259, 278, 283, 286, 288, 289, 299, 311, 312, 324, 325, 329, 350, 357, 378, 379, 380, 410, 413, 414, 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373, 377, 380, 382, 383, 385, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 428, 440, 472, 473, 494, 498, 499, 500, 501, 502, 503, 506, 507, 509, 510, 520, 521, 564, 566, 581, 583, 589, 591, 592, 670, 671, 672, 673, 674, 676, 691, 693, 694, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 716, 720, 723, 724, 732, 734, 735, 736, 737, 740, 741, 749, 758, 768, 791, 1117, 1133, 1135, 1137, 1165, 1181, 1198, 1199, 1200, 1201, 1208, 1225, 1237, 1238, 1302, 1323, 1395, 1402, 1406, 1407, 1412, 1413], "cycl": [11, 38, 44, 95, 120, 214, 227, 228, 229, 230, 231, 232, 263, 293, 294, 295, 338, 341, 343, 358, 449, 450, 451, 452, 453, 457, 462, 463, 464, 466, 467, 468, 480, 496, 501, 504, 505, 508, 519, 584, 585, 587, 608, 628, 629, 630, 632, 652, 657, 658, 663, 697, 727, 742, 743, 758, 791, 1043, 1052, 1135, 1137, 1148, 1149, 1152, 1163, 1186, 1190, 1242, 1244, 1260, 1264, 1325, 1395, 1397, 1398, 1401, 1403, 1404, 1406, 1407, 1408, 1411, 1412, 1414, 1424, 1425], "requir": [11, 38, 65, 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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": 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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, 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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, 1406, 1412, 1413], "come": [54, 93, 100, 101, 102, 517, 577, 588, 598, 608, 677, 698, 699, 1046, 1243, 1326, 1402, 1413], "piec": [54, 374], "move": [54, 94, 95, 100, 101, 230, 231, 377, 380, 1117, 1207, 1210, 1393, 1395, 1404, 1405, 1406, 1407, 1411, 1413, 1416, 1419, 1421, 1425], "chessboard": 54, "from_datafram": [54, 55, 57, 58], "built": [54, 69, 93, 102, 103, 106, 230, 231, 362, 464, 1102, 1103, 1105, 1182, 1183, 1184, 1296, 1328, 1396, 1426], "relev": [54, 93, 99, 101, 103, 104, 106, 132, 168, 176, 184, 189, 497, 501, 504, 505, 508, 657, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998, 1084, 1307, 1312, 1323, 1411, 1417], "delaunay_graph": 54, "merg": [54, 57, 58, 93, 99, 100, 106, 383, 584, 585, 587, 1322, 1403], "nice": [54, 57, 58, 101, 214, 494, 1061, 1328, 1410], "basemap": [54, 57, 58], "lightblu": [54, 58], "cornsilk": 54, "695": [54, 59], "plot_delaunai": [54, 59], "sometim": [55, 63, 92, 94, 99, 102, 109, 199, 346, 347, 611, 729, 731, 887, 925, 968, 1007, 1043, 1117, 1155, 1247, 1328, 1404], "linestr": 55, "altern": 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1406, 1408], "commit": [91, 92, 93, 94, 99, 100, 105, 106, 1407, 1409, 1411, 1412, 1413, 1414, 1415, 1417, 1419, 1425], "git": [91, 93, 94, 97, 99, 106, 111, 1416, 1419], "repositori": [91, 93, 99, 106, 1406], "grep": [91, 97], "uniq": 91, "histor": [91, 99, 101, 1217], "earlier": [91, 299, 363, 364, 365, 739, 1199, 1393, 1402, 1408, 1413], "acknowledg": [91, 92, 96], "nonlinear": [91, 1213, 1215, 1222], "lo": 91, "alamo": 91, "nation": [91, 92, 457, 720], "laboratori": 91, "pi": [91, 653, 1114], "program": [91, 105, 110, 362, 455, 488, 490, 678, 1119, 1120, 1125, 1226, 1302, 1324, 1326, 1328, 1414], "offic": [91, 1267], "complex": [91, 94, 101, 105, 210, 217, 229, 230, 231, 239, 240, 274, 290, 293, 294, 300, 314, 327, 330, 331, 332, 333, 337, 346, 347, 355, 356, 371, 372, 376, 385, 386, 423, 434, 438, 452, 453, 494, 500, 519, 520, 521, 574, 616, 619, 625, 659, 692, 698, 699, 749, 1120, 1126, 1175, 1179, 1196, 1197, 1198, 1341, 1342, 1344, 1381, 1407, 1408, 1409, 1410, 1411, 1412, 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168, 189, 429, 471, 472, 473, 474, 475, 496, 606, 626, 627, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 693, 721, 760, 770, 789, 796, 853, 865, 878, 898, 910, 915, 934, 946, 960, 979, 991, 996, 1037, 1038, 1039, 1040, 1135, 1326, 1392, 1393, 1394, 1396, 1398, 1399, 1402, 1406, 1407, 1408, 1410], "cheong": 93, "se": 93, "hang": 93, "yain": 93, "whar": 93, "schemat": 93, "placement": [93, 614], "survei": [93, 110, 564, 566, 581, 786, 1201], "2020": [93, 99, 100, 101, 102, 569, 1406, 1412], "1177": 93, "2f1473871618821740": 93, "upload": [93, 106, 217], "pdf": [93, 105, 110, 112, 128, 214, 215, 216, 217, 220, 235, 305, 311, 312, 315, 322, 324, 325, 330, 342, 355, 356, 373, 410, 411, 412, 413, 414, 415, 417, 426, 427, 430, 442, 447, 448, 476, 483, 490, 494, 511, 512, 519, 564, 566, 567, 570, 571, 573, 618, 619, 690, 693, 748, 749, 750, 760, 762, 1193, 1197, 1198, 1326, 1407, 1412, 1426], "docx": 93, "ppt": 93, "lectur": [93, 110, 412, 431, 498, 616, 1203], "wayback": [93, 1413], "machin": [93, 312, 331, 494, 511, 512, 762, 1396, 1406, 1413], "snapshot": 93, "unreach": 93, "pyarg": [93, 111, 1038], "tell": [93, 99, 102, 760, 1275, 1278, 1279, 1296, 1328, 1412], "compar": [93, 464, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 559, 560, 561, 562, 563, 615, 760, 782, 1165, 1302, 1414], "baselin": [93, 1134, 1136], "ones": [93, 99, 107, 109, 282, 680, 1038, 1395, 1402, 1404], "savefig": [93, 1426], "mpl_image_compar": 93, "test_barbel": 93, "barbel": [93, 293, 294, 391, 424, 1146, 1157, 1276, 1426], "conduct": [93, 96, 100, 109, 447, 448, 758], "contributor": [94, 96, 99, 105, 106, 110, 1271, 1323, 1403], "shepherd": [94, 99], "mission": [94, 96, 97, 100, 107], "approv": [94, 100], "nuclear": 94, "launch": 94, "carefulli": 94, "clean": [94, 106, 530, 540, 1300, 1406, 1407, 1411, 1413, 1420], "nearli": 94, "volunt": [94, 107, 1413], "tremend": 94, "felt": 94, "evalu": [94, 130, 152, 157, 158, 195, 330, 618, 619, 626, 627, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1296, 1417], "novic": 94, "strongli": [94, 217, 232, 388, 391, 397, 398, 399, 403, 405, 406, 423, 480, 491, 492, 519, 588, 633, 697, 699, 751, 753, 1185, 1402, 1406, 1411, 1414, 1417, 1425], "mentorship": [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": [95, 1405, 1407, 1413], "_pagerank_numpi": 95, "convert_matrix": [95, 1387, 1407, 1411, 1413], "to_pandas_edgelist": [95, 1100, 1407, 1408, 1413], "binari": [95, 110, 429, 476, 586, 593, 730, 739, 1414], "asmatrix": 95, "wrapper": [95, 1119, 1125, 1296, 1405, 1413], "google_matrix": [95, 566, 1414], "futurewarn": [95, 1413, 1414], "attrmatrix": 95, "reflect": [95, 99, 103, 199, 296, 301, 302, 303, 308, 309, 323, 466, 887, 925, 968, 1007, 1061, 1066, 1082, 1085, 1086, 1326, 1406, 1407, 1420], "ndarrai": [95, 107, 565, 629, 1098, 1102, 1278, 1387, 1405, 1414], "distance_measur": [95, 217, 1411], "extrema_bound": [95, 1416], "maxcardin": [95, 581, 583, 1416, 1425], "min_weight_match": [95, 758, 1416, 1425], "scale_free_graph": [95, 1413, 1420], "nx_pydot": [95, 1041, 1042, 1124, 1125, 1126, 1127, 1128, 1396, 1408, 1425, 1426], "5723": [95, 1425], "node_link": [95, 1407, 1422, 1425], "node_link_graph": [95, 1363, 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1098, 1099, 1102, 1192], "breviti": 97, "offici": [97, 99, 1402], "inclus": [97, 99, 109, 220, 534, 544, 729, 731, 1188, 1214], "criteria": [97, 1425], "addit": [97, 99, 100, 103, 107, 111, 115, 184, 350, 423, 476, 534, 544, 545, 734, 736, 761, 791, 796, 873, 916, 947, 955, 979, 992, 998, 1036, 1037, 1039, 1040, 1088, 1117, 1195, 1272, 1296, 1302, 1326, 1345, 1348, 1349, 1350, 1381, 1382, 1383, 1395, 1403, 1404, 1405, 1406, 1407, 1413, 1414, 1425, 1426], "fit": [97, 110, 1326], "enhanc": [98, 99, 107, 341, 508, 1296, 1412, 1425], "berkelei": [99, 100, 103, 618, 619], "draft": [99, 100, 102, 103, 104, 1411, 1412, 1413, 1416], "stand": [99, 545, 1387], "primari": [99, 103, 1414], "gone": 99, "concis": [99, 110, 791, 1413, 1414], "rational": 99, "consensu": [99, 100], "dissent": 99, "opinion": [99, 100, 104], "revis": [99, 444, 732], "track": [99, 101, 102, 103, 104, 107, 115, 370, 387, 389, 390, 394, 598, 1296, 1302, 1406, 1411, 1412], "codebas": [99, 1296, 1404, 1405, 1412], "meta": 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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], "presum": [102, 1297], "rewritten": [102, 1395, 1402, 1406], "gradual": 102, "accomplish": [102, 109, 1165], "wrap": [102, 1045, 1047, 1296, 1301, 1304], "custom_graph": 102, "ichain": 102, "tripl": [102, 114, 249, 250, 711, 1411], "overli": 102, "empty_graph": [102, 753, 1057, 1158, 1297, 1323, 1406, 1409, 1410], "3036": 102, "1393": 102, "canon": [102, 684, 730, 1412], "huge": 102, "path_edgelist": 102, "disallow": [102, 796, 1037, 1039, 1040, 1187, 1417], "2022": [103, 105, 693, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424], "pseudo": [103, 104, 676, 1320, 1321, 1405, 1407], "nep19": 103, "legaci": [103, 1395, 1402, 1408], "randomst": [103, 1100, 1111, 1117, 1299, 1301, 1304, 1305, 1328, 1405, 1409], "statist": [103, 110, 128, 274, 358, 383, 385, 438, 1222, 1328, 1405], "strategi": [103, 123, 222, 362, 366, 370, 453], "engin": [103, 107, 729, 731, 1412], "modern": [103, 110, 1405], "prng": 103, "np_random_st": [103, 1301, 1405, 1414], "random_st": [103, 208, 213, 217, 222, 223, 227, 230, 231, 271, 272, 274, 275, 296, 297, 306, 368, 373, 377, 378, 380, 381, 589, 625, 681, 682, 683, 684, 686, 692, 693, 694, 701, 722, 738, 747, 1164, 1165, 1168, 1169, 1170, 1171, 1173, 1175, 1177, 1179, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1193, 1195, 1196, 1197, 1198, 1199, 1202, 1203, 1204, 1205, 1210, 1222, 1223, 1224, 1225, 1226, 1227, 1228, 1229, 1230, 1231, 1232, 1233, 1234, 1235, 1236, 1237, 1238, 1239, 1240, 1241, 1269, 1273, 1275, 1276, 1277, 1296, 1299, 1301, 1304, 1305, 1319, 1328, 1414], "mtrand": 103, "12345": [103, 1301, 1405], "rng": [103, 1041, 1100, 1299, 1301, 1328, 1405, 1409], "default_rng": [103, 1041, 1405, 1414], "_gener": 103, "stream": [103, 1405], "slight": 103, "guarante": [103, 127, 133, 184, 210, 215, 216, 235, 281, 311, 338, 380, 422, 465, 497, 501, 504, 505, 508, 511, 512, 549, 550, 551, 564, 566, 589, 653, 660, 667, 722, 728, 730, 873, 916, 955, 998, 1100, 1119, 1120, 1123, 1181, 1241, 1294, 1405], "upheld": 103, "exact": [103, 125, 210, 215, 216, 238, 269, 271, 273, 276, 671, 672, 673, 674, 691, 780, 1175, 1177, 1222, 1402, 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1183, 1184, 1189, 1326, 1404, 1407, 1416, 1426], "descend": [132, 454, 456, 465, 709, 758, 1272, 1401, 1404, 1406, 1413, 1414], "unblock": 132, "commonli": [132, 280, 454, 684, 782], "probabilist": [132, 378], "causal": 132, "markov": [132, 462, 565, 692, 1188], "hmm": 132, "s1": [132, 1242, 1313, 1363], "s2": [132, 1242, 1313], "s3": [132, 1313], "s4": 132, "s5": 132, "o1": 132, "o2": 132, "o3": 132, "o4": 132, "o5": 132, "ob": 132, "d_separ": [132, 758, 1412], "darwich": 132, "shachter": 