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| author | jarrodmillman <jarrod.millman@gmail.com> | 2023-01-04 19:41:37 +0000 |
|---|---|---|
| committer | jarrodmillman <jarrod.millman@gmail.com> | 2023-01-04 19:41:37 +0000 |
| commit | 5aa1fdf768771b57f057c25a320c034cb59644ca (patch) | |
| tree | 327d79a6fed4ccc98525c02939f2f96e7ed0d6e0 | |
| parent | 9d17431d8d530159a2c9176b54ae9717f0152907 (diff) | |
| download | networkx-5aa1fdf768771b57f057c25a320c034cb59644ca.tar.gz | |
Deploying to gh-pages from @ networkx/networkx@a7d50d2c40be1261ecd7372c08ce6ec0a0106c55 🚀
109 files changed, 594 insertions, 596 deletions
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--- a/_modules/networkx/classes/digraph.html +++ b/_modules/networkx/classes/digraph.html @@ -949,7 +949,7 @@ <span class="sd"> add_node</span> <span class="sd"> Notes</span> -<span class="sd"> -------</span> +<span class="sd"> -----</span> <span class="sd"> When adding nodes from an iterator over the graph you are changing,</span> <span class="sd"> a `RuntimeError` can be raised with message:</span> <span class="sd"> `RuntimeError: dictionary changed size during iteration`. This</span> @@ -1063,7 +1063,7 @@ <span class="sd"> remove_node</span> <span class="sd"> Notes</span> -<span class="sd"> -------</span> +<span class="sd"> -----</span> <span class="sd"> When removing nodes from an iterator over the graph you are changing,</span> <span class="sd"> a `RuntimeError` will be raised with message:</span> <span class="sd"> `RuntimeError: dictionary changed size during iteration`. This</span> diff --git a/_modules/networkx/classes/graph.html b/_modules/networkx/classes/graph.html index 84a65957..1f4ff6c6 100644 --- a/_modules/networkx/classes/graph.html +++ b/_modules/networkx/classes/graph.html @@ -1043,7 +1043,7 @@ <span class="sd"> add_node</span> <span class="sd"> Notes</span> -<span class="sd"> -------</span> +<span class="sd"> -----</span> <span class="sd"> When adding nodes from an iterator over the graph you are changing,</span> <span class="sd"> a `RuntimeError` can be raised with message:</span> <span class="sd"> `RuntimeError: dictionary changed size during iteration`. This</span> @@ -1155,7 +1155,7 @@ <span class="sd"> remove_node</span> <span class="sd"> Notes</span> -<span class="sd"> -------</span> +<span class="sd"> -----</span> <span class="sd"> When removing nodes from an iterator over the graph you are changing,</span> <span class="sd"> a `RuntimeError` will be raised with message:</span> <span class="sd"> `RuntimeError: dictionary changed size during iteration`. This</span> diff --git a/auto_examples/3d_drawing/plot_basic.html b/auto_examples/3d_drawing/plot_basic.html index c5e9834c..061456ac 100644 --- a/auto_examples/3d_drawing/plot_basic.html +++ b/auto_examples/3d_drawing/plot_basic.html @@ -540,7 +540,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.090 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.078 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-3d-drawing-plot-basic-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/79beefddd68fa45123e60db5559f52aa/plot_basic.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_basic.py</span></code></a></p> diff --git a/auto_examples/3d_drawing/sg_execution_times.html b/auto_examples/3d_drawing/sg_execution_times.html index a19c07a3..ef451a34 100644 --- a/auto_examples/3d_drawing/sg_execution_times.html +++ b/auto_examples/3d_drawing/sg_execution_times.html @@ -463,11 +463,11 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-3d-drawing-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.090</strong> total execution time for <strong>auto_examples_3d_drawing</strong> files:</p> +<p><strong>00:00.078</strong> total execution time for <strong>auto_examples_3d_drawing</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_basic.html#sphx-glr-auto-examples-3d-drawing-plot-basic-py"><span class="std std-ref">Basic matplotlib</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_basic.py</span></code>)</p></td> -<td><p>00:00.090</p></td> +<td><p>00:00.078</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="mayavi2_spring.html#sphx-glr-auto-examples-3d-drawing-mayavi2-spring-py"><span class="std std-ref">Mayavi2</span></a> (<code class="docutils literal notranslate"><span class="pre">mayavi2_spring.py</span></code>)</p></td> diff --git a/auto_examples/algorithms/plot_beam_search.html b/auto_examples/algorithms/plot_beam_search.html index 4b5b42b1..d8f2a7f2 100644 --- a/auto_examples/algorithms/plot_beam_search.html +++ b/auto_examples/algorithms/plot_beam_search.html @@ -612,7 +612,7 @@ the progressive widening search in order to find a node of high centrality.</p> <img src="../../_images/sphx_glr_plot_beam_search_001.png" srcset="../../_images/sphx_glr_plot_beam_search_001.png" alt="plot beam search" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>found node 73 with centrality 0.12598283530728402 </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.250 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.210 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-beam-search-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/ccbccb63fd600240faf98d07876c0e92/plot_beam_search.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_beam_search.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_betweenness_centrality.html b/auto_examples/algorithms/plot_betweenness_centrality.html index b397270d..3949a764 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 4.096 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.667 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 7f577dbb..42fbb098 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 0.410 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.360 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 5d30436b..58b278a2 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 0.122 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.105 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-circuits-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/bd2ce07c5ba253eb7b45764c94237a4c/plot_circuits.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_circuits.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_davis_club.html b/auto_examples/algorithms/plot_davis_club.html index 0193d4d6..e106138b 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 0.083 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.072 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 35509c1c..7f82feea 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 0.277 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.242 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-dedensification-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/868e28431bab2565b22bfbab847e1153/plot_dedensification.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_dedensification.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_iterated_dynamical_systems.html b/auto_examples/algorithms/plot_iterated_dynamical_systems.html index 54be3ab3..aeccead0 100644 --- a/auto_examples/algorithms/plot_iterated_dynamical_systems.html +++ b/auto_examples/algorithms/plot_iterated_dynamical_systems.html @@ -699,7 +699,7 @@ fixed points are [] <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"fixed points are </span><span class="si">{</span><span class="n">fixed_points</span><span class="p">(</span><span class="n">G</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.108 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-algorithms-plot-iterated-dynamical-systems-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/d947686c24b50c278c1228ff766cda27/plot_iterated_dynamical_systems.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_iterated_dynamical_systems.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_krackhardt_centrality.html b/auto_examples/algorithms/plot_krackhardt_centrality.html index 2c83cd35..a22aab5d 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 0.069 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.060 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-krackhardt-centrality-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/e77acafa90a347f4353549d3bffbb72c/plot_krackhardt_centrality.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_krackhardt_centrality.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_parallel_betweenness.html b/auto_examples/algorithms/plot_parallel_betweenness.html index 2c5312a8..0f14cdf1 100644 --- a/auto_examples/algorithms/plot_parallel_betweenness.html +++ b/auto_examples/algorithms/plot_parallel_betweenness.html @@ -517,29 +517,29 @@ faster. This is a limitation of our CI/CD pipeline running on a single core.</p> <img src="../../_images/sphx_glr_plot_parallel_betweenness_001.png" srcset="../../_images/sphx_glr_plot_parallel_betweenness_001.png" alt="plot parallel betweenness" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Computing betweenness centrality for: Graph with 1000 nodes and 2991 edges Parallel version - Time: 2.1512 seconds - Betweenness centrality for node 0: 0.07223 + Time: 1.8304 seconds + Betweenness centrality for node 0: 0.07023 Non-Parallel version - Time: 3.4002 seconds - Betweenness centrality for node 0: 0.07223 + Time: 2.9404 seconds + Betweenness centrality for node 0: 0.07023 Computing betweenness centrality for: -Graph with 1000 nodes and 5070 edges +Graph with 1000 nodes and 4915 edges Parallel version - Time: 2.6347 seconds - Betweenness centrality for node 0: 0.00053 + Time: 2.2285 seconds + Betweenness centrality for node 0: 0.00264 Non-Parallel version - Time: 4.4795 seconds - Betweenness centrality for node 0: 0.00053 + Time: 3.8838 seconds + Betweenness centrality for node 0: 0.00264 Computing betweenness centrality for: Graph with 1000 nodes and 2000 edges Parallel version - Time: 1.7567 seconds - Betweenness centrality for node 0: 0.00362 + Time: 1.5142 seconds + Betweenness centrality for node 0: 0.01260 Non-Parallel version - Time: 3.0709 seconds - Betweenness centrality for node 0: 0.00362 + Time: 2.6764 seconds + Betweenness centrality for node 0: 0.01260 </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 23.868 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 20.608 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 620641e4..6e558f7f 100644 --- a/auto_examples/algorithms/plot_rcm.html +++ b/auto_examples/algorithms/plot_rcm.html @@ -615,7 +615,7 @@ bandwidth: 7 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.270 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.065 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 2d533fe6..2c03f924 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 0.199 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.174 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-algorithms-plot-snap-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/0a756ab7ea4b899fa151e327a4dce8d2/plot_snap.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_snap.py</span></code></a></p> diff --git a/auto_examples/algorithms/plot_subgraphs.html b/auto_examples/algorithms/plot_subgraphs.html index 65c88137..6c4ce644 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 0.793 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 94b2659a..2414b620 100644 --- a/auto_examples/algorithms/sg_execution_times.html +++ b/auto_examples/algorithms/sg_execution_times.html @@ -463,55 +463,55 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-algorithms-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:31.544</strong> total execution time for <strong>auto_examples_algorithms</strong> files:</p> +<p><strong>00:27.311</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>00:23.868</p></td> +<td><p>00:20.608</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>00:04.096</p></td> +<td><p>00:03.667</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_rcm.html#sphx-glr-auto-examples-algorithms-plot-rcm-py"><span class="std std-ref">Reverse Cuthill–McKee</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_rcm.py</span></code>)</p></td> -<td><p>00:01.270</p></td> +<td><p>00:01.065</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>00:00.793</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>00:00.410</p></td> +<td><p>00:00.360</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>00:00.277</p></td> +<td><p>00:00.242</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_beam_search.html#sphx-glr-auto-examples-algorithms-plot-beam-search-py"><span class="std std-ref">Beam Search</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_beam_search.py</span></code>)</p></td> -<td><p>00:00.250</p></td> +<td><p>00:00.210</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_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>00:00.199</p></td> +<td><p>00:00.174</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_circuits.html#sphx-glr-auto-examples-algorithms-plot-circuits-py"><span class="std std-ref">Circuits</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_circuits.py</span></code>)</p></td> -<td><p>00:00.122</p></td> +<td><p>00:00.105</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_iterated_dynamical_systems.html#sphx-glr-auto-examples-algorithms-plot-iterated-dynamical-systems-py"><span class="std std-ref">Iterated Dynamical Systems</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_iterated_dynamical_systems.py</span></code>)</p></td> -<td><p>00:00.108</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_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>00:00.083</p></td> +<td><p>00:00.072</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>00:00.069</p></td> +<td><p>00:00.060</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/basic/plot_properties.html b/auto_examples/basic/plot_properties.html index 517753d1..59a7438d 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 0.105 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.089 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 a9be9b8c..4c89e004 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 0.071 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.062 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 85f42b9d..2b8ee136 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 0.400 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 0.377 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 e8508f89..af273b95 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>00:00.577</strong> total execution time for <strong>auto_examples_basic</strong> files:</p> +<p><strong>00:00.528</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>00:00.400</p></td> +<td><p>00:00.377</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_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>00:00.105</p></td> +<td><p>00:00.089</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>00:00.071</p></td> +<td><p>00:00.062</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/drawing/plot_center_node.html b/auto_examples/drawing/plot_center_node.html index 9cac3b3b..19a9dc50 100644 --- a/auto_examples/drawing/plot_center_node.html +++ b/auto_examples/drawing/plot_center_node.html @@ -530,7 +530,7 @@ to download the full example code</p> <span class="n">nx</span><span class="o">.</span><span class="n">draw</span><span class="p">(</span><span class="n">G</span><span class="p">,</span> <a href="https://docs.python.org/3/library/stdtypes.html#dict" title="builtins.dict" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">pos</span></a><span class="p">,</span> <span class="n">with_labels</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.077 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.066 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-center-node-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/8561539ed0b99621dbdbe53646ac5075/plot_center_node.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_center_node.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_chess_masters.html b/auto_examples/drawing/plot_chess_masters.html index 7f8698b9..c5622a3c 100644 --- a/auto_examples/drawing/plot_chess_masters.html +++ b/auto_examples/drawing/plot_chess_masters.html @@ -536,7 +536,7 @@ to black and contains selected game info.</p> <img src="../../_images/sphx_glr_plot_chess_masters_001.png" srcset="../../_images/sphx_glr_plot_chess_masters_001.png" alt="World Chess Championship Games: 1886 - 1985" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Loaded 685 chess games between 25 players Note the disconnected component consisting of: -['Karpov, Anatoly', 'Korchnoi, Viktor L', 'Kasparov, Gary'] +['Korchnoi, Viktor L', 'Karpov, Anatoly', 'Kasparov, Gary'] From a total of 237 different openings, the following games used the Sicilian opening @@ -702,7 +702,7 @@ findfont: Font family 'Helvetica' not found. <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.445 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.391 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 381d9125..cc453c1a 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 0.322 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.278 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 71a87735..ca0e7052 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 0.300 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.262 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 299a41d4..7cf18be1 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 0.251 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-directed-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/6c2f9c3544cb695b31867eecc0f7fb1e/plot_directed.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_directed.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_edge_colormap.html b/auto_examples/drawing/plot_edge_colormap.html index bfadf5f7..ae49d06d 100644 --- a/auto_examples/drawing/plot_edge_colormap.html +++ b/auto_examples/drawing/plot_edge_colormap.html @@ -534,7 +534,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.070 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.062 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-edge-colormap-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/7ea4dc8cf44604668540ed81d6abebda/plot_edge_colormap.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_edge_colormap.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_ego_graph.html b/auto_examples/drawing/plot_ego_graph.html index 7ffc13f2..373ab30b 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 0.117 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.097 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-ego-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/773fa56bdb128b8bd2a4f4a0e4dd38aa/plot_ego_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_ego_graph.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_eigenvalues.html b/auto_examples/drawing/plot_eigenvalues.html index 29a89eac..5d114003 100644 --- a/auto_examples/drawing/plot_eigenvalues.html +++ b/auto_examples/drawing/plot_eigenvalues.html @@ -541,7 +541,7 @@ Smallest eigenvalue: -2.5363890312656235e-16 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.747 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.654 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 12208f91..2791cbc4 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 0.385 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.326 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 c5956347..593bd91b 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 0.087 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.074 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-house-with-colors-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/98363b3c011ceaffb10684a5ba5de25b/plot_house_with_colors.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_house_with_colors.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_knuth_miles.html b/auto_examples/drawing/plot_knuth_miles.html index b7610fbf..c4d8cc74 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 0.116 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.099 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 b05e61a7..1495ac50 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 0.215 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.184 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-labels-and-colors-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/cff4f78bc18685caa50507ced57e7c6f/plot_labels_and_colors.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_labels_and_colors.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_multipartite_graph.html b/auto_examples/drawing/plot_multipartite_graph.html index 6141cf44..d87b0728 100644 --- a/auto_examples/drawing/plot_multipartite_graph.html +++ b/auto_examples/drawing/plot_multipartite_graph.html @@ -553,7 +553,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.088 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.077 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-multipartite-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/6cb4bf689cf53c849bce13cbab13eaec/plot_multipartite_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_multipartite_graph.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_node_colormap.html b/auto_examples/drawing/plot_node_colormap.html index 89262b51..b7f2592d 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 0.058 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.050 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 a2a305e0..13c816b8 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 0.148 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.128 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-rainbow-coloring-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/b64fd85d6e5ba509e65b2cb30a8274ed/plot_rainbow_coloring.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_rainbow_coloring.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_random_geometric_graph.html b/auto_examples/drawing/plot_random_geometric_graph.html index 74b92ad9..c6470c45 100644 --- a/auto_examples/drawing/plot_random_geometric_graph.html +++ b/auto_examples/drawing/plot_random_geometric_graph.html @@ -555,7 +555,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.121 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.104 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-random-geometric-graph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/f8f8cacecc651443537b92fc341fba08/plot_random_geometric_graph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_random_geometric_graph.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_sampson.html b/auto_examples/drawing/plot_sampson.html index b0beb9f0..a898fa4a 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 0.282 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.238 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 e9ac38bd..b6343948 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 0.093 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-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 ad8590e3..032ca9d7 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 0.066 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.057 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-simple-path-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/2c281c05b18d8d3cf43a312fc3d67a3b/plot_simple_path.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_simple_path.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_spectral_grid.html b/auto_examples/drawing/plot_spectral_grid.html index 49b2de1a..9c470593 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 0.268 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.223 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-spectral-grid-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/5479a9bd23bf1ace2ef03c13b4ac9d7f/plot_spectral_grid.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_spectral_grid.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_tsp.html b/auto_examples/drawing/plot_tsp.html index 2df99b52..9acee241 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 0.102 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.086 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-tsp-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/cc9848c15dd2eeae1872b955a8f34d15/plot_tsp.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_tsp.py</span></code></a></p> diff --git a/auto_examples/drawing/plot_unix_email.html b/auto_examples/drawing/plot_unix_email.html index 13c1497c..2b16ae17 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 0.121 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.114 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-drawing-plot-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 458922e6..81235216 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 0.095 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-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 f4c9efe6..9fc8e3cf 100644 --- a/auto_examples/drawing/sg_execution_times.html +++ b/auto_examples/drawing/sg_execution_times.html @@ -463,99 +463,99 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-drawing-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:04.573</strong> total execution time for <strong>auto_examples_drawing</strong> files:</p> +<p><strong>00:03.947</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>00:00.747</p></td> +<td><p>00:00.654</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>00:00.445</p></td> +<td><p>00:00.391</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>00:00.385</p></td> +<td><p>00:00.326</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>00:00.322</p></td> +<td><p>00:00.278</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>00:00.300</p></td> +<td><p>00:00.262</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>00:00.282</p></td> +<td><p>00:00.238</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>00:00.268</p></td> +<td><p>00:00.223</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_directed.html#sphx-glr-auto-examples-drawing-plot-directed-py"><span class="std std-ref">Directed Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_directed.py</span></code>)</p></td> -<td><p>00:00.251</p></td> +<td><p>00:00.213</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>00:00.215</p></td> +<td><p>00:00.184</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_rainbow_coloring.html#sphx-glr-auto-examples-drawing-plot-rainbow-coloring-py"><span class="std std-ref">Rainbow Coloring</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_rainbow_coloring.py</span></code>)</p></td> -<td><p>00:00.148</p></td> +<td><p>00:00.128</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_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>00:00.121</p></td> +<td><p>00:00.114</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>00:00.121</p></td> +<td><p>00:00.104</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_ego_graph.html#sphx-glr-auto-examples-drawing-plot-ego-graph-py"><span class="std std-ref">Ego Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_ego_graph.py</span></code>)</p></td> -<td><p>00:00.117</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_knuth_miles.html#sphx-glr-auto-examples-drawing-plot-knuth-miles-py"><span class="std std-ref">Knuth Miles</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_knuth_miles.py</span></code>)</p></td> +<td><p>00:00.099</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_knuth_miles.html#sphx-glr-auto-examples-drawing-plot-knuth-miles-py"><span class="std std-ref">Knuth Miles</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_knuth_miles.py</span></code>)</p></td> -<td><p>00:00.116</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_ego_graph.html#sphx-glr-auto-examples-drawing-plot-ego-graph-py"><span class="std std-ref">Ego Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_ego_graph.py</span></code>)</p></td> +<td><p>00:00.097</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_tsp.html#sphx-glr-auto-examples-drawing-plot-tsp-py"><span class="std std-ref">Traveling Salesman Problem</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_tsp.py</span></code>)</p></td> 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the script:</strong> ( 0 minutes 0.069 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.060 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-erdos-renyi-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/1dae7040b667b61c3253579b3b21fe83/plot_erdos_renyi.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_erdos_renyi.py</span></code></a></p> diff --git a/auto_examples/graph/plot_expected_degree_sequence.html b/auto_examples/graph/plot_expected_degree_sequence.html index e6556539..26f93912 100644 --- a/auto_examples/graph/plot_expected_degree_sequence.html +++ b/auto_examples/graph/plot_expected_degree_sequence.html @@ -536,52 +536,50 @@ degree (#nodes) **** 26 ( 0) 27 ( 0) 28 ( 0) -29 ( 0) +29 ( 1) * 30 ( 0) -31 ( 1) * +31 ( 0) 32 ( 0) 33 ( 0) -34 ( 2) ** -35 ( 0) -36 ( 4) **** -37 ( 4) **** -38 (12) ************ -39 ( 6) ****** -40 ( 9) ********* -41 (16) **************** -42 (20) ******************** -43 (20) ******************** -44 (19) ******************* -45 (22) ********************** +34 ( 1) * +35 ( 3) *** +36 ( 3) *** +37 ( 7) ******* +38 ( 6) ****** +39 ( 2) ** +40 ( 8) ******** +41 (14) ************** +42 (19) ******************* +43 (22) ********************** +44 (25) ************************* +45 (21) ********************* 46 (33) ********************************* -47 (21) ********************* -48 (33) ********************************* -49 (31) ******************************* +47 (12) ************ +48 (28) **************************** +49 (34) ********************************** 50 (31) ******************************* -51 (14) ************** -52 (23) *********************** -53 (21) ********************* -54 (26) ************************** -55 (24) ************************ -56 (26) ************************** -57 (14) ************** -58 (16) **************** -59 (12) ************ -60 (11) *********** -61 ( 8) ******** -62 ( 4) **** -63 ( 6) ****** -64 ( 5) ***** +51 (30) ****************************** +52 (21) ********************* +53 (22) ********************** +54 (20) ******************** +55 (18) ****************** +56 (24) ************************ +57 (19) ******************* +58 (14) ************** +59 (16) **************** +60 (14) ************** +61 (15) *************** +62 ( 3) *** +63 ( 5) ***** +64 ( 2) ** 65 ( 1) * 66 ( 1) * -67 ( 3) *** -68 ( 0) -69 ( 0) +67 ( 1) * +68 ( 2) ** +69 ( 1) * 70 ( 0) 71 ( 0) -72 ( 0) -73 ( 0) -74 ( 1) * +72 ( 1) * </pre></div> </div> <div class="line-block"> @@ -601,7 +599,7 @@ degree (#nodes) **** <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">i</span></a><span class="si">:</span><span class="s2">2</span><span class="si">}</span><span class="s2"> (</span><span class="si">{</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span></a><span class="si">:</span><span class="s2">2</span><span class="si">}</span><span class="s2">) </span><span class="si">{</span><span class="s1">'*'</span><span class="o">*</span><a href="https://docs.python.org/3/library/functions.html#int" title="builtins.int" class="sphx-glr-backref-module-builtins sphx-glr-backref-type-py-class sphx-glr-backref-instance"><span class="n">d</span></a><span class="si">}</span><span class="s2">"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.036 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.031 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-expected-degree-sequence-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/7378087382f40e96e66bce4a35ba0e52/plot_expected_degree_sequence.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_expected_degree_sequence.py</span></code></a></p> diff --git a/auto_examples/graph/plot_football.html b/auto_examples/graph/plot_football.html index 3aedc39f..bb426807 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 0.435 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-graph-plot-football-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/ca0a30060f60faf520286faa348f4700/plot_football.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_football.py</span></code></a></p> diff --git a/auto_examples/graph/plot_karate_club.html b/auto_examples/graph/plot_karate_club.html index 8cbe205e..26fbb806 100644 --- a/auto_examples/graph/plot_karate_club.html +++ b/auto_examples/graph/plot_karate_club.html @@ -562,7 +562,7 @@ Journal of Anthropological Research, 33, 452-473.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.103 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.090 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-karate-club-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/373a1e407e4caee6fc7b7b46704a985c/plot_karate_club.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_karate_club.py</span></code></a></p> diff --git a/auto_examples/graph/plot_morse_trie.html b/auto_examples/graph/plot_morse_trie.html index 93d2f414..60d5de7c 100644 --- a/auto_examples/graph/plot_morse_trie.html +++ b/auto_examples/graph/plot_morse_trie.html @@ -602,7 +602,7 @@ the path.</p> <span class="nb">print</span><span class="p">(</span><span class="s2">" "</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="n">morse_encode</span><span class="p">(</span><span class="n">ltr</span><span class="p">)</span> <span class="k">for</span> <span class="n">ltr</span> <span class="ow">in</span> <span class="s2">"ilovenetworkx"</span><span class="p">]))</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.219 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.184 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-morse-trie-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/60379a4283563d425090aaae07ab115a/plot_morse_trie.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_morse_trie.py</span></code></a></p> diff --git a/auto_examples/graph/plot_napoleon_russian_campaign.html b/auto_examples/graph/plot_napoleon_russian_campaign.html index 1f2deb0a..55414d65 100644 --- a/auto_examples/graph/plot_napoleon_russian_campaign.html +++ b/auto_examples/graph/plot_napoleon_russian_campaign.html @@ -632,7 +632,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.147 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.126 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-napoleon-russian-campaign-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/87e75a2d09fb817a4616bb71aa44546f/plot_napoleon_russian_campaign.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_napoleon_russian_campaign.py</span></code></a></p> diff --git a/auto_examples/graph/plot_roget.html b/auto_examples/graph/plot_roget.html index f1148b07..986e6017 100644 --- a/auto_examples/graph/plot_roget.html +++ b/auto_examples/graph/plot_roget.html @@ -588,7 +588,7 @@ DiGraph with 1022 nodes and 5075 edges <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.268 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.228 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graph-plot-roget-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/118b3a0c87610e4910d74143c904d290/plot_roget.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_roget.py</span></code></a></p> diff --git a/auto_examples/graph/plot_triad_types.html b/auto_examples/graph/plot_triad_types.html index fc1519fe..1421c03f 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 1.223 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 1.057 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 336e5df3..d04e7f34 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 0.443 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-graph-plot-words-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/e6a489a8b2deb49ed237fac38a28f429/plot_words.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_words.py</span></code></a></p> diff --git a/auto_examples/graph/sg_execution_times.html b/auto_examples/graph/sg_execution_times.html index 659f54fb..c473af6f 100644 --- a/auto_examples/graph/sg_execution_times.html +++ b/auto_examples/graph/sg_execution_times.html @@ -463,51 +463,51 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-graph-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:03.143</strong> total execution time for <strong>auto_examples_graph</strong> files:</p> +<p><strong>00:02.593</strong> total execution time for <strong>auto_examples_graph</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_triad_types.html#sphx-glr-auto-examples-graph-plot-triad-types-py"><span class="std std-ref">Triads</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_triad_types.py</span></code>)</p></td> -<td><p>00:01.223</p></td> +<td><p>00:01.057</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_words.html#sphx-glr-auto-examples-graph-plot-words-py"><span class="std std-ref">Words/Ladder Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_words.py</span></code>)</p></td> -<td><p>00:00.443</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_football.html#sphx-glr-auto-examples-graph-plot-football-py"><span class="std std-ref">Football</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_football.py</span></code>)</p></td> -<td><p>00:00.435</p></td> +<td><p>00:00.266</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_roget.html#sphx-glr-auto-examples-graph-plot-roget-py"><span class="std std-ref">Roget</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_roget.py</span></code>)</p></td> -<td><p>00:00.268</p></td> +<td><p>00:00.228</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_morse_trie.html#sphx-glr-auto-examples-graph-plot-morse-trie-py"><span class="std std-ref">Morse Trie</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_morse_trie.py</span></code>)</p></td> -<td><p>00:00.219</p></td> +<td><p>00:00.184</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_napoleon_russian_campaign.html#sphx-glr-auto-examples-graph-plot-napoleon-russian-campaign-py"><span class="std std-ref">Napoleon Russian Campaign</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_napoleon_russian_campaign.py</span></code>)</p></td> -<td><p>00:00.147</p></td> +<td><p>00:00.126</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_dag_layout.html#sphx-glr-auto-examples-graph-plot-dag-layout-py"><span class="std std-ref">DAG - Topological Layout</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_dag_layout.py</span></code>)</p></td> -<td><p>00:00.133</p></td> +<td><p>00:00.119</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_karate_club.html#sphx-glr-auto-examples-graph-plot-karate-club-py"><span class="std std-ref">Karate Club</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_karate_club.py</span></code>)</p></td> -<td><p>00:00.103</p></td> +<td><p>00:00.090</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_erdos_renyi.html#sphx-glr-auto-examples-graph-plot-erdos-renyi-py"><span class="std std-ref">Erdos Renyi</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_erdos_renyi.py</span></code>)</p></td> -<td><p>00:00.069</p></td> +<td><p>00:00.060</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_degree_sequence.html#sphx-glr-auto-examples-graph-plot-degree-sequence-py"><span class="std std-ref">Degree Sequence</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_degree_sequence.py</span></code>)</p></td> -<td><p>00:00.068</p></td> +<td><p>00:00.059</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_expected_degree_sequence.html#sphx-glr-auto-examples-graph-plot-expected-degree-sequence-py"><span class="std std-ref">Expected Degree Sequence</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_expected_degree_sequence.py</span></code>)</p></td> -<td><p>00:00.036</p></td> +<td><p>00:00.031</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/graphviz_drawing/plot_attributes.html b/auto_examples/graphviz_drawing/plot_attributes.html index 2e9da75a..1028d82e 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 0.119 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-graphviz-drawing-plot-attributes-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/52bb0ebd52824aa460a3ecb45c1cb5e5/plot_attributes.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_attributes.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/plot_conversion.html b/auto_examples/graphviz_drawing/plot_conversion.html index 31482f29..f4d70c95 100644 --- a/auto_examples/graphviz_drawing/plot_conversion.html +++ b/auto_examples/graphviz_drawing/plot_conversion.html @@ -514,7 +514,7 @@ to download the full example code</p> <a href="https://pygraphviz.github.io/documentation/stable/reference/agraph.html#pygraphviz.AGraph.draw" title="pygraphviz.AGraph.draw" class="sphx-glr-backref-module-pygraphviz sphx-glr-backref-type-py-method"><span class="n">A</span><span class="o">.</span><span class="n">draw</span></a><span class="p">(</span><span class="s2">"k5.png"</span><span class="p">,</span> <span class="n">prog</span><span class="o">=</span><span class="s2">"neato"</span><span class="p">)</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.031 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.025 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-conversion-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/27aa0c08bacf20ba3f5ce4f8d02ac226/plot_conversion.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_conversion.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/plot_grid.html b/auto_examples/graphviz_drawing/plot_grid.html index bac3f2c2..a13b9ea5 100644 --- a/auto_examples/graphviz_drawing/plot_grid.html +++ b/auto_examples/graphviz_drawing/plot_grid.html @@ -519,7 +519,7 @@ Graphviz command line interface to create visualizations.</p> <img src="../../_images/sphx_glr_plot_grid_001.png" srcset="../../_images/sphx_glr_plot_grid_001.png" alt="plot grid" class = "sphx-glr-single-img"/><div class="sphx-glr-script-out highlight-none notranslate"><div class="highlight"><pre><span></span>Now run: neato -Tps grid.dot >grid.ps </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.080 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.072 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-drawing-plot-grid-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/26e3cd745ae317a76a0df34cbf4999d8/plot_grid.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_grid.py</span></code></a></p> diff --git a/auto_examples/graphviz_drawing/plot_mini_atlas.html b/auto_examples/graphviz_drawing/plot_mini_atlas.html index 77bf7674..c580e2a0 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 0.095 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.081 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 4e7f4f0c..0568cd49 100644 --- a/auto_examples/graphviz_drawing/sg_execution_times.html +++ b/auto_examples/graphviz_drawing/sg_execution_times.html @@ -463,23 +463,23 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-graphviz-drawing-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.324</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> +<p><strong>00:00.227</strong> total execution time for <strong>auto_examples_graphviz_drawing</strong> files:</p> <table class="table"> <tbody> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_attributes.html#sphx-glr-auto-examples-graphviz-drawing-plot-attributes-py"><span class="std std-ref">Attributes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_attributes.py</span></code>)</p></td> -<td><p>00:00.119</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_mini_atlas.html#sphx-glr-auto-examples-graphviz-drawing-plot-mini-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mini_atlas.py</span></code>)</p></td> +<td><p>00:00.081</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-even"><td><p><a class="reference internal" href="plot_mini_atlas.html#sphx-glr-auto-examples-graphviz-drawing-plot-mini-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_mini_atlas.py</span></code>)</p></td> -<td><p>00:00.095</p></td> +<tr class="row-even"><td><p><a class="reference internal" href="plot_grid.html#sphx-glr-auto-examples-graphviz-drawing-plot-grid-py"><span class="std std-ref">2D Grid</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_grid.py</span></code>)</p></td> +<td><p>00:00.072</p></td> <td><p>0.0 MB</p></td> </tr> -<tr class="row-odd"><td><p><a class="reference internal" href="plot_grid.html#sphx-glr-auto-examples-graphviz-drawing-plot-grid-py"><span class="std std-ref">2D Grid</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_grid.py</span></code>)</p></td> -<td><p>00:00.080</p></td> +<tr class="row-odd"><td><p><a class="reference internal" href="plot_attributes.html#sphx-glr-auto-examples-graphviz-drawing-plot-attributes-py"><span class="std std-ref">Attributes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_attributes.py</span></code>)</p></td> +<td><p>00:00.049</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_conversion.html#sphx-glr-auto-examples-graphviz-drawing-plot-conversion-py"><span class="std std-ref">Conversion</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_conversion.py</span></code>)</p></td> -<td><p>00:00.031</p></td> +<td><p>00:00.025</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/graphviz_layout/plot_atlas.html b/auto_examples/graphviz_layout/plot_atlas.html index 92bac9b7..414159d6 100644 --- a/auto_examples/graphviz_layout/plot_atlas.html +++ b/auto_examples/graphviz_layout/plot_atlas.html @@ -549,7 +549,7 @@ We don’t plot the empty graph nor the single node graph. <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 4.433 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 3.772 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 d9969e9d..c585e64f 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 0.176 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.150 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 907a75a9..dac19b7b 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 0.349 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.296 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-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 d3b972a6..ae099273 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 0.942 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.795 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-giant-component-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/f5d29b33ff492f40e4749050b3f5e7dd/plot_giant_component.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_giant_component.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/plot_lanl_routes.html b/auto_examples/graphviz_layout/plot_lanl_routes.html index e819247b..6cc3d86c 100644 --- a/auto_examples/graphviz_layout/plot_lanl_routes.html +++ b/auto_examples/graphviz_layout/plot_lanl_routes.html @@ -561,7 +561,7 @@ to download the full example code</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.393 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.335 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-graphviz-layout-plot-lanl-routes-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/30e04b92b8aefc7afe7f634d84ae925a/plot_lanl_routes.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_lanl_routes.py</span></code></a></p> diff --git a/auto_examples/graphviz_layout/sg_execution_times.html b/auto_examples/graphviz_layout/sg_execution_times.html index 2edf268b..2da7d7d1 100644 --- a/auto_examples/graphviz_layout/sg_execution_times.html +++ b/auto_examples/graphviz_layout/sg_execution_times.html @@ -463,27 +463,27 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-graphviz-layout-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:06.294</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> +<p><strong>00:05.347</strong> total execution time for <strong>auto_examples_graphviz_layout</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_atlas.html#sphx-glr-auto-examples-graphviz-layout-plot-atlas-py"><span class="std std-ref">Atlas</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_atlas.py</span></code>)</p></td> -<td><p>00:04.433</p></td> +<td><p>00:03.772</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>00:00.942</p></td> +<td><p>00:00.795</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-odd"><td><p><a class="reference internal" href="plot_lanl_routes.html#sphx-glr-auto-examples-graphviz-layout-plot-lanl-routes-py"><span class="std std-ref">Lanl Routes</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_lanl_routes.py</span></code>)</p></td> -<td><p>00:00.393</p></td> +<td><p>00:00.335</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_decomposition.html#sphx-glr-auto-examples-graphviz-layout-plot-decomposition-py"><span class="std std-ref">Decomposition</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_decomposition.py</span></code>)</p></td> -<td><p>00:00.349</p></td> +<td><p>00:00.296</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>00:00.176</p></td> +<td><p>00:00.150</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/auto_examples/subclass/plot_antigraph.html b/auto_examples/subclass/plot_antigraph.html index a9c85611..bb4c5775 100644 --- a/auto_examples/subclass/plot_antigraph.html +++ b/auto_examples/subclass/plot_antigraph.html @@ -680,7 +680,7 @@ algorithms.</p> <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.103 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.091 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-subclass-plot-antigraph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/652afbfc3c52c8cdd7689321df2e696a/plot_antigraph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_antigraph.py</span></code></a></p> diff --git a/auto_examples/subclass/plot_printgraph.html b/auto_examples/subclass/plot_printgraph.html index 3bb9bc51..dc92f3ce 100644 --- a/auto_examples/subclass/plot_printgraph.html +++ b/auto_examples/subclass/plot_printgraph.html @@ -616,7 +616,7 @@ Add edge: 9-12 <a href="https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html#matplotlib.pyplot.show" title="matplotlib.pyplot.show" class="sphx-glr-backref-module-matplotlib-pyplot sphx-glr-backref-type-py-function"><span class="n">plt</span><span class="o">.</span><span class="n">show</span></a><span class="p">()</span> </pre></div> </div> -<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.066 seconds)</p> +<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes 0.058 seconds)</p> <div class="sphx-glr-footer sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-subclass-plot-printgraph-py"> <div class="sphx-glr-download sphx-glr-download-python docutils container"> <p><a class="reference download internal" download="" href="../../_downloads/1b5e7bf8d2514d71280314171170de85/plot_printgraph.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_printgraph.py</span></code></a></p> diff --git a/auto_examples/subclass/sg_execution_times.html b/auto_examples/subclass/sg_execution_times.html index 30936be8..940d44f9 100644 --- a/auto_examples/subclass/sg_execution_times.html +++ b/auto_examples/subclass/sg_execution_times.html @@ -463,15 +463,15 @@ <section id="computation-times"> <span id="sphx-glr-auto-examples-subclass-sg-execution-times"></span><h1>Computation times<a class="headerlink" href="#computation-times" title="Permalink to this heading">#</a></h1> -<p><strong>00:00.168</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p> +<p><strong>00:00.149</strong> total execution time for <strong>auto_examples_subclass</strong> files:</p> <table class="table"> <tbody> <tr class="row-odd"><td><p><a class="reference internal" href="plot_antigraph.html#sphx-glr-auto-examples-subclass-plot-antigraph-py"><span class="std std-ref">Antigraph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_antigraph.py</span></code>)</p></td> -<td><p>00:00.103</p></td> +<td><p>00:00.091</p></td> <td><p>0.0 MB</p></td> </tr> <tr class="row-even"><td><p><a class="reference internal" href="plot_printgraph.html#sphx-glr-auto-examples-subclass-plot-printgraph-py"><span class="std std-ref">Print Graph</span></a> (<code class="docutils literal notranslate"><span class="pre">plot_printgraph.py</span></code>)</p></td> -<td><p>00:00.066</p></td> +<td><p>00:00.058</p></td> <td><p>0.0 MB</p></td> </tr> </tbody> diff --git a/reference/introduction-7.hires.png b/reference/introduction-7.hires.png Binary files differindex f1171717..878d8984 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 b1354389..492344b1 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 8417b6a9..0891f7fe 100644 --- a/reference/introduction-7.png +++ b/reference/introduction-7.png diff --git a/reference/introduction.ipynb b/reference/introduction.ipynb index 5c2d176b..92d06d3c 100644 --- a/reference/introduction.ipynb +++ b/reference/introduction.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7a8960fd", + "id": "6bcc213e", "metadata": {}, "source": [ "## Introduction\n", @@ -34,7 +34,7 @@ { "cell_type": "code", "execution_count": null, - "id": "e3b56c58", + "id": "f3e7eb3b", "metadata": {}, "outputs": [], "source": [ @@ -43,7 +43,7 @@ }, { "cell_type": "markdown", - "id": "f82971c9", + "id": "0f401fa9", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -82,7 +82,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ee8cf743", + "id": "9569f602", "metadata": {}, "outputs": [], "source": [ @@ -94,7 +94,7 @@ }, { "cell_type": "markdown", - "id": "ffd8b3d9", + "id": "d8278fdf", "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": "9905e01f", + "id": "32835ff7", "metadata": {}, "outputs": [], "source": [ @@ -205,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "8aa73c16", + "id": "0f0a1321", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -214,7 +214,7 @@ { "cell_type": "code", "execution_count": null, - "id": "fb4483bc", + "id": "6a0f1dea", "metadata": {}, "outputs": [], "source": [ @@ -225,7 +225,7 @@ }, { "cell_type": "markdown", - "id": "025b8142", + "id": "4ab56196", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -234,7 +234,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3321272a", + "id": "63c4dd31", "metadata": {}, "outputs": [], "source": [ @@ -246,7 +246,7 @@ }, { "cell_type": "markdown", - "id": "a614e3a5", + "id": "a995335c", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -311,7 +311,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f4931113", + "id": "cd32082f", "metadata": {}, "outputs": [], "source": [ @@ -323,7 +323,7 @@ }, { "cell_type": "markdown", - "id": "21505cdc", + "id": "75b1bc51", "metadata": {}, "source": [ "# Drawing\n", @@ -344,7 +344,7 @@ { "cell_type": "code", "execution_count": null, - "id": "58ec1ac9", + "id": "884a0ace", "metadata": {}, "outputs": [], "source": [ @@ -358,7 +358,7 @@ }, { "cell_type": "markdown", - "id": "d7434695", + "id": "92e36b92", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -398,7 +398,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cab35b17", + "id": "c2642a0b", "metadata": {}, "outputs": [], "source": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "5608f047", + "id": "0fa58dcf", "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": "2320ac72", + "id": "00336d72", "metadata": {}, "outputs": [], "source": [ diff --git a/reference/introduction_full.ipynb b/reference/introduction_full.ipynb index 2f685085..bb57fce2 100644 --- a/reference/introduction_full.ipynb +++ b/reference/introduction_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7a8960fd", + "id": "6bcc213e", "metadata": {}, "source": [ "## Introduction\n", @@ -34,13 +34,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "e3b56c58", + "id": "f3e7eb3b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:36.538058Z", - "iopub.status.busy": "2023-01-04T17:44:36.537791Z", - "iopub.status.idle": "2023-01-04T17:44:36.618596Z", - "shell.execute_reply": "2023-01-04T17:44:36.617850Z" + "iopub.execute_input": "2023-01-04T19:40:09.673336Z", + "iopub.status.busy": "2023-01-04T19:40:09.672876Z", + "iopub.status.idle": "2023-01-04T19:40:09.748014Z", + "shell.execute_reply": "2023-01-04T19:40:09.747254Z" } }, "outputs": [], @@ -50,7 +50,7 @@ }, { "cell_type": "markdown", - "id": "f82971c9", + "id": "0f401fa9", "metadata": {}, "source": [ "To save repetition, in the documentation we assume that\n", @@ -89,13 +89,13 @@ { "cell_type": "code", "execution_count": 2, - "id": "ee8cf743", + "id": "9569f602", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:36.622510Z", - "iopub.status.busy": "2023-01-04T17:44:36.622233Z", - "iopub.status.idle": "2023-01-04T17:44:36.626201Z", - "shell.execute_reply": "2023-01-04T17:44:36.625453Z" + "iopub.execute_input": "2023-01-04T19:40:09.752421Z", + "iopub.status.busy": "2023-01-04T19:40:09.751885Z", + "iopub.status.idle": "2023-01-04T19:40:09.755765Z", + "shell.execute_reply": "2023-01-04T19:40:09.755075Z" } }, "outputs": [], @@ -108,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "ffd8b3d9", + "id": "d8278fdf", "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": "9905e01f", + "id": "32835ff7", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:36.630175Z", - "iopub.status.busy": "2023-01-04T17:44:36.629923Z", - "iopub.status.idle": "2023-01-04T17:44:36.633919Z", - "shell.execute_reply": "2023-01-04T17:44:36.633168Z" + "iopub.execute_input": "2023-01-04T19:40:09.759096Z", + "iopub.status.busy": "2023-01-04T19:40:09.758849Z", + "iopub.status.idle": "2023-01-04T19:40:09.762780Z", + "shell.execute_reply": "2023-01-04T19:40:09.762108Z" } }, "outputs": [], @@ -226,7 +226,7 @@ }, { "cell_type": "markdown", - "id": "8aa73c16", + "id": "0f0a1321", "metadata": {}, "source": [ "Edge attributes can be anything:" @@ -235,13 +235,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "fb4483bc", + "id": "6a0f1dea", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:36.637330Z", - "iopub.status.busy": "2023-01-04T17:44:36.637100Z", - "iopub.status.idle": "2023-01-04T17:44:36.640768Z", - "shell.execute_reply": "2023-01-04T17:44:36.640016Z" + "iopub.execute_input": "2023-01-04T19:40:09.766078Z", + "iopub.status.busy": "2023-01-04T19:40:09.765835Z", + "iopub.status.idle": "2023-01-04T19:40:09.769330Z", + "shell.execute_reply": "2023-01-04T19:40:09.768667Z" } }, "outputs": [], @@ -253,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "025b8142", + "id": "4ab56196", "metadata": {}, "source": [ "You can add many edges at one time:" @@ -262,13 +262,13 @@ { "cell_type": "code", "execution_count": 5, - "id": "3321272a", + "id": "63c4dd31", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:36.644674Z", - "iopub.status.busy": "2023-01-04T17:44:36.644430Z", - "iopub.status.idle": "2023-01-04T17:44:36.649010Z", - "shell.execute_reply": "2023-01-04T17:44:36.648237Z" + "iopub.execute_input": "2023-01-04T19:40:09.772705Z", + "iopub.status.busy": "2023-01-04T19:40:09.772472Z", + "iopub.status.idle": "2023-01-04T19:40:09.778016Z", + "shell.execute_reply": "2023-01-04T19:40:09.777433Z" } }, "outputs": [], @@ -281,7 +281,7 @@ }, { "cell_type": "markdown", - "id": "a614e3a5", + "id": "a995335c", "metadata": {}, "source": [ "See the Tutorial for more examples.