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| author | MridulS <mail@mriduls.com> | 2022-12-27 10:13:11 +0000 |
|---|---|---|
| committer | MridulS <mail@mriduls.com> | 2022-12-27 10:13:11 +0000 |
| commit | 6ae99ab58d8b8ba50f66768c0f3aa4bb82b22196 (patch) | |
| tree | 2876df8670321def66944cd21b30718417c16ca8 | |
| parent | 1021b7dd279c29c98ef4a57b6d818ee02532fa1a (diff) | |
| download | networkx-6ae99ab58d8b8ba50f66768c0f3aa4bb82b22196.tar.gz | |
Deploying to gh-pages from @ networkx/networkx@8f16a2f3e860c4b95c469788e7dc3d1a734725bb 🚀
115 files changed, 601 insertions, 596 deletions
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index c3b3beab..d663ba40 100644 --- a/_modules/networkx/algorithms/isomorphism/isomorph.html +++ b/_modules/networkx/algorithms/isomorphism/isomorph.html @@ -487,6 +487,10 @@ <span class="sd"> Notes</span> <span class="sd"> -----</span> <span class="sd"> Checks for matching degree, triangle, and number of cliques sequences.</span> +<span class="sd"> The triangle sequence contains the number of triangles each node is part of.</span> +<span class="sd"> The clique sequence contains for each node the number of maximal cliques</span> +<span class="sd"> involving that node.</span> + <span class="sd"> """</span> <span class="c1"># Check global properties</span> @@ -528,7 +532,8 @@ <span class="sd"> Notes</span> <span class="sd"> -----</span> -<span class="sd"> Checks for matching degree and triangle sequences.</span> +<span class="sd"> Checks for matching degree and triangle sequences. The triangle</span> +<span class="sd"> sequence contains the number of triangles each node is part of.</span> <span class="sd"> """</span> <span class="c1"># Check global properties</span> <span class="k">if</span> <span class="n">G1</span><span class="o">.</span><span class="n">order</span><span class="p">()</span> <span class="o">!=</span> <span class="n">G2</span><span class="o">.</span><span class="n">order</span><span class="p">():</span> diff --git a/auto_examples/3d_drawing/plot_basic.html b/auto_examples/3d_drawing/plot_basic.html index 6f79d8f2..92b69005 100644 --- a/auto_examples/3d_drawing/plot_basic.html +++ b/auto_examples/3d_drawing/plot_basic.html @@ -540,7 +540,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.073 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.078 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-3d-drawing-plot-basic-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/79beefddd68fa45123e60db5559f52aa/plot_basic.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_basic.py</span></code></a></p> diff --git a/auto_examples/3d_drawing/sg_execution_times.html b/auto_examples/3d_drawing/sg_execution_times.html index d95d3be1..2a8c64d4 100644 --- a/auto_examples/3d_drawing/sg_execution_times.html +++ b/auto_examples/3d_drawing/sg_execution_times.html @@ -463,11 +463,11 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-3d-drawing-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.073</strong> total execution time for <strong>auto_examples_3d_drawing</strong> files:</p> +<p><strong>00:00.078</strong> total execution time for <strong>auto_examples_3d_drawing</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_basic.html#sphx-glr-auto-examples-3d-drawing-plot-basic-py"><span class="std std-ref">Basic matplotlib</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_basic.py</span></code>)</p></td> -<td><p>00:00.073</p></td> +<td><p>00:00.078</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="mayavi2_spring.html#sphx-glr-auto-examples-3d-drawing-mayavi2-spring-py"><span class="std std-ref">Mayavi2</span></a> (<code class="docutils literal notranslate"><span class="pre">mayavi2_spring.py</span></code>)</p></td> diff --git a/auto_examples/algorithms/plot_beam_search.html b/auto_examples/algorithms/plot_beam_search.html index fa68d87f..6ada0913 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 0.206 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.208 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 913a6b2e..2d4650d6 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 3.365 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.814 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 233da7a2..f9b47d10 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 0.390 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.366 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 0c0bc9f7..8fa22897 100644 --- a/auto_examples/algorithms/plot_circuits.html +++ b/auto_examples/algorithms/plot_circuits.html @@ -603,7 +603,7 @@ fourth layer.</p> <img src="../../_images/sphx_glr_plot_circuits_001.png" srcset="../../_images/sphx_glr_plot_circuits_001.png" alt="((x ∨ y) ∧ (y ∨ ¬(z)))" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>((x ∨ y) ∧ (y ∨ ¬(z))) </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.100 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.105 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-circuits-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/bd2ce07c5ba253eb7b45764c94237a4c/plot_circuits.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_circuits.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_davis_club.html b/auto_examples/algorithms/plot_davis_club.html index 52ae906d..74f684a5 100644 --- a/auto_examples/algorithms/plot_davis_club.html +++ b/auto_examples/algorithms/plot_davis_club.html @@ -639,7 +639,7 @@ The graph is bipartite (clubs, women).</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.069 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.071 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-davis-club-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/6a1e333663010969e61d07b33c7845f0/plot_davis_club.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_davis_club.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_dedensification.html b/auto_examples/algorithms/plot_dedensification.html index 2bb822ac..d93246e1 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 0.229 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.242 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 3e5c13cd..2c82fb26 100644 --- a/auto_examples/algorithms/plot_krackhardt_centrality.html +++ b/auto_examples/algorithms/plot_krackhardt_centrality.html @@ -569,7 +569,7 @@ Closeness centrality <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.058 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.060 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-krackhardt-centrality-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/e77acafa90a347f4353549d3bffbb72c/plot_krackhardt_centrality.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_krackhardt_centrality.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_parallel_betweenness.html b/auto_examples/algorithms/plot_parallel_betweenness.html index b0e3afab..a48e7d16 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: 1.6727 seconds - Betweenness centrality for node 0: 0.08378 + Time: 1.7654 seconds + Betweenness centrality for node 0: 0.14984 Non-Parallel version - Time: 2.7735 seconds - Betweenness centrality for node 0: 0.08378 + Time: 2.9646 seconds + Betweenness centrality for node 0: 0.14984 Computing betweenness centrality for: -Graph with 1000 nodes and 5114 edges +Graph with 1000 nodes and 4947 edges Parallel version - Time: 2.1571 seconds - Betweenness centrality for node 0: 0.00246 + Time: 2.2769 seconds + Betweenness centrality for node 0: 0.00263 Non-Parallel version - Time: 3.6963 seconds - Betweenness centrality for node 0: 0.00246 + Time: 3.9447 seconds + Betweenness centrality for node 0: 0.00263 Computing betweenness centrality for: Graph with 1000 nodes and 2000 edges Parallel version - Time: 1.4595 seconds - Betweenness centrality for node 0: 0.01364 + Time: 1.5304 seconds + Betweenness centrality for node 0: 0.00269 Non-Parallel version - Time: 2.5259 seconds - Betweenness centrality for node 0: 0.01364 + Time: 2.7154 seconds + Betweenness centrality for node 0: 0.00269 </pre></div> </div> <div class="line-block"> @@ -611,7 +611,7 @@ Graph with 1000 nodes and 2000 edges <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 19.655 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 20.575 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 1f716857..b3944d15 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 0.815 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.931 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 e71caaec..af826549 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 0.163 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.174 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 da9655ef..fc539258 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 0.631 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 0.652 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 20710ad7..5469f6ce 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>00:25.774</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p> +<p><strong>00:27.291</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_parallel_betweenness.html#sphx-glr-auto-examples-algorithms-plot-parallel-betweenness-py"><span class="std std-ref">Parallel Betweenness</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_parallel_betweenness.py</span></code>)</p></td> -<td><p>00:19.655</p></td> +<td><p>00:20.575</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>00:03.365</p></td> +<td><p>00:03.814</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>00:00.815</p></td> +<td><p>00:00.931</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>00:00.631</p></td> +<td><p>00:00.652</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>00:00.390</p></td> +<td><p>00:00.366</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>00:00.229</p></td> +<td><p>00:00.242</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>00:00.206</p></td> +<td><p>00:00.208</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>00:00.163</p></td> +<td><p>00:00.174</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>00:00.100</p></td> +<td><p>00:00.105</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_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>00:00.069</p></td> +<td><p>00:00.071</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>00:00.058</p></td> +<td><p>00:00.060</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 7aaa7277..2d8d36fd 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 0.083 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.090 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-basic-plot-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 f16d4e4c..877a8762 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 0.060 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.061 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 40d63254..d52ab919 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 0.323 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 0.318 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 5b31203a..69c56292 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>00:00.465</strong> total execution time for <strong>auto_examples_basic</strong> files:</p> +<p><strong>00:00.469</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>00:00.323</p></td> +<td><p>00:00.318</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>00:00.083</p></td> +<td><p>00:00.090</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>00:00.060</p></td> +<td><p>00:00.061</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 70c4ac51..420d7356 100644 --- a/auto_examples/drawing/plot_center_node.html +++ b/auto_examples/drawing/plot_center_node.html @@ -530,7 +530,7 @@ to download the full example code</p> <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 0.063 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.066 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 7a8e1139..586e38c8 100644 --- a/auto_examples/drawing/plot_chess_masters.html +++ b/auto_examples/drawing/plot_chess_masters.html @@ -536,7 +536,7 @@ to black and contains selected game info.</p> <img src="../../_images/sphx_glr_plot_chess_masters_001.png" srcset="../../_images/sphx_glr_plot_chess_masters_001.png" alt="World Chess Championship Games: 1886 - 1985" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Loaded 685 chess games between 25 players Note the disconnected component consisting of: -['Korchnoi, Viktor L', 'Karpov, Anatoly', 'Kasparov, Gary'] +['Kasparov, Gary', 'Karpov, Anatoly', 'Korchnoi, Viktor L'] From a total of 237 different openings, the following games used the Sicilian opening @@ -702,7 +702,7 @@ findfont: Font family 'Helvetica' not found. <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.370 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.384 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 f932a216..c399eff7 100644 --- a/auto_examples/drawing/plot_custom_node_icons.html +++ b/auto_examples/drawing/plot_custom_node_icons.html @@ -585,7 +585,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.264 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.279 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-custom-node-icons-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/b580b9776494e714c1fb1880f03524a8/plot_custom_node_icons.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_custom_node_icons.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_degree.html b/auto_examples/drawing/plot_degree.html index 866a2860..0961e0be 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 0.253 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.266 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-degree-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/70eaef0d99343cf8d3d6e70c803ad5a8/plot_degree.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_degree.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_directed.html b/auto_examples/drawing/plot_directed.html index a1b865fe..741c697d 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 0.200 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.221 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 612cafde..4f0b7e62 100644 --- a/auto_examples/drawing/plot_edge_colormap.html +++ b/auto_examples/drawing/plot_edge_colormap.html @@ -534,7 +534,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.059 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.062 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-edge-colormap-py"> <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 57c1b3dc..fda8da3f 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 0.094 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.097 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 865df2e2..dd96864c 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 0.614 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.630 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 97afe41c..65fa6559 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 0.304 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.327 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 a88ea746..245a380a 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 0.070 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-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 edfa72ac..ec71c1be 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 0.096 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.098 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 fe102824..a2fcec41 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 0.179 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.189 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 aa2b3c29..f8074c0e 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 0.074 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.076 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 7111f1ab..1bd41cdd 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 0.049 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.051 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 9aba3676..30212364 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 0.123 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.125 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 23c817eb..7b17d369 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 0.100 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.102 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 e3370279..624fb262 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 0.227 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.245 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-sampson-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/838bbb120e1c43a61657821eddf29c25/plot_sampson.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_sampson.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_selfloops.html b/auto_examples/drawing/plot_selfloops.html index 8cbfd887..4b115143 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 0.075 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.079 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 af558e58..f5a07323 100644 --- a/auto_examples/drawing/plot_simple_path.html +++ b/auto_examples/drawing/plot_simple_path.html @@ -526,7 +526,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.054 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.057 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-simple-path-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/2c281c05b18d8d3cf43a312fc3d67a3b/plot_simple_path.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_simple_path.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_spectral_grid.html b/auto_examples/drawing/plot_spectral_grid.html index 2c232a9f..07f590c3 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 0.215 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.223 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 30fb7240..705ad268 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 0.082 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.086 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 194858bf..423cc453 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 0.135 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.109 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 f61991f9..0ae4df28 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 0.078 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.084 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 ddcda98f..6d5a256b 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>00:03.776</strong> total execution time for <strong>auto_examples_drawing</strong> files:</p> +<p><strong>00:03.931</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>00:00.614</p></td> +<td><p>00:00.630</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>00:00.370</p></td> +<td><p>00:00.384</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>00:00.304</p></td> +<td><p>00:00.327</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>00:00.264</p></td> +<td><p>00:00.279</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_degree.html#sphx-glr-auto-examples-drawing-plot-degree-py"><span class="std std-ref">Degree Analysis</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_degree.py</span></code>)</p></td> -<td><p>00:00.253</p></td> +<td><p>00:00.266</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_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>00:00.227</p></td> +<td><p>00:00.245</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_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>00:00.215</p></td> +<td><p>00:00.223</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>00:00.200</p></td> +<td><p>00:00.221</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>00:00.179</p></td> +<td><p>00:00.189</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>00:00.135</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_rainbow_coloring.html#sphx-glr-auto-examples-drawing-plot-rainbow-coloring-py"><span class="std std-ref">Rainbow Coloring</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_rainbow_coloring.py</span></code>)</p></td> +<td><p>00:00.125</p></td> <td><p>0.0 MB</p></td> </tr> -<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> +<tr class="row-odd"><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>00:00.109</p></td> <td><p>0.0 MB</p></td> </tr> <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>00:00.102</p></td> <td><p>0.0 MB</p></td> </tr> <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>00:00.098</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_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>00:00.097</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>00:00.082</p></td> 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the script:</strong> ( 0 minutes 0.056 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.058 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-erdos-renyi-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/1dae7040b667b61c3253579b3b21fe83/plot_erdos_renyi.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_erdos_renyi.py</span></code></a></p> diff --git a/auto_examples/graph/plot_expected_degree_sequence.html b/auto_examples/graph/plot_expected_degree_sequence.html index dc5e3b44..704bc77c 100644 --- a/auto_examples/graph/plot_expected_degree_sequence.html +++ b/auto_examples/graph/plot_expected_degree_sequence.html @@ -535,51 +535,47 @@ degree (#nodes) **** 25 ( 0) 26 ( 0) 27 ( 0) -28 ( 1) * +28 ( 0) 29 ( 0) 30 ( 0) -31 ( 1) * +31 ( 0) 32 ( 1) * -33 ( 1) * -34 ( 2) ** -35 ( 2) ** +33 ( 2) ** +34 ( 0) +35 ( 1) * 36 ( 4) **** -37 ( 1) * -38 ( 7) ******* -39 (10) ********** -40 (12) ************ -41 (15) *************** -42 (15) *************** -43 (15) *************** -44 (19) ******************* -45 (22) ********************** -46 (19) ******************* -47 (30) ****************************** -48 (33) ********************************* -49 (26) ************************** -50 (24) ************************ -51 (31) ******************************* -52 (32) ******************************** -53 (30) ****************************** -54 (24) ************************ -55 (22) ********************** -56 (20) ******************** -57 (15) *************** -58 (13) ************* -59 (12) ************ -60 ( 8) ******** -61 ( 6) ****** +37 ( 5) ***** +38 ( 5) ***** +39 ( 7) ******* +40 ( 9) ********* +41 (18) ****************** +42 (12) ************ +43 (10) ********** +44 (21) ********************* +45 (33) ********************************* +46 (27) *************************** +47 (28) **************************** +48 (38) ************************************** +49 (29) ***************************** +50 (36) ************************************ +51 (30) ****************************** +52 (30) ****************************** +53 (25) ************************* +54 (20) ******************** +55 (13) ************* +56 (18) ****************** +57 (17) ***************** +58 (15) *************** +59 (11) *********** +60 ( 9) ********* +61 ( 9) ********* 62 ( 6) ****** -63 ( 3) *** -64 ( 3) *** -65 ( 4) **** -66 ( 3) *** -67 ( 3) *** -68 ( 2) ** -69 ( 1) * -70 ( 1) * -71 ( 0) -72 ( 1) * +63 ( 1) * +64 ( 4) **** +65 ( 3) *** +66 ( 2) ** +67 ( 0) +68 ( 1) * </pre></div> </div> <div class="line-block"> @@ -599,7 +595,7 @@ 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 0.029 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.030 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 125a906e..b16a80db 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 0.276 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.542 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 47e203c7..31b1bd13 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 0.083 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.088 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 d2f0de3f..bb9e03cd 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 0.167 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.180 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 3524c0a7..d42c00cc 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 0.118 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.126 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 142b77ca..66a1df98 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 0.225 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.228 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 ad3cce88..34b5ecaf 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 0.994 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.054 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 d6f3c2e6..69b9ca4c 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 0.360 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.375 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 aba05039..a70bf79b 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>00:02.471</strong> total execution time for <strong>auto_examples_graph</strong> files:</p> +<p><strong>00:02.852</strong> total execution time for <strong>auto_examples_graph</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_triad_types.html#sphx-glr-auto-examples-graph-plot-triad-types-py"><span class="std std-ref">Triads</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_triad_types.py</span></code>)</p></td> -<td><p>00:00.994</p></td> +<td><p>00:01.054</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>00:00.360</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_football.html#sphx-glr-auto-examples-graph-plot-football-py"><span class="std std-ref">Football</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_football.py</span></code>)</p></td> +<td><p>00:00.542</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>00:00.276</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_words.html#sphx-glr-auto-examples-graph-plot-words-py"><span class="std std-ref">Words/Ladder Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_words.py</span></code>)</p></td> +<td><p>00:00.375</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>00:00.225</p></td> +<td><p>00:00.228</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>00:00.167</p></td> +<td><p>00:00.180</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>00:00.118</p></td> +<td><p>00:00.126</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>00:00.108</p></td> +<td><p>00:00.115</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="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>00:00.083</p></td> +<td><p>00:00.088</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>00:00.056</p></td> +<td><p>00:00.058</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_degree_sequence.html#sphx-glr-auto-examples-graph-plot-degree-sequence-py"><span class="std std-ref">Degree Sequence</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_degree_sequence.py</span></code>)</p></td> -<td><p>00:00.054</p></td> +<td><p>00:00.057</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>00:00.029</p></td> +<td><p>00:00.030</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 6489b35c..ec4c31c4 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 0.031 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.028 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 8d898868..bebe3865 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 0.024 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.026 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 f8ade20a..5daf25fd 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 0.067 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.068 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-grid-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/26e3cd745ae317a76a0df34cbf4999d8/plot_grid.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_grid.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/plot_mini_atlas.html b/auto_examples/graphviz_drawing/plot_mini_atlas.html index 386e69c7..21820239 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 0.075 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.079 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 41526b3e..4aeb056a 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>00:00.197</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> +<p><strong>00:00.201</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>00:00.075</p></td> +<td><p>00:00.079</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>00:00.067</p></td> +<td><p>00:00.068</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>00:00.031</p></td> +<td><p>00:00.028</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>00:00.024</p></td> +<td><p>00:00.026</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 178f94af..d3ac832e 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 3.584 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.703 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_decomposition.html b/auto_examples/graphviz_layout/plot_decomposition.html index 25c2f669..f7e2f015 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 0.279 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.295 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 ee47ed81..4b2ba59e 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 0.766 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.812 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 e166d291..3d70c4f0 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 0.332 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.334 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 69fd038a..e3bf5b59 100644 --- a/auto_examples/graphviz_layout/sg_execution_times.html +++ b/auto_examples/graphviz_layout/sg_execution_times.html @@ -463,23 +463,23 @@ <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>00:05.112</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> +<p><strong>00:05.294</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>00:03.584</p></td> +<td><p>00:03.703</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>00:00.766</p></td> +<td><p>00:00.812</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>00:00.332</p></td> +<td><p>00:00.334</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>00:00.279</p></td> +<td><p>00:00.295</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> diff --git a/auto_examples/subclass/plot_antigraph.html b/auto_examples/subclass/plot_antigraph.html index f9c381b4..6201cfff 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 0.087 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.090 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 06700e68..b3dd8913 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 0.054 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.057 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 0ccfc692..2023d65a 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>00:00.142</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p> +<p><strong>00:00.147</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>00:00.087</p></td> +<td><p>00:00.090</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>00:00.054</p></td> +<td><p>00:00.057</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/developer/index.html b/developer/index.html index 81b41316..61efe80e 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 25, 2022</p> +<dd class="field-even"><p>Dec 27, 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 25, 2022</p> +<dd class="field-even"><p>Dec 27, 2022</p> </dd> </dl> <p>NetworkX is a Python package for the creation, manipulation, and study diff --git a/reference/algorithms/generated/networkx.algorithms.isomorphism.could_be_isomorphic.html b/reference/algorithms/generated/networkx.algorithms.isomorphism.could_be_isomorphic.html index cc1d7ad7..70e80bb9 100644 --- a/reference/algorithms/generated/networkx.algorithms.isomorphism.could_be_isomorphic.html +++ b/reference/algorithms/generated/networkx.algorithms.isomorphism.could_be_isomorphic.html @@ -574,7 +574,10 @@ True does NOT guarantee isomorphism.</p> </dd> </dl> <p class="rubric">Notes</p> -<p>Checks for matching degree, triangle, and number of cliques sequences.</p> +<p>Checks for matching degree, triangle, and number of cliques sequences. +The triangle sequence contains the number of triangles each node is part of. +The clique sequence contains for each node the number of maximal cliques +involving that node.</p> </dd></dl> </section> diff --git a/reference/algorithms/generated/networkx.algorithms.isomorphism.fast_could_be_isomorphic.html b/reference/algorithms/generated/networkx.algorithms.isomorphism.fast_could_be_isomorphic.html index b6b171fa..20bdb4e4 100644 --- a/reference/algorithms/generated/networkx.algorithms.isomorphism.fast_could_be_isomorphic.html +++ b/reference/algorithms/generated/networkx.algorithms.isomorphism.fast_could_be_isomorphic.html @@ -574,7 +574,8 @@ </dd> </dl> <p class="rubric">Notes</p> -<p>Checks for matching degree and triangle sequences.</p> +<p>Checks for matching degree and triangle sequences. The triangle +sequence contains the number of triangles each node is part of.