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| author | MridulS <mail@mriduls.com> | 2023-01-03 21:21:58 +0000 |
|---|---|---|
| committer | MridulS <mail@mriduls.com> | 2023-01-03 21:21:58 +0000 |
| commit | 7bd598bdb6b0ad1a1b9f546c77f3060b1b34a9c6 (patch) | |
| tree | aee9fba014099cdd84dd8e2c5a214fb9e6f594b9 | |
| parent | 01d9427f67a41e4fc88ad09f8ae42ce3c82234d7 (diff) | |
| download | networkx-7bd598bdb6b0ad1a1b9f546c77f3060b1b34a9c6.tar.gz | |
Deploying to gh-pages from @ networkx/networkx@83c22dae4423ec65be4881bf30231a4843373435 🚀
120 files changed, 606 insertions, 608 deletions
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a/_images/tutorial-35.png +++ b/_images/tutorial-35.png diff --git a/_modules/networkx/algorithms/isomorphism/isomorph.html b/_modules/networkx/algorithms/isomorphism/isomorph.html index 640975ae..10747814 100644 --- a/_modules/networkx/algorithms/isomorphism/isomorph.html +++ b/_modules/networkx/algorithms/isomorphism/isomorph.html @@ -687,7 +687,7 @@ <span class="sd"> "An Improved Algorithm for Matching Large Graphs",</span> <span class="sd"> 3rd IAPR-TC15 Workshop on Graph-based Representations in</span> <span class="sd"> Pattern Recognition, Cuen, pp. 149-159, 2001.</span> -<span class="sd"> https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.101.5342</span> +<span class="sd"> https://www.researchgate.net/publication/200034365_An_Improved_Algorithm_for_Matching_Large_Graphs</span> <span class="sd"> """</span> <span class="k">if</span> <span class="n">G1</span><span class="o">.</span><span class="n">is_directed</span><span class="p">()</span> <span class="ow">and</span> <span class="n">G2</span><span class="o">.</span><span class="n">is_directed</span><span class="p">():</span> <span class="n">GM</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">algorithms</span><span class="o">.</span><span class="n">isomorphism</span><span class="o">.</span><span class="n">DiGraphMatcher</span> diff --git a/auto_examples/3d_drawing/plot_basic.html b/auto_examples/3d_drawing/plot_basic.html index a8a85335..f9381a52 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 0.080 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.115 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 3452e135..41010b76 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>00:00.080</strong> total execution time for <strong>auto_examples_3d_drawing</strong> files:</p> +<p><strong>00:00.115</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>00:00.080</p></td> +<td><p>00:00.115</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 e27f68f9..fd39a01d 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 0.211 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.297 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 b108be3d..6b047341 100644 --- a/auto_examples/algorithms/plot_betweenness_centrality.html +++ b/auto_examples/algorithms/plot_betweenness_centrality.html @@ -582,7 +582,7 @@ using WormNet v.3-GS.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.702 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 5.712 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 675da717..39c8060b 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 0.374 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.494 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 2a26da29..2b7cb1e1 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 0.107 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.159 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 96430796..4079ab3f 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 0.074 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.105 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-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 75c6ef9c..5ba987e6 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 0.244 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.381 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 91cb1c1f..1bd4a113 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 0.094 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.128 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 5397ef5e..c925ce24 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 0.061 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-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 af681ab2..67d469f3 100644 --- a/auto_examples/algorithms/plot_parallel_betweenness.html +++ b/auto_examples/algorithms/plot_parallel_betweenness.html @@ -517,29 +517,29 @@ faster. This is a limitation of our CI/CD pipeline running on a single core.</p> <img src="../../_images/sphx_glr_plot_parallel_betweenness_001.png" srcset="../../_images/sphx_glr_plot_parallel_betweenness_001.png" alt="plot parallel betweenness" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Computing betweenness centrality for: Graph with 1000 nodes and 2991 edges Parallel version - Time: 1.8126 seconds - Betweenness centrality for node 0: 0.27818 + Time: 2.3492 seconds + Betweenness centrality for node 0: 0.09607 Non-Parallel version - Time: 3.0053 seconds - Betweenness centrality for node 0: 0.27818 + Time: 3.8726 seconds + Betweenness centrality for node 0: 0.09607 Computing betweenness centrality for: -Graph with 1000 nodes and 5065 edges +Graph with 1000 nodes and 4959 edges Parallel version - Time: 2.3279 seconds - Betweenness centrality for node 0: 0.00104 + Time: 3.1350 seconds + Betweenness centrality for node 0: 0.00268 Non-Parallel version - Time: 4.0067 seconds - Betweenness centrality for node 0: 0.00104 + Time: 5.2007 seconds + Betweenness centrality for node 0: 0.00268 Computing betweenness centrality for: Graph with 1000 nodes and 2000 edges Parallel version - Time: 1.5613 seconds - Betweenness centrality for node 0: 0.00307 + Time: 2.0758 seconds + Betweenness centrality for node 0: 0.02492 Non-Parallel version - Time: 2.7442 seconds - Betweenness centrality for node 0: 0.00307 + Time: 3.5621 seconds + Betweenness centrality for node 0: 0.02492 </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 20.926 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 29.022 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 92feb87d..d928e6c7 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 1.131 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.328 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 b4142ac7..b4fd7f66 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 0.177 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.272 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 681a3349..01470664 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 0.685 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 0.966 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 bdca6374..df4c7a09 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>00:27.785</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p> +<p><strong>00:38.947</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>00:20.926</p></td> +<td><p>00:29.022</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_betweenness_centrality.html#sphx-glr-auto-examples-algorithms-plot-betweenness-centrality-py"><span class="std std-ref">Betweeness Centrality</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_betweenness_centrality.py</span></code>)</p></td> -<td><p>00:03.702</p></td> +<td><p>00:05.712</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>00:01.131</p></td> +<td><p>00:01.328</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>00:00.685</p></td> +<td><p>00:00.966</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>00:00.374</p></td> +<td><p>00:00.494</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>00:00.244</p></td> +<td><p>00:00.381</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>00:00.211</p></td> +<td><p>00:00.297</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>00:00.177</p></td> +<td><p>00:00.272</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>00:00.107</p></td> +<td><p>00:00.159</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>00:00.094</p></td> +<td><p>00:00.128</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>00:00.074</p></td> +<td><p>00:00.105</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>00:00.061</p></td> +<td><p>00:00.083</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 7165a1d8..132804be 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 0.098 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.130 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 068ab5a5..b8efe2b8 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 0.063 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.090 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 d5379f6a..4cd67857 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 0.426 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 0.549 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 9e8b9dd4..0c442590 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>00:00.587</strong> total execution time for <strong>auto_examples_basic</strong> files:</p> +<p><strong>00:00.769</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>00:00.426</p></td> +<td><p>00:00.549</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>00:00.098</p></td> +<td><p>00:00.130</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>00:00.063</p></td> +<td><p>00:00.090</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 52a5c01d..9c68d416 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 0.068 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-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 76555a20..77d452d0 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: -['Korchnoi, Viktor L', 'Karpov, Anatoly', 'Kasparov, Gary'] +['Korchnoi, Viktor L', 'Kasparov, Gary', 'Karpov, Anatoly'] 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 0.390 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.539 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 c0c1abe3..90997060 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 0.279 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.435 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 7c7fa28a..6f88dc6f 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 0.266 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.410 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 16436076..8e10635e 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 0.215 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.348 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 76f698aa..d0d1490e 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 0.063 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-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 18cd1c8e..6a31c014 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 0.098 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.142 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 0aed5d7a..23fa05ac 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: 1.592461791177574 -Smallest eigenvalue: -2.5363890312656235e-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: 1.5924617911775805 +Smallest eigenvalue: 4.0699282104742547e-16 </pre></div> </div> <div class="line-block"> @@ -541,7 +541,7 @@ Smallest eigenvalue: -2.5363890312656235e-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 0.645 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.994 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 18db3904..71a2dea5 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 0.324 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.532 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 f3fb9fc1..b79eebf0 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 0.076 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.111 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 ba6d5dfc..cbd9ca0c 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 0.101 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.133 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 48f9a37b..3d0c2ace 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 0.185 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-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 d87b0728..e44c90f8 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 0.077 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.153 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 1e184179..0d8f7561 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 0.052 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.088 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 308d7915..e15723bf 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 0.127 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.173 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 1fd7553f..3fea9848 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 0.105 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-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 82d517f3..332fa86f 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 0.245 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-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 b6343948..ab5826a8 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 0.080 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.123 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 032ca9d7..ebbd3d07 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 0.057 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.082 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 83fed910..9a32ae25 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 0.227 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.345 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 9f2fdef7..1d9ec9c2 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 0.087 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.125 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 2f0d32ca..09231b0b 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 0.162 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.166 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 078092b7..88853e64 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 0.085 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.121 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 dcfc8b64..73635fc2 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>00:04.015</strong> total execution time for <strong>auto_examples_drawing</strong> files:</p> +<p><strong>00:05.980</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>00:00.645</p></td> +<td><p>00:00.994</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>00:00.390</p></td> +<td><p>00:00.539</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>00:00.324</p></td> +<td><p>00:00.532</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>00:00.279</p></td> +<td><p>00:00.435</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>00:00.266</p></td> +<td><p>00:00.410</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> 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the script:</strong> ( 0 minutes 0.425 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.595 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-geospatial-plot-polygons-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/9be63872be08214edeb4d5a2d5f66987/plot_polygons.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_polygons.py</span></code></a></p> diff --git a/auto_examples/geospatial/sg_execution_times.html b/auto_examples/geospatial/sg_execution_times.html index 6acb5513..8b0eff9a 100644 --- a/auto_examples/geospatial/sg_execution_times.html +++ b/auto_examples/geospatial/sg_execution_times.html @@ -463,27 +463,27 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-geospatial-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:15.561</strong> total execution time for <strong>auto_examples_geospatial</strong> files:</p> +<p><strong>00:21.383</strong> total execution time for <strong>auto_examples_geospatial</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_osmnx.html#sphx-glr-auto-examples-geospatial-plot-osmnx-py"><span class="std std-ref">OpenStreetMap with OSMnx</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_osmnx.py</span></code>)</p></td> -<td><p>00:05.609</p></td> +<td><p>00:05.558</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_delaunay.html#sphx-glr-auto-examples-geospatial-plot-delaunay-py"><span class="std std-ref">Delaunay graphs from geographic points</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_delaunay.py</span></code>)</p></td> -<td><p>00:03.281</p></td> +<td><p>00:05.344</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_points.html#sphx-glr-auto-examples-geospatial-plot-points-py"><span class="std std-ref">Graphs from geographic points</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_points.py</span></code>)</p></td> -<td><p>00:03.236</p></td> +<td><p>00:05.292</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_lines.html#sphx-glr-auto-examples-geospatial-plot-lines-py"><span class="std std-ref">Graphs from a set of lines</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_lines.py</span></code>)</p></td> -<td><p>00:03.009</p></td> +<td><p>00:04.593</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>00:00.425</p></td> +<td><p>00:00.595</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 cb638069..3d4c5f21 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 0.116 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.188 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 9205188a..cb55f342 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 0.058 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.084 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 338423c4..22e6aeba 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 0.059 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.087 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 1bd249b2..6fe8d05d 100644 --- a/auto_examples/graph/plot_expected_degree_sequence.html +++ b/auto_examples/graph/plot_expected_degree_sequence.html @@ -535,54 +535,52 @@ degree (#nodes) **** 25 ( 0) 26 ( 0) 27 ( 0) -28 ( 1) * +28 ( 0) 29 ( 0) 30 ( 0) 31 ( 0) -32 ( 0) -33 ( 0) +32 ( 1) * +33 ( 1) * 34 ( 2) ** -35 ( 0) -36 ( 4) **** -37 ( 5) ***** -38 ( 4) **** -39 ( 6) ****** -40 (13) ************* -41 (13) ************* -42 (13) ************* -43 (23) *********************** -44 (27) *************************** -45 (35) *********************************** -46 (28) **************************** -47 (32) ******************************** +35 ( 3) *** +36 ( 7) ******* +37 ( 7) ******* +38 ( 6) ****** +39 (12) ************ +40 (15) *************** +41 (11) *********** +42 (10) ********** +43 (21) ********************* +44 (13) ************* +45 (14) ************** +46 (24) ************************ +47 (26) ************************** 48 (27) *************************** -49 (30) ****************************** +49 (32) ******************************** 50 (31) ******************************* -51 (27) *************************** -52 (28) **************************** -53 (24) ************************ -54 (23) *********************** -55 (16) **************** -56 (19) ******************* -57 (12) ************ -58 ( 6) ****** -59 (10) ********** +51 (26) ************************** +52 (31) ******************************* +53 (22) ********************** +54 (24) ************************ +55 (29) ***************************** +56 (21) ********************* +57 (17) ***************** +58 (10) ********** +59 (13) ************* 60 (12) ************ -61 ( 4) **** -62 ( 4) **** -63 ( 7) ******* -64 ( 3) *** -65 ( 2) ** -66 ( 3) *** -67 ( 2) ** -68 ( 1) * +61 (11) *********** +62 ( 6) ****** +63 ( 3) *** +64 ( 6) ****** +65 ( 3) *** +66 ( 0) +67 ( 1) * +68 ( 0) 69 ( 0) -70 ( 1) * -71 ( 0) +70 ( 0) +71 ( 1) * 72 ( 0) 73 ( 1) * -74 ( 0) -75 ( 1) * </pre></div> </div> <div class="line-block"> @@ -602,7 +600,7 @@ 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 0.031 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.041 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 403704f2..6dd7c282 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 0.303 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.393 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-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 6dd520a8..b4052214 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 0.089 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.132 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 a7054f60..6a46f709 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 0.181 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.280 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 1e13ba38..b398f00c 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 0.127 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.181 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-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 493b2f1d..6f351461 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 0.229 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.302 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 5479b87b..0a4c2465 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 1.064 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.716 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 1b1c5b09..e826b74c 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 0.393 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.539 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 45cfd4c5..fc6df6c9 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>00:02.651</strong> total execution time for <strong>auto_examples_graph</strong> files:</p> +<p><strong>00:03.943</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>00:01.064</p></td> +<td><p>00:01.716</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>00:00.393</p></td> +<td><p>00:00.539</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>00:00.303</p></td> +<td><p>00:00.393</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>00:00.229</p></td> +<td><p>00:00.302</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_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>00:00.181</p></td> +<td><p>00:00.280</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_napoleon_russian_campaign.html#sphx-glr-auto-examples-graph-plot-napoleon-russian-campaign-py"><span class="std std-ref">Napoleon Russian Campaign</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_napoleon_russian_campaign.py</span></code>)</p></td> -<td><p>00:00.127</p></td> +<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>00:00.188</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_dag_layout.html#sphx-glr-auto-examples-graph-plot-dag-layout-py"><span class="std std-ref">DAG - Topological Layout</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_dag_layout.py</span></code>)</p></td> -<td><p>00:00.116</p></td> +<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>00:00.181</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>00:00.089</p></td> +<td><p>00:00.132</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>00:00.059</p></td> +<td><p>00:00.087</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>00:00.058</p></td> +<td><p>00:00.084</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>00:00.031</p></td> +<td><p>00:00.041</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 968d63f6..0c5bb4c5 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 0.083 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.039 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-attributes-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/52bb0ebd52824aa460a3ecb45c1cb5e5/plot_attributes.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_attributes.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/plot_conversion.html b/auto_examples/graphviz_drawing/plot_conversion.html index 4ed8440f..e0373657 100644 --- a/auto_examples/graphviz_drawing/plot_conversion.html +++ b/auto_examples/graphviz_drawing/plot_conversion.html @@ -514,7 +514,7 @@ to download the full example code</p> <a href="https://pygraphviz.github.io/documentation/stable/reference/agraph.html#pygraphviz.AGraph.draw" title="pygraphviz.AGraph.draw" class="sphx-glr-backref-module-pygraphviz sphx-glr-backref-type-py-method"><span class="n">A</span><span class="o">.</span><span class="n">draw</span></a><span class="p">(</span><span class="s2">"k5.png"</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"neato"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.027 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-conversion-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/27aa0c08bacf20ba3f5ce4f8d02ac226/plot_conversion.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_conversion.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/plot_grid.html b/auto_examples/graphviz_drawing/plot_grid.html index adbfdcf8..ac822ba5 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 0.068 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-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 1798acd3..f747ad20 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 0.080 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.105 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 93125a86..254e399e 100644 --- a/auto_examples/graphviz_drawing/sg_execution_times.html +++ b/auto_examples/graphviz_drawing/sg_execution_times.html @@ -463,23 +463,23 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-graphviz-drawing-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.258</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> +<p><strong>00:00.262</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_attributes.html#sphx-glr-auto-examples-graphviz-drawing-plot-attributes-py"><span class="std std-ref">Attributes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_attributes.py</span></code>)</p></td> -<td><p>00:00.083</p></td> +<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>00:00.105</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_mini_atlas.html#sphx-glr-auto-examples-graphviz-drawing-plot-mini-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mini_atlas.py</span></code>)</p></td> -<td><p>00:00.080</p></td> +<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>00:00.085</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_grid.html#sphx-glr-auto-examples-graphviz-drawing-plot-grid-py"><span class="std std-ref">2D Grid</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_grid.py</span></code>)</p></td> -<td><p>00:00.068</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_attributes.html#sphx-glr-auto-examples-graphviz-drawing-plot-attributes-py"><span class="std std-ref">Attributes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_attributes.py</span></code>)</p></td> +<td><p>00:00.039</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_conversion.html#sphx-glr-auto-examples-graphviz-drawing-plot-conversion-py"><span class="std std-ref">Conversion</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_conversion.py</span></code>)</p></td> -<td><p>00:00.027</p></td> +<td><p>00:00.033</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/graphviz_layout/plot_atlas.html b/auto_examples/graphviz_layout/plot_atlas.html index 3a7bde2d..7bdac4fd 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 3.826 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 4.994 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 cfa0c5c0..9af4dfcf 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 0.153 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-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 54b05499..023bb247 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 0.301 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.454 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 9d6dd1e5..6db50cba 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 0.806 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.102 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 6d8b4360..25c79269 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 0.336 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.443 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 bc043ffd..ca50727e 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>00:05.422</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> +<p><strong>00:07.191</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>00:03.826</p></td> +<td><p>00:04.994</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>00:00.806</p></td> +<td><p>00:01.102</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_lanl_routes.html#sphx-glr-auto-examples-graphviz-layout-plot-lanl-routes-py"><span class="std std-ref">Lanl Routes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_lanl_routes.py</span></code>)</p></td> -<td><p>00:00.336</p></td> +<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>00:00.454</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_decomposition.html#sphx-glr-auto-examples-graphviz-layout-plot-decomposition-py"><span class="std std-ref">Decomposition</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_decomposition.py</span></code>)</p></td> -<td><p>00:00.301</p></td> +<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 std-ref">Lanl Routes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_lanl_routes.py</span></code>)</p></td> +<td><p>00:00.443</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_circular_tree.html#sphx-glr-auto-examples-graphviz-layout-plot-circular-tree-py"><span class="std std-ref">Circular Tree</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_circular_tree.py</span></code>)</p></td> -<td><p>00:00.153</p></td> +<td><p>00:00.199</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/subclass/plot_antigraph.html b/auto_examples/subclass/plot_antigraph.html index f3cd57d8..39bf62de 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 0.098 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.125 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-subclass-plot-antigraph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/652afbfc3c52c8cdd7689321df2e696a/plot_antigraph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_antigraph.py</span></code></a></p> diff --git a/auto_examples/subclass/plot_printgraph.html b/auto_examples/subclass/plot_printgraph.html index dc92f3ce..370121c7 100644 --- a/auto_examples/subclass/plot_printgraph.html +++ b/auto_examples/subclass/plot_printgraph.html @@ -616,7 +616,7 @@ Add edge: 9-12 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.058 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.086 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-subclass-plot-printgraph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/1b5e7bf8d2514d71280314171170de85/plot_printgraph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_printgraph.py</span></code></a></p> diff --git a/auto_examples/subclass/sg_execution_times.html b/auto_examples/subclass/sg_execution_times.html index 3b0fea01..e2665b00 100644 --- a/auto_examples/subclass/sg_execution_times.html +++ b/auto_examples/subclass/sg_execution_times.html @@ -463,15 +463,15 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-subclass-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.157</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p> +<p><strong>00:00.211</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>00:00.098</p></td> +<td><p>00:00.125</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>00:00.058</p></td> +<td><p>00:00.086</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/reference/algorithms/generated/networkx.algorithms.isomorphism.is_isomorphic.html b/reference/algorithms/generated/networkx.algorithms.isomorphism.is_isomorphic.html index 6f3923e8..cfa18b0a 100644 --- a/reference/algorithms/generated/networkx.algorithms.isomorphism.is_isomorphic.html +++ b/reference/algorithms/generated/networkx.algorithms.isomorphism.is_isomorphic.html @@ -610,7 +610,7 @@ of the edges under consideration.</p> “An Improved Algorithm for Matching Large Graphs”, 3rd IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition, Cuen, pp. 149-159, 2001. -<a class="reference external" href="https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.101.5342">https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.101.5342</a></p> +<a class="reference external" href="https://www.researchgate.net/publication/200034365_An_Improved_Algorithm_for_Matching_Large_Graphs">https://www.researchgate.net/publication/200034365_An_Improved_Algorithm_for_Matching_Large_Graphs</a></p> </div> </div> <p class="rubric">Examples</p> diff --git a/reference/introduction-7.hires.png b/reference/introduction-7.hires.png Binary files differindex cb016e3b..cc9a99ef 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 f8df25ed..f84a4ecc 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 341bc319..1b022418 100644 --- a/reference/introduction-7.png +++ b/reference/introduction-7.png diff --git a/reference/introduction.ipynb b/reference/introduction.ipynb index a44e5edf..cae82d4c 100644 --- a/reference/introduction.ipynb +++ b/reference/introduction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "e2f57e72", + "id": "032fb79d", "metadata": {}, "source": [ "## Introduction\n", @@ -34,7 +34,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bc29f559", + "id": "99f6bdf8", "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "ec570782", + "id": "8ed4bfef", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b95145e0", + "id": "f145b972", "metadata": {}, "outputs": [], "source": [ @@ -94,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "cc9e3ee7", + "id": "3080a91f", "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": "0376d71c", + "id": "8355bc13", "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "55e825f0", + "id": "618c2ee3", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -214,7 +214,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f1cb48d5", + "id": "6c1f6eef", "metadata": {}, "outputs": [], "source": [ @@ -225,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "10559968", + "id": "eaebd399", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -234,7 +234,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2c55ecf6", + "id": "c831b73c", "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "aac0c4d6", + "id": "d70bbeff", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -311,7 +311,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ca3d9b8b", + "id": "11bef07a", "metadata": {}, "outputs": [], "source": [ @@ -323,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "3d3aa733", + "id": "1f73282d", "metadata": {}, "source": [ "# Drawing\n", @@ -344,7 +344,7 @@ { "cell_type": "code", "execution_count": null, - "id": "adfd3c21", + "id": "e37c43b3", "metadata": {}, "outputs": [], "source": [ @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "b8a18d63", + "id": "5af7e732", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -398,7 +398,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a7bbcdc2", + "id": "4aacda5f", "metadata": {}, "outputs": [], "source": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "12b7f9bb", + "id": "570035bb", "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": "030fd58c", + "id": "c7e74c22", "metadata": {}, "outputs": [], "source": [ diff --git a/reference/introduction_full.ipynb b/reference/introduction_full.ipynb index 795dff66..fb2591c7 100644 --- a/reference/introduction_full.ipynb +++ b/reference/introduction_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "e2f57e72", + "id": "032fb79d", "metadata": {}, "source": [ "## Introduction\n", @@ -34,13 +34,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "bc29f559", + "id": "99f6bdf8", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:35.044503Z", - "iopub.status.busy": "2023-01-03T20:27:35.044263Z", - "iopub.status.idle": "2023-01-03T20:27:35.117993Z", - "shell.execute_reply": "2023-01-03T20:27:35.117269Z" + "iopub.execute_input": "2023-01-03T21:20:09.005963Z", + "iopub.status.busy": "2023-01-03T21:20:09.005709Z", + "iopub.status.idle": "2023-01-03T21:20:09.112055Z", + "shell.execute_reply": "2023-01-03T21:20:09.111154Z" } }, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "markdown", - "id": "ec570782", + "id": "8ed4bfef", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -89,13 +89,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "b95145e0", + "id": "f145b972", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:35.122086Z", - "iopub.status.busy": "2023-01-03T20:27:35.121843Z", - "iopub.status.idle": "2023-01-03T20:27:35.125364Z", - "shell.execute_reply": "2023-01-03T20:27:35.124717Z" + "iopub.execute_input": "2023-01-03T21:20:09.116588Z", + "iopub.status.busy": "2023-01-03T21:20:09.116303Z", + "iopub.status.idle": "2023-01-03T21:20:09.120925Z", + "shell.execute_reply": "2023-01-03T21:20:09.119959Z" } }, "outputs": [], @@ -108,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "cc9e3ee7", + "id": "3080a91f", "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": "0376d71c", + "id": "8355bc13", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:35.128497Z", - "iopub.status.busy": "2023-01-03T20:27:35.128273Z", - "iopub.status.idle": "2023-01-03T20:27:35.131710Z", - "shell.execute_reply": "2023-01-03T20:27:35.131075Z" + "iopub.execute_input": "2023-01-03T21:20:09.125798Z", + "iopub.status.busy": "2023-01-03T21:20:09.125426Z", + "iopub.status.idle": "2023-01-03T21:20:09.129842Z", + "shell.execute_reply": "2023-01-03T21:20:09.128767Z" } }, "outputs": [], @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "55e825f0", + "id": "618c2ee3", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -235,13 +235,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "f1cb48d5", + "id": "6c1f6eef", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:35.134730Z", - "iopub.status.busy": "2023-01-03T20:27:35.134521Z", - "iopub.status.idle": "2023-01-03T20:27:35.137665Z", - "shell.execute_reply": "2023-01-03T20:27:35.137040Z" + "iopub.execute_input": "2023-01-03T21:20:09.133724Z", + "iopub.status.busy": "2023-01-03T21:20:09.133474Z", + "iopub.status.idle": "2023-01-03T21:20:09.137388Z", + "shell.execute_reply": "2023-01-03T21:20:09.136596Z" } }, "outputs": [], @@ -253,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "10559968", + "id": "eaebd399", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -262,13 +262,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "2c55ecf6", + "id": "c831b73c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:35.140639Z", - "iopub.status.busy": "2023-01-03T20:27:35.140430Z", - "iopub.status.idle": "2023-01-03T20:27:35.144275Z", - "shell.execute_reply": "2023-01-03T20:27:35.143626Z" + "iopub.execute_input": "2023-01-03T21:20:09.142122Z", + "iopub.status.busy": "2023-01-03T21:20:09.141793Z", + "iopub.status.idle": "2023-01-03T21:20:09.146367Z", + "shell.execute_reply": "2023-01-03T21:20:09.145606Z" } }, "outputs": [], @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "aac0c4d6", + "id": "d70bbeff", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -346,13 +346,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "ca3d9b8b", + "id": "11bef07a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:35.147402Z", - "iopub.status.busy": "2023-01-03T20:27:35.147194Z", - "iopub.status.idle": "2023-01-03T20:27:35.151505Z", - "shell.execute_reply": "2023-01-03T20:27:35.150853Z" + "iopub.execute_input": "2023-01-03T21:20:09.150056Z", + "iopub.status.busy": "2023-01-03T21:20:09.149675Z", + "iopub.status.idle": "2023-01-03T21:20:09.156548Z", + "shell.execute_reply": "2023-01-03T21:20:09.155416Z" } }, "outputs": [ @@ -373,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "3d3aa733", + "id": "1f73282d", "metadata": {}, "source": [ "# Drawing\n", @@ -394,19 +394,19 @@ { "cell_type": "code", "execution_count": 7, - "id": "adfd3c21", + "id": "e37c43b3", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:35.156101Z", - "iopub.status.busy": "2023-01-03T20:27:35.155889Z", - "iopub.status.idle": "2023-01-03T20:27:35.741006Z", - "shell.execute_reply": "2023-01-03T20:27:35.740353Z" + "iopub.execute_input": "2023-01-03T21:20:09.162652Z", + "iopub.status.busy": "2023-01-03T21:20:09.162394Z", + "iopub.status.idle": "2023-01-03T21:20:09.921522Z", + "shell.execute_reply": "2023-01-03T21:20:09.920601Z" } }, "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": "b8a18d63", + "id": "5af7e732", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -466,13 +466,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "a7bbcdc2", + "id": "4aacda5f", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:35.744553Z", - "iopub.status.busy": "2023-01-03T20:27:35.743997Z", - "iopub.status.idle": "2023-01-03T20:27:35.748034Z", - "shell.execute_reply": "2023-01-03T20:27:35.747494Z" + "iopub.execute_input": "2023-01-03T21:20:09.925805Z", + "iopub.status.busy": "2023-01-03T21:20:09.925376Z", + "iopub.status.idle": "2023-01-03T21:20:09.930536Z", + "shell.execute_reply": "2023-01-03T21:20:09.929703Z" } }, "outputs": [ @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "12b7f9bb", + "id": "570035bb", "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": "030fd58c", + "id": "c7e74c22", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:35.750994Z", - "iopub.status.busy": "2023-01-03T20:27:35.750631Z", - "iopub.status.idle": "2023-01-03T20:27:35.754891Z", - "shell.execute_reply": "2023-01-03T20:27:35.754237Z" + "iopub.execute_input": "2023-01-03T21:20:09.936229Z", + "iopub.status.busy": "2023-01-03T21:20:09.935979Z", + "iopub.status.idle": "2023-01-03T21:20:09.941219Z", + "shell.execute_reply": "2023-01-03T21:20:09.940350Z" } }, "outputs": [ diff --git a/searchindex.js b/searchindex.js index e7a6ec0d..76bd4c2f 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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1309, 1312, 1342, 1356, 1357, 1376, 1409, 1413, 1426], "child": [8, 1147, 1272], "must": [8, 11, 93, 94, 95, 99, 100, 103, 110, 151, 152, 158, 161, 171, 204, 206, 207, 214, 215, 216, 219, 230, 231, 232, 252, 253, 257, 258, 259, 260, 261, 262, 264, 267, 268, 269, 271, 273, 276, 281, 285, 297, 298, 306, 307, 315, 316, 317, 318, 319, 324, 325, 327, 329, 330, 342, 361, 362, 363, 378, 382, 385, 391, 410, 411, 412, 413, 425, 429, 440, 471, 472, 473, 474, 475, 545, 546, 547, 548, 549, 550, 551, 553, 555, 556, 557, 558, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 577, 578, 579, 580, 584, 585, 586, 587, 588, 589, 593, 597, 599, 601, 602, 603, 604, 615, 626, 627, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 680, 690, 692, 698, 699, 707, 721, 734, 735, 736, 737, 789, 796, 853, 854, 857, 867, 891, 892, 898, 899, 902, 912, 928, 934, 938, 972, 973, 979, 983, 1010, 1037, 1038, 1039, 1040, 1063, 1071, 1085, 1102, 1133, 1137, 1146, 1162, 1165, 1173, 1176, 1186, 1188, 1190, 1193, 1197, 1199, 1209, 1213, 1217, 1219, 1235, 1239, 1240, 1270, 1275, 1276, 1277, 1278, 1279, 1295, 1296, 1298, 1307, 1309, 1310, 1311, 1312, 1315, 1333, 1337, 1338, 1339, 1340, 1359, 1361, 1362, 1363, 1364, 1365, 1366, 1376, 1393, 1394, 1395, 1407, 1426], "NOT": [8, 110, 199, 549, 550, 551, 748, 887, 925, 968, 1007], "util": [8, 14, 36, 44, 45, 93, 97, 102, 103, 229, 230, 231, 316, 374, 423, 425, 426, 429, 460, 496, 678, 679, 758, 1044, 1242, 1299, 1301, 1303, 1310, 1319, 1320, 1321, 1325, 1402, 1406, 1407, 1411, 1413, 1416, 1419], "arbitrary_el": [8, 1392, 1413], "nb": [8, 1331, 1334], "left": [8, 71, 115, 183, 311, 312, 322, 324, 325, 385, 559, 560, 584, 616, 688, 689, 739, 1106, 1134, 1136, 1146, 1179, 1206, 1280, 1355, 1358, 1404], "right": [8, 71, 110, 111, 115, 152, 206, 322, 385, 427, 428, 500, 559, 560, 584, 585, 587, 588, 615, 616, 688, 689, 739, 854, 935, 980, 1134, 1136, 1146, 1155, 1157, 1179, 1206, 1213, 1215, 1270, 1280], "littl": [8, 94, 298, 307], "mislead": 8, "That": [8, 97, 132, 165, 212, 221, 227, 295, 385, 436, 465, 525, 535, 555, 588, 657, 671, 672, 673, 674, 691, 704, 717, 791, 862, 907, 943, 988, 1046, 1162, 1210, 1296, 1388, 1404, 1409], "okai": 8, "becaus": [8, 11, 54, 69, 94, 99, 101, 102, 103, 112, 132, 161, 215, 216, 220, 255, 311, 378, 387, 389, 390, 394, 411, 412, 427, 494, 498, 499, 500, 510, 569, 585, 587, 615, 616, 632, 652, 934, 979, 1038, 1236, 1273, 1296, 1303, 1326, 1345, 1350, 1404, 1407, 1416], "AND": [8, 110, 598, 748, 762], "OR": [8, 110, 157, 175, 188, 856, 869, 877, 901, 937, 947, 950, 959, 982, 992], "symmetr": [8, 145, 148, 237, 545, 586, 593, 761, 1173, 1192, 1235, 1246, 1250, 1251, 1256, 1258, 1269, 1320, 1321, 1387], "It": [8, 52, 56, 58, 92, 93, 94, 97, 99, 101, 102, 104, 107, 110, 112, 115, 132, 172, 184, 207, 214, 215, 216, 229, 230, 231, 249, 260, 261, 262, 264, 278, 310, 316, 324, 325, 326, 343, 346, 347, 351, 353, 412, 414, 415, 416, 417, 418, 419, 429, 438, 440, 452, 457, 464, 480, 496, 500, 508, 530, 540, 545, 559, 560, 565, 566, 567, 582, 588, 594, 595, 598, 600, 601, 615, 619, 628, 629, 630, 652, 658, 659, 663, 671, 674, 692, 717, 718, 719, 760, 761, 762, 791, 796, 868, 873, 892, 913, 916, 928, 949, 955, 973, 994, 998, 1010, 1012, 1013, 1018, 1037, 1038, 1039, 1040, 1054, 1117, 1170, 1174, 1200, 1201, 1206, 1207, 1210, 1217, 1223, 1227, 1234, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1253, 1254, 1258, 1261, 1263, 1264, 1269, 1275, 1276, 1277, 1280, 1296, 