132, "1998": [132, 1143, 1144, 1225, 1241, 1407], "bay": 132, "ball": 132, "ration": 132, "pastim": 132, "irrelev": [132, 1407], "requisit": 132, "influenc": [132, 324, 325, 512, 786], "fourteenth": [132, 1186], "uncertainti": [132, 590, 732], "artifici": [132, 574, 590, 732], "480": [132, 426, 514, 518, 1398, 1406], "487": 132, "francisco": [132, 732], "morgan": [132, 732], "kaufmann": [132, 732], "koller": 132, "friedman": 132, "mit": [132, 342, 519, 618], "causal_markov_condit": 132, "ness": [133, 684, 782], "classmethod": [141, 1047], "auxiliari": [141, 142, 143, 220, 411, 412, 413, 415, 416, 417, 418, 419, 423, 430, 431, 1402], "sink": [141, 302, 309, 416, 418, 494, 495, 498, 499, 501, 502, 503, 506, 507, 509, 510, 565], "pick": [141, 217, 331, 657, 1188, 1207, 1210, 1407], "st": [141, 415, 417], "cut": [141, 222, 223, 293, 377, 382, 387, 389, 390, 394, 411, 412, 414, 415, 416, 417, 419, 427, 428, 429, 442, 443, 444, 445, 447, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 619, 758, 760, 1038, 1066, 1115, 1262, 1325, 1395, 1402, 1406, 1413], "refin": [143, 215, 423, 438], "auxgraph": [143, 423], "node_partit": 144, "permut": [144, 368, 452, 453, 455, 466, 748, 1285, 1320, 1321], "containin": 144, "frozenset": [144, 267, 339, 383, 586, 588, 752, 1165, 1333, 1337, 1338, 1412], "abc": [144, 545, 1154, 1206, 1303, 1412, 1413], "interchang": [144, 362], "bool": [145, 146, 148, 149, 165, 168, 171, 176, 184, 189, 196, 204, 208, 232, 237, 238, 242, 243, 245, 249, 250, 258, 265, 266, 267, 268, 272, 275, 286, 287, 288, 291, 294, 295, 296, 297, 298, 299, 301, 302, 305, 306, 307, 308, 309, 310, 314, 315, 322, 324, 325, 326, 327, 330, 343, 350, 355, 362, 393, 394, 395, 396, 397, 398, 439, 454, 462, 463, 467, 479, 480, 488, 489, 491, 494, 498, 499, 509, 510, 513, 514, 515, 516, 517, 518, 520, 521, 522, 545, 562, 564, 578, 579, 580, 581, 588, 613, 614, 616, 617, 622, 623, 625, 640, 652, 663, 673, 679, 685, 690, 696, 698, 699, 700, 704, 708, 719, 723, 724, 725, 726, 728, 730, 733, 734, 735, 736, 737, 738, 740, 741, 742, 743, 862, 865, 867, 870, 873, 878, 885, 891, 907, 910, 912, 916, 927, 931, 943, 946, 948, 951, 955, 960, 966, 972, 976, 988, 991, 993, 998, 1039, 1040, 1045, 1057, 1068, 1070, 1071, 1072, 1084, 1091, 1097, 1116, 1133, 1134, 1135, 1136, 1169, 1179, 1185, 1189, 1209, 1211, 1212, 1213, 1215, 1224, 1228, 1230, 1231, 1232, 1275, 1276, 1277, 1278, 1279, 1282, 1295, 1296, 1307, 1309, 1312, 1335, 1336, 1337, 1339, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1360, 1364, 1379, 1380], "account": [145, 148, 398, 448, 749, 761, 1270, 1393, 1413], "graph_nod": [145, 148], "subgraph_nod": [145, 148], "find_isomorph": [147, 150], "induc": [148, 167, 199, 211, 226, 342, 388, 392, 406, 427, 436, 437, 470, 487, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 512, 586, 589, 752, 761, 762, 864, 887, 909, 925, 945, 968, 990, 1007, 1038, 1061, 1066, 1087, 1102, 1103, 1105, 1189, 1283, 1284, 1393], "u_of_edg": [151, 853, 898], "v_of_edg": [151, 853, 898], "capac": [151, 265, 296, 301, 302, 303, 308, 309, 323, 411, 412, 415, 416, 417, 418, 419, 430, 431, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 758, 853, 898, 934, 979, 1335, 1402], "342": [151, 853, 898, 934, 979, 1255], "ebunch_to_add": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "add_weighted_edges_from": [152, 229, 230, 231, 508, 581, 630, 657, 659, 721, 854, 899, 935, 980, 1070, 1326, 1404, 1407, 1426], "runtimeerror": [152, 157, 158, 195, 464, 465, 466, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005], "happen": [152, 157, 158, 195, 380, 584, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1403, 1404, 1425], "iterator_of_edg": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "wn2898": [152, 854, 899, 935, 980], "wrong": [152, 157, 158, 722, 854, 856, 857, 899, 901, 902, 935, 937, 938, 980, 982, 983, 1406, 1411, 1416, 1425], "start_nod": [153, 154, 155], "end_nod": [153, 154, 155], "reference_neighbor": [153, 154], "half": [153, 154, 155, 164, 177, 183, 206, 297, 298, 615, 653], "clockwis": [153, 154, 169, 182, 197, 615], "networkxexcept": [153, 154, 161, 331, 588, 593, 724, 726, 1043, 1110, 1138, 1180, 1325], "add_half_edge_cw": [153, 155, 164, 615], "connect_compon": [153, 154, 155, 615], "add_half_edge_first": [153, 154, 164, 615], "add_half_edge_ccw": [154, 155, 164, 615], "node_for_ad": [156, 855, 900, 936, 981], "mutabl": [156, 855, 900, 936, 981, 1061, 1066, 1082, 1085, 1086], "hash": [156, 511, 512, 758, 855, 900, 936, 981, 1324, 1325, 1414, 1426], "hello": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1303], "k3": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1217], "utm": [156, 855, 900, 936, 981], "382871": [156, 855, 900, 936, 981], "3972649": [156, 855, 900, 936, 981], "nodes_for_ad": [157, 856, 901, 937, 982], "iterator_of_nod": [157, 195, 856, 884, 901, 923, 937, 965, 982, 1005], "datadict": [159, 190, 200, 207, 734, 736, 858, 879, 888, 892, 903, 928, 939, 969, 973, 1010, 1084, 1312, 1326], "foovalu": [159, 190, 200, 858, 879, 888, 903, 939, 969], "nbrdict": [160, 859, 904, 940, 985, 1019, 1094], "fulfil": [161, 615], "cw": [161, 615], "ccw": [161, 615], "planar": [161, 614, 616, 617, 758, 1110, 1138, 1243, 1246, 1247, 1249, 1325, 1409, 1410], "first_nbr": [161, 615], "invalid": [161, 615, 1413], "alter": [163, 861, 906, 942, 987], "afterward": 164, "as_view": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1089, 1090], "shallow": [165, 202, 204, 284, 285, 286, 287, 288, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1394], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1409], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1404, 1407, 1409, 1410, 1411], "fresh": [165, 862, 907, 943, 988, 1404], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1226, 1323, 1404], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1265, 1394], "degreeview": [166, 863, 908, 944, 950, 989, 1404, 1426], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1404, 1418], "ddict": [168, 176, 184, 189, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998], "in_edg": [168, 189, 865, 878, 946, 960, 1404, 1406, 1407], "out_edg": [168, 865, 946, 1062, 1404, 1406, 1407, 1426], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1426], "outedgedataview": [168, 189, 865, 878, 1404, 1411], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1404, 1412], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1406, 1407, 1417], "indegreeview": [175, 869, 1404], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1165, 1179, 1222, 1404], "inedgeview": [176, 870, 1404], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1127, 1353, 1354, 1359, 1363, 1406, 1413], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1414], "__iter__": [180, 871, 914, 952, 995, 1303], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1394, 1426], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1216], "nedg": [185, 588, 874, 917, 956, 999], "__len__": [186, 187, 875, 876, 918, 919, 957, 958, 1000, 1001], "outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 882, 921, 963, 1003, 1404, 1414], "get_data": [197, 616], "inplac": [199, 690, 887, 925, 968, 1007, 1066, 1393], "reduct": [199, 469, 618, 786, 887, 925, 968, 1007, 1066, 1320, 1321, 1413, 1414], "sg": [199, 887, 925, 968, 1007], "largest_wcc": [199, 887, 925, 968, 1007], "is_multigraph": [199, 758, 887, 925, 968, 1007, 1154, 1412], "keydict": [199, 207, 887, 892, 925, 928, 968, 973, 1007, 1010, 1039, 1040], "contrast": [202, 204, 301, 302, 308, 309, 890, 891, 926, 927, 971, 972, 1008, 1009, 1066, 1233, 1241, 1426], "reciproc": [204, 299, 320, 322, 356, 411, 430, 447, 476, 620, 758, 891, 972, 1325, 1416, 1425], "mark_half_edg": 206, "li": [206, 619, 670, 675, 685, 775, 1207, 1210, 1425], "straightforward": [207, 892, 928, 973, 1010], "slightli": [207, 326, 437, 520, 521, 581, 892, 928, 973, 1010, 1165, 1326, 1404, 1407, 1412, 1414, 1425], "singleton": [207, 588, 892, 928, 973, 1010, 1218, 1251, 1407], "preserve_attr": [208, 723, 724, 725, 726], "optimum": [208, 231, 583, 720, 722, 791, 1395, 1406], "arboresc": [208, 460, 719, 720, 722, 724, 726, 740, 743, 758, 1272, 1395, 1406], "span": [208, 226, 227, 228, 295, 508, 618, 619, 624, 719, 720, 722, 724, 726, 732, 733, 734, 735, 736, 737, 738, 758, 1394, 1397, 1406, 1407, 1420], "max_ind_cliqu": 209, "networkxnotimpl": [209, 210, 211, 212, 220, 224, 227, 293, 294, 295, 318, 319, 321, 328, 343, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 403, 404, 405, 406, 407, 422, 424, 425, 426, 427, 429, 455, 457, 458, 459, 460, 468, 481, 482, 500, 589, 590, 608, 680, 732, 1043, 1216, 1275, 1276, 1298, 1325, 1353, 1354, 1379, 1407, 1408], "boppana": [209, 211, 212], "halld\u00f3rsson": [209, 211, 212], "1992": [209, 211, 212, 517, 518, 1407], "exclud": [209, 211, 212, 215, 216, 261, 262, 453, 688, 719, 723, 724, 725, 726, 733, 751, 1036, 1038, 1088, 1217, 1412], "196": [209, 211, 212], "heurist": [210, 220, 228, 233, 234, 377, 380, 381, 427, 494, 509, 626, 627, 652, 663, 703, 758, 1173, 1320, 1321, 1325, 1395, 1408, 1412, 1413], "max_cliqu": 210, "rigor": 210, "pattabiraman": 210, "bharath": 210, "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], "fraction": [213, 257, 259, 286, 289, 297, 299, 304, 306, 315, 317, 318, 319, 321, 322, 326, 328, 330, 356, 358, 359, 519, 1165, 1234], "schank": 213, "thoma": [213, 751, 1407, 1409, 1413], "dorothea": [213, 1168], "wagner": [213, 429, 758, 1168, 1402, 1406], "universit\u00e4t": 213, "karlsruh": 213, "fakult\u00e4t": 213, "f\u00fcr": 213, "informatik": [213, 412], "5445": 213, "ir": [213, 606], "1000001239": 213, "erdos_renyi_graph": [213, 1224, 1232, 1326, 1406, 1426], "214": 213, "cutoff": [214, 215, 310, 326, 383, 410, 411, 412, 418, 419, 494, 495, 498, 499, 510, 637, 638, 640, 641, 642, 643, 644, 647, 648, 649, 656, 660, 661, 662, 667, 668, 669, 677, 678, 1234, 1398, 1402, 1406, 1413, 1416, 1424, 1425], "distinct": [214, 215, 255, 281, 288, 352, 391, 452, 453, 460, 578, 595, 608, 618, 700, 701, 734, 735, 736, 737, 789, 1150, 1244, 1271, 1323, 1326, 1328, 1395, 1417], "nonadjac": [214, 215, 480, 584, 585, 587], "cutset": [214, 215, 414, 415, 416, 417, 427, 428, 500, 506, 758], "menger": [214, 215, 216], "theorem": [214, 215, 216, 220, 235, 281, 311, 312, 322, 411, 506, 507, 514, 517, 518, 618, 1190, 1205], "local_node_connect": [214, 216, 408, 409, 410, 411, 413], "node_connect": [214, 215, 409, 410, 411, 412, 414, 415, 416, 417, 419, 427, 428, 1402], "dougla": [214, 215, 216, 220, 1413, 1425], "035": [214, 215, 216, 220], "eclect": [214, 215, 216], "ss": [214, 215, 216], "uci": [214, 215, 216, 467, 704, 706, 707, 708, 