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| author | MridulS <mail@mriduls.com> | 2022-12-20 11:41:34 +0000 |
|---|---|---|
| committer | MridulS <mail@mriduls.com> | 2022-12-20 11:41:34 +0000 |
| commit | 0f58b11d05d40689b53b018224e0b6a545b8bfec (patch) | |
| tree | 68bc8f92a3a5451588f7896d6f848bca0ee4b71f | |
| parent | 2a8e7829bc111282c4e0a5ca091d8a27b6f81bed (diff) | |
| download | networkx-0f58b11d05d40689b53b018224e0b6a545b8bfec.tar.gz | |
Deploying to gh-pages from @ networkx/networkx@9d5e11f27033049282e2d244132b0e946df6557d 🚀
106 files changed, 640 insertions, 618 deletions
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b/_images/sphx_glr_plot_subgraphs_007.png Binary files differindex d3daa6fa..4498007e 100644 --- a/_images/sphx_glr_plot_subgraphs_007.png +++ b/_images/sphx_glr_plot_subgraphs_007.png diff --git a/_images/sphx_glr_plot_words_001.png b/_images/sphx_glr_plot_words_001.png Binary files differindex 1b7fcbbc..b5f57dbc 100644 --- a/_images/sphx_glr_plot_words_001.png +++ b/_images/sphx_glr_plot_words_001.png diff --git a/_images/sphx_glr_plot_words_thumb.png b/_images/sphx_glr_plot_words_thumb.png Binary files differindex e146d1a0..a1e694d1 100644 --- a/_images/sphx_glr_plot_words_thumb.png +++ b/_images/sphx_glr_plot_words_thumb.png diff --git a/_modules/networkx/algorithms/smallworld.html b/_modules/networkx/algorithms/smallworld.html index 34ab4845..a7215f9d 100644 --- a/_modules/networkx/algorithms/smallworld.html +++ b/_modules/networkx/algorithms/smallworld.html @@ -509,6 +509,11 @@ <span class="sd"> G : graph</span> <span class="sd"> The randomized graph.</span> +<span class="sd"> Raises</span> +<span class="sd"> ------</span> +<span class="sd"> NetworkXError</span> +<span class="sd"> If there are fewer than 4 nodes or 2 edges in `G`</span> + <span class="sd"> Notes</span> <span class="sd"> -----</span> <span class="sd"> The implementation is adapted from the algorithm by Maslov and Sneppen</span> @@ -521,7 +526,9 @@ <span class="sd"> Science 296.5569 (2002): 910-913.</span> <span class="sd"> """</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="o"><</span> <span class="mi">4</span><span class="p">:</span> - <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXError</span><span class="p">(</span><span class="s2">"Graph has less than four nodes."</span><span class="p">)</span> + <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXError</span><span class="p">(</span><span class="s2">"Graph has fewer than four nodes."</span><span class="p">)</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">)</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span> + <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXError</span><span class="p">(</span><span class="s2">"Graph has fewer that 2 edges"</span><span class="p">)</span> <span class="kn">from</span> <span class="nn">networkx.utils</span> <span class="kn">import</span> <span class="n">cumulative_distribution</span><span class="p">,</span> <span class="n">discrete_sequence</span> @@ -582,7 +589,7 @@ <span class="sd"> Parameters</span> <span class="sd"> ----------</span> <span class="sd"> G : graph</span> -<span class="sd"> An undirected graph with 4 or more nodes.</span> +<span class="sd"> An undirected graph.</span> <span class="sd"> niter : integer (optional, default=1)</span> <span class="sd"> An edge is rewired approximatively niter times.</span> @@ -602,6 +609,11 @@ <span class="sd"> G : graph</span> <span class="sd"> The latticized graph.</span> +<span class="sd"> Raises</span> +<span class="sd"> ------</span> +<span class="sd"> NetworkXError</span> +<span class="sd"> If there are fewer than 4 nodes or 2 edges in `G`</span> + <span class="sd"> Notes</span> <span class="sd"> -----</span> <span class="sd"> The implementation is adapted from the algorithm by Sporns et al. [1]_.</span> @@ -623,7 +635,9 @@ <span class="n">local_conn</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">connectivity</span><span class="o">.</span><span class="n">local_edge_connectivity</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">G</span><span class="p">)</span> <span class="o"><</span> <span class="mi">4</span><span class="p">:</span> - <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXError</span><span class="p">(</span><span class="s2">"Graph has less than four nodes."</span><span class="p">)</span> + <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXError</span><span class="p">(</span><span class="s2">"Graph has fewer than four nodes."</span><span class="p">)</span> + <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">G</span><span class="o">.</span><span class="n">edges</span><span class="p">)</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span> + <span class="k">raise</span> <span class="n">nx</span><span class="o">.</span><span class="n">NetworkXError</span><span class="p">(</span><span class="s2">"Graph has fewer that 2 edges"</span><span class="p">)</span> <span class="c1"># Instead of choosing uniformly at random from a generated edge list,</span> <span class="c1"># this algorithm chooses nonuniformly from the set of nodes with</span> <span class="c1"># probability weighted by degree.</span> diff --git a/auto_examples/algorithms/plot_betweenness_centrality.html b/auto_examples/algorithms/plot_betweenness_centrality.html index 0d5ec585..9e5c3092 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 3.381 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.830 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 92c59655..b66538cb 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 0.380 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.358 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..757286d1 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 0.101 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 74f684a5..235cdc64 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 0.071 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.070 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-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 094464b3..8bb6bde7 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 0.238 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.230 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..452e6dfb 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 0.057 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 623ac24c..3fa49c83 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: 1.7256 seconds - Betweenness centrality for node 0: 0.06802 + Time: 1.7270 seconds + Betweenness centrality for node 0: 0.16254 Non-Parallel version - Time: 2.7711 seconds - Betweenness centrality for node 0: 0.06802 + Time: 2.7598 seconds + Betweenness centrality for node 0: 0.16254 Computing betweenness centrality for: -Graph with 1000 nodes and 4870 edges +Graph with 1000 nodes and 5013 edges Parallel version - Time: 2.0861 seconds - Betweenness centrality for node 0: 0.00115 + Time: 2.1628 seconds + Betweenness centrality for node 0: 0.00323 Non-Parallel version - Time: 3.6317 seconds - Betweenness centrality for node 0: 0.00115 + Time: 3.6820 seconds + Betweenness centrality for node 0: 0.00323 Computing betweenness centrality for: Graph with 1000 nodes and 2000 edges Parallel version - Time: 1.4399 seconds - Betweenness centrality for node 0: 0.00189 + Time: 1.4452 seconds + Betweenness centrality for node 0: 0.01172 Non-Parallel version Time: 2.5251 seconds - Betweenness centrality for node 0: 0.00189 + Betweenness centrality for node 0: 0.01172 </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 19.471 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 19.774 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 aa5670b8..430d73a6 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 0.944 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.988 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..8ea0f75a 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 0.164 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 5653c78c..1b665e4d 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 0.640 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 0.651 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 1bf6ba23..63e73f9e 100644 --- a/auto_examples/algorithms/sg_execution_times.html +++ b/auto_examples/algorithms/sg_execution_times.html @@ -463,31 +463,31 @@ <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>00:25.750</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p> +<p><strong>00:26.527</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>00:19.471</p></td> +<td><p>00:19.774</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>00:03.381</p></td> +<td><p>00:03.830</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>00:00.944</p></td> +<td><p>00:00.988</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>00:00.640</p></td> +<td><p>00:00.651</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>00:00.380</p></td> +<td><p>00:00.358</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>00:00.238</p></td> +<td><p>00:00.230</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> @@ -495,11 +495,11 @@ <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>00:00.164</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>00:00.101</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>00:00.071</p></td> +<td><p>00:00.070</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>00:00.057</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 47f4d1f0..7e60a0f6 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 0.084 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.085 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 f21f9c0c..f16d4e4c 100644 --- a/auto_examples/basic/plot_read_write.html +++ b/auto_examples/basic/plot_read_write.html @@ -545,7 +545,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.059 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.060 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-basic-plot-read-write-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/63b2264e53e5d28aeb43b6aa768515b9/plot_read_write.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_read_write.py</span></code></a></p> diff --git a/auto_examples/basic/plot_simple_graph.html b/auto_examples/basic/plot_simple_graph.html index f17dd227..ce05f6b1 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 0.376 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 0.378 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 e95b4f5f..5fe91f2c 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>00:00.519</strong> total execution time for <strong>auto_examples_basic</strong> files:</p> +<p><strong>00:00.522</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>00:00.376</p></td> +<td><p>00:00.378</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>00:00.084</p></td> +<td><p>00:00.085</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_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>00:00.059</p></td> +<td><p>00:00.060</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/drawing/plot_chess_masters.html b/auto_examples/drawing/plot_chess_masters.html index a4df74c7..dd5dd3eb 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', 'Korchnoi, Viktor L', 'Karpov, Anatoly'] From a total of 237 different openings, the following games used the Sicilian opening @@ -702,7 +702,7 @@ findfont: Font family 'Helvetica' not found. <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.371 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.374 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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..3f747993 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 0.266 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..160b5204 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 0.254 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 53af2914..c51f6737 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 0.199 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.202 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_ego_graph.html b/auto_examples/drawing/plot_ego_graph.html index fda8da3f..c8bac49b 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 0.097 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.095 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 002470cb..b3fb0300 100644 --- a/auto_examples/drawing/plot_eigenvalues.html +++ b/auto_examples/drawing/plot_eigenvalues.html @@ -517,8 +517,8 @@ to download the full example code</p> <section class="sphx-glr-example-title" id="eigenvalues"> <span id="sphx-glr-auto-examples-drawing-plot-eigenvalues-py"></span><h1>Eigenvalues<a class="headerlink" href="#eigenvalues" title="Permalink to this heading">#</a></h1> <p>Create an G{n,m} random graph and compute the eigenvalues.</p> -<img src="../../_images/sphx_glr_plot_eigenvalues_001.png" srcset="../../_images/sphx_glr_plot_eigenvalues_001.png" alt="plot eigenvalues" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Largest eigenvalue: 1.5924617911776051 -Smallest eigenvalue: -5.239273865810698e-16 +<img src="../../_images/sphx_glr_plot_eigenvalues_001.png" srcset="../../_images/sphx_glr_plot_eigenvalues_001.png" alt="plot eigenvalues" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Largest eigenvalue: 1.592461791177574 +Smallest eigenvalue: -2.5363890312656235e-16 </pre></div> </div> <div class="line-block"> @@ -541,7 +541,7 @@ Smallest eigenvalue: -5.239273865810698e-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 0.653 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.624 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 1495873e..97afe41c 100644 --- a/auto_examples/drawing/plot_four_grids.html +++ b/auto_examples/drawing/plot_four_grids.html @@ -562,7 +562,7 @@ customize the visualization of a simple Graph comprising a 4x4 grid.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.301 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.304 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-four-grids-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/4136c066ab1d073cf527e9dc02bfec77/plot_four_grids.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_four_grids.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_house_with_colors.html b/auto_examples/drawing/plot_house_with_colors.html index a88ea746..fa33e411 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 0.071 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 252945ae..faf231c0 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 0.097 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.100 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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..ce3da2b9 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 0.180 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_node_colormap.html b/auto_examples/drawing/plot_node_colormap.html index c0c2035a..7111f1ab 100644 --- a/auto_examples/drawing/plot_node_colormap.html +++ b/auto_examples/drawing/plot_node_colormap.html @@ -526,7 +526,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.050 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.049 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-node-colormap-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/19db6fb1da12c9b9c0afca26691448c8/plot_node_colormap.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_node_colormap.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_rainbow_coloring.html b/auto_examples/drawing/plot_rainbow_coloring.html index d70f19bd..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 0.124 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_sampson.html b/auto_examples/drawing/plot_sampson.html index e3370279..0c22e33c 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 0.230 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..a849449c 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 0.076 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..9796d449 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 0.055 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 7ccd3117..b6fb7e7a 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 0.211 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.213 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 ad2acc15..30fb7240 100644 --- a/auto_examples/drawing/plot_tsp.html +++ b/auto_examples/drawing/plot_tsp.html @@ -568,7 +568,7 @@ that the traveler has to follow in order to minimize the total cost.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.083 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.082 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-tsp-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/cc9848c15dd2eeae1872b955a8f34d15/plot_tsp.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_tsp.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_unix_email.html b/auto_examples/drawing/plot_unix_email.html index 29a21ec5..afdb9b21 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 0.145 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.095 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 1352a46d..d500eaf2 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 0.079 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.080 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 93efb927..ddf89bea 100644 --- a/auto_examples/drawing/sg_execution_times.html +++ b/auto_examples/drawing/sg_execution_times.html @@ -463,75 +463,75 @@ <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>00:03.831</strong> total execution time for <strong>auto_examples_drawing</strong> files:</p> +<p><strong>00:03.771</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>00:00.653</p></td> +<td><p>00:00.624</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>00:00.371</p></td> +<td><p>00:00.374</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>00:00.301</p></td> +<td><p>00:00.304</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_custom_node_icons.html#sphx-glr-auto-examples-drawing-plot-custom-node-icons-py"><span class="std std-ref">Custom node icons</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_custom_node_icons.py</span></code>)</p></td> -<td><p>00:00.264</p></td> +<td><p>00:00.266</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>00:00.254</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>00:00.230</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>00:00.211</p></td> +<td><p>00:00.213</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>00:00.199</p></td> +<td><p>00:00.202</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>00:00.180</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>00:00.145</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>00:00.124</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_random_geometric_graph.html#sphx-glr-auto-examples-drawing-plot-random-geometric-graph-py"><span class="std std-ref">Random Geometric Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_random_geometric_graph.py</span></code>)</p></td> +<td><p>00:00.101</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>00:00.101</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_knuth_miles.html#sphx-glr-auto-examples-drawing-plot-knuth-miles-py"><span class="std std-ref">Knuth Miles</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_knuth_miles.py</span></code>)</p></td> +<td><p>00:00.100</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>00:00.097</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_ego_graph.html#sphx-glr-auto-examples-drawing-plot-ego-graph-py"><span class="std std-ref">Ego Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_ego_graph.py</span></code>)</p></td> +<td><p>00:00.095</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>00:00.097</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_unix_email.html#sphx-glr-auto-examples-drawing-plot-unix-email-py"><span class="std std-ref">Unix Email</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_unix_email.py</span></code>)</p></td> +<td><p>00:00.095</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>00:00.083</p></td> +<td><p>00:00.082</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_weighted_graph.html#sphx-glr-auto-examples-drawing-plot-weighted-graph-py"><span class="std std-ref">Weighted Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_weighted_graph.py</span></code>)</p></td> -<td><p>00:00.079</p></td> +<td><p>00:00.080</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_selfloops.html#sphx-glr-auto-examples-drawing-plot-selfloops-py"><span class="std std-ref">Self-loops</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_selfloops.py</span></code>)</p></td> -<td><p>00:00.075</p></td> +<td><p>00:00.076</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_multipartite_graph.html#sphx-glr-auto-examples-drawing-plot-multipartite-graph-py"><span class="std std-ref">Multipartite Layout</span></a> (<code class="docutils literal 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id="sphx-glr-download-auto-examples-external-plot-igraph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/a390de783800778d34acc914fb8a3f84/plot_igraph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_igraph.py</span></code></a></p> diff --git a/auto_examples/external/sg_execution_times.html b/auto_examples/external/sg_execution_times.html index 1b553c37..c7d9d82b 100644 --- a/auto_examples/external/sg_execution_times.html +++ b/auto_examples/external/sg_execution_times.html @@ -463,11 +463,11 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-external-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> 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container"> <p><a class="reference download internal" download="" href="../../_downloads/2b63e3aade568b3f182ba20240be7234/plot_delaunay.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_delaunay.py</span></code></a></p> diff --git a/auto_examples/geospatial/plot_lines.html b/auto_examples/geospatial/plot_lines.html index 43d51170..0aebffe5 100644 --- a/auto_examples/geospatial/plot_lines.html +++ b/auto_examples/geospatial/plot_lines.html @@ -598,7 +598,7 @@ Create PySAL weights (graph).</p> <span class="n">G_dual</span> <span class="o">=</span> <span class="n">W</span><span class="o">.</span><span class="n">to_networkx</span><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.254 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.376 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-geospatial-plot-lines-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/8ca1ed8a4cf00870baa5a8020931ba46/plot_lines.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_lines.py</span></code></a></p> diff --git a/auto_examples/geospatial/plot_osmnx.html b/auto_examples/geospatial/plot_osmnx.html index 1aa0954e..2a35a916 100644 --- a/auto_examples/geospatial/plot_osmnx.html +++ b/auto_examples/geospatial/plot_osmnx.html @@ -541,7 +541,7 @@ retrieve any other spatial data from OSM as geopandas GeoDataFrames. See <span class="n">ox</span><span class="o">.</span><span class="n">save_graphml</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">filepath</span><span class="o">=</span><span class="s2">"./graph.graphml"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 14.726 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 5.745 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-geospatial-plot-osmnx-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/769ba4a0ffbf9feb2f308b434010db7f/plot_osmnx.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_osmnx.py</span></code></a></p> diff --git a/auto_examples/geospatial/plot_points.html b/auto_examples/geospatial/plot_points.html index 653652ad..178b2b49 100644 --- a/auto_examples/geospatial/plot_points.html +++ b/auto_examples/geospatial/plot_points.html @@ -552,7 +552,7 @@ centroids as representative points.</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 2.988 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 2.997 seconds)</p> <div class="sphx-glr-footer 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****************** +46 (29) ***************************** +47 (32) ******************************** +48 (29) ***************************** +49 (25) ************************* +50 (27) *************************** +51 (31) ******************************* 52 (31) ******************************* -53 (34) ********************************** -54 (26) ************************** -55 (32) ******************************** -56 (16) **************** -57 (10) ********** -58 (14) ************** -59 (10) ********** -60 (13) ************* +53 (31) ******************************* +54 (32) ******************************** +55 (21) ********************* +56 (23) *********************** +57 (20) ******************** +58 (17) ***************** +59 (16) **************** +60 ( 9) ********* 61 ( 4) **** -62 ( 6) ****** -63 ( 1) * -64 ( 4) **** -65 ( 3) *** -66 ( 5) ***** -67 ( 0) -68 ( 0) -69 ( 1) * -70 ( 1) * -71 ( 0) -72 ( 0) -73 ( 1) * +62 ( 3) *** +63 ( 2) ** +64 ( 5) ***** +65 ( 4) **** +66 ( 3) *** +67 ( 4) **** +68 ( 1) * +69 ( 3) *** </pre></div> </div> <div class="line-block"> diff --git a/auto_examples/graph/plot_football.html b/auto_examples/graph/plot_football.html index 9ece2f8e..d9d65dba 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 0.341 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.555 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_triad_types.html b/auto_examples/graph/plot_triad_types.html index 1d35f5d1..ea745d44 100644 --- a/auto_examples/graph/plot_triad_types.html +++ b/auto_examples/graph/plot_triad_types.html @@ -563,7 +563,7 @@ the Orientation as Up (U), Down (D) , Cyclical (C) or Transitive (T).</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.021 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.000 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 bb96efbd..88df253b 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 0.354 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.362 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-words-py"> <div 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notranslate"><span class="pre">plot_roget.py</span></code>)</p></td> @@ -491,7 +491,7 @@ <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>00:00.110</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_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> diff --git a/auto_examples/graphviz_drawing/plot_attributes.html b/auto_examples/graphviz_drawing/plot_attributes.html index b6dea393..0236b13f 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 0.064 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.046 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_mini_atlas.html b/auto_examples/graphviz_drawing/plot_mini_atlas.html index 386e69c7..150af660 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 0.077 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 742ba524..c60ed8a8 100644 --- a/auto_examples/graphviz_drawing/sg_execution_times.html +++ b/auto_examples/graphviz_drawing/sg_execution_times.html @@ -463,11 +463,11 @@ <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>00:00.230</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> +<p><strong>00:00.214</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>00:00.077</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> @@ -475,7 +475,7 @@ <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>00:00.064</p></td> +<td><p>00:00.046</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_conversion.html#sphx-glr-auto-examples-graphviz-drawing-plot-conversion-py"><span class="std std-ref">Conversion</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_conversion.py</span></code>)</p></td> diff --git a/auto_examples/graphviz_layout/plot_atlas.html b/auto_examples/graphviz_layout/plot_atlas.html index be2afb7a..85bbc3a7 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 3.571 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.621 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-atlas-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/37c712582f2a7575f32a59a1389228a7/plot_atlas.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_atlas.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/plot_circular_tree.html b/auto_examples/graphviz_layout/plot_circular_tree.html index 78048af9..a6f75eae 100644 --- a/auto_examples/graphviz_layout/plot_circular_tree.html +++ b/auto_examples/graphviz_layout/plot_circular_tree.html @@ -510,7 +510,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.145 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.146 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-circular-tree-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/e854482dd498b1c5f7f158a5717b999d/plot_circular_tree.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_circular_tree.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/plot_decomposition.html b/auto_examples/graphviz_layout/plot_decomposition.html index 0daffa26..5aa94922 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 0.281 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.282 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 92f71834..5d316683 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 0.775 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.798 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/sg_execution_times.html b/auto_examples/graphviz_layout/sg_execution_times.html index 6316c4b2..e128d0ee 100644 --- a/auto_examples/graphviz_layout/sg_execution_times.html +++ b/auto_examples/graphviz_layout/sg_execution_times.html @@ -463,15 +463,15 @@ <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>00:05.099</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> +<p><strong>00:05.174</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>00:03.571</p></td> +<td><p>00:03.621</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>00:00.775</p></td> +<td><p>00:00.798</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> @@ -479,11 +479,11 @@ <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>00:00.281</p></td> +<td><p>00:00.282</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_circular_tree.html#sphx-glr-auto-examples-graphviz-layout-plot-circular-tree-py"><span class="std std-ref">Circular Tree</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_circular_tree.py</span></code>)</p></td> -<td><p>00:00.145</p></td> +<td><p>00:00.146</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/subclass/sg_execution_times.html b/auto_examples/subclass/sg_execution_times.html index 821daedb..9661f7dc 100644 --- a/auto_examples/subclass/sg_execution_times.html +++ b/auto_examples/subclass/sg_execution_times.html @@ -463,7 +463,7 @@ <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>00:00.144</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> diff --git a/developer/index.html b/developer/index.html index bcb7e2b1..828a899e 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 15, 2022</p> +<dd class="field-even"><p>Dec 20, 2022</p> </dd> </dl> <div class="toctree-wrapper compound"> diff --git a/developer/nxeps/nxep-0004.html b/developer/nxeps/nxep-0004.html index 2fe06280..480903ba 100644 --- a/developer/nxeps/nxep-0004.html +++ b/developer/nxeps/nxep-0004.html @@ -495,7 +495,7 @@ <article class="bd-article" role="main"> <section id="nxep-4-adopting-numpy-random-generator-as-default-random-interface"> -<span id="nxep4"></span><h1>NXEP 4 — Adopting <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> as default random interface<a class="headerlink" href="#nxep-4-adopting-numpy-random-generator-as-default-random-interface" title="Permalink to this heading">#</a></h1> +<span id="nxep4"></span><h1>NXEP 4 — Adopting <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> as default random interface<a class="headerlink" href="#nxep-4-adopting-numpy-random-generator-as-default-random-interface" title="Permalink to this heading">#</a></h1> <dl class="field-list simple"> <dt class="field-odd">Author<span class="colon">:</span></dt> <dd class="field-odd"><p>Ross Barnowski (<a class="reference external" href="mailto:rossbar%40berkeley.edu">rossbar<span>@</span>berkeley<span>.</span>edu</a>)</p> @@ -515,29 +515,29 @@ <p>Pseudo-random numbers play an important role in many graph and network analysis algorithms in NetworkX. NetworkX provides a <a class="reference internal" href="../../reference/randomness.html#randomness"><span class="std std-ref">standard interface to random number generators</span></a> -that includes support for <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> and the Python built-in <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a> module. -<a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> is used extensively within NetworkX and in several cases is the +that includes support for <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> and the Python built-in <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a> module. +<a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> is used extensively within NetworkX and in several cases is the preferred package for random number generation. -NumPy introduced a new interface in the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> package in NumPy version +NumPy introduced a new interface in the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> package in NumPy version 1.17. According to <span class="xref std std-doc">NEP19</span>, the new interface based on -<a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> -is recommended over the legacy <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> as the former has +<a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> +is recommended over the legacy <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> as the former has <a class="reference external" href="https://www.pcg-random.org/index.html">better statistical properties</a>, <span class="xref std std-doc">more features</span>, and <span class="xref std std-doc">improved performance</span>. -This NXEP proposes a strategy for adopting <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> as the +This NXEP proposes a strategy for adopting <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> as the <strong>default</strong> interface for random number generation within NetworkX.</p> </section> <section id="motivation-and-scope"> <h2>Motivation and Scope<a class="headerlink" href="#motivation-and-scope" title="Permalink to this heading">#</a></h2> -<p>The primary motivation for adopting <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> as the default +<p>The primary motivation for adopting <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> as the default random number generation engine in NetworkX is to allow users to benefit from -the improvements in <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a>, including: +the improvements in <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a>, including: - Advances in statistical quality of modern pRNG’s - Improved performance - Additional features</p> -<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> API is very similar to the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> +<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> API is very similar to the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> API, so users can benefit from these improvements without any additional changes <a class="footnote-reference brackets" href="#f1" id="id1" role="doc-noteref"><span class="fn-bracket">[</span>1<span class="fn-bracket">]</span></a> to their existing NetworkX code.</p> <p>In principle this change would impact NetworkX users that use any of the @@ -565,14 +565,14 @@ See the next section for details.</p> <p>The decorator is responsible for mapping various different inputs into an instance of a random number generator within the function. Currently, the random number generator instance that is returned is a -<a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> object:</p> +<a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> object:</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span> <span class="go">numpy.random.mtrand.RandomState</span> <span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="mi">12345</span><span class="p">))</span> <span class="go">numpy.random.mtrand.RandomState</span> </pre></div> </div> -<p>The only way to get a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instance from the random state +<p>The only way to get a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instance from the random state decorators is to pass the instance in directly:</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> <span class="gp">>>> </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">()</span> @@ -581,7 +581,7 @@ decorators is to pass the instance in directly:</p> </pre></div> </div> <p>This NXEP proposes to change the behavior so that when e.g. and integer or -<a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a> is given for the <code class="docutils literal notranslate"><span class="pre">seed</span></code> parameter, a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instance +<a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a> is given for the <code class="docutils literal notranslate"><span class="pre">seed</span></code> parameter, a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instance is returned instead, i.e.:</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="kc">None</span><span class="p">))</span> <span class="go">numpy.random._generator.Generator</span> @@ -589,7 +589,7 @@ is returned instead, i.e.:</p> <span class="go">numpy.random._generator.Generator</span> </pre></div> </div> -<p><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instances can still be used as <code class="docutils literal notranslate"><span class="pre">seed</span></code>, but they +<p><a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instances can still be used as <code class="docutils literal notranslate"><span class="pre">seed</span></code>, but they must be explicitly passed in:</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">rs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">RandomState</span><span class="p">(</span><span class="mi">12345</span><span class="p">)</span> <span class="gp">>>> </span><span class="nb">type</span><span class="p">(</span><span class="n">foo</span><span class="p">(</span><span class="n">rs</span><span class="p">))</span> @@ -606,11 +606,11 @@ thus the results of the <code class="docutils literal notranslate"><span class=" as the corresponding <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> methods.</p></li> <li><p>There are a few slight differences in method names and availability between the <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> and <code class="docutils literal notranslate"><span class="pre">Generator</span></code> APIs.</p></li> -<li><p>There is no global <code class="docutils literal notranslate"><span class="pre">Generator</span></code> instance internal to <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> as is -the case for <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>.</p></li> +<li><p>There is no global <code class="docutils literal notranslate"><span class="pre">Generator</span></code> instance internal to <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> as is +the case for <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>.</p></li> </ol> -<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> interface breaks the stream-compatibility -guarantee that <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> upheld of exact reproducibility of +<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> interface breaks the stream-compatibility +guarantee that <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> upheld of exact reproducibility of values. Switching the default random number generator from <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> to <code class="docutils literal notranslate"><span class="pre">Generator</span></code> would mean functions decorated with <code class="docutils literal notranslate"><span class="pre">np_random_state</span></code> would @@ -659,18 +659,18 @@ major release, e.g. the transition from NetworkX 2.X to NetworkX 3.0.</p> <p>The second point is only a concern for users who are using <a class="reference internal" href="../../reference/generated/networkx.utils.misc.create_random_state.html#networkx.utils.misc.create_random_state" title="networkx.utils.misc.create_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_random_state</span></code></a> and the corresponding decorator <a class="reference internal" href="../../reference/generated/networkx.utils.decorators.np_random_state.html#networkx.utils.decorators.np_random_state" title="networkx.utils.decorators.np_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_random_state</span></code></a> in their own libraries. -For example, the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.RandomState.randint.html#numpy.random.RandomState.randint" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState.randint</span></code></a> method has been replaced -by <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.integers.html#numpy.random.Generator.integers" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator.integers</span></code></a>. +For example, the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.RandomState.randint.html#numpy.random.RandomState.randint" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState.randint</span></code></a> method has been replaced +by <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generated/numpy.random.Generator.integers.html#numpy.random.Generator.integers" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator.integers</span></code></a>. Thus any code that uses <code class="xref py py-obj docutils literal notranslate"><span class="pre">create_random_state</span></code> or <code class="xref py py-obj docutils literal notranslate"><span class="pre">create_py_random_state</span></code> and relies on the <code class="docutils literal notranslate"><span class="pre">randint</span></code> method of the returned rng would result in an <a class="reference external" href="https://docs.python.org/3/library/exceptions.html#AttributeError" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">AttributeError</span></code></a>. This can be addressed with a compatiblity class similar to the <code class="xref py py-obj docutils literal notranslate"><span class="pre">networkx.utils.misc.PythonRandomInterface</span></code> class, which provides a compatibility -layer between <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a> and <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>.</p> +layer between <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a> and <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>.</p> <p><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_random_state</span></code> currently returns the global <code class="docutils literal notranslate"><span class="pre">numpy.random.mtrand._rand</span></code> <code class="xref py py-obj docutils literal notranslate"><span class="pre">RandomState</span></code> instance when the input is <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a> or the <code class="docutils literal notranslate"><span class="pre">numpy.random</span></code> module. -By switching to <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a>, this will no longer be possible as -there is no global, internal <code class="xref py py-obj docutils literal notranslate"><span class="pre">Generator</span></code> instance in the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> module. +By switching to <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a>, this will no longer be possible as +there is no global, internal <code class="xref py py-obj docutils literal notranslate"><span class="pre">Generator</span></code> instance in the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> module. This should have no effect on users, as <code class="docutils literal notranslate"><span class="pre">seed=None</span></code> currently does not guarantee reproducible results.</p> </section> @@ -678,8 +678,8 @@ guarantee reproducible results.