1297, 1323, 1324, 1326, 1328, 1343, 1382, 1383, 1393, 1395, 1398, 1402, 1404, 1407, 1408, 1409, 1411, 1412, 1413, 1426], "just": [8, 99, 102, 103, 104, 184, 199, 338, 374, 439, 464, 559, 560, 577, 660, 661, 662, 692, 791, 873, 887, 916, 925, 946, 955, 960, 968, 991, 998, 1007, 1120, 1126, 1229, 1278, 1279, 1296, 1328, 1393, 1404, 1406], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1325, 1402, 1406, 1412], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1202, 1383], "doe": [8, 77, 93, 94, 99, 101, 102, 103, 104, 114, 115, 132, 147, 153, 154, 165, 168, 189, 207, 208, 227, 228, 229, 230, 231, 232, 293, 308, 339, 340, 342, 343, 352, 357, 373, 382, 385, 410, 414, 426, 450, 469, 494, 495, 496, 497, 498, 499, 500, 502, 503, 506, 507, 509, 510, 511, 512, 534, 544, 549, 550, 551, 564, 566, 583, 584, 586, 589, 601, 612, 626, 627, 678, 691, 693, 694, 698, 699, 717, 718, 721, 722, 723, 724, 725, 726, 762, 862, 865, 878, 892, 907, 910, 928, 943, 946, 960, 973, 988, 991, 1010, 1038, 1043, 1066, 1070, 1072, 1081, 1102, 1103, 1105, 1106, 1107, 1109, 1114, 1173, 1175, 1177, 1192, 1207, 1222, 1223, 1227, 1229, 1234, 1241, 1296, 1300, 1303, 1326, 1333, 1334, 1341, 1342, 1344, 1351, 1353, 1354, 1355, 1356, 1357, 1358, 1371, 1379, 1380, 1381, 1383, 1393, 1404, 1405, 1406, 1410, 1417, 1426], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1219], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1229, 1296, 1395, 1404], "everi": [8, 11, 57, 88, 93, 109, 112, 120, 144, 157, 161, 177, 211, 212, 220, 221, 229, 230, 231, 235, 243, 264, 287, 295, 300, 324, 325, 343, 352, 380, 397, 437, 439, 440, 450, 462, 471, 472, 473, 474, 475, 477, 483, 484, 491, 512, 516, 565, 606, 614, 615, 619, 632, 633, 635, 636, 663, 685, 687, 688, 717, 718, 791, 856, 901, 937, 982, 1052, 1053, 1054, 1070, 1071, 1072, 1085, 1086, 1102, 1103, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1148, 1162, 1195, 1216, 1217, 1257, 1264, 1278, 1279, 1296, 1407], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1177, 1207, 1208, 1404, 1406, 1407, 1426], "ha": [8, 11, 16, 44, 67, 88, 91, 93, 94, 95, 97, 99, 100, 101, 102, 103, 105, 107, 110, 112, 116, 120, 127, 152, 161, 165, 166, 173, 174, 175, 184, 188, 198, 207, 212, 214, 215, 219, 220, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 241, 242, 243, 244, 247, 249, 252, 269, 271, 272, 273, 274, 275, 276, 282, 289, 291, 293, 294, 295, 300, 305, 310, 324, 331, 343, 352, 355, 356, 363, 364, 365, 373, 378, 380, 381, 383, 384, 385, 386, 391, 393, 394, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 424, 427, 428, 429, 439, 450, 458, 460, 466, 467, 468, 471, 472, 473, 474, 475, 476, 477, 480, 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 522, 564, 566, 577, 578, 581, 590, 593, 605, 607, 610, 611, 622, 623, 624, 628, 629, 630, 632, 633, 634, 635, 636, 638, 646, 647, 649, 652, 657, 658, 682, 688, 690, 692, 697, 711, 717, 718, 729, 730, 731, 739, 749, 786, 791, 854, 862, 863, 869, 873, 877, 886, 892, 899, 907, 908, 916, 924, 928, 935, 943, 944, 948, 950, 955, 959, 967, 973, 980, 988, 989, 993, 998, 1006, 1010, 1040, 1043, 1045, 1066, 1068, 1070, 1072, 1075, 1080, 1084, 1098, 1099, 1101, 1102, 1103, 1105, 1122, 1130, 1145, 1154, 1160, 1162, 1165, 1176, 1180, 1185, 1193, 1195, 1196, 1197, 1198, 1199, 1207, 1210, 1211, 1215, 1217, 1222, 1234, 1239, 1243, 1244, 1248, 1249, 1254, 1259, 1261, 1264, 1267, 1269, 1270, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1283, 1284, 1285, 1286, 1289, 1291, 1293, 1296, 1300, 1326, 1328, 1330, 1333, 1334, 1353, 1354, 1371, 1372, 1379, 1382, 1393, 1394, 1395, 1398, 1403, 1404, 1405, 1406, 1407, 1409, 1413, 1414, 1416, 1423, 1425], "output": [8, 13, 16, 89, 93, 101, 102, 103, 109, 197, 287, 288, 345, 374, 380, 494, 498, 499, 509, 510, 575, 588, 677, 678, 691, 722, 1045, 1193, 1197, 1199, 1269, 1296, 1326, 1334, 1341, 1344, 1355, 1358, 1399, 1402, 1404, 1406, 1411, 1413, 1414, 1426], "two": [8, 11, 16, 27, 34, 38, 43, 54, 55, 57, 58, 65, 67, 71, 88, 93, 95, 99, 100, 102, 109, 112, 114, 115, 120, 132, 151, 171, 175, 184, 185, 188, 202, 207, 211, 212, 213, 214, 215, 216, 217, 220, 221, 226, 227, 230, 231, 232, 245, 249, 251, 252, 253, 257, 258, 260, 261, 262, 265, 269, 270, 271, 272, 273, 274, 275, 276, 282, 285, 286, 287, 289, 305, 311, 315, 316, 322, 326, 329, 330, 337, 341, 343, 345, 351, 352, 358, 359, 377, 380, 381, 383, 391, 411, 412, 419, 423, 428, 429, 430, 431, 442, 443, 444, 445, 447, 452, 453, 454, 457, 462, 471, 472, 473, 474, 475, 476, 480, 491, 494, 498, 499, 500, 502, 503, 506, 508, 509, 510, 511, 521, 545, 549, 550, 551, 555, 559, 560, 561, 562, 563, 564, 565, 566, 568, 569, 572, 574, 578, 584, 585, 586, 587, 588, 593, 598, 605, 607, 608, 610, 611, 615, 619, 626, 627, 629, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 680, 692, 694, 731, 732, 738, 739, 760, 761, 762, 780, 786, 791, 796, 853, 867, 869, 873, 874, 877, 890, 892, 898, 912, 916, 917, 926, 928, 934, 946, 948, 950, 955, 956, 959, 960, 971, 973, 979, 991, 993, 998, 999, 1008, 1010, 1019, 1020, 1021, 1022, 1036, 1037, 1039, 1040, 1056, 1084, 1088, 1098, 1100, 1101, 1106, 1107, 1108, 1109, 1114, 1116, 1134, 1146, 1147, 1149, 1151, 1152, 1156, 1174, 1185, 1186, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1204, 1207, 1210, 1211, 1215, 1217, 1218, 1243, 1244, 1253, 1271, 1272, 1275, 1276, 1294, 1295, 1296, 1323, 1324, 1326, 1328, 1359, 1360, 1363, 1393, 1394, 1395, 1397, 1402, 1404, 1405, 1406, 1407, 1410, 1411, 1413, 1425], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1420], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1217, 1226, 1262, 1263, 1326, 1407], "appear": [8, 83, 93, 95, 99, 100, 102, 179, 204, 230, 231, 238, 243, 246, 247, 277, 363, 364, 365, 378, 451, 452, 453, 455, 466, 470, 584, 585, 587, 588, 675, 679, 707, 730, 734, 736, 891, 972, 1036, 1088, 1102, 1136, 1150, 1152, 1154, 1157, 1159, 1187, 1188, 1277, 1282, 1323, 1324, 1345, 1348, 1349, 1350, 1382, 1407, 1413, 1414], "both": [8, 52, 55, 92, 93, 94, 100, 101, 102, 103, 115, 161, 164, 204, 214, 215, 216, 217, 240, 257, 258, 259, 264, 282, 286, 287, 289, 337, 358, 379, 383, 415, 417, 418, 419, 423, 427, 440, 470, 502, 506, 545, 575, 581, 598, 600, 601, 602, 603, 604, 605, 606, 607, 610, 611, 615, 621, 635, 636, 653, 654, 655, 676, 711, 720, 760, 761, 762, 782, 891, 972, 1020, 1036, 1066, 1075, 1080, 1084, 1088, 1097, 1120, 1126, 1144, 1165, 1189, 1192, 1199, 1207, 1210, 1211, 1213, 1215, 1282, 1296, 1326, 1328, 1358, 1363, 1364, 1387, 1393, 1395, 1402, 1413, 1416, 1417, 1425, 1426], "negat": 8, "sole": [8, 786, 1278, 1279, 1326], "fourth": [8, 230, 231, 1326, 1404], "digraph": [8, 10, 11, 16, 21, 25, 41, 45, 56, 61, 67, 69, 70, 82, 88, 101, 102, 115, 132, 151, 152, 156, 157, 158, 160, 162, 163, 165, 166, 168, 170, 171, 172, 175, 176, 185, 186, 187, 188, 189, 192, 193, 194, 195, 196, 198, 199, 202, 204, 207, 208, 216, 227, 229, 230, 231, 240, 246, 247, 299, 308, 314, 318, 319, 321, 327, 328, 334, 335, 336, 337, 339, 340, 342, 343, 388, 391, 393, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 430, 431, 437, 450, 452, 453, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 481, 482, 492, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 513, 514, 518, 519, 523, 555, 566, 575, 576, 577, 588, 590, 613, 615, 623, 630, 636, 643, 644, 652, 656, 657, 658, 659, 663, 678, 688, 690, 693, 696, 697, 698, 699, 700, 701, 702, 706, 707, 708, 709, 711, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 740, 741, 744, 745, 746, 747, 748, 749, 750, 752, 760, 789, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 911, 912, 913, 915, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 957, 958, 963, 964, 965, 966, 967, 968, 972, 973, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 1000, 1001, 1003, 1004, 1005, 1006, 1007, 1010, 1035, 1037, 1038, 1039, 1040, 1041, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1135, 1150, 1154, 1168, 1169, 1170, 1173, 1177, 1178, 1180, 1182, 1183, 1184, 1185, 1189, 1217, 1270, 1272, 1273, 1274, 1283, 1284, 1287, 1290, 1292, 1298, 1323, 1326, 1333, 1337, 1342, 1356, 1357, 1362, 1365, 1366, 1371, 1393, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "add_nod": [8, 11, 26, 34, 69, 74, 89, 102, 157, 184, 246, 339, 340, 398, 422, 491, 492, 496, 504, 505, 508, 522, 523, 605, 607, 610, 611, 691, 796, 856, 873, 901, 916, 937, 955, 982, 998, 1037, 1039, 1040, 1086, 1275, 1326, 1345, 1407, 1408, 1417, 1426], "get_node_attribut": [8, 39, 44, 71, 1213, 1404], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1133, 1134, 1136], "22": [8, 35, 64, 66, 383, 384, 1271, 1323, 1403, 1408, 1412, 1422], "multipartite_layout": [8, 36, 61, 67, 1412, 1414, 1420], "subset_kei": [8, 36, 61, 67, 1109], "equal": [8, 36, 81, 144, 214, 215, 216, 230, 231, 238, 269, 271, 273, 276, 288, 297, 298, 300, 303, 306, 307, 310, 311, 312, 315, 316, 320, 323, 324, 325, 329, 330, 331, 373, 410, 411, 412, 413, 418, 419, 428, 471, 474, 476, 491, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 525, 535, 545, 552, 553, 554, 555, 568, 572, 605, 623, 657, 671, 672, 673, 674, 687, 688, 689, 690, 721, 722, 740, 741, 753, 761, 791, 1112, 1116, 1162, 1165, 1198, 1204, 1230, 1239, 1271, 1280, 1291, 1307, 1309, 1312, 1398, 1399], "107": [8, 17, 241, 244, 1201], "plot_circuit": [8, 17], "southern": [9, 1265], "women": [9, 1265, 1398, 1406], "unipartit": [9, 115, 258, 259, 358], "properti": [9, 11, 18, 22, 33, 63, 86, 101, 102, 103, 112, 134, 159, 161, 166, 168, 175, 176, 179, 184, 188, 189, 190, 200, 284, 285, 286, 287, 288, 363, 364, 365, 388, 476, 500, 545, 569, 619, 685, 858, 863, 865, 869, 870, 873, 877, 878, 879, 888, 903, 908, 910, 916, 939, 944, 946, 950, 951, 955, 959, 960, 969, 984, 989, 991, 998, 1085, 1086, 1122, 1134, 1136, 1193, 1202, 1217, 1219, 1269, 1283, 1284, 1326, 1328, 1383, 1398, 1405, 1406, 1407, 1408, 1413, 1417, 1426], "These": [9, 52, 58, 73, 79, 86, 93, 94, 105, 112, 336, 385, 494, 512, 559, 671, 673, 732, 748, 779, 786, 1038, 1045, 1047, 1323, 1326, 1385, 1387, 1392, 1394, 1395, 1397, 1399, 1404, 1405, 1411, 1426], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1199, 1393, 1395, 1399, 1402, 1405, 1406, 1407, 1413, 1416], "et": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202], "al": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202, 1407, 1413], "1930": [9, 1396], "thei": [9, 54, 58, 65, 71, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 112, 132, 151, 165, 207, 213, 220, 249, 285, 287, 288, 296, 297, 298, 301, 302, 306, 307, 308, 309, 351, 362, 374, 391, 396, 427, 451, 452, 453, 454, 464, 465, 471, 472, 473, 474, 475, 496, 504, 505, 508, 512, 546, 547, 548, 559, 560, 576, 583, 586, 588, 600, 604, 675, 676, 704, 717, 750, 760, 786, 853, 862, 892, 898, 907, 928, 934, 943, 962, 973, 979, 988, 1002, 1010, 1036, 1038, 1066, 1085, 1088, 1109, 1120, 1126, 1133, 1135, 1137, 1151, 1159, 1165, 1193, 1197, 1198, 1217, 1271, 1272, 1323, 1328, 1353, 1354, 1356, 1357, 1359, 1363, 1394, 1396, 1402, 1404, 1406, 1409, 1414, 1426], "repres": [9, 11, 26, 43, 52, 54, 57, 67, 92, 99, 107, 115, 230, 231, 265, 281, 283, 286, 287, 288, 291, 292, 338, 350, 361, 362, 363, 377, 378, 380, 381, 382, 385, 386, 391, 448, 452, 453, 455, 457, 460, 465, 466, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 521, 565, 577, 578, 579, 580, 586, 588, 609, 615, 618, 619, 656, 660, 664, 667, 676, 679, 691, 692, 695, 697, 698, 699, 700, 702, 728, 730, 731, 734, 736, 739, 752, 786, 791, 796, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1045, 1081, 1102, 1140, 1151, 1185, 1193, 1194, 1196, 1197, 1198, 1199, 1209, 1217, 1240, 1243, 1246, 1250, 1258, 1267, 1269, 1272, 1273, 1278, 1279, 1323, 1324, 1326, 1329, 1330, 1346, 1347, 1388, 1393, 1406], "observ": [9, 13, 132, 223, 1414, 1426], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 64, 66, 71, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1150, 1242, 1250, 1262, 1406, 1408, 1426], "event": [9, 25, 99, 100, 110, 1165, 1229, 1300], "18": [9, 44, 64, 66, 93, 324, 325, 345, 383, 384, 618, 1169, 1249, 1255, 1258, 1260, 1263, 1269, 1393, 1406, 1416, 1417, 1421, 1426], "bipartit": [9, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 350, 351, 358, 377, 439, 440, 443, 581, 588, 758, 1043, 1106, 1151, 1203, 1204, 1205, 1265, 1325, 1395, 1398, 1399, 1400, 1401, 1406, 1407, 1411, 1413, 1417, 1421, 1425], "biadjac": [9, 282, 283, 1400, 1406], "7": [9, 12, 14, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 89, 99, 101, 102, 115, 125, 151, 158, 170, 171, 192, 207, 232, 268, 297, 299, 314, 322, 327, 332, 333, 339, 340, 342, 362, 374, 380, 391, 403, 410, 413, 414, 415, 423, 424, 425, 426, 441, 445, 446, 483, 496, 501, 508, 511, 512, 555, 581, 586, 618, 619, 630, 652, 658, 663, 671, 674, 680, 695, 703, 706, 707, 708, 730, 747, 750, 761, 796, 853, 857, 866, 867, 881, 892, 898, 902, 911, 912, 915, 920, 928, 934, 938, 947, 973, 979, 983, 992, 996, 1010, 1037, 1039, 1040, 1052, 1053, 1085, 1100, 1104, 1148, 1212, 1242, 1248, 1250, 1251, 1255, 1258, 1260, 1273, 1323, 1326, 1330, 1339, 1340, 1345, 1348, 1349, 1350, 1382, 1392, 1394, 1402, 1403, 1405, 1408, 1409, 1410, 1411, 1412, 1413, 1426], "12": [9, 11, 19, 25, 44, 50, 55, 58, 64, 65, 66, 89, 91, 93, 229, 230, 231, 265, 345, 380, 381, 392, 399, 405, 406, 407, 449, 486, 501, 516, 568, 572, 574, 606, 616, 1052, 1053, 1054, 1133, 1136, 1150, 1244, 1245, 1249, 1254, 1257, 1263, 1335, 1406, 1408, 1412, 1426], "9": [9, 11, 12, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 82, 89, 101, 102, 111, 115, 125, 232, 293, 295, 339, 340, 342, 346, 347, 356, 374, 380, 405, 406, 424, 438, 449, 494, 496, 501, 504, 505, 508, 545, 566, 581, 586, 676, 706, 707, 708, 761, 1100, 1104, 1148, 1150, 1194, 1199, 1212, 1217, 1235, 1246, 1255, 1267, 1273, 1283, 1284, 1323, 1326, 1328, 1396, 1403, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "11": [9, 25, 33, 44, 64, 65, 66, 68, 89, 102, 110, 115, 157, 210, 239, 240, 297, 298, 303, 306, 307, 323, 392, 399, 405, 406, 407, 413, 415, 417, 422, 501, 514, 517, 606, 618, 680, 721, 738, 856, 901, 937, 982, 1052, 1053, 1054, 1100, 1150, 1287, 1403, 1410, 1413, 1414, 1419, 1424, 1425, 1426], "13": [9, 11, 38, 44, 64, 66, 89, 91, 156, 229, 230, 231, 343, 501, 703, 855, 900, 936, 981, 1150, 1192, 1406, 1420, 1426], "16": [9, 19, 31, 44, 45, 64, 66, 70, 229, 230, 231, 346, 347, 387, 389, 390, 394, 453, 508, 511, 512, 519, 571, 592, 606, 748, 749, 750, 1109, 1205, 1256, 1271, 1286, 1323, 1406, 1411, 1426], "17": [9, 21, 44, 64, 66, 103, 229, 230, 231, 297, 508, 680, 693, 1405, 1406, 1426], "friend": [9, 545, 1407, 1412], "member": [9, 92, 93, 94, 100, 112, 315, 317, 318, 319, 330, 391, 483, 484, 586, 691, 1222, 1267, 1403], "evelyn": 9, "jefferson": 9, "laura": 9, "mandevil": 9, "theresa": 9, "anderson": 9, "brenda": 9, "roger": 9, "charlott": 9, "mcdowd": 9, "franc": 9, "eleanor": 9, "nye": 9, "pearl": [9, 132], "oglethorp": 9, "ruth": 9, "desand": 9, "vern": 9, "sanderson": 9, "myra": 9, "liddel": 9, "katherina": 9, "sylvia": 9, "avondal": 9, "nora": 9, "fayett": 9, "helen": 9, "lloyd": 9, "dorothi": 9, "murchison": 9, "olivia": 9, "carleton": 9, "flora": 9, "price": 9, "meet": [9, 94, 1165, 1196, 1197, 1198], "50": [9, 25, 30, 34, 40, 50, 54, 55, 56, 57, 64, 65, 272, 312, 1117, 1193, 1197, 1198, 1251, 1297, 1302], "45": [9, 58, 64, 110, 226, 300, 409, 1175], "57": [9, 64], "46": [9, 64, 235, 564, 619, 1264], "24": [9, 19, 37, 64, 66, 68, 103, 383, 384, 496, 505, 508, 703, 1212, 1229, 1244, 1262, 1271, 1403], "32": [9, 64, 66, 68, 209, 211, 212, 383, 384, 564, 703, 1403, 1411], "36": [9, 21, 64, 68, 752, 1150, 1262, 1271, 1353, 1354, 1379, 1403], "31": [9, 64, 66, 229, 230, 231, 260, 261, 262, 289, 383, 384, 409, 703, 1226, 1235, 1403], "40": [9, 50, 64, 80, 101, 297, 300, 555, 672, 1173, 1240, 1271], "38": [9, 64, 688, 1271], "33": [9, 58, 64, 66, 68, 93, 383, 384, 500, 514, 703, 1267, 1271, 1403, 1414], "37": [9, 56, 64, 68, 303, 311, 312, 323, 324, 325, 496, 508, 1039, 1040, 1271, 1393, 1403, 1408, 1425], "43": [9, 64, 324, 325, 606, 1244, 1271], "34": [9, 64, 68, 331, 508, 762, 1271, 1403], "algorithm": [9, 14, 15, 44, 52, 54, 88, 93, 94, 95, 96, 102, 103, 107, 109, 110, 111, 112, 114, 115, 117, 120, 121, 122, 125, 127, 128, 132, 133, 136, 141, 151, 210, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 228, 229, 230, 231, 232, 235, 249, 251, 252, 253, 254, 255, 256, 258, 260, 261, 262, 263, 264, 265, 266, 267, 272, 275, 277, 278, 280, 282, 284, 285, 286, 287, 288, 289, 290, 293, 296, 297, 298, 299, 301, 302, 303, 306, 307, 308, 309, 311, 312, 315, 320, 322, 323, 324, 325, 326, 329, 330, 331, 332, 333, 337, 339, 340, 341, 342, 343, 345, 346, 347, 352, 358, 361, 362, 366, 371, 372, 373, 374, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 389, 390, 394, 399, 405, 406, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 421, 422, 424, 425, 426, 427, 428, 429, 430, 432, 433, 435, 437, 440, 449, 451, 452, 453, 454, 455, 460, 464, 466, 468, 481, 482, 483, 488, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 512, 513, 514, 516, 519, 520, 521, 527, 537, 546, 547, 548, 552, 553, 554, 555, 556, 557, 558, 564, 566, 569, 577, 581, 582, 583, 589, 591, 592, 593, 600, 614, 616, 618, 619, 624, 625, 626, 627, 628, 629, 630, 632, 633, 635, 636, 639, 652, 653, 657, 658, 659, 660, 663, 664, 667, 671, 672, 673, 674, 676, 677, 678, 680, 681, 682, 683, 686, 690, 691, 692, 693, 695, 696, 697, 698, 699, 700, 701, 702, 711, 717, 721, 722, 729, 731, 732, 734, 735, 736, 737, 738, 749, 764, 765, 768, 770, 775, 776, 780, 786, 789, 790, 791, 853, 898, 934, 979, 1011, 1038, 1042, 1043, 1105, 1106, 1107, 1109, 1114, 1116, 1117, 1125, 1126, 1155, 1165, 1168, 1169, 1177, 1178, 1179, 1180, 1181, 1185, 1186, 1187, 1188, 1193, 1195, 1200, 1201, 1202, 1205, 1207, 1209, 1210, 1216, 1223, 1224, 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1235, 1239, 1260, 1269, 1275, 1276, 1277, 1298, 1302, 1319, 1320, 1321, 1323, 1325, 1328, 1367, 1368, 1386, 1393, 1394, 1395, 1400, 1401, 1402, 1403, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1422, 1424, 1425, 1426], "davis_southern_women_graph": [9, 88, 263], "top": [9, 34, 52, 67, 106, 111, 112, 115, 125, 260, 272, 284, 350, 381, 670, 675, 770, 1106, 1134, 1136, 1252, 1396, 1399, 1407, 1412, 1413, 1416], "bottom": [9, 91, 115, 260, 272, 274, 284, 285, 286, 287, 288, 350, 381, 1134, 1136, 1155, 1404, 1416], "biadjacency_matrix": [9, 283], "onto": [9, 284, 285, 286, 287, 288, 559, 560], "projected_graph": [9, 115, 284, 285, 286, 288, 351], "keep": [9, 92, 93, 94, 115, 204, 345, 346, 347, 362, 377, 387, 389, 390, 394, 583, 598, 693, 694, 891, 972, 1117, 1207, 1210, 1278, 1279, 1296, 1376, 1394, 1411, 1414], "co": [9, 26, 94, 99, 144, 752, 1326], "occur": [9, 93, 95, 100, 230, 231, 277, 278, 280, 383, 581, 582, 583, 588, 1043, 1117, 1120, 1126, 1282, 1296], "count": [9, 185, 237, 238, 242, 243, 245, 297, 298, 310, 315, 330, 360, 386, 443, 568, 597, 619, 749, 753, 874, 917, 944, 950, 956, 959, 999, 1060, 1179, 1278, 1279, 1406, 1407, 1416], "share": [9, 54, 58, 92, 94, 112, 165, 199, 214, 215, 216, 221, 278, 285, 287, 288, 294, 358, 359, 376, 418, 419, 460, 462, 480, 569, 578, 691, 732, 862, 887, 907, 925, 943, 968, 988, 1007, 1217, 1328], "contact": [9, 92, 688, 1195, 1326], "weighted_projected_graph": [9, 284, 285, 286, 287, 1417], "648": 9, "074": [9, 17], "plot_davis_club": [9, 17], "retain": [10, 102, 110, 230, 284, 285, 286, 287, 288, 1100, 1187, 1295], "pattern": [10, 54, 93, 103, 236, 241, 244, 248, 385, 494, 519, 555, 671, 672, 673, 674, 690, 691, 693, 762, 786, 1036, 1088, 1388, 1413], "add": [10, 11, 26, 34, 41, 45, 49, 52, 61, 71, 88, 89, 91, 93, 94, 101, 102, 105, 106, 115, 151, 152, 153, 154, 156, 157, 158, 164, 207, 222, 223, 229, 282, 285, 341, 374, 411, 412, 423, 428, 430, 431, 450, 460, 581, 582, 583, 589, 614, 615, 618, 619, 654, 690, 701, 717, 718, 796, 850, 853, 854, 855, 856, 857, 892, 895, 898, 899, 900, 901, 902, 928, 931, 934, 935, 936, 937, 938, 973, 976, 979, 980, 981, 982, 983, 984, 1010, 1037, 1038, 1039, 1040, 1042, 1049, 1052, 1053, 1054, 1100, 1154, 1165, 1172, 1185, 1207, 1210, 1217, 1219, 1233, 1234, 1236, 1302, 1326, 1353, 1354, 1356, 1357, 1379, 1380, 1383, 1393, 1394, 1395, 1398, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "compressor": [10, 690, 786], "do": [10, 55, 75, 88, 92, 93, 94, 96, 99, 101, 102, 106, 107, 109, 111, 115, 133, 165, 184, 199, 202, 204, 230, 231, 238, 243, 277, 278, 280, 362, 380, 410, 411, 412, 418, 419, 458, 459, 467, 470, 589, 598, 632, 690, 692, 734, 735, 736, 737, 791, 796, 862, 873, 887, 890, 891, 907, 916, 925, 926, 927, 943, 954, 955, 968, 971, 972, 988, 997, 998, 1007, 1008, 1009, 1037, 1038, 1039, 1040, 1061, 1082, 1102, 1165, 1177, 1189, 1193, 1207, 1210, 1216, 1217, 1227, 1272, 1328, 1393, 1401, 1402, 1407, 1411, 1426], "would": [10, 92, 93, 95, 96, 100, 101, 102, 103, 104, 105, 107, 289, 305, 414, 415, 416, 417, 422, 428, 579, 583, 588, 632, 679, 690, 693, 717, 718, 751, 1217, 1236, 1295, 1296, 1300, 1303, 1326, 1416, 1417], "result": [10, 11, 25, 71, 92, 95, 101, 103, 109, 110, 112, 142, 165, 209, 218, 220, 230, 231, 255, 269, 271, 273, 276, 283, 284, 285, 286, 287, 288, 289, 299, 300, 305, 324, 325, 330, 374, 380, 381, 382, 385, 386, 391, 411, 412, 416, 418, 440, 464, 466, 467, 490, 494, 498, 499, 509, 510, 511, 512, 564, 565, 566, 584, 585, 587, 601, 609, 615, 626, 627, 629, 676, 678, 690, 692, 704, 710, 717, 786, 791, 862, 907, 943, 984, 988, 1038, 1042, 1082, 1094, 1098, 1099, 1102, 1103, 1105, 1112, 1113, 1114, 1116, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1150, 1152, 1154, 1157, 1159, 1160, 1163, 1175, 1177, 1180, 1201, 1222, 1225, 1239, 1278, 1279, 1281, 1296, 1299, 1303, 1308, 1326, 1328, 1331, 1334, 1359, 1402, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1425, 1426], "fewer": [10, 420, 421, 681, 683, 690, 692, 693, 694, 762, 786, 1213, 1215], "compress": [10, 25, 268, 512, 577, 690, 786, 1116, 1242, 1333, 1334, 1339, 1340, 1344, 1350, 1357, 1358, 1371, 1372, 1376], "suptitl": [10, 15], "original_graph": [10, 15, 690], "white_nod": 10, "red_nod": 10, "250": [10, 32, 1165], "white": [10, 21, 25, 82, 83, 127, 214, 215, 216, 220, 427, 1395, 1398, 1406], "add_nodes_from": [10, 15, 16, 36, 70, 71, 82, 89, 115, 156, 165, 199, 207, 236, 237, 248, 265, 267, 268, 423, 425, 426, 469, 555, 690, 796, 855, 862, 887, 892, 900, 907, 925, 928, 936, 943, 968, 973, 981, 988, 1007, 1010, 1037, 1039, 1040, 1065, 1194, 1217, 1291, 1404, 1406, 1413, 1426], "add_edges_from": [10, 15, 16, 36, 41, 70, 82, 89, 115, 132, 151, 158, 165, 199, 204, 207, 236, 248, 287, 327, 376, 422, 423, 425, 426, 460, 469, 501, 511, 512, 572, 574, 588, 688, 690, 705, 706, 707, 709, 730, 742, 743, 796, 853, 857, 862, 887, 891, 892, 898, 902, 907, 925, 927, 928, 934, 938, 943, 956, 962, 963, 968, 972, 973, 979, 983, 988, 999, 1002, 1003, 1007, 1009, 1010, 1037, 1039, 1040, 1070, 1085, 1094, 1135, 1154, 1217, 1287, 1291, 1326, 1404, 1407, 1426], "base_opt": [10, 15], "dict": [10, 15, 19, 25, 39, 54, 57, 58, 67, 70, 88, 101, 102, 107, 109, 144, 145, 148, 157, 159, 160, 165, 168, 169, 176, 179, 184, 189, 190, 195, 197, 200, 202, 204, 207, 220, 237, 239, 240, 252, 290, 309, 310, 329, 334, 336, 353, 408, 411, 412, 416, 422, 427, 470, 473, 481, 482, 496, 502, 512, 545, 561, 563, 565, 566, 575, 577, 578, 579, 580, 588, 614, 628, 631, 636, 637, 638, 640, 642, 644, 645, 646, 647, 648, 649, 662, 666, 669, 687, 688, 691, 705, 706, 707, 713, 715, 749, 750, 760, 796, 849, 856, 858, 859, 862, 865, 870, 873, 878, 879, 884, 888, 890, 891, 892, 894, 901, 903, 904, 907, 910, 916, 923, 926, 927, 928, 930, 931, 935, 937, 939, 940, 943, 946, 947, 951, 955, 960, 965, 969, 971, 972, 973, 975, 976, 980, 982, 984, 985, 988, 991, 992, 998, 1005, 1008, 1009, 1010, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1041, 1045, 1047, 1085, 1086, 1091, 1094, 1097, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1120, 1122, 1126, 1134, 1136, 1193, 1196, 1197, 1198, 1207, 1208, 1213, 1295, 1296, 1302, 1303, 1307, 1324, 1326, 1345, 1348, 1349, 1350, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1382, 1393, 1394, 1395, 1402, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1415, 1416, 1425, 1426], "edgecolor": [10, 15, 21, 32, 34, 35, 38, 54, 58, 82, 83, 1137], "black": [10, 15, 21, 25, 65, 69, 93, 598, 1133, 1134, 1136, 1412, 1413, 1414, 1416, 1426], "ax1": [10, 15, 27, 50, 82], "number_of_edg": [10, 15, 25, 28, 198, 690, 886, 924, 967, 1006, 1059, 1154, 1271, 1406, 1407, 1426], "nonexp_graph": 10, "compression_nod": 10, "summar": [10, 15, 100, 101, 690, 691, 758, 791, 1325, 1328, 1413], "dedensifi": [10, 758], "threshold": [10, 57, 83, 112, 220, 229, 231, 380, 381, 690, 692, 695, 696, 758, 786, 1117, 1193, 1194, 1196, 1197, 1198, 1325, 1398, 1406, 1407, 1408, 1412, 1414], "copi": [10, 16, 38, 44, 93, 95, 106, 167, 196, 199, 202, 203, 204, 205, 284, 285, 286, 287, 288, 341, 388, 390, 392, 406, 433, 434, 435, 436, 437, 453, 460, 469, 521, 584, 585, 587, 596, 599, 602, 603, 605, 606, 607, 610, 611, 613, 614, 633, 636, 690, 864, 885, 887, 890, 891, 909, 925, 926, 927, 945, 963, 966, 968, 971, 972, 990, 1003, 1007, 1008, 1009, 1035, 1038, 1057, 1061, 1063, 1066, 1082, 1083, 1122, 1183, 1189, 1217, 1223, 1227, 1251, 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180, 222, 226, 232, 274, 331, 338, 373, 383, 405, 406, 440, 455, 464, 465, 466, 592, 624, 691, 760, 786, 871, 914, 952, 995, 1045, 1100, 1175, 1177, 1180, 1216, 1219, 1222, 1225, 1245, 1280, 1290, 1295, 1296, 1299, 1301, 1383, 1395, 1407, 1408, 1412, 1413, 1414, 1419, 1426], "follow": [11, 25, 44, 49, 52, 53, 65, 67, 83, 86, 91, 92, 93, 94, 95, 97, 99, 100, 101, 102, 103, 108, 110, 111, 128, 132, 151, 161, 171, 183, 207, 213, 227, 229, 230, 231, 243, 280, 305, 338, 343, 351, 362, 373, 378, 380, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 440, 452, 453, 465, 466, 496, 502, 503, 504, 505, 506, 507, 508, 588, 598, 599, 602, 615, 636, 679, 748, 750, 760, 762, 791, 853, 867, 892, 898, 912, 928, 934, 948, 973, 979, 993, 1010, 1102, 1103, 1105, 1144, 1165, 1175, 1179, 1185, 1188, 1200, 1201, 1209, 1219, 1225, 1233, 1234, 1241, 1251, 1260, 1274, 1275, 1276, 1277, 1281, 1296, 1315, 1323, 1326, 1328, 1329, 1388, 1393, 1395, 1399, 1404, 1406, 1407, 1409, 1411, 1412, 1413, 1425, 1426], "given": [11, 38, 44, 62, 64, 67, 91, 99, 101, 103, 112, 116, 141, 142, 144, 152, 158, 193, 197, 208, 211, 212, 227, 229, 235, 236, 248, 249, 260, 264, 266, 269, 271, 273, 274, 276, 279, 281, 283, 284, 285, 286, 287, 288, 320, 329, 331, 338, 344, 351, 353, 357, 362, 363, 364, 365, 373, 378, 380, 381, 385, 439, 454, 455, 460, 462, 470, 477, 478, 480, 497, 511, 512, 513, 559, 560, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 576, 578, 579, 580, 588, 589, 590, 614, 615, 616, 622, 623, 659, 660, 661, 662, 676, 677, 678, 679, 681, 683, 684, 686, 690, 691, 693, 697, 698, 699, 700, 702, 703, 704, 706, 707, 708, 709, 728, 729, 730, 731, 732, 739, 748, 753, 761, 782, 786, 854, 857, 882, 899, 902, 921, 935, 938, 963, 980, 983, 1003, 1046, 1085, 1086, 1094, 1101, 1102, 1135, 1144, 1151, 1162, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1189, 1199, 1200, 1201, 1206, 1207, 1208, 1209, 1210, 1221, 1222, 1240, 1269, 1273, 1274, 1276, 1295, 1300, 1302, 1315, 1323, 1353, 1354, 1379, 1380, 1394, 1395, 1406], "digit": [11, 70, 99], "base": [11, 15, 38, 43, 55, 58, 69, 93, 94, 100, 101, 102, 103, 107, 128, 132, 199, 203, 205, 212, 216, 220, 229, 296, 297, 301, 302, 303, 308, 309, 310, 311, 312, 322, 323, 324, 325, 329, 330, 337, 343, 346, 347, 362, 371, 373, 374, 380, 381, 382, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 425, 426, 427, 428, 430, 431, 449, 464, 466, 494, 498, 499, 500, 509, 510, 545, 555, 564, 566, 569, 574, 581, 614, 616, 660, 667, 680, 688, 691, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 717, 732, 738, 758, 761, 762, 786, 791, 796, 887, 925, 934, 935, 968, 979, 980, 1007, 1036, 1037, 1038, 1041, 1043, 1082, 1088, 1182, 1229, 1235, 1253, 1267, 1296, 1320, 1321, 1323, 1326, 1383, 1387, 1392, 1395, 1402, 1403, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1421, 1425], "obtain": [11, 91, 165, 207, 282, 345, 346, 347, 380, 383, 387, 388, 389, 390, 394, 465, 511, 606, 618, 619, 656, 722, 742, 743, 760, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1164, 1253, 1272, 1278, 1279, 1323, 1326, 1356, 1357, 1402, 1426], "seri": [11, 444, 616, 680, 1215, 1286], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1177, 1179, 1192, 1222], "end": [11, 25, 36, 52, 95, 101, 106, 153, 154, 206, 215, 227, 267, 268, 300, 332, 333, 342, 371, 372, 427, 614, 618, 619, 626, 627, 631, 632, 634, 635, 636, 639, 640, 650, 651, 652, 653, 654, 655, 660, 664, 667, 677, 678, 680, 734, 736, 1038, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1133, 1135, 1152, 1165, 1206, 1229, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1342, 1344, 1350, 1353, 1357, 1358, 1368, 1371, 1372, 1375, 1376, 1379, 1404, 1413], "In": [11, 16, 27, 43, 54, 57, 58, 88, 92, 93, 94, 95, 97, 99, 100, 101, 103, 110, 115, 127, 132, 133, 175, 184, 199, 217, 229, 230, 231, 235, 240, 257, 258, 259, 278, 283, 286, 288, 289, 299, 311, 312, 324, 325, 329, 350, 357, 378, 379, 380, 410, 413, 414, 415, 422, 429, 443, 447, 450, 458, 460, 494, 498, 499, 501, 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546, 547, 548, 611], "node_typ": [16, 1342, 1356, 1357], "supported_nod": 16, "unsupported_nod": 16, "remove_edges_from": [16, 89, 192, 453, 602, 881, 920, 962, 1002, 1175, 1177, 1222, 1393, 1394, 1412, 1420, 1426], "nbr": [16, 88, 159, 190, 199, 200, 207, 229, 230, 231, 285, 500, 506, 796, 858, 879, 887, 888, 892, 903, 925, 928, 939, 968, 969, 973, 984, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1426], "adj": [16, 88, 199, 200, 207, 324, 325, 796, 849, 887, 888, 892, 894, 915, 925, 928, 930, 968, 969, 973, 975, 996, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1411, 1417, 1425, 1426], "g_minus_h": 16, "strip": [16, 25, 69, 1215], "_node_color": 16, "_po": 16, "draw_networkx_edg": [16, 25, 26, 27, 28, 33, 35, 38, 39, 40, 41, 44, 46, 68, 83, 1130, 1133, 1134, 1136, 1137, 1411, 1413], "draw_networkx_label": [16, 25, 35, 38, 46, 71, 1130, 1133, 1134, 1135, 1137], "ncl": 16, "undirect": [16, 25, 34, 71, 93, 112, 177, 185, 204, 205, 209, 211, 212, 214, 215, 216, 217, 218, 219, 220, 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343, 371, 372, 373, 380, 381, 423, 425, 438, 452, 453, 468, 479, 481, 482, 490, 496, 500, 504, 505, 508, 514, 517, 518, 594, 608, 624, 632, 677, 678, 679, 680, 686, 693, 758, 775, 780, 796, 873, 916, 955, 998, 1037, 1038, 1039, 1040, 1098, 1099, 1100, 1130, 1133, 1175, 1177, 1180, 1181, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1252, 1269, 1296, 1323, 1325, 1326, 1328, 1330, 1351, 1352, 1353, 1354, 1382, 1388, 1395, 1401, 1404, 1406, 1407, 1412, 1413, 1421, 1426], "lollipop": [19, 1157, 1426], "vertex": [19, 115, 211, 235, 249, 281, 289, 315, 322, 330, 338, 359, 360, 373, 387, 394, 397, 427, 428, 432, 438, 477, 491, 580, 606, 615, 616, 619, 622, 623, 624, 688, 689, 758, 1164, 1185, 1190, 1206, 1218, 1219, 1222, 1251, 1323, 1326, 1400, 1406, 1407], "length": [19, 39, 52, 67, 102, 120, 151, 232, 288, 295, 297, 298, 299, 306, 307, 310, 314, 315, 316, 320, 322, 326, 327, 329, 330, 332, 333, 341, 343, 345, 346, 347, 371, 372, 383, 384, 451, 459, 462, 467, 469, 470, 473, 513, 515, 516, 517, 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1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "426": [21, 22], "plot_simple_graph": [21, 22], "587": 22, "auto_examples_bas": 22, "custom": [23, 32, 33, 35, 47, 86, 102, 115, 204, 285, 464, 546, 547, 548, 552, 553, 554, 556, 557, 558, 704, 706, 707, 708, 796, 891, 934, 935, 979, 980, 1037, 1039, 1040, 1094, 1100, 1193, 1197, 1198, 1202, 1302, 1383, 1407, 1408, 1412, 1413, 1426], "chess": [23, 47, 86, 1406], "master": [23, 47, 86, 476, 1406], "icon": [23, 47, 86, 93, 1413], "ego": [23, 47, 86, 305, 688, 1325, 1406, 1407], "eigenvalu": [23, 47, 86, 311, 312, 313, 324, 325, 332, 371, 566, 593, 1115, 1191, 1275, 1276, 1277, 1289, 1290, 1291, 1292, 1293, 1327, 1406, 1413], "hous": [23, 47, 86, 1252, 1253, 1413], "With": [23, 47, 54, 86, 101, 103, 110, 337, 511, 760, 1118, 1130, 1184, 1229, 1297, 1330, 1338, 1388, 1394, 1402, 1404, 1405, 1407], "knuth": [23, 47, 69, 71, 86, 455, 1226, 1268, 1302, 1413], "mile": [23, 47, 86, 1406, 1413], "multipartit": [23, 47, 86, 1109, 1151, 1162, 1395, 1406, 1407, 1413], "rainbow": [23, 47, 86, 1413], "geometr": [23, 47, 86, 105, 356, 1196, 1197, 1198, 1264, 1325, 1407, 1408, 1413], "sampson": [23, 47, 86, 1406], "self": [23, 45, 47, 52, 69, 86, 88, 89, 101, 152, 158, 168, 176, 180, 189, 224, 246, 247, 304, 321, 328, 331, 335, 345, 346, 347, 355, 356, 360, 432, 433, 434, 435, 436, 437, 438, 453, 467, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 519, 567, 575, 584, 585, 587, 593, 612, 619, 625, 675, 700, 735, 737, 854, 857, 865, 870, 871, 878, 899, 902, 910, 914, 935, 938, 946, 951, 952, 959, 960, 980, 983, 991, 995, 1038, 1060, 1075, 1102, 1103, 1105, 1135, 1173, 1175, 1177, 1179, 1185, 1193, 1196, 1197, 1198, 1199, 1217, 1222, 1239, 1281, 1325, 1326, 1330, 1353, 1354, 1389, 1401, 1403, 1406, 1408, 1411, 1412, 1413, 1414, 1417, 1425], "loop": [23, 45, 47, 52, 69, 86, 224, 230, 231, 246, 247, 304, 321, 328, 331, 345, 346, 347, 355, 356, 360, 432, 433, 434, 435, 436, 437, 438, 449, 450, 451, 453, 467, 494, 495, 498, 499, 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1404, 1419], "speed": [52, 56, 107, 215, 291, 292, 346, 347, 423, 427, 509, 796, 1037, 1039, 1040, 1133, 1135, 1173, 1194, 1393, 1402, 1406, 1408, 1410, 1411, 1412, 1413, 1414, 1425], "bear": 52, "also": [52, 54, 55, 56, 57, 58, 63, 75, 88, 92, 93, 94, 95, 97, 99, 101, 102, 103, 107, 110, 111, 156, 159, 162, 168, 176, 177, 180, 184, 189, 190, 200, 207, 208, 211, 226, 230, 280, 287, 293, 301, 302, 303, 308, 309, 323, 324, 325, 342, 369, 388, 391, 411, 412, 416, 417, 418, 419, 423, 424, 425, 427, 435, 440, 450, 464, 465, 466, 467, 470, 500, 501, 502, 503, 506, 507, 508, 509, 511, 512, 545, 555, 577, 581, 585, 587, 597, 600, 604, 605, 607, 610, 611, 612, 615, 618, 676, 679, 688, 690, 691, 741, 760, 761, 786, 796, 850, 855, 858, 860, 865, 870, 871, 873, 878, 879, 888, 892, 895, 900, 903, 905, 910, 914, 916, 928, 931, 936, 939, 941, 946, 948, 951, 952, 955, 960, 969, 973, 976, 981, 984, 986, 991, 993, 995, 998, 1010, 1037, 1039, 1040, 1082, 1094, 1102, 1103, 1117, 1130, 1133, 1134, 1135, 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245, 246, 247, 265, 266, 268, 282, 283, 327, 512, 555, 629, 728, 730, 762, 786, 890, 891, 926, 971, 972, 1008, 1091, 1092, 1094, 1095, 1098, 1099, 1100, 1101, 1117, 1120, 1126, 1130, 1270, 1281, 1326, 1332, 1335, 1336, 1339, 1341, 1347, 1370, 1383, 1393, 1399, 1405, 1406, 1413], "primal": [52, 55, 508, 581], "dual": [52, 54, 55, 581, 1227, 1410, 1413], "sens": [52, 97, 99, 104, 199, 310, 460, 586, 791, 887, 925, 968, 1007, 1217, 1234, 1269, 1326, 1403, 1404], "approach": [52, 55, 99, 101, 103, 104, 107, 115, 341, 345, 462, 464, 466, 500, 519, 616, 678, 1094, 1175, 1188, 1202, 1222, 1407, 1413], "segment": [52, 55, 338], "major": [52, 95, 98, 99, 100, 102, 103, 104, 106, 107, 1393, 1394, 1403, 1404, 1407], "studi": [52, 91, 110, 606, 1192, 1196, 1323, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "topologi": [52, 55, 435, 436, 512, 681, 683, 748, 1202, 1217, 1225, 1229, 1233, 1241, 1326], "encod": [52, 55, 58, 67, 99, 141, 249, 267, 268, 619, 758, 775, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1345, 1348, 1349, 1350, 1354, 1355, 1358, 1363, 1368, 1371, 1372, 1375, 1376, 1382, 1406, 1407, 1412], "angular": [52, 55], "inform": [52, 66, 92, 93, 99, 100, 101, 102, 103, 107, 111, 112, 121, 132, 159, 165, 200, 202, 204, 220, 226, 230, 231, 249, 301, 302, 303, 308, 309, 314, 323, 324, 325, 338, 405, 406, 438, 453, 455, 480, 488, 500, 512, 564, 566, 568, 572, 573, 574, 583, 592, 614, 619, 624, 691, 775, 782, 786, 796, 858, 862, 888, 890, 891, 903, 907, 926, 927, 939, 943, 969, 971, 972, 984, 988, 1008, 1009, 1037, 1039, 1040, 1042, 1112, 1141, 1143, 1185, 1206, 1214, 1216, 1217, 1218, 1219, 1267, 1280, 1290, 1296, 1356, 1373, 1375, 1376, 1381, 1383, 1389, 1390, 1393, 1394, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "angl": [52, 55, 1114, 1116], "instead": [52, 93, 94, 101, 102, 103, 106, 141, 165, 170, 282, 320, 338, 366, 370, 390, 392, 399, 405, 406, 407, 411, 412, 416, 417, 418, 419, 424, 425, 427, 500, 561, 562, 563, 585, 587, 632, 727, 729, 731, 733, 734, 735, 736, 737, 796, 862, 866, 907, 911, 943, 947, 988, 992, 1037, 1038, 1039, 1040, 1097, 1102, 1103, 1124, 1127, 1135, 1172, 1179, 1184, 1186, 1192, 1193, 1199, 1207, 1217, 1300, 1342, 1375, 1383, 1393, 1394, 1395, 1397, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1420, 1421, 1423, 1424, 1425, 1426], "nonplanar": [52, 1250], "form": [52, 55, 110, 151, 170, 220, 238, 377, 381, 391, 422, 427, 440, 449, 450, 451, 488, 500, 517, 521, 567, 568, 569, 570, 571, 572, 573, 574, 578, 579, 580, 588, 589, 677, 679, 697, 711, 717, 718, 719, 729, 730, 731, 748, 752, 767, 786, 791, 853, 866, 898, 911, 934, 947, 979, 992, 1038, 1064, 1085, 1146, 1167, 1199, 1206, 1215, 1217, 1222, 1240, 1243, 1245, 1248, 1252, 1399, 1406, 1407, 1426], "flow": [52, 66, 105, 278, 296, 301, 302, 303, 308, 309, 323, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 427, 428, 430, 431, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 519, 559, 756, 758, 1267, 1325, 1395, 1399, 1400, 1403, 1406, 1407, 1408, 1411, 1414, 1425], "dead": 52, "detail": [52, 53, 86, 92, 93, 97, 99, 100, 128, 252, 253, 256, 257, 258, 259, 260, 277, 278, 281, 282, 284, 285, 286, 287, 288, 297, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 422, 427, 476, 494, 498, 499, 500, 509, 510, 511, 512, 574, 691, 711, 720, 735, 737, 791, 796, 1037, 1039, 1040, 1102, 1105, 1133, 1137, 1140, 1207, 1296, 1319, 1345, 1348, 1349, 1350, 1393, 1399, 1400, 1401, 1402, 1406, 1413, 1414, 1426], "methodologi": 52, "avail": [52, 93, 99, 100, 101, 103, 141, 184, 226, 232, 280, 422, 425, 426, 585, 587, 780, 873, 916, 955, 998, 1039, 1042, 1194, 1196, 1197, 1198, 1328, 1331, 1334, 1393, 1394, 1396, 1402, 1405, 1406, 1409, 1412, 1413, 1426], "1016": [52, 112, 226, 231, 274, 297, 298, 299, 303, 306, 307, 313, 322, 323, 338, 346, 347, 455, 1233], "compenvurbsi": 52, "2017": [52, 227, 512, 1207, 1208, 1406, 1407], "004": [52, 341], "scienc": [52, 91, 101, 105, 107, 109, 110, 112, 219, 228, 249, 296, 301, 302, 303, 308, 309, 323, 346, 347, 409, 412, 431, 441, 445, 446, 453, 476, 498, 618, 619, 680, 681, 683, 1203, 1223, 1255], "pydata": [52, 1413, 1423, 1424, 1425], "stack": [52, 111, 346, 693, 1045, 1046], "showcas": [53, 86, 93, 109], "analys": [53, 70, 86, 310], "ecosystem": [53, 86, 99, 100, 104, 107, 110, 1425], "descript": [53, 86, 93, 97, 464, 466, 704, 717, 786, 1130, 1131, 1132, 1133, 1138, 1139, 1140, 1141, 1142, 1207, 1222, 1242, 1407, 1411, 1413, 1421, 1422, 1425], "plu": [54, 386, 583, 1036, 1088, 1148, 1253], "voronoi": [54, 752, 758, 1325, 1407], "cholera": [54, 57], "broad": [54, 57, 1296], "pump": [54, 57], "record": [54, 57, 94, 99, 691, 1426], "john": [54, 57, 91, 278, 568, 572, 685, 1205, 1250, 1408, 1413], "snow": [54, 57], "1853": [54, 57], "method": [54, 57, 58, 75, 88, 92, 93, 95, 101, 102, 103, 107, 112, 143, 161, 164, 165, 185, 186, 187, 190, 200, 202, 204, 206, 207, 226, 231, 232, 250, 260, 261, 262, 299, 301, 302, 303, 308, 309, 311, 312, 323, 324, 336, 374, 376, 379, 380, 381, 385, 423, 440, 451, 462, 476, 500, 514, 527, 537, 545, 564, 566, 568, 572, 581, 583, 600, 604, 615, 632, 633, 635, 636, 654, 655, 656, 671, 672, 673, 674, 684, 692, 719, 720, 733, 738, 752, 775, 786, 852, 862, 874, 875, 876, 879, 888, 890, 891, 892, 897, 907, 917, 918, 919, 926, 927, 928, 933, 934, 935, 943, 956, 957, 958, 971, 972, 973, 978, 979, 980, 988, 999, 1000, 1001, 1008, 1009, 1010, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1033, 1038, 1043, 1044, 1045, 1046, 1066, 1174, 1182, 1184, 1193, 1197, 1275, 1276, 1277, 1280, 1296, 1301, 1302, 1323, 1326, 1363, 1395, 1399, 1403, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1422, 1425, 1426], "shown": [54, 57, 100, 102, 517, 518, 947, 992, 1275, 1276, 1277, 1300, 1349, 1404], "centroid": [54, 57, 58], "libpys": [54, 55, 57, 58], "cg": [54, 102, 296, 301, 302, 303, 308, 309, 323, 588], "voronoi_fram": 54, "contextili": [54, 55, 57], "add_basemap": [54, 55, 57], "geopackag": [54, 55, 56, 57], "sqlite": [54, 57], "reli": [54, 57, 99, 103, 362, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 502, 503, 506, 507, 1393, 1407, 1411, 1425], "fiona": [54, 57], "level": [54, 57, 101, 103, 104, 106, 111, 112, 115, 125, 165, 220, 322, 334, 336, 374, 380, 381, 387, 389, 390, 394, 423, 427, 640, 691, 770, 786, 862, 907, 943, 988, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1094, 1108, 1155, 1202, 1207, 1208, 1236, 1296, 1323, 1328, 1396, 1399, 1407, 1412, 1413, 1414], "interfac": [54, 57, 58, 75, 76, 96, 98, 99, 101, 102, 107, 109, 110, 184, 429, 496, 673, 758, 761, 762, 780, 873, 916, 955, 998, 1042, 1044, 1326, 1328, 1393, 1396, 1398, 1402, 1404, 1405, 1406, 1409, 1413, 1414, 1426], "kind": [54, 57, 58, 92, 93, 94, 99, 208, 466, 722, 1202, 1326, 1383], "read_fil": [54, 55, 57, 58], "cholera_cas": [54, 57], "gpkg": [54, 56, 57], "correctli": [54, 164, 324, 325, 1393, 1404, 1406, 1411, 1412, 1419], "construct": [54, 55, 56, 57, 58, 67, 94, 102, 227, 229, 230, 231, 232, 269, 273, 276, 352, 423, 450, 460, 513, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 609, 685, 695, 708, 716, 732, 1046, 1047, 1052, 1053, 1101, 1102, 1103, 1104, 1105, 1153, 1154, 1175, 1177, 1178, 1180, 1186, 1190, 1191, 1192, 1195, 1203, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1229, 1236, 1251, 1259, 1263, 1269, 1272, 1278, 1279, 1296, 1323, 1327, 1395, 1399, 1406, 1409, 1415], "column_stack": [54, 57, 58], "could": [54, 93, 101, 102, 103, 165, 215, 216, 224, 581, 679, 862, 907, 943, 988, 1066, 1094, 1102, 1103, 1120, 1126, 1174, 1296, 1300, 1326, 1393, 1404, 1414, 1426], "present": [54, 58, 93, 107, 110, 132, 184, 220, 226, 315, 316, 330, 357, 359, 429, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 567, 581, 594, 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467, 865, 878, 891, 910, 946, 960, 991, 1328, 1402, 1408, 1409, 1413], "cell": [54, 58, 752, 758, 1271, 1323, 1325, 1407], "convex": 54, "hull": 54, "contigu": [54, 58, 438, 1102, 1277, 1278], "being": [54, 92, 94, 95, 99, 101, 102, 109, 217, 227, 464, 465, 466, 559, 560, 711, 1038, 1045, 1144, 1175, 1236, 1296, 1393, 1394, 1407, 1412, 1413, 1416, 1425], "face": [54, 101, 102, 115, 183, 206, 615, 1043, 1262, 1263], "analogu": [54, 58, 230], "von": 54, "neuman": 54, "neighborhood": [54, 58, 114, 213, 240, 249, 285, 286, 324, 325, 512, 690, 786, 1189], "cardin": [54, 115, 218, 221, 264, 277, 278, 279, 280, 339, 341, 343, 345, 414, 415, 416, 417, 428, 440, 441, 444, 446, 581, 583, 611, 691, 1395], "regular": [54, 58, 65, 88, 99, 477, 478, 479, 480, 622, 623, 624, 758, 1038, 1185, 1190, 1191, 1192, 1239, 1245, 1250, 1251, 1254, 1258, 1261, 1262, 1263, 1264, 1280, 1290, 1323, 1325, 1394, 1395, 1398, 1406, 1412, 1413], "come": [54, 93, 100, 101, 102, 517, 577, 588, 598, 608, 677, 698, 699, 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128, 313, 329, 396, 438, 455, 457, 458, 464, 465, 466, 468, 1398, 1401, 1404, 1406, 1414], "sequenc": [60, 72, 80, 86, 101, 102, 107, 180, 269, 271, 273, 274, 276, 363, 364, 365, 374, 386, 488, 512, 513, 514, 515, 516, 517, 518, 549, 550, 551, 625, 671, 672, 673, 674, 678, 679, 693, 702, 728, 729, 731, 758, 791, 871, 914, 952, 995, 1102, 1133, 1134, 1135, 1136, 1137, 1144, 1165, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1199, 1206, 1207, 1208, 1218, 1222, 1237, 1238, 1272, 1273, 1297, 1311, 1315, 1316, 1325, 1398, 1406, 1407, 1413], "renyi": [60, 72, 86, 593, 1398, 1406], "expect": [60, 61, 72, 83, 86, 100, 103, 105, 109, 275, 280, 429, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 671, 672, 673, 674, 686, 727, 1038, 1043, 1085, 1175, 1177, 1179, 1230, 1235, 1236, 1287, 1296, 1319, 1323, 1328, 1398, 1404, 1405, 1406, 1413, 1414], "footbal": [60, 72, 86, 1406], "karat": [60, 72, 86, 1267, 1398, 1406, 1414], "mors": [60, 72, 86, 1421], "trie": [60, 72, 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1424, 1425], "release_dev": [93, 106], "rst": [93, 99, 106, 1407, 1408, 1411, 1412, 1413, 1414, 1422, 1425], "deprec": [93, 96, 103, 106, 1186, 1363, 1364, 1394, 1395, 1403, 1404, 1406, 1420, 1422], "curly_hair": 93, "deprecationwarn": 93, "conftest": [93, 95, 1413], "filterwarn": 93, "remind": [93, 94], "misc": [93, 103, 1413, 1416], "generate_unique_nod": [93, 1413], "4281": [93, 1413], "read_yaml": [93, 1405, 1413], "write_yaml": [93, 1405, 1413], "123": [93, 380, 1106], "longer": [93, 94, 99, 102, 103, 107, 215, 216, 511, 512, 579, 1117, 1217, 1275, 1393, 1394, 1396, 1398, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1413, 1416, 1425], "fetch": 93, "unmerg": 93, "modifi": [93, 94, 99, 101, 103, 109, 152, 157, 158, 195, 226, 322, 377, 585, 587, 677, 678, 692, 693, 694, 719, 733, 734, 736, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1045, 1063, 1102, 1103, 1105, 1154, 1177, 1270, 1281, 1295, 1393, 1406, 1413, 1426], "file_with_conflict": 93, "insid": 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1174, 1188, 1217, 1276, 1325, 1395, 1399, 1400, 1406, 1408, 1411, 1412, 1413, 1414], "97": [93, 110, 357], "troubl": [93, 