710, 734, 736], "drwhite": [214, 215, 216], "pprint": [214, 577, 711], "all_pairs_node_connect": [215, 216, 1402, 1424, 1425], "bf": [215, 216, 217, 363, 588, 704, 706, 707, 708, 717, 1397, 1401, 1406, 1409, 1412, 1413], "lose": [215, 796, 1037, 1039, 1040], "accuraci": [215, 312, 786], "platon": [215, 216, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1245, 1248, 1254, 1257, 1261, 1263], "octahedr": [215, 216, 1257], "approx": [215, 216, 227, 229, 230, 231, 1413], "octahedral_graph": [215, 216], "vari": [217, 238, 243, 373, 378, 569, 695], "sweep": [217, 1412], "dsweep": 217, "a_1": [217, 477], "a_2": 217, "magnien": [217, 260, 261, 262, 289], "cl\u00e9menc": 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"mentored-projects"]], "Pedagogical Interactive Notebooks for Algorithms Implemented in NetworkX": [[105, "pedagogical-interactive-notebooks-for-algorithms-implemented-in-networkx"]], "Completed Projects": [[105, "completed-projects"]], "Release Process": [[106, "release-process"]], "Roadmap": [[107, "roadmap"]], "Installation": [[107, "installation"]], "Sustainability": [[107, "sustainability"]], "Performance": [[107, "performance"]], "Documentation": [[107, "documentation"], [1406, "documentation"], [1406, "id71"], [1406, "id75"]], "Linear Algebra": [[107, "linear-algebra"]], "Interoperability": [[107, "interoperability"]], "Visualization": [[107, "visualization"]], "Mission and Values": [[109, "mission-and-values"]], "Our mission": [[109, "our-mission"]], "Our values": [[109, "our-values"]], "Software for Complex Networks": [[110, "software-for-complex-networks"]], "Citing": [[110, "citing"]], "Audience": [[110, "audience"]], "Python": [[110, "python"]], "License": [[110, "license"]], "Bibliography": [[110, "bibliography"]], "Install": [[111, "install"]], "Install the released version": [[111, "install-the-released-version"]], "Install the development version": [[111, "install-the-development-version"]], "Extra packages": [[111, "extra-packages"]], "Test a source distribution": [[111, "test-a-source-distribution"]], "Test an installed package": [[111, "test-an-installed-package"]], "Approximations and Heuristics": [[112, "module-networkx.algorithms.approximation"]], "Connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"], [126, "connectivity"], [127, "module-networkx.algorithms.connectivity"]], "K-components": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "Clique": [[112, "module-networkx.algorithms.approximation.clique"], [121, "module-networkx.algorithms.clique"]], "Clustering": [[112, "module-networkx.algorithms.approximation.clustering_coefficient"], [115, "module-networkx.algorithms.bipartite.cluster"], [122, "module-networkx.algorithms.cluster"]], "Distance Measures": [[112, "module-networkx.algorithms.approximation.distance_measures"], [134, "module-networkx.algorithms.distance_measures"]], "Dominating Set": [[112, "module-networkx.algorithms.approximation.dominating_set"]], "Matching": [[112, "module-networkx.algorithms.approximation.matching"], [115, "module-networkx.algorithms.bipartite.matching"], [766, "module-networkx.algorithms.matching"]], "Ramsey": [[112, "module-networkx.algorithms.approximation.ramsey"]], "Steiner Tree": [[112, "module-networkx.algorithms.approximation.steinertree"]], "Traveling Salesman": [[112, "module-networkx.algorithms.approximation.traveling_salesman"]], "Travelling Salesman Problem (TSP)": [[112, "travelling-salesman-problem-tsp"]], "Treewidth": [[112, "module-networkx.algorithms.approximation.treewidth"]], "Vertex Cover": [[112, "module-networkx.algorithms.approximation.vertex_cover"]], "Max Cut": [[112, 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"module-networkx.generators.spectral_graph_forge"]], "Redundancy": [[115, "module-networkx.algorithms.bipartite.redundancy"]], "Centrality": [[115, "module-networkx.algorithms.bipartite.centrality"], [118, "module-networkx.algorithms.centrality"]], "Generators": [[115, "module-networkx.algorithms.bipartite.generators"]], "Covering": [[115, "module-networkx.algorithms.bipartite.covering"], [129, "module-networkx.algorithms.covering"]], "Boundary": [[116, "module-networkx.algorithms.boundary"]], "Bridges": [[117, "module-networkx.algorithms.bridges"]], "Degree": [[118, "degree"]], "Eigenvector": [[118, "eigenvector"]], "Closeness": [[118, "closeness"]], "Current Flow Closeness": [[118, "current-flow-closeness"]], "(Shortest Path) Betweenness": [[118, "shortest-path-betweenness"]], "Current Flow Betweenness": [[118, "current-flow-betweenness"]], "Communicability Betweenness": [[118, "communicability-betweenness"]], "Group Centrality": [[118, "group-centrality"]], "Load": [[118, "load"]], 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"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, 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"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": 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"DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, 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"MultiDiGraph.__iter__": [[932, "multidigraph-iter"]], "MultiDiGraph.__len__": [[933, "multidigraph-len"]], "MultiDiGraph.add_edge": [[934, "multidigraph-add-edge"]], "MultiDiGraph.add_edges_from": [[935, "multidigraph-add-edges-from"]], "MultiDiGraph.add_node": [[936, "multidigraph-add-node"]], "MultiDiGraph.add_nodes_from": [[937, "multidigraph-add-nodes-from"]], "MultiDiGraph.add_weighted_edges_from": [[938, "multidigraph-add-weighted-edges-from"]], "MultiDiGraph.adj": [[939, "multidigraph-adj"]], "MultiDiGraph.adjacency": [[940, "multidigraph-adjacency"]], "MultiDiGraph.clear": [[941, "multidigraph-clear"]], "MultiDiGraph.clear_edges": [[942, "multidigraph-clear-edges"]], "MultiDiGraph.copy": [[943, "multidigraph-copy"]], "MultiDiGraph.degree": [[944, "multidigraph-degree"]], "MultiDiGraph.edge_subgraph": [[945, "multidigraph-edge-subgraph"]], "MultiDiGraph.edges": [[946, "multidigraph-edges"]], "MultiDiGraph.get_edge_data": [[947, "multidigraph-get-edge-data"]], 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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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"networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[144, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[151, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": 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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, 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"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": 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"networkx.Graph.to_undirected"]], "update() (graph method)": [[928, "networkx.Graph.update"]], "__contains__() (multidigraph method)": [[929, "networkx.MultiDiGraph.__contains__"]], "__getitem__() (multidigraph method)": [[930, "networkx.MultiDiGraph.__getitem__"]], "__init__() (multidigraph method)": [[931, "networkx.MultiDiGraph.__init__"]], "__iter__() (multidigraph method)": [[932, "networkx.MultiDiGraph.__iter__"]], "__len__() (multidigraph method)": [[933, "networkx.MultiDiGraph.__len__"]], "add_edge() (multidigraph method)": [[934, "networkx.MultiDiGraph.add_edge"]], "add_edges_from() (multidigraph method)": [[935, "networkx.MultiDiGraph.add_edges_from"]], "add_node() (multidigraph method)": [[936, "networkx.MultiDiGraph.add_node"]], "add_nodes_from() (multidigraph method)": [[937, "networkx.MultiDiGraph.add_nodes_from"]], "add_weighted_edges_from() (multidigraph method)": [[938, "networkx.MultiDiGraph.add_weighted_edges_from"]], "adj (multidigraph property)": [[939, 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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, "networkx.convert_matrix.from_pandas_adjacency"]], "from_pandas_edgelist() (in module networkx.convert_matrix)": [[1100, "networkx.convert_matrix.from_pandas_edgelist"]], "from_scipy_sparse_array() (in module networkx.convert_matrix)": [[1101, "networkx.convert_matrix.from_scipy_sparse_array"]], "to_numpy_array() (in module networkx.convert_matrix)": [[1102, "networkx.convert_matrix.to_numpy_array"]], "to_pandas_adjacency() (in module networkx.convert_matrix)": [[1103, "networkx.convert_matrix.to_pandas_adjacency"]], "to_pandas_edgelist() (in module networkx.convert_matrix)": [[1104, "networkx.convert_matrix.to_pandas_edgelist"]], "to_scipy_sparse_array() (in module networkx.convert_matrix)": [[1105, "networkx.convert_matrix.to_scipy_sparse_array"]], "bipartite_layout() (in module networkx.drawing.layout)": [[1106, "networkx.drawing.layout.bipartite_layout"]], "circular_layout() (in module networkx.drawing.layout)": [[1107, "networkx.drawing.layout.circular_layout"]], "kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], "spring_layout() (in module networkx.drawing.layout)": [[1117, "networkx.drawing.layout.spring_layout"]], "from_agraph() (in module networkx.drawing.nx_agraph)": [[1118, "networkx.drawing.nx_agraph.from_agraph"]], "graphviz_layout() (in module networkx.drawing.nx_agraph)": [[1119, "networkx.drawing.nx_agraph.graphviz_layout"]], "pygraphviz_layout() (in module networkx.drawing.nx_agraph)": [[1120, "networkx.drawing.nx_agraph.pygraphviz_layout"]], "read_dot() (in module networkx.drawing.nx_agraph)": [[1121, "networkx.drawing.nx_agraph.read_dot"]], "to_agraph() (in module networkx.drawing.nx_agraph)": [[1122, "networkx.drawing.nx_agraph.to_agraph"]], "write_dot() (in module networkx.drawing.nx_agraph)": [[1123, "networkx.drawing.nx_agraph.write_dot"]], "from_pydot() (in module networkx.drawing.nx_pydot)": [[1124, "networkx.drawing.nx_pydot.from_pydot"]], "graphviz_layout() (in module networkx.drawing.nx_pydot)": [[1125, "networkx.drawing.nx_pydot.graphviz_layout"]], "pydot_layout() (in module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, 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module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() 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"networkx.generators.degree_seq.degree_sequence_tree"]], "directed_configuration_model() (in module networkx.generators.degree_seq)": [[1177, "networkx.generators.degree_seq.directed_configuration_model"]], "directed_havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1178, "networkx.generators.degree_seq.directed_havel_hakimi_graph"]], "expected_degree_graph() (in module networkx.generators.degree_seq)": [[1179, "networkx.generators.degree_seq.expected_degree_graph"]], "havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1180, "networkx.generators.degree_seq.havel_hakimi_graph"]], "random_degree_sequence_graph() (in module networkx.generators.degree_seq)": [[1181, "networkx.generators.degree_seq.random_degree_sequence_graph"]], "gn_graph() (in module networkx.generators.directed)": [[1182, "networkx.generators.directed.gn_graph"]], "gnc_graph() (in module networkx.generators.directed)": [[1183, "networkx.generators.directed.gnc_graph"]], "gnr_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.