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| author | MridulS <mail@mriduls.com> | 2022-12-25 00:25:32 +0000 |
|---|---|---|
| committer | MridulS <mail@mriduls.com> | 2022-12-25 00:25:32 +0000 |
| commit | 1021b7dd279c29c98ef4a57b6d818ee02532fa1a (patch) | |
| tree | 46476878d66d2c6a137f2670943169b13fdc2705 | |
| parent | d6eaa555f03e31c5a596807f507c7571b1a353e6 (diff) | |
| download | networkx-1021b7dd279c29c98ef4a57b6d818ee02532fa1a.tar.gz | |
Deploying to gh-pages from @ networkx/networkx@b7f9a8016c6289b21d13ac0234419f683930516d 🚀
147 files changed, 627 insertions, 627 deletions
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differindex d6676f81..6f7f6e48 100644 --- a/_images/sphx_glr_plot_words_001.png +++ b/_images/sphx_glr_plot_words_001.png diff --git a/_images/sphx_glr_plot_words_thumb.png b/_images/sphx_glr_plot_words_thumb.png Binary files differindex c431da4b..daac95cb 100644 --- a/_images/sphx_glr_plot_words_thumb.png +++ b/_images/sphx_glr_plot_words_thumb.png diff --git a/auto_examples/3d_drawing/plot_basic.html b/auto_examples/3d_drawing/plot_basic.html index 92b69005..6f79d8f2 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 0.078 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.073 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 2a8c64d4..d95d3be1 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>00:00.078</strong> total execution time for <strong>auto_examples_3d_drawing</strong> files:</p> +<p><strong>00:00.073</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>00:00.078</p></td> +<td><p>00:00.073</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 6ada0913..fa68d87f 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 0.208 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.206 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 2930e4a3..913a6b2e 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 3.548 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.365 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 7d26aa6a..233da7a2 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 0.337 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.390 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 8fa22897..0c0bc9f7 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 0.105 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.100 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 a54d6865..52ae906d 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 0.072 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.069 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 798d7d31..2bb822ac 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 0.229 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_krackhardt_centrality.html b/auto_examples/algorithms/plot_krackhardt_centrality.html index 2c82fb26..3e5c13cd 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 0.060 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.058 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 c966fde4..b0e3afab 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: 1.7626 seconds - Betweenness centrality for node 0: 0.16268 + Time: 1.6727 seconds + Betweenness centrality for node 0: 0.08378 Non-Parallel version - Time: 3.0021 seconds - Betweenness centrality for node 0: 0.16268 + Time: 2.7735 seconds + Betweenness centrality for node 0: 0.08378 Computing betweenness centrality for: -Graph with 1000 nodes and 4963 edges +Graph with 1000 nodes and 5114 edges Parallel version - Time: 2.2925 seconds - Betweenness centrality for node 0: 0.00144 + Time: 2.1571 seconds + Betweenness centrality for node 0: 0.00246 Non-Parallel version - Time: 3.9409 seconds - Betweenness centrality for node 0: 0.00144 + Time: 3.6963 seconds + Betweenness centrality for node 0: 0.00246 Computing betweenness centrality for: Graph with 1000 nodes and 2000 edges Parallel version - Time: 1.5464 seconds - Betweenness centrality for node 0: 0.00304 + Time: 1.4595 seconds + Betweenness centrality for node 0: 0.01364 Non-Parallel version - Time: 2.7455 seconds - Betweenness centrality for node 0: 0.00304 + Time: 2.5259 seconds + Betweenness centrality for node 0: 0.01364 </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 20.719 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 19.655 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 ef9b6cd4..1f716857 100644 --- a/auto_examples/algorithms/plot_rcm.html +++ b/auto_examples/algorithms/plot_rcm.html @@ -615,7 +615,7 @@ bandwidth: 7 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.751 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.815 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 af826549..e71caaec 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 0.174 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.163 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 022e2b73..da9655ef 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 0.664 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 0.631 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 727ccc2b..20710ad7 100644 --- a/auto_examples/algorithms/sg_execution_times.html +++ b/auto_examples/algorithms/sg_execution_times.html @@ -463,43 +463,43 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-algorithms-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:26.976</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p> +<p><strong>00:25.774</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>00:20.719</p></td> +<td><p>00:19.655</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>00:03.548</p></td> +<td><p>00:03.365</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>00:00.751</p></td> +<td><p>00:00.815</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>00:00.664</p></td> +<td><p>00:00.631</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>00:00.337</p></td> +<td><p>00:00.390</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>00:00.229</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>00:00.208</p></td> +<td><p>00:00.206</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>00:00.174</p></td> +<td><p>00:00.163</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>00:00.105</p></td> +<td><p>00:00.100</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> @@ -507,11 +507,11 @@ <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>00:00.072</p></td> +<td><p>00:00.069</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>00:00.060</p></td> +<td><p>00:00.058</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 d758be4e..7aaa7277 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 0.088 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-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 877a8762..f16d4e4c 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 0.061 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.060 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 b2566e5a..40d63254 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 0.324 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 0.323 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 0a2ed0dc..5b31203a 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>00:00.473</strong> total execution time for <strong>auto_examples_basic</strong> files:</p> +<p><strong>00:00.465</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>00:00.324</p></td> +<td><p>00:00.323</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>00:00.088</p></td> +<td><p>00:00.083</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>00:00.061</p></td> +<td><p>00:00.060</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 420d7356..70c4ac51 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 0.066 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.063 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 e1ed06e6..7a8e1139 100644 --- a/auto_examples/drawing/plot_chess_masters.html +++ b/auto_examples/drawing/plot_chess_masters.html @@ -536,7 +536,7 @@ to black and contains selected game info.</p> <img src="../../_images/sphx_glr_plot_chess_masters_001.png" srcset="../../_images/sphx_glr_plot_chess_masters_001.png" alt="World Chess Championship Games: 1886 - 1985" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Loaded 685 chess games between 25 players Note the disconnected component consisting of: -['Karpov, Anatoly', 'Kasparov, Gary', 'Korchnoi, Viktor L'] +['Korchnoi, Viktor L', 'Karpov, Anatoly', 'Kasparov, Gary'] From a total of 237 different openings, the following games used the Sicilian opening @@ -702,7 +702,7 @@ findfont: Font family 'Helvetica' not found. <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.384 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.370 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 c399eff7..f932a216 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 0.264 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 eba3796d..866a2860 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 0.260 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.253 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 0bd7f700..a1b865fe 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 0.212 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.200 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 4f0b7e62..612cafde 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 0.062 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.059 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 438e6e61..57c1b3dc 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 0.096 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.094 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 62fe4f47..865df2e2 100644 --- a/auto_examples/drawing/plot_eigenvalues.html +++ b/auto_examples/drawing/plot_eigenvalues.html @@ -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 0.637 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.614 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 d763dfcb..97afe41c 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 0.321 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.304 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 245a380a..a88ea746 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 0.074 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.070 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 1f9f1330..edfa72ac 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 0.099 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-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 d2b999b5..fe102824 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 0.184 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.179 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 f8074c0e..aa2b3c29 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 0.076 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.074 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 1bd41cdd..7111f1ab 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 0.051 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.049 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 30212364..9aba3676 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 0.125 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-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 48b24d8a..23c817eb 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 0.103 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.100 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 7018077c..e3370279 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 0.240 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.227 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 4b115143..8cbfd887 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 0.079 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.075 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 e6211be8..af558e58 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 0.056 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.054 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 2577c1cd..2c232a9f 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 0.222 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.215 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 d2f2e75e..30fb7240 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 0.088 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-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 e978b5bd..194858bf 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 0.088 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.135 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 8e20c746..f61991f9 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 0.083 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.078 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 7d50008a..ddcda98f 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>00:03.883</strong> total execution time for <strong>auto_examples_drawing</strong> files:</p> +<p><strong>00:03.776</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>00:00.637</p></td> +<td><p>00:00.614</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>00:00.384</p></td> +<td><p>00:00.370</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>00:00.321</p></td> +<td><p>00:00.304</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>00:00.264</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>00:00.260</p></td> +<td><p>00:00.253</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_sampson.html#sphx-glr-auto-examples-drawing-plot-sampson-py"><span class="std std-ref">Sampson</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_sampson.py</span></code>)</p></td> -<td><p>00:00.240</p></td> +<td><p>00:00.227</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_spectral_grid.html#sphx-glr-auto-examples-drawing-plot-spectral-grid-py"><span class="std std-ref">Spectral Embedding</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_spectral_grid.py</span></code>)</p></td> -<td><p>00:00.222</p></td> +<td><p>00:00.215</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_directed.html#sphx-glr-auto-examples-drawing-plot-directed-py"><span class="std std-ref">Directed Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_directed.py</span></code>)</p></td> -<td><p>00:00.212</p></td> +<td><p>00:00.200</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_labels_and_colors.html#sphx-glr-auto-examples-drawing-plot-labels-and-colors-py"><span class="std std-ref">Labels And Colors</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_labels_and_colors.py</span></code>)</p></td> -<td><p>00:00.184</p></td> +<td><p>00:00.179</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_rainbow_coloring.html#sphx-glr-auto-examples-drawing-plot-rainbow-coloring-py"><span class="std std-ref">Rainbow Coloring</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_rainbow_coloring.py</span></code>)</p></td> -<td><p>00:00.125</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_unix_email.html#sphx-glr-auto-examples-drawing-plot-unix-email-py"><span class="std std-ref">Unix Email</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_unix_email.py</span></code>)</p></td> +<td><p>00:00.135</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_random_geometric_graph.html#sphx-glr-auto-examples-drawing-plot-random-geometric-graph-py"><span class="std std-ref">Random Geometric Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_random_geometric_graph.py</span></code>)</p></td> -<td><p>00:00.103</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_rainbow_coloring.html#sphx-glr-auto-examples-drawing-plot-rainbow-coloring-py"><span class="std std-ref">Rainbow Coloring</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_rainbow_coloring.py</span></code>)</p></td> +<td><p>00:00.123</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_knuth_miles.html#sphx-glr-auto-examples-drawing-plot-knuth-miles-py"><span class="std std-ref">Knuth Miles</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_knuth_miles.py</span></code>)</p></td> -<td><p>00:00.099</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_random_geometric_graph.html#sphx-glr-auto-examples-drawing-plot-random-geometric-graph-py"><span class="std std-ref">Random Geometric Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_random_geometric_graph.py</span></code>)</p></td> +<td><p>00:00.100</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_ego_graph.html#sphx-glr-auto-examples-drawing-plot-ego-graph-py"><span class="std std-ref">Ego Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_ego_graph.py</span></code>)</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_knuth_miles.html#sphx-glr-auto-examples-drawing-plot-knuth-miles-py"><span class="std std-ref">Knuth Miles</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_knuth_miles.py</span></code>)</p></td> <td><p>00:00.096</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_unix_email.html#sphx-glr-auto-examples-drawing-plot-unix-email-py"><span class="std std-ref">Unix Email</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_unix_email.py</span></code>)</p></td> -<td><p>00:00.088</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_ego_graph.html#sphx-glr-auto-examples-drawing-plot-ego-graph-py"><span class="std std-ref">Ego Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_ego_graph.py</span></code>)</p></td> +<td><p>00:00.094</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_tsp.html#sphx-glr-auto-examples-drawing-plot-tsp-py"><span class="std std-ref">Traveling Salesman Problem</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_tsp.py</span></code>)</p></td> -<td><p>00:00.088</p></td> +<td><p>00:00.082</p></td> <td><p>0.0 MB</p></td> </tr> <tr 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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 5a0e568e..dc5e3b44 100644 --- a/auto_examples/graph/plot_expected_degree_sequence.html +++ b/auto_examples/graph/plot_expected_degree_sequence.html @@ -535,51 +535,51 @@ degree (#nodes) **** 25 ( 0) 26 ( 0) 27 ( 0) -28 ( 0) +28 ( 1) * 29 ( 0) 30 ( 0) 31 ( 1) * 32 ( 1) * -33 ( 0) +33 ( 1) * 34 ( 2) ** -35 ( 5) ***** -36 ( 9) ********* -37 ( 3) *** -38 ( 8) ******** -39 ( 9) ********* -40 ( 7) ******* -41 (10) ********** -42 (25) 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******************************* +52 (32) ******************************** +53 (30) ****************************** +54 (24) ************************ +55 (22) ********************** +56 (20) ******************** +57 (15) *************** +58 (13) ************* 59 (12) ************ -60 ( 7) ******* -61 ( 5) ***** +60 ( 8) ******** +61 ( 6) ****** 62 ( 6) ****** -63 ( 4) **** -64 ( 5) ***** -65 ( 2) ** -66 ( 0) -67 ( 1) * -68 ( 1) * -69 ( 2) ** +63 ( 3) *** +64 ( 3) *** +65 ( 4) **** +66 ( 3) *** +67 ( 3) *** +68 ( 2) ** +69 ( 1) * 70 ( 1) * 71 ( 0) -72 ( 3) *** +72 ( 1) * </pre></div> </div> <div class="line-block"> @@ -599,7 +599,7 @@ 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 0.030 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.029 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 06dc0db0..125a906e 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 0.337 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.276 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 31b1bd13..47e203c7 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 0.088 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-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 bb9e03cd..d2f0de3f 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 0.180 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.167 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 d9f0abef..3524c0a7 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 0.124 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.118 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-napoleon-russian-campaign-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/87e75a2d09fb817a4616bb71aa44546f/plot_napoleon_russian_campaign.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_napoleon_russian_campaign.py</span></code></a></p> diff --git a/auto_examples/graph/plot_roget.html b/auto_examples/graph/plot_roget.html index d25a8e19..142b77ca 100644 --- a/auto_examples/graph/plot_roget.html +++ b/auto_examples/graph/plot_roget.html @@ -588,7 +588,7 @@ DiGraph with 1022 nodes and 5075 edges <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.226 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.225 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 882987a6..ad3cce88 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 1.053 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-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 fba1e463..d6f3c2e6 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 0.377 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.360 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 8794a9dd..aba05039 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>00:02.657</strong> total execution time for <strong>auto_examples_graph</strong> files:</p> +<p><strong>00:02.471</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>00:01.053</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_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>00:00.377</p></td> +<td><p>00:00.360</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>00:00.337</p></td> +<td><p>00:00.276</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>00:00.226</p></td> +<td><p>00:00.225</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>00:00.180</p></td> +<td><p>00:00.167</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>00:00.124</p></td> +<td><p>00:00.118</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>00:00.122</p></td> +<td><p>00:00.108</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>00:00.088</p></td> +<td><p>00:00.083</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>00:00.061</p></td> +<td><p>00:00.056</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>00:00.059</p></td> +<td><p>00:00.054</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>00:00.030</p></td> +<td><p>00:00.029</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 e1856817..6489b35c 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 0.034 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.031 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 bebe3865..8d898868 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 0.026 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.024 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 5daf25fd..f8ade20a 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 0.067 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 8d54b0f0..386e69c7 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 0.082 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.075 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 4b194405..41526b3e 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>00:00.211</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> +<p><strong>00:00.197</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_mini_atlas.html#sphx-glr-auto-examples-graphviz-drawing-plot-mini-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mini_atlas.py</span></code>)</p></td> -<td><p>00:00.082</p></td> +<td><p>00:00.075</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_grid.html#sphx-glr-auto-examples-graphviz-drawing-plot-grid-py"><span class="std std-ref">2D Grid</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_grid.py</span></code>)</p></td> -<td><p>00:00.068</p></td> +<td><p>00:00.067</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_attributes.html#sphx-glr-auto-examples-graphviz-drawing-plot-attributes-py"><span class="std std-ref">Attributes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_attributes.py</span></code>)</p></td> -<td><p>00:00.034</p></td> +<td><p>00:00.031</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>00:00.026</p></td> +<td><p>00:00.024</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 225f6c68..178f94af 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 3.704 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.584 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 6403bd07..7ef9aa5c 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 0.150 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.151 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 71be1e4e..25c2f669 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 0.296 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.279 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 f7b56a85..ee47ed81 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 0.803 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.766 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 1246d99b..e166d291 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 0.335 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.332 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 9898e30d..69fd038a 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>00:05.288</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> +<p><strong>00:05.112</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>00:03.704</p></td> +<td><p>00:03.584</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>00:00.803</p></td> +<td><p>00:00.766</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>00:00.335</p></td> +<td><p>00:00.332</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>00:00.296</p></td> +<td><p>00:00.279</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_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>00:00.150</p></td> +<td><p>00:00.151</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 6201cfff..f9c381b4 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 0.090 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-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 b3dd8913..06700e68 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 0.057 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.054 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 2023d65a..0ccfc692 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>00:00.147</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p> +<p><strong>00:00.142</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>00:00.090</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_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>00:00.057</p></td> +<td><p>00:00.054</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/developer/index.html b/developer/index.html index 828a899e..81b41316 100644 --- a/developer/index.html +++ b/developer/index.html @@ -493,7 +493,7 @@ <dd class="field-odd"><p>3.0rc2.dev0</p> </dd> <dt class="field-even">Date<span class="colon">:</span></dt> -<dd class="field-even"><p>Dec 20, 2022</p> +<dd class="field-even"><p>Dec 25, 2022</p> </dd> </dl> <div class="toctree-wrapper compound"> @@ -464,7 +464,7 @@ <dd class="field-odd"><p>3.0rc2.dev0</p> </dd> <dt class="field-even">Date<span class="colon">:</span></dt> -<dd class="field-even"><p>Dec 20, 2022</p> +<dd class="field-even"><p>Dec 25, 2022</p> </dd> </dl> <p>NetworkX is a Python package for the creation, manipulation, and study diff --git a/objects.inv b/objects.inv Binary files differindex 2c8bc0fb..d6f4358e 100644 --- a/objects.inv +++ b/objects.inv diff --git a/reference/index.html b/reference/index.html index 69a45d4d..136dd738 100644 --- a/reference/index.html +++ b/reference/index.html @@ -496,7 +496,7 @@ <dd class="field-odd"><p>3.0rc2.dev0</p> </dd> <dt class="field-even">Date<span class="colon">:</span></dt> -<dd class="field-even"><p>Dec 20, 2022</p> +<dd class="field-even"><p>Dec 25, 2022</p> </dd> </dl> </div></blockquote> diff --git a/reference/introduction-7.hires.png b/reference/introduction-7.hires.png Binary files differindex fed2f58a..23606e4e 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 4fa03468..515fbfc8 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 c17fb000..95eebca3 100644 --- a/reference/introduction-7.png +++ b/reference/introduction-7.png diff --git a/reference/introduction.ipynb b/reference/introduction.ipynb index 9668a57b..c3b2199b 100644 --- a/reference/introduction.ipynb +++ b/reference/introduction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "b9ced8bc", + "id": "3edd04de", "metadata": {}, "source": [ "## Introduction\n", @@ -34,7 +34,7 @@ { "cell_type": "code", "execution_count": null, - "id": "27f6fee8", + "id": "d6e34ab8", "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "055f9dd5", + "id": "543d8846", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0323906d", + "id": "1834c1ed", "metadata": {}, "outputs": [], "source": [ @@ -94,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "7fb1bffe", + "id": "c9a9f991", "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": "43c845be", + "id": "fac78201", "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "9076be36", + "id": "40b2c17c", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -214,7 +214,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0754cd75", + "id": "3b240003", "metadata": {}, "outputs": [], "source": [ @@ -225,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "a0c29cc3", + "id": "d7f5a9c8", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -234,7 +234,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c1894241", + "id": "09b6b0e0", "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "69bd6df7", + "id": "9fb6eed6", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -311,7 +311,7 @@ { "cell_type": "code", "execution_count": null, - "id": "51689b8f", + "id": "2b6e1953", "metadata": {}, "outputs": [], "source": [ @@ -323,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "56830072", + "id": "6dbe7c93", "metadata": {}, "source": [ "# Drawing\n", @@ -344,7 +344,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9460eb6d", + "id": "5fed2cc7", "metadata": {}, "outputs": [], "source": [ @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "91b9ddb7", + "id": "b46d02fc", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -398,7 +398,7 @@ { "cell_type": "code", "execution_count": null, - "id": "95c37e59", + "id": "2e684aa6", "metadata": {}, "outputs": [], "source": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "b2160e09", + "id": "4f6b2e3d", "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": "37b11de0", + "id": "67501e28", "metadata": {}, "outputs": [], "source": [ diff --git a/reference/introduction_full.ipynb b/reference/introduction_full.ipynb index 7b2c7f64..ea5bfe20 100644 --- a/reference/introduction_full.ipynb +++ b/reference/introduction_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "b9ced8bc", + "id": "3edd04de", "metadata": {}, "source": [ "## Introduction\n", @@ -34,13 +34,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "27f6fee8", + "id": "d6e34ab8", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:22.862085Z", - "iopub.status.busy": "2022-12-20T17:04:22.861672Z", - "iopub.status.idle": "2022-12-20T17:04:22.935402Z", - "shell.execute_reply": "2022-12-20T17:04:22.934212Z" + "iopub.execute_input": "2022-12-25T00:24:15.560964Z", + "iopub.status.busy": "2022-12-25T00:24:15.560749Z", + "iopub.status.idle": "2022-12-25T00:24:15.630514Z", + "shell.execute_reply": "2022-12-25T00:24:15.629557Z" } }, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "markdown", - "id": "055f9dd5", + "id": "543d8846", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -89,13 +89,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "0323906d", + "id": "1834c1ed", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:22.939255Z", - "iopub.status.busy": "2022-12-20T17:04:22.939019Z", - "iopub.status.idle": "2022-12-20T17:04:22.942464Z", - "shell.execute_reply": "2022-12-20T17:04:22.941814Z" + "iopub.execute_input": "2022-12-25T00:24:15.633466Z", + "iopub.status.busy": "2022-12-25T00:24:15.633249Z", + "iopub.status.idle": "2022-12-25T00:24:15.636642Z", + "shell.execute_reply": "2022-12-25T00:24:15.636041Z" } }, "outputs": [], @@ -108,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "7fb1bffe", + "id": "c9a9f991", "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": "43c845be", + "id": "fac78201", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:22.945501Z", - "iopub.status.busy": "2022-12-20T17:04:22.945288Z", - "iopub.status.idle": "2022-12-20T17:04:22.948719Z", - "shell.execute_reply": "2022-12-20T17:04:22.948087Z" + "iopub.execute_input": "2022-12-25T00:24:15.639467Z", + "iopub.status.busy": "2022-12-25T00:24:15.639240Z", + "iopub.status.idle": "2022-12-25T00:24:15.642454Z", + "shell.execute_reply": "2022-12-25T00:24:15.641883Z" } }, "outputs": [], @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "9076be36", + "id": "40b2c17c", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -235,13 +235,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "0754cd75", + "id": "3b240003", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:22.951667Z", - "iopub.status.busy": "2022-12-20T17:04:22.951457Z", - "iopub.status.idle": "2022-12-20T17:04:22.954600Z", - "shell.execute_reply": "2022-12-20T17:04:22.953948Z" + "iopub.execute_input": "2022-12-25T00:24:15.645319Z", + "iopub.status.busy": "2022-12-25T00:24:15.645115Z", + "iopub.status.idle": "2022-12-25T00:24:15.648148Z", + "shell.execute_reply": "2022-12-25T00:24:15.647555Z" } }, "outputs": [], @@ -253,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "a0c29cc3", + "id": "d7f5a9c8", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -262,13 +262,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "c1894241", + "id": "09b6b0e0", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:22.957509Z", - "iopub.status.busy": "2022-12-20T17:04:22.957302Z", - "iopub.status.idle": "2022-12-20T17:04:22.961196Z", - "shell.execute_reply": "2022-12-20T17:04:22.960535Z" + "iopub.execute_input": "2022-12-25T00:24:15.650968Z", + "iopub.status.busy": "2022-12-25T00:24:15.650772Z", + "iopub.status.idle": "2022-12-25T00:24:15.654456Z", + "shell.execute_reply": "2022-12-25T00:24:15.653858Z" } }, "outputs": [], @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "69bd6df7", + "id": "9fb6eed6", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -346,13 +346,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "51689b8f", + "id": "2b6e1953", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:22.963995Z", - "iopub.status.busy": "2022-12-20T17:04:22.963788Z", - "iopub.status.idle": "2022-12-20T17:04:22.968042Z", - "shell.execute_reply": "2022-12-20T17:04:22.967411Z" + "iopub.execute_input": "2022-12-25T00:24:15.657126Z", + "iopub.status.busy": "2022-12-25T00:24:15.656919Z", + "iopub.status.idle": "2022-12-25T00:24:15.661029Z", + "shell.execute_reply": "2022-12-25T00:24:15.660431Z" } }, "outputs": [ @@ -373,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "56830072", + "id": "6dbe7c93", "metadata": {}, "source": [ "# Drawing\n", @@ -394,19 +394,19 @@ { "cell_type": "code", "execution_count": 7, - "id": "9460eb6d", + "id": "5fed2cc7", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:22.972491Z", - "iopub.status.busy": "2022-12-20T17:04:22.972274Z", - "iopub.status.idle": "2022-12-20T17:04:23.537686Z", - "shell.execute_reply": "2022-12-20T17:04:23.536381Z" + "iopub.execute_input": "2022-12-25T00:24:15.665328Z", + "iopub.status.busy": "2022-12-25T00:24:15.665124Z", + "iopub.status.idle": "2022-12-25T00:24:16.193043Z", + "shell.execute_reply": "2022-12-25T00:24:16.191242Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -426,7 +426,7 @@ }, { "cell_type": "markdown", - "id": "91b9ddb7", + "id": "b46d02fc", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -466,13 +466,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "95c37e59", + "id": "2e684aa6", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:23.541139Z", - "iopub.status.busy": "2022-12-20T17:04:23.540733Z", - "iopub.status.idle": "2022-12-20T17:04:23.544779Z", - "shell.execute_reply": "2022-12-20T17:04:23.544087Z" + "iopub.execute_input": "2022-12-25T00:24:16.196176Z", + "iopub.status.busy": "2022-12-25T00:24:16.195850Z", + "iopub.status.idle": "2022-12-25T00:24:16.199558Z", + "shell.execute_reply": "2022-12-25T00:24:16.198937Z" } }, "outputs": [ @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "b2160e09", + "id": "4f6b2e3d", "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": "37b11de0", + "id": "67501e28", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:23.548603Z", - "iopub.status.busy": "2022-12-20T17:04:23.548378Z", - "iopub.status.idle": "2022-12-20T17:04:23.552573Z", - "shell.execute_reply": "2022-12-20T17:04:23.551917Z" + "iopub.execute_input": "2022-12-25T00:24:16.203164Z", + "iopub.status.busy": "2022-12-25T00:24:16.202967Z", + "iopub.status.idle": "2022-12-25T00:24:16.206851Z", + "shell.execute_reply": "2022-12-25T00:24:16.206225Z" } }, "outputs": [ diff --git a/release/api_0.99.html b/release/api_0.99.html index 61bee31b..da4fb2ce 100644 --- a/release/api_0.99.html +++ b/release/api_0.99.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/api_1.0.html b/release/api_1.0.html index 03fa146b..67251a96 100644 --- a/release/api_1.0.html +++ b/release/api_1.0.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/api_1.10.html b/release/api_1.10.html index 699843ec..a4602f25 100644 --- a/release/api_1.10.html +++ b/release/api_1.10.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/api_1.11.html b/release/api_1.11.html index 6b94ef16..fbb2f853 100644 --- a/release/api_1.11.html +++ b/release/api_1.11.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/api_1.4.html b/release/api_1.4.html index bb33ebdc..8b524c42 100644 --- a/release/api_1.4.html +++ b/release/api_1.4.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/api_1.5.html b/release/api_1.5.html index 0af8c660..42d466ef 100644 --- a/release/api_1.5.html +++ b/release/api_1.5.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/api_1.6.html b/release/api_1.6.html index 8880b393..5f76049b 100644 --- a/release/api_1.6.html +++ b/release/api_1.6.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/api_1.7.html b/release/api_1.7.html index 74976086..e167ea4e 100644 --- a/release/api_1.7.html +++ b/release/api_1.7.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/api_1.8.html b/release/api_1.8.html index 535537b1..c8f0348f 100644 --- a/release/api_1.8.html +++ b/release/api_1.8.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/api_1.9.html b/release/api_1.9.html index 76afca09..b2211328 100644 --- a/release/api_1.9.html +++ b/release/api_1.9.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/index.html b/release/index.html index 806cf103..fd1e0bee 100644 --- a/release/index.html +++ b/release/index.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> @@ -531,7 +531,7 @@ for you to use <code class="docutils literal notranslate"><span class="pre">~=</ <li class="toctree-l2"><a class="reference internal" href="release_dev.html#contributors">Contributors</a></li> </ul> </li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a><ul> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a><ul> <li class="toctree-l2"><a class="reference internal" href="release_2.8.8.html#highlights">Highlights</a></li> <li class="toctree-l2"><a class="reference internal" href="release_2.8.8.html#merged-prs">Merged PRs</a></li> <li class="toctree-l2"><a class="reference internal" href="release_2.8.8.html#contributors">Contributors</a></li> diff --git a/release/old_release_log.html b/release/old_release_log.html index 181505a0..9565b0ba 100644 --- a/release/old_release_log.html +++ b/release/old_release_log.html @@ -441,7 +441,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.0.html b/release/release_2.0.html index 7e729c0f..d5e4e1f8 100644 --- a/release/release_2.0.html +++ b/release/release_2.0.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.1.html b/release/release_2.1.html index f3d2dac7..4d436013 100644 --- a/release/release_2.1.html +++ b/release/release_2.1.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.2.html b/release/release_2.2.html index 9d33e0ef..f5659261 100644 --- a/release/release_2.2.html +++ b/release/release_2.2.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.3.html b/release/release_2.3.html index b2c88c91..0ec70cf9 100644 --- a/release/release_2.3.html +++ b/release/release_2.3.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.4.html b/release/release_2.4.html index 4cdb9e2c..4651b114 100644 --- a/release/release_2.4.html +++ b/release/release_2.4.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.5.html b/release/release_2.5.html index 3210a21b..774f3baf 100644 --- a/release/release_2.5.html +++ b/release/release_2.5.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.6.html b/release/release_2.6.html index 739b754e..a7c68fb5 100644 --- a/release/release_2.6.html +++ b/release/release_2.6.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.7.1.html b/release/release_2.7.1.html index 69d94403..43895056 100644 --- a/release/release_2.7.1.html +++ b/release/release_2.7.1.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.7.html b/release/release_2.7.html index 66944ac5..2a14f79d 100644 --- a/release/release_2.7.html +++ b/release/release_2.7.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.8.1.html b/release/release_2.8.1.html index 8e070b1f..01d5e71b 100644 --- a/release/release_2.8.1.html +++ b/release/release_2.8.1.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.8.2.html b/release/release_2.8.2.html index 74527a4f..f17a026f 100644 --- a/release/release_2.8.2.html +++ b/release/release_2.8.2.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.8.3.html b/release/release_2.8.3.html index f2e1f8e2..88be6062 100644 --- a/release/release_2.8.3.html +++ b/release/release_2.8.3.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.8.4.html b/release/release_2.8.4.html index b749ce6a..92d790e1 100644 --- a/release/release_2.8.4.html +++ b/release/release_2.8.4.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.8.5.html b/release/release_2.8.5.html index 9dbf27b6..cdaf0878 100644 --- a/release/release_2.8.5.html +++ b/release/release_2.8.5.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1 current active"><a class="current reference internal" href="#">NetworkX 2.8.5</a></li> diff --git a/release/release_2.8.6.html b/release/release_2.8.6.html index 0564e860..d5781d39 100644 --- a/release/release_2.8.6.html +++ b/release/release_2.8.6.