224, 1409, 1413], "anywai": [93, 101, 1409], "gather": [93, 99], "assembl": [93, 1046, 1047, 1296], "idea": [93, 94, 97, 99, 102, 105, 132, 217, 373, 423, 428, 687, 689, 1326, 1382, 1404, 1407], "plot_": 93, "plot_new_exampl": 93, "highlight": [93, 106, 1403], "resourc": [93, 96, 476, 477, 478, 572, 573, 618, 1165, 1200], "docstr": [93, 94, 95, 97, 109, 1345, 1348, 1349, 1350, 1399, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1416, 1417, 1420, 1421, 1422, 1423, 1425], "chicago": [93, 1265], "citat": [93, 97, 346, 347, 566, 1239, 1412], "quickest": 93, "scholar": 93, "paywal": 93, "arxiv": [93, 110, 128, 217, 220, 300, 305, 332, 333, 355, 358, 371, 372, 373, 385, 386, 427, 432, 433, 437, 512, 573, 619, 625, 685, 693, 1153, 1169, 1170, 1171, 1185, 1227, 1269, 1280], "access": [93, 101, 112, 125, 151, 168, 189, 429, 471, 472, 473, 474, 475, 496, 606, 626, 627, 645, 646, 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"machin": [93, 312, 331, 494, 511, 512, 762, 1396, 1406, 1413], "snapshot": 93, "unreach": 93, "pyarg": [93, 111, 1038], "tell": [93, 99, 102, 760, 1275, 1278, 1279, 1296, 1328, 1412], "compar": [93, 464, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 559, 560, 561, 562, 563, 615, 760, 782, 1165, 1302, 1414], "baselin": [93, 1134, 1136], "ones": [93, 99, 107, 109, 282, 680, 1038, 1395, 1402, 1404], "savefig": [93, 1426], "mpl_image_compar": 93, "test_barbel": 93, "barbel": [93, 293, 294, 391, 424, 1146, 1157, 1276, 1426], "conduct": [93, 96, 100, 109, 447, 448, 758], "contributor": [94, 96, 99, 105, 106, 110, 1271, 1323, 1403], "shepherd": [94, 99], "mission": [94, 96, 97, 100, 107], "approv": [94, 100], "nuclear": 94, "launch": 94, "carefulli": 94, "clean": [94, 106, 530, 540, 1300, 1406, 1407, 1411, 1413, 1420], "nearli": 94, "volunt": [94, 107, 1413], "tremend": 94, "felt": 94, "evalu": [94, 130, 152, 157, 158, 195, 330, 618, 619, 626, 627, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1296, 1417], "novic": 94, "strongli": [94, 217, 232, 388, 391, 397, 398, 399, 403, 405, 406, 423, 480, 491, 492, 519, 588, 633, 697, 699, 751, 753, 1185, 1402, 1406, 1411, 1414, 1417, 1425], "mentorship": [94, 1413], "handhold": 94, "liber": 94, "workflow": [94, 96, 97, 100, 106, 1413, 1420], "realiz": [94, 513, 514, 515, 516, 517, 518, 693, 1175, 1177, 1180, 1207, 1208, 1209, 1210, 1222, 1264], "gentl": 94, "abandon": 94, "difficult": [94, 1405], "carri": [94, 100, 508], "polici": [94, 96, 99, 1412, 1414], "readabl": [94, 107, 109, 169, 172, 460, 868, 913, 949, 994, 1393, 1414], "effici": [94, 102, 112, 212, 275, 290, 377, 387, 389, 390, 392, 394, 399, 405, 406, 407, 422, 425, 426, 486, 487, 508, 512, 581, 614, 680, 688, 691, 698, 699, 758, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1179, 1203, 1230, 1325, 1386, 1390, 1398, 1399, 1406, 1407, 1408, 1411, 1413], "explor": [94, 105, 107, 110, 704, 711, 717], "corner": [94, 1407, 1414], "tempt": 94, "nitpicki": 94, "spell": [94, 1406, 1412, 1413], "suggest": [94, 102, 105, 632, 635, 636, 1165, 1326, 1402, 1406, 1412, 1414, 1425], "latter": [94, 100, 102, 440, 729, 731, 791, 1299], "choic": [94, 102, 204, 385, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 479, 502, 503, 506, 507, 734, 735, 736, 737, 780, 891, 972, 1038, 1042, 1225, 1241, 1280, 1326, 1426], "wish": [94, 619, 1066, 1393], "bring": [94, 101, 566], "advis": [94, 110, 1414], "aris": [94, 110, 238, 243, 1217, 1245], "experienc": 94, "credit": [94, 105], "send": [94, 99, 496, 497, 501, 504, 505, 508, 1393, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "notif": 94, "maintain": [94, 95, 99, 100, 103, 105, 107, 109, 230, 231, 614, 796, 1037, 1039, 1040, 1406, 1425], "concern": [94, 101, 103, 132, 789, 791, 1382], "mere": [94, 1146, 1157], "understood": 94, "made": [94, 99, 100, 102, 222, 282, 284, 285, 286, 287, 288, 324, 325, 331, 693, 694, 1122, 1210, 1326, 1393, 1403, 1404, 1407, 1412, 1425], "freeli": 94, "consult": [94, 111], "extern": [94, 107, 619, 1326, 1383, 1407], "insight": 94, "opportun": [94, 99], "patch": [94, 99, 102, 1042, 1133, 1135, 1412, 1413], "vouch": 94, "fulli": [94, 761, 1042, 1188], "behind": [94, 105], "clarif": [94, 299, 322], "deem": 94, "nich": 94, "devot": 94, "sustain": [94, 96], "effort": [94, 107, 1326], "priorit": 94, "similarli": [94, 103, 115, 207, 356, 598, 621, 796, 892, 928, 973, 1010, 1037, 1039, 1040, 1148, 1175, 1177, 1193, 1198, 1207, 1296, 1394, 1404, 1426], "worth": [94, 761, 1426], "mainten": 94, "burden": 94, "necessari": [94, 95, 100, 104, 527, 537, 954, 997, 1135, 1137, 1296, 1406, 1412], "valid": [94, 101, 161, 177, 256, 277, 278, 281, 282, 377, 386, 439, 458, 464, 466, 497, 513, 514, 515, 516, 517, 518, 559, 560, 578, 579, 580, 588, 614, 615, 734, 735, 736, 737, 746, 758, 1038, 1043, 1071, 1087, 1100, 1104, 1105, 1165, 1187, 1193, 1237, 1238, 1274, 1278, 1279, 1296, 1331, 1334, 1407, 1412, 1413, 1414, 1417, 1419, 1422, 1425], "wari": 94, "alien": 94, "visibl": [94, 97], "thread": [94, 97, 99, 103, 104, 1413], "appeal": [94, 100], "empow": 94, "regardless": [94, 99, 1135, 1191, 1404], "outcom": [94, 105, 1036, 1088, 1382, 1417], "past": [94, 106, 1405], "pep8": [94, 1407, 1412, 1416], "pep257": 94, "superset": [94, 582], "stackoverflow": 94, "monitor": [94, 101], "signatur": [95, 97, 103, 109, 545, 1045, 1296, 1399, 1404, 1407, 1413, 1419, 1422, 1425], "buggi": 95, "usual": [95, 101, 168, 176, 189, 291, 292, 329, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 438, 440, 467, 615, 753, 762, 796, 865, 870, 878, 910, 946, 951, 960, 991, 1039, 1040, 1045, 1094, 1174, 1199, 1217, 1272, 1296, 1326, 1403], "minor": [95, 100, 106, 584, 758, 1325, 1394, 1395, 1403, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "strict": [95, 110, 214, 215, 216, 619, 1408, 1413], "rule": [95, 100, 199, 508, 760, 887, 925, 968, 1007, 1061, 1082, 1144, 1298], "procedur": [95, 97, 99, 217, 220, 281, 305, 377, 508, 680, 1188, 1417], "upon": [95, 102, 580, 1296, 1413, 1416], "justif": [95, 104], "literal_string": [95, 1345, 1350, 1384, 1412], "literal_destring": [95, 1347, 1349, 1384, 1412], "coreview": [95, 1413], "filter": [95, 322, 453, 1036, 1061, 1082, 1088, 1269, 1324, 1325, 1413], "link_analysi": [95, 1405], "pagerank_alg": [95, 1405], "replac": [95, 99, 102, 103, 202, 232, 270, 385, 411, 412, 430, 431, 512, 583, 796, 890, 926, 934, 971, 979, 1008, 1037, 1039, 1040, 1051, 1094, 1225, 1241, 1295, 1296, 1297, 1311, 1317, 1326, 1347, 1363, 1364, 1393, 1394, 1396, 1399, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1417, 1422, 1424, 1425], "pagerank": [95, 311, 312, 324, 325, 565, 758, 1283, 1284, 1394, 1398, 1405, 1406, 1407, 1413], "pagerank_scipi": [95, 1405, 1411, 1413], "renam": [95, 102, 106, 597, 601, 604, 609, 1295, 1348, 1349, 1357, 1394, 1407, 1412, 1421, 1424, 1425], "pagerank_numpi": 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1425], "prototyp": 99, "sound": 99, "principl": [99, 100, 103, 132], "impract": 99, "wip": [99, 1407, 1408, 1412], "incorpor": [99, 1399, 1426], "stabil": [99, 334, 335, 681, 683], "provision": 99, "short": [99, 104, 161, 227, 1038, 1066, 1195, 1406], "unlik": [99, 100, 212, 366, 425, 426, 1383], "reject": [99, 100, 104, 1319], "withdrawn": [99, 104], "wherev": [99, 1282], "defer": [99, 101, 104, 280], "challeng": 99, "wider": 99, "done": [99, 101, 102, 230, 231, 249, 373, 440, 466, 517, 564, 566, 614, 690, 762, 1046, 1219, 1296, 1326, 1404], "fact": [99, 352, 460, 619, 1207, 1210, 1404], "actual": [99, 115, 132, 165, 210, 213, 214, 215, 216, 220, 288, 385, 450, 577, 625, 692, 717, 718, 862, 907, 943, 988, 1102, 1103, 1199, 1296, 1324, 1326, 1402, 1416], "compet": [99, 583], "accordingli": [99, 454, 1110, 1407, 1425], "supersed": [99, 104], "render": [99, 216, 410, 413, 1406], "obsolet": [99, 267, 1337, 1406, 1407], "never": [99, 184, 388, 608, 873, 916, 955, 998, 1236], "meant": [99, 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268, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1355, 1358, 1368, 1371, 1372, 1375, 1376, 1387, 1406], "restructuredtext": 99, "restructuredtextprim": 99, "dd": [99, 104, 1094], "mmm": 99, "yyyi": [99, 104], "dom": 99, "ain": 99, "separ": [99, 102, 106, 152, 157, 158, 195, 214, 215, 258, 265, 266, 267, 268, 299, 322, 343, 427, 428, 454, 464, 758, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1045, 1112, 1116, 1193, 1195, 1325, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1369, 1370, 1371, 1372, 1395, 1406, 1407, 1412, 1413, 1425, 1426], "older": 99, "brows": 99, "colgat": [100, 110], "deadlock": 100, "websit": [100, 106, 1165, 1382, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "ongo": [100, 1405], "trust": [100, 1381, 1383], "cast": [100, 101, 1412, 1422, 1425], "vote": [100, 337, 1412], "therebi": 100, "adher": 100, "nomin": 100, "lazi": [100, 1283, 1284], "unanim": 100, "agreement": [100, 1202], "initi": [100, 102, 141, 230, 231, 282, 315, 324, 325, 338, 373, 377, 378, 466, 495, 511, 512, 525, 535, 615, 692, 719, 733, 796, 850, 895, 931, 976, 1037, 1039, 1040, 1102, 1105, 1108, 1117, 1185, 1186, 1187, 1188, 1223, 1227, 1234, 1278, 1279, 1296, 1302, 1323, 1394, 1395, 1406, 1411, 1412, 1413, 1414], "voic": 100, "smooth": 100, "strateg": 100, "plan": [100, 1394, 1405, 1407, 1413], "fund": [100, 1414, 1425], "theirs": 100, "pursu": 100, "pictur": 100, "perspect": [100, 104, 1195, 1326], "timefram": 100, "entiti": [100, 1345, 1348, 1349, 1350, 1382, 1426], "occasion": [100, 230], "seek": [100, 762, 1352, 1354, 1378, 1380, 1387], "tri": [100, 112, 343, 380, 931, 976, 1039, 1040, 1175, 1181, 1225, 1237, 1238, 1404], "distinguish": [100, 934, 962, 979, 1002, 1040], "fundament": [100, 107, 110, 338, 449, 618, 619, 1217, 1413], "flaw": 100, "forward": [100, 217, 450, 711, 717, 718], "typo": [100, 1396, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1421], "land": 100, "outlin": [100, 249, 336, 462, 1407], "templat": [100, 1413], "taken": [100, 101, 145, 148, 207, 443, 450, 717, 718, 749, 761, 892, 928, 973, 1010, 1117, 1409], "suffici": [100, 101, 1326], "scikit": [100, 103, 109], "expos": [101, 374, 1405], "nodeview": [101, 184, 391, 598, 599, 601, 602, 603, 604, 695, 873, 916, 955, 998, 1036, 1088, 1349, 1362, 1404, 1407], "nodedataview": [101, 184, 391, 591, 592, 600, 873, 916, 955, 998, 1217, 1426], "edgeview": [101, 590, 591, 592, 598, 599, 600, 601, 602, 603, 604, 612, 624, 770, 910, 1036, 1088, 1098, 1404, 1413], "edgedataview": [101, 168, 189, 865, 878, 910, 946, 960, 991, 1098, 1217, 1362, 1412, 1426], "semant": [101, 531, 541, 762, 1403, 1405], "inher": [101, 220, 427], "impli": [101, 110, 132, 220, 312, 314, 327, 455, 466, 511, 512, 545, 1296], "element": [101, 102, 230, 231, 270, 291, 292, 311, 350, 371, 391, 457, 464, 518, 559, 560, 578, 579, 580, 586, 640, 656, 671, 673, 675, 677, 728, 730, 739, 749, 752, 1036, 1038, 1048, 1049, 1050, 1051, 1087, 1088, 1135, 1137, 1173, 1206, 1211, 1212, 1217, 1237, 1238, 1240, 1249, 1272, 1277, 1278, 1279, 1282, 1287, 1288, 1296, 1302, 1303, 1311, 1318, 1323, 1355, 1358, 1361, 1362, 1405], "intend": [101, 104, 107, 111, 327, 567, 1038, 1269, 1296, 1393], "impos": [101, 103, 545, 791], "due": [101, 102, 109, 231, 264, 440, 581, 583, 626, 627, 1217, 1405, 1412, 1414, 1423, 1425], "bit": [101, 209, 211, 212, 453, 511, 512, 786, 1345, 1348, 1349, 1350, 1382, 1411], "lot": [101, 452, 1326, 1405], "screen": 101, "instinct": 101, "error": [101, 102, 152, 157, 158, 195, 280, 288, 296, 311, 324, 414, 422, 471, 472, 473, 474, 475, 489, 497, 501, 504, 505, 508, 556, 557, 558, 564, 566, 581, 584, 653, 660, 667, 675, 676, 796, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1037, 1043, 1117, 1144, 1396, 1401, 1404, 1406, 1407, 1411, 1412, 1413, 1414, 1417, 1419, 1425], "definit": [101, 132, 235, 238, 243, 289, 291, 292, 303, 323, 342, 356, 398, 435, 437, 464, 467, 549, 550, 551, 608, 618, 619, 620, 625, 676, 685, 687, 700, 735, 737, 791, 1192, 1193, 1197, 1217, 1235, 1287, 1326, 1406, 1413, 1426], "coupl": [101, 102, 132, 1257, 1402, 1404], "realis": 101, "But": [101, 102, 107, 143, 170, 238, 243, 256, 277, 278, 281, 297, 298, 583, 796, 866, 911, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1094, 1328, 1393, 1425], "seem": [101, 102, 298, 307, 791, 1234], "eas": [101, 107, 1409], "idiom": [101, 159, 190, 200, 858, 879, 888, 903, 939, 969, 984, 1296, 1394, 1404, 1411], "subscript": [101, 151, 159, 200, 796, 853, 858, 888, 898, 903, 934, 939, 969, 979, 984, 1037, 1039, 1040, 1394, 1426], "repr": [101, 1347, 1413], "4950": [101, 1414], "traceback": [101, 450, 464, 584, 652, 658, 1302, 1303], "recent": [101, 437, 450, 464, 584, 652, 658, 963, 1003, 1302, 1303, 1411], "typeerror": [101, 382, 464, 1206, 1302, 1404], "opaqu": 101, "ambigu": [101, 103, 115, 252, 253, 464, 762, 1043, 1406], "ambigi": 101, "counter": [101, 153, 357], "nativ": [101, 109], "caveat": 101, "nodes_it": [101, 1404, 1407], "toward": [101, 685, 1407, 1413], "inner": [101, 230, 231, 380, 796, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1086], "synonym": 101, "primarili": [101, 1426], "becam": [101, 1411], "concept": [101, 132, 220, 310, 427, 688, 1043], "intuit": [101, 109], "On": [101, 105, 156, 217, 294, 297, 298, 306, 307, 315, 380, 405, 406, 514, 515, 518, 593, 855, 900, 936, 981, 1180, 1202, 1224, 1228, 1232], "front": [101, 619, 1036, 1088], "constuct": 101, "indx": 101, "desir": [101, 102, 142, 143, 204, 346, 347, 422, 425, 426, 598, 629, 647, 891, 972, 1085, 1094, 1102, 1103, 1105, 1150, 1152, 1157, 1159, 1160, 1163, 1165, 1187, 1218, 1220, 1221, 1234, 1281, 1356, 1357, 1414, 1426], "prelimanari": 101, "impelement": 101, "4086": 101, "rid": [101, 1413], "getitem": 101, "dunder": [101, 107, 1296, 1413], "isinst": [101, 103, 464, 1086, 1411, 1412, 1413], "_node": [101, 1422, 1425], "exclus": [101, 449, 476], "necess": 101, "unhash": [101, 1404], "impel": 101, "insipir": 101, "colon": [101, 1421], "syntax": [101, 102, 171, 796, 867, 912, 948, 993, 1037, 1039, 1040, 1296, 1382, 1383, 1410, 1412], "introspect": 101, "neither": [101, 110, 305, 427, 625, 635, 636, 671, 672, 673, 674, 676, 700, 748], "downsid": 101, "drawback": 101, "discover": 101, "complic": [101, 1296, 1326], "nix": 101, "background": 101, "pertain": 101, "arguabl": [101, 102], "overrid": [101, 671, 672, 673, 674, 1411], "mix": [101, 236, 237, 238, 241, 242, 243, 244, 245, 248, 445, 758, 1100, 1341, 1342, 1344, 1355, 1356, 1357, 1358, 1381, 1383, 1393, 1406, 1407, 1411], "pervas": 101, "unforeseen": 101, "preced": [101, 152, 157, 464, 598, 703, 854, 856, 899, 901, 935, 937, 980, 982, 1045, 1363, 1364], "un": [101, 464, 732, 1407, 1413], "sliceabl": 101, "notabl": [101, 1042], "dict_kei": [101, 1303, 1414], "dict_valu": [101, 379, 1404, 1413], "cpython": [101, 107, 429, 496, 1038, 1402, 1413], "consider": [101, 103, 324, 325, 346, 347, 353, 525, 535, 555, 671, 672, 673, 674, 732, 760, 1168, 1413], "cours": [101, 105, 217, 618, 1326, 1426], "action": [101, 106, 1413, 1417], "allevi": 101, "dig": 101, "enough": [101, 468, 509, 1165], "satisfactorili": 101, "reconsid": [101, 1412], "went": [101, 502], "ahead": 101, "4300": [101, 1413], "4304": [101, 1413], "path_edg": 102, "former": [102, 103, 791], "stylist": 102, "creation": [102, 107, 110, 249, 275, 788, 1154, 1170, 1224, 1228, 1230, 1232, 1325, 1399, 1404, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "cleaner": [102, 1401, 1406], "creativ": [102, 464, 466], "demand": [102, 496, 497, 501, 504, 505, 508], "had": [102, 652, 1217, 1296, 1409, 1416], "node_iter": 102, "isn": [102, 346, 347, 720, 1331, 1334, 1406, 1414, 1425], "leav": [102, 231, 388, 500, 508, 584, 585, 586, 587, 678, 1145, 1155, 1296, 1404, 1409, 1426], "dg": [102, 207, 322, 455, 456, 457, 458, 459, 461, 462, 464, 465, 466, 467, 468, 469, 892, 928, 973, 1010, 1041, 1404, 1426], "mdg": [102, 207, 892, 928, 973, 1010, 1420], "customgraph": 102, "elist": [102, 1326], "isol": [102, 355, 380, 435, 491, 492, 522, 524, 621, 735, 737, 758, 1218, 1325, 1330, 1398, 1401, 1406, 1407, 1417], "ekei": [102, 207, 892, 928, 934, 973, 979, 1010, 1084, 1104], "protocol": [102, 1404], "hashabl": [102, 144, 151, 156, 171, 180, 267, 545, 546, 547, 548, 761, 796, 853, 855, 867, 871, 898, 900, 912, 914, 934, 936, 947, 948, 952, 962, 979, 981, 992, 993, 995, 1002, 1037, 1038, 1039, 1040, 1087, 1207, 1278, 1279, 1295, 1310, 1324, 1326, 1333, 1337, 1338, 1426], "logic": [102, 103, 220, 760, 762, 1298, 1406, 1407, 1419, 1425], "denot": [102, 114, 212, 219, 299, 300, 322, 567, 568, 569, 570, 571, 572, 573, 608, 619, 687, 688, 689, 690, 691, 1174], "multiedg": [102, 553, 934, 979, 1039, 1040, 1085, 1326, 1356, 1357, 1393, 1406, 1412, 1414], "attrdict": [102, 157, 856, 901, 937, 982, 1406], "edge_kei": [102, 489, 1039, 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692, 728, 739, 1055, 1077, 1194, 1206, 1213, 1249, 1254, 1269, 1272, 1273, 1278, 1279, 1299, 1301, 1310, 1350, 1411], "mode": [115, 260, 261, 262, 267, 268, 289, 1300, 1333, 1334, 1337, 1338, 1339, 1340, 1371, 1372, 1426], "bipart": [115, 290], "routin": [116, 180, 343, 355, 559, 560, 577, 760, 871, 914, 952, 995, 1042, 1091, 1326, 1395, 1396, 1404, 1406, 1411, 1412, 1413], "outsid": [116, 310, 1404, 1406, 1413], "chord": [120, 341, 343, 1190, 1208, 1215], "chordal_graph": [120, 341], "clique_problem": 121, "character": [122, 313, 782], "triangl": [122, 213, 227, 295, 356, 357, 358, 359, 437, 549, 550, 758, 1098, 1101, 1215, 1219, 1222, 1234, 1243, 1247, 1252, 1263, 1323, 1326, 1406, 1412], "greedy_color": [123, 758, 1395, 1406, 1411], "communities_gener": 125, "girvan_newman": 125, "top_level_commun": 125, "next_level_commun": 125, "kernighan": [125, 377, 1413], "lin": [125, 377, 1407, 1413], "luke": [125, 382, 1412], "asynchron": [125, 373, 378, 379, 1407, 1414], "edge_kcompon": [127, 424], "determen": 127, "maxim": [127, 209, 220, 221, 222, 315, 316, 330, 339, 346, 347, 348, 349, 350, 351, 353, 354, 366, 370, 380, 383, 384, 389, 390, 422, 425, 426, 427, 432, 433, 437, 517, 549, 579, 581, 582, 583, 589, 682, 691, 732, 758, 1043, 1201, 1323, 1325, 1398, 1406, 1407, 1413, 1414], "moodi": [127, 220, 427, 1395], "kanevski": [127, 427, 428, 1395], "recurs": [128, 141, 224, 346, 347, 352, 387, 389, 390, 394, 406, 452, 460, 530, 540, 697, 728, 730, 760, 1045, 1046, 1061, 1082, 1147, 1296, 1406, 1412, 1413], "prune": [128, 760, 1236], "vladimir": [128, 275, 432, 433, 494, 588, 749, 1230], "batagelj": [128, 275, 432, 433, 588, 749, 1230], "matjaz": [128, 432, 433], "zaversnik": [128, 432, 433], "0310049": [128, 432, 433], "0202039": 128, "degeneraci": 128, "christo": 128, "giatsidi": 128, "thiliko": 128, "michali": 128, "vazirgianni": 128, "icdm": 128, "2011": [128, 331, 377, 383, 385, 441, 445, 446, 511, 512, 519, 619, 682, 1179, 1397, 1398, 1399, 1406, 1407], "graphdegeneraci": 128, "dcores_icdm_2011": 128, "anomali": [128, 438], "onion": [128, 438, 1411], "h\u00e9bert": [128, 438], "dufresn": [128, 438], "grochow": [128, 438], "allard": [128, 438, 1411], "31708": [128, 438], "2016": [128, 337, 352, 385, 438, 476, 690, 1197, 1251, 1396, 1406], "1038": [128, 337, 376, 380, 438, 569], "srep31708": [128, 438], "factor": [132, 226, 293, 294, 299, 300, 324, 325, 370, 462, 497, 501, 504, 505, 508, 513, 565, 592, 624, 676, 697, 1106, 1107, 1108, 1109, 1110, 1114, 1115, 1116, 1117, 1145, 1155, 1178, 1180, 1275, 1276, 1277], "graphic": [132, 454, 517, 518, 693, 758, 1175, 1177, 1180, 1181, 1222, 1325, 1383, 1398, 1401, 1406], "overview": [132, 476, 1038, 1296], "collid": [132, 454], "triplet": [132, 745], "successor": [132, 159, 174, 181, 191, 200, 240, 282, 387, 389, 390, 394, 501, 687, 707, 715, 858, 872, 880, 888, 903, 939, 953, 961, 969, 984, 1055, 1183, 1184, 1189, 1326, 1404, 1407, 1416, 1426], "descend": [132, 454, 456, 465, 709, 758, 1272, 1401, 1404, 1406, 1413, 1414], "unblock": 132, "commonli": [132, 280, 454, 684, 782], "probabilist": [132, 378], "causal": 132, "markov": [132, 462, 565, 692, 1188], "hmm": 132, "s1": [132, 1242, 1313, 1363], "s2": [132, 1242, 1313], "s3": [132, 1313], "s4": 132, "s5": 132, "o1": 132, "o2": 132, "o3": 132, "o4": 132, "o5": 132, "ob": 132, "d_separ": [132, 758, 1412], "darwich": 132, "shachter": 132, "1998": [132, 1143, 1144, 1225, 1241, 1407], "bay": 132, "ball": 132, "ration": 132, "pastim": 132, "irrelev": [132, 1407], "requisit": 132, "influenc": [132, 324, 325, 512, 786], "fourteenth": [132, 1186], "uncertainti": [132, 590, 732], "artifici": [132, 574, 590, 732], "480": [132, 426, 514, 518, 1398, 1406], "487": 132, "francisco": [132, 732], "morgan": [132, 732], "kaufmann": [132, 732], "koller": 132, "friedman": 132, "mit": [132, 342, 519, 618], "causal_markov_condit": 132, "ness": [133, 684, 782], "classmethod": [141, 1047], "auxiliari": [141, 142, 143, 220, 411, 412, 413, 415, 416, 417, 418, 419, 423, 430, 431, 1402], "sink": [141, 302, 309, 416, 418, 494, 495, 498, 499, 501, 502, 503, 506, 507, 509, 510, 565], "pick": [141, 217, 331, 657, 1188, 1207, 1210, 1407], "st": [141, 415, 417], "cut": [141, 222, 223, 293, 377, 382, 387, 389, 390, 394, 411, 412, 414, 415, 416, 417, 419, 427, 428, 429, 442, 443, 444, 445, 447, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 619, 758, 760, 1038, 1066, 1115, 1262, 1325, 1395, 1402, 1406, 1413], "refin": [143, 215, 423, 438], "auxgraph": [143, 423], "node_partit": 144, "permut": [144, 368, 452, 453, 455, 466, 748, 1285, 1320, 1321], "containin": 144, "frozenset": [144, 267, 339, 383, 586, 588, 752, 1165, 1333, 1337, 1338, 1412], "abc": [144, 545, 1154, 1206, 1303, 1412, 1413], "interchang": [144, 362], "bool": [145, 146, 148, 149, 165, 168, 171, 176, 184, 189, 196, 204, 208, 232, 237, 238, 242, 243, 245, 249, 250, 258, 265, 266, 267, 268, 272, 275, 286, 287, 288, 291, 294, 295, 296, 297, 298, 299, 301, 302, 305, 306, 307, 308, 309, 310, 314, 315, 322, 324, 325, 326, 327, 330, 343, 350, 355, 362, 393, 394, 395, 396, 397, 398, 439, 454, 462, 463, 467, 479, 480, 488, 489, 491, 494, 498, 499, 509, 510, 513, 514, 515, 516, 517, 518, 520, 521, 522, 545, 562, 564, 578, 579, 580, 581, 588, 613, 614, 616, 617, 622, 623, 625, 640, 652, 663, 673, 679, 685, 690, 696, 698, 699, 700, 704, 708, 719, 723, 724, 725, 726, 728, 730, 733, 734, 735, 736, 737, 738, 740, 741, 742, 743, 862, 865, 867, 870, 873, 878, 885, 891, 907, 910, 912, 916, 927, 931, 943, 946, 948, 951, 955, 960, 966, 972, 976, 988, 991, 993, 998, 1039, 1040, 1045, 1057, 1068, 1070, 1071, 1072, 1084, 1091, 1097, 1116, 1133, 1134, 1135, 1136, 1169, 1179, 1185, 1189, 1209, 1211, 1212, 1213, 1215, 1224, 1228, 1230, 1231, 1232, 1275, 1276, 1277, 1278, 1279, 1282, 1295, 1296, 1307, 1309, 1312, 1335, 1336, 1337, 1339, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1360, 1364, 1379, 1380], "account": [145, 148, 398, 448, 749, 761, 1270, 1393, 1413], "graph_nod": [145, 148], "subgraph_nod": [145, 148], "find_isomorph": [147, 150], "induc": [148, 167, 199, 211, 226, 342, 388, 392, 406, 427, 436, 437, 470, 487, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 512, 586, 589, 752, 761, 762, 864, 887, 909, 925, 945, 968, 990, 1007, 1038, 1061, 1066, 1087, 1102, 1103, 1105, 1189, 1283, 1284, 1393], "u_of_edg": [151, 853, 898], "v_of_edg": [151, 853, 898], "capac": [151, 265, 296, 301, 302, 303, 308, 309, 323, 411, 412, 415, 416, 417, 418, 419, 430, 431, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 758, 853, 898, 934, 979, 1335, 1402], "342": [151, 853, 898, 934, 979, 1255], "ebunch_to_add": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "add_weighted_edges_from": [152, 229, 230, 231, 508, 581, 630, 657, 659, 721, 854, 899, 935, 980, 1070, 1326, 1404, 1407, 1426], "runtimeerror": [152, 157, 158, 195, 464, 465, 466, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005], "happen": [152, 157, 158, 195, 380, 584, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1403, 1404, 1425], "iterator_of_edg": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "wn2898": [152, 854, 899, 935, 980], "wrong": [152, 157, 158, 722, 854, 856, 857, 899, 901, 902, 935, 937, 938, 980, 982, 983, 1406, 1411, 1416, 1425], "start_nod": [153, 154, 155], "end_nod": [153, 154, 155], "reference_neighbor": [153, 154], "half": [153, 154, 155, 164, 177, 183, 206, 297, 298, 615, 653], "clockwis": [153, 154, 169, 182, 197, 615], "networkxexcept": [153, 154, 161, 331, 588, 593, 724, 726, 1043, 1110, 1138, 1180, 1325], "add_half_edge_cw": [153, 155, 164, 615], "connect_compon": [153, 154, 155, 615], "add_half_edge_first": [153, 154, 164, 615], "add_half_edge_ccw": [154, 155, 164, 615], "node_for_ad": [156, 855, 900, 936, 981], "mutabl": [156, 855, 900, 936, 981, 1061, 1066, 1082, 1085, 1086], "hash": [156, 511, 512, 758, 855, 900, 936, 981, 1324, 1325, 1414, 1426], "hello": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1303], "k3": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1217], "utm": [156, 855, 900, 936, 981], "382871": [156, 855, 900, 936, 981], "3972649": [156, 855, 900, 936, 981], "nodes_for_ad": [157, 856, 901, 937, 982], "iterator_of_nod": [157, 195, 856, 884, 901, 923, 937, 965, 982, 1005], "datadict": [159, 190, 200, 207, 734, 736, 858, 879, 888, 892, 903, 928, 939, 969, 973, 1010, 1084, 1312, 1326], "foovalu": [159, 190, 200, 858, 879, 888, 903, 939, 969], "nbrdict": [160, 859, 904, 940, 985, 1019, 1094], "fulfil": [161, 615], "cw": [161, 615], "ccw": [161, 615], "planar": [161, 614, 616, 617, 758, 1110, 1138, 1243, 1246, 1247, 1249, 1325, 1409, 1410], "first_nbr": [161, 615], "invalid": [161, 615, 1413], "alter": [163, 861, 906, 942, 987], "afterward": 164, "as_view": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1089, 1090], "shallow": [165, 202, 204, 284, 285, 286, 287, 288, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1394], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1409], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1404, 1407, 1409, 1410, 1411], "fresh": [165, 862, 907, 943, 988, 1404], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1226, 1323, 1404], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1265, 1394], "degreeview": [166, 863, 908, 944, 950, 989, 1404, 1426], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1404, 1418], "ddict": [168, 176, 184, 189, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998], "in_edg": [168, 189, 865, 878, 946, 960, 1404, 1406, 1407], "out_edg": [168, 865, 946, 1062, 1404, 1406, 1407, 1426], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1426], "outedgedataview": [168, 189, 865, 878, 1404, 1411], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1404, 1412], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1406, 1407, 1417], "indegreeview": [175, 869, 1404], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1165, 1179, 1222, 1404], "inedgeview": [176, 870, 1404], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1127, 1353, 1354, 1359, 1363, 1406, 1413], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1414], "__iter__": [180, 871, 914, 952, 995, 1303], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1394, 1426], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1216], "nedg": [185, 588, 874, 917, 956, 999], "__len__": [186, 187, 875, 876, 918, 919, 957, 958, 1000, 1001], "outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 882, 921, 963, 1003, 1404, 1414], "get_data": [197, 616], "inplac": [199, 690, 887, 925, 968, 1007, 1066, 1393], "reduct": [199, 469, 618, 786, 887, 925, 968, 1007, 1066, 1320, 1321, 1413, 1414], "sg": [199, 887, 925, 968, 1007], "largest_wcc": [199, 887, 925, 968, 1007], "is_multigraph": [199, 758, 887, 925, 968, 1007, 1154, 1412], "keydict": [199, 207, 887, 892, 925, 928, 968, 973, 1007, 1010, 1039, 1040], "contrast": [202, 204, 301, 302, 308, 309, 890, 891, 926, 927, 971, 972, 1008, 1009, 1066, 1233, 1241, 1426], "reciproc": [204, 299, 320, 322, 356, 411, 430, 447, 476, 620, 758, 891, 972, 1325, 1416, 1425], "mark_half_edg": 206, "li": [206, 619, 670, 675, 685, 775, 1207, 1210, 1425], "straightforward": [207, 892, 928, 973, 1010], "slightli": [207, 326, 437, 520, 521, 581, 892, 928, 973, 1010, 1165, 1326, 1404, 1407, 1412, 1414, 1425], "singleton": [207, 588, 892, 928, 973, 1010, 1218, 1251, 1407], "preserve_attr": [208, 723, 724, 725, 726], "optimum": [208, 231, 583, 720, 722, 791, 1395, 1406], "arboresc": [208, 460, 719, 720, 722, 724, 726, 740, 743, 758, 1272, 1395, 1406], "span": [208, 226, 227, 228, 295, 508, 618, 619, 624, 719, 720, 722, 724, 726, 732, 733, 734, 735, 736, 737, 738, 758, 1394, 1397, 1406, 1407, 1420], "max_ind_cliqu": 209, "networkxnotimpl": [209, 210, 211, 212, 220, 224, 227, 293, 294, 295, 318, 319, 321, 328, 343, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 403, 404, 405, 406, 407, 422, 424, 425, 426, 427, 429, 455, 457, 458, 459, 460, 468, 481, 482, 500, 589, 590, 608, 680, 732, 1043, 1216, 1275, 1276, 1298, 1325, 1353, 1354, 1379, 1407, 1408], "boppana": [209, 211, 212], "halld\u00f3rsson": [209, 211, 212], "1992": [209, 211, 212, 517, 518, 1407], "exclud": [209, 211, 212, 215, 216, 261, 262, 453, 688, 719, 723, 724, 725, 726, 733, 751, 1036, 1038, 1088, 1217, 1412], "180": [209, 211, 212, 238], "196": [209, 211, 212], "heurist": [210, 220, 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How do I find it in the source code?": [[97, "q-i-want-to-work-on-a-specific-function-how-do-i-find-it-in-the-source-code"]], "Q: What is the policy for deciding whether to include a new algorithm?": [[97, "q-what-is-the-policy-for-deciding-whether-to-include-a-new-algorithm"]], "NXEPs": [[98, "nxeps"], [1413, "nxeps"]], "NXEP 0 \u2014 Purpose and Process": [[99, "nxep-0-purpose-and-process"]], "What is a NXEP?": [[99, "what-is-a-nxep"]], "Types": [[99, "types"]], "NXEP Workflow": [[99, "nxep-workflow"]], "Review and Resolution": [[99, "review-and-resolution"]], "How a NXEP becomes Accepted": [[99, "how-a-nxep-becomes-accepted"]], "Maintenance": [[99, "maintenance"]], "Format and Template": [[99, "format-and-template"]], "Header Preamble": [[99, "header-preamble"]], "References and Footnotes": [[99, "references-and-footnotes"]], "NXEP 1 \u2014 Governance and Decision Making": [[100, "nxep-1-governance-and-decision-making"]], "Abstract": [[100, "abstract"], [101, "abstract"], [102, 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"difference": [[599, "difference"]], "disjoint_union": [[600, "disjoint-union"]], "full_join": [[601, "full-join"]], "intersection": [[602, "intersection"]], "symmetric_difference": [[603, "symmetric-difference"]], "union": [[604, "union"]], "cartesian_product": [[605, "cartesian-product"]], "corona_product": [[606, "corona-product"]], "lexicographic_product": [[607, "lexicographic-product"]], "power": [[608, "power"]], "rooted_product": [[609, "rooted-product"]], "strong_product": [[610, "strong-product"]], "tensor_product": [[611, "tensor-product"]], "complement": [[612, "complement"]], "reverse": [[613, "reverse"]], "combinatorial_embedding_to_pos": [[614, "combinatorial-embedding-to-pos"]], "networkx.algorithms.planarity.PlanarEmbedding": [[615, "networkx-algorithms-planarity-planarembedding"]], "check_planarity": [[616, "check-planarity"]], "is_planar": [[617, "is-planar"]], "chromatic_polynomial": [[618, "chromatic-polynomial"]], "tutte_polynomial": [[619, "tutte-polynomial"]], "overall_reciprocity": [[620, "overall-reciprocity"]], "reciprocity": [[621, "reciprocity"]], "is_k_regular": [[622, "is-k-regular"]], "is_regular": [[623, "is-regular"]], "k_factor": [[624, "k-factor"]], "rich_club_coefficient": [[625, "rich-club-coefficient"]], "astar_path": [[626, "astar-path"]], "astar_path_length": [[627, "astar-path-length"]], "floyd_warshall": [[628, "floyd-warshall"]], "floyd_warshall_numpy": [[629, "floyd-warshall-numpy"]], "floyd_warshall_predecessor_and_distance": [[630, "floyd-warshall-predecessor-and-distance"]], "reconstruct_path": [[631, "reconstruct-path"]], "all_shortest_paths": [[632, "all-shortest-paths"]], "average_shortest_path_length": [[633, "average-shortest-path-length"]], "has_path": [[634, "has-path"]], "shortest_path": [[635, "shortest-path"]], "shortest_path_length": [[636, "shortest-path-length"]], "all_pairs_shortest_path": [[637, "all-pairs-shortest-path"]], "all_pairs_shortest_path_length": [[638, "all-pairs-shortest-path-length"]], "bidirectional_shortest_path": [[639, "bidirectional-shortest-path"]], "predecessor": [[640, "predecessor"]], "single_source_shortest_path": [[641, "single-source-shortest-path"]], "single_source_shortest_path_length": [[642, "single-source-shortest-path-length"]], "single_target_shortest_path": [[643, "single-target-shortest-path"]], "single_target_shortest_path_length": [[644, "single-target-shortest-path-length"]], "all_pairs_bellman_ford_path": [[645, "all-pairs-bellman-ford-path"]], "all_pairs_bellman_ford_path_length": [[646, "all-pairs-bellman-ford-path-length"]], "all_pairs_dijkstra": [[647, "all-pairs-dijkstra"]], "all_pairs_dijkstra_path": [[648, "all-pairs-dijkstra-path"]], "all_pairs_dijkstra_path_length": [[649, "all-pairs-dijkstra-path-length"]], "bellman_ford_path": [[650, "bellman-ford-path"]], "bellman_ford_path_length": [[651, "bellman-ford-path-length"]], "bellman_ford_predecessor_and_distance": [[652, "bellman-ford-predecessor-and-distance"]], "bidirectional_dijkstra": [[653, "bidirectional-dijkstra"]], "dijkstra_path": [[654, "dijkstra-path"]], "dijkstra_path_length": [[655, "dijkstra-path-length"]], "dijkstra_predecessor_and_distance": [[656, "dijkstra-predecessor-and-distance"]], "find_negative_cycle": [[657, "find-negative-cycle"]], "goldberg_radzik": [[658, "goldberg-radzik"]], "johnson": [[659, "johnson"]], "multi_source_dijkstra": [[660, "multi-source-dijkstra"]], "multi_source_dijkstra_path": [[661, "multi-source-dijkstra-path"]], "multi_source_dijkstra_path_length": [[662, "multi-source-dijkstra-path-length"]], "negative_edge_cycle": [[663, "negative-edge-cycle"]], "single_source_bellman_ford": [[664, "single-source-bellman-ford"]], "single_source_bellman_ford_path": [[665, "single-source-bellman-ford-path"]], "single_source_bellman_ford_path_length": [[666, "single-source-bellman-ford-path-length"]], "single_source_dijkstra": [[667, "single-source-dijkstra"]], "single_source_dijkstra_path": [[668, "single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[669, "single-source-dijkstra-path-length"]], "generate_random_paths": [[670, "generate-random-paths"]], "graph_edit_distance": [[671, "graph-edit-distance"]], "optimal_edit_paths": [[672, "optimal-edit-paths"]], "optimize_edit_paths": [[673, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[674, "optimize-graph-edit-distance"]], "panther_similarity": [[675, "panther-similarity"]], "simrank_similarity": [[676, "simrank-similarity"]], "all_simple_edge_paths": [[677, "all-simple-edge-paths"]], "all_simple_paths": [[678, "all-simple-paths"]], "is_simple_path": [[679, "is-simple-path"]], "shortest_simple_paths": [[680, "shortest-simple-paths"]], "lattice_reference": [[681, "lattice-reference"]], "omega": [[682, "omega"]], "random_reference": [[683, "random-reference"]], "sigma": [[684, "sigma"]], "s_metric": [[685, "s-metric"]], "spanner": [[686, "spanner"]], "constraint": [[687, "constraint"]], "effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "Recognition Tests": [[791, "recognition-tests"]], "Branchings and Spanning Arborescences": [[791, "module-networkx.algorithms.tree.branchings"]], "Encoding and decoding": [[791, "module-networkx.algorithms.tree.coding"]], "Operations": [[791, "module-networkx.algorithms.tree.operations"]], "Spanning Trees": [[791, "module-networkx.algorithms.tree.mst"]], "Exceptions": [[791, "exceptions"], [1043, "module-networkx.exception"]], "Vitality": [[793, "module-networkx.algorithms.vitality"]], "Voronoi cells": [[794, "module-networkx.algorithms.voronoi"]], "Wiener index": [[795, "module-networkx.algorithms.wiener"]], "DiGraph\u2014Directed graphs with self loops": [[796, "digraph-directed-graphs-with-self-loops"]], "Overview": [[796, "overview"], [1037, "overview"], [1039, "overview"], [1040, "overview"]], "Methods": [[796, "methods"], [1037, "methods"], [1039, "methods"], [1040, "methods"]], "Adding and removing nodes and edges": [[796, "adding-and-removing-nodes-and-edges"], [1037, "adding-and-removing-nodes-and-edges"], [1040, "adding-and-removing-nodes-and-edges"]], "Reporting nodes edges and neighbors": [[796, "reporting-nodes-edges-and-neighbors"], [1037, "reporting-nodes-edges-and-neighbors"], [1039, "reporting-nodes-edges-and-neighbors"], [1040, "reporting-nodes-edges-and-neighbors"]], "Counting nodes edges and neighbors": [[796, "counting-nodes-edges-and-neighbors"], [1037, "counting-nodes-edges-and-neighbors"], [1039, "counting-nodes-edges-and-neighbors"], [1040, "counting-nodes-edges-and-neighbors"]], "Making copies and subgraphs": [[796, "making-copies-and-subgraphs"], [1037, "making-copies-and-subgraphs"], [1039, "making-copies-and-subgraphs"], [1040, "making-copies-and-subgraphs"]], "AdjacencyView.copy": [[797, "adjacencyview-copy"]], "AdjacencyView.get": [[798, "adjacencyview-get"]], "AdjacencyView.items": [[799, "adjacencyview-items"]], "AdjacencyView.keys": [[800, "adjacencyview-keys"]], "AdjacencyView.values": [[801, "adjacencyview-values"]], "AtlasView.copy": [[802, "atlasview-copy"]], "AtlasView.get": [[803, "atlasview-get"]], "AtlasView.items": [[804, "atlasview-items"]], "AtlasView.keys": [[805, "atlasview-keys"]], "AtlasView.values": [[806, "atlasview-values"]], "FilterAdjacency.get": [[807, "filteradjacency-get"]], "FilterAdjacency.items": [[808, "filteradjacency-items"]], "FilterAdjacency.keys": [[809, "filteradjacency-keys"]], "FilterAdjacency.values": [[810, "filteradjacency-values"]], "FilterAtlas.get": [[811, "filteratlas-get"]], "FilterAtlas.items": [[812, "filteratlas-items"]], "FilterAtlas.keys": [[813, "filteratlas-keys"]], "FilterAtlas.values": [[814, "filteratlas-values"]], "FilterMultiAdjacency.get": [[815, "filtermultiadjacency-get"]], "FilterMultiAdjacency.items": [[816, "filtermultiadjacency-items"]], "FilterMultiAdjacency.keys": [[817, "filtermultiadjacency-keys"]], "FilterMultiAdjacency.values": [[818, "filtermultiadjacency-values"]], "FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": [[836, "unionatlas-keys"]], "UnionAtlas.values": [[837, "unionatlas-values"]], "UnionMultiAdjacency.copy": [[838, "unionmultiadjacency-copy"]], "UnionMultiAdjacency.get": [[839, "unionmultiadjacency-get"]], "UnionMultiAdjacency.items": [[840, "unionmultiadjacency-items"]], "UnionMultiAdjacency.keys": [[841, "unionmultiadjacency-keys"]], "UnionMultiAdjacency.values": [[842, "unionmultiadjacency-values"]], "UnionMultiInner.copy": [[843, "unionmultiinner-copy"]], "UnionMultiInner.get": [[844, "unionmultiinner-get"]], "UnionMultiInner.items": [[845, "unionmultiinner-items"]], "UnionMultiInner.keys": [[846, "unionmultiinner-keys"]], "UnionMultiInner.values": [[847, "unionmultiinner-values"]], "DiGraph.__contains__": [[848, "digraph-contains"]], "DiGraph.__getitem__": [[849, "digraph-getitem"]], "DiGraph.__init__": [[850, "digraph-init"]], "DiGraph.__iter__": [[851, "digraph-iter"]], "DiGraph.__len__": [[852, "digraph-len"]], "DiGraph.add_edge": [[853, "digraph-add-edge"]], "DiGraph.add_edges_from": [[854, "digraph-add-edges-from"]], "DiGraph.add_node": [[855, "digraph-add-node"]], "DiGraph.add_nodes_from": [[856, "digraph-add-nodes-from"]], "DiGraph.add_weighted_edges_from": [[857, "digraph-add-weighted-edges-from"]], "DiGraph.adj": [[858, "digraph-adj"]], "DiGraph.adjacency": [[859, "digraph-adjacency"]], "DiGraph.clear": [[860, "digraph-clear"]], "DiGraph.clear_edges": [[861, "digraph-clear-edges"]], "DiGraph.copy": [[862, "digraph-copy"]], "DiGraph.degree": [[863, "digraph-degree"]], "DiGraph.edge_subgraph": [[864, "digraph-edge-subgraph"]], "DiGraph.edges": [[865, "digraph-edges"]], "DiGraph.get_edge_data": [[866, "digraph-get-edge-data"]], "DiGraph.has_edge": [[867, "digraph-has-edge"]], "DiGraph.has_node": [[868, "digraph-has-node"]], "DiGraph.in_degree": [[869, "digraph-in-degree"]], "DiGraph.in_edges": [[870, "digraph-in-edges"]], "DiGraph.nbunch_iter": [[871, "digraph-nbunch-iter"]], "DiGraph.neighbors": [[872, "digraph-neighbors"]], "DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, "digraph-to-undirected"]], "DiGraph.update": [[892, "digraph-update"]], "Graph.__contains__": [[893, "graph-contains"]], "Graph.__getitem__": [[894, "graph-getitem"]], "Graph.__init__": [[895, "graph-init"]], "Graph.__iter__": [[896, "graph-iter"]], "Graph.__len__": [[897, "graph-len"]], "Graph.add_edge": [[898, "graph-add-edge"]], "Graph.add_edges_from": [[899, "graph-add-edges-from"]], "Graph.add_node": [[900, "graph-add-node"]], "Graph.add_nodes_from": [[901, "graph-add-nodes-from"]], "Graph.add_weighted_edges_from": [[902, "graph-add-weighted-edges-from"]], "Graph.adj": [[903, "graph-adj"]], "Graph.adjacency": [[904, "graph-adjacency"]], "Graph.clear": [[905, "graph-clear"]], "Graph.clear_edges": [[906, "graph-clear-edges"]], "Graph.copy": [[907, "graph-copy"]], "Graph.degree": [[908, "graph-degree"]], "Graph.edge_subgraph": [[909, "graph-edge-subgraph"]], "Graph.edges": [[910, "graph-edges"]], "Graph.get_edge_data": [[911, "graph-get-edge-data"]], "Graph.has_edge": [[912, "graph-has-edge"]], "Graph.has_node": [[913, "graph-has-node"]], "Graph.nbunch_iter": [[914, "graph-nbunch-iter"]], "Graph.neighbors": [[915, "graph-neighbors"]], "Graph.nodes": [[916, "graph-nodes"]], "Graph.number_of_edges": [[917, "graph-number-of-edges"]], "Graph.number_of_nodes": [[918, "graph-number-of-nodes"]], "Graph.order": [[919, "graph-order"]], "Graph.remove_edge": [[920, "graph-remove-edge"]], "Graph.remove_edges_from": [[921, "graph-remove-edges-from"]], "Graph.remove_node": [[922, "graph-remove-node"]], "Graph.remove_nodes_from": [[923, "graph-remove-nodes-from"]], "Graph.size": [[924, "graph-size"]], "Graph.subgraph": [[925, "graph-subgraph"]], "Graph.to_directed": [[926, "graph-to-directed"]], "Graph.to_undirected": [[927, "graph-to-undirected"]], "Graph.update": [[928, "graph-update"]], "MultiDiGraph.__contains__": [[929, "multidigraph-contains"]], "MultiDiGraph.__getitem__": [[930, "multidigraph-getitem"]], "MultiDiGraph.