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1242, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1243, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1244, "networkx.generators.small.chvatal_graph"]], "cubical_graph() (in module networkx.generators.small)": [[1245, "networkx.generators.small.cubical_graph"]], "desargues_graph() (in module networkx.generators.small)": [[1246, "networkx.generators.small.desargues_graph"]], "diamond_graph() (in module networkx.generators.small)": [[1247, "networkx.generators.small.diamond_graph"]], "dodecahedral_graph() (in module networkx.generators.small)": [[1248, "networkx.generators.small.dodecahedral_graph"]], "frucht_graph() (in module networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, 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"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 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[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], "119": [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, 559, 560, 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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, 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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, 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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, 1046, 1243, 1326, 1402, 1413], 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1424, 1425], "barnowski": [91, 103, 108, 1412, 1413, 1414, 1416, 1417, 1419, 1420, 1421, 1422, 1424, 1425], "rossbar": [91, 103, 105, 108, 1412], "stefan": [91, 108, 1411, 1412, 1413, 1415, 1417], "van": [91, 108, 380, 511, 512, 1239, 1407, 1411, 1412, 1413, 1414, 1415, 1417, 1425], "der": [91, 108, 1411, 1412, 1413, 1415, 1417], "walt": [91, 108, 1411, 1412, 1413, 1415, 1417], "stefanv": [91, 108, 1411], "vadim": [91, 108, 1414], "abzalov": [91, 108], "vdshk": [91, 105, 108, 1414], "dimitrio": [91, 108, 128, 1413, 1414, 1421], "papageorgi": [91, 108, 1413, 1414, 1421], "z3y50n": [91, 105, 108, 1414], "benjamin": [91, 108, 1409, 1410], "edward": [91, 108, 1409, 1410], "bjedward": [91, 108], "chebee7i": [91, 108, 1407, 1409], "jfinkel": [91, 108, 1407], "jordi": [91, 108, 1407, 1408], "torrent": [91, 108, 220, 427, 1407, 1408], "jtorrent": [91, 108], "lo\u00efc": [91, 108], "s\u00e9guin": [91, 108], "charbonneau": [91, 108], "loicseguin": [91, 108], "ysitu": [91, 108, 1402], "feel": 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"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], "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], "sultanorazbayev": 91, "supplementari": 91, "incomplet": [91, 112, 1406, 1408], "commit": [91, 92, 93, 94, 99, 100, 105, 106, 1407, 1409, 1411, 1412, 1413, 1414, 1415, 1417, 1419, 1425], "git": [91, 93, 94, 97, 99, 106, 111, 1416, 1419], "repositori": [91, 93, 99, 106, 1406], "grep": [91, 97], "uniq": 91, "histor": [91, 99, 101, 1217], "earlier": [91, 299, 363, 364, 365, 739, 1199, 1393, 1402, 1408, 1413], "acknowledg": [91, 92, 96], "nonlinear": [91, 1213, 1215, 1222], "lo": 91, "alamo": 91, "nation": [91, 92, 457, 720], "laboratori": 91, "pi": [91, 653, 1114], "program": [91, 105, 110, 362, 455, 488, 490, 678, 1119, 1120, 1125, 1226, 1302, 1324, 1326, 1328, 1414], "offic": [91, 1267], "complex": [91, 94, 101, 105, 210, 217, 229, 230, 231, 239, 240, 274, 290, 293, 294, 300, 314, 327, 330, 331, 332, 333, 337, 346, 347, 355, 356, 371, 372, 376, 385, 386, 423, 434, 438, 452, 453, 494, 500, 519, 520, 521, 574, 616, 619, 625, 659, 692, 698, 699, 749, 1120, 1126, 1175, 1179, 1196, 1197, 1198, 1341, 1342, 1344, 1381, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "depart": [91, 494], "physic": [91, 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1099, 1100, 1103, 1412], "stat": [93, 244, 380, 381, 748, 750, 1193, 1197, 1224, 1228, 1232], "optim": [93, 107, 112, 125, 208, 212, 226, 230, 231, 330, 353, 362, 380, 381, 382, 385, 422, 429, 496, 508, 672, 692, 720, 722, 723, 724, 725, 726, 729, 731, 732, 760, 780, 1108, 1117, 1235, 1320, 1321, 1402, 1411, 1412, 1416], "subpackag": [93, 767, 1326, 1413, 1425], "particular": [93, 97, 110, 115, 357, 374, 517, 618, 750, 1175, 1278, 1279, 1328, 1350, 1409], "decor": [93, 102, 103, 1045, 1046, 1047, 1297, 1298, 1299, 1300, 1301, 1325, 1405, 1407, 1411, 1413, 1414, 1417], "not_implemented_for": [93, 1296, 1407, 1417], "doesn": [93, 94, 97, 101, 102, 156, 170, 561, 562, 563, 761, 796, 855, 866, 900, 911, 936, 947, 981, 992, 1037, 1039, 1040, 1117, 1175, 1177, 1179, 1216, 1222, 1296, 1326, 1404, 1406, 1407, 1412, 1414, 1425], "function_not_for_multidigraph": 93, "function_only_for_graph": 93, "framework": [93, 102, 1358], "submodul": [93, 1413], "specif": [93, 96, 99, 101, 107, 110, 111, 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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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1405, 1406, 1411], "algebra": [96, 110, 313, 380, 385, 1266, 1275, 1286, 1325, 1395, 1402, 1405, 1406], "nxep": [96, 107, 109, 1403, 1412, 1416], "govern": [96, 98, 109, 1412], "slice": [96, 98, 107, 1413], "builder": [96, 98, 1151, 1323, 1413], "frequent": [97, 378, 675], "newcom": [97, 109, 1326], "few": [97, 100, 101, 103, 362, 1402, 1404, 1411, 1412, 1413, 1414], "known": [97, 227, 280, 293, 301, 302, 303, 308, 309, 323, 369, 424, 450, 468, 618, 740, 741, 742, 743, 762, 791, 1068, 1097, 1145, 1148, 1200, 1201, 1224, 1228, 1230, 1232, 1247, 1272, 1324, 1412], "Of": [97, 1426], "sprint": [97, 1425], "permiss": [97, 110, 111, 457], "forget": 97, "sai": [97, 99, 101, 211, 512, 517, 518, 675, 676, 762, 1206, 1411], "rememb": [97, 101], "stick": [97, 1394], "plot_circular_layout": 97, "perhap": [97, 99, 102, 107], "deal": [97, 102], "worri": [97, 583, 1296, 1326], "ipython": 97, "field": [97, 99, 591, 593, 770, 1098, 1099, 1102, 1192], "breviti": 97, "offici": [97, 99, 1402], "inclus": 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330, 496, 608, 692, 1180, 1242, 1426], "tend": [99, 593, 1175, 1326], "doubt": [99, 1426], "champion": 99, "attempt": [99, 101, 194, 202, 204, 282, 284, 285, 286, 287, 288, 361, 362, 377, 425, 426, 584, 692, 693, 694, 786, 883, 890, 891, 922, 926, 927, 964, 971, 972, 1004, 1008, 1009, 1041, 1122, 1225, 1237, 1238, 1302, 1333, 1347, 1371, 1393, 1394, 1406, 1411, 1412, 1421, 1425], "ascertain": 99, "suitabl": [99, 110, 659, 693, 694, 1165, 1359, 1363, 1365, 1385, 1390], "0000": 99, "backward": [99, 217, 1199, 1402, 1404, 1406], "compat": [99, 429, 496, 691, 1302, 1404, 1405, 1406, 1412, 1414], "impact": [99, 100, 107, 329, 796, 1037, 1039, 1040], "broader": 99, "scope": [99, 107, 1045, 1413], "earliest": [99, 465], "conveni": [99, 101, 152, 497, 501, 504, 505, 508, 615, 796, 854, 899, 935, 980, 1037, 1038, 1039, 1040, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1270, 1296, 1326, 1394, 1405, 1409, 1426], "expand": [99, 101, 373, 653, 1038, 1190, 1325, 1395, 1406, 1407, 1408, 1413, 1424, 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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": 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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, 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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, 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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], "presum": [102, 1297], "rewritten": [102, 1395, 1402, 1406], "gradual": 102, "accomplish": [102, 109, 1165], "wrap": [102, 1045, 1047, 1296, 1301, 1304], "custom_graph": 102, "ichain": 102, "tripl": [102, 114, 249, 250, 711, 1411], "overli": 102, "empty_graph": [102, 753, 1057, 1158, 1297, 1323, 1406, 1409, 1410], "3036": 102, "1393": 102, "canon": [102, 684, 730, 1412], "huge": 102, "path_edgelist": 102, "disallow": [102, 796, 1037, 1039, 1040, 1187, 1417], "2022": [103, 105, 693, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424], "pseudo": [103, 104, 676, 1320, 1321, 1405, 1407], "nep19": 103, "legaci": [103, 1395, 1402, 1408], "randomst": [103, 1100, 1111, 1117, 1299, 1301, 1304, 1305, 1328, 1405, 1409], "statist": [103, 110, 128, 274, 358, 383, 385, 438, 1222, 1328, 1405], "strategi": [103, 123, 222, 362, 366, 370, 453], "engin": [103, 107, 729, 731, 1412], "modern": [103, 110, 1405], "prng": 103, "np_random_st": [103, 1301, 1405, 1414], "random_st": [103, 208, 213, 217, 222, 223, 227, 230, 231, 271, 272, 274, 275, 296, 297, 306, 368, 373, 377, 378, 380, 381, 589, 625, 681, 682, 683, 684, 686, 692, 693, 694, 701, 722, 738, 747, 1164, 1165, 1168, 1169, 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1219, 1243, 1244, 1245, 1246, 1248, 1249, 1250, 1251, 1256, 1257, 1258, 1259, 1261, 1262, 1263, 1264, 1271, 1323], "greedi": [112, 222, 229, 230, 231, 232, 330, 362, 366, 383, 384, 722, 1395, 1407], "simul": [112, 229, 230, 231, 331, 692, 1117], "anneal": [112, 229, 230, 231], "sa": 112, "ta": 112, "travelling_salesman_problem": 112, "bag": 112, "minu": [112, 340, 583, 1148], "notion": [112, 125, 128, 260, 261, 262, 289, 791], "partli": 112, "intract": 112, "solvabl": [112, 114], "constant": [112, 497, 501, 504, 505, 508, 675, 1175, 1195, 1215], "treewidth_min_degre": 112, "treewidth_min_fill_in": 112, "han": [112, 358, 1181, 1239, 1412, 1413], "bodlaend": 112, "ari": [112, 1145, 1155, 1397, 1406], "koster": 112, "2010": [112, 241, 244, 311, 312, 324, 325, 361, 379, 693, 1171, 1202, 1269, 1394, 1406, 1407], "inf": [112, 274, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 629, 753, 1411, 1413], "march": [112, 1286, 1406, 1415], "259": 112, "275": 112, "dx": [112, 257, 258, 259, 297, 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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": 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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, 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], "fraction": [213, 257, 259, 286, 289, 297, 299, 304, 306, 315, 317, 318, 319, 321, 322, 326, 328, 330, 356, 358, 359, 519, 1165, 1234], "schank": 213, "thoma": [213, 751, 1407, 1409, 1413], "dorothea": [213, 1168], "wagner": [213, 429, 758, 1168, 1402, 1406], "universit\u00e4t": 213, "karlsruh": 213, "fakult\u00e4t": 213, "f\u00fcr": 213, "informatik": [213, 412], "5445": 213, "ir": [213, 606], "1000001239": 213, "erdos_renyi_graph": [213, 1224, 1232, 1326, 1406, 1426], "214": 213, "cutoff": [214, 215, 310, 326, 383, 410, 411, 412, 418, 419, 494, 495, 498, 499, 510, 637, 638, 640, 641, 642, 643, 644, 647, 648, 649, 656, 660, 661, 662, 667, 668, 669, 677, 678, 1234, 1398, 1402, 1406, 1413, 1416, 1424, 1425], "distinct": [214, 215, 255, 281, 288, 352, 391, 452, 453, 460, 578, 595, 608, 618, 700, 701, 734, 735, 736, 737, 789, 1150, 1244, 1271, 1323, 1326, 1328, 1395, 1417], "nonadjac": [214, 215, 480, 584, 585, 587], "cutset": [214, 215, 414, 415, 416, 417, 427, 428, 500, 506, 758], "menger": [214, 215, 216], "theorem": [214, 215, 216, 220, 235, 281, 311, 312, 322, 411, 506, 507, 514, 517, 518, 618, 1190, 1205], "local_node_connect": [214, 216, 408, 409, 410, 411, 413], "node_connect": [214, 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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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"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"]], "MultiDiGraph.