</p> <h2>Detailed description<a class="headerlink" href="#detailed-description" title="Permalink to this heading">#</a></h2> <p>This NXEP proposes to change the default random number generator produced by the <a class="reference internal" href="../../reference/generated/networkx.utils.misc.create_random_state.html#networkx.utils.misc.create_random_state" title="networkx.utils.misc.create_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">create_random_state</span></code></a> function (and the related -decorator <a class="reference internal" href="../../reference/generated/networkx.utils.decorators.np_random_state.html#networkx.utils.decorators.np_random_state" title="networkx.utils.decorators.np_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_random_state</span></code></a>) from a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> -instance to a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instance when the input to the +decorator <a class="reference internal" href="../../reference/generated/networkx.utils.decorators.np_random_state.html#networkx.utils.decorators.np_random_state" title="networkx.utils.decorators.np_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_random_state</span></code></a>) from a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> +instance to a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instance when the input to the function is either an integer or <a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a>.</p> </section> <section id="related-work"> @@ -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 15, 2022</p> +<dd class="field-even"><p>Dec 20, 2022</p> </dd> </dl> <p>NetworkX is a Python package for the creation, manipulation, and study diff --git a/reference/algorithms/generated/networkx.algorithms.smallworld.lattice_reference.html b/reference/algorithms/generated/networkx.algorithms.smallworld.lattice_reference.html index ca8644c6..d64546b7 100644 --- a/reference/algorithms/generated/networkx.algorithms.smallworld.lattice_reference.html +++ b/reference/algorithms/generated/networkx.algorithms.smallworld.lattice_reference.html @@ -567,7 +567,7 @@ <dl class="field-list simple"> <dt class="field-odd">Parameters<span class="colon">:</span></dt> <dd class="field-odd"><dl class="simple"> -<dt><strong>G</strong><span class="classifier">graph</span></dt><dd><p>An undirected graph with 4 or more nodes.</p> +<dt><strong>G</strong><span class="classifier">graph</span></dt><dd><p>An undirected graph.</p> </dd> <dt><strong>niter</strong><span class="classifier">integer (optional, default=1)</span></dt><dd><p>An edge is rewired approximatively niter times.</p> </dd> @@ -586,6 +586,12 @@ See <a class="reference internal" href="../../randomness.html#randomness"><span </dd> </dl> </dd> +<dt class="field-odd">Raises<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>NetworkXError</dt><dd><p>If there are fewer than 4 nodes or 2 edges in <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code></p> +</dd> +</dl> +</dd> </dl> <p class="rubric">Notes</p> <p>The implementation is adapted from the algorithm by Sporns et al. <a class="reference internal" href="#rc5083992428c-1" id="id1">[1]</a>. diff --git a/reference/algorithms/generated/networkx.algorithms.smallworld.random_reference.html b/reference/algorithms/generated/networkx.algorithms.smallworld.random_reference.html index 5fffcd50..422903e1 100644 --- a/reference/algorithms/generated/networkx.algorithms.smallworld.random_reference.html +++ b/reference/algorithms/generated/networkx.algorithms.smallworld.random_reference.html @@ -584,6 +584,12 @@ See <a class="reference internal" href="../../randomness.html#randomness"><span </dd> </dl> </dd> +<dt class="field-odd">Raises<span class="colon">:</span></dt> +<dd class="field-odd"><dl class="simple"> +<dt>NetworkXError</dt><dd><p>If there are fewer than 4 nodes or 2 edges in <code class="xref py py-obj docutils literal notranslate"><span class="pre">G</span></code></p> +</dd> +</dl> +</dd> </dl> <p class="rubric">Notes</p> <p>The implementation is adapted from the algorithm by Maslov and Sneppen diff --git a/reference/generated/networkx.utils.decorators.np_random_state.html b/reference/generated/networkx.utils.decorators.np_random_state.html index ddeb4694..443e4f3d 100644 --- a/reference/generated/networkx.utils.decorators.np_random_state.html +++ b/reference/generated/networkx.utils.decorators.np_random_state.html @@ -522,18 +522,18 @@ <dl class="py function"> <dt class="sig sig-object py" id="networkx.utils.decorators.np_random_state"> <span class="sig-name descname"><span class="pre">np_random_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">random_state_argument</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/networkx/utils/decorators.html#np_random_state"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#networkx.utils.decorators.np_random_state" title="Permalink to this definition">#</a></dt> -<dd><p>Decorator to generate a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instance.</p> +<dd><p>Decorator to generate a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instance.</p> <p>The decorator processes the argument indicated by <code class="xref py py-obj docutils literal notranslate"><span class="pre">random_state_argument</span></code> using <code class="xref py py-func docutils literal notranslate"><span class="pre">nx.utils.create_random_state()</span></code>. -The argument value can be a seed (integer), or a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> -instance or (<a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a>). The latter options use the glocal -random number generator used by <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a>. -The result is a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instance.</p> +The argument value can be a seed (integer), or a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> +instance or (<a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">None</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a>). The latter options use the glocal +random number generator used by <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a>. +The result is a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instance.</p> <dl class="field-list simple"> <dt class="field-odd">Parameters<span class="colon">:</span></dt> <dd class="field-odd"><dl class="simple"> <dt><strong>random_state_argument</strong><span class="classifier">string or int</span></dt><dd><p>The name or index of the argument to be converted -to a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instance.</p> +to a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instance.</p> </dd> </dl> </dd> diff --git a/reference/generated/networkx.utils.misc.create_random_state.html b/reference/generated/networkx.utils.misc.create_random_state.html index 3799ee6b..1e84053e 100644 --- a/reference/generated/networkx.utils.misc.create_random_state.html +++ b/reference/generated/networkx.utils.misc.create_random_state.html @@ -528,8 +528,8 @@ depending on input.</p> <dt class="field-odd">Parameters<span class="colon">:</span></dt> <dd class="field-odd"><dl class="simple"> <dt><strong>random_state</strong><span class="classifier">int or NumPy RandomState or Generator instance, optional (default=None)</span></dt><dd><p>If int, return a numpy.random.RandomState instance set with seed=int. -if <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instance, return it. -if <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instance, return it. +if <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instance, return it. +if <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instance, return it. if None or numpy.random, return the global random number generator used by numpy.random.</p> </dd> diff --git a/reference/index.html b/reference/index.html index a9940f00..69a45d4d 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 15, 2022</p> +<dd class="field-even"><p>Dec 20, 2022</p> </dd> </dl> </div></blockquote> diff --git a/reference/introduction-7.hires.png b/reference/introduction-7.hires.png Binary files differindex 53cc5d7a..b85eefba 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 fbfb1345..71ec386f 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 8bb8edae..7d2313f3 100644 --- a/reference/introduction-7.png +++ b/reference/introduction-7.png diff --git a/reference/introduction.ipynb b/reference/introduction.ipynb index 91fb240d..16153f65 100644 --- a/reference/introduction.ipynb +++ b/reference/introduction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "78882f52", + "id": "70823638", "metadata": {}, "source": [ "## Introduction\n", @@ -34,7 +34,7 @@ { "cell_type": "code", "execution_count": null, - "id": "b1e0dcc9", + "id": "b5fa0c35", "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "5dfeb316", + "id": "ab9af010", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f036b34a", + "id": "cf1980fc", "metadata": {}, "outputs": [], "source": [ @@ -94,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "62377211", + "id": "55f31619", "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": "35fc0967", + "id": "87c124c8", "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "b59836b4", + "id": "c085d136", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -214,7 +214,7 @@ { "cell_type": "code", "execution_count": null, - "id": "a0af949c", + "id": "c17b567e", "metadata": {}, "outputs": [], "source": [ @@ -225,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "0b169000", + "id": "04edde96", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -234,7 +234,7 @@ { "cell_type": "code", "execution_count": null, - "id": "af2e02a5", + "id": "2d632a67", "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "0f49dfeb", + "id": "fed905e5", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -311,7 +311,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d99a99dd", + "id": "be556332", "metadata": {}, "outputs": [], "source": [ @@ -323,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "98b46216", + "id": "b3b15584", "metadata": {}, "source": [ "# Drawing\n", @@ -344,7 +344,7 @@ { "cell_type": "code", "execution_count": null, - "id": "11a0be27", + "id": "fba91ea0", "metadata": {}, "outputs": [], "source": [ @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "a3e7daf4", + "id": "4dc3c4a1", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -398,7 +398,7 @@ { "cell_type": "code", "execution_count": null, - "id": "2af1ffc6", + "id": "e319f22c", "metadata": {}, "outputs": [], "source": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "95c97c18", + "id": "ad581721", "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": "fbdfaf69", + "id": "389d61af", "metadata": {}, "outputs": [], "source": [ diff --git a/reference/introduction_full.ipynb b/reference/introduction_full.ipynb index a153c1db..a4a8c9ac 100644 --- a/reference/introduction_full.ipynb +++ b/reference/introduction_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "78882f52", + "id": "70823638", "metadata": {}, "source": [ "## Introduction\n", @@ -34,13 +34,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "b1e0dcc9", + "id": "b5fa0c35", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:57.025156Z", - "iopub.status.busy": "2022-12-15T16:09:57.024939Z", - "iopub.status.idle": "2022-12-15T16:09:57.095130Z", - "shell.execute_reply": "2022-12-15T16:09:57.094503Z" + "iopub.execute_input": "2022-12-20T11:40:15.823150Z", + "iopub.status.busy": "2022-12-20T11:40:15.822808Z", + "iopub.status.idle": "2022-12-20T11:40:15.892563Z", + "shell.execute_reply": "2022-12-20T11:40:15.891938Z" } }, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "markdown", - "id": "5dfeb316", + "id": "ab9af010", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -89,13 +89,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "f036b34a", + "id": "cf1980fc", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:57.098384Z", - "iopub.status.busy": "2022-12-15T16:09:57.098162Z", - "iopub.status.idle": "2022-12-15T16:09:57.101457Z", - "shell.execute_reply": "2022-12-15T16:09:57.100828Z" + "iopub.execute_input": "2022-12-20T11:40:15.895911Z", + "iopub.status.busy": "2022-12-20T11:40:15.895541Z", + "iopub.status.idle": "2022-12-20T11:40:15.899060Z", + "shell.execute_reply": "2022-12-20T11:40:15.898448Z" } }, "outputs": [], @@ -108,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "62377211", + "id": "55f31619", "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": "35fc0967", + "id": "87c124c8", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:57.104166Z", - "iopub.status.busy": "2022-12-15T16:09:57.103954Z", - "iopub.status.idle": "2022-12-15T16:09:57.107235Z", - "shell.execute_reply": "2022-12-15T16:09:57.106619Z" + "iopub.execute_input": "2022-12-20T11:40:15.901893Z", + "iopub.status.busy": "2022-12-20T11:40:15.901694Z", + "iopub.status.idle": "2022-12-20T11:40:15.904888Z", + "shell.execute_reply": "2022-12-20T11:40:15.904301Z" } }, "outputs": [], @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "b59836b4", + "id": "c085d136", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -235,13 +235,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "a0af949c", + "id": "c17b567e", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:57.110117Z", - "iopub.status.busy": "2022-12-15T16:09:57.109918Z", - "iopub.status.idle": "2022-12-15T16:09:57.113026Z", - "shell.execute_reply": "2022-12-15T16:09:57.112424Z" + "iopub.execute_input": "2022-12-20T11:40:15.907610Z", + "iopub.status.busy": "2022-12-20T11:40:15.907405Z", + "iopub.status.idle": "2022-12-20T11:40:15.910514Z", + "shell.execute_reply": "2022-12-20T11:40:15.909910Z" } }, "outputs": [], @@ -253,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "0b169000", + "id": "04edde96", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -262,13 +262,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "af2e02a5", + "id": "2d632a67", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:57.115703Z", - "iopub.status.busy": "2022-12-15T16:09:57.115492Z", - "iopub.status.idle": "2022-12-15T16:09:57.119229Z", - "shell.execute_reply": "2022-12-15T16:09:57.118617Z" + "iopub.execute_input": "2022-12-20T11:40:15.913408Z", + "iopub.status.busy": "2022-12-20T11:40:15.913197Z", + "iopub.status.idle": "2022-12-20T11:40:15.916933Z", + "shell.execute_reply": "2022-12-20T11:40:15.916336Z" } }, "outputs": [], @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "0f49dfeb", + "id": "fed905e5", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -346,13 +346,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "d99a99dd", + "id": "be556332", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:57.122042Z", - "iopub.status.busy": "2022-12-15T16:09:57.121840Z", - "iopub.status.idle": "2022-12-15T16:09:57.126072Z", - "shell.execute_reply": "2022-12-15T16:09:57.125445Z" + "iopub.execute_input": "2022-12-20T11:40:15.919679Z", + "iopub.status.busy": "2022-12-20T11:40:15.919467Z", + "iopub.status.idle": "2022-12-20T11:40:15.923685Z", + "shell.execute_reply": "2022-12-20T11:40:15.923073Z" } }, "outputs": [ @@ -373,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "98b46216", + "id": "b3b15584", "metadata": {}, "source": [ "# Drawing\n", @@ -394,19 +394,19 @@ { "cell_type": "code", "execution_count": 7, - "id": "11a0be27", + "id": "fba91ea0", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:57.130192Z", - "iopub.status.busy": "2022-12-15T16:09:57.129981Z", - "iopub.status.idle": "2022-12-15T16:09:57.669598Z", - "shell.execute_reply": "2022-12-15T16:09:57.667825Z" + "iopub.execute_input": "2022-12-20T11:40:15.928124Z", + "iopub.status.busy": "2022-12-20T11:40:15.927911Z", + "iopub.status.idle": "2022-12-20T11:40:16.465653Z", + "shell.execute_reply": "2022-12-20T11:40:16.464246Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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ZMGCAUwr2HhXXli1bnF6ncLvq1asze/ZsFi5cyOjRow2LQwg9XL0KL78MLVuqzXTi49XVAYP/6nucp55SVwXmzYO1a9XEa8UKo6NyH5IMOMHhw4epW7cuH3zwAe+++y5btmyhbNmyRocFQGxsLCkpKYYmAwDPPfccH3/8MR9++CFz5swxNBbh5ZKTYdcu9fq8XbvU/35Cv/8OISGwYAF8/z2sWgVFimgWqbiLyaQezYyPV+9yaNMGXnoJrlzJxKAaPg8uTRG6cTgcypQpUxR/f38lKChI2bp1q9Eh3eOzzz5TsmXLpqSmphodiuJwOJTevXsrvr6+SlRUlNHhCG+yd6+iDByoKKVKKYrJpChqcbr6j8mk/v+BA9XPewwpKeqng6KEhyvK8eP6hq+XmBj1a4iJMTqSjHM4FGX6dEXJmVNRihZVlHXrMvBijZ8HdyDJgE7Onj2rtGzZUgGUV155RUlKSjI6pPtq27atEhERYXQY/0pNTVXCw8OVgIAA5ciRI0aHIzzdsWOKEhmpvslbLHe+6d/9T/rHIyPV1z3A5s2KUrq0omTLpihffaUoaWlO/Ho05s7JQLoTJxSlYUP16xgwQFGSkx/yyTo8D+5CkgEdLFmyRMmfP79SoEABZcWKFUaH80BpaWlKvnz5lFGjRhkdyh0SExOV0qVLK2XLllUuXbpkdDjCU33/vaJkzfroN/37fRPImlV9/W1u3FCUYcMUxWxWlFq1FOXgQYO+Lg15QjKgKGpC9s03aoJWqpSi3HfhUePnwd1IMqChq1evKi+99JICKG3atFHOnz9vdEgPFRcXpwDK+vXrjQ7lHocOHVLy5cunNGzYULl586bR4QhPM2ZMxt7wH/TPmDGKoqjfLCtUUBRfX0X55BNFsdsN/vo04inJQLpDhxSlTh11pX/oUEW5fv2fD2j8PLgjSQY0EhUVpZQoUULJkSOHMn36dMXhcBgd0iNNmjRJsVgsSvJD182M88cffyhZsmRR+vbt6xa/n8JNfP+9Nm/8//yzrM00xWJRlNBQRdmzx+gvTluelgwoipqojRunJm7lyinK8Xe1fR6UadOM/hKfiJwmyKSbN28yYsQIGjRowNNPP83u3bt56aWXDD8y+DiioqKoVq0a2bNnNzqU+2rQoAHff/8906ZN4/PPPzc6HOEJjh+HgQM1G04BIpe/zrj+x9m2TT05IFybjw+8/TbYbFDKfJyCYweiaDnB66+rz5mbkWQgE/bt20etWrUYP348H374IVarlZIlSxod1mNRFAWr1Wr4kcJH6dWrFyNGjOA///kPS5cuNToc4e769QO7XbPhTEBWHzuDD/TD11ezYYUTVKgAy57uh6/ZjqY/utnt6nPmZrwqGUhJtbP3zBViE/5m75krpKQ+2ZuCw+Hg66+/pmrVqty4cYPo6GhGjBhhSAe/J3Xs2DHOnDlDWFiY0aE80ocffshzzz1H9+7diYmJMToc4a727YN16zRNBgBMaXZ13P37NR1X6GzfPszr1+Hj0PZ5wO6ez4PF6AD0dvh8EnOjE9hw8AIJl67dsRxkAgLz+RNRtgDdawZSuuCj7w39888/6d27N+vXr2fgwIF8+umnZMuWTbf49WK1WjGZTNStW9foUB7JbDYza9YswsPDad26Ndu3b+eZZ54xOizhbqZMAYtF82QAUMedPBm+/lr7sYU+5Hm4g0lRFE23S1zFqUvXGLEkjqgjF/Exm0hzPPjLTP94WFB+PmofQtF8/vf9vIULF/Lqq6/i7+/PDz/8QJMmTfQKX3cvvfQSNpuNXbt2GR3KYzt37hw1a9YkX758REVFkSNHDqNDEu4kKAiOHtV3/MOH9RvfADab2skvJgaqVDE6Go3J83AHj9wmWLAjgcYTNrLlmHpTxcMSgds/vuVYIo0nbGTBjoQ7Pn758mW6d+9Oly5daNKkCXFxcW6dCABuUS9wt0KFCrFy5UqOHj1Kt27dSEtLMzok4S6SkuDYMX3nOHrUc1vVehp5Hu7hccnAxA2HGb44jlS745FJwN3SHAqpdgfDF8cxcYOa0f3++++EhISwatUq5syZw4IFC8iXL58eoTvN6dOnOXr0qFvUC9wtJCSEhQsXsmrVKv7zn/8YHY5wF0ePqge/9KQocOSIvnMIbcjzcA+PSgYW7Ejgs7WHNBnrs7WH6PD2ZzRq1IigoCD27NlD9+7d3eLI4KNERUUBuGUyANC8eXO++uorvvjiC6ZOnWp0OMIdpKZ61jwic+R5uIfHFBCeunSNUcv3ajegohCjlGTkuK8ZNeQ1zGbPyZuioqIoU6YMhQoVMjqUJ/b6669z8OBBXnvtNUqWLElkZKTRIQlX5ufnWfOIzJHn4R4e8x1uxJI47BncFngokwkfXz+O5avhUYkAuGe9wP1MmDCBJk2a0LFjR/bt22d0OMKVBQWp99vqyWRS5xGuT56He3jEd7nD55OIOnIxwzUCj+JQIOrIRY5cSNJ0XCMlJiYSHx/vtlsEt7NYLCxYsIDAwEBatWrFhQsXjA5JuKocOUDvhmClSqnzCNcnz8M9PCIZmBudgI9ZnyzPx2xizraER3+im9i8eTOAR6wMAOTKlYuVK1dy7do12rVrx40bN4wOSbiqFi3U8996sFigeXN9xhb6kOfhDh6RDGw4eEHzVYF0aQ6FDYc85ydOq9VK0aJFKVasmNGhaKZYsWIsX76c2NhYXnrpJTy0dYbIrFdf1afBDKjj9u+vz9hCH/I83MHtk4HkVDsJl67pOkdC4rUnbl3satLrBTzhVMTtatSowaxZs5g/fz4ffPCB0eEIV1S+PERGav7ToB0L1+pGQrlymo4rdKbT84DFoo7rZs+D2ycDJxNTtL1x6j4U4ERiis6z6C85ORmbzeYR9QL306lTJ8aOHcvo0aOZN2+e0eEIVzR1qqZv/gpgN1moFjOVb78Fh0OzoYUzaPw8AOp4bnjk2e2TgZt25/ztc9Y8etq6dStpaWkeUy9wP++88w69evWid+/e/9ZHCPGvEiXgm280G84E8M1EwnuX4LXXoGlTSPCcEiPPp/HzAMDEieq4bsbtkwFfi3O+BGfNoyer1Ur+/Pl59tlnjQ5FNyaTie+++45atWrRrl07jundclS4n759YcwYbcYaO5asr/Xh229h7Vo4cABCQuDHH/VvcCc0ovHzQJ8+2ozlZG7/Ha54QHZt76K+D9M/87g7q9VKWFiYx9UL3M3X15fFixeTJ08eWrVqxeXLl40OSbiad9+F77+HrFkzvkxssaivmzYNRoz4939HRkJcHHToAL17Q9u2cO6cxnELfejwPLgbt08GsvtZCHzALYNaCQzwJ7ufezdrTE1NJTo62qO3CG4XEBDAqlWrOHfuHJ06deLWrVtGhyRcTd++sG8fRESo//2obwLpH4+IUF93n58A8+SBH36AZctg+3aoUAEWLtQ2bKETHZ4Hd+L2yQBARNkCuvYZiChTQJexnWnHjh2kpqZ6TTIAUKZMGRYvXswff/zBwIED5cihuFeJEur6/t696lGw+3WmS+8k17+/+qa/du0j94TbtIH4eGjcGLp0gc6d4eJFHb8OoY3HeB4UTBw1BZHW7/GfB3fgEclA95qBuvYZ6FErUJexnclqtZIzZ04qVapkdChOFR4eznfffcfUqVOZMGGC0eEIV1W+PHz9tXr//NWr7J8XS022sX9eLFy9qv7/r7/O0HGx/PnVVYEFC2D9eggOhuXLdfwahHYe8jzEbLhKkHKY7d0z9jy4Oo9IBkoXzElYUH7NVwd8zCbCgvITVCCnpuMawWq1UrduXXx8fIwOxel69+7N8OHDGTp0KMvl3Vg8So4cXC9bme3U5HrZypluKdu5s/qDZvXqah1B795w5Yo2oQonuOt5qFwvBzlzwoYNRgemLY9IBgA+ah+CReNkwGI28VH7EE3HNILdbmfz5s1etUVwt7Fjx9KhQwe6du1KbGys0eEIL1OokLoq8MMPsHixukqwbp3RUYknYbFAWBj88YfRkWjLY5KBovn8eb9NBU3H/KBNBYrqXJzoDLt37yY5OdmrkwGz2cysWbMoX748rVu35vTp00aHJLyMyQQvvqieOHj2WWjSBAYMgORkoyMTGRURAZs3w82bRkeiHY9JBgC6VA9kaJMymRxFrT0IStnH89WKZj4oF2C1WsmaNSvVqlUzOhRD+fv7s3z5csxmM61btyYlxf27Sgr3ExgIa9bApEkwcyZUqgRRUUZHJTIiIgKuXVNPjHgKj0oGAF6PKM0nHULws5gzXEPgYzbhZ/Gh7dPJ/PbNfxg2bJhHVKBbrVZq1qyJn5+f0aEY7umnn2bFihUcPnyY7t27k5aWZnRIwguZzeqqwO7dULgwNGgAQ4fC9etGRyYeR+XKkDu3Z20VeFwyAOoKwfrBDahTMgDgkUlB+sfrlAxg/eAGfDWoM1999RXjx4/n008/1T1ePTkcDqKiorx6i+BulSpVYsGCBaxYsYLhw4cbHY7wYkFB6jeU8ePVLrZVqsCOHUZHJR7Fxwfq1/esIkKPTAZArSGY3acm696sT8+axSDpwj39QU1AsQB/etYsxvrB9Zndp+a/NQKDBg1i9OjRvPPOO0yZMsWAr0AbBw4cIDExUZKBu7Rs2ZIvvviCzz77jO+//97ocIQX8/GBIUPAZlMPLtSuDf/9r2ftR3uiiAjYsgVSU42ORBvu3VbvMZQumJOBdQvxftuX+GH2PKo3bMFNuwNfi5niAdkf2llw5MiRXLp0iQEDBpA7d266du3qxMi1YbVasVgs1K5d2+hQXM6gQYM4dOgQAwYMoGTJkjRq1MjokIQXK19e/ebyySfwwQewYgXMmgUVKxodmbif8HC4cQOio9VVAnfnsSsDt7PZbADUqVGVCoVzExqYlwqFcz+yxbDJZGLChAm88MILvPDCC6xatcoZ4WrKarVSpUoVsmd3/7sVtGYymfjqq69o1KgRzz33HPv37zc6JOHlsmRRVwW2b1evQ65WDT7+GOx2oyMTd6tUCfLm9ZytAq9IBmJiYsiZMydBQUEZfq3ZbGbatGm0atWKjh07EuVGZb+KomC1WmWL4CEsFgsLFy7kmWeeoVWrVlyUnrHCBYSGqrUDQ4fCe+9B3brqjYjCdZjNauGnJANuJCYmhipVqmA2P9mXa7FYmD9/PnXq1KFVq1b/rjS4uhMnTnD69GlJBh4hd+7crFy5kuTkZNq1a0eqp2wCCrfm5wcffaSeZ798WU0QvvxSXTEQriEiArZtU7cL3J1XJQOZkTVrVpYuXcqzzz5Ls2bNOHjwoEbR6cdqtWIymahXr57Robi84sWLs2zZMnbu3Enfvn094kip8Ay1akFsLPTrB4MHQ8OGcPy40VEJUOsGUlNh61ajI8k8j08GLl26xPHjx6latWqmx8qZMyerV6+mQIECREZGkpCQoEGE+rFarQQHB5M3b16jQ3ELtWrVYubMmcyZM4cxY8YYHY4Q//L3V1cFNmyAkychJAS+++6eA1LCyYKDISDAM7YKPD4ZSF/S1yIZAAgICGDt2rVYLBYiIyO5cOGCJuPqQfoLZFznzp358MMPGTlyJAsWLDA6HCHuEB4Oe/ZA9+7qSkHz5vDnn0ZH5b3MZvXPxBOaD3lFMpAjRw7KlMlsm+L/V7hwYdatW8fVq1dp1qwZV1zwCrKzZ89y+PBhSQaewLvvvkvPnj158cUX2eoJ63/Co+TMCVOnwi+/qPccBAfD7NmySmCU8HC1buDaNaMjyRyPTwZiYmIIDQ194uLBBylVqhRr167lxIkTtGrVimsu9iSkn3oICwszOBL3YzKZ+P7776levTpt27bluGzQChfUrBnEx0Pr1vDCC9ChA5w/b3RU3iciAm7dUntEuDOvSAa02iK4W0hICKtXryY2NpaOHTty04VahlmtVoKCgnj66aeNDsUt+fn5sWTJEnLlykWrVq1ccvVHiLx51VWBxYvVUwfBwbBokdFReZfy5eGpp9y/bsCjk4HLly9z9OjRTJ8keJhatWqxdOlSfvvtN1544QWXufhG6gUyL3/+/KxcuZIzZ87w/PPPY5fOL8JFtW8Pe/eqnfA6dYJu3eDSJaOj8g4mk2fUDXh0MqB18eCDNG7cmPnz5/PTTz/x2muvGX4s7dKlS8TFxUkyoIFnn32Wn3/+md9//52BAwca/mcrxIM89ZS6KjB3rlpPEBwMbtg01S1FRKhdI5OTjY7kyXl0MhATE0P27NkpW7as7nN16NCBadOmMXXqVEaMGKH7fA+zefNmFEWRegGNNGzYkMmTJzNlyhS++uoro8MR4oFMJnVVYO9e9ZrdVq2gb1+4etXoyDxbRITaMnrzZqMjeXIenQzYbDYqV66Mj4+PU+br3bs3X3zxBZ988gnjxo1zypz3Y7VaKVKkCCVKlDAsBk/Tt29f3n77bd566y1WrFhhdDhCPFThwuqqwLRpsHCh2pfg99+NjspzlS0LhQq591aBRycDehYPPsjgwYP573//y7Bhwwy7Gje9XsBkMhkyv6f65JNPaNu2LV27dmXXrl0P/dyUVDt7z1whNuFv9p65Qkqq1BsI5zKZoE8f9fhhqVLQqBEMHAgpKUZH5nnS6wbcuYjQY68wvnLlCocPH+a9995z+tzvv/8+f//9N/369SN37tw8//zzTps7OTmZmJgYXnzxRafN6S3MZjNz5syhfv36tG7dmujoaAoXLvzvxw+fT2JudAIbDl4g4dI1bq8uMAGB+fyJKFuA7jUDKV0wp9PjF96peHFYvx4mTYJhw+DXX2HmTKhTx+jIPEtEBPz0EyQlqb0g3I3HrgzExsYC6HqS4EHSr8bt3r07PXr04Ndff3Xa3Nu2bcNut0u9gE6yZ8/OihUrUBSFNm3akJKSwqlL1+g5PZrIL63Mjj7JybsSAQAFOHnpGrOjTxL5pZWe06M5dcm1elMIz2U2q6sCu3ZB/vwQFqYmBp5wwY6rCA+HtDTYtMnoSJ6MxyYDMTExZMuWjWeffdaQ+c1mMzNmzKBZs2Z06NCBTU56QqKioggICKBcuXJOmc8bFS5cmJUrV3LgwAGav/4hjSdsZMuxRADSHA8/bZD+8S3HEmk8YSMLdrj2/RbCs5Qpo36z+vhj9a6DatUgJsboqDxD6dJqrYa7bhV4dDJQuXJlLBbjdkKyZMnC//73P2rWrEmrVq0euc+sBavVSlhYmOYdF8WdKleuzIvj55NQKIzUW2mPTALuluZQSLU7GL44jokbDusUpRD38vGB//xHTQJ8fdVbEUePVrvoiSdnMqlbBZIMuBibzeb04sH7yZo1K8uWLaN06dI0bdqUQ4cO6TZXamoq27Ztk/4CTrBgRwIrE/7565PJQs3P1h5ioawQCCcLDoboaHj3XRgzRk0K4uONjsq9hYeDzQbu2LDUI5OBpKQkDh065BLJAECuXLn45ZdfCAgIIDIyklOnTukyz86dO7lx44bUC+js1KVrjFq+V9MxRy7fKzUEwumyZFFXBaKj1fqBqlVh3Dh171tkXEQEOBzwz9UwbsUjk4HY2FgURTGkePBB8ufPz9q1azGZTERGRvLXX39pPkdUVBQ5cuSgcuXKmo8t/t+IJXHYM7gt8Ch2h8KIJXGajinE46paVd02ePNNGD5cLTA8LLtXGVayJBQt6p5bBR6ZDMTExJA1a1bKly9vdCh3eOaZZ1i/fj2XL1/W5epjq9VK3bp1Da2T8HSHzycRdeRihmsEHiXNoRB15CJHLiRpOq4QjytrVvj0U/Wn2r/+gkqVYMECo6NyL+58T4HHJgOVKlVyyW+KQUFBrFmzhmPHjtGmTRuuX7+uybhpaWls2rRJ6gV0Njc6AR+zPs2cfMwm5myT2gFhrLp11SOIffrA+PHq/zt71tCQ3EpEBMTGwt9/Gx1JxnhsMuAq9QL3U6lSJVatWsXOnTvp1KkTtzQo4929ezdJSUlSL6CzDQcvaL4qkC7NobDh0AVdxhYiI7Jnh2++gcmT1f9+/nmYPh3knq5Hi4hQf5+sVqMjyRiPSwaSk5M5ePCgSycDAHXq1GHx4sWsXbuWF198EYfDkanxoqKi8PPzo3r16hpFKO6WnGonQeciv4TEa9K6WLiMGjXUXyMj1QuPWrWCM2eMjcnVFS8OxYq5X92AxyUDu3btQlEUl08GAJo2bcq8efNYsGABr7/+eqaux7VardSsWZOsWbNqGKG43cnElHs6C2pNAU4kSvN44VpGjoSVK9Vjc8HBMG+erBI8TESE+9UNeFwyEBMTg5+fn8sVDz5Ix44dmTp1KpMnT+a///3vE42hKMq/zYaEfm7aM7d642rzCJERLVuqfQiaNYPu3aFTJ7XQUNwrIgJ274bERKMjeXwemQxUrFiRLFmyGB3KY+vbty/jx49n7NixfP755xl+/YEDB7h48aIUD+rM1+Kcvy7OmkeIjAoIUFcF/vc/9SffChVg6VKjo3I94eHqrxs3GhpGhnjcu46rFw8+yNChQxkxYgRDhw5l+vTpGXptVFQUPj4+1K5dW6foBEDxgOzofSm06Z95hHBlnTrB3r3qzYft28MLL7hf9byeAgPVngPutFXgUclASkoKBw4ccMtkAGDMmDH079+fV155hUWLFj3266xWK1WqVCGnO96b6Uay+1kIzOev6xyBAf5k93O9I7FC3K1gQViyBGbNguXLISQE1qwxOirX4W73FHhUMrB7924cDofbJgMmk4mJEyfSuXNnunXrxtq1ax/5GkVR2Lhxo9QLOElE2QK69hmIKFNAl7GF0IPJBD17qrUEFSqo9QT9+kGS9M4iIkL9fXGXugqPSgZiYmLw9fWlQoUKRofyxMxmMzNnzqRJkya0b9+eLVu2PPTzT548yZ9//in1Ak7SvWagrn0GetQK1GVsIfT0zDPw668wZQrMnQsVK7rXfrke3K1uwOOSgZCQEHx9fY0OJVOyZMnCTz/9RLVq1WjZsiV79ux54OdG/XMjRr169ZwVnlcrXTAnNYvlBkXbin8fs4mwoPwEFZCtHuGeTCZ1VWDPHnXPPDwcBg8GjZqsup0iRaB0affZKvC4ZMBdtwjuli1bNpYvX07JkiVp0qQJhx9wa4jVaiU4OJiAgAAnR+idoqOj2fb1IJQ0bRsDWcwmPmofoumYQhihZEn1G+CECepKQWioeiuiN3KnugGPSQauXbvGvn37PCYZAMidOze//vorefLkITIyktOnT9/zOdJfwDkUReGrr74iLCyMInmyMqxxCU3H/6BNBYrqXJwohLOYzeoNiLGxkDu3eupgxAhITTU6MucKD4f9++H8eaMjeTSPSQb27Nnj1sWDD/LUU0+xbt06FEUhMjKSixcv/vux8+fPc+jQIakX0NmVK1fo2LEjb775JoMGDWLjxo0MaFqZoU3KaDL+203K0rm61AoIz/Pss7B5M3z4IXz2GVSvrl6C5C3S6wbc4YihxyQDMTExZMmSheDgYKND0VzRokVZt24diYmJNG/enKtXrwL/Xy8gKwP6iY2NpWrVqvz2228sWbKEzz777N+GVq9HlOaTDiH4WcwZPmHgYzbhZzHzaYcQXosI0iN0IVyCxaKuCuzYoa4YVK8OY8aA3Quu4Hj6aTUhcoetAo9KBkJCQvDz8zM6FF2UKVOGNWvWcOjQIdq2bcv169exWq2UKlWKIkWKGB2ex1EUhalTp1K7dm3y5MmDzWajXbt293xel+qBrB/cgDol1ZoNn0fkBOlJQ52SAawf3EBWBITXqFQJtm+H4cNh9Gh162D/fqOj0l94uKwMOFVMTAxVqlQxOgxdVa5cmVWrVhEdHU3nzp2lv4BOkpOT6dGjB6+++ip9+/Zl8+bNlCxZ8oGfXzSfP7P71GTdm/VpGJiFW5fuvdbNBBQL8KdnzWKsH1yf2X1qSo2A8Dq+vuqWwZYtai+C0FD4/HNISzM6Mv1ERMDBg65/26NHtDq7fv06e/fupX///kaHort69erx888/07p1a9LS0hg0aJDRIXmU+Ph4OnXqxJ9//smCBQvo3LnzY7+2dMGcBKfuZ86s4Zy7+Dd/Xknlpt2Br8VM8YDs0llQiH/UqKHegPjee/D22+r9Bj/+CKVKGR2Z9m6vG+jWzchIHs4jVgb27NlDWlqaxxUPPkjz5s0ZMmQIABs3bszU1cfi/82cOZMaNWqQJUsWYmJiMpQIpLPZbFSsWJHc2bNSoXBuQgPzUqFwbkkEhLhLtmzqqsDGjepPzRUrwuTJnnc1coECUL6869cNeEQyYLPZsFgshIR41zntPHnyMHv2bEaNGmV0KG7t2rVr9OnThxdffJFu3bqxbds2ypR5spMCNpuN0NBQjSMUwnOFhanX/fbqBQMGQNOmcOqU0VFpKyLC9esGPCIZiImJITg4mKxZsxoditNYrVaaNm3KJ598wocffsiECROMDsktHTx4kFq1ajF//nx+/PFHpk2bhr//k+3lX79+nf3793t87YoQWsuRA779FtauVYsKg4PVbQNPWSWIiIAjR+DPP42O5ME8Jhnwli0CUH+S3blzJ/Xr12fYsGEMGzaMt956ix9++MHo0NzKggULqFatGrdu3WL79u306tUrU+PFxcWRlpYmyYAQTygyEuLioEMH6N0b2raFc+eMjirzGjRQf3XlrQK3TwZu3LhBfHy8V70Bb9u2Dbvd/m+zoY8//ph+/frRt29fFi9ebHB0ru/GjRsMGDCArl270qZNG3bs2KFJf4r07SpP7HUhhLPkyQM//ADLlqlHEStUgIULjY4qc/LnV694duWtArdPBuLi4rDb7V61MmC1WsmbNy/ly5cH1KuPJ02aRKdOnejatSvr1683OELXdezYMerWrcuMGTOYOnUqc+bMIUeOHJqMbbPZKF++vFdtVwmhlzZt1CuAGzeGLl2gc2e4rQGr23H1ewrcPhmIiYnBx8eHihUrGh2K06TfR2A2//8fn4+PD7NmzaJRo0a0a9eObdu2GRiha1q6dClVqlThypUrbN26lVdeeQWTKWOdAx8mNjbWq1aohNBb/vzqqsCCBbB+vVpLsHy50VE9mfBwOH4cTp40OpL7c/tkwGazUaFCBbJly2Z0KE5x8+ZNtm3bdt/7CHx9fVm0aBGhoaG0aNGCuLg4AyJ0Pbdu3WLIkCG0b9+exo0bExMTo3nF/61bt9izZ48kA0LooHNn2LtXbWXctq1aT3DlitFRZUyDBuo1z666VeD2yYC3FQ/GxMRw/fr1B15O5O/vz4oVKyhWrBhNmjTh6NGjTo7QtSQkJFC/fn2++eYbvv76a3766Sdy586t+Tz79u3j5s2bcqxQCJ0UKqSuCvzwAyxerK4SrFtndFSPL18+tSWzq24VuHUykJqaSlxcnFclA1arlezZsz/0m06ePHlYs2YNuXLlIjIykjOu3gdTJ6tXryY0NJSzZ8+yadMmBg4cqOm2wO1sNhsmk4lKlSrpMr4QQv3J+sUX1RMHzz4LTZqovQmSk42O7PGEh6vJgCsemXTrZCA+Pp5bt2551dJsVFQUderUwWJ5eEe7AgUKsG7dOm7dukVkZCSJiYlOitB4drudESNG0LJlS+rUqYPNZqNGjRq6zhkbG0uZMmXImTOnrvMIISAwENasgUmTYOZM9Sfufy5xdWkREZCQACdOGB3Jvdw6GYiJicFsNnvNT2NpaWls2rTpgVsEdwsMDGTdunVcuHCBFi1akJSUpHOExjtz5gyNGjVi3LhxfPrppyxbtox8+fLpPq/NZvOqpFQIo5nN6qrA7t1QuLC6Jz90KFy/bnRkD1a/vrq64YpbBW6fDJQvX/6JO8a5m7i4OK5cufLYyQDAs88+y5o1azhw4ADt2rXjxo0bOkZorN9++43Q0FCOHDnCH3/8wX/+8587TlzoJS0tjV27dkm9gBAGCApSi/LGj4eJE6FKFdixw+io7i9PHvWmRkkGNGaz2byuXsDX1zfDS95VqlRhxYoVbNmyha5du2K323WK0BhpaWl88MEHREZGUqlSJWJjY6lXr57T5j98+DApKSmyMiCEQXx8YMgQ9SbEHDmgdm3473/h5k2jI7tX+j0FrlY34LbJwM2bN9mzZ49XJQNRUVHUqFHjiZra1K9fn0WLFrFy5Ur69u2Lw+HQIULnu3DhAs2bN2f06NG8//77/PLLLxQoUMCpMcTGxgLIyoAQBitfHrZsgVGj4JNP1KuS9+wxOqo7RUSodxS42kEvt00G9u7dy82bN70mGVAUBavVmqEtgru1bNmSWbNmMWvWLN566y23v/o4KiqK0NBQdu/ezbp16/jvf/+Lj4+P0+Ow2WwUL17cKbUJQoiHy5JFXRXYvh0cDqhWDT7+GFxlQbRePbXewdW2Ctw2GfC24sFDhw5x4cKFTCUDAF27duXbb7/lq6++4oMPPtAoOudyOByMGzeOiIgISpcuza5du2jUqJFh8ci1xUK4ntBQtXZg6FB47z2oWxcOHDA6KsidG6pWlWRAMzExMTz77LNkz57d6FCcwmq1YjabqV27dqbHevXVV/noo48YPXo0X331lQbROc+lS5do27btv7c1rl+/nqefftqweBRFkZMEQrgoPz/46CPYvBkuX1YThC+/VFcMjOSKdQNunQx4yxYB/P+SeK5cuTQZb/jw4QwdOpQ333yTmTNnajKm3qKjowkNDWXLli2sXr2asWPHPrLfgt5OnjzJ5cuXJRkQwoXVqgWxsdCvHwweDA0bqvcEGCUiAs6ehUOHjIvhbm6ZDKT3gfemZCCz9QJ3M5lMjBs3jr59+9KnTx+WLl2q2dhaUxSFr7/+mrCwMIoUKcKuXbto3ry50WEB6hYBIMmAEC7O319dFdiwQb0sKCQEvvvOmJ/O69ZVT0C40laBWyYD+/btIzU11WuSgZMnT3Ly5ElNkwFQE4IpU6bQoUMHOnfuzO+//67p+Fq4cuUKnTp14o033mDgwIFs3LiRokWLGh3Wv2w2G4UKFaJQoUJGhyKEeAzh4eoJg+7d1ZWC5s3V6n5nyplTvXTJlS4tcstkICYmBpPJROXKlY0OxSmi/umzqcfZeR8fH+bMmUN4eDht2rRh+/btms/xpGJjY6latSrr169n8eLFfP7552TJksXosO4g9QJCuJ+cOWHqVPjlF/Weg+BgmD3buasErlY34LbJQNmyZcmRI4fRoThFVFQU5cuXJ3/+/LqM7+vry+LFi6lUqRLNmzcnPj5el3kel6IoTJ06ldq1a5M7d25sNhvt27c3NKYHiY2NlWRACDfVrBnEx0Pr1vDCC9ChA5w/75y5w8PVufbvd858j+K2yYC3bBGA9vUC95M9e3ZWrlzJM888Q5MmTThuUHVNcnIyPXr04NVXX6VPnz5s3ryZkiVLGhLLo5w9e5Zz585JMiCEG8ubV10VWLxYPXUQHAyLFuk/b926ak8EV9kqcLtkwG63s3v3bq9JBi5cuMCBAwcICwvTfa68efOydu1asmfPTuPGjTl79qzuc94uPj6e6tWrs3z5cubPn8+kSZOeqNuis6QXD0qPASHcX/v2sHeveplQp07QrRtcuqTffNmzqx0SXaWI0O2Sgf3793Pjxg2vSQbS6wWckQwAFCxYkHXr1pGamkqTJk249Bh/G1JS7ew9c4XYhL/Ze+YKKakZb/U1c+ZMatSoQZYsWdi5cyddunR5kvCdymazkTdvXooVK2Z0KEIIDTz1lLoqMHeuWk8QHAyrVuk3X3i4ujJgdN8DAGMPaT+BmJgYwHt+GouKiqJEiRJOraAvXrw469atIywsjBYtWrB+/fp76jMOn09ibnQCGw5eIOHSNW6vgTEBgfn8iShbgO41AyldMOcD57p27RoDBw5kxowZ9OnTh6+//tptbqFMrxcwmUxGhyKE0IjJpK4KhIdD377QqhW0bavPXBERMHYs7NunJh5GcruVgZiYGMqUKUPOnA/+BuNJnFEvcD/lypXj119/Zd++fbRv357U1FQATl26Rs/p0UR+aWV29ElO3pUIACjAyUvXmB19ksgvrfScHs2pS9fumePgwYPUqlWL+fPn8+OPPzJt2jS3SQRAThII4ckKF1ZXBaZNg7Vr1f+n9WGr2rXB19c1tgrcMhnwli2CK1eusGvXLqdtEdytWrVqLF++nKioKLp168bcbSdoPGEjW44lApDmePiZmPSPbzmWSOMJG1mwI+Hfjy1cuJBq1apx69Yttm/fTq9evfT7QnSQmJjIyZMnvWaFSghvZDJBnz7wv/+p/92/PwwcCCkp2ozv7w81a0oykGF2u51du3Z5TTKwZcsWFEUxZGUgXXh4OD/99BO/n/fl3WV7SbU7HpkE3C3NoZBqdzB8cRxfrjvAa6+9RpcuXWjTpg07duwg2Oj1sSeQfm2xrAwI4fkKF1Z/ffttmD4dKldWr0rWQkQEbNxofN2AWyUDBw8e5Pr1616TDFitVgoVKkRQUJChcaQUqkTusB6ajPXl70eZt+0EU6dOZc6cOW7bKyI2NpYcOXJQunRpo0MRQjhJly6waxfkzw9hYTBsGNy4kbkxIyLUUwtxcZqE+MTcKhnwtuLB9HoBIwvUTl26xqjle7UbUFF4qtkAmnfs4daFdzabjcqVK2M2u9VfISFEJpUpA5s2wccfq3cdVKsG/3xreiK1aqm3Kxq9VeBW72QxMTGULl2a3LlzGx2K7q5fv86OHTsMqxdIN2JJHPYMbgs8lMlEGiZGLDE4Dc4km83mNUmpEOJOPj7wn/+oSYCvr/oNffRouHUr42NlzaoWEkoykAExMTFes0cbHR3NrVu3DK0XOHw+iagjFzNcI/AoaQ6FqCMXOXIhSdNxnSUpKYlDhw55zbPoEpKT1fXZ6Gj11+RkoyMSguBg9ZF8910YM0ZNCp6km3tEBFitkHbFuOfcbZKBtLS0fy+u8QZWq5U8efIYWlw3NzoBH7M+S/k+ZhNztiU8+hNd0O7duwEpHtTdvn0waBAEBUGuXBAaqr7bhoaq/x0UpH583z6jIxVeLEsWdVUgOlqtH6haFcaNg7S0xxxg3z767B7EjstBmPMa95y7TTJw8OBBrl275lXJQFhYmKF70hsOXtB8VSBdmkNhw6ELuoytN5vNhp+fH+XKlTM6FM90/Dg0aQIVKsDkyXD06L1XuymK+v8nT1Y/r0kT9XVCGKRqVXXb4M03YfhwtcDw8OGHvOC257zwsskEcRSTgc+52yQD6X3gveGnsVu3brF161ZD6wWSU+0k3KdRkJYSEq89Uetio9lsNkJCQlzuOmWPMG0alC///xuo9kc8H+kf37BBfd20afrGJ8RDZM0Kn34KUVHw119QqRJ88819jg3e9Zyb0ox/zt0mGYiJiaFUqVLkyZPH6FB0Z7PZuHbtmqH1AicTU+7pLKg1BTiRqFH3DieSzoM6GTsWXn5ZXWt9VBJwN7tdfd3LL6vjCGGgunXVLf8+fdQV/saN4eTJfz7oos+5WyUD3rRF4O/vb+g3nJt253TAcNY8Wrlx4wb79u2TZEBr06bBe+9pM9Z776mdYYQwUPbs6qrA+vXqSn9ICET1ct3n3C2SAYfD8e+lMN7AarVSp04dQ5ehfS3OeTScNY9W4uLiSEtL85pn0SmOH1d7vGrp9delhkC4hEaN1IZCA5ofp9qsgdquuGr4nLvFO/GhQ4dITk722JWB268Ajv/zMpu2Gd9foHhAdvRuCWT6Zx53YrPZ8PHxISQkxOhQPEe/fhlfLn0Uu10dVwgXkCsXfPJ3P/zMdm3fVzV8zt3iCuP0zoOe9NPYw64AztVnGisx41i+95FXAOslu5+FwHz+nNSxiDAwwJ/sfm7xCP7LZrNRvnx5smbNanQonmHfPli3Tvtx7XZ13P37QU59CKP985xr/tO3hs+5W6wM2Gw2SpQoQb58+YwOJdMe5wpgk8nEhevKI68A1ltE2QK69hmIKFNAl7H15E3bVU4xZQpYdEoILRb1SJYQRnOD59wtkgFPKR5csCNBsyuAnaF7zUBd+wz0qBWoy9h6uXXrFnv27JFkQEurV2u/RZDObodfftFnbCEywg2ec5dPBhwOBzabze2TgYkbDjN8cVymrwCeuOFhXSy0VbpgTsKC8mu+OuBjNhEWlJ+gAs7f/siM/fv3k5qaKncSaCUpCY4d03eOo0eldbEwlps85y6/YXvkyBGSkpLc+qexBTsS+GztIU3G+mztIZ7K4Ufn6s75qfqj9iE0nrBR0xUCi9nER+3drwAvvfFV5cqVjQ3EU9yvs6DWFIX9K45wvWzlDL1s//47f/VW8vugyszvQ7aDRynnhOecI0cgE+9NLp8MpBcPuuvKgOZXAAMjl++lTqn8FM3nr+m491M0nz/vt6nA8MXa3TL4QZsKTolda7GxsZQpU4acOd1rRcNlpaY6ZZoXu6Wy/Qlf26OHpqG4Lfl9UD3J70MNUonWPpR7ZfLvk1skA8WKFSMgIMDoUJ6I5lcAA3aHwoglcczuU1PTcR+kS/VALianarK68XaTsk5b1dCadB7UmJ+fU6b5cZ4f18tm7DX796tv/HPmePdhBPl9UGXm9yHbQT/opk9cd8jk3yeXTwbcuV4g/Qpgrd1+BbCz9t1fjyhN/hx+jFq+F7tDydC2gY/ZhMVs4oM2Fdw2EUhvfNW6dWujQ/EcQUFgMum7VWAyUa51EOR4speXKweS/8nvQ7on+n0o45znnKCgTA3h0gWEiqK4dTLgaVcAd6keyPrBDahTUl2lMT2il1b6116nZADrBzdw20QA4PDhw6SkpMjKgJZy5ICSJfWdo1QpdR4hjOImz7lLJwNHjx7lypUrbpsMeOIVwEXz+TO7T03WvVkf/9M78bt19Z6OWiagWIA/PWsWY/3g+szuU9MtawRuFxsbCyAnCbTWooW+56+bN9dnbCEywg2ec5feJnDnzoPOvALYiC5+RXL6cHjBR0yYMIEX+3biRGIKN+0OfC1migdkd7vOgo9is9ncunbFZb36qnqbix7sdujfX5+xhcgIN3jOXXplICYmhqJFi/LUU08ZHUqGefoVwNHR0dy6dYv69euT3c9ChcK5CQ3MS4XCuT0uEQA1GZBVAR2ULw+RkZr/1JRmsmBvGOndVW/Cdej0nGOxqONq8Jy7fDLgrlsEnn4FsNVqJW/evFSoUMGQ+Z1JURRpQ6ynqVM1fZNUgJuKhUaHpxIVpdmwQmSOxs85oI43daomQ7lsMuDuxYOefgWw1WolLCwMs9llHyHNJCQkcOnSJUkG9FKihKZLqCbg6kcTSQssQYMGMHQoXL+u2fBCPBmNn3MAJk5Ux9WAy76THz9+nMuXL7ttMuDJVwDfvHmTrVu3Ur9+fafPbYT0zoOSDOiob18YM0abscaOpeA7fdi4EcaNU98vq1SBHTu0GV6IJ6bxc06fPtqMhQsnA+5cPAj/fwWwnoy6AjgmJobr1697VTJQsGBBnn76aaND8Wzvvgvffw9Zs2Z8OdViUV83bRqMGAGAj4+6KmCzQfbsULs2/Pe/cPOmDrEL8bg0fs614tLJQJEiRShYsKDRoTwxT70C2Gq1kj17dq8pqJN6ASfq21e9+z0iQv3vR71Zpn88IkJ93X1+UipfHrZuhVGj4JNPoEYN2LNH47iFyAgdnvPMculkwF23CNJ56hXAVquVunXrYtHr3KyLkTbETlaiBKxdC3v3qkem0jsV3kbBxFFzEMqr/dU3x7VrH7p3miWLuiqwfTukpUG1avDxx/rdKivEIz3Gc/5vZ8H+j/ecZ4ZLvpsrikJMTAxvvvmm0aFkSumCOakXFMDmIxdRNKwg8DGbqFMywJArgNPS0ti0aRP/+c9/nD63Ec6ePcvZs2e9ZhXEpZQvD19/rf57crJ6K1tqKvj5selsEPVb5GBXX6iUgVNVoaGwcyeMHg3vvQdLl8LMmfDss3p8AUI8hoc85wQFOa2DpkuuDJw8eZK///7b7VcG/vrrL84s+4K0WzdBw64DRl4BvGfPHq5eveo19QLpnQdlZcBgOXKo17PWrAmVK1M9Igd+frBhQ8aH8vNTVwU2b4bLl9UE4csvwWHMKV0h/t9dz7kzW2m7ZDLg7tcWA2zZsoXQ0FDitm6gV7A/aLgyYOQVwFarFT8/P6pXr27I/M4WGxtLnjx5KF68uNGhiNtkzQp16jxZMpCuVi2IjYV+/WDwYGjYEI4f1y5GIdyJyyYDTz/9NIUKFTI6lAxTFIUvvviCBg0aULx4cXbt2sWHLzZjaJMymR0YgN5VAwy98MdqtVKzZk2yZs1qWAzOlF4vYLp7L08YLjwcrFa1BuBJ+furqwK//w4nTkBICHz3nb4XzAnhilw2GXDHVYHLly/ToUMHhgwZwuDBg9mwYQNFihQB1CuAP+kQgp/FnOETBj5mE34WMxbbQn4Y2oXTp0/rEf4jKYqC1Wr1mi0CkDbEriwiQl3m371bm7Hi4qBbN3WlYODAzI8phDtxuWQgvXjQ3ZKBmJgYqlSpwh9//MGyZcsYN24cWbJkueNz7r4C+FFJwR1XAL8VzoZpY3E4HERGRnLx4kV9vpCHOHDgABcvXvSaZODSpUucOHFC6gVcVI0akC1b5rYKbpczp7oqsHq1WsMFsGqVrBII7+ByycCpU6dITEx0m2RAURSmTJlCnTp1CAgIwGaz0aZNmwd+/u1XAPesWYxiAf6PfQVw0aJFWb9+PRcvXqRFixYkJSXp+rXdzWq14uPjQ+3atZ06r1F27doFSPGgq/LzU+sG/vhD23GbN4f//U/995EjoUMHOH9e2zmEcDUud7TQnYoHk5OT6devH/PmzeO1117j888/x8/P77FeW7pgTka3qcBoKpCSan/sK4DLlCnDmjVrCA8Pp23btqxevdpp+/dWq5WqVauSw4kVrkay2Wxkz56d0qVLGx2KeICICLXlsN2u7R0wuXKpv44fr44fHAyTJ0PHjtrNIYQrcbmVgZiYGAoVKkThwoWNDuWh4uPjqV69OsuXL2fBggVMnDjxsROBu2X0CuDQ0FBWrlzJ1q1b6dKlC3YndE5RFIWNGzd6zRYBqMlApUqV8PHxMToU8QAREXD1qnoqQA8NG0J8PNSvD506qTUFly7pM5cQRnLJZMDVl2VnzZpFjRo1sFgs7Ny5k86dOzs9hrCwMBYtWsSqVavo27cvDp0PSZ84cYLTp097XTLg6s+it6tWTT0RoPVWwe0KFIBFi2DOHPjlF3WVYNUq/eYTwggulQy4evHg9evX6du3L7169aJz585ER0dTtmxZw+Jp2bIls2bNYtasWbz11lsoOlY6Wa1WTCYT9erV020OV5KcnMyhQ4ckGXBxvr5Qr552RYQPYjJB9+7qKkHlytCqldpe/upVfecVwllcKhn4888/+euvv1wyGTh8+DC1atVi7ty5zJgxgx9++AF/f2Ma/9yua9euTJo0ia+++ooxWl2NeR9Wq5WQkBDy5s2r2xyuZPfu3SiKIsmAG4iIgKgouHVL/7mKFFFXBb7/HhYuVPsS/P67/vMKoTeXSgbS7413tWRg0aJFVK1alRs3bhAdHU3v3r2NDukO/fv3Z8yYMYwcOZKJEyfqMoc39hfw9fWlfPnyRociHiEiQm3p/s/bh+5MJnVVIC4OSpaERo3UvgQpKc6ZXwg9uFQyEBMTQ4ECBf5t1GO0mzdv8sYbb9CpUyeaN2/Ojh07qFixotFh3deIESN46623GDhwIHPnztV07DNnznDkyBGvSwZCQkLu6RUhXE+VKmoLd723Cu5WvDj89ht89RVMn65uH2zZ4twYhNCKyyUDVatWdYnWrydPniQsLIzJkyczceJEFixYQK7080YuyGQy8dlnn/HSSy/Rq1cvVqxYodnYUVFRgFq06C1iY2Nli8BNZMkCYWHOTwYAzGYYNAh27YL8+dU4hg2DGzecH4sQmeEyyUB68aArvAGvWrWK0NBQLly4wObNm3nttddcIkF5FJPJxNSpU2nbti2dOnXiD41KrK1WK2XKlHHLuyKexI0bN9i7d69LPIvi8UREwKZNzqkbuJ8yZdT5P/pIveugWjX4p2WKEG7BZZKBM2fOcP78eUPrBex2O++88w6tWrWiXr162Gw2t7udz2KxMG/ePOrVq0ebNm3+beKUGd5WLxAfH4/dbpc7CdxIeDhcuwY7dhgXg4+Puiqwc6d6yqFWLRg92rgERYiMcJlkwOjOg2fOnKFRo0aMHz+ecePGsWzZMretnPfz82Pp0qWUL1+eZs2aceDAgSceKzExkfj4eK9KBmw2Gz4+Pi5bHyLuFRqqdg00YqvgbiEhsG0bjBgBY8aoSUF8vNFRCfFwLpMM2Gw28ufPT9GiRZ0+92+//UZoaChHjhxhw4YNvP32226xLfAwOXLkYNWqVRQsWJDIyEgSEhKeaJxNmzYBeFUyEBsbS7ly5ciWLZvRoYjHZLGoXQJdIRkAdWXg/ffVpODGDahaVW1rnJnrloXQk8skA0YUDzocDj788EMiIyOpWLEisbGxHlUkFxAQwNq1a7FYLERGRnLhwoUMj2G1WgkMDKRYsWI6ROiapPOgewoPV6v5U1ONjuT/pdcOvPEGDB+uFhgePmx0VELcy+WSAWf566+/aN68OaNGjWLUqFH8+uuvFChQwGnzO0vhwoVZv349V69epVmzZly5ciVDr/e2eoFbt26xe/duqRdwQxERcP06bN9udCR3yppVXRWIioILF6BSJfjmG9C5g7gQGeISycDZs2c5e/as034a27JlC6GhocTGxrJmzRpGjRrl0ZfRlCpVirVr13L8+HFat27NtWvXHut1SUlJ2Gw2r0oGDhw4QGpqqqwMuKFKlSBPHtfZKrhb3bqweze89JJ6HLFxYzh50uiohFC5RDLgrOJBRVH4/PPPadCgASVKlCA2NpbIyEhd53QVISEhrF69mpiYGDp16sStxyhx3rJlCw6Hw6uSgdh/rr+rXLmysYGIDPPxUesG9Ly0KLOyZ4eJE2H9ejhyRC02nD4ddLxWRIjH4jLJQL58+XTdl758+TLt27dn6NChvPXWW/z+++8u0+nQWWrXrs2SJUtYt24dvXr1euRNh1arlQIFClCmTBknRWg8m81G6dKlXbrBlHiwiAi1bsDVm/40aqS2M+7USW1t3KoVnDljdFTCm7lEMmCz2XQtHkxvZrRx40aWLVvGp59+6rVtZps0acK8efNYuHAhAwcOfOhNh+n1Au5+siIjbDab1Au4sYgItYBw2zajI3m03LnVVYEVK9R7FYKDYd48WSUQxnCJZECv4kFFUZg8eTJ16tQhICAAm81GmzZtNJ/H3XTs2JGpU6fy7bffMnLkyPt+zvXr19m+fbtXbRE4HA5pQ+zmQkIgXz7X3iq4W6tWah+Cpk3Va5I7dYK//jI6KuFtDE8Gzp8/z+nTpzVPBpKSkujevTsDBgzg5ZdfZtOmTZQoUULTOdxZ3759GTduHGPGjOGLL7645+Pbt2/n5s2bXpUMHD16lOTkZEkG3JjZDA0auG4R4YMEBMD8+fC//6mJTIUKsHSp0VEJb2J4MpBePKjlG3B8fDzVq1dnxYoVLFiwgIkTJ+Ln56fZ+J7i7bff5p133mHIkCH88MMPd3zMarWSJ08egoODDYrO+dKv0JZtAvcWEaFuE1y/bnQkGdepE+zdC7VrQ/v28MIL8PffRkclvIFLJAN58+bV7Kf2mTNnUqNGDXx9fdm5cyedO3fWZFxPNXbsWPr160ffvn1ZvHjxv//farVSr149jz5yeTebzUbRokXJnz+/0aGITAgPh5s3YetWoyN5MgULqqsCM2fC8uXq1seaNUZHJTydSyQDVapUyXSR2vXr1+nbty8vvvgiXbp0Ydu2bZQtW1ajKD2XyWRi0qRJdOrUia5du7J+/Xpu3brFli1bvGqLAKTzoKeoUEG9TtjdtgpuZzKpqwJxcVC+PDRrBv36QVKS0ZEJT2V4MpB+kiAzDh8+TK1atZg3bx4zZsxgxowZ+Pv7axSh5/Px8WHWrFk0bNiQdu3aMWvWLK5du+ZVyYCiKFI86CHMZnV1wJ2TgXRFi6qrAlOmwNy5ULEibNxodFTCExmaDPz111+cOnUqU8nATz/9RNWqVblx4wbR0dH07t1bwwi9h6+vLz///DOVK1dm4MCBZM2a1au+MZ46dYrExESv+po9WXi42pY4JcXoSDLPZFJXBfbsUZOD8HAYPNg9ayKE6zI0GchM8eDNmzcZNGgQzz//PC1atGDnzp2EhIRoHaJX8ff3Z+XKlWTJkgWHw8Gff/5pdEhOI8WDniUiAm7dUhsQeYqSJdWTBl98AZMnq9c2R0cbHZXwFIYnA7lz56ZUqVIZet3JkycJCwtj6tSpTJo0ifnz55MzZ06dovQuOXPmRFEUcuTIQePGjTl79qzRITmFzWajQIECFC5c2OhQhAbKlYMCBTxjq+B2ZrO6KhAbC7lyQZ06MGKEa93UKNyT05OBlFQ7e89cITbhb6Lij1O5Ws0MFQ+uXLmS0NBQLly4wObNmxkwYIBXdcjTW3x8PElJSUyaNInU1FSaNm3K315wtim9XkCeJc9gMnlO3cD9lCunrnp8+CF89hlUrw67dhkdlXBnTkkGDp9PYvTyvTQYv4Hg0Wto+c0m2k/ewoHi7TlRbSANxm9g9PK9HD7/4FJZu93O8OHDad26NfXq1cNms1GtWjVnhO9VrFYrvr6+tG3blrVr13LmzBlatmxJiidsvj6EnCTwPBERsGMHJCcbHYk+LBZ1VWDHDjX5qV4dxowBu93oyIQ70jUZOHXpGj2nRxP5pZXZ0Sc5eeka97TdNpk4eekas6NPEvmllZ7Tozl16c4rds+cOUPDhg357LPPGDduHMuWLSNv3rx6hu61rFYrNWrUIFu2bJQvX55ffvmFuLg4OnToQKqHrkWeO3eOM2fOSL2Ah4mIgLQ02LTJ6Ej0VamSmhAMGwajRqlbB/v3Gx2VcDe6JQMLdiTQeMJGthxLBCDN8fDbN9I/vuVYIo0nbGTBjgQAfvvtN0JDQzl69Ch//PEHb7/9tizl6kRRlH8vJ0pXvXp1li9fzsaNG+nRowdpaWkGRqiP9GuLZWXAs5QpA08/7blbBbfz9VVXBbZsgatX1eLCzz9XkyEhHocuycDEDYcZvjiOVLvjkUnA3dIcCql2B8MXx/HcyO+JjIykYsWKxMbGUq9ePT3CFf84dOgQFy5cuKe/QEREBAsXLmTJkiW8+uqrD73p0B3FxsaSO3duubvCw6TXDbjTpUWZVbOmWlw4YAC8/bb69R89anRUwh1ongws2JHAZ2sPaTJWzK3CdH33G3799VcKFCigyZjiwaxWK2azmTp16tzzsbZt2zJ9+nSmTZvG8OHDDYhOP+n1ArLi5HkiIiAmRv1p2Vtky6YeP/zjDzh9Wm1UNHmyXI0sHk7TZODUpWuMWr5XyyHZSUnOXPHMvWpXY7VaqVKlygOPafbq1Ysvv/yScePG8emnnzo5Ov3YbDapF/BQ6XUDUVFGR+J89eurjYpeeEFdKWjaFE6dMjoq4ao0TQZGLInDnsFtgUexOxRGLInTdExxf3fXC9zPG2+8wahRoxg+fDjfffedkyLTz99//83x48elXsBDlSoFRYp411bB7XLkUFcFfv0V9u2D4GD48UdZJRD30iwZOHw+iagjFzNcI/AoaQ6FqCMXOXJBbujQ08mTJ0lISHis+whGjRrFwIEDefXVV1m4cKETotPPrn8OZ0sy4JlMJnV1wBuKCB+maVOIj4d27aB3b2jbFs6dMzoq4Uo0SwbmRifgY9Znz9XHbGLOtgRdxhYqq9UK8FhFmiaTiS+//JLu3bvTs2dPfv31V73D043NZsPf358yZcoYHYrQSUSEWlR3+bLRkRgrTx71WuSlS9U2xhUqwP/+Z3RUwlVolgxsOHhB81WBdGkOhQ2HLugytlBZrVaCg4MJCAh4rM83m83MmDGDZs2a0aFDBzZv3qxzhPqw2WxUqlQJHx8fo0MROgkPB4fDO+sG7qdtW9i7Fxo2hM6doUsXSEw0OiphNE2SgeRUOwl3NQrSWkLiNVJSpbWWXh6nXuBuWbJkYeHChdSoUYOWLVuye/dunaLTj1xb7PlKlIDAQNkquF3+/OqqwPz5sHatukqwYoXRUQkjaZIMnExMubezoMYU4ESiZ7fENcq5c+c4dOhQhpMBgGzZsrF8+XJKlSpF06ZNOXz4sA4R6iMlJYUDBw5IMuDhpG7g/kwmdVVg716oVg3atFHrCa5cMToyYQRNkoGbdocWw7jMPN4m6p/107CwsCd6fa5cufj111/JmzcvkZGRnD59WsvwdLN7924URZFjhV4gPBx274ZLl4yOxPU8/bS6KjBjBvz8M4SEwPr1RkclnE2TZMDX4pzLD501j7exWq0EBQVl6vrep556irVr16IoCpGRkVy8eFHDCPVhs9nIkiULFSpUMDoUobOICPU43T91suIuJpO6KhAXB6VLQ2QkvPYaXNN391e4EE2+uxYPyI7evdtM/8wjtPck9QL3U7RoUdatW8fFixdp0aIFSUmufRw0NjaWkJAQfH19jQ5F6KxYMbV2QLYKHq5YMVi3DiZOVPsRdO1qdETCWTRJBrL7WQjM56/FUA8UGOBPdj+LrnN4o0uXLhEXF6dJMgBQpkwZ1qxZw8GDB2nbti03btzQZFw9yLXF3iU8XJKBx2E2q6sCu3ZB+uGiCRPAhf8qCw1otu4eUbaArn0GIsrI3QR62Lx5M4qiaJYMAISGhrJy5Uq2bt1Kly5dsLvgBeupqanEx8dLvYAXiYhQl8HdYAfLJZQuDd9/r/77woVQpYp6VbLwTJolA91rBuraZ6BHrUBdxvZ2VquVZ555huLFi2s6blhYGIsWLWLVqlX07dsXh8O1ij/j4+Ox2+2yMuBFwsPVXzduNDQMt5LefmPuXPD3h9q1YeRIuHnT2LiE9jRLBkoXzElYUH7NVwd8zCbCgvITVOD+l+eIzEmvF9Djxr6WLVsya9YsZs2axVtvveVSVx/HxsZiNpupWLGi0aEIJylaVL2rQLYKMq5UKdi6VU0EPv5YvSo5Tq6M8Sialud/1D4Ei8bJgMVs4qP2IZqOKVTJycnExMRoukVwt65duzJp0iS++uorxowZo9s8GWWz2ShXrhz+/vrWugjXEhHhvZcWZVaWLGoyEB0Nt25B1apqYuCCu4DiCWiaDBTN58/7bbQ9pvVBmwoU1bk40Vtt3bqVtLQ0XZMBgP79+zN27FhGjhzJxIkTdZ3rccm1xd4pIkJtsnNBups/sSpVICYGhgyB996DevXg4EGjoxKZpfnB/S7VAxnaJHOXvqQvJ7/dpCydq0utgF6sViv58+fn2Wef1X2ud955hyFDhjBw4EDmzp2r+3wPY7fb2b17t9QLeKH0ugFZHcgcPz91VWDTJrWRU+XK8NVX6h0Qwj3p0sXn9YjSfNIhBD+LOcM1BD5mExaTQuLqr8h2XDqE6EnPeoG7mUwmxo8fz0svvUSvXr1YYWAj9IMHD3Ljxg1JBrxQ4cJQpowkA1qpXVs9gvjKK/Dmm+rlR8ePGx2VeBK6tfTrUj2Q9YMbUKekelD1UUlB+sfrlAxgw9CG9KhTin79+vGH/K3VxY0bN4iOjtZ9i+B2JpOJqVOn0rZtWzp16mTYn63NZgOgcuXKhswvjCX3FGjL319dFfj9dzhxAipWhO++Uzs+Cveha3/fovn8md2nJuverE/PmsUoFuB/T6dCE1AswJ+eNYuxfnB9ZvepSWBAdr7++mvCw8N57rnn3OryG3exY8cOUlNTnZoMAFgsFubNm0e9evVo06YNMTExTp0f1GSgVKlS5M6d2+lzC+NFRMCBA3D2rNGReJaICNizR+1a2K8fNG8ObnJNiQCc0tKvdMGcjG5TgdFUICXVzonEFG7aHfhazBQPyH7fzoJZsmThp59+onbt2rRq1Ypt27aRN29eZ4TrFaxWK7ly5TLkaJ2fnx9Lly6lcePGNGvWjKioKKfULaSTzoPerUED9dc//pB2u1rLlUtdFWjfHvr0geBg+OYb6N5dvf9AuC6n3/yT3c9ChcK5CQ3MS4XCuR/aYjhPnjysXLmSxMREOnbsyK1bt5wYqWezWq3Uq1cPn/SuIk6WI0cOVq9eTaFChYiMjCQhIcEp8zocDnbt2iXJgBcrVAjKlZO6AT01bw7x8dCyJfTsCc89Jyc4XJ3LXwNYqlQpFi9eTFRUFK+//rpLNa5xV3a7nc2bNzt9i+Bu+fLlY82aNWTJkoXIyEguOOHd4tixY1y9elWSAS8ndQP6y5cP5syBRYsgKgoqVFCvSBauyeWTAYD69evz3Xff8d133/Hll18aHY7bi42NJSUlxfBkAKBw4cKsW7eOq1ev0qxZM65cuaLrfOnFg9JjwLuFh8Phw7Kn7QzPPaf2dggLg44d1S2Dv/82OipxN7dIBgBefPFFhg0bxpAhQww9luYJrFYr2bJlo2rVqkaHAqirP2vXruX48eO0bt2a69ev6zaXzWbjmWee4amnntJtDuH6pN+AcxUooK4KzJkDq1ertQS//GJ0VOJ2bpMMAHz00Ue0bduWbt26sWfPHqPDcVtWq5XatWvj6+trdCj/CgkJYfXq1cTExNCpUyfd6kNiY2Nli0Dw1FPqNyTZKnAek0ldFYiPV48ftmgBL78MV68aHZkAN0sGzGYzc+bMoXTp0rRu3Zpz584ZHZLbcTgcREVFucQWwd1q167NkiVLWLt2Lb169dL8pkNFUeQkgfhXeLgkA0YoUkRdHfjuO1iwQE0M5M/BeG6VDABkz56dFStWYLfbadeuna5Lyp5o7969/P333y6ZDAA0adKEefPmsXDhQgYOHKhpweiff/7JxYsXpV5AAGoR4bFj4KSDLOI2JpO6KhAXByVKqJ0L33gDrl0zOjLv5XbJAECRIkVYvnw5e/bsoXfv3nLCIAOsVitZsmShZs2aRofyQB07duS7777j22+/ZeTIkZqNm148KCsDAu7sNyCMUbw4/Pab2sHwu+/UOw62bjU6Ku/klskAQNWqVZkzZw4LFy7k/fffNzoct2G1WqlevbrLX93bp08fxo8fz5gxY/jiiy80GTM2NpannnqKIkWKaDKecG8BAbJE7QrMZhg0SL3jICBAvQVx+HBITTU6Mu/itskAQIcOHfj44495//33mTdvntHhuDxFUf69nMgdDB069N/bDn/44YdMj5deL+CMi5mEe4iIkJUBV1G2rNqPYOxY+OILqFYN/lnME07g1skAwLBhw+jVqxcvvfQSW2V96aGOHDnCuXPn3CYZABg7diz9+vWjb9++LFmyJFNj2Ww2qRcQd4iIUC/XOXHC6EgEgMWirgrExKj/XrMmfPABSPNZ/bl9MpB+E16NGjVo164dJ+Rv9QNZrVbMZjN16tQxOpTHZjKZmDRpEp06daJLly6sX7/+ica5cOECp0+flnoBcYf69dViNtkqcC0hIRAdDe+8oyYDtWurjYuEftw+GQD14pvFixeTI0cOWrduzVU5uHpfVquVypUru91tfT4+PsyaNYuGDRvSrl07oqOjMzxGbGwsIMWD4k5586pFa7JV4Hp8fdVEYOtW9ZRBlSowfjykpRkdmWfyiGQAIH/+/KxcuZJTp07RpUsX7Ha70SG5HHeqF7ibr68vP//8M5UrV6Z58+bEx8c/8jUpqXb2nrlCbMLfrN2xj9wBBShZsqQTohXuJP2eAjmU5JqqV1drBwYNgmHD1NWcI0eMjsrzeEwyAFCuXDl++ukn1q5dy9ChQ40Ox6UkJCRw4sQJt00GAPz9/Vm5ciWBgYE0adKE48eP3/M5h88nMXr5XhqM30Dw6DW0/GYT7Sdv4eeUMuTpO53wz/5g9PK9HD6fZMBXIFxReDicOqX2HBCuKWtWdVXAaoVz56BSJZg0CTTuS+bVPCoZAIiMjOSbb77hq6++YvLkyUaH4zKioqIAqFevnsGRZE6ePHlYs2YN2bNnp3Hjxpw9exaAU5eu0XN6NJFfWpkdfZKTl65xzw96JhMnL11jdvRJIr+00nN6NKcuSZcTb1e/vnq8TeoGXF+9erB7N7z4Irz+OjRpIk2jtOJxyQBA//79GTRoEAMHDmTdunVGh+MSrFYr5cuX94gLegoWLMi6detITU2ladOmTP/jAI0nbGTLsUQA0hwPX+9N//iWY4k0nrCRBTvk3cSb5c6t7kdL3YB7yJFDXRVYuxYOHlTvmJgxQ7Z5MssjkwGAzz//nCZNmtCpUycOHDhgdDiGc+d6gfspXrw4a9eu5a8CVflwzVFS7Y5HJgF3S3MopNodDF8cx8QNh3WKVLiD9HsK5BuK+4iMVC896tgR+vSB1q3hn4VC8QQ8NhmwWCwsWLCAZ555hlatWnHx4kWjQzLMhQsXOHDggEclAwB7UnLgV72jJmN9tvYQC2WFwGtFRMCZM3BYckK3kju3uiqwfDns3AkVKqiXH0lSl3EemwwA5MqVi5UrV3L16lU6dOhAqpf2t0yvFwgLCzM4Eu2cunSNUcu1PXg8cvleqSHwUvXqgY+PbBW4q9at1T4ETZpA167QuTN48c9/T8SjkwFQl5OXLl3K9u3b6devn1deamS1WilZsiTPPPOM0aFoZsSSOOwZ3BZ4FLtDYcSSOE3HFO4hVy6oWlWKCN1ZQIC6KrBggXr5UYUKsGyZ0VG5D49PBgDq1KnDjBkzmDlzJuPGjTM6HKfztHqBw+eTiDpyMcM1Ao+S5lCIOnKRIxfk2KE3+rffQFIy2Q7uogbRZDu4C5KTjQ5NZEDnzuoqQc2a0K4d9OoFly9nYsBk73gevCIZAOjWrRsjR45k+PDhLF682OhwnOby5cvs3r3bo5KBudEJ+Jj1uWzIx2xizjapHfA6+/bRf/8gNp0Pgty5KNctlGhqUa5bqLpsEBSkdr3Zt8/oSMVjKFRIXRX48UdYulQ9cbB2bQYG2LdP/fMOCoJc3vE8eE0yADB69Gg6d+5Mjx49iImJMTocp9i8eTOKonhUMrDh4AXNVwXSpTkUNhy6oMvYwgUdP65uNFeoQOCqyQRxFNPdW4mKAkePwuTJ6tpzkybq64RLM5nUVYH4eChXDpo2hf79H/GD/W3PA5Mnq3/uXvI8eFUyYDKZ+OGHHwgJCaFNmzacPn3a6JB0Z7VaKVy4sMe04U1OtZOgc5FfQuI1UlKlnbXHmzYNypf/t1DAlPaIP/P0FucbNqivmzZN5wCFFooWVVcFvv0WZs2CihXVTob3uOt54FEt7T3sefCqZAAgW7ZsLFu2DB8fH9q0aUNKSorRIekqvV7AZNJnWd3ZTiam3NtZUGMKcCLRs58Lrzd2LLz8Mty48eg3/bvZ7errXn5ZHUe4PJNJXRXYsweKFFH7Srz1Fly//s8nyPPgfckAQKFChVi5ciWHDh2iZ8+eODy0wXVKSgo7d+70qC2Cm3bn/Fk5ax5hgGnT4L33tBnrvfdg+nRtxhK6K1VKPT762WfqSkFoKBx/V54H8NJkAKBixYrMnz+fpUuX8u677xodji62bduG3W73qGTA1+KcR9ZZ8wgnO34cBg7UdszXX/eIPWNv4eOjrgrExkJZ3+MU+migtquNbvo8ePU7XqtWrfjss8/45JNP+PHHH40OR3NWq5WAgADKlStndCiaKR6QHb03PEz/zCM8UL9+GV8GfhS7XR1XuJVy5WBJwX74mu3avqe46fPg1ckAwODBg3n55Zd55ZVXsN63qsR9Wa1WwsLCMJs95485u5+FwHz+us4RGOBPdj+LrnMIA+zbB+vW6ZMMrFsH+/drO67Q1759mNevw8chzwNIMoDJZGLSpEmEhYXRvn17jh49anRImkhNTWXbtm0etUWQLqJsAV37DESUKaDL2MJgU6aARackz2JRj5oJ9yHPwx28PhkAyJIlC4sWLSJ//vy0atWKy5lqV+Uadu7cyY0bNzwyGeheM1DXPgM9agXqMrYw2OrV2q8KpLPb4Zdf9Blb6EOehztIMvCPvHnzsnLlSs6fP8/zzz/PrVu3jA4pU6xWKzlz5qRy5cpGh6I5f/tV/K+cRHnUufAM8jGbCAvKT1CBnJqOK1xAUhIcO6bvHEePemyrWo8jz8M9JBm4TenSpVm8eDEbNmxg0KBBbn2pkdVqpV69evj4+BgdimYURWH+/PkEBwdz5bcp+GbRdonPYjbxUfsQTccULuJ+neS0pihw5Ii+cwhtyPNwD0kG7hIeHs6UKVOYMmUK33zzjdHhPBG73c7mzZs9aosgMTGRLl260K1bN5o1a0bc1j/4sJ2237g/aFOBojoXJwqDOOv6ci+9Jt3tyPNwDymZvo8+ffpw8OBBBg8eTFBQEC1atDA6pAzZvXs3SUlJHpMM/PLLL7z00kukpqYyf/58unTpAkCXfPm4mJzKZ2sPZXqOt5uUpXN1qRXwWH5+njWPyBx5Hu4hKwMP8PHHH9O6dWu6dOlCXJx73XFvtVrJmjUr1apVMzqUTElOTubVV1+lRYsWVK5cmfj4+H8TgXSvR5Tmkw4h+FnMGT5h4GM24Wcx82mHEF6LCNIydOFqgoLUnrR6MpnUeYTrk+fhHpIMPICPjw9z5syhVKlStG7dmvPnzxsd0mOzWq3Url0bX19fo0N5Yps3b6ZSpUrMnj2byZMns3r1agoXLnzfz+1SPZD1gxtQp2QAwCOTgvSP1ykZwPrBDWRFwBvkyAF6X9ZVqpQ6j3B98jzcQ5KBh8iRIwcrVqzg5s2btGvXjhs3bhgd0iM5HA6ioqLcdosgNTWVd955h/r161OwYEF2797Nq6+++siLlorm82d2n5qse7M+PWsWo1iA/z1dxUxAsQB/etYsxvrB9Zndp6bUCHiTFi30PVfevLk+Ywt9yPNwB5PiziXzTpJ+2U+7du2YO3euS98AuHfvXoKDg/ntt99o2LCh0eFkyJ49e+jZsyf79+/n/fff5+2338aSib+sKal2TiSmcNPuwNdipnhAduks6M327VPvn9dzfA9q/Q1gs0HVqhATA1WqGB2NxuR5uIOsDDyGatWqMWvWLObPn8+HH35odDgPZbVasVgs1KpVy+hQHltaWhrjxo2jevXqOBwOtm/fzjvvvJOpRADU1sUVCucmNDAvFQrnlkTA25UvD5GRmv80aMdCSt1It3rjF+B4tjwJz0ZyS+s6eotFfc7c7HmQZOAxdezYkTFjxjBq1CgWLFhgdDgPZLVaqV69Ov7+7rH8fezYMcLDwxk+fDhvvPEGO3bs8MhGScJFTJ2qaTKgAHaThWo7p/LNN+Cht6F7nIQEaNIEGhyYiuJj0fbWQotFfc7cjCQDGTBixAh69OjBiy++yLZt24wO5x6KomC1Wt2iXkBRFL7//nsqVqzI6dOn2bhxI+PGjSNr1qxGhyY8WYkSoGH/EBNgmjiRxi+XYNAgaNwYTpzQbHihMUWBH36AkBA4eBC+W1sC3ynfaHtr4cSJ6nPmbhSRITdu3FDq1q2rFCxYUDl58qTR4dzhyJEjCqCsWrXK6FAe6uzZs0rLli0VQOnbt69y9epVo0MS3mbMGEVRvzdk7p+xY/8dcv16RQkMVJQcORTl++8VxeEw8OvTSEyM+mXGxBgdSeadOaMorVqpX8+LLyrK33/f9kEdngd3I8nAE7hw4YJSokQJpWLFii71jWzGjBmKyWRSLl++bHQoD/TTTz8pAQEBSsGCBZUVK1YYHY7wZt9/ryhZsyqKxZKxN3yLRX3dtGn3DHnliqL06aN+WvPminL6tAFfl4Y8IRlwOBRl/nxFyZdPUQoWVJTlyx/wiTo8D+5EkoEnFB8fr+TKlUtp1aqVYrfbjQ5HURRFefHFF5XQ0FCjw7ivS5cuKd27d1cA5bnnnlP++usvo0MSQlGOHVOUyMj/f1N/1Js+qJ9/7NhDh125UlGeflpR8uZVlLlz3XeVwN2Tgb/+UpROndSvoXNnRbl48REv0Ol5cAeSDGTCr7/+qpjNZuWtt94yOhRFURSlZMmSyhtvvGF0GPdYt26dUqRIESV37tzK7NmzFYe7vjMKz7V3r6IMHKgoQUGKYjLd+aZvMqn/f+BARdm377GHTExUlG7d1CGee05RLlzQMX6duHMysHSpohQooK4ILFiQwRfr8Dy4OkkGMmnixIkKoEydOtXQOE6dOqUAys8//2xoHLdLSUlRBg4cqABKo0aNlISEBKNDEuLRkpIUJTZWUbZtU39NSsrUcD/9pCj58yvKU08pyuLFmkToNO6YDPz9t6K88IIad+vWinL2bCYH1Ph5cFWSDGjg9ddfVywWi7J+/XrDYpg3b54CKBdc5MeP6OhopUyZMkrWrFmVr7/+WklLSzM6JCEMc+6corRrp36D6tFDUS5dMjqix+NuycCaNYpSpIii5MqlKD/+6L7bM0aQo4UamDBhAo0aNaJjx44cPHjQkBisVivlypXjqaeeMmT+dLdu3WLkyJHUqVOH3LlzExsby8CBAzGb5VET3qtgQVi8GGbNghUrIDgYfv3V6Kg8R3Iy9O8PTZuqvX7i46FXL/3vIvIk8g6tAYvFwsKFCylcuDCtWrUiMTHR6TG4Qn+Bffv2UatWLT766CNGjhzJ5s2befbZZw2NSQhXYTJBz57qN6qQELV1fb9+kJRkdGTuzWqFihXVROvbb2HtWiha1Oio3I8kAxrJnTs3K1eu5PLlyzz33HPcvHnTaXP/9ddf7Nu3z7BkwOFwMGHCBKpUqcK1a9fYtm0bI0eOJEuWLIbEI4Qre+YZ+OUXtUnd3LnqN7I//jA6Kvdz/Tq89RaEh0ORIrBnj7o6IKsBT0aSAQ2VKFGCpUuXsnXrVvr374/ipDugNm3aBEBYWJhT5rvdyZMnadSoEW+99Rb9+/fHZrNRrVo1p8chhDsxmeCVV9RvYIGBEBEBb74J164ZHZl72L4dQkPVlYDPPlOTqVKljI7KvUkyoLG6desyffp0ZsyYwWeffeaUOa1WKyVKlKCoE9fGFEXhxx9/JCQkhKNHj/Lbb78xYcIEsmXL5rQYhHB3JUvChg0wYYK6UhAaCi7Y6dxl3LwJ770HtWtDzpwQG6uuDvj4GB2Z+5NkQAc9evTg3XffZdiwYSxdulT3+ZxdL3DhwgU6dOhA79696dChA3FxcW53XbIQrsJsVlcFdu2CvHmhbl0YMQJSU42OzLXs3g3Vq8Onn8L778PWrW53MaBLk2RAJx988AHPPfcc3bt3JzY2Vrd5rly5wq5du5yWDCxbtozg4GA2bdrE4sWL+fHHH8mdO7dT5hbCk5UtC5s2wZgx6tJ39erqT77ezm6HsWPV3w9FgR071NUBjW+i9nqSDOjEbDYzc+ZMypcvT+vWrTlz5owu82zZsgWHw6F7MnD16lVeeukl2rVrR61atYiPj6d9+/a6zimEt7FY4J13YOdOdcWgRg348EO4dcvoyIxx4ADUqQMjR8Lbb6uJgNxwrg9JBnTk7+/P8uXLMZlMtG3blms6VAdZrVaefvppSulYPfPHH39QsWJFFi1axIwZM1i2bBkFCxbUbT4hvF3FimqR3PDh6pJ4nTqwb5/RUTmPw6HWUYSGwpUrsGWLujrg52d0ZJ5LkgGdPf3006xYsYL9+/fTq1cvHA6HpuOn1wuYdDhPc+PGDYYMGULDhg0pVqwYe/bsoXfv3rrMJYS4k6+vuiqwdavaVKdKFfj8c0hLMzoyfR07pp6ueOstePVVdaukZk2jo/J8kgw4QeXKlZk7dy4///wzI0eO1Gzca9eusWPHDl22CGw2G1WrVmXixImMHz+eDRs2ULx4cc3nEUI8XPXqYLPB66+rS+UNGsCRI0ZHpT1FgSlT1FWRhIT/P2Xh7290ZN5BkgEnadu2LePGjWPs2LHMnj1bkzGjo6O5deuWpsmA3W5nzJgx1KxZEz8/P2JiYhgyZIi0ExbCQNmyqUWFGzfC2bNQqZJ6xl7jhUbDnDoFzZqpTYN69FD7L4SHGx2Vd5F3eCcaMmQIffr0oW/fvv82CsoMq9VKvnz5KF++vAbRwaFDh6hXrx6jRo1i2LBhbNu2jeDgYE3GFkJkXliYesSuVy947TW1F39CgtFRPTlFUdsIh4TA3r1qZ8YpU9QeAsK5JBlwIpPJxLfffkudOnVo3749x44dy9R4VquVsLCwTP/U7nA4mDRpEpUrV+bSpUts3ryZMWPG4Ovrm6lxhRDay5Hj/3vwHzigfiP98Uf1G6s7OX8e2rdXE5s2bSAuTl0dEMaQZMDJfH19+fnnn8mbNy+tWrXiypUrTzTOzZs32bp1a6a3CP7880+aNWvG66+/zksvvURsbCy1atXK1JhCCP1FRqrfQDt0gN69oW1bOHfO6Kgez08/QYUK6imB9Nsc8+Y1OirvJsmAAfLly8fKlSs5d+4czz//PHa7PcNjxMTEcP369SdOBhRFYd68eYSEhLB3717WrFnDxIkTyZ49+xONJ4Rwvjx54IcfYNky9ShihQqwcKHRUT1YYiJ07QrPP6/WBOzdq64OCONJMmCQMmXKsGjRIn7//XfefPPNDL/earWSI0cOKj9BB47ExEQ6d+5M9+7dadGiBfHx8TRp0iTD4wghXEObNurVyI0bQ5cu0LkzXLxodFR3WrkSgoNhzRqYN09dHXjqKaOjEukkGTBQw4YN+fbbb5k0aRITJ0586OempNrZe+YKsQl/s/fMFTZs2kLdunWxZLAn5+rVqwkODua3335j4cKFzJ07l7yyPieE28ufX10VWLAA1q9XVwmWL8/EgMnJZDu4ixpEk+3gLrXZwRO4cgX69IHWrdVeCfHx6uqAtCtxLSbFWffsigcaMmQIX375JatWraLZbRU0h88nMTc6gQ0HL5Bw6Rq3/0EpikJu80061CpL95qBlC748PLb5ORkhgwZwnfffUfz5s2ZNm0ahQsX1ukrEkIY6dw5ePll9afxXr3gyy/VLYVH2rdPLedfvVrt/nP7tweTSb1msUULtRvQY5xi+u03tZ7h8mW1Z8BLL0kS4KokGXABaWlptGvXjo0bN7J161ZyPV2CEUviiDpyER+ziTTHg/+I0j8eFpSfj9qHUDTfvR06Nm/ezAsvvMC5c+f44osveOWVV6SLoBAeTlFg5kx44w3IlQumT4cH7gYePw79+sG6deoFCQ+rY0r/eGSkeu9yiRL3fEpKCgwbBpMmqd0Ef/gBihXT5usS+pBkwEUkJSVRr149ruSvgF/tHtgVHpoE3M3HbMJiNvF+mwp0qR4IQGpqKqNGjWLcuHHUrl2bmTNnEhQUpNeXIIRwQQkJ6jL9+vXqD/Tjx6vHE/81bRoMHKh+g89IMbPFov7zzTfQt++//3vzZnU14swZGDcOBgxQL10Srk2SARcyZslOpm0/r6b0mfjJfWiTMtQPuE7Pnj3Zv38/H3zwAW+//TY+Pj4aRiuEcBcOh7r6//bbUKiQ+pN6/fqot/+8917mJxgzhhtD3mXkSLVTYq1a6qpE6dKZH1o4hyQDLmLBjgSGL47TbLzLayYSaD/N7NmzqVSpkmbjCiHc15Ej6h7+5s2woPE0nl/3smZj//fpaYxL7MOHH8KQISA/e7gXSQZcwKlL12g8YSOpdm0ajSuKggUHa96sT1ChPJqMKYTwDGlpMOO/x+nxcXmycgMtqocUINWUlZOr91G22b01BML1yU6OCxixJA57BuoDHsVkMoGPhfdXHdRsTCGEZ/DxgZd39iOrj12TRADABPj52Cn7RT+NRhTOJsmAwQ6fTyLqyMUMFQs+jjSHQtSRixy5kKTpuEIIN7dvH6xbhykt451PH8Zkt6unEfbv13Rc4RySDBhsbnQCPmZ9jvn5mE3M2ebGV5oJIbQ3ZYp6CkAPFgtMnqzP2EJXkgwYbMPBC5qvCqRLcyhsOHRBl7GFEG5q9eqMHSHMCLtdvYdYuB1JBgyUnGon4dI1XedISLxGSqpOf/GFEO4lKUntLKino0efuHWxMI4kAwY6mZiC3kc5FOBEYorOswgh3MLRo3e2GNaDoqhnGIVbkWTAQDc1OkroKvMIIVxcaqpnzSM0I8mAgXwtzvntd9Y8QggX5+fnWfMIzch3CQMVD8iu2TnfBzH9M48QQhAUpP+1gSaTOo9wK5IMGCi7n4XA+9wyqKXAAH+y++l0jEgI4V5y5FCvIdZTqVJ33YQk3IEkAwaLKFtA1z4DEWUK6DK2EMJNtWihb5+B5s31GVvoSpIBg3WvGahrn4EetQJ1GVsI4aZefVXfPgP9++szttCVJAMGK10wJ2FB+TVfHfAxmwgLyk9QgZyajiuEcHPly0NkpParAxaLOm65ctqOK5xCkgEX8FH7ECwaJwMWs4mP2odoOqYQwkNMnapPMjB1qrZjCqeRZMAFFM3nz/ttKmg65gdtKlBU5+JEIYSbKlECvvlG2zEnTlTHFW5JkgEX0aV6IEOblNFkrLeblKVzdakVEEI8RN++MGaMNmONHQt9+mgzljCESVH07k0pMmLBjgRGLd+L3aFkqLDQx2zCYjbxQZsKkggIIR7ftGkwcKBa/JeRwkKLRf1n4kRJBDyAJAMu6NSla4xYEkfUkYv4mE0PTQrSPx4WlJ+P2ofI1oAQIuOOH4d+/WDdOvUb/MOSgvSPR0aqNQKyNeARJBlwYYfPJzE3OoENhy6QkHjtjkuNTKgNhSLKFKBHrUA5NSCEyLx9+2DKFPUa4rsvNTKZ1IZCzZurxwfl1IBHkWTATaSk2jmRmMJNuwNfi5niAdmls6AQQj/Jyertg6mp6l0DQUHSWdCDSTIghBBCeDk5TSCEEEJ4OUkGhBBCCC8nyYAQQgjh5SQZEEIIIbycJANCCCGEl5NkQAghhPBykgwIIYQQXk6SASGEEMLLSTIghBBCeDlJBoQQQggvJ8mAEEII4eUkGRBCCCG8nCQDQgghhJeTZEAIIYTwcpIMCCGEEF5OkgEhhBDCy0kyIIQQQng5SQaEEEIILyfJgBBCCOHlJBkQQgghvJwkA0IIIYSXk2RACCGE8HKSDAghhBBeTpIBIYQQwstJMiCEEEJ4OUkGhBBCCC8nyYAQQgjh5f4PGUrOCFDKahgAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -426,7 +426,7 @@ }, { "cell_type": "markdown", - "id": "a3e7daf4", + "id": "4dc3c4a1", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -466,13 +466,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "2af1ffc6", + "id": "e319f22c", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:57.672801Z", - "iopub.status.busy": "2022-12-15T16:09:57.672448Z", - "iopub.status.idle": "2022-12-15T16:09:57.676336Z", - "shell.execute_reply": "2022-12-15T16:09:57.675684Z" + "iopub.execute_input": "2022-12-20T11:40:16.468911Z", + "iopub.status.busy": "2022-12-20T11:40:16.468445Z", + "iopub.status.idle": "2022-12-20T11:40:16.472096Z", + "shell.execute_reply": "2022-12-20T11:40:16.471629Z" } }, "outputs": [ @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "95c97c18", + "id": "ad581721", "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": "fbdfaf69", + "id": "389d61af", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:57.680069Z", - "iopub.status.busy": "2022-12-15T16:09:57.679850Z", - "iopub.status.idle": "2022-12-15T16:09:57.683843Z", - "shell.execute_reply": "2022-12-15T16:09:57.683217Z" + "iopub.execute_input": "2022-12-20T11:40:16.474767Z", + "iopub.status.busy": "2022-12-20T11:40:16.474437Z", + "iopub.status.idle": "2022-12-20T11:40:16.478210Z", + "shell.execute_reply": "2022-12-20T11:40:16.477741Z" } }, "outputs": [ diff --git a/reference/randomness.html b/reference/randomness.html index 680ef2ce..6160e758 100644 --- a/reference/randomness.html +++ b/reference/randomness.html @@ -492,7 +492,7 @@ <span id="id1"></span><h1>Randomness<a class="headerlink" href="#randomness" title="Permalink to this heading">#</a></h1> <p>Random Number Generators (RNGs) are often used when generating, drawing and computing properties or manipulating networks. NetworkX provides -functions which use one of two standard RNGs: NumPy’s package <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> +functions which use one of two standard RNGs: NumPy’s package <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> or Python’s built-in package <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a>. They each provide the same algorithm for generating numbers (Mersenne Twister). Their interfaces are similar (dangerously similar) and yet distinct. @@ -532,10 +532,10 @@ RNG package to use, and whether to use a global or local RNG.</p> </pre></div> </div> <p>Each NetworkX function that uses an RNG was written with one RNG package -in mind. It either uses <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> by default. +in mind. It either uses <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a> or <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> by default. But some users want to only use a single RNG for all their code. This <code class="xref py py-obj docutils literal notranslate"><span class="pre">seed</span></code> argument provides a mechanism so that any function -can use a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> RNG even if the function is written for <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a>. +can use a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a> RNG even if the function is written for <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a>. It works as follows.</p> <p>The default behavior (when <code class="xref py py-obj docutils literal notranslate"><span class="pre">seed=None</span></code>) is to use the global RNG for the function’s preferred package. diff --git a/reference/readwrite/matrix_market.html b/reference/readwrite/matrix_market.html index 0f89dffb..29e7d094 100644 --- a/reference/readwrite/matrix_market.html +++ b/reference/readwrite/matrix_market.html @@ -509,7 +509,7 @@ Matrix Market supports both a <strong>coordinate format</strong> for sparse matr an <strong>array format</strong> for dense matrices. The <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/io.html#module-scipy.io" title="(in SciPy v1.9.3)"><code class="xref py py-mod docutils literal notranslate"><span class="pre">scipy.io</span></code></a> module provides the <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.mmread.html#scipy.io.mmread" title="(in SciPy v1.9.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.io.mmread</span></code></a> and <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.mmwrite.html#scipy.io.mmwrite" title="(in SciPy v1.9.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.io.mmwrite</span></code></a> functions to read and write data in Matrix Market format, respectively. -These functions work with either <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a> or <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.html#scipy.sparse.coo_matrix" title="(in SciPy v1.9.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse.coo_matrix</span></code></a> +These functions work with either <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a> or <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.coo_matrix.html#scipy.sparse.coo_matrix" title="(in SciPy v1.9.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse.coo_matrix</span></code></a> objects depending on whether the data is in <strong>array</strong> or <strong>coordinate</strong> format. These functions can be combined with those of NetworkX’s <a class="reference internal" href="../convert.html#module-networkx.convert_matrix" title="networkx.convert_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">convert_matrix</span></code></a> module to read and write Graphs in Matrix Market format.</p> diff --git a/reference/utils.html b/reference/utils.html index 18ea1694..860548f1 100644 --- a/reference/utils.html +++ b/reference/utils.html @@ -621,7 +621,7 @@ random selections.</p> <td><p>Decorator to allow number of nodes or container of nodes.</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="generated/networkx.utils.decorators.np_random_state.html#networkx.utils.decorators.np_random_state" title="networkx.utils.decorators.np_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">np_random_state</span></code></a>(random_state_argument)</p></td> -<td><p>Decorator to generate a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instance.</p></td> +<td><p>Decorator to generate a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> instance.</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="generated/networkx.utils.decorators.py_random_state.html#networkx.utils.decorators.py_random_state" title="networkx.utils.decorators.py_random_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">py_random_state</span></code></a>(random_state_argument)</p></td> <td><p>Decorator to generate a random.Random instance (or equiv).</p></td> diff --git a/release/migration_guide_from_2.x_to_3.0.html b/release/migration_guide_from_2.x_to_3.0.html index 5f51f1e6..e729ccfa 100644 --- a/release/migration_guide_from_2.x_to_3.0.html +++ b/release/migration_guide_from_2.x_to_3.0.html @@ -495,13 +495,13 @@ only available if these additional libraries are installed.</p> <ul class="simple"> <li><p><a class="reference internal" href="#matrix-to-array"><span class="std std-ref">Removal of matrix semantics</span></a>.</p> <ul> -<li><p>Removing all uses of <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.matrix.html#numpy.matrix" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.matrix</span></code></a> in favor of <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>.</p></li> +<li><p>Removing all uses of <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.matrix.html#numpy.matrix" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.matrix</span></code></a> in favor of <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>.</p></li> <li><p>Adoption of the scipy.sparse <strong>array</strong> interface.</p></li> </ul> </li> <li><p><a class="reference internal" href="#scipy-default-impl"><span class="std std-ref">NumPy or SciPy implementations of some algorithms by default (e.g. pagerank)</span></a>.</p></li> -<li><p><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> support for random number generation.</p></li> +<li><p><a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> support for random number generation.</p></li> <li><p><a class="reference internal" href="#recarray-to-structured"><span class="std std-ref">Replace recarray support</span></a> with more generic support for structured dtypes.</p></li> </ul> @@ -546,17 +546,17 @@ purposes as <code class="docutils literal notranslate"><span class="pre">network but is not exposed publicly to discourage it’s use.</p> </section> <section id="supporting-numpy-random-generator"> -<h2>Supporting <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a><a class="headerlink" href="#supporting-numpy-random-generator" title="Permalink to this heading">#</a></h2> +<h2>Supporting <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a><a class="headerlink" href="#supporting-numpy-random-generator" title="Permalink to this heading">#</a></h2> <p>NumPy v1.17 introduced a new interface for pseudo-random number generation. The <code class="xref py py-obj docutils literal notranslate"><span class="pre">py_random_state</span></code> and <code class="xref py py-obj docutils literal notranslate"><span class="pre">np_random_state</span></code> -decorators have added support for the new <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instances; -in other words, the <code class="docutils literal notranslate"><span class="pre">seed</span></code> argument now accepts <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instances:</p> +decorators have added support for the new <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instances; +in other words, the <code class="docutils literal notranslate"><span class="pre">seed</span></code> argument now accepts <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> instances:</p> <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">G</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">barbell_graph</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="gp">>>> </span><span class="n">pos</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">spring_layout</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">(</span><span class="mi">123456789</span><span class="p">))</span> </pre></div> </div> -<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> interface includes several improvements over the -original <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>, including better statistical properties +<p>The <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> interface includes several improvements over the +original <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a>, including better statistical properties and improved performance. However <code class="docutils literal notranslate"><span class="pre">Generator</span></code> is not stream-compatibile with <code class="docutils literal notranslate"><span class="pre">RandomState</span></code> and does not guarantee stream-compatibility with future versions of NumPy. @@ -584,7 +584,7 @@ possible but may require specific versions of numpy to be installed.</p> <span id="recarray-to-structured"></span><h2>NumPy structured dtypes for multi-attribute adjacency matrices<a class="headerlink" href="#numpy-structured-dtypes-for-multi-attribute-adjacency-matrices" title="Permalink to this heading">#</a></h2> <p>Prior to NetworkX 3.0, multi-attribute adjacency matrices were supported through the <code class="docutils literal notranslate"><span class="pre">nx.to_numpy_recarray</span></code> conversion function. -<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.recarray.html#numpy.recarray" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.recarray</span></code></a> is a convenience wrapper around <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> with structured +<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.recarray.html#numpy.recarray" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.recarray</span></code></a> is a convenience wrapper around <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> with structured dtypes. As such, thisconversion function has been removed in NetworkX 3.0 and support for structured dtypes has been added to <code class="docutils literal notranslate"><span class="pre">to_numpy_array</span></code> instead, generally diff --git a/release/release_2.2.html b/release/release_2.2.html index 94474f25..9d33e0ef 100644 --- a/release/release_2.2.html +++ b/release/release_2.2.html @@ -539,8 +539,8 @@ used. More precisely, the <a class="reference external" href="https://docs.pytho default RNG. You can also create your own RNG and pass it into the <code class="xref py py-obj docutils literal notranslate"><span class="pre">seed</span></code> argument. Finally, you can use an integer to indicate the state to set for the RNG. In this case a local RNG is created leaving the global RNG untouched. -Some functions use <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a> and some use <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a>, but we have written -a translater so that all functions CAN take a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> +Some functions use <a class="reference external" href="https://docs.python.org/3/library/random.html#module-random" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">random</span></code></a> and some use <a class="reference external" href="https://numpy.org/doc/stable/reference/random/index.html#module-numpy.random" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random</span></code></a>, but we have written +a translater so that all functions CAN take a <a class="reference external" href="https://numpy.org/doc/stable/reference/random/legacy.html#numpy.random.RandomState" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.RandomState</span></code></a> object. So a single RNG can be used for the entire package.</p> <p>Cyclic references between graph classes and views have been removed to ease subclassing without memory leaks. Graphs no longer hold references to views.</p> diff --git a/release/release_2.7.html b/release/release_2.7.html index c85ffe18..66944ac5 100644 --- a/release/release_2.7.html +++ b/release/release_2.7.html @@ -539,9 +539,9 @@ problem: <a class="reference internal" href="../reference/algorithms/generated/n <li><p>Added the Louvain community detection algorithm: <a class="reference internal" href="../reference/algorithms/generated/networkx.algorithms.community.louvain.louvain_communities.html#networkx.algorithms.community.louvain.louvain_communities" title="networkx.algorithms.community.louvain.louvain_communities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">louvain_communities</span></code></a> and <a class="reference internal" href="../reference/algorithms/generated/networkx.algorithms.community.louvain.louvain_partitions.html#networkx.algorithms.community.louvain.louvain_partitions" title="networkx.algorithms.community.louvain.louvain_partitions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">louvain_partitions</span></code></a></p></li> -<li><p>Removed all internal usage of the <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.matrix.html#numpy.matrix" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.matrix</span></code></a> class, and added a -<a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FutureWarning</span></code></a> to all functions that return a <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.matrix.html#numpy.matrix" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.matrix</span></code></a> instance. -The <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.matrix.html#numpy.matrix" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.matrix</span></code></a> class will be replaced with 2D <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a> instances +<li><p>Removed all internal usage of the <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.matrix.html#numpy.matrix" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.matrix</span></code></a> class, and added a +<a class="reference external" href="https://docs.python.org/3/library/exceptions.html#FutureWarning" title="(in Python v3.11)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">FutureWarning</span></code></a> to all functions that return a <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.matrix.html#numpy.matrix" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.matrix</span></code></a> instance. +The <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.matrix.html#numpy.matrix" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.matrix</span></code></a> class will be replaced with 2D <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a> instances in NetworkX 3.0.</p></li> <li><p>Added support for the <a class="reference external" href="https://docs.scipy.org/doc/scipy/reference/sparse.html#module-scipy.sparse" title="(in SciPy v1.9.3)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scipy.sparse</span></code></a> array interface. This includes <a class="reference internal" href="../reference/generated/networkx.convert_matrix.to_scipy_sparse_array.html#networkx.convert_matrix.to_scipy_sparse_array" title="networkx.convert_matrix.to_scipy_sparse_array"><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_scipy_sparse_array</span></code></a> and @@ -703,7 +703,7 @@ Improve performance of <code class="docutils literal notranslate"><span class="p <li><p>[<a class="reference external" href="https://github.com/networkx/networkx/pull/5324">#5324</a>] Add support for structured dtypes to <code class="docutils literal notranslate"><span class="pre">to_numpy_array</span></code>.</p></li> <li><p>[<a class="reference external" href="https://github.com/networkx/networkx/pull/5336">#5336</a>] -Add support for the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> interface for random number +Add support for the <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> interface for random number generation.</p></li> </ul> </section> @@ -711,7 +711,7 @@ generation.</p></li> <h2>API Changes<a class="headerlink" href="#api-changes" title="Permalink to this heading">#</a></h2> <ul class="simple"> <li><p>The values in the dictionary returned by -<a class="reference internal" href="../reference/generated/networkx.drawing.layout.rescale_layout_dict.html#networkx.drawing.layout.rescale_layout_dict" title="networkx.drawing.layout.rescale_layout_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rescale_layout_dict</span></code></a> are now <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a> objects +<a class="reference internal" href="../reference/generated/networkx.drawing.layout.rescale_layout_dict.html#networkx.drawing.layout.rescale_layout_dict" title="networkx.drawing.layout.rescale_layout_dict"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rescale_layout_dict</span></code></a> are now <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a> objects instead of tuples. This makes the return type of <code class="docutils literal notranslate"><span class="pre">rescale_layout_dict</span></code> consistent with that of all of the other layout functions.</p></li> <li><p>A <code class="docutils literal notranslate"><span class="pre">FutureWarning</span></code> has been added to <code class="docutils literal notranslate"><span class="pre">google_matrix</span></code> to indicate that the @@ -870,7 +870,7 @@ Deprecate redundant <code class="docutils literal notranslate"><span class="pre" <li><p>Add support for finding maximal cliques containing a set of nodes (#5172)</p></li> <li><p>MAINT: Remove unnecessary helper functions, use inbuilt methods for line graph generator (#5327)</p></li> <li><p>sampling from dict_keys objects is deprecated. (#5337)</p></li> -<li><p>Add support for <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.23)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> (#5336)</p></li> +<li><p>Add support for <a class="reference external" href="https://numpy.org/doc/stable/reference/random/generator.html#numpy.random.Generator" title="(in NumPy v1.24)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.random.Generator</span></code></a> (#5336)</p></li> <li><p>Update matching functions for error validation and speed (#4897)</p></li> <li><p>Update release requirements (#5338)</p></li> <li><p>Add structured dtypes to <code class="xref py py-obj docutils literal notranslate"><span class="pre">to_numpy_array</span></code> (#5324)</p></li> diff --git a/searchindex.js b/searchindex.js index fde3e0eb..fe1e1be0 100644 --- 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94, 97, 99, 100, 101, 102, 103, 107, 109, 110, 111, 114, 115, 121, 127, 128, 143, 165, 172, 198, 199, 202, 204, 215, 216, 218, 219, 220, 221, 230, 231, 235, 256, 267, 277, 278, 281, 289, 299, 310, 314, 324, 325, 335, 338, 361, 378, 383, 385, 387, 389, 390, 392, 399, 405, 406, 407, 422, 427, 428, 432, 433, 437, 460, 464, 480, 520, 521, 559, 560, 581, 582, 583, 590, 593, 614, 619, 626, 631, 635, 653, 656, 660, 661, 662, 676, 679, 681, 683, 691, 698, 699, 703, 711, 717, 718, 735, 737, 748, 760, 782, 786, 796, 862, 868, 886, 887, 890, 891, 907, 913, 924, 925, 926, 927, 943, 949, 967, 968, 971, 972, 988, 994, 1006, 1007, 1008, 1009, 1037, 1039, 1040, 1042, 1043, 1071, 1094, 1100, 1116, 1119, 1120, 1123, 1130, 1131, 1132, 1133, 1135, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1185, 1192, 1193, 1206, 1214, 1217, 1218, 1219, 1272, 1287, 1288, 1295, 1296, 1297, 1323, 1326, 1328, 1337, 1345, 1348, 1349, 1350, 1390, 1394, 1395, 1397, 1398, 1399, 1401, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "express": [8, 92, 110, 184, 315, 329, 330, 383, 384, 618, 619, 873, 916, 955, 998, 1199, 1287, 1326], "than": [8, 11, 34, 43, 55, 97, 99, 101, 102, 103, 115, 128, 142, 143, 144, 161, 199, 214, 215, 216, 218, 219, 221, 227, 231, 235, 241, 256, 277, 278, 281, 288, 289, 297, 298, 299, 304, 306, 307, 310, 311, 315, 316, 321, 324, 325, 326, 328, 329, 330, 341, 352, 358, 361, 374, 380, 381, 383, 384, 385, 387, 389, 390, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 425, 426, 429, 435, 464, 468, 469, 500, 527, 537, 559, 560, 581, 582, 583, 590, 625, 626, 635, 636, 652, 653, 656, 658, 659, 673, 676, 678, 679, 686, 690, 692, 693, 694, 698, 699, 711, 731, 735, 737, 748, 752, 761, 786, 887, 925, 947, 968, 992, 1007, 1038, 1042, 1043, 1060, 1102, 1135, 1154, 1162, 1165, 1167, 1172, 1174, 1185, 1187, 1194, 1198, 1226, 1230, 1231, 1236, 1237, 1238, 1239, 1275, 1276, 1296, 1297, 1326, 1328, 1345, 1348, 1349, 1350, 1353, 1354, 1358, 1365, 1366, 1379, 1382, 1395, 1402, 1404, 1405, 1408, 1413, 1423, 1425], "worst": [8, 210, 211, 212, 221, 228, 235, 264, 293, 294, 338, 345, 346, 347, 440, 513, 515, 516, 517, 518], "reus": [8, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1328, 1402], "subcircuit": 8, "multipl": [8, 11, 25, 40, 45, 77, 93, 94, 99, 103, 107, 109, 143, 157, 158, 166, 175, 188, 195, 207, 287, 311, 357, 385, 386, 423, 443, 447, 458, 460, 464, 485, 486, 487, 594, 595, 597, 615, 616, 641, 643, 678, 690, 691, 697, 705, 738, 762, 786, 796, 856, 857, 863, 869, 877, 884, 892, 901, 902, 908, 923, 928, 937, 938, 944, 946, 950, 959, 960, 962, 963, 965, 973, 982, 983, 989, 991, 1002, 1003, 1005, 1010, 1037, 1039, 1040, 1045, 1046, 1102, 1103, 1105, 1127, 1135, 1137, 1216, 1217, 1219, 1285, 1291, 1296, 1298, 1326, 1352, 1378, 1393, 1405, 1406, 1412, 1413, 1417, 1425, 1426], "wherea": [8, 103, 682, 762, 786, 791, 1165, 1417], "cannot": [8, 101, 103, 127, 132, 199, 232, 300, 362, 394, 476, 581, 582, 583, 584, 632, 722, 887, 925, 934, 968, 979, 1007, 1043, 1165, 1208, 1209, 1296, 1298, 1302, 1303, 1326, 1345, 1347, 1348, 1349, 1350], "subformula": 8, "onc": [8, 38, 54, 55, 88, 93, 94, 99, 100, 112, 127, 199, 227, 230, 231, 232, 246, 247, 360, 374, 380, 388, 422, 423, 428, 488, 491, 492, 581, 582, 583, 652, 678, 679, 717, 718, 887, 925, 968, 1007, 1046, 1066, 1087, 1217, 1311, 1326, 1403, 1407], "thu": [8, 88, 101, 103, 115, 215, 216, 220, 256, 258, 331, 418, 419, 427, 428, 462, 477, 500, 512, 583, 679, 698, 699, 760, 762, 796, 1037, 1039, 1040, 1043, 1087, 1112, 1148, 1215, 1217, 1234, 1278, 1279, 1296, 1328, 1402, 1405, 1407], "wai": [8, 27, 52, 53, 55, 75, 86, 88, 93, 97, 99, 100, 101, 102, 103, 104, 107, 110, 115, 132, 152, 157, 158, 165, 184, 226, 281, 297, 298, 315, 330, 337, 356, 588, 598, 615, 618, 678, 691, 730, 760, 791, 796, 854, 856, 857, 862, 873, 899, 901, 902, 907, 915, 916, 935, 937, 938, 943, 955, 980, 982, 983, 988, 996, 998, 1037, 1039, 1040, 1041, 1097, 1165, 1213, 1215, 1217, 1239, 1262, 1269, 1272, 1326, 1328, 1330, 1393, 1394, 1404, 1406, 1411, 1426], "infeas": [8, 422], "circuit_to_formula": 8, "dag_to_branch": [8, 758, 1408], "transfer": [8, 202, 204, 230, 231, 469, 890, 891, 926, 927, 971, 972, 1008, 1009, 1420], "oper": [8, 30, 52, 95, 101, 112, 115, 168, 184, 189, 227, 374, 423, 460, 546, 547, 548, 552, 553, 554, 577, 595, 598, 601, 671, 672, 673, 674, 679, 680, 758, 786, 865, 873, 878, 910, 916, 946, 955, 960, 991, 998, 1036, 1068, 1088, 1103, 1164, 1218, 1219, 1295, 1302, 1319, 1323, 1325, 1326, 1393, 1394, 1400, 1404, 1405, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1417], "variabl": [8, 94, 132, 373, 530, 540, 618, 619, 732, 796, 1037, 1038, 1039, 1040, 1154, 1165, 1326, 1408, 1412, 1413, 1414, 1420], "formula_to_str": 8, "_to_str": 8, "root": [8, 67, 84, 293, 294, 338, 387, 389, 390, 394, 449, 460, 559, 577, 609, 671, 673, 678, 704, 728, 730, 739, 760, 791, 1119, 1120, 1125, 1126, 1145, 1147, 1235, 1271, 1272, 1323, 1365, 1366, 1393, 1406, 1407, 1408, 1412, 1413, 1423, 1425], "children": [8, 460, 577, 1145, 1155, 1272, 1365, 1366], "otherwis": [8, 92, 110, 146, 149, 171, 178, 184, 185, 198, 217, 230, 249, 250, 284, 297, 298, 303, 306, 307, 311, 315, 316, 322, 323, 324, 325, 326, 329, 330, 343, 353, 358, 393, 394, 395, 396, 397, 398, 410, 411, 412, 418, 419, 422, 425, 426, 462, 463, 464, 470, 479, 488, 490, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 521, 555, 562, 563, 568, 572, 574, 584, 586, 588, 597, 601, 616, 618, 619, 633, 663, 673, 687, 688, 689, 696, 698, 699, 734, 735, 736, 737, 751, 848, 867, 873, 874, 886, 893, 912, 916, 917, 924, 929, 934, 948, 955, 956, 967, 974, 979, 993, 998, 999, 1006, 1068, 1091, 1135, 1137, 1165, 1185, 1197, 1217, 1270, 1282, 1283, 1284, 1307, 1309, 1312, 1342, 1356, 1357, 1376, 1409, 1413, 1426], "child": [8, 1147, 1272], "must": [8, 11, 93, 94, 95, 99, 100, 103, 110, 151, 152, 158, 161, 171, 204, 206, 207, 214, 215, 216, 219, 230, 231, 232, 252, 253, 257, 258, 259, 260, 261, 262, 264, 267, 268, 269, 271, 273, 276, 281, 285, 297, 298, 306, 307, 315, 316, 317, 318, 319, 324, 325, 327, 329, 330, 342, 361, 362, 363, 378, 382, 385, 391, 410, 411, 412, 413, 425, 429, 440, 471, 472, 473, 474, 475, 545, 546, 547, 548, 549, 550, 551, 553, 555, 556, 557, 558, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 577, 578, 579, 580, 584, 585, 586, 587, 588, 589, 593, 597, 599, 601, 602, 603, 604, 615, 626, 627, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 680, 690, 692, 698, 699, 707, 721, 734, 735, 736, 737, 789, 796, 853, 854, 857, 867, 891, 892, 898, 899, 902, 912, 928, 934, 938, 972, 973, 979, 983, 1010, 1037, 1038, 1039, 1040, 1063, 1071, 1085, 1102, 1133, 1137, 1162, 1165, 1173, 1176, 1186, 1188, 1190, 1193, 1197, 1199, 1209, 1213, 1217, 1219, 1235, 1239, 1240, 1270, 1275, 1276, 1277, 1278, 1279, 1295, 1296, 1298, 1307, 1309, 1310, 1311, 1312, 1315, 1333, 1337, 1338, 1339, 1340, 1359, 1361, 1362, 1363, 1364, 1365, 1366, 1376, 1393, 1394, 1395, 1407, 1426], "NOT": [8, 110, 199, 549, 550, 551, 748, 887, 925, 968, 1007], "util": [8, 14, 36, 44, 45, 93, 97, 102, 103, 229, 230, 231, 316, 374, 423, 425, 426, 429, 460, 496, 678, 679, 758, 1044, 1242, 1299, 1301, 1303, 1310, 1319, 1320, 1321, 1325, 1402, 1406, 1407, 1411, 1413, 1416, 1419], "arbitrary_el": [8, 1392, 1413], "nb": [8, 1331, 1334], "left": [8, 71, 115, 183, 311, 312, 322, 324, 325, 385, 559, 560, 584, 616, 688, 689, 739, 1106, 1134, 1136, 1146, 1179, 1206, 1280, 1355, 1358, 1404], "right": [8, 71, 110, 111, 115, 152, 206, 322, 385, 427, 428, 500, 559, 560, 584, 585, 587, 588, 615, 616, 688, 689, 739, 854, 935, 980, 1134, 1136, 1146, 1155, 1157, 1179, 1206, 1213, 1215, 1270, 1280], "littl": [8, 94, 298, 307], "mislead": 8, "That": [8, 97, 132, 165, 212, 221, 227, 295, 385, 436, 465, 525, 535, 555, 588, 657, 671, 672, 673, 674, 691, 704, 717, 791, 862, 907, 943, 988, 1046, 1162, 1210, 1296, 1388, 1404, 1409], "okai": 8, "becaus": [8, 11, 54, 69, 94, 99, 101, 102, 103, 112, 132, 161, 215, 216, 220, 255, 311, 378, 387, 389, 390, 394, 411, 412, 427, 494, 498, 499, 500, 510, 569, 585, 587, 615, 616, 632, 652, 934, 979, 1038, 1236, 1273, 1296, 1303, 1326, 1345, 1350, 1404, 1407, 1416], "AND": [8, 110, 598, 748, 762], "OR": [8, 110, 157, 175, 188, 856, 869, 877, 901, 937, 947, 950, 959, 982, 992], "symmetr": [8, 145, 148, 237, 545, 586, 593, 761, 1173, 1192, 1235, 1246, 1250, 1251, 1256, 1258, 1269, 1320, 1321, 1387], "It": [8, 52, 56, 58, 92, 93, 94, 97, 99, 101, 102, 104, 107, 110, 112, 115, 132, 172, 184, 207, 214, 215, 216, 229, 230, 231, 249, 260, 261, 262, 264, 278, 310, 316, 324, 325, 326, 343, 346, 347, 351, 353, 412, 414, 415, 416, 417, 418, 419, 429, 438, 440, 452, 457, 464, 480, 496, 500, 508, 530, 540, 545, 559, 560, 565, 566, 567, 582, 588, 594, 595, 598, 600, 601, 615, 619, 628, 629, 630, 652, 658, 659, 663, 671, 674, 692, 717, 718, 719, 760, 761, 762, 791, 796, 868, 873, 892, 913, 916, 928, 949, 955, 973, 994, 998, 1010, 1012, 1013, 1018, 1037, 1038, 1039, 1040, 1054, 1117, 1170, 1174, 1200, 1201, 1206, 1207, 1210, 1217, 1223, 1227, 1234, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1253, 1254, 1258, 1261, 1263, 1264, 1269, 1275, 1276, 1277, 1280, 1296, 1297, 1323, 1324, 1326, 1328, 1343, 1382, 1383, 1393, 1395, 1398, 1402, 1404, 1407, 1408, 1409, 1411, 1412, 1413, 1426], "just": [8, 99, 102, 103, 104, 184, 199, 338, 374, 439, 464, 559, 560, 577, 660, 661, 662, 692, 791, 873, 887, 916, 925, 946, 955, 960, 968, 991, 998, 1007, 1120, 1126, 1229, 1278, 1279, 1296, 1328, 1393, 1404, 1406], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1325, 1402, 1406, 1412], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1202, 1383], "doe": [8, 77, 93, 94, 99, 101, 102, 103, 104, 114, 115, 132, 147, 153, 154, 165, 168, 189, 207, 208, 227, 228, 229, 230, 231, 232, 293, 308, 339, 340, 342, 343, 352, 357, 373, 382, 385, 410, 414, 426, 450, 469, 494, 495, 496, 497, 498, 499, 500, 502, 503, 506, 507, 509, 510, 511, 512, 534, 544, 549, 550, 551, 564, 566, 583, 584, 586, 589, 601, 612, 626, 627, 678, 691, 693, 694, 698, 699, 717, 718, 721, 722, 723, 724, 725, 726, 762, 862, 865, 878, 892, 907, 910, 928, 943, 946, 960, 973, 988, 991, 1010, 1038, 1043, 1066, 1070, 1072, 1081, 1102, 1103, 1105, 1106, 1107, 1109, 1114, 1173, 1175, 1177, 1192, 1207, 1222, 1223, 1227, 1229, 1234, 1241, 1296, 1300, 1303, 1326, 1333, 1334, 1341, 1342, 1344, 1351, 1353, 1354, 1355, 1356, 1357, 1358, 1371, 1379, 1380, 1381, 1383, 1393, 1404, 1405, 1406, 1410, 1417, 1426], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1219], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1229, 1296, 1395, 1404], "everi": [8, 11, 57, 88, 93, 109, 112, 120, 144, 157, 161, 177, 211, 212, 220, 221, 229, 230, 231, 235, 243, 264, 287, 295, 300, 324, 325, 343, 352, 380, 397, 437, 439, 440, 450, 462, 471, 472, 473, 474, 475, 477, 483, 484, 491, 512, 516, 565, 606, 614, 615, 619, 632, 633, 635, 636, 663, 685, 687, 688, 717, 718, 791, 856, 901, 937, 982, 1052, 1053, 1054, 1070, 1071, 1072, 1085, 1086, 1102, 1103, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1148, 1162, 1195, 1216, 1217, 1257, 1264, 1278, 1279, 1296, 1407], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1177, 1207, 1208, 1404, 1406, 1407, 1426], "ha": [8, 11, 16, 44, 67, 88, 91, 93, 94, 95, 97, 99, 100, 101, 102, 103, 105, 107, 110, 112, 116, 120, 127, 152, 161, 165, 166, 173, 174, 175, 184, 188, 198, 207, 212, 214, 215, 219, 220, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 241, 242, 243, 244, 247, 249, 252, 269, 271, 272, 273, 274, 275, 276, 282, 289, 291, 293, 294, 295, 300, 305, 310, 324, 331, 343, 352, 355, 356, 363, 364, 365, 373, 378, 380, 381, 383, 384, 385, 386, 391, 393, 394, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 424, 427, 428, 429, 439, 450, 458, 460, 466, 467, 468, 471, 472, 473, 474, 475, 476, 477, 480, 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 522, 564, 566, 577, 578, 581, 590, 593, 605, 607, 610, 611, 622, 623, 624, 628, 629, 630, 632, 633, 634, 635, 636, 638, 646, 647, 649, 652, 657, 658, 682, 688, 690, 692, 697, 711, 717, 718, 729, 730, 731, 739, 749, 786, 791, 854, 862, 863, 869, 873, 877, 886, 892, 899, 907, 908, 916, 924, 928, 935, 943, 944, 948, 950, 955, 959, 967, 973, 980, 988, 989, 993, 998, 1006, 1010, 1040, 1043, 1045, 1066, 1068, 1070, 1072, 1075, 1080, 1084, 1098, 1099, 1101, 1102, 1103, 1105, 1122, 1130, 1145, 1154, 1160, 1162, 1165, 1176, 1180, 1185, 1193, 1195, 1196, 1197, 1198, 1199, 1207, 1210, 1211, 1215, 1217, 1222, 1234, 1239, 1243, 1244, 1248, 1249, 1254, 1259, 1261, 1264, 1267, 1269, 1270, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1283, 1284, 1285, 1286, 1289, 1291, 1293, 1296, 1300, 1326, 1328, 1330, 1333, 1334, 1353, 1354, 1371, 1372, 1379, 1382, 1393, 1394, 1395, 1398, 1403, 1404, 1405, 1406, 1407, 1409, 1413, 1414, 1416, 1423, 1425], "output": [8, 13, 16, 89, 93, 101, 102, 103, 109, 197, 287, 288, 345, 374, 380, 494, 498, 499, 509, 510, 575, 588, 677, 678, 691, 722, 1045, 1193, 1197, 1199, 1269, 1296, 1326, 1334, 1341, 1344, 1355, 1358, 1399, 1402, 1404, 1406, 1411, 1413, 1414, 1426], "two": [8, 11, 16, 27, 34, 38, 43, 54, 55, 57, 58, 65, 67, 71, 88, 93, 95, 99, 100, 102, 109, 112, 114, 115, 120, 132, 151, 171, 175, 184, 185, 188, 202, 207, 211, 212, 213, 214, 215, 216, 217, 220, 221, 226, 227, 230, 231, 232, 245, 249, 251, 252, 253, 257, 258, 260, 261, 262, 265, 269, 270, 271, 272, 273, 274, 275, 276, 282, 285, 286, 287, 289, 305, 311, 315, 316, 322, 326, 329, 330, 337, 341, 343, 345, 351, 352, 358, 359, 377, 380, 381, 383, 391, 411, 412, 419, 423, 428, 429, 430, 431, 442, 443, 444, 445, 447, 452, 453, 454, 457, 462, 471, 472, 473, 474, 475, 476, 480, 491, 494, 498, 499, 500, 502, 503, 506, 508, 509, 510, 511, 521, 545, 549, 550, 551, 555, 559, 560, 561, 562, 563, 564, 565, 566, 568, 569, 572, 574, 578, 584, 585, 586, 587, 588, 593, 598, 605, 607, 608, 610, 611, 615, 619, 626, 627, 629, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 680, 692, 694, 731, 732, 738, 739, 760, 761, 762, 780, 786, 791, 796, 853, 867, 869, 873, 874, 877, 890, 892, 898, 912, 916, 917, 926, 928, 934, 946, 948, 950, 955, 956, 959, 960, 971, 973, 979, 991, 993, 998, 999, 1008, 1010, 1019, 1020, 1021, 1022, 1036, 1037, 1039, 1040, 1056, 1084, 1088, 1098, 1100, 1101, 1106, 1107, 1108, 1109, 1114, 1116, 1134, 1146, 1147, 1149, 1151, 1152, 1156, 1174, 1185, 1186, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1204, 1207, 1210, 1211, 1215, 1217, 1218, 1243, 1244, 1253, 1271, 1272, 1275, 1276, 1294, 1295, 1296, 1323, 1324, 1326, 1328, 1359, 1360, 1363, 1393, 1394, 1395, 1397, 1402, 1404, 1405, 1406, 1407, 1410, 1411, 1413, 1425], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1420], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1217, 1226, 1262, 1263, 1326, 1407], "appear": [8, 83, 93, 95, 99, 100, 102, 179, 204, 230, 231, 238, 243, 246, 247, 277, 363, 364, 365, 378, 451, 452, 453, 455, 466, 470, 584, 585, 587, 588, 675, 679, 707, 730, 734, 736, 891, 972, 1036, 1088, 1102, 1136, 1150, 1152, 1154, 1157, 1159, 1187, 1188, 1277, 1282, 1323, 1324, 1345, 1348, 1349, 1350, 1382, 1407, 1413, 1414], "both": [8, 52, 55, 92, 93, 94, 100, 101, 102, 103, 115, 161, 164, 204, 214, 215, 216, 217, 240, 257, 258, 259, 264, 282, 286, 287, 289, 337, 358, 379, 383, 415, 417, 418, 419, 423, 427, 440, 470, 502, 506, 545, 575, 581, 598, 600, 601, 602, 603, 604, 605, 606, 607, 610, 611, 615, 621, 635, 636, 653, 654, 655, 676, 711, 720, 760, 761, 762, 782, 891, 972, 1020, 1036, 1066, 1075, 1080, 1084, 1088, 1097, 1120, 1126, 1144, 1165, 1189, 1192, 1199, 1207, 1210, 1211, 1213, 1215, 1282, 1296, 1326, 1328, 1358, 1363, 1364, 1387, 1393, 1395, 1402, 1413, 1416, 1417, 1425, 1426], "negat": 8, "sole": [8, 786, 1278, 1279, 1326], "fourth": [8, 230, 231, 1326, 1404], "digraph": [8, 10, 11, 16, 21, 25, 41, 45, 56, 61, 67, 69, 70, 82, 88, 101, 102, 115, 132, 151, 152, 156, 157, 158, 160, 162, 163, 165, 166, 168, 170, 171, 172, 175, 176, 185, 186, 187, 188, 189, 192, 193, 194, 195, 196, 198, 199, 202, 204, 207, 208, 216, 227, 229, 230, 231, 240, 246, 247, 299, 308, 314, 318, 319, 321, 