__init__": [[931, "multidigraph-init"]], 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"removed-functionalities"]], "Miscellaneous changes": [[1395, "miscellaneous-changes"], [1396, "miscellaneous-changes"], [1402, "miscellaneous-changes"]], "NetworkX 1.11": [[1396, "networkx-1-11"], [1406, "networkx-1-11"]], "NetworkX 1.4": [[1397, "networkx-1-4"], [1406, "networkx-1-4"]], "Algorithms changed": [[1397, "algorithms-changed"]], "Shortest path": [[1397, "shortest-path"]], "astar_path(), astar_path_length(), shortest_path(), shortest_path_length(),": [[1397, "astar-path-astar-path-length-shortest-path-shortest-path-length"]], "bidirectional_shortest_path(), dijkstra_path(), dijkstra_path_length(),": [[1397, "bidirectional-shortest-path-dijkstra-path-dijkstra-path-length"]], "bidirectional_dijkstra()": [[1397, "bidirectional-dijkstra"]], "NetworkX 1.5": [[1398, "networkx-1-5"], [1406, "networkx-1-5"]], "Weighted graph algorithms": [[1398, "weighted-graph-algorithms"], [1399, "weighted-graph-algorithms"]], "Random geometric graph": [[1398, "random-geometric-graph"]], "NetworkX 1.6": [[1399, "networkx-1-6"], [1406, "networkx-1-6"]], "Graph Classes": [[1399, "graph-classes"]], "Isomorphisms": [[1399, "isomorphisms"]], "Other": [[1399, "other"], [1400, "other"]], "NetworkX 1.7": [[1400, "networkx-1-7"], [1406, "networkx-1-7"]], "NetworkX 1.8": [[1401, "networkx-1-8"], [1406, "networkx-1-8"]], "NetworkX 1.9": [[1402, "networkx-1-9"], [1406, "networkx-1-9"]], "Flow package": [[1402, "flow-package"]], "Main changes": [[1402, "main-changes"]], "Connectivity package": [[1402, "connectivity-package"]], "Other new functionalities": [[1402, "other-new-functionalities"]], "Releases": [[1403, "releases"]], "Migration guide from 1.X to 2.0": [[1404, "migration-guide-from-1-x-to-2-0"]], "Writing code that works for both versions": [[1404, "writing-code-that-works-for-both-versions"]], "Using Pickle with v1 and v2": [[1404, "using-pickle-with-v1-and-v2"]], "Migration guide from 2.X to 3.0": [[1405, "migration-guide-from-2-x-to-3-0"]], "Default dependencies": [[1405, "default-dependencies"]], "Improved integration with scientific Python": [[1405, "improved-integration-with-scientific-python"]], "Replacing NumPy/SciPy matrices with arrays": [[1405, "replacing-numpy-scipy-matrices-with-arrays"]], "Switch to NumPy/SciPy implementations by default for some algorithms": [[1405, "switch-to-numpy-scipy-implementations-by-default-for-some-algorithms"]], "Supporting numpy.random.Generator": [[1405, "supporting-numpy-random-generator"]], "NumPy structured dtypes for multi-attribute adjacency matrices": [[1405, "numpy-structured-dtypes-for-multi-attribute-adjacency-matrices"]], "Deprecated code": [[1405, "deprecated-code"]], "Old Release Log": [[1406, "old-release-log"]], "NetworkX 2.5": [[1406, "networkx-2-5"], [1412, "networkx-2-5"]], "Release notes": [[1406, "release-notes"], [1406, "id1"], [1406, "id2"], [1406, "id3"], [1406, "id4"], [1406, "id5"]], "NetworkX 2.4": [[1406, "networkx-2-4"], [1411, "networkx-2-4"]], "NetworkX 2.3": [[1406, 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"networkx-0-30"]], "NetworkX 0.29": [[1406, "networkx-0-29"]], "NetworkX 0.28": [[1406, "networkx-0-28"]], "NetworkX 0.27": [[1406, "networkx-0-27"]], "NetworkX 0.26": [[1406, "networkx-0-26"]], "NetworkX 0.25": [[1406, "networkx-0-25"]], "NetworkX 0.24": [[1406, "networkx-0-24"]], "NetworkX 0.23": [[1406, "networkx-0-23"]], "Important Change": [[1406, "important-change"]], "NetworkX 0.22": [[1406, "networkx-0-22"]], "API Changes": [[1407, "api-changes"], [1408, "api-changes"], [1409, "api-changes"], [1410, "api-changes"], [1411, "api-changes"], [1412, "api-changes"], [1413, "api-changes"], [1414, "api-changes"], [1416, "api-changes"], [1425, "api-changes"]], "Merged PRs": [[1407, "merged-prs"], [1408, "merged-prs"], [1411, "merged-prs"], [1412, "merged-prs"], [1413, "merged-prs"], [1414, "merged-prs"], [1415, "merged-prs"], [1416, "merged-prs"], [1417, "merged-prs"], [1418, "merged-prs"], [1419, "merged-prs"], [1420, "merged-prs"], [1421, "merged-prs"], [1422, "merged-prs"], [1423, "merged-prs"], [1424, "merged-prs"], [1425, "merged-prs"]], "Improvements": [[1408, "improvements"], [1409, "improvements"], [1410, "improvements"], [1411, "improvements"], [1412, "improvements"], [1413, "improvements"], [1414, "improvements"], [1416, "improvements"], [1417, "improvements"], [1422, "improvements"], [1423, "improvements"], [1425, "improvements"]], "NetworkX 2.6": [[1413, "networkx-2-6"]], "NetworkX 2.7": [[1414, "networkx-2-7"]], "GSoC PRs": [[1414, "gsoc-prs"]], "NetworkX 2.7.1": [[1415, "networkx-2-7-1"]], "NetworkX 2.8": [[1416, "networkx-2-8"]], "NetworkX 2.8.1": [[1417, "networkx-2-8-1"]], "NetworkX 2.8.2": [[1418, "networkx-2-8-2"]], "NetworkX 2.8.3": [[1419, "networkx-2-8-3"]], "NetworkX 2.8.4": [[1420, "networkx-2-8-4"]], "NetworkX 2.8.5": [[1421, "networkx-2-8-5"]], "NetworkX 2.8.6": [[1422, "networkx-2-8-6"]], "NetworkX 2.8.7": [[1423, "networkx-2-8-7"]], "NetworkX 2.8.8": [[1424, "networkx-2-8-8"]], "NetworkX 3.0 (unreleased)": [[1425, "networkx-3-0-unreleased"]], "Tutorial": [[1426, "tutorial"]], "Creating a graph": [[1426, "creating-a-graph"]], "Examining elements of a graph": [[1426, "examining-elements-of-a-graph"]], "Removing elements from a graph": [[1426, "removing-elements-from-a-graph"]], "Using the graph constructors": [[1426, "using-the-graph-constructors"]], "What to use as nodes and edges": [[1426, "what-to-use-as-nodes-and-edges"]], "Accessing edges and neighbors": [[1426, "accessing-edges-and-neighbors"]], "Adding attributes to graphs, nodes, and edges": [[1426, "adding-attributes-to-graphs-nodes-and-edges"]], "Edge Attributes": [[1426, "edge-attributes"]], "Directed graphs": [[1426, "directed-graphs"]], "Multigraphs": [[1426, "multigraphs"]], "Graph generators and graph operations": [[1426, "graph-generators-and-graph-operations"]], "1. Applying classic graph operations, such as:": [[1426, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1426, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1426, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1426, "using-a-stochastic-graph-generator-e-g"]], "5. Reading a graph stored in a file using common graph formats": [[1426, "reading-a-graph-stored-in-a-file-using-common-graph-formats"]], "Analyzing graphs": [[1426, "analyzing-graphs"]], "Drawing graphs": [[1426, "drawing-graphs"]]}, "indexentries": {"module": [[112, "module-networkx.algorithms.approximation"], [112, "module-networkx.algorithms.approximation.clique"], [112, "module-networkx.algorithms.approximation.clustering_coefficient"], [112, "module-networkx.algorithms.approximation.connectivity"], [112, "module-networkx.algorithms.approximation.distance_measures"], [112, "module-networkx.algorithms.approximation.dominating_set"], [112, "module-networkx.algorithms.approximation.kcomponents"], [112, "module-networkx.algorithms.approximation.matching"], [112, "module-networkx.algorithms.approximation.maxcut"], [112, "module-networkx.algorithms.approximation.ramsey"], [112, "module-networkx.algorithms.approximation.steinertree"], [112, 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[117, "module-networkx.algorithms.bridges"], [118, "module-networkx.algorithms.centrality"], [119, "module-networkx.algorithms.chains"], [120, "module-networkx.algorithms.chordal"], [121, "module-networkx.algorithms.clique"], [122, "module-networkx.algorithms.cluster"], [123, "module-networkx.algorithms.coloring"], [124, "module-networkx.algorithms.communicability_alg"], [125, "module-networkx.algorithms.community"], [125, "module-networkx.algorithms.community.asyn_fluid"], [125, "module-networkx.algorithms.community.centrality"], [125, "module-networkx.algorithms.community.community_utils"], [125, "module-networkx.algorithms.community.kclique"], [125, "module-networkx.algorithms.community.kernighan_lin"], [125, "module-networkx.algorithms.community.label_propagation"], [125, "module-networkx.algorithms.community.louvain"], [125, "module-networkx.algorithms.community.lukes"], [125, "module-networkx.algorithms.community.modularity_max"], [125, 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"module-networkx.algorithms.approximation.clustering_coefficient"]], "networkx.algorithms.approximation.connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"]], "networkx.algorithms.approximation.distance_measures": [[112, "module-networkx.algorithms.approximation.distance_measures"]], "networkx.algorithms.approximation.dominating_set": [[112, "module-networkx.algorithms.approximation.dominating_set"]], "networkx.algorithms.approximation.kcomponents": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "networkx.algorithms.approximation.matching": [[112, "module-networkx.algorithms.approximation.matching"]], "networkx.algorithms.approximation.maxcut": [[112, "module-networkx.algorithms.approximation.maxcut"]], "networkx.algorithms.approximation.ramsey": [[112, "module-networkx.algorithms.approximation.ramsey"]], "networkx.algorithms.approximation.steinertree": [[112, "module-networkx.algorithms.approximation.steinertree"]], "networkx.algorithms.approximation.traveling_salesman": [[112, "module-networkx.algorithms.approximation.traveling_salesman"]], "networkx.algorithms.approximation.treewidth": [[112, "module-networkx.algorithms.approximation.treewidth"]], "networkx.algorithms.approximation.vertex_cover": [[112, "module-networkx.algorithms.approximation.vertex_cover"]], "networkx.algorithms.assortativity": [[113, "module-networkx.algorithms.assortativity"]], "networkx.algorithms.asteroidal": [[114, "module-networkx.algorithms.asteroidal"]], "networkx.algorithms.bipartite": [[115, "module-networkx.algorithms.bipartite"]], "networkx.algorithms.bipartite.basic": [[115, "module-networkx.algorithms.bipartite.basic"]], "networkx.algorithms.bipartite.centrality": [[115, "module-networkx.algorithms.bipartite.centrality"]], "networkx.algorithms.bipartite.cluster": [[115, "module-networkx.algorithms.bipartite.cluster"]], "networkx.algorithms.bipartite.covering": [[115, 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"networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, 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"networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[751, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[752, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[753, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[755, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[757, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[759, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[760, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[764, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[766, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[767, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[768, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[769, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[770, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[771, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[772, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[772, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[772, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[772, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[774, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[775, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[777, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[778, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "networkx.algorithms.shortest_paths.weighted": [[779, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[780, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[781, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[782, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[783, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[785, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[786, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[787, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[788, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[789, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "networkx.algorithms.traversal.breadth_first_search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "networkx.algorithms.traversal.depth_first_search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "networkx.algorithms.traversal.edgebfs": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "networkx.algorithms.traversal.edgedfs": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "networkx.algorithms.tree.branchings": [[791, "module-networkx.algorithms.tree.branchings"]], "networkx.algorithms.tree.coding": [[791, "module-networkx.algorithms.tree.coding"]], "networkx.algorithms.tree.decomposition": [[791, "module-networkx.algorithms.tree.decomposition"]], "networkx.algorithms.tree.mst": [[791, "module-networkx.algorithms.tree.mst"]], "networkx.algorithms.tree.operations": [[791, "module-networkx.algorithms.tree.operations"]], "networkx.algorithms.tree.recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "networkx.algorithms.triads": [[792, "module-networkx.algorithms.triads"]], "networkx.algorithms.vitality": [[793, "module-networkx.algorithms.vitality"]], "networkx.algorithms.voronoi": [[794, "module-networkx.algorithms.voronoi"]], "networkx.algorithms.wiener": [[795, "module-networkx.algorithms.wiener"]], "digraph (class in networkx)": [[796, "networkx.DiGraph"]], "copy() (adjacencyview method)": [[797, "networkx.classes.coreviews.AdjacencyView.copy"]], "get() (adjacencyview method)": [[798, "networkx.classes.coreviews.AdjacencyView.get"]], "items() (adjacencyview method)": [[799, "networkx.classes.coreviews.AdjacencyView.items"]], "keys() (adjacencyview method)": [[800, "networkx.classes.coreviews.AdjacencyView.keys"]], "values() (adjacencyview method)": [[801, "networkx.classes.coreviews.AdjacencyView.values"]], "copy() (atlasview method)": [[802, "networkx.classes.coreviews.AtlasView.copy"]], "get() (atlasview method)": [[803, "networkx.classes.coreviews.AtlasView.get"]], "items() (atlasview method)": [[804, "networkx.classes.coreviews.AtlasView.items"]], "keys() (atlasview method)": [[805, "networkx.classes.coreviews.AtlasView.keys"]], "values() (atlasview method)": [[806, "networkx.classes.coreviews.AtlasView.values"]], "get() (filteradjacency method)": [[807, "networkx.classes.coreviews.FilterAdjacency.get"]], "items() (filteradjacency method)": [[808, "networkx.classes.coreviews.FilterAdjacency.items"]], "keys() (filteradjacency method)": [[809, "networkx.classes.coreviews.FilterAdjacency.keys"]], "values() (filteradjacency method)": [[810, "networkx.classes.coreviews.FilterAdjacency.values"]], "get() (filteratlas method)": [[811, "networkx.classes.coreviews.FilterAtlas.get"]], "items() (filteratlas method)": [[812, "networkx.classes.coreviews.FilterAtlas.items"]], "keys() (filteratlas method)": [[813, "networkx.classes.coreviews.FilterAtlas.keys"]], "values() (filteratlas method)": [[814, "networkx.classes.coreviews.FilterAtlas.values"]], "get() (filtermultiadjacency method)": [[815, "networkx.classes.coreviews.FilterMultiAdjacency.get"]], "items() (filtermultiadjacency method)": [[816, "networkx.classes.coreviews.FilterMultiAdjacency.items"]], "keys() (filtermultiadjacency method)": [[817, "networkx.classes.coreviews.FilterMultiAdjacency.keys"]], "values() (filtermultiadjacency method)": [[818, "networkx.classes.coreviews.FilterMultiAdjacency.values"]], "get() (filtermultiinner method)": [[819, "networkx.classes.coreviews.FilterMultiInner.get"]], "items() (filtermultiinner method)": [[820, "networkx.classes.coreviews.FilterMultiInner.items"]], "keys() (filtermultiinner method)": [[821, "networkx.classes.coreviews.FilterMultiInner.keys"]], "values() (filtermultiinner method)": [[822, "networkx.classes.coreviews.FilterMultiInner.values"]], "copy() (multiadjacencyview method)": [[823, "networkx.classes.coreviews.MultiAdjacencyView.copy"]], "get() (multiadjacencyview method)": [[824, "networkx.classes.coreviews.MultiAdjacencyView.get"]], "items() (multiadjacencyview method)": [[825, "networkx.classes.coreviews.MultiAdjacencyView.items"]], "keys() (multiadjacencyview method)": [[826, "networkx.classes.coreviews.MultiAdjacencyView.keys"]], "values() (multiadjacencyview method)": [[827, "networkx.classes.coreviews.MultiAdjacencyView.values"]], "copy() (unionadjacency method)": [[828, "networkx.classes.coreviews.UnionAdjacency.copy"]], "get() (unionadjacency method)": [[829, "networkx.classes.coreviews.UnionAdjacency.get"]], "items() (unionadjacency method)": [[830, "networkx.classes.coreviews.UnionAdjacency.items"]], "keys() (unionadjacency method)": [[831, "networkx.classes.coreviews.UnionAdjacency.keys"]], "values() (unionadjacency method)": [[832, "networkx.classes.coreviews.UnionAdjacency.values"]], "copy() (unionatlas method)": [[833, "networkx.classes.coreviews.UnionAtlas.copy"]], "get() (unionatlas method)": [[834, "networkx.classes.coreviews.UnionAtlas.get"]], "items() (unionatlas method)": [[835, "networkx.classes.coreviews.UnionAtlas.items"]], "keys() (unionatlas method)": [[836, "networkx.classes.coreviews.UnionAtlas.keys"]], "values() (unionatlas method)": [[837, "networkx.classes.coreviews.UnionAtlas.values"]], "copy() (unionmultiadjacency method)": [[838, "networkx.classes.coreviews.UnionMultiAdjacency.copy"]], "get() (unionmultiadjacency method)": [[839, "networkx.classes.coreviews.UnionMultiAdjacency.get"]], "items() (unionmultiadjacency method)": [[840, "networkx.classes.coreviews.UnionMultiAdjacency.items"]], "keys() (unionmultiadjacency method)": [[841, "networkx.classes.coreviews.UnionMultiAdjacency.keys"]], "values() (unionmultiadjacency method)": [[842, "networkx.classes.coreviews.UnionMultiAdjacency.values"]], "copy() (unionmultiinner method)": [[843, "networkx.classes.coreviews.UnionMultiInner.copy"]], "get() (unionmultiinner method)": [[844, "networkx.classes.coreviews.UnionMultiInner.get"]], "items() (unionmultiinner method)": [[845, "networkx.classes.coreviews.UnionMultiInner.items"]], "keys() (unionmultiinner method)": [[846, "networkx.classes.coreviews.UnionMultiInner.keys"]], "values() (unionmultiinner method)": [[847, "networkx.classes.coreviews.UnionMultiInner.values"]], "__contains__() (digraph method)": [[848, "networkx.DiGraph.__contains__"]], "__getitem__() (digraph method)": [[849, "networkx.DiGraph.__getitem__"]], "__init__() (digraph method)": [[850, "networkx.DiGraph.__init__"]], "__iter__() (digraph method)": [[851, "networkx.DiGraph.__iter__"]], "__len__() (digraph method)": [[852, "networkx.DiGraph.__len__"]], "add_edge() (digraph method)": [[853, "networkx.DiGraph.add_edge"]], "add_edges_from() (digraph method)": [[854, "networkx.DiGraph.add_edges_from"]], "add_node() (digraph method)": [[855, "networkx.DiGraph.add_node"]], "add_nodes_from() (digraph method)": [[856, "networkx.DiGraph.add_nodes_from"]], "add_weighted_edges_from() (digraph method)": [[857, "networkx.DiGraph.add_weighted_edges_from"]], "adj (digraph property)": [[858, "networkx.DiGraph.adj"]], "adjacency() (digraph method)": [[859, "networkx.DiGraph.adjacency"]], "clear() (digraph method)": [[860, "networkx.DiGraph.clear"]], "clear_edges() (digraph method)": [[861, "networkx.DiGraph.clear_edges"]], "copy() (digraph method)": [[862, "networkx.DiGraph.copy"]], "degree (digraph property)": [[863, "networkx.DiGraph.degree"]], "edge_subgraph() (digraph method)": [[864, "networkx.DiGraph.edge_subgraph"]], "edges (digraph property)": [[865, "networkx.DiGraph.edges"]], "get_edge_data() (digraph method)": [[866, "networkx.DiGraph.get_edge_data"]], "has_edge() (digraph method)": [[867, "networkx.DiGraph.has_edge"]], "has_node() (digraph method)": [[868, "networkx.DiGraph.has_node"]], "in_degree (digraph property)": [[869, "networkx.DiGraph.in_degree"]], "in_edges (digraph property)": [[870, "networkx.DiGraph.in_edges"]], "nbunch_iter() (digraph method)": [[871, "networkx.DiGraph.nbunch_iter"]], "neighbors() (digraph method)": [[872, "networkx.DiGraph.neighbors"]], "nodes (digraph property)": [[873, "networkx.DiGraph.nodes"]], "number_of_edges() (digraph method)": [[874, "networkx.DiGraph.number_of_edges"]], "number_of_nodes() (digraph method)": [[875, "networkx.DiGraph.number_of_nodes"]], "order() (digraph method)": [[876, "networkx.DiGraph.order"]], "out_degree (digraph property)": [[877, "networkx.DiGraph.out_degree"]], "out_edges (digraph property)": [[878, "networkx.DiGraph.out_edges"]], "pred (digraph property)": [[879, "networkx.DiGraph.pred"]], "predecessors() (digraph method)": [[880, "networkx.DiGraph.predecessors"]], "remove_edge() (digraph method)": [[881, "networkx.DiGraph.remove_edge"]], "remove_edges_from() (digraph method)": [[882, "networkx.DiGraph.remove_edges_from"]], "remove_node() (digraph method)": [[883, "networkx.DiGraph.remove_node"]], "remove_nodes_from() (digraph method)": 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"add_edges_from() (graph method)": [[899, "networkx.Graph.add_edges_from"]], "add_node() (graph method)": [[900, "networkx.Graph.add_node"]], "add_nodes_from() (graph method)": [[901, "networkx.Graph.add_nodes_from"]], "add_weighted_edges_from() (graph method)": [[902, "networkx.Graph.add_weighted_edges_from"]], "adj (graph property)": [[903, "networkx.Graph.adj"]], "adjacency() (graph method)": [[904, "networkx.Graph.adjacency"]], "clear() (graph method)": [[905, "networkx.Graph.clear"]], "clear_edges() (graph method)": [[906, "networkx.Graph.clear_edges"]], "copy() (graph method)": [[907, "networkx.Graph.copy"]], "degree (graph property)": [[908, "networkx.Graph.degree"]], "edge_subgraph() (graph method)": [[909, "networkx.Graph.edge_subgraph"]], "edges (graph property)": [[910, "networkx.Graph.edges"]], "get_edge_data() (graph method)": [[911, "networkx.Graph.get_edge_data"]], "has_edge() (graph method)": [[912, "networkx.Graph.has_edge"]], "has_node() (graph method)": [[913, "networkx.Graph.has_node"]], "nbunch_iter() (graph method)": [[914, "networkx.Graph.nbunch_iter"]], "neighbors() (graph method)": [[915, "networkx.Graph.neighbors"]], "nodes (graph property)": [[916, "networkx.Graph.nodes"]], "number_of_edges() (graph method)": [[917, "networkx.Graph.number_of_edges"]], "number_of_nodes() (graph method)": [[918, "networkx.Graph.number_of_nodes"]], "order() (graph method)": [[919, "networkx.Graph.order"]], "remove_edge() (graph method)": [[920, "networkx.Graph.remove_edge"]], "remove_edges_from() (graph method)": [[921, "networkx.Graph.remove_edges_from"]], "remove_node() (graph method)": [[922, "networkx.Graph.remove_node"]], "remove_nodes_from() (graph method)": [[923, "networkx.Graph.remove_nodes_from"]], "size() (graph method)": [[924, "networkx.Graph.size"]], "subgraph() (graph method)": [[925, "networkx.Graph.subgraph"]], "to_directed() (graph method)": [[926, "networkx.Graph.to_directed"]], "to_undirected() (graph method)": [[927, 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"networkx.MultiDiGraph.remove_edges_from"]], "remove_node() (multidigraph method)": [[964, "networkx.MultiDiGraph.remove_node"]], "remove_nodes_from() (multidigraph method)": [[965, "networkx.MultiDiGraph.remove_nodes_from"]], "reverse() (multidigraph method)": [[966, "networkx.MultiDiGraph.reverse"]], "size() (multidigraph method)": [[967, "networkx.MultiDiGraph.size"]], "subgraph() (multidigraph method)": [[968, "networkx.MultiDiGraph.subgraph"]], "succ (multidigraph property)": [[969, "networkx.MultiDiGraph.succ"]], "successors() (multidigraph method)": [[970, "networkx.MultiDiGraph.successors"]], "to_directed() (multidigraph method)": [[971, "networkx.MultiDiGraph.to_directed"]], "to_undirected() (multidigraph method)": [[972, "networkx.MultiDiGraph.to_undirected"]], "update() (multidigraph method)": [[973, "networkx.MultiDiGraph.update"]], "__contains__() (multigraph method)": [[974, "networkx.MultiGraph.__contains__"]], "__getitem__() (multigraph method)": [[975, "networkx.MultiGraph.__getitem__"]], "__init__() (multigraph method)": [[976, "networkx.MultiGraph.__init__"]], "__iter__() (multigraph method)": [[977, "networkx.MultiGraph.__iter__"]], "__len__() (multigraph method)": [[978, "networkx.MultiGraph.__len__"]], "add_edge() (multigraph method)": [[979, "networkx.MultiGraph.add_edge"]], "add_edges_from() (multigraph method)": [[980, "networkx.MultiGraph.add_edges_from"]], "add_node() (multigraph method)": [[981, "networkx.MultiGraph.add_node"]], "add_nodes_from() (multigraph method)": [[982, "networkx.MultiGraph.add_nodes_from"]], "add_weighted_edges_from() (multigraph method)": [[983, "networkx.MultiGraph.add_weighted_edges_from"]], "adj (multigraph property)": [[984, "networkx.MultiGraph.adj"]], "adjacency() (multigraph method)": [[985, "networkx.MultiGraph.adjacency"]], "clear() (multigraph method)": [[986, "networkx.MultiGraph.clear"]], "clear_edges() (multigraph method)": [[987, "networkx.MultiGraph.clear_edges"]], "copy() (multigraph method)": [[988, "networkx.MultiGraph.copy"]], "degree (multigraph property)": [[989, "networkx.MultiGraph.degree"]], "edge_subgraph() (multigraph method)": [[990, "networkx.MultiGraph.edge_subgraph"]], "edges (multigraph property)": [[991, "networkx.MultiGraph.edges"]], "get_edge_data() (multigraph method)": [[992, "networkx.MultiGraph.get_edge_data"]], "has_edge() (multigraph method)": [[993, "networkx.MultiGraph.has_edge"]], "has_node() (multigraph method)": [[994, "networkx.MultiGraph.has_node"]], "nbunch_iter() (multigraph method)": [[995, "networkx.MultiGraph.nbunch_iter"]], "neighbors() (multigraph method)": [[996, "networkx.MultiGraph.neighbors"]], "new_edge_key() (multigraph method)": [[997, "networkx.MultiGraph.new_edge_key"]], "nodes (multigraph property)": [[998, "networkx.MultiGraph.nodes"]], "number_of_edges() (multigraph method)": [[999, "networkx.MultiGraph.number_of_edges"]], "number_of_nodes() (multigraph method)": [[1000, "networkx.MultiGraph.number_of_nodes"]], "order() (multigraph method)": [[1001, "networkx.MultiGraph.order"]], "remove_edge() (multigraph method)": [[1002, "networkx.MultiGraph.remove_edge"]], "remove_edges_from() (multigraph method)": [[1003, "networkx.MultiGraph.remove_edges_from"]], "remove_node() (multigraph method)": [[1004, "networkx.MultiGraph.remove_node"]], "remove_nodes_from() (multigraph method)": [[1005, "networkx.MultiGraph.remove_nodes_from"]], "size() (multigraph method)": [[1006, "networkx.MultiGraph.size"]], "subgraph() (multigraph method)": [[1007, "networkx.MultiGraph.subgraph"]], "to_directed() (multigraph method)": [[1008, "networkx.MultiGraph.to_directed"]], "to_undirected() (multigraph method)": [[1009, "networkx.MultiGraph.to_undirected"]], "update() (multigraph method)": [[1010, "networkx.MultiGraph.update"]], "_dispatch() (in module networkx.classes.backends)": [[1011, "networkx.classes.backends._dispatch"]], "adjacencyview (class in networkx.classes.coreviews)": [[1012, "networkx.classes.coreviews.AdjacencyView"]], "__init__() (adjacencyview method)": [[1012, "networkx.classes.coreviews.AdjacencyView.__init__"]], "atlasview (class in networkx.classes.coreviews)": [[1013, "networkx.classes.coreviews.AtlasView"]], "__init__() (atlasview method)": [[1013, "networkx.classes.coreviews.AtlasView.__init__"]], "filteradjacency (class in networkx.classes.coreviews)": [[1014, "networkx.classes.coreviews.FilterAdjacency"]], "__init__() (filteradjacency method)": [[1014, "networkx.classes.coreviews.FilterAdjacency.__init__"]], "filteratlas (class in networkx.classes.coreviews)": [[1015, "networkx.classes.coreviews.FilterAtlas"]], "__init__() (filteratlas method)": [[1015, "networkx.classes.coreviews.FilterAtlas.__init__"]], "filtermultiadjacency (class in networkx.classes.coreviews)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency"]], "__init__() (filtermultiadjacency method)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency.__init__"]], "filtermultiinner (class in networkx.classes.coreviews)": [[1017, "networkx.classes.coreviews.FilterMultiInner"]], "__init__() (filtermultiinner method)": [[1017, "networkx.classes.coreviews.FilterMultiInner.__init__"]], "multiadjacencyview (class in networkx.classes.coreviews)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView"]], "__init__() (multiadjacencyview method)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView.__init__"]], "unionadjacency (class in networkx.classes.coreviews)": [[1019, "networkx.classes.coreviews.UnionAdjacency"]], "__init__() (unionadjacency method)": [[1019, "networkx.classes.coreviews.UnionAdjacency.__init__"]], "unionatlas (class in networkx.classes.coreviews)": [[1020, "networkx.classes.coreviews.UnionAtlas"]], "__init__() (unionatlas method)": [[1020, "networkx.classes.coreviews.UnionAtlas.__init__"]], "unionmultiadjacency (class in networkx.classes.coreviews)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency"]], "__init__() (unionmultiadjacency method)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency.__init__"]], "unionmultiinner (class in networkx.classes.coreviews)": [[1022, "networkx.classes.coreviews.UnionMultiInner"]], "__init__() (unionmultiinner method)": [[1022, "networkx.classes.coreviews.UnionMultiInner.__init__"]], "hide_diedges() (in module networkx.classes.filters)": [[1023, "networkx.classes.filters.hide_diedges"]], "hide_edges() (in module networkx.classes.filters)": [[1024, "networkx.classes.filters.hide_edges"]], "hide_multidiedges() (in module networkx.classes.filters)": [[1025, "networkx.classes.filters.hide_multidiedges"]], "hide_multiedges() (in module networkx.classes.filters)": [[1026, "networkx.classes.filters.hide_multiedges"]], "hide_nodes() (in module networkx.classes.filters)": [[1027, "networkx.classes.filters.hide_nodes"]], "no_filter() (in module networkx.classes.filters)": [[1028, "networkx.classes.filters.no_filter"]], "show_diedges() (in module networkx.classes.filters)": [[1029, "networkx.classes.filters.show_diedges"]], "show_edges() (in module networkx.classes.filters)": [[1030, "networkx.classes.filters.show_edges"]], "show_multidiedges() (in module networkx.classes.filters)": [[1031, "networkx.classes.filters.show_multidiedges"]], "show_multiedges() (in module networkx.classes.filters)": [[1032, "networkx.classes.filters.show_multiedges"]], "__init__() (show_nodes method)": [[1033, "networkx.classes.filters.show_nodes.__init__"]], "show_nodes (class in networkx.classes.filters)": [[1033, "networkx.classes.filters.show_nodes"]], "generic_graph_view() (in module networkx.classes.graphviews)": [[1034, "networkx.classes.graphviews.generic_graph_view"]], "reverse_view() (in module networkx.classes.graphviews)": [[1035, "networkx.classes.graphviews.reverse_view"]], "subgraph_view() (in module networkx.classes.graphviews)": [[1036, "networkx.classes.graphviews.subgraph_view"]], "graph (class in networkx)": [[1037, "networkx.Graph"]], "networkx.classes.backends": [[1038, "module-networkx.classes.backends"]], "networkx.classes.coreviews": [[1038, "module-networkx.classes.coreviews"]], "networkx.classes.filters": [[1038, "module-networkx.classes.filters"]], "networkx.classes.graphviews": [[1038, "module-networkx.classes.graphviews"]], "multidigraph (class in networkx)": [[1039, "networkx.MultiDiGraph"]], "multigraph (class in networkx)": [[1040, "networkx.MultiGraph"]], "networkx.convert": [[1041, "module-networkx.convert"]], "networkx.convert_matrix": [[1041, "module-networkx.convert_matrix"]], "networkx.drawing.layout": [[1042, "module-networkx.drawing.layout"]], "networkx.drawing.nx_agraph": [[1042, "module-networkx.drawing.nx_agraph"]], "networkx.drawing.nx_pydot": [[1042, "module-networkx.drawing.nx_pydot"]], "networkx.drawing.nx_pylab": [[1042, "module-networkx.drawing.nx_pylab"]], "ambiguoussolution (class in networkx)": [[1043, "networkx.AmbiguousSolution"]], "exceededmaxiterations (class in networkx)": [[1043, "networkx.ExceededMaxIterations"]], "hasacycle (class in networkx)": [[1043, "networkx.HasACycle"]], "networkxalgorithmerror (class in networkx)": [[1043, "networkx.NetworkXAlgorithmError"]], "networkxerror (class in networkx)": [[1043, "networkx.NetworkXError"]], "networkxexception (class in networkx)": [[1043, "networkx.NetworkXException"]], "networkxnocycle (class in networkx)": [[1043, "networkx.NetworkXNoCycle"]], "networkxnopath (class in networkx)": [[1043, "networkx.NetworkXNoPath"]], "networkxnotimplemented (class in networkx)": [[1043, "networkx.NetworkXNotImplemented"]], "networkxpointlessconcept (class in networkx)": [[1043, "networkx.NetworkXPointlessConcept"]], "networkxunbounded (class in networkx)": [[1043, "networkx.NetworkXUnbounded"]], "networkxunfeasible (class in networkx)": [[1043, "networkx.NetworkXUnfeasible"]], "nodenotfound (class in networkx)": [[1043, "networkx.NodeNotFound"]], "poweriterationfailedconvergence (class in networkx)": [[1043, "networkx.PowerIterationFailedConvergence"]], "networkx.exception": [[1043, "module-networkx.exception"]], "networkx.classes.function": [[1044, "module-networkx.classes.function"]], "assemble() (argmap method)": [[1045, "networkx.utils.decorators.argmap.assemble"]], "compile() (argmap method)": [[1046, "networkx.utils.decorators.argmap.compile"]], "signature() (argmap class method)": [[1047, "networkx.utils.decorators.argmap.signature"]], "pop() (mappedqueue method)": [[1048, "networkx.utils.mapped_queue.MappedQueue.pop"]], "push() (mappedqueue method)": [[1049, "networkx.utils.mapped_queue.MappedQueue.push"]], "remove() (mappedqueue method)": [[1050, "networkx.utils.mapped_queue.MappedQueue.remove"]], "update() (mappedqueue method)": [[1051, "networkx.utils.mapped_queue.MappedQueue.update"]], "add_cycle() (in module networkx.classes.function)": [[1052, "networkx.classes.function.add_cycle"]], "add_path() (in module networkx.classes.function)": [[1053, "networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, 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"kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], 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module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, 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module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() 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networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, 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networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, 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\ No newline at end of file +Search.setIndex({"docnames": ["auto_examples/3d_drawing/index", "auto_examples/3d_drawing/mayavi2_spring", "auto_examples/3d_drawing/plot_basic", "auto_examples/3d_drawing/sg_execution_times", "auto_examples/algorithms/index", "auto_examples/algorithms/plot_beam_search", "auto_examples/algorithms/plot_betweenness_centrality", "auto_examples/algorithms/plot_blockmodel", "auto_examples/algorithms/plot_circuits", "auto_examples/algorithms/plot_davis_club", "auto_examples/algorithms/plot_dedensification", "auto_examples/algorithms/plot_iterated_dynamical_systems", "auto_examples/algorithms/plot_krackhardt_centrality", "auto_examples/algorithms/plot_parallel_betweenness", "auto_examples/algorithms/plot_rcm", "auto_examples/algorithms/plot_snap", "auto_examples/algorithms/plot_subgraphs", "auto_examples/algorithms/sg_execution_times", "auto_examples/basic/index", "auto_examples/basic/plot_properties", "auto_examples/basic/plot_read_write", 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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], "159": [8, 17, 555, 762], "plot_circuit": [8, 17], "southern": [9, 1265], "women": [9, 1265, 1398, 1406], "unipartit": [9, 115, 258, 259, 358], "properti": [9, 11, 18, 22, 33, 63, 86, 101, 102, 103, 112, 134, 159, 161, 166, 168, 175, 176, 179, 184, 188, 189, 190, 200, 284, 285, 286, 287, 288, 363, 364, 365, 388, 476, 500, 545, 569, 619, 685, 858, 863, 865, 869, 870, 873, 877, 878, 879, 888, 903, 908, 910, 916, 939, 944, 946, 950, 951, 955, 959, 960, 969, 984, 989, 991, 998, 1085, 1086, 1122, 1134, 1136, 1193, 1202, 1217, 1219, 1269, 1283, 1284, 1326, 1328, 1383, 1398, 1405, 1406, 1407, 1408, 1413, 1417, 1426], "These": [9, 52, 58, 73, 79, 86, 93, 94, 105, 112, 336, 385, 494, 512, 559, 671, 673, 732, 748, 779, 786, 1038, 1045, 1047, 1323, 1326, 1385, 1387, 1392, 1394, 1395, 1397, 1399, 1404, 1405, 1411, 1426], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1199, 1393, 1395, 1399, 1402, 1405, 1406, 1407, 1413, 1416], "et": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202], "al": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202, 1407, 1413], "1930": [9, 1396], "thei": [9, 54, 58, 65, 71, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 112, 132, 151, 165, 207, 213, 220, 249, 285, 287, 288, 296, 297, 298, 301, 302, 306, 307, 308, 309, 351, 362, 374, 391, 396, 427, 451, 452, 453, 454, 464, 465, 471, 472, 473, 474, 475, 496, 504, 505, 508, 512, 546, 547, 548, 559, 560, 576, 583, 586, 588, 600, 604, 675, 676, 704, 717, 750, 760, 786, 853, 862, 892, 898, 907, 928, 934, 943, 962, 973, 979, 988, 1002, 1010, 1036, 1038, 1066, 1085, 1088, 1109, 1120, 1126, 1133, 1135, 1137, 1151, 1159, 1165, 1193, 1197, 1198, 1217, 1271, 1272, 1323, 1328, 1353, 1354, 1356, 1357, 1359, 1363, 1394, 1396, 1402, 1404, 1406, 1409, 1414, 1426], "repres": [9, 11, 26, 43, 52, 54, 57, 67, 92, 99, 107, 115, 230, 231, 265, 281, 283, 286, 287, 288, 291, 292, 338, 350, 361, 362, 363, 377, 378, 380, 381, 382, 385, 386, 391, 448, 452, 453, 455, 457, 460, 465, 466, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 521, 565, 577, 578, 579, 580, 586, 588, 609, 615, 618, 619, 656, 660, 664, 667, 676, 679, 691, 692, 695, 697, 698, 699, 700, 702, 728, 730, 731, 734, 736, 739, 752, 786, 791, 796, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1045, 1081, 1102, 1140, 1151, 1185, 1193, 1194, 1196, 1197, 1198, 1199, 1209, 1217, 1240, 1243, 1246, 1250, 1258, 1267, 1269, 1272, 1273, 1278, 1279, 1323, 1324, 1326, 1329, 1330, 1346, 1347, 1388, 1393, 1406], "observ": [9, 13, 132, 223, 1414, 1426], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 64, 66, 71, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1150, 1242, 1250, 1262, 1406, 1408, 1426], "event": [9, 25, 99, 100, 110, 1165, 1229, 1300], "18": [9, 44, 64, 66, 93, 324, 325, 345, 383, 384, 618, 1169, 1249, 1255, 1258, 1260, 1263, 1269, 1393, 1406, 1416, 1417, 1421, 1426], "bipartit": [9, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 350, 351, 358, 377, 439, 440, 443, 581, 588, 758, 1043, 1106, 1151, 1203, 1204, 1205, 1265, 1325, 1395, 1398, 1399, 1400, 1401, 1406, 1407, 1411, 1413, 1417, 1421, 1425], "biadjac": [9, 282, 283, 1400, 1406], "7": [9, 12, 14, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 89, 99, 101, 102, 115, 125, 151, 158, 170, 171, 192, 207, 232, 268, 297, 299, 314, 322, 327, 332, 333, 339, 340, 342, 362, 374, 380, 391, 403, 410, 413, 414, 415, 423, 424, 425, 426, 441, 445, 446, 483, 496, 501, 508, 511, 512, 555, 581, 586, 618, 619, 630, 652, 658, 663, 671, 674, 680, 695, 703, 706, 707, 708, 730, 747, 750, 761, 796, 853, 857, 866, 867, 881, 892, 898, 902, 911, 912, 915, 920, 928, 934, 938, 947, 973, 979, 983, 992, 996, 1010, 1037, 1039, 1040, 1052, 1053, 1085, 1100, 1104, 1148, 1212, 1242, 1248, 1250, 1251, 1255, 1258, 1260, 1273, 1323, 1326, 1330, 1339, 1340, 1345, 1348, 1349, 1350, 1382, 1392, 1394, 1402, 1403, 1405, 1408, 1409, 1410, 1411, 1412, 1413, 1426], "12": [9, 11, 19, 25, 44, 50, 55, 58, 64, 65, 66, 89, 91, 93, 229, 230, 231, 265, 345, 380, 381, 392, 399, 405, 406, 407, 449, 486, 501, 516, 568, 572, 574, 606, 616, 1052, 1053, 1054, 1133, 1136, 1150, 1244, 1245, 1249, 1254, 1257, 1263, 1335, 1406, 1408, 1412, 1426], "9": [9, 11, 12, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 82, 89, 101, 102, 111, 115, 125, 232, 293, 295, 339, 340, 342, 346, 347, 356, 374, 380, 405, 406, 424, 438, 449, 494, 496, 501, 504, 505, 508, 545, 566, 581, 586, 676, 706, 707, 708, 761, 1100, 1104, 1148, 1150, 1194, 1199, 1212, 1217, 1235, 1246, 1255, 1267, 1273, 1283, 1284, 1323, 1326, 1328, 1396, 1403, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "11": [9, 25, 33, 44, 64, 65, 66, 68, 89, 102, 110, 115, 157, 210, 239, 240, 297, 298, 303, 306, 307, 323, 392, 399, 405, 406, 407, 413, 415, 417, 422, 501, 514, 517, 606, 618, 680, 721, 738, 856, 901, 937, 982, 1052, 1053, 1054, 1100, 1150, 1287, 1403, 1410, 1413, 1414, 1419, 1424, 1425, 1426], "13": [9, 11, 38, 44, 64, 66, 89, 91, 156, 229, 230, 231, 343, 501, 703, 855, 900, 936, 981, 1150, 1192, 1406, 1420, 1426], "16": [9, 19, 31, 44, 45, 64, 66, 70, 229, 230, 231, 346, 347, 387, 389, 390, 394, 453, 508, 511, 512, 519, 571, 592, 606, 748, 749, 750, 1109, 1205, 1256, 1271, 1286, 1323, 1406, 1411, 1426], "17": [9, 21, 44, 64, 66, 103, 229, 230, 231, 297, 508, 680, 693, 1405, 1406, 1426], "friend": [9, 545, 1407, 1412], "member": [9, 92, 93, 94, 100, 112, 315, 317, 318, 319, 330, 391, 483, 484, 586, 691, 1222, 1267, 1403], "evelyn": 9, "jefferson": 9, "laura": 9, "mandevil": 9, "theresa": 9, "anderson": 9, "brenda": 9, "roger": 9, "charlott": 9, "mcdowd": 9, "franc": 9, "eleanor": 9, "nye": 9, "pearl": [9, 132], "oglethorp": 9, "ruth": 9, "desand": 9, "vern": 9, "sanderson": 9, "myra": 9, "liddel": 9, "katherina": 9, "sylvia": 9, "avondal": 9, "nora": 9, "fayett": 9, "helen": 9, "lloyd": 9, "dorothi": 9, "murchison": 9, "olivia": 9, "carleton": 9, "flora": 9, "price": 9, "meet": [9, 94, 1165, 1196, 1197, 1198], "50": [9, 25, 30, 34, 40, 50, 54, 55, 56, 57, 64, 65, 272, 312, 1117, 1193, 1197, 1198, 1251, 1297, 1302], "45": [9, 58, 64, 110, 226, 300, 409, 1175], "57": [9, 64], "46": [9, 64, 235, 564, 619, 1264], "24": [9, 19, 37, 64, 66, 68, 103, 383, 384, 496, 505, 508, 703, 1212, 1229, 1244, 1262, 1271, 1403], "32": [9, 64, 66, 68, 209, 211, 212, 383, 384, 564, 703, 1403, 1411], "36": [9, 21, 64, 68, 752, 1150, 1262, 1271, 1353, 1354, 1379, 1403], "31": [9, 64, 66, 229, 230, 231, 260, 261, 262, 289, 383, 384, 409, 703, 1226, 1235, 1403], "40": [9, 50, 64, 80, 101, 297, 300, 555, 672, 1173, 1240, 1271], "38": [9, 17, 64, 688, 1271], "33": [9, 58, 64, 66, 68, 93, 383, 384, 500, 514, 703, 1267, 1271, 1403, 1414], "37": [9, 56, 64, 68, 303, 311, 312, 323, 324, 325, 496, 508, 1039, 1040, 1271, 1393, 1403, 1408, 1425], "43": [9, 64, 324, 325, 606, 1244, 1271], "34": [9, 64, 68, 331, 508, 762, 1271, 1403], "algorithm": [9, 14, 15, 44, 52, 54, 88, 93, 94, 95, 96, 102, 103, 107, 109, 110, 111, 112, 114, 115, 117, 120, 121, 122, 125, 127, 128, 132, 133, 136, 141, 151, 210, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 228, 229, 230, 231, 232, 235, 249, 251, 252, 253, 254, 255, 256, 258, 260, 261, 262, 263, 264, 265, 266, 267, 272, 275, 277, 278, 280, 282, 284, 285, 286, 287, 288, 289, 290, 293, 296, 297, 298, 299, 301, 302, 303, 306, 307, 308, 309, 311, 312, 315, 320, 322, 323, 324, 325, 326, 329, 330, 331, 332, 333, 337, 339, 340, 341, 342, 343, 345, 346, 347, 352, 358, 361, 362, 366, 371, 372, 373, 374, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 389, 390, 394, 399, 405, 406, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 421, 422, 424, 425, 426, 427, 428, 429, 430, 432, 433, 435, 437, 440, 449, 451, 452, 453, 454, 455, 460, 464, 466, 468, 481, 482, 483, 488, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 512, 513, 514, 516, 519, 520, 521, 527, 537, 546, 547, 548, 552, 553, 554, 555, 556, 557, 558, 564, 566, 569, 577, 581, 582, 583, 589, 591, 592, 593, 600, 614, 616, 618, 619, 624, 625, 626, 627, 628, 629, 630, 632, 633, 635, 636, 639, 652, 653, 657, 658, 659, 660, 663, 664, 667, 671, 672, 673, 674, 676, 677, 678, 680, 681, 682, 683, 686, 690, 691, 692, 693, 695, 696, 697, 698, 699, 700, 701, 702, 711, 717, 721, 722, 729, 731, 732, 734, 735, 736, 737, 738, 749, 764, 765, 768, 770, 775, 776, 780, 786, 789, 790, 791, 853, 898, 934, 979, 1011, 1038, 1042, 1043, 1105, 1106, 1107, 1109, 1114, 1116, 1117, 1125, 1126, 1155, 1165, 1168, 1169, 1177, 1178, 1179, 1180, 1181, 1185, 1186, 1187, 1188, 1193, 1195, 1200, 1201, 1202, 1205, 1207, 1209, 1210, 1216, 1223, 1224, 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1235, 1239, 1260, 1269, 1275, 1276, 1277, 1298, 1302, 1319, 1320, 1321, 1323, 1325, 1328, 1367, 1368, 1386, 1393, 1394, 1395, 1400, 1401, 1402, 1403, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1422, 1424, 1425, 1426], "davis_southern_women_graph": [9, 88, 263], "top": [9, 34, 52, 67, 106, 111, 112, 115, 125, 260, 272, 284, 350, 381, 670, 675, 770, 1106, 1134, 1136, 1252, 1396, 1399, 1407, 1412, 1413, 1416], "bottom": [9, 91, 115, 260, 272, 274, 284, 285, 286, 287, 288, 350, 381, 1134, 1136, 1155, 1404, 1416], "biadjacency_matrix": [9, 283], "onto": [9, 284, 285, 286, 287, 288, 559, 560], "projected_graph": [9, 115, 284, 285, 286, 288, 351], "keep": [9, 92, 93, 94, 115, 204, 345, 346, 347, 362, 377, 387, 389, 390, 394, 583, 598, 693, 694, 891, 972, 1117, 1207, 1210, 1278, 1279, 1296, 1376, 1394, 1411, 1414], "co": [9, 26, 94, 99, 144, 752, 1326], "occur": [9, 93, 95, 100, 230, 231, 277, 278, 280, 383, 581, 582, 583, 588, 1043, 1117, 1120, 1126, 1282, 1296], "count": [9, 185, 237, 238, 242, 243, 245, 297, 298, 310, 315, 330, 360, 386, 443, 568, 597, 619, 749, 753, 874, 917, 944, 950, 956, 959, 999, 1060, 1179, 1278, 1279, 1406, 1407, 1416], "share": [9, 54, 58, 92, 94, 112, 165, 199, 214, 215, 216, 221, 278, 285, 287, 288, 294, 358, 359, 376, 418, 419, 460, 462, 480, 569, 578, 691, 732, 862, 887, 907, 925, 943, 968, 988, 1007, 1217, 1328], "contact": [9, 92, 688, 1195, 1326], "weighted_projected_graph": [9, 284, 285, 286, 287, 1417], "648": 9, "105": [9, 17, 77, 78, 517, 518, 1166, 1167], "plot_davis_club": [9, 17], "retain": [10, 102, 110, 230, 284, 285, 286, 287, 288, 1100, 1187, 1295], "pattern": [10, 54, 93, 103, 236, 241, 244, 248, 385, 494, 519, 555, 671, 672, 673, 674, 690, 691, 693, 762, 786, 1036, 1088, 1388, 1413], "add": [10, 11, 26, 34, 41, 45, 49, 52, 61, 71, 88, 89, 91, 93, 94, 101, 102, 105, 106, 115, 151, 152, 153, 154, 156, 157, 158, 164, 207, 222, 223, 229, 282, 285, 341, 374, 411, 412, 423, 428, 430, 431, 450, 460, 581, 582, 583, 589, 614, 615, 618, 619, 654, 690, 701, 717, 718, 796, 850, 853, 854, 855, 856, 857, 892, 895, 898, 899, 900, 901, 902, 928, 931, 934, 935, 936, 937, 938, 973, 976, 979, 980, 981, 982, 983, 984, 1010, 1037, 1038, 1039, 1040, 1042, 1049, 1052, 1053, 1054, 1100, 1154, 1165, 1172, 1185, 1207, 1210, 1217, 1219, 1233, 1234, 1236, 1302, 1326, 1353, 1354, 1356, 1357, 1379, 1380, 1383, 1393, 1394, 1395, 1398, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "compressor": [10, 690, 786], "do": [10, 55, 75, 88, 92, 93, 94, 96, 99, 101, 102, 106, 107, 109, 111, 115, 133, 165, 184, 199, 202, 204, 230, 231, 238, 243, 277, 278, 280, 362, 380, 410, 411, 412, 418, 419, 458, 459, 467, 470, 589, 598, 632, 690, 692, 734, 735, 736, 737, 791, 796, 862, 873, 887, 890, 891, 907, 916, 925, 926, 927, 943, 954, 955, 968, 971, 972, 988, 997, 998, 1007, 1008, 1009, 1037, 1038, 1039, 1040, 1061, 1082, 1102, 1165, 1177, 1189, 1193, 1207, 1210, 1216, 1217, 1227, 1272, 1328, 1393, 1401, 1402, 1407, 1411, 1426], "would": [10, 92, 93, 95, 96, 100, 101, 102, 103, 104, 105, 107, 289, 305, 414, 415, 416, 417, 422, 428, 579, 583, 588, 632, 679, 690, 693, 717, 718, 751, 1217, 1236, 1295, 1296, 1300, 1303, 1326, 1416, 1417], "result": [10, 11, 25, 71, 92, 95, 101, 103, 109, 110, 112, 142, 165, 209, 218, 220, 230, 231, 255, 269, 271, 273, 276, 283, 284, 285, 286, 287, 288, 289, 299, 300, 305, 324, 325, 330, 374, 380, 381, 382, 385, 386, 391, 411, 412, 416, 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89, 115, 156, 165, 199, 207, 236, 237, 248, 265, 267, 268, 423, 425, 426, 469, 555, 690, 796, 855, 862, 887, 892, 900, 907, 925, 928, 936, 943, 968, 973, 981, 988, 1007, 1010, 1037, 1039, 1040, 1065, 1194, 1217, 1291, 1404, 1406, 1413, 1426], "add_edges_from": [10, 15, 16, 36, 41, 70, 82, 89, 115, 132, 151, 158, 165, 199, 204, 207, 236, 248, 287, 327, 376, 422, 423, 425, 426, 460, 469, 501, 511, 512, 572, 574, 588, 688, 690, 705, 706, 707, 709, 730, 742, 743, 796, 853, 857, 862, 887, 891, 892, 898, 902, 907, 925, 927, 928, 934, 938, 943, 956, 962, 963, 968, 972, 973, 979, 983, 988, 999, 1002, 1003, 1007, 1009, 1010, 1037, 1039, 1040, 1070, 1085, 1094, 1135, 1154, 1217, 1287, 1291, 1326, 1404, 1407, 1426], "base_opt": [10, 15], "dict": [10, 15, 19, 25, 39, 54, 57, 58, 67, 70, 88, 101, 102, 107, 109, 144, 145, 148, 157, 159, 160, 165, 168, 169, 176, 179, 184, 189, 190, 195, 197, 200, 202, 204, 207, 220, 237, 239, 240, 252, 290, 309, 310, 329, 334, 336, 353, 408, 411, 412, 416, 422, 427, 470, 473, 481, 482, 496, 502, 512, 545, 561, 563, 565, 566, 575, 577, 578, 579, 580, 588, 614, 628, 631, 636, 