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1 current active"><a class="current reference internal" href="#">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.8.7.html b/release/release_2.8.7.html index 14b5b785..e9e3c112 100644 --- a/release/release_2.8.7.html +++ b/release/release_2.8.7.html @@ -52,7 +52,7 @@ <link rel="index" title="Index" href="../genindex.html" /> <link rel="search" title="Search" href="../search.html" /> <link rel="next" title="NetworkX 2.8.6" href="release_2.8.6.html" /> - <link rel="prev" title="NetworkX 2.8.7" href="release_2.8.8.html" /> + <link rel="prev" title="NetworkX 2.8.8" href="release_2.8.8.html" /> <meta name="viewport" content="width=device-width, initial-scale=1" /> <meta name="docsearch:language" content="en"> </head> @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1 current active"><a class="current reference internal" href="#">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/release/release_2.8.8.html b/release/release_2.8.8.html index fe8d8fc6..5ac27404 100644 --- a/release/release_2.8.8.html +++ b/release/release_2.8.8.html @@ -6,7 +6,7 @@ <meta charset="utf-8" /> <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.19: https://docutils.sourceforge.io/" /> - <title>NetworkX 2.8.7 — NetworkX 3.0rc2.dev0 documentation</title> + <title>NetworkX 2.8.8 — NetworkX 3.0rc2.dev0 documentation</title> @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1 current active"><a class="current reference internal" href="#">NetworkX 2.8.7</a></li> +<li class="toctree-l1 current active"><a class="current reference internal" href="#">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> @@ -508,8 +508,8 @@ <article class="bd-article" role="main"> - <section id="networkx-2-8-7"> -<h1>NetworkX 2.8.7<a class="headerlink" href="#networkx-2-8-7" title="Permalink to this heading">#</a></h1> + <section id="networkx-2-8-8"> +<h1>NetworkX 2.8.8<a class="headerlink" href="#networkx-2-8-8" title="Permalink to this heading">#</a></h1> <p>Release date: 1 November 2022</p> <p>Supports Python 3.8, 3.9, 3.10, and 3.11.</p> <p>NetworkX is a Python package for the creation, manipulation, and study of the diff --git a/release/release_2.8.html b/release/release_2.8.html index 7ff32506..00f928ab 100644 --- a/release/release_2.8.html +++ b/release/release_2.8.html @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1"><a class="reference internal" href="release_dev.html">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li 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initial-scale=1" /> <meta name="docsearch:language" content="en"> @@ -442,7 +442,7 @@ <div class="bd-toc-item navbar-nav"> <ul class="current nav bd-sidenav"> <li class="toctree-l1 current active"><a class="current reference internal" href="#">NetworkX 3.0 (unreleased)</a></li> -<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.7</a></li> +<li class="toctree-l1"><a class="reference internal" href="release_2.8.8.html">NetworkX 2.8.8</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.7.html">NetworkX 2.8.7</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.6.html">NetworkX 2.8.6</a></li> <li class="toctree-l1"><a class="reference internal" href="release_2.8.5.html">NetworkX 2.8.5</a></li> diff --git a/searchindex.js b/searchindex.js index e7653de6..706f77fd 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["auto_examples/3d_drawing/index", 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1417, 1425, 1426], "wherea": [8, 103, 682, 762, 786, 791, 1165, 1417], "cannot": [8, 101, 103, 127, 132, 199, 232, 300, 362, 394, 476, 581, 582, 583, 584, 632, 722, 887, 925, 934, 968, 979, 1007, 1043, 1165, 1208, 1209, 1296, 1298, 1302, 1303, 1326, 1345, 1347, 1348, 1349, 1350], "subformula": 8, "onc": [8, 38, 54, 55, 88, 93, 94, 99, 100, 112, 127, 199, 227, 230, 231, 232, 246, 247, 360, 374, 380, 388, 422, 423, 428, 488, 491, 492, 581, 582, 583, 652, 678, 679, 717, 718, 887, 925, 968, 1007, 1046, 1066, 1087, 1217, 1311, 1326, 1403, 1407], "thu": [8, 88, 101, 103, 115, 215, 216, 220, 256, 258, 331, 418, 419, 427, 428, 462, 477, 500, 512, 583, 679, 698, 699, 760, 762, 796, 1037, 1039, 1040, 1043, 1087, 1112, 1148, 1215, 1217, 1234, 1278, 1279, 1296, 1328, 1402, 1405, 1407], "wai": [8, 27, 52, 53, 55, 75, 86, 88, 93, 97, 99, 100, 101, 102, 103, 104, 107, 110, 115, 132, 152, 157, 158, 165, 184, 226, 281, 297, 298, 315, 330, 337, 356, 588, 598, 615, 618, 678, 691, 730, 760, 791, 796, 854, 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"littl": [8, 94, 298, 307], "mislead": 8, "That": [8, 97, 132, 165, 212, 221, 227, 295, 385, 436, 465, 525, 535, 555, 588, 657, 671, 672, 673, 674, 691, 704, 717, 791, 862, 907, 943, 988, 1046, 1162, 1210, 1296, 1388, 1404, 1409], "okai": 8, "becaus": [8, 11, 54, 69, 94, 99, 101, 102, 103, 112, 132, 161, 215, 216, 220, 255, 311, 378, 387, 389, 390, 394, 411, 412, 427, 494, 498, 499, 500, 510, 569, 585, 587, 615, 616, 632, 652, 934, 979, 1038, 1236, 1273, 1296, 1303, 1326, 1345, 1350, 1404, 1407, 1416], "AND": [8, 110, 598, 748, 762], "OR": [8, 110, 157, 175, 188, 856, 869, 877, 901, 937, 947, 950, 959, 982, 992], "symmetr": [8, 145, 148, 237, 545, 586, 593, 761, 1173, 1192, 1235, 1246, 1250, 1251, 1256, 1258, 1269, 1320, 1321, 1387], "It": [8, 52, 56, 58, 92, 93, 94, 97, 99, 101, 102, 104, 107, 110, 112, 115, 132, 172, 184, 207, 214, 215, 216, 229, 230, 231, 249, 260, 261, 262, 264, 278, 310, 316, 324, 325, 326, 343, 346, 347, 351, 353, 412, 414, 415, 416, 417, 418, 419, 429, 438, 440, 452, 457, 464, 480, 496, 500, 508, 530, 540, 545, 559, 560, 565, 566, 567, 582, 588, 594, 595, 598, 600, 601, 615, 619, 628, 629, 630, 652, 658, 659, 663, 671, 674, 692, 717, 718, 719, 760, 761, 762, 791, 796, 868, 873, 892, 913, 916, 928, 949, 955, 973, 994, 998, 1010, 1012, 1013, 1018, 1037, 1038, 1039, 1040, 1054, 1117, 1170, 1174, 1200, 1201, 1206, 1207, 1210, 1217, 1223, 1227, 1234, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1253, 1254, 1258, 1261, 1263, 1264, 1269, 1275, 1276, 1277, 1280, 1296, 1297, 1323, 1324, 1326, 1328, 1343, 1382, 1383, 1393, 1395, 1398, 1402, 1404, 1407, 1408, 1409, 1411, 1412, 1413, 1426], "just": [8, 99, 102, 103, 104, 184, 199, 338, 374, 439, 464, 559, 560, 577, 660, 661, 662, 692, 791, 873, 887, 916, 925, 946, 955, 960, 968, 991, 998, 1007, 1120, 1126, 1229, 1278, 1279, 1296, 1328, 1393, 1404, 1406], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1325, 1402, 1406, 1412], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1202, 1383], "doe": [8, 77, 93, 94, 99, 101, 102, 103, 104, 114, 115, 132, 147, 153, 154, 165, 168, 189, 207, 208, 227, 228, 229, 230, 231, 232, 293, 308, 339, 340, 342, 343, 352, 357, 373, 382, 385, 410, 414, 426, 450, 469, 494, 495, 496, 497, 498, 499, 500, 502, 503, 506, 507, 509, 510, 511, 512, 534, 544, 549, 550, 551, 564, 566, 583, 584, 586, 589, 601, 612, 626, 627, 678, 691, 693, 694, 698, 699, 717, 718, 721, 722, 723, 724, 725, 726, 762, 862, 865, 878, 892, 907, 910, 928, 943, 946, 960, 973, 988, 991, 1010, 1038, 1043, 1066, 1070, 1072, 1081, 1102, 1103, 1105, 1106, 1107, 1109, 1114, 1173, 1175, 1177, 1192, 1207, 1222, 1223, 1227, 1229, 1234, 1241, 1296, 1300, 1303, 1326, 1333, 1334, 1341, 1342, 1344, 1351, 1353, 1354, 1355, 1356, 1357, 1358, 1371, 1379, 1380, 1381, 1383, 1393, 1404, 1405, 1406, 1410, 1417, 1426], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1219], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1229, 1296, 1395, 1404], "everi": [8, 11, 57, 88, 93, 109, 112, 120, 144, 157, 161, 177, 211, 212, 220, 221, 229, 230, 231, 235, 243, 264, 287, 295, 300, 324, 325, 343, 352, 380, 397, 437, 439, 440, 450, 462, 471, 472, 473, 474, 475, 477, 483, 484, 491, 512, 516, 565, 606, 614, 615, 619, 632, 633, 635, 636, 663, 685, 687, 688, 717, 718, 791, 856, 901, 937, 982, 1052, 1053, 1054, 1070, 1071, 1072, 1085, 1086, 1102, 1103, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1148, 1162, 1195, 1216, 1217, 1257, 1264, 1278, 1279, 1296, 1407], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1177, 1207, 1208, 1404, 1406, 1407, 1426], "ha": [8, 11, 16, 44, 67, 88, 91, 93, 94, 95, 97, 99, 100, 101, 102, 103, 105, 107, 110, 112, 116, 120, 127, 152, 161, 165, 166, 173, 174, 175, 184, 188, 198, 207, 212, 214, 215, 219, 220, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 241, 242, 243, 244, 247, 249, 252, 269, 271, 272, 273, 274, 275, 276, 282, 289, 291, 293, 294, 295, 300, 305, 310, 324, 331, 343, 352, 355, 356, 363, 364, 365, 373, 378, 380, 381, 383, 384, 385, 386, 391, 393, 394, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 424, 427, 428, 429, 439, 450, 458, 460, 466, 467, 468, 471, 472, 473, 474, 475, 476, 477, 480, 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 522, 564, 566, 577, 578, 581, 590, 593, 605, 607, 610, 611, 622, 623, 624, 628, 629, 630, 632, 633, 634, 635, 636, 638, 646, 647, 649, 652, 657, 658, 682, 688, 690, 692, 697, 711, 717, 718, 729, 730, 731, 739, 749, 786, 791, 854, 862, 863, 869, 873, 877, 886, 892, 899, 907, 908, 916, 924, 928, 935, 943, 944, 948, 950, 955, 959, 967, 973, 980, 988, 989, 993, 998, 1006, 1010, 1040, 1043, 1045, 1066, 1068, 1070, 1072, 1075, 1080, 1084, 1098, 1099, 1101, 1102, 1103, 1105, 1122, 1130, 1145, 1154, 1160, 1162, 1165, 1176, 1180, 1185, 1193, 1195, 1196, 1197, 1198, 1199, 1207, 1210, 1211, 1215, 1217, 1222, 1234, 1239, 1243, 1244, 1248, 1249, 1254, 1259, 1261, 1264, 1267, 1269, 1270, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1283, 1284, 1285, 1286, 1289, 1291, 1293, 1296, 1300, 1326, 1328, 1330, 1333, 1334, 1353, 1354, 1371, 1372, 1379, 1382, 1393, 1394, 1395, 1398, 1403, 1404, 1405, 1406, 1407, 1409, 1413, 1414, 1416, 1423, 1425], "output": [8, 13, 16, 89, 93, 101, 102, 103, 109, 197, 287, 288, 345, 374, 380, 494, 498, 499, 509, 510, 575, 588, 677, 678, 691, 722, 1045, 1193, 1197, 1199, 1269, 1296, 1326, 1334, 1341, 1344, 1355, 1358, 1399, 1402, 1404, 1406, 1411, 1413, 1414, 1426], "two": [8, 11, 16, 27, 34, 38, 43, 54, 55, 57, 58, 65, 67, 71, 88, 93, 95, 99, 100, 102, 109, 112, 114, 115, 120, 132, 151, 171, 175, 184, 185, 188, 202, 207, 211, 212, 213, 214, 215, 216, 217, 220, 221, 226, 227, 230, 231, 232, 245, 249, 251, 252, 253, 257, 258, 260, 261, 262, 265, 269, 270, 271, 272, 273, 274, 275, 276, 282, 285, 286, 287, 289, 305, 311, 315, 316, 322, 326, 329, 330, 337, 341, 343, 345, 351, 352, 358, 359, 377, 380, 381, 383, 391, 411, 412, 419, 423, 428, 429, 430, 431, 442, 443, 444, 445, 447, 452, 453, 454, 457, 462, 471, 472, 473, 474, 475, 476, 480, 491, 494, 498, 499, 500, 502, 503, 506, 508, 509, 510, 511, 521, 545, 549, 550, 551, 555, 559, 560, 561, 562, 563, 564, 565, 566, 568, 569, 572, 574, 578, 584, 585, 586, 587, 588, 593, 598, 605, 607, 608, 610, 611, 615, 619, 626, 627, 629, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 680, 692, 694, 731, 732, 738, 739, 760, 761, 762, 780, 786, 791, 796, 853, 867, 869, 873, 874, 877, 890, 892, 898, 912, 916, 917, 926, 928, 934, 946, 948, 950, 955, 956, 959, 960, 971, 973, 979, 991, 993, 998, 999, 1008, 1010, 1019, 1020, 1021, 1022, 1036, 1037, 1039, 1040, 1056, 1084, 1088, 1098, 1100, 1101, 1106, 1107, 1108, 1109, 1114, 1116, 1134, 1146, 1147, 1149, 1151, 1152, 1156, 1174, 1185, 1186, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1204, 1207, 1210, 1211, 1215, 1217, 1218, 1243, 1244, 1253, 1271, 1272, 1275, 1276, 1294, 1295, 1296, 1323, 1324, 1326, 1328, 1359, 1360, 1363, 1393, 1394, 1395, 1397, 1402, 1404, 1405, 1406, 1407, 1410, 1411, 1413, 1425], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1420], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1217, 1226, 1262, 1263, 1326, 1407], "appear": [8, 83, 93, 95, 99, 100, 102, 179, 204, 230, 231, 238, 243, 246, 247, 277, 363, 364, 365, 378, 451, 452, 453, 455, 466, 470, 584, 585, 587, 588, 675, 679, 707, 730, 734, 736, 891, 972, 1036, 1088, 1102, 1136, 1150, 1152, 1154, 1157, 1159, 1187, 1188, 1277, 1282, 1323, 1324, 1345, 1348, 1349, 1350, 1382, 1407, 1413, 1414], "both": [8, 52, 55, 92, 93, 94, 100, 101, 102, 103, 115, 161, 164, 204, 214, 215, 216, 217, 240, 257, 258, 259, 264, 282, 286, 287, 289, 337, 358, 379, 383, 415, 417, 418, 419, 423, 427, 440, 470, 502, 506, 545, 575, 581, 598, 600, 601, 602, 603, 604, 605, 606, 607, 610, 611, 615, 621, 635, 636, 653, 654, 655, 676, 711, 720, 760, 761, 762, 782, 891, 972, 1020, 1036, 1066, 1075, 1080, 1084, 1088, 1097, 1120, 1126, 1144, 1165, 1189, 1192, 1199, 1207, 1210, 1211, 1213, 1215, 1282, 1296, 1326, 1328, 1358, 1363, 1364, 1387, 1393, 1395, 1402, 1413, 1416, 1417, 1425, 1426], "negat": 8, "sole": [8, 786, 1278, 1279, 1326], "fourth": [8, 230, 231, 1326, 1404], "digraph": [8, 10, 11, 16, 21, 25, 41, 45, 56, 61, 67, 69, 70, 82, 88, 101, 102, 115, 132, 151, 152, 156, 157, 158, 160, 162, 163, 165, 166, 168, 170, 171, 172, 175, 176, 185, 186, 187, 188, 189, 192, 193, 194, 195, 196, 198, 199, 202, 204, 207, 208, 216, 227, 229, 230, 231, 240, 246, 247, 299, 308, 314, 318, 319, 321, 327, 328, 334, 335, 336, 337, 339, 340, 342, 343, 388, 391, 393, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 430, 431, 437, 450, 452, 453, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 481, 482, 492, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 513, 514, 518, 519, 523, 555, 566, 575, 576, 577, 588, 590, 613, 615, 623, 630, 636, 643, 644, 652, 656, 657, 658, 659, 663, 678, 688, 690, 693, 696, 697, 698, 699, 700, 701, 702, 706, 707, 708, 709, 711, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 740, 741, 744, 745, 746, 747, 748, 749, 750, 752, 760, 789, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 911, 912, 913, 915, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 957, 958, 963, 964, 965, 966, 967, 968, 972, 973, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 1000, 1001, 1003, 1004, 1005, 1006, 1007, 1010, 1035, 1037, 1038, 1039, 1040, 1041, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1135, 1150, 1154, 1168, 1169, 1170, 1173, 1177, 1178, 1180, 1182, 1183, 1184, 1185, 1189, 1217, 1270, 1272, 1273, 1274, 1283, 1284, 1287, 1290, 1292, 1298, 1323, 1326, 1333, 1337, 1342, 1356, 1357, 1362, 1365, 1366, 1371, 1393, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "add_nod": [8, 11, 26, 34, 69, 74, 89, 102, 157, 184, 246, 339, 340, 398, 422, 491, 492, 496, 504, 505, 508, 522, 523, 605, 607, 610, 611, 691, 796, 856, 873, 901, 916, 937, 955, 982, 998, 1037, 1039, 1040, 1086, 1275, 1326, 1345, 1407, 1408, 1417, 1426], "get_node_attribut": [8, 39, 44, 71, 1213, 1404], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1133, 1134, 1136], "22": [8, 35, 64, 66, 383, 384, 1271, 1323, 1403, 1408, 1412, 1422], "multipartite_layout": [8, 36, 61, 67, 1412, 1414, 1420], "subset_kei": [8, 36, 61, 67, 1109], "equal": [8, 36, 81, 144, 214, 215, 216, 230, 231, 238, 269, 271, 273, 276, 288, 297, 298, 300, 303, 306, 307, 310, 311, 312, 315, 316, 320, 323, 324, 325, 329, 330, 331, 373, 410, 411, 412, 413, 418, 419, 428, 471, 474, 476, 491, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 525, 535, 545, 552, 553, 554, 555, 568, 572, 605, 623, 657, 671, 672, 673, 674, 687, 688, 689, 690, 721, 722, 740, 741, 753, 761, 791, 1112, 1116, 1162, 1165, 1198, 1204, 1230, 1239, 1271, 1280, 1291, 1307, 1309, 1312, 1398, 1399], "105": [8, 17, 517, 518, 1166, 1167], "plot_circuit": [8, 17], "southern": [9, 1265], "women": [9, 1265, 1398, 1406], "unipartit": [9, 115, 258, 259, 358], "properti": [9, 11, 18, 22, 33, 63, 86, 101, 102, 103, 112, 134, 159, 161, 166, 168, 175, 176, 179, 184, 188, 189, 190, 200, 284, 285, 286, 287, 288, 363, 364, 365, 388, 476, 500, 545, 569, 619, 685, 858, 863, 865, 869, 870, 873, 877, 878, 879, 888, 903, 908, 910, 916, 939, 944, 946, 950, 951, 955, 959, 960, 969, 984, 989, 991, 998, 1085, 1086, 1122, 1134, 1136, 1193, 1202, 1217, 1219, 1269, 1283, 1284, 1326, 1328, 1383, 1398, 1405, 1406, 1407, 1408, 1413, 1417, 1426], "These": [9, 52, 58, 73, 79, 86, 93, 94, 105, 112, 336, 385, 494, 512, 559, 671, 673, 732, 748, 779, 786, 1038, 1045, 1047, 1323, 1326, 1385, 1387, 1392, 1394, 1395, 1397, 1399, 1404, 1405, 1411, 1426], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1199, 1393, 1395, 1399, 1402, 1405, 1406, 1407, 1413, 1416], "et": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202], "al": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202, 1407, 1413], "1930": [9, 1396], "thei": [9, 54, 58, 65, 71, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 112, 132, 151, 165, 207, 213, 220, 249, 285, 287, 288, 296, 297, 298, 301, 302, 306, 307, 308, 309, 351, 362, 374, 391, 396, 427, 451, 452, 453, 454, 464, 465, 471, 472, 473, 474, 475, 496, 504, 505, 508, 512, 546, 547, 548, 559, 560, 576, 583, 586, 588, 600, 604, 675, 676, 704, 717, 750, 760, 786, 853, 862, 892, 898, 907, 928, 934, 943, 962, 973, 979, 988, 1002, 1010, 1036, 1038, 1066, 1085, 1088, 1109, 1120, 1126, 1133, 1135, 1137, 1151, 1159, 1165, 1193, 1197, 1198, 1217, 1271, 1272, 1323, 1328, 1353, 1354, 1356, 1357, 1359, 1363, 1394, 1396, 1402, 1404, 1406, 1409, 1414, 1426], "repres": [9, 11, 26, 43, 52, 54, 57, 67, 92, 99, 107, 115, 230, 231, 265, 281, 283, 286, 287, 288, 291, 292, 338, 350, 361, 362, 363, 377, 378, 380, 381, 382, 385, 386, 391, 448, 452, 453, 455, 457, 460, 465, 466, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 521, 565, 577, 578, 579, 580, 586, 588, 609, 615, 618, 619, 656, 660, 664, 667, 676, 679, 691, 692, 695, 697, 698, 699, 700, 702, 728, 730, 731, 734, 736, 739, 752, 786, 791, 796, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1045, 1081, 1102, 1140, 1151, 1185, 1193, 1194, 1196, 1197, 1198, 1199, 1209, 1217, 1240, 1243, 1246, 1250, 1258, 1267, 1269, 1272, 1273, 1278, 1279, 1323, 1324, 1326, 1329, 1330, 1346, 1347, 1388, 1393, 1406], "observ": [9, 13, 132, 223, 1414, 1426], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 64, 66, 71, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1150, 1242, 1250, 1262, 1406, 1408, 1426], "event": [9, 25, 99, 100, 110, 1165, 1229, 1300], "18": [9, 44, 59, 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, 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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, 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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, 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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, 1270, 1294, 1295, 1296, 1403, 1404, 1406, 1407, 1408, 1409, 1412, 1413, 1422, 1425], "nonexp_node_color": 10, "nonexp_node_s": 10, "yellow": [10, 15, 598, 760, 1426], "nonexp_po": 10, "75": [10, 34, 239, 260, 299, 314, 355, 356, 386, 682, 1169, 1170, 1171, 1173, 1404, 1408, 1426], "c_node": [10, 690], "spot": 10, "244": [10, 17, 338], "plot_dedensif": [10, 17], "153": [11, 455], "curiou": 11, "let": [11, 55, 58, 93, 97, 101, 103, 217, 257, 280, 282, 299, 300, 313, 322, 371, 372, 383, 586, 619, 762, 1219, 1278, 1279, 1326, 1425], "defin": [11, 24, 52, 58, 69, 97, 112, 127, 213, 222, 223, 239, 240, 260, 261, 262, 263, 285, 289, 311, 316, 329, 334, 335, 345, 346, 347, 356, 385, 386, 390, 424, 425, 426, 429, 432, 433, 434, 435, 436, 437, 449, 464, 465, 466, 469, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 519, 567, 569, 570, 571, 573, 574, 575, 577, 586, 614, 615, 619, 621, 625, 652, 671, 673, 674, 676, 684, 685, 686, 687, 688, 689, 728, 730, 738, 751, 752, 753, 762, 791, 796, 1037, 1038, 1039, 1040, 1045, 1047, 1071, 1081, 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112, 116, 141, 142, 144, 152, 158, 193, 197, 208, 211, 212, 227, 229, 235, 236, 248, 249, 260, 264, 266, 269, 271, 273, 274, 276, 279, 281, 283, 284, 285, 286, 287, 288, 320, 329, 331, 338, 344, 351, 353, 357, 362, 363, 364, 365, 373, 378, 380, 381, 385, 439, 454, 455, 460, 462, 470, 477, 478, 480, 497, 511, 512, 513, 559, 560, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 576, 578, 579, 580, 588, 589, 590, 614, 615, 616, 622, 623, 659, 660, 661, 662, 676, 677, 678, 679, 681, 683, 684, 686, 690, 691, 693, 697, 698, 699, 700, 702, 703, 704, 706, 707, 708, 709, 728, 729, 730, 731, 732, 739, 748, 753, 761, 782, 786, 854, 857, 882, 899, 902, 921, 935, 938, 963, 980, 983, 1003, 1046, 1085, 1086, 1094, 1101, 1102, 1135, 1144, 1151, 1162, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1189, 1199, 1200, 1201, 1206, 1207, 1208, 1209, 1210, 1221, 1222, 1240, 1269, 1273, 1274, 1276, 1295, 1300, 1302, 1315, 1323, 1353, 1354, 1379, 1380, 1394, 1395, 1406], "digit": [11, 70, 99], "base": [11, 15, 38, 43, 55, 58, 69, 93, 94, 100, 101, 102, 103, 107, 128, 132, 199, 203, 205, 212, 216, 220, 229, 296, 297, 301, 302, 303, 308, 309, 310, 311, 312, 322, 323, 324, 325, 329, 330, 337, 343, 346, 347, 362, 371, 373, 374, 380, 381, 382, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 425, 426, 427, 428, 430, 431, 449, 464, 466, 494, 498, 499, 500, 509, 510, 545, 555, 564, 566, 569, 574, 581, 614, 616, 660, 667, 680, 688, 691, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 717, 732, 738, 758, 761, 762, 786, 791, 796, 887, 925, 934, 935, 968, 979, 980, 1007, 1036, 1037, 1038, 1041, 1043, 1082, 1088, 1182, 1229, 1235, 1253, 1267, 1296, 1320, 1321, 1323, 1326, 1383, 1387, 1392, 1395, 1402, 1403, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1421, 1425], "obtain": [11, 91, 165, 207, 282, 345, 346, 347, 380, 383, 387, 388, 389, 390, 394, 465, 511, 606, 618, 619, 656, 722, 742, 743, 760, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1164, 1253, 1272, 1278, 1279, 1323, 1326, 1356, 1357, 1402, 1426], "seri": [11, 444, 616, 680, 1215, 1286], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1177, 1179, 1192, 1222], "end": [11, 25, 36, 52, 95, 101, 106, 153, 154, 206, 215, 227, 267, 268, 300, 332, 333, 342, 371, 372, 427, 614, 618, 619, 626, 627, 631, 632, 634, 635, 636, 639, 640, 650, 651, 652, 653, 654, 655, 660, 664, 667, 677, 678, 680, 734, 736, 1038, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1133, 1135, 1152, 1165, 1206, 1229, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1342, 1344, 1350, 1353, 1357, 1358, 1368, 1371, 1372, 1375, 1376, 1379, 1404, 1413], "In": [11, 16, 27, 43, 54, 57, 58, 88, 92, 93, 94, 95, 97, 99, 100, 101, 103, 110, 115, 127, 132, 133, 175, 184, 199, 217, 229, 230, 231, 235, 240, 257, 258, 259, 278, 283, 286, 288, 289, 299, 311, 312, 324, 325, 329, 350, 357, 378, 379, 380, 410, 413, 414, 415, 422, 429, 443, 447, 450, 458, 460, 494, 498, 499, 501, 510, 565, 568, 572, 574, 590, 591, 615, 619, 621, 652, 653, 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1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "108": [11, 1216], "513": [11, 1398, 1406], "reach": [11, 99, 100, 314, 324, 327, 376, 383, 387, 389, 390, 394, 410, 411, 412, 418, 419, 494, 498, 499, 510, 564, 566, 626, 627, 632, 640, 643, 652, 693, 711, 758, 1188, 1207, 1210, 1407], "orbit": 11, "up": [11, 70, 80, 93, 94, 97, 99, 100, 101, 104, 107, 132, 133, 346, 347, 377, 423, 427, 509, 530, 540, 577, 619, 652, 653, 657, 748, 1036, 1038, 1061, 1066, 1082, 1088, 1102, 1144, 1148, 1173, 1213, 1215, 1272, 1326, 1328, 1355, 1358, 1395, 1396, 1402, 1404, 1406, 1410, 1411, 1413, 1414, 1416, 1417, 1420, 1426], "reveal": [11, 711, 786], "maximum": [11, 112, 115, 209, 210, 211, 212, 214, 215, 217, 222, 224, 227, 257, 259, 264, 277, 278, 279, 281, 288, 296, 304, 311, 312, 315, 316, 317, 318, 319, 321, 324, 328, 330, 339, 341, 342, 343, 346, 347, 352, 356, 361, 373, 377, 380, 382, 383, 385, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 428, 440, 472, 473, 494, 498, 499, 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1398, 1406, 1412], "stanford": [15, 34, 65, 69, 71, 566, 691, 1268], "analysi": [15, 23, 47, 50, 52, 55, 86, 100, 101, 103, 105, 107, 110, 228, 232, 257, 258, 259, 260, 261, 262, 286, 288, 289, 299, 305, 379, 383, 412, 431, 437, 462, 494, 500, 619, 691, 751, 758, 760, 762, 1042, 1201, 1233, 1325, 1405, 1409, 1410, 1412, 1414], "uniqu": [15, 27, 238, 255, 278, 311, 312, 378, 460, 464, 469, 559, 560, 565, 585, 587, 600, 604, 618, 619, 641, 643, 691, 732, 748, 934, 979, 1047, 1244, 1250, 1251, 1296, 1326, 1343, 1359, 1360, 1363, 1364, 1426], "combin": [15, 61, 102, 204, 207, 379, 380, 385, 411, 412, 416, 418, 423, 575, 598, 600, 604, 678, 691, 891, 892, 928, 973, 1010, 1387, 1408], "type": [15, 70, 93, 95, 97, 100, 101, 102, 103, 104, 110, 165, 208, 241, 242, 243, 244, 247, 266, 267, 269, 270, 271, 273, 274, 276, 282, 283, 296, 301, 302, 303, 308, 309, 315, 323, 350, 351, 429, 496, 549, 550, 551, 555, 584, 585, 587, 588, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 652, 658, 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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, 221, 224, 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1426], "to_undirect": [16, 25, 69, 796, 1037, 1039, 1040, 1182, 1184, 1404, 1413, 1426], "magenta": 16, "six": 16, "classifi": [16, 512, 684, 750], "four": [16, 23, 47, 86, 99, 102, 165, 263, 585, 587, 692, 862, 907, 943, 988, 1039, 1040, 1164, 1193, 1199, 1211, 1323, 1407, 1408, 1414, 1426], "green": [16, 32, 38, 70, 93, 115, 464, 598, 760, 1302, 1330, 1394, 1412, 1426], "goal": [16, 88, 92, 99, 105, 107, 127, 383, 626, 627, 717, 718, 1042], "g_ex": 16, "m": [16, 25, 28, 30, 31, 63, 65, 67, 91, 93, 96, 102, 106, 110, 112, 128, 181, 191, 201, 209, 211, 212, 219, 227, 231, 235, 236, 238, 239, 240, 241, 243, 244, 248, 257, 258, 259, 263, 272, 274, 275, 278, 280, 282, 284, 293, 294, 296, 300, 301, 302, 308, 309, 315, 316, 317, 330, 338, 341, 343, 345, 352, 355, 356, 361, 362, 370, 380, 383, 385, 412, 429, 431, 432, 433, 451, 462, 479, 494, 498, 499, 509, 510, 511, 512, 519, 545, 555, 569, 582, 584, 585, 587, 588, 606, 614, 619, 625, 652, 658, 659, 684, 686, 691, 692, 706, 748, 749, 761, 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1411, 1412, 1413, 1414, 1416, 1417, 1423, 1424, 1425, 1426], "sender": [45, 92], "receiv": [45, 92, 299, 496, 504, 505, 508, 525, 535, 555, 671, 672, 673, 674], "messag": [45, 92, 93, 94, 100, 101, 152, 157, 158, 195, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1412, 1413, 1414, 1425], "hold": [45, 88, 100, 151, 159, 166, 175, 188, 190, 196, 198, 200, 208, 227, 239, 240, 241, 242, 243, 244, 247, 252, 266, 297, 298, 303, 306, 307, 311, 315, 316, 323, 324, 325, 326, 329, 330, 352, 355, 356, 380, 381, 383, 384, 385, 491, 593, 647, 687, 688, 689, 738, 796, 853, 858, 863, 869, 877, 879, 885, 886, 888, 898, 903, 908, 924, 939, 944, 950, 959, 966, 967, 969, 984, 989, 1006, 1020, 1037, 1039, 1040, 1102, 1103, 1105, 1108, 1112, 1115, 1117, 1287, 1288, 1393, 1407, 1409, 1426], "call": [45, 55, 58, 63, 93, 94, 97, 101, 102, 112, 114, 132, 141, 164, 168, 184, 189, 206, 212, 230, 231, 244, 249, 338, 341, 346, 347, 394, 410, 412, 414, 416, 417, 418, 419, 426, 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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, 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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], 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309, 323, 588], "voronoi_fram": 54, "contextili": [54, 55, 57], "add_basemap": [54, 55, 57], "geopackag": [54, 55, 56, 57], "sqlite": [54, 57], "reli": [54, 57, 99, 103, 362, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 502, 503, 506, 507, 1393, 1407, 1411, 1425], "fiona": [54, 57], "level": [54, 57, 101, 103, 104, 106, 111, 112, 115, 125, 165, 220, 322, 334, 336, 374, 380, 381, 387, 389, 390, 394, 423, 427, 640, 691, 770, 786, 862, 907, 943, 988, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1094, 1108, 1155, 1202, 1207, 1208, 1236, 1296, 1323, 1328, 1396, 1399, 1407, 1412, 1413, 1414], "interfac": [54, 57, 58, 75, 76, 96, 98, 99, 101, 102, 107, 109, 110, 184, 429, 496, 673, 758, 761, 762, 780, 873, 916, 955, 998, 1042, 1044, 1326, 1328, 1393, 1396, 1398, 1402, 1404, 1405, 1406, 1409, 1413, 1414, 1426], "kind": [54, 57, 58, 92, 93, 94, 99, 208, 466, 722, 1202, 1326, 1383], "read_fil": [54, 55, 57, 58], "cholera_cas": [54, 57], "gpkg": [54, 56, 57], "correctli": [54, 164, 324, 325, 1393, 1404, 1406, 1411, 1412, 1419], "construct": [54, 55, 56, 57, 58, 67, 94, 102, 227, 229, 230, 231, 232, 269, 273, 276, 352, 423, 450, 460, 513, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 609, 685, 695, 708, 716, 732, 1046, 1047, 1052, 1053, 1101, 1102, 1103, 1104, 1105, 1153, 1154, 1175, 1177, 1178, 1180, 1186, 1190, 1191, 1192, 1195, 1203, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1229, 1236, 1251, 1259, 1263, 1269, 1272, 1278, 1279, 1296, 1323, 1327, 1395, 1399, 1406, 1409, 1415], "column_stack": [54, 57, 58], "could": [54, 93, 101, 102, 103, 165, 215, 216, 224, 581, 679, 862, 907, 943, 988, 1066, 1094, 1102, 1103, 1120, 1126, 1174, 1296, 1300, 1326, 1393, 1404, 1414, 1426], "present": [54, 58, 93, 107, 110, 132, 184, 220, 226, 315, 316, 330, 357, 359, 429, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 567, 581, 594, 595, 597, 600, 601, 604, 632, 633, 635, 636, 659, 670, 749, 786, 873, 916, 955, 998, 1043, 1045, 1061, 1082, 1150, 1152, 1157, 1159, 1160, 1163, 1165, 1278, 1279, 1353, 1354, 1357, 1381, 1383, 1407, 1411, 1426], "alongsid": [54, 438], "diagram": [54, 132, 381, 752], "intrins": 54, "put": [54, 92, 95, 102, 226, 1326, 1404, 1406], "underli": [54, 101, 102, 132, 152, 157, 158, 161, 195, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 427, 428, 490, 491, 500, 615, 742, 743, 791, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1038, 1225, 1233, 1241, 1326, 1393, 1394, 1402], "quickli": [54, 1239], "Be": [54, 92, 1038, 1135, 1404], "care": [54, 92, 100, 102, 106, 107, 109, 115, 156, 855, 900, 936, 981, 1038, 1326, 1404, 1406], "bound": [54, 112, 214, 215, 216, 217, 220, 224, 227, 264, 300, 342, 352, 437, 440, 675, 1043, 1165, 1235, 1319, 1413, 1414, 1416], "box": [54, 107, 1134, 1136, 1271, 1323], "control": [54, 168, 179, 189, 204, 230, 231, 324, 325, 450, 467, 865, 878, 891, 910, 946, 960, 991, 1328, 1402, 1408, 1409, 1413], "cell": [54, 58, 752, 758, 1271, 1323, 1325, 1407], "convex": 54, "hull": 54, "contigu": [54, 58, 438, 1102, 1277, 1278], "being": [54, 92, 94, 95, 99, 101, 102, 109, 217, 227, 464, 465, 466, 559, 560, 711, 1038, 1045, 1144, 1175, 1236, 1296, 1393, 1394, 1407, 1412, 1413, 1416, 1425], "face": [54, 101, 102, 115, 183, 206, 615, 1043, 1262, 1263], "analogu": [54, 58, 230], "von": 54, "neuman": 54, "neighborhood": [54, 58, 114, 213, 240, 249, 285, 286, 324, 325, 512, 690, 786, 1189], "cardin": [54, 115, 218, 221, 264, 277, 278, 279, 280, 339, 341, 343, 345, 414, 415, 416, 417, 428, 440, 441, 444, 446, 581, 583, 611, 691, 1395], "regular": [54, 58, 65, 88, 99, 477, 478, 479, 480, 622, 623, 624, 758, 1038, 1185, 1190, 1191, 1192, 1239, 1245, 1250, 1251, 1254, 1258, 1261, 1262, 1263, 1264, 1280, 1290, 1323, 1325, 1394, 1395, 1398, 1406, 1412, 1413], "come": [54, 93, 100, 101, 102, 517, 577, 588, 598, 608, 677, 698, 699, 1046, 1243, 1326, 1402, 1413], "piec": [54, 374], "move": [54, 94, 95, 100, 101, 230, 231, 377, 380, 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322, 618, 619, 671, 672, 673, 674, 767, 1154, 1300, 1326, 1352, 1354, 1378, 1380, 1393, 1394, 1406, 1407, 1413, 1425], "rest": [93, 107, 184, 214, 410, 414, 873, 916, 955, 998, 1425], "advanc": [93, 103, 574, 592, 618, 673, 758, 796, 1037, 1039, 1040, 1192, 1280, 1290, 1413, 1414], "rebas": [93, 94], "squash": [93, 94], "often": [93, 94, 99, 101, 102, 105, 378, 383, 384, 388, 464, 732, 780, 786, 796, 1037, 1038, 1039, 1040, 1234, 1296, 1326, 1328, 1405, 1425], "typic": [93, 97, 103, 127, 305, 796, 1037, 1039, 1040, 1102, 1103, 1175, 1323, 1413], "propos": [93, 97, 98, 99, 101, 102, 103, 104, 105, 107, 215, 230, 299, 578, 688, 1382, 1412, 1413, 1414, 1422, 1425], "easi": [93, 97, 102, 107, 109, 297, 298, 384, 760, 1326, 1328, 1383, 1412], "demonstr": [93, 100, 310, 1404, 1406], "spread": [93, 301, 302, 308, 309, 329], "sp": [93, 470, 473, 1101, 1387, 1426], "pd": [93, 1099, 1100, 1103, 1412], "stat": [93, 244, 380, 381, 748, 750, 1193, 1197, 1224, 1228, 1232], "optim": [93, 107, 112, 125, 208, 212, 226, 230, 231, 330, 353, 362, 380, 381, 382, 385, 422, 429, 496, 508, 672, 692, 720, 722, 723, 724, 725, 726, 729, 731, 732, 760, 780, 1108, 1117, 1235, 1320, 1321, 1402, 1411, 1412, 1416], "subpackag": [93, 767, 1326, 1413, 1425], "particular": [93, 97, 110, 115, 357, 374, 517, 618, 750, 1175, 1278, 1279, 1328, 1350, 1409], "decor": [93, 102, 103, 1045, 1046, 1047, 1297, 1298, 1299, 1300, 1301, 1325, 1405, 1407, 1411, 1413, 1414, 1417], "not_implemented_for": [93, 1296, 1407, 1417], "doesn": [93, 94, 97, 101, 102, 156, 170, 561, 562, 563, 761, 796, 855, 866, 900, 911, 936, 947, 981, 992, 1037, 1039, 1040, 1117, 1175, 1177, 1179, 1216, 1222, 1296, 1326, 1404, 1406, 1407, 1412, 1414, 1425], "function_not_for_multidigraph": 93, "function_only_for_graph": 93, "framework": [93, 102, 1358], "submodul": [93, 1413], "specif": [93, 96, 99, 101, 107, 110, 111, 157, 184, 232, 346, 347, 370, 458, 502, 503, 506, 507, 517, 681, 683, 703, 856, 873, 901, 916, 937, 947, 955, 982, 992, 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"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": 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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": 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"visibl": [94, 97], "thread": [94, 97, 99, 103, 104, 1413], "appeal": [94, 100], "empow": 94, "regardless": [94, 99, 1135, 1191, 1404], "outcom": [94, 105, 1036, 1088, 1382, 1417], "past": [94, 106, 1405], "pep8": [94, 1407, 1412, 1416], "pep257": 94, "superset": [94, 582], "stackoverflow": 94, "monitor": [94, 101], "signatur": [95, 97, 103, 109, 545, 1045, 1296, 1399, 1404, 1407, 1413, 1419, 1422, 1425], "buggi": 95, "usual": [95, 101, 168, 176, 189, 291, 292, 329, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 438, 440, 467, 615, 753, 762, 796, 865, 870, 878, 910, 946, 951, 960, 991, 1039, 1040, 1045, 1094, 1174, 1199, 1217, 1272, 1296, 1326, 1403], "minor": [95, 100, 106, 584, 758, 1325, 1394, 1395, 1403, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "strict": [95, 110, 214, 215, 216, 619, 1408, 1413], "rule": [95, 100, 199, 508, 760, 887, 925, 968, 1007, 1061, 1082, 1144, 1298], "procedur": [95, 97, 99, 217, 220, 281, 305, 377, 508, 680, 1188, 1417], "upon": [95, 102, 580, 1296, 1413, 1416], "justif": [95, 104], "literal_string": [95, 1345, 1350, 1384, 1412], "literal_destring": [95, 1347, 1349, 1384, 1412], "coreview": [95, 1413], "filter": [95, 322, 453, 1036, 1061, 1082, 1088, 1269, 1324, 1325, 1413], "link_analysi": [95, 1405], "pagerank_alg": [95, 1405], "replac": [95, 99, 102, 103, 202, 232, 270, 385, 411, 412, 430, 431, 512, 583, 796, 890, 926, 934, 971, 979, 1008, 1037, 1039, 1040, 1051, 1094, 1225, 1241, 1295, 1296, 1297, 1311, 1317, 1326, 1347, 1363, 1364, 1393, 1394, 1396, 1399, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1417, 1422, 1424, 1425], "pagerank": [95, 311, 312, 324, 325, 565, 758, 1283, 1284, 1394, 1398, 1405, 1406, 1407, 1413], "pagerank_scipi": [95, 1405, 1411, 1413], "renam": [95, 102, 106, 597, 601, 604, 609, 1295, 1348, 1349, 1357, 1394, 1407, 1412, 1421, 1424, 1425], "pagerank_numpi": [95, 1405, 1407, 1413], "_pagerank_numpi": 95, "convert_matrix": [95, 1387, 1407, 1411, 1413], "to_pandas_edgelist": [95, 1100, 1407, 1408, 1413], "binari": [95, 110, 429, 476, 586, 593, 730, 739, 1414], "asmatrix": 95, "wrapper": [95, 1119, 1125, 1296, 1405, 1413], "google_matrix": [95, 566, 1414], "futurewarn": [95, 1413, 1414], "attrmatrix": 95, "reflect": [95, 99, 103, 199, 296, 301, 302, 303, 308, 309, 323, 466, 887, 925, 968, 1007, 1061, 1066, 1082, 1085, 1086, 1326, 1406, 1407, 1420], "ndarrai": [95, 107, 565, 629, 1098, 1102, 1278, 1387, 1405, 1414], "distance_measur": [95, 217, 1411], "extrema_bound": [95, 1416], "maxcardin": [95, 581, 583, 1416, 1425], "min_weight_match": [95, 758, 1416, 1425], "scale_free_graph": [95, 1413, 1420], "nx_pydot": [95, 1041, 1042, 1124, 1125, 1126, 1127, 1128, 1396, 1408, 1425, 1426], "5723": [95, 1425], "node_link": [95, 1407, 1422, 1425], "node_link_graph": [95, 1363, 1384], "0rc2": [96, 110, 1325], "dev0": [96, 110, 1325], "dec": [96, 110, 342, 606, 1271, 1323, 1325], "2022": [96, 103, 105, 110, 693, 1325, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424], "about": [96, 99, 100, 101, 103, 111, 115, 230, 231, 249, 413, 423, 488, 494, 498, 499, 509, 510, 619, 761, 762, 1038, 1061, 1066, 1141, 1217, 1296, 1323, 1326, 1406, 1407, 1411, 1412, 1413, 1414, 1416, 1422, 1426], "emeritu": 96, "introduct": [96, 110, 311, 312, 324, 325, 383, 385, 464, 466, 618, 619, 1155, 1269, 1302, 1325, 1411], "guidelin": [96, 99, 1416, 1419], "divers": [96, 107], "enforc": [96, 115, 693, 694, 1419, 1425], "endnot": 96, "diverg": [96, 1187, 1325, 1395], "upstream": [96, 464, 1419], "comparison": [96, 107, 231, 464, 494, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 561, 562, 563, 615, 671, 673, 1413], "mentor": [96, 109, 1413, 1414, 1425], "pedagog": [96, 109, 347, 452, 722, 1405, 1414], "me": [96, 1393], "roadmap": [96, 1412, 1413], "linear": [96, 110, 112, 132, 142, 217, 280, 296, 301, 302, 303, 308, 309, 313, 323, 325, 338, 343, 378, 405, 406, 423, 488, 515, 614, 619, 686, 1108, 1133, 1135, 1180, 1182, 1269, 1275, 1276, 1277, 1286, 1325, 1401, 1402, 1405, 1406, 1411], "algebra": [96, 110, 313, 380, 385, 1266, 1275, 1286, 1325, 1395, 1402, 1405, 1406], "nxep": [96, 107, 109, 1403, 1412, 1416], "govern": [96, 98, 109, 1412], "slice": [96, 98, 107, 1413], "builder": [96, 98, 1151, 1323, 1413], "frequent": [97, 378, 675], "newcom": [97, 109, 1326], "few": [97, 100, 101, 103, 362, 1402, 1404, 1411, 1412, 1413, 1414], "known": [97, 227, 280, 293, 301, 302, 303, 308, 309, 323, 369, 424, 450, 468, 618, 740, 741, 742, 743, 762, 791, 1068, 1097, 1145, 1148, 1200, 1201, 1224, 1228, 1230, 1232, 1247, 1272, 1324, 1412], "Of": [97, 1426], "sprint": [97, 1425], "permiss": [97, 110, 111, 457], "forget": 97, "sai": [97, 99, 101, 211, 512, 517, 518, 675, 676, 762, 1206, 1411], "rememb": [97, 101], "stick": [97, 1394], "plot_circular_layout": 97, "perhap": [97, 99, 102, 107], "deal": [97, 102], "worri": [97, 583, 1296, 1326], "ipython": 97, "field": [97, 99, 591, 593, 770, 1098, 1099, 1102, 1192], "breviti": 97, "offici": [97, 99, 1402], "inclus": [97, 99, 109, 220, 534, 544, 729, 731, 1188, 1214], "criteria": [97, 1425], "addit": [97, 99, 100, 103, 107, 111, 115, 184, 350, 423, 476, 534, 544, 545, 734, 736, 761, 791, 796, 873, 916, 947, 955, 979, 992, 998, 1036, 1037, 1039, 1040, 1088, 1117, 1195, 1272, 1296, 1302, 1326, 1345, 1348, 1349, 1350, 1381, 1382, 1383, 1395, 1403, 1404, 1405, 1406, 1407, 1413, 1414, 1425, 1426], "fit": [97, 110, 1326], "enhanc": [98, 99, 107, 341, 508, 1296, 1412, 1425], "berkelei": [99, 100, 103, 618, 619], "draft": [99, 100, 102, 103, 104, 1411, 1412, 1413, 1416], "stand": [99, 545, 1387], "primari": [99, 103, 1414], "gone": 99, "concis": [99, 110, 791, 1413, 1414], "rational": 99, "consensu": [99, 100], "dissent": 99, "opinion": [99, 100, 104], "revis": [99, 444, 732], "track": [99, 101, 102, 103, 104, 107, 115, 370, 387, 389, 390, 394, 598, 1296, 1302, 1406, 1411, 1412], "codebas": [99, 1296, 1404, 1405, 1412], "meta": [99, 106], "inject": 99, "repo": [99, 106, 1413, 1425], "success": [99, 315, 330, 496, 608, 692, 1180, 1242, 1426], "tend": [99, 593, 1175, 1326], "doubt": [99, 1426], "champion": 99, "attempt": [99, 101, 194, 202, 204, 282, 284, 285, 286, 287, 288, 361, 362, 377, 425, 426, 584, 692, 693, 694, 786, 883, 890, 891, 922, 926, 927, 964, 971, 972, 1004, 1008, 1009, 1041, 1122, 1225, 1237, 1238, 1302, 1333, 1347, 1371, 1393, 1394, 1406, 1411, 1412, 1421, 1425], "ascertain": 99, "suitabl": [99, 110, 659, 693, 694, 1165, 1359, 1363, 1365, 1385, 1390], "0000": 99, "backward": [99, 217, 1199, 1402, 1404, 1406], "compat": [99, 429, 496, 691, 1302, 1404, 1405, 1406, 1412, 1414], "impact": [99, 100, 107, 329, 796, 1037, 1039, 1040], "broader": 99, "scope": [99, 107, 1045, 1413], "earliest": [99, 465], "conveni": [99, 101, 152, 497, 501, 504, 505, 508, 615, 796, 854, 899, 935, 980, 1037, 1038, 1039, 1040, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1270, 1296, 1326, 1394, 1405, 1409, 1426], "expand": [99, 101, 373, 653, 1038, 1190, 1325, 1395, 1406, 1407, 1408, 1413, 1424, 1425], "prototyp": 99, "sound": 99, "principl": [99, 100, 103, 132], "impract": 99, "wip": [99, 1407, 1408, 1412], "incorpor": [99, 1399, 1426], "stabil": [99, 334, 335, 681, 683], "provision": 99, "short": [99, 104, 161, 227, 1038, 1066, 1195, 1406], "unlik": [99, 100, 212, 366, 425, 426, 1383], "reject": [99, 100, 104, 1319], "withdrawn": [99, 104], "wherev": [99, 1282], "defer": [99, 101, 104, 280], "challeng": 99, "wider": 99, "done": [99, 101, 102, 230, 231, 249, 373, 440, 466, 517, 564, 566, 614, 690, 762, 1046, 1219, 1296, 1326, 1404], "fact": [99, 352, 460, 619, 1207, 1210, 1404], "actual": [99, 115, 132, 165, 210, 213, 214, 215, 216, 220, 288, 385, 450, 577, 625, 692, 717, 718, 862, 907, 943, 988, 1102, 1103, 1199, 1296, 1324, 1326, 1402, 1416], "compet": [99, 583], "accordingli": [99, 454, 1110, 1407, 1425], "supersed": [99, 104], "render": [99, 216, 410, 413, 1406], "obsolet": [99, 267, 1337, 1406, 1407], "never": [99, 184, 388, 608, 873, 916, 955, 998, 1236], "meant": [99, 291, 292, 631, 1217, 1326, 1413, 1417], "concret": [99, 100], "think": [99, 102, 230, 231, 299, 761, 1426], "bodi": [99, 1243], "briefli": 99, "sentenc": [99, 100], "substant": 99, "pipermail": 99, "2018": [99, 315, 330, 437, 1406, 1408, 1409], "june": [99, 691, 1255, 1398, 1402, 1406, 1419, 1420], "078345": 99, "verg": 99, "chanc": [99, 230, 1234, 1296], "period": [99, 1211, 1212, 1213, 1215, 1297, 1403, 1406, 1412], "beyond": [99, 107, 383, 1210, 1236], "fine": 99, "shouldn": [99, 102], "rigid": 99, "compromis": 99, "followup": [99, 1413], "notifi": [99, 1414], "celebratori": 99, "emoji": 99, "again": [99, 428, 761, 1217, 1403, 1407, 1411, 1416], "unusu": [99, 1393], "disagr": [99, 100], "escal": [99, 100], "controversi": [99, 107], "ultim": 99, "practic": [99, 210, 220, 481, 482, 494, 619, 653, 1328, 1405], "precis": [99, 312, 568, 572, 581, 1269, 1395, 1409], "natur": [99, 102, 109, 376, 443, 466, 585, 587, 618, 753, 1154, 1217, 1225, 1241, 1296, 1326, 1393, 1410], "utf": [99, 267, 268, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1355, 1358, 1368, 1371, 1372, 1375, 1376, 1387, 1406], "restructuredtext": 99, "restructuredtextprim": 99, "dd": [99, 104, 1094], "mmm": 99, "yyyi": [99, 104], "dom": 99, "ain": 99, "separ": [99, 102, 106, 152, 157, 158, 195, 214, 215, 258, 265, 266, 267, 268, 299, 322, 343, 427, 428, 454, 464, 758, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1045, 1112, 1116, 1193, 1195, 1325, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1369, 1370, 1371, 1372, 1395, 1406, 1407, 1412, 1413, 1425, 1426], "older": 99, "brows": 99, "colgat": [100, 110], "deadlock": 100, "websit": [100, 106, 1165, 1382, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "ongo": [100, 1405], "trust": [100, 1381, 1383], "cast": [100, 101, 1412, 1422, 1425], "vote": [100, 337, 1412], "therebi": 100, "adher": 100, "nomin": 100, "lazi": [100, 1283, 1284], "unanim": 100, "agreement": [100, 1202], "initi": [100, 102, 141, 230, 231, 282, 315, 324, 325, 338, 373, 377, 378, 466, 495, 511, 512, 525, 535, 615, 692, 719, 733, 796, 850, 895, 931, 976, 1037, 1039, 1040, 1102, 1105, 1108, 1117, 1185, 1186, 1187, 1188, 1223, 1227, 1234, 1278, 1279, 1296, 1302, 1323, 1394, 1395, 1406, 1411, 1412, 1413, 1414], "voic": 100, "smooth": 100, "strateg": 100, "plan": [100, 1394, 1405, 1407, 1413], "fund": [100, 1414, 1425], "theirs": 100, "pursu": 100, "pictur": 100, "perspect": [100, 104, 1195, 1326], "timefram": 100, "entiti": [100, 1345, 1348, 1349, 1350, 1382, 1426], "occasion": [100, 230], "seek": [100, 762, 1352, 1354, 1378, 1380, 1387], "tri": [100, 112, 343, 380, 931, 976, 1039, 1040, 1175, 1181, 1225, 1237, 1238, 1404], "distinguish": [100, 934, 962, 979, 1002, 1040], "fundament": [100, 107, 110, 338, 449, 618, 619, 1217, 1413], "flaw": 100, "forward": [100, 217, 450, 711, 717, 718], "typo": [100, 1396, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1421], "land": 100, "outlin": [100, 249, 336, 462, 1407], "templat": [100, 1413], "taken": [100, 101, 145, 148, 207, 443, 450, 717, 718, 749, 761, 892, 928, 973, 1010, 1117, 1409], "suffici": [100, 101, 1326], "scikit": [100, 103, 109], "expos": [101, 374, 1405], "nodeview": [101, 184, 391, 598, 599, 601, 602, 603, 604, 695, 873, 916, 955, 998, 1036, 1088, 1349, 1362, 1404, 1407], "nodedataview": [101, 184, 391, 591, 592, 600, 873, 916, 955, 998, 1217, 