\n", @@ -346,13 +346,13 @@ { "cell_type": "code", "execution_count": 6, - "id": "f4931113", + "id": "cd32082f", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:36.653200Z", - "iopub.status.busy": "2023-01-04T17:44:36.652959Z", - "iopub.status.idle": "2023-01-04T17:44:36.661028Z", - "shell.execute_reply": "2023-01-04T17:44:36.660070Z" + "iopub.execute_input": "2023-01-04T19:40:09.780952Z", + "iopub.status.busy": "2023-01-04T19:40:09.780731Z", + "iopub.status.idle": "2023-01-04T19:40:09.785121Z", + "shell.execute_reply": "2023-01-04T19:40:09.784477Z" } }, "outputs": [ @@ -373,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "21505cdc", + "id": "75b1bc51", "metadata": {}, "source": [ "# Drawing\n", @@ -394,19 +394,19 @@ { "cell_type": "code", "execution_count": 7, - "id": "58ec1ac9", + "id": "884a0ace", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:36.666231Z", - "iopub.status.busy": "2023-01-04T17:44:36.665975Z", - "iopub.status.idle": "2023-01-04T17:44:37.320140Z", - "shell.execute_reply": "2023-01-04T17:44:37.319399Z" + "iopub.execute_input": "2023-01-04T19:40:09.789919Z", + "iopub.status.busy": "2023-01-04T19:40:09.789687Z", + "iopub.status.idle": "2023-01-04T19:40:10.389535Z", + "shell.execute_reply": "2023-01-04T19:40:10.387305Z" } }, "outputs": [ { "data": { - "image/png": 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NjsqzSDJQRCdOnKBly5YcPXqUxMREoqOjzQ4JUMlAamoqWVlZpsZx1113sXXrVmrXro3T6eS1114jVzYKCx9y9iz07w93362a6ezYoWYHLBazI/Mut9yiZgVmzoRly1Ti9e23ZkflOSQZKII//vgDp9PJiRMnWLVqFZGRkWaH9BeHw0F2dja7du0yOxQqV67MihUrGD58OCNGjKBt27Yck8U94a7On4ctW9TxeVu2qL8X0sqVEBUFs2fDJ5/A999DlSq6RSr+xmJRWzN37FBnOXTqBI8+CkXqiabj/eDOJBkopN9//534+HhOnTrFqlWrqFu3rtkhXSF/hsLMpYLL2Ww2Ro0axYoVK9i5cycxMTH88MMPZoclhJKaCk8/DWFh6ui8+vVV27/69dXfw8LU46mpNzXchQvqy1u1Usf0bt8O/frJbICrVK6sZgU+/VQtx0RFwYoVBRhA5/vBI2iiwH799VctPDxcq1KlirZnzx6zw/lHNWrU0IYMGWJ2GFc5duyY1rp1a81isWivvPKKlp2dbXZIwlcdOKBpCQmaBppms6k//+mf/McTEtTz/sFPP2la7dqaVry4pr33nqbl5rrw+9HZxo3qW9640exICu/QIU1r2VJ9H08+qWnnz1/niw24HzyFJAMFdOTIES0sLEyrWrWqtm/fPrPDua577rlHa926tdlhXFNubq722muvaVarVWvevLn266+/mh2S8DWffKJpgYE3ftG/1ptAYKB6/mUuXdK0f/1L06xWTWvSRNN27zbp+9KRNyQDmqYSsg8+UAlarVqatnr1Nb5I5/vB08gyQQGkpaXRokULsrOzSUpKolatWmaHdF35Owo0N+zGYbVaefnll1m1ahX79+8nJiaGxYsXmx2W8BWjR6uqvkuXCn4+bk6Oel7//mocYNMmtUY9bhyMGaP6B4SHGxC3KBSrFQYOVN0LK1ZU2zpfeEH9GgHd7wePZHY24ikOHjyoVa9eXatRo4Z26NAhs8O5KXPnztUA7ejRo2aHcl0nTpzQ2rdvrwHaiy++qGVlZZkdkvBmn3xSsE9+N/hnQafJms2mafXra9q2bWZ/c/rylpmBy+XkaNpbb2mav7+m1a2raQdf1vd+0CZPNvtbLBSZGbgJBw4coEWLFvj5+ZGUlES1atXMDummmNGWuDDKly/Pt99+y9tvv80777xDixYtSEtLMzss4Y0OHoRBg3QbTgMSFg7krScOsn69KlQT7s3PT80KbNoEtawHqTh6ELrOnQ4cqO4zDyPJwA3s27ePFi1aEBAQQFJSElWrVjU7pJtWvXp1SpYs6fbJAKhlg+eff57Vq1dz9OhRYmJiWLBggdlhCW8zYEDBp4GvwwIE+uXw7K4B+PvrNqxwgYgIWHDrAPytOei6ySMnR91nHkaSgevYs2cPLVq0oESJEiQlJVHFwzYIW61WoqOjPSIZyNekSRM2b95MixYtuOeeexg8eLDpjZOEl0hNheXLdU0GACy5OWrcX37RdVxhsNRUrCuW45en7/1AjmfeD5IM/INdu3bRokULypQpQ2JiIrfeeqvZIRWKpyUDAGXLlmXu3Lm89957fPTRR9xxxx0cOHDA7LCEp5s4EWw2Y8a22WDCBGPGFsaQ++EKkgxcw86dO4mPj6d8+fIkJiZSqVIls0MqNIfDwe7du7n0V9msZ7BYLDz99NOsXbuWU6dOUb9+fb7++muzwxKebNEi3WcF/pKTA7IbxrPI/XAFSQb+Zvv27TidTipVqkRiYiIVPPwkEYfDQW5uLjt37jQ7lEJp2LAhmzZt4q677qJbt248+eSTHpfYCDdw7hwYPbu0f7/Xtqr1OnI/XEWSgcts3boVp9PJbbfdxg8//ED58uXNDqnIoqKisFgsHrdUcLnSpUsze/ZsJk6cyJQpU2jSpAl79uwxOyzhSfbvVxu/jKRpsG+fsdcQ+pD74SqSDPzXpk2baNmyJTVq1OCHH34gJCTE7JB0ERwcTFhYmEcnA6CWDQYMGEBycjIXL17k9ttvZ8aMGYUaKyMzh51Hz7A57TQ7j54hI9OgqULhPjIzves6omjkfriKQdUTnuXnn38mISGB8PBwli5dSpkyZcwOSVf5nQi9gcPhYOPGjTzxxBM89NBDJCYm8v777xMUFHTd5+394xwzktNI3H2ctFMXrthXbAFCywXhtFegZ2wotSuWNPR7ECYICPCu64iikfvhKj4/M5CcnEzr1q2pU6cOy5Yt87pEANy7LXFhlChRgi+++IIpU6Ywc+ZMGjduTOo/nB525NQFen2aTMK7PzIt+TCH/5YIgGocc/jUBaYlHybh3R/p9WkyR05dMPz7EC4UFmb8kYEWi7qOcH9yP1zFp5OBtWvXkpCQQGRkJEuXLqV06dJmh2QIh8PBn3/+yZEjR8wORTcWi4VHHnmEn3/+GVCFhp999tkVCc/sDWm0HpfE2gPpAOTmXT8Zyn987YF0Wo9LYvYG6YLoNUqUUGcJG6lWLXUd4f7kfriKzyYDa9asoW3bttSvX58lS5ZQqlQps0MyjKe0JS6MevXqkZKSQo8ePXj00Ufp06cP58+fZ3ziXobO3U5mTt4Nk4C/y83TyMzJY+jc7YxP3GtQ5MLl2rc3dl95u3bGjC2MIffDFXwyGUhKSuKuu+6iUaNGLFq0iBIelL0VRtWqVSlTpoxXJgMAQUFBTJ48menTpzN37lxi7n2Sscv02W0wdtke5sgMgXd4/HFj95U/8YQxYwtjyP1wBZ9LBlauXEm7du1o2rQp3333HcHBwWaHZDiLxeJVRYT/pGfPnnyflEyOo6uu9REjFu6UGgJvUK8eJCTo/mkwBxsX7kiAunV1HVcYzKD7AZtNjeth94NPJQPLly/n7rvvJi4ujoULF96wAt2b+EIyADB5y3n8ivlj0bE4KCdPY9i87bqNJ0w0aZKuL/4akGOx0XDjJD76CPLydBtauILO9wOgxps0Sd8xXcBnkoElS5bQsWNHnE4nCxYsoHjx4maH5FIOh4N9+/aRkZFhdiiG2fvHOVbvO1ngGoEbyc3TWL3vJPuOn9N1XGGCGjXggw90G84C8MF44h+pwVNPQdu2IKdvexCd7wcAxo9X43oYn0gGFi1aROfOnUlISGDevHkEBgaaHZLLORwONE1j+3bv/YQ7IzkNP6sx24X8rBamr5dXea/Qrx+89po+Y40eTeBTffnoI1i2DHbtgqgo+Pxz4xvcCZ3ofD/Qt68+Y7mY1ycD3377Lffccw/t2rXjm2++IcCDmkDoKSIiAj8/P69eKkjcfVz3WYF8uXkaiXuOGzK2MMHLL8Mnn0BgYMGniW029bzJk2HYsL/+c0ICbN8OXbvCI49A585w7JjOcQtjGHA/eBqvTgbmzZvHvffeS6dOnfjqq6/w9/c3OyTTBAYGYrfbvTYZOJ+ZQ5rBRX5p6RekdbE36dcPUlPB6VR/v9GbQP7jTqd63jU+AZYpA599BgsWQEoKRETAnDn6hi0MYsD94Em8Nhn45ptvuP/+++nSpQuzZs2iWLFiZodkOm8uIjycnnFVZ0G9acChdO+tufBJNWqo+f2dO9VWsGt1psvvJPfEE+pFf9myG64Jd+oEO3ZA69bwwAPQvTucPGng9yH0cRP3g4aF/ZYwcgfc/P3gCbwyGfjyyy/p3r073bp1Y8aMGZII/JfD4WDbtm3keWHJc1aOa74nV11HuFi9evD++7B3L5w9yy8zNxPLen6ZuRnOnlX//f33C7RdrHx5NSswezasWAGRkbBwoYHfg9DPde6HjYlnCdP2ktKzYPeDu/O6ZGDmzJk8+OCD9OjRg2nTpmEzqsOUB3I4HJw/f56DBw+aHYru/G2uuZVddR1hohIluGiPIYVYLtpjitxStnt39UGzUSNVR/DII3DmjD6hChf42/0Qc2cJSpaExESzA9OXV72yTZs2jV69etG7d28+++wz/Pz8zA7JrXhzW+LqIcEYfOwIlv9eR4iCqlRJzQp89hnMnatmCZYvNzsqURg2G8TFwapVZkeiL69JBj7//HP69OnDo48+yqeffiqJwDVUqlSJW265xSuTgeAAG6HljG0iFRoSRHCAzDSJwrFY4OGH1Y6DOnWgTRt48kk4f97syERBOZ3w00+QlWV2JPrximRg8uTJPProozz22GNMmjQJq9Urvi3deXtbYqe9gqF9BpzhFQwZW/iW0FBYuhQ+/BCmTgWHA1avNjsqURBOJ1y4oHaMeAuPf9ecNGkS/fv358knn2TChAmSCNyANycDPWNDDe0z8FCTUEPGFr7HalWzAlu3QuXK0KIFPP88XLxodmTiZsTEQOnS3rVU4NHvnB9++CGPP/44zzzzDB988IGu/ei9lcPh4NChQ5zxwgqm2hVLEhdWHr0nB/ysFuLCyhNWoaS+AwufFxam3lDeflt1sb39dtiwweyoxI34+UHz5t5VROixycB7773HwIEDee655xg3bpwkAjcpv4hw27ZtJkdijJaljpObnaVrL1ib1cKYLlG6jSfE5fz8YMgQ2LRJbVxo2hReecW71qO9kdMJa9dCZqbZkejDI5OBd955h8GDB/Piiy8yduxYSQQKoE6dOhQrVszrlgo0TeOdd96hb/fOVDuZfHXjmCL4d6cIqhpcnChEvXrqzeXVV+GNN6BxY/DSnN0rxMfDpUuQnGx2JPrwuGTgrbfeYsiQIQwbNow33nhDEoEC8vf3p169el6VDGRnZ/P4448zZMgQXnzxRVZ9Oobn24TrMvYLbex0byS1AsI1ihVTswIpKeo45IYN4fXXIUe6YLsdhwPKlvWepQKPSgbGjBnDv/71L0aMGMFrr70miUAheVMR4enTp2nXrh1TpkxhypQpvPHGG1itVgY6a/NG1ygCbNYC7zDws1oIsFl5s2sUTznDDIpciH9Wv76qHXj+eRg+HO64Q52IKNyH1aoKPyUZcLF///vfvPzyy4waNYpRo0ZJIlAEDoeDHTt2kJuba3YoRbJv3z6aNm3K5s2bWb58OY888sgVjz/QKJQVz7agWc0QgBsmBfmPN6sZwopnW8iMgDBVQACMGaP2s//5p0oQ3n1XzRgI9+B0wvr1arnA07k8GcjIzGHn0TNsTjvNzqNnbngKnKZpjBgxgldffZXRo0czYsQIF0XqvRwOBxcvXmTv3r1mh1JoP/74I7GxsWiaxvr164mPj7/m11UtF8S0vrEsH9ycXrHVqBYSdI1OhRrVQoLoFVuNFc82Z1rfWKkREG6jSRPYvBkGDIBnn4WWLcELO4p7pPh4VUC4bp3ZkRSdS9qp7f3jHDOS00jcfZy0UxeuOF3OAoSWC8Jpr0DP2FBqV/zf9i1N0xg+fDhjxozhzTff5MUXX3RFuF7v8rbEderUMTmagps6dSr9+/fnzjvv5Ouvv6ZcuXI3fE7tiiUZ2SmCkUSQkZnDofQMsnLyePD+brRsHMVHr49zQeRCFE5QkJoVuOcedbZBVBS88w70769rrawooMhICAlRSwX5Jx97KkNnBo6cukCvT5NJePdHpiUf5vDfEgFQx8IePnWBacmHSXj3R3p9msyRUxfQNI2hQ4cyZswY/u///k8SAR2VL1+eypUre1zdQF5eHsOGDePhhx+mT58+LF269KYSgb8LDrARUbk09UPLUvfWkuzfnWpAtELoLz5e7TDo2VPNFLRrB7/+anZUvstqVb8Tb2g+ZFgyMHtDGq3HJbH2QDrADTvD5T++9kA6rcclce+L/8dbb73Fe++9x3PPPWdUmD4r/zhjT3HhwgW6devGG2+8wdixY/n44491OZrabreze/duHSIUwjVKloRJk2DxYnXOQWQkTJuma2sNUQDx8apu4MIFsyMpGkOSgfGJexk6dzuZOXkFbg+bm6eRmZ3LJltdeo6ZxtNPP21EiD7Pk3YUHD16lObNm7NkyRLmzZvHkCFDdCsgtdvtpKWlcVH6wAoPc9ddsGMHdOwIvXtD167wxx9mR+V7nE7IzlY9IjyZ7snA7A1pjF22p2iD/PeFfs3ZsszZkKZDVOLvHA4Hv/76K6dOnTI7lOvasmULsbGxHDt2jDVr1tC5c2ddxw8PD0fTNPbt26fruEK4QtmyalZg7ly16yAyEr7+2uyofEu9enDLLZ6/xVDXZODIqQu8unCnnkMyYuFOjpzy8PkXN3R5EaG7WrhwIXfeeScVK1YkJSWF+vXr634Nu90OIEsFwqN16QI7d6p++d26QY8e4OZ5vtewWLyjbkDXZGDYvO3k6HxqXE6exrB523UdU0Dt2rUJDAx0y2RA0zTGjh3LPffcQ9u2bfnxxx+pXLmyIdcqX7485cqVk2RAeLxbblGzAjNmqHqCyEj4/nuzo/INTqfqGnn+vNmRFJ5uycDeP86xet9J3Y+Qzc3TWL3vJPuOn9N1XF9ns9mIjIx0u2QgOzubxx57jBdeeIGhQ4fy1VdfERRk7J7/8PBw9uwp4tKWEG7AYlGzAjt3qmN2O3SAfv3g7FmzI/NuTqdqGf3TT2ZHUni6JQMzktMK3Pb1ZvlZLUxfL7UDenO3IsLTp09z1113MXXqVD7//HPGjBmD1Wp8XyzZUSC8TeXKalZg8mSYM0f1JVi50uyovJfdDpUqefZSgW6vtIm7j+s+K5AvN08jcc9xQ8b2ZQ6Hg507d5KdnW12KOzdu5cmTZqwZcsWVqxYQZ8+fVx27fxkQJO9WcKLWCzQt6/aflirFrRqBYMGQUaG2ZF5n/y6AU8uItQlGTifmUOawUV+aekXbti6WBSMw+EgKyvL9E/FSUlJNGnSBIvFQnJyMs2bN3fp9e12O3/++ScnT5506XWFcIXq1WHFCnj/ffj0U7V84Onb4NyR0wk//wznPHRFW5dk4HB6xlWdBfWmAYfSJaXVU3R0NGDujoLPPvuMhIQE6tevz7p16wgLc/0pgeHh6rhjs5MiIYxitapZgS1boHx5iIuDf/3LOw7YcRfx8ZCbC2vWmB1J4eiSDGTluOYYLVddx1eUKVOGatWqmZIM5OXlMXToUB599FEeeeQRFi9eTNmyZV0eB0BYWBgWi0WSAeH1wsPVm9Xrr6uzDho2hI0bzY7KO9SurWo1PHWpQJdkwN/mmsMPXXUdX2JGEWFGRgb33Xcfb731Fv/3f//HxIkTdWktXFiBgYFUr15dkgHhE/z84MUXVRLg769ORRw5UnXRE4VnsailAp9OBqqHBF/jWFh9Wf57HaEvVycDv/32G82bN2fZsmUsWLCA5557TrfWwkUh2wuFr4mMhORkePlleO01lRTs2GF2VJ4tPh42bYIzZ8yOpOB0SQaCA2yEGnz+e2hIEMEBLjlx2adER0fzxx9/8IcLmppv2rSJxo0bc/z4cX766Sc6duxo+DVvlmwvFL6oWDE1K5CcrOoHGjSAt95Sa9+i4JxOyMuD1avNjqTgdJt3d9orGNpnwBlewZCxfZ2r2hLPnz+fuLg4KleuTEpKyl/XdRd2u539+/eTkyM7VoTvadBALRsMHgxDh6oCw717zY7K89SsCVWreuZSgW7JQM/YUEP7DDzUJNSQsX1drVq1CA4ONiwZ0DSNt99+m65du9K+fXuSkpK49dZbDblWUYSHh5Odnc2hQ4fMDkUIUwQGwptvqk+1J06AwwGzZ5sdlWfx5HMKdEsGalcsSVxYed1nB/ysFuLCyhNWoaSu4wrFarUSFRVlSDKQlZVFv379ePHFFxk2bBhz5swxvLVwYcmBRUIod9yhtiD27Qtvv63+2++/mxqSR3E6YfNmOH3a7EgKRtfy/DFdorDpmAxomkZeThZPN7tFtzHF1YwoIjx16hRt27Zl2rRpTJ06lddee80lrYULq0qVKgQFBUkyIAQQHAwffAATJqi/33+/algkTTpvzOlUP6cffzQ7koLR9dW5arkgRnWK0G08i8VC9vqZ3HVnIxYsWKDbuOJKDoeDXbt2kZmZqct4e/bsoUmTJmzfvp0ffviB3r176zKukaxWK+Hh4ZIMCHGZxo3VnwkJ6sCjDh3g6FFzY3J31atDtWqeVzeg+0e1BxqF8nybcF3GeqGNna1zJxAXF8c999zDk08+ycWLF3UZW/yPw+EgJyeH1NTUIo+VmJhIkyZN8PPzIzk5mbi4OB0idA3ZXijEtY0YAd99p7bNRUbCzJkyS3A9Tqfn1Q0YMm870FmbN7pGEWCzFriGwM9qIcBm5c2uUTzlDCMkJIR58+YxYcIEPvvsMxo2bMj27duNCNtnRUVFAUXfUTBlyhTatGlDgwYNWLduHbVq1dIjPJeR7YVC/LO771Z9CO66C3r2hG7dVKGhuJrTCVu3Qnq62ZHcPMMWcR9oFMqKZ1vQrGYIwA2TgvzHm9UMYcWzLeje6H+7BywWC48//jg///wzfn5+NGrUiPHjx8spczopWbIktWrVKnQykJeXx4svvkjfvn3p27cvixYtokyZMvoG6QJ2u53ff/+ds3L4uxDXFBKiZgW+/FJ98o2IgPnzzY7K/cTHqz+TkkwNo0AMreiqWi6IaX1jWT64Ob1iq1EtJOianQqrhQTRK7YaK55tzrS+sVT9hwZGERERpKSk8NhjjzFo0CA6derECUlNdVHYIsKMjAzuvfdexo4dy7hx45gwYYKprYWLIv/Aor2ywVqI6+rWDXbuhGbNoEsX6N3b86rnjRQaqnoOeNRSgeZi5y9lazt++1NL3HZQK1ahhjZ99leFGufbb7/Vypcvr916663a8uXLdY7S94waNUorV66clpeXd9PPOXLkiFa/fn2tRIkS2rfffmtgdK5x5swZDdBmzJhhdijCZBs3ahqoP33ZjX4OeXma9sUXmla6tKZVqaJpS5a4NDyXKcz90LevpkVGGheT3ly+1ys4wEZE5dLER1WnVO5ZDu7dVahxOnTowLZt24iIiCAhIYEXX3yRrKwsnaP1HQ6Hg1OnTvHbb7/d1Ndv3LiR2NhYTp48yU8//USHDh0MjtB4pUqVolKlSlI3IMRNsligVy9VSxARoeoJBgyAc+fMjsx8Tqf6uXjK5LWpG7/r1KlTpBfeW2+9laVLl/LWW28xbtw4mjVrJlO8hVSQtsTz5s2jefPm3HbbbaSkpBAdHW10eC4jRYRCFNxtt8GSJTBxIsyYAdHRnrVebgRPqxswNRmw2+3s2lW4mYF8VquVF154gXXr1nH27Fnq16/P559/LsWFBVStWjVKly593WRA0zTefPNNunbtSocOHVi1ahWVKlVyYZTGk+2FQhSOxaJmBbZtU2vm8fHw7LPgq7vBq1SB2rU9p9+A6TMDu3bt0uWNu2HDhmzatIn777+fRx55hB49evDnn38WPUgfYbFYiI6O/sdkICsri759+zJ06FCGDx/OrFmzKF68uIujNJ7dbmfPnj2STApRSDVrqjfAcePUTEH9+upURF/kdEoycFPq1KnD+fPnOapTS6sSJUowZcoUZs2axaJFi4iJiWHt2rW6jO0L/mlHQXp6OgkJCcyYMYNp06bxn//8x61bCxeF3W4nIyPjpmsnhBBXs1rVCYibN0Pp0mrXwbBhoFOTU48RHw+//AIuOCG+yExPBoAiLxX83QMPPMDWrVupUqUKzZs359///je5ckD3DTkcDvbu3cuFCxf++m+7d++mSZMmpKamsnLlSh566CETIzRe/vZCWSoQoujq1IGffoL//AfGjoVGjdQhSL4iv27AE7YYmpoM1KhRg2LFihlSsFW9enWSkpJ4+eWXGTVqFE6nk7S0NN2v400cDgd5eXns2LEDgJUrV9KkSROKFStGcnIyd9xxh8kRGq9GjRrYbDYpIhRCJzabmhXYsEHNGDRqBK+9Bjk5ZkdmvFtvVQmRJywVmJoM2Gw2wsLCdJ8ZuHz8UaNGsWrVKg4dOoTD4eDrr7825FreIDIyEqvVytatW5k8eTJt27alUaNGrF27lpo1a5odnksUK1aMWrVqSTIghM4cDkhJgaFDYeRItXTwyy9mR2W8+HiZGbgp+UWERoqLi2Pr1q20bt2abt260b9/fzIyMgy9picqXrw4tWvX5qOPPqJ///7079/fY1sLF4VsLxTCGP7+aslg7VrVi6B+ffi//wNvXsV1OmH3bvc/7dEnkgGAsmXL8uWXXzJ58mRmzpxJgwYN2Lx5s+HX9STnz5/n3LlzbNmyhffff58PP/wQm81mdlguJ9sLhTBW48bqBMSnnoIXXlCfnvfvNzsqY3hK3YBbJANHjhxxySd1i8VC37592bhxI0FBQcTGxvLOO++Ql5dn+LXd3a+//kpcXBwnT56kePHiDBw4EIulYCdOegu73c6hQ4fI9LXSZyFcqHhxNSuQlKQ+NUdHw4QJ3nc0coUKUK+e+9cNmJ4M2O12wLXV23Xq1GHdunU8/fTTDBkyhPbt23Ps2DGXXd/d/PzzzzRu3JhTp04xbtw4Ll68yKFDh8wOyzR2u528vDz27dtndihCeL24OHXcb58+8OST0LYtHDlidlT6cjplZuCG8pMBVywVXC4gIICxY8eyZMkStmzZgsPhYPHixS6NwR188803NG/enNDQUFJSUujcuTNwc22JvZVsLxTCtUqUgI8+gmXLVFFhZCR8/rn3zBI4nbBvH/z6q9mR/DPTk4EyZcpQqVIllycD+dq2bcu2bdto0KAB7du3Z/DgwT4xPaxpGq+//jr33XcfnTp1IjExkYoVK1K5cmVCQkJ8OhmoUKECpUuXliJCIVwsIQG2b4euXeGRR6BzZ/CGSdsWLdSf7rxUYHoyAK4rIvwnFSpU4Pvvv+fdd99lwoQJxMbG8osX73nJzMzkkUceYdiwYYwYMYKZM2f+1VrYYrH8YydCX2GxWGRHgRAmKVMGPvsMFixQWxEjImDOHLOjKpry5SEqyr2XCtwiGXCHF16LxcIzzzxDSkoKmZmZNGjQgI8//tjretSfPHmShIQEZs2axfTp0xk1atRVrYV9PRkA97gnhfBlnTqpI4Bbt4YHHoDu3eHkSbOjKjx3P6fALZKB/KOM3aGq3+FwsHHjRnr16sWAAQO47777OHXqlNlh6WLXrl00adKEXbt2kZiYSM+ePa/5dQ6HgwMHDnD27FkXR+g+ZHuhEOYrX17NCsyeDStWqFqChQvNjqpw4uPh4EE4fNjsSK7NbZKBS5cuuU274KCgICZNmsQ333xDYmIiDoeDJE85lPof/PDDDzRt2pSAgACSk5Np1qzZP36tw+EAYPv27a4Kz+3Y7XbS09NJT083OxQhfF737rBzp2pl3Lmzqic4c8bsqAqmRQt1zLO7LhW4TTIArt9RcCNdu3Zl69at1KpVC6fTySuvvEJ2drbZYRXYxx9/TNu2bYmNjWXt2rXUqFHjul9ft25dbDabTy8V5O9ykaUCIdxDpUpqVuCzz2DuXDVLsHy52VHdvHLlVEtmd10qcItkIDQ0lMDAQLd84a1atSo//PAD//nPf3j99ddp3rw5Bw8eNDusm5Kbm8tzzz3HgAEDePzxx/nuu+8oXbr0DZ8XEBBA3bp1fToZCAsLA2R7oRDuxGKBhx9WOw7q1IE2bVRvgvPnzY7s5sTHq2TAHUvR3CIZsFqthIeHu93MQD4/Pz9efvllVq9ezbFjx4iJiWHmzJlmh3Vd58+fp0uXLrz33nt88MEHjB8/vkCthX29iDAoKIjQ0FC3TFCF8HWhobB0KXz4IUydqj5xr15tdlQ35nRCWhq4Y083t0gGwPzthTejadOmbNmyhQ4dOtCzZ0/69OnDuXPnzA7rKkeOHOHOO+9k1apVfPfddwwcOLDAYzgcDrZv3+4WRZ1mkR0FQrgvq1XNCmzdCpUrqzX555+HixfNjuyfNW+uZjfccalAkoECKl26NNOnT2fq1KnMnTuX+vXrs2HDBrPD+ktKSgqNGzfmzz//ZO3atbRr165Q4zgcDi5cuMB+bz095CaEh4dLMiCEmwsLU0V5b78N48fD7beDG70kX6FMGXVSoyQD11GnTh2OHTvGGQ8oEbVYLPTu3ZvNmzdTrlw5mjVrxptvvmn6p+ivv/6aFi1aUL16dZKTk4mMjCz0WPk7Cnx5qcBut7Nv3z5yvfl8VSG8gJ8fDBmiTkIsUQKaNoVXXoGsLLMju1r+OQXuVjfgNsmAJ1Zvh4WFsWbNGp5//nleeuklEhISOFrIQ6szMnPYefQMm9NOs/PoGTIyc276uZqmMWbMGLp160aXLl3+ai1cFBUqVKBSpUo+nwxkZWVx2F03BgshrlCvHqxdC6++Cm+8oY5K3rbN7Kiu5HSqMwrcbdLVbQ6rzz8cZteuXTRu3NjkaG6ev78/r7/+Oq1bt6ZXr15ER0czZcoUOnXqdMPn7v3jHDOS00jcfZy0Uxe4PFG0AKHlgnDaK9AzNpTaFUtec4zMzEz69+/PtGnTGDlyJCNGjNDt6GFfLyK8PEGtWbOmydEIIW5GsWJqVqBDB3USYsOGMGoUvPACFKCG2jB33qnqHRIT1RKHu3CbmYESJUpQtWpVj6gbuJZWrVqxbds27rjjDjp37sxTTz3FxX+oZDly6gK9Pk0m4d0fmZZ8mMN/SwQANODwqQtMSz5Mwrs/0uvTZI6cunDF15w8eZLWrVvz5ZdfMnPmTF599VXdEgGQZKBq1apuu+VVCHF99eur2oHnn4fhw+GOO8Ad3l5Kl4YGDdyvbsBtkgHwnCLCf1K+fHnmz5/Phx9+yJQpU2jUqNFVXfxmb0ij9bgk1h5Qne1y866/cJT/+NoD6bQel8TsDapL4y+//EJsbCx79uwhMTGRBx98UPfvx+FwkJaWxunTp3Uf2xNYrVZq164tvQaE8FABATBmDPz0E/z5p0oQ3n0XzN4k5Y51A26VDHjDVi6LxcKTTz7Jhg0bsFgsNGrUiPHjx6NpGuMT9zJ07nYyc/JumAT8XW6eRmZOHkPnbmfwJ0to2rQpQUFBJCcn07RpU0O+l/wiwm3utujmQt5wTwrh65o0gc2bYcAAePZZaNlSnRNgFqcTfv8d3OlzhlslA3Xq1GHv3r3k5Nx88Zy7ioyMJCUlhX79+jFo0CDu7PMvxi7T5zc//0Aude9+lJ9++onq1avrMua12O12AgICfHqpQLYXCuEdgoLUrEBiojosKCoKPv7YnE/nd9yhdkC401KB2yUD2dnZHtPu90aKFy/O+PHjmfLlQn6t2FS/45A1jVM1EziTY2w1jM1mIyIiwqeTAbvdzm+//cZ5T+l3KoS4rvh4tcOgZ081U9Cunarud6WSJdWhS+50aJHbJQPgfgcWFdXKsxWwFfPXr7jPYiEnT2PYPONPFfT1IsL8HQV79+41ORIhhF5KloRJk2DxYnXOQWQkTJvm2lkCd6sbcKtkoHLlypQoUcKrpmX3/nGO1ftOkqvzLzw3T2P1vpPsO25sO2SHw8GOHTu8YummMPK3vHrTPSmEUO66C3bsgI4doXdv6NoV/vjDNdeOj1fX+uUX11zvRtwqGbBYLNjtdq+aGZiRnIafVb/tfpfzs1qYvj7NkLHzRUdHk5mZ6bMV9WXLluWWW26RZEAIL1W2rJoVmDtX7TqIjISvvzb+unfcoXoiuMtSgVslA+D52wv/LnH38QLvHLhZuXkaiXuOGzJ2PmlLrJYKfDUZEsJXdOkCO3eqw4S6dYMePeDUKeOuFxysOiS6SxGhJAMGOp+ZQ9rfGgXpLS39QoFaFxdUuXLluO2223w+GZCZASG83y23qFmBGTNUPUFkJHz/vXHXi49XMwNm9z0AN0wG7HY76enpnDx50uxQiuxwesZVnQX1pgGH0jMMvYavFxHmby/UbTeIEMJtWSxqVmDnToiJUW2N//1vY67ldMLJk5Caasz4BeF2yUD+jgJv+CSWleOadM/o6/h6MmC32zl//jzHjh0zOxQhhItUrqxmBSZPhmXL1H9LSdH3Gk2bgr+/eywVuF0yULt2bSwWi1csFfjbXPPjNfo6DoeD33//nRMnThh6HXfliSdqCiGKzmKBvn3hyy/V3594AgYNggydJmODgiA2VpKBawoMDKRGjRpekQxUDwnGmH0E/2P573WM5OtFhDVr1sTPz0+SASF8VOXK6s8XXoBPP1XLB2vX6jO20wlJSebXDbhdMgDeU0QYHGAjtFyQodcIDQkiOMDYToRhYWEUL17cZ5MBf39/atSoIcmAED7ugQdgyxYoXx7i4uBf/4JLl4o2ptOpdi1sN76H3HW5ZTLgTdXbTnsFQ/sMOMMrGDL2Fdfx8yMqKspnkwGQ7YVCCCU8HNasgddfV2cdNGwIGzcWfrwmTdTpimYvFbhlMlCnTh0OHDhAZmam2aEUWc/YUEP7DDzUJNSQsf9Oigi9J0EVQhSNnx+8+KJKAvz91Rv6yJGQnV3wsQIDVSGhJAPXUKdOHXJzc9m/f7/ZoRRZ7YoliQsrr/vsgJ/VQlxYecIqlNR13H/icDj45ZdfyMrKcsn13E14eDgHDx702e/fdOfPq/nZ5GT1pxwcJdxAZKS6JV9+GV57TSUFO3YUfBynE378EXLPmHefu20yAN5zYNGYLlHYdE4GbFYLY7pE6Trm9TgcDrKzs/nFXRppu5jdbic3N5cDBw6YHYrvSE2Fp5+GsDAoVQrq11evtvXrq7+HhanH3WGTtvBZxYqpWYHkZFU/0KABvPUW5Obe5ACpqfTd+jQb/gzDWta8+9wtk4FbbrmFMmXKeM20bNVyQYzqFKHrmP/uFEFVg4sTLxcdHQ347o4C2V7oQgcPQps2EBEBEybA/v1XH+2maeq/T5igvq5NG/U8IUzSoIFaNhg8GIYOVQWG1z3s9LL7vPKCCYSxH4uJ97lbJgMWi8VrdhTke6BRKM+3CddlrBfa2OneyDW1AvlKlSpFjRo1fDYZqFSpEiVLlpRkwGiTJ0O9ev9bQL3RaZn5jycmqudNnmxsfEJcR2AgvPkmrF4NJ06AwwEffHCNbYN/u88tuebf526ZDID3bC+83EBnbd7oGkWAzVrgGgI/q4UAm5U3u0bxlDPMoAivz5eLCC0Wy19tiYVBRo+G/v3VXGtBj8zOyVHP699fjSOEie64Qy359+2rZvhbt4bDh//7oJve526fDHhbP/gHGoWy4tkWNKsZAnDDpCD/8WY1Q1jxbAuXzwhcLj8Z8Lbfyc2S7YUGmjwZhg/XZ6zhw1VnGCFMFBysZgVWrFAz/VFRsLqP+97nbpsM2O12zp4965X94KuWC2Ja31iWD25Or9hqVAsJuqpToaZplLXl0Cu2Giuebc60vrEurRG4FofDwcmTJ/n9999NjcMssr3QIAcPqh6veho4UGoIhFto1Uo1FHqy3UEafjFI38PrdLzPjW1dVwSXH1h06623mhyNMWpXLMnIThGMJIKMzBwOpWeQlZOHv81Kt7viaRbXjJGdOpsd5l8ub0tcOb8/pw8JDw/nxIkTnD59mrJly5odjvcYMKDg06U3kpOjxs0/YUYIE5UqBW+cHkCeNQeLnm2HdbzP3XZmoFatWthsNq+rG/gnwQE2IiqXpn5oWSIqlyYmsi5btmwxO6wrVK9enZIlS/ps3UD+jgJZKtBRaiosX25MMrB8OfjoVljhZv57n1vz3Pc+d9tkoFixYtSqVctnkoG/i4mJYdu2beSZfXrFZaxWK9HR0T6bDNSuXRuQ7YW6mjgRbAZNUNpsakuWEGbzgPvcbZMB8M4dBTfL4XBw/vx5t2ty48s7CkqUKEGVKlUkGdDTokX6zwrky8mBxYuNGVuIgvCA+9ytkwFfLtiKiYkB3K/Jj8PhYPfu3Vy8eNHsUEzhy/ek7s6dA6OT3f37pXWxMJeH3OduW0AIambg8OHDXLhwgaAgcyvpXa1ixYpUrFiRLVu2cO+995odzl8cDgd5eXns3LmThg0bmh2Oy9ntdtasWWN2GN7hWp0F9aZp/PLtPi7aYwr0tPwlWF8vOZCfg1KUn0Px3fup64L7nH374L8fIgvD7ZMBTdPYu3fvX5XsvsQdp+QjIyOxWCxs3brVZ5OBzz77jLy8PKxWt55Yc38uOpX04R6ZpBTyuQ89pGsoHkt+Dkphfg6NySRZ/1CuVsT/n9w6Gciv3t61a5dPJgMxMTHMnj3b7DCuEBwcTO3atd0uSXGV8PBwLl26RFpaGtWrVzc7HM8WEOCSy3w+M4CL9oI955df1Av/9OlQt64xcXkC+TkoRfk5FN8dAD2MiesKRfz/ya2TgXLlynHLLbf4dBHhW2+9xalTpyhXrpzZ4fzFHWcsXOXy7YWSDBRRWBhYLMYuFVgs1O0YBiUK9/S6deH22/UNyRPJz0Ep1M8h3DX3OWFFa1Pv9vOcderU8dmCrfwiwm3btpkbyN/4clviatWq4e/v77P3pK5KlICaNY29Rq1a6jpCmMVD7nOPSAZ8dWYgPDycgIAAt2s+5HA4OHPmDGlpaWaH4nJ+fn6EhYVJMqCX9u2N3X/drp0xYwtREB5wn3tEMrB79263ar7jKjabjcjISLebkr+8LbEvku2FOnr8cWP3Xz/xhDFjC1EQHnCfu30yYLfbuXDhAr/++qvZoZgiJibG7WYGbrvtNsqWLevTyYC0JNZJvXqQkKD7p6Zci42clgm+XfUm3IdB9zk2mxpXh/vc7ZOByw8s8kUOh4PU1FSys7PNDuUvFovF54sI09LSuHDhgtmheIdJk3R9kdSALM1Gq72TWL1at2GFKBqd73NAjTdpki5DuX0yUL16dfz9/X22biAmJoasrCy3+/4dDgdbdvzCzqNn2Jx2mp1Hz5CRadA0mJsJDw8HYO/evSZH4iVq1FAHv+vEApwdM57c0Bq0aAHPPw8+2jBTuBOd73MAxo9X4+rArbcWgirYCg8Pd7s3Q1eJjo4GYMuWLURFRZkcDez94xwzktNIKuUk+57W3P3B/7rxWYDQckE47RXoGRtK7YolzQvUQJdvL/TF/heG6NcP/vgDhg8v+lijR1Pxpb4kvQjjxqkhv/8evvgCGjUq+vBCFJrO9zl9+xZ9nP9y+5kB8O0dBaVLl6ZGjRqmT8kfOXWBXp8mk/Duj0xLPszpbBsWi+WKr9GAw6cuMC35MAnv/kivT5M5csr7ptJDQkIICQnx2aUrw7z8MnzyCQQGFnw61WZTz5s8GYYNA8DPT80KbNoEwcHQtCm88gpkZRkQuxA3S+f7XC8ekQzY7XafTQbgv1PyJhYRzt6QRutxSaw9kA5Abt71+wvkP772QDqtxyUxe4P3bUEMDw+XZMAI/fqps9+dTvX3G71Y5j/udKrnXeOTUr16sG4dvPoqvPEGNG4Mbta6Q/gaA+7zovKIZKBOnTocPXqUc+fOmR2KKcxs8jM+cS9D524nMyfvhknA3+XmaWTm5DF07nbGJ3rX+rpsLzRQjRqwbBns3Km2TOV3KryMhoX91jC0x59QL47Lll137bRYMTUrkJICubnQsCG8/rpxu72EuKGbuM//6iz4xM3d50Xh9jUDcOWOAl88HCcmJoaTJ0/y+++/U7lyZZddd/aGNMYu02cL3dhle7ilRADdG4XqMp7Z7HY78+bNQ9O0q5ZLhE7q1YP331f/fv68OpUtMxMCAljzexjN25dgSz9wFGBXVf368PPPMHKkWradPx+mToX/vsQI4XrXuc8JC3NZB02PmBm4/MAiX5RfpObKpYIjpy7w6sKduo45YuFOr6khsNvtnDlzhuPHj5sdim8oUUIdzxobCzExNHKWICAAEhMLPlRAgJoV+Okn+PNPlSC8+y74YF8z4W7+dp+7spW2RyQDJUuWpHLlyj6bDFSvXp1SpUq5tIhw2Lzt5BRwWeBGcvI0hs3bruuYZsnfXihLBeYIDIRmzQqXDORr0gQ2b4YBA+DZZ6FlSzh4UL8YhfAkHpEMgG8fWJTf5MdVMwN7/zjH6n0nC1wjcCO5eRqr951k33HPr/0ICwvDarVKJ0ITxcfDjz+qGoDCCgpSswIrV8KhQxAVBR9/bOwBc0K4I49KBnx1ZgBce2zwjOQ0/KzGrIP7WS1MX+/5uwsCAgKoXr26zyao7sDpVNP8evxv4XTC9u3Qo4eaKRg0qOhjCuFJPCoZ2LNnD7lF+RjgwWJiYtizZw8ZGRmGXytx93HdZwXy5eZpJO7xjnV22V5orsaNoXjxoi0VXK5kSTUrsGiRquEC1axIZgmEL/CYZMBut5OVlcWhQ4fMDsUUDocDTdPYsWOHodc5n5lDmsFFfmnpF7yidbFsLzRXQICqG1i1St9x27WDL79U/z5iBHTtqprGCeHNPCYZ8PUDiyIiIvDz8zN8qeBwegZGfxDSgEPpxs9wGM1ut3PgwAG3OkTK1zidqm5A734BpUqpP99+W+06iIyEr7/W9xpCuBOPSQZuu+02goKCfLZuoHjx4tjtdsOLCLNyXLO/ylXXMZLdbicnJ4eDUoJuGqcTzp5VuwKM0LIl7NgBzZtDt26qpuDUKWOuJYSZPCYZsFqtPt+WOCYmxvCZAX+ba24JV13HSLK90HwNG6odAXovFVyuQgU1KzB9OixerGYJvv/euOsJYQaPekWWHQVqR0Gegd1RqocEY3Q/Pct/r+PpqlSpQnBwsGwvNJG/P9x5p35FhP/EYoGePdUsQUwMdOig2sufPWvsdYVwFY9KBnx9ZsDhcJCRkcGBAwcMu0ZwgI3QckGGjQ8QGhJEcIBHdMK+LovFIjsK3IDTCatXgytKN6pUUbMCn3wCc+aovgQrVxp/XSGM5lHJQJ06dThx4gSnfHTRLiYmBsDwpQKnvYKhfQac4RUMGdsMkgyYz+lULd03bXLN9SwWNSuwfTvUrAmtWqm+BC7Y9SuEYTwuGQDfXaOtWLEiFStWNLyIsGdsqKF9Bh5q4h2HFYFsL3QHt9+uWrgbvVTwd9Wrww8/wHvvwaefquWDtWtdG4MQevGoZKB27dpYLBafXipwRRFh7YoliQsrr//sQF4uMZUCCatQUt9xTWS32/njjz84c+aM2aH4rGLFIC7O9ckAgNUKTz8NW7ZA+fIqjn/9Cy5dcn0sQhSFRyUDQUFBhIaG+nQy4KozCoa3rUVeTjaanu3XtDyWj36E9957z9AiSFfK31EgRYTmcjphzRrX1A1cS3i4uv6YMeqsg4YNYeNGc2IRojA8KhkA3z6wCFQycOTIEUPrJn777Td6dG7L2cRPsVj0mx34zz1R9HuwC4MHD6Z9+/b8/vvvuo1tFtle6B7i4+HCBdiwwbwY/PzUrMDPP6tdDk2awMiR5iUoQhSERyYDvjwzYHQR4caNG2ncuDEnTpxg5Sf/4fk24bqM+0IbO72a1eK9995jyZIlbN26laioKObPn6/L+GYpVaoUt956qyQDJqtfX3UNNGOp4O+iomD9ehg2DF57TSUFBncRF6LIPDIZ2L9/v8+2gA0PDycgIMCQZGDu3LnExcVx2223kZKSgsPhYKCzNm90jSLAZi1wDYGf1UKAzcqbXaN4yhn2139v27Yt27Zt484776RLly489thjLjmAySh2u12WCUxms6kuge6QDICaGRg1SiUFly5Bgwbw1ltFO25ZCCN5XDKQ3wJ2//79ZodiCpvNRlRUlK7JgKZpvP7669x777107NiRVatWUalSpb8ef6BRKCuebUGzmiEAN0wK8h9vVjOEFc+2oHujq3cP3HLLLcybN4+PP/6YGTNmUL9+fTaYOcdbBLK90D3Ex6tq/sxMsyP5n/zagWeegaFDVYHh3r1mRyXE1TwuGcjfXujLSwV6FhFmZmby8MMPM2zYMEaMGMGsWbMoXrz4VV9XtVwQ0/rGsnxwc3rFVqNaSNBVnQotQLWQIHrFVmPFs82Z1jeWqtdpYGSxWOjfvz+bN2+mdOnSNGvWjNGjR3vcMdX5MwPeUhTpqZxOuHgRUlLMjuRKgYFqVmD1ajh+HBwO+OADkNtFuBOPawNXqVIlSpUq5dOfxGJiYpg2bRpZWVn4+/sXepwTJ07QtWtXNmzYwIwZM+jRo8cNn1O7YklGdopgJBFkZOZwKD2DrJw8/G1WqocEF6qzYHh4OGvXrmXkyJG88sorLFmyhGnTplG9evVCfFeuZ7fbuXjxIr/99htVq1Y1Oxyf5XBAmTJqqSAuzuxornbHHbB1qyoyfPppmDcPPvsMqlUzOzIhPHBmwGKx+HwRocPhICsrq0g/g9TUVGJjY9mzZw+JiYk3lQj8XXCAjYjKpakfWpaIyqWL1GK4WLFijB49mqSkJI4cOYLD4WDGjBmFHs+VZEeBe/DzU3UDRh5aVFTBwTB+PKxYAfv2qWLDTz8FPXfwClEYHpcMgOwoiI6OBgq/o2Dp0qU0bdqU4OBgUlJSaNq0qZ7hFUlcXBxbt26lY8eOPPTQQ/To0YM///zT7LCuq0aNGhQrVkySATfgdKq6AXdv+tOqlWpn3K2bam3coQMcPWp2VMKXeWQykH9gka4NcTxI6dKlqVGjRqGSgfHjx9O+fXvi4uJYu3Yt1dxwjrJ06dJMnz6dmTNnsmjRIhwOB0lJSWaH9Y9sNhu1atWSZMANOJ2qgHD9erMjubHSpdWswLffqnMVIiNh5kyZJRDm8MhkoE6dOvz555+cOHHC7FBMU9AiwpycHAYOHMigQYN45plnWLBgASVLundb4AcffJCtW7dSvXp1nE4nL730EllZWWaHdU2yvdA9REVBuXLuvVTwdx06qD4EbduqY5K7dQMffmkTJvHYZAB8e0dB/hkFNzM7cubMGe6++24mTZrEpEmTeOedd/Dz83NBlEVXrVo1Vq5cyZgxYxg7dixNmzZ1y9+7bC90D1YrtGjhPv0GblZICMyaBV9+qRKZiAjw8H5cwsN4ZDJQq1Yt/Pz83PJNwVUcDgcnT57k6A0WGg8cOEDTpk1JSUlhyZIlPPbYYy6KUD9+fn4MHTqU9evXk5GRwe23387EiRPdapnIbrdz+PBhLl68aHYoPs/pVMsEnvir6NYNdu6Epk2hSxfo3RtOnzY7KuELPDIZCAgIoEaNGj6dDNxMW+LVq1fTuHFjsrOzWb9+Pa1atXJRdMZo0KABGzdupE+fPjzxxBN07tyZ48ePmx0WoJIBTdN8thmWO4mPh6wsWLfO7EgKp2JFNSswdSosXKiWPpYuNTsq4e08MhkAObCoWrVqlC5d+h/rBqZOnUqrVq2Iiopi/fr12O121wZokODgYCZMmMDChQtZt24d0dHRLF682OywZHuhG4mIUMcJe9pSweUsFjUrsH071KsHd90FAwbAuXNmRya8lUcnA748M2CxWIiOjr5qZiAvL4+XXnqJhx9+mN69e7N06VJCQkJMitI4HTt2ZPv27dSvX5/27dszaNAgU6fob7nlFsqUKSPJgBuwWtXsgCcnA/mqVlWzAhMnwowZEB0NbryxRngwj04GDh48yCV331BsoPwiwnwZGRl069aNN998k7Fjx/LJJ58UqUOhu6tUqRKLFi3igw8+YPLkyTRs2FC3Ns0FZbFYsNvtkgy4ifh41ZbYg8+/+ovFomYFtm1TyUF8PDz7rGfWRAj35bHJQP4a7V4fPvXD4XCwZ88eMjIy+O2332jevDlLly5l/vz5DBkyBIulYKcMeiKLxcLAgQP5+eefKVasGI0bN2bs2LGmnBMg2wvdh9MJ2dmqAZG3qFlT7TR45x2YMEEd25ycbHZUwlt4bDIg2wvVzICmaXz99dc0btyYEydO8NNPP9GpUyezQ3O5iIgIkpOTeeaZZ3jhhRdISEjg119/dWkM+dsL3WmXg6+qWxcqVPCOpYLLWa1qVmDzZihVCpo1g2HD3OukRuGZPDYZKF++PCEhIT49LRsREYHVaqV///7cdtttpKSk4HA4zA7LNAEBAbz99tv88MMP7N69m+joaL766iuXXd9ut3P69GlOnjzpsmuKa7NYvKdu4Frq1lWzHv/5D4wdC40agUkrZMJLeGwyAL5dRKhpGuPGjSMvL4/Q0FBWrVpFpUqVzA7LLbRs2ZJt27bRunVr7r//fh5++GHOnj1r+HXzd2zIUoF7cDphwwY4f97sSIxhs6lZgQ0bVPLTqBG89hrk5JgdmfBEkgx4oMzMTB5++GGGDRtGZGQkFSpUoHjx4maH5VbKlSvHnDlz+Pzzz/nmm2+IiYlhrcELyGFhYVgsFp+erXInTifk5sKaNWZHYiyHQyUE//oXvPqqWjr45RezoxKexqOTgfzqbV9aoz1x4gStW7dmzpw5zJgxg169erFt2zZTCubcncVioU+fPmzZsoVKlSoRFxfHq6++So5BH52KFy9OaGioJANuIjwcbr3Ve5cKLufvr2YF1q6Fs2dVceH//Z9KhoS4GR6dDNSpU4fz58/fsCWvt0hNTSU2NpY9e/aQmJhIjx49iImJISMjgwMHDpgdntuqVasWP/74IyNGjGD06NHceeedhnUKlO2F7iO/bsCTDi0qqthYVVz45JPwwgvq+5emmOJmeHwyAL6xo2Dp0qU0bdqU4OBgUlJSaNq0KcBfBYNm7a/3FDabjVdffZXVq1dz4sQJYmJi+Oyzz3SfVZLthe7F6YSNG9WnZV9RvLjafrhqFfz2m2pUNGGCHI0srs+jk4EaNWpQrFgxr08Gxo8fT/v27YmLi2Pt2rVUq1btr8cqVqxIpUqVrntGgfifpk2bsmXLFrp168ajjz5Kt27dSE9P12388PBw9u3bZ9hShCiY/LqB1avNjsT1mjdXjYp691YzBW3bwpEjZkcl3JVHJwM2m42wsDCvTQZycnIYOHAggwYN4plnnmHBggWULFnyqq9zOBwyM1AAJUuWZMqUKXz11VesXLmS6OhoVqxYocvYdrud7OxsDh06pMt4omhq1YIqVXxrqeByJUqoWYElSyA1FSIj4fPPZZZAXM2jkwHw3h0FZ86c4e6772bSpElMmjSJd955Bz8/v2t+rcPhkJmBQrjvvvvYtm0bdevWJSEhgSFDhpBZxO4tsr3QvVgsanbAF4oIr6dtW9ixA+65Bx55BDp3hmPHzI5KuBOvSAa8rWDrwIEDNG3alJSUFJYsWcJjjz123a+PiYnhyJEjnDp1ykUReo/bbruNZcuWMXbsWMaPH0/jxo3ZuXNnkcYrXry4192TnszpVEV1f/5pdiTmKlNGHYs8f75qYxwRAV9+aXZUwl14RTJw5MgRzntJZ5HVq1fTuHFjsrOzWb9+Pa1atbrhc/KLCGV2oHCsVitDhgwhJSWFnJwcGjRowPvvv1+o4kKr1Urt2rUlGXAj8fGQl+ebdQPX0rkz7NwJLVtC9+7wwAOgY9mM8FAenwx407Ts1KlTadWqFVFRUaxfv/6v7+1GwsPDCQwMlGSgiBwOBz///DOPPfYYzzzzDO3ateP3338v8Dh2u51f9h5g59EzbE47zc6jZ8jIlIJCs9SoAaGhslRwufLl1azArFmwbJmaJfj2W7OjEmbymmTAk+sG8vLyeOmll3j44Yfp3bs3S5cuJSQk5Kafb7PZiIyMlCJCHRQvXpz333+fxYsXs2XLFqKjo1mwYMFNPXfvH+cYuXAnqbW6c6jR09z9wRq6TFjL3R+sIXLkUlq8ncjIhTvZ+8c5g78LcTmpG7g2i0XNCuzcCQ0bQqdOqp7gzBmzIxNm8PhkoEyZMlSqVMljp2UzMjLo1q0bb775JmPHjuWTTz7B39+/wONIEaG+7rrrLrZv306zZs245557GDBgABkZGdf82iOnLtDr02QS3v2RacmHOU/gVcdHa8DhUxeYlnyYhHd/pNenyRw5dcEF34kAtVSwdStIWc3Vbr1VzQpMmQLffANRUaDT5hrhQTw+GQDP3VHw22+/0bx5c5YuXcr8+fMZMmTIVW8iNysmJobU1FSysrJ0jtJ33XLLLcyfP59JkyYxffp0br/9dn7++ecrvmb2hjRaj0ti7QG16Jqbd/06g/zH1x5Ip/W4JGZvSDMmeHEFp1Ntp/vxR7MjcU8Wi5oV2L4dateGhAR46im4IPmqz5BkwCQbN26kcePGnDhxgp9++olOnToVaTyHw0FWVpbH/RzcncVi4bHHHmPTpk2ULFmSpk2bMmbMGHJzcxmfuJehc7eTmZN3wyTg73LzNDJz8hg6dzvjE/caFL3IV62aqh2QpYLrq1YNli+H8eNVP4IHHzQ7IuEqXpEM5LeAzfWQUznmzp1LXFwct912GykpKX/tBiiK6OhoQHYUGMVut7N27VpeeOEFhg8fTqMHnmHsMn2KVscu28McmSEwXHy8JAM3w2pVswJbtkB+6dK4cXDpkqlhCYN5RTJQp04dLl26RFqae7+gaprGG2+8wb333kvHjh1ZtWoVlSpV0mXs0qVLU6NGDSkiNJC/vz9jxozhq0UrSa/eStdzDUYs3Ck1BAZzOtU0+MmTZkfiGWrXhk8+Uf8+Zw7cfrs6Kll4J69JBgC3LiLMzMzk4Ycf5qWXXmLEiBHMmjWL4sWL63qNmJgYmRlwgfm/FcdWzL/Q9R3XkpOnMWzedt3GE1eLj1d/JiWZGoZHyW96OmMGBAVB06YwYgRIaZL38YpkIDQ0lMDAQLddLz958iStW7dmzpw5TJ8+nVGjRmG16v+jzz+jQO+T+MT/7P3jHKv3nSRX5x9xbp7G6n0n2Xdcth0apWpVdVaBLBUUXK1asG6dSgRef10dlbxdclev4hXJgNVqJTw83C2TgdTUVBo3bsyePXtITEykZ8+ehl3L4XCQnp7O0aNHDbuGr5uRnIafVb8Zgcv5WS1MX+/eS12ezun03UOLiqpYMZUMJCdDdjY0aKASAzmg0zt4RTIA7rmjYOnSpTRt2pTg4GCSk5Np2rSpodeLiYkBpIjQSIm7jxd458DNys3TSNxz3JCxheJ0qiY7x+XHXGi33w4bN8KQITB8ONx5J7jxCq24SV6VDLhTzcCHH37I3XffzZ133slPP/1E9erVDb9mtWrVKF26tBQRGuR8Zg5pBhf5paVfkNbFBsqvG5DZgaIJCFCzAmvWqEZOMTHw3nvqDAjhmbwqGTh27Bh/mnw0WU5ODoMGDWLgwIEMGjSIhQsXUqpUKZdc22KxSCdCAx1Oz8DoagwNOJR+7U6HougqV4bwcEkG9NK0qdqC+NhjMHiwOvzo4EGzoxKF4TXJQP4ZBWbODpw5c4YOHTowYcIEJk6cyLhx4/DLL8d1kfwiQqG/rBzXfOxx1XV8lZxToK+gIDUrsHIlHDoE0dHw8ceq46PwHF6TDISHhwPmHVh04MABmjVrRnJyMkuWLGHAgAGmxOFwONi7d+8/9tEXhedvc83/Lq66jq9yOmHXLijEgZTiOpxO2LZNdS0cMADatYPffjM7KnGzvOZVp0SJElStWtWUmYE1a9YQGxtLVlYW69evp3Xr1i6PIV9MTAyaprFd9v3ornpIMMbsI/gfy3+vI4zTooX6U5YK9FeqlJoVWLRIJQaRkTB9uswSeAKvSQbAnB0FX3zxBa1atSIiIoL169f/tVxhloiICPz8/KRuwADBATZCywUZeo3QkCCCA2yGXsPXVaoEdetKMmCkdu1gxw64+27o1QvuvVd2cLg7SQYKKS8vj2HDhtGnTx8eeughli1bRkh+I28TBQYGUqdOHUkGDOK0VzC0z4AzvIIhY4srSd2A8cqVU7MCX38Nq1dDRIQ6Ilm4J69KBux2O/v27SM7O9vQ62RkZNCtWzfeeOMN3n77bSZPnoy/v7+h1ywIKSI0Ts/YUEP7DDzUJNSQscWV4uNh715Z03aFe+9VvR3i4uC++6BnTzh92uyoxN95VTJQp04dsrOzOWjg3pbffvuN5s2bs3TpUubPn8/zzz+va496PcTExLBt2zbyZNOv7mpXLElcWHndZwf8rBbiwsoTVqGkruOKa5N+A65VoYKaFZg+XdUTREbC4sVmRyUu53XJABi3vXDTpk00btyY48ePs2bNGjp16mTIdYrK4XCQkZHB/v37zQ7FK43pEoWfBV3PgLBZLYzpEqXbeOL6brlFvSHJUoHrWCxqVmDHDrX9sH176N8fzp41OzIBXpYMVK5cmRIlShhSNzB37lzi4uKoUqUKKSkpf7X+dUcOhwOQtsRGuXjyVy6umarrjNC/O0VQ1eDiRHGl+HhJBsxQpYqaHfj4Y5g9WyUG8nswn1clAxaLBbvdrmsyoGkab7zxBvfeey8dOnQgKSmJW2+9VbfxjVCxYkUqVaokyYABdu3aRXx8PCX+2MaApvrcBy+0sdO9kdQKuJrTCQcOQJqcDeVyFouaFdi+HWrUUJ0Ln3kGLhjb7Vtch1clA6DvjoLMzEwefvhhXnrpJUaMGMGsWbMoXry4LmMbTYoI9bdz507i4+MpV64cq1at4qVOt/NG1ygCbNYC1xD4WS0E2Ky82TWKp5xhBkUsrkf6DZivenX44QfVwfDjj9UZB+vWmR2Vb/LKZECPmoGTJ0/SunVr5syZw/Tp0xk1ahRWq+f8uGJiYmRmQEfbtm3D6XRSoUIFEhMTqVixIgAPNAplxbMtaFZTbSu9UVKQ/3izmiGseLaFzAiYKCREpqjdgdUKTz+tzjgICVGnIA4dCpmZZkfmWzzn3e0m1alTh/T0dE6ePFnoMVJTU2ncuDF79uwhMTGRnj176hihazgcDo4cOcKpU6fMDsXjbdmyhZYtW1KlShVWrlzJLbfccsXjVcsFMa1vLMsHN6dXbDWqhQRd1anQAlQLCaJXbDVWPNucaX1jpUbADTidMjPgLux21Y9g9Gh45x1o2BA2bTI7Kt/hda3O7HY7lmKBLE3eSb2oaPxtVqqHBN90V7elS5dy//33ExoaysqVK11y9LARLi8idDqdJkfjuTZu3EhCQgI1a9Zk2bJllCtX7h+/tnbFkozsFMFIIsjIzOFQegZZOXkFvgeF6zidaor60CE1ZS3MZbOpWYG774bevSE2Fl55BV56CYoVMzs67+Y1r057/zjHjOQ0Vu46QdXnvuLlNedhzVpAfSoLLReE016BnrGh1K547b3cH374Ic888wxt27Zl1qxZLjt62Ajh4eEEBgayZcsWSQYKKSUlhTZt2mC321m6dCllypS56ecGB9iIqFzauOCELpo3V8VsiYnwyCNmRyPyRUVBcjK89hr8+9+wcCFMnaq6GApjePwywZFTF+j1aTIJ7/7ItOTDpJ2+eNWWLw04fOoC05IPk/Duj/T6NJkjp/5XtpqTk8OgQYMYOHAggwYNYuHChR6dCADYbDYiIyOlbqCQ1q1bR0JCAvXq1WPZsmUFSgSE5yhbVhWtyVKB+/H3V4nAunVql8Htt8Pbb0NurtmReSePTgZmb0ij9bgk1h5IB7hhm9j8x9ceSKf1uCRmb0jjzJkzdOjQgQkTJjBx4kTGjRuHn5+f4bG7ghQRFs6aNWto06YN0dHRLF26lNKl5RO+N8s/p0BO1nNPjRqp2oGnn4Z//UvN5uzbZ3ZU3sdjk4HxiXsZOnc7mTl5Be4Vn5unkZmTx9C522nYZzjJycksWbKEAQMGGBStORwOBzt37iQrK8vsUDxGUlISd911Fw0bNmTx4sWULCntgb1dfDwcOaJ6Dgj3FBioZgV+/BGOHQOHAz78EKTjun48MhmYvSGNscv26DJWdt27GPnFMlq3bq3LeO4kJiaG7Oxslx/r7KlWrlxJu3btaNKkCd9//z0lSpQwOyThAs2bq+1tssXQ/d15J2zdCg8/DAMHQps20jRKLx6XDBw5dYFXF+7UdcyPUk5eUUPgLaKjowGk+dBNWL58OXfffTdxcXF8++23BAXJtj9fUbq0Wo+WugHPUKKEmhVYtgx271ZnTEyZIss8ReVxycCwedvJ0fkI2Zw8jWHztus6pjsoVaoUNWrUkLqBG1i8eDEdO3akZcuWLFiwwGO6TAr95J9TIG8oniMhQR16dN990LcvdOwIv/9udlSey6OSgb1/nGP1vpO6nyefm6exet9J9h0/p+u47kCKCK/vu+++45577qFNmzbMnTuXwMBAs0MSJnA64ehR2LvX7EhEQZQurWYFFi6En39WWw9nz5akrjA8KhmYkZym+zny+fysFqav977Fp/wzCvQ8btdbzJ8/n65du3L33Xfz9ddfExAQYHZIwiR33gl+frJU4Kk6doSdO1UNwYMPQvfuUIQmtD7Jo5KBxN3HdZ8VyJebp5G457ghY5spJiaG9PR0jh49anYobuWbb76hW7dudO7cmTlz5uDv7292SMJEpUpBgwZSROjJQkLUrMDs2erwo4gIWLDA7Kg8h8ckA+czc0gzuMgvLf0CGZk5hl7D1fLbEksR4f/MmTOH7t27c9999zFr1iyKSZ9TwWX9Bs6dp/juLTQmmeK7t8D582aHJgqge3c1SxAbC/fcA336wJ9/FmHA875xP3hMMnA4PQOjJ7o14FB6hsFXca1q1apRunRpqRv4r5kzZ9KjRw8efPBBpk2bhs3mNR25RVGkpvLEL0+z5o8wKF2Kuj3qk0wT6vaor6YNwsJU15vUVLMjFTehUiU1K/D55zB/vtpxsGxZAQZITVW/77AwKOUb94PHJANZOa7pLuGq67iKxWL5q27A133xxRf06tWL3r178/nnn0siIODgQbXQHBFB6PcTCGM/lr/X12ga7N8PEyaouec2bdTzhFuzWNSswI4dULcutG0LTzxxgw/2l90PTJigfu8+cj94TDLgb3NNqK66jis5HA6fnxmYMmUKDz/8MI8++iiffvqp17ScFkUweTLUq/dXoYAl9wZLhDn/fTwxUT1v8mSDAxR6qFpVzQp89BF88QVER6tOhlf52/3w1+/7n3jZ/eAx73zVQ4KvOiNeb5b/XsfbxMTEsHfvXjIyvGsJ5GZ9/PHH9O3blwEDBjBp0iSsVo+57YVRRo+G/v3h0qUbv+j/XU6Oel7//moc4fYsFjUrsG0bVKmi+ko89xxcvPjfL5D7wXOSgeAAG6HljO0KFxoS5JVnzjscDjRNY/t272usdCMfffQRAwYMYODAgXz00UeSCAj1CW74cH3GGj4cPv1Un7GE4WrVUttHx45VMwX168PBl+V+AA9KBgCc9gqG9hlwhlcwZGyzRURE4Ofn53NLBe+//z5PPfUUgwcP5v3337/qaGvhgw4ehEGD9B1z4ECvWDP2FX5+alZg82aw+x+k0phB+hane+j94FHJQM/YUEP7DDzUJNSQsc0WGBhInTp1fKqI8P/+7/945plneOGFF3jnnXckERDKgAEFnwa+kZwcNa7wKHXrwryKA/C35ui7BO2h94NHJQO1K5YkLqy8/rMDebk0qBJEWAXvPa7Wl4oI33zzTZ5//nleeukl3nzzTUkEhJKaCsuXG5MMLF8Ov/yi77jCWKmpWFcsxy9P7gfwsGQAYEyXKGwGJAM/vN6PmTNn6juuG4mJiWHbtm3kefkB4K+99hpDhw5lxIgRjB49WhIB8T8TJ4JR20ltNrXVTHgOuR+u4HHJQNVyQYzqFKHrmCM7RdDB2ZSePXvy6KOPemXVvcPhICMjg/3795sdiiE0TWPkyJG88sor/Pvf/2bUqFGSCIgrLVqk/6xAvpwcWLzYmLGFMeR+uILHJQMADzQK5fk24bqM9UIbOw/HhTNt2jQ+//xz5syZQ4MGDbxufT2/LbE3LhVomsYrr7zCqFGjGDNmDK+88orZIQl3c+4cHDhg7DX27/faVrVeR+6Hq3hkMgAw0FmbN7pGEWCzFriGwM9qIcBm5c2uUTzlDANUp74+ffqwadMmAgMDadKkCePHj/ea0/4qVqxIpUqVvC7J0TSNl156idGjR/P222/z0ksvmR2ScEfX6iSnN02DffuMvYbQh9wPV/HYZADUDMGKZ1vQrGYIwA2TgvzHm9UMYcWzLeje6OrdA3a7nfXr1/PYY48xaNAgunTpwqlTp/QP3gQxMTFeNTOgaRrPP/88b775JuPGjeP55583OyThrjIzves6omjkfriKRycDoGoIpvWNZfng5vSKrUa1kKCrtolYgGohQfSKrcaKZ5szrW8sVa/TwCgwMJD333+f+fPns3r1ahwOB6tXrzb0+3AFbzqjQNM0Bg8ezDvvvMMHH3zA4MGDzQ5JuLOAAO+6jigauR+uYtG8ZR78MhmZORxKzyArJw9/m5XqIcGF7ix45MgRevbsyU8//cTIkSMZNmyYx/a1nzVrFj169CA9PZ1y5cqZHU6h5eXlMWjQID766CMmTJjA448/bnZIwt2dP69OmzPy5c5igbNnoUQJ467hYps2QYMGsHEj3H672dHoSO6Hq3j8zMC1BAfYiKhcmvqhZYmoXLpILYarVq3KypUreeWVVxg5ciStW7fmt99+0zFa14mJiQE8u4gwLy+Pxx9/nAkTJvDJJ59IIiBuTokSULOmsdeoVctjXvh9ntwPV/HKZEBvNpuNkSNHsnLlSvbu3YvD4eC7774zO6wCq127NoGBgR67VJCbm0u/fv2YPHkyn332Gf369TM7JOFJ2rc3dl95u3bGjC2MIffDFSQZKIAWLVqwZcsWmjZtSseOHRk8eDCZHlQgYrPZiIqK8siZgdzcXB555BGmTp3KF198QZ8+fcwOSXiaxx83dl/5E08YM7YwhtwPV5BkoIDKly/PwoULee+995gwYQJNmzZl7969Zod10zyxiDAnJ4fevXszc+ZMZsyYwUMPPWR2SMIT1asHCQm6fxrMwUbGHQmq2b3wGHl16pFWJ4FsdJ4dsNnUfeZh94MkA4VgsVh4+umnWbduHefPn+f2229n2rRpZod1UxwOB6mpqWRlZZkdyk3Jzs6mZ8+efPnll8yePZsHHnjA7JCEJ5s0SddkQANyLDYa/jyJDz4AL+/27TXS0qBNG2ixaxKan03fUwttNnWfeRhJBorg9ttvZ+PGjXTt2pXevXvTp08fzrt5x6mYmBiys7PZtWuX2aHcUFZWFg888ADz5s3jyy+/5L777jM7JOHpatSADz7QbTgLYBk/ntb9a/D009C6NRw6pNvwQmeaBp99BlFRsHs3fLysBv4TP9D31MLx49V95mk0oYsvvvhCCw4O1mrXrq1t3LjR7HD+0ZkzZzRAmzp1qtmhXNelS5e0zp07a/7+/trChQvNDkd4m9de0zT13lC0f0aP/mvIFSs0LTRU00qU0LRPPtG0vDwTvz+dbNyovk03fkm7aUePalqHDur7efhhTTt9+rIHDbgfPI0kAzravXu3Vr9+fc3f31979913tTw3fTWoWbOm9txzz5kdxj+6ePGidvfdd2sBAQHa999/b3Y4wlt98ommBQZqms1WsBd8m009b/Lkq4Y8c0bT+vZVX9aunab99psJ35eOvCEZyMvTtFmzNK1cOU2rWFHT/vGzhQH3gyeRZEBnly5d0gYPHqwBWseOHbUTJ06YHdJVunTporVs2dLsMK7pwoUL2l133aUFBgZqS5cuNTsc4e0OHNC0hIT/vajf6EUf1NcfOHDdYb/7TtNuvVXTypbVtBkzPHeWwNOTgRMnNK1bN/U9dO+uaSdP3uAJBt0PnkCSAYN8++23WkhIiFalShVt1apVZodzhVGjRmkhISFuN3ORkZGhJSQkaMWLF9dWrFhhdjjCl+zcqWmDBmlaWJimWSxXvuhbLOq/DxqkaampNz1kerqm9eihhrj3Xk07ftzA+A3iycnA/PmaVqGCmhGYPbuATzbgfnB3kgwY6Ndff9VatGihWa1W7dVXX9Wys7PNDknTNE2bP3++BmhHjhwxO5S/nD9/XnM6nVpwcLCWmJhodjjCl507p2mbN2va+vXqz3PnijTcV19pWvnymnbLLZo2d64uEbqMJyYDp09rWu/eKu6OHTXt99+LOKDO94O7kmTAYDk5OdqoUaM0q9WqxcXFaWlpaWaHpB08eFADtO+++87sUDRN07SzZ89qzZs310qUKKH9+OOPZocjhO6OHdO0e+5Rb1APPaRpp06ZHdHN8bRkYOlSTatSRdNKldK0zz/33OUZM8jWQoP5+fkxYsQIVq1axcGDB4mJiWHhwoWmxlStWjVKly7tFp0Iz549S7t27di8eTNLly4lLi7O7JCE0F3FijB3LnzxBXz7LURGwpIlZkflPc6fVw3/2rZVvX527IA+fdRZQeLmSDLgInFxcWzZsoW4uDg6d+7M008/zaVLl0yJxWKxuEUnwjNnztC2bVt27NjB8uXLadasmanxCGEkiwV69VJvVFFRqnX9gAFw7pzZkXm2H3+E6GiVaH30ESxbBlWrmh2V55FkwIVCQkKYN28e48ePZ9KkSTRp0oTdu3ebEktMTIypMwOnT58mISGB3bt3s2LFCmJjY02LRQhXuu02WLxYNambMUO9ka1aZXZUnufiRXjuOYiPhypVYNs2NTsgswGFI8mAi1ksFp566imSk5O5dOkSDRo0YOrUqWhGnqt9DQ6Hg71795KRkeHS6wKkp6fTunVrDhw4wA8//EDDhg1dHoMQZrJY4LHH1BtYaCg4nTB4MFy4YHZkniElBerXVzMBY8eqZKpWLbOj8mySDJgkJiaGjRs3cv/99/Pwww/Tq1cvzrlwvtDhcKBpGtu3b3fZNQFOnjxJq1atSEtLY+XKldSvX9+l1xfCndSsCYmJMG6cmimoXx/Wrzc7KveVlQXDh0PTplCyJGzerGYH/PzMjszzSTJgouDgYKZMmcKMGTNYuHDhX2cduEJERAR+fn4uXSo4fvw4LVu25PfffycxMZHo6GiXXVsId2W1qlmBLVugbFm44w4YNgw86HR0l9i6FRo1gjffhFGjYN06jzsY0K1JMuAGevTowaZNmyhdujRNmzblnXfeIc/g488CAwOpU6eOy4oIjx07htPp5MSJE6xatYrIyEiXXFcIT2G3w5o18Nprauq7USP1ydfX5eTA6NHq56FpsGGDmh3Q+SRqnyfJgJsICwtj7dq1PP300wwZMoSOHTty4sQJQ6/pqiLCo0ePEh8fz59//smqVauoK+m8ENdks8FLL8HPP6sZg8aN4T//gexssyMzx65d0KwZjBgBL7ygEoGYGLOj8k6SDLgRf39/xo4dy/fff09KSgoOh4PExETDrudwONi2bZuhsxC//vor8fHxXLhwgaSkJOx2u2HXEsJbREerIrmhQ9WUeLNmkJpqdlSuk5en6ijq14czZ2DtWjU7EBBgdmTeS5IBN9S+fXu2bt1K3bp1adWqFa+88go5OTm6XycmJoaMjAz279+v+9gAaWlptGjRgqysLJKSkggLCzPkOkJ4I39/NSuwbp1qqnP77fB//we5uWZHZqwDB9Tuiueeg8cfV0slsvPYeJIMuKnKlSuzbNky/vOf//D6668THx9PWlqartdwOBwAhtQNHDp0iBYtWqBpGklJSdSoUUP3awjhCxo1gk2bYOBANVXeogXs22d2VPrTNJg4Uc2KpKX9b5dFUJDZkfkGSQbcmJ+fHy+//DJJSUkcOXKEmJgY5s2bp9v4FSpUoFKlSrrXDezfv58WLVrg5+dHUlIS1apV03V8IXxN8eKqqDApCX7/HRwOtcfe4DpjlzlyBO66SzUNeugh1X8hPt7sqHyLJAMe4I477mDLli04nU66du3KU089pVsrY72LCPfu3UuLFi0IDAwkKSmJqtIXVAjdxMWpLXZ9+sBTT6le/DpPGLqUpqk2wlFRsHOn6sw4caLqISBcS5IBD1G2bFm+/vprPvroIz799FNiY2P55ZdfijyunmcU7N69mxYtWlCyZElWrVpFlSpVdBlXCPE/JUr8rwf/rl3qjfTzz9Ubqyf54w/o0kUlNp06wfbtanZAmEOSAQ9isVh44oknSElJITs7m4YNGzJlypQitTKOiYnh119/JT09vUixpaam0qJFC8qVK8eqVau49dZbizSeEOL6EhLUG2jXrvDII9C5Mxw7ZnZUN+erryAiQu0SyD/NsWxZs6PybZIMeKDo6Gg2bNjAgw8+SN++fenRowdnzpwp1Fj5RYRFWSrYsWMH8fHxVKxYkcTERCpWrFjosYQQN69MGfjsM1iwQG1FjIiAOXPMjuqfpafDgw/C/fermoCdO9XsgDCfJAMeKjg4mMmTJzNr1iy+//57br/9djZs2FDgcWrXrk1gYGChk4GtW7cSHx9PlSpVWLlyJbfcckuhxhFCFF6nTupo5Nat4YEHoHt3OHnS7Kiu9N13EBkJS5fCzJlqdkBeLtyHJAMe7oEHHmDLli2UL1+eZs2aMXbs2AI1EbLZbERFRbFx6w52Hj3D5rTT7Dx6hozMG/c12LRpEy1btqR69er88MMPhISEFOVbEUIUQfnyalZg9mxYsULNEixcWIQBz5+n+O4tNCaZ4ru3qGYHhXDmDPTtCx07ql4JO3ao2QE5ati9WDRXn50rDJGVlcXw4cN5++23ueuuu5g6dSoVKlS47nP2/nGOGclpzFmzg4vW4Cv+77QAoeWCcNor0DM2lNoVryzv3bBhA23atCE8PJylS5dSpkwZA74rIURhHDsG/furT+N9+sC776olhRtKTVXl/IsWqe4/l789WCzqmMX27VU3oHr1bjjcDz+oeoY//1Q9Ax59VJIAdyXJgJdZunQpvXv3xmq1Mm3aNFq3bn3V1xw5dYFh87azet9J/KwWcvP++RbIfzwurDxjukRRtVwQ69evp23btkRERLB48WJKly5t5LckhCgETYOpU+GZZ6BUKfj0U2jT5h+++OBBGDAAli9XByRcr+Np/uMJCerc5Ws0FMvIgH/9Cz78UHUT/OwzkHYj7k2SAS907NgxevXqxQ8//MDQoUMZNWoUxYoVA2D2hjReXbiTnDztuknA3/lZLdisFnrVC+CtAffgcDhYtGgRJWVDsBBuLS1NTdOvWKE+0L/9ttqe+JfJk2HQIPUGX5C25zab+ueDD6Bfv7/+808/qdmIo0fhrbfgySfVoUvCvUky4KXy8vJ46623GD58OI0bN2bmzJl8dzCbscv2FHnssmmrWT1pOCWueEURQrirvDw1+//CC1Cpkvqk3rw56vSf4cOLfoHXXuPSkJcZMUJ1SmzSRM1K1K5d9KGFa0gy4OXWrVvHgw8+yIVbYwiK76/buG92jaJ7o1DdxhNCGG/fPrWG/9NPMLv1ZO5frt9rwiu3Tuat9L785z8wZAj4+ek2tHABSQZ8wM5Dx+gwIZk8ix8Wnap3AmxWVjzbgqrl5BQRITxJbi5MeeUgD71ej0AuoccrggZkWgI5vCgV+11yKJknkpUcH/DGD4ex2orplggA5ORpDJu3XbfxhBCu4ecH/X8eQKBfji6JAKjdRwF+OdjfGaDTiMLVJBnwcnv/OMfqfScLVCx4M3LzNFbvO8m+4+d0HVcIYbDUVFi+HEtuAYoFb4IlJ0ftRtDhzBThepIMeLkZyWn4WY3Z2OtntTB9vQcfmSaEL5o4Ue0CMILNBhMmGDO2MJQkA14ucfdx3WcF8uXmaSTuOW7I2EIIgyxaVLAthAWRk6POIRYeR5IBL3Y+M4e0UxcMvUZa+oWbal0shHAD586pzoJG2r+/0K2LhXkkGfBih9MzMHqriAYcSs8w+CpCCF3s339li2EjaJrawyg8iiQDXiwr5+YPLPKE6wghiigz07uuI3QjyYAX87e55tfrqusIIYooIMC7riN0I6/iXqx6SLBu+4j/ieW/1xFCeICwMOOPDbRY1HWER5FkwIsFB9gINbhDYGhIEMEBBm1TEkLoq0QJdQyxkWrV+ttJSMITSDLg5Zz2Cob2GXCGVzBkbCGEQdq3N7bPQLt2xowtDCXJgJfrGRtqaJ+Bh5rIYUVCeJTHHze2z8ATTxgztjCUJANernbFksSFldd9dsDPaiEurDxhFUrqOq4QwmD16kFCgv6zAzabGrduXX3HFS4hyYAPGNMlCpvOyYDNamFMlyhdxxRCuMikScYkA5Mm6TumcBlJBnxA1XJBjOoUoeuY/+4UIccXC+GpatSADz7Qd8zx49W4wiNJMuAjHmgUyvNtwnUZ64U2dro3kloBITxav37w2mv6jDV6NPTtq89YwhQWTTO6N6VwJ7M3pPHqwp3k5GkFKiz0s1qwWS38u1OEJAJCeJPJk2HQIFX8V5DCQptN/TN+vCQCXkCSAR905NQFhs3bzup9J/GzWq6bFOQ/HhdWnjFdomRpQAhvdPAgDBgAy5erN/jrJQX5jyckqBoBWRrwCpIM+LC9f5xjRnIaiXuOk5Z+4YpDjSyohkLO8Ao81CRUdg0I4QtSU2HiRHUM8d8PNbJYVEOhdu3U9kHZNeBVJBkQAGRk5nAoPYOsnDz8bVaqhwRLZ0EhfNn58+r0wcxMddZAWJh0FvRikgwIIYQQPk52EwghhBA+TpIBIYQQwsdJMiCEEEL4OEkGhBBCCB8nyYAQQgjh4yQZEEIIIXycJANCCCGEj5NkQAghhPBxkgwIIYQQPk6SASGEEMLHSTIghBBC+DhJBoQQQggfJ8mAEEII4eMkGRBCCCF8nCQDQgghhI+TZEAIIYTwcZIMCCGEED5OkgEhhBDCx0kyIIQQQvg4SQaEEEIIHyfJgBBCCOHjJBkQQgghfJwkA0IIIYSPk2RACCGE8HGSDAghhBA+TpIBIYQQwsdJMiCEEEL4uP8H2qZram9FYZMAAAAASUVORK5CYII=\n", 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"text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -426,7 +426,7 @@ }, { "cell_type": "markdown", - "id": "d7434695", + "id": "92e36b92", "metadata": {}, "source": [ "See the examples for more ideas.\n", @@ -466,13 +466,13 @@ { "cell_type": "code", "execution_count": 8, - "id": "cab35b17", + "id": "c2642a0b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:37.324177Z", - "iopub.status.busy": "2023-01-04T17:44:37.323741Z", - "iopub.status.idle": "2023-01-04T17:44:37.328223Z", - "shell.execute_reply": "2023-01-04T17:44:37.327460Z" + "iopub.execute_input": "2023-01-04T19:40:10.393348Z", + "iopub.status.busy": "2023-01-04T19:40:10.392933Z", + "iopub.status.idle": "2023-01-04T19:40:10.397160Z", + "shell.execute_reply": "2023-01-04T19:40:10.396494Z" } }, "outputs": [ @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "5608f047", + "id": "0fa58dcf", "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": "2320ac72", + "id": "00336d72", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:37.332966Z", - "iopub.status.busy": "2023-01-04T17:44:37.332699Z", - "iopub.status.idle": "2023-01-04T17:44:37.337389Z", - "shell.execute_reply": "2023-01-04T17:44:37.336633Z" + "iopub.execute_input": "2023-01-04T19:40:10.401628Z", + "iopub.status.busy": "2023-01-04T19:40:10.401366Z", + "iopub.status.idle": "2023-01-04T19:40:10.405820Z", + "shell.execute_reply": "2023-01-04T19:40:10.405091Z" } }, "outputs": [ diff --git a/searchindex.js b/searchindex.js index f3fcb0ce..dfa6c83a 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["auto_examples/3d_drawing/index", "auto_examples/3d_drawing/mayavi2_spring", "auto_examples/3d_drawing/plot_basic", "auto_examples/3d_drawing/sg_execution_times", "auto_examples/algorithms/index", "auto_examples/algorithms/plot_beam_search", 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1429], "formula": [8, 299, 316, 322, 380, 385, 618, 688, 1424], "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, 1124, 1125, 1126, 1132, 1136, 1138, 1140, 1151, 1154, 1157, 1167, 1168, 1169, 1170, 1177, 1178, 1180, 1196, 1199, 1200, 1201, 1209, 1210, 1220, 1221, 1222, 1225, 1238, 1249, 1251, 1253, 1261, 1266, 1267, 1272, 1275, 1278, 1279, 1281, 1282, 1284, 1285, 1286, 1287, 1298, 1299, 1300, 1302, 1304, 1305, 1306, 1323, 1324, 1326, 1327, 1329, 1331, 1332, 1333, 1336, 1337, 1350, 1352, 1355, 1357, 1359, 1360, 1365, 1366, 1374, 1375, 1381, 1383, 1385, 1388, 1390, 1391, 1395, 1396, 1397, 1398, 1399, 1402, 1405, 1407, 1408, 1409, 1411, 1412, 1415, 1428, 1429], "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, 1133, 1134, 1135, 1136, 1138, 1140, 1141, 1142, 1143, 1144, 1145, 1146, 1188, 1195, 1196, 1209, 1217, 1220, 1221, 1222, 1275, 1290, 1291, 1298, 1299, 1300, 1326, 1329, 1331, 1340, 1348, 1351, 1352, 1353, 1393, 1397, 1398, 1400, 1401, 1402, 1404, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429], "express": [8, 92, 110, 184, 315, 329, 330, 383, 384, 618, 619, 873, 916, 955, 998, 1202, 1290, 1329], "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, 1138, 1149, 1157, 1165, 1168, 1170, 1175, 1177, 1188, 1190, 1197, 1201, 1229, 1233, 1234, 1239, 1240, 1241, 1242, 1278, 1279, 1299, 1300, 1329, 1331, 1348, 1351, 1352, 1353, 1356, 1357, 1361, 1368, 1369, 1382, 1385, 1398, 1405, 1407, 1408, 1411, 1416, 1426, 1428], "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, 1134, 1135, 1141, 1142, 1143, 1144, 1145, 1331, 1405], "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, 1124, 1126, 1130, 1138, 1140, 1219, 1220, 1222, 1288, 1294, 1299, 1301, 1329, 1355, 1381, 1396, 1408, 1409, 1415, 1416, 1420, 1428, 1429], "wherea": [8, 103, 682, 762, 786, 791, 1168, 1420], "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, 1168, 1211, 1212, 1299, 1301, 1305, 1306, 1329, 1348, 1350, 1351, 1352, 1353], "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, 1220, 1314, 1329, 1406, 1410], "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, 1151, 1218, 1220, 1237, 1281, 1282, 1299, 1331, 1405, 1408, 1410], "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, 1168, 1216, 1218, 1220, 1242, 1265, 1272, 1275, 1329, 1331, 1333, 1396, 1397, 1407, 1409, 1414, 1429], "infeas": [8, 422], "circuit_to_formula": 8, "dag_to_branch": [8, 758, 1411], "transfer": [8, 202, 204, 230, 231, 469, 890, 891, 926, 927, 971, 972, 1008, 1009, 1423], "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, 1167, 1221, 1222, 1298, 1305, 1322, 1326, 1328, 1329, 1396, 1397, 1403, 1407, 1408, 1409, 1410, 1411, 1414, 1415, 1416, 1417, 1420], "variabl": [8, 94, 132, 373, 530, 540, 618, 619, 732, 796, 1037, 1038, 1039, 1040, 1042, 1124, 1126, 1157, 1168, 1329, 1411, 1415, 1416, 1417, 1423], "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, 1128, 1129, 1148, 1150, 1238, 1274, 1275, 1326, 1368, 1369, 1396, 1409, 1410, 1411, 1415, 1416, 1426, 1428], "children": [8, 460, 577, 1148, 1158, 1275, 1368, 1369], "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, 1124, 1138, 1140, 1168, 1188, 1200, 1220, 1273, 1285, 1286, 1287, 1310, 1312, 1315, 1345, 1359, 1360, 1379, 1412, 1416, 1429], "child": [8, 1150, 1275], "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, 1136, 1140, 1149, 1165, 1168, 1176, 1179, 1189, 1191, 1193, 1196, 1200, 1202, 1212, 1216, 1220, 1222, 1238, 1242, 1243, 1273, 1278, 1279, 1280, 1281, 1282, 1298, 1299, 1301, 1310, 1312, 1313, 1314, 1315, 1318, 1336, 1340, 1341, 1342, 1343, 1362, 1364, 1365, 1366, 1367, 1368, 1369, 1379, 1396, 1397, 1398, 1410, 1429], "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, 1124, 1245, 1302, 1304, 1306, 1313, 1322, 1323, 1324, 1328, 1405, 1409, 1410, 1414, 1416, 1419, 1422], "arbitrary_el": [8, 1395, 1416], "nb": [8, 1334, 1337], "left": [8, 71, 115, 183, 311, 312, 322, 324, 325, 385, 559, 560, 584, 616, 688, 689, 739, 1106, 1137, 1139, 1149, 1182, 1209, 1283, 1358, 1361, 1407], "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, 1137, 1139, 1149, 1158, 1160, 1182, 1209, 1216, 1218, 1273, 1283], "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, 1165, 1213, 1299, 1391, 1407, 1412], "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, 1239, 1276, 1299, 1306, 1329, 1348, 1353, 1407, 1410, 1419], "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, 1176, 1195, 1238, 1249, 1253, 1254, 1259, 1261, 1272, 1323, 1324, 1390], "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, 1124, 1126, 1173, 1177, 1203, 1204, 1209, 1210, 1213, 1220, 1226, 1230, 1237, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1256, 1257, 1261, 1264, 1266, 1267, 1272, 1278, 1279, 1280, 1283, 1299, 1300, 1326, 1327, 1329, 1331, 1346, 1385, 1386, 1396, 1398, 1401, 1405, 1407, 1410, 1411, 1412, 1414, 1415, 1416, 1429], "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, 1042, 1120, 1125, 1129, 1232, 1281, 1282, 1299, 1331, 1396, 1407, 1409], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1328, 1405, 1409, 1415], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1124, 1125, 1126, 1205, 1386], "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, 1176, 1178, 1180, 1195, 1210, 1225, 1226, 1230, 1232, 1237, 1244, 1299, 1303, 1306, 1329, 1336, 1337, 1344, 1345, 1347, 1354, 1356, 1357, 1358, 1359, 1360, 1361, 1374, 1382, 1383, 1384, 1386, 1396, 1407, 1408, 1409, 1413, 1420, 1429], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1222], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1232, 1299, 1398, 1407], "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, 1151, 1165, 1198, 1219, 1220, 1260, 1267, 1281, 1282, 1299, 1410], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1180, 1210, 1211, 1407, 1409, 1410, 1429], "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, 1133, 1148, 1157, 1163, 1165, 1168, 1179, 1183, 1188, 1196, 1198, 1199, 1200, 1201, 1202, 1210, 1213, 1214, 1218, 1220, 1225, 1237, 1242, 1246, 1247, 1251, 1252, 1257, 1262, 1264, 1267, 1270, 1272, 1273, 1275, 1278, 1279, 1280, 1281, 1282, 1284, 1285, 1286, 1287, 1288, 1289, 1292, 1294, 1296, 1299, 1303, 1329, 1331, 1333, 1336, 1337, 1356, 1357, 1374, 1375, 1382, 1385, 1396, 1397, 1398, 1401, 1406, 1407, 1408, 1409, 1410, 1412, 1416, 1417, 1419, 1426, 1428], "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, 1196, 1200, 1202, 1272, 1299, 1329, 1337, 1344, 1347, 1358, 1361, 1402, 1405, 1407, 1409, 1414, 1416, 1417, 1429], "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, 1137, 1149, 1150, 1152, 1154, 1155, 1159, 1177, 1188, 1189, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1207, 1210, 1213, 1214, 1218, 1220, 1221, 1246, 1247, 1256, 1274, 1275, 1278, 1279, 1297, 1298, 1299, 1326, 1327, 1329, 1331, 1362, 1363, 1366, 1396, 1397, 1398, 1400, 1405, 1407, 1408, 1409, 1410, 1413, 1414, 1416, 1428], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1423], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1220, 1229, 1265, 1266, 1329, 1410], "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, 1042, 1088, 1102, 1139, 1153, 1155, 1157, 1160, 1162, 1190, 1191, 1280, 1285, 1326, 1327, 1348, 1351, 1352, 1353, 1385, 1410, 1416, 1417], "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, 1129, 1147, 1168, 1192, 1195, 1202, 1210, 1213, 1214, 1216, 1218, 1285, 1299, 1329, 1331, 1361, 1366, 1367, 1390, 1396, 1398, 1405, 1416, 1419, 1420, 1428, 1429], "negat": 8, "sole": [8, 786, 1281, 1282, 1329], "fourth": [8, 230, 231, 1329, 1407], "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, 1042, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1138, 1153, 1157, 1171, 1172, 1173, 1176, 1180, 1181, 1183, 1185, 1186, 1187, 1188, 1192, 1220, 1273, 1275, 1276, 1277, 1286, 1287, 1290, 1293, 1295, 1301, 1326, 1329, 1336, 1340, 1345, 1359, 1360, 1365, 1368, 1369, 1374, 1396, 1402, 1404, 1405, 1407, 1408, 1409, 1410, 1411, 1412, 1414, 1415, 1416, 1417, 1419, 1420, 1427, 1428, 1429], "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, 1278, 1329, 1348, 1410, 1411, 1420, 1429], "get_node_attribut": [8, 39, 44, 71, 1216, 1407], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1136, 1137, 1139], "22": [8, 35, 64, 66, 383, 384, 1274, 1326, 1406, 1411, 1415, 1425], "multipartite_layout": [8, 36, 61, 67, 1415, 1417, 1423], "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, 1165, 1168, 1201, 1207, 1233, 1242, 1274, 1283, 1294, 1310, 1312, 1315, 1401, 1402], "122": [8, 17, 56, 1238, 1329, 1429], "plot_circuit": [8, 17], "southern": [9, 1268], "women": [9, 1268, 1401, 1409], "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, 1137, 1139, 1196, 1205, 1220, 1222, 1272, 1286, 1287, 1329, 1331, 1386, 1401, 1408, 1409, 1410, 1411, 1416, 1420, 1429], "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, 1326, 1329, 1388, 1390, 1395, 1397, 1398, 1400, 1402, 1407, 1408, 1414, 1429], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1202, 1396, 1398, 1402, 1405, 1408, 1409, 1410, 1416, 1419], "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, 1205], "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, 1205, 1410, 1416], "1930": [9, 1399], "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, 1124, 1125, 1126, 1129, 1136, 1138, 1140, 1154, 1162, 1168, 1196, 1200, 1201, 1220, 1274, 1275, 1326, 1331, 1356, 1357, 1359, 1360, 1362, 1366, 1397, 1399, 1405, 1407, 1409, 1412, 1417, 1429], "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, 1143, 1154, 1188, 1196, 1197, 1199, 1200, 1201, 1202, 1212, 1220, 1243, 1246, 1249, 1253, 1261, 1270, 1272, 1275, 1276, 1281, 1282, 1326, 1327, 1329, 1332, 1333, 1349, 1350, 1391, 1396, 1409], "observ": [9, 13, 132, 223, 1417, 1429], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 64, 66, 71, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1153, 1245, 1253, 1265, 1409, 1411, 1429], "event": [9, 25, 99, 100, 110, 1168, 1232, 1303], "18": [9, 44, 64, 66, 93, 324, 325, 345, 383, 384, 618, 1172, 1252, 1258, 1261, 1263, 1266, 1272, 1396, 1409, 1419, 1420, 1424, 1429], "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, 1154, 1206, 1207, 1208, 1268, 1328, 1398, 1401, 1402, 1403, 1404, 1409, 1410, 1414, 1416, 1420, 1424, 1428], "biadjac": [9, 282, 283, 1403, 1409], "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, 1042, 1052, 1053, 1085, 1100, 1104, 1151, 1215, 1245, 1251, 1253, 1254, 1258, 1261, 1263, 1276, 1326, 1329, 1333, 1342, 1343, 1348, 1351, 1352, 1353, 1385, 1395, 1397, 1405, 1406, 1408, 1411, 1412, 1413, 1414, 1415, 1416, 1429], "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, 1136, 1139, 1153, 1247, 1248, 1252, 1257, 1260, 1266, 1338, 1409, 1411, 1415, 1429], "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, 1151, 1153, 1197, 1202, 1215, 1220, 1238, 1249, 1258, 1270, 1276, 1286, 1287, 1326, 1329, 1331, 1399, 1406, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429], "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, 1153, 1290, 1406, 1413, 1416, 1417, 1422, 1427, 1428, 1429], "13": [9, 11, 38, 44, 64, 66, 89, 91, 156, 229, 230, 231, 343, 501, 703, 855, 900, 936, 981, 1153, 1195, 1409, 1423, 1429], "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, 1208, 1259, 1274, 1289, 1326, 1409, 1414, 1429], "17": [9, 21, 44, 64, 66, 103, 229, 230, 231, 297, 508, 680, 693, 1408, 1409, 1429], "friend": [9, 545, 1410, 1415], "member": [9, 92, 93, 94, 100, 112, 315, 317, 318, 319, 330, 391, 483, 484, 586, 691, 1225, 1270, 1406], "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, 1168, 1199, 1200, 1201], "50": [9, 25, 30, 34, 40, 50, 54, 55, 56, 57, 64, 65, 272, 312, 1117, 1196, 1200, 1201, 1254, 1300, 1305], "45": [9, 58, 64, 110, 226, 300, 409, 1178], "57": [9, 64], "46": [9, 64, 235, 564, 619, 1267], "24": [9, 19, 37, 64, 66, 68, 103, 383, 384, 496, 505, 508, 703, 1215, 1232, 1247, 1265, 1274, 1406], "32": [9, 64, 66, 68, 209, 211, 212, 383, 384, 564, 703, 1406, 1414], "36": [9, 21, 64, 68, 752, 1153, 1265, 1274, 1356, 1357, 1382, 1406], "31": [9, 17, 64, 66, 229, 230, 231, 260, 261, 262, 289, 383, 384, 409, 703, 1229, 1238, 1406], "40": [9, 50, 64, 80, 101, 297, 300, 555, 672, 1176, 1243, 1274], "38": [9, 64, 688, 1274], "33": [9, 58, 64, 66, 68, 93, 383, 384, 500, 514, 703, 1270, 1274, 1406, 1417], "37": [9, 56, 64, 68, 303, 311, 312, 323, 324, 325, 496, 508, 1039, 1040, 1274, 1396, 1406, 1411, 1428], "43": [9, 64, 324, 325, 606, 1247, 1274], "34": [9, 59, 64, 68, 331, 508, 762, 1274, 1406], "algorithm": [9, 14, 15, 44, 52, 54, 88, 93, 94, 95, 96, 102, 103, 107, 109, 110, 111, 112, 114, 115, 117, 120, 121, 122, 125, 127, 128, 132, 133, 136, 141, 151, 210, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 228, 229, 230, 231, 232, 235, 249, 251, 252, 253, 254, 255, 256, 258, 260, 261, 262, 263, 264, 265, 266, 267, 272, 275, 277, 278, 280, 282, 284, 285, 286, 287, 288, 289, 290, 293, 296, 297, 298, 299, 301, 302, 303, 306, 307, 308, 309, 311, 312, 315, 320, 322, 323, 324, 325, 326, 329, 330, 331, 332, 333, 337, 339, 340, 341, 342, 343, 345, 346, 347, 352, 358, 361, 362, 366, 371, 372, 373, 374, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 389, 390, 394, 399, 405, 406, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 421, 422, 424, 425, 426, 427, 428, 429, 430, 432, 433, 435, 437, 440, 449, 451, 452, 453, 454, 455, 460, 464, 466, 468, 481, 482, 483, 488, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 512, 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632, 679, 690, 693, 717, 718, 751, 1220, 1239, 1298, 1299, 1303, 1306, 1329, 1419, 1420], "result": [10, 11, 25, 71, 92, 95, 101, 103, 109, 110, 112, 142, 165, 209, 218, 220, 230, 231, 255, 269, 271, 273, 276, 283, 284, 285, 286, 287, 288, 289, 299, 300, 305, 324, 325, 330, 374, 380, 381, 382, 385, 386, 391, 411, 412, 416, 418, 440, 464, 466, 467, 490, 494, 498, 499, 509, 510, 511, 512, 564, 565, 566, 584, 585, 587, 601, 609, 615, 626, 627, 629, 676, 678, 690, 692, 704, 710, 717, 786, 791, 862, 907, 943, 984, 988, 1038, 1042, 1082, 1094, 1098, 1099, 1102, 1103, 1105, 1112, 1113, 1114, 1116, 1124, 1134, 1135, 1141, 1142, 1143, 1144, 1145, 1153, 1155, 1157, 1160, 1162, 1163, 1166, 1178, 1180, 1183, 1204, 1225, 1228, 1242, 1281, 1282, 1284, 1299, 1302, 1306, 1311, 1329, 1331, 1334, 1337, 1362, 1405, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1428, 1429], "fewer": [10, 420, 421, 681, 683, 690, 692, 693, 694, 762, 786, 1216, 1218], "compress": [10, 25, 268, 512, 577, 690, 786, 1116, 1245, 1336, 1337, 1342, 1343, 1347, 1353, 1360, 1361, 1374, 1375, 1379], "suptitl": [10, 15], "original_graph": [10, 15, 690], "white_nod": 10, "red_nod": 10, "white": [10, 21, 25, 82, 83, 127, 214, 215, 216, 220, 427, 1398, 1401, 1409], "add_nodes_from": [10, 15, 16, 36, 70, 71, 82, 89, 115, 156, 165, 199, 207, 236, 237, 248, 265, 267, 268, 423, 425, 426, 469, 555, 690, 796, 855, 862, 887, 892, 900, 907, 925, 928, 936, 943, 968, 973, 981, 988, 1007, 1010, 1037, 1039, 1040, 1065, 1197, 1220, 1294, 1407, 1409, 1416, 1429], "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, 1138, 1157, 1220, 1290, 1294, 1329, 1407, 1410, 1429], "base_opt": [10, 15], "dict": [10, 15, 19, 25, 39, 54, 57, 58, 67, 70, 88, 101, 102, 107, 109, 144, 145, 148, 157, 159, 160, 165, 168, 169, 176, 179, 184, 189, 190, 195, 197, 200, 202, 204, 207, 220, 237, 239, 240, 252, 290, 309, 310, 329, 334, 336, 353, 408, 411, 412, 416, 422, 427, 470, 473, 481, 482, 496, 502, 512, 545, 561, 563, 565, 566, 575, 577, 578, 579, 580, 588, 614, 628, 631, 636, 637, 638, 640, 642, 644, 645, 646, 647, 648, 649, 662, 666, 669, 687, 688, 691, 705, 706, 707, 713, 715, 749, 750, 760, 796, 849, 856, 858, 859, 862, 865, 870, 873, 878, 879, 884, 888, 890, 891, 892, 894, 901, 903, 904, 907, 910, 916, 923, 926, 927, 928, 930, 931, 935, 937, 939, 940, 943, 946, 947, 951, 955, 960, 965, 969, 971, 972, 973, 975, 976, 980, 982, 984, 985, 988, 991, 992, 998, 1005, 1008, 1009, 1010, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1041, 1042, 1045, 1047, 1085, 1086, 1091, 1094, 1097, 1106, 1107, 1108, 1109, 1110, 1111, 1114, 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1336, 1337, 1340, 1341, 1342, 1343, 1345, 1347, 1353, 1356, 1360, 1361, 1371, 1374, 1375, 1378, 1379, 1382, 1407, 1416], "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, 1157, 1171, 1202, 1206, 1209, 1210, 1211, 1213, 1219, 1220, 1225, 1229, 1234, 1236, 1244, 1298, 1299, 1303, 1323, 1324, 1329, 1331, 1353, 1397, 1401, 1402, 1407, 1408, 1409, 1410, 1411, 1412, 1416, 1417, 1429], "languag": [11, 92, 99, 110, 1042, 1327, 1344, 1345, 1347, 1384, 1385, 1386, 1414], "discret": [11, 104, 235, 249, 362, 409, 513, 517, 518, 618, 1167, 1168, 1181, 1183, 1189, 1193, 1207, 1281, 1282, 1285, 1317, 1318, 1326, 1409], "global": [11, 103, 314, 341, 410, 477, 486, 487, 509, 592, 1045, 1272, 1299, 1304, 1307, 1308, 1331, 1410, 1412, 1414], "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, 1136, 1138, 1140, 1220, 1272, 1298, 1299, 1309, 1313, 1320, 1321, 1327, 1328, 1364, 1365, 1396, 1405, 1409, 1411, 1415, 1416, 1428, 1429], "restrict": [11, 102, 128, 353, 791, 1038, 1082, 1407], "For": [11, 54, 67, 88, 92, 93, 95, 97, 99, 101, 102, 103, 105, 107, 110, 115, 125, 128, 132, 143, 151, 158, 159, 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686, 731, 786, 1138, 1165, 1177, 1188, 1190, 1197, 1278, 1279, 1356, 1357, 1382, 1407, 1408, 1411, 1414, 1416, 1417], "smallest": [11, 31, 211, 221, 264, 362, 370, 376, 381, 440, 483, 490, 679, 729, 731, 1048, 1203, 1252, 1262, 1278, 1279, 1305, 1323, 1324, 1410], "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, 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592, 1204, 1236, 1326, 1406], "1458": 11, "702": 11, "351": 11, "test": [11, 52, 88, 94, 95, 96, 97, 99, 103, 106, 109, 132, 180, 267, 268, 310, 338, 343, 397, 398, 420, 421, 454, 520, 525, 535, 555, 616, 671, 740, 741, 742, 743, 755, 757, 760, 762, 871, 914, 952, 995, 1042, 1070, 1072, 1168, 1329, 1336, 1337, 1340, 1342, 1343, 1347, 1352, 1353, 1374, 1375, 1378, 1379, 1396, 1398, 1399, 1401, 1404, 1408, 1409, 1410, 1411, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1426, 1427, 1428, 1429], "softwar": [11, 91, 107, 111, 481, 482, 729, 731, 1429], "power": [11, 45, 94, 110, 207, 311, 312, 324, 371, 372, 520, 521, 564, 566, 692, 758, 892, 928, 973, 1010, 1043, 1168, 1178, 1240, 1241, 1258, 1319, 1322, 1398, 1409, 1410, 1429], "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, 1124, 1125, 1126, 1151, 1168, 1188, 1222, 1237, 1277, 1281, 1282, 1303, 1402, 1407, 1410, 1420], "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, 1138, 1146, 1147, 1178, 1180, 1181, 1182, 1183, 1184, 1196, 1197, 1215, 1225, 1274, 1275, 1277, 1279, 1280, 1281, 1282, 1284, 1326, 1335, 1336, 1338, 1339, 1358, 1361, 1362, 1363, 1366, 1367, 1373, 1397, 1408, 1409], "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, 1147, 1178, 1180, 1220, 1225, 1245, 1278, 1279, 1280, 1299, 1352, 1396, 1405, 1407, 1420, 1429], "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, 1195, 1220, 1228, 1236, 1244, 1281, 1282, 1291, 1329, 1331, 1396, 1405, 1407, 1408, 