637, 638, 640, 642, 644, 645, 646, 647, 648, 649, 662, 666, 669, 687, 688, 691, 705, 706, 707, 713, 715, 749, 750, 760, 796, 849, 856, 858, 859, 862, 865, 870, 873, 878, 879, 884, 888, 890, 891, 892, 894, 901, 903, 904, 907, 910, 916, 923, 926, 927, 928, 930, 931, 935, 937, 939, 940, 943, 946, 947, 951, 955, 960, 965, 969, 971, 972, 973, 975, 976, 980, 982, 984, 985, 988, 991, 992, 998, 1005, 1008, 1009, 1010, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1041, 1045, 1047, 1085, 1086, 1091, 1094, 1097, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1120, 1122, 1126, 1134, 1136, 1193, 1196, 1197, 1198, 1207, 1208, 1213, 1295, 1296, 1302, 1303, 1307, 1324, 1326, 1345, 1348, 1349, 1350, 1355, 1356, 1357, 1358, 1359, 1360, 1361, 1362, 1363, 1364, 1365, 1366, 1382, 1393, 1394, 1395, 1402, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1415, 1416, 1425, 1426], "edgecolor": [10, 15, 21, 32, 34, 35, 38, 54, 58, 82, 83, 1137], "black": [10, 15, 21, 25, 65, 69, 93, 598, 1133, 1134, 1136, 1412, 1413, 1414, 1416, 1426], "ax1": [10, 15, 27, 50, 82], "number_of_edg": [10, 15, 25, 28, 198, 690, 886, 924, 967, 1006, 1059, 1154, 1271, 1406, 1407, 1426], "nonexp_graph": 10, "compression_nod": 10, "summar": [10, 15, 100, 101, 690, 691, 758, 791, 1325, 1328, 1413], "dedensifi": [10, 758], "threshold": [10, 57, 83, 112, 220, 229, 231, 380, 381, 690, 692, 695, 696, 758, 786, 1117, 1193, 1194, 1196, 1197, 1198, 1325, 1398, 1406, 1407, 1408, 1412, 1414], "copi": [10, 16, 38, 44, 93, 95, 106, 167, 196, 199, 202, 203, 204, 205, 284, 285, 286, 287, 288, 341, 388, 390, 392, 406, 433, 434, 435, 436, 437, 453, 460, 469, 521, 584, 585, 587, 596, 599, 602, 603, 605, 606, 607, 610, 611, 613, 614, 633, 636, 690, 864, 885, 887, 890, 891, 909, 925, 926, 927, 945, 963, 966, 968, 971, 972, 990, 1003, 1007, 1008, 1009, 1035, 1038, 1057, 1061, 1063, 1066, 1082, 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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, 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1302, 1315, 1323, 1353, 1354, 1379, 1380, 1394, 1395, 1406], "digit": [11, 70, 99], "base": [11, 15, 38, 43, 55, 58, 69, 93, 94, 100, 101, 102, 103, 107, 128, 132, 199, 203, 205, 212, 216, 220, 229, 296, 297, 301, 302, 303, 308, 309, 310, 311, 312, 322, 323, 324, 325, 329, 330, 337, 343, 346, 347, 362, 371, 373, 374, 380, 381, 382, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 425, 426, 427, 428, 430, 431, 449, 464, 466, 494, 498, 499, 500, 509, 510, 545, 555, 564, 566, 569, 574, 581, 614, 616, 660, 667, 680, 688, 691, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 717, 732, 738, 758, 761, 762, 786, 791, 796, 887, 925, 934, 935, 968, 979, 980, 1007, 1036, 1037, 1038, 1041, 1043, 1082, 1088, 1182, 1229, 1235, 1253, 1267, 1296, 1320, 1321, 1323, 1326, 1383, 1387, 1392, 1395, 1402, 1403, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1421, 1425], "obtain": [11, 91, 165, 207, 282, 345, 346, 347, 380, 383, 387, 388, 389, 390, 394, 465, 511, 606, 618, 619, 656, 722, 742, 743, 760, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1164, 1253, 1272, 1278, 1279, 1323, 1326, 1356, 1357, 1402, 1426], "seri": [11, 444, 616, 680, 1215, 1286], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1177, 1179, 1192, 1222], "end": [11, 25, 36, 52, 95, 101, 106, 153, 154, 206, 215, 227, 267, 268, 300, 332, 333, 342, 371, 372, 427, 614, 618, 619, 626, 627, 631, 632, 634, 635, 636, 639, 640, 650, 651, 652, 653, 654, 655, 660, 664, 667, 677, 678, 680, 734, 736, 1038, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1133, 1135, 1152, 1165, 1206, 1229, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1342, 1344, 1350, 1353, 1357, 1358, 1368, 1371, 1372, 1375, 1376, 1379, 1404, 1413], "In": [11, 16, 27, 43, 54, 57, 58, 88, 92, 93, 94, 95, 97, 99, 100, 101, 103, 110, 115, 127, 132, 133, 175, 184, 199, 217, 229, 230, 231, 235, 240, 257, 258, 259, 278, 283, 286, 288, 289, 299, 311, 312, 324, 325, 329, 350, 357, 378, 379, 380, 410, 413, 414, 415, 422, 429, 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86, 455, 1226, 1268, 1302, 1413], "mile": [23, 47, 86, 1406, 1413], "multipartit": [23, 47, 86, 1109, 1151, 1162, 1395, 1406, 1407, 1413], "rainbow": [23, 47, 86, 1413], "geometr": [23, 47, 86, 105, 356, 1196, 1197, 1198, 1264, 1325, 1407, 1408, 1413], "sampson": [23, 47, 86, 1406], "self": [23, 45, 47, 52, 69, 86, 88, 89, 101, 152, 158, 168, 176, 180, 189, 224, 246, 247, 304, 321, 328, 331, 335, 345, 346, 347, 355, 356, 360, 432, 433, 434, 435, 436, 437, 438, 453, 467, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 519, 567, 575, 584, 585, 587, 593, 612, 619, 625, 675, 700, 735, 737, 854, 857, 865, 870, 871, 878, 899, 902, 910, 914, 935, 938, 946, 951, 952, 959, 960, 980, 983, 991, 995, 1038, 1060, 1075, 1102, 1103, 1105, 1135, 1173, 1175, 1177, 1179, 1185, 1193, 1196, 1197, 1198, 1199, 1217, 1222, 1239, 1281, 1325, 1326, 1330, 1353, 1354, 1389, 1401, 1403, 1406, 1408, 1411, 1412, 1413, 1414, 1417, 1425], "loop": [23, 45, 47, 52, 69, 86, 224, 230, 231, 246, 247, 304, 321, 328, 331, 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411, 412, 413, 414, 415, 416, 417, 418, 419, 791, 1042, 1402, 1407], "closer": [43, 752, 1394, 1414], "particularli": [43, 94, 97, 1269], "strike": 43, "pull": [43, 91, 93, 96, 97, 99, 100, 101, 104, 106, 107, 111, 1408, 1410, 1411, 1412, 1413, 1414, 1416, 1425], "apart": [43, 1117, 1193], "effect": [43, 102, 103, 112, 152, 303, 323, 434, 438, 450, 476, 688, 762, 791, 796, 854, 899, 935, 980, 1037, 1039, 1040, 1177, 1222, 1302, 1404], "c0": 43, "332": 43, "remove_edg": [43, 89, 193, 390, 391, 397, 500, 690, 699, 740, 741, 882, 921, 963, 1003, 1393, 1394, 1426], "334": 43, "335": 43, "336": [43, 441, 445, 446], "337": 43, "338": 43, "339": 43, "plot_spectral_grid": [43, 47], "christofid": [44, 112, 232, 1413], "calcul": [44, 56, 223, 280, 295, 297, 298, 299, 305, 306, 307, 315, 316, 317, 318, 319, 320, 329, 335, 336, 341, 380, 385, 391, 470, 476, 564, 566, 614, 619, 626, 627, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 658, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 673, 749, 776, 791, 1165, 1199, 1404, 1407, 1412, 1413, 1416], "rout": [44, 49, 55, 79, 85, 86, 112, 1038, 1039, 1040, 1199], "minim": [44, 56, 102, 112, 115, 144, 227, 228, 229, 230, 231, 232, 280, 341, 422, 449, 470, 494, 501, 583, 619, 657, 691, 786, 1043, 1106, 1107, 1109, 1114, 1117, 1199, 1200, 1250, 1323, 1405, 1425], "cost": [44, 101, 102, 112, 227, 229, 230, 231, 235, 458, 459, 471, 472, 473, 474, 475, 494, 496, 497, 501, 504, 505, 508, 626, 627, 632, 633, 635, 636, 652, 663, 671, 672, 673, 674, 719, 733, 758, 1036, 1081, 1085, 1088, 1098, 1100, 1102, 1104, 1108, 1296, 1399, 1402, 1405, 1406, 1412], "19": [44, 64, 66, 77, 93, 300, 362, 485, 486, 487, 500, 501, 1406, 1409, 1426], "approxim": [44, 93, 209, 210, 211, 212, 213, 214, 215, 216, 218, 219, 220, 221, 226, 227, 228, 229, 230, 231, 232, 235, 296, 297, 306, 422, 673, 674, 675, 681, 682, 683, 684, 758, 1043, 1115, 1165, 1234, 1269, 1325, 1395, 1399, 1400, 1406, 1407, 1413, 1422, 1425], "nx_app": 44, "depot": 44, 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45, "tos": 45, "get_al": 45, "cc": [45, 71, 127, 142, 143, 322, 423, 425, 1413], "resent_to": 45, "resent": 45, "resent_cc": 45, "all_recipi": 45, "now": [45, 54, 75, 76, 93, 97, 101, 132, 380, 754, 762, 963, 1003, 1177, 1217, 1278, 1279, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1425, 1426], "mail": [45, 92, 93, 94, 99, 100, 103, 104, 106, 1393, 1404, 1405, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "target_nam": 45, "target_addr": 45, "227": 45, "166": [45, 47, 1414], "plot_unix_email": [45, 47], "elarg": 46, "esmal": 46, "700": 46, "font_famili": [46, 1133, 1134, 1136], "san": [46, 132, 732, 1133, 1134, 1136, 1239], "serif": [46, 1133, 1134, 1136], "edge_label": [46, 67, 1134], "get_edge_attribut": [46, 1085, 1404], "draw_networkx_edge_label": [46, 67, 1130, 1133, 1135, 1136, 1137, 1413], "121": [46, 47, 1326, 1426], 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1414, 1416, 1422, 1425, 1426], "geopanda": [52, 54, 55, 56, 57, 58, 107], "interoper": [52, 96, 1347], "storag": [52, 101, 786, 1326], "mechan": [52, 99, 101, 102, 110, 274, 358, 383, 385, 1328, 1383, 1408, 1410], "databas": [52, 426, 786], "panda": [52, 54, 57, 93, 101, 107, 1097, 1099, 1100, 1103, 1104, 1325, 1395, 1405, 1406, 1412, 1413, 1414], "tabular": 52, "orient": [52, 70, 92, 164, 206, 338, 450, 615, 618, 619, 636, 701, 708, 716, 717, 718, 752, 753, 789, 791, 1282, 1365, 1395], "well": [52, 55, 58, 92, 97, 99, 103, 104, 105, 107, 109, 110, 165, 166, 168, 175, 179, 184, 188, 189, 210, 305, 329, 380, 398, 468, 545, 601, 629, 688, 733, 761, 762, 862, 863, 865, 869, 873, 877, 878, 907, 908, 910, 916, 943, 944, 946, 950, 955, 960, 988, 989, 991, 998, 1055, 1148, 1199, 1278, 1279, 1302, 1303, 1326, 1393, 1404, 1425, 1426], "wide": [52, 93, 105, 568, 572, 619, 775], "predic": [52, 58], "intersect": [52, 55, 211, 477, 478, 616, 617, 732, 758, 772, 1110, 1203, 1204, 1205, 1206, 1217, 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100, 103, 105, 106, 107, 109, 1326, 1328, 1393, 1394, 1403, 1404, 1419], "speed": [52, 56, 107, 215, 291, 292, 346, 347, 423, 427, 509, 796, 1037, 1039, 1040, 1133, 1135, 1173, 1194, 1393, 1402, 1406, 1408, 1410, 1411, 1412, 1413, 1414, 1425], "bear": 52, "also": [52, 54, 55, 56, 57, 58, 63, 75, 88, 92, 93, 94, 95, 97, 99, 101, 102, 103, 107, 110, 111, 156, 159, 162, 168, 176, 177, 180, 184, 189, 190, 200, 207, 208, 211, 226, 230, 280, 287, 293, 301, 302, 303, 308, 309, 323, 324, 325, 342, 369, 388, 391, 411, 412, 416, 417, 418, 419, 423, 424, 425, 427, 435, 440, 450, 464, 465, 466, 467, 470, 500, 501, 502, 503, 506, 507, 508, 509, 511, 512, 545, 555, 577, 581, 585, 587, 597, 600, 604, 605, 607, 610, 611, 612, 615, 618, 676, 679, 688, 690, 691, 741, 760, 761, 786, 796, 850, 855, 858, 860, 865, 870, 871, 873, 878, 879, 888, 892, 895, 900, 903, 905, 910, 914, 916, 928, 931, 936, 939, 941, 946, 948, 951, 952, 955, 960, 969, 973, 976, 981, 984, 986, 991, 993, 995, 998, 1010, 1037, 1039, 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94, 105, 161, 679], "represent": [52, 110, 202, 204, 237, 242, 245, 246, 247, 265, 266, 268, 282, 283, 327, 512, 555, 629, 728, 730, 762, 786, 890, 891, 926, 971, 972, 1008, 1091, 1092, 1094, 1095, 1098, 1099, 1100, 1101, 1117, 1120, 1126, 1130, 1270, 1281, 1326, 1332, 1335, 1336, 1339, 1341, 1347, 1370, 1383, 1393, 1399, 1405, 1406, 1413], "primal": [52, 55, 508, 581], "dual": [52, 54, 55, 581, 1227, 1410, 1413], "sens": [52, 97, 99, 104, 199, 310, 460, 586, 791, 887, 925, 968, 1007, 1217, 1234, 1269, 1326, 1403, 1404], "approach": [52, 55, 99, 101, 103, 104, 107, 115, 341, 345, 462, 464, 466, 500, 519, 616, 678, 1094, 1175, 1188, 1202, 1222, 1407, 1413], "segment": [52, 55, 338], "major": [52, 95, 98, 99, 100, 102, 103, 104, 106, 107, 1393, 1394, 1403, 1404, 1407], "studi": [52, 91, 110, 606, 1192, 1196, 1323, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "topologi": [52, 55, 435, 436, 512, 681, 683, 748, 1202, 1217, 1225, 1229, 1233, 1241, 1326], "encod": [52, 55, 58, 67, 99, 141, 249, 267, 268, 619, 758, 775, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1345, 1348, 1349, 1350, 1354, 1355, 1358, 1363, 1368, 1371, 1372, 1375, 1376, 1382, 1406, 1407, 1412], "angular": [52, 55], "inform": [52, 66, 92, 93, 99, 100, 101, 102, 103, 107, 111, 112, 121, 132, 159, 165, 200, 202, 204, 220, 226, 230, 231, 249, 301, 302, 303, 308, 309, 314, 323, 324, 325, 338, 405, 406, 438, 453, 455, 480, 488, 500, 512, 564, 566, 568, 572, 573, 574, 583, 592, 614, 619, 624, 691, 775, 782, 786, 796, 858, 862, 888, 890, 891, 903, 907, 926, 927, 939, 943, 969, 971, 972, 984, 988, 1008, 1009, 1037, 1039, 1040, 1042, 1112, 1141, 1143, 1185, 1206, 1214, 1216, 1217, 1218, 1219, 1267, 1280, 1290, 1296, 1356, 1373, 1375, 1376, 1381, 1383, 1389, 1390, 1393, 1394, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "angl": [52, 55, 1114, 1116], "instead": [52, 93, 94, 101, 102, 103, 106, 141, 165, 170, 282, 320, 338, 366, 370, 390, 392, 399, 405, 406, 407, 411, 412, 416, 417, 418, 419, 424, 425, 427, 500, 561, 562, 563, 585, 587, 632, 727, 729, 731, 733, 734, 735, 736, 737, 796, 862, 866, 907, 911, 943, 947, 988, 992, 1037, 1038, 1039, 1040, 1097, 1102, 1103, 1124, 1127, 1135, 1172, 1179, 1184, 1186, 1192, 1193, 1199, 1207, 1217, 1300, 1342, 1375, 1383, 1393, 1394, 1395, 1397, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1420, 1421, 1423, 1424, 1425, 1426], "nonplanar": [52, 1250], "form": [52, 55, 110, 151, 170, 220, 238, 377, 381, 391, 422, 427, 440, 449, 450, 451, 488, 500, 517, 521, 567, 568, 569, 570, 571, 572, 573, 574, 578, 579, 580, 588, 589, 677, 679, 697, 711, 717, 718, 719, 729, 730, 731, 748, 752, 767, 786, 791, 853, 866, 898, 911, 934, 947, 979, 992, 1038, 1064, 1085, 1146, 1167, 1199, 1206, 1215, 1217, 1222, 1240, 1243, 1245, 1248, 1252, 1399, 1406, 1407, 1426], "flow": [52, 66, 105, 278, 296, 301, 302, 303, 308, 309, 323, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 427, 428, 430, 431, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 519, 559, 756, 758, 1267, 1325, 1395, 1399, 1400, 1403, 1406, 1407, 1408, 1411, 1414, 1425], "dead": 52, "detail": [52, 53, 86, 92, 93, 97, 99, 100, 128, 252, 253, 256, 257, 258, 259, 260, 277, 278, 281, 282, 284, 285, 286, 287, 288, 297, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 422, 427, 476, 494, 498, 499, 500, 509, 510, 511, 512, 574, 691, 711, 720, 735, 737, 791, 796, 1037, 1039, 1040, 1102, 1105, 1133, 1137, 1140, 1207, 1296, 1319, 1345, 1348, 1349, 1350, 1393, 1399, 1400, 1401, 1402, 1406, 1413, 1414, 1426], "methodologi": 52, "avail": [52, 93, 99, 100, 101, 103, 141, 184, 226, 232, 280, 422, 425, 426, 585, 587, 780, 873, 916, 955, 998, 1039, 1042, 1194, 1196, 1197, 1198, 1328, 1331, 1334, 1393, 1394, 1396, 1402, 1405, 1406, 1409, 1412, 1413, 1426], "1016": [52, 112, 226, 231, 274, 297, 298, 299, 303, 306, 307, 313, 322, 323, 338, 346, 347, 455, 1233], "compenvurbsi": 52, "2017": [52, 227, 512, 1207, 1208, 1406, 1407], "004": [52, 341], "scienc": [52, 91, 101, 105, 107, 109, 110, 112, 219, 228, 249, 296, 301, 302, 303, 308, 309, 323, 346, 347, 409, 412, 431, 441, 445, 446, 453, 476, 498, 618, 619, 680, 681, 683, 1203, 1223, 1255], "pydata": [52, 1413, 1423, 1424, 1425], "stack": [52, 111, 346, 693, 1045, 1046], "showcas": [53, 86, 93, 109], "analys": [53, 70, 86, 310], "ecosystem": [53, 86, 99, 100, 104, 107, 110, 1425], "descript": [53, 86, 93, 97, 464, 466, 704, 717, 786, 1130, 1131, 1132, 1133, 1138, 1139, 1140, 1141, 1142, 1207, 1222, 1242, 1407, 1411, 1413, 1421, 1422, 1425], "plu": [54, 386, 583, 1036, 1088, 1148, 1253], "voronoi": [54, 752, 758, 1325, 1407], "cholera": [54, 57], "broad": [54, 57, 1296], "pump": [54, 57], "record": [54, 57, 94, 99, 691, 1426], "john": [54, 57, 91, 278, 568, 572, 685, 1205, 1250, 1408, 1413], "snow": [54, 57], "1853": [54, 57], "method": [54, 57, 58, 75, 88, 92, 93, 95, 101, 102, 103, 107, 112, 143, 161, 164, 165, 185, 186, 187, 190, 200, 202, 204, 206, 207, 226, 231, 232, 250, 260, 261, 262, 299, 301, 302, 303, 308, 309, 311, 312, 323, 324, 336, 374, 376, 379, 380, 381, 385, 423, 440, 451, 462, 476, 500, 514, 527, 537, 545, 564, 566, 568, 572, 581, 583, 600, 604, 615, 632, 633, 635, 636, 654, 655, 656, 671, 672, 673, 674, 684, 692, 719, 720, 733, 738, 752, 775, 786, 852, 862, 874, 875, 876, 879, 888, 890, 891, 892, 897, 907, 917, 918, 919, 926, 927, 928, 933, 934, 935, 943, 956, 957, 958, 971, 972, 973, 978, 979, 980, 988, 999, 1000, 1001, 1008, 1009, 1010, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1033, 1038, 1043, 1044, 1045, 1046, 1066, 1174, 1182, 1184, 1193, 1197, 1275, 1276, 1277, 1280, 1296, 1301, 1302, 1323, 1326, 1363, 1395, 1399, 1403, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1422, 1425, 1426], "shown": [54, 57, 100, 102, 517, 518, 947, 992, 1275, 1276, 1277, 1300, 1349, 1404], "centroid": [54, 57, 58], "libpys": [54, 55, 57, 58], "cg": [54, 102, 296, 301, 302, 303, 308, 309, 323, 588], "voronoi_fram": 54, "contextili": [54, 55, 57], "add_basemap": [54, 55, 57], "geopackag": [54, 55, 56, 57], "sqlite": [54, 57], "reli": [54, 57, 99, 103, 362, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 502, 503, 506, 507, 1393, 1407, 1411, 1425], "fiona": [54, 57], "level": [54, 57, 101, 103, 104, 106, 111, 112, 115, 125, 165, 220, 322, 334, 336, 374, 380, 381, 387, 389, 390, 394, 423, 427, 640, 691, 770, 786, 862, 907, 943, 988, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1094, 1108, 1155, 1202, 1207, 1208, 1236, 1296, 1323, 1328, 1396, 1399, 1407, 1412, 1413, 1414], "interfac": [54, 57, 58, 75, 76, 96, 98, 99, 101, 102, 107, 109, 110, 184, 429, 496, 673, 758, 761, 762, 780, 873, 916, 955, 998, 1042, 1044, 1326, 1328, 1393, 1396, 1398, 1402, 1404, 1405, 1406, 1409, 1413, 1414, 1426], "kind": [54, 57, 58, 92, 93, 94, 99, 208, 466, 722, 1202, 1326, 1383], "read_fil": [54, 55, 57, 58], "cholera_cas": [54, 57], "gpkg": [54, 56, 57], "correctli": [54, 164, 324, 325, 1393, 1404, 1406, 1411, 1412, 1419], "construct": [54, 55, 56, 57, 58, 67, 94, 102, 227, 229, 230, 231, 232, 269, 273, 276, 352, 423, 450, 460, 513, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 609, 685, 695, 708, 716, 732, 1046, 1047, 1052, 1053, 1101, 1102, 1103, 1104, 1105, 1153, 1154, 1175, 1177, 1178, 1180, 1186, 1190, 1191, 1192, 1195, 1203, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1229, 1236, 1251, 1259, 1263, 1269, 1272, 1278, 1279, 1296, 1323, 1327, 1395, 1399, 1406, 1409, 1415], "column_stack": [54, 57, 58], "could": [54, 93, 101, 102, 103, 165, 215, 216, 224, 581, 679, 862, 907, 943, 988, 1066, 1094, 1102, 1103, 1120, 1126, 1174, 1296, 1300, 1326, 1393, 1404, 1414, 1426], "present": [54, 58, 93, 107, 110, 132, 184, 220, 226, 315, 316, 330, 357, 359, 429, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 567, 581, 594, 595, 597, 600, 601, 604, 632, 633, 635, 636, 659, 670, 749, 786, 873, 916, 955, 998, 1043, 1045, 1061, 1082, 1150, 1152, 1157, 1159, 1160, 1163, 1165, 1278, 1279, 1353, 1354, 1357, 1381, 1383, 1407, 1411, 1426], "alongsid": [54, 438], "diagram": [54, 132, 381, 752], "intrins": 54, "put": [54, 92, 95, 102, 226, 1326, 1404, 1406], "underli": [54, 101, 102, 132, 152, 157, 158, 161, 195, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 427, 428, 490, 491, 500, 615, 742, 743, 791, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1038, 1225, 1233, 1241, 1326, 1393, 1394, 1402], "quickli": [54, 1239], "Be": [54, 92, 1038, 1135, 1404], "care": [54, 92, 100, 102, 106, 107, 109, 115, 156, 855, 900, 936, 981, 1038, 1326, 1404, 1406], "bound": [54, 112, 214, 215, 216, 217, 220, 224, 227, 264, 300, 342, 352, 437, 440, 675, 1043, 1165, 1235, 1319, 1413, 1414, 1416], "box": [54, 107, 1134, 1136, 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1407, 1411, 1412, 1413], "levenson": 91, "haochen": [91, 1409, 1411], "wu": [91, 593, 729, 731, 1409, 1411], "roper": 91, "christoph": [91, 1410, 1412], "ellison": 91, "eppstein": [91, 277, 467, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 734, 736, 1407], "federico": [91, 1409, 1412], "rosato": [91, 1409, 1412], "aitor": 91, "almeida": 91, "ferran": [91, 1407], "par\u00e9": [91, 373, 1407], "christian": [91, 297], "olsson": 91, "fredrik": [91, 1410], "erlandsson": [91, 1410], "nanda": [91, 1411], "krishna": [91, 1411], "nichola": [91, 1185], "fred": 91, "morstatt": 91, "olli": 91, "glass": 91, "rodrigo": [91, 1408], "dorant": [91, 1408], "gilardi": [91, 1408], "pranai": [91, 1409], "kanwar": [91, 1409], "balint": 91, "tillman": [91, 1207, 1208, 1210], "diederik": 91, "lier": 91, "ferdinando": 91, "papal": 91, "miguel": [91, 334, 335, 1409], "sozinho": [91, 1409], "ramalho": [91, 1409], "brandon": 91, "liu": [91, 426, 512], "nima": 91, "mohammadi": 91, "jason": [91, 1413], 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427, 432, 433, 437, 512, 573, 619, 625, 685, 693, 1153, 1169, 1170, 1171, 1185, 1227, 1269, 1280], "access": [93, 101, 112, 125, 151, 168, 189, 429, 471, 472, 473, 474, 475, 496, 606, 626, 627, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 693, 721, 760, 770, 789, 796, 853, 865, 878, 898, 910, 915, 934, 946, 960, 979, 991, 996, 1037, 1038, 1039, 1040, 1135, 1326, 1392, 1393, 1394, 1396, 1398, 1399, 1402, 1406, 1407, 1408, 1410], "cheong": 93, "se": 93, "hang": 93, "yain": 93, "whar": 93, "schemat": 93, "placement": [93, 614], "survei": [93, 110, 564, 566, 581, 786, 1201], "2020": [93, 99, 100, 101, 102, 569, 1406, 1412], "1177": 93, "2f1473871618821740": 93, "upload": [93, 106, 217], "pdf": [93, 105, 110, 112, 128, 214, 215, 216, 217, 220, 235, 305, 311, 312, 315, 322, 324, 325, 330, 342, 355, 356, 373, 410, 411, 412, 413, 414, 415, 417, 426, 427, 430, 442, 447, 448, 476, 483, 490, 494, 511, 512, 519, 564, 566, 567, 570, 571, 573, 618, 619, 690, 693, 748, 749, 750, 760, 762, 1193, 1197, 1198, 1326, 1407, 1412, 1426], "docx": 93, "ppt": 93, "lectur": [93, 110, 412, 431, 498, 616, 1203], "wayback": [93, 1413], "machin": [93, 312, 331, 494, 511, 512, 762, 1396, 1406, 1413], "snapshot": 93, "unreach": 93, "pyarg": [93, 111, 1038], "tell": [93, 99, 102, 760, 1275, 1278, 1279, 1296, 1328, 1412], "compar": [93, 464, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 559, 560, 561, 562, 563, 615, 760, 782, 1165, 1302, 1414], "baselin": [93, 1134, 1136], "ones": [93, 99, 107, 109, 282, 680, 1038, 1395, 1402, 1404], "savefig": [93, 1426], "mpl_image_compar": 93, "test_barbel": 93, "barbel": [93, 293, 294, 391, 424, 1146, 1157, 1276, 1426], "conduct": [93, 96, 100, 109, 447, 448, 758], "contributor": [94, 96, 99, 105, 106, 110, 1271, 1323, 1403], "shepherd": [94, 99], "mission": [94, 96, 97, 100, 107], "approv": [94, 100], "nuclear": 94, "launch": 94, "carefulli": 94, "clean": [94, 106, 530, 540, 1300, 1406, 1407, 1411, 1413, 1420], "nearli": 94, "volunt": [94, 107, 1413], "tremend": 94, "felt": 94, "evalu": [94, 130, 152, 157, 158, 195, 330, 618, 619, 626, 627, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1296, 1417], "novic": 94, "strongli": [94, 217, 232, 388, 391, 397, 398, 399, 403, 405, 406, 423, 480, 491, 492, 519, 588, 633, 697, 699, 751, 753, 1185, 1402, 1406, 1411, 1414, 1417, 1425], "mentorship": [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, 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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, 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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, 1384], "0rc2": [96, 110, 1325], "dev0": [96, 110, 1325], "2023": [96, 110, 1325], "about": [96, 99, 100, 101, 103, 111, 115, 230, 231, 249, 413, 423, 488, 494, 498, 499, 509, 510, 619, 761, 762, 1038, 1061, 1066, 1141, 1217, 1296, 1323, 1326, 1406, 1407, 1411, 1412, 1413, 1414, 1416, 1422, 1426], "emeritu": 96, "introduct": [96, 110, 311, 312, 324, 325, 383, 385, 464, 466, 618, 619, 1155, 1269, 1302, 1325, 1411], "guidelin": [96, 99, 1416, 1419], "divers": [96, 107], "enforc": [96, 115, 693, 694, 1419, 1425], "endnot": 96, "diverg": [96, 1187, 1325, 1395], "upstream": [96, 464, 1419], "comparison": [96, 107, 231, 464, 494, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 561, 562, 563, 615, 671, 673, 1413], "mentor": [96, 109, 1413, 1414, 1425], "pedagog": [96, 109, 347, 452, 722, 1405, 1414], "me": [96, 1393], "roadmap": [96, 1412, 1413], 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"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": [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], 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"compet": [99, 583], "accordingli": [99, 454, 1110, 1407, 1425], "supersed": [99, 104], "render": [99, 216, 410, 413, 1406], "obsolet": [99, 267, 1337, 1406, 1407], "never": [99, 184, 388, 608, 873, 916, 955, 998, 1236], "meant": [99, 291, 292, 631, 1217, 1326, 1413, 1417], "concret": [99, 100], "think": [99, 102, 230, 231, 299, 761, 1426], "bodi": [99, 1243], "briefli": 99, "sentenc": [99, 100], "substant": 99, "pipermail": 99, "2018": [99, 315, 330, 437, 1406, 1408, 1409], "june": [99, 691, 1255, 1398, 1402, 1406, 1419, 1420], "078345": 99, "verg": 99, "chanc": [99, 230, 1234, 1296], "period": [99, 1211, 1212, 1213, 1215, 1297, 1403, 1406, 1412], "beyond": [99, 107, 383, 1210, 1236], "fine": 99, "shouldn": [99, 102], "rigid": 99, "compromis": 99, "followup": [99, 1413], "notifi": [99, 1414], "celebratori": 99, "emoji": 99, "again": [99, 428, 761, 1217, 1403, 1407, 1411, 1416], "unusu": [99, 1393], "disagr": [99, 100], "escal": [99, 100], "controversi": [99, 107], "ultim": 99, "practic": [99, 210, 220, 481, 482, 494, 619, 653, 1328, 1405], "precis": [99, 312, 568, 572, 581, 1269, 1395, 1409], "natur": [99, 102, 109, 376, 443, 466, 585, 587, 618, 753, 1154, 1217, 1225, 1241, 1296, 1326, 1393, 1410], "utf": [99, 267, 268, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1355, 1358, 1368, 1371, 1372, 1375, 1376, 1387, 1406], "restructuredtext": 99, "restructuredtextprim": 99, "dd": [99, 104, 1094], "mmm": 99, "yyyi": [99, 104], "dom": 99, "ain": 99, "separ": [99, 102, 106, 152, 157, 158, 195, 214, 215, 258, 265, 266, 267, 268, 299, 322, 343, 427, 428, 454, 464, 758, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1045, 1112, 1116, 1193, 1195, 1325, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1369, 1370, 1371, 1372, 1395, 1406, 1407, 1412, 1413, 1425, 1426], "older": 99, "brows": 99, "colgat": [100, 110], "deadlock": 100, "websit": [100, 106, 1165, 1382, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "ongo": [100, 1405], "trust": [100, 1381, 1383], "cast": [100, 101, 1412, 1422, 1425], "vote": [100, 337, 1412], "therebi": 100, "adher": 100, "nomin": 100, "lazi": [100, 1283, 1284], "unanim": 100, "agreement": [100, 1202], "initi": [100, 102, 141, 230, 231, 282, 315, 324, 325, 338, 373, 377, 378, 466, 495, 511, 512, 525, 535, 615, 692, 719, 733, 796, 850, 895, 931, 976, 1037, 1039, 1040, 1102, 1105, 1108, 1117, 1185, 1186, 1187, 1188, 1223, 1227, 1234, 1278, 1279, 1296, 1302, 1323, 1394, 1395, 1406, 1411, 1412, 1413, 1414], "voic": 100, "smooth": 100, "strateg": 100, "plan": [100, 1394, 1405, 1407, 1413], "fund": [100, 1414, 1425], "theirs": 100, "pursu": 100, "pictur": 100, "perspect": [100, 104, 1195, 1326], "timefram": 100, "entiti": [100, 1345, 1348, 1349, 1350, 1382, 1426], "occasion": [100, 230], "seek": [100, 762, 1352, 1354, 1378, 1380, 1387], "tri": [100, 112, 343, 380, 931, 976, 1039, 1040, 1175, 1181, 1225, 1237, 1238, 1404], "distinguish": [100, 934, 962, 979, 1002, 1040], "fundament": [100, 107, 110, 338, 449, 618, 619, 1217, 1413], "flaw": 100, "forward": [100, 217, 450, 711, 717, 718], "typo": [100, 1396, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1421], "land": 100, "outlin": [100, 249, 336, 462, 1407], "templat": [100, 1413], "taken": [100, 101, 145, 148, 207, 443, 450, 717, 718, 749, 761, 892, 928, 973, 1010, 1117, 1409], "suffici": [100, 101, 1326], "scikit": [100, 103, 109], "expos": [101, 374, 1405], "nodeview": [101, 184, 391, 598, 599, 601, 602, 603, 604, 695, 873, 916, 955, 998, 1036, 1088, 1349, 1362, 1404, 1407], "nodedataview": [101, 184, 391, 591, 592, 600, 873, 916, 955, 998, 1217, 1426], "edgeview": [101, 590, 591, 592, 598, 599, 600, 601, 602, 603, 604, 612, 624, 770, 910, 1036, 1088, 1098, 1404, 1413], "edgedataview": [101, 168, 189, 865, 878, 910, 946, 960, 991, 1098, 1217, 1362, 1412, 1426], "semant": [101, 531, 541, 762, 1403, 1405], "inher": [101, 220, 427], "impli": [101, 110, 132, 220, 312, 314, 327, 455, 466, 511, 512, 545, 1296], "element": [101, 102, 230, 231, 270, 291, 292, 311, 350, 371, 391, 457, 464, 518, 559, 560, 578, 579, 580, 586, 640, 656, 671, 673, 675, 677, 728, 730, 739, 749, 752, 1036, 1038, 1048, 1049, 1050, 1051, 1087, 1088, 1135, 1137, 1173, 1206, 1211, 1212, 1217, 1237, 1238, 1240, 1249, 1272, 1277, 1278, 1279, 1282, 1287, 1288, 1296, 1302, 1303, 1311, 1318, 1323, 1355, 1358, 1361, 1362, 1405], "intend": [101, 104, 107, 111, 327, 567, 1038, 1269, 1296, 1393], "impos": [101, 103, 545, 791], "due": [101, 102, 109, 231, 264, 440, 581, 583, 626, 627, 1217, 1405, 1412, 1414, 1423, 1425], "bit": [101, 209, 211, 212, 453, 511, 512, 786, 1345, 1348, 1349, 1350, 1382, 1411], "lot": [101, 452, 1326, 1405], "screen": 101, "instinct": 101, "error": [101, 102, 152, 157, 158, 195, 280, 288, 296, 311, 324, 414, 422, 471, 472, 473, 474, 475, 489, 497, 501, 504, 505, 508, 556, 557, 558, 564, 566, 581, 584, 653, 660, 667, 675, 676, 796, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1037, 1043, 1117, 1144, 1396, 1401, 1404, 1406, 1407, 1411, 1412, 1413, 1414, 1417, 1419, 1425], "definit": [101, 132, 235, 238, 243, 289, 291, 292, 303, 323, 342, 356, 398, 435, 437, 464, 467, 549, 550, 551, 608, 618, 619, 620, 625, 676, 685, 687, 700, 735, 737, 791, 1192, 1193, 1197, 1217, 1235, 1287, 1326, 1406, 1413, 1426], "coupl": [101, 102, 132, 1257, 1402, 1404], "realis": 101, "But": [101, 102, 107, 143, 170, 238, 243, 256, 277, 278, 281, 297, 298, 583, 796, 866, 911, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1094, 1328, 1393, 1425], "seem": [101, 102, 298, 307, 791, 1234], "eas": [101, 107, 1409], "idiom": [101, 159, 190, 200, 858, 879, 888, 903, 939, 969, 984, 1296, 1394, 1404, 1411], "subscript": [101, 151, 159, 200, 796, 853, 858, 888, 898, 903, 934, 939, 969, 979, 984, 1037, 1039, 1040, 1394, 1426], "repr": [101, 1347, 1413], "4950": [101, 1414], "traceback": [101, 450, 464, 584, 652, 658, 1302, 1303], "recent": [101, 437, 450, 464, 584, 652, 658, 963, 1003, 1302, 1303, 1411], "typeerror": [101, 382, 464, 1206, 1302, 1404], "opaqu": 101, "ambigu": [101, 103, 115, 252, 253, 464, 762, 1043, 1406], "ambigi": 101, "counter": [101, 153, 357], "nativ": [101, 109], "caveat": 101, "nodes_it": [101, 1404, 1407], "toward": [101, 685, 1407, 1413], "inner": [101, 230, 231, 380, 796, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1086], "synonym": 101, "primarili": [101, 1426], "becam": [101, 1411], "concept": [101, 132, 220, 310, 427, 688, 1043], "intuit": [101, 109], "On": [101, 105, 156, 217, 294, 297, 298, 306, 307, 315, 380, 405, 406, 514, 515, 518, 593, 855, 900, 936, 981, 1180, 1202, 1224, 1228, 1232], "front": [101, 619, 1036, 1088], "constuct": 101, "indx": 101, "desir": [101, 102, 142, 143, 204, 346, 347, 422, 425, 426, 598, 629, 647, 891, 972, 1085, 1094, 1102, 1103, 1105, 1150, 1152, 1157, 1159, 1160, 1163, 1165, 1187, 1218, 1220, 1221, 1234, 1281, 1356, 1357, 1414, 1426], "prelimanari": 101, "impelement": 101, "4086": 101, "rid": [101, 1413], "getitem": 101, "dunder": [101, 107, 1296, 1413], "isinst": [101, 103, 464, 1086, 1411, 1412, 1413], "_node": [101, 1422, 1425], "exclus": [101, 449, 476], "necess": 101, "unhash": [101, 1404], "impel": 101, "insipir": 101, "colon": [101, 1421], "syntax": [101, 102, 171, 796, 867, 912, 948, 993, 1037, 1039, 1040, 1296, 1382, 1383, 1410, 1412], "introspect": 101, "neither": [101, 110, 305, 427, 625, 635, 636, 671, 672, 673, 674, 676, 700, 748], "downsid": 101, "drawback": 101, "discover": 101, "complic": [101, 1296, 1326], "nix": 101, "background": 101, "pertain": 101, "arguabl": [101, 102], "overrid": [101, 671, 672, 673, 674, 1411], "mix": [101, 236, 237, 238, 241, 242, 243, 244, 245, 248, 445, 758, 1100, 1341, 1342, 1344, 1355, 1356, 1357, 1358, 1381, 1383, 1393, 1406, 1407, 1411], "pervas": 101, "unforeseen": 101, "preced": [101, 152, 157, 464, 598, 703, 854, 856, 899, 901, 935, 937, 980, 982, 1045, 1363, 1364], "un": [101, 464, 732, 1407, 1413], "sliceabl": 101, "notabl": [101, 1042], "dict_kei": [101, 1303, 1414], "dict_valu": [101, 379, 1404, 1413], "cpython": [101, 107, 429, 496, 1038, 1402, 1413], "consider": [101, 103, 324, 325, 346, 347, 353, 525, 535, 555, 671, 672, 673, 674, 732, 760, 1168, 1413], "cours": [101, 105, 217, 618, 1326, 1426], "action": [101, 106, 1413, 1417], "allevi": 101, "dig": 101, "enough": [101, 468, 509, 1165], "satisfactorili": 101, "reconsid": [101, 1412], "went": [101, 502], "ahead": 101, "4300": [101, 1413], "4304": [101, 1413], "path_edg": 102, "former": [102, 103, 791], "stylist": 102, "creation": [102, 107, 110, 249, 275, 788, 1154, 1170, 1224, 1228, 1230, 1232, 1325, 1399, 1404, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "cleaner": [102, 1401, 1406], "creativ": [102, 464, 466], "demand": [102, 496, 497, 501, 504, 505, 508], "had": [102, 652, 1217, 1296, 1409, 1416], "node_iter": 102, "isn": [102, 346, 347, 720, 1331, 1334, 1406, 1414, 1425], "leav": [102, 231, 388, 500, 508, 584, 585, 586, 587, 678, 1145, 1155, 1296, 1404, 1409, 1426], "dg": [102, 207, 322, 455, 456, 457, 458, 459, 461, 462, 464, 465, 466, 467, 468, 469, 892, 928, 973, 1010, 1041, 1404, 1426], "mdg": [102, 207, 892, 928, 973, 1010, 1420], "customgraph": 102, "elist": [102, 1326], "isol": [102, 355, 380, 435, 491, 492, 522, 524, 621, 735, 737, 758, 1218, 1325, 1330, 1398, 1401, 1406, 1407, 1417], "ekei": [102, 207, 892, 928, 934, 973, 979, 1010, 1084, 1104], "protocol": [102, 1404], "hashabl": [102, 144, 151, 156, 171, 180, 267, 545, 546, 547, 548, 761, 796, 853, 855, 867, 871, 898, 900, 912, 914, 934, 936, 947, 948, 952, 962, 979, 981, 992, 993, 995, 1002, 1037, 1038, 1039, 1040, 1087, 1207, 1278, 1279, 1295, 1310, 1324, 1326, 1333, 1337, 1338, 1426], "logic": [102, 103, 220, 760, 762, 1298, 1406, 1407, 1419, 1425], "denot": [102, 114, 212, 219, 299, 300, 322, 567, 568, 569, 570, 571, 572, 573, 608, 619, 687, 688, 689, 690, 691, 1174], "multiedg": [102, 553, 934, 979, 1039, 1040, 1085, 1326, 1356, 1357, 1393, 1406, 1412, 1414], "attrdict": [102, 157, 856, 901, 937, 982, 1406], "edge_kei": [102, 489, 1039, 1040, 1100, 1104, 1413], "networkxinvalidedgelist": 102, "flexibl": [102, 110, 467, 1326, 1382, 1383, 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, 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"greedy_color": [123, 758, 1395, 1406, 1411], "communities_gener": 125, "girvan_newman": 125, "top_level_commun": 125, "next_level_commun": 125, "kernighan": [125, 377, 1413], "lin": [125, 377, 1407, 1413], "luke": [125, 382, 1412], "asynchron": [125, 373, 378, 379, 1407, 1414], "edge_kcompon": [127, 424], "determen": 127, "maxim": [127, 209, 220, 221, 222, 315, 316, 330, 339, 346, 347, 348, 349, 350, 351, 353, 354, 366, 370, 380, 383, 384, 389, 390, 422, 425, 426, 427, 432, 433, 437, 517, 549, 579, 581, 582, 583, 589, 682, 691, 732, 758, 1043, 1201, 1323, 1325, 1398, 1406, 1407, 1413, 1414], "moodi": [127, 220, 427, 1395], "kanevski": [127, 427, 428, 1395], "recurs": [128, 141, 224, 346, 347, 352, 387, 389, 390, 394, 406, 452, 460, 530, 540, 697, 728, 730, 760, 1045, 1046, 1061, 1082, 1147, 1296, 1406, 1412, 1413], "prune": [128, 760, 1236], "vladimir": [128, 275, 432, 433, 494, 588, 749, 1230], "batagelj": [128, 275, 432, 433, 588, 749, 1230], "matjaz": [128, 432, 433], "zaversnik": [128, 432, 433], "0310049": [128, 432, 433], "0202039": 128, "degeneraci": 128, "christo": 128, "giatsidi": 128, "thiliko": 128, "michali": 128, "vazirgianni": 128, "icdm": 128, "2011": [128, 331, 377, 383, 385, 441, 445, 446, 511, 512, 519, 619, 682, 1179, 1397, 1398, 1399, 1406, 1407], "graphdegeneraci": 128, "dcores_icdm_2011": 128, "anomali": [128, 438], "onion": [128, 438, 1411], "h\u00e9bert": [128, 438], "dufresn": [128, 438], "grochow": [128, 438], "allard": [128, 438, 1411], "31708": [128, 438], "2016": [128, 337, 352, 385, 438, 476, 690, 1197, 1251, 1396, 1406], "1038": [128, 337, 376, 380, 438, 569], "srep31708": [128, 438], "factor": [132, 226, 293, 294, 299, 300, 324, 325, 370, 462, 497, 501, 504, 505, 508, 513, 565, 592, 624, 676, 697, 1106, 1107, 1108, 1109, 1110, 1114, 1115, 1116, 1117, 1145, 1155, 1178, 1180, 1275, 1276, 1277], "graphic": [132, 454, 517, 518, 693, 758, 1175, 1177, 1180, 1181, 1222, 1325, 1383, 1398, 1401, 1406], "overview": [132, 476, 1038, 1296], "collid": [132, 454], "triplet": [132, 745], "successor": [132, 159, 174, 181, 191, 200, 240, 282, 387, 389, 390, 394, 501, 687, 707, 715, 858, 872, 880, 888, 903, 939, 953, 961, 969, 984, 1055, 1183, 1184, 1189, 1326, 1404, 1407, 1416, 1426], "descend": [132, 454, 456, 465, 709, 758, 1272, 1401, 1404, 1406, 1413, 1414], "unblock": 132, "commonli": [132, 280, 454, 684, 782], "probabilist": [132, 378], "causal": 132, "markov": [132, 462, 565, 692, 1188], "hmm": 132, "s1": [132, 1242, 1313, 1363], "s2": [132, 1242, 1313], "s3": [132, 1313], "s4": 132, "s5": 132, "o1": 132, "o2": 132, "o3": 132, "o4": 132, "o5": 132, "ob": 132, "d_separ": [132, 758, 1412], "darwich": 132, "shachter": 132, "1998": [132, 1143, 1144, 1225, 1241, 1407], "bay": 132, "ball": 132, "ration": 132, "pastim": 132, "irrelev": [132, 1407], "requisit": 132, "influenc": [132, 324, 325, 512, 786], "fourteenth": [132, 1186], "uncertainti": [132, 590, 732], "artifici": [132, 574, 590, 732], "480": [132, 426, 514, 518, 1398, 1406], "487": 132, "francisco": [132, 732], "morgan": [132, 732], "kaufmann": [132, 732], "koller": 132, "friedman": 132, "mit": [132, 342, 519, 618], "causal_markov_condit": 132, "ness": [133, 684, 782], "classmethod": [141, 1047], "auxiliari": [141, 142, 143, 220, 411, 412, 413, 415, 416, 417, 418, 419, 423, 430, 431, 1402], "sink": [141, 302, 309, 416, 418, 494, 495, 498, 499, 501, 502, 503, 506, 507, 509, 510, 565], "pick": [141, 217, 331, 657, 1188, 1207, 1210, 1407], "st": [141, 415, 417], "cut": [141, 222, 223, 293, 377, 382, 387, 389, 390, 394, 411, 412, 414, 415, 416, 417, 419, 427, 428, 429, 442, 443, 444, 445, 447, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 619, 758, 760, 1038, 1066, 1115, 1262, 1325, 1395, 1402, 1406, 1413], "refin": [143, 215, 423, 438], "auxgraph": [143, 423], "node_partit": 144, "permut": [144, 368, 452, 453, 455, 466, 748, 1285, 1320, 1321], "containin": 144, "frozenset": [144, 267, 339, 383, 586, 588, 752, 1165, 1333, 1337, 1338, 1412], "abc": [144, 545, 1154, 1206, 1303, 1412, 1413], "interchang": [144, 362], "bool": [145, 146, 148, 149, 165, 168, 171, 176, 184, 189, 196, 204, 208, 232, 237, 238, 242, 243, 245, 249, 250, 258, 265, 266, 267, 268, 272, 275, 286, 287, 288, 291, 294, 295, 296, 297, 298, 299, 301, 302, 305, 306, 307, 308, 309, 310, 314, 315, 322, 324, 325, 326, 327, 330, 343, 350, 355, 362, 393, 394, 395, 396, 397, 398, 439, 454, 462, 463, 467, 479, 480, 488, 489, 491, 494, 498, 499, 509, 510, 513, 514, 515, 516, 517, 518, 520, 521, 522, 545, 562, 564, 578, 579, 580, 581, 588, 613, 614, 616, 617, 622, 623, 625, 640, 652, 663, 673, 679, 685, 690, 696, 698, 699, 700, 704, 708, 719, 723, 724, 725, 726, 728, 730, 733, 734, 735, 736, 737, 738, 740, 741, 742, 743, 862, 865, 867, 870, 873, 878, 885, 891, 907, 910, 912, 916, 927, 931, 943, 946, 948, 951, 955, 960, 966, 972, 976, 988, 991, 993, 998, 1039, 1040, 1045, 1057, 1068, 1070, 1071, 1072, 1084, 1091, 1097, 1116, 1133, 1134, 1135, 1136, 1169, 1179, 1185, 1189, 1209, 1211, 1212, 1213, 1215, 1224, 1228, 1230, 1231, 1232, 1275, 1276, 1277, 1278, 1279, 1282, 1295, 1296, 1307, 1309, 1312, 1335, 1336, 1337, 1339, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1360, 1364, 1379, 1380], "account": [145, 148, 398, 448, 749, 761, 1270, 1393, 1413], "graph_nod": [145, 148], "subgraph_nod": [145, 148], "find_isomorph": [147, 150], "induc": [148, 167, 199, 211, 226, 342, 388, 392, 406, 427, 436, 437, 470, 487, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 512, 586, 589, 752, 761, 762, 864, 887, 909, 925, 945, 968, 990, 1007, 1038, 1061, 1066, 1087, 1102, 1103, 1105, 1189, 1283, 1284, 1393], "u_of_edg": [151, 853, 898], "v_of_edg": [151, 853, 898], "capac": [151, 265, 296, 301, 302, 303, 308, 309, 323, 411, 412, 415, 416, 417, 418, 419, 430, 431, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 758, 853, 898, 934, 979, 1335, 1402], "342": [151, 853, 898, 934, 979, 1255], "ebunch_to_add": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "add_weighted_edges_from": [152, 229, 230, 231, 508, 581, 630, 657, 659, 721, 854, 899, 935, 980, 1070, 1326, 1404, 1407, 1426], "runtimeerror": [152, 157, 158, 195, 464, 465, 466, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005], "happen": [152, 157, 158, 195, 380, 584, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1403, 1404, 1425], "iterator_of_edg": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "wn2898": [152, 854, 899, 935, 980], "wrong": [152, 157, 158, 722, 854, 856, 857, 899, 901, 902, 935, 937, 938, 980, 982, 983, 1406, 1411, 1416, 1425], "start_nod": [153, 154, 155], "end_nod": [153, 154, 155], "reference_neighbor": [153, 154], "half": [153, 154, 155, 164, 177, 183, 206, 297, 298, 615, 653], "clockwis": [153, 154, 169, 182, 197, 615], "networkxexcept": [153, 154, 161, 331, 588, 593, 724, 726, 1043, 1110, 1138, 1180, 1325], "add_half_edge_cw": [153, 155, 164, 615], "connect_compon": [153, 154, 155, 615], "add_half_edge_first": [153, 154, 164, 615], "add_half_edge_ccw": [154, 155, 164, 615], "node_for_ad": [156, 855, 900, 936, 981], "mutabl": [156, 855, 900, 936, 981, 1061, 1066, 1082, 1085, 1086], "hash": [156, 511, 512, 758, 855, 900, 936, 981, 1324, 1325, 1414, 1426], "hello": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1303], "k3": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1217], "utm": [156, 855, 900, 936, 981], "382871": [156, 855, 900, 936, 981], "3972649": [156, 855, 900, 936, 981], "nodes_for_ad": [157, 856, 901, 937, 982], "iterator_of_nod": [157, 195, 856, 884, 901, 923, 937, 965, 982, 1005], "datadict": [159, 190, 200, 207, 734, 736, 858, 879, 888, 892, 903, 928, 939, 969, 973, 1010, 1084, 1312, 1326], "foovalu": [159, 190, 200, 858, 879, 888, 903, 939, 969], "nbrdict": [160, 859, 904, 940, 985, 1019, 1094], "fulfil": [161, 615], "cw": [161, 615], "ccw": [161, 615], "planar": [161, 614, 616, 617, 758, 1110, 1138, 1243, 1246, 1247, 1249, 1325, 1409, 1410], "first_nbr": [161, 615], "invalid": [161, 615, 1413], "alter": [163, 861, 906, 942, 987], "afterward": 164, "as_view": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1089, 1090], "shallow": [165, 202, 204, 284, 285, 286, 287, 288, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1394], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1409], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1404, 1407, 1409, 1410, 1411], "fresh": [165, 862, 907, 943, 988, 1404], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1226, 1323, 1404], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1265, 1394], "degreeview": [166, 863, 908, 944, 950, 989, 1404, 1426], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1404, 1418], "ddict": [168, 176, 184, 189, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998], "in_edg": [168, 189, 865, 878, 946, 960, 1404, 1406, 1407], "out_edg": [168, 865, 946, 1062, 1404, 1406, 1407, 1426], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1426], "outedgedataview": [168, 189, 865, 878, 1404, 1411], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1404, 1412], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1406, 1407, 1417], "indegreeview": [175, 869, 1404], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1165, 1179, 1222, 1404], "inedgeview": [176, 870, 1404], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1127, 1353, 1354, 1359, 1363, 1406, 1413], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1414], "__iter__": [180, 871, 914, 952, 995, 1303], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1394, 1426], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1216], "nedg": [185, 588, 874, 917, 956, 999], "__len__": [186, 187, 875, 876, 918, 919, 957, 958, 1000, 1001], "outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 882, 921, 963, 1003, 1404, 1414], "get_data": [197, 616], "inplac": [199, 690, 887, 925, 968, 1007, 1066, 1393], "reduct": [199, 469, 618, 786, 887, 925, 968, 1007, 1066, 1320, 1321, 1413, 1414], "sg": [199, 887, 925, 968, 1007], "largest_wcc": [199, 887, 925, 968, 1007], "is_multigraph": [199, 758, 887, 925, 968, 1007, 1154, 1412], "keydict": [199, 207, 887, 892, 925, 928, 968, 973, 1007, 1010, 1039, 1040], "contrast": [202, 204, 301, 302, 308, 309, 890, 891, 926, 927, 971, 972, 1008, 1009, 1066, 1233, 1241, 1426], "reciproc": [204, 299, 320, 322, 356, 411, 430, 447, 476, 620, 758, 891, 972, 1325, 1416, 1425], "mark_half_edg": 206, "li": [206, 619, 670, 675, 685, 775, 1207, 1210, 1425], "straightforward": [207, 892, 928, 973, 1010], "slightli": [207, 326, 437, 520, 521, 581, 892, 928, 973, 1010, 1165, 1326, 1404, 1407, 1412, 1414, 1425], "singleton": [207, 588, 892, 928, 973, 1010, 1218, 1251, 1407], "preserve_attr": [208, 723, 724, 725, 726], "optimum": [208, 231, 583, 720, 722, 791, 1395, 1406], "arboresc": [208, 460, 719, 720, 722, 724, 726, 740, 743, 758, 1272, 1395, 1406], "span": [208, 226, 227, 228, 295, 508, 618, 619, 624, 719, 720, 722, 724, 726, 732, 733, 734, 735, 736, 737, 738, 758, 1394, 1397, 1406, 1407, 1420], "max_ind_cliqu": 209, "networkxnotimpl": [209, 210, 211, 212, 220, 224, 227, 293, 294, 295, 318, 319, 321, 328, 343, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 403, 404, 405, 406, 407, 422, 424, 425, 426, 427, 429, 455, 457, 458, 459, 460, 468, 481, 482, 500, 589, 590, 608, 680, 732, 1043, 1216, 1275, 1276, 1298, 1325, 1353, 1354, 1379, 1407, 1408], "boppana": [209, 211, 212], "halld\u00f3rsson": [209, 211, 212], "1992": [209, 211, 212, 517, 518, 1407], "exclud": [209, 211, 212, 215, 216, 261, 262, 453, 688, 719, 723, 724, 725, 726, 733, 751, 1036, 1038, 1088, 1217, 1412], "180": [209, 211, 212, 238], "196": [209, 211, 212], "heurist": [210, 