</p> </dd></dl> </section> diff --git a/reference/index.html b/reference/index.html index 136dd738..af120adb 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 25, 2022</p> +<dd class="field-even"><p>Dec 27, 2022</p> </dd> </dl> </div></blockquote> diff --git a/reference/introduction-7.hires.png b/reference/introduction-7.hires.png Binary files differindex 23606e4e..7ee912b5 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 515fbfc8..6e401c8a 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 95eebca3..a8f22d18 100644 --- a/reference/introduction-7.png +++ b/reference/introduction-7.png diff --git a/reference/introduction.ipynb b/reference/introduction.ipynb index c3b2199b..8982e097 100644 --- a/reference/introduction.ipynb +++ b/reference/introduction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "3edd04de", + "id": "9d5533ae", "metadata": {}, "source": [ "## Introduction\n", @@ -34,7 +34,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d6e34ab8", + "id": "76e4aa8f", "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "543d8846", + "id": "bfd347b0", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1834c1ed", + "id": "7a7f6002", "metadata": {}, "outputs": [], "source": [ @@ -94,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "c9a9f991", + "id": "ab96731c", "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": "fac78201", + "id": "22ef1e39", "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "40b2c17c", + "id": "1ee1e93a", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -214,7 +214,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3b240003", + "id": "8ff42fba", "metadata": {}, "outputs": [], "source": [ @@ -225,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "d7f5a9c8", + "id": "3ebfa9f2", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -234,7 +234,7 @@ { "cell_type": "code", "execution_count": null, - "id": "09b6b0e0", + "id": "e03def6b", "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "9fb6eed6", + "id": "17934fe1", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -311,7 +311,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2b6e1953", + "id": "7d1c8923", "metadata": {}, "outputs": [], "source": [ @@ -323,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "6dbe7c93", + "id": "89d24b8a", "metadata": {}, "source": [ "# Drawing\n", @@ -344,7 +344,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5fed2cc7", + "id": "1ba5345b", "metadata": {}, "outputs": [], "source": [ @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "b46d02fc", + "id": "646a54d7", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -398,7 +398,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2e684aa6", + "id": "7e22369f", "metadata": {}, "outputs": [], "source": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "4f6b2e3d", + "id": "b526c8f3", "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": "67501e28", + "id": "bde32d2a", "metadata": {}, "outputs": [], "source": [ diff --git a/reference/introduction_full.ipynb b/reference/introduction_full.ipynb index ea5bfe20..623ebc6f 100644 --- a/reference/introduction_full.ipynb +++ b/reference/introduction_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "3edd04de", + "id": "9d5533ae", "metadata": {}, "source": [ "## Introduction\n", @@ -34,13 +34,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "d6e34ab8", + "id": "76e4aa8f", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:45.908976Z", + "iopub.status.busy": "2022-12-27T10:11:45.908396Z", + "iopub.status.idle": "2022-12-27T10:11:45.981875Z", + "shell.execute_reply": "2022-12-27T10:11:45.981223Z" } }, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "markdown", - "id": "543d8846", + "id": "bfd347b0", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -89,13 +89,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "1834c1ed", + "id": "7a7f6002", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:45.985479Z", + "iopub.status.busy": "2022-12-27T10:11:45.985259Z", + "iopub.status.idle": "2022-12-27T10:11:45.988574Z", + "shell.execute_reply": "2022-12-27T10:11:45.988041Z" } }, "outputs": [], @@ -108,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "c9a9f991", + "id": "ab96731c", "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": "fac78201", + "id": "22ef1e39", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:45.991227Z", + "iopub.status.busy": "2022-12-27T10:11:45.991018Z", + "iopub.status.idle": "2022-12-27T10:11:45.994342Z", + "shell.execute_reply": "2022-12-27T10:11:45.993719Z" } }, "outputs": [], @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "40b2c17c", + "id": "1ee1e93a", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -235,13 +235,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "3b240003", + "id": "8ff42fba", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:45.997271Z", + "iopub.status.busy": "2022-12-27T10:11:45.997066Z", + "iopub.status.idle": "2022-12-27T10:11:46.000114Z", + "shell.execute_reply": "2022-12-27T10:11:45.999488Z" } }, "outputs": [], @@ -253,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "d7f5a9c8", + "id": "3ebfa9f2", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -262,13 +262,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "09b6b0e0", + "id": "e03def6b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:46.003106Z", + "iopub.status.busy": "2022-12-27T10:11:46.002901Z", + "iopub.status.idle": "2022-12-27T10:11:46.008311Z", + "shell.execute_reply": "2022-12-27T10:11:46.007368Z" } }, "outputs": [], @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "9fb6eed6", + "id": "17934fe1", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -346,13 +346,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "2b6e1953", + "id": "7d1c8923", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:46.011509Z", + "iopub.status.busy": "2022-12-27T10:11:46.011111Z", + "iopub.status.idle": "2022-12-27T10:11:46.017660Z", + "shell.execute_reply": "2022-12-27T10:11:46.017053Z" } }, "outputs": [ @@ -373,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "6dbe7c93", + "id": "89d24b8a", "metadata": {}, "source": [ "# Drawing\n", @@ -394,19 +394,19 @@ { "cell_type": "code", "execution_count": 7, - "id": "5fed2cc7", + "id": "1ba5345b", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:46.020632Z", + "iopub.status.busy": "2022-12-27T10:11:46.020289Z", + "iopub.status.idle": "2022-12-27T10:11:46.584477Z", + "shell.execute_reply": "2022-12-27T10:11:46.583674Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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/d94JO3bAU0+pkxA7dlTbG4vikzBQTBcuXKB79+7ceuutjBw5slhjSRgw1iUNHyU0w3WEuZ08CQ8/rE7xu/9+tX9Ap05GV2V+pUqprsDq1erE5YgItbhQXjaLR8JAMY0cOZJDhw6xaNEifH19izVWUTbHEdrxtbnmn4OrriPM67PPIDxcbcP72WfqND850rdwOnZUAeqhh9SWxg89BH/9ZXBRbkxelYph/fr1TJ06lfHjxxMeHq7ZuNIZMEbNCoHoHccs/1xHeKdTp6BbN3jkEWjbVr2Zde1qdFXuq2xZ1RVYuRJ+/FF1CT75xOiq3JOEgSI6d+4cPXv2pE2bNgwZMkSTMWWawFiBfjZCdN4hMKRCAIF+sr2HN/rmG/Vm9c03ap3Ap5+qE/tE8T30kApWUVHwf/8Hjz8OKSlGV+VeJAwU0QsvvEBycjILFiy44fa2BSXTBMaz1wvGx6rP98HHasFeV179vc25c9C3L9x3n9pMZ9cu1R2Qf+7aqlRJdQWWLYM1a1Tw+vJLo6tyHxIGiuDLL79k7ty5TJ06ldDQUM3Hl86Acbo1D9F0G+IrZWY5eaqFHFZkemlpaj/c+Hj1a1pakYf6/nuIjITly+GDD+Drr+GmmzSrVFzFYlGPZu7apbZR7twZeveGs2eLMaiG94OZSRgopOTkZPr27ct9991HTEyMpmPLNIHx6lQuRZuwipp3B3ysFtqEVZStiM1qzx4YPBjCwqB0abXdXYsW6tfSpdXvDx6sPq8A0tPVp999N9SqpR4b7NNHugGuUq2a6grMnaumYyIj4bvvCjGAxveDO5AwUAhOp5P+/ftz+fJlPvjgA83b+hIGzGFCl0hsGocBm9XChC6Rmo4pNHD4MHTooJb2z5yptra7+t+f06l+f+ZM9XkdOqivu4YtW9R0QGwsTJsG//sf1Kyp659C5MNiUV2BnTuhTh1o3x4GDoTz19vmQ4f7wV1IGCiEZcuW8dlnnzFr1iyqVq1qdDlCJ9XLB/B6Z+2eDgEY2zlcji82m9hYaNBAbWsH6vzc68n+eFyc+rrY2FwfzshQBwu1aQMVKqiO8uDBYJVXWUPVqAFr16qDn+bPV7sXbtqUzydqfD+4G7lNC+j3339n0KBBPPnkkzz66KO6XEM6A+bxeNMQhnaoCxT/+/FSh3o81lTWCpjK+PFqVd/Fizd+0b+aw6G+rm9fNQ7/HvU7dSpMmKDebOrW1aFuUSRWKwwapHYvrFxZPdb50kvq2whofj+4IwkDBeB0OunduzcBAQHMmDFDt+tIGDCX7o2DyfpxCVano9BrCHysFvxsViZ1jWSgPUynCkWRxMZCMXcLzTFyJKsenEvz5uDrCz//DC+/DBo9YCQ0VqcObNgAkybBu+9C48ZwZKS29wNz52ozlotJGCiAmTNnsnbtWubNm0c52SbMawwZMoQz277mw6fCaVWrAsANQ0H2x1vVqsB3z98pHQGzOXwYnn1Ws+GcQPtVg3hzwGF+/FEtVBPm5uOjugK//AK1rYepPP5Zbc8kGTTILdcQyO4nN5CQkMDQoUMZMGAAHTt21PVa0hkwj1WrVrFgwQLmzZtHi8g6tIiEhBOpLI1PIu7ASZJS0nO9gFhQGwrZ6wbzVIsQeWrArPr1K3wb+DosgL+Pg+f39QPfNZqNK/QXHg5fVO2Hc68Di5bHhTgc6j5b4173g8Up7zzX5HA4aNu2LSdPnmTHjh0EBuq7jezZs2cpW7YsH3/8sW7rEsSN/f3330RERNC8eXO++OKLfJ8aOZ/h4EjKefr0609w+fIsmzNNdhY0uz171DuAnuPXr6/f+AbIXguxbZtqqXsUuR9ykWmC65g8eTLx8fEsWrRI9yAAsgOhGTidTgYMGEBmZiZz5sy55vck0M9GeLUyVHCmwpnfJQi4g1mzwKbT98lmU4+aCfch90Mu8gp2DTt27GD06NEMGzaMVq1aufTa0qwxzocffshnn33Gxx9/TJUqVW74+YGBgaTIJuju4ZtvNJ0iyMXhgG+/1WdsoQ+5H3KRzkA+MjIy6N69O/Xr12fMmDEuu66sGTDWn3/+ycCBA3n88ccLPE0TGBjI+evuYiJMITUVDh3S9xoHD3rsVrUeR+6HPCQM5GP06NHs27ePxYsX4+fn57LrShgwjtPpJCYmBn9/f957770Cf52EATeR305yWnM6ITFR32sIbcj9kIdME1xl8+bNvPnmm0yYMIFbb73V6HKEi3zwwQesXr2ar7/+mvLlyxf46yQMuImMDM+6jigeuR/ykM7AFdLS0ujRowctW7bkpZdecvn1pTNgjEOHDvHCCy/Qp08f7r333kJ9rYQBN+GqDp8LO4miGOR+yEM6A1d46aWXOH78OKtXr8bHgC3EJAy4XlZWFr169aJSpUq8/fbbhf56CQNuIixMnVyj578ti0VdR5if3A95SGfgH6tXr2bWrFlMmTKFMDf6BorimTZtGhs2bGD+/PmUKlX4jYKyw4AEOJMLClJnCeupdm11HWF+cj/kIWEAOHXqFDExMXTs2JH+/fsbVod0Blxr7969vPLKKwwZMoSoqKgijREYGIjT6eRizoknwrTuvVff58o7ddJnbKEPuR9ykTAADBo0iPT0dObOnWvoxj8SBlzn8uXLREdHU7NmTSZMmFDkcbI3o5KpAjfQv7++z5UPGKDP2EIfcj/k4vVh4OOPP+bDDz/kvffe46abbjK6HOEi//nPf9i+fTuLFi2iZMmSRR5HwoAbadAA2rfX/KdBBzbSW7d3q61nBbrdD9hsalw3ux+8OgwcP36cAQMG8Oijj/LEE08YXY50Blzkl19+YezYsbzyyis0a9asWGNJGHAzs2dr+uLvBBwWG022zeb99yFLywNvhP40vh8ANd7s2dqO6QJeGwacTid9+vTB19eX999/3xTnAkgY0F9GRgbR0dFERETw2muvFXs8CQNuJjQUpk/XbDgLwPQZRPUKZeBA6NgRkpI0G17oTeP7AYAZM9S4bsZrw0BsbCzffPMNsbGxVKxY0ehyhIuMGjWKAwcOsGjRInx9fYs9noQBN9SnD4wbp81Y48fjPzCG999XJ9bu2weRkbBggf4b3AmNaHw/EBOjzVgu5pVh4MpNZu677z6jy8khnQF9bd68mcmTJzN27FgiIyM1GVPCgJt69VX44APw9y98m9hmU18XGwsjRuT8dvv2sHMndO0KvXrBgw/CX39pXLfQhw73g7vxujCQmZlJz549qVixYpE2mdGThAH9nD9/nh49etCiRQtNd5eUMODG+vRRZ87b7er/3+hNIPvjdrv6unx+AixbFubPhy++gJ9+gvBw+OgjbcsWOtHhfnAnXhcGpk6dyqZNm1i4cGGRNpnRk4QB/QwbNow///yThQsXarq7pIQBNxcaqvr7u3erR8Gyd6a7UvZOcgMGqBf9NWtuOCfcuTPs2gXt2sHjj8Njj0Fyso5/DqGNAtwPTiwctISR2a/g94M78KrtiHft2sWrr77KCy+8QNu2bY0uR7jI2rVref/995k+fTp16tTRdGybzYavr6+EAXfXoAG8+67677Q09n6ZSM8nM1iwzI/6D4QVaSe5ihVVV6BrV3jmGYiIgDlzVFAQJned++F81TCa2oPY0g1autfTg9flNZ2BS5cuER0dTVhYGOO0WiyiMekMaO/MmTP07t2bu+++m2eeeUaXa8j5BB4mKIgL9W7jJ5pzod5txd5S9rHH1A+aTZuqdQS9esHZs9qUKlzgqvvhtjuCKFUK4uKMLkxbXhMG3njjDXbu3MnixYvx9/c3upx8SRjQ3nPPPce5c+eYN28eVqs+t7uEAXEjVarAqlVqPcGKFapLsHat0VWJorDZoE0bWLfO6Eq05RVhID4+ngkTJjBq1CgaN25sdDnCRT7//HMWLVrEtGnTCAkJ0e06EgZEQVgs0LOneuLgllugQwc1fZCWZnRlorDsdti8GS5dMroS7Xh8GEhPTyc6OpomTZrwyiuvGF3OdUlnQDt///03/fr144EHHqBHjx66XkvCgCiMkBD473/hvfdg4UJo2BA2bjS6KlEYdjukp6snRjyFx4eB4cOHk5SUxMKFC7HpdUKVxiQMFI/T6aR///5kZmYyZ84c3XeXlDAgCstqVV2BHTugWjW4804YOhQuXDC6MlEQt90GZcp41lSBR4eB//3vf0yfPp1JkyZxyy23GF2OcJFly5axYsUKZs2aRZUqVXS/noQBUVRhYeoNZfJktYtt48awdavRVYkb8fGBtm09axGhx4aBM2fO0LNnT+666y4GDRpkdDkFZrFYpDNQDH/88QeDBg3iiSee4JFHHnHJNYOCgiQMiCLz8YEXX4RfflEPLrRsCa+95lnz0Z7IboctWyAjw+hKtOGxYSB7Ffn8+fN1W0WuBwkDRZd9+FTJkiWZMWOGy64rnQGhhQYN1JvL6NHwn/9As2bw229GVyWuJSoKLl6E+HijK9GG+7xLFsLKlStZtGgR7777rq6ryPUiYaBo5syZw+rVq4mNjaV8+fIuu66EAaGVEiVUV+Cnn9RxyE2awMSJ4HAYXZm4WsOGUK6c50wVeFwYOHnyJP369eOhhx4iOjra6HIKzQxHKbujQ4cO8eKLL9K3b1/uvfdel15bwoDQWqNGau3A0KEwciS0bq1ORBTmYbWqhZ8SBkzI6XTy9NNPAzB79my3fGOVaYLCyz58qlKlSrz11lsuv76EAaEHPz+YMEE9z37mjAoI77yjOgbCHOx2+PFHNV3g7jwqDCxcuJAvvviCOXPmEBwcbHQ5RSJhoPCmTZvGpk2bWLBggSGHT0kYEHpq0QK2b4d+/eD55+Guu+DwYaOrEqDWDWRkwA8/GF1J8XlMGDh69CjPPfccPXr04KGHHjK6HOEie/bsYcSIEQwZMoQ777zTkBoCAwNJS0uTECd0ExCgugJxcXD0KERGqkOP5JYzVkQEVKjgGVMFHhEGsrKy6NWrF2XKlGHatGlGl1Ms0hkouMuXLxMdHU1oaCjjx483rI7AwEAyMzO5JM+CCZ1FRaknDLp1U52CTp3g99+Nrsp7Wa3qe+IJmw95RBiYPn06cXFxLFiwgDJlyhhdTrFIGCi4iRMn8uuvv7Jw4UJKlixpWB2BgYEAMlUgXKJUKZg9G779Vp1zEBEBixdLl8AoUVFq3UB6utGVFI/bh4F9+/YxfPhwBg8ezF133WV0OcJFtm3bxhtvvMErr7xCs2bNDK1FwoAwwj33wK5d8MADEB0NXbvCiRNGV+V97Ha4fFntEeHO3DoMXL58me7du1OjRg0mTpxodDmakM7AjV28eJEePXoQGRnJa6+9ZnQ5EgaEYcqVU12BFSvUUwcREfDpp0ZX5V0aNIBKldx/3YBbh4GJEyeyfft2Fi1aREBAgNHlaELCwI2NGjWKhIQEFi5ciK+vr9HlSBgQhuvSBXbvVvvlP/ooPPkknDpldFXewWLxjHUDbhsGstvEI0aMMLxNLFxn06ZNTJkyhbFjxxIZGWl0OYCEAWEOlSqprsDSpWo9QUQEfP210VV5B7td7RqZlmZ0JUXnlmHgwoULdO/enVtvvZWRI0caXY6mpDNwbWlpafTo0YMWLVowdOhQo8vJIWFAmIXForoCu3erY3bvvx/69IFz54yuzLPZ7WrL6M2bja6k6NwyDIwcOZJDhw6xaNEiU7SJtSRh4NqGDRvGX3/9xcKFC/Hx8TG6nBwSBoTZVKumugKxsfDRR2pfgu+/N7oqz1WvHlSp4t5TBW4XBtavX8/UqVMZP3484eHhRpejCwkDea1Zs4aZM2fy5ptvUqdOHaPLyUXCgDAjiwViYtTjh7Vrw913w7PPgtym2steN+DOiwjdKgycO3eOnj170qZNG4YMGWJ0Obpwx/MU9HbmzBl69+5Nu3btGDBggNHl5OHr64vNZpMwIEypZk347jt4912YO1dNH7j7Y3BmZLfDzz9DaqrRlRSNW4WBF154geTkZBYsWGCqNrGWZJogr8GDB5Oamsq8efOwWs15y8r5BMLMrFbVFfj1V6hYEdq0gZdf9owDdswiKgoyM2HTJqMrKRpzvrLm48svv2Tu3LlMnTqV0NBQo8vRjYSB3FauXMnixYt59913qV69utHlXJOEAeEO6tZVb1YTJ6qzDpo0gW3bjK7KM9Spo9ZquOtUgVuEgeTkZPr27ct9991HTEyM0eUIFzl58iT9+vWjc+fOREdHG13OdUkYEO7CxweGDVMhwNdXnYo4ZozaRU8UncWipgokDOjE6XTSv39/HA4HsbGxHj+nLp0Bxel0MmDAAJxOJ3PmzDH9913CgHA3EREQHw+vvgrjxqlQsGuX0VW5t6go+OUXOHvW6EoKz/RhYNmyZXz22WfMnDmTKlWqGF2O7iQMKEuXLmXFihXMnDmTypUrG13ODUkYEO6oRAnVFYiPV+sHbr8d3nxTzX2LwrPbISsLNm40upLCM3UY+P333xk0aBBPPvkkjz76qNHlCBe58vv+yCOPGF1OgUgYEO7s9tvVtMGQITB8uFpgmJBgdFXup1YtqF7dPacKTBsGnE4nvXv3JiAggBkzZhhdjst4e2fA6XQSExNDYGCgW33fJQwId+fvD5MmqZ9q//4bGjaE5cuNrsq9uPM5BaYNAzNnzmTt2rXMmzePcuXKGV2Oy3h7GJg9ezZr1qwhNjbWrb7vEgaEp2jdWj2CGBMDkyer3zt+3NCS3IrdDtu3w+nTRldSOKYMAwkJCbz00ksMGDCAjh07Gl2Oy3lrGDh48CBDhw7l6aefplOnTkaXUygSBoQnCQyE6dNh5kz1///v/9SGRV760lQodrv6e9qwwehKCsflYeB8hoPdf55le9Jpdv95lvMZjlwfdzgc9OjRg6pVqzI5O5Z6EbOvmtdLZmYmPXv2JDg4mClTphhdTqFJGBCeKPtA2Pbt1YFH998Pf/5pbE1mV7Mm1KjhfusGbK64SMKJVJbGJxG3/yRJp9K5MlxagJDyAdjrBdOteQifzptBfHw8GzduzNnz3Zt46zTBO++8w+bNm1m3bh2lSpUyupxCkzAgPNmoUdC3rwoEEREwYwY88YSaIxd52e3ut25A1zBw7FQ6I1buZGNiMj5WC5lZed/knMDRU+ksjj/Kgh+OcPFIJs8Me41WrVrpWZqpeVsY2L17N6+++irPP/88bdu2NbqcIpEwIDzdffepfQiefRa6dYMVK9Q0QqVKRldmPnY7LFgAKSlQoYLR1RSMbtMEy7cm0W7qerYcSgHINwhcKfvj/iG3ssbWnOVbk/QqzdS8bZrg8uXLREdHU6tWLcaNG2d0OUUmYUB4gwoVYNky+Phj9ZNveDh8/rnRVZlPVJT6df16Q8soFF3CwIy4BIav2EmGI+uGISBvRT5kOLIYvmInM+K870FXb5smmDBhAjt27GDhwoWULFnS6HKKLDAwkEuXLuFwOG78yUK4uUcfhd27oVUr6NIFoqPdb/W8nkJC1J4D7jRVoHkYWL41iSlrDmgy1pQ1B/jIyzoE3hQGtm3bxrhx4xgxYgRNmzY1upxiyV7fIt0B4S0qV4aVK2HRIli1CiIj4b//Nboq83C3cwo0DQPHTqUzetVuLYdk1KrdHDuVrumYwngXL14kOjqayMhIRo4caXQ5xSZhQHgjiwW6d1drCcLD4Z57oF8/SE01ujLj2e3q7+Xvv42upGA0DQMjVu7EUdhpgRtwZDkZsXKnpmOambd0Bl577TUSExNZtGgRvr6+RpdTbBIGhDe7+WZYvRpmzYKlS+HWW91rvlwP7rZuQLMwkHAilY2JyYVfI3ADmVlONiYmk3jSO6KmN4SBjRs38tZbb/HGG28QERFhdDmakDAgvJ3ForoCv/2m5syjouD55+HCBaMrM8ZNN0GdOu4zVaBZGFgan4SPVZ+V8D5WC0t+9J61A54cBtLS0ujZsyctW7bkxRdfNLoczUgYEEKpVUu9AU6dqjoFjRqpUxG9kTutG9AsDMTtP6l5VyBbZpaTuAMndRnbbDz90cKXXnqJv/76i4ULF+Lj42N0OZqRMCDEv6xWdQLi9u1Qpox66mDECMjIMLoy14qKgr174cQJoyu5MU3CQFqGgySdF/klpaTn2brYE3nyNMF///tfZs2axeTJkwkLCzO6HE1JGBAir1tugc2b4Y03YMoUaNpUHYLkLbLXDbjDI4aahIGjKefR++3LCRxJ8Y4XWk8MA6dPnyYmJoZ27drRv39/o8vRnIQBIfJns6muwNatqmPQtCmMGwfesCVH1aoqELnDVIEmYeCSI0uLYUxzHSN56jTB4MGDSUtLY968eVitpjwss1j8/f2xWCwSBoS4hoYN4aefYPhwGDNGTR3s3Wt0VfqLivKizoCvzTUv7r2iu/PEE08watQoFi9eTHx8PKdOnXLJtV3FE6cJVqxYwZIlS3j33XepXr260eXowmKxyJbEQtyAr6+aMtiyRe1F0KgRvPUWZGYaXZl+7HbYv9/8pz1qclBRzQqBWEDnqQInt4XdxOGEfaxfv57jx4/nfKR8+fLUqVOHunXrUqdOnVz/K126tK5V6cGTwsDJkyfp378/Dz74IN27dze6HF1JGBCiYJo1g19+gZEj4aWX1PkGCxZA7dpGV6a9K9cNPPmkkZVcnyZhINDPRkj5AI7quIiwRoVAFkycnfP/U1NTSUxMJCEhIed/Bw4c4NtvvyU5OTnn8ypXrpwrHGQHhrCwMAICAnSrt6g8aZrA6XTSr18/nE4ns2fP9qg/W34kDAhRcCVLqq7AQw9Bz55qo6IpU6B/f886Gjk4GBo0UOsGPD4MANjrBbM4/qgujxf6WC3Y6wbn+r1SpUrRqFEjGjVqlOfzz5w5kxMOsoPCrl27WLFiBWfPns35vJtvvjnfoFCrVi38/Pw0/3MUhLtME5zPcHAk5TyXHFn42qzUrBBIoF/u22nJkiV8/vnnfPrpp1SuXNmgSl1HwoAQhdemDezYAcOGwTPPqPMO5s4FT5pRtNvNf26DZmGgW/MQFvxwRKvhcsnMcvJUi5ACf37ZsmVp2rRpnsNvnE4nycnJeboJP/30E0uXLs15IbdarYSEhOQ77VCzZk1KlCih6Z/vamYNAwknUlkan0Tc/pMknUrPNS1kAULKB2CvF0y35iH4XzrDs88+S7du3Xj44YeNKtmlJAwIUTRBQfD+++oExN69ISICpk2DHj08o0tgt8N778Hvv6utm81IszBQp3Ip2oRVZMuhFE27Az5WC61qVSAsuFSxx7JYLFSqVIlKlSrRqlWrXB9zOp389ddfuboJCQkJrFu3jtjYWDL+2S3DZrMRGhqa7xqF6tWrF3sjHTO20o+dSmfEyp1sTEzGx2rJ9/vrBI6eSmdx/FEW/HCEgLNHCawcwvTp011fsEEkDAhRPO3bw86dahvjXr1gxQqYMweqVDG6suK58071a1ycOtjJjDQLAwATukTSbup6TcOAzWphQpdIzca7FovFQtWqValatSp3Zn/n/pGVlcXvv/+eZ+rh22+/ZcaMGTln2Pv5+VG7du18px6qVatWoDd6s00TLN+axOhVu3MOoLrR9zb74+eDbiLgkYn8NzGVx5uW071OM5AwIETxlS0L8+erLsHTT6vTEN9/Hx57zOjKiq5iRXXE87p1XhIGqpcP4PXO4Qxfod0pg2M7h1O9vLEL/bKnDUJCQrj77rtzfczhcHD06NFc0w4JCQmsWLGCI0eOkJWl9kYICAggLCwsTzehbt26VKpUKScomCkMzIhLYMqaA0X6WouPjctZMHzFTpLTMhhkr6NxdeYTGBiY6ykXIUTRde6s9iIYOBAef1x1Cd57T72xuiO7Hb780ugqrk3TMADweNMQktMyivwmcqWXOtTjsaYFXytgBJvNRu3atalduzb33HNPro9dunSJw4cP55l6WLJkCceOHcv5vNKlS+eEg9OnT7Nr1y7i4+OpU6cO5cuXd/UfCVAdAS2+hwBT1hygUpCf6b+XxRUYGEhaWprRZQjhMSpWhI8+gq5d1eLCiAg1bdC5s9GVFV5UFLz7Lhw9CjVqGF1NXpqHAYBB9jpUDPLLaS8XZtrAx2rBZrUwtnO42795+Pr6Uq9ePerVq5fnYxcuXODgwYN5ph7OnTvHV199xVdffQVAhQoV8p12qFOnDqVKFX8dRX6OnUpn9Krdmo45atVuWtWuaHiXR08yTSCEPh57TM279+0LDz6oHkV85x11CJK7uPNOtRhy3Tq1MNJsdAkDoDoErWtXvOHCs2zZH29VqwITukR69JsGQMmSJYmIiCAiIiLX79epU4f777+f6OjoPFMP+e2hkN+0Q+3atYu1h8KIlTtz1ghoxZHlZMTKnSyOaa7puGYiYUAI/VSpAqtWwcKF8Nxz8N13MG+eWnToDsqXV1syx8V5WRgAtYZgcUzzfx9JO3CSpJR8HkmrEIC9bjBPtQjR5KkBd1eiRIlr7qFw+vTpXFMOCQkJ/Pbbb3z22Wf57qFwdVi40R4KCSdS2ZiYfM2PF1VmlpONickknkz12O+xhAEh9GWxqK7AXXdBTAx06AADBsCbb6rHE80uKkqtfXA6zffIpK5hIFudyqUY0zmcMYRzPsPB0DGTWL32O75a9Xm+m9V4sxs9cVCuXDmaNWtGs2bNcv3+1XsoZHcT4uPjWbJkSa49FGrUqJHvtEPNmjVZGp90wy5OUflYLSz5MYkxncM1H9sMJAwI4RohIWoTn1mz1HbG//2v2s64TRujK7s+u11Nbxw5AqGhRleTm8vfhQP9bNwcBOnH9hBezY0mfFykqE8T3GgPhePHj+fZbCm/PRRu6h8LQfos183MchJ34CRj8NwwcPHiRTIzM4u934QQ4vqsVrWosEMHtSfBnXfCCy+og5BKljS6uvy1bas6AnFxEgYACAoKklXX16H1o4UWi4Vq1apRrVq1a+6hcODAAXbtT+SdYxU0vfbVklLSOZ/h8MhuUGBgIADp6em6Le4UQuQWFqYW5b3zDrz6Knz9NSxaBFdtQGsKZcuqkxrj4tROi2ZiyMHygYGBpKen5zyDL/7l6h0Is/dQaNeuHe27PKH7RJYTOJLima30oH8mLWWqQAjX8vGBF19UJyEGBUHLlvDaa3DpktGV5WW3q/Biku1kchgSBrJfNNPT9Tvl0F0ZuenQJYdrwpmrruNq2Z0BCQNCGKNBA9iyBUaPhv/8Rx2V/NtvRleVm92uzig4eNDoSnIzrDMA8qJ5LUaFAV+ba24HV13H1eS+FsJ4JUqorsBPP0FWFjRpAhMnwj+7xhvujjvUeoe4OKMryc3QMCDrBvIysjNQs0Igek9SWP65jieSMCCEeTRqBFu3wtChMHIktG4N+/YZXZXaKOn22yUMADK3ej1GnloY6GcjROfNnkIqBHjk4kGQMCCE2fj5wYQJsHkznDmjAsI776iOgZHMuG5AOgMmY/RBRfZ6wfhY9QkkPlYL9rrBuoxtBhIGhDCnFi1g+3bo108dj3zXXXD4sHH12O1w/Dgc0Ob4F01IZ8CEjAwD3ZqH6LLhEKh9Bp5q4d7nTVyPhAEhzCsgQHUF4uLUYUGRkerQIyNeblu3Vk9AmGmqQDoDJmPkNAGo3SLbhFXUvDvgY7XQJqyix25FDOq8CZAwIISZRUWpJwy6dVOdgk6d1Op+VypVSu2DsG6da697PdIZMBmjpwkAJnSJxKZxGLBZLUzoEqnpmGZjtVoJCAiQ+1oIkytVCmbPhm+/hZ071dHIixe7tktgtnUDhoQBPz8/rFarvGheg9FhoHr5AF7X+PyAsZ3DPf4kSpDzCYRwJ/fcA7t2wQMPQHQ0dO0KJ0645tpRUepae/e65no3YkgYsFgssiXxNRg9TZDt8aYhDO1QV5OxXupQj8eaeu5agStJGBDCvZQrp7oCK1aopw4iIuDTT/W/buvWak8Es0wVGLb7i7xo5s8M0wTZBtnr8J+ukfjZrIVeQ+BjteBnszKpayQD7WE6VWg+cl8L4Z66dIHdu9VhQo8+Ck8+CadO6Xe9wEC1Q6JZFhEaGgakM5A/s4QBUB2C756/k1a11AFGNwoF2R9vVasC3z1/p9d0BLJJGBDCfVWqpLoCS5eq9QQREergI71ERanOgNH7HoBBpxaCWkQoL5p5mWWa4ErVywewOKY5CSdSWRqfRNyBkySlpHNlZLGgNhSy1w3mqRYhHv3UwPVIGBDCvVksqisQFQV9+sD998ODD+pzLbsdxo+HPXtU8DCSYWFAOgP5M9M0wdXqVC7FmM7hjCGc8xkO3pm7lLHjJrDtp3hCKwV57M6ChSFhQAjPUK2a6grMmwfPPqt+76efoHFj7a7RsiX4+qqpAqPDgGHTBNIZuDazhoErBfrZqOqfyaXjB6hftZQEgX9IGBDCc1gsEBMDH3+s/v+AASoYaPVPPCAAmjc3x7oBWTNgMmbuDFwte0rDXep1BQkDQnieatXUry+9BHPnwm23qaOStWC3w/r1xq8bkM6AyZhxzcC1WK3q9pEw8C8JA0J4rscfh19/hYoVoU0bePlluHixeGPa7eqphZ07NSmxyOTRQpNxx85AltGR1kTkvhbCs9WtC5s2wcSJ6qyDJk1g27aij9eihTpd0eipAkM7AzJNkD93CwPuUq8rSBgQwvP5+MCwYSoE+PqqN/QxY+Dy5cKP5e+vFhJ6bRiQF838udM0gYSBvOS+1klamurPxserX+UHCWECERHqlnz1VRg3ToWCXbsKP47dDhs2QOZZ4+5zWUBoMu44TeAu9bpCYGAg6enp8neihT17YPBgCAuD0qWhUSP1atuokfr/YWHq43v2GF2p8GIlSqiuQHy8Wj9w++3w5puQmVnAAfbsIWbHYLaeCcNazrj73PAFhPKimZe7/J1IGMgrMDAQp9PJhQsXjC7FfR0+DB06QHg4zJwJBw/mPdrN6VS/P3Om+rwOHdTXCWGQ229X0wZDhsDw4WqBYULCdb7givu82hczCeMgFgPvc0M7A5mZmWRkZBhVginJNIF7CwwMBOR47iKLjYUGDf6dQHU4rv/52R+Pi1NfFxurb31CXIe/P0yaBBs3wt9/Q8OGMH16Po8NXnWfWzKNv88N7QyAvGheTaYJ3JuEgWIYPx769lW91huFgKs5HOrr+vZV4whhoNat1ZR/TIzq8LdrB0eP/vNBk97nhnYGAFk3kA93eXOVMJCXhIEiio2FkSO1GWvkSLUzjBAGCgxUXYHvvlOd/shI2NjDvPe5dAZMRjoD7k3CQBEcPvzv5u9aGTRI1hAIU7j7brWh0DOdDtNk0bNo+mqp4X1ueGdAXjRzc6c1A7IDYV5yXxdBv36Fb5feiMOhxhXCBEqXhv+c7oef1YGmr/Aa3ueGdwZkmiAvd3lzlc5AXhIGCmnPHli7Vp8wsHYt7N2r7bhCFMU/97k1y7z3uXQGTEamCdyb3NeFNGsW2HQ68dJmU49kCWE0N7jPpTNgMu40TSBnE+QlYaCQvvlG+65ANocDvv1Wn7GFKAw3uM8NCwMBAQGAvGheTToD7s3Hxwc/Pz+5rwsiNRUOHdL3GgcPytbFwlhucp/r1Le4MavVSsmSJaUzkA93eXOVMJA/OZ+ggPLbWVBrTid7v0zkQr3bCvVl2VOw3r7kQP4elOL8PZTcf5D6LrjPSUyE224r8hCGhQH4d0ti8S93nCaQMJCbhIECctHuoz2fzOCnIn7tU09pWorbkr8HpSh/D83IIF77UvIq5r8nQ8OAHFaUl0wTuD8JAwXk5+eSyyxY5seFeoX7mr171Qv/kiVQv74+dbkD+XtQivP3UHK/HzypT125FPPfk3QGTMhd3lwlDORPQm4BhYWBxaLvVIHFQv0HwiCoaF9evz40bqxtSe5I/h6UIv091HXNfU5YWLGGMGwBIchPUPmRzoD7k/u6gIKCoFYtfa9Ru7a6jhBGcZP73NAwEBQUJD9BXUXWDLg/CQOFcO+9+j5/3amTPmMLURhucJ9LZ8CE3OXNVbYjzp/c14XQv7++z18PGKDP2EIUhhvc59IZMBmZJnB/EgYKoUEDaN9e85+aMi02HHe19+5Vb8I8dLrPsdnUuBrc59IZMBmZJnB/cl8X0uzZmr5IOoFLTht3J8xm40bNhhWieDS+zwE13uzZmgxleBiQzkBe7vLmKtsR50/CQCGFhqqD3zViAc5NmEFmSCh33glDh8KFC5oNL0TRaHyfAzBjhhpXA4ZPE8iLZm4yTeD+JAwUQZ8+MG6cNmONH0/lV2JYvx7efFO9XjZuDFu3ajO8EEWm8X1OTIw2YyGdAdORaQL3J2GgiF59FT74APz9C99OtdnU18XGwogRAPj4qK7AL79AYCC0bAmvvQaXLulQuxAFpfF9rhXpDJiQu7y5ShjIX3YYkL+XIujTR539brer/3+jF8vsj9vt6uvy+UmpQQP44QcYPRr+8x9o1gx++03juoUoDB3u8+IyvDOQkZGBQ69HLtyQTBO4v8DAQLKysshw0d77Hic0FNasgd271SNT2TsVXsGJhYPWMJz9B6gXxzVrrjt3WqKE6gr89BNkZkKTJjBxon5PewlxQwW4z3N2FhxQsPu8OAzfjhjUMcZlypQxshTTkGkC9xcYGAio+9rf39/gatxYgwbw7rvqv9PS1KlsGRng58em42G0vTeIX/tAw0I8VdWoEfz8M4wZAyNHwuefw8KFcMstevwBhCiA69znhIW5bAdNwzsDgEwVXEE6A+5P7msdBAWp41mbN4fbbqOpPQg/P4iLK/xQfn6qK7B5M5w5owLCO++APBQjDHfVfe7KrbQNXzMAyCLCq7jLm6vsQJg/CQP68/eHVq2KFgaytWgB27dDv37w/PNw111w+LB2NQrhTqQzYDLSGXB/cl+7RlQUbNig1gAUVUCA6gp8/z0cOQKRkTBnjr4HzAlhRqYIA9IZ+JesGXB/EgZcw25Xbf4dO7QZa+dOePJJ1Sl49tnijymEOzHFNIG8aObmLm+uEgbyJ2HANZo1g5IlizdVcKVSpVRX4Jtv1BougK+/li6B8A7SGTAZd5wmkO2Ic5Mw4Bp+fmrdwLp12o7bqRN8/LH671GjoGtXOHFC22sIYTamCAPyoqmcz3BwsWRFzpesxO4/z3I+w9wPQUtnIH9yX7uO3a7WDWi9X0Dp0urXyZPVUwcREfDpp9peQwgzMXSfAV9fX0qUKOHVL5oJJ1JZGp9E3P6TJJ1Kxxn2KAD3Td+EBQgpH4C9XjDdmodQp3IpY4u9ioSB/Pn6+mKz2bz6vnYVu13tF7B9OzRtqv34d90F0dFqz5dHH4UnnlBnHZQvr/21hDCSoZ0BUOsGvHGa4NipdLrPjaf9OxtYHH+Uo6fSufot1QkcPZXO4vijtH9nA93nxnPsVLoR5eZLwsC1yVbbrtGkiXoiQOupgisFB6uuwJIl8O23qkvw9df6XU8IIxgeBrzxUJflW5NoN3U9Ww6lAJCZdf030+yPbzmUQrup61m+NUn3GgtCwsC1eeN9bQRfX7jjDu0WEV6LxQLdusGuXWovmPvvV9vLnzun73WFcBXDw4C3dQZmxCUwfMVOMhxZNwwBV8vMcpLhyGL4ip3MiEvQqcKCkzBwbRIGXMduh40b4fJl/a91002qK/DBB/DRR2pfgu+/1/+6QujN8DDgTS+ay7cmMWXNAU3GmrLmAB8Z3CGQMHBt3nRfG81uV1u6//KLa65nsaiuwM6dUKsW3H232pdAvt3CnZkiDHhDZ+DYqXRGr9qt6ZijVu02dA2BbEd8bRIGXKdxY7WFu95TBVerWRP+9z+YNg3mzlXTB1u2uLYGIbRieBjwloVWI1buxFHIaYEbcWQ5GbFyp6ZjFoZ0Bq5NwoDrlCgBbdq4PgwAWK0weDD8+itUrKjqePlluHjR9bUIURyGhwFv6AwknEhlY2JyodcI3EhmlpONickknkzVdNyCkjBwbRIGXMtuh02bXLNuID9166rrT5igzjpo0gS2bTOmFiGKwvAw4A2dgaXxSfhY9TlzwMdqYcmPxqwdkDBwbRIGXCsqCtLTYetW42rw8VFdgZ9/Vk85tGgBY8YYF1CEKAzDw4A3vGjG7T+peVcgW2aWk7gDJ3UZ+0ZkO+Jr84b72kwaNVK7BhoxVXC1yEj48UcYMQLGjVOhYNcuo6sS4voMDwOe/mhhWoaDJJ0X+SWlpBuydbF0Bq5NwoBr2WzQtq05wgCozsDrr6tQcPEi3H47vPlm8Y5bFkJPhocBT3/RPJpyPs/OglpzAkdSXP93KGHg2jz9vjajqCi1mj8jw+hK/pW9duC552D4cLXAMMH4LUKEyMPwMODpnYFLDte00JN+/5NMF//YIWHg2iQMuJ7dDhcuwE8/GV1Jbv7+qiuwcSOcPAkNG8L06SCza8JMDD2oCNSLZnp6OllZWTnPrXsSX5tr/kwPPnAfljN/EBoaSlhYGLVr1871a82aNfH19dX0mhIGrk3CgOs1bAhly6qpgjZtjK4mr9atYccOtchw8GBYuRLmz4caNYyuTAiThAGn08mFCxdyjn71JDUrBGIB3acKlsdO548jh0hMTOTgwYP897//ZdasWVy6dAlQGwSFhITkhIMrg0KtWrWK9HcvYeDaAgMDuXz5MpcvX6ZEiRJGl+MVfHzUuoF162DUKKOryV9goDr1sEsX6NVLLTacOhV691Y7GwphFMPDQFBQEKDOfvfEMBDoZyOkfABHdVxEWKNCAF0fsOf5/czMTH7//XcOHjyYExISExP58ccfWbJkSa6fXKtWrZqnm5D9a7ly5fK9ruxAeG02/0BKBIfyY8JflC9bmpoVAgn0M/yfm8ez29Xc/MWLqj1vVnffrbYzfuEFtbXxihXqvINq1YyuTHgrw1+dsgNAWloawcHBBlejD3u9YBbHH9Xl8UIfqwV73fz/3nx8fKhRowY1atTgrrvuyvUxp9PJyZMnc4WEgwcPsmfPHlatWsWpU6dyPrdcuXL5hgQJA7klnEhlaXwScftPcvRUANV6T6f74t8AsAAh5QOw1wumW/MQ6lQuZWyxHspuVwsIf/xRLSg0szJl1DbGXbpA377qaOQZM+CJJ6RLIFzP8DBwZWfAU3VrHsKCH47oMnZmlpOnWoQU+ussFguVK1emcuXKtG7dOs/HT58+zcGDB/N0FTZs2MCff/6Z63N79+5Nw4YN8wSG6tWr4+PjU+Q/m7s4diqdESt3sjExGR+rJd/Q5wSOnkpncfxRFvxwhDZhFZnQJZLq5QNcX7AHi4yE8uXVVIHZw0C2++9X+xAMGqSOSV6xAmbOhEqVjK5MeBPDw8CVnQFPVadyKdqEVWTLoRRNuwM+VgutalUgLFj7nzLLlStHkyZNaNKkSZ6Ppaenc+jQITZu3MgzzzxDeHg4aWlpfPrppxw9ejRnE6ISJUpcd0Gjn5+f5nW72vKtSYxetTvn3IkbfX+zP77lUArtpq7n9c7hPN608GFO5M9qhTvvVIsIx4wxupqCq1ABPvwQunaFAQMgPBzmzIGHHjK6MuEtDA8D3tAZAJjQJZJ2U9drGgZsVgsTukRqNl5BBQQEEBERQUCA+ql2yJAhOdMQly5d4ujRo3k6CmvXrmX27Nk5CxotFkuuBY1X/lq7du2c+8LMZsQlFPlI6swsJ5lZToav2ElyWgaD7HU0rs572e0wdKh6zLBkSaOrKZxHH1WLIJ9+Wk0fdO+uTkW8xrIdITRjeBjI7gx4ehioXj6A1zuHM3yFdqcMju0cbmibOb/tiH19falTpw516uR9c8vMzOSPP/7IExR++uknli1blqs7VKVKlWsuaCxfvrz+f7gbWL41qchB4GpT1hygUpAfj0mHQBNRUXDpEvzwA1y1VMYtVK4Mn38OixerRxC//16tLejY0ejKhCczPAxk/wToydME2R5vGkJyWgZT1hzA6XTmvJkWxUsd6hn+5lHYRwt9fHwICQkhJCQEuz330w9Op5O///47z4LGffv28fXXX5OcnJzzuWXLls03JNSuXZuqVasW6++1II6dSmf0qt2ajjlq1W5a1a4oawg0EB6ujhOOi3PPMABqAWF0tOpyxMTAPfeobsGUKVBK1p4KHRgeBvz9/bFYLB7fGcjWsvQ5Tq2eQaV7ngGrtVDTBj5WCzarhbGdww0PAqDtPgMWi4Xg4GCCg4Np1apVno+fPXs2T0fh4MGDbNq0iT/++CPn8wICAqhVq1a+YaF69erYbMW/5Ues3JmzRkArjiwnI1buZHFMc03H9UZWq+oOmOWcguKoXh3++1+1fuDFF2HNGliwQK2LEEJLhocBi8VCYGCgV3QGLl68SHR0NGF+fnwypC1jvtp33RXo2bI/3qpWBVOtQHflpkNlypShcePGNG7cOM/HLly4wKFDh/IEhZUrV3L06NGcbZptNhuhoaH5brwUGhpaoAWNCSdS2ZiYfMPPK6zMLCcbE5NJPJmqy4JQbxMVBc8/D+fPq41+3JnFAv36Qfv20LOn+rMNGQITJrjfmghhXoaHAVBTBd7QGRg5ciSJiYls27aN2pXLsDim+b/Pph84SVJKeq6dCi1ASIUA7HWDeapFiOneJMyyA2HJkiUJDw8nPDw8z8cuX76c74LG77//ng8++ICMf061sVgsVK9e/ZoLGkv905tdGp90w/BWVD5WC0t+TGJM57x/DlE4djtcvqwOLmrf3uhqtFGrlnpkcto0eOUV+PZbWLgQmkszSWjAFGHAGzoDGzZs4O2332bSpElERETk/H6dyqUY0zmcMYRzPsPBkZTzXHJk4Wuzmn7XOrOEgespUaJETheg41UrsLKysvjzzz/zdBR+/vlnli9fTmpqas7nBgcHExYWRkqrZ8m06RPKMrOcxB04yRgkDBRX/foQHKymCjwlDICaAnn+ebWGoEcPaNVKnXUwejR4wJO6wkCmeKfx9M5AamoqPXv2pHXr1rzwwgvX/LxAPxvh1cq4sLLicfcdCK1WKzfffDM333wzUVftUON0OklOTs4VFPYfOsofPvo+8piUks75DIepQ6A7sFg8Z91AfurXV12PN99U+yl89RUsWgS33WZ0ZcJdmeIVx9M7A0OHDuXkyZOsXbvWo3bkc4fOQFFZLBYqVapEpUqVaNmyJQC7/zzLfdM36XpdJ3Ak5bxbhUKzstvVrn5paeAG21YUms0GI0bAffepJw+aNlUdguHD1ceEKAxTnBnsyZ2Bb7/9ljlz5jBlyhRq165tdDma8uQwkJ9LDtccQO+q63g6ux0yM2GTvvnNcA0bwtat/04XtGoFe/caXZVwN6YIA5569vupU6eIiYmhY8eO9OvXz+hyNOdtYcDX5pp/Lq66jqerWxeqVvXcqYIr+frCuHFq6uDcOWjUCN56S4UhIQrCFK86QUFBHjlNMGjQIC5cuMDcuXN13wjHCN4WBmpWCETv76Lln+uI4steN7BundGVuE7z5rB9OzzzDLz0kvrzHzxodFXCHZgiDHhiZ+CTTz7hww8/ZMaMGdx0001Gl6OL/LYj9mSBfjZCdN7jIaRCgCwe1JDdDtu2qZ+WvUXJkvD22yoE/fEH3HqrOgXRSzK7KCLThAFP6gz89ddfDBgwgIcffpgnn3zS6HJ0422dAQB7vWB8rPr0B3ysFux1g3UZ21tlrxvYuNHoSlyvbVv47Te1uPCZZ9TZBseOGV2VMCtThAFPWkDodDp5+umn8fHxYebMmR45PZDNG8NAt+Yhumw4BGqfgadaGL/NtCepXRtuusm7pgquFBSkugKrV8OePRARobYz9qJ/sqKATBEGPKkzsGDBAr788kvmzJlDpUqVjC5HV94YBupULkWbsIqadwd8rBbahFU03S6T7s5iUd0Bb1hEeD0dO8KuXfDQQ9CrFzz4IPz1l9FVCTMxRRjI7gy4+5vK0aNHee655+jRowcPPvig0eXozhvDAMCELpHYNA4DNquFCV0iNR1TKHa7WlR35ozRlRirbFm1ffHnn0N8vDrd8eOPja5KmIUpwkBgYCAOh4NLly4ZXUqRZWVl0atXL8qWLcu0adOMLscl3H0HwqKqXj6A1zU+P2Bs53DTHEDlaaKiICvLO9cN5OfBB2H3bnW882OPweOPQ0qK0VUJo5kiDAT9sz2YO68bmDFjBnFxccyfP58yZbxj9zhv7QwAPN40hKEd6gLF//O/1KGeKY6k9lShoRASIlMFV6pYUXUFPvxQHYscHg5ffml0VcJIpggDgf+cMequYWD//v28/PLLDBo0iLvvvtvoclzGm8MAQNVTv5HyzbuUsFLoNQQ+Vgt+NiuTukYy0B6mU4UCZN3AtVgsqiuwezc0aQKdO6v1BGfPGl2ZMIIpwkB2Z8AdFxE6HA6io6OpXr06kyZNMrocl/LmMHDy5En69+9P+9qBfP+inVa1KgA3DgXZH29VqwLfPX+ndARcJCoKduyAU6eMrsR8qlZVXYF58+CzzyAyEr77zuiqhKuZYncTd+4MvPnmm/z8889s3ryZgADvmvP11jDgdDoZMGAAALNmzSK4QiCLY5qTcCKVpfFJxB04SVJKOlf+rVhQGwrZ6wbzVIsQeWrAxex29Tjdhg1qRb3IzWJRXYG77oLevdWxz888A088YXRlwlVMEQbctTOwY8cOxowZw8svv0yLFi2MLsflvDUMLFu2jBUrVvDZZ58RHPzvJkF1KpdiTOdwxhDO+QwHR1LOc8mRha/NSs0KgbKzoIFq1FBrB+LiJAxcT40asHat2ptg2DBYtcroioSrmGKawB07AxkZGXTv3p369eszevRoo8sxhLdtRwzwxx9/MGjQILp160bXrl2v+XmBfjbCq5WhUUg5wquVkSBgAlFRsm6gIKxWGDgQfv0VKqjZL6ZOhYsXDS1L6MxUYcCdOgNjxoxh3759LFq0CD8/P6PLMYS3dQacTid9+vQhICCA6dOnG12OKCS7HXbuhORkoytxD3XqwAcfqP/+6CNo3FgdlSw8k6nCgLt0BrZs2cKbb77JmDFjaNiwodHlGMbbwkBsbCyrV68mNjaWcuXKGV2OKKSoKPXr+vWGluFWfHzUr0uXQkAAtGwJo0aBG28JI67BFGHAx8cHf39/t+gMnD9/nh49etCsWTOGDRtmdDmG8qYwcPjwYV544QX69u1Lp06djC5HFEH16uqsApkqKLzateGHH1QQmDhRHZW8c6fRVQktmSIMgPscVvTyyy/zxx9/sHDhQmw2754H9pYwkL27ZMWKFXnrrbeMLkcUg93uvYcWFVeJEioMxMfD5ctw++0qGDgcRlcmtGCaMBAYGGj6MPDdd9/x3nvvMWnSJOrWrWt0OYbzlu2Ip0+fzvr165k/fz6lSskjge7Mbleb7Jw8aXQl7qtxY9i2DV58EUaOhDvugP37ja5KFJdpwkBQUJCppwnOnDlDr169uOuuuxg4cKDR5ZiCN3QG9u/fz/Dhw3nuueeIyp50Fm4r+1so3YHi8fNTXYFNm9RGTrfdBtOmqTMghHsyTRgwe2dgyJAhnDt3jvnz5+f8ROztPD0MOBwOevToQfXq1ZkwYYLR5QgNVKsGdetKGNBKy5bqEcSnn4YhQ9SmRYcPG12VKArTvKuZuTPwxRdfsHDhQqZNm0ZIiGwfm83Tw8DkyZPZunUrCxcu9LrdJT2ZnFOgrYAA1RX4/ns4cgRuvRXmzFE7Pgr3YZowYNbOwN9//83TTz9N586d6dGjh9HlmJInhoHffvuN0aNHM2zYMFq2bGl0OUJDdjvs2wfHjxtdiWex2+G339QWxv36QadO8McfRlclCspUYcBsnQGn00n//v3Jyspizpw5OT8Ji39ZLBaPCwOXLl0iOjqaevXqMWbMGKPLERq78071q0wVaK90adUV+OYbFQwiImDJEukSuAPThAEzPlq4dOlSVqxYwcyZM6lcubLR5ZiSJ4aBN954g927d3v17pKerEoVqF9fwoCeOnWCXbvgvvuge3d4+GF5gsPsTBMGzNYZ+P333xk0aBBPPvkkjzzyiNHlmJbFYvGoswm2bt3KxIkTGTVqFI0aNTK6HKETWTegv/LlVVfg009h40YID1dHJAtzMk0YMFNnwOl0EhMTQ2BgIDNmzDC6HFPzpM7AhQsX6NGjB40aNWL48OFGlyN0FBUFCQkyp+0KDz+s9nZo0wYeeQS6dYPTp42uSlzNNGHATAsIZ82axZo1a5g7d67sQX8DnhQGXnvtNQ4dOsTChQspUaKE0eUIHcl+A64VHKy6AkuWqPUEERHw7bdGVyWuZJowYJZHCxMTExk6dCj9+vXjnnvuMboc07NarR4RBjZu3Mjbb7/N+PHjadCggdHlCJ1VqqTekGSqwHUsFtUV2LVLPX54773Qty+cO2d0ZQJMFAYCAwO5ePEimZmZhtWQmZlJz549qVKlClOmTDGsDnfiCZ2BtLQ0evbsSevWrRkyZIjR5QgXiYqSMGCEm25S3YE5c2D5chUM5PtgPNOEgaCgIMDYY4zffvtttmzZwoIFC3LqEdfnCWFg2LBh/PXXXyxYsACf7DNbhcez2+HQIUhKMroS72OxqK7Azp0QGqp2LnzuOUhPN7oy72WaMBAYGAgYFwZ27drFyJEjefHFF2nTpo0hNbgjdw8Da9asYebMmUyZMoXatWsbXY5wIdlvwHg1a8L//qd2MJwzR51x8MMPRlflnUwXBoxYN5C9yUydOnV44403XH59d+bOYeDMmTPExMTQvn17+vfvb3Q5wsUqVJAWtRlYrTB4sDrjoEIFdQri8OGQkWF0Zd7FNGHAyGmCcePGsXPnThYtWoS/v7/Lr+/O3DkMZB8+NXfuXNld0kvZ7dIZMIt69dR+BOPHw9tvQ5Mm8MsvRlflPUwTBozqDGzdupUJEybw2muv0bhxY5de2xO4axi48vCp6tWrG12OMIjdrg7XOXLE6EoEgM2mugLbtqn/bt4cxo6Fy5eNrszzmSYMGNEZuHDhAtHR0TRq1IhXXnnFZdf1JO4YBpKTk3n66ad54IEH5PApL9e2rVrMJlMF5hIZCfHx8MorKgy0bKk2LhL6MU0YMGIB4YgRIzh8+LBsMlMM7rYdsdPpZMCAATgcDjl8SlCunFq0JlMF5uPrq4LADz+opwwaN4bJk8HAp889mmnCQHZnwFXTBOvWreOdd95hwoQJsslMMbhbZ+Cjjz7i008/ZebMmVSpUsXocoQJZJ9T4Ea3sVdp2lStHRg8GF5+WXVzEhONrsrzmCYM+Pr6YrPZXNIZSE1NpWfPnrRt21Y2mSkmdwoDx48f55lnnuGxxx7j//7v/4wuR5hEVBQcO6b2HBDm5O+vugIbNsBff0HDhvDee+BGTUnTM00YANdtSfzCCy+QkpLCggULsFpN9VfgdtxlO2Kn00nfvn3x9fXlvffeM7ocYSJt26rH22TdgPndcQfs2AE9e8KgQdChg2wapRVTvRO64rCir7/+mtjYWN5++21CQ0N1vZY3cJfOwPz58/n666/54IMPqFChgtHlCBMpU0bNR8u6AfcQFKS6AmvWwP796oyJefNkmqe4TBcG9OwMpKSk0KdPHzp16kSfPn10u443cYcwcPToUYYMGUKvXr144IEHjC5HmFD2OQUmv5XFFdq3V4cePfIIxMTAAw/A8eNGV+W+TBUGgoKCdO0MDBw4kIyMDGJjY2UVuUbMHgaysrLo3bs3ZcuWZerUqUaXI0zKboc//4SEBKMrEYVRpozqCqxaBT//DOHh6vAjE78kmZapwoCenYGPPvqIjz76iPfff59q1arpcg1vZPYwMHPmTL7//nvmzZtHmTJljC5HmNQdd4CPj0wVuKsHHlD7EHToAE88AY89BsnJRlflXkwVBvTqDGSvIv+///s/Hn/8cc3H92ZmDgMJCQkMGzaMgQMH0q5dO6PLESZWujTcfrssInRnFSqorsDy5erwo/Bw+OILo6tyH6YKA3p0BpxOJ3369MHX15f3339f07GFecNAZmYmPXv2pGrVqkyaNMnocoQbyNlvIDWNkvt/pRnxlNz/KxhweJoousceU12C5s3hoYegRw84c6YYA6Z5x/1gqjCgR2dg7ty5fPPNN7KKXCdmDQNvv/02P/zwAwsXLszZ3VKIa9qzhwF7B7PpRBiUKU39JxsRTwvqP9lItQ3CwtSuN3v2GF2pKIAqVVRXYMEC+Pxz9cTBmjWFGGDPHvX9DguD0t5xP5gqDGj9aOGRI0d4/vnn6d27N/fff79m44p/mXE74t27dzNy5EhefPFFWrdubXQ5wswOH1YTzeHhhHw9kzAOYrk63DqdcPAgzJypes8dOqivE6ZmsaiuwK5dUL8+dOwIAwbc4Af7K+4HZs5U33cvuR9MFQa03HQoKyuLnj17UqFCBVlFriOzdQYuX75MdHQ0tWvX5o033jC6HGFmsbHQoEHOQgFLpuP6n+/45+NxcerrYmN1LlBooXp11RV4/31YtAhuvVXtZJjHVfdDzvf7WjzsfjBVGNCyM/Duu++yfv165s+fT+nSpTUZU+Rlth0IJ0yYwI4dO1i0aBH+/v5GlyPMavx46NsXLl688Yv+1RwO9XV9+6pxhOlZLKor8NtvcNNNal+JF16ACxf++QS5H8wXBrToDOzbt49XXnmF5557DrvdrkFl4lrM1BnYtm0b48aN49VXX6VJkyZGlyPMKjYWRo7UZqyRI2HuXG3GErqrXVs9PjpliuoUNGoEh1+V+wFMFgayFxAW583F4XAQHR1NjRo1mDhxoobVifyYJQxcvHiRHj16EBkZyauvvmp0OcKsDh+GZ5/VdsxBgzxizthb+PiorsD27VDP9zBVJjyLpq9gbno/mCoMBAYG4nQ6uZDTuym8iRMn8ssvv7Bo0SJKliypYXUiP2YJA6NHjyYhIYGFCxfi6+trdDnCrPr1K3wb+EYcDjWucCv168PKyv3wtTrQdD9aN70fTBUGgoKCAIq8buCXX35h7NixvPLKKzRr1kzL0sQ1mCEMbNmyhcmTJzN27FgiIyMNrUWY2J49sHatPmFg7VrYu1fbcYW+9uzB+t1afLLkfgCThYHs58GLsm7g4sWLREdHExkZyWuvvaZ1aeIajA4D58+fp0ePHjRv3pyhQ4caVodwA7Nmgc2mz9g2m3rUTLgPuR9y0elvomiK0xkYNWoUCQkJbNu2TdrELmR0GBg+fDh//PEHX3/9NT4+PobVIdzAN99o3xXI5nDAt9/qM7bQh9wPuZiyM1DYMLB582amTJnCG2+8QUREhB6liWswMgx8//33zJgxg0mTJlG3bl1DahBuIjUVDh3S9xoHD3rsVrUeR+6HPEwVBrI7A4WZJkhLS6NHjx60bNmSF198Ua/SxDUYFQbOnTtHr169sNvtDBw40OXXF24mv53ktOZ0QmKivtcQ2pD7IQ9TTRMUpTMwbNgwjh8/zurVq6VNbACjtiN+4YUXOH36NPPmzcNqNVWmFWaUkeFZ1xHFI/dDHqYMAwXtDKxZs4aZM2fy3nvvERYWpmdp4hqM6Ax8/fXXzJ07l9jYWGrWrOnSaws35efnWdcRxSP3Qx6m+pGqZMmSWCyWAnUGTp8+Te/evWnfvj0DBgxwQXUiP67ejjglJYU+ffpw77330rt3b5ddV7i5sDC1J62eLBZ1HWF+cj/kYaowYLVaCQgIKFBnYPDgwaSlpTFv3jwsen9TxTW5ujPw7LPPkpGRwQcffCDfd1FwQUFQq5a+16hdW11HmJ/cD3mYKgzAv1sSX8+KFStYsmQJ06dP5+abb3ZRZSI/rgwDn3zyCR9++CEzZsygWrVqLrmm8CD33qvvc+WdOukzttCH3A+5mCoMnM9wEHBTXY6mWdj951nOZ+R9BvTEiRP069ePLl268NRTTxlQpbiSq8LAiRMnGDBgAA8//DBPPPGE7tcTHqh/f32fK5fpSvci90Muhi8gTDiRytL4JOL2nyTpVDrODi+zFlg7fRMWIKR8APZ6wXRrHkJYcBD9+vXDYrEwe/ZsaRObgCvCgNPp5Omnn8ZqtTJz5kz5vouiadAA2rdX589r+CbgwEZGazuB9etrNqbQX9YtDfj9lvZU3RdHCTQMBTYb2O3q8AM3YlgYOHYqnRErd7IxMRkfq4XMrLxvKE7g6Kl0FscfZcEPR6gdeIkN637kkwVzqFSpkuuLFnm4IgwsXryYVatWsXLlSvm+i+KZPVuFAo3CgBNwWGw0+Xk2z0yHgQNBnnQ1v6Qk6N0bDu6bzX6fBjgzNTysyGZT95mbMeS2Xb41iXZT17PlUApAvkHgStkfT0z1oXr/2Vy8qbHuNYqC0TsMHDt2jMGDB9O9e3ceeugh3a4jvERoKEyfrtlwFsAyYwbt+oYyeDC0awdHjmg2vNCY0wnz50NkJOzfD3PWhOI7a7q2pxbOmKHuMzfj8jAwIy6B4St2kuHIumEIuJrF6kOWxcbwFTuZEZegU4WiMPQMA06nk5iYGIKCgpg2bZou1xBeqE8fGDdOm7HGj8fvmRimT4fvvlMb20VGQmys/hvcicI5fhw6d1Ydga5dYedONWuk9f1ATIw2Y7mYS8PA8q1JTFlzQJOxpqw5wEdbkzQZSxSdnmFg9uzZrF27lrlz51KuXDldriG81KuvwgcfgL9/4VeU22zq62JjYcSInN+++271BvPYY9C3L9x3H/z5p8Z1i0JzOmH5coiIgK1bYdUq1R0oW/aKT9LhfnA3LgsDx06lM3rVbk3HHLVqN8dOpWs6pigcvbYjPnjwIEOHDqVfv3507NhR8/GFoE8f2LNHLfaCG78JZH/cbldfl89PgKVLq/eEr76CX39Vb0DLlkmXwCjJySqcPfGE6gLs3g0PPHCNT9bhfnAnLgsDI1buxFHIaYEbcWQ5GbFyp6ZjisLRYwfCrKwsevXqRXBwMJMnT9Z0bCFyCQ2FNWvUu8SAAfnvTJe9k9yAAepFf82aG84J33cf7NqlHjXv1g0efRT+/lvHP4fI44svIDwc/vc/1RlYvhwqVLjBF+l0P7gDlzxNkHAilY2JyZqPm5nlZGNiMoknUwkLLqX5+OLG9JgmmDZtGps2bSIuLo5SpeT7KlygQQN4913132lp6rS5jAy1t3xYWJF2kitfHpYuhS5d1PtGeLhaZN6li8a1i1zOnIHnnoNFi1QXYM4cqFKlkIPocD+YnUs6A0vjk/Cx6vNsuI/VwpIfZe2AUbQOA3v37uWVV15hyJAh3HnnnZqNK0SBBQXBbbdB8+bq12K+8D/yiOoStG6tFq517w6nT2tSqbjKmjVqaubzz2HBAtUdKHQQuJrG94NZuSQMxO0/WegnBwoqM8tJ3IGTuowtbkzLMOBwOOjRowc1a9Zk/PjxmowphBlUrgwrVqifVr/8Ur1hrV5tdFWeIy1NdV86dlR7/ezaBT166H8WkSfRPQykZThI0nmRX1JKer5bFwv9aRkGJk2axLZt21i0aBElS5bUZEwhzMJiUV2BXbvU44edOkG/fpCaanRl7m3DBrj1VhW03n9fdQeqVze6Kvejexg4mnIevRfSOoEjKTc+9lhoT6sw8Ouvv/L6668zfPhwmjVrpkFlQpjTzTfDt9+q9QNLl6o3snXrjK7K/Vy4AC+8AFFRcNNN8Ntvqjsg3YCi0T0MXHJo/9iZkdcRuWkRBjIyMujRowf169dn1KhRGlUmhHlZLPD00+oNLCREPZ02ZAiky5PSBfLTT9CokeoETJmiwlTt2kZX5d50DwO+Ntc8veiq64jctAgDY8eOZe/evSxatAg/Pz+NKhPC/GrVUucmTZ2qOgWNGsGPPxpdlXldugQjR0LLllCqFGzfrroDPj5GV+b+dH8HrVkhUNt9n/PjdDLnrfF8+eWXnD17Vu+riSsUNwzEx8fzn//8h9GjR9OwYUMNKxPCPVitqivw669Qrpx66mDECPUkm/jXjh3QtClMmgSvvw4//OB2BwOamu5hINDPRkj5AF2v4Z+ZxoqPP6Rz586UL1+eJk2aMHToUL7++msJBzorThhIT08nOjqa22+/nZdfflnjyoRwL/XqwaZNapv8KVPUG9/27UZXZTyHQ23537Sp2slx61bVHSjsrsHi+lzSW7fXC9Z1n4HH20SSlJREYmIic+bMoX79+ixfvpz777+f8uXL07RpU4YNG8Y333zDuXPndKnDWxVnO+JXX32VpKQkFi1ahE3+ZQuBzQavvAI//6w6Bs2awRtvwOXLRldmjH37oFUrGDUKXnpJBYHbbjO6Ks/kkjDQrXmIrvsMPNUiBIvFQu3atYmJiWHx4sUcO3aMhIQEZs+eTd26dVmyZAn33Xcf5cuXp3nz5rz88st8++23pMpzPcVS1O2I161bxzvvvMOECRO45ZZbdKhMCPd1661qkdzw4aol3qqV2vnWW2RlqXUUjRrB2bOwZYvqDsiSIv1YnHoeRn+F7nPj2XIoRdNQ4GO10KpWBRbHNL/h5zqdThITE4mLi2PdunXExcXx119/4ePjQ5MmTYiKisJut9O6dWuCPHSHKT3cfffdBAcH8+GHHxb4a1JTU7n11lsJCQkhLi4Oq1UWfwpxLVu3QnQ0HD6s3hCHDHHdgrlffoHbb4dt26BxY9dc89Ah6NVL7R8wZIj6MwfoO9MscOFBRRO6RGLTeKrAZrUwoUtkgT7XYrFQp04dnn76aZYtW8aff/7Jvn37eO+99wgNDWXBggXcc889lCtXjpYtWzJixAjWrFnD+fOyf8H1FGXNwNChQ/n777+ZP3++BAEhbqBpU/WmPGiQapXfeafaKt/TOJ0wa5bqiiQl/fuUhQQB13DZK3H18gG83jlc0zHHdg6nehEXJ1osFurVq0e/fv348MMPOX78OHv37mX69OnUqFGDuXPn0rFjR8qWLUvr1q159dVXWbt2LenyIHAuhQ0Dq1evZs6cObz11lvUqlVLx8qE8BwlS6pFhevXw/Hj0LChesZeh9PDDXHsGNxzj9o06Kmn1P4LUVFGV+VdXDZNkG1GXAJT1hwo9jgvdajHQHuYBhXlz+l0sm/fvpxphXXr1vH3339TokQJmjVrljOt0LJlSwK8OLp26NCBsmXL8vHHH9/wc0+fPk1ERAQRERGsXr0ai2wVJkShpaXBsGEwcya0awdz56qNi/Sg9zSB0wmLF8Pgwer8n9hYFQqE67k8DAAs35rE6FW7cWQ5C7WGwMdqwWa1MLZzOI811enuvwan08mePXtygsG6detITk6mRIkSNG/eHLvdTlRUFC1btvSqffU7duxI6dKl+eSTT274udHR0axatYpdu3Zx8803u6A6ITzX2rXQuzecOwfTpulzMI+eYeDECXU2wxdfqDMbpk1T+ywIYxgSBgCOnUpnxMqdbExMxsdquW4oyP54m7CKTOgSWeSpAS1lZWXlhIO4uDjWr19PSkoKvr6+tGjRgqioqJxw4O/vb3S5urnnnnsICgri008/ve7nrVy5kq5du7Jo0SK6d+/uouqE8GxnzsDzz6vjeh94AObM0eDI3ivoFQY++URNCVitaufFLl20G1sUjWFhIFvCiVSWxicRd+AkSSnpuQ41sgAhFQKw1w3mqRYhhAWXMqrMG8rKymL37t050wrr16/n1KlT+Pn55YQDu91O8+bNPSocdOrUiYCAAD777LNrfs7ff/9NeHg4rVq1YuXKlTI9IITGVq1SZx1cvqzWEjz2mDbjah0GUlLUQsjly+Hhh9VUR6VKxR9XFJ/hYeBK5zMcHEk5zyVHFr42KzUrBBLo556b0WRlZbFz586cKYX169dz+vRp/Pz8aNmyZc60QvPmzd16P/57770Xf39/VqxYke/HnU4njzzyCOvXr2f37t1UrlzZxRUK4R2Sk2HgQPj4Y/i//4P33oOKFYs3ppZh4KuvoG9ftc3ye+/B44/LCYNmYqow4MmysrL47bffcqYVNmzYwJkzZ/D396dVq1Y50wrNmjVzm3BwPsPBA0/2xubrz9S3Jucb3pYtW0a3bt345JNPeOSRRwyqVAjv8dFH8MwzajfDDz6Azp2LOFBaGnu/TKTnkxksWOZH/QfC1Cq/Qjp7Vh0mNG8e3HuvqqlatSLWJHQjYcAgmZmZ/PbbbznTChs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"text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -426,7 +426,7 @@ }, { "cell_type": "markdown", - "id": "b46d02fc", + "id": "646a54d7", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -466,13 +466,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "2e684aa6", + "id": "7e22369f", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:46.588763Z", + "iopub.status.busy": "2022-12-27T10:11:46.588111Z", + "iopub.status.idle": "2022-12-27T10:11:46.592193Z", + "shell.execute_reply": "2022-12-27T10:11:46.591664Z" } }, "outputs": [ @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "4f6b2e3d", + "id": "b526c8f3", "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": "67501e28", + "id": "bde32d2a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:46.594914Z", + "iopub.status.busy": "2022-12-27T10:11:46.594501Z", + "iopub.status.idle": "2022-12-27T10:11:46.598557Z", + "shell.execute_reply": "2022-12-27T10:11:46.598051Z" } }, "outputs": [ diff --git a/searchindex.js b/searchindex.js index 706f77fd..298f13e1 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["auto_examples/3d_drawing/index", "auto_examples/3d_drawing/mayavi2_spring", "auto_examples/3d_drawing/plot_basic", "auto_examples/3d_drawing/sg_execution_times", "auto_examples/algorithms/index", "auto_examples/algorithms/plot_beam_search", 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"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, 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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, 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[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, 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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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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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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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1217], "earlier": [91, 