1410, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1428, 1429], "converg": [11, 311, 324, 373, 564, 565, 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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, 1126, 1136, 1148, 1150, 1154, 1157, 1168, 1177, 1183, 1189, 1197, 1198, 1200, 1201, 1225, 1232, 1272, 1281, 1282, 1284, 1289, 1292, 1294, 1296, 1299, 1305, 1327, 1328, 1329, 1331, 1340, 1341, 1342, 1348, 1351, 1352, 1353, 1385, 1386, 1397, 1399, 1401, 1406, 1407, 1408, 1409, 1410, 1411, 1413, 1414, 1415, 1416, 1417, 1420, 1428, 1429], "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, 1125, 1168, 1182, 1184, 1192, 1215, 1239, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1395, 1402, 1409, 1411, 1419, 1420], "infer": [15, 695, 1104, 1118, 1361, 1415], "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, 1136, 1168, 1172, 1173, 1174, 1196, 1201, 1210, 1258, 1272, 1290, 1299, 1329, 1368, 1369, 1385, 1397, 1407, 1408, 1409, 1416, 1417, 1428, 1429], "relat": [15, 34, 67, 92, 93, 95, 99, 100, 115, 129, 132, 220, 230, 297, 366, 370, 586, 588, 619, 688, 762, 767, 795, 1205, 1208, 1272, 1326, 1398, 1405, 1409, 1416, 1419, 1428], "strong": [15, 397, 511, 512, 517, 610, 619, 691, 699, 758, 1411], "weak": [15, 398, 691, 758, 1428], "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, 1157, 1274, 1429], "7482934": 15, "_": [15, 16, 26, 38, 93, 105, 300, 333, 356, 372, 405, 406, 425, 426, 502, 503, 506, 507, 569, 588, 630, 1355, 1357, 1381, 1383, 1414], "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, 1416], "prefix": [15, 67, 512, 690, 691, 1275, 1329, 1350, 1416, 1424], "aggreg": [15, 511, 512, 691, 786], "summary_po": 15, "8375428": 15, "edge_typ": 15, "get_edge_data": [15, 25, 1414], "199": [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, 1149, 1305, 1329, 1344, 1345, 1347, 1356, 1357, 1358, 1359, 1360, 1361, 1382, 1383, 1384, 1386, 1390, 1397, 1398, 1399, 1401, 1405, 1407, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429], "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, 549, 550, 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, 1042, 1052, 1053, 1054, 1061, 1066, 1085, 1086, 1087, 1094, 1097, 1100, 1102, 1103, 1105, 1106, 1118, 1130, 1143, 1153, 1154, 1155, 1157, 1160, 1167, 1176, 1203, 1204, 1209, 1210, 1211, 1214, 1254, 1289, 1299, 1300, 1301, 1305, 1325, 1326, 1327, 1329, 1334, 1337, 1355, 1359, 1362, 1363, 1366, 1367, 1374, 1381, 1393, 1398, 1406, 1407, 1409, 1410, 1412, 1414, 1415, 1417, 1426, 1428, 1429], "entir": [16, 95, 101, 165, 179, 184, 260, 360, 375, 577, 862, 873, 907, 916, 943, 955, 988, 998, 1038, 1085, 1100, 1228, 1409, 1412], "adopt": [16, 96, 98, 101, 102, 107, 1408, 1417], "lobpcg": [16, 91, 1278, 1279, 1280], "python_exampl": 16, "graph_partit": 16, "categor": [16, 546, 547, 548, 611], "node_typ": [16, 1345, 1359, 1360], "supported_nod": 16, "unsupported_nod": 16, "remove_edges_from": [16, 89, 192, 453, 602, 881, 920, 962, 1002, 1178, 1180, 1225, 1396, 1397, 1415, 1423, 1429], "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, 1329, 1407, 1429], "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, 1329, 1407, 1414, 1420, 1428, 1429], "g_minus_h": 16, "strip": [16, 25, 69, 1218], "_node_color": 16, "_po": 16, "draw_networkx_edg": [16, 25, 26, 27, 28, 33, 35, 38, 39, 40, 41, 44, 46, 68, 83, 1133, 1136, 1137, 1139, 1140, 1414, 1416], "draw_networkx_label": [16, 25, 35, 38, 46, 71, 1133, 1136, 1137, 1138, 1140], "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, 1124, 1125, 1126, 1136, 1138, 1149, 1169, 1170, 1176, 1178, 1185, 1187, 1190, 1192, 1193, 1194, 1196, 1199, 1200, 1201, 1202, 1205, 1209, 1210, 1220, 1222, 1233, 1246, 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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, 1042, 1043, 1061, 1070, 1071, 1072, 1081, 1094, 1095, 1096, 1098, 1099, 1104, 1117, 1133, 1136, 1137, 1138, 1139, 1140, 1154, 1157, 1168, 1178, 1180, 1181, 1184, 1185, 1192, 1196, 1199, 1200, 1201, 1202, 1205, 1210, 1213, 1214, 1215, 1222, 1225, 1238, 1245, 1278, 1279, 1280, 1281, 1282, 1297, 1298, 1299, 1300, 1303, 1318, 1326, 1327, 1329, 1331, 1334, 1337, 1339, 1340, 1341, 1342, 1343, 1344, 1347, 1348, 1351, 1352, 1353, 1359, 1360, 1363, 1366, 1367, 1385, 1396, 1400, 1401, 1402, 1405, 1406, 1407, 1409, 1410, 1415, 1419, 1429], "to_undirect": [16, 25, 69, 796, 1037, 1039, 1040, 1185, 1187, 1407, 1416, 1429], "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, 1167, 1196, 1202, 1214, 1326, 1410, 1411, 1417, 1429], "green": [16, 32, 38, 70, 93, 115, 464, 598, 760, 1042, 1305, 1333, 1397, 1415, 1429], "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, 1154, 1158, 1160, 1172, 1178, 1180, 1182, 1184, 1202, 1204, 1205, 1206, 1207, 1208, 1210, 1211, 1212, 1213, 1214, 1216, 1218, 1219, 1221, 1222, 1223, 1225, 1226, 1229, 1232, 1233, 1234, 1236, 1237, 1238, 1243, 1259, 1268, 1272, 1274, 1281, 1282, 1283, 1290, 1291, 1295, 1326, 1390, 1409, 1412, 1429], "node_color_list": 16, "nc": [16, 56], "spectral_layout": [16, 43, 1144, 1402, 1409], "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, 1180, 1189, 1275, 1318, 1329, 1407, 1429], "g_ex_r": 16, "compos": [16, 269, 270, 271, 272, 273, 274, 275, 276, 600, 604, 758, 1403, 1409, 1410, 1420, 1426, 1428], "previous": [16, 91, 108, 112, 322, 614, 1185, 1186, 1187, 1398, 1410, 1420], "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, 510, 585, 587, 615, 660, 664, 667, 719, 733, 739, 762, 786, 796, 857, 902, 938, 983, 1037, 1038, 1039, 1040, 1042, 1046, 1085, 1086, 1101, 1102, 1104, 1168, 1173, 1196, 1199, 1200, 1201, 1202, 1216, 1218, 1281, 1297, 1299, 1333, 1336, 1337, 1348, 1351, 1352, 1353, 1362, 1363, 1366, 1367, 1368, 1369, 1374, 1385, 1391, 1393, 1397, 1407, 1417], "assert": [16, 67, 88, 102, 1414, 1417, 1427, 1428, 1429], "is_isomorph": [16, 584, 585, 587, 588, 608, 671, 690, 739, 758, 761, 762, 1402, 1409], "793": [16, 17], "plot_subgraph": [16, 17, 1417], "544": [17, 296, 301, 302, 303, 308, 309, 323, 1401, 1409], "auto_examples_algorithm": 17, "04": [17, 47, 85, 96, 110, 1328], "read": [18, 22, 25, 40, 52, 54, 55, 57, 58, 65, 75, 86, 93, 94, 100, 115, 159, 165, 167, 190, 200, 267, 583, 618, 796, 858, 862, 864, 879, 888, 903, 907, 909, 939, 943, 945, 947, 969, 984, 988, 990, 992, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1035, 1036, 1037, 1038, 1039, 1040, 1042, 1043, 1061, 1066, 1082, 1083, 1088, 1121, 1146, 1147, 1273, 1299, 1328, 1329, 1332, 1333, 1336, 1340, 1341, 1345, 1346, 1348, 1351, 1352, 1353, 1354, 1355, 1357, 1359, 1360, 1370, 1371, 1374, 1378, 1380, 1381, 1383, 1384, 1385, 1386, 1389, 1390, 1391, 1392, 1393, 1397, 1398, 1400, 1401, 1404, 1405, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1416, 1417, 1421, 1427, 1428], "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, 1042, 1047, 1123, 1126, 1132, 1303, 1328, 1329, 1332, 1333, 1337, 1340, 1342, 1343, 1347, 1348, 1351, 1352, 1353, 1355, 1357, 1360, 1361, 1375, 1378, 1379, 1381, 1383, 1384, 1385, 1386, 1390, 1391, 1393, 1398, 1400, 1401, 1402, 1404, 1405, 1408, 1409, 1414, 1415, 1417, 1428, 1429], "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, 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104, 107, 115, 341, 345, 462, 464, 466, 500, 519, 616, 678, 1094, 1178, 1191, 1205, 1225, 1410, 1416], "segment": [52, 55, 338], "major": [52, 95, 98, 99, 100, 102, 103, 104, 106, 107, 1396, 1397, 1406, 1407, 1410], "studi": [52, 91, 110, 606, 1195, 1199, 1326, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428], "topologi": [52, 55, 435, 436, 512, 681, 683, 748, 1205, 1220, 1228, 1232, 1236, 1244, 1329], "encod": [52, 55, 58, 67, 99, 141, 249, 267, 268, 619, 758, 775, 1329, 1336, 1337, 1340, 1341, 1342, 1343, 1344, 1347, 1348, 1351, 1352, 1353, 1357, 1358, 1361, 1366, 1371, 1374, 1375, 1378, 1379, 1385, 1409, 1410, 1415], "angular": [52, 55], "inform": [52, 66, 92, 93, 99, 100, 101, 102, 103, 107, 111, 112, 121, 132, 159, 165, 200, 202, 204, 220, 226, 230, 231, 249, 301, 302, 303, 308, 309, 314, 323, 324, 325, 338, 405, 406, 438, 453, 455, 480, 488, 500, 512, 564, 566, 568, 572, 573, 574, 583, 592, 614, 619, 624, 691, 775, 782, 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101, 1220], "earlier": [91, 299, 363, 364, 365, 739, 1202, 1396, 1405, 1411, 1416], "acknowledg": [91, 92, 96], "nonlinear": [91, 1216, 1218, 1225], "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, 1128, 1229, 1305, 1327, 1329, 1331, 1417], "offic": [91, 1270], "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, 1129, 1178, 1182, 1199, 1200, 1201, 1344, 1345, 1347, 1384, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428], "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, 1172, 1173, 1174, 1196, 1225, 1232, 1236], 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1407, 1409, 1410, 1414, 1415, 1416, 1417, 1420, 1422, 1428], "definit": [101, 132, 235, 238, 243, 289, 291, 292, 303, 323, 342, 356, 398, 435, 437, 464, 467, 549, 550, 551, 608, 618, 619, 620, 625, 676, 685, 687, 700, 735, 737, 791, 1195, 1196, 1200, 1220, 1238, 1290, 1329, 1409, 1416, 1429], "coupl": [101, 102, 132, 1260, 1405, 1407], "realis": 101, "But": [101, 102, 107, 143, 170, 238, 243, 256, 277, 278, 281, 297, 298, 583, 796, 866, 911, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1094, 1331, 1396, 1428], "seem": [101, 102, 298, 307, 791, 1237], "eas": [101, 107, 1412], "idiom": [101, 159, 190, 200, 858, 879, 888, 903, 939, 969, 984, 1299, 1397, 1407, 1414], "subscript": [101, 151, 159, 200, 796, 853, 858, 888, 898, 903, 934, 939, 969, 979, 984, 1037, 1039, 1040, 1397, 1429], "repr": [101, 1350, 1416], "4950": [101, 1417], "traceback": [101, 450, 464, 584, 652, 658, 1305, 1306], "recent": [101, 437, 450, 464, 584, 652, 658, 963, 1003, 1305, 1306, 1414], "typeerror": [101, 382, 464, 1209, 1305, 1407], "opaqu": 101, "ambigu": [101, 103, 115, 252, 253, 464, 762, 1043, 1409], "ambigi": 101, "counter": [101, 153, 357], "nativ": [101, 109], "caveat": 101, "nodes_it": [101, 1407, 1410], "toward": [101, 685, 1410, 1416], "inner": [101, 230, 231, 380, 796, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1086], "synonym": 101, "primarili": [101, 1429], "becam": [101, 1414], "concept": [101, 132, 220, 310, 427, 688, 1043], "intuit": [101, 109], "On": [101, 105, 156, 217, 294, 297, 298, 306, 307, 315, 380, 405, 406, 514, 515, 518, 593, 855, 900, 936, 981, 1183, 1205, 1227, 1231, 1235], "front": [101, 619, 1036, 1088], "constuct": 101, "indx": 101, "desir": [101, 102, 142, 143, 204, 346, 347, 422, 425, 426, 598, 629, 647, 891, 972, 1085, 1094, 1102, 1103, 1105, 1124, 1125, 1153, 1155, 1160, 1162, 1163, 1166, 1168, 1190, 1221, 1223, 1224, 1237, 1284, 1359, 1360, 1417, 1429], "prelimanari": 101, "impelement": 101, "4086": 101, "rid": [101, 1416], 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1417, 1428], "leav": [102, 231, 388, 500, 508, 584, 585, 586, 587, 678, 1148, 1158, 1299, 1407, 1412, 1429], "dg": [102, 207, 322, 455, 456, 457, 458, 459, 461, 462, 464, 465, 466, 467, 468, 469, 892, 928, 973, 1010, 1041, 1407, 1429], "mdg": [102, 207, 892, 928, 973, 1010, 1423], "customgraph": 102, "elist": [102, 1329], "isol": [102, 355, 380, 435, 491, 492, 522, 524, 621, 735, 737, 758, 1221, 1328, 1333, 1401, 1404, 1409, 1410, 1420], "ekei": [102, 207, 892, 928, 934, 973, 979, 1010, 1084, 1104], "protocol": [102, 1407], "hashabl": [102, 144, 151, 156, 171, 180, 267, 545, 546, 547, 548, 761, 796, 853, 855, 867, 871, 898, 900, 912, 914, 934, 936, 947, 948, 952, 962, 979, 981, 992, 993, 995, 1002, 1037, 1038, 1039, 1040, 1087, 1210, 1281, 1282, 1298, 1313, 1327, 1329, 1336, 1340, 1341, 1429], "logic": [102, 103, 220, 760, 762, 1301, 1409, 1410, 1422, 1428], "denot": [102, 114, 212, 219, 299, 300, 322, 567, 568, 569, 570, 571, 572, 573, 608, 619, 687, 688, 689, 690, 691, 1124, 1125, 1126, 1177], "multiedg": [102, 553, 934, 979, 1039, 1040, 1085, 1329, 1359, 1360, 1396, 1409, 1415, 1417], "attrdict": [102, 157, 856, 901, 937, 982, 1409], "edge_kei": [102, 489, 1039, 1040, 1100, 1104, 1416], "networkxinvalidedgelist": 102, "flexibl": [102, 110, 467, 1329, 1385, 1386, 1398, 1404, 1409, 1410, 1414, 1429], "wheel": [102, 106, 1166, 1264, 1414, 1424, 1428], "spoke": 102, "wheel_graph": [102, 341, 671, 672, 674], "star": [102, 260, 300, 615, 626, 627, 779, 1054, 1154, 1163, 1226, 1230, 1397, 1407, 1409, 1410, 1414], "mycustomgraph": 102, "configuration_model_graph": 102, "deg_sequ": [102, 515, 517, 518, 1178, 1179, 1180, 1181, 1183, 1225], "graph_build": 102, "py_random_st": [102, 103, 1299, 1302, 1408], "extended_barabasi_albert_graph": 102, "node_and_edge_build": 102, "ladder_graph": 102, "incompat": [102, 1202, 1405, 1406, 1409], "thrust": 102, "incept": 102, "attach": [102, 214, 274, 357, 569, 571, 621, 1036, 1088, 1122, 1185, 1188, 1226, 1230, 1232, 1329, 1429], "presum": [102, 1300], "rewritten": [102, 1398, 1405, 1409], "gradual": 102, "accomplish": [102, 109, 1168], "wrap": [102, 1045, 1047, 1124, 1126, 1299, 1304, 1307], "custom_graph": 102, "ichain": 102, "tripl": [102, 114, 249, 250, 711, 1414], "overli": 102, "empty_graph": [102, 753, 1057, 1161, 1300, 1326, 1409, 1412, 1413], "3036": 102, "1393": 102, "canon": [102, 684, 730, 1415], "huge": 102, "path_edgelist": 102, "disallow": [102, 796, 1037, 1039, 1040, 1190, 1420], "2022": [103, 105, 693, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427], "pseudo": [103, 104, 676, 1323, 1324, 1408, 1410], "nep19": 103, "legaci": [103, 1398, 1405, 1411], "randomst": [103, 1100, 1111, 1117, 1302, 1304, 1307, 1308, 1331, 1408, 1412], "statist": [103, 110, 128, 274, 358, 383, 385, 438, 1225, 1331, 1408], "strategi": [103, 123, 222, 362, 366, 370, 453], "engin": [103, 107, 729, 731, 1415], "modern": [103, 110, 1408], "prng": 103, "np_random_st": [103, 1304, 1408, 1417], "random_st": 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"upheld": 103, "exact": [103, 125, 210, 215, 216, 238, 269, 271, 273, 276, 671, 672, 673, 674, 691, 780, 1178, 1180, 1225, 1405, 1408], "instanti": [103, 1299, 1397, 1429], "num": 103, "uniform": [103, 565, 566, 625, 738, 1184, 1196, 1208, 1239, 1242, 1322, 1412, 1415], "92961609": 103, "31637555": 103, "18391881": 103, "20456028": 103, "56772503": 103, "5955447": 103, "96451452": 103, "6531771": 103, "74890664": 103, "65356987": 103, "22733602": 103, "31675834": 103, "79736546": 103, "67625467": 103, "39110955": 103, "33281393": 103, "59830875": 103, "18673419": 103, "67275604": 103, "94180287": 103, "recov": [103, 357, 729, 731, 1275, 1350, 1351, 1352, 1405, 1408, 1423], "create_random_st": [103, 1302], "randint": [103, 1100], "create_py_random_st": [103, 1304, 1415, 1419], "attributeerror": 103, "compatibl": 103, "pythonrandominterfac": [103, 1304, 1307], "_rand": 103, "implicitli": 103, "16988": 103, "14042": 103, "higher": [103, 258, 297, 299, 304, 306, 314, 316, 320, 321, 322, 327, 328, 331, 378, 520, 521, 616, 703, 1060, 1188, 1237], "constraint": [103, 616, 688, 689, 693, 694, 758, 791, 1416], "releat": 103, "slep": 103, "quit": [103, 466, 1082, 1168, 1237, 1396, 1429], "encapsul": 103, "valueerror": [103, 226, 280, 346, 347, 383, 422, 425, 426, 470, 584, 594, 595, 596, 597, 608, 632, 633, 635, 636, 660, 661, 662, 686, 749, 752, 1102, 1107, 1114, 1116, 1117, 1188, 1209, 1277, 1306, 1314, 1322, 1348, 1349, 1350, 1351, 1352, 1353, 1354, 1356, 1357, 1382, 1416], "captur": [103, 1416], "reorgan": [103, 1416], "quo": 103, "perpetu": [103, 331], "toggl": 103, "backend": [103, 1011, 1328, 1416, 1428], "pkg": 103, "_random_backend": 103, "bullet": [103, 104, 1415], "regard": [103, 104, 1407, 1411, 1415], "mm": 104, "achiev": [104, 301, 302, 308, 309, 380, 512, 1407, 1429], "elong": 104, "solv": [104, 112, 227, 280, 325, 413, 415, 417, 508, 589, 671, 672, 673, 674, 1043, 1303, 1326, 1398, 1416, 1417, 1420, 1424, 1426, 1427, 1428], "mainli": [104, 1405], "wouldn": 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[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, 1124, 1126, 1136, 1137, 1138, 1139, 1172, 1182, 1188, 1192, 1212, 1214, 1215, 1216, 1218, 1227, 1231, 1233, 1234, 1235, 1278, 1279, 1280, 1281, 1282, 1285, 1298, 1299, 1310, 1312, 1315, 1338, 1339, 1340, 1342, 1344, 1345, 1347, 1356, 1357, 1358, 1359, 1360, 1361, 1363, 1367, 1382, 1383], "account": [145, 148, 398, 448, 749, 761, 1273, 1396, 1416], "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, 1192, 1286, 1287, 1396], "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, 1338, 1405], "342": [151, 853, 898, 934, 979, 1258], "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, 1329, 1407, 1410, 1429], "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, 1406, 1407, 1428], "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, 1409, 1414, 1419, 1428], "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, 1141, 1183, 1328], "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, 1327, 1328, 1417, 1429], "hello": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1306], "k3": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1220], "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, 1315, 1329], "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, 1141, 1246, 1249, 1250, 1252, 1328, 1412, 1413], "first_nbr": [161, 615], "invalid": [161, 615, 1416], "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, 1397], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1412], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1407, 1410, 1412, 1413, 1414], "fresh": [165, 862, 907, 943, 988, 1407], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1229, 1326, 1407], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1268, 1397], "degreeview": [166, 863, 908, 944, 950, 989, 1407, 1429], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1407, 1421], "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, 1407, 1409, 1410], "out_edg": [168, 865, 946, 1062, 1407, 1409, 1410, 1429], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1429], "outedgedataview": [168, 189, 865, 878, 1407, 1414], "set_data": 169, "edge_dict": [170, 866, 911, 947, 992], "safe": [170, 866, 911, 1407, 1415], "edge_ind": [171, 867, 912, 948, 993], "data_dictionari": [171, 867, 912], "simpler": [172, 184, 868, 873, 913, 916, 949, 955, 994, 998, 1409, 1410, 1420], "indegreeview": [175, 869, 1407], "deg": [175, 188, 243, 259, 356, 361, 685, 869, 877, 950, 959, 1168, 1182, 1225, 1407], "inedgeview": [176, 870, 1407], "inedgedataview": [176, 870], "silent": [180, 193, 195, 320, 871, 882, 884, 914, 921, 923, 952, 963, 965, 995, 1003, 1005, 1085, 1086, 1130, 1356, 1357, 1362, 1366, 1409, 1416], "niter": [180, 681, 682, 683, 684, 851, 871, 896, 914, 932, 952, 977, 995, 1417], "__iter__": [180, 871, 914, 952, 995, 1306], "nodedata": [184, 873, 916, 955, 998], "5pm": [184, 796, 873, 916, 955, 998, 1037, 1039, 1040, 1397, 1429], "Not": [184, 379, 432, 433, 434, 435, 436, 437, 438, 476, 873, 916, 955, 998, 1117, 1219], "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, 1407, 1417], "get_data": [197, 616], "inplac": [199, 690, 887, 925, 968, 1007, 1066, 1396], "reduct": [199, 469, 618, 786, 887, 925, 968, 1007, 1066, 1323, 1324, 1416, 1417], "sg": [199, 887, 925, 968, 1007], "largest_wcc": [199, 887, 925, 968, 1007], "is_multigraph": [199, 758, 887, 925, 968, 1007, 1157, 1415], "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, 1236, 1244, 1429], "reciproc": [204, 299, 320, 322, 356, 411, 430, 447, 476, 620, 758, 891, 972, 1328, 1419, 1428], "mark_half_edg": 206, "li": [206, 619, 670, 675, 685, 775, 1210, 1213, 1428], "straightforward": [207, 892, 928, 973, 1010], "slightli": [207, 326, 437, 520, 521, 581, 892, 928, 973, 1010, 1168, 1329, 1407, 1410, 1415, 1417, 1428], "singleton": [207, 588, 892, 928, 973, 1010, 1221, 1254, 1410], "preserve_attr": [208, 723, 724, 725, 726], "optimum": [208, 231, 583, 720, 722, 791, 1398, 1409], "arboresc": [208, 460, 719, 720, 722, 724, 726, 740, 743, 758, 1275, 1398, 1409], "span": [208, 226, 227, 228, 295, 508, 618, 619, 624, 719, 720, 722, 724, 726, 732, 733, 734, 735, 736, 737, 738, 758, 1397, 1400, 1409, 1410, 1423], "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, 1219, 1278, 1279, 1301, 1328, 1356, 1357, 1382, 1410, 1411], "boppana": [209, 211, 212], "halld\u00f3rsson": [209, 211, 212], "1992": [209, 211, 212, 517, 518, 1410], "exclud": [209, 211, 212, 215, 216, 261, 262, 453, 688, 719, 723, 724, 725, 726, 733, 751, 1036, 1038, 1088, 1220, 1415], "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, 1176, 1323, 1324, 1328, 1398, 1411, 1415, 1416], "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, 1417], "maximum_cliqu": 211, "1007": [211, 226, 296, 301, 302, 303, 308, 309, 323, 324, 325, 341, 431, 451, 498, 574, 1147, 1184], "bf01994876": 211, "iset": 212, "trial": [213, 230, 231, 1198, 1240, 1241], "estim": [213, 224, 297, 306, 313, 564, 625, 626, 627, 782, 1283, 1410], "coeffici": [213, 248, 260, 261, 262, 263, 289, 355, 356, 358, 570, 618, 619, 625, 682, 684, 778, 782, 1400, 1401, 1402, 1409, 1416], "fraction": [213, 257, 259, 286, 289, 297, 299, 304, 306, 315, 317, 318, 319, 321, 322, 326, 328, 330, 356, 358, 359, 519, 1124, 1126, 1168, 1237], "schank": 213, "thoma": [213, 751, 1410, 1412, 1416], "dorothea": [213, 1171], "wagner": [213, 429, 758, 1171, 1405, 1409], "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, 1227, 1235, 1329, 1409, 1429], "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, 1237, 1401, 1405, 1409, 1416, 1419, 1427, 1428], "distinct": [214, 215, 255, 281, 288, 352, 391, 452, 453, 460, 578, 595, 608, 618, 700, 701, 734, 735, 736, 737, 789, 1153, 1247, 1274, 1326, 1329, 1331, 1398, 1420], "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, 1193, 1208], "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, 1405], "dougla": [214, 215, 216, 220, 1416, 1428], "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, 1405, 1427, 1428], "bf": [215, 216, 217, 363, 588, 704, 706, 707, 708, 717, 1400, 1404, 1409, 1412, 1415, 1416], "lose": [215, 796, 1037, 1039, 1040], "accuraci": [215, 312, 786], "platon": [215, 216, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 1248, 1251, 1257, 1260, 1264, 1266], "octahedr": [215, 216, 1260], "approx": [215, 216, 227, 229, 230, 231, 1416], "octahedral_graph": [215, 216], "vari": [217, 238, 243, 373, 378, 569, 695], "sweep": [217, 1415], "dsweep": 217, "a_1": [217, 477, 1124, 1125, 1126], "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, 1168, 1416], "marino": 217, "symposium": [217, 619, 1189, 1198, 1242], "berlin": [217, 520, 521, 1416], "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, 1328, 1398, 1403, 1409, 1410], "opt": [218, 221, 1428], "min_weight_dominating_set": 219, "vazirani": [219, 221], "vijai": [219, 221, 517], "min_dens": 220, "95": [220, 590, 1286, 1287, 1385], "nest": [220, 427, 728, 730, 791, 1038, 1045, 1061, 1094, 1299, 1311, 1351, 1358, 1359, 1360, 1361, 1386, 1409], "forth": [220, 427], "relax": [220, 227, 1174, 1416], "narrow": [220, 1168], "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], "2307": [220, 297, 1258], "3088904": 220, "petersen": [220, 427, 761, 1254, 1259, 1262], "triconnect": [220, 427], "apxa": 220, "petersen_graph": [220, 380, 427, 492, 761, 1119, 1120, 1429], "fo": 221, "initial_cut": 222, "highest": [222, 269, 273, 276, 337, 357, 374, 387, 389, 390, 394, 428, 509, 688, 703, 1183], "suppli": [222, 256, 277, 278, 280, 281, 594, 1200, 1323, 1324, 1329, 1348, 1351, 1352, 1353, 1385, 1411, 1416], "cut_valu": [222, 429, 500, 506, 507, 1405], "probabl": [223, 227, 230, 231, 236, 237, 238, 241, 242, 243, 245, 274, 275, 296, 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How do I find it in the source code?": [[97, "q-i-want-to-work-on-a-specific-function-how-do-i-find-it-in-the-source-code"]], "Q: What is the policy for deciding whether to include a new algorithm?": [[97, "q-what-is-the-policy-for-deciding-whether-to-include-a-new-algorithm"]], "NXEPs": [[98, "nxeps"], [1416, "nxeps"]], "NXEP 0 \u2014 Purpose and Process": [[99, "nxep-0-purpose-and-process"]], "What is a NXEP?": [[99, "what-is-a-nxep"]], "Types": [[99, "types"]], "NXEP Workflow": [[99, "nxep-workflow"]], "Review and Resolution": [[99, "review-and-resolution"]], "How a NXEP becomes Accepted": [[99, "how-a-nxep-becomes-accepted"]], "Maintenance": [[99, "maintenance"]], "Format and Template": [[99, "format-and-template"]], "Header Preamble": [[99, "header-preamble"]], "References and Footnotes": [[99, "references-and-footnotes"]], "NXEP 1 \u2014 Governance and Decision Making": [[100, "nxep-1-governance-and-decision-making"]], "Abstract": [[100, "abstract"], [101, "abstract"], [102, 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"single-source-dijkstra-path"]], "single_source_dijkstra_path_length": [[669, "single-source-dijkstra-path-length"]], "generate_random_paths": [[670, "generate-random-paths"]], "graph_edit_distance": [[671, "graph-edit-distance"]], "optimal_edit_paths": [[672, "optimal-edit-paths"]], "optimize_edit_paths": [[673, "optimize-edit-paths"]], "optimize_graph_edit_distance": [[674, "optimize-graph-edit-distance"]], "panther_similarity": [[675, "panther-similarity"]], "simrank_similarity": [[676, "simrank-similarity"]], "all_simple_edge_paths": [[677, "all-simple-edge-paths"]], "all_simple_paths": [[678, "all-simple-paths"]], "is_simple_path": [[679, "is-simple-path"]], "shortest_simple_paths": [[680, "shortest-simple-paths"]], "lattice_reference": [[681, "lattice-reference"]], "omega": [[682, "omega"]], "random_reference": [[683, "random-reference"]], "sigma": [[684, "sigma"]], "s_metric": [[685, "s-metric"]], "spanner": [[686, "spanner"]], "constraint": [[687, "constraint"]], "effective_size": [[688, "effective-size"]], "local_constraint": [[689, "local-constraint"]], "dedensify": [[690, "dedensify"]], "snap_aggregation": [[691, "snap-aggregation"]], "connected_double_edge_swap": [[692, "connected-double-edge-swap"]], "directed_edge_swap": [[693, "directed-edge-swap"]], "double_edge_swap": [[694, "double-edge-swap"]], "find_threshold_graph": [[695, "find-threshold-graph"]], "is_threshold_graph": [[696, "is-threshold-graph"]], "hamiltonian_path": [[697, "hamiltonian-path"]], "is_reachable": [[698, "is-reachable"]], "is_tournament": [[700, "is-tournament"]], "random_tournament": [[701, "random-tournament"]], "score_sequence": [[702, "score-sequence"]], "bfs_beam_edges": [[703, "bfs-beam-edges"]], "bfs_edges": [[704, "bfs-edges"]], "bfs_layers": [[705, "bfs-layers"]], "bfs_predecessors": [[706, "bfs-predecessors"]], "bfs_successors": [[707, "bfs-successors"]], "bfs_tree": [[708, "bfs-tree"]], "descendants_at_distance": [[709, "descendants-at-distance"]], "dfs_edges": [[710, "dfs-edges"]], "dfs_labeled_edges": [[711, "dfs-labeled-edges"]], "dfs_postorder_nodes": [[712, "dfs-postorder-nodes"]], "dfs_predecessors": [[713, "dfs-predecessors"]], "dfs_preorder_nodes": [[714, "dfs-preorder-nodes"]], "dfs_successors": [[715, "dfs-successors"]], "dfs_tree": [[716, "dfs-tree"]], "edge_bfs": [[717, "edge-bfs"]], "edge_dfs": [[718, "edge-dfs"]], "networkx.algorithms.tree.branchings.ArborescenceIterator": [[719, "networkx-algorithms-tree-branchings-arborescenceiterator"]], "networkx.algorithms.tree.branchings.Edmonds": [[720, "networkx-algorithms-tree-branchings-edmonds"]], "branching_weight": [[721, "branching-weight"]], "greedy_branching": [[722, "greedy-branching"]], "maximum_branching": [[723, "maximum-branching"]], "maximum_spanning_arborescence": [[724, "maximum-spanning-arborescence"]], "minimum_branching": [[725, "minimum-branching"]], "minimum_spanning_arborescence": [[726, "minimum-spanning-arborescence"]], "NotATree": [[727, "notatree"]], "from_nested_tuple": [[728, "from-nested-tuple"]], "from_prufer_sequence": [[729, "from-prufer-sequence"]], "to_nested_tuple": [[730, "to-nested-tuple"]], "to_prufer_sequence": [[731, "to-prufer-sequence"]], "junction_tree": [[732, "junction-tree"]], "networkx.algorithms.tree.mst.SpanningTreeIterator": [[733, "networkx-algorithms-tree-mst-spanningtreeiterator"]], "maximum_spanning_edges": [[734, "maximum-spanning-edges"]], "maximum_spanning_tree": [[735, "maximum-spanning-tree"]], "minimum_spanning_edges": [[736, "minimum-spanning-edges"]], "minimum_spanning_tree": [[737, "minimum-spanning-tree"]], "random_spanning_tree": [[738, "random-spanning-tree"]], "join": [[739, "join"]], "is_arborescence": [[740, "is-arborescence"]], "is_branching": [[741, "is-branching"]], "is_forest": [[742, "is-forest"]], "is_tree": [[743, "is-tree"]], "all_triads": [[744, "all-triads"]], "all_triplets": [[745, "all-triplets"]], "is_triad": [[746, "is-triad"]], "random_triad": [[747, "random-triad"]], "triad_type": [[748, "triad-type"]], "triadic_census": [[749, "triadic-census"]], "triads_by_type": [[750, "triads-by-type"]], "closeness_vitality": [[751, "closeness-vitality"]], "voronoi_cells": [[752, "voronoi-cells"]], "wiener_index": [[753, "wiener-index"]], "Graph Hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "Graphical degree sequence": [[755, "module-networkx.algorithms.graphical"]], "Hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "Hybrid": [[757, "module-networkx.algorithms.hybrid"]], "Isolates": [[759, "module-networkx.algorithms.isolate"]], "Isomorphism": [[760, "isomorphism"]], "VF2++": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "VF2++ Algorithm": [[760, "vf2-algorithm"]], "Tree Isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "Advanced Interfaces": [[760, "advanced-interfaces"]], "ISMAGS Algorithm": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "Notes": [[761, "notes"], [762, "notes"], [1042, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "Recognition Tests": [[791, "recognition-tests"]], "Branchings and Spanning Arborescences": [[791, "module-networkx.algorithms.tree.branchings"]], "Encoding and decoding": [[791, "module-networkx.algorithms.tree.coding"]], "Operations": [[791, "module-networkx.algorithms.tree.operations"]], "Spanning Trees": [[791, "module-networkx.algorithms.tree.mst"]], "Exceptions": [[791, "exceptions"], [1043, "module-networkx.exception"]], "Vitality": [[793, "module-networkx.algorithms.vitality"]], "Voronoi cells": [[794, "module-networkx.algorithms.voronoi"]], "Wiener index": [[795, "module-networkx.algorithms.wiener"]], "DiGraph\u2014Directed graphs with self loops": [[796, "digraph-directed-graphs-with-self-loops"]], "Overview": [[796, "overview"], [1037, "overview"], [1039, "overview"], [1040, "overview"]], "Methods": [[796, "methods"], [1037, "methods"], [1039, "methods"], [1040, "methods"]], "Adding and removing nodes and edges": [[796, "adding-and-removing-nodes-and-edges"], [1037, "adding-and-removing-nodes-and-edges"], [1040, "adding-and-removing-nodes-and-edges"]], "Reporting nodes edges and neighbors": [[796, "reporting-nodes-edges-and-neighbors"], [1037, "reporting-nodes-edges-and-neighbors"], [1039, "reporting-nodes-edges-and-neighbors"], [1040, "reporting-nodes-edges-and-neighbors"]], "Counting nodes edges and neighbors": [[796, "counting-nodes-edges-and-neighbors"], [1037, "counting-nodes-edges-and-neighbors"], [1039, "counting-nodes-edges-and-neighbors"], [1040, "counting-nodes-edges-and-neighbors"]], "Making copies and subgraphs": [[796, "making-copies-and-subgraphs"], [1037, "making-copies-and-subgraphs"], [1039, "making-copies-and-subgraphs"], [1040, "making-copies-and-subgraphs"]], "AdjacencyView.copy": [[797, "adjacencyview-copy"]], "AdjacencyView.get": [[798, "adjacencyview-get"]], "AdjacencyView.items": [[799, "adjacencyview-items"]], "AdjacencyView.keys": [[800, "adjacencyview-keys"]], "AdjacencyView.values": [[801, "adjacencyview-values"]], "AtlasView.copy": [[802, "atlasview-copy"]], "AtlasView.get": [[803, "atlasview-get"]], "AtlasView.items": [[804, "atlasview-items"]], "AtlasView.keys": [[805, "atlasview-keys"]], "AtlasView.values": [[806, "atlasview-values"]], "FilterAdjacency.get": [[807, "filteradjacency-get"]], "FilterAdjacency.items": [[808, "filteradjacency-items"]], "FilterAdjacency.keys": [[809, "filteradjacency-keys"]], "FilterAdjacency.values": [[810, "filteradjacency-values"]], "FilterAtlas.get": [[811, "filteratlas-get"]], "FilterAtlas.items": [[812, "filteratlas-items"]], "FilterAtlas.keys": [[813, "filteratlas-keys"]], "FilterAtlas.values": [[814, "filteratlas-values"]], "FilterMultiAdjacency.get": [[815, "filtermultiadjacency-get"]], "FilterMultiAdjacency.items": [[816, "filtermultiadjacency-items"]], "FilterMultiAdjacency.keys": [[817, "filtermultiadjacency-keys"]], "FilterMultiAdjacency.values": [[818, "filtermultiadjacency-values"]], "FilterMultiInner.get": [[819, "filtermultiinner-get"]], "FilterMultiInner.items": [[820, "filtermultiinner-items"]], "FilterMultiInner.keys": [[821, "filtermultiinner-keys"]], "FilterMultiInner.values": [[822, "filtermultiinner-values"]], "MultiAdjacencyView.copy": [[823, "multiadjacencyview-copy"]], "MultiAdjacencyView.get": [[824, "multiadjacencyview-get"]], "MultiAdjacencyView.items": [[825, "multiadjacencyview-items"]], "MultiAdjacencyView.keys": [[826, "multiadjacencyview-keys"]], "MultiAdjacencyView.values": [[827, "multiadjacencyview-values"]], "UnionAdjacency.copy": [[828, "unionadjacency-copy"]], "UnionAdjacency.get": [[829, "unionadjacency-get"]], "UnionAdjacency.items": [[830, "unionadjacency-items"]], "UnionAdjacency.keys": [[831, "unionadjacency-keys"]], "UnionAdjacency.values": [[832, "unionadjacency-values"]], "UnionAtlas.copy": [[833, "unionatlas-copy"]], "UnionAtlas.get": [[834, "unionatlas-get"]], "UnionAtlas.items": [[835, "unionatlas-items"]], "UnionAtlas.keys": 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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, "digraph-to-undirected"]], "DiGraph.update": [[892, "digraph-update"]], "Graph.__contains__": [[893, "graph-contains"]], "Graph.__getitem__": [[894, "graph-getitem"]], "Graph.__init__": [[895, "graph-init"]], "Graph.__iter__": [[896, "graph-iter"]], "Graph.__len__": [[897, "graph-len"]], "Graph.add_edge": [[898, "graph-add-edge"]], "Graph.add_edges_from": [[899, "graph-add-edges-from"]], "Graph.add_node": [[900, "graph-add-node"]], "Graph.add_nodes_from": [[901, "graph-add-nodes-from"]], "Graph.add_weighted_edges_from": [[902, "graph-add-weighted-edges-from"]], "Graph.adj": [[903, "graph-adj"]], "Graph.adjacency": [[904, "graph-adjacency"]], "Graph.clear": [[905, "graph-clear"]], "Graph.clear_edges": [[906, "graph-clear-edges"]], "Graph.copy": [[907, "graph-copy"]], "Graph.degree": [[908, "graph-degree"]], "Graph.edge_subgraph": [[909, "graph-edge-subgraph"]], "Graph.edges": [[910, "graph-edges"]], "Graph.get_edge_data": [[911, "graph-get-edge-data"]], "Graph.has_edge": [[912, "graph-has-edge"]], "Graph.has_node": [[913, "graph-has-node"]], "Graph.nbunch_iter": [[914, "graph-nbunch-iter"]], "Graph.neighbors": [[915, "graph-neighbors"]], "Graph.nodes": [[916, "graph-nodes"]], "Graph.number_of_edges": [[917, "graph-number-of-edges"]], "Graph.number_of_nodes": [[918, "graph-number-of-nodes"]], "Graph.order": [[919, "graph-order"]], "Graph.remove_edge": [[920, "graph-remove-edge"]], "Graph.remove_edges_from": [[921, "graph-remove-edges-from"]], "Graph.remove_node": [[922, "graph-remove-node"]], "Graph.remove_nodes_from": [[923, "graph-remove-nodes-from"]], "Graph.size": [[924, "graph-size"]], "Graph.subgraph": [[925, "graph-subgraph"]], "Graph.to_directed": [[926, "graph-to-directed"]], "Graph.to_undirected": [[927, "graph-to-undirected"]], "Graph.update": [[928, "graph-update"]], "MultiDiGraph.__contains__": [[929, "multidigraph-contains"]], "MultiDiGraph.__getitem__": [[930, "multidigraph-getitem"]], "MultiDiGraph.__init__": [[931, "multidigraph-init"]], "MultiDiGraph.__iter__": [[932, "multidigraph-iter"]], "MultiDiGraph.__len__": [[933, "multidigraph-len"]], "MultiDiGraph.add_edge": [[934, "multidigraph-add-edge"]], "MultiDiGraph.add_edges_from": [[935, "multidigraph-add-edges-from"]], "MultiDiGraph.add_node": [[936, "multidigraph-add-node"]], "MultiDiGraph.add_nodes_from": [[937, "multidigraph-add-nodes-from"]], "MultiDiGraph.add_weighted_edges_from": [[938, "multidigraph-add-weighted-edges-from"]], "MultiDiGraph.adj": [[939, "multidigraph-adj"]], "MultiDiGraph.adjacency": [[940, "multidigraph-adjacency"]], "MultiDiGraph.clear": [[941, "multidigraph-clear"]], "MultiDiGraph.clear_edges": [[942, "multidigraph-clear-edges"]], "MultiDiGraph.copy": [[943, "multidigraph-copy"]], "MultiDiGraph.degree": [[944, "multidigraph-degree"]], "MultiDiGraph.edge_subgraph": [[945, "multidigraph-edge-subgraph"]], "MultiDiGraph.edges": [[946, "multidigraph-edges"]], "MultiDiGraph.get_edge_data": [[947, "multidigraph-get-edge-data"]], "MultiDiGraph.has_edge": [[948, "multidigraph-has-edge"]], "MultiDiGraph.has_node": [[949, "multidigraph-has-node"]], "MultiDiGraph.in_degree": [[950, "multidigraph-in-degree"]], "MultiDiGraph.in_edges": [[951, "multidigraph-in-edges"]], "MultiDiGraph.nbunch_iter": [[952, "multidigraph-nbunch-iter"]], "MultiDiGraph.neighbors": [[953, "multidigraph-neighbors"]], "MultiDiGraph.new_edge_key": [[954, "multidigraph-new-edge-key"]], "MultiDiGraph.nodes": [[955, "multidigraph-nodes"]], "MultiDiGraph.number_of_edges": [[956, "multidigraph-number-of-edges"]], "MultiDiGraph.number_of_nodes": [[957, "multidigraph-number-of-nodes"]], "MultiDiGraph.order": [[958, "multidigraph-order"]], "MultiDiGraph.out_degree": [[959, "multidigraph-out-degree"]], "MultiDiGraph.out_edges": [[960, "multidigraph-out-edges"]], "MultiDiGraph.predecessors": [[961, "multidigraph-predecessors"]], "MultiDiGraph.remove_edge": [[962, "multidigraph-remove-edge"]], "MultiDiGraph.remove_edges_from": [[963, 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Applying classic graph operations, such as:": [[1429, "applying-classic-graph-operations-such-as"]], "2. Using a call to one of the classic small graphs, e.g.,": [[1429, "using-a-call-to-one-of-the-classic-small-graphs-e-g"]], "3. Using a (constructive) generator for a classic graph, e.g.,": [[1429, "using-a-constructive-generator-for-a-classic-graph-e-g"]], "4. Using a stochastic graph generator, e.g,": [[1429, "using-a-stochastic-graph-generator-e-g"]], "5. 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property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], "random_triad() (in module networkx.algorithms.triads)": [[747, "networkx.algorithms.triads.random_triad"]], "triad_type() (in module networkx.algorithms.triads)": [[748, "networkx.algorithms.triads.triad_type"]], "triadic_census() (in module networkx.algorithms.triads)": [[749, "networkx.algorithms.triads.triadic_census"]], "triads_by_type() (in module networkx.algorithms.triads)": [[750, "networkx.algorithms.triads.triads_by_type"]], "closeness_vitality() (in module networkx.algorithms.vitality)": [[751, "networkx.algorithms.vitality.closeness_vitality"]], "voronoi_cells() (in module networkx.algorithms.voronoi)": [[752, "networkx.algorithms.voronoi.voronoi_cells"]], "wiener_index() (in module networkx.algorithms.wiener)": [[753, "networkx.algorithms.wiener.wiener_index"]], "networkx.algorithms.graph_hashing": [[754, "module-networkx.algorithms.graph_hashing"]], "networkx.algorithms.graphical": [[755, "module-networkx.algorithms.graphical"]], "networkx.algorithms.hierarchy": [[756, "module-networkx.algorithms.hierarchy"]], "networkx.algorithms.hybrid": [[757, "module-networkx.algorithms.hybrid"]], "networkx.algorithms.isolate": [[759, "module-networkx.algorithms.isolate"]], "networkx.algorithms.isomorphism": [[760, "module-networkx.algorithms.isomorphism"]], "networkx.algorithms.isomorphism.tree_isomorphism": [[760, "module-networkx.algorithms.isomorphism.tree_isomorphism"]], "networkx.algorithms.isomorphism.vf2pp": [[760, "module-networkx.algorithms.isomorphism.vf2pp"]], "networkx.algorithms.isomorphism.ismags": [[761, "module-networkx.algorithms.isomorphism.ismags"]], "networkx.algorithms.isomorphism.isomorphvf2": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "networkx.algorithms.link_analysis.hits_alg": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "networkx.algorithms.link_analysis.pagerank_alg": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "networkx.algorithms.link_prediction": [[764, "module-networkx.algorithms.link_prediction"]], "networkx.algorithms.lowest_common_ancestors": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "networkx.algorithms.matching": [[766, "module-networkx.algorithms.matching"]], "networkx.algorithms.minors": [[767, "module-networkx.algorithms.minors"]], "networkx.algorithms.mis": [[768, "module-networkx.algorithms.mis"]], "networkx.algorithms.moral": [[769, "module-networkx.algorithms.moral"]], "networkx.algorithms.node_classification": [[770, "module-networkx.algorithms.node_classification"]], "networkx.algorithms.non_randomness": [[771, "module-networkx.algorithms.non_randomness"]], "networkx.algorithms.operators.all": [[772, "module-networkx.algorithms.operators.all"]], "networkx.algorithms.operators.binary": [[772, "module-networkx.algorithms.operators.binary"]], "networkx.algorithms.operators.product": [[772, "module-networkx.algorithms.operators.product"]], "networkx.algorithms.operators.unary": [[772, "module-networkx.algorithms.operators.unary"]], "networkx.algorithms.planar_drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "networkx.algorithms.planarity": [[774, "module-networkx.algorithms.planarity"]], "networkx.algorithms.polynomials": [[775, "module-networkx.algorithms.polynomials"]], "networkx.algorithms.reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "networkx.algorithms.regular": [[777, "module-networkx.algorithms.regular"]], "networkx.algorithms.richclub": [[778, "module-networkx.algorithms.richclub"]], "networkx.algorithms.shortest_paths.astar": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "networkx.algorithms.shortest_paths.dense": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "networkx.algorithms.shortest_paths.generic": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "networkx.algorithms.shortest_paths.unweighted": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "networkx.algorithms.shortest_paths.weighted": [[779, "module-networkx.algorithms.shortest_paths.weighted"]], "networkx.algorithms.similarity": [[780, "module-networkx.algorithms.similarity"]], "networkx.algorithms.simple_paths": [[781, "module-networkx.algorithms.simple_paths"]], "networkx.algorithms.smallworld": [[782, "module-networkx.algorithms.smallworld"]], "networkx.algorithms.smetric": [[783, "module-networkx.algorithms.smetric"]], "networkx.algorithms.sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "networkx.algorithms.structuralholes": [[785, "module-networkx.algorithms.structuralholes"]], "networkx.algorithms.summarization": [[786, "module-networkx.algorithms.summarization"]], "networkx.algorithms.swap": [[787, "module-networkx.algorithms.swap"]], "networkx.algorithms.threshold": [[788, "module-networkx.algorithms.threshold"]], "networkx.algorithms.tournament": [[789, "module-networkx.algorithms.tournament"]], "networkx.algorithms.traversal.beamsearch": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "networkx.algorithms.traversal.breadth_first_search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "networkx.algorithms.traversal.depth_first_search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "networkx.algorithms.traversal.edgebfs": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "networkx.algorithms.traversal.edgedfs": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "networkx.algorithms.tree.branchings": [[791, "module-networkx.algorithms.tree.branchings"]], "networkx.algorithms.tree.coding": [[791, "module-networkx.algorithms.tree.coding"]], "networkx.algorithms.tree.decomposition": [[791, "module-networkx.algorithms.tree.decomposition"]], "networkx.algorithms.tree.mst": [[791, "module-networkx.algorithms.tree.mst"]], "networkx.algorithms.tree.operations": [[791, "module-networkx.algorithms.tree.operations"]], "networkx.algorithms.tree.recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "networkx.algorithms.triads": [[792, "module-networkx.algorithms.triads"]], "networkx.algorithms.vitality": [[793, "module-networkx.algorithms.vitality"]], "networkx.algorithms.voronoi": [[794, "module-networkx.algorithms.voronoi"]], "networkx.algorithms.wiener": [[795, "module-networkx.algorithms.wiener"]], "digraph (class in networkx)": [[796, "networkx.DiGraph"]], "copy() (adjacencyview method)": [[797, "networkx.classes.coreviews.AdjacencyView.copy"]], "get() (adjacencyview method)": [[798, "networkx.classes.coreviews.AdjacencyView.get"]], "items() (adjacencyview method)": [[799, "networkx.classes.coreviews.AdjacencyView.items"]], "keys() (adjacencyview method)": [[800, "networkx.classes.coreviews.AdjacencyView.keys"]], "values() (adjacencyview method)": [[801, "networkx.classes.coreviews.AdjacencyView.values"]], "copy() (atlasview method)": [[802, "networkx.classes.coreviews.AtlasView.copy"]], "get() (atlasview method)": [[803, 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method)": [[824, "networkx.classes.coreviews.MultiAdjacencyView.get"]], "items() (multiadjacencyview method)": [[825, "networkx.classes.coreviews.MultiAdjacencyView.items"]], "keys() (multiadjacencyview method)": [[826, "networkx.classes.coreviews.MultiAdjacencyView.keys"]], "values() (multiadjacencyview method)": [[827, "networkx.classes.coreviews.MultiAdjacencyView.values"]], "copy() (unionadjacency method)": [[828, "networkx.classes.coreviews.UnionAdjacency.copy"]], "get() (unionadjacency method)": [[829, "networkx.classes.coreviews.UnionAdjacency.get"]], "items() (unionadjacency method)": [[830, "networkx.classes.coreviews.UnionAdjacency.items"]], "keys() (unionadjacency method)": [[831, "networkx.classes.coreviews.UnionAdjacency.keys"]], "values() (unionadjacency method)": [[832, "networkx.classes.coreviews.UnionAdjacency.values"]], "copy() (unionatlas method)": [[833, "networkx.classes.coreviews.UnionAtlas.copy"]], "get() (unionatlas method)": [[834, "networkx.classes.coreviews.UnionAtlas.get"]], "items() (unionatlas method)": [[835, "networkx.classes.coreviews.UnionAtlas.items"]], "keys() (unionatlas method)": [[836, "networkx.classes.coreviews.UnionAtlas.keys"]], "values() (unionatlas method)": [[837, "networkx.classes.coreviews.UnionAtlas.values"]], "copy() (unionmultiadjacency method)": [[838, "networkx.classes.coreviews.UnionMultiAdjacency.copy"]], "get() (unionmultiadjacency method)": [[839, "networkx.classes.coreviews.UnionMultiAdjacency.get"]], "items() (unionmultiadjacency method)": [[840, "networkx.classes.coreviews.UnionMultiAdjacency.items"]], "keys() (unionmultiadjacency method)": [[841, "networkx.classes.coreviews.UnionMultiAdjacency.keys"]], "values() (unionmultiadjacency method)": [[842, "networkx.classes.coreviews.UnionMultiAdjacency.values"]], "copy() (unionmultiinner method)": [[843, "networkx.classes.coreviews.UnionMultiInner.copy"]], "get() (unionmultiinner method)": [[844, "networkx.classes.coreviews.UnionMultiInner.get"]], "items() (unionmultiinner method)": [[845, "networkx.classes.coreviews.UnionMultiInner.items"]], "keys() (unionmultiinner method)": [[846, "networkx.classes.coreviews.UnionMultiInner.keys"]], "values() (unionmultiinner method)": [[847, "networkx.classes.coreviews.UnionMultiInner.values"]], "__contains__() (digraph method)": [[848, "networkx.DiGraph.__contains__"]], "__getitem__() (digraph method)": [[849, "networkx.DiGraph.__getitem__"]], "__init__() (digraph method)": [[850, "networkx.DiGraph.__init__"]], "__iter__() (digraph method)": [[851, "networkx.DiGraph.__iter__"]], "__len__() (digraph method)": [[852, "networkx.DiGraph.__len__"]], "add_edge() (digraph method)": [[853, "networkx.DiGraph.add_edge"]], "add_edges_from() (digraph method)": [[854, "networkx.DiGraph.add_edges_from"]], "add_node() (digraph method)": [[855, "networkx.DiGraph.add_node"]], "add_nodes_from() (digraph method)": [[856, "networkx.DiGraph.add_nodes_from"]], "add_weighted_edges_from() (digraph method)": [[857, "networkx.DiGraph.add_weighted_edges_from"]], "adj (digraph property)": [[858, "networkx.DiGraph.adj"]], "adjacency() (digraph method)": [[859, "networkx.DiGraph.adjacency"]], "clear() (digraph method)": [[860, "networkx.DiGraph.clear"]], "clear_edges() (digraph method)": [[861, "networkx.DiGraph.clear_edges"]], "copy() (digraph method)": [[862, "networkx.DiGraph.copy"]], "degree (digraph property)": [[863, "networkx.DiGraph.degree"]], "edge_subgraph() (digraph method)": [[864, "networkx.DiGraph.edge_subgraph"]], "edges (digraph property)": [[865, "networkx.DiGraph.edges"]], "get_edge_data() (digraph method)": [[866, "networkx.DiGraph.get_edge_data"]], "has_edge() (digraph method)": [[867, "networkx.DiGraph.has_edge"]], "has_node() (digraph method)": [[868, "networkx.DiGraph.has_node"]], "in_degree (digraph property)": [[869, "networkx.DiGraph.in_degree"]], "in_edges (digraph property)": [[870, "networkx.DiGraph.in_edges"]], 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"networkx.Graph.has_node"]], "nbunch_iter() (graph method)": [[914, "networkx.Graph.nbunch_iter"]], "neighbors() (graph method)": [[915, "networkx.Graph.neighbors"]], "nodes (graph property)": [[916, "networkx.Graph.nodes"]], "number_of_edges() (graph method)": [[917, "networkx.Graph.number_of_edges"]], "number_of_nodes() (graph method)": [[918, "networkx.Graph.number_of_nodes"]], "order() (graph method)": [[919, "networkx.Graph.order"]], "remove_edge() (graph method)": [[920, "networkx.Graph.remove_edge"]], "remove_edges_from() (graph method)": [[921, "networkx.Graph.remove_edges_from"]], "remove_node() (graph method)": [[922, "networkx.Graph.remove_node"]], "remove_nodes_from() (graph method)": [[923, "networkx.Graph.remove_nodes_from"]], "size() (graph method)": [[924, "networkx.Graph.size"]], "subgraph() (graph method)": [[925, "networkx.Graph.subgraph"]], "to_directed() (graph method)": [[926, "networkx.Graph.to_directed"]], "to_undirected() (graph method)": [[927, "networkx.Graph.to_undirected"]], "update() (graph method)": [[928, "networkx.Graph.update"]], "__contains__() (multidigraph method)": [[929, "networkx.MultiDiGraph.__contains__"]], "__getitem__() (multidigraph method)": [[930, "networkx.MultiDiGraph.__getitem__"]], "__init__() (multidigraph method)": [[931, "networkx.MultiDiGraph.__init__"]], "__iter__() (multidigraph method)": [[932, "networkx.MultiDiGraph.__iter__"]], "__len__() (multidigraph method)": [[933, "networkx.MultiDiGraph.__len__"]], "add_edge() (multidigraph method)": [[934, "networkx.MultiDiGraph.add_edge"]], "add_edges_from() (multidigraph method)": [[935, "networkx.MultiDiGraph.add_edges_from"]], "add_node() (multidigraph method)": [[936, "networkx.MultiDiGraph.add_node"]], "add_nodes_from() (multidigraph method)": [[937, "networkx.MultiDiGraph.add_nodes_from"]], "add_weighted_edges_from() (multidigraph method)": [[938, "networkx.MultiDiGraph.add_weighted_edges_from"]], "adj (multidigraph property)": [[939, 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"networkx.MultiDiGraph.remove_edges_from"]], "remove_node() (multidigraph method)": [[964, "networkx.MultiDiGraph.remove_node"]], "remove_nodes_from() (multidigraph method)": [[965, "networkx.MultiDiGraph.remove_nodes_from"]], "reverse() (multidigraph method)": [[966, "networkx.MultiDiGraph.reverse"]], "size() (multidigraph method)": [[967, "networkx.MultiDiGraph.size"]], "subgraph() (multidigraph method)": [[968, "networkx.MultiDiGraph.subgraph"]], "succ (multidigraph property)": [[969, "networkx.MultiDiGraph.succ"]], "successors() (multidigraph method)": [[970, "networkx.MultiDiGraph.successors"]], "to_directed() (multidigraph method)": [[971, "networkx.MultiDiGraph.to_directed"]], "to_undirected() (multidigraph method)": [[972, "networkx.MultiDiGraph.to_undirected"]], "update() (multidigraph method)": [[973, "networkx.MultiDiGraph.update"]], "__contains__() (multigraph method)": [[974, "networkx.MultiGraph.__contains__"]], "__getitem__() (multigraph method)": [[975, "networkx.MultiGraph.__getitem__"]], "__init__() (multigraph method)": [[976, "networkx.MultiGraph.__init__"]], "__iter__() (multigraph method)": [[977, "networkx.MultiGraph.__iter__"]], "__len__() (multigraph method)": [[978, "networkx.MultiGraph.__len__"]], "add_edge() (multigraph method)": [[979, "networkx.MultiGraph.add_edge"]], "add_edges_from() (multigraph method)": [[980, "networkx.MultiGraph.add_edges_from"]], "add_node() (multigraph method)": [[981, "networkx.MultiGraph.add_node"]], "add_nodes_from() (multigraph method)": [[982, "networkx.MultiGraph.add_nodes_from"]], "add_weighted_edges_from() (multigraph method)": [[983, "networkx.MultiGraph.add_weighted_edges_from"]], "adj (multigraph property)": [[984, "networkx.MultiGraph.adj"]], "adjacency() (multigraph method)": [[985, "networkx.MultiGraph.adjacency"]], "clear() (multigraph method)": [[986, "networkx.MultiGraph.clear"]], "clear_edges() (multigraph method)": [[987, "networkx.MultiGraph.clear_edges"]], "copy() (multigraph 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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__() (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_latex": [[1042, "module-networkx.drawing.nx_latex"]], "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)": 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networkx.generators.community)": [[1169, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1170, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1171, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() (in module networkx.generators.community)": [[1172, "networkx.generators.community.planted_partition_graph"]], "random_partition_graph() (in module networkx.generators.community)": [[1173, "networkx.generators.community.random_partition_graph"]], "relaxed_caveman_graph() (in module networkx.generators.community)": [[1174, "networkx.generators.community.relaxed_caveman_graph"]], "ring_of_cliques() (in module networkx.generators.community)": [[1175, "networkx.generators.community.ring_of_cliques"]], "stochastic_block_model() (in module networkx.generators.community)": [[1176, 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networkx.generators.duplication)": [[1191, "networkx.generators.duplication.partial_duplication_graph"]], "ego_graph() (in module networkx.generators.ego)": [[1192, "networkx.generators.ego.ego_graph"]], "chordal_cycle_graph() (in module networkx.generators.expanders)": [[1193, "networkx.generators.expanders.chordal_cycle_graph"]], "margulis_gabber_galil_graph() (in module networkx.generators.expanders)": [[1194, "networkx.generators.expanders.margulis_gabber_galil_graph"]], "paley_graph() (in module networkx.generators.expanders)": [[1195, "networkx.generators.expanders.paley_graph"]], "geographical_threshold_graph() (in module networkx.generators.geometric)": [[1196, "networkx.generators.geometric.geographical_threshold_graph"]], "geometric_edges() (in module networkx.generators.geometric)": [[1197, "networkx.generators.geometric.geometric_edges"]], "navigable_small_world_graph() (in module networkx.generators.geometric)": [[1198, 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networkx.generators.random_graphs)": [[1233, "networkx.generators.random_graphs.fast_gnp_random_graph"]], "gnm_random_graph() (in module networkx.generators.random_graphs)": [[1234, "networkx.generators.random_graphs.gnm_random_graph"]], "gnp_random_graph() (in module networkx.generators.random_graphs)": [[1235, "networkx.generators.random_graphs.gnp_random_graph"]], "newman_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1236, "networkx.generators.random_graphs.newman_watts_strogatz_graph"]], "powerlaw_cluster_graph() (in module networkx.generators.random_graphs)": [[1237, "networkx.generators.random_graphs.powerlaw_cluster_graph"]], "random_kernel_graph() (in module networkx.generators.random_graphs)": [[1238, "networkx.generators.random_graphs.random_kernel_graph"]], "random_lobster() (in module networkx.generators.random_graphs)": [[1239, "networkx.generators.random_graphs.random_lobster"]], "random_powerlaw_tree() (in module networkx.generators.random_graphs)": [[1240, "networkx.generators.random_graphs.random_powerlaw_tree"]], "random_powerlaw_tree_sequence() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.random_powerlaw_tree_sequence"]], "random_regular_graph() (in module networkx.generators.random_graphs)": [[1242, "networkx.generators.random_graphs.random_regular_graph"]], "random_shell_graph() (in module networkx.generators.random_graphs)": [[1243, "networkx.generators.