1426], "edgeview": [101, 590, 591, 592, 598, 599, 600, 601, 602, 603, 604, 612, 624, 770, 910, 1036, 1088, 1098, 1404, 1413], "edgedataview": [101, 168, 189, 865, 878, 910, 946, 960, 991, 1098, 1217, 1362, 1412, 1426], "semant": [101, 531, 541, 762, 1403, 1405], "inher": [101, 220, 427], "impli": [101, 110, 132, 220, 312, 314, 327, 455, 466, 511, 512, 545, 1296], "element": [101, 102, 230, 231, 270, 291, 292, 311, 350, 371, 391, 457, 464, 518, 559, 560, 578, 579, 580, 586, 640, 656, 671, 673, 675, 677, 728, 730, 739, 749, 752, 1036, 1038, 1048, 1049, 1050, 1051, 1087, 1088, 1135, 1137, 1173, 1206, 1211, 1212, 1217, 1237, 1238, 1240, 1249, 1272, 1277, 1278, 1279, 1282, 1287, 1288, 1296, 1302, 1303, 1311, 1318, 1323, 1355, 1358, 1361, 1362, 1405], "intend": [101, 104, 107, 111, 327, 567, 1038, 1269, 1296, 1393], "impos": [101, 103, 545, 791], "due": [101, 102, 109, 231, 264, 440, 581, 583, 626, 627, 1217, 1405, 1412, 1414, 1423, 1425], "bit": [101, 209, 211, 212, 453, 511, 512, 786, 1345, 1348, 1349, 1350, 1382, 1411], "lot": [101, 452, 1326, 1405], "screen": 101, "instinct": 101, "error": [101, 102, 152, 157, 158, 195, 280, 288, 296, 311, 324, 414, 422, 471, 472, 473, 474, 475, 489, 497, 501, 504, 505, 508, 556, 557, 558, 564, 566, 581, 584, 653, 660, 667, 675, 676, 796, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1037, 1043, 1117, 1144, 1396, 1401, 1404, 1406, 1407, 1411, 1412, 1413, 1414, 1417, 1419, 1425], "definit": [101, 132, 235, 238, 243, 289, 291, 292, 303, 323, 342, 356, 398, 435, 437, 464, 467, 549, 550, 551, 608, 618, 619, 620, 625, 676, 685, 687, 700, 735, 737, 791, 1192, 1193, 1197, 1217, 1235, 1287, 1326, 1406, 1413, 1426], "coupl": [101, 102, 132, 1257, 1402, 1404], "realis": 101, "But": [101, 102, 107, 143, 170, 238, 243, 256, 277, 278, 281, 297, 298, 583, 796, 866, 911, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1094, 1328, 1393, 1425], "seem": [101, 102, 298, 307, 791, 1234], "eas": [101, 107, 1409], "idiom": [101, 159, 190, 200, 858, 879, 888, 903, 939, 969, 984, 1296, 1394, 1404, 1411], "subscript": [101, 151, 159, 200, 796, 853, 858, 888, 898, 903, 934, 939, 969, 979, 984, 1037, 1039, 1040, 1394, 1426], "repr": [101, 1347, 1413], "4950": [101, 1414], "traceback": [101, 450, 464, 584, 652, 658, 1302, 1303], "recent": [101, 437, 450, 464, 584, 652, 658, 963, 1003, 1302, 1303, 1411], "typeerror": [101, 382, 464, 1206, 1302, 1404], "opaqu": 101, "ambigu": [101, 103, 115, 252, 253, 464, 762, 1043, 1406], "ambigi": 101, "counter": [101, 153, 357], "nativ": [101, 109], "caveat": 101, "nodes_it": [101, 1404, 1407], "toward": [101, 685, 1407, 1413], "inner": [101, 230, 231, 380, 796, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1086], "synonym": 101, "primarili": [101, 1426], "becam": [101, 1411], "concept": [101, 132, 220, 310, 427, 688, 1043], "intuit": [101, 109], "On": [101, 105, 156, 217, 294, 297, 298, 306, 307, 315, 380, 405, 406, 514, 515, 518, 593, 855, 900, 936, 981, 1180, 1202, 1224, 1228, 1232], "front": [101, 619, 1036, 1088], "constuct": 101, "indx": 101, "desir": [101, 102, 142, 143, 204, 346, 347, 422, 425, 426, 598, 629, 647, 891, 972, 1085, 1094, 1102, 1103, 1105, 1150, 1152, 1157, 1159, 1160, 1163, 1165, 1187, 1218, 1220, 1221, 1234, 1281, 1356, 1357, 1414, 1426], "prelimanari": 101, "impelement": 101, "4086": 101, "rid": [101, 1413], "getitem": 101, "dunder": [101, 107, 1296, 1413], "isinst": [101, 103, 464, 1086, 1411, 1412, 1413], "_node": [101, 1422, 1425], "exclus": [101, 449, 476], "necess": 101, "unhash": [101, 1404], "impel": 101, "insipir": 101, "colon": [101, 1421], "syntax": [101, 102, 171, 796, 867, 912, 948, 993, 1037, 1039, 1040, 1296, 1382, 1383, 1410, 1412], "introspect": 101, "neither": [101, 110, 305, 427, 625, 635, 636, 671, 672, 673, 674, 676, 700, 748], "downsid": 101, "drawback": 101, "discover": 101, "complic": [101, 1296, 1326], "nix": 101, "background": 101, "pertain": 101, "arguabl": [101, 102], "overrid": [101, 671, 672, 673, 674, 1411], "mix": [101, 236, 237, 238, 241, 242, 243, 244, 245, 248, 445, 758, 1100, 1341, 1342, 1344, 1355, 1356, 1357, 1358, 1381, 1383, 1393, 1406, 1407, 1411], "pervas": 101, "unforeseen": 101, "preced": [101, 152, 157, 464, 598, 703, 854, 856, 899, 901, 935, 937, 980, 982, 1045, 1363, 1364], "un": [101, 464, 732, 1407, 1413], "sliceabl": 101, "notabl": [101, 1042], "dict_kei": [101, 1303, 1414], "dict_valu": [101, 379, 1404, 1413], "cpython": [101, 107, 429, 496, 1038, 1402, 1413], "consider": [101, 103, 324, 325, 346, 347, 353, 525, 535, 555, 671, 672, 673, 674, 732, 760, 1168, 1413], "cours": [101, 105, 217, 618, 1326, 1426], "action": [101, 106, 1413, 1417], "allevi": 101, "dig": 101, "enough": [101, 468, 509, 1165], "satisfactorili": 101, "reconsid": [101, 1412], "went": [101, 502], "ahead": 101, "4300": [101, 1413], "4304": [101, 1413], "path_edg": 102, "former": [102, 103, 791], "stylist": 102, "creation": [102, 107, 110, 249, 275, 788, 1154, 1170, 1224, 1228, 1230, 1232, 1325, 1399, 1404, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "cleaner": [102, 1401, 1406], "creativ": [102, 464, 466], "demand": [102, 496, 497, 501, 504, 505, 508], "had": [102, 652, 1217, 1296, 1409, 1416], "node_iter": 102, "isn": [102, 346, 347, 720, 1331, 1334, 1406, 1414, 1425], "leav": [102, 231, 388, 500, 508, 584, 585, 586, 587, 678, 1145, 1155, 1296, 1404, 1409, 1426], "dg": [102, 207, 322, 455, 456, 457, 458, 459, 461, 462, 464, 465, 466, 467, 468, 469, 892, 928, 973, 1010, 1041, 1404, 1426], "mdg": [102, 207, 892, 928, 973, 1010, 1420], "customgraph": 102, "elist": [102, 1326], "isol": [102, 355, 380, 435, 491, 492, 522, 524, 621, 735, 737, 758, 1218, 1325, 1330, 1398, 1401, 1406, 1407, 1417], "ekei": [102, 207, 892, 928, 934, 973, 979, 1010, 1084, 1104], "protocol": [102, 1404], "hashabl": [102, 144, 151, 156, 171, 180, 267, 545, 546, 547, 548, 761, 796, 853, 855, 867, 871, 898, 900, 912, 914, 934, 936, 947, 948, 952, 962, 979, 981, 992, 993, 995, 1002, 1037, 1038, 1039, 1040, 1087, 1207, 1278, 1279, 1295, 1310, 1324, 1326, 1333, 1337, 1338, 1426], "logic": [102, 103, 220, 760, 762, 1298, 1406, 1407, 1419, 1425], "denot": [102, 114, 212, 219, 299, 300, 322, 567, 568, 569, 570, 571, 572, 573, 608, 619, 687, 688, 689, 690, 691, 1174], "multiedg": [102, 553, 934, 979, 1039, 1040, 1085, 1326, 1356, 1357, 1393, 1406, 1412, 1414], "attrdict": [102, 157, 856, 901, 937, 982, 1406], "edge_kei": [102, 489, 1039, 1040, 1100, 1104, 1413], "networkxinvalidedgelist": 102, "flexibl": [102, 110, 467, 1326, 1382, 1383, 1395, 1401, 1406, 1407, 1411, 1426], "wheel": [102, 106, 1163, 1261, 1411, 1421, 1425], "spoke": 102, "wheel_graph": [102, 341, 671, 672, 674], "star": [102, 260, 300, 615, 626, 627, 779, 1054, 1151, 1160, 1223, 1227, 1394, 1404, 1406, 1407, 1411], "mycustomgraph": 102, "configuration_model_graph": 102, "deg_sequ": [102, 515, 517, 518, 1175, 1176, 1177, 1178, 1180, 1222], "graph_build": 102, "py_random_st": [102, 103, 1296, 1299, 1405], "extended_barabasi_albert_graph": 102, "node_and_edge_build": 102, "ladder_graph": 102, "incompat": [102, 1199, 1402, 1403, 1406], "thrust": 102, "incept": 102, "attach": [102, 214, 274, 357, 569, 571, 621, 1036, 1088, 1122, 1182, 1185, 1223, 1227, 1229, 1326, 1426], "presum": [102, 1297], "rewritten": [102, 1395, 1402, 1406], "gradual": 102, "accomplish": [102, 109, 1165], "wrap": [102, 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[127, 424], "determen": 127, "maxim": [127, 209, 220, 221, 222, 315, 316, 330, 339, 346, 347, 348, 349, 350, 351, 353, 354, 366, 370, 380, 383, 384, 389, 390, 422, 425, 426, 427, 432, 433, 437, 517, 579, 581, 582, 583, 589, 682, 691, 732, 758, 1043, 1201, 1323, 1325, 1398, 1406, 1407, 1413, 1414], "moodi": [127, 220, 427, 1395], "kanevski": [127, 427, 428, 1395], "recurs": [128, 141, 224, 346, 347, 352, 387, 389, 390, 394, 406, 452, 460, 530, 540, 697, 728, 730, 760, 1045, 1046, 1061, 1082, 1147, 1296, 1406, 1412, 1413], "prune": [128, 760, 1236], "vladimir": [128, 275, 432, 433, 494, 588, 749, 1230], "batagelj": [128, 275, 432, 433, 588, 749, 1230], "matjaz": [128, 432, 433], "zaversnik": [128, 432, 433], "0310049": [128, 432, 433], "0202039": 128, "degeneraci": 128, "christo": 128, "giatsidi": 128, "thiliko": 128, "michali": 128, "vazirgianni": 128, "icdm": 128, "2011": [128, 331, 377, 383, 385, 441, 445, 446, 511, 512, 519, 619, 682, 1179, 1397, 1398, 1399, 1406, 1407], "graphdegeneraci": 128, "dcores_icdm_2011": 128, "anomali": [128, 438], "onion": [128, 438, 1411], "h\u00e9bert": [128, 438], "dufresn": [128, 438], "grochow": [128, 438], "allard": [128, 438, 1411], "31708": [128, 438], "2016": [128, 337, 352, 385, 438, 476, 690, 1197, 1251, 1396, 1406], "1038": [128, 337, 376, 380, 438, 569], "srep31708": [128, 438], "factor": [132, 226, 293, 294, 299, 300, 324, 325, 370, 462, 497, 501, 504, 505, 508, 513, 565, 592, 624, 676, 697, 1106, 1107, 1108, 1109, 1110, 1114, 1115, 1116, 1117, 1145, 1155, 1178, 1180, 1275, 1276, 1277], "graphic": [132, 454, 517, 518, 693, 758, 1175, 1177, 1180, 1181, 1222, 1325, 1383, 1398, 1401, 1406], "overview": [132, 476, 1038, 1296], "collid": [132, 454], "triplet": [132, 745], "successor": [132, 159, 174, 181, 191, 200, 240, 282, 387, 389, 390, 394, 501, 687, 707, 715, 858, 872, 880, 888, 903, 939, 953, 961, 969, 984, 1055, 1183, 1184, 1189, 1326, 1404, 1407, 1416, 1426], "descend": [132, 454, 456, 465, 709, 758, 1272, 1401, 1404, 1406, 1413, 1414], "unblock": 132, "commonli": [132, 280, 454, 684, 782], "probabilist": [132, 378], "causal": 132, "markov": [132, 462, 565, 692, 1188], "hmm": 132, "s1": [132, 1242, 1313, 1363], "s2": [132, 1242, 1313], "s3": [132, 1313], "s4": 132, "s5": 132, "o1": 132, "o2": 132, "o3": 132, "o4": 132, "o5": 132, "ob": 132, "d_separ": [132, 758, 1412], "darwich": 132, "shachter": 132, "1998": [132, 1143, 1144, 1225, 1241, 1407], "bay": 132, "ball": 132, "ration": 132, "pastim": 132, "irrelev": [132, 1407], "requisit": 132, "influenc": [132, 324, 325, 512, 786], "fourteenth": [132, 1186], "uncertainti": [132, 590, 732], "artifici": [132, 574, 590, 732], "480": [132, 426, 514, 518, 1398, 1406], "487": 132, "francisco": [132, 732], "morgan": [132, 732], "kaufmann": [132, 732], "koller": 132, "friedman": 132, "mit": [132, 342, 519, 618], "causal_markov_condit": 132, "ness": [133, 684, 782], "classmethod": [141, 1047], "auxiliari": [141, 142, 143, 220, 411, 412, 413, 415, 416, 417, 418, 419, 423, 430, 431, 1402], "sink": [141, 302, 309, 416, 418, 494, 495, 498, 499, 501, 502, 503, 506, 507, 509, 510, 565], "pick": [141, 217, 331, 657, 1188, 1207, 1210, 1407], "st": [141, 415, 417], "cut": [141, 222, 223, 293, 377, 382, 387, 389, 390, 394, 411, 412, 414, 415, 416, 417, 419, 427, 428, 429, 442, 443, 444, 445, 447, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 619, 758, 760, 1038, 1066, 1115, 1262, 1325, 1395, 1402, 1406, 1413], "refin": [143, 215, 423, 438], "auxgraph": [143, 423], "node_partit": 144, "permut": [144, 368, 452, 453, 455, 466, 748, 1285, 1320, 1321], "containin": 144, "frozenset": [144, 267, 339, 383, 586, 588, 752, 1165, 1333, 1337, 1338, 1412], "abc": [144, 545, 1154, 1206, 1303, 1412, 1413], "interchang": [144, 362], "bool": [145, 146, 148, 149, 165, 168, 171, 176, 184, 189, 196, 204, 208, 232, 237, 238, 242, 243, 245, 249, 250, 258, 265, 266, 267, 268, 272, 275, 286, 287, 288, 291, 294, 295, 296, 297, 298, 299, 301, 302, 305, 306, 307, 308, 309, 310, 314, 315, 322, 324, 325, 326, 327, 330, 343, 350, 355, 362, 393, 394, 395, 396, 397, 398, 439, 454, 462, 463, 467, 479, 480, 488, 489, 491, 494, 498, 499, 509, 510, 513, 514, 515, 516, 517, 518, 520, 521, 522, 545, 562, 564, 578, 579, 580, 581, 588, 613, 614, 616, 617, 622, 623, 625, 640, 652, 663, 673, 679, 685, 690, 696, 698, 699, 700, 704, 708, 719, 723, 724, 725, 726, 728, 730, 733, 734, 735, 736, 737, 738, 740, 741, 742, 743, 862, 865, 867, 870, 873, 878, 885, 891, 907, 910, 912, 916, 927, 931, 943, 946, 948, 951, 955, 960, 966, 972, 976, 988, 991, 993, 998, 1039, 1040, 1045, 1057, 1068, 1070, 1071, 1072, 1084, 1091, 1097, 1116, 1133, 1134, 1135, 1136, 1169, 1179, 1185, 1189, 1209, 1211, 1212, 1213, 1215, 1224, 1228, 1230, 1231, 1232, 1275, 1276, 1277, 1278, 1279, 1282, 1295, 1296, 1307, 1309, 1312, 1335, 1336, 1337, 1339, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1360, 1364, 1379, 1380], "account": [145, 148, 398, 448, 749, 761, 1270, 1393, 1413], "graph_nod": [145, 148], "subgraph_nod": [145, 148], "find_isomorph": [147, 150], "induc": [148, 167, 199, 211, 226, 342, 388, 392, 406, 427, 436, 437, 470, 487, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 512, 586, 589, 752, 761, 762, 864, 887, 909, 925, 945, 968, 990, 1007, 1038, 1061, 1066, 1087, 1102, 1103, 1105, 1189, 1283, 1284, 1393], "u_of_edg": [151, 853, 898], "v_of_edg": [151, 853, 898], "capac": [151, 265, 296, 301, 302, 303, 308, 309, 323, 411, 412, 415, 416, 417, 418, 419, 430, 431, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 758, 853, 898, 934, 979, 1335, 1402], "342": [151, 853, 898, 934, 979, 1255], "ebunch_to_add": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "add_weighted_edges_from": [152, 229, 230, 231, 508, 581, 630, 657, 659, 721, 854, 899, 935, 980, 1070, 1326, 1404, 1407, 1426], "runtimeerror": [152, 157, 158, 195, 464, 465, 466, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005], "happen": [152, 157, 158, 195, 380, 584, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1403, 1404, 1425], "iterator_of_edg": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "wn2898": [152, 854, 899, 935, 980], "wrong": [152, 157, 158, 722, 854, 856, 857, 899, 901, 902, 935, 937, 938, 980, 982, 983, 1406, 1411, 1416, 1425], "start_nod": [153, 154, 155], "end_nod": [153, 154, 155], "reference_neighbor": [153, 154], "half": [153, 154, 155, 164, 177, 183, 206, 297, 298, 615, 653], "clockwis": [153, 154, 169, 182, 197, 615], "networkxexcept": [153, 154, 161, 331, 588, 593, 724, 726, 1043, 1110, 1138, 1180, 1325], "add_half_edge_cw": [153, 155, 164, 615], "connect_compon": [153, 154, 155, 615], "add_half_edge_first": [153, 154, 164, 615], "add_half_edge_ccw": [154, 155, 164, 615], "node_for_ad": [156, 855, 900, 936, 981], "mutabl": [156, 855, 900, 936, 981, 1061, 1066, 1082, 1085, 1086], "hash": [156, 511, 512, 758, 855, 900, 936, 981, 1324, 1325, 1414, 1426], "hello": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1303], "k3": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1217], "utm": [156, 855, 900, 936, 981], "382871": [156, 855, 900, 936, 981], "3972649": [156, 855, 900, 936, 981], "nodes_for_ad": [157, 856, 901, 937, 982], "iterator_of_nod": [157, 195, 856, 884, 901, 923, 937, 965, 982, 1005], "datadict": [159, 190, 200, 207, 734, 736, 858, 879, 888, 892, 903, 928, 939, 969, 973, 1010, 1084, 1312, 1326], "foovalu": [159, 190, 200, 858, 879, 888, 903, 939, 969], "nbrdict": [160, 859, 904, 940, 985, 1019, 1094], "fulfil": [161, 615], "cw": [161, 615], "ccw": [161, 615], "planar": [161, 614, 616, 617, 758, 1110, 1138, 1243, 1246, 1247, 1249, 1325, 1409, 1410], "first_nbr": [161, 615], "invalid": [161, 615, 1413], "alter": [163, 861, 906, 942, 987], "afterward": 164, "as_view": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1089, 1090], "shallow": [165, 202, 204, 284, 285, 286, 287, 288, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1394], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1409], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1404, 1407, 1409, 1410, 1411], "fresh": [165, 862, 907, 943, 988, 1404], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1226, 1323, 1404], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1265, 1394], "degreeview": [166, 863, 908, 944, 950, 989, 1404, 1426], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1404, 1418], "ddict": [168, 176, 184, 189, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998], "in_edg": [168, 189, 865, 878, 946, 960, 1404, 1406, 1407], "out_edg": [168, 865, 946, 1062, 1404, 1406, 1407, 1426], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1426], "outedgedataview": [168, 189, 865, 878, 1404, 1411], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1404, 1412], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1406, 1407, 1417], "indegreeview": [175, 869, 1404], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1165, 1179, 1222, 1404], "inedgeview": [176, 870, 1404], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1127, 1353, 1354, 1359, 1363, 1406, 1413], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1414], "__iter__": [180, 871, 914, 952, 995, 1303], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1394, 1426], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1216], "nedg": [185, 588, 874, 917, 956, 999], "__len__": [186, 187, 875, 876, 918, 919, 957, 958, 1000, 1001], "outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 882, 921, 963, 1003, 1404, 1414], "get_data": [197, 616], "inplac": [199, 690, 887, 925, 968, 1007, 1066, 1393], "reduct": [199, 469, 618, 786, 887, 925, 968, 1007, 1066, 1320, 1321, 1413, 1414], "sg": [199, 887, 925, 968, 1007], "largest_wcc": [199, 887, 925, 968, 1007], "is_multigraph": [199, 758, 887, 925, 968, 1007, 1154, 1412], "keydict": [199, 207, 887, 892, 925, 928, 968, 973, 1007, 1010, 1039, 1040], "contrast": [202, 204, 301, 302, 308, 309, 890, 891, 926, 927, 971, 972, 1008, 1009, 1066, 1233, 1241, 1426], "reciproc": [204, 299, 320, 322, 356, 411, 430, 447, 476, 620, 758, 891, 972, 1325, 1416, 1425], "mark_half_edg": 206, "li": [206, 619, 670, 675, 685, 775, 1207, 1210, 1425], "straightforward": [207, 892, 928, 973, 1010], "slightli": [207, 326, 437, 520, 521, 581, 892, 928, 973, 1010, 1165, 1326, 1404, 1407, 1412, 1414, 1425], "singleton": [207, 588, 892, 928, 973, 1010, 1218, 1251, 1407], "preserve_attr": [208, 723, 724, 725, 726], "optimum": [208, 231, 583, 720, 722, 791, 1395, 1406], "arboresc": [208, 460, 719, 720, 722, 724, 726, 740, 743, 758, 1272, 1395, 1406], "span": [208, 226, 227, 228, 295, 508, 618, 619, 624, 719, 720, 722, 724, 726, 732, 733, 734, 735, 736, 737, 738, 758, 1394, 1397, 1406, 1407, 1420], "max_ind_cliqu": 209, "networkxnotimpl": [209, 210, 211, 212, 220, 224, 227, 293, 294, 295, 318, 319, 321, 328, 343, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 403, 404, 405, 406, 407, 422, 424, 425, 426, 427, 429, 455, 457, 458, 459, 460, 468, 481, 482, 500, 589, 590, 608, 680, 732, 1043, 1216, 1275, 1276, 1298, 1325, 1353, 1354, 1379, 1407, 1408], "boppana": [209, 211, 212], "halld\u00f3rsson": [209, 211, 212], "1992": [209, 211, 212, 517, 518, 1407], "exclud": [209, 211, 212, 215, 216, 261, 262, 453, 688, 719, 723, 724, 725, 726, 733, 751, 1036, 1038, 1088, 1217, 1412], "196": [209, 211, 212], "heurist": [210, 220, 228, 233, 234, 377, 380, 381, 427, 494, 509, 626, 627, 652, 663, 703, 758, 1173, 1320, 1321, 1325, 1395, 1408, 1412, 1413], "max_cliqu": 210, "rigor": 210, "pattabiraman": 210, "bharath": 210, "massiv": [210, 217], "421": 210, "448": 210, "1080": [210, 297, 298, 306, 307, 329], "15427951": 210, "986778": 210, "apx": [211, 212], "subseteq": [211, 280, 289, 618, 675], "omega": [211, 758, 782, 1414], "maximum_cliqu": 211, "1007": [211, 226, 296, 301, 302, 303, 308, 309, 323, 324, 325, 341, 431, 451, 498, 574, 1144, 1181], "bf01994876": 211, "iset": 212, "trial": [213, 230, 231, 1195, 1237, 1238], "estim": [213, 224, 297, 306, 313, 564, 625, 626, 627, 782, 1280, 1407], "coeffici": [213, 248, 260, 261, 262, 263, 289, 355, 356, 358, 570, 618, 619, 625, 682, 684, 778, 782, 1397, 1398, 1399, 1406, 1413], "fraction": [213, 257, 259, 286, 289, 297, 299, 304, 306, 315, 317, 318, 319, 321, 322, 326, 328, 330, 356, 358, 359, 519, 1165, 1234], "schank": 213, "thoma": [213, 751, 1407, 1409, 1413], "dorothea": [213, 1168], 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How do I find it in the source code?": [[97, "q-i-want-to-work-on-a-specific-function-how-do-i-find-it-in-the-source-code"]], "Q: What is the policy for deciding whether to include a new algorithm?": [[97, "q-what-is-the-policy-for-deciding-whether-to-include-a-new-algorithm"]], "NXEPs": [[98, "nxeps"], [1413, "nxeps"]], "NXEP 0 \u2014 Purpose and Process": [[99, "nxep-0-purpose-and-process"]], "What is a NXEP?": [[99, "what-is-a-nxep"]], "Types": [[99, "types"]], "NXEP Workflow": [[99, "nxep-workflow"]], "Review and Resolution": [[99, "review-and-resolution"]], "How a NXEP becomes Accepted": [[99, "how-a-nxep-becomes-accepted"]], "Maintenance": [[99, "maintenance"]], "Format and Template": [[99, "format-and-template"]], "Header Preamble": [[99, "header-preamble"]], "References and Footnotes": [[99, "references-and-footnotes"]], "NXEP 1 \u2014 Governance and Decision Making": [[100, "nxep-1-governance-and-decision-making"]], "Abstract": [[100, "abstract"], [101, "abstract"], [102, 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"difference": [[599, "difference"]], "disjoint_union": [[600, "disjoint-union"]], "full_join": [[601, "full-join"]], "intersection": [[602, "intersection"]], "symmetric_difference": [[603, "symmetric-difference"]], "union": [[604, "union"]], "cartesian_product": [[605, "cartesian-product"]], "corona_product": [[606, "corona-product"]], "lexicographic_product": [[607, "lexicographic-product"]], "power": [[608, "power"]], "rooted_product": [[609, "rooted-product"]], "strong_product": [[610, "strong-product"]], "tensor_product": [[611, "tensor-product"]], "complement": [[612, "complement"]], "reverse": [[613, "reverse"]], "combinatorial_embedding_to_pos": [[614, "combinatorial-embedding-to-pos"]], "networkx.algorithms.planarity.PlanarEmbedding": [[615, "networkx-algorithms-planarity-planarembedding"]], "check_planarity": [[616, "check-planarity"]], "is_planar": [[617, "is-planar"]], "chromatic_polynomial": [[618, "chromatic-polynomial"]], "tutte_polynomial": [[619, "tutte-polynomial"]], "overall_reciprocity": [[620, "overall-reciprocity"]], "reciprocity": [[621, "reciprocity"]], "is_k_regular": [[622, "is-k-regular"]], "is_regular": [[623, "is-regular"]], "k_factor": [[624, "k-factor"]], "rich_club_coefficient": [[625, "rich-club-coefficient"]], "astar_path": [[626, "astar-path"]], "astar_path_length": [[627, "astar-path-length"]], "floyd_warshall": [[628, "floyd-warshall"]], "floyd_warshall_numpy": [[629, "floyd-warshall-numpy"]], "floyd_warshall_predecessor_and_distance": [[630, "floyd-warshall-predecessor-and-distance"]], "reconstruct_path": [[631, "reconstruct-path"]], "all_shortest_paths": [[632, "all-shortest-paths"]], "average_shortest_path_length": [[633, "average-shortest-path-length"]], "has_path": [[634, "has-path"]], "shortest_path": [[635, "shortest-path"]], "shortest_path_length": [[636, "shortest-path-length"]], "all_pairs_shortest_path": [[637, "all-pairs-shortest-path"]], "all_pairs_shortest_path_length": [[638, "all-pairs-shortest-path-length"]], "bidirectional_shortest_path": [[639, "bidirectional-shortest-path"]], "predecessor": [[640, "predecessor"]], "single_source_shortest_path": [[641, "single-source-shortest-path"]], "single_source_shortest_path_length": [[642, "single-source-shortest-path-length"]], "single_target_shortest_path": [[643, "single-target-shortest-path"]], "single_target_shortest_path_length": [[644, "single-target-shortest-path-length"]], "all_pairs_bellman_ford_path": [[645, "all-pairs-bellman-ford-path"]], "all_pairs_bellman_ford_path_length": [[646, "all-pairs-bellman-ford-path-length"]], "all_pairs_dijkstra": [[647, "all-pairs-dijkstra"]], "all_pairs_dijkstra_path": [[648, "all-pairs-dijkstra-path"]], "all_pairs_dijkstra_path_length": [[649, "all-pairs-dijkstra-path-length"]], "bellman_ford_path": [[650, "bellman-ford-path"]], "bellman_ford_path_length": [[651, "bellman-ford-path-length"]], "bellman_ford_predecessor_and_distance": [[652, "bellman-ford-predecessor-and-distance"]], "bidirectional_dijkstra": [[653, "bidirectional-dijkstra"]], "dijkstra_path": [[654, "dijkstra-path"]], "dijkstra_path_length": [[655, "dijkstra-path-length"]], "dijkstra_predecessor_and_distance": [[656, "dijkstra-predecessor-and-distance"]], "find_negative_cycle": [[657, "find-negative-cycle"]], "goldberg_radzik": [[658, "goldberg-radzik"]], "johnson": [[659, "johnson"]], "multi_source_dijkstra": [[660, "multi-source-dijkstra"]], "multi_source_dijkstra_path": [[661, "multi-source-dijkstra-path"]], "multi_source_dijkstra_path_length": [[662, "multi-source-dijkstra-path-length"]], "negative_edge_cycle": [[663, "negative-edge-cycle"]], "single_source_bellman_ford": [[664, "single-source-bellman-ford"]], "single_source_bellman_ford_path": [[665, "single-source-bellman-ford-path"]], "single_source_bellman_ford_path_length": [[666, "single-source-bellman-ford-path-length"]], "single_source_dijkstra": [[667, "single-source-dijkstra"]], "single_source_dijkstra_path": [[668, "single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[669, "single-source-dijkstra-path-length"]], "generate_random_paths": [[670, "generate-random-paths"]], "graph_edit_distance": [[671, "graph-edit-distance"]], "optimal_edit_paths": [[672, "optimal-edit-paths"]], "optimize_edit_paths": [[673, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[674, "optimize-graph-edit-distance"]], "panther_similarity": [[675, "panther-similarity"]], "simrank_similarity": [[676, "simrank-similarity"]], "all_simple_edge_paths": [[677, "all-simple-edge-paths"]], "all_simple_paths": [[678, "all-simple-paths"]], "is_simple_path": [[679, "is-simple-path"]], "shortest_simple_paths": [[680, "shortest-simple-paths"]], "lattice_reference": [[681, "lattice-reference"]], "omega": [[682, "omega"]], "random_reference": [[683, "random-reference"]], "sigma": [[684, "sigma"]], "s_metric": [[685, "s-metric"]], "spanner": [[686, "spanner"]], "constraint": [[687, "constraint"]], "effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "Recognition Tests": [[791, "recognition-tests"]], "Branchings and Spanning Arborescences": [[791, "module-networkx.algorithms.tree.branchings"]], "Encoding and decoding": [[791, "module-networkx.algorithms.tree.coding"]], "Operations": [[791, "module-networkx.algorithms.tree.operations"]], "Spanning Trees": [[791, "module-networkx.algorithms.tree.mst"]], "Exceptions": [[791, "exceptions"], [1043, "module-networkx.exception"]], "Vitality": [[793, "module-networkx.algorithms.vitality"]], "Voronoi cells": [[794, "module-networkx.algorithms.voronoi"]], "Wiener index": [[795, "module-networkx.algorithms.wiener"]], "DiGraph\u2014Directed graphs with self loops": [[796, "digraph-directed-graphs-with-self-loops"]], "Overview": [[796, "overview"], [1037, "overview"], [1039, "overview"], [1040, "overview"]], "Methods": [[796, "methods"], [1037, "methods"], [1039, "methods"], [1040, "methods"]], "Adding and removing nodes and edges": [[796, "adding-and-removing-nodes-and-edges"], [1037, "adding-and-removing-nodes-and-edges"], [1040, "adding-and-removing-nodes-and-edges"]], "Reporting nodes edges and neighbors": [[796, "reporting-nodes-edges-and-neighbors"], [1037, "reporting-nodes-edges-and-neighbors"], [1039, "reporting-nodes-edges-and-neighbors"], [1040, "reporting-nodes-edges-and-neighbors"]], "Counting nodes edges and neighbors": [[796, "counting-nodes-edges-and-neighbors"], [1037, "counting-nodes-edges-and-neighbors"], [1039, "counting-nodes-edges-and-neighbors"], [1040, "counting-nodes-edges-and-neighbors"]], "Making copies and subgraphs": [[796, "making-copies-and-subgraphs"], [1037, "making-copies-and-subgraphs"], [1039, "making-copies-and-subgraphs"], [1040, "making-copies-and-subgraphs"]], "AdjacencyView.copy": [[797, "adjacencyview-copy"]], "AdjacencyView.get": [[798, "adjacencyview-get"]], "AdjacencyView.items": [[799, "adjacencyview-items"]], "AdjacencyView.keys": [[800, "adjacencyview-keys"]], "AdjacencyView.values": [[801, "adjacencyview-values"]], "AtlasView.copy": [[802, "atlasview-copy"]], "AtlasView.get": [[803, "atlasview-get"]], "AtlasView.items": [[804, "atlasview-items"]], "AtlasView.keys": [[805, "atlasview-keys"]], "AtlasView.values": [[806, "atlasview-values"]], "FilterAdjacency.get": [[807, "filteradjacency-get"]], "FilterAdjacency.items": [[808, "filteradjacency-items"]], "FilterAdjacency.keys": [[809, "filteradjacency-keys"]], "FilterAdjacency.values": [[810, "filteradjacency-values"]], "FilterAtlas.get": [[811, "filteratlas-get"]], "FilterAtlas.items": [[812, "filteratlas-items"]], "FilterAtlas.keys": [[813, "filteratlas-keys"]], "FilterAtlas.values": [[814, "filteratlas-values"]], "FilterMultiAdjacency.get": [[815, "filtermultiadjacency-get"]], "FilterMultiAdjacency.items": [[816, "filtermultiadjacency-items"]], "FilterMultiAdjacency.keys": [[817, "filtermultiadjacency-keys"]], "FilterMultiAdjacency.values": [[818, "filtermultiadjacency-values"]], "FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": [[836, "unionatlas-keys"]], "UnionAtlas.values": [[837, "unionatlas-values"]], "UnionMultiAdjacency.copy": [[838, "unionmultiadjacency-copy"]], "UnionMultiAdjacency.get": [[839, "unionmultiadjacency-get"]], "UnionMultiAdjacency.items": [[840, "unionmultiadjacency-items"]], "UnionMultiAdjacency.keys": [[841, "unionmultiadjacency-keys"]], "UnionMultiAdjacency.values": [[842, "unionmultiadjacency-values"]], "UnionMultiInner.copy": [[843, "unionmultiinner-copy"]], "UnionMultiInner.get": [[844, "unionmultiinner-get"]], "UnionMultiInner.items": [[845, "unionmultiinner-items"]], "UnionMultiInner.keys": [[846, "unionmultiinner-keys"]], "UnionMultiInner.values": [[847, "unionmultiinner-values"]], "DiGraph.__contains__": [[848, "digraph-contains"]], "DiGraph.__getitem__": [[849, "digraph-getitem"]], "DiGraph.__init__": [[850, "digraph-init"]], "DiGraph.__iter__": [[851, "digraph-iter"]], "DiGraph.__len__": [[852, "digraph-len"]], "DiGraph.add_edge": [[853, "digraph-add-edge"]], "DiGraph.add_edges_from": [[854, "digraph-add-edges-from"]], "DiGraph.add_node": [[855, "digraph-add-node"]], "DiGraph.add_nodes_from": [[856, "digraph-add-nodes-from"]], "DiGraph.add_weighted_edges_from": [[857, "digraph-add-weighted-edges-from"]], "DiGraph.adj": [[858, "digraph-adj"]], "DiGraph.adjacency": [[859, "digraph-adjacency"]], "DiGraph.clear": [[860, "digraph-clear"]], "DiGraph.clear_edges": [[861, "digraph-clear-edges"]], "DiGraph.copy": [[862, "digraph-copy"]], "DiGraph.degree": [[863, "digraph-degree"]], "DiGraph.edge_subgraph": [[864, "digraph-edge-subgraph"]], "DiGraph.edges": [[865, "digraph-edges"]], "DiGraph.get_edge_data": [[866, "digraph-get-edge-data"]], "DiGraph.has_edge": [[867, "digraph-has-edge"]], "DiGraph.has_node": [[868, "digraph-has-node"]], "DiGraph.in_degree": [[869, "digraph-in-degree"]], "DiGraph.in_edges": [[870, "digraph-in-edges"]], "DiGraph.nbunch_iter": [[871, "digraph-nbunch-iter"]], "DiGraph.neighbors": [[872, "digraph-neighbors"]], "DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, "digraph-to-undirected"]], "DiGraph.update": [[892, "digraph-update"]], "Graph.__contains__": [[893, "graph-contains"]], "Graph.__getitem__": [[894, "graph-getitem"]], "Graph.__init__": [[895, "graph-init"]], "Graph.__iter__": [[896, "graph-iter"]], "Graph.__len__": [[897, "graph-len"]], "Graph.add_edge": [[898, "graph-add-edge"]], "Graph.add_edges_from": [[899, "graph-add-edges-from"]], "Graph.add_node": [[900, "graph-add-node"]], "Graph.add_nodes_from": [[901, "graph-add-nodes-from"]], "Graph.add_weighted_edges_from": [[902, "graph-add-weighted-edges-from"]], "Graph.adj": [[903, "graph-adj"]], "Graph.adjacency": [[904, "graph-adjacency"]], "Graph.clear": [[905, "graph-clear"]], "Graph.clear_edges": [[906, "graph-clear-edges"]], "Graph.copy": [[907, "graph-copy"]], "Graph.degree": [[908, "graph-degree"]], "Graph.edge_subgraph": [[909, "graph-edge-subgraph"]], "Graph.edges": [[910, "graph-edges"]], "Graph.get_edge_data": [[911, "graph-get-edge-data"]], "Graph.has_edge": [[912, "graph-has-edge"]], "Graph.has_node": [[913, "graph-has-node"]], "Graph.nbunch_iter": [[914, "graph-nbunch-iter"]], "Graph.neighbors": [[915, "graph-neighbors"]], "Graph.nodes": [[916, "graph-nodes"]], "Graph.number_of_edges": [[917, "graph-number-of-edges"]], "Graph.number_of_nodes": [[918, "graph-number-of-nodes"]], "Graph.order": [[919, "graph-order"]], "Graph.remove_edge": [[920, "graph-remove-edge"]], "Graph.remove_edges_from": [[921, "graph-remove-edges-from"]], "Graph.remove_node": [[922, "graph-remove-node"]], "Graph.remove_nodes_from": [[923, "graph-remove-nodes-from"]], "Graph.size": [[924, "graph-size"]], "Graph.subgraph": [[925, "graph-subgraph"]], "Graph.to_directed": [[926, "graph-to-directed"]], "Graph.to_undirected": [[927, "graph-to-undirected"]], "Graph.update": [[928, "graph-update"]], "MultiDiGraph.__contains__": [[929, "multidigraph-contains"]], "MultiDiGraph.__getitem__": [[930, "multidigraph-getitem"]], "MultiDiGraph.__init__": [[931, "multidigraph-init"]], 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"removed-functionalities"]], "Miscellaneous changes": [[1395, "miscellaneous-changes"], [1396, "miscellaneous-changes"], [1402, "miscellaneous-changes"]], "NetworkX 1.11": [[1396, "networkx-1-11"], [1406, "networkx-1-11"]], "NetworkX 1.4": [[1397, "networkx-1-4"], [1406, "networkx-1-4"]], "Algorithms changed": [[1397, "algorithms-changed"]], "Shortest path": [[1397, "shortest-path"]], "astar_path(), astar_path_length(), shortest_path(), shortest_path_length(),": [[1397, "astar-path-astar-path-length-shortest-path-shortest-path-length"]], "bidirectional_shortest_path(), dijkstra_path(), dijkstra_path_length(),": [[1397, "bidirectional-shortest-path-dijkstra-path-dijkstra-path-length"]], "bidirectional_dijkstra()": [[1397, "bidirectional-dijkstra"]], "NetworkX 1.5": [[1398, "networkx-1-5"], [1406, "networkx-1-5"]], "Weighted graph algorithms": [[1398, "weighted-graph-algorithms"], [1399, "weighted-graph-algorithms"]], "Random geometric graph": [[1398, "random-geometric-graph"]], "NetworkX 1.6": [[1399, "networkx-1-6"], [1406, "networkx-1-6"]], "Graph Classes": [[1399, "graph-classes"]], "Isomorphisms": [[1399, "isomorphisms"]], "Other": [[1399, "other"], [1400, "other"]], "NetworkX 1.7": [[1400, "networkx-1-7"], [1406, "networkx-1-7"]], "NetworkX 1.8": [[1401, "networkx-1-8"], [1406, "networkx-1-8"]], "NetworkX 1.9": [[1402, "networkx-1-9"], [1406, "networkx-1-9"]], "Flow package": [[1402, "flow-package"]], "Main changes": [[1402, "main-changes"]], "Connectivity package": [[1402, "connectivity-package"]], "Other new functionalities": [[1402, "other-new-functionalities"]], "Releases": [[1403, "releases"]], "Migration guide from 1.X to 2.0": [[1404, "migration-guide-from-1-x-to-2-0"]], "Writing code that works for both versions": [[1404, "writing-code-that-works-for-both-versions"]], "Using Pickle with v1 and v2": [[1404, "using-pickle-with-v1-and-v2"]], "Migration guide from 2.X to 3.0": [[1405, "migration-guide-from-2-x-to-3-0"]], "Default dependencies": [[1405, "default-dependencies"]], "Improved integration with scientific Python": [[1405, "improved-integration-with-scientific-python"]], "Replacing NumPy/SciPy matrices with arrays": [[1405, "replacing-numpy-scipy-matrices-with-arrays"]], "Switch to NumPy/SciPy implementations by default for some algorithms": [[1405, "switch-to-numpy-scipy-implementations-by-default-for-some-algorithms"]], "Supporting numpy.random.Generator": [[1405, "supporting-numpy-random-generator"]], "NumPy structured dtypes for multi-attribute adjacency matrices": [[1405, "numpy-structured-dtypes-for-multi-attribute-adjacency-matrices"]], "Deprecated code": [[1405, "deprecated-code"]], "Old Release Log": [[1406, "old-release-log"]], "NetworkX 2.5": [[1406, "networkx-2-5"], [1412, "networkx-2-5"]], "Release notes": [[1406, "release-notes"], [1406, "id1"], [1406, "id2"], [1406, "id3"], [1406, "id4"], [1406, "id5"]], "NetworkX 2.4": [[1406, "networkx-2-4"], [1411, "networkx-2-4"]], "NetworkX 2.3": [[1406, 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"networkx-0-30"]], "NetworkX 0.29": [[1406, "networkx-0-29"]], "NetworkX 0.28": [[1406, "networkx-0-28"]], "NetworkX 0.27": [[1406, "networkx-0-27"]], "NetworkX 0.26": [[1406, "networkx-0-26"]], "NetworkX 0.25": [[1406, "networkx-0-25"]], "NetworkX 0.24": [[1406, "networkx-0-24"]], "NetworkX 0.23": [[1406, "networkx-0-23"]], "Important Change": [[1406, "important-change"]], "NetworkX 0.22": [[1406, "networkx-0-22"]], "API Changes": [[1407, "api-changes"], [1408, "api-changes"], [1409, "api-changes"], [1410, "api-changes"], [1411, "api-changes"], [1412, "api-changes"], [1413, "api-changes"], [1414, "api-changes"], [1416, "api-changes"], [1425, "api-changes"]], "Merged PRs": [[1407, "merged-prs"], [1408, "merged-prs"], [1411, "merged-prs"], [1412, "merged-prs"], [1413, "merged-prs"], [1414, "merged-prs"], [1415, "merged-prs"], [1416, "merged-prs"], [1417, "merged-prs"], [1418, "merged-prs"], [1419, "merged-prs"], [1420, "merged-prs"], [1421, "merged-prs"], [1422, "merged-prs"], [1423, "merged-prs"], [1424, "merged-prs"], [1425, "merged-prs"]], "Improvements": [[1408, "improvements"], [1409, "improvements"], [1410, "improvements"], [1411, "improvements"], [1412, "improvements"], [1413, "improvements"], [1414, "improvements"], [1416, "improvements"], [1417, "improvements"], [1422, "improvements"], [1423, "improvements"], [1425, "improvements"]], "NetworkX 2.6": [[1413, "networkx-2-6"]], "NetworkX 2.7": [[1414, "networkx-2-7"]], "GSoC PRs": [[1414, "gsoc-prs"]], "NetworkX 2.7.1": [[1415, "networkx-2-7-1"]], "NetworkX 2.8": [[1416, "networkx-2-8"]], "NetworkX 2.8.1": [[1417, "networkx-2-8-1"]], "NetworkX 2.8.2": [[1418, "networkx-2-8-2"]], "NetworkX 2.8.3": [[1419, "networkx-2-8-3"]], "NetworkX 2.8.4": [[1420, "networkx-2-8-4"]], "NetworkX 2.8.5": [[1421, "networkx-2-8-5"]], "NetworkX 2.8.6": [[1422, "networkx-2-8-6"]], "NetworkX 2.8.7": [[1423, "networkx-2-8-7"], [1424, "networkx-2-8-7"]], "NetworkX 3.0 (unreleased)": [[1425, "networkx-3-0-unreleased"]], "Tutorial": [[1426, "tutorial"]], "Creating a graph": [[1426, "creating-a-graph"]], "Examining elements of a graph": [[1426, "examining-elements-of-a-graph"]], "Removing elements from a graph": [[1426, "removing-elements-from-a-graph"]], "Using the graph constructors": [[1426, "using-the-graph-constructors"]], "What to use as nodes and edges": [[1426, "what-to-use-as-nodes-and-edges"]], "Accessing edges and neighbors": [[1426, "accessing-edges-and-neighbors"]], "Adding attributes to graphs, nodes, and edges": [[1426, "adding-attributes-to-graphs-nodes-and-edges"]], "Edge Attributes": [[1426, "edge-attributes"]], "Directed graphs": [[1426, "directed-graphs"]], "Multigraphs": [[1426, "multigraphs"]], "Graph generators and graph operations": [[1426, "graph-generators-and-graph-operations"]], "1. Applying classic graph operations, such as:": [[1426, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1426, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1426, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1426, "using-a-stochastic-graph-generator-e-g"]], "5. Reading a graph stored in a file using common graph formats": [[1426, "reading-a-graph-stored-in-a-file-using-common-graph-formats"]], "Analyzing graphs": [[1426, "analyzing-graphs"]], "Drawing graphs": [[1426, "drawing-graphs"]]}, "indexentries": {"module": [[112, "module-networkx.algorithms.approximation"], [112, "module-networkx.algorithms.approximation.clique"], [112, "module-networkx.algorithms.approximation.clustering_coefficient"], [112, "module-networkx.algorithms.approximation.connectivity"], [112, "module-networkx.algorithms.approximation.distance_measures"], [112, "module-networkx.algorithms.approximation.dominating_set"], [112, "module-networkx.algorithms.approximation.kcomponents"], [112, "module-networkx.algorithms.approximation.matching"], [112, "module-networkx.algorithms.approximation.maxcut"], [112, "module-networkx.algorithms.approximation.ramsey"], [112, "module-networkx.algorithms.approximation.steinertree"], [112, "module-networkx.algorithms.approximation.traveling_salesman"], [112, "module-networkx.algorithms.approximation.treewidth"], [112, "module-networkx.algorithms.approximation.vertex_cover"], [113, "module-networkx.algorithms.assortativity"], [114, "module-networkx.algorithms.asteroidal"], [115, "module-networkx.algorithms.bipartite"], [115, "module-networkx.algorithms.bipartite.basic"], [115, "module-networkx.algorithms.bipartite.centrality"], [115, "module-networkx.algorithms.bipartite.cluster"], [115, "module-networkx.algorithms.bipartite.covering"], [115, "module-networkx.algorithms.bipartite.edgelist"], [115, "module-networkx.algorithms.bipartite.generators"], [115, "module-networkx.algorithms.bipartite.matching"], [115, "module-networkx.algorithms.bipartite.matrix"], [115, "module-networkx.algorithms.bipartite.projection"], [115, "module-networkx.algorithms.bipartite.redundancy"], [115, "module-networkx.algorithms.bipartite.spectral"], [116, "module-networkx.algorithms.boundary"], [117, "module-networkx.algorithms.bridges"], [118, "module-networkx.algorithms.centrality"], [119, "module-networkx.algorithms.chains"], [120, "module-networkx.algorithms.chordal"], [121, "module-networkx.algorithms.clique"], [122, "module-networkx.algorithms.cluster"], [123, "module-networkx.algorithms.coloring"], [124, "module-networkx.algorithms.communicability_alg"], [125, "module-networkx.algorithms.community"], [125, "module-networkx.algorithms.community.asyn_fluid"], [125, "module-networkx.algorithms.community.centrality"], [125, "module-networkx.algorithms.community.community_utils"], [125, "module-networkx.algorithms.community.kclique"], [125, "module-networkx.algorithms.community.kernighan_lin"], [125, "module-networkx.algorithms.community.label_propagation"], [125, "module-networkx.algorithms.community.louvain"], [125, "module-networkx.algorithms.community.lukes"], [125, "module-networkx.algorithms.community.modularity_max"], [125, "module-networkx.algorithms.community.quality"], [126, "module-networkx.algorithms.components"], [127, "module-networkx.algorithms.connectivity"], [127, "module-networkx.algorithms.connectivity.connectivity"], [127, "module-networkx.algorithms.connectivity.cuts"], [127, "module-networkx.algorithms.connectivity.disjoint_paths"], [127, "module-networkx.algorithms.connectivity.edge_augmentation"], [127, "module-networkx.algorithms.connectivity.edge_kcomponents"], [127, "module-networkx.algorithms.connectivity.kcomponents"], [127, "module-networkx.algorithms.connectivity.kcutsets"], [127, "module-networkx.algorithms.connectivity.stoerwagner"], [127, "module-networkx.algorithms.connectivity.utils"], [128, "module-networkx.algorithms.core"], [129, "module-networkx.algorithms.covering"], [130, "module-networkx.algorithms.cuts"], [131, "module-networkx.algorithms.cycles"], [132, "module-networkx.algorithms.d_separation"], [133, "module-networkx.algorithms.dag"], [134, 