327, 328, 334, 335, 336, 337, 339, 340, 342, 343, 388, 391, 393, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 430, 431, 437, 450, 452, 453, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 481, 482, 492, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 513, 514, 518, 519, 523, 555, 566, 575, 576, 577, 588, 590, 613, 615, 623, 630, 636, 643, 644, 652, 656, 657, 658, 659, 663, 678, 688, 690, 693, 696, 697, 698, 699, 700, 701, 702, 706, 707, 708, 709, 711, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 740, 741, 744, 745, 746, 747, 748, 749, 750, 752, 760, 789, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 911, 912, 913, 915, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 957, 958, 963, 964, 965, 966, 967, 968, 972, 973, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 1000, 1001, 1003, 1004, 1005, 1006, 1007, 1010, 1035, 1037, 1038, 1039, 1040, 1041, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1135, 1150, 1154, 1168, 1169, 1170, 1173, 1177, 1178, 1180, 1182, 1183, 1184, 1185, 1189, 1217, 1270, 1272, 1273, 1274, 1283, 1284, 1287, 1290, 1292, 1298, 1323, 1326, 1333, 1337, 1342, 1356, 1357, 1362, 1365, 1366, 1371, 1393, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "add_nod": [8, 11, 26, 34, 69, 74, 89, 102, 157, 184, 246, 339, 340, 398, 422, 491, 492, 496, 504, 505, 508, 522, 523, 605, 607, 610, 611, 691, 796, 856, 873, 901, 916, 937, 955, 982, 998, 1037, 1039, 1040, 1086, 1275, 1326, 1345, 1407, 1408, 1417, 1426], "get_node_attribut": [8, 39, 44, 71, 1213, 1404], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1133, 1134, 1136], "22": [8, 35, 64, 66, 383, 384, 1271, 1323, 1403, 1408, 1412, 1422], "multipartite_layout": [8, 36, 61, 67, 1412, 1414, 1420], "subset_kei": [8, 36, 61, 67, 1109], "equal": [8, 36, 81, 144, 214, 215, 216, 230, 231, 238, 269, 271, 273, 276, 288, 297, 298, 300, 303, 306, 307, 310, 311, 312, 315, 316, 320, 323, 324, 325, 329, 330, 331, 373, 410, 411, 412, 413, 418, 419, 428, 471, 474, 476, 491, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 525, 535, 545, 552, 553, 554, 555, 568, 572, 605, 623, 657, 671, 672, 673, 674, 687, 688, 689, 690, 721, 722, 740, 741, 753, 761, 791, 1112, 1116, 1162, 1165, 1198, 1204, 1230, 1239, 1271, 1280, 1291, 1307, 1309, 1312, 1398, 1399], "plot_circuit": [8, 17], "southern": [9, 1265], "women": [9, 1265, 1398, 1406], "unipartit": [9, 115, 258, 259, 358], "properti": [9, 11, 18, 22, 33, 63, 86, 101, 102, 103, 112, 134, 159, 161, 166, 168, 175, 176, 179, 184, 188, 189, 190, 200, 284, 285, 286, 287, 288, 363, 364, 365, 388, 476, 500, 545, 569, 619, 685, 858, 863, 865, 869, 870, 873, 877, 878, 879, 888, 903, 908, 910, 916, 939, 944, 946, 950, 951, 955, 959, 960, 969, 984, 989, 991, 998, 1085, 1086, 1122, 1134, 1136, 1193, 1202, 1217, 1219, 1269, 1283, 1284, 1326, 1328, 1383, 1398, 1405, 1406, 1407, 1408, 1413, 1417, 1426], "These": [9, 52, 58, 73, 79, 86, 93, 94, 105, 112, 336, 385, 494, 512, 559, 671, 673, 732, 748, 779, 786, 1038, 1045, 1047, 1323, 1326, 1385, 1387, 1392, 1394, 1395, 1397, 1399, 1404, 1405, 1411, 1426], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1199, 1393, 1395, 1399, 1402, 1405, 1406, 1407, 1413, 1416], "et": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202], "al": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202, 1407, 1413], "1930": [9, 1396], "thei": [9, 54, 58, 65, 71, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 112, 132, 151, 165, 207, 213, 220, 249, 285, 287, 288, 296, 297, 298, 301, 302, 306, 307, 308, 309, 351, 362, 374, 391, 396, 427, 451, 452, 453, 454, 464, 465, 471, 472, 473, 474, 475, 496, 504, 505, 508, 512, 546, 547, 548, 559, 560, 576, 583, 586, 588, 600, 604, 675, 676, 704, 717, 750, 760, 786, 853, 862, 892, 898, 907, 928, 934, 943, 962, 973, 979, 988, 1002, 1010, 1036, 1038, 1066, 1085, 1088, 1109, 1120, 1126, 1133, 1135, 1137, 1151, 1159, 1165, 1193, 1197, 1198, 1217, 1271, 1272, 1323, 1328, 1353, 1354, 1356, 1357, 1359, 1363, 1394, 1396, 1402, 1404, 1406, 1409, 1414, 1426], "repres": [9, 11, 26, 43, 52, 54, 57, 67, 92, 99, 107, 115, 230, 231, 265, 281, 283, 286, 287, 288, 291, 292, 338, 350, 361, 362, 363, 377, 378, 380, 381, 382, 385, 386, 391, 448, 452, 453, 455, 457, 460, 465, 466, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 521, 565, 577, 578, 579, 580, 586, 588, 609, 615, 618, 619, 656, 660, 664, 667, 676, 679, 691, 692, 695, 697, 698, 699, 700, 702, 728, 730, 731, 734, 736, 739, 752, 786, 791, 796, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1045, 1081, 1102, 1140, 1151, 1185, 1193, 1194, 1196, 1197, 1198, 1199, 1209, 1217, 1240, 1243, 1246, 1250, 1258, 1267, 1269, 1272, 1273, 1278, 1279, 1323, 1324, 1326, 1329, 1330, 1346, 1347, 1388, 1393, 1406], "observ": [9, 13, 132, 223, 1414, 1426], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 56, 59, 64, 66, 71, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1150, 1242, 1250, 1262, 1406, 1408, 1426], "event": [9, 25, 99, 100, 110, 1165, 1229, 1300], "18": [9, 44, 64, 66, 93, 324, 325, 345, 383, 384, 618, 1169, 1249, 1255, 1258, 1260, 1263, 1269, 1393, 1406, 1416, 1417, 1421, 1426], "bipartit": [9, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 350, 351, 358, 377, 439, 440, 443, 581, 588, 758, 1043, 1106, 1151, 1203, 1204, 1205, 1265, 1325, 1395, 1398, 1399, 1400, 1401, 1406, 1407, 1411, 1413, 1417, 1421, 1425], "biadjac": [9, 282, 283, 1400, 1406], "7": [9, 12, 14, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 89, 99, 101, 102, 115, 125, 151, 158, 170, 171, 192, 207, 232, 268, 297, 299, 314, 322, 327, 332, 333, 339, 340, 342, 362, 374, 380, 391, 403, 410, 413, 414, 415, 423, 424, 425, 426, 441, 445, 446, 483, 496, 501, 508, 511, 512, 555, 581, 586, 618, 619, 630, 652, 658, 663, 671, 674, 680, 695, 703, 706, 707, 708, 730, 747, 750, 761, 796, 853, 857, 866, 867, 881, 892, 898, 902, 911, 912, 915, 920, 928, 934, 938, 947, 973, 979, 983, 992, 996, 1010, 1037, 1039, 1040, 1052, 1053, 1085, 1100, 1104, 1148, 1212, 1242, 1248, 1250, 1251, 1255, 1258, 1260, 1273, 1323, 1326, 1330, 1339, 1340, 1345, 1348, 1349, 1350, 1382, 1392, 1394, 1402, 1403, 1405, 1408, 1409, 1410, 1411, 1412, 1413, 1426], "12": [9, 11, 19, 25, 44, 50, 55, 58, 64, 65, 66, 89, 91, 93, 229, 230, 231, 265, 345, 380, 381, 392, 399, 405, 406, 407, 449, 486, 501, 516, 568, 572, 574, 606, 616, 1052, 1053, 1054, 1133, 1136, 1150, 1244, 1245, 1249, 1254, 1257, 1263, 1335, 1406, 1408, 1412, 1426], "9": [9, 11, 12, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 82, 89, 101, 102, 111, 115, 125, 232, 293, 295, 339, 340, 342, 346, 347, 356, 374, 380, 405, 406, 424, 438, 449, 494, 496, 501, 504, 505, 508, 545, 566, 581, 586, 676, 706, 707, 708, 761, 1100, 1104, 1148, 1150, 1194, 1199, 1212, 1217, 1235, 1246, 1255, 1267, 1273, 1283, 1284, 1323, 1326, 1328, 1396, 1403, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "11": [9, 25, 33, 44, 64, 65, 66, 68, 89, 102, 110, 115, 157, 210, 239, 240, 297, 298, 303, 306, 307, 323, 392, 399, 405, 406, 407, 413, 415, 417, 422, 501, 514, 517, 606, 618, 680, 721, 738, 856, 901, 937, 982, 1052, 1053, 1054, 1100, 1150, 1287, 1403, 1410, 1413, 1414, 1419, 1424, 1425, 1426], "13": [9, 11, 38, 44, 64, 66, 89, 91, 156, 229, 230, 231, 343, 501, 703, 855, 900, 936, 981, 1150, 1192, 1406, 1420, 1426], "16": [9, 19, 31, 44, 45, 64, 66, 70, 229, 230, 231, 346, 347, 387, 389, 390, 394, 453, 508, 511, 512, 519, 571, 592, 606, 748, 749, 750, 1109, 1205, 1256, 1271, 1286, 1323, 1406, 1411, 1426], "17": [9, 21, 44, 64, 66, 103, 229, 230, 231, 297, 508, 680, 693, 1405, 1406, 1426], "friend": [9, 545, 1407, 1412], "member": [9, 92, 93, 94, 100, 112, 315, 317, 318, 319, 330, 391, 483, 484, 586, 691, 1222, 1267, 1403], "evelyn": 9, "jefferson": 9, "laura": 9, "mandevil": 9, "theresa": 9, "anderson": 9, "brenda": 9, "roger": 9, "charlott": 9, "mcdowd": 9, "franc": 9, "eleanor": 9, "nye": 9, "pearl": [9, 132], "oglethorp": 9, "ruth": 9, "desand": 9, "vern": 9, "sanderson": 9, "myra": 9, "liddel": 9, "katherina": 9, "sylvia": 9, "avondal": 9, "nora": 9, "fayett": 9, "helen": 9, "lloyd": 9, "dorothi": 9, "murchison": 9, "olivia": 9, "carleton": 9, "flora": 9, "price": 9, "meet": [9, 94, 1165, 1196, 1197, 1198], "50": [9, 25, 30, 34, 40, 50, 54, 55, 56, 57, 64, 65, 272, 312, 1117, 1193, 1197, 1198, 1251, 1297, 1302], "45": [9, 58, 64, 110, 226, 300, 409, 1175], "57": [9, 64], "46": [9, 64, 235, 564, 619, 1264], "24": [9, 19, 37, 64, 66, 68, 103, 383, 384, 496, 505, 508, 703, 1212, 1229, 1244, 1262, 1271, 1403], "32": [9, 64, 66, 68, 209, 211, 212, 383, 384, 564, 703, 1403, 1411], "36": [9, 21, 64, 68, 752, 1150, 1262, 1271, 1353, 1354, 1379, 1403], "31": [9, 64, 66, 229, 230, 231, 260, 261, 262, 289, 383, 384, 409, 703, 1226, 1235, 1403], "40": [9, 50, 64, 80, 101, 297, 300, 555, 672, 1173, 1240, 1271], "38": [9, 64, 688, 1271], "33": [9, 58, 64, 66, 68, 93, 383, 384, 500, 514, 703, 1267, 1271, 1403, 1414], "37": [9, 56, 64, 68, 303, 311, 312, 323, 324, 325, 496, 508, 1039, 1040, 1271, 1393, 1403, 1408, 1425], "43": [9, 64, 324, 325, 606, 1244, 1271], "34": [9, 64, 68, 331, 508, 762, 1271, 1403], "algorithm": [9, 14, 15, 44, 52, 54, 88, 93, 94, 95, 96, 102, 103, 107, 109, 110, 111, 112, 114, 115, 117, 120, 121, 122, 125, 127, 128, 132, 133, 136, 141, 151, 210, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 228, 229, 230, 231, 232, 235, 249, 251, 252, 253, 254, 255, 256, 258, 260, 261, 262, 263, 264, 265, 266, 267, 272, 275, 277, 278, 280, 282, 284, 285, 286, 287, 288, 289, 290, 293, 296, 297, 298, 299, 301, 302, 303, 306, 307, 308, 309, 311, 312, 315, 320, 322, 323, 324, 325, 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853, 898, 934, 979, 1011, 1038, 1042, 1043, 1105, 1106, 1107, 1109, 1114, 1116, 1117, 1125, 1126, 1155, 1165, 1168, 1169, 1177, 1178, 1179, 1180, 1181, 1185, 1186, 1187, 1188, 1193, 1195, 1200, 1201, 1202, 1205, 1207, 1209, 1210, 1216, 1223, 1224, 1226, 1227, 1228, 1230, 1231, 1232, 1234, 1235, 1239, 1260, 1269, 1275, 1276, 1277, 1298, 1302, 1319, 1320, 1321, 1323, 1325, 1328, 1367, 1368, 1386, 1393, 1394, 1395, 1400, 1401, 1402, 1403, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1419, 1422, 1424, 1425, 1426], "davis_southern_women_graph": [9, 88, 263], "top": [9, 34, 52, 67, 106, 111, 112, 115, 125, 260, 272, 284, 350, 381, 670, 675, 770, 1106, 1134, 1136, 1252, 1396, 1399, 1407, 1412, 1413, 1416], "bottom": [9, 91, 115, 260, 272, 274, 284, 285, 286, 287, 288, 350, 381, 1134, 1136, 1155, 1404, 1416], "biadjacency_matrix": [9, 283], "onto": [9, 284, 285, 286, 287, 288, 559, 560], "projected_graph": [9, 115, 284, 285, 286, 288, 351], "keep": [9, 92, 93, 94, 115, 204, 345, 346, 347, 362, 377, 387, 389, 390, 394, 583, 598, 693, 694, 891, 972, 1117, 1207, 1210, 1278, 1279, 1296, 1376, 1394, 1411, 1414], "co": [9, 26, 94, 99, 144, 752, 1326], "occur": [9, 93, 95, 100, 230, 231, 277, 278, 280, 383, 581, 582, 583, 588, 1043, 1117, 1120, 1126, 1282, 1296], "count": [9, 185, 237, 238, 242, 243, 245, 297, 298, 310, 315, 330, 360, 386, 443, 568, 597, 619, 749, 753, 874, 917, 944, 950, 956, 959, 999, 1060, 1179, 1278, 1279, 1406, 1407, 1416], "share": [9, 54, 58, 92, 94, 112, 165, 199, 214, 215, 216, 221, 278, 285, 287, 288, 294, 358, 359, 376, 418, 419, 460, 462, 480, 569, 578, 691, 732, 862, 887, 907, 925, 943, 968, 988, 1007, 1217, 1328], "contact": [9, 92, 688, 1195, 1326], "weighted_projected_graph": [9, 284, 285, 286, 287, 1417], "648": 9, "071": [9, 17], "plot_davis_club": [9, 17], "retain": [10, 102, 110, 230, 284, 285, 286, 287, 288, 1100, 1187, 1295], "pattern": [10, 54, 93, 103, 236, 241, 244, 248, 385, 494, 519, 555, 671, 672, 673, 674, 690, 691, 693, 762, 786, 1036, 1088, 1388, 1413], "add": [10, 11, 26, 34, 41, 45, 49, 52, 61, 71, 88, 89, 91, 93, 94, 101, 102, 105, 106, 115, 151, 152, 153, 154, 156, 157, 158, 164, 207, 222, 223, 229, 282, 285, 341, 374, 411, 412, 423, 428, 430, 431, 450, 460, 581, 582, 583, 589, 614, 615, 618, 619, 654, 690, 701, 717, 718, 796, 850, 853, 854, 855, 856, 857, 892, 895, 898, 899, 900, 901, 902, 928, 931, 934, 935, 936, 937, 938, 973, 976, 979, 980, 981, 982, 983, 984, 1010, 1037, 1038, 1039, 1040, 1042, 1049, 1052, 1053, 1054, 1100, 1154, 1165, 1172, 1185, 1207, 1210, 1217, 1219, 1233, 1234, 1236, 1302, 1326, 1353, 1354, 1356, 1357, 1379, 1380, 1383, 1393, 1394, 1395, 1398, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "compressor": [10, 690, 786], "do": [10, 55, 75, 88, 92, 93, 94, 96, 99, 101, 102, 106, 107, 109, 111, 115, 133, 165, 184, 199, 202, 204, 230, 231, 238, 243, 277, 278, 280, 362, 380, 410, 411, 412, 418, 419, 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1413, 1426], "add_edges_from": [10, 15, 16, 36, 41, 70, 82, 89, 115, 132, 151, 158, 165, 199, 204, 207, 236, 248, 287, 327, 376, 422, 423, 425, 426, 460, 469, 501, 511, 512, 572, 574, 588, 688, 690, 705, 706, 707, 709, 730, 742, 743, 796, 853, 857, 862, 887, 891, 892, 898, 902, 907, 925, 927, 928, 934, 938, 943, 956, 962, 963, 968, 972, 973, 979, 983, 988, 999, 1002, 1003, 1007, 1009, 1010, 1037, 1039, 1040, 1070, 1085, 1094, 1135, 1154, 1217, 1287, 1291, 1326, 1404, 1407, 1426], "base_opt": [10, 15], "dict": [10, 15, 19, 25, 39, 54, 57, 58, 67, 70, 88, 101, 102, 107, 109, 144, 145, 148, 157, 159, 160, 165, 168, 169, 176, 179, 184, 189, 190, 195, 197, 200, 202, 204, 207, 220, 237, 239, 240, 252, 290, 309, 310, 329, 334, 336, 353, 408, 411, 412, 416, 422, 427, 470, 473, 481, 482, 496, 502, 512, 545, 561, 563, 565, 566, 575, 577, 578, 579, 580, 588, 614, 628, 631, 636, 637, 638, 640, 642, 644, 645, 646, 647, 648, 649, 662, 666, 669, 687, 688, 691, 705, 706, 707, 713, 715, 749, 750, 760, 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198, 690, 886, 924, 967, 1006, 1059, 1154, 1271, 1406, 1407, 1426], "nonexp_graph": 10, "compression_nod": 10, "summar": [10, 15, 100, 101, 690, 691, 758, 791, 1325, 1328, 1413], "dedensifi": [10, 758], "threshold": [10, 57, 83, 112, 220, 229, 231, 380, 381, 690, 692, 695, 696, 758, 786, 1117, 1193, 1194, 1196, 1197, 1198, 1325, 1398, 1406, 1407, 1408, 1412, 1414], "copi": [10, 16, 38, 44, 93, 95, 106, 167, 196, 199, 202, 203, 204, 205, 284, 285, 286, 287, 288, 341, 388, 390, 392, 406, 433, 434, 435, 436, 437, 453, 460, 469, 521, 584, 585, 587, 596, 599, 602, 603, 605, 606, 607, 610, 611, 613, 614, 633, 636, 690, 864, 885, 887, 890, 891, 909, 925, 926, 927, 945, 963, 966, 968, 971, 972, 990, 1003, 1007, 1008, 1009, 1035, 1038, 1057, 1061, 1063, 1066, 1082, 1083, 1122, 1183, 1189, 1217, 1223, 1227, 1251, 1270, 1294, 1295, 1296, 1403, 1404, 1406, 1407, 1408, 1409, 1412, 1413, 1422, 1425], "nonexp_node_color": 10, "nonexp_node_s": 10, "yellow": [10, 15, 598, 760, 1426], "nonexp_po": 10, "75": [10, 34, 239, 260, 299, 314, 355, 356, 386, 682, 1169, 1170, 1171, 1173, 1404, 1408, 1426], "c_node": [10, 690], "spot": 10, "238": [10, 17, 217], "plot_dedensif": [10, 17], "153": [11, 455], "curiou": 11, "let": [11, 55, 58, 93, 97, 101, 103, 217, 257, 280, 282, 299, 300, 313, 322, 371, 372, 383, 586, 619, 762, 1219, 1278, 1279, 1326, 1425], "defin": [11, 24, 52, 58, 69, 97, 112, 127, 213, 222, 223, 239, 240, 260, 261, 262, 263, 285, 289, 311, 316, 329, 334, 335, 345, 346, 347, 356, 385, 386, 390, 424, 425, 426, 429, 432, 433, 434, 435, 436, 437, 449, 464, 465, 466, 469, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 519, 567, 569, 570, 571, 573, 574, 575, 577, 586, 614, 615, 619, 621, 625, 652, 671, 673, 674, 676, 684, 685, 686, 687, 688, 689, 728, 730, 738, 751, 752, 753, 762, 791, 796, 1037, 1038, 1039, 1040, 1045, 1047, 1071, 1081, 1098, 1147, 1154, 1170, 1172, 1195, 1197, 1280, 1286, 1287, 1288, 1296, 1320, 1321, 1326, 1344, 1353, 1354, 1359, 1363, 1379, 1395, 1402, 1407, 1408, 1412, 1426], "an": [11, 15, 24, 25, 31, 34, 38, 41, 44, 46, 49, 52, 54, 55, 58, 63, 66, 67, 71, 75, 76, 77, 88, 91, 92, 93, 94, 95, 96, 99, 100, 101, 102, 103, 104, 107, 110, 112, 114, 115, 116, 120, 121, 127, 128, 132, 141, 151, 152, 157, 158, 160, 165, 166, 167, 168, 170, 175, 179, 180, 181, 184, 188, 189, 191, 192, 193, 194, 195, 198, 199, 201, 204, 206, 207, 208, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 243, 249, 250, 251, 255, 256, 264, 266, 267, 269, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 291, 292, 293, 294, 295, 297, 298, 299, 301, 302, 306, 307, 308, 309, 311, 312, 315, 316, 318, 319, 320, 322, 324, 325, 326, 329, 330, 332, 341, 342, 343, 345, 346, 347, 348, 349, 350, 351, 353, 357, 362, 363, 364, 365, 366, 370, 373, 374, 375, 377, 378, 379, 380, 381, 383, 384, 385, 387, 388, 389, 390, 392, 394, 395, 400, 402, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 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740, 743, 748, 752, 760, 761, 762, 767, 775, 782, 791, 796, 801, 806, 810, 814, 818, 822, 827, 832, 837, 842, 847, 849, 850, 851, 853, 854, 856, 857, 859, 862, 863, 864, 865, 866, 869, 871, 872, 873, 877, 878, 880, 881, 882, 883, 884, 886, 887, 889, 891, 892, 894, 895, 896, 898, 899, 901, 902, 904, 907, 908, 909, 910, 911, 914, 915, 916, 920, 921, 922, 923, 924, 925, 927, 928, 930, 931, 932, 934, 935, 937, 938, 940, 943, 944, 945, 946, 947, 948, 950, 952, 953, 954, 955, 959, 960, 961, 962, 963, 964, 965, 967, 968, 970, 972, 973, 975, 976, 977, 979, 980, 982, 983, 985, 988, 989, 990, 991, 992, 993, 995, 996, 997, 998, 1002, 1003, 1004, 1005, 1006, 1007, 1009, 1010, 1012, 1013, 1018, 1020, 1036, 1037, 1038, 1039, 1040, 1042, 1043, 1045, 1046, 1049, 1050, 1051, 1061, 1062, 1066, 1068, 1074, 1075, 1081, 1082, 1084, 1085, 1086, 1087, 1088, 1090, 1094, 1098, 1099, 1100, 1101, 1102, 1103, 1105, 1115, 1117, 1122, 1133, 1135, 1137, 1143, 1144, 1146, 1149, 1150, 1151, 1152, 1154, 1155, 1157, 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1290, 1295, 1296, 1299, 1301, 1383, 1395, 1407, 1408, 1412, 1413, 1414, 1419, 1426], "follow": [11, 25, 44, 49, 52, 53, 65, 67, 83, 86, 91, 92, 93, 94, 95, 97, 99, 100, 101, 102, 103, 108, 110, 111, 128, 132, 151, 161, 171, 183, 207, 213, 227, 229, 230, 231, 243, 280, 305, 338, 343, 351, 362, 373, 378, 380, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 440, 452, 453, 465, 466, 496, 502, 503, 504, 505, 506, 507, 508, 588, 598, 599, 602, 615, 636, 679, 748, 750, 760, 762, 791, 853, 867, 892, 898, 912, 928, 934, 948, 973, 979, 993, 1010, 1102, 1103, 1105, 1144, 1165, 1175, 1179, 1185, 1188, 1200, 1201, 1209, 1219, 1225, 1233, 1234, 1241, 1251, 1260, 1274, 1275, 1276, 1277, 1281, 1296, 1315, 1323, 1326, 1328, 1329, 1388, 1393, 1395, 1399, 1404, 1406, 1407, 1409, 1411, 1412, 1413, 1425, 1426], "given": [11, 38, 44, 62, 64, 67, 91, 99, 101, 103, 112, 116, 141, 142, 144, 152, 158, 193, 197, 208, 211, 212, 227, 229, 235, 236, 248, 249, 260, 264, 266, 269, 271, 273, 274, 276, 279, 281, 283, 284, 285, 286, 287, 288, 320, 329, 331, 338, 344, 351, 353, 357, 362, 363, 364, 365, 373, 378, 380, 381, 385, 439, 454, 455, 460, 462, 470, 477, 478, 480, 497, 511, 512, 513, 559, 560, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 576, 578, 579, 580, 588, 589, 590, 614, 615, 616, 622, 623, 659, 660, 661, 662, 676, 677, 678, 679, 681, 683, 684, 686, 690, 691, 693, 697, 698, 699, 700, 702, 703, 704, 706, 707, 708, 709, 728, 729, 730, 731, 732, 739, 748, 753, 761, 782, 786, 854, 857, 882, 899, 902, 921, 935, 938, 963, 980, 983, 1003, 1046, 1085, 1086, 1094, 1101, 1102, 1135, 1144, 1151, 1162, 1175, 1176, 1177, 1178, 1179, 1180, 1181, 1189, 1199, 1200, 1201, 1206, 1207, 1208, 1209, 1210, 1221, 1222, 1240, 1269, 1273, 1274, 1276, 1295, 1300, 1302, 1315, 1323, 1353, 1354, 1379, 1380, 1394, 1395, 1406], "digit": [11, 70, 99], "base": [11, 15, 38, 43, 55, 58, 69, 93, 94, 100, 101, 102, 103, 107, 128, 132, 199, 203, 205, 212, 216, 220, 229, 296, 297, 301, 302, 303, 308, 309, 310, 311, 312, 322, 323, 324, 325, 329, 330, 337, 343, 346, 347, 362, 371, 373, 374, 380, 381, 382, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 425, 426, 427, 428, 430, 431, 449, 464, 466, 494, 498, 499, 500, 509, 510, 545, 555, 564, 566, 569, 574, 581, 614, 616, 660, 667, 680, 688, 691, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 717, 732, 738, 758, 761, 762, 786, 791, 796, 887, 925, 934, 935, 968, 979, 980, 1007, 1036, 1037, 1038, 1041, 1043, 1082, 1088, 1182, 1229, 1235, 1253, 1267, 1296, 1320, 1321, 1323, 1326, 1383, 1387, 1392, 1395, 1402, 1403, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1421, 1425], "obtain": [11, 91, 165, 207, 282, 345, 346, 347, 380, 383, 387, 388, 389, 390, 394, 465, 511, 606, 618, 619, 656, 722, 742, 743, 760, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1164, 1253, 1272, 1278, 1279, 1323, 1326, 1356, 1357, 1402, 1426], "seri": [11, 444, 616, 680, 1215, 1286], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1177, 1179, 1192, 1222], "end": [11, 25, 36, 52, 95, 101, 106, 153, 154, 206, 215, 227, 267, 268, 300, 332, 333, 342, 371, 372, 427, 614, 618, 619, 626, 627, 631, 632, 634, 635, 636, 639, 640, 650, 651, 652, 653, 654, 655, 660, 664, 667, 677, 678, 680, 734, 736, 1038, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1133, 1135, 1152, 1165, 1206, 1229, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1342, 1344, 1350, 1353, 1357, 1358, 1368, 1371, 1372, 1375, 1376, 1379, 1404, 1413], "In": [11, 16, 27, 43, 54, 57, 58, 88, 92, 93, 94, 95, 97, 99, 100, 101, 103, 110, 115, 127, 132, 133, 175, 184, 199, 217, 229, 230, 231, 235, 240, 257, 258, 259, 278, 283, 286, 288, 289, 299, 311, 312, 324, 325, 329, 350, 357, 378, 379, 380, 410, 413, 414, 415, 422, 429, 443, 447, 450, 458, 460, 494, 498, 499, 501, 510, 565, 568, 572, 574, 590, 591, 615, 619, 621, 652, 653, 654, 657, 658, 663, 670, 675, 676, 690, 691, 701, 703, 717, 718, 719, 730, 732, 740, 741, 742, 743, 761, 762, 767, 770, 789, 791, 796, 869, 873, 887, 916, 925, 954, 955, 968, 997, 998, 1007, 1037, 1038, 1039, 1040, 1042, 1043, 1066, 1100, 1101, 1117, 1154, 1168, 1199, 1203, 1206, 1207, 1208, 1210, 1216, 1217, 1222, 1226, 1231, 1233, 1241, 1295, 1296, 1300, 1320, 1321, 1326, 1328, 1350, 1394, 1398, 1399, 1404, 1405, 1406, 1407, 1408, 1409, 1413, 1414, 1426], "languag": [11, 92, 99, 110, 1042, 1324, 1341, 1342, 1344, 1381, 1382, 1383, 1411], "discret": [11, 104, 235, 249, 362, 409, 513, 517, 518, 618, 1164, 1165, 1178, 1180, 1186, 1190, 1204, 1278, 1279, 1282, 1314, 1315, 1323, 1406], "global": [11, 103, 314, 341, 410, 477, 486, 487, 509, 592, 1045, 1269, 1296, 1301, 1304, 1305, 1328, 1407, 1409, 1411], "attractor": [11, 388], "map": [11, 34, 38, 52, 67, 101, 102, 103, 115, 125, 144, 145, 148, 166, 169, 197, 238, 243, 264, 350, 369, 391, 412, 416, 417, 418, 419, 423, 424, 425, 426, 431, 440, 460, 530, 531, 534, 540, 541, 544, 545, 559, 560, 561, 563, 588, 614, 670, 676, 678, 751, 752, 760, 762, 863, 908, 944, 947, 989, 992, 1012, 1013, 1018, 1019, 1038, 1039, 1040, 1045, 1133, 1135, 1137, 1217, 1269, 1295, 1296, 1306, 1310, 1317, 1318, 1324, 1325, 1361, 1362, 1393, 1402, 1406, 1408, 1412, 1413, 1425, 1426], "restrict": [11, 102, 128, 353, 791, 1038, 1082, 1404], "For": [11, 54, 67, 88, 92, 93, 95, 97, 99, 101, 102, 103, 105, 107, 110, 115, 125, 128, 132, 143, 151, 158, 159, 160, 165, 168, 185, 189, 199, 200, 204, 226, 230, 231, 235, 238, 239, 240, 246, 247, 255, 259, 282, 297, 298, 299, 301, 302, 304, 306, 307, 308, 309, 311, 312, 314, 315, 316, 321, 322, 324, 325, 326, 328, 329, 330, 338, 346, 347, 356, 357, 358, 380, 385, 392, 395, 397, 398, 400, 402, 403, 404, 407, 410, 411, 412, 413, 414, 416, 417, 418, 419, 422, 429, 431, 432, 433, 434, 435, 436, 450, 453, 460, 479, 480, 488, 494, 495, 496, 498, 499, 502, 503, 506, 507, 509, 510, 522, 523, 524, 555, 565, 568, 572, 574, 585, 587, 598, 614, 615, 618, 619, 625, 633, 636, 641, 643, 659, 677, 678, 686, 687, 688, 691, 717, 718, 719, 733, 734, 735, 736, 737, 742, 743, 752, 753, 754, 762, 770, 775, 782, 786, 789, 791, 796, 853, 857, 858, 859, 862, 865, 874, 878, 887, 888, 891, 898, 902, 903, 904, 907, 910, 917, 925, 934, 938, 939, 940, 943, 946, 956, 960, 962, 968, 969, 979, 983, 984, 985, 988, 991, 999, 1002, 1007, 1037, 1038, 1039, 1040, 1042, 1062, 1064, 1066, 1071, 1085, 1094, 1098, 1099, 1101, 1102, 1103, 1105, 1111, 1115, 1131, 1132, 1133, 1135, 1138, 1139, 1140, 1141, 1142, 1143, 1146, 1148, 1151, 1154, 1175, 1177, 1179, 1180, 1185, 1188, 1189, 1193, 1195, 1196, 1197, 1198, 1199, 1213, 1214, 1217, 1219, 1224, 1228, 1232, 1242, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1285, 1286, 1289, 1291, 1293, 1296, 1298, 1326, 1328, 1333, 1345, 1348, 1349, 1350, 1356, 1357, 1358, 1371, 1382, 1390, 1393, 1395, 1400, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "108": [11, 1216], "513": [11, 1398, 1406], "reach": [11, 99, 100, 314, 324, 327, 376, 383, 387, 389, 390, 394, 410, 411, 412, 418, 419, 494, 498, 499, 510, 564, 566, 626, 627, 632, 640, 643, 652, 693, 711, 758, 1188, 1207, 1210, 1407], "orbit": 11, "up": [11, 70, 80, 93, 94, 97, 99, 100, 101, 104, 107, 132, 133, 346, 347, 377, 423, 427, 509, 530, 540, 577, 619, 652, 653, 657, 748, 1036, 1038, 1061, 1066, 1082, 1088, 1102, 1144, 1148, 1173, 1213, 1215, 1272, 1326, 1328, 1355, 1358, 1395, 1396, 1402, 1404, 1406, 1410, 1411, 1413, 1414, 1416, 1417, 1420, 1426], "reveal": [11, 711, 786], "maximum": [11, 112, 115, 209, 210, 211, 212, 214, 215, 217, 222, 224, 227, 257, 259, 264, 277, 278, 279, 281, 288, 296, 304, 311, 312, 315, 316, 317, 318, 319, 321, 324, 328, 330, 339, 341, 342, 343, 346, 347, 352, 356, 361, 373, 377, 380, 382, 383, 385, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 428, 440, 472, 473, 494, 498, 499, 500, 501, 502, 503, 506, 507, 509, 510, 520, 521, 564, 566, 581, 583, 589, 591, 592, 670, 671, 672, 673, 674, 676, 691, 693, 694, 704, 706, 707, 708, 710, 711, 712, 713, 714, 715, 716, 720, 723, 724, 732, 734, 735, 736, 737, 740, 741, 749, 758, 768, 791, 1117, 1133, 1135, 1137, 1165, 1181, 1198, 1199, 1200, 1201, 1208, 1225, 1237, 1238, 1302, 1323, 1395, 1402, 1406, 1407, 1412, 1413], "cycl": [11, 38, 44, 95, 120, 214, 227, 228, 229, 230, 231, 232, 263, 293, 294, 295, 338, 341, 343, 358, 449, 450, 451, 452, 453, 457, 462, 463, 464, 466, 467, 468, 480, 496, 501, 504, 505, 508, 519, 584, 585, 587, 608, 628, 629, 630, 632, 652, 657, 658, 663, 697, 727, 742, 743, 758, 791, 1043, 1052, 1135, 1137, 1148, 1149, 1152, 1163, 1186, 1190, 1242, 1244, 1260, 1264, 1325, 1395, 1397, 1398, 1401, 1403, 1404, 1406, 1407, 1408, 1411, 1412, 1414, 1424, 1425], "requir": [11, 38, 65, 93, 94, 95, 99, 100, 101, 102, 104, 106, 107, 109, 111, 115, 165, 207, 291, 292, 293, 296, 301, 302, 308, 309, 316, 437, 476, 500, 520, 521, 615, 680, 698, 699, 700, 720, 729, 731, 786, 791, 796, 862, 892, 907, 928, 943, 973, 988, 1010, 1037, 1039, 1040, 1046, 1111, 1143, 1192, 1193, 1199, 1215, 1217, 1235, 1296, 1326, 1345, 1348, 1349, 1350, 1382, 1393, 1394, 1396, 1402, 1405, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1419, 1420, 1425, 1426], "less": [11, 34, 43, 99, 101, 128, 142, 144, 227, 289, 324, 325, 380, 381, 383, 384, 385, 422, 425, 426, 429, 464, 520, 521, 636, 673, 686, 731, 786, 1135, 1162, 1174, 1185, 1187, 1194, 1275, 1276, 1353, 1354, 1379, 1404, 1405, 1408, 1411, 1413, 1414], "smallest": [11, 31, 211, 221, 264, 362, 370, 376, 381, 440, 483, 490, 679, 729, 731, 1048, 1200, 1249, 1259, 1275, 1276, 1302, 1320, 1321, 1407], "177": [11, 297, 298, 306, 307, 329], "e": [11, 15, 16, 31, 34, 38, 46, 52, 61, 65, 67, 69, 71, 76, 82, 89, 91, 92, 93, 94, 95, 97, 99, 101, 102, 103, 104, 107, 110, 111, 112, 115, 127, 141, 144, 151, 152, 157, 158, 168, 170, 171, 177, 189, 192, 195, 207, 211, 217, 218, 221, 226, 233, 236, 241, 244, 248, 249, 267, 275, 278, 280, 282, 284, 288, 289, 290, 293, 295, 300, 301, 302, 305, 306, 307, 308, 309, 311, 312, 313, 322, 324, 325, 326, 331, 332, 333, 339, 340, 341, 343, 345, 355, 356, 358, 361, 371, 372, 374, 378, 383, 385, 398, 405, 406, 429, 434, 449, 452, 453, 455, 467, 468, 469, 471, 472, 474, 475, 476, 479, 488, 490, 491, 492, 494, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 517, 518, 565, 566, 575, 577, 582, 586, 588, 590, 593, 598, 602, 615, 616, 618, 619, 625, 626, 675, 677, 678, 686, 688, 691, 692, 693, 732, 734, 736, 762, 796, 850, 853, 854, 856, 857, 865, 866, 867, 878, 881, 884, 892, 895, 898, 899, 901, 902, 910, 911, 912, 920, 923, 928, 931, 934, 935, 937, 938, 946, 947, 948, 960, 962, 965, 973, 976, 979, 980, 982, 983, 984, 991, 992, 993, 1002, 1005, 1010, 1037, 1038, 1039, 1040, 1047, 1097, 1100, 1104, 1133, 1134, 1135, 1136, 1146, 1154, 1165, 1175, 1177, 1179, 1180, 1182, 1183, 1184, 1187, 1192, 1193, 1194, 1203, 1204, 1205, 1207, 1210, 1219, 1222, 1226, 1230, 1233, 1234, 1260, 1266, 1268, 1278, 1279, 1280, 1287, 1288, 1292, 1295, 1302, 1303, 1310, 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1043, 1165, 1175, 1237, 1238, 1255, 1316, 1319, 1395, 1406, 1407, 1426], "abov": [11, 92, 93, 100, 101, 102, 103, 110, 291, 292, 315, 316, 325, 330, 380, 383, 386, 453, 460, 491, 494, 498, 499, 502, 503, 509, 510, 521, 686, 692, 730, 762, 1038, 1102, 1148, 1165, 1185, 1219, 1234, 1274, 1278, 1279, 1300, 1399, 1404, 1407, 1417], "correspond": [11, 67, 101, 103, 144, 161, 167, 222, 223, 227, 228, 229, 230, 231, 232, 233, 234, 265, 266, 281, 311, 312, 324, 325, 331, 332, 350, 361, 362, 380, 391, 415, 417, 418, 419, 422, 460, 476, 482, 511, 514, 581, 583, 588, 609, 615, 616, 624, 628, 629, 630, 677, 678, 679, 728, 729, 731, 732, 742, 743, 748, 791, 850, 864, 895, 909, 931, 945, 976, 990, 1098, 1099, 1101, 1102, 1103, 1105, 1109, 1115, 1135, 1143, 1144, 1175, 1177, 1178, 1179, 1180, 1181, 1193, 1194, 1212, 1222, 1271, 1272, 1274, 1276, 1277, 1278, 1279, 1281, 1323, 1332, 1333, 1335, 1336, 1355, 1358, 1359, 1360, 1363, 1364, 1370, 1394, 1405, 1406], "below": [11, 13, 25, 92, 94, 99, 100, 111, 151, 206, 330, 383, 408, 410, 411, 412, 413, 414, 415, 417, 419, 429, 464, 491, 492, 494, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 565, 615, 692, 796, 853, 898, 934, 979, 1037, 1039, 1040, 1117, 1144, 1175, 1177, 1217, 1222, 1242, 1275, 1276, 1277, 1296, 1349, 1393, 1402, 1404, 1417, 1426], "powersum": 11, "over": [11, 34, 38, 49, 71, 88, 94, 95, 99, 101, 102, 103, 109, 152, 157, 158, 159, 160, 168, 175, 176, 180, 181, 184, 188, 189, 190, 191, 195, 200, 201, 213, 214, 220, 230, 235, 291, 295, 299, 314, 315, 316, 320, 329, 330, 345, 346, 347, 362, 363, 364, 365, 369, 373, 374, 381, 385, 408, 409, 429, 477, 488, 489, 496, 497, 523, 526, 529, 533, 536, 539, 543, 598, 636, 678, 690, 703, 704, 705, 706, 707, 708, 710, 711, 719, 733, 734, 736, 738, 762, 849, 851, 854, 856, 857, 858, 859, 865, 869, 870, 871, 872, 873, 877, 878, 879, 880, 884, 888, 889, 894, 896, 899, 901, 902, 903, 904, 910, 914, 915, 916, 923, 930, 932, 935, 937, 938, 939, 940, 946, 951, 952, 953, 955, 960, 961, 965, 969, 970, 975, 977, 980, 982, 983, 984, 985, 991, 995, 996, 998, 1005, 1074, 1075, 1084, 1100, 1192, 1217, 1225, 1233, 1241, 1278, 1279, 1288, 1326, 1328, 1393, 1402, 1404, 1405, 1407, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1425, 1426], "converg": [11, 311, 324, 373, 564, 565, 566, 676, 1043, 1407, 1408], "singl": [11, 13, 58, 80, 93, 94, 99, 101, 102, 104, 107, 143, 151, 152, 156, 158, 166, 168, 175, 176, 180, 188, 189, 193, 220, 265, 274, 290, 293, 294, 299, 315, 322, 327, 331, 344, 353, 354, 391, 393, 424, 427, 443, 462, 464, 491, 494, 498, 499, 502, 503, 509, 510, 577, 584, 585, 587, 598, 621, 635, 660, 661, 662, 677, 678, 690, 705, 742, 743, 786, 791, 796, 853, 854, 855, 857, 863, 865, 869, 870, 871, 877, 878, 882, 898, 899, 900, 902, 908, 910, 914, 921, 934, 935, 936, 938, 944, 946, 950, 951, 952, 959, 960, 962, 963, 979, 980, 981, 983, 989, 991, 995, 1002, 1003, 1037, 1039, 1040, 1041, 1045, 1046, 1058, 1085, 1086, 1091, 1092, 1093, 1097, 1098, 1099, 1101, 1102, 1104, 1120, 1126, 1133, 1135, 1137, 1140, 1147, 1151, 1156, 1164, 1167, 1172, 1189, 1197, 1272, 1274, 1295, 1296, 1318, 1320, 1321, 1323, 1324, 1328, 1331, 1334, 1335, 1345, 1363, 1364, 1369, 1401, 1404, 1406, 1407, 1409, 1412, 1413], "fix": [11, 91, 93, 94, 95, 100, 106, 512, 693, 694, 709, 1117, 1269, 1394, 1396, 1400, 1402, 1403, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425], "appli": [11, 35, 52, 88, 92, 99, 220, 230, 231, 299, 322, 339, 340, 342, 358, 462, 509, 586, 588, 618, 625, 647, 786, 791, 1036, 1085, 1086, 1088, 1094, 1135, 1137, 1164, 1188, 1197, 1242, 1269, 1282, 1296, 1323, 1356, 1357, 1394, 1404, 1407, 1425], "lead": [11, 99, 101, 230, 231, 383, 471, 472, 473, 474, 475, 567, 1175, 1177, 1222, 1326, 1405, 1426], "370": [11, 1244], "371": [11, 25, 47, 274], "407": [11, 346, 347], "modulo": [11, 586, 1190], "ad": [11, 16, 27, 41, 71, 88, 94, 95, 96, 97, 99, 100, 101, 102, 103, 127, 141, 151, 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1104, 1118, 1358, 1412], "differ": [15, 25, 27, 28, 33, 41, 53, 54, 57, 63, 71, 86, 92, 93, 94, 95, 99, 103, 112, 161, 164, 165, 204, 207, 215, 216, 223, 280, 282, 297, 298, 314, 315, 326, 330, 334, 335, 337, 341, 358, 361, 371, 372, 373, 374, 378, 410, 413, 414, 415, 435, 437, 509, 511, 512, 593, 602, 615, 704, 717, 718, 738, 750, 758, 772, 786, 862, 891, 892, 907, 928, 943, 972, 973, 988, 1010, 1102, 1105, 1133, 1165, 1169, 1170, 1171, 1193, 1198, 1207, 1255, 1269, 1287, 1296, 1326, 1365, 1366, 1382, 1394, 1404, 1405, 1406, 1413, 1414, 1425, 1426], "relat": [15, 34, 67, 92, 93, 95, 99, 100, 115, 129, 132, 220, 230, 297, 366, 370, 586, 588, 619, 688, 762, 767, 795, 1202, 1205, 1269, 1323, 1395, 1402, 1406, 1413, 1416, 1425], "strong": [15, 397, 511, 512, 517, 610, 619, 691, 699, 758, 1408], "weak": [15, 398, 691, 758, 1425], "number_of_nod": [15, 25, 80, 156, 187, 311, 324, 337, 383, 564, 581, 852, 855, 876, 897, 900, 919, 933, 936, 958, 978, 981, 1001, 1154, 1271, 1426], "7482934": 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1381, 1383, 1387, 1394, 1395, 1396, 1398, 1402, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "unsupport": 16, "contain": [16, 25, 34, 45, 65, 69, 71, 88, 99, 102, 104, 114, 115, 151, 152, 157, 158, 165, 166, 167, 168, 172, 175, 176, 177, 180, 188, 189, 193, 195, 199, 207, 212, 214, 220, 226, 236, 237, 238, 240, 241, 243, 245, 248, 249, 252, 253, 255, 256, 257, 258, 259, 260, 264, 266, 267, 270, 277, 278, 280, 281, 290, 293, 294, 299, 315, 320, 322, 338, 344, 346, 347, 350, 352, 353, 355, 356, 357, 358, 360, 373, 377, 379, 380, 381, 388, 400, 408, 414, 415, 427, 432, 433, 437, 440, 457, 481, 482, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 512, 513, 514, 516, 564, 568, 572, 574, 589, 593, 596, 599, 602, 621, 624, 631, 632, 652, 656, 658, 660, 661, 662, 687, 688, 689, 695, 723, 724, 725, 726, 749, 786, 796, 853, 854, 856, 857, 862, 863, 864, 865, 868, 869, 870, 871, 877, 878, 882, 884, 887, 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"supported_nod": 16, "unsupported_nod": 16, "remove_edges_from": [16, 89, 192, 453, 602, 881, 920, 962, 1002, 1175, 1177, 1222, 1393, 1394, 1412, 1420, 1426], "nbr": [16, 88, 159, 190, 199, 200, 207, 229, 230, 231, 285, 500, 506, 796, 858, 879, 887, 888, 892, 903, 925, 928, 939, 968, 969, 973, 984, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1426], "adj": [16, 88, 199, 200, 207, 324, 325, 796, 849, 887, 888, 892, 894, 915, 925, 928, 930, 968, 969, 973, 975, 996, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1411, 1417, 1425, 1426], "g_minus_h": 16, "strip": [16, 25, 69, 1215], "_node_color": 16, "_po": 16, "draw_networkx_edg": [16, 25, 26, 27, 28, 33, 35, 38, 39, 40, 41, 44, 46, 68, 83, 1130, 1133, 1134, 1136, 1137, 1411, 1413], "draw_networkx_label": [16, 25, 35, 38, 46, 71, 1130, 1133, 1134, 1135, 1137], "ncl": 16, "undirect": [16, 25, 34, 71, 93, 112, 177, 185, 204, 205, 209, 211, 212, 214, 215, 216, 217, 218, 219, 220, 221, 224, 227, 228, 229, 230, 231, 232, 237, 239, 240, 246, 247, 264, 267, 275, 277, 278, 280, 281, 293, 294, 295, 297, 298, 300, 313, 315, 318, 319, 321, 322, 328, 330, 331, 332, 333, 337, 338, 341, 345, 346, 347, 348, 349, 350, 352, 353, 371, 372, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 428, 430, 431, 437, 439, 440, 450, 463, 464, 465, 466, 467, 478, 479, 480, 481, 482, 485, 486, 487, 488, 490, 491, 492, 500, 559, 560, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 581, 582, 583, 590, 594, 595, 598, 600, 601, 605, 606, 607, 610, 611, 613, 615, 618, 619, 624, 625, 652, 658, 681, 682, 683, 684, 686, 687, 688, 689, 692, 694, 717, 718, 727, 730, 731, 732, 734, 735, 736, 737, 738, 742, 743, 753, 760, 761, 762, 767, 779, 791, 874, 891, 917, 927, 956, 972, 999, 1009, 1036, 1038, 1056, 1060, 1088, 1090, 1098, 1101, 1115, 1133, 1135, 1166, 1167, 1173, 1175, 1182, 1184, 1187, 1189, 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1040, 1182, 1184, 1404, 1413, 1426], "magenta": 16, "six": 16, "classifi": [16, 512, 684, 750], "four": [16, 23, 47, 86, 99, 102, 165, 263, 585, 587, 692, 862, 907, 943, 988, 1039, 1040, 1164, 1193, 1199, 1211, 1323, 1407, 1408, 1414, 1426], "green": [16, 32, 38, 70, 93, 115, 464, 598, 760, 1302, 1330, 1394, 1412, 1426], "goal": [16, 88, 92, 99, 105, 107, 127, 383, 626, 627, 717, 718, 1042], "g_ex": 16, "m": [16, 25, 28, 30, 31, 63, 65, 67, 91, 93, 96, 102, 106, 110, 112, 128, 181, 191, 201, 209, 211, 212, 219, 227, 231, 235, 236, 238, 239, 240, 241, 243, 244, 248, 257, 258, 259, 263, 272, 274, 275, 278, 280, 282, 284, 293, 294, 296, 300, 301, 302, 308, 309, 315, 316, 317, 330, 338, 341, 343, 345, 352, 355, 356, 361, 362, 370, 380, 383, 385, 412, 429, 431, 432, 433, 451, 462, 479, 494, 498, 499, 509, 510, 511, 512, 519, 545, 555, 569, 582, 584, 585, 587, 588, 606, 614, 619, 625, 652, 658, 659, 684, 686, 691, 692, 706, 748, 749, 761, 762, 775, 872, 880, 889, 953, 961, 970, 1060, 1151, 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1013, 1018, 1019, 1020, 1021, 1022, 1035, 1036, 1037, 1038, 1039, 1040, 1042, 1043, 1061, 1066, 1082, 1083, 1088, 1121, 1143, 1144, 1270, 1296, 1325, 1326, 1329, 1330, 1333, 1337, 1338, 1342, 1343, 1345, 1348, 1349, 1350, 1351, 1352, 1354, 1356, 1357, 1367, 1368, 1371, 1375, 1377, 1378, 1380, 1381, 1382, 1383, 1386, 1387, 1388, 1389, 1390, 1394, 1395, 1397, 1398, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1413, 1414, 1418, 1424, 1425], "write": [18, 22, 49, 52, 75, 76, 77, 86, 89, 93, 99, 105, 110, 115, 267, 268, 470, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1047, 1123, 1129, 1300, 1325, 1326, 1329, 1330, 1334, 1337, 1339, 1340, 1344, 1345, 1348, 1349, 1350, 1352, 1354, 1357, 1358, 1372, 1375, 1376, 1378, 1380, 1381, 1382, 1383, 1387, 1388, 1390, 1395, 1397, 1398, 1399, 1401, 1402, 1405, 1406, 1411, 1412, 1414, 1425, 1426], "simpl": [18, 22, 23, 32, 47, 86, 93, 94, 97, 100, 103, 109, 110, 132, 184, 220, 229, 230, 231, 249, 287, 293, 300, 304, 313, 321, 328, 332, 333, 338, 343, 371, 372, 373, 380, 381, 423, 425, 438, 452, 453, 468, 479, 481, 482, 490, 496, 500, 504, 505, 508, 514, 517, 518, 594, 608, 624, 632, 677, 678, 679, 680, 686, 693, 758, 775, 780, 796, 873, 916, 955, 998, 1037, 1038, 1039, 1040, 1098, 1099, 1100, 1130, 1133, 1175, 1177, 1180, 1181, 1207, 1208, 1209, 1210, 1217, 1219, 1222, 1252, 1269, 1296, 1323, 1325, 1326, 1328, 1330, 1351, 1352, 1353, 1354, 1382, 1388, 1395, 1401, 1404, 1406, 1407, 1412, 1413, 1421, 1426], "lollipop": [19, 1157, 1426], "vertex": [19, 115, 211, 235, 249, 281, 289, 315, 322, 330, 338, 359, 360, 373, 387, 394, 397, 427, 428, 432, 438, 477, 491, 580, 606, 615, 616, 619, 622, 623, 624, 688, 689, 758, 1164, 1185, 1190, 1206, 1218, 1219, 1222, 1251, 1323, 1326, 1400, 1406, 1407], "length": [19, 39, 52, 67, 102, 120, 151, 232, 288, 295, 297, 298, 299, 306, 307, 310, 314, 315, 316, 320, 322, 326, 327, 329, 330, 332, 333, 341, 343, 345, 346, 347, 371, 372, 383, 384, 451, 459, 462, 467, 469, 470, 473, 513, 515, 516, 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1425, 1426], "carol": [45, 1255], "getaddress": 45, "parseaddr": 45, "recip": [45, 660, 667], "doc": [45, 93, 99, 101, 106, 165, 202, 204, 282, 566, 620, 749, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1042, 1105, 1197, 1373, 1375, 1376, 1389, 1396, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1420, 1421, 1422, 1424, 1425], "mbox_graph": 45, "pars": [45, 65, 266, 1332, 1336, 1348, 1349, 1351, 1352, 1370, 1374, 1377, 1378, 1381, 1383, 1385, 1398, 1406, 1408, 1414, 1419, 1425], "msg": [45, 93, 103], "source_nam": 45, "source_addr": 45, "recipi": 45, "tos": 45, "get_al": 45, "cc": [45, 71, 127, 142, 143, 322, 423, 425, 1413], "resent_to": 45, "resent": 45, "resent_cc": 45, "all_recipi": 45, "now": [45, 54, 75, 76, 93, 97, 101, 132, 380, 754, 762, 963, 1003, 1177, 1217, 1278, 1279, 1393, 1394, 1395, 1396, 1397, 1398, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1425, 1426], "mail": [45, 92, 93, 94, 99, 100, 103, 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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": 91, "institut": [91, 112, 214, 215, 216, 220], "discoveri": [91, 670, 675, 676, 690], "madison": 91, "jessica": 91, "flack": 91, 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"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], "448": 210, "1080": [210, 297, 298, 306, 307, 329], "15427951": 210, "986778": 210, "apx": [211, 212], "subseteq": [211, 280, 289, 618, 675], "omega": [211, 758, 782, 1414], "maximum_cliqu": 211, "1007": [211, 226, 296, 301, 302, 303, 308, 309, 323, 324, 325, 341, 431, 451, 498, 574, 1144, 1181], "bf01994876": 211, "iset": 212, "trial": [213, 230, 231, 1195, 1237, 1238], "estim": [213, 224, 297, 306, 313, 564, 625, 626, 627, 782, 1280, 1407], "coeffici": [213, 248, 260, 261, 262, 263, 289, 355, 356, 358, 570, 618, 619, 625, 682, 684, 778, 782, 1397, 1398, 1399, 1406, 1413], "fraction": [213, 257, 259, 286, 289, 297, 299, 304, 306, 315, 317, 318, 319, 321, 322, 326, 328, 330, 356, 358, 359, 519, 1165, 1234], "schank": 213, "thoma": [213, 751, 1407, 1409, 1413], "dorothea": [213, 1168], "wagner": [213, 429, 758, 1168, 1402, 1406], "universit\u00e4t": 213, "karlsruh": 213, "fakult\u00e4t": 213, "f\u00fcr": 213, "informatik": [213, 412], "5445": 213, "ir": [213, 606], "1000001239": 213, "erdos_renyi_graph": [213, 1224, 1232, 1326, 1406, 1426], "214": 213, "cutoff": [214, 215, 310, 326, 383, 410, 411, 412, 418, 419, 494, 495, 498, 499, 510, 637, 638, 640, 641, 642, 643, 644, 647, 648, 649, 656, 660, 661, 662, 667, 668, 669, 677, 678, 1234, 1398, 1402, 1406, 1413, 1416, 1424, 1425], "distinct": [214, 215, 255, 281, 288, 352, 391, 452, 453, 460, 578, 595, 608, 618, 700, 701, 734, 735, 736, 737, 789, 1150, 1244, 1271, 1323, 1326, 1328, 1395, 1417], "nonadjac": [214, 215, 480, 584, 585, 587], "cutset": [214, 215, 414, 415, 416, 417, 427, 428, 500, 506, 758], "menger": [214, 215, 216], "theorem": [214, 215, 216, 220, 235, 281, 311, 312, 322, 411, 506, 507, 514, 517, 518, 618, 1190, 1205], "local_node_connect": [214, 216, 408, 409, 410, 411, 413], "node_connect": [214, 215, 409, 410, 411, 412, 414, 415, 416, 417, 419, 427, 428, 1402], "dougla": [214, 215, 216, 220, 1413, 1425], "035": [214, 215, 216, 220], "eclect": [214, 215, 216], "ss": [214, 215, 216], "uci": [214, 215, 216, 467, 704, 706, 707, 708, 710, 734, 736], "drwhite": [214, 215, 216], "pprint": [214, 577, 711], "all_pairs_node_connect": [215, 216, 1402, 1424, 1425], "bf": [215, 216, 217, 363, 588, 704, 706, 707, 708, 717, 1397, 1401, 1406, 1409, 1412, 1413], "lose": [215, 796, 1037, 1039, 1040], "accuraci": [215, 312, 786], "platon": [215, 216, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1245, 1248, 1254, 1257, 1261, 1263], "octahedr": [215, 216, 1257], "approx": [215, 216, 227, 229, 230, 231, 1413], "octahedral_graph": [215, 216], "vari": [217, 238, 243, 373, 378, 569, 695], "sweep": [217, 1412], "dsweep": 217, "a_1": [217, 477], "a_2": 217, "magnien": [217, 260, 261, 262, 289], "cl\u00e9menc": [217, 260, 261, 262, 289], "matthieu": [217, 260, 261, 262, 274, 289], "latapi": [217, 260, 261, 262, 274, 289], "michel": 217, "habib": 217, "empir": 217, "tight": 217, "jea": 217, "0904": 217, "2728": 217, "crescenzi": 217, "pierluigi": 217, "roberto": 217, "grossi": 217, "leonardo": 217, "lanzi": 217, "andrea": [217, 1165, 1413], "marino": 217, "symposium": [217, 619, 1186, 1195, 1239], "berlin": [217, 520, 521, 1413], "heidelberg": [217, 520, 521], "ut": 217, "ee": [217, 313], "mtat": 217, "2014_fall": 217, "domin": [218, 219, 311, 410, 414, 481, 482, 483, 484, 758, 1325, 1395, 1400, 1406, 1407], "opt": [218, 221, 1425], "min_weight_dominating_set": 219, "vazirani": [219, 221], "vijai": [219, 221, 517], "min_dens": 220, "95": [220, 590, 1283, 1284, 1382], "nest": [220, 427, 728, 730, 791, 1038, 1045, 1061, 1094, 1296, 1308, 1348, 1355, 1356, 1357, 1358, 1383, 1406], "forth": [220, 427], "relax": [220, 227, 1171, 1413], "narrow": [220, 1165], "whitnei": 220, "bicompon": [220, 387, 389, 390, 394], "ferraro": [220, 427], "cohes": [220, 427, 437], "1503": [220, 427], "04476v1": [220, 427], "santaf": 220, "ind": 