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1244, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1245, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1246, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1247, "networkx.generators.small.chvatal_graph"]], 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"networkx.generators.spectral_graph_forge.spectral_graph_forge"]], "stochastic_graph() (in module networkx.generators.stochastic)": [[1273, "networkx.generators.stochastic.stochastic_graph"]], "sudoku_graph() (in module networkx.generators.sudoku)": [[1274, "networkx.generators.sudoku.sudoku_graph"]], "prefix_tree() (in module networkx.generators.trees)": [[1275, "networkx.generators.trees.prefix_tree"]], "random_tree() (in module networkx.generators.trees)": [[1276, "networkx.generators.trees.random_tree"]], "triad_graph() (in module networkx.generators.triads)": [[1277, "networkx.generators.triads.triad_graph"]], "algebraic_connectivity() (in module networkx.linalg.algebraicconnectivity)": [[1278, "networkx.linalg.algebraicconnectivity.algebraic_connectivity"]], "fiedler_vector() (in module networkx.linalg.algebraicconnectivity)": [[1279, "networkx.linalg.algebraicconnectivity.fiedler_vector"]], "spectral_ordering() (in module networkx.linalg.algebraicconnectivity)": [[1280, 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"laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1288, "networkx.linalg.laplacianmatrix.laplacian_matrix"]], "normalized_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1289, "networkx.linalg.laplacianmatrix.normalized_laplacian_matrix"]], "directed_modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1290, "networkx.linalg.modularitymatrix.directed_modularity_matrix"]], "modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1291, "networkx.linalg.modularitymatrix.modularity_matrix"]], "adjacency_spectrum() (in module networkx.linalg.spectrum)": [[1292, "networkx.linalg.spectrum.adjacency_spectrum"]], "bethe_hessian_spectrum() (in module networkx.linalg.spectrum)": [[1293, "networkx.linalg.spectrum.bethe_hessian_spectrum"]], "laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1294, "networkx.linalg.spectrum.laplacian_spectrum"]], "modularity_spectrum() (in module networkx.linalg.spectrum)": [[1295, 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[[1327, "term-ebunch"]], "edge": [[1327, "term-edge"]], "edge attribute": [[1327, "term-edge-attribute"]], "nbunch": [[1327, "term-nbunch"]], "node": [[1327, "term-node"]], "node attribute": [[1327, "term-node-attribute"]], "networkx.linalg.algebraicconnectivity": [[1330, "module-networkx.linalg.algebraicconnectivity"]], "networkx.linalg.attrmatrix": [[1330, "module-networkx.linalg.attrmatrix"]], "networkx.linalg.bethehessianmatrix": [[1330, "module-networkx.linalg.bethehessianmatrix"]], "networkx.linalg.graphmatrix": [[1330, "module-networkx.linalg.graphmatrix"]], "networkx.linalg.laplacianmatrix": [[1330, "module-networkx.linalg.laplacianmatrix"]], "networkx.linalg.modularitymatrix": [[1330, "module-networkx.linalg.modularitymatrix"]], "networkx.linalg.spectrum": [[1330, "module-networkx.linalg.spectrum"]], "networkx.readwrite.adjlist": [[1332, "module-networkx.readwrite.adjlist"]], "networkx.readwrite.edgelist": [[1333, "module-networkx.readwrite.edgelist"]], "generate_adjlist() (in module networkx.readwrite.adjlist)": [[1334, "networkx.readwrite.adjlist.generate_adjlist"]], "parse_adjlist() (in module networkx.readwrite.adjlist)": [[1335, "networkx.readwrite.adjlist.parse_adjlist"]], "read_adjlist() (in module networkx.readwrite.adjlist)": [[1336, "networkx.readwrite.adjlist.read_adjlist"]], "write_adjlist() (in module networkx.readwrite.adjlist)": [[1337, "networkx.readwrite.adjlist.write_adjlist"]], "generate_edgelist() (in module networkx.readwrite.edgelist)": [[1338, "networkx.readwrite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.readwrite.edgelist)": [[1339, "networkx.readwrite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.readwrite.edgelist)": [[1340, "networkx.readwrite.edgelist.read_edgelist"]], "read_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1341, "networkx.readwrite.edgelist.read_weighted_edgelist"]], "write_edgelist() (in module networkx.readwrite.edgelist)": [[1342, 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\ No newline at end of file +Search.setIndex({"docnames": ["auto_examples/3d_drawing/index", "auto_examples/3d_drawing/mayavi2_spring", "auto_examples/3d_drawing/plot_basic", "auto_examples/3d_drawing/sg_execution_times", "auto_examples/algorithms/index", "auto_examples/algorithms/plot_beam_search", "auto_examples/algorithms/plot_betweenness_centrality", "auto_examples/algorithms/plot_blockmodel", "auto_examples/algorithms/plot_circuits", "auto_examples/algorithms/plot_davis_club", "auto_examples/algorithms/plot_dedensification", "auto_examples/algorithms/plot_iterated_dynamical_systems", "auto_examples/algorithms/plot_krackhardt_centrality", "auto_examples/algorithms/plot_parallel_betweenness", "auto_examples/algorithms/plot_rcm", "auto_examples/algorithms/plot_snap", "auto_examples/algorithms/plot_subgraphs", "auto_examples/algorithms/sg_execution_times", "auto_examples/basic/index", "auto_examples/basic/plot_properties", "auto_examples/basic/plot_read_write", 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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, 1136, 1140, 1149, 1165, 1168, 1176, 1179, 1189, 1191, 1193, 1196, 1200, 1202, 1212, 1216, 1220, 1222, 1238, 1242, 1243, 1273, 1278, 1279, 1280, 1281, 1282, 1298, 1299, 1301, 1310, 1312, 1313, 1314, 1315, 1318, 1336, 1340, 1341, 1342, 1343, 1362, 1364, 1365, 1366, 1367, 1368, 1369, 1379, 1396, 1397, 1398, 1410, 1429], "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, 1124, 1245, 1302, 1304, 1306, 1313, 1322, 1323, 1324, 1328, 1405, 1409, 1410, 1414, 1416, 1419, 1422], "arbitrary_el": [8, 1395, 1416], "nb": [8, 1334, 1337], "left": [8, 71, 115, 183, 311, 312, 322, 324, 325, 385, 559, 560, 584, 616, 688, 689, 739, 1106, 1137, 1139, 1149, 1182, 1209, 1283, 1358, 1361, 1407], "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, 1137, 1139, 1149, 1158, 1160, 1182, 1209, 1216, 1218, 1273, 1283], "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, 1165, 1213, 1299, 1391, 1407, 1412], "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, 1239, 1276, 1299, 1306, 1329, 1348, 1353, 1407, 1410, 1419], "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, 1176, 1195, 1238, 1249, 1253, 1254, 1259, 1261, 1272, 1323, 1324, 1390], "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, 1124, 1126, 1173, 1177, 1203, 1204, 1209, 1210, 1213, 1220, 1226, 1230, 1237, 1246, 1247, 1248, 1249, 1250, 1251, 1252, 1253, 1254, 1256, 1257, 1261, 1264, 1266, 1267, 1272, 1278, 1279, 1280, 1283, 1299, 1300, 1326, 1327, 1329, 1331, 1346, 1385, 1386, 1396, 1398, 1401, 1405, 1407, 1410, 1411, 1412, 1414, 1415, 1416, 1429], "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, 1042, 1120, 1125, 1129, 1232, 1281, 1282, 1299, 1331, 1396, 1407, 1409], "operand": 8, "predict": [8, 567, 568, 569, 570, 571, 572, 573, 574, 591, 592, 758, 1328, 1405, 1409, 1415], "henc": [8, 168, 189, 521, 865, 878, 910, 946, 960, 991, 1059, 1124, 1125, 1126, 1205, 1386], "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, 1176, 1178, 1180, 1195, 1210, 1225, 1226, 1230, 1232, 1237, 1244, 1299, 1303, 1306, 1329, 1336, 1337, 1344, 1345, 1347, 1354, 1356, 1357, 1358, 1359, 1360, 1361, 1374, 1382, 1383, 1384, 1386, 1396, 1407, 1408, 1409, 1413, 1420, 1429], "necessarili": [8, 99, 341, 451, 483, 559, 560, 641, 643, 1038, 1222], "behav": [8, 88, 103, 159, 190, 200, 220, 351, 858, 879, 888, 903, 939, 969, 984, 1232, 1299, 1398, 1407], "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, 1151, 1165, 1198, 1219, 1220, 1260, 1267, 1281, 1282, 1299, 1410], "left_subformula": 8, "right_subformula": 8, "in_degre": [8, 166, 188, 491, 678, 863, 877, 944, 959, 1180, 1210, 1211, 1407, 1409, 1410, 1429], "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, 1133, 1148, 1157, 1163, 1165, 1168, 1179, 1183, 1188, 1196, 1198, 1199, 1200, 1201, 1202, 1210, 1213, 1214, 1218, 1220, 1225, 1237, 1242, 1246, 1247, 1251, 1252, 1257, 1262, 1264, 1267, 1270, 1272, 1273, 1275, 1278, 1279, 1280, 1281, 1282, 1284, 1285, 1286, 1287, 1288, 1289, 1292, 1294, 1296, 1299, 1303, 1329, 1331, 1333, 1336, 1337, 1356, 1357, 1374, 1375, 1382, 1385, 1396, 1397, 1398, 1401, 1406, 1407, 1408, 1409, 1410, 1412, 1416, 1417, 1419, 1426, 1428], "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, 1196, 1200, 1202, 1272, 1299, 1329, 1337, 1344, 1347, 1358, 1361, 1402, 1405, 1407, 1409, 1414, 1416, 1417, 1429], "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, 1137, 1149, 1150, 1152, 1154, 1155, 1159, 1177, 1188, 1189, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1207, 1210, 1213, 1214, 1218, 1220, 1221, 1246, 1247, 1256, 1274, 1275, 1278, 1279, 1297, 1298, 1299, 1326, 1327, 1329, 1331, 1362, 1363, 1366, 1396, 1397, 1398, 1400, 1405, 1407, 1408, 1409, 1410, 1413, 1414, 1416, 1428], "layer": [8, 36, 55, 61, 67, 103, 438, 705, 1038, 1109, 1423], "third": [8, 102, 114, 249, 422, 467, 585, 587, 734, 736, 1220, 1229, 1265, 1266, 1329, 1410], "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, 1042, 1088, 1102, 1139, 1153, 1155, 1157, 1160, 1162, 1190, 1191, 1280, 1285, 1326, 1327, 1348, 1351, 1352, 1353, 1385, 1410, 1416, 1417], "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, 1129, 1147, 1168, 1192, 1195, 1202, 1210, 1213, 1214, 1216, 1218, 1285, 1299, 1329, 1331, 1361, 1366, 1367, 1390, 1396, 1398, 1405, 1416, 1419, 1420, 1428, 1429], "negat": 8, "sole": [8, 786, 1281, 1282, 1329], "fourth": [8, 230, 231, 1329, 1407], "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, 1042, 1052, 1062, 1066, 1070, 1072, 1075, 1080, 1083, 1084, 1098, 1099, 1101, 1118, 1138, 1153, 1157, 1171, 1172, 1173, 1176, 1180, 1181, 1183, 1185, 1186, 1187, 1188, 1192, 1220, 1273, 1275, 1276, 1277, 1286, 1287, 1290, 1293, 1295, 1301, 1326, 1329, 1336, 1340, 1345, 1359, 1360, 1365, 1368, 1369, 1374, 1396, 1402, 1404, 1405, 1407, 1408, 1409, 1410, 1411, 1412, 1414, 1415, 1416, 1417, 1419, 1420, 1427, 1428, 1429], "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, 1278, 1329, 1348, 1410, 1411, 1420, 1429], "get_node_attribut": [8, 39, 44, 71, 1216, 1407], "600": [8, 10, 12], "font_siz": [8, 16, 21, 25, 32, 35, 38, 45, 46, 1136, 1137, 1139], "22": [8, 35, 59, 64, 66, 383, 384, 1274, 1326, 1406, 1411, 1415, 1425], "multipartite_layout": [8, 36, 61, 67, 1415, 1417, 1423], "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, 1165, 1168, 1201, 1207, 1233, 1242, 1274, 1283, 1294, 1310, 1312, 1315, 1401, 1402], "105": [8, 17, 517, 518, 1169, 1170], "plot_circuit": [8, 17], "southern": [9, 1268], "women": [9, 1268, 1401, 1409], "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, 1137, 1139, 1196, 1205, 1220, 1222, 1272, 1286, 1287, 1329, 1331, 1386, 1401, 1408, 1409, 1410, 1411, 1416, 1420, 1429], "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, 1326, 1329, 1388, 1390, 1395, 1397, 1398, 1400, 1402, 1407, 1408, 1414, 1429], "were": [9, 65, 88, 99, 101, 104, 215, 216, 220, 289, 305, 410, 437, 460, 588, 962, 1002, 1202, 1396, 1398, 1402, 1405, 1408, 1409, 1410, 1416, 1419], "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, 1205], "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, 1205, 1410, 1416], "1930": [9, 1399], "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, 1124, 1125, 1126, 1129, 1136, 1138, 1140, 1154, 1162, 1168, 1196, 1200, 1201, 1220, 1274, 1275, 1326, 1331, 1356, 1357, 1359, 1360, 1362, 1366, 1397, 1399, 1405, 1407, 1409, 1412, 1417, 1429], "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, 1143, 1154, 1188, 1196, 1197, 1199, 1200, 1201, 1202, 1212, 1220, 1243, 1246, 1249, 1253, 1261, 1270, 1272, 1275, 1276, 1281, 1282, 1326, 1327, 1329, 1332, 1333, 1349, 1350, 1391, 1396, 1409], "observ": [9, 13, 132, 223, 1417, 1429], "attend": 9, "14": [9, 11, 16, 19, 25, 38, 44, 64, 66, 71, 229, 230, 231, 383, 384, 405, 406, 501, 619, 690, 1153, 1245, 1253, 1265, 1409, 1411, 1429], "event": [9, 25, 99, 100, 110, 1168, 1232, 1303], "18": [9, 44, 64, 66, 93, 324, 325, 345, 383, 384, 618, 1172, 1252, 1258, 1261, 1263, 1266, 1272, 1396, 1409, 1419, 1420, 1424, 1429], "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, 1154, 1206, 1207, 1208, 1268, 1328, 1398, 1401, 1402, 1403, 1404, 1409, 1410, 1414, 1416, 1420, 1424, 1428], "biadjac": [9, 282, 283, 1403, 1409], "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, 1042, 1052, 1053, 1085, 1100, 1104, 1151, 1215, 1245, 1251, 1253, 1254, 1258, 1261, 1263, 1276, 1326, 1329, 1333, 1342, 1343, 1348, 1351, 1352, 1353, 1385, 1395, 1397, 1405, 1406, 1408, 1411, 1412, 1413, 1414, 1415, 1416, 1429], "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, 1136, 1139, 1153, 1247, 1248, 1252, 1257, 1260, 1266, 1338, 1409, 1411, 1415, 1429], "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, 1151, 1153, 1197, 1202, 1215, 1220, 1238, 1249, 1258, 1270, 1276, 1286, 1287, 1326, 1329, 1331, 1399, 1406, 1415, 1416, 1417, 1418, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429], "11": [9, 25, 33, 44, 56, 59, 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, 1153, 1290, 1406, 1413, 1416, 1417, 1422, 1427, 1428, 1429], "13": [9, 11, 38, 44, 64, 66, 89, 91, 156, 229, 230, 231, 343, 501, 703, 855, 900, 936, 981, 1153, 1195, 1409, 1423, 1429], "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, 1208, 1259, 1274, 1289, 1326, 1409, 1414, 1429], "17": [9, 21, 44, 64, 66, 103, 229, 230, 231, 297, 508, 680, 693, 1408, 1409, 1429], "friend": [9, 545, 1410, 1415], "member": [9, 92, 93, 94, 100, 112, 315, 317, 318, 319, 330, 391, 483, 484, 586, 691, 1225, 1270, 1406], "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, 1168, 1199, 1200, 1201], "50": [9, 25, 30, 34, 40, 50, 54, 55, 56, 57, 64, 65, 272, 312, 1117, 1196, 1200, 1201, 1254, 1300, 1305], "45": [9, 58, 64, 110, 226, 300, 409, 1178], "57": [9, 64], "46": [9, 64, 235, 564, 619, 1267], "24": [9, 19, 37, 64, 66, 68, 103, 383, 384, 496, 505, 508, 703, 1215, 1232, 1247, 1265, 1274, 1406], "32": [9, 64, 66, 68, 209, 211, 212, 383, 384, 564, 703, 1406, 1414], "36": [9, 21, 64, 68, 752, 1153, 1265, 1274, 1356, 1357, 1382, 1406], "31": [9, 64, 66, 229, 230, 231, 260, 261, 262, 289, 383, 384, 409, 703, 1229, 1238, 1406], "40": [9, 50, 64, 80, 101, 297, 300, 555, 672, 1176, 1243, 1274], "38": [9, 64, 688, 1274], "33": [9, 58, 64, 66, 68, 93, 383, 384, 500, 514, 703, 1270, 1274, 1406, 1417], "37": [9, 56, 64, 68, 303, 311, 312, 323, 324, 325, 496, 508, 1039, 1040, 1274, 1396, 1406, 1411, 1428], "43": [9, 64, 324, 325, 606, 1247, 1274], "34": [9, 64, 68, 331, 508, 762, 1274, 1406], "algorithm": [9, 14, 15, 44, 52, 54, 88, 93, 94, 95, 96, 102, 103, 107, 109, 110, 111, 112, 114, 115, 117, 120, 121, 122, 125, 127, 128, 132, 133, 136, 141, 151, 210, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 228, 229, 230, 231, 232, 235, 249, 251, 252, 253, 254, 255, 256, 258, 260, 261, 262, 263, 264, 265, 266, 267, 272, 275, 277, 278, 280, 282, 284, 285, 286, 287, 288, 289, 290, 293, 296, 297, 298, 299, 301, 302, 303, 306, 307, 308, 309, 311, 312, 315, 320, 322, 323, 324, 325, 326, 329, 330, 331, 332, 333, 337, 339, 340, 341, 342, 343, 345, 346, 347, 352, 358, 361, 362, 366, 371, 372, 373, 374, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 389, 390, 394, 399, 405, 406, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 421, 422, 424, 425, 426, 427, 428, 429, 430, 432, 433, 435, 437, 440, 449, 451, 452, 453, 454, 455, 460, 464, 466, 468, 481, 482, 483, 488, 494, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 512, 513, 514, 516, 519, 520, 521, 527, 537, 546, 547, 548, 552, 553, 554, 555, 556, 557, 558, 564, 566, 569, 577, 581, 582, 583, 589, 591, 592, 593, 600, 614, 616, 618, 619, 624, 625, 626, 627, 628, 629, 630, 632, 633, 635, 636, 639, 652, 653, 657, 658, 659, 660, 663, 664, 667, 671, 672, 673, 674, 676, 677, 678, 680, 681, 682, 683, 686, 690, 691, 692, 693, 695, 696, 697, 698, 699, 700, 701, 702, 711, 717, 721, 722, 729, 731, 732, 734, 735, 736, 737, 738, 749, 764, 765, 768, 770, 775, 776, 780, 786, 789, 790, 791, 853, 898, 934, 979, 1011, 1038, 1042, 1043, 1105, 1106, 1107, 1109, 1114, 1116, 1117, 1128, 1129, 1158, 1168, 1171, 1172, 1180, 1181, 1182, 1183, 1184, 1188, 1189, 1190, 1191, 1196, 1198, 1203, 1204, 1205, 1208, 1210, 1212, 1213, 1219, 1226, 1227, 1229, 1230, 1231, 1233, 1234, 1235, 1237, 1238, 1242, 1263, 1272, 1278, 1279, 1280, 1301, 1305, 1322, 1323, 1324, 1326, 1328, 1331, 1370, 1371, 1389, 1396, 1397, 1398, 1403, 1404, 1405, 1406, 1409, 1410, 1411, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1422, 1425, 1427, 1428, 1429], "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, 1137, 1139, 1255, 1399, 1402, 1410, 1415, 1416, 1419], "bottom": [9, 91, 115, 260, 272, 274, 284, 285, 286, 287, 288, 350, 381, 1137, 1139, 1158, 1407, 1419], "biadjacency_matrix": [9, 283], "onto": [9, 284, 285, 286, 287, 288, 559, 560, 1126], "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, 1210, 1213, 1281, 1282, 1299, 1379, 1397, 1414, 1417], "co": [9, 26, 94, 99, 144, 752, 1329], "occur": [9, 93, 95, 100, 230, 231, 277, 278, 280, 383, 581, 582, 583, 588, 1043, 1117, 1120, 1129, 1285, 1299], "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, 1182, 1281, 1282, 1409, 1410, 1419], "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, 1220, 1331], "contact": [9, 92, 688, 1198, 1329], "weighted_projected_graph": [9, 284, 285, 286, 287, 1420], "648": 9, "072": [9, 17, 76, 78], "plot_davis_club": [9, 17], "retain": [10, 102, 110, 230, 284, 285, 286, 287, 288, 1100, 1190, 1298], "pattern": [10, 54, 93, 103, 236, 241, 244, 248, 385, 494, 519, 555, 671, 672, 673, 674, 690, 691, 693, 762, 786, 1036, 1088, 1391, 1416], "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, 1124, 1126, 1157, 1168, 1175, 1188, 1210, 1213, 1220, 1222, 1236, 1237, 1239, 1305, 1329, 1356, 1357, 1359, 1360, 1382, 1383, 1386, 1396, 1397, 1398, 1401, 1407, 1409, 1410, 1411, 1412, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429], "compressor": [10, 690, 786], "do": [10, 55, 75, 88, 92, 93, 94, 96, 99, 101, 102, 106, 107, 109, 111, 115, 133, 165, 184, 199, 202, 204, 230, 231, 238, 243, 277, 278, 280, 362, 380, 410, 411, 412, 418, 419, 458, 459, 467, 470, 589, 598, 632, 690, 692, 734, 735, 736, 737, 791, 796, 862, 873, 887, 890, 891, 907, 916, 925, 926, 927, 943, 954, 955, 968, 971, 972, 988, 997, 998, 1007, 1008, 1009, 1037, 1038, 1039, 1040, 1042, 1061, 1082, 1102, 1168, 1180, 1192, 1196, 1210, 1213, 1219, 1220, 1230, 1275, 1331, 1396, 1404, 1405, 1410, 1414, 1429], "would": [10, 92, 93, 95, 96, 100, 101, 102, 103, 104, 105, 107, 289, 305, 414, 415, 416, 417, 422, 428, 579, 583, 588, 632, 679, 690, 693, 717, 718, 751, 1220, 1239, 1298, 1299, 1303, 1306, 1329, 1419, 1420], "result": [10, 11, 25, 71, 92, 95, 101, 103, 109, 110, 112, 142, 165, 209, 218, 220, 230, 231, 255, 269, 271, 273, 276, 283, 284, 285, 286, 287, 288, 289, 299, 300, 305, 324, 325, 330, 374, 380, 381, 382, 385, 386, 391, 411, 412, 416, 418, 440, 464, 466, 467, 490, 494, 498, 499, 509, 510, 511, 512, 564, 565, 566, 584, 585, 587, 601, 609, 615, 626, 627, 629, 676, 678, 690, 692, 704, 710, 717, 786, 791, 862, 907, 943, 984, 988, 1038, 1042, 1082, 1094, 1098, 1099, 1102, 1103, 1105, 1112, 1113, 1114, 1116, 1124, 1134, 1135, 1141, 1142, 1143, 1144, 1145, 1153, 1155, 1157, 1160, 1162, 1163, 1166, 1178, 1180, 1183, 1204, 1225, 1228, 1242, 1281, 1282, 1284, 1299, 1302, 1306, 1311, 1329, 1331, 1334, 1337, 1362, 1405, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1428, 1429], "fewer": [10, 420, 421, 681, 683, 690, 692, 693, 694, 762, 786, 1216, 1218], "compress": [10, 25, 268, 512, 577, 690, 786, 1116, 1245, 1336, 1337, 1342, 1343, 1347, 1353, 1360, 1361, 1374, 1375, 1379], "suptitl": [10, 15], "original_graph": [10, 15, 690], "white_nod": 10, "red_nod": 10, "250": [10, 32, 1168], "white": [10, 21, 25, 82, 83, 127, 214, 215, 216, 220, 427, 1398, 1401, 1409], "add_nodes_from": [10, 15, 16, 36, 70, 71, 82, 89, 115, 156, 165, 199, 207, 236, 237, 248, 265, 267, 268, 423, 425, 426, 469, 555, 690, 796, 855, 862, 887, 892, 900, 907, 925, 928, 936, 943, 968, 973, 981, 988, 1007, 1010, 1037, 1039, 1040, 1065, 1197, 1220, 1294, 1407, 1409, 1416, 1429], "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, 1138, 1157, 1220, 1290, 1294, 1329, 1407, 1410, 1429], "base_opt": [10, 15], "dict": [10, 15, 19, 25, 39, 54, 57, 58, 67, 70, 88, 101, 102, 107, 109, 144, 145, 148, 157, 159, 160, 165, 168, 169, 176, 179, 184, 189, 190, 195, 197, 200, 202, 204, 207, 220, 237, 239, 240, 252, 290, 309, 310, 329, 334, 336, 353, 408, 411, 412, 416, 422, 427, 470, 473, 481, 482, 496, 502, 512, 545, 561, 563, 565, 566, 575, 577, 578, 579, 580, 588, 614, 628, 631, 636, 637, 638, 640, 642, 644, 645, 646, 647, 648, 649, 662, 666, 669, 687, 688, 691, 705, 706, 707, 713, 715, 749, 750, 760, 796, 849, 856, 858, 859, 862, 865, 870, 873, 878, 879, 884, 888, 890, 891, 892, 894, 901, 903, 904, 907, 910, 916, 923, 926, 927, 928, 930, 931, 935, 937, 939, 940, 943, 946, 947, 951, 955, 960, 965, 969, 971, 972, 973, 975, 976, 980, 982, 984, 985, 988, 991, 992, 998, 1005, 1008, 1009, 1010, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1038, 1039, 1040, 1041, 1042, 1045, 1047, 1085, 1086, 1091, 1094, 1097, 1106, 1107, 1108, 1109, 1110, 1111, 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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, 1186, 1192, 1220, 1226, 1230, 1254, 1273, 1297, 1298, 1299, 1406, 1407, 1409, 1410, 1411, 1412, 1415, 1416, 1425, 1428], "nonexp_node_color": 10, "nonexp_node_s": 10, "yellow": [10, 15, 598, 760, 1429], "nonexp_po": 10, "75": [10, 34, 239, 260, 299, 314, 355, 356, 386, 682, 1172, 1173, 1174, 1176, 1407, 1411, 1429], "c_node": [10, 690], "spot": 10, "242": [10, 17], "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, 1042, 1222, 1281, 1282, 1329, 1428], "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, 1124, 1125, 1126, 1150, 1157, 1173, 1175, 1198, 1200, 1283, 1289, 1290, 1291, 1299, 1323, 1324, 1329, 1347, 1356, 1357, 1362, 1366, 1382, 1398, 1405, 1410, 1411, 1415, 1429], "an": [11, 15, 24, 25, 31, 34, 38, 41, 44, 46, 49, 52, 54, 55, 58, 63, 66, 67, 71, 75, 76, 77, 88, 91, 92, 93, 94, 95, 96, 99, 100, 101, 102, 103, 104, 107, 110, 112, 114, 115, 116, 120, 121, 127, 128, 132, 141, 151, 152, 157, 158, 160, 165, 166, 167, 168, 170, 175, 179, 180, 181, 184, 188, 189, 191, 192, 193, 194, 195, 198, 199, 201, 204, 206, 207, 208, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 226, 227, 229, 230, 231, 232, 235, 238, 239, 240, 243, 249, 250, 251, 255, 256, 264, 266, 267, 269, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 291, 292, 293, 294, 295, 297, 298, 299, 301, 302, 306, 307, 308, 309, 311, 312, 315, 316, 318, 319, 320, 322, 324, 325, 326, 329, 330, 332, 341, 342, 343, 345, 346, 347, 348, 349, 350, 351, 353, 357, 362, 363, 364, 365, 366, 370, 373, 374, 375, 377, 378, 379, 380, 381, 383, 384, 385, 387, 388, 389, 390, 392, 394, 395, 400, 402, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 427, 428, 429, 431, 432, 433, 437, 438, 439, 440, 449, 450, 451, 455, 456, 457, 460, 462, 466, 467, 468, 469, 471, 472, 473, 474, 475, 477, 480, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 516, 517, 519, 520, 521, 522, 523, 524, 525, 530, 534, 535, 540, 544, 545, 555, 559, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 577, 578, 579, 580, 584, 586, 588, 589, 590, 593, 594, 595, 596, 597, 598, 601, 604, 605, 607, 610, 611, 615, 616, 618, 619, 624, 626, 627, 631, 632, 633, 635, 636, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675, 676, 678, 679, 680, 681, 682, 683, 684, 686, 690, 691, 692, 694, 695, 696, 697, 701, 703, 704, 705, 706, 707, 708, 716, 717, 719, 721, 722, 723, 724, 725, 726, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 743, 748, 752, 760, 761, 762, 767, 775, 782, 791, 796, 801, 806, 810, 814, 818, 822, 827, 832, 837, 842, 847, 849, 850, 851, 853, 854, 856, 857, 859, 862, 863, 864, 865, 866, 869, 871, 872, 873, 877, 878, 880, 881, 882, 883, 884, 886, 887, 889, 891, 892, 894, 895, 896, 898, 899, 901, 902, 904, 907, 908, 909, 910, 911, 914, 915, 916, 920, 921, 922, 923, 924, 925, 927, 928, 930, 931, 932, 934, 935, 937, 938, 940, 943, 944, 945, 946, 947, 948, 950, 952, 953, 954, 955, 959, 960, 961, 962, 963, 964, 965, 967, 968, 970, 972, 973, 975, 976, 977, 979, 980, 982, 983, 985, 988, 989, 990, 991, 992, 993, 995, 996, 997, 998, 1002, 1003, 1004, 1005, 1006, 1007, 1009, 1010, 1012, 1013, 1018, 1020, 1036, 1037, 1038, 1039, 1040, 1042, 1043, 1045, 1046, 1049, 1050, 1051, 1061, 1062, 1066, 1068, 1074, 1075, 1081, 1082, 1084, 1085, 1086, 1087, 1088, 1090, 1094, 1098, 1099, 1100, 1101, 1102, 1103, 1105, 1115, 1117, 1122, 1124, 1125, 1126, 1136, 1138, 1140, 1146, 1147, 1149, 1152, 1153, 1154, 1155, 1157, 1158, 1160, 1162, 1163, 1166, 1169, 1170, 1178, 1180, 1181, 1182, 1184, 1185, 1188, 1189, 1190, 1191, 1195, 1196, 1197, 1198, 1199, 1200, 1201, 1202, 1205, 1208, 1209, 1210, 1211, 1212, 1213, 1214, 1215, 1216, 1219, 1220, 1221, 1225, 1227, 1228, 1230, 1231, 1232, 1233, 1235, 1237, 1238, 1239, 1242, 1245, 1247, 1253, 1262, 1265, 1266, 1270, 1272, 1273, 1274, 1275, 1276, 1278, 1279, 1280, 1281, 1282, 1284, 1285, 1290, 1291, 1294, 1297, 1298, 1299, 1303, 1305, 1306, 1322, 1323, 1324, 1326, 1327, 1329, 1331, 1332, 1334, 1336, 1337, 1339, 1344, 1347, 1355, 1365, 1366, 1368, 1374, 1380, 1381, 1382, 1383, 1384, 1386, 1390, 1396, 1397, 1398, 1400, 1401, 1402, 1405, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1415, 1416, 1417, 1419, 1420, 1427, 1428, 1429], "process": [11, 13, 52, 76, 92, 93, 94, 96, 97, 98, 102, 104, 180, 222, 226, 232, 274, 331, 338, 373, 383, 405, 406, 440, 455, 464, 465, 466, 592, 624, 691, 760, 786, 871, 914, 952, 995, 1045, 1100, 1124, 1125, 1126, 1178, 1180, 1183, 1219, 1222, 1225, 1228, 1248, 1283, 1293, 1298, 1299, 1302, 1304, 1386, 1398, 1410, 1411, 1415, 1416, 1417, 1422, 1429], "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, 1147, 1168, 1178, 1182, 1188, 1191, 1203, 1204, 1212, 1222, 1228, 1236, 1237, 1244, 1254, 1263, 1277, 1278, 1279, 1280, 1284, 1299, 1318, 1326, 1329, 1331, 1332, 1391, 1396, 1398, 1402, 1407, 1409, 1410, 1412, 1414, 1415, 1416, 1428, 1429], "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, 1138, 1147, 1154, 1165, 1178, 1179, 1180, 1181, 1182, 1183, 1184, 1192, 1202, 1203, 1204, 1209, 1210, 1211, 1212, 1213, 1224, 1225, 1243, 1272, 1276, 1277, 1279, 1298, 1303, 1305, 1318, 1326, 1356, 1357, 1382, 1383, 1397, 1398, 1409], "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, 1185, 1232, 1238, 1256, 1270, 1299, 1323, 1324, 1326, 1329, 1386, 1390, 1395, 1398, 1405, 1406, 1407, 1409, 1410, 1411, 1412, 1414, 1415, 1424, 1428], "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, 1167, 1256, 1275, 1281, 1282, 1326, 1329, 1359, 1360, 1405, 1429], "seri": [11, 444, 616, 680, 1218, 1289], "finit": [11, 462, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 514, 518, 1180, 1182, 1195, 1225], "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, 1042, 1061, 1066, 1075, 1080, 1082, 1084, 1117, 1124, 1136, 1138, 1155, 1168, 1209, 1232, 1329, 1336, 1337, 1340, 1341, 1342, 1343, 1345, 1347, 1353, 1356, 1360, 1361, 1371, 1374, 1375, 1378, 1379, 1382, 1407, 1416], "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, 1157, 1171, 1202, 1206, 1209, 1210, 1211, 1213, 1219, 1220, 1225, 1229, 1234, 1236, 1244, 1298, 1299, 1303, 1323, 1324, 1329, 1331, 1353, 1397, 1401, 1402, 1407, 1408, 1409, 1410, 1411, 1412, 1416, 1417, 1429], "languag": [11, 92, 99, 110, 1042, 1327, 1344, 1345, 1347, 1384, 1385, 1386, 1414], "discret": [11, 104, 235, 249, 362, 409, 513, 517, 518, 618, 1167, 1168, 1181, 1183, 1189, 1193, 1207, 1281, 1282, 1285, 1317, 1318, 1326, 1409], "global": [11, 103, 314, 341, 410, 477, 486, 487, 509, 592, 1045, 1272, 1299, 1304, 1307, 1308, 1331, 1410, 1412, 1414], "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, 1136, 1138, 1140, 1220, 1272, 1298, 1299, 1309, 1313, 1320, 1321, 1327, 1328, 1364, 1365, 1396, 1405, 1409, 1411, 1415, 1416, 1428, 1429], "restrict": [11, 102, 128, 353, 791, 1038, 1082, 1407], "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, 1124, 1125, 1126, 1134, 1135, 1136, 1138, 1141, 1142, 1143, 1144, 1145, 1146, 1151, 1154, 1157, 1178, 1180, 1182, 1183, 1188, 1191, 1192, 1196, 1198, 1199, 1200, 1201, 1202, 1216, 1217, 1220, 1222, 1227, 1231, 1235, 1245, 1275, 1278, 1279, 1280, 1281, 1282, 1284, 1285, 1288, 1289, 1292, 1294, 1296, 1299, 1301, 1329, 1331, 1336, 1348, 1351, 1352, 1353, 1359, 1360, 1361, 1374, 1385, 1393, 1396, 1398, 1403, 1404, 1405, 1407, 1408, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429], "108": [11, 1219], "513": [11, 1401, 1409], "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, 1191, 1210, 1213, 1410], "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, 1124, 1126, 1147, 1151, 1176, 1216, 1218, 1275, 1329, 1331, 1358, 1361, 1398, 1399, 1405, 1407, 1409, 1413, 1414, 1416, 1417, 1419, 1420, 1423, 1429], "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, 1136, 1138, 1140, 1168, 1184, 1201, 1202, 1203, 1204, 1211, 1228, 1240, 1241, 1305, 1326, 1398, 1405, 1409, 1410, 1415, 1416], "cycl": [11, 38, 44, 95, 120, 214, 227, 228, 229, 230, 231, 232, 263, 293, 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1218, 1274, 1281, 1282, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1326, 1398, 1409, 1414, 1415], "left_nod": 21, "middle_nod": 21, "right_nod": 21, "accord": [21, 70, 94, 100, 103, 197, 233, 240, 282, 289, 345, 377, 380, 385, 565, 566, 588, 619, 670, 690, 691, 728, 729, 731, 1102, 1103, 1105, 1168, 1176, 1188, 1189, 1225, 1283, 1284, 1285, 1286, 1287, 1288, 1289, 1290, 1291, 1298, 1347, 1351, 1352, 1393, 1416], "coord": [21, 34], "updat": [21, 93, 94, 95, 99, 101, 102, 106, 111, 151, 152, 156, 157, 158, 199, 204, 233, 322, 337, 362, 366, 370, 373, 378, 460, 500, 506, 511, 598, 600, 604, 626, 627, 692, 796, 853, 854, 855, 856, 857, 887, 891, 898, 899, 900, 901, 902, 925, 934, 935, 936, 937, 938, 968, 979, 980, 981, 982, 983, 1007, 1037, 1039, 1040, 1085, 1086, 1122, 1299, 1305, 1395, 1396, 1397, 1401, 1402, 1407, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428, 1429], "377": [21, 22, 1242], "plot_simple_graph": 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1199, 1200, 1201, 1267, 1328, 1410, 1411, 1416], "sampson": [23, 47, 86, 1409], "self": [23, 45, 47, 52, 69, 86, 88, 89, 101, 152, 158, 168, 176, 180, 189, 224, 246, 247, 304, 321, 328, 331, 335, 345, 346, 347, 355, 356, 360, 432, 433, 434, 435, 436, 437, 438, 453, 467, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 519, 567, 575, 584, 585, 587, 593, 612, 619, 625, 675, 700, 735, 737, 854, 857, 865, 870, 871, 878, 899, 902, 910, 914, 935, 938, 946, 951, 952, 959, 960, 980, 983, 991, 995, 1038, 1060, 1075, 1102, 1103, 1105, 1124, 1125, 1126, 1138, 1176, 1178, 1180, 1182, 1188, 1196, 1199, 1200, 1201, 1202, 1220, 1225, 1242, 1284, 1328, 1329, 1333, 1356, 1357, 1392, 1404, 1406, 1409, 1411, 1414, 1415, 1416, 1417, 1420, 1428], "loop": [23, 45, 47, 52, 69, 86, 224, 230, 231, 246, 247, 304, 321, 328, 331, 345, 346, 347, 355, 356, 360, 432, 433, 434, 435, 436, 437, 438, 449, 450, 451, 453, 467, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 567, 584, 585, 587, 593, 612, 619, 625, 700, 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45, "address": [45, 97, 99, 103, 104, 107, 1284, 1408, 1411, 1416], "link": [45, 49, 52, 54, 93, 97, 99, 101, 103, 104, 105, 111, 239, 240, 284, 289, 305, 324, 325, 380, 385, 386, 387, 389, 390, 394, 412, 431, 434, 451, 564, 566, 567, 568, 569, 570, 571, 572, 573, 574, 593, 758, 796, 1037, 1039, 1040, 1150, 1172, 1174, 1175, 1185, 1186, 1187, 1205, 1230, 1237, 1290, 1328, 1362, 1366, 1367, 1368, 1388, 1399, 1405, 1409, 1410, 1414, 1415, 1416, 1417, 1419, 1420, 1426, 1427, 1428, 1429], "sender": [45, 92], "receiv": [45, 92, 299, 496, 504, 505, 508, 525, 535, 555, 671, 672, 673, 674], "messag": [45, 92, 93, 94, 100, 101, 152, 157, 158, 195, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1415, 1416, 1417, 1428], "hold": [45, 88, 100, 151, 159, 166, 175, 188, 190, 196, 198, 200, 208, 227, 239, 240, 241, 242, 243, 244, 247, 252, 266, 297, 298, 303, 306, 307, 311, 315, 316, 323, 324, 325, 326, 329, 330, 352, 355, 356, 380, 381, 383, 384, 385, 491, 593, 647, 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1428], "momepi": [52, 55], "focus": [52, 99, 786, 1272], "urban": 52, "morphologi": 52, "enabl": [52, 102, 103, 107, 132, 165, 311, 786, 862, 907, 934, 943, 979, 988, 1042, 1237, 1299, 1398, 1399, 1413, 1415, 1416, 1417], "multigraph": [52, 88, 93, 101, 102, 151, 152, 156, 157, 158, 160, 162, 163, 165, 170, 171, 172, 178, 186, 187, 193, 194, 195, 198, 199, 202, 204, 207, 209, 210, 211, 212, 224, 226, 269, 271, 273, 276, 283, 287, 291, 293, 295, 304, 321, 328, 337, 339, 340, 342, 343, 386, 422, 424, 425, 426, 429, 443, 447, 448, 450, 460, 467, 488, 490, 494, 498, 499, 502, 503, 509, 510, 515, 555, 561, 562, 563, 565, 585, 587, 588, 598, 601, 602, 605, 607, 610, 611, 612, 615, 652, 657, 658, 677, 696, 717, 718, 732, 734, 736, 742, 743, 762, 796, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 859, 860, 861, 862, 866, 867, 868, 875, 876, 882, 883, 884, 886, 887, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 910, 911, 912, 913, 915, 918, 919, 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102, 107, 110, 1042, 1196, 1200, 1329, 1410, 1414], "retriev": [52, 56, 99, 564, 566, 1100, 1397], "analyz": [52, 56, 110, 144, 257, 258, 259, 286, 288, 385, 388, 393, 401, 691, 792, 1329, 1401, 1409], "infrastructur": [52, 110, 1409, 1417, 1428], "elev": 52, "grade": [52, 71], "googl": [52, 91, 93, 105, 565, 751, 1329, 1396, 1417], "api": [52, 93, 94, 95, 96, 98, 99, 100, 103, 105, 106, 107, 109, 1329, 1331, 1396, 1397, 1406, 1407, 1422], "speed": [52, 56, 107, 215, 291, 292, 346, 347, 423, 427, 509, 796, 1037, 1039, 1040, 1136, 1138, 1176, 1197, 1396, 1405, 1409, 1411, 1413, 1414, 1415, 1416, 1417, 1428], "bear": 52, "also": [52, 54, 55, 56, 57, 58, 63, 75, 88, 92, 93, 94, 95, 97, 99, 101, 102, 103, 107, 110, 111, 156, 159, 162, 168, 176, 177, 180, 184, 189, 190, 200, 207, 208, 211, 226, 230, 280, 287, 293, 301, 302, 303, 308, 309, 323, 324, 325, 342, 369, 388, 391, 411, 412, 416, 417, 418, 419, 423, 424, 425, 427, 435, 440, 450, 464, 465, 466, 467, 470, 500, 501, 502, 503, 506, 507, 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748, 750, 758, 761, 1205, 1237, 1238, 1249, 1286, 1287, 1398, 1407, 1409, 1411, 1414, 1416], "etc": [52, 88, 94, 95, 99, 101, 102, 107, 111, 151, 152, 156, 157, 158, 160, 162, 163, 165, 168, 170, 171, 172, 186, 187, 189, 192, 193, 194, 195, 198, 199, 202, 204, 232, 267, 345, 615, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 859, 860, 861, 862, 865, 866, 867, 868, 875, 876, 878, 881, 882, 883, 884, 886, 887, 890, 891, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 904, 905, 906, 907, 908, 910, 911, 912, 913, 915, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 929, 930, 931, 932, 933, 935, 936, 937, 938, 940, 941, 942, 943, 949, 954, 957, 958, 963, 964, 965, 967, 968, 972, 974, 975, 977, 978, 980, 981, 982, 983, 985, 986, 987, 988, 989, 994, 996, 997, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1038, 1052, 1066, 1075, 1080, 1084, 1133, 1137, 1139, 1157, 1299, 1306, 1327, 1336, 1340, 1341, 1398, 1407, 1408, 1410, 1429], "essenti": [52, 103, 346, 1038, 1220, 1237, 1329], 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616, 678, 1094, 1178, 1191, 1205, 1225, 1410, 1416], "segment": [52, 55, 338], "major": [52, 95, 98, 99, 100, 102, 103, 104, 106, 107, 1396, 1397, 1406, 1407, 1410], "studi": [52, 91, 110, 606, 1195, 1199, 1326, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428], "topologi": [52, 55, 435, 436, 512, 681, 683, 748, 1205, 1220, 1228, 1232, 1236, 1244, 1329], "encod": [52, 55, 58, 67, 99, 141, 249, 267, 268, 619, 758, 775, 1329, 1336, 1337, 1340, 1341, 1342, 1343, 1344, 1347, 1348, 1351, 1352, 1353, 1357, 1358, 1361, 1366, 1371, 1374, 1375, 1378, 1379, 1385, 1409, 1410, 1415], "angular": [52, 55], "inform": [52, 66, 92, 93, 99, 100, 101, 102, 103, 107, 111, 112, 121, 132, 159, 165, 200, 202, 204, 220, 226, 230, 231, 249, 301, 302, 303, 308, 309, 314, 323, 324, 325, 338, 405, 406, 438, 453, 455, 480, 488, 500, 512, 564, 566, 568, 572, 573, 574, 583, 592, 614, 619, 624, 691, 775, 782, 786, 796, 858, 862, 888, 890, 891, 903, 907, 926, 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"form": [52, 55, 110, 151, 170, 220, 238, 377, 381, 391, 422, 427, 440, 449, 450, 451, 488, 500, 517, 521, 567, 568, 569, 570, 571, 572, 573, 574, 578, 579, 580, 588, 589, 677, 679, 697, 711, 717, 718, 719, 729, 730, 731, 748, 752, 767, 786, 791, 853, 866, 898, 911, 934, 947, 979, 992, 1038, 1064, 1085, 1149, 1170, 1202, 1209, 1218, 1220, 1225, 1243, 1246, 1248, 1251, 1255, 1402, 1409, 1410, 1429], "flow": [52, 66, 105, 278, 296, 301, 302, 303, 308, 309, 323, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 423, 427, 428, 430, 431, 494, 495, 496, 497, 498, 499, 500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 519, 559, 756, 758, 1270, 1328, 1398, 1402, 1403, 1406, 1409, 1410, 1411, 1414, 1417, 1428], "dead": 52, "detail": [52, 53, 86, 92, 93, 97, 99, 100, 128, 252, 253, 256, 257, 258, 259, 260, 277, 278, 281, 282, 284, 285, 286, 287, 288, 297, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 422, 427, 476, 494, 498, 499, 500, 509, 510, 511, 512, 574, 691, 711, 720, 735, 737, 791, 796, 1037, 1039, 1040, 1042, 1102, 1105, 1136, 1140, 1143, 1210, 1299, 1322, 1348, 1351, 1352, 1353, 1396, 1402, 1403, 1404, 1405, 1409, 1416, 1417, 1429], "methodologi": 52, "avail": [52, 93, 99, 100, 101, 103, 141, 184, 226, 232, 280, 422, 425, 426, 585, 587, 780, 873, 916, 955, 998, 1039, 1042, 1197, 1199, 1200, 1201, 1331, 1334, 1337, 1396, 1397, 1399, 1405, 1408, 1409, 1412, 1415, 1416, 1429], "1016": [52, 112, 226, 231, 274, 297, 298, 299, 303, 306, 307, 313, 322, 323, 338, 346, 347, 455, 1236], "compenvurbsi": 52, "2017": [52, 227, 512, 1210, 1211, 1409, 1410], "004": [52, 341], "scienc": [52, 91, 101, 105, 107, 109, 110, 112, 219, 228, 249, 296, 301, 302, 303, 308, 309, 323, 346, 347, 409, 412, 431, 441, 445, 446, 453, 476, 498, 618, 619, 680, 681, 683, 1206, 1226, 1258], "pydata": [52, 1416, 1426, 1427, 1428], "stack": [52, 111, 346, 693, 1045, 1046], "showcas": [53, 86, 93, 109], "analys": [53, 70, 86, 310], "ecosystem": [53, 86, 99, 100, 104, 107, 110, 1428], "descript": [53, 86, 93, 97, 464, 466, 704, 717, 786, 1124, 1125, 1126, 1133, 1134, 1135, 1136, 1141, 1142, 1143, 1144, 1145, 1210, 1225, 1245, 1410, 1414, 1416, 1424, 1425, 1428], "plu": [54, 386, 583, 1036, 1088, 1151, 1256], "voronoi": [54, 752, 758, 1328, 1410], "cholera": [54, 57], "broad": [54, 57, 1299], "pump": [54, 57], "record": [54, 57, 94, 99, 691, 1429], "john": [54, 57, 91, 278, 568, 572, 685, 1208, 1253, 1411, 1416], "snow": [54, 57], "1853": [54, 57], "method": [54, 57, 58, 75, 88, 92, 93, 95, 101, 102, 103, 107, 112, 143, 161, 164, 165, 185, 186, 187, 190, 200, 202, 204, 206, 207, 226, 231, 232, 250, 260, 261, 262, 299, 301, 302, 303, 308, 309, 311, 312, 323, 324, 336, 374, 376, 379, 380, 381, 385, 423, 440, 451, 462, 476, 500, 514, 527, 537, 545, 564, 566, 568, 572, 581, 583, 600, 604, 615, 632, 633, 635, 636, 654, 655, 656, 671, 672, 673, 674, 684, 692, 719, 720, 733, 738, 752, 775, 786, 852, 862, 874, 875, 876, 879, 888, 890, 891, 892, 897, 907, 917, 918, 919, 926, 927, 928, 933, 934, 935, 943, 956, 957, 958, 971, 972, 973, 978, 979, 980, 988, 999, 1000, 1001, 1008, 1009, 1010, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1033, 1038, 1043, 1044, 1045, 1046, 1066, 1177, 1185, 1187, 1196, 1200, 1278, 1279, 1280, 1283, 1299, 1304, 1305, 1326, 1329, 1366, 1398, 1402, 1406, 1407, 1409, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1425, 1428, 1429], "shown": [54, 57, 100, 102, 517, 518, 947, 992, 1042, 1278, 1279, 1280, 1303, 1352, 1407], "centroid": [54, 57, 58], "libpys": [54, 55, 57, 58], "cg": [54, 102, 296, 301, 302, 303, 308, 309, 323, 588], "voronoi_fram": 54, "contextili": [54, 55, 57], "add_basemap": [54, 55, 57], "geopackag": [54, 55, 56, 57], "sqlite": [54, 57], "reli": [54, 57, 99, 103, 362, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 502, 503, 506, 507, 1396, 1410, 1414, 1428], "fiona": [54, 57], "level": [54, 57, 101, 103, 104, 106, 111, 112, 115, 125, 165, 220, 322, 334, 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720], "laboratori": 91, "pi": [91, 653, 1114], "program": [91, 105, 110, 362, 455, 488, 490, 678, 1119, 1120, 1128, 1229, 1305, 1327, 1329, 1331, 1417], "offic": [91, 1270], "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, 1129, 1178, 1182, 1199, 1200, 1201, 1344, 1345, 1347, 1384, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428], "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, 1172, 1173, 1174, 1196, 1225, 1232, 1236], "crutchfield": 91, "institut": [91, 112, 214, 215, 216, 220], "discoveri": [91, 670, 675, 676, 690], "madison": 91, "jessica": 91, "flack": 91, "david": [91, 277, 362, 437, 442, 447, 448, 624, 685, 710, 711, 712, 713, 714, 715, 734, 736, 1149, 1160, 1258, 1411, 1412, 1415], "krakauer": 91, "financi": 91, "summer": [91, 105, 1408, 1416, 1417], "foundat": [91, 110, 412, 431, 441, 445, 446, 619, 751], "grant": [91, 100, 105, 1205], "w911nf": 91, "0288": 91, "darpa": 91, "intellig": [91, 132, 494, 574, 590, 732, 762, 1210, 1213], "subcontract": 91, "No": [91, 92, 228, 282, 284, 285, 286, 287, 288, 444, 450, 460, 680, 1038, 1396, 1397, 1399, 1414], "9060": 91, "000709": 91, "nsf": 91, "phy": [91, 275, 284, 313, 371, 372, 383, 385, 434, 573, 1168, 1180, 1185, 1186, 1187, 1190, 1233, 1237, 1290], "0748828": 91, "templeton": 91, "santa": [91, 214, 215, 216, 220], "fe": [91, 214, 215, 216, 220], "under": [91, 324, 325, 525, 535, 555, 566, 577, 586, 588, 606, 671, 672, 673, 674, 739, 1329, 1415, 1416, 1420], "contract": [91, 110, 391, 500, 584, 585, 587, 618, 619, 767, 1177, 1398, 1416], "0340": 91, "space": [92, 101, 109, 231, 296, 301, 302, 308, 309, 355, 423, 628, 629, 630, 760, 786, 1112, 1147, 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132, 217, 373, 423, 428, 687, 689, 1329, 1385, 1407, 1410], "plot_": 93, "plot_new_exampl": 93, "highlight": [93, 106, 1406], "resourc": [93, 96, 476, 477, 478, 572, 573, 618, 1168, 1203], "docstr": [93, 94, 95, 97, 109, 1348, 1351, 1352, 1353, 1402, 1409, 1410, 1411, 1414, 1415, 1416, 1417, 1419, 1420, 1423, 1424, 1425, 1426, 1428], "chicago": [93, 1268], "citat": [93, 97, 346, 347, 566, 1242, 1415], "quickest": 93, "scholar": 93, "paywal": 93, "arxiv": [93, 110, 128, 217, 220, 300, 305, 332, 333, 355, 358, 371, 372, 373, 385, 386, 427, 432, 433, 437, 512, 573, 619, 625, 685, 693, 1156, 1172, 1173, 1174, 1188, 1230, 1272, 1283], "access": [93, 101, 112, 125, 151, 168, 189, 429, 471, 472, 473, 474, 475, 496, 606, 626, 627, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659, 660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 693, 721, 760, 770, 789, 796, 853, 865, 878, 898, 910, 915, 934, 946, 960, 979, 991, 996, 1037, 1038, 1039, 1040, 1138, 1329, 1395, 1396, 1397, 1399, 1401, 1402, 1405, 1409, 1410, 1411, 1413], "cheong": 93, "se": 93, "hang": 93, "yain": 93, "whar": 93, "schemat": 93, "placement": [93, 614], "survei": [93, 110, 564, 566, 581, 786, 1204], "2020": [93, 99, 100, 101, 102, 569, 1409, 1415], "1177": 93, "2f1473871618821740": 93, "upload": [93, 106, 217], "pdf": [93, 105, 110, 112, 128, 214, 215, 216, 217, 220, 235, 305, 311, 312, 315, 322, 324, 325, 330, 342, 355, 356, 373, 410, 411, 412, 413, 414, 415, 417, 426, 427, 430, 442, 447, 448, 476, 483, 490, 494, 511, 512, 519, 564, 566, 567, 570, 571, 573, 618, 619, 690, 693, 748, 749, 750, 760, 762, 1042, 1196, 1200, 1201, 1329, 1410, 1415, 1429], "docx": 93, "ppt": 93, "lectur": [93, 110, 412, 431, 498, 616, 1206], "wayback": [93, 1416], "machin": [93, 312, 331, 494, 511, 512, 762, 1399, 1409, 1416], "snapshot": 93, "unreach": 93, "pyarg": [93, 111, 1038], "tell": [93, 99, 102, 760, 1278, 1281, 1282, 1299, 1331, 1415], "compar": [93, 464, 545, 546, 547, 548, 552, 553, 554, 556, 557, 558, 559, 560, 561, 562, 563, 615, 760, 782, 1168, 1305, 1417], "baselin": [93, 1137, 1139], "ones": [93, 99, 107, 109, 282, 680, 1038, 1398, 1405, 1407], "savefig": [93, 1429], "mpl_image_compar": 93, "test_barbel": 93, "barbel": [93, 293, 294, 391, 424, 1149, 1160, 1279, 1429], "conduct": [93, 96, 100, 109, 447, 448, 758], "contributor": [94, 96, 99, 105, 106, 110, 1274, 1326, 1406], "shepherd": [94, 99], "mission": [94, 96, 97, 100, 107], "approv": [94, 100], "nuclear": 94, "launch": 94, "carefulli": 94, "clean": [94, 106, 530, 540, 1303, 1409, 1410, 1414, 1416, 1423], "nearli": 94, "volunt": [94, 107, 1416], "tremend": 94, "felt": 94, "evalu": [94, 130, 152, 157, 158, 195, 330, 618, 619, 626, 627, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1124, 1126, 1299, 1420], "novic": 94, "strongli": [94, 217, 232, 388, 391, 397, 398, 399, 403, 405, 406, 423, 480, 491, 492, 519, 588, 633, 697, 699, 751, 753, 1188, 1405, 1409, 1414, 1417, 1420, 1428], 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411, 412, 413, 414, 415, 416, 417, 418, 419, 479, 502, 503, 506, 507, 734, 735, 736, 737, 780, 891, 972, 1038, 1042, 1228, 1244, 1283, 1329, 1429], "wish": [94, 619, 1066, 1396], "bring": [94, 101, 566], "advis": [94, 110, 1417], "aris": [94, 110, 238, 243, 1220, 1248], "experienc": 94, "credit": [94, 105], "send": [94, 99, 496, 497, 501, 504, 505, 508, 1396, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428], "notif": 94, "maintain": [94, 95, 99, 100, 103, 105, 107, 109, 230, 231, 614, 796, 1037, 1039, 1040, 1409, 1428], "concern": [94, 101, 103, 132, 789, 791, 1385], "mere": [94, 1149, 1160], "understood": 94, "made": [94, 99, 100, 102, 222, 282, 284, 285, 286, 287, 288, 324, 325, 331, 693, 694, 1122, 1213, 1329, 1396, 1406, 1407, 1410, 1415, 1428], "freeli": 94, "consult": [94, 111], "extern": [94, 107, 619, 1329, 1386, 1410], "insight": 94, "opportun": [94, 99], "patch": [94, 99, 102, 1042, 1136, 1138, 1415, 1416], "vouch": 94, "fulli": [94, 761, 1042, 1191], "behind": [94, 105], "clarif": [94, 299, 322], "deem": 94, "nich": 94, "devot": 94, "sustain": [94, 96], "effort": [94, 107, 1329], "priorit": 94, "similarli": [94, 103, 115, 207, 356, 598, 621, 796, 892, 928, 973, 1010, 1037, 1039, 1040, 1042, 1151, 1178, 1180, 1196, 1201, 1210, 1299, 1397, 1407, 1429], "worth": [94, 761, 1429], "mainten": 94, "burden": 94, "necessari": [94, 95, 100, 104, 527, 537, 954, 997, 1138, 1140, 1299, 1409, 1415], "valid": [94, 101, 161, 177, 256, 277, 278, 281, 282, 377, 386, 439, 458, 464, 466, 497, 513, 514, 515, 516, 517, 518, 559, 560, 578, 579, 580, 588, 614, 615, 734, 735, 736, 737, 746, 758, 1038, 1043, 1071, 1087, 1100, 1104, 1105, 1168, 1190, 1196, 1240, 1241, 1277, 1281, 1282, 1299, 1334, 1337, 1410, 1415, 1416, 1417, 1420, 1422, 1425, 1428], "wari": 94, "alien": 94, "visibl": [94, 97], "thread": [94, 97, 99, 103, 104, 1416], "appeal": [94, 100], "empow": 94, "regardless": [94, 99, 1138, 1194, 1407], "outcom": 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955, 979, 992, 998, 1036, 1037, 1039, 1040, 1088, 1117, 1198, 1275, 1299, 1305, 1329, 1348, 1351, 1352, 1353, 1384, 1385, 1386, 1398, 1406, 1407, 1408, 1409, 1410, 1416, 1417, 1428, 1429], "fit": [97, 110, 1329], "enhanc": [98, 99, 107, 341, 508, 1299, 1415, 1428], "berkelei": [99, 100, 103, 618, 619], "draft": [99, 100, 102, 103, 104, 1414, 1415, 1416, 1419], "stand": [99, 545, 1390], "primari": [99, 103, 1417], "gone": 99, "concis": [99, 110, 791, 1416, 1417], "rational": 99, "consensu": [99, 100], "dissent": 99, "opinion": [99, 100, 104], "revis": [99, 444, 732], "track": [99, 101, 102, 103, 104, 107, 115, 370, 387, 389, 390, 394, 598, 1299, 1305, 1409, 1414, 1415], "codebas": [99, 1299, 1407, 1408, 1415], "meta": [99, 106], "inject": 99, "repo": [99, 106, 1416, 1428], "success": [99, 315, 330, 496, 608, 692, 1183, 1245, 1429], "tend": [99, 593, 1178, 1329], "doubt": [99, 1429], "champion": 99, "attempt": [99, 101, 194, 202, 204, 282, 284, 285, 286, 287, 288, 361, 362, 377, 425, 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335, 681, 683], "provision": 99, "short": [99, 104, 161, 227, 1038, 1066, 1198, 1409], "unlik": [99, 100, 212, 366, 425, 426, 1386], "reject": [99, 100, 104, 1322], "withdrawn": [99, 104], "wherev": [99, 1285], "defer": [99, 101, 104, 280], "challeng": 99, "wider": 99, "done": [99, 101, 102, 230, 231, 249, 373, 440, 466, 517, 564, 566, 614, 690, 762, 1046, 1222, 1299, 1329, 1407], "fact": [99, 352, 460, 619, 1210, 1213, 1407], "actual": [99, 115, 132, 165, 210, 213, 214, 215, 216, 220, 288, 385, 450, 577, 625, 692, 717, 718, 862, 907, 943, 988, 1102, 1103, 1202, 1299, 1327, 1329, 1405, 1419], "compet": [99, 583], "accordingli": [99, 454, 1110, 1410, 1428], "supersed": [99, 104], "render": [99, 216, 410, 413, 1409], "obsolet": [99, 267, 1340, 1409, 1410], "never": [99, 184, 388, 608, 873, 916, 955, 998, 1239], "meant": [99, 291, 292, 631, 1220, 1329, 1416, 1420], "concret": [99, 100], "think": [99, 102, 230, 231, 299, 761, 1429], "bodi": [99, 1246], "briefli": 99, "sentenc": [99, 100], 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"dd": [99, 104, 1094], "mmm": 99, "yyyi": [99, 104], "dom": 99, "ain": 99, "separ": [99, 102, 106, 152, 157, 158, 195, 214, 215, 258, 265, 266, 267, 268, 299, 322, 343, 427, 428, 454, 464, 758, 854, 856, 857, 884, 899, 901, 902, 923, 935, 937, 938, 965, 980, 982, 983, 1005, 1045, 1112, 1116, 1196, 1198, 1328, 1334, 1335, 1336, 1337, 1338, 1339, 1340, 1341, 1342, 1343, 1372, 1373, 1374, 1375, 1398, 1409, 1410, 1415, 1416, 1428, 1429], "older": 99, "brows": 99, "colgat": [100, 110], "deadlock": 100, "websit": [100, 106, 1168, 1385, 1410, 1411, 1412, 1413, 1414, 1415, 1416, 1417, 1419, 1420, 1421, 1422, 1423, 1424, 1425, 1426, 1427, 1428], "ongo": [100, 1408], "trust": [100, 1384, 1386], "cast": [100, 101, 1415, 1425, 1428], "vote": [100, 337, 1415], "therebi": 100, "adher": 100, "nomin": 100, "lazi": [100, 1286, 1287], "unanim": 100, "agreement": [100, 1205], "initi": [100, 102, 141, 230, 231, 282, 315, 324, 325, 338, 373, 377, 378, 466, 495, 511, 512, 525, 535, 615, 692, 719, 733, 796, 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718, 749, 761, 892, 928, 973, 1010, 1117, 1412], "suffici": [100, 101, 1329], "scikit": [100, 103, 109], "expos": [101, 374, 1408], "nodeview": [101, 184, 391, 598, 599, 601, 602, 603, 604, 695, 873, 916, 955, 998, 1036, 1088, 1352, 1365, 1407, 1410], "nodedataview": [101, 184, 391, 591, 592, 600, 873, 916, 955, 998, 1220, 1429], "edgeview": [101, 590, 591, 592, 598, 599, 600, 601, 602, 603, 604, 612, 624, 770, 910, 1036, 1088, 1098, 1407, 1416], "edgedataview": [101, 168, 189, 865, 878, 910, 946, 960, 991, 1098, 1220, 1365, 1415, 1429], "semant": [101, 531, 541, 762, 1406, 1408], "inher": [101, 220, 427], "impli": [101, 110, 132, 220, 312, 314, 327, 455, 466, 511, 512, 545, 1299], "element": [101, 102, 230, 231, 270, 291, 292, 311, 350, 371, 391, 457, 464, 518, 559, 560, 578, 579, 580, 586, 640, 656, 671, 673, 675, 677, 728, 730, 739, 749, 752, 1036, 1038, 1048, 1049, 1050, 1051, 1087, 1088, 1138, 1140, 1176, 1209, 1214, 1215, 1220, 1240, 1241, 1243, 1252, 1275, 1280, 1281, 1282, 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1238, 1290, 1329, 1409, 1416, 1429], "coupl": [101, 102, 132, 1260, 1405, 1407], "realis": 101, "But": [101, 102, 107, 143, 170, 238, 243, 256, 277, 278, 281, 297, 298, 583, 796, 866, 911, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1094, 1331, 1396, 1428], "seem": [101, 102, 298, 307, 791, 1237], "eas": [101, 107, 1412], "idiom": [101, 159, 190, 200, 858, 879, 888, 903, 939, 969, 984, 1299, 1397, 1407, 1414], "subscript": [101, 151, 159, 200, 796, 853, 858, 888, 898, 903, 934, 939, 969, 979, 984, 1037, 1039, 1040, 1397, 1429], "repr": [101, 1350, 1416], "4950": [101, 1417], "traceback": [101, 450, 464, 584, 652, 658, 1305, 1306], "recent": [101, 437, 450, 464, 584, 652, 658, 963, 1003, 1305, 1306, 1414], "typeerror": [101, 382, 464, 1209, 1305, 1407], "opaqu": 101, "ambigu": [101, 103, 115, 252, 253, 464, 762, 1043, 1409], "ambigi": 101, "counter": [101, 153, 357], "nativ": [101, 109], "caveat": 101, "nodes_it": [101, 1407, 1410], "toward": [101, 685, 1410, 1416], "inner": [101, 230, 231, 380, 796, 1012, 1013, 1018, 1019, 1020, 1021, 1022, 1037, 1039, 1040, 1086], "synonym": 101, "primarili": [101, 1429], "becam": [101, 1414], "concept": [101, 132, 220, 310, 427, 688, 1043], "intuit": [101, 109], "On": [101, 105, 156, 217, 294, 297, 298, 306, 307, 315, 380, 405, 406, 514, 515, 518, 593, 855, 900, 936, 981, 1183, 1205, 1227, 1231, 1235], "front": [101, 619, 1036, 1088], "constuct": 101, "indx": 101, "desir": [101, 102, 142, 143, 204, 346, 347, 422, 425, 426, 598, 629, 647, 891, 972, 1085, 1094, 1102, 1103, 1105, 1124, 1125, 1153, 1155, 1160, 1162, 1163, 1166, 1168, 1190, 1221, 1223, 1224, 1237, 1284, 1359, 1360, 1417, 1429], "prelimanari": 101, "impelement": 101, "4086": 101, "rid": [101, 1416], "getitem": 101, "dunder": [101, 107, 1299, 1416], "isinst": [101, 103, 464, 1086, 1414, 1415, 1416], "_node": [101, 1425, 1428], "exclus": [101, 449, 476], "necess": 101, "unhash": [101, 1407], "impel": 101, "insipir": 101, "colon": [101, 1424], "syntax": 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1222, 1246, 1247, 1248, 1249, 1251, 1252, 1253, 1254, 1259, 1260, 1261, 1262, 1264, 1265, 1266, 1267, 1274, 1326], "greedi": [112, 222, 229, 230, 231, 232, 330, 362, 366, 383, 384, 722, 1398, 1410], "simul": [112, 229, 230, 231, 331, 692, 1117], "anneal": [112, 229, 230, 231], "sa": 112, "ta": 112, "travelling_salesman_problem": 112, "bag": 112, "minu": [112, 340, 583, 1151], "notion": [112, 125, 128, 260, 261, 262, 289, 791], "partli": 112, "intract": 112, "solvabl": [112, 114], "constant": [112, 497, 501, 504, 505, 508, 675, 1178, 1198, 1218], "treewidth_min_degre": 112, "treewidth_min_fill_in": 112, "han": [112, 358, 1184, 1242, 1415, 1416], "bodlaend": 112, "ari": [112, 1148, 1158, 1400, 1409], "koster": 112, "2010": [112, 241, 244, 311, 312, 324, 325, 361, 379, 693, 1174, 1205, 1272, 1397, 1409, 1410], "inf": [112, 274, 494, 495, 498, 499, 502, 503, 506, 507, 509, 510, 629, 753, 1414, 1416], "march": [112, 1289, 1409, 1418], "259": 112, "275": 112, "dx": [112, 257, 258, 259, 297, 752, 1238], "ic": [112, 467, 704, 706, 707, 708, 710, 734, 736], "2009": [112, 132, 217, 300, 573, 593, 616, 624, 729, 731, 1204, 1225, 1274, 1326, 1397, 1410], "discov": [112, 293, 345, 385, 1038, 1396], "utrecht": 112, "uu": [112, 333, 1182], "018": 112, "nl": [112, 476, 1253, 1262], "wang": [112, 423, 425, 513, 729, 731, 1181, 1183, 1415], "lu": [112, 296, 301, 302, 303, 308, 309, 323, 520, 521, 573, 1182, 1278, 1279, 1280, 1416], "hick": [112, 352], "20210507025929": 112, "eec": 112, "utk": 112, "cphill25": 112, "cs594_spring2015_project": 112, "vertic": [114, 115, 211, 212, 249, 281, 322, 373, 387, 389, 390, 437, 477, 478, 479, 480, 488, 491, 492, 514, 515, 518, 618, 619, 767, 1098, 1101, 1106, 1109, 1124, 1126, 1137, 1139, 1167, 1172, 1183, 1193, 1195, 1209, 1216, 1218, 1220, 1221, 1222, 1253, 1256, 1266, 1267, 1274, 1326, 1429], "v_j": [114, 282, 332], "v_k": 114, "v_i": 114, "AT": [114, 249, 250, 1414], "polynomi": [114, 264, 440, 618, 619, 758, 762, 1274, 1326, 1328, 1419, 1423], "amongst": 114, "opposit": [115, 177, 259, 615, 762, 962, 1002, 1177, 1256, 1290], "literatur": [115, 468, 616, 732, 762], "analogi": 115, "namespac": [115, 125, 269, 270, 271, 272, 273, 274, 275, 276, 411, 412, 416, 417, 494, 498, 499, 509, 510, 770, 1395, 1398, 1399, 1402, 1405, 1407, 1410, 1415, 1416, 1417], "easiest": [115, 1038, 1329], "is_connect": [115, 394, 396, 397, 398, 1409], "bottom_nod": 115, "top_nod": [115, 256, 277, 278, 279, 280, 281], "refus": [115, 1043], "temptat": [115, 1043], "guess": [115, 1041, 1043], "ambiguoussolut": [115, 256, 277, 278, 281, 1043, 1328], "rb": [115, 267, 1336, 1340, 1341, 1374, 1408], "random_graph": 115, "rb_top": 115, "rb_bottom": 115, "maximum_match": [115, 278, 281], "complete_bipartite_graph": [115, 252, 253, 281, 285, 588, 1154, 1429], "minimum_weight_full_match": 115, "whose": [115, 116, 144, 218, 219, 226, 229, 235, 281, 291, 292, 293, 294, 295, 311, 350, 351, 352, 375, 380, 387, 460, 490, 501, 584, 585, 587, 619, 692, 728, 739, 1055, 1077, 1197, 1209, 1216, 1252, 1257, 1272, 1275, 1276, 1281, 1282, 1302, 1304, 1313, 1353, 1414], "mode": [115, 260, 261, 262, 267, 268, 289, 1303, 1336, 1337, 1340, 1341, 1342, 1343, 1374, 1375, 1429], "bipart": [115, 290], "routin": [116, 180, 343, 355, 559, 560, 577, 760, 871, 914, 952, 995, 1042, 1091, 1329, 1398, 1399, 1407, 1409, 1414, 1415, 1416], "outsid": [116, 310, 1407, 1409, 1416], "chord": [120, 341, 343, 1193, 1211, 1218], "chordal_graph": [120, 341], "clique_problem": 121, "character": [122, 313, 782], "triangl": [122, 213, 227, 295, 356, 357, 358, 359, 437, 549, 550, 758, 1098, 1101, 1218, 1222, 1225, 1237, 1246, 1250, 1255, 1266, 1326, 1329, 1409, 1415], "greedy_color": [123, 758, 1398, 1409, 1414], "communities_gener": 125, "girvan_newman": 125, "top_level_commun": 125, "next_level_commun": 125, "kernighan": [125, 377, 1416], "lin": [125, 377, 1410, 1416], "luke": [125, 382, 1415], "asynchron": [125, 373, 378, 379, 1410, 1417], "edge_kcompon": [127, 424], "determen": 127, "maxim": [127, 209, 220, 221, 222, 315, 316, 330, 339, 346, 347, 348, 349, 350, 351, 353, 354, 366, 370, 380, 383, 384, 389, 390, 422, 425, 426, 427, 432, 433, 437, 517, 549, 579, 581, 582, 583, 589, 682, 691, 732, 758, 1043, 1204, 1326, 1328, 1401, 1409, 1410, 1416, 1417], "moodi": [127, 220, 427, 1398], "kanevski": [127, 427, 428, 1398], "recurs": [128, 141, 224, 346, 347, 352, 387, 389, 390, 394, 406, 452, 460, 530, 540, 697, 728, 730, 760, 1045, 1046, 1061, 1082, 1150, 1299, 1409, 1415, 1416], "prune": [128, 760, 1239], "vladimir": [128, 275, 432, 433, 494, 588, 749, 1233], "batagelj": [128, 275, 432, 433, 588, 749, 1233], "matjaz": [128, 432, 433], "zaversnik": [128, 432, 433], "0310049": [128, 432, 433], "0202039": 128, "degeneraci": 128, "christo": 128, "giatsidi": 128, "thiliko": 128, "michali": 128, "vazirgianni": 128, "icdm": 128, "2011": [128, 331, 377, 383, 385, 441, 445, 446, 511, 512, 519, 619, 682, 1182, 1400, 1401, 1402, 1409, 1410], "graphdegeneraci": 128, "dcores_icdm_2011": 128, "anomali": [128, 438], "onion": [128, 438, 1414], "h\u00e9bert": [128, 438], "dufresn": [128, 438], "grochow": [128, 438], "allard": [128, 438, 1414], "31708": [128, 438], "2016": [128, 337, 352, 385, 438, 476, 690, 1200, 1254, 1399, 1409], "1038": [128, 337, 376, 380, 438, 569], "srep31708": [128, 438], "factor": [132, 226, 293, 294, 299, 300, 324, 325, 370, 462, 497, 501, 504, 505, 508, 513, 565, 592, 624, 676, 697, 1106, 1107, 1108, 1109, 1110, 1114, 1115, 1116, 1117, 1148, 1158, 1181, 1183, 1278, 1279, 1280], "graphic": [132, 454, 517, 518, 693, 758, 1178, 1180, 1183, 1184, 1225, 1328, 1386, 1401, 1404, 1409], "overview": [132, 476, 1038, 1299], "collid": [132, 454], "triplet": [132, 745], "successor": [132, 159, 174, 181, 191, 200, 240, 282, 387, 389, 390, 394, 501, 687, 707, 715, 858, 872, 880, 888, 903, 939, 953, 961, 969, 984, 1055, 1186, 1187, 1192, 1329, 1407, 1410, 1419, 1429], "descend": [132, 454, 456, 465, 709, 758, 1275, 1404, 1407, 1409, 1416, 1417], "unblock": 132, "commonli": [132, 280, 454, 684, 782], "probabilist": [132, 378], "causal": 132, "markov": [132, 462, 565, 692, 1191], "hmm": 132, "s1": [132, 1245, 1316, 1366], "s2": [132, 1245, 1316], "s3": [132, 1316], "s4": 132, "s5": 132, "o1": 132, "o2": 132, "o3": 132, "o4": 132, "o5": 132, "ob": 132, "d_separ": [132, 758, 1415], "darwich": 132, "shachter": 132, "1998": [132, 1146, 1147, 1228, 1244, 1410], "bay": 132, "ball": 132, "ration": 132, "pastim": 132, "irrelev": [132, 1410], "requisit": 132, "influenc": [132, 324, 325, 512, 786], "fourteenth": [132, 1189], "uncertainti": [132, 590, 732], "artifici": [132, 574, 590, 732], "480": [132, 426, 514, 518, 1401, 1409], "487": 132, "francisco": [132, 732], "morgan": [132, 732], "kaufmann": [132, 732], "koller": 132, "friedman": 132, "mit": [132, 342, 519, 618], "causal_markov_condit": 132, "ness": [133, 684, 782], "classmethod": [141, 1047], "auxiliari": [141, 142, 143, 220, 411, 412, 413, 415, 416, 417, 418, 419, 423, 430, 431, 1405], "sink": [141, 302, 309, 416, 418, 494, 495, 498, 499, 501, 502, 503, 506, 507, 509, 510, 565], "pick": [141, 217, 331, 657, 1191, 1210, 1213, 1410], "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, 1265, 1328, 1398, 1405, 1409, 1416], "refin": [143, 215, 423, 438], "auxgraph": [143, 423], "node_partit": 144, "permut": [144, 368, 452, 453, 455, 466, 748, 1288, 1323, 1324], "containin": 144, "frozenset": [144, 267, 339, 383, 586, 588, 752, 1168, 1336, 1340, 1341, 1415], "abc": [144, 545, 1157, 1209, 1306, 1415, 1416], "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, 1124, 1126, 1136, 1137, 1138, 1139, 1172, 1182, 1188, 1192, 1212, 1214, 1215, 1216, 1218, 1227, 