220, 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, 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"difference": [[599, "difference"]], "disjoint_union": [[600, "disjoint-union"]], "full_join": [[601, "full-join"]], "intersection": [[602, "intersection"]], "symmetric_difference": [[603, "symmetric-difference"]], "union": [[604, "union"]], "cartesian_product": [[605, "cartesian-product"]], "corona_product": [[606, "corona-product"]], "lexicographic_product": [[607, "lexicographic-product"]], "power": [[608, "power"]], "rooted_product": [[609, "rooted-product"]], "strong_product": [[610, "strong-product"]], "tensor_product": [[611, "tensor-product"]], "complement": [[612, "complement"]], "reverse": [[613, "reverse"]], "combinatorial_embedding_to_pos": [[614, "combinatorial-embedding-to-pos"]], "networkx.algorithms.planarity.PlanarEmbedding": [[615, "networkx-algorithms-planarity-planarembedding"]], "check_planarity": [[616, "check-planarity"]], "is_planar": [[617, "is-planar"]], "chromatic_polynomial": [[618, "chromatic-polynomial"]], "tutte_polynomial": [[619, "tutte-polynomial"]], "overall_reciprocity": [[620, "overall-reciprocity"]], "reciprocity": [[621, "reciprocity"]], "is_k_regular": [[622, "is-k-regular"]], "is_regular": [[623, "is-regular"]], "k_factor": [[624, "k-factor"]], "rich_club_coefficient": [[625, "rich-club-coefficient"]], "astar_path": [[626, "astar-path"]], "astar_path_length": [[627, "astar-path-length"]], "floyd_warshall": [[628, "floyd-warshall"]], "floyd_warshall_numpy": [[629, "floyd-warshall-numpy"]], "floyd_warshall_predecessor_and_distance": [[630, "floyd-warshall-predecessor-and-distance"]], "reconstruct_path": [[631, "reconstruct-path"]], "all_shortest_paths": [[632, "all-shortest-paths"]], "average_shortest_path_length": [[633, "average-shortest-path-length"]], "has_path": [[634, "has-path"]], "shortest_path": [[635, "shortest-path"]], "shortest_path_length": [[636, "shortest-path-length"]], "all_pairs_shortest_path": [[637, "all-pairs-shortest-path"]], "all_pairs_shortest_path_length": [[638, "all-pairs-shortest-path-length"]], "bidirectional_shortest_path": [[639, "bidirectional-shortest-path"]], "predecessor": [[640, "predecessor"]], "single_source_shortest_path": [[641, "single-source-shortest-path"]], "single_source_shortest_path_length": [[642, "single-source-shortest-path-length"]], "single_target_shortest_path": [[643, "single-target-shortest-path"]], "single_target_shortest_path_length": [[644, "single-target-shortest-path-length"]], "all_pairs_bellman_ford_path": [[645, "all-pairs-bellman-ford-path"]], "all_pairs_bellman_ford_path_length": [[646, "all-pairs-bellman-ford-path-length"]], "all_pairs_dijkstra": [[647, "all-pairs-dijkstra"]], "all_pairs_dijkstra_path": [[648, "all-pairs-dijkstra-path"]], "all_pairs_dijkstra_path_length": [[649, "all-pairs-dijkstra-path-length"]], "bellman_ford_path": [[650, "bellman-ford-path"]], "bellman_ford_path_length": [[651, "bellman-ford-path-length"]], "bellman_ford_predecessor_and_distance": [[652, "bellman-ford-predecessor-and-distance"]], "bidirectional_dijkstra": [[653, "bidirectional-dijkstra"]], "dijkstra_path": [[654, "dijkstra-path"]], "dijkstra_path_length": [[655, "dijkstra-path-length"]], "dijkstra_predecessor_and_distance": [[656, "dijkstra-predecessor-and-distance"]], "find_negative_cycle": [[657, "find-negative-cycle"]], "goldberg_radzik": [[658, "goldberg-radzik"]], "johnson": [[659, "johnson"]], "multi_source_dijkstra": [[660, "multi-source-dijkstra"]], "multi_source_dijkstra_path": [[661, "multi-source-dijkstra-path"]], "multi_source_dijkstra_path_length": [[662, "multi-source-dijkstra-path-length"]], "negative_edge_cycle": [[663, "negative-edge-cycle"]], "single_source_bellman_ford": [[664, "single-source-bellman-ford"]], "single_source_bellman_ford_path": [[665, "single-source-bellman-ford-path"]], "single_source_bellman_ford_path_length": [[666, "single-source-bellman-ford-path-length"]], "single_source_dijkstra": [[667, "single-source-dijkstra"]], "single_source_dijkstra_path": [[668, "single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[669, "single-source-dijkstra-path-length"]], "generate_random_paths": [[670, "generate-random-paths"]], "graph_edit_distance": [[671, "graph-edit-distance"]], "optimal_edit_paths": [[672, "optimal-edit-paths"]], "optimize_edit_paths": [[673, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[674, "optimize-graph-edit-distance"]], "panther_similarity": [[675, "panther-similarity"]], "simrank_similarity": [[676, "simrank-similarity"]], "all_simple_edge_paths": [[677, "all-simple-edge-paths"]], "all_simple_paths": [[678, "all-simple-paths"]], "is_simple_path": [[679, "is-simple-path"]], "shortest_simple_paths": [[680, "shortest-simple-paths"]], "lattice_reference": [[681, "lattice-reference"]], "omega": [[682, "omega"]], "random_reference": [[683, "random-reference"]], "sigma": [[684, "sigma"]], "s_metric": [[685, "s-metric"]], "spanner": [[686, "spanner"]], "constraint": [[687, "constraint"]], "effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "Recognition Tests": [[791, "recognition-tests"]], "Branchings and Spanning Arborescences": [[791, "module-networkx.algorithms.tree.branchings"]], "Encoding and decoding": [[791, "module-networkx.algorithms.tree.coding"]], "Operations": [[791, "module-networkx.algorithms.tree.operations"]], "Spanning Trees": [[791, "module-networkx.algorithms.tree.mst"]], "Exceptions": [[791, "exceptions"], [1043, "module-networkx.exception"]], "Vitality": [[793, "module-networkx.algorithms.vitality"]], "Voronoi cells": [[794, "module-networkx.algorithms.voronoi"]], "Wiener index": [[795, "module-networkx.algorithms.wiener"]], "DiGraph\u2014Directed graphs with self loops": [[796, "digraph-directed-graphs-with-self-loops"]], "Overview": [[796, "overview"], [1037, "overview"], [1039, "overview"], [1040, "overview"]], "Methods": [[796, "methods"], [1037, "methods"], [1039, "methods"], [1040, "methods"]], "Adding and removing nodes and edges": [[796, "adding-and-removing-nodes-and-edges"], [1037, "adding-and-removing-nodes-and-edges"], [1040, "adding-and-removing-nodes-and-edges"]], "Reporting nodes edges and neighbors": [[796, "reporting-nodes-edges-and-neighbors"], [1037, "reporting-nodes-edges-and-neighbors"], [1039, "reporting-nodes-edges-and-neighbors"], [1040, "reporting-nodes-edges-and-neighbors"]], "Counting nodes edges and neighbors": [[796, "counting-nodes-edges-and-neighbors"], [1037, "counting-nodes-edges-and-neighbors"], [1039, "counting-nodes-edges-and-neighbors"], [1040, "counting-nodes-edges-and-neighbors"]], "Making copies and subgraphs": [[796, "making-copies-and-subgraphs"], [1037, "making-copies-and-subgraphs"], [1039, "making-copies-and-subgraphs"], [1040, "making-copies-and-subgraphs"]], "AdjacencyView.copy": [[797, "adjacencyview-copy"]], "AdjacencyView.get": [[798, "adjacencyview-get"]], "AdjacencyView.items": [[799, "adjacencyview-items"]], "AdjacencyView.keys": [[800, "adjacencyview-keys"]], "AdjacencyView.values": [[801, "adjacencyview-values"]], "AtlasView.copy": [[802, "atlasview-copy"]], "AtlasView.get": [[803, "atlasview-get"]], "AtlasView.items": [[804, "atlasview-items"]], "AtlasView.keys": [[805, "atlasview-keys"]], "AtlasView.values": [[806, "atlasview-values"]], "FilterAdjacency.get": [[807, "filteradjacency-get"]], "FilterAdjacency.items": [[808, "filteradjacency-items"]], "FilterAdjacency.keys": [[809, "filteradjacency-keys"]], "FilterAdjacency.values": [[810, "filteradjacency-values"]], "FilterAtlas.get": [[811, "filteratlas-get"]], "FilterAtlas.items": [[812, "filteratlas-items"]], "FilterAtlas.keys": [[813, "filteratlas-keys"]], "FilterAtlas.values": [[814, "filteratlas-values"]], "FilterMultiAdjacency.get": [[815, "filtermultiadjacency-get"]], "FilterMultiAdjacency.items": [[816, "filtermultiadjacency-items"]], "FilterMultiAdjacency.keys": [[817, "filtermultiadjacency-keys"]], "FilterMultiAdjacency.values": [[818, "filtermultiadjacency-values"]], "FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": [[836, "unionatlas-keys"]], "UnionAtlas.values": [[837, "unionatlas-values"]], "UnionMultiAdjacency.copy": [[838, "unionmultiadjacency-copy"]], "UnionMultiAdjacency.get": [[839, "unionmultiadjacency-get"]], "UnionMultiAdjacency.items": [[840, "unionmultiadjacency-items"]], "UnionMultiAdjacency.keys": [[841, "unionmultiadjacency-keys"]], "UnionMultiAdjacency.values": [[842, "unionmultiadjacency-values"]], "UnionMultiInner.copy": [[843, "unionmultiinner-copy"]], "UnionMultiInner.get": [[844, "unionmultiinner-get"]], "UnionMultiInner.items": [[845, "unionmultiinner-items"]], "UnionMultiInner.keys": [[846, "unionmultiinner-keys"]], "UnionMultiInner.values": [[847, "unionmultiinner-values"]], "DiGraph.__contains__": [[848, "digraph-contains"]], "DiGraph.__getitem__": [[849, "digraph-getitem"]], "DiGraph.__init__": [[850, "digraph-init"]], "DiGraph.__iter__": [[851, "digraph-iter"]], "DiGraph.__len__": [[852, "digraph-len"]], "DiGraph.add_edge": [[853, "digraph-add-edge"]], "DiGraph.add_edges_from": [[854, "digraph-add-edges-from"]], "DiGraph.add_node": [[855, "digraph-add-node"]], "DiGraph.add_nodes_from": [[856, "digraph-add-nodes-from"]], "DiGraph.add_weighted_edges_from": [[857, "digraph-add-weighted-edges-from"]], "DiGraph.adj": [[858, "digraph-adj"]], "DiGraph.adjacency": [[859, "digraph-adjacency"]], "DiGraph.clear": [[860, "digraph-clear"]], "DiGraph.clear_edges": [[861, "digraph-clear-edges"]], "DiGraph.copy": [[862, "digraph-copy"]], "DiGraph.degree": [[863, "digraph-degree"]], "DiGraph.edge_subgraph": [[864, "digraph-edge-subgraph"]], "DiGraph.edges": [[865, "digraph-edges"]], "DiGraph.get_edge_data": [[866, "digraph-get-edge-data"]], "DiGraph.has_edge": [[867, "digraph-has-edge"]], "DiGraph.has_node": [[868, "digraph-has-node"]], "DiGraph.in_degree": [[869, "digraph-in-degree"]], "DiGraph.in_edges": [[870, "digraph-in-edges"]], "DiGraph.nbunch_iter": [[871, "digraph-nbunch-iter"]], "DiGraph.neighbors": [[872, "digraph-neighbors"]], "DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, "digraph-to-undirected"]], "DiGraph.update": [[892, "digraph-update"]], "Graph.__contains__": [[893, "graph-contains"]], "Graph.__getitem__": [[894, "graph-getitem"]], "Graph.__init__": [[895, "graph-init"]], "Graph.__iter__": [[896, "graph-iter"]], "Graph.__len__": [[897, "graph-len"]], "Graph.add_edge": [[898, "graph-add-edge"]], "Graph.add_edges_from": [[899, "graph-add-edges-from"]], "Graph.add_node": [[900, "graph-add-node"]], "Graph.add_nodes_from": [[901, "graph-add-nodes-from"]], "Graph.add_weighted_edges_from": [[902, "graph-add-weighted-edges-from"]], "Graph.adj": [[903, "graph-adj"]], "Graph.adjacency": [[904, "graph-adjacency"]], "Graph.clear": [[905, "graph-clear"]], "Graph.clear_edges": [[906, "graph-clear-edges"]], "Graph.copy": [[907, "graph-copy"]], "Graph.degree": [[908, "graph-degree"]], "Graph.edge_subgraph": [[909, "graph-edge-subgraph"]], "Graph.edges": [[910, "graph-edges"]], "Graph.get_edge_data": [[911, "graph-get-edge-data"]], "Graph.has_edge": [[912, 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"networkx-3-0-unreleased"]], "Tutorial": [[1426, "tutorial"]], "Creating a graph": [[1426, "creating-a-graph"]], "Examining elements of a graph": [[1426, "examining-elements-of-a-graph"]], "Removing elements from a graph": [[1426, "removing-elements-from-a-graph"]], "Using the graph constructors": [[1426, "using-the-graph-constructors"]], "What to use as nodes and edges": [[1426, "what-to-use-as-nodes-and-edges"]], "Accessing edges and neighbors": [[1426, "accessing-edges-and-neighbors"]], "Adding attributes to graphs, nodes, and edges": [[1426, "adding-attributes-to-graphs-nodes-and-edges"]], "Edge Attributes": [[1426, "edge-attributes"]], "Directed graphs": [[1426, "directed-graphs"]], "Multigraphs": [[1426, "multigraphs"]], "Graph generators and graph operations": [[1426, "graph-generators-and-graph-operations"]], "1. Applying classic graph operations, such as:": [[1426, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1426, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1426, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1426, "using-a-stochastic-graph-generator-e-g"]], "5. Reading a graph stored in a file using common graph formats": [[1426, "reading-a-graph-stored-in-a-file-using-common-graph-formats"]], "Analyzing graphs": [[1426, "analyzing-graphs"]], "Drawing graphs": [[1426, "drawing-graphs"]]}, "indexentries": {"module": [[112, "module-networkx.algorithms.approximation"], [112, "module-networkx.algorithms.approximation.clique"], [112, "module-networkx.algorithms.approximation.clustering_coefficient"], [112, "module-networkx.algorithms.approximation.connectivity"], [112, "module-networkx.algorithms.approximation.distance_measures"], [112, "module-networkx.algorithms.approximation.dominating_set"], [112, "module-networkx.algorithms.approximation.kcomponents"], [112, "module-networkx.algorithms.approximation.matching"], [112, "module-networkx.algorithms.approximation.maxcut"], [112, "module-networkx.algorithms.approximation.ramsey"], [112, "module-networkx.algorithms.approximation.steinertree"], [112, 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"module-networkx.algorithms.approximation.clustering_coefficient"]], "networkx.algorithms.approximation.connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"]], "networkx.algorithms.approximation.distance_measures": [[112, "module-networkx.algorithms.approximation.distance_measures"]], "networkx.algorithms.approximation.dominating_set": [[112, "module-networkx.algorithms.approximation.dominating_set"]], "networkx.algorithms.approximation.kcomponents": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "networkx.algorithms.approximation.matching": [[112, "module-networkx.algorithms.approximation.matching"]], "networkx.algorithms.approximation.maxcut": [[112, "module-networkx.algorithms.approximation.maxcut"]], "networkx.algorithms.approximation.ramsey": [[112, "module-networkx.algorithms.approximation.ramsey"]], "networkx.algorithms.approximation.steinertree": [[112, "module-networkx.algorithms.approximation.steinertree"]], "networkx.algorithms.approximation.traveling_salesman": [[112, "module-networkx.algorithms.approximation.traveling_salesman"]], "networkx.algorithms.approximation.treewidth": [[112, "module-networkx.algorithms.approximation.treewidth"]], "networkx.algorithms.approximation.vertex_cover": [[112, "module-networkx.algorithms.approximation.vertex_cover"]], "networkx.algorithms.assortativity": [[113, "module-networkx.algorithms.assortativity"]], "networkx.algorithms.asteroidal": [[114, "module-networkx.algorithms.asteroidal"]], "networkx.algorithms.bipartite": [[115, "module-networkx.algorithms.bipartite"]], "networkx.algorithms.bipartite.basic": [[115, "module-networkx.algorithms.bipartite.basic"]], "networkx.algorithms.bipartite.centrality": [[115, "module-networkx.algorithms.bipartite.centrality"]], "networkx.algorithms.bipartite.cluster": [[115, "module-networkx.algorithms.bipartite.cluster"]], "networkx.algorithms.bipartite.covering": [[115, 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"networkx.algorithms.chains": [[119, "module-networkx.algorithms.chains"]], "networkx.algorithms.chordal": [[120, "module-networkx.algorithms.chordal"]], "networkx.algorithms.clique": [[121, "module-networkx.algorithms.clique"]], "networkx.algorithms.cluster": [[122, "module-networkx.algorithms.cluster"]], "networkx.algorithms.coloring": [[123, "module-networkx.algorithms.coloring"]], "networkx.algorithms.communicability_alg": [[124, "module-networkx.algorithms.communicability_alg"]], "networkx.algorithms.community": [[125, "module-networkx.algorithms.community"]], "networkx.algorithms.community.asyn_fluid": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "networkx.algorithms.community.centrality": [[125, "module-networkx.algorithms.community.centrality"]], "networkx.algorithms.community.community_utils": [[125, "module-networkx.algorithms.community.community_utils"]], "networkx.algorithms.community.kclique": [[125, "module-networkx.algorithms.community.kclique"]], "networkx.algorithms.community.kernighan_lin": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "networkx.algorithms.community.label_propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "networkx.algorithms.community.louvain": [[125, "module-networkx.algorithms.community.louvain"]], "networkx.algorithms.community.lukes": [[125, "module-networkx.algorithms.community.lukes"]], "networkx.algorithms.community.modularity_max": [[125, "module-networkx.algorithms.community.modularity_max"]], "networkx.algorithms.community.quality": [[125, "module-networkx.algorithms.community.quality"]], "networkx.algorithms.components": [[126, "module-networkx.algorithms.components"]], "networkx.algorithms.connectivity": [[127, "module-networkx.algorithms.connectivity"]], "networkx.algorithms.connectivity.connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "networkx.algorithms.connectivity.cuts": [[127, 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"networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[144, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[151, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": [[166, "networkx.algorithms.planarity.PlanarEmbedding.degree"]], "edge_subgraph() (planarembedding method)": [[167, "networkx.algorithms.planarity.PlanarEmbedding.edge_subgraph"]], "edges (planarembedding property)": [[168, "networkx.algorithms.planarity.PlanarEmbedding.edges"]], "get_data() (planarembedding method)": [[169, "networkx.algorithms.planarity.PlanarEmbedding.get_data"]], "get_edge_data() (planarembedding method)": [[170, "networkx.algorithms.planarity.PlanarEmbedding.get_edge_data"]], "has_edge() (planarembedding method)": [[171, "networkx.algorithms.planarity.PlanarEmbedding.has_edge"]], "has_node() (planarembedding method)": [[172, "networkx.algorithms.planarity.PlanarEmbedding.has_node"]], "has_predecessor() (planarembedding method)": [[173, "networkx.algorithms.planarity.PlanarEmbedding.has_predecessor"]], "has_successor() (planarembedding method)": [[174, "networkx.algorithms.planarity.PlanarEmbedding.has_successor"]], "in_degree (planarembedding property)": [[175, "networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[751, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[752, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[753, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[755, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[757, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[759, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[760, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[764, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[766, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[767, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[768, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[769, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[770, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[771, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[772, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[772, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[772, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[772, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[774, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[775, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[777, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[778, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "networkx.algorithms.shortest_paths.weighted": [[779, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[780, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[781, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[782, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[783, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[785, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[786, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[787, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[788, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[789, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "networkx.algorithms.traversal.breadth_first_search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "networkx.algorithms.traversal.depth_first_search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "networkx.algorithms.traversal.edgebfs": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "networkx.algorithms.traversal.edgedfs": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "networkx.algorithms.tree.branchings": [[791, "module-networkx.algorithms.tree.branchings"]], "networkx.algorithms.tree.coding": [[791, "module-networkx.algorithms.tree.coding"]], "networkx.algorithms.tree.decomposition": [[791, "module-networkx.algorithms.tree.decomposition"]], "networkx.algorithms.tree.mst": [[791, "module-networkx.algorithms.tree.mst"]], "networkx.algorithms.tree.operations": [[791, "module-networkx.algorithms.tree.operations"]], "networkx.algorithms.tree.recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "networkx.algorithms.triads": [[792, "module-networkx.algorithms.triads"]], "networkx.algorithms.vitality": [[793, "module-networkx.algorithms.vitality"]], "networkx.algorithms.voronoi": [[794, "module-networkx.algorithms.voronoi"]], "networkx.algorithms.wiener": [[795, "module-networkx.algorithms.wiener"]], "digraph (class in networkx)": [[796, "networkx.DiGraph"]], "copy() (adjacencyview method)": [[797, "networkx.classes.coreviews.AdjacencyView.copy"]], "get() (adjacencyview method)": [[798, "networkx.classes.coreviews.AdjacencyView.get"]], "items() (adjacencyview method)": [[799, "networkx.classes.coreviews.AdjacencyView.items"]], "keys() (adjacencyview method)": [[800, "networkx.classes.coreviews.AdjacencyView.keys"]], "values() (adjacencyview method)": [[801, "networkx.classes.coreviews.AdjacencyView.values"]], "copy() (atlasview method)": [[802, "networkx.classes.coreviews.AtlasView.copy"]], "get() (atlasview method)": [[803, "networkx.classes.coreviews.AtlasView.get"]], "items() (atlasview method)": [[804, "networkx.classes.coreviews.AtlasView.items"]], "keys() (atlasview method)": [[805, "networkx.classes.coreviews.AtlasView.keys"]], "values() (atlasview method)": [[806, "networkx.classes.coreviews.AtlasView.values"]], "get() (filteradjacency method)": [[807, "networkx.classes.coreviews.FilterAdjacency.get"]], "items() (filteradjacency method)": [[808, "networkx.classes.coreviews.FilterAdjacency.items"]], "keys() (filteradjacency method)": [[809, "networkx.classes.coreviews.FilterAdjacency.keys"]], "values() (filteradjacency method)": [[810, "networkx.classes.coreviews.FilterAdjacency.values"]], "get() (filteratlas method)": [[811, "networkx.classes.coreviews.FilterAtlas.get"]], "items() (filteratlas method)": [[812, "networkx.classes.coreviews.FilterAtlas.items"]], "keys() (filteratlas method)": [[813, "networkx.classes.coreviews.FilterAtlas.keys"]], "values() (filteratlas method)": [[814, "networkx.classes.coreviews.FilterAtlas.values"]], "get() (filtermultiadjacency method)": [[815, "networkx.classes.coreviews.FilterMultiAdjacency.get"]], "items() (filtermultiadjacency method)": [[816, "networkx.classes.coreviews.FilterMultiAdjacency.items"]], "keys() (filtermultiadjacency method)": [[817, "networkx.classes.coreviews.FilterMultiAdjacency.keys"]], "values() (filtermultiadjacency method)": [[818, "networkx.classes.coreviews.FilterMultiAdjacency.values"]], "get() (filtermultiinner method)": [[819, "networkx.classes.coreviews.FilterMultiInner.get"]], "items() (filtermultiinner method)": [[820, "networkx.classes.coreviews.FilterMultiInner.items"]], "keys() (filtermultiinner method)": [[821, "networkx.classes.coreviews.FilterMultiInner.keys"]], "values() (filtermultiinner method)": [[822, "networkx.classes.coreviews.FilterMultiInner.values"]], "copy() (multiadjacencyview method)": [[823, "networkx.classes.coreviews.MultiAdjacencyView.copy"]], "get() (multiadjacencyview 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"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, 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"freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, 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"kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], "spring_layout() (in module networkx.drawing.layout)": [[1117, "networkx.drawing.layout.spring_layout"]], "from_agraph() (in module networkx.drawing.nx_agraph)": [[1118, "networkx.drawing.nx_agraph.from_agraph"]], "graphviz_layout() (in module networkx.drawing.nx_agraph)": [[1119, "networkx.drawing.nx_agraph.graphviz_layout"]], "pygraphviz_layout() (in module networkx.drawing.nx_agraph)": [[1120, "networkx.drawing.nx_agraph.pygraphviz_layout"]], "read_dot() (in module networkx.drawing.nx_agraph)": [[1121, "networkx.drawing.nx_agraph.read_dot"]], "to_agraph() (in module networkx.drawing.nx_agraph)": [[1122, "networkx.drawing.nx_agraph.to_agraph"]], "write_dot() (in module networkx.drawing.nx_agraph)": [[1123, "networkx.drawing.nx_agraph.write_dot"]], "from_pydot() (in module networkx.drawing.nx_pydot)": [[1124, "networkx.drawing.nx_pydot.from_pydot"]], "graphviz_layout() (in module networkx.drawing.nx_pydot)": [[1125, "networkx.drawing.nx_pydot.graphviz_layout"]], "pydot_layout() (in module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, 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module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() 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networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, 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"networkx.generators.random_graphs.newman_watts_strogatz_graph"]], "powerlaw_cluster_graph() (in module networkx.generators.random_graphs)": [[1234, "networkx.generators.random_graphs.powerlaw_cluster_graph"]], "random_kernel_graph() (in module networkx.generators.random_graphs)": [[1235, "networkx.generators.random_graphs.random_kernel_graph"]], "random_lobster() (in module networkx.generators.random_graphs)": [[1236, "networkx.generators.random_graphs.random_lobster"]], "random_powerlaw_tree() (in module networkx.generators.random_graphs)": [[1237, "networkx.generators.random_graphs.random_powerlaw_tree"]], "random_powerlaw_tree_sequence() (in module networkx.generators.random_graphs)": [[1238, "networkx.generators.random_graphs.random_powerlaw_tree_sequence"]], "random_regular_graph() (in module networkx.generators.random_graphs)": [[1239, "networkx.generators.random_graphs.random_regular_graph"]], "random_shell_graph() (in module networkx.generators.random_graphs)": [[1240, "networkx.generators.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1242, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1243, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1244, "networkx.generators.small.chvatal_graph"]], "cubical_graph() (in module networkx.generators.small)": [[1245, "networkx.generators.small.cubical_graph"]], "desargues_graph() (in module networkx.generators.small)": [[1246, "networkx.generators.small.desargues_graph"]], "diamond_graph() (in module networkx.generators.small)": [[1247, "networkx.generators.small.diamond_graph"]], "dodecahedral_graph() (in module networkx.generators.small)": [[1248, "networkx.generators.small.dodecahedral_graph"]], "frucht_graph() (in module networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, "networkx.generators.small.octahedral_graph"]], "pappus_graph() (in module networkx.generators.small)": [[1258, "networkx.generators.small.pappus_graph"]], "petersen_graph() (in module networkx.generators.small)": [[1259, "networkx.generators.small.petersen_graph"]], "sedgewick_maze_graph() (in module networkx.generators.small)": [[1260, "networkx.generators.small.sedgewick_maze_graph"]], "tetrahedral_graph() (in module networkx.generators.small)": [[1261, "networkx.generators.small.tetrahedral_graph"]], "truncated_cube_graph() (in module networkx.generators.small)": [[1262, "networkx.generators.small.truncated_cube_graph"]], "truncated_tetrahedron_graph() (in module networkx.generators.small)": [[1263, "networkx.generators.small.truncated_tetrahedron_graph"]], "tutte_graph() (in module networkx.generators.small)": [[1264, "networkx.generators.small.tutte_graph"]], "davis_southern_women_graph() (in module networkx.generators.social)": [[1265, "networkx.generators.social.davis_southern_women_graph"]], "florentine_families_graph() (in module networkx.generators.social)": [[1266, "networkx.generators.social.florentine_families_graph"]], "karate_club_graph() (in module networkx.generators.social)": [[1267, "networkx.generators.social.karate_club_graph"]], "les_miserables_graph() (in module networkx.generators.social)": [[1268, "networkx.generators.social.les_miserables_graph"]], "spectral_graph_forge() (in module networkx.generators.spectral_graph_forge)": [[1269, "networkx.generators.spectral_graph_forge.spectral_graph_forge"]], "stochastic_graph() (in module networkx.generators.stochastic)": [[1270, "networkx.generators.stochastic.stochastic_graph"]], "sudoku_graph() (in module networkx.generators.sudoku)": [[1271, "networkx.generators.sudoku.sudoku_graph"]], "prefix_tree() (in module networkx.generators.trees)": [[1272, "networkx.generators.trees.prefix_tree"]], "random_tree() (in module networkx.generators.trees)": [[1273, "networkx.generators.trees.random_tree"]], "triad_graph() (in module networkx.generators.triads)": [[1274, "networkx.generators.triads.triad_graph"]], "algebraic_connectivity() (in module networkx.linalg.algebraicconnectivity)": [[1275, "networkx.linalg.algebraicconnectivity.algebraic_connectivity"]], "fiedler_vector() (in module networkx.linalg.algebraicconnectivity)": [[1276, "networkx.linalg.algebraicconnectivity.fiedler_vector"]], "spectral_ordering() (in module networkx.linalg.algebraicconnectivity)": [[1277, "networkx.linalg.algebraicconnectivity.spectral_ordering"]], "attr_matrix() (in module networkx.linalg.attrmatrix)": [[1278, "networkx.linalg.attrmatrix.attr_matrix"]], "attr_sparse_matrix() (in module networkx.linalg.attrmatrix)": [[1279, "networkx.linalg.attrmatrix.attr_sparse_matrix"]], "bethe_hessian_matrix() (in module networkx.linalg.bethehessianmatrix)": [[1280, "networkx.linalg.bethehessianmatrix.bethe_hessian_matrix"]], "adjacency_matrix() (in module networkx.linalg.graphmatrix)": [[1281, "networkx.linalg.graphmatrix.adjacency_matrix"]], "incidence_matrix() (in module networkx.linalg.graphmatrix)": [[1282, "networkx.linalg.graphmatrix.incidence_matrix"]], "directed_combinatorial_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1283, "networkx.linalg.laplacianmatrix.directed_combinatorial_laplacian_matrix"]], "directed_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1284, "networkx.linalg.laplacianmatrix.directed_laplacian_matrix"]], "laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1285, "networkx.linalg.laplacianmatrix.laplacian_matrix"]], "normalized_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1286, "networkx.linalg.laplacianmatrix.normalized_laplacian_matrix"]], "directed_modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1287, "networkx.linalg.modularitymatrix.directed_modularity_matrix"]], "modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1288, "networkx.linalg.modularitymatrix.modularity_matrix"]], "adjacency_spectrum() (in module networkx.linalg.spectrum)": [[1289, "networkx.linalg.spectrum.adjacency_spectrum"]], "bethe_hessian_spectrum() (in module networkx.linalg.spectrum)": [[1290, "networkx.linalg.spectrum.bethe_hessian_spectrum"]], "laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1291, "networkx.linalg.spectrum.laplacian_spectrum"]], "modularity_spectrum() (in module networkx.linalg.spectrum)": [[1292, "networkx.linalg.spectrum.modularity_spectrum"]], "normalized_laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1293, "networkx.linalg.spectrum.normalized_laplacian_spectrum"]], "convert_node_labels_to_integers() (in module networkx.relabel)": [[1294, "networkx.relabel.convert_node_labels_to_integers"]], "relabel_nodes() (in module networkx.relabel)": [[1295, "networkx.relabel.relabel_nodes"]], "__init__() (argmap method)": [[1296, "networkx.utils.decorators.argmap.__init__"]], "argmap (class in networkx.utils.decorators)": [[1296, "networkx.utils.decorators.argmap"]], "nodes_or_number() (in module networkx.utils.decorators)": [[1297, "networkx.utils.decorators.nodes_or_number"]], "not_implemented_for() (in module networkx.utils.decorators)": [[1298, "networkx.utils.decorators.not_implemented_for"]], "np_random_state() (in module networkx.utils.decorators)": [[1299, "networkx.utils.decorators.np_random_state"]], "open_file() (in module networkx.utils.decorators)": [[1300, "networkx.utils.decorators.open_file"]], "py_random_state() (in module networkx.utils.decorators)": [[1301, "networkx.utils.decorators.py_random_state"]], "mappedqueue (class in networkx.utils.mapped_queue)": [[1302, "networkx.utils.mapped_queue.MappedQueue"]], "__init__() (mappedqueue method)": [[1302, "networkx.utils.mapped_queue.MappedQueue.__init__"]], "arbitrary_element() (in module networkx.utils.misc)": [[1303, "networkx.utils.misc.arbitrary_element"]], "create_py_random_state() (in module networkx.utils.misc)": [[1304, "networkx.utils.misc.create_py_random_state"]], "create_random_state() (in module networkx.utils.misc)": [[1305, "networkx.utils.misc.create_random_state"]], "dict_to_numpy_array() (in module networkx.utils.misc)": [[1306, "networkx.utils.misc.dict_to_numpy_array"]], "edges_equal() (in module networkx.utils.misc)": [[1307, "networkx.utils.misc.edges_equal"]], "flatten() (in module networkx.utils.misc)": [[1308, "networkx.utils.misc.flatten"]], "graphs_equal() (in module networkx.utils.misc)": [[1309, "networkx.utils.misc.graphs_equal"]], "groups() (in module networkx.utils.misc)": [[1310, "networkx.utils.misc.groups"]], "make_list_of_ints() (in module networkx.utils.misc)": [[1311, "networkx.utils.misc.make_list_of_ints"]], "nodes_equal() (in module networkx.utils.misc)": [[1312, "networkx.utils.misc.nodes_equal"]], "pairwise() (in module networkx.utils.misc)": [[1313, "networkx.utils.misc.pairwise"]], "cumulative_distribution() (in module networkx.utils.random_sequence)": [[1314, "networkx.utils.random_sequence.cumulative_distribution"]], "discrete_sequence() (in module networkx.utils.random_sequence)": [[1315, "networkx.utils.random_sequence.discrete_sequence"]], "powerlaw_sequence() (in module networkx.utils.random_sequence)": [[1316, "networkx.utils.random_sequence.powerlaw_sequence"]], "random_weighted_sample() (in module networkx.utils.random_sequence)": [[1317, "networkx.utils.random_sequence.random_weighted_sample"]], "weighted_choice() (in module networkx.utils.random_sequence)": [[1318, "networkx.utils.random_sequence.weighted_choice"]], "zipf_rv() (in module networkx.utils.random_sequence)": [[1319, "networkx.utils.random_sequence.zipf_rv"]], "cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1320, "networkx.utils.rcm.cuthill_mckee_ordering"]], "reverse_cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1321, "networkx.utils.rcm.reverse_cuthill_mckee_ordering"]], "union() (unionfind method)": [[1322, "networkx.utils.union_find.UnionFind.union"]], "networkx.generators.atlas": [[1323, "module-networkx.generators.atlas"]], "networkx.generators.classic": [[1323, "module-networkx.generators.classic"]], "networkx.generators.cographs": [[1323, "module-networkx.generators.cographs"]], "networkx.generators.community": [[1323, "module-networkx.generators.community"]], "networkx.generators.degree_seq": [[1323, "module-networkx.generators.degree_seq"]], "networkx.generators.directed": [[1323, "module-networkx.generators.directed"]], "networkx.generators.duplication": [[1323, "module-networkx.generators.duplication"]], "networkx.generators.ego": [[1323, "module-networkx.generators.ego"]], "networkx.generators.expanders": [[1323, "module-networkx.generators.expanders"]], "networkx.generators.geometric": [[1323, "module-networkx.generators.geometric"]], "networkx.generators.harary_graph": [[1323, "module-networkx.generators.harary_graph"]], "networkx.generators.internet_as_graphs": [[1323, "module-networkx.generators.internet_as_graphs"]], "networkx.generators.intersection": [[1323, "module-networkx.generators.intersection"]], "networkx.generators.interval_graph": [[1323, "module-networkx.generators.interval_graph"]], "networkx.generators.joint_degree_seq": [[1323, "module-networkx.generators.joint_degree_seq"]], "networkx.generators.lattice": [[1323, "module-networkx.generators.lattice"]], "networkx.generators.line": [[1323, "module-networkx.generators.line"]], "networkx.generators.mycielski": [[1323, "module-networkx.generators.mycielski"]], "networkx.generators.nonisomorphic_trees": [[1323, "module-networkx.generators.nonisomorphic_trees"]], "networkx.generators.random_clustered": [[1323, "module-networkx.generators.random_clustered"]], "networkx.generators.random_graphs": [[1323, "module-networkx.generators.random_graphs"]], "networkx.generators.small": [[1323, "module-networkx.generators.small"]], "networkx.generators.social": [[1323, "module-networkx.generators.social"]], "networkx.generators.spectral_graph_forge": [[1323, "module-networkx.generators.spectral_graph_forge"]], "networkx.generators.stochastic": [[1323, "module-networkx.generators.stochastic"]], "networkx.generators.sudoku": [[1323, "module-networkx.generators.sudoku"]], "networkx.generators.trees": [[1323, "module-networkx.generators.trees"]], "networkx.generators.triads": [[1323, "module-networkx.generators.triads"]], "dictionary": [[1324, "term-dictionary"]], "ebunch": [[1324, "term-ebunch"]], "edge": [[1324, "term-edge"]], "edge attribute": [[1324, "term-edge-attribute"]], "nbunch": [[1324, "term-nbunch"]], "node": [[1324, "term-node"]], "node attribute": [[1324, "term-node-attribute"]], "networkx.linalg.algebraicconnectivity": [[1327, "module-networkx.linalg.algebraicconnectivity"]], "networkx.linalg.attrmatrix": [[1327, "module-networkx.linalg.attrmatrix"]], "networkx.linalg.bethehessianmatrix": [[1327, "module-networkx.linalg.bethehessianmatrix"]], "networkx.linalg.graphmatrix": [[1327, "module-networkx.linalg.graphmatrix"]], "networkx.linalg.laplacianmatrix": [[1327, "module-networkx.linalg.laplacianmatrix"]], "networkx.linalg.modularitymatrix": [[1327, "module-networkx.linalg.modularitymatrix"]], "networkx.linalg.spectrum": [[1327, "module-networkx.linalg.spectrum"]], "networkx.readwrite.adjlist": [[1329, "module-networkx.readwrite.adjlist"]], "networkx.readwrite.edgelist": [[1330, "module-networkx.readwrite.edgelist"]], "generate_adjlist() (in module networkx.readwrite.adjlist)": [[1331, "networkx.readwrite.adjlist.generate_adjlist"]], "parse_adjlist() (in module networkx.readwrite.adjlist)": [[1332, "networkx.readwrite.adjlist.parse_adjlist"]], "read_adjlist() (in module networkx.readwrite.adjlist)": [[1333, "networkx.readwrite.adjlist.read_adjlist"]], "write_adjlist() (in module networkx.readwrite.adjlist)": [[1334, "networkx.readwrite.adjlist.write_adjlist"]], "generate_edgelist() (in module networkx.readwrite.edgelist)": [[1335, "networkx.readwrite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.readwrite.edgelist)": [[1336, "networkx.readwrite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.readwrite.edgelist)": [[1337, "networkx.readwrite.edgelist.read_edgelist"]], "read_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1338, "networkx.readwrite.edgelist.read_weighted_edgelist"]], "write_edgelist() (in module networkx.readwrite.edgelist)": [[1339, "networkx.readwrite.edgelist.write_edgelist"]], "write_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1340, "networkx.readwrite.edgelist.write_weighted_edgelist"]], "generate_gexf() (in module networkx.readwrite.gexf)": [[1341, "networkx.readwrite.gexf.generate_gexf"]], "read_gexf() (in module networkx.readwrite.gexf)": [[1342, "networkx.readwrite.gexf.read_gexf"]], "relabel_gexf_graph() (in module networkx.readwrite.gexf)": [[1343, "networkx.readwrite.gexf.relabel_gexf_graph"]], "write_gexf() (in module networkx.readwrite.gexf)": [[1344, "networkx.readwrite.gexf.write_gexf"]], "generate_gml() (in module networkx.readwrite.gml)": [[1345, "networkx.readwrite.gml.generate_gml"]], "literal_destringizer() (in module networkx.readwrite.gml)": [[1346, "networkx.readwrite.gml.literal_destringizer"]], "literal_stringizer() (in module networkx.readwrite.gml)": [[1347, "networkx.readwrite.gml.literal_stringizer"]], "parse_gml() (in module networkx.readwrite.gml)": [[1348, "networkx.readwrite.gml.parse_gml"]], "read_gml() (in module networkx.readwrite.gml)": [[1349, "networkx.readwrite.gml.read_gml"]], "write_gml() (in module networkx.readwrite.gml)": [[1350, "networkx.readwrite.gml.write_gml"]], "from_graph6_bytes() (in module networkx.readwrite.graph6)": [[1351, "networkx.readwrite.graph6.from_graph6_bytes"]], "read_graph6() (in module networkx.readwrite.graph6)": [[1352, "networkx.readwrite.graph6.read_graph6"]], "to_graph6_bytes() (in module networkx.readwrite.graph6)": [[1353, "networkx.readwrite.graph6.to_graph6_bytes"]], "write_graph6() (in module networkx.readwrite.graph6)": [[1354, "networkx.readwrite.graph6.write_graph6"]], "generate_graphml() (in module networkx.readwrite.graphml)": [[1355, "networkx.readwrite.graphml.generate_graphml"]], "parse_graphml() (in module networkx.readwrite.graphml)": [[1356, "networkx.readwrite.graphml.parse_graphml"]], "read_graphml() (in module networkx.readwrite.graphml)": [[1357, "networkx.readwrite.graphml.read_graphml"]], "write_graphml() (in module networkx.readwrite.graphml)": [[1358, "networkx.readwrite.graphml.write_graphml"]], "adjacency_data() (in module networkx.readwrite.json_graph)": [[1359, "networkx.readwrite.json_graph.adjacency_data"]], "adjacency_graph() (in module networkx.readwrite.json_graph)": [[1360, "networkx.readwrite.json_graph.adjacency_graph"]], "cytoscape_data() (in module networkx.readwrite.json_graph)": [[1361, "networkx.readwrite.json_graph.cytoscape_data"]], "cytoscape_graph() (in module networkx.readwrite.json_graph)": [[1362, "networkx.readwrite.json_graph.cytoscape_graph"]], "node_link_data() (in module networkx.readwrite.json_graph)": [[1363, "networkx.readwrite.json_graph.node_link_data"]], "node_link_graph() (in module networkx.readwrite.json_graph)": [[1364, "networkx.readwrite.json_graph.node_link_graph"]], "tree_data() (in module networkx.readwrite.json_graph)": [[1365, "networkx.readwrite.json_graph.tree_data"]], "tree_graph() (in module networkx.readwrite.json_graph)": [[1366, "networkx.readwrite.json_graph.tree_graph"]], "parse_leda() (in module networkx.readwrite.leda)": [[1367, "networkx.readwrite.leda.parse_leda"]], "read_leda() (in module networkx.readwrite.leda)": [[1368, "networkx.readwrite.leda.read_leda"]], "generate_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1369, "networkx.readwrite.multiline_adjlist.generate_multiline_adjlist"]], "parse_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1370, "networkx.readwrite.multiline_adjlist.parse_multiline_adjlist"]], "read_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1371, "networkx.readwrite.multiline_adjlist.read_multiline_adjlist"]], "write_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1372, "networkx.readwrite.multiline_adjlist.write_multiline_adjlist"]], "generate_pajek() (in module networkx.readwrite.pajek)": [[1373, "networkx.readwrite.pajek.generate_pajek"]], "parse_pajek() (in module networkx.readwrite.pajek)": [[1374, "networkx.readwrite.pajek.parse_pajek"]], "read_pajek() (in module networkx.readwrite.pajek)": [[1375, "networkx.readwrite.pajek.read_pajek"]], "write_pajek() (in module networkx.readwrite.pajek)": [[1376, "networkx.readwrite.pajek.write_pajek"]], "from_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1377, "networkx.readwrite.sparse6.from_sparse6_bytes"]], "read_sparse6() (in module networkx.readwrite.sparse6)": [[1378, "networkx.readwrite.sparse6.read_sparse6"]], "to_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1379, "networkx.readwrite.sparse6.to_sparse6_bytes"]], "write_sparse6() (in module networkx.readwrite.sparse6)": [[1380, "networkx.readwrite.sparse6.write_sparse6"]], "networkx.readwrite.gexf": [[1381, "module-networkx.readwrite.gexf"]], "networkx.readwrite.gml": [[1382, "module-networkx.readwrite.gml"]], "networkx.readwrite.graphml": [[1383, "module-networkx.readwrite.graphml"]], "networkx.readwrite.json_graph": [[1385, "module-networkx.readwrite.json_graph"]], "networkx.readwrite.leda": [[1386, "module-networkx.readwrite.leda"]], "networkx.readwrite.multiline_adjlist": [[1388, "module-networkx.readwrite.multiline_adjlist"]], "networkx.readwrite.pajek": [[1389, "module-networkx.readwrite.pajek"]], "networkx.readwrite.graph6": [[1390, "module-networkx.readwrite.graph6"]], "networkx.readwrite.sparse6": [[1390, "module-networkx.readwrite.sparse6"]], "networkx.relabel": [[1391, "module-networkx.relabel"]], "networkx.utils": [[1392, "module-networkx.utils"]], "networkx.utils.decorators": [[1392, "module-networkx.utils.decorators"]], "networkx.utils.mapped_queue": [[1392, "module-networkx.utils.mapped_queue"]], "networkx.utils.misc": [[1392, "module-networkx.utils.misc"]], "networkx.utils.random_sequence": [[1392, "module-networkx.utils.random_sequence"]], "networkx.utils.rcm": [[1392, "module-networkx.utils.rcm"]], "networkx.utils.union_find": [[1392, "module-networkx.utils.union_find"]]}})