220, "embedded": [220, 305, 427], "sociolog": [220, 427, 748], "103": [220, 427, 1222, 1288, 1292], "2307": [220, 297, 1255], "3088904": 220, "petersen": [220, 427, 761, 1251, 1256, 1259], "triconnect": [220, 427], "apxa": 220, "petersen_graph": [220, 380, 427, 492, 761, 1119, 1120, 1426], "fo": 221, "initial_cut": 222, "highest": [222, 269, 273, 276, 337, 357, 374, 387, 389, 390, 394, 428, 509, 688, 703, 1180], "suppli": [222, 256, 277, 278, 280, 281, 594, 1197, 1320, 1321, 1326, 1345, 1348, 1349, 1350, 1382, 1408, 1413], "cut_valu": [222, 429, 500, 506, 507, 1402], "probabl": [223, 227, 230, 231, 236, 237, 238, 241, 242, 243, 245, 274, 275, 296, 358, 452, 468, 593, 675, 738, 758, 796, 1037, 1039, 1040, 1168, 1169, 1170, 1171, 1173, 1175, 1179, 1182, 1184, 1185, 1186, 1187, 1188, 1193, 1195, 1196, 1197, 1198, 1199, 1203, 1205, 1224, 1225, 1227, 1228, 1229, 1230, 1232, 1233, 1234, 1235, 1236, 1239, 1241, 1278, 1279, 1283, 1284, 1319, 1403, 1404, 1406, 1414, 1417, 1426], "cut_siz": [223, 442, 447, 448, 758], "ramsei": [224, 758], "max_pair": 224, "closur": [225, 226, 467, 468, 1036, 1088, 1395, 1406, 1408, 1411], "terminal_nod": 226, "steiner": [226, 758, 1408, 1425], "leaf": [226, 355, 460, 465, 678, 1155, 1236, 1272], "across": [226, 248, 625, 1038, 1100, 1326, 1405], "kou": 226, "mehlhorn": [226, 511, 512, 1425], "proce": [226, 231, 232, 373, 378, 518, 1165], "steiner_tree_problem": 226, "markowski": 226, "berman": 226, "1981": [226, 1164, 1323], "acta": [226, 508], "informatica": [226, 508], "bf00288961": 226, "kurt": [226, 511, 512], "1988": [226, 1199, 1407], "0020": [226, 455], "0190": [226, 455], "88": [226, 513, 1178, 1180], "90066": 226, "held": [227, 1105], "karp": [227, 277, 278, 280, 499, 758, 1169, 1395, 1402, 1406], "entropi": 227, "scheme": [227, 337, 719, 733, 1393], "lceil": 227, "rceil": 227, "augment": [227, 422, 496, 510, 581, 758, 1408], "tour": [227, 488, 490], "pari": 227, "inequ": [227, 1283, 1284], "trip": [227, 229, 230, 231], "goeman": 227, "madri": 227, "gharan": 227, "saberi": [227, 1181], "1043": 227, "1061": 227, "set_edge_attribut": 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"transposit": [230, 231], "move_one_nod": [230, 231], "enact": [230, 231], "declar": [230, 231], "outer": [230, 231, 380, 436, 606, 615, 796, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1086, 1160, 1326], "percentag": [230, 231, 1269], "metaheurist": [230, 231], "characterist": [230, 231, 682, 775], "thoughtfulli": [230, 231], "exp": [230, 1197, 1199], "n_i": 230, "n_o": 230, "simulated_ann": 230, "incycl": [230, 231], "amount": [231, 496, 504, 505, 508, 676, 786, 1042, 1296, 1424, 1425], "minima": 231, "slowli": 231, "simulated_annealing_tsp": [231, 232, 1413], "unchang": [231, 1112, 1296], "presenc": [231, 652, 658, 1425], "0021": 231, "9991": 231, "90": [231, 274, 332, 333, 1286], "90201": 231, "asadpour_atsp": [232, 1414], "biggest": 232, "callabl": [232, 525, 535, 545, 552, 553, 554, 555, 671, 672, 673, 674, 796, 1036, 1037, 1039, 1040, 1045, 1046, 1047, 1088, 1102, 1296, 1345, 1348, 1349, 1350, 1406, 1413, 1414, 1425], "tsp": [232, 1413], "curri": 232, "sa_tsp": 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"Subgraph": [[118, "subgraph"]], "Harmonic Centrality": [[118, "harmonic-centrality"]], "Dispersion": [[118, "dispersion"]], "Reaching": [[118, "reaching"]], "Percolation": [[118, "percolation"]], "Second Order Centrality": [[118, "second-order-centrality"]], "Trophic": [[118, "trophic"]], "VoteRank": [[118, "voterank"]], "Chains": [[119, "module-networkx.algorithms.chains"]], "Chordal": [[120, "chordal"]], "Coloring": [[123, "module-networkx.algorithms.coloring"]], "Communicability": [[124, "module-networkx.algorithms.communicability_alg"]], "Communities": [[125, "module-networkx.algorithms.community"]], "Bipartitions": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "K-Clique": [[125, "module-networkx.algorithms.community.kclique"]], "Modularity-based communities": [[125, "module-networkx.algorithms.community.modularity_max"]], "Tree partitioning": [[125, "module-networkx.algorithms.community.lukes"]], "Label propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "Louvain Community Detection": [[125, "module-networkx.algorithms.community.louvain"]], "Fluid Communities": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "Measuring partitions": [[125, "module-networkx.algorithms.community.quality"]], "Partitions via centrality measures": [[125, "module-networkx.algorithms.community.centrality"]], "Validating partitions": [[125, "module-networkx.algorithms.community.community_utils"]], "Components": [[126, "module-networkx.algorithms.components"]], "Strong connectivity": [[126, "strong-connectivity"]], "Weak connectivity": [[126, "weak-connectivity"]], "Attracting components": [[126, "attracting-components"]], "Biconnected components": [[126, "biconnected-components"]], "Semiconnectedness": [[126, "semiconnectedness"]], "Edge-augmentation": [[127, "module-networkx.algorithms.connectivity.edge_augmentation"]], "See Also": [[127, "see-also"], [762, "see-also"], [1041, "see-also"], [1041, "id2"], [1042, "see-also"], [1042, "id3"], [1042, "id5"]], "K-edge-components": [[127, "module-networkx.algorithms.connectivity.edge_kcomponents"]], "K-node-components": [[127, "module-networkx.algorithms.connectivity.kcomponents"]], "K-node-cutsets": [[127, "module-networkx.algorithms.connectivity.kcutsets"]], "Flow-based disjoint paths": [[127, "module-networkx.algorithms.connectivity.disjoint_paths"]], "Flow-based Connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "Flow-based Minimum Cuts": [[127, "module-networkx.algorithms.connectivity.cuts"]], "Stoer-Wagner minimum cut": [[127, "module-networkx.algorithms.connectivity.stoerwagner"]], "Utils for flow-based connectivity": [[127, "module-networkx.algorithms.connectivity.utils"]], "Cores": [[128, "module-networkx.algorithms.core"]], "Cuts": [[130, "module-networkx.algorithms.cuts"]], "Cycles": [[131, "module-networkx.algorithms.cycles"]], "D-Separation": [[132, "module-networkx.algorithms.d_separation"]], "Blocking paths": [[132, "blocking-paths"]], "Illustration of D-separation with examples": [[132, "illustration-of-d-separation-with-examples"]], "D-separation and its applications in probability": [[132, "d-separation-and-its-applications-in-probability"]], "Examples": [[132, "examples"], [760, "examples"], [762, "examples"], [1041, "examples"], [1041, "id1"], [1042, "examples"], [1042, "id2"], [1042, "id4"], [1387, "examples"], [1393, "examples"], [1394, "examples"], [1402, "examples"], [1406, "examples"], [1406, "id29"], [1406, "id32"], [1406, "id35"], [1406, "id44"], [1406, "id47"], [1406, "id50"], [1406, "id53"], [1406, "id57"], [1406, "id60"], [1406, "id63"], [1406, "id66"], [1406, "id70"], [1406, "id74"]], "Directed Acyclic Graphs": [[133, "module-networkx.algorithms.dag"]], "Distance-Regular Graphs": [[135, "module-networkx.algorithms.distance_regular"]], "Dominance": [[136, "module-networkx.algorithms.dominance"]], "Dominating Sets": [[137, "module-networkx.algorithms.dominating"]], "Efficiency": [[138, "module-networkx.algorithms.efficiency_measures"]], "Eulerian": [[139, "module-networkx.algorithms.euler"]], "Flows": [[140, "module-networkx.algorithms.flow"]], "Maximum Flow": [[140, "maximum-flow"]], "Edmonds-Karp": [[140, "edmonds-karp"]], "Shortest Augmenting Path": [[140, "shortest-augmenting-path"]], "Preflow-Push": [[140, "preflow-push"]], "Dinitz": [[140, "dinitz"]], "Boykov-Kolmogorov": [[140, "boykov-kolmogorov"]], "Gomory-Hu Tree": [[140, "gomory-hu-tree"]], "Utils": [[140, "utils"]], "Network Simplex": [[140, "network-simplex"]], "Capacity Scaling Minimum Cost Flow": [[140, "capacity-scaling-minimum-cost-flow"]], "EdgeComponentAuxGraph.construct": [[141, "edgecomponentauxgraph-construct"]], "EdgeComponentAuxGraph.k_edge_components": [[142, "edgecomponentauxgraph-k-edge-components"]], "EdgeComponentAuxGraph.k_edge_subgraphs": [[143, "edgecomponentauxgraph-k-edge-subgraphs"]], "ISMAGS.analyze_symmetry": [[144, "ismags-analyze-symmetry"]], "ISMAGS.find_isomorphisms": [[145, "ismags-find-isomorphisms"]], "ISMAGS.is_isomorphic": [[146, "ismags-is-isomorphic"]], "ISMAGS.isomorphisms_iter": [[147, "ismags-isomorphisms-iter"]], "ISMAGS.largest_common_subgraph": [[148, "ismags-largest-common-subgraph"]], "ISMAGS.subgraph_is_isomorphic": [[149, "ismags-subgraph-is-isomorphic"]], "ISMAGS.subgraph_isomorphisms_iter": [[150, "ismags-subgraph-isomorphisms-iter"]], "PlanarEmbedding.add_edge": [[151, "planarembedding-add-edge"]], "PlanarEmbedding.add_edges_from": [[152, "planarembedding-add-edges-from"]], "PlanarEmbedding.add_half_edge_ccw": [[153, "planarembedding-add-half-edge-ccw"]], "PlanarEmbedding.add_half_edge_cw": [[154, "planarembedding-add-half-edge-cw"]], "PlanarEmbedding.add_half_edge_first": [[155, "planarembedding-add-half-edge-first"]], "PlanarEmbedding.add_node": [[156, "planarembedding-add-node"]], "PlanarEmbedding.add_nodes_from": [[157, "planarembedding-add-nodes-from"]], "PlanarEmbedding.add_weighted_edges_from": [[158, "planarembedding-add-weighted-edges-from"]], "PlanarEmbedding.adj": [[159, "planarembedding-adj"]], "PlanarEmbedding.adjacency": [[160, "planarembedding-adjacency"]], "PlanarEmbedding.check_structure": [[161, "planarembedding-check-structure"]], "PlanarEmbedding.clear": [[162, "planarembedding-clear"]], "PlanarEmbedding.clear_edges": [[163, "planarembedding-clear-edges"]], "PlanarEmbedding.connect_components": [[164, "planarembedding-connect-components"]], "PlanarEmbedding.copy": [[165, "planarembedding-copy"]], "PlanarEmbedding.degree": [[166, "planarembedding-degree"]], "PlanarEmbedding.edge_subgraph": [[167, "planarembedding-edge-subgraph"]], "PlanarEmbedding.edges": [[168, "planarembedding-edges"]], "PlanarEmbedding.get_data": [[169, "planarembedding-get-data"]], "PlanarEmbedding.get_edge_data": [[170, "planarembedding-get-edge-data"]], "PlanarEmbedding.has_edge": [[171, 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"PlanarEmbedding.succ": [[200, "planarembedding-succ"]], "PlanarEmbedding.successors": [[201, "planarembedding-successors"]], "PlanarEmbedding.to_directed": [[202, "planarembedding-to-directed"]], "PlanarEmbedding.to_directed_class": [[203, "planarembedding-to-directed-class"]], "PlanarEmbedding.to_undirected": [[204, "planarembedding-to-undirected"]], "PlanarEmbedding.to_undirected_class": [[205, "planarembedding-to-undirected-class"]], "PlanarEmbedding.traverse_face": [[206, "planarembedding-traverse-face"]], "PlanarEmbedding.update": [[207, "planarembedding-update"]], "Edmonds.find_optimum": [[208, "edmonds-find-optimum"]], "clique_removal": [[209, "clique-removal"]], "large_clique_size": [[210, "large-clique-size"]], "max_clique": [[211, "max-clique"]], "maximum_independent_set": [[212, "maximum-independent-set"]], "average_clustering": [[213, "average-clustering"], [260, "average-clustering"], [355, "average-clustering"]], "all_pairs_node_connectivity": [[214, 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"node_degree_xy": [[247, "node-degree-xy"]], "numeric_assortativity_coefficient": [[248, "numeric-assortativity-coefficient"]], "find_asteroidal_triple": [[249, "find-asteroidal-triple"]], "is_at_free": [[250, "is-at-free"]], "color": [[251, "color"]], "degrees": [[252, "degrees"]], "density": [[253, "density"], [1060, "density"]], "is_bipartite": [[254, "is-bipartite"]], "is_bipartite_node_set": [[255, "is-bipartite-node-set"]], "sets": [[256, "sets"]], "betweenness_centrality": [[257, "betweenness-centrality"], [297, "betweenness-centrality"]], "closeness_centrality": [[258, "closeness-centrality"], [299, "closeness-centrality"]], "degree_centrality": [[259, "degree-centrality"], [304, "degree-centrality"]], "clustering": [[261, "clustering"], [356, "clustering"]], "latapy_clustering": [[262, "latapy-clustering"]], "robins_alexander_clustering": [[263, "robins-alexander-clustering"]], "min_edge_cover": [[264, "min-edge-cover"], [440, "min-edge-cover"]], "generate_edgelist": [[265, 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"minimum_weight_full_matching": [[280, "minimum-weight-full-matching"]], "to_vertex_cover": [[281, "to-vertex-cover"]], "biadjacency_matrix": [[282, "biadjacency-matrix"]], "from_biadjacency_matrix": [[283, "from-biadjacency-matrix"]], "collaboration_weighted_projected_graph": [[284, "collaboration-weighted-projected-graph"]], "generic_weighted_projected_graph": [[285, "generic-weighted-projected-graph"]], "overlap_weighted_projected_graph": [[286, "overlap-weighted-projected-graph"]], "projected_graph": [[287, "projected-graph"]], "weighted_projected_graph": [[288, "weighted-projected-graph"]], "node_redundancy": [[289, "node-redundancy"]], "spectral_bipartivity": [[290, "spectral-bipartivity"]], "edge_boundary": [[291, "edge-boundary"]], "node_boundary": [[292, "node-boundary"]], "bridges": [[293, "bridges"]], "has_bridges": [[294, "has-bridges"]], "local_bridges": [[295, "local-bridges"]], "approximate_current_flow_betweenness_centrality": [[296, 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"local-reaching-centrality"]], "out_degree_centrality": [[328, "out-degree-centrality"]], "percolation_centrality": [[329, "percolation-centrality"]], "prominent_group": [[330, "prominent-group"]], "second_order_centrality": [[331, "second-order-centrality"]], "subgraph_centrality": [[332, "subgraph-centrality"]], "subgraph_centrality_exp": [[333, "subgraph-centrality-exp"]], "trophic_differences": [[334, "trophic-differences"]], "trophic_incoherence_parameter": [[335, "trophic-incoherence-parameter"]], "trophic_levels": [[336, "trophic-levels"]], "voterank": [[337, "voterank"]], "chain_decomposition": [[338, "chain-decomposition"]], "chordal_graph_cliques": [[339, "chordal-graph-cliques"]], "chordal_graph_treewidth": [[340, "chordal-graph-treewidth"]], "complete_to_chordal_graph": [[341, "complete-to-chordal-graph"]], "find_induced_nodes": [[342, "find-induced-nodes"]], "is_chordal": [[343, "is-chordal"]], "cliques_containing_node": [[344, "cliques-containing-node"]], "enumerate_all_cliques": [[345, "enumerate-all-cliques"]], "find_cliques": [[346, "find-cliques"]], "find_cliques_recursive": [[347, "find-cliques-recursive"]], "graph_clique_number": [[348, "graph-clique-number"]], "graph_number_of_cliques": [[349, "graph-number-of-cliques"]], "make_clique_bipartite": [[350, "make-clique-bipartite"]], "make_max_clique_graph": [[351, "make-max-clique-graph"]], "max_weight_clique": [[352, "max-weight-clique"]], "node_clique_number": [[353, "node-clique-number"]], "number_of_cliques": [[354, "number-of-cliques"]], "generalized_degree": [[357, "generalized-degree"]], "square_clustering": [[358, "square-clustering"]], "transitivity": [[359, "transitivity"]], "triangles": [[360, "triangles"]], "equitable_color": [[361, "equitable-color"]], "greedy_color": [[362, "greedy-color"]], "strategy_connected_sequential": [[363, "strategy-connected-sequential"]], "strategy_connected_sequential_bfs": [[364, "strategy-connected-sequential-bfs"]], 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[[457, "antichains"]], "dag_longest_path": [[458, "dag-longest-path"]], "dag_longest_path_length": [[459, "dag-longest-path-length"]], "dag_to_branching": [[460, "dag-to-branching"]], "descendants": [[461, "descendants"]], "is_aperiodic": [[462, "is-aperiodic"]], "is_directed_acyclic_graph": [[463, "is-directed-acyclic-graph"]], "lexicographical_topological_sort": [[464, "lexicographical-topological-sort"]], "topological_generations": [[465, "topological-generations"]], "topological_sort": [[466, "topological-sort"]], "transitive_closure": [[467, "transitive-closure"]], "transitive_closure_dag": [[468, "transitive-closure-dag"]], "transitive_reduction": [[469, "transitive-reduction"]], "barycenter": [[470, "barycenter"]], "center": [[471, "center"]], "eccentricity": [[473, "eccentricity"]], "periphery": [[474, "periphery"]], "radius": [[475, "radius"]], "resistance_distance": [[476, "resistance-distance"]], "global_parameters": [[477, "global-parameters"]], "intersection_array": [[478, "intersection-array"]], "is_distance_regular": [[479, "is-distance-regular"]], "is_strongly_regular": [[480, "is-strongly-regular"]], "dominance_frontiers": [[481, "dominance-frontiers"]], "immediate_dominators": [[482, "immediate-dominators"]], "dominating_set": [[483, "dominating-set"]], "is_dominating_set": [[484, "is-dominating-set"]], "efficiency": [[485, "efficiency"]], "global_efficiency": [[486, "global-efficiency"]], "local_efficiency": [[487, "local-efficiency"]], "eulerian_circuit": [[488, "eulerian-circuit"]], "eulerian_path": [[489, "eulerian-path"]], "eulerize": [[490, "eulerize"]], "has_eulerian_path": [[491, "has-eulerian-path"]], "is_eulerian": [[492, "is-eulerian"]], "is_semieulerian": [[493, "is-semieulerian"]], "boykov_kolmogorov": [[494, "boykov-kolmogorov"]], "build_residual_network": [[495, "build-residual-network"]], "capacity_scaling": [[496, "capacity-scaling"]], "cost_of_flow": [[497, "cost-of-flow"]], "dinitz": [[498, "dinitz"]], "edmonds_karp": [[499, "edmonds-karp"]], "gomory_hu_tree": [[500, "gomory-hu-tree"]], "max_flow_min_cost": [[501, "max-flow-min-cost"]], "maximum_flow": [[502, "maximum-flow"]], "maximum_flow_value": [[503, "maximum-flow-value"]], "min_cost_flow": [[504, "min-cost-flow"]], "min_cost_flow_cost": [[505, "min-cost-flow-cost"]], "minimum_cut": [[506, "minimum-cut"]], "minimum_cut_value": [[507, "minimum-cut-value"]], "network_simplex": [[508, "network-simplex"]], "preflow_push": [[509, "preflow-push"]], "shortest_augmenting_path": [[510, "shortest-augmenting-path"]], "weisfeiler_lehman_graph_hash": [[511, "weisfeiler-lehman-graph-hash"]], "weisfeiler_lehman_subgraph_hashes": [[512, "weisfeiler-lehman-subgraph-hashes"]], "is_digraphical": [[513, "is-digraphical"]], "is_graphical": [[514, "is-graphical"]], "is_multigraphical": [[515, "is-multigraphical"]], "is_pseudographical": [[516, "is-pseudographical"]], "is_valid_degree_sequence_erdos_gallai": [[517, "is-valid-degree-sequence-erdos-gallai"]], "is_valid_degree_sequence_havel_hakimi": [[518, "is-valid-degree-sequence-havel-hakimi"]], "flow_hierarchy": [[519, "flow-hierarchy"]], "is_kl_connected": [[520, "is-kl-connected"]], "kl_connected_subgraph": [[521, "kl-connected-subgraph"]], "is_isolate": [[522, "is-isolate"]], "isolates": [[523, "isolates"]], "number_of_isolates": [[524, "number-of-isolates"]], "DiGraphMatcher.__init__": [[525, "digraphmatcher-init"]], "DiGraphMatcher.candidate_pairs_iter": [[526, "digraphmatcher-candidate-pairs-iter"]], "DiGraphMatcher.initialize": [[527, "digraphmatcher-initialize"]], "DiGraphMatcher.is_isomorphic": [[528, "digraphmatcher-is-isomorphic"]], "DiGraphMatcher.isomorphisms_iter": [[529, "digraphmatcher-isomorphisms-iter"]], "DiGraphMatcher.match": [[530, "digraphmatcher-match"]], "DiGraphMatcher.semantic_feasibility": [[531, "digraphmatcher-semantic-feasibility"]], "DiGraphMatcher.subgraph_is_isomorphic": [[532, "digraphmatcher-subgraph-is-isomorphic"]], "DiGraphMatcher.subgraph_isomorphisms_iter": [[533, "digraphmatcher-subgraph-isomorphisms-iter"]], "DiGraphMatcher.syntactic_feasibility": [[534, "digraphmatcher-syntactic-feasibility"]], "GraphMatcher.__init__": [[535, "graphmatcher-init"]], "GraphMatcher.candidate_pairs_iter": [[536, "graphmatcher-candidate-pairs-iter"]], "GraphMatcher.initialize": [[537, "graphmatcher-initialize"]], "GraphMatcher.is_isomorphic": [[538, "graphmatcher-is-isomorphic"]], "GraphMatcher.isomorphisms_iter": [[539, "graphmatcher-isomorphisms-iter"]], "GraphMatcher.match": [[540, "graphmatcher-match"]], "GraphMatcher.semantic_feasibility": [[541, "graphmatcher-semantic-feasibility"]], "GraphMatcher.subgraph_is_isomorphic": [[542, "graphmatcher-subgraph-is-isomorphic"]], "GraphMatcher.subgraph_isomorphisms_iter": [[543, "graphmatcher-subgraph-isomorphisms-iter"]], "GraphMatcher.syntactic_feasibility": [[544, "graphmatcher-syntactic-feasibility"]], "networkx.algorithms.isomorphism.ISMAGS": [[545, "networkx-algorithms-isomorphism-ismags"]], "categorical_edge_match": [[546, "categorical-edge-match"]], "categorical_multiedge_match": [[547, "categorical-multiedge-match"]], "categorical_node_match": [[548, "categorical-node-match"]], "could_be_isomorphic": [[549, "could-be-isomorphic"]], "fast_could_be_isomorphic": [[550, "fast-could-be-isomorphic"]], "faster_could_be_isomorphic": [[551, "faster-could-be-isomorphic"]], "generic_edge_match": [[552, "generic-edge-match"]], "generic_multiedge_match": [[553, "generic-multiedge-match"]], "generic_node_match": [[554, "generic-node-match"]], "is_isomorphic": [[555, "is-isomorphic"]], "numerical_edge_match": [[556, "numerical-edge-match"]], "numerical_multiedge_match": [[557, "numerical-multiedge-match"]], "numerical_node_match": [[558, "numerical-node-match"]], "rooted_tree_isomorphism": [[559, "rooted-tree-isomorphism"]], "tree_isomorphism": [[560, "tree-isomorphism"]], "vf2pp_all_isomorphisms": [[561, "vf2pp-all-isomorphisms"]], "vf2pp_is_isomorphic": [[562, "vf2pp-is-isomorphic"]], "vf2pp_isomorphism": [[563, "vf2pp-isomorphism"]], "hits": [[564, "hits"]], "google_matrix": [[565, "google-matrix"]], "pagerank": [[566, "pagerank"]], "adamic_adar_index": [[567, "adamic-adar-index"]], "cn_soundarajan_hopcroft": [[568, "cn-soundarajan-hopcroft"]], "common_neighbor_centrality": [[569, "common-neighbor-centrality"]], "jaccard_coefficient": [[570, "jaccard-coefficient"]], "preferential_attachment": [[571, "preferential-attachment"]], "ra_index_soundarajan_hopcroft": [[572, "ra-index-soundarajan-hopcroft"]], "resource_allocation_index": [[573, "resource-allocation-index"]], "within_inter_cluster": [[574, "within-inter-cluster"]], "all_pairs_lowest_common_ancestor": [[575, "all-pairs-lowest-common-ancestor"]], "lowest_common_ancestor": [[576, "lowest-common-ancestor"]], "tree_all_pairs_lowest_common_ancestor": [[577, "tree-all-pairs-lowest-common-ancestor"]], "is_matching": [[578, "is-matching"]], "is_maximal_matching": [[579, "is-maximal-matching"]], "is_perfect_matching": [[580, "is-perfect-matching"]], "max_weight_matching": [[581, "max-weight-matching"]], "maximal_matching": [[582, "maximal-matching"]], "min_weight_matching": [[583, "min-weight-matching"]], "contracted_edge": [[584, "contracted-edge"]], "contracted_nodes": [[585, "contracted-nodes"]], "equivalence_classes": [[586, "equivalence-classes"]], "identified_nodes": [[587, "identified-nodes"]], "quotient_graph": [[588, "quotient-graph"]], "maximal_independent_set": [[589, "maximal-independent-set"]], "moral_graph": [[590, "moral-graph"]], "harmonic_function": [[591, "harmonic-function"]], "local_and_global_consistency": [[592, "local-and-global-consistency"]], "non_randomness": [[593, "non-randomness"]], "compose_all": [[594, "compose-all"]], "disjoint_union_all": [[595, "disjoint-union-all"]], "intersection_all": [[596, "intersection-all"]], "union_all": [[597, "union-all"]], "compose": [[598, "compose"]], "difference": [[599, "difference"]], "disjoint_union": [[600, "disjoint-union"]], "full_join": [[601, "full-join"]], "intersection": [[602, "intersection"]], "symmetric_difference": [[603, "symmetric-difference"]], "union": [[604, "union"]], "cartesian_product": [[605, "cartesian-product"]], "corona_product": [[606, "corona-product"]], "lexicographic_product": [[607, "lexicographic-product"]], "power": [[608, "power"]], "rooted_product": [[609, "rooted-product"]], "strong_product": [[610, "strong-product"]], "tensor_product": [[611, "tensor-product"]], "complement": [[612, "complement"]], "reverse": [[613, "reverse"]], "combinatorial_embedding_to_pos": [[614, "combinatorial-embedding-to-pos"]], "networkx.algorithms.planarity.PlanarEmbedding": [[615, "networkx-algorithms-planarity-planarembedding"]], "check_planarity": [[616, "check-planarity"]], "is_planar": [[617, "is-planar"]], "chromatic_polynomial": [[618, "chromatic-polynomial"]], "tutte_polynomial": [[619, "tutte-polynomial"]], "overall_reciprocity": [[620, "overall-reciprocity"]], "reciprocity": [[621, "reciprocity"]], "is_k_regular": [[622, "is-k-regular"]], "is_regular": [[623, "is-regular"]], "k_factor": [[624, "k-factor"]], "rich_club_coefficient": [[625, "rich-club-coefficient"]], "astar_path": [[626, "astar-path"]], "astar_path_length": [[627, "astar-path-length"]], "floyd_warshall": [[628, "floyd-warshall"]], "floyd_warshall_numpy": [[629, "floyd-warshall-numpy"]], "floyd_warshall_predecessor_and_distance": [[630, "floyd-warshall-predecessor-and-distance"]], "reconstruct_path": [[631, "reconstruct-path"]], "all_shortest_paths": [[632, "all-shortest-paths"]], "average_shortest_path_length": [[633, "average-shortest-path-length"]], "has_path": [[634, "has-path"]], "shortest_path": [[635, "shortest-path"]], "shortest_path_length": [[636, "shortest-path-length"]], "all_pairs_shortest_path": [[637, "all-pairs-shortest-path"]], "all_pairs_shortest_path_length": [[638, "all-pairs-shortest-path-length"]], "bidirectional_shortest_path": [[639, "bidirectional-shortest-path"]], "predecessor": [[640, "predecessor"]], "single_source_shortest_path": [[641, "single-source-shortest-path"]], "single_source_shortest_path_length": [[642, "single-source-shortest-path-length"]], "single_target_shortest_path": [[643, "single-target-shortest-path"]], "single_target_shortest_path_length": [[644, "single-target-shortest-path-length"]], "all_pairs_bellman_ford_path": [[645, "all-pairs-bellman-ford-path"]], "all_pairs_bellman_ford_path_length": [[646, "all-pairs-bellman-ford-path-length"]], "all_pairs_dijkstra": [[647, "all-pairs-dijkstra"]], "all_pairs_dijkstra_path": [[648, "all-pairs-dijkstra-path"]], "all_pairs_dijkstra_path_length": [[649, "all-pairs-dijkstra-path-length"]], "bellman_ford_path": [[650, "bellman-ford-path"]], "bellman_ford_path_length": [[651, "bellman-ford-path-length"]], "bellman_ford_predecessor_and_distance": [[652, "bellman-ford-predecessor-and-distance"]], "bidirectional_dijkstra": [[653, "bidirectional-dijkstra"]], "dijkstra_path": [[654, "dijkstra-path"]], "dijkstra_path_length": [[655, "dijkstra-path-length"]], "dijkstra_predecessor_and_distance": [[656, "dijkstra-predecessor-and-distance"]], "find_negative_cycle": [[657, "find-negative-cycle"]], "goldberg_radzik": [[658, "goldberg-radzik"]], "johnson": [[659, "johnson"]], "multi_source_dijkstra": [[660, "multi-source-dijkstra"]], "multi_source_dijkstra_path": [[661, "multi-source-dijkstra-path"]], "multi_source_dijkstra_path_length": [[662, "multi-source-dijkstra-path-length"]], "negative_edge_cycle": [[663, "negative-edge-cycle"]], "single_source_bellman_ford": [[664, "single-source-bellman-ford"]], "single_source_bellman_ford_path": [[665, "single-source-bellman-ford-path"]], "single_source_bellman_ford_path_length": [[666, "single-source-bellman-ford-path-length"]], "single_source_dijkstra": [[667, "single-source-dijkstra"]], "single_source_dijkstra_path": [[668, "single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[669, "single-source-dijkstra-path-length"]], "generate_random_paths": [[670, "generate-random-paths"]], "graph_edit_distance": [[671, "graph-edit-distance"]], "optimal_edit_paths": [[672, "optimal-edit-paths"]], "optimize_edit_paths": [[673, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[674, "optimize-graph-edit-distance"]], "panther_similarity": [[675, "panther-similarity"]], "simrank_similarity": [[676, "simrank-similarity"]], "all_simple_edge_paths": [[677, "all-simple-edge-paths"]], "all_simple_paths": [[678, "all-simple-paths"]], "is_simple_path": [[679, "is-simple-path"]], "shortest_simple_paths": [[680, "shortest-simple-paths"]], "lattice_reference": [[681, "lattice-reference"]], "omega": [[682, "omega"]], "random_reference": [[683, "random-reference"]], "sigma": [[684, "sigma"]], "s_metric": [[685, "s-metric"]], "spanner": [[686, "spanner"]], "constraint": [[687, "constraint"]], "effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, 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"FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": 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"DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, 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"MultiDiGraph.__iter__": [[932, "multidigraph-iter"]], "MultiDiGraph.__len__": [[933, "multidigraph-len"]], "MultiDiGraph.add_edge": [[934, "multidigraph-add-edge"]], "MultiDiGraph.add_edges_from": [[935, "multidigraph-add-edges-from"]], "MultiDiGraph.add_node": [[936, "multidigraph-add-node"]], "MultiDiGraph.add_nodes_from": [[937, "multidigraph-add-nodes-from"]], "MultiDiGraph.add_weighted_edges_from": [[938, "multidigraph-add-weighted-edges-from"]], "MultiDiGraph.adj": [[939, "multidigraph-adj"]], "MultiDiGraph.adjacency": [[940, "multidigraph-adjacency"]], "MultiDiGraph.clear": [[941, "multidigraph-clear"]], "MultiDiGraph.clear_edges": [[942, "multidigraph-clear-edges"]], "MultiDiGraph.copy": [[943, "multidigraph-copy"]], "MultiDiGraph.degree": [[944, "multidigraph-degree"]], "MultiDiGraph.edge_subgraph": [[945, "multidigraph-edge-subgraph"]], "MultiDiGraph.edges": [[946, "multidigraph-edges"]], "MultiDiGraph.get_edge_data": [[947, "multidigraph-get-edge-data"]], "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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[[1013, "networkx-classes-coreviews-atlasview"]], "networkx.classes.coreviews.FilterAdjacency": [[1014, "networkx-classes-coreviews-filteradjacency"]], "networkx.classes.coreviews.FilterAtlas": [[1015, "networkx-classes-coreviews-filteratlas"]], "networkx.classes.coreviews.FilterMultiAdjacency": [[1016, "networkx-classes-coreviews-filtermultiadjacency"]], "networkx.classes.coreviews.FilterMultiInner": [[1017, "networkx-classes-coreviews-filtermultiinner"]], "networkx.classes.coreviews.MultiAdjacencyView": [[1018, "networkx-classes-coreviews-multiadjacencyview"]], "networkx.classes.coreviews.UnionAdjacency": [[1019, "networkx-classes-coreviews-unionadjacency"]], "networkx.classes.coreviews.UnionAtlas": [[1020, "networkx-classes-coreviews-unionatlas"]], "networkx.classes.coreviews.UnionMultiAdjacency": [[1021, "networkx-classes-coreviews-unionmultiadjacency"]], "networkx.classes.coreviews.UnionMultiInner": [[1022, "networkx-classes-coreviews-unionmultiinner"]], "hide_diedges": [[1023, "hide-diedges"]], "hide_edges": [[1024, "hide-edges"]], "hide_multidiedges": [[1025, "hide-multidiedges"]], "hide_multiedges": [[1026, "hide-multiedges"]], "hide_nodes": [[1027, "hide-nodes"]], "no_filter": [[1028, "no-filter"]], "show_diedges": [[1029, "show-diedges"]], "show_edges": [[1030, "show-edges"]], "show_multidiedges": [[1031, "show-multidiedges"]], "show_multiedges": [[1032, "show-multiedges"]], "networkx.classes.filters.show_nodes": [[1033, "networkx-classes-filters-show-nodes"]], "generic_graph_view": [[1034, "generic-graph-view"]], "reverse_view": [[1035, "reverse-view"], [1083, "reverse-view"]], "subgraph_view": [[1036, "subgraph-view"], [1088, "subgraph-view"]], "Graph\u2014Undirected graphs with self loops": [[1037, "graph-undirected-graphs-with-self-loops"]], "Graph types": [[1038, "graph-types"]], "Which graph class should I use?": [[1038, "which-graph-class-should-i-use"]], "Basic graph types": [[1038, "basic-graph-types"]], "Graph Views": [[1038, "module-networkx.classes.graphviews"]], "Core Views": [[1038, "module-networkx.classes.coreviews"]], "Filters": [[1038, "filters"]], "Backends": [[1038, "backends"]], "Create a Dispatcher": [[1038, "create-a-dispatcher"]], "MultiDiGraph\u2014Directed graphs with self loops and parallel edges": [[1039, "multidigraph-directed-graphs-with-self-loops-and-parallel-edges"]], "Adding and Removing Nodes and Edges": [[1039, "adding-and-removing-nodes-and-edges"]], "MultiGraph\u2014Undirected graphs with self loops and parallel edges": [[1040, "multigraph-undirected-graphs-with-self-loops-and-parallel-edges"]], "Converting to and from other data formats": [[1041, "converting-to-and-from-other-data-formats"]], "To NetworkX Graph": [[1041, "module-networkx.convert"]], "Dictionaries": [[1041, "dictionaries"]], "Lists": [[1041, "lists"]], "Numpy": [[1041, "module-networkx.convert_matrix"]], "Scipy": [[1041, "scipy"]], "Pandas": [[1041, "pandas"]], "Matplotlib": [[1042, 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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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"module-networkx.algorithms.approximation.clustering_coefficient"]], "networkx.algorithms.approximation.connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"]], "networkx.algorithms.approximation.distance_measures": [[112, "module-networkx.algorithms.approximation.distance_measures"]], "networkx.algorithms.approximation.dominating_set": [[112, "module-networkx.algorithms.approximation.dominating_set"]], "networkx.algorithms.approximation.kcomponents": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "networkx.algorithms.approximation.matching": [[112, "module-networkx.algorithms.approximation.matching"]], "networkx.algorithms.approximation.maxcut": [[112, "module-networkx.algorithms.approximation.maxcut"]], "networkx.algorithms.approximation.ramsey": [[112, "module-networkx.algorithms.approximation.ramsey"]], "networkx.algorithms.approximation.steinertree": [[112, "module-networkx.algorithms.approximation.steinertree"]], "networkx.algorithms.approximation.traveling_salesman": [[112, "module-networkx.algorithms.approximation.traveling_salesman"]], "networkx.algorithms.approximation.treewidth": [[112, "module-networkx.algorithms.approximation.treewidth"]], "networkx.algorithms.approximation.vertex_cover": [[112, "module-networkx.algorithms.approximation.vertex_cover"]], "networkx.algorithms.assortativity": [[113, "module-networkx.algorithms.assortativity"]], "networkx.algorithms.asteroidal": [[114, "module-networkx.algorithms.asteroidal"]], "networkx.algorithms.bipartite": [[115, "module-networkx.algorithms.bipartite"]], "networkx.algorithms.bipartite.basic": [[115, "module-networkx.algorithms.bipartite.basic"]], "networkx.algorithms.bipartite.centrality": [[115, "module-networkx.algorithms.bipartite.centrality"]], "networkx.algorithms.bipartite.cluster": [[115, "module-networkx.algorithms.bipartite.cluster"]], "networkx.algorithms.bipartite.covering": [[115, "module-networkx.algorithms.bipartite.covering"]], "networkx.algorithms.bipartite.edgelist": [[115, "module-networkx.algorithms.bipartite.edgelist"]], "networkx.algorithms.bipartite.generators": [[115, "module-networkx.algorithms.bipartite.generators"]], "networkx.algorithms.bipartite.matching": [[115, "module-networkx.algorithms.bipartite.matching"]], "networkx.algorithms.bipartite.matrix": [[115, "module-networkx.algorithms.bipartite.matrix"]], "networkx.algorithms.bipartite.projection": [[115, "module-networkx.algorithms.bipartite.projection"]], "networkx.algorithms.bipartite.redundancy": [[115, "module-networkx.algorithms.bipartite.redundancy"]], "networkx.algorithms.bipartite.spectral": [[115, "module-networkx.algorithms.bipartite.spectral"]], "networkx.algorithms.boundary": [[116, "module-networkx.algorithms.boundary"]], "networkx.algorithms.bridges": [[117, "module-networkx.algorithms.bridges"]], "networkx.algorithms.centrality": [[118, "module-networkx.algorithms.centrality"]], "networkx.algorithms.chains": [[119, "module-networkx.algorithms.chains"]], "networkx.algorithms.chordal": [[120, "module-networkx.algorithms.chordal"]], "networkx.algorithms.clique": [[121, "module-networkx.algorithms.clique"]], "networkx.algorithms.cluster": [[122, "module-networkx.algorithms.cluster"]], "networkx.algorithms.coloring": [[123, "module-networkx.algorithms.coloring"]], "networkx.algorithms.communicability_alg": [[124, "module-networkx.algorithms.communicability_alg"]], "networkx.algorithms.community": [[125, "module-networkx.algorithms.community"]], "networkx.algorithms.community.asyn_fluid": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "networkx.algorithms.community.centrality": [[125, "module-networkx.algorithms.community.centrality"]], "networkx.algorithms.community.community_utils": [[125, "module-networkx.algorithms.community.community_utils"]], "networkx.algorithms.community.kclique": [[125, "module-networkx.algorithms.community.kclique"]], "networkx.algorithms.community.kernighan_lin": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "networkx.algorithms.community.label_propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "networkx.algorithms.community.louvain": [[125, "module-networkx.algorithms.community.louvain"]], "networkx.algorithms.community.lukes": [[125, "module-networkx.algorithms.community.lukes"]], "networkx.algorithms.community.modularity_max": [[125, "module-networkx.algorithms.community.modularity_max"]], "networkx.algorithms.community.quality": [[125, "module-networkx.algorithms.community.quality"]], "networkx.algorithms.components": [[126, "module-networkx.algorithms.components"]], "networkx.algorithms.connectivity": [[127, "module-networkx.algorithms.connectivity"]], "networkx.algorithms.connectivity.connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "networkx.algorithms.connectivity.cuts": [[127, "module-networkx.algorithms.connectivity.cuts"]], "networkx.algorithms.connectivity.disjoint_paths": [[127, "module-networkx.algorithms.connectivity.disjoint_paths"]], "networkx.algorithms.connectivity.edge_augmentation": [[127, "module-networkx.algorithms.connectivity.edge_augmentation"]], "networkx.algorithms.connectivity.edge_kcomponents": [[127, "module-networkx.algorithms.connectivity.edge_kcomponents"]], "networkx.algorithms.connectivity.kcomponents": [[127, "module-networkx.algorithms.connectivity.kcomponents"]], "networkx.algorithms.connectivity.kcutsets": [[127, "module-networkx.algorithms.connectivity.kcutsets"]], "networkx.algorithms.connectivity.stoerwagner": [[127, "module-networkx.algorithms.connectivity.stoerwagner"]], "networkx.algorithms.connectivity.utils": [[127, "module-networkx.algorithms.connectivity.utils"]], "networkx.algorithms.core": [[128, "module-networkx.algorithms.core"]], "networkx.algorithms.covering": [[129, "module-networkx.algorithms.covering"]], "networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[144, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[151, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": [[166, "networkx.algorithms.planarity.PlanarEmbedding.degree"]], "edge_subgraph() (planarembedding method)": [[167, "networkx.algorithms.planarity.PlanarEmbedding.edge_subgraph"]], "edges (planarembedding property)": [[168, "networkx.algorithms.planarity.PlanarEmbedding.edges"]], "get_data() (planarembedding method)": [[169, "networkx.algorithms.planarity.PlanarEmbedding.get_data"]], "get_edge_data() (planarembedding method)": [[170, "networkx.algorithms.planarity.PlanarEmbedding.get_edge_data"]], "has_edge() (planarembedding method)": [[171, "networkx.algorithms.planarity.PlanarEmbedding.has_edge"]], "has_node() (planarembedding method)": [[172, "networkx.algorithms.planarity.PlanarEmbedding.has_node"]], "has_predecessor() (planarembedding method)": [[173, "networkx.algorithms.planarity.PlanarEmbedding.has_predecessor"]], "has_successor() (planarembedding method)": [[174, "networkx.algorithms.planarity.PlanarEmbedding.has_successor"]], "in_degree (planarembedding property)": [[175, "networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, 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"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__() 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networkx.classes.filters)": [[1029, "networkx.classes.filters.show_diedges"]], "show_edges() (in module networkx.classes.filters)": [[1030, "networkx.classes.filters.show_edges"]], "show_multidiedges() (in module networkx.classes.filters)": [[1031, "networkx.classes.filters.show_multidiedges"]], "show_multiedges() (in module networkx.classes.filters)": [[1032, "networkx.classes.filters.show_multiedges"]], "__init__() (show_nodes method)": [[1033, "networkx.classes.filters.show_nodes.