1231, 1233, 1234, 1235, 1278, 1279, 1280, 1281, 1282, 1285, 1298, 1299, 1310, 1312, 1315, 1338, 1339, 1340, 1342, 1344, 1345, 1347, 1356, 1357, 1358, 1359, 1360, 1361, 1363, 1367, 1382, 1383], "account": [145, 148, 398, 448, 749, 761, 1273, 1396, 1416], "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, 1192, 1286, 1287, 1396], "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, 1338, 1405], "342": [151, 853, 898, 934, 979, 1258], "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, 1329, 1407, 1410, 1429], "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, 1406, 1407, 1428], "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, 1409, 1414, 1419, 1428], "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, 1141, 1183, 1328], "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, 1327, 1328, 1417, 1429], "hello": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1306], "k3": [156, 157, 855, 856, 900, 901, 936, 937, 981, 982, 1220], "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, 1315, 1329], "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, 1141, 1246, 1249, 1250, 1252, 1328, 1412, 1413], "first_nbr": [161, 615], "invalid": [161, 615, 1416], "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, 1397], "deepcopi": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1412], "__class__": [165, 199, 862, 887, 907, 925, 943, 968, 988, 1007, 1407, 1410, 1412, 1413, 1414], "fresh": [165, 862, 907, 943, 988, 1407], "inspir": [165, 230, 231, 342, 681, 862, 907, 943, 988, 1229, 1326, 1407], "deep": [165, 202, 204, 862, 890, 891, 907, 926, 927, 943, 971, 972, 988, 1008, 1009, 1268, 1397], "degreeview": [166, 863, 908, 944, 950, 989, 1407, 1429], "didegreeview": [166, 863], "outedgeview": [168, 189, 467, 468, 613, 747, 750, 865, 878, 1035, 1083, 1407, 1421], "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, 1407, 1409, 1410], "out_edg": [168, 865, 946, 1062, 1407, 1409, 1410, 1429], "quietli": [168, 189, 865, 878, 910, 946, 960, 991, 1087, 1429], "outedgedataview": [168, 189, 865, 878, 1407, 1414], "set_data": 169, 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"outdegreeview": [188, 877], "Will": [193, 362, 605, 607, 610, 882, 921, 963, 1003, 1407, 1417], "get_data": [197, 616], "inplac": [199, 690, 887, 925, 968, 1007, 1066, 1396], "reduct": [199, 469, 618, 786, 887, 925, 968, 1007, 1066, 1323, 1324, 1416, 1417], "sg": [199, 887, 925, 968, 1007], "largest_wcc": [199, 887, 925, 968, 1007], "is_multigraph": [199, 758, 887, 925, 968, 1007, 1157, 1415], "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, 1236, 1244, 1429], "reciproc": [204, 299, 320, 322, 356, 411, 430, 447, 476, 620, 758, 891, 972, 1328, 1419, 1428], "mark_half_edg": 206, "li": [206, 619, 670, 675, 685, 775, 1210, 1213, 1428], "straightforward": [207, 892, 928, 973, 1010], "slightli": [207, 326, 437, 520, 521, 581, 892, 928, 973, 1010, 1168, 1329, 1407, 1410, 1415, 1417, 1428], "singleton": [207, 588, 892, 928, 973, 1010, 1221, 1254, 1410], "preserve_attr": [208, 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228, 233, 234, 377, 380, 381, 427, 494, 509, 626, 627, 652, 663, 703, 758, 1176, 1323, 1324, 1328, 1398, 1411, 1415, 1416], "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, 1417], "maximum_cliqu": 211, "1007": [211, 226, 296, 301, 302, 303, 308, 309, 323, 324, 325, 341, 431, 451, 498, 574, 1147, 1184], "bf01994876": 211, "iset": 212, "trial": [213, 230, 231, 1198, 1240, 1241], "estim": [213, 224, 297, 306, 313, 564, 625, 626, 627, 782, 1283, 1410], "coeffici": [213, 248, 260, 261, 262, 263, 289, 355, 356, 358, 570, 618, 619, 625, 682, 684, 778, 782, 1400, 1401, 1402, 1409, 1416], "fraction": [213, 257, 259, 286, 289, 297, 299, 304, 306, 315, 317, 318, 319, 321, 322, 326, 328, 330, 356, 358, 359, 519, 1124, 1126, 1168, 1237], "schank": 213, "thoma": [213, 751, 1410, 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"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, "planarembedding-has-edge"]], "PlanarEmbedding.has_node": [[172, "planarembedding-has-node"]], "PlanarEmbedding.has_predecessor": [[173, "planarembedding-has-predecessor"]], "PlanarEmbedding.has_successor": [[174, "planarembedding-has-successor"]], "PlanarEmbedding.in_degree": [[175, "planarembedding-in-degree"]], "PlanarEmbedding.in_edges": [[176, "planarembedding-in-edges"]], "PlanarEmbedding.is_directed": [[177, "planarembedding-is-directed"]], "PlanarEmbedding.is_multigraph": [[178, "planarembedding-is-multigraph"]], "PlanarEmbedding.name": [[179, "planarembedding-name"]], "PlanarEmbedding.nbunch_iter": [[180, "planarembedding-nbunch-iter"]], "PlanarEmbedding.neighbors": [[181, "planarembedding-neighbors"]], "PlanarEmbedding.neighbors_cw_order": [[182, "planarembedding-neighbors-cw-order"]], "PlanarEmbedding.next_face_half_edge": [[183, "planarembedding-next-face-half-edge"]], "PlanarEmbedding.nodes": [[184, "planarembedding-nodes"]], "PlanarEmbedding.number_of_edges": [[185, "planarembedding-number-of-edges"]], "PlanarEmbedding.number_of_nodes": [[186, "planarembedding-number-of-nodes"]], "PlanarEmbedding.order": [[187, "planarembedding-order"]], "PlanarEmbedding.out_degree": [[188, "planarembedding-out-degree"]], "PlanarEmbedding.out_edges": [[189, "planarembedding-out-edges"]], "PlanarEmbedding.pred": [[190, "planarembedding-pred"]], "PlanarEmbedding.predecessors": [[191, "planarembedding-predecessors"]], "PlanarEmbedding.remove_edge": [[192, "planarembedding-remove-edge"]], "PlanarEmbedding.remove_edges_from": [[193, "planarembedding-remove-edges-from"]], "PlanarEmbedding.remove_node": [[194, "planarembedding-remove-node"]], "PlanarEmbedding.remove_nodes_from": [[195, "planarembedding-remove-nodes-from"]], "PlanarEmbedding.reverse": [[196, "planarembedding-reverse"]], "PlanarEmbedding.set_data": [[197, "planarembedding-set-data"]], "PlanarEmbedding.size": [[198, "planarembedding-size"]], "PlanarEmbedding.subgraph": [[199, "planarembedding-subgraph"]], "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, "all-pairs-node-connectivity"], [408, "all-pairs-node-connectivity"]], "local_node_connectivity": [[215, "local-node-connectivity"], [412, "local-node-connectivity"]], "node_connectivity": [[216, "node-connectivity"], [413, "node-connectivity"]], "diameter": [[217, "diameter"], [472, "diameter"]], "min_edge_dominating_set": [[218, "min-edge-dominating-set"]], "min_weighted_dominating_set": [[219, "min-weighted-dominating-set"]], "k_components": [[220, "k-components"], [427, "k-components"]], "min_maximal_matching": [[221, "min-maximal-matching"]], "one_exchange": [[222, "one-exchange"]], "randomized_partitioning": [[223, "randomized-partitioning"]], "ramsey_R2": [[224, "ramsey-r2"]], "metric_closure": [[225, "metric-closure"]], "steiner_tree": [[226, "steiner-tree"]], "asadpour_atsp": [[227, "asadpour-atsp"]], "christofides": [[228, "christofides"]], "greedy_tsp": [[229, "greedy-tsp"]], "simulated_annealing_tsp": [[230, "simulated-annealing-tsp"]], "threshold_accepting_tsp": [[231, "threshold-accepting-tsp"]], "traveling_salesman_problem": [[232, "traveling-salesman-problem"]], "treewidth_min_degree": [[233, "treewidth-min-degree"]], "treewidth_min_fill_in": [[234, "treewidth-min-fill-in"]], "min_weighted_vertex_cover": [[235, "min-weighted-vertex-cover"]], "attribute_assortativity_coefficient": [[236, "attribute-assortativity-coefficient"]], "attribute_mixing_dict": [[237, "attribute-mixing-dict"]], "attribute_mixing_matrix": [[238, "attribute-mixing-matrix"]], "average_degree_connectivity": [[239, "average-degree-connectivity"]], "average_neighbor_degree": [[240, "average-neighbor-degree"]], "degree_assortativity_coefficient": [[241, "degree-assortativity-coefficient"]], "degree_mixing_dict": [[242, "degree-mixing-dict"]], "degree_mixing_matrix": [[243, "degree-mixing-matrix"]], "degree_pearson_correlation_coefficient": [[244, "degree-pearson-correlation-coefficient"]], "mixing_dict": [[245, "mixing-dict"]], "node_attribute_xy": [[246, "node-attribute-xy"]], "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, "generate-edgelist"], [1338, "generate-edgelist"]], "parse_edgelist": [[266, "parse-edgelist"], [1339, "parse-edgelist"]], "read_edgelist": [[267, "read-edgelist"], [1340, "read-edgelist"]], "write_edgelist": [[268, "write-edgelist"], [1342, "write-edgelist"]], "alternating_havel_hakimi_graph": [[269, "alternating-havel-hakimi-graph"]], "complete_bipartite_graph": [[270, "complete-bipartite-graph"]], "configuration_model": [[271, "configuration-model"], [1178, "configuration-model"]], "gnmk_random_graph": [[272, "gnmk-random-graph"]], "havel_hakimi_graph": [[273, "havel-hakimi-graph"], [1183, "havel-hakimi-graph"]], "preferential_attachment_graph": [[274, "preferential-attachment-graph"]], "random_graph": [[275, "random-graph"]], "reverse_havel_hakimi_graph": [[276, "reverse-havel-hakimi-graph"]], "eppstein_matching": [[277, "eppstein-matching"]], "hopcroft_karp_matching": [[278, "hopcroft-karp-matching"]], "maximum_matching": [[279, "maximum-matching"]], "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, "approximate-current-flow-betweenness-centrality"]], "betweenness_centrality_subset": [[298, "betweenness-centrality-subset"]], "communicability_betweenness_centrality": [[300, "communicability-betweenness-centrality"]], "current_flow_betweenness_centrality": [[301, "current-flow-betweenness-centrality"]], "current_flow_betweenness_centrality_subset": [[302, "current-flow-betweenness-centrality-subset"]], "current_flow_closeness_centrality": [[303, "current-flow-closeness-centrality"]], "dispersion": [[305, "dispersion"]], "edge_betweenness_centrality": [[306, "edge-betweenness-centrality"]], "edge_betweenness_centrality_subset": [[307, "edge-betweenness-centrality-subset"]], "edge_current_flow_betweenness_centrality": [[308, "edge-current-flow-betweenness-centrality"]], "edge_current_flow_betweenness_centrality_subset": [[309, "edge-current-flow-betweenness-centrality-subset"]], "edge_load_centrality": [[310, "edge-load-centrality"]], "eigenvector_centrality": [[311, "eigenvector-centrality"]], "eigenvector_centrality_numpy": [[312, "eigenvector-centrality-numpy"]], "estrada_index": [[313, "estrada-index"]], "global_reaching_centrality": [[314, "global-reaching-centrality"]], "group_betweenness_centrality": [[315, "group-betweenness-centrality"]], "group_closeness_centrality": [[316, "group-closeness-centrality"]], "group_degree_centrality": [[317, "group-degree-centrality"]], "group_in_degree_centrality": [[318, "group-in-degree-centrality"]], "group_out_degree_centrality": [[319, "group-out-degree-centrality"]], "harmonic_centrality": [[320, "harmonic-centrality"]], "in_degree_centrality": [[321, "in-degree-centrality"]], "incremental_closeness_centrality": [[322, "incremental-closeness-centrality"]], "information_centrality": [[323, "information-centrality"]], "katz_centrality": [[324, "katz-centrality"]], "katz_centrality_numpy": [[325, "katz-centrality-numpy"]], "load_centrality": [[326, "load-centrality"]], "local_reaching_centrality": [[327, "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"]], "strategy_connected_sequential_dfs": [[365, "strategy-connected-sequential-dfs"]], "strategy_independent_set": [[366, "strategy-independent-set"]], "strategy_largest_first": [[367, "strategy-largest-first"]], "strategy_random_sequential": [[368, "strategy-random-sequential"]], "strategy_saturation_largest_first": [[369, "strategy-saturation-largest-first"]], "strategy_smallest_last": [[370, "strategy-smallest-last"]], "communicability": [[371, "communicability"]], "communicability_exp": [[372, "communicability-exp"]], "asyn_fluidc": [[373, "asyn-fluidc"]], "girvan_newman": [[374, "girvan-newman"]], "is_partition": [[375, "is-partition"]], "k_clique_communities": [[376, "k-clique-communities"]], "kernighan_lin_bisection": [[377, "kernighan-lin-bisection"]], "asyn_lpa_communities": [[378, "asyn-lpa-communities"]], "label_propagation_communities": [[379, "label-propagation-communities"]], "louvain_communities": [[380, "louvain-communities"]], "louvain_partitions": [[381, "louvain-partitions"]], "lukes_partitioning": [[382, "lukes-partitioning"]], "greedy_modularity_communities": [[383, "greedy-modularity-communities"]], "naive_greedy_modularity_communities": [[384, "naive-greedy-modularity-communities"]], "modularity": [[385, "modularity"]], "partition_quality": [[386, "partition-quality"]], "articulation_points": [[387, "articulation-points"]], "attracting_components": [[388, "attracting-components"]], "biconnected_component_edges": [[389, "biconnected-component-edges"]], "biconnected_components": [[390, "biconnected-components"]], "condensation": [[391, "condensation"]], "connected_components": [[392, "connected-components"]], "is_attracting_component": [[393, "is-attracting-component"]], "is_biconnected": [[394, "is-biconnected"]], "is_connected": [[395, "is-connected"]], "is_semiconnected": [[396, "is-semiconnected"]], "is_strongly_connected": [[397, "is-strongly-connected"], [699, "is-strongly-connected"]], "is_weakly_connected": [[398, "is-weakly-connected"]], "kosaraju_strongly_connected_components": [[399, "kosaraju-strongly-connected-components"]], "node_connected_component": [[400, "node-connected-component"]], "number_attracting_components": [[401, "number-attracting-components"]], "number_connected_components": [[402, "number-connected-components"]], "number_strongly_connected_components": [[403, "number-strongly-connected-components"]], "number_weakly_connected_components": [[404, "number-weakly-connected-components"]], "strongly_connected_components": [[405, "strongly-connected-components"]], "strongly_connected_components_recursive": [[406, "strongly-connected-components-recursive"]], "weakly_connected_components": [[407, "weakly-connected-components"]], "average_node_connectivity": [[409, "average-node-connectivity"]], "edge_connectivity": [[410, "edge-connectivity"]], "local_edge_connectivity": [[411, "local-edge-connectivity"]], "minimum_edge_cut": [[414, "minimum-edge-cut"]], "minimum_node_cut": [[415, "minimum-node-cut"]], "minimum_st_edge_cut": [[416, "minimum-st-edge-cut"]], "minimum_st_node_cut": [[417, "minimum-st-node-cut"]], "edge_disjoint_paths": [[418, "edge-disjoint-paths"]], "node_disjoint_paths": [[419, "node-disjoint-paths"]], "is_k_edge_connected": [[420, "is-k-edge-connected"]], "is_locally_k_edge_connected": [[421, "is-locally-k-edge-connected"]], "k_edge_augmentation": [[422, "k-edge-augmentation"]], "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph": [[423, "networkx-algorithms-connectivity-edge-kcomponents-edgecomponentauxgraph"]], "bridge_components": [[424, "bridge-components"]], "k_edge_components": [[425, "k-edge-components"]], "k_edge_subgraphs": [[426, "k-edge-subgraphs"]], "all_node_cuts": [[428, "all-node-cuts"]], "stoer_wagner": [[429, "stoer-wagner"]], "build_auxiliary_edge_connectivity": [[430, "build-auxiliary-edge-connectivity"]], "build_auxiliary_node_connectivity": [[431, "build-auxiliary-node-connectivity"]], "core_number": [[432, "core-number"]], "k_core": [[433, "k-core"]], "k_corona": [[434, "k-corona"]], "k_crust": [[435, "k-crust"]], "k_shell": [[436, "k-shell"]], "k_truss": [[437, "k-truss"]], "onion_layers": [[438, "onion-layers"]], "is_edge_cover": [[439, "is-edge-cover"]], "boundary_expansion": [[441, "boundary-expansion"]], "conductance": [[442, "conductance"]], "cut_size": [[443, "cut-size"]], "edge_expansion": [[444, "edge-expansion"]], "mixing_expansion": [[445, "mixing-expansion"]], "node_expansion": [[446, "node-expansion"]], "normalized_cut_size": [[447, "normalized-cut-size"]], "volume": [[448, "volume"]], "cycle_basis": [[449, "cycle-basis"]], "find_cycle": [[450, "find-cycle"]], "minimum_cycle_basis": [[451, "minimum-cycle-basis"]], "recursive_simple_cycles": [[452, "recursive-simple-cycles"]], "simple_cycles": [[453, "simple-cycles"]], "d_separated": [[454, "d-separated"]], "all_topological_sorts": [[455, "all-topological-sorts"]], "ancestors": [[456, "ancestors"]], "antichains": [[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"], [1042, "notes"]], "ISMAGS object": [[761, "ismags-object"]], "VF2 Algorithm": [[762, "module-networkx.algorithms.isomorphism.isomorphvf2"]], "Subgraph Isomorphism": [[762, "subgraph-isomorphism"]], "Graph Matcher": [[762, "graph-matcher"]], "DiGraph Matcher": [[762, "digraph-matcher"]], "Match helpers": [[762, "match-helpers"]], "Link Analysis": [[763, "link-analysis"]], "PageRank": [[763, "module-networkx.algorithms.link_analysis.pagerank_alg"]], "Hits": [[763, "module-networkx.algorithms.link_analysis.hits_alg"]], "Link Prediction": [[764, "module-networkx.algorithms.link_prediction"]], "Lowest Common Ancestor": [[765, "module-networkx.algorithms.lowest_common_ancestors"]], "Minors": [[767, "module-networkx.algorithms.minors"]], "Maximal independent set": [[768, "module-networkx.algorithms.mis"]], "Moral": [[769, "module-networkx.algorithms.moral"]], "Node Classification": [[770, "module-networkx.algorithms.node_classification"]], "non-randomness": [[771, "module-networkx.algorithms.non_randomness"]], "Operators": [[772, "operators"]], "Planar Drawing": [[773, "module-networkx.algorithms.planar_drawing"]], "Planarity": [[774, "module-networkx.algorithms.planarity"]], "Graph Polynomials": [[775, "module-networkx.algorithms.polynomials"]], "Reciprocity": [[776, "module-networkx.algorithms.reciprocity"]], "Regular": [[777, "module-networkx.algorithms.regular"]], "Rich Club": [[778, "module-networkx.algorithms.richclub"]], "Shortest Paths": [[779, "module-networkx.algorithms.shortest_paths.generic"]], "Advanced Interface": [[779, "module-networkx.algorithms.shortest_paths.unweighted"]], "Dense Graphs": [[779, "module-networkx.algorithms.shortest_paths.dense"]], "A* Algorithm": [[779, "module-networkx.algorithms.shortest_paths.astar"]], "Similarity Measures": [[780, "module-networkx.algorithms.similarity"]], "Simple Paths": [[781, "module-networkx.algorithms.simple_paths"]], "Small-world": [[782, "module-networkx.algorithms.smallworld"]], "s metric": [[783, "module-networkx.algorithms.smetric"]], "Sparsifiers": [[784, "module-networkx.algorithms.sparsifiers"]], "Structural holes": [[785, "module-networkx.algorithms.structuralholes"]], "Summarization": [[786, "module-networkx.algorithms.summarization"]], "Swap": [[787, "module-networkx.algorithms.swap"]], "Threshold Graphs": [[788, "module-networkx.algorithms.threshold"]], "Tournament": [[789, "module-networkx.algorithms.tournament"]], "Traversal": [[790, "traversal"]], "Depth First Search": [[790, "module-networkx.algorithms.traversal.depth_first_search"]], "Breadth First Search": [[790, "module-networkx.algorithms.traversal.breadth_first_search"]], "Beam search": [[790, "module-networkx.algorithms.traversal.beamsearch"]], "Depth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgedfs"]], "Breadth First Search on Edges": [[790, "module-networkx.algorithms.traversal.edgebfs"]], "Tree": [[791, "tree"]], "Recognition": [[791, "module-networkx.algorithms.tree.recognition"]], "Recognition Tests": [[791, "recognition-tests"]], "Branchings and Spanning Arborescences": [[791, "module-networkx.algorithms.tree.branchings"]], "Encoding and decoding": [[791, "module-networkx.algorithms.tree.coding"]], "Operations": [[791, "module-networkx.algorithms.tree.operations"]], "Spanning Trees": [[791, "module-networkx.algorithms.tree.mst"]], "Exceptions": [[791, "exceptions"], [1043, "module-networkx.exception"]], "Vitality": [[793, "module-networkx.algorithms.vitality"]], "Voronoi cells": [[794, "module-networkx.algorithms.voronoi"]], "Wiener index": [[795, "module-networkx.algorithms.wiener"]], "DiGraph\u2014Directed graphs with self loops": [[796, "digraph-directed-graphs-with-self-loops"]], "Overview": [[796, "overview"], [1037, "overview"], [1039, "overview"], [1040, "overview"]], "Methods": [[796, "methods"], [1037, "methods"], [1039, "methods"], [1040, "methods"]], "Adding and removing nodes and edges": [[796, 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"networkx.algorithms.approximation.traveling_salesman": [[112, "module-networkx.algorithms.approximation.traveling_salesman"]], "networkx.algorithms.approximation.treewidth": [[112, "module-networkx.algorithms.approximation.treewidth"]], "networkx.algorithms.approximation.vertex_cover": [[112, "module-networkx.algorithms.approximation.vertex_cover"]], "networkx.algorithms.assortativity": [[113, "module-networkx.algorithms.assortativity"]], "networkx.algorithms.asteroidal": [[114, "module-networkx.algorithms.asteroidal"]], "networkx.algorithms.bipartite": [[115, "module-networkx.algorithms.bipartite"]], "networkx.algorithms.bipartite.basic": [[115, "module-networkx.algorithms.bipartite.basic"]], "networkx.algorithms.bipartite.centrality": [[115, "module-networkx.algorithms.bipartite.centrality"]], "networkx.algorithms.bipartite.cluster": [[115, "module-networkx.algorithms.bipartite.cluster"]], "networkx.algorithms.bipartite.covering": [[115, "module-networkx.algorithms.bipartite.covering"]], "networkx.algorithms.bipartite.edgelist": [[115, "module-networkx.algorithms.bipartite.edgelist"]], "networkx.algorithms.bipartite.generators": [[115, "module-networkx.algorithms.bipartite.generators"]], "networkx.algorithms.bipartite.matching": [[115, "module-networkx.algorithms.bipartite.matching"]], "networkx.algorithms.bipartite.matrix": [[115, "module-networkx.algorithms.bipartite.matrix"]], "networkx.algorithms.bipartite.projection": [[115, "module-networkx.algorithms.bipartite.projection"]], "networkx.algorithms.bipartite.redundancy": [[115, "module-networkx.algorithms.bipartite.redundancy"]], "networkx.algorithms.bipartite.spectral": [[115, "module-networkx.algorithms.bipartite.spectral"]], "networkx.algorithms.boundary": [[116, "module-networkx.algorithms.boundary"]], "networkx.algorithms.bridges": [[117, "module-networkx.algorithms.bridges"]], "networkx.algorithms.centrality": [[118, "module-networkx.algorithms.centrality"]], "networkx.algorithms.chains": [[119, "module-networkx.algorithms.chains"]], "networkx.algorithms.chordal": [[120, "module-networkx.algorithms.chordal"]], "networkx.algorithms.clique": [[121, "module-networkx.algorithms.clique"]], "networkx.algorithms.cluster": [[122, "module-networkx.algorithms.cluster"]], "networkx.algorithms.coloring": [[123, "module-networkx.algorithms.coloring"]], "networkx.algorithms.communicability_alg": [[124, "module-networkx.algorithms.communicability_alg"]], "networkx.algorithms.community": [[125, "module-networkx.algorithms.community"]], "networkx.algorithms.community.asyn_fluid": [[125, "module-networkx.algorithms.community.asyn_fluid"]], "networkx.algorithms.community.centrality": [[125, "module-networkx.algorithms.community.centrality"]], "networkx.algorithms.community.community_utils": [[125, "module-networkx.algorithms.community.community_utils"]], "networkx.algorithms.community.kclique": [[125, "module-networkx.algorithms.community.kclique"]], "networkx.algorithms.community.kernighan_lin": [[125, "module-networkx.algorithms.community.kernighan_lin"]], "networkx.algorithms.community.label_propagation": [[125, "module-networkx.algorithms.community.label_propagation"]], "networkx.algorithms.community.louvain": [[125, "module-networkx.algorithms.community.louvain"]], "networkx.algorithms.community.lukes": [[125, "module-networkx.algorithms.community.lukes"]], "networkx.algorithms.community.modularity_max": [[125, "module-networkx.algorithms.community.modularity_max"]], "networkx.algorithms.community.quality": [[125, "module-networkx.algorithms.community.quality"]], "networkx.algorithms.components": [[126, "module-networkx.algorithms.components"]], "networkx.algorithms.connectivity": [[127, "module-networkx.algorithms.connectivity"]], "networkx.algorithms.connectivity.connectivity": [[127, "module-networkx.algorithms.connectivity.connectivity"]], "networkx.algorithms.connectivity.cuts": [[127, "module-networkx.algorithms.connectivity.cuts"]], "networkx.algorithms.connectivity.disjoint_paths": [[127, "module-networkx.algorithms.connectivity.disjoint_paths"]], "networkx.algorithms.connectivity.edge_augmentation": [[127, "module-networkx.algorithms.connectivity.edge_augmentation"]], "networkx.algorithms.connectivity.edge_kcomponents": [[127, "module-networkx.algorithms.connectivity.edge_kcomponents"]], "networkx.algorithms.connectivity.kcomponents": [[127, "module-networkx.algorithms.connectivity.kcomponents"]], "networkx.algorithms.connectivity.kcutsets": [[127, "module-networkx.algorithms.connectivity.kcutsets"]], "networkx.algorithms.connectivity.stoerwagner": [[127, "module-networkx.algorithms.connectivity.stoerwagner"]], "networkx.algorithms.connectivity.utils": [[127, "module-networkx.algorithms.connectivity.utils"]], "networkx.algorithms.core": [[128, "module-networkx.algorithms.core"]], "networkx.algorithms.covering": [[129, "module-networkx.algorithms.covering"]], "networkx.algorithms.cuts": [[130, "module-networkx.algorithms.cuts"]], "networkx.algorithms.cycles": [[131, "module-networkx.algorithms.cycles"]], "networkx.algorithms.d_separation": [[132, "module-networkx.algorithms.d_separation"]], "networkx.algorithms.dag": [[133, "module-networkx.algorithms.dag"]], "networkx.algorithms.distance_measures": [[134, "module-networkx.algorithms.distance_measures"]], "networkx.algorithms.distance_regular": [[135, "module-networkx.algorithms.distance_regular"]], "networkx.algorithms.dominance": [[136, "module-networkx.algorithms.dominance"]], "networkx.algorithms.dominating": [[137, "module-networkx.algorithms.dominating"]], "networkx.algorithms.efficiency_measures": [[138, "module-networkx.algorithms.efficiency_measures"]], "networkx.algorithms.euler": [[139, "module-networkx.algorithms.euler"]], "networkx.algorithms.flow": [[140, "module-networkx.algorithms.flow"]], "construct() (edgecomponentauxgraph class method)": [[141, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.construct"]], "k_edge_components() (edgecomponentauxgraph method)": [[142, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_components"]], "k_edge_subgraphs() (edgecomponentauxgraph method)": [[143, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.k_edge_subgraphs"]], "analyze_symmetry() (ismags method)": [[144, "networkx.algorithms.isomorphism.ISMAGS.analyze_symmetry"]], "find_isomorphisms() (ismags method)": [[145, "networkx.algorithms.isomorphism.ISMAGS.find_isomorphisms"]], "is_isomorphic() (ismags method)": [[146, "networkx.algorithms.isomorphism.ISMAGS.is_isomorphic"]], "isomorphisms_iter() (ismags method)": [[147, "networkx.algorithms.isomorphism.ISMAGS.isomorphisms_iter"]], "largest_common_subgraph() (ismags method)": [[148, "networkx.algorithms.isomorphism.ISMAGS.largest_common_subgraph"]], "subgraph_is_isomorphic() (ismags method)": [[149, "networkx.algorithms.isomorphism.ISMAGS.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (ismags method)": [[150, "networkx.algorithms.isomorphism.ISMAGS.subgraph_isomorphisms_iter"]], "add_edge() (planarembedding method)": [[151, "networkx.algorithms.planarity.PlanarEmbedding.add_edge"]], "add_edges_from() (planarembedding method)": [[152, "networkx.algorithms.planarity.PlanarEmbedding.add_edges_from"]], "add_half_edge_ccw() (planarembedding method)": [[153, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_ccw"]], "add_half_edge_cw() (planarembedding method)": [[154, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_cw"]], "add_half_edge_first() (planarembedding method)": [[155, "networkx.algorithms.planarity.PlanarEmbedding.add_half_edge_first"]], "add_node() (planarembedding method)": [[156, "networkx.algorithms.planarity.PlanarEmbedding.add_node"]], "add_nodes_from() (planarembedding method)": [[157, "networkx.algorithms.planarity.PlanarEmbedding.add_nodes_from"]], "add_weighted_edges_from() (planarembedding method)": [[158, "networkx.algorithms.planarity.PlanarEmbedding.add_weighted_edges_from"]], "adj (planarembedding property)": [[159, "networkx.algorithms.planarity.PlanarEmbedding.adj"]], "adjacency() (planarembedding method)": [[160, "networkx.algorithms.planarity.PlanarEmbedding.adjacency"]], "check_structure() (planarembedding method)": [[161, "networkx.algorithms.planarity.PlanarEmbedding.check_structure"]], "clear() (planarembedding method)": [[162, "networkx.algorithms.planarity.PlanarEmbedding.clear"]], "clear_edges() (planarembedding method)": [[163, "networkx.algorithms.planarity.PlanarEmbedding.clear_edges"]], "connect_components() (planarembedding method)": [[164, "networkx.algorithms.planarity.PlanarEmbedding.connect_components"]], "copy() (planarembedding method)": [[165, "networkx.algorithms.planarity.PlanarEmbedding.copy"]], "degree (planarembedding property)": [[166, "networkx.algorithms.planarity.PlanarEmbedding.degree"]], "edge_subgraph() (planarembedding method)": [[167, "networkx.algorithms.planarity.PlanarEmbedding.edge_subgraph"]], "edges (planarembedding property)": [[168, "networkx.algorithms.planarity.PlanarEmbedding.edges"]], "get_data() (planarembedding method)": [[169, "networkx.algorithms.planarity.PlanarEmbedding.get_data"]], "get_edge_data() (planarembedding method)": [[170, "networkx.algorithms.planarity.PlanarEmbedding.get_edge_data"]], "has_edge() (planarembedding method)": [[171, "networkx.algorithms.planarity.PlanarEmbedding.has_edge"]], "has_node() (planarembedding method)": [[172, "networkx.algorithms.planarity.PlanarEmbedding.has_node"]], "has_predecessor() (planarembedding method)": [[173, "networkx.algorithms.planarity.PlanarEmbedding.has_predecessor"]], "has_successor() (planarembedding method)": [[174, "networkx.algorithms.planarity.PlanarEmbedding.has_successor"]], "in_degree (planarembedding property)": [[175, "networkx.algorithms.planarity.PlanarEmbedding.in_degree"]], "in_edges (planarembedding property)": [[176, "networkx.algorithms.planarity.PlanarEmbedding.in_edges"]], "is_directed() (planarembedding method)": [[177, "networkx.algorithms.planarity.PlanarEmbedding.is_directed"]], "is_multigraph() (planarembedding method)": [[178, "networkx.algorithms.planarity.PlanarEmbedding.is_multigraph"]], "name (planarembedding property)": [[179, "networkx.algorithms.planarity.PlanarEmbedding.name"]], "nbunch_iter() (planarembedding method)": [[180, "networkx.algorithms.planarity.PlanarEmbedding.nbunch_iter"]], "neighbors() (planarembedding method)": [[181, "networkx.algorithms.planarity.PlanarEmbedding.neighbors"]], "neighbors_cw_order() (planarembedding method)": [[182, "networkx.algorithms.planarity.PlanarEmbedding.neighbors_cw_order"]], "next_face_half_edge() (planarembedding method)": [[183, "networkx.algorithms.planarity.PlanarEmbedding.next_face_half_edge"]], "nodes (planarembedding property)": [[184, "networkx.algorithms.planarity.PlanarEmbedding.nodes"]], "number_of_edges() (planarembedding method)": [[185, "networkx.algorithms.planarity.PlanarEmbedding.number_of_edges"]], "number_of_nodes() (planarembedding method)": [[186, "networkx.algorithms.planarity.PlanarEmbedding.number_of_nodes"]], "order() (planarembedding method)": [[187, "networkx.algorithms.planarity.PlanarEmbedding.order"]], "out_degree (planarembedding property)": [[188, "networkx.algorithms.planarity.PlanarEmbedding.out_degree"]], "out_edges (planarembedding property)": [[189, "networkx.algorithms.planarity.PlanarEmbedding.out_edges"]], "pred (planarembedding property)": [[190, "networkx.algorithms.planarity.PlanarEmbedding.pred"]], "predecessors() (planarembedding method)": [[191, "networkx.algorithms.planarity.PlanarEmbedding.predecessors"]], "remove_edge() (planarembedding method)": [[192, "networkx.algorithms.planarity.PlanarEmbedding.remove_edge"]], "remove_edges_from() (planarembedding method)": [[193, "networkx.algorithms.planarity.PlanarEmbedding.remove_edges_from"]], "remove_node() (planarembedding method)": [[194, "networkx.algorithms.planarity.PlanarEmbedding.remove_node"]], "remove_nodes_from() (planarembedding method)": [[195, "networkx.algorithms.planarity.PlanarEmbedding.remove_nodes_from"]], "reverse() (planarembedding method)": [[196, "networkx.algorithms.planarity.PlanarEmbedding.reverse"]], "set_data() (planarembedding method)": [[197, "networkx.algorithms.planarity.PlanarEmbedding.set_data"]], "size() (planarembedding method)": [[198, "networkx.algorithms.planarity.PlanarEmbedding.size"]], "subgraph() (planarembedding method)": [[199, "networkx.algorithms.planarity.PlanarEmbedding.subgraph"]], "succ (planarembedding property)": [[200, "networkx.algorithms.planarity.PlanarEmbedding.succ"]], "successors() (planarembedding method)": [[201, "networkx.algorithms.planarity.PlanarEmbedding.successors"]], "to_directed() (planarembedding method)": [[202, "networkx.algorithms.planarity.PlanarEmbedding.to_directed"]], "to_directed_class() (planarembedding method)": [[203, "networkx.algorithms.planarity.PlanarEmbedding.to_directed_class"]], "to_undirected() (planarembedding method)": [[204, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected"]], "to_undirected_class() (planarembedding method)": [[205, "networkx.algorithms.planarity.PlanarEmbedding.to_undirected_class"]], "traverse_face() (planarembedding method)": [[206, "networkx.algorithms.planarity.PlanarEmbedding.traverse_face"]], "update() (planarembedding method)": [[207, "networkx.algorithms.planarity.PlanarEmbedding.update"]], "find_optimum() (edmonds method)": [[208, "networkx.algorithms.tree.branchings.Edmonds.find_optimum"]], "clique_removal() (in module networkx.algorithms.approximation.clique)": [[209, "networkx.algorithms.approximation.clique.clique_removal"]], "large_clique_size() (in module networkx.algorithms.approximation.clique)": [[210, "networkx.algorithms.approximation.clique.large_clique_size"]], "max_clique() (in module networkx.algorithms.approximation.clique)": [[211, "networkx.algorithms.approximation.clique.max_clique"]], "maximum_independent_set() (in module networkx.algorithms.approximation.clique)": [[212, "networkx.algorithms.approximation.clique.maximum_independent_set"]], "average_clustering() (in module networkx.algorithms.approximation.clustering_coefficient)": [[213, "networkx.algorithms.approximation.clustering_coefficient.average_clustering"]], "all_pairs_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[214, "networkx.algorithms.approximation.connectivity.all_pairs_node_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[215, "networkx.algorithms.approximation.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.approximation.connectivity)": [[216, "networkx.algorithms.approximation.connectivity.node_connectivity"]], "diameter() (in module networkx.algorithms.approximation.distance_measures)": [[217, "networkx.algorithms.approximation.distance_measures.diameter"]], "min_edge_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[218, "networkx.algorithms.approximation.dominating_set.min_edge_dominating_set"]], "min_weighted_dominating_set() (in module networkx.algorithms.approximation.dominating_set)": [[219, "networkx.algorithms.approximation.dominating_set.min_weighted_dominating_set"]], "k_components() (in module networkx.algorithms.approximation.kcomponents)": [[220, "networkx.algorithms.approximation.kcomponents.k_components"]], "min_maximal_matching() (in module networkx.algorithms.approximation.matching)": [[221, "networkx.algorithms.approximation.matching.min_maximal_matching"]], "one_exchange() (in module networkx.algorithms.approximation.maxcut)": [[222, "networkx.algorithms.approximation.maxcut.one_exchange"]], "randomized_partitioning() (in module networkx.algorithms.approximation.maxcut)": [[223, "networkx.algorithms.approximation.maxcut.randomized_partitioning"]], "ramsey_r2() (in module networkx.algorithms.approximation.ramsey)": [[224, "networkx.algorithms.approximation.ramsey.ramsey_R2"]], "metric_closure() (in module networkx.algorithms.approximation.steinertree)": [[225, "networkx.algorithms.approximation.steinertree.metric_closure"]], "steiner_tree() (in module networkx.algorithms.approximation.steinertree)": [[226, "networkx.algorithms.approximation.steinertree.steiner_tree"]], "asadpour_atsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[227, "networkx.algorithms.approximation.traveling_salesman.asadpour_atsp"]], "christofides() (in module networkx.algorithms.approximation.traveling_salesman)": [[228, "networkx.algorithms.approximation.traveling_salesman.christofides"]], "greedy_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[229, "networkx.algorithms.approximation.traveling_salesman.greedy_tsp"]], "simulated_annealing_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[230, "networkx.algorithms.approximation.traveling_salesman.simulated_annealing_tsp"]], "threshold_accepting_tsp() (in module networkx.algorithms.approximation.traveling_salesman)": [[231, "networkx.algorithms.approximation.traveling_salesman.threshold_accepting_tsp"]], "traveling_salesman_problem() (in module networkx.algorithms.approximation.traveling_salesman)": [[232, "networkx.algorithms.approximation.traveling_salesman.traveling_salesman_problem"]], "treewidth_min_degree() (in module networkx.algorithms.approximation.treewidth)": [[233, "networkx.algorithms.approximation.treewidth.treewidth_min_degree"]], "treewidth_min_fill_in() (in module networkx.algorithms.approximation.treewidth)": [[234, "networkx.algorithms.approximation.treewidth.treewidth_min_fill_in"]], "min_weighted_vertex_cover() (in module networkx.algorithms.approximation.vertex_cover)": [[235, "networkx.algorithms.approximation.vertex_cover.min_weighted_vertex_cover"]], "attribute_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[236, "networkx.algorithms.assortativity.attribute_assortativity_coefficient"]], "attribute_mixing_dict() (in module networkx.algorithms.assortativity)": [[237, "networkx.algorithms.assortativity.attribute_mixing_dict"]], "attribute_mixing_matrix() (in module networkx.algorithms.assortativity)": [[238, "networkx.algorithms.assortativity.attribute_mixing_matrix"]], "average_degree_connectivity() (in module networkx.algorithms.assortativity)": [[239, "networkx.algorithms.assortativity.average_degree_connectivity"]], "average_neighbor_degree() (in module networkx.algorithms.assortativity)": [[240, "networkx.algorithms.assortativity.average_neighbor_degree"]], "degree_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[241, "networkx.algorithms.assortativity.degree_assortativity_coefficient"]], "degree_mixing_dict() (in module networkx.algorithms.assortativity)": [[242, "networkx.algorithms.assortativity.degree_mixing_dict"]], "degree_mixing_matrix() (in module networkx.algorithms.assortativity)": [[243, "networkx.algorithms.assortativity.degree_mixing_matrix"]], "degree_pearson_correlation_coefficient() (in module networkx.algorithms.assortativity)": [[244, "networkx.algorithms.assortativity.degree_pearson_correlation_coefficient"]], "mixing_dict() (in module networkx.algorithms.assortativity)": [[245, "networkx.algorithms.assortativity.mixing_dict"]], "node_attribute_xy() (in module networkx.algorithms.assortativity)": [[246, "networkx.algorithms.assortativity.node_attribute_xy"]], "node_degree_xy() (in module networkx.algorithms.assortativity)": [[247, "networkx.algorithms.assortativity.node_degree_xy"]], "numeric_assortativity_coefficient() (in module networkx.algorithms.assortativity)": [[248, "networkx.algorithms.assortativity.numeric_assortativity_coefficient"]], "find_asteroidal_triple() (in module networkx.algorithms.asteroidal)": [[249, "networkx.algorithms.asteroidal.find_asteroidal_triple"]], "is_at_free() (in module networkx.algorithms.asteroidal)": [[250, "networkx.algorithms.asteroidal.is_at_free"]], "color() (in module networkx.algorithms.bipartite.basic)": [[251, "networkx.algorithms.bipartite.basic.color"]], "degrees() (in module networkx.algorithms.bipartite.basic)": [[252, "networkx.algorithms.bipartite.basic.degrees"]], "density() (in module networkx.algorithms.bipartite.basic)": [[253, "networkx.algorithms.bipartite.basic.density"]], "is_bipartite() (in module networkx.algorithms.bipartite.basic)": [[254, "networkx.algorithms.bipartite.basic.is_bipartite"]], "is_bipartite_node_set() (in module networkx.algorithms.bipartite.basic)": [[255, "networkx.algorithms.bipartite.basic.is_bipartite_node_set"]], "sets() (in module networkx.algorithms.bipartite.basic)": [[256, "networkx.algorithms.bipartite.basic.sets"]], "betweenness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[257, "networkx.algorithms.bipartite.centrality.betweenness_centrality"]], "closeness_centrality() (in module networkx.algorithms.bipartite.centrality)": [[258, "networkx.algorithms.bipartite.centrality.closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.bipartite.centrality)": [[259, "networkx.algorithms.bipartite.centrality.degree_centrality"]], "average_clustering() (in module networkx.algorithms.bipartite.cluster)": [[260, "networkx.algorithms.bipartite.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.bipartite.cluster)": [[261, "networkx.algorithms.bipartite.cluster.clustering"]], "latapy_clustering() (in module networkx.algorithms.bipartite.cluster)": [[262, "networkx.algorithms.bipartite.cluster.latapy_clustering"]], "robins_alexander_clustering() (in module networkx.algorithms.bipartite.cluster)": [[263, "networkx.algorithms.bipartite.cluster.robins_alexander_clustering"]], "min_edge_cover() (in module networkx.algorithms.bipartite.covering)": [[264, "networkx.algorithms.bipartite.covering.min_edge_cover"]], "generate_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[265, "networkx.algorithms.bipartite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[266, "networkx.algorithms.bipartite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[267, "networkx.algorithms.bipartite.edgelist.read_edgelist"]], "write_edgelist() (in module networkx.algorithms.bipartite.edgelist)": [[268, "networkx.algorithms.bipartite.edgelist.write_edgelist"]], "alternating_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[269, "networkx.algorithms.bipartite.generators.alternating_havel_hakimi_graph"]], "complete_bipartite_graph() (in module networkx.algorithms.bipartite.generators)": [[270, "networkx.algorithms.bipartite.generators.complete_bipartite_graph"]], "configuration_model() (in module networkx.algorithms.bipartite.generators)": [[271, "networkx.algorithms.bipartite.generators.configuration_model"]], "gnmk_random_graph() (in module networkx.algorithms.bipartite.generators)": [[272, "networkx.algorithms.bipartite.generators.gnmk_random_graph"]], "havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[273, "networkx.algorithms.bipartite.generators.havel_hakimi_graph"]], "preferential_attachment_graph() (in module networkx.algorithms.bipartite.generators)": [[274, "networkx.algorithms.bipartite.generators.preferential_attachment_graph"]], "random_graph() (in module networkx.algorithms.bipartite.generators)": [[275, "networkx.algorithms.bipartite.generators.random_graph"]], "reverse_havel_hakimi_graph() (in module networkx.algorithms.bipartite.generators)": [[276, "networkx.algorithms.bipartite.generators.reverse_havel_hakimi_graph"]], "eppstein_matching() (in module networkx.algorithms.bipartite.matching)": [[277, "networkx.algorithms.bipartite.matching.eppstein_matching"]], "hopcroft_karp_matching() (in module networkx.algorithms.bipartite.matching)": [[278, "networkx.algorithms.bipartite.matching.hopcroft_karp_matching"]], "maximum_matching() (in module networkx.algorithms.bipartite.matching)": [[279, "networkx.algorithms.bipartite.matching.maximum_matching"]], "minimum_weight_full_matching() (in module networkx.algorithms.bipartite.matching)": [[280, "networkx.algorithms.bipartite.matching.minimum_weight_full_matching"]], "to_vertex_cover() (in module networkx.algorithms.bipartite.matching)": [[281, "networkx.algorithms.bipartite.matching.to_vertex_cover"]], "biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[282, "networkx.algorithms.bipartite.matrix.biadjacency_matrix"]], "from_biadjacency_matrix() (in module networkx.algorithms.bipartite.matrix)": [[283, "networkx.algorithms.bipartite.matrix.from_biadjacency_matrix"]], "collaboration_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[284, "networkx.algorithms.bipartite.projection.collaboration_weighted_projected_graph"]], "generic_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[285, "networkx.algorithms.bipartite.projection.generic_weighted_projected_graph"]], "overlap_weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[286, "networkx.algorithms.bipartite.projection.overlap_weighted_projected_graph"]], "projected_graph() (in module networkx.algorithms.bipartite.projection)": [[287, "networkx.algorithms.bipartite.projection.projected_graph"]], "weighted_projected_graph() (in module networkx.algorithms.bipartite.projection)": [[288, "networkx.algorithms.bipartite.projection.weighted_projected_graph"]], "node_redundancy() (in module networkx.algorithms.bipartite.redundancy)": [[289, "networkx.algorithms.bipartite.redundancy.node_redundancy"]], "spectral_bipartivity() (in module networkx.algorithms.bipartite.spectral)": [[290, "networkx.algorithms.bipartite.spectral.spectral_bipartivity"]], "edge_boundary() (in module networkx.algorithms.boundary)": [[291, "networkx.algorithms.boundary.edge_boundary"]], "node_boundary() (in module networkx.algorithms.boundary)": [[292, "networkx.algorithms.boundary.node_boundary"]], "bridges() (in module networkx.algorithms.bridges)": [[293, "networkx.algorithms.bridges.bridges"]], "has_bridges() (in module networkx.algorithms.bridges)": [[294, "networkx.algorithms.bridges.has_bridges"]], "local_bridges() (in module networkx.algorithms.bridges)": [[295, "networkx.algorithms.bridges.local_bridges"]], "approximate_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[296, "networkx.algorithms.centrality.approximate_current_flow_betweenness_centrality"]], "betweenness_centrality() (in module networkx.algorithms.centrality)": [[297, "networkx.algorithms.centrality.betweenness_centrality"]], "betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[298, "networkx.algorithms.centrality.betweenness_centrality_subset"]], "closeness_centrality() (in module networkx.algorithms.centrality)": [[299, "networkx.algorithms.centrality.closeness_centrality"]], "communicability_betweenness_centrality() (in module networkx.algorithms.centrality)": [[300, "networkx.algorithms.centrality.communicability_betweenness_centrality"]], "current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[301, "networkx.algorithms.centrality.current_flow_betweenness_centrality"]], "current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[302, "networkx.algorithms.centrality.current_flow_betweenness_centrality_subset"]], "current_flow_closeness_centrality() (in module networkx.algorithms.centrality)": [[303, "networkx.algorithms.centrality.current_flow_closeness_centrality"]], "degree_centrality() (in module networkx.algorithms.centrality)": [[304, "networkx.algorithms.centrality.degree_centrality"]], "dispersion() (in module networkx.algorithms.centrality)": [[305, "networkx.algorithms.centrality.dispersion"]], "edge_betweenness_centrality() (in module networkx.algorithms.centrality)": [[306, "networkx.algorithms.centrality.edge_betweenness_centrality"]], "edge_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[307, "networkx.algorithms.centrality.edge_betweenness_centrality_subset"]], "edge_current_flow_betweenness_centrality() (in module networkx.algorithms.centrality)": [[308, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality"]], "edge_current_flow_betweenness_centrality_subset() (in module networkx.algorithms.centrality)": [[309, "networkx.algorithms.centrality.edge_current_flow_betweenness_centrality_subset"]], "edge_load_centrality() (in module networkx.algorithms.centrality)": [[310, "networkx.algorithms.centrality.edge_load_centrality"]], "eigenvector_centrality() (in module networkx.algorithms.centrality)": [[311, "networkx.algorithms.centrality.eigenvector_centrality"]], "eigenvector_centrality_numpy() (in module networkx.algorithms.centrality)": [[312, "networkx.algorithms.centrality.eigenvector_centrality_numpy"]], "estrada_index() (in module networkx.algorithms.centrality)": [[313, "networkx.algorithms.centrality.estrada_index"]], "global_reaching_centrality() (in module networkx.algorithms.centrality)": [[314, "networkx.algorithms.centrality.global_reaching_centrality"]], "group_betweenness_centrality() (in module networkx.algorithms.centrality)": [[315, "networkx.algorithms.centrality.group_betweenness_centrality"]], "group_closeness_centrality() (in module networkx.algorithms.centrality)": [[316, "networkx.algorithms.centrality.group_closeness_centrality"]], "group_degree_centrality() (in module networkx.algorithms.centrality)": [[317, "networkx.algorithms.centrality.group_degree_centrality"]], "group_in_degree_centrality() (in module networkx.algorithms.centrality)": [[318, "networkx.algorithms.centrality.group_in_degree_centrality"]], "group_out_degree_centrality() (in module networkx.algorithms.centrality)": [[319, "networkx.algorithms.centrality.group_out_degree_centrality"]], "harmonic_centrality() (in module networkx.algorithms.centrality)": [[320, "networkx.algorithms.centrality.harmonic_centrality"]], "in_degree_centrality() (in module networkx.algorithms.centrality)": [[321, "networkx.algorithms.centrality.in_degree_centrality"]], "incremental_closeness_centrality() (in module networkx.algorithms.centrality)": [[322, "networkx.algorithms.centrality.incremental_closeness_centrality"]], "information_centrality() (in module networkx.algorithms.centrality)": [[323, "networkx.algorithms.centrality.information_centrality"]], "katz_centrality() (in module networkx.algorithms.centrality)": [[324, "networkx.algorithms.centrality.katz_centrality"]], "katz_centrality_numpy() (in module networkx.algorithms.centrality)": [[325, "networkx.algorithms.centrality.katz_centrality_numpy"]], "load_centrality() (in module networkx.algorithms.centrality)": [[326, "networkx.algorithms.centrality.load_centrality"]], "local_reaching_centrality() (in module networkx.algorithms.centrality)": [[327, "networkx.algorithms.centrality.local_reaching_centrality"]], "out_degree_centrality() (in module networkx.algorithms.centrality)": [[328, "networkx.algorithms.centrality.out_degree_centrality"]], "percolation_centrality() (in module networkx.algorithms.centrality)": [[329, "networkx.algorithms.centrality.percolation_centrality"]], "prominent_group() (in module networkx.algorithms.centrality)": [[330, "networkx.algorithms.centrality.prominent_group"]], "second_order_centrality() (in module networkx.algorithms.centrality)": [[331, "networkx.algorithms.centrality.second_order_centrality"]], "subgraph_centrality() (in module networkx.algorithms.centrality)": [[332, "networkx.algorithms.centrality.subgraph_centrality"]], "subgraph_centrality_exp() (in module networkx.algorithms.centrality)": [[333, "networkx.algorithms.centrality.subgraph_centrality_exp"]], "trophic_differences() (in module networkx.algorithms.centrality)": [[334, "networkx.algorithms.centrality.trophic_differences"]], "trophic_incoherence_parameter() (in module networkx.algorithms.centrality)": [[335, "networkx.algorithms.centrality.trophic_incoherence_parameter"]], "trophic_levels() (in module networkx.algorithms.centrality)": [[336, "networkx.algorithms.centrality.trophic_levels"]], "voterank() (in module networkx.algorithms.centrality)": [[337, "networkx.algorithms.centrality.voterank"]], "chain_decomposition() (in module networkx.algorithms.chains)": [[338, "networkx.algorithms.chains.chain_decomposition"]], "chordal_graph_cliques() (in module networkx.algorithms.chordal)": [[339, "networkx.algorithms.chordal.chordal_graph_cliques"]], "chordal_graph_treewidth() (in module networkx.algorithms.chordal)": [[340, "networkx.algorithms.chordal.chordal_graph_treewidth"]], "complete_to_chordal_graph() (in module networkx.algorithms.chordal)": [[341, "networkx.algorithms.chordal.complete_to_chordal_graph"]], "find_induced_nodes() (in module networkx.algorithms.chordal)": [[342, "networkx.algorithms.chordal.find_induced_nodes"]], "is_chordal() (in module networkx.algorithms.chordal)": [[343, "networkx.algorithms.chordal.is_chordal"]], "cliques_containing_node() (in module networkx.algorithms.clique)": [[344, "networkx.algorithms.clique.cliques_containing_node"]], "enumerate_all_cliques() (in module networkx.algorithms.clique)": [[345, "networkx.algorithms.clique.enumerate_all_cliques"]], "find_cliques() (in module networkx.algorithms.clique)": [[346, "networkx.algorithms.clique.find_cliques"]], "find_cliques_recursive() (in module networkx.algorithms.clique)": [[347, "networkx.algorithms.clique.find_cliques_recursive"]], "graph_clique_number() (in module networkx.algorithms.clique)": [[348, "networkx.algorithms.clique.graph_clique_number"]], "graph_number_of_cliques() (in module networkx.algorithms.clique)": [[349, "networkx.algorithms.clique.graph_number_of_cliques"]], "make_clique_bipartite() (in module networkx.algorithms.clique)": [[350, "networkx.algorithms.clique.make_clique_bipartite"]], "make_max_clique_graph() (in module networkx.algorithms.clique)": [[351, "networkx.algorithms.clique.make_max_clique_graph"]], "max_weight_clique() (in module networkx.algorithms.clique)": [[352, "networkx.algorithms.clique.max_weight_clique"]], "node_clique_number() (in module networkx.algorithms.clique)": [[353, "networkx.algorithms.clique.node_clique_number"]], "number_of_cliques() (in module networkx.algorithms.clique)": [[354, "networkx.algorithms.clique.number_of_cliques"]], "average_clustering() (in module networkx.algorithms.cluster)": [[355, "networkx.algorithms.cluster.average_clustering"]], "clustering() (in module networkx.algorithms.cluster)": [[356, "networkx.algorithms.cluster.clustering"]], "generalized_degree() (in module networkx.algorithms.cluster)": [[357, "networkx.algorithms.cluster.generalized_degree"]], "square_clustering() (in module networkx.algorithms.cluster)": [[358, "networkx.algorithms.cluster.square_clustering"]], "transitivity() (in module networkx.algorithms.cluster)": [[359, "networkx.algorithms.cluster.transitivity"]], "triangles() (in module networkx.algorithms.cluster)": [[360, "networkx.algorithms.cluster.triangles"]], "equitable_color() (in module networkx.algorithms.coloring)": [[361, "networkx.algorithms.coloring.equitable_color"]], "greedy_color() (in module networkx.algorithms.coloring)": [[362, "networkx.algorithms.coloring.greedy_color"]], "strategy_connected_sequential() (in module networkx.algorithms.coloring)": [[363, "networkx.algorithms.coloring.strategy_connected_sequential"]], "strategy_connected_sequential_bfs() (in module networkx.algorithms.coloring)": [[364, "networkx.algorithms.coloring.strategy_connected_sequential_bfs"]], "strategy_connected_sequential_dfs() (in module networkx.algorithms.coloring)": [[365, "networkx.algorithms.coloring.strategy_connected_sequential_dfs"]], "strategy_independent_set() (in module networkx.algorithms.coloring)": [[366, "networkx.algorithms.coloring.strategy_independent_set"]], "strategy_largest_first() (in module networkx.algorithms.coloring)": [[367, "networkx.algorithms.coloring.strategy_largest_first"]], "strategy_random_sequential() (in module networkx.algorithms.coloring)": [[368, "networkx.algorithms.coloring.strategy_random_sequential"]], "strategy_saturation_largest_first() (in module networkx.algorithms.coloring)": [[369, "networkx.algorithms.coloring.strategy_saturation_largest_first"]], "strategy_smallest_last() (in module networkx.algorithms.coloring)": [[370, "networkx.algorithms.coloring.strategy_smallest_last"]], "communicability() (in module networkx.algorithms.communicability_alg)": [[371, "networkx.algorithms.communicability_alg.communicability"]], "communicability_exp() (in module networkx.algorithms.communicability_alg)": [[372, "networkx.algorithms.communicability_alg.communicability_exp"]], "asyn_fluidc() (in module networkx.algorithms.community.asyn_fluid)": [[373, "networkx.algorithms.community.asyn_fluid.asyn_fluidc"]], "girvan_newman() (in module networkx.algorithms.community.centrality)": [[374, "networkx.algorithms.community.centrality.girvan_newman"]], "is_partition() (in module networkx.algorithms.community.community_utils)": [[375, "networkx.algorithms.community.community_utils.is_partition"]], "k_clique_communities() (in module networkx.algorithms.community.kclique)": [[376, "networkx.algorithms.community.kclique.k_clique_communities"]], "kernighan_lin_bisection() (in module networkx.algorithms.community.kernighan_lin)": [[377, "networkx.algorithms.community.kernighan_lin.kernighan_lin_bisection"]], "asyn_lpa_communities() (in module networkx.algorithms.community.label_propagation)": [[378, "networkx.algorithms.community.label_propagation.asyn_lpa_communities"]], "label_propagation_communities() (in module networkx.algorithms.community.label_propagation)": [[379, "networkx.algorithms.community.label_propagation.label_propagation_communities"]], "louvain_communities() (in module networkx.algorithms.community.louvain)": [[380, "networkx.algorithms.community.louvain.louvain_communities"]], "louvain_partitions() (in module networkx.algorithms.community.louvain)": [[381, "networkx.algorithms.community.louvain.louvain_partitions"]], "lukes_partitioning() (in module networkx.algorithms.community.lukes)": [[382, "networkx.algorithms.community.lukes.lukes_partitioning"]], "greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[383, "networkx.algorithms.community.modularity_max.greedy_modularity_communities"]], "naive_greedy_modularity_communities() (in module networkx.algorithms.community.modularity_max)": [[384, "networkx.algorithms.community.modularity_max.naive_greedy_modularity_communities"]], "modularity() (in module networkx.algorithms.community.quality)": [[385, "networkx.algorithms.community.quality.modularity"]], "partition_quality() (in module networkx.algorithms.community.quality)": [[386, "networkx.algorithms.community.quality.partition_quality"]], "articulation_points() (in module networkx.algorithms.components)": [[387, "networkx.algorithms.components.articulation_points"]], "attracting_components() (in module networkx.algorithms.components)": [[388, "networkx.algorithms.components.attracting_components"]], "biconnected_component_edges() (in module networkx.algorithms.components)": [[389, "networkx.algorithms.components.biconnected_component_edges"]], "biconnected_components() (in module networkx.algorithms.components)": [[390, "networkx.algorithms.components.biconnected_components"]], "condensation() (in module networkx.algorithms.components)": [[391, "networkx.algorithms.components.condensation"]], "connected_components() (in module networkx.algorithms.components)": [[392, "networkx.algorithms.components.connected_components"]], "is_attracting_component() (in module networkx.algorithms.components)": [[393, "networkx.algorithms.components.is_attracting_component"]], "is_biconnected() (in module networkx.algorithms.components)": [[394, "networkx.algorithms.components.is_biconnected"]], "is_connected() (in module networkx.algorithms.components)": [[395, "networkx.algorithms.components.is_connected"]], "is_semiconnected() (in module networkx.algorithms.components)": [[396, "networkx.algorithms.components.is_semiconnected"]], "is_strongly_connected() (in module networkx.algorithms.components)": [[397, "networkx.algorithms.components.is_strongly_connected"]], "is_weakly_connected() (in module networkx.algorithms.components)": [[398, "networkx.algorithms.components.is_weakly_connected"]], "kosaraju_strongly_connected_components() (in module networkx.algorithms.components)": [[399, "networkx.algorithms.components.kosaraju_strongly_connected_components"]], "node_connected_component() (in module networkx.algorithms.components)": [[400, "networkx.algorithms.components.node_connected_component"]], "number_attracting_components() (in module networkx.algorithms.components)": [[401, "networkx.algorithms.components.number_attracting_components"]], "number_connected_components() (in module networkx.algorithms.components)": [[402, "networkx.algorithms.components.number_connected_components"]], "number_strongly_connected_components() (in module networkx.algorithms.components)": [[403, "networkx.algorithms.components.number_strongly_connected_components"]], "number_weakly_connected_components() (in module networkx.algorithms.components)": [[404, "networkx.algorithms.components.number_weakly_connected_components"]], "strongly_connected_components() (in module networkx.algorithms.components)": [[405, "networkx.algorithms.components.strongly_connected_components"]], "strongly_connected_components_recursive() (in module networkx.algorithms.components)": [[406, "networkx.algorithms.components.strongly_connected_components_recursive"]], "weakly_connected_components() (in module networkx.algorithms.components)": [[407, "networkx.algorithms.components.weakly_connected_components"]], "all_pairs_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[408, "networkx.algorithms.connectivity.connectivity.all_pairs_node_connectivity"]], "average_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[409, "networkx.algorithms.connectivity.connectivity.average_node_connectivity"]], "edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[410, "networkx.algorithms.connectivity.connectivity.edge_connectivity"]], "local_edge_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[411, "networkx.algorithms.connectivity.connectivity.local_edge_connectivity"]], "local_node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[412, "networkx.algorithms.connectivity.connectivity.local_node_connectivity"]], "node_connectivity() (in module networkx.algorithms.connectivity.connectivity)": [[413, "networkx.algorithms.connectivity.connectivity.node_connectivity"]], "minimum_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[414, "networkx.algorithms.connectivity.cuts.minimum_edge_cut"]], "minimum_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[415, "networkx.algorithms.connectivity.cuts.minimum_node_cut"]], "minimum_st_edge_cut() (in module networkx.algorithms.connectivity.cuts)": [[416, "networkx.algorithms.connectivity.cuts.minimum_st_edge_cut"]], "minimum_st_node_cut() (in module networkx.algorithms.connectivity.cuts)": [[417, "networkx.algorithms.connectivity.cuts.minimum_st_node_cut"]], "edge_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[418, "networkx.algorithms.connectivity.disjoint_paths.edge_disjoint_paths"]], "node_disjoint_paths() (in module networkx.algorithms.connectivity.disjoint_paths)": [[419, "networkx.algorithms.connectivity.disjoint_paths.node_disjoint_paths"]], "is_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[420, "networkx.algorithms.connectivity.edge_augmentation.is_k_edge_connected"]], "is_locally_k_edge_connected() (in module networkx.algorithms.connectivity.edge_augmentation)": [[421, "networkx.algorithms.connectivity.edge_augmentation.is_locally_k_edge_connected"]], "k_edge_augmentation() (in module networkx.algorithms.connectivity.edge_augmentation)": [[422, "networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation"]], "edgecomponentauxgraph (class in networkx.algorithms.connectivity.edge_kcomponents)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph"]], "__init__() (edgecomponentauxgraph method)": [[423, "networkx.algorithms.connectivity.edge_kcomponents.EdgeComponentAuxGraph.