\ No newline at end of file diff --git a/tutorial-34.pdf b/tutorial-34.pdf Binary files differindex c86e8d29..75c5e84a 100644 --- a/tutorial-34.pdf +++ b/tutorial-34.pdf diff --git a/tutorial-35.hires.png b/tutorial-35.hires.png Binary files differindex 95fa708b..51b54532 100644 --- a/tutorial-35.hires.png +++ b/tutorial-35.hires.png diff --git a/tutorial-35.pdf b/tutorial-35.pdf Binary files differindex e4fd054c..1ce89295 100644 --- a/tutorial-35.pdf +++ b/tutorial-35.pdf diff --git a/tutorial-35.png b/tutorial-35.png Binary files differindex d7651092..5ae1b78d 100644 --- a/tutorial-35.png +++ b/tutorial-35.png diff --git a/tutorial-36.pdf b/tutorial-36.pdf Binary files differindex 75aaec5d..0fb18923 100644 --- a/tutorial-36.pdf +++ b/tutorial-36.pdf diff --git a/tutorial.ipynb b/tutorial.ipynb index ebf2f0dc..d81d1d09 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "e59716aa", + "id": "e94e9fa9", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,7 +17,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4c02a2be", + "id": "a229eaa1", "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "a68e7605", + "id": "fe98dbb0", "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": "04184847", + "id": "8b198e62", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "b9be2699", + "id": "5eebe239", "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": "1f327dd5", + "id": "f983e2e2", "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "markdown", - "id": "4b467036", + "id": "ed7ca5a6", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": null, - "id": "90bd0361", + "id": "0e97adf2", "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "ac638797", + "id": "f2460766", "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": "308e8588", + "id": "58bf775f", "metadata": {}, "outputs": [], "source": [ @@ -125,7 +125,7 @@ }, { "cell_type": "markdown", - "id": "490aef47", + "id": "5bafb2aa", "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": "7b4d5d97", + "id": "6e83f2e8", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +154,7 @@ }, { "cell_type": "markdown", - "id": "1e95cd0a", + "id": "bf6c3bd4", "metadata": {}, "source": [ "by adding a list of edges," @@ -163,7 +163,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2b2cddc0", + "id": "77dafe76", "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "7735d204", + "id": "aa532e9e", "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": "1a1f832d", + "id": "98cca3d2", "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "bd6b6dbe", + "id": "2ed337ea", "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": "63b6eff3", + "id": "85d84eb0", "metadata": {}, "outputs": [], "source": [ @@ -213,7 +213,7 @@ }, { "cell_type": "markdown", - "id": "463d8334", + "id": "0a2e3f8a", "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": "ddf23c52", + "id": "366065d8", "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "da3789cc", + "id": "84d014fb", "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": "b0e854a0", + "id": "4add5e21", "metadata": {}, "outputs": [], "source": [ @@ -257,7 +257,7 @@ { "cell_type": "code", "execution_count": null, - "id": "de2b7208", + "id": "7163af50", "metadata": {}, "outputs": [], "source": [ @@ -272,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "1f9cb39c", + "id": "24f0059e", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -292,7 +292,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3ad2f7b8", + "id": "b1b8ecad", "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "markdown", - "id": "2dd63975", + "id": "1ad3b3af", "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": "3b3a853a", + "id": "33627768", "metadata": {}, "outputs": [], "source": [ @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "c1d5867d", + "id": "80f5aa84", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -343,7 +343,7 @@ { "cell_type": "code", "execution_count": null, - "id": "336c3714", + "id": "38808384", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "81796970", + "id": "12955ef4", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -370,7 +370,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d5a68853", + "id": "6e1176ac", "metadata": {}, "outputs": [], "source": [ @@ -387,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "67496885", + "id": "ac25aeb4", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -416,7 +416,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5dd56a05", + "id": "1d6d8fb3", "metadata": {}, "outputs": [], "source": [ @@ -428,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "5963217f", + "id": "dc6c043f", "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": "2a14156d", + "id": "81f5148a", "metadata": {}, "outputs": [], "source": [ @@ -450,7 +450,7 @@ }, { "cell_type": "markdown", - "id": "b50cdc42", + "id": "9747bd1c", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -461,7 +461,7 @@ { "cell_type": "code", "execution_count": null, - "id": "297c4229", + "id": "b3ebc5ff", "metadata": {}, "outputs": [], "source": [ @@ -475,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "79600d0d", + "id": "f6b61148", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -484,7 +484,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d613ab0a", + "id": "b3cb7f30", "metadata": {}, "outputs": [], "source": [ @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "3d16bd90", + "id": "366448ac", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -517,7 +517,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ed61843c", + "id": "c4171db9", "metadata": {}, "outputs": [], "source": [ @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "c987375f", + "id": "39a5e273", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -536,7 +536,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9c28c878", + "id": "7f926fcf", "metadata": {}, "outputs": [], "source": [ @@ -546,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "536f6b52", + "id": "7448bf4e", "metadata": {}, "source": [ "# Node attributes\n", @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8c483d57", + "id": "79c57af5", "metadata": {}, "outputs": [], "source": [ @@ -570,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "676ea883", + "id": "b7346935", "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": "f5bd44ff", + "id": "8d4375dd", "metadata": {}, "outputs": [], "source": [ @@ -598,7 +598,7 @@ }, { "cell_type": "markdown", - "id": "07ff2c77", + "id": "6627979e", "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": "9c32a36f", + "id": "6dc40410", "metadata": {}, "outputs": [], "source": [ @@ -633,7 +633,7 @@ }, { "cell_type": "markdown", - "id": "8df9a6bc", + "id": "a7506961", "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": "aabbcd4b", + "id": "66c55ec9", "metadata": {}, "outputs": [], "source": [ @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "0852b1fb", + "id": "283d6899", "metadata": {}, "source": [ "# Multigraphs\n", @@ -675,7 +675,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cdc9ff8c", + "id": "1d408dce", "metadata": {}, "outputs": [], "source": [ @@ -693,7 +693,7 @@ }, { "cell_type": "markdown", - "id": "8636eabf", + "id": "0b95c68e", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -713,7 +713,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bf582f5d", + "id": "02686d3c", "metadata": {}, "outputs": [], "source": [ @@ -725,7 +725,7 @@ }, { "cell_type": "markdown", - "id": "b7049764", + "id": "051f39a8", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -736,7 +736,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0d1da7f1", + "id": "064eaaa3", "metadata": {}, "outputs": [], "source": [ @@ -748,7 +748,7 @@ }, { "cell_type": "markdown", - "id": "fe33bd32", + "id": "e98ae0ab", "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": "e4118e78", + "id": "06e2199a", "metadata": {}, "outputs": [], "source": [ @@ -770,7 +770,7 @@ }, { "cell_type": "markdown", - "id": "88168e19", + "id": "7cb6eafd", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -785,7 +785,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6b8bddf8", + "id": "3629c227", "metadata": {}, "outputs": [], "source": [ @@ -799,7 +799,7 @@ }, { "cell_type": "markdown", - "id": "7f9bca65", + "id": "3dee69eb", "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": "21c663f1", + "id": "9c1e726c", "metadata": {}, "outputs": [], "source": [ @@ -819,7 +819,7 @@ }, { "cell_type": "markdown", - "id": "5c8acf2c", + "id": "cc26616b", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -838,7 +838,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f7d43a30", + "id": "2bb836ef", "metadata": {}, "outputs": [], "source": [ @@ -847,7 +847,7 @@ }, { "cell_type": "markdown", - "id": "1e3ac651", + "id": "b1d71ebf", "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": "c32216be", + "id": "7f78afc5", "metadata": {}, "outputs": [], "source": [ @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "fd7250c1", + "id": "40b05aaf", "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": "79362d66", + "id": "6aa13f9c", "metadata": {}, "outputs": [], "source": [ @@ -889,7 +889,7 @@ }, { "cell_type": "markdown", - "id": "d7711d4b", + "id": "cdb34214", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -898,7 +898,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8be5197e", + "id": "6bae2d6b", "metadata": {}, "outputs": [], "source": [ @@ -919,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "751d3c6c", + "id": "bd41ce21", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -930,7 +930,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b9a82a38", + "id": "c8483998", "metadata": {}, "outputs": [], "source": [ @@ -941,7 +941,7 @@ }, { "cell_type": "markdown", - "id": "4a4e4c97", + "id": "e3525408", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -950,7 +950,7 @@ { "cell_type": "code", "execution_count": null, - "id": "331810ca", + "id": "5f16fecb", "metadata": {}, "outputs": [], "source": [ @@ -960,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "0eb4eea0", + "id": "5eccd196", "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": "1802df64", + "id": "49be0aff", "metadata": {}, "outputs": [], "source": [ @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "66284130", + "id": "7601eacf", "metadata": {}, "source": [ "See Drawing for additional details." diff --git a/tutorial_full.ipynb b/tutorial_full.ipynb index 38bb21a2..022ea528 100644 --- a/tutorial_full.ipynb +++ b/tutorial_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "e59716aa", + "id": "e94e9fa9", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,13 +17,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "4c02a2be", + "id": "a229eaa1", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:36.989696Z", - "iopub.status.busy": "2023-01-03T20:27:36.989182Z", - "iopub.status.idle": "2023-01-03T20:27:37.063917Z", - "shell.execute_reply": "2023-01-03T20:27:37.063168Z" + "iopub.execute_input": "2023-01-03T21:20:11.517279Z", + "iopub.status.busy": "2023-01-03T21:20:11.516765Z", + "iopub.status.idle": "2023-01-03T21:20:11.611529Z", + "shell.execute_reply": "2023-01-03T21:20:11.610444Z" } }, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "markdown", - "id": "a68e7605", + "id": "fe98dbb0", "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": "04184847", + "id": "8b198e62", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.067875Z", - "iopub.status.busy": "2023-01-03T20:27:37.067600Z", - "iopub.status.idle": "2023-01-03T20:27:37.070782Z", - "shell.execute_reply": "2023-01-03T20:27:37.070123Z" + "iopub.execute_input": "2023-01-03T21:20:11.616295Z", + "iopub.status.busy": "2023-01-03T21:20:11.616011Z", + "iopub.status.idle": "2023-01-03T21:20:11.619858Z", + "shell.execute_reply": "2023-01-03T21:20:11.619092Z" } }, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "b9be2699", + "id": "5eebe239", "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": "1f327dd5", + "id": "f983e2e2", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.074272Z", - "iopub.status.busy": "2023-01-03T20:27:37.074059Z", - "iopub.status.idle": "2023-01-03T20:27:37.076984Z", - "shell.execute_reply": "2023-01-03T20:27:37.076369Z" + "iopub.execute_input": "2023-01-03T21:20:11.623539Z", + "iopub.status.busy": "2023-01-03T21:20:11.623279Z", + "iopub.status.idle": "2023-01-03T21:20:11.626811Z", + "shell.execute_reply": "2023-01-03T21:20:11.625917Z" } }, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "4b467036", + "id": "ed7ca5a6", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -117,13 +117,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "90bd0361", + "id": "0e97adf2", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.079996Z", - "iopub.status.busy": "2023-01-03T20:27:37.079786Z", - "iopub.status.idle": "2023-01-03T20:27:37.083144Z", - "shell.execute_reply": "2023-01-03T20:27:37.082519Z" + "iopub.execute_input": "2023-01-03T21:20:11.630627Z", + "iopub.status.busy": "2023-01-03T21:20:11.630296Z", + "iopub.status.idle": "2023-01-03T21:20:11.634526Z", + "shell.execute_reply": "2023-01-03T21:20:11.633742Z" } }, "outputs": [], @@ -134,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "ac638797", + "id": "f2460766", "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": "308e8588", + "id": "58bf775f", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.085998Z", - "iopub.status.busy": "2023-01-03T20:27:37.085791Z", - "iopub.status.idle": "2023-01-03T20:27:37.088562Z", - "shell.execute_reply": "2023-01-03T20:27:37.087944Z" + "iopub.execute_input": "2023-01-03T21:20:11.638139Z", + "iopub.status.busy": "2023-01-03T21:20:11.637837Z", + "iopub.status.idle": "2023-01-03T21:20:11.641492Z", + "shell.execute_reply": "2023-01-03T21:20:11.640549Z" } }, "outputs": [], @@ -160,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "490aef47", + "id": "5bafb2aa", "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": "7b4d5d97", + "id": "6e83f2e8", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.091380Z", - "iopub.status.busy": "2023-01-03T20:27:37.091175Z", - "iopub.status.idle": "2023-01-03T20:27:37.094215Z", - "shell.execute_reply": "2023-01-03T20:27:37.093601Z" + "iopub.execute_input": "2023-01-03T21:20:11.649435Z", + "iopub.status.busy": "2023-01-03T21:20:11.646640Z", + "iopub.status.idle": "2023-01-03T21:20:11.653187Z", + "shell.execute_reply": "2023-01-03T21:20:11.652163Z" } }, "outputs": [], @@ -196,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "1e95cd0a", + "id": "bf6c3bd4", "metadata": {}, "source": [ "by adding a list of edges," @@ -205,13 +205,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "2b2cddc0", + "id": "77dafe76", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.097130Z", - "iopub.status.busy": "2023-01-03T20:27:37.096918Z", - "iopub.status.idle": "2023-01-03T20:27:37.099845Z", - "shell.execute_reply": "2023-01-03T20:27:37.099220Z" + "iopub.execute_input": "2023-01-03T21:20:11.656885Z", + "iopub.status.busy": "2023-01-03T21:20:11.656635Z", + "iopub.status.idle": "2023-01-03T21:20:11.660189Z", + "shell.execute_reply": "2023-01-03T21:20:11.659550Z" } }, "outputs": [], @@ -221,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "7735d204", + "id": "aa532e9e", "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": "1a1f832d", + "id": "98cca3d2", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.102842Z", - "iopub.status.busy": "2023-01-03T20:27:37.102633Z", - "iopub.status.idle": "2023-01-03T20:27:37.105499Z", - "shell.execute_reply": "2023-01-03T20:27:37.104880Z" + "iopub.execute_input": "2023-01-03T21:20:11.664187Z", + "iopub.status.busy": "2023-01-03T21:20:11.663951Z", + "iopub.status.idle": "2023-01-03T21:20:11.667656Z", + "shell.execute_reply": "2023-01-03T21:20:11.666671Z" } }, "outputs": [], @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "bd6b6dbe", + "id": "2ed337ea", "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": "63b6eff3", + "id": "85d84eb0", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.108401Z", - "iopub.status.busy": "2023-01-03T20:27:37.108195Z", - "iopub.status.idle": "2023-01-03T20:27:37.110937Z", - "shell.execute_reply": "2023-01-03T20:27:37.110320Z" + "iopub.execute_input": "2023-01-03T21:20:11.671619Z", + "iopub.status.busy": "2023-01-03T21:20:11.671312Z", + "iopub.status.idle": "2023-01-03T21:20:11.675608Z", + "shell.execute_reply": "2023-01-03T21:20:11.674778Z" } }, "outputs": [], @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "463d8334", + "id": "0a2e3f8a", "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": "ddf23c52", + "id": "366065d8", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.113805Z", - "iopub.status.busy": "2023-01-03T20:27:37.113597Z", - "iopub.status.idle": "2023-01-03T20:27:37.117439Z", - "shell.execute_reply": "2023-01-03T20:27:37.116735Z" + "iopub.execute_input": "2023-01-03T21:20:11.679800Z", + "iopub.status.busy": "2023-01-03T21:20:11.679482Z", + "iopub.status.idle": "2023-01-03T21:20:11.684503Z", + "shell.execute_reply": "2023-01-03T21:20:11.683571Z" } }, "outputs": [], @@ -307,7 +307,7 @@ }, { "cell_type": "markdown", - "id": "da3789cc", + "id": "84d014fb", "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": "b0e854a0", + "id": "4add5e21", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.120616Z", - "iopub.status.busy": "2023-01-03T20:27:37.120406Z", - "iopub.status.idle": "2023-01-03T20:27:37.126709Z", - "shell.execute_reply": "2023-01-03T20:27:37.126087Z" + "iopub.execute_input": "2023-01-03T21:20:11.688080Z", + "iopub.status.busy": "2023-01-03T21:20:11.687807Z", + "iopub.status.idle": "2023-01-03T21:20:11.696717Z", + "shell.execute_reply": "2023-01-03T21:20:11.695793Z" } }, "outputs": [ @@ -345,13 +345,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "de2b7208", + "id": "7163af50", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.131018Z", - "iopub.status.busy": "2023-01-03T20:27:37.130805Z", - "iopub.status.idle": "2023-01-03T20:27:37.134888Z", - "shell.execute_reply": "2023-01-03T20:27:37.134393Z" + "iopub.execute_input": "2023-01-03T21:20:11.702535Z", + "iopub.status.busy": "2023-01-03T21:20:11.701800Z", + "iopub.status.idle": "2023-01-03T21:20:11.708943Z", + "shell.execute_reply": "2023-01-03T21:20:11.708041Z" } }, "outputs": [], @@ -367,7 +367,7 @@ }, { "cell_type": "markdown", - "id": "1f9cb39c", + "id": "24f0059e", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -387,13 +387,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "3ad2f7b8", + "id": "b1b8ecad", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.137616Z", - "iopub.status.busy": "2023-01-03T20:27:37.137273Z", - "iopub.status.idle": "2023-01-03T20:27:37.141541Z", - "shell.execute_reply": "2023-01-03T20:27:37.141046Z" + "iopub.execute_input": "2023-01-03T21:20:11.712984Z", + "iopub.status.busy": "2023-01-03T21:20:11.712299Z", + "iopub.status.idle": "2023-01-03T21:20:11.718249Z", + "shell.execute_reply": "2023-01-03T21:20:11.717361Z" } }, "outputs": [ @@ -417,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "2dd63975", + "id": "1ad3b3af", "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": "3b3a853a", + "id": "33627768", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.144343Z", - "iopub.status.busy": "2023-01-03T20:27:37.143933Z", - "iopub.status.idle": "2023-01-03T20:27:37.147983Z", - "shell.execute_reply": "2023-01-03T20:27:37.147494Z" + "iopub.execute_input": "2023-01-03T21:20:11.723590Z", + "iopub.status.busy": "2023-01-03T21:20:11.723050Z", + "iopub.status.idle": "2023-01-03T21:20:11.729191Z", + "shell.execute_reply": "2023-01-03T21:20:11.728304Z" } }, "outputs": [ @@ -457,7 +457,7 @@ }, { "cell_type": "markdown", - "id": "c1d5867d", + "id": "80f5aa84", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -474,13 +474,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "336c3714", + "id": "38808384", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.150735Z", - "iopub.status.busy": "2023-01-03T20:27:37.150331Z", - "iopub.status.idle": "2023-01-03T20:27:37.153442Z", - "shell.execute_reply": "2023-01-03T20:27:37.152965Z" + "iopub.execute_input": "2023-01-03T21:20:11.734382Z", + "iopub.status.busy": "2023-01-03T21:20:11.733993Z", + "iopub.status.idle": "2023-01-03T21:20:11.738282Z", + "shell.execute_reply": "2023-01-03T21:20:11.737466Z" } }, "outputs": [], @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "81796970", + "id": "12955ef4", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -508,13 +508,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "d5a68853", + "id": "6e1176ac", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.156203Z", - "iopub.status.busy": "2023-01-03T20:27:37.155789Z", - "iopub.status.idle": "2023-01-03T20:27:37.423994Z", - "shell.execute_reply": "2023-01-03T20:27:37.423327Z" + "iopub.execute_input": "2023-01-03T21:20:11.742182Z", + "iopub.status.busy": "2023-01-03T21:20:11.741699Z", + "iopub.status.idle": "2023-01-03T21:20:12.076557Z", + "shell.execute_reply": "2023-01-03T21:20:12.075388Z" } }, "outputs": [ @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "67496885", + "id": "ac25aeb4", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -572,13 +572,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "5dd56a05", + "id": "1d6d8fb3", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.427516Z", - "iopub.status.busy": "2023-01-03T20:27:37.427168Z", - "iopub.status.idle": "2023-01-03T20:27:37.433317Z", - "shell.execute_reply": "2023-01-03T20:27:37.432709Z" + "iopub.execute_input": "2023-01-03T21:20:12.080317Z", + "iopub.status.busy": "2023-01-03T21:20:12.079923Z", + "iopub.status.idle": "2023-01-03T21:20:12.088033Z", + "shell.execute_reply": "2023-01-03T21:20:12.087097Z" } }, "outputs": [ @@ -602,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "5963217f", + "id": "dc6c043f", "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": "2a14156d", + "id": "81f5148a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.436078Z", - "iopub.status.busy": "2023-01-03T20:27:37.435866Z", - "iopub.status.idle": "2023-01-03T20:27:37.440431Z", - "shell.execute_reply": "2023-01-03T20:27:37.439815Z" + "iopub.execute_input": "2023-01-03T21:20:12.091873Z", + "iopub.status.busy": "2023-01-03T21:20:12.091596Z", + "iopub.status.idle": "2023-01-03T21:20:12.097372Z", + "shell.execute_reply": "2023-01-03T21:20:12.096381Z" } }, "outputs": [ @@ -642,7 +642,7 @@ }, { "cell_type": "markdown", - "id": "b50cdc42", + "id": "9747bd1c", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -653,13 +653,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "297c4229", + "id": "b3ebc5ff", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.444156Z", - "iopub.status.busy": "2023-01-03T20:27:37.443946Z", - "iopub.status.idle": "2023-01-03T20:27:37.448614Z", - "shell.execute_reply": "2023-01-03T20:27:37.447967Z" + "iopub.execute_input": "2023-01-03T21:20:12.102309Z", + "iopub.status.busy": "2023-01-03T21:20:12.101977Z", + "iopub.status.idle": "2023-01-03T21:20:12.108489Z", + "shell.execute_reply": "2023-01-03T21:20:12.107799Z" } }, "outputs": [ @@ -685,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "79600d0d", + "id": "f6b61148", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -694,13 +694,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "d613ab0a", + "id": "b3cb7f30", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.452219Z", - "iopub.status.busy": "2023-01-03T20:27:37.452008Z", - "iopub.status.idle": "2023-01-03T20:27:37.455593Z", - "shell.execute_reply": "2023-01-03T20:27:37.454948Z" + "iopub.execute_input": "2023-01-03T21:20:12.112429Z", + "iopub.status.busy": "2023-01-03T21:20:12.111906Z", + "iopub.status.idle": "2023-01-03T21:20:12.116676Z", + "shell.execute_reply": "2023-01-03T21:20:12.115762Z" } }, "outputs": [ @@ -721,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "3d16bd90", + "id": "366448ac", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -743,13 +743,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "ed61843c", + "id": "c4171db9", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.459277Z", - "iopub.status.busy": "2023-01-03T20:27:37.459065Z", - "iopub.status.idle": "2023-01-03T20:27:37.463179Z", - "shell.execute_reply": "2023-01-03T20:27:37.462539Z" + "iopub.execute_input": "2023-01-03T21:20:12.121627Z", + "iopub.status.busy": "2023-01-03T21:20:12.121164Z", + "iopub.status.idle": "2023-01-03T21:20:12.126621Z", + "shell.execute_reply": "2023-01-03T21:20:12.125824Z" } }, "outputs": [ @@ -771,7 +771,7 @@ }, { "cell_type": "markdown", - "id": "c987375f", + "id": "39a5e273", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -780,13 +780,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "9c28c878", + "id": "7f926fcf", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.466906Z", - "iopub.status.busy": "2023-01-03T20:27:37.466690Z", - "iopub.status.idle": "2023-01-03T20:27:37.470836Z", - "shell.execute_reply": "2023-01-03T20:27:37.470189Z" + "iopub.execute_input": "2023-01-03T21:20:12.131169Z", + "iopub.status.busy": "2023-01-03T21:20:12.130669Z", + "iopub.status.idle": "2023-01-03T21:20:12.135877Z", + "shell.execute_reply": "2023-01-03T21:20:12.135015Z" } }, "outputs": [ @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "536f6b52", + "id": "7448bf4e", "metadata": {}, "source": [ "# Node attributes\n", @@ -819,13 +819,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "8c483d57", + "id": "79c57af5", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.474738Z", - "iopub.status.busy": "2023-01-03T20:27:37.474521Z", - "iopub.status.idle": "2023-01-03T20:27:37.479492Z", - "shell.execute_reply": "2023-01-03T20:27:37.478858Z" + "iopub.execute_input": "2023-01-03T21:20:12.140788Z", + "iopub.status.busy": "2023-01-03T21:20:12.140283Z", + "iopub.status.idle": "2023-01-03T21:20:12.146562Z", + "shell.execute_reply": "2023-01-03T21:20:12.145656Z" } }, "outputs": [ @@ -850,7 +850,7 @@ }, { "cell_type": "markdown", - "id": "676ea883", + "id": "b7346935", "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": "f5bd44ff", + "id": "8d4375dd", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.483264Z", - "iopub.status.busy": "2023-01-03T20:27:37.483046Z", - "iopub.status.idle": "2023-01-03T20:27:37.487127Z", - "shell.execute_reply": "2023-01-03T20:27:37.486481Z" + "iopub.execute_input": "2023-01-03T21:20:12.151186Z", + "iopub.status.busy": "2023-01-03T21:20:12.150794Z", + "iopub.status.idle": "2023-01-03T21:20:12.156775Z", + "shell.execute_reply": "2023-01-03T21:20:12.155876Z" } }, "outputs": [], @@ -885,7 +885,7 @@ }, { "cell_type": "markdown", - "id": "07ff2c77", + "id": "6627979e", "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": "9c32a36f", + "id": "6dc40410", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.490364Z", - "iopub.status.busy": "2023-01-03T20:27:37.490147Z", - "iopub.status.idle": "2023-01-03T20:27:37.496909Z", - "shell.execute_reply": "2023-01-03T20:27:37.495166Z" + "iopub.execute_input": "2023-01-03T21:20:12.160961Z", + "iopub.status.busy": "2023-01-03T21:20:12.160376Z", + "iopub.status.idle": "2023-01-03T21:20:12.167987Z", + "shell.execute_reply": "2023-01-03T21:20:12.166903Z" } }, "outputs": [ @@ -938,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "8df9a6bc", + "id": "a7506961", "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": "aabbcd4b", + "id": "66c55ec9", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.499837Z", - "iopub.status.busy": "2023-01-03T20:27:37.499596Z", - "iopub.status.idle": "2023-01-03T20:27:37.502609Z", - "shell.execute_reply": "2023-01-03T20:27:37.501972Z" + "iopub.execute_input": "2023-01-03T21:20:12.173703Z", + "iopub.status.busy": "2023-01-03T21:20:12.173121Z", + "iopub.status.idle": "2023-01-03T21:20:12.177225Z", + "shell.execute_reply": "2023-01-03T21:20:12.176426Z" } }, "outputs": [], @@ -967,7 +967,7 @@ }, { "cell_type": "markdown", - "id": "0852b1fb", + "id": "283d6899", "metadata": {}, "source": [ "# Multigraphs\n", @@ -987,13 +987,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "cdc9ff8c", + "id": "1d408dce", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.505489Z", - "iopub.status.busy": "2023-01-03T20:27:37.505275Z", - "iopub.status.idle": "2023-01-03T20:27:37.511722Z", - "shell.execute_reply": "2023-01-03T20:27:37.511078Z" + "iopub.execute_input": "2023-01-03T21:20:12.181256Z", + "iopub.status.busy": "2023-01-03T21:20:12.180925Z", + "iopub.status.idle": "2023-01-03T21:20:12.189938Z", + "shell.execute_reply": "2023-01-03T21:20:12.189002Z" } }, "outputs": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "markdown", - "id": "8636eabf", + "id": "0b95c68e", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -1043,13 +1043,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "bf582f5d", + "id": "02686d3c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.515192Z", - "iopub.status.busy": "2023-01-03T20:27:37.514977Z", - "iopub.status.idle": "2023-01-03T20:27:37.519299Z", - "shell.execute_reply": "2023-01-03T20:27:37.518654Z" + "iopub.execute_input": "2023-01-03T21:20:12.194990Z", + "iopub.status.busy": "2023-01-03T21:20:12.194481Z", + "iopub.status.idle": "2023-01-03T21:20:12.200621Z", + "shell.execute_reply": "2023-01-03T21:20:12.199726Z" } }, "outputs": [], @@ -1062,7 +1062,7 @@ }, { "cell_type": "markdown", - "id": "b7049764", + "id": "051f39a8", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -1073,13 +1073,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "0d1da7f1", + "id": "064eaaa3", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.522243Z", - "iopub.status.busy": "2023-01-03T20:27:37.522023Z", - "iopub.status.idle": "2023-01-03T20:27:37.603202Z", - "shell.execute_reply": "2023-01-03T20:27:37.602379Z" + "iopub.execute_input": "2023-01-03T21:20:12.205008Z", + "iopub.status.busy": "2023-01-03T21:20:12.204729Z", + "iopub.status.idle": "2023-01-03T21:20:12.278236Z", + "shell.execute_reply": "2023-01-03T21:20:12.277298Z" } }, "outputs": [], @@ -1092,7 +1092,7 @@ }, { "cell_type": "markdown", - "id": "fe33bd32", + "id": "e98ae0ab", "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": "e4118e78", + "id": "06e2199a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:37.606633Z", - "iopub.status.busy": "2023-01-03T20:27:37.606380Z", - "iopub.status.idle": "2023-01-03T20:27:38.447980Z", - "shell.execute_reply": "2023-01-03T20:27:38.447176Z" + "iopub.execute_input": "2023-01-03T21:20:12.283118Z", + "iopub.status.busy": "2023-01-03T21:20:12.282791Z", + "iopub.status.idle": "2023-01-03T21:20:13.302482Z", + "shell.execute_reply": "2023-01-03T21:20:13.301386Z" } }, "outputs": [], @@ -1121,7 +1121,7 @@ }, { "cell_type": "markdown", - "id": "88168e19", + "id": "7cb6eafd", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -1136,13 +1136,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "6b8bddf8", + "id": "3629c227", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:38.451965Z", - "iopub.status.busy": "2023-01-03T20:27:38.451649Z", - "iopub.status.idle": "2023-01-03T20:27:38.460902Z", - "shell.execute_reply": "2023-01-03T20:27:38.460277Z" + "iopub.execute_input": "2023-01-03T21:20:13.309196Z", + "iopub.status.busy": "2023-01-03T21:20:13.308857Z", + "iopub.status.idle": "2023-01-03T21:20:13.317836Z", + "shell.execute_reply": "2023-01-03T21:20:13.317011Z" } }, "outputs": [ @@ -1168,7 +1168,7 @@ }, { "cell_type": "markdown", - "id": "7f9bca65", + "id": "3dee69eb", "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": "21c663f1", + "id": "9c1e726c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:38.464246Z", - "iopub.status.busy": "2023-01-03T20:27:38.463778Z", - "iopub.status.idle": "2023-01-03T20:27:38.468581Z", - "shell.execute_reply": "2023-01-03T20:27:38.467927Z" + "iopub.execute_input": "2023-01-03T21:20:13.322359Z", + "iopub.status.busy": "2023-01-03T21:20:13.321791Z", + "iopub.status.idle": "2023-01-03T21:20:13.328915Z", + "shell.execute_reply": "2023-01-03T21:20:13.327882Z" } }, "outputs": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "markdown", - "id": "5c8acf2c", + "id": "cc26616b", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -1225,13 +1225,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "f7d43a30", + "id": "2bb836ef", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:38.472362Z", - "iopub.status.busy": "2023-01-03T20:27:38.472007Z", - "iopub.status.idle": "2023-01-03T20:27:38.867315Z", - "shell.execute_reply": "2023-01-03T20:27:38.866577Z" + "iopub.execute_input": "2023-01-03T21:20:13.335269Z", + "iopub.status.busy": "2023-01-03T21:20:13.334633Z", + "iopub.status.idle": "2023-01-03T21:20:13.786076Z", + "shell.execute_reply": "2023-01-03T21:20:13.784800Z" } }, "outputs": [], @@ -1241,7 +1241,7 @@ }, { "cell_type": "markdown", - "id": "1e3ac651", + "id": "b1d71ebf", "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": "c32216be", + "id": "7f78afc5", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:38.871475Z", - "iopub.status.busy": "2023-01-03T20:27:38.871088Z", - "iopub.status.idle": "2023-01-03T20:27:39.078550Z", - "shell.execute_reply": "2023-01-03T20:27:39.077938Z" + "iopub.execute_input": "2023-01-03T21:20:13.790813Z", + "iopub.status.busy": "2023-01-03T21:20:13.789642Z", + "iopub.status.idle": "2023-01-03T21:20:14.119423Z", + "shell.execute_reply": "2023-01-03T21:20:14.118422Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -1282,7 +1282,7 @@ }, { "cell_type": "markdown", - "id": "fd7250c1", + "id": "40b05aaf", "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": "79362d66", + "id": "6aa13f9c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:39.082686Z", - "iopub.status.busy": "2023-01-03T20:27:39.082278Z", - "iopub.status.idle": "2023-01-03T20:27:39.085334Z", - "shell.execute_reply": "2023-01-03T20:27:39.084817Z" + "iopub.execute_input": "2023-01-03T21:20:14.124525Z", + "iopub.status.busy": "2023-01-03T21:20:14.124248Z", + "iopub.status.idle": "2023-01-03T21:20:14.128175Z", + "shell.execute_reply": "2023-01-03T21:20:14.127330Z" } }, "outputs": [], @@ -1308,7 +1308,7 @@ }, { "cell_type": "markdown", - "id": "d7711d4b", + "id": "cdb34214", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -1317,19 +1317,19 @@ { "cell_type": "code", "execution_count": 36, - "id": "8be5197e", + "id": "6bae2d6b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:39.088269Z", - "iopub.status.busy": "2023-01-03T20:27:39.087917Z", - "iopub.status.idle": "2023-01-03T20:27:39.369281Z", - "shell.execute_reply": "2023-01-03T20:27:39.368719Z" + "iopub.execute_input": "2023-01-03T21:20:14.132120Z", + "iopub.status.busy": "2023-01-03T21:20:14.131507Z", + "iopub.status.idle": "2023-01-03T21:20:14.560218Z", + "shell.execute_reply": "2023-01-03T21:20:14.554717Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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s2XjkyBF88+aNyh9OrsjPz8fRo0cznvfy5ctV0unZmAoQEZOTkxnNTVeuXFG6zATuUXSc0VRl4NmzZ7RzOX36tEplKh6xkWm2TpP48eNHqexbSlcGFI0/ULFiRTx16hRru31BQQE+efIEly9fji1atJAam5qpGBkZYeXKlbFatWpSg1PIUgwNDbFhw4Y4evRo3Lx5M965c4e2XEXdycnJkeh5reo1zmxMBYi/U84WtwVaW1tjTEyMkiUmcI2iEVBnz56NDx8+1EjFvWnTpmLn0q5dO5XJ8vTpU9q1PXPmjMrk4QJF+xbT7LY6oHRlAJGb+AO1a9fGS5cuyfz1mZycjGfOnMHx48fL5b/g6uqKPXv2xBEjRmDfvn2xSpUqnMVSqFixIvbp0wcXL16M586dw8jISLWcUsrIyMDOnTvT5NfR0cGDBw+qWjxWpgIRGzdupJ1H69attSoMaWmEqwioZmZm2K1bN/z333/x2bNnGtEvDh48SDuPd+/eqUSW4jOHjo6OGqlgFeX79+8K9SkyM1AELuMPeHh44I0bN+SSg6IofP/+Pfr5+WHXrl1ldkQ0MDDADh064IoVK/Dw4cO4bds2nDBhAjZt2pQzp0YLCwts1aoV+vj44L59+/DZs2dKS4LCRFJSEjZv3pwmp76+Pp47d05lchWFrakA8Xcf6NmzJ+18Fi5cqGSpCVzCVwRUCwsL7NWrF/r6+mJYWJharizKysqimUenTZumdDl+/fpFc2hctmyZ0uXgkqCgIMbw5mwK8RmQANv1v2wf5rZt2yqc/S47OxuvX7+Of/31l1xOIg4ODjhq1Cg8ceIExsXF4YcPH/DkyZP4999/Y7du3dDBwYGTAUlXVxdr1qyJ3t7euG7dOrx27ZrEL18u+fnzJy0POcDvUNDXr1/nvX1ZYGsqQPwdjtTJyYnW7+RVMgnqgTIioFpbW6OXlxdu3boV3759qzYD/Zw5c8TkLFOmjNLDsxefddPV1dVYp+r3799jt27dFOorZDWBFNhGbzp//jz+9ddfrOz9Xbt2xWfPnnEiX0xMDO7fvx8HDRokNfgEUxEKhdikSRNcuHAhhoSEFE6NxcfH4/Xr13H9+vU4bNgwrFWrltSY2LKU8uXLY5cuXXDevHl4/PhxfP/+PWfTml+/fqUFNQH4vQTr4cOHnLTBJUymAmmR4+7fv0/rX3Z2dvjjxw8lSk3gElVEQLWzs8PBgwfjrl27MDw8XGXKQUREBG1M3bVrl9LaLygooAX4GjhwoNLa5wo2eQvYvg9InIESEMUfYIrr7OfnJ+ZY9+rVK2zRogWri9+vXz9OI4bl5+fjo0ePcOnSpdi8eXOZHREtLCzQy8sLd+/eTUvSk52djc+fP8f9+/fjtGnTsE2bNmhhYcHJ4GRsbIxNmjTBcePG4bZt2/D+/fuYlpYm07l/+PCB9uUsGvhevXrF2TXmkvT0dNoUZUmD4Zo1a2jn2L59e7WcCiawg6sIhMX7Etvi6OiIw4YNw3379uGXL1+Ueu7Fv2Tr1q2rNOXk6tWrtGtx+/ZtpbTNBaKMhpaWlhLvbcWKFWWKQBgUFKTq05KKypUBERRFYUJCAkZGRhYGFpK03/79+1ktGRQIBDh06FAMDw/nXN6kpCQ8ffo0jhs3Dp2dnWUeJKpXr44zZszAwMBAxoBFFEXh169f8fz587h06VLs27cvTWGStwgEAqxcuTJ6eXnhsmXL8OLFixgVFcV4zUNDQ9HW1pbxQSgpi5eqkcVUgPj7a6ZLly60c12xYoWSJCbwAdsZyEOHDuHgwYOlDupt27bFQYMGyR08rWLFijhq1Cg8dOgQRkdH83reAQEBtPZDQkJ4bVNE8QA9NWrUUBsTSklcvnwZq1WrJvEelitXDvfs2YP5+fmsousaGBiovSKAqEbKgKwkJCTg2LFjWT2AOjo6OGbMGN5CzlIUhe/evUNfX1/s0qWLzF8RhoaG2LFjR9ywYQO+efNG6kOTkpKC9+7dwy1btuDYsWOxUaNGUnOty1IsLS2xbdu2OGPGDDxw4ADu378fy5Qpw6jI8D2QcYGspgJExLi4OCxfvjztJXDnzh0lSU3gA1lmIENCQmhr44sWc3NzXLt2Lb579w737t2L3t7ecvsDValSBcePH4/Hjx/n3CSVn59Pi6Xh7e3NaRtMREVF0b6Wt27dynu7ivLmzRvGVVJFX+pz587FlJQUseMk9S1RqV27tkYoQhqrDIgICQlhHXVQX18fp06dyrsTS1ZWFl69ehVnzpyJNWvWlHmAcHR0xNGjR+PJkydZJUvKy8vDhw8f4vDhwzkzL0gqVatWxY8fP/J6/bhCHlMBIuKtW7dog5mDg4NaZqsjyAbbGciCggI8fPiw1Jd8pUqV8MyZM0hRVGGI9J07d+KgQYOwXLlycj1f1atXx8mTJ+Pp06c5WYK2du1a2hjIt7Px/Pnzxdo0NTXF1NRUXttUhPj4eJw8ebJU02///v0xIiJCaj2ivrV3717a8eroV1UcjVcGEH+/DDds2ICmpqasHjgjIyOcPXu20tZ7RkdH4759+3DAgAEyBy4SCoXo4eGBixcvxgcPHjA6BJY0DQrw25O3QoUKMvs6SCpOTk7YvXt3nD9/Pp46dQrDw8PV0rYuq6lAxNKlS2nn3LVrV7U8RwJ/pKen4+LFi6UuFW7dujU+f/5c7DiKovDNmze4ZcsW7Nevn8wOyKJnt06dOjh9+nQ8f/68XM5n8fHxtMRcq1ev5ujq0MnJyaEpQpMmTeKtPUXIycnBDRs2SP2Aql+/vsyzgvn5+TQz0vDhw3k6C+4oFcqAiG/fvjHGnJdUzMzMcNGiRUqN/pefn48PHz7EJUuWYLNmzWR2bCpbtiz2798f9+7di9++fWPtICVyYjl//jw+efIE9+zZg1OmTMGWLVuiubk5JwqCqakpNmvWDCdOnIg7d+7Ehw8fqjwzmTymAsTf96ldu3a0c1y3bp0SpCaoG9++fcNhw4ZJfXGPHj1a4qxjQUEBvnz5Ejdu3Ig9e/ZkNL+VVIRCITZs2BD/+usvvHz5Muuv7eHDh4vVU6FCBd6CJx07dowmt7o5GVMUhRcuXGBMZy4q9vb26O/vL7fyv3r1arH6DAwM1H5msVQpAyKuXLmClSpVYv2QWVpa4urVq5W+Dhfxd2COU6dO4ZgxYxg99tl8PcgymDAtb6EoCiMiInDkyJGcKAXF26xatSoOHDgQV65ciZcvX8aYmBil2dDkNRUg/o40VvwrR1dXFx88eMCz1AR15dGjR9isWTOpCvHKlSsxKytLaj35+fn49OlTXLduHXbt2pX1rGZxxbZp06Y4b948vHbtmkTF++HDh7RjL168yMflwZYtW4q106pVK17akZewsDBs3769xGtqaGiI8+fPl3nFVXHi4uJQX19frO41a9ZwdBb8UCqVAUTEzMxMXLBgAe2GSCvlypVDPz+/Eh9kvqAoCt++fYsbN27ETp06ceYYWFx5KB74gqIoXLhwIeP+PXv2xI0bN+KoUaOwfv36Ml1PacXGxgbbt2+Ps2bNwkOHDuGrV694Sxcrr6kAEfHatWs0hatChQqsfDkIpROKovD48eNSVxG5uLjgyZMnWSu9ubm5+ODBA1y5ciV26NBBrqWMenp62LJlS1y4cCHevHmzcByjKArr168vtm+XLl04vy5hYWE0mY4fP855O/Lw8+dPHD9+vNQZ1MGDB3PqZO7t7S1Wf8WKFdU6nHWpVQZEfPjwgXG6V1pxdHTEnTt3qjyXeWZmJgYFBeGff/6JNWrU4EwZKBoSs6CgAH18fBj3nTdvHm0wy83NxVevXuHhw4dx1qxZ2KFDB7SxseFENgMDA6xfvz6OGjUK/fz88Pbt25wE6ZDXVCCiuEMUAGDv3r01wkOYwB+ZmZm4fPlyNDExkdinW7RogU+ePJG57pycHLx79y4uWbIE27RpQ7P7symGhobo6emJy5Ytw7lz59LGAa6XBk+cOFGsjXLlymFOTg6nbchKdnY2rlu3TqoptHHjxgpHr2UiJCSE1lZAQADn7XBFqVcGEH9rxkePHkU7OzuZHqZKlSrhwYMH1Uab+/btG+7Zswf79+8vl82xaElISMC8vDwcMWIE4++yOBlRFIWxsbF4+fJlXLVqFQ4cOBCrVavGWShYFxcX7NWrFy5atAjPnDmDERERMr2IFTEVIP52UC0+/QkAuGnTJtZ1EEovsbGxOHLkSKn9fcSIEQplw8zKysLg4GBcsGABNm/enJOopTNnzuTsGqSkpNBMHfPnz+esflmhKArPnDkjNTaLg4MDHj58mDenYIqiaOHbu3XrxktbXKAVyoCI5ORknDJlisxOe+7u7jKlTVYGnz59UmggeP/+PS0wCMDvL4YdO3ZwImN6ejo+fPgQd+7ciRMnTsRmzZpJ/YqSpZibm2PLli1xypQpuGfPHnzy5IlU844ipgLE3ytCinuE6+vr49OnTxW9TIRSwrNnzxiVRlExNjbGpUuXcuJUm56ejkFBQTh37lxs0qSJXKuEBAIBdu7cmZOMjFu3bhWrW0dHB799+6bwecpDaGgotmnTRuJ5GxkZ4eLFi5XiI7Z7927aNS9piaKq0CplQMTTp0+xYcOGMj889erVkyttMh8omqK1VatWtG26urp47NgxXuUuKCjA8PBwPHXqFM6fPx+7d+8ul+MkU9HR0cHq1avjkCFDcM2aNRgUFFQYyEVRUwEic0S3SpUqYXJyMlIUhfHx8RgZGYnx8fFq0UcIyoeiKPzvv/9owX6KFicnJzx69CinfSQlJQUDAgJw5syZWL9+fblm5cqWLYu9e/dGPz8/mTIyUhRFy+TXp08fzs6NLd+/f8fRo0dLPfdhw4YpVUlJT0+nzeLOnj1bae3LglYqA4i/vXm3bt0q13S7ImmTuULeFK0CgYDR/mhoaIiXLl1S2fkkJCRgcHAwbtiwAUeMGIF16tRBPT09TpQEOzs7bN++PW1qVZ7ELX/99Ret/rp16zJGtvP19VXrxCQE/sjKysLVq1ejmZmZ1HGEr2A0v379wnPnzuG0adPkysAK8P8ZGbdt24bv3r2TqLzcunWLduy1a9d4OS8msrKycNWqVVJXZHh4eOCjR4+UJlNRpk2bJiaLlZWVypzUpaG1yoCI79+/49ChQ+V6WNq2bau0WN9MzJ49m5OXpZmZGd66dUtl5yGJnJwcfPHiBR44cABnzJiBnp6eUhOHyFrMzc1x7NixuGXLFrx37x6rddu5ubnYtGlTVkqXKOZ9YGCgEq4WQR358eMHjh07VqrSPnToUFryMq6Ji4vDU6dOSQ23W1Kxt7dnzMg4YMAAsf2qVKmiFJMqRVF48uRJqXkinJ2d8fjx4yqdqXv//j1NroMHD6pMHklovTIg4saNG1i1alW5HpKuXbvSIpDxzdevX9He3l7hF6KVlZVG2b0pisJv377hxYsXcfny5ejl5SU1eIisxdXVFfv27YtLly7F8+fP49evX2kDyZcvX1ivCxcFeyIKgXYTGhqKbdu2ldhPjIyMcOHChbzbsfPy8tDR0ZH2MSDPs+Lk5IReXl40f4WNGzfyeg6Iv0290jLYmpiY4PLlyxmTwKmC4ivamjZtqmqRaBBloAjZ2dm4fPlyudf39+vXD9+8ecO7nImJieju7q7wi8/BwUEp8iqDtLQ0DAkJwW3btuG4ceOwSZMmcqedLV7Kli2Lbdq0wWnTpuH+/fvx9u3bMi310oRc5gT+oSgKz507J9XDvXz58njw4EFev6yXLVsm1qahoSG+evUKDx48iCNHjsQKFSrI/azo6Ojgzp07FVo5IY2YmBiJK6AAfs/IjRw5krf25eX06dM0WZ89e6ZqscQgygADnz9/xq5du0rt8NI6o7e3Ny9pkxF/r21u3rw564dT0vSkq6srRkZG8iKjupCfn4/v37/H48eP47x587BLly68BHJiex+KB3siaCfZ2dn477//SvVXatSoES9r3xF/m0aL++Ns2LBBbJ+IiAjOMjKeOHECf/78qZDMmZmZuGzZMqmrkVq2bKm2s5x5eXm06zhmzBhViyUGUQYkQFEUnj59mjalVrRIi8ano6ODY8eO5TSiVX5+PuNyQGlyMCkDtWrVwtjYWM7k0iSKryoQCATo7u7OWQInafehaLAnAiEuLg4nTpwodanzwIED8cuXL5y3PWjQILF2KleuLHE2omhGxoEDB6Ktra1cz0CNGjVwypQpMmVkFMWIkbbiyMXFBf/77z+1f7aKJz8zMjJSq0imRBkogbS0NJw5c6bEl4W+vr7Ur02u0iZTFIUTJkyg1c/0YAYGBuK+ffsYp7KbNm2KiYmJHF0dzUNSAKKsrCx89uwZ7tu3D318fLB169YKB3ZiKsrKlEnQHF69eoUdOnSQ2GcMDAzwn3/+UTheflHu3LlDaycoKIjVsaKMjPL6WImU47p16+KMGTPwwoULjMniHj58iB4eHhLrMDMzw9WrV6ulZz4TsbGxtBVNyvCvYAtRBljy8uVLqQlKypUrJ9VGrWja5OJ2PgDAMmXK4JgxY8S2VahQAY8ePcoYoaxdu3acDiiaCtsARBRF4ZcvX/DcuXO4ZMkS7NOnj8IxEUq7aYYgHxRF4aVLl7BKlSoS+46dnR3u27ePE38CiqKwZs2aYvX36tWL9fHv3r2jyTdkyBBOMjIePHiQNnNRXJEYO3ZsYQwRTaL4ygs3Nze1CWZHlAEZKCgowD179khd3la7dm2pMwXypE3es2cP49fCzZs3adkZO3bsyGga6N27t8Zo0HyjSAAiRYM9kZkBgjRyc3PR19cXLSwsJPah+vXr4+3btxVua9u2bbQXMluzprS180UzMnbp0kWujIySSps2bTA0NFThc1cVqo7JIA2iDMhBfHw8jho1SmKHtbS0xHbt2kmNH842bfKlS5doJgqBQID//fcfPnnyhNUDNHz4cMzLy1PS1VF/FMlVoEiwJ+IzQGBLQkICTp06VaovS79+/fDz589yt5GamkpbVvjPP/+UeJysUfW4yMgIAFi9enVcuHAh3rp1C7Ozs+U+b1VCURQt6ZwqojUyQZQBBbh79y5tqq1oady4Mfbt21fqAy1Km8zUuR8+fMj44GzevBkREWfNmlXiAzR16lS1mYZSJxTJVeDr6yuXMkBWExBk5e3bt9ilSxeJ/UpfXx/nzJmDKSkpctU/efJksfpsbW1LzDSoaLz97OxsvHPnDi5ZsgTr1asnV+jkohkZ79+/r/IMs7JQPI+DUChUWR6HohBlQEFyc3Nx3bp1Epe86Orq4tixY3HAgAFSO72TkxPu2rWrsFO/f/+elhgH4HdaYcTfGqa0fOoAgAsXLiRfohJQxFSQlJSEJiYmrBNekTgDBEW5cuWK1Ngitra2uGvXLpmTDb1+/ZpW19GjRyXuz1Umvi9fvkj1C5C1mJiYYKdOnXD16tX46NEjtZ4JVbcMjyKIMsARUVFR2KdPH4md1dnZGf38/KTuA/B7/f/mzZsZQ2yOGDGi8OXOlCu7aCm+bpggjqJpjQMDA1l90YgiELL11CYQJJGbm4tbtmwp0WdJ1rwprVu3FqujRYsWEvdlGncCAgJYt5WWlob//POPVL+qtm3b4o4dOxTKyGhubo7du3fH9evX4/Pnz9VudnTixIli8pYrV67EGRm+IcoAx1y8eFFqrOwePXrghQsXZI4R3rlzZ7GpsOLTe0VfPnv37lXhFdAcFDEV5Ofno42NjcT7VTQ3AVEECFzy69cvnDFjhlSfpF69euHHjx9Z1XfixAna8S9fvmTc19vbW2y/ihUrspqNKCgowP3796OdnZ1EmatUqYIXL16kzWampKTgpUuXOMnIuGnTJnz16pXKZ0zDwsJoMh4/flylMhFlgAcyMjLw77//lph1z8jICFetWoXBwcE0rZypuLm5iSXRSU5OZgw0pKenh6dOnVLhmWsWipgKLly4IPWeubq6op+fn0yrRggEWfjw4QP26NFDYh/U09PDmTNnlmieysnJob2kJ0yYQNsvLi6ONu6sWbOmRDlv376N9evXlyinhYUF+vr6sv4y5iIjo42NDfbv37/EjIx80rJlSzGZWrVqpXQZikKUAR55+/YttmnTRmKHrF69Ot68eROvXbuGjRo1ktp5RWmTExISsFq1arTfDQ0NSTIcGVHEVFB8ZqdBgwaYkJCAkZGRmJCQoPIvD4L2cO3aNamOzNbW1rht2zapdvQFCxaIHWNiYkJzSly9erXYPgYGBlKV54iICPTy8pIol46ODk6ZMkXh5baijIyTJk2SO2eLvb09DhkyBHfv3o2fPn1SyvN77NgxmhyvXr3ivV1JEGWAZyiKwkOHDkkN4Tl8+HAcP348q07L5KgoFArx3r17qj5VjaT4YNWhQ4cSj/n06RPtHuzbt08J0hIIzOTl5eGOHTvQ2tpa4thRo0YNvHr1KuPx3759o9nmt2zZUvh7fn4+zfw5fPhwxrpSUlJwzpw5UsOkd+nShbckabGxsXj06FEcO3YsVq5cWS7lwMnJCUeMGIH+/v6chpQvSk5ODpYrV06s3UmTJvHSFhuIMqAkfv36hRMnTmRt75LFLqbKDqTpyGMqKL6k08LCAjMyMpQkMYEgmeTkZPzrr78kmigBALt3747v37+nHdu3b1+x/apXr174hXzp0iVaPQ8fPhQ7Pj8/H3fv3i31w8fd3R0vX76slGshIioqSuGMjJUqVcLRo0fj4cOHOc2IOH/+fLF2TE1N5V4mqihEGVAyjx49kmo/A/i9HPHy5ct44MABrFixYokdVdkPV2lCVlNBZmYmzZt7xowZSpRYO6EoCuPj4zEyMhLj4+OJGaYEwsPDpa5c0tXVxWnTponlKbl+/Tptv5s3byIi0mId1K9fX+weBAcHY506dSS2Z2lpiVu2bFGLeABcZGSsWrUqTpgwQeGMjFFRUbQlymvWrFFJPyfKgArIz8/HTZs2SYzEJRAIcPr06Ziamor37t1DY2PjEmcRvL298dOnT6o+NY1EFlOBv78/7fp/+PBBidJqF0lJSejr64uurq5i19zV1RV9fX1J7IYSYPOS3rx5M+bm5iJFUbTkQ/3798dPnz7RZir37NmDiL+VDqZMqkWVjunTp6tVdr6iUBSFHz58wB07diickXHq1Kl45swZmRPBSbt+yuznRBlQEc+fPy8xZreVlZXU9bjFiyhtclRUlKpPT6OQxVTQuHFj1ooDQTECAwPRxMSkcJlmcQVYtHSTOM5KJz8/H/fs2UOzTxct1apVw8uXL6Ovry/tZV58TXyZMmUwNjYWZ82aJdUc0aNHD0ZzhDojysi4ZcsW7Nevn9SYDtI+zkrKyFiUlStXSq1LWf2cKAMqICIiQuqDqWjR19dHHx8fhdMmawtsTQVPnz6lXeuzZ88qX2AtIDAwEHV0dEqM8igK6kQUgpJJSUnBuXPnSnXsa9euHe0DpPiz4enpKdVRsWbNmhIdFTWNgoICfPHiBW7cuFGhjIyNGjXC2bNn45UrV8Qyx4r6OZs6+O7nRBlQMvHx8ejm5ka72f369cNFixahgYGBxA5RPKlI+fLlpc4uGBsb45w5c0imPBawMRWMHj1abB9HR0e1DnuqqZBwz/wSERFBC7hV/GtUno8Qa2tr3L59e6l+JvLz8/HJkye4du1auTMy6urqYrNmzXDmzJloaGioNv1cgIgIBKWQkZEB7dq1g0ePHoltb9OmDQQGBoKBgQEsXrwYlixZwqq+9evXw/Dhw2HNmjWwZcsWyM7OZtzP3Nwc/vzzT5gxYwaYm5srfB6lkVOnTsGAAQMK/9bR0YEfP36AtbU1AAAkJSWBg4MDZGVlFe6zbNkymD9/vtJlLe34+fnBjBkzQJahSSAQwPLly2Hs2LE8Sla6ePjwIcyfPx/CwsIUqkdPTw/Gjh0LM2bMgDJlynAknWaQl5cHL1++hHv37sH9+/fh0aNHEsdhLhAIBODr6ws+Pj7cV86LikGgkZeXh926daNpe7Vr18bk5GSkKApXrFghk4b55MmTwvpjY2Nx8uTJUm14bNMmayMlmQo2btxI0+6JGYZ75E0RTQop2lD4TIVOlAElQFEUjho1inZjnZ2dMSYmBimKkpiOWFosbnNzc9y0aZNYbPAvX77g6NGj5U6brM1IMhUUFBTQTDsDBw5UsbSlk/j4eJUPuKSQou6FD9MvUQaUQPHAEgC/E2e8ffsW8/PzccyYMYw3fNmyZUhRFD548ECqXal+/fr4+PFjsTY/fPiAQ4YMkSltsrYjaVXBtWvXaNfu9u3bqha3VBIZGanygZYUUtS9REZGcv7sEWWAZ7Zv3067kYaGhnj//n3MycnBAQMGMN7szZs3F9Zx+vTpEjuHQCDAiRMn0tbzhoWFsUqbfOjQIZlzoZc2JJkKil+/GjVqkKA3PEFmBkghpeTCx8wAcSDkkbNnz4KXlxdQFFW4TSgUwtmzZ6F9+/bQr18/CAwMFDtGR0cH9u3bB8OHDy/cNmjQIDhx4kTh33Z2dvDjxw/GNm1tbWH9+vUwdOhQEAgEhdufPn0KCxYsoLVXlOrVq8PSpUuhT58+IBQKZT7f0kD//v3hv//+K/y7ZcuWcP/+fbF7uGXLFpg8ebIqxCv1ICK4ublBRESETA6EAAD6+vpw+vRpaNKkCU/SlT5+/foF69atA39/fygoKJD5eBcXF1i0aBF07dpVbLwhSAcRoUmTJvD161eZHWUrVaoE4eHh3F9vztULAiIi3r17l3GZ4M6dOzE5ORlbtGhB+01fX5+2bj09PZ0WgXD79u14+/ZtrF69ukTNsW3btvju3TtGuUpKm1yvXj0MCAjQyq/f4qaC4mYWpmxuBG7x9fVVyIFw0KBB+OXLF1WfhlqTk5ODGzduRAsLC06+VNu0aYOhoaGqPi2NQp5+LhAI0M/Pjxd5iDLAA2/evGF8yBYtWoRxcXFYr1492m8mJiZ4/fp1Wl0nTpwQ209HRwfj4uIQ8fcDvXr1aolhjfX09PDvv/+mJdGhKAqvXbtGi6ZXvDRr1gyDg4OVcs3UBSZTQdHClOedwC2yxhlgKoaGhvjPP/+IBXgh/H72L168iFWqVJF47aTFOinpRTVmzBiyyoYl6hZPgygDHPPt2zd0cnKi3cixY8fi169fabG/AX47ExbPACaieCYxpmA4X758wZ49e0rsRC4uLnjp0iXacRRF4YULF7B27dpSO6GnpyeGhIRwfq3UFWk52F++fKlq8bQCthEISyr29va4f/9+LCgoUPUpqZywsDBs3769VAVq6tSptGtuZWUl9nenTp3Qw8NDYj2mpqa4atUqzMrKUvUpqz2yRtoMCgriTRaiDHBIUlIS1qxZk3Yje/TogW/fvkVnZ2fab3Z2dhgWFsZYX2pqKi00qChBCBPnz59nbENU+vTpw5i3oKCgAE+cOMGoqBQt3bp1w+fPn3N2vdSV4qYCUWnRooWqRdMqSspNIItSUL9+fa1dARIXF4cTJkyQ+sIRmVb++ecf2ot92bJlYtv09fXxx48feOzYManjjYuLC548eVIrzY2ywDYHB5+KACJRBjgjKysLW7VqRXsgPDw88MGDB4zZsFxcXDA8PFxinYcPHxbbX1dXt8SMWOnp6ThnzhzU1dVlfEBNTExw3bp1jMsJ8/Ly0N/fH11cXKQOrF5eXvjmzRuFr5m6kp6ezjhVevToUVWLpnUkJSWhn58fY9bCmTNnoomJCe0+SVMUvLy8MCIiQtWnpRSys7Nx3bp1aG5uLvF6NG7cGO/fv4+Iv82OxcepSZMmYXx8PO15WLVqFSL+Tum9fPlyxvtQVIl++vSpKi+F2iOtn/v5+ZWY7IgLiDLAAfn5+divXz/aQ1C1alW8fPkyo/+Au7s7RkdHS623R48eYsd07dqVtUyvX7/Gli1bSnxAa9asiffu3WM8NicnB7dv347ly5eXeHxpT5tc/KHU19cnQZpUCEVRmJCQgJGRkZiQkFD4tRkcHMyouEmb5dLX18c5c+aUWkdQiqLw7NmztD5ctDg4OOChQ4fEzCfHjh2j7ff69WtERBw+fLjY9goVKogtRY6JicGRI0dKVMQEAgH+8ccfGBMTo/TroUlI6ufKgCgDCkJRFE6ZMoXW+e3t7fHQoUO0lQAAgA0aNJCYIldEUlISLbTwgQMHZJbN399faoaxUaNGSZQlMzMTN27ciDY2NhKP19XVxXHjxpWqtMlpaWk0J0KBQFDiPSOohrNnzzJOgfv4+KC7u7vEvmtra4u7d+8uVfE1QkNDsU2bNhLP2cjICBcvXswYkrz4x0Pr1q0Lf3v06BGtrosXL9LqePr0qdSPEBMTE1y2bBlmZmbyeRkIckCUAQVZtWoVrcObm5vj+vXrGVOFtm7dmtUXyf79+2lfM/JOFSUmJuK4ceMkPqCWlpa4Z88eiU5WaWlpuHLlSqnLkPT19XHatGn448cPuWRUJ3bs2MF4jkxpjQnqQfHnBeC309WxY8dw8+bNUvPS16lTR+NXzfz48QPHjBkj1UTi7e2N3759Yzw+LCyMtv+JEycKf6coChs0aCD2e+fOnRnroigKT506JdXc6OTkhMeOHSP+BGoEUQYUwN/fn/GlOHv2bMYvlW7durHWiDt37ix2bK9evRSW98GDB1inTh2JD2izZs2kessnJSXhggULWKVNLsm3QV2hKEri6gqmlRwE9WHDhg20e6anp4dXr17FX79+4fTp0yX60gAA9u7dW6oPjzqSlZWFq1atoqU3L1o8PDzw0aNHUuuZMGGC2DF2dnaYk5Mjts/evXtpdUszE2ZlZeHq1atLlE3SSiqCciHKgJxcvnyZMRlQcduaqAwePJh1DoCEhATaoMWV81peXh5u3LhR4gtdR0cHZ86cKXV9dlxcHM6aNYu20qFoMTc3x8WLF2ucXfbevXsSz0mUq4CgvhT3hgf4PTX94MEDRER8//49du/eXeI91tPTw1mzZinFYUsRuPz6TklJoTkALliwgLZfRkYGbXZw1qxZJcr6/ft3hWYtCMqBKANy8PjxY0ZfgE6dOjF29AkTJshkl9y1a5fY8UZGRpwHT4mOjpa6nt7R0RFPnz4tdSCJiYlhlTZ5zZo1GpM2eciQIWLyFx/AiKlAvaEoCidOnEjrh2XLlsVXr14V7nf16lXGZcCiYm1tjdu3b8e8vDwVng0zXNvlt2zZQlN6Jb2YZ8yYQXu+2bbDxp9h0aJFGjNWlDaIMiAj4eHhjA51xe1pojJ37lyZ7WLt2rUTq8PLy4uns0G8cuUKVqpUSeID2rVrV/z8+bPUOiIjI3HUqFElpk3etGmTWnvk//z5k6bY1KpVS+xvYipQfwoKCnDQoEG0Pmhvby+2rDAvLw+3b98u1cG2Zs2aePXqVRWezf8TExODf/zxh9QvbFk99imKooU179Onj8T9P378SGvT399fpvbkWelA4B+iDMjAjx8/GF+ckl6morW4srZR3N/g1KlTPJzN/5OZmYkLFy5kdHgE+B2ZbPny5SW+yD98+ICDBw/W2LTJK1euFJPVwMCAZiclpgLNICcnB7t06ULrf66urrRwuUlJSThr1iypM1zdu3fH9+/fq+RcMjMzcdmyZbys5b916xatLqaw6EXp2LGj2P6NGzeWud3s7Gz8999/pcZAaNSoUWEMBAL/EGWAJampqVi/fn1ah2UKJiQQCHD79u1ytbN161axukxMTGi5Bfjiw4cPtFmJoqVq1ap448aNEusJCwvD3r17S6xHNCirU9rk/Px8rFChgpiMI0aMkJjWmKD+ZGRkYPPmzWl9r3bt2ozx3cPDw6X2W11dXZw+fTotTThfUBTFKsrfqVOn5PbK79+/P+0ZL6muc+fO0eR48uSJXO3//PmTdXREAr8QZYAFOTk5NG0YABi9ZHV1dRVy9isexXDw4MEcnknJiAYgOzs7iQ/n0KFDWS0hfPLkCW1VRPFSo0aNEn0TlMHFixdpsok8sIv7VhBTgeaQlJTEuDqkWbNmEm3TwcHBUlfdWFpa4ubNm3md3Xr06BHv8f9jYmJojsq+vr4lHpeXl0fLvzJy5Ei55UBklzeBJJ7iF6IMlEBBQQF6e3vTOidT1DNDQ0PGhEBsiYmJoU2xF09prCySk5MZk5aISpkyZXDr1q2svuzv3LnDGKq5aKlfv75K0yYXn1Ju0KBBoSxMmSOJqUBz+P79O6ONunPnzrTlcyLy8/Nx9+7djDN/ouLu7o5XrlzhVNZv374xjjeiwmVmwCVLlojVbWRkxDoj3vLly2ljn6LLidlkVCSJp/iDKAMlMHv2bFqHZHKUMzMzw1u3binUlq+vr1id5ubmKs/89fTpU2zUqJHEh7NRo0asbJUUReHVq1fVMm3yp0+faEpY0YRQxFSg+URERDCG1x40aJBUhTYlJQXnzp0r0Z9GpFQomqsjPT0dFy1aJDV9dps2bTA0NFShdkTk5ubSrseYMWNYH//9+3eaj8X69es5kS0nJwd9fX2lBjnT5sRTfEGUASkUfzmLNPPi26ysrOS2mRWl+LTgsGHDODgLxcnPz8etW7dimTJlGB9MoVCIU6ZMYbU2m6IoPH/+fIlpk9u1a1e4Npxv/vrrL9qsR3E/DWIq0HxevXqFZcuWpfW1iRMnljgj9fnzZ6lLcXV0dHDKlCmYkJAgk0wFBQV46NAhdHBwkFi3q6srnj17ltNZs9OnT9PakTUjafEVG5UrV+b0iz0hIQGnTJkidZWSNiWe4huiDEjgxIkTrNKkli9fnpMMfl+/fqXVrYjJgQ++f/8udQrTzs4Ojx49ymrQKigowOPHj6s8bXJmZiYtVO306dNp+xFTQengwYMHjF75//zzD6vjb9++zehILCoWFha4ceNGieaHooSEhEidKTM3N8d169bxshy3uKOwh4eHzHXcvXuXJjMfaXbfvHnDuDJEVEp74illQZQBBoKDg6VOCxbV2LnSStetWydWd9myZVkNKKrgxo0bUl/i7du3xw8fPrCqS5a0yW/fvuX8XA4cOEBri2kJGTEVlB6uXr3KuIyQ7TR3QUEB7t+/H+3t7SX21ypVquDFixcZFeMvX74wxkEQFaFQiBMmTMCfP39yfeqIiPju3Ttam4cOHZK5HoqiaHE4evbsyYPEv7l8+bLWJZ5SJkQZKMbLly+lrn0VlZo1a2JsbCxn7TZs2FCs/lGjRnFWNx9kZ2fjihUrJIYk1tfXxwULFrCOTsYmbbJQKMRhw4Zxmja5SZMmNEVGEsRUUHo4deoUo3Psvn37WNeRlpaG8+fPlxqWu3379hgWFibX/nzh4+Mj1qa1tbXcvknbt2+nPaN8LgPMzc3VisRTqoAoA0X48uWLVG1fVJo0acJpIp5Pnz7R2uBjuo0PIiIisGvXrlJnT2TxuM7MzMQNGzYoJW3y06dPaXWfOXNG4v7EVFC62L17N6PCKesKHjZf+m3btpW6MkHaTAKXpKen0z525syZI3d9qamptCXWf//9N4cSM1NaE0+pEqIM/I+EhASsVq1aiYpAu3btOF/rWjzynbW1tVrGRJcERVF4+vRpdHR0lHjdvLy8MDo6mnWdaWlpuGLFCqkexQYGBgqlTR49erRYfQ4ODlKvOzEVlD7WrFlD61f6+vqsgmsV5/79+yWulileZPEx4ILieU8EAoHCps7JkyeL1Wlra6u0sOOlJfGUOkCUAfwdqUxagA9R6dWrFy9L/YoHOBk/fjznbSiDtLQ0nDlzpkTvX1NTU9ywYYNMik5SUhLOnz+/xLTJc+fOlWm2JikpifZiX7p0aYnHEVNB6WPOnDmMffXx48cy1yVaHSAtaJdotmDSpElKnVmiKArr1q0rJke3bt0Urvf169e08+MqyypbNDnxlLqg9cpAXl4e9uzZs0RFYNiwYbx0pPfv39Pa0nR718uXL7FZs2YSr2WdOnUwJCREpjrj4uJw5syZJaZNXrJkCSuv4uLLRnV1dVn5gBBTQemDoigcO3YsrT9ZWVnJ7LQqiksgLc+BqHAZN4ANISEhNBkuX77MSd3FsxG2aNGCk3plQdMST6kbWq0MUBSF48aNK/GhnTJlCm8Rr4pHAbOzsysV3rAFBQW4d+9etLKyknhdx44dK7PvRUxMDE6aNIlV2mRJOR0oiqJFORswYACr9ompoHSSn59Pi9MP8DuVNxuHOFHEwnLlypU4nhQtooiC8pq6ZGHo0KFibVesWJGzce3kyZO0c3v58iUndcuKuieeUle0WhlYvHhxiQ/rggULeHXqKZ4+dMqUKby1pQri4+NptvmixdraGvfv3y/zNRalTZaW4ERS2uTr16/T9r158ybrtompoHSSnZ2NHTp0oPUNNzc3qcv82OQymDlzJjZt2lTiPmZmZgrnGpDGz58/acul165dy1n9ubm5NNOIqs2d6pZ4St3RWmWguCMNU+EqvKYkXr16RWvz7t27vLapKu7duyfVpteyZUt8/fq1zPWyTZu8e/fuwsQyffv2Ffvd3d1dJmWEmApKL2lpaYwv7Xr16tGc0D59+oR9+vSR+rKZNm1a4eyXKAlY8SQ/RYuiWQglsWrVKrF2DAwMOO+zCxcuFGvDxMRELRz31CHxlCaglcrA+fPnpX5RCoVCsdj0fDF//nyxdh0cHEp1Ao7c3Fxct26dxLzsurq6OHv2bInZ5KTBJm1y5cqV0c/Pj+bguHnzZpnaIqaC0k1iYiKj4tqyZUvMzMzE5ORkhaahMzMzcdmyZRKfA1FbbHJ+sIEpPffw4cM5qbso3759U/jZ4gtVJZ7SJLROGQgJCZGaDERPTw9PnTrFuxwURaGbm5tY2zNmzOC9XXUgKipK6heVs7Mznjt3Tq66Hz9+jJ06dSpx1kfRrxdiKijdxMTEYMWKFWn9pVatWlId1GrUqMHaQS0mJgb/+OMPiXUJBAL8448/MCYmRqFzYUrP/fDhQ4XqlISis258o6zEU5qIVikD7969kxq5ysjICAMDA5UiS2hoqNIeUHXl0qVLUsMQ9+jRQ+5oZmzSJgMAdunSRa7BipgKSj+fPn0qcYmgqCiydO3p06fYokULqQrrsmXLWEfzLI609Nxcc+PGDZr8svjjKAu+Ek9pMlqjDMTExKCzs7PEm1+mTBml2uvnzp0r1r6Li4taadDKIiMjA//++2+JU65GRka4atUquYKysE2b3Lx5c5kHLGIq0A7Onz8v1Rygp6eHM2fOxKSkJIXaoSgKT506JVU5dnZ2xmPHjsk0TjCl5967d69CspZ0HsXzlvTv35+39hSFy8RTmo5WKAPJyclSU+ba2Ngodb0vRVG0KcjZs2crrX115O3bt7S1ykVL9erV8datW3LVLUqbLM1GCyB72mRiKtA8KIrC+Ph4jIyMxPj4eIkvVjbhbqtVq8Z5uNusrCxctWqV1CBbHh4e+OjRI1b1zZo1i/Zyk7Tkliv8/PzE2tTV1VXY1MEnosRT0maB2ISLZtu31JVSrwxkZ2dLfck4OTkpfb3p48ePaXI8e/ZMqTKoIxRF4aFDh6Q6+QwfPlyubG5v376VqggULd27d2elHBJTgeaQlJSEvr6+6OrqKnbPXF1d0dfXt/DLnk0inKLFz8+PF3m/f/+OY8aMkbpKxtvbG799+yaxDqb03MrwS0pKSkJjY2OxdhcvXsx7u4qSlpaG//zzj8yJpNj2LXWnVCsDBQUFOGDAAKna3tevX5Uu18yZM8XkqFy5ssZpkXzy69cvnDhxosSBsGzZsrhjxw6ZVl5MnTpVrA4zMzOah3Xx0r9/f6kR6IipQDMIDAxEExMTFAgEtD4l2mZiYoLLly+XmiLX3NycsU/Kk/6XLaGhoVI/ZoyMjHDRokWMK3D8/f1p+7NNLa4oxSM6li9fXmOW7rFNMR0XF8e6bynLF00RSq0yQFEUTps2TeINrVOnjlKifhWnoKCAts74n3/+UbocmsCjR4+k2vOaNGnC6gs+LS2Nlqlt7ty5mJOTg9u2bWOVNvnz58+MdRNTgXoTGBiIOjo6UpcSl1T09fVxzpw5mJKSQkvZC/B7RujChQu8nQNFUXj27Fnal2fR4uDggIcOHRJTkIv7yiizbzI5SP/3339Ka58LSko8ZWxsjAKBoMS+JRQKUUdHR+0VglKrDKxdu1bizWnevLnKpm7u379Pk4fv/OWaTH5+Pm7atIn2Mi/6oE2bNk1qPoKdO3fSNPbIyMjC32VJm1x8Wra4qUAoFOKzZ8800mZY2khKSkITExOFFAEvLy+aIrhixQrafoaGhnj79m1ezyc7OxvXrVsn8VkAAGzcuDGGhITgkydPaL/Ju1xXXornJ/H09FRq+1wgSjzl4OAgdx8SjQsmJiZqbTIQICKCBoOIkJiYCOnp6WBqagpWVlZw5MgRGDZsGOP+HTt2hDNnzoCJiYmSJf3NtGnTYNOmTYV/u7u7w5s3b0AgEKhEHk3h+/fv8Oeff8Lx48cZfy9fvjxs3LgR+vfvL3YtERHq1q0LYWFhhdu6d+8OFy9epNWRnp4OmzZtgnXr1kFycjJjOwYGBjBhwgSYN28elCtXDjIyMsDa2hqys7Np+7q6usLUqVNhxIgRYGFhIdsJExTGz88PZsyYAfIMcTVr1oQVK1aAh4cH7TdEhEWLFsGOHTvEtpuamsK5c+egdu3acsvMhvj4eFi7di0cPHhQ4rk5OztDVFRU4d/ly5eHp0+fgq6uLq+yFeW///6DSZMmiW27d+8eVKlSRWkycEVGRgZs3boVtmzZwviss0EgEICvry/4+PhwLB1HqFARUQhJThv29vYSbc1eXl5Ky7PNRH5+Ptrb24vJpAmONerE1atXacGaipZOnTqJeXgzzcSUlKlNlrTJp06dkpiyWdNshqUJiqLQ1dVVqgMeKaQoswgEAnR1dVXbGUONVAakOW1IKqNGjVJ5NsBbt27R5Hr37p1KZdJEsrKycMmSJWhgYMB4rw0MDHDJkiWYlZWlUKY2NmmT2RRNsRmWJuLj41U++JNCClNR10BGGqcMyOMQNH36dLXQxiZOnCgmV+3atVUtkkYTHh4uNfRwxYoVaQFj5MnUxiZtcklFE2yGpYnIyEiVD/qkkMJUivorqRMapQzI4xCkp6enFikq8/LyaA5qK1asULVYGg9FUXjy5EmpKwJERdFMbZGRkThy5Ei5HdIEAgFv69IJ4pCZAVLUtajrzIBGORDK4xCkaqcN/J+DY2BgIM2p8dOnT+Dq6qoSuUobqampsGjRIti0aRNQFMW4T6NGjeDu3buQlZUFaWlpcpX09HRITEyE2NhYmWUUCARQqVIlCA8PJw6jPIOI4ObmBhEREXI5EErDwcEBWrRoAc2bN4cWLVqAo6MjJCYmQs+ePSE8PFxs3+bNm8OxY8fA0NCQUxkkMXz4cAgMDCxxPz09PZgwYQJMnz4dzMzMeJXpxo0bMHjwYLFtly5dgsaNG/PaLltyc3MhNDQU7t+/D/fv34cnT57I7SQoDXV//jVGGVDk4ba2tobdu3eDo6MjODg4gK2tLejo6PAk6W+Sk5PhwIEDsHnzZvj8+TPt9zp16sCLFy94laE0QFEUpKen017Ikl7WX758gTt37sCvX79ULbpEEhISwMrKStVilHoUWU0gC66urtC2bVuoXbs2rFmzBmJiYsR+7927N5w6dYp3T/6oqCioWLGimDI8efJkCA4Ohnfv3jEeY2trCytWrICRI0fyNiZSFFU4dosYOnQoHD58mJf2SiI/Px+ePXsGN2/ehJs3b8K9e/cgMzNTpjqEQqHEjw5JqPrDtCQ0RhlISEgAGxsbTurS0dEBe3t7cHBwkFrkXX4YFBQE/fr1K+xgTJdYX18fLly4AJ06dVLoXNQNRITMzExWX9hsvsQzMjJUfUqcExkZCS4uLqoWo9STnJwMjo6OkJWVxWrgFgqFYGhoCKtWrYJHjx7BzZs34fv37zK3q6OjAwUFBWLb/vjjD9i7dy8IhUKZ62PLP//8AytXriz828zMDGJiYsDQ0BB27twJixYtkqgk16lTBzZu3Aht27blRbZ///0X/vrrr8K/9fX14du3b2Bra8tLe0UpKCiAly9fFr7879y5A2lpaTLVoaOjAw0bNoS2bdtC27ZtoUaNGlC1alWZ+paRkRFER0er7TJjjVEGvnz5AhUrVlRqm2XKlCmcTZBUbGxsxB7woKAg6NatGyCi1E4iEAhAKBRCQECAShUCRIScnBy5psslbZdVY9Y2yMyA8mD7PAqFQhAIBHD58mXo2LEjAPx+Nj5+/AjBwcFw8+ZNuHXrFsTHx8stS48ePWD//v283PucnBxwdnaGuLi4wm2TJ0+GLVu2FP7969cvWLp0KWzduhXy8/MZ6+nduzesW7cOKleuzKl8iYmJ4ODgADk5OYXbVq5cCfPmzeO0HYDfMxFv3rwpfPnfvn0bkpKSZKpDIBBAvXr1oG3btuDp6QktWrQAc3NzsX0U6VvqiMYoA1zODHCJnp5e4SyDjY0NXLlyBfLz81lNTcqrLebm5sr0dV3SC13SwKAtiO6hpaUlmJmZlVhMTU1h4sSJ8P37d5n9V9TZZlhakTZTJ7oPxsbGcObMGamDNSKKvWRu3bol10umbt26hV+YrVq1or1k5OHYsWMwZMgQsW2vX7+GGjVq0Pb98OEDzJo1Cy5dusRYl56eHkybNg3mz58PZcqUUVg2EX/88QccOHCg8G9nZ2eIiIhQ2DyBiPDhwwcxpS0hIUHmemrXri12X8qWLVviMVz1LbVAGV6KXKBIEBEDAwN0dHSUmo5UlaV9+/Y4f/58/Pvvv9HHxwdHjhyJXl5e2KlTJ2zWrBnWqlULXVxc0MrKCvX19VUuryqLgYEBWltbo4uLC9aqVQubNWuGnTp1Qi8vLxw5ciROmjSJljyoe/fueOXKFZw8ebLEmAF6enr4999/s07v6uvrK3NfJKsJVEdSUhL6+fkxZpbz8/PD5ORkmessKCjA58+f4/r167F79+5SwwRLKkKhEBs3boxz5szBwMBAxoRDbGjRooVYva1bty7xmKtXr2LNmjUlymZjY4Pbt2/HvLw8uWQqzqNHj2htyJPTgaIoDA8Px127duHgwYOlph6WVtzd3XHSpEl46tQpjIuLk/u8+OhbqkBjZgYAFF9NUFBQAPHx8RAdHQ0xMTESS2pqKo9noV3o6urSvqrZfH1L+iLX19eX2t6hQ4dg+PDhYtvev38PVatWBQCAr1+/go+PD1y4cIHxeBcXF9iyZQt069ZNajvy2KPV3WaoDSAi/Pr1C9LS0sDMzAwsLS05m6XJz8+H0NDQwi/Ue/fuyezzoqurC40bNy6cnvbw8AAjIyOpx4SFhUGdOnXEtp04cQIGDBjASuY9e/bAggULJH5N16xZEzZs2AAdOnRgfyIMICI0atQInj17Vritc+fOcOXKlRKP/fr1a+GMTHBwMERHR8vcfuXKlQuva5s2bcDOzk7mOqTBZ99SBhqlDChrAE5PT2dUEooqET9+/CiVtnGBQCD1ZSzrC9zAwECpD4SHhwc8fPiw8O927drB9evXaftduHABpk6dKha/vSh9+vQBPz8/cHJykthWabMZErglLy8Pnjx5AqtWrZI4JV8S+vr64OHhUTh93aRJEzAwMBDbZ+LEiWJ5Euzs7CAqKgr09PRYt5OcnAzLly+HTZs2QV5eHuM+3bt3h3///bdQsZaH/fv3w6hRo8S2MS2xjomJKXz537x5EyIjI2Vuy8XFpfC6tW3bFhwdHeWWWytQ2ZyEnLCNQCgKARsUFMSLHHl5eRgdHY2PHj3CM2fO4OLFi1U+hV78/E1NTdHe3h6rVq2KHh4e2LNnTxw3bhwuXrwY9+7di9euXcNnz57hx48f8fv375ienq4WkRrl5dmzZ7TrcPr0aYn7p6en45w5cySaj0xMTHDdunVS87CzzWfOVz8kaAZcjQ9GRkbYvn17XLFiBYaEhGB8fDyamJiI7bNw4UK55QwPD8fevXtLbF9XVxenT58udyC3jIwMLFu2rFidM2fOxB8/fuDx48dx/PjxWKVKFbmujYODA3p7e+PevXsxIiJC7mugrWicMoCongOwJkY8EwgEaGtri/Xr18cePXrghAkTcNmyZbhv3z4MCgrC169fY1JSksYoCGPGjBE7v/Lly7Oyd75+/RpbtWol8TrVrFkT7927J/H40mIzJPAHRVHo4+PDqHCOHj0aPTw85PJpKp6fQygU0tJsy0NwcDDWqVNHYruWlpa4efNmqYqyJP7880+azPKMX7a2tjhw4EDcuXMnfvz4UWPGKXVFI5UBRPUbgEtzljRjY2N0c3PDNm3a4NChQ3H27Nno5+eHp0+fxocPH+K3b984czKSl6SkJDQ2NhaTe8mSJayPpygK/f390draWuJ1GDVqlNRwxhRFYUJCAkZGRmJCQgIZnAhiFBQUoLe3N61flStXDsPDwzEtLQ2vXLmCs2fPxkaNGsn1ktTT08MePXrghg0bMDQ0lHVSLiby8/Nx9+7daGtrK7E9d3d3vHLlSol1JSUl4fnz53H69OlYrVo1ucYhS0tL7NevH27ZsgXfvHlDni+O0SifASZQjZw25HVwbNmyJVAUBaGhoRobZEcoFEK5cuVKDOTExTIqJjZt2gTTpk0r/FtXVxe+fv0K5cuXl6meX79+wbx582DXrl2Mv1taWsLatWth5MiRvAaQIZRO8vLyoF+/fnDx4kWx7S4uLnDv3j1wcHAo3JaSkgJ37twptJu/fPlS5miKlpaW0KZNm0K7efXq1WUeH1NTU2HlypWwceNGyM3NZdynS5cusH79enB3dwcAgLS0NLh3716h7M+fP5fZx6pMmTLQunXrQtlr1apFnjke0XhlQJ1Q1MGxoKAAwsPD4dmzZ/Ds2TN4/vw5PH/+XOZoWcXR19cHCwsL0NPTg7y8PEhOTpb4UPONqakpODg4SA3mVK5cOZnWHiMiuLu7w4cPHwq3eXl5walTp+SW8+HDhzBhwgR4+fIl4+/NmjWD7du3Q+3atSXKlJiYCOnp6WBqagpWVlYa5VlM4I+srCzo3Lkz3LlzR2x7jRo14M6dO2Bpacl43K9fv+D27duFqxXevHkjc9u2trbQpk0b8PT0hLZt24KbmxvrfhkREQFz5syB//77j/F3oVAI9erVA4FAAKGhobQojCVhbGwMrVu3LpStbt26vIeNJ/w/RBngGK49zCmKgs+fP9MUhOTkZIXkNDU1hSpVqoCTkxNYW1uDoaEh5OTkiK2eSExMVKgNedHR0QE7OztGRaGoEiEKFx0cHAzt2rUTqyM4OFjh0Kr5+fmwZcsWWLBgAaSnpzPKOX36dFi8eDGYmpoCgOScFK6urjB16lQYMWIEWVpIgJSUFGjbti2EhoaKbW/SpAlcv369sD9JIjY2FpydnWV+4RanfPnyhV/enp6erKK83rlzB6ZNm6ZwbhWBQCA20zFixAjw9/dXqE6C/BBlgAf4jkqFiBAZGSmmIDx79kzh5DwmJiZQt25dqF+/PjRo0ABq1qwJZmZm8PPnT4kxGWJjY1U2y1CmTBlwcHCAhIQEsTCsjo6OcPbsWXB0dARbW1uFpxZjYmJgxowZEmcaHB0dwc/PD4yNjcHLy6vE+3769OlSl5OCIDtxcXHQsmVL+Pjxo9j29u3bw6VLl2hLCIuyZMkSWLx4ceHfxsbG8PTpU3j+/Hnh1HzRxEBsqVChgthyPNHS2tzcXHjy5Elh3SEhITJn9jMwMBBbJnnz5k1YtGhR4e+GhoYQHR1NQnWrCKIM8ERycjIcPHgQNm3aRPtC9PHxgREjRnAa6hMRISoqiqYgKBJLHQDAyMgI6tSpU6ggNGjQAKpXr164hpmiKEhMTCwxkJOsYVu5QldXF+zt7UvMMVFSYBcAgMDAQJgyZQpjFkoRxb92iiOaEVJ1TgqCehAVFQXNmzenBdHx8vKC48ePM06T5+XlgYuLi1ga7bFjx9L8XKKiogqD9Ny8eRO+ffsms3yWlpZgZGQECQkJYnkFZKFixYowf/58GDx4sNhz9vPnT3BychKLa/Dvv//CzJkz5WqHoBhEGeAZVTo4IiLExMTQFIQfP34oVK+BgQHUrl27UEGoX78+1KxZU+qXTGZmJsTGxkoN5vT9+3eV5UkoW7ZsiWYJa2tryM7OhtWrV8Pq1avlnhEh0QgJRXn37h20bNmSZpYbM2YM7Nq1izZenD59Gry8vMS2hYaGQt26dSW2gYgQEREhFshHnoyM0pCmCAuFQhg3bhwsXbpULMfMkCFD4NixY4V/u7q6wsePH4mjoAogyoAWEhsbW6gYiP4tnoNdVvT09KBWrVpiCkLt2rXB0NCQdR0URUFcXJzUGYaYmBhISUlRSFZ50dfXh/Llyxeuinjz5o3ECIYloe65zQnK5enTp9C2bVuab8rs2bNhzZo1Yg6pw4YNg3v37hXu4+HhASEhITK1V1BQAAEBAXD48GG4f/++zEm3RBgaGoK7uzt07doVxo4dC9HR0TBjxgx48uQJ4/7m5uawYMECmDp1KhgYGMC9e/egZcuWYvucOHECGjduTBxvlQxRBggA8HvKrriCIO+LToSuri7UqFFDTEGoU6cOGBsby1WfyDnPz89PYnjSGjVqQHJyMvz48UNh5yo+IRkMCcW5efMmdOnShTYd3717d3j37p1E89ShQ4fA29tbat34v8x+IrOBvJn9pFE0I6NAIICjR49KnH1wdXWFf//9F3r27Al169aFV69eSdyPON4qB6IMECQSHx8PoaGhYgqCPDHCiyIUCqF69epiCkLdunVL9J6W5pRZtO7Lly9Dp06doKCgQKLjY1H/BqZVAsokISGBOEwRCjl//jz069dPJkX24sWL0L17d7FtiAifP38WS7csj1mgXLlyYGhoCHFxcZCVlSXTsaJVQT9//pRo/mvbti3Y2dmJmQqKQhxvlQdRBggy8evXL5qC8OnTJ4XqFAgEUK1aNTEFoV69eoUBitgu1xQIBCAUClk752VnZ8P79+8hLCwM3r9/D58/f4aoqCiIi4uDX79+QUZGhsSkLVwRGRkJLi4uvLZB0CwOHDgAf/zxB+v9dXR0ICAgAKpVqybmEyCPw6Aos1/btm2hTZs2YG9vDwC/l9kWXakgT0ZGRSCOt/xDlAGCwiQnJ8OLFy/EFISPHz/KZYMsSpUqVaBmzZpw6dIlyMvLY1WfyDnv7du3hdkno6OjC2cDRP+Pjo5WWRyFopCZAUJxkpOToVy5cjI5qJa0ikUSoqWEorS+0rJ0FkWUkVFkdpBnqaGsEMdbfiHKAIEX0tLSaArC+/fvS2XaZ3kgPgMEScgT1pwtDg4OYnEE2AQZYkN2djY8evSocObg4cOHvMQfIY63/EGUAYLSyMjIgJcvX4otdXz79q1aO/qJ0NfXF1tq6OjoWPj/+/fvg5+fn8w5KcigRigOIoKbmxtERERwogzY2tqKvfxlCT+sCJmZmRASElKoHDx58oSTZcNEieYPogwQVEpWVhaEhYWJKQivX79WarwBExMTcHJyEnvJF3/pW1tbSxx8FM1JQSCISEhIEFuHLysWFhbg6elZGN/f3d1dLV6a6enphYmLgoOD4dmzZwopO8S8xj1EGSCoHdnZ2fD69WsICgqC+fPnK1SXlZUV7Uu++Avf3Nxc4QGT65wUBO3ky5cvCk3df/78GSpVqsShRPwQFhYGderUkft44njLPUQZIKgtin4lffv2DRwdHTmUSDp856QglH4U7fOa8sWsLeepSZCYjwS1xcrKClxdXWX+ahcIBODq6iqWG14ZdOrUCaKjo8HX15f2dVapUiXw9fWFmJgYoggQJKJon5eU/ljd0Jbz1CSIMkBQWwQCAUydOlWuY318fFRiK7WwsAAfHx8IDw+HhIQEiIyMhISEBAgPDwcfHx9Ok1MRSh+a2OflQVvOU5MgZgKCWkOc8wjahrb0eW05T02BzAwQ1BoLCws4ffp0YXRBaYic886cOUMGC4LGoi19XlvOU1MgygBB7enUqRMEBASAkZERCAQC2hShaJuRkRHx0ieUCrSlz2vLeWoCRBkgaATEOY+gbWhLn9eW81R3iM8AQeNARPj16xekpaWBmZkZWFpaEociQqlGW/q8tpynOkKUAQKBQCAQtBxiJiAQCAQCQcshygCBQCAQCFoOUQYIBAKBQNByiDJAIBAIBIKWQ5QBAoFAIBC0HKIMEAgEAoGg5RBlgEAgEAgELYcoAwQCgUAgaDlEGSAQCAQCQcshygCBQCAQCFoOUQYIBAKBQNByiDJAIBAIBIKWQ5QBAoFAIBC0HKIMEAgEAoGg5RBlgEAgEAgELYcoAwQCgUAgaDlEGSAQCAQCQcshygCBQCAQCFoOUQYIBAKBQNByiDJAIBAIBIKWQ5QBAoFAIBC0HKIMEAgEAoGg5RBlgEAgEAgELYcoAwQCgUAgaDlEGSAQCAQCQcshygCBQCAQCFoOUQYIBAKBQNByiDJAIBAIBIKWQ5QBAoFAIBC0HKIMEAgEAoGg5RBlgEAgEAgELYcoAwQCgUAgaDlEGSAQCAQCQcshygCBQCAQCFoOUQYIBAKBQNByiDJAIBAIBIKWQ5QBAoFAIBC0HKIMEAgEAoGg5RBlgEAgEAgELYcoAwQCgUAgaDlEGSAQCAQCQcshygCBQCAQCFoOUQYIBAKBQNByiDJAIBAIBIKWQ5QBAoFAIBC0HF1lN4iIkJiYCOnp6WBqagpWVlYgEAiULQaBQCCUCsiYSuACpc0MJCcng5+fH7i5uYGNjQ1UrFgRbGxswM3NDfz8/CA5OVlZohAIBILGQ8ZUApcIEBH5biQoKAj69esHmZmZAPBbky0U4H8arLGxMZw+fRo6derEtzgEAoGg0ZAxlcA1vCsDQUFB0K1bN0BEoChK4n5CoRAEAgEEBASQzksgEAgSIGMqgQ94VQaSk5PB0dERsrKypHZaEUKhEIyMjCA6OhosLCz4EotAIBA0EjKmEviCV5+BAwcOQGZmJqtOCwBAURRkZmbCwYMH+RSLQCAQNBIyphL4greZAUQENzc3iIiIAFmaEAgEUKlSJQgPDycesQQCgfA/yJhK4BPelIGEhASwsbFR6HgrKysOJSIQCATNRdExtXv37uDp6QnNmzeHevXqgZ6eHofSETQd3pSBL1++QMWKFeU+fvHixTBp0iSFOj+BQCCUFhQdU4tiZGQEjRs3hubNm0Pz5s3Bw8MDypYty0ndBM1EbWcGAAB0dXWhc+fO4O3tDT179gQjIyOOpCMQCATNgosxVRo1atQoVA6aN28OlSpVImYFLULtfAYkYWZmBv369YNhw4ZB69atQUdHhwMpCQQCQTNARKhcuTJEREQopb1y5cqJKQf16tUDfX19pbRNUD68Li308/ODGTNmcKIMFMXBwQGGDBkC3t7eULt2bU7rJhAIBHUkOzsbGjRoAG/fvpXreD09PbC2tobv37/LdbyhoWGhaaFZs2bQrFkzsLS0lKsugvqhdnEG9PX1oVu3bnD58mXIysoq8ZhatWqBt7c3DBkyBBwdHbkQm0AgENSKrKws6N27N1y9elXhuvr37w/9+vWD0NBQuH//Pjx58gRycnLkqsvd3V1s9qBy5crEtKChqF0EwsuXL0PHjh0hNTUVzp49C4cPH4YbN26UOLsgEAigbdu24O3tDf369QNzc3OuT4VAIBCUTkZGBvTs2ROCg4NlOk4gEEgcNytVqgRHjx6FJk2aQE5ODjx//hzu379fWOLj4+WS1dbWFpo1a1aoHNSvXx8MDAzkqougXJSem6B4c0XjaJ85cwY6duxIOz4mJgaOHTsGhw8fhpcvX5bYnqGhIfTs2ROGDRsGnTp1IktoCASCRpKWlgbdunWDu3fvim03NjYGRITs7GwAkJyb4PDhw3D+/Hnw9/en1a2jowNLly6FOXPmiPlgISJ8+vQJ7t+/DyEhIXD//n25TRMGBgbQqFGjQuWgWbNmZMm4uoJKIikpCefNm4cAIFacnJzQz88Pk5OTWdUTFhaGc+bMQUdHR1pdTMXa2honT56MDx48QIqieD5LAoFA4Ibk5GT08PCgjWkWFhb46NEjTEpKQj8/P3R1dRX73dXVlTamHjt2DM3NzRnHyNatW2NUVJRUWRITE/HSpUs4b948bNWqFRoaGrIaf5lKtWrVcPTo0bhv3z788OEDGZfVBKXMDIh49+4dVK9eXWzbmzdvaNvYQFEU3LlzBw4dOgT//fcfpKamlnhM5cqVwdvbG4YOHQqVK1eWuU0CgUBQBklJSdCpUyd48uSJ2HZLS0u4du0a1K9fv3AbIsKvX78gLS0NzMzMwNLSktFu/+XLFxg6dCiEhITQfitbtizs2bMH+vbty0q+3NzcQp8DUfn586eMZ/kba2trMdNCw4YNiWlBBahcGXj79i24u7srVG9WVhZcunQJDh8+DJcvX4b8/PwSj2natCl4e3vDwIEDwdraWqH2CQQCgSsSEhKgQ4cO8OLFC7HtNjY2cP36dYVWUOXn58Py5cth2bJljD5c48aNgw0bNoCJiYlM9SIiREREiCkHb968kUtGfX19aNiwoZhpgQSf459SoQwUJSEhAU6dOgWHDh2CBw8elLi/rq4udOnSBby9vaFHjx4ksBGBQFAZcXFx0L59e3j16pXYdjs7O7hx44Zcs6hM3Lt3D4YOHQpRUVG036pVqwbHjh2DunXrKtRGUlISPHjwoFA5ePz4MasVYkxUqVJFbNVC1apVyaoFjil1ykBRPn/+DEeOHIFDhw7Bp0+fStzfzMwMvLy8wNvbmwQ2IhAISuX79+/Qrl07ePfundh2BwcHCA4OhipVqnDaXnJyMowfPx5OnjxJ+01fXx9Wr14N06ZNA6GQm+S2eXl58OLFC7HZA3ljHlhZWdFMC4aGhpzIqa2UamVABCLC48eP4fDhw3D8+HFISEgo8RgHBwcYOnQoeHt7Q61atXiVj0AgaDfR0dHg6ekJ4eHhYtudnZ0hODgYXF1deWkXEcHf3x+mTp0KGRkZtN87deoE/v7+YGdnx0vbX758EVMOXr9+LVeQOn19fWjQoIGYacHW1pZzmUszWqEMFCUvLw+uXr0Khw8fhnPnzhUuzZFG7dq1CwMbOTg4KEFKAoGgLXz9+hU8PT1pYYYrVqwIwcHB4OLiwrsMHz9+hCFDhsCzZ89ov9na2oK/vz906dKFdzmSk5Ph4cOHhcrBo0ePIDMzU666KleuLGZaqFatGmezHKURrVMGipKamgpnzpyBw4cPQ3BwMKvARp6enuDt7Q19+/YlgY0IBIJCREREgKenJ3z9+lVse+XKlSE4OBicnJyUJktubi4sWLAA1q5dy/j7tGnTYPXq1Uqdjs/Ly4OXL1+KzR7ExsbKVZelpSV4eHgUKgeNGjUiPmJF0GploCjyBDbq1asXDBs2DDp27EgCGxEIBJkIDw8HT09PiI6OFtterVo1uHHjBpQvX14lcl2/fh2GDx/OaM+vU6cOHD16lDNHRllBRPj69WthMKT79+9DWFiYXKYFPT09qF+/vtjsQbly5XiQWjMgygADr169gsOHD8ORI0cgJiamxP2tra1h0KBB4O3tDY0bNyZergQCQSrv3r2Ddu3a0V64NWrUgBs3bqj8pRQfHw+jR4+Gixcv0n4zMjKCjRs3wrhx49RirEtNTRUzLTx8+JDR/4ENrq6uYsqBu7u71pgWiDIghYKCArHARmlpaSUeIwps5O3tzZvTD4FA0Fxev34N7dq1g7i4OLHtderUgWvXrqnNmnpEhO3bt8PMmTMZfat69+4Ne/bsUbvwwvn5+RAWFiZmWig++8KWsmXL0kwLxsbGHEusHhBlgCVZWVlw8eJFOHz4MFy5coVVYCMPDw/w9vaGAQMGkMBGBAIBXrx4Ae3bt4fExESx7Q0aNICrV6+qZUrg169fw+DBg+H169e038qXLw+HDx+Gtm3bqkAy9kRFRYkpB2FhYawy6RZHV1cX6tevL7as0d7enjM5ERESExMhPT0dTE1NwcrKSnmzL7wHPC7C27dvaXGq3759q0wROCE+Ph63bNmCTZs2ZRWLW1dXF3v06IEnTpzAzMxMVYtPIBBUwJMnT7Bs2bK08aFp06aYlJSkavGkkpmZiVOnTmUc3wQCAc6bNw9zc3NVLSZrUlNT8erVq7ho0SLs0KEDmpqayp1roWLFiujt7Y3bt2/HsLAwLCgokFmepKQk9PX1Zcwz4evrq5T+QZQBBQkPD8fFixdj5cqVWXUcc3NzHDVqFAYHB8vVaQgEguYREhLCmCioRYsWmJKSomrxWHPx4kW0trZmHNsaNWqE4eHhqhZRLvLz8zE0NBS3bNmCgwcPRmdnZ7mVgzJlymDnzp1x2bJlGBwcjOnp6VLbDgwMRBMTExQIBCgQCGiKlkAgQBMTEwwMDOT1GhBlgCMoisKHDx/i5MmT0crKilWncXR0xDlz5uCrV69ULT6BQOCJu3fvMn55tm3bFtPS0lQtnszExsZihw4dGMc0U1NTPHDgQKnIRPjt2zc8fvw4Tp06FevXr486OjpyKQc6OjrYsGFDnDZtGp48eRJjYmIK2wgMDEQdHR0UCoVS6xAKhaijo8OrQkCUAR7Izc3Fixcv4sCBA1mn+qxTpw6uW7cOo6OjVS0+gUDgiODgYDQ2NqY97x06dMCMjAxViyc3BQUF+O+//6Kenh7jeDZ48GDWaek1hbS0NLx+/TouWbIEO3bsiGZmZnLPHri4uGD//v1RX1+fNhsgTSEwMTHhzWRAlAGeSUlJwX379qGnpyermy4QCLB9+/bo7++PqampqhafQCDISVBQEOPHQNeuXTErK0vV4nHCs2fPsEqVKhJfeCEhIaoWkTfy8/PxxYsXuHXrVhwyZAhWqFBBbuWAbREIBOjn58fL+RBlQIl8+/YN165di7Vq1WJ1442MjHDQoEF46dIljXLOIRC0nYCAADQwMKA907169cLs7GxVi8cpaWlpOHr0aIlT5EuXLsX8/HxVi6kUoqOj8eTJkzht2jRs2LCh3KYFacqAq6srL2YYsrRQRYSFhRUGNmITXtPGxqYwsFGjRo1kWm6CqlyuQiBoGefPn4f+/ftDXl6e2HYvLy84evRoqYtWKhpfTpw4AX///TekpqbS9mnZsiUcPnwYnJ2dVSCh6sjIyIDHjx8XLml88OABpKSkKFxvQkIC9/EdOFcvpKDtMwNM5Ofn440bN3DkyJGsbVBubm64ZMkS/PTpk9S61WG5CoGgTZw8eRJ1dXVpz+zgwYMxLy9P1eJxiqTxRZKflIWFBZ48eVLVYquUgoICDAsLw+XLlys0QxAZGcm5bEQZUCMyMjLw+PHj2L17d8YBhal4eHjgtm3bMCEhQawudVmuQiBoC0eOHGH0Ch8+fHipmyYvaXyRNmaNGjWqxOV2pZ34+HiFlIHi4z0XEGVATYmLi5M5sFHPnj3x5MmTeOHCBbVZrkIgaAP+/v6ML8ExY8aUungibJfDSVMKqlSpgs+ePVP1qagMiqLQ1dWV9UqCoteUL58BogxoAB8/fsRFixbRpuO4KHwvVyEQSju7du1iHNQnTZpU6hSBpKQkNDExKVERKDq+SJrl1NPTw3Xr1pW6a8QWX19fuZQBvlYTaEc6Jg3Hzc0NFi9eDOHh4fDgwQOYNGkSZ84jFEVBZmYmHDx4kJP6CARtYtu2bTBu3DhaCt3p06fDli1bSl3GuwMHDkBmZibruP4URUFBQQEMHToUTE1NxX7Ly8uDv/76Czp37syYLrm0M2LECNDX12e9v1AoBGNjYxg+fDgv8pSunlrKEQgE0LRpU9i6dSvExsbChQsXYMCAAWBgYKBw3Zs2bZIrJziBoK34+vrC5MmTadtnz54NGzZsKHUrdhARNm/eLNexDx8+hOfPn0OjRo1ov127dg1q164Nly5dUlREjeLHjx+s9xUKhSAQCODMmTNgYWHBj0C8zDdIgJgJ+CE5ORn37t2LzZs3VzunFAKhNLJmzRrGZ2jBggWlIhQvExEREQqPL7m5uThv3jyJ0+NTpkzRimRuWVlZWLt2bVZmAZGzd1BQEK8ykZmBUoC5uTnUq1ePFsNBVtLS0jiSiEAovSxbtgzmzJlD27506VJYunRpqZoRyM3NhQsXLsDAgQOhWrVqCtWVlpYGenp6sHLlSrhx4wY4ODjQ9tmyZQs0btyYMV1yaeKvv/6CsLAwsW1MsyYVKlQAX19fiImJgY4dO/IrFK+qRjHIzAC3hIeH49KlS7FatWqcOBOSmQECQTIUReH8+fMZn53Vq1erWjzOKCgowNu3b+O4ceMYUy5zNb4kJCRg7969Gfc1NDTErVu3lspZlrNnz9LOt0aNGvj161fa9p8/fypNLqIMaBixsbG4ceNGbNSoEWcPKZ/LVQiE0gBFUTh79mzG52fDhg2qFo8TXr58ibNnz0YnJyfOxpaSxheKonDHjh1oZGTEeGyPHj0wPj5eBVeDH6KiomgKlpGREb5+/Rrj4uJo5x8XF6c02YgyoAEkJSXh3r17sV27dqyX9Mj6sPK1XIVA0HQoisLp06czPjtbtmxRtXgK8eXLF1y5ciXWrFmT1Vghz/jDZnx58+aNRBu6vb09Xrt2TUlXhD/y8vKwRYsWtPPbuXMnIiJRBogywExmZiaeOnUK+/Tpg/r6+qweOhcXF/zzzz/RyMhIpodWKBRiRESEqk+ZQFA7CgoKcNKkSYwvuF27dqlaPLmIj4/Hbdu2Mb6YJJUWLVrg9u3b8fPnz4WRB9mOLWzjmGRlZUlUugQCAc6ePRtzcnL4v0A8sXDhQtp59e/fv3DGhCgDRBkoJC8vDwMDA3H48OGs8xTY2NjglClTMCQkpLBTsY0QVrQ0aNCABB4iEIpQUFCAY8aMYXwx+fv7q1o8mcjIyMBjx47JFOq8Zs2auGrVKloc/AsXLsj0oSGrF3xAQADa2NhIHKc+fPjA4ZVRDjdv3qQpUBUqVBAbc4kyoOXKAEVRGBISglOmTEFbW1tWD5iZmRmOGDECg4KCJCY/kSd2eNOmTTE1NVXJV4BAUD/y8/NxxIgRtGdER0cHjxw5omrxWJGXl4dXrlxBb29vNDExYTW2ODk54Zw5c/Dly5cS6921axdrZaBTp05yyf79+3fs1KkTY50mJia4b98+jfFxio+Px/Lly9P60YMHD8T2I8qAlioDr169wnnz5qGLiwurh0pfXx/79OmDp06dYr0ONykpCf38/BizFi5cuJDWQQF+TwdqexIRgnaTl5eHQ4YMoT0burq6ap91j6IofPDgAU6ZMkXi13XxUrZsWRw/fjzevn27xNDABQUFWKVKFbHjJTn/icat2NhYuc6loKAAN27cKNFMOmDAALWfzaQoCrt3706TfdWqVbR9iTKgRcpAZGQkrlq1CmvVqsXqIRUKhdiuXTvcu3evQp2eoihMSEjAyMhITEhIKNSoP378iPb29rR2PT09tSLwB4FQnNzcXPTy8qI9E3p6enju3DlViyeRd+/e4fz587FSpUqsxhZDQ0McMGAAnj9/XiY7PNOyuCtXrhSOL2/evEE9PT2x3+fOnavQuT1//lzi8mlnZ2e8e/euQvXzia+vL03m9u3bMypdRBko5cpAXFwcbt26VabogI0bN0ZfX1+5NWpZePv2LaN5onPnzpidnc17+wSCupCdnY29evWiPQsGBgYYEBCgavFoREdH47///ov169dn/XHRsWNHPHDggNzmwGbNmonVWbt2bdp0/dixY8X2KVOmjMLmx/T0dBw3bpzE81q0aJFEk6mqePbsGU0xsrW1xe/fvzPuT5SBUqgMpKam4sGDB7FLly6oo6PD6kGtWrUqLl26FMPDw5Uub1hYGFpZWdFk6tGjh0Z77xIIbMnKysKuXbsyfkHzHQZWFpKSknDPnj3o6enJ2qO/cePG6OfnJ/ElxJZ79+7R6j506BBtv/fv39NkW79+vUJtizh9+rTEQEjNmjWjOTuqitTUVHRzc6PJKK0vEWWglCgD2dnZeO7cORwwYIBUG1rR4uDggLNmzcLnz5+r3Bnm+fPnaGFhQZOxX79+aqdxEwhckpGRgR06dKD1fWNjYwwODla1eJiVlYWnT5/Gvn37ooGBAauxpUqVKrhkyRL8+PEjZ3IUnzVxcnLC3NxchfeVlaioKGzdujXjeZubm+OxY8c4aUcRhg0bRpNt9uzZUo8hyoAGKwP5+fl448YNHDNmDOOLlKlYWlqydtZRNo8ePWJc0jho0CDMz89XtXgEAuekpaVhmzZtaH3e1NQU79y5ozK5RGPLqFGjsEyZMqzGFjs7O5w+fTo+efKE84+Ld+/e0b72pUVevH//PqtZBHnJz8/HFStWSJx5/eOPP1S2MurAgQOMszMlKUNEGdAwZYCiKHzy5AnOmDGD0fmOqRgbG+PgwYPx4sWLaj/tfu/ePcZlSCNGjFA75YVAUITU1FTGwDvm5uYYEhKidHkoisJnz57hn3/+ybjSh6mYmZnhH3/8gdeuXeNVYS8eb4GNH0Bx/4JatWpxrqQ8fPhQotNk5cqV8fHjx5y2VxIfPnygjZ/m5ub4+fPnEo8lyoCGKAPv37/HRYsWMdqBmIquri52794djx49qnFL9W7evMlo6hg7dqzKzRkEAhckJSVh06ZNaX3cwsICnzx5olRZPn36JFPCMX19fezdu7dMy4wV4fv377TlffPmzSvxOKaVB4GBgZzLl5KSwjgtLxqH16xZo5QPmezsbKxXrx5NhuPHj7M6nigDaqwMREdH4/r167FBgwasHlIAwFatWuGOHTs0PgPg1atXGe2TU6ZMIQoBQaNJTEzEhg0b0vq2lZUVhoaGKkWGnz9/4qZNmxgVEqYiEAiwTZs2uHv3bvz165dSZBTx999/05QRNiudmGIStGvXjjc5Dx8+LDFyq6enJ0ZHR/PWNiLitGnTaO2OGTOG9fFEGVAzZSAxMRF37dqFbdq0Ye2tW7duXVy7di1GRUWpWnxOCQgIoC2NAQCcOXMmUQgIGkl8fDzWrVuX1qdtbW3x1atXvLYtWmXUuXNn1quMRGPLt2/feJVNmszF/aFGjx7N+nimaIXPnj3jTd7Pnz9jkyZNGK+llZUVb7EimEI0u7u7Y0ZGBus6iDKgBspARkYGHj9+HHv27Mn48mMqrq6uOH/+fHzz5o2qxeeVs2fPMg5cf//9N1EICBrFjx8/GLPz2dvb8zYO5eTk4MWLF3HQoEGsVxm5uLjg33//rRZjy8aNG2nyvXv3jvXxWVlZWK5cObHjBw0axKPEvwNHzZ8/X+LH3MSJE2nmFYqiMD4+HiMjIzE+Pl6msS06Opq2NNvAwADDwsJkkpsoAypSBnJzczEgIECmuN3lypXDadOm4aNHj7TqRXjixAnGpEdLlixRtWgEAitiYmIYbfKOjo6cLr9D/D09fvfuXZwwYQJj/A5pxcHBATdu3KgWYXZzc3PR2dlZTL6ePXvKXM+KFSvE6tDR0VFKltRbt26ho6Mj43WuXr06vnz5EpOSktDX15cxZLuvr2+J9yE/P59xmeO2bdtkllfrlQFlar+ih3TixImsH1Jzc3McOXIk79666s6hQ4cYNe3Vq1erWjQCQSpRUVFYuXJlWt+tUKECKy9vtrx69Qrnzp2LFSpUkEkBKFpEicVMTEx4cbaThcOHD9Pku3fvnsz1/Pr1i/bBNXXqVB4kppOYmIh9+/ZlvNZ6enqor68vMZkbm/uwZMkSWr19+/aV62NRa5SBpKQknDdvHu1knZycWGlg8kJRFL58+RLnzJlD03IlFQMDA/Ty8sIzZ85gVlYWL3JpInv37mW8Xhs3blS1aAQCI5GRkVixYkVan61UqRJ++fJF4fq/fv2Kq1evxtq1a7MaW0QzbCX5IwmFQtTR0VGZQkBRFNapU0dMJg8PD7nrmz59ulhdxsbGSnOypigKd+/ejcbGxjIrZ9Luw507d2gzps7OznI7eGqFMlA0na6yNOHPnz/j8uXLsUaNGqxveseOHdHf3x+Tk5M5k6O0sW3bNsbrJ8+0GIHAJ58+fWL8AKhSpYpCDnmJiYm4Y8cObNWqFeuXSrNmzXDt2rVobGzMaHKTNCaZmJioxGQQFBREk+fMmTNy1/flyxea79HSpUs5lLhk3r17x+g8Ks99SEhIoJkgdHR05Jo5EaFqZUAXeCYoKAi6desG+FvxoP0u2paVlQXdunWDgIAA6NSpk1xt/fz5E06ePAlHjx6Fhw8fsjqmadOmMGTIEBgwYACUK1dOrna1iYkTJ0Jubi5Mnz5dbPukSZNAX18fRo8erRrBCIQifPz4ETw9PSEmJkZsu7u7O9y4cQPs7e1lqi8zMxMuXrwIR48ehStXrkBeXl6Jx7i7u8PQoUNhyJAhULFiRfDz84OsrCzGcZAJiqIgMzMTtm3bBmPHjpVJXkVZsWKF2N+VKlUCDw8PiI+Pl6s+Y2Nj6NWrF5w5c6Zwm5+fH4wYMQKMjIwUkpUtVlZWcPHiRRg0aBDcv3+f9XGi+3Dw4EHw8fEBRITRo0dDdHS02H5LliyB5s2bcy228uBT00hKSkITExNeNeGUlBT09/fHjh07sm6nevXquGLFCk7thdrGmjVrGGd5Dh48qGrRCFrOmzdv0M7OjtY/a9WqhT9//mRdT15eHgYFBeHw4cPR1NSU1dgiyjcSGhoqZjemKApdXV1ZL1cmRb2KQCBAV1dXpCgKN2/eTPvd09NTYZ8yVc8M8KoM+Pr6ytz5BQIB+vn5Sa1XlLijX79+rBN3ODs745w5c/Dly5datRKAT5icZ4RCIeuIWwQC14SFhaGNjQ2tX9atWxfj4+NLPJ6iKHz06BH6+PjQlsRJKhYWFjhmzBi8efOmxBdCfHy8yl9opChebt68SYvGaGNjgzExMQr33VKrDMirCRfVwIqSn5+P165dw5EjR6K5uTmruqytrXHSpEl49+5dElefByiKokUnA/htO1PEvkggyMPz588ZVwk1atSoRKeuDx8+4MKFCxlXHTAVkZPx2bNnMTs7u0TZIiMjVf4iI0XxwpQH4fLly5z031KrDCiqCSckJCBFUfjw4UOcNm0a47QfUzExMUFvb2+8fPkyZykzCZKhKApnzpxJuw96enp46dIlVYtH0BIeP37MmDnUw8NDokNwbGwsbtiwgTE0MVMRCoXYvn173Ldvn8xOxmRmoHSWmTNnctF9EVH1yoAAkaU3i4x8+fIFKlasKPfxU6ZMgcuXL0NERESJ++rp6UGXLl1gyJAh0KNHDzA2Npa7XYLsICJMmzYNNm/eLLbdwMAALly4AB07dlSRZARtICQkBLp06QKpqali21u2bAkBAQFgZmZWuC0lJQXOnDkDR48eheDgYKAoqsT6GzZsCEOGDIFBgwbJ7HgoAhHBzc0NIiIiWDsQFqdOnTowd+5c8PT0BIFAIFcd0khKSoK6detCVlZW4baxY8fSnAkV5fTp0zBx4kSxbRcuXICmTZty2o6IN2/ewJo1ayAwMFDuOgQCAe2+NWzYEO7fvw/6+vqKiggAAPHx8WBrayu2LS4uDmxsbDipv0T40jL41oQFAgG2bdsWd+3ahYmJiXydBoElFEXhuHHjaPfJ0NAQb968qWrxCBqOpHCxt2/fZnTu8/T0LMwWmp2djWfPnkUvLy/WPkaurq64cOFCfP/+PWfnII8PFVNp3rw5L8/U8uXLxdrR0dHhJBZDcXJzc2mBmXr06MF5O+/evcOBAwfy8v4xMzPD8PBwTuVV9cwArz4DLi4unN+EBg0a4Pr163nPQEWQnYKCAhwxYgTtnpmYmCi0/pagvUgLFztp0iTGeP8dO3bE9PR0vHnzJo4ZM4bRfMBUbG1t0cfHh7dw47KuriqpeHp6YkhICCeyZWVloa2trVj9gwcP5qRuJnx9fWnnw1Vo+s+fP+Pw4cOlXmemqIOylCNHjnAia1FKrTKAiNinTx9OOr2bmxsuWrSIUy2dwA/5+fk4ePBgRk360aNHqhaPoEEUDVbGFC6Waaxo0aIFTp8+HR0cHFiNLaampjh8+HAMCgrCvLw8pZyTjo5OiQqBKPLdvHnzaGmAi5euXbsqnAlw586dtHqfP3/O0VnTSUtLw7Jly4q1N2rUKIXqjIqKwnHjxqGurq7Ea2VlZYXr1q3Dc+fOsboPTGXkyJEcXQVxSq0yQFEU6/C/TMXMzAxnzJiBT548IUsBNYy8vDzs168f7Z5aWFjwmr6UUHpg+9IsPmaw2U9XVxd79OiBx48flynFLJfnJknJERUTExMMCgpCxN/P0/79+0ucae3bt69caZgLCgpoCkf79u25Pm0a//zzj1ib+vr6GBsbK3M9379/Rx8fH9qSv6KlTJkyuGzZMkxNTS08riRlk+neVK1atdD8xDWlVhngwmdg3LhxZEWAhpKTk4M9evSg3VNLS0uZU3sStAuup9NFpWXLlrhjxw6lxcQv6Rz9/Pxo5g+A37MVTIHXcnJycPv27VJnPQQCAQ4ePBg/fPjAWpazZ8/S6rl69SqHZ8vMjx8/aD4cc+fOZX18fHw8/vXXX1JTQ5uamuL8+fMlLi2VdB8qVKhAS65kYGCAoaGhHJ09nVKrDERERHDyALdr107uxA8E1ZKdnY2dO3em3VMbGxuVpa4mqD9cOdoB/I46uHr1avz69auqT4sRiqLw8ePHNLkjIyMlHpOVlYW+vr40G3/RIhQKceTIkVLrEeHh4SF2bN26dZU2Gzt27FixtsuUKSP29c5EUlISLliwQGpUSENDQ5w1axbrlylFUZiQkICRkZH48+dPbNu2La3OzZs3c3HKEim1ysD79+850+irVKnCuecmQTlkZmaip6cn7Z7a29tznkeeoPlwEbbX2dkZ586dqzEzUBRFoaWlpdg5nDhxosTj0tPTcfXq1bRjixY9PT2cOHGiRIfre/fu0Y45fPgw16cokffv39Pu9fr16xn3TU1NxeXLl0t1CNXX18cpU6bIZW4QUXxVBQBgr169eFeQSq0ywNXMgKhYWlrirVu3+BKXwCPp6enYsmVL2j11dHTEiIgIVYtHUCMUNS9evHhRI6ONFp9B+/PPP1kfm5KSgkuWLJEamdXAwACnT5+OP378EDu2Z8+eNEVK2abZ3r1708aFojJkZmbiv//+i9bW1hLPT0dHB8eOHavwDNC9e/do2RUdHR2Vsny91CoDij7UTPHF9fT0cN++fXyJTOCR1NRUbNq0Ke2euri4qO0ULkH5KBq2l820uDqyaNEisfNo0aKFzHUkJibivHnz0NjYWOL1MTY2xrlz52JiYiK+e/eO9vvGjRu5P7kSCAkJoclx8OBBzM7Oxi1btqC9vb3E8xEIBDhs2DBOZo5//fpFc3oXCoV4584dDs6yZEqtMkBRFFasWFHuh3r8+PFYo0YNxt/mzJmjkdq/tpOUlIQNGjSg3U9XV1dOEn0QNB8uwphrIgEBAWLnYWRkJPdSx58/f+KMGTOkBlgyNzfHevXqiW2zsLDAtLQ0js+MHc2bNxeTxcHBocTVaP3798c3b95w0j5FUdi3b19aG0uWLOGkfjaUWmUAUXFHIAsLC6xevTrjb3369OFtiQeBPxITE7FOnTq0+1mtWjXaFCZB++A6wZmmwPQiUNRzPTo6GidNmoR6enqsruHff//NzcnIwblz51jf6549e3Lu1b9t2zZaO61bt1Y4LbEsqFoZEAKPjBgxAoyNjeWOo52cnAxv374Fc3Nz2m9nz56FVq1aQUxMjKJiEpSIpaUlXLt2DapXry62/f3799C+fXtISEhQkWQEdUAgEMDUqVNlPg4RYfLkybzE7FcGNjY2UKlSJbFtjx8/VqhOBwcH2Lp1K3z8+BFGjRoFOjo6Uvc3MDCA7OxshdqUB4qiIDs7u8QY/x07doRHjx7B+fPnoW7dupy1HxYWBjNmzBDbZmVlBUeOHCnxmpUq+NY22AYPEQV5kHVtcfny5UkgGw3k+/fvjJHV6tatS3JNaDnyxhno2rUr5uTkqFp8uRk0aJDY+Sgaka84Hz9+xKFDh0qddXFwcMBt27Yp5TpSFIUXLlxgnCksWlq1asWb3T49PR3d3d1pbV68eJGX9qSh6pkB3pUBRPFIT5KUAFHErY8fP6KXl5dMg4CxsTGePXtWGadC4JDo6GjG/OANGzaUOUUsoXQhTwRCAMBu3bphVlaWqsWXi40bN4qdS82aNXlpZ/bs2SVeRxcXF9y3bx8vIZopisKrV69i48aNS5SjdevWvJp+xowZQ2tz+vTpvLUnDa1QBhB/a/vFA0wAANrZ2aGfnx9t8H/w4AHjcjRpMwurV6/WWJuhtvLlyxdGRyEPD48Sg48QSjfyhIsF+J2oKDMzU9Xiy8z9+/dp58j1M5Cbm4tOTk6sx1U3Nzc8cuQIZ7bz27dvY6tWrVi3LxQKeVt+fPz4cVp79erVw+zsbF7aKwmtVwbKlSuHvr6+jOE3RdNITNM4ksrIkSM1eqpQG/n8+TNjiNXWrVurJHY8QX2QFC7W1dUV/fz88PLly4yR6Nq2batxDsaZmZm0JDtcpyo+fPgw7Vrt3LkT27dvL3VcrVGjBp4+fVruj62HDx9ihw4dpLZRvXp13L9/P21p5NSpUzm9Boi/x5zicRlMTU1VGghNK5QBtmaCwMBAxuPz8vJw9+7dWL58eVYKQatWrTR2iZG28uHDByxXrhztXrZv315jp30J3FE0XGxCQoLYSykkJIQx4E7Lli01bnapfv36YuewZs0azuqmKApr164tVn+zZs0Kf7958ya2aNFC6thav359DAgIYK0UhIaGMuYoKa7YHT58uHD2Yfr06WK/Gxsbczqe5+bmMpooDh48yFkb8lDqlQFZU3ZKUggQETMyMnDFihWsspNVqlSJpDzWMF6/fs0YZaxLly4qm7ojaAaPHz9mDFPr4eGhUf4nEyZMEJO/b9++nNUdFBREuz7nzp0T24eiKAwMDMSGDRtKHV89PDzwxo0bEtt68+ZNib5fzs7OuGfPHlrEw69fv9KiAC5dupSz68DkMzF8+HDO6peXUq0MyOoVLBQK0cTEhNFkUJS4uDj08fEpcf2siYkJXrt2jc9TJHDMixcvaHnOAX7HBicZLAnSeP78OVpZWdH6TsOGDTUm2dn+/fvFZHd0dOSs7nbt2onVXbVqVYnB2yiKwvPnz9NmEoqXNm3a4L179wqPCw8PR29vb6krFuzt7XHLli1SFfyhQ4eKHWNjY8OJH0hgYCBNHjc3N5UFWypKqVYG5Ak6JBAI0M/Pj1X9nz59woEDB5ZY3z///MPnaRI45unTp1imTBnavezfvz8v3s2E0kNYWBhjKPO6detifHy8qsUrkTdv3tBk5yI657Nnz2j17tq1q8TjCgoK8MSJE1i1alWp42zr1q2xV69etC/6osXa2hrXr1/P6qX+4sUL2vHbt29X6Bp8//6dlulRX19fbZaml1plQJmRxB4/foxt2rSRWm+1atVI5kMN4sGDB4yOYUOHDlVqVDCC5vHmzRu0s7Oj9Z2aNWviz58/VS2eVPLz82lmUC6WTQ8ePFisznLlysnki5OXl4cHDhxgXApcUrGwsMAVK1bI/PXdsWNHsXoqV64s97NfUFDA6CTp6+srV318UGqVAWXHGKcoCgMCAiSGLwb4bYaYPHkycS7UEO7cucOYdGXkyJEkNwVBKh8+fGBcoeLu7q5QeltlUDzl97x58xSqLzIykvbFvmLFCrnqys3NxV27dklNHiQqxsbGuHDhwhLNvpK4du0arc7//vtPrrpWr15Nq6t79+5qtRS91CoDqso+lp+fj3v27JHqZGhmZoarVq3SyLXI2saNGzfQ0NCQdg8nTJigVg8yQf349OkTYwwLNzc3/Pbtm6rFk8jcuXPF5PX09FSoPh8fH7H6TExM5Pah+PXrF/7zzz9oYmJS4hguEAhwxIgR+PnzZ7naoiiKlkypSZMmMj/3Dx48oClD5cuXVzuzUalVBhSdGahVqxZu3rxZ7tC0mZmZJXqzOjg44L59+8i0s5oTGBiI+vr6tPvn4+NDFAKCVL58+cKYPbVSpUr45csXVYvHyNmzZ2kfL/LOhCUkJNBm1+SJsJeSkoJLly5l9OUpqejq6uK4ceMwKipK5naPHj1Kq0+W0MRJSUno4uIidrxQKMRbt27JLAvflFplQF6fgeJFX18fBw4ciEFBQXK9tC9cuCA1lSfAb1uiLGtnCcrnwoULtIAsAIB//fUXuW8EqXz79g3d3NxofcfZ2Vnur1Y+iYmJocn69u1buepatmyZWD06OjoyKUEZGRm4du1axlUaRV/2o0aNwlmzZklVFvT19dHHxwe/f//Ouv28vDysUKGCWD3du3dndSxFUdi/f3+aHAsXLmTdvjIptcoAouIpjIsXJycnXLBggcwPcHh4OOPXQfHSpk0bfPz4MU9Xg6Aop0+fZvRWXrBggapFI6g5sbGxWK1aNVrfcXBwUGnUOUk4OjqKybl//36Z68jKyqJ5zw8dOpT1sX5+foyBwIp+YRc3A4jMCEzOv6JiZGSEf/31F+tpej8/P1odb968KfG4Xbt20Y5r2bKl2q5IKtXKgKxxBqTFGy9ePD098fDhw6zt/r9+/aKts5VUBgwYgJ8+feLz0hDk5NixY4z9afny5aoWjaDm/PjxA2vWrEnrO3Z2dnJ/efNF3759xWScOHGizHXs3LmTdq4vXryQekxubi7u3LmTpowUL4MGDcJ3795JrCcuLg5nzZrF6O8jKqamprhgwYISHQzT0tJosUdKyuj4+vVrWtuWlpZymSqURalWBhBlj0B4+fJlDAoKwoEDBzLaiYuXMmXK4IQJE/Dx48clThfn5ubiuHHjWCkEenp6OHXqVKXeDAI7/P39GZXGdevWqVo0gpoTHx+PdevWpfUdGxsbDAsLU7V4hRT3fq9fv75Mx+fn59NMIx06dJC6P5ulg71798aXL1+yliM2NhanTJkiNUAcm6WH8+fPp43PkuIvZGZmYo0aNWjtFI+2qG6UemUAUbYUxkVJTEzEzZs30zxKJZWaNWvihg0bpF5AiqJww4YNrGcgzMzMcPny5SRhjprBNAUIAKwDVhG0l8TERMZwu1ZWVvj8+XNVi4eIv/MEFJVNV1dXptVPZ86coZ0fUzTWgoICPH78OKMJpWjp3LmzQibUL1++4JgxY+QOSvTjxw+a79ecOXMY2xo/fjytbj6SHXGNVigDiL9NBvPmzaOdrJOTE2MK4+I8f/4cp06dyhiqtnjR1dXFvn374qVLlyTahy5evCjVrlW82Nvb4+7du9XW3qSNbNmyhfFe7dixQ9WiEdSc5ORkbNq0KeNXqjr4DaWmptI+WEJCQlgdS1EU7dzq1q0rNnNKURSeO3dO5nDDihIeHo7Dhg2TK1xx8Vldc3NzTElJEdvn1KlTtPrq1KmjEcnOtEYZQER8+/Yt7WTZOIIUJSsrC48fP44dO3Zk9XVvb2+Pc+fOxQ8fPtDqevnypUy5vQF+By05f/488WBXE9avX894n/bt26dq0QhqTmpqKmOWPnNzc9YvXj4p7t+wceNGqftTFIXx8fF48uRJ2jkdPXq0cJ8rV64onIhIUd68ecPo6V+0FE9k9OHDB9qYv2TJEoyMjMT4+HiMiIigrWYwMTHRmIR1Wq8MKOK48/XrV1y6dCmrlQIAgC1atMB9+/aJ2aa+f//OmM6ypNKyZUt8+PAhF5eFoCArV66k3R+BQIBHjhxRtWgENSc9PR3btm1L6z+mpqYyrWfng1GjRonJNHjwYMb9kpKS0NfXF11dXRnHKicnJ8zLy8ObN29i8+bNpY5r9erVU+oy69DQUOzZs6dUmVxdXfHQoUOYn5+Pffr0kbgf0xJyeVZhqAqiDHDgxVtQUIDBwcHo7e0t1Xu1qLY4atQovH//PlIUhZmZmThgwADGfZlS6hYtXl5eark0SdtYtGgR7d7o6OjgqVOnVC0aQc3JyMjADh060PqPsbExBgcHq0yuHTt2iMlTqVIl2j5F/bEkzZTq6ekxOk0WLTVq1MAzZ86obMbz0aNHtFwExYu7uzuOGDGC9Qfb0KFDNWoGlygDHC/pSU5Oxh07drD+2q9atSquWbMGo6OjccGCBYz7VKhQgdE7VVR0dXVx0qRJ+OPHD07PhcAeiqJwzpw5jPdG3b2ICaonKysLu3btSus/hoaGNMdmZREaGkqTp+jafLYrtaQVNzc3PHr0qNpEYb19+za2bNlS7vMpWs6cOaPq05EJogzwuL731atX+Oeff5b4dS/6iuzRowdOnz6dcRmMtbU1Lly4kBbasmgxNTXFJUuWqEVubG2EoiicPn0645fR5cuXVS0eQc3Jzs7GXr160fqPvr4+Xrp0Seny5OXloZGRkZgson4sawwXpg+cffv2qaVDNEVRePXqVbnMt6IiWqEmb5IkVUCUASUE+8jJycEzZ85g9+7dWT08ZcuWpT2EAL9tUv7+/rhhwwa0tLSUeHy5cuVwx44davmglXYoisJJkyYx3jumpVUEQlFyc3MZHdv09PQ4SSUsK8UdHBctWoSI8kd3NTc3x23btmFOTo7Sz0VWKIrCixcvlmjikKYQaNJSY6IMKDnyV0xMDK5atYoxVjnbsnDhQvz16xfOmTNHqo9C1apV8ezZsxpltyoNFBQU4JgxY2j3w8jICG/fvq1q8QhqTl5eHg4ZMoTWf3R1dfHkyZNKleXPP/8Uk6FLly4K5X0xMTHB6dOn48aNG/HMmTP49OlTjIuLU+sxKj8/X2pYZGnKgKurq1qfW1GIMqCiMKAUReHdu3dx5MiRrNJxFi8DBw7EzMxMjIqKwj/++EPqg9msWTO8f/++Ss5TW8nPz8dhw4bR7oWpqalaLBsjqDf5+fmMzmpCoVCpq1SOHz8u1r6VlRXjS0PRYmhoiFWqVMH27dvj6NGjccmSJbh//368ceMGhoeHq3SdvqIZcBMSElQmuywQZUANYoKnpqbi3r17S1x2U7zUrVu3MANXWFgYowNS0dKnTx+NWfNaGsjLy2NcJWJubq4WgWUI6o2kGSaBQID+/v5KkSEyMpLW/q1btzhXBtiUcuXKYePGjdHLywv//PNP9PX1VcrsAtM1kKVERkbyIhfXEGVADZSBorx//x7nzJmDdnZ2rDXqdevWFUbLCg4OlhrQQ0dHB8ePHy9TGk+C/OTm5jKuTbawsMDQ0FBVi0dQcwoKChh9UAQCAe7atYv39imKQhsbG7G2iy85VJdiZGSEVatWxQ4dOojNLgQHB+OnT59oEQXZQmYGlANRBiSQl5eHFy9exD59+qCurm6JHc7MzAx9fHzwxYsXhfG+pQVDMjExwYULF2JqaqqqT7XUk5OTg926daPdAysrK3z16pWqxSOoOZJWqQAAbtmyhff2u3fvLtamj4+P3D4DFhYWOHjwYGzRogU6OztLzRXAR7GzsxObXfDz88OzZ8/is2fPMD4+nnF2QV4fCeIzIBtEGWDBz58/8d9//8Xq1auz6oT169fHLVu24I8fP9DPzw+trKwk7mtra4tbt24tDLlJ4IesrCzGoCa2trZSU7ESCIiS41gAAG7YsIHXtpcuXSrWnoeHh1yrCZi86/Py8vDr16949+5dPHLkCK5atQonTpyI3bp1w1q1aqG5ublSlYWiswtjxozBpUuXor+/P06ePJmT81VntF4ZkDU3gSqhKAofPnyIw4YNY6VRGxgY4KBBg/Ds2bM4b948xuWKouLm5oanTp3SGC1WE8nIyMA2bdrQrr29vT2Gh4erWjyCmkNRlMTAZKtXr+at3aCgINq48vPnT5niDAiFQrnX3ScnJ2NYWBheunQJt23bhnPnzsUhQ4Zg8+bN0cnJSaGgR3wVRc5XVahaGRAgIoISSE5OhrVr18KqVavEtjs5OcHMmTNhxIgRYGFhoQxROOHXr1/QvXt3ePDgAav9nZ2doW/fvhAbGwv//fcfUBTFuF/Tpk1h7dq10LJlSy7FJfyP9PR06Ny5M9y/f19su5OTE9y5cwdcXFxUIxhBY1i+fDksWLCAtn3p0qWM2xXl169fYGVlJbbt6dOnkJCQAN26dQNElDieAAAIhUIQCARw+fJl6NixI+fy5efnQ2xsLERFRUFUVBR8/fq18P+iv9PS0jhvtyTq168P9evXB2dnZ3B2doYKFSqAs7MzODo6gr6+vtLlKYn4+HiwtbUV2/bz50/aNt5QhsZRNH42MEzliKJFBQYGKkMczqAoCpctWyaz1tq4cWOsV6+e1H169uypESYUTSQlJYUxulnFihUxKipK1eIRNIA1a9YwPrfz58/nZXaveFyUbdu2IaL03ARFx1ZVhVQWUXR2YevWrThnzhwcPHiwSmYXBAIB2tvbY5MmTbB///44c+ZM3LRpE547dw6fP3+OCQkJSp+hTUpKwuXLl9NkdXFxQV9fX6XMcPCuDLCNny0UClFHR0fjFAJExBMnTrBKkFS8mJiYoK2trdRrMnbsWIyJiVH1KZY6fv36xaiQubm5YWxsrKrFI2gAGzduZHxuZ8+ezfnLxNvbW6yNESNGFP6WlJSEfn5+tKyFrq6u6Ofnh8nJyZzKwgci34U7d+7g4cOHceXKlThhwgTs2rUr1qxZE83MzJSmLAD8TlJVrVo17NSpE44dOxaXLVuGBw4cwJs3b+Lnz585jeCoLh/LvJoJkpOTwdHREbKysqROY4kQCoVgZGQE0dHRGmUyAAB4/Pgx9OzZE37+/CnX8fr6+pCbm8v4m5GREfz5558we/ZsMDc3V0RMQhESExOhbdu28OrVK7Ht7u7ucOvWLeVNzxE0lm3btsHkyZNp26dNmwYbN24EgUDASTubN28GHx+fwr/d3d3h7du3YvsgIvz69QvS0tLAzMwMLC0tOWtfHUhOTqaZHz59+gRfv36F2NhY+PHjB/D4OhNDIBCAvb19oQmiaBGZI8qWLVvi9Q8KCpLJ1BMQEACdOnXi+nQAAIBXZcDPzw9mzJgh0w0SCATg6+sr1vE1haioKOjRoweEhYXRfmvQoAFERERAUlKS3PVbW1vDwoULYfz48Wpp89JE4uLioE2bNvDu3Tux7bVq1YKbN2/SbLUEQnH27NkD48aNo41zEydOhC1btoBQKFS4jcePH0OTJk0K/xYIBJCUlARlypRRuO7SQl5enpjvQnEfhq9fv0J6errS5DExMZGoKDg7O4OJiQlUqlRJfT6W+Zpy0Ja1ocVJTU2lrQsWlV69eqG/vz926NBBrjXCouLq6oonTpzQ2GukbsTGxmLlypVp17l+/foa5Y1MUB3+/v6MptAxY8ZgQUGBwvVnZ2fTsqlev36dA8m1B4qiMCkpCV++fIkXL17ErVu34uzZs3HQoEHYrFkzdHR0VMuVEcXfj3wtl+RNGdCWqFFM5Ofn44wZMxjPq0GDBhgTE4NfvnzBJUuWSE2JXFJp1KgR3rx5U9WnWyqIiopiDBLVuHFjTElJUbV4BA3gyJEjjEuOhw8fjvn5+QrX36hRI7F6V65cyYHUhKLk5ubily9f8M6dO3jo0CFcsWIFjh8/Hrt06YI1atRAU1NTlSsDfH0s86YMKBpPesSIERgYGKjSBBmKsmPHDsbBwcHBAZ89e4aIv8Od3rhxA4cOHSqXEyIAYLdu3aRG0qMoCuPj4zEyMlJilC/C7z7r5OREu77NmzfHtLQ0VYtH0ABOnTrFGLF08ODBCqc0nzJlilidvXr14kZoAmtEswsvXrzACxcu4JYtW1Qyu8DHx7LazgyIiomJCfbq1Qt3796tkV71165dwzJlytDOy9jYmJYfPSkpCbdv3077AmBThEIhjhw5Er99+yZWn6+vL6OXsbKWq2ga4eHhWL58edr1bdOmDWZkZKhaPIIGcO7cOdqUPgBgv379FIo0evDgQbH67OzsiGKvhuTm5mJkZCTevn0b9+zZg6NGjcKGDRuitbU1Z4oCH8mX1M5noKRSr149XLBgAT58+JATW5wyePfuHe2FDPB7ymft2rWMD3RYWBhOnz5daihjpmJoaIhz587F06dPs1p/rIlLOfnm3bt3jPnTO3TooNEzVQTlERAQgAYGBrQ+1LNnT7kT9nz48IFWH4mLoV5ER0fjiRMn0MfHBxs0aMBb7geNmhlARLniZ8tSbG1tccSIEXjq1Cm1X0ubkJCArVq1YjyPUaNGSVy3mpOTg//99x927dpVZq2ypGuvybEd+ObVq1eMili3bt04XWNMKL1cvXqV0fTXpUsXuZTKgoICtLCwEKvrv//+40FyAhvy8/MxNDQUt2zZgoMHD0ZnZ2fe3nVFx3SN8xlA/D1NLWv8bCMjI/znn3/Qw8NDJkVCV1cX27Zti+vXr8cPHz7weVpyk5OTg3/88Qej/G3atMHExESpx0dHR+PKlSsZPd/lLZoYw1tZPH/+nDb4AgD26dOHJJYisCI4OBiNjY1pfahDhw5ymZ06dOggVs/s2bN5kJrARGpqKl67dg0XL16MHTp0UCgQUsWKFaWmupemDGjcagIRskYgLBo2My4uDg8ePIgDBgxgtLtLK5UrV8Zp06bhtWvX1OpLjqIoXL16NaPMbm5urBQZiqLwzp07+McffzAONOrUwTSdx48fM2ZuGzhwoMIOYQTt4O7du4xe6G3atJHZMXX+/PlidbRu3ZofoQkYFRWFx44dwylTpmC9evXktvfr6upio0aNcPr06Xjq1KnCCKfyfCzz+eGm9NwE8sbPzs3NxVu3buGsWbPQ3d1dppthamqKffv2xb179+L379+Vccolcvr0acYshmXLlsXg4GDW9aSmpuKePXuwWbNmCikEZcqUwQ0bNuCpU6fwwYMHGB0dzclyqNLA/fv30cTEhHbNhg0bRq4RgRUPHjxgVCqbN28u09LVCxcuiB1vYmJC+iAH5OXl4fPnz3Hz5s04aNAgxlVFbIuFhQV26dIFly9fjjdv3pQ6A6TIxzLXKC2FMdfxsz9//oybNm3Cjh07or6+vkw3q2HDhrho0SJ88uSJSp0Qnz59yui5rquri7t375a5vpCQEIUUguJFR0cHHR0dsWnTpti/f3+cMWMGrl+/Hk+cOIEhISEYFRWlNV/Ht2/fZlTeRo8erTGOrATV8uTJEyxbtiytDzVp0oT1196PHz9ox4eFhfEreCkkJSUFg4KCcOHChdi+fXuF4gdUqlQJhw0bhjt27MBXr17JPB6oS7IppaUwFoE8xM9OT0+HGzduwKVLlyAgIAC+f//O+thy5cpB165doXv37tChQwcwMzNTSBZZiYmJgR49ekBoaCjtt5kzZ8KaNWtAR0eHVV1fvnyBihUrci2iVIRCIdjb24Ojo2NhcXJyEvu7fPnyoKenp1S5+OD69evQvXt3yMnJEds+ceJE2Lp1a6mKA0/ghxcvXkD79u0hMTFRbHuDBg3g6tWrYGlpWWIdLi4u8PXr18K/9+zZA6NHj+Zc1tICIkJUVBTcv3+/sLx69YpVCODi6OrqQoMGDaBZs2bQvHlzaN68OdjZ2SksY3JyMhw8eBA2bdoEnz9/Ltzu6uoKPj4+MGLECN5DTytdGeAbRITQ0FAICAiAS5cuwZMnT1jnRtDT04PWrVtDt27doHv37lC5cmWepf1NRkYGeHt7w7lz52i/9ezZE44cOQKmpqYl1pOQkAA2NjY8SKgYAoEA7OzsGJUF0f/Lly+vEfkWLl++DL1794a8vDyx7dOnT4cNGzYQhYBQIq9fv4Z27dpBXFyc2PY6derAtWvXSnyGBw4cCCdPniz8e9y4cbBz505eZNVE8vPz4eXLl2Iv/5iYGLnqKlu2rNiLv1GjRmBkZMSxxP8PHx/LbCl1ykBxfv78CVeuXIGAgAC4evUqpKamsj62SpUqhYpBixYteH1ZURQFf//9N6xZs4b2W506deDixYvg5OQktQ5EBDc3N4iIiJA5e5epqSnUq1cPYmJiIDo6WmIGRT4pV64cbVah6N8ODg5gYGCgdLmKc/78efDy8oL8/Hyx7XPnzoWVK1eCQCAARITExERIT08HU1NTsLKyIooCoZD379+Dp6cnbRazRo0acOPGDShXrpzEY9evXw+zZs0q/LtOnTrw4sULvkRVe1JSUuDhw4eFL/5Hjx5BRkaGXHVVrly58MXfvHlzqFatGieJpjSBUq8MFCU3Nxfu3btXOGvw8eNH1seamZlBp06doFu3btClSxepD6si7N+/H8aPH0/78rSzs4MLFy5Ao0aNpB7PRaZIiqIgISEBoqOjITo6Gr59+1b4/6J/F58uVwa2trZSTRKOjo5gaGjIuxynTp2CQYMG0aYa58yZA/b29rB582badN/UqVNhxIgRGpeem8AP4eHh4OnpCdHR0WLbq1WrBjdu3IDy5cszHnf37l1o1apV4d86OjqQkpICJiYmvMqrDiAifPnyBUJCQsSm/OV5jenp6UGDBg0KX/zNmjXjbVzXBLRKGSjOp0+fChWD27dv017AkhAIBNCoUaPCWYN69epx+tV3+/Zt6Nu3L/z69Utsu6GhIRw6dAi8vLwkHpucnAyOjo68p8UUffkWVxaK/z87O5t1nVxhbW3NqCiI/nZwcABjY2OF2zl69Ch4e3szDkSi2YGifwMAGBsbw+nTp3nLSU7QLCIjI6Ft27ZiPgAAv79Qg4ODGWcDMzIyoEyZMlBQUFC47c6dO9CyZUve5VU2eXl58OLFi8IXf0hICMTGxspVl6WlpdiUf8OGDXmd8tc0tFoZKEpaWhpcu3YNAgICICAgAH7+/Mn6WHt7e+jWrRt069YN2rdvz8q+XxLh4eHQvXt3xtmLFStWwLx58yQqIEFBQdCtWzdARKkKgVAoBIFAAJcvX4aOHTsqLHNxRPYvplmFon9nZmZy3nZJWFpaSjVJODo6svrS2r9/P4waNYp1u6JrHhAQQBQCAgAAfP36FTw9PSEiIkJse8WKFSE4OBhcXFxox9StWxdevnxZ+Pe///4LM2fO5FtU3klOToYHDx4UvvwfP34s9/jg5uYmNuVftWpVrZnylweiDDBAURQ8f/68cNbg6dOnrI/V19eHNm3aQPfu3aFbt25QqVIlueVISkoCLy8vCA4Opv02bNgw2L17t0QbelBQEPTr16/wQZL0lXrmzBleFAG2ICIkJydLnFkQ/SuvDVARypYtK1FREP1tamoKGzduhD///JN1vfLOxhBKL9HR0dCuXTua8u/k5AQ3b94EV1dXse3jx4+HXbt2Ff49YMAAOHHihFJk5QpEhMjISDFHvzdv3sg15a+vrw8NGzYUm/JXR2dqdYYoAyz48eMHXL58udAJMT09nfWx1apVK1QMmjdvLvMSu7y8PJg8eTLs3r2b9luLFi3g7NmzYG1tzXisOixX4QJEhNTUVIm+C6Iii3MoV5QpUwaMjIzgx48fMh1X3E+DQPj+/Tu0a9cO3r17J7a9fPnyEBwcDFWrVi3ctnfvXhgzZkzh3xUqVIAvX74oS1S5yMvLg9DQULGXv6zPjQgrKyuxr/4GDRooxVeoNEOUARnJycmBu3fvFsY0+PTpE+tjy5QpI+aEyFZzRUTYuHEjzJo1i6Y1V6xYES5dugTVq1eXeryqlqsok9TUVInKguj/KSkpqhYTAH4rA5UqVYLw8PBSeS8I8hEXFwft/6+9+3tp6g/jAP4+mzFHlCu1zXkRW1FkoGGULAiKyjGY2V101XUX7S/orrpICET/gugmEA1qFalEShGkCSFIPxCjdBtttURqrpj7XnzZwY9n022ebefH+wUH8rjjJvY5n+fznOc85/x5zM7OCvudTidevHghj/PZ2Vm0t7cLr4nFYpoqgEsmk0Kh39TUFFKpVFk/6/Dhw8Lkf+jQIY4blTEY2KZPnz7JgcHk5KTidrNCJElCV1eXnDXo6OjY8j/348ePceXKFUXKfPfu3RgaGqppul8vVlZWsLS0lDfL8PXr16oHDIlEAo2NjVV7P9K+Hz9+4MKFC4pGZM3NzRgfH0d7ezsymQwaGhqEc8GjR4/Q09NT7Y8L4P8Fx/z8vLDqn5ubK+tn2Ww2Rcq/UPaT1MNgQEXLy8tCEWI8Hi/62NbWVrkI8dy5cwWL196/f4+enh58+/ZN2G+1WjE4OIhr165t63cwoj9//iASiSASiSAajcr/3riVcvlHLQsLC3kLxMjckskk/H4/pqamhP179+7F2NgYOjs7cebMGUxMTMjfu3HjBm7evFmVz/f371/MzMwIVf6lFF2v19zcLFT5Hz9+XBP9RMyGwUCFrK2tYXp6Ws4azMzMFH2szWbD2bNn5azBxskiGo2it7dXcaIAgFAohLt376Kurk7eZ9QGOOl0etPJPbdp5dJAPswMUCHLy8sIBAJ48+aNsN/hcOD58+cYHh5GX1+fvP/06dMYGRmpyPj++fOnIuVf7m3DR44cEVL+Bw8eNMT5SO8YDFRJJBKRixDHxsZKqo5va2uTA4NTp06hrq4OqVQKV69exdDQkOL1gUAADx48wNraGu7du6e7Bjj//v1DLBYTJvR8k/7G/u7V1tjYiJaWFszPz5d8LZQ1A1SMlZUVBINBTE5OCvt37dqFUCiE27dvK47Z7vjOZrP4/PmzvOJ//fq1oqixWPX19Thx4oQ88ft8Pga/GsVgoAbS6TQmJiYQDocRDoexsLBQ9LF79uyB3+9HMBhEd3c3BgYGcOvWLcXr9u/fj3g8Lk9SWmiAk8lk8P379y1X8vF4vKzbi9TicDjgdruFraWlRfja5XLJ1ctqdH0kKuT379+4ePFi3luM8yl1fKfTabx7905I+ZdyiXO9ffv2Cav+zs5OXTxzhBgM1Fw2m8WHDx/kngavXr0SOottxmKxwOfzwe124+HDh0UXL64/Xo0GOLn2xfkm9vUr+lgsVtaTwtSyc+dOtLa2Kib6jZN+qd0Jq9X1kcwrlUrh0qVLGB0dLfqYQuM7kUgIKf/p6emyW4u3tbUJk/+BAweY6dIpBgMa8+vXL4yOjiIcDuPZs2dIJBJFH2uxWEqebDebmLLZLJLJ5JYr+Wg0WnIgoqb6+nphkt+4is9tlXw8tVa6PpJxra6uore3t+SAwGazoa+vT77H/+PHj2W9v91ux8mTJ+UKf5/PV9Qjl0kfGAxoWCaTwdu3b+W7Eyr1ZDJJkhAMBuHxeBQr+lo8jChnx44dm67ic1tDQ4MmViN66fpI+rXxiYWV5HK5hFX/sWPHmPI3MAYDOrK4uIinT58iHA5jfHy87AYetWa1WuXVe6FVvNvt1uVdD0bp+kjas51HlG9FkiQcPXpUmPw9Ho/uxh+Vj8GATqVSKbx8+VLOGmihFakkSXA6nVuu5JuammC1Wmv9cSvKLF0fqXoSiYRq/fbtdju6urqEKn/Wr5gbgwEDyGazmJubw/3793Hnzp2KvEdTU9OWk7zT6RT6GxCRer58+QKPx1P28YFAAN3d3XLKv9TnpJCxMRgwkO2uHC5fvgyv16uY5F0uF68VEtXYdsc3G1zRZhgMGEi51xTZAIdI+zi+qZIstf4ApB5JknD9+vWyjg2FQjxREGkYxzdVEjMDBsMGOETGxfFNlcLMgME4HA4MDw9DkiRYLJv/eXMNcEZGRniiINIBjm+qFAYDBuT3+/HkyRPY7XZIkqRID+b22e12dsIj0hmOb6oEBgMG5ff7sbi4iP7+fni9XuF7Xq8X/f39WFpa4omCSIc4vkltrBkwATbAITIujm9SA4MBIiIik+NlAiIiIpNjMEBERGRyDAaIiIhMjsEAERGRyTEYICIiMjkGA0RERCbHYICIiMjkGAwQERGZHIMBIiIik2MwQEREZHIMBoiIiEyOwQAREZHJMRggIiIyOQYDREREJsdggIiIyOT+A3/mSlV594toAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 640x480 with 4 Axes>" ] @@ -1356,7 +1356,7 @@ }, { "cell_type": "markdown", - "id": "751d3c6c", + "id": "bd41ce21", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -1367,13 +1367,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "b9a82a38", + "id": "c8483998", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:39.373376Z", - "iopub.status.busy": "2023-01-03T20:27:39.372932Z", - "iopub.status.idle": "2023-01-03T20:27:39.479368Z", - "shell.execute_reply": "2023-01-03T20:27:39.478782Z" + "iopub.execute_input": "2023-01-03T21:20:14.564227Z", + "iopub.status.busy": "2023-01-03T21:20:14.563927Z", + "iopub.status.idle": "2023-01-03T21:20:14.714875Z", + "shell.execute_reply": "2023-01-03T21:20:14.714076Z" } }, "outputs": [ @@ -1396,7 +1396,7 @@ }, { "cell_type": "markdown", - "id": "4a4e4c97", + "id": "e3525408", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -1405,19 +1405,19 @@ { "cell_type": "code", "execution_count": 38, - "id": "331810ca", + "id": "5f16fecb", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:39.482770Z", - "iopub.status.busy": "2023-01-03T20:27:39.482444Z", - "iopub.status.idle": "2023-01-03T20:27:39.616195Z", - "shell.execute_reply": "2023-01-03T20:27:39.615592Z" + "iopub.execute_input": "2023-01-03T21:20:14.718449Z", + "iopub.status.busy": "2023-01-03T21:20:14.718195Z", + "iopub.status.idle": "2023-01-03T21:20:14.903716Z", + "shell.execute_reply": "2023-01-03T21:20:14.902905Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1433,7 +1433,7 @@ }, { "cell_type": "markdown", - "id": "0eb4eea0", + "id": "5eccd196", "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": "1802df64", + "id": "49be0aff", "metadata": { "execution": { - "iopub.execute_input": "2023-01-03T20:27:39.619306Z", - "iopub.status.busy": "2023-01-03T20:27:39.618836Z", - "iopub.status.idle": "2023-01-03T20:27:39.849633Z", - "shell.execute_reply": "2023-01-03T20:27:39.849038Z" + "iopub.execute_input": "2023-01-03T21:20:14.907434Z", + "iopub.status.busy": "2023-01-03T21:20:14.906989Z", + "iopub.status.idle": "2023-01-03T21:20:15.098668Z", + "shell.execute_reply": "2023-01-03T21:20:15.097945Z" } }, "outputs": [ @@ -1476,7 +1476,7 @@ }, { "cell_type": "markdown", - "id": "66284130", + "id": "7601eacf", "metadata": {}, "source": [ "See Drawing for additional details." |