__init__"]], "show_nodes (class in networkx.classes.filters)": [[1033, "networkx.classes.filters.show_nodes"]], "generic_graph_view() (in module networkx.classes.graphviews)": [[1034, "networkx.classes.graphviews.generic_graph_view"]], "reverse_view() (in module networkx.classes.graphviews)": [[1035, "networkx.classes.graphviews.reverse_view"]], "subgraph_view() (in module networkx.classes.graphviews)": [[1036, "networkx.classes.graphviews.subgraph_view"]], "graph (class in networkx)": [[1037, "networkx.Graph"]], "networkx.classes.backends": [[1038, "module-networkx.classes.backends"]], "networkx.classes.coreviews": [[1038, "module-networkx.classes.coreviews"]], "networkx.classes.filters": [[1038, "module-networkx.classes.filters"]], "networkx.classes.graphviews": [[1038, "module-networkx.classes.graphviews"]], "multidigraph (class in networkx)": [[1039, "networkx.MultiDiGraph"]], "multigraph (class in networkx)": [[1040, "networkx.MultiGraph"]], "networkx.convert": [[1041, "module-networkx.convert"]], "networkx.convert_matrix": [[1041, "module-networkx.convert_matrix"]], "networkx.drawing.layout": [[1042, "module-networkx.drawing.layout"]], "networkx.drawing.nx_agraph": [[1042, "module-networkx.drawing.nx_agraph"]], "networkx.drawing.nx_pydot": [[1042, "module-networkx.drawing.nx_pydot"]], "networkx.drawing.nx_pylab": [[1042, "module-networkx.drawing.nx_pylab"]], "ambiguoussolution (class in networkx)": [[1043, "networkx.AmbiguousSolution"]], "exceededmaxiterations (class in networkx)": [[1043, "networkx.ExceededMaxIterations"]], "hasacycle (class in networkx)": [[1043, "networkx.HasACycle"]], "networkxalgorithmerror (class in networkx)": [[1043, "networkx.NetworkXAlgorithmError"]], "networkxerror (class in networkx)": [[1043, "networkx.NetworkXError"]], "networkxexception (class in networkx)": [[1043, "networkx.NetworkXException"]], "networkxnocycle (class in networkx)": [[1043, "networkx.NetworkXNoCycle"]], "networkxnopath (class in networkx)": [[1043, "networkx.NetworkXNoPath"]], "networkxnotimplemented (class in networkx)": [[1043, "networkx.NetworkXNotImplemented"]], "networkxpointlessconcept (class in networkx)": [[1043, "networkx.NetworkXPointlessConcept"]], "networkxunbounded (class in networkx)": [[1043, "networkx.NetworkXUnbounded"]], "networkxunfeasible (class in networkx)": [[1043, "networkx.NetworkXUnfeasible"]], "nodenotfound (class in networkx)": [[1043, "networkx.NodeNotFound"]], "poweriterationfailedconvergence (class in networkx)": [[1043, "networkx.PowerIterationFailedConvergence"]], "networkx.exception": [[1043, "module-networkx.exception"]], "networkx.classes.function": [[1044, "module-networkx.classes.function"]], "assemble() (argmap method)": [[1045, "networkx.utils.decorators.argmap.assemble"]], "compile() (argmap method)": [[1046, "networkx.utils.decorators.argmap.compile"]], "signature() (argmap class method)": [[1047, "networkx.utils.decorators.argmap.signature"]], "pop() (mappedqueue method)": [[1048, "networkx.utils.mapped_queue.MappedQueue.pop"]], "push() (mappedqueue method)": [[1049, "networkx.utils.mapped_queue.MappedQueue.push"]], "remove() (mappedqueue method)": [[1050, "networkx.utils.mapped_queue.MappedQueue.remove"]], "update() (mappedqueue method)": [[1051, "networkx.utils.mapped_queue.MappedQueue.update"]], "add_cycle() (in module networkx.classes.function)": [[1052, "networkx.classes.function.add_cycle"]], "add_path() (in module networkx.classes.function)": [[1053, "networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, "networkx.convert_matrix.from_pandas_adjacency"]], "from_pandas_edgelist() (in module networkx.convert_matrix)": [[1100, "networkx.convert_matrix.from_pandas_edgelist"]], "from_scipy_sparse_array() (in module networkx.convert_matrix)": [[1101, "networkx.convert_matrix.from_scipy_sparse_array"]], "to_numpy_array() (in module networkx.convert_matrix)": [[1102, "networkx.convert_matrix.to_numpy_array"]], "to_pandas_adjacency() (in module networkx.convert_matrix)": [[1103, "networkx.convert_matrix.to_pandas_adjacency"]], "to_pandas_edgelist() (in module networkx.convert_matrix)": [[1104, "networkx.convert_matrix.to_pandas_edgelist"]], "to_scipy_sparse_array() (in module networkx.convert_matrix)": [[1105, "networkx.convert_matrix.to_scipy_sparse_array"]], "bipartite_layout() (in module networkx.drawing.layout)": [[1106, "networkx.drawing.layout.bipartite_layout"]], "circular_layout() (in module networkx.drawing.layout)": [[1107, "networkx.drawing.layout.circular_layout"]], "kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], "spring_layout() (in module networkx.drawing.layout)": [[1117, "networkx.drawing.layout.spring_layout"]], "from_agraph() (in module networkx.drawing.nx_agraph)": [[1118, "networkx.drawing.nx_agraph.from_agraph"]], "graphviz_layout() (in module networkx.drawing.nx_agraph)": [[1119, "networkx.drawing.nx_agraph.graphviz_layout"]], "pygraphviz_layout() (in module networkx.drawing.nx_agraph)": [[1120, "networkx.drawing.nx_agraph.pygraphviz_layout"]], "read_dot() (in module networkx.drawing.nx_agraph)": [[1121, "networkx.drawing.nx_agraph.read_dot"]], "to_agraph() (in module networkx.drawing.nx_agraph)": [[1122, "networkx.drawing.nx_agraph.to_agraph"]], "write_dot() (in module networkx.drawing.nx_agraph)": [[1123, "networkx.drawing.nx_agraph.write_dot"]], "from_pydot() (in module networkx.drawing.nx_pydot)": [[1124, "networkx.drawing.nx_pydot.from_pydot"]], "graphviz_layout() (in module networkx.drawing.nx_pydot)": [[1125, "networkx.drawing.nx_pydot.graphviz_layout"]], "pydot_layout() (in module networkx.drawing.nx_pydot)": [[1126, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1130, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1131, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1132, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1143, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1144, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1145, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1146, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1147, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1152, "networkx.generators.classic.cycle_graph"]], "dorogovtsev_goltsev_mendes_graph() (in module networkx.generators.classic)": [[1153, "networkx.generators.classic.dorogovtsev_goltsev_mendes_graph"]], "empty_graph() (in module networkx.generators.classic)": [[1154, "networkx.generators.classic.empty_graph"]], "full_rary_tree() (in module networkx.generators.classic)": [[1155, "networkx.generators.classic.full_rary_tree"]], "ladder_graph() (in module networkx.generators.classic)": [[1156, "networkx.generators.classic.ladder_graph"]], "lollipop_graph() (in module networkx.generators.classic)": [[1157, "networkx.generators.classic.lollipop_graph"]], "null_graph() (in module networkx.generators.classic)": [[1158, "networkx.generators.classic.null_graph"]], "path_graph() (in module networkx.generators.classic)": [[1159, "networkx.generators.classic.path_graph"]], "star_graph() (in module networkx.generators.classic)": [[1160, "networkx.generators.classic.star_graph"]], "trivial_graph() (in 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() (in module networkx.generators.community)": [[1169, "networkx.generators.community.planted_partition_graph"]], "random_partition_graph() (in module networkx.generators.community)": [[1170, "networkx.generators.community.random_partition_graph"]], "relaxed_caveman_graph() (in module networkx.generators.community)": [[1171, "networkx.generators.community.relaxed_caveman_graph"]], "ring_of_cliques() (in module networkx.generators.community)": [[1172, "networkx.generators.community.ring_of_cliques"]], "stochastic_block_model() (in module networkx.generators.community)": [[1173, "networkx.generators.community.stochastic_block_model"]], "windmill_graph() (in module networkx.generators.community)": [[1174, "networkx.generators.community.windmill_graph"]], "configuration_model() (in module networkx.generators.degree_seq)": [[1175, "networkx.generators.degree_seq.configuration_model"]], "degree_sequence_tree() (in module networkx.generators.degree_seq)": [[1176, "networkx.generators.degree_seq.degree_sequence_tree"]], "directed_configuration_model() (in module networkx.generators.degree_seq)": [[1177, "networkx.generators.degree_seq.directed_configuration_model"]], "directed_havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1178, "networkx.generators.degree_seq.directed_havel_hakimi_graph"]], "expected_degree_graph() (in module networkx.generators.degree_seq)": [[1179, "networkx.generators.degree_seq.expected_degree_graph"]], "havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1180, "networkx.generators.degree_seq.havel_hakimi_graph"]], "random_degree_sequence_graph() (in module networkx.generators.degree_seq)": [[1181, "networkx.generators.degree_seq.random_degree_sequence_graph"]], "gn_graph() (in module networkx.generators.directed)": [[1182, "networkx.generators.directed.gn_graph"]], "gnc_graph() (in module networkx.generators.directed)": [[1183, "networkx.generators.directed.gnc_graph"]], "gnr_graph() (in module networkx.generators.directed)": [[1184, "networkx.generators.directed.gnr_graph"]], "random_k_out_graph() (in module networkx.generators.directed)": [[1185, "networkx.generators.directed.random_k_out_graph"]], "scale_free_graph() (in module networkx.generators.directed)": [[1186, "networkx.generators.directed.scale_free_graph"]], "duplication_divergence_graph() (in module networkx.generators.duplication)": [[1187, "networkx.generators.duplication.duplication_divergence_graph"]], "partial_duplication_graph() (in module networkx.generators.duplication)": [[1188, "networkx.generators.duplication.partial_duplication_graph"]], "ego_graph() (in module networkx.generators.ego)": [[1189, "networkx.generators.ego.ego_graph"]], "chordal_cycle_graph() (in module networkx.generators.expanders)": [[1190, "networkx.generators.expanders.chordal_cycle_graph"]], "margulis_gabber_galil_graph() (in module networkx.generators.expanders)": [[1191, "networkx.generators.expanders.margulis_gabber_galil_graph"]], "paley_graph() (in module networkx.generators.expanders)": [[1192, "networkx.generators.expanders.paley_graph"]], "geographical_threshold_graph() (in module networkx.generators.geometric)": [[1193, "networkx.generators.geometric.geographical_threshold_graph"]], "geometric_edges() (in module networkx.generators.geometric)": [[1194, "networkx.generators.geometric.geometric_edges"]], "navigable_small_world_graph() (in module networkx.generators.geometric)": [[1195, "networkx.generators.geometric.navigable_small_world_graph"]], "random_geometric_graph() (in module networkx.generators.geometric)": [[1196, "networkx.generators.geometric.random_geometric_graph"]], "soft_random_geometric_graph() (in module networkx.generators.geometric)": [[1197, "networkx.generators.geometric.soft_random_geometric_graph"]], "thresholded_random_geometric_graph() (in module networkx.generators.geometric)": [[1198, "networkx.generators.geometric.thresholded_random_geometric_graph"]], "waxman_graph() (in module networkx.generators.geometric)": [[1199, "networkx.generators.geometric.waxman_graph"]], "hkn_harary_graph() (in module networkx.generators.harary_graph)": [[1200, "networkx.generators.harary_graph.hkn_harary_graph"]], "hnm_harary_graph() (in module networkx.generators.harary_graph)": [[1201, "networkx.generators.harary_graph.hnm_harary_graph"]], "random_internet_as_graph() (in module networkx.generators.internet_as_graphs)": [[1202, "networkx.generators.internet_as_graphs.random_internet_as_graph"]], "general_random_intersection_graph() (in module networkx.generators.intersection)": [[1203, "networkx.generators.intersection.general_random_intersection_graph"]], "k_random_intersection_graph() (in module networkx.generators.intersection)": [[1204, "networkx.generators.intersection.k_random_intersection_graph"]], "uniform_random_intersection_graph() (in module networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, "networkx.generators.random_graphs.dense_gnm_random_graph"]], "dual_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1227, "networkx.generators.random_graphs.dual_barabasi_albert_graph"]], "erdos_renyi_graph() (in module networkx.generators.random_graphs)": [[1228, "networkx.generators.random_graphs.erdos_renyi_graph"]], "extended_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1229, "networkx.generators.random_graphs.extended_barabasi_albert_graph"]], "fast_gnp_random_graph() (in module networkx.generators.random_graphs)": [[1230, "networkx.generators.random_graphs.fast_gnp_random_graph"]], "gnm_random_graph() (in module networkx.generators.random_graphs)": [[1231, "networkx.generators.random_graphs.gnm_random_graph"]], "gnp_random_graph() (in module networkx.generators.random_graphs)": [[1232, "networkx.generators.random_graphs.gnp_random_graph"]], "newman_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1233, "networkx.generators.random_graphs.newman_watts_strogatz_graph"]], "powerlaw_cluster_graph() (in module networkx.generators.random_graphs)": [[1234, "networkx.generators.random_graphs.powerlaw_cluster_graph"]], "random_kernel_graph() (in module networkx.generators.random_graphs)": [[1235, "networkx.generators.random_graphs.random_kernel_graph"]], "random_lobster() (in module networkx.generators.random_graphs)": [[1236, "networkx.generators.random_graphs.random_lobster"]], "random_powerlaw_tree() (in module networkx.generators.random_graphs)": [[1237, "networkx.generators.random_graphs.random_powerlaw_tree"]], "random_powerlaw_tree_sequence() (in module networkx.generators.random_graphs)": [[1238, "networkx.generators.random_graphs.random_powerlaw_tree_sequence"]], "random_regular_graph() (in module networkx.generators.random_graphs)": [[1239, "networkx.generators.random_graphs.random_regular_graph"]], "random_shell_graph() (in module networkx.generators.random_graphs)": [[1240, "networkx.generators.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1242, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1243, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1244, "networkx.generators.small.chvatal_graph"]], "cubical_graph() (in module networkx.generators.small)": [[1245, "networkx.generators.small.cubical_graph"]], "desargues_graph() (in module networkx.generators.small)": [[1246, "networkx.generators.small.desargues_graph"]], "diamond_graph() (in module networkx.generators.small)": [[1247, "networkx.generators.small.diamond_graph"]], "dodecahedral_graph() (in module networkx.generators.small)": [[1248, "networkx.generators.small.dodecahedral_graph"]], "frucht_graph() (in module networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, "networkx.generators.small.octahedral_graph"]], "pappus_graph() (in module networkx.generators.small)": [[1258, "networkx.generators.small.pappus_graph"]], "petersen_graph() (in module networkx.generators.small)": [[1259, "networkx.generators.small.petersen_graph"]], "sedgewick_maze_graph() (in module networkx.generators.small)": [[1260, "networkx.generators.small.sedgewick_maze_graph"]], "tetrahedral_graph() (in module networkx.generators.small)": [[1261, "networkx.generators.small.tetrahedral_graph"]], "truncated_cube_graph() (in module networkx.generators.small)": [[1262, "networkx.generators.small.truncated_cube_graph"]], "truncated_tetrahedron_graph() (in module networkx.generators.small)": [[1263, "networkx.generators.small.truncated_tetrahedron_graph"]], "tutte_graph() (in module networkx.generators.small)": [[1264, "networkx.generators.small.tutte_graph"]], "davis_southern_women_graph() (in module networkx.generators.social)": [[1265, "networkx.generators.social.davis_southern_women_graph"]], "florentine_families_graph() (in module networkx.generators.social)": [[1266, "networkx.generators.social.florentine_families_graph"]], "karate_club_graph() (in module networkx.generators.social)": [[1267, "networkx.generators.social.karate_club_graph"]], "les_miserables_graph() (in module networkx.generators.social)": [[1268, "networkx.generators.social.les_miserables_graph"]], "spectral_graph_forge() (in module networkx.generators.spectral_graph_forge)": [[1269, "networkx.generators.spectral_graph_forge.spectral_graph_forge"]], "stochastic_graph() (in module networkx.generators.stochastic)": [[1270, "networkx.generators.stochastic.stochastic_graph"]], "sudoku_graph() (in module networkx.generators.sudoku)": [[1271, "networkx.generators.sudoku.sudoku_graph"]], "prefix_tree() (in module networkx.generators.trees)": [[1272, "networkx.generators.trees.prefix_tree"]], "random_tree() (in module networkx.generators.trees)": [[1273, "networkx.generators.trees.random_tree"]], "triad_graph() (in module networkx.generators.triads)": [[1274, "networkx.generators.triads.triad_graph"]], "algebraic_connectivity() (in module networkx.linalg.algebraicconnectivity)": [[1275, "networkx.linalg.algebraicconnectivity.algebraic_connectivity"]], "fiedler_vector() (in module networkx.linalg.algebraicconnectivity)": [[1276, "networkx.linalg.algebraicconnectivity.fiedler_vector"]], "spectral_ordering() (in module networkx.linalg.algebraicconnectivity)": [[1277, "networkx.linalg.algebraicconnectivity.spectral_ordering"]], "attr_matrix() (in module networkx.linalg.attrmatrix)": [[1278, "networkx.linalg.attrmatrix.attr_matrix"]], "attr_sparse_matrix() (in module networkx.linalg.attrmatrix)": [[1279, "networkx.linalg.attrmatrix.attr_sparse_matrix"]], "bethe_hessian_matrix() (in module networkx.linalg.bethehessianmatrix)": [[1280, "networkx.linalg.bethehessianmatrix.bethe_hessian_matrix"]], "adjacency_matrix() (in module networkx.linalg.graphmatrix)": [[1281, "networkx.linalg.graphmatrix.adjacency_matrix"]], "incidence_matrix() (in module networkx.linalg.graphmatrix)": [[1282, "networkx.linalg.graphmatrix.incidence_matrix"]], "directed_combinatorial_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1283, "networkx.linalg.laplacianmatrix.directed_combinatorial_laplacian_matrix"]], "directed_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1284, "networkx.linalg.laplacianmatrix.directed_laplacian_matrix"]], "laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1285, "networkx.linalg.laplacianmatrix.laplacian_matrix"]], "normalized_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1286, "networkx.linalg.laplacianmatrix.normalized_laplacian_matrix"]], "directed_modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1287, "networkx.linalg.modularitymatrix.directed_modularity_matrix"]], "modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1288, "networkx.linalg.modularitymatrix.modularity_matrix"]], "adjacency_spectrum() (in module networkx.linalg.spectrum)": [[1289, "networkx.linalg.spectrum.adjacency_spectrum"]], "bethe_hessian_spectrum() (in module networkx.linalg.spectrum)": [[1290, "networkx.linalg.spectrum.bethe_hessian_spectrum"]], "laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1291, "networkx.linalg.spectrum.laplacian_spectrum"]], "modularity_spectrum() (in module networkx.linalg.spectrum)": [[1292, "networkx.linalg.spectrum.modularity_spectrum"]], "normalized_laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1293, "networkx.linalg.spectrum.normalized_laplacian_spectrum"]], "convert_node_labels_to_integers() (in module networkx.relabel)": [[1294, "networkx.relabel.convert_node_labels_to_integers"]], "relabel_nodes() (in module networkx.relabel)": [[1295, "networkx.relabel.relabel_nodes"]], "__init__() (argmap method)": [[1296, "networkx.utils.decorators.argmap.__init__"]], "argmap (class in networkx.utils.decorators)": [[1296, "networkx.utils.decorators.argmap"]], "nodes_or_number() (in module networkx.utils.decorators)": [[1297, "networkx.utils.decorators.nodes_or_number"]], "not_implemented_for() (in module networkx.utils.decorators)": [[1298, "networkx.utils.decorators.not_implemented_for"]], "np_random_state() (in module networkx.utils.decorators)": [[1299, "networkx.utils.decorators.np_random_state"]], "open_file() (in module networkx.utils.decorators)": [[1300, "networkx.utils.decorators.open_file"]], "py_random_state() (in module networkx.utils.decorators)": [[1301, "networkx.utils.decorators.py_random_state"]], "mappedqueue (class in networkx.utils.mapped_queue)": [[1302, "networkx.utils.mapped_queue.MappedQueue"]], "__init__() (mappedqueue method)": [[1302, "networkx.utils.mapped_queue.MappedQueue.__init__"]], "arbitrary_element() (in module networkx.utils.misc)": [[1303, "networkx.utils.misc.arbitrary_element"]], "create_py_random_state() (in module networkx.utils.misc)": [[1304, "networkx.utils.misc.create_py_random_state"]], "create_random_state() (in module networkx.utils.misc)": [[1305, "networkx.utils.misc.create_random_state"]], "dict_to_numpy_array() (in module networkx.utils.misc)": [[1306, "networkx.utils.misc.dict_to_numpy_array"]], "edges_equal() (in module networkx.utils.misc)": [[1307, "networkx.utils.misc.edges_equal"]], "flatten() (in module networkx.utils.misc)": [[1308, "networkx.utils.misc.flatten"]], "graphs_equal() (in module networkx.utils.misc)": [[1309, "networkx.utils.misc.graphs_equal"]], "groups() (in module networkx.utils.misc)": [[1310, "networkx.utils.misc.groups"]], "make_list_of_ints() (in module networkx.utils.misc)": [[1311, "networkx.utils.misc.make_list_of_ints"]], "nodes_equal() (in module networkx.utils.misc)": [[1312, "networkx.utils.misc.nodes_equal"]], "pairwise() (in module networkx.utils.misc)": [[1313, "networkx.utils.misc.pairwise"]], "cumulative_distribution() (in module networkx.utils.random_sequence)": [[1314, "networkx.utils.random_sequence.cumulative_distribution"]], "discrete_sequence() (in module networkx.utils.random_sequence)": [[1315, "networkx.utils.random_sequence.discrete_sequence"]], "powerlaw_sequence() (in module networkx.utils.random_sequence)": [[1316, "networkx.utils.random_sequence.powerlaw_sequence"]], "random_weighted_sample() (in module networkx.utils.random_sequence)": [[1317, "networkx.utils.random_sequence.random_weighted_sample"]], "weighted_choice() (in module networkx.utils.random_sequence)": [[1318, "networkx.utils.random_sequence.weighted_choice"]], "zipf_rv() (in module networkx.utils.random_sequence)": [[1319, "networkx.utils.random_sequence.zipf_rv"]], "cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1320, "networkx.utils.rcm.cuthill_mckee_ordering"]], "reverse_cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1321, "networkx.utils.rcm.reverse_cuthill_mckee_ordering"]], "union() (unionfind method)": [[1322, "networkx.utils.union_find.UnionFind.union"]], "networkx.generators.atlas": [[1323, "module-networkx.generators.atlas"]], "networkx.generators.classic": [[1323, "module-networkx.generators.classic"]], "networkx.generators.cographs": [[1323, "module-networkx.generators.cographs"]], "networkx.generators.community": [[1323, "module-networkx.generators.community"]], "networkx.generators.degree_seq": [[1323, "module-networkx.generators.degree_seq"]], "networkx.generators.directed": [[1323, "module-networkx.generators.directed"]], "networkx.generators.duplication": [[1323, "module-networkx.generators.duplication"]], "networkx.generators.ego": [[1323, "module-networkx.generators.ego"]], "networkx.generators.expanders": [[1323, "module-networkx.generators.expanders"]], "networkx.generators.geometric": [[1323, "module-networkx.generators.geometric"]], "networkx.generators.harary_graph": [[1323, 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"PlanarEmbedding.connect_components", "PlanarEmbedding.copy", "PlanarEmbedding.degree", "PlanarEmbedding.edge_subgraph", "PlanarEmbedding.edges", "PlanarEmbedding.get_data", "PlanarEmbedding.get_edge_data", "PlanarEmbedding.has_edge", "PlanarEmbedding.has_node", "PlanarEmbedding.has_predecessor", "PlanarEmbedding.has_successor", "PlanarEmbedding.in_degree", "PlanarEmbedding.in_edges", "PlanarEmbedding.is_directed", "PlanarEmbedding.is_multigraph", "PlanarEmbedding.name", "PlanarEmbedding.nbunch_iter", "PlanarEmbedding.neighbors", "PlanarEmbedding.neighbors_cw_order", "PlanarEmbedding.next_face_half_edge", "PlanarEmbedding.nodes", "PlanarEmbedding.number_of_edges", "PlanarEmbedding.number_of_nodes", "PlanarEmbedding.order", "PlanarEmbedding.out_degree", "PlanarEmbedding.out_edges", "PlanarEmbedding.pred", "PlanarEmbedding.predecessors", "PlanarEmbedding.remove_edge", "PlanarEmbedding.remove_edges_from", "PlanarEmbedding.remove_node", "PlanarEmbedding.remove_nodes_from", 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717, 730, 753, 762, 786, 1036, 1085, 1086, 1088, 1112, 1117, 1140, 1150, 1168, 1175, 1185, 1196, 1197, 1198, 1215, 1235, 1295, 1307, 1309, 1312, 1326, 1336, 1393, 1405, 1412, 1414, 1426], "posbm": 7, "xy": [7, 245], "212": 7, "358": [7, 17], "plot_blockmodel": [7, 17], "convert": [8, 34, 50, 52, 54, 55, 56, 57, 58, 74, 75, 99, 102, 112, 169, 266, 267, 293, 375, 464, 565, 566, 615, 676, 679, 850, 895, 931, 934, 976, 979, 1038, 1085, 1097, 1098, 1099, 1166, 1167, 1273, 1281, 1296, 1297, 1299, 1301, 1306, 1310, 1325, 1332, 1333, 1336, 1337, 1338, 1342, 1345, 1346, 1347, 1348, 1349, 1350, 1353, 1356, 1357, 1361, 1362, 1363, 1364, 1370, 1371, 1376, 1379, 1403, 1404, 1406, 1409, 1411, 1412, 1413, 1416, 1421, 1426], "formula": [8, 299, 316, 322, 380, 385, 618, 688, 1421], "can": [8, 15, 24, 34, 38, 40, 43, 52, 54, 55, 56, 57, 58, 67, 69, 70, 71, 75, 76, 84, 88, 91, 92, 93, 94, 95, 96, 99, 100, 101, 102, 103, 105, 107, 110, 111, 112, 115, 125, 132, 141, 142, 143, 144, 151, 152, 156, 157, 158, 165, 168, 171, 176, 180, 184, 185, 189, 190, 193, 199, 200, 207, 220, 222, 224, 227, 229, 230, 231, 238, 239, 240, 243, 251, 260, 261, 262, 264, 278, 281, 282, 297, 298, 301, 302, 305, 306, 307, 308, 309, 315, 316, 324, 325, 329, 330, 332, 333, 337, 339, 340, 342, 344, 345, 346, 347, 353, 354, 357, 358, 361, 362, 374, 376, 380, 382, 383, 385, 387, 388, 389, 390, 394, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 422, 423, 427, 439, 440, 449, 454, 456, 458, 460, 461, 464, 465, 466, 471, 472, 473, 474, 475, 491, 492, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515, 516, 517, 518, 519, 530, 540, 553, 575, 577, 581, 586, 588, 597, 598, 601, 602, 604, 615, 616, 617, 619, 626, 628, 629, 630, 633, 641, 643, 647, 652, 653, 654, 655, 657, 658, 660, 661, 662, 667, 668, 669, 676, 677, 678, 679, 680, 687, 688, 689, 690, 691, 720, 722, 723, 724, 725, 726, 729, 730, 731, 748, 749, 751, 762, 767, 770, 775, 786, 791, 796, 850, 853, 854, 855, 856, 857, 862, 865, 867, 870, 871, 873, 874, 878, 879, 882, 887, 888, 892, 895, 898, 899, 900, 901, 902, 907, 910, 912, 914, 916, 917, 921, 925, 928, 931, 934, 935, 936, 937, 938, 943, 946, 947, 948, 951, 952, 955, 956, 960, 963, 968, 973, 976, 979, 980, 981, 982, 983, 988, 991, 992, 993, 995, 998, 999, 1003, 1007, 1010, 1036, 1037, 1038, 1039, 1040, 1042, 1045, 1047, 1059, 1060, 1061, 1063, 1066, 1068, 1082, 1085, 1088, 1102, 1103, 1105, 1129, 1133, 1135, 1137, 1148, 1151, 1154, 1164, 1165, 1166, 1167, 1174, 1175, 1177, 1193, 1196, 1197, 1198, 1206, 1207, 1217, 1218, 1219, 1222, 1235, 1246, 1248, 1250, 1258, 1263, 1264, 1269, 1272, 1275, 1276, 1278, 1279, 1281, 1282, 1283, 1284, 1295, 1296, 1297, 1299, 1301, 1302, 1303, 1320, 1321, 1323, 1324, 1326, 1328, 1329, 1330, 1333, 1334, 1347, 1349, 1352, 1354, 1356, 1357, 1362, 1363, 1371, 1372, 1378, 1380, 1382, 1385, 1387, 1388, 1392, 1393, 1394, 1395, 1396, 1399, 1402, 1404, 1405, 1406, 1408, 1409, 1412, 1425, 1426], "more": [8, 43, 53, 67, 86, 92, 93, 94, 97, 99, 100, 101, 102, 103, 107, 109, 110, 111, 114, 115, 121, 127, 128, 143, 165, 172, 198, 199, 202, 204, 215, 216, 218, 219, 220, 221, 230, 231, 235, 256, 267, 277, 278, 281, 289, 299, 310, 314, 324, 325, 335, 338, 361, 378, 383, 385, 387, 389, 390, 392, 399, 405, 406, 407, 422, 427, 428, 432, 433, 437, 460, 464, 480, 520, 521, 559, 560, 581, 582, 583, 590, 593, 614, 619, 626, 631, 635, 653, 656, 660, 661, 662, 676, 679, 683, 691, 698, 699, 703, 711, 717, 718, 735, 737, 748, 760, 782, 786, 796, 862, 868, 886, 887, 890, 891, 907, 913, 924, 925, 926, 927, 943, 949, 967, 968, 971, 972, 988, 994, 1006, 1007, 1008, 1009, 1037, 1039, 1040, 1042, 1043, 1071, 1094, 1100, 1116, 1119, 1120, 1123, 1130, 1131, 1132, 1133, 1135, 1137, 1138, 1139, 1140, 1141, 1142, 1143, 1185, 1192, 1193, 1206, 1214, 1217, 1218, 1219, 1272, 1287, 1288, 1295, 1296, 1297, 1323, 1326, 1328, 1337, 1345, 1348, 1349, 1350, 1390, 1394, 1395, 1397, 1398, 1399, 1401, 1404, 1405, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "express": [8, 92, 110, 184, 315, 329, 330, 383, 384, 618, 619, 873, 916, 955, 998, 1199, 1287, 1326], "than": [8, 11, 34, 43, 55, 97, 99, 101, 102, 103, 115, 128, 142, 143, 144, 161, 199, 214, 215, 216, 218, 219, 221, 227, 231, 235, 241, 256, 277, 278, 281, 288, 289, 297, 298, 299, 304, 306, 307, 310, 311, 315, 316, 321, 324, 325, 326, 328, 329, 330, 341, 352, 358, 361, 374, 380, 381, 383, 384, 385, 387, 389, 390, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 425, 426, 429, 435, 464, 468, 469, 500, 527, 537, 559, 560, 581, 582, 583, 590, 625, 626, 635, 636, 652, 653, 656, 658, 659, 673, 676, 678, 679, 681, 683, 686, 690, 692, 693, 694, 698, 699, 711, 731, 735, 737, 748, 752, 761, 786, 887, 925, 947, 968, 992, 1007, 1038, 1042, 1043, 1060, 1102, 1135, 1154, 1162, 1165, 1167, 1172, 1174, 1185, 1187, 1194, 1198, 1226, 1230, 1231, 1236, 1237, 1238, 1239, 1275, 1276, 1296, 1297, 1326, 1328, 1345, 1348, 1349, 1350, 1353, 1354, 1358, 1365, 1366, 1379, 1382, 1395, 1402, 1404, 1405, 1408, 1413, 1423, 1425], "worst": [8, 210, 211, 212, 221, 228, 235, 264, 293, 294, 338, 345, 346, 347, 440, 513, 515, 516, 517, 518], "reus": [8, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1131, 1132, 1138, 1139, 1140, 1141, 1142, 1328, 1402], "subcircuit": 8, "multipl": [8, 11, 25, 40, 45, 77, 93, 94, 99, 103, 107, 109, 143, 157, 158, 166, 175, 188, 195, 207, 287, 311, 357, 385, 386, 423, 443, 447, 458, 460, 464, 485, 486, 487, 594, 595, 597, 615, 616, 641, 643, 678, 690, 691, 697, 705, 738, 762, 786, 796, 856, 857, 863, 869, 877, 884, 892, 901, 902, 908, 923, 928, 937, 938, 944, 946, 950, 959, 960, 962, 963, 965, 973, 982, 983, 989, 991, 1002, 1003, 1005, 1010, 1037, 1039, 1040, 1045, 1046, 1102, 1103, 1105, 1127, 1135, 1137, 1216, 1217, 1219, 1285, 1291, 1296, 1298, 1326, 1352, 1378, 1393, 1405, 1406, 1412, 1413, 1417, 1425, 1426], "wherea": [8, 103, 682, 762, 786, 791, 1165, 1417], "cannot": [8, 101, 103, 127, 132, 199, 232, 300, 362, 394, 476, 581, 582, 583, 584, 632, 722, 887, 925, 934, 968, 979, 1007, 1043, 1165, 1208, 1209, 1296, 1298, 1302, 1303, 1326, 1345, 1347, 1348, 1349, 1350], "subformula": 8, "onc": [8, 38, 54, 55, 88, 93, 94, 99, 100, 112, 127, 199, 227, 230, 231, 232, 246, 247, 360, 374, 380, 388, 422, 423, 428, 488, 491, 492, 581, 582, 583, 652, 678, 679, 717, 718, 887, 925, 968, 1007, 1046, 1066, 1087, 1217, 1311, 1326, 1403, 1407], "thu": [8, 88, 101, 103, 115, 215, 216, 220, 256, 258, 331, 418, 419, 427, 428, 462, 477, 500, 512, 583, 679, 698, 699, 760, 762, 796, 1037, 1039, 1040, 1043, 1087, 1112, 1148, 1215, 1217, 1234, 1278, 1279, 1296, 1328, 1402, 1405, 1407], "wai": [8, 27, 52, 53, 55, 75, 86, 88, 93, 97, 99, 100, 101, 102, 103, 104, 107, 110, 115, 132, 152, 157, 158, 165, 184, 226, 281, 297, 298, 315, 330, 337, 356, 588, 598, 615, 618, 678, 691, 730, 760, 791, 796, 854, 856, 857, 862, 873, 899, 901, 902, 907, 915, 916, 935, 937, 938, 943, 955, 980, 982, 983, 988, 996, 998, 1037, 1039, 1040, 1041, 1097, 1165, 1213, 1215, 1217, 1239, 1262, 1269, 1272, 1326, 1328, 1330, 1393, 1394, 1404, 1406, 1411, 1426], "infeas": [8, 422], "circuit_to_formula": 8, "dag_to_branch": [8, 758, 1408], "transfer": [8, 202, 204, 230, 231, 469, 890, 891, 926, 927, 971, 972, 1008, 1009, 1420], "oper": [8, 30, 52, 95, 101, 112, 115, 168, 184, 189, 227, 374, 423, 460, 546, 547, 548, 552, 553, 554, 577, 595, 598, 601, 671, 672, 673, 674, 679, 680, 758, 786, 865, 873, 878, 910, 916, 946, 955, 960, 991, 998, 1036, 1068, 1088, 1103, 1164, 1218, 1219, 1295, 1302, 1319, 1323, 1325, 1326, 1393, 1394, 1400, 1404, 1405, 1406, 1407, 1408, 1411, 1412, 1413, 1414, 1417], "variabl": [8, 94, 132, 373, 530, 540, 618, 619, 732, 796, 1037, 1038, 1039, 1040, 1154, 1165, 1326, 1408, 1412, 1413, 1414, 1420], "formula_to_str": 8, "_to_str": 8, "root": [8, 67, 84, 293, 294, 338, 387, 389, 390, 394, 449, 460, 559, 577, 609, 671, 673, 678, 704, 728, 730, 739, 760, 791, 1119, 1120, 1125, 1126, 1145, 1147, 1235, 1271, 1272, 1323, 1365, 1366, 1393, 1406, 1407, 1408, 1412, 1413, 1423, 1425], "children": [8, 460, 577, 1145, 1155, 1272, 1365, 1366], "otherwis": [8, 92, 110, 146, 149, 171, 178, 184, 185, 198, 217, 230, 249, 250, 284, 297, 298, 303, 306, 307, 311, 315, 316, 322, 323, 324, 325, 326, 329, 330, 343, 353, 358, 393, 394, 395, 396, 397, 398, 410, 411, 412, 418, 419, 422, 425, 426, 462, 463, 464, 470, 479, 488, 490, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 521, 555, 562, 563, 568, 572, 574, 584, 586, 588, 597, 601, 616, 618, 619, 633, 663, 673, 687, 688, 689, 696, 698, 699, 734, 735, 736, 737, 751, 848, 867, 873, 874, 886, 893, 912, 916, 917, 924, 929, 934, 948, 955, 956, 967, 974, 979, 993, 998, 999, 1006, 1068, 1091, 1135, 1137, 1165, 1185, 1197, 1217, 1270, 1282, 1283, 1284, 1307, 1309, 1312, 1342, 1356, 1357, 1376, 1409, 1413, 1426], "child": [8, 1147, 1272], "must": [8, 11, 93, 94, 95, 99, 100, 103, 110, 151, 152, 158, 161, 171, 204, 206, 207, 214, 215, 216, 219, 230, 231, 232, 252, 253, 257, 258, 259, 260, 261, 262, 264, 267, 268, 269, 271, 273, 276, 281, 285, 297, 298, 306, 307, 315, 316, 317, 318, 319, 324, 325, 327, 329, 330, 342, 361, 362, 363, 378, 382, 385, 391, 410, 411, 412, 413, 425, 429, 440, 471, 472, 473, 474, 475, 545, 546, 547, 548, 549, 550, 551, 553, 555, 556, 557, 558, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 577, 578, 579, 580, 584, 585, 586, 587, 588, 589, 593, 597, 599, 601, 602, 603, 604, 615, 626, 627, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 680, 690, 692, 698, 699, 707, 721, 734, 735, 736, 737, 789, 796, 853, 854, 857, 867, 891, 892, 898, 899, 902, 912, 928, 934, 938, 972, 973, 979, 983, 1010, 1037, 1038, 1039, 1040, 1063, 1071, 1085, 1102, 1133, 1137, 1162, 1165, 1173, 1176, 1186, 1188, 1190, 1193, 1197, 1199, 1209, 1213, 1217, 1219, 1235, 1239, 1240, 1270, 1275, 1276, 1277, 1278, 1279, 1295, 1296, 1298, 1307, 1309, 1310, 1311, 1312, 1315, 1333, 1337, 1338, 1339, 1340, 1359, 1361, 1362, 1363, 1364, 1365, 1366, 1376, 1393, 1394, 1395, 1407, 1426], "NOT": [8, 110, 199, 549, 550, 551, 748, 887, 925, 968, 1007], "util": [8, 14, 36, 44, 45, 93, 97, 102, 103, 229, 230, 231, 316, 374, 423, 425, 426, 429, 460, 496, 678, 679, 758, 1044, 1242, 1299, 1301, 1303, 1310, 1319, 1320, 1321, 1325, 1402, 1406, 1407, 1411, 1413, 1416, 1419], "arbitrary_el": [8, 1392, 1413], "nb": [8, 1331, 1334], "left": [8, 71, 115, 183, 311, 312, 322, 324, 325, 385, 559, 560, 584, 616, 688, 689, 739, 1106, 1134, 1136, 1146, 1179, 1206, 1280, 1355, 1358, 1404], "right": [8, 71, 110, 111, 115, 152, 206, 322, 385, 427, 428, 500, 559, 560, 584, 585, 587, 588, 615, 616, 688, 689, 739, 854, 935, 980, 1134, 1136, 1146, 1155, 1157, 1179, 1206, 1213, 1215, 1270, 1280], "littl": [8, 94, 298, 307], "mislead": 8, "That": [8, 97, 132, 165, 212, 221, 227, 295, 385, 436, 465, 525, 535, 555, 588, 657, 671, 672, 673, 674, 691, 704, 717, 791, 862, 907, 943, 988, 1046, 1162, 1210, 1296, 1388, 1404, 1409], "okai": 8, "becaus": [8, 11, 54, 69, 94, 99, 101, 102, 103, 112, 132, 161, 215, 216, 220, 255, 311, 378, 387, 389, 390, 394, 411, 412, 427, 494, 498, 499, 500, 510, 569, 585, 587, 615, 616, 632, 652, 934, 979, 1038, 1236, 1273, 1296, 1303, 1326, 1345, 1350, 1404, 1407, 1416], "AND": [8, 110, 598, 748, 762], "OR": [8, 110, 157, 175, 188, 856, 869, 877, 901, 937, 947, 950, 959, 982, 992], "symmetr": [8, 145, 148, 237, 545, 586, 593, 761, 1173, 1192, 1235, 1246, 1250, 1251, 1256, 1258, 1269, 1320, 1321, 1387], "It": [8, 52, 56, 58, 92, 93, 94, 97, 99, 101, 102, 104, 107, 110, 112, 115, 132, 172, 184, 207, 214, 215, 216, 229, 230, 231, 249, 260, 261, 262, 264, 278, 310, 316, 324, 325, 326, 343, 346, 347, 351, 353, 412, 414, 415, 416, 417, 418, 419, 429, 438, 440, 452, 457, 464, 480, 496, 500, 508, 530, 540, 545, 559, 560, 565, 566, 567, 582, 588, 594, 595, 598, 600, 601, 615, 619, 628, 629, 630, 652, 658, 659, 663, 671, 674, 692, 717, 718, 719, 760, 761, 762, 791, 796, 868, 873, 892, 913, 916, 928, 949, 955, 973, 994, 998, 1010, 1012, 1013, 1018, 1037, 1038, 1039, 1040, 1054, 1117, 1170, 1174, 1200, 1201, 1206, 1207, 1210, 1217, 1223, 1227, 1234, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1251, 1253, 1254, 1258, 1261, 1263, 1264, 1269, 1275, 1276, 1277, 1280, 1296, 1297, 1323, 1324, 1326, 1328, 1343, 1382, 1383, 1393, 1395, 1398, 1402, 1404, 1407, 1408, 1409, 1411, 1412, 1413, 1426], "just": [8, 99, 102, 103, 104, 184, 199, 338, 374, 439, 464, 559, 560, 577, 660, 661, 662, 692, 791, 873, 887, 916, 925, 946, 955, 960, 968, 991, 998, 1007, 1120, 1126, 1229, 1278, 1279, 1296, 1328, 1393, 1404, 1406], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1325, 1402, 1406, 1412], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1202, 1383], "doe": [8, 77, 93, 94, 99, 101, 102, 103, 104, 114, 115, 132, 147, 153, 154, 165, 168, 189, 207, 208, 227, 228, 229, 230, 231, 232, 293, 308, 339, 340, 342, 343, 352, 357, 373, 382, 385, 410, 414, 426, 450, 469, 494, 495, 496, 497, 498, 499, 500, 502, 503, 506, 507, 509, 510, 511, 512, 534, 544, 549, 550, 551, 564, 566, 583, 584, 586, 589, 601, 612, 626, 627, 678, 691, 693, 694, 698, 699, 717, 718, 721, 722, 723, 724, 725, 726, 762, 862, 865, 878, 892, 907, 910, 928, 943, 946, 960, 973, 988, 991, 1010, 1038, 1043, 1066, 1070, 1072, 1081, 1102, 1103, 1105, 1106, 1107, 1109, 1114, 1173, 1175, 1177, 1192, 1207, 1222, 1223, 1227, 1229, 1234, 1241, 1296, 1300, 1303, 1326, 1333, 1334, 1341, 1342, 1344, 1351, 1353, 1354, 1355, 1356, 1357, 1358, 1371, 1379, 1380, 1381, 1383, 1393, 1404, 1405, 1406, 1410, 1417, 1426], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1219], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1229, 1296, 1395, 1404], "everi": [8, 11, 57, 88, 93, 109, 112, 120, 144, 157, 161, 177, 211, 212, 220, 221, 229, 230, 231, 235, 243, 264, 287, 295, 300, 324, 325, 343, 352, 380, 397, 437, 439, 440, 450, 462, 471, 472, 473, 474, 475, 477, 483, 484, 491, 512, 516, 565, 606, 614, 615, 619, 632, 633, 635, 636, 663, 685, 687, 688, 717, 718, 791, 856, 901, 937, 982, 1052, 1053, 1054, 1070, 1071, 1072, 1085, 1086, 1102, 1103, 1105, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 1115, 1116, 1117, 1148, 1162, 1195, 1216, 1217, 1257, 1264, 1278, 1279, 1296, 1407], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1177, 1207, 1208, 1404, 1406, 1407, 1426], "ha": [8, 11, 16, 44, 67, 88, 91, 93, 94, 95, 97, 99, 100, 101, 102, 103, 105, 107, 110, 112, 116, 120, 127, 152, 161, 165, 166, 173, 174, 175, 184, 188, 198, 207, 212, 214, 215, 219, 220, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 241, 242, 243, 244, 247, 249, 252, 269, 271, 272, 273, 274, 275, 276, 282, 289, 291, 293, 294, 295, 300, 305, 310, 324, 331, 343, 352, 355, 356, 363, 364, 365, 373, 378, 380, 381, 383, 384, 385, 386, 391, 393, 394, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 424, 427, 428, 429, 439, 450, 458, 460, 466, 467, 468, 471, 472, 473, 474, 475, 476, 477, 480, 491, 492, 493, 494, 495, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 522, 564, 566, 577, 578, 581, 590, 593, 605, 607, 610, 611, 622, 623, 624, 628, 629, 630, 632, 633, 634, 635, 636, 638, 646, 647, 649, 652, 657, 658, 682, 688, 690, 692, 697, 711, 717, 718, 729, 730, 731, 739, 749, 786, 791, 854, 862, 863, 869, 873, 877, 886, 892, 899, 907, 908, 916, 924, 928, 935, 943, 944, 948, 950, 955, 959, 967, 973, 980, 988, 989, 993, 998, 1006, 1010, 1040, 1043, 1045, 1066, 1068, 1070, 1072, 1075, 1080, 1084, 1098, 1099, 1101, 1102, 1103, 1105, 1122, 1130, 1145, 1154, 1160, 1162, 1165, 1176, 1180, 1185, 1193, 1195, 1196, 1197, 1198, 1199, 1207, 1210, 1211, 1215, 1217, 1222, 1234, 1239, 1243, 1244, 1248, 1249, 1254, 1259, 1261, 1264, 1267, 1269, 1270, 1272, 1275, 1276, 1277, 1278, 1279, 1281, 1282, 1283, 1284, 1285, 1286, 1289, 1291, 1293, 1296, 1300, 1326, 1328, 1330, 1333, 1334, 1353, 1354, 1371, 1372, 1379, 1382, 1393, 1394, 1395, 1398, 1403, 1404, 1405, 1406, 1407, 1409, 1413, 1414, 1416, 1423, 1425], "output": [8, 13, 16, 89, 93, 101, 102, 103, 109, 197, 287, 288, 345, 374, 380, 494, 498, 499, 509, 510, 575, 588, 677, 678, 691, 722, 1045, 1193, 1197, 1199, 1269, 1296, 1326, 1334, 1341, 1344, 1355, 1358, 1399, 1402, 1404, 1406, 1411, 1413, 1414, 1426], "two": [8, 11, 16, 27, 34, 38, 43, 54, 55, 57, 58, 65, 67, 71, 88, 93, 95, 99, 100, 102, 109, 112, 114, 115, 120, 132, 151, 171, 175, 184, 185, 188, 202, 207, 211, 212, 213, 214, 215, 216, 217, 220, 221, 226, 227, 230, 231, 232, 245, 249, 251, 252, 253, 257, 258, 260, 261, 262, 265, 269, 270, 271, 272, 273, 274, 275, 276, 282, 285, 286, 287, 289, 305, 311, 315, 316, 322, 326, 329, 330, 337, 341, 343, 345, 351, 352, 358, 359, 377, 380, 381, 383, 391, 411, 412, 419, 423, 428, 429, 430, 431, 442, 443, 444, 445, 447, 452, 453, 454, 457, 462, 471, 472, 473, 474, 475, 476, 480, 491, 494, 498, 499, 500, 502, 503, 506, 508, 509, 510, 511, 521, 545, 549, 550, 551, 555, 559, 560, 561, 562, 563, 564, 565, 566, 568, 569, 572, 574, 578, 584, 585, 586, 587, 588, 593, 598, 605, 607, 608, 610, 611, 615, 619, 626, 627, 629, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 671, 672, 673, 674, 675, 676, 680, 692, 694, 731, 732, 738, 739, 760, 761, 762, 780, 786, 791, 796, 853, 867, 869, 873, 874, 877, 890, 892, 898, 912, 916, 917, 926, 928, 934, 946, 948, 950, 955, 956, 959, 960, 971, 973, 979, 991, 993, 998, 999, 1008, 1010, 1019, 1020, 1021, 1022, 1036, 1037, 1039, 1040, 1056, 1084, 1088, 1098, 1100, 1101, 1106, 1107, 1108, 1109, 1114, 1116, 1134, 1146, 1147, 1149, 1151, 1152, 1156, 1174, 1185, 1186, 1193, 1194, 1195, 1196, 1197, 1198, 1199, 1204, 1207, 1210, 1211, 1215, 1217, 1218, 1243, 1244, 1253, 1271, 1272, 1275, 1276, 1294, 1295, 1296, 1323, 1324, 1326, 1328, 1359, 1360, 1363, 1393, 1394, 1395, 1397, 1402, 1404, 1405, 1406, 1407, 1410, 1411, 1413, 1425], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1420], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1217, 1226, 1262, 1263, 1326, 1407], "appear": [8, 83, 93, 95, 99, 100, 102, 179, 204, 230, 231, 238, 243, 246, 247, 277, 363, 364, 365, 378, 451, 452, 453, 455, 466, 470, 584, 585, 587, 588, 675, 679, 707, 730, 734, 736, 891, 972, 1036, 1088, 1102, 1136, 1150, 1152, 1154, 1157, 1159, 1187, 1188, 1277, 1282, 1323, 1324, 1345, 1348, 1349, 1350, 1382, 1407, 1413, 1414], "both": [8, 52, 55, 92, 93, 94, 100, 101, 102, 103, 115, 161, 164, 204, 214, 215, 216, 217, 240, 257, 258, 259, 264, 282, 286, 287, 289, 337, 358, 379, 383, 415, 417, 418, 419, 423, 427, 440, 470, 502, 506, 545, 575, 581, 598, 600, 601, 602, 603, 604, 605, 606, 607, 610, 611, 615, 621, 635, 636, 653, 654, 655, 676, 711, 720, 760, 761, 762, 782, 891, 972, 1020, 1036, 1066, 1075, 1080, 1084, 1088, 1097, 1120, 1126, 1144, 1165, 1189, 1192, 1199, 1207, 1210, 1211, 1213, 1215, 1282, 1296, 1326, 1328, 1358, 1363, 1364, 1387, 1393, 1395, 1402, 1413, 1416, 1417, 1425, 1426], "negat": 8, "sole": [8, 786, 1278, 1279, 1326], "fourth": [8, 230, 231, 1326, 1404], "digraph": [8, 10, 11, 16, 21, 25, 41, 45, 56, 61, 67, 69, 70, 82, 88, 101, 102, 115, 132, 151, 152, 156, 157, 158, 160, 162, 163, 165, 166, 168, 170, 171, 172, 175, 176, 185, 186, 187, 188, 189, 192, 193, 194, 195, 196, 198, 199, 202, 204, 207, 208, 216, 227, 229, 230, 231, 240, 246, 247, 299, 308, 314, 318, 319, 321, 327, 328, 334, 335, 336, 337, 339, 340, 342, 343, 388, 391, 393, 396, 397, 398, 399, 401, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 430, 431, 437, 450, 452, 453, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467, 468, 469, 481, 482, 492, 494, 495, 496, 497, 498, 499, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 513, 514, 518, 519, 523, 555, 566, 575, 576, 577, 588, 590, 613, 615, 623, 630, 636, 643, 644, 652, 656, 657, 658, 659, 663, 678, 688, 690, 693, 696, 697, 698, 699, 700, 701, 702, 706, 707, 708, 709, 711, 716, 717, 718, 719, 721, 722, 723, 724, 725, 726, 740, 741, 744, 745, 746, 747, 748, 749, 750, 752, 760, 789, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 911, 912, 913, 915, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 957, 958, 963, 964, 965, 966, 967, 968, 972, 973, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 1000, 1001, 1003, 1004, 1005, 1006, 1007, 1010, 1035, 1037, 1038, 1039, 1040, 1041, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1135, 1150, 1154, 1168, 1169, 1170, 1173, 1177, 1178, 1180, 1182, 1183, 1184, 1185, 1189, 1217, 1270, 1272, 1273, 1274, 1283, 1284, 1287, 1290, 1292, 1298, 1323, 1326, 1333, 1337, 1342, 1356, 1357, 1362, 1365, 1366, 1371, 1393, 1399, 1401, 1402, 1404, 1405, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1424, 1425, 1426], "add_nod": [8, 11, 26, 34, 69, 74, 89, 102, 157, 184, 246, 339, 340, 398, 422, 491, 492, 496, 504, 505, 508, 522, 523, 605, 607, 610, 611, 691, 796, 856, 873, 901, 916, 937, 955, 982, 998, 1037, 1039, 1040, 1086, 1275, 1326, 1345, 1407, 1408, 1417, 1426], "get_node_attribut": [8, 39, 44, 71, 1213, 1404], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1133, 1134, 1136], "22": [8, 35, 64, 66, 383, 384, 1271, 1323, 1403, 1408, 1412, 1422], "multipartite_layout": [8, 36, 61, 67, 1412, 1414, 1420], "subset_kei": [8, 36, 61, 67, 1109], "equal": [8, 36, 81, 144, 214, 215, 216, 230, 231, 238, 269, 271, 273, 276, 288, 297, 298, 300, 303, 306, 307, 310, 311, 312, 315, 316, 320, 323, 324, 325, 329, 330, 331, 373, 410, 411, 412, 413, 418, 419, 428, 471, 474, 476, 491, 494, 495, 496, 498, 499, 502, 503, 504, 505, 506, 507, 508, 509, 510, 525, 535, 545, 552, 553, 554, 555, 568, 572, 605, 623, 657, 671, 672, 673, 674, 687, 688, 689, 690, 721, 722, 740, 741, 753, 761, 791, 1112, 1116, 1162, 1165, 1198, 1204, 1230, 1239, 1271, 1280, 1291, 1307, 1309, 1312, 1398, 1399], "101": [8, 17, 39, 47, 239, 240, 555, 762], "plot_circuit": [8, 17], "southern": [9, 1265], "women": [9, 1265, 1398, 1406], "unipartit": [9, 115, 258, 259, 358], "properti": [9, 11, 18, 22, 33, 63, 86, 101, 102, 103, 112, 134, 159, 161, 166, 168, 175, 176, 179, 184, 188, 189, 190, 200, 284, 285, 286, 287, 288, 363, 364, 365, 388, 476, 500, 545, 569, 619, 685, 858, 863, 865, 869, 870, 873, 877, 878, 879, 888, 903, 908, 910, 916, 939, 944, 946, 950, 951, 955, 959, 960, 969, 984, 989, 991, 998, 1085, 1086, 1122, 1134, 1136, 1193, 1202, 1217, 1219, 1269, 1283, 1284, 1326, 1328, 1383, 1398, 1405, 1406, 1407, 1408, 1413, 1417, 1426], "These": [9, 52, 58, 73, 79, 86, 93, 94, 105, 112, 336, 385, 494, 512, 559, 671, 673, 732, 748, 779, 786, 1038, 1045, 1047, 1323, 1326, 1385, 1387, 1392, 1394, 1395, 1397, 1399, 1404, 1405, 1411, 1426], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1199, 1393, 1395, 1399, 1402, 1405, 1406, 1407, 1413, 1416], "et": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202], "al": [9, 210, 226, 227, 315, 316, 322, 330, 334, 337, 345, 352, 358, 373, 380, 381, 423, 425, 426, 451, 569, 591, 592, 681, 682, 684, 693, 1202, 1407, 1413], "1930": [9, 1396], "thei": [9, 54, 58, 65, 71, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 112, 132, 151, 165, 207, 213, 220, 249, 285, 287, 288, 296, 297, 298, 301, 302, 306, 307, 308, 309, 351, 362, 374, 391, 396, 427, 451, 452, 453, 454, 464, 465, 471, 472, 473, 474, 475, 496, 504, 505, 508, 512, 546, 547, 548, 559, 560, 576, 583, 586, 588, 600, 604, 675, 676, 704, 717, 750, 760, 786, 853, 862, 892, 898, 907, 928, 934, 943, 962, 973, 979, 988, 1002, 1010, 1036, 1038, 1066, 1085, 1088, 1109, 1120, 1126, 1133, 1135, 1137, 1151, 1159, 1165, 1193, 1197, 1198, 1217, 1271, 1272, 1323, 1328, 1353, 1354, 1356, 1357, 1359, 1363, 1394, 1396, 1402, 1404, 1406, 1409, 1414, 1426], "repres": [9, 11, 26, 43, 52, 54, 57, 67, 92, 99, 107, 115, 230, 231, 265, 281, 283, 286, 287, 288, 291, 292, 338, 350, 361, 362, 363, 377, 378, 380, 381, 382, 385, 386, 391, 448, 452, 453, 455, 457, 460, 465, 466, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 521, 565, 577, 578, 579, 580, 586, 588, 609, 615, 618, 619, 656, 660, 664, 667, 676, 679, 691, 692, 695, 697, 698, 699, 700, 702, 728, 730, 731, 734, 736, 739, 752, 786, 791, 796, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1045, 1081, 1102, 1140, 1151, 1185, 1193, 1194, 1196, 1197, 1198, 1199, 1209, 1217, 1240, 1243, 1246, 1250, 1258, 1267, 1269, 1272, 1273, 1278, 1279, 1323, 1324, 1326, 1329, 1330, 1346, 1347, 1388, 1393, 1406], "observ": [9, 13, 132, 223, 1414, 1426], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 64, 66, 71, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1150, 1242, 1250, 1262, 1406, 1408, 1426], "event": [9, 25, 99, 100, 110, 1165, 1229, 1300], "18": [9, 44, 64, 66, 93, 324, 325, 345, 383, 384, 618, 1169, 1249, 1255, 1258, 1260, 1263, 1269, 1393, 1406, 1416, 1417, 1421, 1426], "bipartit": [9, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 350, 351, 358, 377, 439, 440, 443, 581, 588, 758, 1043, 1106, 1151, 1203, 1204, 1205, 1265, 1325, 1395, 1398, 1399, 1400, 