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"module-networkx.algorithms.approximation.clustering_coefficient"]], "networkx.algorithms.approximation.connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"]], "networkx.algorithms.approximation.distance_measures": [[112, "module-networkx.algorithms.approximation.distance_measures"]], "networkx.algorithms.approximation.dominating_set": [[112, "module-networkx.algorithms.approximation.dominating_set"]], "networkx.algorithms.approximation.kcomponents": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "networkx.algorithms.approximation.matching": [[112, "module-networkx.algorithms.approximation.matching"]], "networkx.algorithms.approximation.maxcut": [[112, "module-networkx.algorithms.approximation.maxcut"]], "networkx.algorithms.approximation.ramsey": [[112, "module-networkx.algorithms.approximation.ramsey"]], "networkx.algorithms.approximation.steinertree": [[112, "module-networkx.algorithms.approximation.steinertree"]], "networkx.algorithms.approximation.traveling_salesman": [[112, "module-networkx.algorithms.approximation.traveling_salesman"]], "networkx.algorithms.approximation.treewidth": [[112, "module-networkx.algorithms.approximation.treewidth"]], "networkx.algorithms.approximation.vertex_cover": [[112, "module-networkx.algorithms.approximation.vertex_cover"]], "networkx.algorithms.assortativity": [[113, "module-networkx.algorithms.assortativity"]], "networkx.algorithms.asteroidal": [[114, "module-networkx.algorithms.asteroidal"]], "networkx.algorithms.bipartite": [[115, "module-networkx.algorithms.bipartite"]], "networkx.algorithms.bipartite.basic": [[115, "module-networkx.algorithms.bipartite.basic"]], "networkx.algorithms.bipartite.centrality": [[115, "module-networkx.algorithms.bipartite.centrality"]], "networkx.algorithms.bipartite.cluster": [[115, "module-networkx.algorithms.bipartite.cluster"]], "networkx.algorithms.bipartite.covering": [[115, 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"networkx.algorithms.chains": [[119, "module-networkx.algorithms.chains"]], "networkx.algorithms.chordal": [[120, "module-networkx.algorithms.chordal"]], "networkx.algorithms.clique": [[121, "module-networkx.algorithms.clique"]], "networkx.algorithms.cluster": [[122, "module-networkx.algorithms.cluster"]], "networkx.algorithms.coloring": [[123, "module-networkx.algorithms.coloring"]], "networkx.algorithms.communicability_alg": [[124, "module-networkx.algorithms.communicability_alg"]], "networkx.algorithms.community": [[125, "module-networkx.algorithms.community"]], "networkx.algorithms.community.asyn_fluid": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "networkx.algorithms.community.centrality": [[125, "module-networkx.algorithms.community.centrality"]], "networkx.algorithms.community.community_utils": [[125, "module-networkx.algorithms.community.community_utils"]], "networkx.algorithms.community.kclique": [[125, "module-networkx.algorithms.community.kclique"]], "networkx.algorithms.community.kernighan_lin": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "networkx.algorithms.community.label_propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "networkx.algorithms.community.louvain": [[125, "module-networkx.algorithms.community.louvain"]], "networkx.algorithms.community.lukes": [[125, "module-networkx.algorithms.community.lukes"]], "networkx.algorithms.community.modularity_max": [[125, "module-networkx.algorithms.community.modularity_max"]], "networkx.algorithms.community.quality": [[125, "module-networkx.algorithms.community.quality"]], "networkx.algorithms.components": [[126, "module-networkx.algorithms.components"]], "networkx.algorithms.connectivity": [[127, "module-networkx.algorithms.connectivity"]], "networkx.algorithms.connectivity.connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "networkx.algorithms.connectivity.cuts": [[127, 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"networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, 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"networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": 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"networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[751, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[752, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[753, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[755, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[757, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[759, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[760, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[764, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[766, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[767, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[768, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[769, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[770, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[771, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[772, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[772, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[772, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[772, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[774, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[775, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[777, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[778, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "networkx.algorithms.shortest_paths.weighted": [[779, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[780, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[781, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[782, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[783, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[785, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[786, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[787, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[788, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[789, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "networkx.algorithms.traversal.breadth_first_search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "networkx.algorithms.traversal.depth_first_search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "networkx.algorithms.traversal.edgebfs": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "networkx.algorithms.traversal.edgedfs": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "networkx.algorithms.tree.branchings": [[791, "module-networkx.algorithms.tree.branchings"]], "networkx.algorithms.tree.coding": [[791, "module-networkx.algorithms.tree.coding"]], "networkx.algorithms.tree.decomposition": [[791, "module-networkx.algorithms.tree.decomposition"]], "networkx.algorithms.tree.mst": [[791, "module-networkx.algorithms.tree.mst"]], "networkx.algorithms.tree.operations": [[791, "module-networkx.algorithms.tree.operations"]], "networkx.algorithms.tree.recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "networkx.algorithms.triads": [[792, "module-networkx.algorithms.triads"]], "networkx.algorithms.vitality": [[793, "module-networkx.algorithms.vitality"]], "networkx.algorithms.voronoi": [[794, "module-networkx.algorithms.voronoi"]], "networkx.algorithms.wiener": [[795, "module-networkx.algorithms.wiener"]], "digraph (class in networkx)": [[796, "networkx.DiGraph"]], "copy() (adjacencyview method)": [[797, "networkx.classes.coreviews.AdjacencyView.copy"]], "get() (adjacencyview method)": [[798, "networkx.classes.coreviews.AdjacencyView.get"]], "items() (adjacencyview method)": [[799, "networkx.classes.coreviews.AdjacencyView.items"]], "keys() (adjacencyview method)": [[800, "networkx.classes.coreviews.AdjacencyView.keys"]], "values() (adjacencyview method)": [[801, "networkx.classes.coreviews.AdjacencyView.values"]], "copy() (atlasview method)": [[802, "networkx.classes.coreviews.AtlasView.copy"]], "get() (atlasview method)": [[803, "networkx.classes.coreviews.AtlasView.get"]], "items() (atlasview method)": [[804, "networkx.classes.coreviews.AtlasView.items"]], "keys() (atlasview method)": [[805, "networkx.classes.coreviews.AtlasView.keys"]], "values() (atlasview method)": [[806, "networkx.classes.coreviews.AtlasView.values"]], "get() (filteradjacency method)": [[807, "networkx.classes.coreviews.FilterAdjacency.get"]], "items() (filteradjacency method)": [[808, "networkx.classes.coreviews.FilterAdjacency.items"]], "keys() (filteradjacency method)": [[809, "networkx.classes.coreviews.FilterAdjacency.keys"]], "values() (filteradjacency method)": [[810, "networkx.classes.coreviews.FilterAdjacency.values"]], "get() (filteratlas method)": [[811, "networkx.classes.coreviews.FilterAtlas.get"]], "items() (filteratlas method)": [[812, "networkx.classes.coreviews.FilterAtlas.items"]], "keys() (filteratlas method)": [[813, "networkx.classes.coreviews.FilterAtlas.keys"]], "values() (filteratlas method)": [[814, "networkx.classes.coreviews.FilterAtlas.values"]], "get() (filtermultiadjacency method)": [[815, "networkx.classes.coreviews.FilterMultiAdjacency.get"]], "items() (filtermultiadjacency method)": [[816, "networkx.classes.coreviews.FilterMultiAdjacency.items"]], "keys() (filtermultiadjacency method)": [[817, "networkx.classes.coreviews.FilterMultiAdjacency.keys"]], "values() (filtermultiadjacency method)": [[818, "networkx.classes.coreviews.FilterMultiAdjacency.values"]], "get() (filtermultiinner method)": [[819, "networkx.classes.coreviews.FilterMultiInner.get"]], "items() (filtermultiinner method)": [[820, "networkx.classes.coreviews.FilterMultiInner.items"]], "keys() (filtermultiinner method)": [[821, "networkx.classes.coreviews.FilterMultiInner.keys"]], "values() (filtermultiinner method)": [[822, "networkx.classes.coreviews.FilterMultiInner.values"]], "copy() (multiadjacencyview method)": [[823, "networkx.classes.coreviews.MultiAdjacencyView.copy"]], "get() (multiadjacencyview method)": [[824, "networkx.classes.coreviews.MultiAdjacencyView.get"]], "items() (multiadjacencyview method)": [[825, "networkx.classes.coreviews.MultiAdjacencyView.items"]], "keys() (multiadjacencyview method)": [[826, "networkx.classes.coreviews.MultiAdjacencyView.keys"]], "values() (multiadjacencyview method)": [[827, "networkx.classes.coreviews.MultiAdjacencyView.values"]], "copy() (unionadjacency method)": [[828, "networkx.classes.coreviews.UnionAdjacency.copy"]], "get() (unionadjacency method)": [[829, "networkx.classes.coreviews.UnionAdjacency.get"]], "items() (unionadjacency method)": [[830, "networkx.classes.coreviews.UnionAdjacency.items"]], "keys() (unionadjacency method)": [[831, "networkx.classes.coreviews.UnionAdjacency.keys"]], "values() (unionadjacency method)": [[832, "networkx.classes.coreviews.UnionAdjacency.values"]], "copy() (unionatlas method)": [[833, "networkx.classes.coreviews.UnionAtlas.copy"]], "get() (unionatlas method)": [[834, "networkx.classes.coreviews.UnionAtlas.get"]], "items() (unionatlas method)": [[835, "networkx.classes.coreviews.UnionAtlas.items"]], "keys() (unionatlas method)": [[836, "networkx.classes.coreviews.UnionAtlas.keys"]], "values() (unionatlas method)": [[837, "networkx.classes.coreviews.UnionAtlas.values"]], "copy() (unionmultiadjacency method)": [[838, "networkx.classes.coreviews.UnionMultiAdjacency.copy"]], "get() (unionmultiadjacency method)": [[839, "networkx.classes.coreviews.UnionMultiAdjacency.get"]], "items() (unionmultiadjacency method)": [[840, "networkx.classes.coreviews.UnionMultiAdjacency.items"]], "keys() (unionmultiadjacency method)": [[841, "networkx.classes.coreviews.UnionMultiAdjacency.keys"]], "values() (unionmultiadjacency method)": [[842, "networkx.classes.coreviews.UnionMultiAdjacency.values"]], "copy() (unionmultiinner method)": [[843, "networkx.classes.coreviews.UnionMultiInner.copy"]], "get() (unionmultiinner method)": [[844, "networkx.classes.coreviews.UnionMultiInner.get"]], "items() (unionmultiinner method)": [[845, "networkx.classes.coreviews.UnionMultiInner.items"]], "keys() (unionmultiinner method)": [[846, "networkx.classes.coreviews.UnionMultiInner.keys"]], "values() (unionmultiinner method)": [[847, "networkx.classes.coreviews.UnionMultiInner.values"]], "__contains__() (digraph method)": [[848, "networkx.DiGraph.__contains__"]], "__getitem__() (digraph method)": [[849, "networkx.DiGraph.__getitem__"]], "__init__() (digraph method)": [[850, "networkx.DiGraph.__init__"]], "__iter__() (digraph method)": [[851, "networkx.DiGraph.__iter__"]], "__len__() (digraph method)": [[852, "networkx.DiGraph.__len__"]], "add_edge() (digraph method)": [[853, "networkx.DiGraph.add_edge"]], "add_edges_from() (digraph method)": [[854, "networkx.DiGraph.add_edges_from"]], "add_node() (digraph method)": [[855, "networkx.DiGraph.add_node"]], "add_nodes_from() (digraph method)": [[856, "networkx.DiGraph.add_nodes_from"]], "add_weighted_edges_from() (digraph method)": [[857, "networkx.DiGraph.add_weighted_edges_from"]], "adj (digraph property)": [[858, "networkx.DiGraph.adj"]], "adjacency() (digraph method)": [[859, "networkx.DiGraph.adjacency"]], "clear() (digraph method)": [[860, "networkx.DiGraph.clear"]], "clear_edges() (digraph method)": [[861, "networkx.DiGraph.clear_edges"]], "copy() (digraph method)": [[862, "networkx.DiGraph.copy"]], "degree (digraph property)": [[863, "networkx.DiGraph.degree"]], "edge_subgraph() (digraph method)": [[864, "networkx.DiGraph.edge_subgraph"]], "edges (digraph property)": [[865, "networkx.DiGraph.edges"]], "get_edge_data() (digraph method)": [[866, "networkx.DiGraph.get_edge_data"]], "has_edge() (digraph method)": [[867, "networkx.DiGraph.has_edge"]], "has_node() (digraph method)": [[868, "networkx.DiGraph.has_node"]], "in_degree (digraph property)": [[869, "networkx.DiGraph.in_degree"]], "in_edges (digraph property)": [[870, "networkx.DiGraph.in_edges"]], "nbunch_iter() (digraph method)": [[871, "networkx.DiGraph.nbunch_iter"]], "neighbors() (digraph method)": [[872, "networkx.DiGraph.neighbors"]], "nodes (digraph property)": [[873, "networkx.DiGraph.nodes"]], "number_of_edges() (digraph method)": [[874, "networkx.DiGraph.number_of_edges"]], "number_of_nodes() (digraph method)": [[875, "networkx.DiGraph.number_of_nodes"]], "order() (digraph method)": [[876, "networkx.DiGraph.order"]], "out_degree (digraph property)": [[877, "networkx.DiGraph.out_degree"]], "out_edges (digraph property)": [[878, "networkx.DiGraph.out_edges"]], "pred (digraph property)": [[879, "networkx.DiGraph.pred"]], "predecessors() (digraph method)": [[880, "networkx.DiGraph.predecessors"]], "remove_edge() (digraph method)": [[881, "networkx.DiGraph.remove_edge"]], "remove_edges_from() (digraph method)": [[882, "networkx.DiGraph.remove_edges_from"]], "remove_node() (digraph method)": [[883, "networkx.DiGraph.remove_node"]], "remove_nodes_from() (digraph method)": 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"add_edges_from() (graph method)": [[899, "networkx.Graph.add_edges_from"]], "add_node() (graph method)": [[900, "networkx.Graph.add_node"]], "add_nodes_from() (graph method)": [[901, "networkx.Graph.add_nodes_from"]], "add_weighted_edges_from() (graph method)": [[902, "networkx.Graph.add_weighted_edges_from"]], "adj (graph property)": [[903, "networkx.Graph.adj"]], "adjacency() (graph method)": [[904, "networkx.Graph.adjacency"]], "clear() (graph method)": [[905, "networkx.Graph.clear"]], "clear_edges() (graph method)": [[906, "networkx.Graph.clear_edges"]], "copy() (graph method)": [[907, "networkx.Graph.copy"]], "degree (graph property)": [[908, "networkx.Graph.degree"]], "edge_subgraph() (graph method)": [[909, "networkx.Graph.edge_subgraph"]], "edges (graph property)": [[910, "networkx.Graph.edges"]], "get_edge_data() (graph method)": [[911, "networkx.Graph.get_edge_data"]], "has_edge() (graph method)": [[912, "networkx.Graph.has_edge"]], "has_node() (graph method)": [[913, "networkx.Graph.has_node"]], "nbunch_iter() (graph method)": [[914, "networkx.Graph.nbunch_iter"]], "neighbors() (graph method)": [[915, "networkx.Graph.neighbors"]], "nodes (graph property)": [[916, "networkx.Graph.nodes"]], "number_of_edges() (graph method)": [[917, "networkx.Graph.number_of_edges"]], "number_of_nodes() (graph method)": [[918, "networkx.Graph.number_of_nodes"]], "order() (graph method)": [[919, "networkx.Graph.order"]], "remove_edge() (graph method)": [[920, "networkx.Graph.remove_edge"]], "remove_edges_from() (graph method)": [[921, "networkx.Graph.remove_edges_from"]], "remove_node() (graph method)": [[922, "networkx.Graph.remove_node"]], "remove_nodes_from() (graph method)": [[923, "networkx.Graph.remove_nodes_from"]], "size() (graph method)": [[924, "networkx.Graph.size"]], "subgraph() (graph method)": [[925, "networkx.Graph.subgraph"]], "to_directed() (graph method)": [[926, "networkx.Graph.to_directed"]], "to_undirected() (graph method)": [[927, 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"networkx.MultiDiGraph.remove_edges_from"]], "remove_node() (multidigraph method)": [[964, "networkx.MultiDiGraph.remove_node"]], "remove_nodes_from() (multidigraph method)": [[965, "networkx.MultiDiGraph.remove_nodes_from"]], "reverse() (multidigraph method)": [[966, "networkx.MultiDiGraph.reverse"]], "size() (multidigraph method)": [[967, "networkx.MultiDiGraph.size"]], "subgraph() (multidigraph method)": [[968, "networkx.MultiDiGraph.subgraph"]], "succ (multidigraph property)": [[969, "networkx.MultiDiGraph.succ"]], "successors() (multidigraph method)": [[970, "networkx.MultiDiGraph.successors"]], "to_directed() (multidigraph method)": [[971, "networkx.MultiDiGraph.to_directed"]], "to_undirected() (multidigraph method)": [[972, "networkx.MultiDiGraph.to_undirected"]], "update() (multidigraph method)": [[973, "networkx.MultiDiGraph.update"]], "__contains__() (multigraph method)": [[974, "networkx.MultiGraph.__contains__"]], "__getitem__() (multigraph method)": [[975, "networkx.MultiGraph.__getitem__"]], "__init__() (multigraph method)": [[976, "networkx.MultiGraph.__init__"]], "__iter__() (multigraph method)": [[977, "networkx.MultiGraph.__iter__"]], "__len__() (multigraph method)": [[978, "networkx.MultiGraph.__len__"]], "add_edge() (multigraph method)": [[979, "networkx.MultiGraph.add_edge"]], "add_edges_from() (multigraph method)": [[980, "networkx.MultiGraph.add_edges_from"]], "add_node() (multigraph method)": [[981, "networkx.MultiGraph.add_node"]], "add_nodes_from() (multigraph method)": [[982, "networkx.MultiGraph.add_nodes_from"]], "add_weighted_edges_from() (multigraph method)": [[983, "networkx.MultiGraph.add_weighted_edges_from"]], "adj (multigraph property)": [[984, "networkx.MultiGraph.adj"]], "adjacency() (multigraph method)": [[985, "networkx.MultiGraph.adjacency"]], "clear() (multigraph method)": [[986, "networkx.MultiGraph.clear"]], "clear_edges() (multigraph method)": [[987, "networkx.MultiGraph.clear_edges"]], "copy() (multigraph method)": [[988, "networkx.MultiGraph.copy"]], "degree (multigraph property)": [[989, "networkx.MultiGraph.degree"]], "edge_subgraph() (multigraph method)": [[990, "networkx.MultiGraph.edge_subgraph"]], "edges (multigraph property)": [[991, "networkx.MultiGraph.edges"]], "get_edge_data() (multigraph method)": [[992, "networkx.MultiGraph.get_edge_data"]], "has_edge() (multigraph method)": [[993, "networkx.MultiGraph.has_edge"]], "has_node() (multigraph method)": [[994, "networkx.MultiGraph.has_node"]], "nbunch_iter() (multigraph method)": [[995, "networkx.MultiGraph.nbunch_iter"]], "neighbors() (multigraph method)": [[996, "networkx.MultiGraph.neighbors"]], "new_edge_key() (multigraph method)": [[997, "networkx.MultiGraph.new_edge_key"]], "nodes (multigraph property)": [[998, "networkx.MultiGraph.nodes"]], "number_of_edges() (multigraph method)": [[999, "networkx.MultiGraph.number_of_edges"]], "number_of_nodes() (multigraph method)": [[1000, "networkx.MultiGraph.number_of_nodes"]], "order() (multigraph method)": [[1001, "networkx.MultiGraph.order"]], "remove_edge() (multigraph method)": [[1002, "networkx.MultiGraph.remove_edge"]], "remove_edges_from() (multigraph method)": [[1003, "networkx.MultiGraph.remove_edges_from"]], "remove_node() (multigraph method)": [[1004, "networkx.MultiGraph.remove_node"]], "remove_nodes_from() (multigraph method)": [[1005, "networkx.MultiGraph.remove_nodes_from"]], "size() (multigraph method)": [[1006, "networkx.MultiGraph.size"]], "subgraph() (multigraph method)": [[1007, "networkx.MultiGraph.subgraph"]], "to_directed() (multigraph method)": [[1008, "networkx.MultiGraph.to_directed"]], "to_undirected() (multigraph method)": [[1009, "networkx.MultiGraph.to_undirected"]], "update() (multigraph method)": [[1010, "networkx.MultiGraph.update"]], "_dispatch() (in module networkx.classes.backends)": [[1011, "networkx.classes.backends._dispatch"]], "adjacencyview (class in networkx.classes.coreviews)": [[1012, "networkx.classes.coreviews.AdjacencyView"]], "__init__() (adjacencyview method)": [[1012, "networkx.classes.coreviews.AdjacencyView.__init__"]], "atlasview (class in networkx.classes.coreviews)": [[1013, "networkx.classes.coreviews.AtlasView"]], "__init__() (atlasview method)": [[1013, "networkx.classes.coreviews.AtlasView.__init__"]], "filteradjacency (class in networkx.classes.coreviews)": [[1014, "networkx.classes.coreviews.FilterAdjacency"]], "__init__() (filteradjacency method)": [[1014, "networkx.classes.coreviews.FilterAdjacency.__init__"]], "filteratlas (class in networkx.classes.coreviews)": [[1015, "networkx.classes.coreviews.FilterAtlas"]], "__init__() (filteratlas method)": [[1015, "networkx.classes.coreviews.FilterAtlas.__init__"]], "filtermultiadjacency (class in networkx.classes.coreviews)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency"]], "__init__() (filtermultiadjacency method)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency.__init__"]], "filtermultiinner (class in networkx.classes.coreviews)": [[1017, "networkx.classes.coreviews.FilterMultiInner"]], "__init__() (filtermultiinner method)": [[1017, "networkx.classes.coreviews.FilterMultiInner.__init__"]], "multiadjacencyview (class in networkx.classes.coreviews)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView"]], "__init__() (multiadjacencyview method)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView.__init__"]], "unionadjacency (class in networkx.classes.coreviews)": [[1019, "networkx.classes.coreviews.UnionAdjacency"]], "__init__() (unionadjacency method)": [[1019, "networkx.classes.coreviews.UnionAdjacency.__init__"]], "unionatlas (class in networkx.classes.coreviews)": [[1020, "networkx.classes.coreviews.UnionAtlas"]], "__init__() (unionatlas method)": [[1020, "networkx.classes.coreviews.UnionAtlas.__init__"]], "unionmultiadjacency (class in networkx.classes.coreviews)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency"]], "__init__() (unionmultiadjacency method)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency.__init__"]], "unionmultiinner (class in networkx.classes.coreviews)": [[1022, "networkx.classes.coreviews.UnionMultiInner"]], "__init__() (unionmultiinner method)": [[1022, "networkx.classes.coreviews.UnionMultiInner.__init__"]], "hide_diedges() (in module networkx.classes.filters)": [[1023, "networkx.classes.filters.hide_diedges"]], "hide_edges() (in module networkx.classes.filters)": [[1024, "networkx.classes.filters.hide_edges"]], "hide_multidiedges() (in module networkx.classes.filters)": [[1025, "networkx.classes.filters.hide_multidiedges"]], "hide_multiedges() (in module networkx.classes.filters)": [[1026, "networkx.classes.filters.hide_multiedges"]], "hide_nodes() (in module networkx.classes.filters)": [[1027, "networkx.classes.filters.hide_nodes"]], "no_filter() (in module networkx.classes.filters)": [[1028, "networkx.classes.filters.no_filter"]], "show_diedges() (in module networkx.classes.filters)": [[1029, "networkx.classes.filters.show_diedges"]], "show_edges() (in module networkx.classes.filters)": [[1030, "networkx.classes.filters.show_edges"]], "show_multidiedges() (in module networkx.classes.filters)": [[1031, "networkx.classes.filters.show_multidiedges"]], "show_multiedges() (in module networkx.classes.filters)": [[1032, "networkx.classes.filters.show_multiedges"]], "__init__() (show_nodes method)": [[1033, "networkx.classes.filters.show_nodes.__init__"]], "show_nodes (class in networkx.classes.filters)": [[1033, "networkx.classes.filters.show_nodes"]], "generic_graph_view() (in module networkx.classes.graphviews)": [[1034, "networkx.classes.graphviews.generic_graph_view"]], "reverse_view() (in module networkx.classes.graphviews)": [[1035, "networkx.classes.graphviews.reverse_view"]], "subgraph_view() (in module networkx.classes.graphviews)": [[1036, "networkx.classes.graphviews.subgraph_view"]], "graph (class in networkx)": [[1037, "networkx.Graph"]], "networkx.classes.backends": [[1038, "module-networkx.classes.backends"]], "networkx.classes.coreviews": [[1038, "module-networkx.classes.coreviews"]], "networkx.classes.filters": [[1038, "module-networkx.classes.filters"]], "networkx.classes.graphviews": [[1038, "module-networkx.classes.graphviews"]], "multidigraph (class in networkx)": [[1039, "networkx.MultiDiGraph"]], "multigraph (class in networkx)": [[1040, "networkx.MultiGraph"]], "networkx.convert": [[1041, "module-networkx.convert"]], "networkx.convert_matrix": [[1041, "module-networkx.convert_matrix"]], "networkx.drawing.layout": [[1042, "module-networkx.drawing.layout"]], "networkx.drawing.nx_agraph": [[1042, "module-networkx.drawing.nx_agraph"]], "networkx.drawing.nx_pydot": [[1042, "module-networkx.drawing.nx_pydot"]], "networkx.drawing.nx_pylab": [[1042, "module-networkx.drawing.nx_pylab"]], "ambiguoussolution (class in networkx)": [[1043, "networkx.AmbiguousSolution"]], "exceededmaxiterations (class in networkx)": [[1043, "networkx.ExceededMaxIterations"]], "hasacycle (class in networkx)": [[1043, "networkx.HasACycle"]], "networkxalgorithmerror (class in networkx)": [[1043, "networkx.NetworkXAlgorithmError"]], "networkxerror (class in networkx)": [[1043, "networkx.NetworkXError"]], "networkxexception (class in networkx)": [[1043, "networkx.NetworkXException"]], "networkxnocycle (class in networkx)": [[1043, "networkx.NetworkXNoCycle"]], "networkxnopath (class in networkx)": [[1043, "networkx.NetworkXNoPath"]], "networkxnotimplemented (class in networkx)": [[1043, "networkx.NetworkXNotImplemented"]], "networkxpointlessconcept (class in networkx)": [[1043, "networkx.NetworkXPointlessConcept"]], "networkxunbounded (class in networkx)": [[1043, "networkx.NetworkXUnbounded"]], "networkxunfeasible (class in networkx)": [[1043, "networkx.NetworkXUnfeasible"]], "nodenotfound (class in networkx)": [[1043, "networkx.NodeNotFound"]], "poweriterationfailedconvergence (class in networkx)": [[1043, "networkx.PowerIterationFailedConvergence"]], "networkx.exception": [[1043, "module-networkx.exception"]], "networkx.classes.function": [[1044, "module-networkx.classes.function"]], "assemble() (argmap method)": [[1045, "networkx.utils.decorators.argmap.assemble"]], "compile() (argmap method)": [[1046, "networkx.utils.decorators.argmap.compile"]], "signature() (argmap class method)": [[1047, "networkx.utils.decorators.argmap.signature"]], "pop() (mappedqueue method)": [[1048, "networkx.utils.mapped_queue.MappedQueue.pop"]], "push() (mappedqueue method)": [[1049, "networkx.utils.mapped_queue.MappedQueue.push"]], "remove() (mappedqueue method)": [[1050, "networkx.utils.mapped_queue.MappedQueue.remove"]], "update() (mappedqueue method)": [[1051, "networkx.utils.mapped_queue.MappedQueue.update"]], "add_cycle() (in module networkx.classes.function)": [[1052, "networkx.classes.function.add_cycle"]], "add_path() (in module networkx.classes.function)": [[1053, "networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, 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"kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], 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module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, 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module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() 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networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, 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networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, 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\ 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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916, 935, 937, 938, 943, 955, 980, 982, 983, 988, 996, 998, 1037, 1039, 1040, 1041, 1097, 1165, 1213, 1215, 1217, 1239, 1262, 1269, 1272, 1326, 1328, 1330, 1393, 1394, 1404, 1406, 1411, 1426], "infeas": [8, 422], "circuit_to_formula": 8, "dag_to_branch": [8, 758, 1408], "transfer": [8, 202, 204, 230, 231, 469, 890, 891, 926, 927, 971, 972, 1008, 1009, 1420], "oper": [8, 30, 52, 95, 101, 112, 115, 168, 184, 189, 227, 374, 423, 460, 546, 547, 548, 552, 553, 554, 577, 595, 598, 601, 671, 672, 673, 674, 679, 680, 758, 786, 865, 873, 878, 910, 916, 946, 955, 960, 991, 998, 1036, 1068, 1088, 1103, 1164, 1218, 1219, 1295, 1302, 1319, 1323, 1325, 1326, 1393, 1394, 1400, 1404, 1405, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1417], "variabl": [8, 94, 132, 373, 530, 540, 618, 619, 732, 796, 1037, 1038, 1039, 1040, 1154, 1165, 1326, 1408, 1412, 1413, 1414, 1420], "formula_to_str": 8, "_to_str": 8, "root": [8, 67, 84, 293, 294, 338, 387, 389, 390, 394, 449, 460, 559, 577, 609, 671, 673, 678, 704, 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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], "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, 59, 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, "069": [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, 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15, 27, 50, 82], "number_of_edg": [10, 15, 25, 28, 198, 690, 886, 924, 967, 1006, 1059, 1154, 1271, 1406, 1407, 1426], "nonexp_graph": 10, "compression_nod": 10, "summar": [10, 15, 100, 101, 690, 691, 758, 791, 1325, 1328, 1413], "dedensifi": [10, 758], "threshold": [10, 57, 83, 112, 220, 229, 231, 380, 381, 690, 692, 695, 696, 758, 786, 1117, 1193, 1194, 1196, 1197, 1198, 1325, 1398, 1406, 1407, 1408, 1412, 1414], "copi": [10, 16, 38, 44, 93, 95, 106, 167, 196, 199, 202, 203, 204, 205, 284, 285, 286, 287, 288, 341, 388, 390, 392, 406, 433, 434, 435, 436, 437, 453, 460, 469, 521, 584, 585, 587, 596, 599, 602, 603, 605, 606, 607, 610, 611, 613, 614, 633, 636, 690, 864, 885, 887, 890, 891, 909, 925, 926, 927, 945, 963, 966, 968, 971, 972, 990, 1003, 1007, 1008, 1009, 1035, 1038, 1057, 1061, 1063, 1066, 1082, 1083, 1122, 1183, 1189, 1217, 1223, 1227, 1251, 1270, 1294, 1295, 1296, 1403, 1404, 1406, 1407, 1408, 1409, 1412, 1413, 1422, 1425], "nonexp_node_color": 10, "nonexp_node_s": 10, "yellow": [10, 15, 598, 760, 1426], "nonexp_po": 10, "75": [10, 34, 239, 260, 299, 314, 355, 356, 386, 682, 1169, 1170, 1171, 1173, 1404, 1408, 1426], "c_node": [10, 690], "spot": 10, "229": [10, 17, 719, 733], "plot_dedensif": [10, 17], "153": [11, 455], "curiou": 11, "let": [11, 55, 58, 93, 97, 101, 103, 217, 257, 280, 282, 299, 300, 313, 322, 371, 372, 383, 586, 619, 762, 1219, 1278, 1279, 1326, 1425], "defin": [11, 24, 52, 58, 69, 97, 112, 127, 213, 222, 223, 239, 240, 260, 261, 262, 263, 285, 289, 311, 316, 329, 334, 335, 345, 346, 347, 356, 385, 386, 390, 424, 425, 426, 429, 432, 433, 434, 435, 436, 437, 449, 464, 465, 466, 469, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 519, 567, 569, 570, 571, 573, 574, 575, 577, 586, 614, 615, 619, 621, 625, 652, 671, 673, 674, 676, 684, 685, 686, 687, 688, 689, 728, 730, 738, 751, 752, 753, 762, 791, 796, 1037, 1038, 1039, 1040, 1045, 1047, 1071, 1081, 1098, 1147, 1154, 1170, 1172, 1195, 1197, 1280, 1286, 1287, 1288, 1296, 1320, 1321, 1326, 1344, 1353, 1354, 1359, 1363, 1379, 1395, 1402, 1407, 1408, 1412, 1426], "an": [11, 15, 24, 25, 31, 34, 38, 41, 44, 46, 49, 52, 54, 55, 58, 63, 66, 67, 71, 75, 76, 77, 88, 91, 92, 93, 94, 95, 96, 99, 100, 101, 102, 103, 104, 107, 110, 112, 114, 115, 116, 120, 121, 127, 128, 132, 141, 151, 152, 157, 158, 160, 165, 166, 167, 168, 170, 175, 179, 180, 181, 184, 188, 189, 191, 192, 193, 194, 195, 198, 199, 201, 204, 206, 207, 208, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 243, 249, 250, 251, 255, 256, 264, 266, 267, 269, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 291, 292, 293, 294, 295, 297, 298, 299, 301, 302, 306, 307, 308, 309, 311, 312, 315, 316, 318, 319, 320, 322, 324, 325, 326, 329, 330, 332, 341, 342, 343, 345, 346, 347, 348, 349, 350, 351, 353, 357, 362, 363, 364, 365, 366, 370, 373, 374, 375, 377, 378, 379, 380, 381, 383, 384, 385, 387, 388, 389, 390, 392, 394, 395, 400, 402, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 427, 428, 429, 431, 432, 433, 437, 438, 439, 440, 449, 450, 451, 455, 456, 457, 460, 462, 466, 467, 468, 469, 471, 472, 473, 474, 475, 477, 480, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 516, 517, 519, 520, 521, 522, 523, 524, 525, 530, 534, 535, 540, 544, 545, 555, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 577, 578, 579, 580, 584, 586, 588, 589, 590, 593, 594, 595, 596, 597, 598, 601, 604, 605, 607, 610, 611, 615, 616, 618, 619, 624, 626, 627, 631, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 678, 679, 680, 681, 682, 683, 684, 686, 690, 691, 692, 694, 695, 696, 697, 701, 703, 704, 705, 706, 707, 708, 716, 717, 719, 721, 722, 723, 724, 725, 726, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 743, 748, 752, 760, 761, 762, 767, 775, 782, 791, 796, 801, 806, 810, 814, 818, 822, 827, 832, 837, 842, 847, 849, 850, 851, 853, 854, 856, 857, 859, 862, 863, 864, 865, 866, 869, 871, 872, 873, 877, 878, 880, 881, 882, 883, 884, 886, 887, 889, 891, 892, 894, 895, 896, 898, 899, 901, 902, 904, 907, 908, 909, 910, 911, 914, 915, 916, 920, 921, 922, 923, 924, 925, 927, 928, 930, 931, 932, 934, 935, 937, 938, 940, 943, 944, 945, 946, 947, 948, 950, 952, 953, 954, 955, 959, 960, 961, 962, 963, 964, 965, 967, 968, 970, 972, 973, 975, 976, 977, 979, 980, 982, 983, 985, 988, 989, 990, 991, 992, 993, 995, 996, 997, 998, 1002, 1003, 1004, 1005, 1006, 1007, 1009, 1010, 1012, 1013, 1018, 1020, 1036, 1037, 1038, 1039, 1040, 1042, 1043, 1045, 1046, 1049, 1050, 1051, 1061, 1062, 1066, 1068, 1074, 1075, 1081, 1082, 1084, 1085, 1086, 1087, 1088, 1090, 1094, 1098, 1099, 1100, 1101, 1102, 1103, 1105, 1115, 1117, 1122, 1133, 1135, 1137, 1143, 1144, 1146, 1149, 1150, 1151, 1152, 1154, 1155, 1157, 1159, 1160, 1163, 1166, 1167, 1175, 1177, 1178, 1179, 1181, 1182, 1185, 1186, 1187, 1188, 1192, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1202, 1205, 1206, 1207, 1208, 1209, 1210, 1211, 1212, 1213, 1216, 1217, 1218, 1222, 1224, 1225, 1227, 1228, 1229, 1230, 1232, 1234, 1235, 1236, 1239, 1242, 1244, 1250, 1259, 1262, 1263, 1267, 1269, 1270, 1271, 1272, 1273, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1287, 1288, 1291, 1294, 1295, 1296, 1300, 1302, 1303, 1319, 1320, 1321, 1323, 1324, 1326, 1328, 1329, 1331, 1333, 1334, 1336, 1341, 1344, 1352, 1362, 1363, 1365, 1371, 1377, 1378, 1379, 1380, 1381, 1383, 1387, 1393, 1394, 1395, 1397, 1398, 1399, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "process": [11, 13, 52, 76, 92, 93, 94, 96, 97, 98, 102, 104, 180, 222, 226, 232, 274, 331, 338, 373, 383, 405, 406, 440, 455, 464, 465, 466, 592, 624, 691, 760, 786, 871, 914, 952, 995, 1045, 1100, 1175, 1177, 1180, 1216, 1219, 1222, 1225, 1245, 1280, 1290, 1295, 1296, 1299, 1301, 1383, 1395, 1407, 1408, 1412, 1413, 1414, 1419, 1426], "follow": [11, 25, 44, 49, 52, 53, 65, 67, 83, 86, 91, 92, 93, 94, 95, 97, 99, 100, 101, 102, 103, 108, 110, 111, 128, 132, 151, 161, 171, 183, 207, 213, 227, 229, 230, 231, 243, 280, 305, 338, 343, 351, 362, 373, 378, 380, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 440, 452, 453, 465, 466, 496, 502, 503, 504, 505, 506, 507, 508, 588, 598, 599, 602, 615, 636, 679, 748, 750, 760, 762, 791, 853, 867, 892, 898, 912, 928, 934, 948, 973, 979, 993, 1010, 1102, 1103, 1105, 1144, 1165, 1175, 1179, 1185, 1188, 1200, 1201, 1209, 1219, 1225, 1233, 1234, 1241, 1251, 1260, 1274, 1275, 1276, 1277, 1281, 1296, 1315, 1323, 1326, 1328, 1329, 1388, 1393, 1395, 1399, 1404, 1406, 1407, 1409, 1411, 1412, 1413, 1425, 1426], "given": [11, 38, 44, 62, 64, 67, 91, 99, 101, 103, 112, 116, 141, 142, 144, 152, 158, 193, 197, 208, 211, 212, 227, 229, 235, 236, 248, 249, 260, 264, 266, 269, 271, 273, 274, 276, 279, 281, 283, 284, 285, 286, 287, 288, 320, 329, 331, 338, 344, 351, 353, 357, 362, 363, 364, 365, 373, 378, 380, 381, 385, 439, 454, 455, 460, 462, 470, 477, 478, 480, 497, 511, 512, 513, 559, 560, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 576, 578, 579, 580, 588, 589, 590, 614, 615, 616, 622, 623, 659, 660, 661, 662, 676, 677, 678, 679, 681, 683, 684, 686, 690, 691, 693, 697, 698, 699, 700, 702, 703, 704, 706, 707, 708, 709, 728, 729, 730, 731, 732, 739, 748, 753, 761, 782, 786, 854, 857, 882, 899, 902, 921, 935, 938, 963, 980, 983, 1003, 1046, 1085, 1086, 1094, 1101, 1102, 1135, 1144, 1151, 1162, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1189, 1199, 1200, 1201, 1206, 1207, 1208, 1209, 1210, 1221, 1222, 1240, 1269, 1273, 1274, 1276, 1295, 1300, 1302, 1315, 1323, 1353, 1354, 1379, 1380, 1394, 1395, 1406], "digit": [11, 70, 99], "base": [11, 15, 38, 43, 55, 58, 69, 93, 94, 100, 101, 102, 103, 107, 128, 132, 199, 203, 205, 212, 216, 220, 229, 296, 297, 301, 302, 303, 308, 309, 310, 311, 312, 322, 323, 324, 325, 329, 330, 337, 343, 346, 347, 362, 371, 373, 374, 380, 381, 382, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 425, 426, 427, 428, 430, 431, 449, 464, 466, 494, 498, 499, 500, 509, 510, 545, 555, 564, 566, 569, 574, 581, 614, 616, 660, 667, 680, 688, 691, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 717, 732, 738, 758, 761, 762, 786, 791, 796, 887, 925, 934, 935, 968, 979, 980, 1007, 1036, 1037, 1038, 1041, 1043, 1082, 1088, 1182, 1229, 1235, 1253, 1267, 1296, 1320, 1321, 1323, 1326, 1383, 1387, 1392, 1395, 1402, 1403, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1421, 1425], "obtain": [11, 91, 165, 207, 282, 345, 346, 347, 380, 383, 387, 388, 389, 390, 394, 465, 511, 606, 618, 619, 656, 722, 742, 743, 760, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1164, 1253, 1272, 1278, 1279, 1323, 1326, 1356, 1357, 1402, 1426], "seri": [11, 444, 616, 680, 1215, 1286], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1177, 1179, 1192, 1222], "end": [11, 25, 36, 52, 95, 101, 106, 153, 154, 206, 215, 227, 267, 268, 300, 332, 333, 342, 371, 372, 427, 614, 618, 619, 626, 627, 631, 632, 634, 635, 636, 639, 640, 650, 651, 652, 653, 654, 655, 660, 664, 667, 677, 678, 680, 734, 736, 1038, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1133, 1135, 1152, 1165, 1206, 1229, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1342, 1344, 1350, 1353, 1357, 1358, 1368, 1371, 1372, 1375, 1376, 1379, 1404, 1413], "In": [11, 16, 27, 43, 54, 57, 58, 88, 92, 93, 94, 95, 97, 99, 100, 101, 103, 110, 115, 127, 132, 133, 175, 184, 199, 217, 229, 230, 231, 235, 240, 257, 258, 259, 278, 283, 286, 288, 289, 299, 311, 312, 324, 325, 329, 350, 357, 378, 379, 380, 410, 413, 414, 415, 422, 429, 443, 447, 450, 458, 460, 494, 498, 499, 501, 510, 565, 568, 572, 574, 590, 591, 615, 619, 621, 652, 653, 654, 657, 658, 663, 670, 675, 676, 690, 691, 701, 703, 717, 718, 719, 730, 732, 740, 741, 742, 743, 761, 762, 767, 770, 789, 791, 796, 869, 873, 887, 916, 925, 954, 955, 968, 997, 998, 1007, 1037, 1038, 1039, 1040, 1042, 1043, 1066, 1100, 1101, 1117, 1154, 1168, 1199, 1203, 1206, 1207, 1208, 1210, 1216, 1217, 1222, 1226, 1231, 1233, 1241, 1295, 1296, 1300, 1320, 1321, 1326, 1328, 1350, 1394, 1398, 1399, 1404, 1405, 1406, 1407, 1408, 1409, 1413, 1414, 1426], "languag": [11, 92, 99, 110, 1042, 1324, 1341, 1342, 1344, 1381, 1382, 1383, 1411], "discret": [11, 104, 235, 249, 362, 409, 513, 517, 518, 618, 1164, 1165, 1178, 1180, 1186, 1190, 1204, 1278, 1279, 1282, 1314, 1315, 1323, 1406], "global": [11, 103, 314, 341, 410, 477, 486, 487, 509, 592, 1045, 1269, 1296, 1301, 1304, 1305, 1328, 1407, 1409, 1411], "attractor": [11, 388], "map": [11, 34, 38, 52, 67, 101, 102, 103, 115, 125, 144, 145, 148, 166, 169, 197, 238, 243, 264, 350, 369, 391, 412, 416, 417, 418, 419, 423, 424, 425, 426, 431, 440, 460, 530, 531, 534, 540, 541, 544, 545, 559, 560, 561, 563, 588, 614, 670, 676, 678, 751, 752, 760, 762, 863, 908, 944, 947, 989, 992, 1012, 1013, 1018, 1019, 1038, 1039, 1040, 1045, 1133, 1135, 1137, 1217, 1269, 1295, 1296, 1306, 1310, 1317, 1318, 1324, 1325, 1361, 1362, 1393, 1402, 1406, 1408, 1412, 1413, 1425, 1426], "restrict": [11, 102, 128, 353, 791, 1038, 1082, 1404], "For": [11, 54, 67, 88, 92, 93, 95, 97, 99, 101, 102, 103, 105, 107, 110, 115, 125, 128, 132, 143, 151, 158, 159, 160, 165, 168, 185, 189, 199, 200, 204, 226, 230, 231, 235, 238, 239, 240, 246, 247, 255, 259, 282, 297, 298, 299, 301, 302, 304, 306, 307, 308, 309, 311, 312, 314, 315, 316, 321, 322, 324, 325, 326, 328, 329, 330, 338, 346, 347, 356, 357, 358, 380, 385, 392, 395, 397, 398, 400, 402, 403, 404, 407, 410, 411, 412, 413, 414, 416, 417, 418, 419, 422, 429, 431, 432, 433, 434, 435, 436, 450, 453, 460, 479, 480, 488, 494, 495, 496, 498, 499, 502, 503, 506, 507, 509, 510, 522, 523, 524, 555, 565, 568, 572, 574, 585, 587, 598, 614, 615, 618, 619, 625, 633, 636, 641, 643, 659, 677, 678, 686, 687, 688, 691, 717, 718, 719, 733, 734, 735, 736, 737, 742, 743, 752, 753, 754, 762, 770, 775, 782, 786, 789, 791, 796, 853, 857, 858, 859, 862, 865, 874, 878, 887, 888, 891, 898, 902, 903, 904, 907, 910, 917, 925, 934, 938, 939, 940, 943, 946, 956, 960, 962, 968, 969, 979, 983, 984, 985, 988, 991, 999, 1002, 1007, 1037, 1038, 1039, 1040, 1042, 1062, 1064, 1066, 1071, 1085, 1094, 1098, 1099, 1101, 1102, 1103, 1105, 1111, 1115, 1131, 1132, 1133, 1135, 1138, 1139, 1140, 1141, 1142, 1143, 1148, 1151, 1154, 1175, 1177, 1179, 1180, 1185, 1188, 1189, 1193, 1195, 1196, 1197, 1198, 1199, 1213, 1214, 1217, 1219, 1224, 1228, 1232, 1242, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1285, 1286, 1289, 1291, 1293, 1296, 1298, 1326, 1328, 1333, 1345, 1348, 1349, 1350, 1356, 1357, 1358, 1371, 1382, 1390, 1393, 1395, 1400, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "108": [11, 61, 72, 1216], "513": [11, 1398, 1406], "reach": [11, 99, 100, 314, 324, 327, 376, 383, 387, 389, 390, 394, 410, 411, 412, 418, 419, 494, 498, 499, 510, 564, 566, 626, 627, 632, 640, 643, 652, 693, 711, 758, 1188, 1207, 1210, 1407], "orbit": 11, "up": [11, 70, 80, 93, 94, 97, 99, 100, 101, 104, 107, 132, 133, 346, 347, 377, 423, 427, 509, 530, 540, 577, 619, 652, 653, 657, 748, 1036, 1038, 1061, 1066, 1082, 1088, 1102, 1144, 1148, 1173, 1213, 1215, 1272, 1326, 1328, 1355, 1358, 1395, 1396, 1402, 1404, 1406, 1410, 1411, 1413, 1414, 1416, 1417, 1420, 1426], "reveal": [11, 711, 786], "maximum": [11, 112, 115, 209, 210, 211, 212, 214, 215, 217, 222, 224, 227, 257, 259, 264, 277, 278, 279, 281, 288, 296, 304, 311, 312, 315, 316, 317, 318, 319, 321, 324, 328, 330, 339, 341, 342, 343, 346, 347, 352, 356, 361, 373, 377, 380, 382, 383, 385, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 428, 440, 472, 473, 494, 498, 499, 500, 501, 502, 503, 506, 507, 509, 510, 520, 521, 564, 566, 581, 583, 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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, 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92, 299, 496, 504, 505, 508, 525, 535, 555, 671, 672, 673, 674], "messag": [45, 92, 93, 94, 100, 101, 152, 157, 158, 195, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1412, 1413, 1414, 1425], "hold": [45, 88, 100, 151, 159, 166, 175, 188, 190, 196, 198, 200, 208, 227, 239, 240, 241, 242, 243, 244, 247, 252, 266, 297, 298, 303, 306, 307, 311, 315, 316, 323, 324, 325, 326, 329, 330, 352, 355, 356, 380, 381, 383, 384, 385, 491, 593, 647, 687, 688, 689, 738, 796, 853, 858, 863, 869, 877, 879, 885, 886, 888, 898, 903, 908, 924, 939, 944, 950, 959, 966, 967, 969, 984, 989, 1006, 1020, 1037, 1039, 1040, 1102, 1103, 1105, 1108, 1112, 1115, 1117, 1287, 1288, 1393, 1407, 1409, 1426], "call": [45, 55, 58, 