__iter__": [[932, "multidigraph-iter"]], "MultiDiGraph.__len__": [[933, "multidigraph-len"]], "MultiDiGraph.add_edge": [[934, "multidigraph-add-edge"]], "MultiDiGraph.add_edges_from": [[935, "multidigraph-add-edges-from"]], "MultiDiGraph.add_node": [[936, "multidigraph-add-node"]], "MultiDiGraph.add_nodes_from": [[937, "multidigraph-add-nodes-from"]], "MultiDiGraph.add_weighted_edges_from": [[938, "multidigraph-add-weighted-edges-from"]], "MultiDiGraph.adj": [[939, "multidigraph-adj"]], "MultiDiGraph.adjacency": [[940, "multidigraph-adjacency"]], "MultiDiGraph.clear": [[941, "multidigraph-clear"]], "MultiDiGraph.clear_edges": [[942, "multidigraph-clear-edges"]], "MultiDiGraph.copy": [[943, "multidigraph-copy"]], "MultiDiGraph.degree": [[944, "multidigraph-degree"]], "MultiDiGraph.edge_subgraph": [[945, "multidigraph-edge-subgraph"]], "MultiDiGraph.edges": [[946, "multidigraph-edges"]], "MultiDiGraph.get_edge_data": [[947, "multidigraph-get-edge-data"]], "MultiDiGraph.has_edge": [[948, "multidigraph-has-edge"]], "MultiDiGraph.has_node": [[949, "multidigraph-has-node"]], "MultiDiGraph.in_degree": [[950, "multidigraph-in-degree"]], "MultiDiGraph.in_edges": [[951, "multidigraph-in-edges"]], "MultiDiGraph.nbunch_iter": [[952, "multidigraph-nbunch-iter"]], "MultiDiGraph.neighbors": [[953, "multidigraph-neighbors"]], "MultiDiGraph.new_edge_key": [[954, "multidigraph-new-edge-key"]], "MultiDiGraph.nodes": [[955, "multidigraph-nodes"]], "MultiDiGraph.number_of_edges": [[956, "multidigraph-number-of-edges"]], "MultiDiGraph.number_of_nodes": [[957, "multidigraph-number-of-nodes"]], "MultiDiGraph.order": [[958, "multidigraph-order"]], "MultiDiGraph.out_degree": [[959, "multidigraph-out-degree"]], "MultiDiGraph.out_edges": [[960, "multidigraph-out-edges"]], "MultiDiGraph.predecessors": [[961, "multidigraph-predecessors"]], "MultiDiGraph.remove_edge": [[962, "multidigraph-remove-edge"]], "MultiDiGraph.remove_edges_from": [[963, 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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. 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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, 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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"]], 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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, "networkx.Graph.to_undirected"]], "update() (graph method)": [[928, "networkx.Graph.update"]], "__contains__() (multidigraph method)": [[929, "networkx.MultiDiGraph.__contains__"]], "__getitem__() (multidigraph method)": [[930, "networkx.MultiDiGraph.__getitem__"]], "__init__() (multidigraph method)": [[931, "networkx.MultiDiGraph.__init__"]], "__iter__() (multidigraph method)": [[932, "networkx.MultiDiGraph.__iter__"]], "__len__() (multidigraph method)": [[933, "networkx.MultiDiGraph.__len__"]], "add_edge() (multidigraph method)": [[934, "networkx.MultiDiGraph.add_edge"]], "add_edges_from() (multidigraph method)": [[935, "networkx.MultiDiGraph.add_edges_from"]], "add_node() (multidigraph method)": [[936, "networkx.MultiDiGraph.add_node"]], "add_nodes_from() (multidigraph method)": [[937, "networkx.MultiDiGraph.add_nodes_from"]], "add_weighted_edges_from() (multidigraph method)": [[938, "networkx.MultiDiGraph.add_weighted_edges_from"]], "adj (multidigraph property)": [[939, 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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, "networkx.convert_matrix.from_pandas_adjacency"]], "from_pandas_edgelist() (in module networkx.convert_matrix)": [[1100, "networkx.convert_matrix.from_pandas_edgelist"]], "from_scipy_sparse_array() (in module networkx.convert_matrix)": [[1101, "networkx.convert_matrix.from_scipy_sparse_array"]], "to_numpy_array() (in module networkx.convert_matrix)": [[1102, "networkx.convert_matrix.to_numpy_array"]], "to_pandas_adjacency() (in module networkx.convert_matrix)": [[1103, "networkx.convert_matrix.to_pandas_adjacency"]], "to_pandas_edgelist() (in module networkx.convert_matrix)": [[1104, "networkx.convert_matrix.to_pandas_edgelist"]], "to_scipy_sparse_array() (in module networkx.convert_matrix)": [[1105, "networkx.convert_matrix.to_scipy_sparse_array"]], "bipartite_layout() (in module networkx.drawing.layout)": [[1106, "networkx.drawing.layout.bipartite_layout"]], "circular_layout() (in module networkx.drawing.layout)": [[1107, "networkx.drawing.layout.circular_layout"]], 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module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, 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module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() 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networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, 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networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, 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"networkx.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 99b3f892..52b8e5b9 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 a6ab10d8..4e1b91c3 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 1ebf5e59..11019820 100644 --- a/tutorial-36.pdf +++ b/tutorial-36.pdf diff --git a/tutorial.ipynb b/tutorial.ipynb index 098f75ea..93c2049a 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7ea0661a", + "id": "556904a6", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,7 +17,7 @@ { "cell_type": "code", "execution_count": null, - "id": "15c0dfff", + "id": "2f70944b", "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "5ef8ce9c", + "id": "ce2e12a6", "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": "3096057b", + "id": "c9b79e88", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "d342f7a4", + "id": "ca159ecf", "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": "e2baaead", + "id": "fc8dd841", "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "markdown", - "id": "9a103282", + "id": "08a65628", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1a5a24df", + "id": "e942f2c4", "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "91326f57", + "id": "e46c6c9e", "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": "329aefb3", + "id": "4e05da68", "metadata": {}, "outputs": [], "source": [ @@ -125,7 +125,7 @@ }, { "cell_type": "markdown", - "id": "6750ca3b", + "id": "de5be6e6", "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": "62550029", + "id": "acbd939c", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +154,7 @@ }, { "cell_type": "markdown", - "id": "e1afed9d", + "id": "fffcbb82", "metadata": {}, "source": [ "by adding a list of edges," @@ -163,7 +163,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8b4e3922", + "id": "01fd2e57", "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "4f7ca0ba", + "id": "cf94d8f8", "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": "52963962", + "id": "38de4c01", "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "c151911e", + "id": "d4b29f95", "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": "118271ac", + "id": "4642592f", "metadata": {}, "outputs": [], "source": [ @@ -213,7 +213,7 @@ }, { "cell_type": "markdown", - "id": "706abb5e", + "id": "ab3bb044", "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": "32640814", + "id": "6a3347ca", "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "98768522", + "id": "992f34ff", "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": "10575e27", + "id": "297c8f54", "metadata": {}, "outputs": [], "source": [ @@ -257,7 +257,7 @@ { "cell_type": "code", "execution_count": null, - "id": "635b8fe4", + "id": "4ea2d38c", "metadata": {}, "outputs": [], "source": [ @@ -272,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "5f057606", + "id": "f996097b", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -292,7 +292,7 @@ { "cell_type": "code", "execution_count": null, - "id": "507722d1", + "id": "b23270c7", "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "markdown", - "id": "abcee75f", + "id": "5cc50bbb", "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": "860270c6", + "id": "b1b77bcd", "metadata": {}, "outputs": [], "source": [ @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "585bbe7d", + "id": "e5b2404c", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -343,7 +343,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cdcf1b41", + "id": "a422dcc2", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "03ceae19", + "id": "b9c7bdae", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -370,7 +370,7 @@ { "cell_type": "code", "execution_count": null, - "id": "06dbab3c", + "id": "4bdf9108", "metadata": {}, "outputs": [], "source": [ @@ -387,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "8d4355de", + "id": "ed15df6b", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -416,7 +416,7 @@ { "cell_type": "code", "execution_count": null, - "id": "97a718f5", + "id": "58ed22c2", "metadata": {}, "outputs": [], "source": [ @@ -428,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "0078ea02", + "id": "39c51bcd", "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": "d288fe15", + "id": "6a6fc51c", "metadata": {}, "outputs": [], "source": [ @@ -450,7 +450,7 @@ }, { "cell_type": "markdown", - "id": "15c9b616", + "id": "657dac76", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -461,7 +461,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f5da306c", + "id": "ca2e8457", "metadata": {}, "outputs": [], "source": [ @@ -475,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "2588bc6e", + "id": "282e9697", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -484,7 +484,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8184d911", + "id": "427c15e1", "metadata": {}, "outputs": [], "source": [ @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "0cf7f0dc", + "id": "d628c908", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -517,7 +517,7 @@ { "cell_type": "code", "execution_count": null, - "id": "86059d0d", + "id": "036176aa", "metadata": {}, "outputs": [], "source": [ @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "d4447035", + "id": "325d8a7f", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -536,7 +536,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4c903f3a", + "id": "afb95d5d", "metadata": {}, "outputs": [], "source": [ @@ -546,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "371561a8", + "id": "9acf916c", "metadata": {}, "source": [ "# Node attributes\n", @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": null, - "id": "34eb8a77", + "id": "a032aeba", "metadata": {}, "outputs": [], "source": [ @@ -570,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "c3a86e3f", + "id": "7b00414e", "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": "84642cdd", + "id": "cdef5897", "metadata": {}, "outputs": [], "source": [ @@ -598,7 +598,7 @@ }, { "cell_type": "markdown", - "id": "1f5004d7", + "id": "c9dab37d", "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": "2311539b", + "id": "16afcf25", "metadata": {}, "outputs": [], "source": [ @@ -633,7 +633,7 @@ }, { "cell_type": "markdown", - "id": "cbd9402f", + "id": "7a167fd6", "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": "6b6eb004", + "id": "c31c382b", "metadata": {}, "outputs": [], "source": [ @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "44a2e7b1", + "id": "26c42ce6", "metadata": {}, "source": [ "# Multigraphs\n", @@ -675,7 +675,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a78c6a9d", + "id": "bd6e9b0d", "metadata": {}, "outputs": [], "source": [ @@ -693,7 +693,7 @@ }, { "cell_type": "markdown", - "id": "9fbe25b5", + "id": "d1d7ab2f", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -713,7 +713,7 @@ { "cell_type": "code", "execution_count": null, - "id": "fbb8725d", + "id": "8ee96e6f", "metadata": {}, "outputs": [], "source": [ @@ -725,7 +725,7 @@ }, { "cell_type": "markdown", - "id": "a3aad792", + "id": "74aa1028", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -736,7 +736,7 @@ { "cell_type": "code", "execution_count": null, - "id": "94e97ff0", + "id": "c6199beb", "metadata": {}, "outputs": [], "source": [ @@ -748,7 +748,7 @@ }, { "cell_type": "markdown", - "id": "3449deee", + "id": "13340f7e", "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": "392b652d", + "id": "a1389e73", "metadata": {}, "outputs": [], "source": [ @@ -770,7 +770,7 @@ }, { "cell_type": "markdown", - "id": "f0f80f8a", + "id": "7fde08c4", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -785,7 +785,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9baf3bec", + "id": "1cc203a1", "metadata": {}, "outputs": [], "source": [ @@ -799,7 +799,7 @@ }, { "cell_type": "markdown", - "id": "599e5c84", + "id": "760be142", "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": "b3942cdd", + "id": "0b019237", "metadata": {}, "outputs": [], "source": [ @@ -819,7 +819,7 @@ }, { "cell_type": "markdown", - "id": "ec583062", + "id": "ff0a53a6", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -838,7 +838,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2379c1f7", + "id": "5ae58915", "metadata": {}, "outputs": [], "source": [ @@ -847,7 +847,7 @@ }, { "cell_type": "markdown", - "id": "96bc1f02", + "id": "c56c1964", "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": "d8d29201", + "id": "059a8dec", "metadata": {}, "outputs": [], "source": [ @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "716993f7", + "id": "199339be", "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": "81a4b499", + "id": "ee8c1c6b", "metadata": {}, "outputs": [], "source": [ @@ -889,7 +889,7 @@ }, { "cell_type": "markdown", - "id": "0140e892", + "id": "ef2fb3cc", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -898,7 +898,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d6d7581d", + "id": "21fe7c4f", "metadata": {}, "outputs": [], "source": [ @@ -919,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "449a7d6c", + "id": "5f44eaab", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -930,7 +930,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d88b6f5b", + "id": "21526fe5", "metadata": {}, "outputs": [], "source": [ @@ -941,7 +941,7 @@ }, { "cell_type": "markdown", - "id": "757e1428", + "id": "f99bb0b4", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -950,7 +950,7 @@ { "cell_type": "code", "execution_count": null, - "id": "08f52c04", + "id": "09eea67d", "metadata": {}, "outputs": [], "source": [ @@ -960,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "a6b06e2a", + "id": "c0a0b338", "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": "cf0800f2", + "id": "2319e3f5", "metadata": {}, "outputs": [], "source": [ @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "26ba3ceb", + "id": "a8415aea", "metadata": {}, "source": [ "See Drawing for additional details." diff --git a/tutorial_full.ipynb b/tutorial_full.ipynb index f400db1e..778e489e 100644 --- a/tutorial_full.ipynb +++ b/tutorial_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7ea0661a", + "id": "556904a6", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,13 +17,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "15c0dfff", + "id": "2f70944b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:00.865803Z", - "iopub.status.busy": "2023-01-04T11:14:00.865346Z", - "iopub.status.idle": "2023-01-04T11:14:00.966612Z", - "shell.execute_reply": "2023-01-04T11:14:00.965436Z" + "iopub.execute_input": "2023-01-04T14:37:25.016282Z", + "iopub.status.busy": "2023-01-04T14:37:25.016007Z", + "iopub.status.idle": "2023-01-04T14:37:25.093941Z", + "shell.execute_reply": "2023-01-04T14:37:25.093233Z" } }, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "markdown", - "id": "5ef8ce9c", + "id": "ce2e12a6", "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": "3096057b", + "id": "c9b79e88", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:00.970956Z", - "iopub.status.busy": "2023-01-04T11:14:00.970716Z", - "iopub.status.idle": "2023-01-04T11:14:00.974737Z", - "shell.execute_reply": "2023-01-04T11:14:00.973897Z" + "iopub.execute_input": "2023-01-04T14:37:25.097892Z", + "iopub.status.busy": "2023-01-04T14:37:25.097666Z", + "iopub.status.idle": "2023-01-04T14:37:25.100890Z", + "shell.execute_reply": "2023-01-04T14:37:25.100129Z" } }, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "d342f7a4", + "id": "ca159ecf", "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": "e2baaead", + "id": "fc8dd841", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:00.978488Z", - "iopub.status.busy": "2023-01-04T11:14:00.978207Z", - "iopub.status.idle": "2023-01-04T11:14:00.981953Z", - "shell.execute_reply": "2023-01-04T11:14:00.981013Z" + "iopub.execute_input": "2023-01-04T14:37:25.104429Z", + "iopub.status.busy": "2023-01-04T14:37:25.104201Z", + "iopub.status.idle": "2023-01-04T14:37:25.107079Z", + "shell.execute_reply": "2023-01-04T14:37:25.106485Z" } }, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "9a103282", + "id": "08a65628", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -117,13 +117,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "1a5a24df", + "id": "e942f2c4", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:00.985697Z", - "iopub.status.busy": "2023-01-04T11:14:00.985446Z", - "iopub.status.idle": "2023-01-04T11:14:00.989349Z", - "shell.execute_reply": "2023-01-04T11:14:00.988579Z" + "iopub.execute_input": "2023-01-04T14:37:25.109882Z", + "iopub.status.busy": "2023-01-04T14:37:25.109644Z", + "iopub.status.idle": "2023-01-04T14:37:25.113271Z", + "shell.execute_reply": "2023-01-04T14:37:25.112582Z" } }, "outputs": [], @@ -134,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "91326f57", + "id": "e46c6c9e", "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": "329aefb3", + "id": "4e05da68", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:00.993571Z", - "iopub.status.busy": "2023-01-04T11:14:00.993322Z", - "iopub.status.idle": "2023-01-04T11:14:00.996721Z", - "shell.execute_reply": "2023-01-04T11:14:00.995839Z" + "iopub.execute_input": "2023-01-04T14:37:25.116349Z", + "iopub.status.busy": "2023-01-04T14:37:25.116126Z", + "iopub.status.idle": "2023-01-04T14:37:25.119072Z", + "shell.execute_reply": "2023-01-04T14:37:25.118448Z" } }, "outputs": [], @@ -160,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "6750ca3b", + "id": "de5be6e6", "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": "62550029", + "id": "acbd939c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.000528Z", - "iopub.status.busy": "2023-01-04T11:14:01.000286Z", - "iopub.status.idle": "2023-01-04T11:14:01.004906Z", - "shell.execute_reply": "2023-01-04T11:14:01.004176Z" + "iopub.execute_input": "2023-01-04T14:37:25.122371Z", + "iopub.status.busy": "2023-01-04T14:37:25.122146Z", + "iopub.status.idle": "2023-01-04T14:37:25.125443Z", + "shell.execute_reply": "2023-01-04T14:37:25.124687Z" } }, "outputs": [], @@ -196,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "e1afed9d", + "id": "fffcbb82", "metadata": {}, "source": [ "by adding a list of edges," @@ -205,13 +205,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "8b4e3922", + "id": "01fd2e57", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.008204Z", - "iopub.status.busy": "2023-01-04T11:14:01.007959Z", - "iopub.status.idle": "2023-01-04T11:14:01.011539Z", - "shell.execute_reply": "2023-01-04T11:14:01.010574Z" + "iopub.execute_input": "2023-01-04T14:37:25.128926Z", + "iopub.status.busy": "2023-01-04T14:37:25.128671Z", + "iopub.status.idle": "2023-01-04T14:37:25.131930Z", + "shell.execute_reply": "2023-01-04T14:37:25.131255Z" } }, "outputs": [], @@ -221,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "4f7ca0ba", + "id": "cf94d8f8", "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": "52963962", + "id": "38de4c01", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.015312Z", - "iopub.status.busy": "2023-01-04T11:14:01.015070Z", - "iopub.status.idle": "2023-01-04T11:14:01.018480Z", - "shell.execute_reply": "2023-01-04T11:14:01.017723Z" + "iopub.execute_input": "2023-01-04T14:37:25.134962Z", + "iopub.status.busy": "2023-01-04T14:37:25.134765Z", + "iopub.status.idle": "2023-01-04T14:37:25.137669Z", + "shell.execute_reply": "2023-01-04T14:37:25.136978Z" } }, "outputs": [], @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "c151911e", + "id": "d4b29f95", "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": "118271ac", + "id": "4642592f", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.022476Z", - "iopub.status.busy": "2023-01-04T11:14:01.022166Z", - "iopub.status.idle": "2023-01-04T11:14:01.025640Z", - "shell.execute_reply": "2023-01-04T11:14:01.024841Z" + "iopub.execute_input": "2023-01-04T14:37:25.140949Z", + "iopub.status.busy": "2023-01-04T14:37:25.140711Z", + "iopub.status.idle": "2023-01-04T14:37:25.143671Z", + "shell.execute_reply": "2023-01-04T14:37:25.143027Z" } }, "outputs": [], @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "706abb5e", + "id": "ab3bb044", "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": "32640814", + "id": "6a3347ca", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.029869Z", - "iopub.status.busy": "2023-01-04T11:14:01.029625Z", - "iopub.status.idle": "2023-01-04T11:14:01.034270Z", - "shell.execute_reply": "2023-01-04T11:14:01.033482Z" + "iopub.execute_input": "2023-01-04T14:37:25.146775Z", + "iopub.status.busy": "2023-01-04T14:37:25.146554Z", + "iopub.status.idle": "2023-01-04T14:37:25.150610Z", + "shell.execute_reply": "2023-01-04T14:37:25.149912Z" } }, "outputs": [], @@ -307,7 +307,7 @@ }, { "cell_type": "markdown", - "id": "98768522", + "id": "992f34ff", "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": "10575e27", + "id": "297c8f54", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.038219Z", - "iopub.status.busy": "2023-01-04T11:14:01.037954Z", - "iopub.status.idle": "2023-01-04T11:14:01.045803Z", - "shell.execute_reply": "2023-01-04T11:14:01.045034Z" + "iopub.execute_input": "2023-01-04T14:37:25.153956Z", + "iopub.status.busy": "2023-01-04T14:37:25.153734Z", + "iopub.status.idle": "2023-01-04T14:37:25.160443Z", + "shell.execute_reply": "2023-01-04T14:37:25.159727Z" } }, "outputs": [ @@ -345,13 +345,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "635b8fe4", + "id": "4ea2d38c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.050584Z", - "iopub.status.busy": "2023-01-04T11:14:01.050338Z", - "iopub.status.idle": "2023-01-04T11:14:01.055444Z", - "shell.execute_reply": "2023-01-04T11:14:01.054563Z" + "iopub.execute_input": "2023-01-04T14:37:25.164807Z", + "iopub.status.busy": "2023-01-04T14:37:25.164566Z", + "iopub.status.idle": "2023-01-04T14:37:25.168807Z", + "shell.execute_reply": "2023-01-04T14:37:25.168205Z" } }, "outputs": [], @@ -367,7 +367,7 @@ }, { "cell_type": "markdown", - "id": "5f057606", + "id": "f996097b", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -387,13 +387,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "507722d1", + "id": "b23270c7", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.059518Z", - "iopub.status.busy": "2023-01-04T11:14:01.059220Z", - "iopub.status.idle": "2023-01-04T11:14:01.065281Z", - "shell.execute_reply": "2023-01-04T11:14:01.064464Z" + "iopub.execute_input": "2023-01-04T14:37:25.171829Z", + "iopub.status.busy": "2023-01-04T14:37:25.171606Z", + "iopub.status.idle": "2023-01-04T14:37:25.176270Z", + "shell.execute_reply": "2023-01-04T14:37:25.175681Z" } }, "outputs": [ @@ -417,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "abcee75f", + "id": "5cc50bbb", "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": "860270c6", + "id": "b1b77bcd", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.070481Z", - "iopub.status.busy": "2023-01-04T11:14:01.070222Z", - "iopub.status.idle": "2023-01-04T11:14:01.075559Z", - "shell.execute_reply": "2023-01-04T11:14:01.074775Z" + "iopub.execute_input": "2023-01-04T14:37:25.180292Z", + "iopub.status.busy": "2023-01-04T14:37:25.180053Z", + "iopub.status.idle": "2023-01-04T14:37:25.184636Z", + "shell.execute_reply": "2023-01-04T14:37:25.183923Z" } }, "outputs": [ @@ -457,7 +457,7 @@ }, { "cell_type": "markdown", - "id": "585bbe7d", + "id": "e5b2404c", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -474,13 +474,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "cdcf1b41", + "id": "a422dcc2", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.078898Z", - "iopub.status.busy": "2023-01-04T11:14:01.078403Z", - "iopub.status.idle": "2023-01-04T11:14:01.082080Z", - "shell.execute_reply": "2023-01-04T11:14:01.081478Z" + "iopub.execute_input": "2023-01-04T14:37:25.188263Z", + "iopub.status.busy": "2023-01-04T14:37:25.188043Z", + "iopub.status.idle": "2023-01-04T14:37:25.191392Z", + "shell.execute_reply": "2023-01-04T14:37:25.190676Z" } }, "outputs": [], @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "03ceae19", + "id": "b9c7bdae", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -508,13 +508,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "06dbab3c", + "id": "4bdf9108", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.086130Z", - "iopub.status.busy": "2023-01-04T11:14:01.085418Z", - "iopub.status.idle": "2023-01-04T11:14:01.419787Z", - "shell.execute_reply": "2023-01-04T11:14:01.418887Z" + "iopub.execute_input": "2023-01-04T14:37:25.194428Z", + "iopub.status.busy": "2023-01-04T14:37:25.194189Z", + "iopub.status.idle": "2023-01-04T14:37:25.476331Z", + "shell.execute_reply": "2023-01-04T14:37:25.475414Z" } }, "outputs": [ @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "8d4355de", + "id": "ed15df6b", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -572,13 +572,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "97a718f5", + "id": "58ed22c2", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.423776Z", - "iopub.status.busy": "2023-01-04T11:14:01.423258Z", - "iopub.status.idle": "2023-01-04T11:14:01.432163Z", - "shell.execute_reply": "2023-01-04T11:14:01.431210Z" + "iopub.execute_input": "2023-01-04T14:37:25.480229Z", + "iopub.status.busy": "2023-01-04T14:37:25.479853Z", + "iopub.status.idle": "2023-01-04T14:37:25.486702Z", + "shell.execute_reply": "2023-01-04T14:37:25.486029Z" } }, "outputs": [ @@ -602,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "0078ea02", + "id": "39c51bcd", "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": "d288fe15", + "id": "6a6fc51c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.435733Z", - "iopub.status.busy": "2023-01-04T11:14:01.435489Z", - "iopub.status.idle": "2023-01-04T11:14:01.441643Z", - "shell.execute_reply": "2023-01-04T11:14:01.440808Z" + "iopub.execute_input": "2023-01-04T14:37:25.489843Z", + "iopub.status.busy": "2023-01-04T14:37:25.489596Z", + "iopub.status.idle": "2023-01-04T14:37:25.494790Z", + "shell.execute_reply": "2023-01-04T14:37:25.494127Z" } }, "outputs": [ @@ -642,7 +642,7 @@ }, { "cell_type": "markdown", - "id": "15c9b616", + "id": "657dac76", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -653,13 +653,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "f5da306c", + "id": "ca2e8457", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.446514Z", - "iopub.status.busy": "2023-01-04T11:14:01.446245Z", - "iopub.status.idle": "2023-01-04T11:14:01.452839Z", - "shell.execute_reply": "2023-01-04T11:14:01.451935Z" + "iopub.execute_input": "2023-01-04T14:37:25.498727Z", + "iopub.status.busy": "2023-01-04T14:37:25.498499Z", + "iopub.status.idle": "2023-01-04T14:37:25.504018Z", + "shell.execute_reply": "2023-01-04T14:37:25.503112Z" } }, "outputs": [ @@ -685,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "2588bc6e", + "id": "282e9697", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -694,13 +694,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "8184d911", + "id": "427c15e1", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.457707Z", - "iopub.status.busy": "2023-01-04T11:14:01.457431Z", - "iopub.status.idle": "2023-01-04T11:14:01.464217Z", - "shell.execute_reply": "2023-01-04T11:14:01.463138Z" + "iopub.execute_input": "2023-01-04T14:37:25.508418Z", + "iopub.status.busy": "2023-01-04T14:37:25.508180Z", + "iopub.status.idle": "2023-01-04T14:37:25.512189Z", + "shell.execute_reply": "2023-01-04T14:37:25.511501Z" } }, "outputs": [ @@ -721,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "0cf7f0dc", + "id": "d628c908", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -743,13 +743,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "86059d0d", + "id": "036176aa", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.467987Z", - "iopub.status.busy": "2023-01-04T11:14:01.467717Z", - "iopub.status.idle": "2023-01-04T11:14:01.474914Z", - "shell.execute_reply": "2023-01-04T11:14:01.474109Z" + "iopub.execute_input": "2023-01-04T14:37:25.516276Z", + "iopub.status.busy": "2023-01-04T14:37:25.516034Z", + "iopub.status.idle": "2023-01-04T14:37:25.520433Z", + "shell.execute_reply": "2023-01-04T14:37:25.519790Z" } }, "outputs": [ @@ -771,7 +771,7 @@ }, { "cell_type": "markdown", - "id": "d4447035", + "id": "325d8a7f", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -780,13 +780,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "4c903f3a", + "id": "afb95d5d", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.479060Z", - "iopub.status.busy": "2023-01-04T11:14:01.478798Z", - "iopub.status.idle": "2023-01-04T11:14:01.486404Z", - "shell.execute_reply": "2023-01-04T11:14:01.485558Z" + "iopub.execute_input": "2023-01-04T14:37:25.524949Z", + "iopub.status.busy": "2023-01-04T14:37:25.524655Z", + "iopub.status.idle": "2023-01-04T14:37:25.529245Z", + "shell.execute_reply": "2023-01-04T14:37:25.528502Z" } }, "outputs": [ @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "371561a8", + "id": "9acf916c", "metadata": {}, "source": [ "# Node attributes\n", @@ -819,13 +819,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "34eb8a77", + "id": "a032aeba", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.490145Z", - "iopub.status.busy": "2023-01-04T11:14:01.489900Z", - "iopub.status.idle": "2023-01-04T11:14:01.502010Z", - "shell.execute_reply": "2023-01-04T11:14:01.501204Z" + "iopub.execute_input": "2023-01-04T14:37:25.533639Z", + "iopub.status.busy": "2023-01-04T14:37:25.533413Z", + "iopub.status.idle": "2023-01-04T14:37:25.538582Z", + "shell.execute_reply": "2023-01-04T14:37:25.537855Z" } }, "outputs": [ @@ -850,7 +850,7 @@ }, { "cell_type": "markdown", - "id": "c3a86e3f", + "id": "7b00414e", "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": "84642cdd", + "id": "cdef5897", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.507816Z", - "iopub.status.busy": "2023-01-04T11:14:01.507497Z", - "iopub.status.idle": "2023-01-04T11:14:01.512455Z", - "shell.execute_reply": "2023-01-04T11:14:01.511621Z" + "iopub.execute_input": "2023-01-04T14:37:25.542567Z", + "iopub.status.busy": "2023-01-04T14:37:25.542342Z", + "iopub.status.idle": "2023-01-04T14:37:25.546841Z", + "shell.execute_reply": "2023-01-04T14:37:25.546160Z" } }, "outputs": [], @@ -885,7 +885,7 @@ }, { "cell_type": "markdown", - "id": "1f5004d7", + "id": "c9dab37d", "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": "2311539b", + "id": "16afcf25", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.516865Z", - "iopub.status.busy": "2023-01-04T11:14:01.516105Z", - "iopub.status.idle": "2023-01-04T11:14:01.522953Z", - "shell.execute_reply": "2023-01-04T11:14:01.522257Z" + "iopub.execute_input": "2023-01-04T14:37:25.550020Z", + "iopub.status.busy": "2023-01-04T14:37:25.549790Z", + "iopub.status.idle": "2023-01-04T14:37:25.555631Z", + "shell.execute_reply": "2023-01-04T14:37:25.554904Z" } }, "outputs": [ @@ -938,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "cbd9402f", + "id": "7a167fd6", "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": "6b6eb004", + "id": "c31c382b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.527250Z", - "iopub.status.busy": "2023-01-04T11:14:01.526376Z", - "iopub.status.idle": "2023-01-04T11:14:01.530710Z", - "shell.execute_reply": "2023-01-04T11:14:01.529976Z" + "iopub.execute_input": "2023-01-04T14:37:25.559653Z", + "iopub.status.busy": "2023-01-04T14:37:25.559409Z", + "iopub.status.idle": "2023-01-04T14:37:25.562660Z", + "shell.execute_reply": "2023-01-04T14:37:25.561971Z" } }, "outputs": [], @@ -967,7 +967,7 @@ }, { "cell_type": "markdown", - "id": "44a2e7b1", + "id": "26c42ce6", "metadata": {}, "source": [ "# Multigraphs\n", @@ -987,13 +987,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "a78c6a9d", + "id": "bd6e9b0d", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.534272Z", - "iopub.status.busy": "2023-01-04T11:14:01.533723Z", - "iopub.status.idle": "2023-01-04T11:14:01.541830Z", - "shell.execute_reply": "2023-01-04T11:14:01.541131Z" + "iopub.execute_input": "2023-01-04T14:37:25.565806Z", + "iopub.status.busy": "2023-01-04T14:37:25.565579Z", + "iopub.status.idle": "2023-01-04T14:37:25.572605Z", + "shell.execute_reply": "2023-01-04T14:37:25.571883Z" } }, "outputs": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "markdown", - "id": "9fbe25b5", + "id": "d1d7ab2f", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -1043,13 +1043,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "fbb8725d", + "id": "8ee96e6f", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.545802Z", - "iopub.status.busy": "2023-01-04T11:14:01.545182Z", - "iopub.status.idle": "2023-01-04T11:14:01.550870Z", - "shell.execute_reply": "2023-01-04T11:14:01.550205Z" + "iopub.execute_input": "2023-01-04T14:37:25.576622Z", + "iopub.status.busy": "2023-01-04T14:37:25.576395Z", + "iopub.status.idle": "2023-01-04T14:37:25.580954Z", + "shell.execute_reply": "2023-01-04T14:37:25.580225Z" } }, "outputs": [], @@ -1062,7 +1062,7 @@ }, { "cell_type": "markdown", - "id": "a3aad792", + "id": "74aa1028", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -1073,13 +1073,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "94e97ff0", + "id": "c6199beb", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.554399Z", - "iopub.status.busy": "2023-01-04T11:14:01.553806Z", - "iopub.status.idle": "2023-01-04T11:14:01.636256Z", - "shell.execute_reply": "2023-01-04T11:14:01.635179Z" + "iopub.execute_input": "2023-01-04T14:37:25.584244Z", + "iopub.status.busy": "2023-01-04T14:37:25.584002Z", + "iopub.status.idle": "2023-01-04T14:37:25.603489Z", + "shell.execute_reply": "2023-01-04T14:37:25.602629Z" } }, "outputs": [], @@ -1092,7 +1092,7 @@ }, { "cell_type": "markdown", - "id": "3449deee", + "id": "13340f7e", "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": "392b652d", + "id": "a1389e73", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:01.641542Z", - "iopub.status.busy": "2023-01-04T11:14:01.640921Z", - "iopub.status.idle": "2023-01-04T11:14:02.951863Z", - "shell.execute_reply": "2023-01-04T11:14:02.950832Z" + "iopub.execute_input": "2023-01-04T14:37:25.607344Z", + "iopub.status.busy": "2023-01-04T14:37:25.607114Z", + "iopub.status.idle": "2023-01-04T14:37:26.161918Z", + "shell.execute_reply": "2023-01-04T14:37:26.160927Z" } }, "outputs": [], @@ -1121,7 +1121,7 @@ }, { "cell_type": "markdown", - "id": "f0f80f8a", + "id": "7fde08c4", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -1136,13 +1136,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "9baf3bec", + "id": "1cc203a1", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:02.957112Z", - "iopub.status.busy": "2023-01-04T11:14:02.956759Z", - "iopub.status.idle": "2023-01-04T11:14:02.968194Z", - "shell.execute_reply": "2023-01-04T11:14:02.967312Z" + "iopub.execute_input": "2023-01-04T14:37:26.166889Z", + "iopub.status.busy": "2023-01-04T14:37:26.166625Z", + "iopub.status.idle": "2023-01-04T14:37:26.174584Z", + "shell.execute_reply": "2023-01-04T14:37:26.173964Z" } }, "outputs": [ @@ -1168,7 +1168,7 @@ }, { "cell_type": "markdown", - "id": "599e5c84", + "id": "760be142", "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": "b3942cdd", + "id": "0b019237", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:02.972163Z", - "iopub.status.busy": "2023-01-04T11:14:02.971866Z", - "iopub.status.idle": "2023-01-04T11:14:02.978228Z", - "shell.execute_reply": "2023-01-04T11:14:02.977502Z" + "iopub.execute_input": "2023-01-04T14:37:26.177636Z", + "iopub.status.busy": "2023-01-04T14:37:26.177402Z", + "iopub.status.idle": "2023-01-04T14:37:26.182170Z", + "shell.execute_reply": "2023-01-04T14:37:26.181443Z" } }, "outputs": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "markdown", - "id": "ec583062", + "id": "ff0a53a6", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -1225,13 +1225,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "2379c1f7", + "id": "5ae58915", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:02.981700Z", - "iopub.status.busy": "2023-01-04T11:14:02.981470Z", - "iopub.status.idle": "2023-01-04T11:14:03.437031Z", - "shell.execute_reply": "2023-01-04T11:14:03.436048Z" + "iopub.execute_input": "2023-01-04T14:37:26.185974Z", + "iopub.status.busy": "2023-01-04T14:37:26.185741Z", + "iopub.status.idle": "2023-01-04T14:37:26.576044Z", + "shell.execute_reply": "2023-01-04T14:37:26.575110Z" } }, "outputs": [], @@ -1241,7 +1241,7 @@ }, { "cell_type": "markdown", - "id": "96bc1f02", + "id": "c56c1964", "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": "d8d29201", + "id": "059a8dec", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:03.441440Z", - "iopub.status.busy": "2023-01-04T11:14:03.441034Z", - "iopub.status.idle": "2023-01-04T11:14:03.753601Z", - "shell.execute_reply": "2023-01-04T11:14:03.752515Z" + "iopub.execute_input": "2023-01-04T14:37:26.581391Z", + "iopub.status.busy": "2023-01-04T14:37:26.580458Z", + "iopub.status.idle": "2023-01-04T14:37:26.797664Z", + "shell.execute_reply": "2023-01-04T14:37:26.797077Z" } }, "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": "716993f7", + "id": "199339be", "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": "81a4b499", + "id": "ee8c1c6b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:03.757572Z", - "iopub.status.busy": "2023-01-04T11:14:03.757285Z", - "iopub.status.idle": "2023-01-04T11:14:03.761080Z", - "shell.execute_reply": "2023-01-04T11:14:03.760191Z" + "iopub.execute_input": "2023-01-04T14:37:26.803591Z", + "iopub.status.busy": "2023-01-04T14:37:26.803134Z", + "iopub.status.idle": "2023-01-04T14:37:26.806331Z", + "shell.execute_reply": "2023-01-04T14:37:26.805812Z" } }, "outputs": [], @@ -1308,7 +1308,7 @@ }, { "cell_type": "markdown", - "id": "0140e892", + "id": "ef2fb3cc", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -1317,19 +1317,19 @@ { "cell_type": "code", "execution_count": 36, - "id": "d6d7581d", + "id": "21fe7c4f", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:03.765369Z", - "iopub.status.busy": "2023-01-04T11:14:03.764951Z", - "iopub.status.idle": "2023-01-04T11:14:04.204225Z", - "shell.execute_reply": "2023-01-04T11:14:04.203240Z" + "iopub.execute_input": "2023-01-04T14:37:26.809288Z", + "iopub.status.busy": "2023-01-04T14:37:26.808878Z", + "iopub.status.idle": "2023-01-04T14:37:27.138222Z", + "shell.execute_reply": "2023-01-04T14:37:27.137045Z" } }, "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": "449a7d6c", + "id": "5f44eaab", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -1367,13 +1367,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "d88b6f5b", + "id": "21526fe5", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:04.208663Z", - "iopub.status.busy": "2023-01-04T11:14:04.208080Z", - "iopub.status.idle": "2023-01-04T11:14:04.369135Z", - "shell.execute_reply": "2023-01-04T11:14:04.368267Z" + "iopub.execute_input": "2023-01-04T14:37:27.142651Z", + "iopub.status.busy": "2023-01-04T14:37:27.142151Z", + "iopub.status.idle": "2023-01-04T14:37:27.262438Z", + "shell.execute_reply": "2023-01-04T14:37:27.261708Z" } }, "outputs": [ @@ -1396,7 +1396,7 @@ }, { "cell_type": "markdown", - "id": "757e1428", + "id": "f99bb0b4", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -1405,19 +1405,19 @@ { "cell_type": "code", "execution_count": 38, - "id": "08f52c04", + "id": "09eea67d", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:04.374287Z", - "iopub.status.busy": "2023-01-04T11:14:04.373072Z", - "iopub.status.idle": "2023-01-04T11:14:04.567777Z", - "shell.execute_reply": "2023-01-04T11:14:04.566856Z" + "iopub.execute_input": "2023-01-04T14:37:27.266526Z", + "iopub.status.busy": "2023-01-04T14:37:27.266275Z", + "iopub.status.idle": "2023-01-04T14:37:27.416551Z", + "shell.execute_reply": "2023-01-04T14:37:27.415934Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1433,7 +1433,7 @@ }, { "cell_type": "markdown", - "id": "a6b06e2a", + "id": "c0a0b338", "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": "cf0800f2", + "id": "2319e3f5", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T11:14:04.571665Z", - "iopub.status.busy": "2023-01-04T11:14:04.571204Z", - "iopub.status.idle": "2023-01-04T11:14:04.858361Z", - "shell.execute_reply": "2023-01-04T11:14:04.857538Z" + "iopub.execute_input": "2023-01-04T14:37:27.421594Z", + "iopub.status.busy": "2023-01-04T14:37:27.420095Z", + "iopub.status.idle": "2023-01-04T14:37:27.570210Z", + "shell.execute_reply": "2023-01-04T14:37:27.569579Z" } }, "outputs": [ @@ -1476,7 +1476,7 @@ }, { "cell_type": "markdown", - "id": "26ba3ceb", + "id": "a8415aea", "metadata": {}, "source": [ "See Drawing for additional details." |