__init__"]], "bridge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[424, "networkx.algorithms.connectivity.edge_kcomponents.bridge_components"]], "k_edge_components() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[425, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_components"]], "k_edge_subgraphs() (in module networkx.algorithms.connectivity.edge_kcomponents)": [[426, "networkx.algorithms.connectivity.edge_kcomponents.k_edge_subgraphs"]], "k_components() (in module networkx.algorithms.connectivity.kcomponents)": [[427, "networkx.algorithms.connectivity.kcomponents.k_components"]], "all_node_cuts() (in module networkx.algorithms.connectivity.kcutsets)": [[428, "networkx.algorithms.connectivity.kcutsets.all_node_cuts"]], "stoer_wagner() (in module networkx.algorithms.connectivity.stoerwagner)": [[429, "networkx.algorithms.connectivity.stoerwagner.stoer_wagner"]], "build_auxiliary_edge_connectivity() (in module networkx.algorithms.connectivity.utils)": [[430, "networkx.algorithms.connectivity.utils.build_auxiliary_edge_connectivity"]], "build_auxiliary_node_connectivity() (in module networkx.algorithms.connectivity.utils)": [[431, "networkx.algorithms.connectivity.utils.build_auxiliary_node_connectivity"]], "core_number() (in module networkx.algorithms.core)": [[432, "networkx.algorithms.core.core_number"]], "k_core() (in module networkx.algorithms.core)": [[433, "networkx.algorithms.core.k_core"]], "k_corona() (in module networkx.algorithms.core)": [[434, "networkx.algorithms.core.k_corona"]], "k_crust() (in module networkx.algorithms.core)": [[435, "networkx.algorithms.core.k_crust"]], "k_shell() (in module networkx.algorithms.core)": [[436, "networkx.algorithms.core.k_shell"]], "k_truss() (in module networkx.algorithms.core)": [[437, "networkx.algorithms.core.k_truss"]], "onion_layers() (in module networkx.algorithms.core)": [[438, "networkx.algorithms.core.onion_layers"]], "is_edge_cover() (in module networkx.algorithms.covering)": [[439, "networkx.algorithms.covering.is_edge_cover"]], "min_edge_cover() (in module networkx.algorithms.covering)": [[440, "networkx.algorithms.covering.min_edge_cover"]], "boundary_expansion() (in module networkx.algorithms.cuts)": [[441, "networkx.algorithms.cuts.boundary_expansion"]], "conductance() (in module networkx.algorithms.cuts)": [[442, "networkx.algorithms.cuts.conductance"]], "cut_size() (in module networkx.algorithms.cuts)": [[443, "networkx.algorithms.cuts.cut_size"]], "edge_expansion() (in module networkx.algorithms.cuts)": [[444, "networkx.algorithms.cuts.edge_expansion"]], "mixing_expansion() (in module networkx.algorithms.cuts)": [[445, "networkx.algorithms.cuts.mixing_expansion"]], "node_expansion() (in module networkx.algorithms.cuts)": [[446, "networkx.algorithms.cuts.node_expansion"]], "normalized_cut_size() (in module networkx.algorithms.cuts)": [[447, "networkx.algorithms.cuts.normalized_cut_size"]], "volume() (in module networkx.algorithms.cuts)": [[448, "networkx.algorithms.cuts.volume"]], "cycle_basis() (in module networkx.algorithms.cycles)": [[449, "networkx.algorithms.cycles.cycle_basis"]], "find_cycle() (in module networkx.algorithms.cycles)": [[450, "networkx.algorithms.cycles.find_cycle"]], "minimum_cycle_basis() (in module networkx.algorithms.cycles)": [[451, "networkx.algorithms.cycles.minimum_cycle_basis"]], "recursive_simple_cycles() (in module networkx.algorithms.cycles)": [[452, "networkx.algorithms.cycles.recursive_simple_cycles"]], "simple_cycles() (in module networkx.algorithms.cycles)": [[453, "networkx.algorithms.cycles.simple_cycles"]], "d_separated() (in module networkx.algorithms.d_separation)": [[454, "networkx.algorithms.d_separation.d_separated"]], "all_topological_sorts() (in module networkx.algorithms.dag)": [[455, "networkx.algorithms.dag.all_topological_sorts"]], "ancestors() (in module networkx.algorithms.dag)": [[456, "networkx.algorithms.dag.ancestors"]], "antichains() (in module networkx.algorithms.dag)": [[457, "networkx.algorithms.dag.antichains"]], "dag_longest_path() (in module networkx.algorithms.dag)": [[458, "networkx.algorithms.dag.dag_longest_path"]], "dag_longest_path_length() (in module networkx.algorithms.dag)": [[459, "networkx.algorithms.dag.dag_longest_path_length"]], "dag_to_branching() (in module networkx.algorithms.dag)": [[460, "networkx.algorithms.dag.dag_to_branching"]], "descendants() (in module networkx.algorithms.dag)": [[461, "networkx.algorithms.dag.descendants"]], "is_aperiodic() (in module networkx.algorithms.dag)": [[462, "networkx.algorithms.dag.is_aperiodic"]], "is_directed_acyclic_graph() (in module networkx.algorithms.dag)": [[463, "networkx.algorithms.dag.is_directed_acyclic_graph"]], "lexicographical_topological_sort() (in module networkx.algorithms.dag)": [[464, "networkx.algorithms.dag.lexicographical_topological_sort"]], "topological_generations() (in module networkx.algorithms.dag)": [[465, "networkx.algorithms.dag.topological_generations"]], "topological_sort() (in module networkx.algorithms.dag)": [[466, "networkx.algorithms.dag.topological_sort"]], "transitive_closure() (in module networkx.algorithms.dag)": [[467, "networkx.algorithms.dag.transitive_closure"]], "transitive_closure_dag() (in module networkx.algorithms.dag)": [[468, "networkx.algorithms.dag.transitive_closure_dag"]], "transitive_reduction() (in module networkx.algorithms.dag)": [[469, "networkx.algorithms.dag.transitive_reduction"]], "barycenter() (in module networkx.algorithms.distance_measures)": [[470, "networkx.algorithms.distance_measures.barycenter"]], "center() (in module networkx.algorithms.distance_measures)": [[471, "networkx.algorithms.distance_measures.center"]], "diameter() (in module networkx.algorithms.distance_measures)": [[472, "networkx.algorithms.distance_measures.diameter"]], "eccentricity() (in module networkx.algorithms.distance_measures)": [[473, "networkx.algorithms.distance_measures.eccentricity"]], "periphery() (in module networkx.algorithms.distance_measures)": [[474, "networkx.algorithms.distance_measures.periphery"]], "radius() (in module networkx.algorithms.distance_measures)": [[475, "networkx.algorithms.distance_measures.radius"]], "resistance_distance() (in module networkx.algorithms.distance_measures)": [[476, "networkx.algorithms.distance_measures.resistance_distance"]], "global_parameters() (in module networkx.algorithms.distance_regular)": [[477, "networkx.algorithms.distance_regular.global_parameters"]], "intersection_array() (in module networkx.algorithms.distance_regular)": [[478, "networkx.algorithms.distance_regular.intersection_array"]], "is_distance_regular() (in module networkx.algorithms.distance_regular)": [[479, "networkx.algorithms.distance_regular.is_distance_regular"]], "is_strongly_regular() (in module networkx.algorithms.distance_regular)": [[480, "networkx.algorithms.distance_regular.is_strongly_regular"]], "dominance_frontiers() (in module networkx.algorithms.dominance)": [[481, "networkx.algorithms.dominance.dominance_frontiers"]], "immediate_dominators() (in module networkx.algorithms.dominance)": [[482, "networkx.algorithms.dominance.immediate_dominators"]], "dominating_set() (in module networkx.algorithms.dominating)": [[483, "networkx.algorithms.dominating.dominating_set"]], "is_dominating_set() (in module networkx.algorithms.dominating)": [[484, "networkx.algorithms.dominating.is_dominating_set"]], "efficiency() (in module networkx.algorithms.efficiency_measures)": [[485, "networkx.algorithms.efficiency_measures.efficiency"]], "global_efficiency() (in module networkx.algorithms.efficiency_measures)": [[486, "networkx.algorithms.efficiency_measures.global_efficiency"]], "local_efficiency() (in module networkx.algorithms.efficiency_measures)": [[487, "networkx.algorithms.efficiency_measures.local_efficiency"]], "eulerian_circuit() (in module networkx.algorithms.euler)": [[488, "networkx.algorithms.euler.eulerian_circuit"]], "eulerian_path() (in module networkx.algorithms.euler)": [[489, "networkx.algorithms.euler.eulerian_path"]], "eulerize() (in module networkx.algorithms.euler)": [[490, "networkx.algorithms.euler.eulerize"]], "has_eulerian_path() (in module networkx.algorithms.euler)": [[491, "networkx.algorithms.euler.has_eulerian_path"]], "is_eulerian() (in module networkx.algorithms.euler)": [[492, "networkx.algorithms.euler.is_eulerian"]], "is_semieulerian() (in module networkx.algorithms.euler)": [[493, "networkx.algorithms.euler.is_semieulerian"]], "boykov_kolmogorov() (in module networkx.algorithms.flow)": [[494, "networkx.algorithms.flow.boykov_kolmogorov"]], "build_residual_network() (in module networkx.algorithms.flow)": [[495, "networkx.algorithms.flow.build_residual_network"]], "capacity_scaling() (in module networkx.algorithms.flow)": [[496, "networkx.algorithms.flow.capacity_scaling"]], "cost_of_flow() (in module networkx.algorithms.flow)": [[497, "networkx.algorithms.flow.cost_of_flow"]], "dinitz() (in module networkx.algorithms.flow)": [[498, "networkx.algorithms.flow.dinitz"]], "edmonds_karp() (in module networkx.algorithms.flow)": [[499, "networkx.algorithms.flow.edmonds_karp"]], "gomory_hu_tree() (in module networkx.algorithms.flow)": [[500, "networkx.algorithms.flow.gomory_hu_tree"]], "max_flow_min_cost() (in module networkx.algorithms.flow)": [[501, "networkx.algorithms.flow.max_flow_min_cost"]], "maximum_flow() (in module networkx.algorithms.flow)": [[502, "networkx.algorithms.flow.maximum_flow"]], "maximum_flow_value() (in module networkx.algorithms.flow)": [[503, "networkx.algorithms.flow.maximum_flow_value"]], "min_cost_flow() (in module networkx.algorithms.flow)": [[504, "networkx.algorithms.flow.min_cost_flow"]], "min_cost_flow_cost() (in module networkx.algorithms.flow)": [[505, "networkx.algorithms.flow.min_cost_flow_cost"]], "minimum_cut() (in module networkx.algorithms.flow)": [[506, "networkx.algorithms.flow.minimum_cut"]], "minimum_cut_value() (in module networkx.algorithms.flow)": [[507, "networkx.algorithms.flow.minimum_cut_value"]], "network_simplex() (in module networkx.algorithms.flow)": [[508, "networkx.algorithms.flow.network_simplex"]], "preflow_push() (in module networkx.algorithms.flow)": [[509, "networkx.algorithms.flow.preflow_push"]], "shortest_augmenting_path() (in module networkx.algorithms.flow)": [[510, "networkx.algorithms.flow.shortest_augmenting_path"]], "weisfeiler_lehman_graph_hash() (in module networkx.algorithms.graph_hashing)": [[511, "networkx.algorithms.graph_hashing.weisfeiler_lehman_graph_hash"]], "weisfeiler_lehman_subgraph_hashes() (in module networkx.algorithms.graph_hashing)": [[512, "networkx.algorithms.graph_hashing.weisfeiler_lehman_subgraph_hashes"]], "is_digraphical() (in module networkx.algorithms.graphical)": [[513, "networkx.algorithms.graphical.is_digraphical"]], "is_graphical() (in module networkx.algorithms.graphical)": [[514, "networkx.algorithms.graphical.is_graphical"]], "is_multigraphical() (in module networkx.algorithms.graphical)": [[515, "networkx.algorithms.graphical.is_multigraphical"]], "is_pseudographical() (in module networkx.algorithms.graphical)": [[516, "networkx.algorithms.graphical.is_pseudographical"]], "is_valid_degree_sequence_erdos_gallai() (in module networkx.algorithms.graphical)": [[517, "networkx.algorithms.graphical.is_valid_degree_sequence_erdos_gallai"]], "is_valid_degree_sequence_havel_hakimi() (in module networkx.algorithms.graphical)": [[518, "networkx.algorithms.graphical.is_valid_degree_sequence_havel_hakimi"]], "flow_hierarchy() (in module networkx.algorithms.hierarchy)": [[519, "networkx.algorithms.hierarchy.flow_hierarchy"]], "is_kl_connected() (in module networkx.algorithms.hybrid)": [[520, "networkx.algorithms.hybrid.is_kl_connected"]], "kl_connected_subgraph() (in module networkx.algorithms.hybrid)": [[521, "networkx.algorithms.hybrid.kl_connected_subgraph"]], "is_isolate() (in module networkx.algorithms.isolate)": [[522, "networkx.algorithms.isolate.is_isolate"]], "isolates() (in module networkx.algorithms.isolate)": [[523, "networkx.algorithms.isolate.isolates"]], "number_of_isolates() (in module networkx.algorithms.isolate)": [[524, "networkx.algorithms.isolate.number_of_isolates"]], "__init__() (digraphmatcher method)": [[525, "networkx.algorithms.isomorphism.DiGraphMatcher.__init__"]], "candidate_pairs_iter() (digraphmatcher method)": [[526, "networkx.algorithms.isomorphism.DiGraphMatcher.candidate_pairs_iter"]], "initialize() (digraphmatcher method)": [[527, "networkx.algorithms.isomorphism.DiGraphMatcher.initialize"]], "is_isomorphic() (digraphmatcher method)": [[528, "networkx.algorithms.isomorphism.DiGraphMatcher.is_isomorphic"]], "isomorphisms_iter() (digraphmatcher method)": [[529, "networkx.algorithms.isomorphism.DiGraphMatcher.isomorphisms_iter"]], "match() (digraphmatcher method)": [[530, "networkx.algorithms.isomorphism.DiGraphMatcher.match"]], "semantic_feasibility() (digraphmatcher method)": [[531, "networkx.algorithms.isomorphism.DiGraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (digraphmatcher method)": [[532, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (digraphmatcher method)": [[533, "networkx.algorithms.isomorphism.DiGraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (digraphmatcher method)": [[534, "networkx.algorithms.isomorphism.DiGraphMatcher.syntactic_feasibility"]], "__init__() (graphmatcher method)": [[535, "networkx.algorithms.isomorphism.GraphMatcher.__init__"]], "candidate_pairs_iter() (graphmatcher method)": [[536, "networkx.algorithms.isomorphism.GraphMatcher.candidate_pairs_iter"]], "initialize() (graphmatcher method)": [[537, "networkx.algorithms.isomorphism.GraphMatcher.initialize"]], "is_isomorphic() (graphmatcher method)": [[538, "networkx.algorithms.isomorphism.GraphMatcher.is_isomorphic"]], "isomorphisms_iter() (graphmatcher method)": [[539, "networkx.algorithms.isomorphism.GraphMatcher.isomorphisms_iter"]], "match() (graphmatcher method)": [[540, "networkx.algorithms.isomorphism.GraphMatcher.match"]], "semantic_feasibility() (graphmatcher method)": [[541, "networkx.algorithms.isomorphism.GraphMatcher.semantic_feasibility"]], "subgraph_is_isomorphic() (graphmatcher method)": [[542, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_is_isomorphic"]], "subgraph_isomorphisms_iter() (graphmatcher method)": [[543, "networkx.algorithms.isomorphism.GraphMatcher.subgraph_isomorphisms_iter"]], "syntactic_feasibility() (graphmatcher method)": [[544, "networkx.algorithms.isomorphism.GraphMatcher.syntactic_feasibility"]], "ismags (class in networkx.algorithms.isomorphism)": [[545, "networkx.algorithms.isomorphism.ISMAGS"]], "__init__() (ismags method)": [[545, "networkx.algorithms.isomorphism.ISMAGS.__init__"]], "categorical_edge_match() (in module networkx.algorithms.isomorphism)": [[546, "networkx.algorithms.isomorphism.categorical_edge_match"]], "categorical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[547, "networkx.algorithms.isomorphism.categorical_multiedge_match"]], "categorical_node_match() (in module networkx.algorithms.isomorphism)": [[548, "networkx.algorithms.isomorphism.categorical_node_match"]], "could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[549, "networkx.algorithms.isomorphism.could_be_isomorphic"]], "fast_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[550, "networkx.algorithms.isomorphism.fast_could_be_isomorphic"]], "faster_could_be_isomorphic() (in module networkx.algorithms.isomorphism)": [[551, "networkx.algorithms.isomorphism.faster_could_be_isomorphic"]], "generic_edge_match() (in module networkx.algorithms.isomorphism)": [[552, "networkx.algorithms.isomorphism.generic_edge_match"]], "generic_multiedge_match() (in module networkx.algorithms.isomorphism)": [[553, "networkx.algorithms.isomorphism.generic_multiedge_match"]], "generic_node_match() (in module networkx.algorithms.isomorphism)": [[554, "networkx.algorithms.isomorphism.generic_node_match"]], "is_isomorphic() (in module networkx.algorithms.isomorphism)": [[555, "networkx.algorithms.isomorphism.is_isomorphic"]], "numerical_edge_match() (in module networkx.algorithms.isomorphism)": [[556, "networkx.algorithms.isomorphism.numerical_edge_match"]], "numerical_multiedge_match() (in module networkx.algorithms.isomorphism)": [[557, "networkx.algorithms.isomorphism.numerical_multiedge_match"]], "numerical_node_match() (in module networkx.algorithms.isomorphism)": [[558, "networkx.algorithms.isomorphism.numerical_node_match"]], "rooted_tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[559, "networkx.algorithms.isomorphism.tree_isomorphism.rooted_tree_isomorphism"]], "tree_isomorphism() (in module networkx.algorithms.isomorphism.tree_isomorphism)": [[560, "networkx.algorithms.isomorphism.tree_isomorphism.tree_isomorphism"]], "vf2pp_all_isomorphisms() (in module networkx.algorithms.isomorphism.vf2pp)": [[561, "networkx.algorithms.isomorphism.vf2pp.vf2pp_all_isomorphisms"]], "vf2pp_is_isomorphic() (in module networkx.algorithms.isomorphism.vf2pp)": [[562, "networkx.algorithms.isomorphism.vf2pp.vf2pp_is_isomorphic"]], "vf2pp_isomorphism() (in module networkx.algorithms.isomorphism.vf2pp)": [[563, "networkx.algorithms.isomorphism.vf2pp.vf2pp_isomorphism"]], "hits() (in module networkx.algorithms.link_analysis.hits_alg)": [[564, "networkx.algorithms.link_analysis.hits_alg.hits"]], "google_matrix() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[565, "networkx.algorithms.link_analysis.pagerank_alg.google_matrix"]], "pagerank() (in module networkx.algorithms.link_analysis.pagerank_alg)": [[566, "networkx.algorithms.link_analysis.pagerank_alg.pagerank"]], "adamic_adar_index() (in module networkx.algorithms.link_prediction)": [[567, "networkx.algorithms.link_prediction.adamic_adar_index"]], "cn_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[568, "networkx.algorithms.link_prediction.cn_soundarajan_hopcroft"]], "common_neighbor_centrality() (in module networkx.algorithms.link_prediction)": [[569, "networkx.algorithms.link_prediction.common_neighbor_centrality"]], "jaccard_coefficient() (in module networkx.algorithms.link_prediction)": [[570, "networkx.algorithms.link_prediction.jaccard_coefficient"]], "preferential_attachment() (in module networkx.algorithms.link_prediction)": [[571, "networkx.algorithms.link_prediction.preferential_attachment"]], "ra_index_soundarajan_hopcroft() (in module networkx.algorithms.link_prediction)": [[572, "networkx.algorithms.link_prediction.ra_index_soundarajan_hopcroft"]], "resource_allocation_index() (in module networkx.algorithms.link_prediction)": [[573, "networkx.algorithms.link_prediction.resource_allocation_index"]], "within_inter_cluster() (in module networkx.algorithms.link_prediction)": [[574, "networkx.algorithms.link_prediction.within_inter_cluster"]], "all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[575, "networkx.algorithms.lowest_common_ancestors.all_pairs_lowest_common_ancestor"]], "lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[576, "networkx.algorithms.lowest_common_ancestors.lowest_common_ancestor"]], "tree_all_pairs_lowest_common_ancestor() (in module networkx.algorithms.lowest_common_ancestors)": [[577, "networkx.algorithms.lowest_common_ancestors.tree_all_pairs_lowest_common_ancestor"]], "is_matching() (in module networkx.algorithms.matching)": [[578, "networkx.algorithms.matching.is_matching"]], "is_maximal_matching() (in module networkx.algorithms.matching)": [[579, "networkx.algorithms.matching.is_maximal_matching"]], "is_perfect_matching() (in module networkx.algorithms.matching)": [[580, "networkx.algorithms.matching.is_perfect_matching"]], "max_weight_matching() (in module networkx.algorithms.matching)": [[581, "networkx.algorithms.matching.max_weight_matching"]], "maximal_matching() (in module networkx.algorithms.matching)": [[582, "networkx.algorithms.matching.maximal_matching"]], "min_weight_matching() (in module networkx.algorithms.matching)": [[583, "networkx.algorithms.matching.min_weight_matching"]], "contracted_edge() (in module networkx.algorithms.minors)": [[584, "networkx.algorithms.minors.contracted_edge"]], "contracted_nodes() (in module networkx.algorithms.minors)": [[585, "networkx.algorithms.minors.contracted_nodes"]], "equivalence_classes() (in module networkx.algorithms.minors)": [[586, "networkx.algorithms.minors.equivalence_classes"]], "identified_nodes() (in module networkx.algorithms.minors)": [[587, "networkx.algorithms.minors.identified_nodes"]], "quotient_graph() (in module networkx.algorithms.minors)": [[588, "networkx.algorithms.minors.quotient_graph"]], "maximal_independent_set() (in module networkx.algorithms.mis)": [[589, "networkx.algorithms.mis.maximal_independent_set"]], "moral_graph() (in module networkx.algorithms.moral)": [[590, "networkx.algorithms.moral.moral_graph"]], "harmonic_function() (in module networkx.algorithms.node_classification)": [[591, "networkx.algorithms.node_classification.harmonic_function"]], "local_and_global_consistency() (in module networkx.algorithms.node_classification)": [[592, "networkx.algorithms.node_classification.local_and_global_consistency"]], "non_randomness() (in module networkx.algorithms.non_randomness)": [[593, "networkx.algorithms.non_randomness.non_randomness"]], "compose_all() (in module networkx.algorithms.operators.all)": [[594, "networkx.algorithms.operators.all.compose_all"]], "disjoint_union_all() (in module networkx.algorithms.operators.all)": [[595, "networkx.algorithms.operators.all.disjoint_union_all"]], "intersection_all() (in module networkx.algorithms.operators.all)": [[596, "networkx.algorithms.operators.all.intersection_all"]], "union_all() (in module networkx.algorithms.operators.all)": [[597, "networkx.algorithms.operators.all.union_all"]], "compose() (in module networkx.algorithms.operators.binary)": [[598, "networkx.algorithms.operators.binary.compose"]], "difference() (in module networkx.algorithms.operators.binary)": [[599, "networkx.algorithms.operators.binary.difference"]], "disjoint_union() (in module networkx.algorithms.operators.binary)": [[600, "networkx.algorithms.operators.binary.disjoint_union"]], "full_join() (in module networkx.algorithms.operators.binary)": [[601, "networkx.algorithms.operators.binary.full_join"]], "intersection() (in module networkx.algorithms.operators.binary)": [[602, "networkx.algorithms.operators.binary.intersection"]], "symmetric_difference() (in module networkx.algorithms.operators.binary)": [[603, "networkx.algorithms.operators.binary.symmetric_difference"]], "union() (in module networkx.algorithms.operators.binary)": [[604, "networkx.algorithms.operators.binary.union"]], "cartesian_product() (in module networkx.algorithms.operators.product)": [[605, "networkx.algorithms.operators.product.cartesian_product"]], "corona_product() (in module networkx.algorithms.operators.product)": [[606, "networkx.algorithms.operators.product.corona_product"]], "lexicographic_product() (in module networkx.algorithms.operators.product)": [[607, "networkx.algorithms.operators.product.lexicographic_product"]], "power() (in module networkx.algorithms.operators.product)": [[608, "networkx.algorithms.operators.product.power"]], "rooted_product() (in module networkx.algorithms.operators.product)": [[609, "networkx.algorithms.operators.product.rooted_product"]], "strong_product() (in module networkx.algorithms.operators.product)": [[610, "networkx.algorithms.operators.product.strong_product"]], "tensor_product() (in module networkx.algorithms.operators.product)": [[611, "networkx.algorithms.operators.product.tensor_product"]], "complement() (in module networkx.algorithms.operators.unary)": [[612, "networkx.algorithms.operators.unary.complement"]], "reverse() (in module networkx.algorithms.operators.unary)": [[613, "networkx.algorithms.operators.unary.reverse"]], "combinatorial_embedding_to_pos() (in module networkx.algorithms.planar_drawing)": [[614, "networkx.algorithms.planar_drawing.combinatorial_embedding_to_pos"]], "planarembedding (class in networkx.algorithms.planarity)": [[615, "networkx.algorithms.planarity.PlanarEmbedding"]], "__init__() (planarembedding method)": [[615, "networkx.algorithms.planarity.PlanarEmbedding.__init__"]], "check_planarity() (in module networkx.algorithms.planarity)": [[616, "networkx.algorithms.planarity.check_planarity"]], "is_planar() (in module networkx.algorithms.planarity)": [[617, "networkx.algorithms.planarity.is_planar"]], "chromatic_polynomial() (in module networkx.algorithms.polynomials)": [[618, "networkx.algorithms.polynomials.chromatic_polynomial"]], "tutte_polynomial() (in module networkx.algorithms.polynomials)": [[619, "networkx.algorithms.polynomials.tutte_polynomial"]], "overall_reciprocity() (in module networkx.algorithms.reciprocity)": [[620, "networkx.algorithms.reciprocity.overall_reciprocity"]], "reciprocity() (in module networkx.algorithms.reciprocity)": [[621, "networkx.algorithms.reciprocity.reciprocity"]], "is_k_regular() (in module networkx.algorithms.regular)": [[622, "networkx.algorithms.regular.is_k_regular"]], "is_regular() (in module networkx.algorithms.regular)": [[623, "networkx.algorithms.regular.is_regular"]], "k_factor() (in module networkx.algorithms.regular)": [[624, "networkx.algorithms.regular.k_factor"]], "rich_club_coefficient() (in module networkx.algorithms.richclub)": [[625, "networkx.algorithms.richclub.rich_club_coefficient"]], "astar_path() (in module networkx.algorithms.shortest_paths.astar)": [[626, "networkx.algorithms.shortest_paths.astar.astar_path"]], "astar_path_length() (in module networkx.algorithms.shortest_paths.astar)": [[627, "networkx.algorithms.shortest_paths.astar.astar_path_length"]], "floyd_warshall() (in module networkx.algorithms.shortest_paths.dense)": [[628, "networkx.algorithms.shortest_paths.dense.floyd_warshall"]], "floyd_warshall_numpy() (in module networkx.algorithms.shortest_paths.dense)": [[629, "networkx.algorithms.shortest_paths.dense.floyd_warshall_numpy"]], "floyd_warshall_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.dense)": [[630, "networkx.algorithms.shortest_paths.dense.floyd_warshall_predecessor_and_distance"]], "reconstruct_path() (in module networkx.algorithms.shortest_paths.dense)": [[631, "networkx.algorithms.shortest_paths.dense.reconstruct_path"]], "all_shortest_paths() (in module networkx.algorithms.shortest_paths.generic)": [[632, "networkx.algorithms.shortest_paths.generic.all_shortest_paths"]], "average_shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[633, "networkx.algorithms.shortest_paths.generic.average_shortest_path_length"]], "has_path() (in module networkx.algorithms.shortest_paths.generic)": [[634, "networkx.algorithms.shortest_paths.generic.has_path"]], "shortest_path() (in module networkx.algorithms.shortest_paths.generic)": [[635, "networkx.algorithms.shortest_paths.generic.shortest_path"]], "shortest_path_length() (in module networkx.algorithms.shortest_paths.generic)": [[636, "networkx.algorithms.shortest_paths.generic.shortest_path_length"]], "all_pairs_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[637, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path"]], "all_pairs_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[638, "networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length"]], "bidirectional_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[639, "networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path"]], "predecessor() (in module networkx.algorithms.shortest_paths.unweighted)": [[640, "networkx.algorithms.shortest_paths.unweighted.predecessor"]], "single_source_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[641, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path"]], "single_source_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[642, "networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length"]], "single_target_shortest_path() (in module networkx.algorithms.shortest_paths.unweighted)": [[643, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path"]], "single_target_shortest_path_length() (in module networkx.algorithms.shortest_paths.unweighted)": [[644, "networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length"]], "all_pairs_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[645, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path"]], "all_pairs_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[646, "networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length"]], "all_pairs_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[647, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra"]], "all_pairs_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[648, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path"]], "all_pairs_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[649, "networkx.algorithms.shortest_paths.weighted.all_pairs_dijkstra_path_length"]], "bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[650, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path"]], "bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[651, "networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length"]], "bellman_ford_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[652, "networkx.algorithms.shortest_paths.weighted.bellman_ford_predecessor_and_distance"]], "bidirectional_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[653, "networkx.algorithms.shortest_paths.weighted.bidirectional_dijkstra"]], "dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[654, "networkx.algorithms.shortest_paths.weighted.dijkstra_path"]], "dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[655, "networkx.algorithms.shortest_paths.weighted.dijkstra_path_length"]], "dijkstra_predecessor_and_distance() (in module networkx.algorithms.shortest_paths.weighted)": [[656, "networkx.algorithms.shortest_paths.weighted.dijkstra_predecessor_and_distance"]], "find_negative_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[657, "networkx.algorithms.shortest_paths.weighted.find_negative_cycle"]], "goldberg_radzik() (in module networkx.algorithms.shortest_paths.weighted)": [[658, "networkx.algorithms.shortest_paths.weighted.goldberg_radzik"]], "johnson() (in module networkx.algorithms.shortest_paths.weighted)": [[659, "networkx.algorithms.shortest_paths.weighted.johnson"]], "multi_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[660, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra"]], "multi_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[661, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path"]], "multi_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[662, "networkx.algorithms.shortest_paths.weighted.multi_source_dijkstra_path_length"]], "negative_edge_cycle() (in module networkx.algorithms.shortest_paths.weighted)": [[663, "networkx.algorithms.shortest_paths.weighted.negative_edge_cycle"]], "single_source_bellman_ford() (in module networkx.algorithms.shortest_paths.weighted)": [[664, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford"]], "single_source_bellman_ford_path() (in module networkx.algorithms.shortest_paths.weighted)": [[665, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path"]], "single_source_bellman_ford_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[666, "networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length"]], "single_source_dijkstra() (in module networkx.algorithms.shortest_paths.weighted)": [[667, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra"]], "single_source_dijkstra_path() (in module networkx.algorithms.shortest_paths.weighted)": [[668, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path"]], "single_source_dijkstra_path_length() (in module networkx.algorithms.shortest_paths.weighted)": [[669, "networkx.algorithms.shortest_paths.weighted.single_source_dijkstra_path_length"]], "generate_random_paths() (in module networkx.algorithms.similarity)": [[670, "networkx.algorithms.similarity.generate_random_paths"]], "graph_edit_distance() (in module networkx.algorithms.similarity)": [[671, "networkx.algorithms.similarity.graph_edit_distance"]], "optimal_edit_paths() (in module networkx.algorithms.similarity)": [[672, "networkx.algorithms.similarity.optimal_edit_paths"]], "optimize_edit_paths() (in module networkx.algorithms.similarity)": [[673, "networkx.algorithms.similarity.optimize_edit_paths"]], "optimize_graph_edit_distance() (in module networkx.algorithms.similarity)": [[674, "networkx.algorithms.similarity.optimize_graph_edit_distance"]], "panther_similarity() (in module networkx.algorithms.similarity)": [[675, "networkx.algorithms.similarity.panther_similarity"]], "simrank_similarity() (in module networkx.algorithms.similarity)": [[676, "networkx.algorithms.similarity.simrank_similarity"]], "all_simple_edge_paths() (in module networkx.algorithms.simple_paths)": [[677, "networkx.algorithms.simple_paths.all_simple_edge_paths"]], "all_simple_paths() (in module networkx.algorithms.simple_paths)": [[678, "networkx.algorithms.simple_paths.all_simple_paths"]], "is_simple_path() (in module networkx.algorithms.simple_paths)": [[679, "networkx.algorithms.simple_paths.is_simple_path"]], "shortest_simple_paths() (in module networkx.algorithms.simple_paths)": [[680, "networkx.algorithms.simple_paths.shortest_simple_paths"]], "lattice_reference() (in module networkx.algorithms.smallworld)": [[681, "networkx.algorithms.smallworld.lattice_reference"]], "omega() (in module networkx.algorithms.smallworld)": [[682, "networkx.algorithms.smallworld.omega"]], "random_reference() (in module networkx.algorithms.smallworld)": [[683, "networkx.algorithms.smallworld.random_reference"]], "sigma() (in module networkx.algorithms.smallworld)": [[684, "networkx.algorithms.smallworld.sigma"]], "s_metric() (in module networkx.algorithms.smetric)": [[685, "networkx.algorithms.smetric.s_metric"]], "spanner() (in module networkx.algorithms.sparsifiers)": [[686, "networkx.algorithms.sparsifiers.spanner"]], "constraint() (in module networkx.algorithms.structuralholes)": [[687, "networkx.algorithms.structuralholes.constraint"]], "effective_size() (in module networkx.algorithms.structuralholes)": [[688, "networkx.algorithms.structuralholes.effective_size"]], "local_constraint() (in module networkx.algorithms.structuralholes)": [[689, "networkx.algorithms.structuralholes.local_constraint"]], "dedensify() (in module networkx.algorithms.summarization)": [[690, "networkx.algorithms.summarization.dedensify"]], "snap_aggregation() (in module networkx.algorithms.summarization)": [[691, "networkx.algorithms.summarization.snap_aggregation"]], "connected_double_edge_swap() (in module networkx.algorithms.swap)": [[692, "networkx.algorithms.swap.connected_double_edge_swap"]], "directed_edge_swap() (in module networkx.algorithms.swap)": [[693, "networkx.algorithms.swap.directed_edge_swap"]], "double_edge_swap() (in module networkx.algorithms.swap)": [[694, "networkx.algorithms.swap.double_edge_swap"]], "find_threshold_graph() (in module networkx.algorithms.threshold)": [[695, "networkx.algorithms.threshold.find_threshold_graph"]], "is_threshold_graph() (in module networkx.algorithms.threshold)": [[696, "networkx.algorithms.threshold.is_threshold_graph"]], "hamiltonian_path() (in module networkx.algorithms.tournament)": [[697, "networkx.algorithms.tournament.hamiltonian_path"]], "is_reachable() (in module networkx.algorithms.tournament)": [[698, "networkx.algorithms.tournament.is_reachable"]], "is_strongly_connected() (in module networkx.algorithms.tournament)": [[699, "networkx.algorithms.tournament.is_strongly_connected"]], "is_tournament() (in module networkx.algorithms.tournament)": [[700, "networkx.algorithms.tournament.is_tournament"]], "random_tournament() (in module networkx.algorithms.tournament)": [[701, "networkx.algorithms.tournament.random_tournament"]], "score_sequence() (in module networkx.algorithms.tournament)": [[702, "networkx.algorithms.tournament.score_sequence"]], "bfs_beam_edges() (in module networkx.algorithms.traversal.beamsearch)": [[703, "networkx.algorithms.traversal.beamsearch.bfs_beam_edges"]], "bfs_edges() (in module networkx.algorithms.traversal.breadth_first_search)": [[704, "networkx.algorithms.traversal.breadth_first_search.bfs_edges"]], "bfs_layers() (in module networkx.algorithms.traversal.breadth_first_search)": [[705, "networkx.algorithms.traversal.breadth_first_search.bfs_layers"]], "bfs_predecessors() (in module networkx.algorithms.traversal.breadth_first_search)": [[706, "networkx.algorithms.traversal.breadth_first_search.bfs_predecessors"]], "bfs_successors() (in module networkx.algorithms.traversal.breadth_first_search)": [[707, "networkx.algorithms.traversal.breadth_first_search.bfs_successors"]], "bfs_tree() (in module networkx.algorithms.traversal.breadth_first_search)": [[708, "networkx.algorithms.traversal.breadth_first_search.bfs_tree"]], "descendants_at_distance() (in module networkx.algorithms.traversal.breadth_first_search)": [[709, "networkx.algorithms.traversal.breadth_first_search.descendants_at_distance"]], "dfs_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[710, "networkx.algorithms.traversal.depth_first_search.dfs_edges"]], "dfs_labeled_edges() (in module networkx.algorithms.traversal.depth_first_search)": [[711, "networkx.algorithms.traversal.depth_first_search.dfs_labeled_edges"]], "dfs_postorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[712, "networkx.algorithms.traversal.depth_first_search.dfs_postorder_nodes"]], "dfs_predecessors() (in module networkx.algorithms.traversal.depth_first_search)": [[713, "networkx.algorithms.traversal.depth_first_search.dfs_predecessors"]], "dfs_preorder_nodes() (in module networkx.algorithms.traversal.depth_first_search)": [[714, "networkx.algorithms.traversal.depth_first_search.dfs_preorder_nodes"]], "dfs_successors() (in module networkx.algorithms.traversal.depth_first_search)": [[715, "networkx.algorithms.traversal.depth_first_search.dfs_successors"]], "dfs_tree() (in module networkx.algorithms.traversal.depth_first_search)": [[716, "networkx.algorithms.traversal.depth_first_search.dfs_tree"]], "edge_bfs() (in module networkx.algorithms.traversal.edgebfs)": [[717, "networkx.algorithms.traversal.edgebfs.edge_bfs"]], "edge_dfs() (in module networkx.algorithms.traversal.edgedfs)": [[718, "networkx.algorithms.traversal.edgedfs.edge_dfs"]], "arborescenceiterator (class in networkx.algorithms.tree.branchings)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator"]], "__init__() (arborescenceiterator method)": [[719, "networkx.algorithms.tree.branchings.ArborescenceIterator.__init__"]], "edmonds (class in networkx.algorithms.tree.branchings)": [[720, "networkx.algorithms.tree.branchings.Edmonds"]], "__init__() (edmonds method)": [[720, "networkx.algorithms.tree.branchings.Edmonds.__init__"]], "branching_weight() (in module networkx.algorithms.tree.branchings)": [[721, "networkx.algorithms.tree.branchings.branching_weight"]], "greedy_branching() (in module networkx.algorithms.tree.branchings)": [[722, "networkx.algorithms.tree.branchings.greedy_branching"]], "maximum_branching() (in module networkx.algorithms.tree.branchings)": [[723, "networkx.algorithms.tree.branchings.maximum_branching"]], "maximum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[724, "networkx.algorithms.tree.branchings.maximum_spanning_arborescence"]], "minimum_branching() (in module networkx.algorithms.tree.branchings)": [[725, "networkx.algorithms.tree.branchings.minimum_branching"]], "minimum_spanning_arborescence() (in module networkx.algorithms.tree.branchings)": [[726, "networkx.algorithms.tree.branchings.minimum_spanning_arborescence"]], "notatree": [[727, "networkx.algorithms.tree.coding.NotATree"]], "from_nested_tuple() (in module networkx.algorithms.tree.coding)": [[728, "networkx.algorithms.tree.coding.from_nested_tuple"]], "from_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[729, "networkx.algorithms.tree.coding.from_prufer_sequence"]], "to_nested_tuple() (in module networkx.algorithms.tree.coding)": [[730, "networkx.algorithms.tree.coding.to_nested_tuple"]], "to_prufer_sequence() (in module networkx.algorithms.tree.coding)": [[731, "networkx.algorithms.tree.coding.to_prufer_sequence"]], "junction_tree() (in module networkx.algorithms.tree.decomposition)": [[732, "networkx.algorithms.tree.decomposition.junction_tree"]], "spanningtreeiterator (class in networkx.algorithms.tree.mst)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator"]], "__init__() (spanningtreeiterator method)": [[733, "networkx.algorithms.tree.mst.SpanningTreeIterator.__init__"]], "maximum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[734, "networkx.algorithms.tree.mst.maximum_spanning_edges"]], "maximum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[735, "networkx.algorithms.tree.mst.maximum_spanning_tree"]], "minimum_spanning_edges() (in module networkx.algorithms.tree.mst)": [[736, "networkx.algorithms.tree.mst.minimum_spanning_edges"]], "minimum_spanning_tree() (in module networkx.algorithms.tree.mst)": [[737, "networkx.algorithms.tree.mst.minimum_spanning_tree"]], "random_spanning_tree() (in module networkx.algorithms.tree.mst)": [[738, "networkx.algorithms.tree.mst.random_spanning_tree"]], "join() (in module networkx.algorithms.tree.operations)": [[739, "networkx.algorithms.tree.operations.join"]], "is_arborescence() (in module networkx.algorithms.tree.recognition)": [[740, "networkx.algorithms.tree.recognition.is_arborescence"]], "is_branching() (in module networkx.algorithms.tree.recognition)": [[741, "networkx.algorithms.tree.recognition.is_branching"]], "is_forest() (in module networkx.algorithms.tree.recognition)": [[742, "networkx.algorithms.tree.recognition.is_forest"]], "is_tree() (in module networkx.algorithms.tree.recognition)": [[743, "networkx.algorithms.tree.recognition.is_tree"]], "all_triads() (in module networkx.algorithms.triads)": [[744, "networkx.algorithms.triads.all_triads"]], "all_triplets() (in module networkx.algorithms.triads)": [[745, "networkx.algorithms.triads.all_triplets"]], "is_triad() (in module networkx.algorithms.triads)": [[746, "networkx.algorithms.triads.is_triad"]], 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"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_latex": [[1042, "module-networkx.drawing.nx_latex"]], "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"]], "to_latex() (in module networkx.drawing.nx_latex)": [[1124, "networkx.drawing.nx_latex.to_latex"]], "to_latex_raw() (in module networkx.drawing.nx_latex)": [[1125, "networkx.drawing.nx_latex.to_latex_raw"]], "write_latex() (in module networkx.drawing.nx_latex)": [[1126, "networkx.drawing.nx_latex.write_latex"]], "from_pydot() (in module networkx.drawing.nx_pydot)": [[1127, "networkx.drawing.nx_pydot.from_pydot"]], "graphviz_layout() (in module networkx.drawing.nx_pydot)": [[1128, "networkx.drawing.nx_pydot.graphviz_layout"]], "pydot_layout() (in module networkx.drawing.nx_pydot)": [[1129, "networkx.drawing.nx_pydot.pydot_layout"]], "read_dot() (in module networkx.drawing.nx_pydot)": [[1130, "networkx.drawing.nx_pydot.read_dot"]], "to_pydot() (in module networkx.drawing.nx_pydot)": [[1131, "networkx.drawing.nx_pydot.to_pydot"]], "write_dot() (in module networkx.drawing.nx_pydot)": [[1132, "networkx.drawing.nx_pydot.write_dot"]], "draw() (in module networkx.drawing.nx_pylab)": [[1133, "networkx.drawing.nx_pylab.draw"]], "draw_circular() (in module networkx.drawing.nx_pylab)": [[1134, "networkx.drawing.nx_pylab.draw_circular"]], "draw_kamada_kawai() (in module networkx.drawing.nx_pylab)": [[1135, "networkx.drawing.nx_pylab.draw_kamada_kawai"]], "draw_networkx() (in module networkx.drawing.nx_pylab)": [[1136, "networkx.drawing.nx_pylab.draw_networkx"]], "draw_networkx_edge_labels() (in module networkx.drawing.nx_pylab)": [[1137, "networkx.drawing.nx_pylab.draw_networkx_edge_labels"]], "draw_networkx_edges() (in module networkx.drawing.nx_pylab)": [[1138, "networkx.drawing.nx_pylab.draw_networkx_edges"]], "draw_networkx_labels() (in module networkx.drawing.nx_pylab)": [[1139, "networkx.drawing.nx_pylab.draw_networkx_labels"]], "draw_networkx_nodes() (in module networkx.drawing.nx_pylab)": [[1140, "networkx.drawing.nx_pylab.draw_networkx_nodes"]], "draw_planar() (in module networkx.drawing.nx_pylab)": [[1141, "networkx.drawing.nx_pylab.draw_planar"]], "draw_random() (in module networkx.drawing.nx_pylab)": [[1142, "networkx.drawing.nx_pylab.draw_random"]], "draw_shell() (in module networkx.drawing.nx_pylab)": [[1143, "networkx.drawing.nx_pylab.draw_shell"]], "draw_spectral() (in module networkx.drawing.nx_pylab)": [[1144, "networkx.drawing.nx_pylab.draw_spectral"]], "draw_spring() (in module networkx.drawing.nx_pylab)": [[1145, "networkx.drawing.nx_pylab.draw_spring"]], "graph_atlas() (in module networkx.generators.atlas)": [[1146, "networkx.generators.atlas.graph_atlas"]], "graph_atlas_g() (in module networkx.generators.atlas)": [[1147, "networkx.generators.atlas.graph_atlas_g"]], "balanced_tree() (in module networkx.generators.classic)": [[1148, "networkx.generators.classic.balanced_tree"]], "barbell_graph() (in module networkx.generators.classic)": [[1149, "networkx.generators.classic.barbell_graph"]], "binomial_tree() (in module networkx.generators.classic)": [[1150, "networkx.generators.classic.binomial_tree"]], "circulant_graph() (in module networkx.generators.classic)": [[1151, "networkx.generators.classic.circulant_graph"]], "circular_ladder_graph() (in module networkx.generators.classic)": [[1152, "networkx.generators.classic.circular_ladder_graph"]], "complete_graph() (in module networkx.generators.classic)": [[1153, "networkx.generators.classic.complete_graph"]], "complete_multipartite_graph() (in module networkx.generators.classic)": [[1154, "networkx.generators.classic.complete_multipartite_graph"]], "cycle_graph() (in module networkx.generators.classic)": [[1155, "networkx.generators.classic.cycle_graph"]], "dorogovtsev_goltsev_mendes_graph() (in module networkx.generators.classic)": [[1156, "networkx.generators.classic.dorogovtsev_goltsev_mendes_graph"]], "empty_graph() (in module networkx.generators.classic)": [[1157, "networkx.generators.classic.empty_graph"]], "full_rary_tree() (in module networkx.generators.classic)": [[1158, "networkx.generators.classic.full_rary_tree"]], "ladder_graph() (in module networkx.generators.classic)": [[1159, "networkx.generators.classic.ladder_graph"]], "lollipop_graph() (in module networkx.generators.classic)": [[1160, "networkx.generators.classic.lollipop_graph"]], "null_graph() (in module networkx.generators.classic)": [[1161, "networkx.generators.classic.null_graph"]], "path_graph() (in module networkx.generators.classic)": [[1162, "networkx.generators.classic.path_graph"]], "star_graph() (in module networkx.generators.classic)": [[1163, "networkx.generators.classic.star_graph"]], "trivial_graph() (in module networkx.generators.classic)": [[1164, "networkx.generators.classic.trivial_graph"]], "turan_graph() (in module networkx.generators.classic)": [[1165, "networkx.generators.classic.turan_graph"]], "wheel_graph() (in module networkx.generators.classic)": [[1166, "networkx.generators.classic.wheel_graph"]], "random_cograph() (in module networkx.generators.cographs)": [[1167, "networkx.generators.cographs.random_cograph"]], "lfr_benchmark_graph() (in module networkx.generators.community)": [[1168, "networkx.generators.community.LFR_benchmark_graph"]], "caveman_graph() (in module networkx.generators.community)": [[1169, "networkx.generators.community.caveman_graph"]], "connected_caveman_graph() (in module networkx.generators.community)": [[1170, "networkx.generators.community.connected_caveman_graph"]], "gaussian_random_partition_graph() (in module networkx.generators.community)": [[1171, "networkx.generators.community.gaussian_random_partition_graph"]], "planted_partition_graph() (in module networkx.generators.community)": [[1172, "networkx.generators.community.planted_partition_graph"]], "random_partition_graph() (in module networkx.generators.community)": [[1173, "networkx.generators.community.random_partition_graph"]], "relaxed_caveman_graph() (in module networkx.generators.community)": [[1174, "networkx.generators.community.relaxed_caveman_graph"]], "ring_of_cliques() (in module networkx.generators.community)": [[1175, "networkx.generators.community.ring_of_cliques"]], "stochastic_block_model() (in module networkx.generators.community)": [[1176, "networkx.generators.community.stochastic_block_model"]], "windmill_graph() (in module networkx.generators.community)": [[1177, "networkx.generators.community.windmill_graph"]], "configuration_model() (in module networkx.generators.degree_seq)": [[1178, "networkx.generators.degree_seq.configuration_model"]], "degree_sequence_tree() (in module networkx.generators.degree_seq)": [[1179, "networkx.generators.degree_seq.degree_sequence_tree"]], "directed_configuration_model() (in module networkx.generators.degree_seq)": [[1180, "networkx.generators.degree_seq.directed_configuration_model"]], "directed_havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1181, "networkx.generators.degree_seq.directed_havel_hakimi_graph"]], "expected_degree_graph() (in module networkx.generators.degree_seq)": [[1182, "networkx.generators.degree_seq.expected_degree_graph"]], "havel_hakimi_graph() (in module networkx.generators.degree_seq)": [[1183, "networkx.generators.degree_seq.havel_hakimi_graph"]], "random_degree_sequence_graph() (in module networkx.generators.degree_seq)": [[1184, "networkx.generators.degree_seq.random_degree_sequence_graph"]], "gn_graph() (in module networkx.generators.directed)": [[1185, "networkx.generators.directed.gn_graph"]], "gnc_graph() (in module networkx.generators.directed)": [[1186, "networkx.generators.directed.gnc_graph"]], "gnr_graph() (in module networkx.generators.directed)": [[1187, "networkx.generators.directed.gnr_graph"]], "random_k_out_graph() (in module networkx.generators.directed)": [[1188, "networkx.generators.directed.random_k_out_graph"]], "scale_free_graph() (in module networkx.generators.directed)": [[1189, "networkx.generators.directed.scale_free_graph"]], "duplication_divergence_graph() (in module networkx.generators.duplication)": [[1190, "networkx.generators.duplication.duplication_divergence_graph"]], "partial_duplication_graph() (in module networkx.generators.duplication)": [[1191, "networkx.generators.duplication.partial_duplication_graph"]], "ego_graph() (in module networkx.generators.ego)": [[1192, "networkx.generators.ego.ego_graph"]], "chordal_cycle_graph() (in module networkx.generators.expanders)": [[1193, "networkx.generators.expanders.chordal_cycle_graph"]], "margulis_gabber_galil_graph() (in module networkx.generators.expanders)": [[1194, "networkx.generators.expanders.margulis_gabber_galil_graph"]], "paley_graph() (in module networkx.generators.expanders)": [[1195, "networkx.generators.expanders.paley_graph"]], "geographical_threshold_graph() (in module networkx.generators.geometric)": [[1196, "networkx.generators.geometric.geographical_threshold_graph"]], "geometric_edges() (in module networkx.generators.geometric)": [[1197, "networkx.generators.geometric.geometric_edges"]], "navigable_small_world_graph() (in module networkx.generators.geometric)": [[1198, "networkx.generators.geometric.navigable_small_world_graph"]], "random_geometric_graph() (in module networkx.generators.geometric)": [[1199, "networkx.generators.geometric.random_geometric_graph"]], "soft_random_geometric_graph() (in module networkx.generators.geometric)": [[1200, "networkx.generators.geometric.soft_random_geometric_graph"]], "thresholded_random_geometric_graph() (in module networkx.generators.geometric)": [[1201, "networkx.generators.geometric.thresholded_random_geometric_graph"]], "waxman_graph() (in module networkx.generators.geometric)": [[1202, "networkx.generators.geometric.waxman_graph"]], "hkn_harary_graph() (in module networkx.generators.harary_graph)": [[1203, "networkx.generators.harary_graph.hkn_harary_graph"]], "hnm_harary_graph() (in module networkx.generators.harary_graph)": [[1204, "networkx.generators.harary_graph.hnm_harary_graph"]], "random_internet_as_graph() (in module networkx.generators.internet_as_graphs)": [[1205, "networkx.generators.internet_as_graphs.random_internet_as_graph"]], "general_random_intersection_graph() (in module networkx.generators.intersection)": [[1206, "networkx.generators.intersection.general_random_intersection_graph"]], "k_random_intersection_graph() (in module networkx.generators.intersection)": [[1207, "networkx.generators.intersection.k_random_intersection_graph"]], "uniform_random_intersection_graph() (in module networkx.generators.intersection)": [[1208, "networkx.generators.intersection.uniform_random_intersection_graph"]], "interval_graph() (in module networkx.generators.interval_graph)": [[1209, "networkx.generators.interval_graph.interval_graph"]], "directed_joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1210, "networkx.generators.joint_degree_seq.directed_joint_degree_graph"]], "is_valid_directed_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1211, "networkx.generators.joint_degree_seq.is_valid_directed_joint_degree"]], "is_valid_joint_degree() (in module networkx.generators.joint_degree_seq)": [[1212, "networkx.generators.joint_degree_seq.is_valid_joint_degree"]], "joint_degree_graph() (in module networkx.generators.joint_degree_seq)": [[1213, "networkx.generators.joint_degree_seq.joint_degree_graph"]], "grid_2d_graph() (in module networkx.generators.lattice)": [[1214, "networkx.generators.lattice.grid_2d_graph"]], "grid_graph() (in module networkx.generators.lattice)": [[1215, "networkx.generators.lattice.grid_graph"]], "hexagonal_lattice_graph() (in module networkx.generators.lattice)": [[1216, "networkx.generators.lattice.hexagonal_lattice_graph"]], "hypercube_graph() (in module networkx.generators.lattice)": [[1217, "networkx.generators.lattice.hypercube_graph"]], "triangular_lattice_graph() (in module networkx.generators.lattice)": [[1218, "networkx.generators.lattice.triangular_lattice_graph"]], "inverse_line_graph() (in module