63, 93, 94, 97, 101, 102, 112, 114, 132, 141, 164, 168, 184, 189, 206, 212, 230, 231, 244, 249, 338, 341, 346, 347, 394, 410, 412, 414, 416, 417, 418, 419, 426, 450, 452, 453, 464, 470, 491, 492, 494, 498, 499, 502, 503, 506, 507, 509, 510, 517, 525, 530, 535, 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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], 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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, 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"add_basemap": [54, 55, 57], "geopackag": [54, 55, 56, 57], "sqlite": [54, 57], "reli": [54, 57, 99, 103, 362, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 502, 503, 506, 507, 1393, 1407, 1411, 1425], "fiona": [54, 57], "level": [54, 57, 101, 103, 104, 106, 111, 112, 115, 125, 165, 220, 322, 334, 336, 374, 380, 381, 387, 389, 390, 394, 423, 427, 640, 691, 770, 786, 862, 907, 943, 988, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1094, 1108, 1155, 1202, 1207, 1208, 1236, 1296, 1323, 1328, 1396, 1399, 1407, 1412, 1413, 1414], "interfac": [54, 57, 58, 75, 76, 96, 98, 99, 101, 102, 107, 109, 110, 184, 429, 496, 673, 758, 761, 762, 780, 873, 916, 955, 998, 1042, 1044, 1326, 1328, 1393, 1396, 1398, 1402, 1404, 1405, 1406, 1409, 1413, 1414, 1426], "kind": [54, 57, 58, 92, 93, 94, 99, 208, 466, 722, 1202, 1326, 1383], "read_fil": [54, 55, 57, 58], "cholera_cas": [54, 57], "gpkg": [54, 56, 57], "correctli": [54, 164, 324, 325, 1393, 1404, 1406, 1411, 1412, 1419], "construct": [54, 55, 56, 57, 58, 67, 94, 102, 227, 229, 230, 231, 232, 269, 273, 276, 352, 423, 450, 460, 513, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 609, 685, 695, 708, 716, 732, 1046, 1047, 1052, 1053, 1101, 1102, 1103, 1104, 1105, 1153, 1154, 1175, 1177, 1178, 1180, 1186, 1190, 1191, 1192, 1195, 1203, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1229, 1236, 1251, 1259, 1263, 1269, 1272, 1278, 1279, 1296, 1323, 1327, 1395, 1399, 1406, 1409, 1415], "column_stack": [54, 57, 58], "could": [54, 93, 101, 102, 103, 165, 215, 216, 224, 581, 679, 862, 907, 943, 988, 1066, 1094, 1102, 1103, 1120, 1126, 1174, 1296, 1300, 1326, 1393, 1404, 1414, 1426], "present": [54, 58, 93, 107, 110, 132, 184, 220, 226, 315, 316, 330, 357, 359, 429, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 567, 581, 594, 595, 597, 600, 601, 604, 632, 633, 635, 636, 659, 670, 749, 786, 873, 916, 955, 998, 1043, 1045, 1061, 1082, 1150, 1152, 1157, 1159, 1160, 1163, 1165, 1278, 1279, 1353, 1354, 1357, 1381, 1383, 1407, 1411, 1426], "alongsid": [54, 438], "diagram": [54, 132, 381, 752], "intrins": 54, "put": [54, 92, 95, 102, 226, 1326, 1404, 1406], "underli": [54, 101, 102, 132, 152, 157, 158, 161, 195, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 427, 428, 490, 491, 500, 615, 742, 743, 791, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1038, 1225, 1233, 1241, 1326, 1393, 1394, 1402], "quickli": [54, 1239], "Be": [54, 92, 1038, 1135, 1404], "care": [54, 92, 100, 102, 106, 107, 109, 115, 156, 855, 900, 936, 981, 1038, 1326, 1404, 1406], "bound": [54, 112, 214, 215, 216, 217, 220, 224, 227, 264, 300, 342, 352, 437, 440, 675, 1043, 1165, 1235, 1319, 1413, 1414, 1416], "box": [54, 107, 1134, 1136, 1271, 1323], "control": [54, 168, 179, 189, 204, 230, 231, 324, 325, 450, 467, 865, 878, 891, 910, 946, 960, 991, 1328, 1402, 1408, 1409, 1413], "cell": [54, 58, 752, 758, 1271, 1323, 1325, 1407], "convex": 54, "hull": 54, "contigu": [54, 58, 438, 1102, 1277, 1278], "being": [54, 92, 94, 95, 99, 101, 102, 109, 217, 227, 464, 465, 466, 559, 560, 711, 1038, 1045, 1144, 1175, 1236, 1296, 1393, 1394, 1407, 1412, 1413, 1416, 1425], "face": [54, 101, 102, 115, 183, 206, 615, 1043, 1262, 1263], "analogu": [54, 58, 230], "von": 54, "neuman": 54, "neighborhood": [54, 58, 114, 213, 240, 249, 285, 286, 324, 325, 512, 690, 786, 1189], "cardin": [54, 115, 218, 221, 264, 277, 278, 279, 280, 339, 341, 343, 345, 414, 415, 416, 417, 428, 440, 441, 444, 446, 581, 583, 611, 691, 1395], "regular": [54, 58, 65, 88, 99, 477, 478, 479, 480, 622, 623, 624, 758, 1038, 1185, 1190, 1191, 1192, 1239, 1245, 1250, 1251, 1254, 1258, 1261, 1262, 1263, 1264, 1280, 1290, 1323, 1325, 1394, 1395, 1398, 1406, 1412, 1413], "come": [54, 93, 100, 101, 102, 517, 577, 588, 598, 608, 677, 698, 699, 1046, 1243, 1326, 1402, 1413], "piec": [54, 374], "move": [54, 94, 95, 100, 101, 230, 231, 377, 380, 1117, 1207, 1210, 1393, 1395, 1404, 1405, 1406, 1407, 1411, 1413, 1416, 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1416, 1417], "ox": [56, 748, 750], "config": [56, 1404, 1411], "use_cach": 56, "log_consol": 56, "graph_from_point": 56, "79": [56, 452, 453, 513, 1178, 1180], "122": [56, 1235, 1326, 1426], "41": [56, 64, 297, 1186, 1271], "750": 56, "network_typ": 56, "drive": 56, "imput": 56, "add_edge_spe": 56, "add_edge_travel_tim": 56, "gdf_node": 56, "gdf_edg": 56, "graph_to_gdf": 56, "graph_from_gdf": 56, "graph_attr": [56, 77, 1118, 1122], "choos": [56, 92, 93, 101, 102, 141, 213, 233, 234, 271, 275, 362, 370, 374, 409, 791, 1066, 1111, 1133, 1135, 1185, 1186, 1224, 1228, 1229, 1230, 1232, 1235, 1320, 1321, 1409, 1425], "travel_tim": 56, "utils_graph": 56, "get_digraph": 56, "bc": [56, 588, 1151], "normal": [56, 99, 237, 238, 242, 243, 245, 257, 258, 259, 296, 297, 298, 299, 300, 301, 302, 304, 305, 306, 307, 308, 309, 314, 315, 321, 322, 324, 325, 326, 327, 328, 330, 356, 447, 564, 569, 598, 625, 685, 688, 689, 733, 734, 735, 736, 737, 1085, 1133, 1134, 1136, 1168, 1275, 1276, 1277, 1278, 1279, 1284, 1286, 1293, 1296, 1300, 1314, 1315, 1401, 1403, 1406, 1413], "set_node_attribut": [56, 238, 251, 598, 760, 1404, 1407], "get_node_colors_by_attr": 56, "plot_graph": 56, "bgcolor": 56, "edge_linewidth": 56, "333333": 56, "save_graph_shapefil": 56, "filepath": [56, 58], "graph_shapefil": 56, "save_graph_geopackag": 56, "save_graphml": 56, "730": [56, 59], "plot_osmnx": [56, 59], "nearest": [57, 239, 662, 1211, 1225, 1233, 1241], "knn3": 57, "knn": 57, "meter": 57, "band": 57, "pair": [57, 88, 102, 112, 115, 127, 132, 144, 184, 210, 214, 215, 220, 222, 223, 228, 229, 230, 231, 232, 237, 238, 242, 245, 246, 247, 257, 264, 289, 296, 297, 298, 300, 306, 307, 312, 315, 316, 329, 330, 371, 372, 374, 377, 383, 384, 396, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 429, 440, 470, 473, 480, 485, 486, 487, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 525, 526, 534, 535, 536, 544, 555, 559, 560, 565, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 584, 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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, 86, 1272], "napoleon": [60, 72, 86, 1406, 1413], "russian": [60, 72, 86, 1406], "campaign": [60, 72, 86, 1406], "roget": [60, 72, 86, 1406], "triad": [60, 72, 86, 359, 744, 746, 747, 748, 749, 750, 758, 1274, 1325, 1395], "word": [60, 69, 72, 86, 92, 235, 460, 512, 565, 701, 789, 1133, 1135, 1326, 1405, 1413, 1425], "ladder": [60, 72, 86, 1149, 1156], "topological_gener": [61, 67, 758, 1413], "numer": [61, 88, 110, 151, 166, 175, 188, 198, 209, 211, 212, 239, 240, 241, 242, 243, 244, 247, 248, 252, 283, 355, 356, 378, 380, 381, 383, 384, 385, 453, 556, 557, 558, 581, 593, 626, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 658, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 853, 863, 869, 877, 886, 898, 908, 924, 944, 950, 959, 967, 989, 1006, 1100, 1101, 1102, 1103, 1105, 1108, 1115, 1117, 1133, 1135, 1137, 1287, 1288, 1295, 1296, 1326, 1338, 1340, 1358, 1393, 1394, 1399, 1402, 1404, 1406, 1407, 1409, 1413, 1414, 1416, 1419, 1426], "plot_dag_layout": [61, 72], "668273": 62, "is_graph": [62, 758, 1175, 1181], "configuration_model": [62, 275, 1177, 1178, 1181], "reproducibl": 62, "plot_degree_sequ": [62, 72], "report": 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92, 290, 315, 358, 1321], "expected_degree_graph": [64, 1235, 1408], "dh": [64, 588], "degree_histogram": [64, 1413], "029": [64, 72], "plot_expected_degree_sequ": [64, 72], "gml": [65, 95, 1325, 1326, 1345, 1347, 1348, 1349, 1350, 1384, 1395, 1398, 1406, 1407, 1410, 1411, 1412, 1413, 1414, 1425, 1426], "statistc": 65, "unpack": [65, 101, 111, 192, 688, 881, 920, 962, 1002, 1393, 1408, 1426], "internet": [65, 84, 92, 93, 210, 320, 435, 436, 1202, 1323, 1411], "person": [65, 92, 93, 94, 97, 238, 565, 566, 688, 1257, 1266, 1407], "umich": 65, "mejn": 65, "netdata": 65, "american": [65, 220, 311, 312, 427, 444, 687, 689], "ia": 65, "colleg": 65, "dure": [65, 74, 93, 97, 99, 152, 157, 158, 195, 329, 345, 346, 347, 494, 525, 535, 555, 614, 640, 671, 672, 673, 674, 703, 704, 717, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1117, 1165, 1412, 1413], "season": 65, "fall": 65, "girvan": [65, 374, 1407], "newman": [65, 110, 214, 215, 216, 220, 236, 241, 244, 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1425, 1426], "answer": [92, 97, 761, 1407], "question": [92, 97, 693, 1326, 1393, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "inadvert": 92, "mistak": [92, 94, 1424, 1425], "easili": [92, 100, 115, 380, 494, 688, 691, 1328, 1399, 1404, 1426], "detect": [92, 95, 105, 128, 210, 322, 373, 374, 378, 379, 380, 381, 383, 385, 386, 438, 519, 593, 652, 658, 663, 758, 786, 1165, 1169, 1170, 1171, 1326, 1407, 1408, 1409, 1412, 1414], "empathet": 92, "welcom": [92, 94, 109], "patient": 92, "resolv": [92, 93, 94, 97, 99, 100, 101, 464, 1411, 1412, 1425], "assum": [92, 93, 94, 97, 101, 106, 111, 132, 184, 219, 235, 265, 291, 292, 314, 316, 327, 378, 429, 471, 472, 473, 474, 475, 577, 581, 588, 600, 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, 688, 689, 691, 753, 761, 873, 916, 931, 955, 976, 998, 1039, 1040, 1086, 1091, 1097, 1143, 1209, 1270, 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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], "longer": [93, 94, 99, 102, 103, 107, 215, 216, 511, 512, 579, 1117, 1217, 1275, 1393, 1394, 1396, 1398, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1413, 1416, 1425], "fetch": 93, "unmerg": 93, "modifi": [93, 94, 99, 101, 103, 109, 152, 157, 158, 195, 226, 322, 377, 585, 587, 677, 678, 692, 693, 694, 719, 733, 734, 736, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1045, 1063, 1102, 1103, 1105, 1154, 1177, 1270, 1281, 1295, 1393, 1406, 1413, 1426], "file_with_conflict": 93, "insid": [93, 101, 111, 220, 719, 1253, 1296, 1413], "kept": [93, 106], "delet": [93, 95, 106, 322, 618, 619, 671, 672, 673, 674, 767, 1154, 1300, 1326, 1352, 1354, 1378, 1380, 1393, 1394, 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720, 722, 723, 724, 725, 726, 729, 731, 732, 760, 780, 1108, 1117, 1235, 1320, 1321, 1402, 1411, 1412, 1416], "subpackag": [93, 767, 1326, 1413, 1425], "particular": [93, 97, 110, 115, 357, 374, 517, 618, 750, 1175, 1278, 1279, 1328, 1350, 1409], "decor": [93, 102, 103, 1045, 1046, 1047, 1297, 1298, 1299, 1300, 1301, 1325, 1405, 1407, 1411, 1413, 1414, 1417], "not_implemented_for": [93, 1296, 1407, 1417], "doesn": [93, 94, 97, 101, 102, 156, 170, 561, 562, 563, 761, 796, 855, 866, 900, 911, 936, 947, 981, 992, 1037, 1039, 1040, 1117, 1175, 1177, 1179, 1216, 1222, 1296, 1326, 1404, 1406, 1407, 1412, 1414, 1425], "function_not_for_multidigraph": 93, "function_only_for_graph": 93, "framework": [93, 102, 1358], "submodul": [93, 1413], "specif": [93, 96, 99, 101, 107, 110, 111, 157, 184, 232, 346, 347, 370, 458, 502, 503, 506, 507, 517, 681, 683, 703, 856, 873, 901, 916, 937, 947, 955, 982, 992, 998, 1123, 1133, 1135, 1137, 1165, 1193, 1199, 1287, 1288, 1296, 1326, 1343, 1345, 1348, 1349, 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946, 960, 979, 991, 996, 1037, 1038, 1039, 1040, 1135, 1326, 1392, 1393, 1394, 1396, 1398, 1399, 1402, 1406, 1407, 1408, 1410], "cheong": 93, "se": 93, "hang": 93, "yain": 93, "whar": 93, "schemat": 93, "placement": [93, 614], "survei": [93, 110, 564, 566, 581, 786, 1201], "2020": [93, 99, 100, 101, 102, 569, 1406, 1412], "1177": 93, "2f1473871618821740": 93, "upload": [93, 106, 217], "pdf": [93, 105, 110, 112, 128, 214, 215, 216, 217, 220, 235, 305, 311, 312, 315, 322, 324, 325, 330, 342, 355, 356, 373, 410, 411, 412, 413, 414, 415, 417, 426, 427, 430, 442, 447, 448, 476, 483, 490, 494, 511, 512, 519, 564, 566, 567, 570, 571, 573, 618, 619, 690, 693, 748, 749, 750, 760, 762, 1193, 1197, 1198, 1326, 1407, 1412, 1426], "docx": 93, "ppt": 93, "lectur": [93, 110, 412, 431, 498, 616, 1203], "wayback": [93, 1413], "machin": [93, 312, 331, 494, 511, 512, 762, 1396, 1406, 1413], "snapshot": 93, "unreach": 93, "pyarg": [93, 111, 1038], "tell": [93, 99, 102, 760, 1275, 1278, 1279, 1296, 1328, 1412], "compar": [93, 464, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 559, 560, 561, 562, 563, 615, 760, 782, 1165, 1302, 1414], "baselin": [93, 1134, 1136], "ones": [93, 99, 107, 109, 282, 680, 1038, 1395, 1402, 1404], "savefig": [93, 1426], "mpl_image_compar": 93, "test_barbel": 93, "barbel": [93, 293, 294, 391, 424, 1146, 1157, 1276, 1426], "conduct": [93, 96, 100, 109, 447, 448, 758], "contributor": [94, 96, 99, 105, 106, 110, 1271, 1323, 1403], "shepherd": [94, 99], "mission": [94, 96, 97, 100, 107], "approv": [94, 100], "nuclear": 94, "launch": 94, "carefulli": 94, "clean": [94, 106, 530, 540, 1300, 1406, 1407, 1411, 1413, 1420], "nearli": 94, "volunt": [94, 107, 1413], "tremend": 94, "felt": 94, "evalu": [94, 130, 152, 157, 158, 195, 330, 618, 619, 626, 627, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1296, 1417], "novic": 94, "strongli": [94, 217, 232, 388, 391, 397, 398, 399, 403, 405, 406, 423, 480, 491, 492, 519, 588, 633, 697, 699, 751, 753, 1185, 1402, 1406, 1411, 1414, 1417, 1425], "mentorship": [94, 1413], "handhold": 94, "liber": 94, "workflow": [94, 96, 97, 100, 106, 1413, 1420], "realiz": [94, 513, 514, 515, 516, 517, 518, 693, 1175, 1177, 1180, 1207, 1208, 1209, 1210, 1222, 1264], "gentl": 94, "abandon": 94, "difficult": [94, 1405], "carri": [94, 100, 508], "polici": [94, 96, 99, 1412, 1414], "readabl": [94, 107, 109, 169, 172, 460, 868, 913, 949, 994, 1393, 1414], "effici": [94, 102, 112, 212, 275, 290, 377, 387, 389, 390, 392, 394, 399, 405, 406, 407, 422, 425, 426, 486, 487, 508, 512, 581, 614, 680, 688, 691, 698, 699, 758, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1179, 1203, 1230, 1325, 1386, 1390, 1398, 1399, 1406, 1407, 1408, 1411, 1413], "explor": [94, 105, 107, 110, 704, 711, 717], "corner": [94, 1407, 1414], "tempt": 94, "nitpicki": 94, "spell": [94, 1406, 1412, 1413], "suggest": [94, 102, 105, 632, 635, 636, 1165, 1326, 1402, 1406, 1412, 1414, 1425], "latter": [94, 100, 102, 440, 729, 731, 791, 1299], "choic": [94, 102, 204, 385, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 479, 502, 503, 506, 507, 734, 735, 736, 737, 780, 891, 972, 1038, 1042, 1225, 1241, 1280, 1326, 1426], "wish": [94, 619, 1066, 1393], "bring": [94, 101, 566], "advis": [94, 110, 1414], "aris": [94, 110, 238, 243, 1217, 1245], "experienc": 94, "credit": [94, 105], "send": [94, 99, 496, 497, 501, 504, 505, 508, 1393, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "notif": 94, "maintain": [94, 95, 99, 100, 103, 105, 107, 109, 230, 231, 614, 796, 1037, 1039, 1040, 1406, 1425], "concern": [94, 101, 103, 132, 789, 791, 1382], "mere": [94, 1146, 1157], "understood": 94, "made": [94, 99, 100, 102, 222, 282, 284, 285, 286, 287, 288, 324, 325, 331, 693, 694, 1122, 1210, 1326, 1393, 1403, 1404, 1407, 1412, 1425], "freeli": 94, "consult": [94, 111], "extern": [94, 107, 619, 1326, 1383, 1407], "insight": 94, "opportun": [94, 99], "patch": 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313, 380, 385, 1266, 1275, 1286, 1325, 1395, 1402, 1405, 1406], "nxep": [96, 107, 109, 1403, 1412, 1416], "govern": [96, 98, 109, 1412], "slice": [96, 98, 107, 1413], "builder": [96, 98, 1151, 1323, 1413], "frequent": [97, 378, 675], "newcom": [97, 109, 1326], "few": [97, 100, 101, 103, 362, 1402, 1404, 1411, 1412, 1413, 1414], "known": [97, 227, 280, 293, 301, 302, 303, 308, 309, 323, 369, 424, 450, 468, 618, 740, 741, 742, 743, 762, 791, 1068, 1097, 1145, 1148, 1200, 1201, 1224, 1228, 1230, 1232, 1247, 1272, 1324, 1412], "Of": [97, 1426], "sprint": [97, 1425], "permiss": [97, 110, 111, 457], "forget": 97, "sai": [97, 99, 101, 211, 512, 517, 518, 675, 676, 762, 1206, 1411], "rememb": [97, 101], "stick": [97, 1394], "plot_circular_layout": 97, "perhap": [97, 99, 102, 107], "deal": [97, 102], "worri": [97, 583, 1296, 1326], "ipython": 97, "field": [97, 99, 591, 593, 770, 1098, 1099, 1102, 1192], "breviti": 97, "offici": [97, 99, 1402], "inclus": [97, 99, 109, 220, 534, 544, 729, 731, 1188, 1214], "criteria": [97, 1425], "addit": [97, 99, 100, 103, 107, 111, 115, 184, 350, 423, 476, 534, 544, 545, 734, 736, 761, 791, 796, 873, 916, 947, 955, 979, 992, 998, 1036, 1037, 1039, 1040, 1088, 1117, 1195, 1272, 1296, 1302, 1326, 1345, 1348, 1349, 1350, 1381, 1382, 1383, 1395, 1403, 1404, 1405, 1406, 1407, 1413, 1414, 1425, 1426], "fit": [97, 110, 1326], "enhanc": [98, 99, 107, 341, 508, 1296, 1412, 1425], "berkelei": [99, 100, 103, 618, 619], "draft": [99, 100, 102, 103, 104, 1411, 1412, 1413, 1416], "stand": [99, 545, 1387], "primari": [99, 103, 1414], "gone": 99, "concis": [99, 110, 791, 1413, 1414], "rational": 99, "consensu": [99, 100], "dissent": 99, "opinion": [99, 100, 104], "revis": [99, 444, 732], "track": [99, 101, 102, 103, 104, 107, 115, 370, 387, 389, 390, 394, 598, 1296, 1302, 1406, 1411, 1412], "codebas": [99, 1296, 1404, 1405, 1412], "meta": [99, 106], "inject": 99, "repo": [99, 106, 1413, 1425], "success": [99, 315, 330, 496, 608, 692, 1180, 1242, 1426], 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"principl": [99, 100, 103, 132], "impract": 99, "wip": [99, 1407, 1408, 1412], "incorpor": [99, 1399, 1426], "stabil": [99, 334, 335, 681, 683], "provision": 99, "short": [99, 104, 161, 227, 1038, 1066, 1195, 1406], "unlik": [99, 100, 212, 366, 425, 426, 1383], "reject": [99, 100, 104, 1319], "withdrawn": [99, 104], "wherev": [99, 1282], "defer": [99, 101, 104, 280], "challeng": 99, "wider": 99, "done": [99, 101, 102, 230, 231, 249, 373, 440, 466, 517, 564, 566, 614, 690, 762, 1046, 1219, 1296, 1326, 1404], "fact": [99, 352, 460, 619, 1207, 1210, 1404], "actual": [99, 115, 132, 165, 210, 213, 214, 215, 216, 220, 288, 385, 450, 577, 625, 692, 717, 718, 862, 907, 943, 988, 1102, 1103, 1199, 1296, 1324, 1326, 1402, 1416], "compet": [99, 583], "accordingli": [99, 454, 1110, 1407, 1425], "supersed": [99, 104], "render": [99, 216, 410, 413, 1406], "obsolet": [99, 267, 1337, 1406, 1407], "never": [99, 184, 388, 608, 873, 916, 955, 998, 1236], "meant": [99, 291, 292, 631, 1217, 1326, 1413, 1417], "concret": [99, 100], "think": [99, 102, 230, 231, 299, 761, 1426], "bodi": [99, 1243], "briefli": 99, "sentenc": [99, 100], "substant": 99, "pipermail": 99, "2018": [99, 315, 330, 437, 1406, 1408, 1409], "june": [99, 691, 1255, 1398, 1402, 1406, 1419, 1420], "078345": 99, "verg": 99, "chanc": [99, 230, 1234, 1296], "period": [99, 1211, 1212, 1213, 1215, 1297, 1403, 1406, 1412], "beyond": [99, 107, 383, 1210, 1236], "fine": 99, "shouldn": [99, 102], "rigid": 99, "compromis": 99, "followup": [99, 1413], "notifi": [99, 1414], "celebratori": 99, "emoji": 99, "again": [99, 428, 761, 1217, 1403, 1407, 1411, 1416], "unusu": [99, 1393], "disagr": [99, 100], "escal": [99, 100], "controversi": [99, 107], "ultim": 99, "practic": [99, 210, 220, 481, 482, 494, 619, 653, 1328, 1405], "precis": [99, 312, 568, 572, 581, 1269, 1395, 1409], "natur": [99, 102, 109, 376, 443, 466, 585, 587, 618, 753, 1154, 1217, 1225, 1241, 1296, 1326, 1393, 1410], "utf": [99, 267, 268, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1355, 1358, 1368, 1371, 1372, 1375, 1376, 1387, 1406], "restructuredtext": 99, "restructuredtextprim": 99, "dd": [99, 104, 1094], "mmm": 99, "yyyi": [99, 104], "dom": 99, "ain": 99, "separ": [99, 102, 106, 152, 157, 158, 195, 214, 215, 258, 265, 266, 267, 268, 299, 322, 343, 427, 428, 454, 464, 758, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1045, 1112, 1116, 1193, 1195, 1325, 1331, 1332, 1333, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1369, 1370, 1371, 1372, 1395, 1406, 1407, 1412, 1413, 1425, 1426], "older": 99, "brows": 99, "colgat": [100, 110], "deadlock": 100, "websit": [100, 106, 1165, 1382, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "ongo": [100, 1405], "trust": [100, 1381, 1383], "cast": [100, 101, 1412, 1422, 1425], "vote": [100, 337, 1412], "therebi": 100, "adher": 100, "nomin": 100, "lazi": [100, 1283, 1284], "unanim": 100, "agreement": [100, 1202], "initi": [100, 102, 141, 230, 231, 282, 315, 324, 325, 338, 373, 377, 378, 466, 495, 511, 512, 525, 535, 615, 692, 719, 733, 796, 850, 895, 931, 976, 1037, 1039, 1040, 1102, 1105, 1108, 1117, 1185, 1186, 1187, 1188, 1223, 1227, 1234, 1278, 1279, 1296, 1302, 1323, 1394, 1395, 1406, 1411, 1412, 1413, 1414], "voic": 100, "smooth": 100, "strateg": 100, "plan": [100, 1394, 1405, 1407, 1413], "fund": [100, 1414, 1425], "theirs": 100, "pursu": 100, "pictur": 100, "perspect": [100, 104, 1195, 1326], "timefram": 100, "entiti": [100, 1345, 1348, 1349, 1350, 1382, 1426], "occasion": [100, 230], "seek": [100, 762, 1352, 1354, 1378, 1380, 1387], "tri": [100, 112, 343, 380, 931, 976, 1039, 1040, 1175, 1181, 1225, 1237, 1238, 1404], "distinguish": [100, 934, 962, 979, 1002, 1040], "fundament": [100, 107, 110, 338, 449, 618, 619, 1217, 1413], "flaw": 100, "forward": [100, 217, 450, 711, 717, 718], "typo": [100, 1396, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1421], "land": 100, "outlin": [100, 249, 336, 462, 1407], "templat": [100, 1413], "taken": [100, 101, 145, 148, 207, 443, 450, 717, 718, 749, 761, 892, 928, 973, 1010, 1117, 1409], "suffici": [100, 101, 1326], "scikit": [100, 103, 109], "expos": [101, 374, 1405], "nodeview": [101, 184, 391, 598, 599, 601, 602, 603, 604, 695, 873, 916, 955, 998, 1036, 1088, 1349, 1362, 1404, 1407], "nodedataview": [101, 184, 391, 591, 592, 600, 873, 916, 955, 998, 1217, 1426], "edgeview": [101, 590, 591, 592, 598, 599, 600, 601, 602, 603, 604, 612, 624, 770, 910, 1036, 1088, 1098, 1404, 1413], "edgedataview": [101, 168, 189, 865, 878, 910, 946, 960, 991, 1098, 1217, 1362, 1412, 1426], "semant": [101, 531, 541, 762, 1403, 1405], "inher": [101, 220, 427], "impli": [101, 110, 132, 220, 312, 314, 327, 455, 466, 511, 512, 545, 1296], "element": [101, 102, 230, 231, 270, 291, 292, 311, 350, 371, 391, 457, 464, 518, 559, 560, 578, 579, 580, 586, 640, 656, 671, 673, 675, 677, 728, 730, 739, 749, 752, 1036, 1038, 1048, 1049, 1050, 1051, 1087, 1088, 1135, 1137, 1173, 1206, 1211, 1212, 1217, 1237, 1238, 1240, 1249, 1272, 1277, 1278, 1279, 1282, 1287, 1288, 1296, 1302, 1303, 1311, 1318, 1323, 1355, 1358, 1361, 1362, 1405], "intend": [101, 104, 107, 111, 327, 567, 1038, 1269, 1296, 1393], "impos": [101, 103, 545, 791], "due": [101, 102, 109, 231, 264, 440, 581, 583, 626, 627, 1217, 1405, 1412, 1414, 1423, 1425], "bit": [101, 209, 211, 212, 453, 511, 512, 786, 1345, 1348, 1349, 1350, 1382, 1411], "lot": [101, 452, 1326, 1405], "screen": 101, "instinct": 101, "error": [101, 102, 152, 157, 158, 195, 280, 288, 296, 311, 324, 414, 422, 471, 472, 473, 474, 475, 489, 497, 501, 504, 505, 508, 556, 557, 558, 564, 566, 581, 584, 653, 660, 667, 675, 676, 796, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1037, 1043, 1117, 1144, 1396, 1401, 1404, 1406, 1407, 1411, 1412, 1413, 1414, 1417, 1419, 1425], "definit": [101, 132, 235, 238, 243, 289, 291, 292, 303, 323, 342, 356, 398, 435, 437, 464, 467, 549, 550, 551, 608, 618, 619, 620, 625, 676, 685, 687, 700, 735, 737, 791, 1192, 1193, 1197, 1217, 1235, 1287, 1326, 1406, 1413, 1426], "coupl": [101, 102, 132, 1257, 1402, 1404], "realis": 101, "But": [101, 102, 107, 143, 170, 238, 243, 256, 277, 278, 281, 297, 298, 583, 796, 866, 911, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1094, 1328, 1393, 1425], "seem": [101, 102, 298, 307, 791, 1234], "eas": [101, 107, 1409], "idiom": [101, 159, 190, 200, 858, 879, 888, 903, 939, 969, 984, 1296, 1394, 1404, 1411], "subscript": [101, 151, 159, 200, 796, 853, 858, 888, 898, 903, 934, 939, 969, 979, 984, 1037, 1039, 1040, 1394, 1426], "repr": [101, 1347, 1413], "4950": [101, 1414], "traceback": [101, 450, 464, 584, 652, 658, 1302, 1303], "recent": [101, 437, 450, 464, 584, 652, 658, 963, 1003, 1302, 1303, 1411], "typeerror": [101, 382, 464, 1206, 1302, 1404], "opaqu": 101, "ambigu": [101, 103, 115, 252, 253, 464, 762, 1043, 1406], "ambigi": 101, "counter": [101, 153, 357], "nativ": [101, 109], "caveat": 101, "nodes_it": [101, 1404, 1407], "toward": [101, 685, 1407, 1413], "inner": [101, 230, 231, 380, 796, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1086], "synonym": 101, "primarili": [101, 1426], "becam": [101, 1411], "concept": [101, 132, 220, 310, 427, 688, 1043], "intuit": [101, 109], "On": [101, 105, 156, 217, 294, 297, 298, 306, 307, 315, 380, 405, 406, 514, 515, 518, 593, 855, 900, 936, 981, 1180, 1202, 1224, 1228, 1232], "front": [101, 619, 1036, 1088], "constuct": 101, "indx": 101, "desir": [101, 102, 142, 143, 204, 346, 347, 422, 425, 426, 598, 629, 647, 891, 972, 1085, 1094, 1102, 1103, 1105, 1150, 1152, 1157, 1159, 1160, 1163, 1165, 1187, 1218, 1220, 1221, 1234, 1281, 1356, 1357, 1414, 1426], "prelimanari": 101, "impelement": 101, "4086": 101, "rid": [101, 1413], "getitem": 101, "dunder": [101, 107, 1296, 1413], "isinst": [101, 103, 464, 1086, 1411, 1412, 1413], "_node": [101, 1422, 1425], "exclus": [101, 449, 476], "necess": 101, "unhash": [101, 1404], "impel": 101, "insipir": 101, "colon": [101, 1421], "syntax": [101, 102, 171, 796, 867, 912, 948, 993, 1037, 1039, 1040, 1296, 1382, 1383, 1410, 1412], "introspect": 101, "neither": [101, 110, 305, 427, 625, 635, 636, 671, 672, 673, 674, 676, 700, 748], "downsid": 101, "drawback": 101, "discover": 101, "complic": [101, 1296, 1326], "nix": 101, "background": 101, "pertain": 101, "arguabl": [101, 102], "overrid": [101, 671, 672, 673, 674, 1411], "mix": [101, 236, 237, 238, 241, 242, 243, 244, 245, 248, 445, 758, 1100, 1341, 1342, 1344, 1355, 1356, 1357, 1358, 1381, 1383, 1393, 1406, 1407, 1411], "pervas": 101, "unforeseen": 101, "preced": [101, 152, 157, 464, 598, 703, 854, 856, 899, 901, 935, 937, 980, 982, 1045, 1363, 1364], "un": [101, 464, 732, 1407, 1413], "sliceabl": 101, "notabl": [101, 1042], "dict_kei": [101, 1303, 1414], "dict_valu": [101, 379, 1404, 1413], "cpython": [101, 107, 429, 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], 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1411], "mode": [115, 260, 261, 262, 267, 268, 289, 1300, 1333, 1334, 1337, 1338, 1339, 1340, 1371, 1372, 1426], "bipart": [115, 290], "routin": [116, 180, 343, 355, 559, 560, 577, 760, 871, 914, 952, 995, 1042, 1091, 1326, 1395, 1396, 1404, 1406, 1411, 1412, 1413], "outsid": [116, 310, 1404, 1406, 1413], "chord": [120, 341, 343, 1190, 1208, 1215], "chordal_graph": [120, 341], "clique_problem": 121, "character": [122, 313, 782], "triangl": [122, 213, 227, 295, 356, 357, 358, 359, 437, 549, 550, 758, 1098, 1101, 1215, 1219, 1222, 1234, 1243, 1247, 1252, 1263, 1323, 1326, 1406, 1412], "greedy_color": [123, 758, 1395, 1406, 1411], "communities_gener": 125, "girvan_newman": 125, "top_level_commun": 125, "next_level_commun": 125, "kernighan": [125, 377, 1413], "lin": [125, 377, 1407, 1413], "luke": [125, 382, 1412], "asynchron": [125, 373, 378, 379, 1407, 1414], "edge_kcompon": [127, 424], "determen": 127, "maxim": [127, 209, 220, 221, 222, 315, 316, 330, 339, 346, 347, 348, 349, 350, 351, 353, 354, 366, 370, 380, 383, 384, 389, 390, 422, 425, 426, 427, 432, 433, 437, 517, 579, 581, 582, 583, 589, 682, 691, 732, 758, 1043, 1201, 1323, 1325, 1398, 1406, 1407, 1413, 1414], "moodi": [127, 220, 427, 1395], "kanevski": [127, 427, 428, 1395], "recurs": [128, 141, 224, 346, 347, 352, 387, 389, 390, 394, 406, 452, 460, 530, 540, 697, 728, 730, 760, 1045, 1046, 1061, 1082, 1147, 1296, 1406, 1412, 1413], "prune": [128, 760, 1236], "vladimir": [128, 275, 432, 433, 494, 588, 749, 1230], "batagelj": [128, 275, 432, 433, 588, 749, 1230], "matjaz": [128, 432, 433], "zaversnik": [128, 432, 433], "0310049": [128, 432, 433], "0202039": 128, "degeneraci": 128, "christo": 128, "giatsidi": 128, "thiliko": 128, "michali": 128, "vazirgianni": 128, "icdm": 128, "2011": [128, 331, 377, 383, 385, 441, 445, 446, 511, 512, 519, 619, 682, 1179, 1397, 1398, 1399, 1406, 1407], "graphdegeneraci": 128, "dcores_icdm_2011": 128, "anomali": [128, 438], "onion": [128, 438, 1411], "h\u00e9bert": [128, 438], "dufresn": [128, 438], "grochow": [128, 438], "allard": [128, 438, 1411], "31708": [128, 438], "2016": [128, 337, 352, 385, 438, 476, 690, 1197, 1251, 1396, 1406], "1038": [128, 337, 376, 380, 438, 569], "srep31708": [128, 438], "factor": [132, 226, 293, 294, 299, 300, 324, 325, 370, 462, 497, 501, 504, 505, 508, 513, 565, 592, 624, 676, 697, 1106, 1107, 1108, 1109, 1110, 1114, 1115, 1116, 1117, 1145, 1155, 1178, 1180, 1275, 1276, 1277], "graphic": [132, 454, 517, 518, 693, 758, 1175, 1177, 1180, 1181, 1222, 1325, 1383, 1398, 1401, 1406], "overview": [132, 476, 1038, 1296], "collid": [132, 454], "triplet": [132, 745], "successor": [132, 159, 174, 181, 191, 200, 240, 282, 387, 389, 390, 394, 501, 687, 707, 715, 858, 872, 880, 888, 903, 939, 953, 961, 969, 984, 1055, 1183, 1184, 1189, 1326, 1404, 1407, 1416, 1426], "descend": [132, 454, 456, 465, 709, 758, 1272, 1401, 1404, 1406, 1413, 1414], "unblock": 132, "commonli": [132, 280, 454, 684, 782], "probabilist": [132, 378], "causal": 132, "markov": [132, 462, 565, 692, 1188], "hmm": 132, "s1": [132, 1242, 1313, 1363], "s2": [132, 1242, 1313], "s3": [132, 1313], "s4": 132, "s5": 132, "o1": 132, "o2": 132, "o3": 132, "o4": 132, "o5": 132, "ob": 132, "d_separ": [132, 758, 1412], "darwich": 132, "shachter": 132, "1998": [132, 1143, 1144, 1225, 1241, 1407], "bay": 132, "ball": 132, "ration": 132, "pastim": 132, "irrelev": [132, 1407], "requisit": 132, "influenc": [132, 324, 325, 512, 786], "fourteenth": [132, 1186], "uncertainti": [132, 590, 732], "artifici": [132, 574, 590, 732], "480": [132, 426, 514, 518, 1398, 1406], "487": 132, "francisco": [132, 732], "morgan": [132, 732], "kaufmann": [132, 732], "koller": 132, "friedman": 132, "mit": [132, 342, 519, 618], "causal_markov_condit": 132, "ness": [133, 684, 782], "classmethod": [141, 1047], "auxiliari": [141, 142, 143, 220, 411, 412, 413, 415, 416, 417, 418, 419, 423, 430, 431, 1402], "sink": [141, 302, 309, 416, 418, 494, 495, 498, 499, 501, 502, 503, 506, 507, 509, 510, 565], "pick": [141, 217, 331, 657, 1188, 1207, 1210, 1407], "st": [141, 415, 417], "cut": [141, 222, 223, 293, 377, 382, 387, 389, 390, 394, 411, 412, 414, 415, 416, 417, 419, 427, 428, 429, 442, 443, 444, 445, 447, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 619, 758, 760, 1038, 1066, 1115, 1262, 1325, 1395, 1402, 1406, 1413], "refin": [143, 215, 423, 438], "auxgraph": [143, 423], "node_partit": 144, "permut": [144, 368, 452, 453, 455, 466, 748, 1285, 1320, 1321], "containin": 144, "frozenset": [144, 267, 339, 383, 586, 588, 752, 1165, 1333, 1337, 1338, 1412], "abc": [144, 545, 1154, 1206, 1303, 1412, 1413], "interchang": [144, 362], "bool": [145, 146, 148, 149, 165, 168, 171, 176, 184, 189, 196, 204, 208, 232, 237, 238, 242, 243, 245, 249, 250, 258, 265, 266, 267, 268, 272, 275, 286, 287, 288, 291, 294, 295, 296, 297, 298, 299, 301, 302, 305, 306, 307, 308, 309, 310, 314, 315, 322, 324, 325, 326, 327, 330, 343, 350, 355, 362, 393, 394, 395, 396, 397, 398, 439, 454, 462, 463, 467, 479, 480, 488, 489, 491, 494, 498, 499, 509, 510, 513, 514, 515, 516, 517, 518, 520, 521, 522, 545, 562, 564, 578, 579, 580, 581, 588, 613, 614, 616, 617, 622, 623, 625, 640, 652, 663, 673, 679, 685, 690, 696, 698, 699, 700, 704, 708, 719, 723, 724, 725, 726, 728, 730, 733, 734, 735, 736, 737, 738, 740, 741, 742, 743, 862, 865, 867, 870, 873, 878, 885, 891, 907, 910, 912, 916, 927, 931, 943, 946, 948, 951, 955, 960, 966, 972, 976, 988, 991, 993, 998, 1039, 1040, 1045, 1057, 1068, 1070, 1071, 1072, 1084, 1091, 1097, 1116, 1133, 1134, 1135, 1136, 1169, 1179, 1185, 1189, 1209, 1211, 1212, 1213, 1215, 1224, 1228, 1230, 1231, 1232, 1275, 1276, 1277, 1278, 1279, 1282, 1295, 1296, 1307, 1309, 1312, 1335, 1336, 1337, 1339, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1360, 1364, 1379, 1380], "account": [145, 148, 398, 448, 749, 761, 1270, 1393, 1413], "graph_nod": [145, 148], "subgraph_nod": [145, 148], "find_isomorph": [147, 150], "induc": [148, 167, 199, 211, 226, 342, 388, 392, 406, 427, 436, 437, 470, 487, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 512, 586, 589, 752, 761, 762, 864, 887, 909, 925, 945, 968, 990, 1007, 1038, 1061, 1066, 1087, 1102, 1103, 1105, 1189, 1283, 1284, 1393], "u_of_edg": [151, 853, 898], "v_of_edg": [151, 853, 898], "capac": [151, 265, 296, 301, 302, 303, 308, 309, 323, 411, 412, 415, 416, 417, 418, 419, 430, 431, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 758, 853, 898, 934, 979, 1335, 1402], "342": [151, 853, 898, 934, 979, 1255], "ebunch_to_add": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "add_weighted_edges_from": [152, 229, 230, 231, 508, 581, 630, 657, 659, 721, 854, 899, 935, 980, 1070, 1326, 1404, 1407, 1426], "runtimeerror": [152, 157, 158, 195, 464, 465, 466, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005], "happen": [152, 157, 158, 195, 380, 584, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1403, 1404, 1425], "iterator_of_edg": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "wn2898": [152, 854, 899, 935, 980], "wrong": [152, 157, 158, 722, 854, 856, 857, 899, 901, 902, 935, 937, 938, 980, 982, 983, 1406, 1411, 1416, 1425], "start_nod": [153, 154, 155], "end_nod": [153, 154, 155], "reference_neighbor": [153, 154], "half": [153, 154, 155, 164, 177, 183, 206, 297, 298, 615, 653], "clockwis": [153, 154, 169, 182, 197, 615], "networkxexcept": [153, 154, 161, 331, 588, 593, 724, 726, 1043, 1110, 1138, 1180, 1325], "add_half_edge_cw": [153, 155, 164, 615], "connect_compon": [153, 154, 155, 615], "add_half_edge_first": [153, 154, 164, 615], "add_half_edge_ccw": [154, 155, 164, 615], "node_for_ad": [156, 855, 900, 936, 981], "mutabl": [156, 855, 900, 936, 981, 1061, 1066, 1082, 1085, 1086], "hash": [156, 511, 512, 758, 855, 900, 936, 981, 1324, 1325, 1414, 1426], "hello": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1303], "k3": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1217], "utm": [156, 855, 900, 936, 981], "382871": [156, 855, 900, 936, 981], "3972649": [156, 855, 900, 936, 981], "nodes_for_ad": [157, 856, 901, 937, 982], "iterator_of_nod": [157, 195, 856, 884, 901, 923, 937, 965, 982, 1005], "datadict": [159, 190, 200, 207, 734, 736, 858, 879, 888, 892, 903, 928, 939, 969, 973, 1010, 1084, 1312, 1326], "foovalu": [159, 190, 200, 858, 879, 888, 903, 939, 969], "nbrdict": [160, 859, 904, 940, 985, 1019, 1094], "fulfil": [161, 615], "cw": [161, 615], "ccw": [161, 615], "planar": [161, 614, 616, 617, 758, 1110, 1138, 1243, 1246, 1247, 1249, 1325, 1409, 1410], "first_nbr": [161, 615], "invalid": [161, 615, 1413], "alter": [163, 861, 906, 942, 987], "afterward": 164, "as_view": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1089, 1090], "shallow": [165, 202, 204, 284, 285, 286, 287, 288, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1394], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1409], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1404, 1407, 1409, 1410, 1411], "fresh": [165, 862, 907, 943, 988, 1404], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1226, 1323, 1404], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1265, 1394], "degreeview": [166, 863, 908, 944, 950, 989, 1404, 1426], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1404, 1418], "ddict": [168, 176, 184, 189, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998], "in_edg": [168, 189, 865, 878, 946, 960, 1404, 1406, 1407], "out_edg": [168, 865, 946, 1062, 1404, 1406, 1407, 1426], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1426], "outedgedataview": [168, 189, 865, 878, 1404, 1411], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1404, 1412], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1406, 1407, 1417], "indegreeview": [175, 869, 1404], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1165, 1179, 1222, 1404], "inedgeview": [176, 870, 1404], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1127, 1353, 1354, 1359, 1363, 1406, 1413], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1414], "__iter__": [180, 871, 914, 952, 995, 1303], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1394, 1426], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1216], "nedg": [185, 588, 874, 917, 956, 999], "__len__": [186, 187, 875, 876, 918, 919, 957, 958, 1000, 1001], "outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 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, 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"vf2pp_is_isomorphic": [[562, "vf2pp-is-isomorphic"]], "vf2pp_isomorphism": [[563, "vf2pp-isomorphism"]], "hits": [[564, "hits"]], "google_matrix": [[565, "google-matrix"]], "pagerank": [[566, "pagerank"]], "adamic_adar_index": [[567, "adamic-adar-index"]], "cn_soundarajan_hopcroft": [[568, "cn-soundarajan-hopcroft"]], "common_neighbor_centrality": [[569, "common-neighbor-centrality"]], "jaccard_coefficient": [[570, "jaccard-coefficient"]], "preferential_attachment": [[571, "preferential-attachment"]], "ra_index_soundarajan_hopcroft": [[572, "ra-index-soundarajan-hopcroft"]], "resource_allocation_index": [[573, "resource-allocation-index"]], "within_inter_cluster": [[574, "within-inter-cluster"]], "all_pairs_lowest_common_ancestor": [[575, "all-pairs-lowest-common-ancestor"]], "lowest_common_ancestor": [[576, "lowest-common-ancestor"]], "tree_all_pairs_lowest_common_ancestor": [[577, "tree-all-pairs-lowest-common-ancestor"]], "is_matching": [[578, "is-matching"]], "is_maximal_matching": [[579, "is-maximal-matching"]], "is_perfect_matching": [[580, "is-perfect-matching"]], "max_weight_matching": [[581, "max-weight-matching"]], "maximal_matching": [[582, "maximal-matching"]], "min_weight_matching": [[583, "min-weight-matching"]], "contracted_edge": [[584, "contracted-edge"]], "contracted_nodes": [[585, "contracted-nodes"]], "equivalence_classes": [[586, "equivalence-classes"]], "identified_nodes": [[587, "identified-nodes"]], "quotient_graph": [[588, "quotient-graph"]], "maximal_independent_set": [[589, "maximal-independent-set"]], "moral_graph": [[590, "moral-graph"]], "harmonic_function": [[591, "harmonic-function"]], "local_and_global_consistency": [[592, "local-and-global-consistency"]], "non_randomness": [[593, "non-randomness"]], "compose_all": [[594, "compose-all"]], "disjoint_union_all": [[595, "disjoint-union-all"]], "intersection_all": [[596, "intersection-all"]], "union_all": [[597, "union-all"]], "compose": [[598, "compose"]], "difference": [[599, "difference"]], "disjoint_union": [[600, "disjoint-union"]], "full_join": [[601, "full-join"]], "intersection": [[602, "intersection"]], "symmetric_difference": [[603, "symmetric-difference"]], "union": [[604, "union"]], "cartesian_product": [[605, "cartesian-product"]], "corona_product": [[606, "corona-product"]], "lexicographic_product": [[607, "lexicographic-product"]], "power": [[608, "power"]], "rooted_product": [[609, "rooted-product"]], "strong_product": [[610, "strong-product"]], "tensor_product": [[611, "tensor-product"]], "complement": [[612, "complement"]], "reverse": [[613, "reverse"]], "combinatorial_embedding_to_pos": [[614, "combinatorial-embedding-to-pos"]], "networkx.algorithms.planarity.PlanarEmbedding": [[615, "networkx-algorithms-planarity-planarembedding"]], "check_planarity": [[616, "check-planarity"]], "is_planar": [[617, "is-planar"]], "chromatic_polynomial": [[618, "chromatic-polynomial"]], "tutte_polynomial": [[619, "tutte-polynomial"]], "overall_reciprocity": [[620, "overall-reciprocity"]], "reciprocity": [[621, "reciprocity"]], "is_k_regular": [[622, "is-k-regular"]], "is_regular": [[623, "is-regular"]], "k_factor": [[624, "k-factor"]], "rich_club_coefficient": [[625, "rich-club-coefficient"]], "astar_path": [[626, "astar-path"]], "astar_path_length": [[627, "astar-path-length"]], "floyd_warshall": [[628, "floyd-warshall"]], "floyd_warshall_numpy": [[629, "floyd-warshall-numpy"]], "floyd_warshall_predecessor_and_distance": [[630, "floyd-warshall-predecessor-and-distance"]], "reconstruct_path": [[631, "reconstruct-path"]], "all_shortest_paths": [[632, "all-shortest-paths"]], "average_shortest_path_length": [[633, "average-shortest-path-length"]], "has_path": [[634, "has-path"]], "shortest_path": [[635, "shortest-path"]], "shortest_path_length": [[636, "shortest-path-length"]], "all_pairs_shortest_path": [[637, "all-pairs-shortest-path"]], "all_pairs_shortest_path_length": [[638, "all-pairs-shortest-path-length"]], "bidirectional_shortest_path": [[639, "bidirectional-shortest-path"]], "predecessor": [[640, "predecessor"]], "single_source_shortest_path": [[641, "single-source-shortest-path"]], "single_source_shortest_path_length": [[642, "single-source-shortest-path-length"]], "single_target_shortest_path": [[643, "single-target-shortest-path"]], "single_target_shortest_path_length": [[644, "single-target-shortest-path-length"]], "all_pairs_bellman_ford_path": [[645, "all-pairs-bellman-ford-path"]], "all_pairs_bellman_ford_path_length": [[646, "all-pairs-bellman-ford-path-length"]], "all_pairs_dijkstra": [[647, "all-pairs-dijkstra"]], "all_pairs_dijkstra_path": [[648, "all-pairs-dijkstra-path"]], "all_pairs_dijkstra_path_length": [[649, "all-pairs-dijkstra-path-length"]], "bellman_ford_path": [[650, "bellman-ford-path"]], "bellman_ford_path_length": [[651, "bellman-ford-path-length"]], "bellman_ford_predecessor_and_distance": [[652, "bellman-ford-predecessor-and-distance"]], "bidirectional_dijkstra": [[653, "bidirectional-dijkstra"]], "dijkstra_path": [[654, "dijkstra-path"]], "dijkstra_path_length": [[655, "dijkstra-path-length"]], "dijkstra_predecessor_and_distance": [[656, "dijkstra-predecessor-and-distance"]], "find_negative_cycle": [[657, "find-negative-cycle"]], "goldberg_radzik": [[658, "goldberg-radzik"]], "johnson": [[659, "johnson"]], "multi_source_dijkstra": [[660, "multi-source-dijkstra"]], "multi_source_dijkstra_path": [[661, "multi-source-dijkstra-path"]], "multi_source_dijkstra_path_length": [[662, "multi-source-dijkstra-path-length"]], "negative_edge_cycle": [[663, "negative-edge-cycle"]], "single_source_bellman_ford": [[664, "single-source-bellman-ford"]], "single_source_bellman_ford_path": [[665, "single-source-bellman-ford-path"]], "single_source_bellman_ford_path_length": [[666, "single-source-bellman-ford-path-length"]], "single_source_dijkstra": [[667, "single-source-dijkstra"]], "single_source_dijkstra_path": [[668, "single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[669, "single-source-dijkstra-path-length"]], "generate_random_paths": [[670, "generate-random-paths"]], "graph_edit_distance": [[671, "graph-edit-distance"]], "optimal_edit_paths": [[672, "optimal-edit-paths"]], "optimize_edit_paths": [[673, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[674, "optimize-graph-edit-distance"]], "panther_similarity": [[675, "panther-similarity"]], "simrank_similarity": [[676, "simrank-similarity"]], "all_simple_edge_paths": [[677, "all-simple-edge-paths"]], "all_simple_paths": [[678, "all-simple-paths"]], "is_simple_path": [[679, "is-simple-path"]], "shortest_simple_paths": [[680, "shortest-simple-paths"]], "lattice_reference": [[681, "lattice-reference"]], "omega": [[682, "omega"]], "random_reference": [[683, "random-reference"]], "sigma": [[684, "sigma"]], "s_metric": [[685, "s-metric"]], "spanner": [[686, "spanner"]], "constraint": [[687, "constraint"]], "effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "Recognition Tests": [[791, "recognition-tests"]], "Branchings and Spanning Arborescences": [[791, "module-networkx.algorithms.tree.branchings"]], "Encoding and decoding": [[791, "module-networkx.algorithms.tree.coding"]], "Operations": [[791, "module-networkx.algorithms.tree.operations"]], "Spanning Trees": [[791, "module-networkx.algorithms.tree.mst"]], "Exceptions": [[791, "exceptions"], [1043, "module-networkx.exception"]], "Vitality": [[793, "module-networkx.algorithms.vitality"]], "Voronoi cells": [[794, "module-networkx.algorithms.voronoi"]], "Wiener index": [[795, "module-networkx.algorithms.wiener"]], "DiGraph\u2014Directed graphs with self loops": [[796, "digraph-directed-graphs-with-self-loops"]], "Overview": [[796, "overview"], [1037, "overview"], [1039, "overview"], [1040, "overview"]], "Methods": [[796, "methods"], [1037, "methods"], [1039, "methods"], [1040, "methods"]], "Adding and removing nodes and edges": [[796, "adding-and-removing-nodes-and-edges"], [1037, "adding-and-removing-nodes-and-edges"], [1040, "adding-and-removing-nodes-and-edges"]], "Reporting nodes edges and neighbors": [[796, "reporting-nodes-edges-and-neighbors"], [1037, "reporting-nodes-edges-and-neighbors"], [1039, "reporting-nodes-edges-and-neighbors"], [1040, "reporting-nodes-edges-and-neighbors"]], "Counting nodes edges and neighbors": [[796, "counting-nodes-edges-and-neighbors"], [1037, "counting-nodes-edges-and-neighbors"], [1039, "counting-nodes-edges-and-neighbors"], [1040, "counting-nodes-edges-and-neighbors"]], "Making copies and subgraphs": [[796, "making-copies-and-subgraphs"], [1037, "making-copies-and-subgraphs"], [1039, "making-copies-and-subgraphs"], [1040, "making-copies-and-subgraphs"]], "AdjacencyView.copy": [[797, "adjacencyview-copy"]], "AdjacencyView.get": [[798, "adjacencyview-get"]], "AdjacencyView.items": [[799, "adjacencyview-items"]], "AdjacencyView.keys": [[800, "adjacencyview-keys"]], "AdjacencyView.values": [[801, "adjacencyview-values"]], "AtlasView.copy": [[802, "atlasview-copy"]], "AtlasView.get": [[803, "atlasview-get"]], "AtlasView.items": [[804, "atlasview-items"]], "AtlasView.keys": [[805, "atlasview-keys"]], "AtlasView.values": [[806, "atlasview-values"]], "FilterAdjacency.get": [[807, "filteradjacency-get"]], "FilterAdjacency.items": [[808, "filteradjacency-items"]], "FilterAdjacency.keys": [[809, "filteradjacency-keys"]], "FilterAdjacency.values": [[810, "filteradjacency-values"]], "FilterAtlas.get": [[811, "filteratlas-get"]], "FilterAtlas.items": [[812, "filteratlas-items"]], "FilterAtlas.keys": [[813, "filteratlas-keys"]], "FilterAtlas.values": [[814, "filteratlas-values"]], "FilterMultiAdjacency.get": [[815, "filtermultiadjacency-get"]], "FilterMultiAdjacency.items": [[816, "filtermultiadjacency-items"]], "FilterMultiAdjacency.keys": [[817, "filtermultiadjacency-keys"]], "FilterMultiAdjacency.values": [[818, "filtermultiadjacency-values"]], "FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": [[836, "unionatlas-keys"]], "UnionAtlas.values": [[837, "unionatlas-values"]], "UnionMultiAdjacency.copy": [[838, "unionmultiadjacency-copy"]], "UnionMultiAdjacency.get": [[839, "unionmultiadjacency-get"]], "UnionMultiAdjacency.items": [[840, "unionmultiadjacency-items"]], "UnionMultiAdjacency.keys": [[841, "unionmultiadjacency-keys"]], "UnionMultiAdjacency.values": [[842, "unionmultiadjacency-values"]], "UnionMultiInner.copy": [[843, "unionmultiinner-copy"]], "UnionMultiInner.get": [[844, "unionmultiinner-get"]], "UnionMultiInner.items": [[845, "unionmultiinner-items"]], "UnionMultiInner.keys": [[846, "unionmultiinner-keys"]], "UnionMultiInner.values": [[847, "unionmultiinner-values"]], "DiGraph.