299, 363, 364, 365, 739, 1199, 1393, 1402, 1408, 1413], "acknowledg": [91, 92, 96], "nonlinear": [91, 1213, 1215, 1222], "lo": 91, "alamo": 91, "nation": [91, 92, 457, 720], "laboratori": 91, "pi": [91, 653, 1114], "program": [91, 105, 110, 362, 455, 488, 490, 678, 1119, 1120, 1125, 1226, 1302, 1324, 1326, 1328, 1414], "offic": [91, 1267], "complex": [91, 94, 101, 105, 210, 217, 229, 230, 231, 239, 240, 274, 290, 293, 294, 300, 314, 327, 330, 331, 332, 333, 337, 346, 347, 355, 356, 371, 372, 376, 385, 386, 423, 434, 438, 452, 453, 494, 500, 519, 520, 521, 574, 616, 619, 625, 659, 692, 698, 699, 749, 1120, 1126, 1175, 1179, 1196, 1197, 1198, 1341, 1342, 1344, 1381, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "depart": [91, 494], "physic": [91, 110, 230, 236, 241, 244, 248, 326, 332, 333, 355, 356, 358, 378, 383, 386, 438, 485, 486, 487, 625, 1169, 1170, 1171, 1193, 1222, 1229, 1233], "crutchfield": 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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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99], "assembl": [93, 1046, 1047, 1296], "idea": [93, 94, 97, 99, 102, 105, 132, 217, 373, 423, 428, 687, 689, 1326, 1382, 1404, 1407], "plot_": 93, "plot_new_exampl": 93, "highlight": [93, 106, 1403], "resourc": [93, 96, 476, 477, 478, 572, 573, 618, 1165, 1200], "docstr": [93, 94, 95, 97, 109, 1345, 1348, 1349, 1350, 1399, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1416, 1417, 1420, 1421, 1422, 1423, 1425], "chicago": [93, 1265], "citat": [93, 97, 346, 347, 566, 1239, 1412], "quickest": 93, "scholar": 93, "paywal": 93, "arxiv": [93, 110, 128, 217, 220, 300, 305, 332, 333, 355, 358, 371, 372, 373, 385, 386, 427, 432, 433, 437, 512, 573, 619, 625, 685, 693, 1153, 1169, 1170, 1171, 1185, 1227, 1269, 1280], "access": [93, 101, 112, 125, 151, 168, 189, 429, 471, 472, 473, 474, 475, 496, 606, 626, 627, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 693, 721, 760, 770, 789, 796, 853, 865, 878, 898, 910, 915, 934, 946, 960, 979, 991, 996, 1037, 1038, 1039, 1040, 1135, 1326, 1392, 1393, 1394, 1396, 1398, 1399, 1402, 1406, 1407, 1408, 1410], "cheong": 93, "se": 93, "hang": 93, "yain": 93, "whar": 93, "schemat": 93, "placement": [93, 614], "survei": [93, 110, 564, 566, 581, 786, 1201], "2020": [93, 99, 100, 101, 102, 569, 1406, 1412], "1177": 93, "2f1473871618821740": 93, "upload": [93, 106, 217], "pdf": [93, 105, 110, 112, 128, 214, 215, 216, 217, 220, 235, 305, 311, 312, 315, 322, 324, 325, 330, 342, 355, 356, 373, 410, 411, 412, 413, 414, 415, 417, 426, 427, 430, 442, 447, 448, 476, 483, 490, 494, 511, 512, 519, 564, 566, 567, 570, 571, 573, 618, 619, 690, 693, 748, 749, 750, 760, 762, 1193, 1197, 1198, 1326, 1407, 1412, 1426], "docx": 93, "ppt": 93, "lectur": [93, 110, 412, 431, 498, 616, 1203], "wayback": [93, 1413], "machin": [93, 312, 331, 494, 511, 512, 762, 1396, 1406, 1413], "snapshot": 93, "unreach": 93, "pyarg": [93, 111, 1038], "tell": [93, 99, 102, 760, 1275, 1278, 1279, 1296, 1328, 1412], "compar": [93, 464, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 559, 560, 561, 562, 563, 615, 760, 782, 1165, 1302, 1414], "baselin": [93, 1134, 1136], "ones": [93, 99, 107, 109, 282, 680, 1038, 1395, 1402, 1404], "savefig": [93, 1426], "mpl_image_compar": 93, "test_barbel": 93, "barbel": [93, 293, 294, 391, 424, 1146, 1157, 1276, 1426], "conduct": [93, 96, 100, 109, 447, 448, 758], "contributor": [94, 96, 99, 105, 106, 110, 1271, 1323, 1403], "shepherd": [94, 99], "mission": [94, 96, 97, 100, 107], "approv": [94, 100], "nuclear": 94, "launch": 94, "carefulli": 94, "clean": [94, 106, 530, 540, 1300, 1406, 1407, 1411, 1413, 1420], "nearli": 94, "volunt": [94, 107, 1413], "tremend": 94, "felt": 94, "evalu": [94, 130, 152, 157, 158, 195, 330, 618, 619, 626, 627, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1296, 1417], "novic": 94, "strongli": [94, 217, 232, 388, 391, 397, 398, 399, 403, 405, 406, 423, 480, 491, 492, 519, 588, 633, 697, 699, 751, 753, 1185, 1402, 1406, 1411, 1414, 1417, 1425], "mentorship": [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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"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, 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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], 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"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, 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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": 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353, 525, 535, 555, 671, 672, 673, 674, 732, 760, 1168, 1413], "cours": [101, 105, 217, 618, 1326, 1426], "action": [101, 106, 1413, 1417], "allevi": 101, "dig": 101, "enough": [101, 468, 509, 1165], "satisfactorili": 101, "reconsid": [101, 1412], "went": [101, 502], "ahead": 101, "4300": [101, 1413], "4304": [101, 1413], "path_edg": 102, "former": [102, 103, 791], "stylist": 102, "creation": [102, 107, 110, 249, 275, 788, 1154, 1170, 1224, 1228, 1230, 1232, 1325, 1399, 1404, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "cleaner": [102, 1401, 1406], "creativ": [102, 464, 466], "demand": [102, 496, 497, 501, 504, 505, 508], "had": [102, 652, 1217, 1296, 1409, 1416], "node_iter": 102, "isn": [102, 346, 347, 720, 1331, 1334, 1406, 1414, 1425], "leav": [102, 231, 388, 500, 508, 584, 585, 586, 587, 678, 1145, 1155, 1296, 1404, 1409, 1426], "dg": [102, 207, 322, 455, 456, 457, 458, 459, 461, 462, 464, 465, 466, 467, 468, 469, 892, 928, 973, 1010, 1041, 1404, 1426], "mdg": [102, 207, 892, 928, 973, 1010, 1420], "customgraph": 102, "elist": [102, 1326], "isol": [102, 355, 380, 435, 491, 492, 522, 524, 621, 735, 737, 758, 1218, 1325, 1330, 1398, 1401, 1406, 1407, 1417], "ekei": [102, 207, 892, 928, 934, 973, 979, 1010, 1084, 1104], "protocol": [102, 1404], "hashabl": [102, 144, 151, 156, 171, 180, 267, 545, 546, 547, 548, 761, 796, 853, 855, 867, 871, 898, 900, 912, 914, 934, 936, 947, 948, 952, 962, 979, 981, 992, 993, 995, 1002, 1037, 1038, 1039, 1040, 1087, 1207, 1278, 1279, 1295, 1310, 1324, 1326, 1333, 1337, 1338, 1426], "logic": [102, 103, 220, 760, 762, 1298, 1406, 1407, 1419, 1425], "denot": [102, 114, 212, 219, 299, 300, 322, 567, 568, 569, 570, 571, 572, 573, 608, 619, 687, 688, 689, 690, 691, 1174], "multiedg": [102, 553, 934, 979, 1039, 1040, 1085, 1326, 1356, 1357, 1393, 1406, 1412, 1414], "attrdict": [102, 157, 856, 901, 937, 982, 1406], "edge_kei": [102, 489, 1039, 1040, 1100, 1104, 1413], "networkxinvalidedgelist": 102, "flexibl": [102, 110, 467, 1326, 1382, 1383, 1395, 1401, 1406, 1407, 1411, 1426], "wheel": [102, 106, 1163, 1261, 1411, 1421, 1425], "spoke": 102, "wheel_graph": [102, 341, 671, 672, 674], "star": [102, 260, 300, 615, 626, 627, 779, 1054, 1151, 1160, 1223, 1227, 1394, 1404, 1406, 1407, 1411], "mycustomgraph": 102, "configuration_model_graph": 102, "deg_sequ": [102, 515, 517, 518, 1175, 1176, 1177, 1178, 1180, 1222], "graph_build": 102, "py_random_st": [102, 103, 1296, 1299, 1405], "extended_barabasi_albert_graph": 102, "node_and_edge_build": 102, "ladder_graph": 102, "incompat": [102, 1199, 1402, 1403, 1406], "thrust": 102, "incept": 102, "attach": [102, 214, 274, 357, 569, 571, 621, 1036, 1088, 1122, 1182, 1185, 1223, 1227, 1229, 1326, 1426], "presum": [102, 1297], "rewritten": [102, 1395, 1402, 1406], "gradual": 102, "accomplish": [102, 109, 1165], "wrap": [102, 1045, 1047, 1296, 1301, 1304], "custom_graph": 102, "ichain": 102, "tripl": [102, 114, 249, 250, 711, 1411], "overli": 102, "empty_graph": [102, 753, 1057, 1158, 1297, 1323, 1406, 1409, 1410], "3036": 102, "1393": 102, "canon": [102, 684, 730, 1412], "huge": 102, "path_edgelist": 102, "disallow": [102, 796, 1037, 1039, 1040, 1187, 1417], "pseudo": [103, 104, 676, 1320, 1321, 1405, 1407], "nep19": 103, "legaci": [103, 1395, 1402, 1408], "randomst": [103, 1100, 1111, 1117, 1299, 1301, 1304, 1305, 1328, 1405, 1409], "statist": [103, 110, 128, 274, 358, 383, 385, 438, 1222, 1328, 1405], "strategi": [103, 123, 222, 362, 366, 370, 453], "engin": [103, 107, 729, 731, 1412], "modern": [103, 110, 1405], "prng": 103, "np_random_st": [103, 1301, 1405, 1414], "random_st": [103, 208, 213, 217, 222, 223, 227, 230, 231, 271, 272, 274, 275, 296, 297, 306, 368, 373, 377, 378, 380, 381, 589, 625, 681, 682, 683, 684, 686, 692, 693, 694, 701, 722, 738, 747, 1164, 1165, 1168, 1169, 1170, 1171, 1173, 1175, 1177, 1179, 1181, 1182, 1183, 1184, 1185, 1186, 1187, 1188, 1193, 1195, 1196, 1197, 1198, 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"dufresn": [128, 438], "grochow": [128, 438], "allard": [128, 438, 1411], "31708": [128, 438], "2016": [128, 337, 352, 385, 438, 476, 690, 1197, 1251, 1396, 1406], "1038": [128, 337, 376, 380, 438, 569], "srep31708": [128, 438], "factor": [132, 226, 293, 294, 299, 300, 324, 325, 370, 462, 497, 501, 504, 505, 508, 513, 565, 592, 624, 676, 697, 1106, 1107, 1108, 1109, 1110, 1114, 1115, 1116, 1117, 1145, 1155, 1178, 1180, 1275, 1276, 1277], "graphic": [132, 454, 517, 518, 693, 758, 1175, 1177, 1180, 1181, 1222, 1325, 1383, 1398, 1401, 1406], "overview": [132, 476, 1038, 1296], "collid": [132, 454], "triplet": [132, 745], "successor": [132, 159, 174, 181, 191, 200, 240, 282, 387, 389, 390, 394, 501, 687, 707, 715, 858, 872, 880, 888, 903, 939, 953, 961, 969, 984, 1055, 1183, 1184, 1189, 1326, 1404, 1407, 1416, 1426], "descend": [132, 454, 456, 465, 709, 758, 1272, 1401, 1404, 1406, 1413, 1414], "unblock": 132, "commonli": [132, 280, 454, 684, 782], "probabilist": [132, 378], "causal": 132, "markov": [132, 462, 565, 692, 1188], "hmm": 132, "s1": [132, 1242, 1313, 1363], "s2": [132, 1242, 1313], "s3": [132, 1313], "s4": 132, "s5": 132, "o1": 132, "o2": 132, "o3": 132, "o4": 132, "o5": 132, "ob": 132, "d_separ": [132, 758, 1412], "darwich": 132, "shachter": 132, "1998": [132, 1143, 1144, 1225, 1241, 1407], "bay": 132, "ball": 132, "ration": 132, "pastim": 132, "irrelev": [132, 1407], "requisit": 132, "influenc": [132, 324, 325, 512, 786], "fourteenth": [132, 1186], "uncertainti": [132, 590, 732], "artifici": [132, 574, 590, 732], "480": [132, 426, 514, 518, 1398, 1406], "487": 132, "francisco": [132, 732], "morgan": [132, 732], "kaufmann": [132, 732], "koller": 132, "friedman": 132, "mit": [132, 342, 519, 618], "causal_markov_condit": 132, "ness": [133, 684, 782], "classmethod": [141, 1047], "auxiliari": [141, 142, 143, 220, 411, 412, 413, 415, 416, 417, 418, 419, 423, 430, 431, 1402], "sink": [141, 302, 309, 416, 418, 494, 495, 498, 499, 501, 502, 503, 506, 507, 509, 510, 565], "pick": [141, 217, 331, 657, 1188, 1207, 1210, 1407], "st": [141, 415, 417], "cut": [141, 222, 223, 293, 377, 382, 387, 389, 390, 394, 411, 412, 414, 415, 416, 417, 419, 427, 428, 429, 442, 443, 444, 445, 447, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 619, 758, 760, 1038, 1066, 1115, 1262, 1325, 1395, 1402, 1406, 1413], "refin": [143, 215, 423, 438], "auxgraph": [143, 423], "node_partit": 144, "permut": [144, 368, 452, 453, 455, 466, 748, 1285, 1320, 1321], "containin": 144, "frozenset": [144, 267, 339, 383, 586, 588, 752, 1165, 1333, 1337, 1338, 1412], "abc": [144, 545, 1154, 1206, 1303, 1412, 1413], "interchang": [144, 362], "bool": [145, 146, 148, 149, 165, 168, 171, 176, 184, 189, 196, 204, 208, 232, 237, 238, 242, 243, 245, 249, 250, 258, 265, 266, 267, 268, 272, 275, 286, 287, 288, 291, 294, 295, 296, 297, 298, 299, 301, 302, 305, 306, 307, 308, 309, 310, 314, 315, 322, 324, 325, 326, 327, 330, 343, 350, 355, 362, 393, 394, 395, 396, 397, 398, 439, 454, 462, 463, 467, 479, 480, 488, 489, 491, 494, 498, 499, 509, 510, 513, 514, 515, 516, 517, 518, 520, 521, 522, 545, 562, 564, 578, 579, 580, 581, 588, 613, 614, 616, 617, 622, 623, 625, 640, 652, 663, 673, 679, 685, 690, 696, 698, 699, 700, 704, 708, 719, 723, 724, 725, 726, 728, 730, 733, 734, 735, 736, 737, 738, 740, 741, 742, 743, 862, 865, 867, 870, 873, 878, 885, 891, 907, 910, 912, 916, 927, 931, 943, 946, 948, 951, 955, 960, 966, 972, 976, 988, 991, 993, 998, 1039, 1040, 1045, 1057, 1068, 1070, 1071, 1072, 1084, 1091, 1097, 1116, 1133, 1134, 1135, 1136, 1169, 1179, 1185, 1189, 1209, 1211, 1212, 1213, 1215, 1224, 1228, 1230, 1231, 1232, 1275, 1276, 1277, 1278, 1279, 1282, 1295, 1296, 1307, 1309, 1312, 1335, 1336, 1337, 1339, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1360, 1364, 1379, 1380], "account": [145, 148, 398, 448, 749, 761, 1270, 1393, 1413], "graph_nod": [145, 148], "subgraph_nod": [145, 148], "find_isomorph": [147, 150], "induc": [148, 167, 199, 211, 226, 342, 388, 392, 406, 427, 436, 437, 470, 487, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 512, 586, 589, 752, 761, 762, 864, 887, 909, 925, 945, 968, 990, 1007, 1038, 1061, 1066, 1087, 1102, 1103, 1105, 1189, 1283, 1284, 1393], "u_of_edg": [151, 853, 898], "v_of_edg": [151, 853, 898], "capac": [151, 265, 296, 301, 302, 303, 308, 309, 323, 411, 412, 415, 416, 417, 418, 419, 430, 431, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 758, 853, 898, 934, 979, 1335, 1402], "342": [151, 853, 898, 934, 979, 1255], "ebunch_to_add": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "add_weighted_edges_from": [152, 229, 230, 231, 508, 581, 630, 657, 659, 721, 854, 899, 935, 980, 1070, 1326, 1404, 1407, 1426], "runtimeerror": [152, 157, 158, 195, 464, 465, 466, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005], "happen": [152, 157, 158, 195, 380, 584, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1403, 1404, 1425], "iterator_of_edg": [152, 158, 854, 857, 899, 902, 935, 938, 980, 983], "wn2898": [152, 854, 899, 935, 980], "wrong": [152, 157, 158, 722, 854, 856, 857, 899, 901, 902, 935, 937, 938, 980, 982, 983, 1406, 1411, 1416, 1425], "start_nod": [153, 154, 155], "end_nod": [153, 154, 155], "reference_neighbor": [153, 154], "half": [153, 154, 155, 164, 177, 183, 206, 297, 298, 615, 653], "clockwis": [153, 154, 169, 182, 197, 615], "networkxexcept": [153, 154, 161, 331, 588, 593, 724, 726, 1043, 1110, 1138, 1180, 1325], "add_half_edge_cw": [153, 155, 164, 615], "connect_compon": [153, 154, 155, 615], "add_half_edge_first": [153, 154, 164, 615], "add_half_edge_ccw": [154, 155, 164, 615], "node_for_ad": [156, 855, 900, 936, 981], "mutabl": [156, 855, 900, 936, 981, 1061, 1066, 1082, 1085, 1086], "hash": [156, 511, 512, 758, 855, 900, 936, 981, 1324, 1325, 1414, 1426], "hello": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1303], "k3": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1217], "utm": [156, 855, 900, 936, 981], "382871": [156, 855, 900, 936, 981], "3972649": [156, 855, 900, 936, 981], "nodes_for_ad": [157, 856, 901, 937, 982], "iterator_of_nod": [157, 195, 856, 884, 901, 923, 937, 965, 982, 1005], "datadict": [159, 190, 200, 207, 734, 736, 858, 879, 888, 892, 903, 928, 939, 969, 973, 1010, 1084, 1312, 1326], "foovalu": [159, 190, 200, 858, 879, 888, 903, 939, 969], "nbrdict": [160, 859, 904, 940, 985, 1019, 1094], "fulfil": [161, 615], "cw": [161, 615], "ccw": [161, 615], "planar": [161, 614, 616, 617, 758, 1110, 1138, 1243, 1246, 1247, 1249, 1325, 1409, 1410], "first_nbr": [161, 615], "invalid": [161, 615, 1413], "alter": [163, 861, 906, 942, 987], "afterward": 164, "as_view": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1089, 1090], "shallow": [165, 202, 204, 284, 285, 286, 287, 288, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1394], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1409], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1404, 1407, 1409, 1410, 1411], "fresh": [165, 862, 907, 943, 988, 1404], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1226, 1323, 1404], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1265, 1394], "degreeview": [166, 863, 908, 944, 950, 989, 1404, 1426], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1404, 1418], "ddict": [168, 176, 184, 189, 865, 870, 873, 878, 910, 916, 946, 951, 955, 960, 991, 998], "in_edg": [168, 189, 865, 878, 946, 960, 1404, 1406, 1407], "out_edg": [168, 865, 946, 1062, 1404, 1406, 1407, 1426], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1426], "outedgedataview": [168, 189, 865, 878, 1404, 1411], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1404, 1412], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1406, 1407, 1417], "indegreeview": [175, 869, 1404], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1165, 1179, 1222, 1404], "inedgeview": [176, 870, 1404], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1127, 1353, 1354, 1359, 1363, 1406, 1413], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1414], "__iter__": [180, 871, 914, 952, 995, 1303], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1394, 1426], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1216], "nedg": [185, 588, 874, 917, 956, 999], "__len__": [186, 187, 875, 876, 918, 919, 957, 958, 1000, 1001], "outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 882, 921, 963, 1003, 1404, 1414], "get_data": [197, 616], "inplac": [199, 690, 887, 925, 968, 1007, 1066, 1393], "reduct": [199, 469, 618, 786, 887, 925, 968, 1007, 1066, 1320, 1321, 1413, 1414], "sg": [199, 887, 925, 968, 1007], "largest_wcc": [199, 887, 925, 968, 1007], "is_multigraph": [199, 758, 887, 925, 968, 1007, 1154, 1412], "keydict": [199, 207, 887, 892, 925, 928, 968, 973, 1007, 1010, 1039, 1040], "contrast": [202, 204, 301, 302, 308, 309, 890, 891, 926, 927, 971, 972, 1008, 1009, 1066, 1233, 1241, 1426], "reciproc": [204, 299, 320, 322, 356, 411, 430, 447, 476, 620, 758, 891, 972, 1325, 1416, 1425], "mark_half_edg": 206, "li": [206, 619, 670, 675, 685, 775, 1207, 1210, 1425], "straightforward": [207, 892, 928, 973, 1010], "slightli": [207, 326, 437, 520, 521, 581, 892, 928, 973, 1010, 1165, 1326, 1404, 1407, 1412, 1414, 1425], "singleton": [207, 588, 892, 928, 973, 1010, 1218, 1251, 1407], "preserve_attr": [208, 723, 724, 725, 726], "optimum": [208, 231, 583, 720, 722, 791, 1395, 1406], "arboresc": [208, 460, 719, 720, 722, 724, 726, 740, 743, 758, 1272, 1395, 1406], "span": [208, 226, 227, 228, 295, 508, 618, 619, 624, 719, 720, 722, 724, 726, 732, 733, 734, 735, 736, 737, 738, 758, 1394, 1397, 1406, 1407, 1420], "max_ind_cliqu": 209, "networkxnotimpl": [209, 210, 211, 212, 220, 224, 227, 293, 294, 295, 318, 319, 321, 328, 343, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 403, 404, 405, 406, 407, 422, 424, 425, 426, 427, 429, 455, 457, 458, 459, 460, 468, 481, 482, 500, 589, 590, 608, 680, 732, 1043, 1216, 1275, 1276, 1298, 1325, 1353, 1354, 1379, 1407, 1408], "boppana": [209, 211, 212], "halld\u00f3rsson": [209, 211, 212], "1992": [209, 211, 212, 517, 518, 1407], "exclud": [209, 211, 212, 215, 216, 261, 262, 453, 688, 719, 723, 724, 725, 726, 733, 751, 1036, 1038, 1088, 1217, 1412], "180": [209, 211, 212, 238], "196": [209, 211, 212], "heurist": [210, 220, 228, 233, 234, 377, 380, 381, 427, 494, 509, 626, 627, 652, 663, 703, 758, 1173, 1320, 1321, 1325, 1395, 1408, 1412, 1413], "max_cliqu": 210, "rigor": 210, "pattabiraman": 210, "bharath": 210, "massiv": [210, 217], "421": 210, "448": 210, "1080": [210, 297, 298, 306, 307, 329], "15427951": 210, "986778": 210, "apx": [211, 212], "subseteq": [211, 280, 289, 618, 675], "omega": [211, 758, 782, 1414], "maximum_cliqu": 211, "1007": [211, 226, 296, 301, 302, 303, 308, 309, 323, 324, 325, 341, 431, 451, 498, 574, 1144, 1181], "bf01994876": 211, "iset": 212, "trial": [213, 230, 231, 1195, 1237, 1238], "estim": [213, 224, 297, 306, 313, 564, 625, 626, 627, 782, 1280, 1407], "coeffici": [213, 248, 260, 261, 262, 263, 289, 355, 356, 358, 570, 618, 619, 625, 682, 684, 778, 782, 1397, 1398, 1399, 1406, 1413], "fraction": [213, 257, 259, 286, 289, 297, 299, 304, 306, 315, 317, 318, 319, 321, 322, 326, 328, 330, 356, 358, 359, 519, 1165, 1234], "schank": 213, "thoma": [213, 751, 1407, 1409, 1413], "dorothea": [213, 1168], "wagner": [213, 429, 758, 1168, 1402, 1406], "universit\u00e4t": 213, "karlsruh": 213, "fakult\u00e4t": 213, "f\u00fcr": 213, "informatik": [213, 412], "5445": 213, "ir": [213, 606], "1000001239": 213, "erdos_renyi_graph": [213, 1224, 1232, 1326, 1406, 1426], "214": 213, "cutoff": [214, 215, 310, 326, 383, 410, 411, 412, 418, 419, 494, 495, 498, 499, 510, 637, 638, 640, 641, 642, 643, 644, 647, 648, 649, 656, 660, 661, 662, 667, 668, 669, 677, 678, 1234, 1398, 1402, 1406, 1413, 1416, 1424, 1425], "distinct": [214, 215, 255, 281, 288, 352, 391, 452, 453, 460, 578, 595, 608, 618, 700, 701, 734, 735, 736, 737, 789, 1150, 1244, 1271, 1323, 1326, 1328, 1395, 1417], "nonadjac": [214, 215, 480, 584, 585, 587], "cutset": [214, 215, 414, 415, 416, 417, 427, 428, 500, 506, 758], "menger": [214, 215, 216], "theorem": [214, 215, 216, 220, 235, 281, 311, 312, 322, 411, 506, 507, 514, 517, 518, 618, 1190, 1205], "local_node_connect": [214, 216, 408, 409, 410, 411, 413], "node_connect": [214, 215, 409, 410, 411, 412, 414, 415, 416, 417, 419, 427, 428, 1402], "dougla": [214, 215, 216, 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How do I find it in the source code?": [[97, "q-i-want-to-work-on-a-specific-function-how-do-i-find-it-in-the-source-code"]], "Q: What is the policy for deciding whether to include a new algorithm?": [[97, "q-what-is-the-policy-for-deciding-whether-to-include-a-new-algorithm"]], "NXEPs": [[98, "nxeps"], [1413, "nxeps"]], "NXEP 0 \u2014 Purpose and Process": [[99, "nxep-0-purpose-and-process"]], "What is a NXEP?": [[99, "what-is-a-nxep"]], "Types": [[99, "types"]], "NXEP Workflow": [[99, "nxep-workflow"]], "Review and Resolution": [[99, "review-and-resolution"]], "How a NXEP becomes Accepted": [[99, "how-a-nxep-becomes-accepted"]], "Maintenance": [[99, "maintenance"]], "Format and Template": [[99, "format-and-template"]], "Header Preamble": [[99, "header-preamble"]], "References and Footnotes": [[99, "references-and-footnotes"]], "NXEP 1 \u2014 Governance and Decision Making": [[100, "nxep-1-governance-and-decision-making"]], "Abstract": [[100, "abstract"], [101, "abstract"], [102, 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"single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[669, "single-source-dijkstra-path-length"]], "generate_random_paths": [[670, "generate-random-paths"]], "graph_edit_distance": [[671, "graph-edit-distance"]], "optimal_edit_paths": [[672, "optimal-edit-paths"]], "optimize_edit_paths": [[673, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[674, "optimize-graph-edit-distance"]], "panther_similarity": [[675, "panther-similarity"]], "simrank_similarity": [[676, "simrank-similarity"]], "all_simple_edge_paths": [[677, "all-simple-edge-paths"]], "all_simple_paths": [[678, "all-simple-paths"]], "is_simple_path": [[679, "is-simple-path"]], "shortest_simple_paths": [[680, "shortest-simple-paths"]], "lattice_reference": [[681, "lattice-reference"]], "omega": [[682, "omega"]], "random_reference": [[683, "random-reference"]], "sigma": [[684, "sigma"]], "s_metric": [[685, "s-metric"]], "spanner": [[686, "spanner"]], "constraint": [[687, "constraint"]], "effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "Recognition Tests": [[791, "recognition-tests"]], "Branchings and Spanning Arborescences": [[791, "module-networkx.algorithms.tree.branchings"]], "Encoding and decoding": [[791, "module-networkx.algorithms.tree.coding"]], "Operations": [[791, "module-networkx.algorithms.tree.operations"]], "Spanning Trees": [[791, "module-networkx.algorithms.tree.mst"]], "Exceptions": [[791, "exceptions"], [1043, "module-networkx.exception"]], "Vitality": [[793, "module-networkx.algorithms.vitality"]], "Voronoi cells": [[794, "module-networkx.algorithms.voronoi"]], "Wiener index": [[795, "module-networkx.algorithms.wiener"]], "DiGraph\u2014Directed graphs with self loops": [[796, "digraph-directed-graphs-with-self-loops"]], "Overview": [[796, "overview"], [1037, "overview"], [1039, "overview"], [1040, "overview"]], "Methods": [[796, "methods"], [1037, "methods"], [1039, "methods"], [1040, "methods"]], "Adding and removing nodes and edges": [[796, "adding-and-removing-nodes-and-edges"], [1037, "adding-and-removing-nodes-and-edges"], [1040, "adding-and-removing-nodes-and-edges"]], "Reporting nodes edges and neighbors": [[796, "reporting-nodes-edges-and-neighbors"], [1037, "reporting-nodes-edges-and-neighbors"], [1039, "reporting-nodes-edges-and-neighbors"], [1040, "reporting-nodes-edges-and-neighbors"]], "Counting nodes edges and neighbors": [[796, "counting-nodes-edges-and-neighbors"], [1037, "counting-nodes-edges-and-neighbors"], [1039, "counting-nodes-edges-and-neighbors"], [1040, "counting-nodes-edges-and-neighbors"]], "Making copies and subgraphs": [[796, "making-copies-and-subgraphs"], [1037, "making-copies-and-subgraphs"], [1039, "making-copies-and-subgraphs"], [1040, "making-copies-and-subgraphs"]], "AdjacencyView.copy": [[797, "adjacencyview-copy"]], "AdjacencyView.get": [[798, "adjacencyview-get"]], "AdjacencyView.items": [[799, "adjacencyview-items"]], "AdjacencyView.keys": [[800, "adjacencyview-keys"]], "AdjacencyView.values": [[801, "adjacencyview-values"]], "AtlasView.copy": [[802, "atlasview-copy"]], "AtlasView.get": [[803, "atlasview-get"]], "AtlasView.items": [[804, "atlasview-items"]], "AtlasView.keys": [[805, "atlasview-keys"]], "AtlasView.values": [[806, "atlasview-values"]], "FilterAdjacency.get": [[807, "filteradjacency-get"]], "FilterAdjacency.items": [[808, "filteradjacency-items"]], "FilterAdjacency.keys": [[809, "filteradjacency-keys"]], "FilterAdjacency.values": [[810, "filteradjacency-values"]], "FilterAtlas.get": [[811, "filteratlas-get"]], "FilterAtlas.items": [[812, "filteratlas-items"]], "FilterAtlas.keys": [[813, "filteratlas-keys"]], "FilterAtlas.values": [[814, "filteratlas-values"]], "FilterMultiAdjacency.get": [[815, "filtermultiadjacency-get"]], "FilterMultiAdjacency.items": [[816, "filtermultiadjacency-items"]], "FilterMultiAdjacency.keys": [[817, "filtermultiadjacency-keys"]], "FilterMultiAdjacency.values": [[818, "filtermultiadjacency-values"]], "FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": [[836, "unionatlas-keys"]], "UnionAtlas.values": [[837, "unionatlas-values"]], "UnionMultiAdjacency.copy": [[838, "unionmultiadjacency-copy"]], "UnionMultiAdjacency.get": [[839, "unionmultiadjacency-get"]], "UnionMultiAdjacency.items": [[840, "unionmultiadjacency-items"]], "UnionMultiAdjacency.keys": [[841, "unionmultiadjacency-keys"]], "UnionMultiAdjacency.values": [[842, "unionmultiadjacency-values"]], "UnionMultiInner.copy": [[843, "unionmultiinner-copy"]], "UnionMultiInner.get": [[844, "unionmultiinner-get"]], "UnionMultiInner.items": [[845, "unionmultiinner-items"]], "UnionMultiInner.keys": [[846, "unionmultiinner-keys"]], "UnionMultiInner.values": [[847, "unionmultiinner-values"]], "DiGraph.__contains__": [[848, "digraph-contains"]], "DiGraph.__getitem__": [[849, "digraph-getitem"]], "DiGraph.__init__": [[850, "digraph-init"]], "DiGraph.__iter__": [[851, "digraph-iter"]], "DiGraph.__len__": [[852, "digraph-len"]], "DiGraph.add_edge": [[853, "digraph-add-edge"]], "DiGraph.add_edges_from": [[854, "digraph-add-edges-from"]], "DiGraph.add_node": [[855, "digraph-add-node"]], "DiGraph.add_nodes_from": [[856, "digraph-add-nodes-from"]], "DiGraph.add_weighted_edges_from": [[857, "digraph-add-weighted-edges-from"]], "DiGraph.adj": [[858, "digraph-adj"]], "DiGraph.adjacency": [[859, "digraph-adjacency"]], "DiGraph.clear": [[860, "digraph-clear"]], "DiGraph.clear_edges": [[861, "digraph-clear-edges"]], "DiGraph.copy": [[862, "digraph-copy"]], "DiGraph.degree": [[863, "digraph-degree"]], "DiGraph.edge_subgraph": [[864, "digraph-edge-subgraph"]], "DiGraph.edges": [[865, "digraph-edges"]], "DiGraph.get_edge_data": [[866, "digraph-get-edge-data"]], "DiGraph.has_edge": [[867, "digraph-has-edge"]], "DiGraph.has_node": [[868, "digraph-has-node"]], "DiGraph.in_degree": [[869, "digraph-in-degree"]], "DiGraph.in_edges": [[870, "digraph-in-edges"]], "DiGraph.nbunch_iter": [[871, "digraph-nbunch-iter"]], "DiGraph.neighbors": [[872, "digraph-neighbors"]], "DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, "digraph-to-undirected"]], "DiGraph.update": [[892, "digraph-update"]], "Graph.__contains__": [[893, "graph-contains"]], "Graph.__getitem__": [[894, "graph-getitem"]], "Graph.__init__": [[895, "graph-init"]], "Graph.__iter__": [[896, "graph-iter"]], "Graph.__len__": [[897, "graph-len"]], "Graph.add_edge": [[898, "graph-add-edge"]], "Graph.add_edges_from": [[899, "graph-add-edges-from"]], "Graph.add_node": [[900, "graph-add-node"]], "Graph.add_nodes_from": [[901, "graph-add-nodes-from"]], "Graph.add_weighted_edges_from": [[902, "graph-add-weighted-edges-from"]], "Graph.adj": [[903, "graph-adj"]], "Graph.adjacency": [[904, "graph-adjacency"]], "Graph.clear": [[905, "graph-clear"]], "Graph.clear_edges": [[906, "graph-clear-edges"]], "Graph.copy": [[907, "graph-copy"]], "Graph.degree": [[908, "graph-degree"]], "Graph.edge_subgraph": [[909, "graph-edge-subgraph"]], "Graph.edges": [[910, "graph-edges"]], "Graph.get_edge_data": [[911, "graph-get-edge-data"]], "Graph.has_edge": [[912, "graph-has-edge"]], "Graph.has_node": [[913, "graph-has-node"]], "Graph.nbunch_iter": [[914, "graph-nbunch-iter"]], "Graph.neighbors": [[915, "graph-neighbors"]], "Graph.nodes": [[916, "graph-nodes"]], "Graph.number_of_edges": [[917, "graph-number-of-edges"]], "Graph.number_of_nodes": [[918, "graph-number-of-nodes"]], "Graph.order": [[919, "graph-order"]], "Graph.remove_edge": [[920, "graph-remove-edge"]], "Graph.remove_edges_from": [[921, "graph-remove-edges-from"]], "Graph.remove_node": [[922, "graph-remove-node"]], "Graph.remove_nodes_from": [[923, "graph-remove-nodes-from"]], "Graph.size": [[924, "graph-size"]], "Graph.subgraph": [[925, "graph-subgraph"]], "Graph.to_directed": [[926, "graph-to-directed"]], "Graph.to_undirected": [[927, "graph-to-undirected"]], "Graph.update": [[928, "graph-update"]], "MultiDiGraph.__contains__": [[929, "multidigraph-contains"]], "MultiDiGraph.__getitem__": [[930, "multidigraph-getitem"]], "MultiDiGraph.__init__": [[931, "multidigraph-init"]], "MultiDiGraph.__iter__": [[932, "multidigraph-iter"]], "MultiDiGraph.__len__": [[933, "multidigraph-len"]], "MultiDiGraph.add_edge": [[934, "multidigraph-add-edge"]], "MultiDiGraph.add_edges_from": [[935, "multidigraph-add-edges-from"]], "MultiDiGraph.add_node": [[936, "multidigraph-add-node"]], "MultiDiGraph.add_nodes_from": [[937, "multidigraph-add-nodes-from"]], "MultiDiGraph.add_weighted_edges_from": [[938, "multidigraph-add-weighted-edges-from"]], "MultiDiGraph.adj": [[939, "multidigraph-adj"]], "MultiDiGraph.adjacency": [[940, "multidigraph-adjacency"]], "MultiDiGraph.clear": [[941, "multidigraph-clear"]], "MultiDiGraph.clear_edges": [[942, "multidigraph-clear-edges"]], "MultiDiGraph.copy": [[943, "multidigraph-copy"]], "MultiDiGraph.degree": [[944, "multidigraph-degree"]], "MultiDiGraph.edge_subgraph": [[945, "multidigraph-edge-subgraph"]], "MultiDiGraph.edges": [[946, "multidigraph-edges"]], "MultiDiGraph.get_edge_data": [[947, "multidigraph-get-edge-data"]], "MultiDiGraph.has_edge": [[948, "multidigraph-has-edge"]], "MultiDiGraph.has_node": [[949, "multidigraph-has-node"]], "MultiDiGraph.in_degree": [[950, "multidigraph-in-degree"]], "MultiDiGraph.in_edges": [[951, "multidigraph-in-edges"]], "MultiDiGraph.nbunch_iter": [[952, "multidigraph-nbunch-iter"]], "MultiDiGraph.neighbors": [[953, "multidigraph-neighbors"]], "MultiDiGraph.new_edge_key": [[954, "multidigraph-new-edge-key"]], "MultiDiGraph.nodes": [[955, "multidigraph-nodes"]], "MultiDiGraph.number_of_edges": [[956, "multidigraph-number-of-edges"]], "MultiDiGraph.number_of_nodes": [[957, "multidigraph-number-of-nodes"]], "MultiDiGraph.order": [[958, "multidigraph-order"]], "MultiDiGraph.out_degree": [[959, "multidigraph-out-degree"]], "MultiDiGraph.out_edges": [[960, "multidigraph-out-edges"]], "MultiDiGraph.predecessors": [[961, "multidigraph-predecessors"]], "MultiDiGraph.remove_edge": [[962, "multidigraph-remove-edge"]], "MultiDiGraph.remove_edges_from": [[963, 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Applying classic graph operations, such as:": [[1426, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1426, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1426, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1426, "using-a-stochastic-graph-generator-e-g"]], "5. 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property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[751, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[752, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[753, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[755, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[757, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[759, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[760, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[764, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[766, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[767, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[768, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[769, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[770, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[771, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[772, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[772, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[772, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[772, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[774, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[775, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[777, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[778, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "networkx.algorithms.shortest_paths.weighted": [[779, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[780, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[781, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[782, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[783, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[785, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[786, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[787, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[788, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[789, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "networkx.algorithms.traversal.breadth_first_search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "networkx.algorithms.traversal.depth_first_search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "networkx.algorithms.traversal.edgebfs": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "networkx.algorithms.traversal.edgedfs": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "networkx.algorithms.tree.branchings": [[791, "module-networkx.algorithms.tree.branchings"]], "networkx.algorithms.tree.coding": [[791, "module-networkx.algorithms.tree.coding"]], "networkx.algorithms.tree.decomposition": [[791, "module-networkx.algorithms.tree.decomposition"]], "networkx.algorithms.tree.mst": [[791, "module-networkx.algorithms.tree.mst"]], "networkx.algorithms.tree.operations": [[791, "module-networkx.algorithms.tree.operations"]], "networkx.algorithms.tree.recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "networkx.algorithms.triads": [[792, "module-networkx.algorithms.triads"]], "networkx.algorithms.vitality": [[793, "module-networkx.algorithms.vitality"]], "networkx.algorithms.voronoi": [[794, "module-networkx.algorithms.voronoi"]], "networkx.algorithms.wiener": [[795, "module-networkx.algorithms.wiener"]], "digraph (class in networkx)": [[796, "networkx.DiGraph"]], "copy() (adjacencyview method)": [[797, "networkx.classes.coreviews.AdjacencyView.copy"]], "get() (adjacencyview method)": [[798, "networkx.classes.coreviews.AdjacencyView.get"]], "items() (adjacencyview method)": [[799, "networkx.classes.coreviews.AdjacencyView.items"]], "keys() (adjacencyview method)": [[800, "networkx.classes.coreviews.AdjacencyView.keys"]], "values() (adjacencyview method)": [[801, "networkx.classes.coreviews.AdjacencyView.values"]], "copy() (atlasview method)": [[802, "networkx.classes.coreviews.AtlasView.copy"]], "get() (atlasview method)": [[803, 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method)": [[824, "networkx.classes.coreviews.MultiAdjacencyView.get"]], "items() (multiadjacencyview method)": [[825, "networkx.classes.coreviews.MultiAdjacencyView.items"]], "keys() (multiadjacencyview method)": [[826, "networkx.classes.coreviews.MultiAdjacencyView.keys"]], "values() (multiadjacencyview method)": [[827, "networkx.classes.coreviews.MultiAdjacencyView.values"]], "copy() (unionadjacency method)": [[828, "networkx.classes.coreviews.UnionAdjacency.copy"]], "get() (unionadjacency method)": [[829, "networkx.classes.coreviews.UnionAdjacency.get"]], "items() (unionadjacency method)": [[830, "networkx.classes.coreviews.UnionAdjacency.items"]], "keys() (unionadjacency method)": [[831, "networkx.classes.coreviews.UnionAdjacency.keys"]], "values() (unionadjacency method)": [[832, "networkx.classes.coreviews.UnionAdjacency.values"]], "copy() (unionatlas method)": [[833, "networkx.classes.coreviews.UnionAtlas.copy"]], "get() (unionatlas method)": [[834, "networkx.classes.coreviews.UnionAtlas.get"]], "items() (unionatlas method)": [[835, "networkx.classes.coreviews.UnionAtlas.items"]], "keys() (unionatlas method)": [[836, "networkx.classes.coreviews.UnionAtlas.keys"]], "values() (unionatlas method)": [[837, "networkx.classes.coreviews.UnionAtlas.values"]], "copy() (unionmultiadjacency method)": [[838, "networkx.classes.coreviews.UnionMultiAdjacency.copy"]], "get() (unionmultiadjacency method)": [[839, "networkx.classes.coreviews.UnionMultiAdjacency.get"]], "items() (unionmultiadjacency method)": [[840, "networkx.classes.coreviews.UnionMultiAdjacency.items"]], "keys() (unionmultiadjacency method)": [[841, "networkx.classes.coreviews.UnionMultiAdjacency.keys"]], "values() (unionmultiadjacency method)": [[842, "networkx.classes.coreviews.UnionMultiAdjacency.values"]], "copy() (unionmultiinner method)": [[843, "networkx.classes.coreviews.UnionMultiInner.copy"]], "get() (unionmultiinner method)": [[844, "networkx.classes.coreviews.UnionMultiInner.get"]], "items() (unionmultiinner method)": [[845, "networkx.classes.coreviews.UnionMultiInner.items"]], "keys() (unionmultiinner method)": [[846, "networkx.classes.coreviews.UnionMultiInner.keys"]], "values() (unionmultiinner method)": [[847, "networkx.classes.coreviews.UnionMultiInner.values"]], "__contains__() (digraph method)": [[848, "networkx.DiGraph.