networkx.generators.line)": [[1219, "networkx.generators.line.inverse_line_graph"]], "line_graph() (in module networkx.generators.line)": [[1220, "networkx.generators.line.line_graph"]], "mycielski_graph() (in module networkx.generators.mycielski)": [[1221, "networkx.generators.mycielski.mycielski_graph"]], "mycielskian() (in module networkx.generators.mycielski)": [[1222, "networkx.generators.mycielski.mycielskian"]], "nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1223, "networkx.generators.nonisomorphic_trees.nonisomorphic_trees"]], "number_of_nonisomorphic_trees() (in module networkx.generators.nonisomorphic_trees)": [[1224, "networkx.generators.nonisomorphic_trees.number_of_nonisomorphic_trees"]], "random_clustered_graph() (in module networkx.generators.random_clustered)": [[1225, "networkx.generators.random_clustered.random_clustered_graph"]], "barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1226, "networkx.generators.random_graphs.barabasi_albert_graph"]], "binomial_graph() (in module networkx.generators.random_graphs)": [[1227, "networkx.generators.random_graphs.binomial_graph"]], "connected_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1228, "networkx.generators.random_graphs.connected_watts_strogatz_graph"]], "dense_gnm_random_graph() (in module networkx.generators.random_graphs)": [[1229, "networkx.generators.random_graphs.dense_gnm_random_graph"]], "dual_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1230, "networkx.generators.random_graphs.dual_barabasi_albert_graph"]], "erdos_renyi_graph() (in module networkx.generators.random_graphs)": [[1231, "networkx.generators.random_graphs.erdos_renyi_graph"]], "extended_barabasi_albert_graph() (in module networkx.generators.random_graphs)": [[1232, "networkx.generators.random_graphs.extended_barabasi_albert_graph"]], "fast_gnp_random_graph() (in module networkx.generators.random_graphs)": [[1233, "networkx.generators.random_graphs.fast_gnp_random_graph"]], "gnm_random_graph() (in module networkx.generators.random_graphs)": [[1234, "networkx.generators.random_graphs.gnm_random_graph"]], "gnp_random_graph() (in module networkx.generators.random_graphs)": [[1235, "networkx.generators.random_graphs.gnp_random_graph"]], "newman_watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1236, "networkx.generators.random_graphs.newman_watts_strogatz_graph"]], "powerlaw_cluster_graph() (in module networkx.generators.random_graphs)": [[1237, "networkx.generators.random_graphs.powerlaw_cluster_graph"]], "random_kernel_graph() (in module networkx.generators.random_graphs)": [[1238, "networkx.generators.random_graphs.random_kernel_graph"]], "random_lobster() (in module networkx.generators.random_graphs)": [[1239, "networkx.generators.random_graphs.random_lobster"]], "random_powerlaw_tree() (in module networkx.generators.random_graphs)": [[1240, "networkx.generators.random_graphs.random_powerlaw_tree"]], "random_powerlaw_tree_sequence() (in module networkx.generators.random_graphs)": [[1241, "networkx.generators.random_graphs.random_powerlaw_tree_sequence"]], "random_regular_graph() (in module networkx.generators.random_graphs)": [[1242, "networkx.generators.random_graphs.random_regular_graph"]], "random_shell_graph() (in module networkx.generators.random_graphs)": [[1243, "networkx.generators.random_graphs.random_shell_graph"]], "watts_strogatz_graph() (in module networkx.generators.random_graphs)": [[1244, "networkx.generators.random_graphs.watts_strogatz_graph"]], "lcf_graph() (in module networkx.generators.small)": [[1245, "networkx.generators.small.LCF_graph"]], "bull_graph() (in module networkx.generators.small)": [[1246, "networkx.generators.small.bull_graph"]], "chvatal_graph() (in module networkx.generators.small)": [[1247, "networkx.generators.small.chvatal_graph"]], "cubical_graph() (in module networkx.generators.small)": [[1248, "networkx.generators.small.cubical_graph"]], "desargues_graph() (in module networkx.generators.small)": [[1249, "networkx.generators.small.desargues_graph"]], "diamond_graph() (in module networkx.generators.small)": [[1250, "networkx.generators.small.diamond_graph"]], "dodecahedral_graph() (in module networkx.generators.small)": [[1251, "networkx.generators.small.dodecahedral_graph"]], "frucht_graph() (in module networkx.generators.small)": [[1252, "networkx.generators.small.frucht_graph"]], "heawood_graph() (in module networkx.generators.small)": [[1253, "networkx.generators.small.heawood_graph"]], "hoffman_singleton_graph() (in module networkx.generators.small)": [[1254, "networkx.generators.small.hoffman_singleton_graph"]], "house_graph() (in module networkx.generators.small)": [[1255, "networkx.generators.small.house_graph"]], "house_x_graph() (in module networkx.generators.small)": [[1256, "networkx.generators.small.house_x_graph"]], "icosahedral_graph() (in module networkx.generators.small)": [[1257, "networkx.generators.small.icosahedral_graph"]], "krackhardt_kite_graph() (in module networkx.generators.small)": [[1258, "networkx.generators.small.krackhardt_kite_graph"]], "moebius_kantor_graph() (in module networkx.generators.small)": [[1259, "networkx.generators.small.moebius_kantor_graph"]], "octahedral_graph() (in module networkx.generators.small)": [[1260, "networkx.generators.small.octahedral_graph"]], "pappus_graph() (in module networkx.generators.small)": [[1261, "networkx.generators.small.pappus_graph"]], "petersen_graph() (in module networkx.generators.small)": [[1262, "networkx.generators.small.petersen_graph"]], "sedgewick_maze_graph() (in module networkx.generators.small)": [[1263, "networkx.generators.small.sedgewick_maze_graph"]], "tetrahedral_graph() (in module networkx.generators.small)": [[1264, "networkx.generators.small.tetrahedral_graph"]], "truncated_cube_graph() (in module networkx.generators.small)": [[1265, "networkx.generators.small.truncated_cube_graph"]], "truncated_tetrahedron_graph() (in module networkx.generators.small)": [[1266, "networkx.generators.small.truncated_tetrahedron_graph"]], "tutte_graph() (in module networkx.generators.small)": [[1267, "networkx.generators.small.tutte_graph"]], "davis_southern_women_graph() (in module networkx.generators.social)": [[1268, "networkx.generators.social.davis_southern_women_graph"]], "florentine_families_graph() (in module networkx.generators.social)": [[1269, "networkx.generators.social.florentine_families_graph"]], "karate_club_graph() (in module networkx.generators.social)": [[1270, "networkx.generators.social.karate_club_graph"]], "les_miserables_graph() (in module networkx.generators.social)": [[1271, "networkx.generators.social.les_miserables_graph"]], "spectral_graph_forge() (in module networkx.generators.spectral_graph_forge)": [[1272, "networkx.generators.spectral_graph_forge.spectral_graph_forge"]], "stochastic_graph() (in module networkx.generators.stochastic)": [[1273, "networkx.generators.stochastic.stochastic_graph"]], "sudoku_graph() (in module networkx.generators.sudoku)": [[1274, "networkx.generators.sudoku.sudoku_graph"]], "prefix_tree() (in module networkx.generators.trees)": [[1275, "networkx.generators.trees.prefix_tree"]], "random_tree() (in module networkx.generators.trees)": [[1276, "networkx.generators.trees.random_tree"]], "triad_graph() (in module networkx.generators.triads)": [[1277, "networkx.generators.triads.triad_graph"]], "algebraic_connectivity() (in module networkx.linalg.algebraicconnectivity)": [[1278, "networkx.linalg.algebraicconnectivity.algebraic_connectivity"]], "fiedler_vector() (in module networkx.linalg.algebraicconnectivity)": [[1279, "networkx.linalg.algebraicconnectivity.fiedler_vector"]], "spectral_ordering() (in module networkx.linalg.algebraicconnectivity)": [[1280, "networkx.linalg.algebraicconnectivity.spectral_ordering"]], "attr_matrix() (in module networkx.linalg.attrmatrix)": [[1281, "networkx.linalg.attrmatrix.attr_matrix"]], "attr_sparse_matrix() (in module networkx.linalg.attrmatrix)": [[1282, "networkx.linalg.attrmatrix.attr_sparse_matrix"]], "bethe_hessian_matrix() (in module networkx.linalg.bethehessianmatrix)": [[1283, "networkx.linalg.bethehessianmatrix.bethe_hessian_matrix"]], "adjacency_matrix() (in module networkx.linalg.graphmatrix)": [[1284, "networkx.linalg.graphmatrix.adjacency_matrix"]], "incidence_matrix() (in module networkx.linalg.graphmatrix)": [[1285, "networkx.linalg.graphmatrix.incidence_matrix"]], "directed_combinatorial_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1286, "networkx.linalg.laplacianmatrix.directed_combinatorial_laplacian_matrix"]], "directed_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1287, "networkx.linalg.laplacianmatrix.directed_laplacian_matrix"]], "laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1288, "networkx.linalg.laplacianmatrix.laplacian_matrix"]], "normalized_laplacian_matrix() (in module networkx.linalg.laplacianmatrix)": [[1289, "networkx.linalg.laplacianmatrix.normalized_laplacian_matrix"]], "directed_modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1290, "networkx.linalg.modularitymatrix.directed_modularity_matrix"]], "modularity_matrix() (in module networkx.linalg.modularitymatrix)": [[1291, "networkx.linalg.modularitymatrix.modularity_matrix"]], "adjacency_spectrum() (in module networkx.linalg.spectrum)": [[1292, "networkx.linalg.spectrum.adjacency_spectrum"]], "bethe_hessian_spectrum() (in module networkx.linalg.spectrum)": [[1293, "networkx.linalg.spectrum.bethe_hessian_spectrum"]], "laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1294, "networkx.linalg.spectrum.laplacian_spectrum"]], "modularity_spectrum() (in module networkx.linalg.spectrum)": [[1295, "networkx.linalg.spectrum.modularity_spectrum"]], "normalized_laplacian_spectrum() (in module networkx.linalg.spectrum)": [[1296, "networkx.linalg.spectrum.normalized_laplacian_spectrum"]], "convert_node_labels_to_integers() (in module networkx.relabel)": [[1297, "networkx.relabel.convert_node_labels_to_integers"]], "relabel_nodes() (in module networkx.relabel)": [[1298, "networkx.relabel.relabel_nodes"]], "__init__() (argmap method)": [[1299, "networkx.utils.decorators.argmap.__init__"]], "argmap (class in networkx.utils.decorators)": [[1299, "networkx.utils.decorators.argmap"]], "nodes_or_number() (in module networkx.utils.decorators)": [[1300, "networkx.utils.decorators.nodes_or_number"]], "not_implemented_for() (in module networkx.utils.decorators)": [[1301, "networkx.utils.decorators.not_implemented_for"]], "np_random_state() (in module networkx.utils.decorators)": [[1302, "networkx.utils.decorators.np_random_state"]], "open_file() (in module networkx.utils.decorators)": [[1303, "networkx.utils.decorators.open_file"]], "py_random_state() (in module networkx.utils.decorators)": [[1304, "networkx.utils.decorators.py_random_state"]], "mappedqueue (class in networkx.utils.mapped_queue)": [[1305, "networkx.utils.mapped_queue.MappedQueue"]], "__init__() (mappedqueue method)": [[1305, "networkx.utils.mapped_queue.MappedQueue.__init__"]], "arbitrary_element() (in module networkx.utils.misc)": [[1306, "networkx.utils.misc.arbitrary_element"]], "create_py_random_state() (in module networkx.utils.misc)": [[1307, "networkx.utils.misc.create_py_random_state"]], "create_random_state() (in module networkx.utils.misc)": [[1308, "networkx.utils.misc.create_random_state"]], "dict_to_numpy_array() (in module networkx.utils.misc)": [[1309, "networkx.utils.misc.dict_to_numpy_array"]], "edges_equal() (in module networkx.utils.misc)": [[1310, "networkx.utils.misc.edges_equal"]], "flatten() (in module networkx.utils.misc)": [[1311, "networkx.utils.misc.flatten"]], "graphs_equal() (in module networkx.utils.misc)": [[1312, "networkx.utils.misc.graphs_equal"]], "groups() (in module networkx.utils.misc)": [[1313, "networkx.utils.misc.groups"]], "make_list_of_ints() (in module networkx.utils.misc)": [[1314, "networkx.utils.misc.make_list_of_ints"]], "nodes_equal() (in module networkx.utils.misc)": [[1315, "networkx.utils.misc.nodes_equal"]], "pairwise() (in module networkx.utils.misc)": [[1316, "networkx.utils.misc.pairwise"]], "cumulative_distribution() (in module networkx.utils.random_sequence)": [[1317, "networkx.utils.random_sequence.cumulative_distribution"]], "discrete_sequence() (in module networkx.utils.random_sequence)": [[1318, "networkx.utils.random_sequence.discrete_sequence"]], "powerlaw_sequence() (in module networkx.utils.random_sequence)": [[1319, "networkx.utils.random_sequence.powerlaw_sequence"]], "random_weighted_sample() (in module networkx.utils.random_sequence)": [[1320, "networkx.utils.random_sequence.random_weighted_sample"]], "weighted_choice() (in module networkx.utils.random_sequence)": [[1321, "networkx.utils.random_sequence.weighted_choice"]], "zipf_rv() (in module networkx.utils.random_sequence)": [[1322, "networkx.utils.random_sequence.zipf_rv"]], "cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1323, "networkx.utils.rcm.cuthill_mckee_ordering"]], "reverse_cuthill_mckee_ordering() (in module networkx.utils.rcm)": [[1324, "networkx.utils.rcm.reverse_cuthill_mckee_ordering"]], "union() (unionfind method)": [[1325, "networkx.utils.union_find.UnionFind.union"]], "networkx.generators.atlas": [[1326, "module-networkx.generators.atlas"]], "networkx.generators.classic": [[1326, "module-networkx.generators.classic"]], "networkx.generators.cographs": [[1326, "module-networkx.generators.cographs"]], "networkx.generators.community": [[1326, "module-networkx.generators.community"]], "networkx.generators.degree_seq": [[1326, "module-networkx.generators.degree_seq"]], "networkx.generators.directed": [[1326, "module-networkx.generators.directed"]], "networkx.generators.duplication": [[1326, "module-networkx.generators.duplication"]], "networkx.generators.ego": [[1326, "module-networkx.generators.ego"]], "networkx.generators.expanders": [[1326, "module-networkx.generators.expanders"]], "networkx.generators.geometric": [[1326, "module-networkx.generators.geometric"]], "networkx.generators.harary_graph": [[1326, "module-networkx.generators.harary_graph"]], "networkx.generators.internet_as_graphs": [[1326, "module-networkx.generators.internet_as_graphs"]], "networkx.generators.intersection": [[1326, "module-networkx.generators.intersection"]], "networkx.generators.interval_graph": [[1326, "module-networkx.generators.interval_graph"]], "networkx.generators.joint_degree_seq": [[1326, "module-networkx.generators.joint_degree_seq"]], "networkx.generators.lattice": [[1326, "module-networkx.generators.lattice"]], "networkx.generators.line": [[1326, "module-networkx.generators.line"]], "networkx.generators.mycielski": [[1326, "module-networkx.generators.mycielski"]], "networkx.generators.nonisomorphic_trees": [[1326, "module-networkx.generators.nonisomorphic_trees"]], "networkx.generators.random_clustered": [[1326, "module-networkx.generators.random_clustered"]], "networkx.generators.random_graphs": [[1326, "module-networkx.generators.random_graphs"]], "networkx.generators.small": [[1326, "module-networkx.generators.small"]], "networkx.generators.social": [[1326, "module-networkx.generators.social"]], "networkx.generators.spectral_graph_forge": [[1326, "module-networkx.generators.spectral_graph_forge"]], "networkx.generators.stochastic": [[1326, "module-networkx.generators.stochastic"]], "networkx.generators.sudoku": [[1326, "module-networkx.generators.sudoku"]], "networkx.generators.trees": [[1326, "module-networkx.generators.trees"]], "networkx.generators.triads": [[1326, "module-networkx.generators.triads"]], "dictionary": [[1327, "term-dictionary"]], "ebunch": [[1327, "term-ebunch"]], "edge": [[1327, "term-edge"]], "edge attribute": [[1327, "term-edge-attribute"]], "nbunch": [[1327, "term-nbunch"]], "node": [[1327, "term-node"]], "node attribute": [[1327, "term-node-attribute"]], "networkx.linalg.algebraicconnectivity": [[1330, "module-networkx.linalg.algebraicconnectivity"]], "networkx.linalg.attrmatrix": [[1330, "module-networkx.linalg.attrmatrix"]], "networkx.linalg.bethehessianmatrix": [[1330, "module-networkx.linalg.bethehessianmatrix"]], "networkx.linalg.graphmatrix": [[1330, "module-networkx.linalg.graphmatrix"]], "networkx.linalg.laplacianmatrix": [[1330, "module-networkx.linalg.laplacianmatrix"]], "networkx.linalg.modularitymatrix": [[1330, "module-networkx.linalg.modularitymatrix"]], "networkx.linalg.spectrum": [[1330, "module-networkx.linalg.spectrum"]], "networkx.readwrite.adjlist": [[1332, "module-networkx.readwrite.adjlist"]], "networkx.readwrite.edgelist": [[1333, "module-networkx.readwrite.edgelist"]], "generate_adjlist() (in module networkx.readwrite.adjlist)": [[1334, "networkx.readwrite.adjlist.generate_adjlist"]], "parse_adjlist() (in module networkx.readwrite.adjlist)": [[1335, "networkx.readwrite.adjlist.parse_adjlist"]], "read_adjlist() (in module networkx.readwrite.adjlist)": [[1336, "networkx.readwrite.adjlist.read_adjlist"]], "write_adjlist() (in module networkx.readwrite.adjlist)": [[1337, "networkx.readwrite.adjlist.write_adjlist"]], "generate_edgelist() (in module networkx.readwrite.edgelist)": [[1338, "networkx.readwrite.edgelist.generate_edgelist"]], "parse_edgelist() (in module networkx.readwrite.edgelist)": [[1339, "networkx.readwrite.edgelist.parse_edgelist"]], "read_edgelist() (in module networkx.readwrite.edgelist)": [[1340, "networkx.readwrite.edgelist.read_edgelist"]], "read_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1341, "networkx.readwrite.edgelist.read_weighted_edgelist"]], "write_edgelist() (in module networkx.readwrite.edgelist)": [[1342, "networkx.readwrite.edgelist.write_edgelist"]], "write_weighted_edgelist() (in module networkx.readwrite.edgelist)": [[1343, "networkx.readwrite.edgelist.write_weighted_edgelist"]], "generate_gexf() (in module networkx.readwrite.gexf)": [[1344, "networkx.readwrite.gexf.generate_gexf"]], "read_gexf() (in module networkx.readwrite.gexf)": [[1345, "networkx.readwrite.gexf.read_gexf"]], "relabel_gexf_graph() (in module networkx.readwrite.gexf)": [[1346, "networkx.readwrite.gexf.relabel_gexf_graph"]], "write_gexf() (in module networkx.readwrite.gexf)": [[1347, "networkx.readwrite.gexf.write_gexf"]], "generate_gml() (in module networkx.readwrite.gml)": [[1348, "networkx.readwrite.gml.generate_gml"]], "literal_destringizer() (in module networkx.readwrite.gml)": [[1349, "networkx.readwrite.gml.literal_destringizer"]], "literal_stringizer() (in module networkx.readwrite.gml)": [[1350, "networkx.readwrite.gml.literal_stringizer"]], "parse_gml() (in module networkx.readwrite.gml)": [[1351, "networkx.readwrite.gml.parse_gml"]], "read_gml() (in module networkx.readwrite.gml)": [[1352, "networkx.readwrite.gml.read_gml"]], "write_gml() (in module networkx.readwrite.gml)": [[1353, "networkx.readwrite.gml.write_gml"]], "from_graph6_bytes() (in module networkx.readwrite.graph6)": [[1354, "networkx.readwrite.graph6.from_graph6_bytes"]], "read_graph6() (in module networkx.readwrite.graph6)": [[1355, "networkx.readwrite.graph6.read_graph6"]], "to_graph6_bytes() (in module networkx.readwrite.graph6)": [[1356, "networkx.readwrite.graph6.to_graph6_bytes"]], "write_graph6() (in module networkx.readwrite.graph6)": [[1357, "networkx.readwrite.graph6.write_graph6"]], "generate_graphml() (in module networkx.readwrite.graphml)": [[1358, "networkx.readwrite.graphml.generate_graphml"]], "parse_graphml() (in module networkx.readwrite.graphml)": [[1359, "networkx.readwrite.graphml.parse_graphml"]], "read_graphml() (in module networkx.readwrite.graphml)": [[1360, "networkx.readwrite.graphml.read_graphml"]], "write_graphml() (in module networkx.readwrite.graphml)": [[1361, "networkx.readwrite.graphml.write_graphml"]], "adjacency_data() (in module networkx.readwrite.json_graph)": [[1362, "networkx.readwrite.json_graph.adjacency_data"]], "adjacency_graph() (in module networkx.readwrite.json_graph)": [[1363, "networkx.readwrite.json_graph.adjacency_graph"]], "cytoscape_data() (in module networkx.readwrite.json_graph)": [[1364, "networkx.readwrite.json_graph.cytoscape_data"]], "cytoscape_graph() (in module networkx.readwrite.json_graph)": [[1365, "networkx.readwrite.json_graph.cytoscape_graph"]], "node_link_data() (in module networkx.readwrite.json_graph)": [[1366, "networkx.readwrite.json_graph.node_link_data"]], "node_link_graph() (in module networkx.readwrite.json_graph)": [[1367, "networkx.readwrite.json_graph.node_link_graph"]], "tree_data() (in module networkx.readwrite.json_graph)": [[1368, "networkx.readwrite.json_graph.tree_data"]], "tree_graph() (in module networkx.readwrite.json_graph)": [[1369, "networkx.readwrite.json_graph.tree_graph"]], "parse_leda() (in module networkx.readwrite.leda)": [[1370, "networkx.readwrite.leda.parse_leda"]], "read_leda() (in module networkx.readwrite.leda)": [[1371, "networkx.readwrite.leda.read_leda"]], "generate_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1372, "networkx.readwrite.multiline_adjlist.generate_multiline_adjlist"]], "parse_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1373, "networkx.readwrite.multiline_adjlist.parse_multiline_adjlist"]], "read_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1374, "networkx.readwrite.multiline_adjlist.read_multiline_adjlist"]], "write_multiline_adjlist() (in module networkx.readwrite.multiline_adjlist)": [[1375, "networkx.readwrite.multiline_adjlist.write_multiline_adjlist"]], "generate_pajek() (in module networkx.readwrite.pajek)": [[1376, "networkx.readwrite.pajek.generate_pajek"]], "parse_pajek() (in module networkx.readwrite.pajek)": [[1377, "networkx.readwrite.pajek.parse_pajek"]], "read_pajek() (in module networkx.readwrite.pajek)": [[1378, "networkx.readwrite.pajek.read_pajek"]], "write_pajek() (in module networkx.readwrite.pajek)": [[1379, "networkx.readwrite.pajek.write_pajek"]], "from_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1380, "networkx.readwrite.sparse6.from_sparse6_bytes"]], "read_sparse6() (in module networkx.readwrite.sparse6)": [[1381, "networkx.readwrite.sparse6.read_sparse6"]], "to_sparse6_bytes() (in module networkx.readwrite.sparse6)": [[1382, "networkx.readwrite.sparse6.to_sparse6_bytes"]], "write_sparse6() (in module networkx.readwrite.sparse6)": [[1383, "networkx.readwrite.sparse6.write_sparse6"]], "networkx.readwrite.gexf": [[1384, "module-networkx.readwrite.gexf"]], "networkx.readwrite.gml": [[1385, "module-networkx.readwrite.gml"]], "networkx.readwrite.graphml": [[1386, "module-networkx.readwrite.graphml"]], "networkx.readwrite.json_graph": [[1388, "module-networkx.readwrite.json_graph"]], "networkx.readwrite.leda": [[1389, "module-networkx.readwrite.leda"]], "networkx.readwrite.multiline_adjlist": [[1391, "module-networkx.readwrite.multiline_adjlist"]], "networkx.readwrite.pajek": [[1392, "module-networkx.readwrite.pajek"]], "networkx.readwrite.graph6": [[1393, "module-networkx.readwrite.graph6"]], "networkx.readwrite.sparse6": [[1393, "module-networkx.readwrite.sparse6"]], "networkx.relabel": [[1394, "module-networkx.relabel"]], "networkx.utils": [[1395, "module-networkx.utils"]], "networkx.utils.decorators": [[1395, "module-networkx.utils.decorators"]], "networkx.utils.mapped_queue": [[1395, "module-networkx.utils.mapped_queue"]], "networkx.utils.misc": [[1395, "module-networkx.utils.misc"]], "networkx.utils.random_sequence": [[1395, "module-networkx.utils.random_sequence"]], "networkx.utils.rcm": [[1395, "module-networkx.utils.rcm"]], "networkx.utils.union_find": [[1395, "module-networkx.utils.union_find"]]}})
\ No newline at end of file diff --git a/tutorial-34.pdf b/tutorial-34.pdf Binary files differindex 4900adba..3e2f9346 100644 --- a/tutorial-34.pdf +++ b/tutorial-34.pdf diff --git a/tutorial-35.pdf b/tutorial-35.pdf Binary files differindex a880aba4..56bd0bd3 100644 --- a/tutorial-35.pdf +++ b/tutorial-35.pdf diff --git a/tutorial-36.pdf b/tutorial-36.pdf Binary files differindex adb9a14c..a7cb4fd6 100644 --- a/tutorial-36.pdf +++ b/tutorial-36.pdf diff --git a/tutorial.ipynb b/tutorial.ipynb index 18ed824b..165472f6 100644 --- a/tutorial.ipynb +++ b/tutorial.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f341cd7a", + "id": "e0170922", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,7 +17,7 @@ { "cell_type": "code", "execution_count": null, - "id": "915e25c5", + "id": "c705cb00", "metadata": {}, "outputs": [], "source": [ @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "3c66bbab", + "id": "2a2c4385", "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": "bf54140f", + "id": "6a6ae66b", "metadata": {}, "outputs": [], "source": [ @@ -56,7 +56,7 @@ }, { "cell_type": "markdown", - "id": "b944c843", + "id": "fbd3d137", "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": "abe72052", + "id": "604bea0d", "metadata": {}, "outputs": [], "source": [ @@ -74,7 +74,7 @@ }, { "cell_type": "markdown", - "id": "600188a8", + "id": "35d340f1", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": null, - "id": "f505d7bc", + "id": "0d31f3e7", "metadata": {}, "outputs": [], "source": [ @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "cdf97fea", + "id": "c6fd9d90", "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": "7bcc267d", + "id": "fa1fefb4", "metadata": {}, "outputs": [], "source": [ @@ -125,7 +125,7 @@ }, { "cell_type": "markdown", - "id": "e5323595", + "id": "6cf23cf6", "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": "37c5e5e0", + "id": "d260cff6", "metadata": {}, "outputs": [], "source": [ @@ -154,7 +154,7 @@ }, { "cell_type": "markdown", - "id": "00ca1374", + "id": "e1e43dc6", "metadata": {}, "source": [ "by adding a list of edges," @@ -163,7 +163,7 @@ { "cell_type": "code", "execution_count": null, - "id": "c6dbe13d", + "id": "56a65798", "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "markdown", - "id": "9b8e3126", + "id": "2151bbf6", "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": "35c254e3", + "id": "7844811a", "metadata": {}, "outputs": [], "source": [ @@ -194,7 +194,7 @@ }, { "cell_type": "markdown", - "id": "d8e3d02a", + "id": "7fd897b2", "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": "3b95cfba", + "id": "20b019af", "metadata": {}, "outputs": [], "source": [ @@ -213,7 +213,7 @@ }, { "cell_type": "markdown", - "id": "7a38efa7", + "id": "ca374508", "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": "de7c39bc", + "id": "3e3c6586", "metadata": {}, "outputs": [], "source": [ @@ -237,7 +237,7 @@ }, { "cell_type": "markdown", - "id": "bba0fc4b", + "id": "95bf3002", "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": "dc32d21c", + "id": "77ea3ed7", "metadata": {}, "outputs": [], "source": [ @@ -257,7 +257,7 @@ { "cell_type": "code", "execution_count": null, - "id": "57a7fc3a", + "id": "5af92400", "metadata": {}, "outputs": [], "source": [ @@ -272,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "2ff133c5", + "id": "cdea6834", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -292,7 +292,7 @@ { "cell_type": "code", "execution_count": null, - "id": "179dfe37", + "id": "0a4ca736", "metadata": {}, "outputs": [], "source": [ @@ -304,7 +304,7 @@ }, { "cell_type": "markdown", - "id": "0ba17135", + "id": "d6d075b2", "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": "600f437d", + "id": "b8b84533", "metadata": {}, "outputs": [], "source": [ @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "2cbcffbf", + "id": "0df09e60", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -343,7 +343,7 @@ { "cell_type": "code", "execution_count": null, - "id": "267bffb7", + "id": "b1579224", "metadata": {}, "outputs": [], "source": [ @@ -355,7 +355,7 @@ }, { "cell_type": "markdown", - "id": "d3b8d158", + "id": "82858da3", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -370,7 +370,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cfadd1c1", + "id": "13a5f95e", "metadata": {}, "outputs": [], "source": [ @@ -387,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "883c013f", + "id": "5bcb612c", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -416,7 +416,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d4240f62", + "id": "9414d971", "metadata": {}, "outputs": [], "source": [ @@ -428,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "5a699950", + "id": "d8fc256d", "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": "e7475845", + "id": "de72574c", "metadata": {}, "outputs": [], "source": [ @@ -450,7 +450,7 @@ }, { "cell_type": "markdown", - "id": "796777aa", + "id": "83227c2d", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -461,7 +461,7 @@ { "cell_type": "code", "execution_count": null, - "id": "ff8d3e4b", + "id": "7d913456", "metadata": {}, "outputs": [], "source": [ @@ -475,7 +475,7 @@ }, { "cell_type": "markdown", - "id": "c0d52456", + "id": "77281896", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -484,7 +484,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7d957260", + "id": "48a57526", "metadata": {}, "outputs": [], "source": [ @@ -495,7 +495,7 @@ }, { "cell_type": "markdown", - "id": "68c56030", + "id": "7ae42a25", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -517,7 +517,7 @@ { "cell_type": "code", "execution_count": null, - "id": "3eb77ff4", + "id": "462a34df", "metadata": {}, "outputs": [], "source": [ @@ -527,7 +527,7 @@ }, { "cell_type": "markdown", - "id": "0639a17f", + "id": "97fff941", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -536,7 +536,7 @@ { "cell_type": "code", "execution_count": null, - "id": "34ad8489", + "id": "9776660a", "metadata": {}, "outputs": [], "source": [ @@ -546,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "b45c21ba", + "id": "224f3c1e", "metadata": {}, "source": [ "# Node attributes\n", @@ -557,7 +557,7 @@ { "cell_type": "code", "execution_count": null, - "id": "1b71fd2c", + "id": "2b229012", "metadata": {}, "outputs": [], "source": [ @@ -570,7 +570,7 @@ }, { "cell_type": "markdown", - "id": "77bdd920", + "id": "fa30a8cf", "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": "3ed49146", + "id": "f27dfb23", "metadata": {}, "outputs": [], "source": [ @@ -598,7 +598,7 @@ }, { "cell_type": "markdown", - "id": "f6e3b9fd", + "id": "48c77a50", "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": "7ece9a9c", + "id": "c5739106", "metadata": {}, "outputs": [], "source": [ @@ -633,7 +633,7 @@ }, { "cell_type": "markdown", - "id": "64f109ba", + "id": "2fd3646e", "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": "434f9992", + "id": "dde53331", "metadata": {}, "outputs": [], "source": [ @@ -655,7 +655,7 @@ }, { "cell_type": "markdown", - "id": "f08e6c52", + "id": "e44f67a1", "metadata": {}, "source": [ "# Multigraphs\n", @@ -675,7 +675,7 @@ { "cell_type": "code", "execution_count": null, - "id": "41ade2d1", + "id": "feea16bb", "metadata": {}, "outputs": [], "source": [ @@ -693,7 +693,7 @@ }, { "cell_type": "markdown", - "id": "ba8d4af7", + "id": "8d615784", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -713,7 +713,7 @@ { "cell_type": "code", "execution_count": null, - "id": "5fc6c20a", + "id": "43c8e071", "metadata": {}, "outputs": [], "source": [ @@ -725,7 +725,7 @@ }, { "cell_type": "markdown", - "id": "2522e834", + "id": "4cb0db6d", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -736,7 +736,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6b849c37", + "id": "f0fab8cd", "metadata": {}, "outputs": [], "source": [ @@ -748,7 +748,7 @@ }, { "cell_type": "markdown", - "id": "8c5b0ba4", + "id": "87e08a78", "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": "6fcf24d5", + "id": "5a652d37", "metadata": {}, "outputs": [], "source": [ @@ -770,7 +770,7 @@ }, { "cell_type": "markdown", - "id": "ebd909bd", + "id": "9b87f8cd", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -785,7 +785,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8d8a1355", + "id": "da7cf48a", "metadata": {}, "outputs": [], "source": [ @@ -799,7 +799,7 @@ }, { "cell_type": "markdown", - "id": "a7db1188", + "id": "6ad339b8", "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": "3a8f10fa", + "id": "7f2fbd1a", "metadata": {}, "outputs": [], "source": [ @@ -819,7 +819,7 @@ }, { "cell_type": "markdown", - "id": "1c8a9cef", + "id": "042ddc38", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -838,7 +838,7 @@ { "cell_type": "code", "execution_count": null, - "id": "464ab17e", + "id": "bea11dcd", "metadata": {}, "outputs": [], "source": [ @@ -847,7 +847,7 @@ }, { "cell_type": "markdown", - "id": "c58cc91e", + "id": "a0e66810", "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": "86ed6e67", + "id": "fbe47396", "metadata": {}, "outputs": [], "source": [ @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "d5151c7e", + "id": "be4106df", "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": "f88f1bce", + "id": "9645f407", "metadata": {}, "outputs": [], "source": [ @@ -889,7 +889,7 @@ }, { "cell_type": "markdown", - "id": "2e6bcc59", + "id": "9d2aad9f", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -898,7 +898,7 @@ { "cell_type": "code", "execution_count": null, - "id": "6e27cd84", + "id": "0372711b", "metadata": {}, "outputs": [], "source": [ @@ -919,7 +919,7 @@ }, { "cell_type": "markdown", - "id": "80dd6400", + "id": "ff09dba7", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -930,7 +930,7 @@ { "cell_type": "code", "execution_count": null, - "id": "d399c4cb", + "id": "35d8b340", "metadata": {}, "outputs": [], "source": [ @@ -941,7 +941,7 @@ }, { "cell_type": "markdown", - "id": "ff07fe90", + "id": "0b0dbcd5", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -950,7 +950,7 @@ { "cell_type": "code", "execution_count": null, - "id": "cbdb9a7b", + "id": "4c25a13b", "metadata": {}, "outputs": [], "source": [ @@ -960,7 +960,7 @@ }, { "cell_type": "markdown", - "id": "78300d30", + "id": "a4f599fb", "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": "7e0e5e01", + "id": "97a8506b", "metadata": {}, "outputs": [], "source": [ @@ -985,7 +985,7 @@ }, { "cell_type": "markdown", - "id": "d999d94b", + "id": "72e1fd01", "metadata": {}, "source": [ "See Drawing for additional details." diff --git a/tutorial_full.ipynb b/tutorial_full.ipynb index 67b5c61f..4ced0a30 100644 --- a/tutorial_full.ipynb +++ b/tutorial_full.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f341cd7a", + "id": "e0170922", "metadata": {}, "source": [ "## Tutorial\n", @@ -17,13 +17,13 @@ { "cell_type": "code", "execution_count": 1, - "id": "915e25c5", + "id": "c705cb00", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.636791Z", - "iopub.status.busy": "2023-01-04T17:44:38.636524Z", - "iopub.status.idle": "2023-01-04T17:44:38.720416Z", - "shell.execute_reply": "2023-01-04T17:44:38.719458Z" + "iopub.execute_input": "2023-01-04T19:40:11.638260Z", + "iopub.status.busy": "2023-01-04T19:40:11.637581Z", + "iopub.status.idle": "2023-01-04T19:40:11.713871Z", + "shell.execute_reply": "2023-01-04T19:40:11.713139Z" } }, "outputs": [], @@ -34,7 +34,7 @@ }, { "cell_type": "markdown", - "id": "3c66bbab", + "id": "2a2c4385", "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": "bf54140f", + "id": "6a6ae66b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.725793Z", - "iopub.status.busy": "2023-01-04T17:44:38.725511Z", - "iopub.status.idle": "2023-01-04T17:44:38.728956Z", - "shell.execute_reply": "2023-01-04T17:44:38.728208Z" + "iopub.execute_input": "2023-01-04T19:40:11.717735Z", + "iopub.status.busy": "2023-01-04T19:40:11.717469Z", + "iopub.status.idle": "2023-01-04T19:40:11.720797Z", + "shell.execute_reply": "2023-01-04T19:40:11.720078Z" } }, "outputs": [], @@ -70,7 +70,7 @@ }, { "cell_type": "markdown", - "id": "b944c843", + "id": "fbd3d137", "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": "abe72052", + "id": "604bea0d", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.732706Z", - "iopub.status.busy": "2023-01-04T17:44:38.732457Z", - "iopub.status.idle": "2023-01-04T17:44:38.735761Z", - "shell.execute_reply": "2023-01-04T17:44:38.735037Z" + "iopub.execute_input": "2023-01-04T19:40:11.724288Z", + "iopub.status.busy": "2023-01-04T19:40:11.724055Z", + "iopub.status.idle": "2023-01-04T19:40:11.727197Z", + "shell.execute_reply": "2023-01-04T19:40:11.726537Z" } }, "outputs": [], @@ -95,7 +95,7 @@ }, { "cell_type": "markdown", - "id": "600188a8", + "id": "35d340f1", "metadata": {}, "source": [ "You can also add nodes along with node\n", @@ -117,13 +117,13 @@ { "cell_type": "code", "execution_count": 4, - "id": "f505d7bc", + "id": "0d31f3e7", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.739236Z", - "iopub.status.busy": "2023-01-04T17:44:38.738991Z", - "iopub.status.idle": "2023-01-04T17:44:38.742757Z", - "shell.execute_reply": "2023-01-04T17:44:38.742053Z" + "iopub.execute_input": "2023-01-04T19:40:11.730395Z", + "iopub.status.busy": "2023-01-04T19:40:11.730171Z", + "iopub.status.idle": "2023-01-04T19:40:11.733732Z", + "shell.execute_reply": "2023-01-04T19:40:11.733066Z" } }, "outputs": [], @@ -134,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "cdf97fea", + "id": "c6fd9d90", "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": "7bcc267d", + "id": "fa1fefb4", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.746202Z", - "iopub.status.busy": "2023-01-04T17:44:38.745961Z", - "iopub.status.idle": "2023-01-04T17:44:38.749100Z", - "shell.execute_reply": "2023-01-04T17:44:38.748390Z" + "iopub.execute_input": "2023-01-04T19:40:11.737057Z", + "iopub.status.busy": "2023-01-04T19:40:11.736501Z", + "iopub.status.idle": "2023-01-04T19:40:11.739789Z", + "shell.execute_reply": "2023-01-04T19:40:11.739114Z" } }, "outputs": [], @@ -160,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "e5323595", + "id": "6cf23cf6", "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": "37c5e5e0", + "id": "d260cff6", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.752536Z", - "iopub.status.busy": "2023-01-04T17:44:38.752295Z", - "iopub.status.idle": "2023-01-04T17:44:38.755823Z", - "shell.execute_reply": "2023-01-04T17:44:38.755105Z" + "iopub.execute_input": "2023-01-04T19:40:11.743014Z", + "iopub.status.busy": "2023-01-04T19:40:11.742649Z", + "iopub.status.idle": "2023-01-04T19:40:11.746184Z", + "shell.execute_reply": "2023-01-04T19:40:11.745533Z" } }, "outputs": [], @@ -196,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "00ca1374", + "id": "e1e43dc6", "metadata": {}, "source": [ "by adding a list of edges," @@ -205,13 +205,13 @@ { "cell_type": "code", "execution_count": 7, - "id": "c6dbe13d", + "id": "56a65798", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.759200Z", - "iopub.status.busy": "2023-01-04T17:44:38.758959Z", - "iopub.status.idle": "2023-01-04T17:44:38.762352Z", - "shell.execute_reply": "2023-01-04T17:44:38.761645Z" + "iopub.execute_input": "2023-01-04T19:40:11.749490Z", + "iopub.status.busy": "2023-01-04T19:40:11.748941Z", + "iopub.status.idle": "2023-01-04T19:40:11.752382Z", + "shell.execute_reply": "2023-01-04T19:40:11.751758Z" } }, "outputs": [], @@ -221,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "9b8e3126", + "id": "2151bbf6", "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": "35c254e3", + "id": "7844811a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.765982Z", - "iopub.status.busy": "2023-01-04T17:44:38.765735Z", - "iopub.status.idle": "2023-01-04T17:44:38.768983Z", - "shell.execute_reply": "2023-01-04T17:44:38.768253Z" + "iopub.execute_input": "2023-01-04T19:40:11.755577Z", + "iopub.status.busy": "2023-01-04T19:40:11.754996Z", + "iopub.status.idle": "2023-01-04T19:40:11.758307Z", + "shell.execute_reply": "2023-01-04T19:40:11.757689Z" } }, "outputs": [], @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "d8e3d02a", + "id": "7fd897b2", "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": "3b95cfba", + "id": "20b019af", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.772486Z", - "iopub.status.busy": "2023-01-04T17:44:38.772288Z", - "iopub.status.idle": "2023-01-04T17:44:38.775228Z", - "shell.execute_reply": "2023-01-04T17:44:38.774543Z" + "iopub.execute_input": "2023-01-04T19:40:11.761419Z", + "iopub.status.busy": "2023-01-04T19:40:11.760879Z", + "iopub.status.idle": "2023-01-04T19:40:11.764128Z", + "shell.execute_reply": "2023-01-04T19:40:11.763486Z" } }, "outputs": [], @@ -276,7 +276,7 @@ }, { "cell_type": "markdown", - "id": "7a38efa7", + "id": "ca374508", "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": "de7c39bc", + "id": "3e3c6586", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.778546Z", - "iopub.status.busy": "2023-01-04T17:44:38.778306Z", - "iopub.status.idle": "2023-01-04T17:44:38.782592Z", - "shell.execute_reply": "2023-01-04T17:44:38.781877Z" + "iopub.execute_input": "2023-01-04T19:40:11.767794Z", + "iopub.status.busy": "2023-01-04T19:40:11.767209Z", + "iopub.status.idle": "2023-01-04T19:40:11.771405Z", + "shell.execute_reply": "2023-01-04T19:40:11.770738Z" } }, "outputs": [], @@ -307,7 +307,7 @@ }, { "cell_type": "markdown", - "id": "bba0fc4b", + "id": "95bf3002", "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": "dc32d21c", + "id": "77ea3ed7", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.786084Z", - "iopub.status.busy": "2023-01-04T17:44:38.785843Z", - "iopub.status.idle": "2023-01-04T17:44:38.793196Z", - "shell.execute_reply": "2023-01-04T17:44:38.792443Z" + "iopub.execute_input": "2023-01-04T19:40:11.774494Z", + "iopub.status.busy": "2023-01-04T19:40:11.774133Z", + "iopub.status.idle": "2023-01-04T19:40:11.780920Z", + "shell.execute_reply": "2023-01-04T19:40:11.780285Z" } }, "outputs": [ @@ -345,13 +345,13 @@ { "cell_type": "code", "execution_count": 12, - "id": "57a7fc3a", + "id": "5af92400", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.797615Z", - "iopub.status.busy": "2023-01-04T17:44:38.797372Z", - "iopub.status.idle": "2023-01-04T17:44:38.802066Z", - "shell.execute_reply": "2023-01-04T17:44:38.801347Z" + "iopub.execute_input": "2023-01-04T19:40:11.785428Z", + "iopub.status.busy": "2023-01-04T19:40:11.784881Z", + "iopub.status.idle": "2023-01-04T19:40:11.789514Z", + "shell.execute_reply": "2023-01-04T19:40:11.788860Z" } }, "outputs": [], @@ -367,7 +367,7 @@ }, { "cell_type": "markdown", - "id": "2ff133c5", + "id": "cdea6834", "metadata": {}, "source": [ "# Examining elements of a graph\n", @@ -387,13 +387,13 @@ { "cell_type": "code", "execution_count": 13, - "id": "179dfe37", + "id": "0a4ca736", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.805442Z", - "iopub.status.busy": "2023-01-04T17:44:38.805202Z", - "iopub.status.idle": "2023-01-04T17:44:38.810217Z", - "shell.execute_reply": "2023-01-04T17:44:38.809508Z" + "iopub.execute_input": "2023-01-04T19:40:11.792855Z", + "iopub.status.busy": "2023-01-04T19:40:11.792263Z", + "iopub.status.idle": "2023-01-04T19:40:11.797376Z", + "shell.execute_reply": "2023-01-04T19:40:11.796717Z" } }, "outputs": [ @@ -417,7 +417,7 @@ }, { "cell_type": "markdown", - "id": "0ba17135", + "id": "d6d075b2", "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": "600f437d", + "id": "b8b84533", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.814662Z", - "iopub.status.busy": "2023-01-04T17:44:38.814437Z", - "iopub.status.idle": "2023-01-04T17:44:38.819707Z", - "shell.execute_reply": "2023-01-04T17:44:38.819097Z" + "iopub.execute_input": "2023-01-04T19:40:11.800956Z", + "iopub.status.busy": "2023-01-04T19:40:11.800494Z", + "iopub.status.idle": "2023-01-04T19:40:11.805211Z", + "shell.execute_reply": "2023-01-04T19:40:11.804553Z" } }, "outputs": [ @@ -457,7 +457,7 @@ }, { "cell_type": "markdown", - "id": "2cbcffbf", + "id": "0df09e60", "metadata": {}, "source": [ "# Removing elements from a graph\n", @@ -474,13 +474,13 @@ { "cell_type": "code", "execution_count": 15, - "id": "267bffb7", + "id": "b1579224", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.822935Z", - "iopub.status.busy": "2023-01-04T17:44:38.822695Z", - "iopub.status.idle": "2023-01-04T17:44:38.826400Z", - "shell.execute_reply": "2023-01-04T17:44:38.825680Z" + "iopub.execute_input": "2023-01-04T19:40:11.809064Z", + "iopub.status.busy": "2023-01-04T19:40:11.808676Z", + "iopub.status.idle": "2023-01-04T19:40:11.812416Z", + "shell.execute_reply": "2023-01-04T19:40:11.811747Z" } }, "outputs": [], @@ -493,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "d3b8d158", + "id": "82858da3", "metadata": {}, "source": [ "# Using the graph constructors\n", @@ -508,13 +508,13 @@ { "cell_type": "code", "execution_count": 16, - "id": "cfadd1c1", + "id": "13a5f95e", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:38.829963Z", - "iopub.status.busy": "2023-01-04T17:44:38.829487Z", - "iopub.status.idle": "2023-01-04T17:44:39.129645Z", - "shell.execute_reply": "2023-01-04T17:44:39.128762Z" + "iopub.execute_input": "2023-01-04T19:40:11.815823Z", + "iopub.status.busy": "2023-01-04T19:40:11.815347Z", + "iopub.status.idle": "2023-01-04T19:40:12.104032Z", + "shell.execute_reply": "2023-01-04T19:40:12.103306Z" } }, "outputs": [ @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "883c013f", + "id": "5bcb612c", "metadata": {}, "source": [ "# What to use as nodes and edges\n", @@ -572,13 +572,13 @@ { "cell_type": "code", "execution_count": 17, - "id": "d4240f62", + "id": "9414d971", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.133953Z", - "iopub.status.busy": "2023-01-04T17:44:39.133316Z", - "iopub.status.idle": "2023-01-04T17:44:39.139339Z", - "shell.execute_reply": "2023-01-04T17:44:39.138599Z" + "iopub.execute_input": "2023-01-04T19:40:12.107603Z", + "iopub.status.busy": "2023-01-04T19:40:12.107170Z", + "iopub.status.idle": "2023-01-04T19:40:12.113447Z", + "shell.execute_reply": "2023-01-04T19:40:12.112778Z" } }, "outputs": [ @@ -602,7 +602,7 @@ }, { "cell_type": "markdown", - "id": "5a699950", + "id": "d8fc256d", "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": "e7475845", + "id": "de72574c", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.143764Z", - "iopub.status.busy": "2023-01-04T17:44:39.143512Z", - "iopub.status.idle": "2023-01-04T17:44:39.148992Z", - "shell.execute_reply": "2023-01-04T17:44:39.148261Z" + "iopub.execute_input": "2023-01-04T19:40:12.116825Z", + "iopub.status.busy": "2023-01-04T19:40:12.116229Z", + "iopub.status.idle": "2023-01-04T19:40:12.121557Z", + "shell.execute_reply": "2023-01-04T19:40:12.120901Z" } }, "outputs": [ @@ -642,7 +642,7 @@ }, { "cell_type": "markdown", - "id": "796777aa", + "id": "83227c2d", "metadata": {}, "source": [ "Fast examination of all (node, adjacency) pairs is achieved using\n", @@ -653,13 +653,13 @@ { "cell_type": "code", "execution_count": 19, - "id": "ff8d3e4b", + "id": "7d913456", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.153045Z", - "iopub.status.busy": "2023-01-04T17:44:39.152796Z", - "iopub.status.idle": "2023-01-04T17:44:39.158419Z", - "shell.execute_reply": "2023-01-04T17:44:39.157684Z" + "iopub.execute_input": "2023-01-04T19:40:12.124912Z", + "iopub.status.busy": "2023-01-04T19:40:12.124661Z", + "iopub.status.idle": "2023-01-04T19:40:12.130071Z", + "shell.execute_reply": "2023-01-04T19:40:12.129387Z" } }, "outputs": [ @@ -685,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "c0d52456", + "id": "77281896", "metadata": {}, "source": [ "Convenient access to all edges is achieved with the edges property." @@ -694,13 +694,13 @@ { "cell_type": "code", "execution_count": 20, - "id": "7d957260", + "id": "48a57526", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.162659Z", - "iopub.status.busy": "2023-01-04T17:44:39.162426Z", - "iopub.status.idle": "2023-01-04T17:44:39.166404Z", - "shell.execute_reply": "2023-01-04T17:44:39.165830Z" + "iopub.execute_input": "2023-01-04T19:40:12.134282Z", + "iopub.status.busy": "2023-01-04T19:40:12.134029Z", + "iopub.status.idle": "2023-01-04T19:40:12.138019Z", + "shell.execute_reply": "2023-01-04T19:40:12.137333Z" } }, "outputs": [ @@ -721,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "68c56030", + "id": "7ae42a25", "metadata": {}, "source": [ "# Adding attributes to graphs, nodes, and edges\n", @@ -743,13 +743,13 @@ { "cell_type": "code", "execution_count": 21, - "id": "3eb77ff4", + "id": "462a34df", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.169949Z", - "iopub.status.busy": "2023-01-04T17:44:39.169289Z", - "iopub.status.idle": "2023-01-04T17:44:39.174180Z", - "shell.execute_reply": "2023-01-04T17:44:39.173463Z" + "iopub.execute_input": "2023-01-04T19:40:12.142175Z", + "iopub.status.busy": "2023-01-04T19:40:12.141928Z", + "iopub.status.idle": "2023-01-04T19:40:12.146445Z", + "shell.execute_reply": "2023-01-04T19:40:12.145772Z" } }, "outputs": [ @@ -771,7 +771,7 @@ }, { "cell_type": "markdown", - "id": "0639a17f", + "id": "97fff941", "metadata": {}, "source": [ "Or you can modify attributes later" @@ -780,13 +780,13 @@ { "cell_type": "code", "execution_count": 22, - "id": "34ad8489", + "id": "9776660a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.177500Z", - "iopub.status.busy": "2023-01-04T17:44:39.177107Z", - "iopub.status.idle": "2023-01-04T17:44:39.181873Z", - "shell.execute_reply": "2023-01-04T17:44:39.181159Z" + "iopub.execute_input": "2023-01-04T19:40:12.150340Z", + "iopub.status.busy": "2023-01-04T19:40:12.150111Z", + "iopub.status.idle": "2023-01-04T19:40:12.154460Z", + "shell.execute_reply": "2023-01-04T19:40:12.153815Z" } }, "outputs": [ @@ -808,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "b45c21ba", + "id": "224f3c1e", "metadata": {}, "source": [ "# Node attributes\n", @@ -819,13 +819,13 @@ { "cell_type": "code", "execution_count": 23, - "id": "1b71fd2c", + "id": "2b229012", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.185846Z", - "iopub.status.busy": "2023-01-04T17:44:39.185596Z", - "iopub.status.idle": "2023-01-04T17:44:39.191126Z", - "shell.execute_reply": "2023-01-04T17:44:39.190539Z" + "iopub.execute_input": "2023-01-04T19:40:12.158458Z", + "iopub.status.busy": "2023-01-04T19:40:12.158224Z", + "iopub.status.idle": "2023-01-04T19:40:12.163433Z", + "shell.execute_reply": "2023-01-04T19:40:12.162757Z" } }, "outputs": [ @@ -850,7 +850,7 @@ }, { "cell_type": "markdown", - "id": "77bdd920", + "id": "fa30a8cf", "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": "3ed49146", + "id": "f27dfb23", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.194480Z", - "iopub.status.busy": "2023-01-04T17:44:39.193914Z", - "iopub.status.idle": "2023-01-04T17:44:39.198721Z", - "shell.execute_reply": "2023-01-04T17:44:39.197997Z" + "iopub.execute_input": "2023-01-04T19:40:12.167421Z", + "iopub.status.busy": "2023-01-04T19:40:12.167174Z", + "iopub.status.idle": "2023-01-04T19:40:12.171564Z", + "shell.execute_reply": "2023-01-04T19:40:12.170880Z" } }, "outputs": [], @@ -885,7 +885,7 @@ }, { "cell_type": "markdown", - "id": "f6e3b9fd", + "id": "48c77a50", "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": "7ece9a9c", + "id": "c5739106", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.201889Z", - "iopub.status.busy": "2023-01-04T17:44:39.201641Z", - "iopub.status.idle": "2023-01-04T17:44:39.207644Z", - "shell.execute_reply": "2023-01-04T17:44:39.206933Z" + "iopub.execute_input": "2023-01-04T19:40:12.174992Z", + "iopub.status.busy": "2023-01-04T19:40:12.174416Z", + "iopub.status.idle": "2023-01-04T19:40:12.180301Z", + "shell.execute_reply": "2023-01-04T19:40:12.179632Z" } }, "outputs": [ @@ -938,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "64f109ba", + "id": "2fd3646e", "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": "434f9992", + "id": "dde53331", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.211577Z", - "iopub.status.busy": "2023-01-04T17:44:39.211328Z", - "iopub.status.idle": "2023-01-04T17:44:39.214846Z", - "shell.execute_reply": "2023-01-04T17:44:39.214113Z" + "iopub.execute_input": "2023-01-04T19:40:12.183469Z", + "iopub.status.busy": "2023-01-04T19:40:12.183222Z", + "iopub.status.idle": "2023-01-04T19:40:12.186572Z", + "shell.execute_reply": "2023-01-04T19:40:12.185862Z" } }, "outputs": [], @@ -967,7 +967,7 @@ }, { "cell_type": "markdown", - "id": "f08e6c52", + "id": "e44f67a1", "metadata": {}, "source": [ "# Multigraphs\n", @@ -987,13 +987,13 @@ { "cell_type": "code", "execution_count": 27, - "id": "41ade2d1", + "id": "feea16bb", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.217835Z", - "iopub.status.busy": "2023-01-04T17:44:39.217615Z", - "iopub.status.idle": "2023-01-04T17:44:39.225013Z", - "shell.execute_reply": "2023-01-04T17:44:39.224276Z" + "iopub.execute_input": "2023-01-04T19:40:12.189609Z", + "iopub.status.busy": "2023-01-04T19:40:12.189153Z", + "iopub.status.idle": "2023-01-04T19:40:12.196198Z", + "shell.execute_reply": "2023-01-04T19:40:12.195537Z" } }, "outputs": [ @@ -1023,7 +1023,7 @@ }, { "cell_type": "markdown", - "id": "ba8d4af7", + "id": "8d615784", "metadata": {}, "source": [ "# Graph generators and graph operations\n", @@ -1043,13 +1043,13 @@ { "cell_type": "code", "execution_count": 28, - "id": "5fc6c20a", + "id": "43c8e071", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.229232Z", - "iopub.status.busy": "2023-01-04T17:44:39.228840Z", - "iopub.status.idle": "2023-01-04T17:44:39.234888Z", - "shell.execute_reply": "2023-01-04T17:44:39.234214Z" + "iopub.execute_input": "2023-01-04T19:40:12.200234Z", + "iopub.status.busy": "2023-01-04T19:40:12.199759Z", + "iopub.status.idle": "2023-01-04T19:40:12.204635Z", + "shell.execute_reply": "2023-01-04T19:40:12.203925Z" } }, "outputs": [], @@ -1062,7 +1062,7 @@ }, { "cell_type": "markdown", - "id": "2522e834", + "id": "4cb0db6d", "metadata": {}, "source": [ "# 4. Using a stochastic graph generator, e.g,\n", @@ -1073,13 +1073,13 @@ { "cell_type": "code", "execution_count": 29, - "id": "6b849c37", + "id": "f0fab8cd", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.238523Z", - "iopub.status.busy": "2023-01-04T17:44:39.237973Z", - "iopub.status.idle": "2023-01-04T17:44:39.260077Z", - "shell.execute_reply": "2023-01-04T17:44:39.259300Z" + "iopub.execute_input": "2023-01-04T19:40:12.207883Z", + "iopub.status.busy": "2023-01-04T19:40:12.207432Z", + "iopub.status.idle": "2023-01-04T19:40:12.282790Z", + "shell.execute_reply": "2023-01-04T19:40:12.281934Z" } }, "outputs": [], @@ -1092,7 +1092,7 @@ }, { "cell_type": "markdown", - "id": "8c5b0ba4", + "id": "87e08a78", "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": "6fcf24d5", + "id": "5a652d37", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.264784Z", - "iopub.status.busy": "2023-01-04T17:44:39.263239Z", - "iopub.status.idle": "2023-01-04T17:44:39.908596Z", - "shell.execute_reply": "2023-01-04T17:44:39.907594Z" + "iopub.execute_input": "2023-01-04T19:40:12.286273Z", + "iopub.status.busy": "2023-01-04T19:40:12.286014Z", + "iopub.status.idle": "2023-01-04T19:40:13.102835Z", + "shell.execute_reply": "2023-01-04T19:40:13.102018Z" } }, "outputs": [], @@ -1121,7 +1121,7 @@ }, { "cell_type": "markdown", - "id": "ebd909bd", + "id": "9b87f8cd", "metadata": {}, "source": [ "For details on graph formats see Reading and writing graphs\n", @@ -1136,13 +1136,13 @@ { "cell_type": "code", "execution_count": 31, - "id": "8d8a1355", + "id": "da7cf48a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.914222Z", - "iopub.status.busy": "2023-01-04T17:44:39.913926Z", - "iopub.status.idle": "2023-01-04T17:44:39.921067Z", - "shell.execute_reply": "2023-01-04T17:44:39.920316Z" + "iopub.execute_input": "2023-01-04T19:40:13.107661Z", + "iopub.status.busy": "2023-01-04T19:40:13.107150Z", + "iopub.status.idle": "2023-01-04T19:40:13.116823Z", + "shell.execute_reply": "2023-01-04T19:40:13.116087Z" } }, "outputs": [ @@ -1168,7 +1168,7 @@ }, { "cell_type": "markdown", - "id": "a7db1188", + "id": "6ad339b8", "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": "3a8f10fa", + "id": "7f2fbd1a", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.926069Z", - "iopub.status.busy": "2023-01-04T17:44:39.925663Z", - "iopub.status.idle": "2023-01-04T17:44:39.930860Z", - "shell.execute_reply": "2023-01-04T17:44:39.930289Z" + "iopub.execute_input": "2023-01-04T19:40:13.120512Z", + "iopub.status.busy": "2023-01-04T19:40:13.119980Z", + "iopub.status.idle": "2023-01-04T19:40:13.125066Z", + "shell.execute_reply": "2023-01-04T19:40:13.124365Z" } }, "outputs": [ @@ -1206,7 +1206,7 @@ }, { "cell_type": "markdown", - "id": "1c8a9cef", + "id": "042ddc38", "metadata": {}, "source": [ "See Algorithms for details on graph algorithms\n", @@ -1225,13 +1225,13 @@ { "cell_type": "code", "execution_count": 33, - "id": "464ab17e", + "id": "bea11dcd", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:39.934323Z", - "iopub.status.busy": "2023-01-04T17:44:39.933627Z", - "iopub.status.idle": "2023-01-04T17:44:40.365672Z", - "shell.execute_reply": "2023-01-04T17:44:40.364794Z" + "iopub.execute_input": "2023-01-04T19:40:13.129246Z", + "iopub.status.busy": "2023-01-04T19:40:13.128848Z", + "iopub.status.idle": "2023-01-04T19:40:13.520231Z", + "shell.execute_reply": "2023-01-04T19:40:13.519480Z" } }, "outputs": [], @@ -1241,7 +1241,7 @@ }, { "cell_type": "markdown", - "id": "c58cc91e", + "id": "a0e66810", "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": "86ed6e67", + "id": "fbe47396", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:40.369734Z", - "iopub.status.busy": "2023-01-04T17:44:40.369331Z", - "iopub.status.idle": "2023-01-04T17:44:40.606107Z", - "shell.execute_reply": "2023-01-04T17:44:40.605429Z" + "iopub.execute_input": "2023-01-04T19:40:13.524011Z", + "iopub.status.busy": "2023-01-04T19:40:13.523640Z", + "iopub.status.idle": "2023-01-04T19:40:13.736925Z", + "shell.execute_reply": "2023-01-04T19:40:13.736108Z" } }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] @@ -1282,7 +1282,7 @@ }, { "cell_type": "markdown", - "id": "d5151c7e", + "id": "be4106df", "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": "f88f1bce", + "id": "9645f407", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:40.612448Z", - "iopub.status.busy": "2023-01-04T17:44:40.611643Z", - "iopub.status.idle": "2023-01-04T17:44:40.615293Z", - "shell.execute_reply": "2023-01-04T17:44:40.614712Z" + "iopub.execute_input": "2023-01-04T19:40:13.740421Z", + "iopub.status.busy": "2023-01-04T19:40:13.740163Z", + "iopub.status.idle": "2023-01-04T19:40:13.743515Z", + "shell.execute_reply": "2023-01-04T19:40:13.742943Z" } }, "outputs": [], @@ -1308,7 +1308,7 @@ }, { "cell_type": "markdown", - "id": "2e6bcc59", + "id": "9d2aad9f", "metadata": {}, "source": [ "command if you are not using matplotlib in interactive mode." @@ -1317,19 +1317,19 @@ { "cell_type": "code", "execution_count": 