1401, 1406, 1407, 1411, 1413, 1417, 1421, 1425], "biadjac": [9, 282, 283, 1400, 1406], "7": [9, 12, 14, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 89, 99, 101, 102, 115, 125, 151, 158, 170, 171, 192, 207, 232, 268, 297, 299, 314, 322, 327, 332, 333, 339, 340, 342, 362, 374, 380, 391, 403, 410, 413, 414, 415, 423, 424, 425, 426, 441, 445, 446, 483, 496, 501, 508, 511, 512, 555, 581, 586, 618, 619, 630, 652, 658, 663, 671, 674, 680, 695, 703, 706, 707, 708, 730, 747, 750, 761, 796, 853, 857, 866, 867, 881, 892, 898, 902, 911, 912, 915, 920, 928, 934, 938, 947, 973, 979, 983, 992, 996, 1010, 1037, 1039, 1040, 1052, 1053, 1085, 1100, 1104, 1148, 1212, 1242, 1248, 1250, 1251, 1255, 1258, 1260, 1273, 1323, 1326, 1330, 1339, 1340, 1345, 1348, 1349, 1350, 1382, 1392, 1394, 1402, 1403, 1405, 1408, 1409, 1410, 1411, 1412, 1413, 1426], "12": [9, 11, 19, 25, 44, 50, 55, 58, 64, 65, 66, 89, 91, 93, 229, 230, 231, 265, 345, 380, 381, 392, 399, 405, 406, 407, 449, 486, 501, 516, 568, 572, 574, 606, 616, 1052, 1053, 1054, 1133, 1136, 1150, 1244, 1245, 1249, 1254, 1257, 1263, 1335, 1406, 1408, 1412, 1426], "9": [9, 11, 12, 19, 25, 35, 44, 46, 63, 64, 65, 66, 68, 82, 89, 101, 102, 111, 115, 125, 232, 293, 295, 339, 340, 342, 346, 347, 356, 374, 380, 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1406, 1426], "friend": [9, 545, 1407, 1412], "member": [9, 92, 93, 94, 100, 112, 315, 317, 318, 319, 330, 391, 483, 484, 586, 691, 1222, 1267, 1403], "evelyn": 9, "jefferson": 9, "laura": 9, "mandevil": 9, "theresa": 9, "anderson": 9, "brenda": 9, "roger": 9, "charlott": 9, "mcdowd": 9, "franc": 9, "eleanor": 9, "nye": 9, "pearl": [9, 132], "oglethorp": 9, "ruth": 9, "desand": 9, "vern": 9, "sanderson": 9, "myra": 9, "liddel": 9, "katherina": 9, "sylvia": 9, "avondal": 9, "nora": 9, "fayett": 9, "helen": 9, "lloyd": 9, "dorothi": 9, "murchison": 9, "olivia": 9, "carleton": 9, "flora": 9, "price": 9, "meet": [9, 94, 1165, 1196, 1197, 1198], "50": [9, 25, 30, 34, 40, 50, 54, 55, 56, 57, 64, 65, 272, 312, 1117, 1193, 1197, 1198, 1251, 1297, 1302], "45": [9, 58, 64, 110, 226, 300, 409, 1175], "57": [9, 64], "46": [9, 64, 235, 564, 619, 1264], "24": [9, 19, 37, 64, 66, 68, 103, 383, 384, 496, 505, 508, 703, 1212, 1229, 1244, 1262, 1271, 1403], "32": [9, 64, 66, 68, 209, 211, 212, 383, 384, 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248, 385, 494, 519, 555, 671, 672, 673, 674, 690, 691, 693, 762, 786, 1036, 1088, 1388, 1413], "add": [10, 11, 26, 34, 41, 45, 49, 52, 61, 71, 88, 89, 91, 93, 94, 101, 102, 105, 106, 115, 151, 152, 153, 154, 156, 157, 158, 164, 207, 222, 223, 229, 282, 285, 341, 374, 411, 412, 423, 428, 430, 431, 450, 460, 581, 582, 583, 589, 614, 615, 618, 619, 654, 690, 701, 717, 718, 796, 850, 853, 854, 855, 856, 857, 892, 895, 898, 899, 900, 901, 902, 928, 931, 934, 935, 936, 937, 938, 973, 976, 979, 980, 981, 982, 983, 984, 1010, 1037, 1038, 1039, 1040, 1042, 1049, 1052, 1053, 1054, 1100, 1154, 1165, 1172, 1185, 1207, 1210, 1217, 1219, 1233, 1234, 1236, 1302, 1326, 1353, 1354, 1356, 1357, 1379, 1380, 1383, 1393, 1394, 1395, 1398, 1404, 1406, 1407, 1408, 1409, 1411, 1412, 1413, 1414, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "compressor": [10, 690, 786], "do": [10, 55, 75, 88, 92, 93, 94, 96, 99, 101, 102, 106, 107, 109, 111, 115, 133, 165, 184, 199, 202, 204, 230, 231, 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1426], "ax1": [10, 15, 27, 50, 82], "number_of_edg": [10, 15, 25, 28, 198, 690, 886, 924, 967, 1006, 1059, 1154, 1271, 1406, 1407, 1426], "nonexp_graph": 10, "compression_nod": 10, "summar": [10, 15, 100, 101, 690, 691, 758, 791, 1325, 1328, 1413], "dedensifi": [10, 758], "threshold": [10, 57, 83, 112, 220, 229, 231, 380, 381, 690, 692, 695, 696, 758, 786, 1117, 1193, 1194, 1196, 1197, 1198, 1325, 1398, 1406, 1407, 1408, 1412, 1414], "copi": [10, 16, 38, 44, 93, 95, 106, 167, 196, 199, 202, 203, 204, 205, 284, 285, 286, 287, 288, 341, 388, 390, 392, 406, 433, 434, 435, 436, 437, 453, 460, 469, 521, 584, 585, 587, 596, 599, 602, 603, 605, 606, 607, 610, 611, 613, 614, 633, 636, 690, 864, 885, 887, 890, 891, 909, 925, 926, 927, 945, 963, 966, 968, 971, 972, 990, 1003, 1007, 1008, 1009, 1035, 1038, 1057, 1061, 1063, 1066, 1082, 1083, 1122, 1183, 1189, 1217, 1223, 1227, 1251, 1270, 1294, 1295, 1296, 1403, 1404, 1406, 1407, 1408, 1409, 1412, 1413, 1422, 1425], "nonexp_node_color": 10, 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1286], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1177, 1179, 1192, 1222], "end": [11, 25, 36, 52, 95, 101, 106, 153, 154, 206, 215, 227, 267, 268, 300, 332, 333, 342, 371, 372, 427, 614, 618, 619, 626, 627, 631, 632, 634, 635, 636, 639, 640, 650, 651, 652, 653, 654, 655, 660, 664, 667, 677, 678, 680, 734, 736, 1038, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1133, 1135, 1152, 1165, 1206, 1229, 1326, 1333, 1334, 1337, 1338, 1339, 1340, 1342, 1344, 1350, 1353, 1357, 1358, 1368, 1371, 1372, 1375, 1376, 1379, 1404, 1413], "In": [11, 16, 27, 43, 54, 57, 58, 88, 92, 93, 94, 95, 97, 99, 100, 101, 103, 110, 115, 127, 132, 133, 175, 184, 199, 217, 229, 230, 231, 235, 240, 257, 258, 259, 278, 283, 286, 288, 289, 299, 311, 312, 324, 325, 329, 350, 357, 378, 379, 380, 410, 413, 414, 415, 422, 429, 443, 447, 450, 458, 460, 494, 498, 499, 501, 510, 565, 568, 572, 574, 590, 591, 615, 619, 621, 652, 653, 654, 657, 658, 663, 670, 675, 676, 690, 691, 701, 703, 717, 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691, 862, 907, 943, 988, 1196, 1197, 1198, 1398, 1406, 1412], "stanford": [15, 34, 65, 69, 71, 566, 691, 1268], "analysi": [15, 23, 47, 50, 52, 55, 86, 100, 101, 103, 105, 107, 110, 228, 232, 257, 258, 259, 260, 261, 262, 286, 288, 289, 299, 305, 379, 383, 412, 431, 437, 462, 494, 500, 619, 691, 751, 758, 760, 762, 1042, 1201, 1233, 1325, 1405, 1409, 1410, 1412, 1414], "uniqu": [15, 27, 238, 255, 278, 311, 312, 378, 460, 464, 469, 559, 560, 565, 585, 587, 600, 604, 618, 619, 641, 643, 691, 732, 748, 934, 979, 1047, 1244, 1250, 1251, 1296, 1326, 1343, 1359, 1360, 1363, 1364, 1426], "combin": [15, 61, 102, 204, 207, 379, 380, 385, 411, 412, 416, 418, 423, 575, 598, 600, 604, 678, 691, 891, 892, 928, 973, 1010, 1387, 1408], "type": [15, 70, 93, 95, 97, 100, 101, 102, 103, 104, 110, 165, 208, 241, 242, 243, 244, 247, 266, 267, 269, 270, 271, 273, 274, 276, 282, 283, 296, 301, 302, 303, 308, 309, 315, 323, 350, 351, 429, 496, 549, 550, 551, 555, 584, 585, 587, 588, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 652, 658, 671, 672, 673, 674, 690, 691, 693, 695, 711, 722, 748, 749, 750, 786, 862, 907, 943, 988, 1041, 1043, 1047, 1087, 1091, 1092, 1093, 1094, 1097, 1098, 1099, 1100, 1101, 1102, 1104, 1105, 1110, 1118, 1145, 1147, 1148, 1150, 1152, 1154, 1155, 1157, 1159, 1160, 1163, 1175, 1177, 1178, 1180, 1182, 1183, 1184, 1190, 1191, 1192, 1200, 1201, 1202, 1211, 1213, 1215, 1217, 1222, 1243, 1244, 1245, 1246, 1247, 1248, 1249, 1250, 1252, 1253, 1254, 1255, 1256, 1257, 1259, 1260, 1261, 1262, 1263, 1264, 1273, 1278, 1279, 1281, 1298, 1325, 1326, 1332, 1333, 1336, 1337, 1338, 1342, 1345, 1348, 1349, 1350, 1356, 1357, 1358, 1370, 1371, 1382, 1386, 1390, 1393, 1395, 1404, 1406, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1417, 1425, 1426], "other": [15, 16, 24, 41, 43, 50, 52, 56, 57, 58, 83, 88, 91, 92, 93, 94, 97, 99, 100, 101, 102, 103, 104, 105, 107, 109, 110, 115, 132, 134, 165, 208, 214, 215, 216, 226, 230, 231, 232, 235, 256, 258, 264, 267, 268, 282, 288, 289, 294, 297, 298, 305, 316, 320, 322, 324, 325, 327, 352, 358, 366, 373, 396, 397, 428, 452, 453, 460, 462, 473, 491, 502, 503, 506, 507, 527, 537, 559, 560, 565, 588, 602, 632, 633, 635, 636, 641, 653, 660, 661, 662, 665, 666, 667, 668, 669, 675, 676, 688, 691, 701, 723, 724, 725, 726, 734, 735, 736, 737, 751, 752, 762, 789, 791, 796, 862, 907, 943, 948, 988, 993, 1037, 1038, 1039, 1040, 1042, 1054, 1102, 1103, 1114, 1116, 1133, 1145, 1147, 1151, 1154, 1165, 1174, 1180, 1186, 1194, 1195, 1197, 1198, 1222, 1229, 1269, 1278, 1279, 1281, 1286, 1289, 1291, 1293, 1296, 1302, 1324, 1325, 1326, 1328, 1337, 1338, 1339, 1345, 1348, 1349, 1350, 1382, 1383, 1394, 1396, 1398, 1403, 1404, 1405, 1406, 1407, 1408, 1410, 1411, 1412, 1413, 1414, 1417, 1425, 1426], "produc": [15, 44, 49, 103, 115, 226, 246, 247, 272, 280, 297, 298, 306, 307, 315, 316, 329, 330, 422, 460, 565, 601, 612, 629, 632, 633, 635, 636, 677, 678, 680, 691, 786, 1097, 1102, 1103, 1105, 1165, 1179, 1181, 1189, 1212, 1236, 1280, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1392, 1399, 1406, 1408, 1416, 1417], "infer": [15, 695, 1104, 1118, 1358, 1412], "differ": [15, 25, 27, 28, 33, 41, 53, 54, 57, 63, 71, 86, 92, 93, 94, 95, 99, 103, 112, 161, 164, 165, 204, 207, 215, 216, 223, 280, 282, 297, 298, 314, 315, 326, 330, 334, 335, 337, 341, 358, 361, 371, 372, 373, 374, 378, 410, 413, 414, 415, 435, 437, 509, 511, 512, 593, 602, 615, 704, 717, 718, 738, 750, 758, 772, 786, 862, 891, 892, 907, 928, 943, 972, 973, 988, 1010, 1102, 1105, 1133, 1165, 1169, 1170, 1171, 1193, 1198, 1207, 1255, 1269, 1287, 1296, 1326, 1365, 1366, 1382, 1394, 1404, 1405, 1406, 1413, 1414, 1425, 1426], "relat": [15, 34, 67, 92, 93, 95, 99, 100, 115, 129, 132, 220, 230, 297, 366, 370, 586, 588, 619, 688, 762, 767, 795, 1202, 1205, 1269, 1323, 1395, 1402, 1406, 1413, 1416, 1425], "strong": [15, 397, 511, 512, 517, 610, 619, 691, 699, 758, 1408], "weak": [15, 398, 691, 758, 1425], "number_of_nod": [15, 25, 80, 156, 187, 311, 324, 337, 383, 564, 581, 852, 855, 876, 897, 900, 919, 933, 936, 958, 978, 981, 1001, 1154, 1271, 1426], "7482934": 15, "_": [15, 16, 26, 38, 93, 105, 300, 333, 356, 372, 405, 406, 425, 426, 502, 503, 506, 507, 569, 588, 630, 1352, 1354, 1378, 1380, 1411], "edge_type_visual_weight_lookup": 15, "edge_weight": [15, 382, 583], "node_attribut": [15, 691], "edge_attribut": [15, 283, 691, 1101], "summary_graph": [15, 691], "snap_aggreg": [15, 758, 1413], "prefix": [15, 67, 512, 690, 691, 1272, 1326, 1347, 1413, 1421], "aggreg": [15, 511, 512, 691, 786], "summary_po": 15, "8375428": 15, "edge_typ": 15, "get_edge_data": [15, 25, 1411], "164": [15, 17], "plot_snap": [15, 17], "support": [16, 52, 77, 92, 93, 96, 100, 101, 102, 103, 226, 308, 322, 339, 340, 342, 343, 356, 373, 410, 411, 412, 418, 419, 464, 494, 496, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 597, 626, 627, 632, 633, 635, 636, 690, 738, 762, 775, 786, 796, 1037, 1038, 1039, 1040, 1114, 1116, 1302, 1326, 1341, 1342, 1344, 1353, 1354, 1355, 1356, 1357, 1358, 1379, 1380, 1381, 1383, 1387, 1394, 1395, 1396, 1398, 1402, 1404, 1406, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426], "unsupport": 16, "contain": [16, 25, 34, 45, 65, 69, 71, 88, 99, 102, 104, 114, 115, 151, 152, 157, 158, 165, 166, 167, 168, 172, 175, 176, 177, 180, 188, 189, 193, 195, 199, 207, 212, 214, 220, 226, 236, 237, 238, 240, 241, 243, 245, 248, 249, 252, 253, 255, 256, 257, 258, 259, 260, 264, 266, 267, 270, 277, 278, 280, 281, 290, 293, 294, 299, 315, 320, 322, 338, 344, 346, 347, 350, 352, 353, 355, 356, 357, 358, 360, 373, 377, 379, 380, 381, 388, 400, 408, 414, 415, 427, 432, 433, 437, 440, 457, 481, 482, 494, 495, 498, 499, 500, 502, 503, 506, 507, 509, 510, 512, 513, 514, 516, 564, 568, 572, 574, 589, 593, 596, 599, 602, 621, 624, 631, 632, 652, 656, 658, 660, 661, 662, 687, 688, 689, 695, 723, 724, 725, 726, 749, 786, 796, 853, 854, 856, 857, 862, 863, 864, 865, 868, 869, 870, 871, 877, 878, 882, 884, 887, 892, 898, 899, 901, 902, 907, 908, 909, 910, 913, 914, 921, 923, 925, 928, 934, 935, 937, 938, 943, 944, 945, 946, 949, 950, 951, 952, 959, 960, 963, 965, 968, 973, 979, 980, 982, 983, 988, 989, 990, 991, 994, 995, 1003, 1005, 1007, 1010, 1037, 1038, 1039, 1040, 1041, 1052, 1053, 1054, 1061, 1066, 1085, 1086, 1087, 1094, 1097, 1100, 1102, 1103, 1105, 1106, 1118, 1127, 1140, 1150, 1151, 1152, 1154, 1157, 1164, 1173, 1200, 1201, 1206, 1207, 1208, 1211, 1251, 1286, 1296, 1297, 1298, 1302, 1322, 1323, 1324, 1326, 1331, 1334, 1352, 1356, 1359, 1360, 1363, 1364, 1371, 1378, 1390, 1395, 1403, 1404, 1406, 1407, 1409, 1411, 1412, 1414, 1423, 1425, 1426], "entir": [16, 95, 101, 165, 179, 184, 260, 360, 375, 577, 862, 873, 907, 916, 943, 955, 988, 998, 1038, 1085, 1100, 1225, 1406, 1409], "adopt": [16, 96, 98, 101, 102, 107, 1405, 1414], "lobpcg": [16, 91, 1275, 1276, 1277], "python_exampl": 16, "graph_partit": 16, "categor": [16, 546, 547, 548, 611], "node_typ": [16, 1342, 1356, 1357], "supported_nod": 16, "unsupported_nod": 16, "remove_edges_from": [16, 89, 192, 453, 602, 881, 920, 962, 1002, 1175, 1177, 1222, 1393, 1394, 1412, 1420, 1426], "nbr": [16, 88, 159, 190, 199, 200, 207, 229, 230, 231, 285, 500, 506, 796, 858, 879, 887, 888, 892, 903, 925, 928, 939, 968, 969, 973, 984, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1426], "adj": [16, 88, 199, 200, 207, 324, 325, 796, 849, 887, 888, 892, 894, 915, 925, 928, 930, 968, 969, 973, 975, 996, 1007, 1010, 1037, 1039, 1040, 1094, 1326, 1404, 1411, 1417, 1425, 1426], "g_minus_h": 16, "strip": [16, 25, 69, 1215], "_node_color": 16, "_po": 16, "draw_networkx_edg": [16, 25, 26, 27, 28, 33, 35, 38, 39, 40, 41, 44, 46, 68, 83, 1130, 1133, 1134, 1136, 1137, 1411, 1413], "draw_networkx_label": [16, 25, 35, 38, 46, 71, 1130, 1133, 1134, 1135, 1137], "ncl": 16, "undirect": [16, 25, 34, 71, 93, 112, 177, 185, 204, 205, 209, 211, 212, 214, 215, 216, 217, 218, 219, 220, 221, 224, 227, 228, 229, 230, 231, 232, 237, 239, 240, 246, 247, 264, 267, 275, 277, 278, 280, 281, 293, 294, 295, 297, 298, 300, 313, 315, 318, 319, 321, 322, 328, 330, 331, 332, 333, 337, 338, 341, 345, 346, 347, 348, 349, 350, 352, 353, 371, 372, 379, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 428, 430, 431, 437, 439, 440, 450, 463, 464, 465, 466, 467, 478, 479, 480, 481, 482, 485, 486, 487, 488, 490, 491, 492, 500, 559, 560, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 581, 582, 583, 590, 594, 595, 598, 600, 601, 605, 606, 607, 610, 611, 613, 615, 618, 619, 624, 625, 652, 658, 681, 682, 683, 684, 686, 687, 688, 689, 692, 694, 717, 718, 727, 730, 731, 732, 734, 735, 736, 737, 738, 742, 743, 753, 760, 761, 762, 767, 779, 791, 874, 891, 917, 927, 956, 972, 999, 1009, 1036, 1038, 1056, 1060, 1088, 1090, 1098, 1101, 1115, 1133, 1135, 1166, 1167, 1173, 1175, 1182, 1184, 1187, 1189, 1190, 1191, 1193, 1196, 1197, 1198, 1199, 1202, 1206, 1207, 1217, 1219, 1230, 1243, 1244, 1247, 1250, 1251, 1252, 1254, 1259, 1273, 1275, 1276, 1278, 1279, 1282, 1298, 1323, 1326, 1327, 1333, 1341, 1342, 1344, 1351, 1352, 1353, 1354, 1371, 1377, 1378, 1379, 1380, 1381, 1383, 1389, 1390, 1395, 1401, 1402, 1404, 1406, 1408, 1411, 1414, 1417, 1426], "And": [16, 23, 47, 86, 93, 101, 107, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 467, 502, 503, 506, 507, 688, 1296, 1297, 1328, 1408, 1409, 1411, 1416, 1425], "specifi": [16, 24, 25, 62, 102, 151, 152, 157, 158, 167, 184, 185, 193, 207, 222, 223, 226, 232, 236, 238, 240, 241, 243, 244, 246, 247, 248, 260, 264, 266, 267, 268, 269, 271, 273, 276, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 299, 305, 310, 311, 320, 324, 326, 329, 338, 348, 349, 353, 356, 357, 374, 377, 410, 411, 412, 413, 414, 415, 418, 419, 433, 435, 436, 440, 442, 443, 444, 445, 447, 448, 449, 458, 473, 491, 494, 495, 498, 499, 510, 518, 552, 553, 554, 555, 564, 565, 566, 575, 577, 584, 588, 597, 601, 604, 608, 609, 635, 636, 660, 671, 672, 673, 674, 676, 686, 691, 692, 704, 705, 706, 707, 708, 710, 711, 712, 713, 714, 715, 716, 721, 722, 751, 760, 853, 854, 856, 857, 864, 873, 874, 882, 892, 898, 899, 901, 902, 909, 916, 917, 921, 928, 934, 935, 937, 938, 945, 947, 948, 955, 956, 962, 963, 973, 979, 980, 982, 983, 990, 992, 993, 998, 999, 1002, 1003, 1010, 1043, 1061, 1070, 1071, 1072, 1081, 1094, 1095, 1096, 1098, 1099, 1104, 1117, 1130, 1133, 1134, 1135, 1136, 1137, 1151, 1154, 1165, 1175, 1177, 1178, 1181, 1182, 1189, 1193, 1196, 1197, 1198, 1199, 1202, 1207, 1210, 1211, 1212, 1219, 1222, 1235, 1242, 1275, 1276, 1277, 1278, 1279, 1294, 1295, 1296, 1297, 1300, 1315, 1323, 1324, 1326, 1328, 1331, 1334, 1336, 1337, 1338, 1339, 1340, 1341, 1344, 1345, 1348, 1349, 1350, 1356, 1357, 1360, 1363, 1364, 1382, 1393, 1397, 1398, 1399, 1402, 1403, 1404, 1406, 1407, 1412, 1416, 1426], "to_undirect": [16, 25, 69, 796, 1037, 1039, 1040, 1182, 1184, 1404, 1413, 1426], "magenta": 16, "six": 16, "classifi": [16, 512, 684, 750], "four": [16, 23, 47, 86, 99, 102, 165, 263, 585, 587, 692, 862, 907, 943, 988, 1039, 1040, 1164, 1193, 1199, 1211, 1323, 1407, 1408, 1414, 1426], "green": [16, 32, 38, 70, 93, 115, 464, 598, 760, 1302, 1330, 1394, 1412, 1426], "goal": [16, 88, 92, 99, 105, 107, 127, 383, 626, 627, 717, 718, 1042], "g_ex": 16, "m": [16, 25, 28, 30, 31, 63, 65, 67, 91, 93, 96, 102, 106, 110, 112, 128, 181, 191, 201, 209, 211, 212, 219, 227, 231, 235, 236, 238, 239, 240, 241, 243, 244, 248, 257, 258, 259, 263, 272, 274, 275, 278, 280, 282, 284, 293, 294, 296, 300, 301, 302, 308, 309, 315, 316, 317, 330, 338, 341, 343, 345, 352, 355, 356, 361, 362, 370, 380, 383, 385, 412, 429, 431, 432, 433, 451, 462, 479, 494, 498, 499, 509, 510, 511, 512, 519, 545, 555, 569, 582, 584, 585, 587, 588, 606, 614, 619, 625, 652, 658, 659, 684, 686, 691, 692, 706, 748, 749, 761, 762, 775, 872, 880, 889, 953, 961, 970, 1060, 1151, 1155, 1157, 1169, 1175, 1177, 1179, 1181, 1199, 1201, 1202, 1203, 1204, 1205, 1207, 1208, 1209, 1210, 1211, 1213, 1215, 1216, 1218, 1219, 1220, 1222, 1223, 1226, 1229, 1230, 1231, 1233, 1234, 1235, 1240, 1256, 1265, 1269, 1271, 1278, 1279, 1280, 1287, 1288, 1292, 1323, 1387, 1406, 1409, 1426], "node_color_list": 16, "nc": [16, 56], "spectral_layout": [16, 43, 1141, 1399, 1406], "subgraphs_of_g_ex": 16, "removed_edg": 16, "node_color_list_c": 16, "One": [16, 52, 55, 101, 102, 103, 115, 545, 559, 560, 679, 684, 761, 1177, 1186, 1272, 1315, 1326, 1404, 1426], "g_ex_r": 16, "compos": [16, 269, 270, 271, 272, 273, 274, 275, 276, 600, 604, 758, 1400, 1406, 1407, 1417, 1423, 1425], "previous": [16, 91, 108, 112, 322, 614, 1182, 1183, 1184, 1395, 1407, 1417], "store": [16, 25, 39, 53, 54, 55, 57, 67, 86, 93, 97, 101, 102, 110, 158, 219, 220, 283, 290, 345, 346, 347, 431, 470, 471, 472, 473, 474, 475, 494, 495, 498, 499, 502, 503, 506, 507, 509, 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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": 91, "institut": [91, 112, 214, 215, 216, 220], "discoveri": [91, 670, 675, 676, 690], "madison": 91, "jessica": 91, "flack": 91, 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1401, 1406, 1414], "subcultur": 92, "technic": [92, 99, 104, 112, 179, 352, 377, 1272, 1405], "abil": [92, 94, 107, 110, 337, 1412], "fluent": 92, "develop": [92, 95, 97, 99, 102, 104, 105, 106, 107, 109, 110, 227, 457, 786, 1165, 1217, 1323, 1326, 1393, 1394, 1395, 1406, 1412, 1413, 1415, 1416, 1419, 1425], "uphold": 92, "interact": [92, 93, 96, 100, 101, 373, 1187, 1267, 1326, 1382, 1407, 1426], "painfulli": 92, "devolv": 92, "obviou": [92, 93, 500, 1404], "flagrant": 92, "abus": [92, 1426], "recogn": [92, 94, 249, 250, 1402], "bad": [92, 99, 1406, 1412, 1413], "dai": [92, 99, 615, 796, 850, 895, 931, 976, 1037, 1039, 1040, 1426], "unawar": 92, "mind": [92, 362, 1278, 1279, 1328], "respond": [92, 94, 99, 100], "breach": 92, "clearli": [92, 94], "steer": [92, 99], "council": [92, 99], "possibli": [92, 104, 180, 199, 232, 470, 722, 871, 887, 914, 925, 952, 968, 995, 1007, 1211, 1212, 1296, 1308, 1426], "unintent": 92, "repli": 92, "whatev": [92, 1416, 1425, 1426], "advic": [92, 99], 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998, 1425], "advanc": [93, 103, 574, 592, 618, 673, 758, 796, 1037, 1039, 1040, 1192, 1280, 1290, 1413, 1414], "rebas": [93, 94], "squash": [93, 94], "often": [93, 94, 99, 101, 102, 105, 378, 383, 384, 388, 464, 732, 780, 786, 796, 1037, 1038, 1039, 1040, 1234, 1296, 1326, 1328, 1405, 1425], "typic": [93, 97, 103, 127, 305, 796, 1037, 1039, 1040, 1102, 1103, 1175, 1323, 1413], "propos": [93, 97, 98, 99, 101, 102, 103, 104, 105, 107, 215, 230, 299, 578, 688, 1382, 1412, 1413, 1414, 1422, 1425], "easi": [93, 97, 102, 107, 109, 297, 298, 384, 760, 1326, 1328, 1383, 1412], "demonstr": [93, 100, 310, 1404, 1406], "spread": [93, 301, 302, 308, 309, 329], "sp": [93, 470, 473, 1101, 1387, 1426], "pd": [93, 1099, 1100, 1103, 1412], "stat": [93, 244, 380, 381, 748, 750, 1193, 1197, 1224, 1228, 1232], "optim": [93, 107, 112, 125, 208, 212, 226, 230, 231, 330, 353, 362, 380, 381, 382, 385, 422, 429, 496, 508, 672, 692, 720, 722, 723, 724, 725, 726, 729, 731, 732, 760, 780, 1108, 1117, 1235, 1320, 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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], "cutoff": [214, 215, 310, 326, 383, 410, 411, 412, 418, 419, 494, 495, 498, 499, 510, 637, 638, 640, 641, 642, 643, 644, 647, 648, 649, 656, 660, 661, 662, 667, 668, 669, 677, 678, 1234, 1398, 1402, 1406, 1413, 1416, 1424, 1425], "distinct": [214, 215, 255, 281, 288, 352, 391, 452, 453, 460, 578, 595, 608, 618, 700, 701, 734, 735, 736, 737, 789, 1150, 1244, 1271, 1323, 1326, 1328, 1395, 1417], "nonadjac": [214, 215, 480, 584, 585, 587], "cutset": [214, 215, 414, 415, 416, 417, 427, 428, 500, 506, 758], "menger": [214, 215, 216], "theorem": [214, 215, 216, 220, 235, 281, 311, 312, 322, 411, 506, 507, 514, 517, 518, 618, 1190, 1205], "local_node_connect": [214, 216, 408, 409, 410, 411, 413], "node_connect": [214, 215, 409, 410, 411, 412, 414, 415, 416, 417, 419, 427, 428, 1402], "dougla": [214, 215, 216, 220, 1413, 1425], "035": [214, 215, 216, 220], "eclect": [214, 215, 216], "ss": [214, 215, 216], "uci": [214, 215, 216, 467, 704, 706, 707, 708, 710, 734, 736], "drwhite": [214, 215, 216], "pprint": [214, 577, 711], "all_pairs_node_connect": [215, 216, 1402, 1424, 1425], "bf": [215, 216, 217, 363, 588, 704, 706, 707, 708, 717, 1397, 1401, 1406, 1409, 1412, 1413], "lose": [215, 796, 1037, 1039, 1040], "accuraci": [215, 312, 786], "platon": [215, 216, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1245, 1248, 1254, 1257, 1261, 1263], "octahedr": [215, 216, 1257], "approx": [215, 216, 227, 229, 230, 231, 1413], "octahedral_graph": [215, 216], "vari": [217, 238, 243, 373, 378, 569, 695], "sweep": [217, 1412], "dsweep": 217, "a_1": [217, 477], "a_2": 217, "magnien": [217, 260, 261, 262, 289], "cl\u00e9menc": [217, 260, 261, 262, 289], "matthieu": [217, 260, 261, 262, 274, 289], "latapi": [217, 260, 261, 262, 274, 289], "michel": 217, "habib": 217, "empir": 217, "tight": 217, "jea": 217, "0904": 217, "2728": 217, "crescenzi": 217, "pierluigi": 217, "roberto": 217, "grossi": 217, "leonardo": 217, "lanzi": 217, "andrea": [217, 1165, 1413], "marino": 217, "symposium": [217, 619, 1186, 1195, 1239], "berlin": [217, 520, 521, 1413], "heidelberg": [217, 520, 521], "ut": 217, "ee": [217, 313], "mtat": 217, "238": 217, "2014_fall": 217, "domin": [218, 219, 311, 410, 414, 481, 482, 483, 484, 758, 1325, 1395, 1400, 1406, 1407], "opt": [218, 221, 1425], "min_weight_dominating_set": 219, "vazirani": [219, 221], "vijai": [219, 221, 517], "min_dens": 220, "95": [220, 590, 1283, 1284, 1382], "nest": [220, 427, 728, 730, 791, 1038, 1045, 1061, 1094, 1296, 1308, 1348, 1355, 1356, 1357, 1358, 1383, 1406], "forth": [220, 427], "relax": [220, 227, 1171, 1413], "narrow": [220, 1165], "whitnei": 220, "bicompon": [220, 387, 389, 390, 394], "ferraro": [220, 427], "cohes": [220, 427, 437], "1503": [220, 427], "04476v1": [220, 427], "santaf": 220, "ind": 220, "embedded": [220, 305, 427], "sociolog": [220, 427, 748], "103": [220, 427, 1222, 1288, 1292], "2307": [220, 297, 1255], 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"license"]], "Bibliography": [[110, "bibliography"]], "Install": [[111, "install"]], "Install the released version": [[111, "install-the-released-version"]], "Install the development version": [[111, "install-the-development-version"]], "Extra packages": [[111, "extra-packages"]], "Test a source distribution": [[111, "test-a-source-distribution"]], "Test an installed package": [[111, "test-an-installed-package"]], "Approximations and Heuristics": [[112, "module-networkx.algorithms.approximation"]], "Connectivity": [[112, "module-networkx.algorithms.approximation.connectivity"], [126, "connectivity"], [127, "module-networkx.algorithms.connectivity"]], "K-components": [[112, "module-networkx.algorithms.approximation.kcomponents"]], "Clique": [[112, "module-networkx.algorithms.approximation.clique"], [121, "module-networkx.algorithms.clique"]], "Clustering": [[112, "module-networkx.algorithms.approximation.clustering_coefficient"], [115, "module-networkx.algorithms.bipartite.cluster"], 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"effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, 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"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": 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"DiGraph.add_edges_from": [[854, "digraph-add-edges-from"]], "DiGraph.add_node": [[855, "digraph-add-node"]], "DiGraph.add_nodes_from": [[856, "digraph-add-nodes-from"]], "DiGraph.add_weighted_edges_from": [[857, "digraph-add-weighted-edges-from"]], "DiGraph.adj": [[858, "digraph-adj"]], "DiGraph.adjacency": [[859, "digraph-adjacency"]], "DiGraph.clear": [[860, "digraph-clear"]], "DiGraph.clear_edges": [[861, "digraph-clear-edges"]], "DiGraph.copy": [[862, "digraph-copy"]], "DiGraph.degree": [[863, "digraph-degree"]], "DiGraph.edge_subgraph": [[864, "digraph-edge-subgraph"]], "DiGraph.edges": [[865, "digraph-edges"]], "DiGraph.get_edge_data": [[866, "digraph-get-edge-data"]], "DiGraph.has_edge": [[867, "digraph-has-edge"]], "DiGraph.has_node": [[868, "digraph-has-node"]], "DiGraph.in_degree": [[869, "digraph-in-degree"]], "DiGraph.in_edges": [[870, "digraph-in-edges"]], "DiGraph.nbunch_iter": [[871, "digraph-nbunch-iter"]], "DiGraph.neighbors": [[872, "digraph-neighbors"]], "DiGraph.nodes": [[873, "digraph-nodes"]], "DiGraph.number_of_edges": [[874, "digraph-number-of-edges"]], "DiGraph.number_of_nodes": [[875, "digraph-number-of-nodes"]], "DiGraph.order": [[876, "digraph-order"]], "DiGraph.out_degree": [[877, "digraph-out-degree"]], "DiGraph.out_edges": [[878, "digraph-out-edges"]], "DiGraph.pred": [[879, "digraph-pred"]], "DiGraph.predecessors": [[880, "digraph-predecessors"]], "DiGraph.remove_edge": [[881, "digraph-remove-edge"]], "DiGraph.remove_edges_from": [[882, "digraph-remove-edges-from"]], "DiGraph.remove_node": [[883, "digraph-remove-node"]], "DiGraph.remove_nodes_from": [[884, "digraph-remove-nodes-from"]], "DiGraph.reverse": [[885, "digraph-reverse"]], "DiGraph.size": [[886, "digraph-size"]], "DiGraph.subgraph": [[887, "digraph-subgraph"]], "DiGraph.succ": [[888, "digraph-succ"]], "DiGraph.successors": [[889, "digraph-successors"]], "DiGraph.to_directed": [[890, "digraph-to-directed"]], "DiGraph.to_undirected": [[891, 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"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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"multigraph-new-edge-key"]], "MultiGraph.nodes": [[998, "multigraph-nodes"]], "MultiGraph.number_of_edges": [[999, "multigraph-number-of-edges"]], "MultiGraph.number_of_nodes": [[1000, "multigraph-number-of-nodes"]], "MultiGraph.order": [[1001, "multigraph-order"]], "MultiGraph.remove_edge": [[1002, "multigraph-remove-edge"]], "MultiGraph.remove_edges_from": [[1003, "multigraph-remove-edges-from"]], "MultiGraph.remove_node": [[1004, "multigraph-remove-node"]], "MultiGraph.remove_nodes_from": [[1005, "multigraph-remove-nodes-from"]], "MultiGraph.size": [[1006, "multigraph-size"]], "MultiGraph.subgraph": [[1007, "multigraph-subgraph"]], "MultiGraph.to_directed": [[1008, "multigraph-to-directed"]], "MultiGraph.to_undirected": [[1009, "multigraph-to-undirected"]], "MultiGraph.update": [[1010, "multigraph-update"]], "_dispatch": [[1011, "dispatch"]], "networkx.classes.coreviews.AdjacencyView": [[1012, "networkx-classes-coreviews-adjacencyview"]], "networkx.classes.coreviews.AtlasView": 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Applying classic graph operations, such as:": [[1426, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1426, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1426, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1426, "using-a-stochastic-graph-generator-e-g"]], "5. Reading a graph stored in a file using common graph formats": [[1426, "reading-a-graph-stored-in-a-file-using-common-graph-formats"]], "Analyzing graphs": [[1426, "analyzing-graphs"]], "Drawing graphs": [[1426, "drawing-graphs"]]}, "indexentries": {"module": [[112, "module-networkx.algorithms.approximation"], [112, "module-networkx.algorithms.approximation.clique"], [112, "module-networkx.algorithms.approximation.clustering_coefficient"], [112, "module-networkx.algorithms.approximation.connectivity"], [112, "module-networkx.algorithms.approximation.distance_measures"], [112, "module-networkx.algorithms.approximation.dominating_set"], [112, "module-networkx.algorithms.approximation.kcomponents"], [112, "module-networkx.algorithms.approximation.matching"], [112, "module-networkx.algorithms.approximation.maxcut"], [112, "module-networkx.algorithms.approximation.ramsey"], [112, "module-networkx.algorithms.approximation.steinertree"], [112, 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"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"]], 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"module-networkx.algorithms.bipartite.covering"]], "networkx.algorithms.bipartite.edgelist": [[115, "module-networkx.algorithms.bipartite.edgelist"]], "networkx.algorithms.bipartite.generators": [[115, "module-networkx.algorithms.bipartite.generators"]], "networkx.algorithms.bipartite.matching": [[115, "module-networkx.algorithms.bipartite.matching"]], "networkx.algorithms.bipartite.matrix": [[115, "module-networkx.algorithms.bipartite.matrix"]], "networkx.algorithms.bipartite.projection": [[115, "module-networkx.algorithms.bipartite.projection"]], "networkx.algorithms.bipartite.redundancy": [[115, "module-networkx.algorithms.bipartite.redundancy"]], "networkx.algorithms.bipartite.spectral": [[115, "module-networkx.algorithms.bipartite.spectral"]], "networkx.algorithms.boundary": [[116, "module-networkx.algorithms.boundary"]], "networkx.algorithms.bridges": [[117, "module-networkx.algorithms.bridges"]], "networkx.algorithms.centrality": [[118, "module-networkx.algorithms.centrality"]], "networkx.algorithms.chains": [[119, "module-networkx.algorithms.chains"]], "networkx.algorithms.chordal": [[120, "module-networkx.algorithms.chordal"]], "networkx.algorithms.clique": [[121, "module-networkx.algorithms.clique"]], "networkx.algorithms.cluster": [[122, "module-networkx.algorithms.cluster"]], "networkx.algorithms.coloring": [[123, "module-networkx.algorithms.coloring"]], "networkx.algorithms.communicability_alg": [[124, "module-networkx.algorithms.communicability_alg"]], "networkx.algorithms.community": [[125, "module-networkx.algorithms.community"]], "networkx.algorithms.community.asyn_fluid": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "networkx.algorithms.community.centrality": [[125, "module-networkx.algorithms.community.centrality"]], "networkx.algorithms.community.community_utils": [[125, "module-networkx.algorithms.community.community_utils"]], "networkx.algorithms.community.kclique": [[125, "module-networkx.algorithms.community.kclique"]], "networkx.algorithms.community.kernighan_lin": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "networkx.algorithms.community.label_propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "networkx.algorithms.community.louvain": [[125, "module-networkx.algorithms.community.louvain"]], "networkx.algorithms.community.lukes": [[125, "module-networkx.algorithms.community.lukes"]], "networkx.algorithms.community.modularity_max": [[125, "module-networkx.algorithms.community.modularity_max"]], "networkx.algorithms.community.quality": [[125, "module-networkx.algorithms.community.quality"]], "networkx.algorithms.components": [[126, "module-networkx.algorithms.components"]], "networkx.algorithms.connectivity": [[127, "module-networkx.algorithms.connectivity"]], "networkx.algorithms.connectivity.connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "networkx.algorithms.connectivity.cuts": [[127, "module-networkx.algorithms.connectivity.cuts"]], "networkx.algorithms.connectivity.disjoint_paths": [[127, "module-networkx.algorithms.connectivity.disjoint_paths"]], "networkx.algorithms.connectivity.edge_augmentation": [[127, "module-networkx.algorithms.connectivity.edge_augmentation"]], "networkx.algorithms.connectivity.edge_kcomponents": [[127, "module-networkx.algorithms.connectivity.edge_kcomponents"]], "networkx.algorithms.connectivity.kcomponents": [[127, "module-networkx.algorithms.connectivity.kcomponents"]], "networkx.algorithms.connectivity.kcutsets": [[127, "module-networkx.algorithms.connectivity.kcutsets"]], "networkx.algorithms.connectivity.stoerwagner": [[127, "module-networkx.algorithms.connectivity.stoerwagner"]], "networkx.algorithms.connectivity.utils": [[127, "module-networkx.algorithms.connectivity.utils"]], "networkx.algorithms.core": [[128, "module-networkx.algorithms.core"]], "networkx.algorithms.covering": [[129, "module-networkx.algorithms.covering"]], "networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[144, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[151, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": 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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, 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"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, 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"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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method)": [[988, "networkx.MultiGraph.copy"]], "degree (multigraph property)": [[989, "networkx.MultiGraph.degree"]], "edge_subgraph() (multigraph method)": [[990, "networkx.MultiGraph.edge_subgraph"]], "edges (multigraph property)": [[991, "networkx.MultiGraph.edges"]], "get_edge_data() (multigraph method)": [[992, "networkx.MultiGraph.get_edge_data"]], "has_edge() (multigraph method)": [[993, "networkx.MultiGraph.has_edge"]], "has_node() (multigraph method)": [[994, "networkx.MultiGraph.has_node"]], "nbunch_iter() (multigraph method)": [[995, "networkx.MultiGraph.nbunch_iter"]], "neighbors() (multigraph method)": [[996, "networkx.MultiGraph.neighbors"]], "new_edge_key() (multigraph method)": [[997, "networkx.MultiGraph.new_edge_key"]], "nodes (multigraph property)": [[998, "networkx.MultiGraph.nodes"]], "number_of_edges() (multigraph method)": [[999, "networkx.MultiGraph.number_of_edges"]], "number_of_nodes() (multigraph method)": [[1000, "networkx.MultiGraph.number_of_nodes"]], "order() (multigraph method)": [[1001, "networkx.MultiGraph.order"]], "remove_edge() (multigraph method)": [[1002, "networkx.MultiGraph.remove_edge"]], "remove_edges_from() (multigraph method)": [[1003, "networkx.MultiGraph.remove_edges_from"]], "remove_node() (multigraph method)": [[1004, "networkx.MultiGraph.remove_node"]], "remove_nodes_from() (multigraph method)": [[1005, "networkx.MultiGraph.remove_nodes_from"]], "size() (multigraph method)": [[1006, "networkx.MultiGraph.size"]], "subgraph() (multigraph method)": [[1007, "networkx.MultiGraph.subgraph"]], "to_directed() (multigraph method)": [[1008, "networkx.MultiGraph.to_directed"]], "to_undirected() (multigraph method)": [[1009, "networkx.MultiGraph.to_undirected"]], "update() (multigraph method)": [[1010, "networkx.MultiGraph.update"]], "_dispatch() (in module networkx.classes.backends)": [[1011, "networkx.classes.backends._dispatch"]], "adjacencyview (class in networkx.classes.coreviews)": [[1012, "networkx.classes.coreviews.AdjacencyView"]], "__init__() (adjacencyview method)": [[1012, "networkx.classes.coreviews.AdjacencyView.__init__"]], "atlasview (class in networkx.classes.coreviews)": [[1013, "networkx.classes.coreviews.AtlasView"]], "__init__() (atlasview method)": [[1013, "networkx.classes.coreviews.AtlasView.__init__"]], "filteradjacency (class in networkx.classes.coreviews)": [[1014, "networkx.classes.coreviews.FilterAdjacency"]], "__init__() (filteradjacency method)": [[1014, "networkx.classes.coreviews.FilterAdjacency.__init__"]], "filteratlas (class in networkx.classes.coreviews)": [[1015, "networkx.classes.coreviews.FilterAtlas"]], "__init__() (filteratlas method)": [[1015, "networkx.classes.coreviews.FilterAtlas.__init__"]], "filtermultiadjacency (class in networkx.classes.coreviews)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency"]], "__init__() (filtermultiadjacency method)": [[1016, "networkx.classes.coreviews.FilterMultiAdjacency.__init__"]], "filtermultiinner (class in networkx.classes.coreviews)": [[1017, "networkx.classes.coreviews.FilterMultiInner"]], "__init__() (filtermultiinner method)": [[1017, "networkx.classes.coreviews.FilterMultiInner.__init__"]], "multiadjacencyview (class in networkx.classes.coreviews)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView"]], "__init__() (multiadjacencyview method)": [[1018, "networkx.classes.coreviews.MultiAdjacencyView.__init__"]], "unionadjacency (class in networkx.classes.coreviews)": [[1019, "networkx.classes.coreviews.UnionAdjacency"]], "__init__() (unionadjacency method)": [[1019, "networkx.classes.coreviews.UnionAdjacency.__init__"]], "unionatlas (class in networkx.classes.coreviews)": [[1020, "networkx.classes.coreviews.UnionAtlas"]], "__init__() (unionatlas method)": [[1020, "networkx.classes.coreviews.UnionAtlas.__init__"]], "unionmultiadjacency (class in networkx.classes.coreviews)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency"]], "__init__() (unionmultiadjacency method)": [[1021, "networkx.classes.coreviews.UnionMultiAdjacency.__init__"]], "unionmultiinner (class in networkx.classes.coreviews)": [[1022, "networkx.classes.coreviews.UnionMultiInner"]], "__init__() (unionmultiinner method)": [[1022, "networkx.classes.coreviews.UnionMultiInner.__init__"]], "hide_diedges() (in module networkx.classes.filters)": [[1023, "networkx.classes.filters.hide_diedges"]], "hide_edges() (in module networkx.classes.filters)": [[1024, "networkx.classes.filters.hide_edges"]], "hide_multidiedges() (in module networkx.classes.filters)": [[1025, "networkx.classes.filters.hide_multidiedges"]], "hide_multiedges() (in module networkx.classes.filters)": [[1026, "networkx.classes.filters.hide_multiedges"]], "hide_nodes() (in module networkx.classes.filters)": [[1027, "networkx.classes.filters.hide_nodes"]], "no_filter() (in module networkx.classes.filters)": [[1028, "networkx.classes.filters.no_filter"]], "show_diedges() (in module networkx.classes.filters)": [[1029, "networkx.classes.filters.show_diedges"]], "show_edges() (in module networkx.classes.filters)": [[1030, "networkx.classes.filters.show_edges"]], "show_multidiedges() (in module networkx.classes.filters)": [[1031, "networkx.classes.filters.show_multidiedges"]], "show_multiedges() (in module networkx.classes.filters)": [[1032, "networkx.classes.filters.show_multiedges"]], "__init__() (show_nodes method)": [[1033, "networkx.classes.filters.show_nodes.