__contains__"]], "__getitem__() (digraph method)": [[849, "networkx.DiGraph.__getitem__"]], "__init__() (digraph method)": [[850, "networkx.DiGraph.__init__"]], "__iter__() (digraph method)": [[851, "networkx.DiGraph.__iter__"]], "__len__() (digraph method)": [[852, "networkx.DiGraph.__len__"]], "add_edge() (digraph method)": [[853, "networkx.DiGraph.add_edge"]], "add_edges_from() (digraph method)": [[854, "networkx.DiGraph.add_edges_from"]], "add_node() (digraph method)": [[855, "networkx.DiGraph.add_node"]], "add_nodes_from() (digraph method)": [[856, "networkx.DiGraph.add_nodes_from"]], "add_weighted_edges_from() (digraph method)": [[857, "networkx.DiGraph.add_weighted_edges_from"]], "adj (digraph property)": [[858, "networkx.DiGraph.adj"]], "adjacency() (digraph method)": [[859, "networkx.DiGraph.adjacency"]], "clear() (digraph method)": [[860, "networkx.DiGraph.clear"]], "clear_edges() (digraph method)": [[861, "networkx.DiGraph.clear_edges"]], "copy() (digraph method)": [[862, "networkx.DiGraph.copy"]], "degree (digraph property)": [[863, "networkx.DiGraph.degree"]], "edge_subgraph() (digraph method)": [[864, "networkx.DiGraph.edge_subgraph"]], "edges (digraph property)": [[865, "networkx.DiGraph.edges"]], "get_edge_data() (digraph method)": [[866, "networkx.DiGraph.get_edge_data"]], "has_edge() (digraph method)": [[867, "networkx.DiGraph.has_edge"]], "has_node() (digraph method)": [[868, "networkx.DiGraph.has_node"]], "in_degree (digraph property)": [[869, "networkx.DiGraph.in_degree"]], "in_edges (digraph property)": [[870, "networkx.DiGraph.in_edges"]], 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"add_edges_from() (graph method)": [[899, "networkx.Graph.add_edges_from"]], "add_node() (graph method)": [[900, "networkx.Graph.add_node"]], "add_nodes_from() (graph method)": [[901, "networkx.Graph.add_nodes_from"]], "add_weighted_edges_from() (graph method)": [[902, "networkx.Graph.add_weighted_edges_from"]], "adj (graph property)": [[903, "networkx.Graph.adj"]], "adjacency() (graph method)": [[904, "networkx.Graph.adjacency"]], "clear() (graph method)": [[905, "networkx.Graph.clear"]], "clear_edges() (graph method)": [[906, "networkx.Graph.clear_edges"]], "copy() (graph method)": [[907, "networkx.Graph.copy"]], "degree (graph property)": [[908, "networkx.Graph.degree"]], "edge_subgraph() (graph method)": [[909, "networkx.Graph.edge_subgraph"]], "edges (graph property)": [[910, "networkx.Graph.edges"]], "get_edge_data() (graph method)": [[911, "networkx.Graph.get_edge_data"]], "has_edge() (graph method)": [[912, "networkx.Graph.has_edge"]], "has_node() (graph method)": [[913, "networkx.Graph.has_node"]], "nbunch_iter() (graph method)": [[914, "networkx.Graph.nbunch_iter"]], "neighbors() (graph method)": [[915, "networkx.Graph.neighbors"]], "nodes (graph property)": [[916, "networkx.Graph.nodes"]], "number_of_edges() (graph method)": [[917, "networkx.Graph.number_of_edges"]], "number_of_nodes() (graph method)": [[918, "networkx.Graph.number_of_nodes"]], "order() (graph method)": [[919, "networkx.Graph.order"]], "remove_edge() (graph method)": [[920, "networkx.Graph.remove_edge"]], "remove_edges_from() (graph method)": [[921, "networkx.Graph.remove_edges_from"]], "remove_node() (graph method)": [[922, "networkx.Graph.remove_node"]], "remove_nodes_from() (graph method)": [[923, "networkx.Graph.remove_nodes_from"]], "size() (graph method)": [[924, "networkx.Graph.size"]], "subgraph() (graph method)": [[925, "networkx.Graph.subgraph"]], "to_directed() (graph method)": [[926, "networkx.Graph.to_directed"]], "to_undirected() (graph method)": [[927, "networkx.Graph.to_undirected"]], "update() (graph method)": [[928, "networkx.Graph.update"]], "__contains__() (multidigraph method)": [[929, "networkx.MultiDiGraph.__contains__"]], "__getitem__() (multidigraph method)": [[930, "networkx.MultiDiGraph.__getitem__"]], "__init__() (multidigraph method)": [[931, "networkx.MultiDiGraph.__init__"]], "__iter__() (multidigraph method)": [[932, "networkx.MultiDiGraph.__iter__"]], "__len__() (multidigraph method)": [[933, "networkx.MultiDiGraph.__len__"]], "add_edge() (multidigraph method)": [[934, "networkx.MultiDiGraph.add_edge"]], "add_edges_from() (multidigraph method)": [[935, "networkx.MultiDiGraph.add_edges_from"]], "add_node() (multidigraph method)": [[936, "networkx.MultiDiGraph.add_node"]], "add_nodes_from() (multidigraph method)": [[937, "networkx.MultiDiGraph.add_nodes_from"]], "add_weighted_edges_from() (multidigraph method)": [[938, "networkx.MultiDiGraph.add_weighted_edges_from"]], "adj (multidigraph property)": [[939, 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"networkx.MultiDiGraph.remove_edges_from"]], "remove_node() (multidigraph method)": [[964, "networkx.MultiDiGraph.remove_node"]], "remove_nodes_from() (multidigraph method)": [[965, "networkx.MultiDiGraph.remove_nodes_from"]], "reverse() (multidigraph method)": [[966, "networkx.MultiDiGraph.reverse"]], "size() (multidigraph method)": [[967, "networkx.MultiDiGraph.size"]], "subgraph() (multidigraph method)": [[968, "networkx.MultiDiGraph.subgraph"]], "succ (multidigraph property)": [[969, "networkx.MultiDiGraph.succ"]], "successors() (multidigraph method)": [[970, "networkx.MultiDiGraph.successors"]], "to_directed() (multidigraph method)": [[971, "networkx.MultiDiGraph.to_directed"]], "to_undirected() (multidigraph method)": [[972, "networkx.MultiDiGraph.to_undirected"]], "update() (multidigraph method)": [[973, "networkx.MultiDiGraph.update"]], "__contains__() (multigraph method)": [[974, "networkx.MultiGraph.__contains__"]], "__getitem__() (multigraph method)": [[975, "networkx.MultiGraph.__getitem__"]], "__init__() (multigraph method)": [[976, "networkx.MultiGraph.__init__"]], "__iter__() (multigraph method)": [[977, "networkx.MultiGraph.__iter__"]], "__len__() (multigraph method)": [[978, "networkx.MultiGraph.__len__"]], "add_edge() (multigraph method)": [[979, "networkx.MultiGraph.add_edge"]], "add_edges_from() (multigraph method)": [[980, "networkx.MultiGraph.add_edges_from"]], "add_node() (multigraph method)": [[981, "networkx.MultiGraph.add_node"]], "add_nodes_from() (multigraph method)": [[982, "networkx.MultiGraph.add_nodes_from"]], "add_weighted_edges_from() (multigraph method)": [[983, "networkx.MultiGraph.add_weighted_edges_from"]], "adj (multigraph property)": [[984, "networkx.MultiGraph.adj"]], "adjacency() (multigraph method)": [[985, "networkx.MultiGraph.adjacency"]], "clear() (multigraph method)": [[986, "networkx.MultiGraph.clear"]], "clear_edges() (multigraph method)": [[987, "networkx.MultiGraph.clear_edges"]], "copy() (multigraph method)": [[988, "networkx.MultiGraph.copy"]], "degree (multigraph property)": [[989, "networkx.MultiGraph.degree"]], "edge_subgraph() (multigraph method)": [[990, "networkx.MultiGraph.edge_subgraph"]], "edges (multigraph property)": [[991, "networkx.MultiGraph.edges"]], "get_edge_data() (multigraph method)": [[992, "networkx.MultiGraph.get_edge_data"]], "has_edge() (multigraph method)": [[993, "networkx.MultiGraph.has_edge"]], "has_node() (multigraph method)": [[994, "networkx.MultiGraph.has_node"]], "nbunch_iter() (multigraph method)": [[995, "networkx.MultiGraph.nbunch_iter"]], "neighbors() (multigraph method)": [[996, "networkx.MultiGraph.neighbors"]], "new_edge_key() (multigraph method)": [[997, "networkx.MultiGraph.new_edge_key"]], "nodes (multigraph property)": [[998, "networkx.MultiGraph.nodes"]], "number_of_edges() (multigraph method)": [[999, "networkx.MultiGraph.number_of_edges"]], "number_of_nodes() (multigraph method)": [[1000, "networkx.MultiGraph.number_of_nodes"]], "order() (multigraph method)": [[1001, "networkx.MultiGraph.order"]], "remove_edge() (multigraph method)": [[1002, "networkx.MultiGraph.remove_edge"]], "remove_edges_from() (multigraph method)": [[1003, "networkx.MultiGraph.remove_edges_from"]], "remove_node() (multigraph method)": [[1004, "networkx.MultiGraph.remove_node"]], "remove_nodes_from() (multigraph method)": [[1005, "networkx.MultiGraph.remove_nodes_from"]], "size() (multigraph method)": [[1006, "networkx.MultiGraph.size"]], "subgraph() (multigraph method)": [[1007, "networkx.MultiGraph.subgraph"]], "to_directed() (multigraph method)": [[1008, "networkx.MultiGraph.to_directed"]], "to_undirected() (multigraph method)": [[1009, "networkx.MultiGraph.to_undirected"]], "update() (multigraph method)": [[1010, "networkx.MultiGraph.update"]], "_dispatch() (in module networkx.classes.backends)": [[1011, "networkx.classes.backends._dispatch"]], "adjacencyview (class in networkx.classes.coreviews)": [[1012, "networkx.classes.coreviews.AdjacencyView"]], "__init__() (adjacencyview method)": [[1012, "networkx.classes.coreviews.AdjacencyView.__init__"]], "atlasview (class in networkx.classes.coreviews)": [[1013, "networkx.classes.coreviews.AtlasView"]], "__init__() (atlasview method)": [[1013, "networkx.classes.coreviews.AtlasView.__init__"]], "filteradjacency (class in networkx.classes.coreviews)": [[1014, "networkx.classes.coreviews.FilterAdjacency"]], "__init__() (filteradjacency method)": [[1014, "networkx.classes.coreviews.FilterAdjacency.__init__"]], "filteratlas (class in networkx.classes.coreviews)": [[1015, "networkx.classes.coreviews.FilterAtlas"]], "__init__() (filteratlas method)": [[1015, "networkx.classes.coreviews.FilterAtlas.__init__"]], "filtermultiadjacency (class in networkx.classes.coreviews)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency"]], "__init__() (filtermultiadjacency method)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency.__init__"]], "filtermultiinner (class in networkx.classes.coreviews)": [[1017, "networkx.classes.coreviews.FilterMultiInner"]], "__init__() (filtermultiinner method)": [[1017, "networkx.classes.coreviews.FilterMultiInner.__init__"]], "multiadjacencyview (class in networkx.classes.coreviews)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView"]], "__init__() (multiadjacencyview method)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView.__init__"]], "unionadjacency (class in networkx.classes.coreviews)": [[1019, "networkx.classes.coreviews.UnionAdjacency"]], "__init__() (unionadjacency method)": [[1019, "networkx.classes.coreviews.UnionAdjacency.__init__"]], "unionatlas (class in networkx.classes.coreviews)": [[1020, "networkx.classes.coreviews.UnionAtlas"]], "__init__() (unionatlas method)": [[1020, "networkx.classes.coreviews.UnionAtlas.__init__"]], "unionmultiadjacency (class in networkx.classes.coreviews)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency"]], "__init__() (unionmultiadjacency method)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency.__init__"]], "unionmultiinner (class in networkx.classes.coreviews)": [[1022, "networkx.classes.coreviews.UnionMultiInner"]], "__init__() (unionmultiinner method)": [[1022, "networkx.classes.coreviews.UnionMultiInner.__init__"]], "hide_diedges() (in module networkx.classes.filters)": [[1023, "networkx.classes.filters.hide_diedges"]], "hide_edges() (in module networkx.classes.filters)": [[1024, "networkx.classes.filters.hide_edges"]], "hide_multidiedges() (in module networkx.classes.filters)": [[1025, "networkx.classes.filters.hide_multidiedges"]], "hide_multiedges() (in module networkx.classes.filters)": [[1026, "networkx.classes.filters.hide_multiedges"]], "hide_nodes() (in module networkx.classes.filters)": [[1027, "networkx.classes.filters.hide_nodes"]], "no_filter() (in module networkx.classes.filters)": [[1028, "networkx.classes.filters.no_filter"]], "show_diedges() (in module networkx.classes.filters)": [[1029, "networkx.classes.filters.show_diedges"]], "show_edges() (in module networkx.classes.filters)": [[1030, "networkx.classes.filters.show_edges"]], "show_multidiedges() (in module networkx.classes.filters)": [[1031, "networkx.classes.filters.show_multidiedges"]], "show_multiedges() (in module networkx.classes.filters)": [[1032, "networkx.classes.filters.show_multiedges"]], "__init__() (show_nodes method)": [[1033, "networkx.classes.filters.show_nodes.__init__"]], "show_nodes (class in networkx.classes.filters)": [[1033, "networkx.classes.filters.show_nodes"]], "generic_graph_view() (in module networkx.classes.graphviews)": [[1034, "networkx.classes.graphviews.generic_graph_view"]], "reverse_view() (in module networkx.classes.graphviews)": [[1035, "networkx.classes.graphviews.reverse_view"]], "subgraph_view() (in module networkx.classes.graphviews)": [[1036, "networkx.classes.graphviews.subgraph_view"]], "graph (class in networkx)": [[1037, "networkx.Graph"]], "networkx.classes.backends": [[1038, "module-networkx.classes.backends"]], "networkx.classes.coreviews": [[1038, "module-networkx.classes.coreviews"]], "networkx.classes.filters": [[1038, "module-networkx.classes.filters"]], "networkx.classes.graphviews": [[1038, "module-networkx.classes.graphviews"]], "multidigraph (class in networkx)": [[1039, "networkx.MultiDiGraph"]], "multigraph (class in networkx)": [[1040, "networkx.MultiGraph"]], "networkx.convert": [[1041, "module-networkx.convert"]], "networkx.convert_matrix": [[1041, "module-networkx.convert_matrix"]], "networkx.drawing.layout": [[1042, "module-networkx.drawing.layout"]], "networkx.drawing.nx_agraph": [[1042, "module-networkx.drawing.nx_agraph"]], "networkx.drawing.nx_pydot": [[1042, "module-networkx.drawing.nx_pydot"]], "networkx.drawing.nx_pylab": [[1042, "module-networkx.drawing.nx_pylab"]], "ambiguoussolution (class in networkx)": [[1043, "networkx.AmbiguousSolution"]], "exceededmaxiterations (class in networkx)": [[1043, "networkx.ExceededMaxIterations"]], "hasacycle (class in networkx)": [[1043, "networkx.HasACycle"]], "networkxalgorithmerror (class in networkx)": [[1043, "networkx.NetworkXAlgorithmError"]], "networkxerror (class in networkx)": [[1043, "networkx.NetworkXError"]], "networkxexception (class in networkx)": [[1043, "networkx.NetworkXException"]], "networkxnocycle (class in networkx)": [[1043, "networkx.NetworkXNoCycle"]], "networkxnopath (class in networkx)": [[1043, "networkx.NetworkXNoPath"]], "networkxnotimplemented (class in networkx)": [[1043, "networkx.NetworkXNotImplemented"]], "networkxpointlessconcept (class in networkx)": [[1043, "networkx.NetworkXPointlessConcept"]], "networkxunbounded (class in networkx)": [[1043, "networkx.NetworkXUnbounded"]], "networkxunfeasible (class in networkx)": [[1043, "networkx.NetworkXUnfeasible"]], "nodenotfound (class in networkx)": [[1043, "networkx.NodeNotFound"]], "poweriterationfailedconvergence (class in networkx)": [[1043, "networkx.PowerIterationFailedConvergence"]], "networkx.exception": [[1043, "module-networkx.exception"]], "networkx.classes.function": [[1044, "module-networkx.classes.function"]], "assemble() (argmap method)": [[1045, "networkx.utils.decorators.argmap.assemble"]], "compile() (argmap method)": [[1046, "networkx.utils.decorators.argmap.compile"]], "signature() (argmap class method)": [[1047, "networkx.utils.decorators.argmap.signature"]], "pop() (mappedqueue method)": [[1048, "networkx.utils.mapped_queue.MappedQueue.pop"]], "push() (mappedqueue method)": [[1049, "networkx.utils.mapped_queue.MappedQueue.push"]], "remove() (mappedqueue method)": [[1050, "networkx.utils.mapped_queue.MappedQueue.remove"]], "update() (mappedqueue method)": [[1051, "networkx.utils.mapped_queue.MappedQueue.update"]], "add_cycle() (in module networkx.classes.function)": [[1052, "networkx.classes.function.add_cycle"]], "add_path() (in module networkx.classes.function)": [[1053, "networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, "networkx.convert_matrix.from_pandas_adjacency"]], "from_pandas_edgelist() (in module networkx.convert_matrix)": [[1100, "networkx.convert_matrix.from_pandas_edgelist"]], "from_scipy_sparse_array() (in module networkx.convert_matrix)": [[1101, "networkx.convert_matrix.from_scipy_sparse_array"]], "to_numpy_array() (in module networkx.convert_matrix)": [[1102, "networkx.convert_matrix.to_numpy_array"]], "to_pandas_adjacency() (in module networkx.convert_matrix)": [[1103, "networkx.convert_matrix.to_pandas_adjacency"]], "to_pandas_edgelist() (in module networkx.convert_matrix)": [[1104, "networkx.convert_matrix.to_pandas_edgelist"]], "to_scipy_sparse_array() (in module networkx.convert_matrix)": [[1105, "networkx.convert_matrix.to_scipy_sparse_array"]], "bipartite_layout() (in module networkx.drawing.layout)": [[1106, "networkx.drawing.layout.bipartite_layout"]], "circular_layout() (in module networkx.drawing.layout)": [[1107, "networkx.drawing.layout.circular_layout"]], 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module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, 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module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() 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networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, 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networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, 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1280], "littl": [8, 94, 298, 307], "mislead": 8, "That": [8, 97, 132, 165, 212, 221, 227, 295, 385, 436, 465, 525, 535, 555, 588, 657, 671, 672, 673, 674, 691, 704, 717, 791, 862, 907, 943, 988, 1046, 1162, 1210, 1296, 1388, 1404, 1409], "okai": 8, "becaus": [8, 11, 54, 69, 94, 99, 101, 102, 103, 112, 132, 161, 215, 216, 220, 255, 311, 378, 387, 389, 390, 394, 411, 412, 427, 494, 498, 499, 500, 510, 569, 585, 587, 615, 616, 632, 652, 934, 979, 1038, 1236, 1273, 1296, 1303, 1326, 1345, 1350, 1404, 1407, 1416], "AND": [8, 110, 598, 748, 762], "OR": [8, 110, 157, 175, 188, 856, 869, 877, 901, 937, 947, 950, 959, 982, 992], "symmetr": [8, 145, 148, 237, 545, 586, 593, 761, 1173, 1192, 1235, 1246, 1250, 1251, 1256, 1258, 1269, 1320, 1321, 1387], "It": [8, 52, 56, 58, 92, 93, 94, 97, 99, 101, 102, 104, 107, 110, 112, 115, 132, 172, 184, 207, 214, 215, 216, 229, 230, 231, 249, 260, 261, 262, 264, 278, 310, 316, 324, 325, 326, 343, 346, 347, 351, 353, 412, 414, 415, 416, 417, 418, 419, 429, 438, 440, 452, 457, 464, 480, 496, 500, 508, 530, 540, 545, 559, 560, 565, 566, 567, 582, 588, 594, 595, 598, 600, 601, 615, 619, 628, 629, 630, 652, 658, 659, 663, 671, 674, 692, 717, 718, 719, 760, 761, 762, 791, 796, 868, 873, 892, 913, 916, 928, 949, 955, 973, 994, 998, 1010, 1012, 1013, 1018, 1037, 1038, 1039, 1040, 1054, 1117, 1170, 1174, 1200, 1201, 1206, 1207, 1210, 1217, 1223, 1227, 1234, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1253, 1254, 1258, 1261, 1263, 1264, 1269, 1275, 1276, 1277, 1280, 1296, 1297, 1323, 1324, 1326, 1328, 1343, 1382, 1383, 1393, 1395, 1398, 1402, 1404, 1407, 1408, 1409, 1411, 1412, 1413, 1426], "just": [8, 99, 102, 103, 104, 184, 199, 338, 374, 439, 464, 559, 560, 577, 660, 661, 662, 692, 791, 873, 887, 916, 925, 946, 955, 960, 968, 991, 998, 1007, 1120, 1126, 1229, 1278, 1279, 1296, 1328, 1393, 1404, 1406], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1325, 1402, 1406, 1412], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1202, 1383], "doe": [8, 77, 93, 94, 99, 101, 102, 103, 104, 114, 115, 132, 147, 153, 154, 165, 168, 189, 207, 208, 227, 228, 229, 230, 231, 232, 293, 308, 339, 340, 342, 343, 352, 357, 373, 382, 385, 410, 414, 426, 450, 469, 494, 495, 496, 497, 498, 499, 500, 502, 503, 506, 507, 509, 510, 511, 512, 534, 544, 549, 550, 551, 564, 566, 583, 584, 586, 589, 601, 612, 626, 627, 678, 691, 693, 694, 698, 699, 717, 718, 721, 722, 723, 724, 725, 726, 762, 862, 865, 878, 892, 907, 910, 928, 943, 946, 960, 973, 988, 991, 1010, 1038, 1043, 1066, 1070, 1072, 1081, 1102, 1103, 1105, 1106, 1107, 1109, 1114, 1173, 1175, 1177, 1192, 1207, 1222, 1223, 1227, 1229, 1234, 1241, 1296, 1300, 1303, 1326, 1333, 1334, 1341, 1342, 1344, 1351, 1353, 1354, 1355, 1356, 1357, 1358, 1371, 1379, 1380, 1381, 1383, 1393, 1404, 1405, 1406, 1410, 1417, 1426], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1219], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1229, 1296, 1395, 1404], "everi": [8, 11, 57, 88, 93, 109, 112, 120, 144, 157, 161, 177, 211, 212, 220, 221, 229, 230, 231, 235, 243, 264, 287, 295, 300, 324, 325, 343, 352, 380, 397, 437, 439, 440, 450, 462, 471, 472, 473, 474, 475, 477, 483, 484, 491, 512, 516, 565, 606, 614, 615, 619, 632, 633, 635, 636, 663, 685, 687, 688, 717, 718, 791, 856, 901, 937, 982, 1052, 1053, 1054, 1070, 1071, 1072, 1085, 1086, 1102, 1103, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1148, 1162, 1195, 1216, 1217, 1257, 1264, 1278, 1279, 1296, 1407], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1177, 1207, 1208, 1404, 1406, 1407, 1426], "ha": [8, 11, 16, 44, 67, 88, 91, 93, 94, 95, 97, 99, 100, 101, 102, 103, 105, 107, 110, 112, 116, 120, 127, 152, 161, 165, 166, 173, 174, 175, 184, 188, 198, 207, 212, 214, 215, 219, 220, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 241, 242, 243, 244, 247, 249, 252, 269, 271, 272, 273, 274, 275, 276, 282, 289, 291, 293, 294, 295, 300, 305, 310, 324, 331, 343, 352, 355, 356, 363, 364, 365, 373, 378, 380, 381, 383, 384, 385, 386, 391, 393, 394, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 424, 427, 428, 429, 439, 450, 458, 460, 466, 467, 468, 471, 472, 473, 474, 475, 476, 477, 480, 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 522, 564, 566, 577, 578, 581, 590, 593, 605, 607, 610, 611, 622, 623, 624, 628, 629, 630, 632, 633, 634, 635, 636, 638, 646, 647, 649, 652, 657, 658, 682, 688, 690, 692, 697, 711, 717, 718, 729, 730, 731, 739, 749, 786, 791, 854, 862, 863, 869, 873, 877, 886, 892, 899, 907, 908, 916, 924, 928, 935, 943, 944, 948, 950, 955, 959, 967, 973, 980, 988, 989, 993, 998, 1006, 1010, 1040, 1043, 1045, 1066, 1068, 1070, 1072, 1075, 1080, 1084, 1098, 1099, 1101, 1102, 1103, 1105, 1122, 1130, 1145, 1154, 1160, 1162, 1165, 1176, 1180, 1185, 1193, 1195, 1196, 1197, 1198, 1199, 1207, 1210, 1211, 1215, 1217, 1222, 1234, 1239, 1243, 1244, 1248, 1249, 1254, 1259, 1261, 1264, 1267, 1269, 1270, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1283, 1284, 1285, 1286, 1289, 1291, 1293, 1296, 1300, 1326, 1328, 1330, 1333, 1334, 1353, 1354, 1371, 1372, 1379, 1382, 1393, 1394, 1395, 1398, 1403, 1404, 1405, 1406, 1407, 1409, 1413, 1414, 1416, 1423, 1425], "output": [8, 13, 16, 89, 93, 101, 102, 103, 109, 197, 287, 288, 345, 374, 380, 494, 498, 499, 509, 510, 575, 588, 677, 678, 691, 722, 1045, 1193, 1197, 1199, 1269, 1296, 1326, 1334, 1341, 1344, 1355, 1358, 1399, 1402, 1404, 1406, 1411, 1413, 1414, 1426], "two": [8, 11, 16, 27, 34, 38, 43, 54, 55, 57, 58, 65, 67, 71, 88, 93, 95, 99, 100, 102, 109, 112, 114, 115, 120, 132, 151, 171, 175, 184, 185, 188, 202, 207, 211, 212, 213, 214, 215, 216, 217, 220, 221, 226, 227, 230, 231, 232, 245, 249, 251, 252, 253, 257, 258, 260, 261, 262, 265, 269, 270, 271, 272, 273, 274, 275, 276, 282, 285, 286, 287, 289, 305, 311, 315, 316, 322, 326, 329, 330, 337, 341, 343, 345, 351, 352, 358, 359, 377, 380, 381, 383, 391, 411, 412, 419, 423, 428, 429, 430, 431, 442, 443, 444, 445, 447, 452, 453, 454, 457, 462, 471, 472, 473, 474, 475, 476, 480, 491, 494, 498, 499, 500, 502, 503, 506, 508, 509, 510, 511, 521, 545, 549, 550, 551, 555, 559, 560, 561, 562, 563, 564, 565, 566, 568, 569, 572, 574, 578, 584, 585, 586, 587, 588, 593, 598, 605, 607, 608, 610, 611, 615, 619, 626, 627, 629, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 680, 692, 694, 731, 732, 738, 739, 760, 761, 762, 780, 786, 791, 796, 853, 867, 869, 873, 874, 877, 890, 892, 898, 912, 916, 917, 926, 928, 934, 946, 948, 950, 955, 956, 959, 960, 971, 973, 979, 991, 993, 998, 999, 1008, 1010, 1019, 1020, 1021, 1022, 1036, 1037, 1039, 1040, 1056, 1084, 1088, 1098, 1100, 1101, 1106, 1107, 1108, 1109, 1114, 1116, 1134, 1146, 1147, 1149, 1151, 1152, 1156, 1174, 1185, 1186, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1204, 1207, 1210, 1211, 1215, 1217, 1218, 1243, 1244, 1253, 1271, 1272, 1275, 1276, 1294, 1295, 1296, 1323, 1324, 1326, 1328, 1359, 1360, 1363, 1393, 1394, 1395, 1397, 1402, 1404, 1405, 1406, 1407, 1410, 1411, 1413, 1425], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1420], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1217, 1226, 1262, 1263, 1326, 1407], "appear": [8, 83, 93, 95, 99, 100, 102, 179, 204, 230, 231, 238, 243, 246, 247, 277, 363, 364, 365, 378, 451, 452, 453, 455, 466, 470, 584, 585, 587, 588, 675, 679, 707, 730, 734, 736, 891, 972, 1036, 1088, 1102, 1136, 1150, 1152, 1154, 1157, 1159, 1187, 1188, 1277, 1282, 1323, 1324, 1345, 1348, 1349, 1350, 1382, 1407, 1413, 1414], "both": [8, 52, 55, 92, 93, 94, 100, 101, 102, 103, 115, 161, 164, 204, 214, 215, 216, 217, 240, 257, 258, 259, 264, 282, 286, 287, 289, 337, 358, 379, 383, 415, 417, 418, 419, 423, 427, 440, 470, 502, 506, 545, 575, 581, 598, 600, 601, 602, 603, 604, 605, 606, 607, 610, 611, 615, 621, 635, 636, 653, 654, 655, 676, 711, 720, 760, 761, 762, 782, 891, 972, 1020, 1036, 1066, 1075, 1080, 1084, 1088, 1097, 1120, 1126, 1144, 1165, 1189, 1192, 1199, 1207, 1210, 1211, 1213, 1215, 1282, 1296, 1326, 1328, 1358, 1363, 1364, 1387, 1393, 1395, 1402, 1413, 1416, 1417, 1425, 1426], "negat": 8, "sole": [8, 786, 1278, 1279, 1326], "fourth": [8, 230, 231, 1326, 1404], "digraph": [8, 10, 11, 16, 21, 25, 41, 45, 56, 61, 67, 69, 70, 82, 88, 101, 102, 115, 132, 151, 152, 156, 157, 158, 160, 162, 163, 165, 166, 168, 170, 171, 172, 175, 176, 185, 186, 187, 188, 189, 192, 193, 194, 195, 196, 198, 199, 202, 204, 207, 208, 216, 227, 229, 230, 231, 240, 246, 247, 299, 308, 314, 318, 319, 321, 327, 328, 334, 335, 336, 337, 339, 340, 342, 343, 388, 391, 393, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 430, 431, 437, 450, 452, 453, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 481, 482, 492, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 513, 514, 518, 519, 523, 555, 566, 575, 576, 577, 588, 590, 613, 615, 623, 630, 636, 643, 644, 652, 656, 657, 658, 659, 663, 678, 688, 690, 693, 696, 697, 698, 699, 700, 701, 702, 706, 707, 708, 709, 711, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 740, 741, 744, 745, 746, 747, 748, 749, 750, 752, 760, 789, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 911, 912, 913, 915, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 957, 958, 963, 964, 965, 966, 967, 968, 972, 973, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 1000, 1001, 1003, 1004, 1005, 1006, 1007, 1010, 1035, 1037, 1038, 1039, 1040, 1041, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1135, 1150, 1154, 1168, 1169, 1170, 1173, 1177, 1178, 1180, 1182, 1183, 1184, 1185, 1189, 1217, 1270, 1272, 1273, 1274, 1283, 1284, 1287, 1290, 1292, 1298, 1323, 1326, 1333, 1337, 1342, 1356, 1357, 1362, 1365, 1366, 1371, 1393, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "add_nod": [8, 11, 26, 34, 69, 74, 89, 102, 157, 184, 246, 339, 340, 398, 422, 491, 492, 496, 504, 505, 508, 522, 523, 605, 607, 610, 611, 691, 796, 856, 873, 901, 916, 937, 955, 982, 998, 1037, 1039, 1040, 1086, 1275, 1326, 1345, 1407, 1408, 1417, 1426], "get_node_attribut": [8, 39, 44, 71, 1213, 1404], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1133, 1134, 1136], "22": [8, 35, 64, 66, 383, 384, 1271, 1323, 1403, 1408, 1412, 1422], "multipartite_layout": [8, 36, 61, 67, 1412, 1414, 1420], "subset_kei": [8, 36, 61, 67, 1109], "equal": [8, 36, 81, 144, 214, 215, 216, 230, 231, 238, 269, 271, 273, 276, 288, 297, 298, 300, 303, 306, 307, 310, 311, 312, 315, 316, 320, 323, 324, 325, 329, 330, 331, 373, 410, 411, 412, 413, 418, 419, 428, 471, 474, 476, 491, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 525, 535, 545, 552, 553, 554, 555, 568, 572, 605, 623, 657, 671, 672, 673, 674, 687, 688, 689, 690, 721, 722, 740, 741, 753, 761, 791, 1112, 1116, 1162, 1165, 1198, 1204, 1230, 1239, 1271, 1280, 1291, 1307, 1309, 1312, 1398, 1399], "105": [8, 17, 517, 518, 1166, 1167], "plot_circuit": [8, 17], "southern": [9, 1265], "women": [9, 1265, 1398, 1406], "unipartit": [9, 115, 258, 259, 358], "properti": [9, 11, 18, 22, 33, 63, 86, 101, 102, 103, 112, 134, 159, 161, 166, 168, 175, 176, 179, 184, 188, 189, 190, 200, 284, 285, 286, 287, 288, 363, 364, 365, 388, 476, 500, 545, 569, 619, 685, 858, 863, 865, 869, 870, 873, 877, 878, 879, 888, 903, 908, 910, 916, 939, 944, 946, 950, 951, 955, 959, 960, 969, 984, 989, 991, 998, 1085, 1086, 1122, 1134, 1136, 1193, 1202, 1217, 1219, 1269, 1283, 1284, 1326, 1328, 1383, 1398, 1405, 1406, 1407, 1408, 1413, 1417, 1426], "These": [9, 52, 58, 73, 79, 86, 93, 94, 105, 112, 336, 385, 494, 512, 559, 671, 673, 732, 748, 779, 786, 1038, 1045, 1047, 1323, 1326, 1385, 1387, 1392, 1394, 1395, 1397, 1399, 1404, 1405, 1411, 1426], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1199, 1393, 1395, 1399, 1402, 1405, 1406, 1407, 1413, 1416], "et": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202], "al": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202, 1407, 1413], "1930": [9, 1396], "thei": [9, 54, 58, 65, 71, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 112, 132, 151, 165, 207, 213, 220, 249, 285, 287, 288, 296, 297, 298, 301, 302, 306, 307, 308, 309, 351, 362, 374, 391, 396, 427, 451, 452, 453, 454, 464, 465, 471, 472, 473, 474, 475, 496, 504, 505, 508, 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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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204, 237, 242, 245, 246, 247, 265, 266, 268, 282, 283, 327, 512, 555, 629, 728, 730, 762, 786, 890, 891, 926, 971, 972, 1008, 1091, 1092, 1094, 1095, 1098, 1099, 1100, 1101, 1117, 1120, 1126, 1130, 1270, 1281, 1326, 1332, 1335, 1336, 1339, 1341, 1347, 1370, 1383, 1393, 1399, 1405, 1406, 1413], "primal": [52, 55, 508, 581], "dual": [52, 54, 55, 581, 1227, 1410, 1413], "sens": [52, 97, 99, 104, 199, 310, 460, 586, 791, 887, 925, 968, 1007, 1217, 1234, 1269, 1326, 1403, 1404], "approach": [52, 55, 99, 101, 103, 104, 107, 115, 341, 345, 462, 464, 466, 500, 519, 616, 678, 1094, 1175, 1188, 1202, 1222, 1407, 1413], "segment": [52, 55, 338], "major": [52, 95, 98, 99, 100, 102, 103, 104, 106, 107, 1393, 1394, 1403, 1404, 1407], "studi": [52, 91, 110, 606, 1192, 1196, 1323, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "topologi": [52, 55, 435, 436, 512, 681, 683, 748, 1202, 1217, 1225, 1229, 1233, 1241, 1326], "encod": [52, 55, 58, 67, 99, 141, 249, 267, 268, 619, 758, 775, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1341, 1344, 1345, 1348, 1349, 1350, 1354, 1355, 1358, 1363, 1368, 1371, 1372, 1375, 1376, 1382, 1406, 1407, 1412], "angular": [52, 55], "inform": [52, 66, 92, 93, 99, 100, 101, 102, 103, 107, 111, 112, 121, 132, 159, 165, 200, 202, 204, 220, 226, 230, 231, 249, 301, 302, 303, 308, 309, 314, 323, 324, 325, 338, 405, 406, 438, 453, 455, 480, 488, 500, 512, 564, 566, 568, 572, 573, 574, 583, 592, 614, 619, 624, 691, 775, 782, 786, 796, 858, 862, 888, 890, 891, 903, 907, 926, 927, 939, 943, 969, 971, 972, 984, 988, 1008, 1009, 1037, 1039, 1040, 1042, 1112, 1141, 1143, 1185, 1206, 1214, 1216, 1217, 1218, 1219, 1267, 1280, 1290, 1296, 1356, 1373, 1375, 1376, 1381, 1383, 1389, 1390, 1393, 1394, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "angl": [52, 55, 1114, 1116], "instead": [52, 93, 94, 101, 102, 103, 106, 141, 165, 170, 282, 320, 338, 366, 370, 390, 392, 399, 405, 406, 407, 411, 412, 416, 417, 418, 419, 424, 425, 427, 500, 561, 562, 563, 585, 587, 632, 727, 729, 731, 733, 734, 735, 736, 737, 796, 862, 866, 907, 911, 943, 947, 988, 992, 1037, 1038, 1039, 1040, 1097, 1102, 1103, 1124, 1127, 1135, 1172, 1179, 1184, 1186, 1192, 1193, 1199, 1207, 1217, 1300, 1342, 1375, 1383, 1393, 1394, 1395, 1397, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1420, 1421, 1423, 1424, 1425, 1426], "nonplanar": [52, 1250], "form": [52, 55, 110, 151, 170, 220, 238, 377, 381, 391, 422, 427, 440, 449, 450, 451, 488, 500, 517, 521, 567, 568, 569, 570, 571, 572, 573, 574, 578, 579, 580, 588, 589, 677, 679, 697, 711, 717, 718, 719, 729, 730, 731, 748, 752, 767, 786, 791, 853, 866, 898, 911, 934, 947, 979, 992, 1038, 1064, 1085, 1146, 1167, 1199, 1206, 1215, 1217, 1222, 1240, 1243, 1245, 1248, 1252, 1399, 1406, 1407, 1426], "flow": [52, 66, 105, 278, 296, 301, 302, 303, 308, 309, 323, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 427, 428, 430, 431, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 519, 559, 756, 758, 1267, 1325, 1395, 1399, 1400, 1403, 1406, 1407, 1408, 1411, 1414, 1425], "dead": 52, "detail": [52, 53, 86, 92, 93, 97, 99, 100, 128, 252, 253, 256, 257, 258, 259, 260, 277, 278, 281, 282, 284, 285, 286, 287, 288, 297, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 422, 427, 476, 494, 498, 499, 500, 509, 510, 511, 512, 574, 691, 711, 720, 735, 737, 791, 796, 1037, 1039, 1040, 1102, 1105, 1133, 1137, 1140, 1207, 1296, 1319, 1345, 1348, 1349, 1350, 1393, 1399, 1400, 1401, 1402, 1406, 1413, 1414, 1426], "methodologi": 52, "avail": [52, 93, 99, 100, 101, 