36, - "id": "6e27cd84", + "id": "0372711b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:40.618340Z", - "iopub.status.busy": "2023-01-04T17:44:40.617672Z", - "iopub.status.idle": "2023-01-04T17:44:40.934656Z", - "shell.execute_reply": "2023-01-04T17:44:40.934013Z" + "iopub.execute_input": "2023-01-04T19:40:13.746478Z", + "iopub.status.busy": "2023-01-04T19:40:13.746052Z", + "iopub.status.idle": "2023-01-04T19:40:14.034019Z", + "shell.execute_reply": "2023-01-04T19:40:14.033407Z" } }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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t27eoS5cuSCAQcCAhu3h6eiIjI6PCv0UiEdq/fz+PEv0jJycHHThwgLRt1KhRyNjYmCeJZMfc3Bxt3rwZRURE0L4TZIUgCJSVlYWOHj3KnnBsw7c2IguSCW169uxZ+JtkZEIDAwNWAn4QBAH79+9nTI7UrFkzrYlWlZ2dTRvXoWzZsnJNne7Zs4fydZCXlwcA/8IYS9aPl2UwikIQBLi4uDBGHpVWzMzMNNZ4VdJTp2LFipCfn8+rTEeOHKGc448fP/Iqk6Lcu3cPatWqpdDsgEAgABcXF7WdZdKITy9pMwNNmzYlae25ubkoNDRU6TYFAgEaM2YMCgsLQ40aNaL8HhQUhOrUqYOOHDlC+1VbUsjLy0P9+/dHAQEBpO0WFhbo7t27qEaNGsXWAQAoMTERnT17lrS9U6dOSE9Pr3AfSTThawyjniQlJaHIyEiF7s20tDQOJFINEydOJP0dHx+Prly5wpM0/9i5cyfp73bt2qEqVarwJI1yeHh4oDt37ih0LACgyMhIlJyczLJU7KARysC3b99IfxdVBszNzSkvJGWXCori6uqKnjx5gpYtW0aZts7IyEAjR45EAwcOVNsLrAz5+flo0KBB6Nq1a6Tt5ubm6M6dO6hOnTpSj09NTUW+vr7I1dUVWVlZoXv37pF+9/DwKPw/3VQuXibAKEpmZqZSx2/cuBGFhIQgkUjEkkSqoV69eqhJkyakbZIvY1USGhqKQkJCSNsmTZrEkzTskJ2drdTxGRkZLEnCMrzOS8iIvb09abrlwoULpN8nTJjAuIzAJsHBweDs7Ew7BVSxYkW4e/cuJ+3yQX5+PgwcOJDSz1KlSkFISEixx0tzuSkoxsbGhRa2dB4jqorvjil5JCQkKGXwVXS8d+3aFTZv3gyhoaEaEZ726NGjlH7wFVV1zJgxJDlsbW15X7ZQFrqYKPIUdV2CUntlIC8vj2IN/PLlS9I+x44dI/1eXNIiZUhPT4dRo0YxXuiZM2dCdnY2J22rCpFIRJvDwcTEBIKCgoo9XlZ3rqIuN58+faL8XmBPgMHIizI2A9KKubk59OzZE3x8fODt27cgFov57iqF7OxsKFu2LEnuadOmqVyO5ORkMDIyIsmxatUqlcvBJgEBAYwxaYor6m4zoPbKAJ1hmaRmFRUVRdnn06dPnMp1/vx5sLCwoL3otWrVUvvMYUyIxWJaZcfIyAgePnxY7PHyunMVuNyEhIRQftP0LwgMv/j4+LCuDEiWsmXLQr9+/WDHjh3w/v17tXnQSyZ2MzMzU7lBrmSEWF1dXY1NPvbx40fo2rWrUmNFIBCAr68v311hRO2Vgfv371O+TiVvOIIgKCFuZU1apAzx8fHQvn172gtvYGAA27ZtU8svByYIgoBx48ZR+mJoaCjzEogiD2CBQAALFiygbNekc4dRP9iIMyBvKV++PAwePBj27t0LX7584U05iIqKotyHe/fuVVn7YrEYXF1dSe0PHDhQZe2zhSx5C+T56FHnOANqrwwcPnyYdFKrV69Ou1+/fv1I+8mTtEgZxGIx+Pj4UFLyFpT27dtDfHy8SmRRBoIgYMqUKRT59fX1ZY6cpejUrEAgADs7O8p2dfnKwmgubEUglJzulrXY2trC8OHD4eDBgxATE6PSvkt+ydatW1dl99Tt27cp50KWmUV1oSCjIdPsL0IInJyc5IpAGBAQwHe3pKL2ysCKFStIJ7ZLly60+ymbtEhZwsPDaTP4IYTAwsICzp8/r1J55IEgCJgxYwZFbj09Pbh69arM9bBltFVQMBg2kDV+/LFjx2Dw4MFSH+pt2rSBQYMGUbLvyVqcnJxg9OjRcOzYMakJvdjg+vXrlPaDg4M5bbOAXr16kdqtUaOGxij3N27cgKpVqzJew3LlysH+/ftBJBLJZChtYGCg9ooAgAYoA5Lr1xMnTqTdjy5pkaoTiuTk5MCsWbMYB9HIkSMhPT1dpTIVB0EQlPVFhP6t7126dEmuuujsOxQtQqGQmw5jtJKUlBTw9fWlzSzn6+tLCn0dHBwMjRs3ZhybpUuXho0bN8KHDx/gwIEDMGzYMKhYsaJC47xy5cowfvx4OH36NOsZUkUiETg5OZHaGzZsGKtt0BEbG0v5Wt6xYwfn7SpLREQEdOrUifFaGRgYwPz58yEtLY10HNPYKii1a9fWCEVI7ZUBDw8P0ondsGED7X50SYsuX76sYmn/cffuXcaHg7Ozs0wW+api6dKltC/is2fPyl0XmzMDOjo6HPQWo+0QBAGJiYkQHR0NiYmJjA9psVgMx48fl/qSd3Z2hosXLwJBEEAQBHz+/Bn27NkDgwYNgnLlyik07qtXrw6TJ0+GCxcusOKCtnHjRlL9+vr68OfPH6XrlYZkxFhTU1O1+wgqSkJCAkyePBl0dHQYr0v//v2LjThbMLYOHDhAOf7Zs2cq6o3iqL0yIOnXf/r0acZ927RpQ9p37ty5KpSUTFJSEgwYMIDxq3fp0qW8u86tWrWKIptAIIDjx48rVB+b7lw6OjoUDRyDUTWZmZmwfPlyyodG0dKqVSt49eoV6TiCICAiIgL8/Pygb9++UrPfMRWBQAB16tSB6dOnw+XLlxUyPktISKDYM61fv56ls0MlNzeXoghNmjSJs/aUITc3F7Zu3Qrm5uaM16BevXrw6NEjueoViUSUZaQRI0Zw1Av2UGtlQCwWU7JFPX36lHF/SY20WbNmKpSWCkEQcOTIEShVqhTtQGvUqBF8/vyZF9k2bNhAK9OhQ4eUqpdNdy4TExMYN24chIWFsdJnDEZRvn//DsOHD5f64h4zZgyj65xYLIY3b97Atm3boEePHmBmZib3/SAUCqFBgwYwZ84cuHHjhsxf2yNGjCDV4+DgwFnwpFOnTlHkDg8P56QtRSEIAq5cuULxdihaKlSoAIcPH1bYo2n9+vWk+gwMDFS+bC0vaq0M0OW4l+anevPmTcoFUIcodlFRUdCsWTPGF96+fftUuqYkaWxZUPbs2aN03Vy5czVu3BgOHz4MWVlZLJwBDEYxQkJCwN3dnXGcmpqawtq1a4sNPCYSieDly5ewadMm6NKlC5iamsp9T+jo6ECTJk1gwYIFcOfOHcaUwM+ePaMcK49hsDy0aNGC1E7Lli05aUdR3r59C+3atWM8p4aGhrB48WLIyMhQqp0/f/6Avr4+qW6mJW51Qa2VgSdPnlBe7tI0tdTUVMpXqbqsz4tEIlizZg2jv2rPnj05X8sDANixYwdt+35+fqy1IY87l1AohDNnztC6NdKVMmXKwIwZMzgPKoXBMEEQBJw+fZoSJr1ocXR0hLNnz8qs5Ofl5cHTp09h7dq10L59e4VcGfX09KBFixawdOlSuH//fqFCQhAE1KtXj7Rv586dWT8vb9++pcgkbVlXlfz+/RvGjx8v9Zk0ePBg+PbtG2ttSkZxdXJyUutw1mqtDJw4cYJ0MitXrlzsMZLpJTdu3KgCSWXn+fPnULlyZdrBWL58ebh58yZnbe/bt4+23a1bt7LeliwuNwghqFatGhAEQfv1Ulzx8PCAc+fO8W57gdFOsrKyYPXq1YxpzhFC0Lx5c3jx4oXcdefm5sLjx49hxYoV0Lp1a8Y4JtKKoaEheHh4wKpVq2D+/Pmk3wQCAXz9+pXV8zFx4kRSG+XKlYPc3FxW25CXnJwc2LRpE5QuXZrxPDVq1IiTj8bg4GBKW9evX2e9HbZQa2VgzZo1pBPZvn37Yo9RVdIiZcjMzKTIWbRMmTKF9enww4cP076UuZy6Ks7lpugNInnjmJqawrJly8DGxqbYh1758uVh8eLFEBsby1lfMBgmfvz4AaNGjZKq9Hp6eioVfCw7OxsCAwNhyZIl0KxZM6Uj4iGEYNasWaydg7S0NMpSx+LFi1mrX14IgoCLFy9KffZUrFgRjh8/zlmkU4IgoG7duqQ2u3btyklbbKDWyoBkaNyxY8cWe4xk0qKyZcuqrY/nlStXwMrKivGLWdJCWVFOnDhB+6BSVdKQou5cP378oFjaurm5wePHj0nbSpcuDQD/pk8vXLjAGPa5aBEKhdC9e3e4ceOGWk/HYUomoaGhlDXzosXY2BhWrlzJuLYvD5mZmRAQEADz58+Hxo0bS3WLYyoCgQA6derESkZGyeVHHR0d+P79u9L9VISwsDBo3bo1Y7+NjIxg+fLlKsnVIDkbKxAIinVR5Au1VgY6duxIOpGrV68u9hi6pEUfP35UgbSK8evXL+jSpQvtoNXT04MNGzYodZOePXuW9kGxZMkSFnshH5IhpgsUk6J/m5ubU477/PkzzJ49W2qI0ILi6OgI69atg9+/f/PQQ4y2QhAEnD9/nhLsp2ixs7ODkydPsvqRkpaWBtevX4dZs2ZBvXr1FPLoKVOmDPTq1Qt8fX3lyshIEAQlk1/v3r1Z65us/Pz5E8aMGSO178OHD1epkpKZmUnxHOHT5V0aaq0MVKlShXQSjx07VuwxdEmLDhw4oAJpFYcgCNi5cyej0VDr1q0VMmy5dOkS7XTivHnzeJ0tEYlElHCfDg4OlAcTE9nZ2XD06FGpVt1FFapBgwbBw4cP1XaGCFPyyM7OhvXr1zO6FSOEoGnTppwFo0lOTgZ/f3+YNm0axY5K1lKQkXHnzp3w4cMHxvvnwYMHlGPv3LnDSb/oyM7OhnXr1kn1yGjatCmEhISoTKaiTJs2jSSLpaWlWqa5V1tlgCAIMDQ0JJ3Ex48fy3Rs//79SceNHj2aY2nZ4cOHDxSr34JiZmYGp06dkrmua9euUWI0IIRgxowZKn8pEgQBCQkJEB0dDQkJCUAQBJw5c0bqg8jS0lKmul+/fg0TJ06UyTWrevXqsH37dlLoWQyGS379+gVjx46V+rU6dOhQzu1d/vz5A+fOnZMabre4UqFCBdqMjJLB1SpXrqySjKMEQcDZs2el5omwt7eH06dP8/oh8PHjR4pcR48e5U0eJtRWGfj16xflBMp6w/j4+JCOq1KlCsfSskdubi4sWLCA8eExdOjQYiOR3bp1i+LjihCCyZMnq/SmSElJAR8fH9p48Nu2bYOaNWtK/SqRh/T0dNi9ezfUqVOn2IeasbExjBkzBl6+fMlRzzEYMmFhYZQIqUWLkZERLF26lPN17Pz8fLC1tSW1LW32Qlqxs7ODfv36UZYht23bxmkfAABevnwJzZs3Z5TNxMQEVq9erTZxSdq2bUuSr0mTJnyLREFtlYGQkBDSydPV1ZV57fzFixeUwaHu0Z8kefjwIaMfs729PWM60Hv37lFmVBBCMG7cOJVo6wXIkimOTs6CYm1trVC7BEFAcHAwjBgxQiZ3rIYNG8KBAwdYMerCYKRBEAT4+/tLtXC3sbGBo0ePcnqvStrnGBoaQnh4OBw9ehRGjRpFWbKTp+jo6MCePXs4S9seHx8Pnp6ejO0LBAIYNWqU2qWNv3DhAkXW0NBQvsUiobbKgOQ0spOTk8zHqlPSImVITU2lBK4oOujnz59P8uN9+PAhbQz1kSNHqlwRkCXokLTfy5cvr7QciYmJsGXLFqlhRwuKmZkZeHt7w/v371k4AxgMMzk5ObB582apIYkbNmzIWcC0nz9/UpYQJWONREVFsZaR8cyZM0ob8mZlZcGqVaukxnRo0aKF2s725efnU86jl5cX32KRUFtlQDLbVuvWreU6Xp2SFinLqVOnGJNp1KtXDz58+ABBQUG0N8qQIUNU6mYnbzhipuWQChUqsCaTWCyGO3fuQN++fWVywWrdujWcPn2a94ApmJLNnz9/YOLEiVLvlYEDB0JMTAzrbQ8aNIjUTqVKlRg/GIpmZBw4cCBYW1srpBzUqFEDpkyZIldGRoIg4OTJk2BnZ8dYr6OjI5w/f17tDYRXrlxJktvIyAiSk5P5FqsQtVUGJk+eTPm6lQfJpEXu7u4cSaoaYmNjGX1nDQwMaKfc+/fvD/n5+SqVk61ERTY2NpzIFx8fDytXrqSsm9IVa2trWLBgAURHR3MiCwYDABAeHi41joaBgQEsWrRI6Xj5RXn06BGlnYCAAJmOLcjIKOntJU8RCARQt25dmDFjBly5coXWqPfZs2fQtGlTxjpKlSoF69evV0vLfDp+/PhB8e5ShX2FrKitMtC1a1fSSVu2bJlcx0smLdLX19eYQcOEWCyGjRs30noJSJZevXqpPEyvoimM6fY3MzPjVNb8/Hzw9/eHTp06FSuvQCCALl26wNWrV2WeZaHzoMBgmCAIAq5du8YYqhyhf0tnBw8eZGXJjyAIigGvPNFaP3z4QJFvyJAhrGRkPHr0KGXmQvJ+HDt2LPz69Uvp86BqJD0vXF1dVbqEKw21VQYkB6q8qXXpkhY9efKEG2FVTFhYGCXIR9HSoEEDXqa4ExISFP5SoHs4qMoFMDIyEubNmwdly5YtVi57e3tYvXo1Y/ZMaR4UPj4+CuWkx2gPeXl54OPjw7gsiNC/pUEmA2J52LlzJ+WekzWeiTTf+aIZGTt37qxQRkam0rp1a41Oac53TAZpqKUyQBAEZQAFBgbKXU/t2rVJdah7Ckl5CA0NlZrZbPz48SoJt1mU6Oho1m56hBAsX75cpfLn5OTAiRMnpIaULSi6urrQv39/CAwMLPzql8WDwsTEBG7duqXSfmE0j8TERJg6dapUG5e+fftCZGSkwm2kp6dT3AoXLVpU7HHyRtVjIyMjQv/ihCxduhQePHigFqnpFYEgCKhRowapX3xEa6RDLZWBpKQkykBQZNBLZtHq0aMHB9Kqno8fP0K5cuWKvXlcXV3h+fPnKpOLzZkBhP6tCcpqaMQ24eHhMGXKFKnZzgpKlSpVYPz48TJ7UOjo6GCFACMT79+/h86dOzOOJ319fZg3bx6kpaUpVL+kbZa1tXWxs4rKxtvPycmBR48ewYoVK8DNzU0hG6OiGRmDgoI0KnOpZB4HoVDIWx6HoqilMvDq1SvKyVJk2vv48eOketQ5aZGsfPnyhTaTn6WlJeMX7OrVq1XiUaCMzUD58uVpf5s3bx7ncksjIyMD9u3bxxgZUpEiFArBxMQELxlgZObmzZtQrVo1xjFlbW0Ne/fulfs+f/fuHaWukydPMu7PVia+mJgYqXYB8hYTExPo2LEjrF+/HkJCQlRuOC0P6pbhsQC1VAYuXrxIOlG2trYK1UM3ba3OSYuKIzo6mtbFxt3dHdLS0mD//v2MfrjNmjVTSbYsRbwJBAIBjBkzhvY3IyMjxvV5VUIQBISEhMCoUaOkBkuSp8++vr58dwujQeTl5YGfn5/URF21a9eGe/fuyVVvq1atSHU0b96ccV/JVOMI/UtBLisZGRmwaNEiqfdQmzZtYPfu3UplZCxdujR069YNtmzZAq9evVIbI70CJGety5Urx7srs1oqA9u2bZN5cEqDIAjKV7S6Jy1iIjY2ljYGd6NGjUiGdp8/f4ZGjRrR3iClSpWCw4cPczo7Im+cgYKv5KNHjzLu4+3tzZm8ipCcnAw+Pj5Ku1a5uLho/EwVRvUkJyfDjBkzaJOQFZSePXvC58+fZaqPLk/ImzdvaPeVDILm5OQk02yEWCyGQ4cOMc4AIvQvSNHVq1cp90RaWhpcu3aNlYyM27dvh/DwcN7vu7dv31JkPH36NK8yqaUyIGmpOnToUIXrkkxaNGrUKBYlVQ1xcXG0IUzr1atHG7QiLy8Pli1bxvhC7t+/PyQlJXEmr6wRCAvKjRs34Pz584y/6+vrc57IRVZSUlLg0aNH4OfnB2PHjpUpuqG0wpdNBEbz+fTpE3Tv3p1xbOnp6cGsWbOKXY7Kzc2lvKQnTJhA2e/Pnz+UnCeyGGU/fPhQ6jKbubk5+Pj4yPxlzEZGRisrK+jfv3+xGRm5RNJQuWXLliqXoShqqQz06tWLdJIWLlyocF2SSYsqV67MoqTc8/PnT9ov0Nq1axf7IgkODgZnZ2fam6FixYpw9+5dzuQuallf3I25ZMkSOHv2rNR9xo4dy5msdOTn50NERAScOnUKFixYAF27dpUaBU3RggMaYZTlzp07xSb92rlzp9R19CVLlpCOMTExoRglrl+/nrSPgYGB1JwvUVFR0K9fP0a5dHR0YMqUKUorxAUZGSdNmiTVrkJaqVChAgwZMgT27dsHX79+VYlycOrUKYoc4eHhnLfLhFoqA25ubqQTtHfvXoXrokta9OfPHxal5Y4/f/7QxhOoXr26zH1IT0+HUaNGMd4EM2fO5CwYU0pKCvj6+hbrZywUCinJU8qUKUN5cHz58oUTOX/9+gW3b9+GzZs3w4gRI6Bu3bq0WR+5KHhmAMMG+fn5sHv3bqmxMmrUqAG3b9+mPf779++UtXk/P7/C30UiEWWZcsSIEbR1paWlwbx586TeQ507d4aIiAhOzsWPHz/g5MmTMHbsWKhUqZJC96WdnR14enrC4cOHZY69IC+5ubkUr7BJkyZx0pYsqKUyIPkikDVMJh35+fkUozp/f38WpeWGxMRESpwEhP7NbChiUHf+/HlGw6NatWpxqpHOnTuX1B5dMiXJh1jVqlUpRkbDhg1TSo6srCx4+fIlHDp0CGbMmAFt27YFKysrlbz0JQu2GcBwQWpqKsyZM0dqlNJu3brRGlL36dOHtF/16tULx+e1a9co9Tx79ox0vEgkgn379knNXVCtWjW4ceOGSs5FAbGxsUpnZHR2doYxY8bA8ePHWc2IKBk239TUVGE3UWVRO2UgLS2NciE+ffqkVJ0eHh6k+ubMmcOStNyQnJxMmR1B6F8Uu7i4OIXrjY+PZ4yBbmBgANu2bePE6tbPz4/ykCnu5qtVqxbMnj2b8gKV5WuCIAiIjo6GK1euwOrVq2HAgAFQtWpVmW0Yiit6enrg6OiolFcB9iaQDxzeWT6+fPkCvXv3Zhx/urq6MG3aNJLt0N27dyn73b9/HwCAEuugXr16pGsQGBgIderUYWzPwsIC/Pz81CIeABsZGatUqQITJkxQOiNjbGws5bm0YcMGXsa52ikDdFaWyk5jS66HqXPSotTUVGjYsCHlHDg6OrIyXSUWi8HHxwcMDAxoB3n79u1ZzwV++fJlUhs2NjYwevRoqTdbnTp1ICEhgbLE0LdvX1LdaWlpEBQUBLt27YKJEydCs2bNZAoUJGuxtbWFzp07w7x58+DEiRPw8OFDilGqvAXHGZAdHN5ZOWR5Sf/333+Ql5cHBEFQ7JP69+8PX79+pdj+7N+/HwD+KR2SNl6SSsf06dPVKjtfUQiCgE+fPsHu3buVzsg4depUuHjxotzG2dLOnyrHudopA1evXiWdDDby2t+6dYtUp7omLUpPTwd3d3fKgLCzs2M9RkB4eDjtMkTBA+L8+fOstSUZREogEMDPnz8py0FFi5ubGwBQFTmEEIwZMwZ69OhB62qpaDE2NoZGjRqBl5cXbN++HR48eEC6qQmCgFOnTjGuyRoZGYFQKJQ5AqEyS1/aAg7vzA4ikQj2798vNWpp1apV4caNGxSDa11dXYpPvJmZGfz48QNmz54tdTmie/fuGhfXpSAjo5+fH/Tt21dqTAemIktGxqKsXbtWal2qGudqpwxITik3btxY6To1IWlRZmYmtGzZkjIYbGxsODOcy8nJgVmzZjEOxFGjRkF6errS7dCFKY6JiYGDBw8ytu3q6grbtm2DIUOGKBR0RFpxcXGB3r17w7Jly+DChQvw5csXqcsjP378gJ49ezLW16VLF4iNjZX55YUVgeKR1T0Vh3eWnbS0NJg/f75Uw762bdtSlr8kcwl4eHhINVSsWbMmo6GipiEWi+H169ewbds2pTIyNmzYEObOnQs3b94kpaIuGOey1MH1OFc7ZUBynXjAgAGs1Cv5Fbx+/XpW6mWDrKwsaNu2LWUAlCtXTiWa9d27dxnXzpydnSEoKEip+gmCoDxgHj9+DFlZWVKnMJUt5ubm0LJlS5gyZQrs3bsXnj17JldOeIIg4NChQ4wZ5MqUKQNHjx4lresVeFDQTWv7+vqqLBOjJqNo4Cq8ZCAbUVFRUpe6FAnqg9A/I+Bdu3apdShgZRGJRPDixQvYuHGjwhkZdXV1wd3dHWbNmgWGhoZqM84FAABIjRgwYAA6d+5c4d9z585FGzZsULreSZMmoV27dhX+3b17d3TlyhWl61WWnJwc1KtXLxQQEEDabmVlhR48eICqV6+uEjmSk5PRxIkT0dmzZym/CYVCtHjxYrR48WKkp6cnd90AgFxcXFB0dHThNltbW/Tr1y8kEomUkhshhHR1dVGVKlVQ7dq1SaVixYpIIBAoVGdsbCwaN24c5boU0Lt3b7Rz505Uvnx52t8BACUnJ6OMjAxUqlQpZGFhobAs2oavry+aMWMGkufRJBAI0OrVq9HYsWM5lKxk8ezZM7R48WL09u1bperR09NDY8eORTNmzEBmZmYsSacZ5Ofnozdv3qAnT56goKAgFBISgnJycjhrTyAQIB8fH+Tt7c1+5ZyoGEogaTy3Y8cOVuqVTFpkaWnJu0Vybm4udOvWjaIBWlhYMIYD5RKCIODIkSOUtKYFpVGjRsWGOM3IyICnT5/C3r17YcqUKdCyZUupudmVKfPnz4fXr1+zms5ULBbDrl27GDV+KysrOHv2LO9jp6SiaLIrXHDRhsKlS7LaKQOS1pzyJMGQRkxMDOXEfvjwgZW6FSEvL4/W9cfc3BxCQ0N5kwvg3zRis2bNaAejiYkJ7Nu3D0QiEXz+/BkuXLgAy5Ytg969e9OGTFa21KhRA7Zt2wY3b96EChUqkH5r2rQpqzdFZGQktGnThlGWIUOGSI24hlEettNg44JLSSxcBCtTK2Xg79+/lE6/e/eOlboJgqCsixe4x6ia/Px8GDBgAKWvpUuXhufPn/MikyQikQjWrFnDmAyFTaM+JycnqcFACsIm7969m/LbtWvXWOmrj48PbTAkhP6FKr18+bLS7WCKhy7TKC644EIuXIQxVytl4MOHD5ROy2PwVRySL2A+khaJRCIYOnQopZ+mpqZKG+opS15eHoSHh8OJEydg3rx50LlzZ4X9bosr9vb2EBQUVBhta+fOnYz7VqxYEZKSkiA3NxecnJxIv7m5uSkVKOnDhw+07pwFZfTo0dgwTYXgmQFccCm+cDEzoIvUiG/fvpH+trS0RKampqzV36xZM5KBXFBQEGt1ywJBEMjLywudOHGCtN3Y2Bhdv34dubu7q0QOAEA/f/5E4eHh6O3bt4Xlw4cPKD8/X+n6hUIhqly5MsmYLykpCY0aNapwn5ycHJn7Gx8fjyZMmIDOnDmDli9fjjw9PQt/CwsLQ5cuXUJ9+/aVS0aRSIS2bNmCli1bhnJzcym/29vbo3379qEOHTrIVS9GOSwtLZGLiwuKioqSy4AQIYT09fXRhQsXUOPGjTmSruSRnJyMNm3ahA4fPozEYrHcxzs6OqJly5ahLl26YANZOQAA1LhxY/Tt2ze5DWWdnZ2RhYUFJ0KpDZLTwPXq1WO1/pcvX1I0LFUlLRKLxTB27FhK+4aGhnDv3j3O2v379y88f/4cDhw4ANOmTYM2bdqApaUl65qqhYUFLF++HEJDQyErK4siR3h4OOWYooZ/kvEl6MqRI0dAJBJB1apVSdurVasmU071At6+fQv169dnbGfSpEmsxFfAKIaPj49SBoSDBg2CmJgYvruh1uTm5sK2bdtYM+5t3bo1hIWF8d0tjUKRcc5lGHO1UgYWLFhA6nifPn1YrZ8uadGlS5dYbYMOgiBg8uTJlAurr6/PWhAJsVgMkZGR4O/vDytXroR+/fpB5cqVWbPK1tfXh7p160L//v0pL+OCoqenBxs2bKB9MaemplL2//r1a+Hv//33X7EylCpVCqKiouDMmTOU344dO1bsOcrNzYXly5czRk1zcXGBBw8esHI9MIojb5wBumJoaAiLFi1idZmxJEAQBFy9ehUqV67MeO6YQpXL8qLy8vJSKJGaNqJu8TTUShkYPHgwqfMzZsxgvQ3JpEWzZ89mvY2iEAQB06dPp31xXr16VaE6U1JS4NGjR7Bjxw4YP348NG3aVKHgF0zFzs4OunbtCgsWLIBTp05BREQEKcEIQRCwc+dOSmSygtK6dWvaPAqSLosFSVAAAHx9fWWSrVmzZpCbm0sJVuTs7Cw1CcrLly8Zwy8LBAKYOXMm/P37V6HrgWEfWSMQFlcqVKgAhw4d4iQBl6bx9u1baNeunVQFaurUqZRzLjmT2LFjR2jatCljPaamprBu3Tq1DPmubsgbaZPL6KVqpQxIGnL5+Piw3oZkrPumTZuy3kYBBEFQ0vci9C8ClSwzEvn5+RAREQGnT5+GhQsXQrdu3cDe3p61l76pqSk0adIExo0bB35+fvDo0SO5Eop8+PAB6tWrR1u3mZkZnDp1irS/ZLbCo0ePFv4mGRO9fPnyjHKvXr0arly5Qtm+d+9eiozZ2dmwYMECRu+HqlWrQnBwsMx9xqiO4sI7yzPW69WrBw8fPuS7S7zw588fmDBhgtQXTsHSyqJFiyjPiFWrVpG26evrw69fv+DUqVNSn0eOjo44JocMqEsYc7VSBiRd//z9/VlvIyAggDKwudJg6ZLsCIVCOHv2LGXfX79+wZ07d2DLli3g6ekJbm5uCk/XSRaBQACVK1eGvn37wooVK+DSpUsQGRnJytdSbm4uLFiwgPHhPHTo0MJprY4dO5J+W7NmTWE9W7duJf3WqlUrxvVMXV1dCAkJgcaNG5O229rakq5lcHAw45KGjo4OLFy4EH+9qDnSwjvPmjWLsuxXnKLQr18/1pN+qSs5OTmwadMmqVk8GzVqVOjFlJubS/EemjRpEiQkJFCeRevWrQOAf6HUV69eTXsdCkrz5s3h5cuXfJ4KtUcdwpirjTKQm5tLuYm5MEhJS0ujaMhnz55lPXe0pDZd8JA6dOgQvHr1Cg4fPgwzZ86Edu3aseq+Z2FhAa1btwZvb2/Yv38/PH/+HDIzM1nrFxMPHz5k/Eqwt7eHhw8fwpgxY0jbJ0yYUHj8li1bSL916NABdu3axdhPZ2dn2L9/P2W7r68v/P37F2bMmMH4UqhduzbvgZ0w8kEQBCQmJkJ0dDQkJiYW3quBgYG0SrNkKt6iRV9fH+bNm1fo1lrSIAgCLl26JDUIWMWKFeHYsWOkD4JTp05R9iuI8zJixAjSdgcHB5JtUHx8PIwaNYrxnhMIBDBy5EjW06OXNJjGuSpQG2Xg69evlAHElaFEzZo1aQcsW7mjN2zYQFu/jY0Na8F69PT0oFatWjB06FDYsGED3Lx5E+Lj43mdkktNTYVhw4YxPgyaN29O2ta1a9fCYzdt2kT6rVOnTiAWi6FRo0ZynZcyZcpQYhEUPWcrVqyA3Nxc3s4Rhn0uXbpEOwXu7e0N1apVYxwr1tbWhdE0SwphYWHQunVrxj4bGRnB8uXLaT8QWrRoQdq3VatWhb+FhIRQ6qKzeXr58iWlnqLFxMQEVq1aRetxhOEXtVEG7t69Sxo0pUuX5qSdW7duMUbVUzR3dHp6OgQFBcHu3bsZw/gqUypWrAidOnWCuXPnwvHjx+Ht27dq/UI7deqUTKk+a9euXXiMpALVpUsXAAB49eqV0kZkCCFo0KABvH37lq9TguGYQ4cOUa65UCiEU6dOwX///Sc1L32dOnUgMDCQ7y4oxa9fv8DLy0vqEsmwYcPg+/fvtMe/ffuWsv+ZM2cKfycIguKO26lTJ9q6CIKAc+fOgaOjI6MsdnZ2cOrUKWxPoEaojTJw4MAB0mCpVasW620UWG4WZ3zElDtaJBLBx48f4dy5c7BkyRLo2bMn41eoIsXIyAgaNmwIY8aMAV9fX7h//z4nkaZUwbdv36R+oSD07yu+gPXr15N+Kzpr0LNnT4XPqYGBAWzYsKFEp1XF/EPS7gShf7NBt2/fhuTkZJg+fTrjhwBCCHr16gVfvnzhuxtykZ2dDevWrWNMLobQPyPpkJAQqfVMmDCBdEz58uUpHxySz2iEyO7BdLKtX7++WNmePXvGyrnAKIfaKANLly4lDZLu3buzWr8iPp2Ghoawdu1aGD16NDRo0IDRlU6R4uLiAr169YKlS5fC+fPn4fPnzyVquhLgX+yDjRs3Mvr1I4QgMjISAADWrl1Le/0Lrpsi59jd3R0+fvzI5ynAqBhJa3iE/k1NP336FAAAPn78SJsptKjyMHv2bJUYbCkDm1/faWlplHtsyZIllP3+/v1LMeqVxTX758+fSs1aYFSD2igDkgYqU6ZMYbV+ZaOaKVrMzc2hRYsWMHnyZNizZw88ffpU66LbhYWFMa7dlilTBq5cuQJr1qwhbe/RowcAKH7ddHR04Pfv3zz3HKNqCIKAiRMn0o6z8PDwwv1u377NaDuEEIKyZcvCrl271HJGie11ecnonzo6Oowv5hkzZpD2tbCwkLkdWewZli1bphKDZwwVtVEGWrVqRRoYmzdvZq1uPnKkDx8+HGJjY/Ga2P+TlZUFU6dOZTxfDRs2JP3dq1cvpa/bnDlz+O42hgfEYjEMGjSIMh4qVKhAcivMz8+HXbt2QdmyZRnHUM2aNeH27ds89uZ/xMfHw8iRI6XeD/Ja7BMEQYn/0bt3b8b9P3/+TGnz8OHDcrWniKcDhnvURhmQTGF77tw51upmOxNa+fLloX379tCtWzfaG3Pp0qWsyV7SkNXGonfv3kpfN0NDQxwaVUvJzc2Fzp07U8aEi4sLZUykpKTA7NmzpS5ndevWjbclp6ysLFi1ahUnvvwPHjyg1FWQMpyJDh06kPZv1KiR3O3m5OTA5s2bpcZAaNiwIe+ZXLUJtVAG8vPzKS53L168YK1+ZXOk9+3bF7Zu3Qp3794tnHq+ePEirZvgvHnz8GyAFLp27SrTOe/YsSNtGGd5i7e3N99dxvDE379/ab17ateuTes+/OXLF+jVqxfjWNLV1YXp06fLFaVTGQiCkCnK37lz5xR+5vTv359UX5UqVYqty9/fnyKHos/r379/yxwdEcMtaqEMfPv2jTIA2MwmqOwXpqRF/9WrV2m/ImbMmIEVgWKQtFqWZt3NRtHX16fNk4DRDlJSUmhzUri7uzOuTQcGBlJyXxQtFhYW8N9//0nNhaEsISEhnMf/j4+Pp9x/soSAz8/PBzs7O9Jxo0aNUlgOANnyJuDEU9yiFsrAw4cPSRfeyMiI1ZeqsmvPbdq0KXR/uXXrFujr61P2mTx5MlYEZEDSULBZs2ZSo8WxUcaOHct3tzE88vPnT9o16k6dOjHG6xCJRLBv3z6p0UGrVasGN2/eZFXW79+/MwbuQojdzIArVqygPHdlDbi2evVqyss6KSlJKXlkyaiIE09xh1ooA0ePHqXcZGzDhjeBu7s7rSIwbtw4PDhlRPJacz0zgNA/62hN8x/HsEtUVBTY2NhQxsagQYOkuvSmpaXB/Pnzae/7okpFRESEUvJlZmbCsmXLpLovt27dmrUQ7Xl5eZTz4eXlJfPxP3/+pMyObtmyhRXZcnNzwcfHhzE3CULanXiKK9RCGVi5ciXl5mIbNnKk05WRI0diRUBGEhMTwdvbm/OXP10ZNmwY393H8Ex4eDiUKVOGMjYmTpxY7KxeZGQk9OvXT6rCOWXKFLmDhInFYjh27BglSVvR4uLiApcuXWJ15vHChQuUdl69eiVXHZIeG5UqVWL1WZiYmAhTpkyRGsJdmxJPcY1aKAPSEtiwiTy5o4VCISWPt2SpXLkyxMXFcSJrSSEvLw/8/f2hd+/eUq21uS4CgaAw6QpGe3n69CmtVf6iRYtkOv7hw4eMabsR+hdXZNu2bTKFCw8ODpaae6N06dKwadMmyMnJUbbbFNq2bUtqS5FU7o8fP6bIzEWa3YiICFrPkIJS0hNPqQq1UAYkB2ZBekwukCd3dHZ2NkybNk3q8oKxsTEsWLBAZRbGmgBBEBAaGgre3t5SfbiLFsm12ZYtW0qNJ0933YpL+dy3b1++Tw1GDbh9+zatYirrNLdYLIZDhw5BhQoVpH4oXL16lfZrPiYmhjYOQtGPkQkTJnAWNOvDhw+UNo8dOyZ3PQRBQK1atUj1FAQL44IbN25oXeIpVaIWykClSpVIF/XkyZOctidr7ujQ0FCp61aSXwTr1q3T6uhZP378gI0bN0qN7EZXduzYAXPmzCFtGzFiBMTHx0P79u2LPb7odRs+fLjUfXHqYgwAwLlz52hnCA8ePChzHRkZGbB48WIwNDRkHG/t2rUrTJAl7/5cIblUV7ZsWYW9EiTTjAuFQk7dAPPy8rQi8RQf8K4MiMViinFOcHCwStqWljv6zZs3tAPOyclJ6hdo+fLlwc/PT62zCrJJVlYWnDp1Cjp16iSTPYaBgQFl3XbPnj0we/Zs0raRI0cCwL/x4ePjw3jOW7VqRVqqSU1NpQSwKloKsiFiMPv27aP9Kr906ZJc9cjypd+mTRupngnSZhLYJDMzkxLoZ968eQrXl56eTklEtHDhQhYlpqekJp7iE96Vgfj4eMpFlCecJhe8e/eOdnq7U6dOkJOTA7GxsTBmzBipLz8nJyc4evRoiZyyIggCHj9+DF5eXlIjiBUtzZo1g71790JKSgolsMvixYth5syZpG2Sfsvh4eG0/uII/fP7Pn/+fOG+jx49knptcFQzTAGSqbMR+rcGfe/ePbnrCgoKkmoDQFfksTFgg71795LaFwgEShvgTZ48mVSntbU1J3YOdJSUxFPqAO/KQHBwMOVG5NM6/+PHj1CuXDnKoGrbti0lIcfHjx8pEbwkS82aNcHf379ExCCIjo6GFStWSI0rXrQ4ODjAkiVLKNq5ZI4CT09PSgKUMWPGUNrPycmBWbNmMbY3atSowiRQCxcuZNyvTZs2KjlfGM1g3rx5lDFiamoKz58/l7uuAu+A8uXLS703hEIhTJo0CRISEjjoET0EQUDdunVJchRNFa4o7969o/SP66VeSTQ58ZS6wLsycPLkSdJFq1SpEm+yfPnyhdYXuVWrVvD371/G40JDQ6Fjx45Sb/4mTZpo5DpWeno6HDhwgJJIiqmYmprCqFGj4P79+4xK3caNG0nHeHh4wLRp00jbpPk83717l9EVy9nZGYKCgiAvLw8aNGjAKKciX36YkglBEDB27FjKGLG0tIT379/LVVdBXAJZPGfYjBsgC5IfXgghuHHjBit1S2YjbN68OSv1yoOmJZ5SN3hXBtatW0f5AueDqKgoSohNhP4FGpI1BOaDBw+khhBFCEH79u1ZzbvABSKRCG7fvg1Dhw6VGgSloAgEAmjXrh0cO3ZMJgNKSQXQ1dWVMlswbtw4qXUkJSUxzsoIhUJYunQphIeHg7GxMaNyVhJmazDsIBKJaMeTra2tTAZxBREL6WYVi7t3vLy84NevX5z3cejQoaS2nZycWJuFPXv2LKVvb968YaVueVH3xFPqCu/KgGSserrpYa759u0bODo6UgZMo0aN5F5rKgipybS+XVD69u0r91cH17x//x7mzZsnNQBK0VKlShVYu3YtxMbGytWOpH+yoaEhZd1x/PjxxdZDEAQcOXKEYsBUUBo3bkwJaFW0XLt2TdFThSmB5OTk0HqvuLq6SnXzkyWXwaxZs6BJkyaM+5QqVUrpXAPS+P37N8VQe+PGjazVn5eXR1kakeUe5hJ1Szyl7vCuDHTq1Il0gVauXKnS9uPi4mjXwOvVq6fUIBGLxXDixAlwdnZmHIxCoRBGjRrFa0auxMRE8PPzg4YNG8qkAJQpUwYmTZoEISEhCn9Zx8TEUOodNWoU6e+JEyfKXF9UVBRtdjqE/sWBYHpQu7m54eiRGBIZGRm0L203NzfKh8HXr1+hd+/eUl8206ZNK4zZX5CFkG4GsqAom4WQCckZWAMDA9btFZYuXUpqw8TERC0M99Qh8ZQmwLsyIBlE4ujRoypr++fPn7RJMWrXri13WFEm8vLyYNeuXVIDlOjr64O3tzdnQUboZJInKqCuri706NEDzp8/z4qVcF5eHiWQk2So10mTJslVZ35+PqxevZrR1YjJNfHs2bOQkJAA0dHRkJCQgJcOMJCUlERrjNaiRQvIysqC1NRUpaahs7KyYNWqVbSREIu29fLlS1b6IxKJKO62I0aMYKXuonz//p0SOvi///5jvR1F4CvxlCbBqzJAEARlTVpVySf+/PkD1atXpwyI6tWrs5o+uYC/f//Chg0baGOjF9WkFy9ezIk2rUhUQDc3N/Dx8eFESZE01JQ0wJwyZYpC9T5//lxq1jPJIvlAd3FxAR8fH5mzt2FKJvHx8eDk5EQZL7Vq1ZJ6/9SoUUNmA7X4+HgYOXIkY10CgQBGjhyptKv11atXKXUXZGFlmz59+lBesOqkYKsq8ZQmwqsy8OfPH8qFUMWUeWJiIu2afpUqVVhJDSqNlJQUWLRokdSvAgsLC9i4cSPFlVER5I0KWL58eZg1axbnUdAaN25Maldymt/b21vhujMzMym2KLKWoiGpb926xWKPMZrG169fi3URLCjKuK69fPkSmjdvLvUjYdWqVQo/DyTj+tevX5+zF/S9e/co8t+/f5+TtpSBq8RTmgyvysCLFy8oF4BrP9Dk5GRwc3OjXPxKlSqpNNjRr1+/YOrUqVKnGm1sbGD37t1yr2UpEhVw4MCBcOPGDZX54UreiJLK2bRp05Ru48qVK2BlZaWQUiAUCkFHRwcrBFrO5cuXpd6jenp6MGvWLKVnkgiCgHPnztEaMhcUe3t7OHXqlFwv8q9fv1KW5A4cOKCUrMX1o0qVKqT2+vfvz1l7ysJm4ilNh1dl4Ny5c6QT7+DgwGl7qamptIZyTk5OclvEs0V0dDR4enpKfWlXqlQJTp48KdXYTdGogHv27OFlSlwyyJCkEeeMGTNYaefXr1/QpUsXhRUCExMTvGRQgiAIQiYbEVnC3VatWpX1cLfZ2dmwbt06MDU1ZWy3adOmEBISIlN9kmG+zc3NpcZMYQNfX19Sm7q6urxHlZVGQeIpabNAsoSLlnVsqSu8KgObN28mnfBWrVpx1lZ6ejq4u7tTLrKdnR1ER0dz1q6sREREUNbbJEvt2rUpA5KtqICqZuvWrSS5JP2zZ86cyVpbBEHAzp07FUqhLBAIwNfXlzVZMPyQkpICPj4+tMnJitqIyJIIp2jhamz8/PkTvLy8pGZMHTZsGHz//p2xjqysLEo/2FKypZGSkkKJ77F8+XLO21WWjIwMWLRokdyJpGQdW+oOr8rAlClTSCePCwtXgH9ryC1btqRcWBsbG/j69SsnbSrK8+fPoV27dlIfQE2aNIG5c+eyGhVQ1UjOCkl+Cc2ePZvV9giCkOrSJU0ZcHFx0TgtH/M/ZE1bvnr1aqkpckuXLk37clYk/a+shIWFUaL7FS1GRkawbNky2mBfhw8fpuz/6dMnzmQtimRERxsbG41x3ZM1xfSfP39kHluasNzIqzLQvXt30slbsmQJ621kZWWBh4cH5YKWL19erSNQ3bt3T+6kJ5IDUZ6ogKrm2bNnlBus6N9z5sxhtb2EhASFzyVCSKsMiUoSt27dAh0dHZlsZ5iKvr4+zJs3D9LS0igpexH6Z+t05coVzvpAEARcunRJ6uxfxYoV4dixYyRlX/L50b59e85klCQsLIwiY9FkYppAcYmnjI2NQSAQFDu2NMX+iFdlQNJojG3DluzsbOjQoQPl4lhZWWmE6whBELB9+3awtLSU+cGlaFRAVUOXrbJoUSatKh3R0dFKKQPqsJSEkY+UlBQwMTFRShHo168fREZGkupds2YNZT9DQ0PO3aJzcnJg06ZNUm2CGjVqBMHBwRTjbIQQ+Pv7cyqfJJLLsh4eHiptnw0KEk/JGpVVmkKg7vZHuohHYmJiSH87ODiwVndeXh7q168fun37Nmm7paUlunfvHqpevTprbbFNUlISOn36NDpy5Ah68eKFTMcIBALUo0cPtH37dmRvb8+xhMpTrlw5pKuri0QiEe3vQqGQ1fZMTU2VOr5UqVIsSYJRFUeOHEFZWVkIAOQ+tmbNmmjNmjWoadOmCCGEEhISCn/z8vJC379/R7t37y7clpOTg7p27Yr8/f1R7dq1lReeAU9PT9SlSxe0ceNGdPToUUrfnj9/jtzd3SnPABsbG9SoUSNSP7hm2LBhKDg4uPDvwMBAFBQUhCpXrqwyGdigY8eOqHnz5mjHjh3Iz88P5eTkyF0HQRAoKysLHT16FHl7e3MgJQvwpYWkpKRQtCe21u/z8vJoY1Kbm5vDq1evWGmDbeSNCshUDAwMYObMmSpNjaooklHRipaFCxey2hZBEODi4iLVIIuuYJsBzUTR640LLlwVdX+WsPv5JQeSswICgQDZ2dkpXa9IJEJDhw5F/v7+pO2lS5dGt2/fRm5ubkq3wRYAgF69eoWmTZuGbGxsUK9evdClS5dQfn4+4zFubm5ow4YNaOHChcjMzIzye25uLtq6dStydnZGK1asQBkZGVx2QSmkXW+2ZwYEAgGaOnWqQsd6e3sjgUDAqjwYbklKSkKRkZEKzQpgMFwAACgyMhIlJyfzLQo9fGkh/v7+JK2pYsWKStcpEolgyJAhFI3M1NQUgoKCWJCaHdiKCpicnAzz58+Xmma4bNmysGXLFs6yoSnD4MGDGeXmwphU3jVkTVjnw9CjrI0ILrhwVdTV/og3ZUAyMIW7u7tS9YnFYvD09KSceGNjY5XlO5AGl1EBf/z4AZMmTZIaIMXW1hb27dunsgiDsjB37lxGeZctW8ZJm7JalxdYAAcEBHAiB4ZblPUewQUXroq6eibxZkDIpvEgQRBowoQJ6MiRI6TthoaG6OrVq6hly5YK160MAICCgoLQkSNH0NmzZ1F6enqxx7i7uyNPT080YMAAZG5uLlM7FSpUQDt27ECzZs1Cy5YtQydOnKBMj8bFxaGxY8eiTZs2oVWrVqF+/fqxPhUvL9KWCbialu/YsSO6fv066tu3L8rKykIIIdK5KmjXyMgIXbx4EXXo0IETOTDcYmlpiVxcXFBUVBTrSwUVK1ZEzZs3R82aNUPNmzdHtra2KCkpCfXo0QN9+fKFtG+zZs3QqVOnkKGhIasyMDFixAh069atYvfT09NDEyZMQNOnT+fcOPbevXto8ODBpG3Xrl1DjRo14rRdWcnLy0NhYWEoKCgIBQUFoRcvXihkJFgcAoEAOTs7IwsLC9brZgW+tBDJaHsLFixQqB6CIGDy5MkU7UtfX5+3rzq+owK+ffsWevToIbVNNzc3uHnzJq/GLJJLRUXLypUrOW07JSUFfH19aaOG+fr6qkUedoxy+Pj4qMSA0MXFBby8vGD79u20Lmi9evVSyYzct2/fKDNekydPlhpIydraGvbt2wcikYgzucRiMTg7O5PaHTp0KGftFUd+fj48e/YM1q1bBx06dKBES5SlKOKuqu7RTHlTBiSTQ+zevVvuOgiCgOnTp1NOup6eHly7do0DqZlJT0+HAwcOqFVUwODgYKnRyxBC0LJlS97sKUJDQxnlWrVqlUpkIAgCEhMTITo6GhITE9XW0hcjP4rYiBgbG4Ovry8MGTIEKlSooJByoKOjQ9k2cuRIzqN/Lly4kNRmqVKlID09XaYQy3Xq1IHAwEDOZNu0aROpPX19fU5So9MhEokgNDQUNm/eDF27doVSpUopdE0bN24M8+fPh4CAAIiLiytx9ke8KQOSgXTkjc5EEATtmrOurq7KgmuIRCK4ffs2DB06VKoRX1HNUNVRAQmCgNu3b0P9+vWlytatWzd4/fq1SmQq4Pfv34zyrFmzRqWyYEomytiIEAQBHz9+hJ07d0L//v0VzoBZULp3787ZenFOTg5YW1uT2ps8eTJpn6SkJJg2bZpU26JevXpxkrckMTERDAwMSG2tXbuW9XYA/s1EvH37Fnx9faFXr15QpkwZua+VQCCAevXqwaxZs+D69euQlpZGaaek2R/xogxkZGRQTtiHDx/kqmPJkiW02tu5c+c4kvp/vH//HubNmydzVCp1iApIEAScP38eqlatKvUGGDx4sMqSGBEEQXlAFJR169apRAZMyUfW+PHFPawJgoDw8HDYvn079O7dW+GXjJubG8ycOROuXr1K+5JRhJMnT1LaevfuHe2+Hz9+hG7dujHKqKenB7Nnz2Z9qUzSwNve3p6V5QmCIODDhw+wY8cO6NevH5QtW1YhZa127dowbdo08Pf3h+TkZJnaZmtsqQO8KAPv3r2jXAh50mquXLmSVvs6ceIEZzInJiaCn58fbQpkulKmTBmYNGkShISEqNXUc35+Phw8eBDs7e0ZZdfV1YXx48erJO0ok13F+vXrOW8boz1wYSMiFovh1atXsGXLFujWrZvMqcMln1uNGjWCefPmwa1btxSeMWzevDmpXlkywN6+fVuqe7OVlRXs2rWLNXuHkJAQShuK5HQgCAK+fPkCe/fuhcGDB0tNPSytVKtWDSZNmgTnzp2DP3/+KNyvkmJ/xIsycP36ddJJs7a2lvnY9evX02rbhw8fZl1OeaMC6urqQvfu3eH8+fOQk5PDujxskpOTAz4+PlKnPg0NDWHOnDmcusIw2TRs3LiRszYx2guXNiL5+fnw/PlzWL9+PXTs2BFMTEzkfkHp6uqCu7s7LFq0CO7duwdZWVnFtvvmzRtKPWfOnJFZ5l27dkn9mq5Zsybcvn1b2dMDBEFQlis7deok07ExMTFw6NAhGDFiBNja2ir08q9UqRKMHTsWTp06BT9//lS6P3T902T7I16UgR07dpAuUsOGDWU6buvWrbQXed++fazJRhAEhIaGgre3t8zTTW5ubuDj46Mygxg2SU9Ph5UrV0r9qildujSsWrUKMjIyWG9/+PDhtG1u2rSJ9bYwGFWSl5cHQUFBUqfkiyv6+vrQqlUrWL58OTx8+JD2I2PChAmkY8qXLy93uuCUlBSYNWuW1I+ebt26KZ3p9eDBg5R66cLQx8XFwbFjx2D06NHg5OSk0LlzdHSEUaNGwdGjR+H79+9Kya0N8KIMSBr+9e/fv9hj/Pz8aC/4zp07WZGJraiAmkpiYiLMmTMHDA0NGftsbW0Nvr6+rM56SFpAF5QtW7aw1gYGwzfLly9XWCEoWoyMjKBdu3awZs0aCA4OhoSEBMoMxNKlSxWW88uXL7R5XQqKrq4uTJ8+XeY1dUn+/v1LsbWYNWsW/Pr1C06fPg3jx4+HypUrK3RuKlasCMOGDYMDBw5AVFSUwudAW+FFGRgwYADpIs6ePVvq/nv27KG9+D4+PkrJwWVUQE0lLi4Oxo8fT+seVVAcHBzg0KFDrBj/0OWHRwjB1q1bWegNBqMeEAQB3t7elHFuYmICY8aMgaZNm0q18pf2TCr6t1AoZOUrODAwEOrUqcPYroWFBfz3339yz0AAAMycOZMisyIvf2traxg4cCDs2bMHPn/+rHHT8uoGL8pA48aNSRf1v//+Y9yXbloJIcXXlAmCgMePH4OXl5fMBj/u7u6wZ88etfYRZZvPnz9LzR2A0D8DnPPnzyt1E167do0TRQ+DUTfEYjEMGzaMMtbLlSsHX758gYyMDLh58ybMnTsXGjZsqNBLUk9PD7p37w5bt26FsLAwpWIbiEQi2LdvH8VlUfIZcPPmzWLrSklJgcuXL8P06dOlejRJKxYWFtC3b1/w8/ODiIgI/PJnGV6UAUnrz6tXr9Lud+zYMdoIYqtXr5a7Tb6jAmoqr1+/hq5du0o9Vw0aNIDbt28rdHPSGT8hhGD79u0c9AaD4Ze8vDzo3r07Zbw7OjpCXFwcad/U1FS4cuUKzJgxA+rWratQNEULCwvo06cP/Pfff/Du3TuF7tG0tDSYN28e6OvrM7bTuXNneP/+feEx6enpcOPGDZgzZw40aNBAIcXGzMwMevToAdu2bYPXr19zHrRJ21G5MpCdnU256HTr7mfOnKEdQPKshykSFXDkyJGcRgXUVB4/fgwtWrSQev7atGkDT58+lave5ORk2rqkzRZhMJpMVlYWtGzZkjLma9SoAUlJSYzHJSUlwcWLF2HKlClQo0YNhb6ura2tYcCAAbB792749OmTXMpBZGQk9OvXj7FuoVAI9evXhwYNGkhdZmQqxsbG0LlzZ9i0aRO8fPmS0xDJGCoqVwY+ffpEGQSSgTcuXrxIO5jmz59f7ODVhKiAmgpBEHDjxg2oW7eu1HPas2dPCA8Pl7lOOhesHTt2cNwbDIY/UlNTwc3NjTLuGzduLJPXTnx8vEIvXMliY2MDQ4cOhf3798tsdPfw4cNinwGyFMmZDk9PTyXPKkYZVK4MBAQEkAZAmTJlSL9fvXqV1r1l5syZUhUBTYwKqKmIxWI4ffo0uLq6Sr3Rhw8fLtMDhm4NkS0vEQxGXfn9+zet5Xy7du2K9diR9E4wNjaG9+/fw/Hjx2HMmDGUxECyFgcHBxg5ciQcOXKE9GzMzc2FJ0+ewKpVq8DDw0Oq1xFTMTAwgNatW8OKFSvg0aNHsGLFCtLvhoaGapveVxtQuTKwd+9e0gCoW7du4W+3bt2iXZeaMmUKrSJQUqICaip5eXmwd+9eqQqYnp4eTJ48GX78+MFYT/v27SnH7dq1S4U9wWD44du3b7RBdPr168c4TZ6Xlwc2Njak/ceOHUtb9+HDh2HEiBFgZ2enkHJgYWEBFStWZAwbLktxcnKCAwcOUAIo/fr1i/Lht3nzZk7OM6Z4VK4MLFq0iHTxe/XqBQAAd+/epdU2x48fT3pxl+SogJpKdnY2bNmyhZJ8qmgxMjKC+fPnU/yTU1JSKN4lCP0Lherj46NVHhwY7eT9+/e0946XlxftR8v58+cp+4aFhUltgyAI+Pr1K+zbt0+pjIzSijQDR6FQCBMmTKCE/ZX0WHJxccH2WjyhcmVgyJAhpIvv7e0NDx48oF3fHzVqFIjFYq2LCqippKWlwbJly8DU1JTxupibm8PatWshMzOzMMkH04OlIMmHvBktMRhN48WLF7T3zdy5cwHg38s8ISEBoqOjKXkImjZtKnd7IpEILl++DP379wcbGxuFPBUQ+je17+bmBosWLYKYmBh48uSJ1Jna0qVLw6ZNmwo/zB4/fkzZ58yZMxAdHQ0JCQl4BleFqEwZSElJAR8fH8rXv5mZGe3SwLBhw+D79+9aHRVQU/nz5w/MmDFD6tRimTJlQCAQyJz+EysEmJJOYGAg7T3TrVs3qS7Rx44dK7bugsx+O3fuVCqzX3EzAwUZGWfNmiV19sHFxQUuXboEYrEYatWqJXU/PEOoGlSiDBRN8yjLoGratCl06NABRwXUcGJjY2HMmDEKRxgrqhCYmJjgBwKmxOPv7y+3lwBdnBbJzH6KLguUK1cOHBwcZPLMkiw6OjpQsWJFqZEV27RpIzW4GZ4hVB0CAADEIQEBAahr164IABBBEMXuLxQKZdrP3d0deXp6ogEDBiBzc3MWJMVwxadPn9CSJUvQuXPnFK5DIBAgHx8f5O3tzaJkGIz6ceTIETRy5EiZ99fR0UHXr19HVatWRffv3y8s379/l7vtSpUqoTZt2qA2bdqg1q1bowoVKiCEEBKJROjVq1eFdT958gT9/ftX7voVRSgUIoFAgK5fv446duyosna1CU6VgdTUVGRra4uys7NlesEXh4ODAxoxYgQaMWIEqlSpEgsSYlRJaGgoWrhwIbp9+7bcxwoEAuTs7Iy+fPmCBAIBB9JhMOpBamoqKleuHMrLy5P5GIFAgBR5lDs4OKA2bdogDw8P1Lp1a2RnZyfTcfn5+ejFixfo/v37KDAwEAUHB6OcnBy525cHoVCIjIyMUFxcHP4A5ABOlQFfX180Y8YMhQZpAaampqhfv37I09MTtWzZEgmFQhYlxKiaxMREZGVlpdTxlpaWLEqEwagXbDw3mahYsWLhl3+bNm2Qk5MTK/Xm5OSgkJCQwpmDZ8+eyaXMyAqeIeQOzpQBAECurq4oKipKoUHdtm1bNHLkSNS7d29kYmLCgYQYPoiJiVHqARQdHY0cHR3ZEwiDUSOUfW5KYm1tTXr5u7q6qmRmLSsrCwUHBxcqBy9evEAikUjpevEMIXdwpgzgL0AMHXhcYDDMKHt/mJubIw8PD+Th4YHatGmDqlWrphYvzczMTPTkyZPCZYXQ0FCllB38HGAfXa4qzszMVOr4jIwMfLFLIJaWlsjFxUXuL5+CLwILCwsOpcNg+EXZ52ZoaChydnZmSRr2MDU1RZ06dUKdOnVCCCH09u1bVKdOHYXrw+8H9uFsAd7U1FSp40uVKsWSJBh1QiAQoKlTpyp0rLe3t1p85WAwXKHsc9PMzIwlSbjFxsZGqePx+4F9OFMGCr4A5X14CwQC5OLigr8ASzCenp7I2NhYZmNQoVCIjI2N0YgRIziWDIPhF215bmpLPzUJzpQB/AWIYcLc3BxduHABCQSCYhWCAv/iixcvYnciTIlHW56b2tJPTUKt4gxgP1LtIiAgAPXt2xdlZWUhhBDJhqDgZjc2NkYXL15EHTp04EVGDEbVaMtzU1v6qSlw6rSPvwAx0ujYsSOKi4tDPj4+FKMnZ2dn5OPjg+Lj47EigNEqtOW5qS391BQ4D0eMEP4CxBQPAKDk5GSUkZGBSpUqhSwsLPBUIEar0Zbnprb0U91RiTKA0L8poaNHj6Lt27ejyMjIwu0uLi7I29sbeXp6aowlLAaDwagCbXluaks/1RmVKQMF4C9ADAaDkQ9teW5qSz/VEZUrAxgMBoPBYNQLnPUHg8FgMBgtBysDGAwGg8FoOVgZwGAwGAxGy8HKAAaDwWAwWg5WBjAYDAaD0XKwMoDBYDAYjJaDlQEMBoPBYLQcrAxgMBgMBqPlYGUAg8FgMBgtBysDGAwGg8FoOVgZwGAwGAxGy8HKAAaDwWAwWg5WBjAYDAaD0XKwMoDBYDAYjJaDlQEMBoPBYLQcrAxgMBgMBqPlYGUAg8FgMBgtBysDGAwGg8FoOVgZwGAwGAxGy8HKAAaDwWAwWg5WBjAYDAaD0XKwMoDBYDAYjJaDlQEMBoPBYLQcrAxgMBgMBqPlYGUAg8FgMBgtBysDGAwGg8FoOVgZwGAwGAxGy8HKAAaDwWAwWg5WBjAYDAaD0XKwMoDBYDAYjJaDlQEMBoPBYLQcrAxgMBgMBqPlYGUAg8FgMBgtBysDGAwGg8FoOVgZwGAwGAxGy8HKAAaDwWAwWg5WBjAYDAaD0XKwMoDBYDAYjJaDlQEMBoPBYLQcrAxgMBgMBqPlYGUAg8FgMBgtBysDGAwGg8FoOVgZwGAwGAxGy8HKAAaDwWAwWg5WBjAYDAaD0XJ0+RYAo74AAEpKSkKZmZnI1NQUWVpaIoFAwLdYGAymCPg+xbABnhnAUEhNTUW+vr7I1dUVWVlZIScnJ2RlZYVcXV2Rr68vSk1N5VtEDEbrwfcphk0EAAB8C4FRHwICAlDfvn1RVlYWQujfV0cBBV8bxsbG6MKFC6hjx468yIjBaDv4PsWwDVYGMIUEBASgrl27IgBABEEw7icUCpFAIEDXr1/HDxoMRsXg+xTDBVgZwCCE/k052traouzsbKkPmAKEQiEyMjJCcXFxyNzcnHsBMRgMvk8xnIFtBjAIIYSOHDmCsrKyZHrAIIQQQRAoKysLHT16lGPJMBhMAfg+xXAFnhnQMgosj6Ojo1FUVFThv8ePH0fZ2dly1SUQCJCzszP68uULtl7GYDgGAJCrqyuKiopC8jy28X2KkQWsDJRAMjMzUXR0NGPJzMxktb3ExERkaWnJap0YDIZMYmIisrKyUvj4bt26IQ8PD9SsWTPk5uaG9PT0WJQOo+lgZUADycvLQ7GxsYwv+4SEBJXKEx0djRwdHVXaJgajbcTExCAnJydW6jIyMkKNGjVCzZo1Q82aNUNNmzZFZcqUYaVujGaClQE1hCAI9PPnT8aXfVxcnMxrhqoAzwxgMNyj7MxAcdSoUaNQOWjWrBlydnbGywpaBFYGeAAAUEpKCuPLPiYmBuXm5nIqg46ODrK3t0dOTk7I0dER+fv7o5SUFLnWIhFCyNbWFsXGxuKHBgbDMQCAKlWqhKKiolTSXrly5UjKgZubG9LX11dJ2xjVg5UBjsjKykIxMTG0L/uoqCiUnp7OuQzly5dHTk5OhcXZ2bnw/7a2tkhX93/RqH19fdGMGTPkVgZ0dHTQjh070Lhx47BCgMFwSE5ODqpfvz56//69Qsfr6emhsmXLop8/fyp0vKGhYeHSgru7O3J3d0cWFhYK1YVRP7AyoCAikQh9//6d8WX/+/dvzmUwMzNjfNk7OjoiIyMjmeuS139ZksGDB6M9e/agUqVKyX0sBoORTnZ2NurVqxe6ffu20nX1798f9e3bF4WFhaGgoCD04sULhWciq1WrRpo9qFSpEv4o0FBUrgxoSlINAEC/f/9mfNl///4dicViTmUwMDBAjo6OtC97Jycn1g1+ZI1sxkTlypXRuXPnUO3atVmVC4PRZv7+/Yt69OiBAgMD5TpOIBAwzvQ5OzujkydPosaNG6Pc3Fz06tUrFBQUVFgUNUK2trZG7u7uhcpBvXr1kIGBgUJ1YVSLypSB1NRUdOTIEfTff/+hyMjIwu0uLi5o6tSpyNPTU+URstLS0hhf9jExMXL73cuLUChEtra2jC/78uXLI6FQtXGhpMU8L0AgEKCePXsif39/ym+Ghobov//+Q2PGjFFLJQ+D0SQyMjJQ165d0ePHj0nbjY2NEQCgnJwchBBzboLjx4+jy5cvo8OHD1Pq1tHRQStXrkTz5s1DOjo6hdsBAH39+hUFBQWh4OBgFBQUpPDShIGBAWrYsGGhcuDu7o6NjdUUlSgDfCXVyMnJQd++faO86Av+n5KSwlpbTBRkE5N80Ts5OSE7Ozu1NMhJTU1FR48eRdu3bycpbkV5/PgxSkxMRCNHjkRpaWmU34cNG4Z27dqFTE1NuRYXgymRpKWloc6dO6OnT5+Stpubm6OAgABUuXJl2vvUxcUFeXt7I09PT2RmZoYQQuj06dNo/PjxtLZKrVq1QseOHUN2dnaMsiQnJ6OnT58Wzhw8f/68UBGRl6pVq5KWFlxdXfGHgxrAuTLAZVINsViM4uPjGV/2P378YKsbjJiamjK+7B0dHTX6ZQgAKDk5GaWlpaG2bduimJiYwt8GDRqETp06haKjo9GAAQPQy5cvKcdXqVIFnTt3DtWqVUuFUmMwmk9KSgrq2LEjevHiBWm7hYUFunPnDqpXr17htoL7NCMjA5UqVQpZWFjQvlxjYmLQ0KFDUXBwMOW3MmXKoP3796M+ffrIJF9eXl6hzUFBUdROqmzZsqSlhQYNGuClBR7gVBlQNqkGAKDExETGl31sbCzKz8/nSnyE0D8LXAcHB9qXvZOTk9raPLDNli1b0OzZswv/1tPTQ7Gxsah8+fIoNzcXzZs3D/n6+lKOMzIyQn5+fmjUqFFacZ4wGGVJTExE7du3R69fvyZtt7KyQnfv3lXKJkckEqHVq1ejVatW0T6Tx40bh7Zu3YpMTEzkqhcAUFRUFEk5iIiIUEhGfX191KBBA9LSApfxFTD/4FQZUNRdrUaNGkggEKDo6Gj09+9fjqT7h0AgQBUrVqR90Ts5OSEbGxvSepq2kpycjCpWrEiaGly1ahVavHhx4d8XL15Eo0ePpl02GD58ONq1a5fcDxkMRpv48+cPateuHQoPDydtL1++PLp37x6qXr06K+08efIEDR06FMXGxlJ+q1q1Kjp16hSqW7euUm2kpKRQlhYUtcOqXLkyaWmhSpUq+OOCZThTBhRNqsEFFhYWjF/2Dg4OeEpKRkaPHo0OHTpU+LetrS2Kjo4mxSuIiopCAwYMQKGhoZTjq1Wrhs6ePYtq1qypEnkxGE3i58+fqG3btujDhw+k7RUrVkSBgYGocuXKrLaXmpqKxo8fj86ePUv5TV9fH61fvx5NmzaNNSPm/Px89Pr1a9LsgaIxDywtLSlLC4aGhqzIqa1wpgxwHTqzKMbGxoxf9k5OTqh06dIqkaOkExoaiho0aEDadvHiRdS7d2/SttzcXDR79mzk5+dHqcPIyAjt3LkTjRw5kktRMRiNIi4uDnl4eKAvX76Qttvb26PAwEDk4uLCSbsAgA4fPoymTp1KOwvbsWNHdPjwYVS+fHlO2o6JiSEpB+/evVPo41FfXx/Vr1+ftLRgbW3NuswlGc6UATaTaujq6haGzqUr1tbWeMpIRTRu3Bg9f/688O927dqhO3fu0O57/vx5NGbMGFoLZk9PT7Rjxw68bIDRer59+4Y8PDwoYYadnJxQYGCgSpKAff78GQ0ZMoR2Rs/a2hodPnwYde7cmXM5UlNT0bNnzwqVg5CQkEIvNHmpVKkSaWmhatWqKnfV1iiAIxISEgAhpHDx9fWFwMBAiI6Ohvz8fK7ExMjJkSNHKNfq48ePjPt/+fIF3NzcaK9x9erVISIiQoXSYzDqRWRkJDg4OFDujUqVKkFsbKxKZcnNzYW5c+cyPpOnTZsG2dnZKpUpLy8PXrx4AT4+PtC/f3+wsbFR+J1iYWEBXbt2hbVr18LDhw8hKytLpX1RdzhTBgiCABcXFxAIBHJfNBsbGyAIgivRMEqQnZ0NlpaWlIdEccdMmjSJ9lobGxvDkSNHVCM8BqNGfP78GWxtbSn3RNWqVSE+Pp43ue7cuQMVKlSgvV/r1KnDqwJPEARER0fDiRMnYNKkSVCnTh2F3jEIIdDT04PGjRvDzJkz4cKFC/Dr1y/e+qUOcKYMAAD4+PgofKGGDh0KP3784FI8jIJIfj2YmZlBZmZmscedPn0aSpUqRXu9R48eDX///lWB9BgM/7x//572hVujRg21eCn9+fMHunfvTnuvGhkZwe7du9Xmgy0tLQ0CAgJg6dKl0LZtWzAxMVF49sDFxQVGjBgBe/bsgXfv3oFYLOa7eyqDU2UgJSUFTExMQCgUKnRhSpUqBVu2bIG8vDwuxcTISVRUFEXJ27t3r0zHfv78GerWrUt7vWvUqAHv37/nWHoMhl/Cw8PB2tqa9qv7z58/fItXCEEQsGPHDjA0NKS9X3v16gWJiYl8i0khPz8fQkNDYfv27TBw4EDa2RdZS5kyZaBLly6wZs0aePDgQYn+YOFUGQAAuHXrFujo6CisEBS8JO7fv8+1qBg56Nq1K+VBJuuXQnZ2NkyYMIH2WpuYmMCxY8c4lh6D4YewsDDKMhtCCOrXrw9JSUl8i0dLeHg41KxZk/Z+tbGxgcDAQL5FLJZv377ByZMnYfLkyVC3bl2F30e6urrQqFEjmD59Opw7d4712WuCICAhIQGio6MhISFBpbMvnCsDAP8UAhMTE9olA4FAAAKBAExMTGDSpElgZmbGeCEGDx7M61oa5n9cv36dcn2CgoLkquPUqVNgampKe629vLywgQ+mRPHixQsoU6YMZaw3adIEUlJS+BZPKllZWTB16lTae1UgEMCCBQs0agY3PT0dbt++DcuWLYP27dszPodkKU5OTjBs2DDYtWsXvH37VqGlhZSUFPDx8QEXFxdS3S4uLuDj46OS8aESZQDgX2fXrFlDOZEODg7g6+sLqampAADw+/dvGDlyJOOJNzU1hc2bN2vUwCuJiMVicHJyIl2bIUOGyF3Pp0+foHbt2rTXulatWlI9FTAYTSE4OBhKly5NGePNmzeHtLQ0vsWTmatXr0LZsmVp79eGDRvCly9f+BZRIUQiEYSFhYGfnx8MHjwY7O3tFVYOzMzMoFOnTrBq1SoIDAws1p6q6Mey5Adz0Y/lW7ducXoOVKYMAPxzE5E8cUyWqUFBQYxrywj9c0vThOmpkszGjRtJ10RPTw9+//4tdz1ZWVkwbtw42utsYmICJ06c4EB6DEY1PH78mPbLs02bNpCRkcG3eHLz48cPaN++PePH2pEjR9TGuFAZvn//DqdPn4apU6dCvXr1QEdHRyHlQEdHBxo0aADTpk2Ds2fPkma3ZV1GFwqFoKOjw6lCoFJlAAAolp7BwcGM+4pEItixYweYm5sznqRBgwZBXFycCnuAKSAxMREMDAxI12Pt2rUK13f8+HFGS+CxY8fiZQOMxhEYGAjGxsaU8dy+fXuNNkYTi8WwefNm0NPTo71fBw8eXDjbW1LIyMiAu3fvwooVK6BDhw6MnlGyFEdHR+jfvz/o6+vL7HEnFArBxMSEsyUDlSsDFStWJHXwxo0bxR7z+/dvGD16NONJMjU1hU2bNuGlAx7w9PQkXQt7e3sQiUQK1/fx40eoVasW7XWuXbs2fPr0iUXpMRjuCAgIoLXE79Kli8qD93BFaGgoVK5cmfGFJ+1jT9MRiUTw+vVr2LFjBwwZMoQ2eBTbRSAQgK+vLyf9UbkyUKNGDVLnTp48KfOxwcHBjNHsEEJQrVo1uHfvHofSYyR5/vw55TpcvnxZqTr//v0LY8aMYVT8Tp06xZL0GAw3XL9+nTJrhhCCnj17Qk5ODt/isUpGRgbj/aqjowMrV65U6gNBk4iLi4OzZ8/CtGnToEGDBgovLUhTBlxcXDhZhlG5MtCsWTNS53bu3CnX8SKRCHbu3Cl16WDgwIF46UCFNGjQgHT+O3TowEq9R48epZ1iRQjBhAkTSszXFaZk4e/vTzt93q9fvxI5e1ngDufn50drJIkQghYtWsC3b9/4FlXlZGZmQmBgIKxatQo6deok1VtOnsJFfAeVKwOS/umKrjH/+fOHURtF6J/h2YYNGyA3N5flHmAkOXToEOX8f/78mZW6379/T5lNKih169ZlrR0Mhg3Onj0Lurq6lLE6ePDgEpdjhckdjilIkbm5OZw9e5ZvsXlFLBbD27dvYfXq1UopA9HR0azLpnJlYOjQoaROzZ07V6n6nj59CvXq1WM8aVWrVoW7d++yJD2GjqysLIr/9IwZM1ir/+/fv4w2I6VKlYIzZ86w1hYGoygnTpygtQofMWJEiZsmL84dTtqLbPTo0TKFLy/JKJvIr0TMDEyePJnUqbFjxypdp0gkgl27dtEG9CgoAwYMgO/fv7PQAwwds2bNonwFsG0tfeTIEcZlg0mTJuFlAwxvHD58mPYl6OXlVeLi28vqDidNKahcuTKEhoby3RXeUDSRX4myGVi0aBGpc/3792et7oSEBPDy8mI8wSYmJrB+/Xq8dMABX79+pZzv/fv3s95OREQEVK9enfb6urm5wdevX1lvE4ORxt69e2mfOZMmTSpxioC8+WaEQiHtsglC/+KSbNq0qcSdI1lRJJFfifIm2Lx5M6lz7du3Z72NkJAQilFb0VKlShW4c+cO6+1qO506daK8nLnQYDMzMykujQWldOnScO7cOdbbxGDo2LFjB+04nD59eokIvCOJoi+woUOHMob8bd++vVZmqE1JSaH1OJGmWJWoOAP79+8ndbBhw4actCMSiWD37t1gYWHBeHL79+8PsbGxnLSvjVy9epVyjp8+fcpZe4cOHQIjIyPaaztlypQS58KFUS+2bdtGO/bmzp1bIhUBZae2P3/+DA0bNqTdp2zZsnD16lW+u6hSPnz4ILMyUBCBMCAggDN5VK4MnD9/ntRJV1dXTttLSEiAsWPHMg5gY2NjWLduHV46YAGRSEQJvDF8+HBO2wwPD4eqVavSXtv69etDZGQkp+1jtJMNGzbQjrklS5aUSEUA4F/qcnmUAMmSmJgIeXl5sGDBAsbn8ZQpU7Qi0mh2djZjThZJRaogNwGXigAAD8rA3bt3SZ21srJSSbvPnz+XunRQuXJlzk+2NrB+/XrSedXX1+c8R3tGRgYMHz6c9rqWLl0azp8/z2n7GO1i5cqVtGNt5cqVfIvGOrm5uXD58mUYMGAA6OvrK6UMFHWHCwwMpESjLSg1a9aE8PBw/jqtAqZMmULpN92siaOjIymRH5eoXBl4+fIlqbN6enoq06RFIhHs2bNH6tJB3759tTI4Blv8+fOH8tBYv3495+0SBAH79+9n9HH29vbGywYYpSAIAhYvXkw7vlQxxlWFWCyGhw8fwrhx46R6aCkyM1CUxMRE6NWrF+2+hoaGsGPHjhI5y3Lp0iVKf2vUqAHfvn2jbFck8ZuiqFwZoLM6V3XCjsTERBg/frzUpYO1a9fil4eCSH6lOzg4qMzP+u3bt1ClShXa69qwYUOIiopSiRyYkgVBEDB37lzacbV161a+xWOFN2/ewNy5c8HOzo41BQAh6e5wBEHA7t27GW1/unfvDgkJCTycDW6IjY2lKFhGRkbw7t07+PPnD6X/XM+qFkXlykBiYiKlw3yFDn7x4gWjQQtC/+wZuM4hXRJ5+vQp5Vyq0jgoIyODEtyqoJiZmcHFixdVJgtG8yEIAqZPn047nvz8/PgWTyliYmJg7dq1ULNmTZle7LK6FEoqA8W5w0VERDCuoVeoUKFEeH/l5+dD8+bNKf3bs2cPAID2KQP5+fmUDr97907VYhQiFoth3759YGlpyTiY+/Tpg5cO5IAgCEpUyE6dOqlchn379jEuG0yfPh0bjWKKRSwWw6RJk2hfcHv37uVbPIVISEiAnTt30r6YmErz5s1h165dEBkZWRh5UFblQVZ3uOzsbEalSyAQwNy5czX6nl26dCmlX/379y+cMdE6ZQAAKHmgnzx5wocYJBITE2HChAmMg9zIyAjWrFmDlw5kRNKFFCHES0CgN2/eMKZYbdSoEScxvjElA7FYDF5eXrQvpsOHD/Mtnlz8/fsXTp06Bd26dWMMAiRZatasCevWraPcI1euXJFZiRAKhXIbZl+/fh2srKxo66tfv75GpjG/f/8+5d3i4OBAUpK0UhmQXJO6du0aH2LQ8uLFC2jUqBHj4HZ1dYWbN2/yLaba8/fvX0pmydmzZ/MiS3p6OgwaNIj2epqbm4O/vz8vcmHUF5FIRBvYSkdHB06cOMG3eDKRn58PN2/ehGHDhoGJiYlML287OzuYN28evHnzhrHevXv3yqwMdOzYUSHZf/78CR07dqSt08TEBA4ePKgxxoUJCQlgY2NDGUeSMVi0UhmoVasWqcPHjx/nQwxGxGIx7N+/X+rSQe/evSEmJoZvUdWaGTNmkM6ZhYUFbz7EBEHAnj17GIN8zJw5U6OnIDHskZ+fD0OGDKGMEV1dXbXPukcQBDx9+hSmTJnC+HUtWcqUKQPjx4+Hhw8fFhsaWCwWU2bamIz/EPrnWqxodEGxWAzbtm1jdGkcMGAAZ9H42IIgCOjWrRtF9nXr1lH21UploEWLFqQOq6sRTlJSEkycOFHq0sGqVatwghwGPn/+TDlnhw4d4lWmsLAwqFSpEu31bNy4MVbwtJy8vDzo168fZWzo6emp9QzShw8fYPHixeDs7CyTAmBoaAgDBgyAy5cvy6UE07nF3bx5ExITEyE6OhoiIiJAT0+P9Pv8+fOV6turV68YA4vZ29vD48ePlaqfS3x8fCgyt2vXjlbp0kploHv37qQOr169mg8xZObly5fQuHFjxhurUqVKcOPGDb7FVEs6dOhAOlcNGjTgWyRIS0uDgQMHMn4laVtYVMw/cnJyoGfPnpQxYWBgANevX+dbPApxcXGwefNmqSncixahUAgdOnSAI0eOQHp6ukJturu7k+qsXbs2Zbp+7NixpH3MzMwUbq+AzMxMGDduHGO/li1bBvn5+Uq1wTahoaEUxcja2hp+/vxJu79WKgOSfuh8rSXLg1gshgMHDkDZsmUZb7ZevXphgzQJLl++TDlPz58/51ssIAgCdu7cyTgFOXv2bMjLy+NbTIyKyM7Ohi5dutB+QatTZNKUlBTYv38/eHh4yGzR36hRI/D19WV8CcnKkydPKHUfO3aMst/Hjx8psm3ZskWptgu4cOECYyAkd3d3tXn+pqeng6urK0VGaWNJK5WBqVOnkjo8ZswYPsRQiKSkJJg0aRKjv62hoSGsXLkSLx38PyKRCOzt7UnnyNPTk2+xCnn16hW4uLjQXsumTZviRFZawN+/f6F9+/aU629sbAyBgYF8iwfZ2dlw4cIF6NOnj8yJbSpXrgwrVqyAz58/syaH5KyJnZ0do8Isz77yEhsbC61ataLtd+nSpeHUqVOstKMMdOHR586dK/UYrVQGlixZQupw3759+RBDKV69egVNmzZlvBldXFzUcmqRD9auXUs6NwYGBpTQpHySmpoK/fv3p72OFhYWauXtgmGXjIwMaN26NeW6m5qawqNHj3iTSyQSwb1792D06NFgZmYmkwJQvnx5mD59Orx48YJ1S/sPHz5QvvalRV4MCgqSaRZBUUQiEaxZswZ0dHRoz8XIkSOVXppQlCNHjtDOzhSnDGmlMrB161ZSh9u2bcuHGEojFovh4MGDUq12e/bsqTZTV3zx+/dvynT8xo0b+RaLBEEQ4Ofnx7hsMHfuXLxsUMJIT0+nDbxTunRpCA4OVrk8BEFAaGgozJw5k+KKxlRKlSoFI0eOhDt37nAa8lsy3oIsdgCS9gW1atViXUl59uwZo9FkpUqVVL4k+enTJ4obZ+nSpWXKnqqVysDBgwdJHa5fvz4fYrBGcnIyTJ48WerSwYoVK7R66UAyPLCTk1Oxbkx88PLlS8aHS7NmzfCyQQkhJSUFmjRpQrnG5ubm8OLFC5XK8vXrV1i5ciWjxbxk0dfXh169esG5c+dU4qr78+dPipK8YMGCYo+j8zzgIrx7WloaY9ZSXV1d2LBhg0qeNTk5OeDm5kaR4fTp0zIdr5XKwMWLF0kddnFx4UMM1gkLC6Now0WLs7Oz1k45000bqusySmpqKvTt25f2GlpaWmLPEQ0nKSmJNp25paUlhIWFqUSG379/w/bt22kVEroiEAigdevWsG/fPkhOTlaJjAUsXLiQoozIEjuALiYBl7PAx48fp0S3LSgeHh6c58CZNm0apV0vLy+Zj9dKZSAwMJByE5YUxGIxHDp0SOrSQffu3bUuex5BEFCnTh3SeejatSvfYjFCEARs376d4hpUUObPn692rkyY4klISIC6detSrqe1tTWEh4dz2nZ6ejocPXoUOnXqxLjWLVnq1q0LGzduhO/fv3MqmzSZJSOJymPwTRetMDQ0lDN5IyMjGd3ALS0tOYsVQReiuVq1anJl5NVKZeDVq1ekDuvo6GhMaElZSUlJgalTp0pdOli+fDlvEfn4QPLBIBAI1F4pev78OTg5OdFew+bNm/OWcRMjP79+/aLNzlehQgV4//49J23m5ubC1atXYdCgQVIj9RUtjo6OsHDhQoiIiOBEJnnYtm0bRb4PHz7IfHx2djaUK1eOdPygQYM4lPhf4KjFixczul5OnDiR8twlCAISEhIgOjoaEhIS5HofxcXFUaLVGhgYwNu3b+WSWyuVgaioKEqn+bL85JqwsDBo1qwZ443v5OSkNUFuMjMzKZbRxbnbqAMpKSnQu3dv2utXtmxZ2nVQZR4uGPaJj4+nXZO3tbVl1f0O4N/s4OPHj2HChAlSQ5rTlYoVK8K2bdvUIsxuXl4exS24R48ectezZs0aysefKj4CHjx4ALa2trTnuXr16vDmzRtISUkBHx8finuxi4sL+Pj4FHsdRCIRrZvjzp075ZZXK5WB5ORkSqdLsmEWQRBw5MgRsLa2ZnwIdOvWTSaLU01Hcl3N0tJSIwwrCYIAHx8fxmWDhQsXQn5+vtIPFwz7xMbG0oagdnBwYPWeCw8Ph/nz54ODg4NcCkDRIhAIQCAQgImJCSfGdvJw/PhxinyKZJhNTk6mWNhPnTqVA4mpJCUlQZ8+fWjPtZ6eHujr6xeec0Wuw4oVKyj19unTRyHlXyuVAZFIROm0vFMqmkhKSgp4e3szLh0YGBjAsmXLSvTSwcePHyn9PnLkCN9iyUxISAjjw75mzZpgbGys1MMFwy7R0dG0yzzOzs6s5KH49u0brF+/HmrXri3Ty77g3i8ueqBQKAQdHR3exgqdjU/Tpk0Vrm/69OmkuoyNjVUWa4QgCNi3bx8YGxvLrZxJuw6PHj2iPMvt7e0VNvDUSmUAACjTxXwG+FA1r1+/pvVvLihOTk5w5coVvsXkjHbt2pH626hRI75Fkovk5GTaGPbKPlww7PL161fKNDdC/6LzKWOQl5SUBLt374aWLVvKfN3d3d1h48aNYGxszPgxQDdWTExMeJlNCggIoMhz8eJFheuLiYmhGE2uXLmSRYmL58OHD7TGo4pch8TERMoShI6OjkIzJwXwrQwIAAAQDzg6OqJv374V/n3lyhXUvXt3PkThBQBAx48fR3PmzEG/f/+m3adr167I19cXubi4qFg6brl06RLq06cPaduLFy9QgwYNeJJIfgAA+fj4oLlz5yKRSCTXsUKhEBkZGaG4uDhkbm7OjYBazufPn5GHhweKj48nba9WrRq6d+8eqlChglz1ZWVloatXr6KTJ0+imzdvovz8/GKPqVatGho6dCgaMmQIcnJyQr6+vmjGjBlInkeuQCBAq1evRmPHjpVLXmXp168fevToUeHfzs7OKCgoCOno6Chc54QJE9DFixcL/7a0tESvXr1CRkZGSskqD7m5uWjQoEEoKChIruMEAgHy8fFB3t7eCABQ79690eXLl0n7rF69Gi1atEhh2RISEpC1tTVp258/f5CVlZXCdcqFytQOCSSnoI4ePcqXKLySmpoK06ZNY3Q1MjAwgCVLlsjloqLu5OfnU7Tq0aNH8y2WQjx9+pTieiVLEQgE4Ovry7f4JZKIiAgoX7485ZzXqlULfv/+LXM9+fn5EBAQACNGjABTU1OZrmvFihVh9uzZEBYWRlo3JggCXFxcZE4uhIt6FYFAAC4uLkAQBPz333+U3z08PJSOAMn3zABvyoCkBeb27dv5EkUtePPmjdSlA0dHR/D39y8xVumrVq0i9c/Q0BCSkpL4FktuCIIAR0dHpR4uGPZ4+/YtbYyPunXrQkJCQrHHEwQBISEh4O3tTXGJYyrm5ubg5eUF9+/fZ3whJCQk8P5Cw0X5cv/+fUo0RisrK4iPj1d67GqtMiC55qrq9SN1hCAIOHbsmNSHUJcuXeDLly98i6o0P3/+pFjms5XmVJV8//5dqYeLOiVs0nRevXpF68rXsGHDYo26Pn36BEuXLqX1OqArBgYG0K9fP7h06RLk5OQUK1t0dDTvLzJclC90ocrZikiqtcqAp6cnqdMzZszgSxS1IzU1FaZPn864dKCvr18ilg4GDRpE6peLi4ta5isoCkEQEBERAdu2bYNOnTrJnFKWqWh7Eiu2eP78Oe1yTdOmTSE1NZX2mB8/fsDWrVtpQxPTFaFQCO3atYODBw8y1skEnhkomWXWrFlsDF8A4F8Z4M2AcPr06cjX17fw71GjRqGDBw/yIYraEh4ejqZMmUIy5CmKg4MD8vHxQT179kQCgUDF0inPkydPUIsWLUjbbt26hTp27MiTRPQkJSWhu3fvotu3b6Pbt2+juLg41upOTExElpaWrNWnjQQHB6POnTuj9PR00vYWLVqg69evo1KlShVuS0tLQxcvXkQnT55EgYGBiCCIYutv0KABGjJkCBo0aJDchocFAABydXVFUVFRchkQFqVOnTpo/vz5yMPDg5P7PSUlBdWtWxdlZ2cXbhs7dixas2YNq+1cuHABTZw4kbTtypUrqEmTJqy2U0BERATasGEDunXrlsJ1CAQCynVr0KABCgoKQvr6+sqKiBDSYgPCZcuWkTSg3r178yWKWkMQBBw/fpzWIKqgdO7cWSOXDgiCgFq1apH60r17d77Fgry8PHj06BEsXrwYGjZsyInRF7YZkA+miI4PHz6kNe7z8PCAzMxMAPiXTe7SpUvQr18/mWdyXFxcYOnSpfDx40fW+uDj48PKWGrWrBncv3+fNbkKWL16NakdHR0dVmIxSJKXl0eJ1cHFff/hwwcYOHAgJzMCpUqVYv2Zy/fMAG/KgI+PD6nTbdq04UsUjSAtLQ1mzpwpdelg8eLFGrd0sHv3bspLko+p869fv8KOHTugZ8+ejJnP2FYGsDdB8UiL6Dhp0iTaeP8dOnSAzMxMuH//Pnh5ecns7WFtbQ3e3t4QEhLCiZKWkpICJiYmMscZKK54eHhAcHAwK7JlZ2dTIqQOHjyYlbrpkHz+I4RYyw8RGRkJI0aMkHqe6QKDyVNOnDjBiqxF0Vpl4PDhw6ROu7m58SWKRhEeHk4bC7ugODg4wMWLFzXmizMjIwNKly5N6sP8+fM5bzctLQ0uXboEEydOpDUKKq7Y2trCmDFj4MyZMxAZGSnXQ57PYDKaxK1bt8DExIQxoiPduW3evDlMnz4dKlasKNO1MDU1hREjRkBAQIBKslDeunULdHR0ih0rBcGpFixYQEkDLFm6dOmidCbAPXv2UOp99eoVS72mkpGRAWXKlCG1p6x7cWxsLIwbNw50dXUZz5WlpSVs2rQJ/P39ZboOdGXUqFEsnQUyWqsM+Pv7kzrt5OTElygaB0EQcOLECahQoQLjgO3YsSPrCVi4YurUqZQbNi4ujtUkPyKRCEJCQmDlypXQvHlzmVPIFhQjIyPo3Lkz+Pj4wPv37ykyFTzkZQ0zGxAQoHSfSjKyvjSLFllndHR1daF79+5w+vRpXmbSpCk5BcXExKRwjOTn58OhQ4eKdWHt06ePQmmYxWIxReFo164d292msGjRIlKb+vr68OPHD7nr+fnzJ3h7e1Nc/ooWMzMzWLVqFSkhXnHKJt21qVKlSuHyE9torTLw4MEDUqfLlCnDlygaS1paGsyaNUvq0sHChQs5G7xs8f79e6kPOUWT/MTGxsL+/fthwIABYGFhIfcXQJ06dWDu3Llw9+5dmZIp3bp1izGRUdHcBFgRkA7b0+kFpUWLFrB79261cOdMSUkBX19fyvIHQv9mK+jGem5uLuzatUvqrIdAIIDBgwfDp0+fZJbl0qVLlHpu377NYm/p+fXrF8WGQ55ZwYSEBJgzZ47U1NCmpqawePFiRtdSpuvg4OBASa5kYGAAYWFhLPWeitYqA69fvyZ1WigUqr1bmbry7t07aN26NeMNYW9vDxcuXFDrpQPJiJRML1JpMf0zMzPhxo0bMG3aNKhWrZrcLwtra2sYNmwYHD16FH7+/KlQPzp27Mio0Pj6+srtkqaNsGVoh9C/qIPr16+Hb9++8d0tWgiCgOfPn1PklmY3k52dDT4+PlKzoAqFQhg1apRM9jdNmzYlHVu3bl2VPSvGjh1LatvMzKzYdPYpKSmwZMkSqVEhDQ0NYfbs2TK/TAmCgMTERIiOjobfv39DmzZtKHX+999/bHSZEa1VBmJiYigdxw9KxSEIAk6dOgU2NjaMN0iHDh3k+mJQFbdu3ZLpK1AyyQ9BEPD69WvYsGEDtG3bVuo0IV3R19cHDw8P2LBhA4SFhbGijEou3fj4+EBiYqJaK2LqBBthe+3t7WH+/PkakwmVIAjKzNWZM2eKPS4zMxPWr18vddZLT08PJk6cCHFxcbR1PHnyhHLM8ePH2e4iIx8/fqRca6bgY+np6bB69WqpBqH6+vowZcoUhZYbCpD0qkAIQc+ePTm/h7VWGUhNTaV0nAs3Fm0jPT0dZs+ezWhEo6enBwsWLJC6dMDkxiUvstQj75SwUCgEAwMDGDhwoMzhYouWatWqwbRp0+DGjRusL5/8+PGD0h6brmnagLLBea5evaqRM4ydOnUi9WPmzJkyH5uWlgYrVqygGOIWLQYGBjB9+nT49esX6dgePXpQFKm8vDy2uyeVXr16kWSwtbUlyZCVlQWbN2+GsmXLMvZPR0cHxo4dq/QM0JMnTyjLrra2tioJla61yoBYLKZohK9fv+ZLnBJHREQE7VRXQbGzs4Pz58+TXtDS3LjkWbOXpx42p4TpSpkyZaB///6wf/9+zqeKr169Smrb1NRUI19MfKJs2F5NjegoGXelefPmcteRlJQECxYsAGNjY8bzY2xsDPPnz4ekpCT48OED5fdt27ax37liCA4Opshx9OhRyMnJAT8/P6mG0gKBAIYPH86Kz39ycjIl5bVQKIRHjx6x0Mvi0VplAAAoriUPHjzgU5wSB0EQcPr0aalLB+3bt4ePHz/KZFlb3Jo9gGwWugX1cJHJTUdHB5o3bw4rV66EkJAQpTOJycPy5ctJsrRo0UJlbZcUlJ0ZUAfjQEW4fv06qR9GRkYKuzr+/v0bZsyYITXAUunSpcHNzY20zdzcHDIyMljumWw0a9aMJEvFihUpL2bJ0r9/f4iIiGClfYIgoE+fPpQ2VqxYwUr9sqDVyoCTkxOp4/7+/nyKU2JJT0+HOXPmMC4dFLjEyer7zKQQyOtDfebMGVYUACcnJ5gwYQJcunSJV7sTySnX6dOn8yaLpqKogqjpER3pXgTKWq7HxcXBpEmTGD1cJMvChQvZ6YwCSLqaSys9evRg3ap/586dlHZatWql0o8JvpUBIeIRc3Nz0t+pqam8yFHSKVWqFNq4cSN6+/Yt8vDwoPwuFosRABQbp50gCAQAqG/fvpRrlZqaivr27StzPQRBoKFDh8rdl6KsXLkSff36FUVGRqJVq1ahunXrovz8fIVjvytLaGgo6e/69evzIocmIxAI0NSpU+U+DgDQ5MmTNTJHB0IIWVlZIWdnZ9K258+fK1VnxYoV0Y4dO9Dnz5/R6NGjkY6OjtT9DQwMUE5OjlJtKgJBECgnJ6fYGP8dOnRAISEh6PLly6hu3bqstf/27Vs0Y8YM0jZLS0t04sSJYs9ZiUJlagcNkmvaPj4+fIqjFRAEAWfOnJE5QhtdoQuly/XaP12JjIxkxcaBDX79+kWRj63wqtqGonEGunTpArm5uXyLrzCSWTyVjcgnyefPn2Ho0KFS79OKFSvCzp07VXIeCYKAK1euSHUrRghBy5YtOVu3z8zMpHVDvnr1KiftSYPvmQFelYHevXuTOr58+XI+xdEqMjIyYO7cuQorAw4ODhAbGwvv37+Hp0+fSrVL4KJYWFiAsbGx0jYObCG55mtiYqLSKcaShiIRCBFC0LVrV5kCRKkj27ZtI/WlZs2anLQjy33v6OgIBw8e5CREM0EQcPv2bWjUqFGxcrRq1YrTpR8vLy9Km3wt72m1MjBq1ChSx8eNG6exa36aQE5ODvz58we+fv0Kr169gsuXL6v0Ba7qUpyNA5usXLmS1LYi1uAYMoqEi0XoXzyNrKwsvsWXm6CgIEofiwvAIy95eXlgZ2cn8z3k6uoKJ06cYE2xffjwIbRs2VKuezgqKoqVtiU5ffo0pT03NzfIycnhpL3i0FplICUlhTbhDh9TvOqMSCSC1NRUiI2NhXfv3sHTp08hICAAzp07BwcOHIBt27bBypUrYfbs2TBu3DgYNGgQdO3aFVq0aAF16tQBJycnsLS0lDsgT0kpqkoK1LNnT1K706ZN47Q9bYEpXGxBRMcbN27QRqJr06aN2ofhliQrK4ti5Mt2quLjx49TztWePXugXbt2Uu+jGjVqKBXF9NmzZ9C+fXupbVSvXh0OHTpEcY2cOnUqq+cA4F9mQ8m4DKamprzmc9FKZaBA46cbEHxM8bINQRCQlZUFv379gs+fP0NoaCjcv38fLl++DMeOHYOdO3fC+vXrYeHChTBlyhQYMWIE9O7dG9q2bQsNGzaEKlWqQIUKFaSG28RF9qKKdMG2trakNo8ePcppe9pG0XCxkhEdg4ODaQPutGjRgvUva66pV68eqQ8bNmxgrW6CIKB27dqk+t3d3Qt/v3//PjRv3lzqvVSvXj24fv26zEpBWFgYdO/eXWqdLi4ucPz48cLZh+nTp5N+NzY2ZtVlNC8vj3aJgu97VuuUAXndz1SpEOTn50NycjLExMRAeHg4BAUFwc2bN+HMmTOwf/9+2Lp1KyxfvhxmzpwJXl5eMHDgQOjcuTM0a9YMatWqBQ4ODmBhYSE1hSYuqi9cu539/v2b0ua7d+84aQtDz/Pnz2nD1DZt2lSjwpxPmDCBJH+fPn1YqzsgIIByfiTduQmCgFu3bkGDBg2k3lNNmzaFe/fuMbYVEREB/fr1k1qHvb097N+/nxLx8Nu3b5QogCtXrmTtPNDZTIwYMYK1+hWFb2VAAKA6P6zU1FRka2uLsrOzi3U/QwghoVCIjIyMUFxcHMUNsQAAQFlZWSg9PR1lZGSg9PT0wiL5d3HbsrKyWO6x+qKjo4NKly6NEEIoJSVF7uMHDhyIxo0bh7Kzs1FKSgq6cOEC8vf3Z1lKdklMTESWlpas13vz5k3UpUuXwr+NjY1Renq6drklqQFhYWGoffv2KCkpibS9QYMG6Pbt26hMmTI8SSY7hw8fRqNGjSr829bWFn3//p2Vutu1a4fu3btX+HeVKlXQ+/fvkVBI9TAHAHT16lW0ZMkS9PbtW8Y6W7dujVavXo2aNWuGEELo69evaMWKFejEiROMLr4VKlRAixYtQl5eXsjAwIB2n2HDhqETJ04U/m1lZYW+ffuGjIyMZOorEwEBAahTp06kba6urujVq1fI1NRUqbqVJSEhAVlbW5O2/fnzB1lZWamkfZUqA76+vmjGjBly+4E3adIE2dnZMb7UZVEsSgomJiaoVKlSqHTp0qQi7zYjIyMkEAjkVtAK0NXVRdu3b0cTJkxQqB6BQIB0dHSQSCQibTc1NUUEQXCimEVHRyNHR0fW612zZg1avHhx4d/u7u4oKCiI9XYwxRMeHo7atm2LEhISSNvr1q2L7ty5g8qWLcuTZLLx/v17VKNGDdK2+Ph4ZGNjo1S9r169osS92Lt3Lxo7dqzU4wiCQOfPn0dLly5Fnz59YtyvVatWyNzcHF27dg2JxWLafcqWLYsWLFiAJk6cWOxL/c2bN5RYArt27UITJkyQepw0fv36herUqYP+/PlTuE1fXx89ffoU1atXT+F62UJrlAEAQK6urigqKoq3oDB8oaurS3kp072si3uhm5qaIl1dXdblCwgIQF27dpUpYJAkw4YNQ7t370YmJiYy1yMUCpFAIEDW1tbo58+fhdutrKzQp0+fUJkyZVBeXh5KSkpCiYmJheXTp09oyZIlCveTq5mBPn36oEuXLhX+PXXqVLR9+3bW28HIxvv371Hbtm3Rr1+/SNtr1qyJ7t27R3ngqhNisRiVKVMGZWRkFG67dOkS6tWrl1L1DhkyBJ06darw73LlyqGYmBhkaGgo0/EikQidPHkSrVixAkVFRcnVtrm5OZozZw7y9vaW6+u7Y8eO6Pbt24V/V6pUCX38+FGhGTeCIFDHjh3R3bt3Sdt9fHzQtGnT5K6PC/hWBlRmM6BszHE+iqmpKdjY2EDVqlWhUaNG0LZtW+jduzd4enrC1KlTYdGiRbB+/XrYuXMnHD9+HK5cuQIPHjyA0NBQ+PLlC/z+/Ruys7M1wl1SFjcuJjuPmjVrFqZGljU3wfDhwyn1HDp0SKqM6hqqVjKG+uHDhzlpByM7nz59og2sVa1aNaXS26oCDw8PkswLFixQqr7o6GjKGvyaNWsUqisvLw/27t0rNXlQQTE2NoalS5cq7Mlz584dSp3nz59XqK7169dT6urWrZtaPZv5thlQmTKgbDYyWYuenh6ULVsWnJ2doU6dOtCiRQvo2rUrDB48GMaPHw+zZ8+GlStXgo+PDxw8eBDOnz8PAQEB8PTpU4iIiIDv379DWlqaVgaMKc6NKzExkTFgSalSpeDChQsy1fP69WswNDQk/ebu7i5Thj9FIh1y6U1Ap+SGh4dz0hZGPr5+/Uqb7MbV1RW+f//Ot3iMzJ8/nySvh4eHUvV5e3uT6jMxMYHk5GSF6kpOToZFixYxeoNJ3neenp4QGRmpUFsEQVCSKTVu3FjuF/jTp08pypCNjQ0kJCQoJBdXaI0yoOzMwMyZM2Hjxo2we/duOHHiBFy9ehUePnwIYWFhEBkZCX/+/OEtWERJQ5obFwDAxYsXGXOnz549uzBqGVM9kq5GQqFQ5sQj8oaq5TrOwK1bt0jtKZNtDsM+MTExlIRoCCFwdnaGmJgYvsWj5dKlSxRFW9FU2ImJiRS/fUUi7KWlpcHKlSvBzMxM7me3rq4ujBs3DmJjY+Vu9+TJk5T65AlNnJKSAo6OjpRngjpmyNUaZUCZdLUCgQC2bNlCcUHB8Mfnz5+hVq1atNerZcuWjFOxV65coewvb1ARed1TAwIC2OgyLWvWrCG12bRpU87awijG9+/fwdXVlTI+7O3tFf5q5ZL4+HiKrIrmuVi1ahWpHh0dHbmUoL9//8LGjRvB0tJS6st+9OjRMHv2bKnKgr6+Pnh7e8PPnz9lbj8/Px8cHBxI9XTr1k2mYwmCgP79+1PkWLp0qcztqxKtUQYAlE9mU716dQgMDFSlyBgp/P37l3btHyEE5cuXh4cPH5L2z8rKonyllStXTqGvdlltE7hUBACAkgN9ypQpnLaHUYwfP35A1apVKeO0YsWKvEadY0IyiFVx9jR0ZGdng7W1NameoUOHynysr68vlCtXTqqyLbkMULCMIC1gmpGREcyZM0fmaXpfX19KHREREcUet3fvXspxLVq0UNuZO61SBhTNRiZZBgwYoNZrftoEQRCwe/du2nDHOjo6sHnz5sLlgWXLllH2USbqV3G2CaoINiP51XLw4EHO28Qoxq9fv6BmzZq0iqu6ZZiUVDInTpwodx179uyh9PX169dSj8nLy4M9e/ZQlBHJMmjQIPjw4QNjPX/+/IHZs2dTbIOKFlNTU1iyZEmxHwMZGRlQpkwZ0rHFZXR89+4dpW0LCwuFlipUhVYpAwDyTfFKm0UwNjaGdevWYTsBNeH58+e0xloI/YuiFhYWBgYGBqTtzZs3Z8WatzgbB65ITEyk9PXNmzcqaRujGAkJCVC3bl3KdbOysoK3b9/yLV4hktbv9erVk+t4kUhEWRpp37691P2PHDkCzs7OUp/LvXr1kmuM//jxA6ZMmQJ6enqMdZqbm8OaNWsgIyODsZ7FixeTjtHT04P4+HjafbOysqBGjRqUdiSjLaobWqcMAMg3xfv06VNKvO6ipXLlyhqbw6CkkZiYCB07dmRU3iRnDTT9xXn79m1SnwwNDdV2ChLzP5KSkmjD7VpaWsKrV6/4Fg8A/uUJKCqbrq6uXJkYL168SOnfnTt3KPuJxWI4ffo07RJK0dKpUyd4/vy5wv2JiYkBLy8vilV/0VK2bFnYsmULbT9//fpF+ZiYN28ebVvjx4+n1M1FsiO20UplAEC+KV6RSAS7d+8GCwsLqRprdHQ0X93B/D8ikQiWL19erG0IXznD2WTdunWkPjVu3JhvkTAykpqaCk2aNKH9SlXmpccW6enplHsoODhYpmMJgqD0rW7duqQZM4IgwN/fn5K4SLK0bt0anjx5wlq/vnz5AsOHD5f6fKhQoQL4+flRZn3HjRtH2q906dKQlpZG2ufcuXOU+urUqQPZ2dms9YErtFYZKECeKd7ExEQYP34840AyNDSEFStWaGQu85LGzZs3GZU3Y2Nj+P37N98iKo1kIpZJkybxLRJGDtLT02mz9JUuXVrmFy+XSNo3bNu2Ter+BEFAQkICnD17ltKnkydPFu5z8+ZNpRMRKUtERAStpX/RIpnI6NOnT5Rn/4oVKyA6OhoSEhIgKiqK4s1gYmICHz9+5KwfbKL1yoAivHz5Eho3bsw4iJycnODy5ctqFV1KG4mJiWGMVNawYUP49u0b3yIqhaRnxIEDB/gWCSMnmZmZ0KZNG8r4NDU1lcufnQtGjx5Nkmnw4MG0+6WkpICPjw9llrWg2NnZQX5+Pty/fx+aNWsm9QXs5uYmV4piZQkLC4MePXpIlcnFxQWOHTsGIpEIevfuzbif5DICQop5YfAFVgYURCwWw8GDB8HKyopxcHTp0gW+fPnCt6hay+fPn2m9DAqKpaWlxtp7JCUlUfpTnKU2Rj35+/cvtG/fnnYGi09X5t27d5PkcXZ2puwjzf6qoOjp6dEaTRYtNWrUgIsXL/L2ARUSEgIdOnSQKmO1atXA09NT6j5Fy9ChQzXqgxArA0qSkpICU6dOZfRO0NfXh0WLFkFmZibfomoVBEEwGhMWLQKBAFasWKFwhDW+kIybbmBggINiaTDZ2dnQpUsXyvg0NDTkPFYFE2FhYRR5ivrmy+qZJa24urrCyZMn1Sb8+sOHD6FFixYK96douXjxIt/dkQusDLDEmzdvpA4iOzs7OHfunEZpiprMhQsXKNdgxIgRUKlSJdrr07lzZ0hMTORbbJmRdP1q2LAh3yJhlCQnJwd69uxJ+0Fx7do1lcuTn58PRkZGJFlu3LgBAMrHbHFwcICDBw+qpfcLQRBw+/ZtaNSokcKKQIFHGldhyLkAKwMsQhAEnDhxQmpGrXbt2qldgJGSRmZmJtjZ2ZHOu42NDaSnp0Nqair06tWL8QH14sULvsWXCUnjpwkTJvAtEoYF8vLyaA3b9PT04NKlSyqXR9LAcdmyZQCgeDTX0qVLw86dOyE3N1flfZEXgiDg6tWrxS5xSFMIuEpQxgVYGeCAtLQ0mD17Nujq6tIOEl1dXZg9ezakp6fzLWqJZMGCBZRzfurUqcLfCYKAjRs30voc6+vrw549e9R+BkfSWGvfvn18i4Rhifz8fBgyZAjtc+Ps2bMqlWXmzJmUGTRl8ryYmJjA9OnTYdu2bXDx4kV4+fIl/PnzR63vN5FIJDUssjRlgMvU5WyDlQEOef/+PbRt25ZxsFSoUAFOnDihMYNFE/j48SMl2piHhwftOX7w4AHjTe7p6Ql///7loQfFk5KSQpFXXYLVYNhBJBLRGqsJhUI4ceKEyuQ4ffo0qX1LS0val4ayxdDQECpXrgzt2rWDMWPGwIoVK+DQoUNw7949+PLlC69++spmvNWU5UesDHAMQRBw7tw5yrR10dKyZUuNj4anDhAEQbHK1tXVlbos8+PHD1pfb4QQ1K5dWy29Qe7du0eZzdCEaVeMfIjFYvDy8qL94jx8+LBKZIiOjqa0/+DBA9aVAVlKuXLloFGjRtCvXz+YOXMm+Pj4qGR2ge4cyFM0JRgdVgZURGZmJixcuJDR1U1HRwe8vb01yuBE3aCL/jV37txij8vLy4NZs2bRXpfSpUurXUzxjRs3kmRs0KAB3yJhOEIsFsOkSZNoFYK9e/dy3j5BEBT3aUmXQ3UpRkZGUKVKFWjfvj1pdiEwMBC+fv2qcB4ZPDOgGrRGGSjg8+fP0LlzZ8aBY2VlBQcPHtQ4Vze+ycjIoGQ6q1ixotTkI5KcO3cOSpUqRXtd5s2bpzaWzwMHDiTJNn78eL5FwnAIQRAwffp02nHp5+fHefvdunUjtent7a2wzYC5uTkMHjwYmjdvDvb29lJzBXBRypcvT5pd8PX1hUuXLkFoaCgkJCTQzi4oaiOBbQbkQ+uUAYB/g+vy5cuUCHJFS5MmTeDly5d8i6oxzJ07l3IOFTG2+vjxI23GMYT+xUn/9esXB9LLh6R7pCq+EDH8QhAEzJs3j3Zcbt26ldO2V65cSWqvadOmCnkT0FnX5+fnw7dv3+Dx48dw4sQJWLduHUycOBG6du0KtWrVgtKlS6tUWSg6u+Dl5QUrV66Ew4cPw+TJk1nprzqDlQEeycrKguXLlzPm3BYIBDBhwgSNmWbii/fv31M8N9q3b6+wRp6ZmUlrzY3QP6NPNhOnyEtqaipFptDQUN7kwagOgiBgyZIltONy/fr1nLUbEBBAasvAwAB+//4tV5wBoVCosN99amoqvH37Fq5duwY7d+6E+fPnw5AhQ6BZs2ZgZ2enVNAjrooy/eULvpUBAQAA0nKio6PRzJkzkb+/P+3vFhYWaO3atcjLywvp6OioVjg1BwBQu3btUGBgYOE2PT09FB4ejqpUqaJUvbt27ULTp09H+fn5pN90dXXRxo0b0fTp05FAIFC4DUW4f/8+8vDwKPxbT08PZWRkIAMDA5XKgeGP1atXoyVLllC2r1y5kna7siQnJyNLS0vStpcvX6LExETUtWtXBACIIAjG44VCIRIIBOjGjRuoQ4cOrMsnEonQjx8/UGxsLIqNjUXfvn0r/H/B3xkZGay3Wxz16tVD9erVQ/b29sje3h45ODgge3t7ZGtri/T19VUuT3EkJCQga2tr0rbfv39TtnGGytQODeDmzZvg6urKqG3Wr18fnj59yreYaoWk6xNCCBYsWMBa/c+ePaPYIhSUfv36qTxWxKZNm0gy1KtXT6XtY9SDDRs20I7JxYsXc7JGLflc2rlzJwBIz01QsM3ExIS3kMoFFJ1d2LFjB8ybNw8GDx7My+yCQCCAChUqQOPGjaF///4wa9Ys2L59O/j7+8OrV6+KzZ7LBSkpKbB69WqKrI6OjuDj46OSGQ6sDEiQk5MD69atA2NjY8bBNGrUqBKRgldZ0tPTwcbGhnRu7OzsWM8D8efPH9pEMgghqFKlCrx7947V9qQxePBgUvtjx45VWdsY9WLbtm20Y3Lu3Lmsv0yGDRtGasPT07Pwt5SUFPD19aUEwnJxcQFfX19ITU1lVRYuKLBdePToERw/fhzWrl0LEyZMgC5dukDNmjUZDYu5KsbGxlC1alXo2LEjjB07FlatWgVHjhyB+/fvQ2RkJKuuxEUVOjrFpUCh4zqpG1YGGIiNjYUBAwYwDhYzMzPYvn272li488Hs2bMp5+XChQuctCUSiRjXa42NjQvztXNN5cqVSW3v3r1bJe1i1JMdO3bQjslp06axqhBs376dVH+1atUo+xAEAYmJiRAdHc3L1y3XpKSkwJs3b+Dq1auwY8cOmDt3LvTp0wfq168PFSpUUMi7QpnZBRsbG2jSpAkMGDAAZs+eXTi7EBYWBklJSTKdf1mTTQmFQtDR0eFUIcA2A8UQGBiIpk6dit6/f0/7e+3atZGfnx9q0aKFiiXjl4iICFS3bl0kEokKt3Xs2BHdvHmT03X869evo+HDh6OUlBTKb1OmTEFbtmzhbD0wPT0dmZmZkba9ePECNWjQgJP2MJrB/v370bhx45Dko3TixInIz88PCYVCpdt4/vw5aty4ceHfAoEApaSkUMajNpOfn0+yXZC0Yfj27RvKzMxUmTwmJiaF9gqSdgv29vbIxMQEOTs7o+zsbKk2HwUIhUJkZGSE4uLikLm5OfsCc6ZmlCDy8vJgy5YtUqeqhg4dCvHx8XyLqhIIgoBWrVqR+q+vrw+fP39WSftRUVFQr1492uvQpEkTiI2N5aRdychvenp6CgdSwZQsDh8+TPt15+XlxUrMkpycHEqY77t377IgufZAEATt7MKgQYPA3d0dbG1t1dIzomjh0l0SKwNy8OPHDxg+fDjjhTI1NYVNmzaV+Lz2J06coPR98eLFKpUhOzsbxo4dS3sdypYtC3fu3GG9zS1btpDacXNzY70NjOZy4sQJ2iA+I0aMAJFIpHT9DRs2JNW7du1aFqTGFCUvLw9iYmLg0aNHcOzYMVizZg2MHz8eOnfuDDVq1ABTU1PelQGuAilhZUABHj9+DLVr12a8YNWqVSuxWntaWhqUL1+e1F8HBwfekgodPHiQNk6EQCCA1atXsxpJUjL2gZeXF2t1Y0oG586do82WOnjwYKXti6ZMmUKqs2fPnuwIjZGZgtmF169fw5UrV8DPz4+X2QUuYt9gZUBB8vPzwc/PD8zNzRkvWP/+/TmbsuaLGTNmUPrJd+6AsLAwiiV1QenatSskJyez0k6VKlVIdRe4d2EwRfH396dM6SOEoG/fvkrNGh49epRUX/ny5UuckWBJIC8vD6Kjo+Hhw4ewf/9+GD16NDRo0ADKli3LmqLARfIlrAwoye/fv2HMmDGMF83Y2BjWrFlTItaW3759S5kG7dKli1o8kFJSUqBHjx6018DJyUnpKIHp6ekUa+WQkBCWpMeUNK5fvw4GBgaUsdijRw+FnwWfPn2i1FfSPjY0nbi4ODhz5gx4e3tD/fr1Ocv9gGcG1Jhnz55B/fr1GS9epUqV4MaNG3yLqTAEQUCLFi1IfTIwMICvX7/yLVohYrEY1q9fT6t9GxgYwP79+xWu+9GjR6T6dHV1ec3xjlF/bt++TbuE1blzZ4XGjlgspsxEnj9/ngPJMbIgEokgLCwM/Pz8YPDgwWBvb8/Ji79owTYDGoJIJIK9e/eCpaUl48Xs0aMHREZG8i2q3EhOUSKEYNmyZXyLRUtgYCBYW1vTnv/Ro0dDVlaW3HVKBpipU6cO+4JjShyBgYG0Aczat2+vkJ2NZPAtWVKEY9ghPT0d7ty5A8uXL4f27dsrFQjJyckJGjRooJAygL0JNIikpCSYOHEiYxAMAwMDWLZsmUIvJT5ISUmhvFydnJzUWv64uDhwd3enPf9169aVWyGTjAA3evRojiTHlDQeP35Ma4XeunVruVJ8AwAsXryYVEerVq24ERoDsbGxcOrUKZgyZQq4ubkpvN6vq6sLDRs2hOnTp8O5c+fgx48fAPDvuaqqZFOygJUBDgkNDYWmTZsyXlxHR0fw9/dXizV3aXh7e1Nkv3r1Kt9iFUteXh5jHnpzc3O4cuWKzHVVq1aNdPyOHTs4lBxT0nj69CltOuBmzZpBWlqazPVcuXKFdLyJiQkrbovaTn5+Prx69Qr+++8/GDRoENjZ2Sn81W9ubg6dO3eG1atXw/3796XOAMkbgZDLHBNYGeAYsVgMhw8fZpy2RghBp06d4NOnT3yLSsvr168pA7V79+58iyUXZ86cARMTE9pzv3DhwmIfphkZGZRZnmfPnqlIekxJ4f/au/+YKOs4DuCfBwQ8SgXOE3+gcIdoaUEp8cvaHJLMYeZG4V8N/2g4S3FmNlZWGw2cZnXY2FzFllarNrk1lyTaXJJnKzAaDKgYxxVkh3Fw8kPglPv01914uDu4O+4X97xf27Pp8/MDPM/z/Xy/z/f5Po2NjRwbG2t3DmZmZrpc2zMYDHbbt7S0+DbwEHTnzh2ur6/nt956i/Py8uY0foBKpeIXXniBz5w5w62trW6/zhwsH5tCMuAnJpOJDx065LR3aUREBJeVlXn9Iz9zMTk5yVu2bBHFuXDhQtbpdIEOzW3t7e12tXvrtG3bthk/PPXjjz+K1g8PDw/qRyQQvJqbmx32Kdq8eTMbjUaX9pGYmCjadi4dY6XAYrGwXq/nL774gl966SVOS0ubU5N/ZmYmHz58mM+fP8///vuvV2IMho9NIRnws5aWFruhfKdOCQkJ/PXXXwfFo4NPP/3ULr7y8vJAh+Wx4eFh3rNnj8Pf+6pVq1ir1TrcTq1Wi9ZNTU31c+QQSlpbWx22FKalpfHt27dn3X76B9RKSkr8EPX8ce/ePW5qauKqqiouKiriVatWeVzrj42N5YKCAq6srORr1675vBIQyI9NIRkIAIvFwl9++aXd53+nTrm5udzW1hawGAcGBlihUNhlqfP9dTqLxcKnT592OErcggULuKqqSnQBWiwWfv7550Xr7d27N4A/AYSCjo4OXrFihd05uHHjRjYYDDNue+rUKbskQspMJhNfunSJ33zzTc7NzXX6SNCVae3atVxcXMwfffQRt7W1eXUE02CHZCCAhoaG+LXXXnNYMFkLp1deecWtDkbe8vLLL9vFM5/HSZhOq9U6rTHs2bOHe3p6WK1WOxzZsLCw0Gc9ekE6/vzzT05ISLA7vx566KEZP3o2fcyL8PDwoHq86EsWi4V1Oh1//vnnvH//fk5NTfX408URERGclZXFR44cYY1GM2sSFuqQDASBjo4OzsvLc3rSLl++nD/77DO/NRndvHnT7pna7t27/XJsf+rr6+Pc3FyHv3Nrxx1HNxprhx5fflscpEGn09n1AbDWUJ2NLjgyMmLX96ihocHPkfuH2WzmX375hT/44AN+7rnnZmxNnW2Ki4vjnTt38vHjx7mhoQH9fqZBMhAkLBYL19bWzjiK1ZNPPsm//fabT+OYnJzkrKws0XFlMhnr9XqfHjdQ7t+/z6+//rrbNxbrqz5ICGCu9Ho9q1Qqu3NMqVQ6HYM+LS1NtO6pU6f8G7SPDA4Ocl1dHb/xxhu8detWhwM2uTqlpKTw3r17+eOPP+b29nZJNfl7AslAkBkdHeVjx45xZGSk00LowIEDXvv4znQ1NTV2x6yoqPDJsYLJhQsXHL4HPltC4MtBQEA6enp6eN26dXbn2OrVqx0O+V1SUiJar6ioKABRz43FYuGuri4+d+4c79u3jx955BGPm/wjIyM5JyeHjx49yt98841LHTFBDMlAkOrs7OSCggKnJ79CoeCamhqvZrtGo5GXLl1ql12HwkeWXDF9dDdXJl8ODwrScuvWLYevv65cuZJ///130bqffPKJaJ3ExMTABO0Gs9nMP//8M7///vtcWFho9yl0dya5XM67du3iEydO8PXr1+d9x+ZgIDAzEwStb7/9lg4dOkQ6nc7h8oyMDKqurqb09PQ5H2v//v105swZ0bxLly5Rfn7+nPcdbO7evUvd3d2k0+lIp9NRV1cX1dTU0N27d93ajyAIpFKpqLOzkwRB8FG0IBW3b9+mvLw8am1tFc2Pj4+nq1ev0oYNG4iIqLW1lVJTU0XrGAwGio+P91ussxkcHKQbN26QVqslrVZLjY2NNDY25tG+1q9fT1u2bLFN69atw/XmZUgG5oHx8XF69913qbKyksbHx+2WC4JAL774IlVWVtLSpUs9OkZTUxNlZGTQ1NOhsLCQzp8/73HcgTQ5OUm3bt0inU4nKvStU19fn1eP19/fT3K53Kv7BGkyGo309NNPU3Nzs2i+QqGg77//nlJTU2lycpKWLFlCo6OjtuUXLlygZ555xt/hEhERM1NXV5et4NdqtdTe3u7RvqKioig9Pd1W8Ofk5Hh8XwPXIRmYR/R6PR05coQ0Go3D5bGxsVRRUUElJSUUHh7u8n4tFgtlZWVRY2OjbV50dDR1dHTQmjVr5hy3rwwNDdkV8taCX6/Xk9ls9lss3d3dlJSU5LfjQWgbHByk/Px80TVJRBQXF0dXrlyhTZs20datW+natWu2ZceOHaN33nnHL/GZzWb69ddfbQX/jRs3PE6wFQoF5eTk2Ar/zZs3U1RUlJcjhtkgGZiHLl++TKWlpfTHH384XP74449TdXU1ZWdnO1zOzGQ0GmlkZIQefPBB0mg0tG/fPtE6x48fp7KyMq/H7o779+9TT0+PXYFvLfSNRmNA45sKLQPgbXfu3KEdO3bQTz/9JJofExND9fX1VFtbSydPnrTNf+qpp0ij0ZBcLvd6E/rAwIBdk7+jVkpXPPzww6Im/7Vr16LJPwggGZinzGYzqdVqKi8vFzUVTlVcXEwnTpywPUc0mUx09uxZ+vDDD6mrq8u2XlhYGFksFtv/169fTy0tLRQZGenTn4GZaWBgwK5Wb53+/vtvmpyc9GkMK1asIKVSSUqlkurq6shkMpE7lwT6DIAvDQ8P086dO6mhoUE0f9GiRVRaWkoVFRV22yQnJ9PBgwepuLiYYmJi3D4mM1NnZ6etxq/Vaqmjo8Oj+BcuXEhPPPGEreDPzs5G0hykkAzMc729vXT06FH66quvHC5fvHgxlZeXU0pKChUVFdk6yM30Z79y5Qrl5eV5Jb6JiQn666+/HDbl63Q6Ghoa8spxnImOjiaVSkUqlYqUSqXt3yqVipKSkig6Otq2blVVFR0+fNjtZECtVlNpaakvwgeg0dFR2rVrF129etWl9a1JaXR0NNXW1s7aAXhiYoJu3rwpavL/77//PIp12bJlolr/pk2bfF6pAO9AMhAifvjhBzpw4AC1tbU5XUcQhFkLOkEQ6LvvvnP5DQJmpr6+Poed9Lq7u6m3t9etwtVdgiBQQkKC0wJ/2bJlLtfYTSYTJSQk0NjYmKilxJmwsDCSyWTU29vrUQ0MwFVjY2O0e/duunz5ssvbhIWFkSAIdPHiRdH13N/fL2ryb2pqoomJCY/i2rBhg6jwT05ORgvZPIVkIITcu3ePqqur6e233/a4xu2ogJv6Gt70Qr+7u9vt1/HctXjxYlEBP7XQT0xM9Gpno/r6eiooKCBmnjEhsN5o6+rqaPv27V47PoAz4+Pj9Oyzz7qdEERFRdHJkyepubmZtFqt075Gs5HJZJSRkWHr4Z+dnU1xcXEe7QuCD5KBEGQwGKisrIzOnj3r8T7S09MpMjKSdDodGQwGL0ZnLzw8nNasWWNX4FsL/bi4OL/WNurr66mwsNDhI5WpTbAajQaJAPjVe++9R6+++qpfjrV8+XJRrf+xxx5Dk38IQzIQwq5fv07btm3z6yt2zsjlcofN+CqVilavXk0LFiwIdIgiJpOJzp07R6dPnxZ1tkxOTqbS0lIqLi6mJUuWBDBCkBpmppSUFNLpdF5/9CYIAm3cuFFU+CuVSjT5SwiSgRDW399PCoXCL8eKjIykpKQkh8/ulUrlvC04rW88DA8P06JFi/zeSgFg5c3rWSaTUWZmpqiXP/q9SFtwVcfAq0ZGRry6v/j4eKfP7leuXOnWQEfzhSAIJJfL8ToUBNxcr+cdO3bQ9u3bbU3+ERERXooMQgFaBkLYXGsSFRUV9Oijj9pew3vggQe8GB0AuGOu1zMGxoKZIBkIYZ4+Y8RAOgDBB9cz+FJYoAMA3xEEgQ4ePOjRtqWlpbhxAAQRXM/gS2gZCHEYSAcgdOB6Bl9By0CIi4mJodraWhIEgcLCZv5zWwfS0Wg0uHEABCFcz+ArSAYkID8/ny5evEgymYwEQbBrLrTOk8lkGFEPIMjhegZfQDIgEfn5+dTb20tqtZpUKpVomUqlIrVaTf/88w9uHADzAK5n8Db0GZAgDKQDEDpwPYM3IBkAAACQODwmAAAAkDgkAwAAABKHZAAAAEDikAwAAABIHJIBAAAAiUMyAAAAIHFIBgAAACQOyQAAAIDEIRkAAACQOCQDAAAAEodkAAAAQOKQDAAAAEgckgEAAACJQzIAAAAgcUgGAAAAJO5/JxVpGF+LySMAAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 640x480 with 4 Axes>" ] @@ -1356,7 +1356,7 @@ }, { "cell_type": "markdown", - "id": "80dd6400", + "id": "ff09dba7", "metadata": {}, "source": [ "You can find additional options via `draw_networkx()` and\n", @@ -1367,13 +1367,13 @@ { "cell_type": "code", "execution_count": 37, - "id": "d399c4cb", + "id": "35d8b340", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:40.939147Z", - "iopub.status.busy": "2023-01-04T17:44:40.938622Z", - "iopub.status.idle": "2023-01-04T17:44:41.055567Z", - "shell.execute_reply": "2023-01-04T17:44:41.054962Z" + "iopub.execute_input": "2023-01-04T19:40:14.037966Z", + "iopub.status.busy": "2023-01-04T19:40:14.037492Z", + "iopub.status.idle": "2023-01-04T19:40:14.144440Z", + "shell.execute_reply": "2023-01-04T19:40:14.143724Z" } }, "outputs": [ @@ -1396,7 +1396,7 @@ }, { "cell_type": "markdown", - "id": "ff07fe90", + "id": "0b0dbcd5", "metadata": {}, "source": [ "To save drawings to a file, use, for example" @@ -1405,19 +1405,19 @@ { "cell_type": "code", "execution_count": 38, - "id": "cbdb9a7b", + "id": "4c25a13b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:41.059201Z", - "iopub.status.busy": "2023-01-04T17:44:41.058689Z", - "iopub.status.idle": "2023-01-04T17:44:41.211293Z", - "shell.execute_reply": "2023-01-04T17:44:41.210643Z" + "iopub.execute_input": "2023-01-04T19:40:14.148147Z", + "iopub.status.busy": "2023-01-04T19:40:14.147842Z", + "iopub.status.idle": "2023-01-04T19:40:14.288867Z", + "shell.execute_reply": "2023-01-04T19:40:14.288124Z" } }, "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": "78300d30", + "id": "a4f599fb", "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": "7e0e5e01", + "id": "97a8506b", "metadata": { "execution": { - "iopub.execute_input": "2023-01-04T17:44:41.214708Z", - "iopub.status.busy": "2023-01-04T17:44:41.214264Z", - "iopub.status.idle": "2023-01-04T17:44:41.370983Z", - "shell.execute_reply": "2023-01-04T17:44:41.370349Z" + "iopub.execute_input": "2023-01-04T19:40:14.292501Z", + "iopub.status.busy": "2023-01-04T19:40:14.292239Z", + "iopub.status.idle": "2023-01-04T19:40:14.512103Z", + "shell.execute_reply": "2023-01-04T19:40:14.511418Z" } }, "outputs": [ @@ -1476,7 +1476,7 @@ }, { "cell_type": "markdown", - "id": "d999d94b", + "id": "72e1fd01", "metadata": {}, "source": [ "See Drawing for additional details." |