__contains__": [[848, "digraph-contains"]], "DiGraph.__getitem__": [[849, "digraph-getitem"]], "DiGraph.__init__": [[850, "digraph-init"]], "DiGraph.__iter__": [[851, "digraph-iter"]], "DiGraph.__len__": [[852, "digraph-len"]], "DiGraph.add_edge": [[853, "digraph-add-edge"]], "DiGraph.add_edges_from": [[854, "digraph-add-edges-from"]], "DiGraph.add_node": [[855, "digraph-add-node"]], "DiGraph.add_nodes_from": [[856, "digraph-add-nodes-from"]], "DiGraph.add_weighted_edges_from": [[857, "digraph-add-weighted-edges-from"]], "DiGraph.adj": [[858, "digraph-adj"]], "DiGraph.adjacency": [[859, "digraph-adjacency"]], "DiGraph.clear": [[860, "digraph-clear"]], "DiGraph.clear_edges": [[861, "digraph-clear-edges"]], "DiGraph.copy": [[862, "digraph-copy"]], "DiGraph.degree": [[863, "digraph-degree"]], "DiGraph.edge_subgraph": [[864, "digraph-edge-subgraph"]], "DiGraph.edges": [[865, "digraph-edges"]], "DiGraph.get_edge_data": [[866, "digraph-get-edge-data"]], "DiGraph.has_edge": [[867, "digraph-has-edge"]], "DiGraph.has_node": [[868, "digraph-has-node"]], "DiGraph.in_degree": [[869, "digraph-in-degree"]], "DiGraph.in_edges": [[870, "digraph-in-edges"]], "DiGraph.nbunch_iter": [[871, "digraph-nbunch-iter"]], "DiGraph.neighbors": [[872, "digraph-neighbors"]], "DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, 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"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, "module-networkx.algorithms.bipartite.covering"]], "networkx.algorithms.bipartite.edgelist": [[115, "module-networkx.algorithms.bipartite.edgelist"]], "networkx.algorithms.bipartite.generators": [[115, "module-networkx.algorithms.bipartite.generators"]], "networkx.algorithms.bipartite.matching": [[115, "module-networkx.algorithms.bipartite.matching"]], "networkx.algorithms.bipartite.matrix": [[115, "module-networkx.algorithms.bipartite.matrix"]], "networkx.algorithms.bipartite.projection": [[115, "module-networkx.algorithms.bipartite.projection"]], "networkx.algorithms.bipartite.redundancy": [[115, "module-networkx.algorithms.bipartite.redundancy"]], "networkx.algorithms.bipartite.spectral": [[115, "module-networkx.algorithms.bipartite.spectral"]], "networkx.algorithms.boundary": [[116, "module-networkx.algorithms.boundary"]], "networkx.algorithms.bridges": [[117, "module-networkx.algorithms.bridges"]], "networkx.algorithms.centrality": [[118, "module-networkx.algorithms.centrality"]], "networkx.algorithms.chains": [[119, "module-networkx.algorithms.chains"]], "networkx.algorithms.chordal": [[120, "module-networkx.algorithms.chordal"]], "networkx.algorithms.clique": [[121, "module-networkx.algorithms.clique"]], "networkx.algorithms.cluster": [[122, "module-networkx.algorithms.cluster"]], "networkx.algorithms.coloring": [[123, "module-networkx.algorithms.coloring"]], "networkx.algorithms.communicability_alg": [[124, "module-networkx.algorithms.communicability_alg"]], "networkx.algorithms.community": [[125, "module-networkx.algorithms.community"]], "networkx.algorithms.community.asyn_fluid": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "networkx.algorithms.community.centrality": [[125, "module-networkx.algorithms.community.centrality"]], "networkx.algorithms.community.community_utils": [[125, "module-networkx.algorithms.community.community_utils"]], "networkx.algorithms.community.kclique": [[125, "module-networkx.algorithms.community.kclique"]], "networkx.algorithms.community.kernighan_lin": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "networkx.algorithms.community.label_propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "networkx.algorithms.community.louvain": [[125, "module-networkx.algorithms.community.louvain"]], "networkx.algorithms.community.lukes": [[125, "module-networkx.algorithms.community.lukes"]], "networkx.algorithms.community.modularity_max": [[125, "module-networkx.algorithms.community.modularity_max"]], "networkx.algorithms.community.quality": [[125, "module-networkx.algorithms.community.quality"]], "networkx.algorithms.components": [[126, "module-networkx.algorithms.components"]], "networkx.algorithms.connectivity": [[127, "module-networkx.algorithms.connectivity"]], "networkx.algorithms.connectivity.connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "networkx.algorithms.connectivity.cuts": [[127, "module-networkx.algorithms.connectivity.cuts"]], "networkx.algorithms.connectivity.disjoint_paths": [[127, "module-networkx.algorithms.connectivity.disjoint_paths"]], "networkx.algorithms.connectivity.edge_augmentation": [[127, "module-networkx.algorithms.connectivity.edge_augmentation"]], "networkx.algorithms.connectivity.edge_kcomponents": [[127, "module-networkx.algorithms.connectivity.edge_kcomponents"]], "networkx.algorithms.connectivity.kcomponents": [[127, "module-networkx.algorithms.connectivity.kcomponents"]], "networkx.algorithms.connectivity.kcutsets": [[127, "module-networkx.algorithms.connectivity.kcutsets"]], "networkx.algorithms.connectivity.stoerwagner": [[127, "module-networkx.algorithms.connectivity.stoerwagner"]], "networkx.algorithms.connectivity.utils": [[127, "module-networkx.algorithms.connectivity.utils"]], "networkx.algorithms.core": [[128, "module-networkx.algorithms.core"]], "networkx.algorithms.covering": [[129, "module-networkx.algorithms.covering"]], "networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[144, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[151, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": [[166, "networkx.algorithms.planarity.PlanarEmbedding.degree"]], "edge_subgraph() (planarembedding method)": [[167, "networkx.algorithms.planarity.PlanarEmbedding.edge_subgraph"]], "edges (planarembedding property)": [[168, "networkx.algorithms.planarity.PlanarEmbedding.edges"]], "get_data() (planarembedding method)": [[169, "networkx.algorithms.planarity.PlanarEmbedding.get_data"]], "get_edge_data() (planarembedding method)": [[170, "networkx.algorithms.planarity.PlanarEmbedding.get_edge_data"]], "has_edge() (planarembedding method)": [[171, "networkx.algorithms.planarity.PlanarEmbedding.has_edge"]], "has_node() (planarembedding method)": [[172, "networkx.algorithms.planarity.PlanarEmbedding.has_node"]], "has_predecessor() (planarembedding method)": [[173, "networkx.algorithms.planarity.PlanarEmbedding.has_predecessor"]], "has_successor() (planarembedding method)": [[174, "networkx.algorithms.planarity.PlanarEmbedding.has_successor"]], "in_degree (planarembedding property)": [[175, "networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[751, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[752, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[753, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[755, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[757, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[759, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[760, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[764, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[766, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[767, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[768, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[769, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[770, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[771, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[772, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[772, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[772, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[772, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[774, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[775, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[777, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[778, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "networkx.algorithms.shortest_paths.weighted": [[779, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[780, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[781, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[782, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[783, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[785, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[786, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[787, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[788, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[789, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "networkx.algorithms.traversal.breadth_first_search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "networkx.algorithms.traversal.depth_first_search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "networkx.algorithms.traversal.edgebfs": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "networkx.algorithms.traversal.edgedfs": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "networkx.algorithms.tree.branchings": [[791, "module-networkx.algorithms.tree.branchings"]], "networkx.algorithms.tree.coding": [[791, "module-networkx.algorithms.tree.coding"]], "networkx.algorithms.tree.decomposition": [[791, "module-networkx.algorithms.tree.decomposition"]], "networkx.algorithms.tree.mst": [[791, "module-networkx.algorithms.tree.mst"]], "networkx.algorithms.tree.operations": [[791, "module-networkx.algorithms.tree.operations"]], "networkx.algorithms.tree.recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "networkx.algorithms.triads": [[792, "module-networkx.algorithms.triads"]], "networkx.algorithms.vitality": [[793, "module-networkx.algorithms.vitality"]], "networkx.algorithms.voronoi": [[794, "module-networkx.algorithms.voronoi"]], "networkx.algorithms.wiener": [[795, "module-networkx.algorithms.wiener"]], "digraph (class in networkx)": [[796, "networkx.DiGraph"]], "copy() (adjacencyview method)": [[797, "networkx.classes.coreviews.AdjacencyView.copy"]], "get() (adjacencyview method)": [[798, "networkx.classes.coreviews.AdjacencyView.get"]], "items() (adjacencyview method)": [[799, "networkx.classes.coreviews.AdjacencyView.items"]], "keys() (adjacencyview method)": [[800, "networkx.classes.coreviews.AdjacencyView.keys"]], "values() (adjacencyview method)": [[801, "networkx.classes.coreviews.AdjacencyView.values"]], "copy() (atlasview method)": [[802, "networkx.classes.coreviews.AtlasView.copy"]], "get() (atlasview method)": [[803, "networkx.classes.coreviews.AtlasView.get"]], "items() (atlasview method)": [[804, "networkx.classes.coreviews.AtlasView.items"]], "keys() (atlasview method)": [[805, "networkx.classes.coreviews.AtlasView.keys"]], "values() (atlasview method)": [[806, "networkx.classes.coreviews.AtlasView.values"]], "get() (filteradjacency method)": [[807, "networkx.classes.coreviews.FilterAdjacency.get"]], "items() (filteradjacency method)": [[808, "networkx.classes.coreviews.FilterAdjacency.items"]], "keys() (filteradjacency method)": [[809, "networkx.classes.coreviews.FilterAdjacency.keys"]], "values() (filteradjacency method)": [[810, "networkx.classes.coreviews.FilterAdjacency.values"]], "get() (filteratlas method)": [[811, "networkx.classes.coreviews.FilterAtlas.get"]], "items() (filteratlas method)": [[812, "networkx.classes.coreviews.FilterAtlas.items"]], "keys() (filteratlas method)": [[813, "networkx.classes.coreviews.FilterAtlas.keys"]], "values() (filteratlas method)": [[814, 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"networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, 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"kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], "spring_layout() (in module networkx.drawing.layout)": [[1117, "networkx.drawing.layout.spring_layout"]], "from_agraph() (in module networkx.drawing.nx_agraph)": [[1118, "networkx.drawing.nx_agraph.from_agraph"]], "graphviz_layout() (in module networkx.drawing.nx_agraph)": [[1119, "networkx.drawing.nx_agraph.graphviz_layout"]], "pygraphviz_layout() (in module networkx.drawing.nx_agraph)": [[1120, "networkx.drawing.nx_agraph.pygraphviz_layout"]], "read_dot() (in module networkx.drawing.nx_agraph)": [[1121, "networkx.drawing.nx_agraph.read_dot"]], "to_agraph() (in module networkx.drawing.nx_agraph)": [[1122, "networkx.drawing.nx_agraph.to_agraph"]], "write_dot() (in module networkx.drawing.nx_agraph)": [[1123, "networkx.drawing.nx_agraph.write_dot"]], "from_pydot() (in module networkx.drawing.nx_pydot)": [[1124, "networkx.drawing.nx_pydot.from_pydot"]], "graphviz_layout() (in module networkx.drawing.nx_pydot)": [[1125, "networkx.drawing.nx_pydot.graphviz_layout"]], "pydot_layout() (in module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, 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module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() 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networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, "networkx.generators.random_graphs.dense_gnm_random_graph"]], "dual_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1227, "networkx.generators.random_graphs.dual_barabasi_albert_graph"]], "erdos_renyi_graph() (in module networkx.generators.random_graphs)": [[1228, "networkx.generators.random_graphs.erdos_renyi_graph"]], "extended_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1229, "networkx.generators.random_graphs.extended_barabasi_albert_graph"]], "fast_gnp_random_graph() (in module networkx.generators.random_graphs)": [[1230, "networkx.generators.random_graphs.fast_gnp_random_graph"]], "gnm_random_graph() (in module networkx.generators.random_graphs)": [[1231, "networkx.generators.random_graphs.gnm_random_graph"]], "gnp_random_graph() (in module networkx.generators.random_graphs)": [[1232, "networkx.generators.random_graphs.gnp_random_graph"]], "newman_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1233, "networkx.generators.random_graphs.newman_watts_strogatz_graph"]], "powerlaw_cluster_graph() (in module networkx.generators.random_graphs)": [[1234, "networkx.generators.random_graphs.powerlaw_cluster_graph"]], "random_kernel_graph() (in module networkx.generators.random_graphs)": [[1235, "networkx.generators.random_graphs.random_kernel_graph"]], "random_lobster() (in module networkx.generators.random_graphs)": [[1236, "networkx.generators.random_graphs.random_lobster"]], "random_powerlaw_tree() (in module networkx.generators.random_graphs)": [[1237, "networkx.generators.random_graphs.random_powerlaw_tree"]], "random_powerlaw_tree_sequence() (in module networkx.generators.random_graphs)": [[1238, "networkx.generators.random_graphs.random_powerlaw_tree_sequence"]], "random_regular_graph() (in module networkx.generators.random_graphs)": [[1239, "networkx.generators.random_graphs.random_regular_graph"]], "random_shell_graph() (in module networkx.generators.random_graphs)": [[1240, "networkx.generators.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1242, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1243, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1244, "networkx.generators.small.chvatal_graph"]], "cubical_graph() (in module networkx.generators.small)": [[1245, "networkx.generators.small.cubical_graph"]], "desargues_graph() (in module networkx.generators.small)": [[1246, "networkx.generators.small.desargues_graph"]], "diamond_graph() (in module networkx.generators.small)": [[1247, "networkx.generators.small.diamond_graph"]], "dodecahedral_graph() (in module networkx.generators.small)": [[1248, "networkx.generators.small.dodecahedral_graph"]], "frucht_graph() (in module networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, "networkx.generators.small.octahedral_graph"]], "pappus_graph() (in module networkx.generators.small)": [[1258, "networkx.generators.small.pappus_graph"]], "petersen_graph() (in module networkx.generators.small)": [[1259, "networkx.generators.small.petersen_graph"]], "sedgewick_maze_graph() (in module networkx.generators.small)": [[1260, "networkx.generators.small.sedgewick_maze_graph"]], "tetrahedral_graph() (in module networkx.generators.small)": [[1261, "networkx.generators.small.tetrahedral_graph"]], "truncated_cube_graph() (in module networkx.generators.small)": [[1262, "networkx.generators.small.truncated_cube_graph"]], "truncated_tetrahedron_graph() (in module networkx.generators.small)": [[1263, "networkx.generators.small.truncated_tetrahedron_graph"]], "tutte_graph() (in module networkx.generators.small)": [[1264, "networkx.generators.small.tutte_graph"]], "davis_southern_women_graph() (in module networkx.generators.social)": [[1265, "networkx.generators.social.davis_southern_women_graph"]], "florentine_families_graph() (in module networkx.generators.social)": [[1266, "networkx.generators.social.florentine_families_graph"]], "karate_club_graph() (in module networkx.generators.social)": [[1267, "networkx.generators.social.karate_club_graph"]], "les_miserables_graph() (in module networkx.generators.social)": [[1268, "networkx.generators.social.les_miserables_graph"]], "spectral_graph_forge() (in module networkx.generators.spectral_graph_forge)": [[1269, "networkx.generators.spectral_graph_forge.spectral_graph_forge"]], "stochastic_graph() (in module networkx.generators.stochastic)": [[1270, "networkx.generators.stochastic.stochastic_graph"]], "sudoku_graph() (in module networkx.generators.sudoku)": [[1271, "networkx.generators.sudoku.sudoku_graph"]], "prefix_tree() (in module networkx.generators.trees)": [[1272, "networkx.generators.trees.prefix_tree"]], "random_tree() (in module networkx.generators.trees)": [[1273, "networkx.generators.trees.random_tree"]], "triad_graph() (in module networkx.generators.triads)": [[1274, "networkx.generators.triads.triad_graph"]], "algebraic_connectivity() (in module networkx.linalg.algebraicconnectivity)": [[1275, "networkx.linalg.algebraicconnectivity.algebraic_connectivity"]], "fiedler_vector() (in module networkx.linalg.algebraicconnectivity)": [[1276, "networkx.linalg.algebraicconnectivity.fiedler_vector"]], "spectral_ordering() (in module networkx.linalg.algebraicconnectivity)": [[1277, "networkx.linalg.algebraicconnectivity.spectral_ordering"]], "attr_matrix() (in module networkx.linalg.attrmatrix)": [[1278, "networkx.linalg.attrmatrix.attr_matrix"]], "attr_sparse_matrix() (in module networkx.linalg.attrmatrix)": [[1279, "networkx.linalg.attrmatrix.attr_sparse_matrix"]], "bethe_hessian_matrix() (in module networkx.linalg.bethehessianmatrix)": [[1280, "networkx.linalg.bethehessianmatrix.bethe_hessian_matrix"]], "adjacency_matrix() (in module networkx.linalg.graphmatrix)": [[1281, "networkx.linalg.graphmatrix.adjacency_matrix"]], "incidence_matrix() (in module networkx.linalg.graphmatrix)": [[1282, "networkx.linalg.graphmatrix.incidence_matrix"]], "directed_combinatorial_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1283, "networkx.linalg.laplacianmatrix.directed_combinatorial_laplacian_matrix"]], "directed_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1284, "networkx.linalg.laplacianmatrix.directed_laplacian_matrix"]], "laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1285, "networkx.linalg.laplacianmatrix.laplacian_matrix"]], "normalized_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1286, "networkx.linalg.laplacianmatrix.normalized_laplacian_matrix"]], "directed_modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1287, "networkx.linalg.modularitymatrix.directed_modularity_matrix"]], "modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1288, "networkx.linalg.modularitymatrix.modularity_matrix"]], "adjacency_spectrum() (in module networkx.linalg.spectrum)": [[1289, "networkx.linalg.spectrum.adjacency_spectrum"]], "bethe_hessian_spectrum() (in module networkx.linalg.spectrum)": [[1290, "networkx.linalg.spectrum.bethe_hessian_spectrum"]], "laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1291, "networkx.linalg.spectrum.laplacian_spectrum"]], "modularity_spectrum() (in module networkx.linalg.spectrum)": [[1292, "networkx.linalg.spectrum.modularity_spectrum"]], "normalized_laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1293, "networkx.linalg.spectrum.normalized_laplacian_spectrum"]], "convert_node_labels_to_integers() (in module networkx.relabel)": [[1294, "networkx.relabel.convert_node_labels_to_integers"]], "relabel_nodes() (in module networkx.relabel)": [[1295, "networkx.relabel.relabel_nodes"]], "__init__() (argmap method)": [[1296, "networkx.utils.decorators.argmap.__init__"]], "argmap (class in networkx.utils.decorators)": [[1296, "networkx.utils.decorators.argmap"]], "nodes_or_number() (in module networkx.utils.decorators)": [[1297, "networkx.utils.decorators.nodes_or_number"]], "not_implemented_for() (in module networkx.utils.decorators)": [[1298, "networkx.utils.decorators.not_implemented_for"]], "np_random_state() (in module networkx.utils.decorators)": [[1299, "networkx.utils.decorators.np_random_state"]], "open_file() (in module networkx.utils.decorators)": [[1300, "networkx.utils.decorators.open_file"]], "py_random_state() (in module networkx.utils.decorators)": [[1301, "networkx.utils.decorators.py_random_state"]], "mappedqueue (class in networkx.utils.mapped_queue)": [[1302, "networkx.utils.mapped_queue.MappedQueue"]], "__init__() (mappedqueue method)": [[1302, "networkx.utils.mapped_queue.MappedQueue.__init__"]], "arbitrary_element() (in module networkx.utils.misc)": [[1303, "networkx.utils.misc.arbitrary_element"]], "create_py_random_state() (in module networkx.utils.misc)": [[1304, "networkx.utils.misc.create_py_random_state"]], "create_random_state() (in module networkx.utils.misc)": [[1305, "networkx.utils.misc.create_random_state"]], "dict_to_numpy_array() (in module networkx.utils.misc)": [[1306, "networkx.utils.misc.dict_to_numpy_array"]], "edges_equal() (in module networkx.utils.misc)": [[1307, "networkx.utils.misc.edges_equal"]], "flatten() (in module networkx.utils.misc)": [[1308, "networkx.utils.misc.flatten"]], "graphs_equal() (in module networkx.utils.misc)": [[1309, "networkx.utils.misc.graphs_equal"]], "groups() (in module networkx.utils.misc)": [[1310, "networkx.utils.misc.groups"]], "make_list_of_ints() (in module networkx.utils.misc)": [[1311, "networkx.utils.misc.make_list_of_ints"]], "nodes_equal() (in module networkx.utils.misc)": [[1312, "networkx.utils.misc.nodes_equal"]], "pairwise() (in module networkx.utils.misc)": [[1313, "networkx.utils.misc.pairwise"]], "cumulative_distribution() (in module networkx.utils.random_sequence)": [[1314, "networkx.utils.random_sequence.cumulative_distribution"]], "discrete_sequence() (in module networkx.utils.random_sequence)": [[1315, "networkx.utils.random_sequence.discrete_sequence"]], "powerlaw_sequence() (in module networkx.utils.random_sequence)": [[1316, "networkx.utils.random_sequence.powerlaw_sequence"]], "random_weighted_sample() (in module networkx.utils.random_sequence)": [[1317, "networkx.utils.random_sequence.random_weighted_sample"]], "weighted_choice() (in module networkx.utils.random_sequence)": [[1318, "networkx.utils.random_sequence.weighted_choice"]], "zipf_rv() (in module networkx.utils.random_sequence)": [[1319, "networkx.utils.random_sequence.zipf_rv"]], "cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1320, "networkx.utils.rcm.cuthill_mckee_ordering"]], "reverse_cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1321, "networkx.utils.rcm.reverse_cuthill_mckee_ordering"]], "union() (unionfind method)": [[1322, "networkx.utils.union_find.UnionFind.union"]], "networkx.generators.atlas": [[1323, "module-networkx.generators.atlas"]], "networkx.generators.classic": [[1323, "module-networkx.generators.classic"]], "networkx.generators.cographs": [[1323, "module-networkx.generators.cographs"]], "networkx.generators.community": [[1323, "module-networkx.generators.community"]], "networkx.generators.degree_seq": [[1323, "module-networkx.generators.degree_seq"]], "networkx.generators.directed": [[1323, "module-networkx.generators.directed"]], "networkx.generators.duplication": [[1323, "module-networkx.generators.duplication"]], "networkx.generators.ego": [[1323, "module-networkx.generators.ego"]], "networkx.generators.expanders": [[1323, "module-networkx.generators.expanders"]], "networkx.generators.geometric": [[1323, "module-networkx.generators.geometric"]], "networkx.generators.harary_graph": [[1323, "module-networkx.generators.harary_graph"]], "networkx.generators.internet_as_graphs": [[1323, "module-networkx.generators.internet_as_graphs"]], "networkx.generators.intersection": [[1323, "module-networkx.generators.intersection"]], "networkx.generators.interval_graph": [[1323, "module-networkx.generators.interval_graph"]], "networkx.generators.joint_degree_seq": [[1323, "module-networkx.generators.joint_degree_seq"]], "networkx.generators.lattice": [[1323, "module-networkx.generators.lattice"]], "networkx.generators.line": [[1323, "module-networkx.generators.line"]], "networkx.generators.mycielski": [[1323, "module-networkx.generators.mycielski"]], "networkx.generators.nonisomorphic_trees": [[1323, "module-networkx.generators.nonisomorphic_trees"]], "networkx.generators.random_clustered": [[1323, "module-networkx.generators.random_clustered"]], "networkx.generators.random_graphs": [[1323, "module-networkx.generators.random_graphs"]], "networkx.generators.small": [[1323, "module-networkx.generators.small"]], "networkx.generators.social": [[1323, "module-networkx.generators.social"]], "networkx.generators.spectral_graph_forge": [[1323, "module-networkx.generators.spectral_graph_forge"]], "networkx.generators.stochastic": [[1323, "module-networkx.generators.stochastic"]], "networkx.generators.sudoku": [[1323, "module-networkx.generators.sudoku"]], "networkx.generators.trees": [[1323, "module-networkx.generators.trees"]], "networkx.generators.triads": [[1323, "module-networkx.generators.triads"]], "dictionary": [[1324, "term-dictionary"]], "ebunch": [[1324, "term-ebunch"]], "edge": [[1324, "term-edge"]], "edge attribute": [[1324, "term-edge-attribute"]], "nbunch": [[1324, "term-nbunch"]], "node": [[1324, "term-node"]], "node attribute": [[1324, "term-node-attribute"]], "networkx.linalg.algebraicconnectivity": [[1327, "module-networkx.linalg.algebraicconnectivity"]], "networkx.linalg.attrmatrix": [[1327, "module-networkx.linalg.attrmatrix"]], "networkx.linalg.bethehessianmatrix": [[1327, "module-networkx.linalg.bethehessianmatrix"]], "networkx.linalg.graphmatrix": [[1327, "module-networkx.linalg.graphmatrix"]], "networkx.linalg.laplacianmatrix": [[1327, "module-networkx.linalg.laplacianmatrix"]], "networkx.linalg.modularitymatrix": [[1327, "module-networkx.linalg.modularitymatrix"]], "networkx.linalg.spectrum": [[1327, "module-networkx.linalg.spectrum"]], "networkx.readwrite.adjlist": [[1329, "module-networkx.readwrite.adjlist"]], "networkx.readwrite.edgelist": [[1330, "module-networkx.readwrite.edgelist"]], "generate_adjlist() (in module networkx.readwrite.adjlist)": [[1331, "networkx.readwrite.adjlist.generate_adjlist"]], "parse_adjlist() (in module networkx.readwrite.adjlist)": [[1332, "networkx.readwrite.adjlist.parse_adjlist"]], "read_adjlist() (in module networkx.readwrite.adjlist)": [[1333, "networkx.readwrite.adjlist.read_adjlist"]], "write_adjlist() (in module networkx.readwrite.adjlist)": [[1334, "networkx.readwrite.adjlist.write_adjlist"]], "generate_edgelist() (in module networkx.readwrite.edgelist)": [[1335, "networkx.readwrite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.readwrite.edgelist)": [[1336, "networkx.readwrite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.readwrite.edgelist)": [[1337, "networkx.readwrite.edgelist.read_edgelist"]], "read_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1338, "networkx.readwrite.edgelist.read_weighted_edgelist"]], "write_edgelist() (in module networkx.readwrite.edgelist)": [[1339, "networkx.readwrite.edgelist.write_edgelist"]], "write_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1340, "networkx.readwrite.edgelist.write_weighted_edgelist"]], "generate_gexf() (in module networkx.readwrite.gexf)": [[1341, "networkx.readwrite.gexf.generate_gexf"]], "read_gexf() (in module networkx.readwrite.gexf)": [[1342, "networkx.readwrite.gexf.read_gexf"]], "relabel_gexf_graph() (in module networkx.readwrite.gexf)": [[1343, "networkx.readwrite.gexf.relabel_gexf_graph"]], "write_gexf() (in module networkx.readwrite.gexf)": [[1344, "networkx.readwrite.gexf.write_gexf"]], "generate_gml() (in module networkx.readwrite.gml)": [[1345, "networkx.readwrite.gml.generate_gml"]], "literal_destringizer() (in module networkx.readwrite.gml)": [[1346, "networkx.readwrite.gml.literal_destringizer"]], "literal_stringizer() (in module networkx.readwrite.gml)": [[1347, "networkx.readwrite.gml.literal_stringizer"]], "parse_gml() (in module networkx.readwrite.gml)": [[1348, "networkx.readwrite.gml.parse_gml"]], "read_gml() (in module networkx.readwrite.gml)": [[1349, "networkx.readwrite.gml.read_gml"]], "write_gml() (in module networkx.readwrite.gml)": [[1350, "networkx.readwrite.gml.write_gml"]], "from_graph6_bytes() (in module networkx.readwrite.graph6)": [[1351, "networkx.readwrite.graph6.from_graph6_bytes"]], "read_graph6() (in module networkx.readwrite.graph6)": [[1352, "networkx.readwrite.graph6.read_graph6"]], "to_graph6_bytes() (in module networkx.readwrite.graph6)": [[1353, "networkx.readwrite.graph6.to_graph6_bytes"]], "write_graph6() (in module networkx.readwrite.graph6)": [[1354, "networkx.readwrite.graph6.write_graph6"]], "generate_graphml() (in module networkx.readwrite.graphml)": [[1355, "networkx.readwrite.graphml.generate_graphml"]], "parse_graphml() (in module networkx.readwrite.graphml)": [[1356, "networkx.readwrite.graphml.parse_graphml"]], "read_graphml() (in module networkx.readwrite.graphml)": [[1357, "networkx.readwrite.graphml.read_graphml"]], "write_graphml() (in module networkx.readwrite.graphml)": [[1358, "networkx.readwrite.graphml.write_graphml"]], "adjacency_data() (in module networkx.readwrite.json_graph)": [[1359, "networkx.readwrite.json_graph.adjacency_data"]], "adjacency_graph() (in module networkx.readwrite.json_graph)": [[1360, "networkx.readwrite.json_graph.adjacency_graph"]], "cytoscape_data() (in module networkx.readwrite.json_graph)": [[1361, "networkx.readwrite.json_graph.cytoscape_data"]], "cytoscape_graph() (in module networkx.readwrite.json_graph)": [[1362, "networkx.readwrite.json_graph.cytoscape_graph"]], "node_link_data() (in module networkx.readwrite.json_graph)": [[1363, "networkx.readwrite.json_graph.node_link_data"]], "node_link_graph() (in module networkx.readwrite.json_graph)": [[1364, "networkx.readwrite.json_graph.node_link_graph"]], "tree_data() (in module networkx.readwrite.json_graph)": [[1365, "networkx.readwrite.json_graph.tree_data"]], "tree_graph() (in module networkx.readwrite.json_graph)": [[1366, "networkx.readwrite.json_graph.tree_graph"]], "parse_leda() (in module networkx.readwrite.leda)": [[1367, "networkx.readwrite.leda.parse_leda"]], "read_leda() (in module networkx.readwrite.leda)": [[1368, "networkx.readwrite.leda.read_leda"]], "generate_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1369, "networkx.readwrite.multiline_adjlist.generate_multiline_adjlist"]], "parse_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1370, "networkx.readwrite.multiline_adjlist.parse_multiline_adjlist"]], "read_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1371, "networkx.readwrite.multiline_adjlist.read_multiline_adjlist"]], "write_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1372, "networkx.readwrite.multiline_adjlist.write_multiline_adjlist"]], "generate_pajek() (in module networkx.readwrite.pajek)": [[1373, "networkx.readwrite.pajek.generate_pajek"]], "parse_pajek() (in module networkx.readwrite.pajek)": [[1374, "networkx.readwrite.pajek.parse_pajek"]], "read_pajek() (in module networkx.readwrite.pajek)": [[1375, "networkx.readwrite.pajek.read_pajek"]], "write_pajek() (in module networkx.readwrite.pajek)": [[1376, "networkx.readwrite.pajek.write_pajek"]], "from_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1377, "networkx.readwrite.sparse6.from_sparse6_bytes"]], "read_sparse6() (in module networkx.readwrite.sparse6)": [[1378, "networkx.readwrite.sparse6.read_sparse6"]], "to_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1379, "networkx.readwrite.sparse6.to_sparse6_bytes"]], "write_sparse6() (in module networkx.readwrite.sparse6)": [[1380, "networkx.readwrite.sparse6.write_sparse6"]], "networkx.readwrite.gexf": [[1381, "module-networkx.readwrite.gexf"]], "networkx.readwrite.gml": [[1382, "module-networkx.readwrite.gml"]], "networkx.readwrite.graphml": [[1383, "module-networkx.readwrite.graphml"]], "networkx.readwrite.json_graph": [[1385, "module-networkx.readwrite.json_graph"]], "networkx.readwrite.leda": [[1386, "module-networkx.readwrite.leda"]], "networkx.readwrite.multiline_adjlist": [[1388, "module-networkx.readwrite.multiline_adjlist"]], "networkx.readwrite.pajek": [[1389, "module-networkx.readwrite.pajek"]], "networkx.readwrite.graph6": [[1390, "module-networkx.readwrite.graph6"]], "networkx.readwrite.sparse6": [[1390, "module-networkx.readwrite.sparse6"]], "networkx.relabel": [[1391, "module-networkx.relabel"]], "networkx.utils": [[1392, "module-networkx.utils"]], "networkx.utils.decorators": [[1392, "module-networkx.utils.decorators"]], "networkx.utils.mapped_queue": [[1392, "module-networkx.utils.mapped_queue"]], "networkx.utils.misc": [[1392, "module-networkx.utils.misc"]], "networkx.utils.random_sequence": [[1392, "module-networkx.utils.random_sequence"]], "networkx.utils.rcm": [[1392, "module-networkx.utils.rcm"]], "networkx.utils.union_find": [[1392, "module-networkx.utils.union_find"]]}})