103, 141, 184, 226, 232, 280, 422, 425, 426, 585, 587, 780, 873, 916, 955, 998, 1039, 1042, 1194, 1196, 1197, 1198, 1328, 1331, 1334, 1393, 1394, 1396, 1402, 1405, 1406, 1409, 1412, 1413, 1426], "1016": [52, 112, 226, 231, 274, 297, 298, 299, 303, 306, 307, 313, 322, 323, 338, 346, 347, 455, 1233], "compenvurbsi": 52, "2017": [52, 227, 512, 1207, 1208, 1406, 1407], "004": [52, 341], "scienc": [52, 91, 101, 105, 107, 109, 110, 112, 219, 228, 249, 296, 301, 302, 303, 308, 309, 323, 346, 347, 409, 412, 431, 441, 445, 446, 453, 476, 498, 618, 619, 680, 681, 683, 1203, 1223, 1255], "pydata": [52, 1413, 1423, 1424, 1425], "stack": [52, 111, 346, 693, 1045, 1046], "showcas": [53, 86, 93, 109], "analys": [53, 70, 86, 310], "ecosystem": [53, 86, 99, 100, 104, 107, 110, 1425], "descript": [53, 86, 93, 97, 464, 466, 704, 717, 786, 1130, 1131, 1132, 1133, 1138, 1139, 1140, 1141, 1142, 1207, 1222, 1242, 1407, 1411, 1413, 1421, 1422, 1425], "plu": [54, 386, 583, 1036, 1088, 1148, 1253], "voronoi": [54, 752, 758, 1325, 1407], "cholera": [54, 57], "broad": [54, 57, 1296], "pump": [54, 57], "record": [54, 57, 94, 99, 691, 1426], "john": [54, 57, 91, 278, 568, 572, 685, 1205, 1250, 1408, 1413], "snow": [54, 57], "1853": [54, 57], 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1275, 1276, 1277, 1300, 1349, 1404], "centroid": [54, 57, 58], "libpys": [54, 55, 57, 58], "cg": [54, 102, 296, 301, 302, 303, 308, 309, 323, 588], "voronoi_fram": 54, "contextili": [54, 55, 57], "add_basemap": [54, 55, 57], "geopackag": [54, 55, 56, 57], "sqlite": [54, 57], "reli": [54, 57, 99, 103, 362, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 502, 503, 506, 507, 1393, 1407, 1411, 1425], "fiona": [54, 57], "level": [54, 57, 101, 103, 104, 106, 111, 112, 115, 125, 165, 220, 322, 334, 336, 374, 380, 381, 387, 389, 390, 394, 423, 427, 640, 691, 770, 786, 862, 907, 943, 988, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1094, 1108, 1155, 1202, 1207, 1208, 1236, 1296, 1323, 1328, 1396, 1399, 1407, 1412, 1413, 1414], "interfac": [54, 57, 58, 75, 76, 96, 98, 99, 101, 102, 107, 109, 110, 184, 429, 496, 673, 758, 761, 762, 780, 873, 916, 955, 998, 1042, 1044, 1326, 1328, 1393, 1396, 1398, 1402, 1404, 1405, 1406, 1409, 1413, 1414, 1426], "kind": [54, 57, 58, 92, 93, 94, 99, 208, 466, 722, 1202, 1326, 1383], "read_fil": [54, 55, 57, 58], "cholera_cas": [54, 57], "gpkg": [54, 56, 57], "correctli": [54, 164, 324, 325, 1393, 1404, 1406, 1411, 1412, 1419], "construct": [54, 55, 56, 57, 58, 67, 94, 102, 227, 229, 230, 231, 232, 269, 273, 276, 352, 423, 450, 460, 513, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 609, 685, 695, 708, 716, 732, 1046, 1047, 1052, 1053, 1101, 1102, 1103, 1104, 1105, 1153, 1154, 1175, 1177, 1178, 1180, 1186, 1190, 1191, 1192, 1195, 1203, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1229, 1236, 1251, 1259, 1263, 1269, 1272, 1278, 1279, 1296, 1323, 1327, 1395, 1399, 1406, 1409, 1415], "column_stack": [54, 57, 58], "could": [54, 93, 101, 102, 103, 165, 215, 216, 224, 581, 679, 862, 907, 943, 988, 1066, 1094, 1102, 1103, 1120, 1126, 1174, 1296, 1300, 1326, 1393, 1404, 1414, 1426], "present": [54, 58, 93, 107, 110, 132, 184, 220, 226, 315, 316, 330, 357, 359, 429, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 567, 581, 594, 595, 597, 600, 601, 604, 632, 633, 635, 636, 659, 670, 749, 786, 873, 916, 955, 998, 1043, 1045, 1061, 1082, 1150, 1152, 1157, 1159, 1160, 1163, 1165, 1278, 1279, 1353, 1354, 1357, 1381, 1383, 1407, 1411, 1426], "alongsid": [54, 438], "diagram": [54, 132, 381, 752], "intrins": 54, "put": [54, 92, 95, 102, 226, 1326, 1404, 1406], "underli": [54, 101, 102, 132, 152, 157, 158, 161, 195, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 427, 428, 490, 491, 500, 615, 742, 743, 791, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1038, 1225, 1233, 1241, 1326, 1393, 1394, 1402], "quickli": [54, 1239], "Be": [54, 92, 1038, 1135, 1404], "care": [54, 92, 100, 102, 106, 107, 109, 115, 156, 855, 900, 936, 981, 1038, 1326, 1404, 1406], "bound": [54, 112, 214, 215, 216, 217, 220, 224, 227, 264, 300, 342, 352, 437, 440, 675, 1043, 1165, 1235, 1319, 1413, 1414, 1416], "box": [54, 107, 1134, 1136, 1271, 1323], "control": [54, 168, 179, 189, 204, 230, 231, 324, 325, 450, 467, 865, 878, 891, 910, 946, 960, 991, 1328, 1402, 1408, 1409, 1413], "cell": [54, 58, 752, 758, 1271, 1323, 1325, 1407], "convex": 54, "hull": 54, "contigu": [54, 58, 438, 1102, 1277, 1278], "being": [54, 92, 94, 95, 99, 101, 102, 109, 217, 227, 464, 465, 466, 559, 560, 711, 1038, 1045, 1144, 1175, 1236, 1296, 1393, 1394, 1407, 1412, 1413, 1416, 1425], "face": [54, 101, 102, 115, 183, 206, 615, 1043, 1262, 1263], "analogu": [54, 58, 230], "von": 54, "neuman": 54, "neighborhood": [54, 58, 114, 213, 240, 249, 285, 286, 324, 325, 512, 690, 786, 1189], "cardin": [54, 115, 218, 221, 264, 277, 278, 279, 280, 339, 341, 343, 345, 414, 415, 416, 417, 428, 440, 441, 444, 446, 581, 583, 611, 691, 1395], "regular": [54, 58, 65, 88, 99, 477, 478, 479, 480, 622, 623, 624, 758, 1038, 1185, 1190, 1191, 1192, 1239, 1245, 1250, 1251, 1254, 1258, 1261, 1262, 1263, 1264, 1280, 1290, 1323, 1325, 1394, 1395, 1398, 1406, 1412, 1413], "come": [54, 93, 100, 101, 102, 517, 577, 588, 598, 608, 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"efraim": [91, 1411], "rodrigu": [91, 352, 1411], "efraimrodrigu": 91, "erwan": [91, 331, 1409, 1411], "le": [91, 103, 331, 1193, 1199, 1268, 1280, 1409, 1410, 1411], "merrer": [91, 331, 1409, 1411], "s\u00f8ren": [91, 1411, 1412], "fugled": [91, 1411, 1412], "j\u00f8rgensen": [91, 1411, 1412], "belhaddad": [91, 1411, 1412, 1413], "salymdotm": 91, "jangwon": [91, 1412], "yie": [91, 1412], "a7960065": 91, "toma": 91, "gavenciak": 91, "luca": [91, 334, 335, 1407, 1409, 1411, 1416, 1420, 1425], "baldesi": [91, 1269, 1409, 1411], "yuto": [91, 1409], "yamaguchi": [91, 1409], "clough": [91, 1407], "mina": [91, 1407], "gjoka": [91, 1207, 1208, 1209, 1210, 1407], "drew": [91, 1412], "alex": [91, 110, 1407, 1411, 1412, 1413], "levenson": 91, "haochen": [91, 1409, 1411], "wu": [91, 593, 729, 731, 1409, 1411], "roper": 91, "christoph": [91, 1410, 1412], "ellison": 91, "eppstein": [91, 277, 467, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 734, 736, 1407], "federico": [91, 1409, 1412], 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"kazimierz": [91, 1412], "wojciechowski": [91, 1412], "256": [91, 110, 1175, 1266, 1344, 1412], "gaetano": [91, 1412], "pietro": 91, "paolo": [91, 320, 1412], "carpinato": [91, 1412], "carghaez": 91, "gaetanocarpinato": 91, "arun": 91, "nampal": 91, "arunwis": [91, 1412], "b57845b7": 91, "duve": [91, 1412], "shashi": [91, 1412], "prakash": 91, "tripathi": [91, 517, 1412], "itsshavar": 91, "itsshashitripathi": 91, "danni": [91, 1412], "niquett": [91, 1412], "trimbl": [91, 1412, 1414], "jamestrimbl": 91, "matthia": [91, 1412, 1413, 1416, 1422], "bruhn": [91, 1412], "mbruhn": 91, "philip": 91, "boalch": 91, "knyazev": [91, 1414], "sultan": [91, 1414, 1416, 1422, 1425], "orazbayev": [91, 1414, 1416, 1422, 1425], "supplementari": 91, "incomplet": [91, 112, 1406, 1408], "commit": [91, 92, 93, 94, 99, 100, 105, 106, 1407, 1409, 1411, 1412, 1413, 1414, 1415, 1417, 1419, 1425], "git": [91, 93, 94, 97, 99, 106, 111, 1416, 1419], "repositori": [91, 93, 99, 106, 1406], "grep": [91, 97], "uniq": 91, "histor": [91, 99, 101, 1217], "earlier": [91, 299, 363, 364, 365, 739, 1199, 1393, 1402, 1408, 1413], "acknowledg": [91, 92, 96], "nonlinear": [91, 1213, 1215, 1222], "lo": 91, "alamo": 91, "nation": [91, 92, 457, 720], "laboratori": 91, "pi": [91, 653, 1114], "program": [91, 105, 110, 362, 455, 488, 490, 678, 1119, 1120, 1125, 1226, 1302, 1324, 1326, 1328, 1414], "offic": [91, 1267], "complex": [91, 94, 101, 105, 210, 217, 229, 230, 231, 239, 240, 274, 290, 293, 294, 300, 314, 327, 330, 331, 332, 333, 337, 346, 347, 355, 356, 371, 372, 376, 385, 386, 423, 434, 438, 452, 453, 494, 500, 519, 520, 521, 574, 616, 619, 625, 659, 692, 698, 699, 749, 1120, 1126, 1175, 1179, 1196, 1197, 1198, 1341, 1342, 1344, 1381, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "depart": [91, 494], "physic": [91, 110, 230, 236, 241, 244, 248, 326, 332, 333, 355, 356, 358, 378, 383, 386, 438, 485, 486, 487, 625, 1169, 1170, 1171, 1193, 1222, 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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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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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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, 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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, 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"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": 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"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], "wagner": [213, 429, 758, 1168, 1402, 1406], "universit\u00e4t": 213, "karlsruh": 213, "fakult\u00e4t": 213, "f\u00fcr": 213, "informatik": [213, 412], "5445": 213, "ir": [213, 606], "1000001239": 213, "erdos_renyi_graph": [213, 1224, 1232, 1326, 1406, 1426], "214": 213, "cutoff": [214, 215, 310, 326, 383, 410, 411, 412, 418, 419, 494, 495, 498, 499, 510, 637, 638, 640, 641, 642, 643, 644, 647, 648, 649, 656, 660, 661, 662, 667, 668, 669, 677, 678, 1234, 1398, 1402, 1406, 1413, 1416, 1424, 1425], "distinct": [214, 215, 255, 281, 288, 352, 391, 452, 453, 460, 578, 595, 608, 618, 700, 701, 734, 735, 736, 737, 789, 1150, 1244, 1271, 1323, 1326, 1328, 1395, 1417], "nonadjac": [214, 215, 480, 584, 585, 587], "cutset": [214, 215, 414, 415, 416, 417, 427, 428, 500, 506, 758], "menger": [214, 215, 216], "theorem": [214, 215, 216, 220, 235, 281, 311, 312, 322, 411, 506, 507, 514, 517, 518, 618, 1190, 1205], "local_node_connect": [214, 216, 408, 409, 410, 411, 413], "node_connect": [214, 215, 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How do I get started?": [[97, "q-i-m-new-to-open-source-and-would-like-to-contribute-to-networkx-how-do-i-get-started"]], "Q: I\u2019ve found an issue I\u2019m interested in, can I have it assigned to me?": [[97, "q-i-ve-found-an-issue-i-m-interested-in-can-i-have-it-assigned-to-me"]], "Q: How do I contribute an example to the Gallery?": [[97, "q-how-do-i-contribute-an-example-to-the-gallery"]], "Q: I want to work on a specific function. How do I find it in the source code?": [[97, "q-i-want-to-work-on-a-specific-function-how-do-i-find-it-in-the-source-code"]], "Q: What is the policy for deciding whether to include a new algorithm?": [[97, "q-what-is-the-policy-for-deciding-whether-to-include-a-new-algorithm"]], "NXEPs": [[98, "nxeps"], [1413, "nxeps"]], "NXEP 0 \u2014 Purpose and Process": [[99, "nxep-0-purpose-and-process"]], "What is a NXEP?": [[99, "what-is-a-nxep"]], "Types": [[99, "types"]], "NXEP Workflow": [[99, "nxep-workflow"]], "Review and Resolution": [[99, "review-and-resolution"]], "How a NXEP becomes Accepted": [[99, "how-a-nxep-becomes-accepted"]], "Maintenance": [[99, "maintenance"]], "Format and Template": [[99, "format-and-template"]], "Header Preamble": [[99, "header-preamble"]], "References and Footnotes": [[99, "references-and-footnotes"]], "NXEP 1 \u2014 Governance and Decision Making": [[100, "nxep-1-governance-and-decision-making"]], "Abstract": [[100, "abstract"], [101, "abstract"], [102, 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"difference": [[599, "difference"]], "disjoint_union": [[600, "disjoint-union"]], "full_join": [[601, "full-join"]], "intersection": [[602, "intersection"]], "symmetric_difference": [[603, "symmetric-difference"]], "union": [[604, "union"]], "cartesian_product": [[605, "cartesian-product"]], "corona_product": [[606, "corona-product"]], "lexicographic_product": [[607, "lexicographic-product"]], "power": [[608, "power"]], "rooted_product": [[609, "rooted-product"]], "strong_product": [[610, "strong-product"]], "tensor_product": [[611, "tensor-product"]], "complement": [[612, "complement"]], "reverse": [[613, "reverse"]], "combinatorial_embedding_to_pos": [[614, "combinatorial-embedding-to-pos"]], "networkx.algorithms.planarity.PlanarEmbedding": [[615, "networkx-algorithms-planarity-planarembedding"]], "check_planarity": [[616, "check-planarity"]], "is_planar": [[617, "is-planar"]], "chromatic_polynomial": [[618, "chromatic-polynomial"]], "tutte_polynomial": [[619, "tutte-polynomial"]], "overall_reciprocity": [[620, "overall-reciprocity"]], "reciprocity": [[621, "reciprocity"]], "is_k_regular": [[622, "is-k-regular"]], "is_regular": [[623, "is-regular"]], "k_factor": [[624, "k-factor"]], "rich_club_coefficient": [[625, "rich-club-coefficient"]], "astar_path": [[626, "astar-path"]], "astar_path_length": [[627, "astar-path-length"]], "floyd_warshall": [[628, "floyd-warshall"]], "floyd_warshall_numpy": [[629, "floyd-warshall-numpy"]], "floyd_warshall_predecessor_and_distance": [[630, "floyd-warshall-predecessor-and-distance"]], "reconstruct_path": [[631, "reconstruct-path"]], "all_shortest_paths": [[632, "all-shortest-paths"]], "average_shortest_path_length": [[633, "average-shortest-path-length"]], "has_path": [[634, "has-path"]], "shortest_path": [[635, "shortest-path"]], "shortest_path_length": [[636, "shortest-path-length"]], "all_pairs_shortest_path": [[637, "all-pairs-shortest-path"]], "all_pairs_shortest_path_length": [[638, "all-pairs-shortest-path-length"]], "bidirectional_shortest_path": [[639, "bidirectional-shortest-path"]], "predecessor": [[640, "predecessor"]], "single_source_shortest_path": [[641, "single-source-shortest-path"]], "single_source_shortest_path_length": [[642, "single-source-shortest-path-length"]], "single_target_shortest_path": [[643, "single-target-shortest-path"]], "single_target_shortest_path_length": [[644, "single-target-shortest-path-length"]], "all_pairs_bellman_ford_path": [[645, "all-pairs-bellman-ford-path"]], "all_pairs_bellman_ford_path_length": [[646, "all-pairs-bellman-ford-path-length"]], "all_pairs_dijkstra": [[647, "all-pairs-dijkstra"]], "all_pairs_dijkstra_path": [[648, "all-pairs-dijkstra-path"]], "all_pairs_dijkstra_path_length": [[649, "all-pairs-dijkstra-path-length"]], "bellman_ford_path": [[650, "bellman-ford-path"]], "bellman_ford_path_length": [[651, "bellman-ford-path-length"]], "bellman_ford_predecessor_and_distance": [[652, "bellman-ford-predecessor-and-distance"]], "bidirectional_dijkstra": [[653, "bidirectional-dijkstra"]], "dijkstra_path": [[654, "dijkstra-path"]], "dijkstra_path_length": [[655, "dijkstra-path-length"]], "dijkstra_predecessor_and_distance": [[656, "dijkstra-predecessor-and-distance"]], "find_negative_cycle": [[657, "find-negative-cycle"]], "goldberg_radzik": [[658, "goldberg-radzik"]], "johnson": [[659, "johnson"]], "multi_source_dijkstra": [[660, "multi-source-dijkstra"]], "multi_source_dijkstra_path": [[661, "multi-source-dijkstra-path"]], "multi_source_dijkstra_path_length": [[662, "multi-source-dijkstra-path-length"]], "negative_edge_cycle": [[663, "negative-edge-cycle"]], "single_source_bellman_ford": [[664, "single-source-bellman-ford"]], "single_source_bellman_ford_path": [[665, "single-source-bellman-ford-path"]], "single_source_bellman_ford_path_length": [[666, "single-source-bellman-ford-path-length"]], "single_source_dijkstra": [[667, "single-source-dijkstra"]], "single_source_dijkstra_path": [[668, "single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[669, "single-source-dijkstra-path-length"]], "generate_random_paths": [[670, "generate-random-paths"]], "graph_edit_distance": [[671, "graph-edit-distance"]], "optimal_edit_paths": [[672, "optimal-edit-paths"]], "optimize_edit_paths": [[673, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[674, "optimize-graph-edit-distance"]], "panther_similarity": [[675, "panther-similarity"]], "simrank_similarity": [[676, "simrank-similarity"]], "all_simple_edge_paths": [[677, "all-simple-edge-paths"]], "all_simple_paths": [[678, "all-simple-paths"]], "is_simple_path": [[679, "is-simple-path"]], "shortest_simple_paths": [[680, "shortest-simple-paths"]], "lattice_reference": [[681, "lattice-reference"]], "omega": [[682, "omega"]], "random_reference": [[683, "random-reference"]], "sigma": [[684, "sigma"]], "s_metric": [[685, "s-metric"]], "spanner": [[686, "spanner"]], "constraint": [[687, "constraint"]], "effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "Recognition Tests": [[791, "recognition-tests"]], "Branchings and Spanning Arborescences": [[791, "module-networkx.algorithms.tree.branchings"]], "Encoding and decoding": [[791, "module-networkx.algorithms.tree.coding"]], "Operations": [[791, "module-networkx.algorithms.tree.operations"]], "Spanning Trees": [[791, "module-networkx.algorithms.tree.mst"]], "Exceptions": [[791, "exceptions"], [1043, "module-networkx.exception"]], "Vitality": [[793, "module-networkx.algorithms.vitality"]], "Voronoi cells": [[794, "module-networkx.algorithms.voronoi"]], "Wiener index": [[795, "module-networkx.algorithms.wiener"]], "DiGraph\u2014Directed graphs with self loops": [[796, "digraph-directed-graphs-with-self-loops"]], "Overview": [[796, "overview"], [1037, "overview"], [1039, "overview"], [1040, "overview"]], "Methods": [[796, "methods"], [1037, "methods"], [1039, "methods"], [1040, "methods"]], "Adding and removing nodes and edges": [[796, "adding-and-removing-nodes-and-edges"], [1037, "adding-and-removing-nodes-and-edges"], [1040, "adding-and-removing-nodes-and-edges"]], "Reporting nodes edges and neighbors": [[796, "reporting-nodes-edges-and-neighbors"], [1037, "reporting-nodes-edges-and-neighbors"], [1039, "reporting-nodes-edges-and-neighbors"], [1040, "reporting-nodes-edges-and-neighbors"]], "Counting nodes edges and neighbors": [[796, "counting-nodes-edges-and-neighbors"], [1037, "counting-nodes-edges-and-neighbors"], [1039, "counting-nodes-edges-and-neighbors"], [1040, "counting-nodes-edges-and-neighbors"]], "Making copies and subgraphs": [[796, "making-copies-and-subgraphs"], [1037, "making-copies-and-subgraphs"], [1039, "making-copies-and-subgraphs"], [1040, "making-copies-and-subgraphs"]], "AdjacencyView.copy": [[797, "adjacencyview-copy"]], "AdjacencyView.get": [[798, "adjacencyview-get"]], "AdjacencyView.items": [[799, "adjacencyview-items"]], "AdjacencyView.keys": [[800, "adjacencyview-keys"]], "AdjacencyView.values": [[801, "adjacencyview-values"]], "AtlasView.copy": [[802, "atlasview-copy"]], "AtlasView.get": [[803, "atlasview-get"]], "AtlasView.items": [[804, "atlasview-items"]], "AtlasView.keys": [[805, "atlasview-keys"]], "AtlasView.values": [[806, "atlasview-values"]], "FilterAdjacency.get": [[807, "filteradjacency-get"]], "FilterAdjacency.items": [[808, "filteradjacency-items"]], "FilterAdjacency.keys": [[809, "filteradjacency-keys"]], "FilterAdjacency.values": [[810, "filteradjacency-values"]], "FilterAtlas.get": [[811, "filteratlas-get"]], "FilterAtlas.items": [[812, "filteratlas-items"]], "FilterAtlas.keys": [[813, "filteratlas-keys"]], "FilterAtlas.values": [[814, "filteratlas-values"]], "FilterMultiAdjacency.get": [[815, "filtermultiadjacency-get"]], "FilterMultiAdjacency.items": [[816, "filtermultiadjacency-items"]], "FilterMultiAdjacency.keys": [[817, "filtermultiadjacency-keys"]], "FilterMultiAdjacency.values": [[818, "filtermultiadjacency-values"]], "FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": [[836, "unionatlas-keys"]], "UnionAtlas.values": [[837, "unionatlas-values"]], "UnionMultiAdjacency.copy": [[838, "unionmultiadjacency-copy"]], "UnionMultiAdjacency.get": [[839, "unionmultiadjacency-get"]], "UnionMultiAdjacency.items": [[840, "unionmultiadjacency-items"]], "UnionMultiAdjacency.keys": [[841, "unionmultiadjacency-keys"]], "UnionMultiAdjacency.values": [[842, "unionmultiadjacency-values"]], "UnionMultiInner.copy": [[843, "unionmultiinner-copy"]], "UnionMultiInner.get": [[844, "unionmultiinner-get"]], "UnionMultiInner.items": [[845, "unionmultiinner-items"]], "UnionMultiInner.keys": [[846, "unionmultiinner-keys"]], "UnionMultiInner.values": [[847, "unionmultiinner-values"]], "DiGraph.__contains__": [[848, "digraph-contains"]], "DiGraph.__getitem__": [[849, "digraph-getitem"]], "DiGraph.__init__": [[850, "digraph-init"]], "DiGraph.__iter__": [[851, "digraph-iter"]], "DiGraph.__len__": [[852, "digraph-len"]], "DiGraph.add_edge": [[853, "digraph-add-edge"]], "DiGraph.add_edges_from": [[854, "digraph-add-edges-from"]], "DiGraph.add_node": [[855, "digraph-add-node"]], "DiGraph.add_nodes_from": [[856, "digraph-add-nodes-from"]], "DiGraph.add_weighted_edges_from": [[857, "digraph-add-weighted-edges-from"]], "DiGraph.adj": [[858, "digraph-adj"]], "DiGraph.adjacency": [[859, "digraph-adjacency"]], "DiGraph.clear": [[860, "digraph-clear"]], "DiGraph.clear_edges": [[861, "digraph-clear-edges"]], "DiGraph.copy": [[862, "digraph-copy"]], "DiGraph.degree": [[863, "digraph-degree"]], "DiGraph.edge_subgraph": [[864, "digraph-edge-subgraph"]], "DiGraph.edges": [[865, "digraph-edges"]], "DiGraph.get_edge_data": [[866, "digraph-get-edge-data"]], "DiGraph.has_edge": [[867, "digraph-has-edge"]], "DiGraph.has_node": [[868, "digraph-has-node"]], "DiGraph.in_degree": [[869, "digraph-in-degree"]], "DiGraph.in_edges": [[870, "digraph-in-edges"]], "DiGraph.nbunch_iter": [[871, "digraph-nbunch-iter"]], "DiGraph.neighbors": [[872, "digraph-neighbors"]], "DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, "digraph-to-undirected"]], "DiGraph.update": [[892, "digraph-update"]], "Graph.__contains__": [[893, "graph-contains"]], "Graph.__getitem__": [[894, "graph-getitem"]], "Graph.__init__": [[895, "graph-init"]], "Graph.__iter__": [[896, "graph-iter"]], "Graph.__len__": [[897, "graph-len"]], "Graph.add_edge": [[898, "graph-add-edge"]], "Graph.add_edges_from": [[899, "graph-add-edges-from"]], "Graph.add_node": [[900, "graph-add-node"]], "Graph.add_nodes_from": [[901, "graph-add-nodes-from"]], "Graph.add_weighted_edges_from": [[902, "graph-add-weighted-edges-from"]], "Graph.adj": [[903, "graph-adj"]], "Graph.adjacency": [[904, "graph-adjacency"]], "Graph.clear": [[905, "graph-clear"]], "Graph.clear_edges": [[906, "graph-clear-edges"]], "Graph.copy": [[907, "graph-copy"]], "Graph.degree": [[908, "graph-degree"]], "Graph.edge_subgraph": [[909, "graph-edge-subgraph"]], "Graph.edges": [[910, "graph-edges"]], "Graph.get_edge_data": [[911, "graph-get-edge-data"]], "Graph.has_edge": [[912, "graph-has-edge"]], "Graph.has_node": [[913, "graph-has-node"]], "Graph.nbunch_iter": [[914, "graph-nbunch-iter"]], "Graph.neighbors": [[915, "graph-neighbors"]], "Graph.nodes": [[916, "graph-nodes"]], "Graph.number_of_edges": [[917, "graph-number-of-edges"]], "Graph.number_of_nodes": [[918, "graph-number-of-nodes"]], "Graph.order": [[919, "graph-order"]], "Graph.remove_edge": [[920, "graph-remove-edge"]], "Graph.remove_edges_from": [[921, "graph-remove-edges-from"]], "Graph.remove_node": [[922, "graph-remove-node"]], "Graph.remove_nodes_from": [[923, "graph-remove-nodes-from"]], "Graph.size": [[924, "graph-size"]], "Graph.subgraph": [[925, "graph-subgraph"]], "Graph.to_directed": [[926, "graph-to-directed"]], "Graph.to_undirected": [[927, "graph-to-undirected"]], "Graph.update": [[928, "graph-update"]], "MultiDiGraph.__contains__": [[929, "multidigraph-contains"]], "MultiDiGraph.__getitem__": [[930, "multidigraph-getitem"]], "MultiDiGraph.__init__": [[931, "multidigraph-init"]], 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"removed-functionalities"]], "Miscellaneous changes": [[1395, "miscellaneous-changes"], [1396, "miscellaneous-changes"], [1402, "miscellaneous-changes"]], "NetworkX 1.11": [[1396, "networkx-1-11"], [1406, "networkx-1-11"]], "NetworkX 1.4": [[1397, "networkx-1-4"], [1406, "networkx-1-4"]], "Algorithms changed": [[1397, "algorithms-changed"]], "Shortest path": [[1397, "shortest-path"]], "astar_path(), astar_path_length(), shortest_path(), shortest_path_length(),": [[1397, "astar-path-astar-path-length-shortest-path-shortest-path-length"]], "bidirectional_shortest_path(), dijkstra_path(), dijkstra_path_length(),": [[1397, "bidirectional-shortest-path-dijkstra-path-dijkstra-path-length"]], "bidirectional_dijkstra()": [[1397, "bidirectional-dijkstra"]], "NetworkX 1.5": [[1398, "networkx-1-5"], [1406, "networkx-1-5"]], "Weighted graph algorithms": [[1398, "weighted-graph-algorithms"], [1399, "weighted-graph-algorithms"]], "Random geometric graph": [[1398, "random-geometric-graph"]], "NetworkX 1.6": [[1399, "networkx-1-6"], [1406, "networkx-1-6"]], "Graph Classes": [[1399, "graph-classes"]], "Isomorphisms": [[1399, "isomorphisms"]], "Other": [[1399, "other"], [1400, "other"]], "NetworkX 1.7": [[1400, "networkx-1-7"], [1406, "networkx-1-7"]], "NetworkX 1.8": [[1401, "networkx-1-8"], [1406, "networkx-1-8"]], "NetworkX 1.9": [[1402, "networkx-1-9"], [1406, "networkx-1-9"]], "Flow package": [[1402, "flow-package"]], "Main changes": [[1402, "main-changes"]], "Connectivity package": [[1402, "connectivity-package"]], "Other new functionalities": [[1402, "other-new-functionalities"]], "Releases": [[1403, "releases"]], "Migration guide from 1.X to 2.0": [[1404, "migration-guide-from-1-x-to-2-0"]], "Writing code that works for both versions": [[1404, "writing-code-that-works-for-both-versions"]], "Using Pickle with v1 and v2": [[1404, "using-pickle-with-v1-and-v2"]], "Migration guide from 2.X to 3.0": [[1405, "migration-guide-from-2-x-to-3-0"]], "Default dependencies": [[1405, "default-dependencies"]], "Improved integration with scientific Python": [[1405, "improved-integration-with-scientific-python"]], "Replacing NumPy/SciPy matrices with arrays": [[1405, "replacing-numpy-scipy-matrices-with-arrays"]], "Switch to NumPy/SciPy implementations by default for some algorithms": [[1405, "switch-to-numpy-scipy-implementations-by-default-for-some-algorithms"]], "Supporting numpy.random.Generator": [[1405, "supporting-numpy-random-generator"]], "NumPy structured dtypes for multi-attribute adjacency matrices": [[1405, "numpy-structured-dtypes-for-multi-attribute-adjacency-matrices"]], "Deprecated code": [[1405, "deprecated-code"]], "Old Release Log": [[1406, "old-release-log"]], "NetworkX 2.5": [[1406, "networkx-2-5"], [1412, "networkx-2-5"]], "Release notes": [[1406, "release-notes"], [1406, "id1"], [1406, "id2"], [1406, "id3"], [1406, "id4"], [1406, "id5"]], "NetworkX 2.4": [[1406, "networkx-2-4"], [1411, "networkx-2-4"]], "NetworkX 2.3": [[1406, 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"networkx-0-30"]], "NetworkX 0.29": [[1406, "networkx-0-29"]], "NetworkX 0.28": [[1406, "networkx-0-28"]], "NetworkX 0.27": [[1406, "networkx-0-27"]], "NetworkX 0.26": [[1406, "networkx-0-26"]], "NetworkX 0.25": [[1406, "networkx-0-25"]], "NetworkX 0.24": [[1406, "networkx-0-24"]], "NetworkX 0.23": [[1406, "networkx-0-23"]], "Important Change": [[1406, "important-change"]], "NetworkX 0.22": [[1406, "networkx-0-22"]], "API Changes": [[1407, "api-changes"], [1408, "api-changes"], [1409, "api-changes"], [1410, "api-changes"], [1411, "api-changes"], [1412, "api-changes"], [1413, "api-changes"], [1414, "api-changes"], [1416, "api-changes"], [1425, "api-changes"]], "Merged PRs": [[1407, "merged-prs"], [1408, "merged-prs"], [1411, "merged-prs"], [1412, "merged-prs"], [1413, "merged-prs"], [1414, "merged-prs"], [1415, "merged-prs"], [1416, "merged-prs"], [1417, "merged-prs"], [1418, "merged-prs"], [1419, "merged-prs"], [1420, "merged-prs"], [1421, "merged-prs"], [1422, "merged-prs"], [1423, "merged-prs"], [1424, "merged-prs"], [1425, "merged-prs"]], "Improvements": [[1408, "improvements"], [1409, "improvements"], [1410, "improvements"], [1411, "improvements"], [1412, "improvements"], [1413, "improvements"], [1414, "improvements"], [1416, "improvements"], [1417, "improvements"], [1422, "improvements"], [1423, "improvements"], [1425, "improvements"]], "NetworkX 2.6": [[1413, "networkx-2-6"]], "NetworkX 2.7": [[1414, "networkx-2-7"]], "GSoC PRs": [[1414, "gsoc-prs"]], "NetworkX 2.7.1": [[1415, "networkx-2-7-1"]], "NetworkX 2.8": [[1416, "networkx-2-8"]], "NetworkX 2.8.1": [[1417, "networkx-2-8-1"]], "NetworkX 2.8.2": [[1418, "networkx-2-8-2"]], "NetworkX 2.8.3": [[1419, "networkx-2-8-3"]], "NetworkX 2.8.4": [[1420, "networkx-2-8-4"]], "NetworkX 2.8.5": [[1421, "networkx-2-8-5"]], "NetworkX 2.8.6": [[1422, "networkx-2-8-6"]], "NetworkX 2.8.7": [[1423, "networkx-2-8-7"]], "NetworkX 2.8.8": [[1424, "networkx-2-8-8"]], "NetworkX 3.0 (unreleased)": [[1425, "networkx-3-0-unreleased"]], "Tutorial": [[1426, "tutorial"]], "Creating a graph": [[1426, "creating-a-graph"]], "Examining elements of a graph": [[1426, "examining-elements-of-a-graph"]], "Removing elements from a graph": [[1426, "removing-elements-from-a-graph"]], "Using the graph constructors": [[1426, "using-the-graph-constructors"]], "What to use as nodes and edges": [[1426, "what-to-use-as-nodes-and-edges"]], "Accessing edges and neighbors": [[1426, "accessing-edges-and-neighbors"]], "Adding attributes to graphs, nodes, and edges": [[1426, "adding-attributes-to-graphs-nodes-and-edges"]], "Edge Attributes": [[1426, "edge-attributes"]], "Directed graphs": [[1426, "directed-graphs"]], "Multigraphs": [[1426, "multigraphs"]], "Graph generators and graph operations": [[1426, "graph-generators-and-graph-operations"]], "1. Applying classic graph operations, such as:": [[1426, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1426, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1426, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1426, "using-a-stochastic-graph-generator-e-g"]], "5. Reading a graph stored in a file using common graph formats": [[1426, "reading-a-graph-stored-in-a-file-using-common-graph-formats"]], "Analyzing graphs": [[1426, "analyzing-graphs"]], "Drawing graphs": [[1426, "drawing-graphs"]]}, "indexentries": {"module": [[112, "module-networkx.algorithms.approximation"], [112, "module-networkx.algorithms.approximation.clique"], [112, "module-networkx.algorithms.approximation.clustering_coefficient"], [112, "module-networkx.algorithms.approximation.connectivity"], [112, "module-networkx.algorithms.approximation.distance_measures"], [112, "module-networkx.algorithms.approximation.dominating_set"], [112, "module-networkx.algorithms.approximation.kcomponents"], [112, "module-networkx.algorithms.approximation.matching"], [112, "module-networkx.algorithms.approximation.maxcut"], [112, "module-networkx.algorithms.approximation.ramsey"], [112, "module-networkx.algorithms.approximation.steinertree"], [112, 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[117, "module-networkx.algorithms.bridges"], [118, "module-networkx.algorithms.centrality"], [119, "module-networkx.algorithms.chains"], [120, "module-networkx.algorithms.chordal"], [121, "module-networkx.algorithms.clique"], [122, "module-networkx.algorithms.cluster"], [123, "module-networkx.algorithms.coloring"], [124, "module-networkx.algorithms.communicability_alg"], [125, "module-networkx.algorithms.community"], [125, "module-networkx.algorithms.community.asyn_fluid"], [125, "module-networkx.algorithms.community.centrality"], [125, "module-networkx.algorithms.community.community_utils"], [125, "module-networkx.algorithms.community.kclique"], [125, "module-networkx.algorithms.community.kernighan_lin"], [125, "module-networkx.algorithms.community.label_propagation"], [125, "module-networkx.algorithms.community.louvain"], [125, "module-networkx.algorithms.community.lukes"], [125, "module-networkx.algorithms.community.modularity_max"], [125, 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"module-networkx.algorithms.approximation.clustering_coefficient"]], "networkx.algorithms.approximation.connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"]], "networkx.algorithms.approximation.distance_measures": [[112, "module-networkx.algorithms.approximation.distance_measures"]], "networkx.algorithms.approximation.dominating_set": [[112, "module-networkx.algorithms.approximation.dominating_set"]], "networkx.algorithms.approximation.kcomponents": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "networkx.algorithms.approximation.matching": [[112, "module-networkx.algorithms.approximation.matching"]], "networkx.algorithms.approximation.maxcut": [[112, "module-networkx.algorithms.approximation.maxcut"]], "networkx.algorithms.approximation.ramsey": [[112, "module-networkx.algorithms.approximation.ramsey"]], "networkx.algorithms.approximation.steinertree": [[112, "module-networkx.algorithms.approximation.steinertree"]], "networkx.algorithms.approximation.traveling_salesman": [[112, "module-networkx.algorithms.approximation.traveling_salesman"]], "networkx.algorithms.approximation.treewidth": [[112, "module-networkx.algorithms.approximation.treewidth"]], "networkx.algorithms.approximation.vertex_cover": [[112, "module-networkx.algorithms.approximation.vertex_cover"]], "networkx.algorithms.assortativity": [[113, "module-networkx.algorithms.assortativity"]], "networkx.algorithms.asteroidal": [[114, "module-networkx.algorithms.asteroidal"]], "networkx.algorithms.bipartite": [[115, "module-networkx.algorithms.bipartite"]], "networkx.algorithms.bipartite.basic": [[115, "module-networkx.algorithms.bipartite.basic"]], "networkx.algorithms.bipartite.centrality": [[115, "module-networkx.algorithms.bipartite.centrality"]], "networkx.algorithms.bipartite.cluster": [[115, "module-networkx.algorithms.bipartite.cluster"]], "networkx.algorithms.bipartite.covering": [[115, 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"networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, 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"networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[751, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[752, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[753, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[755, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[757, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[759, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[760, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[764, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[766, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[767, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[768, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[769, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[770, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[771, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[772, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[772, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[772, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[772, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[774, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[775, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[777, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[778, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "networkx.algorithms.shortest_paths.weighted": [[779, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[780, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[781, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[782, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[783, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[785, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[786, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[787, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[788, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[789, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "networkx.algorithms.traversal.breadth_first_search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "networkx.algorithms.traversal.depth_first_search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "networkx.algorithms.traversal.edgebfs": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "networkx.algorithms.traversal.edgedfs": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "networkx.algorithms.tree.branchings": [[791, "module-networkx.algorithms.tree.branchings"]], "networkx.algorithms.tree.coding": [[791, "module-networkx.algorithms.tree.coding"]], "networkx.algorithms.tree.decomposition": [[791, "module-networkx.algorithms.tree.decomposition"]], "networkx.algorithms.tree.mst": [[791, "module-networkx.algorithms.tree.mst"]], "networkx.algorithms.tree.operations": [[791, "module-networkx.algorithms.tree.operations"]], "networkx.algorithms.tree.recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "networkx.algorithms.triads": [[792, "module-networkx.algorithms.triads"]], "networkx.algorithms.vitality": [[793, "module-networkx.algorithms.vitality"]], "networkx.algorithms.voronoi": [[794, "module-networkx.algorithms.voronoi"]], "networkx.algorithms.wiener": [[795, "module-networkx.algorithms.wiener"]], "digraph (class in networkx)": [[796, "networkx.DiGraph"]], "copy() (adjacencyview method)": [[797, "networkx.classes.coreviews.AdjacencyView.copy"]], "get() (adjacencyview method)": [[798, "networkx.classes.coreviews.AdjacencyView.get"]], "items() (adjacencyview method)": [[799, "networkx.classes.coreviews.AdjacencyView.items"]], "keys() (adjacencyview method)": [[800, "networkx.classes.coreviews.AdjacencyView.keys"]], "values() (adjacencyview method)": [[801, "networkx.classes.coreviews.AdjacencyView.values"]], "copy() (atlasview method)": [[802, "networkx.classes.coreviews.AtlasView.copy"]], "get() (atlasview method)": [[803, "networkx.classes.coreviews.AtlasView.get"]], "items() (atlasview method)": [[804, "networkx.classes.coreviews.AtlasView.items"]], "keys() (atlasview method)": [[805, "networkx.classes.coreviews.AtlasView.keys"]], "values() (atlasview method)": [[806, "networkx.classes.coreviews.AtlasView.values"]], "get() (filteradjacency method)": [[807, "networkx.classes.coreviews.FilterAdjacency.get"]], "items() (filteradjacency method)": [[808, "networkx.classes.coreviews.FilterAdjacency.items"]], "keys() (filteradjacency method)": [[809, "networkx.classes.coreviews.FilterAdjacency.keys"]], "values() (filteradjacency method)": [[810, "networkx.classes.coreviews.FilterAdjacency.values"]], "get() (filteratlas method)": [[811, "networkx.classes.coreviews.FilterAtlas.get"]], "items() (filteratlas method)": [[812, "networkx.classes.coreviews.FilterAtlas.items"]], "keys() (filteratlas method)": [[813, "networkx.classes.coreviews.FilterAtlas.keys"]], "values() (filteratlas method)": [[814, "networkx.classes.coreviews.FilterAtlas.values"]], "get() (filtermultiadjacency method)": [[815, "networkx.classes.coreviews.FilterMultiAdjacency.get"]], "items() (filtermultiadjacency method)": [[816, "networkx.classes.coreviews.FilterMultiAdjacency.items"]], "keys() (filtermultiadjacency method)": [[817, "networkx.classes.coreviews.FilterMultiAdjacency.keys"]], "values() (filtermultiadjacency method)": [[818, "networkx.classes.coreviews.FilterMultiAdjacency.values"]], "get() (filtermultiinner method)": [[819, "networkx.classes.coreviews.FilterMultiInner.get"]], "items() (filtermultiinner method)": [[820, "networkx.classes.coreviews.FilterMultiInner.items"]], "keys() (filtermultiinner method)": [[821, "networkx.classes.coreviews.FilterMultiInner.keys"]], "values() (filtermultiinner method)": [[822, "networkx.classes.coreviews.FilterMultiInner.values"]], "copy() (multiadjacencyview method)": [[823, "networkx.classes.coreviews.MultiAdjacencyView.copy"]], "get() (multiadjacencyview method)": [[824, "networkx.classes.coreviews.MultiAdjacencyView.get"]], "items() (multiadjacencyview method)": [[825, "networkx.classes.coreviews.MultiAdjacencyView.items"]], "keys() (multiadjacencyview method)": [[826, "networkx.classes.coreviews.MultiAdjacencyView.keys"]], "values() (multiadjacencyview method)": [[827, "networkx.classes.coreviews.MultiAdjacencyView.values"]], "copy() (unionadjacency method)": [[828, "networkx.classes.coreviews.UnionAdjacency.copy"]], "get() (unionadjacency method)": [[829, "networkx.classes.coreviews.UnionAdjacency.get"]], "items() (unionadjacency method)": [[830, "networkx.classes.coreviews.UnionAdjacency.items"]], "keys() (unionadjacency method)": [[831, "networkx.classes.coreviews.UnionAdjacency.keys"]], "values() (unionadjacency method)": [[832, "networkx.classes.coreviews.UnionAdjacency.values"]], "copy() (unionatlas method)": [[833, "networkx.classes.coreviews.UnionAtlas.copy"]], "get() (unionatlas method)": [[834, "networkx.classes.coreviews.UnionAtlas.get"]], "items() (unionatlas method)": [[835, "networkx.classes.coreviews.UnionAtlas.items"]], "keys() (unionatlas method)": [[836, "networkx.classes.coreviews.UnionAtlas.keys"]], "values() (unionatlas method)": [[837, "networkx.classes.coreviews.UnionAtlas.values"]], "copy() (unionmultiadjacency method)": [[838, "networkx.classes.coreviews.UnionMultiAdjacency.copy"]], "get() (unionmultiadjacency method)": [[839, "networkx.classes.coreviews.UnionMultiAdjacency.get"]], "items() (unionmultiadjacency method)": [[840, "networkx.classes.coreviews.UnionMultiAdjacency.items"]], "keys() (unionmultiadjacency method)": [[841, "networkx.classes.coreviews.UnionMultiAdjacency.keys"]], "values() (unionmultiadjacency method)": [[842, "networkx.classes.coreviews.UnionMultiAdjacency.values"]], "copy() (unionmultiinner method)": [[843, "networkx.classes.coreviews.UnionMultiInner.copy"]], "get() (unionmultiinner method)": [[844, "networkx.classes.coreviews.UnionMultiInner.get"]], "items() (unionmultiinner method)": [[845, "networkx.classes.coreviews.UnionMultiInner.items"]], "keys() (unionmultiinner method)": [[846, "networkx.classes.coreviews.UnionMultiInner.keys"]], "values() (unionmultiinner method)": [[847, "networkx.classes.coreviews.UnionMultiInner.values"]], "__contains__() (digraph method)": [[848, "networkx.DiGraph.__contains__"]], "__getitem__() (digraph method)": [[849, "networkx.DiGraph.__getitem__"]], "__init__() (digraph method)": [[850, "networkx.DiGraph.__init__"]], "__iter__() (digraph method)": [[851, "networkx.DiGraph.__iter__"]], "__len__() (digraph method)": [[852, "networkx.DiGraph.__len__"]], "add_edge() (digraph method)": [[853, "networkx.DiGraph.add_edge"]], "add_edges_from() (digraph method)": [[854, "networkx.DiGraph.add_edges_from"]], "add_node() (digraph method)": [[855, "networkx.DiGraph.add_node"]], "add_nodes_from() (digraph method)": [[856, "networkx.DiGraph.add_nodes_from"]], "add_weighted_edges_from() (digraph method)": [[857, "networkx.DiGraph.add_weighted_edges_from"]], "adj (digraph property)": [[858, "networkx.DiGraph.adj"]], "adjacency() (digraph method)": [[859, "networkx.DiGraph.adjacency"]], "clear() (digraph method)": [[860, "networkx.DiGraph.clear"]], "clear_edges() (digraph method)": [[861, "networkx.DiGraph.clear_edges"]], "copy() (digraph method)": [[862, "networkx.DiGraph.copy"]], "degree (digraph property)": [[863, "networkx.DiGraph.degree"]], "edge_subgraph() (digraph method)": [[864, "networkx.DiGraph.edge_subgraph"]], "edges (digraph property)": [[865, "networkx.DiGraph.edges"]], "get_edge_data() (digraph method)": [[866, "networkx.DiGraph.get_edge_data"]], "has_edge() (digraph method)": [[867, "networkx.DiGraph.has_edge"]], "has_node() (digraph method)": [[868, "networkx.DiGraph.has_node"]], "in_degree (digraph property)": [[869, "networkx.DiGraph.in_degree"]], "in_edges (digraph property)": [[870, "networkx.DiGraph.in_edges"]], "nbunch_iter() (digraph method)": [[871, "networkx.DiGraph.nbunch_iter"]], "neighbors() (digraph method)": [[872, "networkx.DiGraph.neighbors"]], "nodes (digraph property)": [[873, "networkx.DiGraph.nodes"]], "number_of_edges() (digraph method)": [[874, "networkx.DiGraph.number_of_edges"]], "number_of_nodes() (digraph method)": [[875, "networkx.DiGraph.number_of_nodes"]], "order() (digraph method)": [[876, "networkx.DiGraph.order"]], "out_degree (digraph property)": [[877, "networkx.DiGraph.out_degree"]], "out_edges (digraph property)": [[878, "networkx.DiGraph.out_edges"]], "pred (digraph property)": [[879, "networkx.DiGraph.pred"]], "predecessors() (digraph method)": [[880, "networkx.DiGraph.predecessors"]], "remove_edge() (digraph method)": [[881, "networkx.DiGraph.remove_edge"]], "remove_edges_from() (digraph method)": [[882, "networkx.DiGraph.remove_edges_from"]], "remove_node() (digraph method)": [[883, "networkx.DiGraph.remove_node"]], "remove_nodes_from() (digraph method)": 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"add_edges_from() (graph method)": [[899, "networkx.Graph.add_edges_from"]], "add_node() (graph method)": [[900, "networkx.Graph.add_node"]], "add_nodes_from() (graph method)": [[901, "networkx.Graph.add_nodes_from"]], "add_weighted_edges_from() (graph method)": [[902, "networkx.Graph.add_weighted_edges_from"]], "adj (graph property)": [[903, "networkx.Graph.adj"]], "adjacency() (graph method)": [[904, "networkx.Graph.adjacency"]], "clear() (graph method)": [[905, "networkx.Graph.clear"]], "clear_edges() (graph method)": [[906, "networkx.Graph.clear_edges"]], "copy() (graph method)": [[907, "networkx.Graph.copy"]], "degree (graph property)": [[908, "networkx.Graph.degree"]], "edge_subgraph() (graph method)": [[909, "networkx.Graph.edge_subgraph"]], "edges (graph property)": [[910, "networkx.Graph.edges"]], "get_edge_data() (graph method)": [[911, "networkx.Graph.get_edge_data"]], "has_edge() (graph method)": [[912, "networkx.Graph.has_edge"]], "has_node() (graph method)": [[913, "networkx.Graph.has_node"]], "nbunch_iter() (graph method)": [[914, "networkx.Graph.nbunch_iter"]], "neighbors() (graph method)": [[915, "networkx.Graph.neighbors"]], "nodes (graph property)": [[916, "networkx.Graph.nodes"]], "number_of_edges() (graph method)": [[917, "networkx.Graph.number_of_edges"]], "number_of_nodes() (graph method)": [[918, "networkx.Graph.number_of_nodes"]], "order() (graph method)": [[919, "networkx.Graph.order"]], "remove_edge() (graph method)": [[920, "networkx.Graph.remove_edge"]], "remove_edges_from() (graph method)": [[921, "networkx.Graph.remove_edges_from"]], "remove_node() (graph method)": [[922, "networkx.Graph.remove_node"]], "remove_nodes_from() (graph method)": [[923, "networkx.Graph.remove_nodes_from"]], "size() (graph method)": [[924, "networkx.Graph.size"]], "subgraph() (graph method)": [[925, "networkx.Graph.subgraph"]], "to_directed() (graph method)": [[926, "networkx.Graph.to_directed"]], "to_undirected() (graph method)": [[927, 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"networkx.MultiDiGraph.remove_edges_from"]], "remove_node() (multidigraph method)": [[964, "networkx.MultiDiGraph.remove_node"]], "remove_nodes_from() (multidigraph method)": [[965, "networkx.MultiDiGraph.remove_nodes_from"]], "reverse() (multidigraph method)": [[966, "networkx.MultiDiGraph.reverse"]], "size() (multidigraph method)": [[967, "networkx.MultiDiGraph.size"]], "subgraph() (multidigraph method)": [[968, "networkx.MultiDiGraph.subgraph"]], "succ (multidigraph property)": [[969, "networkx.MultiDiGraph.succ"]], "successors() (multidigraph method)": [[970, "networkx.MultiDiGraph.successors"]], "to_directed() (multidigraph method)": [[971, "networkx.MultiDiGraph.to_directed"]], "to_undirected() (multidigraph method)": [[972, "networkx.MultiDiGraph.to_undirected"]], "update() (multidigraph method)": [[973, "networkx.MultiDiGraph.update"]], "__contains__() (multigraph method)": [[974, "networkx.MultiGraph.__contains__"]], "__getitem__() (multigraph method)": [[975, "networkx.MultiGraph.__getitem__"]], "__init__() (multigraph method)": [[976, "networkx.MultiGraph.__init__"]], "__iter__() (multigraph method)": [[977, "networkx.MultiGraph.__iter__"]], "__len__() (multigraph method)": [[978, "networkx.MultiGraph.__len__"]], "add_edge() (multigraph method)": [[979, "networkx.MultiGraph.add_edge"]], "add_edges_from() (multigraph method)": [[980, "networkx.MultiGraph.add_edges_from"]], "add_node() (multigraph method)": [[981, "networkx.MultiGraph.add_node"]], "add_nodes_from() (multigraph method)": [[982, "networkx.MultiGraph.add_nodes_from"]], "add_weighted_edges_from() (multigraph method)": [[983, "networkx.MultiGraph.add_weighted_edges_from"]], "adj (multigraph property)": [[984, "networkx.MultiGraph.adj"]], "adjacency() (multigraph method)": [[985, "networkx.MultiGraph.adjacency"]], "clear() (multigraph method)": [[986, "networkx.MultiGraph.clear"]], "clear_edges() (multigraph method)": [[987, "networkx.MultiGraph.clear_edges"]], "copy() (multigraph method)": [[988, "networkx.MultiGraph.copy"]], "degree (multigraph property)": [[989, "networkx.MultiGraph.degree"]], "edge_subgraph() (multigraph method)": [[990, "networkx.MultiGraph.edge_subgraph"]], "edges (multigraph property)": [[991, "networkx.MultiGraph.edges"]], "get_edge_data() (multigraph method)": [[992, "networkx.MultiGraph.get_edge_data"]], "has_edge() (multigraph method)": [[993, "networkx.MultiGraph.has_edge"]], "has_node() (multigraph method)": [[994, "networkx.MultiGraph.has_node"]], "nbunch_iter() (multigraph method)": [[995, "networkx.MultiGraph.nbunch_iter"]], "neighbors() (multigraph method)": [[996, "networkx.MultiGraph.neighbors"]], "new_edge_key() (multigraph method)": [[997, "networkx.MultiGraph.new_edge_key"]], "nodes (multigraph property)": [[998, "networkx.MultiGraph.nodes"]], "number_of_edges() (multigraph method)": [[999, "networkx.MultiGraph.number_of_edges"]], "number_of_nodes() (multigraph method)": [[1000, "networkx.MultiGraph.number_of_nodes"]], "order() (multigraph method)": [[1001, "networkx.MultiGraph.order"]], "remove_edge() (multigraph method)": [[1002, "networkx.MultiGraph.remove_edge"]], "remove_edges_from() (multigraph method)": [[1003, "networkx.MultiGraph.remove_edges_from"]], "remove_node() (multigraph method)": [[1004, "networkx.MultiGraph.remove_node"]], "remove_nodes_from() (multigraph method)": [[1005, "networkx.MultiGraph.remove_nodes_from"]], "size() (multigraph method)": [[1006, "networkx.MultiGraph.size"]], "subgraph() (multigraph method)": [[1007, "networkx.MultiGraph.subgraph"]], "to_directed() (multigraph method)": [[1008, "networkx.MultiGraph.to_directed"]], "to_undirected() (multigraph method)": [[1009, "networkx.MultiGraph.to_undirected"]], "update() (multigraph method)": [[1010, "networkx.MultiGraph.update"]], "_dispatch() (in module networkx.classes.backends)": [[1011, "networkx.classes.backends._dispatch"]], "adjacencyview (class in networkx.classes.coreviews)": [[1012, "networkx.classes.coreviews.AdjacencyView"]], "__init__() (adjacencyview method)": [[1012, "networkx.classes.coreviews.AdjacencyView.__init__"]], "atlasview (class in networkx.classes.coreviews)": [[1013, "networkx.classes.coreviews.AtlasView"]], "__init__() (atlasview method)": [[1013, "networkx.classes.coreviews.AtlasView.__init__"]], "filteradjacency (class in networkx.classes.coreviews)": [[1014, "networkx.classes.coreviews.FilterAdjacency"]], "__init__() (filteradjacency method)": [[1014, "networkx.classes.coreviews.FilterAdjacency.__init__"]], "filteratlas (class in networkx.classes.coreviews)": [[1015, "networkx.classes.coreviews.FilterAtlas"]], "__init__() (filteratlas method)": [[1015, "networkx.classes.coreviews.FilterAtlas.__init__"]], "filtermultiadjacency (class in networkx.classes.coreviews)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency"]], "__init__() (filtermultiadjacency method)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency.__init__"]], "filtermultiinner (class in networkx.classes.coreviews)": [[1017, "networkx.classes.coreviews.FilterMultiInner"]], "__init__() (filtermultiinner method)": [[1017, "networkx.classes.coreviews.FilterMultiInner.__init__"]], "multiadjacencyview (class in networkx.classes.coreviews)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView"]], "__init__() (multiadjacencyview method)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView.__init__"]], "unionadjacency (class in networkx.classes.coreviews)": [[1019, "networkx.classes.coreviews.UnionAdjacency"]], "__init__() (unionadjacency method)": [[1019, "networkx.classes.coreviews.UnionAdjacency.__init__"]], "unionatlas (class in networkx.classes.coreviews)": [[1020, "networkx.classes.coreviews.UnionAtlas"]], "__init__() (unionatlas method)": [[1020, "networkx.classes.coreviews.UnionAtlas.__init__"]], "unionmultiadjacency (class in networkx.classes.coreviews)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency"]], "__init__() (unionmultiadjacency method)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency.__init__"]], "unionmultiinner (class in networkx.classes.coreviews)": [[1022, "networkx.classes.coreviews.UnionMultiInner"]], "__init__() (unionmultiinner method)": [[1022, "networkx.classes.coreviews.UnionMultiInner.__init__"]], "hide_diedges() (in module networkx.classes.filters)": [[1023, "networkx.classes.filters.hide_diedges"]], "hide_edges() (in module networkx.classes.filters)": [[1024, "networkx.classes.filters.hide_edges"]], "hide_multidiedges() (in module networkx.classes.filters)": [[1025, "networkx.classes.filters.hide_multidiedges"]], "hide_multiedges() (in module networkx.classes.filters)": [[1026, "networkx.classes.filters.hide_multiedges"]], "hide_nodes() (in module networkx.classes.filters)": [[1027, "networkx.classes.filters.hide_nodes"]], "no_filter() (in module networkx.classes.filters)": [[1028, "networkx.classes.filters.no_filter"]], "show_diedges() (in module networkx.classes.filters)": [[1029, "networkx.classes.filters.show_diedges"]], "show_edges() (in module networkx.classes.filters)": [[1030, "networkx.classes.filters.show_edges"]], "show_multidiedges() (in module networkx.classes.filters)": [[1031, "networkx.classes.filters.show_multidiedges"]], "show_multiedges() (in module networkx.classes.filters)": [[1032, "networkx.classes.filters.show_multiedges"]], "__init__() (show_nodes method)": [[1033, "networkx.classes.filters.show_nodes.__init__"]], "show_nodes (class in networkx.classes.filters)": [[1033, "networkx.classes.filters.show_nodes"]], "generic_graph_view() (in module networkx.classes.graphviews)": [[1034, "networkx.classes.graphviews.generic_graph_view"]], "reverse_view() (in module networkx.classes.graphviews)": [[1035, "networkx.classes.graphviews.reverse_view"]], "subgraph_view() (in module networkx.classes.graphviews)": [[1036, "networkx.classes.graphviews.subgraph_view"]], "graph (class in networkx)": [[1037, "networkx.Graph"]], "networkx.classes.backends": [[1038, "module-networkx.classes.backends"]], "networkx.classes.coreviews": [[1038, "module-networkx.classes.coreviews"]], "networkx.classes.filters": [[1038, "module-networkx.classes.filters"]], "networkx.classes.graphviews": [[1038, "module-networkx.classes.graphviews"]], "multidigraph (class in networkx)": [[1039, "networkx.MultiDiGraph"]], "multigraph (class in networkx)": [[1040, "networkx.MultiGraph"]], "networkx.convert": [[1041, "module-networkx.convert"]], "networkx.convert_matrix": [[1041, "module-networkx.convert_matrix"]], "networkx.drawing.layout": [[1042, "module-networkx.drawing.layout"]], "networkx.drawing.nx_agraph": [[1042, "module-networkx.drawing.nx_agraph"]], "networkx.drawing.nx_pydot": [[1042, "module-networkx.drawing.nx_pydot"]], "networkx.drawing.nx_pylab": [[1042, "module-networkx.drawing.nx_pylab"]], "ambiguoussolution (class in networkx)": [[1043, "networkx.AmbiguousSolution"]], "exceededmaxiterations (class in networkx)": [[1043, "networkx.ExceededMaxIterations"]], "hasacycle (class in networkx)": [[1043, "networkx.HasACycle"]], "networkxalgorithmerror (class in networkx)": [[1043, "networkx.NetworkXAlgorithmError"]], "networkxerror (class in networkx)": [[1043, "networkx.NetworkXError"]], "networkxexception (class in networkx)": [[1043, "networkx.NetworkXException"]], "networkxnocycle (class in networkx)": [[1043, "networkx.NetworkXNoCycle"]], "networkxnopath (class in networkx)": [[1043, "networkx.NetworkXNoPath"]], "networkxnotimplemented (class in networkx)": [[1043, "networkx.NetworkXNotImplemented"]], "networkxpointlessconcept (class in networkx)": [[1043, "networkx.NetworkXPointlessConcept"]], "networkxunbounded (class in networkx)": [[1043, "networkx.NetworkXUnbounded"]], "networkxunfeasible (class in networkx)": [[1043, "networkx.NetworkXUnfeasible"]], "nodenotfound (class in networkx)": [[1043, "networkx.NodeNotFound"]], "poweriterationfailedconvergence (class in networkx)": [[1043, "networkx.PowerIterationFailedConvergence"]], "networkx.exception": [[1043, "module-networkx.exception"]], "networkx.classes.function": [[1044, "module-networkx.classes.function"]], "assemble() (argmap method)": [[1045, "networkx.utils.decorators.argmap.assemble"]], "compile() (argmap method)": [[1046, "networkx.utils.decorators.argmap.compile"]], "signature() (argmap class method)": [[1047, "networkx.utils.decorators.argmap.signature"]], "pop() (mappedqueue method)": [[1048, "networkx.utils.mapped_queue.MappedQueue.pop"]], "push() (mappedqueue method)": [[1049, "networkx.utils.mapped_queue.MappedQueue.push"]], "remove() (mappedqueue method)": [[1050, "networkx.utils.mapped_queue.MappedQueue.remove"]], "update() (mappedqueue method)": [[1051, "networkx.utils.mapped_queue.MappedQueue.update"]], "add_cycle() (in module networkx.classes.function)": [[1052, "networkx.classes.function.add_cycle"]], "add_path() (in module networkx.classes.function)": [[1053, "networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, 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"kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], 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module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, 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module networkx.generators.classic)": [[1161, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1164, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1165, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1166, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1167, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() 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networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, 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networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, "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 88c4ea8b..f9991169 100644 --- a/tutorial-34.pdf +++ b/tutorial-34.pdf diff --git a/tutorial-35.pdf b/tutorial-35.pdf Binary files differindex f1576b95..dac636b8 100644 --- a/tutorial-35.pdf +++ b/tutorial-35.pdf diff --git a/tutorial-36.pdf b/tutorial-36.pdf Binary files differindex 92b2b3ab..acc3626a 100644 --- a/tutorial-36.pdf +++ b/tutorial-36.pdf diff --git a/tutorial.ipynb b/tutorial.ipynb index e6124418..d23d7651 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "910be702", + "id": "90937821", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,7 +17,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e81d4b41", + "id": "59049f51", "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "7a9bad7e", + "id": "538cf811", "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": "1afd3f57", + "id": "f6698902", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "d80564b3", + "id": "5de3e8ef", "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": "87021f0b", + "id": "6405beaa", "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "markdown", - "id": "8d61f686", + "id": "c8f17656", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bb477285", + "id": "0ada7cc5", "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "646415bb", + "id": "8ca64757", "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": "6c49602d", + "id": "88575925", "metadata": {}, "outputs": [], "source": [ @@ -125,7 +125,7 @@ }, { "cell_type": "markdown", - "id": "fd829173", + "id": "b37b79c1", "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": "f427deb8", + "id": "9d5a38ba", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +154,7 @@ }, { "cell_type": "markdown", - "id": "af6f6202", + "id": "08482fe6", "metadata": {}, "source": [ "by adding a list of edges," @@ -163,7 +163,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2b82af46", + "id": "d66dcae5", "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "de9e6182", + "id": "bddd430d", "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": "27402789", + "id": "7e713e69", "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "e666008d", + "id": "b0fd3d0c", "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": "b63010db", + "id": "cf723a75", "metadata": {}, "outputs": [], "source": [ @@ -213,7 +213,7 @@ }, { "cell_type": "markdown", - "id": "35e3dd39", + "id": "c1cba503", "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": "979aba61", + "id": "7ef878a4", "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "1313cf91", + "id": "c5c7da7b", "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": "feee6953", + "id": "d4197f8c", "metadata": {}, "outputs": [], "source": [ @@ -257,7 +257,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8cc20d74", + "id": "e428178e", "metadata": {}, "outputs": [], "source": [ @@ -272,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "06bc9a00", + "id": "a7334ec7", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -292,7 +292,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3ccfeb23", + "id": "6ad604cf", "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "markdown", - "id": "18d910fc", + "id": "253640bb", "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": "5e673c8a", + "id": "9f801d29", "metadata": {}, "outputs": [], "source": [ @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "1d415acd", + "id": "809a5504", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -343,7 +343,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5fe562ad", + "id": "29738670", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "0950da01", + "id": "0f10edf8", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -370,7 +370,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9883bd1c", + "id": "5058d816", "metadata": {}, "outputs": [], "source": [ @@ -387,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "12932883", + "id": "78b07e07", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -416,7 +416,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1880bacb", + "id": "9fd5b954", "metadata": {}, "outputs": [], "source": [ @@ -428,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "90ba3f56", + "id": "6e5cdbe0", "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": "f05f1ff0", + "id": "a2fae2a3", "metadata": {}, "outputs": [], "source": [ @@ -450,7 +450,7 @@ }, { "cell_type": "markdown", - "id": "cb687f1c", + "id": "929a414f", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -461,7 +461,7 @@ { "cell_type": "code", "execution_count": null, - "id": "000f5628", + "id": "4eb00c76", "metadata": {}, "outputs": [], "source": [ @@ -475,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "42f4f049", + "id": "40710665", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -484,7 +484,7 @@ { "cell_type": "code", "execution_count": null, - "id": "734b4a38", + "id": "3d18acf6", "metadata": {}, "outputs": [], "source": [ @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "374e7e55", + "id": "428e300d", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -517,7 +517,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c3c5158c", + "id": "13ebf06e", "metadata": {}, "outputs": [], "source": [ @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "51e4aca7", + "id": "4c50340d", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -536,7 +536,7 @@ { "cell_type": "code", "execution_count": null, - "id": "134aef74", + "id": "9ba4b2dc", "metadata": {}, "outputs": [], "source": [ @@ -546,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "e88e3f11", + "id": "8c966e8e", "metadata": {}, "source": [ "# Node attributes\n", @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b65fa5e1", + "id": "81d527a6", "metadata": {}, "outputs": [], "source": [ @@ -570,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "dee786f2", + "id": "bab5d9b5", "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": "199205bf", + "id": "7dff455a", "metadata": {}, "outputs": [], "source": [ @@ -598,7 +598,7 @@ }, { "cell_type": "markdown", - "id": "e950cba1", + "id": "0e40282f", "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": "725a73a5", + "id": "1ce9ad47", "metadata": {}, "outputs": [], "source": [ @@ -633,7 +633,7 @@ }, { "cell_type": "markdown", - "id": "d6f47b84", + "id": "5425c7f1", "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": "b8d42578", + "id": "00e70ab7", "metadata": {}, "outputs": [], "source": [ @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "81f8afa6", + "id": "0e62e4f9", "metadata": {}, "source": [ "# Multigraphs\n", @@ -675,7 +675,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6408a9e1", + "id": "8cdebcee", "metadata": {}, "outputs": [], "source": [ @@ -693,7 +693,7 @@ }, { "cell_type": "markdown", - "id": "ba84061f", + "id": "83d8c968", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -713,7 +713,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e38785b7", + "id": "0854d6d5", "metadata": {}, "outputs": [], "source": [ @@ -725,7 +725,7 @@ }, { "cell_type": "markdown", - "id": "fe2deba1", + "id": "7e19840b", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -736,7 +736,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1edcecef", + "id": "337abf3c", "metadata": {}, "outputs": [], "source": [ @@ -748,7 +748,7 @@ }, { "cell_type": "markdown", - "id": "204c93d0", + "id": "b5bef502", "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": "30a8c595", + "id": "75cdd2c4", "metadata": {}, "outputs": [], "source": [ @@ -770,7 +770,7 @@ }, { "cell_type": "markdown", - "id": "50e1412d", + "id": "8ba69968", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -785,7 +785,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b86d1f14", + "id": "42b38c47", "metadata": {}, "outputs": [], "source": [ @@ -799,7 +799,7 @@ }, { "cell_type": "markdown", - "id": "7be00c0a", + "id": "d731579b", "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": "750d84ae", + "id": "ce458635", "metadata": {}, "outputs": [], "source": [ @@ -819,7 +819,7 @@ }, { "cell_type": "markdown", - "id": "0e53b504", + "id": "020161fc", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -838,7 +838,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d7d732e2", + "id": "1db0fd84", "metadata": {}, "outputs": [], "source": [ @@ -847,7 +847,7 @@ }, { "cell_type": "markdown", - "id": "654b18e1", + "id": "13602902", "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": "64acafa1", + "id": "5a6bd124", "metadata": {}, "outputs": [], "source": [ @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "79ba0ba2", + "id": "4b0563c7", "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": "ad8840dd", + "id": "a102647c", "metadata": {}, "outputs": [], "source": [ @@ -889,7 +889,7 @@ }, { "cell_type": "markdown", - "id": "ac7f96cc", + "id": "d9c5131e", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -898,7 +898,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5399df9e", + "id": "26bd2caf", "metadata": {}, "outputs": [], "source": [ @@ -919,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "c4066cae", + "id": "af3fc4c6", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -930,7 +930,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a297b883", + "id": "a97da429", "metadata": {}, "outputs": [], "source": [ @@ -941,7 +941,7 @@ }, { "cell_type": "markdown", - "id": "5af59003", + "id": "67ffbe4d", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -950,7 +950,7 @@ { "cell_type": "code", "execution_count": null, - "id": "be51076a", + "id": "4091ed0e", "metadata": {}, "outputs": [], "source": [ @@ -960,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "62817648", + "id": "d33f4f6d", "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": "99fe891a", + "id": "83db308c", "metadata": {}, "outputs": [], "source": [ @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "90ee80e2", + "id": "020647dc", "metadata": {}, "source": [ "See Drawing for additional details." diff --git a/tutorial_full.ipynb b/tutorial_full.ipynb index 4fda47f8..fca1e3c2 100644 --- a/tutorial_full.ipynb +++ b/tutorial_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "910be702", + "id": "90937821", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,13 +17,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "e81d4b41", + "id": "59049f51", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.820385Z", + "iopub.status.busy": "2022-12-27T10:11:47.820143Z", + "iopub.status.idle": "2022-12-27T10:11:47.893309Z", + "shell.execute_reply": "2022-12-27T10:11:47.892670Z" } }, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "markdown", - "id": "7a9bad7e", + "id": "538cf811", "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": "1afd3f57", + "id": "f6698902", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.896732Z", + "iopub.status.busy": "2022-12-27T10:11:47.896508Z", + "iopub.status.idle": "2022-12-27T10:11:47.899552Z", + "shell.execute_reply": "2022-12-27T10:11:47.898903Z" } }, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "d80564b3", + "id": "5de3e8ef", "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": "87021f0b", + "id": "6405beaa", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.902915Z", + "iopub.status.busy": "2022-12-27T10:11:47.902692Z", + "iopub.status.idle": "2022-12-27T10:11:47.907055Z", + "shell.execute_reply": "2022-12-27T10:11:47.906305Z" } }, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "8d61f686", + "id": "c8f17656", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -117,13 +117,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "bb477285", + "id": "0ada7cc5", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.910073Z", + "iopub.status.busy": "2022-12-27T10:11:47.909537Z", + "iopub.status.idle": "2022-12-27T10:11:47.912880Z", + "shell.execute_reply": "2022-12-27T10:11:47.912388Z" } }, "outputs": [], @@ -134,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "646415bb", + "id": "8ca64757", "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": "6c49602d", + "id": "88575925", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.915782Z", + "iopub.status.busy": "2022-12-27T10:11:47.915443Z", + "iopub.status.idle": "2022-12-27T10:11:47.918410Z", + "shell.execute_reply": "2022-12-27T10:11:47.917785Z" } }, "outputs": [], @@ -160,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "fd829173", + "id": "b37b79c1", "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": "f427deb8", + "id": "9d5a38ba", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.921639Z", + "iopub.status.busy": "2022-12-27T10:11:47.921149Z", + "iopub.status.idle": "2022-12-27T10:11:47.924411Z", + "shell.execute_reply": "2022-12-27T10:11:47.923781Z" } }, "outputs": [], @@ -196,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "af6f6202", + "id": "08482fe6", "metadata": {}, "source": [ "by adding a list of edges," @@ -205,13 +205,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "2b82af46", + "id": "d66dcae5", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.927571Z", + "iopub.status.busy": "2022-12-27T10:11:47.927233Z", + "iopub.status.idle": "2022-12-27T10:11:47.930371Z", + "shell.execute_reply": "2022-12-27T10:11:47.929744Z" } }, "outputs": [], @@ -221,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "de9e6182", + "id": "bddd430d", "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": "27402789", + "id": "7e713e69", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.933397Z", + "iopub.status.busy": "2022-12-27T10:11:47.932989Z", + "iopub.status.idle": "2022-12-27T10:11:47.935997Z", + "shell.execute_reply": "2022-12-27T10:11:47.935372Z" } }, "outputs": [], @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "e666008d", + "id": "b0fd3d0c", "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": "b63010db", + "id": "cf723a75", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.938907Z", + "iopub.status.busy": "2022-12-27T10:11:47.938569Z", + "iopub.status.idle": "2022-12-27T10:11:47.941506Z", + "shell.execute_reply": "2022-12-27T10:11:47.940878Z" } }, "outputs": [], @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "35e3dd39", + "id": "c1cba503", "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": "979aba61", + "id": "7ef878a4", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.944530Z", + "iopub.status.busy": "2022-12-27T10:11:47.944191Z", + "iopub.status.idle": "2022-12-27T10:11:47.948098Z", + "shell.execute_reply": "2022-12-27T10:11:47.947471Z" } }, "outputs": [], @@ -307,7 +307,7 @@ }, { "cell_type": "markdown", - "id": "1313cf91", + "id": "c5c7da7b", "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": "feee6953", + "id": "d4197f8c", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.951231Z", + "iopub.status.busy": "2022-12-27T10:11:47.950822Z", + "iopub.status.idle": "2022-12-27T10:11:47.957230Z", + "shell.execute_reply": "2022-12-27T10:11:47.956604Z" } }, "outputs": [ @@ -345,13 +345,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "8cc20d74", + "id": "e428178e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.961521Z", + "iopub.status.busy": "2022-12-27T10:11:47.961183Z", + "iopub.status.idle": "2022-12-27T10:11:47.965476Z", + "shell.execute_reply": "2022-12-27T10:11:47.964871Z" } }, "outputs": [], @@ -367,7 +367,7 @@ }, { "cell_type": "markdown", - "id": "06bc9a00", + "id": "a7334ec7", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -387,13 +387,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "3ccfeb23", + "id": "6ad604cf", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.968541Z", + "iopub.status.busy": "2022-12-27T10:11:47.967950Z", + "iopub.status.idle": "2022-12-27T10:11:47.972703Z", + "shell.execute_reply": "2022-12-27T10:11:47.972053Z" } }, "outputs": [ @@ -417,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "18d910fc", + "id": "253640bb", "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": "5e673c8a", + "id": "9f801d29", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.976042Z", + "iopub.status.busy": "2022-12-27T10:11:47.975638Z", + "iopub.status.idle": "2022-12-27T10:11:47.979944Z", + "shell.execute_reply": "2022-12-27T10:11:47.979328Z" } }, "outputs": [ @@ -457,7 +457,7 @@ }, { "cell_type": "markdown", - "id": "1d415acd", + "id": "809a5504", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -474,13 +474,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "5fe562ad", + "id": "29738670", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.983715Z", + "iopub.status.busy": "2022-12-27T10:11:47.983210Z", + "iopub.status.idle": "2022-12-27T10:11:47.986550Z", + "shell.execute_reply": "2022-12-27T10:11:47.985913Z" } }, "outputs": [], @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "0950da01", + "id": "0f10edf8", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -508,13 +508,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "9883bd1c", + "id": "5058d816", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:47.989557Z", + "iopub.status.busy": "2022-12-27T10:11:47.989066Z", + "iopub.status.idle": "2022-12-27T10:11:48.249192Z", + "shell.execute_reply": "2022-12-27T10:11:48.248411Z" } }, "outputs": [ @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "12932883", + "id": "78b07e07", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -572,13 +572,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "1880bacb", + "id": "9fd5b954", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.252503Z", + "iopub.status.busy": "2022-12-27T10:11:48.252153Z", + "iopub.status.idle": "2022-12-27T10:11:48.258091Z", + "shell.execute_reply": "2022-12-27T10:11:48.257484Z" } }, "outputs": [ @@ -602,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "90ba3f56", + "id": "6e5cdbe0", "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": "f05f1ff0", + "id": "a2fae2a3", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.261035Z", + "iopub.status.busy": "2022-12-27T10:11:48.260622Z", + "iopub.status.idle": "2022-12-27T10:11:48.265416Z", + "shell.execute_reply": "2022-12-27T10:11:48.264785Z" } }, "outputs": [ @@ -642,7 +642,7 @@ }, { "cell_type": "markdown", - "id": "cb687f1c", + "id": "929a414f", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -653,13 +653,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "000f5628", + "id": "4eb00c76", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.269074Z", + "iopub.status.busy": "2022-12-27T10:11:48.268660Z", + "iopub.status.idle": "2022-12-27T10:11:48.273531Z", + "shell.execute_reply": "2022-12-27T10:11:48.272886Z" } }, "outputs": [ @@ -685,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "42f4f049", + "id": "40710665", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -694,13 +694,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "734b4a38", + "id": "3d18acf6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.277120Z", + "iopub.status.busy": "2022-12-27T10:11:48.276900Z", + "iopub.status.idle": "2022-12-27T10:11:48.280647Z", + "shell.execute_reply": "2022-12-27T10:11:48.280132Z" } }, "outputs": [ @@ -721,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "374e7e55", + "id": "428e300d", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -743,13 +743,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "c3c5158c", + "id": "13ebf06e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.283373Z", + "iopub.status.busy": "2022-12-27T10:11:48.282850Z", + "iopub.status.idle": "2022-12-27T10:11:48.289648Z", + "shell.execute_reply": "2022-12-27T10:11:48.288348Z" } }, "outputs": [ @@ -771,7 +771,7 @@ }, { "cell_type": "markdown", - "id": "51e4aca7", + "id": "4c50340d", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -780,13 +780,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "134aef74", + "id": "9ba4b2dc", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.292611Z", + "iopub.status.busy": "2022-12-27T10:11:48.292290Z", + "iopub.status.idle": "2022-12-27T10:11:48.297434Z", + "shell.execute_reply": "2022-12-27T10:11:48.296821Z" } }, "outputs": [ @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "e88e3f11", + "id": "8c966e8e", "metadata": {}, "source": [ "# Node attributes\n", @@ -819,13 +819,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "b65fa5e1", + "id": "81d527a6", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.300814Z", + "iopub.status.busy": "2022-12-27T10:11:48.300404Z", + "iopub.status.idle": "2022-12-27T10:11:48.305410Z", + "shell.execute_reply": "2022-12-27T10:11:48.304785Z" } }, "outputs": [ @@ -850,7 +850,7 @@ }, { "cell_type": "markdown", - "id": "dee786f2", + "id": "bab5d9b5", "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": "199205bf", + "id": "7dff455a", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.309070Z", + "iopub.status.busy": "2022-12-27T10:11:48.308663Z", + "iopub.status.idle": "2022-12-27T10:11:48.312887Z", + "shell.execute_reply": "2022-12-27T10:11:48.312237Z" } }, "outputs": [], @@ -885,7 +885,7 @@ }, { "cell_type": "markdown", - "id": "e950cba1", + "id": "0e40282f", "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": "725a73a5", + "id": "1ce9ad47", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.315706Z", + "iopub.status.busy": "2022-12-27T10:11:48.315488Z", + "iopub.status.idle": "2022-12-27T10:11:48.320816Z", + "shell.execute_reply": "2022-12-27T10:11:48.320205Z" } }, "outputs": [ @@ -938,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "d6f47b84", + "id": "5425c7f1", "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": "b8d42578", + "id": "00e70ab7", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.323477Z", + "iopub.status.busy": "2022-12-27T10:11:48.323136Z", + "iopub.status.idle": "2022-12-27T10:11:48.326282Z", + "shell.execute_reply": "2022-12-27T10:11:48.325642Z" } }, "outputs": [], @@ -967,7 +967,7 @@ }, { "cell_type": "markdown", - "id": "81f8afa6", + "id": "0e62e4f9", "metadata": {}, "source": [ "# Multigraphs\n", @@ -987,13 +987,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "6408a9e1", + "id": "8cdebcee", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.329127Z", + "iopub.status.busy": "2022-12-27T10:11:48.328788Z", + "iopub.status.idle": "2022-12-27T10:11:48.335265Z", + "shell.execute_reply": "2022-12-27T10:11:48.334630Z" } }, "outputs": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "markdown", - "id": "ba84061f", + "id": "83d8c968", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -1043,13 +1043,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "e38785b7", + "id": "0854d6d5", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.338758Z", + "iopub.status.busy": "2022-12-27T10:11:48.338417Z", + "iopub.status.idle": "2022-12-27T10:11:48.342841Z", + "shell.execute_reply": "2022-12-27T10:11:48.342199Z" } }, "outputs": [], @@ -1062,7 +1062,7 @@ }, { "cell_type": "markdown", - "id": "fe2deba1", + "id": "7e19840b", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -1073,13 +1073,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "1edcecef", + "id": "337abf3c", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.345806Z", + "iopub.status.busy": "2022-12-27T10:11:48.345587Z", + "iopub.status.idle": "2022-12-27T10:11:48.425762Z", + "shell.execute_reply": "2022-12-27T10:11:48.425042Z" } }, "outputs": [], @@ -1092,7 +1092,7 @@ }, { "cell_type": "markdown", - "id": "204c93d0", + "id": "b5bef502", "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": "30a8c595", + "id": "75cdd2c4", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:48.429564Z", + "iopub.status.busy": "2022-12-27T10:11:48.429103Z", + "iopub.status.idle": "2022-12-27T10:11:49.525847Z", + "shell.execute_reply": "2022-12-27T10:11:49.524708Z" } }, "outputs": [], @@ -1121,7 +1121,7 @@ }, { "cell_type": "markdown", - "id": "50e1412d", + "id": "8ba69968", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -1136,13 +1136,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "b86d1f14", + "id": "42b38c47", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:49.529841Z", + "iopub.status.busy": "2022-12-27T10:11:49.529428Z", + "iopub.status.idle": "2022-12-27T10:11:49.535729Z", + "shell.execute_reply": "2022-12-27T10:11:49.535088Z" } }, "outputs": [ @@ -1168,7 +1168,7 @@ }, { "cell_type": "markdown", - "id": "7be00c0a", + "id": "d731579b", "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": "750d84ae", + "id": "ce458635", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:49.539801Z", + "iopub.status.busy": "2022-12-27T10:11:49.539579Z", + "iopub.status.idle": "2022-12-27T10:11:49.543928Z", + "shell.execute_reply": "2022-12-27T10:11:49.543283Z" } }, "outputs": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "markdown", - "id": "0e53b504", + "id": "020161fc", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -1225,13 +1225,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "d7d732e2", + "id": "1db0fd84", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:49.547545Z", + "iopub.status.busy": "2022-12-27T10:11:49.547333Z", + "iopub.status.idle": "2022-12-27T10:11:49.912590Z", + "shell.execute_reply": "2022-12-27T10:11:49.911608Z" } }, "outputs": [], @@ -1241,7 +1241,7 @@ }, { "cell_type": "markdown", - "id": "654b18e1", + "id": "13602902", "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": "64acafa1", + "id": "5a6bd124", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:49.916523Z", + "iopub.status.busy": "2022-12-27T10:11:49.916149Z", + "iopub.status.idle": "2022-12-27T10:11:50.121281Z", + "shell.execute_reply": "2022-12-27T10:11:50.120219Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -1282,7 +1282,7 @@ }, { "cell_type": "markdown", - "id": "79ba0ba2", + "id": "4b0563c7", "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": "ad8840dd", + "id": "a102647c", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:50.124828Z", + "iopub.status.busy": "2022-12-27T10:11:50.124473Z", + "iopub.status.idle": "2022-12-27T10:11:50.129078Z", + "shell.execute_reply": "2022-12-27T10:11:50.128356Z" } }, "outputs": [], @@ -1308,7 +1308,7 @@ }, { "cell_type": "markdown", - "id": "ac7f96cc", + "id": "d9c5131e", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -1317,19 +1317,19 @@ { "cell_type": "code", "execution_count": 36, - "id": "5399df9e", + "id": "26bd2caf", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:50.132031Z", + "iopub.status.busy": "2022-12-27T10:11:50.131728Z", + "iopub.status.idle": "2022-12-27T10:11:50.416679Z", + "shell.execute_reply": "2022-12-27T10:11:50.415878Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 640x480 with 4 Axes>" ] @@ -1356,7 +1356,7 @@ }, { "cell_type": "markdown", - "id": "c4066cae", + "id": "af3fc4c6", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -1367,13 +1367,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "a297b883", + "id": "a97da429", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:50.421526Z", + "iopub.status.busy": "2022-12-27T10:11:50.420201Z", + "iopub.status.idle": "2022-12-27T10:11:50.527815Z", + "shell.execute_reply": "2022-12-27T10:11:50.527174Z" } }, "outputs": [ @@ -1396,7 +1396,7 @@ }, { "cell_type": "markdown", - "id": "5af59003", + "id": "67ffbe4d", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -1405,19 +1405,19 @@ { "cell_type": "code", "execution_count": 38, - "id": "be51076a", + "id": "4091ed0e", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:50.531455Z", + "iopub.status.busy": "2022-12-27T10:11:50.531020Z", + "iopub.status.idle": "2022-12-27T10:11:50.665361Z", + "shell.execute_reply": "2022-12-27T10:11:50.664761Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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8LhXBRDlRQUkI+Y+HDx/i559/xuHDh1GjRg0sWLAA48aN4zrbMS8vD/3794e/vz98fX3Rtm1bblmI6pLJZLh9+3bRxp7bt29DJBLB3t6+qPeyRYsW3IaqxySl42BoAvwfJyMhLQsfvkCLAJga6sPZygTDHU1hUU1xPcuEfA0VlISQIg8ePMDPP/+Mo0ePonbt2liwYAHGjh3LfWNDQUEBhg0bhtOnT+P8+fPo0qUL1zxEfSQlJeHSpUu4ePEifHx88O7dO5iYmKB79+7o0aMHunbtqpBRVIlpWVh4KgLXYlOgJRahQPb5l+bCj7c3N8aKfjaoY0hjkwh/VFASQvDXX39h+fLlOH78OMzMzLBw4UK4u7tDR0eHdzTIZDKMHz8e+/btw7Fjx9CvXz/ekYiays/Px40bN4o29kREREBLSwtt27YtWr1s0qSJ4EPVD4clYMnZSEhl7IuF5Me0xCJIxCIsdbPGUHtTQTMRUlJUUBKiwe7evYvly5fj1KlTqFevHhYtWoRRo0YpzRnYjDHMmjULf/zxB/bv348RI0bwjkQ0SGJiYlFx6efnh6ysLNSpU+dfQ9XLOuFgk38M1vpGlznrnK6WmOZsUebrEFJaVFASooHCw8OxbNkynD17Fubm5li0aBGGDx+uNIVkoR9//BHLly/H5s2bMXnyZN5xiAbLzc1FUFBQUe9lTEwMdHR00KFDh6Kd4xYWJSvoDoclYP7JCMEyru5vgyG0Ukk4oYKSEA1y69YtLFu2DBcuXIClpSV++OEHfPPNN0o5PmXNmjWYN28efv31V8ydO5d3HEL+JSYmpuhIyICAAOTl5cHc3LzovHEnJ6cvbmJLTMuCy/pA5EplgmXSlYjhN7sD9VQSLqigJEQD3LhxA0uXLoWPjw8aNmyIxYsXY8iQIdDS0uId7ZO2bNmCyZMn44cffsDy5ct5xyHkizIzM3H16tWi1cvExETo6+ujc+fORY/HTU3/vXI4cmcoQuJSS9Qz+TVaYhHa1DfC/nGOgl2TkOKigpIQNRYcHIylS5fCz88P1tbWWLx4MQYOHKi0hSQAHDhwAKNGjcL06dOxYcMGwTdAECJPjDFERkYW9V4GBwejoKAATZo0KSouq5o3RY9NIXLL4DfbCeYmNFKIKBYVlISoocDAQCxduhT+/v6wsbHBjz/+iP79+3ObrVdcp0+fxsCBAzFq1Cjs2LFD6fMS8jVv377F5cuXcfHiRXh7eyMpKQnVe0yHrk0XQCT897eWWISRjmb4yc1a8GsT8iVUUBKiJhhj8Pf3x9KlSxEUFARbW1v8+OOP6NOnD5fCrKSnfFy+fBm9evVCnz59cOjQIaVeRSWkNGQyGe7evQv3E8+QzuR3SICZkT4C5zjL7fqEfIrydeITQkqEMQY/Pz8sW7YMwcHBaNGiBc6cOYPevXsr/HFxaU/5CA4ORt++feHi4oIDBw5QMUnUklgshlWTZsg4/kqu90lIzUJmrpSOaSQKRd9thKgoxhh8fHywbNky3LhxA/b29jh//jx69Oih8EKyOKd8MADxaVnYHxqPPTeeFZ3y8frZI/Ts2RP29vY4fvy4UgxTJ0Re4lMzIe/HggzAs9RMWNesJOc7EfL/qKAkRMUwxnDx4kUsW7YMt27dQqtWreDt7Q1XV1cuG1g+POUDwFd3rRZ+PCQuFZ3XBeC9/040bNgQ586dQ7ly5eSelxCe8gQcE6QM9yGkEBWUhKgIxhjOnTuHZcuWITw8HG3btoWvry9cXFy47YQuyykfBTIGKWPQcxqLAe3qwMCAdqUS9acjUUw/s6LuQ0gh+o4jRMnJZDKcOnUKzZs3R58+fVC+fHlcuXIF165dQ5cuXbgVk4fDEsp8ZFxh9s3BiTgSliBELEKUWl2j8pD331jR/+5DiCJRQUmIkpLJZDh+/Djs7OzQv39/VKlSBQEBAQgMDESnTp24zmdMTMvCkrORgl7zx7ORSEzLEvSahCib8roSmMr5JBtTI33akEMUjgpKQpRMQUEBjhw5gqZNm2LQoEGoVq0agoKCcPXqVXTo0IF3PADAwlMRRT2TQpHKGBaeEu5cY0KUlbOVCbTE8nlDqCUWwdnSRC7XJuRLqKAkREkUFBTgzz//hI2NDYYOHYo6deogJCQEvr6+aN++Pe94RWKS0nEtNkXQI+OAf3oqr8WmIDY5XdDrEqJshjuaCv73p1CBjGFEK9Ov/0ZCBEYFJSGcSaVS7N+/H40bN8bw4cNRv3593Lx5E97e3mjdujXveP9xMDRBrqsrB25SLyVRbxbVDNDe3Fjwv0daYhHamxvTsYuECyooCeFEKpViz549aNSoEUaNGoWGDRsiLCwM58+fh6OjI+94n+X/OFmuqyv+0clyuTYhymRFPxtIBC4oJWIRVvSzEfSahBQXFZSEKFh+fj527twJKysrjBkzBjY2Nrhz5w7OnDmDli1b8o73RRm5UiTIeeNM4SkfhKizOob6WCrwedvL3KxRR84bfgj5HCooCVGQvLw8bNu2DZaWlhg/fjyaN2+Oe/fu4eTJk7Czs+Mdr1gUecoHIepuqL0p5nS1FORabwL34tnVQ4Jci5DSoLkChMhZbm4udu3ahZUrV+L58+cYPHgwzp07hyZNmvCOVmJ0ygchwprmbAHjCrpFp02VpJ1ESyyCRCzCUjdrPNRphPnz5yMrKws//fQT17FiRDNRQUk0WmauFM9SM5EnlUFHIkZdo/KCzW/LycnBjh07sGrVKrx8+RJDhw7FokWL0LhxY0GuzwOd8kGI8Ibam6JtA2MsPBWBa7EpEIGBfWH8uZZYhAIZQ5v6RljRz+afx9z2S6Gvr19UVP76669UVBKFooKSaJyYpHQcDE2A/+NkJKRl/esRrgiAqaE+nK1MMNzRFBbVSr5bMjs7G9u3b8fq1avx6tUrDB8+HIsWLYKVlZVgXwMvhad8yPOxN53yQTRRHUN97B/niJikdIxctg2vtU0gK2f4359PRvpwtjTBiFam/9nN/f3330NfXx8zZsxAVlYWNm7cCLGY3pwRxaCCkmiMxLSsohWAwnf4H2MA4tOysD80HntuPEN7c+P/XwH4iqysLGzduhW//vorXr9+jZEjR2LhwoWwsLCQw1fDR+EpH/Fy3JhDp3wQTWZRzQBv/Lahp6srVv60rsRPUKZPn45y5cph4sSJRW9utbS0FJSeaDJ660I0wuGwBLisD0RIXCoAfLVPqfDjIXGpcFkfiMNfOGc6MzMTa9euRb169TBv3jz07NkTjx8/xu7du9WqmCzkbGUCLXk9SWMyNDGkx3REc71//x7R0dFo0aIFyutKYF2zEuxMq8C6ZqViv9EaP3489u/fj3379mHEiBHIz8+Xc2pCqKAkGmCTfwzmn4xArlRW4vmJBTKGXKkM809GYJN/zL8+lp6ejtWrV6Nu3bpYuHAh+vbti+joaOzYsQMNGjQQ8ktQGgUFBdB7EYYCeT3zFomxbd5IdOzYEadPn0ZBQYGcbkSIcrp79y4AoHnz5mW6zvDhw3HkyBGcOHECgwcPRm5urhDxCPksKiiJWjscloC1vtGCXGutbzSOhCXg/fv3WLFiBerWrYsff/wRgwYNQkxMDLZu3Yp69eoJci9l5Ofnh+bNm2PBlDGolP1S8FVKLbEIbRsY4U+vdcjPz0e/fv1gaWmJ33//He/fvxf2ZoQoqTt37qBcuXJo1KhRma81YMAAnD59Gt7e3ujbty+ysuQ7Q5ZoNiooidpKTMvCkrORgl5z4cn7qGdjj6VLl+Kbb75BbGwsNm/eDDMzM0Hvo0wePnyIXr16oUuXLjAwMEBoaCjO/zgMEi1hf3xIxCKs6t8UgwYNwvXr1xEaGgpHR0fMmTMHderUwbfffounT58Kek9ClE14eDiaNWsGiUSYPuIePXrgwoULCAoKQs+ePZGeni7IdQn5GBWURG0tPBUBqcBHBEplDPWHLERcXBw2bdqEOnXqCHp9ZfL69WtMmzYNNjY2iIqKwrFjx3Dt2jU4ODgo5JQPBwcH/Pnnn3j69CmmTp2KvXv3wtzcHP3790dQUBAYk/eIdUIULzw8vMyPuz/WuXNn+Pj4IDw8HK6urnj79q2g1ycEoIKSqKmYpHRci00R/MxpkVgLr7WMka1dUdDrKpPc3FysWbMG5ubmOHDgAFatWoWHDx9i4MCB/5prJ+QpH3O7WmGIveknP1a7dm2sWLECiYmJ2Lx5Mx4+fIgOHTqgZcuW2L9/P/Ly8gTJQAhvGRkZePz4MVq0aCH4tdu1a4crV67g0aNH6Ny5M1JSUgS/B9FsVFAStXQwNAFaYvnsFtYSi3Dg5ud3fasqxhiOHTuGRo0aYcGCBRg1ahRiY2MxZ84c6OrqfvJzpjlbYFV/G+hKxCX+89YSi6ArEWN1fxtMdTb/6u/X19eHh4cHIiMj4e3tjapVq2LUqFEwMzPD8uXL8fr16xLdnxBlc+/ePTDG5FJQAoC9vT0CAgKQmJgIZ2dnvHr1Si73IZqJCkqilvwfJwu+OlmoQMbgH50sl2vzEhoainbt2mHw4MGwtrbGgwcPsHHjRhgbG3/1c4fam8Jvdge0qW8EAF8tLAs/3qa+Efxmd/jsyuTniMVidOvWDZcuXUJkZCT69OmDlStXok6dOhg/fjwiIiJKdD1ClEV4eDh0dXXleppW06ZNERQUhLS0NHTo0AHPnz+X272IZqGCkqidjFwpEuQ4eBsAElKzkJkrles9FCE+Ph7Dhg1Dq1atkJGRAT8/P5w7dw4NGzYs0XUKT/m4PMsJIx3NYGak/5+D40QAzIz0MdLRDH6znbB/nGOxBsZ/SePGjbFlyxYkJiZiyZIl8Pb2RtOmTdGlSxdcuHABMhmdCU5Ux507d9C0aVNoa2vL9T4NGzZEUFAQcnNz0b59e9rsRgQhYtTZTtRM5N/v0HNjsNzvc2F6O1jXrCT3+8jD+/fvsWrVKqxbtw5VqlTBL7/8gtGjRwt6ooY8z0n/nPz8fBw/fhzr169HWFgYLCwsMHPmTIwePRoVKlSQ670JKasmTZqgffv28PLyUsj9EhIS0LlzZ2RnZ+PKlStqcTws4YdWKInayZMqZlVKUfcRklQqxdatW2FhYYENGzZg3rx5iImJwdixYwU/nq20p3yUhba2Nr755huEhobi+vXrsLW1xYwZM1CnTh3MmzcPCQnq1/tK1ENmZiYePnwot/7JTzE1NUVQUBAqVaoEJycnahchZUIFJVE7OhLFfFsr6j5C8fHxga2tLSZNmoRu3bohOjoay5YtU8uVO5FIhDZt2uDo0aOIi4vD+PHjsW3bNtSvXx+DBw/GjRs3aOwQUSr379+HTCYTfGTQ19SoUQMBAQGoWbMmOnbsiPDwcIXen6gP1XpFJKQY6hqV/0//ntBE/7uPKoiMjET37t3RrVs3GBkZISwsDHv37kXt2rV5R1MIMzMzrFmzBs+fP8fvv/+Oe/fuoU2bNmjVqhUOHTpE5xwTpXDnzh3o6OigSZMmCr931apVcfXqVVhYWKBTp04ICQlReAai+qigJGqnvK4EpmXc7PE1pkb6CnmEWxZJSUmYNGkSmjZtipiYGJw8eRIBAQFo2bIl72hcVKhQAVOnTsWjR49w7tw5GBgYYNiwYahXrx5WrVqF1NRU3hGJBgsPD4eNjQ10dHS43L9KlSq4fPkybG1t0bVrV/j7+3PJQVQXFZRELTlbmch1DqWzpYlcri2EnJwcrFq1ChYWFjhy5Ah+++03REVFoV+/fv8aTK6pxGIxevXqBT8/P/z1119wdXXFTz/9hDp16mDSpEl4+PAh74hEA8njhJySMjAwgLe3N9q0aYMePXrg0qVLXPMQ1UIFJVFL/WyM5TqHckSrks1OVATGGA4dOoSGDRti8eLFGDt2LGJjYzFr1ixuqx7KzsbGBjt37kRCQgIWLFiA06dPo3HjxujevTt8fHyoz5IoRHZ2NqKiohS6Iedz9PX1cfbsWXTp0gVubm44ffo070hERVBBSdSOr68v+jg7Ii/+PkQQtiDQEovQ3twY5iYGgl63rEJCQtC6dWsMGzYMdnZ2iIyMxIYNG2BkZMQ7mkowMTHB4sWLER8fj3379iEpKQndunWDtbU1tm3bhqws+c41JZrtr7/+QkFBgVIUlACgp6eHEydOoG/fvhg4cCAOHz7MOxJRAVRQErXx5s0bjBkzBq6urjA3N8exef2hIxFyFA6DRCzCin42Al6zbJ4+fYohQ4agbdu2yMvLg7+/P06dOgVLS2HO2NY0urq6GDlyJMLDwxEYGIiGDRti0qRJqFOnDhYuXIgXL17wjkjUUHh4OCQSCZcNOZ+jra2NP//8E8OHD8ewYcOwe/du3pGIkqOCkqiFkydPonHjxjh16hR27NiBy5cvo61tQyx1sxbwLiI0yYlCrcp6Al6zdN69e4d58+ahYcOGCA4Oxp49e3D79m107NiRdzS1IBKJ4OTkhJMnTyI2NhajRo3Cpk2bULduXQwfPhxhYWG8IxI1cufOHTRp0gR6evx/tnxIIpFg9+7d8PDwwNixY+Hp6ck7ElFiVFASlfbq1SsMHDgQAwYMgKOjI6KiojBu3LiizSdD7U0xp6swq3XtK77Bqd/mwt3dnduoGalUis2bN8Pc3Byenp5YuHAhoqOjMXr0aIjF9NdZHurXr4/169fj+fPnWLt2LW7cuAEHBwe0bdsWx44dg1Sq+kdwEr7Cw8OV5nH3x8RiMTZv3ozZs2dj2rRpWLt2Le9IREnRKxBRSYwx7N27F40bN0ZQUBCOHDmCU6dOoWbNmv/5vdOcLbCqvw10JeIS7/zWEougKxFjdX8b7F8wAocOHcLhw4fRt29fZGZmCvXlfBVjDBcuXEDTpk0xbdo09O7dGzExMViyZAnKl1eNeZiqrmLFipg5cyZiYmJw6tQpaGtrY/DgwWjQoAHWrl2Lt2/f8o5IVFBOTg4ePHjAfYf3l4hEIvz2229YtGgR5s6di2XLltGGNfIfVFASlRMfH4/u3bvD3d0dPXv2RFRUFAYPHvzFkThD7U3hN7sD2tT/Z5PK1wrLwo+3qW8Ev9kdMMT+n13dQ4YMwYULFxAYGAgXFxekpaUJ9FV93l9//YWuXbuiV69eqF69Ou7cuYNdu3Z9sngm8qelpYW+ffsiICAAd+7cgbOzMxYuXIjatWtj+vTpiImJ4R2RqJAHDx5AKpUq7QplIZFIhJ9//hm//PILlixZggULFlBRSf5FxOg7gqgImUyGzZs3Y/78+ahSpQq2bNmCnj17lvg6MUnpOBiaAP/oZCSkZv1rH7gI/wwtd7Y0wYhWpp/dzX379m10794dJiYm8PHxkcupM69evcLixYuxa9cumJubY+3atejVqxfNklRCr169gpeXF7y8vJCSkoKePXti1qxZ6NSpE/33Il+0detWTJ06Fenp6ShXrhzvOMWyYcMGzJ49G9OnT8eGDRuo3Yb8gxGiAh49esTatm3LALDJkyezd+/eCXLdjJx89uDFW3YnPo09ePGWZeTkF/tzHz9+zMzMzFidOnVYVFSUIHkYYywzM5MtX76clS9fnhkaGrI//viD5eXlCXZ9Ij/Z2dls586dzMbGhgFgNjY2bOfOnSw7O5t3NKKkJkyYwGxsbHjHKLEtW7YwkUjExo8fz6RSKe84RAlQQUmUWl5eHluxYgXT1dVl5ubmLCAggHekf3nx4gVr0qQJMzQ0ZDdv3izTtQoKCtj+/ftZ7dq1mba2Nvv2229ZWlqaQEmJIslkMnblyhXWu3dvJhKJWNWqVdnixYvZy5cveUcjSqZFixbM3d2dd4xS2bt3LxOLxWz48OEsP7/4b8aJeqKCkiitO3fuMDs7OyYWi9m8efNYVlYW70iflJaWxtq1a8f09fWZt7d3qa4RFBTEWrZsyQCwAQMGsNjYWIFTEl6io6PZtGnTWPny5Zm2tjYbNWoUu3PnDu9YRAnk5uYyHR0dtnHjRt5RSu3o0aNMIpGwAQMGsNzcXN5xCEfU+ECUTk5ODhYtWgR7e3tIpVKEhoZi9erVSttfVKVKFfj6+qJz587o3bs3/vzzz2J/bmxsLAYMGAAnJycAQFBQEI4fP44GDRrIKy5RMAsLC2zcuBHPnz/HihUrEBAQgObNm6NDhw44ffo0CgoKeEcknDx48AB5eXlKvcP7awYNGoSTJ0/i3Llz6NevH7Kzs3lHIrzwrmgJ+VBwcDCzsrJiOjo6bPny5Sr1jjc/P5+NGTOGAWAbNmz44u9NS0tj3377LdPW1ma1a9dm+/fvZwUFBQpKSnjKz89nx44dK+oJrlevHlu/fr1gfcFEdWzfvp2JxWKWmZnJO0qZ+fr6snLlyrFOnTqxjIwM3nEIB1RQEqWQnp7Opk+fzkQiEWvVqhWLjIzkHalUZDIZ+/777xkAtmDBAiaTyf718by8PPb7778zQ0NDVr58efbzzz+rxYsJKZ3Q0FA2bNgwJpFImIGBAZs1axZ78uQJ71hEQSZNmsSsra15xxBMYGAgq1ChAmvbti17+/Yt7zhEwaigJNz5+PgwMzMzpq+vzzZs2KAWOwZ/++03BoCNGzeO5efnM5lMxs6cOcMsLS2ZWCxm48ePpw0apMjz58/ZwoULmaGhIROJRKxv374sMDDwP29IiHqxt7dnI0eO5B1DUDdv3mSVK1dmLVu2ZKmpqbzjEAWiOZSEm7S0NHz33XfYs2cPOnfujG3btqF+/fq8Ywlm//79GDt2LNq2bQsARcPQf/vtNzRt2pRzOqKMsrKycODAAWzYsAEPHz6EnZ0dZs2ahSFDhkBXV5d3PCKg/Px8GBgYYPXq1Zg5cybvOIK6d+8eunTpgpo1a+Ly5cswMTHhHYkoAG3KIVycOHECjRs3xqlTp7Bjxw5cvnxZrYpJAOjcuTOcnZ0RGBiIW7du4ciRI/D19aViknyWvr4+Jk6ciMjISPj4+KBatWoYPXo0zMzMsGzZMiQnJ/OOSAQSFRWF3NxcpT8hpzRsbW0RGBiI5ORkdOjQAS9evOAdiSgAFZREoV69eoWBAwdi4MCBaNWqFaKiojBu3Di1Ok0kMzMTS5cuhYWFBe7evYvvvvsOurq6+OWXX/Dq1Sve8YgKEIlE6Nq1K7y9vREVFYV+/fph1apVMDU1xbhx4/DXX3/xjkjKKDw8HCKRCLa2tryjyEXjxo0RFBSEzMxMODk54dmzZ7wjETmjgpIoBGMMe/fuLfohc+TIEZw6dUqtzqOWyWTYs2cPLC0tsWLFCkybNg2xsbFYu3Ytrl+/jrS0NLRp04bOeiYl0qhRI3h5eSExMRE//fQTfHx80KxZM3Tu3Bnnz5+HTCbjHZGUQnh4OKysrFChQgXeUeTGwsIC165dAwA4OTnRzz41RwUlkbv4+Hh0794d7u7u6NmzJx4+fIjBgwer1aqkv78/WrZsiTFjxqBdu3Z49OgRVq9ejUqVKgH45916SEgI9PT00LZtW4SHh3NOTFSNkZER5s+fj6dPn+LQoUPIyMhA79690bBhQ2zatAkZGRm8I5ISuHPnjlo+7v6YmZkZgoKCUL58eTg5OSEyMpJ3JCInVFASuZHJZNi0aROsra0RGRmJCxcuYP/+/TAyMuIdTTDR0dHo27cvOnXqBB0dHVy/fh1HjhxBvXr1/vN769Spg+DgYNSvXx8dO3bE1atXOSQmqk5bWxtDhw5FaGgoQkJCijbu1K5dG3PnzkV8fDzviOQrpFIp7t+/rxEFJQDUqlULgYGBMDExQceOHXH37l3ekYgcUEFJ5OLx48dwcnLC9OnTMWrUKERGRqJHjx68YwkmLS0Ns2bNgrW1Ne7evYtDhw7hxo0baNOmzRc/z8jICFeuXEG7du3QvXt3HD9+XEGJiTpq3bo1jhw5gri4OEycOBE7duxA/fr1MWjQIISEhICGeCinhw8fIjs7W6VPyCkpExMT+Pv7o169eujUqRNCQ0N5RyJC4zu1iKibvLw8tmLFCqarq8ssLCxYYGAg70iCys3NZevWrWOVK1dmBgYGbOXKlaU6YzwvL48NHz6ciUQitnnzZjkkJZooPT2deXp6MktLSwaA2dvbs4MHD7K8vDze0cgH9uzZwwBo5OlI7969Y+3atWMVKlRQu9cHTUcFJRHMnTt3mJ2dHROLxWzevHmlKrSUlUwmYydPnmTm5uZMLBazSZMmsaSkpDJds6CggM2aNYsBYEuWLKEh1kQwBQUF7Pz588zFxYUBYDVr1mQrVqxgKSkpvKMRxtj06dOZpaUl7xjcZGRksM6dO7Ny5coxHx8f3nGIQKigJGWWnZ3NFixYwLS0tFjTpk1ZWFgY70iCun37NnNycmIAWLdu3diDBw8Eu7ZMJmMrV65kANjkyZPV4pQgolwiIiLY+PHjma6uLitXrhybOHGiyh5tqi7atGnDhg4dyjsGV9nZ2axnz55MR0eHnT17lnccIgAqKEmZBAcHMysrK6ajo8OWL1/OcnNzeUcSTGJiIhs5ciQDwKytrdmlS5fkdq8dO3YwsVjMBg4cyHJycuR2H6K5kpOT2fLly1n16tUZAObq6sq8vb1ZQUEB72gaRSqVMn19fbZmzRreUbjLzc1lAwYMYBKJhB05coR3HFJGVFCSUklPT2fTpk1jIpGItWrVSq1WPNLT09nixYtZuXLlmImJCdu6dSvLz8+X+31Pnz7N9PT0WKdOnTSyt4ooRm5uLtu3bx+zs7NjAFjDhg3Zli1bWGZmJu9oGiEyMpIBYFevXuUdRSnk5+ezESNGMLFYzPbu3cs7DikDKihJifn4+DAzMzOmr6/PNmzYoDaPaaVSKduxYwerXr0609XVZQsWLFB4YRcUFMQqVarEmjdvzl69eqXQexPNIpPJWFBQEOvfvz8Ti8XM0NCQzZ8/nyUmJvKOptb27dvHALA3b97wjqI0CgoK2IQJExgAtmXLFt5xSClRQUmKLTU1lbm7uzMArHPnzuzJkye8Iwnm8uXLrGnTpgwAGzZsGHv27Bm3LPfv32c1atRg5ubmLC4ujlsOojni4uLY7NmzmYGBAZNIJOybb75hoaGhcr9vRk4+e/DiLbsTn8YevHjLMnLk/ySAt1mzZrEGDRrwjqF0ZDIZmzFjBgPA1q9fzzsOKQURYzSojHzdiRMnMHXqVOTk5GDdunUYM2aMWpx08+jRI8ydOxfnz59HmzZtsG7dOjg6OvKOhadPn8LV1RXp6em4dOkSmjVrxjsS0QDv37/Hnj178PvvvyMuLg6tW7fGrFmz0L9/f0gkEkHuEZOUjoOhCfB/nIyEtCx8+AIkAmBqqA9nKxMMdzSFRTUDQe6pTJycnFCjRg0cOXKEdxSlwxjDwoULsWrVKvz8889YtGgR70ikBGiwOfmiV69eYeDAgRg4cCBatWqFqKgojB07VuWLyZSUFEybNg1NmjRBZGQkjh49iuDgYKUoJgGgXr16CA4ORs2aNeHk5ISgoCDekYgGqFixImbMmIHo6GicPn0aurq6GDJkCOrXr481a9bgzZs3pb52YloWRu4MRZcNQdgfGo/4j4pJAGAA4tOysD80Hl02BGHkzlAkpmWV6WtSJjKZDHfv3tWogeYlIRKJsGLFCixfvhw//PADFi1aRMP5VQitUJJPYoxh7969+Pbbb6GtrY1NmzZh4MCBKl9I5ubmYuPGjfj555/BGMPixYsxffp06Orq8o72Se/fv0e/fv2KjnTs06cP70hEw9y7dw8bNmzAoUOHIJFI4O7ujpkzZ8LS0rLY1zgcloAlZyMhlTEUyIr/kqMlFkEiFmGpmzWG2puWJr5Sefz4MRo2bIjLly/DxcWFdxyl9ttvv2HOnDmYNWsW1q1bp/KvPZqAVijJfzx79gzdunXDmDFj0KtXL0RFRWHQoEEq/ReaMYZjx46hUaNGmD9/PkaMGIHY2FjMmTNHaYtJ4J8Vo4sXL8LNzQ39+/fHzp07eUciGsbW1hZ79uxBfHw85s6di2PHjsHKygq9evWCn5/fV1eQNvnHYP7JCORKZSUqJgGgQMaQK5Vh/skIbPKPKcuXoRTCw8MBgFYoi+G7776Dp6cnNmzYgMmTJ0Mmk/GORL6CCkpSRCaTYePGjWjSpAkePnyIixcvYt++fTAyMuIdrUxu3bqFdu3aYfDgwbC2tkZERAQ2bdqEqlWr8o5WLLq6ujh06BAmTZqE8ePHY+XKlfQYiChc9erV8dNPPyEhIQG7du1CYmIiunTpgqZNm2Lnzp3Izs7+z+ccDkvAWt9oQe6/1jcaR8ISBLkWL+Hh4ahbty4MDQ15R1EJU6ZMwe7du7F9+3a4u7tDKpXyjkS+gApKAuCfzSlOTk6YMWMGRo0ahQcPHqB79+68Y5VJQkIChg8fDkdHR2RkZODy5cs4d+4cGjVqxDtaiWlpaWHTpk346aefsHDhQsyePZvesRMu9PT0MGbMGNy7dw9Xr15FvXr1MGHCBJiammLx4sV4+fIlgH96JpecjRT03j+ejVTpnso7d+6gRYsWvGOoFHd3dxw8eBB//vknhg0bhry8PN6RyGdQQanh8vPzsXLlStja2iI5ORmBgYHYvHkzKlasyDtaqb1//x4LFy6ElZUVrl69ih07duDOnTsq37MkEomwZMkSeHl54Y8//sDIkSPphyvhRiQSwdnZGWfPnsXjx4/xzTffYP369TAzM8OoUaMwbd91SEv4iPtrpDKGhaciBL2moshkMiooS2no0KE4ceIEzpw5g4EDByInJ4d3JPIJVFBqsLt378LR0RE//PADZs6cifv378PJyYl3rFKTSqXYtm0bLCwssGHDBsydOxcxMTEYN24ctLS0eMcTzKRJk3D06FEcP34cbm5uyMjI4B2JaDgLCwv88ccfeP78OVatWoWge9G4n5RX4p7JrymQMVyLTUFscrqg11WEJ0+e4P3799Q/WUp9+vTB2bNncfnyZbi5uSErS3VXqtUVFZQaKCcnBwsXLoS9vT0KCgoQGhqK1atXo1y5cryjlZqPjw9sbW3h4eEBV1dXPH78GMuWLUOFChV4R5OLgQMHwtvbGyEhIejcuTNSUlJ4RyIElStXxrfffotRy7dD/J+hQMLQEotw4Kbq9VLeuXMHAGiFsgxcXV2Lfu5169YN6elff2ORmStF5N/vcDfhDSL/fofMXOrDlBdhJtUSlXH9+nWMGzcOT58+xU8//YTvv/8e2travGOVWmRkJObMmYNLly6hffv2CAsLQ8uWLXnHUohOnTohICAA3bt3R7t27eDr6wtTU9UfrUJUX2B0CmSQz1SIAhmDf3QyfoK1XK4vL+Hh4TA1NYWxsTHvKCqtY8eOuHz5Mrp37w4XFxdcunQJVapU+dfv0fTh+bzQCqWGSE9Px/Tp09G+fXtUqVIFd+/exQ8//KCyxWRycjImT56Mpk2bIiYmBidOnEBgYKDGFJOFmjdvjuvXryMvLw9t2rRBVFQU70hEw2XkSpEg540zCalZKrfSFB4eTo+7BdK6dWtcvXoVT548QadOnfD69WsANDyfNyooNYCPjw+aNGmCXbt2Yf369QgODkbjxo15xyqVnJwcrFq1Cubm5jh8+DDWrl2LqKgo9O/fX6XnZJaFubk5rl+/DiMjI7Rr1w43btzgHYlosPjUTDk97P5/DMCz1Ew530U4jDHakCOw5s2bIyAgAC9fvkTHjh2x9XIEXNYHIiQuFQC+2r9b+PGQuFS4rA/EYRUfSaUM6JG3GktLS8O3336LvXv3wsXFBQEBAahXr55c75mZK8Wz1EzkSWXQkYhR16g8yuuW/duMMYYjR45g/vz5ePHiBaZOnYrFixer/IxModSoUQOBgYHo06cPOnfujOPHj6NHjx68YxENlCdVzDirtu07QD87GZUqVULFihW/+uun/l2FChUU8kb06dOnePv2LRWUAmvSpAmCgoLgMnMtVl4tXUFY8L/Tm+afjEBKRi6mOVsInFJzUEGppk6cOIGpU6ciJycHO3fuxJgxY+T2g1Pe/So3btzAt99+i5s3b6JPnz7w9fUt0bFvmqJy5cq4dOkShg0bBjc3N+zevRsjR47kHYtoGB2JYh58TfGYCN3s13j//j3evXtX9OuLFy/+9f+/NAVBLBbDwMCgREXop37V1dX94s9XOiFHfu6804PYVpgjadf6RqNqBV0MUYNjPnmgs7zVzKtXrzB16lScPHkSffv2haenJ2rWrCmXeyWmZWHhqQhci02Bllj0xUcMhR9vb26MFf1sUMdQ/6vXf/r0KRYsWIAjR47A1tYW69atg7Ozs5BfglqSSqWYNGkSdu7cibVr1+K7777jHYlokMxcKZr85CPXx94iAA9+ci3W04+CggKkp6f/q/D8uAgtzq9fmn2ora39xSL0zp07iIqKwqpVqz5bsFasWFFle9p5SUzLgsv6QOQKuCquKxHDb3aHYr1GkX+jglJNMMawd+9efPvtt9DW1samTZswcOBAua1KHg5LwJKzkZD+73FBcWmJRZCIRVjqZo2hn3kX+O7dO6xYsQIbNmyAsbExfvnlF4wcOVKtZknKG2MMP/zwA1asWIG5c+di9erVGttjShSvwxp/xMtxo4OZkT4C5yj2zWVeXl6Ji9HC/x0dHQ2pVAqZTIaCgoLP3qNcuXKlenT/4a8VKlSAWKwZ2yNG7gxFSFyqoPNOtcQitKlvhP3jHAW7pqagR95q4NmzZ/Dw8ICvry9GjhyJ9evXy7W3cJN/TKnP5/1Sv0rhYPIlS5YgKysLCxcuxJw5c1C+fHmhomsMkUiEX375BdWqVcPMmTPx+vVrbN++HRIJ/ZUn8udsZYL9ofGCDzYH/nnBd7Y0Efy6X6OjowNjY+MSj/1hjMHY2BgzZ87E4sWLkZ2d/cXi81O/vnz58l//Pz09HZ9bCxKJRDAwMCjVo/sPf2+5cuWU+k1oTFI6rsUKP3/3w+H55iY0Uqgk6NVFhclkMnh6emLBggUwNDTExYsX5X7+9uGwhFIXkx8r7FcZ3LIOvL29MWfOHDx69AijR4/Gzz//jFq1aglyH002Y8YMGBsbY/To0UhJScGRI0egr0+Pcoh8DXc0xZ4bz+Ry7QIZw4hWqtPjFh8fj7S0NDRv3hwikQj6+vrQ19dH9erVS31NmUyGjIyMEq2WpqWl4enTp//6d186bUYikRS7GP3Sx3R0dEr9dX7JwdCEr7ZalVbh8Pyf3FRr1ilv9MhbRT169Ajjx4/H9evXMWXKFKxcuVLu52/Lo19FR0uEWvd2IeDCCXTs2BHr1q2DnZ2dYNcn//Dx8UH//v1hZ2eHc+fO/WcQMCFCo8eR/zh58iQGDBiAv//+GzVq1OAd51/y8/ORnp7+1Uf2X/s1Pz//s/fQ09Mr04anihUrwsDA4D8tT+rYVqHqaIVSxeTn52PNmjVYunQpzMzMEBgYqLDztxeeioBU4HeDuXn5eGbSGmfOjELv3r2V+hGLKnN1dcXVq1fRs2dPODk54dKlS7QCTORqRT8buKwPFLSglIhFWNHPRrDrKUJ4eDhq1KihdMUk8M9mIkNDQxgaGpb6Gowx5ObmlrgIffLkyb8+5/3795DJPr9YUaFChf/vE61ijOT28wA5vl4UDs8XYuydpqA/KRVy9+5djB07FhEREZgzZw6WLFmisPO35dWvItKSgFVriMatnKiYlDNHR0cEBweja9euaNu2LXx8fGBlZcU7FlFTdQz1sdTNGvNPRgh2zWVu1iq3+1bdT8gRiUTQ09ODnp4eqlWrVurrMMaQmZlZrI1OzzOBZDm/XhQOz7euWUmu91EnVFCqgJycHCxduhRr1qxBkyZNEBoaqvABudSvoh4aNmyIkJAQuLq6ol27drh48SLs7e15xyJqaqi9KVIycgXpu57b1Url5gMWnpAzefJk3lGUnkgkQoUKFVChQoWvPj25m/AG/bxC5J5JUUP61YVmzBZQYcHBwWjWrBnWrVuHpUuXIiwsjMtpC/6Pk+VSTAL/NNn7RyfL5drkv2rXro1r167BwsICzs7OuHz5Mu9IRI1Nc7bAqv420JWIIWIle4HWEougKxFjdX8bTHU2l1NC+Xn+/Dlev35NJ+QITFHD8xV1H3VBf1pKKj09HdOnT4eTkxOMjIxw7949LFq0iMvg24xcKRLk2PwM/H+/ClEMQ0ND+Pn5oUOHDujZsycOHz7MOxJRY0PtTXHGoyVyEx8A+KdQ/JLCj7epbwS/2R1UbmWyEJ2QIx91jcpD3g1Sov/dhxQfPfJWQj4+Ppg4cSJSUlKwfv16TJs2jetQ7/jUTLmeegFQvwoP+vr6OH36NMaPH49hw4bh9evXmD59Ou9YRE0FXjiJpMM/wD/8Ia4m5MM/OhkJqZ84qtVIH86WJhjRylTl5wDeuXMHJiYmtAFOYOV1JTA11JfrLm9TI33akFNC9KelRNLS0vDtt99i7969cHFxQUBAAOrVq8c7lsL6SKhfRfG0tbWxe/dumJiYYMaMGUhOTsayZctogxQRFGMMXl5e6NWrF5xsLeFkC/wEa2TmSvEsNRN5Uhl0JGLUNSqvVi/i4eHhaNGiBf19kgN1HJ6v6tTnb66KO3HiBKZOnYqcnBzs3LkTY8aMUZofQtSvot7EYjHWrFkDExMTzJs3D8nJydi8eTMddUkEc/PmTdy/fx+rVq36178vrytR26cSjDGEh4dj/PjxvKOoJRqer3zoFZyzly9fYsCAARg4cCBat26NqKgojB07VmmKSYD6VTTF3LlzsXv3buzcuRODBg1CTk4O70hETXh5eaF+/fro2rUr7ygK8/LlSyQlJdGGHDmxqGaA9ubGX+3HLSktsQjtzY1Vvt2CByooOWGMYc+ePWjcuDGCg4Nx9OhRnDx5EjVr1uQdDQCQl5cHf39/zJ8/H+1a2SPvzd9yvR/1qygHd3d3nDp1Ct7e3ujWrRvevXvHOxJRcYVHfk6aNAlisea85BRuyKGCUn5W9LOBROCCUhWH5ysLzfnbrUSePXsGV1dXjBkzBm5uboiKisKgQYO4r0rGxsbC09MTbm5uMDIyQqdOnbB79240adIETg2MoCWneNSvolx69+4NPz8/3L9/Hx07dsSrV694RyIqbPfu3RCJRBgzZgzvKAoVHh4OIyMj1KlTh3cUtVU4PF9Iqjg8X1nQWd4KJJPJ4OnpiQULFsDQ0BBbt25F9+7dueVJT0+Hv78/fHx8cOnSJcTFxUEikaBdu3ZwdXWFq6srmjVrBrFYjJikdHTZECS3LH6znegRg5J58OABXF1doaenB19fXzRo0IB3JKJiZDIZLCws0KZNG+zfv593HIVyc3NDbm4ufHx8eEdRe5v8YwQbnq+K806VBT1jVJBHjx5h3LhxCAkJwdSpU7Fy5UoYGCi2gJLJZLh//z4uXboEHx8fhISEID8/Hw0aNEC3bt3g6uoKZ2fnT+Yq7FcJiUsVdFedlliENvWNqJhUQk2aNEFISEjRUY3e3t6ws7PjHYuoEF9fX8TFxWlcMQn8s0I5evRo3jE0wjRnCxhX0MWSs5GQyliJXqO0xCJIxCIsc7NW2XmnyoJWKOUsPz8fa9aswdKlS2FmZoadO3eiffv2Crt/cnIyfH194ePjA19fXyQnJ6N8+fLo1KlT0SqkuXnx3pElpmXBZX0gcgUc76MrEcNvdgd6xKDEXr9+jZ49e+LRo0c4e/YsOnbsyDsSURF9+vRBfHw87t69y72lR5FevXqFGjVq4NixYxg4cCDvOBojMS0LC09F4FpsylePCi78eHtzY6zoZ0OvQQKgglKO7t69i7FjxyIiIgJz587Fjz/+iHLlysn1nnl5ebhx4wZ8fHzg4+ODO3fuAABsbW3h6uqKbt26oU2bNtDR0SnV9Q+HJWD+yQjB8q7ub0PvClVARkYG+vfvj8DAQBw6dAj9+/fnHYkouYSEBNSrVw+bN2+Gh4cH7zgKdfHiRfTs2RNxcXFKMUtY08QkpeNgaMInh+eDMejLsjC4nbVaDM9XJlRQykFOTg6WLl2KNWvWoEmTJti5c6dcd/rFxcUV9UFevXoVGRkZMDY2LlqB7NKlC6pXry7Y/ahfRTPl5eVh9OjROHr0KLy8vDBx4kTekYgSW7x4MX7//Xf8/fffqFChAu84CrV8+XKsX78eqampGrUyq4w+Hp7/8/czEf8kGiEhIbyjqR3qoRRYcHAwxo0bh2fPnmHp0qWYN2+e4OdvZ2RkFG2m8fHxQWxsLCQSCdq0aYMFCxbA1dUVdnZ2chvRQf0qmklHRwcHDx6EsbExPDw8kJSUhB9++IFeMMl/5OXlYfv27Rg1apTGFZMAnZCjTD4ent+iWROcPXkMBQUFdHiDwDS+oBTq6K/09HQsWLAAnp6eaN26NU6fPo1GjRoJkpExhvv37xcVkMHBwcjPz0e9evXg6uqKNWvWoFOnTqhYsaIg9yuOofamaNvAuNj9KkxWAJFYC23qG1G/igoTi8X4448/UK1aNSxevBhJSUn4448/NGq+IPm606dPIykpCZMnT+YdhYvw8HAMGzaMdwzyCba2tsjKykJsbCysrKx4x1ErGvnIu6i/4nEyEtL+3V8hAmBqqA9nKxMMdzSFRbWv91f4+Phg4sSJSElJwcqVKzF16tQyv/N5/fo1Ll++XFREJiUlQV9fH87OzkU7ss3NzZXiHfCX+lVE+GdoufbraNw7thHPo25DT0+PV1QioG3btmHy5MkYNGgQ9u7dC11dXd6RiJJwdnZGQUEBgoLkN2pMWSUnJ6NatWo4cuQIBg8ezDsO+UhKSgqqVq2Kw4cPY8iQIbzjqBWNKiiF3gGWlpaG2bNnY9++fXBxccG2bdtK3YCdn5//n800jDE0bdq0qIBs27at0r9of27FNyYmBpaWlti/fz9GjBjBOyYRyMmTJzFs2DC0b98eJ0+eVPgoLKJ8oqKiYG1tjUOHDmHo0KG84yjcpUuX0L17d8TGxtLsViVVp04dDB8+/D9ny5Oy0ZiC8nBYQpl6/pa6WWPoBz1/x48fx9SpU5GXl4d169bB3d29xKuFT58+LSogr1y5gvT0dBgZGaFr165wdXVF165dUaNGjRJdU5l17twZeXl5uHbtGu8oREABAQHo06cPLC0tcfHiRVStWpV3JMLRjBkzcOTIESQmJpZ6moQq++WXX7BmzRq8efNGKZ4gkf/q3bs38vPzcenSJd5R1IpG9FCWZVdywf8K0PknI5CSkYsBDStg2rRpOHnyJPr16wdPT89iF32ZmZkICAgoKiKjo6OhpaWF1q1bY968eejWrRuaN2+utv1oHh4eGDJkCCIjI2FtLexxWYSfjh07IjAwEN26dUO7du3g4+ODunXr8o5FOMjMzMTevXsxdepUjSomP3wyExz5DLYtHamYVGJ2dnbYtm0b7xhqR+1XKIWem5gVsB2iuBvw9PTEgAEDvvhDgzGGiIiIopE+wcHByMvLg5mZWdFMyE6dOqFSpUqfvYY6ycvLQ506dTB06FD8/vvvvOMQgT158gSurq7Izs7GpUuXYGNjwzsSUbDt27fDw8MDcXFxav+m4ku9+GAMZkblS9SLTxTn5MmTGDBgAF6+fCnoSD1Np9YFpdAnuzDGoIUCnJ7QEk0b1Prk70lJSSnaTOPr64uXL1+iXLlycHZ2LpoLaWlpqbHvXufPn4+tW7fi77//lvuQd6J4SUlJ6NatG549e4Zz586hXbt2vCMRBWGMoUWLFqhVqxbOnTvHO47c0Gksqi8uLg4NGjTAxYsX0b17d95x1IZ6Plv9n4WnIiAV8NxpkUgEkZY21gQ8L/p3UqkUwcHBWLx4MRwcHGBiYoJhw4bhzp07GD58OC5fvoy0tDRcuHABM2bMgJWVlcYWkwAwYcIEvH37FkePHuUdhchBtWrVEBAQAFtbW3Tp0kWtCwvyb7du3cLdu3fVelTQ4bAEuKwPREhcKgB8tR+/8OMhcalwWR+Iw2EJcs9Ivq5u3bqoWLEi7t27xzuKWlHbFcqYpHR02SC/kRUTayUh3P8C/Pz88P79exgaGqJLly5Fm2lq1fr0CiYBunTpgqysLFy/fp13FCInOTk5GD58OM6cOYPt27djzJgxvCMROXN3d0dgYCBiY2PVcmC0UCeEzelqiWnOFgIkImXRoUMHVK9eHUeOHOEdRW2o7aacg6EJX30cUVpMVoDfzt5Cw8xXmDNnDlxdXdGiRQu1/CEqDx4eHhg0aBAePHiAJk2a8I5D5EBPTw9Hjx7FlClTMHbsWLx+/Rpz584t9uq8UAcOEMVITU3F4cOHsXTpUrX8OXg4LEGQYhIA1vpGo2oFXTopjDNbW1t4e3vzjqFW1PYntP/jZLkUkwAgEmuhscsgXJu3RS7XV3d9+vRBtWrVsHXrVmzcuJF3HCInWlpa2LJlC6pVq4bvv/8eSUlJWLNmzWenGAh94ABRnD179oAxhrFjx/KOIrjEtCwsORsp6DV/PBuJNg2MqaeSI1tbW2zcuBHp6ek0P1cgatlDmZErRUJallzv8fxNDjJzpXK9h7rS1tbG2LFjsX//fmRlyfe/E+FLJBJh2bJl2LhxI9avXw93d3fk5+f/6/ckpmVh5M5QdNkQhP2h8Yj/eMcsAAYgPi0L+0Pj0WVDEEbuDEWinP+Ok+KRyWTYsmULBg4cqJYzSIXuxQcAqYxh4Snhpo+QkrOzsyuaxEKEoZYFZXxq5n9ekITGADxLzZTzXdTXhAkT8P79e+pf0RDTpk3DoUOHcPjwYfTt2xeZmf/83aFNDqrPz88PsbGxmDJlCu8ogotJSse12BTBn3YVyBiuxaYgNjld0OuS4mvcuDG0tbVpY46A1LKgzBNoTJCy3Ecd1atXD127dsXWrVt5RyEKMmTIEFy4cAGBgYFwcXHBrxf+wvyTEciVykr8gl0gY8iVyjD/ZAQ2+cfIKTEpDi8vL9jY2KBNmza8owiusBdfHrTEIhy4SW+IeNHR0UHjxo1x9+5d3lHUhloWlDoSxXxZirqPuvLw8EBoaCju37/POwpRkC5duiAgIADPtGpic3CiINdc6xuNI7RSycXz589x9uxZTJ48WS3HocmzF79AxuAfnSyXa5PisbW1pRVKAallRVTXqDzk/aNN9L/7kNLr1asXqlevTkdgaZhq9RvDoOM4QMCJZT+ejaSeSg62bdsGfX19jBgxgncUwSmiFz8hNYt68Tmys7NDREQEpFL6byAEtdzlXV5XAlNDfcTL8YeBqZE+jTEpI21tbYwbNw4bN27Er7/+ivLlqUDXBAtPRaCAARBwRatwk8P+cY6CXZN8WX5+Pnbs2IGRI0cqxS5ZxhikUimys7ORk5NT5l9TCvTAavaUb2b804tvXVMzjt9VNra2tsjNzcWjR49ohJ0A1LYicrYywf7QeLk8rtASi+BsaSL4dTXRhAkTsGLFChw+fBjjxo3jHYfIWeEmB6F9uMnB3IR/caMJzpw5g5cvX/7nZBzGGPLz8wUr7Eryq0xWsr52HR0dlCtXDnp6ev/5VWRcD6gp5J/Yp1EvPj/NmjUDANy7d48KSgGobUE53NEUe248k8u1C2QMI1rRUFohmJmZoVu3bti6dSsVlBpAngcOFG5y+MnNWvBrqyLGGPLy8uRWwN27dw/6+vro16/ffz5e0gPYdHV1P1vYFf5asWJFmJiYfPX3FfdXPT29z85EBYDIv9+h58bgsv5n+CrqxeencuXKqFevHu7du6eWbRuKprYFpUU1A7Q3N0ZIXKqgL15aYhHa1DeiVRABeXh4oG/fvrh79y7s7Ox4xyFypIhNDj9BuQpKxhhyc3