__init__"]], "show_nodes (class in networkx.classes.filters)": [[1033, "networkx.classes.filters.show_nodes"]], "generic_graph_view() (in module networkx.classes.graphviews)": [[1034, "networkx.classes.graphviews.generic_graph_view"]], "reverse_view() (in module networkx.classes.graphviews)": [[1035, "networkx.classes.graphviews.reverse_view"]], "subgraph_view() (in module networkx.classes.graphviews)": [[1036, "networkx.classes.graphviews.subgraph_view"]], "graph (class in networkx)": [[1037, "networkx.Graph"]], "networkx.classes.backends": [[1038, "module-networkx.classes.backends"]], "networkx.classes.coreviews": [[1038, "module-networkx.classes.coreviews"]], "networkx.classes.filters": [[1038, "module-networkx.classes.filters"]], "networkx.classes.graphviews": [[1038, "module-networkx.classes.graphviews"]], "multidigraph (class in networkx)": [[1039, "networkx.MultiDiGraph"]], "multigraph (class in networkx)": [[1040, "networkx.MultiGraph"]], "networkx.convert": [[1041, "module-networkx.convert"]], "networkx.convert_matrix": [[1041, "module-networkx.convert_matrix"]], "networkx.drawing.layout": [[1042, "module-networkx.drawing.layout"]], "networkx.drawing.nx_agraph": [[1042, "module-networkx.drawing.nx_agraph"]], "networkx.drawing.nx_pydot": [[1042, "module-networkx.drawing.nx_pydot"]], "networkx.drawing.nx_pylab": [[1042, "module-networkx.drawing.nx_pylab"]], "ambiguoussolution (class in networkx)": [[1043, "networkx.AmbiguousSolution"]], "exceededmaxiterations (class in networkx)": [[1043, "networkx.ExceededMaxIterations"]], "hasacycle (class in networkx)": [[1043, "networkx.HasACycle"]], "networkxalgorithmerror (class in networkx)": [[1043, "networkx.NetworkXAlgorithmError"]], "networkxerror (class in networkx)": [[1043, "networkx.NetworkXError"]], "networkxexception (class in networkx)": [[1043, "networkx.NetworkXException"]], "networkxnocycle (class in networkx)": [[1043, "networkx.NetworkXNoCycle"]], "networkxnopath (class in networkx)": [[1043, "networkx.NetworkXNoPath"]], "networkxnotimplemented (class in networkx)": [[1043, "networkx.NetworkXNotImplemented"]], "networkxpointlessconcept (class in networkx)": [[1043, "networkx.NetworkXPointlessConcept"]], "networkxunbounded (class in networkx)": [[1043, "networkx.NetworkXUnbounded"]], "networkxunfeasible (class in networkx)": [[1043, "networkx.NetworkXUnfeasible"]], "nodenotfound (class in networkx)": [[1043, "networkx.NodeNotFound"]], "poweriterationfailedconvergence (class in networkx)": [[1043, "networkx.PowerIterationFailedConvergence"]], "networkx.exception": [[1043, "module-networkx.exception"]], "networkx.classes.function": [[1044, "module-networkx.classes.function"]], "assemble() (argmap method)": [[1045, "networkx.utils.decorators.argmap.assemble"]], "compile() (argmap method)": [[1046, "networkx.utils.decorators.argmap.compile"]], "signature() (argmap class method)": [[1047, "networkx.utils.decorators.argmap.signature"]], "pop() (mappedqueue method)": [[1048, "networkx.utils.mapped_queue.MappedQueue.pop"]], "push() (mappedqueue method)": [[1049, "networkx.utils.mapped_queue.MappedQueue.push"]], "remove() (mappedqueue method)": [[1050, "networkx.utils.mapped_queue.MappedQueue.remove"]], "update() (mappedqueue method)": [[1051, "networkx.utils.mapped_queue.MappedQueue.update"]], "add_cycle() (in module networkx.classes.function)": [[1052, "networkx.classes.function.add_cycle"]], "add_path() (in module networkx.classes.function)": [[1053, "networkx.classes.function.add_path"]], "add_star() (in module networkx.classes.function)": [[1054, "networkx.classes.function.add_star"]], "all_neighbors() (in module networkx.classes.function)": [[1055, "networkx.classes.function.all_neighbors"]], "common_neighbors() (in module networkx.classes.function)": [[1056, "networkx.classes.function.common_neighbors"]], "create_empty_copy() (in module networkx.classes.function)": [[1057, "networkx.classes.function.create_empty_copy"]], "degree() (in module networkx.classes.function)": [[1058, "networkx.classes.function.degree"]], "degree_histogram() (in module networkx.classes.function)": [[1059, "networkx.classes.function.degree_histogram"]], "density() (in module networkx.classes.function)": [[1060, "networkx.classes.function.density"]], "edge_subgraph() (in module networkx.classes.function)": [[1061, "networkx.classes.function.edge_subgraph"]], "edges() (in module networkx.classes.function)": [[1062, "networkx.classes.function.edges"]], "freeze() (in module networkx.classes.function)": [[1063, "networkx.classes.function.freeze"]], "get_edge_attributes() (in module networkx.classes.function)": [[1064, "networkx.classes.function.get_edge_attributes"]], "get_node_attributes() (in module networkx.classes.function)": [[1065, "networkx.classes.function.get_node_attributes"]], "induced_subgraph() (in module networkx.classes.function)": [[1066, "networkx.classes.function.induced_subgraph"]], "is_directed() (in module networkx.classes.function)": [[1067, "networkx.classes.function.is_directed"]], "is_empty() (in module networkx.classes.function)": [[1068, "networkx.classes.function.is_empty"]], "is_frozen() (in module networkx.classes.function)": [[1069, "networkx.classes.function.is_frozen"]], "is_negatively_weighted() (in module networkx.classes.function)": [[1070, "networkx.classes.function.is_negatively_weighted"]], "is_path() (in module networkx.classes.function)": [[1071, "networkx.classes.function.is_path"]], "is_weighted() (in module networkx.classes.function)": [[1072, "networkx.classes.function.is_weighted"]], "neighbors() (in module networkx.classes.function)": [[1073, "networkx.classes.function.neighbors"]], "nodes() (in module networkx.classes.function)": [[1074, "networkx.classes.function.nodes"]], "nodes_with_selfloops() (in module networkx.classes.function)": [[1075, "networkx.classes.function.nodes_with_selfloops"]], "non_edges() (in module networkx.classes.function)": [[1076, "networkx.classes.function.non_edges"]], "non_neighbors() (in module networkx.classes.function)": [[1077, "networkx.classes.function.non_neighbors"]], "number_of_edges() (in module networkx.classes.function)": [[1078, "networkx.classes.function.number_of_edges"]], "number_of_nodes() (in module networkx.classes.function)": [[1079, "networkx.classes.function.number_of_nodes"]], "number_of_selfloops() (in module networkx.classes.function)": [[1080, "networkx.classes.function.number_of_selfloops"]], "path_weight() (in module networkx.classes.function)": [[1081, "networkx.classes.function.path_weight"]], "restricted_view() (in module networkx.classes.function)": [[1082, "networkx.classes.function.restricted_view"]], "reverse_view() (in module networkx.classes.function)": [[1083, "networkx.classes.function.reverse_view"]], "selfloop_edges() (in module networkx.classes.function)": [[1084, "networkx.classes.function.selfloop_edges"]], "set_edge_attributes() (in module networkx.classes.function)": [[1085, "networkx.classes.function.set_edge_attributes"]], "set_node_attributes() (in module networkx.classes.function)": [[1086, "networkx.classes.function.set_node_attributes"]], "subgraph() (in module networkx.classes.function)": [[1087, "networkx.classes.function.subgraph"]], "subgraph_view() (in module networkx.classes.function)": [[1088, "networkx.classes.function.subgraph_view"]], "to_directed() (in module networkx.classes.function)": [[1089, "networkx.classes.function.to_directed"]], "to_undirected() (in module networkx.classes.function)": [[1090, "networkx.classes.function.to_undirected"]], "from_dict_of_dicts() (in module networkx.convert)": [[1091, "networkx.convert.from_dict_of_dicts"]], "from_dict_of_lists() (in module networkx.convert)": [[1092, "networkx.convert.from_dict_of_lists"]], "from_edgelist() (in module networkx.convert)": [[1093, "networkx.convert.from_edgelist"]], "to_dict_of_dicts() (in module networkx.convert)": [[1094, "networkx.convert.to_dict_of_dicts"]], "to_dict_of_lists() (in module networkx.convert)": [[1095, "networkx.convert.to_dict_of_lists"]], "to_edgelist() (in module networkx.convert)": [[1096, "networkx.convert.to_edgelist"]], "to_networkx_graph() (in module networkx.convert)": [[1097, "networkx.convert.to_networkx_graph"]], "from_numpy_array() (in module networkx.convert_matrix)": [[1098, "networkx.convert_matrix.from_numpy_array"]], "from_pandas_adjacency() (in module networkx.convert_matrix)": [[1099, "networkx.convert_matrix.from_pandas_adjacency"]], "from_pandas_edgelist() (in module networkx.convert_matrix)": [[1100, "networkx.convert_matrix.from_pandas_edgelist"]], "from_scipy_sparse_array() (in module networkx.convert_matrix)": [[1101, "networkx.convert_matrix.from_scipy_sparse_array"]], "to_numpy_array() (in module networkx.convert_matrix)": [[1102, "networkx.convert_matrix.to_numpy_array"]], "to_pandas_adjacency() (in module networkx.convert_matrix)": [[1103, "networkx.convert_matrix.to_pandas_adjacency"]], "to_pandas_edgelist() (in module networkx.convert_matrix)": [[1104, "networkx.convert_matrix.to_pandas_edgelist"]], "to_scipy_sparse_array() (in module networkx.convert_matrix)": [[1105, "networkx.convert_matrix.to_scipy_sparse_array"]], "bipartite_layout() (in module networkx.drawing.layout)": [[1106, "networkx.drawing.layout.bipartite_layout"]], "circular_layout() (in module networkx.drawing.layout)": [[1107, "networkx.drawing.layout.circular_layout"]], "kamada_kawai_layout() (in module networkx.drawing.layout)": [[1108, "networkx.drawing.layout.kamada_kawai_layout"]], "multipartite_layout() (in module networkx.drawing.layout)": [[1109, "networkx.drawing.layout.multipartite_layout"]], "planar_layout() (in module networkx.drawing.layout)": [[1110, "networkx.drawing.layout.planar_layout"]], "random_layout() (in module networkx.drawing.layout)": [[1111, "networkx.drawing.layout.random_layout"]], "rescale_layout() (in module networkx.drawing.layout)": [[1112, "networkx.drawing.layout.rescale_layout"]], "rescale_layout_dict() (in module networkx.drawing.layout)": [[1113, "networkx.drawing.layout.rescale_layout_dict"]], "shell_layout() (in module networkx.drawing.layout)": [[1114, "networkx.drawing.layout.shell_layout"]], "spectral_layout() (in module networkx.drawing.layout)": [[1115, "networkx.drawing.layout.spectral_layout"]], "spiral_layout() (in module networkx.drawing.layout)": [[1116, "networkx.drawing.layout.spiral_layout"]], 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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.degree_seq.degree_sequence_tree"]], "directed_configuration_model() (in module networkx.generators.degree_seq)": [[1177, "networkx.generators.degree_seq.directed_configuration_model"]], "directed_havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1178, "networkx.generators.degree_seq.directed_havel_hakimi_graph"]], "expected_degree_graph() (in module networkx.generators.degree_seq)": [[1179, "networkx.generators.degree_seq.expected_degree_graph"]], "havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1180, "networkx.generators.degree_seq.havel_hakimi_graph"]], "random_degree_sequence_graph() (in module networkx.generators.degree_seq)": [[1181, "networkx.generators.degree_seq.random_degree_sequence_graph"]], "gn_graph() (in module networkx.generators.directed)": [[1182, "networkx.generators.directed.gn_graph"]], "gnc_graph() (in module networkx.generators.directed)": [[1183, "networkx.generators.directed.gnc_graph"]], "gnr_graph() 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networkx.generators.intersection)": [[1205, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1206, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1207, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1208, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1209, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1211, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1212, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1213, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1216, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1217, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1218, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1219, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1220, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1221, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1222, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1223, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1224, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1225, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1226, 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"networkx.generators.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1242, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1243, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1244, "networkx.generators.small.chvatal_graph"]], "cubical_graph() (in module networkx.generators.small)": [[1245, "networkx.generators.small.cubical_graph"]], "desargues_graph() (in module networkx.generators.small)": [[1246, "networkx.generators.small.desargues_graph"]], "diamond_graph() (in module networkx.generators.small)": [[1247, "networkx.generators.small.diamond_graph"]], "dodecahedral_graph() (in module networkx.generators.small)": [[1248, "networkx.generators.small.dodecahedral_graph"]], "frucht_graph() (in module networkx.generators.small)": [[1249, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1257, "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 1fcafcd5..8f8300ee 100644 --- a/tutorial-34.pdf +++ b/tutorial-34.pdf diff --git a/tutorial-35.pdf b/tutorial-35.pdf Binary files differindex f5571d06..61ce168e 100644 --- a/tutorial-35.pdf +++ b/tutorial-35.pdf diff --git a/tutorial-36.pdf b/tutorial-36.pdf Binary files differindex ae5081bf..b188ab3a 100644 --- a/tutorial-36.pdf +++ b/tutorial-36.pdf diff --git a/tutorial.ipynb b/tutorial.ipynb index b9afd1c2..6b3063e6 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "17631eeb", + "id": "c1df7792", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,7 +17,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8b43999d", + "id": "c8da7c6c", "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "26ff4033", + "id": "fb8cff6d", "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": "0fe5988f", + "id": "160a4cb7", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "222e2bae", + "id": "bd486052", "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": "57fe5cf9", + "id": "89778ad6", "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "markdown", - "id": "6df8a05b", + "id": "df75a4d8", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": null, - "id": "289a49be", + "id": "8965f552", "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "c0cfeabf", + "id": "e8d79910", "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": "15930f02", + "id": "8130eaa1", "metadata": {}, "outputs": [], "source": [ @@ -125,7 +125,7 @@ }, { "cell_type": "markdown", - "id": "4fe1f15d", + "id": "fd4cb72d", "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": "8ec67ec0", + "id": "25ac86f8", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +154,7 @@ }, { "cell_type": "markdown", - "id": "b8a64f73", + "id": "a7a507ca", "metadata": {}, "source": [ "by adding a list of edges," @@ -163,7 +163,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d8138ac4", + "id": "3551b660", "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "b078e838", + "id": "dbeb878d", "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": "fa225e53", + "id": "bb0bed7b", "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "7d1b3654", + "id": "c978377d", "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": "947e7828", + "id": "118590f5", "metadata": {}, "outputs": [], "source": [ @@ -213,7 +213,7 @@ }, { "cell_type": "markdown", - "id": "34cc62da", + "id": "8128daf6", "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": "1abaca76", + "id": "29d285b0", "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "9c8b3551", + "id": "73405a2b", "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": "8d945779", + "id": "7930b9ae", "metadata": {}, "outputs": [], "source": [ @@ -257,7 +257,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8106e641", + "id": "0bf4652e", "metadata": {}, "outputs": [], "source": [ @@ -272,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "7776ebf0", + "id": "e46e1f97", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -292,7 +292,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f1db6fe4", + "id": "e8fc834c", "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "markdown", - "id": "326966d8", + "id": "2fae0e4a", "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": "a887bd22", + "id": "e1508072", "metadata": {}, "outputs": [], "source": [ @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "0e95153e", + "id": "aa2f21b1", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -343,7 +343,7 @@ { "cell_type": "code", "execution_count": null, - "id": "93137ba0", + "id": "29a3c316", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "6bece1a1", + "id": "bffa3a12", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -370,7 +370,7 @@ { "cell_type": "code", "execution_count": null, - "id": "34430c44", + "id": "a6204886", "metadata": {}, "outputs": [], "source": [ @@ -387,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "631ff23d", + "id": "51d764d6", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -416,7 +416,7 @@ { "cell_type": "code", "execution_count": null, - "id": "99a5b8f8", + "id": "45c6451a", "metadata": {}, "outputs": [], "source": [ @@ -428,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "d8c15355", + "id": "7d632e8f", "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": "f6c0f87c", + "id": "dc2f2e19", "metadata": {}, "outputs": [], "source": [ @@ -450,7 +450,7 @@ }, { "cell_type": "markdown", - "id": "3a5e2995", + "id": "963c5720", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -461,7 +461,7 @@ { "cell_type": "code", "execution_count": null, - "id": "030a4a4d", + "id": "d902e4cd", "metadata": {}, "outputs": [], "source": [ @@ -475,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "a8a9a94a", + "id": "c301ade8", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -484,7 +484,7 @@ { "cell_type": "code", "execution_count": null, - "id": "55f2a3e8", + "id": "77b61e77", "metadata": {}, "outputs": [], "source": [ @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "967fe442", + "id": "90b14046", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -517,7 +517,7 @@ { "cell_type": "code", "execution_count": null, - "id": "35759e03", + "id": "088afb6c", "metadata": {}, "outputs": [], "source": [ @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "536cb1e0", + "id": "16b697e9", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -536,7 +536,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cee37c27", + "id": "67ab7cdb", "metadata": {}, "outputs": [], "source": [ @@ -546,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "e5859bc2", + "id": "7bdc4675", "metadata": {}, "source": [ "# Node attributes\n", @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": null, - "id": "509abb93", + "id": "7adb48a4", "metadata": {}, "outputs": [], "source": [ @@ -570,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "c87af800", + "id": "72624cdc", "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": "c669880f", + "id": "5e6a24ae", "metadata": {}, "outputs": [], "source": [ @@ -598,7 +598,7 @@ }, { "cell_type": "markdown", - "id": "cdd119f1", + "id": "279603ae", "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": "188a0581", + "id": "fb770685", "metadata": {}, "outputs": [], "source": [ @@ -633,7 +633,7 @@ }, { "cell_type": "markdown", - "id": "67ef6a71", + "id": "31452e73", "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": "bef2a5dd", + "id": "6ad4b4f4", "metadata": {}, "outputs": [], "source": [ @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "32f625b5", + "id": "0745992c", "metadata": {}, "source": [ "# Multigraphs\n", @@ -675,7 +675,7 @@ { "cell_type": "code", "execution_count": null, - "id": "9afbc429", + "id": "cda91c67", "metadata": {}, "outputs": [], "source": [ @@ -693,7 +693,7 @@ }, { "cell_type": "markdown", - "id": "5f3fb92c", + "id": "298eb127", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -713,7 +713,7 @@ { "cell_type": "code", "execution_count": null, - "id": "dfbfbccd", + "id": "74d76d3e", "metadata": {}, "outputs": [], "source": [ @@ -725,7 +725,7 @@ }, { "cell_type": "markdown", - "id": "ff571c7d", + "id": "945ac4c7", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -736,7 +736,7 @@ { "cell_type": "code", "execution_count": null, - "id": "dbd5ef2a", + "id": "93f74903", "metadata": {}, "outputs": [], "source": [ @@ -748,7 +748,7 @@ }, { "cell_type": "markdown", - "id": "b8546121", + "id": "f12a0272", "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": "618f48a4", + "id": "54196e80", "metadata": {}, "outputs": [], "source": [ @@ -770,7 +770,7 @@ }, { "cell_type": "markdown", - "id": "cbf17408", + "id": "22b76e1e", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -785,7 +785,7 @@ { "cell_type": "code", "execution_count": null, - "id": "bab63117", + "id": "ff743238", "metadata": {}, "outputs": [], "source": [ @@ -799,7 +799,7 @@ }, { "cell_type": "markdown", - "id": "76a41131", + "id": "a1540523", "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": "07fbc523", + "id": "bf51122c", "metadata": {}, "outputs": [], "source": [ @@ -819,7 +819,7 @@ }, { "cell_type": "markdown", - "id": "3978385c", + "id": "7ad44534", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -838,7 +838,7 @@ { "cell_type": "code", "execution_count": null, - "id": "661afff3", + "id": "63d9498e", "metadata": {}, "outputs": [], "source": [ @@ -847,7 +847,7 @@ }, { "cell_type": "markdown", - "id": "d3b9e423", + "id": "39c9d7a5", "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": "d33340f6", + "id": "c1a1256b", "metadata": {}, "outputs": [], "source": [ @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "449ba321", + "id": "159f63cd", "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": "60189d8d", + "id": "9e94fc92", "metadata": {}, "outputs": [], "source": [ @@ -889,7 +889,7 @@ }, { "cell_type": "markdown", - "id": "d967d169", + "id": "9905d091", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -898,7 +898,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f4cc346e", + "id": "9d4bb899", "metadata": {}, "outputs": [], "source": [ @@ -919,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "4c682dc4", + "id": "805e8019", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -930,7 +930,7 @@ { "cell_type": "code", "execution_count": null, - "id": "50b65c1a", + "id": "572d2502", "metadata": {}, "outputs": [], "source": [ @@ -941,7 +941,7 @@ }, { "cell_type": "markdown", - "id": "657979bb", + "id": "ddedda46", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -950,7 +950,7 @@ { "cell_type": "code", "execution_count": null, - "id": "0bf2381c", + "id": "11cfb1cb", "metadata": {}, "outputs": [], "source": [ @@ -960,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "0745862f", + "id": "364d1dfc", "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": "cd4aad41", + "id": "50b2da8a", "metadata": {}, "outputs": [], "source": [ @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "30bc0300", + "id": "3e79a049", "metadata": {}, "source": [ "See Drawing for additional details." diff --git a/tutorial_full.ipynb b/tutorial_full.ipynb index 1c97a5b6..2b52c557 100644 --- a/tutorial_full.ipynb +++ b/tutorial_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "17631eeb", + "id": "c1df7792", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,13 +17,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "8b43999d", + "id": "c8da7c6c", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:58.892605Z", - "iopub.status.busy": "2022-12-15T16:09:58.892265Z", - "iopub.status.idle": "2022-12-15T16:09:58.962180Z", - "shell.execute_reply": "2022-12-15T16:09:58.961564Z" + "iopub.execute_input": "2022-12-20T11:40:17.686365Z", + "iopub.status.busy": "2022-12-20T11:40:17.686152Z", + "iopub.status.idle": "2022-12-20T11:40:17.756154Z", + "shell.execute_reply": "2022-12-20T11:40:17.755523Z" } }, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "markdown", - "id": "26ff4033", + "id": "fb8cff6d", "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": "0fe5988f", + "id": "160a4cb7", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:58.965271Z", - "iopub.status.busy": "2022-12-15T16:09:58.965047Z", - "iopub.status.idle": "2022-12-15T16:09:58.967968Z", - "shell.execute_reply": "2022-12-15T16:09:58.967351Z" + "iopub.execute_input": "2022-12-20T11:40:17.759727Z", + "iopub.status.busy": "2022-12-20T11:40:17.759186Z", + "iopub.status.idle": "2022-12-20T11:40:17.762362Z", + "shell.execute_reply": "2022-12-20T11:40:17.761771Z" } }, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "222e2bae", + "id": "bd486052", "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": "57fe5cf9", + "id": "89778ad6", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:58.970772Z", - "iopub.status.busy": "2022-12-15T16:09:58.970569Z", - "iopub.status.idle": "2022-12-15T16:09:58.973424Z", - "shell.execute_reply": "2022-12-15T16:09:58.972817Z" + "iopub.execute_input": "2022-12-20T11:40:17.765005Z", + "iopub.status.busy": "2022-12-20T11:40:17.764803Z", + "iopub.status.idle": "2022-12-20T11:40:17.767828Z", + "shell.execute_reply": "2022-12-20T11:40:17.767239Z" } }, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "6df8a05b", + "id": "df75a4d8", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -117,13 +117,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "289a49be", + "id": "8965f552", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:58.976257Z", - "iopub.status.busy": "2022-12-15T16:09:58.976058Z", - "iopub.status.idle": "2022-12-15T16:09:58.979272Z", - "shell.execute_reply": "2022-12-15T16:09:58.978648Z" + "iopub.execute_input": "2022-12-20T11:40:17.770562Z", + "iopub.status.busy": "2022-12-20T11:40:17.770360Z", + "iopub.status.idle": "2022-12-20T11:40:17.773835Z", + "shell.execute_reply": "2022-12-20T11:40:17.773236Z" } }, "outputs": [], @@ -134,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "c0cfeabf", + "id": "e8d79910", "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": "15930f02", + "id": "8130eaa1", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:58.981908Z", - "iopub.status.busy": "2022-12-15T16:09:58.981696Z", - "iopub.status.idle": "2022-12-15T16:09:58.984470Z", - "shell.execute_reply": "2022-12-15T16:09:58.983874Z" + "iopub.execute_input": "2022-12-20T11:40:17.776574Z", + "iopub.status.busy": "2022-12-20T11:40:17.776377Z", + "iopub.status.idle": "2022-12-20T11:40:17.779142Z", + "shell.execute_reply": "2022-12-20T11:40:17.778563Z" } }, "outputs": [], @@ -160,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "4fe1f15d", + "id": "fd4cb72d", "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": "8ec67ec0", + "id": "25ac86f8", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:58.987099Z", - "iopub.status.busy": "2022-12-15T16:09:58.986885Z", - "iopub.status.idle": "2022-12-15T16:09:58.989895Z", - "shell.execute_reply": "2022-12-15T16:09:58.989298Z" + "iopub.execute_input": "2022-12-20T11:40:17.781851Z", + "iopub.status.busy": "2022-12-20T11:40:17.781654Z", + "iopub.status.idle": "2022-12-20T11:40:17.784675Z", + "shell.execute_reply": "2022-12-20T11:40:17.784071Z" } }, "outputs": [], @@ -196,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "b8a64f73", + "id": "a7a507ca", "metadata": {}, "source": [ "by adding a list of edges," @@ -205,13 +205,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "d8138ac4", + "id": "3551b660", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:58.992728Z", - "iopub.status.busy": "2022-12-15T16:09:58.992526Z", - "iopub.status.idle": "2022-12-15T16:09:58.995489Z", - "shell.execute_reply": "2022-12-15T16:09:58.994833Z" + "iopub.execute_input": "2022-12-20T11:40:17.787358Z", + "iopub.status.busy": "2022-12-20T11:40:17.787156Z", + "iopub.status.idle": "2022-12-20T11:40:17.790061Z", + "shell.execute_reply": "2022-12-20T11:40:17.789451Z" } }, "outputs": [], @@ -221,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "b078e838", + "id": "dbeb878d", "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": "fa225e53", + "id": "bb0bed7b", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:58.998184Z", - "iopub.status.busy": "2022-12-15T16:09:58.997979Z", - "iopub.status.idle": "2022-12-15T16:09:59.000786Z", - "shell.execute_reply": "2022-12-15T16:09:59.000186Z" + "iopub.execute_input": "2022-12-20T11:40:17.792886Z", + "iopub.status.busy": "2022-12-20T11:40:17.792679Z", + "iopub.status.idle": "2022-12-20T11:40:17.795507Z", + "shell.execute_reply": "2022-12-20T11:40:17.794935Z" } }, "outputs": [], @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "7d1b3654", + "id": "c978377d", "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": "947e7828", + "id": "118590f5", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.003559Z", - "iopub.status.busy": "2022-12-15T16:09:59.003352Z", - "iopub.status.idle": "2022-12-15T16:09:59.006076Z", - "shell.execute_reply": "2022-12-15T16:09:59.005471Z" + "iopub.execute_input": "2022-12-20T11:40:17.798307Z", + "iopub.status.busy": "2022-12-20T11:40:17.798102Z", + "iopub.status.idle": "2022-12-20T11:40:17.800850Z", + "shell.execute_reply": "2022-12-20T11:40:17.800254Z" } }, "outputs": [], @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "34cc62da", + "id": "8128daf6", "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": "1abaca76", + "id": "29d285b0", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.008868Z", - "iopub.status.busy": "2022-12-15T16:09:59.008660Z", - "iopub.status.idle": "2022-12-15T16:09:59.012320Z", - "shell.execute_reply": "2022-12-15T16:09:59.011712Z" + "iopub.execute_input": "2022-12-20T11:40:17.803651Z", + "iopub.status.busy": "2022-12-20T11:40:17.803443Z", + "iopub.status.idle": "2022-12-20T11:40:17.807088Z", + "shell.execute_reply": "2022-12-20T11:40:17.806503Z" } }, "outputs": [], @@ -307,7 +307,7 @@ }, { "cell_type": "markdown", - "id": "9c8b3551", + "id": "73405a2b", "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": "8d945779", + "id": "7930b9ae", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.014910Z", - "iopub.status.busy": "2022-12-15T16:09:59.014700Z", - "iopub.status.idle": "2022-12-15T16:09:59.020831Z", - "shell.execute_reply": "2022-12-15T16:09:59.020228Z" + "iopub.execute_input": "2022-12-20T11:40:17.809715Z", + "iopub.status.busy": "2022-12-20T11:40:17.809512Z", + "iopub.status.idle": "2022-12-20T11:40:17.815660Z", + "shell.execute_reply": "2022-12-20T11:40:17.815071Z" } }, "outputs": [ @@ -345,13 +345,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "8106e641", + "id": "0bf4652e", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.024547Z", - "iopub.status.busy": "2022-12-15T16:09:59.024344Z", - "iopub.status.idle": "2022-12-15T16:09:59.028517Z", - "shell.execute_reply": "2022-12-15T16:09:59.027910Z" + "iopub.execute_input": "2022-12-20T11:40:17.819620Z", + "iopub.status.busy": "2022-12-20T11:40:17.819416Z", + "iopub.status.idle": "2022-12-20T11:40:17.823451Z", + "shell.execute_reply": "2022-12-20T11:40:17.822858Z" } }, "outputs": [], @@ -367,7 +367,7 @@ }, { "cell_type": "markdown", - "id": "7776ebf0", + "id": "e46e1f97", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -387,13 +387,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "f1db6fe4", + "id": "e8fc834c", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.031217Z", - "iopub.status.busy": "2022-12-15T16:09:59.030997Z", - "iopub.status.idle": "2022-12-15T16:09:59.035317Z", - "shell.execute_reply": "2022-12-15T16:09:59.034716Z" + "iopub.execute_input": "2022-12-20T11:40:17.826229Z", + "iopub.status.busy": "2022-12-20T11:40:17.826031Z", + "iopub.status.idle": "2022-12-20T11:40:17.830373Z", + "shell.execute_reply": "2022-12-20T11:40:17.829790Z" } }, "outputs": [ @@ -417,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "326966d8", + "id": "2fae0e4a", "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": "a887bd22", + "id": "e1508072", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.038895Z", - "iopub.status.busy": "2022-12-15T16:09:59.038690Z", - "iopub.status.idle": "2022-12-15T16:09:59.042738Z", - "shell.execute_reply": "2022-12-15T16:09:59.042151Z" + "iopub.execute_input": "2022-12-20T11:40:17.834022Z", + "iopub.status.busy": "2022-12-20T11:40:17.833816Z", + "iopub.status.idle": "2022-12-20T11:40:17.837812Z", + "shell.execute_reply": "2022-12-20T11:40:17.837222Z" } }, "outputs": [ @@ -457,7 +457,7 @@ }, { "cell_type": "markdown", - "id": "0e95153e", + "id": "aa2f21b1", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -474,13 +474,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "93137ba0", + "id": "29a3c316", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.046004Z", - "iopub.status.busy": "2022-12-15T16:09:59.045797Z", - "iopub.status.idle": "2022-12-15T16:09:59.048909Z", - "shell.execute_reply": "2022-12-15T16:09:59.048305Z" + "iopub.execute_input": "2022-12-20T11:40:17.841056Z", + "iopub.status.busy": "2022-12-20T11:40:17.840862Z", + "iopub.status.idle": "2022-12-20T11:40:17.843931Z", + "shell.execute_reply": "2022-12-20T11:40:17.843344Z" } }, "outputs": [], @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "6bece1a1", + "id": "bffa3a12", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -508,13 +508,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "34430c44", + "id": "a6204886", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.051519Z", - "iopub.status.busy": "2022-12-15T16:09:59.051317Z", - "iopub.status.idle": "2022-12-15T16:09:59.299859Z", - "shell.execute_reply": "2022-12-15T16:09:59.299260Z" + "iopub.execute_input": "2022-12-20T11:40:17.846646Z", + "iopub.status.busy": "2022-12-20T11:40:17.846439Z", + "iopub.status.idle": "2022-12-20T11:40:18.094746Z", + "shell.execute_reply": "2022-12-20T11:40:18.094090Z" } }, "outputs": [ @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "631ff23d", + "id": "51d764d6", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -572,13 +572,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "99a5b8f8", + "id": "45c6451a", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.303483Z", - "iopub.status.busy": "2022-12-15T16:09:59.303001Z", - "iopub.status.idle": "2022-12-15T16:09:59.310194Z", - "shell.execute_reply": "2022-12-15T16:09:59.309623Z" + "iopub.execute_input": "2022-12-20T11:40:18.097862Z", + "iopub.status.busy": "2022-12-20T11:40:18.097544Z", + "iopub.status.idle": "2022-12-20T11:40:18.102894Z", + "shell.execute_reply": "2022-12-20T11:40:18.102295Z" } }, "outputs": [ @@ -602,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "d8c15355", + "id": "7d632e8f", "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": "f6c0f87c", + "id": "dc2f2e19", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.313133Z", - "iopub.status.busy": "2022-12-15T16:09:59.312602Z", - "iopub.status.idle": "2022-12-15T16:09:59.319403Z", - "shell.execute_reply": "2022-12-15T16:09:59.318849Z" + "iopub.execute_input": "2022-12-20T11:40:18.105640Z", + "iopub.status.busy": "2022-12-20T11:40:18.105435Z", + "iopub.status.idle": "2022-12-20T11:40:18.109850Z", + "shell.execute_reply": "2022-12-20T11:40:18.109251Z" } }, "outputs": [ @@ -642,7 +642,7 @@ }, { "cell_type": "markdown", - "id": "3a5e2995", + "id": "963c5720", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -653,13 +653,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "030a4a4d", + "id": "d902e4cd", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.323339Z", - "iopub.status.busy": "2022-12-15T16:09:59.322123Z", - "iopub.status.idle": "2022-12-15T16:09:59.328488Z", - "shell.execute_reply": "2022-12-15T16:09:59.327934Z" + "iopub.execute_input": "2022-12-20T11:40:18.113355Z", + "iopub.status.busy": "2022-12-20T11:40:18.113139Z", + "iopub.status.idle": "2022-12-20T11:40:18.117783Z", + "shell.execute_reply": "2022-12-20T11:40:18.117152Z" } }, "outputs": [ @@ -685,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "a8a9a94a", + "id": "c301ade8", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -694,13 +694,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "55f2a3e8", + "id": "77b61e77", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.332429Z", - "iopub.status.busy": "2022-12-15T16:09:59.331244Z", - "iopub.status.idle": "2022-12-15T16:09:59.336688Z", - "shell.execute_reply": "2022-12-15T16:09:59.336101Z" + "iopub.execute_input": "2022-12-20T11:40:18.121210Z", + "iopub.status.busy": "2022-12-20T11:40:18.121003Z", + "iopub.status.idle": "2022-12-20T11:40:18.124547Z", + "shell.execute_reply": "2022-12-20T11:40:18.123943Z" } }, "outputs": [ @@ -721,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "967fe442", + "id": "90b14046", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -743,13 +743,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "35759e03", + "id": "088afb6c", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.339815Z", - "iopub.status.busy": "2022-12-15T16:09:59.339366Z", - "iopub.status.idle": "2022-12-15T16:09:59.345359Z", - "shell.execute_reply": "2022-12-15T16:09:59.344799Z" + "iopub.execute_input": "2022-12-20T11:40:18.128075Z", + "iopub.status.busy": "2022-12-20T11:40:18.127875Z", + "iopub.status.idle": "2022-12-20T11:40:18.131728Z", + "shell.execute_reply": "2022-12-20T11:40:18.131159Z" } }, "outputs": [ @@ -771,7 +771,7 @@ }, { "cell_type": "markdown", - "id": "536cb1e0", + "id": "16b697e9", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -780,13 +780,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "cee37c27", + "id": "67ab7cdb", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.347997Z", - "iopub.status.busy": "2022-12-15T16:09:59.347787Z", - "iopub.status.idle": "2022-12-15T16:09:59.353597Z", - "shell.execute_reply": "2022-12-15T16:09:59.353057Z" + "iopub.execute_input": "2022-12-20T11:40:18.135036Z", + "iopub.status.busy": "2022-12-20T11:40:18.134827Z", + "iopub.status.idle": "2022-12-20T11:40:18.138669Z", + "shell.execute_reply": "2022-12-20T11:40:18.138091Z" } }, "outputs": [ @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "e5859bc2", + "id": "7bdc4675", "metadata": {}, "source": [ "# Node attributes\n", @@ -819,13 +819,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "509abb93", + "id": "7adb48a4", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.356191Z", - "iopub.status.busy": "2022-12-15T16:09:59.355985Z", - "iopub.status.idle": "2022-12-15T16:09:59.362567Z", - "shell.execute_reply": "2022-12-15T16:09:59.362016Z" + "iopub.execute_input": "2022-12-20T11:40:18.141880Z", + "iopub.status.busy": "2022-12-20T11:40:18.141681Z", + "iopub.status.idle": "2022-12-20T11:40:18.146279Z", + "shell.execute_reply": "2022-12-20T11:40:18.145680Z" } }, "outputs": [ @@ -850,7 +850,7 @@ }, { "cell_type": "markdown", - "id": "c87af800", + "id": "72624cdc", "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": "c669880f", + "id": "5e6a24ae", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.365306Z", - "iopub.status.busy": "2022-12-15T16:09:59.365110Z", - "iopub.status.idle": "2022-12-15T16:09:59.369068Z", - "shell.execute_reply": "2022-12-15T16:09:59.368445Z" + "iopub.execute_input": "2022-12-20T11:40:18.149692Z", + "iopub.status.busy": "2022-12-20T11:40:18.149491Z", + "iopub.status.idle": "2022-12-20T11:40:18.153420Z", + "shell.execute_reply": "2022-12-20T11:40:18.152800Z" } }, "outputs": [], @@ -885,7 +885,7 @@ }, { "cell_type": "markdown", - "id": "cdd119f1", + "id": "279603ae", "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": "188a0581", + "id": "fb770685", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.371856Z", - "iopub.status.busy": "2022-12-15T16:09:59.371657Z", - "iopub.status.idle": "2022-12-15T16:09:59.376683Z", - "shell.execute_reply": "2022-12-15T16:09:59.376082Z" + "iopub.execute_input": "2022-12-20T11:40:18.156229Z", + "iopub.status.busy": "2022-12-20T11:40:18.156010Z", + "iopub.status.idle": "2022-12-20T11:40:18.161034Z", + "shell.execute_reply": "2022-12-20T11:40:18.160436Z" } }, "outputs": [ @@ -938,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "67ef6a71", + "id": "31452e73", "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": "bef2a5dd", + "id": "6ad4b4f4", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.380045Z", - "iopub.status.busy": "2022-12-15T16:09:59.379848Z", - "iopub.status.idle": "2022-12-15T16:09:59.382711Z", - "shell.execute_reply": "2022-12-15T16:09:59.382115Z" + "iopub.execute_input": "2022-12-20T11:40:18.164497Z", + "iopub.status.busy": "2022-12-20T11:40:18.164295Z", + "iopub.status.idle": "2022-12-20T11:40:18.167195Z", + "shell.execute_reply": "2022-12-20T11:40:18.166598Z" } }, "outputs": [], @@ -967,7 +967,7 @@ }, { "cell_type": "markdown", - "id": "32f625b5", + "id": "0745992c", "metadata": {}, "source": [ "# Multigraphs\n", @@ -987,13 +987,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "9afbc429", + "id": "cda91c67", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.385450Z", - "iopub.status.busy": "2022-12-15T16:09:59.385250Z", - "iopub.status.idle": "2022-12-15T16:09:59.391730Z", - "shell.execute_reply": "2022-12-15T16:09:59.391153Z" + "iopub.execute_input": "2022-12-20T11:40:18.169999Z", + "iopub.status.busy": "2022-12-20T11:40:18.169794Z", + "iopub.status.idle": "2022-12-20T11:40:18.175983Z", + "shell.execute_reply": "2022-12-20T11:40:18.175395Z" } }, "outputs": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "markdown", - "id": "5f3fb92c", + "id": "298eb127", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -1043,13 +1043,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "dfbfbccd", + "id": "74d76d3e", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.394310Z", - "iopub.status.busy": "2022-12-15T16:09:59.394108Z", - "iopub.status.idle": "2022-12-15T16:09:59.398324Z", - "shell.execute_reply": "2022-12-15T16:09:59.397712Z" + "iopub.execute_input": "2022-12-20T11:40:18.179345Z", + "iopub.status.busy": "2022-12-20T11:40:18.179135Z", + "iopub.status.idle": "2022-12-20T11:40:18.183262Z", + "shell.execute_reply": "2022-12-20T11:40:18.182658Z" } }, "outputs": [], @@ -1062,7 +1062,7 @@ }, { "cell_type": "markdown", - "id": "ff571c7d", + "id": "945ac4c7", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -1073,13 +1073,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "dbd5ef2a", + "id": "93f74903", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.400914Z", - "iopub.status.busy": "2022-12-15T16:09:59.400704Z", - "iopub.status.idle": "2022-12-15T16:09:59.415729Z", - "shell.execute_reply": "2022-12-15T16:09:59.415171Z" + "iopub.execute_input": "2022-12-20T11:40:18.185934Z", + "iopub.status.busy": "2022-12-20T11:40:18.185721Z", + "iopub.status.idle": "2022-12-20T11:40:18.242120Z", + "shell.execute_reply": "2022-12-20T11:40:18.241464Z" } }, "outputs": [], @@ -1092,7 +1092,7 @@ }, { "cell_type": "markdown", - "id": "b8546121", + "id": "f12a0272", "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": "618f48a4", + "id": "54196e80", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.418492Z", - "iopub.status.busy": "2022-12-15T16:09:59.418290Z", - "iopub.status.idle": "2022-12-15T16:09:59.850088Z", - "shell.execute_reply": "2022-12-15T16:09:59.849443Z" + "iopub.execute_input": "2022-12-20T11:40:18.245207Z", + "iopub.status.busy": "2022-12-20T11:40:18.244981Z", + "iopub.status.idle": "2022-12-20T11:40:19.036083Z", + "shell.execute_reply": "2022-12-20T11:40:19.035455Z" } }, "outputs": [], @@ -1121,7 +1121,7 @@ }, { "cell_type": "markdown", - "id": "cbf17408", + "id": "22b76e1e", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -1136,13 +1136,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "bab63117", + "id": "ff743238", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.853313Z", - "iopub.status.busy": "2022-12-15T16:09:59.853057Z", - "iopub.status.idle": "2022-12-15T16:09:59.861263Z", - "shell.execute_reply": "2022-12-15T16:09:59.860728Z" + "iopub.execute_input": "2022-12-20T11:40:19.039526Z", + "iopub.status.busy": "2022-12-20T11:40:19.039282Z", + "iopub.status.idle": "2022-12-20T11:40:19.045070Z", + "shell.execute_reply": "2022-12-20T11:40:19.044486Z" } }, "outputs": [ @@ -1168,7 +1168,7 @@ }, { "cell_type": "markdown", - "id": "76a41131", + "id": "a1540523", "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": "07fbc523", + "id": "bf51122c", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.864076Z", - "iopub.status.busy": "2022-12-15T16:09:59.863870Z", - "iopub.status.idle": "2022-12-15T16:09:59.868065Z", - "shell.execute_reply": "2022-12-15T16:09:59.867461Z" + "iopub.execute_input": "2022-12-20T11:40:19.049208Z", + "iopub.status.busy": "2022-12-20T11:40:19.049003Z", + "iopub.status.idle": "2022-12-20T11:40:19.053156Z", + "shell.execute_reply": "2022-12-20T11:40:19.052554Z" } }, "outputs": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "markdown", - "id": "3978385c", + "id": "7ad44534", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -1225,13 +1225,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "661afff3", + "id": "63d9498e", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:09:59.871575Z", - "iopub.status.busy": "2022-12-15T16:09:59.871373Z", - "iopub.status.idle": "2022-12-15T16:10:00.172789Z", - "shell.execute_reply": "2022-12-15T16:10:00.172148Z" + "iopub.execute_input": "2022-12-20T11:40:19.056612Z", + "iopub.status.busy": "2022-12-20T11:40:19.056397Z", + "iopub.status.idle": "2022-12-20T11:40:19.404203Z", + "shell.execute_reply": "2022-12-20T11:40:19.403564Z" } }, "outputs": [], @@ -1241,7 +1241,7 @@ }, { "cell_type": "markdown", - "id": "d3b9e423", + "id": "39c9d7a5", "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": "d33340f6", + "id": "c1a1256b", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:10:00.176199Z", - "iopub.status.busy": "2022-12-15T16:10:00.175872Z", - "iopub.status.idle": "2022-12-15T16:10:00.412388Z", - "shell.execute_reply": "2022-12-15T16:10:00.411845Z" + "iopub.execute_input": "2022-12-20T11:40:19.407789Z", + "iopub.status.busy": "2022-12-20T11:40:19.407450Z", + "iopub.status.idle": "2022-12-20T11:40:19.601936Z", + "shell.execute_reply": "2022-12-20T11:40:19.601394Z" } }, "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": "449ba321", + "id": "159f63cd", "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": "60189d8d", + "id": "9e94fc92", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:10:00.415429Z", - "iopub.status.busy": "2022-12-15T16:10:00.415080Z", - "iopub.status.idle": "2022-12-15T16:10:00.417827Z", - "shell.execute_reply": "2022-12-15T16:10:00.417352Z" + "iopub.execute_input": "2022-12-20T11:40:19.605138Z", + "iopub.status.busy": "2022-12-20T11:40:19.604927Z", + "iopub.status.idle": "2022-12-20T11:40:19.607919Z", + "shell.execute_reply": "2022-12-20T11:40:19.607453Z" } }, "outputs": [], @@ -1308,7 +1308,7 @@ }, { "cell_type": "markdown", - "id": "d967d169", + "id": "9905d091", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -1317,19 +1317,19 @@ { "cell_type": "code", "execution_count": 36, - "id": "f4cc346e", + "id": "9d4bb899", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:10:00.420457Z", - "iopub.status.busy": "2022-12-15T16:10:00.420147Z", - "iopub.status.idle": "2022-12-15T16:10:00.682477Z", - "shell.execute_reply": "2022-12-15T16:10:00.681840Z" + "iopub.execute_input": "2022-12-20T11:40:19.610312Z", + "iopub.status.busy": "2022-12-20T11:40:19.610095Z", + "iopub.status.idle": "2022-12-20T11:40:19.878445Z", + "shell.execute_reply": "2022-12-20T11:40:19.877790Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 4 Axes>" ] @@ -1356,7 +1356,7 @@ }, { "cell_type": "markdown", - "id": "4c682dc4", + "id": "805e8019", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -1367,13 +1367,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "50b65c1a", + "id": "572d2502", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:10:00.685937Z", - "iopub.status.busy": "2022-12-15T16:10:00.685600Z", - "iopub.status.idle": "2022-12-15T16:10:00.789968Z", - "shell.execute_reply": "2022-12-15T16:10:00.789322Z" + "iopub.execute_input": "2022-12-20T11:40:19.881517Z", + "iopub.status.busy": "2022-12-20T11:40:19.881269Z", + "iopub.status.idle": "2022-12-20T11:40:19.984218Z", + "shell.execute_reply": "2022-12-20T11:40:19.983630Z" } }, "outputs": [ @@ -1396,7 +1396,7 @@ }, { "cell_type": "markdown", - "id": "657979bb", + "id": "ddedda46", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -1405,19 +1405,19 @@ { "cell_type": "code", "execution_count": 38, - "id": "0bf2381c", + "id": "11cfb1cb", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:10:00.793315Z", - "iopub.status.busy": "2022-12-15T16:10:00.792900Z", - "iopub.status.idle": "2022-12-15T16:10:00.923470Z", - "shell.execute_reply": "2022-12-15T16:10:00.922798Z" + "iopub.execute_input": "2022-12-20T11:40:19.987088Z", + "iopub.status.busy": "2022-12-20T11:40:19.986863Z", + "iopub.status.idle": "2022-12-20T11:40:20.117930Z", + "shell.execute_reply": "2022-12-20T11:40:20.117298Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -1433,7 +1433,7 @@ }, { "cell_type": "markdown", - "id": "0745862f", + "id": "364d1dfc", "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": "cd4aad41", + "id": "50b2da8a", "metadata": { "execution": { - "iopub.execute_input": "2022-12-15T16:10:00.927106Z", - "iopub.status.busy": "2022-12-15T16:10:00.926739Z", - "iopub.status.idle": "2022-12-15T16:10:01.060822Z", - "shell.execute_reply": "2022-12-15T16:10:01.060165Z" + "iopub.execute_input": "2022-12-20T11:40:20.120937Z", + "iopub.status.busy": "2022-12-20T11:40:20.120709Z", + "iopub.status.idle": "2022-12-20T11:40:20.255796Z", + "shell.execute_reply": "2022-12-20T11:40:20.255203Z" } }, "outputs": [ @@ -1476,7 +1476,7 @@ }, { "cell_type": "markdown", - "id": "30bc0300", + "id": "3e79a049", "metadata": {}, "source": [ "See Drawing for additional details." |