\ No newline at end of file diff --git a/tutorial-34.pdf b/tutorial-34.pdf Binary files differindex 29ca3c9d..88c4ea8b 100644 --- a/tutorial-34.pdf +++ b/tutorial-34.pdf diff --git a/tutorial-35.pdf b/tutorial-35.pdf Binary files differindex eed3c33c..f1576b95 100644 --- a/tutorial-35.pdf +++ b/tutorial-35.pdf diff --git a/tutorial-36.pdf b/tutorial-36.pdf Binary files differindex f3500420..92b2b3ab 100644 --- a/tutorial-36.pdf +++ b/tutorial-36.pdf diff --git a/tutorial.ipynb b/tutorial.ipynb index 40f853a5..e6124418 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7ccab446", + "id": "910be702", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,7 +17,7 @@ { "cell_type": "code", "execution_count": null, - "id": "71ea016d", + "id": "e81d4b41", "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "aa8ac53f", + "id": "7a9bad7e", "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": "0980a693", + "id": "1afd3f57", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "d2e028f1", + "id": "d80564b3", "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": "88d2ec03", + "id": "87021f0b", "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "markdown", - "id": "fc693aa8", + "id": "8d61f686", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e27a376e", + "id": "bb477285", "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "7c54e3d0", + "id": "646415bb", "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": "f46a7b22", + "id": "6c49602d", "metadata": {}, "outputs": [], "source": [ @@ -125,7 +125,7 @@ }, { "cell_type": "markdown", - "id": "d51b1b17", + "id": "fd829173", "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": "9531ea66", + "id": "f427deb8", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +154,7 @@ }, { "cell_type": "markdown", - "id": "fd9ba5a4", + "id": "af6f6202", "metadata": {}, "source": [ "by adding a list of edges," @@ -163,7 +163,7 @@ { "cell_type": "code", "execution_count": null, - "id": "744c9caa", + "id": "2b82af46", "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "be753840", + "id": "de9e6182", "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": "4b2cbb84", + "id": "27402789", "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "6feeafeb", + "id": "e666008d", "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": "c0dbd368", + "id": "b63010db", "metadata": {}, "outputs": [], "source": [ @@ -213,7 +213,7 @@ }, { "cell_type": "markdown", - "id": "537f3ffc", + "id": "35e3dd39", "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": "46722cac", + "id": "979aba61", "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "db505c0e", + "id": "1313cf91", "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": "a2c93eb4", + "id": "feee6953", "metadata": {}, "outputs": [], "source": [ @@ -257,7 +257,7 @@ { "cell_type": "code", "execution_count": null, - "id": "83185796", + "id": "8cc20d74", "metadata": {}, "outputs": [], "source": [ @@ -272,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "445b92d4", + "id": "06bc9a00", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -292,7 +292,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5245b501", + "id": "3ccfeb23", "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "markdown", - "id": "71f9a0a3", + "id": "18d910fc", "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": "3f1923c8", + "id": "5e673c8a", "metadata": {}, "outputs": [], "source": [ @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "3f6ee189", + "id": "1d415acd", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -343,7 +343,7 @@ { "cell_type": "code", "execution_count": null, - "id": "403ea631", + "id": "5fe562ad", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "c441bb61", + "id": "0950da01", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -370,7 +370,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a64e20fe", + "id": "9883bd1c", "metadata": {}, "outputs": [], "source": [ @@ -387,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "3b4797c3", + "id": "12932883", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -416,7 +416,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6398381f", + "id": "1880bacb", "metadata": {}, "outputs": [], "source": [ @@ -428,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "a78f4789", + "id": "90ba3f56", "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": "7bd5354e", + "id": "f05f1ff0", "metadata": {}, "outputs": [], "source": [ @@ -450,7 +450,7 @@ }, { "cell_type": "markdown", - "id": "1951d601", + "id": "cb687f1c", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -461,7 +461,7 @@ { "cell_type": "code", "execution_count": null, - "id": "296db177", + "id": "000f5628", "metadata": {}, "outputs": [], "source": [ @@ -475,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "293bf78c", + "id": "42f4f049", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -484,7 +484,7 @@ { "cell_type": "code", "execution_count": null, - "id": "08403ca8", + "id": "734b4a38", "metadata": {}, "outputs": [], "source": [ @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "e61d611a", + "id": "374e7e55", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -517,7 +517,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2bfc4734", + "id": "c3c5158c", "metadata": {}, "outputs": [], "source": [ @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "bfcd2122", + "id": "51e4aca7", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -536,7 +536,7 @@ { "cell_type": "code", "execution_count": null, - "id": "17d63f27", + "id": "134aef74", "metadata": {}, "outputs": [], "source": [ @@ -546,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "234658ab", + "id": "e88e3f11", "metadata": {}, "source": [ "# Node attributes\n", @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bf6a969f", + "id": "b65fa5e1", "metadata": {}, "outputs": [], "source": [ @@ -570,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "70449c66", + "id": "dee786f2", "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": "5327a56d", + "id": "199205bf", "metadata": {}, "outputs": [], "source": [ @@ -598,7 +598,7 @@ }, { "cell_type": "markdown", - "id": "d3bbc698", + "id": "e950cba1", "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": "7686cfe1", + "id": "725a73a5", "metadata": {}, "outputs": [], "source": [ @@ -633,7 +633,7 @@ }, { "cell_type": "markdown", - "id": "0750c9c6", + "id": "d6f47b84", "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": "0b8346a7", + "id": "b8d42578", "metadata": {}, "outputs": [], "source": [ @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "b51c8e14", + "id": "81f8afa6", "metadata": {}, "source": [ "# Multigraphs\n", @@ -675,7 +675,7 @@ { "cell_type": "code", "execution_count": null, - "id": "fc17806b", + "id": "6408a9e1", "metadata": {}, "outputs": [], "source": [ @@ -693,7 +693,7 @@ }, { "cell_type": "markdown", - "id": "4e97bc06", + "id": "ba84061f", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -713,7 +713,7 @@ { "cell_type": "code", "execution_count": null, - "id": "648fcb02", + "id": "e38785b7", "metadata": {}, "outputs": [], "source": [ @@ -725,7 +725,7 @@ }, { "cell_type": "markdown", - "id": "75e3bbba", + "id": "fe2deba1", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -736,7 +736,7 @@ { "cell_type": "code", "execution_count": null, - "id": "dcfb0edf", + "id": "1edcecef", "metadata": {}, "outputs": [], "source": [ @@ -748,7 +748,7 @@ }, { "cell_type": "markdown", - "id": "2a88b3d3", + "id": "204c93d0", "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": "18fa7468", + "id": "30a8c595", "metadata": {}, "outputs": [], "source": [ @@ -770,7 +770,7 @@ }, { "cell_type": "markdown", - "id": "f3ec0545", + "id": "50e1412d", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -785,7 +785,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1236e5b7", + "id": "b86d1f14", "metadata": {}, "outputs": [], "source": [ @@ -799,7 +799,7 @@ }, { "cell_type": "markdown", - "id": "17248966", + "id": "7be00c0a", "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": "89719c2f", + "id": "750d84ae", "metadata": {}, "outputs": [], "source": [ @@ -819,7 +819,7 @@ }, { "cell_type": "markdown", - "id": "b6625f86", + "id": "0e53b504", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -838,7 +838,7 @@ { "cell_type": "code", "execution_count": null, - "id": "163cad99", + "id": "d7d732e2", "metadata": {}, "outputs": [], "source": [ @@ -847,7 +847,7 @@ }, { "cell_type": "markdown", - "id": "2060a04e", + "id": "654b18e1", "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": "674713cf", + "id": "64acafa1", "metadata": {}, "outputs": [], "source": [ @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "c4ffb67e", + "id": "79ba0ba2", "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": "930f5359", + "id": "ad8840dd", "metadata": {}, "outputs": [], "source": [ @@ -889,7 +889,7 @@ }, { "cell_type": "markdown", - "id": "7bbdb213", + "id": "ac7f96cc", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -898,7 +898,7 @@ { "cell_type": "code", "execution_count": null, - "id": "29427b11", + "id": "5399df9e", "metadata": {}, "outputs": [], "source": [ @@ -919,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "fc153443", + "id": "c4066cae", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -930,7 +930,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ec75cd12", + "id": "a297b883", "metadata": {}, "outputs": [], "source": [ @@ -941,7 +941,7 @@ }, { "cell_type": "markdown", - "id": "f6eada6e", + "id": "5af59003", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -950,7 +950,7 @@ { "cell_type": "code", "execution_count": null, - "id": "589f977d", + "id": "be51076a", "metadata": {}, "outputs": [], "source": [ @@ -960,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "6da45806", + "id": "62817648", "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": "5ad73987", + "id": "99fe891a", "metadata": {}, "outputs": [], "source": [ @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "02850e5d", + "id": "90ee80e2", "metadata": {}, "source": [ "See Drawing for additional details." diff --git a/tutorial_full.ipynb b/tutorial_full.ipynb index 1241bcc1..4fda47f8 100644 --- a/tutorial_full.ipynb +++ b/tutorial_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7ccab446", + "id": "910be702", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,13 +17,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "71ea016d", + "id": "e81d4b41", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.785268Z", - "iopub.status.busy": "2022-12-20T17:04:24.784877Z", - "iopub.status.idle": "2022-12-20T17:04:24.864377Z", - "shell.execute_reply": "2022-12-20T17:04:24.863695Z" + "iopub.execute_input": "2022-12-25T00:24:17.407338Z", + "iopub.status.busy": "2022-12-25T00:24:17.406845Z", + "iopub.status.idle": "2022-12-25T00:24:17.476748Z", + "shell.execute_reply": "2022-12-25T00:24:17.475560Z" } }, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "markdown", - "id": "aa8ac53f", + "id": "7a9bad7e", "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": "0980a693", + "id": "1afd3f57", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.868032Z", - "iopub.status.busy": "2022-12-20T17:04:24.867805Z", - "iopub.status.idle": "2022-12-20T17:04:24.870817Z", - "shell.execute_reply": "2022-12-20T17:04:24.870191Z" + "iopub.execute_input": "2022-12-25T00:24:17.479712Z", + "iopub.status.busy": "2022-12-25T00:24:17.479501Z", + "iopub.status.idle": "2022-12-25T00:24:17.482326Z", + "shell.execute_reply": "2022-12-25T00:24:17.481754Z" } }, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "d2e028f1", + "id": "d80564b3", "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": "88d2ec03", + "id": "87021f0b", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.873850Z", - "iopub.status.busy": "2022-12-20T17:04:24.873642Z", - "iopub.status.idle": "2022-12-20T17:04:24.876512Z", - "shell.execute_reply": "2022-12-20T17:04:24.875896Z" + "iopub.execute_input": "2022-12-25T00:24:17.485172Z", + "iopub.status.busy": "2022-12-25T00:24:17.484967Z", + "iopub.status.idle": "2022-12-25T00:24:17.487801Z", + "shell.execute_reply": "2022-12-25T00:24:17.487176Z" } }, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "fc693aa8", + "id": "8d61f686", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -117,13 +117,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "e27a376e", + "id": "bb477285", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.879372Z", - "iopub.status.busy": "2022-12-20T17:04:24.879165Z", - "iopub.status.idle": "2022-12-20T17:04:24.882529Z", - "shell.execute_reply": "2022-12-20T17:04:24.881916Z" + "iopub.execute_input": "2022-12-25T00:24:17.490488Z", + "iopub.status.busy": "2022-12-25T00:24:17.490291Z", + "iopub.status.idle": "2022-12-25T00:24:17.493452Z", + "shell.execute_reply": "2022-12-25T00:24:17.492862Z" } }, "outputs": [], @@ -134,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "7c54e3d0", + "id": "646415bb", "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": "f46a7b22", + "id": "6c49602d", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.885418Z", - "iopub.status.busy": "2022-12-20T17:04:24.885211Z", - "iopub.status.idle": "2022-12-20T17:04:24.887927Z", - "shell.execute_reply": "2022-12-20T17:04:24.887311Z" + "iopub.execute_input": "2022-12-25T00:24:17.496183Z", + "iopub.status.busy": "2022-12-25T00:24:17.495983Z", + "iopub.status.idle": "2022-12-25T00:24:17.498678Z", + "shell.execute_reply": "2022-12-25T00:24:17.498101Z" } }, "outputs": [], @@ -160,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "d51b1b17", + "id": "fd829173", "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": "9531ea66", + "id": "f427deb8", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.890836Z", - "iopub.status.busy": "2022-12-20T17:04:24.890629Z", - "iopub.status.idle": "2022-12-20T17:04:24.893625Z", - "shell.execute_reply": "2022-12-20T17:04:24.893001Z" + "iopub.execute_input": "2022-12-25T00:24:17.501486Z", + "iopub.status.busy": "2022-12-25T00:24:17.501282Z", + "iopub.status.idle": "2022-12-25T00:24:17.504240Z", + "shell.execute_reply": "2022-12-25T00:24:17.503660Z" } }, "outputs": [], @@ -196,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "fd9ba5a4", + "id": "af6f6202", "metadata": {}, "source": [ "by adding a list of edges," @@ -205,13 +205,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "744c9caa", + "id": "2b82af46", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.896341Z", - "iopub.status.busy": "2022-12-20T17:04:24.896136Z", - "iopub.status.idle": "2022-12-20T17:04:24.899029Z", - "shell.execute_reply": "2022-12-20T17:04:24.898409Z" + "iopub.execute_input": "2022-12-25T00:24:17.506881Z", + "iopub.status.busy": "2022-12-25T00:24:17.506676Z", + "iopub.status.idle": "2022-12-25T00:24:17.509565Z", + "shell.execute_reply": "2022-12-25T00:24:17.508982Z" } }, "outputs": [], @@ -221,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "be753840", + "id": "de9e6182", "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": "4b2cbb84", + "id": "27402789", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.901782Z", - "iopub.status.busy": "2022-12-20T17:04:24.901575Z", - "iopub.status.idle": "2022-12-20T17:04:24.904369Z", - "shell.execute_reply": "2022-12-20T17:04:24.903752Z" + "iopub.execute_input": "2022-12-25T00:24:17.512151Z", + "iopub.status.busy": "2022-12-25T00:24:17.511954Z", + "iopub.status.idle": "2022-12-25T00:24:17.514730Z", + "shell.execute_reply": "2022-12-25T00:24:17.514130Z" } }, "outputs": [], @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "6feeafeb", + "id": "e666008d", "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": "c0dbd368", + "id": "b63010db", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.907080Z", - "iopub.status.busy": "2022-12-20T17:04:24.906874Z", - "iopub.status.idle": "2022-12-20T17:04:24.909580Z", - "shell.execute_reply": "2022-12-20T17:04:24.908971Z" + "iopub.execute_input": "2022-12-25T00:24:17.517467Z", + "iopub.status.busy": "2022-12-25T00:24:17.517271Z", + "iopub.status.idle": "2022-12-25T00:24:17.519942Z", + "shell.execute_reply": "2022-12-25T00:24:17.519342Z" } }, "outputs": [], @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "537f3ffc", + "id": "35e3dd39", "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": "46722cac", + "id": "979aba61", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.912452Z", - "iopub.status.busy": "2022-12-20T17:04:24.912247Z", - "iopub.status.idle": "2022-12-20T17:04:24.915903Z", - "shell.execute_reply": "2022-12-20T17:04:24.915282Z" + "iopub.execute_input": "2022-12-25T00:24:17.522546Z", + "iopub.status.busy": "2022-12-25T00:24:17.522346Z", + "iopub.status.idle": "2022-12-25T00:24:17.525929Z", + "shell.execute_reply": "2022-12-25T00:24:17.525351Z" } }, "outputs": [], @@ -307,7 +307,7 @@ }, { "cell_type": "markdown", - "id": "db505c0e", + "id": "1313cf91", "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": "a2c93eb4", + "id": "feee6953", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.918641Z", - "iopub.status.busy": "2022-12-20T17:04:24.918433Z", - "iopub.status.idle": "2022-12-20T17:04:24.924738Z", - "shell.execute_reply": "2022-12-20T17:04:24.924113Z" + "iopub.execute_input": "2022-12-25T00:24:17.528529Z", + "iopub.status.busy": "2022-12-25T00:24:17.528333Z", + "iopub.status.idle": "2022-12-25T00:24:17.534352Z", + "shell.execute_reply": "2022-12-25T00:24:17.533781Z" } }, "outputs": [ @@ -345,13 +345,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "83185796", + "id": "8cc20d74", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.928620Z", - "iopub.status.busy": "2022-12-20T17:04:24.928412Z", - "iopub.status.idle": "2022-12-20T17:04:24.932616Z", - "shell.execute_reply": "2022-12-20T17:04:24.931981Z" + "iopub.execute_input": "2022-12-25T00:24:17.538036Z", + "iopub.status.busy": "2022-12-25T00:24:17.537841Z", + "iopub.status.idle": "2022-12-25T00:24:17.541865Z", + "shell.execute_reply": "2022-12-25T00:24:17.541263Z" } }, "outputs": [], @@ -367,7 +367,7 @@ }, { "cell_type": "markdown", - "id": "445b92d4", + "id": "06bc9a00", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -387,13 +387,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "5245b501", + "id": "3ccfeb23", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.935444Z", - "iopub.status.busy": "2022-12-20T17:04:24.935238Z", - "iopub.status.idle": "2022-12-20T17:04:24.939628Z", - "shell.execute_reply": "2022-12-20T17:04:24.939022Z" + "iopub.execute_input": "2022-12-25T00:24:17.544603Z", + "iopub.status.busy": "2022-12-25T00:24:17.544403Z", + "iopub.status.idle": "2022-12-25T00:24:17.548619Z", + "shell.execute_reply": "2022-12-25T00:24:17.548051Z" } }, "outputs": [ @@ -417,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "71f9a0a3", + "id": "18d910fc", "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": "3f1923c8", + "id": "5e673c8a", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.943369Z", - "iopub.status.busy": "2022-12-20T17:04:24.943159Z", - "iopub.status.idle": "2022-12-20T17:04:24.947317Z", - "shell.execute_reply": "2022-12-20T17:04:24.946703Z" + "iopub.execute_input": "2022-12-25T00:24:17.552089Z", + "iopub.status.busy": "2022-12-25T00:24:17.551885Z", + "iopub.status.idle": "2022-12-25T00:24:17.555843Z", + "shell.execute_reply": "2022-12-25T00:24:17.555266Z" } }, "outputs": [ @@ -457,7 +457,7 @@ }, { "cell_type": "markdown", - "id": "3f6ee189", + "id": "1d415acd", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -474,13 +474,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "403ea631", + "id": "5fe562ad", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.950758Z", - "iopub.status.busy": "2022-12-20T17:04:24.950552Z", - "iopub.status.idle": "2022-12-20T17:04:24.953660Z", - "shell.execute_reply": "2022-12-20T17:04:24.953030Z" + "iopub.execute_input": "2022-12-25T00:24:17.559137Z", + "iopub.status.busy": "2022-12-25T00:24:17.558941Z", + "iopub.status.idle": "2022-12-25T00:24:17.561976Z", + "shell.execute_reply": "2022-12-25T00:24:17.561391Z" } }, "outputs": [], @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "c441bb61", + "id": "0950da01", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -508,13 +508,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "a64e20fe", + "id": "9883bd1c", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:24.956452Z", - "iopub.status.busy": "2022-12-20T17:04:24.956245Z", - "iopub.status.idle": "2022-12-20T17:04:25.222164Z", - "shell.execute_reply": "2022-12-20T17:04:25.221480Z" + "iopub.execute_input": "2022-12-25T00:24:17.564620Z", + "iopub.status.busy": "2022-12-25T00:24:17.564415Z", + "iopub.status.idle": "2022-12-25T00:24:17.807859Z", + "shell.execute_reply": "2022-12-25T00:24:17.806966Z" } }, "outputs": [ @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "3b4797c3", + "id": "12932883", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -572,13 +572,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "6398381f", + "id": "1880bacb", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.225563Z", - "iopub.status.busy": "2022-12-20T17:04:25.225212Z", - "iopub.status.idle": "2022-12-20T17:04:25.232132Z", - "shell.execute_reply": "2022-12-20T17:04:25.231557Z" + "iopub.execute_input": "2022-12-25T00:24:17.811430Z", + "iopub.status.busy": "2022-12-25T00:24:17.810768Z", + "iopub.status.idle": "2022-12-25T00:24:17.817226Z", + "shell.execute_reply": "2022-12-25T00:24:17.816680Z" } }, "outputs": [ @@ -602,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "a78f4789", + "id": "90ba3f56", "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": "7bd5354e", + "id": "f05f1ff0", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.235050Z", - "iopub.status.busy": "2022-12-20T17:04:25.234836Z", - "iopub.status.idle": "2022-12-20T17:04:25.239417Z", - "shell.execute_reply": "2022-12-20T17:04:25.238807Z" + "iopub.execute_input": "2022-12-25T00:24:17.819750Z", + "iopub.status.busy": "2022-12-25T00:24:17.819540Z", + "iopub.status.idle": "2022-12-25T00:24:17.824043Z", + "shell.execute_reply": "2022-12-25T00:24:17.823472Z" } }, "outputs": [ @@ -642,7 +642,7 @@ }, { "cell_type": "markdown", - "id": "1951d601", + "id": "cb687f1c", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -653,13 +653,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "296db177", + "id": "000f5628", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.242861Z", - "iopub.status.busy": "2022-12-20T17:04:25.242651Z", - "iopub.status.idle": "2022-12-20T17:04:25.247498Z", - "shell.execute_reply": "2022-12-20T17:04:25.246854Z" + "iopub.execute_input": "2022-12-25T00:24:17.827411Z", + "iopub.status.busy": "2022-12-25T00:24:17.827191Z", + "iopub.status.idle": "2022-12-25T00:24:17.831955Z", + "shell.execute_reply": "2022-12-25T00:24:17.831331Z" } }, "outputs": [ @@ -685,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "293bf78c", + "id": "42f4f049", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -694,13 +694,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "08403ca8", + "id": "734b4a38", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.251067Z", - "iopub.status.busy": "2022-12-20T17:04:25.250854Z", - "iopub.status.idle": "2022-12-20T17:04:25.256004Z", - "shell.execute_reply": "2022-12-20T17:04:25.255436Z" + "iopub.execute_input": "2022-12-25T00:24:17.835440Z", + "iopub.status.busy": "2022-12-25T00:24:17.835211Z", + "iopub.status.idle": "2022-12-25T00:24:17.838861Z", + "shell.execute_reply": "2022-12-25T00:24:17.838388Z" } }, "outputs": [ @@ -721,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "e61d611a", + "id": "374e7e55", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -743,13 +743,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "2bfc4734", + "id": "c3c5158c", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.258871Z", - "iopub.status.busy": "2022-12-20T17:04:25.258661Z", - "iopub.status.idle": "2022-12-20T17:04:25.262728Z", - "shell.execute_reply": "2022-12-20T17:04:25.262119Z" + "iopub.execute_input": "2022-12-25T00:24:17.841180Z", + "iopub.status.busy": "2022-12-25T00:24:17.840977Z", + "iopub.status.idle": "2022-12-25T00:24:17.845047Z", + "shell.execute_reply": "2022-12-25T00:24:17.844480Z" } }, "outputs": [ @@ -771,7 +771,7 @@ }, { "cell_type": "markdown", - "id": "bfcd2122", + "id": "51e4aca7", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -780,13 +780,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "17d63f27", + "id": "134aef74", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.266096Z", - "iopub.status.busy": "2022-12-20T17:04:25.265889Z", - "iopub.status.idle": "2022-12-20T17:04:25.269943Z", - "shell.execute_reply": "2022-12-20T17:04:25.269335Z" + "iopub.execute_input": "2022-12-25T00:24:17.848310Z", + "iopub.status.busy": "2022-12-25T00:24:17.848100Z", + "iopub.status.idle": "2022-12-25T00:24:17.851926Z", + "shell.execute_reply": "2022-12-25T00:24:17.851328Z" } }, "outputs": [ @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "234658ab", + "id": "e88e3f11", "metadata": {}, "source": [ "# Node attributes\n", @@ -819,13 +819,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "bf6a969f", + "id": "b65fa5e1", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.273432Z", - "iopub.status.busy": "2022-12-20T17:04:25.273222Z", - "iopub.status.idle": "2022-12-20T17:04:25.278019Z", - "shell.execute_reply": "2022-12-20T17:04:25.277389Z" + "iopub.execute_input": "2022-12-25T00:24:17.855285Z", + "iopub.status.busy": "2022-12-25T00:24:17.855083Z", + "iopub.status.idle": "2022-12-25T00:24:17.859698Z", + "shell.execute_reply": "2022-12-25T00:24:17.859110Z" } }, "outputs": [ @@ -850,7 +850,7 @@ }, { "cell_type": "markdown", - "id": "70449c66", + "id": "dee786f2", "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": "5327a56d", + "id": "199205bf", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.281567Z", - "iopub.status.busy": "2022-12-20T17:04:25.281359Z", - "iopub.status.idle": "2022-12-20T17:04:25.285354Z", - "shell.execute_reply": "2022-12-20T17:04:25.284701Z" + "iopub.execute_input": "2022-12-25T00:24:17.862865Z", + "iopub.status.busy": "2022-12-25T00:24:17.862667Z", + "iopub.status.idle": "2022-12-25T00:24:17.866584Z", + "shell.execute_reply": "2022-12-25T00:24:17.865969Z" } }, "outputs": [], @@ -885,7 +885,7 @@ }, { "cell_type": "markdown", - "id": "d3bbc698", + "id": "e950cba1", "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": "7686cfe1", + "id": "725a73a5", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.288264Z", - "iopub.status.busy": "2022-12-20T17:04:25.288054Z", - "iopub.status.idle": "2022-12-20T17:04:25.293194Z", - "shell.execute_reply": "2022-12-20T17:04:25.292542Z" + "iopub.execute_input": "2022-12-25T00:24:17.869197Z", + "iopub.status.busy": "2022-12-25T00:24:17.868988Z", + "iopub.status.idle": "2022-12-25T00:24:17.873927Z", + "shell.execute_reply": "2022-12-25T00:24:17.873351Z" } }, "outputs": [ @@ -938,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "0750c9c6", + "id": "d6f47b84", "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": "0b8346a7", + "id": "b8d42578", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.296570Z", - "iopub.status.busy": "2022-12-20T17:04:25.296361Z", - "iopub.status.idle": "2022-12-20T17:04:25.299286Z", - "shell.execute_reply": "2022-12-20T17:04:25.298647Z" + "iopub.execute_input": "2022-12-25T00:24:17.877129Z", + "iopub.status.busy": "2022-12-25T00:24:17.876920Z", + "iopub.status.idle": "2022-12-25T00:24:17.879775Z", + "shell.execute_reply": "2022-12-25T00:24:17.879173Z" } }, "outputs": [], @@ -967,7 +967,7 @@ }, { "cell_type": "markdown", - "id": "b51c8e14", + "id": "81f8afa6", "metadata": {}, "source": [ "# Multigraphs\n", @@ -987,13 +987,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "fc17806b", + "id": "6408a9e1", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.302048Z", - "iopub.status.busy": "2022-12-20T17:04:25.301839Z", - "iopub.status.idle": "2022-12-20T17:04:25.308135Z", - "shell.execute_reply": "2022-12-20T17:04:25.307502Z" + "iopub.execute_input": "2022-12-25T00:24:17.882542Z", + "iopub.status.busy": "2022-12-25T00:24:17.882333Z", + "iopub.status.idle": "2022-12-25T00:24:17.888473Z", + "shell.execute_reply": "2022-12-25T00:24:17.887879Z" } }, "outputs": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "markdown", - "id": "4e97bc06", + "id": "ba84061f", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -1043,13 +1043,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "648fcb02", + "id": "e38785b7", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.311540Z", - "iopub.status.busy": "2022-12-20T17:04:25.311331Z", - "iopub.status.idle": "2022-12-20T17:04:25.315491Z", - "shell.execute_reply": "2022-12-20T17:04:25.314872Z" + "iopub.execute_input": "2022-12-25T00:24:17.891740Z", + "iopub.status.busy": "2022-12-25T00:24:17.891538Z", + "iopub.status.idle": "2022-12-25T00:24:17.895671Z", + "shell.execute_reply": "2022-12-25T00:24:17.895032Z" } }, "outputs": [], @@ -1062,7 +1062,7 @@ }, { "cell_type": "markdown", - "id": "75e3bbba", + "id": "fe2deba1", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -1073,13 +1073,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "dcfb0edf", + "id": "1edcecef", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.318276Z", - "iopub.status.busy": "2022-12-20T17:04:25.318067Z", - "iopub.status.idle": "2022-12-20T17:04:25.333367Z", - "shell.execute_reply": "2022-12-20T17:04:25.332716Z" + "iopub.execute_input": "2022-12-25T00:24:17.898383Z", + "iopub.status.busy": "2022-12-25T00:24:17.898178Z", + "iopub.status.idle": "2022-12-25T00:24:17.953402Z", + "shell.execute_reply": "2022-12-25T00:24:17.952789Z" } }, "outputs": [], @@ -1092,7 +1092,7 @@ }, { "cell_type": "markdown", - "id": "2a88b3d3", + "id": "204c93d0", "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": "18fa7468", + "id": "30a8c595", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.336279Z", - "iopub.status.busy": "2022-12-20T17:04:25.336066Z", - "iopub.status.idle": "2022-12-20T17:04:25.799147Z", - "shell.execute_reply": "2022-12-20T17:04:25.798420Z" + "iopub.execute_input": "2022-12-25T00:24:17.956475Z", + "iopub.status.busy": "2022-12-25T00:24:17.956246Z", + "iopub.status.idle": "2022-12-25T00:24:18.976800Z", + "shell.execute_reply": "2022-12-25T00:24:18.976187Z" } }, "outputs": [], @@ -1121,7 +1121,7 @@ }, { "cell_type": "markdown", - "id": "f3ec0545", + "id": "50e1412d", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -1136,13 +1136,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "1236e5b7", + "id": "b86d1f14", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.803162Z", - "iopub.status.busy": "2022-12-20T17:04:25.802907Z", - "iopub.status.idle": "2022-12-20T17:04:25.809103Z", - "shell.execute_reply": "2022-12-20T17:04:25.808432Z" + "iopub.execute_input": "2022-12-25T00:24:18.980003Z", + "iopub.status.busy": "2022-12-25T00:24:18.979772Z", + "iopub.status.idle": "2022-12-25T00:24:18.985491Z", + "shell.execute_reply": "2022-12-25T00:24:18.984908Z" } }, "outputs": [ @@ -1168,7 +1168,7 @@ }, { "cell_type": "markdown", - "id": "17248966", + "id": "7be00c0a", "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": "89719c2f", + "id": "750d84ae", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.813424Z", - "iopub.status.busy": "2022-12-20T17:04:25.813194Z", - "iopub.status.idle": "2022-12-20T17:04:25.817681Z", - "shell.execute_reply": "2022-12-20T17:04:25.817049Z" + "iopub.execute_input": "2022-12-25T00:24:18.989280Z", + "iopub.status.busy": "2022-12-25T00:24:18.989065Z", + "iopub.status.idle": "2022-12-25T00:24:18.993210Z", + "shell.execute_reply": "2022-12-25T00:24:18.992626Z" } }, "outputs": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "markdown", - "id": "b6625f86", + "id": "0e53b504", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -1225,13 +1225,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "163cad99", + "id": "d7d732e2", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:25.821484Z", - "iopub.status.busy": "2022-12-20T17:04:25.821269Z", - "iopub.status.idle": "2022-12-20T17:04:26.135538Z", - "shell.execute_reply": "2022-12-20T17:04:26.134817Z" + "iopub.execute_input": "2022-12-25T00:24:18.996436Z", + "iopub.status.busy": "2022-12-25T00:24:18.996236Z", + "iopub.status.idle": "2022-12-25T00:24:19.335531Z", + "shell.execute_reply": "2022-12-25T00:24:19.334896Z" } }, "outputs": [], @@ -1241,7 +1241,7 @@ }, { "cell_type": "markdown", - "id": "2060a04e", + "id": "654b18e1", "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": "674713cf", + "id": "64acafa1", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:26.139618Z", - "iopub.status.busy": "2022-12-20T17:04:26.139247Z", - "iopub.status.idle": "2022-12-20T17:04:26.417892Z", - "shell.execute_reply": "2022-12-20T17:04:26.417214Z" + "iopub.execute_input": "2022-12-25T00:24:19.338997Z", + "iopub.status.busy": "2022-12-25T00:24:19.338655Z", + "iopub.status.idle": "2022-12-25T00:24:19.532064Z", + "shell.execute_reply": "2022-12-25T00:24:19.531316Z" } }, "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": "c4ffb67e", + "id": "79ba0ba2", "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": "930f5359", + "id": "ad8840dd", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:26.421010Z", - "iopub.status.busy": "2022-12-20T17:04:26.420770Z", - "iopub.status.idle": "2022-12-20T17:04:26.424009Z", - "shell.execute_reply": "2022-12-20T17:04:26.423499Z" + "iopub.execute_input": "2022-12-25T00:24:19.535173Z", + "iopub.status.busy": "2022-12-25T00:24:19.534962Z", + "iopub.status.idle": "2022-12-25T00:24:19.537928Z", + "shell.execute_reply": "2022-12-25T00:24:19.537463Z" } }, "outputs": [], @@ -1308,7 +1308,7 @@ }, { "cell_type": "markdown", - "id": "7bbdb213", + "id": "ac7f96cc", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -1317,19 +1317,19 @@ { "cell_type": "code", "execution_count": 36, - "id": "29427b11", + "id": "5399df9e", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:26.426865Z", - "iopub.status.busy": "2022-12-20T17:04:26.426478Z", - "iopub.status.idle": "2022-12-20T17:04:26.709508Z", - "shell.execute_reply": "2022-12-20T17:04:26.708792Z" + "iopub.execute_input": "2022-12-25T00:24:19.540483Z", + "iopub.status.busy": "2022-12-25T00:24:19.540276Z", + "iopub.status.idle": "2022-12-25T00:24:19.804209Z", + "shell.execute_reply": "2022-12-25T00:24:19.803609Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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6peIQXlggEMCcOXOkRolVNBUH52YlUhkgCIISGtPAwAC+fv3KtWhyCQsLg6lTp6psV3DgwAHKuc2aNYP8/HypZd69e5fixEicunbtqpXTokWRnBWoVKkSZZ93y5YtpXb2mJKJOhFKS5UqBdu2bZP53mgbAoGA4udk5cqVXIsFOTk5lBG2t7c312IpRVhYGPTp00cthUDb3Z6XSGXg7NmzlAcxf/58rsVSCmXsCoYMGVJoV0AQBAwdOpRy3oIFC2SWFxcXBx4eHrRlVKxYUWvdddLNCuzatYtyHl0IaHHSltEThjlUDVolVrSL63ZbLy8vyrtbUFDAqUxHjhyh3ONv375xKpOq3Lt3j3Y5VlFlQJsDopU4ZSA9PR0qVqxIegj29vbFNkKVsnYFFy5cgJSUFNppcXne9oRCISxfvlzqssG6deu0btlAkVkBAICpU6dKvW/FwZ87Rjl0NZz1mzdvKHXh2p+GpMfGDh06cCqPuvz9+7dEtq0SpwzMmzePcvPPnTvHtVhqQxAEPHjwAHr37i13tOPs7Ey7zmVnZ6dQ5LR79+5JXTbo0qWL1iwbKDorAACwatUqqfdrwoQJGpYcwzZRUVFqddg+Pj7w/PlzzkfVqiDpiKx9+/acyfL69WvKvb148SJn8jCBum1Lmg0X15QoZeDz588UxxGdOnXS2mkZVVHUroDOa1rHjh0VGt3HxcVJjQmuDcsGBEFQgrRImxUAoIZMLZrc3Nw0LD2GbdSdGRAnCwsL6N69O2zevBnevHlTLNzTHj16lFIPruylxo0bR5LD3t6+WCpYRYmLi1OrTeGZAZYhCIKyVc7AwAC+f//OtWiskZycDOvXr6csi8hL69atUyh/oVAIK1as0Mplg+DgYIpM0mYFAACuXr0q857ExsZqUHoM26hjMyArWVtbQ+/evcHPzw8+fPigdctmAP8M9sqUKUOSe+bMmRqXIyUlhWLQuGrVKo3LwSRBQUFQo0YNldoOthnQEKdOnaLc/P/++49rsTRCfn4+nDhxQiG7AoT+bXNRZmQfHBxMCYYiTl26dIGEhAQWa0dF2VkBAICXL1/KvCfHjh3TYA0wmkCd3QSKpjJlysCAAQNg586d8OXLF63p6OfPn0+S08rKCjIzMzUqg6+vL0kGfX19hZYptZFv375B9+7d1WoreDeBBkhLS6P4EHdwcNB44+caZewK9PX1Ye3atQp7Y/z796/MZYOHDx+yXLv/R9lZAQCAmJgYmfdj3LhxGpIeoymY8DOgbCpXrhx4enrC3r17ITw8nDPlIDIyktIH7N27V2Pli0QicHNzI5U/ePBgjZXPFIrELVAkYT8DGmL27NmUm3/p0iWuxeKU8PBwmDZtmly7AgsLC5g7dy78/PlTbp5CoRBWrlxJ27nq6enB2rVrWZ82VWVWAAAgNzdX5n1wcXFhVW4MNzDlgVByulvRZG9vDyNGjICDBw9CdHS0RusuOZKtV6+expST27dvU+7FgwcPNFI2E4gjGpYuXVrqs3V2dlbKA6G83VxcU+yVgY8fP1IidnXt2lVrpuu4JiUlBdavX6+Qv4LBgwfDixcv5OYpa9mgc+fOrC4bqDIrIEbWi40QUkghwhQ/FPUff+zYMfD09JTZqbu7u8OQIUMo0fcUTc7OzjB27Fg4duwY63YqN27coJT/9OlTVssUI+mgp2bNmsWmT7558yZUq1ZN6jMsW7Ys7N+/H4RCocy2JU5GRkZarwgAFHNlgG6UaGhoCOHh4VyLpnWINV1JxYkutWzZEs6fPy/Tcvrv378UL4/iVKFCBVZGAarOCoiRNPyRjFdw+PBhxmXGaAcCgQD8/f1pI8v5+/uTItE9ffqUsje+aLK0tISNGzfC169f4cCBAzB8+HCljXjFqUqVKjBx4kQ4ffo0/P37l9E6C4VCcHZ2JpU3fPhwRsugIyYmhjJa3rlzJ+vlqsvnz5+hS5cuUp+VkZERLFiwANLS0kjXSWtb4lSnTp1ioQgVa2Xg2LFjlBu/ZMkSrsXSauRZ1UuOYvz8/KTaFchaNuDz+bBmzRpGlw3UmRUAAIqHRUlPYqNGjWJMVox2QhAEJCUlQVRUFCQlJUntpEUiERw/flzmR97FxQUuXrwIBEEAQRAQFhYGe/bsgSFDhkj10yEv1ahRA6ZOnQoXLlxgZAvaxo0bKYMltg1+Fy9eTFG6uY4UK4vExESYOnWqzIHSwIEDITIyUmY+4rZF5xL++fPnGqqN6hRbZSA1NZXywjk5OUF2djbXomk93t7eSnVQlpaWMGfOHKnT6CEhIVKXDTp16sRImGB1ZwUAgDIFLGkQ6ejoqLacmJJFZmYmLF++XKbtTdu2beHt27ek6wiCgM+fP8OOHTugf//+MqPfSUs8Hg/q1q0L3t7ecOXKFZWMzxITE8HIyIiU7/r16xm6O1Ty8vIo/fKUKVNYK08d8vLyYOvWrWBtbS31GTRo0EBp42ihUEhZRho5ciRLtWCOYqsMzJw5k/Lgrl69yrVYxYLc3FzabYilSpWS2TnJsiv4+/cvdOzYkfY6JpYN1J0VAACYNWsW6foePXpQ8pSn/WN0k1+/fsGIESNkfrjHjRsndeucSCSC9+/fg6+vL/Tq1QusrKyUVg74fD40atQI5s2bBzdv3lR4tD1y5EiK0suW8yS6Ld4fP35kpSxVIQgCrl69StntUDSVL18eDh8+rPLM5vr160n5GRkZaY3nVmkUS2Xg3bt3lKnpHj16cC1WsSIsLIyyZm5hYQFbt26Fhg0byu2YWrRoQbErEIlEsHr1aqnLBqtXr1bp5ZI2K5CXl6dUPhs2bCDl0bFjR7C1tSUdO3DggNLyYXSHFy9eQIsWLaS+F+bm5rB27VrIycmRmY9QKITXr1/Dpk2boFu3bpR3UZGkp6cHzZo1g4ULF8KdO3ekhgR+/vw55dpr166xcXugdevWpHLatGnDSjmq8uHDB6m2Tgj989q6ePFitWPZJCQkgKGhISnvDRs2MFQLdih2yoBIJKKEozUyMoIfP35wLVqx48SJE5SXoWHDhpCTkwMPHz6EPn36KBQHwdfXl2RUc//+fYrfB3FSZdmAblZg9+7dStdXMnpanTp1YODAgaRjmjCwwhRvCIKA06dPg4ODg9T3wsnJCc6ePauw4Vh+fj48e/YM1q5dCx07dlRpK6OBgQG0bt0ali5dCiEhIYUKCUEQlJnArl27Mn5fPnz4QJHp9OnTjJejCvHx8TBx4kSZ2wA9PT0Z3VE0fPhwSl+pze6si50ycPjwYcpDXL58OddiFVvGjBlDuZ+zZs0q/D08PBymT58OZmZmMjsisV2BeC91fHy81GWD8uXLw/379xWSj6lZAYB/rkSL5mNnZwcBAQGkYxUrViwWlr8Y7snOzobVq1fLfDdatWoFr169UjrvvLw8ePToEaxYsQLatWtHWfdXJBkbG4OHhwesWrUKFixYQPqNx+NBREQEo/dj8uTJpDLKli2r0nvKJLm5ubBp0yawtLSUep+aNGkCT548Ybzsp0+fUsq6ceMG4+UwRbFSBgQCAWVa18XFRe6UHEY6mZmZULVqVUqjlZxGTElJgQ0bNoC9vb3MDkhPTw8GDRoEz58/l7tssGrVKrnLBkzNCgAAvH//ntIh0o1mwsLCVMofo5v8+fMHxowZI3MWbdSoUfD792+Vy8jJyYHg4GBYsmQJtGzZUm2PeAghmDNnDmP3IC0tjbLUsXjxYsbyVxaCIODixYtSt/uJFf/jx4+z5iiNIAioV68eqczu3buzUhYTFCtlYNq0aZQHev36da7FKva8e/eOMvKwsbGhdYqSn58PJ0+ehEaNGsntbFq0aAHnzp2Du3fvSl026NixI2XZgCAISExMhMjISEo4VlVnBQDo45D//v2bshNCk25bMSWHN2/eUNbMiyZTU1NYuXKl1LV9ZcjMzISgoCBYsGABNG3aVCH/IZKJx+NBly5dGInIuHPnTsqg4NevX2rXUxVCQ0OhXbt2UuttYmICy5cv14i7esloqTweT2uNlIuNMvD27VvKCLN3795ci1VikHyZEfq3ZUpaB0EQBDx8+BD69u0r167AyckJVq5cSdnnL07ly5eHkJAQEAgE4OfnJ1ObV3VWAOCf0ZZkG3r79i0MGTKEdMzT01PlMjC6DUEQcP78eYqzH0mF9uTJk4wuR6WlpcGNGzdgzpw50KBBA5UCNJUqVQr69OkD/v7+SkVkJAiC4tCrb9++jNVNUeLi4mDcuHEy6z5ixAiNKimZmZmUnSM+Pj4aK18ZNK4MiEd9UVFRkJiYqNALIRKJKCNEExMTiIqKYl9gHYEgCOjXrx/l5VHEHiMiIkJhu4KWLVvSvqw8Hg8MDQ1luvXk8XhqW0FLzgLcunUL9uzZQzpWrlw5bDeAUYucnBxYv349WFhYSH0fmjdvzpozmpSUFLh8+TLMnDmT4lxL0SSOyBgQEABfv36V+k7cv3+fcu2dO3dYqRcdOTk5sG7dOpk7Mpo3b66Qq3U2kNwGb2Njo5VL2xpTBqSN+lxdXcHPz0+mQw06j06rV6/WlOg6Q0pKCsVCms/nK2zsl5KSAhs3blTIrsDY2FjpzonH44Genh4EBgaqXEfJNbxDhw5BWFgYpayvX7+qXAYGI+bv378wfvx4maPVYcOGQUxMDKtyJCQkwLlz52S625WXypcvTxuRcdCgQaTzqlSpwnrAMoB/A5izZ8/KjBPh4OAAp0+f5lS5//btG0Wuo0ePciaPNDSiDCgaKISuk09OToYyZcqQrqlcubJSnucwivPkyRPK+mPFihWVcpihjF2BskndUKCSneG6deuAIAhKIKeAgACV8sdg6AgNDQV3d3ep7drExASWLl3K+jp2QUEBRVmXNXshK1WqVAkGDBhA6S98fX1ZrQMAwOvXr6FVq1ZSZTMzM4PVq1drjUdaSW+nzZo141okCqwrA4qGEBWHebx16xZpGWHSpEmUc2/dusW22DrN2rVrKfe8R48eSmvXBEHAo0ePFLIrUCbxeDzw9/dXqW6jR48m5TVz5kwAABg2bBjp+KBBg1TKH4ORBkEQcPnyZZk2MRUqVICjR4+yOrJetWoVqUxjY2P4+PEjHD16FMaMGQOOjo4qv5t6enqwZ88etXZOyOL3798watQomX3DmDFjWCtfVS5cuECR9c2bN1yLRYJVZUAgEICZmZnCscRlrReLU79+/dgUGQP/bDTovHSpo/FHRETAjBkz5NoVKNpOXF1dVZr6mz9/PimvwYMHAwDA/v37Scft7Oyw3QCGFXJzc2Hz5s0yXRI3btyYlb3vAP8M7QwMDEjlbd26lXROZGQkYxEZz5w5o3Z8kuzsbFi1apXM/qN169bw+vVrtcphi4KCAsp99PLy4losEqwqA35+foyOCI2MjHDMeQ0RFxcHdnZ2pPtvYGCg9ssmEAgUsitQJKkS1c3X15eUR9u2bQEA4MePH5T8P336pFZdMRhZJCQkwOTJk2UOlgYPHlzoyItJJHfQVK5cWepsRNGIjIMHD6b0C4qmmjVrwrRp05SKyEgQBJw8eRIqVaokNV8nJyc4f/681ivvK1euJMltYmICKSkpXItVCGvKAEEQ4Orqyvj0MFs+tTFUJD32IfTP4FMynrcq5Ofnw7Zt29RqD6rsJpEMpFKtWjUA+NdeJY0nt2/frnY9MRh5fPz4Uaq3TvEgaNGiRWr7yy/Kw4cPKeUEBQUpdK04IiOdszJl+vJ69erBrFmz4OrVq5Camkop5/nz59C8eXOpeVhYWMD69eu10jKfjj9//lCcRWnCvkJReAAAiAWSkpKQra0tG1kjExMTVKpUKWRtbV34r+T/pf1maWmJ9PT0WJGrJLJgwQK0YcMG0rGhQ4ei48ePIx6Pp1be6raRpKQkZGNjo9Q19+/fR+7u7oV/W1tbI4FAgBBCaNSoUejo0aOFv/Xr1w9duHBBZfkwGEUBAHTz5k00e/ZsFBYWRntOuXLl0Nq1a9GoUaMQn89Xu7w6deqgT58+FR7r3bs3unz5skLXf/v2DVWvXp10bOjQoSgzMxM9ePAApaWlKSUPn89HDRo0QO7u7qh27dro5s2b6PTp07Tn8ng85OXlhVatWoXKli2rVDlcM3jwYHT27NnCv93c3NC3b9/Ufp5MwJoyEB0djZydndnIWm2srKzkKg3SfjMzM1P7I1icKCgoQG3atEHPnz8nHT906BAaPXq0WnkDAHJzc0ORkZFI2WZoZWWFQkNDlW5jdJ1YTk4OMjY2RocPH0ZjxowpPG5jY4MSEhK04kXF6AYFBQUoICAALV++HKWmptKe06BBA+Tr64vatGmjVlm7du1CU6ZMKfybz+ejqKgo5ODgIPdab29v5O/vX/i3jY0Nio2NRcbGxkgkEqF3796hkJAQFBwcjB49eoQyMzPVklVMu3btkK+vL6pXrx4j+WmaBw8eoHbt2pGO3blzB3Xo0IEbgYpQLGcGuERPT08lJUL8f2NjY66roDTR0dGoXr16JG3f1NQUvX79mvJhVRZ/f380a9YspZUBhP51Xv3790ezZ89GzZo1U+ia1NRUVKpUKdKx6Oho5OjoiH7+/ImcnJxIv71//x7VqVNHadkwGHVITk5GK1asQAEBAUgkEtGe079/f7Rx40bk4uKiUhkZGRmoYsWKKCMjo/DYokWL0OrVq2Vel5WVhSpWrEjqD3x8fCgziGIKCgrQmzdvUEhICAoJCUGPHz9GOTk5Sstbo0YNNGDAAOTh4YGaNWuGjIyMlM6DawAA1a5dG33+/LnwWN++fdHFixc5lOofrCkD6oz6SjLGxsYqz0pYWVkhfX19TuQ+f/48GjhwIOlYnTp10PPnz5GJiYnK+aampiJ7e3uUk5ODCIJQOZ9mzZqh2bNno759+8q8RwCAjI2NUX5+fuGxwMBA1KlTJ8Tj8ZCLiwuKiooq/M3Pzw/NnDlTZbkwGHX4+vUrmjNnDrp16xbt74aGhmjWrFnov//+Q5aWlkrnP23aNLRz587Cv+3s7NCvX7+QoaGh1Gv279+Pxo8fX/g3j8dDP378UHiWLi8vD718+RKFhISgy5cvo3fv3in9jTA2NkYtWrRA7u7uyMPDAzVu3BgZGBgolQdXBAQEoKlTpxb+zefz0c+fP5G9vT2HUrGoDCCk3qhPHocOHUJGRkYoNTUVpaamIoFAQPt/8d/StOvihrm5ucqzEhYWFmpNeU+ePBnt3r2bdGzKlCmkzkQVgoKCUPfu3REAqKUQIISQo6MjmjlzJho3bhxt55iamoqcnZ0pU7Curq5o+vTp6NWrV+jEiROFx5VZR8Vg2CIwMBDNnj0bff36lfZ3Ozs7tHr1ajR27FilbKI+f/6MatWqRTp28uRJ5OnpSXs+AKAGDRqgd+/eFR7r3r07un79usJlIoTQz58/0YIFC6TaBSiLmZkZatWqFXJ3d0fu7u6oQYMGnA2c5JGeno4qVqxIWjpZvHgxWrVqFYdSsawMMDXqo6N27dpo4sSJaOjQoZRpX0kAAGVlZUlVFGQpEampqUobw2grfD6fZC+hrEKBEEJNmzYlGR0hhNCFCxdQv3791JItKCgI9e/fH2VnZyOEEEWB5PF4yNTUFO3YsQOFhoaigwcPylyHtLCwQF5eXmjGjBmFU//iMrKysijni+1ADA0NUV5eXuFxa2trlJSUhI1OMZxTUFCA9u7di5YuXYpSUlJoz6lTpw7y9fVFHh4eCufbrl079ODBg8K/W7VqhR49ekR77rNnz1CLFi1Ix27cuIG6deumUFmZmZlo/fr1aMuWLSg3N5f2HHd3dzR48GAUHR2NQkJC0OvXr5UezFlaWqI2bdoUKgd169bVKtufKVOmoF27dhX+XbZsWRQTEyNzRoZtWFUGEGJ21EeHsbEx6t+/Pxo3bhxq27YtKw9cJBKh9PR0pZUI8f/FH7jijoGBATI3N0epqamkj7WhoSEaO3YscnR0lKlgyGvoqamp6OjRo2jDhg3oz58/hcf5fD7y9fVFo0aNQlZWVoXn7t+/H23btg39+vVLap5iu4LmzZujefPmyW2HfD6f8vvbt29R/fr1ZcqOwWgKgUCAVq1ahbZv346EQiHtOb1790abNm1Cbm5ucvM7e/YsGjx4MOmYNFuZESNGoOPHjxf+7ezsjMLDw+UqywRBoKNHj6KFCxeiv3//0p5TpUoVtGXLFtS9e3eSkXZ6ejp69OhRoc1BaGio0rPNpUqVQm3btkUeHh7I3d0d1axZk1ND8I8fP1Lu7+nTpynPQZOwrgwgJH/UxxQuLi5o7NixaPTo0ahixYqslKEK+fn5KC0tTWklQvxvQUEB11VgBFNTU4VmJbKysijr9GlpabTT/gUFBejixYtoy5Yt6NWrV6zIvWXLFjR79mxW8sZgVCUsLAzNnTsXXbt2jfZ3AwMDNGPGDLR48eLCmT068vPzkaOjI+kjPWnSJNLIFSGEEhMTkb29PcneZsOGDcjHx0emnA8fPkSzZs1Cb9++pf3d2toaLV++HE2ePFmhkbFAIEAPHz4s3K3w8eNHuddIYmtri9q1a1c4c1C1alWNKwdt2rQhzcC0adOGNEOjaTSiDCD0/6O+bdu2oR8/fvy/AP97AEyKwefzUZcuXZCXlxfq0aNHsTEsoQMAUG5urtJKRNHExowMF8jaEmr9P38BT548QW/fvmW0zj169JDa4WIwXHP37l00a9YsyvKdmDJlyqCVK1ei8ePHS11HX7p0KWnN2szMDP3584ekgG/YsAEtWLCg8G8jIyMUGxuLypQpQ5tnVFQU8vHxQefPn6f9XU9PD02ePBktX75caX8hRUlMTEQPHjwonDmQZlchi/LlyxcqBu7u7sjFxYV15eD06dMU24yPHz9SbDg0hcaUATEAgFJSUlBGRgaysLBAr169Qj169GBtGcHOzg6NHDkSjRs3DlWrVo3x/LUdgiBQZmamyvYSRbcd6SqWlpYoOTlZaw2SMBihUIgOHDiAFi9ejJKSkmjPqVmzJvL19UUdO3ak/BYbG4ucnJxIa/M7duwotHoXiUSocuXKKDo6uvD3kSNHoiNHjlDySk9PR2vXrkW+vr6kWYSidO3aFW3evBnVqFFDmWoqRFxcHLp//36hchAREaF0HpUqVSpcUnB3d1fI94Ky5OfnIwcHBxQfH194jAmDbJVhx7GhcsgLcYykuKNUNrVo0QIOHDjAqFvPkk5BQQEkJSVBREQEvH79Gu7evQvnzp2Dffv2wYwZMyh+1Q0MDMDNzQ3Kly8PxsbGjD07rtPLly+5fhQYjFxSU1Nh3rx5lEBERVOPHj3g27dvlGv79etHOq9GjRqF/v6vX79Oyef58+ek64VCIezbt09m7ILq1avDzZs3NXIvxMTExKgdkdHFxQXGjRsHx48fZzQi4uLFi0nlmJubM+LuXRW0QhkA+BfAxt/fnxLe09XVFfz9/eHGjRtQu3ZtRjp2c3Nz8PLygmfPnml9cAttZ8uWLZT726lTp8KgJzk5OfD371/49u0bPHv2DG7dugWnTp2CgIAAWLt2Lfj4+MCECRNg4MCB0LFjR2jcuDG4ubmBoaEh5wpA0bRhwwaO77RuQxAEKbQ5fm9lEx4eDn379pXanvX19WHmzJmQnJxceM3du3cp54WEhAAAQNeuXUnHGzRoQHoGwcHBULduXanllS5dGnbs2AH5+fmavhUUmIjIWLVqVZg0aZLaERljYmIoA6oNGzZw0s61RhkQQxAEJCUlQVRUFCQlJZFuRkFBAWzbtk1m6E9lU40aNWDLli2QkJDAYa2LLwRBQPfu3Sn3df369WrlO3HiRFJ+/fv3h0ePHsG1a9fg6NGjsG3bNli5ciXMmjULxowZA3379gV3d3eoV68eODk5gbW1NaOzSl27dmXojmGUQSAQgJ+fH+0gwc/PDwQCAdciajWKfKS3b98O+fn5QBAEJfjQwIEDISIigvIu7d+/HwD+KR19+vSRqXR4e3trVXS+ohAEAd+/f4fdu3erHZFx+vTpcPHiRZKCpQiy7p8m27nWKQOKEB8fD2PHjmWso0fo3/R2//794ebNmyAUCrmuYrEiISEBKlSoQLqfenp68PTpU5XzXLVqldofY5FIBCkpKeDg4KC0YiB5vrm5uVaManQJecuHPB4PzMzMIDAwkGtRtRqhUAj79++HsmXLSm3v1apVg5s3b4Kfnx/lYz558mTSMSsrK/jz5w/MnTtX5nJEz549aZcjtBlxRMYdO3ZA//79oXTp0kp/SxSJyFiUtWvXysxLU+28WCoDYp49ewYNGjRgVClACIG9vT0sWbIEIiMjua5isSEkJITSYTs6Oqqs0R4+fJiUV61atVSWzc/PT21lACEEz549U1kGjHIEBgaCnp4eZQpVMvH5fNDT08MKgQKkpaXBggULZC7BtW/fnmLrY2JiQvrbw8MDypQpIzWPWrVqwe3bt7muLiOIRCJ49+4d+Pr6Qq9evVSalebz+dC4cWPw8fGBW7dukWzWxO1ckTzYbufFWhkA+Kf17tmzRyUNTpHUvn17OHnyZLGJmc0lS5cupdy//v37q7Tude/ePVI+1tbWKsslEAjAzMxM7oel6ItnZmYG1apVIx1fu3atyjJgFEfV54WXDBQjMjISBg4cqJQirEgqU6YM7Nq1CwoKCriuImsIhUJ49eoVbNy4Ebp27Qrm5uZK3yd9fX1o0aIFzJkzB4yNjbWmnWt8ayFbJCcno8WLF6M9e/aw4tSoVKlSaNiwYcjLywvVrVuX8fxLAkKhELVv3x49fPiQdHzXrl1o0qRJSuUVFhaGqlatSjqWkZGBzM3NVZJNUU+YfD4f8Xg8dPPmTXT16lXSNp+OHTui27dvq1Q+RnFUiWnC4/HQ6tWrSQF0MLJ5/vw5Wrx4Mfrw4YNa+RgYGKDx48ejWbNmFXoI1RUKCgrQ+/fv0ePHj9GTJ0/QixcvpLpZZgIej4f8/PzQjBkzmM+cFRWDQ968eQPNmjVjZZZAnBo2bAgBAQF4JELDr1+/KLM0RkZG8P79e6XyycrKotz3L1++qCWbomvQQUFBAABw/vx50jmmpqaQl5enlgwY2RAEAa6urowaf+KEU0lJPB4PXF1dWdllUOKUAYB/6zyHDh1S2TJU0WRsbAzDhw+H+/fv461ORbh69SrlXlWvXh0yMzOVykdSqWBiHVLeFtaixj6JiYmUejx+/FhtGTDSobvnOOGEEzklJSUx/u6VSGVAjEAggJkzZypkoKHuSKRy5cqwdu1aRh1SFGdmzpxJuUfjxo1TKg/JLVEHDhxgTD5ZW1iLUqdOHZIMq1atYkwGDJWoqCjOO1qccNL2FBUVxfi7pz0xHVnA2toa+fn5odDQUNSmTRuZ58L/1ieNjIxUKisiIgL9999/yMHBAfXs2RNdvny5xAQYUoUNGzagBg0akI4dOHAAnTp1SuE8KlWqRPo7NjaWEdkQ+rf2ZmNjg5ycnJCNjY1UP+Tu7u6kv0NCQhiTAUNFVZsQDEaXsLCwYDzPEmNAKA8AQKdPn0Zz584lhcelg8/nI1tbW5LPaFUoW7YsGjVqFBo7dizFGE4XCA8PRw0aNECZmZmFxywsLNDbt29R5cqV5V4/efJktHv37sK/vby80L59+1iRVRqXL19Gffv2Lfzb2NgYpaamqqw0YmQDAMjNzQ1FRkYqbQhsaGiILly4gJo2bcqSdCWPlJQUtGnTJnT48GFSXAJFcXJyQsuWLUPdunXjNCRwcQMAUNOmTdHPnz+VNpR1cXFB4eHhzN9vxucatJz09HTw8fEBfX19uVMxtra20KhRI0amdVq1agWHDh1Set28uHP8+HHKvWjYsKFChnhr1qwhXde5c2cNSEwmJSWFsoT04MEDjcuhS6jiF6JoGjJkCERHR3NdDa0mLy8PfH19wdrampH+rV27dhAaGsp1tYoVqvo/8ff3Z0UenVMGxHz9+hU6dOig0ANo3bo1eHp6yg28o8iDtbCwgPHjx8OLFy90xuhw9OjRlPswe/ZsudcdOXKEdE3NmjU1IC2V+vXrk+RYvnw5J3LoCsr6GaBLxsbGsGjRIhyUTAKCIODatWtQpUoVqffOyMhIpXvO4/HAy8sL4uLiuK5msUDb/GnorDIA8O/FuHDhAjg4OMh9EEZGRuDj4wNLliwBW1tbuQ9NkYdbq1Yt8PX1hcTERK5vBatkZGRQfJ4jhOD69esyrwsODiadb2lpqSGJycyePZskR9u2bTmRQ5dQ1AOhvFS+fHk4dOhQYeAsXebDhw8yB0DGxsYwffp0yj23sbEh/d25c2do3ry51HzMzc1h3bp12FGbAijraVO87ZkNdFoZEJOVlQWLFy9WKFJe5cqV4dq1a7Bv3z6oXr263PMVydPQ0BAGDhwIgYGBJTYuwrt37ygjjjJlykBsbKzUa8LDwyn3iovwnteuXaM8r+zsbI3LoWswGdq8QYMGOru8k5CQAJMmTZL5wREvrSxatIjyYZeME2JoaAh///6FU6dOyRxIOTk5wdmzZ3VmBlRVlPV/whZYGShCeHg49OjRQ6HOZeDAgRATEwPXr18Hd3d3uedbWVkp1IE5ODjAsmXLSuSa586dOyn1bdeunVQFKDs7m3L+58+fNSz1vxjxkh3pvXv3NC6HLiLLL8ScOXPAzMyM0kZkvWcDBgzQmZgjubm5sGnTJrC0tJR6P5o0aQJPnjwBgH92BJK+WaZMmQKJiYkURX7dunUA8O8dXb16Ne1zEKdWrVrB69evubwVWo8y/k/YAisDNFy7dg1cXFzkfrjNzc1hy5YtkJ+fD2/fvoXhw4fLNUy0srJSKI4Cj8eDjh07wunTpyE3N5frW8IIBEHQxlhfsWKF1GskA6JwFZBG0pB0yZIlnMihq0jzCxEcHEy7xk23LFV0ZDt//nxOZpk0AUEQcOnSJcqHpWiqWLEiHDt2jLR8curUKcp5nz59AgCAkSNHko47OjqSlPjfv3/DmDFjpCpiPB4PRo8ejf2wyEFR/ydsgJUBKeTk5MCqVasoEbvoUu3atQs90/369Qt8fHzkRrcyMzOD6tWrK2SsU7p0aZgxY4bSLn21EXFI4aL14/P5Uqdw69WrRzpXHEdd08ybN48y2sFoB5cuXaKdAp8xY4bMpTw7OzvYt29fiVqaCw0NhXbt2kmts4mJCSxfvpx2V1Pr1q1J5xa1jXnx4gUlr2vXrlHyeP36NSUfyX5v1apVeJlNC8HKgByio6Ohf//+cj/YCCEYM2YMJCQkAMC/LYx+fn7g6Ogo8xo9PT1o1KiRzJFM0dS4cWPYvXu3RqaN2OLJkycUr5AVK1akdbHZs2dP0nnLli3TvMAAcPPmTZIcBgYGkJWVxYksGCqHDh2ivCt8Ph9OnToF27dvlzkbV7duXQgODua6Cmrx9+9f8PLykrlEMnz4cPj16xft9R8+fKCcf+bMmcLfCYKAhg0bkn7v0qULbV4EQcC5c+fAyclJqiyVKlWCU6dOYXsCLQIrAwpy+/ZthT7YpUqVgj179hROvxUUFMCZM2egSZMmcq9t2LAhdOvWTaGY2SYmJjBy5Eh48OBBsXyh1q5dS6lTz549KXWZMmUK6RxlXRozRXp6OkWBKSkx20sKW7dupbQpAwMDuH37NqSkpIC3t7fMZbw+ffpAeHg419VQipycHFi3bh1YWFhIrVfz5s3hxYsXMvOZNGkS6Zpy5cpRfIEcOHCAkndERIRM2davXy9XtufPnzNyLzDqgZUBJcjLy4ONGzfKNJYRp6ZNm8Lbt28LryUIAh49egR9+vSRa0hYpUoVGDt2LLRp00ah2YIqVarA+vXri9X+XpFIRLvNyc/Pj3SepNLQqVMnjiQGSjTMhQsXciYLhh5Ja3iE/k1NP3v2DAAAvn37JtNI2MDAAObOnav1M29Mjr7T0tIofRqdTUxWVhbFSdHcuXPlyhoXF6fWrAVGM2BlQAViY2PB09NT7keaz+fD9OnTKR1LWFgYTJkyRa49gq2tLUyfPh28vb2hQoUKcsvT09ODXr16wZUrV6CgoICju6M4cXFxFOtlAwMDkuXxsWPHSL9Xr16dM3kXLFhAkqVZs2acyYKhhyAImDx5MuXdKFWqFHz8+LHwvNu3b0OtWrWkvktlypSBXbt2aeV7xPS6/I4dOyj9iLQP86xZs0jnli5dWuFyFLFnWLZsmc55adUWsDKgBvfv35fZoYhTuXLl4MSJExQNPSkpCVavXg1ly5aVeb2xsTF4eXnBrl27oE+fPgq5Ui5fvjwsWLAAwsLCOLo7ihEYGEiRvXLlypCeng4AACEhIaTfLCwsOJM1KCiI0mliD3fah0gkgiFDhtC+E0W3FRYUFMCuXbsoO1aKplq1amnNctDv379h9OjRMkfYylrsEwQBNWrUIOXRt29fqeeHhYVRyjx8+LBS5amy0wHDPlgZUJOCggLw9/eXuZdXnDw8PODr16+UPHJzc+HgwYMKKRY9evSACxcuwIYNGxQ2OmzTpg0cOXJEaw3efHx8KDIPGzYMCIKAiIgIym9cTeFmZmaCgYEBSZZbt25xIgtGNnl5edC1a1dK23F1daUspwkEApg7dy7l2Uq+d9++feOkLtnZ2bBq1SpW9vLfv3+fktfdu3dlXtOpUyfS+U2aNFG63NzcXNi8ebPMfrNx48aFPhAw7IOVAYb4+/cvrQ9+yWRgYAD//fcf7YeZIAgIDAyEjh07ys2nQYMGcPz4cbh//z6MGTMGTE1N5V5jaWkJEydOhFevXmmV0WF+fj40bdqUIu+hQ4cgJyeHcly895kLWrZsSZLFx8eHM1kwssnKyqI8L4QQ1KlTh9a/e3h4OPTp00fq+6Ovrw/e3t6QkpKiEfkJglDIy9+5c+dUfp8HDhxIyq9q1apy87p8+TJFjlevXqlUfnx8vMLeETHsgpUBhnn69Ck0aNBA7ofZ0dERrl69KjWf9+/fw6hRo2SOVhD6ZyS0adMmiImJgb1799J+VOlSnTp1wN/fn3Y7HxdERkZSdlGYmprC169fKbEguByNL168mDJ6wWgvAoEA6tSpQ2n/LVq0kLo2HRwcDHXr1pX67pQuXRq2b98O+fn5rMn94sUL1v3///79m7LkKGnAS0dBQQFUqlSJdN2YMWNUlgNAsbgJOPAUu2BlgAWEQiHs3r0bSpUqJfej3LNnT4iKipKa1+/fv2HhwoVy87KwsIBZs2ZBdHQ0fPr0CWbNmiVzLVScDA0NYfDgwXD79m3O1+jOnj1Lq7RIOh7au3cvZzLevXuXJAufz9d6y3NdJy4ujnaNukuXLlJDaQuFQti3bx/FwLVoql69OuOK6a9fv2D48OFSy2QyMuCKFStIeZuYmCgcEW/16tWUj3VycrJa8igSUREHnmIPrAywSFJSEkycOFHuB9nExATWrFkj0+1wRkYGbN++Xa6bZD09PRg8eDC8fPkS8vLy4Ny5c9C5c2eF4iI4OjrCihUr4OfPnxq8S2To7pfk9qmlS5dyJl92djYl+JS86IsY7omMjKTdkTNkyBCZHgjT0tJgwYIFMgOOdenSRe2YGZmZmbBs2TKZO4zatWsHoaGhapUjJj8/n3I/vLy8FL4+Li6OMmu5ZcsWRmTLy8sDPz8/yjbGokmXA0+xBVYGNMCrV68Umr6vWrWqXOMdoVAIFy5cgBYtWsjNr3Xr1nD58mUQiUTw8+dPWLFihVyPiOLRR+fOneHs2bMaj4uQnZ0t15BS3SlJdZH0/zBnzhxO5cEoxsePH2ln2CZPnix3nfzHjx8wYMAAmUr4tGnTlF52E4lEcOzYMahYsaLUvF1dXeHSpUuM2vlcuHCBUk5RvyiKILljo3LlyoyO2JOSkmDatGkUZ19Fky4FnmIbrAxoCJFIBAcPHlRo6t7T0xP+/PkjN89nz57BgAED5MbCdnNzg4CAAMjKygKRSAR37tyBIUOGKBRe2cbGBry9vUl7tNnm8+fPMkdIHTt21JgsdCxbtowySsEUD549e0Zrlb9o0SKFrn/w4IFMmyBra2vw9fWVuvxQlKdPn8r0TGppaQmbNm1iRSFv3749qazmzZsrncejR48oMrMRZvfz58+0O0PEqaQHntIUWBnQMAKBAKZPny532t7S0hL8/f0Vcnry48cPmDFjhlzPiDY2NrB48WL4+/cvAPzTvP39/WkNrOhS06ZNYe/evRp56fbv3y9VjmrVqrFeviwkt2PxeDyNWZhj1Of27du0hrmKTnOLRCI4dOgQlC9fXmobrVKlCly7do12NB8dHU3rB0Gc+Hw+TJo0CeLj45muOgAAfP36lVLmsWPHlM6HIAioXbs2KZ9evXqxIPE/bt68qXOBpzQJVgY44v379zK9iIlTvXr1Cl2pyiMlJQXWr18v11uhoaEhjB07tnCLHkEQ8OrVK5g0aZJC/hJMTU1h9OjR8OjRI9a2KBIEIbXDNDMz43RrZE5ODiXa5OXLlzmTB6M8586do51RO3jwoMJ5ZGRkwOLFi8HY2Fjqu9KhQwf48OGDSuezxYwZM0hllilTRuVdCbt27aIoMmxuA8zPz9eJwFNcgJUBDiEIAk6cOAHlypWT+wEeP368wuuReXl5cPToUZnbo8SpS5cucPfu3cKPa1ZWFhw9ehTatm2r0GxB1apVYePGjYWzDUySmpoq1WBSUatntnB3dyfJ4+3tzak8GOXZt28f7aj80qVLSuWjyEjf3d1d5s4EWTMJTJKZmUlR+OfPn69yfunp6ZRARP/99x+DEtNTUgNPcQlWBrSA9PR0mDt3rkxDGYT+TfMfPHhQYSMdgiDg7t27MtfbimrTR44cIa11hoWFwYIFCxRSVvT19aFPnz5w7do1Rv25v3z5knZKNyQkhLEyVGHlypWU+4cpfmzYsIHStgwNDeHevXtK5/XkyROFopMWTcrYGDDB3r17SeXzeDy1DfCmTp1KytPOzk5jhsclJfCUNoCVAS3iy5cvFMMeutSyZUt4//69Unl/+vQJxo0bJ9dosEKFCrBu3TrSGnhBQQFcvXoVevXqJVdhEefx33//yQxvqgxbtmyhlNGgQQNO9xrTGU9piwMnjHLMnz+f8izNzc3h5cuXSucl3h0gT4Hm8/kwZcoUSExMZKFG9BAEQfHZ0b17d7Xz/fTpE6V+J0+eZEBixSnOgae0BawMaBkEQcD58+fB3t5eZmeip6cHs2fPLgzooyhxcXGwePFisLGxkZm/mZkZTJ8+HX78+EG6/s+fP7B+/Xpwc3NTaOTTrl07OHbsmMKRzegQiUS0tgwbNmxQOU91ycvLo7iAvnDhAmfyYFSHIAgYP348pX3Z2NjAly9flMpL7JdAnudQ8bvBlN8ARXj69ClFhps3bzKSt2Q0wlatWjGSrzIUt8BT2gZWBrSUzMxMWLRokdxOpXz58nD27Fml1xqzsrIgICBA7kedz+fDgAEDKEaMBEHAw4cPYdSoUXJDMSOEwMrKCiZPngxv3rxR6X7QLXXo6+srbFzJBpIxJKZNm8aZLBj1EAqFFD/9CCGwt7dXyCBO7LFQXgRSyST2KMiGzY0kw4YNI5Xt7OzM2OwanfdQZWcvmULbA09pK1gZ0HLCw8OhW7ducjuVjh07qhSuWCQSweXLlxXa2dCiRQu4cOECZetOamoq7N69Gxo3bqxQB1ivXj3Yvn27Utvxpk2bRpuXk5MTZ8aEa9asoYw6MMWX3Nxc2iBhbm5uMrf5KRLLYM6cOdCsWTOp51hYWKgda0AW8fHxlCXCjRs3MpZ/fn4+ZWlk4sSJjOWvCtoWeErbwcpAMYAgCLh69apc74EGBgawZMkSlafkX758CYMHD5ZrF+Dq6grbt2+nDfTy4cMHmDlzpsytP+JkZGQEnp6ecPfuXbkjFDpDL3EaOHAgJ1sN6aZdExISNC4HhjkyMjJoP9r169enGKFFRERA3759ZX5sZs6cWeizXxyFUDLIj6Ryq04UQmmsW7eO8u4xba+wdOlSUhlmZmZaYbinDYGnigNYGShG5OTkwMqVK+UaATo6OsKNGzdULic6OhpmzZpF2TIkmUqVKgULFy6E379/U/LIzc2FM2fOQKdOnRSKi+Ds7AyrVq2CX79+0cp08uRJmdfv3r1b5fqqSn5+PsXR09mzZzUuB4ZZkpOTaY3RWrduDdnZ2ZCamqrWNHR2djasWrVKppOw1q1bw+vXrxmpj1AopAwkRo4cyUjeRfn16xdlILF9+3bGy1EFrgJPFSewMlAMiYqKkjn9JU59+vRRK+hQamoqbNq0Sa4xo4GBAYwaNUrqGmF0dDQsW7ZMZlx2ceLz+dC1a1c4f/48abvVw4cPZV5nbGzMurMWOrp06UKSY/LkyRqXAcM8v3//BmdnZ0o7q127tkwDtZo1aypsoPb7928YPXq01Lx4PB6MHj2aVtlWhmvXrlHyfv78uVp5SqNfv36UDyyXDsIk0VTgqeIIVgaKMYGBgVC5cmW5H8kNGzaoNQWWn58PJ06ckOmTXZw6duwIgYGBtB2AUCiEoKAgGDRokEJxEWxtbWH27Nnw+fNniIqKknt+9erVpcaoZwvJ5QuuXSVjmCMiIkIhHxsIqbd17fXr19CqVSupeZuZmcGqVatUXv6TNL5t2LAhax/oe/fuUeTn2icIHWwFnirOYGWgmJOXlwcbNmyQa9FfpUoVtUN+EgQBISEhMp18iFOtWrXg4MGDUp2PJCYmgp+fn9wIheJEt447dOhQyjFlwrAywYsXLygyKBJkClM8uHLliszlAAMDA5gzZ47aRqwEQcC5c+co4bqLJgcHBzh16pRSH/KIiAjKMt2BAwfUklVePapWrUoqb+DAgayVpy5MBp4q7mBloITw69cvGDx4sNyP6vDhwxnZxvT161eYMGGCTD/rCCEoV64crF69WqqGTRAEvHjxAiZMmCDXRkEyXbx4EerXr085furUKbXrpygFBQUUuTVZPkY5CIKAxMREiIqKgsTERKkfVkXc3VarVo1xd7c5OTmwbt06MDc3l1pu8+bN4cWLFwrlN3fuXMrHLSsri1GZJfH39yeVqa+vr/ZSB5uIA0/JmgVSxF20om1LW8HKQAkjJCSEoplLJnNzcwgICGAkuldCQgIsX74cbG1tZZZpYmICU6ZMkdl5ZmZmwuHDhxXa5ihWNObPn09x/mNhYcGY90NF6N69O6n8CRMmaKxsjGIIBALw8/MDV1dX0rNydXUFPz+/wpG9IoFwiiZ/f39W5I2LiwMvLy+ZxrfDhw+XanAL8M9QUbIes2bNYkXeoggEAso7uXz5ctbLVZeMjAxYtGiR0oGkFG1b2g5WBkog+fn54OvrS3khJVOdOnXg1atXjJSZnZ0Ne/fuhWrVqsksk8fjQZ8+feRGPPz27Rv4+Pgo5MSFbitko0aNNDa1t3nzZlLZbm5uGikXoxiBgYFgZmYGPB6P8nEVHzMzM4PVq1fLDJFraWlJ+3FWJfyvooSGhlK8+0kq2cuWLaO1lTl8+DDl/O/fv7Mma1EkPTpWqFCh2GzdUzTEdEJCgsJtKzAwkOtqyQUrAyWYuLg4GDFihNyP88SJExlzvCESieD69euUqH50qUmTJnDmzBmZRlf5+flw+fJl6Nmzp0JbFIumOXPmMFInebx584ZSdmxsrEbKxsgmMDAQ9PT0aMMVK5oMDQ1h/vz5kJaWRgnZK1ZGr169ylodCIKAS5cuUUaeRVPFihXh2LFjJH8dkkGTOnbsyJqMkoSGhlJkPH/+vMbKZwJ5gadMTU2Bx+PJbVt8Ph/09PS0XiHAyoAO8PjxY6hZs6bMBmttbQ1Hjx5ldJ3rzZs3MGzYMJnrrgj9c7Ti6+srN87CokWLlO7I582bx5pXNzFCoRCsra01NlrEKIZAIAAzMzO1FIEBAwZQ4nNIep5E6N+uHXUNdOWRm5sLmzZtoo3TUVTBfvr0Kbx69Yry2+XLl1mVT5IWLVqQyvfw8NBo+UwgDjxVsWJFlduQWCEwMzPT6iUDHgAAwpR4RCIR2rt3L/Lx8UGZmZlSz2vSpAk6ePAgqlmzJmNl//r1C23fvh3t2bMHpaenSz3PysoKTZgwAc2YMQPZ29tTfj99+jTy9PQs/NvCwgIJhUKUk5Mjs3wrKys0fPhwNG7cOFS/fn3VKyKD3r17o6tXrxb+PW7cOLR//35WysIohr+/P5o1axZSpYurVasWWrNmDWrevDnlNwBAy5YtQ7t37yYdNzc3R5cvX0Z16tRRWWZFSExMRBs3bkRHjx6VWjcHBwcUExNT+HeFChXQ69evkb6+PquyFeX8+fNoypQppGOPHz9GVapU0ZgMTJGVlYV27tyJduzYgXJzc1XKg8fjIT8/PzRjxgyGpWMITlURjMZJTEyEcePGydViZ8+eDRkZGYyWnZ6eDr6+vnLdKuvr68OwYcPg7du3pOsfP35MOs/ExAQEAgHs2rULGjVqpJCG3qBBA9i5cyfjGrqvry+pHBcXF0bzxygHQRDg6uqq9NISTjixlXg8Hri6umrtLgOsDOgoL1++lOmvG6F/jlQuXLjAeOMtKCiAM2fOKBTYyN3dHa5fvw4ikQiio6Mpvxfdsvju3TuYNm2aQuFjjY2NYdiwYRAcHMxI5LZ3795RylDH+yNGPRITEznv/HHCiS5pqyMjrAzoMCKRCA4cOCB3f3/btm0p66ZMIA6D3Lt3b7kjuOrVq9Mab9HFg//9+zfY2Ngo/HK6uLjA6tWr1TL6E4lElG1chw8fVuPuYNRBEY+VOOHERYqKiuL69aAFKwMYSElJgYkTJ8pswPr6+rB48WLWjPHCwsJg8uTJcj0pShqDXbt2jTa/x48fy42+SJd39+7d4eLFiyptg5KMYDdq1Cg17wpGVfDMAE7amrR1ZgAbEGIKef/+PRo7dix6+/at1HPKly+PDh06hDp37syKDMnJyWj37t1o+/btKD4+Xu75y5YtQ8uXL6f9be3atWjRokWkY+XLl0dJSUmooKBAZr52dnZo5MiRaNy4cahatWoKyb59+3aScZCjoyOKjo5W6FoMswAAcnNzQ5GRkSoZEMqiYsWKqFWrVqhly5aoVatWyN7eHiUnJ6NevXqh8PBw0rktW7ZEp06dQsbGxozKII2RI0eiwMBAuecZGBigSZMmIW9vb2RhYcGqTPfu3SMZ/iKE0PXr11GTJk1YLVdR8vPzUWhoKHry5Al68uQJevXqlcpGgrLg8XjIxcUFhYeHIx6Px3j+asOtLoLRNgiCgKNHj4KVlZVM7bZLly6s7qXPzc2FAwcOyN0SiRCCnj17wv379ym2DUKhENq3b085f82aNbB161aoUaOGQpp8ixYt4ODBg3INKj9+/Ei59uHDh8XSNWlJwM/PTyMGhK6uruDl5QXbtm2j3YLWp08flQIYKcvPnz8pM2dTp06V6UjJzs4O9u3bx4g3UmmIRCJwcXEhlTts2DDWypNHQUEBPH/+HNatWwedOnWS65yNLqmyXZXH47HmsZIJsDKAoSUtLQ2mTZsmszM1MDCAtWvXstrREQQBt27dgg4dOsh92Ro2bAgnT54kTfH/+fOH4irZ0NAQ3rx5AwRBwLNnz8DLy0umL3hxMjc3By8vL3j27Bntx50gCKm2CsXNNWlJQFk/A3w+H0xNTcHf3x+GDh0K5cuXV0k5oFueGj16NCOGqrL477//SGVaWFhAenq6Qi6W69atC8HBwazJtmnTJso7GB8fz1p5RREKhfDmzRvYvHkzdO/eXekYKOJn2rRpU1iwYAEEBQVBbGys0m1L2/0MYGUAI5PPnz/Ltfp3cHCAR48esS7Lu3fvoGHDhnJfvEqVKsHmzZshNTUVAABu3bpFOcfNzQ3S09MLg4t8+vQJ/P39KY5SpKUaNWrAli1bICEhoVA+sbc7aaOC4uSatKSgqAdCsZe4oKCgwmsJgoBv375BQEAADBw4UG78DXmpZ8+erK0X5+bmgp2dHam8qVOnks5JTk6GmTNnynQC1qdPH8aDLwEAJCUlgZGREamstWvXMl4OwL+ZiA8fPoC/vz/06dMHSpUqpfSz4vF40KBBA5gzZw7cuHED0tLSKOWo07a0EawMYORCEAScPn1a7kvVp08fSExMZFWW06dPK/xCW1hYwOzZs+Hnz5/g4+ND+b1Ro0a0wUUWLlwI06ZNo3SudMnAwAAGDBgAq1evBj09PbnT0sXFNWlJQlH/8fI6a4Ig4OPHj7Bt2zbo27evyh+Z+vXrw+zZs+HatWu0HxlVOHnyJKWsT58+0Z777ds3mWHIDQwMYO7cuYXKNFOMGjWKVI6DgwMjyxMEQcDXr19h586dMGDAAChTpoxKylqdOnVg5syZcPnyZYXdszPVtrQBrAxgFCYzMxO8vb1lasLGxsbg5+fH2pTokydPSOXp6+uDs7OzzJdcT08PBg0aBLVq1aLtnKW9wNevX4eLFy9C9+7d1XJpS6cQaPuUYUlDIBCAv78/rfLn7++v0odPJBLB27dvYcuWLdCjRw+ZboJltYUmTZrA/PnzITAwkDbgkCK0atWKlG/btm3lXnP79m3ad0KcbG1tYdeuXYwtA7548YJShioxHQiCgPDwcNi7dy94enrKDD0sK1WvXh2mTJkC586dI83wKQsbbYsLsDKAUZqwsDBo1qyZzBfNxcUFXr9+zXjZMTExlLL+/v0L58+fh+bNmyvU+SraSRcdwcfGxsKaNWtkBotRJmm7MVFJhSAISEpKgqioKEhKSmLUsLOgoABevnwJ69evh86dO4OZmZnS7UJfXx9atGgBixYtgnv37kF2drbcct+/f0/J58yZMwrLvGvXLpmj6Vq1asHt27fVvT1AEARlma9Lly4KXRsdHQ2HDh2CkSNHgr29vUrvXOXKlWH8+PFw6tQpiIuLU7s+dPVjq21pAqwMYFSCIAi4ePGi3LjvgwcPZlQzLigooHzQi7otfvr0KfTv35+RkbzkCD4/Px9iY2Nh37594OHhoZCnQ1nKgDa7JsWoT35+Pjx58kTmlLy8ZGhoCG3btoXly5fDgwcPIDc3l1LOpEmTSNeUK1dOaT8ZAoEA5syZI7NN9+jRA759+6bWPTl48CAl34iICMp5sbGxcOzYMRg7dqzcmT9pycnJCcaMGQNHjx6FX79+qSW3LoCVAYxaZGdnw+zZs2V+fE1MTGDPnj2Mffgkt29duXKFcs6PHz9g+vTpKo3OJJOtra1K68OKJG11QIJhluXLlzPSXkxMTKBDhw6wZs0aePr0KSQmJlLa+NKlS1WWMzw8HPr06SO1fH19ffD29lY55HlWVhblXZozZw78/fsXTp8+DRMnToQqVaqodG8qVqwIw4cPhwMHDkBkZKTK90BXwcoAhhEiIyPlWuJXrVpVqlGTMjRt2pSU786dO6Wem5KSAuvWrVN5XZHtpK2uSTHMQhAEzJgxg/L8zczMYNy4cdC8eXO5ob7pkqSFPp/PZ2QUHBwcLDN2SenSpWH79u0qeeqcPXs2RWZV3h07OzsYPHgw7NmzB8LCwvAsm5pgZQDDKFeuXJEZF4DH48HIkSMhKytL5TIGDBhAynPBggVyr/n9+zfnH366hGcGdAeRSATDhw+ntIGyZctCeHg4ZGRkwK1bt8DHxwcaN26s0kfSwMAAevbsCVu3boXQ0FC1DHmFQiHs27dP5q6a6tWrw61bt+TmJRAI4MqVK+Dt7Q3VqlVT6V0pXbo09O/fH3bs2AGfP3/GH3+GwcoAhnFyc3Nh3rx5MjszMzMzOHr0qEr5z5o1i5TX8OHD5V7DZOAaHo8HZcqUgRo1akCbNm1UWorANgO6SX5+PvTs2ZPSHpycnCgePVNTU+Hq1aswa9YsqFevnkreFEuXLg39+vWD7du3w6dPn1Rqb2lpaTB//nwwNDSUWk7Xrl3hy5cvhdekp6fDzZs3Yd68edCoUSOVFBsrKyvo1asX+Pr6wrt371h32qTrYGUAwxo/f/6Eli1bynzha9WqpbSTky1btpDyUGQblbqBay5fvgyfPn2ChIQEyt5oVdze4t0Eukt2dja0adOG0iZq1qwJycnJUq9LTk6GixcvwrRp0xRy002X7OzsYNCgQbB79274/v27UsrBjx8/KLNyRROfz4eGDRtCo0aNlA4ShhACU1NT6Nq1K2zatAlev37NqotkDBWsDGBY5/r163KXDsaPHw95eXkK5Xf27FnS9S4uLnKvIQgCXF1dVfpoyxvBq+L2FvsZ0G1SU1Ohfv36lLbRtGlTuTEwAP4te6nywZVMFSpUgGHDhsH+/fsVNrp78OAB1KtXT+2yJd9FHOWTW7AygNEI+fn54OPjI7MDs7S0VGh/9NOnT0nXGRoaKjTCYXMEX9Jck2LYJz4+ntZyvkOHDrRbCIsiuTvB1NQUvnz5AsePH4dx48ZRAgMpmhwdHWH06NFw5MgRiImJKSwvLy8PHj9+DKtWrQIPDw8wNjZWOm8jIyNo164drFixAh4+fAgrVqwg/W5sbIxtaDgEKwMYjfLnzx+5Swf16tWD6OhoqXn8+vWLco0iQU/YHsGXJNekGM3w8+dPWic6AwYMkDpNnp+fDxUqVCCdP378eNq8Dx8+DCNHjoRKlSqppByULl0aKlasSNm1oExydnaGAwcOUBwo/f37l+LXYPPmzazcZ4x8sDKA4YSbN2/KXDrg8/kwZcoUWleoQqGQMsPw5s0bhcplewRfUlyTYjTHly9faN8FLy8v2hmv8+fPU84NDQ2VWQZBEBAREQH79u1TKyKjrCRr1o3P58OkSZMobn89PT0p7wk2FOQGrAxgOEMoFMpdOrCysoJLly5RrpUcTR09elRhYyhNjOCLu2tSjGZ59eoVbRhtHx8fAIDC6JpRUVGUOATNmzdXujyhUAhXrlyBgQMHQoUKFVTaqYDQv6n9+vXrw6JFiyA6OhoeP34sM8qppaUlbNq0qXAZ5NGjR5Rzzpw5A1FRUZCYmIjfGw2ClQEM58TFxcldOmjUqBH8/v0bAP6Nvp2cnCjnuLq6gp+fn0LT+ngEj9E2goODaafje/ToITMmxrFjx+TmLY7sFxAQoFZkP3kzA+KIjHPmzJE5++Dq6gqXLl0CkUgEtWvXlnmeou80Rj14AAAIg9ECAgMD0fDhw1FycjLt73w+H/Xq1QvduXMHZWVlUX7n8XgIIYRMTU3RhQsXUOfOneWWCQAoJSUFZWRkIAsLC1S6dOnCfDAYTXPlyhXUv39/JBKJFL7m2rVrqEePHqRjAIB+/PiBQkJCUEhICLp//z6Ki4tTWp6yZcsiY2NjlJCQgHJycpS6Vk9PD5UrVw7Fx8cjoVBIe467uzsqV64cOnXqFO3vqrzTGNXAygBGqyAIAi1YsABt3bpVqQ6xKHw+H/F4PHTjxg3ceWCKHUeOHEGjR49W+Hw9PT1048YNVK1atcKPf0hICPr165fSZVeuXBm5u7sjd3d31K5dO1S+fHmEEEJCoRC9ffu2MO/Hjx/TKuRsgd9p9sHKAEYriY+PR3379kXPnj1T6Xo+n49MTExQbGwssra2ZlY4DIZFUlNTUdmyZVF+fr7C1/B4PKRKV+7o6Ijc3d2Rh4cHateuHapUqZJC1xUUFKBXr16hkJAQFBwcjJ4+fYpyc3OVLl8Z8DvNLlgZwGg18pYOZMHj8ZCfnx+aMWMGC5JhMOzg7++PZs2apdLHXR4VK1YsHPm7u7sjZ2dnRvLNzc1FL168KJw5eP78uVLKjKLgd5o9sDKA0XoIgkClS5dGaWlpSl3H4/GQi4sLCg8Px3YAmGIBACA3NzcUGRnJiDJgZ2dH+vi7ublp5F3Izs5GT58+LVQOXr16JdVuQBnwO80eWBnAaD1JSUnI1tZWrettbGwYlAiDYQd127q1tTXy8PBAHh4eyN3dHVWvXl0rPpqZmZno8ePHhcsKb968UUvZwe808+hzLQAGI4/MzEy1rs/IyMAdB6ZYoG5bf/PmDXJxcWFIGuYwNzdHXbp0QV26dEEIIfThwwdUt25dlfPD7zTz8LkWAIORh7m5uVrXW1hYMCQJBsMu6rZ1KysrhiRhlwoVKqh1PX6nmQcrAxitx8bGBrm6uio93cnj8ZCrqysqXbo0S5JhMMyiK21dV+pZnMDKAEbr4fF4aPr06SpdO2PGDK1YM8VgFEFX2rqu1LM4gQ0IMcWC1NRUZG9vj3JychBBEHLPx3uSMcUVXWnrulLP4gKeGcAUC6ytrdGFCxcQj8dDfL7sZiv2Vnbx4kXcaWCKHbrS1nWlnsUFrAxgig2dO3dGN27cQCYmJojH41GmCsXHTExM0M2bN1GnTp04khSDUQ9daeu6Us/iAFYGMMWKzp07o9jYWOTn50fZQuXi4oL8/PzQ79+/caeBKfboSlvXlXpqO9hmAFNswREHMbqCrrR1XamnNoKVAQwGg8FgdBy8TIDBYDAYjI6DlQEMBoPBYHQcrAxgMBgMBqPjYGUAg8FgMBgdBysDGAwGg8HoOFgZwGAwGAxGx8HKAAaDwWAwOg5WBjAYDAaD0XGwMoDBYDAYjI6DlQEMBoPBYHQcrAxgMBgMBqPjYGUAg8FgMBgdBysDGAwGg8HoOFgZwGAwGAxGx8HKAAaDwWAwOg5WBjAYDAaD0XGwMoDBYDAYjI6DlQEMBoPBYHQcrAxgMBgMBqPjYGUAg8FgMBgdBysDGAwGg8HoOFgZwGAwGAxGx8HKAAaDwWAwOg5WBjAYDAaD0XGwMoDBYDAYjI6DlQEMBoPBYHQcrAxgMBgMBqPjYGUAg8FgMBgdBysDGAwGg8HoOFgZwGAwGAxGx8HKAAaDwWAwOg5WBjAYDAaD0XGwMoDBYDAYjI6DlQEMBoPBYHQcrAxgMBgMBqPjYGUAg8FgMBgdBysDGAwGg8HoOFgZwGAwGAxGx8HKAAaDwWAwOg5WBjAYDAaD0XGwMoDBYDAYjI6DlQEMBoPBYHQcrAxgMBgMBqPjYGUAg8FgMBgdBysDGAwGg8HoOPpcC4DRXgAAJScno8zMTGRubo5sbGwQj8fjWiwMBlME/J5imADPDGAopKamIn9/f+Tm5oZsbW2Rs7MzsrW1RW5ubsjf3x+lpqZyLSIGo/Pg9xTDJDwAAK6FwGgPQUFBqH///ig7Oxsh9G/UIUY82jA1NUUXLlxAnTt35kRGDEbXwe8phmmwMoApJCgoCHXv3h0BACIIQup5fD4f8Xg8dOPGDdzRYDAaBr+nGDbAygAGIfRvytHe3h7l5OTI7GDE8Pl8ZGJigmJjY5G1tTX7AmIwGPyeYlgD2wxgEEIIHTlyBGVnZyvUwSCEEEEQKDs7Gx09epRlyTAYjBj8nmLYAs8M6Bhiy+OoqCgUGRlZ+O/x48dRTk6OUnnxeDzk4uKCwsPDsfUyBsMyAIDc3NxQZGQkUqbbxu8pRhGwMlACyczMRFFRUVJTZmYmo+UlJSUhGxsbRvPEYDBkkpKSkK2trcrX9+jRA3l4eKCWLVui+vXrIwMDAwalwxR3sDJQDMnPz0cxMTFSP/aJiYkalScqKgo5OTlptEwMRteIjo5Gzs7OjORlYmKCmjRpglq2bIlatmyJmjdvjkqVKsVI3pjiCVYGtBCCIFBcXJzUj31sbKzCa4aaAM8MYDDso+7MgDxq1qxZqBy0bNkSubi44GUFHQIrAxwAAEggEEj92EdHR6O8vDxWZdDT00MODg7I2dkZOTk5ocuXLyOBQKDUWiRCCNnb26OYmBjcaWAwLAMAqHLlyigyMlIj5ZUtW5akHNSvXx8ZGhpqpGyM5sHKAEtkZ2ej6Oho2o99ZGQkSk9PZ12GcuXKIWdn58Lk4uJS+H97e3ukr///3qj9/f3RrFmzlFYG9PT00M6dO9GECROwQoDBsEhubi5q2LAh+vLli0rXGxgYoDJlyqC4uDiVrjc2Ni5cWmjRogVq0aIFKl26tEp5YbQPrAyoiFAoRL9+/ZL6sY+Pj2ddBisrK6kfeycnJ2RiYqJwXsruX5bE09MT7dmzB1lYWCh9LQaDkU1OTg7q06cPun37ttp5DRw4EPXv3x+FhoaiJ0+eoFevXqk8E1m9enXS7EHlypXxoKCYonFloLgE1QAAFB8fL/Vj/+vXLyQSiViVwcjICDk5OdF+7J2dnRk3+FHUs5k0qlSpgs6dO4fq1KnDqFwYjC6TlZWFevXqhYKDg5W6jsfjSZ3pc3FxQSdPnkRNmzZFeXl56O3bt+jJkyeFSVUjZDs7O9SiRYtC5aBBgwbIyMhIpbwwmkVjykBqaio6cuQI2r59O/rx40fhcVdXVzR9+nQ0atQojXvISktLk/qxj46OVnrfvbLw+Xxkb28v9WNfrlw5xOdr1i+ULJ/nYng8Hurduze6fPky5TdjY2O0fft2NG7cOK1U8jCY4kRGRgbq3r07evToEem4qakpAgCUm5uLEJIem+D48ePoypUr6PDhw5S89fT00MqVK9H8+fORnp5e4XEAQBEREejJkyfo6dOn6MmTJyovTRgZGaHGjRsXKgctWrTAxsZaikaUAa6CauTm5qKfP39SPvTi/wsEAsbKkoY4mpjkh97Z2RlVqlRJKw1yUlNT0dGjR9G2bdtIiltRHj16hJKSktDo0aNRWloa5ffhw4ejXbt2IXNzc7bFxWBKJGlpaahr167o2bNnpOPW1tYoKCgIValShfY9dXV1RTNmzECjRo1CVlZWCCGETp8+jSZOnEhrq9S2bVt07NgxVKlSJamypKSkoGfPnhXOHLx8+bJQEVGWatWqkZYW3Nzc8MBBC2BdGWAzqIZIJEK/f/+W+rH/8+cPU9WQirm5udSPvZOTU7H+GAIASklJQWlpaah9+/YoOjq68LchQ4agU6dOoaioKDRo0CD0+vVryvVVq1ZF586dQ7Vr19ag1BhM8UcgEKDOnTujV69ekY6XLl0a3blzBzVo0KDwmPg9zcjIQBYWFqh06dK0H9fo6Gg0bNgw9PTpU8pvpUqVQvv370f9+vVTSL78/PxCmwNxUtVOqkyZMqSlhUaNGuGlBQ5gVRlQN6gGAKCkpCSpH/uYmBhUUFDAlvgIoX8WuI6OjrQfe2dnZ621eWCaLVu2oLlz5xb+bWBggGJiYlC5cuVQXl4emj9/PvL396dcZ2Jignbs2IHGjBmjE/cJg1GXpKQk1LFjR/Tu3TvScVtbW3T37l21bHKEQiFavXo1WrVqFW2fPGHCBLR161ZkZmamVL4AgCIjI0nKwefPn1WS0dDQEDVq1Ii0tMCmfwXMP1hVBlTdrlazZk3E4/FQVFQUysrKYkm6f/B4PFSxYkXaD72zszOqUKECaT1NV0lJSUEVK1YkTQ2uWrUKLV68uPDvixcvorFjx9IuG4wYMQLt2rVL6U4Gg9ElEhISUIcOHdDHjx9Jx8uVK4fu3buHatSowUg5jx8/RsOGDUMxMTGU36pVq4ZOnTqF6tWrp1YZAoGAsrSgqh1WlSpVSEsLVatWxYMLhmFNGVA1qAYblC5dWurI3tHREU9JKcjYsWPRoUOHCv+2t7dHUVFRJH8FkZGRaNCgQejNmzeU66tXr47Onj2LatWqpRF5MZjiRFxcHGrfvj36+vUr6XjFihVRcHAwqlKlCqPlpaamookTJ6KzZ89SfjM0NETr169HM2fOZMyIuaCgAL179440e6CqzwMbGxvK0oKxsTEjcuoqrCkDbLvOLIqpqanUkb2zszOytLTUiBwlnTdv3qBGjRqRjl28eBH17duXdCwvLw/NnTsX7dixg5KHiYkJCggIQKNHj2ZTVAymWBEbG4s8PDxQeHg46biDgwMKDg5Grq6urJQLAOjw4cNo+vTptLOwnTt3RocPH0blypVjpezo6GiScvDp0yeVBo+GhoaoYcOGpKUFOzs7xmUuybCmDDAZVENfX7/QdS5dsrOzw1NGGqJp06bo5cuXhX936NAB3blzh/bc8+fPo3HjxtFaMI8aNQrt3LkTLxtgdJ6fP38iDw8PipthZ2dnFBwcrJEgYGFhYWjo0KG0M3p2dnbo8OHDqGvXrqzLkZqaip4/f16oHLx48aJwF5qyVK5cmbS0UK1aNY1v1S5WAEskJiYCQkjl5O/vD8HBwRAVFQUFBQVsiYlRkiNHjlCe1bdv36SeHx4eDvXr16d9xjVq1IDPnz9rUHoMRrv48eMHODo6Ut6NypUrQ0xMjEZlycvLAx8fH6l98syZMyEnJ0ejMuXn58OrV6/Az88PBg4cCBUqVFD5m1K6dGno3r07rF27Fh48eADZ2dkarYu2w5oyQBAEuLq6Ao/HU/qhVahQAQiCYEs0jBrk5OSAjY0NpZOQd82UKVNon7WpqSkcOXJEM8JjMFpEWFgY2NvbU96JatWqwe/fvzmT686dO1C+fHna97Vu3bqcKvAEQUBUVBScOHECpkyZAnXr1lXpG4MQAgMDA2jatCnMnj0bLly4AH///uWsXtoAa8oAAICfn5/KD2rYsGHw588fNsXDqIjk6MHKygoyMzPlXnf69GmwsLCgfd5jx46FrKwsDUiPwXDPly9faD+4NWvW1IqPUkJCAvTs2ZP2XTUxMYHdu3drzYAtLS0NgoKCYOnSpdC+fXswMzNTefbA1dUVRo4cCXv27IFPnz6BSCTiunoag1VlQCAQgJmZGfD5fJUejIWFBWzZsgXy8/PZFBOjJJGRkRQlb+/evQpdGxYWBvXq1aN93jVr1oQvX76wLD0Gwy0fP34EOzs72lF3QkIC1+IVQhAE7Ny5E4yNjWnf1z59+kBSUhLXYlIoKCiAN2/ewLZt22Dw4MG0sy+KplKlSkG3bt1gzZo1cP/+/RI9YGFVGQAACAwMBD09PZUVAvFHIiQkhG1RMUrQvXt3Skem6EghJycHJk2aRPuszczM4NixYyxLj8FwQ2hoKGWZDSEEDRs2hOTkZK7Fo+Xjx49Qq1Yt2ve1QoUKEBwczLWIcvn58yecPHkSpk6dCvXq1VP5e6Svrw9NmjQBb29vOHfuHOOz1wRBQGJiIkRFRUFiYqJGZ19YVwYA/ikEZmZmtEsGPB4PeDwemJmZwZQpU8DKykrqg/D09OR0LQ3z/9y4cYPyfJ48eaJUHqdOnQJzc3PaZ+3l5YUNfDAlilevXkGpUqUobb1Zs2YgEAi4Fk8m2dnZMH36dNp3lcfjwcKFC4vVDG56ejrcvn0bli1bBh07dpTaDymSnJ2dYfjw4bBr1y748OGDSksLAoEA/Pz8wNXVlZS3q6sr+Pn5aaR9aEQZAPhX2TVr1lBupKOjI/j7+0NqaioAAMTHx8Po0aOl3nhzc3PYvHlzsWp4JRGRSATOzs6kZzN06FCl8/n+/TvUqVOH9lnXrl1b5k4FDKa48PTpU7C0tKS08VatWkFaWhrX4inMtWvXoEyZMrTva+PGjSE8PJxrEVVCKBRCaGgo7NixAzw9PcHBwUFl5cDKygq6dOkCq1atguDgYLn2VEUHy5ID5qKD5cDAQFbvgcaUAYB/20Qkb5w0y9QnT55IXVtG6N+2tOIwPVWS2bhxI+mZGBgYQHx8vNL5ZGdnw4QJE2ifs5mZGZw4cYIF6TEYzfDo0SPakae7uztkZGRwLZ7S/PnzBzp27Ch1sHbkyBGtMS5Uh1+/fsHp06dh+vTp0KBBA9DT01NJOdDT04NGjRrBzJkz4ezZs6TZbUWX0fl8Pujp6bGqEGhUGQAAiqXn06dPpZ4rFAph586dYG1tLfUmDRkyBGJjYzVYA4yYpKQkMDIyIj2PtWvXqpzf8ePHpVoCjx8/Hi8bYIodwcHBYGpqSmnPHTt2LNbGaCKRCDZv3gwGBga076unp2fhbG9JISMjA+7evQsrVqyATp06Sd0ZpUhycnKCgQMHgqGhocI77vh8PpiZmbG2ZKBxZaBixYqkCt68eVPuNfHx8TB27FipN8nc3Bw2bdqElw44YNSoUaRn4eDgAEKhUOX8vn37BrVr16Z9znXq1IHv378zKD0Gwx5BQUG0lvjdunXTuPMetnjz5g1UqVJF6gdP1mCvuCMUCuHdu3ewc+dOGDp0KK3zKKYTj8cDf39/VuqjcWWgZs2apMqdPHlS4WufPn0q1ZsdQgiqV68O9+7dY1F6jCQvX76kPIcrV66olWdWVhaMGzdOquJ36tQphqTHYNjhxo0blFkzhBD07t0bcnNzuRaPUTIyMqS+r3p6erBy5Uq1BgjFidjYWDh79izMnDkTGjVqpPLSgixlwNXVlZVlGI0rAy1btiRVLiAgQKnrhUIhBAQEyFw6GDx4MF460CCNGjUi3f9OnToxku/Ro0dpp1gRQjBp0qQSM7rClCwuX75MO30+YMCAEjl7Kd4Ot2PHDlojSYQQtG7dGn7+/Mm1qBonMzMTgoODYdWqVdClSxeZu+WUSWz4d9C4MiC5P13VNeaEhASp2ihC/wzPNmzYAHl5eQzXACPJoUOHKPc/LCyMkby/fPlCmU0Sp3r16jFWDgbDBGfPngV9fX1KW/X09CxxMVakbYeT5qTI2toazp49y7XYnCISieDDhw+wevVqtZSBqKgoxmXTuDIwbNgwUqV8fHzUyu/Zs2fQoEEDqTetWrVqcPfuXYakx9CRnZ1N2T89a9YsxvLPysqSajNiYWEBZ86cYawsDEZVTpw4QWsVPnLkyBI3TS5vO5ysD9nYsWMVcl9eklE3kF+JmBmYOnUqqVLjx49XO0+hUAi7du2ideghToMGDYJfv34xUAMMHXPmzKGMApi2lj5y5IjUZYMpU6bgZQMMZxw+fJj2I+jl5VXi/Nsruh1OllJQpUoVePPmDddV4QxVA/mVKJuBRYsWkSo3cOBAxvJOTEwELy8vqTfYzMwM1q9fj5cOWCAiIoJyv/fv3894OZ8/f4YaNWrQPt/69etDREQE42ViMLLYu3cvbZ8zZcqUEqcIKBtvhs/n0y6bIPTPL8mmTZtK3D1SFFUC+ZWo3QSbN28mVa5jx46Ml/HixQuKUVvRVLVqVbhz5w7j5eo6Xbp0oXyc2dBgMzMzKVsaxcnS0hLOnTvHeJkYDB07d+6kbYfe3t4lwvGOJKp+wIYNGybV5W/Hjh11MkKtQCCg3XEiS7EqUX4G9u/fT6pg48aNWSlHKBTC7t27oXTp0lJv7sCBAyEmJoaV8nWRa9euUe7xs2fPWCvv0KFDYGJiQvtsp02bVuK2cGG0C19fX9q25+PjUyIVAXWntsPCwqBx48a055QpUwauXbvGdRU1ytevXxVWBsQeCIOCgliTR+PKwPnz50mVdHNzY7W8xMREGD9+vNQGbGpqCuvWrcNLBwwgFAopjjdGjBjBapkfP36EatWq0T7bhg0bwo8fP1gtH6ObbNiwgbbNLVmypEQqAgD/QpcrowRIpqSkJMjPz4eFCxdK7Y+nTZumE55Gc3JypMZkkVSkxLEJ2FQEADhQBu7evUuqrK2trUbKffnypcylgypVqrB+s3WB9evXk+6roaEh6zHaMzIyYMSIEbTP1dLSEs6fP89q+RjdYuXKlbRtbeXKlVyLxjh5eXlw5coVGDRoEBgaGqqlDBTdDhccHEzxRitOtWrVgo8fP3JXaQ0wbdo0Sr3pZk2cnJxIgfzYROPKwOvXr0mVNTAw0JgmLRQKYc+ePTKXDvr376+TzjGYIiEhgdJprF+/nvVyCYKA/fv3S93jPGPGDLxsgFELgiBg8eLFtO1LE21cU4hEInjw4AFMmDBB5g4tVWYGipKUlAR9+vShPdfY2Bh27txZImdZLl26RKlvzZo14efPn5TjqgR+UxWNKwN0VueaDtiRlJQEEydOlLl0sHbtWvzxUBHJUbqjo6PG9ll/+PABqlatSvtcGzduDJGRkRqRA1OyIAgCfHx8aNvV1q1buRaPEd6/fw8+Pj5QqVIlxhQAhGRvhyMIAnbv3i3V9qdnz56QmJjIwd1gh5iYGIqCZWJiAp8+fYKEhARK/dmeVS2KxpWBpKQkSoW5ch386tUrqQYtCP2zZ2A7hnRJ5NmzZ5R7qUnjoIyMDIpzK3GysrKCixcvakwWTPGHIAjw9vambU87duzgWjy1iI6OhrVr10KtWrUU+rAruqVQUhmQtx3u8+fPUtfQy5cvXyJ2fxUUFECrVq0o9duzZw8AgO4pAwUFBZQKf/r0SdNiFCISiWDfvn1gY2MjtTH369cPLx0oAUEQFK+QXbp00bgM+/btk7ps4O3tjY1GMXIRiUQwZcoU2g/c3r17uRZPJRITEyEgIID2wyQttWrVCnbt2gU/fvwo9DyoqPKg6Ha4nJwcqUoXj8cDHx+fYv3OLl26lFKvgQMHFs6Y6JwyAACUONCPHz/mQgwSSUlJMGnSJKmN3MTEBNasWYOXDhREcgspQogTh0Dv37+XGmK1SZMmrPj4xpQMRCIReHl50X6YDh8+zLV4SpGVlQWnTp2CHj16SHUCJJlq1aoF69ato7wjV69eVViJ4PP5Shtm37hxA2xtbWnza9iwYbEMYx4SEkL5tjg6OpKUJJ1UBiTXpK5fv86FGLS8evUKmjRpIrVxu7m5wa1bt7gWU+vJysqiRJacO3cuJ7Kkp6fDkCFDaJ+ntbU1XL58mRO5MNqLUCikdWylp6cHJ06c4Fo8hSgoKIBbt27B8OHDwczMTKGPd6VKlWD+/Pnw/v17qfnu3btXYWWgc+fOKskeFxcHnTt3ps3TzMwMDh48WGyMCxMTE6FChQqUdiTpg0UnlYHatWuTKnz8+HEuxJCKSCSC/fv3y1w66Nu3L0RHR3MtqlYza9Ys0j0rXbo0Z3uICYKAPXv2SHXyMXv27GI9BYlhjoKCAhg6dCiljejr62t91D2CIODZs2cwbdo0qaNryVSqVCmYOHEiPHjwQK5rYJFIRJlpk2b8h9C/rcWqehcUiUTg6+srdUvjoEGDWPPGxxQEQUCPHj0osq9bt45yrk4qA61btyZVWFuNcJKTk2Hy5Mkylw5WrVqFA+RIISwsjHLPDh06xKlMoaGhULlyZdrn2bRpU6zg6Tj5+fkwYMAAStswMDDQ6hmkr1+/wuLFi8HFxUUhBcDY2BgGDRoEV65cUUoJptsWd+vWLUhKSoKoqCj4/PkzGBgYkH5fsGCBWnV7+/atVMdiDg4O8OjRI7XyZxM/Pz+KzB06dKBVunRSGejZsyepwqtXr+ZCDIV5/fo1NG3aVOqLVblyZbh58ybXYmolnTp1It2rRo0acS0SpKWlweDBg6WOknTNLSrmH7m5udC7d29KmzAyMoIbN25wLR6F2NhY2Lx5s8wQ7kUTn8+HTp06wZEjRyA9PV2lMlu0aEHKs06dOpTp+vHjx5POsbKyUrk8MZmZmTBhwgSp9Vq2bBkUFBSoVQbTvHnzhqIY2dnZQVxcHO35OqkMSO5D52otWRlEIhEcOHAAypQpI/Vl69OnDzZIk+DKlSuU+/Ty5UuuxQKCICAgIEDqFOTcuXMhPz+fazExGiInJwe6detGO4LWJs+kAoEA9u/fDx4eHgpb9Ddp0gT8/f2lfoQU5fHjx5S8jx07Rjnv27dvFNm2bNmiVtliLly4INURUosWLbSm/01PTwc3NzeKjLLakk4qA9OnTydVeNy4cVyIoRLJyckwZcoUqfttjY2NYeXKlXjp4H8IhUJwcHAg3aNRo0ZxLVYhb9++BVdXV9pn2bx5cxzISgfIysqCjh07Up6/qakpBAcHcy0e5OTkwIULF6Bfv34KB7apUqUKrFixAsLCwhiTQ3LWpFKlSlIVZmXOVZaYmBho27Ytbb0tLS3h1KlTjJSjDnTu0X18fGReo5PKwJIlS0gV7t+/PxdiqMXbt2+hefPmUl9GV1dXrZxa5IK1a9eS7o2RkRHFNSmXpKamwsCBA2mfY+nSpbVqtwuGWTIyMqBdu3aU525ubg4PHz7kTC6hUAj37t2DsWPHgpWVlUIKQLly5cDb2xtevXrFuKX9169fKaN9WZ4Xnzx5otAsgqoIhUJYs2YN6Onp0d6L0aNHq700oSpHjhyhnZ2RpwzppDKwdetWUoXbt2/PhRhqIxKJ4ODBgzKtdnv37q01U1dcER8fT5mO37hxI9dikSAIAnbs2CF12cDHxwcvG5Qw0tPTaR3vWFpawtOnTzUuD0EQ8ObNG5g9ezZlK5q0ZGFhAaNHj4Y7d+6w6vJb0t+CInYAkvYFtWvXZlxJef78uVSjycqVK2t8SfL79++UbZyWlpYKRU/VSWXg4MGDpAo3bNiQCzEYIyUlBaZOnSpz6WDFihU6vXQg6R7Y2dlZ7jYmLnj9+rXUzqVly5Z42aCEIBAIoFmzZpRnbG1tDa9evdKoLBEREbBy5UqpFvOSydDQEPr06QPnzp3TyFbduLg4ipK8cOFCudfR7Txgw717Wlqa1Kil+vr6sGHDBo30Nbm5uVC/fn2KDKdPn1boep1UBi5evEiqsKurKxdiME5oaChFGy6aXFxcdHbKmW7aUFuXUVJTU6F///60z9DGxgbvHCnmJCcn04Yzt7GxgdDQUI3IEB8fD9u2baNVSOgSj8eDdu3awb59+yAlJUUjMor577//KMqIIr4D6HwSsDkLfPz4cYp3W3Hy8PBgPQbOzJkzKeV6eXkpfL1OKgPBwcGUl7CkIBKJ4NChQzKXDnr27Klz0fMIgoC6deuS7kP37t25FksqBEHAtm3bKFuDxGnBggVat5UJI5/ExESoV68e5Xna2dnBx48fWS07PT0djh49Cl26dJG61i2Z6tWrBxs3boRfv36xKpssmSU9iSpj8E3nrfDNmzesyfvjxw+p28BtbGxY8xVB56K5evXqSkXk1Ull4O3bt6QK6+npFRvXkooiEAhg+vTpMpcOli9fzplHPi6Q7Bh4PJ7WK0UvX74EZ2dn2mfYqlUrziJuYpTn79+/tNH5ypcvD1++fGGlzLy8PLh27RoMGTJEpqe+osnJyQn+++8/+Pz5MysyKYOvry9Fvq9fvyp8fU5ODpQtW5Z0/ZAhQ1iU+J/jqMWLF0vdejl58mRKv0sQBCQmJkJUVBQkJiYq9T2KjY2leKs1MjKCDx8+KCW3TioDkZGRlEpzZfnJNqGhodCyZUupL76zs7POOLnJzMykWEbL226jDQgEAujbty/t8ytTpgztOqg6nQuGeX7//k27Jm9vb8/o9juAf7ODjx49gkmTJsl0aU6XKlasCL6+vlrhZjc/P5+yLbhXr15K57NmzRrK4E8Tg4D79++Dvb097X2uUaMGvH//HgQCAfj5+VG2F7u6uoKfn5/c5yAUCmm3OQYEBCgtr04qAykpKZRKl2TDLIIg4MiRI2BnZye1E+jRo4dCFqfFHcl1NRsbm2JhWEkQBPj5+UldNvjvv/+goKBA7c4FwzwxMTG0LqgdHR0Zfec+fvwICxYsAEdHR6UUgKKJx+MBj8cDMzMzVoztlOH48eMU+VSJMJuSkkKxsJ8+fToLElNJTk6Gfv360d5rAwMDMDQ0LLznqjyHFStWUPLt16+fSsq/TioDQqGQUmllp1SKIwKBAGbMmCF16cDIyAiWLVtWopcOvn37Rqn3kSNHuBZLYV68eCG1s69VqxaYmpqq1blgmCUqKop2mcfFxYWROBQ/f/6E9evXQ506dRT62IvffXneA/l8Pujp6XHWVuhsfJo3b65yft7e3qS8TE1NNeZrhCAI2LdvH5iamiqtnMl6Dg8fPqT05Q4ODiobeOqkMgAAlOliLh18aJp3797R7m8WJ2dnZ7h69SrXYrJGhw4dSPVt0qQJ1yIpRUpKCq0Pe3U7FwyzREREUKa5EfrnnU8dg7zk5GTYvXs3tGnTRuHn3qJFC9i4cSOYmppKHQzQtRUzMzNOZpOCgoIo8ly8eFHl/KKjoylGkytXrmRQYvl8/fqV1nhUleeQlJREWYLQ09NTaeZEDNfKAA8AAHGAk5MT+vnzZ+HfV69eRT179uRCFE4AAHT8+HE0b948FB8fT3tO9+7dkb+/P3J1ddWwdOxy6dIl1K9fP9KxV69eoUaNGnEkkfIAAPLz80M+Pj5IKBQqdS2fz0cmJiYoNjYWWVtbsyOgjhMWFoY8PDzQ79+/ScerV6+O7t27h8qXL69UftnZ2ejatWvo5MmT6NatW6igoEDuNdWrV0fDhg1DQ4cORc7Ozsjf3x/NmjULKdPl8ng8tHr1ajR+/Hil5FWXAQMGoIcPHxb+7eLigp48eYL09PRUznPSpEno4sWLhX/b2Nigt2/fIhMTE7VkVYa8vDw0ZMgQ9OTJE6Wu4/F4yM/PD82YMQMBAOrbty+6cuUK6ZzVq1ejRYsWqSxbYmIisrOzIx1LSEhAtra2KuepFBpTOySQnII6evQoV6JwSmpqKsycOVPqViMjIyNYsmSJUltUtJ2CggKKVj127FiuxVKJZ8+eUbZeKZJ4PB74+/tzLX6J5PPnz1CuXDnKPa9duzbEx8crnE9BQQEEBQXByJEjwdzcXKHnWrFiRZg7dy6EhoaS1o0JggBXV1eFgwvhpF2Jx+OBq6srEAQB27dvp/zu4eGhtgdIrmcGOFMGJC0wt23bxpUoWsH79+9lLh04OTnB5cuXS4xV+qpVq0j1MzY2huTkZK7FUhqCIMDJyUmtzgXDHB8+fKD18VGvXj1ITEyUez1BEPDixQuYMWMGZUuctGRtbQ1eXl4QEhIi9YOQmJjI+QcNJ/VTSEgIxRujra0t/P79W+22q7PKgOSaq6bXj7QRgiDg2LFjMjuhbt26QXh4ONeiqk1cXBzFMp+pMKea5NevX2p1LtoUsKm48/btW9qtfI0bN5Zr1PX9+3dYunQp7a4DumRkZAQDBgyAS5cuQW5urlzZoqKiOP+Q4aR+onNVzpRHUp1VBkaNGkWq9KxZs7gSRetITU0Fb29vqUsHhoaGJWLpYMiQIaR6ubq6amW8gqIQBAGfP38GX19f6NKli8IhZaUlXQ9ixRQvX76kXa5p3rw5pKam0l7z588f2Lp1K61rYrrE5/OhQ4cOcPDgQal5SgPPDJTMNGfOHCaaLwBwrwxwZkDo7e2N/P39C/8eM2YMOnjwIBeiaC0fP35E06ZNIxnyFMXR0RH5+fmh3r17Ix6Pp2Hp1Ofx48eodevWpGOBgYGoc+fOHElET3JyMrp79y66ffs2un37NoqNjWUs76SkJGRjY8NYfrrI06dPUdeuXVF6ejrpeOvWrdGNGzeQhYVF4bG0tDR08eJFdPLkSRQcHIwIgpCbf6NGjdDQoUPRkCFDlDY8FAMAyM3NDUVGRiplQFiUunXrogULFiAPDw9W3neBQIDq1auHcnJyCo+NHz8erVmzhtFyLly4gCZPnkw6dvXqVdSsWTNGyxHz+fNntGHDBhQYGKhyHjwej/LcGjVqhJ48eYIMDQ3VFREhpMMGhMuWLSNpQH379uVKFK2GIAg4fvw4rUGUOHXt2rVYLh0QBAG1a9cm1aVnz55ciwX5+fnw8OFDWLx4MTRu3JgVoy9sM6Ac0jw6PnjwgNa4z8PDAzIzMwHgXzS5S5cuwYABAxSeyXF1dYWlS5fCt2/fGKuDn58fI22pZcuWEBISwphcYlavXk0qR09PjxFfDJLk5+dTfHWw8d5//foVBg8ezMqMgIWFBeN9LtczA5wpA35+fqRKu7u7cyVKsSAtLQ1mz54tc+lg8eLFxW7pYPfu3ZSPJBdT5xEREbBz507o3bu31MhnTCsDeDeBfGR5dJwyZQqtv/9OnTpBZmYmhISEgJeXl8K7Pezs7GDGjBnw4sULVpQ0gUAAZmZmCvsZkJc8PDzg6dOnjMiWk5ND8ZDq6enJSN50SPb/CCHG4kP8+PEDRo4cKfM+0zkGUyadOHGCEVmLorPKwOHDh0mVrl+/PleiFCs+fvxI6wtbnBwdHeHixYvFZsSZkZEBlpaWpDosWLCA9XLT0tLg0qVLMHnyZFqjIHnJ3t4exo0bB2fOnIEfP34o1clz6UymOBEYGAhmZmZSPTrS3dtWrVqBt7c3VKxYUaFnYW5uDiNHjoSgoCCNRKEMDAwEPT09uW1F7Jxq4cKFlDDAkqlbt25qRwLcs2cPJd+3b98yVGsqGRkZUKpUKVJ56m4vjomJgQkTJoC+vr7Ue2VjYwObNm2Cy5cvK/Qc6NKYMWMYugtkdFYZuHz5MqnSzs7OXIlS7CAIAk6cOAHly5eX2mA7d+7MeAAWtpg+fTrlhY2NjWU0yI9QKIQXL17AypUroVWrVgqHkBUnExMT6Nq1K/j5+cGXL18oMok7eUXdzAYFBaldp5KMoh/NoknRGR19fX3o2bMnnD59mpOZNFlKjjiZmZkVtpGCggI4dOiQ3C2s/fr1UykMs0gkoigcHTp0YLraFBYtWkQq09DQEP78+aN0PnFxcTBjxgzKlr+iycrKClatWkUKiCdP2aR7NlWrVi1cfmIanVUG7t+/T6p0qVKluBKl2JKWlgZz5syRuXTw33//sdZ4meLLly8yOzlVg/zExMTA/v37YdCgQVC6dGmlRwB169YFHx8fuHv3rkLBlAIDA6UGMioamwArArJhejpdnFq3bg27d+/Wiu2cAoEA/P39KcsfCP2braBr63l5ebBr1y6Zsx48Hg88PT3h+/fvCsty6dIlSj63b99msLb0/P37l2LDocysYGJiIsybN09maGhzc3NYvHix1K2l0p6Do6MjJbiSkZERhIaGMlR7KjqrDLx7945UaT6fr/XbyrSVT58+Qbt27aS+EA4ODnDhwgWtXjqQ9Egp7UMqy6d/ZmYm3Lx5E2bOnAnVq1dX+mNhZ2cHw4cPh6NHj0JcXJxK9ejcubNUhcbf31/pLWm6CFOGdgj98zq4fv16+PnzJ9fVooUgCHj58iVFbll2Mzk5OeDn5yczCiqfz4cxY8YoZH/TvHlz0rX16tXTWF8xfvx4UtlWVlZyw9kLBAJYsmSJTK+QxsbGMHfuXIU/pgRBQFJSEkRFRUF8fDy4u7tT8ty+fTsTVZaKzioD0dHRlIrjjlJ1CIKAU6dOQYUKFaS+IJ06dVJqxKApAgMDFRoFSgb5IQgC3r17Bxs2bID27dvLnCakS4aGhuDh4QEbNmyA0NBQRpRRyaUbPz8/SEpK0mpFTJtgwm2vg4MDLFiwoNhEQiUIgjJzdebMGbnXZWZmwvr162XOehkYGMDkyZMhNjaWNo/Hjx9Trjl+/DjTVZTKt2/fKM9amvOx9PR0WL16tUyDUENDQ5g2bZpKyw1iJHdVIISgd+/erL/DOqsMpKamUirOxjYWXSM9PR3mzp0r1YjGwMAAFi5cKHPpQNo2LmVRJB9lp4T5fD4YGRnB4MGDFXYXWzRVr14dZs6cCTdv3mR8+eTPnz+U8pjcmqYLqOuc59q1a8VyhrFLly6kesyePVvha9PS0mDFihUUQ9yiycjICLy9veHv37+ka3v16kVRpPLz85munkz69OlDksHe3p4kQ3Z2NmzevBnKlCkjtX56enowfvx4tWeAHj9+TFl2tbe314irdJ1VBkQiEUUjfPfuHVfilDg+f/5MO9UlTpUqVYLz58+TPtCytnEps2avTD5MTgnTpVKlSsHAgQNh//79rE8VX7t2jVS2ubl5sfwwcYm6bnuLq0dHSb8rrVq1UjqP5ORkWLhwIZiamkq9P6amprBgwQJITk6Gr1+/Un739fVlvnJyePr0KUWOo0ePQm5uLuzYsUOmoTSPx4MRI0Ywsuc/JSWFEvKaz+fDw4cPGailfHRWGQAAytaS+/fvcylOiYMgCDh9+rTMpYOOHTvCt2/fFLKslbdmD6CYha44HzYiuenp6UGrVq1g5cqV8OLFC7UjiSnD8uXLSbK0bt1aY2WXFNSdGdAG40BVuHHjBqkeJiYmKm91jI+Ph1mzZsl0sGRpaQn169cnHbO2toaMjAyGa6YYLVu2JMlSsWJFyodZMg0cOBA+f/7MSPkEQUC/fv0oZaxYsYKR/BVBp5UBZ2dnUsUvX77MpTgllvT0dJg3b57UpQPxljhF9z5LUwiU3UN95swZRhQAZ2dnmDRpEly6dIlTuxPJKVdvb2/OZCmuqKogFnePjnQfAnUt12NjY2HKlClSd7hIpv/++4+ZyqiA5FZzWalXr16MW/UHBARQymnbtq1GBxNcKwN8xCHW1takv1NTUzmRo6RjYWGBNm7ciD58+IA8PDwov4tEIgQAcv20EwSBAAD179+f8qxSU1NR//79Fc6HIAg0bNgwpetSlJUrV6KIiAj048cPtGrVKlSvXj1UUFCgsu93dXnz5g3p74YNG3IiR3GGx+Oh6dOnK30dAKCpU6cWyxgdCCFka2uLXFxcSMdevnypVp4VK1ZEO3fuRGFhYWjs2LFIT09P5vlGRkYoNzdXrTJVgSAIlJubK9fHf6dOndCLFy/QlStXUL169Rgr/8OHD2jWrFmkYzY2NujEiRNy71mJQmNqBw2Sa9p+fn5ciqMTEAQBZ86cUdhDG12ic6XL9to/Xfrx4wcjNg5M8PfvX4p8TLlX1TVU9TPQrVs3yMvL41p8lZGM4qmuRz5JwsLCYNiwYTLf04oVK0JAQIBG7iNBEHD16lWZ24oRQtCmTRvW1u0zMzNptyFfu3aNlfJkwfXMAKfKQN++fUkVX758OZfi6BQZGRng4+OjsjLg6OgIMTEx8OXLF3j27JlMuwQ2UunSpcHU1FRtGwemkFzzNTMz0+gUY0lDFQ+ECCHo3r27Qg6itBFfX19SXWrVqsVKOYq8905OTnDw4EFWXDQTBAG3b9+GJk2ayJWjbdu2rC79eHl5UcrkanlPp5WBMWPGkCo+YcKEYrvmVxzIzc2FhIQEiIiIgLdv38KVK1c0+gHXdJJn48AkK1euJJWtijU4howq7mIR+udPIzs7m2vxlebJkyeUOspzwKMs+fn5UKlSJYXfITc3Nzhx4gRjiu2DBw+gTZs2Sr3DkZGRjJQtyenTpynl1a9fH3Jzc1kpTx46qwwIBALagDtcTPFqM0KhEFJTUyEmJgY+ffoEz549g6CgIDh37hwcOHAAfH19YeXKlTB37lyYMGECDBkyBLp37w6tW7eGunXrgrOzM9jY2CjtkKekJE0FBerduzep3JkzZ7Janq4gzV2s2KPjzZs3aT3Rubu7a70bbkmys7MpRr5Mhyo+fvw45V7t2bMHOnToIPM9qlmzplpeTJ8/fw4dO3aUWUaNGjXg0KFDlK2R06dPZ/QeAPyLbCjpl8Hc3JzTeC46qQyINX66BsHFFC/TEAQB2dnZ8PfvXwgLC4M3b95ASEgIXLlyBY4dOwYBAQGwfv16+O+//2DatGkwcuRI6Nu3L7Rv3x4aN24MVatWhfLly8t0t4mT4kkT4YLt7e1JZR49epTV8nSNou5iJT06Pn36lNbhTuvWrRkfWbNNgwYNSHXYsGEDY3kTBAF16tQh5d+iRYvC30NCQqBVq1Yy36UGDRrAjRs3FFYKQkNDoWfPnjLzdHV1hePHjxfOPnh7e5N+NzU1ZXTLaH5+Pu0SBdfvrM4pA8puP9OkQlBQUAApKSkQHR0NHz9+hCdPnsCtW7fgzJkzsH//fti6dSssX74cZs+eDV5eXjB48GDo2rUrtGzZEmrXrg2Ojo5QunRpmSE0cdJ8YnvbWXx8PKXMT58+sVIWhp6XL1/Suqlt3rx5sXJzPmnSJJL8/fr1YyzvoKAgyv2R3M5NEAQEBgZCo0aNZL5TzZs3h3v37kkt6/PnzzBgwACZeTg4OMD+/fspHg9//vxJ8QK4cuVKxu4Dnc3EyJEjGctfVbhWBngAmtuHlZqaiuzt7VFOTo7c7WcIIcTn85GJiQmKjY2lbEMUAwAoOzsbpaeno4yMDJSenl6YJP+Wdyw7O5vhGmsvenp6yNLSEiGEkEAgUPr6wYMHowkTJqCcnBwkEAjQhQsX0OXLlxmWklmSkpKQjY0N4/neunULdevWrfBvU1NTlJ6erlvbkrSA0NBQ1LFjR5ScnEw63qhRI3T79m1UqlQpjiRTnMOHD6MxY8YU/m1vb49+/frFSN4dOnRA9+7dK/y7atWq6MuXL4jPp+4wBwB07do1tGTJEvThwwepebZr1w6tXr0atWzZEiGEUEREBFqxYgU6ceKE1C2+5cuXR4sWLUJeXl7IyMiI9pzhw4ejEydOFP5ta2uLfv78iUxMTBSqqzSCgoJQly5dSMfc3NzQ27dvkbm5uVp5q0tiYiKys7MjHUtISEC2trYaKV+jyoC/vz+aNWuW0vvAmzVrhipVqiT1o66IYlFSMDMzQxYWFsjS0pKUlD1mYmKCeDye0gqaGH19fbRt2zY0adIklfLh8XhIT08PCYVC0nFzc3NEEAQrillUVBRycnJiPN81a9agxYsXF/7dokUL9OTJE8bLwcjn48ePqH379igxMZF0vF69eujOnTuoTJkyHEmmGF++fEE1a9YkHfv9+zeqUKGCWvm+ffuW4vdi7969aPz48TKvIwgCnT9/Hi1duhR9//5d6nlt27ZF1tbW6Pr160gkEtGeU6ZMGbRw4UI0efJkuR/19+/fU3wJ7Nq1C02aNEnmdbL4+/cvqlu3LkpISCg8ZmhoiJ49e4YaNGigcr5MoTPKAAAgNzc3FBkZyZlTGK7Q19enfJTpPtbyPujm5uZIX1+fcfmCgoJQ9+7dFXIYJMnw4cPR7t27kZmZmcL58Pl8xOPxkJ2dHYqLiys8bmtri75//45KlSqF8vPzUXJyMkpKSipM379/R0uWLFG5nmzNDPTr1w9dunSp8O/p06ejbdu2MV4ORjG+fPmC2rdvj/7+/Us6XqtWLXTv3j1Kh6tNiEQiVKpUKZSRkVF47NKlS6hPnz5q5Tt06FB06tSpwr/Lli2LoqOjkbGxsULXC4VCdPLkSbRixQoUGRmpVNnW1tZo3rx5aMaMGUqNvjt37oxu375d+HflypXRt2/fVJpxIwgCde7cGd29e5d03M/PD82cOVPp/NiAa2VAYzYD6voc5yKZm5tDhQoVoFq1atCkSRNo37499O3bF0aNGgXTp0+HRYsWwfr16yEgIACOHz8OV69ehfv378ObN28gPDwc4uPjIScnp1hsl1RkG5c0O49atWoVhkZWNDbBiBEjKPkcOnRIpoza6qpW0of64cOHWSkHozjfv3+ndaxVvXp1tcLbagIPDw+SzAsXLlQrv6ioKMoa/Jo1a1TKKz8/H/bu3SszeJA4mZqawtKlS1XeyXPnzh1KnufPn1cpr/Xr11Py6tGjh1b1zVzbDGhMGVA3GpmiycDAAMqUKQMuLi5Qt25daN26NXTv3h08PT1h4sSJMHfuXFi5ciX4+fnBwYMH4fz58xAUFATPnj2Dz58/w69fvyAtLU0nHcbI28aVlJQk1WGJhYUFXLhwQaF83r17B8bGxqTfWrRooVCEP1U8HbK5m4BOyf348SMrZWGUIyIigjbYjZubG/z69Ytr8aSyYMECkrweHh5q5TdjxgxSfmZmZpCSkqJSXikpKbBo0SKpu8Ek37tRo0bBjx8/VCqLIAhKMKWmTZsq/QF/9uwZRRmqUKECJCYmqiQXW+iMMqDuzMDs2bNh48aNsHv3bjhx4gRcu3YNHjx4AKGhofDjxw9ISEjgzFlESUPWNi4AgIsXL0qNnT537txCr2XS8pHcasTn8xUOPKKsq1q2/QwEBgaSylMn2hyGeaKjoykB0RBC4OLiAtHR0VyLR8ulS5coiraqobCTkpIo+/ZV8bCXlpYGK1euBCsrK6X7bn19fZgwYQLExMQoXe7Jkycp+SnjmlggEICTkxOlT9DGCLk6owyoE66Wx+PBli1bKFtQMNwRFhYGtWvXpn1ebdq0kToVe/XqVcr5yjoVUXZ7alBQEBNVpmXNmjWkMps3b85aWRjV+PXrF7i5uVHah4ODg8qjVjb5/fs3RVZV41ysWrWKlI+enp5SSlBWVhZs3LgRbGxsZH7sx44dC3PnzpWpLBgaGsKMGTMgLi5O4fILCgrA0dGRlE+PHj0UupYgCBg4cCBFjqVLlypcvibRGWUAQP1gNjVq1IDg4GBNioyRQVZWFu3aP0IIypUrBw8ePCCdn52dTRmllS1bVqVRu6K2CWwqAgBAiYE+bdo0VsvDqMafP3+gWrVqlHZasWJFTr3OSUPSiZU8exo6cnJywM7OjpTPsGHDFL7W398fypYtK1PZllwGEC8jyHKYZmJiAvPmzVN4mt7f35+Sx+fPn+Vet3fvXsp1rVu31tqZO51SBlSNRiaZBg0apNVrfroEQRCwe/duWnfHenp6sHnz5sLlgWXLllHOUcfrlzzbBE04m5EctRw8eJD1MjGq8ffvX6hVqxat4qptESYllczJkycrnceePXsodX337p3Ma/Lz82HPnj0UZUQyDRkyBL5+/So1n4SEBJg7dy7FNqhoMjc3hyVLlsgdDGRkZECpUqVI18qL6Pjp0ydK2aVLl1ZpqUJT6JQyAKDcFK+sWQRTU1NYt24dthPQEl6+fElrrIXQPy9qoaGhYGRkRDreqlUrRqx55dk4sEVSUhKlru/fv9dI2RjVSExMhHr16lGem62tLXz48IFr8QqRtH5v0KCBUtcLhULK0kjHjh1lnn/kyBFwcXGR2S/36dNHqTb+588fmDZtGhgYGEjN09raGtasWQMZGRlS81m8eDHpGgMDA/j9+zftudnZ2VCzZk1KOZLeFrUNnVMGAJSb4n327BnFX3fRVKVKlWIbw6CkkZSUBJ07d5aqvEnOGhT3D+ft27dJdTI2NtbaKUjM/5OcnEzrbtfGxgbevn3LtXgA8C9OQFHZ9PX1lYrEePHiRUr97ty5QzlPJBLB6dOnaZdQiqYuXbrAy5cvVa5PdHQ0eHl5Uaz6i6YyZcrAli1baOv59+9fymBi/vz5tGVNnDiRkjcbwY6YRieVAQDlpniFQiHs3r0bSpcuLVNjjYqK4qo6mP8hFAph+fLlcm1DuIoZziTr1q0j1alp06Zci4RRkNTUVGjWrBntKFWdjx5TpKenU96hp0+fKnQtQRCUutWrV480Y0YQBFy+fJkSuEgytWvXDh4/fsxYvcLDw2HEiBEy+4fy5cvDjh07KLO+EyZMIJ1naWkJaWlppHPOnTtHya9u3bqQk5PDWB3YQmeVATHKTPEmJSXBxIkTpTYkY2NjWLFiRbGMZV7SuHXrllTlzdTUFOLj47kWUW0kA7FMmTKFa5EwSpCenk4bpc/S0lLhDy+bSNo3+Pr6yjyfIAhITEyEs2fPUup08uTJwnNu3bqldiAidfn8+TOtpX/RJBnI6Pv375S+f8WKFRAVFQWJiYkQGRlJ2c1gZmYG3759Y60eTKLzyoAqvH79Gpo2bSq1ETk7O8OVK1e0yruULhIdHS3VU1njxo3h58+fXIuoFpI7Iw4cOMC1SBglyczMBHd3d0r7NDc3V2o/OxuMHTuWJJOnpyfteQKBAPz8/CizrOJUqVIlKCgogJCQEGjZsqXMD3D9+vWVClGsLqGhodCrVy+ZMrm6usKxY8dAKBRC3759pZ4nuYyAkGq7MLgCKwMqIhKJ4ODBg2Brayu1cXTr1g3Cw8O5FlVnCQsLo91lIE42NjbF1t4jOTmZUh95ltoY7SQrKws6duxIO4PF5Vbm3bt3k+RxcXGhnCPL/kqcDAwMaI0mi6aaNWvCxYsXORtAvXjxAjp16iRTxurVq8OoUaNknlM0DRs2rFgNCLEyoCYCgQCmT58udXeCoaEhLFq0CDIzM7kWVacgCEKqMWHRxOPxYMWKFSp7WOMKSb/pRkZG2ClWMSYnJwe6detGaZ/Gxsas+6qQRmhoKEWeonvzFd2ZJSu5ubnByZMntcb9+oMHD6B169Yq16dounjxItfVUQqsDDDE+/fvZTaiSpUqwblz54qVplicuXDhAuUZjBw5EipXrkz7fLp27QpJSUlci60wklu/GjduzLVIGDXJzc2F3r170w4orl+/rnF5CgoKwMTEhCTLzZs3AUB9ny2Ojo5w8OBBrdz9QhAE3L59G5o0aaKyIiDekcaWG3I2wMoAgxAEASdOnJAZUatDhw5a52CkpJGZmQmVKlUi3fcKFSpAeno6pKamQp8+faR2UK9eveJafIWQNH6aNGkS1yJhGCA/P5/WsM3AwAAuXbqkcXkkDRyXLVsGAKp7c7W0tISAgADIy8vTeF2UhSAIuHbtmtwlDlkKAVsBytgAKwMskJaWBnPnzgV9fX3aRqKvrw9z586F9PR0rkUtkSxcuJByz0+dOlX4O0EQsHHjRto9x4aGhrBnzx6tn8GRNNbat28f1yJhGKKgoACGDh1K22+cPXtWo7LMnj2bMoOmTpwXMzMz8Pb2Bl9fX7h48SK8fv0aEhIStPp9EwqFMt0iy1IG2AxdzjRYGWCRL1++QPv27aU2lvLly8OJEyeKTWMpDnz79o3ibczDw4P2Ht+/f1/qSz5q1CjIysrioAbyEQgEFHm1xVkNhhmEQiGtsRqfz4cTJ05oTI7Tp0+TyrexsaH9aKibjI2NoUqVKtChQwcYN24crFixAg4dOgT37t2D8PBwTvfpqxvxtrgsP2JlgGUIgoBz585Rpq2LpjZt2hR7b3jaAEEQFKtsfX19mcsyf/78od3rjRCCOnXqaOVukHv37lFmM4rDtCtGOUQiEXh5edGOOA8fPqwRGaKioijl379/n3FlQJFUtmxZaNKkCQwYMABmz54Nfn5+GpldoLsHyqTi4owOKwMaIjMzE/777z+pW9309PRgxowZxcrgRNug8/7l4+Mj97r8/HyYM2cO7XOxtLTUOp/iGzduJMnYqFEjrkXCsIRIJIIpU6bQKgR79+5lvXyCICjbpyW3HGpLMjExgapVq0LHjh1JswvBwcEQERGhchwZPDOgGXRGGRATFhYGXbt2ldpwbG1t4eDBg8VuqxvXZGRkUCKdVaxYUWbwEUnOnTsHFhYWtM9l/vz5WmP5PHjwYJJsEydO5FokDIsQBAHe3t607XLHjh2sl9+jRw9SmTNmzFDZZsDa2ho8PT2hVatW4ODgIDNWABupXLlypNkFf39/uHTpErx58wYSExNpZxdUtZHANgPKoXPKAMC/xnXlyhWKB7miqVmzZvD69WuuRS02+Pj4UO6hKsZW3759o404htA/P+l///5lQXrlkNweqYkRIoZbCIKA+fPn07bLrVu3slr2ypUrSeU1b95cpd0EdNb1BQUF8PPnT3j06BGcOHEC1q1bB5MnT4bu3btD7dq1wdLSUqPKQtHZBS8vL1i5ciUcPnwYpk6dykh9tRmsDHBIdnY2LF++XGrMbR6PB5MmTSo200xc8eXLF8rOjY4dO6qskWdmZtJacyP0z+iTycApypKamkqR6c2bN5zJg9EcBEHAkiVLaNvl+vXrWSs3KCiIVJaRkRHEx8cr5WeAz+ervO8+NTUVPnz4ANevX4eAgABYsGABDB06FFq2bAmVKlVSy+kRW0md+nIF18oADwAA6ThRUVFo9uzZ6PLly7S/ly5dGq1duxZ5eXkhPT09zQqn5QAA6tChAwoODi48ZmBggD5+/IiqVq2qVr67du1C3t7eqKCggPSbvr4+2rhxI/L29kY8Hk/lMlQhJCQEeXh4FP5tYGCAMjIykJGRkUblwHDH6tWr0ZIlSyjHV65cSXtcXVJSUpCNjQ3p2OvXr1FSUhLq3r07AgBEEITU6/l8PuLxeOjmzZuoU6dOjMsnFArRnz9/UExMDIqJiUE/f/4s/L/474yMDMbLlUeDBg1QgwYNkIODA3JwcECOjo7IwcEB2dvbI0NDQ43LI4/ExERkZ2dHOhYfH085xhoaUzuKAbdu3QI3Nzep2mbDhg3h2bNnXIupVUhufUIIwcKFCxnL//nz5xRbBHEaMGCAxn1FbNq0iSRDgwYNNFo+RjvYsGEDbZtcvHgxK2vUkv1SQEAAAMiOTSA+ZmZmxplLZTFFZxd27twJ8+fPB09PT05mF3g8HpQvXx6aNm0KAwcOhDlz5sC2bdvg8uXL8PbtW7nRc9lAIBDA6tWrKbI6OTmBn5+fRmY4sDIgQW5uLqxbtw5MTU2lNqYxY8aUiBC86pKeng4VKlQg3ZtKlSoxHgciISGBNpAMQgiqVq0Knz59YrQ8WXh6epLKHz9+vMbKxmgXvr6+tG3Sx8eH8Y/J8OHDSWWMGjWq8DeBQAD+/v4UR1iurq7g7+8PqampjMrCBmLbhYcPH8Lx48dh7dq1MGnSJOjWrRvUqlVLqmExW8nU1BSqVasGnTt3hvHjx8OqVavgyJEjEBISAj9+/GB0K3FRhY5OcRErdGwHdcPKgBRiYmJg0KBBUhuLlZUVbNu2TWss3Llg7ty5lPty4cIFVsoSCoVS12tNTU0L47WzTZUqVUhl7969WyPlYrSTnTt30rbJmTNnMqoQbNu2jZR/9erVKecQBAFJSUkQFRXFyeiWbQQCAbx//x6uXbsGO3fuBB8fH+jXrx80bNgQypcvr9LuCnVmFypUqADNmjWDQYMGwdy5cwtnF0JDQyE5OVmh+69osCk+nw96enqsKgTYZkAOwcHBaPr06ejLly+0v9epUwft2LEDtW7dWsOSccvnz59RvXr1kFAoLDzWuXNndOvWLVbX8W/cuIFGjBiBBAIB5bdp06ahLVu2sLYemJ6ejqysrEjHXr16hRo1asRKeZjiwf79+9GECROQZFc6efJktGPHDsTn89Uu4+XLl6hp06aFf/N4PCQQCCjtUZcpKCgg2S5I2jD8/PkTZWZmakweMzOzQnsFSbsFBwcHZGZmhlxcXFBOTo5Mmw8xfD4fmZiYoNjYWGRtbc28wKypGSWI/Px82LJli8ypqmHDhsHv37+5FlUjEAQBbdu2JdXf0NAQwsLCNFJ+ZGQkNGjQgPY5NGvWDGJiYlgpV9Lzm4GBgcqOVDAli8OHD9OO7ry8vBjxWZKbm0tx83337l0GJNcdCIKgnV0YMmQItGjRAuzt7bVyZ0TRxOZ2SawMKMGfP39gxIgRUh+Uubk5bNq0qcTHtT9x4gSl7osXL9aoDDk5OTB+/Hja51CmTBm4c+cO42Vu2bKFVE79+vUZLwNTfDlx4gStE5+RI0eCUChUO//GjRuT8l27di0DUmOKkp+fD9HR0fDw4UM4duwYrFmzBiZOnAhdu3aFmjVrgrm5OefKAFuOlLAyoAKPHj2COnXqSH1g1atXL7Fae1paGpQrV45UX0dHR86CCh08eJDWTwSPx4PVq1cz6klS0veBl5cXY3ljSgbnzp2jjZbq6emptn3RtGnTSHn27t2bGaExCiOeXXj37h1cvXoVduzYwcnsAhu+b7AyoCIFBQWwY8cOsLa2lvrABg4cyNqUNVfMmjWLUk+uYweEhoZSLKnFqXv37pCSksJIOVWrViXlLd7ehcEU5fLly5QpfYQQ9O/fX61Zw6NHj5LyK1euXIkzEiwJ5OfnQ1RUFDx48AD2798PY8eOhUaNGkGZMmUYUxTYCL6ElQE1iY+Ph3Hjxkl9aKamprBmzZoSsbb84cMHyjRot27dtKJDEggE0KtXL9pn4OzsrLaXwPT0dIq18osXLxiSHlPSuHHjBhgZGVHaYq9evVTuC75//07Jr6QNNoo7sbGxcObMGZgxYwY0bNiQtdgPeGZAi3n+/Dk0bNhQ6sOrXLky3Lx5k2sxVYYgCGjdujWpTkZGRhAREcG1aIWIRCJYv349rfZtZGQE+/fvVznvhw8fkvLT19fnNMY7Rvu5ffs27RJW165dVWo7IpGIMhN5/vx5FiTHKIJQKITQ0FDYsWMHeHp6goODAysf/qIJ2wwUE/6vvXuPaer6AwD+vTwtTAUq4AOEFtGpG/hAnltGEGQEZSZsuH8WjFkwbFjnnAtxbCxsYHRuKxoSoyNRt8WZSWPYZCILm2h1GyAGAswRChPGwFEobyi0398fv7Tx0hba0hft95PcZOt9fS2993zPueeeMzMzg+fPn0cul6vzj5mWlobt7e3WDtVgs5soAQDz8/OtHZZW1dXV6Ofnp/X7P3DgAI6Pjxt8zNkDzISHh5s+cGJ3qqurtQ5glpSUZFQ/m9mDb+kzRTgxjeHhYayqqsKPP/4Yk5KSFjQQEo/Hw4iICKOSAXqbYBGRSqWYnZ2tcxAMd3d3zM/PN6pQsobBwUGNwpXH49l0/N3d3RgbG6v1+9+yZYvBCdnsEeAOHDhgpsiJvblz547WXujx8fEGTfGNiJiXl8c6xksvvWSeoAk+fvwYr1y5gjk5Obh161ajn/e7uLjgjh078J133sHvv/8ee3p6EPH/91VLTTalD0oGzKi+vh5jYmJ0/nGDg4Px+vXrNvHMfS4CgUAj9h9++MHaYc1LLpfrnIfey8sLy8vL9T7Wxo0bWfuXlJSYMXJib+7fv691OuC4uDgcGhrS+zjl5eWs/T09PU3y2qKjm56exgcPHuDZs2fx9ddfx8DAQKNr/V5eXpiSkoKffvop/vLLL3O2ABk6AqE555igZMDMFAoFXrx4UWezNQDgyy+/jI8ePbJ2qFo9fPhQ44e6Z88ea4dlkKtXr6Knp6fW7/748ePz3kxHRkY0Wnl+++03C0VP7EVtbS16e3tr/AajoqL0ru319vZq7N/Y2GjewO3Q0NAQVlZW4kcffYSJiYkLGj+Az+fjG2+8gefOncOmpiaDX2e2lcmmKBmwEJlMhocPH9bZu9TV1RVzc3NNPsnPQigUCoyLi2PFuWTJEpRIJNYOzWAtLS0atXvVsnPnzjknnrpz5w5re2dnZ5t+REJsV0NDg9Y+Rdu3b0epVKrXMYKCglj7LqRjrCNQKpXY2dmJ3377Lb711lsYHh6+oCb/qKgoPHLkCF67dg3//fdfk8RoC5NNUTJgYY2NjRpD+T69BAQE4NWrV23i0cHFixc14isoKLB2WEYbGRnBffv2af3e16xZg2KxWOt+QqGQtW1YWJiFIyf2pKmpSWtLYXh4OD558mTe/WdPoJaVlWWBqBeP6elprKurw+LiYszIyMA1a9YYXev39vbG1NRULCoqwtu3b5u9EmDNyaYoGbACpVKJV65c0Zj+9+klISEBm5ubrRbjwMAA+vr6amSpi/11OqVSiWfOnNE6SpyLiwsWFxezLkClUomvvfYaa7v9+/db8V9A7EFrayuuWrVK4ze4efNm7O3tnXPf06dPayQRjkwmk+HNmzfxww8/xISEBJ2PBPVZ1q1bh5mZmXj+/Hlsbm426Qimto6SASsaHh7G999/X2vBpCqc3n33XYM6GJnK22+/rRHPYh4nYTaxWKyzxrBv3z7s6upCoVCodWTD9PR0s/XoJY7jr7/+woCAAI3f17PPPjvnpGezx7xwdna2qceL5qRUKlEikeA333yD2dnZGBYWZvTUxa6urhgdHY1Hjx5FkUg0bxJm7ygZsAGtra2YmJio80e7cuVK/Prrry3WZFRfX6/xTG3v3r0WObcl9fX1YUJCgtbvXNVxR9uNRtWhx5xzixPHIJFINPoAqGqoukYXHB0d1eh7VFNTY+HILUMul+Mff/yBX375Jb766qtztqbOt/j4+ODu3bvxxIkTWFNTQ/1+ZqFkwEYolUosKyubcxSrF154AR8+fGjWOBQKBUZHR7POy+FwsLOz06zntZaZmRk8fvy4wTcW1as+lBCQhers7EQ+n6/xG+PxeDrHoA8PD2dte/r0acsGbSaDg4NYUVGBH3zwAcbHx2sdsEnfJTQ0FPfv348XLlzAlpYWh2ryNwYlAzZmbGwM8/Ly0M3NTWchlJOTY7LJd2YrLS3VOGdhYaFZzmVLysvLtb4HPl9CYM5BQIjj6OrqwvXr12v8xgIDA7UO+Z2VlcXaLiMjwwpRL4xSqcT29na8fPkyHjx4EJ977jmjm/zd3NwwNjYWjx07htevX9erIyZho2TARrW1tWFqaqrOH7+vry+WlpaaNNuVSqW4YsUKjezaHiZZ0sfs0d30Wcw5PChxLD09PVpff129ejX++eefrG2/+uor1jZBQUHWCdoAcrkcf//9d/ziiy8wPT1dYyp0QxYul4tpaWl48uRJvHv37qLv2GwLGEREIDbrxx9/hMOHD4NEItG6PjIyEkpKSiAiImLB58rOzoZz586xPrt58yYkJycv+Ni2Znx8HDo6OkAikYBEIoH29nYoLS2F8fFxg47DMAzw+Xxoa2sDhmHMFC1xFE+ePIHExERoampife7v7w/V1dWwadMmAABoamqCsLAw1ja9vb3g7+9vsVjnMzg4CPfu3QOxWAxisRhqa2thYmLCqGNt2LAB4uLi1Mv69evpejMxSgYWgcnJSfjss8+gqKgIJicnNdYzDANvvvkmFBUVwYoVK4w6R11dHURGRsLTP4f09HS4du2a0XFbk0KhgJ6eHpBIJKxCX7X09fWZ9Hz9/f3A5XJNekzimKRSKSQlJUFDQwPrc19fX/j5558hLCwMFAoFLF++HMbGxtTry8vLYc+ePZYOFwAAEBHa29vVBb9YLIaWlhajjuXu7g4RERHqgj82Ntbo+xrRHyUDi0hnZyccPXoURCKR1vXe3t5QWFgIWVlZ4OzsrPdxlUolREdHQ21trfozDw8PaG1thbVr1y44bnMZHh7WKORVBX9nZyfI5XKLxdLR0QHBwcEWOx+xb4ODg5CcnMy6JgEAfHx8oKqqCrZt2wbx8fFw+/Zt9bq8vDz45JNPLBKfXC6HBw8eqAv+e/fuGZ1g+/r6QmxsrLrw3759O7i7u5s4YjIfSgYWoVu3boFAIIBHjx5pXb9161YoKSmBmJgYresREaRSKYyOjsIzzzwDIpEIDh48yNrmxIkTkJuba/LYDTEzMwNdXV0aBb6q0JdKpVaN72nUMkBMbWhoCFJSUuD+/fusz728vKCyshLKysrg1KlT6s9ffPFFEIlEwOVyTd6EPjAwoNHkr62VUh8bN25kNfmvW7eOmvxtACUDi5RcLgehUAgFBQWspsKnZWZmwsmTJ9XPEWUyGVy6dAnOnj0L7e3t6u2cnJxAqVSq/3/Dhg3Q2NgIbm5uZv03ICIMDAxo1OpVy+PHj0GhUJg1hlWrVgGPxwMejwcVFRUgk8nAkEuC+gwQcxoZGYHdu3dDTU0N6/OlS5eCQCCAwsJCjX1CQkLg0KFDkJmZCV5eXgafExGhra1NXeMXi8XQ2tpqVPxLliyBHTt2qAv+mJgYSpptFCUDi1x3dzccO3YMvvvuO63rly1bBgUFBRAaGgoZGRnqDnJz/dmrqqogMTHRJPFNTU3B33//rbUpXyKRwPDwsEnOo4uHhwfw+Xzg8/nA4/HU/83n8yE4OBg8PDzU2xYXF8ORI0cMTgaEQiEIBAJzhE8IjI2NQVpaGlRXV+u1vSop9fDwgLKysnk7AE9NTUF9fT2ryf+///4zKlY/Pz9WrX/btm1mr1QQ06BkwE78+uuvkJOTA83NzTq3YRhm3oKOYRj46aef9H6DABGhr69Paye9jo4O6O7uNqhwNRTDMBAQEKCzwPfz89O7xi6TySAgIAAmJiZYLSW6ODk5AYfDge7ubqNqYIToa2JiAvbu3Qu3bt3Sex8nJydgGAZu3LjBup77+/tZTf51dXUwNTVlVFybNm1iFf4hISHUQrZIUTJgR6anp6GkpATy8/ONrnFrK+Cefg1vdqHf0dFh8Ot4hlq2bBmrgH+60A8KCjJpZ6PKykpITU0FRJwzIVDdaCsqKmDXrl0mOz8hukxOTsIrr7xicELg7u4Op06dgoaGBhCLxTr7Gs2Hw+FAZGSkuod/TEwM+Pj4GHUsYnsoGbBDvb29kJubC5cuXTL6GBEREeDm5gYSiQR6e3tNGJ0mZ2dnWLt2rUaBryr0fXx8LFrbqKyshPT0dK2PVJ5ughWJRJQIEIv6/PPP4b333rPIuVauXMmq9W/ZsoWa/O0YJQN27O7du7Bz506LvmKnC5fL1dqMz+fzITAwEFxcXKwdIotMJoPLly/DmTNnWJ0tQ0JCQCAQQGZmJixfvtyKERJHg4gQGhoKEonE5I/eGIaBzZs3swp/Ho9HTf4OhJIBO9bf3w++vr4WOZebmxsEBwdrfXbP4/EWbcGpeuNhZGQEli5davFWCkJUTHk9czgciIqKYvXyp34vjs22qmPEpEZHR016PH9/f53P7levXm3QQEeLBcMwwOVy6XUoYnULvZ5TUlJg165d6iZ/V1dXE0VG7AG1DNixhdYkCgsL4fnnn1e/hufp6WnC6Aghhljo9UwDY5G5UDJgx4x9xkgD6RBie+h6JubkZO0AiPkwDAOHDh0yal+BQEA3DkJsCF3PxJyoZcDO0UA6hNgPup6JuVDLgJ3z8vKCsrIyYBgGnJzm/nOrBtIRiUR04yDEBtH1TMyFkgEHkJycDDdu3AAOhwMMw2g0F6o+43A4NKIeITaOrmdiDpQMOIjk5GTo7u4GoVAIfD6ftY7P54NQKIR//vmHbhyELAJ0PRNToz4DDogG0iHEftD1TEyBkgFCCCHEwdFjAkIIIcTBUTJACCGEODhKBgghhBAHR8kAIYQQ4uAoGSCEEEIcHCUDhBBCiIOjZIAQQghxcJQMEEIIIQ6OkgFCCCHEwVEyQAghhDg4SgYIIYQQB0fJACGEEOLgKBkghBBCHBwlA4QQQoiDo2SAEEIIcXD/A5FbErSU0AusAAAAAElFTkSuQmCC\n", "text/plain": [ "<Figure size 640x480 with 4 Axes>" ] @@ -1356,7 +1356,7 @@ }, { "cell_type": "markdown", - "id": "fc153443", + "id": "c4066cae", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -1367,13 +1367,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "ec75cd12", + "id": "a297b883", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:26.713001Z", - "iopub.status.busy": "2022-12-20T17:04:26.712757Z", - "iopub.status.idle": "2022-12-20T17:04:26.819333Z", - "shell.execute_reply": "2022-12-20T17:04:26.818702Z" + "iopub.execute_input": "2022-12-25T00:24:19.807009Z", + "iopub.status.busy": "2022-12-25T00:24:19.806780Z", + "iopub.status.idle": "2022-12-25T00:24:19.906497Z", + "shell.execute_reply": "2022-12-25T00:24:19.905934Z" } }, "outputs": [ @@ -1396,7 +1396,7 @@ }, { "cell_type": "markdown", - "id": "f6eada6e", + "id": "5af59003", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -1405,19 +1405,19 @@ { "cell_type": "code", "execution_count": 38, - "id": "589f977d", + "id": "be51076a", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:26.823233Z", - "iopub.status.busy": "2022-12-20T17:04:26.822855Z", - "iopub.status.idle": "2022-12-20T17:04:26.962634Z", - "shell.execute_reply": "2022-12-20T17:04:26.961990Z" + "iopub.execute_input": "2022-12-25T00:24:19.910579Z", + "iopub.status.busy": "2022-12-25T00:24:19.909468Z", + "iopub.status.idle": "2022-12-25T00:24:20.039889Z", + "shell.execute_reply": "2022-12-25T00:24:20.039345Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1433,7 +1433,7 @@ }, { "cell_type": "markdown", - "id": "6da45806", + "id": "62817648", "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": "5ad73987", + "id": "99fe891a", "metadata": { "execution": { - "iopub.execute_input": "2022-12-20T17:04:26.966195Z", - "iopub.status.busy": "2022-12-20T17:04:26.965935Z", - "iopub.status.idle": "2022-12-20T17:04:27.106777Z", - "shell.execute_reply": "2022-12-20T17:04:27.106157Z" + "iopub.execute_input": "2022-12-25T00:24:20.043512Z", + "iopub.status.busy": "2022-12-25T00:24:20.043265Z", + "iopub.status.idle": "2022-12-25T00:24:20.229431Z", + "shell.execute_reply": "2022-12-25T00:24:20.228829Z" } }, "outputs": [ @@ -1476,7 +1476,7 @@ }, { "cell_type": "markdown", - "id": "02850e5d", + "id": "90ee80e2", "metadata": {}, "source": [ "See Drawing for additional details." |