MVvmKXk5NT4sKuOIVY5cqVkZubi/T0dLi6usLW1rZMhZ2uru4XCzteCnvx5TmCjnrx+aONOcJR24ISAFb0s4HL+kBBX8AkYhFW9LMR7HoE6NmzJ2rWrImtW7diyxY6fUhdKXKTw6f6m2UyGbfCriREIlGxCjFDQ0NBVuo+LOyKu1N71qxZqFq1Ks6cOQNdXd0SfX2qgnrxNYOtrS1+//13MMbUclKBIqn1d3IdQ30sdbPG/JPCTcJf5mZNx2UJTCKRYNy4cVi/fj3Wrl2LChUq8I5E5EBRBw607uoG6etn/ynscnNzS3QtkUiEcuXKfbUQMzY2FrSw09HRUeoXtszMTOzZsweTJk1S22KyEPXiqz9bW1ukpaXh+fPnqFOnDu84Kk2tC0oAGGpvipSMXKz1jS7zteZ2tcIQe+qdlIfx48fjl19+waFDhzBhwgTecYgcKGrzQZOmtqiu3bDMhZ22trZSF3a8HD58GO/fv4eHhwfvKHJHvfjqr7DN6t69e1RQlpGIlbTJRkUdDkvAkrORkMpYid5taolFkIhFWOZmTcWknPXq1QuvXr3C7du3eUchcqCoTQ4XprejMSxy1LJlS1SrVg0XLlzgHUUhRu4MlVsvPo254o8xBmNjY8yaNQuLFy/mHUelKV8ntJwMtTeF3+wOaFPfCAC+epxW4cfb1DeC3+wOVEwqgIeHB8LDwxEeHs47CpEDOnBA9YWFhSE8PPw/o4LU2Yp+NpAIfPwi9eIrD5FIBFtbWzqCUQAaU1AC//RU7h/niMuznDDS0QxmRvr/eYFjjMHMSB8jHc3gN9sJ+8c5Us+kgnTv3h21a9em873VVOEmB3miTQ7y5eXlBTMzM3Tv3p13FIUp7MUXEvXiKxc7Ozva6S0AjSooC1lUM8BPbtYInOOMBz+54sL0djg1uQ3mN2NIXDcI+wbVx09u1jQaSMEkEgnGjx+PP//8E+/fv+cdh8iBs5XJV58OlBZtcpCvtLQ0HDp0CB4eHtDS0uIdR6GG2ptiTldLQa5FvfjKx9bWFk+fPsXbt295R1FpGllQfqi8rgTWNSvBzrQK+jnbg+Xn4NatW7xjaaxx48YhOzsbf/75J+8oRA6GO5rKdQ4lbXKQn71796KgoEBjDyCY5myBVf1toCsRl/hNkZZYBF2JGKv722Cqs7mcEpLSsrW1BQDcv3+fbxAVp/EF5YeqVasGMzMzKig5ql27Nnr27ImtW7eWeCgzUX6FBw4IvUqpJRahvbkxPVWQE5lMBi8vLwwYMAAmJpq7ClzSXnzICgBQL76ya9iwIXR1demxdxlRQfkRR0dHhIaG8o6h0Tw8PHDv3j3a7a2maJOD6rl69SpiYmIwZcoU3lG4K04vvgiAsR7D+zsXsLG7CfXiKzmJRAIbGxsqKMtIY8YGFddvv/2GH3/8Ee/evYNEQs39PBQUFKB+/fro0qULduzYwTsOkYPDYQmCHjiwur8Nrf7I0YABA/D48WNERETQbM5PyMyV4llqJvKkMuhIxKhrVB46YobatWtj2LBhWL9+Pe+I5CsmTJiA27dv027vMqAVyo84ODggKysLUVFRvKNoLC0tLYwfPx6HDh3Cu3fveMchckCbHFTHixcvcObMGUyePJmKyc/4sBffumYllNeVQFtbGyNGjMDBgweRn5/POyL5CltbW0RGRiIvL493FJVFBeVHmjdvDi0tLeqj5GzcuHHIzc3FwYMHeUchcvLhJgdRCQ9lpE0OirN9+3bo6elh5MiRvKOonNGjR+P169fw9vbmHYV8hZ2dHfLz82kxqQyooPxI+fLl0aRJE+qj5KxmzZro3bs3bc5Rc0PtTeEzox3Yy0cA6MABZZOfn4/t27djxIgRqFixIu84Kqdp06aws7PDnj17eEchX2FjYwORSER9lGVABeUnODg40AqlEvDw8MBff/1Fxb2ai7gZgPi9c7HOxfCLmxzowAHFO3fuHP7++2+NOhlHaO7u7jh37hxev37NOwr5AgMDA5ibm1MPZRnQppxP2LFjBzw8PPD+/XuUL0/HuPFSUFCABg0aoFOnTti1axfvOEROunXrhrS0tH+9ifvUJgc6AUfxunTpgszMTISEhPCOorJSUlJQs2ZNrF27FjNmzOAdh3zBkCFD8OrVKwQGBvKOopJohfITHBwcIJPJcOfOHd5RNJqWlhYmTJiAw4cP0wkGaiomJgY+Pj6YOnXqv/79pzY5EMWKjo6Gn58fjQoqI2NjY/Tq1Ysee6sAW1tb3Lt3j9qsSokKyk+wtrZG+fLl6VGrEhg7dizy8/Nx4MAB3lGIHHh5ecHIyAhDhgzhHYV8ZMuWLTAyMsLAgQN5R1F5o0ePxt27d/HXX3/xjkK+wNbWFu/fv8ezZ894R1FJVFB+gpaWFlq0aEF9lEqgRo0acHNzo805aigrKwu7d+/GuHHjoKenxzsO+UBWVhb27NmDsWPH0n8bAfTo0QNVq1bF3r17eUchX1B4BCP1UZYOFZSfQRtzlIeHhwcePHiAGzdu8I5CBPTnn3/i3bt3mDRpEu8o5CNHjhzB27dv4eHhwTuKWtDW1sbw4cNx4MABmkmpxGrUqIFq1arRTu9SooLyMxwdHREfH4+kpCTeUTSei4sL6tevj61bt/KOQgTCGIOnpyd69uyJevXq8Y5DPuLl5QVXV1c0aNCAdxS14e7ujuTkZFy6dIl3FPIFhX2UpOSooPwMBwcHAKBVSiUgFosxYcIEHD16FG/evOEdhwjgxo0buHfv3n824xD+wsPDERYWRqOCBNasWTPY2trS5hwlZ2trS4+8S4kKys+oU6cOqlWrRgWlknB3d4dUKsX+/ft5RyEC8PT0hLm5Obp27co7CvmIl5cXTE1N0bNnT95R1E7hTMrU1FTeUchn2NnZ4fnz50hJSeEdReVQQfkZIpGI+iiVSPXq1dG3b1/anKMGkpKScOzYMUyePBliMf0IUiZv3rzBn3/+iYkTJ0JLS4t3HLUzbNgwMMZw6NAh3lHIZxRuzLl//z7fICqIfpp/gaOjI27dugWZTMY7CsE/m3OioqJw/fp13lFIGezYsQMSiQRjxozhHYV8ZN++fcjPz8e4ceN4R1FLVatWRc+ePemxtxIzNzeHvr4+9VGWAhWUX+Dg4IC3b98iNjaWdxQCoFOnTmjQoAFtzlFhUqkUW7ZswbBhw1ClShXeccgHGGPw8vJC//79Ub16dd5x1Ja7uzvCw8MRERHBOwr5BC0tLTRt2pT6KEuBCsovaNmyJQDamKMsxGIxJk6ciGPHjlEPkoo6d+4cnj9/TptxlJC/vz8eP35MJ+PIWY8ePWBsbEwzKZWYnZ0drVCWAhWUX1ClShVYWlpSQalE3N3dIZPJsG/fPt5RSCl4enqidevWsLOz4x2FfMTLywuNGzeGk5MT7yhqTUdHp2gmpVQq5R2HfIKtrS0ePXqE7Oxs3lFUChWUX+Ho6EhHMCoRExMT9OvXjzbnqKBHjx7hypUrtDqphP7++2+cPn0akyZNgkgk4h1H7Y0ePRpJSUnw8fHhHYV8gq2tLQoKCvDgwQPeUVQKFZRf4eDggHv37iE3N5d3FPI/Hh4eePz4Ma5du8Y7CimBzZs3o2rVqnQ2tBLasWMHdHR0MGrUKN5RNIKtrS2aNm1Km3OUlI2NDcRiMT32LiEqKL/CwcEBeXl5+Ouvv3hHIf/j7OwMCwsL2pyjQjIyMrB3715MmDABurq6vOOQD0ilUmzbtg3Dhw9HpUqVeMfRCCKRCO7u7jh79iz1gyuhcuXKoWHDhlRQlhAVlF/RrFkz6Ojo0GNvJSISiTBx4kQcP36chs+qiAMHDiAjI4POhlZC58+fx4sXL+hkHAUbPnw4ZDIZDh8+zDsK+QQ6grHkqKD8Cl1dXdja2tLGHCXj7u4OALRTUgUUntvt5uYGU1NT3nHIR7y8vNCqVSvaKKVgJiYm6NGjBz32VlK2tra4f/8+CgoKeEdRGVRQFgOdmKN8jI2NMWDAAGzbto025yi5a9eu4cGDB7QZRwnFxMTA19eXVic5cXd3x+3btxEZGck7CvmInZ0dMjMz8eTJE95RVAYVlMXg4OCAx48f4+3bt7yjkA94eHggOjoaAQEBvKOQL/D09ISVlRU6d+7MOwr5yNatW2FoaIjBgwfzjqKRevbsCSMjI3rSooSaNWsGAPTYuwSooCwGBwcHAEBYWBjnJORDTk5OsLKyos05Suzly5c4efIkpkyZQuNolEx2djZ2796NMWPGQE9Pj3ccjaSjo4Nhw4Zh//79NJNSyVStWhW1atWigrIEqKAsBgsLC1SuXJkeeyuZws05J0+exOvXr3nHIZ+wbds26OrqYvTo0byjkI8cPXoUaWlpmDRpEu8oGs3d3R2vXr2Cr68v7yjkI3Z2dnQEYwlQQVkMYrEY9vb2VFAqodGjR0MsFlNjuxLKz8/H1q1bMWLECBpHo4S8vLzQtWtXmJub846i0ezs7GBjY0M/w5QQ7fQuGSooi8nBwQGhoaG0AUTJGBkZYeDAgdi2bRtkMhnvOOQDp0+fxsuXL2kzjhK6c+cOQkNDaTOOEiicSXnmzBmkpaXxjkM+YGtri1evXuHVq1e8o6gEKiiLycHBAUlJSUhMTOQdhXzEw8MDsbGx8Pf35x2FfMDT0xPt27eHjY0N7yjkI15eXqhduzZ69erFOwrBPzMpCwoKcOTIEd5RyAdsbW0B0Mac4qKCspgKN+bQY2/l065dOzRq1Ig25yiRBw8eIDAwkFYnldC7d+/w559/YuLEiZBIJLzjEADVqlVD9+7d6bG3kqlXrx4qVqxIBWUxUUFZTNWrV4epqSkVlEpIJBLBw8MDp06dQlJSEu84BP+c2129enX069ePdxTykX379iEvLw/jx4/nHYV8wN3dHbdu3UJUVBTvKOR/xGIxmjVrRgVlMVFBWQKFfZRE+YwcORJaWlrYvXs37yga7/3799i/fz8mTpwIHR0d3nHIBxhj8PLyQr9+/VCjRg3eccgHevXqBUNDQ5pJqWRoY07xUUFZAo6Ojrh9+zbNC1NChcOZt2/fTptzONu3bx+ys7MxceJE3lHIRwIDA/Hw4UPajKOEdHV1aSalErKzs0N0dDQyMjJ4R1F6VFCWgIODA7KysvDw4UPeUcgneHh4IC4uDleuXOEdRWMxxrB582b069cPtWrV4h2HfMTLywsNGzZEx44deUchnzB69Gi8fPkSfn5+vKOQ/7G1tQVjDBEREbyjKD0qKEugefPmEIvF1EeppNq0aQNra2vanMORv78/Hj58SJtxlNCrV69w8uRJTJo0iU4tUlItWrSAtbU1bc5RIo0bN4ZEIqHH3sVABWUJVKhQAdbW1tRHqaQKN+ecOXOG5oZx4unpCWtra3To0IF3FPKRHTt2QFtbm04tUmKFMylPnz6NN2/e8I5D8E8rgrW1NZ2YUwxUUJaQo6MjrVAqsZEjR0JbWxu7du3iHUXjPH/+HGfOnKFzu5VQQUEBtm3bhmHDhqFy5cq845AvGD58OKRSKc2kVCK0Mad4qKAsIQcHBzx48ACZmZm8o5BPqFy5MoYMGUKbczjYunUr9PX1MXLkSN5RyEcuXLiAxMRE2oyjAmrUqIFu3brRY28lYmtri4iICNos9RVUUJaQg4MDCgoKaPlbiU2cOBHPnj2Dr68v7ygaIy8vD9u3b8eoUaNgYGDAOw75iJeXFxwcHNCiRQveUUgxuLu7IzQ0FI8ePeIdheCfgjInJwfR0dG8oyg1KihLyNraGvr6+tRHqcRatWoFGxsbbNu2jXcUjXHixAkkJSVhypQpvKOQjzx58gSXLl2i1UkV0rt3b1SpUoVmUiqJwiMYaSHpy6igLCGJRIIWLVpQH6USK9ycc/bsWfz999+842gET09PODs7o3HjxryjkI9s3boVVapUwZAhQ3hHIcWkq6uLb775Bvv27UNBQQHvOBqvcuXKqFu3LvVRfgUVlKXg4OBABaWSGzFiBHR1dWlzjgLcv38f169fp1FBSignJwe7du3CmDFjUK5cOd5xSAm4u7vj77//ppmUSoI25nwdFZSl4ODggGfPniE5OZl3FPIZlSpVwtChQ7F9+3Z6hy9nnp6eqFWrFvr06cM7CvnIsWPHkJqaikmTJvGOQkqoZcuWaNy4MW3OURJ2dna4e/cuGGO8oygtKihLwcHBAQBolVLJeXh4ICEhAT4+PryjqK23b9/i4MGD8PDwgEQi4R2HfMTLywsuLi6wsLDgHYWUUOFMylOnTuHt27e842g8W1tbpKam4sWLF7yjKC0qKEvBzMwMJiYmVFAqOXt7ezRr1oxOzpGjPXv2ID8/HxMmTOAdhXzk3r17uHHjBm2UUmEjRoxAfn4+jh49yjuKxivcmEOPvT+PCspSEIlE1EepAgo355w/fx7Pnz/nHUftyGQybN68GQMGDED16tV5xyEf8fLyQq1atdC7d2/eUUgp1ahRA66urvTYWwnUqVMHVapUoYLyC6igLKXCgpL6KZTb8OHDUa5cOdqcIwd+fn6IiYmhzThK6P379zh48CAmTJhArQgqzt3dHTdu3MDjx495R9FoIpGoqI+SfBoVlKXk4OCAN2/eIDY2lncU8gUVK1bEN998gx07dtDmHIF5enqiadOmaNu2Le8o5CP79+9HTk4Oxo8fzzsKKSM3NzdUrlyZZlIqAdrp/WVUUJaSvb09ANqYowo8PDyQmJgIb29v3lHURnx8PM6fP4+pU6fSud1KhjEGLy8v9O3bF7Vq1eIdh5SRnp4ezaRUEra2toiLi8O7d+94R1FKVFCWkqGhISwsLKigVAEtW7ZE8+bNaXOOgLZs2QIDAwMMHz6cdxTykWvXriEyMpJOxlEj7u7uePHiBa5cucI7ikazs7MD8M/sXfJfVFCWgYODAx3BqCI8PDxw8eJFJCYm8o6i8nJycrBjxw64u7ujfPnyvOOQj3h5ecHS0hKdOnXiHYUIxN7eHg0bNqTH3pxZWVlBV1eXHnt/BhWUZeDg4IC7d+8iLy+PdxTyFd988w309fWxY8cO3lFU3rFjx5CSkkLjaJRQUlISTpw4gcmTJ1MrghopnEl58uRJetzKkba2Npo0aUIF5WdQQVkGjo6OyMvLw19//cU7CvkKAwMDDBs2DDt27IBUKuUdR6V5enqiS5cusLS05B1FY2XmShH59zvcTXiDyL/fITP3n+/pnTt3QiKRYPTo0ZwTEqGNGDECeXl5NJOSM9qY83k0T6IMmjVrBm1tbdy6dQstW7bkHYd8hYeHB7Zt24aLFy/Czc2NdxyVFB4ejtDQUJw+fZp3FI0Tk5SOg6EJ8H+cjIS0LHw4sEwEoI5hOcTfTkPv4RNQpUoVXjGJnNSqVQtdu3bFnj176CABjuzs7LBv3z7k5eVBR0eHdxylQiuUZaCnp4dmzZpRH6WKaN68OVq2bEmbc8rA09MTpqam6NWrF+8oGiMxLQsjd4aiy4Yg7A+NR/xHxSQAMAAJadmAhRNCjbti5M5QJKZl8YhL5Mjd3R0hISGIjo7mHUVj2draIj8/Hw8fPuQdRelQQVlGjo6OtNNbhXh4eMDb2xvx8fG8o6ic1NRUHDp0CJMmTYKWlhbvOBrhcFgCXNYHIiQuFQBQIPvyQQoi8T//XULiUuGyPhCHwxLknpEoTp8+fVCpUiXs27ePdxSN1bRpU4hEInrs/QlUUJaRg4MDHj16RI3SKmLo0KGoUKHCvzbnfK4fjfzb7t27IZPJaFi2gmzyj8H8kxHIlcq+Wkh+rEDGkCuVYf7JCGzyj5FTQqJoenp6GDp0KPbu3UszKTkxMDCAubk5FZSfQD2UZeTg4AAACAsLg4uLC+c05GsqVKiAESNGYOfxi0CLwQiKSflkP5qpoT6crUww3NEUFtUMeMVVGjKZDF5eXhg8eDCqVq3KO47aOxyWgLW+wjzWXOsbjaoVdDHE3lSQ6xG+3N3dsXXrVvj7+9NrDie2trZ0BOMn0AplGVlaWqJSpUr02FtFJKZl4W/L/tDpuwwHv9CPFp+Whf2h8eiyIYj60QBcunQJcXFxdG63AiSmZWHJ2UhBr/nj2UiN/x5WF46OjrCyssKePXt4R9FYhTu9GSvZkwN1RwVlGYnFYtjb21NBqQIK+9EiXv8zN1SGL8/pK3zMSP1o/2zGad68ORwdHXlHUXsLT0VAWsJH3F8jlTEsPBUh6DUJHx/OpHz//j3vOBrJ1tYW7969o178j1BBKYDCE3Po3Yryon600ouLi4O3tzed260AMUnpuBabUuLv0a8pkDFci01BbHK6oNclfIwcORK5ubk4duwY7ygaqfAIRnrs/W9UUArAwcEBr169wvPnz3lHIZ8gdD/aEQ1bqfTy8kLlypUxdOhQ3lHU3sHQBGiJ5VO0a4lFOHBTs7531VWtWrXg4uJCj705qV69OkxMTGhjzkeooBRA4cYceuytfKgfrWyys7Oxa9cujB07Fvr6+rzjqD3/x8mCr04WKpAx+Ecny+XaRPHc3d0RHByM2NhY3lE0jkgkohNzPoEKSgHUqFEDderUoYJSCVE/WtkcPnwYb968weTJk3lHUXsZuVIkyPmNSkJqFo3FUhN9+/ZFxYoVsXfvXt5RNJKdnR3uPXhII+c+IGLU+CeIgQMHIjU1Ff7+/ryjkP+JSUpHlw1Bcru+32wnmJuo70ghxhjs7e1hYmKCixcv8o6jUIwx5OfnIz8/H3l5eQr5NVVWDuFVu8r9a7swvR2sa1aS+32I/BUe1PDs2TOIxbQ+pAiFR6CeDY9Daq7oX33lmj5yjuZQCsTBwQHLly9HQUEBnSKiJAr70eTxCLGwH+0nN2vBr60sbt26hfDwcJw/f77U1ygoKFBYUSbktaRS4VYatLS0oKOjA21t7S/+ygzNAAWM+MyTyuR/E6IQ7u7u2LZtG/z9/dG5c2fecdRaYloWFp6KwLXYlP+9rojx8R7FD0fO7bnxDO3NjbGinw3qGGpGuxCtUAokICAAzs7OiIiIQJMmTXjHIQA6rPFHvBwfIZoZ6SNwjrPcrl+Ix2pZXl4eAgMDkZycDBcXl1LfXyYTrnjR0dEpVmGmTL9qa2sXe+Uo8u936LkxWLA/r8+hFUr1wRiDlZUVWrVqRccxytHhsAQsORsJqYyVaIFCSyyCRCzCUjdrDNWAgwVohVIgLVu2hFgsxq1bt6igVAKK6EeLT83EL6vXguXnyHV1TcjVMolEUqxCSCQSIT4+HhYWFigoKICenh4MDAy4FWZaWlpqP7KorlF5iID/DNoXkuh/9yHqoXAm5c8//4xNmzahYsWKvCOpnU3+MaWeElLwvwJ0/skIpGTkYpqzhcDplAsVlAKpUKECGjdujNDQUIwdO5Z3HI0Xn5op1xfmf4iwYedBaL1/WezCyMDAgNuKmUQiKfZq2cqVK/HXX38hJCQERkZGcv5zJABQXlcCU0N9ua6qmxrpo7wu/dhXJyNHjsQPP/yA48eP02uPwOgI1JKhnywCcnBwoJ3eSkJRfWK+fldhZ1pFIfdSlIKCAmzZsgVDhw6lYlLBnK1MsD80Xm59v86WJoJfl/BVp06dopmUVFAKR14j59o0MFbbnkraFiYgBwcHREREICtLM2YUKjMdiWK+tRV1H0U6f/48EhIS6NxuDoY7msp1DuWIVuq7OqLJ3N3dce3aNTx58oR3FLVBI+dKTv1eDTlydHREQUEBHcekBAr70eRJXfvRPD094eDggJYtW/KOonEsqhmgvbmx4KflaIlFaG9urNZjrjRZ3759YWBgQBtzBEJHoJYOFZQCsra2Rrly5RAaGso7isYr7EeTJ3XsR4uOjsbly5dpdZKjFf1sIBGyoGQMErEIK/rZCHdNolT09fUxZMgQ7N27V9DJCpqKjkAtHSooBaStrY3mzZtTH6WScLYykesPBXXsR9u8eTOMjY0xePBg3lE0Vh1DfSwVcr6pSIT6b26jRkUd4a5JlI67uzvi4+MRGBjIO4rKoyNQS4cKSoE5OjpSQakkqB+tZDIzM7Fnzx6MGzcOenp6vONotKH2ppjT1VKQa7lUzcLlLUsxZMgQ5ObmCnJNonzatGkDc3Nz7Nmzh3cUlUZHoJYeFZQCc3BwwNOnT/H69WveUTQe9aOVzMGDB/H+/XtMmjSJdxQCYJqzBVb1t4GuRFzi72EtsQi6EjFW97fBjm8H4dSpU7hw4QJ69uyJ9HT17N/SdIUzKY8fP07/jctAESPnGIBnqZlyvoviUUEpMAcHBwCgVUolIXg/GqCW/WiMMXh6eqJXr16oW7cu7zjkf4bam8Jvdge0qf/P+KavFZaFH29T3wh+szsUzbzr3bs3fHx8cOvWLbi4uCA1NVW+wQkXI0eORHZ2No4fP847ispS1Mg5dTwClQpKgdWtWxfGxsZUUCoJwfvRACxzs1a7OWLXr1/HX3/9RZtxlFAdQ33sH+eIy7OcMNLRDGZG+v+ZYCDCP0eBjnQ0g99sJ+wf5/if79EOHTogICAAT58+hZOTE168eKGwr4EohqmpKTp16oS9e/fyjqKyaORc6dFZ3nLQq1cvFBQUwNvbm3cU8j9lOT7rQ3O7WmGqs7kAiZTLN998g9u3b+Px48fFPk2H8JOZK8Wz1EzkSWXQkYhR16h8sScOPH78GF26dIFYLMbly5dhYaHex8FpmgMHDmDkyJF48uQJ6tevzzuOSmCMIS4uDn5+fvC9Gojb9YbJ9ahXEYAHP7mq3ZQQeuWQg8ITc6hWVx5C9aOpYzH56tUrnDhxAlOmTKFiUkWU15XAumYl2JlWgXXNSiV6YbKyssL169dRrlw5tGvXDvfu3ZNfUKJw/fr1o5mUxZCcnIzDhw9jwoQJqF+/PszNzTF16lS8THwGA5F8N6+p48g5gApKuXBwcEBaWhqdWqBkPu5HA/tyD8vn+tHUzfbt2yGRSODu7s47ClGQOnXq4Nq1azA1NUXHjh0RHBzMOxIRSPny5TF48GCaSfmRzMxMeHt7Y86cObC1tUW1atXwzTffICQkBG5ubjh79izS0tIQEhKCga0b0si5UqBH3nKQmpoKY2NjHDx4EMOGDeMdh3xCTFI6ukxZhvIWDsgS6f9rV58I/7yDdLY0wYhWpmq3m/tDUqkUdevWRffu3bF9+3becYiCpaeno0+fPrh58yaOHz+OHj168I5EBBAcHIz27dvD398fHTt25B2Hi/z8fISFheHKlSvw8/PDjRs3kJ+fj1q1asHFxQUuLi7o1KkTatas+Z/PjUlKR5cNQXLL5jfbSS1fV6iglBMLCwv07NkTGzZs4B2FfEJycjKqVauGw4cPo1ffAaXuR1N1J06cwMCBA3H37l3Y2tryjkM4yMnJwdChQ3HhwgXs27cP33zzDe9IpIwYY7CwsED79u2xe/du3nEUgjGGqKioogIyICAA6enpqFSpEpydneHi4oLOnTvDysrqi/2ROTk5WL58OXbGlYOeaVNArCVYRi2xCG3qG2H/OEfBrqlMqKCUk+HDhyMuLg43btzgHYV8wvnz59G7d288ffpUo8fkdOrUCbm5ubh+/TrvKIQjqVSKCRMmYO/evdi0aROmTJnCOxIpo+XLl2P16tV49eoVKlSowDuOXDx//ryogPTz88OrV6+go6ODtm3bFhWQLVq0gERSvAWCoKAgTJgwAU+fPsXMhctwtqAZcgUc76MrEcNvdge1mxJSSDOWYThwcHDAiRMnkJeXBx0dOvJM2dy6dQtVq1aFmZkZ7yjcREVFwd/fHwcPHuQdhXAmkUiwc+dOVKlSBVOnTkVqaip++OEHue50JfI1atQo/Pjjjzhx4gRGjx7NO44g3r59i4CAgKIC8vHjxxCJRLCzs8PIkSPh4uKCdu3aQV+/ZAXbu3fv8P3332Pr1q1o06YNTp06hcaNG6NFWALmn4wQLL86jpz7EBWUcuLg4IDc3FxERESgRYsWvOOQj4SGhsLR0VGjXzA3b94MExMTDBgwgHcUogTEYjF+++03GBkZ4YcffkBqairWrVtHO/9VlJmZGTp16oQ9e/YUFZRlGTfFQ05ODm7cuFFUQN6+fRsymQwNGjRA586dsXz5cjg7O8PY2LjU9zhz5gymTJmC9+/fY9OmTZg8eXLR9/xQe1OkZOQKNnJOXTd2FlLe7yQVZ2dnB4lEglu3blFBqWQYY7h16xa+/fZb3lG4SU9Px759+zBjxgzo6uryjkOUhEgkwqJFi2BoaIipU6fizZs32LlzZ7EfGRLl4u7ujnGzF2H2gRu48zIHCWlZ/92AaKgPZysTDHc0hUU1vhtFZDIZ7t27V1RABgcHIzs7G1WrVkWnTp0wYcIEdO7cGfXq1SvzvV69eoXp06cXbUbz8vKCqel/C75pzhYwrqCLJWcjIZUxFMiK3yWoJRZBIhZhmZu12heTAPVQylXLli3RpEkT7Nmzh3cU8oGYmBhYWlri0qVLcHV15R2Hi82bN2P69Ol49uwZ6tSpwzsOUUKHDh3CqFGj0KNHDxw5cgR6enq8I5ESSEzLwvfH7yHk6RuIwMD+c77S/9MSi1AgY2hvbowV/WwU9lj2w4Hifn5+8Pf3R2pqKvT19eHk5FS0G9vGxkawlXLGGPbs2YPvvvsOWlpa+OOPPzB06NCvPq1KTMvCwlMRuBabUvTn9Tm8/jx5o4JSjqZMmYKAgABERUXxjkI+UHiSRGpqKgwNDXnHUTjGGJo0aQIrKyucPHmSdxyixC5evIiBAwfC0dERZ86cQcWKFXlHIsVwOCyhTCtqS92sMVROK2rJycm4evVqUREZHx8PLS0tODg4FBWQrVq1ksvegydPnsDDwwNXrlzBqFGj8Ntvv5X4cXlMUjoOhibAPzoZCamfWPHVkJFzn0IFpRzt2bMHY8eOxYukFKTla6lM34q6mzFjBi5duoTo6LL3xaiigIAAODs7w8/PD507d+Ydhyi54OBg9OrVC+bm5vD29kbVqlV5RyJfINQxs3O6WmKac9mP5czIyMC1a9fg5+eHK1eu4P79+wCAxo0bFxWQHTp0kOubFalUig0bNuDHH3+EiYkJtm7dKsjTKVXrSZU3KijlJCYpHRsv3cOJG4+gXeXfg1OVrW9F0zg6OsLS0hL79+/nHYWLQYMG4cGDB4iKitLoTUmk+O7fvw9XV1dUrlwZly9fpjYJJXVY4F3Jq/vblLj3r3CgeGEBWZKB4vJw7949jB8/Hnfu3MHMmTOxfPlytR2jxBsVlAKjPgvllpubi4oVK2Lt2rWYPn067zgK9+LFC5iZmWH9+vUa+fWT0ouNjUWXLl1QUFCAy5cvw8rKinck8oHEtCy4rA9U+NzE4gwUd3FxgaWlpULfwGZnZ2PZsmVYs2YNGjVqhB07dsDRUT0HiisLKigFpMx9K+QfYWFhcHBwwM2bNzXyh8uSJUvw22+/4cWLF6hUqRLvOETFvHjxAq6urkhKSoKPjw+aN2/OOxL5n5E7QxESl1qi156v+dzJLs+fPy9agfzUQHEXFxc0b96c23SAwMBATJgwAfHx8fjhhx/w/fff0zxoBdDch/0CK0vfSsH/CtD5JyOQkpErSN8K+bTQ0FBoa2tr5DGDeXl52LZtG0aOHEnFJCmVWrVqISgoCD169EDHjh1x7tw5dOjQgXcsjReTlI5rsSmCX7dAxnAtNgV3Yv9GfERoUQH54UDxUaNGoXPnzqUaKC60d+/eYd68edi2bRvatm2LM2fOoFGjRlwzaRIqKAVwOCxBkCZoAFjrG42qFXQ1YmYVD7du3YKtra1Gzl48deoUXr16halTp/KOQlSYoaEh/Pz80K9fP7i6uuLo0aNwc3PjHUujHQxN+GqLVanJCtBlyjKkXd6KBg0awMXFRZCB4kI7ffo0pkyZgvT0dHh6emLSpEk0lF/B6JF3GfHqWyGlY2Vlha5du2Ljxo28oyick5MTRCIRAgMDeUchaiA3NxfDhw/H6dOnsWvXLowaNYp3JI3VYY0/4tOy5Hb9KhIpTo5tKshAcaF9OKC8V69e2Lx5M20a44TK9zJaeCoCUoHfFUplDAtPCbdTj/zjzZs3iI6OhoODA+8oChcREYFr167R6iQRjK6uLo4cOQJ3d3eMHj0av//+O+9IGikjV4oEORaTAPBWKoFJTeUq0hhj2LlzJxo1aoTAwEAcPnwYZ8+epWKSI3rkXQby7luJTU7XuMGo8nT79m0A0MiC0tPTEzVq1EC/fv14RyFqREtLC9u3b4eRkRFmzZqF1NRULF26lMZRKVB8aibk/ZiRAXiWmgnrmsrRex0bGwsPDw9cvXoVo0aNwrp162BkZMQ7lsajgrIM5Nm3oiUW4cDNBPzkZi34tTVVaGgoKleuDAsL9dz09Lkhu+/evcOBAwcwZ84caGtr845J1IxIJMLq1athaGiI+fPnIy0tDX/88Qf1rylInoDtVspwny+RSqVYv349fvzxR1SvXh0+Pj7o2rUr71jkf6igLAP/x8nyaYLGP6uU/tHJ+AlUUArl1q1bsLe3V6sXuqJjwB4nIyHtE8eAGeqjSs7fKKhQFRMnTuQVk2iA77//HoaGhvDw8MCbN2+wZ88eegOjADoSxfw8U9R9PufevXsYN24c7t27VzSgvHz58lwzkX+jgrKUFNG3kpCahcxcqUYf5SQUxhhCQ0PVpqgqzgB9BiA+LQvxMgNUG7MJ33snYkW/yrTZi8jNhAkTULlyZQwfPhzv3r3D0aNHuY+SUXd1jcpDBMj1sbfof/fh4cMB5Y0bN8aNGzc0sm1JFajPUo2CKapvZf+pSwgODsaDBw/w/PlzZGRkgDbml1xCQgKSk5PV4gfR4bAEuKwPREhcKgB8fZVcrAUACIlLhcv6QBwOS5B3RKLBBg0ahPPnz8Pf3x+urq54+/Yt70hqrbyuBKZyfpNoaqTPZWEjICAATZs2xbp167B06VLcvn1bLX6Gqyta+iolRfWTzPz2O+S9/PeMSy0tLVSuXPmL/1SqVOmzH6tQoYLGNc3funULgOpvyKEB+kQVdO3aFVeuXEGPHj3g7OyMS5cuoVq1arxjqS1nKxPsD42XWz+/s6WJ4Nf9krdv32LevHnYvn072rVrh3PnzqFhw4YKzUBKjgrKUlJUP8llH2+YaOfh7du3X/3n+fPnRf/7zZs3yMvL++Q1tbS0vlhwfu0fVSxIQ0NDYWZmptIvajRAn6iSVq1aISgoCF27dkW7du1w+fJl1K1bl3cstWQueiXXfv4RrRT3c+LUqVOYOnUqMjIysHnzZnh4eKhV37s6o4KylBTVt9LC0rTUjxpycnKKVYi+e/cOb9++xd9///2vf5+Tk/PJ64rF4hKtiH6qIFX0D4hbt26p9OpkYloWlpyNFPSaP56NRJsGxtRTSeSmSZMmuH79Orp06YJ27drB19cXjRs35h1LbTx9+hRz587FiRMnYDHxD0iN6kPIurLwLG9FjK97+fIlpk2bhpMnT6J3797YvHkzateuLff7EuFQQVlKhX0r8jydoKx9K3p6eqhevTqqV69eqs/PyckpKjaL88/Lly+LXZCWZoW08HMMDAxKVJBKpVKEh4dj2bJlpfpzUAbyHKC/f5yjoNcl5EP16tVDcHAwXF1d0b59e3h7e6v0mztlkJmZiVWrVmHNmjUwMjLCgQMH0L5bX3TZECToqW0SsQgr+tkIdr1PYYxh165dmDNnDnR0dHDkyBEMGjRI5Z6CESooy0Td+lY+pqenBz09vVI/Jv5UQfqlAvXRo0f/+v/Z2dmfvK5IJCpRQZqSkoKsrCyYmpri3bt3JS5IeaMB+kTVVa9eHQEBAejVqxc6deqE06dPw8XFhXcslcMYw6FDhzBv3jykpKRgzpw5mD9/PipUqAAAWOpmjfknhTtlbZmbtVyfYMTGxmLixInw9/eHu7s71q5dSwPKVRid5V0GMUnp6LIhSG7X95vtpNEv9Lm5uSVaIf34n7IWpJ/7PRUrVlRoQfrT2Ui5vnEZ6WhGA/SJQmRmZmLgwIG4evUqDh06hP79+/OOpDJu376NmTNnIiQkBP3798fatWs/ebZ2WTbufWhuVytMdTYv83U+RSqVYt26dViyZAlq1KiBrVu3okuXLnK5F1EcKijLaOTOUITEpQr6Yl/Yt0KPIssmLy8P7969w5QpU3D//n14enqWqCDNyvp0O4NIJELFihVLvamppAVphzX+cm2tMDPSR+AcZ7ldn5AP5eXlYdSoUTh27Bi2b9+OsWPH8o6k1F69eoVFixZh9+7dsLa2xu+//45OnTp98XMOhyVgydlISP832aG4tMQiSMQiLHOzltuGvbt372LcuHG4f/8+Zs2ahWXLltGAcjVBj7zLaEU/G7isDxS0oFRE34om0NHRQdWqVfH48WN07NixxO+ACwvS4m5qio2N/de/z8zM/OR1P1WQfm41VM+gMuLTdIT44/gsGqBPFElHRwcHDx5ElSpVMG7cOKSlpWHOnDm8YymdvLw8/PHHH1i2bBm0tbWxadMmTJw4ERLJ1/+eDrU3RdsGxl89/KBQ4cfb1DfCin42cnnMnZ2djZ9++gm//fYbrK2tcfPmTdjb2wt+H8IPvYKUUR1DfcH7VibYVaKdtwLJyMhAZGQkZs6cWeLPLSxIq1atWqp75+fnl+iR/ZMnT/5TkGqb1EPNsRtLdf/iYgCepWbCumYlud6HkEJaWlrYvHkzjIyMMHfuXKSlpeGXX36hjRj4p0/y4sWLmD17NuLi4jB58mQsXboUhoaGJbpOHUN97B/n+P/Hs0YnIyH1E8ezGunD2dIEI1qZyq3Fyt/fHxMnTkRiYiKWLVuGuXPn0rGcaogKSgEMtTdFSkauIH0r+jF+WLF1Lzqa+qJly5YCpNNs4eHhkMlkXHaVamtrw9jYGMbGxqX6/Pz8fFx/9ALufwo7LuhTFDWon5BCIpEIP//8MwwNDfHdd98hLS0Nnp6e0NLS4h2Nm0ePHmH27Nm4dOkSOnfujJMnT6JJkyZluqZFNQP85GaNn2CNzFwpnqVmIk8qg45EjLpG5eX6ZOLt27eYO3cuduzYgfbt2+P8+fOwsrKS2/0IX1RQCmSaswWMK+iWuW/F1aItune/ic6dO+PSpUto3bq1HFOrv1u3bqF8+fIqOftOW1sbVY2qKOReihrUT8jHvv32W1SpUgXjx4/HmzdvsH//fujoyLfNQ9m8ffsWy5Ytw8aNG1GnTh2cOnUKffr0EXzFtryuRGFPIk6ePImpU6ciMzMTXl5emDhxokpN1yAlR/91BTTU3hR+szugTf1/xh5oib/8w6Dw423qG8FvdgcMsTdF5cqV4evri2bNmqFr164ICpLfLnJNEBoaipYtW6rsqkfhAH15Ev3vPoTwMmbMGBw/fhynT5+Gm5vbZ/uP1U1BQQG2b98OS0tLbNu2DcuWLUNUVBT69u2rso////77b/Tv3x8DBgyAg4MDHj58iEmTJlExqQHov7DACvtWLs9ywkhHM5gZ6f+nIBDhn521Ix3N4DfbCfvHOf6rZ9LAwKBo+G+3bt1w5coVhX4N6kTVT8gpHKAvT2UdoE+IEPr16wdvb++ik3XS0tJ4R5Kra9euwd7eHhMnTkS3bt3w+PFjLFiwAHp6eryjlQpjDNu3b0fjxo1x/fp1HD16FKdPn0atWrV4RyMKQmODFKC0fSvZ2dno378//P39cerUKXTv3l0BadXHy5cvUbNmTRw/fhwDBgzgHafU5DmHUgSGYfa18Ut/W8GvTUhphIWFoXv37qhRowZ8fX1Ro0YN3pEElZCQgHnz5uHIkSOwt7fHH3/8gVatWvGOVSYxMTGYOHEiAgICMGbMGKxdu7bEm4iI6qMVSgUo7FuxM60C65qVir0aVK5cOZw+fRpdu3ZF3759cebMGTknVS+3bt0CAJVeoQSA4Y6mcikmAYBBhO3fj8Ly5cvVfkWIqAZ7e3tcu3YNb968Qdu2bfHkyRPekQSRlZWFpUuXomHDhggMDMSePXtw8+ZNlS4m8/PzsXr1ajRt2hQJCQm4fPkydu3aRcWkhqKCUsnp6uri+PHjcHNzw8CBA3Hs2DHekVRGaGgoatSogdq1a/OOUiYW1QzQ3tz4qz25JaUlFqFlrfIY7OqEFStWwNTUFLNnz0ZiYqKg9yGkpBo1aoTr169DIpGgXbt2iIgQbiybojHGcPToUTRq1AgrVqzAjBkzEB0djdGjR6t0X+GdO3fg6OiIhQsXYtq0aYiIiKDjNDWc6n43axAdHR0cOnQIgwcPxtChQ3Hw4EHekVRCYf+kqja3f2hFPxtIBC4oJWIR1g9zwKZNmxAfH4/Zs2dj7969qF+/Ptzd3REVFSXo/QgpCTMzMwQHB6N69epwcnJCSEgI70gldu/ePXTs2BFDhgyBra0tIiMjsWrVKhgYqO6RullZWZg3bx4cHBxQUFCA0NBQrFmzBvr6NDtZ01FBqSIkEgn27duHUaNGYeTIkdi9ezfvSEpNJpMhLCxM5R93FyocoC+kZW7WRZvBTExMsHz5csTHx2P16tXw8/ODtbU1+vTpgxs3bgh6X0KKy8TEBAEBAbCxsUGXLl3g4+PDO1KxvH79Gh4eHmjevDlev34NHx8fnDlzBubm8jkbW1GuXr2Kpk2b4o8//sDy5ctx+/ZtmpdMilBBqUK0tLSwc+dOTJw4EWPHjsXWrVt5R1Jajx8/xvv37+HoqD7noQ+1N8WcrpaCXGtuV6tPntVrYGCAb7/9FnFxcdi9ezeio6PRpk0bODk54eLFi6A9fETRKlWqBB8fH3Tq1Am9e/fG0aNHeUf6rPz8fGzYsAEWFhY4evQoNmzYgPv376Nr1668o5XJmzdvMH78eHTu3Bm1atXCX3/9hQULFtBpN+RfqKBUMWKxGF5eXpgxYwYmTZqEP/74g3ckpXTr1i2IRCK1e/c8zdkCq/rbQFciLnFPpZZYBF2JGKv722Cq85dXSnR0dODu7o7IyEicPn0a+fn56NmzJ5o1a4YDBw4gPz+/LF8GISVSrlw5nDx5sqjtZ9u2bbwj/YePjw+aNm2K7777Dt988w2io6MxY8YMlS+6Tpw4gcaNG+PYsWPYunUr/P39YWkpzBtbol6ooFRBIpEIGzZswNy5czFz5kysWbOGdySlExoaioYNG6JSJfU7n1qIAfrFJRaL0adPH4SEhCAwMBB16tTByJEjYWFhgY0bNyIrK6v0XwghJaCtrY19+/Zh2rRp8PDwwKpVq5RixTwmJgZubm7o1q0bqlWrhjt37sDLywtVq1blHa1MCgeUDxw4EI6OjoiKiqLTbsgX0TRjFSUSibB69Wro6upi3rx5yMnJweLFi3nHUhqqPtD8awoH6MckpeNgaAL8o5ORkJqFD19eRfhnaLmzpQlGtDKFuUnpNwKIRCI4OTnByckJf/31F3799VfMnj0bS5cuxYwZMzB16lQYGRmV+esi5EvEYjF+//13GBoaYsGCBUhNTcWvv/7KZePd+/fv8fPPP2PDhg2oWbMmjh07hgEDBqj8JkCZTIYdO3Zg7ty5KFeunNp8XUQBGFF5y5cvZwDYokWLmEwm4x2Hu+zsbCaRSJinpyfvKAqVkZPPHrx4y+7Ep7EHL96yjJx8ud7v6dOnbNq0aaxcuXJMX1+fzZw5k8XHx8v1noQU+v333xkANnbsWJafL9/v9Q8VFBSwXbt2sWrVqrFy5cqxZcuWsaysLIXdX54eP37MOnToUPTnmpaWxjsSUSFUUKqJX3/9lQFgc+bM0fiiMiQkhAFgt2/f5h1FIyQnJ7PFixezKlWqMIlEwkaNGsUePHjAOxbRAPv27WNaWlqsf//+LDs7W+73CwkJYS1btmQA2DfffMMSEhLkfk9FyMvLYytXrmS6urqsfv36zM/Pj3ckooKooFQjhe/Yp0+frtFF5YYNG5iuri7Lzc3lHUWjpKens3Xr1rHatWszAKx3797s+vXrvGMRNXf27Fmmp6fHOnXqxN6/fy+Xezx//pyNGDGCAWDNmzdn165dk8t9eLh9+zaztbVlYrGYzZ07l2VmZvKORFQUddeqkRkzZmDLli3YuHEjJk2aBJlMxjsSF6GhoWjevDl0dHR4R9EoFSpUwOzZs/HkyRPs2bMHsbGxaNu2Ldq3b48LFy4oxQYKon569+4NHx8f3L59G507d0Zqaqpg187JycEvv/wCS0tL+Pr6YseOHbh16xbatWsn2D14ycrKwty5c+Hg4ADGGG7duoVff/2VBpST0uNd0RLh7dq1i4lEIubu7s6kUinvOArXoEEDNnPmTN4xNF5BQQE7c+YMa926NQPAmjRpwvbt28fy8vJ4RyNqKDw8nFWtWpU1atSIJSYmlulaMpmMnThxgtWtW5dJJBL23Xffsbdv3wqUlD8/Pz9Wv359pqury1auXEl/J4kgaIVSDY0ZMwYHDhzA/v37MWrUKEilUt6RFCY1NRVPnjxR6x3eqkIsFsPNzQ3Xr19HUFAQzMzMMGrUKJibm+OPP/5AZmYm74hEjTRv3hzBwcHIzMxEu3btEBMTU6rrFJ5JPWDAADRu3BgPHjzA2rVr1WIE2Zs3bzBu3Di4uLigTp06iIiIwPz581V+ViZRDlRQqqlhw4bh8OHDOHr0KIYOHYq8vDzekRTi1q1bAKBWJ+SoOpFIhPbt2+P8+fP466+/4OTkhG+//RZmZmZYunSpoI8oiWaztLREcHAwypUrh3bt2uHevXvF/tzU1FRMmzYNtra2eP78OS5cuIALFy7AyspKfoEVhDGG48ePo1GjRjhx4gS2bduGq1evwsLCgnc0ok54L5ES+Tpz5gzT0dFhvXv3Zjk5ObzjyN1PP/3EjIyMNHpTkip4+vQpmz59Oo0cInLx+vVr1rJlS1axYkUWFBT0xd+bn5/PNm7cyKpUqcIqVqzIfvvtN7Xa0Pf8+XPWp08fBoD169ePvXjxgnckoqaooNQA3t7eTFdXl3Xr1k1t5qV9Tvfu3Vn37t15xyDFlJyczH788UdmaGjIJBIJGzlyJIuIiOAdi6iB9+/fM2dnZ6anp8fOnz//yd/j5+fHrK2tmUgkYuPHj2evXr1ScEr5KSgoYFu2bGEVK1Zk1atXZ8ePH+cdiag5euStAbp164YLFy4gMDAQvXr1UtveNfa/nYrUP6k6qlatiqVLlyI+Ph5r1qxBQEAAbGxs0Lt3b1y/fp13PKLCDAwMcPHiRbi6uqJv3774888/iz4WFxeH/v37w8XFBZUrV0ZYWBi2b9+OatWqcUwsnOjoaDg7O2PSpEkYNGgQoqKiMGDAAN6xiJqjglJDdO7cGZcuXcKtW7fQvXt3pKen844kuKdPnyI1NZUKShVUoUIFzJo1C0+ePMHevXsRFxeHdu3aoV27djh//rzGjsAiZaOnp4fjx49jxIgRGDFiBNatW4dFixahcePGCAsLw59//olr166hRYsWvKMKIj8/HytXrkTTpk3x4sULXLlyBTt27ECVKlV4RyOagPcSKVGskJAQVrFiRdaqVSv25s0b3nEE9eeffzIA7PXr17yjkDIqKChgZ8+eZW3atGEAmLW1Ndu7dy+NNyGlkp+fz1xdXRkAJpFI2OLFi1lGRgbvWIIKCwtjzZo1Y1paWmzevHk0oJwoHK1QapjWrVvjypUrePz4MVxcXJCWlsY7kmBu3bqF+vXrw9jYmHcUUkZisbjosfe1a9dQr149jB49Gubm5vi/9u48Lsqqfx/4NTMIKu4YmguYoiSKe7kigoqAMkn64JpbmfbkmoKKqBVumAoupaVmahb2FZcZETdEQCxwSSFQQRGGlERAERlim/v3RzW/ejITZ7kHuN5/NnDOB18xXHPucz5n06ZN1XbbBulfQkICnJyccPLkSXTu3Bnl5eUoKChAnTp1xC5NL4qKirBw4UL07t0bEokECQkJCAoKYoNyMjoGyhqoV69eiIqKQmZmJlxcXPDgwQOxS9KL+Ph4tguqhgYMGAClUomkpCQ4OztjwYIFsLGxwYcffojc3FyxyyMTlZ2djalTp6J3795Qq9WIiopCUlISPvvsM2zZsgVTpkxBWVmZ2GXq5MyZM3B0dMSnn36KNWvWICEhAT169BC7LKqpxF4iJfH89NNPQrNmzQQHBwfh3r17Ypejk9LSUsHCwkIIDg4WuxQysIyMDGHOnDnalkNz5swRMjIyxC6LTMSvv/4qBAUFCfXq1ROsrKyEbdu2/e3GsG+++UYwMzMTvLy8qmTni7y8PGHq1KkCAGHQoEFCamqq2CURsW1QTXfjxg2hRYsWQocOHXS+rkxMly9fFgAIFy5cELsUMpIHDx4IK1asEJo0aSLIZDJh4sSJQmJiothlkUg0Go2gUCgEOzs7QSaTCXPnzhXy8/P/8euPHz8u1KlTR3B2dhYKCgqMWOmL02g0woEDBwRra2uhYcOGwo4dO9hzl0wGH3nXcPb29oiJicGvv/4KZ2dnZGZmil3SC4mPj4eZmRm6desmdilkJE2bNsWHH34IlUqFDRs2IDo6Gl26dMGIESNw/vx5scsjI0pJSYG7uzvkcjleeeUVJCYmIiQk5Jmnmz08PHD69GlcvXq1Smz9uXv3LkaOHIkxY8ZgwIABSElJwTvvvAOJRCJ2aUQAuIeSALRr1w4xMTEAgIEDB+L27dsiV1R5CQkJ6NKlS7XZaE/Pz9LSEnPnztW2HMrIyICTkxP69+8PpVLJlkPV2MOHDzFv3jx06dIFt2/fxtGjR3Hy5Ek4ODg81/f3798f0dHRuHv3LpycnKBSqQxcceVpNBps374dDg4OSEhIQFhYGMLCwtCiRQuxSyP6CwZKAgDY2toiOjoatWvXxsCBA3Hz5k2xS6oUHsihWrVqYdKkSUhMTIRSqYREIoFcLoejoyP27NlT5Q9g0P9XUVGBzz//HB06dMCuXbuwatUqJCcnQy6XV3rFrmvXrjh//jxKSkrQv39/3Lhxw0BVV97NmzcxaNAgvPfeexgzZgyuX7+ON998U+yyiJ6KgZK0WrVqhejoaDRq1AjOzs5ITk4Wu6TnUlBQgBs3brChOQH4reXQH4+9z58/j3bt2mHKlClo164dQkJC8OTJE7FLJB1ER0ejZ8+emDlzJkaMGIHU1FQsWrQIFhYWLzymnZ0dzp8/j4YNG8LJyQmXL1/WY8WVV1ZWhlWrVqFr167Izs7G2bNn8cUXX6BRo0ai1kX0LAyU9BfNmzfHuXPn0Lx5cwwaNAjXrl0Tu6R/dfnyZQiCwBVK+pv+/ftDoVAgKSkJLi4u8PX1ha2tLVasWMGWQ1VMZmYmfHx8MGjQINSuXRvx8fHYvXs3Xn75Zb2M37JlS8TExKBdu3ZwcXHBuXPnnvt7i0rKkXyvAD+qHiL5XgGKSspfuI6LFy+iZ8+eWLFiBebNm4fExES4uLi88HhExiIRBEEQuwgyPfn5+XBzc0N6ejpOnTqFXr16iV3SP1qzZg3WrFmDR48eQSrlZyT6ZyqVChs3bsSOHTsgCALeeecdLFiwALa2tmKXRv9ArVYjKCgI69atQ+PGjREUFIQJEyYY7Hf9yZMn8Pb2RmxsLL777jvI5fKnfl3a/ULsj1ch6mYOVPlq/PkPqQSATZO6cLG3xoTeNmjfrP6/zltUVITly5cjJCQE3bp1w86dO9G9e3f9/FBERsBASf/o0aNH8PDwQEpKCk6cOIG+ffuKXdJTeXt74/Hjx4iMjBS7FKoi8vLysHXrVmzZsgWPHj3C2LFjsWjRIjg6OopdGv1OEAQcOHAAvr6+yMnJwcKFC7FkyRLUq1fP4HOXlJRgwoQJOHLkCL788ktMmjRJ+1pWvhr+h5MQeysXMqkEFZp//hP6x+tOdk2x2tsRrZs8/faa06dPY8aMGcjOzsZHH32EDz74AGZmZnr/uYgMics59I8aNWqEU6dOoWvXrnBzc9OeBDclgiDwQA5VmpWVFVasWIHMzExs3LgRsbGx6NKlC4YPH47Y2Fjwc7a4rly5goEDB2LcuHHo1asXrl+/jlWrVhklTAKAhYUFDhw4gClTpmDy5MkICQkBAIReVGFIcDQupOcBwDPD5J9fv5CehyHB0Qi9+NdT5Pn5+ZgyZQrc3NzQpk0bJCUlwc/Pj2GSqiQGSnqm+vXrIyIiAq+//jrc3d1NbhXw7t27yM7O5oEceiGWlpaYM2cObt26hb1790KlUmHgwIHavZdsOWRcOTk5mD59Onr16oWHDx/i9OnTOHz4MNq2bWv0WmQyGXbs2AE/Pz/Mnz8fI5duw+JDSSgp1/xrkPxfFRoBJeUaLD6UhK1RadrV144dO+LIkSPYuXMnIiMjYWdnZ6Cfhsjw+MibnktxcTHefPNNREVF4fDhw/Dw8BC7JABAWFgYRo8ejXv37ultcz7VXIIg4Pjx41i7di3Onz8PBwcH+Pn5Ydy4cTA3Nxe7vGqrtLQUW7duxUcffQSZTIaPP/4YM2fONJmVuskff4Ho4pZ6G6/V3XOI27ceo0aNwpYtW/jeRdUCVyjpudSpUwdHjhyBm5sbRo4cCYVCIXZJAH5raN6qVSu+IZNeSCQS7WPvuLg42NnZaVsOBQcHs+WQAURERKBLly7w9fXFW2+9hbS0NMyaNctkwmRWvho/lLUGoJ+1F0EQkNWsH3Z8ewgHDx7kexdVGwyU9NwsLCxw8OBBeHl5YdSoUTh48KDYJSEhIYGPu8kg+vXrh6NHj+Knn37C4MGD4efnBxsbGyxfvtzkr+mrClJTUzF8+HB4enqiRYsWuHr1KrZu3QorKyuxS/sL/8NJKNcI+O3stu4kEgnMapkjuog33VD1wkBJlWJubo7Q0FD4+PhgzJgx+Oabb0SrpaKiApcuXeKBHDKoTp064auvvsLt27cxefJkbNiwAba2tpg9ezYyMjLELq/KKSgowMKFC9G5c2ekpKQgLCwMkZGRJnnCPu1+IWJv5VZ6z+S/qRCA2Fu5uJVTqNdxicTEQEmVZmZmhr1792LSpEmYOHEidu/eLUod169fx5MnT7hCSUZhY2OD4OBgqFQqLF68GN9++y3s7OwwceJEJCYmil2eydNoNNi1axc6dOiAbdu2YcWKFdqrBCt7XaKx7I9XQSY1TG0yqQRf/2B6d4cTvSgGSnohMpkMu3btwrvvvotp06bh888/N3oN8fHxkEqlJt10naofKysrLF++HJmZmQgODsb58+fRtWtXDB8+HDExMWw59BRxcXF4/fXX8c4772Do0KFITU3F0qVLUbt2bbFLe6aomzl6X538Q4VGQFRqjkHGJhIDAyW9MKlUim3btmHOnDmYOXMmNm/ebNT5ExIS4ODgYLTedER/ZmlpidmzZyMtLQ379u2DSqWCs7Ozdu8lWw4BP//8M8aPH48BAwZAIpEgLi4OX3/9NVq21N+JaUN5UlIOVb7aoHOo8tQ6XdNIZEoYKEknEokEISEh8PX1xdy5c/HJJ58YbW4eyCFTUKtWLe1j7/DwcNSqVQsjR45E586d8dVXX6G0tFTsEo2uuLgYgYGBsLe3x9mzZ/Hll18iPj4e/fr1E7u055aZV6Snc93/TACQkVdk4FmIjIOBknQmkUgQFBSEgIAA+Pn5ITAw0OBzqtVqJCUl8UAOmQyJRAJPT0/ExMQgLi4O7du3x9SpU9GuXTts3LgRhYXV/wCGIAg4ePAgOnbsiMDAQLz//vtITU3F1KlTDXb3tqGUlhtnhdlY8xAZWtX6DSeTJZFIEBgYiMDAQCxfvhwBAQEG3Ut25coVVFRUcIWSTNIfj72Tk5MxZMgQLFq0CLa2tli2bFm1bTmUmJgIV1dX/Oc//4GjoyOSk5Oxbt06NGjQQOzSXoi5mXH+PBprHiJD4//JpFcBAQFYt24dVq1aBT8/P4OFyvj4eNSpUwedO3c2yPhE+uDg4IDdu3cjPT0dU6ZMQXBwMGxsbDBr1izcuXNH7PL0Ijc3F++99x66d++OX375BREREVAqlWjfvr3YpemkjZWlnjpP/jPJ7/MQVQcMlKR3vr6+2LRpE9avX4+5c+caJFQmJCSgZ8+eJnObBtGztG7dGhs3boRKpYK/vz8OHDiA9u3bY8KECbh27ZrY5b2QsrIybN68Ge3bt8e3336LDRs2IDExEe7u7mKXpheWFmawaVLXoHPYWNWFpQXfw6h6YKAkg5gzZw62b9+OLVu2YObMmXo/8coDOVQVNWnSBMuWLUNmZiZCQkIQFxeHbt26wdPTE9HR0Qb58FVUUo7kewX4UfUQyfcK9HKq+PTp0+jWrRvmzZsHHx8fpKWlYd68eahVq5YeKjYdHeqVAYJh9jjKpBK4dLA2yNhEYuBHIzKYGTNmwNzcHG+//TZKS0uxc+dOyGQyncfNyclBRkYGD+RQlVW3bl3MmjULM2bMwHfffYegoCAMGjQIffr0waJFiyCXy3U6xJJ2vxD741WIupkDVb76L6eVJQBsmtSFi701JvS2Qftm9Z973Nu3b2PBggU4evQonJyccPnyZXTv3v2F6zRFgiDgzJkzCAwMxA8pGWgxfZtB5qnQCJjYx8YgYxOJgSuUZFBTp07F119/jX379mHSpEkoL9d9dSQhIQEAuEJJVV6tWrW0j72PHz8Oc3NzeHt7o1OnTti9e3elWw5l5avx1q54DA2Jwb74TGT+T5gEfmtVk5mvxr74TAwNicFbu+KR9S/9FgsLC7FkyRI4ODjgypUrOHDgAKKjo6tVmBQEAceOHUPfvn3h5uaG4uJiHPxyKwbYWen9thyZVAInu6aws37+ME9k6hgoyeDGjx+P0NBQfPfddxg7dqzOffni4+NhbW0NW1tbPVVIJC6JRAIPDw9ER0fjwoULsLe3x7Rp09C2bVts2LDhuVoOhV5UYUhwNC6k5wHAv97w8sfrF9LzMCQ4GqEX/34NoEajwd69e2Fvb49NmzbB398fN27cgI+Pj8lel1hZGo0Ghw4dQs+ePeHl5QWZTIaIiAgkJCRALpdjjXcXmOk5UJpJJVjtbXp3lxPpgoGSjGL06NEICwuDUqnE6NGjUVJS8sJj/bF/srr8QSP6s759++LIkSNISUmBm5sblixZAhsbGwQEBCAn5+lX9W2NSsPiQ0koKddU+qrACo2AknINFh9KwtaoNO1/j4+PR9++fTF58mQMHDgQN27cwIoVK1C3rmEPqhhLRUUFQkND0bVrV4waNQoNGzZEZGQkzp8/D3d3d+37S+smdfGRvJNe5/5Y3gmtDXzgh8jYGCjJaORyOY4ePYpTp05h5MiRKC4ufq7v+8uhgrsFSLhyjY+7qdrr2LEjvvzyS6Snp2PatGkICQmBra0t3n//faSnp2u/LvSiCutPpeplzvWnUvH56SRMnjwZffr0QWlpKWJiYhAaGgobm+qx36+8vBx79uyBg4MDxo0bh5YtWyI2NhZRUVFwdXV96gfVsa/ZYKFbB73M7+tmjzGvVY9/S6I/kwiG7D5N9BSRkZHw8vJC3759oVAoYGn59z5szzpUIAgCrOtIMLx7m0ofKiCqqvLz8/HZZ59h06ZNyM/Px5gxYzDl/QWYfSIHJXq7bUWAUF6G4oP+WOX/AaZNm6aXg3SmoLS0FHv27MGaNWtw584deHl5ISAgoFIfTkMvqrBCkYxyjVCplWCZVAIzqQQfyzsxTFK1xUBJooiJicHw4cPRvXt3hIeHo37930JhVr4a/oeTEHsrFzKp5Jlv2n+87mTXFKu9HfkIiWoEtVqN3bt3Y/369VD3noY6bboBEv09bJIIGvR+pTFCZwzQ25hi+vXXX7Fr1y4EBQUhKysLo0aNQkBAALp16/ZC4/E9iujpGChJNN9//z3c3d3h4OCAiIgInEh7rNOn/4/knTCWn/6phrh+7xE8tsQZbPwz8wdW6VPIarUan3/+OT755BPcv38fY8eOhb+/Pzp10s9+SO1TlNQcqPKe0prJqi5cOlhjYh+bKv3vSPS8GChJVJcuXYKbmxuaD54Ctd1gncdb6NYBs1yq9pVvRM/jQ0Uy9sVnVvoQzvOQSSV4q7ctPtTzYRRjKCwsxGeffYYNGzYgPz8fb731FpYsWYIOHfSzB/JpikrKkZFXhNJyDczNpGhjZckbcKjGYaAk0X0SFodPLz3S23hBbzpynxJVe86fRCHzX/pH6sLWqi6iF7oYbHx9e/ToETZv3oyQkBA8efIEU6dOxeLFi/HKK6+IXRpRjcCPUCSqrHw1dl59rNcxlyuS0a9dU+5XomrrSUk5VAYMkwCgylOjqKTc5FfacnNzERISgi1btqC0tBTTp0+Hn58fWrVqJXZpRDUK2waRqPwPJ6Fcz4/syjUC/A8n6XVMIlOSmVf0txtw9E0AkJFXZOBZXtz9+/fh5+eHNm3aIDg4GNOnT8edO3ewefNmhkkiEZj2R0+q1tLuFyL2Vq7ex63QCIi9lYtbOYXcDE/VUqne2gSZxjyVcffuXaxbtw5ffPEFatWqhTlz5mD+/Pl46aWXxC6NqEbjCiWJZn+8Su935P5BJpXg6x/+fpUcUXVgbmact25jzfM8MjMz8d5776Ft27bYu3cvFi9ejMzMTKxevZphksgEmM67BdU4UTdzDHJCFfhtlTIq9enX1BFVdW2sLGHoi0clv88jtlu3buHtt9+GnZ0dDh48iA8//BCZmZlYsWIFGjduLHZ5RPQ7BkoShTEPFRBVN5YWZrAx8KEzG6u6oh7IuX79OiZOnAh7e3uEh4dj7dq1yMjIwJIlS9CgQQPR6iKip2OgJFHwUAGRblzsrQ26ZcSlg7VBxv43iYmJ8PHxQadOnRAdHY1Nmzbhzp07WLBgwVOvaSUi08BASaKoyYcKiPRhQm8bg24ZmdjHuL1cL126hJEjR6Jr1664ePEitm/fjlu3bmHWrFmoU6eOUWshospjoCRR1MRDBUT61L5ZfTjZNdX7KqVMKoGTXVOjdUi4cOECPDw88NprryElJQVfffUVUlNT8e6778LCwsIoNRCR7vjXlkRRkw4VEBnKam9HmEklgB4vPDOTSrDa21Fv4z2NIAg4d+4cBg8ejP79+0OlUuGbb77B9evXMXnyZNSqVcug8xOR/jFQkiiMcajAUlDjx4s/oKKiwqDzEImlef1aaJkdB0j09/HsY3kng90yJQgCTp48iYEDB8LFxQX5+fk4ePAgkpKSMG7cOMhkMoPMS0SGx0BJojHkoQKJoMGjlAtwcnJCs2bNMHnyZISFhaGwsNAg8xEZW2lpKcaNG4fYPUHwaKmfbga+bvYY85r+904KggClUok+ffrA3d0dpaWlUCqVuHLlCkaNGgWplH+KiKo6/haTaAx5qECQSHH2i4/w/fff491338Xly5cxevRoNG3aFO7u7vjss8+QlZVlkLmJDK2kpAQ+Pj5QKBQICwvDtllvYO2bjrAwk1b6Q5pMKoGFmRRBbzrifRc7vdap0WgQFhaGHj16QC6Xw9zcHCdPnsQPP/yAESNGQKLHlVUiEpdEEPS4+Yaokt7aFY8L6Xl6DZYyqQT92lph39u9//Lf09PToVQqoVAoEBMTg/LycnTr1g1yuRxyuRw9evTgHzgyeb/++itGjx6NM2fO4NChQ/D09NS+lpWvhv/hJMTeyoVMKnnm79UfrzvZNcVqb0e9PuauqKjAgQMHsGrVKqSkpMDV1RXLli2Ds7Mzf8eIqikGShJVVr4aQ4KjUaLH9j4WZlKcme/8zD+Qjx49wokTJ6BQKHD8+HEUFBSgZcuWGDFiBORyOVxdXVG7dm291USkD8XFxfD29kZ0dDSOHj0KNze3p35d2v1C7I9XISo1B6o89V96vkrwW9Nylw7WmNjHRq+nucvKyrB//36sXr0aaWlp8PDwQEBAAPr166e3OYjINDFQkuhCL6qw+FCS3sYLetOxUvvAysrKEBsbC6VSiaNHj+LOnTuwtLSEm5sbvLy8MHz4cFhbi9PkmegParUab7zxBuLi4qBUKjF48ODn+r6iknJk5BWhtFwDczMp2lhZ6v0GnJKSEuzZswdr1qxBRkYG3njjDQQEBKBXr156nYeITBcDJZmErVFpWH8qVedxfN3sddoHJggCUlJStI/Gf/jhBwBA3759IZfL4eXlhY4dO/KxHRlVUVERvLy8kJCQgPDwcDg7O4tdEoDfVkx37tyJdevW4e7duxg9ejQCAgLQpUsXsUsjIiNjoCSTEXpRhRWKZJRrhErtqZRJJTCTSvCxvJPeT6jev38f4eHhUCqVOHXqFNRqNdq1a6fdd9m/f3/2zCODKiwsxPDhw/Hjjz8iIiICAwYMELskFBUVYfv27Vi/fj1ycnIwfvx4+Pv7o2PHjmKXRkQiYaAkk2Iqhwqepri4GGfPntWuXmZnZ6NRo0bw9PSEXC6Hu7s7GjZsaNAaqGZ5/PgxPDw88NNPP+HEiRPo27ev6PV8+umn2LhxIx49eoRJkyZhyZIlsLPT7+lwIqp6GCjJJIl1qOB5aTQaXLlyBQqFAkqlElevXoWZmRmcnZ21j8ZfeeUVo9dF1cejR48wbNgwpKam4uTJk3j99ddFq+Xhw4fYtGkTNm3aBLVajWnTpmHRokVo06aNaDURkWlhoCSTZ4xDBbpSqVTalcuoqCiUlZXB0dERXl5ekMvleO2119i8mZ5bfn4+3NzckJ6ejjNnzqBHjx6i1PHgwQMEBwdj69atKCsrw4wZM+Dr64uWLVuKUg8RmS4GSiI9e/z4MU6dOgWFQoHw8HDk5+ejWbNm2pZEQ4YMQd26hn08T1VXbm4uhg4diqysLERGRqJr165GryE7OxsbNmzAtm3bIJFI8N///hcLFixAs2bNjF4LEVUNDJREBlReXo7vv/8eCoUCCoUCqampqF27NoYOHQovLy+MGDECL7/8sthlkonIycnBkCFD8MsvvyAyMhKOjo5GnT8rKwvr1q3Djh07YGFhgdmzZ2PevHlo2rSpUesgoqqHgZLIiG7evKl9NB4XFweNRoPXX39d+2jc0dGRLYlqqF9++QWDBw9GXl4ezp49CwcHB6PNfefOHaxduxa7d+9GvXr1MH/+fMyePRuNGjUyWg1EVLUxUBKJJDc3FxEREVAoFDhx4gSePHkCW1tb7aEeZ2dnmJubi10mGcG9e/fg6uqKwsJCnD17Fvb29kaZNzU1FWvWrMG+ffvQpEkTLFiwAP/9739Rv77xD7oRUdXGQElkAkpKShAdHa19NJ6VlYUGDRrA3d0dcrkcHh4eaNKkidhlkgH8/PPPcHV1RXFxMaKioozSgic5ORmrVq3CgQMH0KxZM/j6+uLdd9+FpaWlwecmouqJgZLIxAiCgGvXrmkfjV+6dAkymQwDBgzQrl62b99e7DJJDzIzM+Hq6ory8nJERUWhbdu2Bp3v6tWrWLlyJcLCwtC6dWssXrwY06ZN4731RKQzBkoiE3f37l0cO3YMSqUSZ86cQUlJCV599VXtbT19+vSBTCYTu0yqpDt37sDFxQVSqRRRUVGwtbU12FwJCQlYuXIllEol2rZtiyVLlmDSpEncUkFEesNASVSFFBUV4fTp01AoFDh27BgePHiApk2bYvjw4ZDL5XBzc0O9evXELpP+xe3bt+Hi4gJzc3NERUWhdevWBpnn/PnzCAwMxKlTp9ChQwcsXboU48ePh5mZafVxJaKqj4GSqIqqqKhAQkKCdt9lSkoKzM3N4erqqn003qpVK7HLpP+RmpoKV1dXWFpa4uzZs3pvEi4IAqKiohAYGIhz586hU6dOCAgIwH/+8x+uZBORwTBQElUTt2/f1u67jImJQUVFBXr06KFtSdS9e3e2JBLZjRs34OrqikaNGiEyMlKvPUgFQcCJEyewcuVKXLhwAd27d8eyZcvwxhtv8JYmIjI4Bkqiaujhw4c4ceIEFAoFIiIiUFBQgFatWsHLywteXl5wcXHhQQwjS05OhqurK6ytrXHmzBm93TojCAIUCgVWrlyJS5cuoXfv3li2bBk8PT35AYKIjIaBkqiaKysrQ2xsrPbR+J07d2BpaYlhw4ZBLpfD09MTL730kthlVmuJiYkYPHgwWrRogTNnzujl31uj0SAsLAwrV65EYmIinJycsGzZMgwZMoRBkoiMjoGSqAYRBAEpKSnacBkfHw8A6Nevn3bf5auvvspAokc//vgjhgwZAltbW5w+fRpWVlY6jVdeXo7Q0FCsXr0a169fx5AhQ7Bs2TIMHDhQTxUTEVUeAyVRDXb//n2Eh4dDoVDg1KlTKC4uhp2dnbYlUf/+/XkiWAeXLl3C0KFD0b59e5w8eRKNGzd+4bHKysqwb98+rFmzBrdu3YKnpycCAgLQt29fPVZMRPRiGCiJCABQXFyMyMhIKJVKKJVKZGdno3HjxvD09IRcLsewYcPQsGFDscusMuLj4zFs2DB07NgRJ06ceOF/u5KSEuzevRtr165FZmYmvL29sXTpUvTs2VPPFRMRvTgGSiL6G41Gg8uXL0OhUECpVOLatWswMzPDoEGDtI/G27RpI3aZJisuLg4eHh7o0qULjh8/jgYNGlR6DLVajR07dmDdunXIzs6Gj48Pli5dCkdHRwNUTESkGwZKIvpXmZmZ2pXLqKgolJWVwdHRUftovFevXmxN87uYmBh4enqiV69eOHbsWKUbzT958gTbtm3D+vXrkZeXh/Hjx8Pf3x+vvvqqgSomItIdAyURVcrjx49x8uRJKBQKHD9+HPn5+WjevDlGjBgBuVyOwYMHo27dumKXKYqzZ8/Cy8sLffr0gUKhgKWl5XN/b0FBAbZu3Yrg4GAUFBRgypQpWLx4Mdq1a2fAiomI9IOBkoheWHl5OS5cuKA9NZ6WloY6depg6NCh8PLywogRI9C8eXOxyzSK06dPQy6XY+DAgThy5Ajq1KnzXN+Xn5+PTZs2YfPmzVCr1XjnnXewaNEi2NjYGLhiIiL9YaAkIr25efOmNlxeuHABGo0GvXv31u677Ny5c7VsSXTixAmMHDkSrq6uOHTo0HM1jc/JyUFwcDC2bt2KiooKzJgxA76+vmjRooURKiYi0i8GSiIyiNzcXBw/fhwKhQInT57EkydP0KZNG+2+SycnJ5ibm4td5l8UlZQjI68IpeUamJtJ0cbKEpYWz26bdOzYMYwaNQrDhg3D//3f/8HCwuKZX5+dnY1PPvkE27dvh0wmw/vvv48PPvgA1tbW+vxRiIiMioGSiAyupKQE586d065e/vzzz2jQoAE8PDwgl8vh4eGhU49GXaTdL8T+eBWibuZAla/Gn98QJQBsmtSFi701JvS2Qftm9f/yvUeOHIGPjw9GjBiB0NDQZwbkrKwsBAUFYefOnahduzbmzJmDuXPn6tzonIjIFDBQEpFRCYKAq1evQqlUQqFQ4PLly5DJZHByctKuXhrjIEpWvhr+h5MQeysXMqkEFZp/fiv843Unu6ZY7e2I1k3q4uDBgxg3bhy8vb2xf/9+1KpV66nfm56ejjVr1mDPnj2oX78+5s+fj1mzZqFRo0YG+smIiIyPgZKIRHX37l0cO3YMCoUCkZGRKCkpQceOHbXhsnfv3pDJZHqdM/SiCisUySjXCM8Mkv9LJpXATCrBiJfV2DRnDHx8fLB3796n3iZ08+ZNrF69Gvv374eVlRUWLlyImTNnon79+k8ZmYioamOgJCKT8eTJE5w+fRpKpRLHjh3DgwcP8NJLL2H48OGQy+UYOnRopfs6/q+tUWlYfypVhxEEABK0KUhE5Ba/v4Xdn376CatWrcKBAwfw8ssvw8/PD9OnT6+xrZSIqGZgoCQik1RRUYH4+HjtbT0pKSmwsLCAq6ur9tR4y5YtKzVm6EUVFh9K0luNQW86Ysxrv7X3+fHHHxEYGIjDhw/DxsYGixcvxtSpU5/rxDcRUVXHQElEVcKtW7e0t/XExMSgoqICPXv2hJeXF+RyObp16/bMlkRZ+WoMCY5GSblGbzVZmEmx3qUhPt+4GuHh4WjXrh38/f0xceJEkzvBTkRkSAyURFTlPHz4EBEREVAoFIiIiMDjx4/RqlUr7cqli4vL39r3vLUrHhfS8yq1Z/JfCRoUZ1xFk2vfYOnSpRg7duxT91MSEVV3DJREVKWVlpYiNjZW25IoIyMD9erVw7BhwyCXy+Hp6YmHFRYYGhJjsBpOzhkA+5cbGmx8IiJTx0BJRNWGIAhITk7Whsv4+HhIpVJ0nLgcRS16QoD+b+mRSSV4q7ctPpR30vvYRERVBQMlEVVbv/zyC8LDwxGUUgel5oZbQbS1qovohS4GG5+IyNRJxS6AiMhQmjdvjjETJ6PMgGESAFR5ahSVlBt0DiIiU8ZASUTVWmZeEQz9GEYAkJFXZOBZiIhMFwMlEVVrpXpsE2QK8xARmSIGSiKq1szNjPM2Z6x5iIhMEd8Biahaa2NlaYCz3X8l+X0eIqKaioGSiKo1Swsz2DQx7D3aNlZ1YWnBhuZEVHMxUBJRtedibw2Z1DDrlDKpBC4drA0yNhFRVcFASUTV3oTeNvq9cvFPKjQCJvaxMcjYRERVBQMlEVV77ZvVh5NdU72vUsqkEjjZNYWddX29jktEVNUwUBJRjbDa2xFmeg6UZlIJVns76nVMIqKqiIGSiGqE1k3q4iM937f9sbwTWhv4wA8RUVXAQElENcbY12yw0K2DXsbydbPHmNe4d5KICAAkgiAY+lYyIiKTEnpRhRWKZJRrhEod1pFJJTCTSvCxvBPDJBHRnzBQElGNlJWvhv/hJMTeyoVMKnlmsPzjdSe7pljt7cjH3ERE/4OBkohqtLT7hdgfr0JUag5UeWr8+Q1Rgt+alrt0sMbEPjY8zU1E9A8YKImIfldUUo6MvCKUlmtgbiZFGytL3oBDRPQcGCiJiIiISCc85U1EREREOmGgJCIiIiKdMFASERERkU4YKImIiIhIJwyURERERKQTBkoiIiIi0gkDJRERERHphIGSiIiIiHTCQElEREREOmGgJCIiIiKdMFASERERkU4YKImIiIhIJwyURERERKQTBkoiIiIi0gkDJRERERHphIGSiIiIiHTCQElEREREOmGgJCIiIiKdMFASERERkU4YKImIiIhIJwyURERERKQTBkoiIiIi0gkDJRERERHphIGSiIiIiHTCQElEREREOmGgJCIiIiKdMFASERERkU4YKImIiIhIJwyURERERKQTBkoiIiIi0gkDJRERERHphIGSiIiIiHTCQElEREREOmGgJCIiIiKdMFASERERkU4YKImIiIhIJwyURERERKST/wf9Ktuxz7a3iwAAAABJRU5ErkJggg==\n", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1433,7 +1433,7 @@ }, { "cell_type": "markdown", - "id": "62817648", + "id": "d33f4f6d", "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": "99fe891a", + "id": "83db308c", "metadata": { "execution": { - "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" + "iopub.execute_input": "2022-12-27T10:11:50.668789Z", + "iopub.status.busy": "2022-12-27T10:11:50.668408Z", + "iopub.status.idle": "2022-12-27T10:11:50.879037Z", + "shell.execute_reply": "2022-12-27T10:11:50.878476Z" } }, "outputs": [ @@ -1476,7 +1476,7 @@ }, { "cell_type": "markdown", - "id": "90ee80e2", + "id": "020647dc", "metadata": {}, "source": [ "See Drawing for additional details." |
